From 0d89d880c0dbc280bd3c10c9744f2d1e7bc91bd5 Mon Sep 17 00:00:00 2001 From: Wenchao Ma <60903466+mawc2019@users.noreply.github.com> Date: Wed, 25 May 2022 21:25:32 -0400 Subject: [PATCH 01/26] fix calculate_fd_gradient to deal with design pixel weights near 0 or 1 (#2073) --- python/adjoint/optimization_problem.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index e86fbe219..8a024520e 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -321,6 +321,8 @@ def calculate_fd_gradient( "The requested number of gradients must be less than or equal to the total number of design parameters." ) + assert db < 0.2, "The step size of finite difference is too large." + # cleanup simulation object self.sim.reset_meep() self.sim.change_sources(self.forward_sources) @@ -339,7 +341,10 @@ def calculate_fd_gradient( self.sim.reset_meep() # assign new design vector - b0[k] -= db + in_interior = True # b0[k] is not too close to the boundaries 0 and 1 + if b0[k] < db or b0[k]+db > 1: in_interior = False # b0[k] is too close to 0 or 1 + + if b0[k] >= db: b0[k] -= db self.design_regions[design_variables_idx].update_design_parameters( b0) @@ -367,7 +372,9 @@ def calculate_fd_gradient( self.sim.reset_meep() # assign new design vector - b0[k] += 2 * db # central difference rule... + if in_interior: b0[k] += 2 * db # central difference rule... + else: b0[k] += db # forward or backward difference... + self.design_regions[design_variables_idx].update_design_parameters( b0) @@ -394,7 +401,7 @@ def calculate_fd_gradient( # estimate derivative # -------------------------------------------- # fd_gradient.append([ - np.squeeze((fp[fi] - fm[fi]) / (2 * db)) + np.squeeze((fp[fi] - fm[fi]) / db / (2 if in_interior else 1)) for fi in range(len(self.objective_functions)) ]) From 543e8e7695b5a4012cfeb26c73be05c294df3c61 Mon Sep 17 00:00:00 2001 From: mochen4 Date: Fri, 27 May 2022 17:51:39 -0400 Subject: [PATCH 02/26] check is_D for FourierFields adjoint (#2085) * Update dft.cpp * is_E_or_D and is_H_or_B * Update dft.cpp * typo * typo * Update src/meep/vec.hpp Co-authored-by: Steven G. Johnson --- src/dft.cpp | 4 ++-- src/meep/vec.hpp | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/dft.cpp b/src/dft.cpp index 6b37de40f..bc18c2fe2 100644 --- a/src/dft.cpp +++ b/src/dft.cpp @@ -1491,7 +1491,7 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher temp_struct.idx_arr.push_back(idx); for (size_t i = 0; i < Nfreq; ++i) { EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]; - if (is_electric(temp_struct.near_fd_comp)) EH0 *= -1; + if (is_E_or_D(temp_struct.near_fd_comp)) EH0 *= -1; EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); } @@ -1503,7 +1503,7 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher temp_struct.idx_arr.push_back(site_ind[j]); for (size_t i = 0; i < Nfreq; ++i) { EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]*0.25; // split the amplitude of the adjoint source into four parts - if (is_electric(temp_struct.near_fd_comp)) EH0 *= -1; + if (is_E_or_D(temp_struct.near_fd_comp)) EH0 *= -1; EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); } diff --git a/src/meep/vec.hpp b/src/meep/vec.hpp index c5c412182..4996b2dc7 100644 --- a/src/meep/vec.hpp +++ b/src/meep/vec.hpp @@ -412,6 +412,8 @@ inline bool is_electric(component c) { return c < Hx; } inline bool is_magnetic(component c) { return c >= Hx && c < Dx; } inline bool is_D(component c) { return c >= Dx && c < Bx; } inline bool is_B(component c) { return c >= Bx && c < Dielectric; } +inline bool is_E_or_D(component c) {return is_electric(c) || is_D(c); } +inline bool is_H_or_B(component c) {return is_magnetic(c) || is_B(c); } inline bool is_derived(int c) { return c >= Sx; } inline bool is_poynting(derived_component c) { return c < EnergyDensity; } inline bool is_energydensity(derived_component c) { return c >= EnergyDensity; } From 708cf829da88063170a32dce4f57422ebbd3bdd2 Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 2 Jun 2022 10:47:14 -0700 Subject: [PATCH 03/26] improvements to unit test for `DiffractedPlanewave` (#2088) * improvements to unit test for DiffractedPlanewave * validate using all propagating diffraction orders rather than a subset --- python/tests/test_diffracted_planewave.py | 147 ++++++++++++---------- 1 file changed, 79 insertions(+), 68 deletions(-) diff --git a/python/tests/test_diffracted_planewave.py b/python/tests/test_diffracted_planewave.py index 962a3e3ea..7c0d8b3c0 100644 --- a/python/tests/test_diffracted_planewave.py +++ b/python/tests/test_diffracted_planewave.py @@ -5,133 +5,144 @@ import numpy as np +# Computes the mode coefficient of the transmitted orders of +# a binary grating given an incident planewave and verifies +# that the results are the same when using either a band number +# or `DiffractedPlanewave` object in `get_eigenmode_coefficients`. + class TestDiffractedPlanewave(unittest.TestCase): - def run_binary_grating_diffraction(self, gp, gh, gdc, theta, bands, orders): - resolution = 50 # pixels/um + @classmethod + def setUp(cls): + cls.resolution = 50 # pixels/um + cls.dpml = 1.0 # PML thickness + cls.dsub = 3.0 # substrate thickness + cls.dpad = 3.0 # length of padding between grating and PML - dpml = 1.0 # PML thickness - dsub = 3.0 # substrate thickness - dpad = 3.0 # length of padding between grating and PML + cls.wvl = 0.5 # center wavelength + cls.fcen = 1/cls.wvl # center frequency - sx = dpml+dsub+gh+dpad+dpml - sy = gp + cls.ng = 1.5 + cls.glass = mp.Medium(index=cls.ng) - cell_size = mp.Vector3(sx,sy,0) - absorber_layers = [mp.Absorber(thickness=dpml,direction=mp.X)] + cls.pml_layers = [mp.PML(thickness=cls.dpml,direction=mp.X)] - wvl = 0.5 # center wavelength - fcen = 1/wvl # center frequency - df = 0.05*fcen # frequency width - ng = 1.5 - glass = mp.Medium(index=ng) + def run_binary_grating_diffraction(self,gp,gh,gdc,theta): + sx = self.dpml+self.dsub+gh+self.dpad+self.dpml + sy = gp + cell_size = mp.Vector3(sx,sy,0) # rotation angle of incident planewave # counter clockwise (CCW) about Z axis, 0 degrees along +X axis theta_in = math.radians(theta) - eig_parity = mp.ODD_Z - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in) + k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(z=1), theta_in) - symmetries = [] - if theta_in == 0: + eig_parity = mp.ODD_Z + if theta == 0: k = mp.Vector3() eig_parity += mp.EVEN_Y symmetries = [mp.Mirror(direction=mp.Y)] + else: + symmetries = [] def pw_amp(k,x0): def _pw_amp(x): return cmath.exp(1j*2*math.pi*k.dot(x+x0)) return _pw_amp - src_pt = mp.Vector3(-0.5*sx+dpml,0,0) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), + src_pt = mp.Vector3(-0.5*sx+self.dpml,0,0) + sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=0.1*self.fcen), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0), amp_func=pw_amp(k,src_pt))] - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=absorber_layers, - k_point=k, - default_material=glass, - sources=sources, - symmetries=symmetries) - - tran_pt = mp.Vector3(0.5*sx-dpml,0,0) - tran_flux = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=10) - - input_flux = mp.get_fluxes(tran_flux) - - sim.reset_meep() - - geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, + geometry = [mp.Block(material=self.glass, + size=mp.Vector3(self.dpml+self.dsub,mp.inf,mp.inf), + center=mp.Vector3(-0.5*sx+0.5*(self.dpml+self.dsub),0,0)), + mp.Block(material=self.glass, size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] + center=mp.Vector3(-0.5*sx+self.dpml+self.dsub+0.5*gh,0,0))] - sim = mp.Simulation(resolution=resolution, + sim = mp.Simulation(resolution=self.resolution, cell_size=cell_size, - boundary_layers=absorber_layers, + boundary_layers=self.pml_layers, geometry=geometry, k_point=k, sources=sources, symmetries=symmetries) - tran_flux = sim.add_mode_monitor(fcen, + tran_pt = mp.Vector3(0.5*sx-self.dpml,0,0) + tran_flux = sim.add_mode_monitor(self.fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) - sim.run(until_after_sources=100) + sim.run(until_after_sources=mp.stop_when_fields_decayed(20,mp.Ez,src_pt,1e-6)) + + m_plus = int(np.floor((self.fcen-k.y)*gp)) + m_minus = int(np.ceil((-self.fcen-k.y)*gp)) + + if theta == 0: + orders = range(m_plus) + else: + # ordering of the modes computed by MPB is according to *decreasing* + # values of kx (i.e., closest to propagation direction of 0° or +x) + ms = range(m_minus,m_plus+1) + kx = lambda m: np.power(self.fcen,2) - np.power(k.y+m/gp,2) + kxs = [kx(m) for m in ms] + ids = np.flip(np.argsort(kxs)) + orders = [ms[d] for d in ids] - for band,order in zip(bands,orders): + for band,order in enumerate(orders): res = sim.get_eigenmode_coefficients(tran_flux, - [band], + [band+1], eig_parity=eig_parity) - tran_ref = abs(res.alpha[0,0,0])**2/input_flux[0] - if (theta_in == 0): + tran_ref = abs(res.alpha[0,0,0])**2 + if theta == 0: tran_ref = 0.5*tran_ref vg_ref = res.vgrp[0] + kdom_ref = res.kdom[0] res = sim.get_eigenmode_coefficients(tran_flux, mp.DiffractedPlanewave((0,order,0), mp.Vector3(0,1,0), 1, 0)) - if res is not None: - tran_dp = abs(res.alpha[0,0,0])**2/input_flux[0] - if ((theta_in == 0) and (order == 0)): - tran_dp = 0.5*tran_dp - else: - tran_dp = 0 + tran_dp = abs(res.alpha[0,0,0])**2 + if (theta == 0) and (order == 0): + tran_dp = 0.5*tran_dp vg_dp = res.vgrp[0] + kdom_dp = res.kdom[0] err = abs(tran_ref-tran_dp)/tran_ref - print("tran:, {:2d} (band), {:2d} (order), {:10.8f} (eigensolver), {:10.8f} (planewave), {:10.8f} (error)".format(band,order,tran_ref,tran_dp,err)) + print("tran:, {:2d} (band), {:2d} (order), " + "{:10.8f} (band num.), {:10.8f} (diff. pw.), " + "{:10.8f} (error)".format(band,order,tran_ref,tran_dp,err)) + + self.assertAlmostEqual(vg_ref,vg_dp,places=4) + self.assertAlmostEqual(tran_ref,tran_dp,places=4) + if theta == 0: + self.assertAlmostEqual(abs(kdom_ref.x),kdom_dp.x,places=5) + self.assertAlmostEqual(abs(kdom_ref.y),kdom_dp.y,places=5) + self.assertAlmostEqual(abs(kdom_ref.z),kdom_dp.z,places=5) + else: + self.assertAlmostEqual(kdom_ref.x,kdom_dp.x,places=5) + self.assertAlmostEqual(kdom_ref.y,kdom_dp.y,places=5) + self.assertAlmostEqual(kdom_ref.z,kdom_dp.z,places=5) - self.assertAlmostEqual(vg_ref,vg_dp,places=5) - self.assertAlmostEqual(tran_ref,tran_dp,places=5) + print("PASSED.") def test_diffracted_planewave(self): - self.run_binary_grating_diffraction(2.6,0.4,0.6,0,range(1,6),range(0,5)) - self.run_binary_grating_diffraction(2.6,0.4,0.6,13.4,range(1,6),[-2,-1,-3,0,-4]) + self.run_binary_grating_diffraction(2.6,0.4,0.6,0) + self.run_binary_grating_diffraction(2.6,0.4,0.6,13.4) - # self.run_binary_grating_diffraction(10.0,0.5,0.5,0,[2,4,6],[1,3,5]) - # self.run_binary_grating_diffraction(10.0,0.5,0.5,10.7,[1,4,8],[-6,-4,-2]) + # self.run_binary_grating_diffraction(10.0,0.5,0.5,0) + # self.run_binary_grating_diffraction(10.0,0.5,0.5,10.7) if __name__ == '__main__': unittest.main() From d1e013e4405d336ea750ba2877cdb04f9b60d446 Mon Sep 17 00:00:00 2001 From: "Steven G. Johnson" Date: Thu, 2 Jun 2022 14:29:18 -0400 Subject: [PATCH 04/26] fix dft_ldos::J() for parallel runs (#2091) --- src/dft_ldos.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/dft_ldos.cpp b/src/dft_ldos.cpp index ee828c58d..c86c1c84c 100644 --- a/src/dft_ldos.cpp +++ b/src/dft_ldos.cpp @@ -86,7 +86,8 @@ complex *dft_ldos::F() const { complex *dft_ldos::J() const { const size_t Nfreq = freq.size(); complex *out = new complex[Nfreq]; - sum_to_all(Jdft, out, Nfreq); + // note: Jdft is the same on all processes, so no sum_to_all + memcpy(out, Jdft, Nfreq * sizeof(complex)); return out; } From 91d36ba73d1ae0eb135d18c783840cff9fcd71ef Mon Sep 17 00:00:00 2001 From: mochen4 Date: Thu, 2 Jun 2022 15:31:10 -0400 Subject: [PATCH 05/26] adjoint for ldos (#2077) * ldos adjoint * ldos_scale * missing ; * resolve * test * ldos adjoint * ldos_scale * missing ; * resolve * test Co-authored-by: Mo Chen Co-authored-by: Mo Chen --- python/adjoint/objective.py | 47 ++++++++++++++++++++++++++ python/adjoint/optimization_problem.py | 41 +++++++++++++--------- python/simulation.py | 1 + python/tests/test_adjoint_solver.py | 47 +++++++++++++++++++++++++- src/dft_ldos.cpp | 9 +++-- src/meep.hpp | 6 ++-- 6 files changed, 129 insertions(+), 22 deletions(-) diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index ac7d16d11..e56207cbc 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -353,3 +353,50 @@ def __call__(self): for far_pt in self.far_pts ]).reshape((self._nfar_pts, self.num_freq, 6)) return self._eval + +class LDOS(ObjectiveQuantity): + def __init__(self, sim, decimation_factor=0, **kwargs): + super().__init__(sim) + self.decimation_factor = decimation_factor + self.srckwarg = kwargs + + def register_monitors(self, frequencies): + self._frequencies = np.asarray(frequencies) + self.forward_src = self.sim.sources + return + + def place_adjoint_source(self, dJ): + time_src = self._create_time_profile() + if dJ.ndim == 2: + dJ = np.sum(dJ, axis=1) + dJ = dJ.flatten() + sources = [] + forward_f_scale = np.array([self.ldos_scale/self.ldos_Jdata[k] for k in range(self.num_freq)]) + if self._frequencies.size == 1: + amp = (dJ * self._adj_src_scale(False) * forward_f_scale)[0] + src = time_src + else: + scale = dJ * self._adj_src_scale(False) * forward_f_scale + src = FilteredSource( + time_src.frequency, + self._frequencies, + scale, + self.sim.fields.dt, + ) + amp = 1 + for forward_src_i in self.forward_src: + if isinstance(forward_src_i, mp.EigenModeSource): + src_i = mp.EigenModeSource(src, component=forward_src_i.component, eig_kpoint = forward_src_i.eig_kpoint, amplitude=amp, eig_band=forward_src_i.eig_band, size = forward_src_i.size,center=forward_src_i.center, **self.srckwarg) + else: + src_i = mp.Source(src, component=forward_src_i.component, amplitude=amp, size = forward_src_i.size,center=forward_src_i.center, **self.srckwarg) + if mp.is_electric(src_i.component): + src_i.amplitude *= -1 + sources += [src_i] + + return sources + + def __call__(self): + self._eval = self.sim.ldos_data + self.ldos_scale = self.sim.ldos_scale + self.ldos_Jdata = self.sim.ldos_Jdata + return np.array(self._eval) diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index 8a024520e..a9b0b656a 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -3,7 +3,7 @@ from autograd import grad, jacobian from collections import namedtuple -from . import utils, DesignRegion +from . import utils, DesignRegion, LDOS class OptimizationProblem(object): """Top-level class in the MEEP adjoint module. @@ -175,11 +175,16 @@ def forward_run(self): self.prepare_forward_run() # Forward run - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time - )) + if any(isinstance(m, LDOS) for m in self.objective_arguments): + self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, + self.minimum_run_time, + self.maximum_run_time)) + else: + self.sim.run(until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, + self.minimum_run_time, + self.maximum_run_time)) # record objective quantities from user specified monitors self.results_list = [] @@ -354,11 +359,13 @@ def calculate_fd_gradient( self.forward_monitors.append( m.register_monitors(self.frequencies)) - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time - )) + if any(isinstance(m, LDOS) for m in self.objective_arguments): + self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) + else: + self.sim.run(until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, + self.minimum_run_time, + self.maximum_run_time)) # record final objective function value results_list = [] @@ -385,11 +392,13 @@ def calculate_fd_gradient( m.register_monitors(self.frequencies)) # add monitor used to track dft convergence - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time - )) + if any(isinstance(m, LDOS) for m in self.objective_arguments): + self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) + else: + self.sim.run(until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, + self.minimum_run_time, + self.maximum_run_time)) # record final objective function value results_list = [] diff --git a/python/simulation.py b/python/simulation.py index 318dcf72d..a3ba603d6 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -5213,6 +5213,7 @@ def _ldos(sim, todo): sim.ldos_data = mp._dft_ldos_ldos(ldos) sim.ldos_Fdata = mp._dft_ldos_F(ldos) sim.ldos_Jdata = mp._dft_ldos_J(ldos) + sim.ldos_scale = ldos.overall_scale() if verbosity.meep > 0: display_csv(sim, 'ldos', zip(mp.get_ldos_freqs(ldos), sim.ldos_data)) return _ldos diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index c0edb934f..39fdbfab4 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -10,7 +10,7 @@ from enum import Enum from utils import ApproxComparisonTestCase -MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT') +MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT LDOS') resolution = 30 @@ -57,6 +57,11 @@ center=mp.Vector3(-0.5*sxy+dpml,0), size=mp.Vector3(), component=mp.Ez)] + +line_source = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), + center=mp.Vector3(-0.85,0), + size=mp.Vector3(0,sxy-2*dpml), + component=mp.Ez)] k_point = mp.Vector3(0.23,-0.38) @@ -81,6 +86,12 @@ def forward_simulation(design_params, mon_type, frequencies=None, mat2=silicon): if not frequencies: frequencies = [fcen] + + if mon_type.name == 'LDOS': + sim.change_sources(line_source) + sim.run(mp.dft_ldos(frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=50.0, decay_by = 1e-11)) + sim.reset_meep() + return np.array(sim.ldos_data) if mon_type.name == 'EIGENMODE': mode = sim.add_mode_monitor(frequencies, @@ -159,6 +170,12 @@ def J(mode_mon): def J(mode_mon): return npa.power(npa.abs(mode_mon[:,4,10]),2) + elif mon_type.name == 'LDOS': + sim.change_sources(line_source) + obj_list = [mpa.LDOS(sim)] + + def J(mode_mon): + return npa.array(mode_mon) opt = mpa.OptimizationProblem( simulation=sim, @@ -410,6 +427,34 @@ def test_adjoint_solver_eigenmode(self): print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) tol = 0.04 if mp.is_single_precision() else 0.01 self.assertClose(adj_scale,fd_grad,epsilon=tol) + + + def test_adjoint_solver_LDOS(self): + print("*** TESTING LDOS ADJOINT ***") + + ## test the single frequency and multi frequency cases + for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: + ## compute gradient using adjoint solver + adjsol_obj, adjsol_grad = adjoint_solver(p, MonitorObject.LDOS, frequencies) + + ## compute unperturbed |Ez|^2 + Ez2_unperturbed = forward_simulation(p, MonitorObject.LDOS, frequencies) + + ## compare objective results + print("|Ez|^2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,Ez2_unperturbed)) + self.assertClose(adjsol_obj,Ez2_unperturbed,epsilon=2e-5) + + ## compute perturbed Ez2 + Ez2_perturbed = forward_simulation(p+dp, MonitorObject.LDOS, frequencies) + + ## compare gradients + if adjsol_grad.ndim < 2: + adjsol_grad = np.expand_dims(adjsol_grad,axis=1) + adj_scale = (dp[None,:]@adjsol_grad).flatten() + fd_grad = Ez2_perturbed-Ez2_unperturbed + print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) + tol = 0.07 if mp.is_single_precision() else 0.006 + self.assertClose(adj_scale,fd_grad,epsilon=tol) def test_gradient_backpropagation(self): diff --git a/src/dft_ldos.cpp b/src/dft_ldos.cpp index c86c1c84c..f55363e45 100644 --- a/src/dft_ldos.cpp +++ b/src/dft_ldos.cpp @@ -29,6 +29,7 @@ dft_ldos::dft_ldos(double freq_min, double freq_max, int Nfreq) { for (int i = 0; i < Nfreq; ++i) Fdft[i] = Jdft[i] = 0.0; Jsum = 1.0; + saved_overall_scale = 1.0; } dft_ldos::dft_ldos(const std::vector freq_) { @@ -39,6 +40,7 @@ dft_ldos::dft_ldos(const std::vector freq_) { for (size_t i = 0; i < Nfreq; ++i) Fdft[i] = Jdft[i] = 0.0; Jsum = 1.0; + saved_overall_scale = 1.0; } dft_ldos::dft_ldos(const double *freq_, size_t Nfreq) : freq(Nfreq) { @@ -49,12 +51,13 @@ dft_ldos::dft_ldos(const double *freq_, size_t Nfreq) : freq(Nfreq) { for (size_t i = 0; i < Nfreq; ++i) Fdft[i] = Jdft[i] = 0.0; Jsum = 1.0; + saved_overall_scale = 1.0; } // |c|^2 static double abs2(complex c) { return real(c) * real(c) + imag(c) * imag(c); } -double *dft_ldos::ldos() const { +double *dft_ldos::ldos() { // we try to get the overall scale factor right (at least for a point source) // so that we can compare against the analytical formula for testing // ... in most practical cases, the scale factor won't matter because @@ -62,14 +65,14 @@ double *dft_ldos::ldos() const { // overall scale factor double Jsum_all = sum_to_all(Jsum); - double scale = 4.0 / pi // from definition of LDOS comparison to power + saved_overall_scale = 4.0 / pi // from definition of LDOS comparison to power * -0.5 // power = -1/2 Re[E* J] / (Jsum_all * Jsum_all); // normalize to unit-integral current const size_t Nfreq = freq.size(); double *sum = new double[Nfreq]; for (size_t i = 0; i < Nfreq; ++i) /* 4/pi * work done by unit dipole */ - sum[i] = scale * real(Fdft[i] * conj(Jdft[i])) / abs2(Jdft[i]); + sum[i] = saved_overall_scale * real(Fdft[i] * conj(Jdft[i])) / abs2(Jdft[i]); double *out = new double[Nfreq]; sum_to_all(sum, out, Nfreq); delete[] sum; diff --git a/src/meep.hpp b/src/meep.hpp index 3d09c6942..0dc5b5227 100644 --- a/src/meep.hpp +++ b/src/meep.hpp @@ -1412,15 +1412,17 @@ class dft_ldos { } void update(fields &f); // to be called after each timestep - double *ldos() const; // returns array of Nomega values (after last timestep) + double *ldos(); // returns array of Nomega values (after last timestep) std::complex *F() const; // returns Fdft std::complex *J() const; // returns Jdft std::vector freq; - + double overall_scale() const { return saved_overall_scale; } + private: std::complex *Fdft; // Nomega array of field * J*(x) DFT values std::complex *Jdft; // Nomega array of J(t) DFT values double Jsum; // sum of |J| over all points + double saved_overall_scale; // saved overall scale for adjoint calculation }; // dft.cpp (normally created with fields::add_dft_fields) From 94c5985271bd19ee05e19baca8bcc86cc91659ef Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 16 Jun 2022 12:31:51 -0700 Subject: [PATCH 06/26] tutorial for triangular/hexagonal lattice diffracted orders (#2103) * tutorial for triangular/hexagonal lattice diffracted orders * minor improvements in text * fix a bug in the example script * Update Mode_Decomposition.md * Update Mode_Decomposition.md Co-authored-by: Steven G. Johnson --- .../Python_Tutorials/Mode_Decomposition.md | 132 ++++++++++++++++++ doc/docs/images/triangular_lattice.png | Bin 0 -> 32400 bytes .../examples/grating2d_triangular_lattice.py | 105 ++++++++++++++ 3 files changed, 237 insertions(+) create mode 100644 doc/docs/images/triangular_lattice.png create mode 100644 python/examples/grating2d_triangular_lattice.py diff --git a/doc/docs/Python_Tutorials/Mode_Decomposition.md b/doc/docs/Python_Tutorials/Mode_Decomposition.md index 35aaad1ed..0c5be2419 100644 --- a/doc/docs/Python_Tutorials/Mode_Decomposition.md +++ b/doc/docs/Python_Tutorials/Mode_Decomposition.md @@ -611,6 +611,138 @@ tran:, 14, 0.02346054, 4, 0.0234605 flux:, 0.90830587, 0.90830748, 0.90911585, 0.00088919 ``` +Diffracted Orders of a Triangular/Hexagonal Lattice +--------------------------------------------------- + +While it is straightforward to compute the diffracted orders of a square lattice, a triangular/hexagonal lattice requires some care. This is because Meep only supports a rectilinear cell lattice. It is therefore not possible to directly simulate the unit cell of a triangular lattice. The workaround is to use a (rectilinear) *supercell* but then the diffracted orders must be defined differently in order to correspond exactly to those of the actual unit cell. This is because the supercell introduces artificial diffraction orders (forbidden by the higher symmetry of the underlying triangular lattice) that carry no power. As a demonstration, we will compute the transmitted orders of a 2D grating with triangular lattice. We will verify that only the "real" orders contain nonzero power whereas the artificial ones contain zero power. + +As shown in the left side of the figure below, the lattice vectors of a triangular lattice are $\vec{a_1} = (\Lambda,0)$ and $\vec{a_2}=(\frac{\Lambda}{2},\frac{\sqrt{3}}{2}\Lambda)$ where $\Lambda$ is the lattice periodicity. The unit cell is marked by the dotted silver line. The [reciprocal lattice vectors](https://en.wikipedia.org/wiki/Reciprocal_lattice#Two_dimensions) are $\vec{b_1}=\frac{2\pi}{\Lambda}(1,-1/\sqrt{3})$ and $\vec{b_2}=\frac{2\pi}{\Lambda}(0,2/\sqrt{3})$. An in-plane ($xy$) diffracted order of the unit cell can be defined as $\vec{k_\parallel}=m_1\vec{b_1}+m_2\vec{b_2}$ where $m_1$ and $m_2$ are integers. + +For the supercell shown in the right side of the figure, the lattice vectors are $\vec{a_1} = (\Lambda,0)$ and $\vec{a_3} = (0,\sqrt{3}\Lambda)$. Its reciprocal lattice vectors are $\vec{b'_1}=\frac{2\pi}{\Lambda}(1,0)$ and $\vec{b'_2}=\frac{2\pi}{\Lambda}(0,1/\sqrt{3})$. Note that the dot product of $\vec{b'_1}$ and $\vec{b'_2}$ is zero because they are orthogonal. An in-plane diffracted order of the supercell can be defined as $\vec{k_{SC}}=n_1\vec{b'_1}+n_2\vec{b'_2}$ where $n_1$ and $n_2$ are integers. + +Given a "real" diffracted order specified by $(m_1,m_2)$, we need to determine the equivalent order of the supercell specified by $(n_1,n_2)$ for use in the simulation when computing the mode coefficients. Setting $\vec{k_{SC}}=\vec{k_\parallel}$ and solving for the pair of equations yields: $n_1=m_1$ and $n_2=-m_1+2m_2$. + +
+![](../images/triangular_lattice.png) +
+ +To demonstrate this in practice, we will compute the power (an absolute quantity) of the $(0,1)$ order of a triangular lattice of cylindrical rods (height: 0.5 µm, radius: 0.1 µm) on a glass ($n=1.5$) substrate with periodicity of 1.0 µm. Note that this is a 3D simulation. In this example, the plane of incidence is $yz$ and the $E_x$ source therefore corresponds to the $\mathcal{S}$ polarization. + +The simulation script is in [examples/grating2d_triangular_lattice.py](https://github.com/NanoComp/meep/blob/master/python/examples/grating2d_triangular_lattice.py). + +```py +import meep as mp +import math +import numpy as np + +resolution = 100 # pixels/μm + +ng = 1.5 +glass = mp.Medium(index=ng) + +wvl = 0.5 # wavelength +fcen = 1/wvl + +# rectangular supercell +sx = 1.0 +sy = np.sqrt(3) + +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dair = 2.0 # air padding +hcyl = 0.5 # cylinder height +rcyl = 0.1 # cylinder radius + +sz = dpml+dsub+hcyl+dair+dpml + +cell_size = mp.Vector3(sx,sy,sz) + +boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] + +# periodic boundary conditions +k_point = mp.Vector3() + +src_pt = mp.Vector3(0,0,-0.5*sz+dpml) +sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.1*fcen), + size=mp.Vector3(sx,sy,0), + center=src_pt, + component=mp.Ex)] + +substrate = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), + center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), + material=glass)] + +cyl_grating = [mp.Cylinder(center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(-0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(-0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass)] + +geometry = substrate + cyl_grating + +sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k_point) + +tran_pt = mp.Vector3(0,0,0.5*sz-dpml) +tran_flux = sim.add_mode_monitor(fcen, + 0, + 1, + mp.ModeRegion(center=tran_pt, + size=mp.Vector3(sx,sy,0))) + +sim.run(until_after_sources=mp.stop_when_fields_decayed(20,mp.Ex,src_pt,1e-6)) + +# unit cell (triangular lattice) +mx = 0 # diffraction order in x direction +my = 1 # diffraction order in y direction + +# check: for diffraction orders of supercell for which +# nx = mx and ny = -mx + 2*my and thus +# only even orders should produce nonzero power +nx = mx +for ny in range(4): + kz2 = fcen**2-(nx/sx)**2-(ny/sy)**2 + if kz2 > 0: + res = sim.get_eigenmode_coefficients(tran_flux, + mp.DiffractedPlanewave((nx,ny,0), + mp.Vector3(0,1,0), + 1, + 0)) + t_coeffs = res.alpha + tran = abs(t_coeffs[0,0,0])**2 + + print("order:, {}, {}, {:.5f}".format(nx,ny,tran)) +``` + +The output lists the power in the orders labeled by $n_1$ and $n_2$. Because $n_1=m_1$ and $n_2=-m_1+2m_2$, only those supercell orders with even $n_2$ contain nonzero power whereas the odd orders (artifacts of the supercell) contain zero power. Note that the $(m_1,m_2)=(0,1)$ order of the unit cell corresponds to the $(n_1,n_2)=(0,2)$ order of the supercell. + +``` +order:, 0, 0, 1.46914 +order:, 0, 1, 0.00000 +order:, 0, 2, 0.03570 +order:, 0, 3, 0.00000 +``` + + Phase Map of a Subwavelength Binary Grating ------------------------------------------- diff --git a/doc/docs/images/triangular_lattice.png b/doc/docs/images/triangular_lattice.png new file mode 100644 index 0000000000000000000000000000000000000000..126a0c1722227dd4c4ceba4e575b8b27e9d5a991 GIT binary patch literal 32400 zcmb4rbySpV+wUL>3KA-U(h|}k(y3B{gh)3i-6A~AFc(&Esa(%pTo z+57#z@0_*HALqE%UR#HUndiCVx_@;|;By6OJRDLS1OkC4^Hkym0&z(lfxy_fiUqH@ z$sS|C|1LW|kx{t{e>|=l1;D?r**(>CL?EuTqW{C-t^a5ZFWz;M)NoR^HF0t=bTCG^ zxVW&J+gLijGPE;hw{C5|33}Kd+9vxDxdr*KcjuxEDrTe){C;H(JK7 zOIg%GM3Rr#B_IF1B&$>rG$mO`eKj@Um*V4UVwLAggx|8bF>ZYPEulpnqw@5s%+sFr z)AJ!Cxy}q*E~bqIw{6a75y!ttoI)q-@edaki6oT>pIp7?M@am2P!IwCe-h9l5ygW3 z%T?T{Ia&0N8sE_&Z=x?MqLwK@|KJ|$)BnpQ7DhI;M<(xleU}CclLx+<*)07@zl@E2 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z6%HMiGa)m76$-=+T{w3UwX!6fmR28{fIUWe^b&{USc3xiZ9Erv72hS@7dDY=e({zr zuu`U?V(jWH$hnRk&w_Z)It^FU#RxaSph5-c8J1Av%qn(VB5)Ex_Y6X43PTv8;x^Ie z_bU(0fXz%V^)lH=Seb%2$q_(AAW?;h%(0j^EDYIA2-j6kpQaAws3>FGj1GOib82n) z*<9Y)Y~{00kLs~mLtg^){Rd1o;q&ouq7AOi`xEP-ih^y&jm z$^}k1CtAlr@lulD$2wm;0ou29}jo4#sp>84)r$VatN-s5aksHCGOe7Gq>HPh~VKQ z(ajhgLZ~0(#PsOd{u=ecBY6k%L;m$PB1JjII}3!70D4^WM*h>7dzwY?JiJ2Q35ID_ zHUGVonPJnCf>Vq} 0: + res = sim.get_eigenmode_coefficients(tran_flux, + mp.DiffractedPlanewave((nx,ny,0), + mp.Vector3(0,1,0), + 1, + 0)) + t_coeffs = res.alpha + tran = abs(t_coeffs[0,0,0])**2 + + print("order:, {}, {}, {:.5f}".format(nx,ny,tran)) From 619c879be3d53a20b882b4ca0443ea2158f03bd7 Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 16 Jun 2022 12:54:04 -0700 Subject: [PATCH 07/26] unit test and tutorial for LDOS in planar cavity using cylindrical coordinates (#2082) * unit test and tutorial for LDOS in planar cavity using cylindrical coordinates * mention impact of grid resolution at Van Hove singularities and update figure * add note in example script describing effect of discretizaiton errors on cavity structure * Update Local_Density_of_States.md * Update Local_Density_of_States.md * Update Local_Density_of_States.md Co-authored-by: Steven G. Johnson --- .../Local_Density_of_States.md | 135 ++++++++++++++--- .../planar_cavity_purcell_enhancement.png | Bin 31856 -> 85449 bytes python/examples/planar_cavity_ldos.py | 127 ++++++++++++---- python/tests/test_ldos.py | 141 ++++++++++++++---- 4 files changed, 329 insertions(+), 74 deletions(-) diff --git a/doc/docs/Python_Tutorials/Local_Density_of_States.md b/doc/docs/Python_Tutorials/Local_Density_of_States.md index 088d6ea82..16859168c 100644 --- a/doc/docs/Python_Tutorials/Local_Density_of_States.md +++ b/doc/docs/Python_Tutorials/Local_Density_of_States.md @@ -10,17 +10,26 @@ $$\operatorname{LDOS}_{\ell}(\vec{x}_0,\omega)=-\frac{2}{\pi}\varepsilon(\vec{x} where the $|\hat{p}(\omega)|^2$ normalization is necessary for obtaining the power exerted by a unit-amplitude dipole assuming linear materials. In FDTD, computing the LDOS is straightforward: excite a point dipole source and accumulate the Fourier transforms of the field at a given point in space to obtain the entire LDOS spectrum in a single calculation. This is implemented in the `dft_ldos` feature which is the subject of this tutorial. +[TOC] Planar Cavity with Lossless Metallic Walls ------------------------------------------ -The spontaneous-emission rate of a point-dipole emitter in a planar cavity bounded by a lossless metallic mirror can be tuned using the thickness of the cavity. A schematic of the cavity is shown in the figure inset below. In this example, the 3D cavity consists of two mirrors separated in the *z* direction by a distance $L$. The cavity consists of a homogeneous dielectric with $n$=2.4. The dipole wavelength ($\lambda$) is 1.0 μm with horizontal polarization along the *x* axis. The Purcell enhancement factor, a dimensionless quantity defined relative to the bulk medium, can be computed analytically in terms of the cavity thickness in units of the medium wavelength ($nL/\lambda$) for this system using equation (7) of [IEEE J. Quantum Electronics, Vol. 34, pp. 71-76 (1998)](https://ieeexplore.ieee.org/abstract/document/655009). We will validate the simulated results using the analytic theory. +The spontaneous-emission rate of a point-dipole emitter in a planar cavity bounded by a lossless metallic mirror can be tuned using the thickness of the cavity. A schematic of the cavity structure is shown in the figure below. The planar cavity consists of two mirrors separated in the $z$ direction by a distance $L$. The cavity consists of a homogeneous dielectric with $n$=2.4. The dipole wavelength ($\lambda$) is 1.0 μm with polarization *parallel* to the mirrors. The Purcell enhancement factor can be computed analytically as a function of the cavity thickness expressed in units of the wavelength in the cavity medium ($nL/\lambda$) using equation 7 of [IEEE J. Quantum Electronics, Vol. 34, pp. 71-76 (1998)](https://ieeexplore.ieee.org/abstract/document/655009). We will validate the simulated results using the analytic theory. Since this system has cylindrical symmetry, we can perform an identical simulation in [cylindrical coordinates](Cylindrical_Coordinates.md) which is significantly faster because it is a 2D calculation. In this demonstration, the cavity thickness is swept over a range of 0.5 to 2.5. Below a thickness of 0.5 there are no guided modes and thus the Purcell enhancement factor is zero. -Two types of simulations are necessary for computing the Purcell enhancement factor: (1) bulk medium and (2) cavity. The `dft_ldos` featured is used to compute the LDOS in each case at a single wavelength. The Purcell enhancement factor is computed as the ratio of the LDOS measured in (2) to that from (1). Each simulation uses three mirror symmetries to reduce the size of the 3D computation by a factor of eight. The cavity is infinitely extended in the *xy* plane and thus the cell is terminated using PMLs in these two directions. Because Meep uses a default boundary condition of a perfect electric conductor, there is no need to explicitly define the boundaries in the *z* direction. The fields are timestepped until they have decayed away sufficiently due to absorption by the PMLs at the location of the pulsed source. +Two types of simulations are necessary for computing the Purcell enhancement factor: (1) bulk cavity medium and (2) cavity. The `dft_ldos` feature is used to compute the LDOS in each case. The Purcell enhancement factor is computed as the ratio of the LDOS measured in (2) to that from (1). -As shown in the plot below, the results from Meep agree well with the analytic theory. +In 3D, each simulation uses three [mirror symmetries](../Exploiting_Symmetry.md#rotations-and-reflections) to reduce the size of the computation by a factor of eight. The dipole is polarized in the $x$ direction. The cavity is infinitely extended in the $xy$ plane and the cell is therefore terminated using PMLs in $x$ and $y$. Because Meep uses a default boundary condition of a perfect-electric conductor, there is no need to explicitly define the boundaries in the $z$ direction. The fields are timestepped until they have decayed away sufficiently due to absorption by the PMLs. + +In cylindrical coordinates, the dipole is polarized in the $r$ direction. Setting up a linearly polarized source in cylindrical coordiantes is demonstrated in [Tutorial/Cylindrical Coordinates/Scattering Cross Section of a Finite Dielectric Cylinder](Cylindrical_Coordinates.md#scattering-cross-section-of-a-finite-dielectric-cylinder). However, all that is necessary in this example which involves a single point dipole rather than a planewave is one simulation involving an $E_r$ source at $r=0$ with $m=-1$. This is actually a circularly polarized source but this is sufficient because the $m=+1$ simulation produces an identical result to the $m=-1$ simulation. It is therefore not necessary to perform two separate simulations for $m=\pm 1$ in order to average the results from the left- and right-circularly polarized sources. + +One important parameter when setting up this calculation is the grid resolution. + +A key feature of the LDOS in this geometry is that it experiences discontinuities, called [Van Hove singularities](https://en.wikipedia.org/wiki/Van_Hove_singularity), any time the cavity thickness/λ passes through the cutoff for a waveguide mode, which occurs for cavity-thickness/λ values of 0.5, 1.5, 2.5, etc. (Mathematically, Van Hove singularities depend strongly on the dimensionality — it is a discontinuity in this case because the waves are propagating along two dimensions, i.e. each cutoff is a minimum in the 2d dispersion relation $\omega(k_x,k_y)$.) This discontinuity also means that the LDOS *exactly at* the cutoff thickness/λ is ill-defined and convergence with discretization can be problematic at this point. (In consequence, the LDOS *exactly* at the Van Hove discontinuity can behave erratically with resolution, and should be viewed with caution.) + +As shown in the plot below, the results from Meep for both coordinate systems agree well with the analytic theory over the entire range of values of the cavity thickness.
![](../images/planar_cavity_purcell_enhancement.png) @@ -36,28 +45,89 @@ matplotlib.use('agg') import matplotlib.pyplot as plt -resolution = 50 # pixels/μm +# important note: +# Meep may round the cell dimensions to an integer number +# of pixels which could modify the cavity structure. +resolution = 70 # pixels/μm + + dpml = 0.5 # thickness of PML L = 6.0 # length of non-PML region n = 2.4 # refractive index of surrounding medium wvl = 1.0 # wavelength (in vacuum) fcen = 1/wvl -sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ex, - center=mp.Vector3())] -symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] + +def bulk_ldos_cyl(): + sr = L+dpml + sz = L+2*dpml + cell_size = mp.Vector3(sr,0,sz) + + pml_layers = [mp.PML(dpml)] + + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Er, + center=mp.Vector3())] + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n)) + + sim.run(mp.dft_ldos(fcen,0,1), + until_after_sources=mp.stop_when_fields_decayed(20, + mp.Er, + mp.Vector3(), + 1e-6)) + + return sim.ldos_data[0] + + +def cavity_ldos_cyl(sz): + sr = L+dpml + cell_size = mp.Vector3(sr,0,sz) + + pml_layers = [mp.PML(dpml,direction=mp.R)] + + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Er, + center=mp.Vector3())] + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n)) + + sim.run(mp.dft_ldos(fcen,0,1), + until_after_sources=mp.stop_when_fields_decayed(20, + mp.Er, + mp.Vector3(), + 1e-6)) + + return sim.ldos_data[0] -def bulk_ldos(): +def bulk_ldos_3D(): s = L+2*dpml cell_size = mp.Vector3(s,s,s) pml_layers = [mp.PML(dpml)] + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Ex, + center=mp.Vector3())] + + symmetries = [mp.Mirror(direction=mp.X,phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z)] + sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml_layers, @@ -74,13 +144,21 @@ def bulk_ldos(): return sim.ldos_data[0] -def cavity_ldos(sz): +def cavity_ldos_3D(sz): sxy = L+2*dpml cell_size = mp.Vector3(sxy,sxy,sz) boundary_layers = [mp.PML(dpml,direction=mp.X), mp.PML(dpml,direction=mp.Y)] + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Ex, + center=mp.Vector3())] + + symmetries = [mp.Mirror(direction=mp.X,phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z)] + sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=boundary_layers, @@ -98,21 +176,27 @@ def cavity_ldos(sz): if __name__ == '__main__': - ldos_bulk = bulk_ldos() - print("ldos_bulk:, {:.6f}".format(ldos_bulk)) + ldos_bulk_cyl = bulk_ldos_cyl() + ldos_bulk_3D = bulk_ldos_3D() - # units of wavelength in medium + # units of wavelength in cavity medium cavity_thickness = np.arange(0.50,2.55,0.05) gap = cavity_thickness*wvl/n - ldos_cavity = np.zeros(len(cavity_thickness)) + ldos_cavity_cyl = np.zeros(len(cavity_thickness)) + ldos_cavity_3D = np.zeros(len(cavity_thickness)) for idx,g in enumerate(gap): - ldos_cavity[idx] = cavity_ldos(g) - print("ldos_cavity:, {:.3f}, {:.6f}".format(g,ldos_cavity[idx])) + ldos_cavity_cyl[idx] = cavity_ldos_cyl(g) + ldos_cavity_3D[idx] = cavity_ldos_3D(g) + print("purcell-enh:, {:.3f}, " + "{:.6f} (cyl.), {:.6f} (3D)".format(cavity_thickness[idx], + ldos_cavity_cyl[idx]/ldos_bulk_cyl, + ldos_cavity_3D[idx]/ldos_bulk_3D)) # Purcell enhancement factor (relative to bulk medium) - pe_meep = ldos_cavity/ldos_bulk + pe_meep_cyl = ldos_cavity_cyl / ldos_bulk_cyl + pe_meep_3D = ldos_cavity_3D / ldos_bulk_3D # equation 7 of reference pe_theory = (3*np.fix(cavity_thickness+0.5)/(4*cavity_thickness) + @@ -121,14 +205,17 @@ if __name__ == '__main__': (16*np.power(cavity_thickness,3))) if mp.am_master(): - plt.plot(cavity_thickness,pe_meep,'b-',label='Meep') - plt.plot(cavity_thickness,pe_theory,'r-',label='theory') + plt.plot(cavity_thickness,pe_meep_3D,'b-',label='Meep (3D)') + plt.plot(cavity_thickness,pe_meep_cyl,'r-',label='Meep (cylin.)') + plt.plot(cavity_thickness,pe_theory,'g-',label='theory') plt.plot(cavity_thickness,np.ones(len(cavity_thickness)),'k--') - plt.xlabel('cavity thickness') - plt.ylabel('Purcell enhancement factor (relative to bulk)') + plt.xlabel('cavity thickness, $nL/\lambda$') + plt.ylabel('Purcell enhancement factor') + plt.title("parallel point dipole at λ=1.0 μm in a planar cavity\n" + "with n=2.4 and lossless metallic walls") plt.axis([0.5,2.5,0.4,3.1]) plt.legend() - plt.savefig('planar_cavity_purcell_factor_vs_thickness', + plt.savefig('cavity_purcell_factor_vs_thickness.png', bbox_inches='tight') 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b/python/examples/planar_cavity_ldos.py index 068b6a5d5..ac9fd5cc5 100644 --- a/python/examples/planar_cavity_ldos.py +++ b/python/examples/planar_cavity_ldos.py @@ -1,8 +1,9 @@ -# Computes the Purcell enhancement factor of a horizontal dipole in a 3D -# homogeneous dielectric cavity with lossless metallic walls on two sides. -# The simulated result is compared with the analytic theory from +# Computes the Purcell enhancement factor of a parallel dipole in a planar +# dielectric cavity with lossless metallic walls. The result is computed in +# cylindrical and 3D coordinates and compared with the analytic theory from: # I. Abram et al., IEEE J. Quantum Electronics, Vol. 34, pp. 71-76 (1998). + import meep as mp import numpy as np import matplotlib @@ -10,28 +11,89 @@ import matplotlib.pyplot as plt -resolution = 50 # pixels/μm +# important note: +# Meep may round the cell dimensions to an integer number +# of pixels which could modify the cavity structure. +resolution = 70 # pixels/μm + + dpml = 0.5 # thickness of PML L = 6.0 # length of non-PML region n = 2.4 # refractive index of surrounding medium wvl = 1.0 # wavelength (in vacuum) fcen = 1/wvl -sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ex, - center=mp.Vector3())] -symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] +def bulk_ldos_cyl(): + sr = L+dpml + sz = L+2*dpml + cell_size = mp.Vector3(sr,0,sz) + + pml_layers = [mp.PML(dpml)] + + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Er, + center=mp.Vector3())] + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n)) + + sim.run(mp.dft_ldos(fcen,0,1), + until_after_sources=mp.stop_when_fields_decayed(20, + mp.Er, + mp.Vector3(), + 1e-6)) + + return sim.ldos_data[0] + + +def cavity_ldos_cyl(sz): + sr = L+dpml + cell_size = mp.Vector3(sr,0,sz) -def bulk_ldos(): + pml_layers = [mp.PML(dpml,direction=mp.R)] + + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Er, + center=mp.Vector3())] + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n)) + + sim.run(mp.dft_ldos(fcen,0,1), + until_after_sources=mp.stop_when_fields_decayed(20, + mp.Er, + mp.Vector3(), + 1e-6)) + + return sim.ldos_data[0] + + +def bulk_ldos_3D(): s = L+2*dpml cell_size = mp.Vector3(s,s,s) pml_layers = [mp.PML(dpml)] + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Ex, + center=mp.Vector3())] + + symmetries = [mp.Mirror(direction=mp.X,phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z)] + sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml_layers, @@ -48,13 +110,21 @@ def bulk_ldos(): return sim.ldos_data[0] -def cavity_ldos(sz): +def cavity_ldos_3D(sz): sxy = L+2*dpml cell_size = mp.Vector3(sxy,sxy,sz) boundary_layers = [mp.PML(dpml,direction=mp.X), mp.PML(dpml,direction=mp.Y)] + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), + component=mp.Ex, + center=mp.Vector3())] + + symmetries = [mp.Mirror(direction=mp.X,phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z)] + sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=boundary_layers, @@ -72,21 +142,27 @@ def cavity_ldos(sz): if __name__ == '__main__': - ldos_bulk = bulk_ldos() - print("ldos_bulk:, {:.6f}".format(ldos_bulk)) + ldos_bulk_cyl = bulk_ldos_cyl() + ldos_bulk_3D = bulk_ldos_3D() # units of wavelength in medium cavity_thickness = np.arange(0.50,2.55,0.05) gap = cavity_thickness*wvl/n - ldos_cavity = np.zeros(len(cavity_thickness)) + ldos_cavity_cyl = np.zeros(len(cavity_thickness)) + ldos_cavity_3D = np.zeros(len(cavity_thickness)) for idx,g in enumerate(gap): - ldos_cavity[idx] = cavity_ldos(g) - print("ldos_cavity:, {:.3f}, {:.6f}".format(g,ldos_cavity[idx])) + ldos_cavity_cyl[idx] = cavity_ldos_cyl(g) + ldos_cavity_3D[idx] = cavity_ldos_3D(g) + print("purcell-enh:, {:.3f}, " + "{:.6f} (cyl.), {:.6f} (3D)".format(cavity_thickness[idx], + ldos_cavity_cyl[idx]/ldos_bulk_cyl, + ldos_cavity_3D[idx]/ldos_bulk_3D)) # Purcell enhancement factor (relative to bulk medium) - pe_meep = ldos_cavity/ldos_bulk + pe_meep_cyl = ldos_cavity_cyl / ldos_bulk_cyl + pe_meep_3D = ldos_cavity_3D / ldos_bulk_3D # equation 7 of reference pe_theory = (3*np.fix(cavity_thickness+0.5)/(4*cavity_thickness) + @@ -95,14 +171,15 @@ def cavity_ldos(sz): (16*np.power(cavity_thickness,3))) if mp.am_master(): - plt.plot(cavity_thickness,pe_meep,'b-',label='Meep') - plt.plot(cavity_thickness,pe_theory,'r-',label='theory') + plt.plot(cavity_thickness,pe_meep_3D,'b-',label='Meep (3D)') + plt.plot(cavity_thickness,pe_meep_cyl,'r-',label='Meep (cylin.)') + plt.plot(cavity_thickness,pe_theory,'g-',label='theory') plt.plot(cavity_thickness,np.ones(len(cavity_thickness)),'k--') - plt.xlabel('cavity thickness') - plt.ylabel('Purcell enhancement factor (relative to bulk)') - plt.title("horizontal point dipole at λ=1.0 μm in a cavity with" - "\n n=2.4 and lossless metallic walls on two sides") + plt.xlabel('cavity thickness, $nL/\lambda$') + plt.ylabel('Purcell enhancement factor') + plt.title("planar point dipole at λ=1.0 μm in a planar cavity\n" + "with n=2.4 and lossless metallic walls") plt.axis([0.5,2.5,0.4,3.1]) plt.legend() - plt.savefig('cavity_purcell_factor_vs_thickness', + plt.savefig('cavity_purcell_factor_vs_thickness.png', bbox_inches='tight') diff --git a/python/tests/test_ldos.py b/python/tests/test_ldos.py index db86ae15f..9807ad6f8 100644 --- a/python/tests/test_ldos.py +++ b/python/tests/test_ldos.py @@ -2,42 +2,99 @@ import numpy as np import meep as mp -# Computes the Purcell enhancement factor of a horizontal dipole in a 3D -# homogeneous dielectric cavity with lossless metallic walls on two sides. -# The simulated result is validated using analytic theory from +# Computes the Purcell enhancement factor of a parallel dipole in a planar +# dielectric cavity with lossless metallic walls. The result is computed in +# cylindrical and 3D coordinates and validated using analytic theory from: # I. Abram et al., IEEE J. Quantum Electronics, Vol. 34, pp. 71-76 (1998). + class TestLDOS(unittest.TestCase): @classmethod def setUp(cls): - cls.resolution = 20 # pixels/μm + cls.resolution = 25 # pixels/μm cls.dpml = 0.5 # thickness of PML cls.L = 6.0 # length of non-PML region cls.n = 2.4 # refractive index of surrounding medium cls.wvl = 1.0 # wavelength (in vacuum) cls.fcen = 1/cls.wvl - cls.sources = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=0.2*cls.fcen), - component=mp.Ex, - center=mp.Vector3())] - cls.symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] + + def bulk_ldos_cyl(self): + sr = self.L+self.dpml + sz = self.L+2*self.dpml + cell_size = mp.Vector3(sr,0,sz) + + pml_layers = [mp.PML(self.dpml)] + + sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), + component=mp.Er, + center=mp.Vector3())] + + sim = mp.Simulation(resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=self.n)) + + sim.run(mp.dft_ldos(self.fcen,0,1), + until_after_sources=mp.stop_when_fields_decayed(20, + mp.Er, + mp.Vector3(), + 1e-6)) + + return sim.ldos_data[0] + + + def cavity_ldos_cyl(self,sz): + sr = self.L+self.dpml + cell_size = mp.Vector3(sr,0,sz) + + pml_layers = [mp.PML(self.dpml,direction=mp.R)] + + sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), + component=mp.Er, + center=mp.Vector3())] + + sim = mp.Simulation(resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=self.n)) + + sim.run(mp.dft_ldos(self.fcen,0,1), + until_after_sources=mp.stop_when_fields_decayed(20, + mp.Er, + mp.Vector3(), + 1e-6)) + + return sim.ldos_data[0] - def bulk_ldos(self): + def bulk_ldos_3D(self): s = self.L+2*self.dpml cell_size = mp.Vector3(s,s,s) pml_layers = [mp.PML(self.dpml)] + sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), + component=mp.Ex, + center=mp.Vector3())] + + symmetries = [mp.Mirror(direction=mp.X,phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z)] + sim = mp.Simulation(resolution=self.resolution, cell_size=cell_size, boundary_layers=pml_layers, - sources=self.sources, - symmetries=self.symmetries, + sources=sources, + symmetries=symmetries, default_material=mp.Medium(index=self.n)) sim.run(mp.dft_ldos(self.fcen,0,1), @@ -49,18 +106,26 @@ def bulk_ldos(self): return sim.ldos_data[0] - def cavity_ldos(self,sz): + def cavity_ldos_3D(self,sz): sxy = self.L+2*self.dpml cell_size = mp.Vector3(sxy,sxy,sz) boundary_layers = [mp.PML(self.dpml,direction=mp.X), mp.PML(self.dpml,direction=mp.Y)] + sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), + component=mp.Ex, + center=mp.Vector3())] + + symmetries = [mp.Mirror(direction=mp.X,phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z)] + sim = mp.Simulation(resolution=self.resolution, cell_size=cell_size, boundary_layers=boundary_layers, - sources=self.sources, - symmetries=self.symmetries, + sources=sources, + symmetries=symmetries, default_material=mp.Medium(index=self.n)) sim.run(mp.dft_ldos(ldos=mp.Ldos(self.fcen,0,1)), @@ -72,26 +137,52 @@ def cavity_ldos(self,sz): return sim.ldos_data[0] - def test_ldos(self): - ldos_bulk = self.bulk_ldos() - print("ldos_bulk:, {:.6f}".format(ldos_bulk)) + def purcell_enh_theory(self,c): + # equation 7 of reference + return (3*np.fix(c+0.5)/(4*c) + + (4*np.power(np.fix(c+0.5),3) - + np.fix(c+0.5))/(16*np.power(c,3))) + + + def test_ldos_cyl(self): + ldos_bulk = self.bulk_ldos_cyl() + + # not a Van Hove singularity + cavity_thickness = 1.63 + gap = cavity_thickness*self.wvl/self.n + + ldos_cavity = self.cavity_ldos_cyl(gap) + + # Purcell enhancement factor (relative to bulk medium) + pe_meep = ldos_cavity/ldos_bulk + + pe_theory = self.purcell_enh_theory(cavity_thickness) + + rel_err = abs(pe_meep-pe_theory)/pe_theory + + print("ldos-cyl:, {:.6f} (Meep), {:.6f} (theory), " + "{:.6f} (error)".format(pe_meep,pe_theory,rel_err)) + + self.assertAlmostEqual(pe_meep, pe_theory, delta=0.1) + + def test_ldos_3D(self): + ldos_bulk = self.bulk_ldos_3D() + + # not a Van Hove singularity cavity_thickness = 0.75 gap = cavity_thickness*self.wvl/self.n - ldos_cavity = self.cavity_ldos(gap) + ldos_cavity = self.cavity_ldos_3D(gap) # Purcell enhancement factor (relative to bulk medium) pe_meep = ldos_cavity/ldos_bulk - # equation 7 of reference - pe_theory = (3*np.fix(cavity_thickness+0.5)/(4*cavity_thickness) + - (4*np.power(np.fix(cavity_thickness+0.5),3) - - np.fix(cavity_thickness+0.5))/(16*np.power(cavity_thickness,3))) + pe_theory = self.purcell_enh_theory(cavity_thickness) rel_err = abs(pe_meep-pe_theory)/pe_theory - print("ldos:, {:.6f} (Meep), {:.6f} (theory), " + print("ldos-3D:, {:.6f} (Meep), {:.6f} (theory), " "{:.6f} (error)".format(pe_meep,pe_theory,rel_err)) self.assertAlmostEqual(pe_meep, pe_theory, delta=0.1) From 5aac79a41c84cb14a64a844379036a12c14fc51d Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 23 Jun 2022 12:16:08 -0700 Subject: [PATCH 08/26] fix bug in mode coefficients with DiffractedPlanewave and nonzero k_point (#2114) --- python/tests/test_mode_decomposition.py | 204 +++++++++++++++++++++++- src/mpb.cpp | 15 +- 2 files changed, 213 insertions(+), 6 deletions(-) diff --git a/python/tests/test_mode_decomposition.py b/python/tests/test_mode_decomposition.py index 373f086ea..1c5cb254c 100644 --- a/python/tests/test_mode_decomposition.py +++ b/python/tests/test_mode_decomposition.py @@ -1,6 +1,9 @@ import unittest import numpy as np import meep as mp +import math +import cmath + class TestModeDecomposition(unittest.TestCase): @@ -159,7 +162,7 @@ def test_oblique_waveguide_backward_mode(self): def test_grating_3d(self): - """Unit test for mode decomposition in 3d. + """Unit test for mode decomposition in 3d with zero k_point. Verifies that the reflectance and transmittance in the z direction at a single wavelength for a unit cell of a @@ -324,5 +327,204 @@ def test_grating_3d(self): self.assertAlmostEqual(Rsum+Tsum,1.00,places=1) + def test_triangular_lattice_oblique(self): + """Unit test for mode decomposition in 3d with nonzero k_point. + + Verifies that the reflectance and transmittance in the z + direction at a single wavelength for a supercell of a + binary grating with triangular lattice using a incident oblique + planewave is equivalent to the sum of the Poynting flux + (normalized by the flux of the input source) for all the + individual reflected and transmitted diffracted orders. + """ + resolution = 25 + + ng = 1.5 + glass = mp.Medium(index=ng) + + wvl = 0.5 + fcen = 1/wvl + + dpml = 1.0 + dsub = 2.0 + dair = 2.0 + rcyl = 0.1 + hcyl = 0.3 + + a = 0.6 + + sx = a + sy = a*np.sqrt(3) + + sz = dpml+dsub+hcyl+dair+dpml + + cell_size = mp.Vector3(sx,sy,sz) + + boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] + + # plane of incidence is yz + # 0° is +z with CCW rotation about x + theta = math.radians(50.0) + + if theta == 0: + k = mp.Vector3() + else: + k = mp.Vector3(0,0,fcen).rotate(mp.Vector3(1,0,0),theta) + + def pw_amp(k,x0): + def _pw_amp(x): + return cmath.exp(1j*2*math.pi*k.dot(x+x0)) + return _pw_amp + + src_pt = mp.Vector3(0,0,-0.5*sz+dpml) + src_cmpt = mp.Ex # S-pol: Ex / P-pol: Ey + sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.1*fcen), + size=mp.Vector3(sx,sy,0), + center=src_pt, + component=src_cmpt, + amp_func=pw_amp(k,src_pt))] + + symmetries = [mp.Mirror(direction=mp.X, phase=-1 if src_cmpt==mp.Ex else +1)] + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=glass, + boundary_layers=boundary_layers, + k_point=k, + symmetries=symmetries) + + refl_pt = mp.Vector3(0,0,-0.5*sz+dpml+0.5*dsub) + refl_flux = sim.add_mode_monitor(fcen, + 0, + 1, + mp.ModeRegion(center=refl_pt, + size=mp.Vector3(sx,sy,0))) + + stop_cond = mp.stop_when_fields_decayed(25,src_cmpt,src_pt,1e-6) + sim.run(until_after_sources=stop_cond) + + input_flux = mp.get_fluxes(refl_flux)[0] + input_flux_data = sim.get_flux_data(refl_flux) + + sim.reset_meep() + + substrate = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), + center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), + material=glass)] + + grating = [mp.Cylinder(center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(-0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass), + mp.Cylinder(center=mp.Vector3(-0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), + radius=rcyl, + height=hcyl, + material=glass)] + + geometry = substrate + grating + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k, + symmetries=symmetries) + + refl_flux = sim.add_mode_monitor(fcen, + 0, + 1, + mp.ModeRegion(center=refl_pt, + size=mp.Vector3(sx,sy,0))) + + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_pt = mp.Vector3(0,0,0.5*sz-dpml) + tran_flux = sim.add_mode_monitor(fcen, + 0, + 1, + mp.ModeRegion(center=tran_pt, + size=mp.Vector3(sx,sy,0))) + + sim.run(until_after_sources=stop_cond) + + Rsum = 0 + Tsum = 0 + m = 5 + tol = 1e-6 + for nx in range(-m,m+1): + for ny in range(-m,m+1): + kz2 = (ng*fcen)**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 + if kz2 > 0: + Rpol = 0 + for S_pol in [True, False]: + res = sim.get_eigenmode_coefficients(refl_flux, + mp.DiffractedPlanewave((nx,ny,0), + mp.Vector3(0,1,0), + 1 if S_pol else 0, + 0 if S_pol else 1)) + + coeffs = res.alpha + refl = abs(coeffs[0,0,1])**2 / input_flux + + if refl > tol: + if (nx + ny) % 2 == 0: + Rpol += refl + else: + print("WARNING: artificial order contains nonzero power") + print("refl:, {}, {:2d}, {:2d}, {:.5f}".format('S' if S_pol else 'P',nx,ny,refl)) + + Rsum += Rpol + + kz2 = fcen**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 + if kz2 > 0: + Tpol = 0 + for S_pol in [True, False]: + res = sim.get_eigenmode_coefficients(tran_flux, + mp.DiffractedPlanewave((nx,ny,0), + mp.Vector3(0,1,0), + 1 if S_pol else 0, + 0 if S_pol else 1)) + coeffs = res.alpha + tran = abs(coeffs[0,0,0])**2 / input_flux + + if tran > tol: + if (nx + ny) % 2 == 0: + Tpol += tran + else: + print("WARNING: artificial order contains nonzero power") + print("tran:, {}, {:2d}, {:2d}, {:.5f}".format('S' if S_pol else 'P',nx,ny,tran)) + + Tsum += Tpol + + + Rflux = -mp.get_fluxes(refl_flux)[0] / input_flux + err = abs(Rflux-Rsum)/Rflux + print("refl:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Rflux,Rsum,err)) + + Tflux = mp.get_fluxes(tran_flux)[0] / input_flux + err = abs(Tflux-Tsum)/Tflux + print("tran:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Tflux,Tsum,err)) + + ## to obtain agreement for two decimal digits, + ## the resolution must be increased to 100 + self.assertAlmostEqual(Rsum,Rflux,places=1) + self.assertAlmostEqual(Tsum,Tflux,places=2) + self.assertAlmostEqual(Rsum+Tsum,1.00,places=1) + + if __name__ == '__main__': unittest.main() diff --git a/src/mpb.cpp b/src/mpb.cpp index 63f701a44..db63ccf9a 100644 --- a/src/mpb.cpp +++ b/src/mpb.cpp @@ -334,9 +334,16 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c // if the mode region extends over the full computational grid and we are bloch-periodic // in any direction, set the corresponding component of the eigenmode initial-guess // k-vector to be the (real part of the) bloch vector in that direction. + grid_volume eig_gv; + if (eig_vol.dim == D1) + eig_gv = vol1d(eig_vol.in_direction(Z), a); + else if (eig_vol.dim == D2) + eig_gv = vol2d(eig_vol.in_direction(X), eig_vol.in_direction(Y), a); + else + eig_gv = vol3d(eig_vol.in_direction(X), eig_vol.in_direction(Y), eig_vol.in_direction(Z), a); vec kpoint(_kpoint); LOOP_OVER_DIRECTIONS(v.dim, dd) { - if (dd != d && float(eig_vol.in_direction(dd)) == float(v.in_direction(dd))) + if (dd != d && eig_gv.num_direction(dd) == user_volume.num_direction(dd)) if (boundaries[High][dd] == Periodic && boundaries[Low][dd] == Periodic) kpoint.set_direction(dd, real(k[dd])); } @@ -344,12 +351,10 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c bool empty_dim[3] = {false, false, false}; // special case: 2d cell in x and y with non-zero kz - if ((eig_vol.dim == D3) && (float(v.in_direction(Z)) == float(1 / a)) && + if ((v.dim == D3) && (float(v.in_direction(Z)) == float(1 / a)) && (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && - (kpoint.z() == 0) && (real(k[Z]) != 0)) { - kpoint.set_direction(Z, real(k[Z])); + (real(k[Z]) != 0)) empty_dim[2] = true; - } if (resolution <= 0.0) resolution = 2 * gv.a; // default to twice resolution int n[3], local_N, N_start, alloc_N, mesh_size[3] = {1, 1, 1}; From 405b7e607f87d7dd4a81baf3d5473aa4c28e7a4a Mon Sep 17 00:00:00 2001 From: Wenchao Ma <60903466+mawc2019@users.noreply.github.com> Date: Thu, 23 Jun 2022 16:25:48 -0400 Subject: [PATCH 09/26] Fix FourierFields adjoint for D and B fields in isotropic media (#2095) * deal with D and B fields * add unit test * adjust tolerance in unit test * adjust tolerance further * adjust tolerance further --- python/tests/test_adjoint_solver.py | 35 +++++++++++++++-------------- src/dft.cpp | 17 +++++++++++--- 2 files changed, 32 insertions(+), 20 deletions(-) diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index 39fdbfab4..53a97d123 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -204,7 +204,7 @@ def forward_simulation_complex_fields(design_params, frequencies=None): material=matgrid)] sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, + cell_size=cell_size,default_material=silicon, k_point=k_point, boundary_layers=pml_x, sources=pt_source, @@ -213,7 +213,7 @@ def forward_simulation_complex_fields(design_params, frequencies=None): if not frequencies: frequencies = [fcen] - mode = sim.add_dft_fields([mp.Ez], + mode = sim.add_dft_fields([mp.Dz], frequencies, center=mp.Vector3(0.9), size=mp.Vector3(0.2,0.5), @@ -221,15 +221,15 @@ def forward_simulation_complex_fields(design_params, frequencies=None): sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Ez2 = [] + Dz2 = [] for f in range(len(frequencies)): - Ez_dft = sim.get_dft_array(mode, mp.Ez, f) - Ez2.append(np.power(np.abs(Ez_dft[3,9]),2)) - Ez2 = np.array(Ez2) + Dz_dft = sim.get_dft_array(mode, mp.Dz, f) + Dz2.append(np.power(np.abs(Dz_dft[3,9]),2)) + Dz2 = np.array(Dz2) sim.reset_meep() - return Ez2 + return Dz2 def adjoint_solver_complex_fields(design_params, frequencies=None): @@ -249,7 +249,7 @@ def adjoint_solver_complex_fields(design_params, frequencies=None): material=matgrid)] sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, + cell_size=cell_size,default_material=silicon, k_point=k_point, boundary_layers=pml_x, sources=pt_source, @@ -261,7 +261,7 @@ def adjoint_solver_complex_fields(design_params, frequencies=None): obj_list = [mpa.FourierFields(sim, mp.Volume(center=mp.Vector3(0.9), size=mp.Vector3(0.2,0.5)), - mp.Ez)] + mp.Dz)] def J(dft_mon): return npa.power(npa.abs(dft_mon[:,3,9]),2) @@ -505,23 +505,24 @@ def test_complex_fields(self): ## compute gradient using adjoint solver adjsol_obj, adjsol_grad = adjoint_solver_complex_fields(p, frequencies) - ## compute unperturbed |Ez|^2 - Ez2_unperturbed = forward_simulation_complex_fields(p, frequencies) + ## compute unperturbed |Dz|^2 + Dz2_unperturbed = forward_simulation_complex_fields(p, frequencies) ## compare objective results - print("Ez2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,Ez2_unperturbed)) - self.assertClose(adjsol_obj,Ez2_unperturbed,epsilon=1e-6) + print("Dz2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,Dz2_unperturbed)) + tol = 1e-5 if mp.is_single_precision() else 1e-6 + self.assertClose(adjsol_obj,Dz2_unperturbed,epsilon=tol) - ## compute perturbed |Ez|^2 - Ez2_perturbed = forward_simulation_complex_fields(p+dp, frequencies) + ## compute perturbed |Dz|^2 + Dz2_perturbed = forward_simulation_complex_fields(p+dp, frequencies) ## compare gradients if adjsol_grad.ndim < 2: adjsol_grad = np.expand_dims(adjsol_grad,axis=1) adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = Ez2_perturbed-Ez2_unperturbed + fd_grad = Dz2_perturbed-Dz2_unperturbed print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.018 if mp.is_single_precision() else 0.002 + tol = 0.06 if mp.is_single_precision() else 0.002 self.assertClose(adj_scale,fd_grad,epsilon=tol) def test_damping(self): diff --git a/src/dft.cpp b/src/dft.cpp index bc18c2fe2..d7780e090 100644 --- a/src/dft.cpp +++ b/src/dft.cpp @@ -1468,7 +1468,10 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher std::vector idx_arr; std::vector > amp_arr; std::complex EH0 = std::complex(0,0); - sourcedata temp_struct = {component(f->c), idx_arr, f->fc->chunk_idx, amp_arr}; + component c = component(f->c); + direction cd = component_direction(c); + sourcedata temp_struct = {c, idx_arr, f->fc->chunk_idx, amp_arr}; + int position_array[3] = {0, 0, 0}; // array indicating the position of a point relative to the minimum corner of the monitor LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { @@ -1491,7 +1494,11 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher temp_struct.idx_arr.push_back(idx); for (size_t i = 0; i < Nfreq; ++i) { EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]; - if (is_E_or_D(temp_struct.near_fd_comp)) EH0 *= -1; + + if (is_electric(c)) EH0 *= -1; + if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; + if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; + EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); } @@ -1503,7 +1510,11 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher temp_struct.idx_arr.push_back(site_ind[j]); for (size_t i = 0; i < Nfreq; ++i) { EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]*0.25; // split the amplitude of the adjoint source into four parts - if (is_E_or_D(temp_struct.near_fd_comp)) EH0 *= -1; + + if (is_electric(c)) EH0 *= -1; + if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; + if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; + EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); } From 5e426857da379618891d185c68b27e112f68f718 Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 30 Jun 2022 12:32:54 -0700 Subject: [PATCH 10/26] fix Bloch-periodic boundary condition in unit test for mode decomposition and triangular lattice (#2120) --- python/tests/test_mode_decomposition.py | 48 +++++++++++++++---------- 1 file changed, 30 insertions(+), 18 deletions(-) diff --git a/python/tests/test_mode_decomposition.py b/python/tests/test_mode_decomposition.py index 1c5cb254c..6d0262475 100644 --- a/python/tests/test_mode_decomposition.py +++ b/python/tests/test_mode_decomposition.py @@ -330,14 +330,13 @@ def test_grating_3d(self): def test_triangular_lattice_oblique(self): """Unit test for mode decomposition in 3d with nonzero k_point. - Verifies that the reflectance and transmittance in the z - direction at a single wavelength for a supercell of a - binary grating with triangular lattice using a incident oblique - planewave is equivalent to the sum of the Poynting flux - (normalized by the flux of the input source) for all the - individual reflected and transmitted diffracted orders. + Verifies that the sum of the diffraction efficiencies of all + the reflected and transmitted orders of a binary grating with + triangular lattice given an oblique planewave incident from + within the high-index medium is equivalent to the reflectance and + transmittance, respectively, obtained using the Poynting flux. """ - resolution = 25 + resolution = 30 ng = 1.5 glass = mp.Medium(index=ng) @@ -364,12 +363,15 @@ def test_triangular_lattice_oblique(self): # plane of incidence is yz # 0° is +z with CCW rotation about x - theta = math.radians(50.0) + theta = math.radians(34.6) if theta == 0: k = mp.Vector3() else: - k = mp.Vector3(0,0,fcen).rotate(mp.Vector3(1,0,0),theta) + # The planewave source is incident from within the high-index + # medium which means ω = c|k|/n where n is the index of medium. + # In Meep units (c=1), this implies |k| = nω. + k = mp.Vector3(0,0,ng*fcen).rotate(mp.Vector3(1,0,0),theta) def pw_amp(k,x0): def _pw_amp(x): @@ -467,6 +469,11 @@ def _pw_amp(x): tol = 1e-6 for nx in range(-m,m+1): for ny in range(-m,m+1): + # convert supercell order to unit cell order + mx = nx + my = (nx + ny) // 2 + + # consider only propagating modes in high-index medium kz2 = (ng*fcen)**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 if kz2 > 0: Rpol = 0 @@ -480,15 +487,19 @@ def _pw_amp(x): coeffs = res.alpha refl = abs(coeffs[0,0,1])**2 / input_flux + pol_str = 'S' if S_pol else 'P' + if refl > tol: + # determine whether diffracted order is for the unit cell or super cell if (nx + ny) % 2 == 0: Rpol += refl + print("refl:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format(pol_str,mx,my,refl)) else: - print("WARNING: artificial order contains nonzero power") - print("refl:, {}, {:2d}, {:2d}, {:.5f}".format('S' if S_pol else 'P',nx,ny,refl)) + print("refl:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format(pol_str,nx,ny,refl)) Rsum += Rpol + # consider only propagating modes in air kz2 = fcen**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 if kz2 > 0: Tpol = 0 @@ -501,12 +512,15 @@ def _pw_amp(x): coeffs = res.alpha tran = abs(coeffs[0,0,0])**2 / input_flux + pol_str = 'S' if S_pol else 'P' + if tran > tol: + # determine whether diffracted order is for the unit cell or super cell if (nx + ny) % 2 == 0: Tpol += tran + print("tran:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format(pol_str,mx,my,tran)) else: - print("WARNING: artificial order contains nonzero power") - print("tran:, {}, {:2d}, {:2d}, {:.5f}".format('S' if S_pol else 'P',nx,ny,tran)) + print("tran:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format(pol_str,nx,ny,tran)) Tsum += Tpol @@ -519,11 +533,9 @@ def _pw_amp(x): err = abs(Tflux-Tsum)/Tflux print("tran:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Tflux,Tsum,err)) - ## to obtain agreement for two decimal digits, - ## the resolution must be increased to 100 - self.assertAlmostEqual(Rsum,Rflux,places=1) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=1) + self.assertAlmostEqual(Rsum,Rflux,places=3) + self.assertAlmostEqual(Tsum,Tflux,places=3) + self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) if __name__ == '__main__': From 0f88943b892afdb381efda3d753ab2f7b7738b81 Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Wed, 13 Jul 2022 10:02:12 -0700 Subject: [PATCH 11/26] improvements to unit test for adjoint solver (#2127) --- python/tests/test_adjoint_solver.py | 941 ++++++++++++---------------- 1 file changed, 410 insertions(+), 531 deletions(-) diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index 53a97d123..f7db4d152 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -10,581 +10,460 @@ from enum import Enum from utils import ApproxComparisonTestCase -MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT LDOS') - -resolution = 30 - -silicon = mp.Medium(epsilon=12) -sapphire = mp.Medium(epsilon_diag=(10.225,10.225,9.95), - epsilon_offdiag=(-0.825,-0.55*np.sqrt(3/2),0.55*np.sqrt(3/2))) - -sxy = 5.0 -cell_size = mp.Vector3(sxy,sxy,0) - -dpml = 1.0 -pml_xy = [mp.PML(thickness=dpml)] -pml_x = [mp.PML(thickness=dpml,direction=mp.X)] - -eig_parity = mp.EVEN_Y + mp.ODD_Z - -design_region_size = mp.Vector3(1.5,1.5) -design_region_resolution = int(2*resolution) -Nx, Ny = int(design_region_size.x*design_region_resolution), int(design_region_size.y*design_region_resolution) - -## ensure reproducible results -rng = np.random.RandomState(9861548) - -## random design region -p = 0.5*rng.rand(Nx*Ny) - -## random epsilon perturbation for design region -deps = 1e-5 -dp = deps*rng.rand(Nx*Ny) - -w = 1.0 -waveguide_geometry = [mp.Block(material=silicon, - center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf))] - -fcen = 1/1.55 -df = 0.2*fcen -wvg_source = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(-0.5*sxy+dpml+0.1,0), - size=mp.Vector3(0,sxy-2*dpml), - eig_parity=eig_parity)] - -pt_source = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(-0.5*sxy+dpml,0), - size=mp.Vector3(), - component=mp.Ez)] - -line_source = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(-0.85,0), - size=mp.Vector3(0,sxy-2*dpml), - component=mp.Ez)] - -k_point = mp.Vector3(0.23,-0.38) - - -def forward_simulation(design_params, mon_type, frequencies=None, mat2=silicon): - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mat2, - weights=design_params.reshape(Nx,Ny)) - - matgrid_geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(design_region_size.x,design_region_size.y,0), - material=matgrid)] - - geometry = waveguide_geometry + matgrid_geometry - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_xy, - sources=wvg_source, - geometry=geometry) - - if not frequencies: - frequencies = [fcen] - - if mon_type.name == 'LDOS': - sim.change_sources(line_source) - sim.run(mp.dft_ldos(frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=50.0, decay_by = 1e-11)) - sim.reset_meep() - return np.array(sim.ldos_data) - - if mon_type.name == 'EIGENMODE': - mode = sim.add_mode_monitor(frequencies, - mp.ModeRegion(center=mp.Vector3(0.5*sxy-dpml-0.1), - size=mp.Vector3(0,sxy-2*dpml,0)), - yee_grid=True, - eig_parity=eig_parity) - elif mon_type.name == 'DFT': - mode = sim.add_dft_fields([mp.Ez], - frequencies, - center=mp.Vector3(1.25), - size=mp.Vector3(0.25,1,0), - yee_grid=False) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - if mon_type.name == 'EIGENMODE': - coeff = sim.get_eigenmode_coefficients(mode,[1],eig_parity).alpha[0,:,0] - S12 = np.power(np.abs(coeff),2) - elif mon_type.name == 'DFT': - Ez2 = [] - for f in range(len(frequencies)): - Ez_dft = sim.get_dft_array(mode, mp.Ez, f) - Ez2.append(np.power(np.abs(Ez_dft[4,10]),2)) - Ez2 = np.array(Ez2) - - sim.reset_meep() - - if mon_type.name == 'EIGENMODE': - return S12 - elif mon_type.name == 'DFT': - return Ez2 - - -def adjoint_solver(design_params, mon_type, frequencies=None, mat2=silicon): - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mat2, - weights=np.ones((Nx,Ny))) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(design_region_size.x, - design_region_size.y, - 0))) - - matgrid_geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] - - geometry = waveguide_geometry + matgrid_geometry - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_xy, - sources=wvg_source, - geometry=geometry) - - if not frequencies: - frequencies = [fcen] - - if mon_type.name == 'EIGENMODE': - obj_list = [mpa.EigenmodeCoefficient(sim, - mp.Volume(center=mp.Vector3(0.5*sxy-dpml-0.1), - size=mp.Vector3(0,sxy-2*dpml,0)), - 1, - eig_parity=eig_parity)] - - def J(mode_mon): - return npa.power(npa.abs(mode_mon),2) - elif mon_type.name == 'DFT': - obj_list = [mpa.FourierFields(sim, - mp.Volume(center=mp.Vector3(1.25), - size=mp.Vector3(0.25,1,0)), - mp.Ez)] - - def J(mode_mon): - return npa.power(npa.abs(mode_mon[:,4,10]),2) - elif mon_type.name == 'LDOS': - sim.change_sources(line_source) - obj_list = [mpa.LDOS(sim)] - - def J(mode_mon): - return npa.array(mode_mon) - - opt = mpa.OptimizationProblem( - simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies) - - f, dJ_du = opt([design_params]) - - sim.reset_meep() - - return f, dJ_du - - -def forward_simulation_complex_fields(design_params, frequencies=None): - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - silicon, - weights=design_params.reshape(Nx,Ny)) - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(design_region_size.x, - design_region_size.y, - 0), - material=matgrid)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size,default_material=silicon, - k_point=k_point, - boundary_layers=pml_x, - sources=pt_source, - geometry=geometry) - - if not frequencies: - frequencies = [fcen] - - mode = sim.add_dft_fields([mp.Dz], - frequencies, - center=mp.Vector3(0.9), - size=mp.Vector3(0.2,0.5), - yee_grid=False) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - Dz2 = [] - for f in range(len(frequencies)): - Dz_dft = sim.get_dft_array(mode, mp.Dz, f) - Dz2.append(np.power(np.abs(Dz_dft[3,9]),2)) - Dz2 = np.array(Dz2) - - sim.reset_meep() - - return Dz2 - - -def adjoint_solver_complex_fields(design_params, frequencies=None): - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - silicon, - weights=np.ones((Nx,Ny))) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(design_region_size.x, - design_region_size.y, - 0))) - - geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size,default_material=silicon, - k_point=k_point, - boundary_layers=pml_x, - sources=pt_source, - geometry=geometry) - - if not frequencies: - frequencies = [fcen] - - obj_list = [mpa.FourierFields(sim, - mp.Volume(center=mp.Vector3(0.9), - size=mp.Vector3(0.2,0.5)), - mp.Dz)] - - def J(dft_mon): - return npa.power(npa.abs(dft_mon[:,3,9]),2) - - opt = mpa.OptimizationProblem( - simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies) - - f, dJ_du = opt([design_params]) - - sim.reset_meep() - - return f, dJ_du - -def forward_simulation_damping(design_params, frequencies=None, mat2=silicon): - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mat2, - weights=design_params.reshape(Nx,Ny), - damping = 3.14*fcen) - - matgrid_geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(design_region_size.x,design_region_size.y,0), - material=matgrid)] - - geometry = waveguide_geometry + matgrid_geometry - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_xy, - sources=wvg_source, - geometry=geometry) - - if not frequencies: - frequencies = [fcen] - - mode = sim.add_mode_monitor(frequencies, - mp.ModeRegion(center=mp.Vector3(0.5*sxy-dpml-0.1), - size=mp.Vector3(0,sxy-2*dpml,0)), - yee_grid=True, - eig_parity=eig_parity) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - - coeff = sim.get_eigenmode_coefficients(mode,[1],eig_parity).alpha[0,:,0] - S12 = np.power(np.abs(coeff),2) - sim.reset_meep() - return S12 - -def adjoint_solver_damping(design_params, frequencies=None, mat2=silicon): - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mat2, - weights=np.ones((Nx,Ny)), - damping = 3.14*fcen) - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_size.x, design_region_size.y, 0))) - - matgrid_geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] - - geometry = waveguide_geometry + matgrid_geometry - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_xy, - sources=wvg_source, - geometry=geometry) - - if not frequencies: - frequencies = [fcen] - obj_list = [mpa.EigenmodeCoefficient(sim, mp.Volume(center=mp.Vector3(0.5*sxy-dpml-0.1), - size=mp.Vector3(0,sxy-2*dpml,0)), 1, eig_parity=eig_parity)] - - def J(mode_mon): - return npa.power(npa.abs(mode_mon),2) - - - opt = mpa.OptimizationProblem( - simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies, - minimum_run_time=150) - - f, dJ_du = opt([design_params]) - - sim.reset_meep() - return f, dJ_du - -def mapping(x,filter_radius,eta,beta): - filtered_field = mpa.conic_filter(x, - filter_radius, - design_region_size.x, - design_region_size.y, - design_region_resolution) - - projected_field = mpa.tanh_projection(filtered_field,beta,eta) - - return projected_field.flatten() +MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT LDOS') class TestAdjointSolver(ApproxComparisonTestCase): + @classmethod + def setUpClass(cls): + cls.resolution = 30 # pixels/μm + + cls.silicon = mp.Medium(epsilon=12) + cls.sapphire = mp.Medium(epsilon_diag=(10.225,10.225,9.95), + epsilon_offdiag=(-0.825,-0.55*np.sqrt(3/2),0.55*np.sqrt(3/2))) + + cls.sxy = 5.0 + cls.cell_size = mp.Vector3(cls.sxy,cls.sxy,0) + + cls.dpml = 1.0 + cls.pml_xy = [mp.PML(thickness=cls.dpml)] + cls.pml_x = [mp.PML(thickness=cls.dpml,direction=mp.X)] + + cls.eig_parity = mp.EVEN_Y+mp.ODD_Z + + cls.design_region_size = mp.Vector3(1.5,1.5) + cls.design_region_resolution = int(2*cls.resolution) + cls.Nx = int(cls.design_region_size.x*cls.design_region_resolution) + cls.Ny = int(cls.design_region_size.y*cls.design_region_resolution) + + # ensure reproducible results + rng = np.random.RandomState(9861548) + + # random design region + cls.p = 0.5*rng.rand(cls.Nx*cls.Ny) + + # random perturbation for design region + deps = 1e-5 + cls.dp = deps*rng.rand(cls.Nx*cls.Ny) + + cls.w = 1.0 + cls.waveguide_geometry = [mp.Block(material=cls.silicon, + center=mp.Vector3(), + size=mp.Vector3(mp.inf,cls.w,mp.inf))] + + cls.fcen = 1/1.55 + cls.df = 0.2*cls.fcen + cls.mode_source = [mp.EigenModeSource(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), + center=mp.Vector3(-0.5*cls.sxy+cls.dpml,0), + size=mp.Vector3(0,cls.sxy-2*cls.dpml), + eig_parity=cls.eig_parity)] + + cls.pt_source = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), + center=mp.Vector3(-0.5*cls.sxy+cls.dpml,0), + size=mp.Vector3(), + component=mp.Ez)] + + cls.line_source = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), + center=mp.Vector3(-0.85,0), + size=mp.Vector3(0,cls.sxy-2*cls.dpml), + component=mp.Ez)] + + cls.k_point = mp.Vector3(0.23,-0.38) + + + def adjoint_solver(self, design_params, mon_type: MonitorObject, frequencies=None, + mat2=None, need_gradient=True): + matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), + mp.air, + self.silicon if mat2 is None else mat2, + weights=np.ones((self.Nx,self.Ny))) + + matgrid_region = mpa.DesignRegion(matgrid, + volume=mp.Volume(center=mp.Vector3(), + size=mp.Vector3(self.design_region_size.x, + self.design_region_size.y, + 0))) + + matgrid_geometry = [mp.Block(center=matgrid_region.center, + size=matgrid_region.size, + material=matgrid)] + + geometry = self.waveguide_geometry + matgrid_geometry + + sim = mp.Simulation(resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.pml_xy, + sources=self.mode_source, + geometry=geometry) + + if not frequencies: + frequencies = [self.fcen] + + if mon_type.name == 'EIGENMODE': + obj_list = [mpa.EigenmodeCoefficient(sim, + mp.Volume(center=mp.Vector3(0.5*self.sxy-self.dpml), + size=mp.Vector3(0,self.sxy-2*self.dpml,0)), + 1, + eig_parity=self.eig_parity)] + + def J(mode_mon): + return npa.power(npa.abs(mode_mon),2) + elif mon_type.name == 'DFT': + obj_list = [mpa.FourierFields(sim, + mp.Volume(center=mp.Vector3(1.25), + size=mp.Vector3(0.25,1,0)), + mp.Ez)] + + def J(mode_mon): + return npa.power(npa.abs(mode_mon[:,4,10]),2) + elif mon_type.name == 'LDOS': + sim.change_sources(self.line_source) + obj_list = [mpa.LDOS(sim)] + + def J(mode_mon): + return npa.array(mode_mon) + + opt = mpa.OptimizationProblem(simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies) + + if need_gradient: + f, dJ_du = opt([design_params]) + return f, dJ_du + else: + f = opt([design_params], need_gradient=False) + return f[0] + + + def adjoint_solver_complex_fields(self, design_params, frequencies=None, need_gradient=True): + matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), + mp.air, + self.silicon, + weights=np.ones((self.Nx,self.Ny))) + + matgrid_region = mpa.DesignRegion(matgrid, + volume=mp.Volume(center=mp.Vector3(), + size=mp.Vector3(self.design_region_size.x, + self.design_region_size.y, + 0))) + + geometry = [mp.Block(center=matgrid_region.center, + size=matgrid_region.size, + material=matgrid)] + + sim = mp.Simulation(resolution=self.resolution, + cell_size=self.cell_size, + default_material=self.silicon, + k_point=self.k_point, + boundary_layers=self.pml_x, + sources=self.pt_source, + geometry=geometry) + + if not frequencies: + frequencies = [self.fcen] - def test_adjoint_solver_DFT_fields(self): - print("*** TESTING DFT ADJOINT ***") - - ## test the single frequency and multi frequency cases - for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver(p, MonitorObject.DFT, frequencies) - - ## compute unperturbed |Ez|^2 - Ez2_unperturbed = forward_simulation(p, MonitorObject.DFT, frequencies) + obj_list = [mpa.FourierFields(sim, + mp.Volume(center=mp.Vector3(0.9), + size=mp.Vector3(0.2,0.5)), + mp.Dz)] + + def J(dft_mon): + return npa.power(npa.abs(dft_mon[:,3,9]),2) + + opt = mpa.OptimizationProblem(simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies) + + if need_gradient: + f, dJ_du = opt([design_params]) + return f, dJ_du + else: + f = opt([design_params], need_gradient=False) + return f[0] + + + def adjoint_solver_damping(self, design_params, frequencies=None, mat2=None, need_gradient=True): + matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), + mp.air, + self.silicon if mat2 is None else mat2, + weights=np.ones((self.Nx,self.Ny)), + damping=np.pi*self.fcen) + + matgrid_region = mpa.DesignRegion(matgrid, + volume=mp.Volume(center=mp.Vector3(), + size=mp.Vector3(self.design_region_size.x, + self.design_region_size.y, + 0))) + + matgrid_geometry = [mp.Block(center=matgrid_region.center, + size=matgrid_region.size, + material=matgrid)] + + geometry = self.waveguide_geometry + matgrid_geometry + + sim = mp.Simulation(resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.pml_xy, + sources=self.mode_source, + geometry=geometry) + + if not frequencies: + frequencies = [self.fcen] - ## compare objective results - print("|Ez|^2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,Ez2_unperturbed)) - self.assertClose(adjsol_obj,Ez2_unperturbed,epsilon=1e-6) + obj_list = [mpa.EigenmodeCoefficient(sim, + mp.Volume(center=mp.Vector3(0.5*self.sxy-self.dpml), + size=mp.Vector3(0,self.sxy-2*self.dpml,0)), + 1, + eig_parity=self.eig_parity)] - ## compute perturbed Ez2 - Ez2_perturbed = forward_simulation(p+dp, MonitorObject.DFT, frequencies) + def J(mode_mon): + return npa.power(npa.abs(mode_mon),2) - ## compare gradients - if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = Ez2_perturbed-Ez2_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) + opt = mpa.OptimizationProblem(simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + minimum_run_time=150) + + if need_gradient: + f, dJ_du = opt([design_params]) + return f, dJ_du + else: + f = opt([design_params], need_gradient=False) + return f[0] + + + def mapping(self,x,filter_radius,eta,beta): + filtered_field = mpa.conic_filter(x, + filter_radius, + self.design_region_size.x, + self.design_region_size.y, + self.design_region_resolution) + + projected_field = mpa.tanh_projection(filtered_field,beta,eta) + + return projected_field.flatten() + + + def test_DFT_fields(self): + print("*** TESTING DFT OBJECTIVE ***") + + # test the single frequency and multi frequency cases + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, + MonitorObject.DFT, + frequencies) + + # compute objective value for perturbed design + perturbed_val = self.adjoint_solver(self.p+self.dp, + MonitorObject.DFT, + frequencies, + need_gradient=False) + + # compare directional derivative + if unperturbed_grad.ndim < 2: + unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) tol = 0.07 if mp.is_single_precision() else 0.006 - self.assertClose(adj_scale,fd_grad,epsilon=tol) - - - def test_adjoint_solver_eigenmode(self): - print("*** TESTING EIGENMODE ADJOINT ***") - - ## test the single frequency and multi frequency case - for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver(p, MonitorObject.EIGENMODE, frequencies) - - ## compute unperturbed S12 - S12_unperturbed = forward_simulation(p, MonitorObject.EIGENMODE, frequencies) - - ## compare objective results - print("S12 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-6) - - ## compute perturbed S12 - S12_perturbed = forward_simulation(p+dp, MonitorObject.EIGENMODE, frequencies) - - ## compare gradients - if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = S12_perturbed-S12_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) + self.assertClose(adj_dd,fnd_dd,epsilon=tol) + + + def test_eigenmode(self): + print("*** TESTING EIGENMODE OBJECTIVE ***") + + # test the single frequency and multi frequency case + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, + MonitorObject.EIGENMODE, + frequencies) + + # compute objective for perturbed design + perturbed_val = self.adjoint_solver(self.p+self.dp, + MonitorObject.EIGENMODE, + frequencies, + need_gradient=False) + + # compare directional derivative + if unperturbed_grad.ndim < 2: + unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) tol = 0.04 if mp.is_single_precision() else 0.01 - self.assertClose(adj_scale,fd_grad,epsilon=tol) - - - def test_adjoint_solver_LDOS(self): - print("*** TESTING LDOS ADJOINT ***") - - ## test the single frequency and multi frequency cases - for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver(p, MonitorObject.LDOS, frequencies) - - ## compute unperturbed |Ez|^2 - Ez2_unperturbed = forward_simulation(p, MonitorObject.LDOS, frequencies) - - ## compare objective results - print("|Ez|^2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,Ez2_unperturbed)) - self.assertClose(adjsol_obj,Ez2_unperturbed,epsilon=2e-5) - - ## compute perturbed Ez2 - Ez2_perturbed = forward_simulation(p+dp, MonitorObject.LDOS, frequencies) - - ## compare gradients - if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = Ez2_perturbed-Ez2_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) + self.assertClose(adj_dd,fnd_dd,epsilon=tol) + + + def test_ldos(self): + print("*** TESTING LDOS OBJECTIVE ***") + + # test the single frequency and multi frequency cases + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, + MonitorObject.LDOS, + frequencies) + + # compute objective for perturbed design + perturbed_val = self.adjoint_solver(self.p+self.dp, + MonitorObject.LDOS, + frequencies, + need_gradient=False) + + # compare directional derivative + if unperturbed_grad.ndim < 2: + unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) tol = 0.07 if mp.is_single_precision() else 0.006 - self.assertClose(adj_scale,fd_grad,epsilon=tol) + self.assertClose(adj_dd,fnd_dd,epsilon=tol) def test_gradient_backpropagation(self): - print("*** TESTING BACKPROP ***") + print("*** TESTING GRADIENT BACKPROPAGATION ***") - for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: - ## filter/thresholding parameters + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + # filter/thresholding parameters filter_radius = 0.21985 eta = 0.49093 beta = 4.0698 - mapped_p = mapping(p,filter_radius,eta,beta) + mapped_p = self.mapping(self.p,filter_radius,eta,beta) - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver(mapped_p, MonitorObject.EIGENMODE, frequencies) + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver(mapped_p, + MonitorObject.EIGENMODE, + frequencies) - ## backpropagate the gradient + # backpropagate the gradient using vector-Jacobian product if len(frequencies) > 1: - bp_adjsol_grad = np.zeros(adjsol_grad.shape) + unperturbed_grad_backprop = np.zeros(unperturbed_grad.shape) for i in range(len(frequencies)): - bp_adjsol_grad[:,i] = tensor_jacobian_product(mapping,0)(p,filter_radius,eta,beta,adjsol_grad[:,i]) + unperturbed_grad_backprop[:,i] = tensor_jacobian_product(self.mapping,0)(self.p, + filter_radius, + eta, + beta, + unperturbed_grad[:,i]) else: - bp_adjsol_grad = tensor_jacobian_product(mapping,0)(p,filter_radius,eta,beta,adjsol_grad) - - ## compute unperturbed S12 - S12_unperturbed = forward_simulation(mapped_p,MonitorObject.EIGENMODE,frequencies) - - ## compare objective results - print("S12 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-6) - - ## compute perturbed S12 - S12_perturbed = forward_simulation(mapping(p+dp,filter_radius,eta,beta),MonitorObject.EIGENMODE,frequencies) - - if bp_adjsol_grad.ndim < 2: - bp_adjsol_grad = np.expand_dims(bp_adjsol_grad,axis=1) - adj_scale = (dp[None,:]@bp_adjsol_grad).flatten() - fd_grad = S12_perturbed-S12_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.02 if mp.is_single_precision() else 0.01 - self.assertClose(adj_scale,fd_grad,epsilon=tol) + unperturbed_grad_backprop = tensor_jacobian_product(self.mapping,0)(self.p, + filter_radius, + eta, + beta, + unperturbed_grad) + + # compute objective for perturbed design + perturbed_val = self.adjoint_solver(self.mapping(self.p+self.dp,filter_radius,eta,beta), + MonitorObject.EIGENMODE, + frequencies, + need_gradient=False) + + # compare directional derivative + if unperturbed_grad_backprop.ndim < 2: + unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + tol = 0.025 if mp.is_single_precision() else 0.01 + self.assertClose(adj_dd,fnd_dd,epsilon=tol) def test_complex_fields(self): print("*** TESTING COMPLEX FIELDS ***") - for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver_complex_fields(p, frequencies) + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver_complex_fields(self.p, + frequencies) - ## compute unperturbed |Dz|^2 - Dz2_unperturbed = forward_simulation_complex_fields(p, frequencies) + # compute objective value perturbed design + perturbed_val = self.adjoint_solver_complex_fields(self.p+self.dp, + frequencies, + need_gradient=False) - ## compare objective results - print("Dz2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,Dz2_unperturbed)) - tol = 1e-5 if mp.is_single_precision() else 1e-6 - self.assertClose(adjsol_obj,Dz2_unperturbed,epsilon=tol) + # compare directional derivative + if unperturbed_grad.ndim < 2: + unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + tol = 0.06 if mp.is_single_precision() else 0.01 + self.assertClose(adj_dd,fnd_dd,epsilon=tol) - ## compute perturbed |Dz|^2 - Dz2_perturbed = forward_simulation_complex_fields(p+dp, frequencies) - - ## compare gradients - if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = Dz2_perturbed-Dz2_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.06 if mp.is_single_precision() else 0.002 - self.assertClose(adj_scale,fd_grad,epsilon=tol) def test_damping(self): - print("*** TESTING CONDUCTIVITIES ***") - - for frequencies in [[1/1.58, fcen, 1/1.53]]: - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver_damping(p, frequencies) + print("*** TESTING CONDUCTIVITY ***") - ## compute unperturbed S12 - S12_unperturbed = forward_simulation_damping(p, frequencies) + for frequencies in [[1/1.58, self.fcen, 1/1.53]]: + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver_damping(self.p, + frequencies) - ## compare objective results - print("S12 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-6) + # compute objective value perturbed design + perturbed_val = self.adjoint_solver_damping(self.p+self.dp, + frequencies, + need_gradient=False) - ## compute perturbed S12 - S12_perturbed = forward_simulation_damping(p+dp, frequencies) + # compare directional derivative + if unperturbed_grad.ndim < 2: + unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + tol = 0.06 if mp.is_single_precision() else 0.04 + self.assertClose(adj_dd,fnd_dd,epsilon=tol) - ## compare gradients - if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = S12_perturbed-S12_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.06 if mp.is_single_precision() else 0.03 - self.assertClose(adj_scale,fd_grad,epsilon=tol) def test_offdiagonal(self): - print("*** TESTING OFFDIAGONAL COMPONENTS ***") - filt = lambda x: mpa.conic_filter(x.reshape((Nx,Ny)),0.25,design_region_size.x,design_region_size.y,design_region_resolution).flatten() - - ## test the single frequency and multi frequency case - for frequencies in [[fcen], [1/1.58, fcen, 1/1.53]]: - ## compute gradient using adjoint solver - adjsol_obj, adjsol_grad = adjoint_solver(filt(p), MonitorObject.EIGENMODE, frequencies, sapphire) - - ## backpropagate the gradient + print("*** TESTING ANISOTROPIC ε ***") + filt = lambda x: mpa.conic_filter(x.reshape((self.Nx,self.Ny)), + 0.25, + self.design_region_size.x, + self.design_region_size.y, + self.design_region_resolution).flatten() + + # test the single frequency and multi frequency case + for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + # compute objective value and its gradient for unperturbed design + unperturbed_val, unperturbed_grad = self.adjoint_solver(filt(self.p), + MonitorObject.EIGENMODE, + frequencies, + self.sapphire) + + # backpropagate the gradient using vector-Jacobian product if len(frequencies) > 1: - bp_adjsol_grad = np.zeros(adjsol_grad.shape) + unperturbed_grad_backprop = np.zeros(unperturbed_grad.shape) for i in range(len(frequencies)): - bp_adjsol_grad[:,i] = tensor_jacobian_product(filt,0)(p,adjsol_grad[:,i]) + unperturbed_grad_backprop[:,i] = tensor_jacobian_product(filt,0)(self.p, + unperturbed_grad[:,i]) else: - bp_adjsol_grad = tensor_jacobian_product(filt,0)(p,adjsol_grad) - - ## compute unperturbed S12 - S12_unperturbed = forward_simulation(filt(p), MonitorObject.EIGENMODE, frequencies, sapphire) - - ## compare objective results - print("S12 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-6) + unperturbed_grad_backprop = tensor_jacobian_product(filt,0)(self.p,unperturbed_grad) + + # compute objective value perturbed design + perturbed_val = self.adjoint_solver(filt(self.p+self.dp), + MonitorObject.EIGENMODE, + frequencies, + self.sapphire, + need_gradient=False) + + # compare directional derivative + if unperturbed_grad_backprop.ndim < 2: + unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) + adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() + fnd_dd = perturbed_val-unperturbed_val + print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + tol = 0.1 if mp.is_single_precision() else 0.04 + self.assertClose(adj_dd,fnd_dd,epsilon=tol) - ## compute perturbed S12 - S12_perturbed = forward_simulation(filt(p+dp), MonitorObject.EIGENMODE, frequencies, sapphire) - ## compare gradients - if bp_adjsol_grad.ndim < 2: - bp_adjsol_grad = np.expand_dims(bp_adjsol_grad,axis=1) - adj_scale = (dp[None,:]@bp_adjsol_grad).flatten() - fd_grad = S12_perturbed-S12_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.1 if mp.is_single_precision() else 0.04 - self.assertClose(adj_scale,fd_grad,epsilon=tol) if __name__ == '__main__': unittest.main() From 0bc1e7027cd9ed1f192daa03d6791b7af57cf2f9 Mon Sep 17 00:00:00 2001 From: mochen4 Date: Thu, 14 Jul 2022 14:17:46 -0400 Subject: [PATCH 12/26] update adjoint tutorials (#2129) * update tutorials * update tutorials Co-authored-by: Mo Chen --- doc/docs/Python_Tutorials/Adjoint_Solver.md | 4 +- .../01-Introduction-checkpoint.ipynb | 541 ++++ .../02-Waveguide_Bend-checkpoint.ipynb | 410 +++ ...3-Filtered_Waveguide_Bend-checkpoint.ipynb | 2201 +++++++++++++++++ .../04-Splitter-checkpoint.ipynb | 1894 ++++++++++++++ .../05-Near2Far-checkpoint.ipynb | 414 ++++ .../06-Near2Far-Epigraph-checkpoint.ipynb | 2042 +++++++++++++++ .../Bend Minimax-checkpoint.ipynb} | 6 +- .../01-Introduction.ipynb | 39 +- .../02-Waveguide_Bend.ipynb | 39 +- .../03-Filtered_Waveguide_Bend.ipynb | 190 +- .../adjoint_optimization/04-Splitter.ipynb | 401 ++- .../adjoint_optimization/05-Near2Far.ipynb | 1990 +++++++++++++++ .../06-Near2Far-Epigraph.ipynb | 2042 +++++++++++++++ .../adjoint_optimization/Bend Minimax.ipynb | 2107 ++++++++++++++++ .../Near2Far-Epigraph.ipynb | 1817 -------------- 16 files changed, 14106 insertions(+), 2031 deletions(-) create mode 100644 python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb create mode 100644 python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb create mode 100644 python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb create mode 100644 python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb create mode 100644 python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb create mode 100644 python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb rename python/examples/adjoint_optimization/{05-Minimax.ipynb => .ipynb_checkpoints/Bend Minimax-checkpoint.ipynb} (99%) create mode 100644 python/examples/adjoint_optimization/05-Near2Far.ipynb create mode 100644 python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb create mode 100644 python/examples/adjoint_optimization/Bend Minimax.ipynb delete mode 100644 python/examples/adjoint_optimization/Near2Far-Epigraph.ipynb diff --git a/doc/docs/Python_Tutorials/Adjoint_Solver.md b/doc/docs/Python_Tutorials/Adjoint_Solver.md index 242741a62..5cf710a91 100644 --- a/doc/docs/Python_Tutorials/Adjoint_Solver.md +++ b/doc/docs/Python_Tutorials/Adjoint_Solver.md @@ -18,8 +18,8 @@ There are six Jupyter notebooks that demonstrate the main features of the adjoin - [Design of a Symmetric Broadband Splitter](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/04-Splitter.ipynb) -- [Broadband Objective Function using Epigraph Formulation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/05-Minimax.ipynb) +- [Broadband Objective Function using Epigraph Formulation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/05-Near2Far.ipynb) -- [Objective Function based on Near to Far-Field Transformation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/Near2Far-Epigraph.ipynb) +- [Objective Function based on Near to Far-Field Transformation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb) More documentation will be available soon. diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb new file mode 100644 index 000000000..321cf70de --- /dev/null +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Meep Adjoint Solver - Introduction\n", + "\n", + "This tutorial serves as a basic introduction to MEEP's adjoint solver. The adjoint solver provides a simple and flexible interface to calculating \"sensitivities\" or gradients of user specified objective functions with respect to **any number** of design variables -- **all with the cost of just two simulations.**\n", + "\n", + "Practical electromagnetic design problems are often constrained by complicated design requirements that aren't easily satisfied with physical intuition. We often formulate our problem as functions of Fourier transformed fields, (e.g. S-parameters, Poynting Fluxes, mode overlap coefficients, etc.). We can \"inverse design\" a structure that maximizes (or minimizes) this cost function with various optimization algorithms. The adjoint method provides an extremely cheap gradient, accelerating many out of the box optimization algorithms.\n", + "\n", + "In this tutorial, we'll demonstrate how you can quickly begin leveraging this interface. As a toy example, we'll examine a 2D integrated photonic waveguide bend. We'll code up a cost function that will tell our optimizer to maximize power transport around the bend. We'll discretize the bend into several individual pixels and calculate the gradient of this cost function w.r.t. all these pixels. Finally, we'll compare our gradients with finite difference approximates, which are much more expensive to calculate. Hopefully by the end of the tutorial, you appreciate the simplicity and flexibility this particular package offers. \n", + "\n", + "First, we'll import `meep` and `autograd`, a widely used automatic differentiation package. `autograd` wraps around `numpy` and allows us to quickly differentiate functions composed of standard `numpy` routines. This will be especially useful when we want to formulate our objective function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'meep'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01madjoint\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpa\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'meep'" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from matplotlib import pyplot as plt\n", + "mp.quiet(quietval=True)\n", + "seed = 240\n", + "np.random.seed(seed)\n", + "Si = mp.Medium(index=3.4)\n", + "SiO2 = mp.Medium(index=1.44)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll begin to specify the domain of our waveguide bend simulation, just as we would with any other simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "resolution = 30\n", + "\n", + "Sx = 6\n", + "Sy = 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "pml_layers = [mp.PML(1.0)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll then define our sources. We'll use a narrowband Gaussian pulse, even though our objective function will only operate over a single wavelength (for this example). While we could use the CW solver, it's often faster (and more dependable) to use the timestepping routines and a narrowband pulse." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [-1,0,0]\n", + "source_size = mp.Vector3(0,2,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll define our waveguide geometry and \"design region\". The design region takes a 10x10 grid of randomly generated design variables and maps them to a point within the specified volume. Since meep operates under the \"illusion of continuitiy\", it's important we provide an interpolator to perform this mapping.\n", + "\n", + "In this case, we'll use a builtin bilinear interpolator to take care of this for us. You can use whatever interpolation function you want, provided it can return either a medium or permittivity (as described in the manual) and you can calculate the gradient of the interpolator with respect to the inputs (often just a matrix multiplication). The built-in interpolator takes care of all of this for us." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "design_region_resolution = 10\n", + "Nx = design_region_resolution\n", + "Ny = design_region_resolution\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "\n", + "\n", + "geometry = [\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now formulate or simulation object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def mapping(x):\n", + " x = x.reshape(Nx,Ny)\n", + " x = (npa.flipud(x) + x)/2 # left-right symmetry\n", + " x = (npa.fliplr(x) + x)/2 # up-down symmetry\n", + " x = (npa.rot90(x) + x) # 90-degree symmetry\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll start defining our objective function. Objective functions must be composed of \"field functions\" that transform the recorded fields. Right now, only the Eigenmode Decomposition monitors are readily accessible from the adjoint API. That being said, it is easy to extend the API to other field functions, like Poynting fluxes.\n", + "\n", + "In our case, we just want to maximize the power in the fundamental mode at the top of the waveguide bend. We'll define a new object that specifies these details. We'll also create a list of our objective \"quantities\" (just one in our case)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "ob_list = [TE0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our objective function will take the magnitude squared of our predefined objective quantity. We'll define the function as a normal python function. We'll use dummy parameters that map sequentially to the list of objective quantities we defined above. We'll also use autograd's version of numpy so that the adjoint solver can easily differentiate our objective function with respect to each of these quantities. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def J(alpha):\n", + " return npa.abs(alpha) ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now define an `OptimizationProblem` using our simulation object, objective function, and objective quantities (or arguments). We'll also tell the solver to examine the `Ez` component of the Fourier transformed fields. The solver will stop the simulation after these components have stopped changing by a certain relative threshold (default is 1e-6)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", + " fcen=fcen,\n", + " df = 0,\n", + " nf = 1,\n", + " decay_fields=[mp.Ez]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now initialize our object with a set of random design parameters. We'll use `numpy`'s `random` library to generate a uniform random variable between 1 and 12, to correspond to the refractive index of air and silicon." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "x0 = np.random.rand(Nx*Ny)\n", + "opt.update_design([x0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we can visualize our final simulation domain with any extra monitors as defined by our objective function. This `plot2D` function is just like `Simulation`'s `plot2D`, only it takes an additional first argument. We'll set it to `True` to tell the solver to initialize the solver and clear any stored fields." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.plot2D(True,frequency=1/1.55)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see an expected monitor on the top of the waveguide, but we also see an additional monitor spanning our design region. This monitor records the Fourier transformed fields needed to calulcate the gradient.\n", + "\n", + "Since everything looks good, we can go now calculate the gradient and the cost function evaluation. We do so by calling our solver object directly. The object returns the objective function evaluation, `f0`, and the gradient, `dJ_du`. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize these gradients." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.imshow(np.rot90(dJ_du.reshape(Nx,Ny)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a gradient with respect to our design parameters. To verify the accuracy of our method, we'll perform a finite difference approximation. \n", + "\n", + "Luckily, our solver has a built finite difference method (`calculate_fd_gradient`). Since the finite difference approximates require several expensive simulations, we'll only estimate 20 of them (randomly sampled by our function)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "db = 1e-3\n", + "choose = 10\n", + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compare the results by fitting a line to the two gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "(m, b) = np.polyfit(np.squeeze(g_discrete), dJ_du[idx], 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and subsequently plot the results" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "min_g = np.min(g_discrete)\n", + "max_g = np.max(g_discrete)\n", + "\n", + "fig = plt.figure(figsize=(12,6))\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", + "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", + "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Adjoint Gradient')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axis(\"square\")\n", + "\n", + "plt.subplot(1,2,2)\n", + "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", + "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.grid(True)\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Relative Error (%)')\n", + "\n", + "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", + "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We notice strong agreement between the adjoint gradients and the finite difference gradients. Let's bump up the resolution to see if the results are consistent." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + } + ], + "source": [ + "resolution = 20\n", + "opt.sim.resolution = resolution\n", + "f0, dJ_du = opt()\n", + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "min_g = np.min(g_discrete)\n", + "max_g = np.max(g_discrete)\n", + "\n", + "fig = plt.figure(figsize=(12,6))\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", + "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", + "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Adjoint Gradient')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.axis(\"square\")\n", + "\n", + "plt.subplot(1,2,2)\n", + "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", + "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.grid(True)\n", + "plt.xlabel('Finite Difference Gradient')\n", + "plt.ylabel('Relative Error (%)')\n", + "\n", + "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", + "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb new file mode 100644 index 000000000..5fed22162 --- /dev/null +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Waveguide Bend Optimization\n", + "\n", + "In this tutorial, we'll examine how we can use Meep's adjoint solver to optimize for a particular design criteria. Specifcally, we'll maximize the transmission around a silicon waveguide bend. This tutorial will illustrate the adjoint solver's ability to quickly calculate gradients for objective functions with multiple objective quantities.\n", + "\n", + "To begin, we'll import meep, our adjoint module, `autograd` (as before) and we will also import `nlopt`, a nonlinear optimization package." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "SiO2 = mp.Medium(index=1.44)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll build a 90 degree bend waveguide with a design region that is 1 micron by 1 micron. We'll discretize our region into a 10 x 10 grid (100 total parameters). We'll send in a narrowband gaussian pulse centered at 1550 nm. We'll also use the same objective function and optimizer object as before." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "resolution = 20\n", + "\n", + "Sx = 6\n", + "Sy = 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "pml_layers = [mp.PML(1.0)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [-1.5,0,0]\n", + "source_size = mp.Vector3(0,2,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]\n", + "\n", + "design_region_resolution = 10\n", + "Nx = design_region_resolution\n", + "Ny = design_region_resolution\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "\n", + "\n", + "geometry = [\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "\n", + "]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution)\n", + "\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "ob_list = [TE_top]\n", + "\n", + "def J(alpha):\n", + " return npa.abs(alpha) ** 2\n", + "\n", + "opt = mpa.OptimizationProblem(\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", + " fcen=fcen,\n", + " df = 0,\n", + " nf = 1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we'll visualize everything to ensure our monitors, boundary layers, and geometry are drawn correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x0 = 0.5*np.ones((Nx*Ny,))\n", + "opt.update_design([x0])\n", + "\n", + "opt.plot2D(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now define a cost function wrapper that we can feed into our `nlopt` optimizer. `nlopt` expects a python function where the gradient is passed in place. In addition, we'll update a list with every objective function call so we can track the cost function evolution each iteration.\n", + "\n", + "Notice the `opt` adjoint solver object requires we pass our numpy array of design parameters within an additional list. This is because the adjoint solver can solve for multiple design regions simultaneously. It's useful to break up each region's parameters into indvidual numpy arrays. In this simple example, we only have one design region." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "sensitivity = [0]\n", + "def f(x, grad):\n", + " f0, dJ_du = opt([x])\n", + " f0 = f0[0] # f0 is an array of length 1 \n", + " if grad.size > 0:\n", + " grad[:] = np.squeeze(dJ_du)\n", + " evaluation_history.append(np.real(f0))\n", + " sensitivity[0] = dJ_du\n", + " return np.real(f0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can set up the actual optimizer engine. We'll select the Method of Moving Asymptotes because it's a gradient based method that allows us to specify linear and nonlinear constraints. For now, we'll simply bound our parameters between 0 and 1.\n", + "\n", + "We'll tell our solver to maximize (rather than minimize) our cost function, since we are trying to maximize the power transmission around the bend.\n", + "\n", + "We'll also tell the optimizer to stop after 10 function calls. This will keep the wait time short and demonstrate how powerful the adjoint solver is." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny\n", + "maxeval = 10\n", + "\n", + "solver = nlopt.opt(algorithm, n)\n", + "solver.set_lower_bounds(0)\n", + "solver.set_upper_bounds(1)\n", + "solver.set_max_objective(f)\n", + "solver.set_maxeval(maxeval)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n", + "Starting adjoint run...\n" + ] + } + ], + "source": [ + "x = solver.optimize(x0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the solver is done running, we can evaluate our progress." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(evaluation_history,'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can update our optimization object and visualize the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt.update_design([x])\n", + "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "plt.axis(\"off\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We quickly see that the solver improves the design, but because of the way we chose to formulate our cost function, it's difficult to quantify our results. After all, how good is a figure of merit (FOM) of 70?\n", + "\n", + "To overcome this, we'll slightly modify our objective function to include an extra monitor just after the source. This monitor will track however much power is transmitted into the waveguide. We can then normalize the upper monitor's response by this parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(-1,0,0),size=mp.Vector3(y=2)),mode=1)\n", + "ob_list = [TE0,TE_top]\n", + "\n", + "def J(source,top):\n", + " return npa.abs(top/source) ** 2\n", + "\n", + "opt.objective_functions = [J]\n", + "opt.objective_arguments = ob_list\n", + "opt.update_design([x0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll refresh our solver and try optimizing again:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "solver = nlopt.opt(algorithm, n)\n", + "solver.set_lower_bounds(0)\n", + "solver.set_upper_bounds(1)\n", + "solver.set_max_objective(f)\n", + "solver.set_maxeval(maxeval)\n", + "x = solver.optimize(x0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view our results and normalize the FOM as the percent power transmission." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.plot(np.array(evaluation_history)*100,'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Transmission (%)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We once again clearly see great improvement, from about 5% transmission to about 85%, after just 10 iterations!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "improvement = max(evaluation_history) / min(evaluation_history)\n", + "print(\"Achieved an improvement of {0:1.1f}x\".format(improvement))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can visualize our new geometry. We see that the design region naturally connected the two waveguide segements in a bend fashion and has placed several other distinctive features around the curve." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt.update_design([x])\n", + "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "plt.axis(\"off\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize the sensitivity to see which geometric areas are most sensitive to perturbations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx,Ny)))));" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb new file mode 100644 index 000000000..049ea8d2e --- /dev/null +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb @@ -0,0 +1,2201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering and Thresholding Design Parameters and Broadband Objective Functions\n", + "\n", + "While cheap gradients are always appreciated, we noticed that the optimizer and adjoint solver often produce devices that are impossible to fabricate. They tend to continuously vary the refractive index and produce small feature sizes. Furthermore, the lack of constraint often throws the optimizer into local minima, stunting the overall progress of the solver.\n", + "\n", + "To overcome this, the Topology Optimization (TO) community often implements linear and nonlinear functional transforms on the design parameters before projecting them onto the simulation domain. For example, we can blur many of the small design parameters together using a conic filter, and subsequently threshold using sigmoid like functions.\n", + "\n", + "The resulting parameters can include constraints, like minimum lengths scales, and project the cost function into a more friendly (and sometimes less friendly) design space.\n", + "\n", + "We'll examine how to accomplish these goals using native `autograd` functions and meep's adjoint solver." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "from scipy import special, signal\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "SiO2 = mp.Medium(index=1.44)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's choose our basic geometry parameters, along with the frequency range to simulate. If our frequency points are too close together than the resulting adjoint simulation may take longer to simulate each iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "waveguide_width = 0.5\n", + "design_region_width = 2.5\n", + "design_region_height = 2.5\n", + "\n", + "waveguide_length = 0.5\n", + "\n", + "pml_size = 1.0\n", + "\n", + "resolution = 30\n", + "\n", + "frequencies = 1/np.linspace(1.5,1.6,10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll specify our smoothing filter radius (in microns). This is determined by our minimum length scale and erosion threshold point. In this example, we won't actually enforce the length scale constraints, but the following machinery is required to do so.\n", + "\n", + "We also need to specify the number of design parameters per meep pixel we want to set up." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20124611797498096\n" + ] + } + ], + "source": [ + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "print(filter_radius)\n", + "design_region_resolution = int(1*resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we'll generate our simulation cell with the specified sources, boundary layers, geometry, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,Sy,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's set up our design region. The defauly grid type behaves just like any other geometry object -- the last object in the tree overides any objects underneath it. In some cases, it's important to *average* overlapping design regions, i.e. to enforce particular symmetries. To enable this capability, we'll set our design variables to use the `U_MEAN` grid type." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The optimizer doesn't know anything about the geometry outside of the design region. We can \"hint\" what parts of the design must be waveguides by forcing boundry constraints. We'll build bitmasks that map the border of the design regions to either silicon or silicon dioxide." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "Si_mask = left_wg_mask | top_wg_mask\n", + "\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", + "SiO2_mask = border_mask.copy()\n", + "SiO2_mask[Si_mask] = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll define various filters that perform smoothing, projection, and material scaling. We'll be sure to use autograd so that we can easily backpropogate our gradient provided by the adjoint solver. We have some smoothing and thresholding filters already defined in the adjoint package that we'll use here." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def mapping(x,eta,beta):\n", + " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's build the geometry and simulation objects. We can force a rotational symmetry by adding extra blocks with the same design variables, but different basis vectors (`e1`, `e2`, etc.) In our case, we need one additional block object rotated by 90 degrees.\n", + "\n", + "Because our design variables are using the `U_MEAN` scheme, the adjoint solver will search for all of the material grids at each point in our design region and average the overlapping design variables. This way, we can enforce our symmetry constraint." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "geometry = [\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + "]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's formulate our broadband objective function. To keep it simple we'll just minimize the sum of the error, where the error is the L2 norm between the transmission and 1 (i.e. the desired profile)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mode = 1\n", + "\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", + "ob_list = [TE0,TE_top]\n", + "def J(source,top):\n", + " power = npa.abs(top/source)\n", + " return npa.mean(power)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's see how well our filtering and projection functions work." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rho_vector = np.random.rand(Nx*Ny)\n", + "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", + "opt.plot2D(True,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can increase our `beta` term, which controls the thresholding, and simultaneously sweep our perturbation term (`delta`) which is used to generate erosion and dilation geometries typically used in the literature. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "beta = 2048\n", + "\n", + "plt.figure()\n", + "ax1 = plt.subplot(1,3,1)\n", + "opt.update_design([mapping(rho_vector,0.498,beta)])\n", + "opt.plot2D(True,ax=ax1,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "plt.title(\"Dilation\")\n", + "\n", + "ax2 = plt.subplot(1,3,2)\n", + "opt.update_design([mapping(rho_vector,0.5,beta)])\n", + "opt.plot2D(True,ax=ax2,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "plt.title(\"Intermediate\")\n", + "\n", + "ax3 = plt.subplot(1,3,3)\n", + "opt.update_design([mapping(rho_vector,0.502,beta)])\n", + "opt.plot2D(True,ax=ax3,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "plt.title(\"Erosion\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our framwork in place, we can define our `nlopt` cost function wrapper that also includes our mapping layers and their corresponding gradients. We'll systematically increase our `beta` term so that the thresholding gradually turns on, as suggested by the literature." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(v, gradient, cur_beta):\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", + " \n", + " if gradient.size > 0:\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", + " \n", + " \n", + " evaluation_history.append(np.real(f0))\n", + " \n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.show()\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1\n", + " \n", + " return np.real(f0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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JOM/Hk+y5ijidHqOxPIddKulp5XUklp+TTtzNxfF+vVqBqIq9hymersvdQ30/gIJ772FCkP95CNJviFUPWk7c8/e7tXNy0J3XHDnFO212YRyv7jZz8u12O4l3SnhN0XkLrQqHJLy69hpXM/vgk29zVt7HZbt4qeFP7l7rPQ+if3sE6TdA7mFbljrKWXhVyAlaLq2409LX+/t73N3dFRpdcjlv5v9JeIp3VOwp3inhl2UKNJ73hUULjbQvn6Igr4nHVE+BFXxu+fXeld3fVX8fWI0g/ZpwtzRnhRRq8ZRYWnjiJbZKuIeHB9ze3uLm5gY3NzeFFB2JqB4DG2pYaku3/vj4GEdHRwtufY7wPt6bNfY8z5yV1+2weFxeGwnPa9PrZwii90sXyxzZl+kA6/w88A2C9BtCY1/vHvO/8aoyf7+SXUU75uJJ+tvb20R6L8Cp1Z6m8rDqbnd3F8fHx+lF8Y4jqXNuuO7cQ+1A8//AkxuuCwx3wuGse2oEel2aTuTvXckvu9cOr22IHP3mCNJvgJyFdzfbf+/98FpuqmTXNlmN45XwKt4BTyRkM02n08H+/n4i+8nJyYKVd7FNwwr/bCe9lg/To9ANMmi5VbGnKOnCmzbp8FyWYVlNgP4+sBpB+g3gVt6rzJhS0gIZfZhJMFpBJbv3rGszDUU9VdFp4TiAkoQ/OjrC6ekpXrx4gZOTExwcHCQrv4zwHlJQQ+BGFpqaY/OO7n9Hb8Nn9enoLgDJ0nOOXo74y6x/WXaEvwusRpB+Dbjl1hSUF63wIVSlmu/VWXZq2XUqjabnVKlnbK1imHfPHR4e4sWLFzg7O8OLFy8KVl7HYOlCxXMh4a+urnB9fY27u7vCrj3cAZdxfLfbTR16jOu5GDF+571hJR8/m4uj39tcmKRhDEmdK2UOwq+PIP2acNec5OVXYHG7aY+bfQCG9sPTspZNwFGlm3G5i3YnJyeJ8CcnJzg8PES32y0U4QBPyjqFQxL+/fv3eP/+PW5ublK1ny4yWsevE3d03zx6Ofyc3ERejeVz4RDvmS8CZb0LuXseKEeQfg2oZdTSWH7VNtOcOFVGeLrwauUZu7sARq+BhGcszbTc8fExzs7OcHZ2htPTUxwdHRVSdEpEra1/eHjAzc0N3r17h8vLS7x79w63t7fo9/vJYjOEoFCorbmdTif1w/O8+/1+Wug0vgeelHkPjXJNNd5xx/fzaxnxA8sRpF8BtUCqsNM1933hKVApydSl1xFX2i2nk290mq12szE1R8Kz8IYxPC380dER9vf3s6W2dLvZpkvCX1xc4PLyEtfX17i/v09Wnp+rAziYEdjb20tWnmJdripvlWCnqUv9mXoCqtaHW//tEKRfAZJEi2ZUZONWzZor1w0etKRVCa9iHRcPVeed8EBxS2mKdiT72dlZculdrQcWC4B6vR5ub2/x7t07vH37Fm/fvk1WXvenYxihIcTx8XHqx280GphOpykc0dy73kNC43L9vabzcosFr8GPE9gca5G+imWPfKgmkwkeHx9xf3+Pm5sbXF1dpRfz5rREdINpidw7UMGONfQ+Q97dYZ1xx5h6b2+vYOF/9KMfJcL7+CvgyZIyxGC13/v37/H27Vu8efMGFxcXuLq6wsPDQ7LytVqtMHHn5OQEL168SJaeVp7hTbvdToU7fi+9uMcbkDz9qa69lysvQxWf1U2xFumrvKrO53P0ej28efMGl5eXSey6u7tLrj1VbR177WWtKtJ5Go66gAt2ShKtp1fRzi289sirt6GTd25ubnB9fY2Liwucn5/j4uIiXdPj42MKV7jI0KU/PT0t6AWctkMPhdfu0KIenx5E0AvhYqnaCO8ntYNcYZQXQwXKEe79CkwmE7x79w5ffPEFzs/PCwMi1RrrPnAEh0tqt1xuUwrNCBC50lqKdrS4p6enCy69178zvHCV/uLiAm/fvsXFxUWK4/v9fromhhJcZEh45v47nQ7q9XrKtWtYk+s41O4/3dzSvREX+fR3OkBTRcJ1PIDAE5aSvt/vL8RVVYAKR19++SU+//xz/PWvf8X19XUhZw48EZ77wKmIpYTT/dxzLasqYumceiU8Y3im5U5PTwsuvS46Xml3f3+P6+vrRHgKdwxTVJDk5pb6uScnJ4UQQnecHQ6HWRec10CQ7Dy+1+p7loRgJ2C9Xk9ZDXpGWidRxWf1OVhK+pubG/zpT3/CX/7yl8KElY8dmn77+uuv8dVXXxXceYIPLuvO1drREpH0vs+c1tDrIuPNLMzDlxHei29cS9D4/fLyMll45uMfHh7SzrO0pozltWlHp+5omk6n16r11WuhxddiIi6Sufn2SmQAaasskl436vQ6Bn1VOSxdhpWW/rPPPsNnn30GAGi320nV/ZjBwYzz+Ty5trplk5a/tlqt9CC65dIKPBba6EYUKm7pMAqtetvb20vnQKWecbWm5dSl16IbpuSo0JPwzMVrnYF+NsU71vC7SMh7wc90d5v3kYTlsalNUGhkilMrHbVWn8fnvW00GoUNO1UTybXqBhaxlPQUsQhuY/SxQ1NDNzc3yfL53PpWq5XI7VVvJAALbHJbR2vOWQtgmBMn4U9OTlI8TYvLnWk8D88c/MPDQ0Gse/PmDc7Pz5Ngp7X8tPAA0jnkJu5wAAeJynvFz9TmHF4T/+2E140yc/dLZ/5RyOO9Z7jEr1x0czMGAotYSvparYZut5u+b7fbhdjzYwYf6mazWai4azabmE6naLVaBVef7ifDAo1RSXZtzAGe3F9awU6nkywsY+mjo6PUE68z7trtdmEhcsLf3Nzg4uICr1+/xqtXr3B+fo7Ly8tUX+AE5blQvOPn+yBNd8cZRtDl5rVqzYJmH9TKA4sttmrxdWHRkEHn8eukXl1Ug/jlWKneq9XTxomqYDwep5iVhSj8XnPKZXXjukjS+tGdpyZAd5p7zNHCknQHBweFgZadTqegH6hK3+/3cXt7i8vLS7x58wavXr3C69evcXl5mQpv1MLrJBytA+DEHX4uP1MXNe0U1I0wAKTjqZVn7z3vAY/B93h/Q65pifeQKVCSPzc+LJBHpOxWQB9EWie1jK5a5yrJ/D0q1NGV5nZTSnZaWbaweiwMPFlBCob39/e4urrC27dv8fr161RfoE00uoU1Fw/d0Zbn4SO2VK/woiPf1FJTdNp7r1aesbtP8eH91ntKHWQwGCTPgaXMtPjh4q+HIP0GUAVa8836wALFbZ60Zp4PK60ep9xo0wzJ5jvKenaAn6Nlwh7Ha2ktqwd1bBXPkYTPDdJkZmAZ4X12H607p/nooA1mgfh3s9mskPIjfPGs1WqF0EHrHnQkuIYFgTyC9GtCB0jwQVaLq7li7/tWkY6uPJV5t+4ceqE70OggS0JjX620u7y8zKr0Snh167mIUUvguezv7xeEQl5jbqyW9iDQylP85OK2s7OTXHvgqXR3OBwW0noEv1frT6+ANf7ameizA8PSlyNIvwY8x8wHWPeA09wyrZjOrqOLS7LTndfYWbeO5mCKnHX39lg2z2jhjTbP0AJ6mpALkQp3bKbhoqPxN61yroeAMTVQ7L3nAqcLCM9hMpkUehXKeuXV6vNnLHTi5/vWW0H6cgTp14C2tFJw29nZSUKVTohxgYxkpyuvhNe+dB4zZ93dnVel/vHxsdA8w0o7puU0zgaeWnTpsWi7rM7UU6vM94/H40Q0juVmSTIbj3yB3N7eLrj2LFzSbAffo7pHo9HAeDxOaT3XS6jiM8TQoSMh5i1HkH4F/CHe3d1NL8anOvdOXdxGo5GsO60p43X2o+/u7iZV3mN3JbxmCnRDDLfwJHzZ5FxV6SnascRWW2ZJUGoGXuGnAzvZpKNpTY/lOYlX72uuMIkZkul0mohP0Iuii689DHyFpV+NIP0KaFmsWnpNY6mYppZIy2hp4WnVuRCoFdTZ8U54Wne1tlptxzz8zc3NQh5e3WbdDMMr/XS8Fi0sj8NFhoTnixN2dKaAZib8ujytqedHC6+WWsdvuW6i6cJcrj6QR5B+BdQKuauuFlHjegCF/DuFOVp1TpClBWSHnsay7tK6gEbCn5+fJ8JfX18vNM9oZZyW92p//NnZWbLynGzrhOdCo6Rnqo4CIe8TwxOvrweKNfZedKNlu56+43uV0PR4cg1MQfpyBOnXAImjRSYkvsa++rD5IqFf3QK6UOcPOR9ur6f31BybgtzC50Q79sfrEE0SHkBawJzwnImvc/g1lucC5jve0Bvi4qUzAGm5udho6pM99no/eH3ay6DhlXoT0XSziCD9GvBmGCW+usP+97oDjLq7ubZSflUr7ykyEp4dcyT71dVVYXMKYNFlpmjHFCHjeE3P6SQcdek5y8830eTfMY7nZ/lCppadDTJawecNOnr9ehweSxcQJb0X9QTyCNKvgBbY0Bo56SlSuTCl1px/o916auVy1swV+ru7O1xdXeHdu3eJ7Noeqw0qWtOvRUBey6+9+Px8TrRVwqtSr/lwTbcp6VUE5HVqx6HW63sPAF18r01Ql98FVG+vDZQjSL8GlPDqtvPFuFwXB2048Tp5fgUWh0CS7Fr1RtLRyl9fX+P29jYVp5CEANJ5sMqOhPciIFp3FhkBSPX7+pm3t7dZwvO+uEfhQ0G1Tp+LmJLd+xYoIObSldqYAzx1+HmDU2A5gvQroIq3No0o4XVjyLJCE2/M8YERarFoDX0zS42pdVy2Dptg+EEvRBt4SHYfnknr7kNAuXkmZ+dpzp8k13vkhNd2WV4X43nfdVe9nWUluSr+6c80RAgsx0rS601kW2kVwAep3W6ndFuuas7z6gAKFkqtkFtxkkDdXf5bi050F1mSXfeH07CDdQHawMPyXhYAUbBjCo0CmFb3XV9fpz3t3JvIzQFQjUJnB/jmIKqw815RE9D7BhTJroumLwJan+/lvIFFxBCNFajVavjxj3+MTz75BC9fvkzjn7URRVttWXFGaI6dDz6FMG1WIZkpcul0GB2zpcUnLhhyA4ycK68pRm9tpcDGGF7z8LniG53h58Idz43E92vQtKZqAMBTaOLpOhXovByX/0dlJbyBRWw0RKPT6RQGR3ys4Cy2+XyOn/70p/j5z3+eCE/xy2NhoLjrqqbbVJBj3bpvZ6UFJmoRSUrNP3tdv07KXWfoBvC0GKlKr3l4qvT9fj9ZbRJJh1syE8GFj+erHgtJz3vB8IBz9njPJ5NJ+pl7C1q34P9XGl6oFhDEz2Mp6Xd2dvC73/0Ov/zlL1NKpirKqApRzWYzVdHRpeeDlbNEGrP77ja65zwJ75tV+qQdwh/wXO28jtTSEVfqleiQCk0HUjNgEwszApPJpPDZJLs2BAHF0Vn0ZHTABhdSAAULr0TVxZKLnrvxej9U8dfagCB8OZaS/vDwEL/5zW/w61//+h91Pt8b8AE8Pz/H69evMRqN0kPv8bm+1Lrr+GttUtF8N5tVvJrM41RVtbV2XmfonZ6eFghP3YGWGCgW/OjeerqvXi4t54Km1hxoek4Jr91vvF/Ak0IPfEN4Wnheq4p+KggqdNrPsmKnwCJWWvqqgym69+/fJzeTirm67XRhSX618E4s3c6KcS6PnVO0dWcY7QHQjS+43VRupp0WAXk5LxcirbTT3Ll2wXmxkQ7XoNegu/nofdGaebXQ6inxunWevab1dCHk/dD5Bu7RBPKIlN0KbG1tJYGMYhRj1ul0mlJcjMu1Flx3tvEpL773vLeDemyq3XE64YZuvROe6Tgq9Iy3vayWaTmmAXleWlpLV1wJ78M11KtRcVIXNeBpMi676EhmnZOnll5DHb5fFX9No5L0WusfWERsYFkCtRSsaqNVG41GAFDoeGP/Oomj3V+53W2WEV7z1VpGq11+OjyTQy98a2oSiCq9Ep65eFXpc3X7Gi/nptnqsWnltYbAZ9jz2nKVdvR2fAw2f69ej94Tpil1eGdY+nLEBpYrQBdSH0xtO2Vum8KcPvBKcs3Fa2mqWjB+1Qdbe9PZjsv2XHbu6XRcAAXLSSvMlBzTcnTpmYdn7p9pOc3D06J61xxTfb64qVrvnXTqueQGhLhWovG8996zgUjrJoL0qxHu/Qpo9xgFJ5JeY3q+OK1G89PaFKKxqVeP5QZKqDVjSeaOYR4AAA2ZSURBVC3bc9WyAShM7+H3eo4s8tGUIS0z35erpadKv4zwPrKKn+2CpOb1vWxXZwZ4eS2PAaCwEPo4Lu2BCOQRpF8Bry8ve0i16kzHNuUeXE2dKfG5oDBeddLrwA0vsBkOh0nd57FVtNOtslVI5LmywQVYHOjpFYdcXMoIr7PsXZNQz0H339PSXZ57jvCarqRbr2PGvEknsIgg/Rpwd9RdUn/xd4S6yto+6+OjtLiEpKB4p3vWkdS0tAAKaS8SSGv4dT695s19gq0W/jjZeVzVB3zuvY+g1uNpr71rD3yPpi1zs/0YbrHkWHsgwrVfD0H6NaDpMy/5zJV/arUZW1aZnsqVkvpxnCT6QNOyK9kfHx/T79UD8d71nCKe82TU7c6l+zykUY9B24W9xVebkzhxCHjSHbSS0YtxXLXXASU5Kx/EL0eQfgPkiK8uMDe01L+nBfaiGz+eCnhOfnXZSQ4W12j/OlBUwklS7ebzun0lubblOtkZwqh11xSfKu7Akxuum3vk8vtMf6oH5YU4uRCBx9Smpyi/XQ9B+g3hhNfdWDW/TUJ52kktfG4R0Z8Rmnoj2XWB8Ifcy4H5M62ddwLpNB+N3bVcl0TPbRzJBcWr93T2AInPKj6KgZoVAYoCp94rt/Q+liti+fUQpN8QapXVbaXlAZ76y3OpOYVbppxW4M02uZoJJ71+74sUz5sWWAdzajFPrj6fhUXq0uuipoudbrmtw0Dp3jPlyc8bjUYFr8bvi95zH8sVcfxmCNJvAHfHfWae5qMbjUaB9Lk5BDkLDaBAdO+0W9Zx5qECX3Sn6/V6sr7ubqtmoITXykIV7bzSDnjKPujobzYEKel5rWzVHo1GCwTmNXi4oGJn1Ng/D0H6ZyAnUrVarYU2Ua24W2aplcS0mnyfpsD0mGUdeOoCA09E9Ko+Tf9pHzsXGAqGmvLTvnittFMLzwWQRTM6mmtnZydlLLgF+HQ6RbvdLoQsWguh16ehkIc/Kv5VsYJ0EwTpvwXKxDD9Pf/GY2yguOGDtvLqmClt4PG2Wz7sms7iuZCM9Eg8r03Sa8u0LlAs6tEiIyW9qv/A0/51auV1Nx+2JVPUVBdf23Tpceg1eJWghkFlTTlB/HIE6TdAWTytxCdplfy0aroAaOrOCa+VdTrPXVt31cqzKCfXhUa9gekt3UFWt5rS/n1V63NTb3LFRt4BSEvPcmHu/cfUJYAC0dV1bzS+2eWG7n1O89B75T0MQfjlCNJviFwhDqEEoHWiNdQiFLXUmrtXq6UWX135sgdaFxXPKuhuOjqDn2RTi5kbU60hhnoqtLz6WVoy7GGEphY1ftcaAQ9TACwscLw3uXPLDdsIFBGk3wCuqutXRVnaTd+rgpwSW6fw6PGUIADSguJxvI6y0q44/ts32lDCawGP70KjlXFa1OMLjLffajrN43QeTxcstfh+b/lvte4aAuWGbQQWEaRfExorOvGXuZTukpJkQHGE87IKNLrgFL6UhK7UayGMl7wq0bXsldZdp/TmGoSAp8yEkl6Jrw06KrjpYuYueS6zkbv/Hv74SO1lmY3AE4L0GyBHdne/gWJBiVtpd9NzRNcafRKdls+J6AIeSZ4ju6cAGXLQUnpNvubf6caziEbz5mrVWXjDz6M+ADx5CepVaNGRezweSrGIx/UO9UbCvV+NIP0acBdzWVwPPBFR1XV9aczPY6rry/eR/B7fO+m9JFgLbYAi2QEUiEvS64hu34iCHsR4PE7nrgsBP0/7+ek1aCqQ/9b5gfqZOt7br1vvMxcJT18G4ddDkP6ZyLm9WpQDIBE3B5ahLntQ3bPQsIC/57G8acZHWWl6jL8j6dWCknjAUxpOc/G53DxfSmj1Ulh8o+W9Ol7Li33c+mtfPpV9FTyD7JshSL8BygQ7JRx/Tgs/mUwWKvmYttK6eD+uf2aZeFj2XhJQCe2VbJ5VUCIxv68CnjcOaejCRYneBI9Htz/XxKMjxVxLKEtP5iy/39/AcgTpN4DXtDvh+SCSHJ6CYgzslXpAsSHGv/L3LiL6IqACH70N1gjweHpOPNfcIqMCojYP6dReXXj0mkhaDTV0Sq2GGzpPUOcH8vcqOnJx8fvv6b4g/nIE6TdEzmqTzAAS+fmwanMI41rtvluW8uO/gWJdvhKcJNOfa55fiZlbqEhuJwsXBbegdNmBvNXVayxrjNGQQmsCdPKOlgPruTvRdQpPtNeuhyD9M5GLo4HF1B5JqH+rcauqzgolHaEWlqHDfD5PnoWKXzlvgsdT5Z0LkH7v58CFTMVJrd7LiYue0tNFBiiKcTpizHPuekyeiw8MjZn3myFIvwbcrfdKMnWXXel35V5fXAzKxKjcw6tpP/0cXWR0eEYur894fTqdpgwBBTt+hh4750WQsGqR9bz0OnOk15DAdwlSwlMb0fvNsmJ28bGsWGfuBfHLEaTfAGXur4p4TnqFx+T8qg+pWsycIp3LX7uV14YdT3t5OjF3vq7UK8G1+cen3uY8Ci4u/Dd/7oU2+pW/433mi4VHnArMUeCcea+kD5QjSL8hlKBlrqR7Bv5eL9QhNC2nP/PvPSwoI75afj0W/4ZNQfw7WnB1wZ306kXkFhWNv7WuX7vm9Dp4DPUYnPC07uwj2NvbSzv8cBtukj7c+9UI0q8Jf5BcYHPry5+VVZb5z3PxfZnX4GW8+pllyMXHbJElKb1oRxcN7/xTAVGvn8f3+8VZgXo97pHoOSrhGb/v7Ozg4OAg7eGnW4f7NNwgfjmC9M9Ezsq6Wu6E9mIS/aq/84abHMpy1d6Y4w+/npdW5GmIwr/Tc1Lh0b2M3HnwGH48v3+5enknPIdy7O/v4+joqLBD78HBwcJ2XkH45QjSPxO0cKqWe118TgTzly8CJJSn2ggSVYlNscy9CP5MK+E8lnedQn+nhM9pEQq3sHTvcyQs83b4Po3huXUVCc9dek9PT3F0dLTg2od7vxpB+jXhFhwokiKXd9eH2q29LwwOJTe/J1Rd90xAWWUaS2P1M/Xc9Np8scpdu56X5/8p2mljjhbWuG6Rq33QgRxq4c/OztK23BzDFRtdbIYg/TOQi2OV2Pq7XMytDzhdaqr4bErJxchlhFZRrNlspsmyuayAnrtX1+n55xYFwkMIrYjzKTh+vjyWpiv1mNq5xy3ClfBnZ2c4OTlJsbyq9roYBsoRpP8WUOvmuXqSmN/TzdYSXXf9c0KgE51EyuXStdhlMBikDrjBYFCwgk5+FxE15HBtQUMKJbnO0Cdx/TOB4gAMEl/hc/aOj49TDH92dpZi+TIrX7YwBp4QpH8G9IHnQ8oiEnfdCf+ZWlHVB5Z5Bb7IOImd9Dq2Wq2hEkS3k8oNs1Dy6Pt1Vx996XQeXZy4sPi8P4Y7XnjT7XZxeHiYCH96epos/MHBQdqemg09WuYbWI4g/ZpQ0vnkVwAp5bUqfeZltfzqFt4/15V5/blaaye97lab27GWffLen56Lu7UE1nfU5VcO1PD2Xi5KLO7RhhoeP6fUMzV3dHSUyN7tdtNn6WIWhF8PQfo14WWgzBsDSMTxUlS+j19dlCtLbS17r7fF8j2M0Ukm3Ua61+vh/v6+sABoWytbWknGHOl99h63quIATFbF6bZVXtXnI731frmVZz6ec/P39vYKG2aoR5G7J4FyBOlXIKcqt9vtVFrKEVJe9cb3Estizpwwpz/PKeX+Hs3xk1TcXXaZtSfpuUVVbvHS+Xu6W2y32y3sD+/bafNa1APRYRnUEaj2K+l9fLYO21Sy56x8EH85gvRrgu6tTpJpNpuF+WyrrHSOtOs8oLm/LVPwtZ5d+9V980ndj24wGGR35PEiGda++wz9nFvPsMMbdLwRCEDBg6K3oJteKtE1fnd9Iiz9egjSrwFNpelgiWazmXXp/b1lbr0/oJssAA4XBrVgyHvWfWiFNs/o9aho51tO6/x8klKtLkmv2YlcVR+viQuL9sf71B0nulr4IPz6qC0TnQDE4DHkB1gsS2sRZZb5OWRf9zxz5NdpNrnXsv3xSGQnpG5FlcsKlJ1P7r6pXuKvZdZ8U4+pgsjelCD9msgp7auUemLZA/khH9ZcWjBX37+sFNgLgtTFd2LmCKnXtOqeecXhsgyF36uI4ddC9saEe78m/CFbh+zfJcrqArzMdtXilUsZPkebyKUjc5+17GvubwObIyx9RZD7f9504foQ5Fu3hiHwQRDufSBQMWRJHx0KgUDFEKQPBCqGIH0gUDEE6QOBiiFIHwhUDEH6QKBiCNIHAhVDkD4QqBiC9IFAxRCkDwQqhiB9IFAxBOkDgYohSB8IVAxB+kCgYgjSBwIVQ5A+EKgYgvSBQMUQpA8EKoYgfSBQMQTpA4GKIUgfCFQMQfpAoGII0gcCFUOQPhCoGIL0gUDFEKQPBCqGIH0gUDEE6QOBiiFIHwhUDEH6QKBiCNIHAhVDkD4QqBiC9IFAxRCkDwQqhiB9IFAxBOkDgYohSB8IVAxB+kCgYgjSBwIVQ5A+EKgYgvSBQMUQpA8EKoYgfSBQMQTpA4GKIUgfCFQMQfpAoGII0gcCFUOQPhCoGIL0gUDFEKQPBCqGIH0gUDEE6QOBiqGx4ve1f8hZBAKBfxjC0gcCFUOQPhCoGIL0gUDFEKQPBCqGIH0gUDEE6QOBiuH/Azx3deYgxb4PAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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Kygo2NjZMFVy7br3X68Xk5CQ++OADLC0tIRqNmlLX82AxTTqdxtbWlhnFdd5rZx3/wMCAac31+XxmrBeLbuQ8AXmmt07K4Znf2qBjt9uNq6/u/dVQ0b8B6I5flnu3/lurx5BufaVSQS6XM279q1evkE6nm6rg2iEej+PDDz/EgwcPMDIyAp/Pd2kLLkdrs8aegbhWsNEmGo1iaGgIAwMDCIfDpvZeNu/IlCb/lqJnoxKfX47XkqPIZGPRRe+p0hoV/TWwNrZY++T5/bK0nHwseY7P5/PY3d3FysoKVlZWsL29jXw+35FbHwwGMTMzg4cPH2JmZubCFJ2kXq+jXC5jd3cXOzs7OD4+bvl7dO2DwSAGBwdNa67f7wcAE8izzgWQzUmsxHM6nSbg12oGn/y3VlOFlPZQ0V+TNzHIQVbcUfBHR0fY29vDy5cvTbT+8PCwqW31Mux2O0ZGRrC0tISFhQXEYrELo/Xyek5OTlAoFLC7u3thAI+FOJFIBMPDw4jH4wgGg01Vc7TWshmIwudcPI/HA6/Xa8pw5VTfVqLnY6jYO0dFf0UusvLt/r18HCn4fD5vBP/kyROsrKwYt76TzbE+nw/z8/NYWlrC0NDQpdF6eW3cmpNOp18rxpE4HA5j5UdHRxGNRs02HLrlpVIJxWLRXD97FHjGZyWez+czR4KLxol1mvpUmlHRXxN5Fm/nd61/J4N20sKvrKzgu+++w7Nnz64UrXe5XBgeHsb8/DxmZ2cvjdZLzs7OkM/nkUgksLe3h0ql0vL3WH0Xj8cxNjaGwcFBBINBuFwuE4QrFArIZrPI5XIoFotm6i7wUzEPBc/NOjKIJ8//FDi7GtvxWpTXUdFfEWuRCS39ZaJsFbDjYIlcLmcE/+TJEzx9+hSbm5vI5/MdufUA0Nvbi8XFRSwuLmJgYMBU3rXzuk5OTnBwcGC66VpZeQo2HA5jfHwck5OTGBgYQG9vL+x2O46Pj1EsFnFwcIBUKoVsNotCoWDm3csAntvtRm9vr5nYw9daqVSaGpdo/TmtSA4BVdpHRX8NpHj53/K7/D35+zIfzZRWNpvF9vY2Xr58iWfPnuHFixfY3t426blO3Hqn04mRkRHcv38fc3Nz8Pv9bS+f5KCM7e1tLC8vI5PJtLyRORwOBAIB0603MTGBvr4+Uy/PgqJkMtk0MZdReAqVZ3paeqfTac7/DO7JXgZ24smfKZ2hor8CVtec+Wbp5rcSfivrzkEYm5ubWFlZwYsXL7C2toZUKmUi9Z0I3m63Ix6P4/bt27h7966pvGvHGjI3n0wmsbKygvX1deTz+dc8GI62YuPO7OwshoeHEQwG0dPTY0ppM5kMdnd3sbe3h3w+b6rqbDZbU0suz/ScvtvT04Narda0MIQRe3bZccKOWvrOUdFfkVaiB5q77vh7/C7HOdP13dnZwdramimv3draQjabNQ0mnbj0drsdoVAIt27dwkcffYSpqSkjxHYj9vl8Hi9evMB3332HZDKJ4+Pj1+oNHA4HotEoZmZmcO/ePUxNTZkWXQYk8/k8kskk9vb2kM1mUSqVmnLtco4/z/Ws2uPRgV4Ah2oCeO13rcNFlctR0V8Ba624HNdss9leKxrh77KhRFr3ly9f4sWLF1hfX0cymUSxWDTufCepQLrbi4uL+M1vfoN3330Xg4ODZihFO6+nVCphc3MT3333HX744QdTdisDb06nE6FQCDMzM1haWsLi4iKGh4cRCARgt9vNthy69pyJLysIObueNxA5H49WnWd23iBYwkuPQIpe6QwVfYdYz+SyPFRuTpXTZOnOl0olM0n21atXePHihaml57y5TrrmgJ+KY8LhMObn5/Hpp5/iN7/5DSYmJkwhzmVWkGfwvb09PHr0CI8ePcL29rax8ryZsb5+YmICS0tLeOedd8xZniOsbDabOc9nMhkTk5DNMXLwJi39eePCeWOgN2VdmaXufeeo6K+AdQ9co9Ew1kgub+DNgTPtMpkMEomEse6vXr1CMplEoVC4knUHfgyE9ff3Y2FhAX/3d3+Hjz/+GHNzc6YM9rL6+kbjx7HW6XQajx8/xpdffonnz5+bNVXy2MIA4YMHD/Duu+9idnYWkUgEbre7qW6eos/lcmaMV6uSWT4m3Xh5g5J19XTfZSHPRUM8lYtR0XeIFHKpVEKhUDCjohhZptvJrbBHR0dIp9NN7nwikcD+/v6VrDvwU8osGo3izp07+OSTT8w5nlH0ds7xtVoN+/v7ePz4Mf7whz/g22+/RSaTaartp+AYIPzggw8wPz9vCnF4tKE1Z4ERb2ZyeYW08hS8zLnzceQoLVm6y6MAg3wq+s5R0XcIhzoUCgXs7++b0ljmm5lOAtA0pnpjYwOrq6vY2NhoimZfxbpTMNFoFO+88w4+/fRTfPDBB5icnGyy8Bc197D4ZX9/H99//z0+//xzfPnll9jd3W0qoAFg1lfPz8/jgw8+wK1btzA0NGQ218g4xtnZGcrlsplp16qgSKbeeD7nzYA3VKbt5ABMWcWnAbyr05botX0R5oNNIe/u7mJzc9OsdJIbWhiMKhaLZi791tYWdnZ2zHz6q1h34nA40N/fj7t37+LTTz/Fhx9+aCL1l6WxaEmlS//ZZ5/hT3/602vddHwMr9eLGzdu4N1338XS0hJGR0fh9/ubKuL4/ezszIy6ZrSe8JwuBe/z+Zom4LJ0l7PuWcxj7WSUr0fpjLZE3+13UwbtyuUyMpmMmRfHJhgWktDS22w/bnOhlU+n09jf379yZF7icDgQDodx+/Zt49JPTk62JXhmEcrlMvb29vD999/jj3/8I7744gusr6+b+IT1+ehRPHjwAJOTk2ZktnXXHb0H1trL/ntZWedyudDb2wu/3w+/3296Angc4oz7UqlkhM+KPNlhp/30V0Pd+0tg+onNJ2tra3j8+DFWVlawv7+PWq3WdD6Vc90KhQIKhULTh/c6Y5vtdjuCwSDm5+eN4OV2mvPcXRmwKxQK2NnZwaNHj/D555/jP//zP7Gzs4NqtfqagLgUY3Z2Fg8fPjQBQpkGlM/H183Z9jJ4xzM5S265zjoUCpniIc70LxaL5njAgh6W9sqdduy+U+F3xoWi5/9x3famMhoPAK9evcL333+P9fV17OzsIJFIYHNzE7lczsxyk+kmVptxzBM/qNed52az2RAMBrG4uIh/+Id/MEG7cDhsct7nCZ5ucy6Xw8uXL/HVV1/hiy++wJMnT5BKpc7tlXc4HBgfHzf5+IGBgZYz8ml16Q1RsHIyDgOPfr8f/f39GBgYQDweNxNzWbTE4CiXWzAQyLM85+XJkVnS9Vcu50LRHx4e4ptvvsHz589NcKgbbgAUfaPRwObmJpaXl7G3t4f9/X0cHR01WR/rQAgA5oPIKPR13zOXy2XSch9//DE++eQTzM7OXhq04zWUy2WkUik8ffoUf/7zn/Hll19ieXkZR0dH516bw+FAKBTCwsIClpaWMDY2Zmr4Wz2XjHcwSMnHAX70Gvx+P6LRKEZHRzE2NoaBgQEEg0FTDchKxXw+j3K53BQI5NGAG3qLxWLTgEztuGufSy397373O/zud78D8ONa4fPmpL1N8PzYaDRMcI4fahnZblV+y39/E8M1uP11cHAQd+7cwccff4z33nsPk5OTJi13keBpebe3t/Htt9/is88+w1/+8hckEomW7rzE4/FgYmICd+/exezsLPr6+loKi6+T23dyuRyOjo5MYQ9r530+HyKRCMbHx3Hz5k2Mjo4iHA7D4XCYRRdy7VWlUkGtVjOfN1p6uv9HR0coFAomA8KMiXI5F4q+0WiYpYEAzEritx157qYreR6tOujeBBT73Nwc7t27h3v37mFhYQEjIyOXBu0Y8CqVStjY2MCf//xnfPbZZ/j222+xt7fX1o2bNfyLi4tmweVFz0crn06ncXh4aKoUWScfjUYxPj6OmZkZ3LhxA7FYzEzXYWCTm2y4zUbOtpc/Pzo6QjabxeHhIQqFgokLtNtJ2O1cKHqbzYbe3l7z3263u+vGDnfinr8Jwfv9fsRiMUxOTmJxcRF3797FwsICxsfHzflXRs5bXUO9XkexWMT6+jq++OIL/P73v8ejR4+QyWTaugav14vR0VHcu3cPN2/efC09J5+LHkWpVML+/r7pDqSXxKPJxMQEZmdnMT09jaGhIfj9fthsNmO5WQpMCy9jIfK5yuUyDg8PcXh4iIODA2SzWfT396O3t1dd/Da5NHovrZ4uDfyvgUGuvr4+TE1NYWlpCQ8ePMDMzAyGhoYQCoXg8/nazsGXy2UkEgn8+7//O/7lX/4FX3/99bkz61sRjUbNxJ14PG4yA61gMU02m8Xe3h7S6TTK5TLsdju8Xq8ZsjE/P4+5uTmMjY2Z4CNFzdp6Oe7aGg+hN8CGpVwuh4ODA+zv7yMejyMUCrVVhahoyu4XQW9vL27evGlq2m/duoWxsTH09/fD6/W21VhCCy8bZ37/+9/ju+++Qy6Xa/ta3G43JiYmcO/ePYyOjqK3t7dl8E5W9XEZx9bWFvb3902xUm9vL4aGhjAzM4O5uTlMTk4iGo3C6/UCgCnCYQyFo61lKk5G5nmDOTo6ahJ+LpdDJBJpa7S3oqL/WeEG2fn5eTx8+BAPHz7E/Pw8YrFYkxvf7gCM4+NjZLNZU1b7zTffIJ1Ot309drsdfX19mJmZwcLCAqLRaMv2VWvwjn0FiUQCR0dHqNfr8Hq9iEQiuHHjBqanp3Hjxg3E43H4/X5Ty9BoNMxrlOOuZRrOKvzj42OTFiwUCuZ8n8/nEQwG1cVvAxX9zwDXLY+OjuL+/fv4+OOPcf/+fYyNjZnBkp20jFKAR0dHePnyJb788kt89dVXODg46Oi6fD4fpqamsLi4aEptrcExKfhqtYpsNoutrS0zD4CuPUdpTU5OYnx8HLFYrOm10a3n419WYSfFzxFj/KK1j8Vi8Hq9KvpLUNH/DZBpPZ/Ph1gshsXFRbz33ntYWlrC3NycGSrZaeeYtLiJRMII/qIFFa2w2+2IRCK4c+cO5ufn0d/f33SWt04AqtVqyOVySCQSZrRWLpdDvV43c/CZj4/H4wgEAqYHno/TqoZe1j3IEWRyDiGHbrLaMZfLmWKpi0ZnKz+ior8CsqwUaJ2Xl0J3uVwIBoOIxWIYHx/H7Ows7t69i9u3b2N8fNyU0XY6EIJiqNVq2N7exqNHj/CnP/0Jq6urHadXQ6EQpqamcOvWLUxMTLQ8H1Pw7JdPJBJYXl7GysoKkskkqtUqbDYbAoEAYrEYRkZGTAGOPK5IwUtBW7vo5HttvQaKnq6+dTqPcj4q+g6RdfZcv2Qts2X1mMfjQSAQQDQaNSmrhYUFTE9Pm+IUtqdexToxd51Op/HXv/4Vf/jDH/Ds2TMcHh529DherxcTExN45513zGAMWnlp4VlazE22T58+xZMnT8xG27OzM/j9foTD4aaddhx4KacJUbzyvWMHHt1+ORuP1wD81L6bz+dNfT6/tFDnclT0HeJ0OuHz+RAIBExAih84upasMR8aGsLNmzdNfnp8fBwDAwPo6+trKwV3Eayn5wCMP/7xj/j666+RzWY7ehyPx4ORkRHcu3cP9+/fx/DwsJmRL8/RPMPTpX/y5Al++OEHvHz50kTsXS6XCeDF4/Gm1ynddetySlY5yik67KPnCC4JvRtZxMNMAMt29Vx/Pir6DmDuORqNmtQTrQ43yXo8HoTDYYyNjWF6etqkqrj9xTrFtVOsAzAeP36Mf/3Xf8VXX31lpte2i8fjwejoKB48eID3338fMzMz6O/vbxr1xVQai282Njbw/PlzPH36FGtrazg4OEC1WjVNR6FQCNFoFJFIBIFAoGlkF49BnBlYrVaNcBnY8/l8ZqaeXF7J107497zZMqrPG4iK/nxU9B3As/ng4CAGBwfR29vbNOmFI6hHR0cxNTWF6elpjIyMGBdXpuCuI/hqtYpMJoPHjx/j888/xxdffIG1tbW2z/Fs0R0dHcWdO3fw0Ucf4c6dO4jH46bAhW43x30lk0msra3h+fPnWF5eRiKRMEMvOSrM5/MhFAqhv7/fLLGkxyAn67Cbjvl2dnPSU+DMAebtW839Z0CP7z33CLCATIN557SqLp4AABfMSURBVKOi7wC3242+vj7EYjGTSwd+WrbIANbExATGx8fNBlc5xPGqFojlrpVKBclkEt9//z3+7d/+DV988QW2trYubaABYKbJhsNhzMzM4P79+1haWsLs7CwGBgbg9/tht9uNJWY6bHNzEy9evMCzZ8+wvr6OdDptBl7KPfM+nw/hcBjBYBBer/e1mIfst8/lckin08hkMmapB/fcU+xsoz0Pip43CDbodEMn6HVQ0XcAy0r7+vrg9/tNsIsz5xmxHh4eNjvarxKVl1gr3zY3N/HNN9/gyy+/xDfffINEIoFyuXzpB93pdKKvrw8TExNYWFjA7du3MT8/j4mJCfT395uGFRmd39nZwcrKCp49e4aVlRXs7Ow0pcYYbWcnot/vRyAQMLly3jyk4IvFInK5HDKZjJkoRI+BmQ65zOKiJhpeK70BVvOp6C9GRd8mMhrP7ap0a3t7e5sEzwaQ657dGd0ulUpIpVJ49eoVvvvuO/zlL3/BkydPkE6nL+2YY4xhcHAQMzMzuH37Nm7dumVKYmWg7fT0FJVKBfv7+1hfX8eTJ0/w/fff49WrV0ilUsa6W4dUsgXY7/cbC8/aAbrbnJB7cHCATCaD/f19MyJbThTq6elpOe22lZB5VJAjyFTwl6OibxN+sDnIkYErn8+Hvr4+xONxxGIxk4brRPDWwhe5HINDOJ8/f45Hjx7hr3/9KxKJhJlBZ/2Q8wjBDALXVd+7d89Y9mg0Cr/fD7fbbYJ2TMflcjm8evUK3377Lb755husrq4im82aYJtMS1LwHHLJuMXJyYnJmzPQyeUX6XQaBwcHZhhJvV43Y61bNfZcJGTOLuR16XTc9rhU9DIgwkUO3QSDSDabrWmlks/ng9frRSgUMmf8VgG785CVZvzw8szOSHkikcDq6qpZjMGGFp7frZNogZ/WW42Pj2NhYQGLi4smg8BCGRljkDeaQqGAra0tPH78GI8ePcLKyoqZ9iuDabJwhjdDuWaa8+5PTk5QKBRwcHBgXHnrXjun0wmv14ve3l5TsyADeBdZcF77ycmJuRbtqb8cHaLRBkNDQ01TZJiu83q9CAQCCIfD5wreWn0GvL7bjm5vKpUy1nBvbw+JRAIbGxvY3d1FLpdrSsfJwhkATV1ts7OzuHPnDu7evYvp6WkTpKNltw6zpPudTqexsrKCx48f4+XLlzg4OHhtJRXh4gneAFkQQ5de7gbgfnpad+bg6SXIYRlOp9NsD+JzX1Rlx+vq6enR/XZt0tEQDY/H09Ha5F8rdrvduM5zc3P46KOPcP/+fczPzzf1l9O6MPjEszHQXEsurTqLXAqFArLZLJLJJLa3t7G5uYmNjQ1sbW0hmUwil8s1zcdvJTwZSOvv78fU1JSJyM/Pz79WAmt1fyl4dudtbGzgyZMnWF1dRS6Xe010/Hv5vNKt5+MwzXdwcICDgwMcHh6aeID8/NCqyxsjq/A4FPOyGQ78O3oMF/X+Kz9yoeh9Ph/+6Z/+CUtLSyaw0g2BEjnRNhAIYHR01FTTMTJNwci2T1lxxseRFW2MXnOUNnPeW1tbyGQyODo6Mq7vRQE6ecPp7e01/e8PHjzA7du3TUReeh78O6C5Y+309BTFYhF7e3tmoWY6nb7QwjPYRrfc7Xaj0WgYt57trrlcrmkvvVxvJYesVqvVptSfFP1FnXcMpHJ5BgOsaukv5kLRh8Nh/P3f/z0++eSTv9X1/KJgNRrwY7qO7jHw03nSWjlG0cvzMtNt6XQaGxsbWF5exvLyMl6+fGnSYO1W0lHwbrcbsVgM09PTePDgAd577z0sLCyYoiGZ7rpoUm6lUkEmk8Hq6iqWl5exu7uLcrn8muBltN7tdsPn88Hv98Pn88Fms5koOodb0LpbN9TwsRgbYo88bwKsE5BezkWi54IRxlukt6W05lJLr+A195ZQVLRgPKtKt7lYLOLw8BC7u7tYWVnB06dP8fz5cyQSiaYUWDuw2cfn82FwcNAsk+Tmmb6+vnNdeSKbXdgPzxvR2toa8vn8axt4rIJnLIN1CPV63Qy14ECLUqnUFIizXoN833hj5L/LnPtFgWOWRTMIKFt3lfPRd6gNWrmL1g40jptmAIpTW5PJJDY2NvDixQusra0Zy85lEO0g03DBYNDsh3///fdx586dpuEbF/XjS8GzWy6RSJjiGwbuWvW206X3+XwIBoMIBAKmIpE5+MPDQ1NW286CD3k9rbrvLlv/5XA44PV60dfX99rmHeV8dIFlG1yWeqM7ynMsK8645HJzcxNbW1vIZrNGEJ1Yd0amI5GIGZz53nvvYXFxEUNDQ20VAlktvGyPffbsGXZ2dpp20rM9mLEDFiH5/X4Eg0HTb398fIx8Pt9UR9/JRh95XTwanRe4tELRs8GH67GUi9EFlldERuSZnkomk9jZ2cH29jYSiYTZVJvNZlEsFpvWMLUD23R9Ph/i8Tjm5ubw4MED3L9/HzMzM4jFYm1PyZWZA1r4p0+f4ocffsD6+jqOjo5M/EJG6GXxDRdOMpjJPDwXT1QqlY4EL48gsquvHdEzXRgIBBCPxxGNRnUabpuoe38NrH3mu7u7Zgf99vY2UqmUCWjJppOLkJF5j8eDUCiEkZERU1V369Yt3Lhxw0x/PS8vba3yY01ANps1Lj3Tc9ls1mykYaWhXCnt8/ng8/maouOMV+TzeRSLxSsJXl63vIm2sz+R5cWRSKRp/p6K/nJU9FdEWs9KpYLDw0Ozi353d9d0j7HU1Jq3t0KxU2hs4GFf/uLiImZmZjAyMmICdjKTYH1ceX30RDKZDLa2trCysoLl5WWsr68jk8mYZhe32w0Axp1npR2rENk1xzP80dFR0065do8t9CKk8KXo2+kWpPfDXgfW/CuXo6K/BnIgBMc30fJd1OYpy2dlDbvL5UIgEEB/fz8GBwfN3jfufqNFk9ZdWnR+p9hZv8821s3NTbx69Qpra2umyo/tsYz6u1wuk/OWYgdgauqtLv1VBN8q4NhqWEYrHA4HgsEghoeHm5ZnaBCvPVT010AGoeR5VFp1eT6WH0qKnRbV7/cjEolgYGAAIyMjGB0dxfDwMAYHB80edznGiulBeS1S7BwYub+/j93dXWxsbCCRSGB3dxcHBwcmF89KNnlup+CZ85Z74xml5yTadqLsfC/k0Es+tozW83VchsPhMHP4BgcHEQgENFXXAfpOXRH54WQduuwFZ005fy4r0RigY767v78f8XgcQ0NDGB4exsDAgGngYWqM8/hktZ686XDeHD2ObDaLTCaDZDKJZDKJdDptml1OT0+Ni8w8N4dfsB/e4XCY4h2W5ObzebOVllNq2jnDWzvyZD6ddfqtZuFZ4Q3U6/Wa96yvr8/cDJX2UNFfEwa/KOBQKIRCoWAsFxtIKAzpPnMKz+DgoBF7NBo1u+tk6ytLWeWUGDkQslwuo1QqGdebCyBkKSyvgcG5UCiEvr4+RKNR9PX1IRAIwOfzoaenx0Tm+VystOPY6XaDdrTq1uh/T0+PqVSk59JOB6fT6UQgEEAkEkF/f7/pzNMAXvuo6K+JrJJjNJkjnhwOhznz8r/dbjf8fn+T4AcHB41l9/v95hzdaDSMuMvlsjlPM3aQz+dNJVypVDJfHAtN15vPzZgBn3tgYADxeByRSMQ05rDZiLvfZS29NRPRztmbIu3v78fAwIAR6unpKfL5PFKplKnIowdyUaCTr4Fe0HUnE3UjKvprQteevfWRSMTUkrtcLhSLRSM8zpELBoPo7+83ffhSdHKuHANxXMucSqWQSqVM55qc+04XWZ6vmX5j+2skEjFeBT2L/v5+s32GzS5s+qEwM5mM6frrRPButxvBYBAjIyO4ceOGWcrpcrlQLpeRSqXQaDSMp1Kr1ZpGbxMZF3E6nWYsFyvwVPCdoaJ/A1Dgvb29CIVCZu4AK+lY2sr/5sgtnm0bjYa5UbCVlGm2g4MDU/Szt7dnxkwVi8Uml58wWMYvl8tlgl4TExOYmJjA6OioWUQhc+9Mx9HqMhAoi4vaCdrRpeco8IWFBczNzWFkZATBYBCNRgP5fB4ul8ukO2U3XqtZBNZYiCy57faK0U5R0V8BmR6TQxxku6mcNkMLyhSdnArLYZSVSsUEt1j4wn77vb09JJNJ7O/vG7Hz5tCqtZeBM/bZj4+PY2pqCjMzM2a3HCfWMtXFczWzApzew5ZfHhfaETx7BEZGRrC4uIh79+7h5s2biEQicLvdODs7Q29vL05OTnBwcGCuhUcHa3syBc+bidPpBICm65U3COViVPRXpFUFmXRB5XomAE2LI7iogTPwPB5Pk7WtVCrGyu/v7zcNopB97nx+2S9PC+/z+cw6rfn5eczOzjaN5Xa73U2Cl6uiGbHnPDsu8mgnLcfjRCwWw9TUlFnjNTQ0ZLbgMrAZCoWa4hgMelLwfG3WtCcbnKxDMZX2UNFfEYqE1oZf1vpxjn7iuVtaMN4cKD6ucObmF1a80brLoRLSGvLx+JjM+U9MTGBubg7z8/O48f/vh5fbY6V7zOtl4I5ju+SAy8uExRteKBTC0NCQOcfHYrGmrjwG5Fgb4PP5zI2Pz8Xfk6+N7ztLiovFYtM0Xa3Iaw8V/RWw5sdpcVp9VSoVM+zSmtu2drPxsRnNptCla22tD7COr5KCn56exszMDMbHx5sm4Mo5fvK1yAUXmUwGh4eHJtXXbi7e7Xab1VbMSFg31raqcaBXdNEGIFp4zhU8OjoywUwW6KiLfzkq+itAy0hXneKW21P5xci0TIHJKTsAXvugyyo/GaTjTUJW/AEw3gJTY2NjY5icnMTNmzcxNDR07lhu+Trk5plMJoNMJtN2G7A81jCmQY+Co8V4g2TQUq6jomXntbW6Ocgbk7U6kNfJ3gHlYlT0V4SCtApefpj5Rat/XmrNavGt0WtaQ+burf/OYh8ZpR8fH2/qPrPulOPj06WXm2cYMJTLIOU1SeRZm3l5h8NhMgEsGALQVGh0eHjYlHa0lhXLa+R3Zjd45GGtAifwajCvPVT0V0DWucuVSvzf0pLLzjHrWC0pegat5KgrOZse+Kkhha60HF/FwqDBwUEMDQ2ZzjPOo2OBkJxSQ+vL+EE6nUY6nTarqyg0Xkurijl5wyLHx8fm8bh2ulgswul0otFomIEjzEjIsl56BfL9I3wdrD6U8Q6+p1qOezkq+g7hB9HqgvNDKuvwZRRfnqNlLT6xWkz5JX8mU1cyRchKu/7+fvj9fjNKulKpAPhRiDJ4J9tu6danUins7++jVCqh0WiY56DYZAxAXre0rqzRT6VSsNvtJg9Pdx9AUysyx33LgiD5/vI953PxOWRRUqfDSbodFf0VkeKnkNkLz0i+dPGt3WhyIiyFzFy//GKDCm8e/C5/zoo7NtAAPwqLVpWTcWXwToqegTHW1nNXvNfrbWoaOm/jDMXIzEM+nzfrrLLZLHZ3d5ui97T8uVzOpCPpotPKy7SkhDcgfsl6BaU9VPTXQJ6raXWltZf5c9k7z2IdfsApep7P2alHK84KNIrcegPged9ms5kyWrmZCGi+Sck8uCz5ZarQZrOZdl++Hrvdbspk6eFYi5Ske85ag2w223IQB4eHsoGHBUfysa1Cls8nG3SsGRDlYlT0V0QKnj3xjUajaaactNh0lVmPL62U9BSky84RVVbhy7w+8JN4mU2wBhHlXjgJbxZyqAWvx+PxtBzvZY1TWMUpbyLHx8dG/LxuHlcYB2HZL130dkaK8flZlyBvesrlqOg7xDoYgyLnh5CiPTk5Ma65dNP5c7m2iUjR0+LLZQ608tblk7TSrAfgV7lcbpps06pykI/P5+RwCwYJecPgTYpeirUi0Qo9AqYDKUyKXpb90mq3I3j+f8AbLfsYdMlF+6jor4gcDMEBF0Czy0wRM6BnFZrc78aGHJlLp7AoOlp2ur8UvKxOY6sth1VKSy/rAuTUHrrycmEEn5evRVYfWrMRF7nidOelyOUNS1Yvtit4lvqGw+GmEWIq+vZQ0V8BeUbnmZofWApSVpgBrfes22w2k6qi6003XebFKZZqtWpuCrKohqKn2KXg6TZLKy+PEzwS0CrL1V0ck0WPQfboW8/1VvjvsmvO2ivPa2pX8MBP23kjkYgZJxYIBFT0HaCivyJSPPILQJPVl5adwblqtWpabtmAIoNXnEvHgh6rt0DRy+g7c9cUKAV/Xv+77NungGu1WlMTjrwGazVhpykya60B/63TPQAejwfxeBxTU1OYmprCwMAAent7te6+A1T010BaFusZV25sucgaSrdZdphJa0xXXObtgZ/SVxQmrbE1MNYq4MbnppfCY4L0TGj9WzUTXRd5Te2Ini27kUgEi4uLuHv3Lm7evKnrrK6Aiv6ayJy3dKVlEItW0lqiK2v3ZY88g4LWI0SrKLWcmSfz3Ly288QlPQrZ60+sN63rFr+0aqJp9+YhBb+wsICHDx/izp07GBoaMuu1lPZR0V8Dq7jl+VyeuTnfTkbV5bJLeTNoNUJbRr5p6eVR4rwafmtzjfwuS3pbvS7+7lWFbj3+WOv323lcORcvHA5jYWEBH330EZaWljA+Pt40I09pHxX9FbHW31P0LCWl+Ol2U/RWF5wWv9VZmaKhJT47OzORd2sMQRb+yHJVptgkUtTWUtc3gbUYSf639fgiewFaQQs/NzeH3/72t/jwww+NW6+Cvxoq+isgz++09LIwRjbhyPO1tK602tzvDsAUrcjUGq28tTzX5XK9dr6Xk3NlxJ3ehLXS7b+idNXabWetK5B5fwAtYx6yTiAajWJ+ft4IfnZ2Fv39/XqOvwYq+isig3C0lrS0PC9LZG5cLoWUvfeytp1uvVwQwVJWa6EOABOMq1arTaOyDw8PX0vjAWhrxnwn0A1nhoJFM9YJvywgkscbGT+Q477i8Thu376Nhw8f4t1338XU1BT6+/vNqG5N0V0NFf01oTg5MIKtpK3GaUmPwDphR7blAmgq/LFWzbHE1zqDj1NluPAil8uZefXcsSdr3XnMuGo0Xk6n5d56dvrJSbsAmoqIeB2lUum1a5A756enp3H//n3cu3cPN27caHLpVfBXR0V/BWQ6jTX3dNOtX9ZUnhxAKXPk1gIa6xFA1u5bI/l8fNayl0ols9oqm82aVVQUvlyWwXJg6WVYq+xk6bH0QDjrPx6PY3Bw0CzPkLP0eWSpVqtNizoY45CiZx4+HA5jZGQEs7OzmJmZwfDwsM65f4Oo6K8Aa799Ph9sth93q7VqQLmoS4zfz8tVy4IfufjROkdOluvKTAIn7XLeXTabNdZVFvLI44U1pShFL/sH+BUMBhGNRjE2NobR0VEMDg6ir6+vaZY+z/EU/dHRkfE8ZA89nyMYDGJgYKBpeSdr61XwbwbbJcEcbVI+B55RrVNwzns/O/mwyrJV65f15/I5KXxZk89zvdXqy4YfayDSmj6UXo3f70c4HG7a0MMtPaFQyIyyblXkw8pBq5Xnc8iVXxyNzbJgDdpdiZYfOhX9NeikoqxdWt0cOrlhyOME4wg853NoBSfVsCDovHFfQPNGXp7duTE2FouZFdo8gsgUnfV65AJOmark8/AII2cGqHW/Fir6bsPq9svpvPzf8kuOq5LReGYazhvuIef6tXM9Mm5gzdvrQIw3ioq+m5E3AKvFlZti5Ex+Wl6mCBlEtMYWlF8sKnqlmVYNQrLTT1pfPVf/KlHRK0qX0VL0eutWlC5DRa8oXYaKXlG6DBW9onQZKnpF6TJU9IrSZajoFaXLUNErSpeholeULkNFryhdhopeUboMFb2idBkqekXpMlT0itJlqOgVpctQ0StKl6GiV5QuQ0WvKF2Gil5RugwVvaJ0GSp6RekyVPSK0mWo6BWly1DRK0qXoaJXlC5DRa8oXYaKXlG6DBW9onQZKnpF6TJU9IrSZajoFaXLUNErSpeholeULkNFryhdhopeUboMFb2idBkqekXpMlT0itJlqOgVpctQ0StKl6GiV5QuQ0WvKF2Gil5RugwVvaJ0GSp6RekyVPSK0mWo6BWly1DRK0qXoaJXlC5DRa8oXYaKXlG6DBW9onQZKnpF6TJU9IrSZTgu+bntb3IViqL8zVBLryhdhopeUboMFb2idBkqekXpMlT0itJlqOgVpcv4/wDCVUZN6c73cgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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xA2fOnBG59dWQTqexsrKCN2/eiN13N5FZrVYMDAxgeHgYXV1dMJvNovJQo9FAp9OJXV7uMyBXJVLJsFxSK3v86d8o7FceAWCqgzsKHgAHvdtQ1l0mk4Hf78fTp09x8+ZNvHz5suY+eCaTCSdPnsSVK1cwODhYlfOO7iGZTGJ2dhbPnz8X2Xe7vVen04nx8XH09/fDbrdDo9Fs6+YjN8Ik5LJkqqXXarXiufJnsVOfPO6aUx+80+8TucvLQafZUjz+n//8Jx49egSfz1fVNeRowuHDh3Ht2jUMDw/DarXWdI+pVAoLCwuYm5vbs2xXoVCgp6cHx44dQ0dHhyjLlVtryedw4IfGm9TAg0prqeNuNpsVWXvy51Ie8mPB1w6L/lcG7fDBYBAvX77El19+idu3b8Pj8dRlURw9ehRnz56F0+msSSCFQgGpVAo+n6+i07C1tRWHDx/G4OAgmpqatrXIyuVyIqMuHA5vy8QDIPrjmc1mWCwWGAwG0XaMcvLLkS0EFn3tsOh/JdAOls1mEQqF8OLFC/ztb3/DrVu3sLKyss2jXc21FAoF+vv7MT4+ju7u7prGUpVKJcTjcXg8HuHAA3ZORNJoNBgeHha7vNFoFLs4Vch5vV4sLy8jEAhsC7MpFAqRsmu1WuFwOITzD4AoqZUhwVPzTRZ97bDo90G5aV+viS877Xw+H54/f46bN2/i5s2bWFhYqCnrju5Dr9fjwoULOH78OCwWS03980qlEgKBAJ48eQK3273t38tpbm7G8ePHcezYMdjtdiFEqoP3+Xx4/fo1FhcXRSpuoVAQzju1Wg2j0Yimpibh8U+n0yITr9z5R4KXB20ytcGi3yf7deKRCZxMJuHxePDs2TN8+eWXuHXrFpaXl2tOs5Xz68+ePYsjR47UPHwym81ibW0Nt2/fxsrKyq6Ps1gsGB8fx/Hjx9Hf3y92eTrHx2IxLC4u4vnz53C73UgkEiJfvlgsirO8xWJBc3MzWltbRSedTCaDcDgMtVot/ANk0uv1enH+ZxO/dlj0+6C8MWMtuz198Wke3erqKqanp3Hz5k1MT09jY2Oj5hFVhN1ux7lz5zA2Ngan01nTMAiac7+wsIBnz57tmZDT29uLy5cvY2xsTIQC5Sw6n8+H7777Dt999x22trbEkIryBp+009N0XJ1Oh1gsJhpqqFSqbf3xjUYjTCYT7/R1wqKvE9mbLFNJ8LIHOp1OC4Hdv38fd+/eFTn19VoQVDZ748YN9Pf3Vx2iIzKZDObm5nDv3j0RLdjpLN/e3o6zZ8/iwoUL6Onp2SZAqoFfWlrCy5cvsbq6Ks7m8pEIgHDi2e12OBwOkcBDLbeoX59arUY+nxfddWinZ9HXDot+H5B4ge2DKHbb7eXdPRaLic43d+7cwd27d7GwsIB4PF73/Wg0Ghw5cgTXr1/H+fPn4XK5ajJ9c7kcvF4v7t27hzt37iCRSIj7ljGZTDh79iyuXbuGoaEh2Gy2bWOoyFqYnZ3FmzdvEIlEdq1EVKvVYmgH9dinrjlUW69Wq1EoFMRUG7PZDKPRKCb7MLXBoq8Tqgqj8JLcFXcnodHjM5kMtra28ObNG0xPT+Pu3bt49uwZPB5Pzed3GaVSiZGREfz+97/HtWvX0NbWVtOU2VKphFAohIcPH+L27dt4/fr1jo/T6/U4duwYrl27hlOnTsHhcIjjA5n21GVndnYWGxsbu1ot5JSjJpq0s8sJOpTgQ4uqTqfb9tifouno2w6Lvg6otp3SQenLS1/U8jxyeTKM2+3GixcvMD09jSdPnmB+fh5bW1v7uh8S4ocffogPPvgAQ0NDMBgMNYkhHA7j8ePH+OSTTzA9Pb3r4/r6+vD+++/j3LlzaG9vFwuLbLZTReDKysqeMX6ayUe7evmQC7KMZO89TaplJ179sOjrgDLmYrEY4vG4cEaROUq7E5nyiUQCfr8fS0tLmJmZwYMHD/D8+XN4PJ66BlTIGAwGTE1N4eOPPxYVdJT3Xu17iUajePLkCT777DN89dVXuy5CbW1tOHfuHN555x309/dDq9X+aCIvhdqCwSCCweCedQLUZUeexUeWAi2ocvadWq0W4TpaIFj0tcOirwPyuFMPdxr6SBllWq1WpJJGo1G43W589913+Pbbb/HixQtsbm6KXvD7wWQy4dSpU/j3f/93XL16Fb29vTCZTFU7t0jwz58/x6efforPP/9811RfpVKJkydP4saNGzh8+PCusX8K1QWDwT39E7TLm0wmmEwm0faaRm9Ho1HE4/FtbbHIgiqfiMvURlWib+TyxfIvVqFQQDKZhNvtxuzsLNxuNwqFAoxGI6xWqyghLRQK2NrawtraGl6/fo1Xr15heXkZwWDwQLq46vV6nDhxAr///e/x7rvvCk99Ld7scDiMp0+f4pNPPsFf//pXrK6u7vg4tVqNsbExvPvuuzh58iQcDseOgicnXjgcht/v39VHQQ48s9kMh8MhLJN8Po94PI6trS2EQiHE43FRQivv+LtFTpjqqEr0vKL+0MwiHA5jcXER9+/fx8zMDLxeL3K5HDQaDQwGAywWC7RaraiQW19fh9vtFi2gDgKtVovJyUl89NFHuH79+o9CZpWgoRWPHz/Gp59+ir/+9a9YXl7e9fGtra24fv06Lly4IM7xu0Gee6/Xu+vRhcx6h8MBl8sFi8UCtVqNdDqNra0teL1eMe9Orpsn8VOVHTfGrA827ytA4bVYLIZkMonl5WXcvXsXt2/fxps3b8S8dKoFJ0+2POG13iSbnaBc948++gjvvfceOjs7q3bakY8hEAjgwYMH+NOf/oS///3ve1bv6fV6jI+P4/Llyzh06NCegidfRyAQgMfj2fE8r1AooNFoxEjrjo4O2Gw2ABCz7nw+HwKBgJjPR3Xz5Dylz1XunMMbU/XsKXoaRthoq2mpVBK75uvXr/HixQu43W54vV4sLS2JUJR8ZpW7wBzkEAa5U41CocDRo0fx8ccf49q1a+jv7xfz4ipBgllZWcHt27fx2Wef4e7duzs67WRv/NDQEN59912MjIzAarXuaU3IBUOBQGDHJhdarRbNzc0YGBjAyMgIurq6YLFYkE6nEY1G4fV6xRQcapNFoiYHXyKRQDweF80xyYfCVMeeog+Hw3j06BFmZ2eFp7YRFgASfalUwtLSEubm5uDxeODz+RAMBneseCvPNDsoSPBWqxWjo6P44IMP8OGHH+Lw4cPbKtr2Ip/PIxQK4dWrV7h16xa++OILPHz4cFcLhN4D+Q0uXryIlpaWihEBqpsPhUKIRCLbRC/v8CMjI5iamsLY2BhcLpdosx0Oh8VnnEgktnXMAb4/FqTTaXHuj0QiaG5urrm2oNGpuNP/4Q9/wB/+8AcAENlSjQR5isl0/SUWPZfLhYsXL+Jf/uVfcPbsWXR0dFQdlstms/D5fHj69Cn++7//G//4xz+wvLxc0RrRaDSYnJzEmTNnMDAwUHEohtwHIBgMIplMbrOYaIcfGxvD+fPnMTExITryxmIx5PN5RKNRBINBxGKxbVNwCDoyxeNx8Trt7e2wWq2cmVcDe4qeVmDiIM+mvxVqqWPfDzvlt/f19WFychJTU1M4fvw4xsbG0NbWti0+vhe5XA4bGxu4ffs2/vSnP+HevXtiJFWl+zCZTDh//jympqZgs9kqvh4NmlxdXRX5B5SpqFKp4HK5MDk5iYsXL2Jqagrd3d0wGo1i9jyJPhqNIpVKiUGY5VBIj+bcR6PRbSW9TGX2FL1Codg23FCn0zVcM8Kfy0NMr2EymdDS0oJDhw7hxIkTOHfuHMbHx+F0OsVE12q+3NlsFhsbG7h58yb+8z//E7dv364qr5/uo6+vDydPnkR/f3/V/fHpCLG5uYlSqSSSaZxOJyYnJ3H16lWcPn0a3d3dMJvNUCqVSKVS0Gg0wmGaSCT2bHpJwztDoRD8fj98Ph+amprE4EumMhU/JXm15UmhPy02mw2nT5/G7373O5w+fRqdnZ1obm6GxWL50XSYvcjn89jc3MTt27fxxz/+EV9//XVNVlp7ezvOnDkjxlJVMp2pQ87Gxgbm5+fh9/uhUCiEST8+Po53330XZ8+eFRmDNOKKGmpQ5iJFQ3ZbaGnOfSQSgd/vh9vtRktLC2w2G4u+SvhT+oWQvfIajQZHjx7F1atXcfHiRYyNjYnWU7WeVWnHffz4Mf74xz/i3r17VQlePl4MDg7i8uXL6Orqqigk8nV4vV6RgBSLxaDRaGAymTAyMoJLly7h9OnT6O/vFzF5inJQMU0ulxPe+L02lmw2i0gkglgsJpKAwuEwMplMxQk6zPew6H9mSFz0xe7s7MTk5CTeeecdXL58WaS41pNmWiwWkUql8Pz5c/zlL3/BN998U/UYaxK8wWDAkSNHRGOMSmd5mr7z+vVrPH36FD6fD4VCASaTCb29vThx4gROnz6Nvr4+WK3WbY5REjxdp5pe9uRnIqsgHo+LaIHJZIJWq63q/TYyLPqfGbkp5KFDh/Dee+/hxo0bOHbsGJqbm2E0Gut2SGUyGayuruLmzZv4n//5H4RCoZqer1QqMT4+jsnJSbS0tFRswEHFNW63GzMzM3j+/LnY5R0OB8bHx3HixAkMDAzAZrP9qBRWDnPSFJtqjo805SaXyyGXy8Hn88Hr9W7r3sPsDov+F6CpqQlnz54VzS4GBweFB7oeKA/d5/Ph66+/xs2bN/dMqy1H9tifPXsWU1NTwtrYDRqq6ff78eTJEzx+/Bibm5vI5XJil5+YmMDQ0BCamppEAs1uvQZqSQKjbr2UoOP1etHc3Iyenh5YLBYWfQVY9D8TWq0WXV1dOHToEEZHR3Hu3DmcOHECnZ2dNTW72A2/34/79+/j008/xczMTE3PpYy3w4cP48SJExXbbFEzT7/fj2fPnuHu3buYnZ1FIpGAQqGAy+XCyMgIRkZG0NraKuoCdhO8HJ6TR17tRSwWQygUQiwWg1qtFim7jZA8tl9Y9D8hSqVSFJYMDAzg/PnzOHfuHIaGhuByuURzx/1QKpUQDocxPT2NP//5z7h//z7S6XRVwpHp7e3FlStXMDIysqcnvFQqIZ1OIxAIYGZmBl9//TUePXok+vqZTCb09PRgdHQUnZ2dMJlMu8bQybdR3oFIpVJVDJXGYjH4/X7xGhQJYNFXhkVfB9TBhb6cVPUl/95oNKKlpQUTExM4deoURkdHMTAwgPb2dthstgNxOJVKJUQiETx9+hR//vOf8be//Q2xWEz8rlosFgumpqbw7rvvoqenZ0fBk6ioy+3MzAz+8Y9/4M6dO3C73chkMtDpdGhqasLAwAAGBwfhcDh2zYuX+wVSmE6j0YjuQ5XMfUrSoR758uLBobu94U+nRpRKJWw2G1wul8gok798KpUKZrMZnZ2dmJiYwJkzZzA+Po6urq49d716iEajmJmZwX/9139tc9zttcuX/85qteL8+fN4//33MTo6CrvdLrzr9Fgy5+PxOFZWVvDo0SPcunUL3377LdbX10Vqtl6vR2dnJwYGBtDa2rprbQBdM5/PI51Oi7Rbmm6byWTEa+4lfNnbTwtSLpfbt/X0tsOirxGLxYKenh709vbCZrMJ0cfjcRQKBRgMBrS1tWF0dBSnTp3C8PAwmpubD7QSjEz6p0+f4rPPPsPnn3++zXG3l1Dod0qlEq2trZiamsJHH32E8+fPw+l0bvN+03k7kUjA4/FgYWEBjx49wr179/D8+XMEg0GxOKhUKlgsFvT29qK7u1tMri1/z7Lgk8kkwuEwtra2kE6nRU97apNFu/du0P0BP3Qzong9t8beHRZ9Deh0OrhcLvT19aGnpwdms1nEl2l3MpvNGBwcxLFjx4SJW+08+EqQib21tYUnT57gk08+weeff461tbWarqNWqzEyMoKrV6/i6tWrIqefwoX0OqlUCuFwGCsrK3j8+DFu3bqFZ8+ewev1ivx4up5Wq0VTUxO6urrQ0tIiet7J9y6b9IlEAj6fD2tra6JMmUZcZbNZ5HK5io0y6GhFkQTZ4mLR7w6LvgYsFgva2trQ2toKq9Uq+rpReyyNRoPW1laMjIygv79fxI1rZaemEPQF93g8ePDgAT755BPcunUL6+vru16n3JTX6/UYGZMqj6sAABesSURBVBnBxMQEjh8/jsnJSRw+fFjcZ/lIqtXVVTx+/Fjs7DSPTs41IAwGA1wuF1paWkT/PLmbLe3Kct380tISXr9+jdXVVdE112AwIJPJiJ+9jipUv5/L5bbF7tmZtzcs+howm81wuVwi0YSgZo1WqxVdXV3o6ur60WNqQRYTnW2DwSAWFxfx7bff4ubNm7h9+3bFEdK0eNjtdrS2tuLIkSM4d+6cKJe12+3bWknTa4VCIczPz4sOQZRpt9v1ybR3uVxiMZQdazTNJxKJiN19aWkJKysr8Hg8iMViYkdXq9XQ6XTCobcXlIFIZ/nyXnrMzrDoa4C6t9L5nHYx+l1zc7Moktlv7J0acNJs9/n5eXz11Ve4desWFhcXq+proFarRajwwoULGBkZQUdHh8j8k52KtMNHo1HMzs7i73//O7744ouKU3OpOYbFYoHdbodWqxUDOamNdSwWQyAQwNraGhYWFrCwsAC3241QKCRm1On1euj1+po+M7Ic5JZlbNZXpqLo5Q9Ro9E0XGmt7Egix5T8byqVChqNBlarFW1tbaKTS61fvkKhIMxU6kDjdrvx5MkTPHjwALOzs1hZWYHf76+4k+n1ehw5cgQnTpzAxMQERkdHcejQITidTiGscmuCUmpXVlbwz3/+E1988QVevXpVceoONbm0Wq3Q6/WiCi6ZTIrw3vLyMhYWFrC0tITNzU0Eg0HhE6BBIWazGfl8HhqNRrTJqhR3pxZguVxO3Ic8MIPZGW6iUQUtLS04fPiwcM5RMgjttlqtFm1tbWhpaYHJZKq6Mo7OudlsFuFwGG63G8vLy0IY6+vrmJ2dxcuXL6sqnNHr9ejq6sL4+DguXryIs2fPYmBgAFarVZjxu5HP5xEIBDA9PY1bt25hbm6uYgMRamVtNBphMBhQLBYRCASQTCbF6O2VlRUsLi7C7Xb/qC02jZ6W22LRoEq53/1ekNORrI1yByLzY2pqoqHX6/c9keW3gFKpFOfD/v5+XL58edtIZjJfqdJLqVTCbrcL87ZShRyZ7tQIIhQKiYEYjx8/xtzcXMXpMOU0NTVhYmIC169fx7lz5zA4OIimpiYYDIaKySq0y7958wa3bt3Cy5cvq+oYpFarodfrYTabRdurN2/eIJVKYXNzEysrK9jc3BRNLssFLJ+/6fcajWZbN529ICuLBmew6Ktjz2+D0WjEf/zHf2Bqakp8uI3gJFEoFMJbbrPZ0NfXh8HBQbS2tkKr1YqdiM6T+XwearVamPUUntrJIZfJZIRn/Ntvv8X9+/exsLCAQCCARCKBSCRS0yBLhUKBY8eO4cqVKzh//jyOHj2K7u7uigUz8n1ls1m43W48fvwYMzMz8Pv9Vb2uVqsVwyRzuRw8Hg9SqRR8Ph98Pp8w8yvlDVBrLQrnyaG4vaA0Z51OJ2bcHVR49G1mT9Hb7XZcvXoVV65c+bnu51eBHKsGsM2jLP/dZDKJXYl2ZfJayxlt8iTXhYUFPH/+HC9fvsTLly8xNzeHSCSy4z0Qu4mmpaUFY2NjuH79Oq5cuYKhoSFRs15Ll51oNIrvvvsO33zzDTY2Nqr6fORps0qlUvSt29raEgtYtZYKCV/+sxrfEZ3jaZw15e0ze1Nxp2d2hs6zZMrTjplKpZDNZsXOlUqlEIlE4PV6MT8/jwcPHuCbb77B8vLynubrXrujVqtFZ2cnLl68iA8++AAnT55ER0cH9Hp9TV/6YrEoBnhMT0/j6dOnVfXRo2k+VDBEyTaRSAThcFh0wKmF8rFV1UBef4qosFlfHRyy2yfU/YWqz5LJpHBMURLK7Owsnj17JmbfRSKRuo9JBoNBnN0vX76MkZERtLS01FzAQ9aH1+vFw4cP8fjxY/j9/oomNe3wZrNZ9JtPJpOihRUdd+qhns9Eo9GgqalJ+FO4lr4yPMCyCip9keQsNr/fLwYwrq6u4tWrV1hYWBDZbOXXrOWzbWtrw8mTJ3Ht2jVcvHgRg4ODoslkLdDZmfrh3759G/Pz83s672ST3mw2i+IhyrCTh03+XFDLrZaWFtFTkEVfGR5geQCQaR+JRLC2tobl5WW8efMGs7OzYlJt+fm2FrEbDAZ0dXXhwoULeO+993D69Gm0tbVBp9PVJfhMJoNAIICnT5/iq6++wpMnTxAIBHZ9Ds3oMxqNMJvNIrGHRnH/EoIHvrc6dDod2trattUOMHvD5v0BIIve7Xbj5cuXmJ+fx+rqKiKRyL6mAjmdTjFA8vz58zhy5AhcLlfNGX9yPbzf78fMzAy+/PJL3LlzB16vd1vnGoKGcup0OrHDU3JPJpNBNBoVYcufW/B6vR5NTU1wOBxoamoSbcKZyrDo94nc/SUej8Pr9WJtbQ2bm5uihVOtKBQKOJ1ODAwM4NixYzh58iSmpqbQ19cnutrUIngy5+PxONbW1jAzM4M7d+5genoabrcb2WxWNAQhH4VKpRIxeEq+ocSZVCr1i+7wwPczAvr7+zE0NCRKgpnqYNEfEBSHl0cz1TIYRKFQQK/Xw2azob29HSMjIzh58iQmJycxMDAAh8MhJtxUCy1GiUQCfr8fy8vLmJmZwfT0tCiRpUo2ErlOp4PRaITJZILFYoHJZBIFNNR6+pcWPPB9ZKm3txcjIyNwOp0cqqsBFv0BQKLRaDRiF5Z/9jq/U+45jW8eGxvD6OgohoaG0NXVBZfLBbPZvC0ktdsuLzeoyGazomBnY2MDc3NzePToEZ4/f46NjQ1Eo1EUi0VotVqoVCqYTCbYbDY4nU44HA7Y7XaRWptMJhEIBBAMBhGNRvftpT8ITCYTXC4XOjs799VJuBHhT+oAUCqVIg2Uwkc00pry62XhU2Ua9dHr6+vD8PAwhoaGMDw8jO7ubjQ3NwsPuVz6SvF/4IfW0fQastiDwSBWV1fx+vVrUeyytraGYDCITCYDpVIJo9EIm82GtrY2dHR0oLOzU5QOU/w9FApheXkZyWQSW1tbiEajByJ4WsBooaoFnU4nFiiqK2AHXvWw6A8AEr3D4UBXVxd6e3uRSCREqykacU0JPXq9Hna7Hd3d3RgeHsb4+DhGRkbQ1dWFpqYmGI1GUS0mt64iZ5ycBpxIJBCLxURiTCQSwdbWFnw+H1ZXV7G0tCR2dio/NZlMaG5uRnt7O3p7ezEwMIDe3l60trYKhxjV8KdSKbHT15t4Q1BSj1arFUcPKr+tRfgmkwmtra1wOp2clFMHLPoDgGLYdrsdfX19iEajotzT7/cjlUqhVCpBpVLBaDSiubkZ/f39GB0dxejoKPr7+9Ha2irMeHkyLYkjm82KrDcSNqW8ejweeDwebG5uwuv1IhwOi3p2amJBC5PdbkdXVxeGh4e3dei12+1idl6xWBR9/xKJBLxeL/x+P+LxeF2Cp3Cf0+kU75OiCNQ6u9qR4Gq1Gg6HQ/QF4DFWtcOi3ycKhQJKpRIajQZmsxltbW1IJpPbyj2j0Sjy+bxYGDo6OoQp39/fL87tcssqueyW8tqXl5fx6tUrvH79Gm63G1tbW2K8cyqVEl1kZAFpNBpRGNPd3Y3R0VGMj49jaGgIPT09cDqdP3ptakudyWSwubkp2lnVI3hqIXbkyBGMjo6iu7sbGo0GoVAIc3NzmJmZERGEalCr1bDb7XA4HDWVMTM/wKI/IOSdtL29XZisOp1OJOfQF7ajowMul2tbbJkqzej8T/ns5HWfm5vD7OwsXr16hdXVVYRCoV2FQg5ElUolnIRDQ0OYmprC1NQUhoeHK1oW6XQaXq9XlMfW00uBpvqcPHkSFy5cwOjoKJxOJ0qlEvx+P4xGI6LRqLBMqol20DRcqjNo9GzRemDR7xO5SSTVdZvNZtjtdjidTqTT6W3NSFQqlchkCwaD4u90jidTNxaLwev1YmFhATMzM3j27BnW1tbE0aFSuSr5Dyi559KlSzh58iQGBgbQ1NQEvV4vCoZkyMKIRCJ48+YNVldXEY/HaxYX7fBnzpzB+++/v20oZqFQgMViQSqVEv3y9lrEZCiHQJ5yy8KvDRb9ASB7oMvDdzSHnto6JZNJxONx0fvO5XKhqalJxMMph39zcxNLS0tYWFgQbbLkLkZ7QT4Gl8uFqakpXL16FadOnUJ/f7+YrlPe6EOucEun0/B4PJifn8fm5mZdZr3FYsGxY8dw6dIlTE1NobOzU/QbIKG2tLSIkBs5DysJmO5ZHpJBQ0aY6mDR7wO5lzt51anPXSaTQSqVQiwWE043qjGnsViU2krprdQ6ihpJejwe+Hy+mmrTSfAtLS04fvw4rl69ivPnz6O3t3fbfPjdRk1Rjzvy/FfquLsTWq0WHR0dmJyc3NZTXxYmhSytVqs45qhUqqoWGFoY6ViQy+U4I68GWPT7oFzwJPR4PI5wOIxgMAi/349AIAC/349kMrltFJPsBCRBUMkrOdIKhULVzUhpMXE6nTh69CiuXLmC06dPixHO5fPhy6EBFxsbG3j9+rXI2KsVk8mEzs5O4aTca2ottQ+vdq483SP5Ara2tkT3Yd7tq4NFXydy9ht5u+nLuLW1JQRPoqcYNzn45JZae81sr7aPO2X2keDPnz+P48ePVyV4ubtPJBLB8vIyFhcXq2rGudN9UHYf7e50fXotWiCpz+BO/fN2g1Kd5XwEyi9g0VcHi34fUNtqeYeXdyDKYKNKtPKduzxLb6c0W/IJlD9e/j0lvTgcDhw+fBinTp3C5OQkuru7fxTWknv30fXkgqHNzU0sLi5ifX29rl2eIgG5XE7kFFBzVbnZSDgchsfjwfr6OmKxWNV+A1qY6Njk9/sRjUbhcrk4Zl8lLPo6oew4Ej3t9OXxcuqfR84r2eknO//k3ZBMYVoEZA+7nNNPZa9GoxEOhwN9fX04evSomJIrn6N3el15tlwqlYLf78fS0hLcbjfC4XDdXnHZEWgymZBOp8XY6kKhgHg8jo2NDczOzmJxcRGBQEAsiJUoFosiN4E69shz9ZjKsOjrRC6plQcuUjcXGuqo1Wp/lFJLz5f/m35Hpa0UmqKpLZSDTw0tZEegw+FAa2sruru70dfXJ3Y9ihqQd7v89ckXkUqlsLW1haWlJSwuLsLn84m8AdnSqPZzSSaTcLvdUKvViEajcLvdaG1thclkQrFYRCQSwerqKubm5kRHIfrsqoHm3MmLKoftqodFXyey556+cBqNRtSgNzc3i7MnnVspJRbAj3YmEhgJm66l1+tFE0qDwbDth7zfVqsVTU1NothHoVCI3P/yyj/Z+Ui+CGrztbq6irW1NSSTSbFokSVQrfDJTA8EAiLVdmFhAQ6HQzRaTaVSCAaD8Hg8CAQCIp+/WuHSjDx5qi2LvnpY9PtEznwjxxuJl8Qld6WRw3byF5V2dxKb2WwWVXskZpvNBovFIppaUK93uQV0JpMRDkT59cmXsFNVHoUWQ6EQQqEQisWiWGzICqk0NlqGHpvJZEQ3IarVl/MW6AhUz04tL7Z7RSSYH8OirxM51ZU847RLk2BoCINOpxNmfjAYRDweF8cB+XpynrzVakVzczNaWlrQ0tICp9MJu90uprjIw0coO42aXMRiMcRisW1FM5QcJAuM/AKy6U+VeAaDYZs1Um7VVIJ2XxI1TQKSnYg7lR1XCx2jZEuGqQ4WfZ3IIgcgTHLqGGswGIT4yUw3Go2ipp1aaZUPyKSzuslkgtlshtVqhc1mg81mg9Vq3dbjXRZ7OBxGIBCAz+cTQycikQji8fiugtdoNOI16fhAbaR1Op2wBEi48vSeWqAwnfzZHYQ5TuXM1HeAqQ7+pPYB7ZTU2IKEXywWxe5OOzx1biWTnIRfnqwj71okaqppV6vVKJVK4k/K0ad4tcfjgdfrFR1uKC+gPMGH/Ae0U2q1WmQyGSF8mk4sD5asdRDFXuz3GvT5mM1mdHR0oKmpiUVfA/xJ1YksHOCHGDQJhLzu5c6z8jO+vOPTeZdy9OnLTSE1Mu3JX0DtsOgM7/f7RWoqDYDcyXym+5HHd9HrptNpcWyg16WQ468lLEZdilpbW7ldVh3wJ7VPyp1IJH7gh6mq5NG32Wyw2+2wWq3Cq59Op4XZTeInUzyVSiGRSGBra0tMlKF0U0pHpZRfMuVJpNUMgJRnx5HlQGdvOaRXixPvp0apVKK1tRXj4+M4evQoWlpaeFJtjbDo94Es+PIQXvkPPZ48+3QkIDOaqsXIW55KpaBWqxGLxcTRgI4KtEPLSUFyxl8t4S8SfqFQEJl0BJn2vyZsNhtOnDiBS5cu4ejRo7DZbJx+WyMs+n0gC1023+VsPRIl5ZnTTyqV2pZkQsIrP3vLCTqU5ENfcno8WQby+bseZ5ucFfhrRK/XY3x8HDdu3Ng25Yc997XBot8H5Yku9ENipKKSaDSKQCAg2kiHw2GRSkqONhJtuVgp608eRiEn2shncuDH1sfbglqtxvHjx/Hxxx/j3Llz6OrqEtN2mNpg0deJXGVH9fNkbtM5WD5zk6MtFAohHA6L2Dn97FRVJ6fm0uIix9XluDeZ5gfpZf+1QJN6//Vf/xXXrl1DX18fDAYDC75OWPT7QG7jTGdqcqRRqI2SY+Re8RTKkxNgaKEAthfiyAk0cviv/Hwvt5OW89JrPeMfFHQ0kSMc8mdWzTFCoVDAbDZjYmIC//Zv/4Zr165hcHCQBb9PWPR1ImfC0W5NJrpsbtO4KIvFglKpJLrmOhwOMR4qmUyKlFS5ZbXs/aeEH5PJBKPRKGL+FF6jRYaqz6gtVzweRyQSEUMqfmqUSqWIVFBHIOr9J+f6x2IxJJPJXUtqqfvPyZMn8d577+HixYvo7+/nybQHAIv+gKC8efrv8mw3m82GdDq9zbEnC54sBTLvqbsuFdaYTCYheMrwI8eeHNunen7Kow+HwyKkJ5f+kmNxv9C90I/dbkdrayva2tpgt9tFOW0mkxH9BqgenvIJKGIgWzhNTU2YmJjAO++8gzNnzqC7u5t3+AOCRV8n5YU2tJvJZ/OdfsiML09xlUtz5WuT8ElUZNbLve7IuqCJN6FQSPgQgsEgtra2xMDJdDqNSCQiHIu025aHGmXzm44Z5bFwvV4vmlu2tbXB6XSKgRYul0vsyjTWmpqLhMNhkVMgWzeU1mwwGNDV1YWpqSmMj4+jvb2dvfQHiKLCWe/t8QYdMOXJK3J3m90aVtCfJLBy553cQkuuqaczvRy+2ylHQK6ai8fjCIVC2NzcxMbGBkKhkPAr0OTZWCwmylqpXZY86IJ2YPI/0KJD4tfpdGhra8ORI0cwNDSEtrY2WCwWkURE6cKyWR8MBkV9AGUjkl9Dr9fD4XCgs7MTXV1d6OjogMPhYC99/ez4obHo98lurayqcZzt9Rj5Sy4LfK8vv7yQkLlPefm0y4ZCoW2VfnLrLjnXnzIFqaZAHmFts9nQ3NwMh8OB5uZmdHZ2or29HU1NTaJgp3xBIhOfagXouEELDvX4s9vtcLlcQuxcQbcvWPSNBok4nU6LIZTr6+vwer1IpVLbLA45/Veu/ivvAGQ0GuFyudDd3S264dDvSfC7sVOnITm/gIRPP5xau29Y9I0K7bTULEPuQEsWAWUKptNpsbsbjUZYLBaYTCYRKaAzN5X5Vtu6erf7Kod39QOFRc9gW/ccOc9Azt8Hfgg10tw4uc+fHINnftWw6JmdkXML5KIfuSEn85uERc8wDcaOomf7jGEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANBoueYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg8GiZ5gGg0XPMA0Gi55hGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIPBomeYBoNFzzANBoueYRoMFj3DNBgseoZpMFj0DNNgsOgZpsFg0TNMg8GiZ5gGg0XPMA0Gi55hGgwWPcM0GCx6hmkwWPQM02Cw6BmmwWDRM0yDwaJnmAaDRc8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabBYNEzTIOhrvB7xc9yFwzD/GzwTs8wDQaLnmEaDBY9wzQYLHqGaTBY9AzTYLDoGabB+P8BUJNiSnUAovAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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cCHvxyWQyHqwTBvCyn5Nl5lEAr3BI9C8ZyWQS0WgUc3NzuHLlCr7++mtMTEwU1H46lUpBKpWivb0dx48fh9FoLHgOHWt4EQqFcv5ceAM4cuQITpw4kfN5EokE782/traW81olJSVQKpU8wh+Px+HxeHinHCEsMCiXyyGTyfa9D38xQKJ/iUilUggGg3jy5AmuXr2Kr776CqOjowUJnolRIpHgvffew/Hjx3eM1mfDymPv378Ph8OR8bhwrQBQXl6Ot956Cz09Pfwcnh2hJZNJeL1eTE5O4vbt2zlFz0Ss1+tRW1uLsrIy3hCEHctl9yCUSqVQqVQoLS3dsgEnsTUk+peEZDIJj8eDmZkZfPfdd/jHP/5RsIUHfhOmwWDA4cOH0djYWLDoWQXfjz/+iKmpqWeuzdBqtejr68PRo0dhtVp5R14mwnA4jNnZWV5DH4vFnnkukUgElUoFi8WC5uZmGAwGuFwuxONxuN1uBIPBZ0Qvl8uhVqtRWlpK47N3Ab1jLwGsGcbY2Bi+/fZb/PDDD5icnCxI8MKOsyaTCe+//z6ampoKnjabSqXg9/sxMzODGzduPFPBJ0zasdlsOH/+PFpaWp55HnadoaEh/Pzzz1uW4paUlECj0aCmpgbNzc0wGo2w2+3weDxYWlqC1+tFPB7P2MsrFAqoVCrew48sfWGQ6F8wbM97584dfPfdd7h+/Xre2XZChNbwxIkT+Pjjj1FTU1OwIJLJJObm5vDrr79yd5zdUITRdIPBgLfeegvHjx9HZWXlM8/DSmVHRkYwMTGxZaRdLBZDr9fDZrOhrq4OOp0OUqkUKysrvKmmcF+fnexTaKyCING/MFgN+fLyMgYHB/H1119jYGBgV7PlGRKJBB0dHTh79iy6u7thNBoLEn06nYbL5cLNmzfx888/c08ju5RWKpXi/PnzuHjxImpra5/ZPqRSKbhcLkxMTGBycpK79bmO4CQSCW+7XV5eDo1Gg3g8Dr1en9N9F4vFkEqlkMlkvDU3URgk+j2QPYop36mrsVgMoVAIMzMz+Omnn/D3v/8dY2NjBZXJ5qKxsRGffPIJ3n77bWi12oKtvMfjwe3bt/H9999jdHQ05+8oFAq88847uHTpEnp7e/nZv/B9SCaTWFlZwc8//5zhtWxVpGMwGDLabrMvmUz2TLIOm6Yj7JxLFAaJfpewzDDWC16YWJILdoMIBoNYWFjA6OgoBgcHcevWLYyPj+8ps0wqleLQoUO4cOECzp8/v2NBTS6Y4P/85z/jl19+2fL32tvb8cknn+DNN9+E0WjM+TusxdW9e/d4l52tYI0zdTodd9eF1XW5invY+03DK3cHiX6XsLrvQCAAmUwGjUaTMRxCaJ0SiQQCgQDW19cxMzODkZERXh7LjqcKmUojRC6Xo7OzEx999BEuXLiAuro6fpSVLz6fD0NDQ/jLX/6CK1euIBgM5lxPbW0tTp8+jRMnTsBsNmf8TCg+1uiC9e7b6rWxyL3BYIBGo+H185FIBMFgEKFQ6JlgJrPy+ebyE89Cot8FqVQK4XAYa2trWFlZgVwuh8lkgsFggFKp5HXqrPbb5/NhcXERN27cwMDAAB48eMBbQDN2I3iFQoHe3l58+umnOH36NGpra3n5ar74fD7cvXsXf/3rX3H58mWefSd0p1kQrru7G2fPnkVlZeWWR2XCghj2+rYSPZusYzQaeSYfq+pzOp0IBAIZkXu2HhL73shL9MVc1JDrwxWLxbCysoLR0VFMTk4ilUqhrKwMJpMJZrOZp7w6nU7MzMxgenoaCwsL/Huu8+pCkUql6OnpwWeffYazZ8+ivr4eMpmsIDF4vV78+uuv+Mtf/oJvv/2WH88JRcoE39nZifPnz6Ojo4MPschFIpHgos0OBGajVqths9lQUVHBx2H7/X6srq5icXGRH9dlw7ZK2TEVIj/yEj29qb8FqiKRCGZnZ/HTTz9hcHAQi4uLiEQikMlkUKlUqKys5D3Z7XY7pqamMD09nRGk260rL/z7jo4OXLx4EefOnUNtbS2fCJMvbrcbt2/fxl//+ld8++23OTPv2Drr6urw+eefo7+/H+Xl5dsmxESjUSwtLWFmZmbLJpgMs9mMpqYmVFZWQiaTIRwOw+Fw8Bl5fr8/Z2mtsM6+mA3SbiH3fgfYaOZgMMiFPDAwgL/97W8YGxvLKAphteBsMgtr/ZTrg7sXDhw4gIsXL+Ldd9/lmXCFCn5wcBB//vOf8c9//pO79Nmk02mUl5fj1KlTOHPmDBobG3fMgItEInjy5Anu37/PB3Dker1swm17ezvMZjNKSkrg9/sxPz+P6elprK6u5hzgwVpnxWIxXpRDx3aFse3/wVAoVJR303Q6zffF09PTGB8fx/r6OlwuF+bn5zE2NobZ2dmCpsrs1rqzmfCMzs5OXLx4Ee+99x4aGhpQWlqa91EhAGxsbGBwcBB/+tOfcPXqVS747GMxdiNrbGzEuXPn8j4RiMViWFxcxPT09JaWXqPRoLu7G/39/WhuboZOp0M0GsXm5iampqYwOzsLv9+fM6EnkUjwIKrP54NWq6WjuwLZVvQejwd3797Fo0ePeEVTMdwAmOjT6TRmZmbw6NEjbGxswOl0wuFw8A/zVv3isq8l/J4Pwuuy59LpdOjq6uLHcvX19SgtLc0raMcCjysrK7h58yb+/ve/44cffkAkEslIqxX+PvB0XtyxY8fQ09OzbX98YbYeSylmZbTZ71FZWRm6u7tx/vx5nDhxAhaLBRKJBMFgEE6nE9PT01hbW9t2a8Cm7WxsbMBoNPLWXER+7Gjpv/jiC3zxxRcAnh4P7bRPe91gotqutnunxwol+xo6nQ6ffPIJPvzwQ3R2dvJstXzc2lgsBo/Hg7m5OVy7dg3ffPMNhoaGMsZK5UKhUODChQs4c+YMqqqqeDFNLpjgEokEQqFQxnZBKHpm4T/99FOcPHkSNTU1kMvliMViiEQicDgcWFlZgdfr3fH9cblccDgcqKmpgU6noxLbAthW9CyZhLGbIYmvOs+rO0sui9vd3Y3u7m60tbVlDI/IN2KdSCTgcDhw584dXL58Gbdu3cL8/HxGDCJb9MLS3L6+PnR3d28brWek02n4fD7Mzc3lDApqNBqcPHkSFy9eRH9/P2w2Gy/SicfjCAaD2NjYgNvt3vbGKeyNzwZgxONxqrYrgG3fKZY8wZDL5UXXZzyXhd9vhOLT6/UoLy9HQ0MD3n33XfT396OhoYGPcWa/vxOs682///1v/O1vf8P169d5Xr+wIi8b9loPHTqEtrY2mM3mvKwoq8G/c+cOZmZmMh7XarXo7e3Fhx9+iHfeeYePsBZm3vn9fmxsbGwZVGQkEgn4fD74/X643W44HA4YDAaeskvszI63R+EHPh6PU1+y3wEmPqVSifPnz+ODDz5Ae3s7KioquCtfCKlUChsbG7h16xa+/PJLXL16NcNL22kL0tLSgosXL6Kuri5vtzkej2N1dZWPsWYoFAp0dHTg0qVLOH36NKqrq3mBjtC7YfPnd8phiMVicDgcqKiowMbGBhYWFlBeXs7z9omdIZ/oBZHdb66urg7vvfcePvzwQ/T09OQ9Zy4b1l9vcnISX331FW7cuJHXtky4npqaGrz99tuwWq15rSGRSMDpdGJ2dhZzc3MZwm1ra8PFixfR19eXUZGXHTj0+/3Y3NzcMWYUj8f5NsDlcmF9fR1er7foPNC9QKJ/zrBTAaHAmpub0dfXhzNnzuDw4cPPjH8qJOsslUphfn4eAwMDGBwczLtUl61HrVbj0KFDqKury2svDzy1vlNTU7hx4wYfiiGRSFBdXY2zZ8/yjMGtOviwNmFerzevxiEsL59NuvF4PPD5fBm1D8TWkOifM0Lr3tDQgEuXLuHcuXNoaWmBXq/PiKEw8hU821cPDg7iX//6144VbsLrs7FUrKAm1zpykUgksLm5ieHhYQwODvI9uVarxenTp/HOO+/gwIEDz2xRsjvmsiKbfLePbNYdAD7KWq1WQ6PR0PHdDpDonwO5ouTHjh3DpUuXeClsoYUyuXC5XLwhx507d/L+u5KSEiQSCYjFYvT29qKnpydvK+/z+TA2Nobbt29jfn4e8XgcMpkMjY2N6O/vR3t7+zNtsYUwr4cVJ+VLOBxGKBRCJBLB0tIS1Go1rFYr1Go1iX4HSPTPASb46upqHDhwAHV1dejr60NfX19eqa35wAT/pz/9CQMDA/zxfDIBmUvd1dWFN954A5WVlXlNtA0EAnj48CG+/fZbDA8P8/14dXU1+vr60NHRAaPRuO3NjCX0xOPxgnJAPB4P7HY7DAYDEokEP7orhuSxvUKi/52Ry+VQKpWorKzE2bNn8e6776KlpQUGgwEqlWpfBO/1enHz5k3893//N77//ntEo1EemMtXBC0tLfjoo49w8ODBvAU/MTGBK1eu4OrVq3z6DhtX3d/fD6vVumMhEMulL1Swbrcba2trqKyspAaZBUKi/50Qi8UwmUzo7OzEm2++iba2NjQ1NaGmpobPbd8PvF4vhoaG8OWXX+Kbb77hLnIhR6sKhQJHjhzBmTNnUFNTs+2NiB2vPXjwAJcvX8Y//vGPDMHbbDYcPnwYra2teb1OZqUjkUhBomfn9ZFIZMfsQiITEv0u0Gg00Ol0UCqVvOddLBZDOp3mNe1arRaHDx/G6dOncfz4cVRXV+/LcAZhJJ8NmPziiy/w448/8vLd7WbAC9OKAUClUuHUqVN49913eT5/LljVoMvlwr179/DNN9/gp59+wuzsLN8eyOVydHV1obu7GyaTaVuPgQk0Go3C7XbzDkKFkEgkMgTPym0pgr89JPoCUSgUqKmpQX19PfR6PRKJBCKRCBccG7dktVrR19eHnp4eWCyWvFzmfGCC93g8GBoawldffYXvvvsODocjI8NtK9jPxGIxLBYLenp6cOnSJfT19UGtVuc8HmQR+oWFBYyNjWFgYAA//fTTM+2w9Ho9enp6eB/87W5uIpEooy6ArT9XOvJO74VIJEIikeDjq/frvX5dIdEXiF6vR01NDZ/DnkqlEIvFeLaiWCyG2WxGa2srDh8+vK+CZ3g8Hp5td/nyZbjdbgCFubfd3d24ePEi3n77bTQ2NvJqNSHxeByhUAh2ux3j4+P48ccfMTAwgPn5+YymIOl0GnK5HHV1dWhsbERFRcW2BTrMO3K5XHjy5AlGR0extLTEe9oXOoJa2GYrHA5DqVRSAc42kOgLQKfTwWazoaqqijdyZOfbALjobTYbWlpaYDabdyV4Yeea7MdYeu1//dd/4dq1a1sm37CuscJkF1ae29bWhiNHjqCnp4ePvcp+rmg0CpfLhbGxMfz73//GjRs3MDU1xZNvgMxtRHl5OTo6OmC1WjNq/IWeAyuWiUQisNvtmJiYwI0bN/Drr7/C4XDwkuaSkpItawOEJJNJfoNgvQhDoRBV3e0Aib4AysrKYLFYYDQaIZFIMho/MsHrdDpUVlbCaDQW3Iaake0Ws95xS0tLGBoa4qOvcjXAEBbTJBIJSKVSGI1G6PV6HDt2DOfOncPRo0dRUVHBe8sLSafTCAQCmJ2dxe3bt7ngl5aWMtaXvY2oqqrK6IIjdL3ZfjsYDPIWYg8fPsTo6CgePHiAtbU17jkIXfydSKVSiEajiMfjiMViiEajGft8Ijck+gJQKpUoKyvj7iPLCmOil0qlqK6u5kG7vVobZr02NjYwOzuLGzdu8EEUQiu41b+Bp9VyH330EXp7e2Gz2XjX3q1uSOFwGNPT0/juu+/w5Zdf4vHjx8+cn2c/h1wuh9VqRV1dHcrKyjI8ADbyenNzE8vLyxgfH8fg4CBGR0exvr6OZDLJLbxQ7PnkF7A6g1AohHQ6TQMw8mRH0Qs/uFKptOgKG5g1l0ql3A1mvdmAp8dU7EOrUqlgNpthMpm23dNuRTweRyQS4W6ry+XC3bt3MTAwgJGRESwtLcHlcj1j1bMFotPp8Oabb6KjowMdHR04fPgwb63F/m6r53c6nbh+/Tq+/PJLPHjwYEerKRKJUFVVhbq6OhiNRt7ei6XWMjf+1q1buH//PhYXF7GxsfFMowzm1m9X9ptNMpnkmXklJSXQ6XT7crN93aEmGnlQXl6O9vZ2HLjtOjUAABYtSURBVD58GHV1dbzbC0sDTaVSGT3c8/3gCV3kcDiMpaUlPHjwAAsLC/D5fPB6vXj06BGGh4czpsdmC4N91+l0qKiowMmTJ/HBBx/gyJEj/Ogsn62G1+vF6Ogorl27hvHxcQDbH/8BT08zDhw4wAtqotEoQqEQ1tfXMTU1hfHxcYyOjuL+/ftYWVnZ8joskagQ0bPXnkqlIJfLuejpyG57CmqioVAoCp6X/ioiFov53tBms6Gvrw/nz5/H4cOHUVZWhmg0CqfTidXVVayvryMWi0Gn08FqtUKr1eb1oYtEItjc3ITD4UAwGITb7cbDhw9x9epVDA8Pb1sdl0sQVVVVOHXqFM6ePYuenh5UV1dDp9PlLYB4PI7l5WX88MMPGXPsthO8RCJBZWUlmpqaYLFY+DVWV1dx7949DAwMYGxs7Jlx19u9rkKi9mKxGAqFAhKJBDKZDKWlpbuOoxQT24q+tLQUf/zjH9Hd3c1HCRVD1pNIJOIutkajQW1tLQ4ePIjKykpu5U0mEyoqKuB2uxEKhSCTyXa08slkMsMSDg0N4dq1a5icnITX60U4HIbT6dxxkGW2O8+O306dOoWmpiYYjcaCtheJRAIbGxv8DH47iyzEaDTi0KFDvLPP2toaZmZmcOfOHdy7dw+Li4sIh8N5r6NQSkpKoFKpoFKpMoZdEtuzreh1Oh1OnTqF/v7+57WelwJhxBl4atHkcjk/flMoFJDJZNBqtbBYLAiHw4jH43ywotBiMUuZTCaxubmJx48f4969e5icnMSDBw8wPj4On8/3zBokEgl3eXNF5kUiEcxmM9rb23Hx4kWcPXsWjY2NuzoijMfjmJycxPXr13mrq50CaTqdDi0tLejs7ITZbIbP58PMzAxu3bqFkZGRjLLevQ732AqJRMLbiP1ez/E6sqOlJ3IjFoshl8shl8tRWlqKSCTCj4+Ap1YomUwiFArB6/XC4XBgenoaP//8M65du4aFhQV+rVxZaLm2UeznUqkUzc3NeOedd3D27FkuvN0Int2MhoaGMDAwwL2M7QSk1WrR2tqK3t5eHDhwACUlJZiensbNmzcxMjKScZa/07X2gkgkyrghk5XPDzqy2wdKSkq4yIPBID+GCgaDWFtbw/j4OG7duoWHDx9idXX1mTzznUQhvCnI5XKcPXsW//Ef/4Fjx47xFtC7DV75fD7cu3ePd8plz7fVmrRaLdra2nDy5El0dHRALpdjcXERo6OjGBsb4/3unwfM4zEajXweHgl/Z2iAZR7s9EFiLnckEuGTcDweD1ZWVjA1NYX79+/j0aNHGWJnbbO2e2+zI9kGgwFnzpzBZ599hhMnTqCysnJP+1i/34/x8XF88803GU03tlqTTqdDa2srTp48iTfeeANarRYrKysYGxvD2NgYz6p7XrByXIvFApvNRg008oQGWO4DTJxM9DMzM5ibm8PDhw8xNTWVcypOPlljTEByuRxmsxnnz5/HpUuX0NvbC71ez69XKMwLYfXwP/74Iy+eyYVYLIbBYEBrayuOHTvG+/jZ7Xbcv38fd+/exfLy8nPPhGMxFBZUpUk3+UHu/T6SSCTg9XqxsrKCJ0+e4NGjR3A4HBliKMSVB54W+LCmmcePH0dTUxP0ev2uP9ypVAo+nw+PHj3C5cuX8fXXX28brZfJZKisrERbWxu6urrQ3NwMhUIBu92OkZERvi3Yj/HbhaDRaGCxWFBeXg6VSgWpVEpJOXlCot8nmAUPhUJwOBxYXV2Fx+PJK6ONpfECmWI/dOgQent7cfLkSXR1daGqqmrLjrKMXMU6wG8dapxOJ8bGxvDtt99m1MNn7+NlMhkqKipgs9lw8OBBHDx4EDabDRKJBIuLixgbG8PIyAhmZmZ+12O5rdDr9WhpaUFHRwfMZjNNuCkAeqf2CbFYDIlEArFYjFAoBJ/Pl5f1E+7ZFQoFysrKUFlZic7OTrzzzjs4cuQIrFYrlEplXh/sXB5ALBbLqIcfHBzEwMBARhENawCiVqthMBhgsVjQ2NiIgwcP8lqCUCiEmZkZjI2N8XLYF5WlKZfLYbPZ0NnZiaqqKqqhLwAS/T5RUlIChUIBg8EArVbLZ9Xnu881GAw4evQo+vr6cOjQIdhsNlgsFhgMhh2tey5YGyq/3w+n08nP4QcHB7GwsJCRXi0Wi6HValFXV4c33ngDra2tqK2thclkgkql4hNzZmZmcPv2bYyPj2fEKV4EYrEYGo0GZrN5T6cXxQiJfp8QiURQq9WoqanBgQMHMDU1BafTuW3aslarxYEDB9Dc3Izm5ma0traira0N1dXVvGlm9h4/lyVn2YOJRIK3jGLJMr/++itGR0cxNzeHhYUFbGxs8DVJJBJYrVa0tbWhpaUFTU1NqK+vh9VqhV6vh1QqRTQa5fPmb926hXv37mFjY6PgKP1+Js+IxWKUlZVBr9dDoVCQ4AuERL9PiMVilJaWwmq1orOzE06nE6FQCHNzcwiHw3zfLJVKoVKpUF5ejra2Npw4cQJHjx5FfX09L9vNlWgiFD+L/rN6co/Hg7W1NaysrMDpdPJpMXNzcxgZGXmmPFYul8NkMqGxsRFdXV04duwY2traUFFRwfPXxWIx4vE4LwRighdOpC0EJniZTAaZTIZEIoFoNLqrG4FOp0N9fT2qq6shlUp5LwMiP0j0+wQTNDvLZiLXaDRYWlpCJBLhHXIPHDiAI0eOoKurCwcOHOBi26mRZCQSgc/ng9vtRiAQQCQSQSAQwOrqKiYmJjA6OoqZmRlsbm4iFovlbDulUqnQ0NCA/v5+9PX18Q4/Go0GCoUio3FmJBLB2toaRkdHcffu3V0LHnjqVZSXl6O2thY6nQ4ejwdLS0sFbxMkEgnMZjOam5tRW1tLx3S7gES/j7B9fUVFBQ4dOgSRSAS9Xo+pqSn4fD5IpVLYbDYcOnQIHR0d3Lozy8oQtphigyCE2X23b9/G1NQUPB4PotEowuEwvF4v3G73tsU69fX1OH78OE6cOIHOzk40NjbCYDBsedwVDAYxNjaGmzdvbnuOvxOVlZU4fvw4enp6UFdXB4VCgfX1ddy5cwe//PILlpaW8o59sHN5tpen9NvCIdHvM0z45eXlaG1thVwuh8FggMvlgkQigcViwYEDB/gIpq06vbCW08xNHx8fx8TEBCYmJjA+Pr5jMg3w21ZAqVTi4MGDOHPmDM6dO8cnz+Sa6c48A5ZoNDIygrGxsV2fw9fV1eH06dP44IMP0NnZCZPJhFQqBZfLBZ1Oh0AgAK/Xm/egTYlEwrdAwjWT8POHRP87IBaLIZPJoNFoUF5eziPlzN0OBAJwu90Qi8VQq9UZZaGsU2wwGOQR81u3buHatWuYmJjI60xcaDXVajUOHz6MDz/8EKdPn0ZjY+O2NxuWM+ByufiNZjc96YGnNf7nz5/Hp59+ijfeeCNj/LZCocDhw4d53n6+omd5DeFwGH6/H7FYjObSFwiJ/neENacMhULY3NzkQl9eXkZlZSWqqqpQXl4OjUYDuVzOm3f4fD7Mzc1heHgYQ0NDvAttodZWq9XizTff5K2u2TCLnaxiIpHAysoKBgYGeKltoajVavT39+PChQvo6uqCwWDI2ELI5XJUVFSgoaEBOp0u7+sK+/47nU7emZgi+PlDot9nWB1+NBqF1+vF0tISHj58iMePH8NutyORSEAikUCr1UKn00Gn00GlUvGmFyyVd2lpCU+ePMHCwkJGoCvfoy/WJ++jjz7C6dOnYbPZ8jrvT6VSCAQCmJ+fx8jIyK6Dd1arFW+99Ra6u7v5eKvsdbNkpHwn5AK/vX6PxwOXy7XrE4BihkS/jzDBx2IxLtzx8XEMDw9jenoaHo8nY/YaS+ARBvJYX/itItr5fMDLyspw5MgRXLp0Cf39/aiurs47wScWi2FlZQWPHj3iM+oKPWNXKpVoaGhAU1MTTCZThhXO7q/PmormCzvF8Pv98Hg8CAQCiMVilIZbAPRO7RNM8PF4HIFAAGtra3jw4AHvkuN0OhGNRnNGqfczd12tVuPYsWP4+OOPcfLkyYIEn0qlEAwG8eDBAwwNDSEUCvHXVggslsFahWcPvAB+62Tr8/kKSuVlk3GYpV9fX+ctvUn4+UEZDXuEJcqwrLhwOAy3242lpSVMTk5ibm4OLpcrw8Lnw26i0cJOuH19fbDZbJDJZNyi7iTeWCwGu93OG2LsNq+exTE8Hg+CwWDG62aBwkgkwj2Kzc3NvK8djUZht9uxubkJp9OJxcVFnpdA5AfdGveAMDuOWXmWQON0OuFwOOD3+3fVQTjfAY6sH39FRQU6Ojpw9uxZnDhxgu/h823FzZpjTkxM4P79+3s6lw8Gg5iensa9e/dQVlaGhoYG3iWYVSKurKzg9u3buH37dkHddtLpNJ9y6/F44HA44PV6i6JL835Bot8jLB2WCV84sYV1EN6PM2R2DdaaSy6X83Rem82G5uZmvPHGG2hvb4fFYuGBwa3OsIU3KzY99tGjRxgcHMTMzMyegmPRaBRzc3O4cuUKPB4Pjh49iurqaiiVSiSTSV7e+8svv2B0dPSZwRc7IawzYMegFMzLHxL9HhC6zUKhq9VqVFVVoaGhAaurq3C73fyDWggSiYR3fFWr1dBoNCgrK+Nfer0eFRUVsFqtqKmpgdVq5Rl22bPbhV/sJsWm0jqdTszOzuLu3bv49ddfsbGxUVCFYC68Xi+vxhseHubtwVOpFPx+P5aXl7G4uAi3270rwZLIdw+Jfg+wRBE2IRb4rYMwK4ZhM9Onp6fh8/nyEj67eSiVSuh0OpjNZlgsFlitVpjNZpSXl0Ov10Or1UKtVkOtVvO23PF4HC6Xi281wuEwgsEgwuEwPxVIJBJ86CMT/cLCAmZmZrC0tIRYLJb35NjtCAaDmJ2dxezsLH9dbAbgXkWbXYBE5A+Jfo+wYzfgtw81690mkUgglUohl8shk8nw5MkTeDwexGKxLT+oQsGbTCbU1NSgubkZTU1NqK6u5sk8LBW1pKSEW22XywWfzweXywWXy8XjCg6HA5ubm3yUM2vVLdyaME+EFQqx17Gfe2X2XPsB27aUlJQ8M/yS2B4S/R4QWnpGtuhlMhnkcjmUSiXUajWPVrNBlcI6efYhZi69VqvlVr66uhpWqxU6nY7XkKfTT+eyBwIBOBwOLC4u8qac6+vrPOAVCoW4x7HV+T9bL/NYhK/tZbSkqVQKSqUSVVVV0Ol0dFxXAPRO7ZFcde/MUgpvAOwcWSKR4OHDhxnCZ9aP/Q27JtsisPZbbK4gm9cWiUTgdruxurqKubk5TE1NYWZmBsvLy3n15xOumVl7Vpv+MgpdCMu5r6+vh9lsphl2BUCi3yPCcdHZjwNPU031ej3q6uoQiUQQiUQQDAa5wLLPl9nxGRP04uIiT4212+3Q6XSQyWRIJpPw+XxYW1vD/Pw85ubmsLq6ypNdChGtMMAn9Fxe1v0ymy7EevmRpS8Meqf2ga32k8zis4h+RUUFLBYLzGYz1tfX4fP5Mqw6+y4Wi3m3HRZom5+fR1lZGW+2wSLvbP/u9XoRDAb3LNKXVehCTCYTurq60NnZCb1eT4IvEHq39plswWT/d3bQiVnY7KGX7Pw8EonA6/ViY2MDUqmUN7xgyUDRaBSxWOy5D5p43jCPSq1W4/jx43x0uEajoSBegZDo9wGhsNm/mZiZq+73++FyueBwOOB2u7klzxZ89nVY9Dwej2ecFADY8m9fR9hrbGtrw8WLF3HixAlUVVWRld8F9I7tkWwLnZ38wiz18vIynjx5gpmZGaytrSEQCPCsskKepxgELkSYJNTZ2YnPPvsMb731FqxWKwl+l9C7tkeys9yYkIV5+Ha7HbOzs5iensbCwgKcTidvbLmb5ysmWHCxvb0dn3/+Od5//33U1tbScIs9QKLfA6ycVvgVj8f5XltYFLKxsQG/349UKgWZTMa7uLIjO+YlZMP2qywWwFx8YWIK8FscIDvlVngk+DzJXiuDrSnfm5dMJkN7ezv++Mc/4sKFC2hqaiLB7xES/R4QdsmJx+MZwbVwOMyTYlgiidlsBvB0mo3f7+dBuFgsxv/NRMpEzfrEs+w+YeIP63PHjvmEwhc21mRxhOcFqwtgx4vCY8BIJAKXy4WNjY0d+wiYTCYcP34cH3zwAU6dOoX6+noS/D5Aot8j2ZYV+C2VVi6XQ6vVQiKR8CKcUCjEbwbhcJgn37DceBaJZyWzpaWlUKlUKC0t5fn12dlzTPQsO8/r9fKUW6/XC5PJxFtLJZNJvu3Yj+YdarUapaWlPBFJr9ejoaEBDQ0NMBqNEIvFvD9/IBCA3+/H5uYmDAYDz1cQvpcsd6GsrAzd3d14//330dfXB6vVSoLfJ0j0e4BZY3aMJnSns91+4WOJRIJbeJawE4lEuMfAhmEqFAqoVKqcohe6zex5WQOP9fV1XmfOOsz4fD7+/F6vF6urq7Db7QgEAjy5SHjyILyJCU8N2O8lk0moVCpUV1ejoaEBBoMBSqUSFRUVaGxsRF1dHbRaLZLJJN/iOJ1OPqzD5XIhFAo902CDtQ4zm804ceIEuru7KUq/z4h22FsVV9RoFwjP2dl/Z4tmq9JW4U2A3RiE3oKwaEdo3XMVmLAgYigU4hbV7/djdXUVi4uLfP6cSCTiCT+so4/wtTCPIRQKIRaLQSwWQ6FQQKlU8o69bO1SqRS1tbXo7u5GS0sLF75KpYJGo4FUKuWeRSAQ4J4HW5OwrDadTkMul8NqtcJms6GyshKVlZUwGAy8NwBRMDkTGEj0zxHhBzzX91xkF/Vsl4iSfYoQiUTgdDpht9vh8Xh4R5/19XUuaqE1Z9dgIo1EIkinnw7LKC0t5YMm9Ho9TCYT7+hbX18Pm80GrVbLPR52YxLGF1KpFEKhEDY2NmC32zP66adSKcjlcl5gxCbRUgXdniDRFxusCo99sWYZy8vL3IozmMfBTh9Y/oBEIuGlwcDTWXhWqxV1dXW8YYdMJoNCocjLBWdFRNkdf9mNgnkV5M7vCyT6Yoc1q8xu5sG2BSwAGI/HUVpairKyMmi1WigUCm5tJRIJ7+DDAnjESwuJnkBGHz/gtyCgcN+dTCYhl8uh0WigUqn4sRuLCbBIPbndLz0kemJrWBCPNZoUBhKJVxYSPUEUGTlFTxsygigySPQEUWSQ6AmiyCDRE0SRQaIniCKDRE8QRQaJniCKDBI9QRQZJHqCKDJI9ARRZJDoCaLIINETRJFBoieIIoNETxBFBomeIIoMEj1BFBkkeoIoMkj0BFFkkOgJosgg0RNEkUGiJ4gig0RPEEUGiZ4gigwSPUEUGSR6gigySPQEUWSQ6AmiyCDRE0SRQaIniCKDRE8QRQaJniCKDBI9QRQZJHqCKDJI9ARRZJDoCaLIINETRJFBoieIIoNETxBFBomeIIoMEj1BFBkkeoIoMkj0BFFkkOgJosgg0RNEkUGiJ4gig0RPEEUGiZ4gigwSPUEUGSR6gigySPQEUWSQ6AmiyCDRE0SRQaIniCKDRE8QRYZkh5+LnssqCIJ4bpClJ4gig0RPEEUGiZ4gigwSPUEUGSR6gigySPQEUWT8f/a/iIHzx79VAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "lb[Si_mask.flatten()] = 1\n", + "ub = np.ones((Nx*Ny,))\n", + "ub[SiO2_mask.flatten()] = 0\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "for iters in range(num_betas):\n", + " solver = nlopt.opt(algorithm, n)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_maxeval(update_factor)\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll wait for a few minutes (or longer) and visualize the results. We see that every time `beta` increases it either drives the cost function out of a local minimum or into a poorer spot. It gets harder to converge as `beta` increases. This is expected as the gradient starts to swing wildy at these thresholded transition regions. Regardless, we are still able to generate a somewhat smoothed structure after just 72 iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(10*np.log10(evaluation_history),'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To be sure, we can plot our results and see the resulting geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course we want to check the final frequency response. We can run another forward run to pull the objective function parameters and calculate the transmisson." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "frequencies = opt.frequencies\n", + "source_coef, top_coef = opt.get_objective_arguments()\n", + "\n", + "top_profile = np.abs(top_coef/source_coef) ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Transmission (%)')\n", + "plt.ylim(98,100)\n", + "plt.show()\n", + "\n", + "plt.figure()\n", + "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Insertion Loss (dB)')\n", + "plt.ylim(-0.1,0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In summary, it is very easy to implement design constraints and density parameter operations using the native adjoint solver interface. One could use this same design flow to implement robus optimization over many frequency points." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb new file mode 100644 index 000000000..f67d5f19a --- /dev/null +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb @@ -0,0 +1,1894 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Design of a Symmetric Broadband Splitter\n", + "\n", + "Many devices of interest can leverage some form of simulation symmetry to reduce the computational cost and storage requirements. The adjoint solver and its corresponding TO filter toolbox are built to work with these symmetries.\n", + "\n", + "To demonstrate this, we look at a symmetric, broadband splitter." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import autograd.numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "import nlopt\n", + "seed = 240\n", + "np.random.seed(seed)\n", + "mp.quiet(quietval=True)\n", + "Si = mp.Medium(index=3.4)\n", + "SiO2 = mp.Medium(index=1.44)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we'll define our geometry, filtering, and wavelength parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "waveguide_width = 0.5\n", + "design_region_width = 2.5\n", + "design_region_height = 2.5\n", + "arm_separation = 1.0\n", + "waveguide_length = 0.5\n", + "pml_size = 1.0\n", + "resolution = 20\n", + "\n", + "minimum_length = 0.09\n", + "eta_e = 0.55\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "eta_i = 0.5\n", + "eta_d = 1-eta_e\n", + "design_region_resolution = int(5*resolution)\n", + "\n", + "frequencies = 1/np.linspace(1.5,1.6,10)\n", + "#print(1/frequencies)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll also define our simulation domain and set up a broadband source." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.56\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,2,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll define our design region. This time, however, we'll include a symmetry across the Y plane (normal direction of the symmetry plane points in the Y direction)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll define a filtering and interpolation function. We need to include the symmetry requirements in the filter too." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def mapping(x,eta,beta): \n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to define a bitmask that describes the boundary conditions of the waveguide and cladding layers." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Define spatial arrays used to generate bit masks\n", + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "\n", + "# Define the core mask\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g + arm_separation/2) <= waveguide_width/2)\n", + "bottom_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g - arm_separation/2) <= waveguide_width/2)\n", + "Si_mask = left_wg_mask | top_right_wg_mask | bottom_right_wg_mask\n", + "\n", + "# Define the cladding mask\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", + "SiO2_mask = border_mask.copy()\n", + "SiO2_mask[Si_mask] = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can formulate our geometry and simulation object." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "geometry = [\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # left waveguide\n", + " mp.Block(center=mp.Vector3(x=Sx/4, y=arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # top right waveguide\n", + " mp.Block(center=mp.Vector3(x=Sx/4, y=-arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # bottom right waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + "]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " symmetries=[mp.Mirror(direction=mp.Y)],\n", + " default_material=SiO2,\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can proceed to define our objective function, its corresponding arguments, and the optimization object." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "mode = 1\n", + "\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=1.5)),mode)\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", + "TE_bottom = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,-arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", + "ob_list = [TE0,TE_top,TE_bottom]\n", + "\n", + "def J(source,top,bottom):\n", + " power = npa.abs(top/source) ** 2 + npa.abs(bottom/source) ** 2\n", + " return npa.mean(power)\n", + "\n", + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the design and ensure we have symmetry." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "x0 = mapping(np.random.rand(Nx*Ny,),eta_i,128)\n", + "opt.update_design([x0])\n", + "opt.plot2D(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll define a simple objective function that returns the gradient. We'll plot the new geometry after each iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(v, gradient, cur_beta):\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)])\n", + " \n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.savefig('media/splitter_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", + " plt.show()\n", + " \n", + " if gradient.size > 0:\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1))\n", + " \n", + " evaluation_history.append(np.max(np.real(f0)))\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1\n", + " \n", + " return np.real(f0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we'll run the optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 4\n", + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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QesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMIbQA8YQesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMCba8udBJ+8CQGdo6QFjCD1gDKEHjCH0gDGEHjCG0APG/B0HFjsW0aWLiAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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DxNTv2SCQSXrVZLRIhxYgycwM9ikYzTDpG6COrn6/X9ntx8fHFZmRTsv58lyPHeRbLpc1MmaERMiNf4ME5Iq6OIfJy2p6ltGGfWqyKOnVj8CONlXHNY6+i+xs5+Pe+DvfM4fzIrZj+zyoWcK3h0nfAiAIS/rZbLZFelbt4cQDAVerVUWo1WpVC73hhV2v1zV1W0nPxOLzMrucSZGRXuvwYR/AdjOr6kx4XaknqxjE0pxV8WzQU78DaybqgLRk//ow6RvALxqkKRM/4j05UAiS8+URrsPAgM/lcrnl2MsIfB/Sq32ubbMZgeM5mWi5XG45HNVeV8Jrhpza4Oy1Z0Krbc73kSUflQANB74NPDv1ieBYawJ3MOnvAaj4cObhpeUyV1yjDvY1XnC2uSPqTjoOw3HYLHNysUONQ27qF1DS4xrw8mOFWV5pNuKO9JmUV5WeQ2nqd1Dvvg5ypQ3QKAc/H4BNMAyuSnATvo5G0nf9YanqudlsKuKD2CC9LtagjrHMScZZaGhfX2KYA0hcwXcmCV70rH0NZbHqH5FPvtGEGXbSqUOvNCnm2yA8RyGyxCHcD9plU8oow5K+AeyA4+qvnIqrYa+sDd5Y6jJZoKbipVZHnob61J5lkqgDTkN8aB9ORvST71kTavi6fP0mAutx/Ez0+ShUspfmBGAgZkkfsb3evXGHRtJ3fdLC3t5eNZWT16TLYuzqKeeXlskNu15DVyAaZ8Wpba1ah84HUKiHm/ezWZBl4bHNrQkvnNSjU4g1nRdmB0jJAxN+z4jP5Nb7xHnZffJgyH0w7mBJX8B6vY7ZbBbT6TTevXsX19fXNfLDYcV2dWaPg/RsB7NtzOp6RL2e3q7EGtYy2PkXUS+EgbZwLhMW5gqr9lkKL8Dhy9vb2yp/gQc9OAe5r+yoVG99ydHG/dckIh60cCwGM82KZAelsYP0f/7znz9UPz4K8MsOGxr535zzzYQFudS+hSMv4j2RdIFLdoBpzB12KqCSFi8xT7Lh+fZQbZW0bJbwNGCQqCkElknakhRlE4WlLwYK1j5KyTU8kDLZtV4BL4vd7/fj6OgoHj9+HI8fP47z8/MYDoe1Qp7GDtL/4Q9/+E5VfJZG32f76tQC6XF+NmuNX2Ycw2vMgUws6bFIBrb5fL6lLQBso2YqME8AYv8Csvn03tAWnI7cT9QEgOnR6/VqNrGaECXVHsfd3NxEr9fbSjfOQnWaPMTXVJOBBzheJwAZksfHx/HJJ5/Ez372s/j000/j4cOH1SxHdnp2HY2k//3vf/+h+vHRAaEsLlIJkihB8YLqIpIcsuM17VnSK5FVmq5Wq+j1elv2PgYZltYYYHR1F/QBbSO8yFmDEXeJQhz64zZY6kZEpd5joIAUhnqv0hzPFedq3B7ORh1sVdIjX2I8HsfJyUmcn5/HgwcP4pNPPomf/OQn8Ytf/CI+/fTTOD8/T2c4dh2NpP/jH//4ofrx0WF/fz+Gw2FMJpM4OzuLk5OTWk49pA7bnbxaDRMxS7Dh2DxIqlEBvKSr1apWGUeLcEBa8wDDti5rDZxTgHM51IXz1bnHpFcfAE/t7ff7tVRh9UNE1B2fHN5Tac/nsR3PE5zOzs7iRz/6UTx9+rT6fPr0aZyennqFmwIaSf/8+fMP1Y+PEqPRKCaTSUyn00p9PDo6qojP6i4IASnE8+ghPXmWHMf8I6KmurKNrQNLRGzN6+diHZD03Cf4H0BMJX3EnXTNCA9g0MD1cW/QOG5ubuLg4KBKIFLzR6MMHMfn2D/6w9cFcA/435yfn8fjx48r0j958qSaF2F1Poe99w1AgcXNZlMVa5jNZtV66HjxAZCCa+MBWf47JDUfB20B0pfj91C7MbBwqS0QPZPUsK85oxADF0iPvrGGopEAvjabBfDYDwaDuLm5qZKWcD6n/OrcAk3sYWcoX5+f2/7+fmXLn52dxdnZWTx48CAePHgQk8kkhsNh9fy6jNKAZ9I3AOoyyI8XcrFYpKSPiJqk1nAVt3twcFCRGGqoFsPgeDpLy6y+Huf5q0edPfN8HvsdoGbf3NxsxcC5HWxc+w/+D15mm3MJcKyaIhF3g2E2O08n6MC8gemFoiXQwLRwiePzOZyc0wC2xxeLRUTcrZDKBTL4eCZn5gyDtAXRUdgy4s4jD9JjH8B2ORfRZNJjoEBfcV0MMGwWgKg4Tkt7oQ1Vtbl2P/p5cHBQ+S1AemgGGAhYu2B/B5s+nMPPxTc4BTgiaiaKRiJK2ZHGezj3vgHsacZ0WJZQTcTGb6r+80SXXq8Xo9GolrjC0j7rB0tOjhbAJOC0V55kw6SHNNQYNif6oA0NdbHvQh2I6DtUe3YccsFQXB+DFDQMnmOQzeZjB6ESvakSsFGH1fsd4Bcfaigkp0pFoET4iDvJDrVeHV7sBVebVj38fCxL+oj6pBl2CLIKzup5NklI75/vhbUS1jCY9DADuMSYRj+4rzp7bz6fx2AwiPl8XiM0QoUlopvwzTDpW4BffKj3LIkz8IuHl5vDaExKDWuptMU+PSarfAPJqUUy4Mjj/RolyAgfcadh8OCkcXTdj4GEzQlemw+aCdrHM2UTY39/PxaLRS3Eh2NLZDfhd8Okb4mmlFFG9tJxoQdoCZpai99US4BkZ3NCk1b4b5bqmRNQs+ciYutvve/S/XF7uC4frwMPayYc3eBkICY37H1dIkzNJx4Ujd3wkzKMjsGSviUyydkkXUrqPVTszMOsZgT2cXw/YrusNTvsWCNRzQQqOu/HvtKElKyP/F0nBnH7uGdNvuE4PZtMWpKLQ3haAUifVdNkIaMOk74FVJXMVpLhY/kc3Z+tbMuqLZNYCczTUDl1lavGYJDgDTF4DFbZOUxY7TObBqV+6nV5sNJBkqMhEXf5D5kjLyvJBVNCB6vSwGXUYdLvAJOdp6Oy91mPV+cW/8apsLoiDWeoASrl0L7Wicd+ZORldfIzGx4hO/YzqKdeBzAedHjA4qQakJ0jCCA7koI465Dj8hqiy0J22raS3yjDyTkNYAJzXDxLn8XxagZgP8CzxDgjLiJq0hvQqjs4tte7W8OOs/d4Rh9LTdYSuG+IQujgwvdTIjwIiL7rIh7oJ9JzkbjDST080GlxTR60eCUc9IfrGmQDQNff3xKcnLMDmq+OeLMSFmCPdUR9jj6AeDkSTDiHnVNPNTedSY856xiUuCQVq8dQkZmcDPzN12iS9Jw9h78hwdku5zkDmobL2o2eywTXMmHQgHAuCpPwenfZPRh1WL1vAKQnJnfw3HrNvdfYOaeDsnTkl5GlfcQdodheZScY1GkQlX0KPOFGF4IEidhph/tDcg2kaFZnDuDsPgwkOBepykx6zQ1AuI4JzwNdVgAU/WT1HSHM4XBYLWWN5ax1QO6ytC/du0nfgNFoFCcnJ3F6ehrHx8cxmUyq2msqsWAf69TaiLoKy0s6QYtg3wDIx8Qv1dFj6csTbqDeM+kxmHAkALPj0EcmrKbe8n1wX7FP6+CXptay7wF9KM2uA9Q8QrbiwcFBXFxcxNu3b+P4+LiWkqwTe4w7NJL+pz/96Yfqx0eHvb29GA6HcXJyUhXRyNJI2TmmRTS4DNWuIho4TiUyS3oMFpB0rEHAmcekx7rubBow6dE+HGqsGmfqMY5BX/GJ63HOfFZEA5/q7WdfBl9bnaicSozBB5WAUGQk4r0GdXR0ZMIX0Ej6X/3qVx+qHx8NWAqhXNbx8XEl3bNZXJBeWi5La881lcsCGdgWjoiKiExINhXwnctlYfovLzfFHm8lvZoXGfH5b15sgyfJ8BJXLL1xPn+ynZ6ZP1nmITQpLUy62WxisVjEdDqt8vRHo1FlWnQVpXtvJP1vfvOb76QzHztYCkXk68txjBovKBxzWcFJEEKngHJhTL622vWahMN59jwXAETMpC5yDBi3t7dbGknmCGOTgPvCYTVIe6j3mW1euif1d5TSjWE2sQ9gsVjEu3fv4u9//3u8efMmrq6u4uLiIh48eFBNge6iR/+Xv/xlur+R9L/+9a+/k858rGDbFdILDqJs6ieXwNaadVkJbJ5HjuvM5/OK4AzNsGNyRNx5zeFshHrP0lc92vid71cHmYyArBWgb+q8A9lhTmQhwGzA5EFAwRNvIOnRV5hLkPDQrL744ot48eJF/OlPf4qzs7NK4uP6XcLXIv3Pf/7z76Qz/whYr9cxn8/j3bt3cXFxUVvsAuWr4fRi1b5UZZansbKUgrRUpxkPBDooMIlAevgCNMzHvgAARELYjdvUwQdgSY92s8UsOeymBOdBgze0r+D7hx8DM+w4IYmxv78fX3zxRa2WYVdJ/7vf/S7db+99AbAL8eIdHBzE9fX1Vv662vNs00NC8cu+Wq1iOBzWHHqcEaczyCLqNerwN6uqcO4BrAXgd1Wx2RcAInHbEXlyDtvimlTD6bRon7WTEtk1JIdr4xN9ZmmfPQ8NN0bE1qBgODmnEZtNvboNpLIm5XAoiifUaMFL2N4cs2avcxZb1heb1WLsj4iaX4D7xETh37gdaCCac8BpullWnjoUeTDk/kZE7Xqq7quTj7/zfbB5kj0XjfkbOSzpG4CXXqu0aCgtImovs7ahGw8OID0Tj8nLkQJ+ufl4JinOibgLBeJ3bl8HmFLKLZMKAwv3Mbs/vVc2PXBORl4G3yPfJ5Nf+8b/F6MM5943IPMgc8wdOe14QVWa4wVlCcjtcqIJBgwlvaqyHG9XEvJ5UPkh5bmffG3+5EEj0zYi3qf/YqBCRR6+Dw7rMVgTyZx2AM5XP4YOcjhWk40yh6VRhyV9S7ATCzHw2WwW6/U6BoNBLY7MFWJB+iyEBfKpNNPwIGLTANRXluScwML9ZQ86XxfXRtkqzgwEmbJyXGy+qKbAAxAPkLgH/J2FI3Fd3jJ7PIsqsJlgsu+GSd8AlSqQ8LPZrMr3vr29rS12yeonq6jsrWepzrFzJq6SngcEzK7LSKzqLzvTMk2Di1tmzkA45rA+HTZe6Qar2ywWixgMBmntera30S9ENXgAaGPv8++MpkiAcQeTfgeYPLzy7HQ6jaurq0rSqwML+fCs0pamnmLAaCpyyURFtEA1BiZ+xLa3nCMOqpmojc+OMXaOaXUb/kSMnqfEMvnZq6+59jzIZE5C/C8Y8BcY94NJ3wAOeUGiz+fzmE6nMZ1O4/LyMjabTZULrrHsbBFLTouN2J6zr55/qNkqmXlSDNvobNuz+qx+hUxdLzkNlaQ6DZYTl3hxCia9Sn88Ax4I+HjVjEpmAJ5R1/1P94FJvwN4ufDiwpaHpL+9va1Wd+GXFwUjQKiMQKxul6afchYaO+Y4RVglNtvCWTgN18xy29V7rzn+TH4MZNnGJM+KYbDqj+elGY+cy8A5+hr6g/POkr8dTPoW4Bg9Z+VdX19XdimTAC+sLuqgEpeJp3Y22/Uq6VViswdez21ydKnnX7326LN6x3kA08GgaUBgwme/cUEMZD0i85FnJurgGhG1RTtt0zfDpN8BttGhtuJlBOmRC64vMC/qoGTiHADY79ivITg4BNnxxW2wlsCkB9TfwH3QQUdj9fwMdFOpyxqB+gPUPFCVniftYFDlT62Og2P7/X7MZrO0f0YOk74F+GXmueNwXEF95zRUXtlWPeuamAPyqvoN8qlzD7+DpPAd6OowQMkRxu1nST7Zc9BPJpgOBJmDTh11TH4QHyYUIiTQqrg67nw+j6urq5hOp5V0z9JwjW2Y9C2gHnz2xHOtOOxn1V7j4Ex4Xt1V1XTsZwJzSE2df5mkV/ucPyPq2XTZOU15603kyjSCklbA+0rE50lO2DB99u3bt1UExMk57WDSt4SG49hBFXE3mQbxbMyZZzubw22YlMPSiVVtDqtF5OvHqfNPHXlNpEWb3Ha2fVvPrKR+sxbCJoBW/8H0Znakvn37No6OjmrOTY31G9sw6e8BTXgB0eHdRygNqapZNhsIiim4mqxTArejKbR8rSz8ptAU2xL5s2PxHErtNT03PpfbUPOAnYCaDwDVfjqdxtnZWYzH462kIvb2G9sw6e8BVZd5EEAsXsNfGhJDFttisYjhcLhVS07JUPK2syOwpN5D0pfy6PnvjOwZ8blvWZu7/AH6tw4kbBKU8gMWi0VcX1/H8fFxjFWuKKMAAAmoSURBVEajrRVwcYyRw6Rvicz+jdiuEIv9fAyfA8k8GAxqpanUw86hMbTRlFyjGoWaCSWy8/cS6fmYXdKzaaBoi+w58ACwXC7j8PCwKk/GAx9f9+rqamvij2HSt4La2Jn6rDaqTu/kAQN2vxaf0I1JjjZgIqgjLbPHlfh8bHaP3I5+z5Cp+Ur6NuRv0g7U4Ye0ZyQ+wXdycHBQrU8wmUzi9PQ0Li8v0wG562q/Sd8SrJ6zJx4veilurej1elWcGTa9+gmY9CztuR+I28PTD9Oi6YVuInvT9wyZOcLnIvmoTT92mRE8D0Fn+eF/MhgM4vj4OM7OzuLRo0fx+vXruL6+7jzBM5j0O8BSE4TnEtcgP0JFmvLKpAVRIa1VfS1lt3Fef9Y3/c7Y5cFn25qJiu+6rxSj5z6UogCZpL3PAMH3ojMTUev+/Pw8fvzjH8d8Pq98Ld80CvFDg0nfAuyQ41p4GADwEnNFWlZHVfXnZB5Na82y2HQgKcXBb29va0k/aoooAUpSOiNrloBTIn2TT0G1iWyg4eMyzz+A/wfU/PF4HOfn57UoCZ6BJf4dTPoWYFucq96ORqOt5aY2m01trntGDiYO/40BAs6qbPJKlrPOZbe59t6uEF4b0iv5sgQbPk8djOr4VNJn2krWz8y+j6gnKUXcFTRFJVxjGyZ9C7ADbTAYVC8VSA/VPiKqVWtYtcxi1MjXB/iFVomPySZcanqxWFR90EU24Nxi8uMeAPYTZM7CjIA6MKm/QROGNKKA9jOS33ewgVagswzxP9JFPYw7+Mk0QL32SKoZj8dxeHhYkT7i/bLJ/KLywpBNCSkgPh/Hef481RRkR1rqeDyuknwy4jP51emoajrAKb8amsx8DjxjkJOF+NqaGsyk19yDTCPR2X08X4H9KnyvmQ+kayj5Mkz6FmCJAkmPNe7Yo6xxYgYm5mQvIsJKTCxV4XkuP0v4jPD4G8TX9NxSLgDutWQSqPaBvuI8dnKiH3p9leaaW8BaAfqqTk5oSgjT4bv6E9Avow6TvgUy9R5r1YPM2UvNWWF7e3u11FCV+iw1mfxcmgqkh08BaizIxd+ZfFrtVqMGLBXZHmcCstnBPga2rfF88IyakmfY5NCSXXycpuWiv71eL8bjceWwHAwGlcZR8g0Y72HS7wC/QFAfod7P5/O4vb1N1Wm8xIvForLf2WufOfciolYzrtfrVQTjqbqckJItnqHHgZBMJI0KaAoxEx/QajdcwUfnFDDpuR+sxutcAh0c8IzUoYnrTSaTuL19X4obC5J0XaVvA5O+JVi9h11/dHRUSR2Qje1MlmCLxaIiMSfUZOEvhP4Q08cnzlVzQr30XKm2VE2Hy0+xXc6ExECB8zKbns+DlAfZeV0/9iso8XkQw3XZLlfSbzabGI1G1YA6Ho8rU8uk3w2TvgWYDFCbx+Nx5amHhJvP56kdjRcZS2IpaTLpD7WZQ2IYDNiMUJNCpTWHzgC+FofAlJRcXAN90T5H1E0CJj6HEdUxyIOVOv/U/6Bhyr29vRiPx7FcLmMwGFRpt6UkJqMOk74l2P7EywyVsiTlWdVGaScuvsHkj6iTMUt8QRgQf5f6ucsbnoXA+HwN8bEvIKJefiuiTnomL/LjWcpze6y1sDmCwQDgmnsYBI+OjqrPhw8fVlV4TfjdMOl3IPM2s5caEjILVbE9jd84/Ia590wunnkXsT29FijF2bnfEdtLVClxS/echdiyfAMdZDhDbj6f1zQN7gcTH8+Jk4vUg89+CGQcYiXh+Xxec/IZzTDp7wGW9iAx59JrIQt8n8/n0e/3K2cc/gbhsT49Yv7L5bKy5QElqcb/d73snCSUtcfHRURKelxLj8fvmE+AvmcZeVkiEA+M2KBxZP1Fu1ynULUkowyTviXUycXZX1DR1Tvd5FWHus+Ovr29vVpJZ3j79SVWE0A1g/tASciaABOaJW/2XKClcP/wTEqDhmpPOouxlPegGYEm+f1g0t8DGZnhvYfaqWq+5urP5/Oq5NN8Po/BYFDTBDi8h/CYhtYUpcGhCdmxUNezfXpdHQSa4uPqnORrQ5pjwMC14MNg4mfhU5gTJc3E2IZJ3wKaRML2PCbXcN48Z9EhqUZLZ3NlV92QY68OPw2zsSedw2ptpH4TMZhk6sxTuxybmjWaWYf+ZGm/TGKdM6A+Adj+k8kkJpNJHB8fx+HhYW0JMaMZJn0LMOnhtQfhQUTNpMvIr5NmUOkVmXZa5pmr62hCDNTbUsmtb6L2Zo68Euk5X4DtcY7xR2wvrwXoYAqTSROLeHAYDocxmUziyZMn8ejRo5hMJjEej7eiBEYOk74Bmdeea9XDnleHWml+PCeYQPLzijm8QSNgiY+wFA8onKuvxLqvnd8U7lMHH39yViCkM2cARtzF+PFdPf9qDmm2IUcHQPqHDx/GkydPqqq4XCDTKMOkbwF+uYfDYSVxNLwWsR0SKxXJ4Bx2LfHM2oAu/qiJKlxwg+1/XBt92YU2CT+6sUqvYUrN9ddngT7p+ZzcxNKeVX0kR52cnMTp6Wmcnp7GaDTamk1o5DDpd4DtWgCSqeRYU8mv6n9pICit+lrKec9Ir0k32YCk94fPjORKIpXyHKbMZtPxc1AzhNvT/HvN4tPsRlTKGY/HVZ6/1ft2MOlbAi8ePjNSMTKyaWxdSaATYXhKaTanXI9VcrVV7zPil+z4khbAE2hKHvws21Db0YEju16W/aczCY0y9na8EA6ARrPkbntu9ndTu6Vt1/Ha7n2gTjrdlx2TDRh6bNYfJn12fmngyQYAHWxM/Arpg7CkbwF9kb8O2dseV1LH2x53n2tnaEuY7Lj7kE2TdErtlNrMBoj79qGrsKT/BviuM8G+zcHlu8KHINmua5joRaQPxqQ3jB8uUtI7qGkYHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHUN/x+97H6QXhmF8MFjSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx/Dvlxi98LdOCjwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 16\n", + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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9ChYYW2FNg53Ku/MYW0WsLr4G4thD4E5DBc7zBrIsQjYUQUd8qqPA728+YNG3BNYeKTukk7IlqxCgi/hoRXVRDf4Mr7lDiIiSNcw6ANDr9YrAVpbiw3Ys4kyUHFDLJspwh6DeBA9hsqg7DynUO+GptVmnwLB3ckrUFnyZWtH7x/qIXoB8c4fD4ZAuQKlReN2Xjpvx3X6/XxKvRt5VsCCr3ss8Az1+ZoV5G3XH2ZJnmYFs2MDw78IThPDbZrEGPbdsfTxfr82wpW+ABtBwMWcVcCqoiChc9ywQp5NO8H117zUYlkXgM9GrdeXP66hK1WkaLes8VIA6qYc7BLyH36aqbTp+v729PfIUXKTTjFrRO1DyAVhbvcW0WrT9fl+a8w53PqIcSddUlgbkFLW4GojjQF5EOb9fldbK6uy13be3t6X8PwcYdXvNHlRlJvg3Ve8C8xiyDlDPDd4V1/pjWMATm7J5CF3Hlr6CTOi4Rx3uSMtLZkUcl4lCNPgsK1VV7yGi2k3PXOgqD4HbU2d1OebAgbT9fh+j0Si22236+yCWwfvd7/el4Jp+rr8vfiMWe5Xg0Vbu0LhQCc9nZ2dFZoVve2X3/yO1ov/uu+9+rnZ8EbCLzlFrXFwQKt+GGhFrROZ3u10Mh8PY7XZHizJin1qYkrn3QC2iBvJAFkvQeAHvX1eJhXXnQCLOi1ONPLkH3gfP8qsTL6PnxOfK20DsbLG5/XyXnLOzs5hMJnF5eRlfffVVXFxcxGQyseiFWtH/+c9//kld/Cp39ufev15oPNbmxS8gEh2P8xgfkX1E8Fn07N5nRTJV+ecsyKaiYiutaS79LkSaHQvgeGdnZ0Vb4U5nc9w5naewSPl8smAjyBb6ZPEjiLpYLOLevXtxeXkZFxcXcXl5Gffv34/Ly8uYz+cxGo06uzBmFbWi/+Mf//hzteOLAcLGBTwcDkur0vJ8d76II6LUQfCYMuJ4XF7l1qu1zlJeClthFj3Atrj4OWqeHVe9C+7ksrnvmnvPzkd/Lx0GaRAP56GTiLA2wWQyiYuLi3j48GE8evQoHj16FL/61a/iwYMHcXFxUQg++z91nV7dD/H06dPO/UpszXu9XnG/uOl0Gufn5zGfz+P8/LyordfVZ/QOM+pqa8EKb6u185yrr/o/8cqx2aQboJYV+8yEmYkzS9VpHcAp11yDfNl+qs6Pf6PRaBTz+Ty+/vrrePz4cTx58iQeP34c9+/fL92z3kTqxtWKvtfrdU70Sq/3sdpusVjE+fl53Lt3rxA+F+DweFcXwWDB8cWti2BkY/Oq8b4KXufTZy51tr9sWw3wseXnqrxM8BFRKk7SMTn2la2Ok7n5+v8YDofx4MGDePz4cTx9+jSePHkSDx8+jMVikU5l7jDpD+Du8AR3d3fFckz8WK/XRY09Li4WsF7kVdaQvYKswEepsqIcTMP7VTEN7Ti4zbr6bjYOZ7c84mPsgNvFQx3sNxN93UQgFj97RrD0FxcXRQeM9Qvszn+kqtOz6BtyOByKJZoiorhYh8PhkQjZUkZEabzKLjWP/3lYwNvzP47H64PBoEgJai0/Xmcr6TDsEUCYOjxRb4E9Fo41cGfE55Ot2Z+JHqnRrKpPhxEY0+uSZHWdpfmIi3NqYEsBC7fZbErjdMyX50BTJhQttYUQeAXdKrHxMzoEFnaW7+ZOJyKOhK+Cx76qxKpDlSw1x/vI7rHHrjqnQVn4nMbkWAKnUHn/uhSZOY1r7xuiRTKbzSYiolS1FhEly8oucZY2g7UeDoeFBYN4s0g+V8ZVibyqEEfH3Lp/Fn+dleboPo7H+9C0ZZXoqyw9i5+Lb7hD0Lvzsgdk4Z/G7n0LcLGjWGW73RYWV6PfLA5Nt2H8nRXO4Dgq9swq47sKtkXunl1w7A/f023U4utNMrWdvC1ce31UiZ7TgChmYoHjoVY8K9bR8zHVWPQtYAvHVl8DZ1n9OQsPlrnX6xWWDBVwQIcIER9vWRVRXfuOfcIj4OcM3UdWeaeZBW4Tb6vDA75LT7bCL9qp7c+8HA4gZhkO0xyL/hNQCw4rDmGxeHXsqxYXbjpXwGk0HK9RO4DPNTCGjketf/ZeRtaJnMr/8/ez9KP+DbT0Fp0ozp8DeFkK0mL/dDz9yHyxOKb002BL/wlkLii7nOze4/ua2mKrGBFHrzOrxkErzkfr8dUKNnWDdSjCFljH/5l7j9eaSuSUpbr3PIchK+/VB+/DfBoWfQuySLrm6ZsG8tj15Tvb8Lp6dYG8zHWuq8yrQoWu1Xd8LrwfFV2/3y8tw8Xf0+FPRB7I0+h9FsnXmgeLvz0WfQsgAASmuCgkS9lFROVEFM1n88w8vT21djScWtNgm3YCmfXH2FnrELTUloNoWeQ+OyeejTcajYqofFaRx6LPBM85ek7Z4Ry0rLiqAtGUcXFODXpRs9hQj6/FISp6dmWBWnnNaVe59+xRZEGzrN5dvQ89N3XjkZHAdzlaztvq9F6cl3Zm2YxDFr3eACOrxdfiHKRNtXIP++76dXsKF+c0BGKD2DHdFu49j7fxN6gq0NEiGHbtecyO70fk962r6wTY2jOczuNUH39PU2p1Zbhom54XD1k4ZZcV3qgbr0MNFrhW72ktgqnG7n1DMH7nKbbz+bw0pufxtE78OFV/z1a6SqgR9RNusgk/dSKA4PEM11lz6HhPg2rZ0EWHHvzMnQeLHkuIYZ86ExFt5RoJ3AkXN8Lc7/fF/8J8oOq3sOhP0OuVp9ZiVheWYlJryhd6lYXEBc1ewSmxahltxOeZWstVgVpxiLZnU2urhM+ZBh12aFt4TM+CPzW1drfbxWg0infv3sXbt2+L24j1+31PrW1AreifPn36c7Xji4IFwu485tPzDSp17M0uLX/G4+Umi2hopLtqqKXCr+o02J3XwBfOt2o6rYq97pZUQDu0LBBYtRgHfhM+P/5dYNG56g/nsVgsio7G5NT+Mr/97W9/rnZ8EXBFHU/j5OWycKFl87Y5iMXTZCOql8tSK6lBN3ZpuTPSdmsQUYWbpRD1uPo9FiOs8acul6WxAR6fZ/MQso6Mf2OO7m82m7i5uYnlchkPHjyIe/fulZbL6ipV514r+t///vc/SWO+ZHDxb7fbwhJyIIotFEeYcZE2WRhTV8JVIbM4sK0Ki7fRzoKLg7AfnBdnE+oCtdpJ6SMrptGhAntA2VAjixOAzHNhr2G1WsVyuYy3b9/Gq1ev4ocffigW1Xjw4IEXxoyI3/zmN+n7taL/3e9+95M05kuFL8rswt5ut7Fer4+WwMbFyhNN6kTfZgnsiOObWFaNfdVLyCLyLHi2zFkHo50UW1eupsui69iXtofbweeC9zKvJ5tVx94Nx1GwBDYsfpeXwK4Sfe0aeRHRrV9JwEWP9e7X63Usl8t49+5dvHv3LpbLZazX6yIwx/l2LIrBVpf3x8UoVW4+CzMrV61ypwHHEXglW7jqOr5n64tj8e2t9ZGN9bP0JNoG9Lwy74XPR4cI2Ad3THju9XrF4qW42QXXB3SJP/3pT14jry2IQPM4PSJKFhupLQSY+FbVmeizktnMxWd4Wwiu3+9Xegg6NFDB4TlLJbJ4+dbTmeAzi68Ziiq4k2EPIessskg84gtarXd3dxc//vjj0ToAdb9v13BxTgPgYsJ1h6B1Aky25h2sE9eeq1XXMSdf5FmVHKNje36t42pGXXuNG7AQtRgH2+i+stiDoulLzRDwvuvaz0MPXlXX1+xpbOkbwJFjLifl6DWPNzXyzC4pl8niomeBqlWDAHkfOh4Huh06kyyFdyqHrWNxWEt+hmBxnjqGr4oXqPAhVu4EsqELnxviLpoJMadx7X0D9GLGhQoXP+JjWW1dkAxox8Dgb3ZJs7w5dzS8X24znnkowWW3GuXnbbmyLmszOqJ+v1/yQjj9p+de51lUBfXqvAUOaFrwzbGlbwkEj9zwzc1NYanZzdeinezCBtyhcDEKr6LDFlVz10yWrsN+ED/AgppswdF+HAexCogbHRoLjSvqMJyBy83eAIStAcqI46GBejL4nv5mVR6BOY1F3wC+UBHRvrm5idVqFdfX16XCFq27ZyuqwS615FV19xAuBw35ODo04E6G94G/EQjUdqjbDYGPRqOjMT4H8Dj9mI2z0WHoeF+9J26rdgTKqc9NNRZ9AzSyvV6vY7Vaxfv37+P9+/dHkXQWHay+jmEhAt4GzzoGZ3ebBa/bscseUZ4Wy94DZwzU/WbPQt3vLNjHotc17LWYBwuAZr+tLvpZJ+Q619+cxqJvwd3dXex2u6JAZ7lcHomePQLM/OKoPVtQXLQqcraAPLbncbluy52FRv6R6sNzVtzD5xhRvitP5naz5WbBowaBC5DwwHuZ8PGM4YTjST8dFn0L2NJfX1+XLL3OPIMYeI489sFWCtY3s27sumdWniPmVdkC9ixGo9HREINRLwFkgcms8AfCZuHz82g0Kv7u9Xoly8/PCAxiaMTeh/4+/DuZZlj0DWEBIYiH+m9Yeh3r4j5ruoAEQIAsC3Lxd1nUcIP587oAn3ZE7GVgPzgG4GBg9jtk++V17lC9CLGv1+vSA1WLm80mNptNyRNBERLvOwvucRoT6wCYZlj0LUBumEW/Wq0iIo6CV7j4eepntqwWLDDeY/c2G6/Dyld1CKemx2bjYT1elhUAGvTjTo5LjCF2znLwAx0Af5/n1utsPh5moOPa7XZHHaA5jUXfAo7es+XCZ+zy4juox8/KbzEjjwWsD8BBOn5fl8ni4iFud/bQTiU7bt1vkVl7rl/g3wkdJF7j/c1mc7Rclj5rqhDH4HQpOgBzGou+IezW8kW+2WxKATpYvc1mU9Th8ywwfj0ej4+i/lnOHp/xFFONxqvodbzL43EmE3zm8vPvoCk1PXd19TNrz+4/L5WFmENm8XlOwM3NTbx9+zaWy6VLcFti0bdAU3d8IfL7iNpvNpvSLakxKQeC3+12MZlM0lzzcDiMiOMpszyllC18tsYeto/Ix+daOnxK9NxxqOg1bsBr3202m5jNZiXviC28xhnYjdepu7e3H24X/vbt27i6uoqXL1+WUoLmNBb9J6LjWRU9rwari0QOh8NYr9cxmUxKU2uzY/BMMR0eZPvmop1sOABU2E2sPNqUFc+w682CRccGV5/H/dxhcrvYO8n2u16v48cff4yLi4sYjUZFu5bLpYN6DbDoG1IlAlyUEC8i8ly7DhGiE8BMPV2+OSK3pugo0A6d1YfjsfA1fafxAX2dCV4LhLRdmXXGzTgh1OFwGPv9vlhrUJe65thCtjBoZv1h6e/fvx/T6bTIaNze3sb79+//nn9zJ7DoW6AudkR5uWjklTkCz+vYQ5i4WQbXqWsBjObzORWn7j3vl9875bJXibyplc9SjFx9yKm3/X5f6ui4TkBXA9YMBB8bHSxWxsECpTj/ly9fxvX1dRHY8zj/GIu+BZnYcGFWuegsfmyLi58FwBafOwE887F132wdq57xfX5u+joiX1tBc/vaIfAEHu4AtPw4yz5olSLagLTpYrEoViWezWZx7969ePToUTx//jyurq7i+vr6qLrPHcAHLPqWqFXV9FjVzC8WIAJcPBdcc9Aset4HR+zVK+Dv6XOVFa/7m8WSWV1+ze/pcXkmn9bYs+j5OctA4HiHw6HIjEwmk7i4uIhvvvkmfv3rX8fV1VW8ePEi3rx5E7vdrpTCtOg/YNG3ABco36uNJ7CwCHUVGGwP9x+VeCpwFX+2Co2O2Xu9XrEtu/NauMLvARUCf86fVUXs6zo5/d3Y/ef3M9HrsETbgDgHbkLy1VdfxZMnT4oVclerVTHEqBqudBWLviFsZTPRR3xMi6lrztYabq5+RyPVWVFKNvGlyuKyVVU3v8m54ln3z+m5TPRtYwh1Vl7bwe2B9catxrSz/JTz7goWfQsgel7mejweF2kiXjqLL0K2gvhcBZ8JSqenZsty433Medd742lgrMqK8znyM7dbPRCOwPP2mSeS7bvpWDvLbmg8Q1OZdaXEXceibwFEPxqNihTUeDyO7XYbEWVrW+WawhVHR4FtI6pvMKE17ZvNJqbTaVH1h/EtvA9emDMr2NE2VYle02ba2ajoTwXl9JHFAXA8zlRk7dTMRV0A0JSx6MngwS8AAAavSURBVFvAln4ymRSP7XZbChZp1D2zqip8FhWLn4WOarbJZBLT6bTU8SD3n93vXsfJOgzIgoUqeq17Z8+DXWn2NDTgyW1Qy8yLhaqHkP2mfCOROi+my1R1fhZ9C1j04/E4ZrNZzGazokosc42RYsrKYDlXje+quFjwmNkHoePeepjCWyV8nn2H42hAjs8R3+MZfdwR8QQZFSJqELLj89yBU1WF2kFx/ADlzBFxNDXZVv40Fn0D+IJCnh1WHqLPxrC8fTb+jfi4Qk1EWfQ8aQWCH4/HRYfDwmKBNRV9FoxDW7Nz4RJjXg4L7edZg2gf2pgVDrHg+V4B2lYcm9va7/djPB5Hr9crztE0x6JvAUfvx+NxzOfzWCwWRWqoahzb7/eLVWJUZBH5HHUsLLHdbo+EweW8WtOP93lcD1eYJ99wMI7bo9ZYRc+TabjOna08OkQIX4cZLHiOQ+j38JtwDAG/9WKxKI43Ho/t0rfAom8JXEu49/P5PO7u7o4uWk7xbTab6Pf7R5NzeHzPwo+IwnvAfjLRaJ09v8cWXqPZPITIUm68P/YOdMYb2qzDHhU9d1SZ6HlYwjf+RNCTg5uHw4fFRrfbbek24hZ9cyz6FnCwCi7+fD6PiChZXLa8Nzc3cXZ2VgTkUMjDq8Fg7MpFPXpcrBuX1d9HlBfXZGHp99ibyI6pQTfuLHQbCA3HgmhZ8HwLMO6k8DfHIqruAchDi8Ph45LcKL/1XPp2WPQtYOsN0cPK6H3p2apxZ6CzzNjyRxzPZgMszLpgFQ8pqoJbWWFQdo6aXkO7uAiJOwmcO1x6WHHtgLB/dBSDwaAIRmI7fA/Hg+jv7u6K8fzl5WWs12uLviUWfUsyaw/RszXTHDLcfLyPsTFec9luJny9qDXqX9depapjwfe1Yk6zETg+dyzwZkajUXHHmyxyzw928Tnqj/fZ2kPYd3d3RWe7Wq2KOwdb9M2x6FvCBSiwSih5ZateNX7FarDI7WtADGNn/lsDf1qsknkK3F6mqhPJtslEz9uyV4GFQzDmhlfDHoMeg6P37CnB0mNbrSe4vb0tBK9rEjhldxqLvgV8kUPEk8kkIuLoFtUccYebC8HzMlpc2BNRvgddVqqbvQ+aCFq/i9csFk3VnfpN0MFpnYHe00+3w+/G43vOUGRxBQQ161YcMvVY9A3RIBkED1eWx+jb7baIYk+n06N133mdOH7onWF4jTidcafpvyor10QUKvjsdfZ9DhJq9qCqJFbjBzznXScicdCSO1xkCdBBuM6+HRZ9CziIN51OC/dVhZktBc3PvEIsrw/PK8Ri4UidbMPH4nXjdDpvlRcQ0W6RTLyn1pZdfwiRU4Vq5dVTwTG5dLeusCjiYy3AfD6Pr7/+Ou7fvx+z2azkFZjTWPQtwAU6Ho9jsVhEv98vKvL4gtYZcFyvjio7XSKaRY/OgJeI1pr3bPZdtvSWxgOAWlw8Z1Za04IseB3GZAU2OB63B0U2HBTlfD1nADg+MhwOYz6fx8OHD+Obb76Ji4uLGI/HtvYtsOgbwhFnLgHlqHJEXteuU1G5Q+A6dhU6Hlz2WiX8LAWYBfwArL1aeM7pa9qOrb+mJCFcndbLx6sq+a2y9LoveBKTySTOz8/j/v37cX5+HqPRyJa+BRZ9CyD6iI+VeVWiqgu+1XUIbLV5DXn2IODOZ+69HkPJrD5b9Ezk7AngmV17rfzLIvbZ76E1Adl0YN4XdzI8w9Dj+nb0TgR6HBoV1KLr71f3t3YOVam3TCB10fu6R13bIpovh519B89ZZ3Hqt+P9NXnwMTlwWBUwNBERkf4oFv3fwaemi+q2a9JpVL2uS9mdEvynvt8k2t+UNpkDvLbYa7Hov3SadiJt8/Bt+JzCrSILIn7O/ZsCi96YjpGK3tEPYzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdIzBic97P0srjDE/G7b0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6Rj/DzU3Ky4oi7HMAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 32\n", + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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6DID9XKlUrNVq2b1798zsWk3vdrshsw62vG/yyCoxvO1IoEnLb09LdGESYWzsyAOZvK0N8rOJwNdlMwJaBTvV2IPO54+tqOuX6GKbPeZUFOE/DkT6jID3vt1u2507d4JK3mq1QjUc1HrY8lDtEUNnVTrWCpvtWt/Wyie8+MzAWBdZL4V9YgxfF1IYv3OdAUt9jptDc4gRncftk3jSyC98WIj0GcAJOfV63brdbviuXq8nOtnAOceedJbevtkGvvNxci/heeOVcszsBulZtWfie9veaxIgeGw5bCa9J793yvnOOjHCf6z/N00qbyHSZwRUfFTImV0TEzH7mBeeXzoQ26vznEmHCYKdd2kE40o/r0EwcbHxd2xb+xx52OI8QbBN7ieimGTn++PPtOf6oYgpwiexlfR6WNfwuejsxQdxeOmmbWWh+PTOOa+ie9Kziu2JzxMJawgcpvMTh9caYMtzWI+v55fkYpJ76b4P4XGvGLvXhIQPB0n6jPBJMVhsIeZ48y81k5wlta82i5GXVW323u8iPa7nyZjlfmMJOfy3v2//rPj5eBwcHOyl6qeV2grZsZX0+5Rc5glMKO9V32yuV165urpKhOh8uM7bypyNZpbMjMO12CEHTzkfyxORjxT4DDzshw3X86RDhOHw8DDk9OMYOCY5rs/Xx/XS3h8/QfL9xogcqwfwsX1GsVgMi2Cyn0O4hiT9HvAJKXCMcfirUCgE9Xe9Xt9w5PHxiJXjWO+MSyM9H+dj7UxIHrPP1+dqPJyfU3lRzAN4h+Pl5eWNajzkHLCJwpJ4W/GOn5DSjuGsQezn/RGbzcaq1ap1u127e/eutdttq1arQZuQZnCNraR/9uzZxxrHDwLeu25miVx4EBDZZ6PRyKbTaejUUiqVbD6fJ3LpIWnYsZYmrT1xzZK2uXfI+Rx8n9HnpSHvg2WuYn4GbqUNab9YLMLfV1dX4Z6gBXjVP2bD49Pb+d7Dz/tymTBrQWhMgqIlPO96vR4yI7vdrkgfwVbS/+EPf/igKv42FfBjnt9LFxCtWCyGUFylUgm/r1arGwkpZpZYrIKz8dibzoTnBS7NbnaPYUdeLFmG02j9IhO4D57A/O88ufiJolwu29XVVSAWr+CDicp7832IDuf1/gY881j83h8XW7QDi2UeHR3Z/fv37Wc/+1lYuppLlRFNyWtjzDRsJf3vfve7jzWOHwxANkgzrDTD5bE+AQYvLvew96TnLDoOg4EoXBrLZIQay045TuE1s8SxfuFJHO8jD6yyc286f81arZYYM0J5PoQHrcPb6TgvNAs/mbHD0ZsEbHZgbHjG3W7XHj16ZL/4xS/siy++sCdPnliv10s8d28qifjXKGx7EI8ePcrdU+IUVjML8XfY6CA0pAirqyiXhXqPMJ5ZcgFI9tizlPZhP7OkRGRJiuN8Pz5fdAP48KCPFqS15eLQHefQxxpcMOH9Ypy+LsAnAbFvw5s6/HlwcGD1et0+++wz++KLL+zLL7+0x48f271796zZbMppl0RUzd0q6V+8ePFhhvIjBUgPlb9WqyXWR+dSWXTWYXuXHX1mybAf9vfrwntJzcexzQ1yxZa38h52fPrr8zp4ManMKcA+H5/vCSo4pDL3CWCnZsy/wU5NHi9PUs1m0x49emSff/65PX782E5OTqxarX6o//afHOS9zwC87ChgQUFNTJX1xPGeat6XG2mydsBg/wRLdSb6arWKkh/XA1i74POg2QeO92m9mIA82b32wctWM+nTioG4AjBW+gutANdotVphua+joyOr1+uJ8mXhGmn+LJH+FlitVmFJKx9X915ms3jiCqvlnvS+xx0ANRymAJMLdnqpVEo41fxS1WznsqYA0mMFXE/+2L34Yhm+H4yLxxdT7z3x8Vx9ZiBMgM1mExb55CrGWCxfiEPJORnh48OeoD7kZpYMSfE+vic+Qnys3vMxbHsvl8sEwQ8PD1PDZv58XtJ7ssKPwVI6rZuP11xQeszE91oHngnIDeegL81lmx+JSev1OhES5W7Bwn5Q7v0twC97zFaOpaJ60oOcICsmkn1ID6nu49sM75GHZPfn47AYTAOQnh2Yu1bX4fg/NBe/5l7M3OHOPmnbfD63crlss9nMrq6uwmSyq9ZBiEPq/TsAZMULx+mpvA8Qy7rjBBuE/ZAlxwA52Uzw6nqxWAzZgSAhTBA/6eCTj01T93etocfXizkFfUQC9woNBQVL5XL5RiXgYrFITILseJSn/nYQ6d8BXrpuKxqJkR9kxMSBSSBGLu+B55g90mfxt0+USRuXT5yJRQV8ZME7BtOcmGlRBP+8fLEQT2R839AIzGzrOYXdEOnfI9IyAP3kgP04rg1yIr2VPdGeAOxJL5VKie/SyJ6Whhojvrf5OSsupk7z/uwn8JpImoaAsXFdACYO/B1r8S3C3w7SjwQhZ5Ckf49IkzysmvN+PtQXk45px7BjLiahfRQhTTKylsC5BFy9xqW1sfHwefx4dl3XVwL6bjyxNmH+ekI2iPTvAP/ixxJhdjny2I7mkFzsWqzist3NSTa+8CYWQuSxxUjv/QU4nv0Nfmys2sfOm+bIS0v15Y2LfRCrT6vmE3ZDpH8H+JAYPmOk32w2Ucnsl7iOhcb43OxY43XxYqm42PgcfkxpZOdmIJzks02b8SnF2NJCdtyTL0Z2DtmhfTdCdj5dV9gfSs7JCC4GiYWivPeZHWqxRJZisRhizvhMy5ln0vvMN+S5c2Yfe9K9tPcOQd/kg73maWnFaQlHnInHxE8r5uEKPi7f5bRnpOlirCj24axAva/7Qck5t0ChUEiQytvmsfLYWBouS0YQ/l3ScJlkPDnESkwRKoO9DunOxIQ6zyp9zO7niTAW4+esPiY9t+jmVFxf0MNZe2Zv06B9uzBhP0i9vwVQbYd15EFwLlP1lXJmydZTHNfep+AGYNuej2VNAZ+xghv2K/A5WYqjGIYnr5hzz/fg8yq+z+PnhiK7Cm64BRdXKCIBqdPphDx9vr4mgLdI03xE+gwA2RuNRiitZSkIMvo0UTO70WEGzi3OYvPqvVmyug3wxOfrcl38tkq/WMPMzea6eYiXyr6Db6wHvq+085oIkx5aBKv0scYiMTPp8PDQ5vO5NZtNOzs7s263a6VSyRqNhrL09sRW0j969OhjjeMHBc6S4yYa3A6Ly2pBHKjnvkeeWbLAJNb22hPYE5WlLOALZvgcsaKfWGsqltycwstj5h59fpmrbTX1sZRcfr4+5z52fz6MeXBwYOPxODTTQCOTYrEYFh8RtmMr6X/1q199rHH8IMBSGaRHz7VYuyy8pFBFodL6dlnYl51WvHiEWdIX4P0BPm69T7ssznTjCYalPN/3crm8oeJjnTougfU993lM3m/BhPUTETfm8GYCH+uz8IrFolUqlXB97hDc6/WsXq/f8CHkFWlm4lbS//rXv/4gg/khAzYnSMCNMTle7R1P6/U6aALozgqb3yxJehzrV5718XWuRPOx7CyNMfE3PtkO5vvG7yyFQXRUuGVtjMnj4+uwB9/H9H1ij49olEolm0wmNhwO7ezszF68eGEPHz4MjTFbrZYdHR3lvjHmL3/5y+j3W3vk/f3vf8/dk2KymVnCKQebd7FY2Hg8tvF4bJPJ5EY3XJgAvNgCyObXiudW1pxVty20xZNFjPhMYK5Z92vFs5rPpgRLeO7ci0kA4+EJwvsJYsT38GTHdz7ngcnPzwg+A14WvNls2snJid27d886nU7wu+Qxkee3v/1tVM3ZSnozy9dTSoEn32w2s9FoZIPBwMbjcbCFmfTsvMI5PAH9Yhf+JTe7uawVh7f2WeyC49zQLvykwVoAd6rxXnV871VyHkcsL8EsviwVrumdibFjfGZhLF14s7le7KLT6WixCzP7/e9/n70xpnANnwILAkPqQRPg5hMIpTEJsS9v3BbKbPuyVovFIhFzT1vwAsdxYw7/vbfD+fuY/R4jvO+Tx0Tch2BewvOnf/78yffh95/P5/b999/bYDAIJhk0LeEaSs7ZAz7e7nPn2fvuu8Yw6WOpsFBTY+c2e0t6jnF7aei1BBxnZtFiGZ+NB/Kwnc6qOxOaSR2Tnjy+tPcnRnRfBpz2f+DvIYb1em2z2cxms1n097xDkj4jeAJgjzo7qtgu93Fy1IujVtwsmSgTs8t9Ugt/Yix8bRxndk169u7f5n75friohh2bvB+0Fg5nxlT4NFMgRmYJoPcH5d5nABMea9pNp9NAaDSqTJNC3kHFCTScrOMdeWbXJC6VSsEvgDZTfC2v3nMkwne0YYmM/Rho0OH3A8FBboQ2fRjRE99ny8UIL2J/HEjSZwQk/Hw+D848M7N6vR6y4mKtpbapxbGQW0xie38AiIV9fCafj7nDNmcNg+1xaBDwXyAVF+24fDIOZ+jxPmxH+8lkl8ovfHiI9BkAsi6XS5tOp3ZxcWFnZ2fBqcaZcZya62PgsRRYlv7eBufEG9/DjiUpO+xgSvC/cT0cy/Y83xvH6TkRh0nvcwe808/H7jnTju9LDraPD5E+I9br67LO4XBop6en9urVKzs8vF7KmUNnkOycHOL9ACBcLC7t7Xy2y+EwNEuq0d5cgHbAWW2Ibfs4P98fp+WyNPeLaGIiQCgQven96rasCeCevbZhJmn/sSDSZwCItFgsbDgc2uvXr+27775LkB5YrVYhFTdWvBLLMQd8fNr7AGBGsJ3Mv+H41Wp1Y6UZv3QUgx2OsdAcaymxeP50Ok1snIDkQ36xJhje7hc+DET6jFgulzabzWw4HNqbN2/s5cuXwf5lYlxeXlqz2UwsvcR2PRxtkL5MasCr7kx4s7cttGN592Y3k23q9XoiMYcJ5k0Kn/wS2zh/YDab2XQ6DVmKo9HIJpNJ2BBCgyYAkwhjYK1HxP+wEOkzgL32w+HQzs/P7c2bN8GTzRJtPp9bu90OBSC+TBQJP4jR499IpuGX36eegug8cbCtzyZGTB1nOz7NpOB7jm08oUDFn81mNplMbDwe22g0sn6/b8Ph0IbDoQ0GAxsOhzadTgP5Dw4OwrhwP1nCeMLtINJnAEg/m81sPB7bYDCwfr9vZjfz3KfTqR0dHVmj0QhtrEBIkBRLSLF9y5Laq/mQ7GaW6H3nHXw+Xu8z7rxazTkFafXoHFbzxOf7ZuIPh0Pr9/t2cXERPgeDgQ0GgwT5UVYLk8NnCYrw7xcifUbApofdOplMgsTkFNbJZGL9fj+srsoLOcKZhoo8ltggoG8IwZKeJwUv5X1Gn9nNFlexqIHf8JsnnCc/E58LikB+hDXPz88T23A4tPF4nCB+TCPx6cLQXjQR3B4ifQbgxfNLLEPdxoSAKrxWqxUqwLxDDTX37Xb7hgfeh/t8OM+H6Lw9H0u7xacnS4zo25KyYhIfRIS0ZvLDzu/1ekHaQ+KPRqMb0h55BHwe7/xDpGA8HofkKGF/iPQZ4VVab5PiO7yUqKuHKo+inHq9bq1WK7zUZknCcdquV719gwkv5WPmAeClfOwz7Vg+Pk3dZzOiXq+HteSPjo7s+Pg4SH5oSvDux3wGPlSIZzsej+3169f2z3/+0169emXj8TiMj/MShDhE+lvAv5isguIlnc/nNzrUspRvNBrhpffJOp4AbKuzNGey8+9eWvNEwv9m7EN4Pgf/HSMschTYjGm1Wtbtdm02myW62XJREJs0nCzECUPj8di+/fZb63Q6Vq/X7ZtvvrF+v59wAgrpEOlvAU8KkJ9JjyWWQUrudosmG7yAg8+MwwtcqVTCNWPx+pha79X1rPcT+85PGJxU5MkPE4T9F2gjxhl7mOy8+eKJ78OEs9nMHj9+bJ999pk9ePDAvvrqK/vLX/5iFxcXe/3/5R0ifUb4JBh2MKHoBEUpXvp6Jx6knU9ZjXmuvdqelq/v94lhn4kgy368f2wCQKUg7t1rNjz2mKaC85m9nWAfPHhgDx8+tF6vF0Kiz549s/Pzc1ssFjfGI7yFSJ8B7EFHQo2XdlAv4c3H39jQwhlpqj691W/++hy2i42Pr+e/3+f+tv3m/QE8Ifm/OVeAtZNYgw1vlnhthYHJpNlsWqPRCCbE3bt37fnz5/by5Ut7/fq1DQaD0NHI/x/lHSJ9RjDp0X4Z8Ko5H89yD/oAAAhbSURBVGP2diKAp5/9Afy3L2rhiYTt+X1e4n1U932RJjljJI4dx6o//5YWLkwDJpJarWYPHjywRqNhT548sbOzM3v58qX94x//sBcvXthsNgvPTIR/C5E+AyBpYaP6fu5myZg4wBKbpTVLdO8Y9H3svJrv1eAYIVnSMvYlQJqEjHnw9z1X7O99w4V8DPtJ6vW6nZyc2KNHj+zx48f25MkTOz09tfl8fiN8KYj0mcGkR1492/dmu+PiIDxSajnWnVbWCvL7mny+BiRpWlbdrhCe32dXYk7s/vgcMYkf23db6DANfpzwF9Trdev1eiHuL8LfhEifEX4Vm2q1mmhMgRctFtrC35BUvF6bDwP6rrl+jXaYBGjCCWeZD++xtN9HIsfi+H4yi008fMy+RI5FAm4D9hmUSiWr1+u3PlceINJnAF4ujrU3Go3gkEMjDUhthiccJgeuNMNxfjGNyWQSUn6R1NJut63ZbCYW1uDVav3KMGlmwDZJ7cfPGglHGWKmx64Qolfx05yCaWPE75Lk2SHSZwTUeySbNJvNkHJqdtOLv4/ji/vLcckqqtdQnjocDu3i4sJ6vZ4dHR0liI/19fzy0MjVj6XmxqR1muecw4i+kw4kNSQtnJw8+cQcdhx63OW447/9BHZbn8VPHWnPVKTPAHYg1et163Q61u127fLyMvFio6EGp+cCMSlbKBQSRSXcuQb566hYa7fbdnp6GlJbkd+PLTYB+EUtOeGFpbbZzaW1QCykGPPiGZxjwL4ONA+B6cETj4/J79IMMF5vRnBKcuz/SUiHSJ8RiLXX63Xrdrt2586d0G02Fh+Hyr8tPRSqvlebuTYfNer1ej1siFWD7H7zxGd12YcKOVEmloCESQjls/AzYLI6PDy0arUalvHGuHi1H4Q7uccfmyOx0mA8HzYlcB50DxLJs0GkzwhP+pOTk9CSulQq2Xg8DgRDhxh22KWBfwMR0Vfu8vLSDg8PbTqd2mg0CiRCAQ8kLP5mJyOX9bK/wZewsqTndtkgFKcX8wb1+vDwMPg5YHrA/IHk94uBwAzxJglHIPx4N5vrWgRMMGkZfEI6RPoMwMt1cHBgtVrNut2u3bt3zzabTaJsFg0zUFDD3vptMW6osJzMgusitXc+n4cxQCr6HH8mFS+vBSL5KkGf7sukxzHe14CIBUveSqUSJD2ID4mPSQnqvl/sk00Sv5Y9L6iJZ91ut61QKCTWCxT2g0ifESBcpVKxdrttJycn4UXEC8yJO5PJJBAekptV1bT4Pn+aJb39PJZYPr6fCNiex7l4HEx6TCQc9sMxnEvgvfa4XxAZ5gfIDCkOkkLCwxTB5IDnx6v6cMfdzWZj9XrdHj58GKQ91HxhP4j0twDCds1m07rdrm02m0T1HCQah9B4PXp21u0iPmNbrDv23S4ixK6V9Vjsi4kCJcUwP9ibD20DExKX3UJD4EkCzwV+DSwJ3m63bblc2vHxsfV6PXnrM0KkvwWgVlarVWs2m7ZerxMlpHjBfb07/ualoXghiH3JD3iPdsxZ+C6EiBHfe8t5koAJgNV4MQGw3e37AbDHH4SH1x/pymxWmFmoo//888/DJKp02/0h0mcESzXYsJwGyqosO6iGw6FNJpOQcAPPPNR2Jn8sRJWF0B9S8sVWqmEzoFgsBl/BwcFBotLNRzbYDGF/CDcSxf3w81ksFnb//n2bTCbql3cLiPS3AF5W2KSFQiGhqkLNbzabYUMraLSBRgON+XyeUPfZ2fepse84QEoPJnpaPJ1TaHkC8OXLnABULpfDhPlDeVY/Joj0twDs0mq1GlT7Wq0W1FAkznQ6HTs+PraTk5PQLns0GgXyj8fj0BE21hX2x9z6iQm9C5wsBJOHnYTsVEQ2ZL1eTzj8hP0h0mcEZ+XVajUrFotWq9US3WA5qQZSHQT3G68AgxAfrwXn15LDNXwTShCHP82yq/qx1Nhtn+yr4FBfjIxcqMPHg8ycb+DVfPhRKpWKnZyc2M9//nM7Pj6+sZCIsBsifUYw6aHW88vMZOQ2ztw2m6vnsEHag/zoG8/78PH4268Th1g2h9U88WPeerPkQplMSG+Ts2OSbXFOBkqzyXlcnFnHppHPJuTsvWq1au122+7du2f379+3SqUiwmeESH8LsNPKk4olLE8EfkLwtfPcR58nAUh/XgeOSY9jfe19rNEmj4/Bkh3+im058RyXRxIQcu1B1BjpY52AmPQ+SQfn4fHgOthXMfrsKOxQ/+Ql2YFd6nNMynIlHktlr8pzNduu1losRX3J6y5V3xPaTwJmyZZfPvTmc+g5TAekrbDDZgHnNcTKg1nD8L8JUUQfjEj/CbBN3faagv8u7XPb/mnX92SJJebsSgjiY3Ydt+1d4+N3jWPfBCJBpBeEvCFK+t3xFEEQflKQI+9HgNskoNxG9X3fiS5+DP78Us8/DaTeC8JPF1LvBUEQ6QUhdxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZxDpBSFnEOkFIWcQ6QUhZzjc8Xvho4xCEISPBkl6QcgZRHpByBlEekHIGUR6QcgZRHpByBlEekHIGf4/I07euGJbMaUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 64\n", + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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NZtO0rbY9k3lRa343qOiXwO/3I5vN4uXLl4jH4/D7/abunaWvdp484OwmO+2V7Z+d9s0y4i1FT+FT9NL9pki5l5Yeh90tl+LmfTlrTw7M7HQ66HQ6phlos9mcmM2nZ+eriYp+CXw+H9LpNPb29pDNZuH1epFIJJDNZs0e3m4hDcDRbXbCPu+WVl6KXrbo4uAIe0Q0a/85SLPVapl9NY/E7IWJr8GFgiOy5aRc+3u7sy69CLXWq4eKfgm8Xi+i0SgKhQJisRgAmMQb26WftS+ViwAwuY+V7rl0sWl1KUZaUwYYpYfB/X+320Wj0TCWuNlsmnpzufBwcZGvQWsux2lfXV1N3OTM+VVEF59JVPRL4PF4zLk8W0T7/f65BD/tAyiFL39nW3jb+tJNDwQC6Pf7xsX3eDxmwCbn7LEEtdVqmU62bGEtE2powfk6tOQUuZ0Pb4+fXjVU8JPMFL2+WZNIEbNSjm600/m6E07HYNMGWtLySpdeCp6iB2CCbKFQCP1+Hx6PxwiYAzbp3tNyy+k7dMkpbIqdgud2gBZ9WtHRQ3xm7JwEZTZq6edgmsX2+XzGyjkF6+zH2oJ3Ond3Snqhuy2FS/EOBgNzLeFwGMPh0HgbDOJJAdOKcw8uRc/XkgE7W/DynF+20Fa+HGaKXs9JnbFdd1m/LQtvnCyfLWhZTCLPz+W/09VmhJwz7rkvZ4bfaDSaCOTJyL0UuixkkTnt/Nkue3U6bZhWEvsQOF0Hx2TRA1M+oZZ+Cex9NlNJA4EAhsPh1KIZ6c4zKGcHxCg4OVGGLnqr1TKVaRcXF+Z1mRpMwVL03NNLwduv4ZTe6yRyO9awyhQKBRSLRaTTaYTDYfh8PvVIBDNF//bt2/u6jpXAdtG9Xu/EAEhacIpJnntT7BwnxQIaKXzprrP6zLbCdKNl4QqP3HjjNFy68n6/f8JKS9HztZy8CVsIdu6+jFHYR4/sEnRfQrK9TtY/sGiJ7zdrIFj3oKK/zkzR/+lPf7pTF/+uUy2XfX5aPWbeMYdeiqnX6xl3m3vrUChkxk9x8gyFz8XCPnKj1Zail5lvFL08MqPgmX4rrfxwODTRewbm5GPsoCHfH7rAzOOXAyztx9A7oPCnbWM+B3vh4W00GsHn8yGTyZjU5729PRSLRdNeLB6Pmz4GMulI+ZGZov/9739/X9exMvADTMtdKBSwvr6ORCJhymll0ku73Uan0zGRc85aZ4+7cDhsjvXoIVDE8jhMWmJ7qqx9XGan3obDYeMdUPT0SmxLL5OC+LdKgREZqON9eWMWIL0hJysqn1PWBNivIU8v5NEfxe5UlxCNRrG7u4tf/epX+Oabb/D1119je3sb8Xh8og+h4sxM0X/33Xf3dR0rgxS93+837jqDQhQUP4Dcm9MCsUkGrQ3dS2ByUIYMqNkBMruwRhbJ8HWYjBMKha6564PBwHzo+ZpOFp73kUU2dpsvafV9Pt/MKD6fi49hezCOy2Y/AXoIcuY9FydZCMSFTV5nMBhELpfD119/jW+++Qa//OUvsb29jWQyeX8fki+cmaL/+PHjfV3HFwXP6OXMd35Q5fx4mSEnI+RSpNIa2oU5ducYABP1+NLyEy5YXGjolcjuvfJ0AcA1S8+58XKGfDgcnvA2ZCCQz0Gxh0Ihs/glEgmkUimz3QmHw0b0ttcjE4bkewPAvBexWAybm5v4+uuv8ZOf/MRYePn33PW28UtHo/dLMBqNTADOhh9+iljuKe3gmSxplUMh5R5ZPoYiYFGNzBGQ1no0GsHv//Rfa1e52XXz8nrlYsGFh96J9Dq4VZBiZ9NPbnGy2ayZrJtIJBCJRCa2HjJQSdGzUIevAXyaHdjv9xGJRLC5uYknT54gn8+bRibTThrczLSFT0V/y1AoTkk7RAbP5J512jAL+ThaSQbQbPdbnhTQStrut9zvyueQNzsPwe5fx+ej1xEOh83gzXw+f633HwUvy37tefd2QhC3PxcXF2i1Wuh2uybOkk6nTUakfE/t75XraHLOHNjvwzzHP7Osjty/S9eaEflpyMQf/syAn11LD3xaVOxqPjsqzoAgXXre7IXEyVOhhY9Go0gmk8hms+ZGl54DO2QgTyYpyWIieaPwu90u6vU6Op0OPB4PUqmUqXtQFkdz7+fgLt8HpySYaWm/smSVrjYDZvJ8X3bqsZ9jlqXn3j0SiZiYhGwCai8mXCzYLzCZTBpXnicXsurPvib+3dyq8OhRHlv2+310Oh0Eg0G0Wi0Mh0NEo9Fr16TMjy6VKwSPwmS0msg9vd3vzu4ZT3fcKWAng4p2TEEG4eiSU7j0JHh/uvVygIfs8y8bgdoxBCKDlrw2xiMCgcDEIsb725WEyuKo6FcEfvDlObqTtZbReXlcZh+h8b7y+AtwnqsnE3GkkOWQDplaLOMIbOktj+bkfadl9Uls8cvtBxc55iLY5/7K4qjoVwjbtZ8lft7f/t6O+tvYAS877VZm5NGiBoNBY7lp7e1/dxL6IpbY6broqUihOwU7lcXQpVKZCz0OezyopV8hbOt1k/vq5LbbCTc2tgcgA4kyHiDP5+XRn1NmHq+VHoKdfDQP8jrk9chEIF4X05qV5VDRrwhSrE6BPP7MgNc019wpvRa43qkH+LSoUEgy57/X602cpzNaLpN42LiD+22Z0+/krjsxTeiyLyDP8Jmgw2ua1lhUmY2KfoWQYrZxEridzSfTeZ2eSxa3AHA8AqRlZ5JRr9ebeH55rfKYLxaLmZTbRCJhovjzHNnxuuyhHPLIrtlsot1um+un+JXF0eScOVgmOWeR57aFPO1+ttDlGTqDbHbASybnSGsqE2xYlMNrYUmuPKIDMOG287GcxxeNRs0k3lwuh0wmY5JoZHKOTBeWyTn2IA2ZpMNuvq1WyyxIyWRyqQEaiibnzMUy74PTMZVMhQWWT8OVQy0YPefv5E0+jxSIfawnYYGOUxquLP2V10RrPy0Nl4U2LFBaNA231+uh2Wzi4uLCtAVLJpNzj8hSJlH3/paheOUMe1kQ4nQeLV10W/C2VyFFz/NzltjyxgWB2MMnZNWefB2W88oEILnPt/v5SYvP64jH46bghkU3suCGf6dTwY1stS2LhNhlaDAYIBqNYjAYIJVKTQzCtIOT6qVqwc2tIi2sDJTJPHYmrcjyWxmksoNeXCiILMOdVlrL9lwMptnlvHwe+dq8VopfWl37OmUTDwpfCtJul8UtBltZcQGQ+/xwOGxe0+7j71RaKxeI8XiMeDyOXq+HeDyOzc1NM1XI6SRDcWam6Le3t+/rOlYKCsJuomEnn0jryCy5SCSCRCJhPuR8HO87q4mGUxEKxWYvFLKJRiQSMTdaetmiC7hedSd71tvehd3cguWucprNrCYa9FxkEw2Z0ktPQr4XssGo9IRkxR+3Es1mE16vF8lkEqFQCDs7O9pEYwFmiv7bb7+9r+tYCWS3GfbIy+fzKBaLE+2ypMvJkk9Or5XtshKJhNlvU0x2j7x522XJ+8gsNZa1yptdsw7A9MeXnoltqeXCZA+sdHLt7ZtEvj92Jp0M5Mn8AHkEJ48f+f/Cr/V63Rwj8v9CtstaJiPwMSI9R8lM0f/2t7+9k4tZZRi9ZtWX3RgT+NFSsj+ePEqyG2NGo9EJV9tujMnutk7CkufldH3lAmE3xqTgucjw76D4OfZKnuPbKbu0vuypR+s7rU/+rIDg5waB7Rx8ebLARXYwGKBWq+Hdu3fY2NgwnhU79rA/oVvF//Of/9zx9zNF/5vf/OZOLmaVkVbI6/Ua91n2amM3XNnNdjT6cZ5cNBqdiFbLGnguJnIund3lVlp7Wnq2wGYTTvmhl/t7uvuya6+0+LI+3k7asSv07Nn09j7ejg/cNvK6+P9CKPaLiwscHBxMtMCOx+PXWmDbi5xbmCZ6zw1vhLvepTmRraW536U1ldFzKTA7EcVuGkFrag+7oJssR023Wi0z7AL41L1GVsNR9OzP32w2zZjqbrdrFhkpeKcMOGnt7fJdp8j5KsAOxm4fdvGHP/zB0cXR6P0S0K3mB5/uuzw/n5ZoQw/CTrKxXWfe6BV0Oh0TqQ+FQmbIBi24Xe1G0fNrKBS6ll3n8/kmova2Kz0tIr7q7nK5XEa73Z4Ya6Upu5/Q5Jw5uCkjzw46TROLfT4vm0NIa8TnpvWVe3WZaUfRAjB58BQ9gAnRS8+EHWp47fQsuGd2SuWVNy5cdiDwoXBKZmKXXeU6aunnwM6k4+9kAold5OIUPaZgpPD5e+69JYwT0HOQyTV8Tbr4Mj9ADrCkkOkxcLKtFK5sZy2vxcklls/Ja+R1yfdKWV00935J5L5+PB5PZJrNwha+jFDbyAQavib3+UxBZVqqFHwwGAQAM7CDj5Vn/T6fb8Liy+g9vYFQKIRut2u2LXZyjrzxOR4CXWgWQy39EvBsmEExACbT7KYWUdM+oPL+MrJuW3imolJogUAAAEyyjjwx4LGjrJBjwgtPAGQyDL0XGcjjaYGdUyC/0qtQ8X0ZqOiXgGfF5XIZ9XrdZIdJiy/dd4lTQouT4Pkz7yc7xtJFZ4Ya8GkqDbcD0q3nPp9eQDQaNXkBskEm/zaW1Mq8eIpeDtOUU3cZVHSajKusFir6JRgOh6jX69jf38fx8TECgQDW1taMO80AnW31nbLYnOIFtndgR/o5v47WXOb8y/HYzAJkxJ/58Dzuk8E8KXw7T0DOuJciZ3IRy14bjQaazaZJJmK8QVktVPRLMBgMUK1W8ebNG7x79w7BYBBPnjyZyIDj2GS7+cQ0wU+z9vydTLsNhUIAPu3npeilpyG3BOxsK3Po5evbwUKZTyDr3eVi0O120W63Ua/XcX5+jlKphEqlgvPzczOcotfrTQQ81fI/PCr6Jbi6ukK1WsX+/j5++OEH+P1+tFqtieSWtbU1UxAis/LsD76damojg30y7ZZWnveRnWql6CleFr7Yve1klZ99LDcrW49/J/PrKfyzszNUKhVzq1arqFaraLfbpjx2Hm4KIuvisTwq+gUZjX6ctlqv13FycoKDgwNzdMZ9Lve6+Xwe8Xh84uycyGo07sFlZN++rzzLl5NwgU9NKZ3aZDkJ166Km5aQIx/P72XOPVOK7azBWq2GcrmMk5MTfPjwAYeHhzg+Psb5+Tna7bYZSa08DCr6BWF0u9PpoF6vo1KpGKEwJ7/T6aBWq6FYLJp6b1maK/vLMV8ewNzC51f5ezsxyEm4087SZ20tJPJxticgawr43pRKJTx58gTb29s4PDzE0dERyuUyarWayShkCvOs11JuFxX9DdgClJVyvV7PfPA7nQ5KpZKpviuXyygWi2bCKivAeOMIqHg8jng8DmBSdE7HfjLzzz7rn5UUJP+WaTgtMvM+hxQ/C444zHJzcxO7u7vG6tPyn5ycGMtP8S9j/aVHo8yHin5BeAzGAhRCi8Xqu2q1itPTU2SzWSN6NuSIxWJIpVKmn5zMhuP+fNr+3v5qC32WpZ4n2eomSz/tumQ2H2fisWfe+vq6mSl/dHSE7e1tY/UbjQa63e5E0g9jDv1+/1o8YTweT3gUKvbFUdEviIxqS8ska9EHg4GpbDs7OzNz4RhBTyQSyOVyWF9fx/b2tomk21baKcAnTwKcxH6XWZQ3LShSgDL+IEdZ5/N57OzsoFqtotFooNPpmPdMittuhS1bdLdaLRwcHODNmzdoNpsT16CW/2ZU9Atil8gSmYrKbLWLiws0Gg2T8ip7xGcyGZyfn0986O09t6zhn3asdx9in5dpcQh6AOwsVCwWJ9pc28d5sq7h8vJyQvA8OXnz5g1isRhev36NarU60WpLmY2K/haxFwKPx2O61cgAXigUMgktbMLBPa1cVHjcZ7eAWkXB28hr4vUzx0AG/+xKPfu40GkIRqfTwbNnz/DixQv87W9/w1/+8hf84x//QK/Xe4g/9YtDRb8gMlJuJ93Ir0SWt3q93mtdc5jPTktPMWQyGYxGI5PTb0f1V1nwEnmdshpRlhbb97WRFpzvz9OnT/HVV19hd3cXkUgE4/EY79+/R7vdNrkA9pZD+REV/YLww+o0H92OPtsLAS0XFwHuVeXelS4s97j8wMte9tLlnxZcW0XkdU77fhESiYTxpgqFAj5+/IhyuYyzs/gbMvEAAAf/SURBVDNUq1VzuqLin0RFvyAyocapoGYWUvy8sammHOsk22Pn83kkk0lEIpGZFv9L4bavN5vN4ttvv8WrV6/QaDRQKpXw9u1bvH//Ho1G41Zf67Ggol8Q7k2XEb2EVt8pw4157exlNxgMkM1mjWBuOtJ7TNxkodkFuFAo4OrqCo1GA1tbW3jx4gW63e4X5QndFyr6G7A/MDIgxbLWZaG1Z2/6Wq02kdXHghVGuDOZzMRASKd8+cf2AZ/37/F4PAiFQshms4hGo9je3p4IqiqfUNEviKx0Y/os8HlWlsK3x0lR/M1mE81mE1tbWybDjz31ZU7/YxP8Mvj9/oksR+U6KvolCAQCiEQipr/95x4V2eW2dPN5zs+KtVqthqdPn6JYLE5YfdnhdlbXnttknjx+J3RhenhU9EtA0XMya7VavTYJdhnkOT+Tdih8lq6enp4ai5/JZJBMJs2wB9k11+67fxvIaj27P57MRXjI0VIapf/EtPdeRb8EPp8PyWQS6+vrKBaLuLq6QqfTmegMu+yHj91uPJ5P47UuLy/RbrdxdnaG/f19FAoFrK2tYW1tDYVCAblcDplMBul0GvF43HggTkM3+RqySMapoYcdK5Dpx3KoJQdm2OO17C6+0zyAeQOSdgLPNNSTuBkV/RJ4vV4kEglsb29jd3cXvV4Pp6enRqjsVvM5yGw05vLXajUcHx+bIZn5fB75fB65XM4U9nAWvJy2a5822Pntcnik7KkvFwq7dp49Ayh6jvTieGouPhw3JcuKZXKT06wAJ+RZu1yglMVR0S8BG2Hu7OyYo6HhcIharWaSa26jMSQfywWEgut2u6jX6zg9PTWxBTmumjf+Xs7VoychcwLYzBKASRXm6YDP5zOLgtP8PS4WFH06nUYulzOeSDabNXkGPPFgS235vZNXIN8H+/2UC5OyGCr6JfB4PIjFYtjY2MCLFy+Max8Oh02kna2hWExyW4xGI1PLz2sBMGFBebpgj6/2+/1G9Gx4yQVAWk/eNxgMTlh6uvV8nKwO9Pv9CIfDSCQSZrx3sVjE2tqa2XZwy8GbvUjJWXxy6KTMWASAYDBoFrVQKKQWf0FU9Evg8XgQDodNmejl5SWCwSDS6TTOzs5QKpVQq9XQ6XQmasKnRbw/B9sbAIBer4dWqwUAxsLLhB67iMUpAi8tL1+Hf8s0Wq0Wms0mqtUqSqUS9vf3zchuuvlywi5LjRkQTafTSCQSiMViE+O2e72eWUj9fj8ymQyKxSI2NzcnFiZlPlT0S0BrmEgkUCwWMRwOEYlEkMlk8PHjR0QiEQQCAVQqFZNVB+DWrf48LNOGWiYMLQq3N+12G6enp8bzkI09WWbLDkLpdBrZbBaFQsFsBzh8cjgcmqYknU4HwWAQm5ub+OlPf4pYLIZEIqEu/oKo6JdAurOpVMqUwcbjcSQSCeOq+v1+lMvlia4v055PfpXMs0g4NY64y8Vl2vVKT4buPwDHQZJyG8LGIgwCMgDJ4B1PL3q9Hnw+H4rFIjweD7a3t7G1tXVnf+djRUW/BLIpRCwWA/BjJhj3mYycyyBao9G4ts93qsJbhvv2Hm5jm8JZgExCqtfrppBJdgimtZc5AfV6HcViEa1WS8/ll0BFvyS09rRIHBfFFFA2hlxfX8fJyYnpAc8WUSyoua8P7TSr/JDIXIFFthOhUMgsoKvwd3xpqOiXhEdG7G4je8ElEgnTCXZvb8/UeJ+enuL09BTlchmVSgX1eh2tVutexP8YxOHxeJBIJMxRIBuMKIuhol8Spp3yK6Pz7IGXSqVQKBSwtbVlBkBUKhWUy2Wcn5+b0U9smcUMN7srrJwoI39mApB0fe9ygITcx8tW3AzO8YTArgMArg/ckMeDMnWX0X0WNfF3fB6/32+Son72s58hn89rEG8JPDdYgC/fPNwh0/bksj7erpqTk1/tqa9yUCTbaMlpsdwSyN/Jx8pOPIwbTDsqlBl4snmlFKKdmccbhc1AHI/eGMfg8RwXRS5I9lRbippRfPkczCSUiTuMm6RSKaytrWFzcxPpdFqFPx1HN0hFf4fctCjIxhnMkJNTYqXQZRyAzTSl+HlUJoXllMnm5A3Y6bAUmTxio+DlYsAtDQOYFC5FS9HTK5GLEl+XxUucB2CL3k7f5UIg/02Ziop+lZHegXTnnVpoyUVC3p8WdFoy0DxHhrZVdyqYkR4BXXqZVus0n88eWMFMPjniSwqaQne6JmVuVPRfGrM8hWn3nef5JMuK6KbquEVPC2ZV4ilLo6JXFJfhKHqNgCiKy9AjuxXjrs7TZf/3WX3gV8m1XqVreUyoe68ojxd17xVFUdEriutQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNEristQ0SuKy1DRK4rLUNErisvw3/Dvnnu5CkVR7g219IriMlT0iuIyVPSK4jJU9IriMlT0iuIyVPSK4jL+Py4wh0w65jqAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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aV3hhW1fZxILjrWjpZeMKLjLyMXwcQ2ASuVh4hdnkFF0+Dyf5NJtN1ySfer3u8sxPS2a6CRX3/aCiX4BQKIRCoYAnT57AcRyEQiGUy2Wsr6+jXC4jk8lcK3sFvJ1xXqEu+99ShLSw/X7f9KiTEQUKn+Kl4DkMk8MvZaNJe5fBHYGsJuRj2ByT23kO+mi1WmbgB73xdrKMshyo6BdgZWUFmUwGjx8/Rj6fRyDwdsZaPp+H4zjXvPBExtgpZBsv0XsNxqTwKFw6GKXoZXhLTuFhHwCv9td8jOyFZ0/KlRNzeR+7yeZ4PNY02CVFRb8AwWAQiUTCOOoAmMSbeRpbyEUAcCffEHmOllt7ipHWVIqer09rzW14q9VCu91Gt9tFv993FQ1xAbJ3Br1ez9UIkxZc3uTM+WVEFx83KvoFCAQCiEQicBwH4XAY4/HYjGi6yQs/DfuMz59Ns/BS9JPJBKFQCBcXF8bSA28bfpyfnxuveqvVQqvVMqKnM5BOPbmwyNewxW6PyJ632vChUcG7mSl6fbPcSBHTsgIwzjT7/G4/RmJnr3md8e0cdAqeFrjX6xmHnIz9y8GPTH/tdrvodDomA45bculkm7a4UPDSok8rOvoQn5nbJBEp71BLfwum5bjLBpG2s25WgovtnLM7y3o575jhxnM5Q2Cj0chcBxcIe0AFFwlabdmzXo62skUvrbtsHMLrlNerfDzMFP2yVW4tC14Zb3Rc3dR2WgpGhsOkqLy89RQtx2b1ej2X9z4cDuPq6spYeoqYorcn09BZZ7e0ltfjtRDJXINlEbvXdXCiDndgyjvU0i+Afc5mKmkoFDLCk22piRSzPVpKilAKkE41bucZJpONH1dWVlwLB1+Touc1SovNBUYWtEyz4l7HjmWmVCphdXUV2WwWsVjMLILLft0PxUzRP3/+/KGuYymwt+jBYNAMWeQUVeCdg6zf75ttNsUuZ8oxdMfH2dZbhr2kKGltZVycYTMmvMjhkSzljUQintt7W/Cyas0rD9/esdg7Fx5tHtLa2zsn+V6nUikz6dZxHFPwlMvlVPQezBT9n//853vd4s9KtfyQz0+LF4lEUCgUUCqVTBIOQ2F0kNEbTs+5HL0sQ3gAjOBtEfOMLYdf8iwtt+UUL73lcr4dFwr5enb2njyT2+8PHxMKhTAej10NQEKh0LXQIp2X0+LxdxGYLPzhroVOUr5uLpcztQ67u7tYW1tzlS6zj4FcdJW3zBT9H/7wh4e6jqWBH+CrqytEo1EUi0Wsrq4ilUq5ymllkQmLSDioIpfLIZvNIpVKIRaLmRAarThFL2PfTGyRVp6i5yJgT5eVgo/FYsaC24uMzNW3K/Tsr9MEIqv5Li8vzfvEdN9pOwY6Gb1yF+RRwqt6UEZE5DEpHo9ja2sLv/nNb/DVV1/h5z//OTY2NuA4jqvoSPFmpui/+eabh7qOpUGKPhQKmZHPjMHLODzvx60zK/BYJy9LagFcO8vbITCv7Ds5P97Os2dzTXkev7y8dGXk0dJPm05rW3v+jRQpfzYajYzvgNvlaWKVLcLk/HnO2uN7JxN8ZPccXpNdBMSjTKFQwM9+9jN89dVX+PWvf43NzU2k0+kP8Gn5OJkp+lqt9lDX8VHBHHd+gAGYbS473XJEtOyCI7u/2OKT52h63mVlHe9PUbLfnn0+Z6KQXJSks06+pow42DkGV1dXiMViLiejV3KOtPByMeI2O51OI5PJIJ1Om8WTr83dksz6kzUBEr4f8Xgc1WoVX3zxBT7//HNj4eXidd/Hxo8d9d4vAC30YDDw/D3r5Gkp7YQdWmwpOK9cfXuB4LY1HA4by2073iaTicvpKD3z8vq4NZ+2Befr82+VvgZZnsvnC4fDiMViSCQSyGazpl1YNps17cJ4xpY5BXRkUvg87vD1JpOJSTu+uLhAPB7Ho0ePsLGxgWKxiGQyee2IoQ67t0xb+FT09wCLVmYhz6z2AiAdY/IDLJ1ZtGZyG85zLI8mvK8UvX0+loK3C3ZWVlZcxw4Zv5e7BgrecRzk83kUi0WUSiXT+49ONekT8co2lFmA/PfFxYXpxDMcDhGJRFCpVJDNZk1GJK/D63vlOpqccwvs9+E24Z/b/F5aakYFZjmgWEI7Go1cZ2e7lt7regmf33a0sWUX23cxpVc6xewYPn9HC59Op00z0Hw+bwTP55OOPJmCbOcsyEVgOByamv12u20898lk0vhKlPnQ3PtbcJ/vg512a6f1SphPD7wdl81ttZ1Jx8dSrLb/gFCEtPAcwkGnWywWc4nftqZcbNjLP5VKIZPJIJVKme283djD/tvpKOSCE4/HXT6E8/NzE/qMRqO4uroyMXk7+Um5HbpULhG0fLK1FrEr72RlnHTUMWQnt/P8SgcahSLj8KwSpNXmjR53e1IudwhygAdvdNjZ8/emLWTyqCOdpKPRCJFIxOyC6PGng1R3oouhol8SeEanqCVyEbDDe1wo7PCZFDdw3crzee1WW7T4MrOQApbHB+kH4P15s+fv2fkA8prk3+2VAcjdDTMjubvxOsYot0NFv0RMCzVJAdv58DfdvITGr9KRKB16svUWrS4z/WTXXblIyLLeRfoJyOvzsv62w1IFvzh6IFJuhfp3Ph3U0i8R06yXvU2f52ZjhwIZ1pM+AdlBR7bvpr+A23vZbosWmbn703YaNyGdmjLLUCYEefk8lNujol8S7G33tN/JDD47vj8tVx247gCUz23n/J+fnxtxy9TeaY482Y5bXq98jZsWIHl9smZA9gRkURKwXPX8Hxsq+iVCZu95iUQKWubFy/OuvQAAblFJR6HMFQgGgzg/PzePY4LRtIGafIxXyC6dTsNxHJfnf1rIzq438IrZs8dfu902TUOWuRHnsqPJObdgkeSceZ7bdqrNui8FSG+6dLjJf1P8MhJgW3t6zWk9Zeous9/s5BxZmstdBuP705JzWGloLxx2cZHsHyDLiTlBp9PpmOuRVYcaq58PTc65BYu8D7MWTJk8My0Nl6/rlYbLZBqGy6SH3U6m4fadUGTyOaW3n1WDckvOn3MLz5x4Iq19oVBwpeHKib0ybi+fk9t2u4cf+wf0+300m030+31Eo1EEAgEUCoVbj8hS3Oj2/j1DsXgV3ADXU2JlWGqeghumyzJzjmOwuQhIqy2/8rlkv3t5o+DlIiBLgWWVnbT4UviZTAbZbBa5XA65XM5VcMMFiYuH7PAre/nJLj+sxmN23mQyQaFQwPn5uef7qlV2b5n2HqjoF8CrtFYKkxaY51nmiFNEdt69tPLyftKjLgtsmLbKZBhmzlH4dmktvxJ7m84MPlpOW+yy6u2m0lpeGzP62EVIltayyk6W1rKpiKypB94l8PAMn0gkcHFxgWw2i83NTRSLRaRSqWsJPcp0Zop+fX39oa5jqZB16dOaaLCRBPvdMZTENNZUKjWziYZskCFDXHbjTH6d1URDNqmQY7FpJRlqW1lZMVtzu/Gl/NtlHz859PKmVtjAOwefrPmX+fzchcj3js9tC972cfBo0+12EYlEkMvlEI/Hsbm5iUwm84CfkI+bmaL/+uuvH+o6lgJZmjoejxGNRlEqlVCpVJBKpUxWGj+wbIzJHnmhUMjMrZftslhEQxHa7bJYQioXAlsQFJxXuyymqHJ3IV9P/k22v8AOe8mYuHSqsfOO3SffvkloxTudjmetPq/Bjsnbi4dXCLLVamEymZjS2tFoZJpp3DUj8FOCnwObmaL//e9/fy8Xs8zQQl5dXZnWTNxC8sPLxpicGNPpdExjTBm2kuOqbQvKMyybR8gR0rMaY56fnxtxcDsty2LtRpz8e/icXo5CeX73Erzdtmta+E9yVyewjGoA7l4CbF4ymUzQbDbx008/YW1tDY7jIBqNwnEcV39Cv4r/l7/8pefPZ4r+d7/73b1czDIjnVjcrsttKXC9BfZwOLzWU0/GqKVTTQpfJp3Yopfi85ocO60FNsNsAMwZnc9lx8spIulDsLPfaNm9pvDI79838jV4rZJGo4F+v49Xr17hm2++MS3Hk8kkKpUKqtWqaYFtlxj7hWmiD9zwRvjrXboltJy0hhxSIUNnXgkpdiKKPf3VHnYhu8rIo4TXsAu+Nl8XeNf3nuOw5MRabtlpuaWnnGO05HlelvDaW/FlExOPZH4fdvHHP/7Rc4uj3vsFoKNKbrMnk4lxXs2aXEvhe1Wq2aKSu4LBYGBq1TnLjk46+Tz2WCtmr7EOfTgcmsYbMpzHI4Gd4uuVxy+95MsopJOTE3S7XddYK7tc2c9ocs4tmJaRZzudbCeSLRZux+X9+XjZj473ldlqsqadvoXBYOCy9jJlFoARvVw8YrGYq1CGOwpet4xcyJ/LG48+yyIkrzwI9tpTrqOW/hZIccufyaaT8pw8zXFEwdhOKuB6owx+5bgsO47P5/EaVS2Ha1CcMtWVgmelHCfYyIaa8nXk4s+FRO4SpMVXQ7H8aO79gtA5NhwOzdae22pgtvjl72nl7Z0D8K6HnBS77EgLvA1X8fVt0VPEfKwd65dxcenEi8ViGAwGrrReGT60HX28lg8leF1o5kMt/QKwSIVOMQAmJ/ymFlHyOSRexwH5eAozHo+7JuNQ1PaZnn3lGMKTZ3X6EOypN9LBKNNjvXrSy0UAeBcpUJYfFf0CjMdj9Pt9nJycoNlsIhgMIpVKuSy+V0074C6isS2710Jhi57xeG6zmaBiW3oZn5cWm2O3mBBkX4MdNaDweUa2i2LsxcBrrr2yXKjoF+Dq6grNZhMvXrzAmzdvEAqFUKlUzHaa53MZPpPxf69MNil429rbDSzp6ac1Z4EPowf2eCzmGnDUFMOMcrtvO/KkxedX2ZOeOfOsdW+1Wuh0Ouh0Ouj1emYnoCwfKvoFuLy8RKPRwA8//IBnz54hEolgY2PD1cqJ2WF2b/Zpgp8leuntZwYe8LaklaKl6OVOQwo4Ho8jmUxeG1sNwCV6mUhEq+012orJSax1r9frODk5wcnJCU5PT9FqtcwuQBbnqOX/8KjoF2A0GqHRaODFixf4xz/+gZWVFXQ6HVe7qXK5jHQ67RK+LXjpxZ/m+JP3obj52HA4DMA9msqrJt8WsHS+8fFe12DnxsusPZk/IIV/enpqhF+v19FoNNBoNEzm4k3jvuTfPQ1dOO6Gin5OxuMxzs/P0Ww2cXBwgJcvX5pOLtz2UgylUgmO47i23ERWo/EMLkN6EtvLD7hDfDJHQAoYmF7UIoVjx+HlY+VzeOXp8/zP7T4XgOPjYxweHmJ/fx+vX7/Gmzdv0Gg00Ol0TOah8mFQ0c+JzLtvNBo4PT01QmEhTq/Xw9nZGVZXV5HP543w2UBCnr/Z+ALArYXPr/LntrX2stj29/Zz83svvApq5IIiE4A4cPLo6Ahv3rzB+vo69vb2sL+/j9PTU9P6in4CL8ut1vz+UNHfgC1A2fyBMfrJZIJer4ejoyOz3T05OcHq6ipKpZKrVxxFzhFQjuPAcRwAbvF5hf1k2E0eD6ZZaq+/ZRrTjhY3Pd7OIKTvgP3yqtUqtre3cXh4iFqthtevX2Nvbw+1Wg2NRgPtdtvVKWde7F2JcjMq+jnh9p6JLYRJMtzmnp2d4fDw0DSIdBzHNJNwHAeZTMaMdZZjn+mJnyVC2+l3G0t90+/muY99f9s/wQgDR1evrq5ifX0dm5ub2NjYwOvXr7G/v4+TkxO0Wi30+31T6COTj+hslI5Au82Win1+VPRzIqvk5LlU1qxfXV1hMBig3W7j6OjI1dmGraILhQIqlQrW19dd7Zyl6Lyca3YnmVkx/vfNrPRiwG1t6X9g95x4PI5UKoVisYjNzU1j5Rnes8t36RSVEQP6D1qtFl69eoVnz56h1+uZ17SjJIo3KvoFkM4sIlNRuRMYDAYmQ44ltxR+LpdDvV5Hr9czH2g7aceO83uF9R5C7Ldlmh+CO4BoNIp0Oo1KpeLqH2An8/C9pch5PzoMG40Gvv/+eziOg2fPnuHs7Eydg3Ogol+AWWdmuRAEAm8LYuh4k3F2JrMwxZUfWvnBZ7hPxvq9zvrLIHgbeU28fm777Q6805yEdlMPWv9ut4vPPvsMn3/+Of72t7/hm2++wT//+U9NBrolKvo5kZ5yr+2k/SalxpQAAAivSURBVAFmxhwTd2TzDaa2cnsrPeG5XA7j8RixWMzTQbfMgpfI6/SqMLRDhzZ21IHvz87ODr788ktsbW0hFothMpng5cuX6PV6xiHI11TcqOjnRCbC2B9Se3s5rVSWi4B0XPHsKjvVSMsvq+fs1N5lFz6xhS9/vgjpdBrA24W1VCrhzZs3qNfrODo6wtnZmYmuqPjdqOjnRMbZp3UbnYZXsku32702sFF2xi0Wi0in04jH455x/I9F8OR9Xu9kMkGxWMTXX3+Np0+fGsfp8+fP8eLFC7Tb7Y9qUXwoVPRzIkV/lxlqsr7dK8GFjTC59c/lcubDa1t88il+uG+y0CwmKpfLGI1GaLVaqFar+Oyzz4xn/1N8X+6Civ4G7A+MdEixrHVRaO2Zj352dubK6mMDTDlkgoU8zLuX5/1P0ard9u8JBAKIRqPI5/NIJBKmAEq5jop+Thh+s0V/F7FR+LT2svV1r9dDq9VCq9XCo0ePUC6XkclkXCOiPtat/n3AgSPMclSuo6JfADmsMRaLYTgc3un5vIpZGOdvtVqmZLXRaGBzc9P0dLfHQNvFNve5CEyLVsx6bV2UlgMV/QJwqEUul0M2m0W9Xr8Wo18E+RxM2un3+2i322g2m2g0Gjg6OkK1WkW5XEYul0M6nTYTYZnbL7vmzsrFX+T6ZJmtLNGVuQgfcrSUeunfMe29V9EvQCgUQiqVQrlcxurqqnG8yc6wi3742O0mEAiYxJTz83N0Oh0cHx/jxYsXKJVKKJVKKJfLZhY8F6BUKmVy/FnZN23uPUXs1dDD9hXILDnZUYctt+TASk4E8ur/75W1ZzMrXj/t97f5nfIWFf0CBINBpNNprK+vY2trC4PBAEdHRyb+bo9wXgQ+nt57pp8eHBwglUqZ2e+lUgmFQgH5fN5Yfk7MlZNiZbTBnqIjx1bL1lxyoZCRBdkbj1lw4XAYiUQCjuO4rkGO95K7APt2065Axtq96hSU26OiXwA2wtzc3MTu7i56vR4uLi7QbDZdcfa7tofiY2XJKXcVzWYTh4eHePHihRE4Z9Xze5a5Unhy8o3MCZAttZkqLHMRuCjYgucwTeCd6LPZLAqFAsrlMsrlMvL5vJlNz/oDNumUI8BuMxVIvp9yYVLmQ0W/AIFAAI7jmHhwv9/H1dUVEomEyamX2/P3ec4cj8dmim2r1XJtm2lJKSpZ3cdtPgBTyCJHUEvryZ0Bm34A7j7/8nF8TCgUQiwWM5V0q6urWF1dRblcRjabNX4HRj5ks07eGBGRuxIeK7hAAUAkEjELGluPK7dHRb8AgUAAsVgMhUIBW1tbOD8/RygUQq1Ww/HxMU5OTnB2doZut2smzNzXwEe7iQVrzQkFzBp9mQgkZ83bf5+0vMC7RpmzKtk4JJMOxxcvXpiR3dxt0MJHo1HTWyCdTiObzSKbzZpdAacEc5HrdDro9/sIhULI5XJYXV1FtVpFJBJR0c+Jin4BaA1TqZRpfR2LxZDL5bC/v49EImGsLr3wAN671b8NPGrMg0wYmhfWDnS7XRweHppkJtnYMxgMGuEnk0lks1nk83mUSiVzHGCh0Xg8Rq/XQ6PRQK/XQyQSQbVaxZdffml6E+gWfz5U9AvArXQsFkM2mzXNIhzHMd7zSCSC/f19HB0dGQs8TfCzYtu3WSS8mljc5+Iy7XrlrkO2v/IaJCkzGxOJBFKplLkx8YjPPxwOTTfdlZUVrK2tIRAIYH19HY8ePbq3v/NTRUW/ABR9JBJBMpkE4PZe2+fU4+Nj0wfeqwf8Xbf9D717eB/HlPF4bM7pg8EAzWbT5UDkcYQDPXgLBoNoNpuoVCrodDoal18AFf2CSOdVIPB2vBRF7ziO8WJXKhUcHBygXq/j7OwM7XYb3W7X5NU/1Id20V3EfSJzBeY5ToTDYTOm+0P/DR8jKvoF4fkUeJePH4vFzFaVnWB3d3dxcnKC4+NjHB0d4ejoCMfHx6jX62g2m8ZBdd8f3k9BHIFAwCRF5fN5s+Aq86GiXxBu8WnxOWJKdrstl8smjVZOgDk9PUWj0TCil9V0chS0PVbK7hIrW0mxTPc+/15+la24ufjJ+L6dhistuoxi2Km7MmYvewvSUcdMyPX1dfziF79AsVhUJ94CBG6wAB+/ebhHpp3J+cGmQGVTR9bIs1UWY+6Mf/Mr22jZE2N5sx8vZ9TJ/ACvUKFcHKQ4+b3sDGQnzfBncpgmk4JkYpCc6sMFyZ5qKysWk8mkeQ6ZyiuzCYPBoEkAKpfLqFaryGazKvzpeG6DVPT3hJcn3Y6py8YZcjCkPSaaM+LlV3uEtOwa6yV6L8ET23LLdFm5AHBB4M9le2sO75B5/3JkNhc/LkqTycQsHFw05OPtwiHZXFMuBvN2L/IZKvplRibNyO28VwstuUjI+9OCTksGuk3IUFp8+3veR+4I5HacyTde8/nkAEwuPPI55ONlkdCscV3KjajoPzamidbr/+wmR92s3y8ipJuq46bF8Gc9nzYDee+o6BXFZ3iKXj0giuIzNGS3ZNxXPF32f5/VB36ZttbLdC2fErq9V5RPF93eK4qiolcU36GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUn6GiVxSfoaJXFJ+holcUnxG64feBB7kKRVEeDLX0iuIzVPSK4jNU9IriM1T0iuIzVPSK4jNU9IriM/4/yKVlJo+n/q0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current beta: 128\n", + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "lb[Si_mask.flatten()] = 1\n", + "ub = np.ones((Nx*Ny,))\n", + "ub[SiO2_mask.flatten()] = 0\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "for iters in range(num_betas):\n", + " print(\"current beta: \",cur_beta)\n", + " \n", + " solver = nlopt.opt(algorithm, n)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_maxeval(update_factor)\n", + " solver.set_xtol_rel(1e-4)\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the optimizer quickly finds a topology that works well and slowly refines it as we continue to \"binarize\" the design." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(10*np.log10(0.5*np.array(evaluation_history)),'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Mean Splitting Ratio (dB)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view the final spectral response to verify that the design performs." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "frequencies = opt.frequencies\n", + "source_coef, top_coef, bottom_ceof = opt.get_objective_arguments()\n", + "\n", + "top_profile = np.abs(top_coef/source_coef) ** 2\n", + "bottom_profile = np.abs(bottom_ceof/source_coef) ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(1/frequencies,top_profile*100,'-o' ,label = 'Top Arm')\n", + "plt.plot(1/frequencies,bottom_profile*100,'--o',label = 'Bottom Arm')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Splitting Ratio (%)')\n", + "plt.ylim(48.5,50)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And of course we'll visualize the final topology. We'll plot the minimum length scale as a circle in the upper corner." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb new file mode 100644 index 000000000..3d6528ff8 --- /dev/null +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Near2Far Optimization with Epigraph Formulation\n", + "\n", + "The adjoint solver in meep now supports the adjoint simulation for near-to-far fields transformation. We present a simple optimization of metalens using the epigraph formulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "from scipy import special, signal\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "SiO2 = mp.Medium(index=1.44)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "design_region_width = 15\n", + "design_region_height = 2\n", + "\n", + "pml_size = 1.0\n", + "\n", + "resolution = 20\n", + "\n", + "Sx = 2*pml_size + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "nf = 2\n", + "frequencies = np.array([1/1.5, 1/1.6])\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "design_region_resolution = int(resolution)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [0,-(design_region_height/2 + 1.5),0]\n", + "source_size = mp.Vector3(design_region_width,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.Source(src,\n", + " component=mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "\n", + "def mapping(x,eta,beta):\n", + "\n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()\n", + "\n", + "geometry = [\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "]\n", + "kpoint = mp.Vector3()\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will have two objective functions, one for each far point. However, we only need one `mpa.Near2FarFields` objective. We also need to provide a near-field monitor, from which the field at far point will be calculated. Only single monitor is supported right now, so the monitor needs to extend to the entire cell to capture all outgoing fields.\n", + "\n", + "When evaluated, mpa.Near2FarFields will return a numpy array with shape npoints by nfreq by 6 numpy array, where the third axis corresponds to the field components $E_x, E_y, E_z, H_x, H_y, H_z$, in that order. We will specify a objective as a function of the field components at frequencies of interest at points of interest. In this case, we would like to optimize $|E_z|^2$, and focus the fields of different frequency at different points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "far_x = [mp.Vector3(0,40,0)]\n", + "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "ob_list = [FarFields]\n", + "def J1(alpha):\n", + " return -npa.abs(alpha[0,:,2])**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = [J1],\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + ")\n", + "opt.plot2D(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our objective function that we pass to nlopt is rather simple. We'll introduce a dummy parameter `t`. The goal of the optimization problem will be to simply minimize the value of `t`. The gradient of this functional is rather straightforward." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(x, grad):\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", + " if grad.size > 0:\n", + " grad[0] = 1\n", + " grad[1:] = 0\n", + " return t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key to the epigraph formulation (and most nonlinear optimization problems) lies in the constraints. We'll define one constraint for every frequency point of interest ($f_i$). We'll define our constraint as \n", + "\n", + "$$f_i < t$$\n", + "\n", + "where $t$ is the previosuly defined dummy parameter. Each constraint function is then defined by\n", + "\n", + "$$ c_i = f_i-t $$\n", + "\n", + "within some tolerance.\n", + "\n", + "More detail about this formulation can be found in the nlopt [documentation](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#equivalent-formulations-of-optimization-problems)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def c(result,x,gradient,eta,beta):\n", + " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", + " \n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + "\n", + " # Backprop the gradients through our mapping function\n", + " my_grad = np.zeros(dJ_du.shape)\n", + " for k in range(opt.nf): \n", + " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + "\n", + " # Assign gradients\n", + " if gradient.size > 0:\n", + " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + " \n", + " result[:] = np.real(f0) - t\n", + " \n", + " # store results\n", + " evaluation_history.append(np.real(f0))\n", + " \n", + " # visualize\n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.show()\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", + "\n", + "# insert dummy parameter bounds and variable\n", + "x = np.insert(x,0,0) # our initial guess for the worst error\n", + "lb = np.insert(lb,0,-np.inf)\n", + "ub = np.insert(ub,0,0)\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "ftol = 1e-5\n", + "for iters in range(num_betas):\n", + " solver = nlopt.opt(algorithm, n+1)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_min_objective(f)\n", + " solver.set_maxeval(update_factor)\n", + " solver.set_ftol_rel(ftol)\n", + " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "lb = -np.min(evaluation_history,axis=1)\n", + "ub = -np.max(evaluation_history,axis=1)\n", + "mean = -np.mean(evaluation_history,axis=1)\n", + "\n", + "num_iters = lb.size\n", + "\n", + "plt.figure()\n", + "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", + "plt.plot(mean,'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot our results and see the resulting geometry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the performance of our final structure, we use a CW source at the desired frequency and plot the fields after the struture." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb new file mode 100644 index 000000000..3e552be49 --- /dev/null +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb @@ -0,0 +1,2042 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Near2Far Optimization with Epigraph Formulation\n", + "\n", + "This tutorial redoes the previous example with epigraph formulation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "from scipy import special, signal\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "Air = mp.Medium(index=1.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "design_region_width = 15\n", + "design_region_height = 2\n", + "\n", + "pml_size = 1.0\n", + "\n", + "resolution = 30\n", + "\n", + "Sx = 2*pml_size + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "nf = 3\n", + "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "design_region_resolution = int(resolution)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [0,-(design_region_height/2 + 1.5),0]\n", + "source_size = mp.Vector3(design_region_width,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.Source(src,\n", + " component=mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "\n", + "def mapping(x,eta,beta):\n", + "\n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()\n", + "\n", + "geometry = [\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "]\n", + "kpoint = mp.Vector3()\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of the calculating the average of intensities, we change our objective function to be vector-valued intensities at the frequencies of interest.\n", + "\n", + "Note that because ``meep.adjoint`` looks at the fields in the frequency domain (i.e. Fourier transformed fields), the ``objective_functions`` from ``meep.adjoint`` can be vector-valued if and only if each component of the vector is at different frequencies.\n", + "\n", + "Also note that this objective function is different from the objective for `nlopt`, which has to be a scalar. In our case, this is a dummpy variable `t` from the epigraph formulation, introduced lated in the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "far_x = [mp.Vector3(0,15,0)]\n", + "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "ob_list = [FarFields]\n", + "def J1(FF):\n", + " return -npa.abs(FF[0,:,2])**2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = [J1],\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + " maximum_run_time = 2000\n", + ")\n", + "opt.plot2D(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our objective function that we pass to nlopt is rather simple. We'll introduce a dummy parameter `t`. The goal of the optimization problem will be to simply minimize the value of `t`. The gradient of this functional is rather straightforward." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(x, grad):\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", + " if grad.size > 0:\n", + " grad[0] = 1\n", + " grad[1:] = 0\n", + " return t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key to the epigraph formulation (and most nonlinear optimization problems) lies in the constraints. We'll define one constraint for every frequency point of interest ($f_i$). We'll define our constraint as \n", + "\n", + "$$f_i < t$$\n", + "\n", + "where $t$ is the previosuly defined dummy parameter. Each constraint function is then defined by\n", + "\n", + "$$ c_i = f_i-t $$\n", + "\n", + "within some tolerance.\n", + "\n", + "More detail about this formulation can be found in the nlopt [documentation](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#equivalent-formulations-of-optimization-problems)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def c(result,x,gradient,eta,beta):\n", + " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", + " \n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + "\n", + " # Backprop the gradients through our mapping function\n", + " my_grad = np.zeros(dJ_du.shape)\n", + " for k in range(opt.nf): \n", + " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k]) \n", + " #Note that we now backpropogate the gradients at individual frequencies\n", + "\n", + " # Assign gradients\n", + " if gradient.size > 0:\n", + " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + " \n", + " result[:] = np.real(f0) - t\n", + " \n", + " # store results\n", + " evaluation_history.append(np.real(f0))\n", + " \n", + " # visualize\n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.show()\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 0; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAC/CAYAAABdcw+KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAD1klEQVR4nO3dQU4jRwBA0WrTICQEB5hL5CZBYpFVLhZpDsGGO8xcB4TkgE1nEXlWAWY0nm8rvLctqau8+S6XSu1pWZYBQGN16AUAfCSiCxASXYCQ6AKERBcgJLoAofmdcffJAH7c9NqAnS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkurBnf2+242nzcuhlcKREF/bsj7++jD8/fz30MjhS86EXAP83v//2acyr6dDL4EhNy7K8Nf7mIAD/6dVvXccLACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AUPo+3XdeIwlwENPUvf84jW75wQCOURbdp6encX9/L7zAUVmWZVxdXY2zs7Nkvl8e3e12O05OTsbd3d24ubkZ5+fn4+XFn/YBh7darcZ6vR63t7fj+vr6W69+pWynu91uxxhjrNfrakqA77LrUyG7vbA7Vphn/4UJHIddj8pjz/zKmKMF4Fgcokfu6QKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE8uiuVjoPHIdD9CibcVmWMcYYm82mmhLgTbse7fpUmLOJ5n+nuri4EF7gKMzzPB4fH7/1qTC9U/i95f/5+Xk8PDyMaZr29UiAn7Ysy7i8vBynp6f7fOyrocuiC/CBvBrdbk892nMTgO9V/gJPo+toAfjo3N8CCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCM3vjE/JKgA+CDtdgJDoAoREFyAkugAh0QUIiS5A6B+NOmaIgLcIiwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ehpLJTmUa5i2R9DLrXj282ygzt4pGyxtbszBrP1te9vN76xOdY+Z8ez4eHW/b0dbDxkTPzmh6PpvceILpxXDUx7z3e9fZZ7DtkvHtK5YYvsQlYzMn3nPWvR0sEtUoE8pkGl4d7QOdn0lUhvclzStHf83o+cHZGdEjligegVfPaI2xMh5632/o/S2R73UmOTsD6dHrZ1u049W8/JKxjCMyo6clkyVvzYoe/TQwyUL0+pI9R52l9B7qMdoeZT0jRtNAr/5RB5kRGW9AHHXs7AynN5MZ1XFrR7azm5k21Da8ba/+o/XtLQNuth/M9LOZ2e0WXagY2EaUX72QCeTRsa31n3SVYXbpwpu6eNPM7NRzS731k7G8zjqzjDKqkxVpTyAytmc7wj0DQq/8rQO893nkh4yIZm1rol9+kM+2+GskwL14jvrAiHuXDL16P0JPqkX46aI7G3xbRrjePtH0qLWWWh/NlGmvzx0tO9hraLPZZ2/t7p4sUjN7H32mrJFfqoI+KisjBFs79lZBzGZxmecwR7Oo0dPHZtauo/rZ40bP9u31W002Th+lJ4+kPNP1nOWNnN56U2ZKaffXx2WE2O436gCRYEeBNQoqzzfanmx7mUjPrrUZPaYys843g+24vZ9HyrSPjZXMQNcT28yAuZVH+Fnq7bW3jYne8dH7mXiL+uQo69TbmfbJCq7VAO8cvMvsvL5474x5Cy+5OUJv96YvGTuae0bkUV2zAaEbfKYj9DIp78YLT3gzeALg+a332itvtE7n1bXXIXoPGM+8P/q9MnvsKHZG55zxX8bPPazg9mJCE8VWL97kf7Y/zSZE2f167TKzRBIlJ3/L5QVLNDLZLNc7xopfFGzRiHbvtHkmcC0j0cra2HLs1uPssZFgjbA+2vqzNr3XWbzZiLC1LbPCPbK5VYAf4YutbWnfu2cZJsq6ZVsPNF6WHMVZL9ONzumZfI1rahSZUW30xKGeHY/sz+Vkp4OZ6yG3ZPK9/aPpcyZTyXaU2Wmg3mfUoex79hhvSSeb1UVZdqaeHqPlp+iYzL5Rm23JJEeZYK+81ratm/bibeaqH1u/0WymN/sdlaGpEOCX3JG2ZVT1OkW0XjSTFelRM6qnl814wSDZd2vjnxaPArp3t1tmDa13HrYcu+7cW//zyhbf6f9efW02klk6kP1mBlEpb5Q1Cd7txplz0f/t51FHtj6OfNDLJKN26NFLACIR7F2S6PUDe3739Lfe8V4Zs7PPrbOCR1Ka6Xpi1RvJWvu8nvqoy4nsmugjn+iUbfSt2a7nh5lfYfXszWZrnthHg+Q92UM2A33EVDEawLzz7E1rR7Z79LJO+3oUI5kyHjnj6pUV9bd7sXex9QS9lzzZ+j6T8kxXP43ey0hHvynmbfcyPm/9Z7fbfcpyvMu49CPhdN2ixo2yMluHng1ry35hIsfoh3p45UTZvjfA2KzBPmbPfq6z2+i8M9PM7ANpdKzYS6TkOtWZjNj6wOuQdn+v3bRvvA7tfdmpfSz1F3rZsmUUF17M2XK8c/DO28tOvdmL7TPafqa/ZfqvrqfNij290LNKry1ecdfiS34jzWvk6IlirX2eyp7P55uG6d3Npe3pANfC4jleC4IEsT7OdlotzLpMK+C96ZXYFxvLsny6rlFPT8UXgv35Iiu0I3HRSJ3F1z2h8YRH27H/9eCnO6xmt/u/n+r2/GE71+Vyaeu63tiwMRL5wfNHdA5SX11/a1d+Wnz0zb8WBStctkzrC72t69Xa/8fFuq43tjMZYC92xZZGC63XJ71lMi22MmDO9F9tU/s7GtxsnGn/zwzUj6RMdK/Xa3t/f2/rut5kunLCImzRN5Syjw6E8/n86X/UaGJbd2K9rYPABkRr7SbQRXiXZWnLsnyUo8Vb113blPO3t4Ha7Mt2XtmW42wniIJWkI7Ty0qjjmltW0GQulm/y38vS+p1NrHnPZ7P2tWvxabEgRYxHVO27TVetqQFzaur3ccTG7ufjW/vtuAoW+3FhRUp6RuRL/b7/cdg5q1x6/qJrXVd27qu7XQ63fQTaS/xre7TmX7WE13pf1JHeS3nLD637ecN6jbm3t/fS6/XfbroihMOh0P74Ycf2rdv39wvcaJRywam7lDn8/njTzId62AvqzkcDm5Gcr1eb4JUj976uP1+397e3trhcGhvb28f4isBrIPaGyC8ukpgii3502UKUj8RcAl+8YWciw14T9R1G1hfS/3EbiRk2q+9bFFsaft6oLSiIee/3+/b8Xi8EQjtC88P67relGvrqf1i407XWcdFb4Dw4sq7lNGzqdtU+1j3ncgP1he6P+g/nUDoOmsfy38bv3K82Hp/f78RailTJyA2M9cx5sVVL9O1ddX+0GVFuiFl6dfir/1+3759++aK9rPYDZT9YbJ/Op3aX3/99fA1lF52lnGgl9lk8DLF6LVn13tvZMejYmQelbUlULf62r72suitdjM80gdbmY2LmTqPYjdjd1TWvTH7aF9fLpf2448/tuPx+EizYSXLRBcA4B9EKLqlX6RVZmUAAFkqZyovuXoBAOCfype7DRgA4O8MogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUMgy+HxXUgsAgH8IZLoAAIUgugAAhSC6AACFILoAAIUgugAAhSC6AACF/C8T8q6hOkFX9wAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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RcW2uRBoRL+74RREvef2z7go31++G4xcI9LdfzSeBe0ChbHyfJFsnJw8Snt2TGHxO3sIkJ/YKwx2V8qT8XKY8Zm5+BvW5AO1k67KZ04NXKhqLQYNgUKN98jPtyffmpO17lC+wkqIsqTtm/J3sPGnwAOBZpeDBTfL0FobroNunVzPKcPnaKxX6ol/Y7IKAVwzespu7KPmlMPxC2nK5PPlWlgTErwdX1YkxOdlJSF4GdeU0S0QaltbTGYK3FXSMGzgvhDkZ8xi/CNORJAn23IUbytGz5nPExL1fXl5OZR6dUk6rh2TdEYuv5Xg8nty+xi9pdPrVmvQsh2LvmOumfAnaA0nIx686vf2oIzfe5aCsiETG+1Tn4DLVHHR8to8+xeGdZGnDPNd/14IZq2eWHJuZtI51G/VKwrNm6Zx3Qsy1mbos10mdD9oH5TCnF43HL7xIPr43Vhyj8KtcSOsiKwUlx1WpzlLUSw1eZOhKTb3Pc7zEcyPVOth74rzeQ3pJady7kwnPp3xI6OfIz+XZzavXc7roenAMKufmln58X8y0JEv/8gLn7DK+br1zpCsdz2WvnrF5huVE6LKgY8/pWnrgN6JYKvOYuT11Y7vfnJvbj3Nd86LTXHCaW4POp395RuktQ/qst7Tcbrwd4plwFwzdVv0LL/pbvt3JcyThVv0KPV0atN73qKjSgjf/d9kiCYEZRaesqtPyWV/l7H7pjHOwuc/xuJ45Y//UDJfoyi5+VvXxmzpc41xv9aWxPYvi3REMYL4GX5/vg87d9QL52m3Cy9pPdYqOPDzL7ZxZczAAsOqYawG4fVEvJAge3yUIHKNDl/l5YkG4jlxPnvHO2bXec/JkGc95vKUkm9IxT09PU5Xl+3Ub45j0PeqIpMsxFOh5UdMTEG/f/SYz3apqFVp1ShyMpoKUpzLer3pT4d5S0PkStAhXD/8NTVeQZ0gkEn9wX8qMvIR0R5cxKyrPXdjyiEyjZBnlAYxEzXN8rx3x+Xn+vpM8x+L4DunZ9e69xK4qoA782O6czsYYTDxYMCvzoMr1e+akZx+Tju2Bx/XsZKbj2eesOr3w02W0OtezT+qsqwhkryRHBiSe53vv/GQuk/S7gXxNnkUz8WHlJz1RdoJzAuXHfXfvf2kMv2XMX7sTq/zgccw6eDeBzte5vL2J4wnMXFar1fSLRozmfkW76w/TkOZaA7q6rGjse9TflIOMyCsCfqWSBiqjIuFy/XO9bWYNkrH3A91oKceudcMSXz00ZShdO4lOq8pGx7sT0rncjriO7lYtzU1ZKVDpuE53lD2P0fjnvtKtvSvL1f68vaI1d20DJ+COeHkrlgd/9lnnAjLXTFtwXbuuRGKuz85WKE8dr2fp3O2abanFYjF93Z/Bmr7CTNdtwxO4rrLoAsOXxND2AsFsgw9GLBk9f2dWDkRj569l8Sf6vCwS5iKbZ3RyTClfxuElmyuQ69bvkmo8ZiI6Xhm8Z/kyrE5Wc/fjMuPr/uZ6WUb7HQveu+suwnAsl6+On8syJA/2GTlHl0WS1OS4IoEu+/XsmcTrWbiXm7rt6lwW5HKR8zNJ0F6UOXZX92n3nZ5ct11wYGDzPfNB22W2yIyRMpyzc7dJ3onE4MLzHXOtNRFt1T9/FF4/zcog6HYrkBtcnl3QcYwg31/1txcY0fxGaY9muhG86uOPc8vApHT/TVQXLrNZZlZyYJIMiZrljJRNo6IDa80iXTmvxvWr+JyLWQLnEnjfcdXHErJzhrm/uQ8ZsWe5LJ/pxB28FNc6uU/pmCQi55KT+F7lOJ7FaU49axzZRdf2oKwYTJhBd3qjQ3eVUDcH20q8Wn4u6+zsXn+fkzl78JQdZdMFZT+O1ZS3fShLT0okD/7GMr8VSd/ySoc+774k3Yh4RbjuH9KPuIFVFcfiLW1zcuAevzTx/mq/MkbhqzVQ9fFLERQMI3rV6S/D6xwpkD9CLbDUZEbBW8L0ngjNSzVd2GPm57c3Mdug4+k9z9C7ssfX3JU+cxm8jqcjE57Z+G032g9LOe2lK90pJ+2Tn0tOnkFJXyI89tX5mmV911Lo9uZleVWdBABmfX6BhsTrF2lkL57depBksGSm7PrkOrpsmvInvB3CoOfr6YKxf86Ax+Cvkt71Tz9hkkJC88qVtuoVWNfa4Dq1jsPhcPKDSkzG5Jdsjcln+S+6dAfNS9nul8avSrpSHBUoJVE4TgBUlGcRjGQ8Xhkn+7hcizuT0Dn7uSzXSz8a7aegI9jPOafLcl2GXCed1+HZv2cuPj8dXY7IMpuEy2yXxMc1duCenJDniIakwfZCVZ3YCstskqbr2WXtY3UOLTmfI6Jzc7meO7l0z59yjsD1MdHxdsfcXD4ffcrvELi4uJhI9Onpn1/NZeLFxIFtKBK12zDP8yDA87svx4zEUNL18oTvOUHomf0Y9nWZ1fB4j+aLxeLkopnuWHCHYobt0Z+vuReuhWvqem06x0sbJ46O6F6a0+HlKmXIrMrXxdd+Ec3JTs9+D7M+m1unB4Gu3PucAMCgMZf1c1+0FX4+pwse3/VFXT9sd8m+lD3O7ZH6oWz1t++pW6fLycm/O97t0ZMaXbB+aU4FKbZsuDcP7jqGFxFll8pM5/ig24/ssNPxnDw6/Y3CMNLtyENwpc4RGY3Cx6KCusxYhCsCZv9ShkVS8tKtIyiunyW7l5e+Nu9jdftwcqIsquqZQ+hckoL3IF/Kclkq8jYj37M7Kfu1LBV5m19HjHPONZfpucz53KEb1wMZdex9Uurbb/h3GyZR8gs13nahPTCL1HisiEQmlGkX2Lsg0ZFM97fbo9vu3MXjqmoJm3esaDw9c92ys65ipT3q3I4kuX9WKecerjMfcwSGZrqeOVWdEi5LO/VqeNsVHdud1wmN5Zqgsbv+pdbADKobn6UOiYi9QBG75mEmI6ckuoxda/OSmwat3zjg7/RqHhIw53Fy8SDm5PpS/4sVght91cf+O+dj8HAH60iRdtL1Px1OLBzf7c1lwtfMhKnDOXl0hOxr7jJptp+kNx3LOyIWi493xTA4aJ1za+WaqV9WG5yjqqb/AUeyVm+cgWXu4lTnT5S5jjnnb3OJCuVKvbEXz7tP3IZoG93YXxpDM10qilkUjWu5XJ78kzpeSfboybH5unO6uYjvBsksy3uxUprfgkMyF+Hq2TNBgSTg/TzOxSyfBqPXfquXxvAymGMxmLFt4sY+lxloPJcVH8LV1dXJj3Rrfl0gcRImNHaX1fm6GNBIloJXPgperFB4qxjvrdaxJMZOHnxNEvBslHecKJP1ANld6GMyslqtno3LYymbLuP347hmTwJoD/Rj3k3jPkEyIwHTrl7yz66acJ1KJk9PTyd+JxlpTdqj1kAeOndHypfAUNLlLSXdLSPeDmCW698c47jd66qPhkuH5O1MVfXsu9pci19J55V4GhQz3NVqddLG0LqcWGhkHK/q1BnmesNVH7PM7nM3ZidxEW9HCOxre+AhmMF05aO3FLzUm+vF6tiq5/9G3bN+ORED2xzxcl5WN93nXXXBtc85KeVGMqeMpV/pz28j9MyVa6KM5wIm5eefeWtMa1bi4Bc+eb7fHdCRr7difC3uezyfFRpxjhApm8fHx5NkTe978OC6uy+tfGkMI105Bv8ttd9xwEyWTll1KniWT5+CLor6bT9ddO0cc86pRLabzeYk2spYu1Ke5Mk59PenlNIdYfm+fD6d5yV7t1+COpqb3x3cHbOrLLq2U7dGb13wWUTvpNiVkb4GJwy9N0fcnVx48agLMudk3GV7czo4p2+hqwS9jPYKhRWTZ8Ba9263OwmCnT3P6ZJ/s2I4l9l2e2bg+BSb4TkXFxcT5+z3+8lvf7Okq0x3LkK6MPVM5xVxz33t1Q3yXKbFC2kdEWt+XsDjje9sfTCzVbbrN5sze+rKScnIsws3SI/Y3gt1MiOxe3/MA4wTkZMRdaO9eLYpwqCuvOR0mUsefqWccmH/sct0ude5rIY25zJhJUadMft3gmDQ19q9xOax3gLqdKJ1eYbGz1TBeebdrauzHfmCt4Rk264D7Wm9Xtd+v59u79L7+rozCdrh/Vetxasf+oeDuiAPsHpxOdK2ZWvOJ7/59kJnZELXs6Vgj8fjSZZ8ruzt3mNp7T8bqXPmfn5QRM2Wh0iXbQU28KVgrqFb21w2xs+6bH2OyM4RgxOgO2xHUF2G5I7lX7ueIyuuV2tyAtAY6mnSiTpC7kiF8/i3pZx0XQddD3DuW2Q6l+OpbL28vDwJJJ2eXc6yt0/Vs8uh2//nZJIkPvZLn56eJsLVg/r1H7HhWN3FLep/rld/ruJixqrxnBPIJ3zfEwrZ7igMJV1mHtykhDN3gaeLTJ7lVp1eMeW8bnTsi/kaZUCEEyfbC/4vus/1KecyUWWGnaN4FkCw9TJHzlp7R1T8zEnnHMHMEa+Co0ptjs+A58HXiZ7tAq59Tg5+3FwAIeFyPd4TF7lXnf4GhQcMzq3zPIBpf7TNrtLo9DGn2zl5zGW9blc6rluHj++JirchqIsukeKdK/IXjd0F/W4tbrveO3Yilw2KT7rM2wPeb5J0n56ept9GoIExGvFnF/lbt1UfM1B3nqrnRMhbW6gcfstNcx6Px5MbuwUd4+Xv3NwdEZE45kjRP/OskobfZaWd8XNcrpPwufzcl4ieuptzFpbpHqC0fl+PSmeugz3HuUyXF2f9fdd51ektfl6dsHXi82iMjiA7+XjQOicv14GP620AyoMZLoNPl+nO2R7PYfDV/FqD62uuZ9+RtxM+s1bP+KUnt/G5uTUXf2xKfCPfZ29d9rHb7Vpi/lIYmunu9/sT4hW8T6pyXVGSBNgJ2ftSXSnHUnMu0+icVOtTRFXJqCDgJFh1/vdOBZa1zID8PPbCuFaNwZ5z57gaw53GjYzrc0PXFzE4np+nh9+HqeO9XOW+u/Jb5Ev7INGw56lM8lxrQ/vSfLzbxC8gecBRBk+n9wtjGl8y8ErN7Y1JB3VDu6CMqQvJwe9e4LhzGaj277bmx3uF4CTd3QHA9zRuF2CoZ1Y97ntzvt2tkbec8gIZAzErWBKufq9lFIZmumq+U9B0VhGuf02X0Yr/g0mQU2pM/gBzV151hln1/O4G763xGD9eRiCwzGak7/pMc6WNtyo0HvtgmudzCJdyoYH7F1Dmem3MGHQuHcSJj4GOstBYPja/gMIAKxvhHPrML9JyXq7RHZny4LyaQ2tlkOBaHU7Ysg+/hcozZ9dRN26XjdMmzo3RJRJ69i8RfEoWy73xNf1BwVqVjfbha5rzze4X29xO1NOlftgSUbbrc1Dfv+me7lyWSaNj1kMie3x8PLndTETMzEIKOh4//h8mJwdmCi8RjAxH5+qZmYeX/F3Z6DffK1tmRvC5kZbG3mV1c+Ury7cum+fvW/h8dBxmasyyWFIrk+Ctgl2VQnkLu91uGl8/jELSZqbrjsSKyvu4LgvZFYlSXzzwqoBzdTLp5E279+yS751LALoq7BxJzNkSgwarS7+f2AO479Pt39ffVVHuY50fal3yO7ZRuD7JQrrWOr114JziiQ6D7W+SdKtOyxhmGVQcHUjEKiHLmdif5djszdKZu16YShCWnoqQUgydlGW+99OckLuHl4VzWaSTiF5fXHz8kW/JpvuyCUvBbl2cQ699fn/fKwZmga5btl20vt1uN+mt64lzTs/A2ZbgT/v52lgq0kZ8L10Z7XrwJKCrmKgb2ZbLrJMj9+xE1hGvdMpbtNTqcnKk3ObImQR0zp794es8Z88iTmbnTDouLi5m7/kl4XKdrrdOBrwNlDri+tme4p5GYvg/phTZkVSr6uT+wKpTB6yqE4IhGbtDdcbC+btyRcbtxsNMt5vjJeLV2HPrE+goXsaxbynI6PxWKMqFc3alpAc9fsb3uC4f33vX3BcJgnsg0XT9ZX2uPirbBjzPs1a2FNhz5zGSAd+j7unMbCs46bJKoTyYUXsmyP05UfA8EicTDmZ2yk65Hx3fZf98by5BcPl3dq3nzu4Jb/2RcLVu9uhJnnN+xPVRLspeu+M4jn+71IPBSOIdTrr+nxRYBvjX92S47FdVPS+93OFoqIygTlQak2TSGarWz16R97rmjN37wnMPZj9OFl7Ocs98UNY03sVicfLlDX6/3jNKZmyPj4/Tbydovy8Zp/bhxs4x52TCfUhvDNT8h6LSqZxutVrVw8PDSVCZI0EPnLyTwYM/M/G5/WoOXgzWa2VeXI/Gl00Kaql083XEz7E8wXB78XFd9vz8XPBnlaWqwh/Kcn0M3m/vvui2LCKUzbHXPpdwVZ3+Xz62G8UvtE3a2G+WdGmIJJmq5z/owai4XC5PbhnzCynMPrpvrLFdQCIXwR+Px5N+MfvGOpb9NO1F/7tJx3blNtGRS0e0XUbqBqx1sN/V9aZ5QWGz2Uy/J+xOwehfVSfH+DfNuE6uQevWemX0h8PhGXkr0M1l0erlbjaburm5qdevX9fr16/r+vr6hHQfHh6e7YdOT110d2V4iT33dVi3ZQYnrVUBYbPZ1GazOZEFr0GwHaE++v39/ZTZ+wVZD0Cyfb941j2cgDWm25IHdPqTZ9q73a4eHh7q7u6ubm9v6/b2th4eHqb2zvF4nMhvuVxOX7mVnz09/fOi+t3dXX348KHu7++n8+kXlIH8i/bKJMh/FMurWlXXtAftl/8KbASGX0ir+kiwzBLkBMw4JECdR2JTn0vKenh4OIm8MnRFzfv7+7q/v58MRb+RIOU8Pj4+G4tkvVwua71e13a7ncaXszFAzEVhBgCt3fvSTmh0MBmqwEjOL5R4T1qBbLPZ1PX19QkZsDWhQCK5k7hYqpMQ3MGdFJjhyJm7lgiDm+YRiW2323r16lW9efOm3r5925LuarWazidpSV5OsF12q3X712A965RMPLDJDrbbbW2329psNicVlmyD80qebju0Ba7ViYW6YDLjevDsWOfrW2V+dd8TD/XlRbTv37+vd+/e1bt37+qnn36qu7u7enh4aO804bmqcNV/lz+KdJm8yC91W9/hcKj1en2SINDGGTQ1P22SQV6ypi+eq2Z+aQzNdCVA3h/JMs6zDZbXary7ITGjJRHK4ZityIAeHh6qqqYox96dl59d+Tt3C9ScY2scfjlgtVpNa5AReaa0Xq8nQ2XGwixM5RONXWsTKa/X67q5uambm5uTMXmVn5WIxqQjX11dTcHGgwNJt8sQ6fRyQl4c8v9rJ3lcX1/Xmzdv6uuvv66vv/663r59W9vt9hnp6m/K5/b29oTEtS7+2ya1Lc5lt1y7l90cd7Va1Xa7rZubm3r16lWt1+spcxWBHY/HZ7+CpYqLmZnsS/KV/q6vr+v6+noiFmb1DJjKLjebzZRIyP5lb/xvKpKB9M52gmTK6x0MoHOtOG976BgmS/f39ydBkkFPuiKpSk/8cSkFOf3+Ce2AQZiJg9asBE1BexQuXmD4X4z+D4dD3d3dzfafni3s4nlzX6+7PlTXt+Lf3Zxzc/t8fq4//OLM3Nhz6z43/kt74TidDLhXvvY9fs6a5/CS4XZzzv3t62apPLd/d/65Mbu/P2ftHTo5d+f7nP+q/s6Ny/G86vpX1szXc20JHvcpcu32OrcX37eve26+uX13fLLdbk8utv0CmDWoYaQbBEHwH4RZ0h3+jyl/iTR+ZP/lHEaWJEHw74Tfkg/+Urz0qUimGwRB8MtjlsVf/tcEQRAEwS+GkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIkG4QBMFAhHSDIAgGIqQbBEEwECHdIAiCgQjpBkEQDERINwiCYCBCukEQBAMR0g2CIBiIyxc+vxiyiiAIgv8QJNMNgiAYiJBuEATBQIR0gyAIBiKkGwRBMBAh3SAIgoEI6QZBEAzE/wdlVWCNkdOiZwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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SFtF46u1puNfQqAxY59SHNTk3IlecOuZSeeFrNV8ZG53YHd3vyToiAxi33XG+vJ2+6Em0yINgaYZKnw7nNWFXeqwNs20/px2yLZ5PZ9W5GhfNu8he/ffg1hIAHthIzFTfHBtXcjyGmZbbCsdW7eCcehmI5ETbVSBsbS3jAqTv9qD/MJtzW2KWSvJmnzT2PJ7Hqf3c3ubjxACzXq9/v6T78PBwtsmZakeOxAHlqjKJj2TJp9JcvbjK0D2lmnSc4GQkQ5IBqs7XIh2lpK10kNGbqpqpne7PcWHq7DXCS3VC/V3XZTDwcdb92X8HycmJyp1f7ZTadcVGpf014vV76RoeaFt1uVY7v3Z9/d+d0oMydy20FObXVCbH9VJpjEGENVsvDVBd095IUpxn2gO35fmxFBJ+bdVEWVKiXXsGpFKDl2/UTvmpFvM4du5bbKMEgcqCrAEza62qYUGPax2tkpXKnr9b0tU2lapzw/BNyr4/lFGQBsHVY6Y//NA4qAiZZrqjqA2z2WzYX6l9hJfIhwGEBMBUmaSr4/Wv2kOCcUdi/9jWS/UokrdvdeMxVU/KgBvgSZJyPHdIXU9/U52QRnypfsm/q18kOV2fmQ0dSaTLcWMG5SSi8bt0f2ZLurfsTPdkECR5/pzx43U1HxyrVkBvzS2J2slVf2cGqH5ThLA0pDnkfmqfN4oD9y8qX+5oYdupmPV/7sRwZd7yMfIFn/5039f8q5yhY9RGBSD5oR7K6YXuD0foCRpFKRLPpcUKd1aqJ68ZugL0qEnDaV2TE6hJms/nZ2kTr99SVPx/Kwhosr2WysU09duDhde9v/bzeDx+9s4A9VF/Zw1PCw1cbPAMRKRKZ1fQEEkpXaXDtMa7pcT5ewYo7gVlOUjnKMUnkXB/NWuILO34/DuhsXzEc0i4ug+fQNTc+zkaaxcaLYFBBcix5Ph5mxkEdH32Udfj9Rn03HbZ/paQYRbHXQxfy4YuZSEtNdpSvbzmJZui0Kh6epqRQkhEq/3rvdD1MWClIfq9ojxT0arn6Z2n1iw7SBGzjOBPrPi16BiMvLp+Kx3iRDFF+6l0ks5J8mSbZBxSGiQTjd2llJkKgESuayvaO3Gwdqv/+4dzx1ROH6+vtp6o0hhS5bT6zzZozHz+pNT50IWefNL5VDokac5JS9V4gNc9/ckl9ZPjoPOdoEkgrC/SFjXP2sXDDEzXvhTcNbZqr9QtA6jmTWUB1p7lg632uB3zfq6YqZxdmeqaDOguuNz/3Yf5VBv9kbsOaE++tuLBgmUWjQ3F1qWM7NdCV6WrtEMDTYXCNJERmI4gwp1MJvX4+DgY8mg0ahIuHUIfTbw/wTWZTIbN6FRLrg78ZS40WhoRFyGYgns9W4QrlVT1RLoC62hUALoOFY8rtVba1qq3utORMNVvvk1MRMIMRXNEwuU1/GcRhOaR7eK8M11mnVtEczqdhk34DJrqH8sRIh6/n+bHSZfEW/W0CMwnyPT0oatTXoNZnMbX0+9WxqMx9axNdsD503jQnngttZuCgYRLe/Hsg7beIj4nW5XjmHH5zgU9PMGFco1Li3jVDu2X5/ZLcoULJvZbHz4NqrcM9kL3hbSbm5sajUaDQYhEWVej+mUtzleoZVByOE956NyKtpp4kq4c39WVjIcqRW3RdamoqJ5bNTBXiVXP01i1qep8tVn9YXBh/1opl8aMKsdTfVc6viix2+3OXvGn5/5dVasvajdJxRf8RHIOjhHJgQtObDevoXMYsBTkPDVvtfFr12dfda5qgyJe2gjVHDMC3pdZGonGU3O1hTaksdIxTu58JaUIzoMKr+VK2lWjfidfoU8KJHzNObMOvt6SpKuHh1xttsoXVT8GPe0NVv24NVYaT/XLCZyLeL/rhbTValU3NzdndVwRpoycjk9HFWm0IjSjmTu3G5DX0zzCVp0/pEEFSRUkovY0VoZIktTWGipwT0F1XRIGHy7QNfkeCY2XXogi0EG5JYhKhu3nyryrmIeHh+EVknzvrO7pKRxLCT62TpKeotMZqESULVxSI606HVWWE2YrY+C1NP7sF+2J6t+3Rp1Op+F9Hcx+tPPFFRfTXgY83o9jyHn3jIH1XJZqaNeaV75shpmbrqcxYKpOgXIpK3BVT7EjwcM2aUzoAz5XLDHs9/thb7Ce7vSgxn5yXHkN7s/VW9t6oRvparD4og13PCollgZaNV2+KWixWFTVuQKlIdD5/SO4EhXo7HIeT8VdZVJh6lWJSodk3HJC1t9EoAKvK+PTU0B6DJXn6Xgaa1WdGabuJ2fQC9bVT91DK9F6kQ3f+8uAyS17rsyZanOcq+qM2GkjzHJaKpRj4mq3Vcag0tdYeEDgvAlcK/DMRmQkstDLs722LsUpFXw4HIYArD7yvRXsC3dLkKC4LiK7Z537kh3yRTZ6+5wyH2V0LM94UPX0nSU7Dwwc36r2omDLB9UXzq/OYSnAXw3L41niY7DxYCvi1ZOmrczrW6H7ljERAFUr05+q85T7cDicrUDLCWTAqumxZsdaYcspGeGVevBF3iR8/cv3lnpw8FSR7dO7ZvUWe6ajIl8qBxkRlb6uz2920DhWPb0PlQZL9cg0T3+fz+fDG6GoPvi6TX+rFdurdJFlGaaCnm3oZ86DFsFY52RpRyCZ6VzOt/7GFJ8lK91Lzss6JInEy0H7/X7IxBSo1HfOM4OViFb2rJ0jHF/Nr4KYL/qqH5xP/V22SlXMGiptRjaqjEVvg5MN6Zoibe5kUZ89SHq5hsRIctf9XTUzKPj1mI36/lrNo78bwstzDIqtchLnltvcfpflharz57arnm8daUXUqvNXupF0RQoiWh3rpQuqVZ1LY/boSeMTQbkRVZ0rDzq8pzgsL4gwqmpovx4akRpSLdLrT/7uVe4J5co5gwnrjiIt1qK5lUyEKdJW21krlUL2dwUzdXZC09xqzDT/rItyUVT94HW4DcsdSHPFOWMmouNZD1c7WiWIVnlGY0qCk1Jl/7m4Iztkf+QHTiq8PudH86k51LyoD1LU2tqohSnNs8idj+tqTr1UoTUW+qnarHv6ljCWvnQvloY0Lyw/SHD4dTQXLkKk7JVVsO+yF98aKeJtZbTkAs3xpdLVt0A30nWlVdV+AkVGpxfj0DBlDJpQTYIMrFXn0h5bRkWPgpdqkWqPvxDDi/VMD7204aTjv5fD+leX8Petr0+RE+rarOWx/kbVydKDBz5PvfwRT16DJKXj/QXgl8oAXsahEzBl9bnRzwoK/ncFAxI9x5cBkjsP1A+2k+mpAqp2uLRIRMdqDv11l7pmy+E9HW/VZjW/6hfrwSxj+HY1ki7JlCUD+ifHoWWjDKoaA46zb/Gi37cEluycfuOP9lPl6h4ULiovXl1dDR9fQOQ1SPQkedrMt0Y30h2Px0P9kFuERFxUAjI4Da6cjV+JQif21IDRVP8XmM7L6amyBN7bjcXT0JYxMU1S6tZSmnx6iUbRKnuQMNxwXLG10iupYz62KRJpqX6SLslO9UyBCxNUxSwXsR0kW7abWQ6VLceE53Hcda6TE6/Bj8iHaLWzqoanEvnVNb4ow7KHj59sQcqYr8X0p7LchmSDnGf2iVmQ5pNZH8dbc6J6LYMM7fFSiu727j7Dv3t/dA/2y2u3VU/+eSlzdLvnlkuRrtY5qNgp2tjn0+k0fOFlL3Qj3el0Wsvlcvg2Vm4b0aQodRMpVT2pM38yzB2EJQmP4F4r9pVpOQXJmmQgI2FNyv/uKs13S2gHgfrkW81aCxKuhKjWq2pYPOF53u/WYgxJUn+TKiIRkJxZI9S4MXhKgVBVOCkylfUSkuDn0InZN/1LJ2zVtB1+35bjs/0iaN/ayAVHZl5M0X38ZMe+k4X9vUR2bDtfh6oxoKqmeuTYcEsXlabs1Z/4U39pzx4c1W4d4z4jv+J1+XALA5KuxcVqzQODIOea2+P4xQOaD5bJNO7yefnecrk8m/9vja6kq6//1uB5qquFs6rzp3Y4cDJgrnq7AiIxyUmq6tnfWOvUZLN+1iJbQec44VKZ8CNDUD+phFuLDK4qdIyCUtXTFwVyLGm8Miwaq6sHLzW0lCfJx3clML3m7z2d9rFv9cnHmH3xMoDg5M7zfJ5I1lyEamUv3g9lZBQBLL24cvPx8/FuEUhLZTrhKFjTlnROy25cHCiDVN98/YJzxbF2GyfhOlG6yGEmyfnQ+STdlp14n2j/VLms52p+xRX+fW+j0Wiwu7u7uyEY9UB3pXt1dXWmBJjmSRHSIBWd+UQLV4yrnlItOo9PXNXzGhSVJA2ROwv4Da5ucC01TJKiQqXB8LyWytK/bI9WklWnddWrftDwuX9XxzF9I/HT8He7XV1fX9fV1dWzr2JX37R7gucRLcJVgGi1hePhRNjKLASqO28HnZ9KW3VH2iaDc+ta6pPI1xeUdD99XbvGT+pLBCc1zNKT+qn6JN+ZQPvUPXwB1b/a/VLZy22PmYeOd7slSXpgJEGqP/xWbX1YOtNc8XrqJ8F+eHs0Vq5y+Xi/rsmSmb4YVwpZ11gul7+v8gIjogyKpEnFOp/PzxYguHVGK5RcFKh6cjqllFIBregqyACUYnNFVG1VGscn2FyJ6VgnXNaQaMSX0KpZeeCR46q26OmlE4/KN/o/AwkfheZeUhm1lBwX7vQ7zqkcQj+35p1zQPXmzkqibZUeqB5bQYpjxnPcedk+kgp/T7LgfZgR+PHa86wvNb25uambm5uhxqj7nE6ns7FXqks713iwtEJRwDf1ae78yUmOhavrFpjJ+H5pJ14fY5GYvhfNx0m+5iUKL1+Q/NkWbvHjee6ftC/uHOKun6qnDJHtbq0/fCt03b3AQWFdUZjP50NU4raPqvPn9Ol4mhgdQ+cgUereJAlOKtWE7sXiPFMXJwYnYydRqjyv21E9yajYPrZ9PB4PNXB/+krEztqxzuV15ditcomO1zH+khIuwOi+bCv/xn46qbma91S4ZfgkDVe6l+yN48t7tUiT48V7yaaYSbEd+h0DGHcStIhK/fTxZBmEdsHFPx5H8uaKPzMh2gdJlCRDMmYpyevorOXSXkR83G9cVWdbvUSCEl28rmyU99euC5Ju1VNpjmUNXzwk4frXyQsKVK2M+Fuj6z5dpto0KBkeN/9LHUi5esStev7lcnRoFulJ3q7OWkqUKRvTRHckKlx9POX04zxoyOjp2Pq9/vUyAmtWrgomk6dvMdDf3GF0HRknVZ8/HcX702k1n75wxj7SQXRPBiM6m8bdFS7beintv6TE3NYYYDnWnA9XmFTJ7Dvv21LmHF9dw3cr6Bq6p2zhaxkTU3evA3ufW8GNgVXzQZL28Tsej2fzwnFmgF4sFs/2NFc9EW+r3Kbr8Z6uuKVU2W8GvVZJj0+C+v57+ajPbU90fbUjt224cTDd16PCIg4nMxo306/RaDS8jEWEW3X+rQs/lT4wzeOGa3/axRcWqs5fokJVwojK+iEVKg2xZeBsX2vsRLi6disNrHoiNpULdC5/z1V5prauEvl7tc0VCJWb5otqyg1eQUXzpKyIY0UFRudnSUrn6ng+gaR2k/RbpQ3aKm2MKpr9IolpoU1zoX5wJ4MCFB9IYabGYOHbnVrBR+c4qdCuaIO6p4sf1Xm9PRxrEq9KViRe+vultnLcdf3WfLFtrr45z/qZCpfv+GUZ0oUfs7ZvXWLo+mpH1maqnqeATAu0v7HqfOuQzmtFZP3enUHncPKdRFzZsV5GJeElBU9h6SS6tqeDMh6qLh5PFSNy8Pt41iADIumSuN3Zqs5VBRWDK0JXfU6gVc/fx6v2eymiFfxc5fs8kXA9K6EypWL0a3sG4u1oBWaRAMUCbcDtu6W2uD/ba7OcX80/CdOFiqtXJ0benwFDbfZtW1xM45qGzueCo5O3L6r54qDeHsa+u9+15oN/875xzHlvjptEg6/VtOZO1+a7OXqgG+lSsXAAmGa70VK1Mq1oqRLd49Kk8h4e5bgPVWCK7E7cIgVNnJ5zV5SV03lJQqAykoEz/eMC16V+VT29g5f7ELlgwRVu7z/rlUy/vN0kYDo0F4J8pwfRSqPZRo2HOx7RqkXrPCoytw2vYVLVsWau80W4fn3ZiRMPAw6DmttlS21q/p08vLTQghM1yZLn+c4R9ptky3ZqPPjK0ZZw4VwwwGvcdH0+UOIBodUvt/eW4OE9NBYkW5ZkNF9e7vL90t8aXUmX9aOq5xvVBSob1ps8tWgRr+5F+PWp5vj4atXzF54zZWfqRsdgNNXjuqonHQ5P27i0R1J9JOHKUNgGEpArRqkUGqY7psZFi4LX19eDM/i+Z3cebufyMgGN3MshOl4OqDZrDHUdjq0rFld2TI25eEVivlQrZNu1cMV5pCN6BkWnlv256mplEyTUFuFysU1zIVLj+zK8Ruokxb6ondyOSdKV3cgG2SfdS+fQR0+n09kLe1r2zyAmpcugyx1K6n+r7HDJhznWrUyJvMJsyIm0xRO9Cbeq80KapxiERy0ZHNNlV1gtwnbQKagYWUMW4VENcMM167DuDFKy4/GP+z+5WspyipSiaq98DLT11BcJ9HQ6f/qLUJta40Mi4cq6+qh7efbhRHJpOw4J0c9naUFqTm1iLZD9IvnTkVxJeltIAEzdnXhZW+XuD6phb7MrXfZb/dG5vtDqGZjawldq6hqaQ/adj33r/myfjtVYcLHQv5FXbZa96ToUN5fGRfbH7MgDgvqmNunRaZ/LyeTHN/ZxrlulHffrFvHSVxiA2DYGyUsq/XdLulQ++r+rCyoXr1G6k10i7ZYCJum6YnNnVS1X+y312LKnvFS5cho33BZBthRGq1Yp5aM26WcaC8eDZQwSFY1bv9N1fFFPzkMDbaVy7B9JlgbONJvz4sqVpEsy41i7wmHfnBTVB/aZ9WVmBewn1THHn87MLVEsK7BdtGWe7+pbC7PeZ1ee/hpD9o99YSbp7RZobwy27ku6DklcxzCr45yxX1U1vNmM894K8B5EW/7bElZUtfKPS5mSwPIer0le6oHupMv0RhMkw+C2FxKvzuegOplQcdAhvL7INJBpvytCKl218Wt1NR3D1E3tYIrO9Fht97SWxuF1UXdMV51SFyQm1rb8+lVPb2sjwTFVpfF6Ss+0k/Ooj8aFJHHJPjgWHpA9MHOBR39n+3mNr2VEJAwumKosxLl30vAAyHKRkyPHmyUNkgUDtwiXRD8ajc4etPDFLF6bOzP0+0vB63j8ce3ASzQUF5eIideh/2oH0nw+H77PkOsD/nG7cbGke+n6GituZ2O5wDM+cY1nJCw/9ULXt4ypjiVyaC0UMO2V0tW/+h1rmVVPdSpdl5vTScDH49MjmNriohdSawJ9hd8jdctoZKBqN+uummQuZFX9aDhSO9pSpvN0TRoqVS5JjsqDgYXHsG7FRSuqPzk6+8Gat9dXqQhbWQXHn3VtzbeTG1NatZ9j4jVtzgnHTMdyoah1Hzo6n+jSvZnye//4L5W2dixwbNUe2QAVIRU5bZsET2XqKlyE5NmFxlk+xjZQZTNAM9thmYsBpmX7LHvIR3VNllP0M/e8k/B0LktaPtetjJhlK44Z7YIlNRIt597FzbdE9/fpOvF4hNdgyeC32+0wASIHLQBxYOXkJF2vkelnKQaRLhXsbvfjFzEqgu73+2f7bP0ptfH4acuNFIoChatuKhVhOp2evYfCVRrTyFbdioansSYBqp96PFXjKRLiu3O144I7KvjYtYxYRk7H4iOp/iWEvljDwMl0WPdgv70W1woynuZzjDWuIipmNrIdEsDhcBi+QURvt1MbSco6R3apbyDQE5UiNq0LXF1d1XK5rNvb27q+vh7sRjXQxWJRj4+PzwhdfdD4yLZYP27Zi5dcaPNqk8ZW5SmSFp9EVH8UKCiUXJ2v1+v69OlTffnyZVhEYy2bXwzL7IEBVot3k8nkWblO88Lgp36yDs77trJN3Ys7lHoo3m7vXqg67yA7KqPTAhSdk0ZeVWcvSuaE8mEGphycoKrnj9fKsE6np3ed6r0D6/W67u7u6vb2tm5vb4cJvL6+ruVyeUZgapO+nkcLZkrdqs6DA1WHrsHI7mTLjID1Nhoq1YwMfLlc1t3dXb1+/bpevXo19ON0Op3tZlDA8McmdQ+NnwyXi0ByEn79j0ieXwOucZKC1kfO4qqNCp+1a6bBJB6v12u+9X+NdeuhF9mXghDJxUsYHHONiwhHNq4xWC6XNZ/P6/b2tl6/fl1/+MMf6u7urq6uroagzi1ZJJjD4fDs3bcMuq7cGDhoP/Q97pyQmqeg0e6bw+Ew2IlEh86Rf9E+d7tdrVarWq1W9fnz5/r8+fOwg6eqzuyewY0BXdfTnOhhC3+qjLt8uDNCvs71BhdJVMusz/dE1yfSRKQcVK8p6WtFRJRUrlV19pq20+l0Rrh6i5mMiSTqtUQq5sfHx1qtVmf7aufzeX348KFevXpVb9++He714sWLurq6Gl5oMp1Oz74O5dOnT/Xx48fhBR+3t7dVVQMpSSnqHvr4AqGIgmQkI6URKiOoOq+bz2azurm5qZcvX9bbt2/r3bt3w6s1FWT0rbVygO12W6vVqrbbbd3f39eXL18GhyCZsybs5Rr1h2Mkcqt6+q48fcOwgp2CSNXzr4VnzVztlF2xLMBFOX5EIFJsL168qLu7u+GFNAoKGtv1en1WclksFgPB+6q4HN1FgN7Rend3V8vlst68eVNv376tN2/e1O3t7WADh8NhGB/PCPjoaqvcoLESOXrpgupN48nSm8Zeouf+/r4+fvxYq9WqDofDYJcaM76PQz4kO7y/v6/vv/++/vnPf9bHjx9rvV4P8yRfV3DWN2xovgX3CRctFBskYXIDv+hgPp/Xcrkc3iSm7ELZCMeafvet1W73hTQ5p1QRU93dbjeseHJBSgZGKPIrkkmN3tzc1Gj09DiwJkCKjYtT/nNVnTnSeDwevsjveDyeqVzdS2pjvV5X1dPEa8vMbDY72y4jQpLRMx2ighNZqB1yOKaTrM1qHER0y+WyXr58Wa9fv643b94MKpe1RP6sYCbHIwEo+FGZs95MBck2aE78yy8fHx8Hpee7Aqqev1Bb4+qpM2ulTuws+VTVWWC+u7urN2/eDAFU7ZLK05iw3KESkMoBdFgKCjmt5vv6+vpsHhT4GCCktDUWrK1ThYlw3AZY0uG3O7PkQFXMQKv+adz8i0gVSPgeEu4YUClFP8tf/CulWj7HwKiAIWK+ubkZXhPJ9rrQEFjj1Y4iqfPr6+vBX2UXPF/974XRpdXk/49f7dk4KRzW3KrOH89lnY/tah3LFU2/5tD40+nZ8c86iPv5MV4f9EUUtslraGzfpcWXS+3y33k/vMbp9ahLCz88hvfhIgod4dJ48P/+d7//pXmpas+3Kw2WhS6Nl/f/a+Pj9d5LNtNqV6sPPMfbJJu5ZDeXxuPSuFwaH+9fa2xav7s0/+5bXjNmn91ufo4vXVKSX+tHa05a8+680Zrvlj99g6/suSiXu5FuEATBvxEukm7XJ9J+Lr6mZr72t1/jPpfwS+/Rozb0rdHKNoLLcAX2Xxn/Efv9lv70tXt8C274lojSDYIg+PVxke377pUIgiD4N0dINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiIkG4QBEFHhHSDIAg6IqQbBEHQESHdIAiCjgjpBkEQdMT0J/4+6tKKIAiCfxNE6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOuL/AS9u6RDcsyw1AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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KwuA5vNxjRnAJa/CUIARXZoQhL0J4+gNh4DkIHd3j8iIQoR1k5GTvxSWEiPujIFh/xowBQ0mx+Hi9eC79fj/lij2H6mGaRyrMHdfgfnlrWx5leDHMC5YeHrtx9TDVf7wgyDyxdozHo4dcBtzLZiyQqofEpJEIg5m3Xq+X5q7X66UuEZ8zojUiLsbD/XxcRDCQKBFVPufSuQuCdfWCKbLDfLouMF8e6XAPUiz8PSc6nCYiDy+0sYZeUHNnBhlxEkannStyhwxZ8Y6OfE29+Ig8kYYiWvW8uhvaslAa6bqCQLZOkoRV3vKDcEjncITv4w0RJrsQI9juqXjxI1ci/o2QIiA+hvV6naqeeDcQHjk4QlvCYrxVCEBSssKQrs8PSsl4yF0hzPv9PpGCzwnkBOmSo8LKe/HDDZp7dYSwXozpdDppzplPT48QwjE+1sDXOO+19rw59/K59iITz0F4D6m5grg3SWSA1+OGGYLJlSwnc1IbkJIbR7woDC9hNx46eUlvx+JZvIibFxo9ikKmkEsnPdJRfg1PVeAxn06n1IblROdzzzzxHZdXZBAZkJS8SMgfOX56eiqklZir3EP1SAa5yOUBufa58tQR67/f79VqtVLk7M4Uuuj3rtfr6na7Gg6HGo1GqQPnksddBkrfBuweBVYRQfVKvlti93Ygt06no263m3KZeY4w7yxwMkXQ8QY9t8QYfTHIp83n80Qu5HUlpcKaEzKkTG6apL/npj2dwRidoEAe+rrCoXTb7bbgHeapGATeBTv/wQvILb/nJzEE5LE9f+vpACd692Dy6ANCuJT3hkgYH/ehuMJ9aAejMOMG2RUQOck3FbjnjXcL0fmzQFykaVgT1h8icZniv06WHgm4EXRZJz+PrOZ/9/u4fPBZNyQeDbK2rCfrBwnTwtloNFKPMREMz0k4TmFvPp+n713SH2Saa7CGjMPH7brrPfkuK3zPjRl64HJHGojIbTAYpPY95q5swpVKzumiECwASgSB9vv91L5DiONk6gWuwWCgwWCQLCHeh1QkI+9YAITT7gmRD3WPjAIcXt1isSi0qZG7InTxcBXy4hl8bE6Kec6N72OUsOAYKIQO4cTaI9heREHgIFvCvjxP7rl1b09i/nwecjJ0zwJD43OQEy4k4VEHP17IgUC9Z9dz2+7hoKwUWVFa5s77Y319vYjjBHEpHPciGYp+yXtnwwdjkYqeG54X68l4kQfm1w2mPysG4dIz8W+Mp3/PDYB7uW7wPBrwjSi5N+j9yfP5XIvFIqUZ3ONmTJdSgtyPsQGXyTwiwNHB6F7y/FkD6hLtdlvdbjcVtd1wIjs+3jJQGukSlngYQahBT16n00ktNHiJTlBYStq4hsNhEnIm0r3TnHwRWM+xunC4EnnrDJ44nQuQn3vseTsQ94ewGZP/SCp4evye8B5LjaDlhQLIMC+YoSh4Z1wPDwNh9/yyC12eN2VOPKfHXFKwyfNjearGr+HzjsH70LphLL3ABuG44blE0J7vc0Po94fc3PjxOR+/RyB5MSovCOe5Zu7nBhXZRx8gafeKnfB8vi5t2PENAk7ovmb5nLoX7LUC5NJDcPcuGSNEi6M0m81SX693L1wydpecD3jC1w19xTnzrhh3iFgD5pFdoqPRSL1eT51OJ80H0TT/JX1VFkrdkfb09JTI0z1XQg7I63g8arlcJq8LRcZzYwHoDICAco/RlcKFmL85STkQVEgXT7bdbmu1WqUCG6QrncmDhXehcovOj+e/8jCYOSHMc6+RsWOhXWBc2SFbwiyiCYwUz8kceF8xhH2JcN2Lc68DMDZXmjyHCqH58zJmZMWVEe+NNfBUBoVKnx/3JPken8e7dQJ14vcquf/N4WkhyM49qm63mwgCZ8DXxx0NqvxERXiNzHeeAvHcJveXziRJestlivXw3vUPpTk8FeWpOE9p4WE66T09PaVUA6TrBS7mDflADlwvSf+gH3mEwzMxZ/nWcF+3ZrOZSBe5xwmiEwWnbj6f/7g2RwD6dCEbQlNXEC9aIZCe85I+friIh1IIsi8G38/zlX4dPovQk8tFSXxnkeePIDmu7wTrJEz4JJ0P2XDF8apqpVJJYZCHqh5q+mddaSiC9Pv91GuMkfJ2LfdWnaDIW/O8eNgeTXi133NtuUfryuWES37ezzHwtAPG2T3/brf7HumyBpAOxhlDw8YRNzaeBmEsnrqQirlIvs86enHo6uoqnb0xGAwKhRqcCyd25tmLcJ7OYq5I4ThRuYzlKaE8SvKCHnIE8UKi3N/TDF778IgATxfSIr2Ah4vByDc7edTkedRcF3MyvrROearDuYA5poWPH+/dxpHiHInD4aCnp6cfl6fLhO337w42kd4pK3kUFtcV+FIoyUIQxmC98Ag93KbIk7eseEIeBXOPVzr3PbpH7E3zXpEn3ZEXnriOd19IZ89SUkEZERIS/BggxuxVd/c2uV4+b8wLrUv0kHoln3nwnLEXnySl72AIIUHIgOv5DiWPKtzDgvBcgUh58H1Iwz0WxkrzPSTK55grnif/PEoH8UjnnGS+7i4X7slDRl5A43no48bADYfDlDuUVNhtCLyNzomYaxBJeNrEvVOfUz7r42+328nIsi6stedT3bt3/UMundycDL2P9+npKZGtb/3N0zKeDnF9ZiyuI+iu54i9I4ezGNhUwnXcI87vRaH16elJ0+m0sHuSXXcun58TpXm6m81Gj4+PaTEoPHnOj9+5F+GeLi1BfOd0Oqnf76tSqRQq697O5MSEokMunkOr1+vvdRV4IYhqL9fudDqFceItSMWjCLkH48XDR7g91MQjYzOCe+7c3wtaKD6erVeGScGQ/+MZ+K/nZXkG/3HCR3DpUWXXneeJ8T4Zp5OvP4fnSn3cjUYj3cvzi27c2OXnnq572J5HBB6RECbn+XdP+Xg4y/pA5lwbImBs/X6/YMi804F0GqSXp75A7qH7unK4jVfpkVmegzlstVqpKI1jgtH01IATrtclfL4YN6B9DWL0LfCeJ85z4lzf+2Qx3r5TFINChOnFOZ73dDppNpul0+EgT08FeaoJwuZYAd/izgaix8fHZJDLQKnpBU57osVHOhPU6XRKHkLecI9SQayuzPv9PjVi48Ew0d5eQwHC85soE6ESAuRhm5OQe2/S+ZwHz8+dTqcknB7WIUx4LR6i+zkS7iF7nyo5Zqw7RsW9PCx8fraA95ZK5w4KFMNJxp+T++MhTCaT1Jfphg9j5l6UFza8BYhOjtz7Y0wYTjYBONyDheBPp3dnQDBfHM5CgcxTPyBPASGfTjq+/swNsrHf7ws7tdrttkajkZbLZSI6T1kxDu4FaTFGnt93lLGeHnF5n6935fT7fdVq786egLQx4t4BA9lyv7yw5//2qIlIFfJ2A4YR4bsYF3c80DvfFs8cInPMLc+W6zIEudu9O8Pl9evXur+/Lxxu414qssS/OZgJwiUdcjy+O5ryR5nT3W63enx8TMTgiXwUgqZ2r5L6JPI7DycqlYp6vV6BSHKFzj1L3zGEhyIV94NLxc6CPBTzzgt+3MJ7QY3UBP2MKLgk9fv99zxrxuXFEISUggUC5XvOUVY8DBSV+fe5dg/HCxQ+dsK48XiczlPl0BHmLL/npeq057edeFhTxpS3IZECcA8VA4XhZix44kRDhN08M0Yc4+weLtdnzKx3nk/E+ErnohAGaTwep3NrvYjpqSh29/lGE+bCU2SMie/xOYwna0ohCIeCdAiGN09TMS+kB3BY/LwN5A+Hwo030WYuL65fnrJCR/G+0TvSiy6jzDfAUUFOGCOn4N3f36e0gBtKnhNPHINPHpd0iMvYeDz+8Xq63kTN4nohxAVdKnqaeYjM3nQ/VIVFy6uxhPQeSqO8EL17eO5le07YvQAvPvgYIXzGs1wuNR6P9fj4mJL3hOb7/V7tdrtw4DkEkueCIUCuw+EofM8rvZ4SwTvwsV86Ps+9F+/L5SBrziBmXT6UvkDIvfeU+WP+pfPxfH5GhB/dyDWIHjzs963Clzx01tu9MifdfLei5y+dsD3FxQ/PzRwxblod/XAk95LxxClCsUU83wCSt0vxbHjHtGcdj8e0O6tef7fjKi8Y8/9el/BzljF4tFfd3NxIOh+r6WkZ92Qv6UE+/3mk6gbBC8deIEN3PU2ATNIaSS4XQnUjydjcifO0oL8wwE+Tm81mhVTP50YphTT+S/jORBIi5Xk1r8CifF7VRTHwMlhQwnZINW/W92Kae7e+A8rPLfA0QrfbLRQcPP+EsuReBUT59u1bvXnzJh1VKSltsSVchrzIwTl5kUudTqfp7FqErVKppLy2K4HnDd3D9MZxL7JBDL6V1JUcj4IdgCiqv9XD9+37WroHlLcvSSp4PRAepOp5VRQSJeXfHo5DQu7huifHfd2zc6PJuqLkGEC8vl6vlwwcaY1arZaKvPP5XIPBQKfTu9Y1PzAJ8vAiLvLmB0D5nOGNYjhoz8LzPxzenZTV6/XStTB+yOJ2u01GAQeA4yGldyTLFnyKgq4z0uVNPXnNxXO0rkMUBb2rwUnVSZ10m0dw7ul7tFWpnI91xJMmheF9yl5PYG7gHwgY/chrAp8DpW4D9qKKkzFA2bCEXpnmuwiRCyHfk85VWe8scCvqiiupICzudfA3SUmZUTBXIJL9fpAGz0U45mfUogwIONVU8rl+Vi8ExsHVXINze/FWJaXnJc2BJ4nA8Txu0Pjxfk7PcbpHhpJj0Mgb4mXj7bGTzOcbQ+KhPMZgt9slUsUgkoKCgMhVurH29I2njYgU3MB6q54Tkxtg98AwUvlpeIyr2+0mI8ezHg7nA2kgBebA59fTN3ye//ccO7LPXEFAXtXHuLZaLQ0Gg3TuMzrBGDabTQqtyclTGzidTum56UN3nfT8PkVqCqXMn+fu0SMnMe/EcC/eU1M4QTgFEDDzSi2HlIo7V7xo4NmzZxqNRoWuDQzaJS66lIosA6UeeOPtODk5sThYWgTKW4S4DhOPNympcMAIyXsXZH7n54LWarVC4cXzjiyEe5yeqM9J18nLc4l8Fi+BkNnzePkZn9L58BEIlx9vnvdc7qXqsOdX8+fKwzr3jNll5JVe5txTKhAcqSPeHgFBuyFyj8jngjXy1AdeJUpNOoP1oFrtbUf83d8a4kSQR0zMn3vUPj68Y7xYQLogT3mtVitVKu9Os/KD7/1ZgM+7e2GuA3mVPy9UekSGQeaeGChPxWE8MSKeL4Wgkb2PpYKcdN1xcj1Bjjwf72km5Bxd9PY97y4i6iAFhXPknR7tdju9k+729lbD4bDQl81h77mz58+IDPD7z43PTrruZXpFlYmAeFBA7zKgMuw5MTxFyJbfE8557i9vwSIMwUvDa86b4sElzxw4eXirkhOF57Dcg3fPKld2LypBxl6Ec08ceIhMesE9SUgmrwhTwEAhEFKKZ4+Pj3p4eEivooEc80IJ4SupCEJevBHmy+czV0Q+i3HudDoprPbcpneGQLoosufD8dRZG54dOYPg+LyHoX7aFrLH/SEMWudIvdCpgoFgZ5qnEphjn383PC67vo4uJy5/3rrlZxuzOcOJ3VMCLo9e5COs93pFnuu+lKvn7x8qYrOGvhkG3SeNQyoQg5PXeFzP8qI4Xu7NzY0Gg0GKrPwNEs4xTtqVSiVtGioLpXm6zWZTo9FIg8GgoIwIDYpG2EKI4WEC4TYCBNl6lwOKi/CjiBRB6F/kRDBvpHYv3IlFev/kJH4nFbeLegeDh1C5V+XnTXjhJc9DeQ7KBd1DdxcY99o8BId4UfbNZlPYNeVERZfEdDpN4SjtNfV6vbCVlIKgp2fca8t7PXMvCmPnyslcsX6eKvAIie97ISfvPrl0ff+3ExlKSfju6QvCebxlNgd4B8Fut0vbT6mSo9Cel/UNPKyLF5p8O3a+5h5peNrIW63yYpynnpA536qbF4Nddl2efO4+pBP5OjvhuhfLuL3oSYE9L8553UQ6p2woQnP4FbsBiU64ptcaiA7It/O+QRyiMlAa6bZaLX355ZcaDoepWOMFCyrxfoCLkxiJcto8UGoPZ6RzGsNbdSSl8BtPF7LIC2wIifQ+mebeMOSeF2Kky6/d8VYi7+9l8SFBlNjHheJgnbHUnmP2Qkw+Vg+fICYvbPkz+Rx4CsTJhe/wji7ujSIReVAhd+VnLiQVvC73sDxacG+XueY5+L2H6Hn/Mp5PbgQw6u6N5zvA8qLkfr9PRU1eGYMxwgv2lBZwL98LdC5DdEb4e/88RcSYW61WwehhsJkXny/k9nA4FOSNIrVvsvBc+yWyz2U814O8XsIc5AUzrseckx4j9+rymxeCIVzqEhTPcKRw0vgehEsk5qTLdXDyykKppHt3d6d2u/3eWwfI6XQ6nfSqGc8vuUB2u92UU8JzcyFOD2ZCyPcRNhSfxDyeW16A8cKe5+W8qHepVcWJKn97LUrD8zAeb31yYaVwg7HC65KUhMxTL96O52EpaQE3El4YY2x0LYxGI41GIz08PCTi8zDVw7WcXLxVyQ2oryfK5ikRxuKFFFfmnHQ9ovDqtJMgnyHH7+Gxe1UY99xYukx5RJC3nkEGnU4nzR2eFx4718rnnmfwLdGkvjxFJCmRDXPuRTRvO3QvE0fEvV30z42Byyyy9KG55zt5dIKcuTecF+LYmIQeMnfeVcJ3PNJzo0mq0Ld6e+snEQWEyw/OgG8iQR/LQmk53UajkY5iJAfGxACOdiTpjyXy4gJeoR/phreBZcRqEablBEdOmFd/5y9whDy9sEMKgLCdZ0MYEW7PH0K4jA2FQFBQMAjJyYpxcJgK5EaF2XuPyUP6Tj5XFsbplX7IBpLjuUhvrFYrTafT9EZY0jqkctz78+4SX/M8v83a83k3DhiIvA8XODnw7IyBv2HYkBcvTDl5+bZTxsY93EPNjawTMmCOIYHr62vd3t7q2bNnGg6HhXelsb70hmMIfF08JSadt59DwNVqNekAIXK/39ezZ880GAwKBO9pN2QC0qbXGHm7lPrwdjU3gHkE5TqCvHvxkYh2NpsVWrqI1Nwj99SOt595BIdueq8442R+LxEuRXPpfGYG+W9k4FKq5FOj1JYxJpdQxquRkgokSCFFOoe83k6CkOTHG5LrcoX2A0AgHzxcSD73dN0a4kkgLJcIgufyqjAESB+lpMLe+H6/n54RC+9khkAPBoP0PN1ut3DQB8/jOVXasNzLkYrtdDm55MLMGOnFpU2N7gvImfQB3gjX8vwdxI736KSVRwu+vj4XXinPPes8x+hRhd+HdfHPutJLStV05NNl1dMDPCPEhIwNBoNUN/AT6Rhrfm+8cc/7Suf+cW9nxNhwXT9kh5PkvHOBsfNf1rff7ye59E0veNhetPIUjqeB8jCfSMy7fLz1kqiSPmaKZ8yBF8ulYhTqjkStdt7s5ONhfUkpcBiPH8jDq69wVFgD98zLQKkvpkRB3TPxkI0CzmQySaRLqJh3CKCMuYeHwhJ24w2Q85FU2MdNqsIr2h7Wj0Yj3dzc6Pr6OhFvXgTz7ZY8KxVSine9Xi8pSn5EpLe+8X3PAxOO5QUG7/7wXWScCObCKJ29X6m4e8y9dw+p80o31+MHBeN6XgT1dA4tf+6pelrCc5De2eB5Z54z9zS9oMjc5F0JHn1AYMiG5/4Zo+esubcXYfJo4lKniq8jn/c5d6V3okUO875YT2Ug0xwn6R41BO8dEQ4n7OFwmFJ9dIH4NfK8rnvJ3s/LPfK1QU+9GOYtezgVfNbbFdFNJ13m2e/j0SXP7YVgisA+595lgUHNC+efE6Ue7UjPnPf48RmUignzVjFCRc/fIqC+uwTklU7vzUUQUSavdktnAqAdZTQa6fr6Op1AD5mQwMc4OKl4MQXCIZThGu4pIoh5n2ReoPAOA5T1Evn6hhFPM/h/Cfmkc7iLknhl3dfKizROZv53r5R7KOnrx7qx9i4H3jbn3qm3u3kRCo8TD9nJUyp6jJ6v9HsiJ17c4fd5XtUNK/PgpJmvA8bX8+5ePIZEPB/NNfyNvJ5GgQQ9L0m47PPoz4FhIvLwNJK/1t07WtyrZ6yc9EYU4PMGgfm2Zh+D65und1gv3izjJO2y4rWE3BjjxbKtl/5yJ1yPipxwfTNLHkl9DpT65ojFYpGEDOQKTO6HDRLSeS+4H1bi3rL/f17tzLsLPC/HgjqRe9XbOwz87REIHt4FQuMpE38ttTfMEwLi3SL4TgY+Xs9t+u/znK2H436ACF6n526Zc/eq/Pf+4slccaRz36f/zsNNr8L7/Hgo7yQCoaLgUnH7OM/l1XW/L9fwuZBUIEMPmT289yo/nh6KzBhyo+jXdnAfCN7fbp178Bhk5tIPaIG4fDfch8bsEZDfx7stpOKmDvKg1DbofqDQ5AU1T3ngjHS73fe8w/1+XzAAOAbIDz+X2try53CnyD32S3qPvDP/m80mnYZ36dVBXicgLUfqrCyU+o40zqH1QpHnExE+9vwTtu/3+5SrdRLwUNfhYZF7jSgnXgT5LD/XwT1Mcox5NZjfe74ubydiN9d+vy+0SZHn9Nwm3o8fKOPJfTdMPBeKJ53brjwsdBLCQ3FvyCv5+TZJlMK9SW8HyiME/3veZM98sYbMBdGLr48ro3tZ/hkP34ETN8rIWN0w4Q1zXzw/73IhR3t1dVUwzMyrE4WToN/Hc9FevOF7Xshk/SkK+9ZtvxdGjbRInpZgTdyzZ335jHep+A43xnhJDnkexurGNa8NeN4373Lwa+NkYFQ8LeT6nIf8ub67kWO+0ev8xabIqfMN802RuCyUntP1lp08tHTLRT4LrwjvkO863NOTij2pHk444dK3B/GSY0RhIQiIw0NMbydyxVyv15pOp3r79q3u7+/TO+EoquRj9zQJz0uBga2f3keKYnke0b2eS8UxPHbaayAs92b8efJCVF4shGh5bkkFhXQPzHPIXuWH+HweIArW3+cq3zTgrUGez/aDVtw4Ohm4p+OFtHycHlHkxgxAFpeKTE44blwgdHKrrAXFSLY45+voxSsfs0c30nnHm/cb87l2u13w1p0QOSNksViksXi0we5OJ3fXCeTS+4ZxFlhH7xhihyURrTtKIF+rS84VZI7XTl9uvuXZ5c3XkpTFj5J0gYcNHipIZ2/Rc3CeZyNnk+cRXUBz7za3mlg2CDfv0UVwvCrsFWEOt0HJ8a7IWY/HYz08POjh4UHz+Tzdm+IDRbF8Lryox0lUfmqWFxPdw+L/ESLPl7nniYft3rMXINyzdc8gT88w7rwYRiTC9y4ZAv7m1XCEnuIWnp4X5ri+t1VBam40MOikMtw79tYiyNoJCPnJvXrmxlMbuXH37gvv12YenXS9Txs5qFbftU/mrXKXQuxLXjWk58Uqj94Yn6feeHZPhRCSS2dDwPPgkbqRw2gjB61WS/1+P+VJIXAnPe7l29p9Y4I/Y+7ZfsjLZR48b+/nbeTdL3wXmcoJ+XOj1ANvXCD5HT/uOXoogmB6fs2J10mWMMiF0lMLHobm1zkezxsHOp2Orq+vdXd3p+vr69RF4V4pPxTnIF1a3mhFg8Tdy0fg3ejkHotbaBq3LxkaPkf+zQ+Jz+fYUxJ4JtI5F0YRi/93YnLy9CIH1/LoJfcWmV9PGbh36LuINptN4V6uLP48kMjHPufetR80j5H1EJ71z+XIic+fGSPseXLWA/ll3rg2c5970BiXfF29bcrfmJLnrp1w83y8pFTI5LN5agvnASJlTijU+cl1HiF51xC5fIrE6/U6tWzhITNnuf55MZK59c6QS6lE9+idG/z9iKwz+pU7YqxPzkufG6WRbqPR0GAwSG1b0pl0XLnzkB2S8kq7kyXeXHqg/+9JeMuUdG4t4d95NV96f1fRzc2NhsNh8rDxxnIldG/NPWAKJm4EAIuf58GcCJw4IC83Ip4DdG/dK9V54SKvztKt4M3jFPnoB/ZQPzcUXCNPfaCEKGvuKbpRYM3wenIv1J8fA+ck7kU6xnSpjcv7dhmTp5O82IQnlHu7XP9SUQfD+/T0JOnc9yqpMBc8t8s76+SepLc+sQ45EeVe74dSTD4HLkO598yzIsOQK4bf5Z5nwbBB7pVKJb3HrNlsJr1xffuYPng9xYurRAHu5fqmpku84aky1wsvjGMUy0JpO9Kazabu7u50dXWVwvpWq1Wwmm7x8g4D6f28sHTOq1ElR9H8hCeUgs/7dkh/gaXn8VwI/BrH4/G9sDkXgvyQbC8ueRHEd9VAniiUh6H0TnrRi7nJPdRarZbSCbwZw/Pokt5TMgqAs9ksER5hIMc6usfrxoGxeucCmwT8XWaubL5uzKHPk6c8pOKZtBCUGx43vJeIgO86GbmSNZvNwpkHXBMShbCQRScHJ+XVaqXJZKLT6aTFYpGIlg0JvHDSi3yeI/WuBX/FE6E48uEdQKw342ZcfrYIa8KOTI9A+KwXxvCcPU3BGjJ/Hs0w/+RmXXf4DM+NvPsbqtEhzwt3u12dTqfkOLhDwXxjoLifk63LK9yQk62fxeL9wp8bpdF7u93W119/rXq9nopXbOX1F0N6foq+PensYXmvZrVaTW8uQME8r4blJWRFoFjUwWCQ8kp40pvNRuPxWH/4wx90Or176SE7xxCufBsvyu9bZ2m/kZSU2LsSfDw8C96eKwOEezweC33OuWeN8jebzffe0OoVZwpLCHuv19NsNkthN72N/HAtyIbCHM8jqdC/ORwOU5TQ6/WSt+Jek6eGWGtI0SMVz2O6wuXtPcwnXSF5isOvwbxyELkbN1IFeNykWYhaUGzu6bvnyC3joVK45NyMWq2W5gglZy0Ph0Oh4g/hoR/80IHQaDQKRIQTwfU8dK9UKul5facc88t9kTGiHlIC7Ijs9/tpTtvtdroH8k3HER7uy5cvC29KYX3YlME5EUS6zDXptFqtptFolLxq3wTFnG2324LxzvPM6Hqj0UgbpPzAH9rf9vv9j+vsBdDpdPSzn/1MlUoltXMgWPP5PL1C5OnpKVl6DyMRet9qyG6vm5sbPXv2LAnGbrcrvMWAk8nIHVar1bRnnc954p3vPz4+6vnz53rx4kXK77ITiANNyNfu93tNp1N9++23evXqlebzefLy+v2+drtdIgEnUowEz0IBwyvVFIwgGydc93QpdCBw5KeHw+F75wDwd5QLZXt4eNBkMkmFQIyAh2YYA4wFhohzB+7u7nR7e5u8O56Bcfva+44oNwZJQM378hSF54Y9esg7DLybAI/Wz+HgWXxzyHK5TIbFC3NEDnm0A1ktFot0n16vp2fPniVCvL291RdffKGbm5tUsee7PB+ySrSDZ+0bAk6nd5uFnPS4n3cYeJcNERVk4xsgvHNisVikN+YeDgf1ej1Vq1UNBgM9e/asYGgc7CJ9+/atXr16pdevX2s8HhdO9fIIFK+b+3qL2+FwSOTILs7j8d270R4eHvT4+JjeaeYFWHQEBw4nodlspvFfX18XPH7k4XQ6pSikDJSWXri6utKLFy90PB4Lb+ncbreaTqeqVCqpSZlJrVaryWvgVSwe4rXbbd3d3emXv/ylvv76a41GI51OJ81mM33zzTd6+fJl4c25hOqe3/UwCo9GUnrD68PDQ3onWafT0d3dXSJdxoTCvH37Vr///e/18uVLrVar5NU3m019+eWXKUTzXlMPyzEmHppJ53eIYZDI9XGeK2ElXhqkQEGQw1B8i6ekAnFgaF6/fq3FYpHetooX7edXkKvEW6dqfXNzk7ZM39zcaDQaFbanQpwQ1Hg81v39faEDxNNAzA1EwvoQkuepDQyZp1S8U4a6wu3tra6vrwsngHEPPFWUnbGRDiINBgFSWPIUQLP57uxo2sJub2/11Vdf6e7uLhGJF25YB6Kl8XicCNlTVqwzz+1GmvXBe8RR8Tz7pf5g8syz2UzffvutvvnmG43HY+33716aut/vdX19ra+//jqRpu+oJBL64x//qN/+9rf65ptvNJ1O3zszAbL24qtvc3YdHQwG+uqrr/Q3f/M36ZyI6XSq//zP/9Rvf/vbwksld7tdSq3QBkoxsF6vp3W4ubnRixcvNBwOC3l+ioQ/yvRCs9nUzc2NDodDOssTBSHZ/vDwUPC+EDqKK9K5dQzv6ssvv9QvfvEL/exnP1O/39fxeEz9sVhffyWPpEQCXmDIc4IQMykDvJj8ABzCwUqlknodvQ2N3BzEibeIZ4Vnxk4oL24wBlrJ/Dt5UZBnoTXM9+bz2pgPFS/wyiaTSXpFtntZhKKkbNhkgQFB4dkuPRqNknfNDjz37DAcpDTwTjylgHfmG2RWq1UaNwTknQkoH6kMPEHmpt1uazQapVPAOGEqz2MyDpwCD+NxGPLuAu9yQCZqtZr6/b7u7u4K6RbPL/s6DAYDLZfLlA93LxyizAtwzBXhszsVXjj1AhXP67lw1gQy49oYdvQGz5A1oTthuVym/D9/5zm5j3vKGA9OA2OtkKUXL17opz/9aTrsiRQYEajrmPcLe5EdGe10Orq5udHd3Z1Go1EhVYheeNva50YF5f0APlkDmwsB/5//DaLNG5XzNg/glX334Px6XnH2a3nVOa/G+2f5r98nbz3JOxq4Fn/3goK3F+Xz833mz+fIx8x48gKgd2d81/W9eJmvQ379S3PkxuJSe05+Py9O5mvg183nKF/P7/qcj5v5/75jy2Ukv/+lbg6p+OJFL85+F3yN3aO/tNZeEM2J/BIu/d1bIb0AhfyS9/YUTj5H3vt7aY58zLls5PPmuubPlN8r5wgfk+Njc+Sy833m70/EBy9WGukGAoHAXxE+SLql70j7LnyXx+f4vpYp957+nHt93/t96JqfOlf0ue/zsbkpI+/13wVlzNN/B5n6r+qtR4bfB5+DJ8pCeLqBQCDw6fFBpi9v71sgEAgEgnQDgUCgTATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAi6t/x90opowgEAoG/EoSnGwgEAiUiSDcQCARKRJBuIBAIlIgg3UAgECgRQbqBQCBQIoJ0A4FAoET8P1pdBj4e4qB1AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", + "\n", + "# insert dummy parameter bounds and variable\n", + "x = np.insert(x,0,-1) # our initial guess for the worst error\n", + "lb = np.insert(lb,0,-np.inf)\n", + "ub = np.insert(ub,0,0)\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "ftol = 1e-5\n", + "for iters in range(num_betas):\n", + " solver = nlopt.opt(algorithm, n+1)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_min_objective(f)\n", + " solver.set_maxeval(update_factor)\n", + " solver.set_ftol_rel(ftol)\n", + " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lb = -np.min(evaluation_history,axis=1)\n", + "ub = -np.max(evaluation_history,axis=1)\n", + "mean = -np.mean(evaluation_history,axis=1)\n", + "\n", + "num_iters = lb.size\n", + "\n", + "plt.figure()\n", + "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", + "plt.plot(mean,'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot our results and see the resulting geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the performance of our final structure, we plot $|𝐸_𝑧|_2 $ at those three frequencies again. The enhancement is universal across all frequencies now." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "frequencies = opt.frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2\n", + "\n", + "plt.figure()\n", + "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use a CW source at individual frequencies and plot the fields." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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pzIlbk7ZuA6oX/+LBvTKl7Jm3DDvwqtPtmcgan61us5YUNd+NgMBaB9ZgwNblT75+D9/6YwIFb36mD017YUBBILsHOODiE/fS7ID344Uv+mrQ8VEfAmLQG6mU/8JCAM51zCEUpffC9+OFX/ObwZdmeVaO682k08GQq0fPev4/a5R2V4EOzrfqAEdyH/zE7xf3QQ8K0v0z1V37EBSsSypGXPyNn8W77n83PvT89+GFz/9ysIlZ4J5RormHXB/JeqAwIeUhvPqpnxEo++1/Ai/8+i8Hjo9EJWUIIMD5yrqQYcF5IFkStN178qKHU7u6+IQEwn7g7aKUmBmcqjQm64Io+T70An3Fp7Dw2oMLfOuPvog//fUv4TkTs5DdDQkeYgcQqnrSe05wwBD34MXrHyvugzc9t3hGwPhZMlGz7xIS7iX3ZQlk1L5lCQc92epX2ynLtt/JpRqNbczpuoBAbj8UDGXddmOhoOc+OAUSbgwYtNKrhDUf0TLQ8BoyAAJgO+NgL6o+/7ZySb3i/U9sz5OvS3QCFKjP8Ee/RaK7N8yDxFwUSifym+22NCKz9fOWZ9OUrGsCQa9ztFDwFvNSqtLX4vTcBG4AAtktEIOU/0e/RQIBv/C3AseHoBhK/Wo0dmshSMqfQyiKMcb6M4CLv/FxvOsn/2186Gv/Pbzweb8JuCqWguUDMA/e+fRXf3Ppj6u+X/yvfwnvuvhOfOi3/3G88EVfnfzjBdzqEbTWHJptnesnufj5n8a7/pt/Q3z+X/I1pYn34h+a4Ek2cReY0rNiLrmOOEr5FQq+4Ctk/1R3VU6ENWtEU37Odedw8Td/Fu+6/7340Avfj7f+2i8DLj8N8j5Dg9ZVjlMgX2CBnAEGApHPVgVyHkwOF6/fx3s+/B58sIJiQkxNhxIVMJaQYN/zXtuPXNr/n1b3Wa6HEpi4BxAq6wH59GPEqw8+Vgcaal2b0fKaJcj+VkECOYBZZpd89Jvz+Tk/y31wsNW/akzQB97+Cp4zlkq30hePICHfk/lf0nyb5FqP6ZZu77GFgutaClRuDBiMTESVaS39tSxHqH0yxVLQNwVV0jXzLFRvnvLycjsSbnfE/jnP2vfaRp1Jd7VU5oJdv/QGFOhLP5BqhLkAAGMlMApGoeCld76C5565U/pz1EDQcxesWlqS/OTr9/BtqVN87pk7QxjoWQUKKDAQgxnxq1sgyEjpx75VzMu//iuBq4c1BERJ5WsBICv/GMFqQdD59bkCEhT8rb+Mb/z4H8IPf9UfxJ3P+03gq8v6Bl2j7FWhZceyGQm7ZNo2Su3VT/0M3vXf/t/wof/Df4AXvuRrU502I+Ghmd1cZ5Bz4OKTH8M3/vi344e//gdx502pU8eynQEo0ASAK6hKlhXdlyPgJoCjxBTc+25xH3zBV5a6da7AgV5nUMdrkuv/n/938MKv/KfBn/4lwKX6c16uk+qWmrpFUvwVLDifrAgTQIR7n/xp3P2xbxHz+5d8DTjOgPPwRGCSmoopSEHjARhcQYLcYh8SLBR/bRNoqIGJFhAAiUWwgNDGIBRIJvH5f+Q9eOUdL+GOjuT1GRs4yNNEN2IOspUsAcLFJ39S3JcayAhpJ2V2Uw0HAIaA0Otrtf98/9vUUlP2sXWix2srb/NEjEDhnomZyu6PVnfs0i/9C2y5D4au6JXHcGPAYOEISmLThtpd9atW6jJPQSMDc3h7wVbf65SXrPS43rEaLzUPqudXVIWmP2nykw+sJM/pigGAJwEFu4Eg/05glOQkf+YdEh2tnd8ICLbcBVYcSUyBQsFbnn0hF2sEAz53epxH+xTnBAU66i/bLj75U/jG//pfwYd+x/vw1s//Z4GjQAExg+fjEgDCXCslhQSgO7K9+Dv/A77pv/s+fPArvwd3Pu83go+XWfFnEIgoyt8V5WMVVoEB+cxJwV986uN410/8m/jQv/gfy/NVxW9M4ezsdyBbfZrnmYtunsG9B/dw98e+Fa+884N4ixmJ5T2bdsiNO4ViRC8eQ2Hq4ud/snZPcITjWACMDCDEIPWi9dyxzLTPINf/V3w37vzKfxrx4S+XetZnQA5wLrnJHdhPS2CYDslKIM+H3SRK9VMfTzEL/ynufNFXiaWJLHRM8ETw+syAZE2jTUjwJIGM39Zxn7XplNuZC5qoSwFBYxBaQJDofVV6z6c69/I8kZoFR2R1egIgACYjpgYypnPo7CZGaofdqP6xaDu0yc3ylM02roZLvws0sGBBwRyj9aqzG+o8CK3tGnXZT7iPFgqqe+xV705Tws0BAysdSGgBgQH8yY75haoD12pxDAMquqDKn9FAlgYGbPvbk/CEmv1ee/1+1ajN69cp5Yg3x2LT1N555i37fNiNC6FnIVDrZF4Q5u0JmnAaEADjwCsgjZR+9EX84De8jLc8e6cLA87Z0Y8qo2TWj6Eophhke44PCLj45E/KlLvf9sfw1l/zzwCXnwaHeQkBFgDS8awKSBVhJ6ju4u/+Fdz9uT+OV778D4ilIMwSxKYKTuvZJeXvPOAn+e4n+W2aoCPXqKPWBA8Xv/AX8a4f/3344a//Adx55g6i8ZGz/jXPKyalIDq6PA95bjrKLw9EAz3/9Ne/hK/4wrfgl66SVaCy1pTkREST3M5iVFqeC+tzYcbF6/fwjT/++/Ch3/Wf4YUv/mq5dgxgDiCf9gMD8yz1n54NYN7uaJ5B82wu/s7/gLt/+Y/gld/8b+LOr/in6pgOAPC+uFyA5BZIFgEDDGhggfwEIodX/9Z/l6HmhS/4CrE0ZSCQf4SjgJnzEp/gPJyBBAYQYx8SXntwL8cUtDE1+t5U1gP9MQUkrgECuLgvqyl96l4ggJmWgJCgQa63bq2p0mTnjInbYnvuXi/eg4K3PPN81XYXfd0KLPRyRKglVwPBn392OftJyre3V17qmxEUXCtGrZEbAwYVrdkfbAtBeRFaKLC713DQl1Hd69+cO73JvT+CgT3Pzt6KZuyzpLtPqgIM96pJ/c3LHSrC7cCAbjfWAb06o9TPB96epkw1ikaLtwUEo/UGfvKT9/F7fvRF/NDbXsbzz9zZtAwsYEBdAQoDMSRLQIop+IW/IJ3689+HF37VbwQ//AeilGI0QGAUDUcghPLM7chUR6smGPDe3//ruPtzfwyv/Obfjxd+zZel6jWmaB2NTlMBAj+Z0akoEzFXp9Gr+f7qz/8kvum/+pfxyjtexvP/xFuRVChCUv4xcPrOiMwJCjiNLuW5FJArz0ef3V/4+fv4jj//It73O1/Cl/36t+DvX5U3tA7qNBac9IwIgCNKgEDpWTk45+Gc3Or9B/dw98d+r5T/TV+LaJ4bxVnqXL87se646SBWHD8BYQYF2c7zLNbu9FzIebz6t/5ygYJf/c9k6Kie2bF+ZhqTQERg71FiCMwzS+B28bf/B7zrp/5tfOj578Nbf+2XgY4PyzMjJwrWAAIy1NWQAHJwbmlJ+NgnZPaBnd1g36Wey+EUQND+5wMpD0JgC3MKfh4gzoCgo/weJMj1Shup+p8tKJBC6hfsGQLZNNNveaZkHBz1iN3tVf9ZQ8L9T5g8Ec+U+kFzRGcMu1p+3f99e6CgU+i4/nOWGwMGVnLgiN1o2sv7/uL78D3/bQ0FtjmNmtYaiOWOkc3sABPIMgqUa8u8JjnQMM9DfnkXFJxsKfjkx8SnN3opO26DJQzIlRUEdMQJlJHG+9/2Cp57U0nzulVPLQl3UwsT4WOflE5R6ueFhXXA6ZmjKBFxDaRlfa1lQGMDsishgucjXv2FvyCBaF/77+HOr/ynwA9/OY1GDQjMx1qZGLN1/p5vJNVXEHPovb//13D3r/6JAgVJwYAINB0KEPgJ0O9+EuuAmxBbGPAHgDw4fb745Mdw90d/D1565wfxNW+6g4czI4ARIycwEBAIzIgxrUDAwBwZzIw5MkJSGgFiPZDv8oB+5m+8hj/46rfgD731B/Gln/fV+Bu/XGIivHlOngjkCD41G0+EyREo/QXJ/s7JbwoKP/1JsUS8/20v42uefQGzU9dQeqbhmJ8nhWOGBLEkHOR5zrM8M44gfwSCbINzePVv/izu/vd/GK98+XfihV/1G9af49FMd3C5ZWVrAQMlpmA6CHT83Z/DN/2l75OYkV/5T4EffTpZFOQ5ugbsyMQmFFAolh+4CeQ8XFLgrxn3op3dkF0NzbvlqFiD7LvVA4QAyPulaZrf9LzMbtATUjleIUEBQU41gAQgg0IXCjoBrVsysvnamILnn73T72t2nN/qlxKrAdx/cD/X/3Nm7Yyslzq3Ysu6pX80Jq4NNLxuAHtPbiQYrMl/9PECBd/euA/az2vCzT6t0tNGsaro7PkGF7TvQ4SYxxUK3vLMGAq2ImbzyZllhJJ8dBcP7puMZT0oMCOkFRgAGiDgYl6Tl1LmUefA8oF1YG36lVxNiyCjzZ9MaWTf/7ZXcOdL71RmaQegjRFYtQ4oLCQlwmGW2QEf+4PSqf+q3yDmZWZwOBaLQB6t1gFuzMvXkyj5v5MFoIKCz//nQP4wBALyk1gH3JRGj+pKMDDgJ7ESuAkRwKv/yz28+BGZx/6VX/gWfPoqYmYuEJAAYOaIOTBmBuYg+3BkzBGIYMwhWRIgx6lF4a/8rZ/EH/3pb8O/+TX/Gb74H/8q/M1/UAdKquMgWwScbHNEmDzBgTA5gBxhIsLkHSYCJk+YyOFnP/Uaft9/9SL+o//jS/jyVH7nAJ8AY3IT/GGSZx1nUEyWAQsJYQa8PF+Ks9RRmEH+iItPfbwobXXfcASCySWg7of2uYbyfImcUJNzYkEi+Xvx9/96cQ9p+wlzfrbsJ/DxSqw/fpLnnaAO0QsgOvlOUa0IIUPE/dfv48UPv6fkAWEgcprFsAIIOZUz13/t+8bM+MnkHvrBbxBLREDjZtCDUmc6siLI+RpIAHDx+n0TaHhC/7MhCj929sGo/2zf0lHfbBdM09DH15r+mRnG2lKff62PHumfERQ8abnRYND63BUK/t//wvfjX/mtp+UpGFkLbDChzoMtpL5UdntgAM0+2u41d/oPDaBgf8hK5zrkcO8TF/gmnXLULOhRZ4yrXQeqnnvWAQUCZuD+g4u8YNHXmlXi9kw3VLHrDUhRiovgY+ml/MDbJfq3shAMgEAtBmtAgPTv1U99HN/4F/5dfPC3fC/ufN5vRLx8iOwiUP+1gYEeCFgh9UGnurz3S/8j7v7V/wCvfPl34q2f/xUFCBIAZHdBax1wBgYSJOjnIEXBPDMuHtzDv/Tn3o3/7Ov+DL7yC57DL19FBABzYBxDFCCIjMsoUBCYExxEzAGYOcq5YkRk4BgiQmSEKBaG//Hv/jR+4L//V/G7f/P78GsOX4lf+MXllEpHBO8UDpJCd4SDd3AETM7BOWAih8nrX7Eu/Nzf+hj+7Ve/BX/4t78f/8yvezP+wVWU3wBMCQ5csjZMJJDgpgl+gkCCWhLSZ44BHOdsRXj1kz+Nd/2Ffxcfesv/Cy/8un8WPB9BYQbPR8CzrEURCOwYmI/5haO4hL4WFogc7v29v467f+19eOU3/Wu484//78FXjyROIcygDCfJcjAdksujAQSuAQE0AzwBMeLi5y9w90e/xaRpRp5uG2MBgi1AaN8/O7vh234sZQx9k6QRt+swKCAABhJGVgRAoMZAwsWDezlPwZ1n7eyGpQxTY6+IzVNgB21rsraP/S1S3T/XGSsp7wP0AaE32G+5oYUCK0/SWgDccDCooOBnChT8qyuzD+xD6bWJHhAAZRW9MuVlbCHY0yBb6UGBDeLK0hlNt58rSVYDXftAX0pu98mf+zCgn0cJidT9oQvC2JgCYB0GVLag4Js/+u68imEVQ2CmGQ5dBiMgSCPMVz/1M/jGj/8hfPArv1tGevNV30KwYh2QMuvsARuc5nHv7/1V3P25P44PfuV344Vf/5XZH53B4HCWYwfUGgA/JQBIsQP+APYHRBBCgoE5jeTvPbjA7/3RF/Enf9dL+PIveB7/4CriMkTMDBzngMsEAldzAYQ5Mq5CzABwTAARY8QxKBCIC+J/+sW/iP/yf/rX8X/+J/84fpX7cnzqFx8u7t2nduQcwSXl7ZIyFzggOOdw8A4HX4DhzDv89b/zU/ijP/1t+N7nfgD/5K/+5/F3Hh5x7gmHyWMi4NzLcZ44Q8KkoEDWksACBeGYIRHhiIvXX8t5HP6FL/ytAgPHq2RdmOq/UVL45mdPOi11/OwvfvGv4t1//T/GK7/h23HnV/0GAUckNxaRHEMOZeZEBE3SNqUt9gEBzoOY8eovvIZv+vO/Fz/8df857nzJ18hxxHDOg0FlNh8ECGKUZMOirOp1G3pw0E55VJ+5nc2QAxOxz4ogV5bfL17/GO4mS4daCriNQei/UP3NqPtyXTDqpXekmC/krg9bmSX3SF7bIsV06O3Kh/2AYMWW6E+uQMFCGuXlcDoc3EgwaFmyBwVbpvaRhaC30UJBz2e1ZSXIqUs7jZxoCQXU/A6UoDqgb2pfnthlIr94vU0z2uzXnK3rKkgb2jgBBiQN60fLgjA5pmCFctsbqFYmNEBA0NkZ78ZL73gFL3xpmX1QlH0dVJgVQvNdYwjy6D+NFi/+5s/iGz/+/8QHv+K7cOfzfhOqQMLWbaDFbkc0NqFQDiAjwHtc/N2/irt/5Y/jh7/6D+KFz/+K2l0wnYl1gDziAgiW1oE5AiEp9RCBmRmvPbiHf+XPv4g/8Tv+DL7s89+MX7ycFzBwlSwDV3PMAPDoGDGHYICAcRUEBI5BrAYhMj7xv/1F/OjrfwC/60v+MH6F+3L8nX9wlZ5veajOtG2XgMCTAIKM9CFWA0c487JdAeF//vsfx3/+c9+Ob/8t/wme/ZVfjV98dMREDmeecDZFeKIMCefpOLE2cLIeELyTz94RJn8G78/EWhCOuPiFj6fZDf8pXvjCr0LkAHICY5hnWQshWw/EtaABjL22QI0L6d4v/rUCBZ/3G1N7j7ljYOdBIQAeyXWR8nMk2KDpkN4nOTdNB/nNRTBYpsz++LfLlMcv+eel3XIUqwLH1NY8iFCSFrmx9aCFgypj6JuWPnN9pjY5UmtFyNv0XTfvtZ3SV60toi/8nmRAAwtChgJNnpaUtq56qXCQL4Vakdrfrdg+Wxdk+8FveBnPvelOVTfKY5SO0fUxiIxFO+2QA9RN2YE60HAEBT2Yq35Pf/cCAj0uKX0uCH0xMX7vZ7sUt3Irt3Irt3Ir/3DIV3z4K/GX/tLPdseON8Zi8Et/YGm6vP+6MR+ZkfBoiltPKldA+vIr/vDT+KU/8HDXwj2nxhTkMllLgCln6z5w5seFqb35i0Xphldf7G1dBr0VDnvpioexA9Y315TH2dK2vsnGUkBpKtti+uFJroM4tBTk/APzEe20Q6DcXzWNLd9ImboGM3UtTzc0vuRe7ECeZuhTYKETn7NaByLEfS2BgsgzBdRSEBi4DAHHmXGZ3AFXcWkduJzL3+Ms+z2aI+b0+WgsBHOUAMQYYnnmaRunIMTcAEZiPSmQWQmUZiJI1TgJPNS4g+RiOHiHaXJ4akquhsnhfHI4M3+LFcHhLB1/7h0OE+Hce3gCvINYDqpYBIJPMxtyLEKYgZhiEjR4Mc6AndGQ/mlbWcxMWZui2msnwKKtaNBp1VY0F4LOXshTF8usBTazF/RznukAO71R/trcIfZ9fpx31nytpwtj+T7rxsoyWJ9yWZCh9Psv+3dr2fbHdQFb4+/S5jrux3/FH3kav/SdD+GorPL4cmdBPJW2SKdkObzz4XHCuhsDBq12twsKKRSsrx2wLnt84ItjTty/9STU3vxBYxq9iM3fHVfPn1oL/xYQ6H7dbTs6lrXiXAsK9kxBTGl1KWXJy4mI8j5GUlIhcl6smr4qIhCS8s9lLwlvNvMOKAxoBz6aWeAPYPKI6LsLMiAks/9lZBznIHECO4FgTu6DLSCIIdYwoCbx7CIaP1+XVgWKatN2XEGCmlnZO8xRzP8xOhwD4xAi5tnhqYPDMTgcZwGEKwMIMztcBU6AIOWfZsKjife5GdwE5ybQJLMYEGZg6s9o0HgUBYQqL4JJaEUKCkBtFg+hbjep7dh2U0mKbQAgGR1DkBl+AIhTxsp0nexmdpDgSABE8ubpSo+eZOKEBxAgNnMbdwDmYqJu/NZVscwP+fk27gKd8sgWBtptXI5rV3bM1VNt2e5HtNjtX0fG3N+4Fcj4D6xrwfbPp0BCGwS/Ry4+MV4l10r7WNrZJdWORu4/uLd6/RsDBlbp30sZp9ZIa7/CbGSFMrek568awQCwTZet8rTHtJ+rwtcl6O5h/27FEPS2QY87FQhMkZ4oFOQc/FxmIsAsXrTixySdjx6DKPL2IU5n5XOT9S5nJCRa5hywMODUOqAWA58DCzWYUCGgQIHkHdgCAvksMw0u1SrQxA9cBa6sBAIKKbhQQeAEKOAOSZMjRGa4FOxFydEaAbjUO0dEOO8QQwQxYYZDZJl9YL2/EyPlW+AUDOlzgOT5xJiZ5L5dsiBExtGlOIRQA4KPJVjRKyyQh5883JTalZ3REOdiHfDLoFXiKDEJKe6AY0rtbLNfAtL7jjoEbUcjSW2WA2RhqTTjhl1qnxwzEGQ4cF72wZOHAxV9xx2o6itz/8xyohyD0NvG/YA8q9zLFnQKtdyrBxn2vnIgINmjkJ+PfRJbkLAn5cIw3gBlULumv6rr6TnNtrUYOtWPz9Czw31uDBioqPllRFqnAEHvHdgiLSu9aNBeo2lf/1OAoNq/+Vtk9DbvN7m1swzy72abHtdaCVog0G+LFxWELShwjvrZC9V9YKGAi2VAO8uSm3/FnuO8FMO5bEkgn7LW2eNswJOFACCbfBUE8joFatJtYYC8pL71B4GCxjqg+QVCmgUwx5JzQIFgTi6DOQJXISbrQD27QIMLR1AQGYiR86yRVigPSZIyT2kYKPWoXiO8ff9NqwJs7WxN41boSYyMSIzoCMecL0DfMAcg4BGQZ0ocokP04iaIYMwksxvmOGP2DlPkAgiO4CMjOM5BkbINtRUhuZ84HCXlsmZZtJCQUzYnS5SCQnJREZBhoUpytdWugKUFwUrOJBghCp0ACFwxS/1kOOCYlhZYwkFvxkILB45p851uAaFS9CZKP6Z32y7mlI1JaKCikaXqH5fIKk8LCqY5S/k2AEEtCCotJPTkVGuBWrpVf/WgpicjTLJi9eP3/tnvGe53Y8CAsM8n0zvOCje/2e86D7Z7nsGUl72NohrpPxEg2Bqdr0NBayUAaigYgsIKFLR1a+WxoIDNKoYZArj5bMwXPesOufph6TNo113Fcp/NBYsqGEhw0IEBkC95BwIjICk6tRSwuA40h4DkHOCcgOg4M+aYLATBJClK8NBCQUgA0EKAcyQmYdUE3oGJU0yBPCALg84cz9aGPKo209PrZ33Wuo1IIKEkQpKZClYEFoCgk+eLeiv7MCFCYgkii8tgjsCBGTORAIKNaYgM7xhzFMuBc2ySJ3k472VGwwgSTJZFTLGOYzEra2ZYyC6Gne1MHlAND7nyOVU9l/Ppe6Dx8Wk2BDmAmdLsmQIHjgG40+FA+8oeIGTrQTq+tR7k6XtmWx5FN1BhlXrfLrCoFFuRw5Z5KiDoma+bO6Y3A02ljYlr91zTWb3fVU7RjzcGDNpVrK4ro+Zl0xx/3Z/9nWV/6piS0t811bx42J2cBHsCC5fn2gKCWkaWAv3ypKBgWU6RHLj0OFAQizugrMYXi7UAaEZklMtI3oMDxCLAaTU+oAxlW8nxA2X6YW+Z3QoENDisDQZrYEBdBZqaOJjPc+rr51DyDygQjJIRWSjQJESt/985whmAKwAHOISkALzzGRyAUhb7nNfcBmsrg9bVKdt0OqO2cx2558+uAIJOc9RpjVbEvUAIUVtqek4+AkHiFs48EEECBgoI3mEKEefeYfIORJzdDHpNHznlXxBXwwISOsGunAAgg4K2V9TLcEs9blizbNvTz7qQk1SweQDFgkCcspESJasBAC6fHwcOgPKejxTSCA4YWIJAb5shgBYK9o6my9FS0p6yZUjfwjDlbABBzf/I5VyevSejMvb4YC3Nfe88e+7/Xkc/rh13Y8DgrpkHe5pq3JZ7TXIMYOQmkKrOc1xXzmk7yREMANe0DvQ6lh2ZwioFvxMKYI8xJ9mKJ2gjmas4CQMFzgYrrUIBl2Vc2789IQUBAiZKx4oLgZuVDikFiJWlpUmOJyoQoNtaEIDLq+OxZiiELHTTgwFdp2DOinycorgFAslKyFXK4qCKPf11RIBzOCBKbn1mBGJxC0BG4bM+w8aaELjsY+U6gbkqrZlYFb03L4W1FmhSJL0XzXboHeCcy4ARooz0QxRlNgcAPiZlJmA1kUPgAggTkWwPMadgnhzBRYZLwYouMo4pxqWFBOcBG+vCmjsDcQEKYMlBoLCQ3V6qZBW6Bm0RQFphMZtZxpWsloT0l1jXtYibcCCGNq7gQJVnhBRX3+W1d37NcsA74UDdCrthYNgPWkvH+EyOTFlRDutCQi7zuowsBde1POyRew/uVfpxj9wYMLA3vZ8gi9QGp/Ldml9GubUpN3D9vl2CJwoDexKAaGa13k/mc9vJtwCg23rHWnFY+iF1e5bGEtILMsyA4BQIChTU7oL0N6ELtXWiCZ3I5REBcXlwbCvXmdeC0kJQOS+7gQCNHE+A0IKAnUYWkVYujKJYxUowXrRIrQM2C+EcSwrjEmugIIBm7QI2q+ZJAJ4qycgEeJdhISecMj7vMGhSazMOnpS4wfvjK9e7q/btpVnW3+Q2WWZCOInPcEyAj4izuBEsIHgAU3CY0myFyYlFYU6/OcditWggQX4jOJrgpyn5zkvw6wIUUpvlNMURCgacZg9wBPxhYb6uJM9S6LRPuxvHtE8igexeUBNCrOAg8VOKAaBuIiSrOLcAwQ16ZavkNRAv/2bgQKUCCnPs8sSDBtzrF/RjOltrkVhYEfTiQLVewimaZzMuofN7z8qxJRfXgALgBoGB5PYfdVo7FHX6y+a7ZszSQMZWaWqT0qa2JxrVHmcv3Ebf9v7m0rWNe6uzzuSyDw6stcD+brftmYXR7Qw68GPdKAsoMNaEFgrIQEFlLeiOFAiAwoGsGb+eb92MxIjSv2IlKNvLsrotCDCKVSCygYCIZjVD5CBOCwMCAWUBI+teiFzWLlAXQTVtTH3zqTPzIBx8sRw8jvher9VIb5c9l37S5ZM6qAsTwQADcyA4knqb2CESI7C4DeYYqoWc/BwrSCDiZNFiAwYSk6BLRTtjTUitbwkKzNBA2eL+SnBgY2UArBurgUWbbUVhQK0GaXxOLON5CwdA8qblvkCsBda1IEaIBhBQXAynSJ6lgmI1sGcZuRT6JzPv/2Y/1QeFkSUhN60OJICW/eaW9HRBbxOt79aVAgUvD/TjuG5uDBiskeD6C0WLb4zik3n5nXUehMjNjqgfri1FV+1YS8F1YaBp7IvRMVArvRbF6zMP5VqDw50kvAkEaad1KNBYAq6sBaUs2h3b+AIT2d2rlxIBt4QApG06/dCAgF5dQYB5bBVghQGulzK2MFCWN5ZiRhQgaFc1zHWbrDS6YuHWA7Hg1qYsBkqYRW+/kQvgFBm5JKxVQmFHjRn2kN5+e8Tej4YizE7cDI4hgOAILgIzMXyIZTnoBhJ0iWh1N1Csl4i21gSybgekLoSb6bRqPYCFXmMVA5Yv5o52XItYCaQLiwAnJd+DAztapyaNslygBgScrhy3ZNB9ld8xaOV7+slFL237Cm0plLt70+0vIAFoQKF/1pP0wspuRpYXFCh4d4KCZClYDCjH17lBYNDaoeoHvHKg7pS3WJ+MQsGiMdhDqs65kbYDfUwY6DXunuh+p6xC1husjSwDOgtDX5SqA+F+4yZzrPytgcBuq10HALpQMGjZyV0gS0krHKD/Vpk204UAaylwU7ESIIEA1+4B7lgFMjCk34OJGWAu0xC3YABABoJWssLudOitgldrgvwmSx1rNXgCyCgy2VamEXpTZRoHYJuY3zGeCeaF0eas8QvMKEtxp3qR7al+U/3ovlpHABb1BKCqqzVjRIzJD+44z8jQegppJgM1kKDTGonKstDekewHcTk4LK0JFGHcDktQAJtcG3ZmTQsLuRJXbsy6GkZ9gcYboIYDEhSXWF1Cdi2oohwBQja9t3Xc6RfaPmFZNF78NupfRrLWZy5+Y9tnXgMS6qPyOYe98A4IGO/Sf+4VFDzzlr4FdWPUd3PAAKhvdgQJwOAFkUdtfTJ3TCCjbQRAx3owkB4I2M8nw8BOMMgmMfUtbmH3DmkDLi0c5OKhjPLR7Ct/y7l6QLC0EqSz9qCgsha0F3TleXWBQGFBf1wDAYcqjWxEtgJoOtm9VoHAEtzHJmaAd8AAgC4QTMYAYhU/0nOwir9V+qrwZSqerMDnkZSe06Vx7bMyMGeeGzXXH9a5He2nL4zy+JjZ5MVAFeDGEKASQJBpk2phYQNWa/UJLOu0rdcCCLLvKiSY+pyCAznGRDDAsGFNiAxN1qXWBZCHd5BFnDTeoAEFGzyYKi79XekbBtMbpV8r8QYWDpDgQEVdC0ANAxYQctIeLAcVbexULlrTLz2RQLzH6Dtzaa4JCS0Y7bnqCJjG2wZQ8IlXcffDaUG8tEplOWS/Ve1mgYEVrYShry2JeWEuHlwk0qqndKzBwZaswwDKGRszYW7Qb0A8wSnSy89AZKu3vpu1Qbkz+9h567uBAFhCwehizB3lb0qRR1IFBKAzDFLiFxsnIKP+vlWAuQ4cBJeZBKrATrUKqLQwYGdROtAmBEwGAHpKC1SsAJ7kuThK53blWeXZIB1Xjz5T+7f9zJ3P+tfOguEEWgpheTZMUtjRgFi2LqzAl3zmdVhwvKjz7LowkAAGXCzwVdd5gCcsrAkKXDrD4Uh1AKNYF4w1gYw1AQTnZFVE56ZUSSZTZ/se9Nxpo8GSfU7MCzgQpSiDia71wABChoH0rNBAQq89dMvR2WFPIPcuuWbcAWk/2oOEJh6h1Q8qp3rZRu9QkeYq6d4uHtwrUGADDU8AApUbAwaWDBf+dWA8Wk4PvphfSvRmrzrXnvEW5Q2tA2t023uoaxaQJ2AZyKe1AIBkDUi/xR2XsFjStQ6kH4ZAACytBECnM7RX1Je2zUG/AgK67QQYOMVFMAoeBEq8ANC3COQ7M+6AFga8Km4DAq2J20KAc0s/eDtyVShw+jzMwlSLuA4uz2to4m5dNkB5Dq21xiz4A7g6kHNoqaEhnG0/D8hMjfQ84OrnUVkU0rNyroDCvufBtcuBG5dDLC6H4KQIRMh5FNQlQW4qz6SakTMAheo91RtZ9hcVHLCsyVBZD4AxIKQhc56xgBoS7JXXpO0v8ucnxAYAlv1pd/ZS2+eqfUSPSW2Yy9oVW0GLi2J0tp2qW+REK1DQ0R2162QMDDcGDKyUqTlGVgChG6hhz4dlFY4e1MJMVgpgyrJhHeDOvmui++g9Kxw8YauB3osFhO7+zbHyV773RpoVEAC1lcDWSVY+awTcsQgA+2CANWagjFgVBnS0ukf5WBfBrMc2gYOAUTyD27FBgAoEdpQ6uaJ8Ju+yS6BEztcgIJn8jPInmf/vFdQ0EC4E0LGz1kRUBVSm2ukUu5ysR5/fSJIZtkoKVQV6erT5IFybGMrrzA8S5R4LLOjzmvV79Ahp+YC1GR/lOQkkKCCMnpEmLHQkmlHN7AoK+pyO5jnZaZAVtHGCtigxDp5ltoQzkOAJIIWE9OyIfJrXz9U7w02wYgGF9b6gwAGQrQepD8lTHfX5tYAAdK0IgACcr3cZl8F+ftLWgj39ane79qsFBMotj6wIdq/mEsNfatl79wsoGPSPe+PTgBsKBqvSjKhzpWZLQf+xNW1/sX28rbEOaBmwHwjWFCHbl+cxQcDGTeg4Qa0GNnnTKtn2QADIlWKtA2XzwEKQvud7w0pdVCPSmuKXQODzQKddetbOJAB3phXyeCbByDJgZxEAqKwFVpbZ/2ogmJzLCkUgwBUQaGBgcmUE6tPnKQFBvcRwWhWQ02qCKaWvXWaYmMuy1GkJ6miXpdbUvul+8kqC+tzsXHFN60uExYJT00G2TwezrDBBl6Emm1LaH+DIw+taE16XoqaUKTI9N2PhmcnVOSIcNTkiyABeFEBQN8Pg2bVT9CwoKCTMag2gZhokO3iE/PwkjwJJwikCfIIDD2CmAnAlRiQt7JkgQd7VALBL75C8vDKyj9kyUJ5Nb0TJUuc6tVHu0gBC6T+tBUEfqXUzqMTcd6Abe7Aog3mXF/0ITjfNL6RRkJv9qx145X1rK8ICEHR/1FaEnnbZO+hc7M1xNxScKv/ogYGRoU8myZql4A0Bgp0wUF1TX+QnKenGLRwANSAsDjGbRzCgm3rWAQD7LQTW+jMCAvPZxg30gEAUN7BnauHIOqDbbNbBFgZWlyOuzNFjIFDrwES1m0DN/5OBgcmVrIATIS8ChKsjKM4FBCwYKACEGXy8BKcVAxFmcDgCaWEghBmS6rdYEmT5aoWD5ehE00iTI8ngp0CgFgJdhtp7kD/k7zRNwOHcLFN9kMQ/5OV7AgVyE7xZhGpmytaE2UDCHDllPbTPVLIeziSAR8ENAUGi4qmeLtkBhR4kTL5Mg8zJlBLgta4fycfEMmWS9Lu48STuIbWb9FKJSygBgnX/cBRLjLEIUOtyNO9UsTCEpBjTO9VaENTlYBSgWhHkfUQ915+KNcFesidr/cmTlK1+1v7OlUOkBoEFILRAkfbqDTC3bmv0+5b+ehy5kWCwZ4reuFJPa33LvTuj/scBgl7D3QsCth6qY/bfo23+vctWNW1f5i4MAF3rAFABQhcINl7g7J8GOhaCZlbBwEIQWQPdlhaCLSAQBVJbB0aWgar+NoCgdRcoEJx7UQBTUgi6bLAjCTKUJYQBBxaFHwwMhGQJSNt4PoKPVwICxyv5Pl+B52QhCAIHPB8R54B4lNS+fJRVAmNkAYHAAgoAuJM6kVLqQiLxX5BzyW/uQAcPIgd38HCTByWrARII0DSBpjPZfjgDDuegg3wXi4GAAfwE9rLtzB/A/iAuh5isA0w4uLJs9eQIM3uwBy5DwEyuAoQpOsxxCQjCPTR8vgqXjggxiCXBOUacCyDwLJaAAFkRc3IJRijUKZkJ4DQLghUIGBkGM7BbQACBnAczFQtCDjAUJddVinabgQTJd4A0ag4gSpYgRteKIMcMICH/mOpq9G4Mto/FDOXU1Akt804z+kp/m+vCKv29gECulK2JRDhtjC/3eB0oYHK73Qk3Bgx2z9cnWlbqNczv1wKCzm+bo+KTClX7168j2Z3QoG23hhoQsJvqv7V1YBcMnHD/uwMKVdl3Ygisy4C5n5Z47JceJx4aQcEICCZf4gfExLx0F0h8QPqdltaBA0EU//EoboJ4zEBA8QjEGThegS8fisI/Xsm/q0fg45WAwHyFeJwRLgUGeJ4R5gCeA8JRrAXxKBYCjtH8TWDQCZzIKymmpYRJocAR3EHcBP4wgSYPP3nQNMFNHv78AHcQMICfBAjOnpK/h7Tt/GngcAa4CXACFWJFkM/OH3CYJhxZ3A0FDsTtMEeGJ4/g+26GOQFCtgidAAhiXQBy+j6WtRsEFgQQ2AkgBJZnzDODXQSn7zG1R+ICCGACQyyGHgKoZR0JUT+OPEBcA0JyEwzhIL9Y5bdsYaisCKl/U0iAgYQ2FgEGElKVWbflqnbs9DPXkuwXPQEUgNpCiQ4g2Ong6XvOI9ONQSiAUP7fDwhVoHw7JdECUe9WKl03rswbAwar0osp2AEFJ6gm83GnlcBsexwo6PrTzfeqIXTu1TZR/bwnR0MvP8MaDADYdhVU970SBATk+1yDArUSiBVgbCWwU+BOdRvY9QqQrtMLJmwzC/YCCjWavQcEB+/E79xxF4i1QHzPAgSXtXUgXBU3wXwlUHB1KSDQwEC4vEK4OoKPAfPVMYMAJ0tBnAPiPAsIzAExKBQwOJhFl7pZmFz6QyBPCQwIzjvQ5OVzggF38AIICRSmswPo4OHPDvDnZ0tIuHwEOjsHDuk3dTf4swwJ7CYcpnMc/IQAgo+yyJKPwESU3QyeHA5clrae1doQZWTviUoMCVkgpGFmRvmODAeaI2FKzXqOaXTuAZ6DrLcBcWcFiOsDaXaCnowJMpuBZETu0ovLyXrgXLL0Qc39eSkjLUiBg4VC6Ty/FLw4hASiOhYBQNfVADmkmu9P/femaj6dPseOYcqPRfHnUbJua+CA27iLNWncL1U8Rif+QAGBO+DQK/1ITdu9dmU07LmITpSbBQYbI+VtKNjG0WtZCp4kFKwF2eV9GiiwZvZabQ9ljcyp89mUqrrfTevAJgyg+b3c6x4o2BtLEGOxKmzNNLBQ0IcA+9kAQQcGNO/A7vgBSrCw5i6Yr5K7IFkKkotA//LxCvHyYf4er64Q5xnhcka4OiJeHTMMqMUgZmuBWAnCUWILwpGLtUAnJayMxrKS8Go5IPgDJWuBfhcocOmfPz9gTpDgzg7wZ1fw55NAxNkZ6OoRcDiHO386uRmKqwGH88pywHEGuwmTP8D7A6Kr3QyeGMEVQOjGIZjVLau2UEFC/eyHLVobnZO4BI6SjlnASp7/HFjmLcY07RRI1gGrZtVWT3Cs3+WPJwB74KCUalTa8rGBhFNcDcC+xECnSFGe6Yw6i4J5CQftsRYONkbcm3Bg9zHWg71wsCb1gkjPlx/WLCCjNrihZ24OGJwCBV/61t4J8qdrNc6TTFMn7KvS3N9uS0EntuA699eDgcUZ14BgaB2wnc2gZO2zJVoEXD4OFDAvoUCLrXPf5VZKOl4rjkqPJqM0+bKViXDv7AJdKfBxgSBePqy/X12J4r86IsxB/l5eIR4DwtUsloFjkN/miHCM4DkiBhZQCAIBHAQOYvorj3VMTGIlSBYDTwiXDuSBMHk4T5inAH9wcJODnzzC1RFumhDPJrjDFeL5GcLVAX7ycGdH+PMruLMrxONlBgKnroXDVQGEeJBZGH4CT2dAOML7A5w/iNUlis9fASG6ZRxC5WZwy9kMFhKkGpIlqWkTtl1YEQYQBR/khJgcgaOs56Bw4JL+CpV6LXCABAce4hbrwoHph5iS33vvQIVqSFi1IgAJEDScGVBXw5pSPLWfWsABkBV9BQdtPofWcpB9HieW4JRZYSfOIOuvkmjKR+VedsmGvrxBYDCuZIGC9wyWnmwUzEkX7ey9x1qwJhsPrFKIe6CgsRRU52ovvVW0xX4GCPa4C1oY6AY/WV9cKx3LSGM1gHEf2CDDFgoYsp25uA/ytqgm3CKhvrJ08A5ZAboOgGV/L5Ypiac0Wm6tAzJHHfmzugo0R8EuIFCXQQIBGz/As7gRwtUR4XKW2IHLGfPV1cJCIJ9jZR1QIIhzqGEgFjdCL76gPLKEkulGBRIiyJGc1wMu+HQ9h3iIcHOAPxerhJs9ODLoOIPPzuBjRJwD/DzDn6WYivmIOB9THMKVAML5nF0MewEhQFwNc2RMCRLVzdBaEdhAQptlEantjURh0UpA6ZwD5P3xkcCOwSguC/FYy6g1ZkuBwAExi8sEyLkGCPqeWCXZjjit8jTSnbaXSpjh2HWsCAkQOjMaRoCwfxw92tf2fQPLQcetAGAJCNeRLavBjjtQsWv33OkGyncAYY+s3N7NAYNBnOc4edHjAMGTkz0+rsV0xL1WghX3wSn3uwoEwLaFYA0IRoQ7ImqigQuhhgLNXJjz53OBBYZ22uUvsL7kr4fkyfeRAUojO9BiVUH7qHprE/QX3dnIPeDSdMMWCDozDMRFkGIIjpcZBHC8Aoe5byU4zogGCBQKNG6gchkkKwGA2kJwTeEo0OYdyXmDAztGOAL+EMFBlGG4PFbHuRAxQ8rizqSMHFiSBJ3FPJOCwjloPoLmGUgxCO78abALYD6AYsguBgsIYiUAAhG8E+jROIQYZfrgBLEiBAMJ1srESTFzZATrAmyqTH/y+bWlnBAIQPUZQLZiRIjOjSR/daDMak0gwDNLHoGYZjCgwAGT5iQxLoVeAeVB9T9LgcsxakngHiAIcBR1VlsQoGWzl12WpCvWbgI0lgNjJdBgwHKVvvUg39qpffMTlh4U1PdoP+2prZHtt5YbBAZW5IZ7aY5b6VXl2kj62t2g9V01PqFdjasKIjwNCIBS7usAQfdMPSuBiSHo5m9vYgxOkx4kmLiCBAURyQKAAgOsVgMzglNrQU/IAQgWBsoITpPH9Z6YduDtAkViLahz5oPGWQmn5HKYnNw1cRDlZvMNtECQQAArswzUTaBQwMcSWJgDCHXdaNQjf+dl9ExSKYCP8HDZagAniwUBWI8iM+4EPa+6FIhczmTtvGlxkWX/EMGOwNHl4McAgMhVFh2OUQIU02ccQoZVikGSMx3O4c7OwW6qLQjJAsN+wsEfMHmPAwhzJAk0TVMWD1GzK4rineABX7ui1E3VLvQELC1QbfsB6lUtpc76VSrtXBZkiokMNMxPISEab0P28hOVeANe2sS2Y35yhTfHoQYE3W2YchkoFoyMLtU4ek+/ZRVmObbtE3cCQrqvkxR/r49+DFEoeDmt8tvaFcaA8PhyY8CgbTgtabVEuXV8+1u/ytfOqLsMAkPWAkb09+r70oXwJCwEo3sbQsHjWgmqnA31/XdnUFT33ax+WMUVIMcJgA0M2Pu3rNIR7yQhDlhGhCC5F3aECVR15pMqK3s8CgQAZkEdVxYecupS0ADEZEWw6xRUmQlTdkL9DPs5TTuMqvyvLtP0wxRboC6FMCMe5zyrQKcV9oIEyTmw5xTABnBeN4AxOYCjS5YCV6YmGiuCfB+/E2QUvgWBPG3RC0Ah5ThwB93uAC/THFuRJEsEjoQ4h3S+OU2DS2VkFutAjJI0iRkxzGnKYwDcBI6T1LWfgPSZ/QRyE7ybEJObISQ4mFhiDaIrM1smR4jsM5jqipBzgiULC9U9NO2pQEJpT/Jd2lNPspU6WQ0IKf6Qy1seYboLG29AA6tBY27P5Ri9u+30PcRM01I0q4DXrAdU9a6jnrbXf/X2o5MBQctoT3xCf721vSldKxYKrPuAO0f1BrJbIHXvwb3V328kGNx7cA/v7vhk9lgH1s6/i8faYJf2N6C2HKydp/rewIDdZycQnAoHqLZfEwoGboNdiTZyHSgI1C4E2aeJKwAqGLDWAqBfBw7FHOsdAdFlhZhfvti8LHnkK19HyxU7MhYDUvdDgQC7aJEHkkUgJHdBLBaCGGogmMUKgJScCCkBUZuYSEzsAd1MhOTgnEN0UfIIAOAouQV48qA5yCj9wIjGipBdDEB3euLu/tNMXwRQTWEEINMYSUjJTR4294E7pOmNmjmxEU73TeSkHqBwGIHpTP76CZr5UZInTQUQnGRURDyAyIOdEzeD8zg4jwCSKbCOFus02BU32REiEw6pAIx6+ehcXx1lb9uWPK9l+1rcd/orHq9iNVBIcGafHG/AATbeoMCBxiIQhjSt51Ifut7Qwr0g3wloUi4bH3x6z4sbkVaV3JoVdAQVW4CAqt9uG/IOJT+wFPSDwZt90t8RFIz23wNGPf34DJ4dnvvGgcG9B/fwYmN+OeX4VlrTzf0FaaVm17EA1JGwKL+f6Dp4I2CgFQsH7d/qLD0o0P17loJ83DoULKwFG1DQuhCAsbXAStVJGOTOcIAUBZ7uPgeApXnZdrRG5oNL36kDAiDNUpdWyTMwoFijyl8WLOqsWxCO0oFrimJ1GzQpi6tshcnFY10C5AV8/AREJ90heQcOETRJ0J8ofgsC5RwlgZEFgU4r62Q+hF92rLWp3DV/SwIkpHJSSgLhnJPvJFkTcwbF1gXBYhkhCweANJK0vjIlqMLhvKRgppRmOcyAcyDnxc2QFnOaUhrmCYSokJAzI1IKdCWAG1BgyZnAkBgBMCR3QVV1y7YGIAMBsGxv1T6N9KwGOjVw4VJYgwMTpNgb+HThoCpFAwf6HHpwkJ9Wf2rjVr+23e8pYHnZm1DurQsJeg87ZdR/L35fPrEWCrZ0094+HhD3+os/chcvvfMVfO+f/Z7hfjcGDIACBS81UDBSw3sq1J5DSUu3L87bRruieYFOiBjdAwO2fKMX5pSkIesFGiv6KqZArpq2L90HwOBFAYwVYCcUGBeCugoq18FAKPWwjsu0MgI0kVtZLc6ITwonQ1PqkB1kJJc8D9K9UnEXqFWAqLgRxMSdLAMGBvJn/cfmNwMAah3If5nzOgaIS++1KFYHdxCLAJNLypKBSS0F0cwqSLVoXAK1q6DfQca1daMbcR2XAICcNlnq2Oyj9W+TJOW1qJ2JUVCYcGhdPVLIAASTaMesHEnMYD8BIa3RwAF5VUfyCRJ8/kfzVV79kZMlQVd8jI7SjBcBBOYCChE6K6YArGVq78cvpW1/+te2wZHo/UZAki9amLAuBakkrMIBIMC51Z/13Apquue0bsMIJE6YzndKH9frt4sVYQUSOsftkfV+vC6XysvG/b16bvN5j46z+nHNEgHcIDC4+MQ9vPjhu3jpHa/gLc/cydN5gX0AsJWoRknr5Xe+gt/1w78zn7eoi+YEDRycJJ14gr0wsLiPkZ3N7L8OB7W1oP6JG0AY1PQp8RS289mAArUWMOr77q3e5kg7xRSgleBAugI1ea4bD+3KkRYCnH43FgEFAZetCZwUf9wJA7JgTV7VMMYCAjGmbaGCguq+nQNFiI+cNMAslqWRgYUVoJXR9lwfA+V+Hbnutax1IW/TBZoAVIs06bWYQSFlAPCHXKeSlego6y/4CZidpFOeDnnmCzsPWeWx/qeWBO88HDnA+WQVoDQdtoCCWhSk3S5hQcuoct02uahj5ry/fe/tFMZ1OACkUXGt0EeyFi2ZLQYJFmyfV03p6w7Bsuzp7xb1Z/SCtUYAG5CQFqK6lnSpjbpF7kHBln7aKlVPP64ddGPA4MUP38VLb38Fzz9TSKitTFuRe2ZZ6ctj3RNj0kpNrCLgpjGM5ilV26rXvhw6+KvlXGxck8aUknOiYO0VTIdca0YB1um/qofSSeyBAi2/Wgs231tCXiI3mrSteljPwKedLVAgQEdo3nyuQQDFKsCxQIEuZ8xcw4Duk5Q3p+l2Wfm3yxyHGdyxDkCXNYYDqAR35RiQYb00z8cq4nxOrS5zFt9Ophucc63dWFhpH6C9xxYettqivX62LtTl1TqUESxAnIIqFRA0hiOt7Oi8z6t1MrkKEogoLw/Nyf3gEkwIKKCAQrIolOm0YlUokFuAQUvcNu1FG203dkRnKDjuuRQ24AAQQLDJja6x1NGa0A5LRPXetxvXDrDHmfdZd9mEBOhOnXb3GH37oJh1eXdsH+m3+w8KFDz/zJ1deuLGgMFLbxf3AYCF4lNZhYGBXebiE/fwng8X90TvMKoO1AbUIepBQGHvwr02v/VC7FXZ+ap7SOAUyVYSa3pEX1u3MGC2VUAArFoK9rAQpXK58sanLLTLm7cvlwKAFk0VvloGdGRmQQCRU4BgSDEXISt8GzOgU+cqy4DOFEjuAcQUNGiBQEFBFaa2L59C1EKQoDpAqKVV3KQjbJ0OQFlZknP1SLtVpmbfrGhNGz7FglBZCPQe8hrVobSZdJ+5HtL+3Nl3UScqoQEoWyeadCeG1OfLPRZASPUQZjAReDoAJNtaSwIyKKSYhAQQRF7ylTgvMxxSPVpQEAgwlgTobRVgUBn1Y6O2uyZ1tzeAA/ICbKyAYEbOVQbE097zJybX6QO1eTV9oB0gtX17/mxBYc+Vd/bzvW2rFpEd+i1DQTNo3pIbAwbPPXtnORN1pRL3EOb91wtptYEgttEs4cDsuUq/daFOhYG2Ke6xchGhrqfHhYOhm8CVEnY7gqXvbWvp5PRDBQWttaAdcebZVpSCmNLv1o1rO1AFALmSsQakDUMQSNPhkGBgYRVAAQaAgXnOCs5aBoYwAAw63kbR+0Op71bht8peFb2f8j7kfQoMK3BmE0jVz6eAm7W6rI346pVGubQd62TnKHWUoKkEvLKM7JMVRi0ntu7k8Kb+gG4ddi0uzMkaY+rQ1l+KM2CiYklQd8U0SR05L5aD9Lm1JoAouSRoCQqOOm3a9AMoSk2Ku2wTbXuub09eeGs1qIFCn7tYMdolmzmNmCsLQj54TS1vxBVdVzp94Z5+MNp64VK6yhZiIKGVNhHTvvvY7u+rMm7tsGZCAnD/E/fw4kfuVoPmqp5WSnpjwMBKCXExsmdoaeT+g1Sp7+hbCvSUfTjA4tvo+LXPWzDAGx1EVRpVvtzX021MxqmskDM4VlaCUWdA1XGyrQME5rsFAi2j7UB7kl/2AQS0hr3WOrAAAY0T0IRO6h5AcQMUq4C6DYxVILkINNhNlZpVZAC2YQAJBLYAwCp/J/7yovzrkS4bhZVhwHmoa0e396w3+jy2+jFqPlfjJgsA+jlq3IRxsygAqDWmtbwEdcdIXAatABfpdMVePev3HiRIZed65gRXmBWujMsh17EH0VGOT3UO50E4ykqKzkssCDl4fQZy5yYOIUEBoVgRNGag20rWDf16PgsHcnqN2C/WgxyhzzUgQN97AJUloSe99/4E2WM12dsn6mAhn8905Hsgoe0jF6CwIVvvynDnnftl/TWAgi25kWAADOBgZV+Y/buVahSnlbaxqOwxUIwaRxsz0IOBXqNfA0gbeKT6+5Q6yuepZlnocFysBlUq0eHxVks3psUBEOi9VVaCtFHrZuHiQ6mPkWfPRnJbEFClla0CqrBUAbXugK6iEldCdhFcwypgR7SV6R8QRaJKSkf8qrzV3O2nDgT44vNWBZV84jrSFSCQ6XR2ISpdmlr0NoMhywerL1yfU+95VI85PxcydZ5mFZCXuA1HYvEnM5NDwUxdNek5cKpzHgVwJiAj46YhfQY6jbHzDBb1H2ZJEGiBLMPYMT8DVpBKz0VcDlTqXl0OGdrKcyjnk22yFLdHYgMDxFS1fTsraAvK8jFUwwHQsx54gOShLwAhBeNlSEgPeZRGeDO1O3B6oDb6fUDbP/b6RruPBYU1SNg9iwtPSAcMZGEdN/K4UADcYDAAthVfW1kRwMc6lbolvRH26LnuhQFbvh4M7IFI3Se3cwMHix2HDd6oWDPTIrsQLBzk83VSifam7aTt/QyOxjRt7sVG046gwJa8XLI2rzqzUw0DZvSq7oDHhYEwr4JA16RdVV3t468sA9NhAQPQefjOiyXAgoA7NBAwZQCIDJl/H4EY4+rS1Iw6UQ/HMk9fxd6VjXQgKimi28RQZYaH7KPZITUPhIeHc5N4QPQ55sRPOutDc0AkWJg60z45guajLKo0H0Ek8QQ2jkNyF6h7ovOMdPqiAnf7bJwD+6lyOSwgIe6ABCLATdKGycGRJC1yMP0GUXY92Pd9TVo4AIr1QLuEZENLZUyAQB5l2WUDCemkeSpkz8XYKv7WYmi3pWuPb2B0X9v9ZHe7AmEHEnoxCSNI6A0Ud5dhh8TO59VB7TWucaPBAOjDwaii7j+4h/fuhIJWl/asBXsa5SnWgfZ8Ww9c77uyaozgoLNvJSaWYJG4qS0JeQwlHbOWzvlxgUBOW4MAsAYD6Wqtm2AYPNhxE3QsA4uZBKPAwZV6yi4Do3QkQ18yX6doeQsDUBjwU1b+nBLywMy1DxGYZ84wECCpeyV7n9TvZQiYA8sKglFWEJzT6oECC2kNCmbIksPlFuyox3aikthJlpJ2qvCT8pelp+uVJ8+9zzNAnEurTMIuNDXB+wl+Apw+x3BMACfQkJ+LX0IC+anEKsxzBQgWDnrPK+d0IFeDgloT0vkoAcESEow1J4o7IbsboloOvJQtQ4NLsJiWX06KvB1UENGmaT3HeBoFqMpv6WNPgIBEHz1IqKwHK/1APmXpnbsp0dvybpxuBAV7+srq3Pk8jRUBNUQBpZ507zWLzUmg0pHRfUT09dd1oAD4RwAM9hqmtFI/cIKlYCRbJqO9sQM9IDjlQVsoWjUKDKR7TLIQ6EtcA8LKuXojggEM6N8RNC3dwZxOS48HBNZVoH5tkwCntQ5oAGE0eQYQg2QfXIOBNkpeRaPltePvAYEvUJCBwCUAoAQD/iCQoJ8VBhIIzMx5BcAMAgwcgyj9y8g4zkEWBIrAVYhp9UBg5ogYgTlGxHRMiIyQAEGz9sWOEtKlqb0TheadWAoO3qUlpp0ofnKYvPw98w6TO2JyhMPkce5kbYmDdzl75OTKypUTAZOb4A9TDQlmASrWzy5ZGkzyqAwJBhB0VovEP6ZWuHiGybLgYx8SwlyeYVqDIUOJblewYyfxEc5Je3Je2kOMMg0yu+5cavMSuEgJEiIb5UXlnbHvhn1n9He7vwUEeXZF6ak/vQ8JxXq4d2pzt2+wv+86y/iYPaWo4gns8Q0gVMHbnb67nRnSK88bIaNBrcP14ODGgsEpnioLBaNVGK8ri1F+qwExdhmsNe6dMT5PSFKXYN0HqRCbPsFeTIGJG2j/7oGBLROpvewwduAkC0EfCHgvEDTKxJY/d9YdKCB1D3QtBIfKHVBZBxIQMHlZOjjISoDHUGAgJBi4DAHHmXEZIo4h4ioy5sC4ChFXMWIOjMtZfjsGlr9zRGT5HCPjKiSrQ7IgwD5HI9ppqoXAOcKZl78CB4TD5HDwDgcv284nh8kTzpzDmQ/pc/rNOxwmsSh4YlmeOp374HXJ6gnTYQJNKa20mwBvrQhzAoQg604khS6K2FgQ5mOyHkCep/f95xkCZGlOL9YEkuWHdRqkTo+kSSwAeUpk3q6AEAUQyOc2yuRASHEiHFM7ScsnO4+8pmIDCFG3dZ7J8l2Sh5RDKdi4LDYhASjpQxMoyEWWF25lpZ94krKn37Tm+S1AcDA7aZ1r91gd8WSkp+i3BrUDu+6q3Cgw2AsDtnJXocCaj058ukMgMD+uKb1sQm/Pe0301OJ33Qin3JtxKWwSSAUNbzwM5CttQYEGFO6ZZjhwGWhq4hxImOMITgQCQJRMayXwU4kh2AICf5Dv/gD2h2IdCIxjgoACBjLKvwwRj+YCA1dzxJUCQYh4dAw4BsblcU5/A64SGMyBcZyjQEaQAERO8QY5m2LncRVDEaWpeuJOmHxS5gkCDt7hzBPODx4HTzg/TDh4wlMHjzPv0j/C2eQSJAQ8NSVQ8A6eGHNUMAAOjjA5Dz95uImT1eAI8nOxIMRZlKsFhCDbJAbByQg/Ww9CWpc7mFF3WVaZ1KLgMQYEP8n1FBD8BOIoz1zzK7gIsIzEKR3HjkURM6dpjyn0k6goaMBk+1yHAyv1e3YqJMgxBGBXUqCenBJ82LHbi/uEhyb9NcnhUwNAICC7YhcWFpSdtD97HEBw5jztzezWX+05zeetp3FjwODUWFaHjZiCDShYe8jXgYKeb6znXniS0quz9r7sC19e9D21XYOA/bwHBk4JtOw9iz4UMKrAwhOtBKxWApuAaKeVAOjAAHACEBiXwQYQHGOxChxjxByB2VgHHs0Bl8kSoJaBqznico54dIy4PM54dBRwuDyGDAKXc0QIETHEdPuyuFKIonlkFcsEBh2TQV41UV0+TtwJuqyyVIGDT5aCw5QsBtOMpyaHpw4O54dJ/k4ugYGAwqPJ4dwTnpq8WBHYYyJZKjo4B+9kSeSDI3h/BucPQDhKboEuIMzLGIRZTPwVIBhQpp6LKIj1QNoIsiVC3qsZHB1Ie+EYJIFSaheYJslArNqYWdoGAKYo7SVCpgoD0haQVpSE1nUNB62svVuqYOWztN9TISH/300KtCbL/kO3ttuuay4fSQ8QWjiQ32k5w8vURzuTzfy0kO59bcDBKTFxrZQW0pcbAwabONbUei96s3eeFgpOvMxuS0F7/HUbutU9rUHOztMvB6R9d+HsPuZtrQL6eS8M2ONG9TBCkzzj4AlDAU6BAitqcrbZ9prAQnUZ7AKCHERYXAaRvFgFYt9C8CgEPJolbsACwaO5WAceHSMeXs3ZMnB5DLg81jAQIyPMIVURIwSpzxBaa4HCweKWSxtMVgMvq03Be1kAyTnATx7HBAsKCecHh/ODx5mfcX7wePpMAEGtCE8Fh0vvcBkY5zPh0cR4aiI85T3mGHHwyYriOFkQEiA4D4SjKC6XACFOxsUwBgSY6Y8y+8HVN90+dwCcAhozHDiA51ngwHmxVgByrXkGJhQ4MC9E5hEHECRAssBBHMPBitVg613LI/ETIAHoWBOegGSlaccrVMpmrQYWHPZYTXrSwoGVJwkHrSzgIJ1kqL+2TrhTbg4YbImppF5GqMcFgk1ZaYyjWQfl9+3Tt1b9LSg4xcKy1pj197XPa3kH1lwna+bo/BKuSO0+MFDAXKCAuTPjoJl+mJV/Kp26D3rX1Ch2a1WZDAjoTWhQoc1BMAICG1Q4iCFQK0H5Jy6DR3PtNng0FwvBp69CshAEXM7mswGCMCsURIRjDQNiOWCt2gwHsWMtUHHJQqA5JIJLSsY7AwkMfyA45xC8QwgOl7PD+THi/OCyS+Py6PHoEPHUwWE+8zibHK6Cw3FyOATGHByOnvHU5BDYYXKMiWVGxsTAgQneeUyTB3kTgxDVguABN40BIQZ5Zhproi4poIBCp330hGPKJugnsUpQlAkIzGAEAwdUwwE7AEHgILXvAgc7ZgUkWXvvbOoGICnILUjQfZuOo+1LTrG8rolecy8cANv9qir8XrmsW2FY+GsqDQtUQKlDfa3uv74CBU+IvG4MGGyNeHOlPljPaPi4QNAzCVXlOPF8wDrlbgGB7DOAgsZacN2XdAsGgBoIRjAwmm0gZadqn4rQ0++ttSB9zFeirMGyJlt+hsm+B5TVCHUk2HkQ5GT5Z81NQL7zWtmERDagkAiYDpKMSKev2RgCtRBUswwmBADHFFQYEgyotUAV56M54jLG7D64CsmNMIuV4DK5Cn75KmSXwaNjwNUcEY5BpiLOEWFmhBjBIWbrgLgRILn9FQYai4ldqlmXUA6hWEoUEpwDmAOYXTbRR3bwDvBTuV5kLwGOLNBzdeCUZ8EjRMZTB495kt/PouRU0BkYT02cYhfSNEsmRCZ4JgQiHLzEIHCyGgiMhY4FIVSrXmouBDIWJGnUMbeHrtj2YCUTs7RuDkEgkSOIqbgumEybTWPZ9Jly4B/ngMTWagBQfr/2vH+2d6hyPGEJCXHFivAkJCv7xmqwBgdaVmA/IFwniHtrwHLKKXuAcO+BWRBpK6Zg62Irv98YMOjdo33eWqnv6VTqnhiCte/XsExtSus3W2ukbUPctBJ0XtS9FG9lLxD0rAOnBhi206v2SN7TAgGQMrPp51h+1331eObls9XRPvQW09Qyn+afV/um73lpYFfBQJWQCJItj50EF64BwRyRYaAXWKhWgtpCIIGFl8lS0IsjUChQt0GcY05ixGlao362VdYTCwX6nQaxKfo4mDhVJ8ERg0nuD3MEJgcKEVfpmKck8g+A1EWMrnLbRXaIYJxFB55kxsR5ZDBLgGKMjOiBKQLRCSh4RzLV0U1gbwCBLSAEIB7l2R3O6oRJvriZinVJgbIZEqy1jUaIuSQMMxkHK1iAfk77cOpByG8OVpbPY7n35uwFqBmdF1aE3rx/PUNrJV+Tdn+Fg+rcei2yRxl4Qd1fKkhsyakxbHuld+kWCPT7xYPlKr+tcW4r6dJeuZFg0GtsunTySytLJ2/BwNq1W7YevYhrgTLtcaNpJltBg6tugxUrwZMGAmBpIRi5EtrztpJfljXzXRLr36ysBcASAtZgpJ1ClR9ISC4ALt+7BTEd/Wi9ArUOaKY7Yy1gE08QgRxHMDOqPAQ69fDRMeQcBJfJatBCgcw2kKmHswKA5iHQGIFB4KBjBqeUezpSjIFAnkAuwYO2PaCuW1OX1p1Qqqlsk7QNy2fMkQGPXF5H8nkOUWISXMSjY/UARPPPJqUzgPNkXeDACI4xobgXYkxTHt0EZwFBXQxxToAgsSiVFWE6g8aiUJtiea2N2PqxaZb1vtfauwKvgQWd4sg54K9kldxKtbs76LdjTagj9EsvvMgemH65DiBYpWk/96wHtkx57+b+2r701L62lVOsBacM6FsoaN0Me8u4t7+/MWBgpb3hiwf38O6GtPYeu1bBPUNb+zL1gmQW56Ji1uudZxho15yjfG6OMzs+DhC05epBQU/xW5fBHiBop/voPm05q8mQVNwIeVs+mOu/7eeeJJMteQ8OEIsAxxQNnq6so8HWdWBdBr1VC51HdGUlvpKuuKxVUKUrTkAQYC0FEkdgpx8eg2Qm7EHBHDkDQYyxJCIymsI5gmOAfcLXyQFzlOn0M0n0u2dwiNl64HyBiV7QYa9agQIC+bMCQcpnQERwk6g0NzkpW/rNmYYRIwOeEthEHAPBuwjvCNmwO2VVBCCAnRMLgi/WgylZDaKTka5vASFDgV2rIWQrAoNBPm0/nC1XgbQuhp502gwAaTegChS6wmzpGcW233Q9hEUQYm+wsvVOViN3tt/k3Lm0zNV2AHkhKA3Ma5X8XutBu39rPcjXQgcQctnq+zlJsa8BWzMAWztPu22kv14xUHDKoPU6cmPAoFcBDLEUtJW6deyplT4655bVwMYO2LnQe8swWl51j8tgq3EC6yOGLSjYYyWw5++NZNqI3p4Qli9oUr/pYrUpd7HAC2nPRFBfrbx4yccLAJMuDOOr6YfdsHugu3RxTKlsYfLiKwy0CxsxeUQgZRisgSAma0HkkqBoDuJKuEwJiTSD4RwgaYpTHIIV78THPuUAOblfT4wjAbMjuMjgySEGBk/qQmAwHDQ5ZPZTZyjYAC4Ua4BmQtSpi84JCJCxKDhfch5oxkNJiCSfpwQNvmko2aIAxhwoPUm5x5A9wREBjHMviYs0fsFFmdpoAcElQKA0i8XmuaggIQW1MkeQTwGvOXaFS8zKRvsBxFLApg3l5a91/QSpvLpu2SxqlBOS+f77rlMuN55X7aIpnxepgLnM8R9ZD3Js0Aoc9GTUN/WOsU1h7GJojt4aKNij2jpvB2K9Y07crnLx4B7uJv31QgcK9uizPddp5caAwbKKCPdMpaqlYH/j22oo5RG19Fq9LOhbDSwcAEtA2JIuCJjC7IGBrSu1cMPN3ycBBTv0SFXWLUqv9cPKyRUIgNQpa0klB70GeVEK8soLw4xGbmbthwwBplPPMECuCwNIMBA4xa0ll0Fk8e3PLK6DEJHhIANALFCgyk3ggRcDVFGg6p93cMSYCZgmh0NKWnSe/PWaxMhmM9RkRgByvEGu7aa6hwmdULdf61bQpEdtlsTD5Kp1FabJpbUUXEqrTJi8XwBCTDEEcxQQCcwpNCGBHwMcg0CGI0SWDIqRxdAQnOSAmGKyqJCH915yFGnyqxYS0iwXboJaLSxIBfWtB22KYB61p41UwuaM0DdIgxC3rJdrA5tct8aKkLs3Awc2QLhYFrbhoFV+ewcxtp/LFgCzMbKCitlW3ffGDbdl2NkH9+5leU/Lnlag4N145Z0v44Vnn6+OWwOj9lr9a/S+F7k5YJCz8ckjunhwkSr11DTHbe82CBqq9i2AcB04ADbe645cBwa2G8pyz80OwvxYWeqx3H5dKFiUjkpnU81GsPuMiktmWVhylXXAjC/yWXhXYhbTSdtOWzt15wE4WdUwxxUUGGAgr1cws6YXxmJlw97CRnOIEnkfJOgrxpJkCEhhC0w4eLkv78R6cHSEySs8RAGOM13zoMDAzMXdoH+DOX87NXHPM22tQNY14DXuwJW/k4EER5TWWtBFlFzedvDlt4NPgGEahoKSc4Q5CLYGJgQ4BA55waYYxdIQHMMzJXgi+MipPLraowd1IIHb9TZM8GuGBSADw7qodllpXzviEdr32AF5dgKY8/dTlSPQUewNHACNu8Bs3zIV7O2zRhkS1iABKJYEoLUm7JfHg4LO/XAUKPjwi3jlHS8Z/WUsLyeVcKnTLh7cW216NwcMVGylvvOVRFpbfLUBA73f1FSXjx/DgUoFB+mw3vizVU8LsaR6Egzsfev38OjgSKs0Otv3lqB7XwMIsELYUWoiiOO8uAsITQCXlKLfU7ZBieoGsmZeSu4EBYEMCFRZBjgBQAaBKKscKhzoAJcVAjTGAAUKOE3Ni6Z2nQMmiLsAPuZIf8AARyzf9bN1ObRKvnVH9KS3eFIrbgcFtyN/+1V/Uwgon8u5FQomUkuDKSMYjgkcGbODBHF4B55luuXkCOwdKAECEeCJERzBRyA4FhhJVggiVJYEQM5TLAexypuBlF4bQNPecP32Boet0cVWrVtYqKL404F7oK+FA6BYDdBsP022Ll7/3lOdm5Cwcpnr9sntz8P+2LSBBRQsXE62kKfrND3/s3h2cOxNAgNW88t93P1IqtRn3tJR4KPjGxgYdHAXr9+vj9kBBxaOq5esZw9DP2hQZWuK4Z6GtynNPW1ZDU448+Ll3Jpu00KB3T7KXdC/sFoEHBgpmYzCAXmwppbjCDG1b50LVYecISD9bUFArQISO8BZ2SsMSCxAsQ4wpG4YBQgCp7n5+bNAQVjUIcGBZBoeGJPGBGBZ34+ryJ/E3PQ1hXOd8hU3RPoL6t5DYMBHBjupYx1t62yPDAgsCpMiwxkrggPBJUgg0mWhKW2fQMASFJL1oMCCWC623AuVVO1P7rDXBsf1VUz51mqQhwMdOLD1uvbOntIcLDD0pJzr8foxav5vB26ezBU6fRJQWxaawpV9nlDffPfDL+KVt7+EF555vtZDRBuQ0JHm3BefuCf68e0v4Xs+9G8ND7s5YAADBVqpKo2bYbE9f1+vZD3/4hwdOFCxDXDzJWsa21qypf7nQWPbax/Mdr/Y7Vi2AGH91FRZE1S2ptvYqZfO/NaDgt2dEhk4gC4Vq78NoCCP0iifo4KA3BkbEGDkFPeRuUAByw4BouhV4UdGnrkRImNO+fX19wAkEOA89c4Wz3GJ2I8bNlpn3UXpo68Gprl20j7pu4kFUGlrbG1A2DYBew8ar6CuCm0vofld9lmeL260zt496/m91meUe2Mu00EpWR68I1Cy6DgCiAQSiBgehCMhuxtcZU0woCClhroVqjwabYDsnvfWtsudCxARxnCQQ1F1mynHYrS9WqzrE+PwyFP7NA5La0s+Pw375goUkqzhyN5+2hTMfFze0ytvfynHFNSHbUGC3Xep2yr92Du/kRsDBvc+cYG7H31vfdOVMwtj2mwaWW8d8YsH9/P5f+ef/br63B04GDU6az0A1l+y9qfd5LnjflqRYLt0nDa6TKRPYFiIAgd7ztbLxdBaD3rdIOX9eibZOo4gBxT26qcX2NWBgKTjK4tACwLlN06gUNwE1jKwBgMAMhAARVmSI0y6D1mFPVbsVumLmZxADvBI6xfoNML0X4nnaJ5H84z2thL7ZGz8SR3AihK8yuUYTbgUwIt6Ago46TltXcHsA9Rwo3AwM6dEQfLb7GRmwkwh11OGBACBONUNwxMwk8zmUGsCQa0IBhRAEp9ATXutMnN23uUT2ulSOJvX2zejjTeotyGXcO8A43GgYCmNJcWUYVe/ZnsJCwor1oSe7IKhle+nDN5eePb54b3lwFTbV7fnWhxUQ8Fbn3kOW/EtNwYMvumj78UH3/YBvPDMc/XttnDQ/mZk9DAuHtyvzt89d2eUvQYEW+1srZEtXhYti+5bbd94efQF4Vg3ugoOlvdia64OoiyWAd1OZNuxeUXSxl5HYhWQXnNhPdB9qg62L0xlzftyE2pJKisd2v3zxaiUYgQCOoLPUJD+ctrWWgXA4hIYKbnR6FllMUr3UsY9in9KFea07vRzUnaiuEqdq5l8Le10H1oHz8L81c8xfenVp+bCULiSz2wgbF+dItVroH6d5vIpIBABgVchwdbpTOKoqq0JEBcEJfeD1mes69M5WTK5vArmHdf3ZO29bvoeXrEe6CXyDAU0lgOphHGSoKaugH3vcLvfWsBwdbYtKFjp46jpl/NdDa0JfUhYg4bFNTe+L8p8wkBOfxsCwuI6HSjYITcGDD74tg+MzSMLutp4EDZQ4/XX8E0f/ebx+bvgUUbZvQa2V/bCwOpLMqL8ljTJ1XCwuPag5OYGKwBImy0ctMXqrvaIGgbk9wYI0o+7FVMq2OLeVkdbVCmuHggwSka9x7UKAP3R7khaC0APAibvstlYRq5FCXmj/F063uUAPrUSyOiVdBnpEIpfXFeg7AXRrZW9dcUAJohOcjwwkTjnyYF9+ptcNOqe0TgNa5GRoE0ggrpxGgIL1HXN9Opcv8/5PSPMqc7n1LB9Dk5csyZI8CJRiU0ITuqXSGY4UA8UAICKZYGrPsBvj+B7yhr122yTAuXpg2lPSnWQ5+UYkKjPOLzc4l1e7LfaIfbvr8piWu0+qo+yXwUKbAFqaZa3AYwrPeCqdPvwatOGBWQAgJuAkL4v3Ac7YzRuDBhYpT1UcJ2Gsw8K3r/pk8nHDoIRdctI+o2uY3bqNaTrxBP0/FUWDgaWFgs6NniwZzXInU/v+iucoefQolXnaKwE1THl5jon7lywaiO0GMlujWJHsQJZ2e+wCvTcAz2xLoERDGSlRBoIJ79ptL5MsashYKJ051VGvwhw+Q77PUGBJuvRdQLsypNSiSvPQNMAp+mbzk8lKZTCQSdNtCcvUz5thki4lO+hhoV2uudELj87hbMpAoEgKy7ueR4GFHy63Tk9jz3WBIUIa03wgPyWLDfZOsO9GBoDCwBAnf5hVaRPauEAWFoPFALytEM0kJCf6foVayDoWwt2BS+2g6A9ENocW8SAgu6yWEEBVf/Qm+VwWn9+6rMa7NdYQBa6ztxrCwV7XC8qNwYMrKyZ0azshoLF7IbryT7iHDSgNevAlh9yWKCx66A6X8edUJ+n/KBWA5vJcS9pn7oK5C4gyOfoGy4fFwasZaAHA5pfYKR4tjIFKgBo/fikaBQIJpcsA0mJ+A4MTGbevycxd1O4EgtAmKGLBAkEpCWGzTLDSP/ifATCDJ5lsSAORyCkRD522WqggIIVk/ZXFT4RAd6D/AEgAk0HWYci/dVlqMkuQ92sOul8+j4dEEGSmyAKGMgMjjRDI0FCIMIED+Yy44OYwUk7yvPmnAaq94zm/Ikxp2mMR/OM5LkJCExeLDSzcePoNMiYLAkhAQEozYCwkEBU0gwTjOvMDDx6+Ta6fUHqRzpvZQ66S8DvdPcBJOyVUYbWrcHB8B4et9/rQIC9pnGimELVg4hFOZ+0rMYMbMABOpYCoLhTd8iNAoO9QNA/eAUKgGtCwV4D1MoLPSJl89tiO7BM/au72TdzNPtg6FIw+6RSt1YDoPgkLSCsnsvsshcG6s/XAwILBRYIsnk63UMbM6D+7bG5uqQybmcRlNiBUqKem9ATjFWgKJupAwRqHZiSi0AS/6REPBUMHIFwBMW0MFAQAKBwBDgAxyvw8Qp8vATPs/wNM/h4BSgUJDCIcwDHKH9TUgaO0SzE1DE6JzCQRZQEDsgT3CRLELvJg9IS1PCTLGd8OBMwOJwLHBzOZVXDwxmIBA7Iyf762fsD2NeQkBNHRUJwKb10BGhymCwgkIzW9bl5lvwF7bTQ6pkFTi4GaU0KCtmawGItmJx+bqZBguGcwIJzlBMGOZL3lcASA5DqTrsVBQTtZYqy18KtvcN9QNDz7YEEWw9b0os3AJbv9+o5hsHjJ/Z9dpZa3nfFiqCDJj3mZOfwfogqp3UnwYGVtdkH2q9vAcKNAYPHggIjXShYk04Ayz7pNJbHtBCMXggr1RKuJ0mBnK7lwPRJy6ClvlS1ZeFg0zKg5dGPg0beofwtC4HOJtgLBCPrwBxiFwbWHpEabnpQMLlkKUguAxmFLoFAFv4Ri8GBIIp/PoplIB4LHCRAiFeXAgDHK/DlQ/B8BZ5nIG2LV1eI84xwOYPnGWEO4DkgHGfwHMAhgiNnUJDHEbujbFIgAAoIOAJ5B5o8/GGSv5MHTRP8+QQ3TXBnZwkGBA5oOgOdP523ubNzsST4AyhBATkBDOcPOPgJR10gKUp6ZI8lIFCQ6YM+JotANFMZnWQ+bJ+h/TyrPmZNoV1DQoBLn6W9WFcDp/gVl9QROywBgaQtCSygAoSqnnvOy413hJr/2fztQoK5fqqqVRm96+PN11CmWO8D7W/cKntLesatKiUZAUJbzp39qlX6JjDrSeiwvVMSOc2qGsmNAYNKViI012QIBb1o9bXrrMo+IAA6VoJTgKA3DDX7Xw8OmvOgWA0A7Mrq2LbF/UlBrmE2XEQkL60EmmegDSZczTXQWAjWgKCdb9+ba6/z61somJzLVoLJkwCBCRCcklXAOcn8pxaCGgiuinVAP6vSv3xYLASXD8ViMF8hXF4hXB0RL48IlzPmqyvwHBCPQbYnOIghIs4RHBQMgBjUYtAHAwCyMJKDgIFPqycmOHCThz87wB08aPKYzs4EEM4P8GcH+PMz0JQsBudPgw7n4EMNCvAHwIulgf0B7M9wSMtXH0ncKrNjuAQIM7HMHPCEib18dxEUivUAMRY42HqeLJYBtSYwJV0wB3EnpCyLMzEmT+BQAkU5AkwCoZ5IAg6ZENN0SHlvGcQogID1ufSbI8/6IZnjqQsJQAEFoIGF/GNHOvC/KOtjyBAKOn0hZWU8AAQTZ1DFXel5FoAAlBs/8Y4MHNTbN6wGjVRQ8KV3+jvtNPHcHDDYo+hGDwDAxesf23QfrEJBl/ZObCBPAgp635uyPg4UtB2FtR6sZXXszQO+FgicartcXKkPBW0cQc9K0MYQaAbCmRUCeAEFawoE6EPB1FgJrNtgSvv5tN0R4eCStYAA4gA6XoHCDAqXSyBQGFDrwCP5Gx89Qrg8Ijy6zDAQL48IVzPiPCNezQjHgPkygOeIcIyIxwhm+RtDWlAplnUWuvebqIacrJzoDg5EDu7g4A9OTPvnHv7g4c4mHKcJ/kzAQCHBP3UOf36Ae+qRWA+eehp0+TDDAZ0/DRzOxXLgz0D+CPbnYD/hMJ3JYktMCEQ4kpjpHUlQYmB5GJ49PCJ85BRP4EDMKciwtBcHWjxb/e5SogxZpTFZHZhzlkVylMEBiNl6EAF4BuCkbHBIcYYStxCQYkxYXrQcNEhtS29se7sC9ZbT+Mq7Waxu9syLhEC2H2hkBDB9q+BpUq2Dkjd27r8BhMrFMIi76k7n3i0dG2ur9BvLAbXgsTE9dRcU6HV2yA0Cg4EZZlGhywdQ5SkYmF+GZp6BD3u3dIhwOB2n2bYJBVti5+qb76eatFrD5eropfneNR9uzVtekVHZ2fxT94G1FLTBhW80FFzHSpBdBY3b4OBKDAHNV8lacJldBjhegq8eFSi4ejgGgkePEC7nbDGYr+YMA/PljDgLCIRjshZELmAgphXE5FKwYQY5PbFMlYAuqewOcr9uEjBwB4fj5DCdTxkSpgQG4ewK/nzCdDkLIFweBRDmKwGE+Qp09rSA0fnToLOngEMAx0OacnkuyyNPYkHwqRwhSjt0kOWZKcoqluQdqLEeCCjENLqX56wA0AMEB8rWJAsHuqTz7JLSiMDkgDnKcw0E9S0AKZsi9DxJNcvyyoXCI/aNxDffJX0NyQkoaEMFDCjUDou2DwD6GQS3yrZaLJuLhGPpy08cWbeKfQgHef8OHDS/bYu9YwWURumbMi0CBVeu8errH1uHgqHuGpf25oDBSKqpHEu/zquvfwx3V6BgaCV4XCBYXGhFqXd+65rNWotIhw65gYCh7Niv5eBhRzTcZycI7H3pd0y31MvG8jHFFKRZ4sZ9YOMJFBY0nkBdCwC6wYVAeRyuuWstVhtLMHm3CC70yW1grQTWbTARcjBhdhvMxxRPcAW+fARcPQJfCRzEy4dAshbMDx8hPLzCfHXE/PAyWwjC1VFg4TIgXIUMBPOjZC1QOJgj4jGUNSCMtQWoR4yqsFwyict6DmlywsHDTw7h4OAnsRrEOcJNDjxPiMcIPwfEOSDOB8RjgLs6YJoDpqsD/DxjevqpNGNihjt/OtGczKag86eQVzycDtDlkZ0/4MxNkq3QuBcEEpLSb6wHgQByHhSiLLWBAgge/aV763jfGg6mDA4ye8czwKDkKkhwlSwGIU1zVCCIqQ4zQ3QsdeXCUrDVQUdb7t6c/xVQ0P9PHJ6s95wtBGzst7AaZIW/3i923aurfcipVoPqanqS9LUDCFzyrqyB3BAKHjNe4QaBwdpDMg/AVL5dUOLOs8+PG/QQCK7TMK7z6iylazYDho2Ve/fQ2Ta2FvTPu6cG9sDAZpBl+1tbzpHZrxGrrPKKj1x+01EfG8WvJdPVBeuilhN6IEewqy9W58K36xDsnW0wpRkG+psCwthKMAsgxDlDQLx8lOMIOEHC/PAK8eqI+eEV5qsrhIdXCytBSP/iLC4Eay3gOWKeIyKAY+SSMwBLKJCKSnDAEnSnWQAPILjLIJaGwOADw0VxS0znPulwcU9wZCDI7AevF9CYhsCYnpaFimKUJZApzHBpdUN3zmCnSyLLKocL60EsqYtnYjiW61Ize8FHyYswMzClTIpTBww1vqT37Kv2k6iJEwSEyMWlkNonQ6Y1xhRz4FVPkrTVvJIh19ezspkDoPduGRAYgkIziDh1UeBBadHrJ7tWA70XAwcA+oAwkCcRc1XKfZ19ua53c1+jvmzoPtito8a/3Rgw6KlaWnwqjefiwT2zCmPHfbAZQ2DMPjvKR4tvrc9pg4g7MoQD83t9jU7Da4GgC0GnvTQdO0X6M4CBXgzBxhQktnHQ1kKwYtqzZ2iVPpsdKrdk59iqXCnqPAMAS0ev3/UFW0tQpPPc2+mHnvrBhTmWIBxBah2wcDAfEa8eAcfLXVAQL49pNB4Q5iCxA3OaisgCAkiKmZMrJcaYrQStjEIMjA402+S5xRjzUsgaqxCOEeQJcZaAxegIwQXAO5BzoMtjzicwQWYStB1atsbHCDp7SqAR5zJCnM5SJseYrAc+rXUgz8VFIEIWSRJIqAFhAmMOJUGSQkJOE9yUZW3dilZ08F+Z6XWQmqwJailQeNAd1wwHCyjouSYTDNRTm5HeswICRUHvh4TTerh0sw0E6CyBfObWrZC22fKf1E/uKlrvmNPvdamjUFsQqmdQzlitIvxsDwoep95vKBg0hpqm8hkXr7+Gux9+z7JSW1khr1FFj17K4cu6xz9moaHZf7NR90bWzfYFFFzjvocwAGwDQdNJrc6ySOVcRBVvuQ+uKWT+ekeYg6y25yPJlDOGBNMhRa+n67cvVm/9AklUVJLe9IDAUYo3SAGGw3wExo3ALRQcr4DjFRDmPKsgJKWPNLWQY0yj8WXwoPOEEClNN0zxAQeP6RgQI+VcFuJvl8paCzzLWRgbdwJRuQZScKKVGMWaoFYDnS3AHBHmADiHcHXE5L3MsEgZFhUO9B0kAOzT6po+yh4xgPwhWw98lPTHM8k9xWitCAIIB5aphznmhAokAAIKw3ZVJa4q2SuR2pltd09c1t43685bHBfS+wbTR8Rq30VyoKYvacbIy6ItrmvgQMtsTO31WgimP2lcyLuV/6mzznYMQtrtozpYQELrBk/lqQa1qr8GfXbHcLdZRuCGgUELBEA9Nick0vqRd+OVd75cQ8FwpNmv5LVKbX+z5Wohpd6x0+A7cRHd4Jjednve/LnjMngMIFjuafa+LhBsWU5MdHH2De4OAloR7YN08JH+OqRENAwZ1UcGp5XzctXz2IZbliquVzAkQl7IqKQrroHAORJ3AoyVwCYmsvkJDBRwAgHMaXuYEY9zlYxoWVAJBPQHSR/sQjF6kyMEJ8s6u4MTq8LkOkGH1E16qDIKQqR0XucIlAIR81TGyZdtGoXZCMcEOYERjzMcObGmEElWxONVNQLPbx9zenYsDztZD8j5vHxyjDUgBMc57bIjQgRh4uXKj3CU12E4qV1ACpjHflT/fSK0sOed68U26U9mlVKRPYCw2QOmY+trVT1oAwE2lXHZv7FA9vqFx+kzN6wFW3pia/Reaml53xcP7pVB7Ze+9Q0rw40BA6CvkGsouIe7P3IXr7zzFQMFPWW7r4K3KrdnuagbfWMqs+duaXjFrLRJuWvWgfb4x3KRmKN6UHASEGwFRxULwSJwiFPUOPnqiN7r7CBuAMrfxRJARHCJCmKs4UCWJSZQ5LSWDWMaxWDQchljtRroSNHp6Jnk3L21DDRlcZWoKMYaCuarHIDH8xEcjqK0rblYCATkUkSbc3CHup6cDyAXJAtf9Hl6oj+P4vOfg1gWzBRFACUOYEMoAYZcrExdlNkJXpZLcGX6osKIT7kNbK4DOJnuaDMp2nYg0JIAgRzIefB8VcMBpH2yj3mRKHKSptk5SWUsUAAEl1wMJAGEYimReJSJCAGEAwOcqlRhYVgX6lZo2ogGFerCV7qfa1RG95y9bfb9y9va927nO2eusQYIVRBw7sv2wQGa7SNFacu8cDGM7mkECyuu166fvzHbP66u0H2WesxYCnqD2sEgbu9Atic3Bgx6EdAtFLw7QcGdlUq1x+393Lt2e339vrxaAwfGMrA6n3Vk5moa8L5ZFafBwFhOg4LNjmk1uDIZiK37QKO3qoCkfkkrA4xaBkjOSvlBFThgRo4U16tPOdtL26HIH9ttKAhoJ+8IeXljNa2LIjAWAnCCgaMEzOk6BkEXO0rrGySLgMDBUeohB1Ekw3aaBqkZBwHI2gCeJJPhNMMdvMwymAN8SncMTdh0FBjgFAgoVge9RA0EXWuE1oVz5nNxF1DyKThHoAQJ/qBrKbgqbbImP2ozJeo+4opw5v6nbA3g+QgikndLHxVHMB+S5SACHAA3ybH+gIOTBZ6io5Q1UVp6YJKgy0h5Vc2JS+ZMZsB7uUqukd7oBdvthchAg7EgtFaFrrSugm5ekN42W05q9qmVMKNx52U434aDPVJDwqgfS1YEMitPttP+zH5D2dOHrsRfbSnkNV01OuZed1C7fd32XHHrQkluDBhYLaw3r5V+8eAeXvyRu3j5na/g+VSpe8w5vf303GsBVio9QGHzV7d14QBAG5Hari3eLfcopgDoNuZTyTafyuzffcW7sw12QsGaK+HUOAKOgLEcONM+yNCBfnba+zIQKaWfZYAhkJHHR8vhTPlqwMB26g7yQVbSq2FA3QmiRhogSNPrKAaBArvyoUJBmAtQVgkEPBAjiBxwdg7vZ5CbQZ7AkwfNsviRSymNNc1xVNN85Jz22KY65sYdUVkKwkrH62swKJ8dSrpkygqefFH0zrmcPpmcg5/EYlAyKE5wh7TwEjlp7648e44xrTXEYlnRRzfpG5isBZ5l9gJ7mbngPOAP8Mm9wI5kemKyIrATSJA8GKntoM6PkYNcV5q2bTcYtB1NYpQXHDOfgWVw50nz+9cKaCkaQAvmxLyEA2Bz+vDjjGpTgcrrVy0ktbQklCO25ZR+9GSFvGO73XyvGdSu1dOeweueir45YKDSAML9B/fwng/fxUvGUtCO5O223vdupe6wDbWAYqFgqVgNHAALQAA6jXVxzbEpbAQDp4DBLnXc64hGOQlOhYJrS4MvlPzFxiqgnzMcyHAnAwJIlmXeimLQq/Q68hJTkD7rPyQY0Kl0CgEWCPQf629ROl21xujvECXLkWTFQkDK7pzM4/cTnJ/h0kqIHMQSMCVFz0GUIoAcjwAU2IhrloFwwrNr4jGsJUHdDFQCErKLgMgli4Iz8OATBDhZgMl+tis3WisCJauC1mNEWmFR6oWcl9gE5ytAoLTNkctWhMgaokBmrQ0BBQYQDSwA+xRgrx3pdtuW7OflC/oGvEuVgh9Y7ao+rBMPtSMW6Lolz5hgIYFQ9TuVRWHzhCPrxD4oOFUht8e89roMal9Kg9rReGTUj1/n+sANAoPW0wUG7r9+Dy9++C5eekdNWlYpWznlgfY4vGruDaDsh4O0ZRRbMJKd0ynXbmtEr2uWj9UidSEA2znNryvkhnW1VlaX4xTEd5wHR5Bt6tfdSvFajeSywm+gwOkIEIBaBlSxc0rCw/pZYYCLlUA/M0scQRrZS2HEOgByoOkgI2SiNLSVcyIEeX5qKdHlklXsM1uLIjTCp45KjdDegFEDDlVbd8UqkEfOCRTIeWjwocRUKGgkK0KMYIrAfJT6guQsAMViPYihAEJ0onDSuch5EDn4dJ0IgEE54VMFB+k7gLxN5XHaVStk/nWFUut7jGc2Gv3XGQQ7VoO1UzZ/Vbb6JD1m2ZcCY2sCFrCwKicMsLrPdaf+aE92/8E9vPgR0V9veWYJBb2q2aPDYmffVm4MGKgkhq0q9fln7yyU8/D4TmXufYXsfhZQToMD1Ft3dZz1S7oHBLovXLuN6n3Xyv9ExDr+R79nWYkQ3qgzjSXIU+vkIIBZ/Md638256xC9qhTdzttaDDIIJP+/Knkyil9HrxkGYsjHqJUA4BoKbOdGBJoOJa5AFXuM5Tnt6Qx79edcs0tvn14NDUTLaGRhgeiByanlN6s5yndft5lkLShwEACmYj0gJ/BmIAMxQQZJDASn35ybACJ4J6o7gwIjWQy0eVMFB3v6l9YJWFkK0g+DlAh1vbT1l985JyVprJT98zzRt357pN3+iMHArOmf2s8VKABLWFiV7T52CwpO0SP3H9zDez9yFy+9/RU8/8yd6pwpO/b6OTag4P6De6vH3zgwACSmoK1UYKng2u1ZOkDQviercXEogJLPtwIH6HxuP41k7f051aTVs7rYoozqr3r59uRlWJMeHCw6ohoKOEWcV2XasB4oHAAGEPR8ep8rRax+b029MIZGq9RZv0cA9rv53cKAOSYDQQj1CJ1cMUWoIvVTLktdbctRtx1Jk09BiWTGnKYemZypZ6rOU1XMHqkC4hTNOH8vU1vZtCcu+StCyPtmoDjF2qF1YSPP56NYArwX60GI+f6Zg3wOc4EEcsaSQCAcwVqfRPDpd6/nAPI/GGAAzChuo8fvtT2gAOjoIMYy9z4jxfq0cGAvtPkunii2vjs/77XUut5vzUhlGxSABSxsyEmDrhUoWHvO9z5xD9/80bv4wNtfwXPP3imDGCnuMNi9unanYBYK3vuRu3gGzw7LcOPAQG/6A28vgYZtaxjOqNoBBEpaNg1pu69u3gsHvc975VQQ2KOy232cnqNjvltaOsrF6lkVReHnjI1Vh2Su3O18lgpose5DBxBasWV25jk1V6gPsKVoAKDsYqwB6vcfgYC1ChjFP4IBQBQhA5Xyk1iCKL50wKTWGyh6U0+q5BmUR9EKUxkA1OxMVOBLFaL53NTCpiyMsaN60/pp645LXXdBKtdTHySscIy19YNjyQLsvXwnSWC1gARQCnKkjjUh1Z0BBWrqzRNl65RhhPXR8kp77Py8FHKlPhMcVMexfQvW3Jgr76PZJtu33ZyLdtPpi60M+zE2vckOULiuXBcKxl7U8sM3f/Qu3v+25D5I3eQCiLTvWnvhVqDgA29/Bf/3D33P8NAbBQZdKFBZaw2dCqxn+MiX116/h2/+6LsX2wFUkGBdcBUc6DVW4MCKLe4Gy+Tztj+cQqo90UZp4UDLv0riI7dAsihUcJALtj+4cg0K6ulF5bOFAlte3z6Lpp0sO90VCBgps50gACBbBqRaVlCOJEofECVWAQARYh7hF+Wvo1w1hUNN4c5LvTkPJDO4rjQpK0UWX3kMGlQHgHkRVLfHCq11KcF1sogRweUZGqJrZeYGKCWA0quYuAzriinrIBRoYK1n37e+aP31AmR5VlCt65j0DlqQCvMmKFADWQoLlNsvVasSsq0ss63XlXXhwFjw6twoBQ7ekPdxZd98X83nVqnutaK0EpuOsxl2ANjhdsFj9r+day6tzv2zvv9tL+MtzzyfB592wFn1xW0hRwVLsqofG7kxYHDKTWfZoDr74BQK3v+2l/G2/+J3LU/VsSB0r2dIrxfUNyjecNsWDPQAZ4/ovVhi7cGB2WQObjojAFWOBtMZAag7pIF0F4GSglbb1rOTLaGg/Q3Vb3b0idNAQEezJ1gE9DpxYAanxvxtYSCP9t2ECgScl23ki5k7BdMx+byCpExAYIQARI5pqWleXXo6gDEHs8okkKct9jL+lUx/dTpgXSuilx5avCR1RkhPHo4meA+4A0pWyBjATRCnTutkY12gOMt3cvkZqJUgt9vqGaRtYa6egVpk0ABZFxSMVYYMKGSIaKwxJV5laZFZe1OGv6V3bwEHADJyUbl/KChsyOribGohaX/XY83nPVCwt/+S7qSUK4PCTkgYDdSqazTfT43+X7uXtxj3dw4SToBQWQ/stTZUz6n68caAQXvTXW4dPIu9UPBDb3sZzz2zXanXlb1qezPadPAy7T0/YdwgW7fCsqNS1YsCAkCBBQsHQAUI4wI1T7MzOjllvYfx1QYgoNsGMFBmC+g+MoJdixNYQMCGZcB+ViBQNwDn0b/LUfoVDPhDshJ4BIiSn0NaPjpK7v+ZZSnpEKV9zZFxDBFz5PRPPwNzjLJPAghJfiiWgxzv2On4XHo2ovApZ3t0DpjISaZH5zA5WRtCPgssHLx81ozI7QJTHg6T83AHXVQogUI4LiGBfHpWIU1VDMBEBRA61oP81eRo4JAArYWFHiikGQ3UgAC7KSmx4rIhAwlAqOJlWlBYl/QuUq0OFxkCLRDAvlPL++9fZgzq1bZOubnzpe3HrtOH1cPzJSS4Tr8J7Avse6OkYaPaYmGsB0NX9UCuM2i+MWCgN71h/FrIFhTcf/0efneCgorksO/VXL02L2l1bd/q4rrdbt7xIq295lp39t56DdKWaTRtyILAMlVp9SqulAjLi3bdBQNrwuYTsgR1Ggwsgwr3wUC0MwmyFl1G6OcIfxtVb6BAlL5xCTgH9oc0MvVgPwkcuKmCgTkp/cBp4Z+k2I8h4jJEHAPjMkbMoYDBVWDMLNsUGK5m2Tcy4zhHRE6QkdpeuxCT3EoCA5I00Y4Ih8nJX084m1wGgMkTJnI48wYMPOHcORw84dzLvgoKsiR1WraaIJDgPbw/A9uMkZobIhyB6HK8AMUARpD6Ddp89z0nDuU5cXCLZ1XiE2YTvyEAQVEups+yhQTZFk27Ny/irrZuxr8tIPQyBLYwsGfOf+f9WwP11dG27rPRl231YzVwLCHBuhvWIGGP7MgCvilr1uG1vrjnqraSZ+cZKNiBejcHDNZXSTSfBw+x1/ZfG0BBe0qgNl3Z96R6aCeSxNqUwlOAYE9D0P16cDDapy2WqQHkjshYCZaroWHZEQFLCwH2goCecI9cwzqgvm1V+lnJN1YDAwOcMuzl/VW57HHEV9tU0TgTD3CoLQRuWgDBHCVf/xwZx8jps7gOHoWA48wJCCKuIuPRHHA1M64SFDw6Bjw6RsxB/0ZcKTQEOd8xRHE5JMtBzo7YicEhZy0FovB9AoGDTyDgHZ46OEze46mDw1MHj8kRzrzD2UR4avI4S8eee4fDRHjKe3hiTMmCcEiWBu8Ik5vg3QR2c1qRUhS0WBAcECVKgpjAMYguDED3zWmfW0gLP4fy3CRmwxWLgp/6kKBuH/teqIXBeRA7IGXfRAICSk2XrSuAVBXudDIM3r8MCuqqym4Fe/8nvKsLKFg46RYy6qtO6c/aYYdVsKVNqgWmY5pXpWsuuhcSyoXKOfX8lQGVqH4/MK6Tnqx6X9P2HhRomcxuXbkxYADgJOvaSNpAwx4ULE45eEJb1ou1xrZn2knPZbD2Ao10kS1+Dw668RMDd0L53IEDAG16Utl1paZOgICtF2vRLZ0QO1Ap/gEQEMelZUBhQCu/N+q0YnMB5JHnVENBchPAT0kJHQQI/GEVCI4KBMeAywQBl7OM/q9C+f7oGPDwasajY8SjOWKeY94+B0YIEVFhYJZkQFINXIGB1q19hgoGRAQ3AYS0gqIjOO/gk2XgqYPH+eQwTQ5PTQIKT59NaXsQOEhWhvPJ4amJce7kOB8ZwXEGhNADhHAEggfoCCIN9AxZT2oWbQ5z6t3N27T1DJkLKDgP5tixJLgcW8NukjTSyRVEJO1JwWEECKwxChm2y9u3/i6szOfPL3Isi5DZjmPtXc379N7ZZbl6boRq0xPo06zSbwHhlMC+NeuuTbPe/b1TbrnmEg560rPgDsVCwYdLHp/uSVfk5oDB49r1jdhAwzam4ElZCj6TULBlCdwR+7dLMkigAwfdkg3kRADYCwTdMdUWFFgrwSgZkVoIbNKhDhBsLi6U3Qd+BQqSu8D5DAIKBUxeXAXqMgiMmRnH9L0FArUOPJoDPn0lFoGHVzMuk5Xg01cBl8cgroIQERIkcIYDWWo5r7TI0hYtGMTIZSVFCBjk5Dy6oqJzcJ7gvaQ7niaHh5OD8w6HyeH84PG/O/N46jDj/OATIDjZNnmcTYTLKeJ8criMBRCmyIhedO6UkkJOXgBBg/4oEJhmaRdR4jaGcACIhcD59eepUyCdTwteKSC4DMqaaRGcrqeKPUKUv/MSJEkuaTWX3iZVX7GGA9ukB597+9RWPvM9ry/C6xaDngzeX73mE7C8y3lWB3jpyh2Te2/As4ijsjsmOQkObKeDAgdanpHloCends2bULDjpDcGDLZMPYuHNngWFgrWLAUtte2BgpPMUR3pvZKPCwVrosVdI9R26mIfDnJpVkYcy2usdXB7b6suS7u1s/9eKDCZCDHPMirsQUEaXS781QAWWfmANMosUIBpAud4AgECKAi4SQIL/QERJBaBZCFQS8ExAsdZRv6P5oDLwAkK5PvDY8AvX4YMBL98FXB5jHh4FTDPAWGOCHNAOLL8DfJXsjULMHAMiGEGc1GW3BlVk6Yudg5EHs5PIOfhvEv/AD95zJ7gJw9/IMyTx9Ux4OGVx9NnHueHgH/sGHB+8Hh0nPCPnUc8ffCYQwKhwDh6whwZT00OAQ4Hx4iQdQ0YQHSEg/NwU1LUQSHhWNoWOeiaCgRrOSj2YM0j0XuubLNNJpCQz6nZpGRKmGQVRyI2KQTS/s6DEBNQxAIHw9exDwhr78pat0iL/2GAYevM62XRv2sjbVWap5rZq+uaQc9ea+iW334ktn/P97XTrQCUvvzaaoJqKHjhS9ct3WsXujFg0N5j25ByMpuVFnb/wTYUfCaAYMs09TjKvifDe7imWDjI1zD/t/uubauAZ//wJ0ubc75rNQCytaD6brfnv7FYGNRSsBcKAHRTC9t0uw0UwEJBdhd4AwVn2XVQXAbJShAljuDRXFsJHs1R3ARHsRR8+nLGp68CHl4FsSpcJSA4BszHWAFBmBlhjuAwI4YZMRwRQ1mzgWOsAKG9V6K0bkFaBMn5Q/o3gfwEPzH8RPCB4WeCnxjTwSEexEpxDB5zYDwdSrCjukxmdpjZIbDMvggAngID3mOZgjjFIPizuq2SS24GQPVxXn0xzMB0kGecenbS59tJEZ2TJyUl38IBOeQ1OqRdae5HKjvZv8bMX3231zR/V5XvyjsD1NH5NNx99FbXlxh9v46MlOuTkC3L6Sn9eKVvjPXAhn/2rAdbsraPQsHLZsHA6thdJRe5MWDQyggUstJNrV0bmlgKJONUXppZCa7zMLrKdAMG1h7ME9b15ZorL097W70YZ5v/v9pno5W1CngLANrva/OCh8ZMQ+aAFMBaNIC9L0dSHSYGobqADfIMG/5mNCNLGAuBug4UCvwkynM6LOMJ8qyDZTzBMSC7DALvg4JfvpxzHIFCwacvZxzniPkYKivBPEeEo7gSxEpwRDheFkvBXKwFmkegl4vB2cWHEiTwpBYHD384B/gAjk4Y7GAj2RnMUl/tjIcYI0Jk9LszDyDgKRQ/esyPkQAPeH+WLAcOBLMcc0w5BSIBE8F5L+4iUitBGtUbpd99zivCIQgASoGQowvN97IIUUR/xY7mnOUMw/wA1c5GsuLqvDCj92gI2+PLLKXpj/Os5o7VoIUDYH8f9yRlT39eWRHM7848j8XEK7NhpIPa/lgXDLRQ8Di3fmPAYItaq4bVwMHHzDxPayl4XOvAKQ/GQGVdxt6+J5Ly1ssx8ugPocAea869BwZGv50yHXNL2rnK0ZTzNEB4DFFlr1/bBYbs73a54GlCzk+QZhfkeAKdhrgTCo4z4zjL36s5ymyDFER4maYbPppjjiOYI4vinyPiDIQQEYJsYxaFzLEAACcLyR4oAESBOx0AAzLojU4WK4qiVJkCIpEsix0iAhFAUSwNiIiOMDvCcY55ZoN3BDdHeBfSMtYEhwgHgkeAb+FAvqa/CQ7cVPcVajmIVCxgEcDhDDTPadbBXFwIakEYLST1RmqoJBYIAFxrLQbFXDIdUgULg75uDxzYfe052n5uLxwAS0B4HNlrLdhzqd7AtGdF6E2V7LkZeuXcCwWj8q7dx40Fg1YB9MzbQD2l47kdyR+2rANtOU59WXplbGm6J2svzUi2gEA+d/a9xkt4Kgj0gitH51oWSb5ZQFA4ILsPOZTE+NQ/cU/MvvW8d2CxoBFgTMh1bgIZPbsMBLBAoMv7tlMRTZBhiCkxkclLMDPyVMLLEHEZytTDObkYJKlRrKYZaqbCVnFQmkEASBBhZA/iCDcdEOcjME0SG0cRnCwjssTAdoyBWEcmEDm46SDfnc/BiuXaRbR8gdP0yDRd8uAJITocA2NynPIvRFDKA0DkcITY7ynKy+T0eQVCdFgGJaYyyBRCD7iUJGmS+qD00CtXQpvzoJ1l0jx3WbDJtKu9soj8N6NMFCho36etZEG5vzSKqYKFnaAAtO/kDjGdWA8ObMGeVF+3uu+G1feUvt3uu2VFAPYP5uwqwi0UXFcXATcIDOpmXk/HsVCgfx0BF5/YTv6wGCV3XoS1xrLH3KT7tXBwCk3ba7UvzZpUZT8BCLb8bSMdu5bC+dTpl62UaOJSkznS2MCB/KK7mJXnMig4aMY54mDslQITojBYlAIZOPBGUXhTc768Zm2WvLy+gc1gqLMOdCqiyV7YgwJV+iGFQiggzCwzBSKXHAOLZ5DqxFPKL+AJzA6MgAkecxlai5JNCxk4f0D0Elsgf8uqj3sCLSm5FJw/5FgDEMHnqYsSgOg8YZq8GE4mmb0gaZFpuLKcvV9O9TAz4CNjSnUHscuUhhMJcMBEXm43ByWaKY1p1kLOO5AAwbHJZunT/ft+19quZmnXtyirV1IGFGl22pZK28nbrdkZYyho36e975KO1NOFAVQpG7qr/o2Wtx91GZv+eMMELSDodfbGRi365xOsotft59eka0Vo9hnpJAsFGmjYQsGyXIMRWSM3BwyqxDm1vaCFAoIszfyedkoHDxpYRyHa0ec2RfZfkR682M8VHOyg6etEtZ463XJP7ESvue0BgsedYRHbF4ul4nr0nX4pMJD+VovkQKPPUo3nnAxOAtNYHgBDlQRApiPV+IOcUx9oFIFdydCubZDgIAUeasBhBCF0oEDTGkcG5hDFehDSNMLsVy+VJyZ4h4NPayFEVz0fHalHF+EmIBwZzsu9FtcCI4YpT0+MkctobmWeP5m4CucoT1+U+EvNZSBK0XsHf9DpjA5+kjwH55NMY9SESAfvZB87EkvPPoLBUeplIoc5RJB3oChrQZRKQYYDTzJTQuDgKPDtUsZEmY4hbdVp5ssoLp8UkOo6MSjLNoAMGbDtwGxb/qXO4mFyzOjVyFNItV7M9jWJnb6gBwmLFMMdQMhlSUe1/V6+zk5AsNdPBVu9l57sHQStrV65t6/f0x+70YkHOun+66dCQVMujqsW0psDBgDqVJ4OFhBaKHjxR+7ipeST6QXaWOm5CnqBeqkQewq6ONro/a51Yw0OgIamd8pa8OGTNBG2AVDAvtHMmjuhFZusJPXvcj5VELS0GuR7UatBggBZp16ggN0k5mO4dFLK52Sdk87lIVRTydxU37YqAKAogXbVQwMEGRLSYkchSoIihQKFgcBIMxNk/YNeVTmS7IKRCZHrrsY5yR1weQw4TA7HOeI4mZiDgyyaNExmxFKW/BxXnlUZock6ByAaJj2iZMFwKU3yIeU1mDzh/OAzFJynZEiSUlnu03XeBWaBAYoMeJJcQayGDS5wQNJInJtEEScYIAo1ILCTZ26WfpYLxdJ42zZg2gKAARAYYFD3Bly1KqO1FrD5Z60FPSjY+07Z/qSyENgd5EaKVc7eKJXA3zXpDSr2mNotJNh3/xTZGgjth4K1iy9/66GC7f9H96/Sm5I4hgL7wLfsREVuDhgUp1j63loQKEPBu3+kBGosGmKnMfcaxLBhnFD5PXgppV2Bg7ST9a91R8VNlbTyRoGAbeQ9sR3UFhTsWUCFzH7aSeyBg4xkGvGdI79dmaqW4CADQJ62mD6TR1kaea2EKB2/FNS4D4rFoAYCBwZhZmR/egAyFARIbAFzuXTNxgQXZR0BAEBwwISUkliC9g7e4RgijnPEU5ND5LQ2wqyWh3HaY5vMqPe8pDws7gdbG435Nic9WkmX7Eng5SwpfYWEgxcYOCRLQrvGggNV12cN7GepssAs5Yip1YquR4SUQ1wLEutRAwJjkejKgtEpbUIrY6H4tb24sjJma0lAgYO9UHDdd8oCAFf7SBn0nRvJnn6k3acdSWdrRFP4ODj54/aBu0fhJ/f7er4+JLTlULn4hFi6e4GGu6FgB0XdHDBQsS0h5/qWpnzx4D7u/shdvGKgwCpjYNuf9CQqfVQ+vUILBRYOgDEgWImQNezH1y8ft0BgLxj07nxtulQPCk7pvNrftaNq4WD7uDRiJzWVGjjgVPskvvfWQiB/NzoF0/m3HX6GAWs1IAlIi0BZEpk5/7VLIjOX+7B15lPsiRgHHJxjOEqxByQZAGP0eaXEY5ApfyEFKMp1YlpkqeQM0KmCcwIVANX0wd5yy61400vnhZUcqTdGYgyoLLTkHZKrQBZb8q5AjSNZkVFXaJy8/hUomJxwgL2mKjNOjzBGYy2ApB72UVMQa3k8ZJ3nkCAgAjzJ4kvMgI8ZGslaDE5oGwD67aOBAWtlMEw4lDUQH0n7TpVvNXTbd06vlZX2itXg1MGGyl5QaC+Uy9WTQV/4RijcUho96RISRnWT3d9Jf60PWDvt7wSzyo0BgzLXN4kibpKLB/dw90fejVfe+QpeMFBQN/nmnN3P2xVOK51BLuMKwIzgQK/eBYRG1hTiKbMo9rzAVSfSfO4FUFbmzPS3Bwz2d2B5HhUb7DTuqPpWAxVKZn1mAjgFlzHl0V8BBC8xBYsb6b/spQ2aTl73yRYDytYBGcXKpTIIIFkLdHuCAu3wY74UgRxj0g7GeVCImBwjgOCJccYOcSpxBXa5ZPsdQNqWPue/5dZCm0/ghI6nNfV78zBKHA/lvzn1A+rlmuvvyUKQwMenOpm8g4dYTqS6S9ugBJAwcBBJXELOpc1MAtkk8QfkvTwplliLYjEw7YIV7XB6O0nf+0BQLAUKBWvWgjUQB/a/U3p8+17Vvy9H5vsHWn2xfUrvuyfzvdPX5HJsXGfUJ15H4W72/9X+y/Zg+3r9rO7vXvKiVXBZLeP4fb0xYACUB1IpXyJcfOJV3P3we/DKO1/OqzC2Crd7vupb0yBMRXcbwqADGJUxHzOAA3Q+XyfF8tpLuTzdno6+7wK5jrR6ZU8Hpr/16qLXUWVLwggOkjtBctA3gACgzEmEpE/u3cciQIzy517nHlF37DGWTj1/jlIqTtuyAjDX9Y6A6MCOZQZklNGz+NYBTACnqY1SF/LXhgpyp6LDdR/oE5Ce1cu6BjSkTxWWp+I6EDDQbYCHuCwshGSrgYEDYsgzTwiogEAAoiN5+iRWhHwqz6gyZALlu37GjjYjN6g3VYDAfN+CgsW5B89v7zvV9pO992qvLJWYXmHrGKq+j2ChAoVrlm38eR0K9uiBdp96Ubna/aQ9q7q/XxlkNGzLu7j2Vhk7cqPAoCcXD+7h7odfxCvveClBQWnmIyW2BwiqCt6q7PZ300AyQa7AAVC/nFtAM5J1Um+18o4GtOEC+UzJXkDq5kXnHhx40Q66whwvwQ7o3+PakrN7R3isI/e8D2cFxmygICmrHDPHMipmkLzYfjmqH9WDim8qkwZfxsG3p8lg7FT90N5B756A8X3ZeyLzQT302m8qHBARmMSK4kAIzGKRIAJFzsflxaCADArJ+5Cus+w7GNudc3eJ8SaeoGpHnXrYI2tJ1N4IWR3Znrg40xosPE7/M+4jx1CwWxd0fquAS98qY0HuWbrXztEt5zXkRoPBxYP7uPsRCwUqNRz05UQg2HoZbYdlAiOzC2QFDrSctuE3pSyXGV2++rYBAXs6lg0XSFvmx6WFUSe26haxVW5+t1YDNT8uR0cE0gVjSH8BuJuSdtnVsfk8mlduQSB/b2DA7m+Pb6/u04cWGqc05LYxjwBlxa7KTY9xkC/VdFyrAFGUoj3vKIhrIabs7f30guWs+dvWjW6z++iyReV8/cuPipctB8xwjAwI4OJpsHWj8YItKOT6SC4OkM/7r1bIQvrtqqqP9KW1FlQjaqp/s6/invfqWjJ4L/eOvrtic4rkE/ZhYa272dNnPhEo2KMTTB/apmC/ePCqDGrf+QpeePb5YVkX5XwCcmPBYAwFKr0uokOwvQbQ+X1T2vDYataE2WfRe/QhpgcJve/13thsvHtMTZWfTBv3wMoBLDsf21FVIEFaBSUFanuekVSdz047ZwSqtMl6jX7dLs9ZjXabThroKDazbdmxL0HAlqvXzCrrc6OgrZISq0Kt8F0yiatCUx+6M4oOYICjTNc0ZvJqBUpjKqetTt4GAOYAOt3eCbRzLs/OQMftEhrAym6XFbjaqlPdFvSKXOpUZzIG1jrNFZXqlSuIasEKnWO2ByipXObzWkDvmrTJ0K77XlXtrvq9PraF9iEUnGLqZjSxZGNY2FezdRm7F1xs6m27RpBfL84MUgcXD+7j7kffi1fe/hJeeOYtHX2xQ3etyMWD+6u/3zgwYHIFCt7eQMGicgcPcAQFo4e/9iBa5d/bpQ2cXJR1qapOIse9PrGd5ryuG2Rg5ahKQ8W3D9QdlXj2l3Bgz7NZNNNBLBTl4JgcMbCnk22qdQ0A7PZTIKBqVifAgB35KwRIniDZroF4njRoL9UJpzn5Qefmc9o2o1olkkMJskNaVTJl+6tWlgSAlQRHmiI4pwS2qYFJ6aTJAklpm5vg22md3s7ioDyLg1kCLrV+JXCTNHokxWf0rTCtxUHbZe4PkuIPBgTGsIBsWcjPKj8/WrTT8nDHVdizuvQOZ/TfK7k3PkFlLqHAdX7L24dAYAr/GK5ZavvxFhYGUf9aon33vdKH2nLsOtWGfmgGjRcP7uGbPvrN+ODbPoA7zz7f6WM75dspTA73PnGBb/roe/Esnhnud2PAQBtGBQVb61EvTrJiKkp/K9La0zB6loFTyrMHZHrHVd8HMPAETF99OGgsAaitBuoTzx1VUp62EytHtkUc+8ft5i0fuDXOjHzb7f62DLprDwKA5ajVmsvrY1YvW4nqpsqEnRV/DQOekFIHWxAQJU/zDAqzjLY0WU/6LkAwA/MsiwSlfzwfwekvmMHhCIQ0VS8GgYIEB7zyXhA56NLSuhYEEQHeg1JaZJoOstJk+ou0JDNNac0IN4mbx09VUijn5beD92CQme5JeboncwmmDAQJJWHO7XH0PKpXg9WiIF9qxc+VErWw4KgcR0SVRcLCsl7jlAC/LkSiwAGAcebA6jz996v3bul96O/2nbOuqMXVTh14daVuY/b8/YA+3fEaffHj+Ov3xp8ZQBAoeL+4D3p9rO6/V4yJ1loivudD/9bwkBsDBsAO98FaZe6Egm/6c797uf/nglwHBtqX7xSy1ShrjCwHxWpgTZYRZUM1ijGWA5Ve0pI1N8FybLDvOCuLKjEQAJwGAj0IaM+zSQSt5aOBAg2K078ZCJxAggOAOINigoH0Gfo5HAUQjlfg4xX4eCnLKB8vBQSOV8B8THBwhXicEecZHBhxFiAIs6yuiMAZCjh0Rlhp/QgiKRw5Bz/JYlFu8iBPcNMEd5hA05lAwXQAHc4EDA7nAgeHc9DhTFY6JJ9Xm6QEBrrgFLkJ3k2y0gNLsqfIAgOROVsQIgguwYFtA7ufUdpHrAhILoUlLNQWHS7TjmGsB1zUGuX3pG67W025ZxWwgAD0363etVR6oN1zLSgAtfvmUvSg4DqW2JUVBYaQ0Jjsrz1geyPEDKo++A0/VLkP1uPQOkJuWXdEEsiYLenPrxbnxoDBxeufGSj44Df8EP7F//Lt+wu29vD0ko/TQE8Fgt0v3uA6bQNN11k0XDDSmEj2gelfyfj2GxOnLd8ptdJ2Z3usCirXgYGea8DCgAUBq2D0MBtZP1pz3TtJ29veXAsFYhUgTOmzdwYIwjEBwVEgIMygeATiDL58JABgoeB4BaS/8eoK4fKIOAeEqyP4GDBfXYHngHgMiAkI4hyAvH5CzFMe7dRHnUJITlIci5UgAUECA3fwoMljOjsDHTz82QFu8vDnB7izswQDAgWU/6Zt509BVlpSK8MB5A5gP8H5A5yb4D0QoizpPDMkNXJqmQoHCydIembtTIjeM9PZD2SOLVYwzkCnsQoS0EjZFKSjbG5cDFbBt+l/cxyJaSOF3807asoM5OSPu2UE2ZWlYAAEallpC7h0Zaq5ZqcltpKOlaDn9tRC23Ncs/9lGzDYU8Zr27snlP0ECuqB1uPCwXJ23rrcGDDIN/2lb73+SXZAwQtvevP+87UPLb/FAyg4yXb45IBgNLe6On3bGEcN1+4DVCMWgnEpJFLQjtKUpvo0mnq1ZgE4pRpb2RUrsBcGGhAoeQMG9Z1+1/n2vfuSfwUKJqcwoCl8AeJggGAGzZcCBOEKFGfEy4diIbh8KABw+VBg4OoRwuUVwtUR8fKI+dEVwuUV4jEgXM0CB3PAfDkjHCN4jvI3RllHIaQFlVYSHzifFk7yaT0E5+APDjTJ3+l8Ak0CBf5sgjt4+PMzTE+dwZ0fZPv5GejsKeBwDnf+tMDB1dPA2VNwZ+fgMAH+DPBHgM8lO2ECBHIeYIBYlhMmBub0sKJB2V6zk7iFBUXmj3NILh3IM/KOjMuhxNf0IIEAyFKdycKQYNkuDKbvw5b1a/TzAio69zc6bnT+Fgqq2SztNZoAu2pbBwpO7pcAVJbNNwoQjIWzCwfm92r7qbIHDlbLqe71eymPj0x53FOWGwMG+aafsFy8/lqBAjXv7JGRwt8LBXstHMC1oWDPi2f3HcJBWxZjNdCugcw3a0IFMFwcBVgHAJXr2lvaJ3kqFFSJhjpAoDCgykSrfpQ2WBPxBDA8y6JJkxmFEunyyMVSoFCgqX+tlYDCZYoRuAKFKyBBAF8+Al89qoBgfvgI4eEV5qsj5oeXGQzi5RHz1Yz5MiBcipUgXIYCBnNEPEbEkMAnSCrl2HlNnAOcS24ESgskHWTVRAUDf+6TlcBjOveYzia480MGg+npc0xnB/inH2F6+inE46UAwvEKdHaJeP4U6Pxp0CGA+UzauJ8APpeH5A84uAmBUq1GXfiqtNCQFLNK7zmOnqFmyybHGRQUEmK6jGMBEuEAmRrpXAqMZCqJt1jKQ1SCZEdwsPsdaF4n+5j2utuqaxkgsKdfuBDyxy5xpb+n902UFXRnFL1wb6IZwDT912gEbgc8edsOOLD3dl04eEwpeRBKcr8yG2h83GcVDIjoPwfwDQD+V2b+srTt/wHgWwH8rbTb9zLzj22d64VnX9h51f3K8OL11yQQRKFgS0YWgua3awOB3WdvUEsHCrovXTd6aefQu/dCdQIR278tIACm0xk9po0ijaZeLeZqV2becX/Vg4LYsRKEdM4REJRsg4PbItnHR8pVqaZpl34vfwUKJrPoUA0FV+I2mI+gcAkKR4GAy4eIl4/AVw/Bjx6KheDRI8wPrzA/uqyAIDy6EsvA5YzjwxnhMmC+DIhzFDCYI+IxYI7AUZd/TnWjz7Ne6lnvJaTyirXjkKDGHbwsq3zu4SaH6Vzg4PD0BH9+RHh0Bf/UmZTv/IDp6hzhcsb09BX8U08hzscMRC7MwPnTcOcS88B8nppN1EcP7yZxY5FcXx6q5C1wSWnrCN87whz2PcuZ06JMQSAuOGkgHJaA4NPxNrESaAwHvTaj7QPpnFb2vAvd3uaEd2/f2gLo91cnuTLX+6cKEDbgYCh7f88QUJQ/G/Co9rOAUM2keCMhQa7ZJkca7deTz7bF4P0A/iSAP9Ns//eZ+Y+ecqK22awYmgdH1HLx4H6JDn3Tc+ZwV//tXuJEIHhCQTCr02dWf9sPS6dL6tjyp6V0O7ANADjVW9CmSrXWCoUDwKQ+tvs2lgI9vgoo7EBBT4m0PuxeuqTKlaCm5uRC8KA08i4LDE0EwLgPdkHB5aeLleDhFeYMCOn7ZcD8cEa4mjE/CpgfyV+eI67mmGHgyIyZGUdGWgKa89/lM0gQQwRHjEMEJiIciDDNjIMjnF0G0OQQnvKYjhPiHOEvI8LTEVOKa5hS0KMsJiGujMm0bx1dRwDuXLaVtifvGhPJyonJnu8c5OlGyiP5HN+RzPqegbnTbyyeaX7WXAGCWoE0dkSX34AUIfv9reWgmxKa1vIi5GYzlNHiavnOTni5aPPzG9i3rJnUV0CgOz38FGkV/KmA0J7jiUmpC4GCuxUUnPIkPqtgwMz3iOifeCLnSn/X1D4tvvWrSqDgvQIFainYUp4rI/9Fmtzm917p+mLK8EabptasBe39bDbyGg6A+jlt3fno9739l72mfraxDja3wua52LgcrPsg/770Q4+goBV1JQBJeRgrgSpVqBsByK6EOqbgmGYcSIBhvLqsZxyopeDyCuFyFteBgYJweayg4PjpGeEqYH4kcQZXgXEZaiA4puWYj8x5WiDQsxhwnjVxIMKRgIMDZmJMTJijTCs8M36Idu0Gcse20gDnQO4K3kA7OwdyHpEc6JykjQb5KwwomljyIqRnSbKAktq51HoQqVgNyAE+0uoqkgFJyTd6S11E2nIVYHRqoq76uNdQ10JBb6R+yjvS23ePMmmP2w0FrU9+rU8bmfWelFy3Px7kT8itaAQIwOk6ZX3n/MlCwZ1OGmVu/vbks20xGMm3E9F7APwMgN/PzH+v3YGIvg3AtwEAflXZvmY5aJVSGQ6UBqnzPD/4tg/ghWespaBV/D2UP9U6UJ/j2jCTXprK13WKbDTA7gIve6Si9gIHqLZ0ijM+4fg6PakCIMv/Fg4A5AyIMVkNrtsBjXL4q3STKacy2IV+Ji/Ky6UySm6COq7AuWQ5AEseAk1CFI5lGqLOMLgqMw80J4EEFEqQYZ5hMIurgDVu4ChBhTHFE8xpkB4gbpSQrAQtFCgQ1DGINZ4dARxAYl2AzvFnBCbMEXBzRDxKoGI8RhBF8EHK47yUNx4D4uURQWc1TEeZsni8yhkTiRI4QJa8ppCWvo4ORA7OO3BaldGbeANOEKNFjwAm74AQZfQfKQOgP7G5hMg5duQUEcuRXZ+h9u3rvyKmYLvekfZqy+37vQw7rj0qD8fc51Ruz8eJKrYl6w3U6kLU+w/3aHqzDgDY6ZLVWVtIqE6+8z5z3zqGglH5t+RzEQz+EwB/CHI/fwjAHwPwL7U7MfOfAvCnAIC+uB+q0qrRrVFqnvL49pcKFGw16pNgYLvBtbIsv7mLU6wGZl8mOi3i154jf66tBftMc3XN95t/p1wrwZbrl0tj9Kasdt1zRsmv4LiM6jVinLnOv2AHLu0gxjsSUzGXmQeeaDwLAf2V/yiVSaHAOXEhZCDQAEQgJRcKoJhSF+tfcMpIyNLRpv2YZTrhKAkRNzMKnCPEVBA3JysFJFDOa72RKHlV8OJKWLqI1FrgYIMoS76FfI9pZ3IE15ykLV/ebu6LYgDHlKmRpR4IDIozmLzUD3kwBYAcnJskFTal5Z1TvAH0c5LIAgcUWVawhDTNreebP5tnbKXqKsywPyt9rRN9JuZ3CwOqiuRPJ8aoW3GNHWsj5qmPC4uTbvyOMojpRfI3fRWA0/urUwYwnf5562rLAai9tp0MurQi1FfCbh2zBTQWCp5vLAWnwsHnHBgw89/Uz0T0AwD+3J7jqoCaVG9rZuvyWdRDnuepyR+2prD0FOKGZeC6D6oPNI21o7Ua7Hzh9hVg/R5Pm3K51VHZWRWNGbk342JLyAGcyqh51dWcbGpTlX/ezil1rqk/lxQiUUmIQ2ymnXENB3CiNCbQMt+Iugz0iiTBeKDaUqBQoHEFOVcBIfnBY0plrFYDUYbESfnHlHyII7pTBQCQl2mDNHm4Q0CMDHdwYHaIgeEi547CHYNMk0wxBhMzDpSsB2oBSPXVeye9gRpPJsagCUSczjxcmsboDq76TCn3gSZMWki6X45aNzHVB6d6CIALoEiQFM0sCjxZC2YTb7CAA0jQ52yecX4/dzxjBT99zmIFKLkpKLUz5N/qNS0okYCFggUQpPrvZhRck/SuAOadbuHa3th1xLxP3b4KQBWo96T6q5Fy3YCCvf10O9jMoLDhaqivrBtOqV85uoUCWy4rcefNfc6BARF9ETN/Kn39PwH4K7sONE8mp97tAELPWpDneWryhzaIxF5mTRmeAAN7G5yWuff5JDgA6hcO6HcYvUZ5ajDl6Dw92TP1cgUWtkykCgOlPiAr5mVAqOEA0NdX6lKDEl0yn7sUuQ4IHMAhLc+LxdLHISYoSPtVxUv11VMUmsLYQoF3Ja5Af0d2IbBRCMlCENqRoBOTOjkZiU8efvLg8wNijPBnqSswGQv9wcFNM8KlQzhGTOce82WAP0aczRExinvBuhCisZZ0wQDLLI2Tk2mMJZeBr6YvSm4Dl6cvao4DmrzkNph8SpREqFIu22YSQtrGdX3FYjVgJw+f1YzUwAERQFGMCZMngCXrAWlCJ7d8qxfPefCsi4uAiuKnki0x/z6CggyEzTvTmZW0LOQSAqrpd6gV2SIiZ+tdb5R8N1ivOsCev3PuPf3W2gBmAAWP21f3xT73Th2aOhjJlq7pQcEqEPR2aOSzPV3xhwG8FcCvI6JPAvh3ALyViL4cUvT/BcDv3X3Cxu7eAwRq/t570MzzbEbZw4d2IgxsPqiOtCv99QFnBxwANSAAS0jo3Vv+3nSyTxoI9sLAKfOcUycnI/5ULoUEaEcX+3DAJd7AMyNoh53gwLMEqWm2PLhUnckCDZSRZSptv4zNqFFHji67E8hYDWxcgdxfXvUQcRxb4jzITxKQ6CcZRZ+dw0UGnxVLQEwjcDd5uKsjprMjwhzgzzzi0wHhKCmQDzZvwRxlRcPIVXKjUaIcQBScTXKk7gKaXM5r4CYphz9QzmngFQLODjK18ewgeQ3ODnBnB7hpAp2dl3UV9L5dL7JD2hlDLAviYiC4NEvBaefhdD0DcfdIi+HsdkoYBspTXlZGtVRUXPu8c8IqAwWO6sWvctvYggJjMVidnmzFvitAeV8AZEw2fckwzfCTAITFbILOwOEa/dZeKBgBwd7+2h67bA0dSCAsQGe/zqFNKNhrIWjlsz0r4Rs7m3/wOufKEb755MhPJr3j1WYGcP+BndLR5I7eMl2tmKB6jezkB9SATQ8K9sAB0HnZVgNflo3yiYBAdcKl0l9NzNR2cN2OTo+vy6Ol5er3KNuN9YCoKBCXBpZqObBwQEgBask8IPEaAKPOtZ9OUW4Jy46iVQ46OnTqOjDKwamZHUjKiqEugzxvMp84rVYYnNzbNAF8Vl3bP+3gDlcIlx7u7ICYQCBeHRGOc5X2OM4z+BjSmggsYBA5TRWEwEAUX751Wds4BjLthDzEFeAUEqTSnRMwIEfwkwcdvKydYNIl+8MEd5asBAoJ52d5bQU6SFZEOpzJfatlyPtmud5Sf6yfieEg9TxbOOAUZJleI5cPryHBSvu8c5+e/o6eubUOVFDQsxRwWFoJ1BqCtfdl8K6YaRTc7qMAoAMO4LQUvdWF2n2MiZ18PVBYHIuFIl079/pouw8F1+23Y/PQW51TFdNuGQHThjwOFMTlpko+51wJjyOL5j6AA0AsBe9u5nlm5QosG9+iMW+TZrtuelvOkTh7TAcQdsMBUAEC0CNyU+4tNwIwOHarIQ+a4MgtsAkEY/cD0KzPzgDgcqcno0QACgTQEVBIykuOtW0lJDhgAiJRGrSIEtFnoxbnNONMlM3aAFKDqoxiGI0WuyskqrXAKAJzcrEY+al2PZGAAqZDWhjpDNOZLJTE87Fa+6CAQATPARyiAEJkcFoPgWP5K49L/65YDDSTo3P5Lzkqf726Oib5nOIJCiiUtRUoLbCkVgJMh3qxJZ8WXPJTWsa5eSCm/ijOaaDsMxxEFOgLaVSvszAWzxxYtZRsPXMLBMWKJM++2kfbQM9KYN+X67wrAMSfMgJqc74WsobKbEffcAIo6LXXZl8t+rGuK3QdCkZKdbPvtvsa/dMG4lpYWMQj7NBBFw/ur05JvG75VW4UGKhU1oMOHFw8uIcXf+QuXjaVWilXYFMBntKg2ocx6j+0/VavYgMI23CAumE1Lyxjh/LP2/swVJ9vcGj3W39v6lgQNju51ViDtmNQ4HBNh1esB+paIFK7gBQ7QhR0BoIWEJIFwUKCXFMCFVvJJTAdxkgp2JFisRTEHG0vwGRGGUQAeXAaeWICnPdg58BhBvkj6Oxclk4OsyjyMOcllF2YS+Be9TkYEIh5ZgBz+dwGNnIn0FFhoFRGUkKeskWBvEKCgoJMO4QT0Kk+61LNfpJz61LNCX4ofWdygPOy8iL5UldScBBLHAhxBLOUxantn4HIBRCYaJkGO3cClM648tzTs6+mGY6evdm3IOsYCmqA7sQYyIOxD6T+ncgcVwChyiZoIGA9UdDevoKWe1ZR/VqI1tyeLHy9zrTXn51g4e0N6Pb24W1K97xYnN026m4NKvSBSWQEBY8LNVZuJBgsxMDBvQYKlrJOuHsbk30IvUbUji56q6YtAGEDDkrpG0CopOkYhtICReceVo7k5vumdFwJtW/0hGCqXuennR4n8zrzEA407kDMvWgy1BVAACErCqB0AvpsfefORyvT7VIIOgVPZx9U95g6bmIAAgd2Ox1k3QAOQeINOIKinovL7AVV6CmqX2465HrnaH0FpgxtsOMe8cb/b61aGhdAlGMEyAYUJnCA82Wf9Dv5KbkN0tjaJRcCOQMFbnFN8YmoImSQ8zlQMqb3Dpqq2EACgAwK+VQrhoN2qeLyfPvPv1gJgGtDwSnvSS5cGV7ldVIWMUvbboO2Kkb9SOdNqbe1sFB2K+XJ2/YN6uzn6/TjW304YECBFzaXISC05bXSy1MwlJV2uCU3FgwWMQeQmIIXPyxQYH0y9ah7KddpSHVHsf2E7D62gSkgbMEBOp+7d0T9YKxcjo3vW9t7UFBbNLgorD0y6nGrQMTluRYrPbZwkEtTwwEYJu6gWA8qQBDtsQQCmIc2kLUla4fz0lUZxLlWBKY+mGQyJZxP+6ejmQEXS+kmha6l8uh3nYP72NGmT5V9U9LsyNv4vyF1wDDPHgkCrIM/fW/jDXJbiABRGvOnNqCAgPTOiZWAqnd+LxjkoncAYWFFQqcNdOIJula1bsDujvdEjzWuyL0+7z0Bfb3vuq29St2XlU/d0mz0a+11e58fty/v/1ZKm1fKbM7fczMsj15PXtS1Fhg5xVoA3GAwaEWh4KV3jElr7X1+XCDY041muOSyepq+l2twoMf2Pp8ie17gtehcWx5bji4cANAgydMLut7Z6fZVOLCjoSq7vpbQfDeAkO/RKPVSJ+Y1HpBmm8++hoB0oIks70Wa10rZQVwlrr7fjfrJzXfhj+0Ea+l1OttXk2CNpCkTDbXqwHW0OH6926sD0Nr7sPE3pS1QOGaQEOhCBgVAfP86A0VhIZeaOw9+oz207aACAqBAgQW6RVs4DQp0excOTpGqDfShoH3Lt/oRK23VrQ3itmQEB29Uf67H2MHeFiD0+vF7p1gKnoDcWDCwzfv+g3t48SMCBc93KnWtoY0akm5Ya0BdxbpSTluO3tKqIzhYHNu57hYsbL64O96E1o82ggTZ0FgNTrEi7JRNOOi6FeROivXAwU5zhCoJ02K6JkHqfkSulSau4uT16RdwYK/tyygaRnnbEXb6nIPyKEcxlOOgJvR6VKwrTgLL9r/HOtYdLQPZnaK/9UzqyH9XRtH6GWt11khqFzL6DmWaaw5KNQW2rgiiuk2jznbYY8aqLupCVGWv4LBzf3vdB9dKk74lK0DZtiGVvRH+eb+mzranA5rirF9iFxRU+69AwahPr/bbAgQq5bCDKaCePXdnJXnRmpRhzj65cWDQdR8MoKBVpi2Vtp/bhj3qFAdjnq60Lo9eg1+z5rVwoDIyy+2yAjTf9zaoKiJXt3WsGtVySkRgNFHGFhKsVUE/m98XMy70FtZGwlaMQqgisDuJkeS8TeVQ4wvtKJ9hxsaTlP9AshndKK2B4uek+FXhy/LRAEdGgLRfTVIUk/JnlkyGaq3OKih97i0xvVnk9CjaNNBqhaF0W0QpZXQCg5wKOs/acIsgTQUIDdTUoMJcuDTSruq6/ds+B/Ev5G85Ta+9mW7kO/rtbjEToH7Ojz91F913ZPX9sGWt7mVwXzBtq+zQhYI9wXxd4bpvbKcDAn0oH0HBrr7P7t/5bQsKetucPa4BhGwNtnVj+vT7afZcGxO3k7EW5RiVuZUbAwa9bn/LUtDKWmPaMjW1UHAKnfXiITbFvCRtIidbju71VkCgLfcplv5MwOacEStw0HRepEdoXeoILp811nAAVJponBhkZB43N6lwoEFW5U7QzQjXk0V0Ukfh7zGVjySXvXTgbPzmavZuQSCtTiwxhmyyFMa0IFLkakEkBjCHiDmlPZ4jI7CspjgHgYQI+RyZJelPupX/P3n/t+vLjuT5Yd8gmb/f2lUjGzAMCLJVZ/QAfgGpq04N4IsGRuqqvhKgOX26x5CtV7BaMuBbPYCvZIzdPdWnZ0ZXXdWG7gz03qdlvYbr1ACydeMLu/vstTJJ+iIiyCCTzMzf2rv/YA2BvVf+8g+TyWQyPowIBstUvsHzOCm/tlOegslrPwTPCxkRcVRBDv3MoZLLP11cSiBgFzKaajRFDhrk4V0LClYlryGSqan/ThCX92New+59mHdi9ueBNmH/Lifp4bZk8i0g7T7h+zAagB4UyA3MMwdQMOhfrvYrEV1V9bDQnd9rKmfpNRrRXR4PnqeAoCaGHRx05fvL3+xnzz2UJipk1x0epTcDBk2iORT0Nt6jdES8gAw+BiqmsSrK3Pfsxq9MPSBMafhAI3DV0aZPQxWZ3GsEB2WLRODbzkw/k5zazjWrQGyMFlcL2O0YX6edbgsIev/5+Xx8IPQHtvEdXExHfXaU6mqZyXQjFgScB8iXUX1KKBCwpQoEUbY30QI8x4gtZmwZWLeILWW8pCz7ErYI+cv5rDHhZUu8qmLk0Mgx1byBYzDwjmHAO8A5h8U7OAJugbcZAgiBHIKH/CXcBBCW4BEEIO7egygjCCiUvIkjT0ayi095kZtRmpC2M51R0avqL4Bb165qhD03AIiLfhqTdOzwaZTFjePghe9jBDgnmqhyDqmOR26JMRSc2eyPEgvPeg87JbCPQj0T1qd94mdKR/28frUWDuTMZhYaMvDtb/aO8rNkNSQaa2N3gskbYPl4lN4OGJiHt46GMyg4S1c1BbvzR3mdQIH9dEdFfBQkho3/FTDw0Dc0U5FJRgoHNt92NJWqScHCAVBU/DtAADpIOEuzEdO+gqed8NmIvxf+A3PBoQv7SI1LunwTGnVvVn27gYIEXu6ABb9oA0QzsOm2wMC6ZTzHxIJeQOAlJrwkFvzPW/27bgnPW8RLzNg2viZlYN0SaxNEe6DBjUadfvEfcFVL4ImwBAaDxTuE4HDzhHvwWILDPTjczN+bc7j5WEBh8RF377AEhgSfMrzjZY1jArzLHDUyy+JTDnAa60HgoMBn+dZrrIidKv/knTV+QYNZE2V/rvvZOnWi1Rolqx1Al0dn/jhOY21HKavuH/qlcOoUA4dQcOSDNU3Nc04goc/UHHsNEOyqF50Qxr6/t9eMTMA9HGiy59pB7Y9/xPKrP/8oDeHAPIDm/wW+mObxZsBAhc6H7z7g9395FKeA02sG7QktaV1t4Eff5wwKeget/tzL6UEg6J/lTOxaR5sZBRfbVq52YHMW/5rCQb3LDhAAAIPIaKP0WjXNIyDQQ4Aa5u0+/nFwP6A8YbHhci0S0l6Fa6AggrUEW+LRf9EUpIy1A4KPW8RzzCz8BQY+rgkf14iPa8LzGvG8me2VYWDdElLkkMhpS+xjYKEgV7NCPwUXYPMBiAockGcfAxckJLJ3WERzcF8c7ovH0+JwD75sP8lfhgSGhvtGWEMugBBTxuJ4PYsAIBNkFgEhO8BbOKAMG8u5gYLiXFHE2+Sdlavb98MPz88MXcgpCtgZULBRCB8Bhemx130XQyAwv/dQQA0UZLRCyULBbNBxpY9pnqQbhGgxOwPgSEV5Kdmep1guiUr5r8DBlWT7TPssX//qK/ziZ9fM3+WeaOtoNgi28vG/+Fd/OM3vzYABUIMX/eIACq68s5m24NvvPuAPfvXVpxYTwF7Ia7n6ee7D86829AkUnAHBmdbDlq23n2nxOF8DB/IVWDjQ+47gQPPZ30U7eNTO+jPZZnZagplpYAYDdqhgBcnIhHAw6iwBeXKDVQBJjAIzZ1vPVdOBQkHxCxAo2CLDwEcZ7X/c+J8CwV89b3jeEv76ecNfv8QCA9+vEXGNiFvCtnGkw7hFxJgZEGIuCymlmJBThF0jIafURDzk1R09nIY+lvUSnHfwXtZI8IQQHHxw8IvHu8UXSPjBzeMH94B7cPjhPeBpcXiJDmtw2FLGEh22kPEUPBIIC9iJ0hPKUCpAFkUix8GMMAjatIOCjFPzQv8Os4BANsJVnVeJQGVbgC/rORUGy31mAvwoPfJdzHwgLBDo/g4KSpnQ9pkjKDjqZ876GODYkU+v6e31l4bbtkPCHA74tnkHB6PneW36Y4WCrs88e4zuEXbpinzU9GbA4NvvziIafnr+f/Crr/BHv/MNfue/+e2H7O82zYAAuKAl+AT5Nxb2xx/r0SPaD1GvG2k0mj7NtOwGIrCHAz6nA4QdEHRBTV75TjTP4m2+y+cACoZAcAAP9YZmm0yFSKCinDiEr8JBZweuZgRqnAwjxLFwAgUfxTTA2xHfrxF/9Rzx/cuGv3qJ+F7A4GVL2F4YCrY1IW4R25YQZQGluEakuPK/bUWSEMo5JeQcpyGRibxELXRwPsCFBc7zP794OLfx0suB10cIS8S2eHx/87iFiOfV4+OW8MObxxozfnhncFDHybup1idebQJABjk+HonDG7OAYSdFNctQlpF7eUX1fVLadm1g9x5N/IKd0NdzsxGsWbUERhvUvO/9V9U6Ar5Kh6gFnOzv7zfWHFgosBqDskM3J5/klX5mZIK1NdKPuPv8X1U7pq+yAr9x2xDtQS+IPwUQ7DM8oikYpdGbPY/426Y3Awa/Nwn+MBMVdOEcTWqT+eOfVZvPLF1pjCMY4O1BPoO3PFMTXbGjHdn5rkJBn1//0Y4+2JG/wRgOYNSYEIHdxUWfTFF8GJyaDt7aS1NdaW5kKz9TNY9s1PK0Y98FeRfF4VEEgw04o/ex6w2oKQE664BH7ynxNMIoRbAagh4K/r8ft6Il+P4l4q+fN7ysEdtLxPaSsG0R2xoR14S4JcR1RdyeGQbWlwIGyAkxrkA8BwN4B+8XgFwFg+UGFxb4cIffFsTg4JeEGD3CJtqJm+fnyxDfiTx0ctTkieCdR0yAYyMVCBnZASmRDM5FO6NAQA42BLSFvAYUTt9jhAIfSSTNrPnnVLUDAKamolJxVPOdzbD5BEg4XTytOS5l0WvN9sivANj3NZ+rn9E8e3X8FUXJaR9q+2LzTDVvc0JnIvkEXKvluJjPFTmmUx57+XhUTW8GDNpVEluhc5Z6+4xNFgp+8sWXZqQ8tjkd3aP5PQCCmXbgqtPkodPJSXrNx3o17ZwR0arH+jRckpQPmExFq3AhFOqwQCZv9mmoebIYsXAwsiQOMx5AQQsEebKmAHlfFmhSONj7FLT1ks00Mb1TBjsa6vTD5xjxnHiqIU85ZJ+C5y0V/4GPG/+dQcH2krCtEWl7QVyfEddnpPW5aAryxpoD1RZA//bPSLymAZFHcs9wfkGKG5xf4eIKt9wlj4SUboft0JFMVXRRZjWoQyMQMmHLGc8pg2LEk/eIucKB1pfqozI5BoTeq1/L3b3PK+8SQGfy6h1Ir3zUVVPUTEsteQyE+qeY1aZwYUwkk78zv4LPmWZw8Gg66k/7Y80KifYZOw0Cb+dLglrTbIB4Ne1kyuAcGzHxpw9oIt4MGPz0i5+g8uNjGgE9vz+vzG4QKGiJcQ8Hp/d4JQxcybuos6ij3s/8cX6WJK9ptuZDm/bvk3e8AgjKtVbXmcrSrvUe3O0UOHgo615TgLp4kblne5FDjrEIlHHGNN4GSqCiZLZzFlBIQBYfgC1mbJFNDavMRohJZxnwSFzPzTmz/0Dk+f45bsV0kDdemXEHBXEt2oKxxiCxup4SgAUJa7HlkvPI24akKyuSQw6ONQXOIftcysbTJDOWLSEGJ89CuAfHUysp4+b0fEL2tU6Saqxk27e9sxlRjLVF5RXq+5y8SwBmMSc970ArcJD6WBW8bw8J8uOhvA/ve7JvBwV/H/sa4JP6VXt+o180faz50stWDwmHxRtAwVkLuZL3HArOX9ibAYOa7MNOhMrkbJs+2CmPB+YDS4uj/e2+uj0yFfRx0wen7FKvGckYw8HZmHfkbHMlvXrWxAAOzk7/XIlsu9BV2wjImQwgGDgAUF3YnRQ9DQtVFjOC2wsNYLxvkg5VvMW/wJxv9bomxcwaBE1W/X6kim9u6zz7B5CsbBg5BkH0DpQdg4BzTYiFfR6uaA30N7yDM7+J1A/hGvjNniVmfu5dEkCwvi7qZ8CF2H9tmRxooAEZJmv+sUkE+TzIEJlVIAmqLchEQFltsg1gJTvLc9i/f1Opz/81UPC30dd8rv7VJm/6qREktEsuD9rRBRnxmjTK5f0OCh5rGW8HDAbkPoKEPtmRqm6rI2OJgyAnHDXoyyDQFWXUWK823NwdsyPvmVmhn35jn79/PmD+0faPOxqzXG34tpxXIOFTg5TsF66hCgmUBRBk2mSWekqxgYMqANT+fHxP8p5HkhPBsNMWUC2dCoJcRovHnU7fAXmiZmTMwYVY9b54B+eS/AbPFAgOlDKcJ/jsAUTk5ADcTSYOaRVB5hfE0PoXTH0MZn4Gyx3OB/jlCX658yyFwLMUnOdpjS7ITAaSQEaOihlBn6c+c7tmQamXBuS74+KYyKaFDPlR34f93h99n8OT6ruVCmqhQJeV3gFBhYEjlf5r0hWz5aP3+Nvsa2Zdzmyxqj6fq2kECepDpRn25tmj/vAK9IzKfgwFf2I06djLyIP3+IbAQJ6yUd3BfLh9LVCzpUc/iKOGTukoNiYDB+W6k9Z0FQaOX/Ts7dEUCnZw0GkNrsIB8NgzNs8xcapsUq/uwEGnM9h/NPY+dELq7tvG2CcGBESxSPOdsixnzPPeecYAe6pLjTnPTmtkNAoleqPc7kBYVGFvBYE6xtlkhQjfq68brnt+r3waxw0InhAy2+CX5LD4hM0Tnpb2HquUxTlgfWHPfaKIFB0L7OXOfgbLvZgR/LYZKBio2dWfw/k6OyEEkHNmVoICgWMoCR5+cVhuOkOBgx794B7w7saxDBZP8o+jIwbPERH1mckdgKrI/dYB0TgilnJn0SzkV7/PRiMA0QIYE0F2oUBBdkZz0ECB12KXf1cWAAJOvge0vj/12efn14znh+zgaeTJ33vxX3IaHBRvtiiX/b6b87u/Ne0fhrozRwPJM00C0GsTLqau/JPDJmWBgt8TKBBN99VIqya9HTDQdAoI5UT5W6tXoWAYhtK2CLSNeqrWehgGuhc2UzsPtCGjtvMoHJRrZs8zKkr/+wAIpvmetNO+Fmy7Pl4ffbKfus9d6qcBhF57IBCQHUCZAMQynbCE13U1qEyWG5CYJ+p0yFGhtHEMBAjZfwYKZkmfQbL0IATKCETIjpCcQ3IAFkA/f+ccHG0SNhh4XhOevcO2OfjAUxXj6hElbkHcIlIMw/gF2TgeWq2BxjLQ6YqzeAaqIfDewS8CB8EhBM+BjBaHd/eAJwEEG8/g5vQfr7UQiJ/fVuOhsFMNEIlmqPQhIqDdJ7zP8ts375O1AlXwW3+CAgvGybQBgrx39HvN90BUIzk032x+nYPfTBswc9J79B6jAcgVKBgDwSN9rr2+zeURSLBpKj8eAgnOfAcFrwACTW8GDJoldoE9Ik8BgZN11FAo2KnkB/3ATCsAvAIGrsRmB9A0uQYS6rqFzYd3AQ74ltcdZmzqR2KXgeAgzWZJvDZ6mi1LcRjNtVMkzbABBGJAEAgopoUUQfClBI3ASBEw8+d1bjrpMPNAX9pGwjOjSufNqFHeVTdbg4in4um79jpa8254W5fYFS44wvOWsHiHp3XDU3D4KMGMeOaCrKOwxeKMmFJC2rgeo8yJ1OmSOdewyMPHdKqBoBIF0XteJdEFgRTPGoMQPIInPC0MBSE4PAXHkRCXgB/ceP/NO9wC4eb4+OId7vJv8Q4eXB8EebdSX7axs2+IxhWQmQoO/D47bVADfJN3Wd6R9QXQdyjvtWqF9Lcea7UEMyCwMPDab0GvLwODTuW9Czn8YN5HgNDf+5F06MhtoKDve1/d75aYE5qRM/k9Bgn29mdQcFXb0UDBj358CgVkZkyN0psBA+ACHAADQMh4/923zXrXfXXt4MD+7c7D4PC0UU4a5NEKfixoBt1AAYQWDvRvWUREy6qqx+Y5Hv9ER+q/ww6k9EDjwyMgOFqoqv+OzwJPFW2Bdh4GEpqY+QYQXK890PsU0wIhZwEApS0b4wAk0yoH7x1ohYf+VghQKHAesIGN2iv4VHkGx8VEUAniXTlHVyx8jg43l/CSCDfv8G7xeImBYx5I+OPnNfI6CoP1EXRbQyHn8nf+HuzorgCC+AboWgmjdRM4PLJneFlY2N9ksSUFAoYBQnBsXihQQEBwEG2I/DPvv23+JtBR40YSjTaoA7NH3mWvERqYDnotgXbfPRBYGHhEa2DfQ68lKN9tc319Z40tfZixXl939ef2KnUbtfCRNLTJGyCwxTntf4dCc5Cyfe97UBhBgiYLCcA1eLs2Td2YD37+zxkKdqdcfD6T3hQYABfhAChffSWt44hQI2e+2Vrgo79HMLBfbW/+4uwtCyQQdcAzhoOZaQHog3dcS1dMKKcPMgP0rpM76wT7bGbvymoLAIEBqpqEHhCck7pR7QGJvVm1B+TYMRG5BQSjcmYpUwqM4VTLIqlagWKhoAqXtuadvMtMPFOeHQ1JTuNncuQQvMMWE54jYQmytHLOWLeMLbXLLPO/uoLi0WqKvIQzh2Iu9T/QGjjzwQSqwvpstUVHQHCuXYLZCQQEMRt4h0DA3fNzegG7fsVFIhQ42H2/5LhhUGaysnDwud7lmYOhY21ToyVIcyDQfcArvoFaYD6/AwQF4NPAQbt6nJyHiUqdrms5mrwG97wEBWd98MX+twGFTourkKD9bnfZOF6CSVdj1zRQMJJfr4AC4A2CwTBNWvXIUWMkTDXZl/Vq7cBZQzzC5w4ANA8NHVyOFzUXTeEAuGYDm5dlv+uR1SuvxFoYQcEUGGweg/yGEc245+PO1QCCjgwTQamgNS8438xcyEZLQKIlyEiowsS877P3C7Qq6MGo0p5bHknK5zMLXMoyFRQEB37GmDMC8ag8Z46OqMsp52zWV5DR/5aAhFyjDeYsv9H8BlAWTroy/VFnD2gQR4eqNXCu/01i8gA7UBIVrQcRyvLMXn57ydeT/maTRQEDrTrqmjDJkE6FtWqDHBj2ADRrJVx9j8AeBoz2oJ9xoOGtj7QEIyA4a/+AGRCU96Dntx9HUlBGCwfjZx3kd5S07+kF4UG5z/Lq7z2Ggs/UD3eagrI1ggTTD8+S3mUmW/bPUf++/+59az7YZf4KVYykfzPAANjBwfvvPuCrX/5+6705SDO5NYKBdvukIR41wpl60jpWdoCw0x6IaWHWKHtqvWyXHGT3iKKh/xD6WR+z1JoYBh2iHa0eZSbC0ubpqNMWEJsY5uYF1h7QZGoj36dCAl9+odMp7bNCQbPanRUkegnad+kcIWYec2YiRGJY4LoiJMfCRYuW4Mu2Ch3IdhQTAcBzNLTJaXwA+xgXZ/k3yY619TF1iiE5cRwEQKJVUAASD4xqbZFz1G1AoYL02m5b36PWn6ZMtmU4Wd3b1fdIvr7H03fI1+q+JpzxZwCCHRDL38O2D8CZ9Rxs++8B4NCpujxrvX6w+zDp9NmmtBNomKWrAvSz98UjU665X8bjgDBLsys+p6PhKL05MLgSVYyh4OvLYSJnL+eTgMC+uDP1Tj/tqweEEzjQJtkNGJp0RPtXnv81SQXaVThoqqzPCG2neJSVnqedpO0gdxqES4AAZFVRmved+3f9QHCjVmOAFgiskEF9D54gIZUBnVPnAYYXqEBpn/nIjwN4bProJ6dJg5pq53p/EXOOPWZt0COHNP7hwC3Dle8Jpt5445K4NPnh5P3NZxpc1hB0bf/otZA5z4GaDsH2DYdmA5sZxoD1SL9QhWlNNubG2fPMfv+N9ce7Y7Y9pObeZ4DQ1PnkOUapjVNwLr9sykbTfJTeDBicAoG09AIFP/+FqdTXk1x7pWmEZw2wC5l7lprQqv2z9nDQ7bdwALQNcpaOjh9fmy+fZUe72tFx93xwnamrmdA6q80y2hoAQqtepWp3PQAEoIUEPjzukABc+jAPY+B3x2QQXOrU6Y7ueft3Mvq1f2sHHeag3b7q2YC9JGrOGX5lp/v2V57sp9r6OEx2V8+PPpvV9pi7WhgAjoEA3T57PnANCErZMH5ujT7K9zowG6AVg9fjA9gSjM8YCcj+zKNj431jIB9qCsolD/bLrUFW/h4Awiv7Y037MMcHmqtJ+XPXHkfpzYDBMHUNfAwFNV35uF5Npa8EAnvuDg5maD/cX5vf6GO7RvyD8p51ljshNhY9jqoaG9DiE3oPa7s+xZU0OrUK724UlWmoPSCianc1gIBc7bHycA0o8L18OTbr/F6frHiR/AdqUTeC02zWgcgihnqfCP1tTCKw05yO/CdG7WIgNNspmvIUzchaflM3ndPY7DkfMvtrfrtlg/ty1J3yx+92lUcaXDVLebA9FuymavOxY6HVEsy0Y0ft/UoaR3ClNg9qoWAPBF0hHugjaLBl94zewfjxRn3VbCTx+r65LKhlzbsdCDTa3E57oD3Flbs9vCCSfWkPmhbeDhictP4dFFwwOexuMdw+gIKJlmDX6C44Mj2cdiYFU1bO2PzfXDjJ75U2rOn8XzKo0sKBy3utgQMLYXvbWdhnmw7AudxXy5CQp9oDa14A0ExzTJAONe89u+Vhd+nMUetsNFTfnmlbOe/bXU6glGAFP+WEsrSwzqjIScI+8/EceTvHTdzi+fy8rfw7m7DHZpniYdTDUmh59yX2P0HXASB1rnQOFJZ6zDmQDwA5OK/xHExgIPB5JOfvPP93TpsOWZZBHo3kNT2ildA0bItm38x8kwfHh861nZbgSpmudB87Wz1haDBxwAkUDEboV/oJXYfiUGtk7zN7qIOBy6fY3C/0zztA6ECAgEum3nJudxsNvjedPUfuGMAelCNvBwwOUgMF//AfdUevVdghlb4WCj7RQeRyaqYylp3n55ff3aj9EVs5YFRpgCXmXntQ7kICB6I1gBlJASqEc3M+MsrCtv2H9Sq2MnDSA8IwUlzOjWf1qIbsaKx31uvLOJuKNYRTCwUq3BUGUqz7UwJy5CiOOXOHvG0s/CP/zdvK29vK+/VvSrw/y98YkTYJgRxz8afIMdcpCqPkHMirNoW3yTm44DnEsA8gcoAPFQrCAvKBgcH8hQ9yPKBEFHSy7WTqX5K/ZWZH5j5UjVaqWaivbmjDB/bv9BA4BwftHptX2yUMgKC/+CAdtXU9pC6WtjER9toC3VdAoT+/2VW1TY+azZosGlNo3D9QEaZneT3WR706WX8voGp2e61uTpfgQJPtw46hwJxZnNQ//dnfDhhMNAB19sFI/TJWfvXNbqwp0JMn5gOz79VQMJ0iNFCPXkk9IMwa0BEIDO1y3bMMOphyqqp/y0fRfhA7fwMDB1ZroHCwD9ykI/62TLPaPqxBUSeU8sjJdh64ffSiOZjm19VrWzEjfOKOuSvP0OGrhwIFgBSBuMlv3leE/vrC2+sL8vrMELC+FEhILy9I28YAsEZE+cv7NuSUkVZdHyHDLp40in5IUvCymJITKFg8BzoKAS4E0OLhgwctHi543n+7MQT4AFpuDAvLHbTcGBQWPu7CguyCaBQ8X5N1bQuw24D22cZsoGAwCjU8ihPwCCT06QwaAFyafgiMR5f2mE09EDQj/85ZcwcFqNecQcFhf3HWV3Q+VHmnu5j0WZ8iFFWtOBt1n6kdgSrscQAHwIGT+OC2YPk1CtNvNZ27VnBVG37QVb0dMBg8ZTPP85VQME+Dju/IqeXR1H0wzbS13bkPmkUuCPeH5vceeuoCjZYgp4EzzgAOgCIsR3AgGZbyzAChluD4zbrZV2K+wEvTqB7UAlk1bgnNjNopI2e4LPsdFTOLRlIjvadoqQoUxFW0BJsAAoNAWl+A9Vlg4AX55SP/3V6QX54RX1bE5w152xCfN2wvL0jPK9LGGoL0vPJaCVtCXBOQEuKaKxhEtNoDfZ5GS4ACBn4hwDn4xcEFXizJ3RcBAt4Otxv8PYBCgL8H+NsCut1B4cZgcHtiWFhuyAoLyw1wATxbJHNn7VHhQKaZgrrRudRvSmMHQOBEiH/uNMjbLO0FYN6/79r0AAgA09ZwAAV0AgUjvypg/z2c9hX8ZF2R+dJ+qmBjQ38NEAxAQPZxBMzuXp+q4Z05ifNB9G/Srt3zZRdnZ3/FESLq8Sv7OL0ZMLD2GQBNRMMjR43XQUGfyUhYdmaGPs0a2gwIgLGmoHeymuQzTTMgmMHA4Hl6n4m8o/ojb91KzBYPzuAAdp8pYxG01HbYpZO8+pJn1Xdy/QxAZpcNy6hAIB1x8W1IPBLxYLk2LmKqmgLREFBckV6eKxA8f88g8PH7AgTb9y9ILyu2718KDMSXTUBhRXyOiC8R2/OGtCWklcFAt3kxJYl7EDPSwJzgOPIQiHjBJHIEp0CwuLId7gH+5uHvHv6+wN8W+FsokBDe3eBuLwjvbgwI6wvo6R1oWxmMxGzibnepe4USQvaEkTDK8l8UAIioMDAyLVyx818W2EcXdlqlS/kMdg8DAA2mdF6HAtO/WS3Bwewr4EpfwU9W0x4SaljiVo1/mEx/q9cX7URONQ+jOSjh0/v7POLheeQoPtonSR0N/2RgPjiGg/OUJf+j9GbAQJM+9Jn35iNAsGc5PfCJmoGDBj0EAnuNVbeNjl9JM1vgiZf5mceuPX7qrdvAQR7DAVqfA5abudg9VYWfs/lgcj4MojJKpyO/7vhVz/CrsyhUE1I6X5H+DrwANAcrykji6EjUwlQJxFP+scYgb2sLBc/fF21B/PiRYeDjM9Lziu37Z2wfGQzWj6w9WL/fEJ8jtmfRFDxHxC1he44cUlkiI0bUsMgaATHmOi9dIx7qqoeOgEVXQrzzKor+7gUOGAyWd6wlWJ4WuPuC9LQibRtvbxHhXYR/klayVM8NAsSngKRjdqVeqPFZoSL4E1ooSDkbLUINDPXIOx0tMBYHI/1GyM86HLPvapTRhjFMWayGQM/bgYLRMFyGggEQHPUXO1Cw/YXVEBi1e4n4agHhKhzo+XJ9Y7pQQFA4kH1TQPhcqTMnHEFBuUT+Urd9dG6f/xf4YlqkNwMG+vC9+uXo8x0dO6vk9yekVdLAbrVrYPa+o8Z2VUNwdG1zkwsgcwAFr/GROPTWvQgHQJ2toCBgAaEsT2TOrStGjt/lqGMfggQMMNDgoNnVC43U7cfkt3VgJOLnJCJk4qmRyLyTMooThnPE2nDzrO1NeHYB5YykzoVqMhhAwfb9M2LZfsH6/Ybt+w3xZcP61xtrCz5GxJcNL5Fh4Dlx+OQ1o8JB5jHgKDSyrlHgqULBQkAgwj0mLI5w+57gbwHxycM/e6QtwT+zdmLZqj+D7zQSxVvAe56l4Bxy3ODCwp37xHac9f1lNh9EGDNCaoEgy7MdfQY7XzlpHf2EPNJ3ty/VIQAciaSZf4vdbXuFI81B78vyGij4bP2FXn9ik2/zOuj/TIXk3ZsBWj+GPSCU86/046/wB+tX+a1l7cs5BoRRGsnHP/xXfzg9/02BwYfvPuDrCRSMKnP2W/f1r1JJ6+HU2bKGEKDnNb8PtAOzBvhAOS6lT5lNceSQo3k3asE9HGiD16A9KbeAYAGgPGbzY/x+0+AdlAEBWnA4Ct9soWAEBKWv7O9vtl1TdlUU5AIIPCeSY/5nOR5FonG9uElHmHnaoToFpgjoNMRtZcfBbWMfgucV8WVFemHtAJsOGAq27zdsL6wx+D6mAgTPiYFgLVqDjCgjb4UETQwDWcCAIYHBgLA4YMsZIRHuifAubazCtz4KjnjdBL8hin9Ccg45eKTVw4WV/Qoim1ByYn+LHCPPVOiTqTMuKyoAoEJB6oBAjw/fI7p2iPoJ2EBZenQIB2aUju7YaMRv06gH6E+b5XEEBDWfFgpKGmgb/0b6i6tpdn6/35oQUCGhrbNe21mvnfbjZ+U4SaNVfmfb1O07knMKBb8YQEef3gwYvP/1B3z9y+q92VfUkXZgluxHa80Tv/0vflsyPxG01o/gkuDeawaACQzs8jsjUlMOS85HH+2VGQhnNsKZms+MAAhoA4AMNAclZ9tbZfPXnNoL/dEjcifeHlB/xR1wdP4L6vsA7Em9h4Lc7R81unKsCI8WEIAKB3AcmY6EEPgcKSvR8awIfWadOZATL5BkZhXENVZHQ/EhiGtCWiOeBQJGULDK6opR1O99q9jE9MMrHhKCLm/ptAL42VwGXMpwa0QkglvYhyGFiLQR4urgbuLTkLj8JYBTSsg5H38JZNaf0PG8Ef68UmSFgphz8y4feo/duSQwOxq1648jx0Cb55HA7x63SbteqAOR5t7NffZQMJyW3afP0V+cwcG0X3xUcKtm19dntFk1/eEDQv9BgBj5FMzSVTmng+Zf/O43+PGPvmziYozSmwGDr3/5FX7x8/rQZWrZhWuPlrzMAL4180iHPgsiYBub1UBtNfWoLfsegYHdJztMtNvKr9McPJT67k9K0I8CjuAAKM9LajZAK4iL6r+vyr5CZhhN1HRTheFUnSnXJuO/kHM2gZjodLYDsBcm0yvkHiWaYjk/IykcZAKJKUEBhURrQOSAZnlmAnmPLNMDM1iAZFnWkKR+nXM1LsPAeO08x22wMewfWUnzSrL5FZ8Ev7+JLZ/rngPOVbW4cxIHwXdREVlTkInrQzUcrCVQqKmagh0U5AvfmzTU8VcweHZQgQJCFfK9UyC6Y/X6USEm9+qO7QBjt98AAXAMBSNtQZP+JvscmP6xGxjML+jAbO/0OIQEc7y90bX+far1tVl1f8tdJ7LqqE0qFPxzkY9X0psBg1/87Bv85Edf1o9SByQHH8nM2cxeOzdP9GNFTjuHFmAMCR0lTn0GBo29v+usUfQlJLvXeuNaqGkyuAIQZcjX7Xs8Def4ajlQP+Mj9Rlw8rF0xxwGPgQCDBYUMpk5FSM4ID7m8tjPuoeCOGh83ol+ILdwEAF4KFShlEUBIRNAzvOUROLQweQccg48M8HzvH4KS4lfQGGBWyJ88MghwC0ebgvwISLdA+Ka4W/gmQYpw8eA8HHD3cn7ThnwBJdQzANryogSVnr0bTmq56rT4eKo+BrcHOHuHC+tfAtwi+PZCbcAFzxPVdS4Bgv/9cFzHISwcDsJC287jmGQySE7DpZUVzMkwPnGt0DhQAV/FrPIDAoO35++6EHf41C1AIwpeyhQIJhBwswH4YjVzjhuf7wDAqCFglelV/YVA23BTrh2/eTVPrK7kdEQFM8ljCABo/5y9OUf9fPdvY/SFVk1SsV88HMxH1yriLcDBo3NxHyUnzLHuIeCYeoELFBfPnWCzabp9MKBVsA+wmx7V6zuuB1x7+Dg5Hm4vKK+Hpofrqn5Dm1y/Rxf6wFsy2jq5/HObtz9OTKE3lMHjMZAAaFoENhZbQgHppocxMZ80hZjykM4IAgIZCANtAaKTdkFsEOY5zpLiQVkWJCXGwc42lZguQE5wWc2EfiUkPN9V55NRuxlaqEnhOeIZSNxPkzYHO3MCHECR9bxkM0JCgjA3Tl2PpSZCWVGwi1g+UFAeMczE/y7O8K7G8K7O/w9wN0W+PutxC4gDYAkgY800BGHV/bIznM9iRlhpC3I2ZiEcA0Kdonar6II+wEUeBoDwZEz4Ohvmz6h4zsISnQlVksTA2DXX5yAgJrwdj5Uo/4R3b62v7wCB/3gouU5XerbQAKhPHsffOmoP7rW378+jZrkt999KJr0R6AAeENgsHMRmQ0nbToYamul2ohTk0FAK0yBHSCM73FNKzADgbO+qR9J5O5vAwcSR39sCqnbDRyUQk0K0tv5D/0jukvtdKQ+/9wHE35loloLIGfqpCgNWDmgO9ACQhQI4JE8gLSHA10CuTwBsXDP4NHlSMAoHDSPXHhsrDVIIJADnFMg8Mgug1xEzgvDQrgVhzybln+AEpaYSKMRkkQdfAEFh/jMo/YojonhOSKuCT/cErYt7aYsjnwMAFQfA3RTFYMDSSwDnabob8FMV/QI727wt4Wh4OmJf7+7Ibx74gBHtyfQ/R3o/g5YOPgRR0BcWFtQQiXzvwRxMJxoC7TeR+9nlPSdUW1G5fcMCpxrIcCbbScX98tE73qIs+Bjnyld0hKM+gpg1y/ur7vYV4xU8SdQ8Jo+0/bztr/caRMMJPC5r/cjs6kv+6UomJ1gaqDgRy0UWOidpTcDBpp2dr1XfCu2UqeaAgDNuHzmVzC87NhX4LRRX3imxk1mUJxLcCD7Xjun93AK5sm1fI8HVJavVW+SAxCbzoY7ax67KSRk86oTETjYUIUFOJHWGYjE3YlH1RwoECSBA6AKExU2PRA0jyc9VB5oDXSGgi4kBB/4zYYWBglgu7tzSM7z1D4fEHyACx9l9B0QnjZsHz8iPN2x/JBnLGwvGzaZrZAk8qENcsThkVMNcgTI1AlJJfIhax7cwhDSBzdik4FHuHsECWrkS7me2JzwdIe/L3BPCgV30O0d3P0JuL8DhRuw3JH9DfABOdwBF5DFpJLBdaYzEXptQanvQZq9syo07Kh/DwXemA5cpyVwjnZAUGEgY2jnBz6x7X+GZLUCHRyUU2ZOyCYdxm65CAWvEqr2XFOEHhTs4YIJE23CaTrRDD+czMXffvcBX//qq2peN+lqS3lzYABcd/oZpVKpPz+f0sGpg4NL6RwGrizicpTKFDhCcca0DbyW5AIcAFNAOE1D1d8JMMzSlVkSFyIzArYTiqIxMAu2UF2Bjzt4vwMEFchqXuCaZNOCz4REMrpPuYZwznUkqKYBAAiOYF7XNCWgxmwQDomZ8SVlWRqafJnUX+BAnO2ICBR4vQHnA/JyQ769IL98D7o9wa8vSM/fI76sCM9PiC8r8hqbsMi6T8Mi55hlMaXqj1DWSzDKnbL6tEw1ZPMEZPEkKuGQafHwt6UJh6z7NByyu79jU4EAQTEjyH6GgDtDgV8qFPgFmXzxgSh+BFqfpp5HyY4ivesd11pHQQsFTs7tTQdehb9sD4FAKzEPVs4Emvb+cHyU2aqG9pxZ6s2QVivQX/va/qJTt8+g4Gofeqn/lHN3vkfY96E7SOjLfyldrBdTtqPUQMEXr4MC4I2CAXANDppRNY4rtU8tXNouY5T2IGC3r8DAI1pCImNa6T6KEmYX9m8HB2C1WOtE2dfWA4UZ7r+Yx4G9c9opXpgixULZPJu1bVKuoCCQwCM+hwxqACFl0RiAIxJS5ilvJfqQU21B1R7kjBLWWEs2qqXT9itQEAVANhCCM3BADogqoWTFwbiBHNvhSdZLwPqEvG3I6zNoe4F7ecZSAiJJfIPRQkopIW8ROdpFlLK8mkGd6ywCWTyJHIG8AwUP59xwASV/Y2dCWnj0X9dIuDPkyNoIBQjcYoBA/Ak8+xooFGxiQuAIh/lcxQyY9Tn278qZfY9oCcr2DghEO2BhwIAADdUa426f8sC2LUBQhHXGXiBP/HravPYAMP7eX9lfmPyL42izfwwFR0BwtR+NpiilDzWd/gwSOkwwJevTtbOO0iPy61Gd0psFg6M0qqT3333AH1yAgl6wt693LARn2qwzsm1l3SNNp5Yj2cZttAeHcCAf+DgqmC3hWTE+k6pS09EoqdRPN6Iq147KHM3oCawhKMvvUYUESlNAcMS2aiJW7Wdqt9WbMVIFBNUYqImhFNGUzNa3FTaaEjKcxDFI+lIFDti5z8NJFEDogkpuAfwKpBv/DhvozgsskS6/vD7Lsst1OWYfV+StrtRYlmHOHEDIrqyoQYlGkQZ1WmHxaShw4CFOEmUFRXaaDCC/lOWVVduB5V6XW3ZBZh2E4lwIt/CUTOcZEAQIYub230c4REYz7bQBAdltfdTP3s8ICAjVVGC3i58BDoBAYcC28QkgtxXOZrKmoAaIyyNaELCzgh5NU4A4KONBHsOZWg9AwefqR4eQIGecTdsdxWLp06dCAXBdfl1N/8aBwah5fvsJlbqHg7p/9PsqDNgG/GhTLssCq7OcCKIeDpprYBrxQEvQONb0sy70nM8EAoe+Bf1c6RkQlPMONA7dyChrRyr+BZR5uD8HBF8HWxllloCdMdADQtKRRgcJo+JpEY9SYQLZyLLQUhKB5MmDggchI6cISov8TQCSbHO0QKQIIPPCS7KNbRMAEBhIkQsp0QULIKiGIF1wDnVsVyDnKhCIILfaDfKBjweeRQA7o8DMMuD8XIUBhQQRHgoEWbQ57HjYLY40SCrwi/l88E56GCBUk8EREBSTgubLy1IeAEHdB1xo10A5t36XqorXb2UCCAff8au/8e66S33HgYO27T5Hfern6E9tsCzuR6lCQjbmBqNFaIrf3euKnHhNUvn1xz/7Br/1GaAAeINg8Giz/RQo0DR7ua/RDlxZ+32WnLlOAeEMDprzoY23B4RxKex69qdp9OFPfAaGq5+N0mgEZTvOs1FV89U6UI4FFlh1mVmJQtzdg1ggjQCh9z/oAcFlfh8OVNpA0ebYR6HxqGYU2U61BgkVDjKR7Oc3Fou6muBcYDMCJqPTFNlnohNElFuhpPYQsvWNtv4PUz8Lp7Ed132ZSN6DqpFd1eQ4L+fIMdHiqKBIBgbKConofsvJfZAqIlnBsieC7j0oCAB7GOinHz4CBKTw1bVrsvV/VNelLel3xO261YlAAOFEQ9Db9/XBJ+e15Zi3hUt9xwUn7Ueg4LV9ar2x7S07bazJc+bs/TkgwJYroYWCI/l13JPv05sBg6tAYNzp5pU6IcCraQQEwBgKZo33UbtY41eAKuR7OCg3GcBBk5/5nzcfd6w8P7VX0AJ25D8MurRL5vhR57kzLWinr6Or1hGLKAOkMRXAkAACxXwJELidGSiACu0xJNSnqS/qSkdS4KC+cDgS9ad5vKIKhR35cnlVoNmDsznz/fbn6e72nb7dPoXqpN9PBQCg/bbmefRQYLcNBJhzmuWxMYcB+7s1GeAaEPSQe7Utl9SaBzTOZ9Z95Zzjb3seTGhUO3b3TPhfbTPjlnbWLoDP169a35Ly1ANAcHqTg0HX/omuJbES7uhiJL+u9NIdlg/TmwGDR5I6agw1BRPau5L6dvaIlmBEs/vvfz+ygTlXf+qn3sOBPTZqxMdUu7eWfR4CplYsGB+HAgdA7RRHGoRZR9k4bQ1Km486xcTFkdkJWYpnAYGX9T3wQXBG3ZjHkOBRR7FAFTqjzo5LtX+Osk/bV3PK5xynfO7cxumVPP7Z7uv6EsxgiWgHXLM4BFMtjWplxLRzCQhG/jPqGzBM5riFh5GmoNEMuIcjDF5L+zd8pW8ZQeOxo+F1KJj1rZa5bL+qB3XQdaQ90L7VluVKGx/2x7Lzc2i6j9LbAYOjmu5qd+q9aemQdrsOb/MaKJg13KvOMtaXQK87skk3JgW98Qnhjp7pKL1uLFD3EtCOZiwc9E5XhwWZqVl7qOjPU2dErcwBIMABpJ35kYmBxYWzWgSBBHkabgeoeZcORdsHtW3J2R2oUFCaWTdS2kPooKM0+2KqcQgicqkeXUyI92vV5XKsqdKDd9SbRLxqaOSD8+U8e0wc+eT6UTAhK65GWhJ7b+q+7QYEHoAAu9+Z66cxCI40BECFhInppqShWczsm6n1r2r91DRzMj1Qnmy4fZj9YN/Va5sB1MmAa5rHhf7V7lfhD3TmvyYzOu1b7XP3v2dJ++KiNQDw7W+uz557LWW/GTCwav+dQ5HBtb8JKOjT3wYUfO706MJT/bWvTaOPpnWCNDBQplM6yLJCQGYhvesNjnwTmtQxfO+dXZIFhGRs37JfTQwKBKZzrZDAws7MZJS64x2qUeBS5TKdTAFCu+SsnYSAQoEDaVfVlo4KGxnYROirwI+ZQxnnlGU7yzZfu6UktvqMBFl1cPdbAKT8Pa9xbWdOno/9H/hZHDFI9b+DcwILDBGBCJ5ItiHbDBBEhCDSWVsM+wPUhXWrkK8fexNlsBP4qg2A5KUaAX2eHQhoWxppB4qgvwAEzd++PT8w/pw69xm/DdNum3OoolA2d931c69IrzXXfso9H01W49oPrrTv4r76MTiYpX5gZuFAVxEuEQ2PMhmkpr4PCvNmwMCmGSQU0poEL5otSPLKttukoeA/OvYKKDjSFkyTaa0Pf2yD86+I4pEjjK17LRJBO6WEElfhKJGsINjABF3UMnSf7VQ9y589O3QZQBCrvs5kANBCgpZvBApgYa8eF9b0AOzNDwoJPFXRFNlAgYYlzhnYYkJExhYZBLaYCgRsiQFgS7n5lzL/XWNCTBlr5O2UEmICb+eMJOskJGk8aaJF0GcF2BFP/wYSxzwiLN7BO8A5h8U7LJ61A4t3CHJOcP0/h+AqLATvEAgInuDBv4m4Dh3EHmw6ei5HFfwjHwFgbxqAnlPaDvYgALQwoL9HToVn5q8pFJyk1mnCtNfjlBtIoGMgMEV5+Pt/gG3GBX38kstdgr3NBTgYHXttMmPZ8vv9dx/w+xKm/+oqicDr4OvNgMFMPaWV8uG7bkEJk0bTTGZ5nqWjj6ac8xk1ASNv9d65ZLZWey0Qzh/2AgRcfayyxK+gcBk50+hddHBQ7jzXGrDpQT7NXdAVNRWkcodpsupX/eLtSK8HhAyU2Mc0iImAWICg0ShA3pF0wp5QtAoWFGykxQhSQoDLhGheUO6g4DmmBgjWLWNLCS8pYYvAlhNetoQ1ZnxcI9aYsUX++7xGvAgUbDFj3RJrFyLDQ9LgRqKtqAGO9o1BzQXkqAhdjoLoWOh71gIswSF4BoKbJ9wXj8UTgue/T/L7FhwCOQQP3JxDcA5LzgwI2SEIoAXvSihq2xR0JoHW/6VIhPx0jQAfBSHi/dl8FKPj+33XoAAYttudP0AF0gIF5dy5tmAGBU3flstT7Yp7lmxcAE1udP0r+qTmciLolMOM1vG8P+dzJAsPTRlfoTUo5ZO/77/jBf1+0S3oNxvMzWTa1fQmwaCHUK3Uw1USuzw+t8YAeJxSj1I/bUrTERQc8kGDveNTjkCg/7hmj0qDX9ZxZwQID8GBLI1aNAc2umGTOp4fdb6zkVWBhB4Q6r4mJkK276F23pmMT8VUq8Cq9LI6owACkMuaDREq9Kg8OzL7C2ypagnWmPCSMl62hJeY8RITtpTwvCV8XBOe1w0f14SPW8LzGrEJKLxsCSnyOggp8sJJSEBMCTkmWYxIAUHBYF6dJCYA5xyP1r2DZ9sBQnAMCp6B4RYcnhaPIIDwFByeFof7EvhvcAjRYfPAzWckOGRtPN7BpwyizGGnydwfAIhNEyXYUAcEjQL9QBsADCBAj5vKaDReI/PArIOYmcX69jkCAvndTvsEXgMFR0BwJU5A00c330Tr/S+lG2d00kfJ4xqF4RgO9Na2HJ8PEE762kkaDUgz6tLJVn5pSc8DLM1/H1365sCgJzILBSPzwWsrThva31Q6HeXjGAhsHqPzhukCEMxgwF56pk7UMpQPVj7OBhDQN3qeDgjKyJmALKaFLMSfEyjrKFqhATwizANA6PWJdq31UdqNBHLdXwQFSmdMZfpjrY0CC+DzqOTbwcLAR8GTbwCBZBGl4pWUidd3yOZbyOw7kBP7D2wJ2GIuUPASE/7qecOzgMBfvUQ8rxHPa8L3LxHbFhG3JOsioPwukJAy4sbBj7IEOco5ShTEfaAjcp6jHZL+5SBGPjgWzgIDPjiE4OECsAaPj/L73S3heXH4uHn8MGas0eMlOPzwrt2YgyNGR3IZPvEqmEHtv+ZVOtHMOMcxC9ic0QHBadAhaU8DIT+EgPK7+0JG7W7XPicQMPrdA4Hue1BLAOyh4MhPCrj+7ZdMaqGbR0mj8/ubTfIeCX4brOjovL+rNLvzh+8+4Ks/+wrfdFAwGgjP8juScbP0ZsBAq6uHgt+TSj3y3jyvuEkDNkd0+wgYZratEcVOyzoi667Ml4Ggf9DBfUdQcNYhnIH3bG6wBQRnOqHWrntBe6DWeh1BmxEXZdsToO2kr7yAPu06/W5hGtu3GyiogCCjFRnRNZoFIpRogBQB5zmSYTmFio4yizMgOzfuy58TOwxuOfG/lPBxjQ0UfP+84a9fWEOwvUTENWJbGQy2jVdSVDCI6zNSXJEkXHJOibdTLHWajAOEc1VAkfNwfmE4CAHOL3B+gV/uBQy2JSIEBx8ywuKQFjZdrNEVPwZN3kVxUCRsOSGA9jNRiavRi7bAuT0UeNKRqkSDvDJ7oG8DR34wj45IR4JquK81ITQwUPY7c/xBLQFQoGAEBJ/87ZsL+5grU1v9Qd81Evx6wRkgnD3DlUHWEV+MtAIzefP+uw/46s9+D9/87jf46QEUzG53fo/5g74hMADsg77/7tsdaR2lYxiY75+BgJ1esrtXp94CWvvXUcPqG2MdBbXEPTr3FBW7h7kCBVe0CeMbyfV9WWV0pyNjrZcj0wJHLWy1B2zjNw9kIQHYg4K9/67TmXT2R884WETIHOxGeMTQMFD9sonCsyYhJ2SX4ZwvAJXEgJlJpkUiM08IZHkirMhs3zcNMmU2NaTEJoZNfAy2mBHXFgrWl4i4ZWxrRNpeENdnxPUZedsQ149IcWNNwbairJmQBxoD1RQ4xwsjkYfzAX55AoWAFFf45Y6UbiJ7u2+NgI0Ia8xYtoTVExZPiMmVZZP15ZXpj8T+DE6uJ5JZEMRQEJzOiBCRmCIo8boQrBmILQjMfALO2oM+wKPHprMJzG17ECjntJqoco4FAj33wHTQawn6b//qd2+n/fVagTKoyvuAbDs4GFXVABQaJ0canCiF6Wu4N2uMUsec/Pfg/c5U/t2bLFsVCv4EP/3iJxgNfI+gYHyPNv+j9HbAwEwzG5EWcK5y2Z3RC4TmIzWdkPm1u0cn4FRrMIODszTSDAATGOi1C5NW1PS/g4c4g4JHfA3InNPPD9YOQuGg7M/HpgXkPSAg6xoFBgDIFFZHT+hMCCN1sEZw688baRhOR4/Z/OleiLEBF2dEImSXi/9AsWuL9gAOCKiaA+jji119yzKSTqw+3wXvAc8kiJlNAzrTIMp2jBkp7qEgrc9I24q4PSNvK9LGcIDGnGDqUoGAZB2EbYMLATkvAAAnfzVtuIHII8YM8lweZ8snf/ukz6hCn5wIf9EWeJLZDFShwANVS6BrR6gZIW21zq1j4OEsgcmovmt7++1esJvcm2Oz83rNAbXnnwCBPkUPBfb7t9/+FbMiUGtj9N0rIJzBwS4zfeJZn9b9bpah7zKzfRLw2GyCHgp2/fCoKQwP9VDwFb75+S/w0x/9GNUJum1jtLtyf49h/r/+C3z1y6/xRf5icCWntwMGkvShGQoqaQHDdyTprDOvx1rSGjObytYjrQHQwsGVdOZwaDOaxevWZIul5zZThwzMXE0jE8PuHHuLrqNotAe5jSo2My3sAIE8yuIwCgloP3zVM5cpZA0w+PLUDTTkVG7KazhYoDCjx6G9qBtlTiunLuDEN2IwIMk3uyxrGaSiPfAuSL+vmgPICJG3794jpyjT+iJ7/5NDcBm+OACywNRphEQsME+XQ+qfZwIFfKr8dmC3C+/twbM7sZOiNHpbXuccvE5dJJnd4CDPS7h7D09sJuDYByhQ4EVTgLRVLUFKAgkCBKlqDKSwl8rbRwis+6n7OzEDDI5JTezzwgACunuMIxhOgEB2jKDgNU6Hu3FHHnjvH6TX9G+746YvLo6NBhIaWMGx5mN335NnmcXFqam9V4ECq+neBbCqPenx3dt+7/13H1g+/vwX+MN/+V9Or3o7YJAzmw9+9XUlrXrwwvXnDkHvf/Mtvvrl1/2F0JfziNZAb3H1A5natlSY0m7Xrghtidt0BDIjWXfUQVxJtgzDxZ5s5hPTQvscAggAShyBTIAR7tUhzIuWoXsIYA4NCgzWZ4Gwd2w8gYKhXdomcVbM+vEXh0oGhOwSj4dFe5ABOOelAZQQSOJtwYGMFu8Qc0bMDrcEjlGQedpfTBlr8EgSs+AWHPsjRI4pkH1C8oSweGx5qY6FIvw9GCAKRGYHooTsojx2Aol/Njo4jwABAABJREFUQdEWkIcLARQWeL+AxMfA+epvEBYP5wneE7xzDACBZynwlEWHe/C4m1kLN0+4+RoDYfGOQcABXkBA4x8wFLDgp7hWLYFdbbIAAf89f3dGuDejvAEczBwESz57KDgU/t3+q6GMs9k+czB8DRTM0vSbN2nWNR4JWjsa78vkyexTJZst/AQUrqSzgdqofJz2g1KWXz/Zt7MiOKp2/HLNWyj4meR/kN4MGBQosA/dVOAgXfEOHuT/2//qH5sPf5x2WgMj2IC9w8tRmpkHRh/IieZtmCzQjMrbfiy5lOkRbYKNrNjfF9h3FE25RqYFc/F+wR8d+XpzILMGAJD3vjcPkNmfd/PKU2uOsD4LRWiYWjFQox/vpdXxlEckwiIAXkZYAEE95FV7gJwAEaYKB+Na9qaj96hYFAoMbrvmvwEIDCUugki3HaLz7Bfw8gKKK7JfkZbEfga5QkGpWwMHFBY45yoQ3G7V+TDcsNw8/OIQFo8QPMLNYbkH3G8eP7h5vLsHvLsF/OAe8MM7T1t8Cg5PweMpEO6et2+esAgULAIEi4ECiisQ11ZLINCjQEBpq+/sSFOgpgL1BRlpDNTJwcYVOHMQLMcwFvz2eHfOSPz0fx9Z2A2DY/1xTaPv/Sg1pTbP0wvZ/bfeplFfuNNYyHYBhZE2QfdffQCM++lzbYEWst5pKrStfCLCXoswz7eBgi+OoQB4Q2BQoEAfupEwZyrctmn3Efbef/ctvvrzP9hXajcq6LUGQ2ELNIBwmi6CwL7hzSGHJlfY+/SreVmtgf24ep+JozwfSUd2x9TVY1O33b2o2yIAdtU326cWC6oCQqNp8AwLWUePWgtcUi5CBwcaYGmUrGoaqBoKiFDIEWoGqaaEAMQkgFC1B1nKyJ7+HhWNID4FCQoHLJsSgACHyDZ54jrzDixMvcNfe4fvvcMaNvgXgvcOMUT4xSGuHnG78+yEm5mdkBLPUDgFgwBybjgbwS9OZiN4hMUh3DyWW8A7gYIf3AUObgH/1lMQGGAQeGcAQaFgcaz5UDAIDqDMWoLG0VC1BCmiAEFvQhi9J21VRwOF4vA3gAIDBKOwxGcj/1oyXPrdaAQNDJRdEyh4REAC59/7aOZU068NOrk+6mR/zazPG/YBaPtobwYjU1C4kg7666m2YKKJGkV6rc7TpmO2eQzSCApINWGT9GbA4E9/54/x0x/9VvuoQ++V7rhJoxfx/rtv8U/+/A/wp7/zx/hyRFoPwAGwF2JHaSTgxtqBvTrqNJkOZlRm130cR1qDkUNlV6pxEU6Oj+CgvzJ1NyugcPGGu9kOAEC8ZE/VXKiA8GXU3kJCBnJEzqo9kApJomSXNqICvKa9sGl/5wYSCNIxpNxoD4pTIgC4jOBkZE+ElTJ4GiS/F+8CPEVxwkMTWvhp8bgvEfew4WnZ8FeLw/PNNzEN1peIHDPiFhFjRtyCaN05pgFyRk6xalzsc8lURRDBB56a6Tzgg2eTQfAgT6wxKLELPO6Lww8FBn5wDwwGGuwoWG2BmBUIHCmx0xIEAkj8CShujW8BRGtQtD8CCKP3Y98Tw0H7UVdNgBH08twNEMhsk+HUQaDmIXcYyHQAk/Z+8PHt3CZt9zHQBJz1KHagcHRO2Z456plj/f5jKDjQwA0L48z11JTfgkJ5JjroUwbpqN++WuZZ+HfdPwWELs2h4Di9GTCYqkcuGIqGFZUT3v/mL/FP/vyfMnR88RPAqgjPoAN7OACuj5yPtQIDEHjQxl8+9/KRtLkXOJAdDiyEi1+E2rcxhoP+Gc7Sma9F8+Hazqt8F3z9ocNcV0XUffC9KrASP/svsLZhFmCJWCVNhJwdCxzHvgysnzVwYMM1m0LlOC49ec/KgxSLiSE7tNoDBRTPwLD4hYUtEVaSOfoEhJThyRc7/FPI+LhFfNwSnoLDu8Xj33oK+KvnDf+TLeGvn7cSCfF55b+rBjlKDAgsU3nGQI7crlJpltXRrIQykBDI5GVWQPAS4MhhCQ73hWFAIx3+4B5wl0BGN+92QMDPcdF0kKJAwXqiJZB6jXEo9Mg6TxoA2EGBqyCQBdgAJ0t2D4BgMG0QqPZ/TUfC/Wqarix44dpeQ3j1W59BAXXHrAlhvD6FKemjfWAfa4TsXJ2xv1gDChfTWKsxK5Opy52Je28SnwKCSY15/YL5wKY3AwavTbOXUKHgj5pKPaatVmsAtHDQn4nB/n7fIWHunOqwP6fJ2Ngod5EAXSm73u01cDB7qn4Z02HxLnxBs4/zVeFMu0tsrTWRGAHTQQkkUNUksOMaCwUSIZMdqvYAAK94FIdwkGk+KgUYGFgQZbmnOiL6vfYACUgMJM4F+UfwiRBSxpo4IuDigJsPiBn4uDo8JwaEdeOoiP/gFkp0xI9rxMc1YYv6l0Mor5FjH6jjoq68WNZLGLxzcnXFxMXzjAh1FHxaHEL569l3YPG4eVeA4OYdlsAgcHfsQOkJBQgqDLBGxANFM0BxA4wZodESlMBMubyLGaiV90IipkZQoIGpnDdaAtEalKBVTrb3MDCbLjhpun9r6cxx+uw7HznpvRoKOiBoo02e94H8KTluA52fxpE2oU+v7suvpNEzdYDQ+JtI+hQoAN4QGIwq5yjNtARABwXNPNLZzffHzxrUuMH0v+dEXB3Z9qAwT8bmW+5gAKHTHlyFA6AdbY8DhBgmvwAAj7zNURcwqoozeGimgyLX50UFhREksPs+C5dMDABsTmDzAtkCGdvtDg5kFFoEknm3ZZCj2oOc2DeREjIlIKuZg4GBcgTRhuwDmw9cQBRAiLK08iLxAG7eIyYg5oDnyHDwbNZW2Mq6CvXvbNXFlDnmwCx5AYOjVRRv9q8n3OTYXaCgTEEUU4jGJuB9cyDY+RLk2C11XLUEff2Xb0Pf0QwKqAJBoyVQs4GAgAJBNkDQw8BsiiBvX2/Ldd/+vCvfWTeEmOYlRy7frwj8h6DA9Ik9EFzpCzttAWnfrZCg5+y0CeMHHg36+uebXn3q+3bgn3SgPTiDgqFjbJfeDBh8cppBwWdKR7KwPTY3E+w+gDw49zA14rvcO6tHdYGDPIcDyKyAXjtgyjLsbK7qGi+mR+2kwAU7oUKOkByhQgEhl1gKFhJ4yd4OEMjxlD4xL2QvsACgmBZAdUaDmBrKIj/es3AqHVUqnYEKLfK+ag/UvIDM93G+jlhTALmA7DyC8/DOIwePBGBLVJdNzrzg0rsQsC2ZIyNmWZGxWZJZfwMJsuyyhFtW84HWc7JtQs0JqoVx7BjJQYZItoG6lLJZWlmmHWrY4/q3LtscHL8Pdiy0ELC1MQmumg1GozPvW4fDbsRvTQdZ34E1Gzg/BoI0hoE6i6S246u2bmdafhkEdKN8ovY7OoOES7B+8p03wnJkVtDtR6DgNf1hhvm+GATICNyxRhWHkPC3miaD1U/VFGj6NxIMZuaAKRQ8pI2oDElmz64Mw+uwo94rMHCkhi6n2uktZmoVcnoMDuSHRhIbhRolnI9oHk1HuU09qXVDy9rlMnsv1elSJXUuYNRDAkfaq4DgyCN7FgSs5hMB5WRBpLRx/Zb4+8QLQ6rD4kh7MGh/M/MC+zhEIPHolUh8HUQgEXlk5+Cch3cBCB4ZhARCSgwDKefyN2Unf7l+I1su2JeDlSqIsqqiitWzWX0A4MHOkd6ZlQ7BTdIT1zPPlqh/vf2rIIDMwn9rAxQV80CJR2AcCzsgKPU8KijOtAS+/G61BMZsoOYEmTGiniUKBCntYcBqEIC27V7qTwpl8p/i26QClSaRBY/y/MTUazF2zoefAwpM4zvtF3M0oDeBhKJJ2GuPAGrq6KF+/koid/wxdenDr9+PZ8+9Ir0dMJi18EHjyNJp23RVUzA0WZyAw/EHNgYC4LjhN43+ioOlnqqA8CAcaCqzK/S33robfTy6UtnRIxzNdBhBwQgI8uAae51NWvYEXneAMiQYCjWQ4BxxMCBUQMgKCC6wgFahIeaFYmqQvxBHxlwEFwNCFvGxMy/Y5zD7yEvQJiLUtSTWIqjICDSytm8Z1Xpj+67q79epus/i5fNf/i1jbQYuQmeq0X+5CASeRZBBq1nYKMU6kyDnVvCrZgAYwsDRl3PZbNDXpzEbWFPCERCU7VLHtT1faa/lM5R6jeBvxmVty1LfVMF9BgejUf2wfj6BHJoes4OXCgOvgwKqDfG0HOURRpBgtao24NROm/RKPcKDgv8o6ey5IRS84kW9HTCYJestM0lcqeeagmlQkfaGFws2UHsdmQxOgWDWwIzXKlGdYvUgHFQ9iORqvtxmzm/3aJdTV21pcHC2rnrpB+wlk85VO1abf980yOSk4EPEYYYpg4U5oUABEcEPAMGpkyLxUso5JyARCLwSpGoVKiDkKSAghNb+DTSdSt7Gqm9+2K3WI5kOrjjL8bG63HN73jAQT9mW+3fOW9NU2vSsLSfYjn63cFEn+KfnQb+lvUZgNJVyZDKQHK4DgZoNGj8Cb4R/CwFxoB1QiLVmhMO2SnWng37OVcLqN+A0OJfUDNnr0ToC8v4W4Ow5n5S67/x8at/ngIKDvlHPk76R75jqcdM38rEOEKSPHD7cMGkP1u+uHVkzcLXmxOb8+jamUPAJ5PZ2wGDUGTXOQ9Q0Fq18G6fgSP0ydW5s9l95EV2jOIKCT2r0/fEKAVM4kPs3KxBONAc2jeKSvybtpg7qiL/kVyHPwsFR6qFgP7odXGPlVeljc2M+ISmvIxbzOVcNQiZWy2vgINf7HzgZ5R4CAqvEexNDAwhmxFGDCdX2kzfM06A9axCi/pxmBNlM0+vq7YGO6GwWxn5n6n52vwfnlP3lpu3zlec1joVzIDAmg0tA4MREA5mtwa8xSRu0GoKUxzBwZeBrR/xlFo3RBoDOP0cLBVcXA3o0YNks0WDb4Kb8seaBvWZ13j8+2DcC+/6xc84u/aMZUNV2dRUQDBxYrUED2p1WeyJ/LkPBUNM9L+HbAYORtaenrU578Be/+e/w1QkUHGoJHoaCPvNPgYKu0Q+HEqN7dnDQl6fUWd/wuZcZ5Tqo+cNzdkWSv566faQq/HL7qtyzcEDtvuE9LBRmU70H5dJzdVDWQwKrcHMBhGJWcKxpyOqkSGornwACedYUuNA6xSXRMogqXafUtT4IMO1I/iaBDH2A1EUitEJ1BxZory2ndcJ6pgI9meIHoF1Ayaad4DbnFUllolZ2gp2Pu/aYOmHqdn8NuaHJYOZUeHWmwWuAYAQDZ+2zP6l2cfOFihQAiulmsm+k4h/ldaFYl65p93dQ0ND6vu1dMh9M+8iLg6dLcHA1TeBAy5XrEvFTn7gRFJwCwQjF9unNgEHLatTuHQDC+19/KN6bX37xk/MPcFrhr0TnM/OB2X4YCnTfK1VJ84Zf7zPTILzmjiOw0M9GA4uUTzdfA4FROho/nI0tippWfkfsAcGaGDIRkhwfA4Jnqe4Cq/qzl+mOsj8nEMUKiSmyU6MAgvVB4BlYHRToNSkBKSHHrezPOmc/RgYmuz5AkqiO8ltH7zll2FUTc8xFJZ+jeQvprCZRhTMA8jpCdXVbl2jW1R69L8KZrJCWUToRcX1SjayYnQf5ADhZxMn5CjyuhYIpEIziDXSxCBpIkLcwAoKynQFk9gGYAcHV8e5MFKWDY6O0gwIDBPtR/PVv/PG+4KI29TXpUh8pNTeDAy3TZ4UDAMj7azvTQp+GYfoPzXk7PcxhejNgoOkUELKsMqWrMH7x5ZwyH9QQ9Lm8Wtt2RL1Xz51Bgey/rPa1DR8wdXChjK/4kKnz+BVFARcB8ukKHBDxdLtea1CcAzMLZ8gxZ0wJNjX7Ro9FsGOKBhB4UScuZTQ+CEmcFOeAABB5PkeFlv7Njqc5ym/2kKYGEFSzkBGr42EyT6NQEDfkbeVz48owEDcgbhUYckJaN6Rt4wiGG4NC3DSPjLRtSAoZiSMcVliQDkyh4QAOqBvRk6MCAeQFBmQ1RRcC4Pm4DzzadxI22YUAtwRujz5AQijKPw/yC3KKoLAgI/D78kELwfdUKCAj5C0QjMwF8ns4y+ACEPQ+BKlsT4Bg0h71vJEocqiCnqBOs8ah00AAAWW5bYUC6/hpblcL8xoB/YjQHJkLzi5RH4HOZFzvP9n/yADqEwZbx7JhLKeae5myN1MS/+GX+5tN5NQjg6g3Awb2oY2SZgcI73/zl/jql7+Pb373m+poePSyD1Uxx5XdluFCetUHdyX31llsuHzrYbnMB3FWxsmHfIX2SxQyoNyPdPQGAwj6gjMAJ7bUnK/DgT6KFLXxVbCNpxSsHtPOOMt+NTE02oMBIFgTA2+zk2IkwBOBnHyKTkf7LPiJZFTvYg2znNTnPIMSFQGIbUXOro6MdYQvUJC3DdhWhoW4Ib28sMDfIvIaGQTWKPs2pDUibfUfosJCRlo5JDLfA0gxF1Ao1WasCmStAgICzhM3QccREN3C+31gk4sLvv5beKlmFwJo8byuwqLHA9ztxjCVEpAXuScvE02UqpZAtRFhKcK9AYLZlMOBdiCDHQiRu5kFdvsCENgmVmpv1rGYz73p/glV2F+AAm+2j4Eg70buwAPfc/lhG8MDgvhKEkG6h4POC+kTnPHacl3XDsyeoJdX/VaTv4GE3aB2dH6Xl73XbLtPbw4Meiiw2++/+xZf/dnv4Zvf/ZO2Uk/TuXZg/KLrsUtN8vL0lStudwPVFCZQcAUUjHPO0Qd7GpZ0RvP2WnLcHykoEDWAAMAIfhm1gzuF+CAc6IszLkY7gO+ThQMyj7QzLxzMYiDRdDjqgiXJTIa6zoLOxRc1dmbNgs7Rz2oDTxEUumKnBMqu9skpNlAQn1ekLSK+rEgvK+K6IZl9eY3YXjaGgC0irlkgIfG+mJE3BgTWItRwyCnuK88VUwGvk0AS3IiCg/MMBi4wEPiFBAgcwk1g4LbwvvuCtAS42wJ/W4opw93k1TkH7drYjCDahLCAfGAokKBPIF+AwJoLHglKlBpBfw0IYP/atjdLAyAoAvwzQ0EDBEemTvsQTVm775lfRL3kyNBx0r+UvKwWzewbw4Gm1xpnBve/kEZPMZIT+/OstMjlfrwgkgxqp/Lr04BA05sBA13/u4cCrab3333AV3/21aBSL6D54KwrmgKbSy2LlGzUuMtF7b6xmuxC4zSEPJxONrFJHYaX7sp6uE7DicNQPWYzNDAgZJ7RAkKZPqnag1RNCT5nXpZAzQxX4MCUYQgIgzSCg6Ltlx0WEJDVkZLQBkxC7axz7ayd42iKznvAZwGEDUhiZiBenRBE7KSo72wRE0xKLABTp5KUd5HFlyBvG2sLnl8QnzeGhOe1AEF8idieN6QtYfsYkbeEKGDAf3kRpS3boEjzerPBigLxIkpu4dUU3cJLL1NwCE8eLjiEe0BaE9zikNcId1/gRVPBDpyE7Ek0BV0bE/8ACgvDlA/AckMmj+wXgQKjNVAg0N+TgEQq5KPZHsEAzPFkfjd/bXtryj6ou+5Q4fkLUKAaKgsIBUaBYyCYTA2cptIJj4XybuDUjI7njWc3jc+q23PtU7Wva+6zg4SDNDO3jjQOu0FVqcV668lt+v09KNinYPk1G9Rek1V2f78oV5/eDBgAqPPpyXTYAD589wG/J1Dw5RdfTswM+3QGA0dwsAeCARzYvLTRN9NX6nYDB+Uik8eg0U7nlg80BI+uNQFMRhGDzmM3Ne1glFECjWQUSCAygKCewVZ7UHT7BgiAYmYgZDE/cL6JstQnoJECVKirkPc4fr/N46BeZ5MFBH7Ea5DgwCBDZBwWVVCpFiGuItjExBAJGQRKK7AYMA7VgTG7rXH8y7IAUtq2YgZIG5sUtueIqP9eNsRn2bcmxJcNWwJWCYe8iuBLOWPV9zCoPPVuX/SZACxECFuW1RABfwvwi0PeEvzdyxIHGT559j8QE0OWctPiQVY7oQ6JfgGFBRTY74CWG7DcABeQHYMCfGiBwC+XZhZchQEAUyDQdlOKfdTAMIYBve5Rf4IrWoJmSW+gfM+ngdWa/skEDdLvGUCpFeN1f7X/see1PZ71AtJbzmGET5j3n/O+0+3KYQ7KfU0+k+3RlVaA298fpoPa47xH974aUvvtgIGR9lZ78OG7D/j6z77Cn/zuN/iJQMFchTPP9mj7pDhzU0KnNWiJWCWVNEBDwgB2kDB0JnwUBo5scDNNwUPzioGpOq+c4ox2RDuWDhAoD7UHEWxaKEBgtAdngMBFqJDQPM5VQuiqQVPUPDtISGwIqJAgGg1PHSRo504Er1oEdVRUQCAHimsJwiPxD2snkxIoBJ6R4APIbSxgtw3JS2dXnAhzcTJkEwJrBxQKXiKv0vicMjaBgZQZFKIZHQ/XSgD7VCyOv9GNMkImJPl3e9kABJ6h4AjkItziSnlsOUlWZ3TBs8lAnA9bGLiXv9kvgFsA58p2CwQcnTAewID6FACtmYDf+xgGZm3o4Gur53QgoHVo4w1cAYLedOANFJwBwUPxAbKWsH3GWVA1PsmN4eCkPxpCAjd2U1NarH2Z95qLXkMw6D9h+s0TE+xopD5LM9kyG9Reyaf/3UeEPUpvBww0SU+YMvDtdx/w+7/8Cr8QKLCnPJKdTXWBmOPrdHRk6a+FBBUEAziod5NM8q7x5Y66d+mKmeAIJkZq2SMJebUjObUf6jMDKi4bQIBEIcswgMCebZ5QHRNhgOAEEEDSycsotqp5paOfKGnOHqkXDAovuoOkrA4SwpbIgAJKR+8EEjyAJCM+7wwgxBVIMlKOK2sPpB0R8XI6jVdKSnC3JBqCAAoRJE5+advKzAAglSmEXORs1lNAI0QtFNi1FjQ5EUbV3gIsjhjm5HuNzCMyy4NTub/OXFAQKP8COybebqBwA93uoNs7uPsTQ8HtCQgLcrgDjoEh+6WYCtSkkABeHRLsOKhmgqMZBcAYAo7aRJ+OtNOAMR8YEAAMDMiPq0Cw0xLkWIEAqFCw+45f8Q0D4PXH7WCsg4NpHid9Uz/CH8BIAwnkd+XOpVAHfWkn/OdQ0GoLRjL46mhdrxsNah+5fnjfi5m8GTBQmy8AIAPf/uYDvv7lV/jFz7/Bj3/0pe5u0qxZzmDg8KQu05lZ4zIcNJ6vA0K3bt7N/g4gRv4Eg/PO8pn6QwANFOz2Wbixec1SNvemNAYEBQJohxDHvgcHgACwdkFVwg5U7W4CCiAqQrWHBfNkw2qZVVN99/WcoqVQWADgFBSIQcYRIRKX2RMQsy4mxB76yNa8sIG2ZzYtkIPzHsm5nctqaUFF2qcy/bBqDRz84oCUkYKTqYsJKWcEMDQlKAhTgQEvv8u9TPPzRAV42NeAtxdHCIH9DHzg+7KfgUO4e/g7Oxv6e0C43RBuC8K7G/zTE+j2BHp6V6Hg/g7u/o6dDP2NgUDgIAscgDyiffzOT2IUgMi+t+E7xrxfOTJZu2b/HgL4nBYE9Ji2E83nEhDMtAQzILjqX6BCvzyA9MwWBHrNwS6P45H4WIDXsmXtG2VAVbWwGD/DrC/t7vVaKNjJjxPhrIPar3+5h4KrMuvsvu+/+3BYjDcDBkCFg2+/+4Cvf8VQoJoCK6g1HVXMoy9zd54BhNfCAYAOEA5ue6SGa47tm1Y+PIph+Wap9SeYdyqz6U66FDEnCwh15JFLZ3NuXrCAAKIykyGLQ0GWfVnfk5RVO+EZLPAz6ONlOV4ft68mWyvD2IDaPhtYyFrsal4QSPCU4RzBJ1mCWAAhu421BuRB7hnZeeT4Iv4NvKpidg6JHOBchQPnWundpRJsyBHwEuGeAZcyPDJCzlgIWDOwZGLNgTxHzBleQYwqFCzEzocL8dLKd0cId49w8/z3XYC/eyzvAsI7BoDwdOO/P3yH8HSvUPD0A5CAAN3fAbcn0P0Jyd8AhQJ/r0DgQgGCTbQDKVUoUBiIOTcQ0KHuMBl3l+ZbGvkG8O/HIKC/xnX7CwyYay8BAVChwEL9a7/dRvDXYdsw4qpNE6Hb3Mee3lzr2zMEFhpQMHlfDZg01LK+BgouypBvf13l149/9OVukHmWzgay77/7gD/41Vf4Eb6Y5vGmwACoD/2Ln32Dn/zoyx3CP6LO6d9CAkOHbs+Ss9fSg3DQ5X44tQc40QTUY1c5px/xzOAlY+APcTEdfZBUqF/vpR2MQsZV80J1UHQCCNk+YKYmmiIECrTTssfOYKF4QRvgsE9oq6bsP4EGfR6IlqCs9CiQEAnwCYiOr6mAEOCdB9yK7BzIb6DNI5Nnk4vY4Z3XkXMA+QAXvi+2eo4X4GXaoIcLK+I98u/7hvAcsd0iwktEWmPjiBiR2V8AakrQdyDLUxMvuRwcFYdDt1Qg8HcPfwtYfhDgbx7LDxb4J4WCO/y7O5YfPCG8uzEICBSUf7cn8Se4AeGGHG5sNvALsl8QQQ0QxCTmEeQGBhQEknlBoxZO3Y9Gc4kWCCwMHJkEbL69NkCPDdc00O0dDEjpLRAAD2sJzgTpI06ETeEh3/uJ0O23+9/UbRHQwkKnMdgBw4VyjvrXUygYff8H6etefsmtHpJdmrprVD7+8c++wf/hX/7h9LI3BQbfWiiw3pvnQ+I2DYDA5g/s25JtO71Z4wwOuFhUL5hM82lv2H+AYwh4BbCOb9fDAVD9IUyHUWZPfGKqIUeN9kBHH732IAPWvGABAcAOEric9R3VD7hCgYcKh6olmMFCsUGLM2POOqshNyAw69SiOtUN6k0FgHfiJEksxBzx/SgTggDCloEggOD9Dc4v1bzgF2BjAUlhAS035OWGvNyRb0/A9/8/LLeP8PcF27sb/Pd3hO+fsX3/DP/0gvS8IrxbEZ83bM8yM2EwfRExI6U0jIzMIQVYMzGanuhvAeHu4O8By9MCd18aIGCtwR1OTAfu3T9gE8L9iTUFy53NBb76FORwQ/YLEghRgGATzYBOs1SnQvWb0Hd19b0A+26FhbcZ1RtBXmDAjOg1z94kMNpvbzhfqlgfpAr5ERAA2GsJ5PwrWoJH08jBrx2RVygYyNXDPqzv5lvNzQQW6iFMzSSTvnZUrkegYCZD/vhn3+C3vvhyL0dsWY/SpJK+NVDwk5M4Pm8GDC4/9AMyy77ID7/+gH/651/hj37nG/zOf/Pb+2y1jVG99gocwGwPu5gDALDZ99t23yWzSJftaPW0Ai8k1mrbsnX03nxcvVVbbm/tfgdpNwIplVztlhWnDCAAxcTA96svh6T8tq51PQbIX52qmOxOeRKFBatZUFAokAAe4Sfsgyn1NRJTLoInyo3aqskgB2yRR9r8GDzi3gjwyIguw2eCo4yNCGEACBRXdr6LLwwJ4Q5a7qD7M/Lz96DbE/LLR9DT9/DP32P5wffYvn9B/PjMMPDxI5YuzsH2LGCwMhjkXAMf8XPU9qGmCA1kRCRgsAgY3D3CLXCcAvUjeHqCvwd4MRu4+7viOzAFAr8A/jYFgi3l4kMQ1ZlS3s12+i7q5+izvr8KB4C18bdQ4Oh45gAw3tdrAcyu7pNtR/f6rTy8MNsIgl6jcm9Sb+Y02oGyj6Ryq9CdClt0B5p8ujvXz7frZ3lPq/HpgGGW6aBsw3JOoGA2btL9P/7Rl8UK0xlVXz26U/P6FSgA3hAYXCWhK6n/DCwUnOVv/WnO4AAYaw9sUnv58F6T7VepsOTc3pjRA0L5KXCQ0WkNyIlIhLR0x4I8D7QNJx1OCwW9r0U1LdhylTG6hQTE+lKInRX7/OuIru7z6k8g76tqGHhHgQLookkApTrjwYFV/hovQQXQCJcicqnCmHWk2j7qJs/jibBlBgUGAV6ngW34GTETNhC8ywwHjhD8Dd7fQPEFOa4MCn4BwhPo9g709Mxg8Pw98voCevke7uP3yNsL0sePHCXxZcUmYJC2DfF5k0iIHCQp54y4mkWXTHwBjXQIAH7hhY+ohDn28HcOd+zuC8Jt4aiG94W1A+FWHAtpue1NBuJYyFCwIPsbIoAtMXhtKSOKuaD6EvA/hQGt/13dl/YhbUIoL7pcQE2ThQL2CdFIgy0QzIIN9aaEFgBMgfqAQ+iE9+D4MK5I38BsarSAD36rjZNe3c5F+NfzignBOvmZf8C+PzssiXlkZ681gNX3vebw7tesX7a3eqTvfUSZ2ssSYIdYl1LxuRNNxJX0ZsBgZz54RRqper79roWCXDqO9g3bJU6ncKD3yUaljXHjM4PMYcP8lMZoy94vzVrXdDflp/Z+jVnBwoEZceSi1jdwwDlLIR60R7YPYEYcVXsAoI3vYOKzVxVm3Uf9/Ufe0NJxUdlPxWehdDy5OjUmhx0gqPbAGziAAIN3hM0IUOvwpiXN3VBpE7LwYL+D4Int9t5hgzgFihZhTQYOiODdDcHfQDmCtjsobkB8BpZ3wP0F7gcrr7nw8hFYn5GeP8Ktz/DrC7A+I8cN+eWZNQd2jYWUkLeIHBMvtgSgsSdIYCXnHC+WJP4Mds0Df1t4uqEPJf4ALff91EMR/o1TYeCIhlvmlZ+3nAsUqHYgJn5fGcAWEyIytii+BZO6LjBT3kl1pgTExAN5n2ihoASn6oDADWBAQaBAwMgEwAUs+5s0ENxDk95uXzo4Zg5d+VZ3M6A6KCjn0QQKaAwFHRBc7c9K/BCgAvkAFOTwqZb+cj9s9zXlnJd7ek855bWAYKGgl49Hz/tmwOBvGgp+/KOfHL7Ifv3z4Uwc0/p6OADaFzXTBui1/cFRA7TlnVKvCtSyR0S8AYSR9uAQDqTZFs0BICP7tO80yvHBOOAyOHTX5n3+Y7+HXk/cKWctEFhoIA1H7FirICq/lAHKtAMEoGoPkMS0kKo2gYjgM49CVUJFVCFVR7H1ZRMRNljtQQbFBC9mhuAdiDI8gFU1ByB4JyYG5+HCO4QFoPQOFFdQ3JCTaBOeNlBa4dMGrC/I6wuvs/DCcBDi1i7MlHVpZ6O67pO+T+dY+JPj9QtCAPmFgxL5ALrdh9EK66yCup1dEJ8BIMZWO7ClairImWFgk3gLCgNZfjf1iypgvGpxHJUZHORYC0BUocBDfCjAAKBaAp5S2gGB/kMHAnamAKTQo4iDeuzh9ICfwGi68tk5zfdTv5lGU3ARCuxgpweCMjgbFLt5h+YaIpqDgin2yIQ6Slf65LZc83Luz82lzPb6qR/bJB1BwVl6M2DQDLEfTKPPZaYpeE0amRSAFg700DSPB2GgIdkLZazlazGFlxWu+VyHA85VVfvSzZovtRfK+wAk9djFr7VJY3tpk2YG5HLbfUfXw0J2gQU7OTiBhJgBn4lV1o6Ff8oZyLyaIGUU4orEgnpLPCrNJPZtHaFma/c2RY8J5AhbzqI9EMAgVtmHDhJWmVrIvn9U1inwjuDhEXyAX/iNIUUGBVmfgeIKXcyJcoSLK4AMbFtZthmytLOu7DhaelmXWi7LGoeFIcsHIPAKh3UNA168SKcX1m3f+A1E+VfXa5jDwJYrCOhaB7Up5KIdAABv6lw1BKwNoAIFQYb6CgU1PkP9681fBQXeNDAgK2nunQEPRvMnbXdf+Re+odE55Xs9ggBNrcagWbDNbF+CAtPnzPq2R/q1LP5IOdfvuvf96fN81fRAs/0Ahr0qHcHBIRRcaApvBww0zYbdB6m3+fZQ8KpiXJRlo/gKdn9JgwZ3BQau8kzREJT7VUDQ1f+KXf0SHGgpqvaAz9c0mCp0WmcXPrWDDrRVyY4qZhBhoHRy4stQOjn5myI/u8TYJ+fLiokQDQKljEhUtAdKh87x6osJFQ7IZfhELLgkTsFmhFgWGzkXtz4Daw8yVgAko1sFheDSzuSg2oQeFErYZRd4ZmN597Gs6IicQCmBo+YlGeVWQcd1PajfIhyoglURFKMVDWuI4pjA/gERSDkdgoA1EWwdCFQtzOj1ZxbcEpHRAwW06jYKFNggTdTDgFzj5LxSjwpeOdW6tCDQAMLEbDBMsel0djb/5ju70jmpkL9w6kDTVu6rx4sj8ONQMAOCK9VitQRXIMGZPPve5rSPPkhEr1TyHKSHoODBsdWbAQP70no70hVAUDjQ2Q2PQkFvq+/zPkvTRvYAEJyZE45T94FAVXEsxM7hAA0clBwJ3NGJRmAfhrRmdD7NcaZVOHae2nWwQ+esY20F6bYZ/WQBAcBxJ+88kDZkF+AcO9hlAiJRMcmw9oA17mpa0BQcAclhcwlBPFWjI1DmVSObEW7pPOv1RdglxbNcbd6O4FcRap4DGwXHgi6Q241+9ypxAtECTwurw8N+MZ5STxj3Q3nwt4YbZsGfwRCQY0bMCcjVaZDPaWcRbDmxwkLiEmwxNRAQ87iubCpzVEyhy9LEIuD7urJQoPWkYBWoBYKiIUixaAcoxQoDCgr6Yu0MAvuy+9Tb7TOKwNUoHbugQ4OnP8x3kh5dmyXvIEG+IfPvDApm/d956jQEONaQluOdHHk0jkA/4GxKRPRJWujLUNC9pgZuDl7zmwEDHYcB9eEfBYT/rpvy2EK2NOSJo0tTllEDtAW9ml4BBY/4F9jiqI+E2rJ0rH8VDjgvFZxAiTiWWZvAERz1ZgIKpYDyLLO6sSN96uIk5IyxIlBOH0zTQhmhdXkPjYP16fhP7eBINAa6Ih/lWAAIaQNcAAkgcOhgKmCwIcOaFmLObF3xBJ89Nn2WlAHvsMUEnzM2U7QkAlNPmxQcQH1XzhHcmlpgQNUu9IJQ1ebWyU7bBRHa6XZG5T5LffusAsB0/rKtUzl1xoAK/94cYAHguE7aVNqvQ6Pn0vqwAFX9NsamA08SYKpoDLSeRkAQCwBQqqtf7tvkyZdb+jeH8uF27mmEmZPvQIhjIPAfNVHY2QW9M+IkRsGnQsFZ/1bPre1T+7dszhn1f+UGV/ttK4jQwkGvNWh9CI7lyqFMkdRAwT9sB7XT2WWT9GbAAGgfNgNlcZbhCfZEjKd0jNQ/tfM7fnnA3w4UPPLB2LoYOT4ewYHeo8AB9j4SnE8vQFEhQTOx0wQtMIzSACKyaiFM2QogDKZGlnz0PKOuhfEAvzJfW1cv5IpjECLVFhAhu9wCQk6A8/Dk+bEz2LzApRUVudY3mxY2AMFzl0IOvKywwEEQjyleSZJfRhWEuYyKRwGG+lRM/lKLZQVEGfHqMW3Xvrxa44hnGr0/IoIuRdtOjT2/V/WrsOdnqqGWdU2G1z5veVZH5RlVuCsUBOrNL7yuQ68l8DOzgTG/WCAoPgUpmrZYoeByOwS6dm4EcT+9dzKFkPPaQ2+TTuDg0XDshgFaR0O0PgXluG5PBkXl2gv9my3HSDv6twEH9lnKJQffzikU0HgZgHINldMupzcDBj0U6LvZaQ8GF45sMmcvEt1xmz4JCIDLUIDJsf74cBqN7NtNm8x510iz3KTY44ASRnjkQCnoVB+bNGQQdnWRp2oCPWEAEb1pInezHxQOdnn1+3LbEQ+1CG3i9QvYnptV0mcqIzaFhAIISVZAdB7OeTjySARE0R44pyYFFO0Bid2cvGO/BJ/Y/u0dtsRdJQHYYoaT6ZA2pYSyCFAvOO2Kh2nWqCW5SWc121+OH8iRMwE+K9MjZa1CX34rCKR92RQKGACA4JzAAe9T04FqAayWoOwXLYF3F4CgwIABAuunARy2v52poOyzH2Eed0zWNwbYzxYw+Z+uwjoo0+BALdLk72j2AT9C3gl+3W+vb/IY7BtNR+wBwB7/rHBgCtrrNKl7XcMsjgaa5pguuNRDQWvu3V12+ChvDgwsFNh9s2ko73/dVap5kbah7iD6lUAwK8dOs3HcD/I1k/1nH82VNOpb1KRQPhgdvcnx/tna72j84HS0shmAslIRDERoUCOd2kWey6aAoHDAPvbSicobnX2FvRYBGPo8lO4qEzhwUnWigzjRIecST4EBISNL559dZv8DmcKWEjslkqeyyh/lqj1wRAjZYyO2p1MEmydiAjzDQXAOW0r8pDqSFihgSDDR/iTyn3rz87kw2woStXVF09BGAjq+tpGhjRqoyQp5bz4qp7EQ5LiXEbpue1Xlqw9AFiDgKFRGI8LHg3MGAFotwdSXgFrnQl6fYmA2SLE4aO41BAOnTQCH7a5IMKMdmAls4wtT22WFgmyO8/nuQO0PnEnDs7efB9uzoEXWhNDk8comNooZszsHc/V8k3pBfbUv15tjDwjAK2RLBwW/L6swfjnwiRuBwBW2eTNgoGkEBb02QdOHrlJTd6F9Gb0Qnjaki42nTzuzx99CGhHllbSjaaM96PO9+kgHirTaoBUiBBZyJiBrVDYFgQM4uFSYtnPOcbgWIsh7OZs1BkQZoISsoZhVg5Azskug7Hi47MTsIIsdkSOAqnOiA+AS6wQcaqQ+EPseeCT4lOHh5G8SLQLDQQA7L7rcQUIG1pgQU8YacwGEVZZQ1t8pyVoCSe+dGxV+TNWGHwtEWC3EeQ3b9qEmi7ICowh4PU+FsXP8rzr2VRBYgiu/F09ImbB41uY0MCDOgwoFwan/gKt+BO41ZoMxEBQfgkMgyLu21lcheW9WJbw4bL0CBZOpg7YMR6/zNd3V0bTrR4S/HTv9XfedZ8f6WAm9bCnn9fn0J9jvhqr8+kUHBX3R5lqCeaW9ITCoD0mmcXfAVn5/+O4Dvv6zut410JkdJqS3Sxe1Akef8rCBS8s/8mw9KpLNExjb30Zlu+JAZlMDB2bfpTRrlwdwRajvl8gDlFtAyFS1BwktHFA2pXOwkRGHxbNQ0Kl39VIGhDwBBNUeMCxkx/EPrSq5MS8IIHgAmwx0FRB8yjydMTiEXOfnPycCxQRyGRQdC+voytKLPSSopmCLsQDCFhNeIgv8NSasZltNEjllpJjKiC6nXPwBsmgmAFx7+arid+ZdOnZ8LCGCvWOtiozuF+/gRPDr9s2zEE85Y/EEwLOwLmBRYSB4/StTOI2W4D5wLgyuBYLRbIOp2aBsiwNJFs3BCRDMYhPkGAuITlMR8FbgVygoU0GHQLB3BtR0NG26vf9x8WbXX+0r1N/LevN/Sj93NZV+vxPKV/LsywYMIMGcqPc6AwKglV8jTUFftrp9jaDeDhg0U3K49m3DsZX04bsP+L0/+wrf/G5rk5kK6FGaNJTB4dNkGWRGv6MPQ6Hh7KO5UkbrV6CbbnCeJmtq6FyfHvr4h6MEkzcg6sCu0DpFjkBjQEiiNMgEdmtzVYmgJcrSSQLV3GAcwch77rRHNl9d8+EAELISU2QHRDV9ZIqg7Of+B6pBYNleNQhORvQGEEJMeCZeWXEFL4HsQHUt5uiQKMFRHT1rslCwxoQtsgZhFehIkRdEShLmmAfC2YACzwxQUOD3mW31NKlqqw0MEP9zspaCIwJ5KisxOu/gPGFLHL0xJYeUWd1fvwAnYKBtw/gBGCi4+aolWIJHIODu3SEQPORY2MQnYC0C1IdAQeEKENg2Rq6DgjriL8IfCgTSnmWWDP/2VUvg/BAI9N9MvV+KM5EpRZExSLMB1ZW8NdtRP8fX5KbvBOYj+aPBjzs41qdHHPlG8v9Um3CQ+QwKzsqyg4LiUzW/5u2AgabijVvhwKb3333AVwIFX3ZQ0Atom3o1+RmNPgIGnZKgwkGnNdAPozkX5x/NURoBATCGgtm5mkbdW//BD+2Ho4Ll9t5qYyVzHDQHBOKLkFPkUbrcpYED56EBino4yARQzgIHes9rgFAcIkVjkcnVF6pOnCNAIIKTGAiZCMmx38EGowSgCggxA0Qs2J5jRCCPNWZsKYHIsdWitBJ+5pQdBwjKDt5lbFLulCoUrFsSGMiIW0TagJgSckyIAgMMDVV7kBJrQbJGPhzF7lfHufKsVUvAfMShpr0nkHfszBcSfPasoTAOB47nUgIQXwPn4B3hFhgSgmMQuHnWBize4S77F88ag7v3e5PBVR+CnK8BwUBLcAoEXEmlPXFT5lY+hAKNJNlAgcySIT1ujo2AIM8dnYFjRcGRh30ThvggNX3bYIBj+zkt4+fo68b9XHvMDkZ251647yyN5IxNo2PfGij46YlPwf5uOG5zJr0dMKitqoMDQKvLQsFPD6Cgp1DgmlrqNXAwu3dRjcmB9sOg8jGcfTRnaQQDfdn7vI6yPgKBRkV5ct3uXjkXkwUBZpEmDAEhO7D2gBwLqyKouVMknUlQbk4DOOAaLp3zESDoIVH7MhxEtOaFE0CAa30QiNc08I7DK0dik0DMhOR4LYCQeX6/J4/ogecYsUVCcBnPnrBuGY4SHAEvXbFTSojJYcup2PA1ihLLMokqaKAgblGiIzM45Lgh54QUV15I6SIYkHO8HDRxSGQXFQ4AgH0pIgAkB5fqMtcASlmdqP/VvPC0ONyDwy043AoYOCyBcO+AwEubUQfDJgYB8ZoSVjtgZxVQEp+WB4AAQAsFszQDAj1WoMBoBXSqrGgFGtNBoyXwUyAoJiJtG7ZNn32b+l7K+b2gvtZ/9HDQ59X4fDV5XBfPR4MfNWM196JzIHi0nx/91n3AXLtrNd06qD2/t3l5D0TSfDtgoEl13M083ixQ8Hv45nf/BD/94iew0DCDgusqmvHvmtM8h9E1RlnA2gPZ2cMBn8z5zz+aeTryOz7TDoySbW8jIBg5GfUahL62yJ5r1Hwus9f+DBDcSHsAAQIH5MzCn4+xbwISSqS4Ag4DQAD8aQdf1MWk+WMMCCnxGgJJQtrmAKQNRJuM8KoWIXhCgmgREo+UU8oIRCU8sAWEsBGeiWMfBE8IG5Wpe9WswCGT1NkwJZ4SOXrndmVRhYIUN+QckbYVOSdeI0EcDux6CeRYdwHHph5KIgDJS7sOIJkDq7NfmvokgGTkX0AgCAwsAT+4eYYC2c9woJoCgQMBAhb6utrkg9oBBQQV+upDMAQCbpjTiJ7dND9rMpgDwZGWQIT/REugiDIDAv1t3zUw+CZLe9B3w3tG4YfJ5FWF/TEcjO9mbggYHSCfctVXYdTnTU0LAyjo/5rCHdx13M/3V8/z3kNBe865oH8ECoC3CAY2SWW8/+4Dvvrl1/jm57/AT3/04x00cBoDwiw91ECmBld7bvWJ6P860/DLFEGq1/E9Jh/NQdnL7xONwFU74ShZKOg7nUaDcKG9ujLyEiDgXqd69DsqFcfyX7QHngVv1R4Qj/qksy0dvQ5ZckbOVd2rnX2uaAOEcCGEs4wSS8MaAEKOYPUGd+akay8QAS4ARAUSOIqia7QIyRFiygiZ7fBRNAkKCEuMWDeHj1ssiyp93HjkXKb1OX4uniHAoPB99xwR7OuQBRqcI6TMfhM5RpDzyPFq9wwJJc3AQM6zWUH9DWRpZh8cQuC/fvF4t3jcF4d394Cn4PCDe8A9OPzwHvC0sJbgKbhiNngKfgcEwVGpB943mF0gWoApDHTaAX7PeyDQ998898SJMJM6AM5gAFDzQPERIF7Ea+dgqFoC5yGIw+GkDRBo+GkL643G4Mr3WLoeqyHgLQ2EdqUvBQ76GHOxxhewd9K+RGv1Nf3eVdPBvs/v1aNX+nnO4Uh70JfTQkFrPrhw/1emtwkGBkkbKLCV2oTZbZuwCuSjNCW2qy9neP85W478DoABIAx/jdMlAJipNMz5+iRVrraCv4eCXedjqm8Uz76s2Wi+IIdcliwuIxVROasGIZHGqieQCyzsBQSKZiAlZF+1AzVErewDmXjzHjYQTQMKtsqmwKCVIIBQ4iDICFvKB3DHT7Q1I0M4L2YSz8smi+6d4YCXBt6yzDSQ2QeBPDYPLIHwtGV83CKCc3iJCYESgiN8v0YeiT9v4nkf4RzheyKQi6BOt0kuIa4oUy2JHFLcWCsg5oQs2pisGhGAzQZiRiDycLLMsg88y8Avjn0FFoaB5eYFDjze3RgKfnjzeHcL+IHAwLvF4yl41hJ4h6fQAkEgdij0MvPAO9EUUBeQKJ44ElqzQB8Uq/nmj3uO/RoDBgS4ktqohqgQMNQQdDMOCigYs0EcaAh00Sl+hFw1Cd0jXP4ejRpBBXgPBxo8aGa+tNXRHM/747XPqVqIweWHaQcEJgPVPNo8h0Bw1t8feeFKrkdl/jAxfzd9ydV0UVsAvFUwAICc8f67b/HVr77GNz/7BX76o5+0FWNbZ+ewCFxpYAe09sALKGYPoCnDSHvguo8CqBqEq+q0K8L/yCGmv781cZTsjPNQa6us9dJGPJurLQFZhliLmblj0k7JIZc3FmlsYuBpcQCRB5HYq2UUWE0Hoikov80IUAUDILZzFQZ+GDWxzjffp0aDAMiIlMtfQIErUUwOLByInDyIK2pikhUJvV/gnMfiPCIIWyIDB7wKYVANggEEdc67eYd7iHhaHP7q2eO+bPirl4j74vD9s8P3K4NC3BK2kBC3BOd4poIPhBQ9UlwanwOuitaUwI8lPgWOZMYBOx1aIOi1BO/uHvfFFyj44Z3NBgoE/LcCwZNoCBZfzQUFCtRnUWEgrvu4A0cmAnnHs9Za4w3IGQ0I7CGAzxmAgB632oEyBdE4Fu6AgL8IhYKU5iaDBgjkUfRbPOrB7PfqMkvQJpCQ+s9A54zo47QdSz9CByZ9j9lXnMB1Rx6cc1D23b0n9z+Fgs/a37cF0XvNfOKmUPBIOU7SmwWDBgq++Mn+BOusCAzhYJwmtNirDC+QHAshU46mwRxrDzQ1kNAX8+AxPmWO7+j+ZfVAOm6f/chk1BHNrs+Sf1QYyOCRvQKCAkEHCCmhRLBT50QiVEDIiYVZzuDpjux5j4E54RgUINMjzUMMhEivUVB/BfK+nF/Aplctu4CyWJWo4XNcCyQEv8A7jxw8tkwVDBz7IATnsQXgaXP4uCV83Agft4yPm8NLzHi3RHy/erx7jvj+ZcPzLeKvXiKe14TvXyK2LSJuCWlLiFsezFbwQ3MmmcfYzz5w8IHgghtqCO5LBQLVENzEVKBA8BQclsCRCxeJVdCAAQE0gIESptiAQGM2MO/OvrfmnU3TZwIBvtEOBtp4BHvHwqSKjhMg6L/Bo++XqJXFCXkHB0d9STNK77UNOL4WqOt1lPIeQYNNB/3h6P6PAsHD/T2X2Ny4hYSHoOAVQPD+u28Pj79JMDiFApuGnjA9hg4qfuLMsWsgB6okKkJgAAgHUy5tmo7uLwj+ft/ZR2lrRbetiUPhwObXaxOO8j1ySgRUE9EWVjumRoMwAwSqHZInVEBQwT6BhOJfYITFFVDgwEZmMSWIqUPP7aat5S0NR9nygyPgxVWEgy7aJKAgPgjsi7AgO4ebX5D9ggRCdIQ1ZWwuY00Z0bEH/7vo8XGL+Lg5rDHh4+bwcfH44S3h+zXg4xrxD142fFwT/vol4nllSOinNaaNoypyNeVh6GQnPgQQ1b4TE4LzLNQX73BfHO6Lxw9unk0Ft4CnRYHAlX/F+VAgYZE8FzEZLE4cC5FBca0wkNYajVD8BCoIxPbdmG83pzpOtu+ljWMBoyEwAhzoYg4cgIC8azUTHMNAcZOcmgyuaAhe8/0pIJz1GW5wTg8FeyF8rS+i7ren/TlXMhzdtz29E8ZHQHC1v7f52EEhgPffvTeO8goFs9HS66Dgq199jS/wxfScNwcGR1BgX+JwKdLGKREYvYz3v/4Lc3jSQB6xO5HbA8JAezADBCukR8fO9x1AT+81PcnBmjhcVr+H1683Pruu3V91fqpB4AWFpEwHgFC0DZ0WoT6aFfip2W5AgQtVIaADhRYSqBzXGAl8vVG7a72rV3838SEDKKsAkeMpf2FpQIEdFj3IhwIJ5AOcX7CEgAjCGlmLsMaMe8h4CoQtAR9jxCpmho9bxktMolWIeN4SPq78l+Eg4iVmPK9xGimxT7NIhveFhft9Ye3APTg8La25oPcfePIewe21A4sXU0HaQOsKiluFgbihWbtAQCBva6n/bOp/mJxjJ0upf9tnHMYa6PwEWtOANrzrIKDtoR35XwAC6Ll7IDj7XkcLrF1NJaIlUL472Ty24ZddvTa1yX0HCf05V/vIQyAASmV9tv5e8zT12kLBQV30ZT+Tb2jl4x/+y/9yWuS3AwZErH65AAWvTe9//Rf46pdf848RFIzuMfrYev8GoDSYY+2B7N59FmepK8NV25SVTLbM5HZlyKizJywcIHP8geY3ofF6PoKbWepXQeOOMKP4IMh5PSAgZ2P2oGbp1U8GhREkyOJG1dkRFQ4keFKOmzkX1eFNt2d14MzUNMe2eyIHhABsHvALQ4JzyH4BBBK8X5B9wObZ1LB69kd4SoQ1ZMQcGkh4jkG0CRwR8SUmPEswpI9rwhYj5xNTs95Cn+w6B4sEMAqeNQOLd2W6YXA0nF3Q+A50moFAAPUwEFcxEUTezhHYuK5z3ACNu5DzYT1LcIX2G1Am8EHiVlQosFME1eQzhoFPBwH+FloY2O3rgEDz0vP47+MQv9MEyOjfQkD/ewQFDRCcqch7Wu5Bhdywd7yOM5O+8m+yv9fr5Lxvfv7Ph8GLHklN3pIKFPSO+IP0ZsBgOPvg0cY+oSygQsE3P/8Ffvtf/uN6yayRHBrajeAv+9K59qCU8eJzHZTpkgqs3I9vWRuaadzdJzeCg1GnU8+rywarf4J1Xjx8PAMHnIGWTuyetaQFEAAUdS8hNytE2hkO1jGKVeC+64Nk9J9bm3Tjn5ATiIwzG6iGaU6xOCGyN7+UwgiqEhNg4Myn18kGJIZwncUQGAIoLPzeFBJ8KJDgZPtpCViNP8IaM95l4iiKOXCo5JjwnDLWLTIcpIwt8oqPWwS2nCS+QmoWXNJkF0YKzg3XMLipsA8ed4lBwGsajP0GFgJrBrYOBlQzEFfuILeVQWBbX1m3MrNCI2Wq86frNAUlmJBvgGAYjXDgNKgNOJt/rdq/FeRau/1+2Gthr79uNhilUbhgB2qE/eiaHgqshqA8cSeAL43IyTVdYZn+2xdSz72aJv3mYZk+ob/vTQs//eJLMyAcJBlkPJLe/+Y6FABvCAy++uXvs6PGj35cdxpPuDxS4eg5J2k25XGqhbgKJL1/g2kMQ+2BnnM171E5H3VaMR+aLgZUISEVOLCjfhuUCUD1NTBag1bDUOGgcoYZRzwIeEV7ID/sLAYATTwErQ1SR0TSa0wHptoG2A7OwIIL0PnuDAEcm4BSYqGQIsjJ/uSQcxRzRuYvcNu4WrMrI1cruHib889mZGtrhZwXZywWUGxKcKCwAD4AMjUQIYBcYN8Ex/uzX3BzAfALcliQgmNIkJH/ljJi5sBHKQXErMeSwAEv6lSmwUm45j55AkiCCRGNlznmcMRtvAGecmh9Bl6AdWUNgYJA2sSRcAO2jWdH6L+NNQesJajamDzREpDzyEnqUZp5lv3cCAQKVDtj/D1AvgDap0Qi1ObRCHqzn//WSi6QMICBUXu5mkaBznRPDwUjbUG92GgUyjF9ENaw7QZZZ999p9Es1xtgqEI3Xurr2/wv9KFXymnPG5RhNLq/nC7IuPe/+baVX8WXZZ7tmwGD1lEDQwF6WPlTm4xETDwirU/xEJ00FgBDmuQD3fkjR6FRQ26GCd0IaZBHO9Wq1Raw/HT1g+u0B+3USm68Fg5Yq58bOODze0DY/ZgmW5VWiWAhQR6svZBQ6qafEtoAA47Uo76OmnwWCJCoeRRRYihQBBKxV0SObFoQOChTEZuYAB0U5ATEWIWCggRQVN5EhOx1VMtCiwKbDywouNu9aBOIfIEE5zy8C+y8GBZkOF6KOcvqjBliMvCIyMiZl2AWq0ozktVUPWVQTO1e6tCuYGiDDgVirQ7FFdjWGpJ4oBVIL88s+BUE9O9JvaGrN3gvfM5AlwGQr9oBlNF/hQLWCNQFsbLGdi7HBBoOAg7NzAH8t8IBAOzcN8zvUdyB3endjiO/gcaCqNVl/Hus+UDP330jMyhQkxtQFhhrCmj6qNP+iclNtisE2CfLo/kKj/alu0J8Qn9/pBU4SxOtwaH54B/+o8vZvxkw+OkXPzW/cq04Q1RN6mzm5kfZasMof/mw+uZvLF1pvAcw0HxkBw2bmkYcZXofoMMoramscw8mcAAASf0BctbowyjAIL1FktF9P9oh2nduV9PoutjvzfWtN8u2Wj2IgkHpCI86QQ5jDOerBiFFBgTauBMlYu0BOVAEEGDvJjdLoMzRGlW45a2fXoeds1zJw4m9WkawFAK/IwGE2IGC7mfhFlgT4jSgEs90gPPIXvaLkBt5w9uy2K63BypfBAbDFKUN2OJgBsFWpxaqaUA0AsmAgPpr7Opp5lDoHEMGyag+Z1AI1X/Ammg6zQvIs1nGagmk3rKFBOLYAgoDl6YRNi/ymsC/mq4OnNthQasuONISzMwHIyhotASqccO1Pqqc0/dRUmLdN4IEAIPpg3+P0kQmccr1HJVxwO5ZxlDQfI3T278ZMLAjxNLF9hXXp4PKb+eRnkx5/JR08pWeqZlOgWBE3U0DOoEdHory9WVXkn2d9mACB73PgdUeqI9AzhleAWAACbW08w/57BOf9QEzU0WE8cIWRoq5dm5WleoMKOjIF0Tw5OG8BxzPn1cVMpyMfJOs4ZCohYMURWsgQj3nNuSwCLusQk9ViGYNh2aOvQg+UgEowp1CAPmlaBUwhAU7MnZVEIr9PHTOdECvbZIiNO2vCoI2qJANNBSLWQDWLGC1AXGtEKAOhV2dTOuFHHShLW7i9TujMupXswG1UCDmmAJPAggVnvh3QgWnommRKkg5X4olcGRKm432z4T/0eHefW8EA3rvc03aCRTY/kqdefU3gNP+CYCsb96ULQ/1VcbUAAzV7n06dVqfDTw/Wxq9qfqUVUvSyrniiD+AgiulfVNgQObvDg76VBpDrUytsHEYyn1exaZj9z/SUC44yMwa7aHzYAcFhx/bWVmJ6vm5AkKFA87vETgoswnUgYqMRgEVEvj5a1EKLJSy74s7s6mOOto0OW73Ee0PaMen4EDEI16diucI2IinzaUSi9/DBc/mhbSBIjuhkcBC1hoLrb8GATwCTsT1StQ+tvohxIiccnGi479rW26NPugcyJF40/OImDxrE8q293tgsCNnAQMnfxvPfL7JoEKLkryuNKg+FCrMxQ8gybYFAMRYZhPYmQXz5+/emz5/yvL80gjMcs5kHAPJOYYiH4DlLlCgGpWlwFWBAl9hQYEgZfDy1WhhIEnbtxoWBYZdteV9V8FlrReU3kzBrLtmOPpvMmt/zoIO9SYDPT72w7kABaav0hk+nB7po+y53SCGCEVlr5VifRFwDAjWbl/v18mU1/b5cr8av6L/dvayCc2RDhCkTI1P3AQK8iBPm94MGADtg+7gYJfGlW6h4MtHpoz0cACMG8vwC78OBMP0Gig48DdoytWo6wQGcq5wUM45hgNPIuR1hGQAQctZIIDqdMPGtaJryj00AGNTREb9dnso6Ktn1y0NXqHLspKcnFBt5mggQQMqWUAIamIgdTR0AK08ko+yDcB5X/0c7DNrYXXpX+eAWIVhTgk5agwFIKsXoApKE5yHvHbyDuRFEBpoKKBgPOyLWULs8daPgfNvIwGqr0ST1L4/sv936n87+rfCf/qcg2etz5nrtnEklB1cdjObg5ZbgQL1x9AZHfBqLliqacUvyKDij6FAkIxfhsKA9cko7xUH4+PcCuKmjql8mejXI9Bze1MAMBf+nCeZbc2jPX+4CJEBgnKePsCjUPCJfRQ7JOYxHGieF/rZ3AFEua8tV+PvcKHff6B/HwnwZuBgYJyh4Pe7QW2Fgqu6jTcDBraihpV2cI3VFPzen32FP/ndb/ATqdShBgIokqYhyhFJHqWucbzaM7Vk8BmhwB5rPqhH4QAgI9IbQDApmZFOU3Yypxpg6B+DDAQUU4RxaCxtgupUSFs9GXsY0Gti5+3lTY9aOt7M97GQkKhqSSwgBEdw/gZyYv9MjrUIRECUue5xhbsB2Tnk9YXvY9pHeZ4o6z0gNkI4R4YH/Z3MaHqWdETtHNXfRrgSmW2jfeC/cs1hiGApe9SZF/IezChfBXzOqYEa+xz2mqNn0VUjkQRwQvucrIERs4qAAPkAWu6sKVjuvC8syE6nedaZHNmYEbKYDbY0BoKU9w6aCWB5aco9a2v65RW1vlyrwFtAgNq22TsKmiyGwr/kjfZce/4OFPT3DAiA10PBmSrfCvdBH3UKBw8k2z83fX5fzgf6/Z22oJ7Ex/W8k7IRxKfgz35vEEa5hYIrcPBmwEDjdKsg32sPOOXJ9vvvPuDrP/sKvxhAwQ4OSsYTOCg3GBBml6YwMGpcRhIOgeRRe9duZsJEpbb7oKoZgc0BqflAGjiQ8tkpjSSPp0Kcr+vK1kGAPr4r27mBBgsWxV/BAEKCnJ/3eTa75Ld20AoR1mFxMw7tHlQ6TO/4GRUSnBsDQko6/c6YF6LjOiXHZgYQRy6k6hdA7oVvagLu1LYJ9tx3jpdB9mR8EwUKZIStvgqpBFQ66Hyd635ajUN7jB4A29y1Pes/kXYahvPyuQIwUl9ISHAVcoqmQDQiohmAD6wZCAsDgQ+g2x1YbqDA+5M6GJoYENlXUEhAmd45A4KUKgzk3LavnTOsJA/CFnPbvswLz/LfbjBKBgr6EXyv8gdOQcGeay/oNQ7UHubCGR+CxqcAqFBQ0jkUDPuoR737dyr7QZ93of9tynRoOsPw+JkJoRfkuy7S7JutwjjLw8bHGKU3AwbAGA4Gg9OhpuDrP/sK//zn1XwwymOogTBwAGCsbrL3HjWgy27C2iPkktfQOWbmV3GSpvlNLzC9UhZYEDKvcAC0gAAAtHsvZHoV3V+ublVAUNuANQeoGjVTCwkEMVFkzjhKz+pyrktWKxwMoCAil6qMA+jakHnanWNgUFAIshTyDBAygJh15T8xL7iVAcF5kBMnOwED5z2S+BhQ3FiT4DxPr3t5LqN7h2eusxXAAnYz0CmNAgVJYiTkxA6NduRuQxnPRuVXNQ+f81q7n5zVWPBCTMkleX4AHuBoCQICCwOWWxjE6HavUHC7g7xoC3rTwUxLIGtQqNkgJoaAbQIEEQAyg4MFgZzGbQrgqZwbMsjx7FciXjHTwoECttr7Vej7DghmToLAfPSv6WixtfF2J9Tl+UaOhvWSBwYzOBhMnaUrg7OjvngCC315hoPEB+93BAVWzgFVfv2JmL/P5N0gWvkuvR0wkI/lSHNgT9X0/tcf8PUvv8Ivfs6agmKrngBGeY0Th8NLjfain8EwddNTdg6QTVkIo/m/zT37kdsQXI7L1iw1axx8qtztAEHybDuW+QdiOyerZWjAwcCCyy0kZJH6CYDPhEQZycBBqSJCjY44gAI9rw+Lw7ZxeWwCyGXkyICQHYEGgBAzEJzYohvzgpfZCx5wmyya5JHTWtTeeXspgg3rC7+fbQXWF9YwhA3kXpC2jZ8Z7NMHymLPUHNDMjb7jLRtvJ8DFoBXS0x1n/1rbPrpSk8zSc6aZcqovv2rqzHCu7ovBNlmzZT6DKjGgJyDDx4kMOBCgLvdxloChYKwtFoC9SUQOFAtgTUbaFyHLeUhEKh2wLano7akhpgowJkTEF0ucEBAI5EtFDhqtQSODqYRSj69eaHfHv2uqVe/7YX9UdCiw74J+Gz9UzNDphudD1X5r+mLy749KEzT5J6Ho/xu+9vvPuD3f1k13bMa3eVxUvVvBwyAIRyMkq3Ur3/5FX7xs2/wkx99WS+YAMbuA7H2pUdsVifOkPOU9/ccwYHeIyezaFAGiu3fSMORjWxWzrNnnHj/lquy+RD7mOeT/Js9DUxUdZujohAo77CPm0BEoCTag8QEoHCgPg9q/PAy2tc6GnXkdmS9leIRtpzhE7CRdOxZVxTcaxCQ2fwt7gAMB87DB48sUMCAsIJSEE/4lWcJbKsIMlFrxw0Iz+yPsK1wzsHFDWndEGkFnEN2hLhFBoV1A/kaSbHY+ddYgCBtbOdP7ElXTRMxtzMAzKvsTQSjZE0OVGYOstB3BQ7AGgBHSD7xSoxqM148cmphAM7BLwEUuP4oBLjg4e8L3CIwEG7Vl2BiNpg5FyocsKmgRoVMAgFbzlMNAWsSJBDUoP2UunAckttL01M44DqjGlYaVVvQQ4FzfMw5ujSFELDfmCnTqW1/LllOI60+MgXwVf1TFfa5AwG7PVflA5f642E//qCmdnLPKRSYav/2uw/4+lc8qP3xj74sMmuWrkIB8NbAANjBgU32W/z211ypf/yzb/BbX3xpYurv87BQYHi3/jpqvPZ4c+2+2GepGVWXPKVzhoED+zQWDgCUOb/UfbBHlNsI7L0abDRnfW9mkEt6U8tV04U42JXs1XRTOk7uBhUSHFSTQBx7IPPLZD7KUBrIxC9aO3PVGnhHyJF4tJaoRPjjx6nA0KSYRQXMnXwWYdEDQk5ApoxMHAuBnRL56XzxPwhwLiD7DRQDD/lHgBBuLNjWF+Tlhry+IK/PwMrw4PwL3BIQn1+QVg96WRFlpkFyGwuj5w0usKtD9hnYYnlGhYK4Vhjg6M8yKyBlJHUY1A/sSIOw0waQmAMSl8lXSPAL+wl4T5y3h2gNHJsEnIO/B9YGhABaGArcbYFbPPz9NgYCcSh8BAh6P4KYFAKy8SngfzMgmLYbQLQDuWpLCMVE5UHFf8WaEDh8tPi1KBwUc4LAhBDAdOogsB/da3rk23wFSBSNZgEB06d1ZtNp/zTqm/p+idxYUzCx7QOv6I/Lzq6cn0Em7MNcMxT8gcivMqglNBpvmx6BAuANgUF1h8OukpqUK2n98c+qo+EunwFg7LUGHS5MhWt7VV+s1yhiS6M0JgRV2bOGQD8u+RCQukiGVsCfdQBzzcEQCsrBkyfbrSt8VA4bF92B9DdRgYasNngDCUlH5plkbYRctAdQhwahiEQAEhAFDogIPqPAwZYzj7YdDUd8QO30fcIhIAQn0xlzRhJASIldDCwgOBfgFRDSwoAgsJDdAgQJC3zfkF6eQesz8npj/4T1BfnlI/L6ghBuyNsL4rOHWyPSy4q4edALj7bTc415UMwPa+QRu2VIAwVxFZ8EAxGNj4IxN6iZAFInakKIicHAw/FDR9d8Rs7J8cWDgpoF5N99gV8C3G3ZAQGFWzEZ0O2p+A/Qctv7EDSOhaGJR2A1BH8TQGCTriMBtFBgTQge7G/ppG7Un0AhwcGYEPQfOhjIg4iD5aU9AurlRV+7ZpAaOAA6QMBjfVPfLw00AmMtQdU+luuvlL1caYqw80E7MyvMtQTABSiwg9pO4z0ssOaL42d8M2CwS+NhPt6bSv3xj77cyS6iPRzYTfueerX2WXFG26PfjySC+QCs9qCcUbUHPEju1PsAAD8X4gPBv4OBR52BGg1Cd99hOWznYDQi3ciAdBQgHQA5z3H4BRBSBigzFDhx4ko5A4k7YVJtQmKFQnCypkFMiC4jgFjAZ4YD3zmP6QwGImoAIYraSQEheNZGBO9YqEwAYctASNzhB9EgkI+gFJFjXTsAkUMBk1+A+xNrD+LGf1+ekNdn5O0FeHlGCHwsvbzAPa9IwcNtEaloFSLiy4bkWCBFp51+RBZvzbTyg7lEtYPpeqImroD8tnDAr5KK+UCnQ7rFwS8MB2wK8MZXIMDfQhH+Fgj8fak+BMuNnQqDaAjUXCDH4AJS4z/gq1Oh87uph9ZkMAKCDF5EKoJXnOxNBrM2ookhQBeXaqEgeCcaAYiJgI9b04FG2uTjNAaCFNupgtMpzaPvrUtEDQy0NvwrPlYWKlJz/b63ud437cwGF7UEI4H8KX1yKZ75/ywdyoQBFPzR77TyS+UWYADB3v7BB3q7YADsKuN9V6nA/kO18Ho1+/70E1gbE+GFNLMfNdoDzrXVHsg+yYXLYCCBT7z40K/1CEZ7v8vRGHcjGNUUuKqoKZoR0Zyo1iCnokXwsshRIiBBzAvIiCRRFsS0QMRTGzX0cQS4c04ZWaAhosJBAJXOfxSeNorPASAwkcHCg4CI2MxiSASBlgyfCY4yNiL4lLE6WW5YQix7f0PO7YJC0O2wFS1C3lbk9QVYn8XMwFoEur3APb0gvzwjvqyIzwvcbUF6WeHWDel5RXzZQNsGHyK2lw1pTSDHz5E2BjESs0OKGU4kZTIq8T45kVoFCByxWcALCCz82y0O4RZaILAagtsCfw/wt8VAQNUOqGbgKA5BiVYo26od0OWnD6cd5naWgXUqPNISjIAAQAMFQaDMO2sW2JsO9Lc1G6g2gbWGsWoHJJDULmbAqbYgtt88kenA2j6mxAoArvcT3Xm7fknv+Ug+Rw6Gndkg42SkfiEd2fV3xex+PyKvLRT85IsvG9nVA0KjPX9FejNgcNQMe9Kylaopdx27rdwzp45Z3V+CgQdeXMPxpjyqvWgAQT6wHSDYD089v069gx9o+X0aOBxdCWhSVZ2zsrXrrvNogLtEyjz8z5nt1rzUbS4aBHKQHkG0BqIt0BGgag9izmVpZvJqBsgIoi4mdUp0HKVhJgyKGSHlxrxAmac7qgahCAOBD0eAFzhwKWOVzj84EsEgNvUA6CJDJEsQI8kaA8sGurF2oZoaFBC+R355hltfEN69ID6/IL6sDAW3DWnbBBpWuHvk/VuE3xLi6pG3hBQ90hZ3fgcAiu8BAONUWLUDCgPOEyiwpsAFnk3g7ktxHvS3heHgLnBwW6q5QLUDt3cNEFhTQVYY0AWO+tDFaT7lkLU80jakOdpZBlsJyNQCwagdAKjOhMZsMAMCNgvU4+pDoH+9+VvMCVyaqiHYAUHVGJx/Y1rAVL+xDDMi1+trH1NnJyXsRu5Xk/ZLl8o2114+CgSfu19uinUh2/6YLc/7A/nVA0KjPZjcq2qTxunNgMEsXYGCq0kFcL/dnzP7PXIAecSH1Q2uGzXEISCQUcnpLIZ6weX08NoN6lgkI/hpQBMLBD1MHL2vYkbI4NENFUhQTUIBBNEgOAnpq7MR1LwQidiSkHnkqEWPOauFYQcIOlqknNnrHyhahGkdGkAAgLzlMothy9Wu7B1hI8AJJJCcs6a8GzE6FxBCgAsAqSZBVysUUwOWd6C4wqUN6fl7YP0h8stzMTXQyzP8y0ejRWAwyGvE9vKC9LwibbH8y1tEXCPSmpqZCynm4fNXLQHKjAM2HYz9B8Ltxg6FvXbg9jQ0Fbj7uwID6UqEwu04QuEMBnrtgL7TPvlOaI20A9aPYAYErDWoZgPVIqjZYLdCpQUCDZ1tYeDR70tggGSlQoN78v3VYVSZunxhGuClvuS1fdPEsdB2wTsg+Mz9su6mNutTudG/DetToJruUdLBrdV6N9qDB9KbB4MjKLAvrk+2MmcUeJX+Rg4k5bxHyNSUw3V59bMnAAsInm9eqEZA4TOmBjxsObrYCvsLB1DQBEI5UXVa/wri3pQoAySrUlKWzrEFBJSogqzCTwoFCggk7gaJ61YFhstSzxJaMYCDJikkBBUa6lxXir5/ft2nkKBTHVfkA8GRRXBkWZuhjiCD01Gkh/ee1/hBFnPDegIJLw0kuI/fI2zV1LBsojF4Zk1C2jak1UCCBkySsJAp7t+Vk0iJFDw0SFEBATEZWM2AC741FTy9ewwGJBCRmo5UK9DHHTjTCsxiDxzBH9CCAHAAAwDUsZB6DcEBEFQ/AhH6ukT1ERCUvxe/qyJl6hiUIN+WDWo2rICJSt8eG6RP6psuTP+bAcHfZL9sS3M1a0cSZ+dXPKX+t77Y+8Tt8h7AwWvS3ykYENH/BcB/BOB/zDn/r2Tf/wzAvwLw7wH4fwL4j3PO/5/zzOSvqbgj0rr0cnot1YVLrmoImu/ycis0pg7ZPAKEFhSMv2zz4Y3u/WiLUk0ESiczmyFRZkf0dzyCgnzgQ9tMoteRShJAkN9EyC5wZ0meAUFVnc7DkyuLHllAyESIVE0MCgUp5RLa+AgSiiYhA3A0FChF3RwrJOhIc0UWlbOqCkeOaVmERIWJQFWYBAKcuyHcbiygkvgfxA05rQwNyw9A8YUh4eW5+iM8f8+Q8PJcZjmkFw6cFJ835G1D3CIQOThSH1Fx9441QJHjVRldCICnEnegTD281SiEBQru7wZmghvSJExxRNUKbDnv4g0UjYC8U3UeHEHAVfMA8DgMqErXzjQg7E0GhBMgEC2BBQJK2xgGjr4nTVmWCSNnNI1UYKDAQX+Z+eakQj49XkB7h8G+fR6jEfgZELymT57a9y8AwjRPufzDdyb43o++PNRi9IPC0xucpL9rjcEfAfg/AfjnZt9/DuD/nnP+r4joP5ff//vLOUqt9lMScx6/7L6OVJWnSbUFnwoFfeMbaS6OEplrbGM8AgTNu2emTsE5vN/VMtkt7uxEO9HPkLg6DQqYd2KTeAhtByVPnIHiLECykIrzrEEQDQZPldoDQiYgEhUY0JFkjXfPDoocNTGXUWbK1yAhqgnBTu0z7WHLuUAEaxBMnRMZO3WCB/sckAMCOXgHNjmgerArKHhHCBTgfYBfqjaBthdkAQbcNiCtcHEtgID1GXnbkNdnuO0FeduwrM+seZAgSrriYRKNQbOqY7fKocYgIOdq8CFd2jiEqhUwCxlhuRcASHZqYbhVrUBivtq2XKYV9oGHVBuwZTZ76DRE6x/S9xPlXXTNlCZAcAUG1FTgYLQ+2AcoslMPG5NBDwQ5Q50Nm2P6LZXvJV/7jtQRxwYtAx4bhioUNEBAWoomXe9vju9/lO+jQHAJDZq2wmUrfbJmQq2f2hUh/u13NczxjyVOgZO87S0VItoijR2hTRHx7XcfDu//dwoGOecPRPTvdbt/DuAfyfYfA/gLXAADVfcCwLe/adUvo7QHAmrbPM3PPUtXoaB/oTPRqb5yWpZDZ5PunsDeFHJVP3EWp6b/JLTB8xTKCgh1+qSo9CHhkrMDOzA5DMFhEEFttJZD07mVnU46Sh3B5AIBDSCkxAsUpQhdDpmcLxoE1hDwtDxFlOQMJCSGBNUkKCTkASSQCCMLCP2Ux1ZI1aiK9ZRc2qnO/qtT3ag4sVlYKCYGAwq8RgPBuxvC/UCbEDdQEjOETIPE+oK8yVoO2woXNyAn5E2mT2rggz6YFYCybHMIXNeyxDH0bzetMDeagLFWIG4ZW0plNkEBgawRCisE1BgDPRD09dzKQE/V5GMBYTbVkCFtbCZoNAMDGGjAQI9p6x46FZ4BQQvWs/VQmgWKrPOg3W4uUKEvWjmrLbBQ0AFBP4LXNOtvrEB9NI0cCkcmgxkQHPXJ9lztl/s+eQYHR6lf5VdDrhTFrDSqIgfOs6yFRA2j/CP6Ynrq37XGYJT+7Zzz/yDb/y8A//boJCL6zwD8ZwCA/ynvc3SsfrFL7nZ5lU7AAUz1A00BgfPX7SsN9QoUnI2lq3VvDAiFpQeAAKA2rFk6eZC+fA7dB6d1RzBwoIDAqv05HEgRiNWTsHEKZsAAdKRui1KFUpmpkFXnPwEE8UGAwkHaAHFShHTwCUB2VNXPRKyedkDOsi37shTPGZU1eULIHls/q8HxS+PRrAEE6dcTclmLIDXvlB0QyzvRd+AIDoTgWIiFDhYW34ICb+tsBw/vA8KCHSggrSx0ZGqkiysLpE2gIG4MBEmEVBoEvXFeJKMHHEMBS9EAnjpophBOwhHr+gTblthvQOrSgsAaUwMBW2ZnyC29vj4jKogBLYQVE46rU097zYA6ERJaM0EBBaMZcHoeN+SdwCetY+tDcKIhaMMUTz746ShTBXwV/L2/wBkUKBDonUdmVuBCX7Mr28Gxg7xfCwT9cQsIh3Bwkuxj/J5ZEAmobTEB1Qm6g4Mmr9F7lF1/+Zu6tsJ/8a/+cFqev49gUFLOORPRsFnknP9rAP81AND/grIK7aH6xVyncDBaYnQGBfpXl7aE2T9ts4MDZ1Aw+l7tO7aNzMp62xj1ml3DvojbV0xrtssnqYTyAVMPCAQiFsA5YQ8HRmvA16RxWekAEkYPIGaCVoMwBoRmJkOOXBbVIkjn5oq5gcq6DFFXbNS+WPapM1vIVALiqCnCeeIojAYQIPW0xQSfc9ESqBBjGMnyu3aUaVAfKnSAOq3RERCcAxEQPMPCXQBBYeEMFBzAsx0koBKlBORY7dipjlpVGA3focIikYAZ+3+APLJzwyBDRyBgIeA5Jmy5BhnaUip1txkYSING7qQhiwsEXOa642GerFiIGmo4eFfMOBYIilA3PgM2OqHGGyDU7XIMZlw9CEh0yaFwYDK4PC1x965G++u3UvyGRlDgPKZA0I3cj4o1iqnYyL0H1QhXzAZ9yz3ql0d98lCVfzA463d/87vjVX4djeHAPk9/X/sW//I37SqMR+nvIxj8v4no38k5/w9E9O8A+B+vXPRtp34B5AUbYVllRh1hA6byDK3Lz/L3/Xd1vevf/he/DXPJaducNfwzKND9V+EAQAMIj6RdXIcL1xAMeavWRT98A1h8Lo8UcyaZ/iwwoFxg4YBkNASg0RqQMxqTk85OKq6oR0tB9oAAIh4TGkhgYJFxWzdaItE2OPnNRhPWJvhcNQhRfkfVLhAKNDiqgEAugWIGvMMWOYgQYlYXrwIFW+wBITdCToGhjHZJwMCJypoIwUM0CoTgHIIDaxG8QyAwMCgoEIqw478OgRxcuBWb907FbafDAdipoQfCI/HjyjTRjG1j6NkEivRvzOwkyADA2wwGDAFbOq+nvo529ZQJLhOCZ9JzREXXRQoAUlcMWa7UUfEDoM5xkGrMgatagcPYAxYArgDB0Xei7wQwToJV9d9rC3ZQ4MzvooXbawkUCCwMvMbPyj7K1I5+ko76uitQoPtHcDA7Z5ZosP3TA6Ft4QCo/gx8r/1gVzPuoaBC6Dj9fQSDXwH4AwD/lfz95ZWLVGj/xJAWABZQDWG1SbUEQP1YezCwUKAv7QoQ/G2kHkQPHU+w/yg0D01XPwx7C6f3lcYZtUwGELir0M4DQHY82hzBgQMokxkNmW0qXSmXoy/xzMSQ7RQrYOeDwCfJs5kRECDRE+eg4KVD9MSjJ9UctJAgcJCIdSWi2oYn+OzhkeBT5vxjAjxHSHROYyuIYBNhp6PhKNPuYuJ/o+QdO7850r+sQXDqg+D1L2sL7l6hoTU/WOGnI2G1gRN5eJntQjTutLOYWZD5EfOWkXG89oDVCGwp4TmKeSCy78AWgS2nT6wTwFFGgANcNXoxLFCjaQne4S71EjxrYVhz0NaJDUBEpt64f3kQBLRdNzEI9K9oaMp2be/T1L2bFggAGK3OznwwgQLW+ui3RFC0t0Bg1fdFg9C0j0vFrQOjnIfC7ZH+7+9DHw6MhDR3nqMnUTgArsk29SmwUHCW/q6nK/4LAP8IwP+ciP41gP8jGAj+GyL6TwH8GsB/fCUvFdpmwNo4JE7tPAMtgVHOCBT8Hr753T/BT7/4CWxTUjjo//5dp7OpNv3R2cd5lI+l9ojaMF35WHWRIMm/dIq6yFGSKYQGDnKtQa7Pgc8BMAQETTtQsMeymWLVAwLAkFBO1s5SHchaWEDXSUI6SZJO0omanEfDDAm6BoJLQALBuaoWJ++KpsQbOMgbCy1n6qYE4hHBGWVEvcoUwSTOf3bGoIQQ4GmCBhBugX8vHQzUf6xV0G32W6ijZXWwK1Ct1TPofaxM01FkRp0lwMIe4g+gQACznRtIWCMDwMvWAsHZ8wOsJYkpy1LGDnAMbgUKUGcK9OaXoZ8GBDAcNbEGdloVM4VwaBooFTSDgFKbZrNt80MomMEAsAcCPX8IBWL+sccMFGSjKeihQIGgNyGc9lfdaLiJG9A/5oX8/q5TP/C08mb8d48InlDq0h087ggK/t6DQc75P5kc+l8/mhcLbaNukr+WrhrPPbS+BD0QAMD7X/8Fvvrl1/jm57/AT3/048GHSXMYeJAStN2fqZ9GH8LVNAOCEQxcLbpSu368iVoNgl3yuapvxe8AkeFAtAI5i2MVpPPJTjpMozEAqgZB8rJP12gFJmnnfT18MDEtWNUTUDpO6jpOjquspoaNO07n4QQSMhGS4wWSNlRA8CBslHmNBCJ45/Ec2c8BMfEXujmknBB8Rtp05F6nNCoUJPHML4CgWgYzatZVDSscqEBzWLwbwkKvWbA+CxqiuZ1GyX+t934fw0FnBmgIYusTMNIE9BBgn1e1A2fPCwCLZ0hYwXCgZVTVP4MAJGCUwxIqFCyBcPe+MbPYqaDqc1DXKzAwMJpSOPIR4ErS2jLt8XqbnkUUlJdjf7T7tE3rvoGWwPqGtE6Gfmc6SLkFAqCFhO4JD1OrHdhDgjWvtmccp76btsMQYN8XXzEPTG90uPvkXWu/08kcJ4Jr58xpNAW/GEJBxlHt/300JXxC0nF7Ky6K5mAABPqX9Gx5Ke9//QFf/eprfPOzX+CnP/qJHSLv7jeEEVPn1r/Bpr4R7rI35/XpNW3Tphm122IeegObVAR+5vpQDYIz+4q/BxnTgjolZoKaFrLzFQaQG0AA/MDGaogKEG2Cdvfjj419CNJ4ZNW8qM71ybyc3h5bV3ZkINDYCISVHRmdB/kF3nl4x3PuLSA44hEvJYA8mxaIgDVmXmIRDohAcir0WaCvpogqJHnULKsCptwIyj45x+pyDoREDTAsoWoXFq9/XRGiwVPxXahe/Cij7lFSUwg/Q/UF2GKNRKhakDVWgb9uqQGAx5+PzTK6mqYzH5U+b9GSkMNNQcA5LF7AQHwvgoBAEHOEAoF32uZtSGrRCjTTDLNpw7UtP+oT0KcdEIwgwO7v1Tv9jIMeCCZaAhyYDqyWYGhKeKCPKadKRq/x/h+lXf9tjr22fx6d2/pcDaDgyElUwlHzxW6nQbAzF4AWCn66Mx+cV/rbAYOiXs6Q9fKayuhfiv4tQKB55Iz3332Lr371Nf70d/4YP/3Rb7VCZafObu90RKBlRgTGjfAsjbqDR/0JgHMoGM39PUpF0U+W7Kv2oMABzUwLAghAoz0oUdaM7TXnWv87b2s5f6iCLXEV+Ld2fuM8Jg9vYc/8bzvaLOpWXrzJFUCA8xwfwHk4v8C50ACCI9YcqHlBzRKeEigReO0/bS3VxLJ4/hsTCyY7S1CFZsx1doMmtb37MqKWdyizEo6AQTUM1iSh+dT89u1SR/R6bzWBWBNArwHoAUCfhfMbP4s+j7PqQjPX0IsPwOKrWeBm/t68w1MgLMHjrqYDT1hcnbUR1KRigUCnd6bIHXmKJghRkv4lVrgFxtqBYWp6M9klvdfMLGB/jyCgz6PZZ2BAz7dagsGsgzMoGALBFTAwAy0FBAKKP9UVOLjST/ZDPhzkNynm7l7nGt5W9gCY+odk+/2TK/1nI2+IZ8+NfAr2sm7+LG8HDAAjrKuAtn8x+FuIXV7Kh1+/x1d//gf409/5ox0UgFwnSNr7SW7m/LpDtQYWDjC6Zlc+s++gcVtTRH/+q21u5rI0aUUOFeUTDNkbGADE9yCjmhaM9qAAggvI5n2ol3s2nWiBBPLIdmaC1Sg0+9BqFSwkCCDsP0T9QDNytCGXJ+pcbQNeVKxiq1UPbSIPDewDF0ApNoDgHLFZgXh6ngfBIXHYY+dBMcLDgygVR7aXtO8GPwKAAMSKVEI72xSNgAVajYMmNQk4VwV/+S3QoL8VHIAWBqyQtvfWZNX+mynnSPhrmYFxSOKSZ6JSZpsUchbPo//gPZ4W1gDcA4PALRBuYk55Cq6YDp7EdBAcsDhnAkNxrIgm3kOK0FUuS6CnrGsXxKIhaNrVSZsCuF1V95du7Hc0kwDYaQN215R3ZuGW0EACHccluOJLsIOCq30L0I+9HkqjPrPvK4f96qCdXemn+xlvo5Oo39XP5NF9fd4i3zJc1SCI9kBFjY2YuIeCed59eltgABg4qJU1fkdWcLAQev/dB/yTP/+n+NP/6P+Kn/67v9VL2QY8ups2ORP2jo9Js8h7gT1r832DvWLbGtvEWk1FX55huvDh2mMKCFZ7oHAA4FB7YEcBjQYhE9gDTyABvM5BP9qyyzhntMf3Dl32oblGpERNZZTOW223OuIrD959XM4hc2g8kGgKnPfSyXqBBA9yGwOCX1iD4AO8X+BdkEiIsnpicnBJtQcegXIZrT57gtsYHnSqnQrD1RFEB4EyhtoSOH5T+x5HgX4AXgq6PJZpTwoAFhyAvdbhSupH+73gTw1EzPOx0NLs15F9cLj5CgX3JWDxhKfFF+3AzRNugSHh7hgMei3BIjCwjIBAgj6p+QCiMVDNgW1LOSVuw/yQ3cO0I3+S3zkqHJhntPZ/9IKc9iDQawNKHgMNgjr29s62HRCUd3OgJQDMvk5LcNSv6PErcKBF7HQlYyE9uG5ytPk1mwFR8zL9f1ceoA6A2tQ+f+2rDkwKSC0gaI9LDh+++7bMnrNxCoYy7yS9HTDYeYVwSzpT3eio9P1v/tJAwX8wqECrxjX3RAsLRknQ7LDCuBb3XAMwKsHVZO8HjOGA7zWIoLV7kItJPuCUKxyous/CQe61B3LP+gExIJDsz/a9SR7F4WqgJVBYGIJCzqJzdwX2ChwojdtHUigoxvGI3UfbrOVEgPNlOhf5IJDgZH9gYeIDkFtAcC7AEckKjxle/m3EAYeCI4TNRjRM7BS4sXr7eUvsGBcz3BrhKGEj4CVyW3Ukay/Io6bBKL5ZNKh7vdQL4EFDPQKEkZDvAw4drVpIBULqTawmY/FVO3DzPKVw8Q73xRcgONISFChwNXhRBQMgEEAjINDt4miYkGNEjhu3TxsVclo5JCp6mKY58BsYQUHxbaHrZgHdxggCAP0SLQhYDQEwdia8AgUPp0GbGqnsLRTYpvlw39mP8uk8PszwfgNtQZOG2oKur2tSlUMFEMjh/Xd/ga9++fsye+7LwXWTe0zS2wEDoMJBY//vz+lsOTnh/Xff4p/83/43+NP/8J/hH/0v//1dpeWBtqCZ+sZnDe09xekx98L4+FF2Dbk7f9T59v2pXTeBVMICIjClPFaLoft35abH6N6kLPfTh2gXGDGAoCBiB/PmORXxyMyVr10Rdu+1aBYaUIjFkVGft3bWVOEgc3RD8h556z5OE/I32xGfbuuCQdqhy4yEHJYCCaQhgAUKCiCkiOwDFr8geI6yuBGwEhCIsFKCJw5AtMSI4B3WLeI5OgRKMreeheMac/n7vEaEmLBFwkvMcC4hiXNfIqu6Z+c8clSE827ed6912L3xz5/KyM9AgfoQPAIEi2gGbo7B4B4c7p59CZ5kxgH7DVSzwVLgABz5cdMQ0VsLBCZEdIGBbTUQitpeurbCzyamJg3LbZ+/aAvICH4/BwLVUs38A7hShwAw+rsf6cvPARCM9o/SWX8CGFPCri9otQH2KY6C1zV5n/SfblD4Hhbm5W7v2d+rrW3ZlzuNpO23bMpR3msFhA/ffYuv/vwPxFH+x/uCd3meEg7eGhjYZAV55+1rVTYKBf/iH/8zNh/IOSWRrMw3MuKP7qWXARMheyFZ8jwjTpPUv6p80JqHftBGe/AaOEDJt62DmRe6ag1KNp1pAXJ/ndoIoNQz2TwmFeAEdviP576uPH+G1QoxKPgSZz6rpziRCHsCxB+HHFjwE/8gh7qMsIWCbrGgrEv4AbXTFydEimxCyD4AkVcTJNYRt4CQqgZhCXe2ZYt5wSeHNWWElOHJ4+6B50BYtoy7Z0BYY8JLSHjZMu7B4XlLeFocPq4JW4xYI8c+eIkZy5ZKsCDre5BSXfehunHstQmaXuvCAowBWQHAAsEIBryA0MhkcOZH0AOBJ2BxMOaCqiVwyKDt+RIQQP/ZNmLbR3lGx9NIdMQOadoGCtQkVaDA+QoFZt2J6gwomoHhdMLqg9BrAIBuUGG2LfhZLUBz7dnxQfuoK6ec9CUPQsEMCOz1s9T3n3pyEwvnalu/ct/TKailQs1OqoMcIrz/zf+DB7XiKJ9Vi13OH8jAC+ntggGwAwKgJbMGCn70W+MezpooRj4GOxNGFU5DODhJo9kT/fZZ0vtqEIyi2m8AgcrzOuyhwZalaVJ5DgJ9QUdU3q/r0AcrqY6GBg5s9qauh4FOSM0RVGDBQkIZleUEiUHIwl/d+bNDzpHvnSKP1iKUFuoIzUBBtiYGfU47PUAWDWJIWEFhmQNCWciJAYHCDc4vMoOBEMTvwCcW5sEAwtPG6wUwHPCc/5eQ8RITfnBLeN48XjaOCdBDQpJgSUWTMJjNkHJGplaTkFNd7fERQGjM5RMQAHCoGXCOdjCwGH+B4FofgptcZ2MSqIZgCgSyLHUBAnUwnABB3tYCA1nNB4NUFGMOtU9Rga4HyHH7UyiQNSUKEMhsF9gohAUeBAyMFjNjbwYo5cl2ewB/3e9HrrX+Vr2JctiXDPoQ3dUL/35/3w801+7vNEx6nhaz9KNy8NFp3If3nw00Z8erxyQ+fPeX+Cf/7X/K5m9xlOfqNVqhB4FA09sGA6CpWBvL/f1v/tJoCv4DoCctAHas2mgNDlLX7mtO5tJifx9ce7bN6ahl1hF3DycWEBKhCuhsJsJQzaMHBOC66vjIzjxb12HchdbE5gbbC8m95NphJ0GQsLQGEpyXmQGiNYCTsMeiPUjgSIxAExah3DluICfrNsQOCorA5N/kPAuMoiIRqFBAIALCAgq3CgjeahA2kAvip7DAB4+YCIt483O0wIzFeWwBeCfC/jllMTPUsMIvkSGBAwYFrDEVOFg3WXdA1yEQbYKCwU6jkMU3wLdahCszYBpHrQPzgM6IsI6EHHWRR/2L+AMsFg4MDIQCAvuphzrVkMMcd1MPr5oMtpdiLlAgyKItmLYFbcjWZOCqoyE7pgowdn4pMLNbmoWnZPaLzoI5iz5o2/Lse35UC3T23ndwcHKuJvtN898TLUEHBGOhfN5/6taIZxpQMGnWr49zPzlvV5/tm3r/m/8e/8l/+7/Fv/jH/wxfFvnF5xEgwdsqRDya3g4YjB7+KhQAGH8i4qH1muLAqAfRNqQrWoG6bQXhRbFcplDuG7kj81S5ggDL3Fw+LvZLkDnCqB/+7KMYFuPk+KOhm2c37qGAHR7riKKAAqkA8oD33NGTQ6YISnWaFlEEEvFIjRx34rSPcsngIKYGwNiS61S0bNTFgACmCgUR/qQjzbCwQFjmJgaSaY5L4GWIV0fYMmsPtpQRHbB5j3cZWIPjVQcNJMTMmgQNLfwi0LDGVLQJGlBojRxUSDUKPRiMphH20yNHneVodkMPATrF8OYl2JKAgMYfuBUnwRqDIDjer+s9KAzYFST74ESBiKMhAgwBq5l2OAKC9YWF/7YK8KX6V81UVkug3yw55BQrHDhfTAXFv8CHAgUIAdnMZtFYGEVLQHVbz9GhDUNbBQHdP1P1X0mPi5b2mjI4eeCamb8AdcecuVCdl/dAoA//aB/al2qftB6PzL6vqb+aOij41/8d/pP/9n/XDGpV86kD3AIHwF6rfQEU3g4YHKTGfHAZCgALBVNtwUUam511CAMTldI0AEZpCH2UrJo7SZG1s6guLHxUIaCEA+ogoS/z54hLvmPjPD94OudZbBCOKjAQMdDoc2iMeyIP5z3gZIpZiiCKyLShhDxOok2IAEKnEVJY2LYGDsj5sXAACigwIJBoDxyQUvEtQNxYQxA2DnGbFlZn+8B5+wXZBQSJpJjJY8sSLMlAQgoeW8p4l4Etemw5Yd3a5YnbRYradQhUoxATmx403PIs8uAMDpr3NICCPpDS4iVegPcl6qLVCPTrOVQtQA1drItCqSbAERoYKDMMcgRFXUramAsMEFBOyNt6DQgOBA8Zv4ACBYFhwGoKoM6pftlrCVzYAUGSNqlLUSsM2G2gNSNc+5a04ObYgfA7SkfnHcUaGM020OPnQND1o6/tQ4FdP/rZkjpB78pBRmtQ3dYrFPyf8dN/99+Xk3W2WetX18DBg+nNg4FtAGOfggdsME1AkG7/6HSMqXxOkuOGvJvfOiueGZ0gqzrJBsKodzwChF5TYCEB5jxg/FE3TzSRESOgmM1zLrt35ra6I6KuKkngTpEgnRzxkscEkcEZcNmueieAQJvUFXGHTGujPaC0AoF46uHmWFCAO/y8rSBKHRxoQbv3ZjQJO0DYthIhMZcR5MrCIoqZYVvZlOEXvo8LuPmFryGPBMKWULUIGYiBV3bcgixfnFooeE68iNFo5UINV3y0aJFGLATaQEZ9spERRxEU7ToNGnZ5tAJk8ByuuF8BUrUCqnnQtRw0IJGDwECqMICkMQjS3lwQjQ9BisjbhsZ/4AwIdORpocAHNh+EhfeppigsxnSwdFqCpZgNFBAS6nLVWUw7CQxmrRkh776h/vtrfHdQ/UYInEd1FoS8v8GjTvqCq5rsvhcdwUA57yoQfI4+FEB58oE21qaz/r494Nry2MBTg3K2UPBbplxSJtUcoMqp/ey5a+ntgIF9+FGliqbgT//Df7YPXjRM1YGjiQSmt9hBQtMk98U7/H2hIe+kYveMTflSzWMYKauWwAJCIRmxgSgk9LdXWOjT8Nlpj16s2RrETjCZ9J2Zntt7QO8y1tuS+h7kYkrwIMScxaQgZhHiniU7wLlQnRPTBoqELNoEiqt02ixQsJCYIRyP8IlYeACAc8ipTonkgHeDDsmaGmTWAoiQxYeBNQSOAcFMd9QRJURgUCcwnPPwfgGCR4JjMBDBXUAhCyjIRIqcgecYywqHm9EqWFjgRY14fQO71DFQQUG3+9RCgW7XJaAdqCxC1EOALnmsKzvevZd3OgYBBQ9e3TDvzQQ2OmEyYYzVmVCnG56ZC44EDbmHtAS50xJkv1SzgQ/yftkZkQFAwSCXvwoDEXn33Rx2eTk3gle/Ue2oynRk6Rt2j9oJb00zkTQTlqNAQU0+dORDMOlHZ858R32o9Jfah44GWnpnLUNfNcMuqpxjho1EXLsdJGQxCSBnvP/X//0ACmzGeV/5o2Sf8eD0twMGB6mNaPgfnHwhwA4KNNkGYVT1RzU8OlL37TUEUyAwjWbvmALU+a2SrSHdq4CQocKyLV7f4tOgAaqGod3HGZhVCuT6YzgYQUFrI22LN3QZzVarwZ1eEkhwAg3p/8/e3zTbklxnmti73GOfA0gy00yyJjNTP0ETdRWBRN6UTNVdbAAJYCYSiQRRBn5Uqbpb0pBWM/0CtUplqmoQBrAzE0CbBo0PEkVVSTLcczNBsn9Bj2TivQkOZJr1gLxnh/vSYH34cg+P2PtcgiXqSG5279k7duzYEeEe7o+/a/laSX0uSBQEMTFQAwT1LxDzQtLBRJy7xD8hSydvg0nKMhBUGWC2gLAzmNhytqgiFADrGWwDikFCtEXrrNOXq6lDWgQFycmgM8/lBEZChQRDdHMAyyD/6YUUGLawwKoyWArkMSOiJUMC0AX0G30M3McObQnimKnRzAU0gYCUxAxhWQ0TNTOEKwJgULkH1rIbprgLRLSeffDvlhoOMCBVdWwumCoEEQgmvgQxMqYBw8ZskE/HQFClfdfhebnqWYE+/zq2eBI0lge/KYvzWCUzx0A75rhP/73t7duARFAG4jEfBASv0od2Z2OGVt1tiDhoZxKG+m7//UJNHVA4kM3U2hsR7l587I6Gbj64psxW0V1ZHhEYhNEsSDFPnz8TKHjnu6oUWIXazQ+VHJvlqBTo623CkrlSYO/3lYILUDA05q4h74ANBWrkzTVdBgRr2BESgAAK48VdKAYQowNjdHCU6TpPDzlCQezk/BEfOj6gd0a0YuGXiVj8DQigKoPNHiDkfNP8DwrpTK60lQZU5oCgPgIyyFSYt7oHVUKeDzCliJd/Cdtt0D/fe0RFG2iwLA0UkqoJur2tdVdYCY5rKS1YfPBpqoKZB+Jg8985kTodNqe2tcaUyQINBQDXloNhBnw2OGQiUJKshAIE5KmbzR/AFJ2USHJHKARkgpsdmhqgA/0aBnwLTTyEJ4aZArRu+MBEAOyAQCnivNpdXOpXHUSTgYLd1I8gOheaKmCrDdQ8BJIUWntAENUBAzZ/RmK3MdaH7ROUgs4HYYCDTX1qnUYg8AF7Jv/PDjCUaxz4pn3oFUAwjQtwcEqzPlTAXRBJxhAbfPU+YXufx7Ggf9/DgWyyMSmJ0v3Tbw4+cXou8dwepBZs7+RYHg0YzCSapxoRqmVJ3GsQPRAA6FKMyvaEbdjQHgqOpaPZhwMUxAZ92Jj3ZiypPSD++1cCgl5ThIS985+2QZpc/7ChDr4LCehiKviKCaZONRgPaR2efW6y9Z7dNCfqYEFmsaIi8AEgVAJyMgdFVQLKWTvyRQahHUCwwYd0ORvVpRt8OhUhDjwxaZNsAEsQYyk5q9lnK1OzBVMKtutofiAdiGjwck/phNyZI3pYqJV1YJKBJ0rVBgbdYDSpe6BVgD055s6Rg6kHHRCogjODAEtpbGaAHQgwswAPywoP/QQ2dXBQjswFtl19CCK0XQsEFWIGOgICq4PKr/ZsQJ9JC1m+83irv06w6wcooHDMTjkYZvzD5unvHG/bTqiAVwGCnT6U21gw70OberCFg8nhdl7HSZjDge2n/fDTF3di/v7id/oliT6hvVCm5zSfyI7l0YAB0N/opy8+wrs/fk/DRA6pk4m62XV3jCOVYLAtHQHBrGxIF+iVAn3fNei9xjxCDtHwuTnJ2C/uA8LGfubHnIcy2nO8Ga7MDzWaJqJSMIUDjt+n7bWGUnT2CkhH2Z+MdorFOiyxbVcYgAB8ARAK2yx1AIRagBIAweRnLqAk79NyAi83V6kIXEUtsEHJO/I+j7IkOrDkOrY95SbV5twNVmZq2JOx92EhYxmXxC3ZHd7AWw94O287ry7ErLEnwqAUBphMaFAglaYOgmGgv6AE2H3eqAAGBTb4x3s8BiCahCu2eAN79/iSOnBVPAI3IfzNgeDwmdCSdXOp80yYVjY5Bw6gwIGA9qX/8fW27Dzre8Hq4mcXgWA04R31of0ky8Pij4594f2RarC5nOF916cS4WfPP8a7P/lGN6llkmvbu3+zlXN7kS/vnt8dnt+jAYN4o++e3+HdH34NH375fbz9+lut4ZBwr+w/IcYJAMxurP3eJTA4mnVP7cyhodK1QDBu98ahjVgByAM0OQz0D4ufn0ECgDGZUDu5g44E8HtHoSHa1zg8Na4gMKNECLAZO7Zhm/eKdYAz869lC6TE4EIXASHVrQ9CcRODAAJlBmfzZl+6gYvrIgMTF1AqoOXULXkjtV9HFcED5aisSKUEOAizIXUA4OFC2z3WduuR8FIDh5xB+eTA0DnCTT3jF9AwiCXKWKiF3ZWBrT0fPNT5pm34X/Y2L2DFHq46+gDs+gTo0kEu6xYAyhmw++fHqD7oj/fOz8/afQxXbLkv9P6N0LXxHUipv4+UUC/EIjAnw2t9CC4BQQFfdIMoYOSDIXo0D5haECEgh9cRCGhT16FFXIojcPSQA8MkagCCsO0QCq7qQ/uJ1DYs/qASTFSDPbXgUnn64mO8++Ov48Mvv48nNn6RXVtyQNiU0f9tUhjic/e1H76L1/HG7n6PBgysCBRI6sm335Cb6i4zZsdh3sosnYlgBALAFIKnSlpjtcxmR1MgiGXqAHPQgDfvDXhSv88BHMh5he9NHpzNGt/xXl1o5RY2V75L3uFakqkREAoIWYm4gJAh0nVi0mNJndE1KK6lTO5troQVfBUg5B0FgarJ24Sszn3M25lsg4QSIEHDHweJm7IG0TE7ow1ugMBBvABL2WtSuM18D5YHerhhHdSQktRH9EswYDAlwcwRE3XBYMBe24x5m9EP6OXO7ayOuEn5HRDo61EF6KILlrUBgKkwMZPhK9wfN8VUdIrBNVA1rhi5BAMbdcAG+toUmIcqBBEIpu3/Cjs0heprg76M9png6s6o9EQg6NBwM+G5AAZjOdr/CAi6z4/61Et96PB+ZjbYUZ/94/B6HCfi5NKO0I1fAFhTxdvcSc4hz8ePmek7TGrvFAo++MqH+P3/8vd3z/nRgIHJI+/5TX2CWCVmt5EyH/iOTAZ2/K/98F05wsEA5Uk3qK/wGSjMHpSpWrDXoO31lXBgx2/SGAZAmJQ95WCnEHq/BaB4IyVKW0Cooh7UKo6BEQ4yEwrJ7MaGSQ/pzOo/UEUa3ZNNrVhneQ0gVLDEPFCsJLA7vyW5oyg0URF0VjuFBAuak1aQzX7zIipCAAQuq8qXSWXJNYReboNeXcV8YRkPmdvrTZ2oJylRAmWSFQ0pgRIJNOSlAcNyksEwqAjTZXa+KoJEHRrAIDrqNtk3zhxZ2mJYFcAaXCiqAK4OMLclhKz7lwKuLPdB74UpAkf3wmFV70WC9LWoAHJTDsjuRzQNXFBZbHXBFAZsv4k6IO+bSsD63vbTeeqDgWBWMlq7twEnobHezO8jQkFKJM9BmgCBKUF7Uv8vs+ytNHgIFNjraR+q/ScG1cB/cxsnYKYUzMaLcZtNKD/4yod4640nbbygpHOjNqmlTkWIJzSMZaE81Unz+3r8o/JowODpX9zh6z8SEnriN9U6K2modrNi1fLQmY22mBEK3v/yh/j8D/7h5ZkroVuqdVE9eGhxT9aJg0nXeOdOKr00dh3FT5f47JSW8pX1lKpSLG8AQRZOA0gWXVGy+6GqXa32cJD0sGUGB0k6yUy020leAwhQbqp63baSoRCQK7SzZOkgKxQaZKmjDTIbJSHLsjlWeXujIqz3oHzWEMkrcH7p7UbOeAVYZG5Pi6wDIRcGr6tHHWQbccZigJAkCZEsbUw+WFImpGURYDCnuWA3l5TROmAOSykBNIe7sT34C3a7/rgksDkKqhkhrBwQeGDUde2uGQoEVeFAK3h63XaOKRFoWQSWioa0rixgoPejKSUWpvq2B4LlpqkDV0YotIF9DwaiU+feCoNXBQJTCyjNocBWiIxQYAO/g4CpCPYPO0BwaWXVhXJNXppY9o+t/T/0AuN+bjLd6Uf/BmWqEly4fNvvzdef+LgjwNUyyILkPrMpBgYJ9hPxOoJaYFBg0DFGwRzLowGD934kg3ZHWlYoNA5Aburmc8DUAaABQWXg2fM7Of6XPsSbrwtpHQ2lyQ5wBRTwULHzo9Vto+7OO267DAWXynyN77jtGCbIvHstGx/JKD4DhITW2VQdlYm5gYJKBFXNCuxqAixbssPBAgIn6TSzJi46AoRMJAp+AAQAfccJaQcEjYdAEg9hhAQiQq6x88zIOSMFSOByFh8DjbDHSfwRKJ3bkkcFBEvXzCkD5Qy+b5BApbSJmA6QNjjyWnzJY43pocfiA2UCZTMxyACaFxno0pLbrHrJvbqw48cgN3DS9oLX/8z+H1WAqIbUVfYra3HgETCocn1yobvXmFICVsg1atCnXFULWux0qcFAXkA3t6B8Ap1u5P3ppgeCtPTRCRUOpqsKWC+Tm9+ADfwPhQEAGx+CIyiYAQEg8SIQBnY3n5HF+WhLRKM/gYcTt+/tAQHXy85/28ryV4fO4Q8uB3AAXOhHj/vQo6iCPHlz1RSM0WXj7f02JCEcwK4YbMYzK3puUyi4UB4NGBgUWLFr9659twJ7dcBeG1E9e36H9378Lv7wSx/is68/8TYV48GP0bpcfGJ9vWnLvZIxlj5OdvyakQZvt3Vlx39i+I3N4Q99Gg4kOaB1+n6fzfxg5osEEE8BQWyZuVMPKhOIoUFzCCQOACrhq2KgNldSQKBkYEegyg4IC2i7FN06WYUDuwQBBLlGUxEA7ELCSqJgrERIxNqhss+y2pI7g4QbsAbc4Xzy+PxcNUdCAAQsJ+B8L/f0rOe4qsvYUr2WmWu7Pp0tGxBwZXBUGIYBlFKSpZxuaxfzwjkl5NMyhYWoLmxMEtGBbywxVfWOCcAgZ4SAcl6765HD7F8T9JooM2qqQErIOcn9MWbNhHTKAjymjqgyQMuNqAQ3t8DpZgsEtopA81ZMQxVXdEs9WW/BXiCiGQxI/fKuc22EgtGHwKoiKgSAKgA22FxhOthVCbjsAwH71Wz7i5ldX66m76vC0sFoBgW2/demz9z0lXasuu0PL/an7TemZbDjxxJn5lZ1vcVjO44UYyJtqgYJVmfaIzYVYXfq3ysFpqRfUx4NGLxlM3luNxCIs/V9eTNCQYsvLlDwWz9+F99950O8+dpbXSX2NBjfye/YmuDZ/ofMayYCINBtaNS2fVOOYeDBpH3k59DZ9YaeapS1GDA/A1ECdgAhAURJO7FBPQCjkIICCwVYFMPolGUPTYXMiFgBAQA4tdkWAFcTppeul2AqAgCsBe7F7WpCo09oPESkxGJaSPLbiQi5yrm2REEL8rIgQX0Siqb4zWegDIBgM9hlAZ9PYAt0pDP1hJcipUNmdWutwBquRaGAa/sr17g3mOognxLKX92DkgyeSAlloixEcwSAzpdhc19H2/9oBhiVgFpRz6UDgOuugRV0BL498BAAZEJeMkiBIC1Z1IHlBnS6Ad18Sv6ebgQSTjfA6aYDAllFcBL/EF1RUEEdDKxBAbjkL2B/9pYZHgmKEQYiCEgdDFA7KAQRCAQWLLjUvkqQ7Gy56iqSHSDYmBEmF2HtoWsr0bTb7PuXHPzkOAMYzZwIZw6xl/rT+PkFkwOHf7EYAMppzMcRz3pp/RmRR6GMtoXou+YqwuRcDAo+DD4L15o1Hg0YjOXounl4HW+WVeCz53f4xk/exXff+QBvvv5We5C12OsUjkeQSrUHsgIece9gqbB+OSgIpHGyO2/YsVHH716hCOw16G4gnykVcyjYOJPNTsucFil0AGoO2AACV1/6llQ9ILu5qh6wAkJieBQ+u3yTYu2e+3OkeaJtGd0eKMz6rcLchA85DXuFc5nPyEgjKaUaQAECBKuqCX0Y3wU5LeCltDS/5T4AwoK0nFBN5jZP45yB+6Qz94zy8h4F8kCv0JAI5xWUpeX2flZNQWjb7EJLUw9UDahrCe/JVQWT6Umd9QwYLpbOQbBO1Y02+DdVwd7vl9p8HQA/V5j6sWQsNyfQKSPfnJBvVQk43QQ4uAXdfgo43SLd3KoiEIAg3zQg0IiEs/DSBXy146Bc13iL+uucrSg4AgEAuzCQdN+oEOwBgasHdjxTCXz1yAQIbMUJcNg/+HUg9A9m7584/1m5tm8bl6Rv7+COZ79/oX1jFt/mUomTTD+ODf5xv+F7BTqusGZNjJBgX9ZTiyoC2mZfnRcdGR9SHhcYhBsW344gYGWUeayNPHv+FN/4ydfwnXc+wGdef9J95sex71kFxd9jHfyuhIEugYaNcgoHvuveJV/zkOydiP5Oe1/74E87EuDcw/yo6MCBqr8xBwTxK8ideUFs+kA0L0RAkAiKJDM1sgdqDgkVEnvfQKFUNTMwA2kfEuwy1/F+6H5ntAQ0Eu43wAILzafKSMTig6CQkBPjnCwNcMayZFAuIIUCAwROJ1AKjm8vVT3IJ9T0VyLzkwzihc5YUkK5P4MooaYVdRUJQQb4pj75IFv6C+YCICdRM5LWeUptJr4WhwQ2KDDyDQNzCq9rnN0Pg/zVMFAm7Sz8fjRrpCWrmWARZ0qFgbRk5NsT0k1TBdLtp5tKcPtphYVb1HwzBQJJcy1xklYWACgBBmL46BECuLZB/xpfvL3H+0gVAI5hQGb+zbFwGpfA/tkho9lAlYKrgOBS3xAgwD38Z86AZu6zG7Lzeb+tH/hboLVwTntm2/hb8fc6WEhXQ4L1SUAbg2amBXvvSgF6SAAaIIxmBjv2s7AkcWo+uKLdPS4w0DKuBhg/ix+MEs+zF3f4Rz/5Gr79zgf4zGtPugqNxZSBCAgRDvqdt6rBGB/Ll1PuxFroglpcM/hf42Ubj2m/F+EAQO+4k7AdNeNUdKfFWURFlQEOAQHo/Q9SFu9oBYSqCkKC2m2J/N66nVY75QgJQKvrmZoQIcFWNsRi8+k4Yz1aKz7mAqCkGQMTbSDBcgUsBCwpIy8ZaWHQ2gMC8gm0vgSWG+D+r4GXf4WUF/D5Bnj5V8h5QTrdo7y8B2UCLwXrvc2aV1BOMkNfC7imZt8/cLDiKjDQBmnorFxUCFNgmo9CuD9RbumawwAGAxCM29rN3Z7nDAjMyfCiSnD7aVEI7PXNp4DlBF5uxYEwAsFyK+aCipaBUnNDzHJJrOr3MeaSuNRu4r0cXcpGB8IjE4HdqQ4G1FQQYcC+Z6sOXD2A9WMMCyzlToaeS2IHCK5RC1wJnUHAxLSACRTsQUJ3rPDa+8/cnR9P9znoY2nImaM/ZP2K9UGzAThCwZ6vQTMpBKXAIGEEBIIrqCCBgq//qC1J3EyEryyPEgyAnZsQYADYUtzdizt88ydfw7e/+AE++6tvbQhvczj9vDMddJ9vfQ02ZVhO2drohLRn3qfjGtprSDo6vGx+z6yILeWnH+foQe9b+fYcODwSARBYfqwBQmGN2qf+BwoQIm0mJCKJTgjIkkYOA752wIl7SDCfhCM1IUKCOS6izjtx6ey1k7dL1gZnWQ1i5y7nLg6UljZ4hITFlQN5nRPhtHyqAcJ6Fh+EtAD5XpzlTjfgl38FPuvgtd4D9y+xLPfItwoIpww+F6S1gM8FdRUFgSur/X7umGive2n+iplRqW5a2Pv8qIy/ISy6fy4GBumUFQyCQqC+BLtmA1MKbj8NnG4bCCw34kOgkFBBOFeZ+a+qDqwsr0cYMGiwhFKiFNg95eHaWhtZdaIxMxnEJFOXYGBUBaLT2rURCw9XGtSKCAQAdJ8dIJj1Bbb9SFLtZuuThHZyA+VQ4xK9sXgfltr56nXL51FB2PHwn/3eFWrBTG2emRaANs50uw+Q4Ap1MDOw1uWzvxCfuPe/9CE+9/p1qw/2yuMCg712NgEC+csuMX+kUPAHX3gfn33trV2lYS8uwdjOo6/B3il1qoEOnrNYC9PvHz0Ml9SC8EBs18M2QGhw0KsGMo4fqAibk53BhWAUqZrA+pugqi4J1TsEipIdWRY7mf04JAQlwSDB6rZ7H0CB0RzCDBIKxHFxrcCiUutaWAMo9aVlFOwf/ip2DyQQzgBIvRQtrTCthCUPkJCBDMaSZCa4JELpAOFW1IJ0AuoNsN6LgnC6AZ3vwacbcUxc/moDCPW8Ir08i1KwrihrAYrEBBjt+8B2ABuLmw3sfU67n22/m/34pJkko3Ng++15Bz2unjA/h7QszbFwWZrJ4LRcAIIbcL65CARnhwLGWqXuBQIaDKylomWd1OvxvsdaeFAGWAN6qboUi6lNSyZXCWyJYYw5YP4rRyDQAOIIBoCZOtCbDORKOpUgmgyOgGCvdKpo2qoFEQpmM/ZXUA265X1Bmb0UlXG/z6XNeLH57nA/NuaD8DqONSMkbABBtz97/gzf+ImsnnvzjSfti7PHMQw7e+XxgMEDoKBL9ALgo+d3+OYfKRS83uSXWf8Yt41wcG3pv0dtcA00vbs+dmb3suOE418qMusY1sMiDYAwwAFVf5DkGkJnEAf9PXUh0pNJkQCkSxvNC3KWGydFFKCuDRK008g6TTIFgEGd8w8z+eytBwfqlo+RbkcmMOuqhqzOe5VREvmSwAgFVe3LQGwj/T2QcYyQQFhKBSXCopBwW6TjP+WEXMWcsSognBPhdAgI9yKJn1+Cbz8tgZHO9/Lv/q9B53ukT90DZUU9r6oaiIKwtyoAGPwCJiVNFIQjtcDvykQ1uPa39lZFtFUGC9LJVnIcrTS4rBCcHQQCEDBwLhVrZbysjLVUrMxg/dzawR5bJW3bKRESixycg+3TTFBLauannOJMfx5v4GGKgNfEXBkABiAYt18wG+xBwaACxNfbkNoTKBiBwPs/x5qLhTpFgLeTpKPz35x3M7hc+/uzsve92IY6SOA2glQCfq7m7+++8wE+F+LsJDv4HhwclMcDBrMSoGBUCWz7CAXAAARH5PWKp0ST1/0gPzMjHEPA7JT3SgTGaVQtn9dcCQdxOeVD4YBIOyS5xg4QRJqQs2TAOgsATUmw3/LXPSxwgIXKhEVBwTzFi55mccVBOnbS/aSjzlitTtSvYWV1xAtR9kRWZp8djmOdmualMyfCkglnhYRzIiwp4ZQrlky4zRm5spsWSgCEZfkU8nIrqxjyvYDS+hJYPgW6uQfVFfX+pYCCAcL5JbDKsshcWg6Cel6xF0sAwGZJoMFPurjM5uFl79gtquI8hoKDQJfb4dT8B8zJ8OYWnBZVCMRvAGmRDJj5hAJRigwImkog1fyyFKyFcS6MtQocGAyshS/XPYDEUvfd02wmBWpQsCTCkpP6BqClnk50MUzxrhoQIQCYgwDQQ/toMhi2tf0nr7tKHKAgPLsPggL/boOBh/Z97TW1bQ4MPSxsD7AF34f8/lHZwGQYezYqgt7nj188wzf/SBzl33z9CXiAhkM4OCiPBgwSTW4stvacOE89hIJZJT3g5o7pSi/s3f/oDgTE05qd5rVrVO2Q0/WwCggSl1vVA51tS+dhWGHQYHAAdI6K15YOFBpkkDssxljkbY1gM9OMkmIvO1LoULL+rQA4yUqGDOnEzQQhrgUNEMypTBzf4rUlkeAJWGftrjZQaBv1m0JZSEUGAYOETIyliopwzuwKwpKBMzFOmXBWSfmUCDnd4HR7A9RVovTVVSIklhVQcEhcUe9fAueXElXwfN/lHyBmTdykJoWyel2MSYhGSPjbLNGHAEAXPMnDNFOa5nWwaIUOA5RkBUdYZcCpgcGZZYWBgwDr4M/NXHBeGS9VHViL7GMwsAaFoE4GxkQkQDgRU7LO8JeUGhAoFOQJEIzRCMecBeYhtFECAB/QdyMSRrOA1cNUEdiBAgC7nd4403bAT+H9dUAQuzm/glfs+8Km1gf6vgEW9g50RZFLpY054bDsTE5NXGKIUvDbf/ye+MTp6jkzLez5vVlJvXS0KY8GDIBm/x89QkfzAXCFUjCWyU3s6JN2KuDCOfe8sf2RWfvYexh6CJr/HkW5IDQ4m2HIW3sIr1APkHyJEaGiXyJ00IF0FznCQTg/Si0eAoAxOlpv99NzjdBgxtjQuSQDhaTx6xUSDAIKNUAglkGAKlTGZlCqGkTIYIGxVol8GNuQDRIiL3cXLIMEQ78nSoaYGEQ5OJcqUFDFzHDKCSsDizoqrkTICaoiLMg3EjBJYiGce0g4fVrTFq+gkLEQlrFwkq8ABgXMQA33fw8OLi5ZnZQ9c1kcQS3UMgBPezzmaZhkgpSARAuqLvP0CIW62sBWGJzXrUOhAcG5VDcXiPkArhSISnBZIUogVJbYFb6dtK6TqQMCBaelOaVaUKFM4pRqQGCRCO1znzs/NArhbPCPn8eyV7eXBrqZ2XMEAt92PRQ0BRC/lL4P6CdJ4xxwHj6oP8xemU2VEtClkbffTP2P7h68MvCnn9zhd/74PfzBF95XpSB0oQDM3OBVoBd1rdD3aMBgcx93bmyFOhoqFLwZfAoOD67FbqwPooMyQJNtoO1LHl7vlSMYiH4T8pc33xtLzIhs3qz+pHUS5CuoB+4roZ1NhATC9YNHNzs5yOxIJD4Hm+0Tx6QwKyEijWVPQFpEwk1ZggIxiaNhFUBAFUgoYLVqEDJnAEWCGQU4sNcmLyeibgbpoMCM1BZVIxFhNTBIhJSqDBCVcdOZGQi3OYkfAglcLJvljjfINzcDJFRwPW9SQ0uGR0ngJAqBpi+25Wi1NJUghDP2qvklKgjdagSrvxCN0ZMzJQvLrMGeNJGRJyrq0hyfWu6CAAPryg4BxWb8wX/gZamdueC+sisHtcLBQFShvn4NAHoQ0G2gAAINCk6Z3HxEBCyqEmRTkxQU5D08w+cYbOjiMsKh/rpytbMgzV9v9ts66jX/gF8OEMT+71X6PiB0zaFDjpBwbYmc4T8T3phqgDBBjUrCdLjaGcN+/uIOv/vT9/Ath4LLCsF4MZeu7dGAAdCT197s31YffPuLH+Czr711fLBQRtKyiohKAYXtQHP8mX5//5enKsGMjuMDMZmjH6p8Ip3LYDeLqvVqgKA/Ys6TERKAQU2wk7xicHnILKa74GB2CJDAKYOqdDxEq0NCSgtSWsQ2R5I1MZNEtktEskRIBxHKGWdUZGK8rAQqFe2xlHNbdXHxDA5Ws6dTBAXRY1IlrFSxMLBS6swMf71W3ObUmRnG1QwtsuINlpsbmVWGTI+kYMD+usKTGWGIaic3u8WoVyVhvP+vAgmb5Y9uKhAJVzrvCHcks36oXVrNCdP0ximDNYnRWoGqMGDS/7i6wB0KJ+YCWXnQ1IHV8lE46KGvywEKEgW/AYUCqbuE2xScTlUFMNizVSxJ1SEHAs210WCgtPqy+gSsg5jW106FTLZdMTyO35speiMM6OtXBYI9nzH/3Wv6Pt3PvfwZ06BB3XH2bgH6ftvHIIJHv5XzkP7R7lg1YAgn3Snfww/+/MUdfuenTSmIJaoGszJOao/KowGDKAHtEWOEguao0colmWX8eAYFVjEzKLhUIbtAoBtGIBgfiJly0B3fx+79oBnVGjL3iu5oXvDVCx0gMMTy2WEy2JKhKCgAO7Dg2yheTP9ZvDt7cuh43V3nFJY/BtVA0uYWIK0KCBk5E4o6i60sORAyCCsJIIASqIhp4Wy+EYlBpYeDBQlrqtYLDKCAbpsNLKYiLAT3RYhmhnG2GZc8+uCiNumcCBkJKWUJg+DtlhUUAgjUCp7MOK2O+zqJU7CHg8F0+RepDfnADs2pvbcERlBPFw80VCTSYDE1AEEZQL/UcHQmbK/FNGRKQVWwODI5Wt2lJCsKIhQIDIhp4DanZjrIpvY0s0FOFtfC6ivUVS0tQ6fVXW1wALS6uuq5sPvNPj0I27irm+6zYdvmeN3noylhAgT2+QwKTLhC3//9jfs+9BOkFiegHTv231EZ2CubCar1qRT20POPZgWfbIbvx/KxKgUzKLi2UPh7dA2PBgyAy1BgEQ0/5zaZJuNYid/fgkDbkkNjmakHe1CwVxnXqASjQjCS8+a7k5JoS8stgpYu1zPAqdhVD1oMhAAIsJO0TsmutgcFVxSAXlUwSLClm5uHvAHBvkS6vXAK//uAQgmoCaAMohWWPlds2qIipHxyQMiVsAJY9b4kkmVmGYSFMzIqiMQR8YyCTAn3lZF0AEo1YZUhF5J6oJ9tlsrIidxhMZGoCJUqUgq+CLmGAYc3durOo51YHdTEVp2o2aebN3sWENJb1MXFtwFG7/HGka2773p7r5Ckd8N4R1XnYKCwdMUV0KWWEnTKBm37K1EJ4UtU19IGfPnXw4BBQFQH9oDA6gtoz7g5kTaTEHzgvzE4WDIWQjAJwdUeMxsYJOQUgKCcmzpQm/ojz0tpz0QEuH1RXW9teC59qXDq9mlf6Z8fqcdBDYj7zUBg+H6nEPhxjlWCCAS/1L5PDtQpCCn0vaN5YTKZ33w2gwOgqQSk527P3qgqxPLRC/Ep+NYX3vcliYj7o+e2o/L/s4rBWCQhkizp+Jx6b9q9j4N9hIQxOFGnBGALBP7ZA4FgLJegIKoEG1Dg4SCTEuWp+KAAQ1QtAwQ73gwQAEQTg7ytkOQktYECAFtxIHkYbHuDgc6BMS5/jMfw46BtN/n7gtQtJxekapOpTYquSa7J7Nn5BKolAMIiHuDRvAACESMxI5FIwy9LkeWNlUFVTA0LE9bCSKliLYSUbTYaKlkbjKXaRSIHBJgpw30R2H0Rol9C1oEperkTtSBKsh5ekzqR3IJZdj1r29JhpvBMLM387/e0O/0HlT3nWanWEYC5DQo6+LPua2GHaxhASm2KADMcBEwtsFDG0V9g9B0YTQVWNzmR2/uBVjdthUkDAquT2+BgeJuz+gw058JsKk8Sp8QMiMkgAAGVs/qI1AYDbjqQZ+Dq9g+AvccMgzPX7UDvcIYG1TNFYAYBcR99P4M+q+trgADDtlhHD+n7Wr9CfYRa5n4bz+Fgr4yfu2nA3nOvHnSAALvlLdz+xy96R0PZ53h82juva8ejRwMGQIMDQEkNwMd/YVkSP8TnNEuiCb2bicsOcs0YugsgEj48AoLx6HttuPNsxzEUzEHieNZmjm+dVDbSMuAPyJ7/gVyTqQiyAysU9KAAWEByl/oiKARFgTkFRaBJ8oD5LKRO+u46RJO93V4+L1xM9leTyLLohWUwL0DVDjcCQl6QVUGoSRSEUiXfQaliXlgrgyhjIbFZL5XwksRmnUmXJ5LMSlOhsNRtCwiADkT6foSEtZJ7vXu63ASkM3XQYGBgsBBzOMSoehJqV6DNumobExJaewda3TsyhIZ9DSDM4oRYm/X+Xdt3a+sNEqCvY3IiAwCLRhkhgBkeeKgf/OfKwHiOpg7MFIIZECTCxmzQmxKa2WAEAvcHsVTcEyAwcwLAzXG0GogfmHRI4j7wqs9RziEHi4YoVx+OJvO315ulhQDcT0CPb9vi+0tBieKYfuRHcM1k6CF9n7fVAQR4sg36W3vte9a3HwFCCjupvur7uzGWNEz/HzWl+/DawrnMAlztneusPCIw2FbFs+d3eO/HEibSb6o2qiOysjKCgPyV955dNtz0a2BgPP5uM9754FooOHo8PDzrhYdE9hkUBfutCQh5wBAHBZuVAFGC7lSFAAruxBgBodoZAgIHRXNK1OlFOhSYd71fUyg2s1EPd0lvnGTZW13RnNhODRD4JBJuyiAFhNVm8Un8DzrzQs5Yiy4trIyXJIGL1qIKAsmMdqm0O1MdVQRbf5GtVylw84Ds3mDB3xswdABhcjcCKFg+B/numAAqZvNraX0N6Nqtvea56oYu7v74YC/X28wENuhLHfd5CGZRB/sBP/hwhPsMNLMDwu/HsqcO2D0dTQamEMyAQOCsOYlGP4KFGhCYL0EHBDUoB8wSi+IBbV3MZMbWk1qygR/YQIE45wYgoOA0aL8RIWAEhdBA4jg+m+3PFIFDleAB/R7pfgm0UQFmINCVA6lgtnlvG0PGDhv4K6uJgdp1kiq1z54/xTc1ouE1PgU+/lxlU4g1sC2PCAwAgH1wunuuWaa+/CHeCqmTOweP4SGa3dA9icaerREG+iNcaqbHcHBkV/N9hodjPFbc39VCzB8SIEpsCg1G0TxZwRAvcQCFeIUEAKRDCyksOBzwxIkxAAIg773j007IJM+wnHHTUdrF19I+t3NTIDAHRE4ZXFZfBkfLAqgpwQEhn2T/WkD5hFM+SYfPhEKSAElUBP0bAOG0kKyZT2rbToy1pt118R0kjIBQAzx4vbUbvzezBXARHOx7Bg8RhMfEULK9/e6YJyEGnx0Xlc6yVI4Jh2K+gTHU8KWB3/fZMQfY57Hs3cMRBkwd2Ft+2MUkuAYIMMSfqAYIPRCYOlAny0rlng3tG5D2CrTnxx6dGLp6cM5tUlHW1R8ZGyAwaJj4CAA9AMS/sS5mJiT5u1VH4/Y9lSD+xl6/Z/uPcBC/NyqmR4nwrunvafifw99MwOiKlamZv7tJbXd9x2PXqBYQ2j+A8fT53c4VSXk8YBBsY3fPJR/1+1/Rm8pw789YLpkO2o66fRj0tjAQKuuCpDeOqEeAMJbNA4K5hDZChIBRO9MRDhAfoMH+BgRA8GO1KAJ2DyoP9y/OJq3/QPNRoAgKCgnM0umRZkhitpTUbaDntKhJgfzBopS2cfhna/EROtFU/btIWQL+LCd1huwBgWqVUTSrslDOh4Bwqho0h4DKGSWLA9zLQp50Z1X/g8KMlUIkPTJveOoAIQ5s8fX9wXLBHBpuG/BoeL9VHoAZMLTt8Tjx2TgKl1y7gdn+tkFd9sEGANr27Uzf7oN9tnePxjK7L/Z6BIK4wqBFqmzqwJKTOxXGqIV7Kw1eBQgsWqXBwKxdW+l6l5S7zwwafDkotWWg7oRr24hEQVNQeHAkwngy6NWi2D/tLbseTQfjMS9Bgb0fu/oudJuxNx8nvotlFwgu9vv9hGmEBAD46BNRum1S25+3HurCeR4pd0+f3+HdH76LN/iN3X0eDxhosYv+MOSjjk4f0zK5xzObDEGgA4g33nB2cIrbK0Rt39BQroWCv+1iD4k32EE9sJKAjlppePhpwHSiARpoCwpEAHMBWBLlcC0Akyyhy6Ia+CkwS/BFQGfwWVdbqaG2DnPUSYAeABLJCADXBKQqnaZKtVNAyKd9QEgZS87ITKip+SAsiTyQzkIJpyXJcrlFoODcqQjRQx6b9fPu5ayGyqLqxN4g2S5frnNMfHSkNPR29bmNfTzGFZmZu3EsDvTyt11HHOjbdfXX+SrX6hAQfAfs+gwIbHXB6DuwF5woqgOzWAR27GYyKHMgMHPCJSA48qOhJJ+PleHLPNW/QEGAbbmuqQQGB3FpqKkHBgqYgMCOAgB/328Y+7wZNFy74uD/E6UbNq7p/y3Mux8gbSABAN7T8evJG33wvc2kC9gfOMJkNqoFMj5+DR9++X38/g/+2d6lPS4wePoXP8O7P/p6d1MJYZA/gKzxI5q8NugA0EnY6AbIA2IE4L6nBghBfrPfMsfJS7TggwRgrkNdAzuCymu4OMJB9JIF0K0Fnn+Z9bzCwdATb5c6lGzWmpuKoLESmHR5VoL4FxCp7RUOB7baEYB0pCr5I+V2T3YSA9mZEafLgMBFOsYACJJKeAVRBucFpyQheUsinNSHoIB8Tf3KjJKAUjNqFm/4c7eUjvSvxD+w7RKQugHCubQbXAv7YGoDYwkz7DaQHrfPraKgdaSDzExpiO/H48Qyzt73BnuHhHAdcfuRCtCfA/s556QsleSFAYFBwSlTBwTd0s+JqcB9B3TAjysMWuwIuA9CW2Ww7jsVPgAI9gJKdQmnxoiRJBEjxekw9VCQ4nsd/C1YlJsTyCda0UkQOJjZ41KLa9/fbpsfb3rdeFi/F9NfX+MwGyX54SwbFOj5Ho4BjLBUuwcFSwr34Vc+xNtvPLHdw+ebQ13syOPHPmn+8n/hx98rjwYMnj6/w7s/es9vapRo9u7dEQxIadUi0PEePvzy+/iHP/i8y95t1yuavw90adC3mm9EbAjN/EEA8+a976sXGuFgPNbs+uLDcamBzQKHHAkjVuJd6Rw4mbt1wr4awh9AiURogCCSg6oHKZxuhAOW/byTSEk61g4OoC4KE/m1VjwYEDTiHpVV4vXXxQFh0dnXslgEPhLPeI4rEmTp403OqGwpfVOIwkfbKHykKxt0JUR3l2X0w3mtqMzdzFt+O+w6GWBHM8BC/fYtGPQA0Y6zlQ7G1Mp7A348X2CbpOrSeUvUR0ItGoVQH5zTklwZOGX7qwN9CBJ15EhoQGDt1MCgjzg5rjDQ2AMGBFVzVYTQ1H9TIADgq2w2ULCcHAqwLBJ/xE0FIxRkUQnsfQgexYD6czQQmEn/8vdveWof+jz5/f14NLa7ldlqGgI6T/7x/XisTW89QsHBeEBRMbb7Ffyl3n79TYWG2EPT5vqOrnc83ztX0j/A269/7mBvKY8GDGzQNhI6goJ+26h3DRXKjKfPn+HdH7+H773zh3j7tTflGHXt9rlYggmBgAYHNlK9QnF1IVDQ3oMCDCBgB4ifH388LbMr353QMastT/dDgAWwRxuzjHEy4CsgkEQsFPWgOUERFaCSXC0XWcWwACmLv0BUCkY4APYBgRKAUncBAeqomOLsS6EAKYGKdKyUFomFkDJyPklWR0RIQAcJS0oanAf4lMbtXxfgvBasiXFvSyMZARDknudUcb9WnAtwWgQObMWn+DPofa/sg2sZ2+5ggVk7MwF1f0doAKJZYb9j7CI/DoO/vY+D/+Ych5JJ7mVKhJQ0YmRlsfWbIqBQcMqEmyX54H6zpA0QzIIRjX4DhzBgvgMeiGhtSwyHJYfEksXSk1cdmAwuqgQjFGgWSss6yVEJoBbQay+kNDSkNEPdHrReDEVnzoJSv/t1NZudX+pnohoQrEDdF1vuEf180jPNJkJzFeABZS8AGHB5XAiKAXHrlKiWDhS26+rHnrxBw+wXGxR8iLffeOuqSeyjAYMIBYYE8wq31rxfgVEJePr8Gd79yW/he+98V0huXBZ0dSjY3oTgcGDHoNSdsZkTWjCMrWpgjoD+sHS/Ru1yd1r+q4CAld2OIM5IJ800mj7kR03ubaCQK6sJvQFCoizZ9VJLBERYm2mBCKjiGEWVxD/hdCMwUVZXDyj1ne4eINj7PUAAEbis4JDQh5Zmq+UUICFNICFl8EZJgMc1KJVRUvNHOCdqKsLCOK8J91Q1bHLCfaluH89JkgABcZaegAAHQBtwZzPwsQ5TIhQ9ZrbQzgYK3dfZgWFW9mb/M1A5Oi8rSf034uqIheBQcMqElAQETpn875ISbnIDghsPUXy8zDBGJ9xVBo5ggBmXVhj8jYFgphKM/gSjSqAZKz0ZFQwEeiAoQSnYWx0wrafg6NcN6BRMAAf1vJnkDBvGO3Sp/2v+Tfr3SC1wFXNQC8aQ7Beigk4LI8xSWnCqmaoA9MqCnnh43XwWrDwdoeDK8mjAYGszmTTSseJwLP08ff4MX/2jf4TvffE7ohREhzYOec0vFI7+BGZCMDiIUceuKITBxBCuJ9Pkqq8Y/K/1xJ3Jgw9dOuTfA6tJQc+JbTbaAi1R1ZC+aDnnBRDMiUpgwPIcSEecRCq19wvJrL4Yhdc5IOTUtQHrjEU90M8MELB2HTGruQLrAAmhM95Agkq2KZ82kFB0qZ5n/0vN1GAqwjlJ8qbzKn4ISwbWknCfKk6ZcC6McyGcS0JKFee1ysBWqqx4SIRkyoH5KRwBgs7IZ9vse/b5OPjvHW/2+mhbLPZbmciVAhusRQ0QleAICG5SEgfCRSITxuyV5jcQs1dahsMNDGgGyy740FEwoiMYAKbtEAggIG/sRuwCwWVfgq3ZgNVsEIHAQko7EJgJCE0p2OsKrWspQcH02b0qXVMV4UKf5DPk0Pf5PZvtuLNpDGf/YPXATMo7UHDVGMGlDxNeyxYWorLgJy/QMPVZoNQcDb/ywYOgAHhEYHAox+85h0xAwbY/ffERvvrH38T3vvBtvP3aZ2UWEH9OnpQL50S+7xQOxnO8QjVwlUDe+izbTuWahn30zKnyvCk2WMvr8X7Jn1lHYftO2bmNSag26a9K+yRmBx4BQWfFyeyfNjsrE/OCJUaqaxusV/EFsM6Zdjrn3eZkbWYPEs73IuGuAi+UEtIg45LKuKQzNIMFNzcMkCDZAMVpcQkqwqdKxcuFcV4LXpaEc6q4yYT7GSBkSb5kqaAXd1RUc0ZlVLqiE8PWpABgqhLEzzcDfSYHiAgX1lByPm7JIwy0VQXHQHCTm5/AbRZzwW16YFTCtQUh6vIWDD4D00BERU2QBzAQy1UwMEtFDY3NsbfiQFWCDRCw/gtA4EtDuT3j0dlwb16c0Pcb1jnNlkj7JXbOePNjxt8bAYL5uj4wHt/u8Ew9uEYt6H4cmEPBhbGigxVVlTpYcFkltAcFgWiGMOf2p89/1vnc9T9mStP++TweMJiVGRAcyD1WkU9ffIyv/vSb+P7nv61KwUEjmD4Wqd+HaAsH+tuXVIOpSSEc25uJP3TTM+nKpQcnrnr2qww/a0k+vKlT2BHooGB0SoqviRDiILCDQtUH0WYTG0AwT3Bq/geyRKuAqIATh9laUxDAVQZlLjJYR9vuDBKATtIFRlnXOoDU1D1KAF565y3pf8ltvhZh0T29teOmlGSlwwQSViYHg3PpVYRPMfByIXxqFTPDuVTcV8b9Slhrwn2tWEvylQ/ig9BWL5wL4xYIKxquk+/9dWi3s5UI7cP+bansnU9l9inftb8fl0+auWD0ITCnwhtdSXCzJNw4FKRN3gJTB055EpFwhIG4vDD6CxgIlFWX216vCFjZwIC+n8FArw40+BRFbasQWDCv0Y/As1JOgCDmpbjmmbbSRU/VyUvXzVH/cpp7Btv+amhKXeFJE7zUJ0YgsN+PULD/YzzxKxigYAMEO+PF5GbSdAKpwaxsu5kiAiQ8ffEx3v3Jb+HDL70vjoZxzLmyPF4w2IOCC1T39JOP8Zs//W18//N/gLdf++xQsdtBYV6CXdfOIcJB2DY58U41iINvTL7hD5qfukpq4UhHkRyvKXZ8Zvb43WPq0Lhksv9u34H4bR+O70ezzwnuiEhEYPAuIFQHhAzKGZQZXBsQdCYG1jgEpLJuXYG0gJZlOrM7UhIaNFjEuTGun15Lyhs7MFJyey7y4jZgdwajDMpLBwk3+QTOJ1QQzqlXEc61YiEJnuRmhsJ4uVSJtFirwgE8qZBBQoyBMC51BNqqAStHKw/SpGFFUJgtMeydEPsfO/ptgwCLDWDLDmcrDI7MBZbZ8JRSpw6cEpCwE3zIVxa0lQRc1gYCZZ37COy0kXZRGvDLVhbIhe7DgAOmwgAdBSmamwwYDweCvWfZyjhjJ5/YzIMH+Wyc+pT1s8H62jKOxaJcbM92T50wKPDPwr/5Vfc/OoeCa8aL+H6YXA4l3g4O483TFx+J+fudP8STN95q48wD4eARgYEPkdtPIhQcEJ1AwTfx/c9/C2+/9hkcV+YvuTDL74XKsyuKzoUJCLJSO8PpQzdumElzkfB32nwNEEJmZzT1wuAgntd4Wfbajjf/GTkfFkgQEwk7vXOCB/hIaQIIMHk5mBg0giLqIp04s3fynJZm+zVI2JF/SZeWsRpeHQ78d+TKNoBQ1qAAakY77eApJfcaR15kKaR5j69Jwy+fgpIgn6flplMRLNdCYRbZvDYzw7o0FcECJ/Vphy2dcA8HwPURA0cgOFQN7LYMx46AcM3vjsGI3CGw+yexB0wdiM6Ep8F34JQGdeB8r3V+7iMRlnNTBWxJoYGAA8FBewjF2oMP+AhQMFlZcBUMuLlgPxbBkQ+BgGEzF9Q6h4HD51f3aROWnUE9SvQTxz+/NWH/7neO+q1BwQSkDzs65+674fgNCEKJE8y/tXLhLkcQ0b8/e3En5u8vfkeXPNbmf9AVGv5uyyMCAyuhVUSpZyS6wRQgUPA7CgVv/rs9400FimoQHzCgBwTftnPMoxUHe83BvmN3ZozhXe3bvI2pIOcicRSg2xNLaN/4DPm570GIHtLtkyznUSs3QKgNEAyWEoDE4iAGIuS0yL2JKgLzgyDBncVSaiqCDgBE6kRml+PpbwGUspmhOCCYn0bWgDPBxMAKBsgL6HQjg0BaBBJMSSg3nYpwyrTxRTinijVnFACfLi14ksRF4JCK2CIs1k0YYqmLeSX1wWH61nQFF2w68k45uPCbY3jmGJGQCJu4A+PKglEdkBTHvTpA631TBnQbr2fw+V5WomyAQOteUx7vreEnIllZA5vANX2PbGmhA+PEb+AhMGD7hTgEhQGwqQTcLz88AIKupzwaDwlxrqvXrAM+hfTewXf+mtUAtm/827WNsDEqrPFUE/YnPrNjjb83UwsuBbP72yvD73LC008+xld/+tviE/f653yiKaNhNFVfJ708GjAYB9H5TvXfIRREO6E2q2v0sLieFe264vXNVh+MR94HgctPhz22bTBTSLCBwxQEZhSFgAyZeSSmzgnxUpntZ4qB0/sEECoBVCXrX1HVIpGs7c+A5CyIkJAWAAoJnZLAiEvJuCokcAEpMHT+CCm7giAnl8DrWaMfAljPMjDY56OPgl+kOgvpzFCWOoY15wYJpxsZHE43QFmA9T6YGkRFyIOKcOIACgkoS8bKwH+PNZ5BUAwsS+HqywXb43FxLTj62eAFf8GulHDocRw9+l2i9jtL0pTRo2KQFRJ038NshqM6UFZQPQsgGgic7xsMmFrAFbyGFQhAV8/tBjUfAU5ZVjEsYiYCkdSxOcaaQqAmpm5VgfsMbM0End9AiFI4woBnowzmArBmsZyYCzp14MB8cG21OxRovRgEWFKuPYe/9hvX913DacvW4USv7UO77VfAAKvZGBRnRaPb5C+vPP3kGX7zp7+r5u83wVeYDS7dyUcDBsCVcDCUXz4UDBUyQoF7hF46U7maEQr8sJNvTAFgEq/hiHRj7nTS83X1YgCEAkLegQMQowbVwE7pmseDwwtCg4SZmaFCl8xx61TKBBKI4KsZEqQ/ZbcVV1UVgjMZC0SgFtkvFV/VYDZl0qWKBDQ4yFWUBqBBQdXEULYtlqR5Ic4iF3tchGhaUAUBywm0yF83NeQFvIp6EH0RSiVZ8sjsoZgLGGtOclqQGRRDYMFSHAMx2+G2bsZm22dXvFCxsY7DbRgDGB39rv0eJWj2SkEGG0g8TwGGxEUkfgqd70BpMOBgsJ7B6738Pd/3MGB/x3qd1Kmcc2oN3vwxcu6hIKhEo89JHQIRzXwGpM0MsQcYXXTCo9UFh+aCAxjYK23m36sFiY6hICsFHK4AeGD/BTuGT7R6YLhmrJgjxmxHhQBdEr0PB8AvExDa+PXtq8eva+rz0YDBw6GgXgEFRz3dhV4wdJi7UGCzxv21ceib816J06+dh2hvaeZ42ujXwrLkWgYNgFAgD3UlAiqDSUHhWjhQwAicAaC3MecUVIsLKgKgnX7ocAwSSLdn63S8I8rIOavfhsj/VNctJFRbvVBApLP7870M4kRtRrieffbHK3zA8MFDo9y1qmKQXiNlszk33wP2gUIh4XQrg8bpVkDBVAQFBCTN4ZAWWdGQT2CQhmCGL01kZpeTGQAvuZstXtFMOkAYYeHoyRi7xM4H5crfnMrTVr8UlzAOWQzv1VRgOQpGdeB8D5xfgtcVfH7Zw4ACga9iAbwurR79PLMN2ADZ3QiqkEEBnW46/xKcbtBFJvSgQxMHQjUbuCpQ++BDm0BEE2XA7vk16sD4XPq1hhcPgQJ7Fi3KqX/mx1Qg4J04AXttZOi/AEgf1jWgPtTw5TL/TdYQ9wYDh3DQnfslgp59voWJfvz6zBXXEc4dx4DwaMAAaBd6zVD69JM/nUDBToXNZvcXZvwbs8GDocCPdOHjK0Dg2nW1I8wQg6j6+coafAEEoja4I1kcBe5AQfR+XYlRd+AAaCsS0DqdmFEvAsKoIoAx5FxoSoJckg0erI5qPSissEEk66KBBWQmh3JuPglZ3rM5olFGqqusBT/f6yxQO+7zS1lRkRJQqigC4cHmyr6yoa5aN6no3wRKZx9cbCbJywm03oPyCXS6n6oInE8CLuOqBhtMFnM+o2mIW28uVzhWHXp0Xyixi+ub5HW/a79nA5ENLinZNoYlLNosMYwJjGbqwPkeXM5iJjDzgZkOSunqrkXHTGoBTD0gxNUFRDIgnW4F8m5uN74kYg44KRSeEMMU2/su3kBt9TeCwLiiwO771G8A2FUHrgGCWBcRClIiNx+kNNSVq3j6zw8l/RVFnx3twx7cf0HzDYQ+TD7bgsKrlmvgYLhVl0t8oHZgQswHcfy65hroIhBYeTRgsFEMxtCRoTz95Oe6JHEivwwD+p5fwEV/gbGxdVOsCRRc43/gP75jHtiDAd50A/MHy+QAAMSqgRI1SODqsxWihAQaTlsJ2UAhyZS+ssj9xACjOSSSzVQOAAHYVxHc52FyHbGjaZ0V96BgHZZG8GuyZvNLcHNDOUunXc+gkjQcsoISJfB6L53/+V7OIWXg/q/1FFeA02YNHtcKVmN7UxIKKBOAswZHkqWYWBUQ8gK6n6sIyAvSzW1b1UDZnRujPTpFe3QmbY/tvm6zbPSFdl5f1+Vsv8lh26UjUPd3mFWeS1tmOktWpKsK6r2qAQfqQPtbUNcW14LL7AxrH3sAgC1NdX+Bm081EBiArmpdQYEO6SRQmU99rIGgChTMHQdnvgJ2j2eOv7Or2YOBeP+PgGBUcmz1yMx00FSCUJceOnruE7avGoRBmG0iFpaIm/qpA7lnuo09yLWQEEwEUzjwu9MKX2lC6MYWUx1CefriI/UpGMavozGExnM5Lo8GDIAZHNDmDvzskxC86PUeCnZn+bNy+NlMYWj7HwLBNREcgQcAwfYz/97sJ6g9UPpCHjJ9wEjPkYlBKYs6Q1CFoDctoBkfAAaqHWMHEEA66x86rpmKcKnYd8gO4h0Zh46MkarObCqrHC3wsNSmJOSckfJJ4KCKdI8sNmnWwdfMC7YUkc/38iPrPXht2R31Soaq7Acd0uUgRDI7pVRAaUU6LYcqAvKCev/X/nm6uW3OaMF5Tcwf5tAWPZbTvO3G0nVSOx31kYPWLjA/AJIn7dodSs3xk5uTaYSBblXBgTpQz7LqwOBtU0fZ6qidNyUSiDMT0OlW4mQsN810EM1AaQGnUw8EwSRUIX4ipgzMzECzFQR7KsC1zw6wow7omxS27wLBBdOBKwh2huYU7HUW+i+nG+vbLvRdspP3/6RZVxnqXEMy6dkAAlFru3v9sA3+tj8AMPtv9y13OE9xbJofd/yN7rrgv/n0xUf4qk1qX9+av6cT1nGye/kMHhcYAO2i7Vaw25Mqnr74uYc5fvL65/obdGn2PjaUsNTo4jkdqQezY08PcmAyCA/OHhDQ8P29zptcZqvt3EmGNOKkA7iYGFivJZEMt5Qgs/UkHRqBxfHP5Ew+BgRmmVEwtUdqVBFi5zZKzyXU6DqIRRnWkcvfnAxygFRlVQORJONJxFhJkjmZkrAkQs43SJmBdAZKUgg4g0sW6ZpEQWDNidDMR0nFGBuIVzGlrJB6Kqk5J6INPoyikFBAlFDXFWlZxNSwnEBlBZ/TVkVQZ7Z6/9fS1syGbcveICoBETU4gN4Mbwhpp51Eapt01Fd1fFHybfco7DDsPzuX2OZrAF/epC9m5u2qgonvgMSvYNR1ncLA9jISKFNTduw+m9+Ariih062YDk43ohKcbhUEFocCW5banEc1VwZk8PclhgxdSaImA70VjDbw23NRDoaAPNzjaBrqoDq8SGHbtUDwSiqBpj9/9b5LBvgGvnULCEEt8LFiBIQjOLDSKQThGLrfBmIuMC+ANrbE71JuYfq/+B0Zv/YA3GCfUjf2MAYoOHhUHw0YbJNx9DXgEaG++J1tPuqJremS7f+yb8Bw7G7bq9u0+pM4gILZQzVz4ImvidAl7aAk6/mpOiCQTumvVQ/EaZD9NbNI+FW3M5HHLIgSaIyEWNFm/lE9sGId4OhVH8s6PAWjdzuRLGk7k4VdVkhQJWFhYKkREERB4LSAsqx9Z5uRL0vzMl/EB8FCI7OtVQeQc0Y9r6hYBTRy3QxC/j4LQJRyRjplUGUHBBTJ/4DzvagIEQaS+hwcRNGT+9B3aN0NtXOZLcurCohdgrGDHqdzSNAOcPLbG2l+ck48OndOolWyBacKkCCpjVsyIwMCrhX1XPyYu0CQyaEgLYsrOeZMaKYDNxsElYDzjSpOC3i52QWCmGnTYEBWlkDYp7KaFLarSY6KtfuijjkREDoguBIGbNfmv9M+63wJ8ACVgEsPfFbvR/0WAE8ipKoAoU4AIal6Z9K/9pev2idvvjc8M9eqBN13trAsWX6/ge+984ctdfLesaeK26BlXDilRwMG06KS0c+ef9xuapRfZiBwrUPKAxSD4aQesO9OxzSS4hEUzB6sPfJ2mc3em4evhy7ya7hGPUiQ/pqIxN1AQcDUBEnoA1cRcAAJrLfOZkiiHkxUA51NAS0PA0+egrM5JyZZSUEknSYlCZqTmURBALAkRmHCqoCQKyRaXgAEsRGvoDWpeWFBWk6oJivbv/NLcVakBJQVicRhzWeqNJ+pctEfBlDPAGWxa5N5xGeBA9JU0BESumBKBgXQwZe8m94vk8F4TD++ifI3m80Mz1QX/Q/ozot3vrN3ft15MXdBhyIM+BJD9R8wINgzGfhpBNOBAQElmqsECgIGBenmVlUBA4JbMSMsNx0QnGufVbNWxqowwCwwvHKVy9J2XjBv3+32qUKm7wv6FNVyTdQraLYd+8oA0NSBCAR5BgfJFIIHqgTc9pVKubLfkoYAu5LWd+3AgV2vQcT8Tu5sH3ez4PEP/6qXYWx5+vwO7/746/jwy+/jyRtPpD27X4Pu1KkL+kwH5eCh5dGBAaOvB7mp7+HDL72PJ2+8te0ANyBAfpzj3xnktivO6yGlHX3nm0d23Nm+nSeSfXdybFcVEO6JSnHEoh6kBaC6rx4QgXRdtQMCA2DxqKYq36loKwrcPqrvq3YqU0hQQDA4yLyVTa3DbGvyGVNX1No6zRYsR0DBYu4XFuUgKyBkENbEOFngnHyDlLJ4ulMG8hlYNb1yCoOGxiRgze7I53vQetaIhqsDgg9UZOe+rWu2NWpaZVRZgufYaoaUGiRY3ZhioNfa3bGcPXJf9zvWZmLAJvvR8L47x1nSMStBCYhe+3aO3ftwrpsyOd/uXGNgqRhvYGd1wREI2HtS1WUXCHbMBlXNBryIWsC6gqRSdoXgrFErC1oEy8qmHtQQrbIFpQIwb9No8j2KgEBRk1i/DzUosMEf+zAwAwEA3Xfj8sNNTAKLOHrgSxBhQSu1fy8btxfsn8dB8AFKwJHp4MoxYdz/VceG6Px799xSJ4csid4vmFLC83OfPDfXXsOjAgMzJ9jF3z1/hnd/9HW5qaP5AOhgwL4z/t0r1+730NLsXQMcRKeX6QntqAUTdaH9wvRA7Uw42uIAo3Cqa+97gCwzNFve2AGCdBlJTQwJ6qDIcflUDwrR3AA0SKi1d1gsJNI+g4BSURIjV8I6qAge5EVnZH67tJxhzxCrk776FZSEJXEXUa8oIGQW5aOZGDKWJYsj4HoPpBOovPSwtLScgEXkZDrfgu9fOiCY4xuZ89sgbwO5zYjH2qqiHMjnCVxXnyU6JFgJgXd2a382wPssuq3bH5fsbUwgk7ZKQ+flg2/IHmjnHuM6cDj32XGmvxvvF9fu3LvztiWHdi5h+BzPixZZSmhAMMaY6IBAB3/kG/UjuJX6Xm7AlCV3RWkmAxvwbeXBGKFyrfWwDct9kb+ZICDA28RFporF4FAgAwmEWT7twkAPEVemKI5A4H0UOywcqgQX+yz7jLYDvPZhM9Xg0Mn1YKJ4bZ//0LHB9rervXt+h/d++C7e/8qH+NwbT5rflftLqTpBmI8Nw/jmx7/ixB4VGMQipPVuIK14N/rKnt60vZunbWk0P14TI/4hxbSCjXJAttAOfWMw29qsXLJxTWSo7kEzb3V3ahweNJRu5cIICCALkCTX0kGCKgnAdp21Lc0ySMiJPEhLYfagRxXAkpPDAbGoCJbJIHaozPAzn6X4LYmAKr+5pALSyHlLTlhKxZItVa925MHEsFRgyRnL6dPid1AWULkHlUWc3/J5Cwi6hl6g4CyOc+sZdLrpouztDWzz6rR9AiTEzw++E48dB/2NxF6bfF+vOKex2KBrKZzJlvcBG9k+7h99D2bXNivHUnvavO7gxNQXd948yUqD3Oe02ABBOvlrzjfuYLgysK4NCAo3haAw8LKUBgya24LVpHDUbpPOiBJIgo9VRknkHbwP/BEKDAIUCLI7C15nIpCbtAMDcuMPgOBgInMJCqb91aQw7w/+Ewdwl97bxu6Xf9mTwb1mWSvw7Pkd3vvxu3j/Sx/ic689Ec7VU+sTPEVIGM9u//l49vzu8NweJRjcPb/D1xQKnrzxBDavHamvgwHr7+zzSaVRGJ+tUl1sn1XyA2Ehqh3bw9Bl1cBoGOoF73a2Or8g/4FLx0X/kM0oHAVAv7TRvOmjnYvVG95mfZnIFitsTA5pDxJq45TC4hBZSOCAKgNZpFdKhFzleyv6UmtLLdt/oFdEhLVKh7sSIZcqSXk4Yy2rA4IpCAYIJ2iehrTgdNJAQ+tLURKKesIvEn2PblfU+5eg80v1mI/r5xUSVBanaB+PKX1n8n83yA8z+WHwHmf8HQAEQOgG/82+BggXzAkxTbO+rrmpGAAAlexln/ZaBmfq9h2hwcrGcbH7zL4TExiFqJMaotodM0MuC4cBdSqFLklMN7cSoGiy0sD8Cc4slo+VGedyDATntSg0wH0Npm3V7iUpzA6glGEKAXWmMTMdZK2SCATuD7AHA9eCQHhvbXVvCeJU3bzGYe+SmcD6HQqrb9Rn6vJx25XNgOABKz+3Xx7K+KQ8fX6H3/rxu/juOx/is68/8Umo164di0ZIQPf/nsrx7Pkd3vvRu3gDb+ye06MCg0RNfvngKx/iLYUCYHuTRhgwSRuT/QG90Qx89EJIa3Rwm4LppDEcNcnYQOzhC20ADgeSIQA+z762kdpDwQE/XYkYz2yHajpAYPROiSbToQswAkCVBJ3PBL8OW05qkeEiKEg96UxI+5Si9yYxHBAqmnpAiYCa1IufsSYCscqn6u0NwDvaNgvbXKiMCVXT+mbS1MWrmBVKwppZsvgFE0NhTePL1ADhZpHZezl7wh4oJFA+AbefUqVAfA46SPBwzJK0hwb7OU3yMNAADbMZcxdc6QAGHCRUNeggwPcNz02ZAIEXje6YE4q9tp4tJYGFVT4nSihZ4TIlpFSlXgFwYlAm8CqQwKW4D8BYZqYJ+z0ADQLss0lSK1vZ4TDgwYkWIC2ok1gEBgkRCIr6EkQgWEvV1Ngc0mJLm14Lexs95CwGAA++7OYwUihYAhQsyQIOyX4p5JXYC0J0ZB4AMA9ZbMpA3DaJqdI5SHf7bmrSLvS4r9rM+i8U7Xd6R705FHSP0QMG+r0yXqqNPwIFH+DN19/qYNBWalUorMXzCPV0dHp3z+/w9R+9i/e//CH+2Q9+f/fcHg0YRCh4X5UCKxvSmwDB0HSn5aMXd/jmT76mx+njw8l4eblB1p1dUjxRav4SsewenSTkrjSUA9Wg/7UBEObHba+p/2uFLUirnj7b98LSIRgo1O4Y0WnRtyvZj6AgagI5IFTWZEkMycegTltUAWRC5iznUBhQM8OaZNaSWGIVAOQ56IHtLKAW6H4sv60qwpISllSwVsLKwFIqbnPCSQFhVb+EERCW0wJaQmjeumrQpCIqQtVt6+o+B7b2Pi69A7MoCsXyOwwZ/hQWfGYdTBHih9Ds6i3yYlQZAhQEeOBSmypQazNZlC0g2HFaU0phewlmAD1HVG+hCQAyQEVBABUVCSlEGOTCTTEIaR03fgoBBDolQLd5wKcs/iG2asPTIM8SHCWNJJlaMCKkxYGASbJZ7gFBZeCsQBAVAgHP6tBqGS9ns9NEUuU2oFs+AlIocPOXQsFtzp1KsAxAkKmBwV6Y4o1ZQCpT7vUGAoCuz9nsXyf7o0mB/veBfZU5Sti2mVpA5BOVvcLhHzA3Me+NFdeIHXtp2QHg2+98gM+8/mT3+Em/L47YWmfaJ0YVAejv2NNxfDy4nY8GDEYomMo+O0Bg74FthVn5+SfP8M0/+hq+/cUP8JX/06/3nZiVCy2CiHZ3icAQY/+P5gXaUw1wDRzovv7QWSCNnZPa2OGOwcdnqrahO2wJD7ldSzzmzoOtgYIyETJlX5FQK/nDUAjugCzpmGUVw6dyxhkVuTIyBA6QxekrmS8By1PV4KC/FwJohPvCSCQqQlUYMQXhlNMhIHhaYBInxbxkpIVh8ftJ4/c3SCia3EfWeYsjYnVQQC0Src+W6RVJ/ISwPK9TFmoVBUVNEuKVb4BQ+xgKSbMV6mDMaHAAiAnA4MBWUVBO4FL9vbeHHUm/Ddy9Td+O3xULIGRmheCHEAMMuYNg9AuYmQQUAmDLN/MCW7a5SX2sYOCpj5PmLzAYoBzUgdziEJS5D8EIBGupOJfaKQQrVwnJ4MrW9tlM+nxIbgiFgs5cQKJkqdPsKScf7JtioCtqKIBBCjAQVxCEvAVXhyjeMU0eZUZsOwU4aFe9v+/4OkDCIRTsqAVTKAh9sl/iQXd/Ke/HDj4BAN587clGwe6+q9cpQLAPCECDhL1J8155NGCwd9FHCsFoQhjp0G7qxy/u8Dt//B7+4Avv47OvvSX72jHjgL5zbi7v7FR0BAYitGGcW6PpKfCBcKA+BLLuVYNiENrDe430tkfXk+9OH35mTBdXDd8XH4QACSV07kRAWpBT9mVRBgiZIGu+tT+xNeBYEqgwKMm9EEiQzniFEMVaYdIDTEUA0MwMwQEyMaFSdQVBwtZWLMxYqFcQoolhrZr+NwmULKoi5NOCdAqQUIuoBpbVsVbgFBIB2ezfUj9rlsAY0IeOVIWyAlnXjQ9L99pafrnvuVZwJdTEQG1+ITSaFNAP7tesqwfgg3zywT7U/QQIroYBne1fVAOS7dvCGFPOAQQWDVrV3kOTHXUwENSBqhCw8naVQQSClQUECjeTwTr4EvRGOoUBUwcUCmTwT1hUBZDXhNOipoNskTvVbKAQIIAwAoG2w734AlK5kzgqV04s9vbxzi8qlOjh4tKxYp8B9BE9XxEKZhNKYLR8XA8AsRwBRlSwff8wJrGq1dwBwRYQ7Hc+evEwKAAeERh8sKcUXAEFs0Zg3//5J3f4nZ8qFLwefBaYfUC3NjtVEbDfOEZgiIBgwJGU/kbTwiEc6EMl4XT1aimDze7v9H/Fg/sqZdPSJ5LiTnEVIT7UVZUDJCAVn+1RygIJUEAgWaqYqszoiRhJB/XMGRnSOVPKoKIdHhJS0s45mXxLDQYMDmBwIJ+vlZESo67yu211Qg8ITcZlZPBBB62QAMCz/4X0zx0ocOy8C7CuOliv4qPAEgKYdkABzB7wx30UhiWSMeAPDSsRoiNiAjbOiNeUCABAUxeuVgZs6WBUBSzq45FZgGgbIlqTS01BIKY7VpWgQhMalSb5R0fBGJRotspgBALzIbgWCqzNLJkUDoCTguhCcCjNJFa0TAKiKcnKniUAQbb2VosrAw4DHpmwQcF1ToHarzC2E4rZAL836FOeb5+UTbTAqESOpoMHQsE1QHDFXQGwVQdmSvU4QR3HpE4N0P7IlnSP75/9haxu+CBAwTW9/qMBg3jRXcViDgWemaxrAH31/tknz/A7P30P3/r8+/jMa082g3OEA2ALCMC+iqA/6YWwBQRXDw7hIAOkszxOwRFoBITwi0bVeHi4zv4C+mY+i8gof3n464/awcHbg0xmXki5QULNLuvmAAi5ElYAq1LzWkVHoQrQIr4LK1ecIeYJcfiSznetVeGiolbqwDIWC1NrvgoGCEtqgGCrGJaScB5iIeQqZono/JUNGACklJFyljErzuImoIBaZZ2kde4jKJip4QpQoNMNHhIMaBM2+AHLFbu4BDmoEcGJ8KJ54BoQsNcDCFjWwmhm2ICA+RBAQK8yUEvIX1AlIFHLZbAfg2ALD3Mg8NsTuu+oEiyUFA6uUwmytrtEZsqiKRB42zEY8CyV8Zm98LxKpQ1mgNq2h7LNH3PUU14oM5ODKweXgQDA1VBw5KC+V2ZPxQwyDoEgvmHopEVKUudrqIpqcPD0haxueP/LH+Jzr18PBcAjAgMDgq7wgVKAUVHoK+dPP7nD7/30Pfyrz7+Pz752nfwyK7NGMXsE7HcjIFg11kEaMjgweOzUA8o9IFBux+PWlNxZKLaUB0FCbUQfGraYKwhbGOC+cxklyZkTEoaZQK2wzIBmVoDN8PJJTQxqZqgyq0tEWHXwXivLGm5VD6TTJlURWNIpM0RxIDggAPs236qNqkI6+lQVEJI5KvaxEPJaO0igKkqCLRsz+2+DBmBJi8Tkxz4oUF2lntVHIYICqYIQQeHIT4EsjLAfv/qgz+H13pLJq00JwHS1QBcR8VX8AxQExD+AehBQmDSFYA8EZPIgMQf6wX+bu4AHGCiYxyAYgSC2q1jch4DabYhAcORLYEpA9G0R5UCUhQSAQirx2H5g7ceUqGscBa3OTbpnWbZsM/UOEnZlfr/y3XazKROVoU9XPByfhjgFg0oAzMaEhwPBA9D4qhKBII4KFeofhTYmMMOVgo+eP8U3fvI1/OGXPsRbr2/HL8IxJDwaMAB24EBLBwVe2/LHcplb+fknd/jHD4SCa8xpVo4UhaAUdYpEk4p6OAhngBgJyxQEIM7kc+vM/QcfAANhIHcVwvwV3Mch/J7CSvuNAQocFrozkoHMHGzsf0oAacTFmgDKoFSkk+cF5qJN+QRSQFiJkLn5H2QQigJCooQTizPYksQ0sCab3SUZ1EEbudfzO4Ti7wdAmMVCMEiQTlt+m7TjXxIUDNiz081AIaUFKS/HioLN+mqRAX8xUKi9nwJXNz/E5EMODDH3gNVtAAGzd8r17wXonZQYRyA+PGElQcztQAESumRQy0nAIPoHUNKkVQ0EOKSePlQEdkCgpTtGF6qYax+50GBgFoPgCAhmMCCOhb3JICoEo3NhNiBQBWpJEu44EbDQARC4o2qZPJvz51Iqvw2ybnSj1H2nMwsiQIHZ/W0f7Azs15Sx8x1/b3MefaTbbqb+ClDwKjAQ507XFJ68diOyDhqsSsHdJ7J67rvvyJL9zW9f8XuPBgz2oKADAQTl4AIU/EuFgl7SG37TBq9h+6UmXYfXD4EDAJulKXF/1Q9gJgY5XgMCzwzwgFY5BYvOGYn1uDI7YE5tBjKWScdDzG3WyaNBB22gyDoYUBZI4EUc8NKqs8VTBwgnHRBWUkfBxA4Iq0YujIBgCkKTf2UW7x08tQ4ewKaTN1NPVUBIkjnKIcG8x8+6rMx8DIgIS7aETVtQoAuKwh4osMeiLw4JqKoG2CDgHufcTBAKBuQXWFpdVouNEFrxngmhcx7beSomkQcdGkwdsv0oNUAI6aMFAiR5lX2HXRmQrJYPBQFTBKyvGEHAVAEOPgbMTRmIMDDeoggFEQSABgPWVqIvyhEQmB+BAUGy1wYEZQACWwFTC4CYo6BsnkevzqEuBcQIZrrs4WAortAENQHYSv3D96/OYhu/P5ynvumu4QgIgFeHgof0/U1QoQ6wdcGUHq+PFBOLTHaDYzQBHz9/it/+4/fwnXc+wOdef8usCptyCQ4eDRgAYTDd+fzSWBih4M2oFFB7gO03YtpR4EEi2FX7Rtlo9GXoAMGOSf135G8k8CED+wMUjg6dgqwoA7zSdy3daghOi3Q2MHlx9qMBCmxAir/RnW/SREM6QOQsnRolMIvPAdXSpOIICPkkAYq4AUKuQKlNQciUPLOdJK0hdxiLnX83C0yqDmDe8UscBJMDZXBH3Xb+CQRaqwemEUVB1QSXjQUUQNg1PURHxmZ6AMyswG5LbmDgS9GYgVPYDhyqOml0vppEYLxU3Ixgx+g69jBIEPkAwTrrh/mdKBggbOdOJcjBr2jrMHiU1niWvCgqAq8KAg+BAaI+HoEFKepMBtgBArDGzFAgqGf3KYCZmUYViUOsip3nEBpPgovBwWanfv8JFBis2T5bmX9yrAeUsavnyfZrHQyPho2HekbE/Ssiy/TXKf1G+/EjNdyKrZ779hc/cJ+CsVwyIVh5RGCwg0ZaLsk9MygYE4gAvUrw0Ob7AOWo+47Bgf1+dEwUR5SmIMSyNTe8WomAQQYYpMBggU8oNRs1E4DS4CDpIM6Qh/7Ie91s2DNZOmVhjJTAJfk6c+2hGiCkDJiZoVbpGFVBWHLGyqSZGTVbIsmKhFLFSbByRslqJ1Yzw5HdOEICADEpBBXB/RKqScYKChPZuIMFHQzyWW3LaYxi15sexih2LWiNmFZytjarqgLXUGc60qHBACPAQawfDG35FR1YZXY5m86EJytAwe569NSWGEL9A0z25yr33yGgbh0GoyJgKwi4tsRFBgczCJiZBqIfSgrXZw6EwLy+l9TAcMnJFSR3WtW+yHwGYiyCjckgKgS7QND8T+rgS7L3/AltHgyHcfbf+XJQU29ine04AXZt5JdQNr4cg0IAXK8SXFOu6Xo7SIgsZZ9rH59YVIMjOPj5izv8rq6ee1ODIx2v6RjvdF8eERi08tDx8E8HnwIDAqBBwUwhGGHhqHS2WNt25fnFMX9UD4DQqFx+0u0XfuBa21jXDXTqiQw6ZKsiKLkTHCm1iICwtg6AJec5uZOSUnnsmKxTGuTrUTLhlDWssIavNY/zPUBI4rR20jXoJUnehZWAAgEFGzjWypJyWX82QsI4exydy6KU06+O4d550bVM7K5TT6u9r1NZeVQUErEPHhEUCM30QKoqwEz1iDOJBnsA+rj3/65LVAmAbhCxbs2kfrAtsGirBhi4CgRMEZitHhghIILensQLtPoEtn4DsjJgCwMeCCs6p9I8WmF2x9RhlcEalrkeAAHU8bRaoq69Zy6adXwb1K+52exbPVEDOGoOnq7kdKCQwIMD4LgS6JX6qIMyHm9k2leFgtkIcGlciBO+eP4paf/O3OBAfQlmbS4uqX9zRynYO8e98ijBYFYS5rnL/+yTZ/i9n34d3/r8B+JoeAAEEQbSAAqXS7/zCAqXGuEIB37UARKA/Rzt8bfHYx8VG8Lt99wR0tWUAAhEIlsDMhOtal2wg5nM4ZEZdQBKqY+zbx7SQDMxeDCdFPYh+Twl8TewmPYgtysTZSCdxQchZclkmBcJMJSyqAcM1KAinFILOFN4Agmb5WiSPCklBQSoglDlRvlg4sDQ7nplHmaWCgkbJSF6p3ODgrWZHsYVDwkNFNyZMcDCLI0ukFub94q/0Ej+FovLvG7F2olFYoCm2xwEMK83g7sGBxKOOEYf3CoD19WbvY+QZ0Bw5DcwwoCZi2JAIlMHMskTZD4lHkGzmg/B4GCofiSWe8OBYPasafGrM2dP226hqQ3W/G/CdPmnqQRq9olAYDAQ6xO4bmC2M7KzvmaSNpZZkKKH/La/H377mlOx78RTSJC5j6vDaO3dnQ21GBR86/M9FDzUxDGWRwYGbfg0Bw5z8ACsoshJ7KMX7aZ+Vm/qCASjMmCV7eHZjyp/NqsH3OFEXvPmMHuNkrH9uYdG3ppJZ9ccy00oYJ8Qj5AgnVgG59TUA2AKB64ksKxTFwEhtRZdxnyI8FHVQaBq7HxmoZe8yHI6A4SsHV/KYF5kFmUe7VWAgVOSZY4WyS6RRqsTODC/g5oaJGR1WJz5I9hAMwLCWvSBHsIvm6JwHpIP5URAMVNBUxOWxDrgCAikde61Tqm62cHT7CokENhN9AmtXTv0jq3sSqnraAY9ls1vXPlbcVmxDSJtQGkDDUPqo4A35oFZoqIYdVBeK8iFOiqDBJeTBsIiTGFuyVsgsGWGM7+B6EhoxxzDFmd7REZ1YLLCYIyW2QGBPV+2EgVo1GoXsqk0XRpMzc+HMSgDF2JCIJh6RhiIsAdc1x+199ZeeDMoPhQVjlrxJWVg1+/s0jhBrW9ORL6cnAmoE0AAxHxg49fn3ngyVbY3gO8/eFweGRhEcTpsUzowEgMIHz9/2tlkxsE/zqCiYuCKQjSDDr/H4YPmXdo+6BMptTczSOiON7m2o3Kt/axe+gFqBzBpS7J7NUiwfPCsHSOlRcNzJjDO+3AAkyX158vakNl8E6YXFyFBAcGcD4nAqgq4H0JdEdezE53dtMBpERXBAiWljFPKKBBTQ9EBYFU14cgf4WVlrNQDwloYyBITwcIvx8uyAccGo1IZ56JwoEWcynT5I6mKYDPRQrtqQvNPEFCQNLyyMoP0mL54y9v5vJVtZF2evryyDKa1odHvzXj8HLj9KZXVhMDgClUCMPUTGFWBPtAQN2fE2pt9rI6sThKRhrsOddQpA2jBhgYguL3gNzALWeymghICEtV1gAEDgon/QEjAtQsDe8WXjRoQbENH70JBPk2dQOXvhSi0oZ67MvRHADzzIIAul4BfwuWrnJa98fwiDMRtcaAejsNxo15DVhgAdBLJvAGEjwefgtlkdqaeXMsHjw4MukJi7o1DSwJw98kdflu9N6P8MgOCKJnrSz9O+BkA7T7H9zkoP06EYWeDhKgiyPtWa9eoCfH47Rj9sTYgYOeA/vNNCeNGZZ3tEXeQkCu71OUyJ+U2+FMBVWoZFfWEIxxgkc+4rLI+3UwH8TRmHZmG6t2oCLW4H4J1aGlY6y7xEMQHgoLpgfOCRaVPg4RTFZNDjInf+SMwcKqMs/oivFRAyMRIBVhhAEBArljNBV5NGAC6v3GGmhN1sGCgIDEYDBJYB6WKhRLORf0KLC5C52NAHmzInJSOpE9rS/FZskBGJcLmFY6IsZ1n68RD7/kq58NqGjAfg1ksgT0YWPVeRzCLf/28Ug8DJuknH/gNzNpywjEQ0SnvryrwHBp7MKCmAocBMwFMVhdwWcETc8FVQGCxIgDEoFH2DGFZBOtmUGDJpYaVIdEh1ICgjDDAV/RF+qE3DY+loKdLcDNt7FPHrLZHoHANDMj74VgTGKDtR937eJ29L0rrpx0IFBDuPnl2OH7FEiezDymPEgxG1SCaECx18nfe+QCfff3JZoCfAQGh/Wv72qjbHjCP2KZ72HnY3ykkTFSEmakhniOwfXCO1IEOCAIM8M7+4/GA1uCImoesQUJKMhtLSbLrserSnAQOkE1+JFABOEM6GSriIwCS97UAC2TwXs9gqvajAA9euTuA4NdrgFDWXkUIHdwWEpL6IyRQCZCQEhad+TQlQR0Rgz9Cqayzw4y1sAdOekmSM+G8Nik71QRLFb1K5CQzInYDkncORUAszlwrE0rVIDaqJBgkLNp2j1Y8AMEktlNixGMObcfyJbTVF+yfXSrR0VL+6nY9N6CHgmvPkbmd28xpsPkObJWBEcQiE8QZn8/qbRXA6COgphwJTzwAwUNCFY9xB2aBiK6EAQBXAwGAPoBUiDB5FEmyixuRF8yiSPrKEAS1oLa+aOyH9vogIPSPKhWQ9kVVO1yvM+/T+nZVh+O1PbblWnVgDwbCdK992ZYEAxhXeWhEGOn2QqdXiXD3F238siWJ8Vr8N2nrA/cQPniUYADITfC+lmXg/ejFM3xDI0J97vW32r4XYYARPJ/kO+W8+U1rpF4j5N3cBlZSaKAAupDH9vVLKsKsC74EBTMbbTyP9v3x2toORHK+RAQmjQioFjBWcGCDBlKbqZoW5McIhKSZFEkuuOr7SrKE7nQDWlfI0kT1nCYCJe3sUmpR+sYSzQxAMzXsQYKF4Z06Laq5oZx7SMgnGfgZOjj3mfVWQgcILytjoYpzkSiM96UipYqkZoBVB/5zgT/lpXIX7MTNUgEQChiW6TEnyKoEzQCZEjS4kmwHGijIcfa7ik2GyeCMZ9t7X4n5oDqWOMiaSWR2XgY1wMPPNy4h7OFgDgRANOfMzzcncig45RaRUMwFYja4yalLaHTbqQaqJNCgDqSdmAMHPgMA+6oCrvUYBq4yFQwKgQHBkGNiVyU4SjYVVAJz3rVloxEIoq+Ilb0+iFQOSAh9JLW2Ksv7VD2A9pW8TXoXy2ZGv+fDgOuAYAMD4/jRrfLR+jJA4KKTVNGvkwLCR8/v8I2fvIv3v/Qh3nyjpWZuClsPP+N4dvGiQ3m0YGDF4ODjv5CEEn/4pT5MZKzkHggUBpjnS7ZG2zfREApYQaGDhOsBAbisIsRjpXCMsYxLgMaHEf6+fT6W+JBKek+4j0EHCKTKiJoXNurBkkDl3JkWpuoBV+kxITESYkx/u6cGCdPZUKyrUsHVanrdzIw82MraQCENsyPS2RHlRSBB0++e8gmnvKBAZuulSvAkybDYAOFUxMTwMiec14IlJdxXicC4MnAPDZZEMmCdi6RrLlUe/FHW3tYxw3pDcYhjTfKEFoFRes8wOG+POQeCNqAC21n26Jw35pOIxQb3ZhLpB924PfpSAH8ToJmf//z82uveZNADQQLhJiQtujHzwZIPgcDCFC+mDlTJiNn5DLg/wCTfxbis98hEMC4xjTPTmToAdAoBRli2IFIzlcC25dNUJTCzgb2fAQHzsQlB7Ovwwb0FetPhPw6A7XGY13O8LfH1JXUgfOEiEIzB4IC+TjZqsIwpVM5aV8UnT3cvPsJ7P/waPvyKjF/eHRDtKyu0f/2XyiMDA9q8Ysjqg69rlqkxdvSuOmCVOUR/M29RGsCAAwBIwB0FBYvqZdV3JSBIR962zVQEt6XFO0Bb0rYySnb+U8NDGc9hU/xhnAOC3D8xLwA76kG+AVJbX01UwCk6VWl8fwuQdMoAFyRmcFl67+o9FWEMpBQjKxZ45DarE7kogQI2WAhx+D0rX1FIyIsoCfkETguWfELOJ13VICaE8xEgVMayFrwkxsKEhXSFA4nkvaTmKFcq45SvG3C7+nbtn7z+U5ofZzb7n0ntcVutFUUH3hEK6gRkUvCPAKIsD6SUOol+hAU3Nwwqw6z0UQfnECA5L3p/gfHzdo5iMmgOhb1j4W2+DAQnBYesviBNHVhbeGL3IWixBqyt8yyfBdC18bjUkMabM7Rxed2rA71joUCxrziA7bcDBDu+BDOVoO4AwVX9zrBPbPbGBzER/eYww2Yf1P82gWBQC8axZFNqgXZSIKp4+uJjvPuT38KHX34fb7/xlp8DqyIyu1+jw+Ogf8y/FMojA4O+EIC753d474ctH7VJS3GfDRB43PAYEQ7o5uTcgwFpwiLoWn75IQECssMbIFis8Unj3QAC9lUEvxI9P8MP86k4cgSLakEs48M5HsGsJVV/fgQEZlEHonqAK3wPUEml+gAIbNki1TFBQYuWpZNS4wyqUxEq/CFsMyqTWeerHTiFhD3newcF5EVgwCChCBTQehYVIZ9AaVHfhRPykrEcAMKyyiqGU2Wc14I1cacgiJMcbWRwYF+qH2X6WZlaXvy4c7v7CAPnwuG9nW8PBGXS9rIP6O3vQvI3UUVOOis/hAQ1lxBpyOnLU6K2jz4nWWzR+eD526z8SP1Kg1EhkNfpGAh0iSGVVdSBcm4wUM49DJSmHEwTWx1ecAabjw0Aiy2w8R0IprSoDnBqvjduMjA/nIP8E3u+BFElqHUHCAZFMxbrP1Xw2vgGiLmYp174fgzqV5eNQHAJBoA5ELS/YdAfFYJxYjkbT+JPWih5VDz95Of46k+/ie998Tt4+1c/I/0WyWovW0/UwVF/yuMlXF0eERj0agFDoOBrP3wXHyoUxL1GwvNkMgYEtQKoG8rzEh9OlX1kuq4pjYlgfvquIADgKPpr5c5K7OQvQ0JLwjHCAThEz9LQmurxgrQDB3tQMG4jRpe3QXKCA6jcqQdsdyKhqQfQAcHWN9v9dwWB0dLBLvDkP0kdrkhAgQZ7q5kaekCoKiD05O5wMCT7cZUumBnIPLOX0wAJJ6BkINn2EyidQMsJKZ+wBEBYmXRNvaxiWBmdgnAqFevCWEuSJXeTNfbSHmwQnlXe/kx6LOPMOpYRCtZSXB04l9oBQdUVAeb0Z98fS2cmSIRcCTUREgMLia8EkJBqhQTVy4hOFil4IW6CC+1ccz9zpIv3bPRzcAfBnWWHM4Wge58gIYrXs8YdODsQUFWlQFNiGwzwKsGJOCwx7EB2kpyKYoRCoA32MyCYmQomOSfG9NTyOjezwgAEDgL6l317rxLUulUIjuavPtjNRj1gE4tjDGV/BAWdPd7vaTg2bTfvjSHyclCagfkEc29cAVwxePqLP8Nv/vR38P0vfAtv/8qvwZOiUasvCmPIzu2ZXRZmOkIsjwgM+nL3/A7vKhS83ZkPhgoZFYIIBBwyAQ7FTQlEsDC/wAAIANrQ29QDjkaCA/XAyiVIiFGyzCnHts/goBw8hsm+N+wyLp2D7kLhHBL0oVf1AIi+B001ILWbbwABAJIAQAOCRRUD7iFBwSFCQuLamxrWMyixdrRyqxmQztezCgIoZaquCI0ToKsXxHSgnapBwelG5NfTjZxrWYF8BqoAAi+axGnJWCvUVNAAwRSETzHwshTZvmhchGUble9aONgrcRVBSnocMzVwX8ezwb3z3J9AgZsULnkgypKW1qB10C+VOwCI59Mfop/R2zY7/P5vTzbtQoFEIZwFJbrNEh3y1YFgBc730lbP99JGy+pw6+1zp20C1jbltYhp6gcToWA5SX90kJ56HwYIbQXCsA2YAoGbDYJKwAxdSjpXCeLVzfqZdsHT6huqdw4F9r0ZKPgHdowJDMTXHRDYzTcT8575mfWqO1gYTAx2/Frw9Bd/it/8k3+CH/z6v8STX/k1nSTJRFOcpgVAWnIqhFVxlzWCp8/vDj9/NGDgszzIRTcoeAuYwcBQgZ6edgSCHTBANQCAzvxrDwga0YeA5n+gjUO2pdZYgENAiOP05SWPqkxwH9ApwkFmQiEZuBNzl2vBr5TaoM9oD+k4WESa9yiINmuc+B5UjX8QAYFYH14SE0MiAEmAoEECKyQEIDB/BIOEugJpcVMDq58BEXmnS1jBWOQeqanBO17zAB+uD2cAyQidwMupQcL9S5l1nW5BpxtxWiyiHEgaaAWEvOC03AggcACEMLAupMGSKmNdyCFBAvVQtz7fmxMaJFiZDcpxk8m8si916aRtdUTlvr4ryyheWRqR+CvoPnpCUm8CFClRdx62b/QXsO3R9yCn5nNgpoWcWorh6Pw3W70wJi0aSxoBA9apyvsY72GeuMoCEKFFM+z2USA436vJ4Nz8CdSngNcz+HwPnF+Cg+mATT1QX4JDeSNlVwmh0IoArR28jn4ygwoQQxXv5zUIMMBQBUD6mREIxPHvgi/BBAqsRCCgzQsb3OEDvg30+QAK7L3V9QgEo5lgfN2/344nALZKASZQMALBBA6evvgYv/Fv/il+8B/+C7z9K39PxxodW4gFELQeOwVhcHbfuxIbH9/AG9grjwYMrGygYPACHW0/noYWKmNbxY5JRfR7T//yz/rtANzyReJwqN1kDwdBHRAJPMCBqg7yuTayA+ozUHBICBMwOxuHAezDQbXcBgxXEQgCBOzfmwPCXnEbYFQSmFFVPSBGBwhm9sj+uocEIqiawCql1WB2qB0kcF3g3tyUe0CwHAlADwelwvKhe/Q4YNspFwgcANKJR0jIiyytPL8En26BvCDd3ILLuQcEjcYYAaFU8T0olXGqstSxMFBrRlEOktC+CRbVjxkOCUCz58eZ5TwQkb7nqDioqQICCQu19f6J4Ksj5LU4lZ5X8QdIpUr9pox1rX4u7icT2orb7nUwBYBFbfLJB36x09sKgJvwOjr/GQzEcMMApvEZZoGTgDZIxABL2dtiiw45y12wUEtk1C05vAAE9f6lKgQKBGd9H2HAQxVPoMAIqELas5/7AAUbc5ctJ5zEHlAY6PIZ6F+omYCxhQHGfoAiUwkOHQx3ivUvIxC0wd3qT+tJIeBIKRjf78HAPghYCX3+HhSEz6+CAubuOADwG//mf4kf/Af/HG//yt+XdmCxGlJugJAgfm3EUo8zXza/kGZAiePj7/+Xv7+5QiuPBgwYwLONUmAfzmUer7ghYEhryfZYSHn6l3+O3/i3/4kes8FAm/mn1nKnJ9nvQ6YwRDiwMgS9GB4Xh4MZJLRBuY+Y1cXcFjIR+Vhp23wB7IZaoI0REK4prP+xCAWwlQvQ4xEMZggg9m3dTADyQGf9IKVFtmf0asJkrTfbezo3QCirdKYpyft4uzRmghjSdW040HfQCgcoK4gSOJvpYPEsj3S+B51uJGvdCAhpAZabBghhqaMFTBqzPNbKqDn1dlntZC0UsJyawkE8XQeGdvoWIVC2W5jnBguWV8CcH5ckfg6nnHAuFfdr9denTDgXxlIZNZP7Hcht2zYWG8CbwyF1DoemCtwsySMEWr6A0fnPIMDCB8eB3gZ5ICoB4dnRR8ucD1toaGkQ1vYsAl3MUjnNamhLDg0I1nuYc+EGCNRs4EBgMTosONHY5gAHAsk7lhog5AzKJwcAWgQGcLrtgUDjChzmMZioAuBh8EcPBnswgG5fOdW9rmO3txyAwOpyVAkIzfRzDRAcOxDGMjnj0R8gjitx28xvYPzeCAXhOw4F5uCumWtNpSbS4HTEALJMikj60d6XDWE8Yjx9/szHx7fUEX+vPBowiI6G4lPQbv7MQzSaDppUXdABQaisp3/55/iN/8v/Cj/4B/8Z/sEffb0d7xIM7BXzsI9wALRj7TWuAAwGC1NIuBIQRElApx4wmnlBvqevbSZ47SXaOYWBiXTDCAmFjfz9wvRhZ4eDHhSySs5BTbA14JZhrhZf1eCAkBL4fC/0vabOYYdXQKM1idhjszdb2VAKKGe9F2amSM1xzABhPXeAICaGVTpgM3foSgZb6sgpY1myz9zj+u+ZzZZZBmOAph1wBDz7Y2aBmFdgNdtw1bwCC6bOj2tlrDnh0yd5fS5VczrwdPniXpktT4zmghEGZs5/Uea3Qdpm+A4IKWhuYZAJb32gsc9mg43nK7C/CgvURScMKwwCEPB6RjWTwQwIFAo8QNGkncmAUJvzq6lfy9Kg4HTjKgGdboDTTQ8EedmNTGiqQNGRP4LARhHQhjRGTb0UJG3aGna6zAfVkaoEljn0EhDsw8BwhtcM7HaMOLa8SumgQI779r/370+OZzMsNScAoh7oBRIy3Nnd1AMYIFQ8ffER3g1xEC6VRwMGXwtLEqflFaDAKv1nf/lf4zf+r/9r/OAf/GdCcnJAHPDug4rDAdA1uvl1BKfHeIzgo2BnRlcAAumD7eFE0XwFeAIJcr7hdK68xthZEAWpm7l78AvasqPWAQArtF/Uz5u8azPPRaIrZl3qaGln07kBQj2DQEjLCfX+pRxHQcFOggE1I63wdZeQzjr+BYpml4MAnnmRj4AQTAwREFhNCxYLwfIzpJCfoYI0Cx0dztyANnsDmplg7LxrNoigjfpg5orer6FlIozJh9YS8w60WAuz0MJjmcUnmMUIsO19xshmy28ZI9HN8mezSt1F21jbEGePs4FFuUvTGwPTvAUWg2APCEaTQQQCc5Lt2hX8vcOBqQR5aVCwCAzQ6cZVgnRzC6YkQKCrZAQITpuERgYDY7yBmMLa2pLBqLSlOQRg5/1YZs6DsSeLpgLb/5oVB5eAoIcBu5i5AnBtoVeFgWnpf3s8tvUzPhnVhHFUdcKBsoEDm0Q+fX6Hd3/0dV+dx5tf25ZHAwbv70DBvvmAu+27UPCLP8dv/N/+N/jB//x/h//pv/fvhxs6gQKisP5Xh7Q4gFN4T7NHZHLIvcbns3gDiuLH3kCCAkKCDSAy0ByZGIwuIiSIm4JM7V+lY+C9faKiEDa0TkKFsSoPfiIBmTMBWR3dGiRk5CxplLkWUFG5P51BJTVAuJXpH+vgbDMx0EvpwGEKzCq/mwMc2My7rqBE+pmABFWLrdDaFRf1QbCMjxEQUhbpOS+b/Aw5ZWT1HD8l6frsoZZZnKo9w33tgGGyfeYgVlIS3wURYEJKafaU0mJ6SIcpi49WS4xOguNywJg6mmiemhgKhGZGIJvZTwYMa0NxFnopbGwbRLQfiMtox9DEISBRBwRlBZv5oKzgcgavAgZuMrC4BKV4ewLgPhGUrU2q42BYHktqKqCbW18Vg9ONticFAoUD9tcNNGUFb1tWWCbtYdt2No/rYaGd1/Hej++9njYTgx7mLoFcB3yxPoEGA7NZ/07ha/pqG7St37djEkFzzAMwhbJObmKbwW1MExR81yIcjH9hAe8aHNw9fybBkVRJvxZ9Hg0YzIIXTUcp9wfYQsMhFPzK39s2EBvoifqB3hqH/aR9vgMF8bj7IDDf3u2vx4k5G2SdK2CDSqciWExxVQYkz4G+13sXlQRAQIExgQU0R0IA0w4llu5q9lpr3O4dvMZrSAILKxFSlTXtERIkqIypCKsAQjrJUsKyiARMGamuqKQ5G2JAo3IGr7JO2Drz1rZKgAOGIE3xZWAkH4BrAadVbb8ngZDTjQNC5xxWRd4lkMwEgnMYmf1X6zO2N47tKrUh0CEs3MYIEuwDeXgd/jIDhTPqIiCxqj+DmRUKGizIa/a6jkmWYhmTJJlZwCDA6s3BIJmcP0BA+GuDQ3w9Gxzgf7cDBMUoghNVMa6AsVwFAger+644BNiKgwgEBhFBJRghE4BCpqoEeZG6NhhQ04EpBGIyUJUg6eCvOTx6IFhEHQhmoxh0aBZfQOoQ+89lLKHD3Rs+RxUg7juC2h4I2L7XqgObuh79Aa6Z7dsEa5y9h2dv0/8GOGDUEN3Q7JPx2LV9Fo6zOe4eHDABqCCm5nDIrT94+uIjfPUn38CHX3q/W7J/TbU+GjCoLA1jAwdWOFTSzPljKD/7y/96DgXxb4QCCkt7VCnowiQPUHAVhep5t9cXqlTNDORZCVWc19+PkJAmKsIICQAugwKk0XawADmGgYIJNMYj0f5th4r271gKuItQZ45iFi4iBVDICgmW3GipMhgtaUE2QFhTGHgXcLnvOmCc7+W+lUXUAyJwSiIdqz8B5V5BGOvL1QNABgMAKEVTSRc3N3i2Ol3ZsM3PoF2gzRxDOzJlioAeRP3zedtzkFAFolqdVAtU0+zLfRhb8hUR7qGu9bZy7VZMSL1tS3QINM//hZL7AxgE2OemBKTUbPxS1xb/wgYSa0y1DfSj9zfQP/8AcLSuvLP7R/Ug5C1Q8wD0vYNBXGmwnlubiOdl9yIqBFpH7k9wpBIsN8ByQs0nIN+4SsBpAS+3QFoE2FQdWLU+Td0pWrf2PHq9BYdWqcf++ZP6k/dZO1xpg/B5ahvIA7OG703NO7bfDgjY/g+FgQ0IXGsuIAqz/v2+ejOpm8CBDOosg3YCmFMX3dDv0AwOrAOd/jiHDlbvPkk7/9mLO3z1j7+J773zh3gSHPHbJCG8mZRHAwa/zOI+BVMoCPhra30n5oOZSnA1DFiZNebQeGaxtj0cs+ygp9oauEMCgDF7l0MC2szSQMEG/wpdraCwkHV26g2N1C5JzX/BTBKs+8THAWidkTnEAb1H/TkacPR2xiVlRISFCWfShENJlINVVYSSLHHNgnyjakBaQOmsgHACpZfNqzsvoPNLmXWdX8pArh0+peQOYx5QZG81iToyYj0LXAAiI9uAobBBuvyRh7z3HrMerS1ZG9oEM/E2ap3vFgwkIUvfTsnbsSoli6CWwAG5ejAmwgHLoMFMYIVKt9BtWmW4PWgDhg0WRG0NuikD5vBHFBQDQM7M8gmsKvHbD1+7VjzAgL7o3ncxTMACAprWWJSg2sIVW9RNA4RylqBEEQTq0D649s6FQAO/EQjyIlAQYmXgdAOkBZxvGxAExUASeim0qTpgsTLWqtAWVCB77sZVLFZW7Ufa0k557kvtly+3mXyrX2uLR2aBuE88TtxuIADsm33s5I/yE0j97vSbXR2xnQDMUfxSieOEtL+qMwiZJhEAMytwWrSdasdYCxrcSxvp4OAB5emLjyWM8jvfxdtvvHW1+SCW/z8YDOXpX/5552jo0k3onAH0UJAkjt8GCGZy71Fh3spI/tkw69H9x9J9rg90mz0yyKbalNzkQCZRg3zZI4cH2yRoIDi+DbCwpypI8BxsAimNJXZOzPN1+mOJS9PMM32hhMKiHGRVEaQDE0A4JULON0j5BKwvgbQA6R5tGWIW50SFA1oW8Si3pYkGCKHT51l9WTE4qBB/BWZRD3J24nfgIM0DkSwITXA+I00PDXi78sEl1jGGDk4quJk4hrZsYNCgoQW4yaaCJVExdJj0aHejOQJA598wKzbr09PamAE6FcAchMuqSoBJ+6UN/r6tDeSbAR4YZura7kO7cvXHnAFj/QLtdyIMhAyHDgTWyc+gwE09YZZpMBD9CQwIZs6FaQEHhQD5RgB2uRUfggqcDQiqxcVgXS3SfEc4mBbiEtaxxDgPkroYyJVQkkUCGeu2h76Z/wfQD/h7CsG+vwBaPUcTwaS+tyrRvMQr8UXbExjg8EwdFVkNZQM7ANZYKQYD1n9WiG6XcmujlPWMjp+lWXn6ycf4zZ/+Nr73hW/j7dc/98Bvt/IowUAnrkp7upESOo9+Hv4i4ekvfi5LEm0dqezcUb0P9mOksAEIOhjoGljfBNvLHdkovJ9KoZuL5/a7Br0hwRNgg0hps84BYoioG0Rs+SNjCwu9goAOFCozwCL7FkIXSCkxpqGXIxQIJHAHCbGcgRCYxqLQcVvqlhMyMVbNYFgqULKYGJZEWJZPgbI6KK5n8T9YszoVimxb71/KjC1EqSO1H9vMsLNRA20wGAP42wqHlEGlNOgsawNNgwT7LKTHZUAhFKEeQwmfdb9N7RiiRtiSS+1qvS0nfx+BwcA3Gax0wCDfacokXQQD+9tme2GFUNkJOhYGZhsUfLvO6gEd4HXQ5sFf0K8AAQAASURBVFgn4f7L6yEweHzuPNFWqM8IDGP+Aq5bKBjqwUQ7xLrV9+JcOIlJEINlpUXMBmkBLzfiR7CIQsCk4barOYpquG03G2iI7Rp8RC48W3KKBgOEXCHKm3eufaUKCKCZiYIKNPqDGBxGheAqCNB62iw/t9e4oo+M/WO3vfWF4pRN+/tf268TwLbG2yBhBISUwA66dkNSOO1JJzlOUvXv01/8HL/509/F9z//7d4Rn/xJu7o8SjCIJc7APYQkgGYLkunc01/8GX7j3/6n+MF/oGEou0GzVYQTY8wyNgOCYHbYr5C41rrZphg7qgGwbfAzM0P8QfUz8O0q8fs01mVmm8VtgeYSLDDmfgqFqAOECulk3DHNwCIRuEg8Qiux41pLkzjH8L+orMFu2MPZelz7Uj2ufWVGSRJAaCXCwgoIWeIHUFrcQZHKS11SqIBQV/DpRpwT1Y7sf8fZIuADi7yuW0CAzKypiNMicgaX2kOC119oQ3F7V8U7MmfsuBQs3LnSjj1k2XO/B/dtyFtgiM/CYJoAJsAST6nruAfp32b9IwB4Xgtu9v24/r/2974b0P2Gz5+nmKq4K13SoqAsjIm3zIkwPodRGbD6t6rTe285OChLLgOPQ7D0sQkiEIhKcOuOhZwWAYFVgUAHfFMLCof8GwYHzLCw2tPnCYBHkCwMZELW5zAqBBktKJSoPrqs0/1FegdRg/doFtiaBIBDCLC6uAQBm8nVCAfonw0iuIfEHjgAk8neXv/evk9kz5qY3XYBgQuY49ii44GZGuw6ZuMSIAmX/vXvKBQ0peDB5mstjxgMQuWSa7k6K5fBUuTViqe/+PMWm/pXfy18b68ThDoapjarCjBgDWX8u3N2+p5aYwCa9BsdpLzM7WdHJgg75uh/cAQL8p0ABOGhsPOzgSIT9aBQRSmoVei7rZm2n2nXVFk7jWQhk+PpyxuzkSJ81QLpxOQ57ltQqqgCJWHN7IBQ1MRQWPY7ASgkKxhOp7aCgcoaAKGA8llk3XUF1ntJ0GT5F+xvnEnqLDLG4Y2DR5ciNsRGAADOB6aJSbk4E4gOUiMUAK4k8JiJz+LsB6UhQgPF5yL4OpAdf3OifXvtZv4Tm77P/Lk2J79ZGmKgKQoIg3f3W7+kMnM4hdSnZP8MAz/Q6l+XH1LIcjjLayBOhQuQFtQkywxHIEBacGY5lZU1yNQOEJxLi0cRfQ2OniHLg5FJEp/FpBOtS+wdRg0KLELkkbNorxZNzAHAHABsu5z58B5dXV9cgug7Rhiwzqmr2Dbhi995YD8v/XtWkbpdN3HV/qJNWjf+B3aPaJgwaJ/8s1/8GX7zX/8evq/mA46fb87kuvKIwQAOBD4Dp9ScQJR1f/bJzz2L1QYKgAAEwWQAiKOPA0EOUmqYQQOHrWVcReFwMIWBoexBwcH3iEOnRv2Dw1FZiAOJd/CDbS02PG+kbWmdqWGVxN+AqigI4jwm/gCksxxApH3UBCZZAsckOQTGMl8vL5kCwUCqbV38WoElFayVcOKMtay4zQlLTg4IlUklUtoFBFRd4lgrkM9iWtB167YMjYIzGkX7s9YJ16LOR0OHNRto9jLq1WHfvdnunjMkIIFurFi+B2A6cMnXtVOcpe8NpjUaJPLo/+C/PbPjA93M/5rBvwMvoEn4e/fl4H60ezFsn6gzNIMdQO4dQtfr9yS35yVkPTQ/EqizqfkTWFwLpJMoPJOlhxEIzLnwHIBgLRUvS8XKwHktHqAqxp0YY02kJM+Uov40+VQmMdlJrojULSVtrykAggKB7uPoaGajiU/AVQrA0eB/qc/0L1KIOFsHODj+3mg68zO9sr+3sYcMErhImzBAAEQdYt6aGOL16bk2KPiWQEHol/sJ68PKowGDWWPuig24tr4UGU8/+Qi/+Se/h+9//lt48qu/1tfpIJGydYZ20/MJYwNpgWOk7LVTP1WlgnjuEQ4OTQrxeDMouPS94dyoS4Qwj67Yed365wEOrCMkaew5ZSTS7H1EoApUBQJzhFqSLINDlQ7GbHIFAClAePYfNCjwsaIpyACApJJDZQEEgYOEta5tGWOpuM0Sj79WMTHktAWE5bSAltKHu60Sj4BrAZbiS9g8F4PObskc1XSAm8FCd08HabpdsL4f/RjCNp5sOywT/wPbZhAwSt6bwS6qDfE69swa3TX11zId+O3z0UQzSXT1N7n+trJkuCfRBDRCwrCioFNhdP8u9fFMhRmSG7ElN/JwxUszGZAm3BqAoNgqAwbOO0AQ03Wvg+LW7pndFxa4Ds89aUwJSmOYaiCntqzUAlV5+OgUgYCbr4jNlIPC002E9tTPWWf6UDXI65p7OLhQZorwg/p8ZY7KEzNKVBF8bFn0foliwMHkFsvTX/yZjF+mFAxm7aOSmmwzLY8GDID+OmXMHQZZAAYHP3txh6/+9Lfx/c9/G2+/9ibC06EHmzgUBjCooE3DEAe8y17ZrNK5ZB7UM5rBgdJkDzSAR9LaI4/YgK6laJ7MXLsTJ1BcnU7xPulMiHXJjQ0cmhfAACFlNTEwsLLcixI7pQqcsl0v5Filihyrh05+v7dyqLxufxOxzobgUd8KChYSQLitkhyosC1rnAFCRl4y0sKgcpaZfwiHyxb0xkIxqzRuy9vIZsNl1QyQbSYsJ9rPlLHILIZsQEx5VynwmTbQBtkxtO5BeGJKTQ6dLZ+bvXdwALoBs5tNT9QCL+H8OlUkAtDewH9wrZeus3sfrpUhALBxDrRyBSCNzqIOAtPlpyn0JQoDqeU1YA17zPnU2uwBEFjeipeV3YdgNdNCYQ9dbXAwlqSKnseMIGrppHPz17lVILBEVwYFll0yqW9Pl0/CgKAOq0pmviWhXlulXtl3XSrWNqONfrpPmASOA+wFKLC+H5iLBraxIvT9QIAEAiUZjiVEeoIvbTY1JYDU008+xm/+a3E0nEHBnnlbf/Ki+fHRgME+/PRwAEq4+4unEvzhi9/Bk9c+a37y4SvzG8zhBpe6hQF7b2VmU/ZJBYs93W34GOFAdr5GNdhd4jjd+QqYuKYEMHD1wPwx0tIgIRXt/BQQLCgKU0uqVKE+Diwd9AQOWJ13RBGQ9L+oAQQ2yxsUDphQqbqCYL4FCzNWItyqajEDhDPpDEnl0SXfyMRPlQIu5y5MLuuMlrkAS70ICj4DjkvbDBKsTktpzoIXTAfbkM2DuehgNk2pDO/DrHoMwiMb22tbMTE7rnVwe2XHQXAWGfBh15PCfu09JWp5CI4GCh3cIxBsYOCBIMBmkpxlOdQ8BqzLQmNgIlldMAeCtbIqBT0QjKGqge0zYlOlZIM5teWFmTCFAhv4F4OHJPt63gsagMBCScOUgrXNfkfTwav2QZvtl+Rj/bmHqAXABgosxkfs/4F4Wdv+H5D7YxPCERLkN7P0gdEPIUDC0xc/x1d/+rv43he+jSevf64fo0bz7s7IeOkOPRowmBWfDfnyDuDpX/wM7/7kt/C9d/5QUjOPjTHaZzT7mFV6jBa11gsJbYZiXSfx0BhYBjofBmk450uqwSYut0pSU0fEcfAc9+GDz8ZdR3+EBgZUi3eGZImIDBDyCSfKev0EqgzKhFRVQWhLFdS8UZFhnTqDNPMfSsKalM5AriJY8dcsn6+VkRKjrqQmiwROsvJBTA1bQEjmpAhCToyzx0zISDkj5RuQBtvhGswKtW0zWyHl1cPsRs96800AszjYRUgoawAEAifpcEUtqvInMNReNMbNgFq2DXT7vfaesnUj526P3dUQr1D2BvnZuR4VO1c73uwcKcz8u8BCwYyyAQJzEjQYWE5NGdPPKESuBBGqDvy2X5fh0JSCAAO1ygqcAoGCUuV5WKum4R6AQP71ia7W2kxtoz9OLDFfRUtg1SsFt5rt0rJeSopraLAw6lJQ52T9WUssZdDcrSyxma86mmplXVm5sS6p/178bM9fIA6acdsD1YI9KDgcB9zJE64ykPpcRUgARE2VuglmBjXFPH3+c41o+N0Wp2DXp+B46fBReTRgYFUdxm7fLkMv9VmmzHuzsy3aqCwNwZpubAhG32tsFOFHZw+hnBj5SSY0tSCbbSEYOizIkMAB9WaQ2eFJl6Jx3cKBvbfr25Xn+od0E6t7tzR/hM5DnZJ0BikDtAKsznsqjycNSwwS04KxrS09JFmqgJwyXhZRH3JlZFS8LAAykGrCCusICZVpOjuqBl/FfBCAyhUVzUHRMvgZIFgchFIZiRi5wiXWPieDQUI/U+Ih6U5b414UFE6SfCeqCacb976XuAqLfL6egazfX88dIEid29I9gJbkx6SUNxI71wrKNIeDnQ6a13nNe1rqX0Jp8HHl/pPZnh2jVzsSNomJgGMgWE7eNkl9AjoYCH4CrgqkjOqKgAEA9aqAqQQpg0FzGOC2csBW8sziEEQgmCW0Aramg6YQNCCQxFXNfHCbSbNaEk6LrOQhVQKW1EwHBgauElg0ymhqu5S59hpTQRjEfW9KsB7y0nf6Pr29b33VzvejWhwGWOvrIwQcpacei/gZ2DmxrgJpkACoGq0mBjMzpLTgZ8+fSkKkL7+PJ6+HiIbjNbY1H/196z49Vg0eDRhYGe0n9vrp8zt87Yfvepap7gvD/k6GwVxgEd5sydxa2SvfK2CnnRMJ+ZM2igojR0YlcuWg6iBpTqgi0TczyGZlhasGQNMkOv1B/5ptNpD0gbKwcWaMy8v2ChvIGHnrg2fpQZnBqcq5VXO0OuGUlqYe6L2yFZOkMyXKGRkqm1YCUZNOU9JOMUVA2J5qZXZ76qgezACBEnkcBPfCNkigOSSIF/aCvCyaN2JHTTAPZDU7UBb/A4CbE2OEhLw080Ne5F4aJLDBSFAaACDLa4/h4dK8zi6Sbjc7fmEfbCMgbABiMrM/kvQvFXdejIcYZvkRGiIQjCAQzQXyefCbMBCw4x/AAFL2fAUzGMCyAJD9XBWwgd+UxkEV6FIdq5ngCAasncYMl2MqbEtWNaoEQA8Fe0Bgq3eO/AlmpgODBFcJPOV0r5jFpFOzZakXWoafvVb8vrPgVAk4AALbTuE3olpg+8X3dsZsY0JUCrZZS4/GBInnovsaKCgkAOJPknTiaMry00/u8N4Pv4YPv/K9fvwayh4M+G3Z/WZfHg0YjECA8P7u+R3e++G7+OArH+KtN57M4gX6/tGhxBLKdK/1oDFhjGUnmxUZIFQmIh3iucEBM7rUxhYNsC1lvGBSgIXWrWhxue2eRI2Z9SaNZ2pw0O5g57MwQEEXYW4oZrcVv4kACTaTDh0lsdp404qUW6ecWBSTtUoQlVXVg0QJCwNUClZKYntVQMjELRY8TDqlrqMci8RTkA42sSgJERAsUFIufTRFz/o3QoL6PXSe2XSlmuDe9gWUmskBGnrXQjH7SgdbEnm61RgKbfleBwpWf7UKKABdgiGpQ1UVlgAJGruaC4OWBgpcWEYJ369vS7OMinslpR3J3wZ3G/RpeH8lBPh3w5LKa8wEvpTQYCFrvJKlmQV4hIErVAEJSRyyGqqZYAYDMYulBy1iVjBoQBBNBSObxZUFBgPAViGIQGAprm9z1jZ8hUrgZgPzJ+iBoPMpAF/uPwBdEq6KQPAD6aEgDPwOAXMY6L47AQL/zWtMCNDLwfB6gILojDgdGxgeNrpCEsH52FI1cXKSvunuxR2+8ZOv4f0vfYjPvv4kpKffL3sqwd77sTwaMAB6OIhQ8LUfvov3v/wh3nx9m4+6Dl84AoIa7Ud1jBE/OR+CO9glMxcEOABZ42mviag9E35dE5MCkigLfAAI1sC5yk9w+6xvrnG/QUUI78ews2PhtbbvAB4Qh1Sp6AAhL0pCMouglHHKJ1VQdKlhYqQqHVSpQtFEGZw1gMsACGthLClIq8keTP07eUIruJF7AIRUJXlM1s6xhVymLi+DxF9gZMgKCM8KaICwpybU9QIkVF/pQDt+CbbNBvvdIEvWQGO4Xq4OD766IQBDhAUHBWLZN2eBhgESbLA/AgTbR5rJPgxEEIgQcFEFsNcGAMBl58EL/gK+giCoDHMYWA5VAUtzXBwMICJR7fMXxJDFFpiIeQsEsW37/aXulnZqwZLJoxruAYGZDcyh0GDAVhyISsANCKLZwPxlOiAoLWCVtbWdwkX6jC4k8agQBFOlNpYeCGbKgH/3AAj8WFsTgt/hMBGUv+31DAoujQ+sqrGMD2g+BlUmGFwZz17c4bf/+D18550P8JnX3urVUKvrYZSPj599xm33q1SDRwUGY4lQ8NbrT3wAiMXnw1qZkQRjjvqRAu1h5cmxvCmGD+sABwB8mRDpd7P+Bqh9BmBrUrCN3AOCyG1W7Y4wsIhZzClAgrUYpXlmuHpgPgtxffGkdDNGd4A0VQN+LMq8BQRmkDkkmnmBK1Ja5B8kWcsK6dRqkk4ya0dpgLCWijWJc+GaRGotIGTibWc6gEKrt/BwKyAAIu2VJCrCGHJZEjixdqLNJ4GINUTsVk3Ie5BwyYGxFjBY/BJiZzs4K/pysEmAIBqCLUWFgYAGDQoM41JIgwUBhSrQUDTMKwAUbfW1yqKSo3IFCDgEXPAJkO/TdvAHtgCwE6Bp13EQtt++4+CeiaDUHVVAqsB9BkQNALj2ysAMBoADIJiAQNL2F2FA/GJSF5NA2u0WCBI1s0FKGsRRgTYu2UVUvCZAwFwP+gp7edBoJv5LDgQjDMzMCkNwtg10XGFCsPtv3WHUUWvrPjZQELih+14KxxJ/M8MQOR6D8dGLO/zeT7+OP/jC+/jMa088r0UF4CHq48E3960fR7Zlv29/RGAQmShAwZcUCtDPkeN4N/Ms7VPLbgnQHnL/6VAsi+AICJVahNQYWWG8AlW5/XLkpTXWKstZrEN3KDCVwA7WmmALKcoKCRUel5vH0JvkdGKRwajKfkxAF57UT3zy0AMt/v8EEDhVQFcsyPssSxxTAdKKnE++vHGpYlqIgLDozCtTwomjPVZiFHCAhLX0kADQZl13dRMEPApc1afeOtkzpKM9a0cb1YQlJ+S1btQENznUuclBpNmMrCYH5kGODQmGOA78YGC53SQScjUnKAAUlkHOoEEuulcaaEgd3MHCoCw0E0QwOeyUmUlgVwmIg7omGhpn/n6so8FfjzsN7ax+AWyDTviuqAJBNdiBgVKvMxGMWQ3XUndVAWmH2zYaS+w/rI0C+zBA5h8Q/WZoHqhoAwR7ZgMFhLbyYAIEeyrBznLRGHa7xY6ZA8EWBnYgwNqWv+7hYJYHobvbob+fte5+RVSDghEIojItvyV/EsOj1Bdm/PmLZ/gn//o9/MsvvI/P/OoTmRSRXI8p0LbCzYwy8VLT8FsjHDx9/nRyFa08IjBoxXwKTCmIqgD8tbwZgcAI0FSCaDLYVPRAg4DWs836EeDgoLgJYSgj7TU4kDNg9x3QTrZ7AHPrwOPFq8rgATOIXEmYA4KM38QkgwpBOtZiY374zakTmkLIAAiuTCQdeGxGZiqCOi0aIGQFhMKydFCWc0n611rFB6ByRs3HkLA7G+PujLerGlg74gkoJIijYksBnbAkDpLtdZBw6JfA87TDDgusMv+J/TNYvQf7LhdzfmwD/iE0BB8GCj4M7tg4URcAHNqQL6kAF5cKHsz8NwP/ZEbJ4yDjMQaSKwZ9FslX9xcw50G297X6zC/6C9jzvqcIWJnBQAQBU7UuwUCiPr/BGKRovvxw4kdgIGvtcQSCPcfUYeXIHrDFOpkCgcEA0VwJaD/S/Xyf+2D4XjAhREVgLNOw5ZMyQkE3XoRJo5Wfv3iG//hP3sO/+I/ex6/9ylvuuA5Qt6LNAaEdrf3mODkN5enzO7z7w6/hDby+e86PBwx0oLl7/kyUgq98iM+p+QAI4+IECGy7qQRmK5pBwZ/+4hnsALNm0esWfUnd61CdEyiYHRdQOAB05s/dj7ms6yXbB+23WDt9284tqlYMvenrjqv8qid10v1kzXwb78WRMPUdgS1X1AAnW0CwIDMFzBmoeQMIVFZwVvNCytKJMaEm0pmaqAixc96DBOucm2QrCZTM5hv9TWZ+Ca2z5ibd1hZ6uXXQpc3USnKTw9WQUA0SCIgmBwMFq8Oh/hDqz81DARA4QILDwwgO6sfg0GA5IBwSGkhMgUFvWpM5h1lhiJp4GDzIYGAMHBRl/2EVjM36OQ76wOXBJMjHMX6JqQKocAh4CAzElQSiEEzMW7zNXTCWaCYYE4ZlHcijiUtUrMswYEGNYoCiTOJDYCBgJoO/ERBYPzBe0AbmWv2wmXICOLZU9zv1p8ecDvpj2SgVbb9hnrBbPHGW1o2nlEeMAtKX8bjer4cP/uM/eQ//h19/H7/2q29J91tl5ZNlqQUBWUca92EDvJ+X85pf+t3zO7z7w3fx4Zf/C/z+D/7Z7rU9HjBAIyFffTDUQhd8AluVwAaEqttnUPCf/Ml7cqydc4h1oQJY30dR2ydRv/+sCUc/hO1vDt8I2bdouhuDg5IA3kbVMhu2mxGoSdOcSDoEJjnEon4DJjvbRY+dQn9F1wFCtCPXRX0KLHqiwENNGjK2ChxcDwljp92bG1ZdYVUVAGarG9plscdGiMvAzkVnbAMkxBUOS07iQ7EDCQR7Lw87kfgmpKTtCtYuDAZamloewOAiPBg4nFpn70vNQmKoGJAp+jjMshxOy54JYOIAKDaZZuu3wZ9fZQa5a0fezg4t3Ial/XYH5B0YYMzjDEQYiDkLZrk+ZmVcTTBzIHyoMhBhwHIaZB9YiqQCH50Ko0PstUCwee7topr03+JAkKg4MTrkHhAcpbq3upYXm5++bn7f9ttTC8Y+W/2P3bTbDcqsXWLYf29CaeV//+vv4+//6lvNbE1yIrKyTRzXJbKLqQUhYB4g/cCECsy8/uFXPmjBkXbKowGDGLzorTeedAAwliMoYBxDwT//h+/jaz/6/PQcKLwYoSChKQMpOCB2OcoPyqVGfV2jt/DF2c/N9RNVCWRAyc27PQJCLQAneEawWlw9kMNUs1RIB2EmAStdZ7EHCAUu52pQJKJVj5V1pim2XoOEU86wuPKVj5WElYPTYmWsVR6ytrIBSJm0I1dKD+GXZ1JvMqWmameuORrMu7iwmhvCMsglJyyldo6LK/WQQKTKQgCF2F7aewIsPqQNJAE6DSC8pUSIiFCoyo4QUghOcxJYjEqSDQru3GiDQnBy3DY/0zf35P82ELQBgtxs4DZ/SIro6eBg9mj0M7+uk+f2zHf9AIb3JvUbJBgM6H6jA6HlKohmgpjRsA5taNOONo6DDQjE/p+CEqB+LcCuE+FlZQA9DMziEOhE4dCp8BIQmFpwoc4x1Pm4zcw7G9+DSV1fU3a0hKu+n2ByvaoGBB2wZVBOzM08wGF/3ocDK3//V95qZugAB4nFR2tc8m4+YR0caLHjmHld4vi81SaHO+XRgMG7P3rPgxeNl2wP+6XC6PuzGRT8/V99a/O9cXa+BwWJGhTYkrZtJ38MCfEqNjQb3u9Vux/b+mgSXCDKmvyGgwlAQIBqdYlVOgwSeTkntX9Lp2zqgTgq1r7xXQIEAFxap4Fl6UIroyY5R7crzyEhKgmVSePNN0hYWTryhZLP6Faum6WPFnq5VnF89LilIIwR5SxrXUrS6SemDhJWFmdEz/RIjIW58xAnDaaUEZQEMhhooEDUbIoWGMXqMVSrQ6iPxd1nsiwt6eTaICJZIzJzhUGAAYObl6LpwJSmwXQx7fr6GXwn/0enP5fzAwB02/uopLDButpzfhy73icNERQaIje1IEwMojJQdMAf/QZGdWCWwGg0G4zO+EdRCWdLDD2UsR7LEhsZDFiej1eBgc6kqMoRuKJGh8JXBgJy5YdnK0AUCPbqf1R5YsVe7PuADRXse+733zcYALO/TzrUV0DMnLbk+QIchFOelggH9hwrA1wstgyeCHj2/A5f/5HE8ZHgSJfHwkcDBh9++f1pRKhYmRa9cE8tiMU6kz/7xTP8p3/yHv75r7+Pv/8rDQpGGLDfap0v1BmI3HxgUJBpb+a3Hbhj2QOBvQ4w7OKldOc42KjsfNMi31QQ4FRlYFZAIBuca1Exi0HVyPkVAMFtkEFFKKt3JGlifyTrSIhA6kFO2pEIJCwOCUtQEhaWpY01SVIaybTYVISXhE1shBhdcdVVDbM2432kmSAUEiyRk2yTTqiwdPZrLW1lAydkcAcJRLKOfAVERgzNI6bKjsph7ABHQPB2qG8MCshs1YD4cxCQThNgGDPl6QAy82nYlEHav+Tw1wFAlXZeVwbDBu15SHIfJybPQxw0ZpMA30ffl8pqUmgwMFtRYAG2ZkBwyYcAGJWB/aiEC0FyGEyWGCbaJjXqfAYs/kVcWhgcT+U5bgGJRlVIbtQ15oJXA4KmCrX4EFEdsCR2zRdsH/pisUejIjwPft9DncdLGPtfHdCP4IAgpieJUWMrsOC+B2Bdkk7BtHAwRsfneASCI9WgmR6Bj5/f4b0fCxQ8OYiYOJZHAwZHYSKtWCdXad5vdfsS8GefqFIQHUHiwYCho27f3VMJpOOli0AwNswOCgIQxAcjdmx9p7e9WAmk1IJr2ENjclRKoiIkk/fTIvJy1Y6cyy8PEOJ7+5tk5QNSAusa+S46HajrNExNIJ1xGCw0SFjUYVFjE7CtbBAVQZYDSYc7AsK5VCSIeSERaccfcjOkbcdfqyoINsJAeoM1VYUFoBKjqLqRSRwiTUVYWRyMrE0JsOlrsyxO7Ih506PJuflWkm0RYmV22tpiHgDW7NFEC1JeHCDowCwBoFvaOrP3zzKXmlzPBe7sd7R82McrhM6duz+bUiaGY49PYspBlcFePkNYSdBWFYxAMDMZ7EHB6DtgKsFCyc0GS5I05CMQzKISTlcUmPPguKLABvcDVYCrOShfAQJ+Ub88IIhLRUc4nJl/Yh3G0mU1JGgsGXRL/awPlA/0UtkUv3Ysq0JbSl5JnfxYlolXIqDqknHWyYNmg2Wwjzukvx+6heGkt+PK5FGfXGsPE8+e3+G3fiyr8x4CBcAjAoOtl+mF3XVgTFYxQ5v600+e4Z/+yXv4F7/ezAc57Jb9OO1v7FyjQnAtEBzJWebgMkLBLOXnNMPX5pnhDkJiA8wAiMnl5kwZiaCSfvnbAYTxoTZnxrBIlwH5nbV1PDSxVZImsplCQj7hlLI4HFZREXKVFLOWyW4EhCUnnNeCtYo5IFWByz1AmBVL5GRBKiozFpiEADCTdzAF0gMa+WcinMEgbyAXqPagjO3WgMPUiSWRKxM243TADX+9nZCEz40mCaCb3HiJTdHaclH53wb8GFRsZtsHt7DBBbwZwIF9j/CHFEs+ZREIS9g+W+66apue+REAE5PBBSCQJEZ5AwQemhh9mOIlhXgD68OWF7oicI2fSCxup/olmQwMCIZVIZVbv1Yw9Hu7/Rt0lt9yEMRlfgDCUr+Y7Ra7fXEKjbva4z4AQkpy3hQAoakK7dzj5HRUDcL6jWMgoNh39zs+e36Hb/xE4/jMoIDS/CHV8njAAAC8mqXYoC+zs+N2niBOHSCFgn8t60g/89pb3ffiLEu+p9t3gODQjyA0wFmHutfm96h5zzzixxkOZL8lY5MM6DLLVUDQ+1dTGBjswR0BQW3Q1gmxQYMDguZfmAHCxLywl5inhdHNStwqQXtn1Ce5gc5EKC8KCSdwXrBojARRETRyIkvExJJ6QHhZChbKOBdLYrMPCHI/tw0txq53NcHkv8LqsEgOCAA0WBVjtUayI0XHDIoxttBee29QYPeUOgiJy93M50HaLG9hYYBfih1v+M0ZuM5WAs0c/Eabfhys2wB+3TXH67ZrPyqz4+9GIwz1Iwm7tkHM+uWGcyBYUsIpU5e3wCGAZomMhhDF9dwFH3KTQVQEhqWncgHXP3vtgvYdSU0R8twSOoHwKJLmQxKAwONFWDuokwyGwWS327dJdwZC6N+0DZta0GBAn8Nhxg3s98sSbChswBwQkkFiUBDMxGBnb34IDgqxfYZnaTPG7JSPXrTcCm/9j560Z7O7qmPoe0RgIJd9dLmuEmCwEzlAED765Cn+cYg4ZR6nY8mhAxxhYASACAdxH/vucdeEi9fFw0NzlMijg5zwwwniTUvVrkfHfH3dBg5VPiIgqEMaUQGn6NSUHBBsnykgAH0HZWUImgPIobpiSpEGvuGw7p3ygrScpIMqGUgnIN0LJOQTUM7I+YSUTx5AaSUIIICxkgFCRsman6HQBhAWqJNjbff6mjXpY6mqHkA7stVnFO2+xFvUZGr9GwaqLkDTFedyTQhd27ZQQk5wWCA0cwTQw0EsY3usFR3YlrCc1CDglxUDIF4n0IDccjf0cUX6c/b7ONzv7ndATTHSc0nDTTCHQgDuVCgOhvtAEE0Gcu8nQFDOGnPg7HEnqCoocJXYFBbFMsalmDxfmxKer367gndMQBWAoFtREJcdpowukqQ5FhoQ1AYB41LRWS6CWT0ZFMT2yKRBglgcM91of9D5xr6Zhu3+u/Z72r9X/Q2HAtgqBTlnj6gKnayiKQl24DEDOT1grPlYoeAPd5SC9jPHo84jAoN5GVUDhwFs4cBiU3/r8x/g114T8wEPD7e9W0JNzSqoex/3QawSm4ZMHkxKmwoczR6tUw0RGycPj7yewIUNPDCVgP38KwFURWIuKn0RqSTGDRBI8xogNbnSAYHVmakmoNYGCQMgMIAuOFJUEK5dI6/KQVwbj5TAefEseWk5gdMCLqIcGCBQWvTz2w4QzmpiMB+ELoGTRlWcLVGrNXgjYyIrU1+ncvn7D6oNiH68YbY6rovfbuPu73ge/je1wXPJQDrb6+qBdLoQ0ICHgI6OkhQbll9E+xMd+rjuhwgu3C/3W8sv59raPYcHqIKqISmRn+verMzrygGhwUVl7mFkqOsIA93SVdomMoo+BKdEPRCsL1UdiEAgSgHV1WGA13MITFXb83ThWWqxJuw/u4gh4JTHIohAEOIQDP4Cs9DSNvDHIFKXwtJzaE+bonM51j5rTGAHJnfYs7T3XkvTvnq/n6ahn04kv2uQQOpPxLClhthCgp6ePeNjX2Dv4jL3S1Dw9htP/Fr2y/6HjwoMxkZCcRvtwwER4aPnd/jdn76HP/jC+3jz9RAcqX/2vXiQmQNTwRiIBuDWuLgFo9lcByUZQIkUELrmOS2+Lhs6+2LugGDPMWtsGs0RUSBBPOzJnXEqtb9UEZZCqaNi0sQ8Jl+SbGse0BlMbYmUrzpYz009QKi70kOBJwACWuZAK9nMCxJJj4kkcI52ZHy6lRmOwgLXk3SueQH0NS03QUEgnIMPQnNSFAWhS+Dk69lbEhyt5lZHQw0eyYF75ZKjm7xnt9GXyj5oRqe7UrlzVDSvdgA4ZUJadRBNwLImn7m6t/xaJ0mlaOPDEEv0AZglDYogsG48/GX7uTQQuHQ9gAzMOZFm6WzXlCCrRcSEY5I/AZV3IW1jFgj71eGJmikQswiFMQbBXqrj06AQ0Hq/BYJyBgwGygqcX7Zolev5qmdHTlajUMZohTEMdV6GDJQDEMRlpxccCt1sFMwFY+K6WfTZ0Sds05fZ4MnoEhSZUy20jxtn5rFs+mxtvNs+W80vajaRMUBGA4MEuY4ABRNI4NBmsjWY4cKi2Xk02c2gYLgd3f261PM8GjDYgwICmuPeAAcEgFhSW37zj76Gb7/zAd587Yl+fzLrAdzJI+85EurD3Si0NSz33Ab2aR0AUYV4aicA1bA9kGNzsJoNPF1aULQHyWZrfs+ssYdrissZHRJYEnjUcG2jmaHq/Ugp6/r44IfAC1AXjOulO0A4EWhdA8gRCCu4Jo2BFDoyT+HKvbpgDouAOiESeL0HZQmhS+d76dhON6IiLDcTQFiBfOpMDOcdJ8WFEgo3ZzgHBLSBL97nciUIzHwGZGnSvM2MULBWdiAo/rqHhFnxQZSkLRgsnHJ1IJC8EG1wjZK4+aUc2e1nzntrbedvrw0QzuVvfh2VCckHWpjsJXLzjGAAvx7gWp+EfnsDpNbZtxwG1JSXZOrb3KnwNJoM9N8GCNZ7+Xu+72GgnD2L5tHyQjJQWJo64CYCDVHtpgP14+GYkjoqAdFcQLT1H6hmSpqbC6K/yawPA1o/NvZhAFS1QudQKFZkkfUrmu9HlPDd9OBHmvTdO+2OUBoBKizJuKCQoL5bo6khQoKfP/lB+9/Q43d+BwA+evFsCgWjyTqWo0km8MjAYLz4+D76b7k3KYBnz5/hH/3ka/juOx/gc68/6QYmq6xoXbNjLg+FAZZtbi+eNTDSMK4MgBhEVT15q3jaI0BO+0p3qHj4GRSMy7Lil9bSJGGgPWC2i6kIWdUCQgMtYl0z7asZCFlTKPvgP/NDCIBAIFUPkodYJgC8rg00/Hy0o5sFWwFEIQDkIV1WgYSySqdmEHA6TwGBlhVc184HoSRCri2qYqYQC0EHn5PeJ7OV++RM1RsrY/ccQQAACrVOj8I+hWUQXmuVO6ODqzm6Ab20XqoMrDaQ1lqbADNpf8nrHUgp+QB7yjrQrvI6QoKbHbBvv5eqsfNqvz+aB2Yw8KrnXznpfbEPpW1X5q2ZIZgYLLpgBBzzrwHCbE7LCAqjR7ntT+pomFP0PdqPQSCrDCY+BOv5IhBwWWUbs+y/83z4iaGBtJ+4LQNeRCEwc5yrBPl0DASDuSCuLhjNBTGtfVQ6C7CZ0JgJalOYfRlvqdp3MbrVACoUT537WhsKaoH14SG2A4AQXnwoejA2IFDF1yAhU0Im8jgGIyRYWTolSs8r/oz/HG1XH9CgLGzPUs4Rx3DwdxYMiOj/AeC/hcYTYeb/yaXv7EkkG0DQwfXjcFPfDI4aVuddZQxUeTLlzbddgAH2YfpALSCQRhEUAU0jC6ZFQMFUA2u8O4cZxa4IBXGd9qysYJC2Ui7NbmyDmTnUmLNZpfbwiYrAHt+/UxFylrTLYRkVhuVUTEm2kSxD9GiL4TqIGZw1cU930ZrzQV9DYyAAkERMKUnHmTJoWUD51APC6RbppoJTAbN4d1NeGiAst8ghFsJKogCU2oddrgAqEW6QL85yAAM06uojgoSrD6RJjiqDqEVlBDT0MsssODHtzqbjoFp8oG772kBZmZBq9QG21AYINvu2vxLXoR9c3cY+aWNHpo9LQHDpvGXJpNTP6CsX923+Bm32vpeQaDSPRDAY40rIPg2qAfjzapMGe25iuOIclIQuBsH6cnAqlNdUV9T7l2IuMCA434PLWSDaViSMzwTQnomK7iZZQitLYiYQsACj+c1XECwdELgq8IrqgO27N5m51G8BQNF+K3OAg/B5RQ9ufu1at96vwk5gDPgU+vHZM8ZyIOmXk0/0jiAhroQwiLW2w9yPQQA6kPHx68sCBREI4t/ZaV4qf2fBQMv/jJn/X9fsaFBgf/duipWPX7QwkU/GhEuTL483PduvXgMDvm0HDJw0k472AggSKThpoiNpWKQd2sQxup0rrlvLXXYGkKyec0UMYB0gVL0JrA/8nopQZJzy7SlRryLUFahx2WMB1QwuGaAzyH0PZOZPKQP3fT3L9GPoKaKCoCOh+GxI5EakJGaJXARSVEGgsqKWFXS6QVoExrjeCLTkk0R9zC0WQk4adjk1FYGT+hakrcMUAHAirfqW6707+wEimoOe+DEsEL+GhcXRcYVmZCyMlCSDJHKcY1QZSJMsicypwcGs+GBb7eskb0wHRbTly8qEpEldBAhiOOjtQxTDAs98Ic6lbswFIxQcmRCsZBuwgznhlJPa8i2MMDyccFwZcMmxUmoPXR8xStBEg9xL+4GjxgiFm2WHCgVU7t2psJ7vHQo6haCsPRDEZ8ErAVtqSlmgIJ8cAuhGgeB0IypBWhQCZJ85ECxX+w6M6sC1QLDbZ+2YhLrLvPQ59YpvDPPd5RG5ui/XKLEHkJD0NdCcYs2ac9THP3t+h6+HiIbx6vfAIKoEl56iv+tg8KCypxgA/c16+tyyTLWIUIn2b1Z/o63zXK+HgSBDHUlQRAyQ5SWQAZUTIB4s5m+gJgWIZGWJNRIYYFmWYz8RzzsnySCY0dKDZqLpg2bb9gAB6Gm8LcuB+yJIRygza4vRngAU7xCDmUE7Q9RV7Z1qStCHhiiJrwAAGCjobzMgcukwPeXOuaq0IEiW+8E60byIiaFW0Crb+HQDWm5AXIBqpoczUE/SMZqJQTvHMTeDtAB7bbNwuPQfWlEw/QSAYDFV2ayrM0+k3GXwW6vOfqsMLKkm+UvssSfORUb6QgISVSM4VubprH4stVZxLI3b7LvJMQ1uu48XGL+jG48cJMffHcsMOLJD59YEcsq6BFCXXPb/+tgBbvufyP42qxwHfKBt8+dBASBOJmJws93cBeUsA1AEgnqW52JdUdd74HwPvn8pTobnl4dAwIODIQ11KBd6agpaAAHoM9BUAgWCfOqBQE0KFQKdDgGAZ6EE96C85zswMxdcAgKghwJTb2L/1A2a1NQiIvJ67KAh9tlDrpBr+3PxOTAguA4SvG+VW7I7Jt09l0nth5Mwx5fx6P/7FQMG8G9IAsL/K2b+z+OHRPS7AH4XAPDfh3vNbw/RF0vN/OFXPpAsU2EfmjKWvQzKANCiil2CgWiP2tPBrIGY+QAtSyExuZe/5ANoMxYCNFiHCw0evMPmpO5sqdvdWSfJ6ezBAbAPCBkCGdHMYHeussyHiYBKso9NZFMwNSy+5DEjL7kHhJRB6SzqQV1dPajm0JOSSKhG53YbuYKxOj/FjlFeD4AAaMQ3ravlBhYillczL9yKmYNPqjicgXzS89omcOLUArQgtYG9xXKnDgDk7xwgGNiaJzimj25LJs9Fkj0JLAg43FeZ0Z+yxF6wGfm5NEA4ZZqqCDbrjgPt6JRo28ykYGV3mR+aA2XbXx9cUynsJiQCkBUYqsjuUyjoz9P8IQwIzB/ipgODbbjhMV2xDOBN9ie7rmHAP05WdeCHtJfIqGgqa4UD4upmAzcZqG8Br6v7EHicggkQAAoFuoSQ8tJDwSIwQKcbNa1Ju+dkILA4FIxAIOYBAQILM8627QoYAOa+T9cAAdD7cPi1BlJLaEAn/SJa3xn+dmaEYEIg69tHKHCp46BPRxVIMFXgCBJi0Ki6+ndai5dXbVK7Hb+OS//sWD+zV/4ug8HnmPkXRPQ/APBviei/YeY7+1BB4T8HAPqVo1QU8MqT1MzvScKl1z/XKvhCma0mkHzlAxDYcqANDByAgS1NZGs8rHZ1yzqoja/lNnZzgogEc9UgOgWaKrwkmYWSyXRmk6t9o9l7GO30xQ8BriIA7WGMpL6BBJOcWd6nRAoMJJEIIyBQBpLYVJkyuIp5AXkR9cACqKQM0Et5wMqqD35VR0M97wEQLFAjQ50R13N7RpjBtcrMibmZF1j9D1JuKxg8siI5JEhEQAvmEt1RW0Y4uzf6AartwwYJso0ZkhVS/xooiLPjsGQyJ7wsFSsLtJ1LxVIJq0ZxXBKrjwAcEEpNfh6z/AFtyd8xEIyBkuw7Y6mscJDYPcMlYqQGGdPlg1mdOm0bchInz2HCOzu/U46RBHuFQEBA/Aduc/g8LBX0+Byw65O/vXIwRHe01xEYELti7R9K6x8cBmwQ6iIVlt5sYE6F9tp8Ca6AAorLEC3+QPAZoNNtMx0ElaDmUzMbKBywvVYgWGvvOxBNBtcqA8AWBo7K1vlTtwdzjw3+UelJ/neuFnRmBK+21pc7FHjysAv9un9GU0gQx0yZzREn6XcMcurqF8eBeJ4+f4Z3f/wePvzS+3j7tTfhzthHZpRITA8of2fBgJl/oX//n0T0XwH4ewDujr81O9AABV96H2+//pY8mJP9Jifin3feqPGhnlJko8lDexTXlmXOZIL4l6ipBuprQJQvqgbJHBGS/mwAhQgIAMCpf0AX0MWHNEICgM5hUS4tgAKrScHNDaxe/kBJCgoDILBCgQDCWcwLlLfqgToWcspAOYPXDKxnURW0w6S8PX8vtUqvEeBAmUu+u4gvAmuMA1p0bTZLjAaiMyyQC6weFRQAAbc2Q4A/7NnqnwCzMQI9QIiDVjNJFAOF2pwdV0oeP/5UikBBYqzZ4isA96VqPIAk23LaSPkANnJ+CnVog94mFgD1AYOAXjGIKyYAMycIEADSJjtHxNTMCg85Nzuv5kwo5oCbvB9q2MxdS0LnDJgoZpo0B9r9AV9eyqi3mUR0K5FK2FbRrarhoByY2cCCE62rmwtGKBh9bKbmgplKkEUhmKsENwEITg0I0tIBwWogENqjwcDeigLg1UEgjnFjIrHO0VBBIFGDgpSuVQua4utqwUYpaP37ft8u21tMmtRN/ohrUxE0hwRp+nkqq/YbDFsG+fTFR3j3J9/A9975Lt5+7TMwJ23offbf8t/Xu2G/88DydxIMiOi/CyAx83+rr/9DAP/bS9+rPA9a8fQv7vDuj9/D9975Q7z9+ptyU62MFTq01n55YVAYYqayIyA4NCNoQzCB1SuadYAfVAPzNaB91SCbBZ6hpgiA0bIo+qCDtizGU4mEWaPBAtAvbZxdRvGeAC1+uKoKcQkkR0ioElgm6yxxJUmPbN7ES1qQ0wLOK2hNzbyQMrjcb9WDvADne1BewcsCXsUEYZ0nTzrRTakFbLCu98m+T6okoGiURDNJpAyuJg9qcJcgHRLQwA/wba6bmzkkAoTBg88uYvx40lmaOD2etFOWuAqqIqgjX/NFIA/fbKaGvSyAs2ID/bjqYAsG1O0/vcXuZ9AG/zG0sW2L+++dVzwni6uw5ztgDohj3IC4MiA5aNiMU5+9es3S42GQGAeOrq8oHSBYQiP3FxijFkYo2ItHEAcATTBmEQqv8SWo+QQEKOC0gJfbDgiKDv5rDWDpcIAOBvZWE0RFcuY0OAMAoEEAMKz60BejL4gpAgKAMvp7ng/sqAXBt6BTC7p6DlCw17+zXAhpoDqOH0R1mDR5WsponWjR1WnyrZ998jG++sffxPe+8G28/auf6SengPchFCCAh/4lliOnRit/J8EAwP8QwH+lDWEB8D1m/pPDb+h4OJanzwMUvPFWq3Cgq8wYj14+i++33zmCgnmDGWuD5jRnZoPu72XVwJIgFQgcMAGotEnaYcmkUjgjB4VM4TRbY6ZA/GZ+mNkAmcXOGG8bAKzawGMmP84JVGTNfjIVgTUiXACEU1qQblQNSAsonSWqYTqB0su2zvp8L97T9y918G6A0HLQXwEHgASEIbujC0h1EV9WWVZwiP7Wph3Bjgi4EkD+2m4oDX91KJ3YIFk7+BTjyisorLWHhFrl/hcGbnP2DtzyD4whnGdZAmdlNvDbqY/r/eUy9sGgzRr1vTmooZ0L0IPD3jnNAiuNIYbNb8BgIJPsM8LAkgIIWHtx+A+Tgr3newMJ6J/9cbKg/QUxi8kg5DHoYMD/FmyiFfr9Ts3ZAmgwoEpWBwTDigOcbtRMcNuAIJ8A/VtBOA9AYKmmffWBwoAlvOLa+oU9S601kcK88RMYY6kAQ9du7TFsj49UBALrG2Ok2pZGHNepBbEuZzOjvT7etw+AENVhAEAVgcFn+Qyo59bTT/4UX/3pb+P7n/8DMR8YVAItXD+Hm6cB8ciU6L1yAQ7+ToIBM//fAfyPH/SlPShw88GbGyjYDzYUKn+k/9ghHEHBLhAgbG8NgRAqkoUkTTnov1YRVQNTSKSZyVDmWb7SNmmHNRWzZ9t3s12q/mycA0VlwfwTbElerr3zYuy6YuAeShJSOFd4ciKDhCUnVxGW1ANCrTqzyxKqGOtLdYZaJf1yWUFJOzmNbEhlbYCw6CyMaz/bmgV7iQ5AXJXJSGtKozDazatF1QuNu2CdMaBqgndZff3NAAFoQGDfMcUhRI8TNUF8GZI6PfKS1QOcNOriGFsBWGtGWeS0x0RFe1EaxzIG+JmFP545go1lBEr7OTuP/rPL59IyQ8YETy0t9GgmiDEDzLmQZo6A0Uy4Y1fuwuNOgcA3yHkzNx8AlpwFrmTZb9Qi7ZRbToMulDEQ2mkCaN6OxeRlfgV5HwjMj0CVAdbXFRqvQ00GEQhWVQeYtzAQxMPN828lGjviZMHqdmYW0JcbpvbPgt+HT5SoqQHTpHYGlFZHXb8dq287TnRjgX1/+0W7EbCz7dThAQ5cQa4rmBKefvJn+M2f/g6+/4VvifmAS9hfJ7OmOCoQOMReMh/sszuAv6Ng8Msovvrgy+JT0FX46C8wA4GwLzCSPo6hoH25OxQxY0zKJB/QnO7ivvZb7lHXR0MMX9LfgQNCIulkbdjx55WkmdrymHj5tnzOzA92NaT5RtfKEkgkbeFATo97NWGtICKYEad16oxFIWGhJIqHmhkKk5sYFgWEZfkUKJfmf5DPQFm2gHC6AanDli1PjJAAYD4DG2PGA9I5Q5+7dQVSFRBghmVL4hBXnlfsPpieurbfKJ1XDn4JgwJBIQ496XIx0jj0KcuSstNiy8bIHcHWOjqFAYUz6nLsEDY9971Z3NBJd5+FEo9ch4322UPPw1Qzk4S7lNDUEj/Zaw8gNC4RjOmJHQbG5Wnsk4k4wG+uc0eV2gQbMkg1IJglOgKkvpXeCZB2m7XrtlF242SY3dmQLAS4hQOPQGB+BIsoBExZTU49EKy1mQtEfRJwnoUAt/rrpP/awwHQQ4GFh44mAanrXgWwbQj7uOmKWl9Iw/4pbMvU9o1qQajAvh8n2oz7oiiO/b3uvunnJ5NArastHMg+d598jN/8k3+M7/9H/wpv/+pnw++E0MsAYH4JCHBwRXn2/O7w80cFBtYo7p7f4V1b0vH653ri24OCHSAAZnQIdJCwW6S6/CtdY9lBNl+6eHBUFp9u9zXQw9nMv+r3LRZ3/O1OHYh/9di+jEWhge1SAyg0s4MqCLqxQGEhnKulT+1+DAjqAWGtpDJwwcIq/6otfQSEExNyylgWCchC6z3Ee3oAhFrEQet06wGMeD27SSF2vFO9M9aBZW20UmtzTrTtcep7UHaHO5MZA2CYEkHUUtpaqloDBdLocwYLsnRywSlL9LmVaTfYDLgtKQNI6/i47cUZWrxN47emaaWHq5/BKLIt59w/j3GQMPtxhAFC8xdYCM1E4KalPupmBwLB5u9txXxVgON285Cix2nZQ0OfYyG9qwwElJf2e/nUjuFT5z6zqCcPCysQsIlHoI6Fy40AAQtznCtvgYC3ZimDge4Zj4W5CycNwNUdSyI1AkF0GBzNAXK9/eAPbNtiVBgQjuOfWVvxfWQ82Etq19/rhG3ud/u1vq/tPzsoHRwAT3/xp/iNP/kn+MGv/0s8ee2zbZyhoa8hgisNzA0OkA5/8qMXd3jvR+/iDbyxu8+jAgNAoMCCF7lPwW6ZQMHG+bB///QXfxo+TNvjuwnAPjsAAN2/OacFnayzU6O1+mheUB8FgwOSsVqWJxqkcgMCoE0w2iW32AYRlVwQsWMx+SBSmDtAYHFoAEFmE5mB4N6pp9psyVZWiG/BqisUZPBPyCjNWUwBYUksjndMyEwSKCllLKdPg5bSA0K98aBE0dPbnLo8/sSr5KXfXNe2U+/KuP1i52NycPibsqSPDqlusYgnc1syqQPDkNXuxt4vIUytwoIBn4qPcnpxkB5PLXR4YycMXOz+uj1m6kH3GB78fvxtmwWaNNw5DdZVIwiWrSowvgcD5v1vMKDf3dTxtXVoZS8+84XvUzr4bmgnnvmQtD2EREctd0GMWDgHgpVlmagl4VoDEJgzqygG1WFg9lwDve+JbwPc5GMmxCbvN2AYYa+rY7t8+ztpk1b8zll7iWqCbx4meHHQdZnQZuI2QZT+WV6FQf2a/mOqGPd1LFDwf1TzQTQxTwAhwsEV5e75Hd77oYRR/mc/+P3d/R4NGCRqFy1Q8AQH87OrC6sjB4jw9JOf4zf+z/9UPjAAkDfeUORdsylNi9mFore6dfIGBQ4MtH+cYE+yPArBSuCHBXq7HjDemZBsRv8KHFDrABiiBrCqBjqoFAUEykmcFLPMKMgG+9HEwP3MsTCQqkVIlDS8YgNOuv5eAKGwmBhKkpC8Je0AgsWV19dsDl252Y/N4YureYUHG6+Vief3nkTcvjPIkfE78XgGYjve5W7CCCqCZ7pbFs9a54ltKHWDwEZNoOSgkILqIL2uD6Pom8D+ML+ne10Gg3ALLr6eHy0269axm0+A1WN1EJDXgyoQYZGrhxTes/HLz1xfZ150my1DG1cNbErMXTD9PDzFBgFRWVpOurplyHoY4g/Y0sOHAoFF2owwMD7LADrHVCtmNox+IBZIas/801YTbKFgHOiHzbtl6BLhULCr/IgPh5sNoHYoJljKeFZwIHcW3zlW7POBfiJon+s+AgWf7ff/JRSbNL8/iZg4lkcDBgYFFjtaaE47vris4xIrxIqwzp0S7l587CT3D374vwgdQKA5Xypix9I/Mw/RTiFIrWFEKEhZjk/ky9nsd2mHJLeNfygzqqUUUn16d6vSszoBAr7k36TFygCpgkAV8oSXJAbFIkoAMSEzbxSEWltFWOdSEjkkrLX4crNrAGEFPAZCWiTmPOcbDUSlAWM0KBVzETMEcwOFKBvH+xQGdhrlllEKHu7xOLD4oMK1828YZ8cN7ozqste12499GVpLh4uwUsKh0waI4CDZYEGh09rX/5u8f4u1LcmuA7ExI2Ltc+4jq4pVfNeDctsNGLC/rG6IZLEyabQbYIuqov5MspglClQLbMgwGjBgmG3Af20YMNA/stHdkNQkVVUUpS8WKVHyE7yZxYfc6LYN23DDaomqzKrio15ZmXnvPXvvFTH9MeeMmBEr1j7nZvHDPgrg3rOfa68VESvmiDHnHFPnQpsXk1l00eV2dyDe0a2be+OW361xP9zAsbkJLVBvow8gi7cBQs8ebYL9rHy3G6fbAiEZZ/Hz191+qwYqG0+/8/SXbjv/fWAxVkKsINGCXDVNtihTJBY1NeYoNrXCGlSYBfDfFRCsAxjw92893SlDYG5CBwqiZYMIaPCAwAqwmX4EDDhgMOjDXBg6dfva2PaYYb0nqlONBQxw8exAkbFxcV8GEDpbv7vu64V5FsGN+xQUvAhA2NxbpDF3Yh8/YaDgApK6N8BgCwpc84CAhkFXRap5k8F48uXfc4EgP1yPU7/LPKDF/ni8QYcDrbRhCSaL9t7E6BbM7W6m5eBO6LJZq3StK/IRRB2hBBJQQKSlU0kr+4mvmhhAJESOUltQcg6x5iJBRhrQZK1mS+j5SCakpC/mQFgLkMLtAMEq41mQYgiEFA+IEWBjDkoGxUYfs48Ch3yRavxJASyVzNTqgOZrBsTwWF8FO45d2La/u8jynFHLRtux6mfb47ownNFcCucTiAisRqgDCupuMEPQAwVdWr0QkwtwJDf2L7QIXYrinjXrs+619/B7Q9yQ7f6qcJBLBexT/oZUwOICUm1cgOZumF2nO1/2mQA5V2pOMEDcLtJW3dDT//p9DwDIP65ZLv06UdcV50bwbqRRutgLE22yDC4AgrU0MLC5Z/X8PSYwN4IVpEoEUZpU10FSYz+Wm27gwPn/OaPTkPBjP2sTwPmedt2jnQgAs7ED1OadCbdUGzDevyOtMVn77fUpqzC5rk3bsRP6HQ8KbmMKrN0bYHDbRVeXgG9EsKjOaSPgyZd/Fz/zjzVl5MM/2ujWEGWCAG6Rsol7YRDHxbeLMbD3JwM9fG+c7H3qlLuBNrsrYDfg0v8OJJeedcEhkij9GCIihEkIRW7wWsgHDCqMXADSvOpYWK4nF6xBziNw08zvKu5VgEAIDASSW/MuAKEGKYIQA+McrHSusgiAMAWa/jVLS6tgQQ0Nc1EwoUZnAAs+KA1EPUDQzaE8zqiR5eMcHMGGDaHfiVNoQY1BDULWeaAyzJwW+Q0bLw8URv+zS63sjI6bX9v5UE9sO2fqe7e4WrrjXrrvbvnN+jtuPPT1LjbAx5E4IFDHej1vAZqNZ/3pYRzkg82IF2zdAnqvwI+FgoJNPIBzBeyBgDIBcnYMVknuFkvQ3AgFgs1zaXUMcmmCWD6o8C6AYBTCklRobuBA3QheT+LKiUsFkqwQE5ayx1YAqysqVZmfoR7NbD50c8avq22TMwO9PDxuAkGlHosJDQxAfPpsNmPMRLMgwrsAl2Fd37AD4/VcahPQYMdrtYEcU3CHdm+AwSt60aO/snMn1EFXNFiDN+btyZu/i5/57b+Gv/cX/zPVQXBHD7GbIAAaUKgnoJ8ffqNDhH4CjJTuLWDAWscKuEl6a+nnkUUY0bYBE3Np6KIWtDJijJIaF0g0+YMyaQGyqCAGSA2sgqjgAFFozCrZjEEytwAwWVwGgh7/EkCoIKFIVcHEhFBEdjmQ1mMwkBCjsIAWpa7sgbkVRkqaaxSmCVoVIKQKFOBpaDUq5ILWTBypAwcXUhe8ITID1eouogLZaqQqUFgrSEBU/cu4VKAgQyoGhp2BYjlY779+0WA53+4iIPVndfzS7/A36X76OpjB+SzPPVszAIFNOuFwzvY+XQQ1E1BgYMxcPgMYqIGkFMAda9graXJlexQIeJYgREDJbgMDhVuq6qrGfa1GHgMY2AKCNfMGDJTSD19TnvQxBK0WhVebTMFUTUWrINGQQlpWly1SgFnxImt76xUwKVTEbZOlQkOgrQiQX5fJ3ALmKqi/H9q6SiTJ3vW9uN183sUGjO9T3Hz+tta5mg0UvPnFCgqMSQfqFuBiTMa9AQbWFNdN3iC4TH54NNg+0ybJkze/iJ/97V/Ar/2lX8bLH/0xOWb3WU0qNgqpKgJoG1FjN9BbVHsRCOxNktHvWo1/Dwgu1nMYr6vrC1caVHcjwh6I8p8BhACAiisrCwKRsAfGgFCQPhcGoQBZFpO1FPkWt1iDbl1WkFDWOUAwuvLKQEIMNYDxHNjJ3vIAEgghJJCeP++K2jT/dDVE/nWlDzuQEEIDCPrYdqwEgJOCuUya6plrAAeVARyMbQQLA1BgtztlHHW8Glgww1UBAdADh80cuGN7Ebbgz+D4m6BOHxx4yWVzVyCwd8rmDrC/rn8rKDAgMAEErWaBAwPV4DsgYAGi7nWesAR2F7e4H6dbMbID2k1rLlpwq2UYXAIEU5yEJkedQqig4EolqJfU4gkSqQYJNZYgqtpkBCogsHigLo10cBVtB8TmUDOMXTVDX4fAAILFCVyg6rnqxej9pYYfrL2yw8b2IP6SDQC8HZDf1L+O0dttM7tlAJICnrz5Oj79hc9sSjPfFW7cG2AwAgJ2r1XWoNZ0Aypd5CvsVPrldfzsP/yr+PynPouXu5TH9ln2ecVAN1DCRLi3Jn6vKQiY0F3bofRXWbaTD2igoLS87GrsvC92BAib1t9gEu2uCx0vFSDEuCCEiJUae2A6HFSkxyOLFjhlAAiIkN0JEDpwAC0SVK+wsnp7AEEyGVbzZ+bSgpyYKotgVRwlDYongU5RLi0kxXu3AAWTRw5bkBDSIr7tqLoKJTeA4IGC7Uw8QKCWKVG9XJdSHg0f7AAFWD78+NwxBC2ADk0o5/+X217g5hizMRj+XSCwszkA0GIC3gsgmMZ6GGszgIHqArgMBIxQrzUlSl/d0LQpRsniS9LYLwQIwhYU7BWpMjYgaQzQEh04IKji5LkFCFvZdZ9K+iJrlHe5mBgYdG0NkHtjtsnyLls7ZveQ26bPMa7GDrB7bZxPY0DubtCtf93pVNSg4w0TMbuOfVAw9t5tAOHeAANgHxz0HxLmYEYlgTR687f+ikt5xHZSAIpE9WuGJt3vziKQgQkL4BDveO7za/RyoYp83Yebxjc3UDCJ0Ibb4crpusXUJqPze0o1r+a/FGMnAEEi9hcscUGMVNmDtRiDIFkLFCPOsqyBQgRlfUxBxFQUt2gU4i5AAAOnVcsPwzIZGIlZFp5MSMoWUEBLj1KQQKTR0EVYBPsb7XwDIZAHChpvUHcxpS5eU5BQViAk0Ud3KXG0nkVoZj2L0aiplAzk8xwgAACVwcWwMzlcR1WgkLGh7kcqnEemoH7wO6D8/6zbHiMxWK8N23IXNsBfp08ZNBBgnxkAAcWlGvWqMmi1M0xp0NgBpf7ZZw4MYKCxPe31pj2hYGAiVOWLGfEEDHBBdRdkViCtktgWQ7BqVKEV1xpbjbFUUJCiGPtI0HLWLchw0TLW5jpYnPtgCboe5LOqTq4NELiy0xezhfz6ZGOTUt0Acohyj9TPQdy+JLECnU2toKCB4+3aS+1/3UgSCVyo5wRUYOCNeD3WbfbADgq0OeE/hz6w0cfGeTsmoODVar9m13MX1uAeAYMpDHDveFSoi6exBY4pEKT1a9Un0/llPLvggAHvmvGxuZ3Z5O8mA2i4pEDtJbJrAuaBlaPbYLzhfNrWjpIb+4kaXVXBkGRHHRY5ZlxgWgAhHXBQ9oAgu9GV2LkWxPe3csEZUkFQfJuy6KylqAAP7wMEfX0tqJkMKRJ4FYBgMrjmZjDBJA8SzsTKFkgdssYmQIMZrfIaaZXDFp8AFoq6E8sxhqZkcEmVnaGwar8sIiiTV5GoXc91HIRBSLBAq0qHOyq8FoECUEu63mUHDIjDeZhWchy9H/ZDHubtO4kRuK29AK3ftbu6Mi4xAu75RWbgAhgQIJ16pcrgxahakCC0SFbLJmjugWweNzP8mLMCxh5kyOsjGPCCRGPK4arKRJdYghEQzFiCXdeB/auxB8oSrCeNwzkrKCjy2GJ+tMokX1ibZIOHxshwEUZGNyHVbvoMgqH+ejPKTcdjuhZPTItkYVC1C/rUbU5fzCb4b5hd4vET9jsArPIiuNTPS22gzygoeOWWM5hDIGv3CBgADgJ0RrdzKQDbCQLpVBN/+LGPvdyl1c2araUOS956ZtZmYkLAdu77z5C/KELVHSBjDSywpvuuuRpauk8FBaOoy6gA6BZKCkGpb41+t0IsJYNNDyCqceSi7MEBIclunghNzKSwqJ5pSuNaGMdCoFxAgUE5iB90AhAA2zXpE3UvBBBOK9eFy1IdSVkEy6eegYTIKp5iLgcSBcbKJhDXNKpMprAXJWAqJACsWQ69op4xCQYSGAyK6lZQtmYECZZTT7ZDugAUUCyVqkyAwjAH/NOuPsSLIoJtoz8D1wPvVA18r21zThNGoAMC/vElIOBEpmpmgQMDXo2yzMCAZwZmQYPKCjAPsQLubxUVY1SF0nwBDMjjnh2w4wGYAoIuuHAnlmCWdTALMFxcLAHlU88S5FXiC/JZ7pnzCeVF1yX7XBL6XcCBMQOhgYJR4s0F6pmJNNAl/TKsx8ParCVSLogu0R2swvTQu7anl4K2jdcMFLzc2b/hFCe/uG33BxjopBndCZg8H1+voOBTn8ePfeRlrAVzI43K9kjUPdzEuNC6ddqAXg1W2X5mbAECEAvEwIm33sCBBAgyMYh2juIAwggKbMdaRV1min8+ulpLDiOvoHQQujwkcBnYAy419iBABIiCYw8CMwIF0R7IBUeSG2Il8X0aQNgsZmE7MDVqWhdMS3UMgZBXAQ4Wi2AgQbTauS50tcoj5LviUmCVaOUugJEILTaBCFGDMKcgYWQSLE4hlg1lyllAQQcUNIjxVqAANFahAwqhW1gpxo0h5g1VdbdGgf7MjPp3cg7d89izgLtAwOsPXAICPoBwygqo738vZmDGDIxggFusgIECCxxkhuoNyFpRWP5aVUMz+uYm8FLFFjsAYBcQWNsDA760tQcEXpvAYgms/kFlCRQU1FiC9dxYgryCylncbusKXk+yJk10JjYtRF0AExCEKcB6ble0HHZZAp0MLdZD2YLKxHC/Nu+uyyzrckYzuEQkzz3Lu/d918bfyDs/6qvjQjdahL4MwMh0j62eGpeL+OD+AIOd5lmgEQAWlipTr37h0/jVT30eP/rRl7GW3jHgxyi4g+QixuMuBObseCNAGD9nzX6jyoJyx32oKhg1/xRpBO5kN0is16bbkg4U+PxuoEPnYowIPnhOXisgPoCS8JMM7tmDkoF0hSUkWWCK5iqTACvRQGAQyQJzzBkrhQoQhP6UnbotjsVqP0wWuBlAAFD1EEIRrSAicT0Iq2AxCcJadMVdmJA9SOAGEnKQ489AAllcQnEBVHu6CV5gySv0mbBSXqW/nTiPLwRFM4GeCVDwIIFSqEwC59wZ19sM9GiIp5+5wCLsAYnv5BwugoHbgMCoJGk7UQscdJkE1T2gfuxOYGhPV8BeC7EzQi8KBtbCzh0wBwMGEoAeUAP7gABADcf2YkUmGja65cwtcBWjgAUyLQL5u8wyDtbjlCWgIoXN+HySue2BwS3rkbkSCEmKoyXdkETU6rLbFlwWmDhj/XjUcdAxGTdv3ZxDW8trDQ/9pN+73NUx1tkI5q3d0Q9VW6DPX9eCSKZoONsU35W58O3eAwNgO7B2s7z+pdfw6m9+Gr/yyc/jRz/yiarr7ydF9z332BcOmQW62nHa44EhmHA9o3hI+wEN2GHoxJZdcJuYsghySC34sEbo3jI1ebgJ1cDoVcpHovm2dcFUeo+SiAVxKVIKmYt8lg0gqADNJHMhBtaMAXEvZBZajKNGTAcaNNppoETV+GPQQYDzl6qboYEEroueMQk0xCQ0YOB2QDGAMl4MJIBqOicBXZbDnm4CWzYCF6n1UFNPW1xIDSQd3A8eKNSxdAI+BLTvuij9LhCRy3SDtdveQ3AipZ3v+Ejuu5zD4B64lRFQAag7uQWGugO2w2SVlrbfuKgrMMsiwHtjBvbAwBgzAOBOYMCvLSMY8OxABGoMQf03US/sdAnCJOOgAwXKGKxncRucjwoKWkzBfmn0LGm3BaiS0yHg1n15XQuh45aqC2EEBcYY2BjcbX3m2ofAFjDI4/mpzZjpdQDG/niy/Iva7Gtvvo6f/y1luj/6stSdeS8oYNL+lQAGgDPEAMDiPvgrv/lp/MonP4cf+egnthPCfRbABnZ5YwTu394DIs1/1YOE2eQo4Ja4wACYKkAAGjgAjGKy/F0SAZtSDDHoTZEbWg5h370804ZfV91lQeluZR7UyMhcVUlhiKEzlUHEhFnmQizNvVCCLHJJF75IAQv7FCuasggjSJj18T5IaO4GZmEkrNKjMQkpiogSaQpkrQinICGQBjsOIIEseLGguh76LAegCiwpMGMHAiqAMDp0AAu+GmDHKnhhpbyCl6seKMwCGm0cgeZygLkh+jYV9rkUiDiAjt02CTi89fd9kKA9fxG3wJ6ugDIBIwjYsAF2X4UWZGh7xgJjBPpdqGUUjGCAjUFwYMC7CcaYgbGI0XfKDBgY2GMHxqJHniEI1NwGIlQ0yTgwcFDkL86n6s6sbEF1HwzztBt/kvVpD1zqnNhow7h1sT62jdUACnxJcis7L/Ox/ynrX4v9IrSNirwhX6qGmtvL6F9qx9QX6qZztD16PALwxS+/jl/4hz+HX/nk5/CjH31Zf3tguL8DkPCvBDAYjYaBgl/+5OfwIx99ud2wbkK4jwOooLAdc1y3Loz4DAj4SX+bH0ukibkBhNDAAUCNIOhYA8lpJaPMLD2Q2kJHIYCLLmoWYxDjgNLtnLlqwXOxX08wZb/qWgCaa4F79qBmLsRFlBMDIRV1KwTRHEhEUu2Nxb+fMGcRLO2Kd0CCZxJGkFDBQwUJrT7DWOkxKYuwMpBIfrsGLiqzkDGCBAUHGrwoLIMTgNLFGJSqLMQIFphbTMEGLJimQhJXxZRVMP0E536wHVkXpwBsF2FjGGYtbLfzfmdU28ydsDOvNu2Ov19BALAFAmb4d/UEHBswpBBOQYClD+6AADhGoL7u1pWiYHoDBuw1F0Aoc9sAAFe9gdvAALDPDlwCAyZh3MUIOHZAWDBl0IjkcTBGgXrlwlGXwFgCyzjYcx3cAgpq8/MqGABsgYSWesg6RjWeAMbyNLZgTxTKAIGt05c4V+82sHUacPZi9GlcQgbabDzZfa5+jQm/9+XX8Nd/+1X87Z/8rNgvVtsQqJ0P6fr2HsHBvxLAAMAUFPyoBwXDhADaokXUNAZsDczgfvGaDPKINrkaqvH1C6dNsgQJVU0y40oPDohIJg8paxCi7EDNitsqZPm+XCSKl1mCdziokUcN4KW9m/MSONBrrq6FtCh7UBx7oIF4ISpAiAIKWAx8CazFGeVx1gVxyyL01OolkDDSgbZ49m4IQsncsQjiqxQWIRZUESV5nLt4BA8SCLYxseBFVKEnEBCKfYZa5Tgdu4BUgYJCusHV4MBC1VTIyipITMeYltoBBXM/WLS3d0HcNhmtddFVPQCYlgsemq9GSQBmJanv9Puz2AAHDmZAoMYGOLGuauxHEOC0BGwa1W7jxjDO6Ge/lhgQYEYFDQYGxriB98IOAPs09yyIcAYGzH0wK4lsxY6MUehrGyiYzWfV81BmwEBoUY0Ccx3ov0ugYD7kAxNU2R8du7TI85QaQHBqrezmBoNqufgZKLDsjxZrsD8Vze57ir+eL9CAQv3CznHcY89YexsEAL/7lSf4xd9+FX/rLzb7Bd10+MfvObhA270EBgN7U9slUGDU3hQhMlcvljfmjH6NHL7iHjdAMIIB+9gMlQb9nMQWABFa78uBAyKq1HUhIFIUdiFE2bUHrpKdHIoIDWlqT5XoBUBU9C/JzjKZKt9wo447wZLb99dz1/cEAEnjDDiqOyEDQQWAFCBEBQkpimBRLKS+WGERbHxK2bIIRrnaDstqxq9ZFkMv2DKChO4yWHb4RVf3ghEgyFrDa8ZqLEJArdewMhDhQIJ2gNVcFOMv7iFjgcjYBB1Towm3teijZHfYdz1YKLkZfF850rsfZkGNpmEBtO/reN7aPBgYb4AJozA2Gn9jYCvu/PtmxIGuONQ2SNC5BWoMgDEDfTphJyjEhqtbXMAlADAak+7e1ucGBjK4Sy/kss0qmJU5noECYA4GgP24AV8KubrIdM6lgKrr4dmBoEGFNgdNmKgDBBOhIkvN7UBBaSm5XVPGsmOhfDlrCxb1GhKqsSIAsImwdWPtAKGNR9WBmICCPBvDcRrqWJP9JdSshOA2HPpzd44zGDeQ9qHf//Lr+Bv/+FX8J3/xs/gLH/mEiwVvgIDNNAy/YRuNu7Z7CQxm7XUXU/BxDdSoO4ABFEzZTbvR9DmzGOWLaHLCEOwCgtt+U3cTHThgAQUwQ1Rs/VMtfKXxOZiK3tKYDx15DupuMF3/vMpia77pnQAzGg0AG5fabvQad5AWWSAoyq6Wogj/hCjpjRTFJxkiKCTEkFAGFsEYhFiayluiIItobEFaaxF3Q6J9FgHoUfmm39kHEkk1yAwtGw0FCMyaK23vAWc0VUXrNgMLgCyw9eakBjZJv9eDBlTQEAI6d0RwYCFGmx9j5cgBKJj7YGHnfigAthUKLzY3HywboJN5vdPy0zrfJGO7bIU7nsO0HHFIwtk45mAfCDQxIQMBs3iAFzH+9ifrBDMQYJdlzACAXUAAAGNQIdDP2TCxMjMw8KKuAh87UEshV3YA2BQ66qSMcwcIOs0UL6bmxbrqkMb20hBDsBsrElNVmKS0yNiHCIQFCKGOMbs00YLmQsg6zsXWhgkouPP6PHymvudZ5zvYCqC3F9b+4Muv42/8k1fxv/13Posf/vAnFAAICjA5B1CzS99puz/A4EKE9OtvtOyDH/voJxT9byfEHnVE1BDhLUtWbSMN3zEI+tcvJsND+V3bHdAWHDC1kqdtAXEuhSB8v7gUFkWsGlhofbWYBoIGblmUr6OZmyXdySdGTx8bC2EnzvaaofsKECJQYi00RHrzSmZCEhYhRCwxooCkYhwTlmDU691dDcYijCABQN2J3daM5gU0iyLIpDCAsEIWVgBYNQooooEFa/YZDxwADOBBtiF1Z6agwdwScQALsrDvAwVhF2QRb1kolQ8XZmExq3Zhhnf3mLrX/Gv+QmeLU0+jyR/7zaU+u/N5sAME5mO+LDHcA4GSW50BAwFjUGBnINyuXy5Bjbgz/IB8d7xkmzvN8Hvg0N6zwLOZEdnzFwd9YwQDPqvghV0FhBY7wFnii0pu8QNlMPITQFBcBk3nwqrDGNqIlywsQHdhbn2ZpZIqU4CUhCmwTUZsAKG6EDRdNHOLK6hjywb8JqBg2MjVc3cv2vrM9jqavUB9/mIG2zPLf/CV1/E/+iev4n/zEwIKCgRwdBtEMArJmLM/kb1G4eJn7g8w2GmvqU7BZ1WnwE8Iu9FnoODSIN+lCUJ0OyOa3+zWZm/Vc+AeHBQCYO4DmSUAdHKokUpEQFyE5qKsf5VwLOJnA2cQRUnZ8pr+Lo+Y/M08iR7vmvcvlyw35HqGlag2gFDTxCwKvISeRShyI5OifQMJKQhIWJQVGF0N68TVYCBidDXURXyC5H2pLT9+3dgUBgVSKVoRTrJ0Vyu/vQ7dE4lwnoy0z8uP7reMeTDWIWn0YqzMRKN8t4WheqDAQwBjcx8UN+mLnv92nDf14rtOGZ+PLIK+PQEGFWoP53DreXTnYGzBNnBwDwjMCg5V8MdNN8Ao/2bI+4SePc2FvHOz+zXh0nFsvajBg+oWm83LqoIatq6CLuXWsQOt6mgfSBjMVeDBgLkEqtvAsQOTLJniNxUurqW2EOqwUzSEPIICZYXGuJExk2RwHSA0gMAKIDguYMga4FkBcyEIKHQ2AJdBgb12ySSM0/+97OI9KPibP9GYAsA8yVotUrCBnDu9mMtgr90jYLDtjtffeA2v/san8dmf+jw+oe6DvTaj+euR3aEr/au7t7ucU+AWtyBBKu439DlNftfOZfyZRhspnaSIMRNVHtxSGCs4AEBFyzy7G5wpV4AALirR2/Li624TaDe5tbv4g21RUKDAGdVlsQEJuuMjd5O3Us+yAFBcEEOsfv2i2QyZCZEYJbS6855FaEFe1EV8p2HH5sftUhtFdrqe2DEK5z1Js51jW4wBAOzrK2jA5AAUaplpcq4HAmJKzt/IlTkwyWx9de/E5H1vlCtgcKDg0rV1zxwYqKCq1NcucjnGdNVzaAGCo2tgLEM8Vh5k7OsFANtd/V3aXYSgxs9z4Wrk5cXNpzbHtzkiILIHA6R/xzTDoK83tkC1B8DoAgn3wEDNaOHNWrFhGId7obKLY5DqGLdiz22NMNZgU67aBxjqY2qPDRTUuBGL6XCMsd+dj21vtGnzADXouF4S2v17V1vh2z91TMFf+PAnqi3guy9TOx+j3Xes3SNg0LfX3ngNn/4NUYT6+Edf3ozwOCl86+5NaoM6DnTbL/UdXJc0N3sCc/U7AQAYLe/0AjjwXyl2DvbYIcZsFxUEHKwgNRaEEBdUyrWQGlm/G3A3NmdQSM1YeB+0GQ7vI5y5GsZOrXyqyS6rWdIdgAcJYZCZHUEC5ZO4HELEEhcgRGRlTlZCZRESUTUCKRAK0wQktEhwlhzHgQa+mxGwtrcDHOnhPTeGPfU2paY2Al0AWav9gN5nXBgR3PmMLU7Byzm3LAhJgaiyAJPrqnt77l8w4yvXxt1n5TrddbjHdVmq91Js75vdoP6zu+dTd4HNgLMDAuYqzBUYyPstJmW/toBcA3fjNBsjoKfzgcaOeEM/MkObdgFM+B1nc0dRZZj2wEAPFrfsQATknl8tkHDFPhiYVDz0zACwC4q7gNHxNadJ4SsmTmNILKNkFJoaskpaTAFtlCY9Y8RAZYwBxxZYv+PCmuztAua2goZ5fLutkO/+7lc0pkBBwV7zm8PZ9NmDAE/eeG33mMA9AgYeRD1RUPB5JxN5G9C3AbR5XQeU+kG2DrZKfNZqsJqj/6DggwgqVayT0E0WMurKXpud5zABvWvDJkbQcqLMQCY7ETXAJCp8LahQb+RgCF+ek+7cTH/AKELmAorVAkwBQ1cWdcYwzNgFAwohoqvkqHQhDXShgQTSnQCtZ3BMSPrcWAQfsCjaAs04RBI3yqL9PkaJe8oY2OpAzXaM9p1Yfc5tB2gUNdCMTFHDZa8Brsvc4XuAoAZ+belncS3VLyz0cRlEaaAGQirO+RiFWRbEpdZ2Vbw1zOZjn3zety4Ewa5L7yMPBO66yzIwUO9v3hcO8rEn3q1k6oECHFzBIvTlh2fj4isPoriiW4EQ1J2U0Xz+QAMEngna9NOwwrfvoH7Hu5gsqNXGswOFjh2wQEJjB2pWgdcdmIAB72bkEQRcYg1n2SOuaFWfRdIDAAAoTpeguhM8QAgRsDoUg9jUmF3iFScZqPN29G4R5hu2foD0UtCzBHu2IryArQCAP/iqZB/8x/+OZB+MjIYHIJfaHs40+/gxfGz3u/cKGIyg4OWJdrS1QABYBCn85Lg0yAEiLgGgBn8BPZqXmDRdKHW3VhdQ4gYQ4NwLaAAB1CPWelz3eOPvhk4qlpkcQBUcsJ580YUhWLnoYJHrxhQoADCgoO9twIJ9xwOGkmWhGYFCDWTMFZTM4xUMILQFw/zFm+p1BhIsi6IIa2BZDUtcsMSIjJ5FKC5g0UACM5RZUKCga5eBBaAFlgF62YE2wWW1/LW5J6C710ASqKifmxmeESTshXF4I9Ry00VoSWhiloI1gZBWQoo8SUfjGtBYfcnq2xqN9szIdzsrmwr6/LZAWjuuf+JCCC8yc3c9r+ovRmMFfDrrmksVC1qLVfAEVi7dWOyVILZmIA0FDgzYySlICNtAQA8GeldAO/YYnCqfsb4QEFD7ktr3jTUwMODjTW5lB4bSx12K4V7w4KxzLsQFeHdABwRC7AHAEEzaWAIDDA4I6GeaS8lJUBtQ5AmTZKBgOH2iNqc6cOAv0T7rxm3PVoyA18awHmtiKwDgF3/7M/hPf/Kz+JGPvKz32L7IEpGtCTsfGJrZx8/95c/jl/7+L+1+7t4AA6BVSRxLT97WiHqq38cRGBULAn5fFacA9c3tH7BbQAla1INpAxBAjUWoiPDCiV9EimygotVViIqKiagFMBIQKNZJTPZlFjqVRrBQAzAcYDAXhGMWUESfQBYf3WWY0fc7C+eK8FHKtj1ntN0EzicBCi41KaQFTGdUxUbNa+ag1R9DRNRYBBNPKqFpI1SQwPM0tKSsAtAbPA8cZGyFaVgAzXiAFF6CMgulVHCwstLNhbvdZw8SuIGEcQ5kt1MlBQcBCGBd+IsDCQEpF6GWV5W31QJRVbWRrZa8/NDeXK6j4xgWf+13Dcy7a4Al0GdmvOi5jeqB5iJYd8DAWu7Y/3D9zw2YgdGlDhqbI9fWQIEFBHrqfy+lFXD2iLbGaGqIBjAQgrkScWcRIuLSV1+tDEHpWcGhVfq/wLkFGijYBAuSBogOTEBXe8ICSh1AuBRX4lmjGkTI+wJUe40c+py5e2ZgwJ6PgIDcGAE789jYEbT1/28rKDBbUdQV7TeN/rfv2jwoePljL1/87L0BBk800NBAAdBuLgPyBFkHiVCNsv0FqKPsxkH+/S+/jn/3H72KX/7k5/Cpf/ATEvWPfUNtClgMTS3U15ibZj+gyNWxCEBzK1yiY8dJZqxBYP0tBRwGCkw9MaNNWDtesN5SPf+KbmG7tB4MVAqRGTV1iRlcUqMhKerf0Eo6AyCsdYHxVdTkuY9cbjSkL/uMtAhIqKImqyw2ZZHfjEkCLrU8tGU0+FgEDxLaLoIavej6frZLTtHkcJ1LgiyQkUXmGRBdhlzAQUYoy7ZA5gY7/6I2282Y8R1FbGrX6M7RajGsmsucdK4mDTxMUSvkGUigvtz0bHc6tpnhH3PvUV/r5+yeGp/fSQPbALoZcLh0fgA2QYO+yFBmYQ2KvtazBNz1+6U+zzDDzRsdgcrkhHlVQtu5z1wAZvzbrrK/zy8Zn00ZcKBnB8Y0Q5MptseOHWAvQDRUOZS+bvcn+fsTDRzspRPOsgdgAMFrTIwgwRl/wAEAIy7Q36v2uREcAB44bNmCAPQxYEMb1909dsCDgeZquGwrmKHpjnJWL//QK3VdAlM9N0a/abTfr7bN/bXJZA9n7vVL7d4Ag1cvIKG98TZ3gi9YBGxvvN/98mtasOLzePmH5PgxbGkiAP0khB1fQcFtIEEHP0AnOzWqaXNNO7OsgoMKeHTH4ECBnelswva7E5vgChpirD64qutv1QIHsRMm3YkkkoDCNYhGOjMktkHOFgBG3X45u7M8N6XFuNSiKwIIUsci0GL6CMvWzaDxFTEIUGAQOLSgJGZqOwoFC22HYSit96XPWAbvkrDgtkRSRpqyMi8iRKHKjCoorUFyorzYQIEBhDxsX2MgnLPLP1eQUDQeq3BAIAE75nZIQVwJI7XtA9nqzLDfrYDgbvK8d/HLA42Kv02/f6TfL53n7ByNHZgBAnvPwMClvpaThDtf/QtqKX4OiEmMRytE1GWRABhdALf5pS8ZHNJ1qkpru/ty6i7wgMAVMxorHIIZnPUenKifstZdqdoDTlkSIYKWgyoRqs8/LPW9mk7olShDBCBrozf+UwPvTsWDgvqae38TMDsxiTbHqlohzd+vbIEbn1vHxh3vVnuha40wlpJlFhxAMAbBt0tuBHv5ru513+4NMPi7P9UjobGvFOhLIB5bNL/5BmnzWUPiv//ma/iF3/o5fPZTn8cnfujlGmW81K/Mull2DDbWAW3g7wQS1KgXcEXktzV/BWOxoFlx8uJw+BgpawcMTud7lOmN5MsKi6oe5bMYYS2mwiWKeyGfm9KiHo/Xs9Ro8CyBq/zXXhNXAVuFx7TIIpaVMVBmAHntAUKIAlZG8ST3PCjdKT1BNVJZ+ocmC9IENLgdSJVYLQClgKSG55wLIgJiYUQUrEVv6AKtMiFtLU70rQAIW0MFDK+ZMzQQ1lIEFFLRTAwZQ3lcEAibQlFnqLF1q61X3wO2QMBAwJobq+HpeGDOdnjDWv30MKPqdt0dUJDj+Fx932ZKgf48183jLUOQC0/72ZoHYLWiYBj/SbBneyyvL7EV2zINAdnV060GBpi/hg4IQIx84a0ioRch8vEDe4DAahZYuW4Am+DCELWYWiParWZBlSc2UBCSgIDQ9ASg6oSsGUU1SLBs4wFmu/2x7ZZCnrzvm5qDPoZlstnqgJq+cFcwENwY+Q3Z7GwitWU6qk0yFnkDEFzz5xLcX7tNXpuAggq8LhiWewMMPnGLz8SauRHIoDv3ucPeAH7xzdfx87/1c/icuicsWAqAGD+Xc90OoJ9SMSFSaiySZA20wJjLIEGYg8Ym+J8e297kt1ctUru+XhfzWw4Mv5tpuv6WI08FVWo1KkhgK7VKURajrP563R7VGxLQAMWoQYvGDepC5vvVXA+m/UkkC1NcLgCEgzAXSl9W8STb0eBcg5hIo5kFKDRS1wBDvZH0xQ1AgLgmWF0VObRcedktRqwKEEyy+VgKKAfpy8wIoSBkwqrXHAg4ZxkAqzy5CxIcOLCxbLER8nshCJMUFCwEZUwsot43n065Fyw5+uVHKn52rrb7riyHAvBAkk0yBlb659m+O3Fz3/l8d0DBrMVA3fkukSorkCggRejfxhJcDYDgUtogAZX6v7TDnBoWEWho4kI+aPhCquFFQJDPPTAf78EuGjLpS+YS6EEBloPKE8trAgLEBWjgoACqZirCYNWlV3p33m3VDa0FyDrnjXtn9CffqcBs1tzLfqd/FzDQrSA7Gh3td5y90BNeCDUerJQtQJiVo+7cCNpee48xd8A9AgZjM+PTNVLDS4TIEhBYXE/6wf69N1/DX/2tT+PXLGahE/YAaD32Sm6b3xJA4INmZKctIKEO/A5IAFBfA3QyULs2azXlxb041hEfizhV+g0OHAwkg+syeVXP16e+BQLOtnsq0CIrQtcjnIGshpcCOOstuDi0ntRtULLeIJa90IITazolAHAASmkAIWdhEEoWxUYDCMsVUEp1OTQJ5iBxCC69ycaIJsFOgh1l7GpaWfB+TwUNLm6BA1r2Q0FfQtoBhFQIR5Io+QCWeAEwQgkIkN2tuAO47vxjmM83M7S+mSLmJm/afFQWxwBT0GT3mfbZ0cDetuu2714GBs3wjvESthuXAL8e0ACNxZKh2J73jKmYZXtEY0mCez7pUw9eZizBksRtcKVAwABBVHDjSxVbgKAHBQYSmvFnN/d74amWKWQ3cWmv1TRkYQvaay7DIK/A+di5DAwodIDApxxDT5JLu2eAdo/YPeZAAUICx6sKCjguAuLjgmyuMw3IbfNpSDHVrnB/aqPhQc0gAOALlckeQoym2YPufnAAbHNsNAbhIoPjmIG6cRxFw/y47TVbdwBQPklclMZ8jQDBNpF1eNx52Tm9NsTcvQgoAO4ZMJAFVB63vZOjg3Q97NgC930rg/v6m6/hM7+pnfrRj4PyaUDmkF0x0KO/eiBDfzpVghhHDxICkQw8zd0NQAMKADqwADTjTvYJW+sVKIwBNjMNcMZ+Hv/YfPS4pb4JNWp6AYTEsp6kQIjxoJM7yI6djnqeoS6CFbw5xT1adSdfJn1bim4NSgUIBEjmRNQCUbqokQUpGqMwk2CepEbVnZHLpzbQUMfBxteDBooooQGFzITVlZA2FsEDhBQIx8JIVLAy4by23PpNkFxoC+jYqhucWrYCMN8NzYrvvFdQcM6lAwSeMSiTCPYiUZiwKpa5MGJQ8SnhTwF4l4oZb5ms9j273lmdCx+nUV/THUEAoRSq1xAitRiSoT99X7Zsjy0gSDHgagAEtTohtUyBRA0s1JXBXAB7BoTr3VrfA1BTiNt37PFWonhT1dCXOjaWwIocjaJlvmkMgbnzrHARpQQsV40piAcgLBokfCUsQTqgUEQupifhyqWXrfDUi6xFmyBORqtGOxj80R1gr48xBB4EAA0I1PccGKiAziTHx7GsfVm6MRz7lvwJWPaIMjKRoixLah8yYwMy/Dl+8c0GCnxMgZqH6RoytnsFDO7UdPIUahMCaAP+u2++hs984dP4/E/9Xfz4h38YWE8NebP68aDAwNFE8qLNtlCfMwUQm9GRVJ1mkGTwSaNvDSREuAHkHhBUKklRcCHqQYIDCNYqmEBjDXxq16z066bb9NpSDIhr6dT2ZIHvAcLChCVGxOUaoGM1urRqFDN6t4I1e85Y246ui2grbQdT5HP+OFzccU32mUtzMUTd+VCu49NoUh2bbizdmJorxOVVV6YhRHEXKWOSQFggGQOyO2rVIbMBhBixZMY5qHtBAcKauUbSrwq2PCVu3WDNq8t6+t3eGwP95PU5QDDwfFu75JP3oCAX9Znq62GUwnXHC3F7TmO7dP7txCU+oQQDN7J8F1aApZ+Rc3LHdoBq1IoYAws7hqCWM0Yfh2CgINiujlEDAnlSr8J0Q4DeiHRAAahyxPqYuP8+m7E3DQIPBlST4M6gwBcwGqoZChi4qn+FGTiA00H+xgROV8iQYNlz4Q4QVFfCRIVSLne+DrXslVbyPJHEchgTS9KrfcqhN+gDG7ALAobv2czRbULP1nSMTu7HtBs7N5Z2Es5VI65YgtWMQbCy61EyJ4YNZB0q6Kb2C3dLSbzU/pUCBgRlDeAGXVskqa3wc7/xaXz+U38XP/7hHwGtJ1S98OrP0wFdT9iSXPYraIbD7UY5mA+s0dakFJ25Gsi+q0DBAICd7x6rYCDBFnfLtticoQMFowrcTA7WtwACDWp7KRQsiXAVYwUIhUkj/WUhXdI1QKfegNcYjNDAgfnb7DNBFlKQW7hqN7cRZGZQznLMECEVHldozL+MWUxgLuLScEIrsgpsAV09tmcQdHzruXq2wXytLsCRFCSUQBqISAKgSisfvRKQYsSaGUtS1iAocxAYawnTwD9g36gB6IDARq53YlOZUQv11JQWhqJoue5giBpq7PVG8iDB4jQMHER3o4UQNoxF505wu/S9WINRIro7VqRh7sr9YGlo5kbp+s/6bQoKSDMP+qBCYQuCgoRWgyCpeySRL1XswIALDNwaFL+b7I1IV0xqNDDohcW6AkbmLjBQUOS3uOQWT2CNFGx74OYyDTqmYLlqTMHhWtiBdCUuBAMFSdiDM8uaci7tnwGCY87KkvUqlMzztUfGnAWIFYCDlF1PDCAWoMj4jESZsQWV8p/EdsCmwsgmuGMYELBxqOxAyRv3joG/i+M7nqW5EtajMJuUQVqFVipGMoKOBdP2MK+/8WcDCoB7CgzavqE1b5RGnxLBojd/Fr/2yV/FKx/+YVEH25QXdTEG5dyjvnpA3YUaV08RFu1IGjhnO07J9eeeRQA6oCCHbJh243pAYw2IGQi6OOoiztT0C6xvmNkBXE9f9yles6BGWzhlByU36VUJWCPjKgYsMcAKlGQGrvSkUzyAHAVP67GOh4CAAAon/RFFyvkMZGoAYWoJ4zaauGQ9toEDe51glR6h+gpVSMkPoY+4tn6jcTahAT4bS9tZDVkQMUSEuAhDoCxC1h1SIpI4hADkElEiNEgxtF0Uc1XsGyWWx7GpZ6gPx2j+qUY/3Pwo6EpTl8JYIXMhlKCP9fcIlYrPBkghr+UJ+jCfvT2OuuM26j5FM8Qta0Eeb1MZb78WkaSGBlkC/e26b3jab1hgrY8rsPONLn4gBgcKjB3oFAbLZC1x1P+O0Zj5pHlMHTQwYIPntQdGUDBkGJAyXa5TXIc4QBCleFnVDjlcAcsBlA4NFET5y/Y3JKxFWIJj5jqX1yLz+5gLzlmCcD1Ddtu6EwhgDqoNUpAQwIGkOwOD3T0QYIzAXq2QC64BG4sZENDnXbyHgb2pm8eU2wb2oB+M9lhTS0mZUQ4C/hD1uCoPT4rdGWK/PCgY7z773F3bvQQG1upOVNvsMQF48sYTfPo3Po1f++SvCCjQmAKf+9shfACSldB3tRU0IkN/RGAqlQEwUQ8iBqjU90YWAcgboCDHV1ZhcD0YSCjqR5C1mgANgDOBI40hrC0DldYzUGBKcJaKNltAAxFC1l1UJgEFRSi9q8K4XiKKuSQ4oCSIGFBICC3PU9kCuZ4QRRqViCSA8HzSVZprfMe4y6mAIO6ZCEgsgo231Yow/x1kkRkbj/WSx2bIfiz8pGNcgYJXZNS6DlIdMmlVSFKfq/wtyiakEHQ8e41/5iYwBOz7YIFt3v+FHpJj2XFD0wVYFSSkoICRgBAKSiEEtBiEpK6QUZjJMwk+wt+eG+XegwB0Co5mqL1Ak13XXa4J76G/TGOg1ZzoswuM3UihMQZRAYGl6lZBIacwOMYA7NYasfO+5fpq87VJgB4U7Ok66z0jqYdJ7iu9JwwMWMYOiJrrIApTEA5Xmo54ANKhBhnyco1CEWcFBeeVcS4FZ2UJbs4Zx8I4rxnHbJk6fVrpfM0BUmStQWFUD0uwIagasrquk617mAd+TsFAM+ibDIIuxqNnB6aAwOI8xvf02qa2w849n8GhwIKtKWSpDYMC5iTuosDCSoKquN/nnLhfd+z5DLjY7jUw2Gt+Ijz50u/g0194Fb/2yV/Bj//gD9cbGyU3hbCxshggqT+bA6vBiFFIfHYgIKgqIIXGIFSAoK8xGkhAqcDAJo0PYASwiU8oJAZYdkhcXwc02tdOkxoP1TIWmob4mrfKcNZsYU9Rdr6WL5+ZcCgMThGZGdcpai8XYA3gCDATDjEiLNcVOBEE8CAfQVcEBNvJq4qho0QRWeMIhp3PUKzFV2iTHbwz/sra1EV0rwhMt72cfEb1D0CqM2FKbyGoZDMBFME5gsICE1yy6pAxLsIiqI80a1DcWNQnBQJzRE6oJNReLQffooOAt0mn+mNZ0JcHIeJmEnfHgcPFGgN7WgYzDYN5zYdQjfIlKWFfP+Au13aXvhoD2dpvYqNoOC1IlM8X6w8Qs0gO1128M+TA7jyr7ZZr7Y4FAa4MyFISIPdWKdJn+jlzlVWDOt5LMaFqFCwHeW7piJZ5oG4DCzI8WTxBbqDglBk3a8ZZ/570/VP2m5H5vJE5wkAOSFFclQb3ZxsFW0F9EGktIkXmnurBANWx6BmcPhBUO9O5c3YDQTG4EzwQBCTuaafJ90yPpQCsM3SIT2AAT958HZ/+wmdqoOGfVbs3wKDRmEq3A537AO6vNAcK/tIvCyjwkqGjhniWILYq52tBO4NPjkLQMY3qBxJ0S2V1LgZjFHqAANiNqgCCIe/Xud9YBCaqkastFbJFr5JFIQZJD+Ig/k4usuNZWdxyqwMJNY9Y/3p1ON9vEkmuSn0QYHBgQtFByLoLZw4oKaB0UR0+KFFuYaKjGNKygigilKZJgFV2X16z3caRjW5z/S/d5Hb0wO2L6wQEbIrFXLiRPQBhqxQ5resgAlAUEzifQUHUG5eQsMQkoK5Qze325WFNi4KZUaLFcOyfUnd6F+yJHMMknu3FVp5aihDJb63B3BpWOTLcWpVwbLOYATO4I33fAEIrJ2yD3weMXbq+OxhT2A7T6GdfhbIBA8ss6NkB+dfXH1gbQOBWf4DHYkQ+6G96UgHA+mLz2Yy+7f6BCgjk7SKA2wwfdu4jA9RDwGEnWhQFHLC6E7ogQ3UTnAtwXsV1cLMWnPXvWhin3NiwUQ7cmijMMhKCpgBLUCk8mNNTrumlwViBVlnS3os6xl0WgWWcdSBgP65jEyA6AIIZSzAtV21tsCO8nsWWVNeBciisG6MIUAaevPE6Pv0P/yo+/1OfxSsf+0TXb7zz12f1bPwNrt0bYGBtFqy8bROm4BIosMIifsVbNV3RkGaIgMbvUMAcHBhJZoE+Ziw51xucKgrI3U3fAQY4ZsFocaIuOGW1zjDXgpVlJtZoXS1JTIQ1tDzykXiaidaYX3mJQW7oAAASJIe1KebVloQBKRXLyx0aU2MOEFQtkSK4iNEMZZXdSFYxqUG7nfwOa2+xDJfcDHnzvAMEnuL1gadorMWoGV93WeeTLMidbPNhyyLEBRQWiUWIC0KIWILVkddI+tLqOnTFYIBuuAq2ugVdq4hquGwXpGq/IUyFAoYoX/HVCo1RsCJFLasFFwGL9+Gbr9dqCXggYPLBLYpcv0d9jfvN9e5c43i9e/nsBA8MGu3sDQrlrOyAMgKz6oQqJsRDMaIqOWydjclcso4y424xMf50x3m9N8+jMJVkb09AcHc8+90aoJtkHQsJJUSgEy1qbEEGKig45aKBhj0oMGBwKgVrhrIFpaa+Am2NqboSBZtslUAac6Kg0kAckbl7ZA5FQgcKjN25mBkiA6L9PgEH7v0uW+RFQcEY8+Ees64dYAVjpW1ybbx+549+Fz/7239d3N8f+bgwEOTh8rzdzT7eI2CwNWk9W+Bf9aDglQ//SBsk/WegAOsZxaf9AP1kqD/uqGkHDm717HpwUF/L7Zj+JyooaIChBjEaPW6TMUQsFKX0sl8pSyszzTHUXehaJDq+ou1JyKtX3rP8c6AomLAbKNTHo5pkG6HSZyzEgzAeppYYzqAsOxzmDIqrAoAdn+wtze+Euu9YBPYANgD0oMB04z3V6ALAak0HoPptWRfUsfgTzicgJvHPdtUhT41F8AGLJiUbxEALe9BX7uxTuu5417umEMuJX0kAl6W8eqYihyCAQN0aa2FwClMXROtmxqyy4piLbmyAp+/HnftouBsH9aKt/8YsMn1TeyD3WQV71QnL6TjXDSguhRDApv4AdC5FKx6EFqwMfalgYCidEQfq9+Thzry/1CsuW6cri2yFjkKYqhjOMg/ORV7zTMEMFJzzviz1THRK4gVQgZukTDdwGQnTgNBaVKqu8ZZO6IIGgQ046AeI588vsT71szugYIaiLc0ayubEBKS2gj756h/gZ/53fwN/7yf/Dl7+8I8oa2Hu5y04mGHk2+6ZewMMfOvoEvQA4ckbryko+FW88pEfdUFCLvPAbvJJPfL2I44pGNos0v3Wtjfp+itru2GiBhZolQA3tjLJEpwSQ2ornfZE0enFBbiKEVwylhSRkYX6ZzitferS0oCe6jM53qIljFcuQBblvlNhUGCcMwPIuEYUCtlNU9lhm5yyU0vUIEEqGVxSvYFFyKhUSu1ON6SP8gb6OBHWAFADCEQgq15gzI8fcx8A2c0FW+wd1bvKIm8gAXmVRTcllPNRUr18dUjHIvisBmGC5G+kbdlZNV0X27gI8ORxZSF4CxKqhv0GLEhsCbNBvnbAi4uRUfbYBwH2WjXQHgw4kDBe36Vrvdw3jRammjXAnQHZrT1g1QnPR5jUMDzTOJs3Nmc6EQqrQ4CtkR/pfb+rd+tNM+r+Gu8AnVyqbqk6HbGCD7YCSCFVFUNxfTV9gnMxAy+gwGIKVpb1YC0ufomb+2kGCMY01poSSkHSRiNhSREpSKq0CUvFsAMKvHvHgzsXTzDd9P1ZNQrKJE/WrJG51PowWM+S9ZFXCXAuwJM/+s/x0//Hfx+//hP/MV7+8A8LC0VB1nyE7gYYr2K0i5favQEGnkFUBr2LNQBclalPfRavfORHdECKThCdLEr/iD9wCwp20be7UTeo26W1dYhuDzxsDB5PHppxUIPGDNAKsAbsqX8qxgUUaEO5Sr46VHFOZYaTp/vtcf3ChtzwiF7iEqgKyBT1M0YUUIhSSCjEznpwPbTe+PEgdGrJNWUHpv+uCzV7n95uf4191qhBASPmY2UpqLWeG0AAQEHHv6CClGkbfYW13oP+joEcNRAU4ra2wyQWgUOSxdzqOtjcCRGEtoDXoFQ56UkXzOdq93lqc3Fb637LVJTSg4UGKJpL4jZXQnUBDCzAKBfc79yBqfGG+8HJ9TYwcrl/qJtT/ZyrbKIFlI0uxtuKEc1qD1jTKoUdQPCdZeqhFgCo8SsGCMgCXXU9sDmx1d/oesO9NDAQNv5uzgFB1oe4aAxNRIEwjVkBgWWlWPbBORdVONQYguLqVRR0AYZWC8TWk7FoVYry9xANEEhdClOctLiPGAiLpZXWTBGuweRdpoiNSzd37rC3ns2j6hrWMQCUgwu6+VDAlvWzdoi9dcXe8+BgPeN3vv5/x0//n/8n+PV/+2/ilR/8C0BZwZH0nqB2HtQ2q3VGO1BA8PfUvN0bYHBbq1Wmfuqz+PGPfBydKAVzW+SL7gB8qo8bQBoQvqfzKKYeFNiObw8QeGQ/XU37Bc/7vLgGMgSZFEo7EjM4FPjUlqCVzLZ7B8ahAgEAK4CEblcfIJr9KxUU1a8HUIMP6+fc41KAQpYKCZBWFzzmjKvYgwNAFhJLewtByzvHBex8gWzBQaM/0PXP2EejweBJkBBKBpaAYAGmFEQlEZAKkEhyozPrYSY38wYsmhEQJqIr/uSrQzo3A8c0ZDWoX9mEkxzAbPEow6I+ttsstH3MwGx9PcyZihCB0AIVW3VJzzB0PdA1T/3fWnym7uyGnfssKvwFr3f6+S6grJ8jlqZcwYDFCsxkhmeAgMtkjlinzA2NFSiqct6WpWNaAspGsZsjmMyR2VrD3bwZ55CBAgcQzKWFVgnRpI1rJo0DBceclR0w0SJlB1wXmHR1XTdCW0NqTIArSnWIAYcYKii4TqKZcogCABYFAwYMYgCCKwPf2ILcAIFfBzBZX7v+mYEq/5p+Tg0zq5EmXZsZuemjcAFDc6INAMxAQsk1M+t3/uQ/x0+//j/Dr/9b/xFe+YF/UxlOWVeEKZZASnag4Dtp9wYY+CEal4euytRHP977d7jUf8YWyHvDbmRsEzrP0Dvrbq8DBHbDAvNFqp786FKY+L8A8Wu6mIP6W7aIxtQWOdjGI3a/HUA4Q5ButECnQoiqoxAIOBWp+lfd8cGCEXtQ4BXrANTPZAaSLh6UIQxCDFJIRXeYEWJDMyRTItQdQwKFVI2FsQXTHOPafXaTj7RgqePtVSyZNN+4CNUfYgSvQetbkNB68gnZuQNazyHWw3rZZp+GJIkmZR8gWMGovFZQwDFJ8JE+px32CRgW+FvadNEDGiDwoFUNC3lgokbD6n4Esh2qfj4Yy4DaX5tz6P7OqXsxxKUfL9Ox6Iy2+5UdYPie+scZjDGmpQYOdkDgboCgmxc+e8bYgkFdsAMFY2aAjguHVv+jUv7VDeAAf50vE6ZyeG3LGqGyRi0QVv8WcStZvYPCKm+sgkbV/YTGEMg6wUBRMahG6bT3qelZpAgcgoCAK3UfXAXC9SLugyUASwgVHJgaJSko2GSMjHEFfvPg3TcKbXnsr67PaPuaZcHo+ucBApcMJKpr0+jGnNoaZjz5k/8SP/27/3P8+o//r/HjP/Bvynx0dutFXB93YQuAewQMxmbuBF9QwqokAugWk03z77no4PocqAi++vdSGgBBRIfePTDY/B7LSNXfDdvzmy6IuT61HSUHhyRDBke3044LlpCAKAFfZ2IAUhDpnBkPFvFxRkQQqfRxCThRqZr9q2UmDEv/pmSuu9YMmWjM8n0i4bVYmQMmObYp7gbpAazwtDIBiLUIDdBPbrcfav97IOEMT1scGjXMFIGQG0DQXTyfTwKo1lBBQqfKONR0IArVCDDL81nxJzADKW0oQ6xr9SXbjp1HfzLgDPot7Q70OtWgSbdkVIOvxkef1+JRA2C4aHyGc9mkhV0EAGM+uIxuXVzz/v08XuetzcZtEnw6phjWmgN6zrwOqobAnUEBxdTUBYlaCWNXyngDCKxKaH1OsFoscMa9N/R9z/g72AM6cw0ZGyTuo/Y4K1Mg+iUCEKoYV2mS6n4P3AABWuphP0rV3emFrg4xIAUoMBDG4FpjCpZohaya62AhoBOZ8lkjG6G6cROW3flM1utxPZcBxbR1a41uaiwWoGTRhLBYFLL5so1be/K1/yt++vf+F/j1T/wv8ePf99+b/xag1zNnC14ktsDavQIGhH6oDRTcqh3NehfsIS8PBuyl5VABAWaAoMYb0IDiZ7+NiipbPMMEHMgH+wA6Ox8VVSL1WVZhDGbZhkMXrFiwxAVBwQEREBSwroUBikiklf9iwHnNSCGoRG8vl9xRg7beoUkmB2jKEGQBIQhalh276poXgIkRSUBB0fOp/ufCF6PQt/XXqxceVra0R8mymFfWoCTUgFMLdixrBQhSMnptAYQ1kJDAtEq/m1HQmg6EsKWOfa6yGYxVzp+tsJOxCT5exeewm6hSu9h525vHO+mdPM5vz4LtMRYzNox8rY+9c7tA2XvwNstC8TvwWVT3Xvqqb3ftt73UVZ+lwqUPKvRj7h53YABo4xqilgUPlQ3wgMDcBkhLAwQ1UyUqU+ACA2tMQNvpV0NvlzK5VJ/Z4l1B3j3k40iKvl5ZBAMFXAQoQO73CKiXk5AigBxqYSuAtLhV6xq/uRA3ArQ2RWwVLCNhsdc1wHCpQYa90JRPP29CdcPc8lPD0ss3kNvNcQPrdf5jf10f53QJHUBAEotFuhlg23hoiumTP/0vBRR8/D/EK9/35/tjX7JXQxtBwV0wwj0CBjLZ7KJNJvKzF0HB4KO0ZoPu17dxx7YcUAGBp/TqLk9RfCd1jDaZ/O5JDQKz0k5AAwdKt5OyA36xrFfudym6kIMLRBiDYX4o2RHLZy1nPkA0D2Q/LvUPzgBSiFiKVP47mr+wCtsofgF3pW99sR6ilq9uUrZ+qe7S4mBdYLXUGxgwY+5LpvoWmvpTYxKqD775rRu4IISQ5PUIsC0YbldB+VwBAoUo71sAoQMIZD5mC/rxRsJ2AZeYqZIlpTFnALkCBBljuwBnrOuF7hje4bc2gbKjH9MD3THlLTTjxQNY2QIG7f3bdlFAB4TNDO0BAJ7t1t11dtd34dq662sv7JzfcF/ZsY2hcIBALoe3vw3Mx8u7DExy+BZAgBEQxEXBQKjBgKDodvQTo+673a7DnWoZ7YvR/459s+O1YzfQwe64gLoGiWCxdglSeTVEX8CK6/o6q4WxV9LaWILqOlBQUF0H6xk1Y8QHG1rAqM0zHd8urTOjm9NtQ9dAQY3nuOPazo4V80GPXDIQkrxmAMG+H1NjCj7+H+KVH/g3HBDvmwRUG2V5uXkXwpM3nqCfBX27R8CgtdfuBAoAwBcrUoqIqRpWYWfcjq0yBABCapSenzR2o1Z0GVwgi1sw/QJZg5uE4haIA33d0GrZjKMHBDUNxowUBSBxU0dkyQ6o7EHJoLhgiQe5MQvhXFiFjrApDbwm6qoxmp7+XlytidcAlrO+NRYWiVz0goOjL+VG8d02ui62rbEHXEFFi37nFvFe4ORRh2BHVSc0v6T5lW8FCBYrMO4kbRxnbdzVaqoaW6Q64ChzYGpwh13DJr1yPP6sRc9EaOAbUOfwKDG9AQxAvxvGxBDDGdqRat+h6+t3Zob5DtfU/bb3H4/9fku/dhoWs9+04LF6zaF/D+jBgHdDXgQES3VZGjhgAwdDMGCl9+Gof7fLN+PeLm9rFKb8JA9//WcVFMxKcFOQCogAgEhIda3Y9rWtFbZOJHJ1KiYlrXuWAFLbZsISVD0asASUe+Bp16f32hY4GtCl/fWd7D6Yr++yoTOgK0DAs5PehdkAAvDka/83/PTr/wF+/RP/K7zy/X9e+1OBuJ2P/pXfvqOrTJtk5/0cPoaP7n7mngEDxmtvvI6fU/fBJy6BAgoYbwUBCVLEmNJSFwBPpf7OH/8X8tl4Nfj4FMUbGHCRwkbpdT+vhovBsvgWydNHIVi2f51mJXfntmndzrDAqgeSBwjMYClY4Cp1cc8ekFTFs9LAS8Gm8h+jKeBxaaI2wDZe35Zfu19M1Kb2t/ono+aVeg+ffADWSX2bAV0HHrzLobEQsviEwsoaSER0rCDB9BQS2AcrRWUQcuwAgkWoj7LNTb5ZtfHfoyEzcDfWhdh8xje/i7U201sYm2k06eLIQNvZAmBv0LADGNz3q0b/9JR7UHArANjbmd8m2pO3xhmABpCet/19WxyC72v7bs4NjMa0fV+PW4N+QxO8msoNm6HxgMAYAgcOClBLFxd2Pv/SU/3j7h4YVrzd+2i89v2nvm4HIAadiwLaSEhWfRPA4r7n1wYvduUVL30Fy2DAgFyNijHAsIKCXmNi5oqqbQAEZvDZQIG5aUIEVKH0Tms8+a40V0Juf72WQgktxgnATz/5n+LX/63/CP/97/83qrtjw2SM7Zb5a2xBzc77y5/DL/39X9r9/L0CBoaEPu9AwZ28MJWmYd2ZKz2UpDstgOd3/vi/wE//n/59+Y7qhl+k9dwO2K/V1aUL2cESya4VLNkAjDMIslA0cNDOTfzaF2hi01ywnQkXATqKXtkCcGICeKk59YuWBo5aNrcERiwiYiPZCAIEEoV6jeOuYbYT2XT3sEOb7TisjccbC+F0caLu3pgVxmmsgTIjQaRTQxDmIhYLfEqIIYENFIQEihaRnpTmFtlmcgI3xEVAApsOgqa4eUo6TnaiL9J2QMaGndjZnbfP98+JwhAtFlpEthpZHna/9X24XTjdLrjUGX9/jjOWZRLMd/EarGVsYjrqNYUIrGs/Dz1OuOvCm3ZYCBez0YkTjToEMQkDSbHJDVd1QXMhNPdB1ktZNTMgazZAhukEaHehUfx7Bbf27hvpiv7+vK1Qlb2foEWNAnyx893P+4JV5vILCgYCNVZvU7RKS1pXXYJLUvaeyRvb6G7yO3KL17jgvjFnmLlvgO06H/RB0DVe9i8ZVavFMwaiLY9f/7f/Jn78+/+89CWZO7nNqebeuBtTYCPhQcGsCqNv9wYYeFDwsgMFm+lJAUwMMmlho+nVjcAU+l2tDsCTr/5T/PT/4X+MX/+J/wT/g9/4H0rxkNgCgUz0w2i9qh8/+PYAdMF0gZvGd6IoMJmCUGMZqkkgB+IgSJxhE8YZHKCflUC9ITgtaNKajj0wSitEgJe6I17igiVGZBAWLepz684kWFEf2u5OLuw4gC2osAWs2gszJAMzwRcAhS837HcliQKi9nksLKCAlUkgQmLxtkgudURKUa5Ig5oQFl2Mkhi2koVVGHYosNoappcx8ZMT3A7aj2PtqAmIMFdR97ELoMAdn3cMLACw30uGUMWaKmAIoao3MrDZkTN2DEiMc/nfmZtlz9Xgr/cCU8AojbWg4AABpN8sALS613h7znsuh/Fzt8RnUN1ZtvgM0zkxMGAGZtcAxQUMqhlBWeN71sJdVoCAA5kuWYMAPZv3ovfL2bnxpBw110sbq1qOssUblUW3CXJPa3B/ZfLI4n+cm6+6+5yk8dp0Cfaq4GJdt6q1mwum3v1DobEFNeujjVGTgI5tTJSNMReOxWG0n6D6U81d4tyX5oIqGciNMXjlIx/X+12Yhm0KLrUOrPEOzVZNuh+vV1Dw+U3BpVm7N8BAQMHn6kXfumtxDuwaZxCixImQm8YOFPy9f+c/xcsfe0WOv1xtVMC6HN/Bv9f9NLjeBATWnSuAQPI4pPodY9Ilil6BQi4tDiKX5uMEtsYlRNHoJ5IFVv3jZIsTZyAsQo/r4kR5BYeIpM8TtIpigf6dFPVRsGB974OVUJ+jvu9Bgymf+TYubLaozWIbZgteDwya/1I0GpwPkwmZWIuvSJBUJEIskpmRlXGI8YAQD83NMPgLO5BgjIz6No3KvFQIqgMN1gH2OpzRZQYgUta2yyXbaXtft9sl30q7j82DCTO2HigAAIfN5+soeHrWIq0nx/bX2R3njkDgTq0TJDPDPfj9AXhXid/xy1uD4ddjbaodToI0W1wSNX+0BwPebz26C1gzXHlbjnuWEeCLW83igC7dJygynVa9V+w9AU9ALDTanG7HD7Rdv3WFZ0b7YGK49a+9F4GanixZCrI+NpGi3BgCo+Mn0tQbQLAXmOrG19iCBtZCcx+4uhAja1PjOcpsreca53TW9SUoMxkCIVWAoJuzLD49TodOiIm71EqqgJMdKLgU8Nvp+NwBFAD3CBiM9Ih10yWAwERaucwvqv0u4clX/yl++n//N/D3fvJv4ZWPviwsAVAripksqN9Ne2rPG0Q9JACZJMSKlBko4mYXRB4wBwc4AxyBoIGRhhADusnf70JL272sKrlrKJREu9+i40FR6LnQFi5aT+AQEVxELgdyrhKVxw1cg6EMFOSqjMcbsBC0X6w6tIADxxrw/iI3VvEbNRXkRXlNop3lsQUyERESB0TkqqyWYsDqWISk52OMgi1aSeMQKAkLUBcpp4vA1YdYGlDghv67Wg36OTLAAO0ov/svBVYqtwMKxqRYPAkg2gp1wikoiEF/Q1+O4cWMr0u1rAJS3Vfa+3IOdzTmt7gG7gQK3O/SYLjH2IcuIwDYggEPBMbdv36/Aw0jAACqIqHt5DYpzDUmiSprMAYT+k2GdxespWcHTFnQjJSvdLnmxrJN7w/rptIuv6rqKpMSC5ADIbL8vrkHevecGfeeARiDf309DAMQtoOGMqbVW89FMnUcGOhUC7v0QxdH4Fi6LpjVNRqBoo5VTfW8UCzKr/erX/Preofa521OAkQsMibB+lTWP4uvSiEixthsTzyAg7/+sj1ojW9oMtiz1oOCl3GHLTOAewQMep9JcyJMu6xSMCz/4FwINgBE+J2v/AF+5p/8e1LF6odeaTcxINXDVBbUqL3skbyeBqP3odsNtZLk3YpPrQEDMayEEoClAwelTpDqcUpan9uMuQKbkaK23SiZsE4heS2EHiC49Esr4NNSMAOs8h9REKCgN9MiuL6Lq5DiOgII7HHT3N/eTAUNHIC1HHQHEtrNaIudpUqatsKsmdBSIDkPAwqJWaOdJRWKculZhMKIg5shFkY2fydp2mNI8KVc2VwHrhhXjbpnFoYmtfxmU9ucVo70gMExDGPkvl8Ma1yKBe5FtMC9iGpsuzTd3spvDbR97JJP0+s03KVNjL4/PvP8eJtz2EsLBBoQANBVLRyDAe273gUgP9YBAPmYgoGdWigNCFD9vjc6Frg5kxk2QzManY2IkGMHBBiUCgb8vWHLzqV7w9J9Q5CKp8QiOJaCCZMp81ZpcQUF5gp1Rt/cAR4I+CJYWxAAoN47NodLLXIEY97q/eMKWXk27g6AoI6dd+/4OA83Js2101eQzEWDsRUYjK6cS+s9CLqOsHNViuuyFCv6pMG96SB9gtUpJzpw4NmCassce6Dt9fcICoB7BAy2TQR1LnYFUWMNAiSaX9uTr/wefuaf/CJ+7S/9ZwIK4gKmiFUPeCxtglTfHzckP4va9367FAgrkdL0jBRQg4ZkAZe7p4ID26nVq4MYIFeOE0DdaVIIG3qagV2AgLzCZJ1F3KfRVeR2PBuZXF3sKEQEey8ECUSCsAmLAgWLU2BGBVCkNxcVIJPcMJylxiF1F9aaX/hs0RsFl6yFoAsfk6RDgiVyehVwthZjDQJWBhLlfTeDYxHGjAZQQkyij8ADe1CBgrEGloFidBIrp5JKzXiwC5/l+NOwCNYL98GOJTe5SR/kd4fMhQ2Y3gEK31G7BUS0YlGTz3W7vj7jAMAcBOhnfTCgvL+NBfAswEyrgc3YO1BgC3SX5z4CAX3N3ARwYGDGOO65CyxleC1lA5aNKTARMmBOvIQAscms4Fl1BcbAQ+lX7VY0UBD1GGOgYPWjOyDQlbA2NkCj82f3Ro2NYQEITSQo18DeTQnjERD4i/asj41zTQ21eA/VilAwwGEOCs6ZqztnVUAggMwAXWNq7OfGrIuVRCY+Ektl2iCaD5bBUeJB3AtEQAmgoloFG+rZ4iK2Y9YCDV8cFAD3ChjMrcgeyVLpVwoa1Ne+/+TLv4uf+e2/jl/7S7+MVxQUWIlRkwS2CSLAQG5wT+3ZJBmj6s3fbRQ2M4FjENYZcnNK0QAAhWQOhwTERgiaKBJHgATWb6+eeQAMDSR0AMHYBhL/Glt5YF0Yw6QgFA27InK7IjI9fV0gg6LvCAEHURFyAEwhWOa/+ByQCUhW61ldC+zYg1nzoGB0o5YsO5QCcREEEE4rV3VGZtJxzA2scUCi3MZJb2aP+C2jAaraaHEiMr6quKhCSmy7Il0MW0EotyDW9xpYALR4lJWa3mEXxtgF0guvTBHQMxEl14BWecsBB2tDwOCt2SbvJcNiiAof2yYwcEgDlD8X4gJmwYD22YsgoBn8WoJ4BAGhdxlMc9tDBMy9BmUFdE6PsUgbMIC2ybC1ZOXmLpgBghlInsUbBtKlIDjhsEkzUTJ/HxgoSKHdA5ZKWGMEFAwEQIy5BwKlAL56pWPTNjUMDAj4ehUTMABgFxDUueDB3wgKNOjTAkCttLS5D0ZQ8B2t+Rq7xDGI0iua5oTtS9cCxBARUpCsKHJMSjtom29Al8L4ZAMKZk3n+U67R8Bg1hiXLr5F9gdBZER48uYX8bO//dfwa5/8Fbz8Qz8OdnnDVl4UEIAgE0VLjBZ2CoG88YPXn1RDYrvUFAgLuK86GNT3FwBS5iDGpRn6qIt/xhwchFDjCapbwQehGUBwr3cgAXA7nbZz6xXvej9qxyrUHVKsflWh3WXxXEl27wECBFBsfjOItcijgQOovgGLoAkKg1lXHUeXXoqxq4ujLoY2NdYMBA0IMoCQIsBrFoAQA9bCSIVbRkOgDiTYjsDrIxDQgqiIlGZNqOsTelbBAoz+TMCCDGwDDDbeHgwMi2ldcIaAR/muMlD2XdhHd4DAXdiFHZfEbhbALRkA3euzOAD9/DYW4M8ABBD1MQNAdakVtW8b4aGC6k4zRtGDAZ9ZsBc/MLoMRobgQgKC7zZ5TDZfW3ogkYoNhR4UJDX6lQpXRcJaEZHQudYqGPBugCkb4OJvHDtmmxjvTrsIBoYL7EDBJE20ggLHFFhGiMUUmPtgBAXHnHFeubpz1lvW/E7RUQHCEgNKYSwRdRd3Lmq3gpSiB6nYmroV2oFtPo6g4OcGUPBibAFw74CBmkbrPFK+rL4H93pBw8sFoCig4B/9Aj7/qc/i5R96GZY77ANOzo4xWHVyrFlAwloKjtX3VzofX+frDkKxZxQJ0FOawIMDO1ud3qBACAoOoL572Rlqxb8id6VUBzy3G4oIFJRms+sfghMBbwysUNB2wWWgMgt+txWsJryxChaj4EACqWqbFHKKKCQLUWCZ21W4UeFzASRoruYvBKy5aOo4Y1XAlACh45jF6PtLM9Dght67wmuVSKVRAxPOq6aFhgYQ1tIyGlLYirGciZUtEDaBiECFO0bB+1tlEW6sgtGstXqkBwu2w/JggdtnN64Iczk5IGHPNwWHxjgFYMMmbBZh/Wzt0jF18g4BgxfliX0w4Szq3z8fjb471lSuWZ/XWICJK6D5b+3zwoBtQUCQKHY0NkDAgMXVwMXVCCPA7jXm3i/t3Y8GBmZBtyZfMnOnAW0Ya7LB5B6QiH9f9EzuRatNQGa8HCgQ94BpCphPXAWHSG7VYIAga6aAZfBUMNDKqM/cAqPI1Z0AwNguuIi2QlKD1LQDBQXUAss9IzCAgqNuBFfnYthb91MIWIlxxQwOAUskZNsUZgaHtumUMVZ1x5BkLttGYAAHFm/w5M0vDqDAAYLOJl7YLGu7Z8BgaFzcojNBTSSJg6CAJ2+8jk//1s/j8z/1WbzyQz/eRQh7UGCBJeswOYQ1QEWOI53XgnyAxAGFWGIaEtQqNnBApeXyN4BgCFKEiowdqOp0tsiVDCxSMrcLzLkEEGaLu9kQDAv5QM+ysgAejQerFa9onIIWHwpJ/OsxIdRaDQJqbLpaV2TZEoEcOIgIetMUUNAdf6Da10WFmACDffM20qfFuLxK5UkQ1gqJLVg1mrgHCVKwauM/BACNQjZGIUDOkyAMggUzUsHAKvQuCOyBBQMEI2CAUv4+UKkCB0iwRwUOTR+jjX8DEQD6mhzjXHFAok0cvsOSo23GCIzG3Rv+rgIkUO+MweDbazx53xv/+nkHADp6dgAB8C6BCRtQH+tfAQv+vUYIjkDgLhk4sONUVoA3rICsETy8NrwPbAABEZz0cGgsge5oA/UqhDE4YBCcxsCszLFLMTRGwFIKrULlTOPD2sWsAjdXbgMEQvkl8Kgf4eWmlTUwt3EPCLgGiI6gQFgD2QiuXHbX/dXcCByQQkbmgCuWdR8x1ss+dwMrC0igCCJlS3nsAxKm4AufcSn7E1DwAu3eAIO6g9684cGBa0bLWKf+5quKtF6RG1dv5uyRokPsM1BwyqVOjJHeCxpYEpiAWPR5AWW5s2JhUAYiCiiGmjCRi65NSi9RIPF/AXWnQ/a4aO0HZQ8Avh0glFL7gsfFvhqISb8acwC9GVXmVXKx5YYLh9LQeVlAMQNhhYkpIS7iIrEFSi8DRZkDSFAioshBUyigDFCISIURUXTxBDLRZuEELlOqYTphFFQ4KVdk1qprAhIs59vrIwCMuBa3yBK6tK7Cen2NQWhR3FtWwY4NusAscFtIuQIEZ0WMPqmvDQBCX6ufqYFTagKX9h0ajjMFEb7NFqPhPpwa+2rce+PNs/erwZ9/x79mhr/+3kDD+vLEHROg3WgMwIwNGLNsmNt3Gajqg1ljASpbgC0QsNegv+O7fVPq3AgUasXIZjED9XP6YCxYZIBgU6cgNIEheayAoTIGwhJsKhqq6JBXISynY607AldbZFaoatrqBsiG3I2xjyHZAQRiYHsX50xhkl0sWdORUE0J1piCrIBBmQK/GVxzY3CKokBhRiUDxOaK+kLb3DTGWOdL6CLEBIDJuEVZHN391YOCl9G1HVBwoacB3CNgABg4oGb06xv74KB1qoCCflfQJsfKXCcHIH6+SiMNoGBkDAA1NurZWDMhRYlIzayyw8QSBQ9Welyj9c1QRhJjyaqQaIEpAARSbnW3qazAEgHOQvd7iVAPEMTpqd3WDMXF4jEuF5wBkAICXximaJGhcLiS32HJDR5rNQS9QVFpfXUxQJgTYmhqT8RKFp0tFSDXwtp3rUgL6zFmecUv2uy7lo1ihtDYhDOAjZhSprrgWgzCjFUw90P9DJn7QWMfaJv3TSRuCOiCTWiAWJYSZ7zZjLcDEP6iOreDdVLBCCQ6EDFjIhyI8G2r2ObfdMZen2929vp4a+j9e3NwsAnK6nyxLVupAwC6GTDK36CVZwO8W8B2+pfcApfYAGALAqxrZ80b/bCHaodm3RFrtwxzVY29AVoDBEFdBxY/YLLEHSi4VKtAGYIKCKwSqaslUssM37K+yJ8WO9LJTM8CCzW7anRv+pinjZy9U5nMOl7i0lEXgYE8P/alvedBwXztl81GAmENxsgGWYNzAVFAtJgq1nuyUN0UrgoOyOaB9ssLgQJlvu6yHN4bYCCg4NIHtuBglFGuoKAIKDCNApsc9hyw1MSGGC3VyIMC7/sLRLup3plRDVsqwGqsQZFJRcF5GywYkdStQEFvykaDUigNILBGKJYMLAGBCzinTiGMgmn7Gzjw9HDBVMPebmqjgVctTBMXeWyVB9OCwiLHbOlKJrDEse1wJYlBbmJSlG43QUCL2PYAwXZhswVY+rX1v48U9ntbrwZ3aXGuuzX3un3eRGLOaIwAAAcOnPLiWro4BdKF+TwAhc794MBCAwiNJrbX4P6GKosI/W57Ov6txt2xB+bLrECic2FIj8yYCN950wXIuRBuNfgdWGi+VHtuV+GNvP9b/Avcnjds05TqDABUQGDfwdYt4IHAWvp5eCk+AEBlFFB/d3+ZHnf/3vsS3WNfuTR2n3evV6M6B6w2Dz0gsKwDcx1s6hX4MscKCoQlWIHzSTYG51MDBOo64Hyeryn1wmOrZRHRrdu31Z6wwGgmnzrqAAEZkPBKk1HcB34TWFR2unBlgNesUtNwjNGkNVeCH2gCgmwiE8TluRZhjGMUexKdbSG3KYRmMxRIBkiBbIBfq/br7kxBfRuXWYN7Awx2G3O7oxw4GEFB3TkYbQg1Jow6OcDiQwJamkrzH259fmO7Tf+FC0u6nlvwKh5gQY7FwAFBdtYWmGLxBUF9dcYYsCobkoqCFE1HTEkkRCnIDU2kN+8qaHs9S+CgV7HzOfMV5Q/6+VH8ZdIxci5UMni5EhuVZGU0kSYGQKuOU1ywhCTrQmmuBYkp7AFCJBVVYsE9QE/ZdqfYAQR9zfe5vpj072whDxhqyVuXsI1UM75n6I5OKUEflTwChTRhFAgQY+7cD5U5UGahAgb9LMAb4w/0xsEe+mnYQIUW87I3ApqqnftsBRAzV4bv9Ettsov3VP6lHb0Ya49hehla+3W/JntQOAUNaADAwIEHAXYf7jECs1LkPitpf95I6wMDG91vXTQCTXldd/7VlQX3XjteAwT63Lm4gLqM1HvNA4IQUFmC4OMJlB0gxw5IbRdxJ/B6Bp9PwPkINpnivDZAYG5NoJ8vXRCqz0TRDYMBgJQ0ZmlIPYSmXIfmOmip2I4hqMGHAhAK0FWrtPgNG3c/v4wpuNQuZUgB3l6QBqprvJLdfbZ0Qoq7Wcq2aV+EcIui4QjS/YS4Y7tXwMC6ZtMNQ+fsFVyyACFDg0IpcZ0cjO265xG/2gL33FN/6COB0f6ONJ+/Fu4WF/kBicxXBoIgefMxCj2v0cACCFTPn7lGB3NIqAFBFBtA0FRFCpLVQIAgfNLVYHWa91yGma+zNVuakZasThpL4IIluRSEQ7tGcSsUOQYXIJYad5Ade5ANIBAh8nwRT2rg6kLuOtL60xdsksVd+twzDkndE8wKXoytYAcOzKA4o9OtF8XAAtW50YK9tkAhxTANaPRxChKH1AL8TOp5ZvABMx7snrv33Oc8wDAjYd+/5Mrwnx+ZiEttavT1hT0q34x7BxLQ33+++0fGZ8wtHwEEu3niQYAHmyMz5VMIRyBwF+XB1tocAeYxAH29j7bjB9DNFQCd0fcPAvzYYgCbBhJ6wSJjCaLNvTGewLkRqKwop6O8dj6ClS1gLU3OzFrDwLbUw2Ia0IMDE6oyUKAMJGKaCBS5gEJ1K4zS0xUgOH2J7ObcqDrpCTI/gmRxRgSwxg5YDAFA8mHTuZ+0LkvKcd11s4I2DzMYYHEjExiFCK9/6TV85guf7uwXupgEO9B23vH2U9N2r4BBa7rzmOxennzpd1xMwct9R7kdgrEFzTA3owK0hUYMvAarqR8JvGUHWiQwNDXIdAyapoGJiegVdL8l6SoAkyDMENRNTyQsld7EwVJwWKi6mh40AwlWlcwAwvmkksfUuWZYGQUOcXMzb7TzA2QRCH34DKgRo7wGyZzIWzAnYEHiDgJFKc7nhJEY6OSWi/Y30IyEB1V+rAqwqQJpRqCnhKlSwpSLpC9C4krCREPB//ZmrXPKiyhc50EOhFBUGpskkGlrAFz8wbDjA5oB8P3Xt2EJGD4U3OdsxwjMDYZ8nnowcMGlUX+S5kbaXrpE4dsVjAAQ9po75uZOv8Pq5z8yAkYAd84YGMFAF3yG2ZzQvz5moAOOQ12PGDRAcEv9wwE3oDf4QBuPOrYYfnsYzy6l1hgEAL540QwUgHN1GVSmwIMCYwncpqKuHWP8iUlZm3vAQMFyANIigGA59ICgVrrdAQP2GtB0JhwINcEp1olRbYD1o/6NpGtPsOBBBlwgocwD6jKkuksjcpvENg6dq7MwKFKtJ2PvMxFe+9Jr+Pnf+jQ+95c/j0/MxIv2GLs7pilauzfAwBuyrtnKxIwnb76OT3/h1akilGcLRvpofli5SYlFICfogpainMkowDXmDUdqYiJjilCKYXMxzEAhbm6EIhOFwDV9vyvnHKIys+LbZ680piCBgtzoEgx0Fiq5rCikyoXnY6XdrPoXAyqeBHETkBTkYZbHVdYQQYCJ5d1rTAKpaBIBwHIAFap7YPIXywy2lEYFPxykXyMEqQOjkWmd1oMC2u5M0fuOU9CbWb9rdPFKIpVMuYgvsGhxKPPvgJohqL5FN690G9DSlvh2kODSIyORlMHVnaKFm47sYBxe8O6TWfOf96BDjm0sBQ+7Td7faVZDo9c5uRnfE32vn2e03TuAjbvoRa959nn/1AMBee/uYOAuqoMVHOysCQYILEugupns/nbjMMai2O80sNCDA2AO5trxbEwBuVmcNoEBhGziRbK5wPnkggytyiFj42baAwVG8acFlBIoLpUVoJSA5aoGMrNpooQFNXaglqxWBUNzF2DQmsAARN3zriLuOHbUi661XhRrIfVjdD4QnKiV+7QDhAIQ9P4JDezvNQbw2puv4Rd+6+fwq5/6PH7soxNQ0H1BL2DnuLfZt3sDDKy1HWjPGjx543VJSfypz25AwV4nzQKD6iJqyJHk10KkbqEwh59PFZrlDPsUob2b35+npdJJ8BOr3K+em1+0iwcKWvAHAEUFCWUV4SFTJ8sBrOWWiULb9YcTsIaeQQDE4pHsALoaPFU9qCiYyK6stcZBrGd5bV01kskiamSB48BNoMdLK5MoD/b2pY+y3SzGpj+OHizUstFhLksbKUjAkWVBOIBALJkkK6hGIxt9aO6d8Tzqc/Uddlr1jE6BcdRQAMTbIsGNd0f9Mxljoyvj8Lr3WQO4GKTWXBvs3Aw9YJmF08zo+woABuN/l6DS8fr2rs1f311aAx/6vGy1BF4UEAB+LWjGIcUmLuSV8ca0wVlQIPS5L1s8Gnl5Mj8P/1b/l/W+HqSMO/VCAQlyP2umwWqVRotuOHLLNKjKS6FuKOpzDSZEiD0oWA7CFiwHUBK2oAxKhV264aQw1SUgoFc63TDU/qmMiezJDBxQQJc6LYWsaKNKidDPh5EZijpuJj89awWM33/zdfy7/+hV/MonP1eZgt3N8Nhc3M5tgMDavQIGex315M19UHDXFoBa4AcQVA9ofj1bQIr4xX0bA4cMDHhq0KR2e3/udmdmzfJj5aL977kdHtkOT49bzG84VAVUnyHCAsSzanNLrEGIErmL8wkUAnhdgbMa8vUsSl0GEAB0mR8GEGI/3bkUUNTPaj48lSyaHUW+QwCYCgjiuuAQZJGqi54LWBva5uaqHGrbI/lKkBYAJIVshP4rxdJTpcpjlyapAGHNBRQUFKiHxehDq/sA7BsJG8dQGQdFEwoYAqhqKESqepTSXz4WZfiBWYqmB7hlckI+9c0WLOBuPu4RMEgP7zf79dsAwF1Efu56PfbL/rp8u2t/juJCo8tgr80AgWcJBADMAYHpCJi4kAUEmv+fCBU0NLLY7dDt5H2bnK4PJK3fYae82TGOBhIUEdd7GdgoX0bJMKjh9AC6WAIDBETVTUDLVWUJaDmI2yAkcFgcIFg2qYYGBmoQoV2Gd1NNrn+6AbQNH7Mlgol8AAGUAhIDiUy6WoTPMmSjYSBhlooK9DYhUV87BxA741e23/vya/jF3/4M/s4nP3c7U9BfwLTdBRzcK2BgzbMGT978orgPNqDgdrwVQGCNCCeG7vZlyGruL5MUsdPFbHHfHyOGG1sw9xHKwqHnPoCB8dqsbYoLuafyfXZBRduqgDEeJO0xnyWrIERQOINzrAWTpKJiAMUzEAIorwIYLOXRpx6585cH6pbQHf+oXEbMYBQQE5hzXTxIRTyk2FXQixm+u+0ebL3N7juk1TQpINAYiKRqZ2rcoz7OQQKSRoBwRkAsIrK0FiATwUqweoCgHpf+jAYfsw9SLYWrmmMnsuSbW2ms2/eEnW7zd49tQ3dOdjiXwAKwpez7U7fd/ov77d/LNQCtf81A5zHy/4ILYq9/7biFuWasWDR6n2XQzuU2V+KS9oWF7LMGCmJdK1gZv0bd92mlcvZDD13uQPtOPV57XEGBggHiYYcdArjoOsIC+iTtMFXJ3/pZohpgOI0lUJaA4wLEA2b1DNp9a+AeGwVKP46ztjddSdmYgJ5lMAASIyEhAtzHKK2Z6vwG2hz3bQayDRiYe44I+P2vCCj42z/52VtAwc4Fvge2ALiHwMCb+66gxEd/7IWOo4BRxHUgSDGyRMcDwBKDag8wUsHUqboXWW6T4aKvdtJmAVnyuj3fuRYSQ2v+SElF0gqDRV5L8YCoAEFqG2i6orIHFpwo7gHVKLDcZMuGmKQekRMdqVWE9hoX6aCCisSIGL2vwn+eh+/i1hWANMqZDbBAQE/UeIYlRGQQYiHVsiBIyeUeIAggkJoNrYgKOgVG71YaI9O7jJW6o5ztdC9f9kwidxRZuUtAXP3dqSHbAwp9doV0Md0KDDxVfxsQmKmI3nrupQGBoOA4BGjkOKFkTe20zzvWbe/Um/HXD1SA0EBGYR6KE7nzGMDVLLZosZoEDhCY0mCXLmgVC/N5u6M3A34XS0hh+9rY/PHq4wvILEQgFBAHsCZes1oZGtaHLg1xBAaeJUgHAQRxQStwJLUMTG9mLFl9tzUR1ehPA227L7Q+KpB6Kj42xirlZgCKFTas2PQ8Joyb2Yd/+uXX8e/9k1fxt/7iCzAF48GHdldwcO+AASAX3+d5fkLfmI9OmwwAk9BHTJI3DyaluYVSMvCdIgFMwhBEz1K0B3tugRqNSkb561cmN6f3hYGpMzDTYC1uk9Jal7NctCpgkcm8ghADIweZmIsxCOsRVmAEOckiRBEhLSino0QGK0BAUcEiK4ICVABwSZBERGvGa9bFp+b0TvSYfTBT/TzawljfG5oHBSZ8Qlo2Oi4gFT2JcUEMESUIcFoJG4AQKGDhVlnTAILktSs9TlRP565CNr47RsrbiAKiARy4sd4zqF50pYKEySmZWJOAADVsG2Ag7i8fOHmG7Xx6NUjfZiI/HgzM5MTvfN56b9o5ynlzBQMogIR5N4Nt7Iy1ad8by8AtOJO5gbh+XPtrHrUIZtTxWI/gkvRwDQK0bCKTH2bGtmTxzvzXE/J1JWDxWGMn+FaP2Xd+E6KSwEGsZ9FBAUBU0CX1u7Vhsy5oCmINLnQgAPEgYkTpCgWEc0EvVQ/uNWW4d1nVMXWbs8DONUC9C9Lm0P66TG5dpi6TpjiwwAoW/Nrs/vSzhRqPE8IACj72cmUu7O/m+2PrXKfYMAavvfHapW/fT2Dw2huv4dXfkJSOlufJUwRlzQLGvNuAWHchxTIQUNPPlrBFozZ/piyALQ40xhLsuAy4/c31ufAhI0U2qrCtdQGe0VeodGVWkJBY6LgYWIxhIKR0DQprdQMYe8DlLO4FLi09SRXNyDIcfJ/cRcO8qtoN4zNjBCwwqlsAXdlhAyhjrvRYic9+XyOZaRU2hBxdGeOixZ7E+EXSWukgrCQgwACCaKerjjpvKXI4n+PYRiM641Qy2s3KhQFC1VZAmPvax2bGtXC/YJrBDUQtgE8jrgLZTtsZWiYUKgIYSs8kWLbIjJ6fgYFLQMafL7B/ztY/AoD71y+1cRcP9GPhx8H6foSp+ztBv/vrBa3GoMLLBYr2tAM0Q8CCANf1heZ+mJSdbvfghFHoL04Nv95/SSXZTCmzFDGmzC0mofu6WxMsrkCrHhaNHzAXAsdDvR+lSJ0Vs4PK0XMFA3YPWsyKb5EI5+wyPtTyR3YEChoosPiNMaDTXYWMvwIBMf40z3Bwn6ur2I7tAID/y1cGUKDvO9JimvWzHbO5C8Hs40fxsZ2D3ENgYBf92Q4UANKttyyeiiSNNUAg3VUAYEFqX/yKIK0l0Ab57eV5R/c4+PeH71vzCK8UEbdgiF42lMXocvZvMUz18uoCxf0CFSWQJjGpPDOwMGGJCdFYA4pASKAs9B6Xs6RC6qJkEssE9AvBnkG+64K0Cwj6sq21QIsuRrVa28S9wQN7URemkMBlAeVVAEJZgXxGTFeIIQmrUgRQxQLk0gCCuRg47mvly+Vs5+Bsdz225MFelB0LwcaZalxClyExK341tF6gydMQ+tcZW5N1laf9Lhw+eBI9Pd8OqTv+FwQFBghmQkG+tO2lNssE8Dt5bwB8ZsbYluH5pfGcBWmasuUYVDitWFhW0HrcFRPyOgFgkzU3l9ow7101VBDVQme+roAU6CntfrRjeHbBvcYBIFYAMBZtc/ce7a0HTqCoWPphDS5M4HQAxwMygHOWqoOr/8fQ9e6u657EAaVIQBFg5qdp1RYgAXCm6+BrkszWam+AfUBzl/3EvGEX2vf74/7iP/5MBwpCGNiC26f75kP2O94+/gd//5d2v31vgAFBYgo8KODh/R4ckPtfWtC1znb5plFg698X33iCX/ztzwAQYFCP7RBlQEOb02jhmvqz4wdUP7jl4dqkKJIcKylyKonrqalat4FVrpmtyIf/Ge4Ww7QSriIjZaEzUwQW1uh8/bcEDVAMURYnikA4S3qjBSKFtUYoT3eKdeG+Axiwz3bHcaDAIqHXVbTY7V/JLX9aAQKPcQ+1j3VRUoYASUquYrlqVGYHEEp1MYSQsCrNvoYGEHIYUh3ttDHPv3+RxhrDIsGtLbDJFBoTc9VXCJG0dgcBsVSxlTUUNCU2Ab3CGNHU4P7/SzNQEENb1E1Extwd/nEKofPz3xZAeYFk1LZdpbd6EPIxWRv6lMMYNH7AwAGhAgHKKygfp+qCfD72mgFVsGxnvlt5dJ3zlMQVyDq/5T4AwAJiuF68uz4iuf+5raEsKUbt/lyibhDUZO6uB3Iu22qHC1jZgkIRuQggsH/CGAggMDfecXe9M2Amab+JlZ7JjBhNlL19Dg4UpGBCRGr2Swu49D9A/gBu3ZZvzbOfxAVsjMO2f/7OT34WP/axV+bM8rCxdFeA0bb5DSbQg4LbsvPuDTDwF/1jH31ZNi+EHXAwb4SeoinqIyZmvP7l1/DX/tGr+OVPfg6f+gc/UYFBp/42AgELErJc4OoLdDeSa74+PJlYh/q7ZRcidDS7zZkdwap+GSg4r1YGlDcLv9FlKQScc8ESA64KY2ECxyg7usAoISBHYQ9iiFiS3MiUk6D7clb3Qaq7+CpcYulLnS9TO0mvUTtweB/QUGa9Ad0uhXWaO/31rmrbTH7VjtcNtGVZJHCMoLhIwNP5hHI+NoAQFwEIJQN8JdcaE5a4IMWIyJKeWrQ4SqYm1+wDoLxUM9yY3akxKqftQYYpNJpWf8ulhoA2lniBQuK7DyVghYmuaKCcdn2Z7O6Bdi94Y3uXoEQ/rN3x7Hc0Zgegym5Ip/jnDcSEKDutOLl3987RFvYREMw0RHZ1GqzdaYfWLs1/hchtGPS4llkwLUy0umqFqxh/yqcOEPD5JPP9fNrWH5jMdQDgtMBSAmugcExovaznnPReNjRj9+refQpUQ2mlv9uORQV/Z2sBeeliYQvYsQVnFgmEc5E17KzBvSvPS94LGJ4H+aYorkAkALkgxlCr2BLaeh0dYNzEdZjL5pa1mxQ8MUnsUqDQRJcg7obGGghA6OLIALzyQ6/UOTSzL5ctWBtPDwqefOk1vPqF0b2+3+4NMPi532gykS06ewsOgHnHGt4ycGAsAUh0EP6qKk4Z0krqlJwzAmsPBEp2YiFlc/PICQRUUSbzwYVUjVFIV+LaQGMwiqcNYLEIl2uEyzkbMGAJQFR/3VUJWCPjKgYsKaBA8nELExYmlEBS5CgIa4BiKYvbwCcGdIHxO44GCLaAAa4/YjtWyYAl91Npl2siKgYKdJHkda3VHbnrb4BzBvlyrjHpTiWB8pUIq+QVlIWNoOUAWq7AvAjLkxaAZWGluGCJC2IkkWxWt1OG/OXQ04b1tF9gc+6zTuy7idpOZIwrySxlY1cq+lqo1dzWzEgaONmU2frgvrFdCj68lM4o3bu9y1rwYXN9jOmdXk7WQMyLnl8tEezEg0Y1wVZueJs27O1gNfR3cFdY63zWg3sxKDPRSw5P4gh81cLzEcWKEp1Pba5bXQJzpe3Nc59ibMbbXFF2bT6dggwQhGq8twGL3cDKy4MGQn3brwMjY6gBv6Ze6IMLR5bgvBYFA4xjKTivbZ1rsQb9Opcio6w61lrJMKN3CVEFlXIfGyig9dhAWhV2cut3t3briFt/O0BADihQSIjWn4AyCto3eupe6rz29AAK9mYju78jKPjsTwkouMtMvjfA4HMT9wGwBQceAMC9NjZNOsBrb7yGn//NhrTss4l0D2g3mYGBjUJYaYazNAPKuXcAkw8GChEcFlBYwenQShOnq5oWU0gWeCKYCi2AFqhY1CCd1v0y0LaAHqJG1UfGVRHjcg0GlD3gKMdLLJeToukfMDi6a2Xj8fb2xL27YMxIqLQji7YBStaYhQywZhP40TKkXQpYXQkVFBiDUFjft3M6i181BEnJjLFmVnASlwgVFXbR8QsHKRHNNoZxkY4oGTFEhCg+2hJEsrmERhsCLSipnfbd0IFRncXd5XtSzgvLR9ZcsIZW68EYpEjKKLmdlffvWxuD+uqO/BYwsOen9y3rRLUS4z5zowYkVtCioCDAAf3+3Oz8tsCgsQMmHGSCYiY5bhuAMNvRV/dWu/67woKxmqUt7NW9WPtSyWarP9BVKpTyxZTPUpTIAwJjDNYzeFXmIOdb5rkwBZ4dAAUJEhR9cX8FCgCsY5rMMBAu3rt1MyCjtdNDBgzs2H3Z4+oONUCQxT1wkzNuVsZ5zTg6N8JJGVEBBsPmJwgoOKRG5XtQYCDQAFoiAZEVFBg4W0+6tp+rK7MGO/s+0JLPUFBQM5+CunJCUia4bfxEo0JLo2mXpon7agQEs/l4GyiY1lbYafcGGEzpkbqY3g4OMHn+uron+toKchQyFMncaCZjBpxkKOVzjeDnUuRGdkFyAOQGMWRe9cFXcFRQkNpNFtO1xIAFi5RnrMOJS9qMMgXOnWCBXBa9vRZZRAuTGhiAOSBLjDe4ZFwvEZxlV1qYJAUHYvyEBhUVxVq0aS/P2e0yZoqF8obz4bEWWjZ6DhALQUFuwOzcET6WgEsFBWXNGpjI1cUhLUtmRSRQyAhJAICkXDbXj7zGKFwQrqCAReMoQhb2QKtVkmZuxCA3udyc1IxuhwXmZma2lFrgkh3CQEYJXAGDl3JOFCo47EBCMpBAu9oBs2YgAMCdRY5u1zGYiRttzwvAnc5tPK9dMEA+INB2721H7413XYDNtzv+9u4VumagStd8+cdtk1DXi7WlHvpYguNzqUFgLoT1VFkCq1y4P8cBIgbFAgqhMR/GDNR4HVSGwBs2W4s25Yq9L921urb6c9hZB1qJbS15rBsZS0GUrANhCTIDN+eMY2HcrOJCOOWCU25MgYFdoK1vVtguRWDNhBBNB8MNZmVslC2w+aGggPLRMTenzoV56xpegzpDreNAYUWtOxMiiNYaDAor9ISWDTOb9u+JKfjUi4EC4B4BAzP2AGrqYW18V3DA9b0mjvQ5vPLRj6NWJjTabD1uXQV2Y9sOWn3ePPrCbTdqCNeCgoLS26r8Fa5Ydqp2nkTgsCKGhKR068qitxAh1fgAt+MBKlNwVnCQC+OcLViLUFgC1UrXEwFA1gpiIuYEUN2tWuaCBPeS7oJiS6mZjM/UPk7GkABYGhZT1iJL9jqDg4CHGmENwFQVZ40zo6yDXjug9RhUQAmOMi5bxQGCzB9KBwEPsQBF3R1h1ZtadgbNzygLYDWS4U6mZPhVPdcKNBp7UMxlAaHmreZDBYDMG5CQU9NyX8uYTtmo/u4swla86E51FHYa4+5yyEB7bXZeQJ8OWNVIh7ojQt3LuRoAsNThrpqgdvs8uMvO/gVaTRvkPnCNy5ZZ9NoE6xllPc1BgRYrMlBQzhLXw5m7+Y0QwMgIiPNbowbJoa49bPQ3OVBQCxa1Sobe+MzabbvaOpeL3FdZAWHVAdF0xJMyAzdrqa4DAwWnopkICiJsbZPulrVtico62WWizZmIFukvMR7yV4DZWkEB5aMwN8fnsp5bfMdta3iSmAnZ6J3FDWmsQa3tEEGs/V1yG4+yAmRBq3vrhl8f+r4F5qDAe/f21mlr9wYYWPMAoWsOHOjT3ZteSjOrjPJHPq4R9+hQdmMLWlS8ZwiMAqyRw+dj9QPyujakac0XEFH/dikFdLgGHRr9RyEihKg1D0gFi0SqmXLRCnx982jabh5D1uLfssnnEX4AUgFVVaeABCs5qv0ZVPiFMBRtGn7f9/JAqW+KvhAQKYJiBAUFW1lozeZqKILGi/xFFiR+cdnuaFabJ0Ei98/1ips/do1t8Tz1QJLAsmBCFmMKelMXt6tCYzq2Ik59GyO3u8+b/9Lv1OyxsjcCCPoYB6sAWdmEIEWhilNlG3O+Zx4OO5X9QkqXffPdENj1ha3wyx5guOt51XojaC6CWcGhmY/f7+TlnnZGHOh2vhfHatK8ewyjzHBXd0B82VhXcREYKKhusbXGzeyBgnF+XwKjpEbMqw7WioWj9LBpmAAuP991j7uP/dgA87XAWLA2d1t9Ep+GeM4Fx8IXQcE5l25dq78baco0NWn60LLGlC1osR6nCgpwukE53WzjOy6s4Zb1ZDUffMYTSm4bi8rKaP9ph1JZKyBgE9VpA6cPegvmL/USU3B5trZ2r4DBDBTYrRKADhxMO4iLMAVfeBWf/9Rn8eMf+bjcrIBzGegvTFkCubHLeGOfj60muTEIMwH9mFog3Gpyp1AxkgDKQW7ccEaMB8SCKlS0kgVUBRBlWG0E6PX6KVxvmPbAeggBjBAKQgEoB5yRgRTbZ6LQMUzCUgRCrQTtDUPXrW4Bqa+5R/LdBioyWf5wRFQtdawAko6xLeAxV1SOUuQmdOmgVJRKZQI7HmVT6MW3UgTlKONjoky8nkHk2Iski74wGOqvtQ4YUi8v3owTq1c/X4FFC/oiBR2sAU7QyGfZgVD1zTfteKpuB2MTmEXpkjl2xrkfl/FctgDAdlzGFPlAu93urYZlUnJ5Ahhe5JyMBTBWwLMD9a/z79eYIIv9MXlhFzzXBdRNWgVttzUPMiwWp4s9yg0UrGdhF7k0ATEDEbcUiqiugqCuskgCAKKm5lqqYmz/SNmBWqQoHeR5ugJCQgYuFieyUfK9MBcFal3hCxvZbj8zNqDgvOYWTM3qPpiAAlvK/NxrGSvNTZSCsHg2X5ICxRjgUkRb4Gc53YCPz+Wfd+VcWMPtH+dVwEFM1SVJ6dBvLGDrSIQFi1FuLgbi0jYEAKpFmzAJjB4UfPxjL7uV3Y3Ndkg27V4Bg0utdtDAHACtozpQ8NGP99Sfv5mBXVDAexSgcymU87oxThQCwtKMHJZcXy9BDILULTiCQ0CIi2QUsCLewlqtUaKxLULdgtMAYQhy4csLNyQyfAUjaBqeMBEBFAoix1bpT4PFJDOD0EVBWuN2XPd0k2tcTTdJ3EQIpO4SIAXNsc6hjhdDFu1u55UOkrqlwCBAQEBIgEgOhdqnAGTR1FiDS41LAZHOBY0Ar+AgF/UvFnez3nbredrk8kK/iXhWq1xrPmhAkzwWNom15oMFdPUVJBUY7Bjozc+7BX40/iOgo+Hzm6t2ANEyK0YjMZ6TfX52XuM5efeA1xKJZPe+pg/XLCFuu3UDA/U+L22c7jBG7snlz7pjjrocLeVwywBMfzaS3BNR63Q6hoAiIaTYrys1fumgO9pDLVjE8SA1CVRUqKoNMqrKoMWxzMZIxsT3gIPisy7RYxQ3H1ft+gYASudeENfDHKD5Nc2i+muAddBA1KjrY8ca6WfAkhWSW4qoxHgcGzA4n2rA561ruC8ylw61G7qNRZsNCjAbCBVXqm0yxNHLFDBmerH7N4KC76TdS2BAaHEG0q3SZujJ2p1BQfUbWoCh7S7XlkPvYgsqBeiChToKEM1AlXVFPCwNicaIcpTFXgJaErgkUT0rGSEk1fKXCFzTI1iJsJKoGq5U1N8mkqCHFDa029j82xJPwCqoA6wQ3QNb1ENQjfKBhRkX9WYE9PVhvAA5VgBUYRAoQdQYETTI0dOVXMC42vrKBlYiXgHlvIoPtbQ+t34HgJASKGgxF1/saSgZDSv9HIKM+7pKwFYBCHnDFIzn0wxM2ynUt4YI53aOcg6jSBRbtUqQC2ZqQIFDkIyJsYIkA1CFy5mBnp6DC8AzAHC7T352LDMZY8yEMR1NWta6Ts5pa1lmgCTqCVQgYKxAnmQMWeCYKx1cU4qH8bltbDbjA/RgYTYfHCNhUt4t6HX4vRjtZgNY6H8BvQFFqp0I5VzPi2pGghkqSgIC6HAtu9ira9DVA2C5EmYgNqVBTodOXMjXJLBsmCqw6PvDXa6MDdexmnWDMUWl6HFYYmG4tNRrX7J4b9ny6X0WO7VEy1KRddAUJq+C1qYIpjqpHhefZl7OUjDufEI53sjm7nwCn27A6xn5dK7BnuMaLhuRLG5YoHdJrrbBU8iU1PAP9WVk8xkVOJjVcuBg0l57Yw4KWlbM3ZgCa/cGGHg0xuOLrhWg6qnYfHrNAg1/6rN45aOfaAu4BwUoDeUD+poFI3K7sZmbGt96bgGICgrqpFrXqm8fgtzI5hMMhRFCAE6a+qIBiTWeIUosg2QENLYg699klR+ZcBhiCCQwp6X2BPUXV/oVtMsoZJZ6abagB9KbWnf83UaY21/tPXhfsrzXLxyJdedXJOc+244XwALCEiKCou9xzLtTJpLF9BSAvCJEc+FkHVb9Xb1QS1kk0zYwMRgTm/JgQW9eUYsLmjOuhsEKPum1bwwLu12gf3xBu7h2qbo09MRlx2cprpUxSBUokAU3WcAYUQMKJH25MdCgXXvu0/Zqf1tArvfJV4O3PRBXAzFEt+s1WMyEfdNAw+x8gAkgsfMxIGBgfswWAjuWINfUs045UFNgLzW2cQFggacyPO7xBti5y2B2c1J/NxgI0AqFRfqJiMDLlfRdXmssjM3h3TltTMHhSsHBDBQs4OW6AwVnBQWSLiiAYFWDDUatxzLewzGIhHvv5rHPDP1n+y3IWNu4j3UOrAVCq9tBrMWn5mtZCgGHGHBQpkD+tfoUrbS1BtJaVojqFnBeG+t7Eua3nE4o64p8XCuj063hKdXMkBqv5LUgQlAtlFIBKHFpTICzOyASEAgFBzSroKJMwRtSMHCXKaDtw9tAwr0BBu+1tSqMn5PSzF2wkGu3vQ7ActvbWzrYpYALC1PgQAGr4y5HqV8QQqmxAMGBC84r6HwSSkpBgSiFrUiq4W9FkIw14BCk/nkyzkRQ8molZ93U6FTj/F+06O1Z8+6ZTTehBwOz+gG+ic+vBZJJ6WPGYoqrEWAmLDEi3gYOapGWUHUNUIoYDKDevGTou9ZwaOpwtVy0iiDVCnCWeTAEds12/2zG34SWAGzqOQBu+3WBOnbnahkZY3pUUM37WhiKbLdikrh9qemNgbaBGJuu3tPI+jGgzi9w9l0fazEI3GxiJtz5xEvnA2wAyaYEsRekcQWHfI2NMfWs1th4gfGQpzbnyGXLBHAOU6CwPVyQnwsArFwxUONIJLhVGCtOi+TYx1TjkDZz2uatAd3lqroOLoGCTLHVJchSKfScTROjD1i9dA/7uhDQyzJSw4a02zTM+oRI9TdsfdIiXoGREJx6Z7+WWQ2YQyQcUsAhEK5CqGxBtPMLpMvj2mILrCCVZn/U9SOvFRSM6zcioSAgrBIIVc5ZGBude1bXBECbkzUncYcGYe6t9/hc50dV/P2pz+MTH335xVRVL7R7BQy8kQDQKRj6DrPXnrzxGj7zBadTMCvxe+uP6i6LQm+QgBqsNjY/qZiLSNjmjLJERKW7y7ntdMmlOlI+S5CQUqJBKfYYZDdfioCDzIzMCgqSIGtRxbMAonZmM9W4QwxVMc4otxSpgYRK10q7DRBYmpyJ7uz5jUWZLiMx4SqKhkIOrPn7yn7EiLhcu++JdgCRGpe0SJS1BQDZ4l8lkrn7TWiAY1cfnoIEabkqcBUQ7CzyG2bAAMHM8NhnasaL7sAu7FLt/Gyn6kvXwkCBnacVqFEGQfQgHFDYMdCuY4ZFawcAGCWP4T3tB3fy9bi17LW5QXzMxHd6Ph4IzHz4GtUPdkWHJmNx6zjYOelYwI1FBQlF+54bmLQlxuZOZRQyQFFcVKzlii1SnUoCr6umykogGy8Huc6cO8NT788B3NLhSljHdBBQMMYULNfIEHfjKZeqOpjV9h1z7mqxXLp/Y4EUFwNXgFCAtmYMXw2QrgLbMUjWRhLdFPlCADIQIiOU0Kl3tuOY4iW0OBzhoGBgiQoKjC2omx8oe6S6JbkxvFUozWIKNPujFAafG/inHMAoKAjTyqhtzQz9XPFz6IUb1U3t390BBTNXH9zfS+1eAYMXaa+/8Rpe/c0mowxABmsPHFQAQO451QWXIgOcZELV6NRYg1AoSIQwVqGdcrepKtU/xYXaTrMY26D+0byiLsAaZ0AaVFgrIyoFf61TNBKBSkHU6omrUm9jEI+vPBfVXyuFlUJXiTGGrQjMrJnLwECBX1BWJ2TTdzEj5iI16jlizWuVZ26rSQEz4ZBIwIEakOq7I2pR18tBmBZTRdR+Hc+bZov6yA5QaNS9fKuLyAagwks+tbWBgs4ImWHyabCVZdjZQcCmn/5+dAwAEfh8qjnUpDLPe2xCZ4B9UKMMwuSHvV+ouc684d0te913tOykur5swOXP5Hy8e8CxAkYTN5bAjY3FFtxhDLpzsowZTZc1UMkliCvA6hIAxi3L14d5BECZPaWlrS+1wBG4gJZS05jZlDlvmc8V2C4SbIjlAIQEjlcacHglBYvSFTIIp5W7gkVehnhlSIbA7r0L3d1LjFPSYkWIAGlq9Gy98IxCIY0XKAGIyoCECCsZSiQbnmCCbd0RtmtYisIULJFwnaKsYRZboOCgZaesbS6ZzLTLBOFS6rpsTN8oKGVCYKRBjhs7ENQ1Wd2AsVeZ3ItL2XRaUJ2dVhtoJybzPbd7Bwzuwhq8/sZr+Cu/2bSj4b/TAYABKFBo/V/FKCB5/QAood2oLkoe6QCUglBY2IBSQEFkfykHd/gtRS0nZ7tPXfCcb5dLlpLASruVQFj0aq4REUm0CFYSadxD2KrL+cI3vshMVY0LTSym+Q37uTvztDCzY3qFlmwCO33REy/Bm0LAWlYsMSjLwGII2aVHrXKuS7puhsR2z1lLQpdV+t7SvurYuDEdfMRbENAMFQOb4J+uPgTpor8WWDRxC3KbgAK346vn5COdR+Pq55/OG9agQw8UWMGAGQcOpkffrrECBR8s5waUKQwqds0IdyDAL6J7dLyn3RV4VTeI0t0bsHDn80E7JwMCBk5c1U3/2nvt+/pWVQ/k1vdFjBivZwUEQCda41imzbwaA8q4AHGR40/6vLIit8xnipJTj2RCRapLEBcgNrbgzJoiWBgnjStYi8gQ7xUrAvp7N5D0i5Q1ljghDiT3f/Ar8hzzMUPKy5MY1sgREQWxMGKKVeEww9wLeg5uDRNwAnj1S3MfpEhYggAPn77aglIdoLT5MQG3skbLWDS3jQv2TFGCmdPSsj9cFgiWq1bi3cSkzFXkmYVx7rlOM/G9sTaQb98JWwDcQ2AAbMGBtQBxH/yV31Txh49Kp7ZsNXngFyEmRbocmkWELsjQ56X5aOmKpr8NLgjckGeEZrMY7g1BEKftqHbESSTYrShaLdWdEIME7/ldrBSQiUiDfv6oLuf17X3FuZmUrPkR7dIBvadc/CwG102G+iRLAwVW4KkUC3zjqntfGBpBb5RlBJCVBVHK3Q4eCUs8NOlkC7yzQKKYNa3R7S43rTeOXH3uDsXv7FzZ71hLBmt2gsQTBbQqgWg7jZlhGv3a01Q1b7hC/RwDAhRscTGQYK6UYQfJyjDsBcwBQKgUt2M19BqYi7hlLjEhu4xB766pAVnqO+/iN5wxJX8uej6bQE4PTjwYKC6w8IX73PW9nVc3DFFSIKNioFrG2M2b0ACnjwGBn2MeBNUHzSXDYIl07+Zy92nf0T3oINuxxpqGaBoFBgpOxhIMoOBGaxNk5hpj0O5Z6D3LWjmzIIUg6wsxCFQNjG6zutoUPtvFLjUzI6gbklJAUqbRFwkTlU9dB9wa5lU5bUOzqJBRDMDiNjpSKAmyeXCB5bsZKLous8aClSXW+UchIKYIWiLCEhGW1MVzWMAn9C9TrDLJdg+2+QDUQEOiLmiVjSn4wmfweQUFlj7qm7cctPP4tnYvgQHQwAHQWIPXv+RAgYve7Ay5uRNsx4egUaGmvNdkRCWatIBCUXAQgXIGXVG9EUf1MfNBFVsUlZqyyPcmSkJthzUiSAA1pFcDqiJFKeLjf4skejhQwKJfMcU7YL4p8kFDpgxWg4rIkmiwmWVa76ZG6RYY4GBEoGofWO6yr/pY3RoMhEIoQajLQwiAyjPnCTiwsavpjAsB+SzR+aWBgrqbtX4bm9/CuEW66sb7z7nFuqsAySzGuJDcVauql22izKWuAudLRqhvI2UJAMhFdyxm3HQuhVB3smy7dXM3OJBQjRXQdvD6uParb97/PgMDY/lf7aPWd8ZwKGBZT6LySSQAjgJwPqm+/N3PxR5PYzf2mJkxIHSvj4G2K7xDs9iDTRaLzSO/c3dlh71rR3+0n6c214AW0HzX+WzrVGgFizguXTrieQIKxoJFpyKAwAC9tcCkNQlYpdXbahrr6VDdvVomgAAF6reyLNLEVkI+aIBjpNAVCbMg5r7v5e/o7mxxUxZbQJU1sIyVupZuB7SuweYKJiYASWLBSstYCklAQbzSlNCDZH7Uf4drdd8sEJG6AO9Cqwy1jlu9Bxwb+uSN1/Hp3xRQ8PLHdmIK6rljk0n0Iu3eAgOg74wvvikxBXsFJXpw4JXyitzEzF3mFAfpOrKdd1SqPwetjHhEiFHEiUIYDHYAhRMoUlf8hCgI4kxRqCgLHDLBDKMJAbTAK1nsQowy+UmyCgji4gsk12b5x6Iu5xTvXBvdBFVbHu2Ys6Iydl9lNjylVF+QgMEx/cgkUOdV0QQ0JHZGT8FBhNCL4tdt3ymQdMYUtO/shi+iF0Hdjb9zO1UwMGEJ7LmtRn5x7tLhpCQyAb1Koy4g4nu26PIg/tecWxS7Pz2PRzoGfWvMiAQUSDeVLUBQf60HCSYh3TEFPsJ+aD6DgqsEOPfysL4EsO8v14fGYogU7CosBnMzkqXc+TzsN7q0z5GxmAEC9/0ZIOjAgD+HoZ9MO6LGGlRDbwwBNUq/goKlAoZatZBim3c7c67GPdh8lhf1xPbndD2u/h5r4aJVQUH2oICBcxb54REUrBmOLejv1xSAUkRh06+6RG3nHiGgwHQGLNtptpZEtjWL6kbCsiCSutNuW7sCHADR37PA6mCMRU1pdUBLx9WnekoKs7C8FILq0BAIESaQFlJCODitiKsHCA8eAVcPEK4eCFMTr4CgRZU0O2gDAoC2HrjXf+fN38Wnf/PVGih/cVthGzrgPYEC4J4DA0A6xVI6PmeBGsCABPxLZhgKvIzu5rPpqn3H/FHMQDwAZQWy7oooADEhhIhiojkUEEJA0GjX+hOB+vzj5apG2DdGQWsCdA46obOj3hHEckMkCCVf1N+3pyzX9ZdOqLHQjK88V/uj0n+6KQZcUKX4EzgGrIWRYsDKXDlIYw1EptdLmiotGQuQA2wZiBRwzKVddgogdyxmDesIJAWdYlQcZeVgL9xK1RBQHVP/t/uo+xQ7URRZMUVGtU6tmboZ3NQzloGL1IMA1B+L3u8dm5EghH3K24wqF0ckRBFiUn8QZwFWVWPBXB0hQrwgGjk/83sD6Kj59SwGy0Vwg8tUFU66OSAs+vulCDgANA5naS5AdZNwuXAOvo/qtUwAgX1uAgqACSMwuDG614wRsEk4ZLMgRPUpmzERP3MHCrQOAcJSy++yE6GaMMPym+6vXm3XJ9Pm5rU6vOqu30obm6qgBBdKoOFRqXsrbWygwFdpBdqOtACqrmo/q9kJZH8FPMRK4/vKnJsLg68gmlnqs2QmFL7DuoXGThBQhbjqYzLXMQ/AVU6EooiDkWX6ZK2JAwAhIMYVIUmZa2udiNT1A9BBQAFdPwSWK5R0LRkgcUEtSqWAbSpY5DcKwAAKXtnOj3rjtHH5TkABcK+BgXTfkzdex6e1dPInPvay3BwTUDBtyhzYY/lri0Ta3MQyELI7ofUoVGlMoCDIMhCBQxA3wjE08Z36c0pdGTDQ/ONO05xCZSv6yy0IFOtNUarzDigmgsKy88h648lr3GEkQ/H+xjW2ILh+rRcMuckzEwI35C6QSnKelxiwFnEGtNRInq6AFm+wZgCxIBTpN4nnkwUsFgZlg236owVgkniGWNp1NGi3TSTyGMb3h3u5a3YkK/UbtOQ0cVY51ZaKJ2WhJcp+Aw5CqJHxzcgMgADYN3JVOnXHKOzR3zmril6pAEE+HpXOJzGuIQIomx1758dnZ5y5lbrmwijruikDLDsr88nqIhyCfD+mBqxLbkyGZ0cugYGxL3b8xFDX37S4UBcg6QCApaS6Pp2lK/qMkB4UOPq4ggKpicJxqTv4oljOXHF7c6+eF2y3LecyHfFqaP29jypt7CsZWv2Bla2YkdyLq7oeR1AwNi+QFvWsFgcKliD6AQYMqlvBn6jbhQmQIa3RQN265cuQG4tpw2HrVh0ad9wKFoDpvcNWQl1r1qAUWX/t4KpnQBZnY2dr67OyA3T9EHT9CDhcg5cH4HQlY52ulKmjid3w56Hzi1ndB1rQ75LM8UVQMG5Nbm/3FBgYKHClkxUUAA3p7jdqCwH1r9sxztx2qt6YiDFNiIeEwBnhZLEGSVwLAIIGm/Dppi6GdTGyCotEtToXlD2oFdBAzXdZL5kBahxHh8Rdr9ikbOxBAwnkJlel2wzoeD1530JEpFA1+lcSw38mBhBQUJBAWJiQEbGWFauuzcGSl7VltxWpuxFucQhcxMcYEZCC+DSpAKuBA3VbZJLzngkz+UXFWrfY1cV0uwLW3UhhYVSK7YIiYopCja9HGTt3OLtJzddLJcPiS+TH3CKzYwy7aGmfVreXbz/ubgFsJJ7r70jcQ6X0fTT97LgWEwDHarhzHuVi5beFAeJQmmEtxTmi+135tN1V8yFGIOd67ZWyLxlTE2ouAfvuwALI6U1cCkDv9vAporX0rgMFk+JEa0EtIFQNL1+YfwB8TRK/lnlwP7axHkWGqz+ga5jVJ2ALDB6AQOF2j3oZYnM1mlthSRFL0rRBBQWSFWAljgGyuhWabdU1EraBQ0IMUfRZQLoWyJX6NcuM/913ym4noL9X50eIrXIrlEGLCXw+amaIuM4IaJ8ncnEF1wiP3oeyPACWa3C6Rjk8qDEdOW/tBdl6WwdQ4qievPlajSkQULC9qtGW9dc/zqG77ojvETAYL3kEBWje3+47GL7XjtPv84wdsLVuzT367v1uGhEbIq6uHiNoOlYBEB65nWoNaERdhOrOQyO1YbEGNcXFie543yTQjDZv67/LT7TF14yUv7ZuQnXa4QW1Epwdsy66Sc9dmJElLoixLbQCPBhXUXQJxOcYNChS+sr6Lg6zvBQAUV0OhZGJat2GtTCIWBCQggNVVHaLWQ8CPIjz8KaL8/KP0TcBG6jxF1LDXSoVLj4Acj3p59txiIJUyczqY09LVa2TE9oaObIT0hRAn7tec/OBO9LJlz6yswP23w8BUkiqgJFacCMzTMPfNxODqc8RQH618QF6VvkvDKJNLo6AgqZqqvofmFGzT2bXmdwOv/bPZLnzrIlL8+w0LSaG1vqpux5yUtVGGXtQcKEOwcpc/eg8IdP8Qu9Pxz+ujgO3Yx7PnAGNBxJQYPdSzVhiXc9ckKGfpuM9WlVTQS3FmYCrGJFIMwEi4aDpgoGzqDa6zQa5CrY+ENNqfkiJaAEJwuLurVdle0+44L0qv107T7JERPjLSrfLAIQDRD02r6B41sqtRcXSHHOmg2CZBwYK+PAQfHiIkq5xzG2sRzthBeMiUHUQCoDX33h9sF9bU79hIsc+udCevPHaxffvDTDwrYKCn/q7KnPcJslcZmOvtS42UGBG5+xuaBlwVL1vAiMScEiEcwl4uFwjARohv4okKdAWP1uEBlEacrnmbL5IssCl1HyUfsIbKMhatrmcezU66gvtQN0cNd1SEXyVj/Ua4hu/nPan+lY5iqBLjAsoHtCIu6LgIKhvUwDCWqS2AxCwTnzS1bV7acwYYNLFDpLqZK1RqGgBS27RHYtJeSSfd26smrVBjAjGEkWsKkdgYcIhDvUciIBMYFrrAkUpdTr5AHalcrvcdasBYG6IMQp/bHu779tYhplx9M2yEuTDMudLEQNfGECWwFq4+a0UVi0BTEFYsMVyvCUwi9KyOVfLOKDglju9BorDEvYi1zwAkdk9KMb+wtho8/oE9V4NqboPPCg45bnkMHOLup+16O6DkRUwg02AModqJMjtqq0r4MDHBfthrjrz+Iy7UwMCFvFvbMGViqIdItV6BYcAhHzSdUnlhyu47deVqoqpbliOC4hWF6wpZ1Y3P27zUplNOZBugBbNhllkTmqnSEVKjdniVIOUueZmB5mf7DJx8iprYwUIOr81NZHjIkzB4SHWdI1nZ9GBOK2qH6PjIpsirW1TIFVk9fp/50uDIu+FNhLD1WLtpW0AVRzpY/jY7nHvFTBguIJIVvug+0Cpk2qPUtkzQXYTvfalJwCgiL/gXIBTlnzfm7VJhaYQ8OAQ8HgRSdTHh2vR6reJZeIkgCxQy0GBQDP2fWCK0pp6o8ClHoGiYzqy0KhlBa0nYD1JtbCOmVDdf4uOpbYrssIz7cYdNMT9Dey1+csieuMxgcuKwAVX6RqAZCaUCGRo9ccYsBaoOwCd9jmAVq8BIm8qDExowZDYbhDt8rJ7btQpw+dCN2nm6Tg7sOCzKVgj/CVXuuiuKCBzwBJZa5/JgrYBB3UOCbMCW9CiGEFW4wpbEH1buDE1RdMvXf2HOp+ALuDv1oj+S/EJZqCVsWq7alHnpOUg4CAEqUIHofUJAOWMsESUM8DIjWrXjBupYnkQNb7l0Gn4e9lpr1FATjPBWIvdOIG7ZDT494Pl+IfGzLl7UIDPZN/NRYJLLSAScCmHVI+7Bwqq7HCWTABTF/QaI974R/eYRgttn4Fjs0yDhFHTtbt45QEQUIBKGauAUAgoXCQNcZX7sBTa3qP2WyFInZZAWJJKEAdhCq4CQOuNxF1N1pVqeP3JpARkBQWaxYK4AGW7VglYVsn42VqXRGSOKQAptvWS4HRqXKyVZhdBY7kYANLV9h5UVVUAEii+XIGTuA9yusbTU8HTc8G754znp1I3P0SE6xRwnSIOERpwHOr4vPoFCZR/eQIKjCUYZ0DHElxgEA0UfP4vfx6/9Pd/afdz9woYvOYu+pWPfaKj1lt7cfbAvv3kjdfw87/1cwDMRwcc14J3TxlPzxlPzytOK9ed8ONDxAeuF/DDBSEQHi/XiOfnbfHThTAcJDilRA00NKQLoNOBJxPC0NQjy0dWSiMESYWUy8zAekQ4P5e64nbjpUXSZ4qWd04LOLDQaUBD3etZ2IZ8Ah9vgPOxoWSokbCd1XJASKsYOGMLlGq7Wh6AY0DmgqR1HFJhpFxwSAEFGcgBJXCl91uAYtM8t7oNViEtQoKYxiH0AMF2YGthHNV/ujJX429uoTgcw16v8X6VUuUaeJgCcBMDrlPAo0NSQFjk7IlwCLEa/jrfNIVSjt3cMZtc9nEFNz9syWKA41lTrUqn9tddxmDM61wahYBGY2nUuXdhKfgDtLsVqJTTEXQ+gm9CjXonLgg4gosox5vct6R0xabZ7wK1sFzJPVANq/2OGg0TalIXwsXzDrHS+tPrH/qoMXNyjDKmE+6Mh92XLfixH0+uIP5QgwytYuEpF5yySA4/Pa24WZuyoLnNjFb2qqTA/ly11zvVv9KqrRK1+NaObCBhGzgTOAh4l7msYDcHlCBVWWf3aIpSyj0FIEUBB0kZhEMMAgrOz+XfegLyCZTPdf7UbBY3ppW5MUGgeIBlBuytVZQFdMzWugJWoCZjLi5gvXbS7CVWdcmg95G5aN1abK5ZcS8cEdIi16CuLotVKMs1nq2Md08FX392xls3Z7x7ytUuHBLh0ZLwaGE8PkRQCioSJT/zq58SUDADANh5rWMJuviJ9mkfiD8DHb7dG2DQgwKJKQBQO6khe9tJOB/UxSbwwRQTf/mTn8On/sFPqJ8OOOaCp+eMb9+s+NazE956dsKz04pIhPc9POAH33cNIuCQgEfXSZAoZ4tCUcnaBSU9AB8eyE0QJLuAcgZqgQ/zpZJWTkzIiAJEzEdfZBcbQXITnp8jv/MWyrO3RekOEK3160eghy8BV7rTSY5rVJqMstzEfPMM5flTqUd+PtbJRyHKbi8m4HzU0tBXoHgG0hnIQr0xM64ODyUKuhDWCFxlwqpgQUSMiu5GdDEkWdt9IZQUCEuKddGZKTJaq+BA/XrHwrg5Z5xyQa5un7bzsd/0zQc9eqW3en4EXC9RgAYDj68SiBhB3UgrCIuyA8wJhNCCD7m0xUbzmbvUtSFuhAwQFJ0PujvisoLiom6fAvO9wxnYGgipixppqiEPbEM9l2HnbIayuqvU/UVlBcIiO30F2TUYsqpzZpBGF1oaLtICHK5BVw9BDx6BHjwCxwNKtPgZdT+oS4yLSM8GVxRLfn84b3O/xT4dDIDL+y/bfgoRhWJH+9eUMg8Ouj7UOiWlAGhunDquFBS8K2AJqQYZmvTwuQDvHlc8XTOOa8HNObvgOjfPQg+U3b56V5o4BsKhMKQ0KUDEUpjI3Su1C1gyZVOU+zAzy6569ZxaUA2D7T16CBIoaGxBqkwBcBUBnJ6BTs8EGORTK0NvGw0tbsXnYxcvIkzSEaFk4JqBqEWVMKxV6xm0HsHHpyjP3kG5edqvdQ/fB3oJMqbLQ2QmHHOpgCqRMHxR71Viq45pbgt128aoFGQG5RPoFBAYoJjl3LO4Xhniqr05rfjG8zO++vYNvvr2Dd5+dkJmxsNDwgceHrA+lN+34k7elpvOTluSLscLjICgqfaGClhbzN3toAC4R8CgBwX7bRcg7H4h4LU3XpfSlp/6PH7kI5+Q7yv1dM6M41rw7Zsz/ujbz/GVbz7H8+eStnV4sODt73mEQwr44APZPVJZgZtn4OdPBWVq+VO+fox8eIRTlhudGEhpwYIMOt+0KFibLlEWm1NhnLJQj1KohPEgStlWPH+K8vY3UN7+FvLNDSgEhOtr4KWMGJOocdkuq8pwluq/49MRfPMM/Pxd8PG5ZFFYrnxM4OXU6OCYhFrT8sR0uJbIYwJKTDiEA05BWIO682cGa9CaVUsDUAuhBJBENxOJ3zKo7nkwjQVsykKzENj1XlqLiLXYOJ1yU370C+7IPBsY8CJMYw73KTMe6uKbggQxRWLEglq5LUbLzz+LaprOG9mJUAMDGqVeiripmtRrlF3NogH8RTIfzE/L3k8r32i73DEwtaixzmdJsfSywUDb9aSkMSwWOHeFLv+6AoATcHou84pk7pW8Cg2uWSGm6hkWqfBHyxXC9UPQw8egh4/BCogRD93xBQzJtXKOQDkLyNGUw9k5gyLKIDVbr70Gm5S+rzzlP2QMzMcCCCmJ/mZZW4AuN4nxOrZalyBDNhGS+ieg4Oac8XTNeHrMeHbOuFlzLzOsQX3mww/EF+eosVpWbphAiJQRUzR8sL1fSF1k5vuOwnadc0FMUXRDgoDdBKopi3aPSmyBMnqxBR7GQDiEgJBPCOdnoNO7wOlG1g+rXKhCWHw+iiE/H5tqZgjA4Rq4eiBUf4ig69QuwtaqINdCnMHH5yhP30Z551soNzfgUhCvr4H1jJgW0NVLQEw4ZcbzVYSdiATAhECIMcl8NrLe4riWa5wRpcYLgBAWHJZrxKBs2PlGrq0UmfOHxwCA4wp87ekJ/9WfvIM//NpTnJ6fQYHw4MGCD3/wAUIQd8I5mzZD4wdqfAh0IbsUXAxsAMHYvIyyz8671O4NMDAktKFfbCGzZjteezrPAK7tyRu/Uzv1xz72Mlbt+xAIpLBzLQXffnbCH/7RW/gX/+yr+JMv/wlOz9/F4dEH8P/58Et467/9vcB/44M4vS8hfe2/xvpH/xI43SA9fIzD93wY9N0fwfqQ8NbxLTxfRVjpwWHB+x9d4QMPr3EdgLCKeE7OjOcZeDe/g28+vcG3nwtNdcoF1yng/VcJH3oQkd76Csof/XOcv/wv8PRPvonz0xuEJeLqfY9x/X0fQvrejyB86AfA1y+hLI9kgWTZCdLpOej8FHj328jvfAvl3bdQnr2L9fkRvGakw4IPfs93433f/wMqtSyodF0zbm5u8Px4xFpYmInHHwAefQjx/d+DsjxAiQfEoHQjUfWvmZSqL4biWYIrrfQYdbcuhlgDsnQwmSGBiJlFTrUUrHnF05sT3np6g7ef3+DpUYIAD4crPH54jetFylangaDzoGAtRUCF/TudwSXj8YMDPvTSNVK8wk0oUu46oIqxFK0jX1mCgeLzgOCcBeCdldXIhSvNa9UuUxBwkOhQKU1k2fWRzWoNqmIWyjQXIJciksUlI5az7OgCkGICDXeA6GRIWuyJCU+fHvH09AwnlkUsq8siEiGWM8L5BnR6inB6F/zsHeDp2+Djc+D0XFimnCXYcDkAB5GHxaMj6OENyuE5+PAIZblGDov41iHusCUSDlTw6BDx6CrhEBaZHYmkXoCNEwAGYc3AqRScyw2yshwUF8QQ5B8Agqlgyp3PsKWBND3xgHUFMiSl1nL9swaqmJzuogF1S0yy0yyrMnvj+ErOus2lqh2QGTerzKl3jid8450bvPv8BAoRh8OCQwo4pICrFAAETbCgzR7GAOvNecXT5zc4Ho8AMx5dJbzvwTU+8Oga6cEBHEkE1Yh08+AUB5kQIKwXWRpuiFhVTGxlICJjLTLmzM21Zi61RA0QlJxxc3qO0/k58re/Bjz9Bvjdt0A3T5EC4cHVFa6vr5GSpseuZ5yfP8Xbf/xH+ObXvo71dAaliPTgCuHhY4THH0B86buAx+8HL4+EVQ1aHKpkhPNT0M07KN/4I6x/+mXc/Mk3cHz7XZRzxvLoGo++74NYPvKvIfzAu1g/8BzfeJ7x7aO4bw4x4PEh4v0PFnzw0TUex4IHEYiRxE27JNzcrHjr2bv49tMjnp/OIBQ8SAEfuIpIz74B/vqXcfraV5CfPQUfrpB+4M9h/Z6n+MO3V7z2h9/EP/2v/hRf+8o7OD19C4cHj/F9H/k+HP/1H8Djq4QPPVzsrlXxJQUGwUgRbqD2QquAwGdiaHvy5uv49BdefSFQANwjYDClR3R3Udvk8dxfI+8/efOL+PRv/Tx+7ZO/ipc/+nF5XftcAnwkKp0ArDnj228/xVff/FN86f/xBzi9+y1cve+78e7z/w6++1HGf/f9wAee3oD/+f8T56/8C+TjiusPfRcehSvg6kN4691v4KtvH/H152cAhA8+foA/9z3vB3FGvApIp+fA6Qan8xlPjyu+dSL88bef4xvPV/zxuyc8P2dcpYAffOkKN48XPH76Fsqf/DGe/uGbeOfLf4r12Q3S9RUefO8H8HghLA++C3R4jrxegQ/CPgQCQjkjnJ4h3LwDPH8X5dtvo7z9beR3v4312Q3KeUVcFlw9eojHJrzDjFIy1tMRz99+C2+/9S08P56Ql2vED3wP6P1HXJWI9L4PIl1HoJZFFX+mDZMFVZmP1Mo9X8Uobm+C20GZ+BI5pk0yRUSKuaiKIqPkjNP5jHef3eCtpzdAiHjfI+D66oCSmhvBdmRdFqGyBCddyN99fsRb7zzD8XjEB196gCVFPFgSHia5FmY5nyKnIymUJK6fGkXtYkXOrMFna8Hzk+wcj+csug0sgVyHGLEEAwaSDRGJRCFw5RY3QCSCOUTIIeC8Ftycz8hrrpkysTAeJqAsUpI2QVAFgev3RY8i4uZc8NbzFV//9rt49+aIk9sxRg0su44B8bwinFbQMYPODD6u4JszsGr2RGBgJRAvoMDgYwZjRVlX5PMZN1mObVkigeTYj6+v8KH3P0KMCeEQ1UCyuNaYq9FdQTiWgudnxrOVkQMj8wqG7JivlwVLCohFCpmRfr/GI4QAzoS8npFByCCVDAZOOeO8FnUTEa6WiOsU8eAQUVh86ktIsrOtLp3gAhfRzweGagaI++Dt52f8ybfewTffeY6rqyt84KWHePxARHWWaAG5bZUa52hhiaN5dnPC2+8+E1/9o2tcp4iSFwRmBGIpu66APBrQ0D+xNPl0EzUiEiB+zBmJYi2bvur5cGHUgkWqcgjovXbzHOvb38Txa18Ff/tPkd/6GuL5Bg+uDsAHvgspfABEVwhKdZec8ezZM3z9T7+OfD4jLAnp4TXiMSPkgIAFWAPKNaEcgBIWFJb7gU4nxOfPwe88xfmb38S7X/0jPP/Tt7DeHJEeXuOlZ0/xaHmIcP0hvHu6wh+9e8ZX3zniuBY8WCK+//EBzx8k0PsfgA6McJVwWMTdtXLCt49H/PG33sW//Nq38c13nwNgfPeDBT/4vit8gJ8D3/g6nv7hP8PNN76FeJWwvP0uaD3gm8dr/PM3/wh/+F//Mb72z/5fOL79dRwefxcy/wg+9H3vx5olWHfxpaCNDAY0fmKSerljqzbPKTRxpB0m/RJIuDfAYBuooZTQ9MP+9bFT5b0nb34RP/sP/yp+7Sf/Dl75yI+KX5cyYhCUdxUBZsLDJeDhEvDoEJCWgOWQcLi6Rr5JiGlBShHf9fgaL11FxOdv4fT1r+DZH30LIQQ8ev9LCMsB5eoRvv1Oxr986wZffusZiAgfPhc8WBIeLwHXp4Krm2+Cn7+D8/mMzAkhPMSyLDg9XfH2ccVbz8+4WiKWEPD+64TH148Rrh4gXi9IVwcQEZZH13jw3R/A8j0fBn3wB5Bf+l4844jjUfogEnCVFlw9eJ/mDyfEwxXCw0cITz+A+Owd4Pgc1w+ucf2h70V8/H6pGgYgMCOWguurK5wfPBDdgRSEnkwRIUjVtaKa/hYfwEHleWOrjrYHCKwOhNVwkHrqqEGkrP5SIpkNa5HAqAdLwoPDgscPrzWHnLEsyaUMWdpVuzNF7U1cGqsarPOa8fTmjG+98xzH4xEpBDx73xnnfCW781vweBdcSFF39OLieHY8461nR7z17IinxzPODMQY8eCw4HqJeLQkXCcRdkpgBF4RuQAroxyPyHmVnWmIWBGr3/q4ZhQWIHGVAh4eFiAGkPpbCnGtNMcEMBHWknGTC945Fbx7XPF8LTgWwvPM1fUcA+OgwXQP0iM8uH4EOr0Eevgc4fxcosTXcxNPUrEfXh4Amud9XIHn54KjsiUmp52inEdcC54eM5Z0RikB11EobVLfeFEgeGbgJgc8Y+BpLnj2/AbHVfzlgQhXKeJ6ibhKAQkZoWSZPzEhXEWACvJ6QqGEFYS1EG7WgqfnFTfnjOenM3LOWAh4dLXgAw+vwHwFYBFKPRIiRZFy4J3F2zWtwYlzlnF/++kR33rnOQ7HjGVZcLUkZQvkPrAgv+QMQ9FItcIFgSRN8NH1FWIgPH6w4MFhwYMl1cBA8WWLroAURDOiWuTScwGKCoQlopqGTRTBETjmjJVCy5xQkaVIVON8wEApIpAUQsCSIjgGpBQQseDBgwe4urpCXBaEJGsS0oJIAdff/b143+mEm+c3Ul/g4UsIjx6DHilTcP0YfPUYp0I4nluMwFV6gIcvLYhcsJxu8ODpM+SbEygGxMOCeC0BiLh+jGcr42tPz/jaszOO54wPPjzgVIBlWRDWI/LpGY5PV5RlAT14CccHjHeOAX/y1lO88a2n+Mq3noKZ8ZEPPJR58NIjhOWAuK44v/UUx1Lw0tVXcPjBfw0vPfgwvuuxMCMxLYhJ7MJySEhJ7IXZjqtIuIrAlcWB5JNzfxXnEmtxZv3C0jNVgAYa/tZfcYqJ/XduYw7uDTDwbQsStI0MArClaZQp+Nl/9Av4e3/xb+OVD/+o5vDr2yThPw+UKSiF8MHriO99mPCRDxzwpQ9e4aUf/NeRXvoQHrz/u/C9P/AS/lsfeogPXUfkL72Bp1/5Op7/6beQHl0j5xU5JrxzXPGlb76L//dXv4U3vv5U6gqsGd/7IOLZw4CnT9/B8VtfQXnra8jrivXB+8Dv+z6k5btAkMyIt56eEALhYQp4erpCft9LWL7re3D47u/GYwa4FBw+8D6kH/hzwPf/N3F86fvw1s2qi19BisB1ilIwJQVcHV5CPDwCrSfQo+8B5SPS6TlCPuEqBlx/z4eQ3vc+8S0zo5xPiEQ4PAaumbFSwAkRnBaUEIRGPZ0Q4wEpJEDpzBSExhf3QV/qeQMIzIdpcQEEvYFUlhdCgwYAKCI8lCPh4SHi8fUBKwMhSKpktF04SfR0UmBiLTFhJcYpA2uQ+I1SCm5ujnj67DmOxxMeHCKOJ0mPCpCFm9Q5GAE4L8e0iW59wc3pjLeePscff+sd/OnbT/HuzSrBi8uCh9dXeN+DKzy/SniwRDxMhAUFVM4IWdxL6/EG5XxEBqFQws1a8OxU8HwttVCO5Zc/ujphfXiFvABHXnEIjEUZAwZhZcKJCc8z4XkG3n5+xPPTiufqBxfWQIMvU0ROssM8ZcISHuDw4CHCQ0gshMkbAxrMl4R9yYzzsQGCm7VUH3sg4FAIYMnaj8+egTjjFIEHkXEgRiKu53tmiLGghHfPwLefHfH0mHHMBec1C/UfCA9SwMODZJEEXhHBCMsVUiZwXFHiAg4Lzgh4tjKenzPePa54+/kRz26OOJ/PSGA8vk64ed8jgF8Sg6ygIO7gAUKbD2cN+0gUVJaXcTyd8e7zGzx9doM1F9zcXP9/yfuzGFvX9L4P+73jN6yphj2dfabuJilSMqALJ5Il0t10EAhQLIvmTYBEFCUZSpQgcJCbIIgTOLlK7OTCiIEYCWQ7EkO1nCA3EhXQdi4M8TTFXEhCFEekLJJid5/57L1r17CGb3inXDzvWlXn9OHQtJEYrQ8o7L1rV62q9Q3v87z/5z+QF80pV8Br/aX3ZyoFnaTRdhp6b2nq7nPReJatp/eGppJ3nSo0FXVSJUozfmpUdeX4VC6EBp350gbhi7JfuK9TKWdiCPKcF2n6tXWotseTaJdLmuUa03Ro36KdpyiFXaxpbEvj1+SUycZTfEc2lX9lPQktqFpMTBHGmIgJWqdZOMXZ5i28djTaYLqW+eYOpTX+0SP0+WNCs2J/JwqBl3cjORcWXgaIVhXK4Zbx7jPicIexFn32mHgeObDiervjuy/v+M1P74gpsz1MLB2c+yUr60gpMm33xP2Idhbz7H0uf/Qdfviy5x++sWIaf5jh9pJudcHqouGtc8+T3nLRGhZW0VlFaxQqTXI+w/TFleKetwLwu42/P/g7/Jm/9ef55k993kb59zpGgB+wxuBLUQMlbNuC/nJyxhc+9977v8yf+cW/yH/wL/5lfvLNP8YXDYJODOfDDY3xsuP3ireXjpvHHbsfusDZzG5Yc7Fs+K9+9ZwfveywNx8xf/IdDi+umfczppFwlWRatmPk07uR77zYcns7YJ3lduWYw5ISJ8bbT0jf/keEq1eUUrAXFxjb0F9e0lqDMzLTDkEUEndT5BANq8UF7o2volcXKGvRF09J6+fcqY5XdzMvdjNDFEhr2VjOWiRvASHpAZTiQDm0W+JaRe8Mi9ahz5eoxqGLFCc1DaJ914ZOG5JtKSkTfE8xnilmpjRhlaNRFl2NbKxWoqHm8w3BMYnt6K1u6+jAqOqUdjT7yRlKlMtTFyulNN42stjpQqzci1IKVsFct6ZOC/LTuyqzqqv7UUsedK5s7kLKmlsKOc6UFNAqY8n46mnQ1kX5mPmujwTJeo99zuq4kqdKKaSUmOaZ/W7Pzd2W17d7bsfAlGQns+w7QorMwbP0lugUnoxOMyrNmBIhzJQUJTVPFcaQGUNmP4uUNpaC05pVY8nFoCmo1jHFCZfnWigqp0IZEpZgHGOCaU7EnL9X944iFYHbM0Kou3e4rw/j59kLoEJdnIRdLvP7msr5BYSvFGmapjmyoxANjClgiNiSJHdEQVGWoD3RKrZjYDsG9lNiO0VCzlglsmFVNBqNKhpbIglQWTFlSCpUZURiRrMPhd0c2U4z19uB/WFgDoHGwDw5PJmV0yy8ofdVfXBkeTy8zuVISIWsFbbIrjzoIveLQu4fMloJDyTHGU2hq7vJ3hsp7BWuP47bYsoELVkkSYOyCqzFG82icWwaS2c1jS54lfGAGg9VPkolcgJKjNK0NujqLmiKIilF1AVdR4ymOrwqpbHl8/HH4qJYiDEwjRNxnGhKxhqPanucLnRG0y16XNdh2x7tm5MhUFEO3T9G+yfMY+AQhCxcvbJOplmpiO/DGBM3Y2Q3RQrQWcOTpedR95jVu0v8+WPs688oMWKWG/LigkPM3E2RfZBRnfBFpLntrUbd3jB+/G3i69eCrl6+wnxVUzaeOQRu9wPXtwMxRHLOvH3esT1r6XUjZN2Ymfcz6sU1/SffwT/9Kj96+ZR/4Q8+5u+1lte7Dcuu4Q+9e8EffNzx9tJx7hUrBy4OMM0nB0gVx/qQPYzjrqNDpShfRLk/xyn4Zf7M3/oL0hS8+43T9z48/qkiH37x+FyT8LA5AODLCR3vvf/L/LdPTcGP37toHfXnRyY3oPZXZMS2c6EbHjeWH7toWJcLfvTcc5gDq8by5srzSE/kz77N8OKaNE6gFaZ1qHZJtC27KXK1G9kfZuYp1aKiaLXCpInp0w84/NZ32H/2WkYQ40S3PEetntE5T+8sfeOYK84rZLnColmhz55hNk/IrmXuL3l5iHxwN/DZbuTmEHFWsWkdvReG8fEWm2NhSoXbKbKbIzEXWmu4XDTQbzhbPKZ0ljzvhYCmDdpYrPUU19K4jhgiIVuyaxlCYMgJUwxBWfrOYK2pJMLqq873prCJ7TA4hRDoosyuyQFOsjExjMopklImosjaoX2P0w1eFTqrKI1GFcMUhEGuybQkOpXpncVac9rhxVQdy1JGU0gpsrCZpVecdQaF4dHKs3KapZOGSXZs6sQHEOQi3nML6o2pkEKhQNjZMZBC1XfnhK4KghhgmjSD1VgyKluIiqSkMTA54lWu+nDxkdBWHulciW4hy3tIWkYG1ijGOWJVoSmJUEmtpWSyMhRVKNYQKScWuq7ER6WMSNAqY14QEcXRZz8kkd4dCZtH+d0R3TmObBqrccaglZIZqxGvkfuAAAEAAElEQVSEIFe+ypF8KjNv8Z+YkZRQFSOqJLHWVZpiCtkapjQzzvLzDyHKjjLn6qGh7xn/QEmJfNSko5lLJulMNpmxaPaxMITIMASmSdACckRr8aJQSa6XsOsbQS9KEWldzvfj+wxFFbS22Kocy0XRGLlflk6xcppHKy8e/MDSKxY201tYesPCG/EDqKgBlTsZIxxyJJFIZJwRhn7jDOtG01mFVwlXFHo6EOYDOgvqgdHCwj96kVQ0B+1Q1mKNwxlHqA2C0eUk/TVZ/FuU1ahUhDycZcx2GCemw0CaJrLOLFwLqce3nsZZfNdimxbtHMrXHAHXUvyCMEaubxLfvn3F1X5ijKl6wVg2jaAhxsj1s1qu51DVYCEWXh4mni5b3l43PH70I9j+AhNGkdw2K6ZYTgZDjTN4a+idpXMaNe+Jrz9j+PgT9h+9IufM4umOvltjls9oK1qTU2KeEvvDzNVuZDdFLm2LapeY1oFWpHESRPizb/PoK2f8kedrnq9atlOk9463zpc8v2h43BQW4Q41TZSSKv263qRh4OhwK6TYXAmXGUr1o/ziOAH4pQ//zpc0Bff18Ps5fuAag4cNgfz9S7gGtVE4aZmP44NfPI4P/tj9jKdqx1W18oyHHQDh1cfkGE+Ro0YZzoLCe8vz5wvmlGVxG+/Q1x8Trl+QY8K0HrvQdI/PUJsLiu85DImQsszXncZ3jsulZ9UY1P414dUnbD95xf6zO4wz6NbR7G7R4UDnOtat5WLhmWNiWclRSkGxDbnbUIxjKIarXeC3Xh/4zvXAq63AVY9WDU+Xmke9kwWl7poPiGPX1WHmw5uR1/sJZzVvXa6wyw0Xc6HtHItW+AgokTpp67DO42yDOsyUqIjKM86RQ0jkOLAwHuc93pmT0uCUvKaOxfVB4EqWXbFKgRJnYYBH8XdQKVByJM8zKcxM4yiFSTuy71HdBu16TFb4AisLthRyyigSPhdMEujUc8x50GQN3hhiUpiSyXNhcBAXlo1bYrVis+h4Y9Vw0Ro6K7vAxogVrBgQHRvKL3T4SdjxmuOuEVoLq8YQe4fXirHa5FoHnlSTG8UEZyZjS0SrhFJZ4r1TFHtXpbHaiuNcNWA6kivtUVJY339SR1266LaL0jXox0kWSJZ7WNXrAVJ8Hn7kophjYkqJkGSscKgkyuMY4wjnt1Z22A1yvRuj8dagVanEzfsP6nOsVYWoEUtbZUHFmXyc5ZfqrJmzyHyRGbkYYulTUfW1aaOOX8hJds/aUooWe+JYiGgZ66aMJ7F00KIxeFqjWLeWVWNorVw3q0CXKAqlk73v6UKf5JVipCNjLlUU2RQuWsO0aiAuOG8kP6T1lrWDlSmsnbDmrdGSM6CEVJtSIueASRM+z2QiRSu00XQWfMmYNKNLIR4OhOEWPR+wOZC0omlbMSXzXt7/UeJpLSSJD8Y4nPVYY08IglaFqApaS5OgUaSiSCURUuQwTuz2gyioHDSNx/keZQuu99i2QduaEms7im8pfskha26mwAfbwK++3PPh1ZYQMxeLhrfOWnqnWXoh+GLBp0wujv0c+SxkXm0nPruDq1XgMEfCRc9ld07X1vXBNqiUaK1h6Qx54fHWsG5lPKfDgby7Zbi6ZffilhSSjGiefoJ54zUrf87l0uM7R4yyToeUOYRE6XvU5oLu8Rlxmikpk2MiXb/AbT7g6dlzHj9dkwpiD50G+t0WM31ILomkariVtSc7cB0nkUpSDdKqYkzIWV8oeLX4H5uCv/6nf45vvPP1h1/wuer3T50q4eHxu40UOP2/FP9f+vBXKqegEg1L+oIPt+iV87An3l0DMH/2IXkeSfPEPI6M88w8R5JxqGZB0y1QrqHEQN5ey27rfCWEmMbRPH2KvnzGpD05j/TecL70TI3l2VnLm5uWTaNRt3fE7S3zbiBOglaUfNyhZJxRbBrH0Gdyzmway1krHTYlU4wjYLmbEh/fTXxwM/L+1Z4pZDad46xzvLFsebKwLNKAPuwo1uO6c9H7Fni1G3n/aqAA+6B4/GjmnSGw6jNN53C2PRXAogwoi8oGFQ1QTjI/6dpn7DhRFn2F2u8LxxGGlz8RH4QgRa8ksXcWQlus1ySJ8988kqaJaXfHsNsyzYFoPfQr6A+o5TnZdifQx1aXNa0KJgS0ChgdMbrFay/z1FpcppToVaRYsL3l3HTE1OCtYdk2bJYt69awsJrG1iIx1x9U7u+x4z137PRVTigUrhQWTnPee1QKdEZx3ok8LJRCUeLnL7LFLMTDnNBKZsvMk/hcpAhaiRV1syRbKdpK2RN5Uuxpa5iNklGGRHjf7xxTUaSUTzvsY7NmjqOREtEIVwNjKcowRthPpcreZGcWi5jimKo1t0bj7T1Zt3eaRVOb2Gr4lXOWYYSy1RBLRjoi9SuUVCS51BlOeR5atuJa1dGQlk7LmftRVWcVrVU0uqCnQaDaXCCL94HzHTFldI54bdBa0TjF0hhK06CKxdX7oXOGZes46z0Lp3EloKLiPtpHxo2nBVgpMTNTgrhYbeitwRSNbg1m07Iyhd1SED9rNF1jWVjoVcQj7oHHZ6WUxBxnzDyi5wkTIr5KYxWaUhLZQDYwx4Fpdw2HazhssXGm8Q6WK9RyjYoNyrfiWqoNJVuUttKIWy/Pl7G1QTAYpQhKEiGP1MWcRO1SUmIcJ4Z5xuaEU4rZZjrboLyqjpdOjIK0pVhHsS1ROw5T4HoIfLyd+fXPdnz4aieoWim8sRGFxaYx2OEaFWfaZolfdMyx5XaMvLgV2fbRWK61BqdbbGvFw6FkGqM4ay3zspFmQGs2jRNV2SxNZcmFFBJxirLebm+xwx2bzSVvblrevez51GiaOuLJuRB0Q3P5jPbNVyhrSFPALlqUMuTbV+ichIQeJhj2pGnPnAIHb8ne49tWCN6+xfTigZCHPbpb1LGBEFNUeeDAeLq3Pt8UfPOnfl6aAnWsd9//COF4/EA2BnDfHBxPhjo2B5Wwdhwr/NIH3xL1wb/0V/jGWz9eoUD1+VeqBYgwkSpiML++YrrbM+/2DHd7Drs9h8OBqAp21eEuzzCbR+jFCtV0mMfPac8uJbLTecz6gtxfElKhtZrHC096tiLlwhvrhnc2HUsL5bAVpKFx+EXCLxuazRLdryqjV8h1j3qHUbD2hk1raPOEGgYwDuUcsRSGmNiOgZDEmvjJpuWds5a3No71/lPyp79F2t2i+xX+jR/ifPkG68biKhkyhcztIfL6MMsMt2a4u5NLnIZiZDtooJocEOe5zpOrteqxMMPJU13c0jTOiCRPfNWjmI4kIdqVGE6GMuWBg1qZZ2na9lumm9cMw0DUFpYDOiRKLKj+TBzuiiAGCmG4OzL6sCMOMDsDTYv1jSg6jMfnTC6BogLew8Y6aWSsoWsaOqfxasaEgIoyZy/HOXv98/hAF6XqDSn/q0tBx4iLgT4NoAOdz6TGEVGy4y6KSFUkZqoMU5oCU22vVazzyQRkaX27ZolWBqfvGdymciC8EfJmOfrCWk9GfW4EIPC/+AnYEtDTiEojOkUUufozyALf6gbrO7wWSWFjNUPIjDGf7H1bq+mcprWKpTN0VmHnHWacatperCiKJhuLNi3GS+GQeXOuktpqkKUt+hiVWzJayyhHIfP4o8JBlDa6NgU78ehIc320BaI11uO1ReVMVgqvTFUxaiwao4w0q2RMSTRarpeLGq2jjG/ujQGqRPHYJBxV/+qEYCo0rsBKFZpWszQtQ6sIMZFyqa6fAV8QRCtqSDNpGojzxDyNxJDQBVrjMUWLA2dSkJUYApZEONzIyPPuCna32Byh63AKstGiSChZxiHGom01wKof0oC7Bw1CI3r7pHio5pKcr0zOSZCokklFSL6SIaDEHrzGyd87Qpo6gipsJ1lXbvaReQiYyvtZN5bzVuNvxZclHbbo5Yb1s6/x1uYZh9hyMwQOs/AStmNgiMKrUUqhwwQp0LsO3TZohJSZCvRemh2sQ/crms2S9mzLvJswjSPHRDlsWV7CO5uOwxuR84XHaMXjhT8ZFLn+EvfOj6LPHlPCLCFpNRSsDDvSfku6fUW4uiFuB2xR9H1Pv1zQrRf45YJmvcDOlVsQJkp1FD3mNcgtpO7XkapuEqLhl40Pfn9IwfH4gW0M4GFT8HCswKlB+NvfFUnHX//TP8dPvv0TMnPUx9mgRnHvNFhypsRInuTizXd7ppst4+2O/esd+7sdh92BqBL+vGfhrTQGq3NyuxF3N1U5DqWQjKW4jpwKrdO8s+m46BxKKc5ax0VrUOMWcsQvOhZPLugvE369oHvzOfbJm8RmSUwiyVo3VqDORuMOV+jxVnbarsOtHF4bnNH0jWXTOZat5SvnHe9uWlb7z4j/2d/l9h//BtPNnvZixfqf2bP4sSVvrHqerFs+ej2wI2JNVSHkLFAspXr1i4MaSsvnjUVbgWS1kfmsMwqtNL4Wp2P+gTf6ZKOqS3V6PEK+xxCoJNyCHI92wDXxMVV76RQpYSbOEzHMRJ1Rs7iq5XkEvQfXYlQNY8oBNQ+UeaCkiVQSs1Yk32B8j25asA1ZVdJiTthqe2pIeKVwk8Fai3YWbe6T+k4xxvpezy7/x2l0JfhhwsSICwPNfEcatjAMMstVhsY2Et3qOpKxAndXnwZDgXGU91GycC64J79poLUNTdN8zk9flYTK88lkvxhLRp3CfY4FVStoLIJQzCP6aGI0HiDOpJwF+vQNyvdo19H6BU27YIqF0VXNe+3QrRLPg8YqMUQa9qgwUOaDOGzGIOfIekzbk73wZWxjiUoceo8oRqo7ZF+lfKoSUZ1SWGMo1nz+PccJNU3SFMTxJPtSSkMOlDDi2+Upe8NqsRA2JYr0cj5AnDBF5t5N19HoFc6D0Z04H1Qb84fXt9Rn5Og6KXsSQSN1zpSUsCHSxIgOibkUEtWF1BjKZBiVQpcIcSJPI3k+EOeJlAVNKqbB+A7tO4p2cv1KJlRHPh1mcn0OyJFotPz9+OxE4VEVY4W/U+2wVRFUTiLCE+SanOramjYpCaOFjNU1SfFIkizVsEerug5Uq+rj5qxUtKp6XJQiaKcq9bw7w7K1PFm3vLHyLOZb4rf/U+5+9R8xvt7SnC3Y/OgVqx/7o7y7ecLdKLLS3Rjp60bGa4XLE/pwLfeZsXTtBttf4oxmrOtmzIXSLLFP3qTf36KMZr7bo4zBLzppWMctF+2KH7lc8njRUEph6S2tE2ZA7s7EMfPszXsZaMmoeUCPt+T9lrjdcfjsmvn6gC2GvJwp6wghUWKNYDuSn2M83S+n+qM0HK3TP9cUVEniu//Cg2p3X/u+34bgePxANwbH43tPjpJshepo+I23//lqDZplZgkozOmBPsazFjj5eacQSVMgDjM5JEoAskI7jfEWv1ljHz8nnr3FTrXsZpGOGSUhGr4oXJJ/bxrNwjlyEftcbzSdzuhpkqCZR89Zr85QxmLW5+jL58TVU/aqYaxkyMYoVl7jD69Qrz4gvv4UckavztHasl4841Hn2a4irdOctY6vnPdc2Ej+zV/j6h/8Kq/+4UeMdzOLRz3aWs4ePefRG3+YdzYdrx/PvN4HLlYNm1bTqYJFoO3jrlVUAbWZ0hprLU2BJiS6rNAmo61j0flKQBPo1xuZ16oc0WGUoJUwcQxxohK9Sgz31yQd/elLRc6MhPNYDzahTEPxHUl7CuLYpnNGqSxz5jii54O4po178jTKHLURSE81PdimQqwyUtJpgulAmUeUKihn0U2HbjtU14sNtHUU70+WqkqLh38pGrSuzH1ZOEjV3njcwu415eol6fZG5IfaiJ57dY5qVyjfY0wjM2G4j8E+HZU3Q+VkxFHg9jidjGTUcaxR8r3FsREJocjShCMgxEIl15EkRSKO5MOWctjWIpPkuXAtpR0EwQoTZd7T2Zb2eyyOJe1TjSMqTOg4kg5b1DhQwvjg9QTCVrruusiAIVcVQ67GRkoVbFH3BM8jF6gumvpolnEaB4YT6/v+fB1XA7mHrbGSRhijhPLMBxi3pO01TIMU9aalbM6gPBYXRqNANYISncaVcEywLFmi1qlIF7Pcy3keycOBOA7EaSCESCyK7FtoeglUq+uOqshQmQ6kw448jcLDaFp0uxCOTU4U20q0cCV9KhRFV8fJeYY03SdbauEhqVIqHDWDMULojWJtrqxkYYiNdgRXnSNdS2Ns9S5QJCOozKpr0EWa98ZomsbSeI+1nJyZjvkZKs4UZbDeYcl0qrBpNc82DV5nLhaOdzYdj1qL/uR97v7xb/Di//Vd9q8OtGtPHGYeLZZc/NgFXznvGWPmZgxsWsejzrP2Br1/SX75AXl7LevRxTP8o8Kqe0SZs/hfxMzeNizP3sL/sMOePybdXUtD1PTi1BknVs0Su3RcdpLMKGRaaUyHVAilYcYzBxl7Oa1YdkuW7QqbImX7mvEz8a8hKEqAHBJxmEltkHryMMb7FAx2j66UB5/72x/8HfEp+Olv8pPv/OT31Lnfb0NwPH6gG4MvnpzjeOG999/jz/6N+2jLQl1DpM0XtnP9HvFAl6hZ5RvprEGMe4w0AdZbfO/QvYLO0L71GP/WD5Mef40Xs+OfXO/5pLptLbzlydLz5qrlvDV0DlmAjl4JSlcDlyxQ2OoSu9zIAuE6crMgNGuuh8jdIPpypxWtU7gyo7avCJ98m/nlK3KINI+3+G5B259z1loeLzyd1Vz0nrNGY3YvGD/5gLv3X3H93TvuQuZyiCzfeMHy1ce0T3+E5+uG7bzgyTqxaD1v9JZeJxoieXdAp1FgRyo0razY2jpHZwxrpTEuMifQ1tA1Lb13NFqaA6+VyO/CJDu6ONc/g+zo472/+smx8tiZa41SDt1KrrmJBd0F8B3R9mL5XG1USwGnFJqMKRGTJ/J0gMMdZZ7ISpOjjD00ipyEnFpyQsWRsruFuysYdticCM6TFz1sLnCrNaXt0f0SFWUUUXQGIwQ3Zaws1JXJT67JfCmQxoM4S16/ZH71knE/UDTorkPt7jDnT1CLM2gWKNPKIoGEykjGPNJ0VPi61NdXSEKmoDnlXkqnFMUkUC0FTvP8ow1wpmBKdQp4OFYrBdBiUoWMRVTJAsGPA7gaMKMOD+acx+89NiUSfJPDLEqScpRCKsgZgz7B8MfrXCqh7xhYRR0ppOproUqu6Xr3tsQSW35scJDzrYTACO6UZYCRv6si8c4qJ0gjatqT9jek6xeU3Q15GARJXHRC4ms8qe1wbUcxVngpYk5Qe6+aBZCPUeuRPA2UMJEPO9J4IGzvGG9fM+wPzGEmagPdEtaXqOVGCn0lnuk4kvf3jZm85xmlFMZ7UvHIlRPvgaItqo6TlNKYtkPPA8Y77GIteRV1BHK6L0K6P3dhlsYgTCjfCQu2ZLBJ7MZdgzeeYgpNEuOs1Lc4AzkmvIFFY+kaj9MJc4zLzhEIFLQgsHOgUZ7eJN7oLbtHPU+WlpU3PF83tHkgvvqY3YcvuP3gjqvtzPp6xK8bNl/7gPatV5x1z3i+bmmtrrJrQ1sm1N0LwmfvM718jXaWMg24psO1a4x2DKEwxMycFaFtOX/8Q7j1Y9y0p4RBLNWVIB162tIrTVe4JxMbS/EL8c+YMh9tR17sZvZzpLGaN1YNP3Te8+zx1/DzyHqcGPVLGBIWqRvG21MtObKEjwmkp6ySmuOBEavyX/rgl/mZX/hzfPOn/zrfeOcb35P282VNwfdqGH7n4we2MfjiyTnCkN96/z1+9m/+DD//L3+Tn3j7G8SjjKku2ErVGbnSIhCuXIRSMqpbkhuJxdJ9g429PIreYENLthp7uUQ9/wrp+Y/xaWr5tVdb/v53X/PB64GcMherhj/05oZ1Y7nsDWq4xexekPd38rpNh2oX4HtUs5BdaNuiXceUYT8n7g6RF9uZ23GGXFh7xcI66cL3t4Tra/afXlFCouSC2bxEn7/BonnERedorWblRedMEKewHPL9eaguhWUaMOHAyp/z9rpjSIl163naaVY6EK8+JKpw2lweU/nQoI3HW4fSDu1bmi6Ssry2tbZKhgR+VGmSWOc4VfRhPIU2lRMMerz9pVirY1iTsaANugHjOqztMFMmKlEljFmLW18GbxWNdXgLVic0M3nyxMkRq3Ng0Qasl+/XrkpSQWdFDoF82BOvrshDwBnLvFlVKPBYdAUqzSiKa8SbxGq0koZTa3OfmVDh1BQjYRoJw4FxPzDvDhQFtmSBbqcB1S6k2FhxN6QWtmKcNJWnYKFyjwogYUTquGMux4VfDKZOObxwsurNpWIPSpqA446lGA+LNWiL6RZSrI2VQqv150iVJY73yM4xFOc4ZrGVgGY6VGlR3VJkqClKw6QNdP2DlFEhsGaVTzthITl+oWGp4yRVjg22AV0TGbU53tj191Sn34d6/xTk/etylL4GCeYZD6TDnrgbqWZ/+MWBMI2kGE8Q+D3ZUJ84NDnlCgsHiaeeJxgPpPFA3N4wXL3m7uVr9rdbQorozmEvFbpby/2mjGj8VRYFihUURimF1gZrHdZ7dNOQm4boWiYcYQrMESgG65a0TU+eD9gSsI3G9B26EXRSnUZy8R6R40HT7bwoXnwrY8MHILWi4LUXoqs3aNWxaJ0EGGloraUxBZeDoG1JzkWu/KBUxNE7FsdKdTztNOW84240dMaw8hodDsRpqBbM9wqbHDJpnCEc6FbiU6IV4rPiNXq6I12/ZPzsiuHFNcoZyWB4fIs6nylYdlPgbi6gI5vZMxXDuj1nsXhEoyGHQRCueURPe5gPlHEv0c6AXqxJyydov2Y7R96/Gfm1j255vZ3QRvP2Rcf0LpinK56/+WMsVKFffId4tUPHjHMW2zbYdY/uG3LTyS3ULSmmAesptpV72IhHjEQn/1l+/qe/yY8/qF9fduiHjwjfX3PwA9sYHI+HMabf+u57/Owv/Aw/91PSFDz8v4c6Z5mP2QrhSKrbEcJJ/QYAtTnHNx7dN6QQaJWCriOfPSE9/gp39pwPPt3ya5/u+M6ne3Y3Iyhhvt5eSHPRGoXZvyJ++Juk7Q0ohT17jH38HNVv0IszgvFM2hGTELpeHwKvdgLr74YZpwoay+PeySYuJUpM5DmQQiLNssCpMNH0It/KRZQAABiPO1uzfL4hDoHVlOgf9yzfeoxeXVAQrfmmNWyUEV2xnkmf/RZj2EJJolFuO+xyhe5WKF+NirRGO4NrPE0lKqKowTa1KTiS51KFmMMg0P5YG4MwUcI9bK5sLUYFWdydR/uWVDTYAmZJCYpxiuzGyJgyiaqd1w7TtPTtEjc79GhIFCZAO09AkX1Halakdk32C2IpNcb9DpMjZXdLMZoQA2masc4SxokUZkyMJ6JZznKtCxlSFttcJLLouJkuypKVIRtL1p5kqoVrblBanRII6ZYSy20bcuU9pOP8XhvQXhb5SnZU1Q3yRN5U1EyF2jQcpwSneej9c6DrJvvIx8kFGcs00gjgl/L1xogC5cinyKn6KczSSE9jlZZKY6Bsg2rEErkYT7H+899bkgQuKSlI2AZMc3ouVR0fPORll+NqVz0VKtOzPq9W5HfGS3KjEQVCqWhgeUjOqq9j6pvXOZFth+qWqMWAigFbkCa7dSTjyFqiopMyaGWrVFQajEyWIK1UuQW5yMex4MZICjOh6v7Hw4FsFN40lKaH1TmhO6O0a5GsKoU2ewyiENHzgKPg2p5mfYZZnZHbFcEviWOkBBhr8Jeh0BrNsj3DNxZcqeQRJaZKc23iKsO1pCDNzPGsu0mUQSGgU0S3ueY3yP2nbMFrB85U0qeFUgPQjELnGR0SKgu/JYeZPGyJuy3zODCFyKgMya3YtGfk3tJbeXlnFCUq9OqC5VuPOb8daF46bGNYPt/gztZyfZFr19p7eaoKE2UaSHMgzgFTMiXKPXa8r3dz4tUhEopiGxWHYniUNRc4OqexeoFuPda15DRTtgfSq0+INy+hFMzqDPu2pu2kJtwOgavbUdb6okhzpms8Z33L5tkFl+/8IUzXoy9fwDCgSsE4h130qHZBWqzl/mhX94mj1gtigOJvV6T7537qm/z4W5+vX6fn9/gX9aCm/TYdwe/UKPzANAYPFQhf1kQ9bAr++be/IQv3l3ydOKLV5gCFMR5tvMy6jWe0C/m6R2/CuEevR1SM4gXfLUmrx0zdY+5uJ17OhauhUJJGWI0yh1w0lnVjMeOW+OIDxg8/YLrdYduG3jco/TY0S4ZiGbNlTop9KuznzO1YeHlIXG9n5jnQO8Xaa0IuFO8wTSdeCX2LjgnbevAyryxV1pUrdDzETLO4xL/zozzOmeXzR+SYaM5XNG9/BfP8h4j9OTlAY2uwjZpRV58yXX1C2r0maEjrDeX8EQDWOIxvZSHXQsQT7TK4CgUf8w6o5MISR0ELwkAaduRhTxkP5PFQ89prCr2x6NLIzluBsh7dVB6BaShOk0xme73legrcjIkhJLTRbIxn3Yhlb79o8clDYyneYpuGYb+nFJjwpHbDNhu2+1B5IbCwC9YrhylV1aA+gjFiOo9pfeUaCM8A31GMISlHrgmKWlmKchjtqpVzAQw5Jeg2mM2EjRHjWpoQBJ7ul9BvyM1SFgvXMVV/gZwf8MLrjvX4DIgXhMM5j7GN8A30RIlGdrdKSZE/EiNVnZkadQrTOeZGJEC5tsKmS1CakCHkXF0LhSfjrKLpFXraoScn6NGk7x2Omp7ienKzJDdLpliq6qJgbDWx0qLX5+jOp+2JRHiMIM7l3vxIHcl+Sgt6ggJd35/11U63JSlDyGLYlEpVihyVI/V9yoagkj7btXgmGIv2HXq5oRx2qCQBYvbsArO5hG5Dti247sS4l2dLkRRkLWFNuiisKzIWyAnd9uhpwLQe03lc6qC1mMdvop99hbh6g7vi2e+D6N+1Ztl0rJZefEIWM42CbrGgWa5R3QraNbPpGNWICophiNxOMzlJWFBQBuUMfb+ieE3RmZwmTCMeECXMgnSEQA7TabSpZoeyI7qV3Isj2lW4byhVReLEAbFyF6pTqYpJ5uXHRnMeibst080Vw/UrDne3TBni8gJ1+Qar1TOUl3jknCH359jnP8S6yLo0XW9lHPlU1q28uGSIkkh6vNVKKdJ0+gbbetyiRVtzek6TcYS5MARxBj2Ewj4qZuVItjCqzCIpFkbhlaWtEmClDflwx/jZlbg7bra0TYdZPmHddCwaW3kmWrhqSXM1FF7Ohbug2GzewDuLWW1Qww5iQFkrvJJ2QaxNTuk2tSHwZCQh9Zfef48//ws/w1/503+NH3/r66fAsYf1TylFrtVefwlE8PBTvxt68APTGHzZcRof1Kbgr/7pb/ITb3295pffw6fw+WZCdl1141E1u1o7tPccvHSHh/4pup0wKcjeQxuy64jdOdsxcRshYmnbjtUmYa2lkLk8b3hj3bJuDObuJdOLD9l9/Io4TPRPzmVm3K6Ybc9dhJuYGIEpwxQL20n8+1PRpHKUtElsb7ENLDfYR09ZlkxOBbc5w549JvsFoYg0aIzV0U9bbNuzePYH8OtH+GkvJ6BZkPoL9rZnNyVSku69MRq1vSa+/Ij86XfY3+xx1pIuJrQxmG6ByZlMtW7V+mSEI2PkuggrZNaYRG1QokCteTyQD9IUpBrfK7BjHXN4L4XPtzIvtgL3F9+STUfMhv1hyz4qXg2R14dIyIW+daysr/PFDu09XknKY9aGYhzR9UxzIuuOu6lwdQh8cjeynSNWKy46xxtLz6Pzd3Bao9sOO2zp+47m/BJ3doldn2EWa6IWG2nZoRopWq6hOE8xpvIilJwD49GmwSiLdwvS+onI1rQhm04cK4tmyoU8SzFWHKF+VQ2B7m1pgVPqpKg/DG2zRFkvo6baZEmKojQHGkGQVJEFQatq6ayFRxCBUiRHYwyZqZq7jDETU8FbzcIZeqdZNUs616Amj3atIAHKiMNds2LEsR0Sh2rfPUexnW6tru6RmtZJRoCqG1OjFQ6JY861sGtVd0c1uZIHxExl3GlhHWP10chyfk6Nhj6GCR15FAUdBSyfUkGrhqZv8YtL9GpEpwGTE84afL/ErDfo1Rm0i9p42xPTvmhHUTOFKjezCUqDKgHbeJK1FKVp0fTWUQ4DsVuhLp8TNm/xaoJPdhOvh0DMhZW3vLFuKb3jfPUYlQecN/i+w7ULdLeEZkEsFpcUbipkO3HIM4cp4oIYNzWtYh9huVyQdaIkRZ6LbGqUpqBIYSKPA3kWSacyNe8gRsxxlKM0EtasoXIY0GKslR9UHH1Ec7TMzzNK5vbTyHR3zfbTF2xf3zDHiD27EXKwbmhWT5li4hAzU4bl+i3a/oL+zT9A/8U1ynRsx8R2llFgYzShFBq/wJ49pn22xXQN2ijso6cS32wbwiDmdEdJcCqaOcHdVCimMOTMVkNL4swq1rbHtyuUcaQ5MLy6JexH3KqnefoO6/VXeWPd8ui8RxWDQtMtGtq2I2K5jXAWwa2eUWyDXg4iu0ZVBKphrvv9yS+lRkVp+N/74D3+4t/6s/x7f+rn+efe/DohH3HJWq+U2Fvrqgw7NgjH5uDYMH0/xw9kY/AQPfjW+19ACh40BcdIXZkJf7FBKF/aVX26FbniJ7OhtUu6ViA0pRQJzTgn5qIoxrLoO958DL5tGccZpyLvnjve2fRsXCG9+pjhxTVhexATG+/Qm0vy8hGvx8yHh4ldsYy5yt2QXaJzlmXniKbQGjE5AmkcdLvBvvFVYYnnLLud9RNyt2EYI1O615fnIkjv4Bva5ZvolXSnQ8wMU+HubiTmTGsNF62tOwBhk0+3O6abLd45nHe004TLYJUVRYfxUFn5hXsO2AnqPjoC1gahpECaRpFkVXJW3O/FTawUtLV4BcU18vg4YVcX21BcT7Ed035izpptKOwi7KLsHhtt0NbL+TVOfOGtEaZ8SVgUVjm0zcxTYTuPfHQ38uHtwMvtCEUcIg+hI5+1PL34Cr5d0JeZZetZrFY0qw3KCdSvtaccUQJEzx1RJ995XY47XovyBmVbfLsmLQfC/sA4DOzHwDhHxikRU6rWxPdmQ5qj172q3ABVXTiFpGfKkUgoJ92ZBmcbacZq4BQ1s0EpSd4Tb/9j0byf54phEYyxsA+Z6yFwPQb2c5QES6NYNpZHvYTi6LahXzboyjVQSlNsy5TgboxcDZFXh5ndFJlTkeAfbzlvHeedk2bFiQ3GMb/CakhF7ttSkIJ+fEKtkIJV5Z8U42qcdZG8h8QpkwFURWzqjlZVb9SiiBVJS7Gczrc1itYsaPsNi1YSAv2ix3cdyjqS1kQUpcopj89UxqKdwTagyegS0TlCnnG+xS6lschnTyhTYI9j7h/zYh/49s3I+zfVnVTB41VLBrw1LNqWZe+xjcY2DaZt0a6R5yKCNk7uc+tJ2jAWxRQLPsI2FM6zZkrQtz0lSqFWOcizNOzF3CrMzMNIjlHMqRqPLULe1EpTrMM4aYYwFlVEysjx/08rKPL86yywOBNJWUKGaZoYdnsOt1vJoVCFZr3FxBGj5Nq9HmO1RtasG0/XvEG30JgKRI0pczgkbqfIYU7VLyMzREXfbVBnz3BaY85vQWv05hG53TDVwbwzinXr8EljvcM5Swa2U2I7CU+k1ZkbFXmrVzxZPkJvLjH+u5ALYXsQ++NXH7O5/ArvbHr+mbcL310GQrG0refxqmHRC0F1LortnGjdGuOWlFIIKUtA2ZwlkA24m+7VCb/y4Xv8937xZ/k//os/zx+pTcGxLmlkhCYtWm0G+O3RgH9qOQYPGwK4Jxoem4ITOvCgKcgVhj2Rr/j8zPU4yj16hX96J43Bt6/2rBrLwoutprMabQwJQ1aW1juenDl80/L4PBBjZGkzlzbzbKkwu08It1fMuwMAdtHSPj5HPXqTu+L57vWB37qL7KLBNw3L1tc4Vcu60eRGkYMEwiytFItYCrnbCDxc/8yuJbcrdnNmipxc8DJiXRtz5naSxTdmgdb2cw2hSZnGGR51joUTbbg9PvhKibGLM5TGEmxDcC3GCHxrtUgHcy0+x6m1UqeTfc9UP10UkXalMImj5CCBLKUUTOPQjRMzJesFsret2Kq6lpAVSWmyNmRtSMfdulJoY2WcocTV7n4XIyx17QumGAyZPB0IqXAIidf7mRd3EyFK/DBKpKTOWp5fvkPjoWstrm1RzoGVXauyDRRNiokQE6Fk0SFnkdcdyfLHWX7JmlQso244mMy2ZF5NgZt9YJgCUPBasWgkftlX/wejZQcdtbgCpiPxu0BRskOLQCgKshKlg/Ycvf2PDPrj6EAj95HsPlLt4sRIKNZsgljSCTHYzYkxZLwVOL9zmVWWVM3OO1y/EIMfJQZZ8xCYc2YfMrs5czNG5phpnTTWvTPVmEajtMJbU7PpBdbOWteI4BojUwuxUgqUqAsKAueLcyREiuxUFfW9ytjCaCn6VonkLGXJeAgZDjGxnxJzli1X1zjOFpZHTYM2LVo3lGIxSYsj3cNbukDOCmpoW0G8Gaw1KJVRcRJZZZxw7ZpuNTKPkWGG1zcHPtrOvH878J1Xe24PAWc1RouKKCR5L7rpMN6g/T35k+qpr7UQqLW16COvgkJS5sGzoQlFYV1LqX4Fyk8yftGGmBJhmkiT8KtyFmku1lWDsS88t6fnWNCX45pZKIIoWE9JiWhbgpF1ItiG0sj6oaXTq+uKJLFOqfBiN/FqkIhkZ3Rdb43YROv7NWuO5ZSEE3NhirCbM8v1U7TvURtpUI9rYUyCkJ21DmsLUVm0a4RnVDRTKuyGwG6cmaeJpU2ktaW9aFg/epP28bcZrm4J2wPz7kC+vcLtXvFs/QZ/UDuePNLsopCsV63jrBML7VxgDIlAIqdEiNIU7OfIdoqMQRqD21H8Mv7uJ7/M//A/+ln+nT/58/yRN75OSCLNljVEUVT1jqmV79golEq+eYgafLFO/m7HD1Rj8PB476g++Klv8hPvfON0H0vh/3xTcIQYjzf0cdE5NQRFLH0PMfNRbQx+6/WeTWu56D2XfUPvLd6BcQZvNefO0hbNuhdzFk3GlchGBTZqgtdb5vGAUhrXt/SPz7DP3iFt3uCT7cSvv9zyT15NZO24WC9prKaznsvO0uqMLQoTFSVHdElY5PefssG059DK+4ylMM+FKYpvvdViS5uLlkS0Iklyh5DZTpFXu4nr3Vw7cLg4OnzVhdNri+6WuPNz3FLiU9XFE9L5M4Z2A3YBbkE2HpWpvAoBHpWqu7Wj0dOJNFa99829TrfkTKaQShbEQCuKceimRfcL8RnwLcq2FONJudoxa2Fqe+9pokg+O2erX/79PJ2TI50cihreYzTOalpnaCuhKiYZweynxO2cOWTFrBvc+QW290LEO1kKCzw5J5FBzVmQpJxrJ5DrbvfB01koxKQYk+E2GV4GxWdD4XofGaeZRsO6sawaaQrE4tdgtKryNGp8scztc73Zjzt/pRVZyf9pVaodtIzLjn2SEVATHROGStpXiqIS2ng654g50DlL5zKdy4QMWme8NXhn8d7j2wbfNLjGY73BKE0qGTcnfJ7xWeOnhJ8zfS5Yk8S3w9nTh7eazjmcSiJjrfdAzsJ5QBs0mlSLUMpQlIwKlNbkIhG1SmtM0RQlSIM10hhIbkM12VJy7VNWEv2cCwOKoabxTRnaqBh1oXQKkoFkiFFhTUGlB9fxKKeU1RmtNCkrchZEwVuL8+JQegxms4uEO8zML19zyAO3c2Y/pVoExDDodC9ajTFaTJ0+h23K/Xy8lkcCXucsg3eUUvDeY70XpYYyJLSgLLYFL1kwuh/Rw67K4xSpZBTVyzNnjgoVVRHSh88vcHL4zJ/bZMlCWownORjtyNBuSOfPUONMa6wYkLkW3S0p2tYGTbJaPr0ZeL2fyZmTbfyjZcOqsfROnunWauGEKWmYUy4cghBAvVth/foku03VTr41isvOcqENSluS9URlGLPmaohcx8j13cDrux06B+LUsPaw2LyBffYO/dUNh1TRsPGAmbZcPHoH0zZcFkdQloyWOHsNnZL1v0SYQ2IOicMcuTpMvD7M3I6RWImRV2Pg117+Cv/T/+Rf4X/3J/7P/Feef12MzWqhz8gachwZ5toIKApZSRjd76U+/k7HD2Rj8N777/Gzf0MkiV9/+17neers6z+OTcERJUgPPn+MFY2pCIwaMvs5cTtJV/dqJzvZzmpy64CMUwavwViF9o4z7ZiLEMI1hYaMjQP67oYwHuTBX7Y0mwXdm2+in36V11Hz/s2O3/psy8vbicZ5Om9odMdlq3hzYVgYjc0FkiHHTExyw4ecGVMmhntN+kOKpdGKlZFkQJAbLKTCdo7E6pw3BQnAmUKicZKAZ+tDlUuhuBZz9ugU+GEWa8rynHF5yezPGZOhHQJNMXinMboIN0xJ4EwqCltnkiofzTsEktRWyFW6aWHu0NOESpKZrjvxCNCrM/RiDb4HtxC/giNMrDTOWfq2ZbNMwuwvkrveOoPRpRbAUlMaZ9GVzzM5zKi50FsJbHm6aJhjxhvNdoxorVh3Hm8t1jXopiUZR9SeojWliO/B0SY6Jomujlkaw5hz3dFSF1tO5zQXKdrDnDjMhbsAV2PkaitudabVNZFPsfSaLk/oYZKdvmtomo7OtzQpC/8kH0l2qjZjx611JiZx5cvqfodhQQpLDuLvgPgYaC0FNpeMtg00cs2V8mJX2yamVLDWsGg8l6ue81XPpm/ovZOAJC0bTOsT2geUExTJGMN+mokx0RjFwhnOOsvSG1aNo1UREyeJ9S5HfkDd/hRL0g6lDIk6MilVwlgK2sgM3BjhurgiMrvjGMYbaI0W/41pIIcJVaA3Dco3hCze+jFntmNmOyuyi3QB/FwIKtIlMbHRFXEA2ckdSY2qVOtmAylKgxvKA7Y+Yl6UiiGbgm5arGvwdmDdeUTQUFi1lmfrlqeL5hQkpOZRxkaq2m9XFYZSTq6dLrTOsPCW3DcUpVj3LX3b4pw97cpjAes7jnJPvYjoeUIPB/Q8o2uh0q6abTWtPJ/WywjhqNxSuvJmNLGuObHUzVaWDckcEtMYGJMh+gvyRlFMhzt7QjpKtZcbkmtPje0RzclZ1iWt5U+Q8dW6say8refzfo1PRXJGQigQ5HkzigdOq8JpEdRIo61I1KO27JOCkrjSmWEc2N3umIJEnD9ZWB53Sy6efpXu5kqe35Tk/I8HXBy4WK9Z244JTaaGpClQOZDnRMowKwlCyzkzzJGbIXC1n0+Nwa+8/x7/61/57/K/+a//Vf7w0x9nSukUS38cDZqC8Dmq5TiFEypW1O+MCrz3/nv8zN/4Gd7hnd/2a37gGoOjedHP/7SMD75UevCF44vNwhebghCT7ABT5jfu/kH9nlK1uprWwNJAqwKeQmM92haKrfB2kW5O54hWhjkHlDGotqd7fI5qe+xbXyOunvB6l/j0buJmPzFPAaM1C5t52ine7g1POrCHW+LtZ6RhR4wR5TpMuwG35BAjh1CLfZKbRkJrTN1pSpEhR9CWKRWWXoqh1QL3d94yzJHGGR4vGy56T+8MVilhyrYbaFYC4XdrhmIYJkWJI23QbJQEw2RVcLbglDjEC+z7cPaYBArNYrmq2x5T/dZVTBCP2m2LWizRm0eY1Tk0K2iWFN8xF0Wo8Dla4ZyhbxvOc8EbQ0mRzhkWVmFLFsvlVIjzAaY9eR6Yp5EwzaQQUdlw0fZoGnqvebr03I2RgkDbZ8uOzbKnaRqUMsxZFj+ozVguzDkTksjFQi6EmATOrrsodYL8avNWBGEYY2I7Rl7vZ+4OM2MILGxh6QyXneOsMbTTLXq4Rs8jxjlM22NYQ69p+yVzkp+ZamSy7LTzg/m8fNRWD10qWnOy6k2SdklBFfGzoERKnPDac+a72qSIT3xCYayjbRpWfcdq0dE3ErNrTSWeloJPBecczgoisPKGcZpIMWAoOKPorKYzChN2InMrEnSljiY8RRCCfIw/Nqaif+V0flV9oLUuGK0lKrkWGGPuMzmYdjBtSYc70niAEMC3tN05Z82aOTq2U2Q7JfYhcHeYeb2fsb4RKD6XikAduQrH9aOcoF5NwVmD00XkslkUBnMS22WoC7gyNE3DZtnzbJppjeL5SnwD1q3lrHOctY6zVqMONySdCMGi4wSplQKeEWvqkrEls7AyP3dG7IgXbUPfNjgnng65IIiPUnjf1fNbMOuEnmZUEndMlaPYXndLdLfC9Et024uu3piT6Q6VS1Q4ci3UqeENsTCEIqTs7cx4GFHJ0nVP6PpL1HCHCiNZaYrx2DpWuug941nGWc0UEp23XC48T5ae56uWlZcwri+uZ2PMDBEOQUiughYpVt7Se1hogw879HiLCQPWWky3xG6esurP0Nlw2yk+sJlPUiRMgZs9fHo38fqsY716gn3ra7QlUcYDqu1RxqBzoHWG7DS9rsZIqmBKRkVpbqd5wqiIMhANtUGRNSBE2S78r/7Of4f/5Tf+ff7w4z8uPgWpSPZMylgjpNxjE/B9EQce1Mdv/vQ3+df+r//ab/t1P1CNwUNHw6+/8w3gfizwn/fQCn7t1a/wt17+GwCc947HvedRbzlvNG64QocDGENerHBnT3CrS5K2xKLq3Kca4SgtRXDzSB641Tll85R9NrweBg6VCd91jqfnDT/yqOVra8eTJuNffpv5w99g/OxD5t1O7E0vn2GefgV73kKBfUjcjJGYMr23p4dn0xj0/go9bk9BMtZ1dN2G1XLNwike9V5mvyljlczhNq1h1WhcXcxyLcqHBLf7zNUwcjcnsrFcbjTPTIfyWfI/jhfAAKXmLKAw2oh/eikoW+HGUqCTallSlnFHEP92VmeweUzpLyj9BmpTMGdqAycwrtGa3ls0Lb0z5JpY2BowJHSCadijpj15EvMUsaQNxBDkd9SOje1Z9h1POpEIxgwYTdu0rDpH5yylkqQyMt+PBaZjE1mVH8fo4XxsPo8PdD1yKacgmTFEtsPM7jAyTxNeF85byxsrz0Wnacdr9K3oqHWYoO2xF49xjUenFrJIFBNWUIOciDHLB7KLysddRZZckGMzLH/Jn19oqnESScyTpL27ozWOxjbQNmCcEEMbx2LRsOganHc4Y+69MpCGydWm1KtMR2S2SYzw0gxhRI2TZGSQBOnS+t5s7OiiWMdAqPK5Zr7UrlMpMKrgTN0hWo21GqPl9zGkWlAnchrJh1vS65fE8UB2DeZsT7t5g4tuw5Q8Q8iEHJmnid1hpGsbtJaQptnkky3u8Tg2CKquGaYiC1PU+KPO3hqskrem5bGgc5Jhojcdj1olRUBzOl8m7FC3B8iBqAqjcyTnmJsO23ToZqQ0I9ovMCRaA48WDUMqaONwztF6i9GaUipBNR3RBo1vl2L/XRRssnBSTENJkewcpV9KWmm3FPWTbatluMg6izan8WwsopIJSTgbU4ZdyLzYBz69Gbm63aJTZO0Nl51l026ELDiLcZDThXWjeXvdsPaWZ8uGWKQRO2st561h01j8fIe+u0UF+b5iPLpd0SwuySVxCGJ5fJijIANK0TuLJaJ3r0iffYd49SlTmPHLJe3Tt/Bv/QhPzt/la2vH7aOW7b7hM+Q6H+bI6yHwuO9Yb57iYiRvr9G+Rbd9VfjIGm+UjDGtKpgUCcMN8eYF7LfolPCu57w9Z06WIXjmmNlpBVv4n/3Ev8s/++wnfv/F6uFxHJ3y+abgG7U+/nbHD0xjcIRHvvmgKfiyfkDV2epDpuIXP6eUrJ6m7naK1vy9T36Zf+NX/hI/+86/zl/+zv+YN9ctb60bHncWe/sx5fpjwu6GbB2cXWLSjMoZs7683+mkCDGRtSX7JfryKTpnshfpzWEU+VfvDc/OWlpn+eqjnj942fNGV1Af/KcM/+T/w81vfsDuxWvCOOLWC9Y/PGNW56j1M8AyR9mxHqVZjZXOWu+v0K8/JL36mHyQaFPV9ujzJ7RnT3CLR5ydLRijq0E+Ans2ukgAzTwLodH3HBK8HhIf3I58spt5fQg0TcOkW7pVYjFnGpdPcb05ywwwZ8g1T0hrB/beTU1m7TDbwOQjczHSRduG0q/Q3Tm2PyfZRvJfshTkUHX9wjiX2WpjNTlK/qxwMDK+RMaba0rNR0hhJM8TaZpIKZBDoFT2njJG4matp7U1KMj2NP2CpjnGuZaKLnHaqRw/Ym0KpPAfzWrhiy2+sNiliZiCcApimHAqs+4tb64bHveWZdiir94nfPo+5XBH0RqTI2qxwlb4uJDIOdbQKQmXKkeTHSTkpyiIqtyPMurvII6Mue6+c6WKVhe/FKpxlsD6ulQ7Zm1R1mOannZ9Rtso2mJwR6LpA3dCRcEQMWUmpwNpvKbc3ZCmAyXOYqldKnlNKYp1JGPIytdwmaq6QFepZS1E9z9AkheVwilwJWNSwBSNyRpjjEQHa4UmoXQhUZinkXK4I21vJfZ5d4eNgeWjr/C4XzInMZu6mTMxTIzTTOttRUI0UWsZ1XzukDtaI0FhwmvI2Cjz8DblE3IncUQFb2DTWNpkmFMgpQNlPKCrAViJM6RE1IJCBucwxmGaAe0bjJPcBNWu8N2KzkoC6KLybrQ16KoaAIHnAxmiJmuIGoztKL1mnjPjlAjFSUaD1TWeuUXpFutaybEwDqylaPc5RUblEZ+a3ikK2fR2Tny8nfj46sA0TVz0IgF+e9NCZ+itl/yS4ZaF8fR9x1lreJ7EI8FWEyM77zE3H1FuXohl9XiQ890vMY+eA9C3FxxiJSgWKLVZB8T463BL/PR97n7zI8LdHte2LJ98ytnVZ9gfuuWNxz/CeNlTUuTbrWUMkd4bYhL+wqK/wKaI7ZegNdkvydoSYwIVRDpY78l4d0W8/pjw4iPmmytSDLA8w54/5/H6jdNG4bWd4SX80effqAmiGkP1fIETT4LPUzt++889OL71fTQF8APUGPzMgzf9xcdUw4OcxPtDipNCfNQrsaMSWMiaUufjf/+DX+Z/8d5f5N/8r/0V3NWGv/wdeL5qeNRZ/N3H5I9/k/TiA8LugHGOfLgDrUmmwRhPsk2FmbPswlwnhjVKoNrkOkIxpJLwVvGkwveb1vLupuPNpUZ/9KsMv/EPuP7P3mf7wTWH64GiEl1IdE/P8UEQANm5ynzPqOOoQ9GWGX14TXrxIdNnnzDf7iEXbN/g93eYwx3m/A7Tn+FdQ1FGkvhigDRxDLkpbgHNgikUbsbIy/3MR68P3I6Z1UJztsnMlSQWj+ROpDDK/EtVPoBcAa0dxSpyUUSVGUvgkCyH0hCUITUGXItSDVPQTEPEZ9GB5/o+HxYIc5LaCalBZ4XOAR1nwvaasL0mjnvSPJKrDbLcMFr+PlU75mk4OcAp36D7Je78MTYPWFNwXUPMEu86ZdjOiSFkpiRjgVQbgljh7ZTL6QY0X3h6j/C+ZCfMeBXpW80bC8fzlWNTBvT1h8RP3yde1wyMvkMbi+06dNPISEYpTpHU80QJQYpuUaIusB6nbbUTLogx0pE4ZoSkp7WkNwvzBlBCOk8S3CMhPoNwNEpB+wa9WIGa0U6hvULpgimeow3xUSqR4oye96j9NWxfUF6/JO235HmS3906Ydz3SxkNaI8y4ggprYWufCAlRNNyJMPfSzmdBp1nCSsqkaKKeBo4R/GNMOuVZEzoppHzZyzERDgM6KkiacayufwKednJjHofiIiTZMmiSkh1TPPwSCe2vtyLY5JZudVgYmaOmskoZifjlEZDozKWTJnuSLefkm9ekK9fkg874jzJPWgtqukk1Kfp0cg8nymg54A2I2Ycse2IS5HF6oJsrdh6Vx5APrpC1vWwZJGAmjpSUCUzT5FD0ExqQWkthBGTE1OxxGRJxdAqj9XCB1Ja0jmPXI/CkXhYTndQLIUEzBkOIfN6F9juJ4YxYBWiNnCa3itJVg3703pjTUOx7n492k1wkAyL9PoF89UN8TCBVvjNgiZnTNvTNktaY2itZl/DRGO5Tw8tYRa54dUdw4sDqhyYrsSO/HwcMD8y89aTP4AqS5becDsKiuutqhwGg+rOqhJJUZoF2XUSalcURieshhwn0t0V4dUnjB9/m/HVK1IIuGWPGfZ4Co9Wz8lF1mqApTeCJlW5rKlcISu+/Sdl09Gl98vq2rHuwT3n7q/9HpsC+AFqDB52Qg/AgNNRQYAK3cn8D2QOplSNsYXT/MYa2V79/Y++xf/kP/kL/Nt/4uf4o29+g390+FUALjuDH64oVx8RP3uf/cevSNOMa1spGsMBNVXZXUSS03JB7F8Vul3JQ5slEz7EjAbOWkdrDb7aED/yCvvyNxl/4//N7tc/YPfRNcPrkThFTK+xXYNbtKheZv5xiid3uMZqlt7QOY2at5T9HWl/R9iPxHGSG0grzOGAsjcA6PEgxCKQYKFjRKtW6H6N2mjxTldOCjOVJauFBW6NqYQsPgexwr3iQ5V7aF0rRSmGSGZImt1cOETNWDwzmjkUpmmmkGhm2BTHslistbVL/nzuuDR7shgbZbCASZlhtyVuXxN214R5lt9dVSMcbSSUxk4YY6WoziN5txU/dgW+a0mHO0qawTty05KbFXPS7EJhF0S1MqdyGm8codWHh9EiIxJFgaAyus6pTck0JLxTbPqGJ71hw4i9/Yjw2fukmyvyHLGtx27O8Y/ewF0+RfVn4HpxSQuBGGYJZppHSYkriMQzF7SX83JU5IA0Lcoo4XNQ7g2YQCSkZgbtAI1yMww7yriXDAtjyXEiWw19D6FHCf0fdcxaqDkOKo51ZLAl316Rb1+R9ztyiuLJ36+EnOg6aGpeiPXcJ/PVuG+ODoP351YhDZcuCTVPkAYhlypIRmbx1IZAWY8xHqU17jLhp4kYAikl4jiTbq7AWpx1nJ+9hVq1GKO5DaBImJKr+6KqZDcheqaKEH3Rle74PFij8CkxG0XKlSfhFM5k8rSF3RXl6kPSpx+QXr9iHsRDw7QevVyh2gWqX5OaBck2YvJVrahNmEkxUOKEQvgci/MnJGOImBPZ8DTSKtIc5CKRRpRCjJHdbuT2ds90OKBINFrjjcEXS4iaMBdSo+kw2GIqd+he8n1sBr7s/Uuhq4mRWhAg+R1qQmqKEpF8eyWbq1xkLaoBdoCMNoYd+e6GdDjI9ZpnWUuswe7v0Ps71OJA5zcsvWE3a6aYq8cHFCfrpVu02K4BNRCHyPB6ROlr0IolCqc0zy++ircdt2NiTkUUEMhYzvmeZL3wWHxLzIoyJ9BSsK0q+DxRpok4SDLmeHVHGEfM7Z5FKbimw9uGy+6CakfDqqme0FT5cL3Bjx4FMi7j5MFxPL/Ca6nPQ32tb73/Hn/ub0pT8JO/x6YA/kvcGCil/iTwbyPT6X+vlPJv/k5f/2Wd0HHOdxxzH0cE0m0d2wc5mamOE44EMV3g//nRt/gf/cc/y//+T/48f+zNr6MVLJyc+paAHm+JWzEoStWIRzkDvqX4jmAcJWvGGInHgS4FXRDvcO0pKhGTZG83VmGMZtMYGqvoVcLcfET85LtML18z7Q7kXPArR3vR4M9bVu8+xT3/KmV5wSHmk09BU9n1nT16h48SMmMsbtFi3NGAxaOWa1S3EMe4JOl35bhzniVlTLnqSd6vUGnGGSEkbhrLo1XLIhbWi47zhWPdGHpraI2uBVrV833Pmk3V7CflQgiRw2Hg7u6O3W7POE8i6UlFNPNRPBjsmNhmw2UxLPoWZ4w8KJrPddVCNBM2sEMTDzvK/pq0vybstyQU2fWSh9AsmZPMp1ujYLzFWSuw+jxSDjM5RGIaMfqa3PXk9Tmx3xBUwxg1uymzjYUxKeYs0tAjtHo8TJVTARwTkp05NgUFnaGojLGFzlrOvGLNhLn+iPjp++TtjVzXzRK3uaR7/hWap+/A5gmpXcrYIiXhO8RMjoE8D9IY1GAnlIXiUUoW9KIgHw2IaqFAaeGCHv9dNEa1+L7D2EZ+35LJOUmAEEVSCavKQ6WAylnCeZSSN1qOYU4ZlQTFOH6Pooj1cNujV2eoxRm5Pyd5uS4pyoNsOPotaEE6ynEMkqXgUw2fSoYcIEjMcCmZfAxKipmCJeoWbQyuWYLtaXQDrkFZR7gVu9u8vSF++j5WG87P3sIsG7q5MFAwKuNkNE+q45KQ7puCI6fkYYOg66y3sYqgFSlGVFS4rPE2w7Aj72/It1ekm2viboQQ0U4kjnp1jnr0BqG/JLUbxqqv9kZsqBnvKOEAYcIoKI2HvqdZXaJRzIX761pJqIkiaBAQUmJ/GLm62XJzsyVOE42Fvq4f3iR8EFnzmGCdoe8VDkFgjm/12AfroxJAiQKkt4Z1YzjvHc82Hb3NdFaxaSy9MzitasT6SD7ckXe3kvCoNcp3FS1poSZkqsVKoHZridNMDqn6OlhZ58JI053T1XVQ/Dtl/HiImdXyAvf8q5ztJ4zWzNcjuRIVp90B9/I1avVdjHZcbp7TWSfPNTKuUogs2RpBM2I9j1nVE43CqixNtnGSfeAalDOUoZAmQSz09hq7uaVtV/X3hLYiqA/r2FEdeiS2isRYnTgEun5OcT9y+NYH903B7xUpOB6/bWOglPpF4H9QSvnO9/WK/wUcSikD/DvAnwA+BP6uUuoXSim/9vt9TU2Vd6AwpdSsFYWqYwTqhTi6R/3KB+/xr/5HP8v/4b/x8/zxt74OCKO5qW2dmgeZVSEdfbNZop3Fn2/Ql29QVpfkdsN+isxFy6hCi1mJqle4lEgpR5IKaCtNitEKE0f0/hq2r8nDjqIUfrPEdS22cfj1gubpJfrpO+TH7zJ2j9kOkUNIaIWEHTWWzipMrrbNvkWfP6ZZbtDWiQSp7aHpJOJTKWH3Tgfp/FOAYD6XnkuWaN2u0Wxay5ulpfOOpBRd2/F4JV4L69rceFMbA32PIOR6PVIppJQY55nb3Z677Z5hHAlJOBJzyuKvMCeGWCBkovZiiWws3hac0ZisiKrQmKPrmkIVJVLJOJEOd4T9DdPNFXGeKf2KZBtmt+T1PrILiZhh1RjOuzMuLno671HOkJpXTLd7SkqYItc9HXbkcc9sVxymwu1Y2CXFISliUQJXqgeQntb1TcvYylS7Yau1EOSUAhLOFGxraMmsmNA3H5NefkzZXYu72foM/+gZzRvvYi7fJvTn3GXFtBPZn84RR8JiqHq3U3xu0feQt6hwHu62jzs+MfoJSWDfOcmoRmuxw176Jf2ZOO1Z35K316g443yD6xYY50FVKWYt1kfG5VGqiVIY53HdArc6g2aiWI9enZMX5+TFBUO27EaxXs5Z9NreKLwGZ46jmM/FIAnS8WAxLdVdM8eAUloCjTDEORHmgawt1hoa09KdvY3xS7p+hf7ku6hXn5KnA2V3TXrhMFqzOXsL3TaMGKIpWItI3ArELJLOnD7fFOQHo4bjLn2OYFUhGLHebbKibRRq3EsewzxgCjTOoVqRMpvzR5Qn7zAun7JNnuvdzHYSqHrpDBfdEh8nCAfKYccUZ3zTkBYrXL/E2A6V1fEsUYo03OVEEszMMXF7GHl1d+BmN0BKdFZRvKG4Qi5V4phGGRnUDBS08DceSnC1krmszaqaayrWjWHuLOPK49KCYaUxpXDRGjatbGDUPNeiWu9LLZbLWItqWvRyA00P2soMPweYBtx4gGqfjqvmZxRMDnRWkheBup4ktpPC909o34Zlv6B/9j7TZ1fMd3viFFDeikx32KG3rzHGs+jPaZv2ZK51LNAZhVhC25M/RMqlpm4mgsos2g1qdYm+fIMuzJjGkUPEtLLZUimi5oG23QDSSIX7Gad8zYPCrx80BMdx+D2aIF/3d6rj7++nKYDfGTH4K8D/Qyn1c8D/tpQSvu9X//0ffxT4zVLKbwEopf4vwL8M/J4bg3s84B41OHIIOJGjRM53/L9SRNv9y++/x3//P/xz/Lt/6q/xx976er0QskCZB0WyGC/yOcBeBFTTYc6fyC5u8wa3U2HMSdAIUxnZ1orzWsliSZoQopiWhYwUUHNAhQMqDtI49CvaZ4r2aZKQpMUae/GE1F8Q+wtu5sLdIXA3iff8MVZ56TULb9DzQEGh+g12sQHjJT7aL5i0I1SLUKMVtgT0eIduV+jDLfmwpcQonXsjNrAlJUwcOWsaWut5tFQoLZKr5UJyIBZWOl9x0ntQJetx3FGnVBinwDBOTEG84R8axegKjxUKOWXGMDNMM20bqssbFCUFNmZBg+zxZ+VMHPbMu1vG6yvC9k64Jv2a4hfcTpH37yY+uhs5zInz3vPDj5Z0TzZs3ljQLnrK8kPi8hXT7kCqE/kcZso8EeaJYSzc7RM3QTFhyBiMuANVlEB2zdqY0zl2RldyUU19rKmbnS2onFmSUdefkl99TL69Eo15t8A/fRv37GuUR+/wOmlubwOvx5lhkvS4lSusHJw7W3fWcg7ljyop045UCz6V0HU8x3DPKB9qQ7YPAsEuG0ekga7l7NE7mH6F3Zyj4yxjq36BOcbFarGCFjWBqlwGyTQopsF0S5qLDG0vHBHriX5F6s+5GSM3c+RqH9hN4YTSrbzBeI0t6kT2KxVCjVWRoIvs4NDi+FepZlXWpcnKcn0Y2QbYBiHxdY3jovVsmiXrZz+Ksx04z/zZB5IZcHuFshatLevzt9FkihUprrLiIEjMdYdXi0Jl/eYaNHUaLxQJwNIkRhLZFVYYglKYuaYYAk3fYfqeZtljzx+hHr/FuH7G9WT58MUtv/lqx/VhpveGN9ctRsMjv6AMN+RphGlk9A2+X2MXG9SiAbSgBLmcnrFYOQYxZ6YQGKaZMcyklE82u3L/qpMzbMxFvnac6KaAc82pSKHuIWxTx2O2VD+CDLkx2E3LpU1Mk6HkhFdFFENxhJRQ1snI0kh8s7JW7N37Dbldkds1UbkTGuOswuUA8x47HYQDUqRt1HFi4ZeVhG3ZzuK0eT1GUims28ecffUJ9unXaA6via9fkPZ3pGmQ56VfyvobB/QkFurGOCFdVt4GxpzGU0lpSoYpRKaqrBpLYtaFzeY5oHDdEn32gjINYB1mdS4R43AaJZhjlS/65Oh4LGrHZuCUj8DnUYIvNgU/+WC8/v0cv21jUEr5vyml/kPgXwf+nlLq53lAAi6l/Fvf58/6fo43gQ8e/PtD4J97+AVKqb8E/CUANl/+IsfmAB40Bw/+P9eTqesOqijFex+8x1/6xZ/l3/9TP89PvP2NB68lZinHXW+xDbldSS765hKUofiO3G4Ydct2H9nFQigKbWHROaxxNI2nMTJ/KsmSZuEhpHEvqWZxRuUKzaJRizVmsZau1HXQrkj9GXdRCG+315GbaWaOAuv21rDyllWjWXuDmfcy21Va7JKbBaNq2M6J7TZxPR6Yqh/50hvOWsu6OafvNuhug14P1V+/gDaSFmgMKk40OdJaj3Ye5w1N62law6LTNE5hnXTTsk9Rp0TL4xyyFDGtyXXBlMZMdtRWSUKs1RpnPG5OzFl2ruLad9ToF9CZkhRHlwCNFAlNJO33DNsbptvXxMMdxTWiNnEt24PYz/7DD265GwIXq5ZsLI8uznn+5BndokU3jti2+Nsr5nFkVpaUEylEwhwYxsTdfuL1pEjaY1yDMYbGGcigjapIkSg8jk2BN1qMVoz4KxRilVYW1M0Lyu1L8s0VJc5ifnP5BurJu0zr57zaJT64G/jobuJ6PxJCZOkVbywt724aemXwSsvO2tSGwDoJZMqioDjuxDVgyKhKXvWulUAuCkNI3I6RORd2oTChMc7TKs/F47dwcRBv+5LF8reREVqugUZK29NDWAoUA8VLc+WNRy/OSUqTbEuwHa8PgW3MfLwPXO9mphjwWqGRJlcj1tAqiPuosb76F3DaqSulaFyLjqKVF5hHmoA5iqvgB7cTn+wiu7ngnOV80fLmuuHttefR+jkmJ2yMhFcfS3bHzZWQ/mzD4uw5Qwo4orjPVXOr43zImTonAtll53xCD6aQSCmRwoTJM7opXJiGYAwuREyKGNfg1wbftvjNJfbRG+SLN5ntGdcfv+bXr0f+wfs3vN6OrDtHenvDo95x2QmHIoeJEiYmaxjW5+jlJcYvCVhCVlVCKB4tmXLytlBFklc7o9He4nURcqShOkQew9DqGCLnSv59oFLhvlAJKa6gakPii6LpNH2xjNozuUiYkyiBQkUKjCE3K5Tr7kdRxpFdR25XHLLmbszcjCO7aufeWMN561g1a1arc9oywbSXKPc4YlFsmkVtWCzXQ+BmDFyPEW8DZ41n012wunjM4smPog432HELQTZltXrU9fgAyYH1aNdg2gXGNxgv5N9YlJCPs2JArOVzDDhVGK1i1T+lbTfozRORZpYE2pF9L/XkOIbRCiW2HTIuuK9539MMHBuxY1371gffixR8v02BnKnf+ZiBPdAAKx40Bv//Pkopfxn4ywDq+ffohU7Hw5NymvE+OJn5CCsUxS+9LylWf/VP/7VTU3BsJo7GJccj2RZtPbldgxYIaYyZIRR288zdnJgyaGNZaMfaWLrG0zcNjdN4DSRHspohzUxkyJGSY1VJSCNQjMTtJtezD5m7OXP16Z7XQ+B6COynKKODxnLRWZaNYd1oNl6hD1foID7hxXeUds1d0rwaZj64nfj4duTF3cAcE703PFm3PF+3PF81rBrN0p/huvOThTClzo5TRMURPe0wJWJVNa/pevxijckbbFljWaBdA7apJEV14kCUI2GpXhBTxyzWahmrlARKSFNjEvfCpAzFNSwqQfNI/hL3SklJy3VdziUS0kw+HBgPe0KNcQbZweYiBiivthMvrgemQ2CaEufrBT/2dmFSFrt+jFUJk6u72faWFEWJkONMyYmcZPd0t0sEHel7Rdc2FGvEUMdqnNE4o2iduW8KrKqzW9AhMYcZWyJq2sJ4R757LY582qAXG9Tlc+blMz6+m/jHr0d+/cWej64HpiGALjzfOJZmSVw6ShG0QFIuTQ2a6ghYDqHKthQ0RsYcKgziwVEyJc10zZoxCpoQoljT7kNBWUs/JTYry6w97XqBJ6FPCaOaYj3JeXRVSZQiGyuUJavAMbxJux6HwhjHjGE/BmYKV5N4cLwe5Pyu6jlrtBgg6fHuvtEtPdr15CRKGCrhUzuLdx05hZpXoGpzIvLS7RD48GrHx7cBsqLpHG+ed1w9WfCjFy3Pl8+wlwN62J9iiNXda8xiDeMK06zJQcKQtDVktJCG6goh/g3p9O9cJJgppcwwzhwOB1yeWBRDXuiaJSbZesZ7vO1oVhv8+WPM5TPi6jHTUHg9F757NfDJqz3jfmbsI6/OWg6PUlXXFEqYKeOB4BvGwx4OB7QfiMYTlGXKYmz0MFXWKLHY3rQOu2hQHkxJtEasg019/vPR2bA+q0c06tjkH8lwVsvo0KAgTrLhGffE3R1ueytOh8OACrOM3pSVHAPbiXHacTeuRCkUUmFX81s+3k58fDfyoqJ83hqerDueb1re3rQ87hyrdo0armUsEyZUHDnrz1FoQjbcVT+CXSVqLxrLeee46ByXXce6W7BYa0w4oEIdGR9lzERU1hgcjVF0fYfxEks+ZzAhEzMc5sicYT8lcorcalh7I7yv5hHtQmPIJ/VFrmgTCJ9GJN3qc5vZLyIFpzpW/3zvS4iGv5+mAH5njsGfBP4t4BeAf7aUcvh9/ozfz/ER8PaDf79VP/f7Or7QD3zuqG6SvPfBe/yFX/heG2X9uZbj/hWmlOssUVcr4sR+Fn/1IYhVrDGaRSuFYdlaFq2nO6kOQGVLVJkyGrIRHkJWULSi6JZiG5Lr2c2J223g093Eh9uJj68HXtxNTCHjrOK8d7z7aMHCCxlw4wr29lPYv6aEWaA43xO1ZTcmPtoGfv3lnm+/2LHdTeScaVpLSPnee1x5QhIXvCOhyGhw2rL0DTZLc5CvXxCnvXi0r9ao80tsfoYx6WT2QSk1jU1m/zxgMYOoGbSW4um0pgx3qGkPOWC0wfsFm8UZ0XiU7/B9g3GWiMw9j5G6hcxUMoec8DmgwwEfkiTGxSQjkXmCeUSTxZoZafrCHDHWsJsyhyTBQ9n30Cwwqw02jMSU0HuReZY43xPqSiKEmSEnjLG0jRNzGmtorBjbNFZjtT4hBc5oGqtwZHIOECcMCeYDZTjIvFRpVNOizh4T+wte7Gb+8asD/+CjLR+82DPuAzlnfG8IC43W4jLpNBAmVE5iWmM8xS84zLka9hSxaq0eEzocUOMdhBnlWmwpLP2aTbRcu8AuiEX2dgzs5sjtlFh0iikrtGtwrj05WiYlxKlSzd0FKzpyArRYKx9z5wsVmk5MSXE7JXZzZDtKcE5joHMyI156jR1v0YdrCCM4XwtpL6+TjmMQyQxwfiFfV69TCROuA6cUWosb5XAYmQ8JrTW7uwOHYYS4wj7qedJfoM4eo8YDZRrJMaCHA/QHbLNkjhMqB5zyFCtFUpPRSkmkuYIp5tpUy1ihlCypgsMBdEIVfyJqljhDLmhjMf0Cu1xhVoLwZd8TDgOHBLspE6ZEmCO+l+XbVmMd5hHmiRIjOSZCyuSQmO+2ZNczP5AuKtQpmlusgS29blipjjIXbJop+xsYjtJBh24WuG5NUIKCqWNzUE4ULWHG6+qcGSaII3rcY4Zb7O0r1KtPoY71YoHcLFDnT6BZkvyC3ZwIQRAJyCglUsM5FV4PgVf7mY+uD7x/NTCNEa01n93N7Ga5jq1txccEhQoD+bBFO48OI5v1U2K23E6Rz3Yzn94MXB8CIRYap3mybnh+3vHWquHZsmHTNCz7XhqEer1VhfOPGRZd43BtS9Eam0CbRCqFKczc7gUx2k+B25S5NYrOGdaNZeFFRu60rtLE+z23qOSo9xSfRwUejBMeVqSjJPH36lPwux2/E2LwPwf+m6WUX/3P/VO+/+PvAj+ilPoq0hD8t4A/8/2+yJd3Sw8qfcmgdHWE+rOf80E4dmknYnH5/KuFLJDcVK03d3PkbkpMMdWwGk1brYgvOgnAWbeW3hsaa7FayH3GGVLjiaMjzYZS3Gkee2oKpsxHW4li/fbLPZ/cSCKbAtqFRHued46ny4bLzmBfv09++T757lq4AaWglhegNHPO7FJhOxWmWRjshWpJ6yW9zFciRUiFQxYC4FwFwGetJRW4cELwKftbwvUVJRbsekdXgjjqeXciJ8ndrFHGn5zhhPV+z97XWovd57BHjVvYvSLvblGAWayw8SnLR2/SrDqK92QtMaZDlE4+lsIcEyEE0jyhwkiXZzZWo40EHUHdUQ079LRj3fY8XbesFp4UMs7JCMBUUlEGsB7T9mTfYtsOO8/oVASqnPa0tmXtNCuvCENE5RmnMq0z9F7TN/bUEFgjlri2NkGNUdicGVJC5YTO+b7hMA66HrXYwOKc0XS82s58dHPg46sdh+1EDBnrNI03PNuIn/66UajhFh32VWZqwDQcYmY/Z26nSMyFhTO0VkGOqDBRttfk3Q3KNeiLQLuxrJuOxwtPyCLH1EoUJFMIYsgUpRBmcx/RnCsJUMZFD2BQjlbB5XPP1tE6WgyeAiFEtCosvGbpNI8XnnWjadMBvXtBfv0ZJUzo5RnKNJAjuUK4+yCpdGBxGhamAXaQApo9Zbhl3ax4umh4tmn57HZiPkTmKZBz4mNTeLwwPO0t67OObnGOWt2h9O3JurvEGZ1FuV9SwquKulTpqdUZayAmjdWZxmaMipRiGJREHNsSWXnN2mlaq1AV+taqYK3DtrILNa3I4Y6jN6MUjTM4b0B5VgvP03XLujXoaUcZdgLLU8AYkvGErLndDgw6Sc6Jb3BOcix8bQ5aq/FKo21BmY7p+pb59cfEm89I+62sh8sNqjySpMhuhdb6nieEdAYG7pnxKUiS5Dygwp60u6W8/hRef8r86Uvmux3RKvT5Jer8McW13E6J6zFyMwqp2xsxZnNVruqNcKa6mo8wZAl3mmZZz3YVxkdplCqU/Z3cLzmj17fYnLi8eIcpN1wPkY+M5jAnxv3MHXB7mNmNgRAX1Y+iBWDpe3lv2aDIGGPwztE2ntZZrFWi9qpum7pY4uwYOsswGOZZLJ3nJDk0h5BYN4Ie9M7QWHHHtPUhMpUH90VEQD34eFjHfum7f5uf+Zt/jm/+9F/jJ9/5+u9W9X5Px+/EMfjyn/D/g6OUEpVS/yrwHyPIyv/p+21QvrcpOOpp8mkLrErml97/2/zM3/rzfPOnfp6ffPsnuF/OBCa7X/DqyOtI8qgz85TF8e4QHkgFjdjyXnSWy4XlvDGcNYaV19WaN5xsZlGFxnti2xFjIKsgRCbbEFJhSjDEzBQL49H3WyuK03hvebxp+drjJe9sOh73Fnf3CenTbxM/e580TLhlj1qdidzLGIxTtL6hayf63qMtrBrNu5c9X7vseXfT0XuZKc6pEGa4myLXw0zMcDNaOGtZrkQLXlIk7AbiYUbPidY19H0P+ztoGrT1lOwo2XNMYC6lwqtJXAvnyoouWUYIKh5I1y9JNy9FdrZY4eJMs+joz8+x7ZoJI4SmwslpcAyRwziz2x/I48BaJ0ynWPoe1S3gxlDCRNrdYg/XnK9XvHvWcvvmhu82DmMMzzYNCyeM6SPrvSiFdh5jHa5psMNETDM67Fj2PY8XniEVvMsop9k0mk1nWDTuHimwMvM/Rv362hiQNMlIceWYYNd0YhoE6NU5qV0xhMx+Fptro8C1GtcZNsuGP/BsyR98suCdtWcRd5jtZ+TttWjA1y3FOqYE+5i4GYM0YgpWXtdt+0i+uyZvX8v7DRNWGzYX7zJ3jligGSNFCVEyxygW0lV6J+vYMQtC1URMOW+l1GJRZcIiXS0n6XBImVhfL8eIU4W10yjr2LSWx70TBOz1Z8TP3hcyJoi6Y3lWx0KKISbu6ljNGkWXNL11qAL5sIUkM/zFWvHOeknIC3KBX3c7bneToFpKclL2c2QIlqZdSWBYJY7SdHUukjEV+TnaHBtdmHUhFoVLmmQLc1TELGompwt50qRWsTGe80aanqXX6MMOnWZxbWwajHVo5ynqfiXSFBZO82zTsHu8JKXEu4963j1rOW8M+u6auLulhAnlGlQnSMNuirwaAnc5oNvMcqHoKqnwmMLYOk2rZV4c5xsYXhNefUB68RFxvwXnIcyYtqeUM3KGOSZcyricIWmUuVeFKBD1Ug6oNJGHnawHhzumqzvGV7eMuz2l9/h1BOOZled6nPnOzch2ilgN552nd/6UhLnwjk0jrqatM3z36sB2yrTe07We1jcY59BGHANTmGTjsjtgdjfYlHDW8Xj1BodNJ7HhMfMSmKsNfc6yzk6xMFQid5MLxncQJ7QqOO9o247Ge7E8zlEaBqXFkMhrUqOZG0NaWFyxGFU4zPHkBnkIWRpHI8mrSqsTn8DUx/KBo/jnm4Faw6R+feu+fr31E6fN7her4EO+3e/l+C+tj0Ep5ReBX/wv4JXqH/cnk0oq+aX3v8Wf+b//K/z1f+mv8JNv/XEh2Sl9ig89mbwgHCaAamNAY3WN2JWbJ2ZdZWeG3osEZ+0tm9aw1hPNeIPWo+SaG/EXV5XG66zDdz1NziQ1VljcCPtfiyPWqjE8WzU4o3m0iqSSWbeOZ+uWd8863lx5uuGK8uK7pM/eZ3h5g8qIXwEZ6yyN9yxTYdVmnp/3tBYsmWdLy9fOO54tHYs8osOBoh2x25yKyN0Yud7PLFt3Ml/qmgWq6SQieY6kMRAPI+kwUcZD1ZIHgSKLeDWcmoIiqMs4Jw7TzGEKEvmrjBALp4FwuyUOE/4w0zQt+nCLiwe8EQ5GrHNwo6RBmFLmdghc3w2keWA0hYX1LNo1pl+jnBdfhv2WcveKrj/n3c0CUDxZNcSieHbWcdEovBbNPdUGWCnxmHDO0cRISpGcRrqw57LvUUqzGBPFepYLx1lr6WqIjavKDGuEPyHNQYUQNdA2lNlRkiXbBnyH1loKq18IMSnJjumid3z1yYIxJBaN563zlq+d97y5tCzHK7h6n3D1KcQou7z1Y4pxTHNmO4oOvVBoozhHqvpslDCR9jtySNic/7/c/VuwbWl214n9vtu8rMu+nnPyWlmFJBTQmDAmAgnRVBV+cEBLQOsVVRWXoKHpCLtth1+aVz/50dHtDmPTMqZLJUW/Cakl6OChySyhpmkHpulA0QgkSpmVlbdzzj77si5zzu/ih/F9c8219tonMwUPjpwRJ/fOvddea16+b4z/GOM//gPTLjDVgsvlKygl7a99TFTOsHCg4kAKhhjAZzMSkpIW1JSFZPKzLj3WVkk0ZLJ2A0AMnhTEiSwcDLVhrh2VViyc4rwxmNuPSC8+IVx9gr+5RTuDaubjmoqpDM4JKBS3W8+yMqRKpkGmzYp4d024eY7drji/fIvfd3nJsq54sqz5/tWWVdfTOMPFzFHlKXbJ1qR6kYc4pTwjoEZrS1052qamcZaoRamusiJp7q0AjJmVtrMhRBoV0XNHEyqUh0eN4aLVtMMKF0StsHYG5xwmD2dSKcn6CwOVilzUih+6aJnpU6xKvLqo+fJpTetlPafVrdwTV6FnJ4TmhLtt5ONVz21QmJ5MkHW0KU88zZFqZRV18ii/Rq+vSdfP6D55Rr/ZYtsa3S6kBKgMnQ9EBlTXS4mwNiJYlAQclDVVuqzIvIew7sQ+bAdC71GNzUqOc24GkUv+/osNd9uB83nF5axi7mSegt1ey4TCZsblbMHry5rXTxs+vBONmIuTGcumYtHU1JXCOksiErue4WZDf7dhBuiqprUVb55copSoGn54s+VmO2CUlHwvZ45lbXYDjkC4PlUtfJe2oWpnOOsE6MYg/LCQx1cPnnq75UQP0Bhcqpg5zfXWsO7zdEi7m/AoZUU9dlPpFO578Ukw+6D/ij77r4LG7wOEz3r8/y0w+Ld6HIKClHj73Xf4mV/5y/zCT/4sX3/jJ0SOFkAl1KTeMx5RhFS0F0Z0Y0AjKdTivGMq3ytOnKGOW9zNx3DriZUlLU9gIZMBcSL1qZRFW0PdtAQUQVtiNxCGIONTrWQnalPzeFbR5f5+rRTzyjBzmrPaUm2fo65+gH/+IcPdBkISdnHbSM94VZOcowmRi1kFMfBKqzhxitfmlnM2mI9+E/+D7+H7DWpxRvXmj3Jx9mWe1RajxeDebj2LxvLmScPj5Ql2tsQ2NWHdk3IWJOX7nILPYEBYz4WkFKOwyDsfuN50PLtZ0622NHiappYUcR7rnHwgDh76DXrosXFAh4GqrulDIeOIQ/IhcdcNXK17uk1HbBUvtpqzumLWLNGzhaRb+w3x2YfY2ZKLszex5w2P5xU+KeZtzYmFmkDa3ILvwHtISTpQtKZxjkgQASFzy4kx2PmMRVOh65Zm3tDOGupKeuALaaiomO36jyWLo1uJRrZ+IFUzER3yksZMriEZh41SxomnDY/mFc4IWezRzHJRK/Tzdwnv/0v8Jz9gWG2wTY1qZ8LIR3gwXRDwNKr0oaTDRGmUNki9WASL4uoG29/ihgXnzYLKCrnTGJOjy4hJAyok6eFPCh+hyzoI2xDwIY2kNGsUjTE4A7XWJI1MTowRkzw1Ea9BNZbgBDjMrcL1N+j+Fr+6Qfkhk+aEVKmUFuVFhPk/BIRnEoRgF5AsTQoD4eZKhIuun2Fvr1i+8Xv50Yu3eDQ74YfOZ1xvB4YQaK0M6pFJoo5YzVFKdFGTdehqhnYV7WzGrG2xzkjZSUGFIZrMtShtt4gUeucUp8azdYHYbZiZxCyuccMtrt+IbLmTspPWJmdyPJiOtLmlVpYTC185a3hcS1fTSaU5YYt58SH+2YekfiMObLaAZkmXDC+2PdfdwLNNom4Nrh04CXksd947JYOlu0H219Cj+g1x8CSf7aJ1JFMTbc1qO7DtEmsc0VSoPIMiKkXS+flIf6Ps/0zeK3NIUohoqzFNjZ4t8c0Jt7cCYD54IQORHi1rTmrLRWOonn8P//3fJN29QFUtJ69/hfmjH+KiPeGDledmSFhXcTGraCqLcxpX1dDOCW1D79aEXjKb+vmH2HbO/ELzldMLzhrD772cSQdBSmMWpTI5o2JVbj/WWOeoa0fTttRNg7ZZJq90kQ0ddHeku1vi7Q30njpZzusTZrOGmVNsvR3F3cpE0ZkVEnLqxbco3+eUwZTuXnxZghR5+71fu++/cjZLsgOZha2OvMdnOL6QwGCHj3aw6zgo+C/4+pt/LIOGjLJSGFN48nc5nRyD1BG9LFyzvaG2Na5uqI20AYE87FoFzPoT9N1z/NXHpGGLbmr0xSPU41epzwJqdoLSWoyNrTAWalMRtCOwIqQtikStYW7CSNSTyooYjlLH0zdr1OqKePUJaegxzmLOF1SzlvryFeqLJ+hmQTAGp+Gk0tjWUs8VZw7a2w8Iv/1PefY//XNuf+cTwhBYvHrGxR++o/mDCy7aMxprCDGx6YXJ//R04I2TOfXZI+rzE/rtgFEGbbPBPjiKwE2CPEdAUmtXqy0fXa/o1htOK4VThtPmBH16SXV3JXPtrcPVDTZ3IugchRZQpjNLN8TI4CP9ENkOkZVRrIZEFxXz+Rnu7BLWN8TVLXQbwrMPsMZytnjMcnmCTwprFUsTYXVFdAlUIPlOIjGlMMbibKSJAnz0sMJog2s1Z6fn4GqqWUPdOLSxKL1jb057jRVFqCRhKwezGUPXMXgPWhN9ZkJnspgzibPaSOpZSUfDzCbM3VP46H369/813Ucf0d2sJS3ppL8aW8kQp5ile7WMbS1qhxFNsjW6nWMXC+zQC8/AWLT3MKypjaVu5xkYSEeFVQETpCc/ZuW3LgtSdV7S8b2Xcc9GSSnFV5IGxmkwWuYapIANnlYlXK3ojSGEnFHopNVWe482llS31FoL8bDNmRR0FgnLwmEZJMYIPkJlq6zo6emvV3RXd9Q3K+q7G6o3nvP4/A0uzi5Ze5sJxXkQU56lXIAZSqOsJbkWW0u5rKkcymhIiqinhNodsRalSFETLXS6odcemkRaXWH6W9ywok5eiGjWYozNZZYo685YAcaDYmkgNooz6ySFvb1B331CePYBdBvRzm9nuLNL0vyMLsr6X/WR7ZDAyv4QLQUm+0ft7StrDbZucE2Nshp3tkSfXhKbE266wItt4LofqL1GVw1VVVFZQ9BplJ0q7Yt7dllplNWYxmGTpjo/QZ89YqUcT9crnt523G09bWVorOGidTTbZwz/8p/w/J/8BncfvsA4w/LL/4LTP/gHuPyhP8Ts7DVeDNBF4fS4LLhkmwX64gnp7poUE/06T2AceuLVJ7KH5mseuxmprkkzt2P5jedLlm/WGGuo6oZmPqduW4wzQlwPAyoEUlZs7F48Zf3Jh6yfP2W77QiuwZw/oVlc4GYXdM7u+YrGarTforedCMoByndC2U1mtJ1Tf/T2e/+Qn/nVA/8F7KSXMjgotdus6PN5yglfSGCwd0w4BfdBwU+w14GZcgkhTW9fzjZELwQxL0NNTHcHwwZsS2Mqaiu3Ug0dZnsNN59IH/SLp3TbnlA3qIwIk7E4W6NsIxLKxgm5MXmMi9g6UIVAN3Sk7R2pu0X1a+iEIZ18XkC59hkzeQ2k7mpmS2xV4RanVJevoE6fwOwEHwRtViqiHcy1otk+I/7OP+fq//vP+OAf/w7Pvn9DTPDojTvsouHyjR/m5LULzhpL4wybPrDpPatexpou21PM2SOabsBGhZu12Fa0DZR1sqhzWivlux3JxMbec73ueL7asN10dIOmsTWL+RnV+avofotrnlNbS3P+CDdfoqxD3JlEnEUjXGVuyNg2pXVuhdJ4ZbGLC2z/BN2t8VoT+g42K9L1UyEW+Q7nanSwRH9LqC2pddI25H0WmY/ozOKurCElL8S2/pbkNHowVK1jNnOYyqKMHqVNI7tluH+IIXVVjatqVus1XjkwdpTRBah1oiZIqjL06PUdrK7wzz7Ef/IBm6fX9CtpHKoWM8x8iV6ckaqZjH4GrDU0SVL5ldWjuFeqZMImWsPQC4t7vhS1OYA4oPoVTimUj6itZM+iVkRTScrd1gxDoO8Dmz7QDZ5N5sTozInR3qIqg4uGyhm874SgFoSRr1KiUiKtI0Ol8jqvG8zZpcxRGHoBBqePiJXMh9BarmdWyWAjaw0BKVWlaoZenMn9eLGiv1uzftrjN71MUnz8MebyVRbzc2b1Qq6nCNigxv5yaWuWEpCrGlxVj7r/5TnuPVW1IzDrolmiHes+0K9viP4W1d/iwkCjobJ58qNWY2tc8p7IRoYdbQZi56lDwgUvBMztDfH6KWykK8guT7Hnj3HnT/DLC/wqyvpPGqVTnkuSMoFN9omssSKiE1HW4eZLmvNHLBN03sPyAs5fpW/PuF0NfLzquRsiTdLM5h3LuWdeV9R2Mishi1slpUS0yFXYtsLNWqrTAa0T6uwRsT1lPcAq2xWAxhU9FY364H1ufvO3+fCffp+n79+hFVy+f0v0gQvjaH6k5qy5ZBXBKgn+fNTUsxPU6RPqLyXUbIm9u8b3fX6uiXhzRbq7QWVZ9RQkM6KsE+nlekaqZqh6iWoXVK7FVo2UTqzsz6SE/JhCFMGz2xesP/mQ1Yff5/bjp3TdFt1U6O0K+6jDBo9qTqmdzH1QwaM3PdpvMDEgandii8p5ohQq7QKtt7//D/lzv/of8As/+f/c919JSObyUs1Yt/1dZg2++MCgHCneBwWHlvpYCSGDghQCRDHKAKq7k17tEMBWol6Vkoiv5DaZtFnj11v8pgcvg3zU6g42HfUiYKNCK5uV6IS4FLUR7XhjMQpC2KLW1/in7xOuPmG4uSX5IAOLZg16vpBa8vwEfXIus8FdhXYNup6RZiek2ZIuyiCREDwpBmqdaHRCr6/on3/I+sPnrD5e8VEnKbXmozWbj18Qrp8y+5LmpLGczhzrXsTruxDpY4L2BHf5Cjp6quhplwv0bIFqWpmvoEyOetUYUQkBRwiHm0GEQFadaO9fbSyLyvLo9FVqZ7GbaxoN7ckZzcVjaXfLm0YjxrfKtVKtkhCTKkuKjnljqKqaqm4wjaO9eIWaQG8M/d0NPtdw03YlRtzXorVuFDFVBNUQbZmxLnXGhEfl6Y2VEelhE3tiv8bWFbavMBtDxRLTtKLCp81oMIvSaZzci6RlXDTa4JNm48XglBpwbUTURw9rYZ/fvSBcPyPeXjG8eEF/uyF0vfTAL1vqR5fYV99CnT4mtqf4LqG1ZlYJG90pmDmRqo4pCY9hEdD1HEiS8bEV0dVZsVHJug8DKg7o6MVxK0XSlljNCLZFmQblAypEbApUJJKSdkWbEiogwEJHhu4W4zcyZjfmUo1SIqClHco4YiZiRluBm6EWl+iU2yBdTarmxCTdAIvaoLVmSOR0vADD1J6iTx9jX5Uas3n6bLxfm6fXVF2Pu7tGL8/Rp5fYxRmxXhCdOIYuSHYrJIkeWyvPSSudVT0ZORSH7WQCqqT+HIYN/eaWur/FhDW+X6NjjyNS5cFjKo9ElrKOZClj38nsi+0Wtj0qJJHQ9R1pu0L5AVNV2LqmWpxQPX4dffEK62ZO5QeqKjBvE0EFmpyx0Uq4OVU+59K0izZoW9NcPCbGiF6c4iL49pRu9ojbIXG1EZLnbZ8IRvZv70VsKCadVQfJyMiAMjIEqWnRswXtZUeoNL22xMtX6NoT+q1INIOidprTmZMOLqsJ10/ZfPyCu4/WfNR5tFI0H69Yf/ic0+cfUq2vaGYXeBI+BkLwdF5Llmr5mGQaGdferdHDljj0xH5L7DYyVO7umri6w6+3WWnR4E6WmPPH2EdviDqhWkj2zwqfZEiKlEnARlliVPgh0G061qs7Nne3dKs1fddjY8TVa2mbbBdoZUSrRslsCO17dOhkhkjxR2EQTAAkbUe/JKDgr/ALP/k3+fobf2zff2UdDSnZZnWk32UZAb6gwEASJ7tD7dVkDkDBlMVZygnjG6UJPyHsCDVA6jtwIOjMIFYv57WVEfngdoYdOiEAGoN3NZ2yKAzeg0uGtO1FKlYJ4SkkJUIiKLC537vfEO+u6T55zubZNWkI6MbRXp5SzxfSunX2KrE9I7RLIhplpG5srSX2EJKXlhkvICcRpR6oDKpqcYsZ7XnDRRb9qBYOZYUAqYYtJ7XlfF6x7jxNZce6m26X2JNLMXC+l4zB8gzdLlBVSzIuL27hYOzkjjPW1QqlraiGJcUmKbx26PkJ85MFTdrSGHBO6rsyZa3U7ROGhNPgVKJWMu/AzypaJ85iNmto2hbXOJrmFTAKV1XoF8/ptxsZXWucjMBOkRS9qDQGLcQ44zIZQOWWS42KBmMSKQ1UyLS1GAd0t8JWDttJuxSxQ9kqqwCKQ5F7IWukPG8fxArEJLXxLoisbp7CKq1MUfTU090V4eoT4u0Vcb0mxoid1zSXS/RsiT67xF68Sjp5TFg85m4Q9U1nDWfaoFTCAk5FSlNCtDXJXIqxUjrrv0vWxWmRlk3DGh09bNf47Z3MYVAKVc+IgyfUAVUlau1IViJQef+d86xtotYJNWwI/Qq6FWnYkLq1ZOm0RjcLVDPPYKAl2loi/4kyZsp7MmUJ2trAqRK+jUc+02gYEtwNicXZG2hbUc1P0ecfUr94Rlzf4re9yBbf3ZJiIMUg/ffKyLjnlHUWgsgHa5OwcSeKVjogioIkKeQSn8+s/LyefI/qO2x3SxpWqG6FiUP+e8ncGGOFN1EYqTpn2WKS8eBByG0pprF0oo2Ddo6dzamalvbsAnf6GE6eMFDRBMtsFjmLCusCtRNSXa1kv7iiN6CK1oRGVTUmnTJ/1VFdvkIdYKsahujw1xs2qadLMmZZaQt6NyMh5f0ds1NKOhOtqxbdLrBLj0uKupqjbMVwcsnQLqGTQUazWrJk5/OKk9qihq3YH2uoFo6LPAemPW+k26pqQRlRY4yRBPReApcYjTwXO8fPalIThNtARG1uUZsXaKWJd9K1sHl2TdwOKGdou4HGVaSTC/EjtsIj2aI+KUJIDEE4ayp6VDIMHgYMnbJ4V2NmNXVtUbVwfVTd5iAp+5cYJKjMBOc0dDtgkGL2NzoTDTVvv//r+6Bg9FHZf6Uk4CDp0YepFCflhM9eRoAvKDA4PN5+97v8zC//RX7+p34239SSfpl8/RR0VWpm41AUlTewyaN7bU0yDhWFPKeNwzZz4vxUep+1Qc0WxNNHbMyMQTWEuy3KJWERG4dS8v4+QB8UISqMnaHahdR9naSnU0zYpqY6XWAfvwGPvkQ3f8LzbWD9oqePCWNEfGNWifEqNXjvAylG6ftXUM/OcV/+UR4NHbatWL73DL/1NGc1yzefoNsFCWis4bJ1pPMZxmgWjcNai7YOOz8VVnMccFWFmYtxx4luPsrI/cors2AvqzW1dbRtTYhRiGlNSzVbUC3mzJctTeqwyWctdi0SokrJc8jiQip6ZjoxN4nOKtLMEqOmzXK3TV3jKoer2jxe2BHcjLheEb0nYCRTo7N8s1IkrcGKBrpSKesviAFSIQBGWkBTFElt8QzoYYva6ixr2svfGzvqq6scRSVtpI6vc7klRUIM+BgYQiJkZ6QQx1c2f8qDuzAWPV9Qn8m4YnNyDstL0uwc35ywGhLbbSQgzr6xeWATAgp0njmQkuhx9EFEZIboc3QsbOm5MyyqGhdETImhI/Y90cvIZZUzQmmwJGXRNjEzjlrvMkSFU2EUEHsR8xk6UugJfScgO3i0rcB0UMlQr8HU3HWB1SBiPSnLODutqAxUJuXvpWQTlWZIAg58hBAiqxDYhkhTXTJ/7RH6/A3M+gpun2FvrkjrW+n9L6Odg898IwGyfUhs87RMU6LS3GVjlMIppNQSpNRICjlLIGUfggcvkWHcrjCDcIfQyBwAlWvvRpj9pWMJKzMfVNZyTzqQrB7JlzFGtGsxBKy1VLM51ckZbnlBak9wfaTxmvN5wCdoKxEDWljF3CRmOpdHc7Y5xsw1sI0QGJsZOklEbFTN+nZDNRjqdaL1CltB29bU1mG13tvXImug89qwMlWwmWOSCKTVMxnjneoTsR82sGgcj08aQohctiICBx7dLli++YQn6y0nLzpsY1l+6ZKzH/0y7ss/Spyd04c85lzFTJaUYWAR+fm6l3kiIQQqrZi5hovTN6iswwYvSpGrLf0QUEaLnXU1ql2Q7IwuKkxQEMD6KJNQE8Qgcs5p6DCqwZsZ6fQJCo2dnxFjQNkKfXIBzZJYL8RPaCNrQxYcxCEHhruM9ZSj8fb3f50/93czKHjz390Pah/yXykdVrc+1/GFBwZv/847fOOX/wI//6f/Fn/ijYPyweHNPCCfyP8Lg1q6EgyxILBmRjS11DnrBUMyDEnaDK2rqdtTVL9GLR+jszGP1jHUC7yb0d9tCTpgqoStE9alTAhLDENgGCKhjzRujptfYp+8Cd5LFB8j7mSJfeUteOX3cNc84oPrju+92HK1HQhRMW8cl4uGy1nkrHUyejkFVMqjYVPidkgMbsns9T+AO32FJz/yERdPPyDdvQBl0KeX6MvX8UYEYy5nFdYYtFY8mkurlrEOq+dUtaHW4JzFuApdN1JeMVacrDYjHtOQe/ul1/9i0VJbQ2U1p7OG2XxJ1bboymHcHBNFSz3l/niUIgap+0vNXf4tTMI7cBjA0lSWk6amcgZt5NlV8zOitihVk6oN0XtiVBJ9ImS4pCI4BXUDlQaVVdxAgEHVQN9J3iIGKQNIs77IRW/XxBTQrpEMh81zA/KAn5SjQ2JFMhGjLCpFGZscd+N7k4Ihaz04WwlRtZ7DIqKamYDF5ZmMKZ4/4qaPQja76umiOLd57VjWmtZpYfmngbS+JfpOiHuuYVCOtU9cbz1XG08XpE9/XlmezKUeeuIatLYo64iZNIu2UvfXUjJKMYLvIfVY0phdk4dushxyvofKEHWFdl44M0oRtc6TIEUi964XotvHq45V7wlB+r7PW8tpIyI3JvSoYYsjom2Nmi0ZlGPlE+sEt11g1QmRs9aKuZsxn885OX8LvXqKXl8Rb19I/3++v9EK2B9iFJBW5jBoUdiMIaBSFElbLy1qKvQZDHhpGwuDfPWe1HfEYYvqOpmHoBIYCzp3SmVQqqpawJarpDzp6pwSriFKJiYlPdqgmCJKJ5K1qLbFnJxi2gV90mgTqFzkpKmJMbLtNZBoHSxMEp5KSKiQwCRikPJYyilzpTRGG5Ku0IOnajWzQXPaQ7CO3kfmTcW8lvKU1XrfD2mTRw5bUhSyKNrgqoY0eOFN6AajHY3zPJrLecaYOK2FJJ2MxVy+zvz397SvvwopoBZn2EevkU5fwc8fsQ6IvkvOTFmdSDEw5PblF5uBZ+uOZ3dbVtsBoxPnjeMrZw2vLR6xeAVsCMyNpbq5RWlNdfEI++RNwvySwc3Z9hGjI64LuBhQSjos/NDju47QbzFxoHJL7MKQ3Bz8IFL6xhJcK2UpL0JsKoEzFmerkZCstYHMHRPBF03Shrff/0f7oKD4pQxejx0qpZ0e3+8SIHyhgcHb777DN37pW/z8n/nbwt6cGqrDQ41VwR1AKDdVKZIFFYXBDRByDTJWczZB1A+3QdjQQB6YU1G7BlernCpOhKjo1wMiiRGhT7ghUdUJYy0xRfp+YL3uiVtPowPn9Tn1pcLVLfr8qURXJ+eks9dY15e8f9vzGx/f8Vsf33G1HrDW8Mppi7OW07aSdiSdMOQUYFJsBmGQP13JCbfunNnlBSev/i+oUi9kxxQJtiEqS2UCl63jpHZoY1g2FbPKCGkq1ejSkWAtGNHMT2US2WQBq8yAtrnFc15bfGioraVyhvNZQ9vUaOvwGHySKEYTxxRtDIHgO0Kf26mGARs8LgZOTWRuJFVfOUtbiexoSZEPaIJtiU2iGzR3w5ZtPxAIaBS1tSyaChYNqjZgQDHIdW3zEK2+F65o8BAkg6N0cXpelk8vhKboZeyxsjZPYZOvSTuwnhQq0BnKKPlX9nFI0lIoJLqK5GakWRTQpSyxWeBnFzzfRp4+7/ngruOmF+VNZy3nsxpXgbOaudM0wx3p5mNCHpdsmjlxdo5pL4kRrrvAJ5ueuy5gtOa8TbnPus6thrWoV7YLyZ5pR3ANqZqRtJOEUJKx3GrYirPMKYNkKnDNuC5inkiXtEFph85Ew2AqkqkZomY1BJ5tep6uBq42PSHGzCVQLCsrPeTbW5FJ3q5kuM3ynObkCbgFQ1YJvuoiV2vp+HBacVIZXlvUPJpdcPH4EWr+HL29QyVPMrXcZ1PJkB92KVg1eUYGRHI5DpIZ8sK/IAwk70nlqx9IvmQU/G59mLJuTJ7QZ1G2hqpC2QqamYBJHMoCZs5wt+Vu22cdgYRB09QV2IamWRBsC5RSkDD055VCR0tvEqSAJeJijw0BhkBSgRANobIYa3MnjcltoFp0GTBoC20TOV9GMIZ+CKKUWtsxG2Vy++Nus2uxAWOJVqNsg3ZeNEuSpVKi+/JoXlNrRQwBp2VwU1SGsHiM+soSozSpmtGrimdDYN0nNs9lzTROdADa3PZnjMIjttanxM3W8/3naz663uB94HzmuOsWxLTgS8vHzN7QVPMl9uYKjMWcPSIsHtPVZ1xtA9sIOvbMlKOKoJUmeE/fdQz9Vgi7BCyJSjtMdYauFSQhwHYxMqz82I2gNTRGMXOaWZ61k3oDOmcD87545wf/iD/3d/+qtCR+6Y/vfBLkssHnKQ58vuMLCwzefvcdvvGL39wpGsawS88kfYRouKvNjO0h2ow1Hkn/JqJr5TXNkuhmrH3kro8y4Gjr2fpIJElErGU4SeP0yDBPqDzuFNARkwpKNwQUPkZW24HbTc9201GlgJ9ZzppT5hcz9PwCUiS6GRs75+OV53debPjXT+94/2rDMERmbUVM0DrDWS11xaWF1K8JOrB1lsHDs97z3vWWj2471p0IrDxZVry2bHhlsWCZN5xJ4sQrCzH3sre1ZV6JeJI2UlJQVoxcskbKKirX1DPDG1UEbkQ6tM0OK2Q1s8paYTc7admSGrxo4BulMcZJCi8O+O2Gbr2i324J3qOTojUiTjNEIb1ZralVwmmpTYOkmPsIm6i5C4qbPnGzCQxBJkzOGwUOZsqh2gUyubEjxT6DgpwxUnl9aI+KmXuSpF5bWltTltBNSgCTAAQnZCxXQaxQJqJMlNqrk9kKtdG5VpslZ6OktOt6IVr3KZJszVZVPL/zvHu95XsvNrz3fMNmSFSV48lpy3zWYo2ltoZWedTNh/Tf/y2Gq0+kXn9ygXnN4uaXKKXoo6TNb7uANdBUka4QY2PEGkcylTxXpYnVXIx1UpJqVwpHEIW40EkXTVHiqxPB1WAbBoxM5TM1VTVH9WtSv5KaqJYuHR+jaM6HxCbIHvMhYI2mj8Kodxr09pbw0XvEm+filM4fU70ZaR/9HjbWYo2lZ+CTlefj6w19P9A6xZcuWr5y1vLWacNFe05TzYUNrjTJtfQh5ZHJYhaMlhbR2moRIkoDyicBQaFDhT6nlfsRDCTvd7yDJBofWqY5CZ/DZclwYwUYuArqFlU1wvvQFZgaFTV9f8dd2PKij6y2QvZzxnCiLFSKKipMhCqWjghZ97VKJB2wSkIRp8GkAb0ZCNtEZy2padAz4Soo6zDGiaZKytNPEUBfO9mfKSV653OpSRyyAINcLlJ5g2hDShZlkHWrDfiA0gGtA9ondIzMKwsxUKtICCp3SWTOjZ3R0XA7RD562vPB7R0f3/Z0Q2BWW15Z1nzptGHuKuZO0aheRNKqGbcGtt7x1Bligk0XWG961p1Ha5hXhtponswvaR81qNNXQGl8vWSF48XG83Tt6ZWhSSIxPVcaq4X30XtP3w+EYYCRLCwdH2XvbofI1gf6KHtIIy2KJ40VrkqlmdUypbd0myXb8Pb3f52f+dW/ys//1M+KTH8uG4yw6yU+bNre+NDxzrvvvPT3X0hg8M677/CNX/yGaEd/6Y+L0VZqAv1VKQrv/p8MCMa0cPn9hLyRIqmaARCrOVsvhrQLifUQWPtA56WP22RFMWcCzZC18vPPZNCI9F07pYQIpCQ1GIIYwLvOc7eRGmUfIj5W9LVl3lyitQxqud1KRHXdeQYvrO6qMpwvKr50OefN05ZXlw3nVYAXH+HXL8AH6nrJor7gk7Xi6V3P//juC26uNsQQaRYVbz1Z8AffPOX3PZrzqLWc1IYqT9DT1mKcxdpEpTw65rSoqUjOkayUDpQ2cj915heQMxZ58zgl7UUzq4lW440Q5FpnZGYCQBIApWKu8RfeToz4vmNY39Lf3RCGjhCi1DWto3Itqp5h0bjk0cmilc2SvQgAQzEkzU0fueo8214iudMEVQWbAF0yNFVNCga8AtI40RGlUD7DDZ1Kp5HMlCfm4XqZV6E1yWuSsTI/YnBSd61btC2kN8vMKpZNRT8MpBQZorRhJiTyMNZgqhkoTecT113gw7uOd683/KuP7ri682hjmOmKpB1VVdE2NbNKo1dP8Z/8gP7977H65AXKGtrgMeePMWHA2YqmcjQ1zJMIWjWVo7I2R4ICajEVySqim9FhuesiWy9dG5VRnNaG2tYC7HI0lVJCN3OUrekxXHeBPqTMXVEs3Jza1nnCo9TQlSptlZamSswaEWVqakNTOZw1mNDD9o5w9TGbj56RfGC+XmHqFjs/ZzZ/hbapqapA0o6N37LeBK7vZCiUVnLORkukXje1ZMnQwmmAkQPgNLRVzpRZKWHo4IVR7ntStxECmRdpZ8aJfJkoEuPOqOc2Y5UnUErpwKGqRpREjQNbk2xLMhXdxuesJDzbeG42PTEmmiqBdbRJy0Ax8jS+lMS5poBLHvD4uCV1a+mY8rK+MJre1ah4grOW2LSyR0vnUJJujMKQt0bTOgPRMGjRe5hZjVVR9nPmTOaeCtmP2uRMkiEpQ9IxA6YBQi/dTCqATdRJEQaZRphQRF2x7iJPN57/+emK/+n717z78R3bux5tNCfnLX/4y+d8+axlUWnq9VNMdys6DLMz6rNXMLph6wPXm57bTY8PAQUMPnHdeZ5temqjiPWMqloQY2LlIzdd4ONVL3MbjGWhPW0rA9mcEvsVVeYjKUVS4HPpyUf51wfZG0MQufwQpWsqkrCD+ALrpeW6qeYkLR1v/+CD/56f+dW/LJnucfZBDggyIV6NP5uWxtX974+AhOIf3+Kte78rxxcOGOxAwXfkpqYJqlI6i0DskNeIriaAIN0DB7u/+MdX/wwQ5rg4bPmtVTqToBiFJMpnlEFMVqusnS9qWtZo6krhKk3lhPCmSWxyVDGEyDBI9CmZ6oqQZyXEJLKzGsXcGZ6cNsxqSZ+/cT7nR5+c8EOPFjyuEubqfbof/Bbhkw8ZhgEuXqV94/dx1ixxVrFd91w/XdOvb6hmJ0QfOZ9XvLlseDJzqH6F7m4xMWCsxdQtJs0xeiYpUWWI2hK1zeSafP9Kj3cGZTpvKBQEFXFELJ5aJyoU1mpqI+I25U9jEtvqdcIkUJl4FfxA6LbEbk3c3BG7rfRoW5FZTfWcNJzB8gxdOanl5nawhJybzwBhGxObIPfY9oG7IXDmRZkxUGNtJfKqCNte1bIekpYpdaPCYwxiFUOQDexFWyJ5ABEXiqWsELz8qxp0HXGqokmReaXpayfRpY/j2pFMibxHSuAzaXDrI+s+0HmJQp11LGcN58sZp7OWs7ZmViX08xXhxVPWT6/ZPL3GVBY3b6j7LVolmsZxFi2DCZwscwughctG01qotPA5EpBMTYflaht4thm46TxGKc4aUXqs6wa1EUCRsjAU2kLVcNclnm88L7YDISVOastl6zhvLI2pUX4rEsBVS2s1l3PJPs1mkj2qtOKsNTSNwoSO1G/xdyu65zcis6sU1cVTqm7F7NxyFhWns8T50nO1Ghg6TxdFC3+dtfKHPITLJoVCiyT5ZM9KR4VmWTuZipd6XNDo1EO3IfZbAQWDTDbED8K3yAAHxHEopQQUFKXJnCmQ8kGNyrycWMjMtiIkw9Z3dD7J+Os+sCnrIqZxDaP1OOUFJEOhY57WefuCtH4hXSDdRqaMKiWTO9sFwVUEL+TSiCJl3YsYd7ZLazAxyXwPq3FJQKvF4zA4FXFKWmH1aACVZIC0kS4WbUU+PSKCUCpAGjBhwPZr1HaF7jYE7wnakOolWrXcdYHvX2343g9uePbB3WinqtrgrBL58e4F6f3fZHj+Ico5qsevUmtPdf4W2ojDNyTenzv6wbNoHHNn0Ch8gq1P9CHkOQaJp+ue6+3Aeog4hPhrSMysYlFpVIzUUTMkRaek5BKidLHgBQiUe6fyOnJaZNEro8fJrj5CH2LmdIja68/88l/iOz/983ztra/uOgmSdByIUJ8aAYLoHKT7vuyBY+of//p/9dcffN0XChjsg4KvUVIESRViTFGGYn9Y4hQQFGGT8a/z1yTlif/kn/wnAOPAJJubl21jaJ0mpETvEzH/tUZhDThdZDbFyDijcVaEXlylME6iaZMUg9PcOsXGKFSU85VWtkiIZkzb1RYu5w5rFOeNI6TErLK8djrntfM5T6pAffcRww9+i/5f/2uu3/0Bm/WG+slHzI3l0Vf+CK8uaqrWoY1Ix8bgSUnGii6y0p69eoa6e46OQVqjKo1OtZAZM4M/mopgKrR2MPZlqz3mbJFGNinhiLgc0UQCUWmMMlidQVBOxamUiErlKVagE0QlRjAl6RGP3Ya4uiX6QVKyTYudexnoYzSpqlBVhdI77X6jDXVlqZylsRbvYyY3JnwI9INnM3i2Q8A1DmUiOpMfFaAbReylZZOhH4GgXG5OHyTpACHKfIKUQA29MM6HHpyknVWKGOepk6FloHMQgiWlYQSegrXU2O1ktaIximVtebyoud0GbrpE27a8erHg1fM5j5YNy8ZR6w7vO2K/IXTFaSGEN2NF5riqOVMa3QjgrLRioRNzE1goLyO8uztAEW1DHxK3feCD246rzYDRUopY1JbYSieG0jq3U0u2JOqKje/58K7j6bonxMR5K+WfudPUCrTvRc1TK87mF9hkaVrDXZT3twpODDQuYvoNIX+OLLFI6AZiv0H5jlonlk3FoyXcDpFtL6PANxvFSa14vKhZ1pbGqHHmBkBUMppZOjfkXs8ry8xCS0+dEsYH6dDYrKAAg8IpyFkCVcCjzi3NctN3oKCUlFyNrmrhaRjRRImmImrHdjuwHTz94PEZcOpc2misrN+6shgte478+xQjqd8Q1jfE9bVooaxuSNuNKHZahyJh6oaUgkTouRNDSnhZjCyXtFQG9lYL+1+rgE4RlxIuWRwRk0IumSjKHBhp5Rb9/pgSgUAwEI0i6UHWdApUyRNTR7++Iq1Xwj1Z9CzO32JRG5zJSp1B+BvaKKrW8eqi5lFrUd/7V6z++f9I9/EL2lmLeWtDq6F2jlcXr6CfLFg6xesL0WIp2henjaUW6ZIsXy12tjh1kUhWzJ3ixGlOnaK1YqcDCq8MnYnSrhgUnY9Uuew0c9JhNvUFlRX9EJfbXEtLso9JplQC/+W//x3++Je+Jnyw0UWZHF8JqbcABKZEw2P+7AH/+Gmjmb8wwODTLnofHEyOvQyBmcj2yjfl1e/8ztv8xV/+Jv/n/+X/hf/4f/hrgDxUq1XuZCe/e8p14TS+l4JxMdRG+sJ1t8VsBqx1VG1L1S7R81NMSgwV9I0hDY5NL5tyUeX6cx6+4XI7mHciletz3bV2hvNZzTLe0NzcoD75PsMHH7F57xNe/Ktn3F7dUH94ha4ds8s3+OHLx/z+109Y33YY+4SqtjzOkxafzBz2+gfwyXuo9TWurmkqg0sRZTXJVeAqgnJ0IRJ8JKiIRTIiRiuMEpSbUoS8qVM/ELsO+h4Xs0ohCaMSTol2u8q1hJTUyNCPidwaKEpqup6hN3do64jGyPhg30OviVWH8VuSl0hOJanvGi2cCRnpajlrRU3PqUQIkcpqHAnve7puy7a2VFZTa4PSFdEiKD1oVLWfGSrKgckIW50YM9IPuyyCUnIfBoPykjUgeFLdYl1DFSMuBGpjGaxB+UDKZRirEVJf9DRK45oZWgm5clZZbgZwdcOj8xNePZnzeNEwqxWm38jAJOtw84YYPHbW4s7OMMszqBqU1tSV41SJONDMaFodaGJPunlOWD0jbO6kBOJaYlbxu+48H7zY5NKZ5rVlyDr8uQc7p9SVEtC88YEXW/mbkFUR130gzqxM4+vvRD00irT35ckrzHXFMhrWIRJjpEoRpQaoGszyDHd2RvXiDu02uHkj3QUpYVJkVlseYxmi3L+zBoau4sTBa/OKJ3PLaW0wwxrlJaq1riFEhdXS319ZQ20ULvZU0WBDIG230G1I/ZbUd9LymLUQivomJg9OiEjAYaRdVblq133galRVE009kjKTrgh5WNG27+i6Ld73OBKLylBpNbYMn7UVM2fzhEdR5dQpEvNgrOQ7mZ45SFto8r3wqY1BWyf/apn/kLRjiElS5GSxzxwVKYUMicvdN0ElrErY0MOmI4ZKpIUrEadKuZU1KRk2F2LK7dIiy+xDJCqHcgEbBpTfkFIUZdn1C2LXkTYrrLY8al/hhy5n/PbljG490M9qFmcNv//1E374ckZz+wPW/+Kf8fSf/Wu6Zx3L8xNa5ZjNZtRNSxMDj6pT6rOW0yrRDfKMSmnX5hkmg6RhGJJmURnJHCloK8fjxnBawcJG5iYRV9f0m1vYbMAPWO2obcOsqemyzkFIerQPCgFzIt++8+QFNJT7DfATb3yVIdtz2AlnmQw2lbYSFI0ZhAd82uR4+3OAAvgCAYPjF13KAVl7AH0/zVLQVc4SFEAQEuMQlF977x3+0i9/k5/90z/H+c0lQDbISiZiDVshLkWfa8KSOhWBDxEvUikKc3m7RfVrdEnraU1satLyjLQ5o65POHMNvlaYVNENhpQSlVG0VrGoshHbblDBUxUhlIworffUPdjUo1YvCE8/YXh+S/e8Y/PxlutPVtgXa9zJu9Rv/Sav/Tuv84deP2XTez54seWkdfyht874/Y/mnPhr4vf/BfHD71OhqR5f0BiNqx2pmRGaGYNtRAHRJwyROgODyoqqHqaAg0G0wPsNcbMirGU0coqgXUs7O8FYhTYyghStsjJgIYBJD7soq2ns7IRqEJIX2RAHV4lCpTHoLCykc8RWNqYqBFSrCM4QaouZVSyt1AUFPMggoWG7ZWMNTil0K5oMZkT/eXkVPQttZIhKOUKQkkoZLBWKsJRE/soY0VkfelQmrcWqw2BlBkGqsdowjK1JiPJgv0L3a4gDxtRctqcszudczh3roIm2Yr5oOJ83nDaO1iVs0ISmxZ5eMHt1Q3W2QM8X2FfeIrSnhGTpgzgEZwy1Mywrw0wpzN0L+rtP6D96V4DB7ATlWtxsjtaKbhBeDSAkq9zzn1IkdVtitxWNjiRRWO+TzKTPU+a6IaC1gGa1viM+/wjWN9AuhJA6X1K1M2yy0As3IHpR3XRYTHuKfeUt5ikRV3fYpsGeXqCbVmrileVUyVpqdOCxDWjvmJnIaaWohxX6+hpCJxoZ1UzS38rmmRLiOHQcMKnDeE0cvAC0AgqyXkEq4A9yySCrGGY+kTJuL0tQvqbcpRKNk04PbRkibDZbNusNw3aL8gOtSVStIWIlY1RVLGrLwhlqK0GHAAOxZSorQCptRSOiboTLYAymmWEXp9jFOdX8FDs7IaAlTU3K9m/nwLQIiaCtwgZF8IGwviENG6KG0LTEOMMwhypniZSTKaqZwNp7AQbdEAleNnZlG1yTZCBXf4sxijj0bJ9dMTx7hh56zn5Py+9/tGT1I5f808pwsxl47azhD71+ymtzR/iN3+TqX7zL89+6wq8TdqjoLjuG57eE5SfS8TO/o1YVJwG8srKlfBGtS9Ia6VpcNcvdJ1KiLcHWSa04c4l684LU3ZBWL6TNddtJ2S9PiVTVjNY2NFk0TWVwrHLr6p5/sDW4Bp9E2KxkoX3OlI5aSNlmFIQhYzxErCwltc+Xm/o1AKVlNPPnAAXwBQIGL73oEQzEIz9TI6iPsXxNI0j4tXff5j/4lW/xN3/q2/zEm1/lN37jNwBxMk6D2t6gtneo7pa0WQkiV1o2fTMTspapJIWYAvgtrO8Id1ei1Z8CyVpYnGCW56iTC5KdsXQLXGPZmFKyUNQ6obfX6O0NdCuJWCZsVVVIYkaTlLDp/bqTlr4IOik6FOGmZ/XhDcMPvkf95gf88MVjfLrgxSsDJ7XlRy5mvGI7+N4/Z/Mv/wX+aoU+WWIuL6iqGjc/wc+WpGZJ3wW6DFqtSiSfyHMRcTahYqYaxEgKQtKKmxv89XP8Zi3gqZqhjKaaz9FWE3VJYyJ65DmVGXPkolAoZXHLC5TSDFWDaRbEfi3EKhTYCtPMcO0CVzcYK50ORovYkzKZ8FApqqBleE+ufyYSWgXC0LFe78TKZk2F01I7R2l0EM6C3PtddDAe/XZM2wOiqZ5EqEekhnu0NdL7P4jYT9A1KmXhoJzBKjLKihxR3T0n3FxBCuh2wezsMe3yFfzijGQbmllN0zhqZ6lMQlc1fn5G9eRLJNdi/ICan5AWjwiLJ1z3gR6N15paR5w2OJ1oNQzrK/wnPyB88n361RpzusGdXDA7eY2ZM8yqcj/IbagCltOwJWzXxO2apA1m2EqrX67ZG2nRYVZZeR+r0ZsbhucfEa6vqOYzfFVRL89pTx9JC6aGTYx0g8fHgCdwuniCehSomhlpdYOxjur8EWZ+hq1qtLUCcBTMdc3WBJRP2PUL1NVHxBefCOBRBnNyjj4x456KOX2OIosB9YTgCbETXYuhg6En5gmEMQSUVqNgUbEzylghFk5BQdXkiYWldCCAICjDEGG97bhbr1mvV4Sho1IB60BhpN6vNZXTtJWkuUs5RASRFMZaXN0Q2kVuO7bQzjOBWFRE7XyJm51iF2cEZXN5DlFXLC5M7coIWoNGE41i291Jealf46PHtzOiuiBpTTLSWZKSiHT5lBh8ovNC+utDwvtx51C3S4yK2LBBVTVdBP9ixermFnu7orWOV77yv+IPv3bCSW256TxnjeOHLxrczQf0H77L6sMbupsebysBRhGSD/h1h17fkLqOlDQqRBF1mpD2lNLj+GfVDJw0S2qrR0fdOkNDR7p6TvRrws1zwu2VyKp7L7oS7Ry1OEfNPMpKJxIpSstu6GG7lqxSihK0tHNSvSQ1C1xzksWYspfK51a+KqUwuRSp876RZ6GynShEpPv+bezO+xygAL5AwODhi56Y6ok8JHCvbFDYuCVb8N133+Gv/Oq3+Js/+W1+/I2vZqchN1xrjUaETfSwwr/4hHj7gtjl0Zl1g16cyhyDZg6ZWCI7L9eeh47QbeQh9lvUeoVa3ZEWp6jmFFfNMbYW5Bk8qtsIKNjcEO+uZaDS0MkVugpjDFhLtIZoLVGBcgZdWdzC0ZzVnK0quiGhjSZse9TmmsvT1/iR85Y+Nsyd5sIM6Pf/Z9a/+Ru8+O0fwDZSKSPKd3UDzQJmp6x62ARFFyDphA2JqOV7FUU8RSuJPGS2vCd0K7YvnrH55CO69S3JOKqzx+imxfgON1swkOWTFaO4zJ6CHlL2MUZhTi6w8xPiyZqw3RB9L2lzrdGuxjYzqmYm8ydK5KYTpAE7rHDbK8LqFjV0Agi1FVZ41ZKSZuh7VkrldRGZNw21q8WY6JwtUKKToCATEvOjjiIoRYwZIAzErOBXnAkdKNOB2zCYit42xOaU5NrcpigjhRVQWyM6DnfXhKc/YLhbiWzx+QXu1beYvfJl6idvUTcW24iok1Wg9YLq7AlBGeLsHBUDwbYMbs5tn1h7WCcvYMloYjComIj9irh6QXj+EesPnzNstlSDx5xeos/eZF41LGvL6awipMS8srTOoImkbk1Y35LWd8Lu79ZoIq0zzCvLopFZ9svaMq8MulsRrz6mf/oJ/dUdvt1StXPiK28QtyuUnRNDovMDq17kgWdKBIeWp69RzU6wfoPVBjdfUJ1eYtsFUVsaI+qRLZZu8HRXH9B/9Dv0H77LcPVcBMMWc1kziwuSNuLIgpQj6ihciegH+u21lKhCD4MnhakxVqikMHmeguhWOInSCyioW9EpcLWAgqLroGTIUjcEVtstq/WGzWbD0PeYJCRd/EbElKKXLIurcWqJrcDWBqftqNqZjKWaLUEbbN1KKSHGrG5YYZpWZqkYK5+tJFsQQraLSUBB4TOAtAw7kxh8h+5XpNU1/YtPhJQ6W0q2zTp01YATR1bGofdR/snob8nOqaiIRqE9nM5OYVhD3RBjpLtdc/fBFVxdg9HM6pbX3vgD1I/nrAap41+2FnV9jV9v0UbjGsfCVTRnNdXCoSuLyq2K0Q8ypdIH8J4Ywo4b5OpsrweZxZEijWulHJIiuluh+hVxe8327pp094K0XeG3HSFFqFt5rrk1lRhQiKIh2xXx7nq01wC6btDLM+yFJrqKRBBuVm4/lAA17UoOKo3kVROlE4IIcVpeOOLfpqDg6299bUdk/AzHFwYYHB6H0dvhTSmgoKSq48jEFWDw6++9w1/51W/yN/69b/Njb3xVeppVGolOxhgMMm2P4NEha6J70bxOWgtDGUT+s2ol3R8HEXTJbTxJKZLvZfJXjoxTBK3EOCvns0qezCwgeumVzrVpldOVWhtR2qtrknN4Z9EktDLYMND0HSdhS2giPva0r5zgltJ6qYYtp3VNJNGqgLl6n+H93+LuB0/ZXK1RETbbLdvoqW2Lt3NWXeK6j9z2kT4KUHIJPJGIFn+Zuyl8rk/6oWNzd8Pdxx+wfvoRw3YtKThjccsziDIkRSGouDwLn1ORkA2VEva4UVKmMNoJCFicZYZuftrFYWs1jk/VJKIfhKF995x08zHp6hlpsxYHVrWkZkFqT6A9waeKzbYnJDWWopRuMVVLSj4jdpUlYJUoLOa1NxaxEsIQrxwpRUKQdHgaPCGK2mFQCe8MXbNkMDNi7ej6yMZLd0pImtoYjJH5GbHbsn12TegG6psVc9/jjMbO5zQnJ1TOkkqrmNa4k0fQLNHbLZu+p+88myGwSZG7KHVVo4U81Q0D3miGfovfrvHbNcNqw7DeoIyhvrvB9LfMmhkXrWN90hBT4qxx1AZUvyZuV8Ruy3C3wRkrzr1fU5uas8bRnzRopbhoHTOnUdtbwt0Nw82G4W4tjPv82fRbPDVdHuk8JEWIWoBvEoa3mbfUtaWtKupGph8WEmwtSwHTecLqGdun7+G//69Yv/8R3c0KUzuMy012pmJIhpWXVjNnNJVNzCsnHT0318TtLXYImKTytE2DchZtDdpodJVHbudyga7bERTo3I6YTJnSJ90HSVn63rPqeu7WHav1lr4T3oKNPWpzI/+2d8R+I7ajnZHipcw8cYZkLdrVRFVmiSgq43Cz5SSalPa6KPkn0StAyfpOipB2xDujFNYo8VemDIpSQvjtt/S3V/TXz4ndBr+6E45AM4NmgZ2JuJAPKUttw2ZIbIbIZoBhSMIX0YhibNLM7ZzgWrbRi71ZbUkbsD94SnXxW7jFGefnb9FY6SZQgzhat5yxfONc2hR1xclrJzSvnWAfn8DpCUMzx6NI1qOGAd11kLZEI/oJlDkUQ4+q/fi+Kkzs7rBiuLlGrQQUCJdIg21Q81PU6SXMzkhVQ9JOiNl99jVe3pthm0tKOre6CsCLxmAwo1/3SUBVzJkAoVupESBoaZLI5R3GoV1Tf3dIxP88oAC+wMAA9sHA4fclPVtKB1NQ8Gvvvc1/+Cvf4v+eQUFJKZEkIgRQxlJZiwpzdDzBlJSaMZICahbo5SnMLwjtGamei/a9AtMO6GaBaZek2+ewuiEVyV1XiUhQcXC5TUU+VKOMQ8+WWOeEvKaUSO7aCpo2GxrHoA1Rgxk2pPaEpp1zfrmgfuuGQMSenKBf+RKpPZX++iSdE3pzDasbwnoNIWIrg3YafdqybZdgZvSrwHW45WpQ9EmBsVRW0ahIVBGUpFR1lafdBURtcdtxe3PD3e0t3epOsh0R2G6phmHMEghlQZ7FdohjX/AIDsjtP0blyXQJo8w4zKZsFJD0Niq/L2LsCJ7Yb4jbO/zVU4anHzLc3YBSxNkCffpIpFyrGRHLEDweDWbAVDWuiln0qZYZC14AyBQglB7uHcNlV5JIcUv0gRAC/ban227YdB2xMaRLSzyFHs1d73m6HtgMQWRiTUXdnqIXZ+iqQgGx9/S3a+qbO1jfYkKHiQPWSG+7sM0TEU2sDOgKrEdbD91AUh5lAgRpVfOJTHoLVEEmfiYYa5YpJRk17TsaqzlrK9Y+EiKctSJUhReiW+w64uCFSNZ34Dtq23LWWtFl0HDWVnkmfUeYTpjLehE+KQiJbT/QefIEUguVDBxKzkBj0bWDykJlidYS8nCi3FCKNYkQB0zoYH2Lv7mjv10Te4+tHbqq0IszYnvKrU9cbQauu0DrDEZrFpVFR/CbDdtnT9HbQFvXMtujqbDOoq3G1NJhoKoZqsqgoG6F4Fk3RFNlLYjduOqkLb0PdCGyGSKrbmDdDYTB41SuUfdr0uqKcP2UtL6TvbA4wZKIzYzYL6CZizhSErJ1yl0hJSU9BkJIdlQypKKJUnrty1o1pSwUFa0z0s6Z2y5jAu8H+u2WfrMh9RshE9Yz1M0NLC6ZnchwsCHrIayHyMYHVj6y8ZHtkOi9kHKfq8i5S5yaQKVnbNsl+rTFvXDEIUKIhPUat7pBz+6o23MhJGtLak9xX/q9PKoa/M0NVhva0yX1+SPixRO6xRkhZ99SDCg/YEMH240QlX2eFGotyTUC2kqZubQEIjNFkjFin2OEqpVukvkJanlBaE+J9ZKgRYTNalB2JYFLmaNhRHbatnPsYoldnhDbOalu6f0u4J/6G6Uy4VqVfrrCwp58zVFwsXvvvPsO35xwCj4vKIAvODCAHSAoX6eAIOXSQSSnrCN89/tv89d+5Vv853/q2/yR1yVTIH8nbW7kkagYSVVVBkkdVhV+ecqwvhNlw3qGb5aE2SWrZLhbeSHpIa1g8+qMeXuGbk+x3a3Uo30vjFNXEV1LdK0QVLSQYHA1mjm1lpnlxohD8knRYxiUpU9GOgSS9KJXjcbNXkGfvYHu7pgPnSia6ZpQL/D1CRsvbXo1YLWRssR8SfPoFNNWuHnL/MtvkV77EZ6rJT/44IofbGGrKlwtEsbzRuHRxNz6hAoymU8pTIp477nd9NxtOoYE0dhMPKyJxklUkRTaJ7wSVu82C4R0Me5lDcrGs1pjdRhVJl0WkdJKdorRwqvQpDw9AYTjj/RK91v8ekW4uyGubjJJTKNnPWV6n4+RLiLtWSHSRhiSEvGhIJrnxmWS6xQU5N5ylIxUTtoQlWSCXCbDBe/xw8Dq9o7VdoOKNeYsgXasusDVpueTVcd6iKy9yE8vli2zsyfYy9do11uJTI3GNRWmqjDWYazLqnMCzGIEH3N7ldYMxtAZg3dWsh46YkMgxkCIot65TpHKWploOT+lOrlCGY3NrH+JRkXw5ryt8DGyrCyVVqjocxeElLGUFaCro5BlF5UAA6vl7yuTSzy5a0KBfJ2fEquWLZZ159kGLcLVTtrzlDEkp/HO0hmDMRabZzKEqLBIVFVlcFjujakqXFNRLWakNlKfL7CXr8HZE7am5cW654OVSEPPnLQZL53CagchEdZr0l0ngkDGUi9aTO1wsxZVtaiqFkCQwYCqxYkkW+eha/VOp0CZPA9D5XVFXveBGGREdVUW/dATN2vS+hZiJAB+tiT0ov5ZyqIxCcAr30dStmMy+dBHyUIVNT4fGb8WzlrJFtRa53K1FrnuJPu080k6KFwt52msTLPcDrDpUb0nWsMQ5bXrIXA3iObGeoisusBqO7DZdgzdlib1vN7A6/Ml7Ws/wuLuFmU0w2qDm7eY+RJcBdoQopR5eqVpT17D1nPsoy9Rx06yia6mrxfEasFgainHZXtYG02lpE26IggvKgvLbQfhS0W0kANzWSBmoCBchJnIWtsKqgZfL4ntGSsPq22kz9LelVYs6pb5Ukq8VbvAdGsRd5stsItTaE8J7Sm9aSH5ERjENPE3qZAPM/laMxJE0aI5sVP0he++9w5//u98g2//9Hf4agYFe7ynz3h84YEB7LeCHAMF8Qgo+LE3vird6GkHKkJiR1rMgj7V7BzX1Kj5gnT6CN9v2PSBTdIkt+Rm68dBMHedaJw31nDWWC4ax7I+ZzG/RPUrIakkxKgbiSjKBldKUTcL5ssFy3lL29Qy5EQZtj5wdXvH9Ytbnt+uebHu2AxiKForUwZn1YK2OcHNZFENPsrkuLWXyWoa5klTNSewuMS99mXmVS3R4fIC8+pX6C7e4vvXHb/xyZr3XvRQzTg9PeX8RBxlSJn1r0KOOAJGK5nmuNqw9oreNKjluWif+0FSq7MTel2zjYrkA0FrhpDY9HGcrNfnqZPTwxqZb1CyBwIU5P93SpNZgllB1IBKKG0JaHzJAJncQqaU9He7WuqLxuEHERIiJKqoCEqcUx8SEElJYY3BuYbS8qrUlkQmoRVtDK3RxpKsJWqNU5oQAjhNH4O0fzUNzJfEas66DzzfDDxbD6w6kSaeVZ6TxjE/e53qza2ICZ0/laEspxdU5zINUzcLfII+QR8YMy/dRIlNVDuRaYQpEZAIO5HYhsBd8NgQmDWn6Eev0Q4D1d0LdN1iTi/AVKPwz6K2hJiorMaaTMxsZpjZSZ7UN5cUs5LfF7EgkwW/tFJgKszpBW2/JXYbzOIM/eg1fHPGuvPchcQWJ8Q2bSU1nES9cu2lJjukwNonmiwr3ThpTZV5F6CbBbpdUJ0/wa9X6KYhKTAnjzCv/x7C2evcrCOfbDzP1p7bjWdeC3h51FqW1Rw9X0LTEFZb+ijPzzSVcBTqFt1kcOAqGb1eNSOfAFuRbEOyAg6GpPC+TLeUZxCUxkdFJ15dDHRZj05miESb5w8YGVTkEwS0CIzFRJ9UJv3lGv9Eia98P1Xpi6QjewuqoPGZ/GwqIXCGGOiiotc1aXaCiRHVbAQsLk7pdcXKK9Jqg507hiQzL6SEIOJMN1vP7Wbgxarn6mbN9fU19Guen1XExzO+fPEW9e9NLJdnxNvnKFtjLp7A4pLUnLDuIisvROFrHWnMgmq2xFkB5ENMbHxkfR3ZDLdssjJn6yxns5qLkxmPz5cslgsaa1BJJmZuth23qw0vbu/oesleGQO2mhH9bByUJUSnilTNuRsit+vI8+3Ai61n68UmLWrDk1gTGsvZ2Rvo4ZZGRdrKYKtWbEw1Y3AztlufU51x52fyV+EOSNYAsiPTQFZFzSQuNIq333uHv/BL3+DbWQfhdwsK4AsODKY8gvKDY6AgpPugQASsSoSa8k2WHlhAVAkj9FFRzc+ws4Xo4g8Dqvds77Y8v9vwYiuytc/WPTdbGaTROM1N47htPI/mjoUzNLamti15yqps6C6MhEiAmXGc1nP0/JTorKR+Q2SrPBtqPry74rc/vuHpasvd1jMEqZEuGsvcGVqraazBmqycmIU8tJJZCJdthSJxsnwiyownj4BIak7oZo/44G7gt5+v+VcfXfO9p2tOFp5gGqq6wdqAMT736qocyUvKvyJwc7tis9pSVwt0SijbiL6BcYRqTm9a+ps1p9UCKstqkHG7Xa71ilGLo0ZEIUTZ8fMmgMAoqlGSWkYOVya3PxJRQ6RPhm00+HpOWpyhXANaoWYnpFbmYGyGwLoLbKMiGnBtxOdoTIWdToXU8RXWNrtJi9qAF9U3GSxlRWfBSNlHuwqnNLrbEK8N1C1peUmandHrmvUwcJdnZtxtB4bgWFSGZ7WhMRWnJ6+h0ZjTRyJUszghnT0mzS7oMMQAXTaQm6zXvvUCsErLYOcjfVbRk75uUFHR956u3xBNhGZGdfoqShn03bVMP1yeMdiWfvDEIMJPCnFkPoDTUu5SZ4/Q/RxVtTBbErXDhyh1cxIqKTnPIZFsKyUcbdF+QC1O8SeP6c2M53dbroImVArqWkoMMaGUcA4qq9nmtV1ZxWaQZ954ER1LSYMGjSHNLkhnd1g08fSxZLfmpwwnr3DdRZ6te67WPc/vtmy6gRgdd7VhPdT0TU09OyMtL6HrRAhpKXoQdr4UIJCJbLidkmEpHZSv0VT4KH3zBRQMQWaC+KTYBikn6DAIqRdD62bodok6uZD1FBOpbvD1nG002GRQQyQho7YFFKY9Sd4+lpr/DiDEDBxgt6+0Ahs03kCzq+wABvqe65s1wbTo9hxtKhkhrDShaonVgtVqy8bdceJm9Bg2QxjX313nWfdexml3ged3HR9/csPN3TVdN6MxIlP92vmXcfUcu70BNKmeyQTRLnDTRZ5teplLk4rwksyj8UHW9sZLUHFoBy/nDT+sKxZnNa2qQFusEcl2bTzJa1Yv7lhvJfLXuaPFGoex1TjDoQuR7d3A3RBkyNd24Ho7sB0izihOGivcoCgj5S8WZ8wWDaayUga2FV5Z+u3AELM/yW2/PstxK3ZaBlZPXHwBB6O4i+LX3n2bv/TL3+Rv/9nv8NUvfW33ssJf/Jy+8wsLDO7VVV4CCn7tveOgICVRoyq8HaVhtpgDMFssqJzB1o5kDcq0GA34iNtsqdWG1rSsTE+jeuZuINW7Reoqg64tsXboxmGrXBNV0k6nUxKVv1TaVDRt25BcwyZqtn0iZqLQqk9cDbAxDbFZMmw1nR4YUmRAEYPBm1xq0GZM8YMsGGc1VWUwjcO1Fus0s/NHUt9Umm1IdFuZaKhazWypeMSM2jVUrhp7lbsh4kzEmTARDwlgErZqaU/OaQw05jWZTJciGEfQjm1I0tOrLIMPEl0MYkymTmwIaQRKpe1NK0SYKGcJqsnXOusTNFaTrKZSCq0t9eklxmjC6SlsX5dedJREdM0JsT3B9BE1JOqQCMpQNzOSkmyBUkCQNRK1JhhwSVGZCq0NqoACL1kdpa10dLhaAELdUjVz5qbmyfKCQTv02SPCyWvc6hkLPfCYmtj0rDpP5Sxni5pm2VAtK5rGMH/1DZkKmFvQzPwMqoYegw+w9pFV71n7CTDIszy2Q9wzRkZD1Fo4G67BOUNdQVUbzh5dojevZVKWAtcQ2xO2QVHliDclaSecO03NCfpkhnr0ijCztSPVM+LsnBmOZhBQrbIWyMwqGnOB3lzKuiCRXENsz3jRReoq4HqIOKIy9F4i3lLmc1azNZrOxTyMS9NHnVPlmpgsySosBrM4Z1m1hPNXxtbW2CxYedhsAw09Z0jHRj945rXl8bJicdoyWziWiwpzMiO+eA0XB+bLE6qzc+EVlFbEusk6Ji5nCaqxAyEqQx8EFPiQGEoqPzvypDR1M+PkFEwKNEV1r3qE3pyjHr+C8nKPlHHQLDDzc8zinKCtcAYSUsfP/7oRWO9AwRB2oDDE/T3ljKKyGm81MQlnyuQatlMWN1vgnKU5v8DkiZIoDa5hGxI6SJTdx8QmhPE8NkOQzNWQx1knqFzFcrmkahyzpUzuDPWM2NQ0Z+c0RkEScux6CLghYqynMgNDH/BeSioDYiuHlNiqwDp61iGwxRK17NHaOFIzZ2MargbQfWJOQvsgM1CSjCFvl+dgW2KMuTND5esvJKGEjQndR+J2QKcBZzwzF3DZvs8bS9M43KyiXVTUswY3b3Ftg7VaOtxCxDaWSllmtkJXecLons8RcTMf98FBad8GxT/8HWmp/1t/5uf441/6Wp6XwR4a+LzZgy8sMID7JMOUIyPK90j3wX/4K9/iP//3vs2Pv/5VYcHHhE9RhNsm/a4mKi4uLgA4P78YI2KjC8FNoU2icQ0X81PaITLrPKdbz93gR2NcUrCNNSxq6eFurKGxeuy1D9nYTkl3IBtg6IPo5ceUdd8jm2Rwi1PO9Ay96DnPnwVSL2xcngpnZEra1Jk2TjOzhkVlOGucTB2zCoPUJ5uQMF2gv+sYlh32dM2T1cA2Z0+s0RJ55AFQ20Gub+tLet+wOD2nubykttlx5/abmMSxdD7SBbjtvDizIY6gYN0XgxLk9ZMWsR04ULjJ9YwOImiGoPHJCMCqLU1lqe2Ms/MLnE5ZTVCmbyZbE7Rl4xOLQVLud70fRVpQim1IpBRwpgyuyRrzmdktpYVWSkJaShJkERxMhYoBlQLae84vX+Xc2gxIlvjmlKttoF57mruOxxshH2qlmNfyfM5bx2ltaZ3IteqD+zgEZAhXvo/rHLFN7+MQogDkHC1qrUQa1lgWi8X4OY/mFQunmbmvUGkxLgEYgkRN/YT74XJpoLUKEz162EIKoAzRNeN97fJ8grI2KyO1XydTrknIBMz1ELFDRPpiB15sB643ns57GT2c36Pzcg/WRtZ44zSzytBb6fTwMREqy6J2mNpRz5e4yyfjfetCks/pPPFkoN4OPO5krbXOcJGldy9nlvPGYLdvoba34qBL55G1ojuhjQACY3P3gYwel24HxtLB7p9EuUOIdDGBtsyXS05OTnBG+BiNVbRud1+V71ApyWe5hiGqEQj0Ada9Z+UDm36aJboPCGUM/A4UgEihd1nUJ8Tdz4tTXFSW08sn1AaRds97L6WUwY3Modj6yF2/yxT0uYbvQxCiYkpYYzhZzjhZzmic4XzueON8xqNlzcWiZlkbKiME0kBi7hOzPlBvBxa9DK0r11NATjfIXl1kEDKuzWwXTtoKN3NskuH51rPykTqXIJUCdMX8/BHzc8b1WQbfTe1ysU0LHznrwhi8xJTGz1o4y2ljx71aGeliSVm0zFioXELXSbhL2a7t+Z2QGxJ0gqhHlUb5veIfff8d/uqvfouf/dMCClL+eczlh8g+GfuzHl9oYHDskMqwAIRfe1daEsfug7Tr4S0PZ7JnCCR0lFtcNMR9lNcHpYAdkXEIKS8USbNaJDpShcgCkGRQT3nQnVc5rSVHzJu2yCyDbEDRWZBSQB93aPx241l3g0TWPozzv4NSIkcaE84o+tyGFaIhuSx3imKjJGU4xChM8QwMJAUojiXEKNG4FYQdE1n6WO5LGVXb+Yg1iq2XkoLwaIQd34c0aoTHHC35mEZHViKM4szWWfGuG4K81pdanBCKtJZ/lZfSQTcI+Gmc1IfLQJMyHCY4Q0hCvqq1wupGZt6XZzfI9L8upyV7H/HleauEicJNCClRJy2DpPLvQwIHRK2wpsJkUJBy/zkxyL8UoS7s8Tx8qpqx6iXVP+RoxRlFKF0peb3cbIWUNESD0aLrEDLBbOsFCGyGyNp71r2sjXUfWHc+8wtEga4Yn3IPQTIHndWsjc9dH4oUJS3qcnamAG6fM24lsxkydyUlRa0NplqM5OkQJaMkw2Zkf0XIAlbyM+1lX8REHhCVuO0LwPHSrjgIKNgOMWf8dtdgrUxF7AbhqAxVrqcnjY8CZGbO0Nh0776t+8BNJ+9NSsKVICsy5j3R+8SqjyyaU5R24qCjF3U7gEwyLYCQDAxCjviGbBd2dX5xBl0o9f448ppiVrXbag+YPMND1rcxs7Ic8L0EB0OQ+3U3BDYTQNiHaaQuTnPIgE64VWncRwDWyjwKsW+7vnitVJ40KfRdIZDGrJMhr/Gx2LGY93HII4djtoWMWR5TRK7QOUgRBxyiEBSvNsNRO7TOmcQCdHq/D3aHfC+7IWRblMa9oxWsuyGv4USf37/SGqu1dIzlgIXcaq3JWdxJtO6D2IM+pHG9KBjXjFUKm7thYpLzLNF+SIwj4ItmTvElKf/8mN8xUZG0CE8FJGPw699/h//o732Lv/mTP8cfe/Or4ySg3y2vYHp8IYHBvTIC97MFv/aeiBf9jZ/8Nj/2uogXkQ1YGWaRjrxRuenCHFXj65Ta/U3JOggIEV1zm8QpGtJI9istWUOuV3sFJu5YQGG3pgUc5Bq7T0K86TNbfzNIva6bbBgfd/oMMv1XgY/EpCkT75QPGQknep3QIUmdMjt2lSOBLn9Gnx2syWxyhdq1EOodYCpRiA8Jr2VDK7EqRCJW6XGjJKALIZPh4r3yQdn0XXbWQ9g5l3KUckJXOAUuO4YgUU/jTAZXRkBVdhZNGR9bogXkfpXU7jZnDPp8LWU9eAU+BkIS4lddPiPl0kJSRK0IWsCgMRXaVKQUJDMxecZCNHUj03sbEkM2oCmJqIzJA2hiUgwBNioS8Wx8wNncf58JZV0ohjkcBVYlwirOutw/mycIaaWwJkh5aYjc6CBclqhxVmPIIC/tQPZ046mERDm5FFbaNWPM0+fSZEDPuI8KZyDvH2DwQjC760LmSCRWOeNRrmG6DoxW8jMvz79klqS+LtmDPhi2PlJnoqrKa3DwYbxvQ5B9LRocopOR8vMYEmxDQvlE7Wa4qhXQd/g8tahWSpCQywY5QxBylkAIoYEuSqZNtCp2NkcpiAbUIKz5hABcG9K410rW0EfYhjCSdTfDLpreDrvnP4TdvYsx7a2Bcg9DSkQjMK+sB9lfYaxXoyASaKyiNqbo8oyZ1q3fdRT5XDI5zE7ovOasUWNG02gJNPqYWA2ByL4d8lFsRBd2fInOS9ujjyK5PNqIWDrN5H75CGQHvemDrLlaMkrWRCot2itW6azymFtdRSZxHLBV7HIRbkr5YRmjUFGNioTWit3P7Jtxlk0RjirPT0C2Gsvbx46cBCAgwnFWK/6797/L//bvfYu/8VPf5o+9+bUc0MrrStbgECHkn36m4wsJDA6Pwxv+D997h7/yK6Jo+GNvfFUQ2sFrpun7h44SORXWaBGzGfvukdSoRsmYTRvxQRF12tWrIDtRiRLCBJmWzTtmDSITFnHeKMMOMe9FUnGXJh7TxUo+w2uFDhGtTM5uRNn8GvByTcVRxFgYzeJ4QKJKazQNYuhBWP829+nuXVcxil48RopahtNMNse0JNLnLEgBBT6EMcIpzmCYRLsgae0S9ZYoyNpyD2I2fjavBYmQuqhpbMkYqJHkUwxQFxNDMTj5PSc2FGtEDMamtMseGE20GQROAGIpN2lEjbC0JSVyxiRnCPoowGDVy2jg8uzLUe5/TFIr3hQOB0X7QTo3CqgSh+DHEsLh/StGM0aJ3EDWpwCI3PqZxVS809R+1/UxPUqatZxv0AmTFGpyw1I2piWjlGBMy06Psl5KT//aewE5JWMUHr4Oo1S+72lcLzFH00Ml90zItzsHMP3MKWN/uoZjgi4KX0Mpi1KRGBVeK6yu0G5ncMszLU63ZFXG904CekqWYNqKO11jWiGiQzoDvvjytToC6Zy63w6RVefZ9D4D3DzAKH/uNNsyXutkHYBGaxnotR0ywdcodATt8+dHzfDAXh787n5O17C8j3yGzq3fxXaYMfMQ6YOAkyHsovVCnOxjHLUXQgacQ4j4xC4L8oD92w4RrSfBVwb1XkuWSJPGdT9mCxQP2uVyWKVHuz6uL5RMYacAgzSyAafiZ5/uaWRN27zK/vv3v8t//N9I+fsn3vjag39TMgi/m+MLCwzSvW/keOe9d/jL//U3ZfbBG1/DfwYAALv+3oeOYw/ZAF7JlLaIgqDRphAfdyk4KItMEcMuzVTAwBQcFEBQ6molbVacZ0j7DuzzHD6knOaKYzZkBCQTHW5rDA0waIWdfJiMktZjx8Du3ohxUyE7n8g949ZnxO8DexFGiLvNXjZ8MaJynzLgQcbkTrYcXsk5agkXAEuegpwdS2JrJfU3lkLSjtvho9RMp88C5LlFL0Y6JU3Skj2QaDdRZ5GrkFJOjyKDmZSoyE0nd5RIesh6CcWwD16iL8l07KK7mOT+icFS4xqKOW3alXbEA7AoayTtRYuArDcD+IjOxEqb/97okIGeJ2IYQszAQKLtkj2AiSCO0SImp9I95dECCHwmwE11KQK7rEdZD5Lx2KXCpynwAgp82S9BhkCFlHBRj+sDJGoPMTE4TedjbpGM9+7d9D7LGs57LkUGr9mqiDUC96NOBK0xOuXocvdMS3YyRFkPPj/jkCQ7JnLLcYxsyzUfrjEdFT7rRfjoGUqte7JWyxyCPuQ6fq7nrzqf10Lm/kxAQVkD0/0jHwo6pjGAKGtzLwOooC9D6cZs224vB8g2Ko6TA8thtPCAymGnPzfCzSmHTxEdtezrXL4dwVX43Rm4KWjUamfPSqutyzX8iELHPAlRpfFZHAY943UYWUM28yEKiJISquyTciQycfBT/EnJnh0e//j97/J/+Pt/nv/bn5LZPfeuEfY+r6QJPk+2AL7AwAC45yB/7b13+Mt5SuJPvPlVJoHBvcMoNZYUpj87HM54eIzZNi0LwOb6MzDWymJMDy6McYDGS0BBKIg4gQ9hNGrlHKOa7vbJuM9ci7eqpAgfOgfGTV3OAQpjWf5o0AqtJSID6ZqYMpqlQ0DvvWdKYjSETCM/T9kB+7AzwvFgR5TzztlNMYyaPeM/fk5MksYvBkDH0YkWsmQ5H58iNuhxQ+/u+y4i8JPvd3+8AyNyrxS7vJQmRSEmWlSOZiWC1mp/HZT0X4kkBRSUVGgcQcwOFKQROOoDpFqAVDH891LHk2jq8LbFmDBm3+j5EOi9wpmIzvy6mGux0ooqTkv2ROG+pDFDUpxFIWzBDgSWGmoBA4dgbMqd6TMALuv88Lz374H8JypZA1OSaqmZOzPJkB25h9NnjFY7IBmlzLD1Oc1uTc4YSWZETZ5rORcBsIzOsrQNFlBQ0uEl03NsjelcRiqRa0lxy0v2/34EBwUE+ngvYDi2Z4Ax4zL9/2ldfVwXKWLROYgQ8DTdy6Ucspdd05qYvZXOtsdZvWc7nNE4I/alPBd5noxN/A8FPEbnTER+7jqpkeczLTVpxXiNxWb6EDBadDjK/gRx9rlOsDuHiR0s5zjOMzDsPZ9CTLdGlwG+5Hd78DD5t0apPLp88jul+B9+8F3+T3//L/Kf/clv80ffuA8K5HzuH3vg7zMeXzhgcG/t5DT/2++9w1/85W/yt/7Mz/ETb35t3ISZZzIKSRitSEFJOnTivVUmK+31leZf68lnxfK7qMHkMMEaVIhYLYYvqNJTvzvb3YaVetPUyMjvd5dUnMDhISI+Coektu3E2BdyVpU3X0Hojdu1+VmzS53Jde1qJJZ8PeiM8BNDUEzx6dgdcCA2JCj64Dnl8w8H96Eg7vJ+ZdNXRhGjZAMGGPNk040wJdFN72sxAINWGD11FgqvIrZs+KlhjzvwcHj/tbQ944PKz3gXL6aUct1fSHjJaFSUa1SqRNgpR5U5pT6JJKV8sYuOppPWCiA8doyp67DLDOwRzCZ/VwiB5ftjTmD/PRM9CWwcgVBIYHJZqpAxpwChGOAJFrsHCArZrjiUkgHwSYh+hUNw7JJ1dgBMfj+9hDCJfIsYlTzHSIhmdBTHDqMFeJq8F/fIgj5gcoK4ZIYUWdtqwjMarzPtMiSH5alDUDDNGMg1ygKJSY+RK3F/nZa/La2IJSswBgwHN09rNe4ddl/GvVOcc2XKFMzdECVppZ4ASBIhyWCfe89HgU6KXYJAj6CgsmU97myHM2JXih3a1fp316uzkS52rQQpWxgzmBDQ2ozk2kMbWAKjw0OyRGl0zocZ3XLde/ZxsoCEj5DF1DIwtrn7qlxP8RlTmxWzG7FaUXJspfNgOizx//OhgIL/9E9+mx9/86u7APSBNazK5xza3fs/Onp84YDBseO774oi1P/7z3yHf/dLwt5U5Q4lRq2IwhNQSoli2uQJljrqmHVTHH04Ksl7WaMwyWBV3vyZxLSLmBIBNRpFY3ZgoTglq4vIkhIjESXqlM2l6X3M6beAVpLq1VqMgZ/sVZuNf0Hj07a+w97/Ihi0W/PS+hJTkkhBTY2YuVc/nNbmSq2tROQj63eazYjCvtZKSgE637t6nDFuAZ83fbmONEbBh4eeXusRh1ciA69KHTURfQEjOye8DxAOQ2w1RhBSa5a0py8RVJD3DkmIrFZpkla7NQdj2SnEkqqWSLLUuA/TsIfH9NrHbELu7T8ED1rn9Qy4vKinzttZMcJTh3Dv80jEKPwYMZrCjjYx82JiRCVpxSoA4fA4zBBMQUEqGY0cZR+7/kOAaLQZdRim1yODjR569uI8xNmqvbLXy6KqUg7zUdOF4lmDDHD6lGcrAGAHEHwujxUS8X7ZcBIMBLlmr6PYqXh8nZaMxjHgKACqPPcoYEmrPBp9em8Z90xl1F5rc2n/HQOHDPQ16uh+DmTBoUxo1jqiQxYgs/ev85jdmNb35djZIR31yOw3+VlvBzVmB2NKDEY9aAdL5lO4UiZ3RehdVqacT/7/qU3ckRLz9ebrl7KamrQ37gCB0Wp01AVEjleVz6/McgHJMquYZDgW8I9zpuD/+r/5L/mjb3yVzFvfC0in76uO7N/Pe3zhgcF3332Hb/3SN/jbf/Y7Y5+nAAEhmsQkN1KRxs09RW8wQViTh2EmiHv6IIpBLDwCYxQ2GZIRwxDYMdyPG8pdRsHnMaml3iYixxI9FLUyoxNDTpmV9Nwkgzqea0nXlU0gTmAHBnb/dm0702M03nbHMj5qvNnfRDIBcX/zTA/x/0lmFGiJ7HTU48ocN2eQVstdjVSPZKN755CjAmv0Xv3y0PDHlEYnf5ilKZHb4Wz0Ynh2f6dGNbQSQamUyMQCEUFSQoyaZpym4lk7Ypo4jb1rUTswWBbfGA0fXLrWmhAlTU6EysgT0TFh1D43Y3qvSiRVGYlyGqdFi8HsHKc+iDOEJ3/fAIUk7ZyefYexyxA9kPE4WEslTV2eX4jCaynRYSGa2uwAHrqmcl1TEKCnoWD5m2LoC6iYOInp4WMWnYkJPCT98mcrPIBdi1vJkJVTLutm6iyPrjV4cJ3eK70pxoBhtBdByH4v2y8FUJf94oyiyQChGtv65Puyr6dlI7mBCjuxZUYpbMqAMvGpduMwiJge0zbtUrLyMeGNxpl9PYNCPH3IFpb1UNb4bs7KzhZOuxT22hkPgMCUb2ON6L9YIyu4BJB6kt089BdjETKV3xcxI8U/ev+7/B///l/gPy3lgwkIEL+VP0Opf2utivAFBwa/9t47fOvviHb0V7MiVNECUErQmlaiD69LhiAKeoO9jNv4MMpiLeme3czs/MIJaS/pHRs7JWk9iwDmvrJi6WIoQMGnhI3Sf22RSWVaR2yUzWCNbAxpy9qR9cDs1dXK12mKX2vGTV7AgNWy4O1ksU+PqWFPdneu8jN5Tbk3D20c2EfLBRzZWKIx0Nbgo1ynVRpv4wQQ6AkwiPt9ypOjpECPGYByHw7Je4eOT+ucTszGeeogphFOOUqkq3UebKJzulxnBxelPFUYRSVNWJ77MYcpnyOZFGckLa+NtJeGKIOhQkxoo4hJwGGd2ybL/XEmTTIKOf2cGNOpJYIq90jrnTjUg9kkre7dr3/TQ2fUrXOmrPwUogwM04re73gtQ/js13W4HmBXcy7f736Wsyhmtz6OZVBCSigEnVkUniQiNPmYgoKU97p83WVG9q9dAMCxtSbXc/9+RUp5oZyz/MboNAYM1uxnkz5tv0xr/QUQWCWZpKmteIiECjvS3EMZIrl/+d4/YDPKz6bHzjaqHTcpliyMps+8nDIHYlpyfZlNnAKSKSDY/ey+bTRkHzApMU9LBeKsMy+tgIN7voLcwr3jG4Gs4QT8d+9/l//9f/Mt/rM/9W1+InMKpiBDZeB6mC04LCO8LBP20PGFBQbvvPsO3/rFb/BzPy2ZAhj3ngACdlmDcaZllF+UBzRN2ajJAwZReVPs0N/9jSEAJCG17Jh2D7+w1WXyGWOLYwEKJaPgJyDBqESVZNpfqX1HnSMOe78+WY6HUvtleppVgm6nC91mpsw0zTptLZtGQ/vXvM9SL4SbaTRV3ndKSJNrVqMR7aIAocomfBBw0Ptdzb0QMF9Wd59GfiP58ggYODT6OteVy72LpKNOcC8zws5hHsviCQDa56zAcTCgcuRmU+GoyNeYy0hj90oxvnE/ytzdDz1yBKa/nxK+gL37UyKnw1rvtL3vocgJeNCgT39m8hoNiPFJOlf0omRbtFFjpkxSw0YIb7poVKSxTa1c38uuDe6DgGnNvPxM/p/x52Wd7F3/A8+3lDGmNeFDkZrD56sTUh4oacoE6IfX2vj9FDSUerhOY9Bj9Y5YN70/n3WfTKPmyqp79qLOYKDUzqd7e7qvYbe3gT17cWgrYGcvShaqlG7H+zmCv7gXSJVMTGn5/DTexnjvDtoRj2UHin08LA98ZiCQ3/vlfmLHNSrn6Yzi1997h//d35OWxCnRsPgjreU8DrMFnxUUvPPuO8d/kY8vFDAoS7KAgm/nedSQ064ZmBdJyjFrkFG3ykhsuqGnaE/r48BgfBiTQ9qW5NUjnyDl75MYgjCS0FJufZO6bcko7G0Cm4dr5LGppT1uyqI/PHQxQqi9hT4lxYxpL8WI/I9dD0baeA4Xc7nWe59d7t/kPk4Pp/P9QN3LojRR7/WU+6jx9n5bVymnHNv8h5mAMfJS9383PYr+OJSNqu7d29GBZONi9S6KLg5zNJZHrn16KEkoiGORtBJFAKPUUi36XofKeL4H132Yjj7kIpRj6gynjvCwzluu7VhJaJpZmrZIvex6U369RZUmE2xMMl0yR5U2auHs5hS0jyqXsO7X44sDfOj6yjXK1/21MP7+gGQ2dRaH90D2zAQEfcqzNSmXVYrjy5ya0uIGO/7Gp6216TkefNLRLqJph1P53eF1l/ea9u4fywxMy42HTnEasbrJfR/tH+U67x+HGdly347bH0hZ2nxK7CzB1LRkU4Krz2MfD4mDh8FSueayp4tKotjO/Lu9n+0DguM+YgIM8gv+8fvf5a/93T/P/+Onvs1PvPm1T/VH5WcFIHwaKHj73Xf45i9+g7d46/gL+IIBAxBQ8M0DUAC7m1T6PAMKkxLo0lKGZA52fmH8Wh62USKOBLIBxgcz+ZPpkcavakSExbmKgZPPTqiRcBX3MgpmXDwhJrxVe1LN01Qd7PgN0whumvaabmpxCHJtWpda1X2Eu3c9aScEFdPuAl9GlHtZyrn8ndwb4RCkVIRydkBhKj5TMih7pLXR8B0/j0P28MuOvffS9+v44/uMBnsfeBVjUAzLGEVMsicgoNBSoswk5EQt3SvGMHavWH3fuB1LB0/P/d7PH4iYpvfjkFxV0qcvKwlN29s+rY332GHH4qrai7AfSkH7uH8vPs3xTa/z3s8fWAf7APLlTuOzPNtAbt9EQYiSIdHSTSAciPS51trLzr0cx7udDt5z8l7TDNA0Qn7IZkyj1nImn2WfHz2PSXpd7759qf0pzz4B0QiIjPa4zfg8NnL6PA9Jg9OMwJgl1CW1r/JrJq/X+37hIf8g/4r9l+Ov/Mq3+H/9aZE5DsUfTW9heQ6UzMHOP03v47Gj+Mfv/PR3+Ov/1V9/8HVfKGBQLvrnDkBBORJy046Bg5QQ9z25qYfI79ffe4e/9MvfBMBpIGWd7BQlV3ZgsMfFrTRJCWEHpTNQkJYvMYqQ9C6rMGVmx5Jl0ErQMozlh8R9Bno5xg6KycI+BAHl/x9aWNP1lbfTASCYpsMe3vwPHWO2IL/PGP2N2RQ19oIXg1Cu+XDIVQFK43s/ZGWPHPtGYxfJHtZDD49PIyJN06PTdCvIxgsxgSFHygkbxSAdgr7iHD/LdR1L49877+nrjxhIDs5f/uZ+Oejf+DD7//tpaehjwKG8dnyPz/Cxn/Ue/m6eKzAqPiadcpkEVEoyzRPJkNgyI4JdZ9KxY1qDL+cE98s3n+XY6x7gftlvdIZMHc8uPV6i4pJlKn//6aS34789Gk1/RvsTil1EwE+ZOZBysBVSIXbyuW1licJLQDgFAlrt+4RiM00JqEjywOPEL8A934CSuTkpj2k32oy+/9t/9jv8sSlRPt23r4e+6WW2uxyf5h+nxxcGGLzsog9v0hQcRKUgiujOYV5wRIBKiIx//pe+wXd++uf4k7/wp2SyWgzCQE/ZXafEXqERJDxQCoV8TXlBoBRaW1AKpw1FU7tkFWI8LD8AOTpOTBbLIXWX3aJR3Ee2Jn/V9xaz6PiLF37ASh0AnV0Oa980fFaXnCZfZcOrSXotjenCw+uPeYrhNBU3vs+xDzhyTlOOA9znTUyBxkOOZN+B3HcY0/bWMb03/fvy7LIgSzI7LkfIqatDLsex46HI/bBr4Jhj33vFwTmO0c5n9D2fBxsee0+3BzbU3nuWtx6f75FnewgspsdUrOyQC/Cy4xg4+jd9rimqvayfnNNnW2PTc5rW4+E+WDv2bMu5Tp9tccxTJ/hQWnx04pOo9d5nveTYf93EXpYAKz7gSMc3yMGVNveCrJgySICciWUMsMRmHm+jZXJtxWYeu/4xM1DsKeyCw+BH+/9ZfQLINN2kFGgDWtzx//orQpSPSbgoKR3PJhWb/rLnUf7s84AC+AIBg298BlAw/b6AA13SCOzW4nTxG5VbHn/xZ/j5P/O3+fprPyav8V0GBhGZljf1UJOFMEpeqRElopSM41UKlCFlxLhb8IakVV4cQFIjQj6Msveu9QDBHyLaspBHABADKkYg7q7l2EKeXotSJJUTgJMFPj2BdGj1H8ozT183BRsyYWo/1XYQKRQQMM1YjAAD7pU50sFrdzVLdR9YHEQYU/AwPY4Z5sPIo1zmMUe4J5Wal481u1bZh0DOpznwaQR2jPS0tw944BcvOybnMr2/ey85ZsgOl8W/5XMoa+bwHKYtxLuf7b/NMcBx6EynP3romcLuuU5NgskcHZd2QGF6bv+ma+vY8753f8u6KXj+IGI/tBnj68Y/zbYhJbEbUzvxkCM/sCXq8AEcOM/PY4PQMokmab2zn6YMsFJjMHFoM46d7stsp57e8xRQIU9HHQPDDA7iDhx8Vn9APm+0QSmRGNWhR2mDUYao5PzNJBA6PN/DZ7V3Xew4BZ8VFMAXCBi8DBQ8ZGuMQjIFaf+FYxoNePt3/lu+8Xe+xc//6b/Fn3j9xyH08tJhm6FckMXyeRa00ijKopDaclnoKi906es3o7N02owOozjMh651fFUZCZsXsmzmMBn/W0ogx87/yCeUkdGT6xh/PvFKI2gY//Rha58mr1PT95lkWMb3L+BhzJdNAYS6BwymRuCYYdgDE+l+NiaakrHZ3ffD49Oc8vjz8ar278W0/nrMqd37vMn7HdZni0E7luI9/PnRi5gee453/5z2uCaovZ8fXsdDH3UsjXzorO6d66ec5+G5Ts/nZc9d/m53TZ927w9P5aFnOn2/Q3BSyLeHYObw/Q8j+sOo9mXP/d4zPwQG448m+34auR+LgMcL2tk59ZDNK68dvz/IBozvUz5nkq0sPz92V5Se2CAjdkOL/Uzays+0xmSbmpQGY8Z7fRA77L37ns8YHX4J/uL9QKoEhTHcv47P5AsMKvuBZER2Svk+ZxAMWht0Bgia+5mDwyzB4RZ5CBQc8yHT4wsDDD4bKEiAGm9guTHTbPx0s7z9vX/AN37pW/z8T/0sf+L1H4PQo8IgLwzDzsGSwHtIkTSmwu6jRJUdvjImO7y8ILTdBwqTVNk0qzA63GOO9tgmni7eDApkcQuYGc85eDnvssiPHWO6K1+LsXJ+5VrK3Ts4v6njP/q2k/sz+ckeCBk/f/LeIzApGYtptuIYkNgDEdlZlO6Q0VnslzIOQQO8HBw8RKTauw72H18BHZ/leMjwH4sa9gx+MfaJ+8b5ZR9U7l/52ZjJUZN7yAjCDlO1DwTfn+u8gZfXah84b4Ckd+tg/9mrg/M+Dhw/67H/PHdXuw+UDvk5n3E9HXH+nz26P8KBKjZiPNnpzw8c/730ypGg4WWgoFzPYWZh4vRVSqQcuKRQhnJ8dltU7BBWAAGj3TQ7sDDJLtwLNg6PvezF7h7u7GbcAwK7LEHYv5ZP8QMojbIWst1P2mbQgfiZZEjJolIk6YRMopRSz8sATf5QQPH2u+/wjUw0/NpbX/vMJV74AgGD6XEfFOzFYpPXKe4vDVkE73zvH/CNX/4L/MJP/ixff/3HBRAED1EWr/KdPDQ/yIKOMf9eqobpgCKtdHH2Sr6WxaE12rp9oFAW9ujgdiUI2He2Rzfz+H0YQYBKaTzX5OVaUj7nKSjYmy0/nntOjuYNqbQmGbt3TUyuqVyv/MnLgcHLDpWnre0BD3nzDCgOMhfl80qYdSTtOBqGknpUGpejiWKujvEdpiWccrvLLZke91PO6t7rPvcd+ZRIb8oNUaNBPSgPwXFjv/c590HYHgArAHUSiZl8L6dOF47XRPdY5+UaUtgRtUpUdmiQpxHY5zn//P2951/WbAHg90pX90tSn/XYXyPHAEJ5zcQ6HVlLnwb+dHnHUg7cS20fef6HtqH8/dFIfXe2BUSk8FlonQ8c+T33nGW2OSk72WO2SF76gD2a2CKyLcJYsA5lLGpqU3N5duR7HQsyynt/mj0dfzaxqfn+7517uaa98y6A1ci5ej2eq+hfi71Tvpffj58tn5+0ZEeOea3Dhfr2u2/zjV/8Jt/56e/w9QwK1L1XPXx8IYHB/vEAulX6+O9S4p3f+Qf8zC//RX7hJ/8Lvv76H5mAgkEML8DQEYOHh5zssbMoCzmnvopjTcbugIKx6DEKPzDQ+bz3lsWR2pxKkRSCgIDgIXiiHyDG8f9TGCC/ZiwpPDBuMk3OWWphCoxBGbe/QafXla93d7/vhVX7x0P3bHpMAcr0/ycgaz+LoRjTjtru/j+/rmw04XQcpB73Ik1132E8cBxjBT/0/fHj+JocDdae88wGqkQtI9dlmhkSuDNGM9P3PTyO3LuxBqomZa9JVJbGe79/33ZZuANzNM1gTNOz03P+LNfxsjQt7F3HNAMnqecCDNWYet6LJHUBpNMS2ZGs1qc/wTFbMP35Z1lDD2Z+DtPah/dorP0f1LwP7l2a7PkEu/1X9mYJcF56oub+zx7a59OA6YgjHe1RWccvsUliWyb2xtidPTJW/l9rcbjGjj+TNbEDusWm3gtejvIddvcvhUl2OAeDR6/r6Hnvg5lkLCn4HUAAKVenhDKJlCIqufw15vV6bE3uzvvtd9/hG3/nz/Odn/45vv7W8SmMn3Z84YDB/WxBPg4Nyb3/F2P19rvvTEDBj8mGK6AgDKOBTkMvDrbvZDH4gTHiLnX9wyMvzLGmdOhQM8o9zCigNNoc2YRwfKOXxer9HhBIPgOZkraLOeMR04hupyh3jPqz2pky2bjmjZeUFoSuVCb9FAehduj3gUbyPTR9YJSORQnHjmOZjCk4KcYf60bQNRqHqcPTO2cxPp/M9diVczLafygFOV4YewZxP436gDM7BEp7qcxJnDmNACfO87CkNT7zvHbvGa2j9/LIvStGdVyb+efFmer8vEtd9zOmae+Bmkmpq/z/mI2bgFliEHXDz3stxo7Xo4yVtHMp3U2Awbgv83WqCak2PWSMj1ynOnjdXjlNqcMuzXv3aEwpT8H+y2rbafezvaAgxbzf0/F79ymR+fF7+wA45zPs9WlWMtvIlNJok0bbVa7hs9ol6w7KCg5l7T5QsHYXqR8JJI6e94Ft3QMyOdC69/NPsf8j8PSD2CXvBXDE3XpWYdijvUm078aof1fiu7/+BRR8i+/8+9/m65Py+u59PgXs5eMLBwz2j8P0GEej1WK83/6dt/mZ//ovCSh444/eyxSoFIl9J380dPcc7z3Ee3iEXSqpRN2jIzJWPuvAOBeDlI6h86lDzUCggJRxg/lJViOXEVJMRO9JIRH97lwfSn2hNdoalFG59GFkMxmLCl4caDG++bryG8hinJz7aIDKM8kb6N59GyPk3TmlyXNUSu8W+KSEMWY1rB2ByhSdK+tGsLBLOU4zCGb3DI6k06fO4qXHvXX2eUHBAyngQydQnnV5xodrwA/3o7DpfS3PeJoRKveu3B9jUa7a3TNX5SyLpGiV9rlOatiVvCb3aQpupvXaMTuwK3kx9Lt1WsD34Xr+PNdy5Fkra4+knXfZosMy3ph1mmTt9o4H10J2YJ/6usO1sJ/9G5/9lCw8Zo7uP+v7pcJw/96VtfaS/bW7jN11j9F6ubcw7vcxAv+U/T6uxxKcpHyeIRB9yGDw89imHmUU2todUDBWMrEZJEzXM3kNjKVdOJ79OLSvh8Bqcm/3rgsetv9TG5mSrMMelN0BibK/98CB0qiYPZpKY8J7Cjrffve7fOOXJqAgRXaZ8U/PVU6PLxQweDBb8LK/maRfBBT8LF9/48d3kczEaJUNBxyPxj8lBSbDzrRsBq0lOlCKZOKuxnSkXv+gfNsBYj0KBvKmi4Mnei8bb5BFHgsiD2nPKIz3RmnIYCB5LwjdGVKMKB3QNsgCN5k4k50vwe8ZjsSw974jQe3gfqWJ8Stp74eihr3zPJLZSNkQTEHLXhQxBQoPpBvHlPmUVDmtX+9u1PTijj+rz5ItOFbnLc7hMCNQHMKh8yxfw3R97gwuQDpQ0ymAbxqBqalhdVUGBDVUNZTUZwEIyUDM7Opp1H34nKbZjgcAAUNP6jvS0AlAGPqjwLZEWMeupayFPQA7ecZ7gGACeorzmGYUphyWvUzA9Jk/5PCP3IN7rz8GDPe4ABMgdVAmnN6TPSAwtUfF+X6GSHz/1Pf3lNzbfdA8lhiLwwOxZwf7XS5z4jRfAgh+t/ZJazXaJ20HtLVoZ8HnLIKxpAOQcJhlPHpMQNRDmYHPUv6Q99i3/bAfwZdVkfwATgkQUBqiyuVQZLR5mfSntKwHpXeg4M/uZwr27hefPWvwhQIGR49PyRYAk/LBz/L1N//oJE1borS4M8hTBDzJFBx1ckAeuTd+loogEcEDAEHpnFqapOrCbqPupeXyNaUYdpHhxIiS4j1AEAoi94GUpxS+TEtWa422lmAUSstEv4hkYKPPJCmlZdFGAD8axJdmOY4AgmNGP6Vs/CeptmNjdiHfI63F0Sn9qVGEMlK6GcFBdhQlLamU3jmJI1yP/WPqHD5l2x0zcuVne+Dgfl1zmhkQx9ntwMAk2o4+EIdscL3fM7LyMTkzUaI8Z8S4WosyW0zlxLFWtWTHXI0KXv75AVwFMe6iMJ0b/lJiJHiV+zS9tsMSwnjuHWno8z/5ngIO+o7oA6EfZE2U6xnC0WvZcxbWoq1BOyPXU0BPBjsCfOp8Hbv1UZzHPb6PfJB87jHH/xAY2L1guhCOrovD2va97qEpGCglzGmp8IHs4L/pXlI67ICCdZByVi3bMo5xi8pxzD4+AAqKjSLIucc4saWHh9ZorYlGo6xB+YCxhugC2vsdQMhrN/n8fEsqvwRjk/P+VFv7KYBgD8RM7P//j71/e7otOe4DsV9m1d5fQ35w+GHs4aVb4Ue/2Z4RRZHobobmQgoQSPiNQKNB0pQoKiiHJ0KecNDzMhHz5It848iShkOJikaDkJ9IgAQ4nvEMzmkAJGcUfvIfYHQ3qAn7wbewifPtVZV+yMyqrFq19re/0z0exidWxDl7f3uvvVZdM3/5y6wsqlCB6V8X6rY8cwDum8ke2gNFKSAkM65UX4yg4PW2BtucenBO7ssTBgYL4TzQmlqevf8ePv+1n8eX/+o/wps/9JfQI09rt9Y8+KfW9vuItCUK8xkUxFdmDSKJA+WTgZMmzkg+lra4WtBO8Fn5BLVnrqyDlXIoWxkXW62qiOdDywFQEpAlWSKpoMKNHZYiEKezdj+8gr5jmw9AwezikFpbvbXpdVdf7xlKvLcicjaAMFkRBhIoBP/I5b4riBA5HP2RPGzPBB5L0fWycmlZG4NvM/qJozJo7IAzBmVDvb9H3TaUF1sbb9kKymUbxjpm2XNrkE/KUqVTBuWE+uICvjshbQXp7gyqtQvF0PIm2LL9zWjWjK63QMc62LkGCu7/RON2LveQ7R7lxT3K/QX1xWXXnurAYGoPJVNeoT0pK2OU7mwelK0xRdguak0GBqnF+zhImPzRbfvwOIIfeS4cBQi2ILdgoBwxRIMxEIFUsL5Xax6AKtgAsFJODSQoA1PBWfuM8qkbBK7wrq19ZlOK9mxi+OqVIiP7Y/W8RU5VrkBlcNG6bbUiuWFRtN2cTekD2rce9W8Mgvc9iLCLt4hxGCtQMFy6ADEOmqT2vgJDRDpLUCuIwm+bYWAyQKqxBSPoevb+e3jraz/XQQFwwGA9zp3wZIDBzU0O4MA79cuf+U0DBTOte0uYhtNnMKXolrMJyeG6hWtg9tfNZUB/U6DQ7DecQIEExVv1dKbDxebCdc6170fYIlG3JhJ1+jlN+4mnnQtDKaWzCL5AwmJviNnBQgAFM5hpv7HfU3LqkyYrQi0IuZS9FeGKwZVEPpmA9cjmyxjrwT0Kv43pQyDoxiLVosOjYJkCnXauq4VFPSjQF/eol2KAq3QmZlakzKB7FfyyFVBOkFMG1y5Y8ydc8SeAXjRl0faTi55doddkawfHBiKCgibwPLDQ2A9tl4KC7U++j/Ji03bdX1AuGySC3WvtMUVWX1zApwS5O4OyKla+OyGZsqDzXbDK7NVZNOauLKydsqErko9x7Nt7BwLztrc4/hFUHQECo+N9LkR20PtrXjvef75+GBkFBclBn5Baq7UOcQRxh8Dx2o85Lmx/PjZQSqAqoGSKryprURIBm8qfGvpH+6vLKm0Dg1AHI6ayKDgQglRq7CYAEHIT0rKhuRekFuyCKw9YZvI5AQyG302y3+fPQzEnvcWBSRQbjIpnH/4BPv+1n78BFCzqj+s688kAAy+7xkYqs31myR+++ramOX71xxvFebUwN/OUmJXuJwaSW08bGjKQ2gPV5vr4vYJfrkWswtiC1QAbqCFODRw0ULIos/91VSgxpNTgR+RBOJBRdG5xrWhZBPodYXdCrDMAINl+3QBovN9cQKx8nhGFr6xeqbUdXKMW42hFoAjolJobpFsRBcjVrOHaLQj3gcdYjxit7+0C1u6SW4rFmuxKiBSfA0obZXz/otPF9y9UcW5FQcELVQ7l/tIUaN027betoC6sLrYxrtsG3jLSOVsTWVNzJ0K9bGBidSPkrPVJGWSKaylknMY8iK9osTHV21ebwlMlZxbyRVmC+uKCcr89rj05m3tKkM4nxNOKpFa1v84ua8vwnvJJ25bNuixAiwHCJC6OxvOWstgquNr+di2g2NlBkdrp+EtZsoMNGNRqcsxkCWvQKCFYuIs9FDuDIMTwDGsf6Os/rH3fJk0Wv8HEDdBVYtSt2CF31rXIqFVllNY9sFbpYXAm5YDdxKicd6Bgeq9yF0DWOU1u6HDqxlni67Lf3QpBVuqOmMC0XgOcVp9nH34Hn//dX8CXP/OP8cZrrx8Dgqnhf7YrIZapc55995ttS8cbr77efDfjbyZMFSLTAahQ9O9ELNgtmVUcut4VzHDrMXJ/3nanH68nhwuJNkkjKBH191E+tWlZL7DMWRX1lIALkE65WxAILEGIK3BAQMwKCgwMuLVN+dwju48CevyeIYDHrSFKJ7N8005AALb0LwCxACQacMPchWjsj/AaOM3BikAlJQYA4ARIJUjlZrm2SGCzIKTYNiJkYzwrpDLA3mfWPgtG1UffCBKIdu0YLJOVYoh+ZP+umIIs6m+v1RihbdPxdVBwUeqzGHswF6lGUGa1ButWQIlReQOdEqhMgK2UQ8kxbs0L8RgGDvScjQMAHrZ4tch0dy9tkSUQcyk80B7qSamICXXbtF1VQJcCIYZUOwCHFNDLZgnMkoEChn7GtQNDH/MojGfl/lBZ7XOPrkcJ83q1syBQzC0uJ4IC+3sFCgaGwsDBUFx5kQekclv/6XwaDYK4NXDeNuz3cjkTA/gCyKHtYoG/9yDeQEnBKDGjmO+dAutBOQ1Wur5wcyV6vIwHSUZDBo3pWhgysf6LIlVjahp4j2AHubsY5nGOWzqDEejPbO6rsPtj1Fk0zLVnH3wbn//6Lyoo+PNvTkDm42Gx/vkABqH05A/v4s1XP9kXsu1VJuEuvIjb9iVKSjWKJaGgbPtKXej5hE/SrY+pHO0B3i0k/2wqOjExBrvUAqGt0bxE3ARXo858oV2KWoAmHHjFKCwCtyjRWiB4tHrYArTbWeHFAUHcWlkyKG2QnFVAXF6oX9jaX61Pirdl21CzCflLtyfGPj5whaAHVh2WWk3wF7UKAAzbhhxzIAjxuY2LshQ0RyDi2n5y4pEheoQQiJZ1ZJI8ir+WikQEYQKu77RXweYC1F5jNknPCzBG3wfmiJMqZOaWy0NjOhS4XiuqKOtt7cm3ArVApa92ncQSBf1KCazq+1CZr3EFOt/TgdMD92Sm5qGbqXhnBWKZ40w84JhOyhTy+dSV61kDNXG62+UKePT6l77V0uN7Ut7AxoKU+wvY4o08ToItTmJXlrsTUnMp7WRX2Ma62p2wXLMRuNk4IY5TRo8Tekj+L3LY+HpxRkYo7opRUP3swz/A577+19T9/drrLyULbin/HAEDzx3taSLNJ4MK8Sz31CM9wbbPlJJaEsRNAQIATmedXDbRFQz0iXKoguaJt2AL4nVDCxx8APpc8z37tiuUzWjegMKzLjLOGTVrYB97dDKwQ95zRL/74tt2r/NdFwan87jdj3uSJu/zXsYIe5IKsuA58oC6nIH7F1rvyz2IdX8ybQWSC8qWwEaPRktkFUwXBVwMoorMB9kZCs5YqIWx8I/GMkf5XhPSU3TzIGwWTJL+pgdJCdCmo1TLOpgqICdjuZRh4VOCSAVXnadOnzs9K0nAIsafjEWvIXBiIAUL0YL4mMn6K4WtfkHIpqw7FDhrbIFnZgvZA8esjSES+4TAIGUgnSBZo8f9mbJtGisSYgce3x5rS9adJsyk8+CUmtsMzBpjkvqhNsukT0flgMnq39/oZjhwuwDQupWicS5VjRIxlo2zinJn2lJ2zwfb47u78KH14oBgYAnPZ93W6TIg97U/b/ccsmf2hunzYnKgtlMgBtFeVAaUDXQeYyeGnRWxTw9kV9x6u2I3mzyfFPMO1HhfDQGFyhqQv8fI+Fw1QXw++bNCEjYAIbcKdZDNCc++94f43Dd+SQPlX3tjytCKqb97n79MecLAYOykeKDEsM/TzEABg0i3D5IJWDJwoIccASLdleBUesz/rZPkAQEQ6SKMbEHMwDUfXbyKWCfbMoaygU7nFrFMaYOczs0PSbZbgcNeZq3yOHFidkNPK3oNDPj7mBxGYnKYWMT2Ip+mADQHB5cXkO0Oks8QyxWeTmfw5V6j7fM2AgSzHA73NwPLqOpm+cT97cTdiphSPbcxCmP2kcvKlzmXlNWN4j58Yh1b787QTsaLw9u4Mq1MGodR6o7uJDJFaQxRustId2e1FO9OSHcnDdI7qXKg09n+vgOf7yCcIXxqvuaetpVbXYEKcNaxJ00zTQD4fGfuHmXkNGZB50Yy1wjv5qm157KnbnUMu7L0MdftlxnpLmubwnZMyud1trwICB4K6pp2Eb10aXIBoe/QjBAHVe6ubNsKy4bZT5+Nhkeixg4+lA/gJkBgbAFOd3sZwFnPy7gmA/IiSVfMx3HuIEHuXyCdN9tlMOZiGLvNY6QW8stBQch+2dgNv+5g58nwjDDXYnrxBvxXOxJW5UAHtOfa+tFX7ddvfu8P8bnf/xv48qf/oTIFAyg4mpu3Bjfuy5MCBkfdcO2UqZa8xk0zMpaAAQuRNSvHbDgxAXC662BgQmbDBNoJFPu7+V/tDsR9Z8OitIj1rBOwCVJfKMEKX2bA82AZVy7Bjzm4MRbZ4nz7Fnzvtye2SScFAtyBQTusJrStU661++Q8mCoX0OkCbK8A2z3ocqcJbvK57c1PpxdKL1r0fTI/uO/J1q6ZrHK+ks/gWtDU7OsLlsORe+daGX6zYoeugQ6jKv1gJKp554LBPQeGhVHMpVPuL6DEKHkDbwX10uMH5v5yq5pzUqV5yuDzCfkTZ1Wg57MqhPMd+O4ToLtPAOdXQHevqCJId7b986TzwsHunMhHqq2tYkDHBONdZxEYQLV281m3nQFAi+RgBr24gJjUBVXrbuwbhXxKGjhr7XEwkO5Oe4X3ECV+ND7ASCcHxqdfVq8GlF2bV8116Nc4M1hLZwf9s7Ih5QrO49bfxhA+sFYG6j26DEKCqwYQDUw1GeCg8LEyoFbgVEDnCyAFFBNdbRfglb49lyxXSzs0LI7BQWDfztU55alYnZ9wJIebVA9yGAjyvoGul9MHLcttOunnNgeffe8P8bn/4JfxW5/6h3qK8AQKjniBa3DhIS7hSQGDVYmg4PWDoycdHAiJsgYea2CIjWpBi651yz2dD5+57PQhKIum12kRYQzimo8tlba96gCBl9IEhZ/1MGx5AvaWzcrnNWWLU4vg1JC258gXBwZuKS5RbLAIva6+n71cgLyB6gWomwoHT3DT9rd/H3J+RQGC52uw3AfAqKCHbG0OBIh3uyeGgKloIVp/3LL1h/ihJYZ+nwgGKDxrwRi1XouWSdlAp9roVyrbnmE5K8PCOaGeT8ie10DqkM9gbIPS6U2Bml85f0LBAJ1fAd19ooECeuXPAac7SL5TF0I+A+zJofohS5ipZKl9vCkBfNG+qZuuQVfsgMaXMCMxg/MLEDFqTgpyTvmmthAx0l1usTJLQBApcZ/zPkduHBfNPVIH91ZUiEOU/rX5ET+afzK5LMnWMmXb0louHfgbi8i+7Xe7WFVlt068z2bATCnruBtD5AChu41OHQz62rdxX48/0OaABNawVkAKUG3bai4mB4oCAWc8PZnTtFNjKA7K4lpOnRmYz0rZMZwuu26Vw9qpfXzia/juZkK/GWhdjn7zj/8In/v9X8Zvffo38MZrn2zgJYKCtU4LbNOtzw/lSQODGRQAx4NEngIVvVNJCBCNXkZDhzYRTnftt8sMaO3GC0CwyLd/eEhLrHNEqJ75qu0Jd9q5dLres8xJPx6UYsBMLEFZxSNLpYEBHoVAOrVc+RUaD1TbuhfIjJoBECUkSmqEQac1lYsKtbqpwigbkO4nFuEesv250Q8ZT4YMfTP04cyAxChgtyAOLMNre9QHluAh+vAKbTjEYkSLIQqj1AVPS7a19b3/ZEyKnD+hWRAv90jnC/gV7avy4h7pFY3oP1XLCzG1wSlkd7dEBeqgoP07v6KgIJ2BfFZg4vMhneDHU9dZYhGBKYEdkJVLS6VL2726FqgDOLYEVP7vlE8KeF5cUM+n1paVoott4VNCujMg4C6D07lnOzzfTQA4t/Hw9VlXyqHlatBG8pyYKOVHz43YhlUZKOvAJC23tbpiPbKw43NnsBxdhhM7gHRugGA57lVTk1yTAbrcsupsQGWUAUaqugODygU4VbQTbaVoH7+kHHNqXtrhWakrWWc46MphWTiQw+2zwIgMf099PhVafCdZjc5n5j74rU//QwUFEYAFUDDrtKa/Qr0fCw6eDDCYG36NKYhGRgxUJ9sW0mMKBBAaTrnzn0p+ZT2JZvQfvuvpdFeTb+WUWLdRIvKWnpGunWMfqLqWJfHUXQ96k/Ck6M7wSNjIAnC246EVGFRo1HOpQBUxUCAaR2biYA7YJgISCCAgEcBMyERIfEZKZ7CoMNB4CAMJ231nEbZNLePZVbKyHIA9A+K+4gOf4i7t7aq4QAiBVMBkPS4HbvZbOuof0f9gubSx9jfS6fi8jtPw0z49VbJc7pHvjs8Z0KoF+t3jLnIABafz6DpIZyApIPBXSac+J3wuLKYYA2AiJCIdcwcIdsIlttRdO2bBSz5Bzq9A7r8POt83wOOpdLXb926R5Q6aCQys/ONH49CHIYxDOMtCRGwcNTCyzQtjFFZzAgD2/uxjxdRqY88ejqA2S9rZgqNcCLsS1slwuFQ+K0hydsDGGexg7YRKCUV0s8QmurW0mKYqkKUMUHcRqXghUllgBkM6n8EwZqQYIKgmD5rboaJlp8ViksHlGHcQYMC/n6qahiO2V/76m+XwMCa9PldPVY2uhRXI4BOeffhtDTT8tLsPKMTujKBg1mejq9zrOurI5+8/v9LCJwQMYjkCBQPraD1VZQIHpKk1hLOlRjY1EDMQAqinVw4n0/y6fPb0WWeijqekJ+MgIrAlMGA2QAybLjNdH9kF8fO9x4k65P9v6Do1WliMFdgqUDfBJoKtiioAAbYqLf8LRJtXJoo3MYFITDkoOEhMyEz6SgkpJc1QWjcVDHmD2HHXyFu3LKSAQv547czQpmCJLZV/oxGNt1i4cnalKQNvZIjR4Dwt/j2JtwQCLrTm70Lg3s0+2rttB6AOD1haWVrTFq7mTz6/ogGG6aSxBFlfxSzISgml6hzYFgDRS1cIAiZCrtCxT2cwJ1DJAJ9AJUO2E4hVgdfzK6D770PuX2kHK/m5ILwt9ja69TvHyPjpkIPCy4+PkQmxPf6+HxAU5kUDCsdzYscSRfp9jtGYy0BlhzpZPebUysvAuOiTJ7b0wH3Nd1fBqYEB4YxN1CjYRFCqyoJSpbGGDgqXMgAASPNMEOkcYFKQkJmQCWDOyKesrKKv+Vo6QyIlKNSFLItryFmAyc0hQbk6u1W7/XezHG5d6egv4LvR6Fy/+thJYwSBb/6zP8LnfUviq5+0eeLgJa1BQdBnwAgQZnDg+vE1vHbYxicHDFbuA2DqQIzvKxbggAQihJbb0ibBsw++DQAonOFJuHwh+GSaSCX9bAAB43WH9dsVaaPMhr6JaLDGiBKYU/ue/XeRYVjdeZp4kRZs1oALAxFjC1whVGxFFYNU/T4uLDIrkVjzqGUmnBIjM5BI9NVAwokJyQRDOkGVnTMJYv7pxoD03P1RQESg4xHSMvkQl0pYKzsNWLAEVgphBgzACBKacpgs0UkRDQF7gyAIdQtWibuK2smfkX519iWeWhhP4IuBp7GeIbYk+pOrBxfmM8RYAgcEl+KKQa1EB4k1ri34nBWQAYINhMRiY2775dMJtGmiG2x+hsUJuHulx51MvuZlG9y37KzBIljW2S9XhKNFhn2/u+CWALBrRXTnNYYuMgrX5oP/fXVuAsdAxXrYn3c4N/scXa0TCQYBnBmk1MAAOKNADYOyCS4BDGzGEm1VcClVP8fDciATkBMhExs4ECQGEjmT6KyiuR9P7n6ErftbZJn+wkeiuvKvI6slBrJuk8GAbms/UPwBLERcx4vrVG6rzGXu13z+az/fku+J32iKKYiApje8PyQavJEtiPrxV//Jrx428UkBg6OYgnnAZ3uJZQEObMB8EgCEb9r9AeB+swXgVlIYJJ+u8/xagYMVeLgCVoe4RUafYG6ROVW/AgzwNs6GiKN88w0WoNGCkRWw4xbwohRsRUwpqDDYzErcbFtUnRrBRMiJwCBkNnCQE+4cJCQgkyqK9o+AzFmD4hwkNApdmoDQPlsI39lqWCneeP3QJ0EJHymEBVjYDWB8xuTT9KDNbskEUBasmF5sDAH1w/sccPq1BXRte6Bg37EHpy6U6hCY5TkJ3FJM524xVuj4GyC41KrKwXTRVqpSydOQJBByYlyqIJMgVeDEjI0FJyZkTsinT1iehDOo3APlZEFpm7Eij2vDDATcR9tep5iIlXKI7NygnKLLrlYgBZDwmPkQAOohQHzkPO2uRelzFVfWidPrYewrzDDYFPRvBgicHVIZUPGiCi5b2cmBlQwAsJMDmRmnpPLhLiUkEvDEJrj7UZkGi1O4IstarOsEANzVEV2eMrm+bpW/rV3tO4JPnJ1HuV3rN5DWNnWzoiVg+9Jnv2xb6kebfwUKdrrMLwzgwGv13vsHW/YX5ckAg9WWxGF8D0CBfzaDg46yaLj/b37mXXzmf/+T+H7pVLoMk28PCHbPlOEFQKfdZgqrhKtSWAVESsEDlmSDAIZOtmug4Sj5n0809w0Wo4QLYGxAxWVTofCiKkNQRHC/1SYsKoxGllEoMJH+Y5hAIJwTI28VZwMGd4lxyl0wJAZOTDuK0YOWVCYshO9OiK5RdhMeB0KAKOkiM4UQ2RcZLMTaBz4I4OlmOKY1Q/BmVUAm9vfSklkwRuqnzUicVZcDGAO6KsTAQaO9mxSsra+6YvDgrG41CiUFBNsICNxidGvR/x1Zi5lrA36nxNhqRWagMCM1gKCMEeXzwBaJ0clyUP/mAmvWv+2YCYGyCnqtv7dez1v7maACnM31xdbXc1zPo+ZD9HVbYFm0ciNw35fUprwnNWx1bs0IfQUs14pT680dEMBAlT7mbhhcNsGLUnEpFfdVgcC9zYHHyIHMhHNmnRuJcGfz45SNTUiEBNGUBE2J3ibLjpS/uzvc7Qmo/I3z9Ujuarfp3ylUoPXzVKcZyhGkk4Z2r+ZKsUf++KtvaG6P0eHQXo9AARAYujCHHRR84UZQADwhYHCUpyBaXEfUPtEeHHTsp6DgC7/9Fv7xT7+LT/+3flI//7f/K//FNOTPyp+VPyt/Vv6s/HNZBMD/69/U966H4nczKFiRUdF9VwX41gfP8fZvv4UvBf14gKlaeTLAwEGBlxVb0P5c/N2on4k5eP6+duo7P/0ufuzVh5HWn5U/K39W/qz8Wfmz8rKl6SfC3siVxXXhb5qQxHsfPMcXf+ctvDPpx4fKkwEGby6SFy0OXrtaItICAij47Lv45KtvoArw//h//3/xX/s7fw7/7N/4/6DKnqp6bJn9Wl7l4V7X2uE0Ipzy7DQVsHAnhN/M96/orpFibKjHUXhcwSYY/Ik9xkAsuKdTiV6cOmSjzXocARtlSJMrAYOPMZtLIQWfHId2OIiLVHBE1I0qDn/P3epdQtR9fo2mb75laT78Fgw5BZ6NwYh+01XQ4aknhjKqvsJ2fkwR/u7eWc2xgY6ctoEl67/k/RUivHc+aL/bTG9Pu1J6FHoPNpwDUaXCKPrQBWRUMCMEnAHJ/MwJ1N+z+p/nqPTH1HnOr9Gj5vuuifpAnzpt7X2azc891M2CYfUck8tLzoXgtpljH2p3K/lavGX+dnlA3dUQLjxaMx4z5X3l63qOMXJXwiZiLsaPJgdi3JEHJt6lFNZij53yuT40HL0dwN6NAMT1D8QZdIt8bf039PX4+piyc/Way/Rf+F/+Ofw/uw9oWW7VM++9/xxvf/UtvPMzjwMFwBMCBjK9Pvr3E9pyUPCl2T1hK+ouESooTLx9aspb50sDA1MjVkGMK+rInxUXy7xrAdhP7LkOsT1xP3KtghMnbFmfv2UeA5FMKDiYqPYbL8wabOSLuwuD7mvO3JWwvqd2rSu6FljZ2jD2eLKGCs0xBdRcRQId6NVYRaHqz2oKdVYArqw8INKCIds2sXjvaaskzP9NFhRHth2M0gmJMyrrdr6tag6gVK0tlbCKw45Bpx6gxTMoiIGbq+BJnwdks4iTJSDSVNFat4TChFMlAwGEE8ugNETSsCOh19FeqYOECPqyv+cQIxH73LdpAvvIerLDr8Dw7bh61Hjuk531rIhCZLEpZBkW92kGHGARen8qYAl92XJu2HbaOgZ5AnJ9LrRdABbkyQkoSefC1OcJQBH1QatyowZ25zkM7OfxPuYA8LWTgpbzNVOEcBJ7tfHNTCgGFDIllAR8wnci5I9HDmTW4FQiIA/zuc/vGfjMpRtWJguiYRAs7nk77TW56nMifsDjnw+WW8bKgzM5jNnqt3N9VyWCgtcnJp0O7hnLkwEGsbwMOIiT4b33lX5xUNAWFfUJcUoULNNxejTWIaLNawhwBwgikzBO4BVaPNoK076j/USO1YmPj4q0VkExsOCW1iaCkrgFJopZY7OlOKdS6ah/FACqxEarsSU/oslaMGzR2wABAABJREFU1zDjMfraSww6dEVBGtQ0AgVaT5CdAN2zA66oEP5uCYbC/vqWX8BvHQ/imbbQwbaDUdgnntIJnE5IbEmDiLT/GT2B3Vj1gel4EBS4AnOLtlnh6NutggUOTpphkROyMRyZyVgNsnmCIcHNYwFMJu3zDgZKZ2RikCfQX4naOHvAIdVR+M3gwEO1yRQGh5XQLmMKyZhggMDqZtn5yLN0+vt5a6hokOo8D2SaB2wJliSddOdQuoCsj6lskJQ16JYVUDx+Httsmbdcxn5sfam7IJh04WWo8XOqZLtO+rZlZYuAu5SaHNh8u+INMsCZI81v0uWAb1d0lmaY01P74phZK4fX7o8fQYJeo59dk6d+/563YC/bb5LroVKHzwv3SaF9R4qc6BgcvPf+c/zcV9X9vQIFt5QnCQweKkedyuhIK0ZvribhiaYPFpG/7ZjO1UKcKwSgR4d7aPEYWR+esr7Nrp5R8OOqUNg/PwEcoqNNOMyZDpXmJhShpl8Gmg4jcNlRggYU3E3QBTFM+fZo7x2NPEdXI9C0cS8zJ5DtA07UGYXYb1GAuiIa3AWmCDwTIyyRkPhhL76/3hTCvFcvpmOWOeHO6Q5SVRGQn9hZCzidwJybxejR1N0q6v3rAiwyK3tQsHVF2+ju2ufJMJn0ruT96DsVPIVxyji1rX++5Y9akphVgphmHfk/BABWJvfMI+roY00iEK7QOPY1OGAHBRAI0dCHPheWwMoYAiovRkBwebFMKHXzPBjSDmegaqZBqkXBgRQQKUCgkKAnAt79PN7P5eUWRmC3hii4ZogT2J55ahlPRzkQE51J4sYWzHN0nKcBINDorolyYN4N1NkjPCjHhCzlkMuAtsLbSj8soxyV1pcNWAEBoT9Cpk+fHanpeSfD6vsVwCA6BgWPLU8KGDzEFMQOjeDAx8tBwZdCoMa8Ja6dqFXux8m52jcck4o0a2cxpHG7lVO5q+xd07X7DhifEVM5tzoN30+UrN2fwnNdkAlnnJjQfbddCfheYd9qt4rtiIzFbJHtfPjbdMDKnCtgXozRaiTuysz2ZrdDsExgENF+SfrYOiiILoNIGW8XVQSX+yH9cMsqGDILtuxsFPane3peyy6I8x2w3YPvPqH77uvZhPmdKYesAIESKil70AJkp/nLmITpzBREhesZJFu2PGBhl4zWeHOFOO3dEwRxTC3LbTTjnTAI2FifGQw4FR9ZjSMytj23n2qooICX4EAI6pVw2jb04ZELydN00/bCzqe4B9UN9cWf9Hlw8blgabulArUOcwCAbZ8MRwGf7kD3L3QeGECgXCBy0j5Kmo5cQY8d6GZtvjqPd2CghL8fXktEFNw0uu0zcQJzxinFGIgOFKIMAI7lwCwDHCzGfoeIZTf1JF4HctWbHeSXNqNDiig/6aXk50eT5+25szw/ylFhR5P77xzMMKFts/QmRB12Kyi4hTl4UsDgsAQuZgYHXr4dfDJvBvfBgBSDBUCXF9j5aY1Gnq2cXcKTZdgQdgtysIYGqtzE7CLhyS6H+EzBruogtS8Yr1N87hQkpTkUPBGKKWAoGh+AwdTMHcUZBcCK5j7qy1DmdM7k5ggnCBVVXtEKiotxuJFbWJOvOLIE4dTH+uL7HRC0w52OzyOIOfxxOqsFeL4HXV6A7j6Bul30kKJTVaUgYgfx3Gnd2DIDmqXIGAXvqm/biZutTRMoaOBAwgl8kwvE+pb8cKGmlPpBNGRzgSbBN/fyLGhbH7exjkBF9LAoG+/lqYD5BKliigttYZPNi+ZWIIZIAds8JQ795/hi6L/g0ogsQblvLEF98SfKFr34E2WO7vUaZ5AenAN+jsN2gWRli9hcDyS1GSQiVUGEVEAsN4Ov12vzOPb1MkB2nTUU1ncgBkU5sMgPwTbupyMZsBBzhzKgTInLDoN6R7nVgEB8yKBwDej4e2/fbm6ORl5//5Asx7qhkbGcZLkf7LZK3wxA55jLXFR4crpYfLpHo3YHCh7S/lfKkwEGD7EFMzgAOkAYQMGfj+4D6QJsQK7AEIAWFtogbO0EQDHroVlmK5QOIJ7wd3jAz4xCQ22HnjhEt7I//GfuqsUzXfHGurXjVf18hSMUPIGVwQKYBUBUENavR/WllEwJWF2j9Vir1lPqYPl4Jst4LvpSYbmgmkCBuEK4vIC4xVg21Pt71G1D3QrEshUNBxW1M+8v4HwPPp9VkZw2PXSnOrCsoLtudWktK0Ts7I50alYEE6br0BRKyy/vfVhNyQZQQNUOIyr+3ThHJc7Lrc9LXpxaR8NcoQlo+tT0Me3C9ugU0FV9mmC3dSK12lG6NqZBWlLdIAyQkCZGsu/dpUQI03TuuxbnEGIJyj2o3ENefB+4/76yBPffN9ZAD6x63BzIbQ7gdLZRRqPKCQBOQWyJ9DbUih2T6PPY+yoybZ6ZUeoOfK3WFtnaauukre8OCHcGg9eH/cyASQZE6j0Cl0Fu1n09/Rpr3zWZ1errc9HqsWMTbpGXQVbGMydeSpa3454NQJncpJiG2mUpVD4qoK0GxAQWMjuwBj7dXwYUPKQvnwww8HK1wTRexBijN9/884EpmBBipDwBgLb7neWFbWt0svgJZ/MpgL4Q/T7zQrLTzsSstBasdAAagFHJAdgvdn9+tL4iyPHiNOVUn6OjTDvq9YlOHUTEewAjOInKYQECHFA1OtbrFovXc1h8ZkGwLahiY8cJoAqhinZmu0TFVVv7uyAN471tjSnYgYLtHuXFPcr9BXIp/SjgIiONnAh+vHE9FaRakYyhMDFv15IqkD5N94FIbp27Vdc7OfRvnd4b+EA4X8H7uXQg1CzzaZ+ZB1AKkfZF6HdeAUlgsMz2FpnsxnqYl16X1aE/4aTMNoOJlCFIrL8hntpPDTlEcDD3W4zHmEEBfA5EUGCMwUvPgbvzMNbePmc6NCqTO+DxfmX90dV57EA7umbsu53RYr8LPdLWypGCmw2GxjB+1PVv4HBWwi1uxeRULBJlpHZil5OYgUPo6ygro5y057Zg4tVprtdkOYfTXV2Oz8d7O0io5pbz30vRuczJTu4c13oEB9+eQcENgGBSg8vy5IBBLHPEaJ0k7dGWjvazRi9PflAg+G97IFo8ErgDhItOqojagbYYZT6TPZyN7ouxTbKUh8hmiQs3FAn3HyxxALujWP2aZQeaEm0MwfHxxQ0wRCptVQ6QeAMB85Gx7TehEA/KCkQQqZDiJ+vZ/TkNfiPXFSrEwj2jO8jHyRQoSVWL2oVDPJCobKiXDfVSIJeC7f4C2QqkVNSFUksnDSxM4fNk/dsOebI2kft2aWsCePCbcwZQx36Obi9xa9DB7fiPRLRdERRE4RfnBTsFz7u+F5+HEbTaGO1kVASm14DAQR1QrK8qg4LkEkDBCXx9kbWRILBX8UN3+LjfpniMvh21g8MGDO+/3wIPHRTUFxcFBVtBuWy78WdmUFKmIwOoxCDewN5vm1HImwWhlg2ckvaNAx7g9nkcGbhgvDSGyIIk574fSpBLcNnzskeXBzfmEgAUPePjo8ioNjeBwahqRsSuSns5Ka0uBg68vxzEznJpJct9HWfdhuyAgLIeW+2Bx+QnfcJZS6uLDa2QAVpzwURl/t4Ha/0F7HXfY8uTBgZziZ31/P3nePt3eqdG3xeAvsAaupXuOgDGLUpuSWzbPgjJUbmdIS9+JsLkMyWrHGXbx0xsgUdsW9l0X7PMaDQshFgGy2/BFLSI6VIwRJDHBeiLjkiBilZW0a2BlB1gcOTe7hHqFhZhRP/Lc+O9jotCARiRCSn3yTdEnKEKgbhbGKIhU/rkFdU9+S/j++BXlEb910Ydl62ohVj0M6n6vtU5CUqt4DIuOWIG072OZTKaNp/0ZMGcATG3CBVlkVZKbteOxWf+PG+Ds0k2pxsocGWBbgnBj7YOFpEczMEmjL1wGufUNZC6YNR6vf1zi+jfNgUHRDrupQCZtV8WPtnWL7N7y+o0gKkWj+FAqjMrYiyRg4R62QZQUF9cDse/cgVDdxeUrXTXwikrKDZ3wTzfet1Tq283+8Y5O8is5i6oDRREhshPqXQw/tCaW8medmS1zQusjJUIkMJzduNvwKC1u4xyopUgX1w2iculODe14iPTNJVD2VMuIxAI7EqPx5GdHNdXUobCjrNuQCAEHZMBD5KzuoxEIL68ajG2TdrcVNagn9rwrQ/2+uvjLE8eGKz669n7Y/Kiq6VRoG7VOo00gQK3Ju6/r5PJfM+yXVC3olalVPU9AnuqNqlfltLF/LhpmFx67O2mCyGfBiXcrLld1UfayxfdEYMhBwql0Zou/CMidtAQFuaA4ld1Apb1QgmU4UpARarOgVItqihgY21xBeT3darTx/BIafh9G6vE2O/Evl5ElClwpSBDWHYFwKi1graCygwqgrptqhi8/bVAtosKEVMOMimxRo3PltnsGnqwwnX60wHkKJzb97gMABEBBOyEMbBWDjNItXofPROACv5SG4OmsQWTxf+odvd4heEerlTDZ2RxGuKgqRatn3RgKEX398tWbht/0S2VjypxrFdBh3Nb4vvQBriRMm+xdUW4UsRAcy0Q2W6Tec3b91rVhy3znWHyCHmkXWAMhveNsRmU825uNvfsNK2WdfJcJAujrpqLSMxF5HEkrTgwMFnOp6Ry3IJLpRYLKBZAugtJ40nOoEp9zFxWScWcgmtOvjf0y6KvHikVADxRYHBt2RxlNLy+1FyoCbybSUTpYncdbBd1Gzgo2MyaeKHAALV23yOgaeK8mO+RWV/plFAvxSZW0UVo1oD4xDKAEC2pXYnW10yFOfp1Gs/9jlfySDckrH80OtFfG5UWBQjQlUlUMlEAHVBzuxKfY79XcFBBVNu9hGobz8GCnBWpCw4RvS8AIoFudFdqX4hUGW0XRD+qBCuEEoFZ2QlmRuUKKdpf1/rzaql2tDDn3jc3ARsxrjn0NTFIefj+ftUf3hdeZjYJ0J0CUEtNUm33UdcQRiUx46qVb3ZlHc7NKqXPodU1oR19Rw/20ecr3/eqSFBQMHblaE7eUBobyKTuBLK1busegBoHMUbH551/hv5dbOMwh2M7W8BeaIdbx75Oam2gYGcsHLR3dn22o8Nh1jvQgQOwX/PNFbuWQQBukkMOTODsqrC1qRjDWjtwKdvD8jEkJ2sxYgEQNMPuojK8OlswyXGtn8WRbAWcE9KdgE+1uwLKpY2lJrPKYAmnO7axVDAZy/P39UC/L322uw8eIgsWoZYPlicDDK4peP/MO/XdR3Sqlr312M6DdyvcAEL0PTsokG3rVLPU7oMGwMzABvU9koICrtW2h/WnayT61hlED0ypRq3PVtAKFAQKvC3I4OKI26ukHAjpMPkHF0hYqHbB0qfXShA8MgOCaClE67N44Jj1TfFIaaWTV2VWDrstQoBNAl2E5s5TgckZJNKpfQ+Sczo2ZXA2K+JSQKdk/l0Gn5RqjHr8qmJAvM4EXvPbWj94vef3Y8dCwBrRTKTBaWRgob1a4KpUpf5FAJi74EhpTlSuWlXGYCU0i16rmrr7YR6PBg4OAMEBZdw7Z3JTmDVIKY1j25Rnj9+QK33W+0fCb2EKOjV32bwqjoAhwLuxh8UY0Ck1VpBzD0pTv/NJ1769F7JtwWYF951AYf4C0FgDA7crULgq1Y6wLqWDAreSgYfXIzDsPpEH1vtwXzNE9PYvIXuqNENFUvbmAxUQFgOTuFk+7kCBAXMHBS7HaxUFBybHtTldllPSebTVquAg1N9jSWQztiWfgv4IRkD/xfDXs/ef460rTPctbMEcp3BUngwwAPYdE/9+bp169RTG5U2DH3cWmo16DW4G80HVbTP6cWsBSaugtFqrBiYByhhVQgUjVfUrSSVIZUP+VxbeXDe3HudiCnasf1+YEiKplwt08wVazPXhi7UMQGGgnI/KbJUtS1ElVtGBxgN90YP5uP9zoTrlNeiFm3+50XvVWA+poHxWZgKwvAB9HLkK5CyDsFwBQEoKClwxEPFSMcCEv7gyc1q0bQmdcl0gUJKUABTbQmifV2hGQGMchJ19UBcVamlAVBWLNAaTXHHPsQLDEHVWC8Q48lO3cuQyiGWOb3Hrz+JKlDKm7sclD8oNgbm23x6ec8Oj6WN/AfZ76WCqBYImiJQOJkL8RwSG9VIa08cwkH9l7NP5ZKAgg853GoR2vlPf80kzYVI+t1TJaBkmbf5Swj663kbbmDoHt/oZQyBLOn05LsBOeffS/6aUwq4QPva8TXEtKyBwVeZAZY5sDkS8nga+atV0qcCxMXKrfAx1bvW0tV7D34eyvFqQqTAKVKRLItRtMyCTOxgwebJjZ2JdTX4+e/853vqdLw5Gbbtkbir2hrIcfH9UnhQwiOUIFHia46OOaR0ahMVogXQLeUDI1xTgSxaiYJWTZ/RzAZW7sIxBQd4Om2xkC0CIQdvFIvWr6lVhM5bU+oiBNDv/WShiSlpQTCABRAJKtdWBjAqkG6NiVtTh4W9dWaawzYd5iJYGunJo0dRRqPIeuKgAIN0NwJYGGdC8MtS3EEbRhHsFdcSEcn9RAZb7ljUGlG6M7qKcjWJ0C9GCkhwg+Hvy+rtiC+85tWQyrf6A7c9PAEnfwQCoT7vFyySAq+7e8HYDGuSIHsAZwQGAQ1ZmBQLb1q1YPwcMxHrzlIBSGgW9fEYEBd4vPtZ+7kTObWw99kX7iYf+AvpZA0AUnpakCQhskQUiVr2v0tNhbFIG8hmoFenO7sSs6aFNgbCNuzZZ6WUyv3M6nxQMnF+x10+A715RUHDW1wYK7ByNYe5GeSN9Sy9Z9isFeNUs6ApyRsf7rlaVGSJKuzubeHB235raLwfv17/tMSwHBsgVd42gGyHeny4XWxCky0Z3s16JefIYF+KkeDZnyAYL9DX21l27JJBHxhs9WOL4OeMVv2vsFuGbH3wbb311DQqGWy7eR4DwGKfmkwQGFF6fNVDwJbz52uvDNbNQjZ/T4NMLVkTzXwYrogmg1AaZuABwK1pMUNvEZ7aFyfZnsCiCANFkKBa8MliVYQvM0e6E2oU9pLatUO7fkrJZWze1UjdNulKxtSP9BHy8WK3+UqQtWF3gCg6k+qvcBA6OrqHZl+g0++SLdUt7pShUwBoocAtzFq6A9hPnzv7AaPdCEOgc4JRQ2bO+6b1rs2LvW7Apz8GmwD4oya3F09myHqrF6OcoiI1xt35HUFCBMcOcu/cJeordrOxSeA8Juzdqv67tQOC+nc2CPFsf6YBN4xd586OxzMFKS3rPPCq4eO+4L5zyScci5wUo6McVg1NTojOIav2Fsc/m/vLdCcKaQEkssn/cztoLm5JS2jmBHxp3i1DH6Q5894k2/jidNTV2OkH41AGBnb7ZE3gt3CFSG1NFNnV1TC8jEHRLNZ+aTx7mjtPYHTaGCWo0JLX0bwX4czkCBe37G0DBGJxNyrR4BknmJht9mzel0zBnlrsSwm4Ed6EAe0ubrc50slMuNwAkQJoMhCDPgWjU9WdT6sCVgt5o7GAMrjZ28NkH38ZbX/s5vPvZLzdQsHKbj6MTIW//5DHg4EkCAy8jKHCkNZCI7ZP5fQU0hSqjbQESFlP42FkRsLgA2S6D79mMLtha3aUfBXQSIZECArMmB+XhE/98t9sTu9si2Cx2Bwal0Z66ALJtV7pANjtcSHRSU9magJNKENIDkoA1xdfcCC7Ig888bt3R714yCjsCAgcIQTHQ6a4pCxW2Z1MUk2BlVgXnStbGeqDYKHWdxrbFsfhCvkCKpwPmTv+/+BNwypDLGbhYiuOyqSvIUuP2JvVtTMOYZgcGriC6tRgVg39WActPP7Gh7kYV6HHCRGDOnXa2AKwuLExwnNCsJBda8B0d0S98bacIJsEbx3Ex5z1rod57fd9dSmbubpcRFJzad35CZQQI3l8QDCeXep9VAwetv7zqCK6VVMftzF63lIB7S/5TNqRpW7JWf1JiDQDeBVCoQKGmE5DOY1semrcwliglWNSrngI5nRfRxtr/znHHSVAc5G4nS8nsa/GRuz8cUKhx0I0FQMGBriMDDAvXYJQvLlvibi3yHVuzseRbmBuLOMpGNApfdyNQy1GSQWmD5Ay5fwGP9UnQuV1N/ggTymau1AN5Tqc0METNZRjB7YodBBo7+Ox7f4DPf+3nGyiYJfBek/mn16+4pTwZYEDT6/MlKPCy77DBkjBB0cGB0eIiKmwAO/lssiLyGTj1xbaaUAAwn1HuE3AABOF4XkpZ6UVTfi3XugsaDyYD+mEhnkAIUHbAWIO2f9b3ZOfcEqr4/uaUu1JrvsC8BwcxGMhfd0BgClYCOpC4WuYgxrA1cgBH57uHQUF7Hw9/WSgJGMYCwJzA/qxyUZ992kBb0tPvOOkzUwZdXkAuZzsz4RMtyptsv/NgCXtbTJA1hsAp5HyC5DttRz5r/Y09EFNyHgztB9cM3WbKTxkVtfyYkroEKATPEYPKpW33ItL2ymb7ru2kx7Yf/oGo/AEUrIIGvXgeEHQr8uo9I/A1hkxzfXQmBXF8gyKd+8uPMF/1mcT+avEIBNrMlQS0A84oMIVysbEpF92x4QmSZmZlVl5x3O9eMRB4Z9uSz70d6fSIOetjzQEc4Do4aEozdUYxbCEc2nLDzoy4xbAZDLsYA9Y4F6DtFhA6li3DGROBIZjXUWNQzXBQuZjtfmmQja0+FkxMJ5OJ20UBfjppEquyqaLPGzhfGitEW9/ZMctzAGsDz9lBdx3O7GBjDBK++cd/uAMFjR0MzODU+6sRgTMHcR689/7za8P4dIBBLEtQcECBAgjndluJA0DoVkSSYDFksyJkj8ncirAJRXwPPiVIzZAi4GnxzPRYm+TGELS//aheo87dwhO39vSO1gQBsu2Z9qQtlm3Ms/fR6dwPfQnnyGO7KBsybWU8Ko3uBzpjESi8FThYlkmhNN/znAXS2ZLmWjmP1qMrinTuoID03PhZqYopjPhMImVemQiJCSmdwREglEtjXogzcPcKyHNaLPaIT40a23BSZcDnu5FCDuBA0kmPvBWNG12dZOfFQQFBIKIUuVrDCdwUBoOwdfajlinDorQDjDywVi2sA8Xw2Fgbm/833dMoWbUMM2CxI8oKjMxABIDeX1W0n0oY61WfMeu1LITE1l/ZXEmb7X7ZXijLYqCxOmPlyc1CoqChOK3tLq8ZyDogSO4+Oh2O+eF8hdWdVDklZ4qIzBW2AAfEkO1e79F2VeXOMtpuJmB8pnbaQSKk9r3HswRQYexAPDr5JrnCgaXLpx5DYP3p8nGUjYQWiNrkY5CNQJePluHU163LxSYfzxfgXplAutyDsxpNnsNiJc8d0PD53A28Bg4UDLr7KMaSAFBQ8Lu/0EBBWegnd2OknfLBQteNhrDvznsVrx32/ZMDBmOgocUUHFBgkZKbF107HU7UikgGDp598C39QQpR3E7JehCc+69TBi73SAdK9nDixwxZOVCPwVKScLpd8z1OhXwbTtueWBRoxIVwue9I2U8HbClT+97m3TnkwF4h+N9u3QO7wLRVUNq+4mG2O6Uc4yhmIeDW48AU5KYohBK2IGAbMKhAxagoSMOcAAISiQpbAHkGCOWiKXQ3Y1rSGXS2g4msD1tq3WGPebB+nQ6npBRyCDST1AVGAbDVruBqlSAsJMxDWN1FD1giAlVBbtbwQmEQmWWowLWl0GVlC3Tc+2E7qzJGyN9CXQbF9uB9eyxJO5ejBReGgNIDALiJAqQqEgwAGUQlg1BE2hHgUoDK1MZcjF0BUXcpcbY+KsD2iiqUFTDw8TZLdzlfJ4agglAqsB2M92q+KltgbhEhVAayMUU+Kg4EpfDIEiVNv0wpMIouqzxIrw3Kw17q1ZbU9tspj8EOSABrttCDrSO4CiArGkq1BeryOHemovEWIblRDoeL2ampDfTlc8tRQyYr+UaZ3gy6hevIDRhd93cA0EDBJ197Q2UWRt1ERDrO6Ctpuep851G45tn7PQ/Cr/6TXz0cwycFDN67GRT0aG4XFtGaqAAg0mhZhg7Edz78Dt763V/Q26Y7tKAdW3BuJVM+9cV23gaaETDEPCvRoOi6RXHX/OnNmhy2X1nk9SogKYCCeJRpS4FbiwqC052eB3D/ogm3BhKCYhuS0ey6Myh9VxKTj1g/C5P0IfbA7qEXd+HamANXqNfo5EnIbqYgtirmn49WWGgOie6EIwJXUeagiioKJuR0VoVRN1A6m0K4KGCoFTiFY5sR3FBNgepY9V0Tk398YTFuIk1BVJ+nRmH6qDCAAtEgyUAviymMZPM4U9KgP2MLiAqEC9pZIG3uGKh0ZTSP/yBs6eDzqQz3CEru8N4U5nfotxCQ6ayBgGyMgVL3ALAG4Tr3GTMZY4AudE3BJs7gBkbMpWTsEWoBUu+3w/G238oQEDvGjjgALFXnaKu/AMUAzow5lC0QkBCYdI4y2XWsx6M784FCbS1RYc29wgnEWXMfGOtBrvBqP9n0cKvqMLQLJsjdCLX2c2ZcnqzGPYz/bsdVkI+NZWtg4LQGjBTnz4F8rFXBcA1nNeQL6HTpoM+y20bjiUJSpNbmKNfncxLOd8oc5AgKTn0uZHVTv/vZL+PHXn2jGQPR/cXWf77enDUYHQbDoFi7Bc/ef6/px2u7G4AnBAxi8iJ1H9yAbO0/VQ4j3Qh0WjZVwfMP3sPPf+0tvPvZL+OnfusnIac7DUxzFO7HkeaTxRqcG7JcnswFDNbwTdR4jLKetqxFoOO+J2r/fAEUs3R1IcCOl1UFd0I7eS1avNdO3QO66yAsiF0wJD0SEOiF/e3q6OloFQRq+Ygl2Ko0BVtMWRQbf4EKYy+JdYldSDROxATuJYIDUus7nzLoJKooYt/WYD3O9HXcJx8BXvCNRwV3TUnE+9Z2e7V+a3OJGDgAGnvAZg2DA2BkUwh2/DQ8huaalUjuI77RnRBp17Z98vr956RUDQy7EliwBHXqMzGmqCWXs9sX2BoRB4NudQOVBFkIlQlMCgg9MFMBwqhQouaWUP/DsQ5gphSbm7UD163qOD80T0FAgjR3SLZYCQFQpINZshgCKls/W8DXthQQby0Nt7NEA9X/QPAhtcvCdQYyVHGWgTW4SaZ4wGlkXCIzMLuThiBjvl0+1g7mG6gvFyBvevT63SsgOyzvo8n1DgRW7iMA+LEffmNgN6MbR8x1lERQiSw+5mGeTgPxv9Dy+DykHZ8MMPjClKdgWYLAGgRq8EE2yrFdKPjWh8/x137vbfzmZ76Ev/TDen9JZxNWTm9eFIXzCUgX9X05Xe+0WZxArjhjQFUTdoso6ymganP9s/A9Oo3ffI9E5mNO4HTWuIhyUdagbBNI6JSaKwYppQcQTYt4iBy/cia6fj4Gv+3KSvC031D7u6dGXluOMxXblKspi83k91YqCjSZjyvbUF0kUjqZiJBZlcaFDRxAfdEOFJjPKnxnITObea1dnmeBBpC3iSk4r2uVARDUisBqjfM4CgeRDhCEAQhBII09cLo5kUZ5UzKBLdX+SX89KENWxtCuOHbLsY3K0+MNHnpOsPraOjGhH/3wkSUo6AAhKtjwMvSbMoSqYB0gFAGy2N9MYErI8ZyOukHEj7I+GOcIDBbj7KC1wECBA5yqaXIVGErsusZoESsTJMm2GZJASFCtb5K5RnbsQU1AzY1JpFogcmqySvtIgBMO1+V+oK6Avskl9RiZ4sBwcKFOsSWRNarwvh3lo1any8goH4kycs62c6wEI2qzgMzN5OJlNKAskBvAKNsdGATZ3gDBFcYI6Oymg9lY2FxHhQBUacGOnUOwsQljEUHBmwYKCLgKJp4MMBgzGsbJOJfRjeDknwSBEv143/nwOX7562/j1z/1JfzFH3odF0MNlwowZ/XZpg1Usg66FFDJ6iN2JRsF7VCVbv1KtH4XYGBn/UqnlB0UxLsz+uRPzQdpwUlESHzWw/xOASRUtXRd2LVTEJ0iXW0783YAGoYZad/QRmCyKv27AytkyPFOXVDoK3Wa0C2DIBScjnXhWgJj4JbYpdQGHMSumQOsiCiAAyAnwl1KyhyQIFW12tiuSeR/Z6Scw+I7GHurr6Bbu67cVoBgthyV5QoWJBTEJO5JcEVUmMjEHkRfurocCMQZPpL6YhEMM389tSH84gaergukfqee7KZVen5ecC3EPlMlMPriP0q/EWydRYAg6k5ygFBsXrDFGShRezzGbrF6zrAZ9M2A9UUp2IoC1e2BuUlMSBDkKjglDTSo1GVYFUBsy2CFzlNOZwWC5WIWvbMdAdw0wLZYh7sBXazjCAzC+yZPTscyusuUkMZ6ANF5bwiIZXZegMOH5COhy8Zs4C9xUqDvJ21GkOAsi7sMr8n2wCavA2VzY7u2pltG+d5uqYMAsIIDZwEhx+4EzZj49uhev6E8GWDwxmtv7BFQnLDTpBb7TxwcLCbStz98D7/yjbfx9/7KO/gRAwW2ywYvqiqGzDQChLrpVsbot1pNHKtTz32+ppMb/W2WxEyFi3RhN9ya1KLVSS8WmW4UcrB2B5CwtHbLMPEPFXZTGL4gaPzcwcOBcBn2A7tCAkYTKfZbAyCpWdA7Kjn0V/HvSsWLUrHZ+03EgIHPBbFW6AJzejkzIRdujEFmQk6MVGXw7TbwxWRuCEuOhTTs1PYcBB7X4pThNcW2BaXmLEcsmnJCrY1MrACBzPBfsAcVZjFBGQS3oLyvuR0+NdYbVp/uhvOhkvbVUWmgYGC1tH/8y5W3KT7XnznvzphZghquLVWwSe3xcKHvNggSqRXvIAFMhwAhkZiL0SwvG2MOp+A1RsfHFXvg4i4Dn5ebsQf6rzYDwKsa56azQWRurQJGkYK7xMiJWwU8U7IlOYWEnQvKHgIQNQDI2SIvrpgOWKBra1nl6wgOOnu2Bw1X5UowApx1mcFAZF2G8Z8A4VBPB4PBFZNI+5MZ5jI8AgnV+q2G+JK5I0a3p0xuzwgIfH4ACgxiDJSOOSyeTYBKIO6uBO2ziQEgxrPvfjOAgs6kj9BhXZ4MMDgsU4CUo/cuNMyPZ0pBTCB/54P38Ld+/2382k+9g7/wg69jq9ICfQDgsgkKq9BO1Raa0VEEDa7pUa8jXTpQsL6jIFCMK7/4NSHykEWRzdrNxEaJCzKb8hqQMiFRBjul5laQCw1gtOyism/BPZ2RiX3dPjgqHqIQ/H/xNZZ4u7b4ZbQWop/W+80tsU2Ay1as/3Tcq11Xp35kD+RjTReTDVBlZmSuOOXerwoMZABgvkPgqB21KdgeUBip71mpuctjFQomYkCmEjaukKLgJYqsWgN7gA4QeiDbw/3uyjYCgXmM62KwGXFwx36J1O5jnu+AwIMLvX+iUli5jOb+20SB/kYdJGTS3CNk91eQKChMw+4VpsWWZYzju1JYm43rZRvBwK1zsjCBKyBs6z8nwNp6Z+eLCAk8gWkGBleS4R9wSOz10HoDbljPgaf2MR7X8+PlyuAiiGCgAWjttxL61df6LYzgAPZtzeYWS7RgEtyIekkZr4kXu6vTQS2gwMAxlFifVQDMYgHEAgjpIbBQgAAJNhgIz95/1s5WuOpePyhPBhhc85fMRew/73ixN4IOCn7l99/Gr/3kO/iRH3y9bRVCEFyXWsEgbNWsRVGrYkMHCR474/WTqQ5Ncc6TvXZ/o1u6rtQu5WEBMiozmpQZdWWWoJQ4dUp8tnYTpbay54QagxXX1nqPoj1UHqHsgIApCMBQcvh8GMN4XwQLcmE1NDC1AAXel9qPvY77voTlBCDkpFZjToRcCDnJBL50NwOZMnGFN7fB29H7qveT4BgQeFa9aPUmImxQC1KtP+hWtVItRsIsdAHApjwDQAB5e/djBExjje7CmtuA+Bp+7wqhCa+pT8iUqyvbx9QlWlctBkNGhsWp+cf0n1AdAYL1GVUfV3n0uM5Ky1mrrZjLq5gLKczHOBf1Va1brj6u6szAVgwcCBIq/GChrUqjmhOAaiC2jYGP0QNrzMd2ZoaO1vUK9HWGxRi0ySBYjXGpa9YlumC0P28HWddk5ClRcxsmkh5LNDOtlFvmdQc/s4yH9V0NYED1TJf3EtoFqJt6B7oaOlC2CjYOQtTXm/V5jylYJffrt7tWngwweLiM0alAWLzok+cPAij4l37gk7j4SYTo1BMAvKhQGplVgLuFyKyCg8136wthLpFCnhWaI99LoL0vW8GLIDxUuGAQIrGMigwGBCrOTMgb4ZQT8oZGPTolPlu7nkAjUr8r6ni2fF2AROF4ODKEEQxE31+wyoD9hF49e2eZGcMi4tZjZ4iqvR73pez7U/R9Lga2Sm2uBuIOEhLUAqFW8X3i1xoe42BgdhdEhdaD0HqHEvk+fPUlFyYkA2qFRd+DWkR2rS5EOkDwfo1CvsnmBQBoym5RdwCDD99Lgs8hChH1EtbJXtmuLNlYr2vAKvbjESjw+m5iAreosF0BhCEWQXql5sOY5zF1xiJasFFxRXB6bU0DBgiEwKKAtAqhQlBE+5NKRUps/uoKSmHGVWnsAVunjmzWQuEfrOne/7thbmUGxfs1vQclK/nihtkR4I8ycquC+/rRZGQiwl0ifD/rWr5LjJMxCY1pneK2HpLzRy6vxoKEjlcQO67vbG5BEr0PWd0rjKWwZ3uegsfGFMzlnwNg0Mir9upuBMAtTf382x+OoCD6JP3XWxw8JlCxCV4FzBqDwKyJZbr1Iw0Rr6ydOWbgRSm4bGITXnDZBPelYpOK+4CIq4GHuIXJizMAp0TgjRQUFMa9UWanohbvpchEifvefRkWdbT2HqsoXDgDXSCnsIg8srorjE4z01SPI0XhdZqpZPXfWZyB1VkMJKgV4ZZZtNJkR9+iupXRaWZli4wx4s4iJMBAAiGXOkSPA6MAiQJgjh14CBDEe0Rw0O4ngmzPLFWQmFpcgS8I3a6nAzbEQLR73za2Hjh1zSLXsabmic9Mi7GHMXMSwIJef1S/Vk/73OsX+yGWo77soAZLgDDHInhZjWccywgGCtDYATUCgttL6vH8gzEGvpdSgFoJlcW2zysoRNLAUxLrP2M0W79VBV4OBo+A4ENM1ryuY7/2sY5ji8OxXQHBlYw5csG8qNXY1O5ivbfPqijL6gzDXBITTonNdaDy75wJLzbGeROcMuH7W1VwkJVFoCpDTALTmnVbsWwRDMx9ullnRmNU+1GAqnUTGzu237IB1Argve8+xxd/560pED+O7O3liQKDNVEi4Y1Pdi/f+fA5fuUbIaagrH26ltUT39/KIPBzYj3VjGA0cldqAIaTuGZh65asTnLBVioupTbke19qm+geUX+/1Tbh/dz30tlDMHMDBj75tyyN7j5XQS6ELelnd5WbkiPuwWtu1a0680hBzJH+UVnsRmpwW/SYiHGrID2oLOa+3Rlb/jyaCb+xuFBeVVeDzzQbYqVq+8YdIChoKOLUZGmBYTF6HOh133VpUNYIfXYNFPQ2ITwH5g9fPEi6YPFucIPDgWm4dAcGomtj9t/2cR/bE9vsAXNHY76be1Dh50xqvNdRXeeSiFCgzElhDRosBqYeBFoVzcXg38FiEVYljqH3jQe4Oq3tCusIEBwsldY4PpBv14palvaetKIeuHYNBM5bJo/GfFVW4+xZRFfGwFGAwzz3XPnPgOC+sQSCF1ttRtOtcvKclRm4L92QOhc2A6riVBjfT4K7EI/QGC7ITi49BK6Ptku/sP5s67iSxpGE+K2pi/D8u8/x81/TjIb75EWPny9PCBh4ly3onPBvnsMigu988By//Htv4+/+1Dv4l3/odUX0GBcCYMLQfncpgo3RApY2o3fiZL8y15tCbQGE2FNh923C6+v3LwUXWwiXrTYU7NZFrX1fK1NFYsKWCDmldt3JAEAVRaDKWBA295mTBeCgNOUyBx8fUdyzAIx+vVUwGtCpPEfsRGj1mAODoq88KoudlbF4TjIl4EKexNw9okE9LNSizv0uVbC3MpgGgIBU7bdQanfTOlb0wLDNlNEWFDjQlc3Yt2vrdi7RSp2t8ThuO2s8Pgt9fkagHKnwlZ9+m8Y7BoABo1987Dpq1lQMfI0AKjNho9JcMREYAuZNn9bV/CSCWoKlwsCAINU9OADGIMQVSIguGiCAhFmOLECcr424HlYugxZX4KBimgPK/nXGqvUl+6vR9Na5CWtQKPbfCggegYEY4OyxEDoH6qPWdvTl7wEzbpY1q5iMWU5Gw2krKjMfkpMviHAyYHBKhFdOCRtr29WAYuQqOJeKC6srNm11kE2rNdb6HQ+7Cb1sxjJ2QNrZH0qW1KiBb8G3PnwPv/C1L+Cdn34Xn3x1TF503UQ+NpCeEDDYl6Pm1/D67Q+e469//W38/U8ZUxCtEF+oso9o3WwT9WYTfDOImCCDtXaLUp39jcoOwCZ6xYttP9EvRU/10gjXUDcz15gJ50QolXCqatm6onvllABUeKbyKjq5RVgVmcgO6QOj8JstxEiNrnylbVEOyrcLuEwMJvfVe6DkGDSpdZxZhMUAA40uTUxAZWxcQQLdylUq2lnYCUAxhFcYlXUc2szhTsXPpYq2U0ENsBVqsRAsGhdRYf5+6nPBLVIUWVv1oSSzXBHGI5aoDGbhuqLovW/Cn95SbZP9MYNXB8lbqUFpjBbwUQAn4IFz0gM5aw/kbJH1rIyLjpHunsiseSm8DTFHvNffOSAfc0Ct4xkcZBCkKs0urJS7fmb1tL5duUBiOQJvvib8mrguXIluu/drtwGAFs8UQYEzU90nboAqgHqyuebMS1snsQ03AoJVLMSK6fAS13cDL8GPr+CAdsAwyhsAg8yZ5c1cl8imzsbTpagBdV/kUE5mAy7OKpSqhtQ5M6oIMqvszAJUZmRmbHVrsukWcOPzxOX+3LZxbKTFvGT04MJV+fYHnnzvXfz4xBQ8DBCOy5MFBnNfNrYgIi3r1F//lOYp2BbCPwqBiOyUISDABjFVndgbdHJcYN/X9f18QswC1lGvI2BHvy+29USvVXZWpS+uewDnNPqsmNj+VgptG3qq7hTZZtRprH/c8z+DARcWblE4Ui919PG50OuxEHUAAO7WOCVGQUURgos3VxaZ+2EiTUkET4ErimzgICVBqoIEtteKrZrgquqqcFCgfkBqgpuTg6juQ4wAp7o2BQ3vPeAL0NTagFmsga6OYxaLC8c8LWv30Xd6/goYsGrMYGBmWVYOFrduInMW56yPcR/zbj22bLdNUahPFuLR9MrUoLCBKf0yswn/YCkl78vYN/G9EQoi2jet62FjD0KGAR3uWwuzx3OkLnwdLMxlRZfHdTfT6itQ0IJdJyubyVkonS6ncGSeAwJXsMPWugPwvHfBjeN+Cyh4ESzz6LuPgGH23c/ru+10MsYjF25GwAwS5twMK7k5GyANpNSetKzUY1CwlJXVUl4LANTBkPJSbcFVIVSuOCeggkwGFWO5aJT7B/NnBSLj90C3V9rni/sB6v7+pa+/jd/4zJfwyVdfb4NaMQeVjkD6lvLEgMGaPlsBrm998By/+LUv4N//9Dv40R9+w3yOauklJmwFnZ6b81LCPio9al9pH3umT756FJg0KtiIfuPCK1V6LEGt3QdZjyf6QyXKt5YgxiKbq3RFJkLLNLXXAIGj9jIs0kjhjas9+vcuhXBKoluFmHFOHIQnAwlNsRKPwpswxh1EpefWpe7XUuFDXEEFIE6gUkEsoMLqUigaULhV0QxywRL2Pmv1d8soWEUHa7hZCqtjUmcraT5/MmKGmYkaXFeIAV5j38z3ifduTZLRNzoXD+CMOzpmULAKnmtbxMSocGNlMjTfQgaPkfUiS+UMxC1v+zbF93HsvSZkc8CFXiFBlp4FUaoKxFHh+4ONTfDPqwxszthPOLTy2AdIgHZi0/T90bzqbMGeJZi3y4I6i7TqJ1kIRt8ircF9HRTMDKZb5Cv/fWuHZaka45w0ADaznkGRScfdt12yM4IPyJ0a5p++xrm27vejUoLhU8328PZcNO+wGlDUwnYB1MayAmzuJnMZ6tdXAoztswgE7O1KNhD3gOvEttONgD/43nP88te/iN/49Dv45KtzTIE9w++xvyvWWrGXJwYMtMj0CowT5r33n+Pnv/YF/CM7+2CrvofafNaw/ONkiU+I1HJelIgZXHHFYKVZQkSLwkFBnOha13HPbSxMqsTdR7aS4myLLJuQUfQ++XidnsSxMhvqbXUEsAyeOgqMjLEQc0ksZi0pQNBr1M2hdDNbvnplZArtFYYvlMF9MykIEWm+aSZCRoIk81ey5qJQf2K119kapia/D6PFvU8nYd6toePAO2AU4is31LzVD+ggoPUDRkWwivZejasnOdIP9EYk9tsbgGebvw8G0PkEV3BQhXYU96oQdVdIBIHzTpVV2/RVfAu46mQjdCgpm3Bygiep1ZsbgA8AwTqoJbCZXBEFQBLbKmp/s1APHx/AgDaGobsK7M7tWfN8UjcbrjIE7kJg+91R/3RmxMHaXoQUkUZzV4FlXO2gwOn66+u7NsYgru9zZjQFa+676H7LRvn4mrlWmBxsot+jyTyYoaO5WrYb5GXs+6ElYttCRcexy2vCVpXp1ER3EmJQFuBmAQauFZcDiSzeBjpL/ujD9/A3f1/T9H/ytTeXaygSDc4WPIY1eJLAADiILxAFBT/31bfUJ/OqJi9S1lKFP/l+edK0k1mAjQkk1BY+YDnTrwyuT4JIK82gYA7acSpRlYo0pV5dajW8yWBRi3abtHp2RcG+DWcMqvHXKFii8ImKbC6a0ElXVqOMpSsEZzgc1LjQcFdGnYRHdQsSwCkzqmj0b+Kk9yS0xehlQM+kfea5F8jqOPLM9l8AX+7rS8Q4SUw6QyHpDGErgrNwc/O09i7QtoMBH8dbAMEMBuYtjQMAsDczE+C/n/eKe53GG0zFurZtfzKWBBXm3yegMoQ0rkCTqfQA3Ln9R0Foq3IUWe+Bc/7attBauxR4TeN91MYmDXvdBB0srqLGU9LAUYidH2TXFFKqPYNa8FgECWQAQeMX7NFVWRBmCsCJgBTn0Qx0+zzKqc+ha+yArwMHx0yLdRCKZnGkYT1GlnS4NoCDKjB6vq9vp+vntc1MqEXPb7hsta3v+6020OzbLZuxA61TXuTEJhqBVgYb29RnO7KDAg0QZFaXQmICl6pzO9EQRwagxRi4vD1ZwHZ0hwxMztxH0EBkZwSlylLuXyuRLWip1M240wSQOp5/9D3dUv/rn/4SPhnTHA/oD23sXwYUAE8KGGiz4xD4ex+X995/jre/+hb+sUdvmvvAk5UwbGAFpqW7UERijRaVrvDTwYCvfEz+uVKQ2FkUPtFZ1P/a+QtGYlW4ei58tQAZXZx3i+c4YmbmtkfXt+KodREzH3ZrJCqxubjrgw29sOwVgVvTmuKztvcAWqCkl0z6GacQ9GO04649sEWSuCkLMmE4n1Ewp9YFEBSAKrZi6F+sXro7I6FMLELJIdiO13nr53oCuAkMHAEBxzE+AxwA+H1X+Rxiwhi03/S6rEq0ptmsIGKfdX3LoQd8etHgQOwCN7dCGjNQu/Bcul2oR9OvAujcNx7/LQEgdbBwW1t17LtM6GBRwnWrLI6RXYCYu4FGZiFXoBCQTUHkKsFVoYxDIXrU/GmxAonbNr+etnfdJ3EN6D178XaJqHFCrLJP3UCW4ZEqcmJs0s+FmYuv7xjlD2Bc31VZzeJr3Na3r/c1IELbYXEsh3o/shkPzBVcfF6JKfQKJigwKRWnRC8lM90V4obUGCy9NwCAfb1XIGEWc/GaOOYRHP9n39M0/f/g0+/gx159oxsH62FqrEHs7f6edmMQyxMCBr3EJTeDgnd+OkZvKgvQTHUhCANsFoH7kNQnreDABzaTXvvo0ijIvUXBlcFG6/pE10C7qkF4VXAphFK5tWsV0MfUg3/m5B1d2HZfZRRCwD4QrgQrSPd1EyIn54jZ66gWBaNUReu1eJ+NwiPSd8wcELpZhq1e2t+DcKSey9wPPOmHFqFldmvF+0tcQeghLJ4gpmVVswjk6kxCpl063RhJ3PJbhEfN0dUPbiF0hY+RJvd7+eKf0wUTpvYCh4pyjh2Iik9BKlq7XWG4//Vk+7UdqB7t6uDK2IJbogb/uRuBPeBsHVV/Z+DVQazO5WAJh1eibl3d1t4JHKCDRYHLihEwOFjo1+u/yCxEoHAEEsDz7ibazZ0VgFwxAzE76Sr7XlM60/yP9WfoMFZ3nZsWEbE072RbmA0kq2uvy5UqDK66D595ZB9yeG6TSWF9O5PJjAkEctvOOO9S8FJIlMkRwVYZRbSfNzJq3+KDtsS4y2MSOJdNwHW5eUrcAyb5oYDJEcTM8UG7cmQ0hnauWKH/9I/fw//g99/G3//0O/jkD78Z5sDeXRTLyqVwS3lSwGDFFgAGCn5HQUFM/lBNKrODAwhi9hfy8M6qgi/Zggd08G6hiFaoN7tLYWFRZFah5BNdaWwOQXDp0I/rj4qRzJ0RGLcJxVwBTlkBY4T7apvNNgXraRi/Ahqg2lbIAldVl81ADWmdz0N9fRH6e2p19wWRmXGXFEGfsiuMDgpUsXTXQhOQPgO8EaaZTiDEA1mq0ZcFNBxmEpmEmk2B1vXphi7r4+J8aNvgCghcSxs755vH9NnMGMTXuUh4rdL3RVezJJ061nvbTo3UQTIOdnVsUAWiuzmwk5IN8OE4qr6f4+FAcK0EHRhw6KPb2qxXVEFQlj3RkcOfETCswcIKKJwEkDTOFYB2p2HOc2YJGm1sV2DAgbCnXncXC8XWxpNlSdsuUHDMDg4IYbkKANadKGDciUCk5z3ZzL15sm3QyZjNS6kAY7e2246jKT9AZsI5dUPlnHkXTDkn6YoyyfsxJlvacs9t0Hct8BAgG4NiV7Jz3gESt1i2rbVX3INaz/0MvFVP+HK5457oKzPhPwug4PUfftOYDRpkxkM26po1OC5PChjMpUoABT+zzwjlzCfDImEncLC55EiELEkRtE2sO6YGEq6V5T511mjmE8ZtUsOe3Yxmqda8Tha0yv0N9InsiNctDxW03K3wkERmprFbMSrVM49txOqHJ93O5HEH99W3QOq2qxdbbYut7w+e+iZQdx7/cJd1R0JO5v7ImkzklUx4JaXGbqhVSf30MzIwUMcz0mOEsx8FTUTgduR1grACha12kDAfkSsWXyJCTXl436wKhTcfFQSsAEBUhE0ZxGNugfEo69YHfnaD9kWifq57rdSYg0K6Pja/1CjhJMkAgeBFpbarI1feJbYCtP9WsRcx0U1OPGSTc5rcreR+aE0ABrMi9AMRrrQb3m5ofAkonp9CnTWQBTiY/34ALGTqABQGkq7OlWmeuEuAhzZ3MJBj+6WASul9cGXeC2cw6zHGGymgIwcH0Da8kjodVFBQwagoOE8U6YU9JgiHaztmFbzLjJh2+Gw5ARQM7uMnbpFJQ/Kt7FkRe+ppkXUypltkZ2QFXH7OrE6YUoc5SW7VEz5FnS0DobkPZlBwlR2dSmQNbi1PBhjI9Aocg4LWSdR/UIlCDwogOlAaQG3b1Wy7E6CD55TWXIasdA+MiFNa3broqFjyAjDgOgK9NcVwV0KjsppLO7WOCGck1GR7ho1ifEEVlwLkyshUbUuTWoCn1PcVu/vD2+v9ElM2n9u/LkjuWJXGXUrIrNefnJI0AZkgoHIxUFAAKfAjUWN0sCtEPc6VQZT0b05g1iNVJaemJNspbgEgeGzCTDGvyjV3wGNBwA4AiB1ZO515D6ArhYO5OR8JC2IkTs0KLaIKwwNht6pgr1RNo0vEyALkuKujCWQa0iLHEq2so6j6bErRrc3MARCwWc7eDz7G87G3V9bk0HZyxcPtiN8ZMPjhUx8FLDx6jsyAYAZDDgTafBed81J3c76PuY410QbhBOKEUzqBE4FCXAiRZnW9Q4LUAmFuWoLhgYO+06naVmNtRVzbMy0/MAS2znU989J9RAjtPZJLyYC6AJK7++9lUzjrVHg4LgjozI6391qJbotr+sLv4yDkj/64xxR88tU3d+BwZgqBPROy6zcMau+wPBlgEIsAeL4ABTPVCgRdL9rZJOo5IBkpVTGA4ON69oQ3C6gW/b1eDsAkTlYpD/CK1qi/zKfWLQwifcaKuqZOR7bJPQmghpOmSrovvirDOFDsOSWIAKdScNkYL0pFZuCcuB34lLfJFSLY+fac1ZiFxikTXglMQQQF/i+zCcntHlQ2oF5UUPq/aEUPY2FqmJMJS7WkiBKEGZxOEE44cUL1WAQTQJ77wYV+szIPSpxzHwsIiBah2GxpQKCO1vIshAalqAoQxS1J7QvmBKaElDpASKQeIyZ1uyVT/A4Q+umBNMRi7Po9CNnh9MkACGJGvMYUREAgpY1xU4qI/TKyJnPbCWjAsH/OHTA5YPiYwAL82ivzY54L0U0ytLsUoFxAtVo/GDP2wHwHMSjMd/13AmpBymcdcxAuw48EdEraHZsBBxCYamNxtsRDIjMvMYWz0+9ncxHNgKAdTDTPgQWAHmoXXT8BsLt70F1jvlZH1866m6yr1gHBOpSTK+94XIE+BXPqZxzcoi9ODPyn0+6DGSTOzGG7l32wmnMOCgDVj9fKkwIG3hfP33+Ot3/7GBS0gQ2/5QAQkuwp1SrwQ80A6OBdK0fHxl47ACVuqQKCvJc+oRaAMzzTn9EFTVxgvBI+oXJ75KkWUBFnNIxirz0ZSiKN5n/Fjj+9FKWYt8I4sx0nm3uKZG+n98McnX42y+EuMV7JrP5JxggIEuFEAJV70HYP1E0Zg7KZAL00JSHFOdLalAGlZJajCktx9sAsKUmnBhISJzBnvQ7UghYhXQDpuKwHJs6DNmU+KgiIluHAGHSAcDxJoishtF+SBhEyG0DIYM4dIJC6hDaLhUmiW3mLCDLpPm6kbrEBYx6AKGxjAp7hhLprgKBuqghrUcWIOjEGL9l++8xdK+39RwALwEvODZpiZGoxwFs6GDBmDMaMtfbDtkoezndtm89tpALICVQLJJ9xSufGoLR/JACp6ygBeEEK/nOicKwxX13XHk9ySrq+71jdhn6UsTILOuYJtKPKU18gk3yyP4Lyj6C9xWCJGVMG1nxmPEaOxs8+mhy/bqf7s/6zP34Pv/yNL+Lf//SX8Pprb+4AQZ8jvcj0nunYYPGjmV/Da4d1eTLAYAcKPvtuywg1g4JBEFshExgqDPRrCgDBg9W+9YEirVPiPR8TRmq2xONkW1zePom3XE3ia5NrDkSZlf84oYIicodbFKhBCJ6Y0QP2VDFuolG+LckRqxLfiihIqDIceCK5+59jmYMh3d/8yknjCRQUqFA5MeGUzHWwvQBtF1B5oYDAwAHVDbJdIGWD1KA0alFLCVBhz0m1EjEoZSBnaLxBUrDhIMFe/TsQKVBwhQFqiuFwXIZX63frh0eDAP9eKjqttLCUZ+UYsyUFdwrKZu2zPpGsE50LwNshQPAtn7USCltwqsiwvTGOdLSyInO1Ciq8CgjcUo7jiqmfvBz1QXMrBOYAqvhbBR8LFmA+Zu5zovfBem4s12LZbE0Gl1hkwSS4y7YNUjabTxXSGBT0+d7YIJ3nnDcIZ4iBAqTc5tMp34EzgYpmCGWIug8yq///UvAiMU6lWrAfo+T1ml7lX7gLAMF3FTn7kNiDoYML4chtFsbSZVTsd4HO1Rk0ABjYvqMS2Ynb5DYmud2NuJHZoPUPppv+8te/iH/4mS/hjVff2LFHDTQCO/0Vb3IEDp4bKPjSZ9/Fr/6TX120RMuTAQZAb/Q7dh51DLq4ap0BAIpZjrrYmciy7imDUAE8/+A5/vrvvQ1gzGcey0g1TRGjtHdnHKsTeE2n98dCJrZT21h6G2sNAmdUPAB2AVvaF10gCrMGLXHGKWWl2Kv5lyWcuMfAOSV8QjDkWPcdDUBfLHMcRM+voH+fbCtbdyEA3FwHxhZs96B6AeoGXO5RL/dAUXCACA6GQUogAwW6deOkAjLlPUigSL+qP77FJpiCoAYSet/t+nSKBRh94TeCgDhWEzhoyXZK2T8TGKxH63ydKcQQ0XahVhBtE+UcAUJCtuj7zacUo4OE4GMHsAQGMcYibrMbA+qkB5EOr6UDAvept/gC2ffBov37PkDrhwaY2vU3ggW71wAYpmcezol5PsR2XQMD9k+2iw1EvT7XUwbyCZIy6HQGnQrAGVJP1m9al5TPoJS6MWO7jy5FQOeEU+lbeLeM3fZdV6pz7oUdO0DjtuPEAQhuEzM0u4lCn7Z+trFwIJRsLDS2YgbxaO+PCh28XxWZXlfM0bzrZVV8tvzmZ76EN/78m4NBx/7rnd7yuaUMEV0BB240f8n047XyZIDBDAqA66CgCZSAHAntgAQIsflblUF49v5zPdryZ97Fp77yk7hbBZw4uqT5mf25qIugMWBv3ezufcV3EZHjYy3OBTAYFpzR7eS0czoBlNT6SCecclKlX6kxCKUqo3BKbNv/sPTzOb2ckz7PBYcm9aBuWaxcB9sL9bduL4DtAtnugcs95P5FE5xSC7BdrJvDOBNBkil5i9AmBwcPgQQHEx7lPSgJYE0y7sd6sPAfYgIWIEA8Ch2AVFPFfs855NoLM8QO9aDGliRVOMGtgmosSQMGY9AaWaBmZWWPThLzQ6zdXgM9CweFGAXfLn7gwGJe0eeiib8e0wcAtB+A3hdHYOEhZsHuMbMSLtLHOTGDxDDG3oaHwIC/ihhTUIFSdvMcACSfzEVmc9ruQ/kMygUCaXWgWkD5DE5nBW4ANiEk6PkJhYFSNQhZ0MF+XNMxvim6i9oOImcI2Ha0SjH2L7gCF4wQMAGrCcR1FmcxdhE4BEbgVrm6LPNYm96IzBG09vCtos2Cv4JK/vJ/8829zmrB1BMrBoBIQYEwDsFBZNLn3Xmr8mSAwQwK1mUCBbWMEdyNYrTFXzeAGM8++Da+8NUv4t3Pfglv2v3zNJ/6FMCI6nZWQenPu4UCvrXEdjygXKIwlepW/LjgXGBSPpklNFrQlDIknSCckdMJKZ1MUVADBjEeQalmIGbRa4+jtZ85OUsQXAeoFwUHRVkDXO4hl3vg8gJi76VcIGZ1qJthsjSAptzFKFbZ7kHp1IQn5QwxoKAUbO8HtUoydkoi9N/4wGglyjTWi3EK381+4wEEtJOdwpzysrAcW0Ydp5eB1n5VjNWEaFEWwQFCc70oSPI4hGTgGcx6ZsAs/BZlBM22BkpYj0fugqYoQ+yIVNQ4vpFKv6EPtP3GHjBDSgALkWEJ7ErrvwksLGMX9GZjHVZzwce6bbNVueQuMTQg0AGClIsCAW9/nA9+e2sXioG6nIF0ajJQqvYlQSAmnySfTU6payHlHoCaqrFD3FOLnw7SF0dXkccNODBQIGg7icz912KEJobE+0G7ru7kVByvIXZoZnfiODVF/hAXcFCOXFKNKUrXY1Pst0fLJDW6oax1CLDTV2CAjMFrQMlKBAWuH6+xJcATAgZHoGBAXgMyLwvrGjqQuqIAInzzw2/jrd/7RXz5M7+JN3/oLwHlXu9bNWN8V+wHVPG8jahRoQf0sJebAUKwECKVGq1JFxr2bDGhgFVsAaAKE1C/ZKAhQQw+30FCEJMDBOJTZxGCqyFu+TuK4o+BkU4/slsTHjtQ7lV4eFxB3SAvvj8CgouxB014BqHp/QIX9mr5E7FelzKEtyY8pZh1dblvFCxSBjEru1A37KxK67vjoZqsnuDKcRAwj1+NY7QCAkH5SVQMO4t5g2/WJlMW4ARU0t+ZYqSUO4tAaXQzcLF+MzYBI3syWExHpS4AcwwmbKC99rUTlUQpCgamOe390PpgxRiEHL+NKQj9oNckxe4OHMu2ZxWADhbs80FJ+GdH5cAl1MBArX0Om6sgAoIZ9Lb1LXWc416Psuk6rQVIBWIAjLz/agXdoY9JtvEQAacT7jijMOFkrsOCsENnsZYjI+RAf3QVXPouoilomKSi3r/QNg2xQjfKqgHI8mDkxPEbWIMHnQX9Wf19X/PNv9/iHdLEJvrfI8NE9nuZ5ooHTy/jjoCgJ7orS2rRDhZ1KxBpO1eg4JbyZIDBg6Cg0bBuzY+Ku30HH6iCZ9/7Q3z+G38dv/Wp38CbP/ijwHbfJhRtL+w3AcHN9H2Mlo4AYUGDyjzxByE/WT5zCf4ruNL3e8R7B2DQnmOCZPYRIlDtCFR7vf9+o9r5fAcpZoUEkABWpcqccUppCFxsanCimRm2jhEESPHth+462EAmTOr9ix0okMt9t65MedTLZt0a+nMrquCZejtdOZrwREraruTBeJfGnkjZusBZUdDHA9XbvPCH16M5MDMCsxKM18bfz6UY5WhtVWXPpljNlSBVAYIDIAMIoA0ixpK48HV3igdjBn87MArffW4FW4chGVWkjlfWc3MVTIBgBwYeaH/rO1cY3g9DHy+AAnDMKlh7Q4vXddArW5/E9e/KEG4lT4CgMQQOCGyOSymQKss5DgB8ysoOVJMFMxVtNeLznXWBgzF1LUjKSOz/jBWq3X/e7hPcRUMAYS2gbTMwsN9BRHVTMGDr9pqrROu3kFU6KCPDZeMk7v7zcXOWKFb6qMRrA8Bo95qZCu47QDwWqdVn4dZY1YE2NT6vxYIJsQ4aCdwpK8ZSCyUAgufvv9cCDW9xH8TyZIDBXHbDHdF5ez9Z83YdkeDZ9/4An/v9v4mv/NTfxxs/+CNmIfb7UrkM99758a8IOHGFbRMfBxb8sNCBY4AQVqfUrmgAdL+jfz7RjjIJ0YZFbTG1RWVUJJmypNNZQcLpDMpn9cWXDLB9H+IRPIFQiyCeqC7M1FnzK1tE+rTrYAUKsF0GUKD9rMKyuUss6w4ltZKJGVTDSWji6LsCcup97hHepkzIaDyP9h6Uhb45GKfe1zsQ4GPjz5zGdckMzPPjFpapgQ770+6zBwjc+gjEQM46JsTKMpgrRQXgFoRcsIiWzw8sySGVbmzatnXA9FEAwar9xoK0j+11qDMnnXvAABS06n2Md+Pv97/y/J1LyEF7BAS1dOW/mUtB/Lt68xyvl63N8da+icom6JTgM7QezmClDNQM4qxBfbaOU1OY0zp2w6tEBijEDRi4R900dmKzNbxdFNjPrEgY+6uyChjllY0VEQ3AQV9uAAbxGizWNne5KESNUSXmtdsx7v6Z4h92NTBd0/QKMOgnEIOkTGOYrO8JEMaz97+Jt37ni3g3gIKH3AexPFlgADzAFmBB51nnP/vw2/jZ/8Ov4Cs/+Xfx5g/+iAllE8wu9BwYRFARt05N/lBHw744pNYxav4KMm7lGnOwshij8g/C061UT0KzAyBAs6gj7Y6Umw9eLuo6oNOdgoLTHeh01kVRsgWqnYziS5ZAyGIVohIJ9W0xGG5JumCwxEUzKPAgquY+EBMeUfhOAtPfk+0qUeFpi6oCoKqmTi2QDRprULZmVZEoddkUaUGzCKJlebVcA3xXfOSrcRoK8W3KUW/WlSMbCAC6MgwAQQVRHayjBhJmP6tW5NqD2/P3LpUFizYDAuuXlwIFXq7Q/H5fYh773xVFMRDk33GCy5JHj/+qTUEmDKAguljqOMdb3RdzvLcntfVBBi7aeLPNaQRwAJMbIupC4s1iTAj0EddwAwGXFxo3cXnRYilWbsAlIwJ/vCt7k8sur4DmSmjvrb6tl66N1QwMFkyqb3t2dysxQ1LWtnmsUkraBxR2/3hAt96413HWLaFfRzdzCQxdbbsSyAKAn3/3Gd762s/h3c++izdfeyPsxJjKFaTwZIDBvDVxKCu2YPe5Wi/PvveH+Nn/8G/hK//av4s3f+AvojELuweW8T6TxUPGCFRb7Dt/4WrhL6KqB5T8kGJodbG3AQA0BVlrEyAia6ECqMXhSJwSgXNWoBCi9iWfdOtgPoMu98DprL74012P6OesoABOp/WAvehbG5RE2JLWtqqZ77GBgWYxHYMlFxpSawMCR9fsSq2mJItGmrqVCoCwQSoDXDs1OSuMW8sM/oDDe8S6DlT4UPi2eXJUosILbIkDhOZzDzR6p9AnZbEqbc5NdLp9t4upOIin2JVrz4zlijI4nAvA2C9xzHzc5+seKjMgiG6/KFu8bw7GlAzUAVjOcWK+2i7fuUMWiwBOqPfOHFidLLdF28pq471bv0eMqbMDRUEAytZZgu2+syET21e3bZBVKxnV3gdZ1cECj4Chd8phf7QS+oxCHIP/XgKLKmQBzBaUDXPDiRtSzS1XO1vgUMyU+tU5tJIReiEAMwZI5+Wz95/j87/3i3j3p9/Bm69+cuwvdCzwUOrkJwMM5t2DS7YgFpHJYnFQ8Cv4yr/2a8oUSDUzcTGRIjsQF4VRoC1Aao4odkCwXZQRMHrwKIjoWl7vhyzI2Vp2ICBFRhp1cR/ZgqBkRt0KOCeQv+YTqGyQLUOyBu3Rpn54GEgYI/pNsXBHzYqYQ996/w19Wnp/tm2IAUBNxSOANfamAOBBeOoju5URg7R8UTeGBOg05JU+blOPr197c7nhPvTQNY8FKKsSmTB3pwCdRp+D8+yzh+/bx+LmuIqpxLl5c/m4xmcuh4L7SlnFiaxKSupSYW56gGAsVjJ9UIoC1el+u3lO3Gn3XRsUlJC5Tuo9DNwbw0qbbWU1izdS4BHszWxp2FkhIcfIESComwKlupVBTj0kowTF3HwEoFg7SwMPxNfXwzXwtGMjLGhZt75e1I3gr8HtSFnXjeSTGSdmLAl1d6QzbUPyo8gOLOYHcb+GVLeJMJ5979v4/Nf/mgbKv/rjLZsIDZpQywPHOzwdYODl0K9pr3SwiEdQ8KPT71eLdw8KottgCQoeGUS0osEfWwYw4CVQ7PpnjFEYqVRmMsuNUQI9yVVAFpA3AITTue+VDklVnGpr/rdp+5dVNvT3SCtD6rGyS9GK21Rwii5EWoGIiV70Rd4AQfRNTgFNNAGHq1bmY8uN/s7++RW2Iy2W9qr/HqPUWpyKg4RHUui7+11xqazKUT+s+u1l+mz5+cNxPR+lNNBqit9jylCL0tUVulOmEITNEidjqsyYaPMcWM/1eZ4nTfF9CHqrKlmVYRsoibFCN67Z1c4KBwTGFhwxBBEQuCyqk8szrjkO6YLUIu5AgRIpeCDGkPhrWQ5YugYseB20nLLK7pQtALDqtudQJwAqB819Q8k3JJorQB/QH/qQW8yZh1CeffgdfO4bv4Tf+tRv4I0f/nG7TnYKMbIG1zx+Tw4YABNbIAcLJpTuPrCYgsMyIrlV8pmmxFpEbVdqUstVUOBoGVBFPgfbXG3zA5YaJRrAwWxFz5+3wKXqU7i2hVdtERGzJaapHRjVom4G2yLlAOFqRP+iSCktOGsIzOwV7X66RrtaQGAt+vkVZmEMIrII5mtgoD1zAgSPiXA+KrMSO7DAH22Ze4kuo1UsCvB4JT1//3EwFKsS+/S/jH4ywd/KY/vpqIS2eAubsqDgJnLXQnIXWx0SGZG7IKf6D+UgII/Sqc3xXT/VCqGqQMXDq1JaPuPqzooACHYxBGUb5N4MCg4NlmUTx8+PXIdDvW+Qr2JxgETdKHK5p2yNGiLA1uI0PINui91h6YyCVOhGbNcbK3DgdzooO1DwB/jc7/8yfuvTv9GZAgcPHhu0YA2ulScJDOayOp8d0AF8/uG3NNDwX/+7ePMH/6J9cTRhBry1v9+MSuOiXV3frPkyuAwewxBEQLD0Mdqp4VIElP25dp1FU6eZrruBctU6VtTNtyX16awTM/obawtyW0X0X/eBmnJvgrJYHgGzavz7lOG5KXY7M1ZljlCetjjtQEC8zsvLUNOrtq6U2mIrnH2x/N2ubrNF68FhwbcPhDk7B7NFxeTlKEjy4ypHfbvaJhb/Puqjl+gf+0ONi1UfpQlcrcbzsTEePne9yYMLp7tYSLoV3VOb4/HzfcF+LXMASB2MU9kWoGARIPpQ7oUVKFjJPObjtTa7kppl/4A8bPUu0vMI3GiAjUHLAqCAMitLgAMZ1oBdDvcp6lI4LDeAAtsG+ex7f4Cf/f2/id/61K/jTWcK/FLxrYuPL08KGBwGHS4vJjz74Du2JfHvKSho116xMnwSkUfymuNPilJEW23WLEQArrp/evd8S66DaoKthOfa9eWK3ysG3txCa4eRPnJVLN0OV57b7ycq1GoFqAKw3AGOtqHCi9zXlu3glinyfW5P+EBfGR0MEOkzLdCJQnsoCuYjNwKwtv7bZuw0XDMrmY/NhXBNuXnQ07XI/1st4skPLDDAzDl8L3q0eMy0SAtrtBbsqGT//NZyxLTEfo9AYAp29P4BtM/mMw76+xvKFBDZ2C/A1nBu/QMAfACqWnnJUIYdgye1M19AZ8JCIGaLcfm45vzBfF/WcQIEupPHA6qNNVhlIW3yZy9nKBEEx8bCDAAGqh+3r0vhLgOj8XRU4nOGHVtel6O55oArsDLNlRCDsG+dtyH3wbM//iMFBX/lH+DNV398fb3vwAsA4bp5q+XJAINoK1wjTTyC+tkH38bnvvE3tFN/6EfR/DFSR0EFjMKvdXAX3AoOkvq1LBBFhIGqUfyRGOpW9QYg+KWIe4yCBxLxHlAst+foB/o6RdOOzVhQogvWYgYOq7KK/PVnkEfGW1S/+PaeatTktil74ZHvgNEO+wUxLnR7n7zqo5C6mci/YqEDKyU9/Hj6+4owWYDS/T2j0p8BQBAUnigl/u3XTvUfH2hgaQi0tfGd8gg0N1hQhA0oRJDg4AwYgwTjeF4rM/tyBQzMQEBmQfr/l75B658BMAzUby+3jfvwi/D//n7xnvMhWREQxzbdVK6B8YfKNVBQ1SBz1ymADgZC/VRu+HgD4rkiFlppVvxz4DCAjyT79M9R/q2fP27hJmKL2fBTWm0bt+3eouzump40bMhxMM9lrXx48GLeEHem4K/8g777wLMrLssYhPiQrHwywOAx5dkH38bnvv5L+K1PK/0iIXgwIqsY+dk/C3S7aBeL+XKoQhkAS3YhxJrYAtNgcAIcSbNl3Svb9UCiCfXHpB4rfzkA0DRJGjiZrTy3Omyxt0V0sIDa/eLCjAFOXpe5OP0PQ+oLKv4oo9x01dCeB8sVSnlIR+oW6PScfblGQ3dFcw2V75XWJBgWiVBaxjS7ZreXfCUUXKE0JSjdiojz3oX7DBQ8X8EKJLiLJ24rvMW9cmChrsCA0LiTpfUDp3XfPLJfOnsi/bMrB5BJ+B0Q3z9y3FtfzOtkPefE73ryD4KCjfPvqovklrIGOS3VMq8V55iFcwEOmYGiBpBuauFmDPX7B9l1oPSHQGH//IrM6xXcyzyreJO3Q74X+67feFEP38UUk78RjaAgHMim81rrvpzTM1COJbTr2YfBfdCYgkcCuwfKkwIGVxUFMYCKZx98B5//+i/iy5/+DfXJiACkgCBmM1uWFjiC4Rxz3+7oAMGpVk4JstlBPYYa4QF5RBq9avuIJdv56MCxj3IOIAKWwXLa3CvW0uxPjgmWYIImZkyso+Db1W9+1hzZDwx1az5Na8uQ0zwoBomLBgfK1Mug4A8Q98E1O2F9UJYCePHZtbPexxvuLd1DRdeERz+QZe6H1VNnK7QlrXF2oOWMEGA4m8AUIfX96NKs5AVIeCgm4SBm4AgMaByKZVcMGeOaQI1HX9tnH2u/TGv7ECzoj7WvFs/ZP5jGV+uH+bPHzMn23KNtbsPnB9ccgRxvZybbJeTgAF0X2ZZVAdRtGpmjlECl9MOqKBgh7n4EWvzTg7KkMUofk7wzWdf65rEy2E6uHACBy/nAHijTlXbzNxoArbqrYFArzz78jgUaekzBAgwPa2Bf/rl1JczNFkOmzz74Nj7/u7+g+zx/uEdvkgfjKa9/5SFduEk6LQQFWzRvUiFbC3CyLXoeqW/sAJ3OSqlLhZzu1sFE8dFTkNzVwCFgRNXoC2fY5xwVfaQE3Q/otOC8kIC+mFblIHd5zBCm26V63oCOqHUBLWm2uJCsLwa/e1tcEUxcF8BTJ68trJXVHT5vSkM7eOzbgzKAnKneEhVhBATo58lX01X29OvTltAPg6Y0nkkBHdd+todbTfq3gwSd0xWwY4+PQMIQnOcR/SvK+ggMNKHZLUNxHy0ssQzp/BFya1GdeR9Xv1iNtN0TWCBnVaajk4FHjrlXAOOcvIntCPN017wFYN2d5zJdtzvV05mRwBz1Mbc9+L67igFXI6pwMihE6LvBpH2TX15uHMm6lStwIesiM3CLrNOf3C6HKedBpnVAwIH1Guf13hCwwqfdcwHNyPu5b/wSfuvT/7AxBTLJw9gXe3Cp7gQfm2uG9JMBBrsyLdJvfvBtvPW1n8OXP/OP8eZrr/drKHWf4VEh2n0vnDvN5guuFhAHWpbNCquaTpSm8xKoeFa/jlSHhDmxCqsFcWBxDZNOf7wQIEGYBSEwpKR1IHNtIQEH1OE+wn/XhpAVbA8IFosoHNZz1ZK2NkeYGNt/i2VH0/toYUoQpLsz0sWOr22XL562UgI7IJBavYtPL7hxKm3Xtf8dn+IxXXMmUCIyZYh2miWDwJzb9qsGaGsB2Me69Kyezd0QktgEkEAi3WUVlEDclroGA8GKcjeBHfM8gAETsK7iauiXKjL0EdDHeu6TNq7WD4AasBT6BZSQAliAlB1Q6OtIjuXIwARMgtzHPPz9cc/b1hOB4Yht8COXIzMynFvh7kUpLe1uZ0QvPfaECMQS3Eu1/R6AzqlZ49wiJ9x/f4Oc2/XTA3Ku7fRw4DDLulUfz4GbC1nmqd99Du/cXwZ0B1ng9fcdDEHvPPvgW5q86K/+I7zx6ieHoOTY9tbsGRRMAP2h8mSAwSH6IcKz7z7HW199G+/+9Dt447XXQ4KHGzrqwLqUfBcWmk06BwUxV7hkSM3tc0+AQZ4yNFpbwI7COrKw3JISEyQyK9EjS1ofYvV2YdZPfexBVyOt7IGRnmcdtQ4sx9WgswNAswMEkToelIMttAWlvLOixRRFHRWm9+rIqvY/om/S33pvRQXiCmO0Ltsd4VYmgKsW5EoZeDsEa4VXq0zt1L+jIpyf2NpABCIxpoDAbO+ZwBXteFzmBE4JlFwpOFDIDTToXOFgWYb5G0ESd8agr7lJkE8n0DWh6ePNaRhrBwNl1S/1o/XJhQCm0C8ASgRTnLSqbcylK9eXGG+E8Y5sRwQ3H+/8TfoZYceOiIwupDbeESR4LhYIqBpAOJEyRhs3BTvIBt9JsSrzVuEdAOiMkRsLMs+VG2ScvowuoJ49VQY5N4MGADuAcCiTOaFeBbkU2sBhLoyKXPJ5eN6z7z7HW7/7C3v9dUtZ6K9rTIGXJwMMehm77dn7Bgp+5h28+ed/4obfD2L++Ckcu64LCAxI/DSeNe+fN7+uibETmvKNgyYuUKPyD3T6zt96QEEfT4Ve75kudaEAO6ugxRtIoJ1nhuFK0OR+MfU6zqh6Rx2zHsIUaWO3okuwoJs1PSlOhO9i8VpGeOgC1hWAAoGuMKLVPf8NIjB15dHo1H2vt9do0VYJbUAABXZhgUAEKCLN9e2Wc5ys/nZ4NkmosyBVrTtXMVAgICKkBhIIzAkpJYDNUo6H4nAHjA4SItBcr57ZWp4EvLMDPt4mSAtGMFDCOHv/SPv85fuECEg2nqmijTuHz9p4k/YTXmK8BwAbAMDu7wUABMZ5vJrD2r2dCYnzOFH/zhmSZO1IKY3uJR9v3YvcXUu1NHdpBwgMNuNh59M/kgcLxdoAAEd5cGAcfGQZF0BtjKuZ3Cg6VUZw0ECL97DXZcEKSDDYjuotw707u/b8/ed466tfxLuf/TLebEcn3wANDkHq0Swdy586YEBE/zaAvw7g/2Yf/U9E5Ou3/Tp0GLGCAjt6Ujt1Pwjz3/F9lcWHVlquD9L/okKI1kSjHmtZUnZNuGJSXI1iij6oQKG7FTVTq65EBJDqilF2VhPgyo1HYccuSGALvwRfcwl+6NIpxqxggQK1OnaaSyhHxw/4knfWYmpgoIpaiKosuqX4kNL02pQrZ08kVvFuQ9rOk3cdlnZWt39/BSxMXRC75UgxuLKr/nmVZim7whBri4II0RiqFY0Nqzfrqe1EhMSEDa70DCgQNSWYSMBMyEKaaoKTUvAeVGZMWPO3R5AAtHm9K8HVM7sPJFpWnHbswCai4x76x8e6gaqP2CcEoDTGQIwlUK8sE6HYGvk4x7r9PbEd/8XPY217ZI6SvTIREmcwd5YItYCoQDjKAx5iTyAVMS/GXtoGOWAyQKIMuMoYpR5HElkVlwELId0ZEzYApB9y6BNnC0TceFvLZwW8j5DPFNpzxAoe6BfXLe+9/xxvf/UtvPMz7+KTr73R3IfUJthexbe/r7LhD4ODP3XAwMr/SkT+F4/7ydi7z95/jrd++wt497NfwpuvvdmumMdiWF/2PtJ2s6/SyyX80Ls4ovSdjzLpXXbRz5gAQSyRagrbW+ICKUaZz4pyplSPgAHgwq8ru2YhgZE5gU9hIQVLgoL/uaHsJs1mgDAKhda2iTGIwkBMGMzW4lblUDlsk2LYbJwKVIgAo7JIkYI14diE56w4zMJsylTIwKAqDnZgSAjzYF3mOeaC4iEwsJWKAsFWDAhZu7xNq7nUrEIAmam1LzMhJ24KkQlIJChMmoHXAEKqa4XRWATJ49jPAW5A75BFfEi0CI8AYLGx7uBg7JPN+kAkjLlbf4v+8LGPY54TIcH6pNg4V9E1ARlAwsc51hEIVOlKbwVy4ph7Wc1nb1cb8zCXsytHElDV3zhzlEVZhULdteSs0QgQBEPsibvQXnLtz8xgBIZbkQb8q8gg3zxt8qHhQ75GI5jvQIgoIVFSLNLGsAMGHbc6zuVxMrW2HYGAGCMU50HsoVguVfCtD57jF772BfzmZ76EH/vhNxpYaHMs6PUYS+Qqv4OHdb2fvf/s8DvgTy8w+EhlBgURDMxIbfbdzX69mb7z7y517FRHx5GKZmgOwJV/2n9zhQBrr40FOAABBV1guuDApCSBvRG3t5jEBAZMKKigTKzvMxESZXC2YDVjFHy3QnObTEzIrm0RZbvP7UAozNbiSjlsVbBJxVYEm1kRW61BaZolfmUhMFxhRoBATYHuFAeidRkVh7sV7L5h0c5jexQrEMfRFcQmtQGdAlWGzeUQ2uilQnrEPWTXtmyJqXKpyImQiZHZGAIR1AEgaJsSgMpkcySwCAs3Wm9sHa2XBZ3axvsRgODWMb+1P4gIWRgJglwFxEAm1nVRjXYXQSLtj65sdHCZdLwOxxnjWN8K/Drg2Y/1Y+dzG3Obyz7mAygUnfNL1shiT7qbgS2OpDxuzc+7S2YjoIQ1P8m3a+xQLD7lOhgigCQA/y7f9mCBkEgNFAeAmMZ2aNs81pHRmBgi4DooAIBn332Gv/Z7b+M3/uqX8KM//AY2EbBdXK1+kIC1HQ2gy5vWDw0uhPubfnwNrx7U4E8vMPhbRPRFAP8UwN8Wkf/7fAER/RKAXwIA/Ff75zeBAhkHzr8/QvadSRB858PnAPbAoNXLLnafHjD6p7XuY2DQqsTJNNONbilHoXKLoFyVWXBkpv4vMYgE2RZSss+ZVLgwJ7MotD0iZQhgaq6TuW3BYmhKYgEIqogJxq4cttoV5otSWnu3UrFJFKi1WWUVZmUc9IEvJlXw1CzCzBwAAZCL9g8FZeqKg6EKVce2uxeuML47RmdlMfqYRiWxidj42nyo0p5TF/3NNu/YhCGDkLmYklCFmKncDBAYHocAwIRoJ15c+h24EvTNaE3JPpjwVkDg4/7R+0P7NRNhqzrPt9AnYqCviq1n64tqy/zjHmdfu9fmM648192C43yWNp/zRshJGki4SwkbAakKMo+sUWYCCzVlyh7v09Z6BIY3rPeVe9DGs615G/fNQJCzQv3f42XbHhR12cxszAmNzMLsJjwaX2Cv/Jv+sIscyF2ZKgCAX/y9L+DXP/Ul/MUffB1bVdDmgIBIH6KGWe9fB6kVKwahg4OoH3/1n/zqYR3+SwEGRPQfAfgXF1/9WwD+HoB/B9qSfwfA3wHw358vFJF/D8C/BwD0g9pFD4KCBZIDFgq4gYcx6Oc7HzzH3/zG2wCAywMHHRE5QIiAoG/l4jjNFmbGyhVQ68gGrATkViQo0VEpzkIyCsim8JNZyUmVxl1inLItpARkkgOQoHEWKaXOhIifkLjoq0C5Da4RWbsMtuqASNv6olRsAly2YoJChae3eSszKJClkvB+YKKlIGWiZkVttRhoYhVWQXEktlMzxBexDAAQYR7EaPnGvtrfkenpQKAzBDNQugX8sM9FBrhq+yp0N4IEhbgDCIkgokBHzFJm8QC2oBQtgl8tZ5/raVePtt4COHfmaxVQ6ODXFeRDgGArcpPiZDL3gBDYFH2FKkIhgbCOeU4MFIFQhUi3PK+N8zzGR+N8DRDEMfY2bcuxftyczqkqCCo6h3OpyEw4ScJWNtwlhiTWUAERWFZ3FAEy+/h3gEC2zRXooGBXm0a1j9tvdTfjMRjw8b5sYmu9y7a5T3Sc97ItgqPMpIxAkG058Q4ogDS5vf5u74rQtiwmNUbFHwHCzEofFe+qv/dT7+BHfuh1bCIdjEIZD2cwJAAWjWPrAAGCgZV2cPDs/WdBP75xtS7/pQADEflXb7mOiH4dwO/ecu2z737TAg17TAEwMgVH9M41QOAD+wcfvIdf+f238Ws/9Q7e+u1PYYuo47AB+hJiV61d/YfBxT0ysFeURUTNs1KM/+KCqyLLgKVkyv2UOjBwEHBmxp+wAoM7YxNOWa2LVAWJNWgtkwYxpYGKg7pNKMiGqcvauIhbUUExGIW4VekuhSq4mOXgwuKyya7tUUk4gDtqf+yD1Ghy7Qe3HKv0vikCFNSmOGQTCI+KA6ZAqSmPxQAvxlhw7DZ4SAlG4DdTzGwJvFgswJAFdXMlpwoRCUCpGhgqaG2CKYpqwtKpdG6vpH54dAtrntfz/L4WbLliwy6TteisyS0geNcXIBW0oS8cICBB2wx1JaBUbb/di1iA2kFgHGe5MsY2vIdrOY7xSvnNc7oaqLhlTgPoa1uoKcnM0kBuQUG2cc9VcEq61kV0zDMDKOY5aABx3J1BpE76ozU+7yAqtq430fYW+/5FKbamBS9qbev7vlZsBQ1I+dp+SK4NBgzP/xQM9Pf9u+h+6HO7P+ea3I7tb7zZLXrCrvkLxhSQzy8iCKnyh5h8QQADDkxFIUA1NBCZg9FofsOA3HGV/tS5EojoB0Tkn9mf/z0A/+dbfvfW77wddh90QBAV/mNAgfsARYA/+PA9/K3ffxv/2596Bz/yg5ocaWsnEuprOejlFCNIbTal2RE0lVXQUXQPHIGBew/CMgV6Kfp+K6Ud8LhC1hpDwDglxilRe83MOCfGeSO8yIwzE06FcUkqVE4psAg1LEbzT86R23OJ/b+yGLuV1JXDiyrYSrW/oW2WUWhEMHS/1WZVHAmQSzEfq7UpsdYhh/MemEVPu0xQE9YUqV2s47tSHNbOufmD4AigQKSPuVOlYqCgSKTK94rQleAuL4tLHLFKmcm7VRMA3Nuk8znyHNVcSlpZMcZI2C0ZtU6671OM7pzv0sdcX/eBsnOcjMgxhfwQKHhsX0DsWoYyRFUtZqlKq5MIUiUIiw07KfB7iTE+An4RFGxSVXGGdjsguJRq81vvfzSvAVPkwuDgIqkgVOHmllSfv8sxJaQjKBRjDzTfhekmMpCI29Z3dBG5Mo/sQAT8l1JxX3Xt3heXayrPXK5dSkWtFaUeM6GJgZySrWtf29oH58TIXAMYkAEkuNthFYR8rfhYRMboId0w64WCEXASNMaAmRSgC3WXQQADERwA3a3w/P3n+MJvv2X68fWr9ffypw4YAPifEdF/G7qO/i8A/sYtP3r3Z95Z0iORtnwZUPAdAwW/9pMKCjzi2Rd3jwZf1+sSJsW8eGJEfIwsboKx9sCjmV6cleGLrQ6L5rJVQ9W1U3bVhaW++nnn3HxvBg5yBwmvnJKCgyI4J8I56z1PiXFXBSfptHMW6VHtFUrLmRB5yIJsvmaMfmV3HTwGFLjgdGDUrMdJSzAzalF3gUlQmx1qGrnPWxUoobIqH2ZShX2j4gBGJQmMlkQEBS1Q1IX+Q/zjDUXrbNbzpBCraLuZScFuspS3Q1cpOCjoSpAhzcfO1WlWaYF4ANr2quFOjtaBppiiq8zXo2APChwsOfXeXCm3goKXKEU0ya/L2wJBEnykMT4Cfr1N0kCB9pEsQcG1uV3t3BcmQpWKU1Lmq9UyVQO67p90D3VsiY47oHVxQKjaSgwgBDA4FZexq7W9chesAMF9qfj+pQxy7VKqgamHZRpzwSkpMIpy7ZQYd5kDQ1qbHEsGfp05mHeveDmS3972h8pmDISDDynuhsLASPk683V6CziopCzYs/ef44u/81Ywmm+TJ3/qgIGIvP0yv+vJi8a9okAHAvH9w0lEOlPwaz9pPh+LGAa6cGqpaavcLMR9Ql2mQYpbzuYIZLcYjhTh/VYVHEwLZ9vq4I+erQtHqbogKnJWhsAX0KUIXjkx7pOyB69UwZYYZ6MB7yrjlARZegBTFg1aIwqTNSgML60q0pXEbvthVA5uWVXvD6cjR6bA/w3PMsHpzEli/YwXp0BWkYbmXan+F12SK2YrbnUnIvU1MgHFIuvZDF9Bd3aD+oTn/Tx0/+jse/VSoW12wAMijbJvUqoOrhJlD3S1iSlHr7N3/Xx/IFDMARCIdDA8U+2uQPU30rbpRRcBuwT9CP2g/nhq3/tR4m373wzqH7AcP64ynEl1IF9umdu+HjjQ4UgVXBmVlZEAWFmvUpECOKDU71OrAgJVQNQi4JZrO6xrBwgeTOiBw5fSXQYNCGz6eikV379UvLhsDRjcF2kyzdfLSqZ5YGkiQs4aZHkp3fC5ywqqTpWVLRAzjkR3o/TgY9ntXgF0XszyG7gdyDvYABBiBPS7rRhL5+vKb0mdELgGDpL9+eyD5/i5r76FL332Xbz+2hvQKxzA83E0Jf4UAoOPs0TrZM4nf/V3AP7QQYG5DxpFVP1+e6seGK0kmSasT4TtYPL0ffaOGscAs5lSnam1CAruS2cLLsWRujRkHQszIW2KqC+lYkuMnJUhKBUoNeGVU/9dt8yUgizCuBOGVEXn8Kh2Ctu70BXG0Ga/Z2BpGiioakE36yooBh0nWd5zVZgZtVYEGQdmbrQjoALF3SHuO/W8Dv3VgpHMmgB0ITrq9/sQEGJMerFl2cWuLXyBxWYIUFjZB0CwMSGDVUMkMncSISej4cWYDAiqma5V1ivehfccZLlSiEeWskCt5EJruhPownO1ne6hwEuBA2MHyWj1afULAEknAA0gSa3kEZjc2geZsYxknynlNry0Hl8g2OA2xg7+iKHskkjfzswKwpgtINJjH1i1gyef8ofV4gpZ0c1qbt9SnHHxyerA0EEhAaCqrkNvk+cUUPZgvbbdyOruwb6mH3IbOEugoKDgxVYaILgUNZAuweDZjTF75k51IRQR1BQZkd72UjWmQteRr6dqsUVVt6GyBupupFs6AWB7ABgeyX5A5cUmArKgZt8BEl0OpUqTIz46Fe1s3+vPBvCt95/h57/2Bbzz0+/ik69eDzRclScLDOKwPJaN/aMPNdDw3/2pd/AXfuj1LpziwGEEBZE5ALDeQvPATobOapjwNFAQ/Y2RWmy+uiqobkFLdxusQIH/zgsToRbblia6SEZK8egMBLsu9+ujfzInbsI/Rm6vzkufLceKPeE1LzStgQlMcUUu5gKwZ1T9O4FQqoDTuKzcsgCAc+YhANGDkTToEIFmpLZ9sSUJ4q40PMeBK4zY2hiE6UPgbEqFUpcCDeYTy0iHolHyxAlUKoQV7EvVJDQDgGzNPp5nq6167i+e8zfEOntpAsusGbUcbewaa/DwPG/MHEZQMFt/kTkpAJJIA0hFqFGrDpKQPp7258S7xEcxQ2IEBN5Fq/FN3kYDB7rToaKwAi9AO0CsXT1+xWl+teh9vVULmkysyhTQdpfFuopxBg56Y4Ctrp81gJJq9fG/bZzcenVqm4j2a3XBBImsXYKPBQWXYPD4VJnlWQt+qcAFmnG+koJsFnfVVBQDhclcatF1CCg4U1Bu9xNYcCqW27CvliKtO4uzUaY3dJipKeOCMDfQ59etxGVPjqRMAYAA/W4rTwwYdDeCl5vcjASjZTRPwd/8xtv4u3/lHfzFH3y9Cy6o4N+u8A4zKHjs3ImAAMBOib9MSUxLVN2eKTJYzbUKKivKZlM2VTSA8cKExLVTsJXBZr2tKMicuFmWzU8msvTFRutxVYgJJGvLKoelo+3hXeT2KY3LogvNSVgSDYCgR3H3CGZXHG5JjlHMHRA0RbFgDmqojgTLewYIKan1nKuCk000cryQIDeXEwCm3bxbReS3/qQu92dAMGeAvFYsIqMpQwWCx9fH6RzX1lFJhgw0j4ACAhK1agm+5qiBpF6vddu9btfafhR0NgO+OXZmNb5iFnUc31PiBv68Tg78UlLFxLDDraDKiovlVqiiDJGB+Bhn8FB0/jzHdQvfmgmLFm4s0v5Dy3URSwwyrdLX9qFL0FyAm3iAoSr/rRRT4MGwEexk2UPy8aO6fGrj7iOSR9vpc2shJbo0TqPKYf8CISixP26YZ/o5LTX9tz54jl+0jImvv/ZGW59Al62rpEdzeULAYOyl6Ea49gv2MSfgDz94D7/8jbfx9z/1Dn70h15vQTMwxF0qVAHCQT312B3zA68s28cWDsqciZqvzt+7dXxKXQn3cHm34PU7pWDZlLwzB+tnOv32UNEFigYaKjSg6oiCjH4yotvAmtcigSDkFhVb1Jc2lZNRfLB9x4VafWqFWVpr4dEtx1X+gr490enlI0AQGQJXpoHxPYzYbovV5pdbWdz+BlLSCPKTAJLM9wiBVBqCXlfpnsVvfNS/wVcKdAUJjIBgps+bgpxKfNotWHZ1CaGvswwL5IS5VQgGCAgZyh5ks+SKiJ5DNjx4X8ujNnewcJ35iUAvsl8Pja8YOvDxFX0YIAo+t1I1sZABPypVEy5V7ttT6Xo+A+Bx83wGvc6M+P5+Z8SiklqV1VqOzB8MoK9cggP7Wdeuxl17mLp4q6NR49+7HEsc8rJkxjnuuEoJp0Q4W0DiyKJYn4X37dkfQ3FQMAPxztLZ7gfq887nW2N43NgI+ODbHzzHL/7uF/CPPvOl0X1gYzHaRoRr8uEJAQPsgg6PCvmKBwBRiudbHzzHX//62/j1T30JP/rDdrRlNapH1KeamICqSz4bomgR+DB/GutWJ2BPwj9kTSV0q6dTpCpyMztl3BOXbEWtY7UIKi5MYK6m4CpqItwXQaJ1AOIcyRuDdk5JF1K2iF71x1vdCLuAvCI6mWYKsrUd3aqcLeiK0C+BXXDLGajKFBQFB6kKEnTbGhVufePCc45KnwVO9DNrPfbZDmNUsgvK/R5nrSvTuHiJAqoHbuLwWtAQgjLByKakzAPtDvRIdwC71LAPBUKN50TYZ82y7nOiXTO9mX3rjylus8RYC8DodnZ6WunVYqgyh3bqgaT9qY9ta2ynt3EFBFYgz+908/iSRZKjj62/rwJQZpwkBF0aMxBTX2+8D0T+KHPdQW93leyzA14DSKs1DIwuolU5crUqA9nrzMzg6rsiGhJQQ471bAek6zIsAgLN1cK7nQlz/pbGhgaZEGNwbDivlpWcf4idc3ekt4GCodESSqGDgggIiKiBgt+YQUGsV3j/0JR9MsDgFjtdO1ZLE0RE+Nb7z/HXf+9t/Man38GPvfpGZxtYBVUx+FtMWQD91a2YmeYs6J3bWIRpNK7RSSuKNCY5yWBspMIhs7QdBFUY91un4+4qcMkewT/GH8xF0532TIZH6Dozq7WROspux7ku2hR97VHQev83OpJ0XGZq3anXlAQZmtUwsx7HK+x72wlnlgfpdK1Pt/Tigs0HizUKSF+Q/tsdEFgs2t7WML5TtQafu1hnGFMk4Zo5k1omRf5+zWDBvQx5tZiSs+vnmlB5SGgCva3xUmcrZoa2QpVU/EWLg70lEqtVrL9dxQZERXd0ZDFwPLZ+Hy+Dy0RsxwecyaRhF5QH6CVSIHwy4LeVipJpyHmgsRXcEl4BPVgZeHi+A2M8hYOBVTzFyjW2Wr8OdICwftG5y6GPzCWYSCAtcZgn3GLUMKaeY8UNnnOiR8kvBwOez8BlmCd0m+OIZtDkfRZlxFEZmOJw2Rx0eOSuc4OjPRNoaZr171G2xGu/NTEFt4D0h0TDkwEGwPXG+gT2TvP5917o1B979Q2b1LolxxcwoNHBnskOQDtHgCyfwQwQMoIVs7CgY1n6cQNFCpjwCMpPBNi4b2GslZsP7xMn38aYdvv556Qgzh6MPne0iP1Topb4JyY98oA8TRSCRqvPB7REOmy2quPYtPcCFZk0Uq+8oNWL0eoAt22OcxDoci64dYEoLK/TyHOK1NVCHZTLom1jJcY/Z0urK3+yV2lWdGvapAwigOj3XffDx7DF/6XLLeDBy2N0/1Fw1gqc+Sez1c/hz9lVsAcUVxqyAAmCcVz7lmmd1y3a38BfJt4FZ/Z5j507CXjcvJ/dRdfYgWuusZ4GGna+hldGb5KdaU3VHO2eTdKsL/ULgYvLIJdDFSURchHcvYTs8sRGLr/mDIjM89bEfRCql6NYm8fK+NUJru3MFbuFn03TXAf2GuWMj0eMKYhMwbW56ezttfJkgEGUlXN8ATmKnTrrvffH6E33BQqFfOjS/ekkXdAmAigRsiRsLRBMg8SKD/ADASqxOg8FebniA7CwHIIvcsoj3nc09MxpMY3oUV6Do1TJ7oPvmQ+P8o73ybzKOw4EoTx0SmdznG4FurB0Wj3nUWiGlxvOqg8L40AIXmMC5oNVZlAQH3CrW3LYYhiqPwMEfS/TZbSjcudrp9vaM6cPhufK0VfLGILDo8OvlJXwGgDj9N3h+SLY93P888iyX7lCViBiABzhVo8e2wbk7M8IDqDyQv828MfYMQs18RCo+3HOf29zXAPtM2/zgUqp0J1HQtTAve5cUBZoBw6yBlsSC4gYXACGBw4ztlyxFQ0mnlMfPySz4s6iUfnvY4dWTKG3+RaZfEIHRdfKygBZuSQBNNnphznxJG9gYzPrr6NnagXRch4QHna5PxlgADzEGPT3DAUFP/+1t/DOT7+L1//8Gwaj9KJuqUrb0lKl/wOAExuFh701u0qLua9PQKPzYm0X7RsWF39Mm7zzSda1T3I+eOQa9RiTwMzIegUImtVBo3/sSMku2ww02lXr6A33sVlbx0NfX0Hvh8IuKH6/bqX8o+Ifrp9eMdTy4dKrTLtO2evv608Z5GYAFJElmPvtGlMxfO/mxsRWgPbb1q6V2IJrVrveuo+ZXn/9+3jN/LBrAOKWv7U8DgTp2PZxHQmf6GLQvzsANpAQ/vbf+fXrg3tuq9fcbzvlfzAecxH7TasTSQMIbAwnWUckSUjQINrNgi030tNKx5MkFRw8dPaF13d1INwtMUOrdMdDOxcyOP4ZQcot8v5od4vLSSAAg8ZO7uXPoL8CKLiFCfznkjGYy45eJO3UL1pGqJZGeRIq7gtkwLJ90eDLOzGDJ+qvBYklV2Lr7l/5OONrpO127fQFoqcU7awHkQ4a4ul8c86F+cz6a9u7jvZ4r4Lx3DfGOA6YWVrYD5Q+4cdtaX0xrvv6mgVI4fu26IIwjMq+30XQIvyCUBjOoT84k/5lyx4wXu81id8PJvg486JiasrGvvC5pP3drVrPqub93hQC+m8f06AVGNP73jY+e0Dm2jGORxinh/Ikf8xjtyvUbe44TsmRKnW7UAJA1vHpyKKPD9DdTPsgQP/+apUWawR4/NpkmxdxTniui1othXZFD7akKdgyu2FDJqP4pWXUvPW0pTfmGfA/Uu62v/U107iWjmbPKHP6fJ5dk4DK1WR/+pkzUX4+N/317mffxRuW0RBAS7dwyxp86JInAwy8NCHlVs0khJ6H3NFvvPbGICJ7Id9NBIEmkoH4xNcrcrJsVQYg9imVH0aPK6vV6zlbQ6tyZEEIaJ9J0C4U7NmGa+c9REptt687LLDoLnAazCdzpMKAPTjQ9r9soat/7qzP3WVB0Yt0xRFTXMa/gTG5yUtQ6A+XG5TT0XNptZ2Q23eA0r1NQdl7ITblxM3674CBBmWkMSDUwALHOU8PMHetmqPCn8HBLAw7CLDxqhrf48f96le1j03rn0VfXum7h8vLz9Td4xbPlUFJj+PmwE5sjPZAAsvXXSbMj2nK9vwufV54/QfZI5ZPxdwL2QypFmyJYNBkHnbXRBdqLEfU/GyRc1DCD0X2HxXf+rsHzyu25na57/WIrCoAnNhPp0UDCF67Z+8/wxfaKYmvt+f7fQ3L78G63aDKbS6wJwUMdq61oGgBZQrejqdMiYXsiIwWH3Tx6WBpIIvnB/dFdmI7phS+CGIwUXj4wqB9LGUd27BqYIypiLRjCe8FCCfZUft8tDzGKk9VGJiNl/XDH1nlUxcd2P+LRTdbhnZJG8+o4O39UnksAcCkVG7naG+7blV2QQJry/fm+gyMwQgIVMGwgQkDBH4Np6aAkr1W7IGCu9wAtNicVfVmEPgQEDAV2BQ/SQVq6cpfDJbYuNIEECaT+XH9tOs3vn7tY8pD1vvBMyK7MAKGBegD2ri299M9rrZvrEmvw/Q+hffjTpo+L3xODMymzx+e4ycouLD2z4y1ekgWMY9yZ0XH+3w7Kns35iRPgxzt/RBueEX2r+SjuxNPKa6Rvgaef/cZ3vraz+HLn/nHePOH/hKklm4IEIMC++ftaq6/qV0PgYMnAwyO4m18IN5rR09+CW+++uNALUuLw4t3tgtTECNxatbzOQFltqQWk9l34A73xSMsIwmWEfCwtWPWn95Qp5dTkp6waT/Bx7+v9enK/7uif1cLMCLf1kZg2badYo/fX1Pw/ne7ZjG+w2ANqiy8vWK1PyRIH6tIDsHA3N7FHLiV+g6KwgEBgfrnPs852WcJ5EDBPmN7TdQPx+rb77TM7MHYDWSvdi1wCAbIQICuU9cqZfyufV777xZA8Ka+6ZXsfTR8R/trY2OOyjWg9xDQjY/ZPXsx36hveYufeVsogoVrgCJ8Pn4Wwcl4fTIGyRmjCACAhfFkbeiKdRFXFEpUZLcYHqOl/RFlqa+NpnjHuJAVQPByq/z3+gJAguz007P3n+Pzv/eL+PKnfwNv/tCP6vcorW5CYuOv9SR0gLDqzzlAfy5PBhjMJSqw995/jrcaKPikTpC6BWsjWCDjXewmk+AEwFLAO9rVkOUVkOJ12k3auqpTGS0i4GGBt1AAWvc0UMUghrC3h4b6dwsAw+Rpwn/3zH37YjvbjSJlv7Lo7e9HKfxZ2UelethvMrgDpEypqPy6BQAgO2+hU75rgTpaZw/QzyuWYm4LFAi1ukqFzG6Pa8WVAzOIGJSStcHngip9oqRtYH/N5hjunwlnDfaa2DQgCJwjhUmrdRAEt60BVf66Btq6CJ8NAMHGU0r5SP3S3qcU6h/G9+Mc2+G7Pr7AYj7Ga6fnUTj/o43nVF83bHbfhe8pAIjht0EGkj/jwMXR2Sc0ABmBgrZ0kpGOJqwsZUyQL3vZEpRojQzSxyM/KchPccVrayARqQ91AgxD+6ZyVQfY+NPl+4Neevbht/H5r/8ifutTv26gYNMeagyfgKhCWPlrimMdnnlLZkkvTxYYAGtQcM0SAcLitdKtKwCUVDgBoO2+g4XJX3sYFB8H39HgQxZRLRiUXLSQrrYcfRKvLEQX8hE8wMBCXPiOmv3va6Ut9iuW/0MAYPh+QQ/P7V/0y05JtGDB2sav+QLr0SFRVgwINmHBCS2NHLEqFH/flK1jdlhfluEeQ9lZ/xMIsLpLrRAXfrW7wFp7amjjUP9g5bEDAOrtsDZwPpmQSfpdNdaAC2BuBZLU14AzC5yaIHILZdqdZ/2xej1iB0oQ9sUEflwbep1slza+4v1ifSKxHw76pI0d93EDp7YGro5tU9DlxnHtPbIaW4h5rOfxBa7OUbE66417G3ZzdAEGh/Uf2xUNIbuvzHJuwTgcxUTEgEv/rsGZQ1kZ5GSd5EKtWLqRagcCa5nZeuygjP3SFKytGVoAaGfUaJKT6RpgXOmBaPSUDe7mfvbBt/C5b/wNfOWn/j7e/IEfAcrFnlNCPWwXiPW1HpvOXQY90OpVeZLAIEZvfuEqKCiY2YOZXooKH7R1Wq7cw1HbIZL2MivD2T/a6uWTpAtFktqVXNm64FgIDam1CzMgWH2jEohCok9wp48nwbADD8AsFNrzw9+708eGv1cgYUL0O7p/XuhlDQBqVd9bVBBReXifHdGIrQFByALdomTrQw7MgfWvlLF/AaA7Z+jK6twzAjK1aaf0Htsmp3o57doiKUMu90DKoJTByeILOEFEGQNi1udRAXECxLek1QYeyebM+jSF0NaZ1n0AEFDdhrUgZbO1UICy7ftjVqxH/XFtbCfwBGsnAF07jxxXAG2u1tW42hgeju1D7bE2reYrrE2ykAUDe0STsRAU5SgHJ0YS6K6FaOs3IGXVv0k+jHJgUOzRMHAZ+QhZ0B61kpOx/5hBKQc56f3nipj7vGiut32cTuyDXTt3hlEAC+UFnCn43H/wK/jKv/538eYP/AWgbL2viQHS01YV0iVtq348gAOxMThyK6zK0wEG3norMyhoE+pICK2oaKDfdFoAzhis/Xer+k1KcHjmaA1h2xoQkO2iQni7BCGov5VGpQJY0Y9OM5IGk/nEb8or5w4e8skWQt4JCiwExWxRxD56qBxSqjMjcMQGmKUoUgHrnyZUj/qolD1LsD5Nyl5TF3YmHCgoDEq5KxIRqNM9QeK5yBEgXJkaLtTE+uIQEPi8uDIHHoyKPmgL5ax9lzLEAAKyp/iugCgYUFBQ9VhoMoDACUQOEMQftq/Awko6dBk4MxDWRC2bCkcDBbIFgHA01lf6Qf/oQGBQmjkvx5WYW8yyrpPr4wpgqbBWQO+oLdp1cvO8Hdq4Wvspj+yRr31mUD7Z2K1YhXH9k1mtcbzligyIXbWUAVfXf1f+UmwdhLVfb1gbu3JFPkrKIGfR8knXCDGQMxqrFpgD/21nGnqfLEtkCGZWY7vg2ff+ED/7H/4KvvKv/Rre/IF/2dwHQc56TIH3OWMAB4oM6gAOYnnvu8+P64anBAzcwKMYaGi7D2pHl522l1FBz5T9VHbBSAN6u0ExHk38FmmtE13KpsrOFZyBA/9cREwZmjBxQePP2PWLCwybsNE6ShlICZRUGOhCCIDBld+MnlcCAwtwdBNQmlgBe39kDVRnTVw5RDBQtiYMZNuC1Rn6ClgLVi9OM1NwqxQeABSZ0KF8glQGZej3LrjZ/pMKKapASGSwmFoXTZTzTaAgtLMpQW9r68tFl89tMcWBqsJWAYIqZbExoHzS+7FtyBXROQF0a6WI9ZX6OrVhi7UwsUKREnZWYMkSzOsiAAJXErt+OOgDkGbM9H4gIkgyASoCQVZct23LcW3EgRsED4xrdBlcHVc3AGrp6/wjzN9o1XosEeXcQKEYOEDKSl3bZz4P9qxiNRkoaHEB5MrH5Z+BhVUZ5vlkgE0GwJIltTm/669y0bH3615SLra1nk8q0x0M5FOTg0i5fc4mKxHcCyjbnnE9KrMxFD579uG38LP/0f8QX/lX/tf4iX/xXzJQYJe0MaA2N4mhwNUiOMR0myhaGAAZE/Dsu8/x9lffwmt47bB6TwcYWHn+/rwlsQYLxema8L4NzJFPSouj42/+sz/UD+rFvuiKsf89ldUicCBiE72uBF/ZIOWii8G/M0FYL6oMpVZIEaWe5yPLvEqpK25KpJYBM/jkAqBP/AEsuGURXh0wDH7p2bpY9Uvsm6Fvj90JjToOCnGgjyOjshAQ43v3xUunaOd+sgNlAHRrK2XVgLVC8gm0XSAZKlxrNXLAUlmlPLZzYAzWIEnpeOnXMyBlrJ/X162hHShwoAiMCmRuH7GdzG3+UFEhKDAF59eNI2MACB0M1H5ds1Yq4L5Oa8QIDI/caQ+5Do5AgYOjBVjWx13pA2Y9vtvHtBQgmdL3/khs4zue1hCDFH38VsXHlVJShqEhCh+n0kCgmA/9qD3NQn7EHNY2dNqbUta14gouJSCd9Dmm/KgWIGW9d8qtzpTELGGxgQ4KSv+IFWjjPPVI++waC9jGdQD7tcnCtsZtbV+ThwCuysSlPLz3/sk6N3IGpVNj0chZtQigcgbQGYNB9l3VB4t+ARQU/OW/Y6AgAC1i+Ok9hgbsFuKTVxeoVECMTRAa6vP8/ed4+3fewjs/8y7+ra/86rJvgCcGDN57X5MX9YyG0Sflkej+PoKDulfYGK05IcI3//gP8bP/8d/W78oF7ufp15hAnMo+IlYGdwGiknOGYLvfAQLZLqhbQb2YsryocKkuKMKC2NXBJ6gthJTdl0bgnEzRJ5AhYolAIaJnWwiDXzr61Y+AgnbAWKlQ16sBhDNLEKyGFZMSAUEUGEAXFLGfvG8o1SYkgAJKUAGArCyACU6tY6Cige6nPAhYWwoKF16EDiA2UyB+WdkUlC0UwUAvA3tLab4eVce8Qu+/XUawQ7QHB8RKw2YK40NaabdW/H5iQWZStN3TcO98qjtXXgTvRhvXumYKjpTolfZDq2jttw6uRV0hi2tjcCICIBjB3vVx9etVLGj/t+DNOoGFBu56zFEEBQ4IXPlpV87zuHRDgEtTeFJrBwi1AnKycdVj2ghQ9quNDyBi1xfoWnBLNSqo3lnWBo+HuHFtRxeKjfFubU9AwOWg1KqvRYBaUTZnEPd9M1SVuMlBZgMGpwR6cQGfUpeDZYNsGZI3Vf753MZFWUPrUwd9WcYATgBHrpUxU+poiH7lL/8d/MQP/IXhGl1PRRU+Nw0Fp7FIyNxd/t51XWcNnpnR/M7PrM9WiOXJAIM9KEBX9va+lSZ82gf20j8btoYB+OZ//k/xs9/8N/FP/vLfwb/yjV/U39hAtd/QJAmH53awEaOpb2IJtnvUy4a6bboQLqUtAtkKpFhAU60qPMrBgkhmETOjMOvfBgpSTro46H5YHEv0XHL3seeTMQkMM6V7cJaQWRkUe3msUwRNi74bArCileX+RalLUKB9PArSQysi2TNKENIwIZsXC9sWPllglwKjgziNK4KCWvi+wFJNaZ+70MTWfNzgCkLu1r1bt65gYmHe06hHtGYEOwdj4Fa+wISOWytkYEFNE7Sop8CCDIyItRXD7pP42gE7iY3XSrhHQXptV8ncdl70gceSuJ85xI8cu9NuH1dIHYFf2XSebBcFfABIGFLdstu3ZwUKVnNZDBTI5iBBr6mXDcTmKoRZug4KbdxluxgzVHugITtrAuzOuRQ/xcENonGcVsZQixFYGUQOEA5Y0pVR9FFlIDOjugzcClJOjZ3kKuBT3bFn6gIycJCNYQF0bki1Pu7uliEOY+i/iTVAB84/8S/8d4BtG3f1RCPD1xymdSVVEZzfn5KybkJ49sG38YXfVv34+mtvPBiE+GSAwRd/5y28M6c5XgqfdYnobPb7fvM//6f42Wf/Y3zlzf8p3vyv/3f1822zxR58jAu2IN5voMUdFDjyNMtJabTSEXTZIKXsQcHFPgsLowsPe15QGG79kCHkytQWCecMyRm4J6ScULcMzgl8KiN6rmcgmZXhPnagU5D6ABXs+aSYqZmTGAXJx1BkVgqTMnwMtTgXYtLx9ahuckWRmgJRZTL6Hj1gSVYR3v3mpjBC7jh3nZjC7YrVfJdAU4Jid5MMs/ZP9t2mkdHVo5XRcQ6wVowfpZiAIqlhTDs4AOa1JEOFroLCj1IcGE2AoLkRAFP4qfuT3UXmgZgOCowR+7jGlVNS0Oo/wQapOrdIROMdXPk4C5MSgLLESHORIgoOpA5A14NGYfMDKbhDalGw8DGUdWCxHBtEqziqsgGX+x0gqJuyhWUrQBGTiVsDAo+Rf8QEMVDApYJzRoHCn9p+QwDZ2mMGim9jjKBJmjtGKvR9cyFKt/QP+yvoBv9wHuhFPMu45qYygfZn332Ot776RbxroECgsQbXNg89GWDwjjEFV9p6teg+bRO8QTBHUPAT/+K/PAyalNICy/p9AoMQP18FIQFLi0dcMPjfE7xrEfmAIW1dDE6vtWuG35XmP3e6XEFB0t/WCmYGioDsrGl/hk8/svsSEAQbLGCrLxCAW9/MPth5MrddHW5FNuEKNJ97hQpqkUZ5NkFaqlnOrAqpuEIonQqGCkmR2mjWWLorgbpLJXVFsXSpxMCk9n2GhIjlwapcBSMNVpWDFwHEAt5OBBaBbBYctV1UwG+m4LaL9ku5qNDyQDkKTFlwde0C01wxxgDUsL2tb8XitS996V9/CHxEVibcZwIHLVbBAZmIsiZmXVM6dYDE8tHbHcd0Cj5zQHA4rn7voQH16ri2fe+cxjENWwLFAKG3k+FxaK700dmuqcz+86GOznS5kpmB7vx3G/8wh3duQh833gVvz4m5BoNoBgWX+x1LUO4vVw0ilXudUdHHjPOJuJpLDiAWcFYI4OyBuqAIkRmRGs4xrbYTZ26wM24O5BZBx9ova70AYGCmr5V4j1uNq2fvv4e3vvZzFnP3Rkg0db08GWDw5uAzCVTjEcx2HyiZv7TFC3BDeM/+r/8nBQU/8T/HT/w3jCmYFZspwHbbgyjl9lv3RzFGX+NQtaDohDvKJVG60CLAxa2BxIr6mYz5Xbe5L5ZOl9et9Kx12RB/JaU2C6NetC9SSi1S3aNefXET1b5A4vOmHQr7NLPxWveV+RhUIGddRNsFEqYq2dumGKZXVwoteLEyhKu2aVEiUKIYdOhgwFwog0WZMnC6a4Cgb2Ga9jlH63KeF0NAnisSo0M1wMEUiQV6etCTWSxIusWwvV7bmunlaDtbjFJ3BeX9kpLGO3twFaJCXCiIQYDF4MoRAIj5quM6FAODqLaupKrvtJJR73kEqK6ICilA8DZH5vvWNu+YH1XeD47r1OY2tlfGlUh3CcBciG1MHQgZ4JPNc4swQJuuw8G18Mg5fRRk/ABTEsHQ0O65mCVLwV20lIcWbOqBl80lKLW5kJrroAQ3itSroGAGBGPVausTQOVm6y/qAMrfO2iYgdRQYmzRFfnWnvMYUDDFtLTPmpExrsO5PPvgW/j87/4C3v3pd3TLftjT/5AB/WSAAaCNjQ3ebTtsYMB8e2TUZrNSbUIj4T/5Z3+In/1P/kf4JxYIMg/gMFhYT/4BJTYfY8cHzdeYT2HIFoou16a8RSqUWOy2Wd028CmhXgDOalErODiwJgKlPCT6eNkyW5jWXrm2F3pds65kRLdyCipwOoO2TZG9W87tuX5/s644Nf+tUqa2LU7qchG2cTSh777k3bal010HBPkEyuc9ILA+aJZlzI7mz5hL899DJ4crFI+pYItlqRtAASDUqtvMyqb9tG0KhGzngm+pPCpX81rsFAOHiGsf5wAUGujp4EcmcACYUPTIaQPh4t3SQHJbooqhLTiuWddlM7Zk6y6DWoAkV+MNHtte4aQpZkOK6D6uBPflDslspnHVV3NpxlwltUCojAAhZch2D0oXC3rbgEtYPxYkqsBva3Nap83BvPY5HQHNwXa8GRjptbmtYQlr+6E1DEv/ROggn1IKgbWdATra3XhrURagtPfXZF4DSszgU4K7UTln0Cm1OKshAPEhxjDGFsUgbNygE2zXSvvOx3ECBOudX5G14h1IffbBt/Rshc/8Jt589ccPdxwdlScDDHZDcOi3DEIpkAQtMJgYzz78Dn72P/7b+Mpf/jt48wd+RKd5vmINr/BXUHD9eulWsAeYEdv+1wcsBmakpBHqNW+grUByQdkS+JJRt80Yg9HfplWYabXoUqAWhMgmRNoiStToSBA3y6Vn3iJdKLOFudrj+xD92pgCE6SURhYha/81CtYFZRPytqVIROMlzGqmsBecVkxK63O+7nNeMQTpZIrSFCc7UOjj2A5hmdsb2t0ZGLWeKAACTfqTITWrQpEC4gyKaYHLBjoZIHC/LTAoj+XcdJo5Jvg58qlz35K1BwUJAyA4UBoec6JXqg8WwBockOVKEB5dKsaO6DZS6ysPBmvzaNHe5mPmFl/gQLBZxZRQJ5DX2t76IU3C+DowaGMbdhrELZnif9NlZBAuL3T3j/9dNsjpPCT0evS8nl0nDo5M6TVWYbX9zuMmrq5fG2RRWNDWr4M9M4QiA6TbI0uTK54gSFwOJQ14RWUQC0CiQYNNHFdzh9r8q+NYLGXdESDIST8/GRjI5+uMYWSdqAemWijhOP8Wc7PpBC+uY+x1uI+DAl+HAewNgBWMZx/o2Qpf/qv/yJgCoAcsot31GmvwZIAB4A2VvXCwDhO48JjAQYsiJjz78A/ws//HfwNf+Vf/N/iJH/gL9nkMpPF7TlG6h750ozGDBeH+c0BU4JXcopUHi6FkUNpU0W26qDjdg8umuxQCQJCax6Cc2uMYVvRa269v0bmcM5A0+FAXgsYfpPNpRM6n87EVHSjYpiijMhkQL7ATqNJdAAoQFCiICMBmcZEheal7y/l0Nsv5rFHW0Wo2in0oR5nPpsVPp/PUrtPIDjRQwIiWpR+q0hw4C6zKlLRr0OevRMtSTqbgZ2VSA0gYk8LERC/64IVZNlGgo3DrSu86IOgsgYTxXY6tjwGlRhk3gEC+1cq3fqlysYgSULXtVycGS4UU3UUhs2J8iXaCuIOBoEiXIC+AXB9XuTKuAPrYJncnxHG1fA216PNqAfEFVAvofKfR+pd7nQth55Jni7ya2c8ZA6DN7QEMHDFDg6Lxdfsya7c2ACOJQXUbGSD7WZSaM1PK5y6nKoDEDGECEoGLoPJHlHPMLwcIDnYfzWfKzC42mtNkzsZr0y8BbMZ7zWOyWIvPPvwOPueg4LXX9/cnxioT4lyeDDA4RD+NkzRw4FaL2BQMQun5B9/Cz/6Hfwtf+df/d3jzB/9iCBryAQu0T0x8MkyAdU1kABIBaEgFOHdll8/NQqCY+vXyQj/f9Pt03sCmEH2vfjrKbwC0vb2ttL3OfS8vWuAdjQvFBcj5blwo57t98B3nkXJ2q+uAcl72VdzS1vy0eaDWReqoFC3imSy4iVwoBcGxB4wLyzlsT4tgoLrV6IrD2AKJAIGSbTo0hhtOgEzHWQNDQCcAtGNYicCUwCkp9vQ4A6kaRxJBQlMOApECZMtWOG0RO+znNgZd8NdGSY5WyTiGwUUygYHx5MEwxh5YClceJpzaGEtvhwMEi6RXv7y2RwNIjVnx9Qnc0M5RuUkUshMz0E4dpR5fYDMSVfSfjytaDcZx9bHtx+sSmDWoleO4+j8poHIxZshAXzop+Lb53VwIMevnY+b2RHtHMFBnMPAS67Ztu43r1gJim1vRZPEu+BLo8iIlyP2LJpNySm27ttjWQo1PyI+Wcbol210JvJdx0chxwyeCqAYGDETtYi6uyLW4W6V9GMfNXAfpNI3jxLYuWAIQ4ZsfBqbAQcEBe3eNLQCeEDAYlK1/0iwQ7xz7LlotBhCefe87+Nzv/0381l/5B+2864Habje1e3AYPCAIxwWFtKtqYA/stVmAKRwZunm61w20vXKY7yDdsIe/PdqRvQvpELncApWS5QV/2eC7a772Bf26i2Qe/LO1j8F0+qQn+BGIClG7Zji97khwhjHanT5n9d2BAVccxiREMGA5VlRQ2Uzz9wDW55+7MUMAQZoSSVCFktTURErJFI6lYq6aHW51EJgM/eVJaK7ZByNVubMOrzEDNobH7pK4FuIcJGiqQVfuKdSbLYfBChzGILUI2OVKC6+0jzM8XsAPGBI2RWAe8mLVKAYECkawtzqe3B+rxqryHswErujvfVwTdN3XAknndrKej/EAEqb53WJKrsxtfbkyvydG7+qancZ3uW4D0BOfn5w6A9bcBvvgy5EpPUHKRWXgdkHKG9hk4UPJnijUcSff8qkBfxA/yAzEeIvWb61/Yr9cYcq8b9p76a/RYFzplui6ucLWPfvwOyGm4JO7cRqecVTHUJ4QMFiX0YIJFgsBLpCevf8ePv/1X8Jvffof4s0f/jFFvRrF1wXSXGIEfgADO6rtAYStr/05g2DPURGOPuWYGAluMXuWtMU5Afq4vQBpiyj4JFdnKBxtzzsEBG5pNetj9sk2x8/wGnqy9c3gg29Z8gJQMGt62B52Cgol9vW+Azr6X/nuvB0LZiCCgYIACipQTVlFy3Lu/WZjGCAgCBiEC/3/yPvXoN2S6zwMe1b3/mZAuypKVKZ4wcwof5I4omTJlEkBczkDyZYlARwAiSspADMDDgXxolLJZVdsRzRjRbFiW6lYjmOVKhTBEUDOnAGYHzaIIUAqsuT5zpkZgFKkKKpc/qQqxjkDsiKVfsrEzLd3r/xYl17du3u/73dmYLI+dtU57/fuvd+9+7J6rWc9a3VvIBM7OFhhxoSQ0oKUNDPfXzJUM7r9/QPW5kjxDspYXuvfU1agUYTtWPZ/64V1bEl3GiQ16QoODCgwt/WvQEF7sQvtndU2n4dhX/zGGO5ZARtPBwUK7h5obAsra6B/J0LGACQsChJKeIFUqayRLyOO8n0g294PUb6HVHQAAsP5qgOG/Tg3c7YJJRTJDWAGp83DJtPky8CU8nrln2DZ8px0O2R6QL1m74vwMMAgz6LPOTkZVrmmngc6eT3DtpwC6Jf338CnfulH8MozP4dbv/vpk2GC8PDpmZsDDKaoeT9gkda//MYbeNY69dEnApOgRqZ/RbDdw+ie5v5VAe0y0U9ScGieawqQylo9Y2bgIryFjjeQIW6l0KHgYfcaWqM9uzis03iqEHavoI1JNn1i0syjPjcue+jISn/NYvDubRZ74U/bbw4YRvkhg3HZKchILydZYSEsgGyVYN5jKWIwinmQhUMIAW5stOpdHeRDVbbqHZY9FxM5UIjGJEHCDRJ20JADUBmF2DfW7i6U4X3Rew6dzDYx04FxmAGBo13VUpgGZP8rUCCy+4Qx07rvdjAN85Hj8VGbemZD5dHDPgrwWMcxsj0R5L0nY1sEDFBhJCLkABIECMqYpqwzxhgibt9E6fId++JAxg+NS0ySnc3VURu79opc5qDquIL5sgGpzJMvk+ZOgIUlCCtupq/WHui05t0WI53WryQIDo7Nd0u4bUNMg36zZ8zKDhAYs9FIzhm2ZczWMaW6T8HHXsKt2TbHs1yGg/G8OcCgL0NKvx3Ey3u2I9QruPXYU41Sdc8d2A0sACAtc+NP6VCpAu2YxFq5B9XXI3iDHPeYf4hh73cwwEAdnc4dwp6tbW/2+fflTX2G8jhJy42pXafG1JVLGcfcJ3BuHoNXQ5KSgoWMukAqGsUjUOAd345RVY7Z692HCAwEmMEoDGzMalS0PWo0tsIKIOYzMINg66NzsvfeA7mwG5PMweskQqJAT2sfSf8QEqRDrH+aPpzWopaZsffxOqAFZmPZXKO/2ZGZoXJJ+4NscNvT12pH/DQwGnMEnCUwIBBAHoexZdaVdWFsAZw9vnFsU2EJHxEjE8l3EFIysIAQcliQl6UCYwsdTXTUrkxlfAIAunka+/CcuVrnqQIgkrwKsuWkDhSW6tyEsAm4ADnoNwUKMWxCwHm67CBMaEm1vsok6K0hqzRgO2PfAL1ctuC2/t0B2+vYlq4el/fuuv16egcKwv2PwMuk3FxgADQGuVcU/kIJ2zsacLTLg0F1dG73WR6SzwkAiBMOaD1km2AjFkmMRPZNtADxshrCNsTZETwJnlDrlrh1inr19pC+mjZSjzEZKU6eJkmrZuIb7Wo0rCngYXw2Dox3RP3TYvDWPxT6owEMB0Zx2l4MjEZgBNyLDAbDWILeWKxbwQZ5j4EZFOB4qXYG+7zNJC9IySCswZhs1HqduaOnxVvjpj+AKDMmV6F7z9QVM5aaZyfOLCMJtDr2/fWgdY2GLc65Rv64DQ1EIGDX9CDPxtfud2p8rQ1xfJcsEntFrGwBIxubAE1CVSBIVPNOiKpuSA2TdtAv4dP1EA/mY/jeXN/fyMpujpqMquwVoObKELKGwCiHlTcckmrDi7X8BWHMwFLqw2N4cNTWJjRGbkg9h2TCDDb5FaptLKxkbbe+G+tt7YdwIKnnYoxn04nv0rZc3ruD59R+PfnYLWxhOIyJI+8HzIHjpNwcYMDcapCJkS4M3P2GvI/6pY/expOP3IK/b4OM6iQVcjE0BBvOOnycH2qOjEDAbOJJdfcJU4VHVOvIGGZREKoVclQQwaPok4GMamfrL6AVmCYfY05j1aStjpIN3oZRstb+GJ/1/gp9cVR869YAEMwgAhyUUr0ueqQjxdl7QTODUfS4hRF6Y7FyaYDABoALB8Mhn73xMMLzCqY8CVdgUCIHC9GQRK/zSsMN0mauskH60io9lxp3W/ttIF+jvj63PIAzsis+/GeCjZG8xCMjY2bG3x7DPAkLHACBOL6AHasPG42xjS9g46njuxUf34USciLJJSnGBrGPaTLgS62s98boSM6Bsf5pDN+7npvya5ubWUHCqvK5BRDvKzT0N87WWui0Dw+iVkr010BvnUG9jxhB19XF+oWnrAmw19PAWP+0jgtg4Ra5Jk/njoQS3A30T/v78hv66mS1X802FlRBmnxV60Wp7bMT43pzgAFQwcEAFJgAXN67gx/+8rP4/DO38cFHnsIatAgRCfIKAwlEIagjuWEfeztC4H3iEtAKXhzbHuMAVfEDdRK2E9AEMKliWRw49J5FI3JHSHLCuLhCOQACFSi0iVqtIW66b9cP1bhXY2ftB6pBhPdN9b5j96VOZUZ8fl1jcQQEnGVww7HrUS+rfmZty6pxZhQIW0CENclW+FcHQAEMp6hFKcd+4tBfbZ9GANDKGs/7rvMQYwc+CD4YAePRydF4+XeOf4/nUvMZz3O93wwIAPsxjseA+Tiv/hcjk4KEJp/AgMLWjO9CScJHgIAByAxsvPEADB5E1vv+GvVTe5/4jFoqkxe9Zkmc3RTIe54M6sqbVEKuDMRYC3u/vPd6irtwEXiql2Pf9E/s9XKcS66T0OvlA6BQG9kAjS2MYQvcgDvfuIMXXn0Wn3/m5cZ+uW2wxpOv1tyDgzMA+M0CBo2GOwIFL+OJR5/aU4Ask9AGQW5ZwUIsV7ZvTkdXjjzPLQhi43naD62eIW65b5oMbk5BABrPca80opD2nkadSincf14ehJbtDW3z+04BbaVtsz1vVK+mD7yD5Po+dt3i7nB//SzdARuHaCSA84GAtbMMntmXBMJqhpV14yvI/F1ZqOU1eJtrkgr34Qf7DSCgAQheatA4Lb5t+6sFXa2St/tFhV14DhqAvUcFdIa/VkHOBW8VGIPmmfFqFPjAsPXzynIC4tjafU0n9KxPPC6/afXrqfHerL8KYyVZdXIOUAAwZI0qIuzHcC7rta77E7N5aO0H9nOxmYdqjJLfPwIZBlmeDK4b/rJvcR+Ytl5VDvjaOinqo74P5no45AMBjR5OqQVGWTtoGGoJ94w2Zu3TEPSPu/fv4EdefQ4vPvMyPvjorcZ+JTZAT/KmdpXlHTiAPui3FWMQCod/ERR8zjqV94JFaAGB2GJVoF0/rkGZyOccDGxRCLkan5H3CbRKaVSqwlAhBQ6oZ/YJOwIQsGPh/r3XEcspb7t+7xR3N/FOKelzSu4UVZ+QDlRDaaVPFBsZB7kOO8q4HR8011u/lE6xxq87Yxnqm1hAAhGAjX2ZoocImV04I1iwF/hScOGjCo1ddA6I8O0GGiWPJmRhv+EQtkAHFAqjFapuWE8BgVNg8lxjb/e2EpVp3CmvD/3Ea3tmYAT+zhl3uz4l2gEFKKvQzmlq5rqN6wwUWjkl89Km9vuDzDsDtVGOetBuBioyHgA3DgymLNW+Tr3+6T38I7ZvFhqS/qhz/ajdM51r4SDaak4UEVAo7k9CsHBQQfTyWxtjsh2H6PV7d/CZX3oOP/uRl/D4+5/ya7ynzOFjljmcCImBuNnisevXlpsHDELWqBniCAoef/RWk4HsF/rv5SMFSxYBg5XR5jWzJCZLYotgYC3V21xLCZ6oXNsbmpmBSfolgYLCmHkiRwCitj+FDjklTLHrZt430CrwI2Pstxh4Lv2k7YHBdUp/rz4XYGYMgDEIOFqiF68ZedJtPWSMnJ4OIMGKGYK1rRSAGteOJXp6Lahklwk7N5QLdTYSm0JTz48VIOixqLQTaAoGgDOVul44U+zWX0fKfeT1HSUL9nLXhwnOsZ+9LOy+601T2PTJwYI2XHIT5Lx7fcEoAQMP/j2cD6N72fNXZg97EQErWK9nrDwBnICzCoABzNa41cLTbyOAeJQL0jN7cT6fmsd1N+VWx0b9uujGSRkbltSBBNh8FpDA1LFtChKM/DGZj+FtBvDG/Tv40a88j5/58Ev4QMyJC50nrIxUOln7bN6640fY5RpMys0BBkGQ1U57ouEUFAThsKGwudpQpYP5ZoPT5wyMljetKrxrYQcE61awMiswkPusG2usXpfDscTu7TlWLNkkvhjR6irL2YzSao8tKe0EuwcO1pVmJIDWAzk35AAcU7ZyXn9zQqH3ityu7w3grBzF+ntFf64XeJ0yAnL+vROuUffu6n/QoHV4tBoY6zMb75j8aDkNq658MCVPRFiSUpT1dkpZikfEqvSt+sUo4/A91uYcT289U+kDLcNzooveVYksrLWvgNtxPRMwxnPFK2ygoZ67gsqN/qD3qI+mZD6errsSjT/QEvhxr39uAGadr8QtUKAErNu70yF9OE+Ozw0/M7CWstOf75VOTURYMiGBsCR53f1ChA0JuUBe8c66DNnAHANMNVeISYR9ZGei7L55/w5+9KvP4699+OfxgUee8pVRfrn+vkAeVcz7YA7yWWWkOoDHY3BzgAGwYwvu3pPVB5JToOEDjD18K97pVA1bAPZe3Oh1Ci7mD0SWYN1YBVYYgrUDBCsXFGUSTIhNuAG5jwn0KA5oFF4KKD2RCTU1Qt0LuXynISKOwAHAjs7053cH9h557Ds91gGCmdcu14zvNYrt9gbdjx8q6QezJKlr9wwEpEYxtvc4R3kfea59H4zabx6p18Vj3QCgyY+a07DkBFZKkglYksQ98wlw0DwP++99PWegwOaNgekICNatnJ3bMeuHWT3PHpdwfOPW6DHXZxSlzPu67BmFgQz3h0ZtOVDu1swePPftB7o+CHWJTBWpsbH5b3M0m9EOBhAQYCFhMf3uYAEw4ZnpiD6kOjL8oiPrMuLe8Nfv7MuNnaI/U4/Wv8WpMp25JAHNCyUUyFbXnKSerMhiY8aSCSjCHmwkjJt49S3b5iE5k2Gt35tv3cGPf/V5/PSHX8IHH7m1EwFGZeoSV3BgIJ2ZUUgZBFyPNbhZwEALA7hzT5Z0fP6Z23ji0adU4bBfMNrWtLkHV2RrA9CfB04rNwsdjECB/S3gAA4gCjOu9HMrrKCggoMyes1qKEmF04Q7kQi8/E0NaDDkGwHDQukQLAD7+OfKbWx7VE6FCoa/8X6Wz1P0X1SyY9Bw1mO9pC5+G5WoKeYR5QhEil4+KSjlU31lxfpsQWsIzXM9CjlZKcxNjoxR2CkRWL3ABYwtETIz1q0AOSGzAGCCKXxq6GBpa40Vkx8LF4jj6MeLxZP1v8TcMkJhXm04BgWWpGVGom9/Yd4Zz7JxM17GbDzoWEUFupfvAMY6sBDrm4gO5XYus+M5lNL+lI9JafskUZv7YB5obM85wJWLLLX132DPxsXQ18g56NmfEQgwByoCAGdag44c6U0AZ+nOkd68IsZFFj1ZQFhY6LWyClCAm98CIGFJJCHjVBwcGKguxcIKBhyrnbHPN9+6g5/4ioKC99+aOjtG0xk42AECrpfFqXt5785hP9wcYBA2prijmz/83Edv48lHbwn94mhMPntQ0IuLdKoiOaNKQ5n9romBcRX6rflUYWdMQcHVVrCVChBKKbjaKtKtgj73ilr2gJBTFfyLTA1YMKFPRFh0B7Yl71mFTLRbYkesVDSOFWpGVQhOQRbxJDanpLm5LtOcFi4+pjYW1if7a/a/rSdGnpcnjJUKDnpqEajgCUADoKy9cQ17bLed70tvXBYERiVpprEpU0A9BaGyW7DUGZdoeNQYQUFpSoS1UAMOuMjfMwVRE8n0O0IfWd9QPWlVs/oGvOD3mZE2Zih6UNADgrHnPR7nUdwYaJky4HrjZq++YeGHsTHjAvB6yzldoRCAQlFjbW2I7MopWR61b7cZYGP86zO0xX7NqERQ0Oc2jPomXhfLyCmYrezZeM8GrJv8vZpzNHCeoq7clC3oWYJT+jIrgr7ItNOVQMJFltAIcgG2hCUzisr0pktMiRkEAjFjgb4yXC1zobZ/CoRxi/365v07+AllCj7w/qfOsFF1zhWWMbPD/js7DuDOvbt49hc/jcfo0cFISbk5wEB7wXeE+uhtPPFYi7TO81GlCO7T34XJ97W37u6ufUAWevxcrgJtgn61Fj8muQl6beEDJBkEPhEWYwCoICfC2woUlpyRUxX+i5ywFqHOjC5LxFiSZLkWErqMSAyUGRJZGkPuOfTFdPLS95d6GQvkt1CQkJn9XC5VsZph0Z+eFcM9FSaw87uwQAAEkR04CrsAcSObfWIfsM8a70vNzZC+WLh6cEthbCQKlWCelQLXJI3uvdBdUmDonzhUG7cKoV3dYJZTZhpRZQsSVUCQ6mVNMaUklTb3hYWKZUYhpVIVMVAI5me0uRM9UNxR84P2njt+18m3GZVN/bJRgiTr2EWwwAXY1PGIdHiCjl/aAwRp8zEI8vYr4JgZfuuT2F+RPTliCaU/2hKn0GjazUDBKDdg7VgCOxedJtGJWwMEigKLlQMzc0JPiq5kLAQsWRnXUnCxJF+anpOAgKO+vE4RVjp+lzr+xFefx//pTwgosHIdojMyA6wOF1imnTnNtz9+Gz/5Cz85vccNAgYVFPz8x27jKc0pAMadSuG/c7d3/fo37+JP//Lz+3tVHSa6WZUcEWnSiQwQq0cs32XP+xQ+j6BLzxKUMMF6Wt0Fd1NwsEns2CaAgYSLnLCVDTkBWxGAsBXGRU4oXMQg5aJ1k1gz1JtJbAa964uBUt13WPfVPWJVOgYS9BwnVCMIUc5GA6OEaaDKNSX19GlM1fZlHxLQz2vkYCyJGiMyW/Eh952XAvU8GeDcZl0vBdgIWJiwFt73Ddd+KV2/zNrbtwvaJlf8RMjQdlBdppWAdvteBww6hoNhtyEg6J4NKvOs7AWThCw2EpaLNwKTxqsTSfIbyyoNY0tGYGgW7nnXY3jm+MU+Z8wz51cFCsshSKgAwWho60XgGPSekutRvwDjvgH2c1uuPbCSekqAvv6daBdGbEIGARTUMKvmmhRhUkeA4GorDRiQbcv3oKAHSZLUKdekJIJRpDJIBzEU6cP2M+v8kDlT547vAUKt7PRdZ3PHmIJzSh/Gi6UghHRIdkz89C8KKNi/W6EtNwYYXN67g2dt7+hHbw1trCixvYdBRE7n7H6jg/W1tyq98z//zz/sx+13xSFaBQdLIvW+4x1tdTiHv6VOiwuipyzhagNyEgWBYoZPlULXkOohh2MWU03AtjGyVAxLYVyhqIJIUJJa42o6MQgohZR+tPhoFcFoSI6WRMZ+HJXFY4vyaQZQzlHjKVclujeEYhjUOKQYzw00qwG4rjrXAQN1iRJ8iVLc/94UAKFS7v2+EaNiIhjzVhYi2JsdmWveyqJswsrSNyvKECAA8xj1qH2LjqW1bUnargAKsvZNVpag3XmzKqk45szscc6ixo/Mtwlsx4YKDsRzs0QC9kx4WT3BO3Ag49p27jlLzZYgr77U7Ns0hmBpbnyvxnjpcg8S1GgmHso1MJbtc8Jgp5iTI9braF57KNbaowBhS5LoummHZap7IgAVIEDbGJkACxsYKLjaSsMQrGtpAME4/DKa/4Q2pKChVw0lPLQkXOSk88FCrvYvYUkIf9dztqJnphNGffjBR1qjzcxDMDrqewPofbFE/Jf13UCnXOEbAwye/VJttO3Zb8XMnhcKlDSFg4NCpIkgv/w8fvojL+EJHTRnVk1RMMAQSpQZNaM7W4JXkW27N4BSBm0FlBi0VeWXSkICC4Pg1SnIaUFKBVeabWXU6ztAG/M4QMZARffFAlHXKD06ps6QmLe1ZLqWt1zCyQtTqgee8hgktEoUIFUK2t5+d6pd28be0zlGxCZ9NJqndp6clbrhltC/YOw2ylqIsCB3K16AJeWxB8pAzvscGcT6BoOw5DQFBSlJ2xNVA7N/yU8Y49hUGwsIO85g2TtfPUSw9CmxUvBlAA4gbBK2MmRKRu0DjtmBJVMDBkbjmLsxxBnjGMewT3SWcUzNcmZJUiZsutHVOSAB+XSYrJdtYAySHmS/E39G98zKmshFCxDkkkEbCzuoF65JmMHUsahHYcJRMUfJcpas3cCEKdBrUyI8lJVRJWFSLxQIJAIeWhIeXtLO6C/KuooMyVxZgg60uTMDmDvGwD53ojW3TXY25vZ4G/Xz7j3ZRvmlj972VzOraE7LjQEG/pbESWulE8Vqm0wmV1a6AUX/Gygo+Orz+OyHX8bjAcn1scYisQPPBDWPwTeFWRIWBjIKcmEsJDH8K2zIlJpVCmthvFPgQnq1FSSC/205Bym19BnyebG0mm8g94+5BjEZcTQRenRsxrKZDNp5/YQA5jSsKRNWWjHLK9kmXhYdbGRCGsslv5+N8bBP3POZK0oDA9EDMCPRGEvYnvAGMCoYmBpMKwxvr8kPA5o/IMbvQoGCvd0xG0jItW/islhTxsB+LwgAU4NgxtLaqQyrswQp7QGBDldrPGPzwviLwSQH0xtp/gqLh0ikc9LmTk4gYlAqoI2RcxoCxFH7ENrYg9eouPs2RibEX1L2wONIupRZ/7bESQVBmQgPIUs8vcRlmhUk7OSbryfbwPWAQD+PrbkPMpcZAGUCWJJZM4oCBEjicmFkFN3YpwWC9UnGnirg8hRl0YlIwEMA1qRJuMV0yKBfDCAFXSgggHaAILIEDzX6sOrBiyU3OqKXqx4MWK1mO8yabbFxndmm5h5h7kXQ8fr9O3jh1efwcx+t9nGAoXflxgCDW0egAJXeV5/D45yW6DOaW7KO9NP47IdfxpNdTCaHWSHirINpk1WFIYKEUoC8JFwEL2GhLBTYVrAyYd1ECSwrYU0aa8sJDy9FkxET3lnl74f5/NUKPU0WVyn0EyER3hU6nk2G6YZRHY1o3hWw77sIFOpy0DFQsN8DwDaYhKMs62mIYGBAyPrTDUprJJu3XoLha4dn7juA+sZKffUra5zZaHb1HvcgIYlhocq0GEiQ9uujLXfDPCa0RqH3nnuWILbVwI+BoWFbrXmhbUlshNLh5Mv3NtItvDv2gAqATMicsZCFUloW6WLSNuvayGTNAA+a7waeq8JNDzCOzWvIkyxf5CQGvqjxL8leOiZJvoVJExTnIYdWvukkMAL2SbG7MdeBMkAr6/br73fz2Ac2lABWIktiQKlw6yQtuuJgxqLWIsdyysip4J1VmNSrrWiOlLAnyzV0ILBfffDQkqaO0UNLEuM/0IERSPd9N2IPR93HXk87QPuTsRhAC4DAxilBdkx84dXn8PlnBBRYOQcc3Bhg4DLQdaAtgfOJTZW+NoAAtGMASKf+2Fefx4sfeQlPPvZ0uJ98jqhLAL5e3JCyeUcFkihWikwS8/aQFSQsAgrWBbhaNzyUqGMRkrMJ33HRJuNcZ73ubJ3uESDoWYKLTNPJkCf0a/+ilOEYAr6uvr57QkBbVJ4xdrsgN0Bh9vKjWTEFCWDoLZlxtGS76DHnYCzdqADwV19vqwrC4BWy+5EBqL4qNusnpwykjKJGpaQ9SMjcep9GY/chGQDgUtsKjBPszm2zK7/YZnvdN9AazmgwKYkS0zfqFQAFGlZQQNCzBxZTp0xYOD9Q24YszwjsdGNJ2wbfjOSaY0n6qt+kryq/SBkF1ACFouGFkiTh0ABfQRtyaFkzedKDyPdovCPN7aB3MIftnqfZAtrNYQuLScK0An0FsysXrJRwtQl7MGJR1yJJt5E9vciEraRG/x3pPgA7/SesqQHGXveN2YGo/0Zz5lT/HZUY/vHw4uA3dmg0LjNQYGk9FuaalRsDDAAMQIH/5f/bOnnr6BJGySaY7U394jMv48lHbzVUmpVlMsjV061VMoMnO2MplZhEmTObl5BRepDgTAI7k2B7HfRLeewY0AKEvsx2RvRJkTV27lt+QpmCMToe0cyifCdUekC5sfTAroAaSr2oh8VpHH8vADhJPoerbK4icbTLWe8pEVUaOVENEcwNIwNlEyBQNlARU4eyqaHkYFhmtJZoZjMkTAlIGUQZTIScshiXtAxAQut9Gkgw8BQT3/bry9p2myE5u91cQEUNZ9nqznmH3jQBKUPeOJORKbm8lFLDCrZbXIEmmBKQuG45nrMwK5ab0q6vPGjXKTBQ1jqWzAJ63uVYUsoAEjilMJYZi7JDpcDHz8ex0xOWm/BeyHfP6PVA/pxQ2NEcbnSe1jcX1YeFsKXKdiUSsLcSY8kF60au+6427HaIXbck+i7L52xnw75EpsD0XdzQrepAOosZMJnp9UTfZyMDHktf3YXquI7sU3+P3j69fv+ug4Jbv3uw+oDDxZNyc4DBCdRc/5Yv1slC9+sXkpjMZ37pOXz+mZfxlDIFI09XMlaPKiQnIzIzYyeZxfo3h4082JbuZJQFHnO0zY9sstT3LtQJ028LKs8+BgfJlbwCg1Q3MBpl2I4S0RZqwcDZFDOHQaC6DKryaPba7ECppxYsbK546uYw8bbNGx8HgxXHtaf8jtqUgepJlk0NY/y7iHvLRa6LBsUbv5cVRENCWb4nMSqcFgEJKSHnC6TIJChzUDQXQWSJmv4YvV/e2o7Q/p5STzgAQgYGylY96NBOCuEENjfe2AKjA7QdIDOYe4BQEtRwWuigAkJg7LfvxrQHBfqZU8cMbFcC7HgDFQEH7+lYKgtEKQOUwSkjJfm8SFlBXgXCBvpibsJoTM+RbzsW+8OM2C6PIoCEZv4Cx3OYkiORZv6WAGSTggR1klZmZ7tizsyaCGs2prRgTYyNE7ZFVyGUd6fz2nceoOYQHQCBHmDap7MtAxB1iraXete/l0Q+rinospExj0mwRDXR0HIKpuXAXgI3CRh0ZUd1HQ0OyUBIp0qihq/zDOg43mJpvodejrFVSk4z20SRq8NkiQABcBRtHrEoRAUKQBN3NLBwtKc40CYn9XvDH2Xen8pKn3ldZK2N3mRjMOZSaUvSKt2cwEatp6R918bfK1gYJH11TERTurGl0QSPnuQRGLB/XMEBcQErDc1mXOZrB6XNajywLFLBAAocJJRVQUJGUpCw8zyZK8sC+AqNWT+MjESMsTeAoHTeNLMciwazG2OZAp3RTAuIN6DkABKWFiCwgj5odjvLckZv0xnjehLgbFc+nrRdBTAQwAEYWNcHGkvKWYCRgQLKFRDpPxvPRUECqA0f9Q7Fu5XvYxCg41hCaOjdzN+UkW3u9myXgoSe7bIQium7tSRfnjt7I62Vkb7rdV2fXHydhNQoT35tcIJq18/tQj9IETwYWC3m2TPcRsXS27i79+WFgS99TEHBCeN/VG4MMHCEFQ+GidEd8j+s716/dwcv6DpPW9IRB7kHAVRkL7ZpTHVQKEwaN3a7JCVRdptO/g0DsJCTJ+SZgWSMN1I5qpah31HscYaQT8VjUbgzHK0n6R7XiT4yRQoiUB+zTfkQLAiVWZfo1WWA4Tnd8xzlj8DN2WBgA9YVvKkBWa+krWUTY1JKqyCahie1WNI+5AXICyglpOUCTAmcLwDKQnOnDOQL0LbKTpT5ApSyJGcB1cPsWJapd40OGBljYj0XAUEfKjHGwI1H51la+6znbVzLpmO5iQddCpC2BiBkkj3mzbuUDVvo3bXHxiuyAw4IKkAgHUMuBdhW+fcAY8lEwHIhxjIvAvoUCByBhKxsQpTx7T2Q70MQ4KB2MnflgeN26/Pi3AWSHiNnvpKCwHNDYhcpqRMw13NHOg441nOEChZniagnk4rLQX7NUcXUFkTgbDMlGe7Vz1leB0F2NLTNi54KTu0IOM64rlhuDDDYFaqAYI/k2nJ57w6e/9JoR6ig4HSgbXLQdtUiaBeEgeHj0qFEm7BGr4akMzV6F4lEeUBpRZsYyi44cHBFweo91+Q86OeRAIgC9Wp48qBMmhYQRC9y5EULQ1DUiyznGw2vTDUeZN9J0QmlqnRSPgkWImbnA7poB/rM8MWY+blgIBqPtQUHbBvYe9bYBuQQFNc4NKvSpLxIO5YLsIGEfCUGxliEbRUAkbLExYNB8c/QB9OEo97Dtr7o8wciIOhDJnZ9D/xc9jcXMjccIKAUUErSZymD0AEESmIcNXPeJOhsBsSax3X8dgBvW1t2YL0Cb3VMeb0CSpHvKs9s7bcSx1PlWMIFBDJQsCzCBvh4ngkS1KiCEpLK+gPL9ykQMMul6EMnB/OXAvir+STKomg4rIaOxsm1BoJiqJUBTV4UcAhcX79ZWMVyAtzQhxBTZFFckzgzNuo34Fy9j4Heb3QdAJjTabrNW9KMgB+xHX/NfkUsYDawxB+eUW4mMAiKQb+21E4Q6st7d/DsLz6P2x97CU8/+oQIALADAjs6bVsxjKmO6NRTcDYKgFKsLkBEqhhlsrFOsr0HgUobw+Zz9SRmSzmBrq9osGFNM0kmXjRXpqAqkwAQOmAwoyOdirQaxWQ8j9eeBgv228jSjB8YJrRP9o4eNzCAyIaoN7mtwHqlBqSCAWMNsG0yDkZP2zNjUQ+TATEWSj1TXsRomVG5eBjYVlBeQGZQ+MLDDOZpw7472JB+yVH57DvC+yLSx54/YGN5ModiML7aXiZxfaLhYEoAi0ybseKUQayJBW5YqBpGawuC4B61pQOtbe6AsgU9wLt6ez6em765YcQamJwlCxsQeH0HxhY0ICEvwHIByouzQg76NIwEu4fqBAMKboTD30PZBjoddQ4IGM1bjMc2Pi7O3YN5a8yBt6dnRxQA9qFCCy28G71mht7CAXuG0ABkJ/+w72ujL66j4+Uz9AsA8jlZ5yZtVy7vwBbyc8h618vOfjlMbCdGouuBg5sJDEKJ3k9v7C/v3cWzr/4wXnnm5/D0I08AvVcH+MRo0SGAcnWArAdgwe934CmrwDiCRDV+phxNeZgg5QgacvbfmSfheHZAKbV12DMsDqbMW4wKtqeSe0NhhpR5H2cH5O9RseRD97pEsaSsCjEqnSRxeJ9cPVjo+nZYGlDQjWcHBqBx5qk3ua3iSa5XorzWKzeibON+0G6iJPchYQrMc6W8+H3IGIRNPE7iTQBCvpDztLZe51ap3MYrsb45lPVOGTpIGAOCZqzHrVQDmcFggAlEDFBpAUIxtmCteQhox/ja7YiMjxq/ISCwMdVPA37D8Twhw5JomPbjmTJYxw9X73SskII+BQYu42ndy/c1ZBvAob5qc0QG8zbc55y5O5y3DlIziK4qmA3AnnUeJLtW2RF2nQbgiP2S01KdmT7jNvepMf4RLEV5n+n0Q+YTDQjY6fQgy3E8aVsRWWSiMrz3yH6xvlLdnE3CPjfhnHKjgMEohrbzHgIo+NQv/Qhe+aHP4elHHq+gAGgGexRrA9Am1ZnRGHjHcYLJrSdeI9AaQ/1OnUFsY3i5ETBjEyICjd5Fg1r7wqFOIw9jGh5oPck+4c4VqP1G+3mqXGL7jVoHdD2/ecGd0qEAmqzfMGp3p0B7tN9T4n3S2bZKW0aAYLuq3qQCoPg3AOkXAP1LZCiRGksbcwECYizZ7+UMgnqeBhDSQw/LOKj3hWJZ71UuqmcCzKOVBzJ+BiDggRHpGqrPKMcAgbLcnzQPwWQbaD2sWRtsTDsD0IeAiBnlnbfngICLsAYREBhzoOM5GksAzXgiCRvClHz8kCVHBHnR0MlVHdNVZUCZhdaoinGhdyHbQAQJ2xC8s81Zu8eDzNtUwwbtnE2NvtqxCR3LMKTbz2FJ9PswX2In22N2c6S/OT6n7YC2H1TG0Ts0YRzN4TP5l4dsAEhYM6rvCNGBAwBc3n99b7+IpP6UoDt/VF2IyhrYbrxH5cYAgygmDUIc0KOX9+7gU1/5DL7w4Rfx9Ps/2IICK33siLv4aU+x9l6yJi2dNIyDicZA9ToMVQNwWjVmPS8XDhxoIHi9N+33AaohjX03mFgjNmSmWIoZw9DePlmrtn3Q7xoXp445kIz8dIbSqZDwVHvjcrpDb4mLGgfexZsbA2Lx6jjuCpTMgIwVawKXVQEC9J7JlT+XDZSXJrbIgHvAhUulqbkAtIHZQGJW4zoASn2ZUaQPAghG7UzxkjMBAolyfOA2zMDreiWyamAgAD5nCSIQPHM8pUnteKIAjFXGk5MalqBHjCGJAGHVfJJtPWTN/Lkz2d55tnv5Hukon7Pht9eet5M5a6BnCOwjqDew8G3RW0GGLcE0AiILV1hfAKcZIv1kA+WdjjJdTRHoFdb2s7ePyqp9wXCAYO3nDZf338CnvtraLwvTGfgWHaF/U795yRxWW7kxwAAAejpchKGNM96597p26s8K0jIkOSozmhloWYLOWy7mVfRZ6aaAAhLfJaXt2mRC14IDSkmptjr5RsBhxDr4fWao2x49VC7XVCwD74N7xWLPMY8KaMGQfTdaFjgECnKr8LcmhU1MiXvyVo8Sl6NZspkCAnCXUGg08zVBAVvSXfhbrtvcmCArRV4ATgxar1pwUOriUy7Sz5QXaa8aXdDaeihHhnVgRIayfxAiapRnHGczfEADEChByY6sb2EuAKMDCKmO3tn139e9mZ9bBXK7XAIf065d73I8OZlTKDLOK4QBBKQ/yybGoxRlGcRYGFvGW9oB5uvIttX3HCCwYwyAdt7GOQu083Y0Z+N8Xa9OA/sDMDBq657eH8sod+0uIz3Vt/cc3azjaLrZVhR5CFjH1eYnF5Fxs/myR3j2/q7vRk0AFBQzcPnWG/jkV38UX/jwZ/H0Ix/wupqsgBUkEMvcggJrDcXEXIOZzAA3CRhQ/2dUDMVBwScdFHwQzgbMyo52i9ePQQFHUBA9ERW6s5LSRizCwIO2xCRobgEFcHASOOi9XNHEew/6QP7UCWR1DJPobDBg99s6cCAH67Nw1WR5yyRHbcOGvdIB6kS0v1MCr+e1z/v9FMCx9nDZj2FXRHGLMekNh1QpKDwNKVibmlI2oZ1VBnm9ciNCWP0ypzkdEMoLiEy57HIvRqWXez/GnngW6dVpSXtPZfY8AweAUqdcACTNoSjBMHDzcU69fW7aGEZQ0IG9FvjHtiRhZjNw7fEcFTPSKUv9ihgYM5rEqQKEaGCsT+3ZK86T61lYIMq2tTsaxsP5Kn3hJWfwVqrBZHnhmdR3DBIAtMBeOm6nl3ZGrNNLTQ5PmKOn5u919XCfAxWTTE0P1/BQAAiA5JIogKIMZf0EMMLChv78FMABACRcvvVmBQXvN6d2q0BMUYawBQVgCSIwkzMxfr8jVICbBAzQ5xagMeaX940p+BkBBREQHCi4o21QKxUd6SsTRG5BQYxDM48TmVSBya3mgKWJY9pEo1RDEJQk2/kANJhgc29U/SGTmCUwDodEuvEMQMA8b59XwZRRxk7hgEjubUp0gyodNQ5J2YejNo3aN2lb703UWwZlLfO5TnYkSMJgasIWHJRsYzgiG2LgwD5Tbq+xojFPSkHhc2RLIPUnglP1AEBzmW/6o5fxrv5N3bm0gCYq1h7oDIwZb1voj8hQGEAgHM3VIxDjgLwxDmWv/Ek9tNmYFjzQeAIYjunu9c0GSO3nBQ7EG5k2kHyqL4DflPkabyF7VGyT+doBKgMLGLRv1qYR0zEy/kHnyu0eXO8i6F2YE8aic5kLKF+AsYCwymdSBipZx2RrZc0/aRjaAA4o4fL+6/jkL/84vvAn/hqefv8HYufWfvEchaRzRe+j7yg325gIJ19pfWOAwR4U1A05ak7Bz7ag4ITHQyfOT4vFDcN3LlsroCFuKTEuUVRcCljflmgo2L57vcLrRGVnNRVYNfKezBZBwwgwyI8a0GD3qQ+YeJY9k2Dtlorr8RNU3InCzFJXWyPuysyq1iocoHg8jospmvps6o3TrkkD9qCvu084qHegCjEvYDPOGXUvhNzdDwDNZt01WaGmeJx6k99xSJSzJCiwxPJB1c6fMb5AL39ZFKlTqd6Atg/z+N4z+dp711bXChDOqWvParShjjbXx4v2HaUMXnTub1THlErNWQKuN57WzmD8HBD4SiK7rjeGxSOdPgqBoZiVI1kezdfhXL0GKJhUAqAE1n61Zw7nqwGFwH419+nb1Rt/re8ppyvqWXkuV7YBc11reQ6UCZQ217NYLtDkAy0XoG1Tpo7qmFuOzIlxG5U799/AJ3/lJwQUPPL4uG8MIFieArOwBqSMFEsuh0lXaozlvtwYYEDNZ/XsL+/drYmGjzyOOstOGysm9Qw8C7oMzm8w8kYqoN6dxSj7EgW5AwXlSunOTX5rwhtfH8oDpZASBcQtQMEAQw8W8I5SXcFzaagwa9coxn9UoqJV42n3qE5n8WcQcEBPdmXkhVmxyaDKxvrMLwtD8EDqTZG8UJ/V8LliZwbyhSg/YOdpySXnPbnxHnvgJhfsGJ9hqMhoTK9ApeqJuX1T21mgrcq3ZUi3RlzHedtAEzCwu+OAYuedBzwwksMSWbyDUEcpATzl2qoCqbeOLxn4yxftfH0Px1Q+xnPsHGB+tDoAQKt7ekAA7EB88zxrb87Atu1ZjVHpx7NvI9CCnm6+6sGuDQNHowcBFgIwIGDJwB0IMABgutX6o381c69f7S26Nv9JWbd0AQAJ0JBezQfSkF9fbPlwM55d/kSf/0WEy7e+hk/MQEFb8bGssCYTMCPmGpwa0RsDDICWLQCUKfilH8ErH3lRYjJR8OIEaG7SKVQTcOMU7R6a6WkJRQwWAVmvhMotYpS5qNfHDM4lAIYOZAQEa6CgFAtHdAxC9/Z1F6zAHDhY6IBCWhZQutrRYbL0LVLWnZeqa2u99EBh5MFal9pE6ZTQKLv4qFD/jEl9qDOKPqa9wTwqzXXBqzW6ztukQMEft5cpGmVxT587aUdPS6durOy3PesQilH1Ag68xjitJrC7Zm/EASznj+cD+qCHd2pAQV8otYSDKnCyt9RoPFoSBavzMBxXA4HnlAGg3nn7/Vg9gFfZlAEosOd6OwwgGdBLLdBzBuHMMT2cm1au41z0YMCAgF1n4dkBI1DWdQcEGn0KAB1TEAtjcwZgU+cqhUx/WsYOmjuFWZcL23LMlJwtaFaSUVxiHpI2FRS89s2vV6bg0SdDBU/YLQ8fUL3e7B3l6MZOy40BBpUZkSbf+cYlPvXqC3WdJzM8SOg/mgi9D1CuAqoPIXO3iKD7AwZwIBvQ1FoEg9H9Hc9hvZJJu22gTMNxFyE3BmF/QUoJWNXzIYUOJtSJQMsCYgJv7HRYWnINObBkzJrx5JSVIje5ytU7H3k2fX9G1Dzz9E55PWeWIa16QtkOf3NustxRXd71HexGkzvtANkePIwARfTQW6NOu2tP1gHXB3XnlMPQXe/Z+rUtm0HM3lbbxHQX6sip7bdSBCT49z2woFEdfjPLoI7V6Acg3Dk6jWykNASyQADzZ5ZpaOOsUJXW3cDKDBQEdqDdL2RDWbcdM8DrOnSuTupRQMKBg7q3YVvZ7pp0cyosF6JndX8K36wq7HBJtsOlgwIDBlWvcsqSaPjLPy6Jho8+ETqK531aW7Gbu5LHIOFEAuHOvTu/PVYl1BBCweU37uiOUJ+XHaEaIVQrPi29crRPoRerd76oEBPA+iKMsgpbcCEeum/VmzKwtsqoBwfOfF8BuABwBSQUFCQBHFuqSXiDYkKeAI9r0ya/LUhI6wqUJPfeBCBJHF6XU2minNzgADANqGygUwxnGtiZJ32SJj2znGX8D1mQMaV70uMDhlT5dcvQ8z1RZhR9FzkNf8Y2dtRmf75XJaeA9ajMjKsrLj/QnPPViqN8gxgjjo9eFmURAguD965fr1uGzx0ZyNG5XZJkaKgtWQtxfACtvinGeHa3uWazpgDgXFAd5nxMhiSl4tmuOZCTJtkwhgwMFFxtO8a15muVISBo2pg1ZyA6VimBLmSjsHSRxamagAK6eMg3Ievfi8G2Z4NtXW57zNi+BgAuv/l1fPJXfhxf+MiLYr+aygG7kEtTDkCDJiNe3n8dz37pOTyGx6aX3hhgYIrk8t4dPPvl52WbyEefaAXME8eOFdqIJiWW7MbL+6/LNUl3JmMLD5AMOm+gQsAiXjrWFby+UxNRKOl2tqsobI8RJ+S8gpKsYiiUUNYNuRRwkbeNoWhIIKDe96REI28epoYSKF/sAUEf1wZaGjve8zqlo+UBzPM0HqTMDP/I6Hee9vAdDsDemFoSUrw2ll2OxEHc/GJ/zW5Tpv2vzqAaa72BGRAYt7OhO0/da1KmbWiysuO5yrV59nYECmbdBqzCbi5TAtJA7Z3oj+l4hmt27WgP7sZztE9IZD13oZGYK9CFspp5Yk5LrOK5AGBk4M9h5M4pHdChmENgoRwqu1wPBiShz56bAPgqpevrgjQBNzNAgJSQlox0ITowXQRjb8zAQ+8bswTLQ7tXqDsgsCWoyiCYDH7yl38MX/jwX8etx57cUf4OdI/YqwNgLu9W+DRuf/xl/OQv/OT0upsDDLjUF0p81F6IZPkA4jkD46nNJ5QdIFNVljz+KfmeLyRWyyycpa5HRkkCEFIRkEC5AoRtBW0ryF7QkuUNebxdyba76xXyItel5crpsbJuoINY2a6cnWugb35LdTcyX7WwyGt8fR1ut2EH9Jw9B575PjCifd924zY4WH8WJkDjcQ1+d9Z2re1B+RjV2wzCbu1/PedoP3w/7x0NsS5dnbsVM73xYAOig2t3XvZRGTEBIxBwVtvmbTV6vzkW6469cQSw30MhtpnCCpWOKm/LOCFOv4zb0rW5ecZ0TA/G86BtDuLiMmguMOaT7dq0NOcIc9DQPPYB5wPQMiTT0NPgd10F4he5dLRiBJAdHuOKkbDUmwB/hwwnWXUgO4GunteVlgUFq7Chyor2ThWAYXjgSF+mRcHAAUPQAIKLh4dvzvSQgerQuK29zK3ar68883ncijkF3oUhcdhlaeAcjOwZ9N0KX7YXLj2JHeoI5cYAg8tvCFNw+6Mv4UMxJmMsgSv6yQ1CZ7Yegxy/vHcHz776Am5/9CX8sV/4sGSi+6ROFSTkIslJJQg+b6Akgk9cwBcPAVfvCChYr/wzvqmP9J/sf9DG0FKXXQu0SiCuUACwW6VguQXIiyLkpYKDESBYlDWwN8PFBJqwfWkxYZenen/u+jiWaewYALj1gG0ZWf+7hmk4FQfegxUeGoDarpgl3L9r3trVvKTI75vaNp+jQI0WZo2fRm/SDUhrNA+N6KkyYgJifzRtSGiUWDwX29w3r3+kj2EYy6jobKlVOEe7a9O+nUdu8RHYaQx+ascz/KZ/y11s977R7ZjGNrO3KQCC0dbrfq21mUOfWT7Bsn/eMb/RnpmEknh3/pq5KCMmKMxn0kRsMrDD7dbEZFtCr/qypZiDQCROFiXd1yGJvkykepLAxLJEb0sCD8NKhNqM0I7Baq5GTzYhgZovEEMGdPFQzSGI7IA6TmwJib2MdfJ167//h9s5E/rSN+zT/LepPevGxl64JE7zUyd1w40BBgYKnn7sqSqILsAT5BwEnHdKrvZ4pV9ewa3Hbsn1+SG4ytMJ7FsvJ5vYsp+B7Gy26braTeNQDzsIMCDQv8SF7LMUJMvAVaAQt2WdJhCF+H+/q96QIciLMB+Gfo0VUJDQJM5YnOzIgMY+PmfJ0wlv2WOQ53jMzfGmU+KXfR2PQIBNZs0k5jCpgfr2Nwb87W+lq+q45BqRsWoRmr05CADb66BNOZjB6AHDrP9m5Rwg0Cuwbq7YE86AIoOlxZ3RD207Cyic2zar7wgA9G2jvB9Pre4DjWkajCfq+PmYmtfcjWe/0Rofyf45oaRzQ0VnsyaDMmOBRoCnrEBaQNpWzgtsC2FjE3hd60uobOvq5UJAhB7LiwLqkHsgVZjryKon6dhhGoCBCBaQMkoEA8YMKGPgsuaMQQCfwd7EXBtqTinY0VBCG5Kb27jLe3fxKX0L463Hnjo9brhBwKDmFIwnxTD2uaNcWgXHAO7cu4PnvvQsXvrYbTz+6C2sesEVA4mMnhIlwFCnhbcGKDC374I3kIClvhveXuxigMD2cI+v8yXdw51Cdq5v4mGly0x26izkByBpeINSZQdsEkSBj2AgZWUEAiVGcbnNRNmG/j2KP4+EnBsjF7XxyNOcMwnjBw4U36wdzUSuBmMLxr8U8YaKVUup4v5116Uznc1mrxEM0Oj98RkgSao349LIWmdQue+raV8csx/nACCEtsfim1R1j0vhS9IlVBQUYAt8RkBBn3hWrPV8EHD2mIbHTsfUgME1x7QBgJaZPwMLCJ1+jryHftmDpdpXcn7ODDW/Hz1uwArxiPViyTVgvqirDMx5inqxFGdReb0C6Z4FtK3CwHKRcKzqRlLdKI/WOvSrNqD6sdeN5igF3ThdXdAzA+G7byo30B9AnUMAmp0I1/B3++po+b2ABQYjh/7djwVxaUBB4zR7DcblxgCDpzskNAoH1O8tSusHyib+3Xt38PyXn8Xnn7mNxx+5ha3AkfqqsX1Teq3Hl0XWYLk8+oIUZQ7sta8ST1s98QYXY6Awe+8CuN4XwHjDoMkOh7u8gRkYMFZAabB+3a29SUwmw954xP492oazyVv0kakjZBRkT8u6B4IePABnZe9GJdgbjZS9LQVmLHTYWHxYNx5FDERRA1KNSWj/sB6MKI1GWiSIbCUSQ5MSB2MioDQTAMrBqMwNKmL/xKePPMQODHAYT2dCQtutbWY8a8vqX3EGRgNp7bZ2CTgiB9sRJMgObl3Y5Jx2HbRp08HdfouMKRGQVH8kYO9o9EwR8ODy3vWPHN97siNDBpw3n+M83rEkZdXJZCzJVoGC7h7L4B1IsPwD2oLuHOhFl48H1YsHQIB3QECNvunJoD+UE5H+KnWU6vC1HRnzyksDoiPrVJ1S+WMPDl+7/4aHD27tQMFxuTHAIJZdLPAACNhnQxUCuPONO3jh1WfxuWdexgcffQorc+OcXIUfRI8oAgTzEjIRQItveW+enk0AihNjAhQABtlb4GavNrYS/55tiDPKF1DB9lfejuiwCAQmgr8FgQ8+63nOXeg7wIwIOeACwUFXBRCs96/Kcuex7B7YsRgdqDEgAJb2MMveENFobBCZ2JgFMOhvGMBWWH/D4CLXjPrA2p2JQAnIkPbmJHuUiSJgeR8LEVJhJCIkYgcHmxsU8b6T92VnVP1wafrA++GAFShmELUfRh60PSE2kbkdWxuvBGoVnV6XfczlXAsS9m3y553ZJgd4XAGeg4Lw+d/mmFJh5A4oWD8Y+HOgEPqA37WsYzjWANyARbAX5/Ko3aM+iHrQ+sQdp7TIqwPAmpcVgcI4DIvlYXesLC/BmAayF2EBrU4c6EPpFtNr5vjEEGu7kmAPBIJjlGqCYaM7StUdMz0YQYH10VXh2n9qj4gUCjBcOdrckVFsgcLlvUsJf3/sJTz92C0BS9S+4+Oo3EhgAOAs1NuDAZsEd+/fwY+8+hxe/KGX8YH3P1WNQfjxViKFWAd3TyNyQyPWCS8ClXPw9kpgDHR3xAocCnBRKqp2z0HQcGKeZyhrP9TXL6MKtntSdYJEVoAHqNiBQGmN5jnGYqZMqO22YDjYj/dUbP1u10pn7hmHcellYuQNe/vUaLCe2zgYGADrVrAWxsaMlQHWv71v9DejkgmgxhgQKBEWEkW6JMKS5S2JGUBKLCDBwQEPmISJUR33ftMPsS+iYot9YWPLqOMePepZqd4z+/gRam5F0fOZqkKs7REFGEN3R22atScyAxEUGBgwWf5vZUwVKCRi5CKggEodV2t/y6po+EWPB1sxLFPd1+k9M17xe3vtu5nLjD6kEvsmpaUCBQulaNjVwrANm8BcgUKnC+11y2JIH1AXDhIGG12Ycss6MZxRHurC0HF96Mn7SI+vpfZVBQXcgoRu4GPI4e69O3j2S8/h9sdvByb9RNitKzcOGMw8HwC7QYoIzgbxzv07+Myrz+FnP/ISPvjIU0MlCABXikL3CJqDIq70oR0nmGfXTnZRBELn58XooxZJR2aBDRzYJPGGzBFh6zFoH+mEaITfKbMOBZe98BdVprF/NmVXnCrr+qmZF11f1UO8o2BJQVYEDTPAYM2M9+3LaPxNMUYP8hQYqP8KNkiYiRlYS6leqDa6z4HyBSQgl4slJSF1MmFRxbmkgiURLnLCwoRNFW0mxpZIjYooiLIzJKY4aGhAZsZiRq1bf9Tr4O94j/fbAtecbc95O6TgIAWgkBKhsIxXsVBckvsnokqrUlSE+xaN2hOBnsnsBt6N61oYV824vvdjmiHfbUwNJKyAMkJ1XAU4aPiBsZPz68i4fB7nTZwCe9zdtzmoY+P9gP1czt08TknaR6V1mmTFoOxGycquNvlaKJVRiKEV14UHnjGlAAoo5ABMQqSBGXUQsA3mR9AXsb9HLEtvIzZU5nlT3eN6DgxiyNJfEifQv8cxIAEFn/7FZ/Hyx2/jqcduVexACTK657EGNw4YxDLyGIA6KYB2orx+7w4+80vP4bMfeQkfeOTWzuDFQV6NZowPO6oJIRg+dq+oomcRAjtWvYRF/3VUouUWDBKzaiv7UoHBqUQsAwKWhDUCAhZ73fSx1s+MlnYF6jzdeN9ROcwSo16lmka/QjwOmELkoGSkPyM1DVTAoM2dj4y2QXt1BwYcEOm1W2GsXLBu4kWuW8HKjHVTz3JjFBajEg2I9KUaEv205FX/TMBCCYnEYKSVxJCQfD6cEtbCbkwuchJjyYxC0gcZYlSTU9TSD+XAgIwAUkOBTsZ7007rxzvqnY2F3bgK2xNHar2o5soQTy8rsElFFKOtR+hZhOu0p5fdESC46kDe26VMx3Tl4mDgaExlzho46MeUHfgtOWHZin5PyImwEpAL6z0ENEWQUA0unZRv+RwzAKbbIph3vdaNLYDh+PbF5rP5IeNQiszjXGQ8Y95FBAopaXgsZw/DxuRMfi/1X59TpOGUsspbSXvGzEKJvQ70px/aBFRvH7U/ANE7RLLkkhgAHwMEIsIGsV8//OVn8dJHb+OJR2/N8NpZ5UYBgxlbMKLNgDAB0IKCDz5yazfwJijW2dsZk2ZU9obPWAQzZtcADAK7d8ug4K2b1qJhUWIfDRmBA2NpymPdShN7NcNh9CtQ+31aK1UoRrtm749RrBa4UrAgXicHZqG2sg/t7Aq3FGlUkiXIzIgdkM/qRQpgqIbDjm9FDEv0oGPJSYyHfBYkBQILJSwsYGFjwkqMhzOpt0nYwHJNJjCL4mAzqgqSONCQhCrv1tf9XGi8yANAYCCJ1WBanwF13K1caf+SMhtXqMBuSTKmrGPKrAajAwgpsZ7bt6dvS9+eWf6AMQErF1ytdVzfDoAgjqkBhOuPqTAEBvxsTK82ZQ42ZQ62hCVV4Mc5gYoYjsTRmajibAzKTLaBCgAQxnfE9owA3rdzLhtLghAiSxZOgYIrDau0oYd9rlHthmO9Z1eMbMN1AUDvBAEPagNa3mst2hcq70yMzPoaP5skCOCYGW/cv4MXXn0On3/mNp547Ja3qWHWrsEa3Bhg0C6dmWdRA1VpANLHr9+7xJ/6yvMOCiIdNBIGQCjHXila7qspxRGatlJR9XzSmKds51svuQ1JjDK9gTpBm74aAKORQeiBQJwMEQisxihMYrDWDaPYWiwJ1ajPYrTWF0LJtkDBUXXoL9uqZfbqWOuLSJmO2nnEEIyMx9VWDceVBqLNmDRtNgWqBiQbE6BAYGGJSz/Eaiw44WIp2FhQIacCQ4dcIEqEIKuXmIRJCJ62d0M0olz7wto+AoJ2bib7cfyBdrParJ28hrGUuSWMArOMJTT8wQwHA5zk+Ymo8Zqg44yuTxtZDsr9KGzwto6rAAQ59s7GuzG92oqOYw2VjMbUQicXWf6+IsZFbsd0yWaYCRsDG2RcNyRsapxNF0g4ReZQle2Wgj5btoEhELBwydFctv49Zy7b+Ezn8iqAqNd5qVgSpgJ/AwqoCZp9GOU8fdf3xzgEENmTc50f4Hx9jwLkAI5N9k1mTAczyXWAhA6FUBA9wYEtuPOWhL8/98zLePLRp2R8CJ6TkAh6l+A2Uuy1fbkxwABAYAvGoGBkCN+4fwd/6ivP42c+XJmCeN6FCNVYAGIEzjGKdq+jKgMPYAiHRrD3mKWkgQCMEolGS7Ls+8g7XLs2m2IpqoSBOuniM2fF6ukx1UQKFnjXPwunZnLtAJXMhF2csy9uFLFXmHGMaxLaMSgwY2OAwMBAKQWz11vkBBROEkdnYCsbCidcZONii774SnoRa5KZuykokOmPJYUGSpBySEWOSjtf5uzQ2vWLybrF4oFW9q2sMBkHVhYKmXXjLc+uTwwUbYfRAUkqJvkH3LYF47k1a8uDggK79p21ZYBOjWlKCVshBXuEwrQfU/+EdHKWcWUiB0UbNg8x+JzmyP7sO2Ek1/YxM3DrVoag3ubzu5nLQJzPNdfCdN2SCLSVhkXKrHqumJ5jZ1Dmeq6t14PquesCpVnZ6fdiLADtZJ83udhAINzAq1m3Y9ZeAt5Qp/bFH3oZjz96CxtqiAGJQsIimlyDw0rjJgGDQQjBOrEffKAFBTF8EGk3hOttMMxLiIoxTqjeOBrDEJOVRmVuFMegYUm0M4oxNCF9YgwC29emWG1inoQdO2eCrAfKw+Lq0va9VzXsA1MgpLHVUpO3tkRIBRJ7JTHQlpi3qn5dSeo0CjsclqA0R+0+FxQYQxD/RQNifdAn5clmmQViMzKQSK8Ry5hI9ntf1WsuYKybLMVei3qQXMSoZgKZp2MGFYykVGTsZytenQkosLlkTMFM9nuGqC8rw/MItkSQjWjIkBEWEFjbuSQCyyUtOHjAtlQ5reBmUxlmHU8Lm6w+bmiAnoGCddsOx1MqosgmJfmIsk3AuuEYHAA+MYnlWt5CboY9cG8Lvf32cU54YGU+BAM1T8b6+dR8rstqAeznM7eg38HACYfImmwIt09Y7ppfu/GM/ugdnXcDjiy3BMBOfy3ZPP5W9rdU71cgYqCYwOtu8g8C3rh/Bz/6lefxsx95CY8/8lS1Var3I3PAkTUwcHBQbhAwqCbQPQZUhBjLEBScUeJdPNZW9h5zn7nsSXwdOJgsr90JVZvEdBo0xIkF7JOBhm0rXbtOIOVzlccoczsqFWun9IFOdiYRfvAOJEg8XV7Mw8RYE2HhFoGTTgRD4R7v7K1IKA74gsKwPrD2y1jP+7AcnDu3FBb6eCvs9bXsZ4DcYOUsCUcLVMZdk7hTLZ6CKpaoUHb1dPBb6dQdoA59E2UfwBAUzJZkWZ/mIln3i90nETYWzzAzwCAnDEzB7dqCiXrjfVu8maYXgtNkTEcp3Cj9KKezsMGDFPcIR1Vn1SlJ6kUg6Y8CbImRGc5SZOOIB2Ukz+cCAs+leC/mM6vumsxnM5prwZAZzNtvTX3W98FefwcHh8VzT8RYUpLN8TIha9+b7C8B3pxyZt68fwc/+lUFBY/e0ja2YSU2VKFzvmFYKB0+5OYAgz7tzpXbfnlOBAVPPHprpygjCCeS3/UYKxNhDRPClKR5HUeZ6ZbAZGXocSB6GTTIcj4NGIA6yaQt9d5xJYDXwydRVfjWtl7pXxcQROUxVa41mwaFLXGQ1HEWJbNuNuHkXC6MNVUEPgMIGYR1Ox2P7Tev6QsF4ZC+FyNligBJKGNpJ7TuIj1i8IGU93Uw6tnG0pLX5L7UhIPSAcCp7THZ7drca52BIT+VWPabVWJbNIVq2p74G/k8ff+UKmpqlLt5q0X+tvE0ee3H06IFNp45tf/imCZqQ30T8ZRcjEIODgBMwxgjWT5lGCUhczyn3+v5XHwOc2M0UxLQj8KVGfSYvPWPgvwD/dX2QQCwZwCAka6WNobnjPT1hjquhNA2UkAAaTtEFxkg8PuE9hACExKaaX+++ZaAgs9+WOwXUKeBgQFf2suMQuSsATV3muuRGwMMOHxGtqAvPSgANKlHe1ZoSotHk2wcZMaAwnpsjd9SIhALnT/b7cQErc9SB6qQjbKbI0iIWc5W53MBA1BBA9AKW2+jYhNi/x3lDcwoxpESkbrTe+J5jbzP4XXmbSHE9GYaGDK2uUjWf2aASb1zpbRFEASkLNFTUG8gUcLVxsgphhQStsKacLbvjz5ZLZHEpS2bPWn4aNFxzAQsCoJCak1tgwHDg3bKBXCvzpQ/GRruL9XjBopJOGFhwJOANFuxMMprsXpFr1D6u1X2szrH4ydXm3R17psU568wTgzWBE9AvfrEWEIOR04yplcbAzmdnUwax9NYr4QwnlSXpfa0ep+YfFQioDuVHX9O6Oe6JXXjVliAVCnVo/ZjDpoF8BeGAwQADUgAhIKXIqzCVWzLQGeN9NUpAGA6bLTiZKafrzYEnSyh3EQqN4nn80DHOM7fHGSaMM4b+/pbdx0UPPnYmUy3qqzIGpwqNwYYADWMALTevf1toOBnAyiw33loEHBPpBBL9nNhDxlqfogYGfXIJFGI9bvF1SSDtgy26QbgBgOAgwWv74SaM2Vjf/dLogwoACM2oc1jgLbZJtjMCy1Nvdr6RS/TwJUZ/SQoS86B9pvADCQ0bgwzu+a9KCPD456IKYfEDg6WRCBD+ZD3w2MTT3EtRcFZQSmSNV8gHkIEggB2yiaW0Vj2BkT+JSxJjEcmy27XpY0prlIxpWIeCDVG1OXdXY22PgmVvjfTmBOBN/FaibWvcsK6FWTuQgNoFXZksIC69GzJqa66UeVoeSF1fXf1oNpBm7QFFeCbGHpb1PCiWGxf52+yTqjcYCoJK1g9/2o8LvJ5SxXr33W5oo1n3bNivPnRbLUScAycvh1sTyKq8mFx8ELt+dHvOno9XhvVjbE/hcMAlspSpC5kYnJ1SjeNQIBdM1p2el19bE6/hf1GTKD1g+thBfUZcOAnnzIXANu/AbsVGcYURPt1pCEtumh/JwQm/OB3wA0CBgYKSkAHcVOP1+/LPgUv/tDLePyR+sIl34Nar7exFZaAXFdIlic7yrjQLWopFdAGVIUiP1hLcRopsWinxNVTzomGy55KZ0Hjt3UTrxRAY0Tsfo1xKR2jUAJ7oLEv8xSBOt/l/HFf++TQ30h+lSwtsmVl0p4wieIDTty3shz6OWE/Rt4ngOZvoBqcUa5BbCqHI1QYnDQpCIxFQeGSsmwyRJaAmbAxsDA5PWn7GBROQ6V02PYA6JaMAAza3RCXnKagwPfg70CBNZua52q97LoOEMOXCso1S06gwpIMVyCbyuQELiwLJZj9RW/xOZEKjku3bItgUkNZV9zUtvQKsgcE9pxMrWNg4CAl8vlrbWnAAQBKGbQVUGLQlhzYrWqgVhobmHPH0sC5gYGUxgzBKKnY++1QbqV4/gELuI2sQYasDrG/I8sjOgoeLiksCa81hNDO6evMZ/k7HO8cgP7avowAgPzdOiijcGbPCBwBAb9f78XEugAeItoUGRgzFNtSx75+RlC/pCSMYDd/rX9s/to+Nl976w5+7KvP40UFBftcAik9aGS5QJkzrX+rvoflRgEDAwWWT2DlzfuyzvPFZ17GEyHRsJfFtrMBAleAoNebEGaCJpBkyNsDyk65pMRIGyOlgnUjV0KJgKsNeGhJeEdSzXfexyx+uGn2eoEYnmqEK3ItbAABbQw8GTKHH3NEwGjuNQIH/bF2Mis9GKxyqzgrVT0qMyVhStSuMTAAdElKdLwpEoB2hYJNwkFdxACSj/sCYCPGwqwggU4kcqWTmcyDN8Dukk4NDJjxME/SAIEvzySEdkt/mEIyQECYMCX+WXNxeoOaklDOxAaOCBkZq7InWUETMxRISVmCTEemAIMxqvt27NsyAjhntcfmroIdY/E21uvC/F3AWCnJOxGSLVtMTrWvqZxMQhsxXia/PZBdzpDZ68grAIB1NQdEf9gqDwI81yCyX6QgTs5L/2xELrtABbs2cHVOnz+frS/a79OfTksPCnpAsGcL5oBg5pDN9C5Qc0fqdxnfh5a0CxXJxmPwDciyH6+g/uGcdvMXsPlVZf1NXX3w4jMv48nAdFshHbP6XT5jdVlZs7i3wVG5McAgMgWxvHFfXoj0uWde9uxNoO20YR+RxJY9DMYMcEXti7IIBQDlBCKSZWuqXK6wyfIyqPcORioJCawvyZBPQARUqHiZmCLgpwW1aPAuolefPR4apdoxKhmzuBdQBSYaY++Sa0zmd8to2rNmFLScGytWuW6/+RFQPdH4jP7v3TIhFkNsmfJgqPI9vQxslgTVl1k7ey/SvEtb6z+jHX1XOPPOaWxUrBRVLolE1i38Y+wBsbSPuLJzlFMDmqSdwq5IZw8M98ALJkRjH8YyAIL3rj06NUpluGhJuGBZ7lqX4VK3hI2aZNxzxrFnSc4CAXSejAJ1jtnKDSV8ArAFGOTsl/3c2S9t01IYm8b2F01KBChkzLfPe9DyoPrDAUjw8Gfl3YCCWRklkxpLYBuSea5Imof+LpY8ZPmSAkbTucYkERHeeEvC35+fgALvCmrZ5XYZ5/5Hpsdm5cYAg5pdXcvr922byJebnII66aqSAfaKxkAByFlVF9iLJEbe1kgnIiycZS09FyyUcbVVz8O2zo3b6kq2KonyUeGVTVTIk5tSoOxMgFNwM/tY5qlybpJiNMy9txf77ttV2vcn1L97j1POtwoWwFTJRoNyzrsUrLArR/NKoHFpapb1RdBg18dEsKPd0WK7TxkNf3fGgB2wLWSjcpi1tTEuxhqoUaXAMAEVd9oGMUm96ZzJMi9wwQD3sZxQRiAtjssR20EUfzMHrMxte2AAn8yzhO4FILlDi7Ynk4C/C2AK/oAHG0MAw3G0ur+XcmnjYkCBlP0yZd+yX1Dj325p7fePCXjfhvyFti3hWeH5mzJ3to9FE+5grm6whcIOSXLFq8rS1lUmVsY6NiZ9G0OQE50FCGZhgwgIsoJfQBKQE4A37t/FZ159Dj/30dvy6uRdLdvx19XKXkbiY7kGJ7ro5gCDCAiY5S1Ttnf0k49KTkGvTHzoqTo3sTOtowvD96p2YLCQUqiyHrWoR5WJsCCDM/D2tmHdKGylS9MNcmzVwkXIdu7jYDsuC7XeJrizhMQeBJhAz6jOmWdufed91BjwMwbqAUpGHLcAEiZU60jh9jul7V+P3XydFvO7XF86IO33ozeZpDB5m5948ckaylEbvP4nDGcv48NixxnVgLIa1Y4xc5CgoQb2tgawFBo3ojdje/BtbxN5vfo22RjJ/FaDkto2jcBfLLNxi+2NTsd7J4P6fGO1fOzY6xPbYM6NtYH0PdYXDCC3QFZ+Gw30twcMzF7EFMEJK5NxAQPYGhKhGu4QdsA2AJOGLhZqLZoITdDwal0plFH7O+ZCRR0bdevhuy9OhAyWTHg4Zw8ZWA6B6WcDB/bki0R4494dvPCqvCWxBwUMWDqIFwfz3o9ychceJ+mbu/fu/DbJMQitvKud+vln5NWTQKuYErADA/ZvBKUykech2tmLJGvmCxvtKJ/FlqgxkCmj6O5060Ky5arFL5MkrknsMilIaJPVejosFlv243UMiPbcZYwxhm2Gf5YRDUy88vDHDBeMFN2DqJv+PiPlK98HCjgoXxpcF5kFO3dOqfuwVyMkx/vzUnpAcVgGBmNU55HB7I3MUWtYL2gUTjCqjRHtv8cfo3pro+ZZHUYG8VwgcK12dXJhAKC2i5p9ThwAME8B4GGZGPgZM9mfP1VGL4mS4+YxUh0TQgPg4nXAXk6Xpg70nsxPq+eolO4Cu659KysNQ3S2/XYECbY6yDYDK0XW8jf6NO2XJXp9On0K4NC5MjBgejSyAxI2GLMDDjAIsJfiOcOnj//aW3fw6S8/i1c+fhtPP/bUoBfJsKDLeM+Y93kH3t1c7eOjeGwyOjcIGFixRv/cRysoAPbKZQcGwlZo/uYp+yTdhZsSLu/fBQA8lAwoSOxy4woSimY/24tblkQoHEDC8LW9CdtSk9VqZvs+0WZU+gx+ADswYMvfDAz025DGDPE+IezIE0c83tfrhP9zRPvNzhw+4xrG1Op9ruEZt6RVovu/aWhceoZrVnaANlRkVtcdwD3xJjXqXkCmTnLnmRAsNmnAwJ7SGhl9+qBRI/q/DfUEINCDhgdom8hubVtWaiIC/DEAoHc3Pl39w9fDv2MZyVRT1wbgyJ1akNMCOP9dAHHy+7Zx5865WK47xyNQkU/2dlwk2b/A26H1r+93IHnTKKPbnr0mira7N1aQIH1wfV1qerRfUXJqhdAoVJACGMh632qLpF7PfelTeOWZn8PT7/8AsL6tAyCVspcFylxJ/isHCVE2umGJoODzz9zGv/fFn5yO2Y0CBiNQMFMyPSAgLhUc2Lu9u3L51ht49iufkXut3wKnBSBCTgkLJM4qhlyyeTdQeHmLhRuAUjK2RR7Vv863ZrenZkcyoM28nZUWFIxDBJEVGL3dDFSzZE1R98vFjgyvlfP8oJ2ql7ZOru6b3ztb/fOH3loAh36d1cJBIe8BYsOT1xru2hkNbcOht5Pbz9Pgd0fF62Pfa72a16meev/roL5MJgPkgMHCRcHhFkDcAZ72acejPwI5+/EINx8Bdzt+Tmn6PJgyC5d5xa45BsC4/8v4/GF9Qx0dREWZsbrq3y3ACSGR5rEVMFjpx+nUnLIyC5ucLjR8/pCd4RqWs/d1MOurx4EBUBi9zIvwUBpvdSzPOa1DY86VJwOmupqkyScIKwtmYYKs9zLA4Cbd7E9ZXU5e+ciL+ND3/iFgfaepF9ucJAIogeX1iiBKoA4gWE5clAfJuWuZ9Fm5McDg7r1Lzyl46rFbZzME8vKZrYIBVqzK3Eziy7fexKd++cfxhQ9/Fn/0P//XQOvbIFzJBE1Zk6YSOGUgZXAiR6sGDBwgBDZhS8kTokzgZ28wBNrs9lEZZbUDxg6MGYFK5Y6Xu8VlY0OatwcKsT4nxq1vychT2jX3eF43Dx3VaygDwB4YmiwAjTzQCCicKjHWZwZgCCAmoCIU7o6PQOy5Re5V082cPRgYpgi0QKnumhnbdsYz29oPjOaMuXMXszNt12x/fP7ZfdkcnwNFmgCG05Uy8BVkI8iEvZEPwTCQXa/fLeQJtKxIE0O/xtwBTjMeo+996ed0M6855nwY42FvltQ3GJpx12s2BQqMVm8KUEiN7gTqu076VSXD5je5VONVQVFvnssKODPGG2jbAiDYWj0D4EPf8wPA9k5fMZ17CgoNJKQ8Bwio+TV3793BD3+5ggKxJ/N+uDHAIIICLzNQULaGIfDBYQMIARgw4/KbX8cn/y9/Bl/8V/8qnv6u75dbX32rmaT2N1HW/fqzA4WLlEXYcQwUmIEtJ3+jnU2Y62ZGXzezfbTMzZJhYoZ7S3tpX3ofllaBR496UlemmDw4MIi9h+1u/zm+y9jgnAQBfqyVgbZNDwgSbFyaNnUR551h3h9vDdvEwz3FPlhdmmonNP5kw3bsmY/dcQRgcVR6r3nGxDTHz+CUTo3BwFsndMY83mcHSHpg0n6/NigYgAGKcmCU3UDPmIHoz5tsDQGcAv2usd3XDpyVMjze9sM15nioVwQ1gKwIMXNVSgUIQv8L+2qh255VcKCgTWK8h3qz05lx86Gj8IB47AEIlE11jdmbTs9YN609KKhjzCoDRBkgVhBQ4AxCyogaIhFwee8Onv+yMOmjJY+jcmOAgYGC3ov1ATpkCTY0dE6pA3f5a1/HJ/7mn8UX/5X/I57+7u+vSG6zzYTN4NaJSsYc+AAKUBAmIeMiydvkI6NgiFaEH53wBxRtukifPiImo6mJ9sVyAmZsQGUZUM8BlfIqoY9OGVRgbgCCgYT1XTjee00UW2WeU/xd+O2udIqMGiV/CggMgCJ0Yxib7AA4giLgdPAy1J1SMAo5a/t1ACi2OQKJ2n7q2z4z4Ad9VMFFt393pLZP3EPKmTQ8gHmwCGOjOqHgd0Z9do+Z0R4Z+2jom/Ps8rMbfwD8ADJAKbn+oJzbsQ+GACSsZG8knFo2oAAEY4v6/VQfeZuDkjmHLXuAOV6N28ADJgKnRfNBkoIF3Ushqa5MEqZtWIXUsgoFGK4oOaUvvfvcGRo7TVFXVkZAdcm2CQjoHc5SmYKdoxH70mxMdBpMF6YMUNHdRQlUVF+QvG6eC9wGEQiv3buDT//is3jpY7fx1KO33Nk8lfB6Y4DBjCnYgYJo2EoBYEhOwYIrecblW2/iE3/r38AX/8j/AR/6ru9v6B1av1UfBuwE3xJDkCowYEoysEjglJBSBqcFF4kAkv0TKyIOIAEmS+z0WY3rjhFwzPwe5QPk8Hca9VfZpH8MQPV9NfSmg5EF5srDO3EMCKgzfj5T9TvtDGW9x2EJ9TnZBpvEBgTWKwcA7P0i/STfW8+KJ4aBOsPAqQIEVllBUjIzpWo8RoZD2z0DDUDZ98ugnwjbHkQA2AGF32rlbFBwfeNvht9Bnxn/ftyBoDPsNifG3vo6Jf2XYSHJZsyXCx0vNZLqbNj3EVAYgsVp/w0Yj2iwrsOUXWOOU2hDdKZcdwYdaXR50s8LUGVfWcIkphsdLHDNqXgQXRn1ZGUKqGFNjQ0AM2hdq77s7Yk5mlE3zpgn67Ltndo/2necFujLeIAUwkgJIE4CFApAVORaFFzefx3Pfek5vPxxcZr3mwDOZeTGAAOgojwAO7o7orUmrjMCBbzh8ptfV1Dwl/GHv/v7wetVO/nf/lb77Oj5ATslLnkIFSyQGgAmUwz1mCuBRa5XczVMLjp6acpR5n0PmoZxrx7pBtAUAdZQmQKtsti9RWniPc885+g9DViaeB9vtA/WgP6cARptt7WHt1U8wfWqGoR13QEEtj7aNr39fFzi2CArsxRAgfUDUmoAgxkOyov3k1C1LYhiSjJ2lIAYX6bU9kXTRwMQcNSGmSF4N+UoDHHK0B0xBCMgEJRyI7vbOgd+34Yx527MaVlULxB4W3WcF5kTvAHKQMa5UMOY73Ye1L4asWQA3tM5vpvfaRFZNtkfMK+ccmUTDEh1YKFfalvHZixDu+TpoY5UBmA1m6H68QgE9M6Fyskp5wFX77R9xNSGH0vtUi5SSSoAiMXOlBWX99/As6/+MG5//DZuPXbL8UCCrFY4Fem6McBgFEIgwI3d7nOE7DpQ8At/+D/G09/5Lzbeogv/1dtN73KsCCBCCzQeQDPYZIZgQQMWIoiwMEQXR/TYnE+8I6XZoVMO+QA9czIJp4ihLC70vQeNUuaeVHz2btCC52SfQVn2fZdmVKvfa6AYd90x8H4G7ECxtq1XgBmLYDR4W90o8LZW0NTTyqPJn5LGBpO3ne3vlMU4BMMhnqMqy+VCQGrK4Ly0cgUWJaLJSOASDMhgHE4oh+aC7rcPnGTXl2C4dvkS8TsfyfiodOwQMAWyRcfWDf56VYGAAkIDAazeYcMSlHJyvJv26JixgbysbGJewNsqY1k5EkwAAQAASURBVJoX0LYCeQFzAW+VRXAwOADMu37bdcsBa3LAmAzBUmzvezDHk7bN9CE7IDB9JzrSVoORsgqgVMGCh1ZiP1Q3cdAhtS/YwsxBN/ZhxRgOMH3oTtI2ZZsYgL9qdzRf4jxQQAgudW4XPeQXkdivAA7A8uXy/pv41C/9CG5/9CU8/eiTft9ENRmxX8rYlxsDDEwE2nXPXAd7ZAQbGlkEJDIFT3/nvwjzIhAnBgC+eud4UsTJGSjD0YSgbkLAJvoAGOziikAFIyOauEJmPRKFvgdNYQLMgMC2tiCg9571WVxadDwt7knsvaehx9wZQ3RAQfqhU8SxnFCGHMdbjQWvqx6rgMANhF7LgVHhbYNlPp+ilSmRtiN4Sesi4EFBAm+rGJCUYS+3p+VC6mOggYvKkbVRvUgOBGqjKEdlDwSGAKCjQPvEM/eMRu3O7X7JVWapDWkosJFrRuN5og0jyrsDfxzBXwAE07E2+X8vx1qNP1J2Q4C8yP3zos4NK0AIrOQOCJYArAaG51R/BGPGASSdZEzCvaeln+PAjiXhqBt1jo+AQmQP+hCEh1d+M/TizEFqlq0e2AnVdQBEHlMGs4FFAIu4+QQd51B/QhHZoII7997Ap776p/DKD30OTz/6hDAlxDBeUXaBrCGSWbkxwCC2sgkhsHnHXeLZznsquPy1Xw3hg39JJ32poMA8RqDxHv2eQJ0sVhdlDhioqDd6EDop0AMGpQ97D9lBQ2cMBUGfKLv2z2nDJqYeafTOc+4pVXYkjWt5E7bkEwA4Z1C+cDDQ0Kzb6gpk5zFrX1UDszVIfN8PA8U4AAXgwBKsV9JGYxJMIaxX4MKiWEsBb4yYjMbhjTuULcSTQJlAaROAmEhoZi6i8PICLpsoBy6gfCFMAxbweiXgYL0ClgtpyrZpP1jb0rj96GnVA0DQ9ZWc62hl/+lpJcjbqn+2NLOHj9ywUzUoyp22AMe+TdoRywgAHoGCbQVvVy0bZGO9rQ4GRmM9GmdgMtbLBcBJnJW8KItEdXRSAq8ryLX0AsbqxqIfa+KtAVRN+5sxOWZMpuEy7RO5JVew1Ny7K3GOA5UZzTXniq+CXtQ57UBBge8OKPSAYKAPvQrjml1PH8aworGGXV9JN7QOpBwb2wREkMQJXLSvtrX+loq8pI8ZjCLOovU3BZDNBZdvfR2f/OUfwxc+/CJuPfK4tAUA61yypYynDcUNAga79eod6tutS7eiA+qrD/7l/xQf+u4/OJ9cZS8AR7HGXlXZxG9iy3KimRxIaQwYdoZQWz2LK/b1D20eeQs7ZRBp1RGNHhWETRqgTqBBX/dsii1xM1oQWwKnVRTIcuHJWe5RRY9ZPYyGdiMShA9SG5jG9QixU29fc1kY6wgmjVbuQEFZVzcSbiDiPawUay8DqxiQtMjYk771zzcaMmNgxyyEVLQe+cwpfMgWEIZGdQCgAGAaZy6hf2bFgHLwfsVbq+MHoBo8846GAKc/Zt/ZWZSzSiO/m8znHhSozJer1QFBWbezxlmM4gZS755yQVoWQMEdJ1RP1GSZBXhQTvKZqhGQ7wCQZf46OABCNpFdXPsEaJmxc0MoBobPmeejOa59sJvn0SGI4ZTIGl6944wqX71zHF7sn3uWHmz7JerBHQAYOEVym/NzTUT/X7X6v2x7NXVueE4BwuVbb+KTv/zj+MJHPounH3lcxs7snWYmEmVnDU6VGwMMqPm0wT6BZgGAEi7fegOf+Jv/Or74R/8Knv6eH9CB6gamF7IICuLEAdr4o33Xwp2nJCg61fjZiWQ0EaABaND7AZ031pXeq+OozM+hDidKolEQSrHKnweTxN9SovQqq/LgJPe07NtcgueRq8cc2hlpt8a4IHhQg9CCvFKYtc8BDu+5ppTa/jIW4txSav+W0A/Jd+bXOkDXIJtnvG2gLEqXzPPvSzfG1hdHCZu1DYMS+4iLApOyU1A75antHMZPI0Aww7atIrdlU9mtj5f+xy7ccO1cktAW8b61D7l4qgKl1Iw1Uhq/49wAoIcNAksg25jqZSfG+dwdFQft6uez6wNgSJNbvcOXPaiL4VE3dvswyiiHpg+lyOOuMc83VKCQNC9gEx1oDBiifrOYu9HtIezg+jP02Vn6r5Pdxvjb8ZH+GzlEfX+XTq5qxar+ZxZ9v8DBQXttO662udV+NVbC5Vtfwyd/5SfwhQ9/Fk8/8kRsLEBZwokkrwRseOUDgHAzgUEfIzool9/8Gj5hmxd97x9SzVTEqixL60sxA0kH3QS2L/0EsmNWRnYlGnZLTLLjAVEbYPBYHIKSiMverH5HJSjtfrndWZNh1MZOUUyzbmvDvVMoA9jMIC2wvXbMW5ZXXXKdQEX7p6HsyoFxSfu/jf4juV5IhqTV0qmRigARoIIUFoNA2ajdDC6aNIYCXuOjBFykGUxPqaWc4+qWmeGz63RvDA8vLTWRdZS1Xn8c/vb+6y30ud72ABSMGAM7lnJlAPzYmUYztie2o2+P060yamw5GJqwhWUBVjM2Uh8Chgsz5PamkLcGLFImMFIzv/tx3hn1TH6McrvKROLrWQxFXjSvxI4v1QB2obPaN11fdWO4Y3q8j9AwJq47R6BgC7T5GfkVcs76YZM+zLk6slvZOQMuH56PsWdOtbPb/o1h21EpppsGxt/OnwMAZrp92Hgda9Mrprf6EvW1zWuTAZvXHkquORaX3/waPvErfxpf+BN/TZiC3fP3rIGV3xY5BrWRHVswvJgAJLz21pv45K/8aXzxj/80nv7eH9ROzELtAUBRIWZN7sBSAUhKYEvtzFkUjZUOVQM4RNYNopYD8ulLdqqx4A4ssB43hiGCgiPkHEszMYA6OeTkGBTE0h2beQ9iuNPwO2t8XB5fZPlN95ppi7cfll5p9kakuTa7EpR4JjmwZArenikUBDkjWU5mwDFR0pCCeXIssT1AvKFegbhxII8/p2UBJV2FYAmrrhwuJO9kuajJanlplYcv6TJFklyBTPuhjz/bWuloUIv1SQVQDbtiHk/oK7lly774eV+xk9vzg7Hr1+57e+RHp9vUtYfs/IJ9ACXX+e1AEACwApw8pyMtCwpWGdcsLzumNB7jPp+kH2OKY2i5Nfrdli76OJtRXBZwBAQz1iCAvQb8Hm0uBaCPiQNojSGqPpPrOwDSzfN6nKuu47I3knYMkHDCtslroY0AYq46N2VnHs4pUx1nN5/puAEbGtt/pM8pZ2GhSPYZ2Ok01/cKFk23KQikZEmmCvZTneMgwuWv/Z0KCh598kAOMGcNJuXGAAOf4idZAkFOl998E5/8FXn3wa33f6BlGQpk1yxKIigXVDOIrUQlsq2SWWunOQnpYAjRhGmCrn2+rSJsMYu5AQt23ABCOBZjdXa9hB0CEj0QnGGyTIibNaX3RsyD56LG3X6zn7R7WpSqd290obXLvCU9Z7tH+jPdkzCjMk7YbJbsDdseEo4WMQeJGbwmcJaER0oZWKuxxrYC2xV41aWlXKTupYASISanYUGTlOZtV+/RktEsY92MBZYL0LLsjUUHFGwvDA5LX6t30bV/xBZwgcSrI9NGfl5i4OJtD9mVUio4sPEp2944dPk0Po4jLziyHkEZNm0ymTloV59hrtwAqAgQTDnLOKeQv2FeasrtGFviJO/HWMa3ZakcFMQxjoCA0n6MDQwaANRjdJB4d1KuQxjFwfe2whe1pyxM6CbjKAYYAMJ4djqtOp4bzp3n0id7fRYu2rdBwQEoiWPQhaSGIAaY6zO95zD0O2BApa/OY0as3Q0Amug0X5IcxpqWC/lNAPtMWcFxXc0CSrJ6zsIHj4bwwdHuo8YeeD/P7eTNAQbu8XRvSoxFlcfl/a/jk1+V7E2JyRQwqvdIpEiyCIXYKxEAoIuHWiWiSqPSyOtgIlUBkyqPhcwQNxcAa8hihirlQZzO18parA7QnIXwjCYONw417CZazpLtbssFzQAo+vV1vzn5pDIK/JC1ifUJgKf2aTeBOnrVd4WzrGUHBWZMBpvAyEMGjY4rCAwcbsBFQuIC3sKSwbKBtlUykbcFlFfwxUO+SgGlgC4eqhSsdOpwCd8OEEVjoW3bKY6GJbiQ7O0ICIw5Mrk8WLrVL9nifi8LAwhla7xtRqmGwdgVV6ghZLBrcPD2uiSy3djNQI6uYT+3XfKXjCdxBTDihS1A0U2ELFyQcgsC47OXC5BtdLatzftBpuMb2T8PGaTKAgW5dtAXj2m/cM+emNwcJth1YZRu7HhbZf5YEiQAolLZkkU9TdNppECDSjPXPXx7VEZz3b7Hv4PuAlAdHWtaBAd9/kvUXbE+BgSAeVhgkh8VdfQRKIhAaLQs1R0K1WkzsA8A6aGHW7BPdb5wyvLunr/xE5po+FSoxMTxU0CgLg8qLTNtzk0CBschBHZQ8Do+9dXP4JWPvIhbjz6pfZM0AU3uw6o8BCAkoWDS5koEAHDx8FyJcAGvkkjjE2lbHRz0qG4kcB7H9Jim/eYgTmeeexHvlZOEOJxJsPgxugkWSnssy2RbOkO/kVB8XUx598IV4DgG1yVNznIqGk8qTLDem+IHodK9w1vGiRMDLNnblDbQcqFKUpZx0rYqQLgCaeyVQtIWddTkUC5H7Y7GYuQ9mgKZKA0HA7R/G9+uyXGsDBQz66oO/V50CVwpwqKxZNfLumlN3LTYNZU6pqNxj/kTXejAx84ZgtS2y9uR23bFfoztCmMpYD/XLO2y+UY1NrcpZd3Z7qqCQN1jgLYVfPGwfj7U7IJp7ZyO70imzUBEkLtjB6TtxdueH1iWK/AV0GdjJ47OlToaqie2tXq2tmrBGLEg07u5fs48j2Nlx2ZA4AwnpmnyCBScAwis7h0gkM/SfI5KZXfts2P+PPxzMWcJ4twGwPliOrcv3/qaLkn867j12JNHtn1cXy4KDo7LjQEGw3e0m4JTIbu8/zo+9ZXP4JVnPt/sCAVEZRIUSdqqB1UCQABAD3/HXImsVyoMi2+O4jHIXNzTMIpql/1+nVJKO/GC8Z/2VaR0rUwmXxPTt8m3AJawJY/sqDpgF4Lol/A0L/EIymC6NbAxBBNA0HhTMyo9eprDUlkm8Zpl6ZhnLINB+UIMCFjySgwIdOvhYUlNMaFp1L9H7TXDMQoZmNLoYo91M6ilAwSzMFKl2YktAz/7ttjucUYWjUsACNwCBJOD3CrU/j0B1wMEbdtg3r2376BtWh8OTAjR5sCvn9sGEAwEOgsUQOB7MrYd2xUTR8sRWwKcL8cHoRRw9jCph800JERlcyYBeZG+KMa67GPz3ttHcxxovX+Tg1mo8wwwEPNZfL+Yo6WyckF3j9N6d5ardbRxVc8QnAQEthIDAOeHu7ktgPi1t3Tzomc+j1ud/artG0AF61e1DdVW/nYKJfQlMgW/9CN45Zmfw63f/XTdcGh4L5tIOvkpAzl4GgB4eXisRNYrp5oFNOhOgbrMz6nIDiQA6BJ66qBRyHSmHlFHQbTj0UsB9vvxA1VZdffadV93bARgKB6LCUrRMOx+NXhGaIdn3XdgwAAXQLoJygkPc+Q9xzb39TZanS/226Fyu7MZcrtVNNa1KlFlEPpM/aGxHGRdN6ERMxZUr3N2gOrfNXxQX1/LAGYryUSs5FXhdSrYyg81HLoBT8uiBYCg8XoOANwUTopxXn+AUf/R2NF+/JT5qLk08/Zh2kZ5k6i3Ly0AxLDV96Ms8D3vywYui3rYm4NAMm/SMvJLqWN7alyBQxlGlOEgB31+hcuv3U8e1DZ3xP54VnpggMxjpuyyDUrSq2FjIwudiXNUl/I1Ouu6c7yv9wPqHNsZsqmHJScbeMmQBEagblFftJ9SzJnQ9jd5EwI2ztLDUQf3ocBRqGiWH6QVYGWjXf5TxuX9N/Cpr3wGtz/2UvPug9pJHWM+s4enQj5abg4wmBVKuLx3F5969QXc/uhLuPW7bzVelHVw7OhGSZoCcUWiHXvxPhG+bRUlkhbZecwAwYhqDlQk62SLiS+0oMbujqhY4DBGt6PirVGd5yLdk/YKbPA868vaNRMB6xXFKQQP1Gf2VLP93b1fogEDVufGS+7o5gMPsxn37igHT1rWOu9f0+3hAmZgMU86eGlxE6nmGeG5k4RJjoAnGsYBGNi/y75iEvPkauQ5PJvqW+RAQCJCMs80qbFMqnSNRSsbKAWPmbK31b3Urm+9Z83g9aGevn0+rgb0ZAVOAfwtcdbG0YZiDkN0MlsbUyKZqwAo9yBhqyDBQKBRzst7N65ny64DhD3zM5Zb+Bg0zk0ECpEFNSaMGUgqu2lRmW/3/Qewy9YH4I7SYem9/4kH3gAG7vVI0Dcd4BYQU+vHpci4MwP5oo7xRsIUmd61HCldgeS617ak7vt2lh8xCwWOQkVhBVFRuTZmwRghXh5uZP/y/ut49tUXcPvjr8hbEmO/2CeZzpJ2N/U+EwzEcnOAgVKaXnTwXrO3TH3sJTz9uz+EoTdlMqY/dfEkQqKMlLMm2GzVQ1jeJ9+zeRpb9SJNwRjVrKCg2Wq0ezmLxcMaYzqjueLEOmIGjoBA76F2hhjYe3l937b9PxO+60TBOk/IDeTAkMxizzah4nEMvOduzK0kqwcBQJbugepyQDa+MsPBlV6NzEKrmNVrm9J3tk1p2zY3IiPPeWAoi1WB96/qHj/Vu9iNJkEMpxhQgFT2YSxCWQHOQsUzS1jNgEBcBtasdrAHpfaBwNgzjoZS2YFiY1csheY6beQG/FAJYEhBUNLxZXuDnoHAUhATFzmMqcftLw6ePBvTngmIMjtjfAZ6ykpqHis6i1SEXW/1LGhamuOec+GyXETWlxqOoGZMH2Be7w6fo0fME9YxDkDFNtmqr4cPRh9onC7RzQbiy35rZ6vlKZ0bcyI6fWsvO9stMw1gwFcYGLDvmT8AfPE+n9+v3buD5770HF766G08/sgtXGn14pjL+4FkEaK8R4IdIDS934Tbj8fv5gADYAcOXrv/Bp798vMOChqvaqBMgT04kM4mZLUOWY1meeg7XIk0nsYQJJRhzPJol0EAGCbUhMk03HM7xjKPQED0xLus/RK9uvjMPvQyCsXMBO6EIO7uGelSioq2AwKRFQh5Bm5MBt6lGZWjanmXIhpReaFVoqUCBjtvLILH6auXBo5P7B7a9KEZj3G7TH43vd2m7dn6tnmbxwYToU2VKVBgYP/YjCeQSbxspFwNi4UZlHYXQ6NeVheWie1slhmOPORBW22eFuYWGJzdTkZSsCftrGAhYz+/BQSygoNoMN+j8TwAraXUsbQ2XVdW9yxQrt97FrQBOwEohDHkfjyvO5fPOd7fswOXbGyUbTAGk7P9tu4UwUAMgxQLB1Yg64DjOrp2pmdHOUGB8ZqCgQD2AeAKGSjAa9+4gxdefRafe+ZlfOCRp/C2ejUJEuywsS4q1/UFgvpyKX/HSMwrGPT1oNwsYGCFUgUFH7+Npx972iffpn/MlCpQvcr6/gV25WErd64g3lRSJSKgQPIJJAZXvzd0ZAQKB3FLLuUYwVoZMQHAARCoxrUBAEfeDRAm8wQknCpTYYzt60MXZkw6kDAIDfTja0DAjMnOcMKua+vFCAgbgAdPCEhUEXhvWIiy1D6hecMn+V1RFU7fF7uY8d5jjAYjymxtr7TNjp9jTETBRCNJ2JirktH2FWcRxBtFzsoWdF4oEOLawHBszVACU3ZHjH9lB6y9hdnBwAZuDGgMnYzaKuqxjhspWLgiINMeKFDHFto/fg/GEgZ2ygkdxNaL9Rm9fErPkh/sgU+CMCMVKBAyZZCxoGpw2UJmznIZyJuBgYN52/fFuaWXmwAOKNYrsBowAKeAgaJuNRAQWQVrU8iZgOnaWTnFtnZLpSX3qQsDJmMMwncNATrrp9W52hh37t3BZ37pOXz2Iy/hD73/Kbyt+6AkakEBgZFZdaQKaVbmiHRDI5Ggdlnp5f3XD4fiZgEDFcTLe3clfBBAQU+5FlWqM+Xa3dIViTFKb28cJp14kXkRWtIoSQsT7ICChRAuujXjAf06sgWqEhq2OQitfp/HrDsQYLRt9MRPeTnhmXsVdar0XsEZsa+GbmxzQlqvsVWuW/i7DMZ3ZDxHhoWIw9+BSXIDg84bbT22lm2osdZGlWr9wbEue7p81JZSIvCpfVPCvfeNkuPVw2QQyT1Fli0eD3vLgBhMNs8aYlwoPKSPa8/KGaAugnZnB4rUr4Sxc/twTnth7VVwR9LuBGBL1ACFRDwcR9I5Je3OO3NoYwhUo29Q4tQ4HoGcsVzGzx709ExQdWwIwNYBhRh6qN3F9f9rz9N6l/PLHnD1QKVhayKzYfp0EAKhgW7t8yTO1q8z3Zp0J8qYPBqBQBcCLFAmrATAC/h7Nv6r//oSP/rV5/DTH34JP/j+p/BOfGNno3MYmQQUZIWM7r653Go9w37fl2+9gU/90o/gMTw6bfbNAgaAZG9aTsEAFJii2WxASut92BgMwQExfvXeXQDA22tpBsdjs4rIkbI4VghAwWLSIXZZaT2uiPgiCH1fmVHcNlSyAQB2jQlnDwI6Or5d4jbxdICdAYvFvo3UQly+ZMrVysTnaBT93pBfT+kOjQm3j9kOXgaTbZMp+wVVYxMVda+kpX3UdMrs1Qn7HIg5mOnb0bdh9pY3G4ecgiLRsSWV50IAlcoiFGUQijbDWAQxPv3KhtCA9sl+ZiRXcW5GwN7Pz1G7r9NmAJKrZWNV2IECEMePqxL2H50xdoDLn7e3k0HgXcohB3Hq5FBkkNEDhbUDCnvQU3VdamZwlzxo7Y1fgk6Qtu3rP9MNQ8BF2IXrLJwg4YG1AwxdGMR0aVxBEQADgMqGAGfpVQtxTZNmwyqCGEIwIACWzSUjEIhOqQ35j37lOfzVP/4SfuB7nsI7K/dVqfNTKoaU2HuIGJHqDuBAoO8dTcR/5Yc+hz/3xT+/GyMrNwcYEMmSxFdfCDkFVfkcgQIbIGZdqNJNUpuEv/rNu/izf+N5AMDbK7unlYlAhZEDUDCEbkozpeyInVTAG7AwQMONF+Z1GSiM3pM/gwWIIGCXexFo66nh1X4ZUfFera6ajbIxBbQ/pE0Ya99RBvoICNi1R4bE7rVBtoDd2IzL8NGhbvKZTZkmMaBW75z8zec+OQHzQvf32bex/dvbGRo9agNQRWU7aITkDMgO3BkU6lyfabLNQQGBWOOZ5K+HJggoBqqXCsg1fWnlRo91iYSjcMHGBgzq/Iztj20/1e6rrU6T/ZhpUcNqzelZo1F50DHrx+s6sjeUuwh4YPpJDAk4epx6nduQ2Mbz5x6wB7PS7mvqhaAPDKj0wMXDOwl1ZQlqeGEPFlp96oABAEZOV+2A8PfAoYr6tAEBg9yYMmanR7JtNfnP/thL+Je+9ylPNPQ+07G1+ZltIAsJsofYnFLYB7Y6LAmX917Ds6/+8HAfn77cGGDgoOCj7eoDGwjr+Bko8DADgHUrO0X7d3/tLv6tv/UC/vIf/Tz+5KvP4O2NXZlcEe8mYqVkyZFvShSSnZRVcLAAqagtrWGdWi7UneaJJQjybr1zR90egYBzY9e9IJ9BNAJolW2sdvVS7Lui34kSjqVnLmasQG9IzIi4sQHAhYOCNuU9L+ZHuTfqQIHCuTGAiL873cZj42990NfV1mBT5+JKJjaq4U8M3ipAsPtI7LIFCKZzEhnDUEFCjH3O+q1P9O0BZ+Eqb+cAgjiOp/pgtYuKjN1VkLE4RkBrcM8p0Wj2Bj/WzerzbuVsTdaO6pysPAZ6hatuMmNruSVy311rBu1r2zkLw11fJ8ivDDSPGLfIzE5zQWZgIYYjgAfTpcHxGq0ciWzAqRDYOY7K7//ux/EbV1UiokwuObV75xYegoMoc5mAO/fu4tlf/LTYx0efwGH4BDcIGFRQcAtx0MAVDBhqm4GCVQdnVQNhCunv/frr+HN/+wX8pT/yefy+3yVI6zeutiF690kZwEJKFrOVyZoUNBhYiJ5X0lcOxwQ2+GcnxIPs2XhlBAAw4BMUcROHL+O4deFj4wq0XtrBPBsq3xGtjUjpTu7VdMNIKYVjR4CAfaw5fG/v23s/o5LckFhbtc5qlE2Rtf1QGzQma9syM3rAsaeMEKPMRFj12ZmBlYV+HAIE1TUUAEImAquiM4XdJ0RZx8XxanXxPskuAtDeizJZPAUIeqM76wNAkohzqOBqU0hlOp8x5nE8RgZ/251r++JcubqC9aUYxzURcpG+ZiJQkg2mZuMYTUDiOlZH4LsZL+xzOGxe1baOQWt/L2CuE1pQw858JJW9RodCKHSTOXewDCxA1hfvVg8B4NjvQx0KRA5lp0dLy3TFVTIGbE2HlnJahq2PTF6+tbYdlgOIB2SPhiWC/QAOKDE2DSsA4qBc3r+D57/0rOTcPfoEfFn8QbkxwMBBAaXWI0b0dBUIKI3jxk+vN1Cwlmok/u6v3cVPvfYn8b/90Iv4vu/8IL6lb0C0z6jso6e4pAFYKHuwEDPCTclSON4nsMmntVqf1uMFrsg+CmUfg5/GbvXTmJNTxhTojZTWrpO+3kj2HvbIu25iw4yTAm21mIUKTgECVvkopcb8intJbUf3u1EDbbjE6PREkW4/BhDWPw9SeuDQ34eIXBHlIkl3gDADGxi5ELYkWc5bQTUqA4BgToqxCLVtJwapM4oGBo5yQE6BOwAN2wO0Hvysb9Z4TedAXU2qPwYD9sy2bSMZiuf9+4Ec9TLETPo+C5kjAgiOAQKAhg1qhmeATeIcknbtHYE4j+qxsT6IfRR1wkgf9LrA9MCSpUdSMTDADo6ILIw7TxxFONb2QKtDe2bkXP15DghYra/QymvPGn1r3RqWKI4tIK+usdUMxDoPGSg68BmMQiInl/fv4oe/LKDg1mO3vEW8c7vacmOAwdO+o2HLFpiSsU/4d26ObUUGcFPhXreCv/frd/FTr30G//6tF/F93/kE1uB1GKqL8cdM5wn7kvbI2CjBEZUGVO8MmMfl41Ts8wAOFfAACERwtJYyNZ72rKj4RiUqOMDYEaqhlybMYn1mfVyVhPRtVXZ9OUXpzkDBWlpAEMFAKZiDhM6gzICBtLm9xpgj6w87Z0ap9ax2TW1KHy6Q9utv9ZOZmz5rXg8L6aMRODDlI4m0qIYHLOwlZCyKeaDHonAy/GPHjkBBc7/OCLUyMC7RqM1KJBlaDFENP4CdvMg5+Dn5nAMDPdiBAV02mmS1RGEBZ4nYAcICxpYIWeXYwF4EeitYjCvD9dfRvOnzNXpG5hSYnrV5VHr5H+mDJcmbZfNaGmdryQkrIhDgTneGxFF7RswViW2Pw9DpTTs/0p2j8FbPNs+cqJlsATJOwmi14E92atT2FsaSCLaPEYP15WfwY3ffuoMfeVU2R3pKt1H2VQongj03Bhj0oKBnC84tNkh//9dfd1DwL3zXEzvhX0tpspATVS8jKnkTdKlia/wEIExCEYFKk59X4CD3Pi6jGKCh2SNhXoMgr2HiW3slo3ZvLE/RomNjqJSgHktJ+mUEFNZUKbUVrH12/MxzEvHk/Ph4bGdst7W5uTZ8j4l3Dg50LppXLW3m2g9l7yHKOR197tmiFiz0Rh5oGYM+jAHUPp4VZm7BAeAyGQGCHDZBO5bNOBd72fTz9VYN8zMqGWIMubC3zRUws7fvKAwDjEHACADIZwsa7dwcHARQMJET+a6fyVadk3SIf2/LxscKfGPpEy7AlgYNDGVEa8+AwMYtgD7VD7Nic9/a3uuDJSUQbciE6kyZ3tzKwMmCymdM9OUH0pkcZPAc5mod9JWMQQVOQJUl6yvvizC8G8t4C9gT8GfjfEKVeXnjvuyD8PlnXsYTj93y3KAGHBw4GzcHGHSgILIFgAKEMzqVCPh7v/Y6fuq1P4m/+KEX8Qd+1xM7bxIQQWgEv4Gd8jHzDE97ymMveRiPr80f1sX+PDfOfgQG1q0HBqYwT/ett5904qsxTGRekH0nLJmQiiyvysTgRA1dmqGezbbPW2i6JInCy4FCH5VM0g+J6ay4r7S3NRb9+VrE80OJnqAuMWJVhBxBQjAK+oCUCLZFCXMLDqSdlaXyY+Gio5yGGtLY92PDLliNWNgDIAAEVIVFdJxIB7TKrQEE4UDsQaJKo1ICcjEAIMcyAwhJkwsEKCCMu7wQtN7Vwih7tsHqUxV5D4B7IxiBQJwTds2s2DXRKIxWc/Sldzhm5WhuAHvgfEofiA7gnXPQ64Rz2i5tjUCYGmCwZNGxLUjgHUiYhR4MLACd/ZtRBaiGX/piv9rlFGAaMSdngySuFSuaM5B8SVCo/mnxwOv37uDHvvo8XnzmZTzx6K0g0/X31GRd7MuNAQaO9vQ/YwuAMcqyXaNseU9OBN4If+fX7+LP/e0X8B/94c/h93/X4x4X2j+Pd3Sh/B09AvuLd16ynK+ecow9zwADAJ8EQOv9Aa0CiEmnowSXc+j0lctu8rti0GZupSqCc/YASDphU2lBwsJJAQIDm/1tXhNXgBTo0tEkyZ2wm6eUNSAuL5KRncW56KYnCYCGTpaUHASBAVvg3zctETVjfdR2G/zCVQ7KpkoxGXq1E/BjIyMxYqDkeMsIjMFC6KeB0RiFaHIadDJCVdHqVwMKszLqpSNGLyfCViQEskABQhJQsBiXZvljYTxY672AGpkHZD44UEjVMwPgK3N2dTxgyo5A8jlzAhiPde9FL7l1Ko4MJAD0oTev02AUeqO3Mg8ZgqgT6t9VJzyoPgDYdcKSCWVtnQbzeLdEWAuwJPYkzDWwinGlBtAzZKG9ndCdqyMNAADHoUfgyC7Ecbc/xqwQIHM4E4VP7TPFDcIky2/ffOsOfvSrz+PFj7yEJx65pW2SnIPEleFT33labgwwcM/DPI5Bq5PYGM/ZTJCOss1d/u6vvY5/62+9gP/9H/k8/sD3PIl1K6AELGCsGmQtHe88Q4UlcKIpaM9UqtdogCHGniNYsGPnGgM7730SJHOU6DJLuDtiCHoFsBV2BTDTA4nkekl+YhQmVQiSSINCWFPBggQU+N/mkdp84ZG7DOzWc8fixkMBAkGUwg4gEGNxD4mAbB6g1Y1QmFBSTTTSWtVGYqwMre2nyohOHTFMwD6HxY5JH4wV4tFSyVH9dkcGTbBuiE7pCUdx99vRM0I4WGKp3QULQl9rw5cI1IK3Z0puI5GnHiwYONz0vqwywKYkigrhAVAclZlhrEYR+hlYtGbsgYXSLsx2jscMjHMJAGVYIKB5tGqt2a/AQdAcFKyqA0wfxLbP+mmkD6AAYd1sLrCwaUxALvq3gIWCllU0kAAIUACkP65UDiiRC9w5elHO7cMAY5bI+mhmB6SYni8cQUBlk+t1MueXJPUmHWcijQCQrcyA51UAwNfeuoMf/+qn8bMfeQlPPHqruafpUQNYp8qNAQY+1twqnJ3ck1E0IlyW4Pb1b97Fv/E3P43/9I/+PL7/ezXRMCdgK+JRKjhYVAklBESB6j02bwe1Yw23WkEB0MaeZcC4MRBAyyygsBsKQKi1KwRAoM8/FVs9JfSOek+Agl3/o1WIOVFF+wMD6XSq9uGMSvV1y0ERLpnmtGF/Gw4GhRgXqHThugELa8b2VrCwLPlZN6EPUxH6XJQgOoBg9dYJn2nALsS/w5gODMG5ORdHKzlg14UHN90xUQynYrDvdTl83gnlVSDjv/uR9v0F9E+dCFthBw4bGIvR6FTHfjEPMRmAVJA4AQft9yi/KtMHsnAkB0vq4+wm6zKuS07DOQD0y3677jGnyWSkMFiX/K2bJpxq2C4z+3thdn3fsCNjIAxUndDrAz9n4ECpc6Ggav/Z2vwEEn0U2TX7DQNxpQZQgbCtOsmQex/pQmCuD+Xn87yqkc7vSyLy69wRHMx7AA4KDAAuOWHRsV4oqXxIV9neDl//poCCz364BQUF1RZYF/Y+zajcGGDgsgQ0S0sAczLFXTDgaOCAifD1+3fwZ3/ledmG8v1PgRnIKMiFkZE0nFB8tzhABm1teroqif69R7NEtUTkoMFBgcWWCnzw/XhIUDMFs1kYorRrki2r1UqfYDeaBKNSAZDdTK288shu7IMy6I2/fc2JOvo0eEvovabKllCYIJKpLJ7RQqlLOqq6cIcvqLY7a8cUEC4YWCnkX6TsKzJWKhpnTU6pLg2lSg4Y5H77sd/15xDw9YYhNQzBMgADU1AUPIhRX5wTo5zotvnx07eclll1ZvV0ivjgnrGe9nfWlzwU6I7jATCMAOJSGBsJm5ALsBGFvBsDhqzAUL7L/eXYUZnJwBE70Mu+jf2ilHIc81nfBd9JgFUiMGQZNXIR9kAmsLOlxOw5MEnp1cTkasDu6CtgAnNmauFIH9inhxijTgj91BcDFp5/w2FTLVV2pv9WjPUfUJlSu+c5iaWnkkrbtnZtnzgDSyJniS+WuiJjSVT/ZdULep9Moh+NKfjsh1/Ck4/dUhkglwP2BlPdB+lEuTHAoM/Ct2JA1MCBqAdBlwmE1+/dwU/88vP46Y+8hA9871NOE9OSsLAs4ZO9DQhrYWR90kVOSEmEasVeSRT0XsQcILQHVdCTeqBKs0lMenScazye24k0333O+saeJTNYPPtjr8hi6/KTmjSXVdpG9GmMJ9p3UwAWN7WEI5skMimqYYyouVnXTDHOZvSaGsqBTrG2x6VHsmkP+fetMFYuLUjYDCRI+OUhTrvkTIAwii9acW9xEjqK9GGzVAutQbC2RzAU2w+0ikEvPVn6Kse+spuM5hlhDhqOSj8+9j01x8Zjed32mLMAROdBAIMBRM5xya7Mdwst2JLdpaQ2F4ds/KUUCzlec/wjGJyBAQfCk3G/jtyb880wectY1fmhwJYim2ctmnQtwJIZ6yayWiDO0kUWfbAVYc0eVB/EVTwzNs36st8bpI80nrPcdMYAHDEDfYLpqPQrk04BAgOCAPDwCUCQAN/o6etv3cVPaPjgyUdv7WSgZ+U4+HhHU/Y3BRgQ0f8MwF8A8D8G8IPM/H8N534SwGcgdu1fZ+a/cc49Wy+hbbJ1jnWIdZ5lb/7sR17GBx+5BbCufScgMU8BAgA8nMnpZgMIhYInGehmAENPciRYBiRs05MCDiMomjkeF0ONFj2fUJtx8mSIJ2S/tbpWLwiVMnfvmBw5+zUNem6fd4o+jYDAJolRp0PUjMqmGJVOChDimw61x5piVSshC6d/a18hwkPIcgxqLJYaWrClSSvTLk9D+q96IMN+CAotMgMzMDAyCP0GWUdtP7Wt7/T9E6T9ExRJzPuIhta9kzMAQqxOBDLxHPl58i+jMT23bay/LGoNR3t7lALkJe0BYiGshbp9Pd7bsTdWyJYwzwBwHHdj1Y76p7Zd66Qrb6ztG8teFIUBygkLWrY0Fw6AoTpDCayggJAyT/XBdcNq/j0Yz/ZYCwbOYcBiORcUnLP0tG0X7b6PHYA9MxR1HQC8b0mHDIFt5vS1t3T1wQ+9jCcfa3MKRmE628fknDn6m8UY/D8A/E8B/LV4kIh+D4BPAPg+AN8L4L8kov8hM8+cXy+jtc69zET68fW4zvPRW64MbJIYez4CCADwHRcZKwvdbBm8a5HEplJYlzOqQWU0VCMggna0NGm0GU5zPrSumWxhwox2GZsVdksgtClwXswNaNG1FfvdfCfAscckcbSqJHvUnPT39kZLQlUaojipo1X37d7vakYaeqpjxdZmyERmACUHoAB2oHDObpBxDGLy6FGIwGjimkuwNwhx//g2lEJDA1zHJ/aHjff+PQbQT8We6mKqYTVjHpmF8Mz4jP75IyBQ32IYmaB63WhcT7exynI77uTHNqXGS6oAcWMIUwUZdNsuXcadhuMO4KyxPxr3GRAYyfrR7qix9OPqO/RxZQ7i9vAztnQhWa2wbowlSVhloWpYR4l48sxjXQDgJAiwZl1fp0mx1SbGktqz7ZJ43OpgSYLO4HY6exQmiO2csQORGYxsqOUKve8i7wBBZVTk84237uBHv/I8Pv/My3iySzQ87JPGTs4Rwm8KMGDm/zcwFOKPAfgiM78N4P9LRP8fAD8I4GsP+qz4DPvzjXt38ELYEQpo6UUGsLEIRtGd4GziZM1s+WcfWmTiJKrbKDM3IGGU6W/xR/PCzqGjjihIOU67yTPao1/OTSZT0Kptpm6bwT1L1JG2TJvSteN4l7NdyGA0SaBehqPooCiDB+UKM9TDkq96T6q+O0L/TtreBN/+dGPGRUoypkv1LGf7n/eljpEatkl4oGdDzEuIb+4046Dh82F7j1Qnh8+9R12NKRM5/SztqrvxRaAAokbVxJUFUezsz3OAQG8Ae0bkeu2s42513xwU1r83ltBCHPMlKQsIAB1APOcNiXHc9+9VeXdjHtvfynltdzXYABPVJXim56Bb6bItT2wBggHhVeeFsCb55O6HszJjUKSPxiDa+u9U8T1Lcm1nXW0i/eoMaeHKujIgyY4VBBQDE8y7batrW8YgZxYmPHJ8AOAiVccnBz1n4//6/bv4TGe/rIxCfUcbhM3Kb7Ucg/cD+Hr4/pYe2xUi+jEAPwYA+B11EvTI2TrblMrr9+/g02HvaIL81reNhdD4GYqsk4IETSq0ZVAyeElik4jouo1Jy988nDiRlp+VnjE4BQb6l/X0b/qz401fBoE/Wt8L7D2i67wlDsC0frsYelCWcZKg+V4ZgvZlKQx936m0b/ImMdaGZxJYfwHJNRAjgQYsbMoEmXw04YdMyjpUFsKGtB9Z6v6IseL27XFjo3Cqvd7WM14rK0CkfftmJt1mFWZA6RAomFF1oBAbPdLhAbTZ197zHQGB2lZ9QP/2PB/UfXsdGOt4s7YTKcneCDrmMq42zi1Q8DCjyQMHJsmf3Ta/a7KDPhtvb3MHdm2cK7uAKgt9H1xHxikJKACCHEu7N/WIN9YEzM4ZKkCXg5GabdNHL4+alXP33nhQfXUBavSVrTZBJl1xZHUm5ExBNwMOjnMfThgDklOb2I1Wk8zCosaGvC+nHRiwef/6W3fwI6/u7Rf0MyGwNrXH5HwTVjsu3zZgQET/JYDvHpz6KWb+xXd7f2b+GQA/AwD0vTRsa69c7t4Lb5l67ClAIxTNkFNCTjJxOIlhuOAKDjalXd+XE+IbGhdKTldthbGGmPS2tEDhQd7gNwoRHAEB9zx0Ep29G1hfeP9n3B0MaDdMGU3U2cTuXxFbvcZ9qMC+p+Z7oFF5E0Oh72T316u6odgrTvNVTAMxEUhfq5rUWF6kDEtMLEwKBowBghuRUew69lHz3ABaCTUs0IdD/G8917TVQEDZurZ2gKAfkPhKbm2/vahFF0mDwutlI1AwsFST1ypIYJY6WjhhJM0j0D4DA0evI9+P7/XaTDrmcbwdLFAGJxtvHXMb4wgOvw3jbezXcLyZQaUbb2/zbCmMsVB1fIkInBaACDnJYu2SBCRsRC7XthIjcw2rNWBIQXBky0ZdfzT/gf2eC++VfrI6XQDgQs3OhbbipC5Z1noncvYDuJ5OBvbOWb+sNOYKxfBgZAYA4OGFXMfFuXDn3iWe/9JzuP2xn9dXJwf7FV4JQARZumkySmP/4Kh824ABM/8rD/CzbwJ4NHx/RI9dq4w8jtfv3cFzX3oWtz/+Mp5+5HHQdlWVDFAlWqXWJpBsvJGxwBSDXPbwQq4o7LXOHp9MhMK5oZrXhaZba2p/ef0j4j7a7/7UWwmPlvI1TEq496nCirwtfttSVvp54h59PUZJdDPPqQcDpIpSDAe7EhXlWY3IdGYEb1IMZPK/gQROCUQJSY2HvYu9mJcZwILnKcS+mDw35gBE4xgp4h0QUNAjbVaD4Aaza+eRJvA2VzAAbTfUOPqxlKU/UkZSkGBGcwcS9J4zM2VtRWhvP0+rnAYw0ICfE+N7jXb7eKdcwULKUq+0KKvQAoVqPCpYsMda6G3/2AMgFAFCbHcEAaUAKON2H7V5Mr5EqwCFlEE6trnTcbI8MzBlqTJlBoR6tkxrf1ji3O+H5YF0EVr6nFmMsM1DsDAdbbhP7j97S2S9t14XnjnTxyOHrE8aNlbAGaKBwwMoMLCn69y/843X8OyrL+CVH/ocnv6eHwSuvtUCXgXzlDL0TTuAztW62pym+qgvv9VCCV8G8AoR/SeQ5MP/AYC/8yA36kHBs196Frc/9vP40Ps/CJQ1GBJVsH2HEYFwtVcWSUTjfYsoiJLIPcgIEHqgwJyHb+Pq9+M+VY42s+lBwJHhjZRunawjpTbWOyNaakclnyo0p5SPvKcGDJjSbIwIB8UZlKlUfN9AwClXUuQtRlFAARQkknmURJJrYmDBDAjCmz1Rjees7cCcJqetBzxmFEttP4sRsd8AqEYDOOk5GwgCzHMO4EC9SpTs7TOgkCk5SHDqkisgaN/X077RsTEC1Lef2/Z1YC+yQRLnOxjfg7az/51AsLZ1QEjBkbyfQxgkTnkXcjLvWTt0OtZ9XoQr/RETcEZ7faxjm7tO3o2vK4Q6loCO+wwkRB2XuEnatJyNEoX+VAly3x16AP1DXf8PgIv+vVAHFlB1MIBhrshRmbGxpoOHCcM9IAg6LhtABpDL2jgBl/dfx6e++hl84U98Fk9/zw8A21XtnMh+pQzoMmui5HrewMHJDP5QfrOWK/5PAPwVAN8J4CtE9A+Y+Y8x8/+TiP7PAP5fkH0p/sw5KxL8vgie2AgUPPKEGpNtPPl2N2zRtikLAMjbO0I3u0FQoMCVbvT4tE6c5v3do7g04BOsF00afIkAANiDgPqO8r3hte/xd829T4D2EpVgqOwo+UW+1wPnGwq9ee85RjDgRsRo5s6Dhm55a4Z2UsiAQc6abU8+/uxjPwELwdt0eQGCAZp0phkFoBr6zkOcGYdWbkMb7V4AuFsjSykYxGyyHNrJi3+ntCnw2UThlCLsCZc6H5Q9SYQWJFhzObQ9dMHhGB8BvnPaf07bZ+OcFgEKASRF6p2UQXJGKY6zoe7BGIOxG+eG9ZmN85Esh3EeFkpIE1kG5RYIptyCBPM8KYkjRDUvwQ1xAIItazie5/Jdx785WP88rXPsIfLDnpkzBqdh70a6V393kVQHY69/uz/bqg7070z3DnOEKIbKOoYIQHrnN9wJuHP/DXzyV34CX/xjfxVPf/cfBNa3Q4U6PcSyQRsBsntnBw7IKs+8a1tffrNWJfwXAP6Lybn/AMB/cN179obPcgqem4EC+9s9zBaNy03VWPReBQB657+pXoVOrOSTTIR2lMBWhfa8uLR7YX17iRqd28cth963XjxN5pKHt4lMJ5SPX9YbQWrPj7WmPjs+y2Lno1hy7zH3ClS9Z2IGr1diHLhUI+HKeNYepeDUMxQFKUBADEnC0Ig4LUvBSzPF2I9cX1oPf8Z6tLkTxY1g00bbRnPSRnbNnKuBs8+c5ffmGbOAAUpJ7k8JhCxKhdQDteP2HTUBS3V3273NJ1cjOQoXjABBYIv6PmCTh0kf+Dfzprsxroa0HWOAQHTl2p9SbsbYAMO1x9jYnmuMMUoBnxhjb6PmDzRyvFxUMJgyuExAgoOHNjfB6GpL3NyPbP93N7f9bzku/TA4v2tPp2dcx4jM7UFLBQiWM7F0S5Kj3rUq9TT7SPeeyhepwKACAgMIo1CRA0SuoJau/huAGZdvvYFP/M0/iy/+0b+Cp7/r+ysoiKwm6XbQKYt/ol1awYEAhUT6aNIxOsGK/FYLJTxwsQEz43cSFGxXQ4UjJXZaEMJAQ9I7v9EahJ5qThlJPY2LVGnmmMDVIFtE4WyXfe3a2rW5ZwF6EOA0vCnjaeIa0CsxANOMZ6ACArI+sgrV0Qjf7TedJ9G6HG09+pjq0Gvc3JPibW2NhCpgMZwFUQFFr5KiojNmICVnAXpDQpSApXrYlVloJ+2o/W1ncNff+zYfGsG+fX6fQTEDYO1N6lEuF+AtgfIiAIELkMTogwUMiEdSZG99k3MuqMl7rEZSnuPeSW1oW7cZIDDvObQtAj/ri7Ktcq/1qu0DYDfOuz4AfHy9DwZgaQcUjCmx8bWxPTW+Ps7j8QUYWNcx0HuX8osgv7ytCgIXUC5adz3PBoICSAhsqdHWZG22OhzN5dj+gwTRc/TLEGwbhR7CehW0GBNAO7DQswtSrevp3SOd2yTOmkxvMXzdsdUNQATo6jfw2jd/FZ/42/8mvvhH/jI+9Lt+P7C909bCADxln0sCBPfgIJHu10s1pPD6/buhRftyg4CB/SFLEp/3RMMnJKdAAUBlCgwQrDVOq5MTmEw6VRaAojofnDZWaV4FKfKONHMOvwEGsUql6ONUiXMtzsNI/09ZgNJ7IlUwdwqq81K1I+QRpxRt8ze13zsDSf3vYunjpmcq1FIKsLUGhaOyBfyce13xsU2TZIw9Qcu9qRYszJkFHYVToQR/eAsMIi1+EgicyRZ4PbRtXu9FM2SJ5Dm8SL0XVkZM/SpSuTfvhOw8V4DgXuW8Dtdjg7YG/GEV0Mfb2gKCyTgf9gMgtGs3tqeBwns7tjh3bPU+I9kFqvxSkE0GKiO0LBUkbCs4L7XtyyL9bgCoGCMSchNiW8PcbVo/YAX2QOB8YMAGMKNecTmrTEaTMKyOmX2vSaRzdgGAJ5HGKscmA+fp25j/07MBDRsYcoOqUyoPfu3e6/jEa/82vvj0/w5P/87fB377W9rtASQtC2hTAJ8WoABEm4MDWUpEggyIQcrmFUh4/YVXn8WjeGzY78ANAgY2aBUU3MbTjz7h4QIqYbA8Vm0orlTq2YwLUCdjkI7X/vH/XY69/RtjYzAABT0F2SQ2NQq1p8mAI1TXTLIjCr7PXj+gME15ATiM2falEVpAPE/vuzFQ4KBghveMICEolka5butesUZDEcGA3WPbdrShvIwie696fDQrJatx11NgITILo34Zlq6PzwICVucIdEL7mqJjAUrAtko7SgQJxevpbQVUO2RQWeUNowWqZCCAQV0kUuUjCplBdCArI9kcek8DUBBZgm31PuEACK/bF5gY0iOgADzYuJ5iAo7kVbouyGyQVytEBLZ3D5u82jhzaYFg2VxWLW/E9Bh1LClwzbka2r7TJ4N+mUG4XU4MgMO8kD6klzI8gfxkSORMPattJGebJwA3goDR8RlIBAQUPP7v4+n/3vcB73xLqxfCnHkBcQEtFzKsADiR6HgooCq6+wIlMJOHFF6/fwc//OVn8dJHb+OnvviT0xbfGGDQMgW6T0Gz5tcGsoSBEsjI21ZBgSqZhpbUcvlP/iE++av/G/nyrX96SDM7WHAQMAALPUVpNJlPxj1Cb4p784GOjhR8zF4/IZyudC2O6agWw77YVSW1CtO9aqB61toWuyYm/Pl9AgiLtGRUKuWanpYrWQMEZjx66n2rL5p1RbgZ9RzaoxQzhfGPBsX7weK8sR9mpff4B/Txrk1W95HhiGVdVbY2qXtRz7JsSjkuahRZjO5yoQZ4dXBQFX12oySAYYFwl8mp06HcRln1ds5BQTNPjckLoYMpKDjVH6uMsQE/zmUImIiTLBPvaXkdT1fS1x1P4KSMet1ncmpFQwMAgCT7C3jYx7FAGOcEAc0GBJmBQlKPlCScFMFPmJc4Y17KR5VXm59Wr+voE2vPTp9EXZuXlsUZOF8eDvHciTYkci0da+2YhTlHetZA7gAE9CDRyhd+8M/j1u/4Pc4UNDrFQBYWkZWFwjNTnSdEIDYWUID73Xt38fyXnsXLH799chvlGwMMdqCgGRj5t98VTkADW2cqm2AgIXpml//kH+JTf/8/wit/8Kfwr37tf4ny9rdar+GMmPQILDQxyxivdKCAQ7S+86r7cMBEQA0IRJbk0LDGfptWxgS4BQU771qPR08NAw9sp9a5UyjvFhT092uK9asBhFW9kM7TzKJwOLa79z5j++x406zw/M4I9GGCk4Cgp5qNBTGDMSpcACT5bV7E4FIB5STygiJKRmov1+pvqGi/EPs5f8poQdFJxVrCcZ2f3g9h3EMfDZt0ok/sPG0bImACxAjYWvcdSAAeaCwPWZ4j2Yz3GRV7fNF6KRNA2zYFByjwGDT0nN2Euc5DDkC5aePBPPQ+mIS5HliXTFg6zssYKJzSsfq3edjTaoxYyzN17BEIGIJ+Lbf+O/8j8JUmGqYMbFC9udQcD00KtvlZwbswQDKO1e699o27ePYXP43bH5dtlMVhmXf/jQEGFRTcQoyhNwIYPOsY16qDFDyRMJACCv4Sbv+Bfwe3fsc/L7+5ersaA6DxIs6hmacx6QgGbHLEERx4YfoloNvSCm6g1jl6XetVO4G3dWxIZx6Lehp93Th4V0Jv1k1yRlS8/LT1yKB9OSznxNNV4YkXCCCLEeCoCCnt2sR9G7fizIZoVK13keV7BhKGNO6G6uVswfsZh4lrHXYGUL2Ec5TqpM8ON4059JZyrYNQAqFOBZZ7UK8pFUwNS9mPG8dj8f6xHrN6j88Rhc1cjjz7vh6UwCo3Ut1N+j1lAOppb0F3TMZylO8wDGeNWIFgJHbyCAR5tAPhu8oiwpxrckuGlW09Vq1Q9/XBGRAOvxnqkl6PhDaZLtnpkZgjoSsumAi8XLh+9dBJlyBc7y9zos1j6OoWwgh7kDAOB5wVLgp92eeNGCCjlJUVsh0NS2X2DLxbd3EBayJiP3cu772BZ199Abc/9hKeNlBwotwYYOCgIKAk+QxhhFAksUUGhFKaTvAKCv5tPP07/nkxqoB/MvR7Q4cnnJvANoxJU0x0AjADBsBOiPv8AKf2+uxtY0R6MGAxewsrREVl6LaP2wKVdgxeFVFd9w1tsykt7yOd4EPaVifFtJDSaEnX7sqDJcbNmqU7AwcGENRZBgCU0ijdkVIWJWZ/h55PuaHsnaJGpSGjF3qyTJLM5IYKaCwRdjAeTQljE4Gsy2Xsc4s767hVqnXGNsiaGrZ6SeX1M8pq15fRyxq2kWQqW2w4oXqzzD7exKkZZ0DpZFaleW7fNPUfFAUHp3JtDsFcz/CMDOXk/jsw0LGVEYiPQMFZQDzWP5ZT4a0jh+LboEeQl50OwbaKLlGgbqttJERkjILqiZj3FJ/l7d0Dgx0A0OvOzRmRy7f23jPZXK/akN+kRLZqx3poOOHOvTfxqa9+Bq8883O49ehTACQRseuBXbkxwMDffcAxCe80XeUrWVMCComR0oG//Cf/EJ/6vwlT8PTv/L4G/XKPso126xPYPGkmxKT1usNEJ6tTBAyh3k0Jk5Tte09dHrADvK7NRI7gwCYvF/Z+GSlHShVkyWdVUL4pUNIES76oHrbR1ynvaU7rzyMGwcYgL4qkN3lOKQIQ/G/LD7BYdgE2AqegpKlVXL4+3drbezV9PZSiJiKZ9BmtB8osbbE6HwGE4LFa+ykvwZCqXJUNWA6o0AELMwqB2RjUpWrKZjUZ+NXLmhZro1QeAg461u6wUAU+ZuBzDsARAGzDoVXoVaoeZzPWzEC+2Hlk0z7yA10brwvkzgUFo5JamaPcqegYzjgCA1rvnY6x9jTp9gfz6kSIwHOTTDeuV3swoHrEth0+V4/4a5WjHsmL6i9t/7L43HPmwa5VWWTTyeckj/ZMsh7bMUAnmIDDZOfR2J/Jasl41qRJf8fJoFy+9QY++dUfwysfeVHfrVCkSv6b+Vy8McDA6MgmMx9oFVGML8FoT5KNP1C7iQBc/uN/gE/9/b+EV77/J3Hrv/t79iCjpwD9eBAso5qBdjK6oNdjRwlsAKrndkqAugl9GHtfryrAUTAwmsysoRXeDBSN6E1F1kqnU0qgwqBEdXMglomN9Up20QPEvlldYTI+UGAxgRGokyEv7diYQg1InXpPx/ohc+ivAe3pzxoAzB4kRCUNtJ7oqIRx7OPUh89Bq1B3hiOUIZgcGYg+xGW0bJf9jRjuCnHafsMfvXlshH6WyvJYXsYOPAgY4LRIUiKUEVhIWLT1CmxAIOU6znE+dp4pdX12VA7HYlD8vk2sflByBrathjjsen9eanVJDgDAygAISJ3ncfiGFbC/Z07GSMfZ78I8aTzhAdPYOxZRhwA4W4+Qy+omEasCMFZxMpQ94lX6gxcb55A8qkDB+yEwdofhvOuETfS6w+RR+92saG7IULfbuOZFwiJ5AZYL/X6hc9TmYp2bl2+9iU/+yo/jCx9+EbcefRIW+hOmLXhgk3JjgAGdir+aIiJ2PcTJhAkNOLj8R38fn/y7fxFf+IF/D7d+5++txpQSyEIJI0UwiE/bsz0mDVSBixO8WzrV0+tym3DdrER0DxzH/+z6PmHqDFBg371YfH0LRkA6Cj6xTfnlsfJtk/XGRsv7E2izpoGWmuwoPwrtj0DB4obWPzRKkDpFRwNDSnrkrXs7w3Vyru2TIwN1wmdvyyxvYxC+snyXBhC4wskdsMaZoKCpDA7BgU1GlvOSPyWJj7Akq4uHkCa0LoqGDwaK/Vp9Nik7cFEkQdOfH9hGlC0wPLkyO1zjwti6Wo2mdUet70CAnYsGsJs3cklq5G06d2w+GPiyeWMeuYV1ZiAoetnX1CGUJYueKOm5gQ4pqPrDnqXOgVPvg0RL38VTKjCuOzDVn37sKHk0/P6szcZisTArIIY/jvMMFKhzKSuD6ni/9s2v45O//OP4wkc+i6cfeaK1/yZ/7P8Ny40BBs0AxAxSIkUAonzCnwoOVNGVDbggXP7//j4+8eafxxef/A/x9D/3LwgFr/F4sjAAFKU2sepKfU7rthUHCfvlRRms50dZ0XIbM5rVKx93RRDqU8UMhYGYlFBzyjZEb483lknLBZTnqrY/R8G7cQOYgsIzRRYSiWAxwphI1CVquli7l37RTHrSBCH5uzUmFJkDBQvWd7ZGeZYEeFgGFO0OCESvvemLA3r3XE92QCse7zEBeH+OkrMGDIH8NjXfGzBwlBfiWe+ACRrrMitZT5/r/DVAYOPIQdmmRZVcHV9gYOBGc+BUiNFK91uKxnDALHn4CmhCGwDa5E/7bb6YP3sEJmchgR48N3MFQAwH6b1dkj1B05g3zVOyuWIAYb0SY7ReaYRI9R6J0W6ofNNjGeBSVzZQpgYMjHRIXcJMzhgY6xidJO/js8I8FbSReedd2enMHgxoX52dQHpUjsJBgABKA34KAiSvYpFzywKmXHc+tH6hhNd+7VcDKHiqyhEb+Kaz5P8GAYMDpT0CBxximUVi35dv/V184vLfwS/8kb+Mp7/zD4C3VQbCdlorpQ7kQ+/bx6qLGtJzBMQmUQnLi4BmyXhEv/IdLbAwpTMzGob4DeUDmCbjAQCZci6O3G0LWi4JnGyyqHEYTPKaY7Cf0JRtVz3JMYjoF5QqKrakRN2i14yQG64Y6+4U3jDj3TJJFmkfHWQVR8BAQfkPl6Edlcjy+LFBvkRvtAdsws67GxrewbEeJOjvCvXP6PpyxgzYNT0YcLBwhl9u+RLqSftqByIwAihAbpMT7W/mmkcjJ+R/CxsYc2VhJHvucC7udUa/pW/PQslHN79j+Co825iK6wH1MO6jUMAUALQsD8exHcnBcNWHAbIKvJLNf2U1JX/GwqPJfgVar2RFwHoleQBYgQKki6Xqj1JU5T6g/ggG0ENJp5Ituz7ty4MuLwUwBgWzMnCMZrkhdPFwdZB0OeYplgBEHVMQQEHT4DOcG9wkYGClUwCmrOT/LH8YLalKiGjD5Vu/ik/8rX9TXljx3X9QJvXFQ77jmgEEKtJldPGQGxCmVb1T2XSiSWgD9kIz8hYfsOxAQcqtiXAAkZokrV2cPVJiZZPsXvN+cvCitw018a2jAjVZqGcIHBDY3zmD8kVFwHESdICAkwGDPPRe+73Ud8UMhhoWDgYGrNv9ngAM3l+dITiMKcdxHRn/ofc+MNAIhnzXzm7ij/qgVw7xew+qgH1/ngMCJgBkWMyzt0t8vlqyWLhmsr/+8HXDZrT746PvzXP9QPh9PdcDjRoKGO/oNwIN3hvnyEuQlX7nvzkAGDM7150b8Dlif6v8k66S2hbJH0hZnIv1CvaeAllFpLsMbpYHUPxvsv5R/cGFG+tD4dWKOzBg/WKe9BEYsPaOgLn1cxiHmGz+QKXPFTHm2M8HdgdomAEZ18AOmE546OEdQwDo+y46QGDb619+80188ld+Al/4yF+XREMMgHr1OE826+YAg36id43fvSrdDQHjtV/7Oj75N/4MvvjHfxpPv/8Dgg7LBvAGogxaFvgb+2yN6cPfIbHqddWtKQeG1nanysBoF7aGXjxYUiaH017gRxQ0IEIVn2MJfsA+SWuUjMcF2OQebDS7Xkv2W1QvrbanQ8U9srfY2QEgwCKCXyy0MJgEovyCQW282EEJXma/Fa9R2BwUowOIi3o+ggYAe6MwKOMdHVtPvPXeAzCw6ykci98RjHg8799n/XEaPOwUygBATJ/bFVbWqV7fX1Epsh29DezmtW14hNG1XiaKfsgm2X3L/jouLdCIIUowGpbCGIqBjADnyclORgJDxkfG3+XFjtFQXob90bAyNaHQdk2VBL5NGMS0CavXOUtY1WhvS91SXhObSTfMchDyoLoDOLkMkzojPG171I/MO2YWatvZGOJS97UwphVAYHkXjPbM2CUiT+ru77HQetH7/hlPAEYMGRgAo/oqcCBJouFXfwyv/NDncOvRJ+eZAzEUp22flZsDDGIZKbKdApMlgZf37uBTX/0xvPJDfx23HnlCDUR9uZJ50MwbKC0gTWel9/0zMjkuijIJbUJbAxSAY28BwHRZWfS8T2UZd20couFugjplrvXv4+uW9T3au30kWCcnsW1MEhJpJLywDCeB53EgrmU2FsEUYPtWtPg3dZ+MqgxtBQs7QAigoWxjlkH70M3TMjYuTKmdoCNq15S8nR9S9hNPfaT4J0b61D73Z5VzwgSTwnETpGGZnB/I13lEaC3Tl391QCAyBe32t0AEkX7P8J072XD2IT5j2cvH/u+BbES50HnQGH/97OcDUPtqNB8QruI4H2yelwKfB+ooMbXOkm9VraFWslysbQVfPLzXHYGC76XplBGVQx0I6M4/iB509o8IFJjePl/Ek5PDkuE4hsPgXqerh/XvQgUAgIe+Qx2j3LTR2QIb/5Rxef9136fg6ceeGrfRk93T3oGelBsDDIi5ozerAp29nOjy3h08++qP4PbHb+PWo0+ihMnBfKFGcg8SAAAPfYd6ksW3GPb4tAIFAA1YsDJev9sJdc8MxNj0IEt/73VYK+P3ENOLHm9Pl5fSxkvjcj9pwLQdu7b0k9jyCQI7UBFwdi/BvnPzXRE0xCcsZsNhdvvYbMjrUQlAFnOb4G9JS9iDBlNoI5ZB+jB4W9h7Qd4HtQLzWP3I06MQK47nDoDQdQ2n3O3Bzp1z/pwyrTOdOH/i3InbTv4OICGyAOE79zIhB6dysWNEgDPkopOJARg2COKvFdbP8+cCZANryjLtAGUXN2UK4/4mQQ/a97TqXNkqi8BFQMLA2ZCuKBMjutd/7073tXqvOTPQfRx0H4gqy2ptyNjnjh3pcWtLz2R0WzjHHKpirMTFd+zAwMgpurz/utqvV3DrsVvVWXFAYGzdniHYvR67KzcGGHiJSNyNSfUq7d+de3fw3JeexUsfu40nH7uFDXop9pODi76dURE0oIPHLExCXmGvMz6V/Q6gXVIVy2hpWR9z3E0EMyCTGHF9ovVK+B/u8TpdDrjX423R88OlYbMyqbdTo32oYAYGUoYpwY1hO1ejMDeAwBVk07iu6Bppf2WqK0cFB4mQNA8lWdWwBww1FjvzMPvnDrz9EXDtvD37V8IXa7M8rhtLLUc+Qc8dNArT8nHCwTS5OA0G/zogYRgAmCCc2J6+7aN7PUj7a9ttWyuVA5MVxPZ1Xnb0zDT88MAyMZCHEQAohR0gX28OaD/oWzLjHEj2N2XkBKS0SFvL1upBrt8NNEgekgHpUo1ul5tzUmdYnL3Lwxm97tp03Z4RGztDOyo9rMRIHVjgUG9f6gzU8RwtgY15RcZkzJJF1SEqgQXwPWse+g44Qzpwiozpfv5Lz+HnP3Ybjz96y/bw1THVPDNisL56mWx1yJmswY0BBju2IE60AApKkU794S8/i88/cxsffP9TeGdVgx0miaPotIBUEfgrm6GDF4CDo0tLZjMqujOwTYLSpMzRcAUBh8vK/DhaLT/tvC7Tu4mzQ3pu5A2dErBgAD1RKk6CyBRQXf5p4QQHA6oINwRQoJ/s1eXOaHZV6cQjoY63+GcEECPTSFl2gAHRUAz6btcPe7ZKe7U1/qUatUbZh++I19sXwDYlPllSVGduJOxrOw/k+uZSN6ClO9/f87B0VY1tls8W8PR90pwbGMJr9wXJDQwEAHvQ2PZJ9bJHoEHqej15GAHBwmMQvDlInMv/6NH+2Eb+BSAkIiRiBwcZCpSJkNMiIIFV/5UNKEqph9djmx5k3mpCr1XkHF2hlfMwnDl0s9BbbNRR6fSVh34s7ONgriAug33g189PAI0nUqPqv97wAwBffIc7TqzAMOqHO9+4gxdefRaff+ZlfPD9T2ErcMc2IeoofYOvJU2E95ScWkF0Y4ABgAHqrqDADIx16ovPvIwPPPIUrgZuytAwUEbO2UFheeifFVDAmpxn2zF3k8Tj0UsNJ1AnqMN2SEVk4lPySdK2swKCXRKS/h7ovJJRaZKwomXd06SYAYOpFuoQvte9bv9cGYTcMAM7MFCADYxSRAmWAjecYPl7s21XJ56aGbacSCTDlT+LQgzAoCrMahxy+NvuZ0BSDuy96cZ48bHxt7o3BqIzANhdH4Zh1Oam/Sbj8t0MhB0L0uNtadoL2TdNuw6l+/253P4RCJj1g5yvfWHX7IFFeM7g2dGI98YS6AwmSPuBu3E/Ag2A5U30SfGNuinj9m7ctr0HwJvZNbwb2QdAHNgyVqNCyCDZcZjRgYSqA5m3kKC4OWNqusITeoE5aG7Q+p4JGIXbgM6ondBpR/ki09UY8gMNGZVmf5Rpzgr1OnewYqRPHPScKgrsKLDlh6bs6Ov3LvGnvvI8fvYjL+EPvV/sl5mCxNI3pci8lRWgAg6YLJxSpC9PALWbAwx6UKAUdAsKLvHCq8/hZz/yEj74/qewbuzeRRRZUX6tQhCaWQwFAFzRgrwslWo2us2BgkG8LokNsBleHxgHqYs9+mfnfU9jkUM6Ulrlj5t1oZ/bT2jfIbCjTf23E2V0iiptgFuYCNsBGNi0imuRybyBsW7sXboB6JdS1u5V5Ri6d8miDInIAUM1iAYYemPBe6M586Ix9opdVZ3wAHsjYB8jQxByq9EtXN0bBkCBkRk9M3iy6RZp3ay9gCgcAwPeXlea+2fFMqL/hyApAKn3ok+O+mXcJxyMPo89bNcP8uR3KwcjGdiY27YGABDl3u5nwckj2Teym6iV+4WlPbmwgISyBwmZgI1CuEFfSmSMagytRCN7UjdI5XbOxCw3THp8XFqpa/NFmlyRJqG4AwvWoQ24OVNfd/pulzS6AwI1TGrC8a3VdF0rG6+/dYkf/+rz+OkPv4QfeH91ao0lKPqZGe6diCMjoISL9U/x+szKDQIG0WiKuEYqOoKCDzxyC2thper2FJyUViGkImAhaebq2xurcSFkIkCBgk0UcFjy40LXCaI/aoDedlTZOQBgQElaeC+08ZzEJPnM9mQYX1pp5/HnqPDgswQgIEouoOMiCrKocnRmQMHAyrL96WqMQmFVovF7eD53XQmdMKooaZUx9O8KBJYUwMIZxkL66qgn0ADRaOzg31uD1xsCLtVgAKeNATAGQ9JetICIgRGDUsiHH1ADksAOBLjxmK0t8/rMQgMzgHTkIff9Yve9br8Addx3/QLUNsP0f1XKlWGyYw8mA/34M9c2ri7jeG9kngh5qzK/JGn3QgkLK0umICGzhhlU/8VwgyX0phBa0e7yz9kIjPQC0DFEJmNur8/VXTFUNAr7DPJE2FiCoKdjZ56RXDzV0yE0wKhAwNihDQIEAODtlV3/mWx87f5d/JlfeR5/9Y+/hB/4nqdwtVUdVAgC5IgUyYdPQGVXQAtDExIPQAFwo4BB74lWZdOAgvffUmNTFY55oOFDbqn/VYXA/kqAt9fiE4XQMgo+URRRK/51QQSwT1oatqmiz312egcCAi05ik2PJh0wjsfO4tA+2Ts6NVzWnLfSe4q7emqdNnDjKRWu165bceW4sijCtRRXiquBCZ1cRsGOirE+Nm9k7Ch4URXwUSIsncFYctrJhvWHAcpZiQ6Ij4l2zBEI6I0BsGdGdq+vaByy6C1GIFQZkQYIQRMvWeWe5HdMCggMIBBUocp5i3Webv81AUHXP0eG8r3rm3PAgrTCAIPcY268ege0BzvrVpqxN1mPIMDYsV7e5XMu8728LwkBGCSV9c1lYckJpIxdUjCUWYBwBuGKxkmM19EFwNhpcbmIF+IcfcWN8zLLFRHdnRXQBR4isATvhY4uoS19aMAZgeAIAcC3ttIAxF996y7+7N94Hv/ZH3sJf/B7n8Jq+MTBmcxPebeIQrEADhLJvSzfgNMyD4louTHAoKenjXaWnILKFERQULh6oVEZx9IrBNv5++2VHSgQEajIZIkJbDEe3SSwNcJ4vrftk+UECDgnOSkqptlTW4NXP/u4dLw2mY8wapQprwOPycAAwrishZ0dWLcSDAN8PA0IiLI8VpR7YKCKgghX21hxEomizGDQVobGomn2QfutRmbkAJwEAtEr5NCPJRi/6BwnAq7sb1USBn4yMdZEyAVY1RBykmdnIjArvayCasu7CYzEcr8CRmJ9/ZG1lSUEMfJWG4JsAFRNVi1kdCQL1kfrQf+YjB31j/+t4y115r1nPQBRNvawa97F+I/G3uR8BHx7WY9tm8u7GMyUgFRE1lcLFxBhLRuWRFhVztfCWAo7WDRZMJCwGoM2Y0u8/XHgtY5h/gMtKJjli8yhVn121FOz0E8mruCg7MFCzy40TRmUoX5W2Y362ZkBPW9OUATAxnJdlSobX/+1O/hf/M0X8B//y5/H7/9dj+NbV1ujd5D85fAW9xNwUBgpyVzUjW91/is42EG4ttwYYNB70oVl9cELrz6LF3/oZXzwkadUuWAHClYujsJ7ClLoRm68BwD41lpkAgGwmGSaxKONVRih6hirnZU4cXoQEIUxCpsBI7s+eqR2L4u7HoHHyJS5MEJecBaVYGUO2kl6sk2D+kVlOQMEYhiKK8k1KEwAndI8AAeqKEVxkivPhZIrTplQjIX50FjE/urj+1b6Pj8XCERD17dTju+f5cuniwEhad+WCKkAKwFLNuUkBsG9pcRASciJUMwJgYYVGABhCA5YwUEc41iixzhiCnpQwCwy0YePouHsPej3on8qkDoPKFxt7+3Yj9q0cnHQez05Z5FzJqDIWCUSo58YKMTglBQYKgAjwgYFCImw8nF4BaiM2az0Rv4oPyTmhJyjn1rdNE+uXKkyHtVJ451uHi3dnbWpYTnCMQG6rXz3bGjPFAHAP31nBTPwd379dfy5v/0C/qMPfQ6/73c9gbe1fwTQM5ZMQEkC2qw/VGYSETaWthVS0EoAwezkbxPGwLLCjbq5+w1ZkvjiMy/j8fc/JQoW7cBEUDCi7PyGqPE0K/90Lchr8dicJ/EkwlUvkMYqHCQuobu/leHyrE6ZzoBAz4SMFFF8xmhvxrgnnedToKdfqxclv6meFCATdVT6RLFZPWchg7W0DEHLGKji9M/22clixBxAQRJDl5hQqDTeVSJgY2n/yOO+AkufqLxcYRxSMOUYAWgchxEg6L1EAA0rEtvZtJHN4GlbvX0CCBKTJK9lQmaW5yYCMSMXfTEYRKmAoOECG689oNXLTpaR6WgULLcXMle5sP4ZGdB14+t50vWFDQoIRUnUMTevi3QDNdGpzAxiYQ8zEVaTe33mOgglxMfHMMcICB6xYNeTcTmXiGQ7fwW90q+EtbDLBFC8rQBLO9dNAAOqMcpUQQLQzfHBwI7mOIBhrkzfJ7G/+hJDPwBOhn+WmBuDChQi4/GbpZdjrhQgTuff+/W7+KnXPoO/+KEX8Xu/63G8XYqz06xtxcYyMAoOSPuOiFAUFGxEIRcImnZIuLz/xiHquTHAgMO/y//6Dp7/sqzz/MAjt2Ti8B6xmsJh3hugSNkCQsua9wAA31q3gQeBEKdWdM179NonrwEVbfdDFedaVJ4xWclyJAx1m3c1i1EeUa69cgHaWDwAp6SBfUKTHKNhwpudi2ULndwzNqM6R0NpZUStyvF5m6LBsPZV5VkBghiHalALceNxO0gAGmNhhTpQFJWftLmCshkgsHrOwiTTdft+2BoIiQtwoBeZGkCwQEMB+pNNPca+jHRKf2gWSiBgV2OiMcOwlVY+gB4gxL66Xh8V5hCfrsbT+kiO8RQgZACrggQA9YVtkzG3uktbzgOBpwDBSLZr+1pwANi41/alcK1R0RsTssqE7NpajdHK7CDBijEmfRttzM4BwXbtu9FHM2Yn5ggtJCwYbZDQAsJyP2c7W9BLnexU+2Gf89UyppP71VMzhhAAvv7WXfyFu5/B//qpF/F7/7kncLVKHSMwXZDAicR+KYDf1YusPsoaFAGGr92Tzf0ew2P7ztVyY4CB2YPLb1RQ8MSjdfWBx7U6cGBKJiob80qBKqBW/t4/fhMAcLVyAxZm2b49el1yEloHmFBx4xLr7bbvDDCwmieoQhvp95H3eX7CXo3LizGdT1KgTtQrm3wDYzMymjOg1tYNcM82VXCQiNzwHylQa/dMeWL3nd2oijelSpS5GgsbzD7rzdrasTRHbTwEPQfU7XXK7G0Gke2pMdxGaw43BupLPUbicQO+9NGaQIoOIkjIiXxJXl/fdXf0+qWopzgb/9Q1xpS6jzmA3I3lg4z5uaDA6z2bDKH4XA1t6OftuJ5tG7POQ2zKGKCdv6vN1w4AHYFeAA+ULxHb4wwI2jYtKTV6eWlAwtaABE51NY7c57xw6E4fs4vxLoH0CAj0OUMA8Bfufgb/qyc+i9/7nR/EWoTJEceWsKCCeEIA87FepPdiWXotexsISrz7jTv49C8+i5c+fhv/7i/85LR9NwcYsIQPntcdDZ989KkdBXVqqQtQ5/Qou/0f/qM38B+++aMAgHe24hPLPMgpUIgxyUHiGoB98tKgOJg5ESJ4EOpd2sqHCqdOSHIF499pDBb6hDdAjVAZU3Te1lAPoehlMiVWqqXoDFAwILFTQmFCSZXCa8HBKUWjn6Ftp0pUopQUKGj9j9o3KyPPOVELehwIAXvXuyttNnoYnzA2zu5QBbOSy1tjyQTzqixWaffax2RH6/etlKbuytawJDOCdCgJrqyJtR5JVkNkFs98ZelvYnaZSIxGHrRHO2Zg3Efyue+nvjzAkGpTzweCwzpq55hMhzuPr5/I9FQedvO1Nf4mz84GdODn3YAeABixIdO+IBq0C8rswPMiTC9zqqE/TkleBlmANYCEJokYKqGTKkQ9bG1/t8nD0XH5ycd/Bt/3nY9LqIfEETHHa+PTRpuZG0EtEJ1km/u99NHbePLRW4e648YAg8tv1G2On3z0KY/zxCKKjt0Am9FmFlTFShOuLJMkeir/4B+9gb/05o/izz3+Wfy7/9W/hpVLjVEWbibckGoO8ehVcz++HclLkSGIsco+cWk16pWrYG7leFLaZMyJAKPdyQyNxWbrBF047WhYwCgxck9rZEAdMJnCSYTF20k+UVzpUGRBCKWQHwPIjwPYJaLF3el6sGPnZ16WKVErfezTj5NN7LbNsX3ZGBMIALLs/6IgKMFAj4SleiNhyiXWceRR9asuzKOSDHQJhRnlKnSr9YH8lkL/2Dxy0ND0TWtYrH8s1glNXEiaSV2YfQUFdI5spPJWRJuvm4U2kshTNgBMWLKxX30/HffRqJ/smnbFQh3rng07Gmu7hpVR2vw6+P4dXg8DTjvAC2EwUmU50LUttsPaIveswD22cda+/f4OpwH8LCww0z8RCMSQyHk6iJGTzsVSdZAkU0r4r4Bd/xSQAkpgQ8ESQiOSLyLnAGFbpe3X18H/f/b+Ltiy4zoPBL+V+1ySmpeJtt0mARTg7omYjphwW7JEkQQBVBUtmSYJEhRnIiYGvxQ5IClqKP+FPbIl22173G172u22umWOKFEQKRcKgB86WiJIkDJtGvcWfkhRP+0OR3TMQ8+Mqgqk5XH7oR9GAurszHlYuTJXrly59z63CpLncjKi6p6z9z5758/Ktb71rZW5gWUwsLScWjN///Efvqcd41TB9pTno4B4TnSvm3NxWKSte0rAlesn+Oizj+LzD1zGvXddQEyLuODsAAMLCkT3e42XuTdlgzUX74PRo3ghgVhg/3sFCr77D99T7qMHM4rnAp7PkvA1AglEbfJS9aKBfb7vKB5fQDtWBHEDKBBAEFMqk1LHdW2ZAoMYnpjEL1sh6S+UePwOhH2IJTbPWdCh8bjEuy73XuCgd9L+ibBrlBEw52uayRd6jwSosfWlohUogEaJyvElI7GUY7BTCifFiuwpJV6HjGpcBQRNEykKlqpyUbe2inRprwbNaNX16+1eBrtQDX8IooDQZHOXl+6gZw1s0SsVSNpLQCRZ3piBWwIQMmOQDaMAScSAaWL5JQIox8L3iBx7zbLQKeGC3/0+sv0E1DEGxsZyaZyBOtaFYs8e95QSEGoYCoQCdjXoE4AD6LEHGvjlyPJIfqV9oxwhaRcfH4MBoAU9PTPbhyxjTrLzAMGh+gfgvgnFw+fr9xHYBUDCf/sQscuKIKj+3qsEUg0QADSJeqP2evqXj28HA2shQM1wBarySHl+koD4QAXAeyOWALx07QSPf/FRfO6BJ9k+Lj8awBkCBp+XRpvj0lkBvDnLVPhK9kZ2U2AUOCdegzdHYEL2TAJ+47dfwN956RP4K/f8PP5YRnK6xFgnYjmXMsrLNFCEn7wEtAIKtApoPxhBL5lHl2pE+rImkLMSYv8G2r0B6lq1fDxy/0ZZGrVghG2oRY4BvnE5ksQ4cB8coc1LkGz5NsO5j+HZvBGvaAOh62ONxCjhUsqifp2qsdzlegPATuqel0hKewFOFESgxjMblaU6a4bAAgJhCQREeCyBbOglgKAJISzbFAAVEJT5QLwXCCfFs4Ek4r9zBhO8i4taOhoTz+EwcWZ3Pq776tB+ApZBwBY5tSUp0EAKLFAGupJzwcezKprIl9tVUNvL7RqA3domPfd20r9AzUEYlOKUrICCNd0TiK8frXICWjalO5566zknTqas+SJ83NO9dim7XcF2GkAQHR2pWavd1G6+tiPOU9vJ9ylkwN4CeOmDl66d4GNfegxPPPAk7rvzAj+9tG08Kc4MMLhXGg0VXswlUI6zJMLsgIM3ThMmREwxYULIfyO++a0X8LeufAx//fwT+O4335M98Drx2OCP6yQ0dqA6qSVhjRN50Cb3oC55mpXwa0NjJ+BE1IEDIpS4a5xTjU/ONVaJSJ0HJRNujn7C3sJ8bK8TT0U8F6oKy0sIskma3GZRbP5DveVPwPISqPZ328rIkxptp8vn9O+3ddqMxC+yBJCyi7u2xa8HCrv6q/raunY7HRajb8IGoLLpyxAQUP29FP05qYNJHdAAgT1m8Fa8xN4x5brEqMInE+eS7Ca4OyFyf/Zjv6WfpK+AdkXNzY6tlJ3IKYGX0MGA3ODJ6rbnLMnqkpweOtd2EksPBMoAgVLKsftUQrHIjGUIKLpnS7Lsku6ZArnAoMt7aj63LIkuHSuLsb5dS47mY8shg67eht0BmAWwDN8uEHYh5L9qZ0oHFAir9/L1E3z8S4/hiQ8wKBCydANhcHaAgQUDUiTTWYODSNUb4Q0hAJo4k5VCBM3Ab/z2y/jrJ4/jP3vXE/iet9yH/ZwK1QfwQAlIWNpAZ2sZJavZbPFGaEWBRRTkjqzM93Om9FXcFVPk2HuhKINKzCMGBBNhCoze1zLVhcoTWrnE+Yify3FsKruriYDXXQTR7QPRbzfLpe+Z7Hnlb3qlhvxZ2szJowZHRQ/LmlJtkpfGle+KvN0639RNcJKyZWOq8ujFDWB0GABlx86lkIH+rMGANLE21YBV9X8C/67JI5WNkhKnEwgRFTP7NgXiBC/K45k9s2kiRBCO0vqYb+2ncuwmx1Qae1Q/AhnMHMEDty0g5Has1HtFNrsxV3UP7Vev6uWKCNQ+LsxMwi7y6hACAXPM4TAOgSGwgdlHYDcl7GfOa9nrySqKOeQxzuPn6R4JX9bPvd5hJ4RXHIhDYrc7537zmcrumfCdiC2J7FLKapfUsxk25AeghFylvjrcJ/k/u9J+w+o5oODeOy+Ue+upsNSCMwMMvEJqJrDySlkZ5g0glDcyJx6kXZrw0rdO8BNf+wj+3g9+Hm+97T7OcpUdwXLPHk3BjfvaEtSIj+KWXNfx5kGjIoMnyB2oFOU0tTH4lIAQoZL0gDBTmwgk8czUro8uk1W3RZQL1eTDOhnrZ0a5lSXQgMDS12KkKoquhgrYosCqIk0pIRaqvtKyS0BiS7HKVeqpD+kxs+M3Ipis7ZL6HgWpewuEvN+Miq5f6Utq+1crFQ0Q6piQCwaaWq3tK58fzKCkkN0dSJiSbGmel1rlW04pJ8MnXuGT8nG9jlz6S79k9pA+kn5S1XU/29/oMhpL+bybyoI4NrjlZPNntawZeqJaZxlrOd7dw9ZZ1VdvVRxjC8b2lLALzNhM4M2QaI55kyxgDwIQG8ckEOW8p7piCFknH2XFN9I53soo7Yi0SxbbHAoPDGzx3yS8MAdmRybK4UrJSwh51VRq2Vrpd7EN1ln08lkA4E076sJ9q4CAapjvxWuKKbjrAk5TzgwwkMmnO7iLeWYlA7BnkjICjiAEMA35wrUT/Pmvfhg//d5LePvt58uaVNmYYp8H+Y0TFcML1AQ4XRdbHy+G6QGCQ+hLTe9xPVqKUi9f3MXQxsBCv4QRqAlDQE7WM3Daz27Wk7TPepe4WPOCFhIvVIUSxEgpQQdagLBUNDAA8vgWLVdjfGlw/akMyYrC9V5KtViMZdD0qzUah3iU8lEAAKCMvAECozBBAwbsS8GA1j0fTQQKAOby0hl+Fj+5AQlEeczEQLHC4u95FRFQmDtvfA/tI91Ppa9GJ0dFPW80dlbu+NptpQAAI3dL4+tdv1QEEHC9qVDiKeTQVg7bBGKQsEv1RWc7Ctjn7xQSdpG3F9+Byu6UO4RulQKAumJI6ZzxipGWlVxKrt0SSpHSbcdM3N4d2AmTnSBBOWcspWbVFF+vAE3jANbn2NwPGZ837SbFGCyHDMp7HfKxF3JOwedyToGUhinYgDzPDDCwRS+d0hS9TKoEhndMjTEqvHL1BJ/6yofxmfdfwt238zbKEwVWRFO7n/WbdtPmbTzttsJAH//TYMC+oAUY0JlQNKVB2nVtLWHOHvOWPdmBbdmzXsa+pewkJuYhXkIV8kA1jm0z3SuqRjFQpS9VfRpPWjxsATpoFbFmDTwg0d5nXGyo05M3F6iaMmqHvsIzGgewmW49RKHIeeljYAEI5Ae7b57L32mhYpxkO2fGYFZWjd9OV/dOCLyUOA9jDwAYHPDj9Zj6AH2teMby9OOlFTE19ejljO92iKx5hn5xTNWPNs+fVPuyvAUwt4VfjZ6X0iZZFg1Mu8AsQsxbzVMozhQ7J5TfxVD1zWl0DeDrG28X2uGeMab9uohRnGPisF5KOTmY9yqoSaOA5FjoVVMpgwhg2Q5YGyA7jL5pFzrHSdgBCfHZF/UBwAvXruDxvCTxfGYKRN4kT3xrOVPAoKHNYLwewEhCVTK8+cMVfOK5x/AL6t0KR4nRo4QadhTKQH/X0aSMr7+U0Cs2RDB6U1upqpn0+re2eDSljbl3W3PuWrAgdR+9372te/5uqDoNBrYg3qAEPCiqjCBtV95pGninpXJVnTevQQ21F8XQSL9oIDFC1qOYos0LGQIBfXwwflsMjpQNCyuGRZ5f5Kl7vgMCgIYhgLy3Pl+jarb4bF4OptRU7rBUWIN8rHwOCkznmU3t+MmYAremX4Z1V5+HgCD5F42AArBNtjq5MgBODhUQB7SMjt68Y2HeJOJQXx0DQgz1xXQxsVfPb+OkAhLmIC+oY6p9h6lxpvYzt3sf/G2AvdUj1rteez12sxX9TehQ6UEOmQCydnEHu1KIcyzmfC/N1Eo5Up+XdH8JqYKBgTAfFgxISM/m+lzJLwy89EMZFIhTRFneUiqzjmgdNJ8ZYLAECoRuAfTkqQeOr57go88+gic/dBn3nbtQ4pszeLBj3nFM3l4IcBwoyQqDVA0vANikIl0WmYAO+RtvYEAd6tLopdRS5ClwnROAuGvrfbMgxxVygkLuVcg1GJC/RfABIM1ATKC4z43IRiix32I9VLdIDLtogNAYIajXdPOxoDpW803V+Nj+LY9aPWbqW6xae7e1d6SXu4nyHpUtWs97ppcjYFmAcm587XpRu3klrm8FDGjGicdQQawyBxwACOWVrfWBe+6wMWiKzOfyXZSOlaPukbDSQoPPq4CtA2s+o+MWFdaRuSLzhF/LvWMPN0yIgarjlPWjBQmdMzWNX+oGnM6R8pKVR3pTi8OS3vRCi6J6KlhgZLrLFTyt3pd6l91Gc8XesGv1ZHWqqLFn8u/4Km9z/NSHLuPCXXl1noDnnFfGB1pwsFTODDAAHFAg/6DUep5EMqmOr17BI8/+MC5/8BIu3nkvQAkJxLGkjJCTEn4Z/Dfl/Q/E+FbDi02JT6NEIZskxNO0ugVezHBUZF1/9YoBicNmEFmS9CRBr1H7vR3pikXlMjGF6rJhAssMaDBA8wzEGRTnXLk5K7vI653KDJWedYxSo7CpPZY7WXumDAhCVYjleP1tCzDsM0wZxdjV8YZqP8DbLr8fnlmo1+Zi6rBg+F0juoW/J+rulXLeQb1GRfc7OW/buaLjBuUQYrUvhB6sAKhyA3SAhrzjtnjyo8GZBmYFDBiwrK87zVyReRImNT8mnhdhwhQmhDABYeKs/cQ7CzZMQnamJNyQEu9YWRwTVaVD9Iscs/ry1uhKOAxi1ZVynQYLAIMFoIZeNEsgZa3uk6r3G6dQnCaxY6I3ydgwsV9PPfCLuHDuHtaj2bEpWDuJtLfgYKmcGWCwDAryBIpzg65Prr6Ah597HE/f/wQu3vY2pBu/W40EhSz8/JqjCMqCzqP3xh0VoJDKuSpcFnl6xQIAbgd/8ZLCoNoov5O2D55QBKCNsVfgIuCgTIh0WDvcNlC/Ec6IGWjBQP4sQCDmOHaaq/JLEUCq2yvPyzsS0MQ+BytsakACx7RDPa5gO1Eo1/AB3xAsFtfzHiv+7jdLx9bqMjx+C8DDZmZg9Pv+UNneqdRv604T3v0H9Rv146H96xl/kRvg9ZEZDYgbgDwABzc7R8r8mPh7CECQzxOIpg4keEwC683UJDACN6Fbbql+hNKPVOqinaM2yRUo7/XY2IaldujEQZ0r8KZJVlwo2zW3totSwvG1F4v9unDH3cB8A8KCpjCBKDRAyYKDJYk8M8DACkkDCuLMtHScM+qecXztRTz03MfxzHs/g4u3vRXY/66a5AGgCUSyZeYEEiqNeBK9cVeBQkook0DT90ITLu3kpesOKCRp2mNRMEEP7Nir672CGmMvEyAJOkZXfw9YNLd3JuEYoMEHAymV8WEwMKswAo9Z2t9AEqAQI1Kc24pFR/mFKdNqVL7zbAygwKBgSSmy16o8KX2vLYreYwYWPDgXNJhrhsUz+I0nGxau9YzfBgCx1dhtKTZE4V7jpmAOzy/25/B++gZ+uAAYGP9CF4fu+oPkxZMVbfw9w59iMz8QM6G9Mj+4Lf78CLujHE4QtmDHQEFAQtiBGbgeJPDqkQwGQtWLaeCs6DLSKXJO68RGhwKn1IlQOpEMu2rAAQYgwu6hLF2s6mfr3+wPosLdR6RCqcVmVQYVAE6uvYiHvvJJPPPez+DC7W9ToGBim5Uij1eYKjhIbVhhqZwdYOB0cgcKstE5uXqFO/U9n8bF294G7G/wPerNCvISlKypNACY4h5TmOQpZRJINq8VejsMWoy8zPBhVnj0Y4sA+mzwlDqF1MRtFV1eYuwLSXrAtgng1jkbeh0msGCgsjozsN+zspv3rPBmvjbFWJiElBVg097aqapyU25mUN4Pj20i6hQi/5xBg8c0cD9u87yXGYIMHa1np36T7Buftjwz9CCAAAOC5Gi9hj877fMo51r9bSBiVA4w9lXuvfODvjT3uFX9Gdb6cgTKlp7jrvJgACDGv7RBy39K/twANs8PPTcosMeZph1/n3Y8N3Y7NjxaJzYgITtTgf8WZyqIi9bqlOowtTrFS45tzLdQ6QKEpG0H6kG+OECHF1POqSgOQnmq1u/SldSAmpGOH+n3JsQtTCmA8NrvNECgZboTjl/5Oh786o/hmXf/Q7Zf841a35AYqKFgAXZsM0PR5BwscAZnBhhMSooIQlXX+LQGBQ9+5ZPcqW/5fmB+rZ2QWjGKxximPKpTVQyv/Q6SGBN3EhRVtRhDy1UufxuDmtSyMEUhlcSiZjJYKnHhWTbGDhTqvIuze5ni0j/6vjbr2auvDhNkZcYAoeYSpP0NpHnPCk7AgICE/Q0gJWYLUgSy8i+T03pFAghkTKes0DSTYJRipUwzCNSAQfpqmjJlvM1jHhl+Ud5Jxssq8yVvb6EIeLXKP4mBk3aYNgEoQIh0giDleo68ZC1vW4xgJ58L7IkDprz+dPvSM4oH9qXLOgF+X+rvqj+3SMmwTVsAgJwDbs3cIALtjgoYSGECph2wZ5DATALLDoOECRT2rY4MEwg3Gj1CazokOTokfx7lV5TfbdB9fLHoOxMmzLqPtN7LgEGcpsmABQAlLDt8nPo7cpbY+EcUsn//u62OVIzR89/6VTz4z/4cnvnBn8K73vK9SPNrEIYbgfNyUuApm8B6PSVCoInvTuDXk68I5ZkBBtrLZlCQDY4KH1RQ8F/j4m1vBc03elQOq/hJTYCMkgHQjf9PTc5ZmATNkrlRSeLtrIOAhk50Yo3lPuq+fV9pirMaB2lrjbO335O6lkZtaiazqkdhCnJ/C1uAVNiBOO+ZvcmhAyi2QBRe2muWoTWoySiF4gUAxcADWalnrwYTjyEpMGC9J8sspD16gzAoKarV2R4AMIq7KH19vRzXRSt6AQLSbuvhBlVXBRoK8AGQ5gp+ilErfoeMeaz96YYuNjIHFHwFbmUmH7NgIGnaHKggQPVl9ZjjretHoMqEvl4xibo/3fs4pciI154FIKBBQJI5pmVHtXU4NzSgyUwB7XZI857127zj+k870O6oAPeGRcjOUwo7YN5XJyPPKdalSs+Ifmk6oQWHjRw0hl/pO3utvU/bYD6twADZ3KLyOWwDC+q+btmi162zBAA3fhcNU5Dl//lvfxMPfu0v4Jl3/T38iTd/L1LclzakEIE0ASlVcJAIKc7le6CJcTwxW7OEDc4MMChr6gGwkUyqc2ccX3uBwwfv/ocMCgxlHZUCSft804L+dwiaVgaA/asVKIhgCVrWQgUoofSHYrjESAMBY1hHMceOcpQik8eidKUYRvF2PhZQl4hRd58yQTqa10xgnTcwz8wOpLgMCPY3WsU371vPUNprkqwSqufWKHZRghSAuTIEmKq3AwUELEjQ7U3GmAyLwwJ4xqsJj2gvsOnTQVFAiCxrYNrCl7dtTSlUo11CKSjfPUrWPntzLH0EDlS7Pbm2cfTGeAJ+mOn16Ee5zvblbK4Fq5nFMpKNQ8BA7pOkAcFgXgBgWbdtIAYAKc5VLnZH/FkYvGlXcn6wO2IgMO1AUwTiHiUfgQIQq77Qy0tdXVHqO3AsHIDo6jnVbq2jFkOEUl8Kta4K2BTdV2S77q2xTacDnXPXrLKam/bS/ndb/Qjg+X/1TTx45SfxzPm/jYt/6LsRX3u1OjC7HTMEAfxfzGxfmvk4kSCFkjMlieCjcmaAQUPVGBR2fO1FPPTlH8Ezf+rTuHjb92cKh0GBNUwA+HfaG7jxaqHTRMDC/tUqTEWwpipAhoZPpJdfqUmiFNQ4+1gJjhIuqywt5ci3V5NGfw5a+Yn3MLmGhIIAHD2ppNdNe3RR9C+3ryr4qEIFBRBIOwQkzDdYAUpowSq/3Af6jYNWSVA+J9tQU2YIMKMHCemIvYMpG8bEO0ciTKAUkGIAsG+9xlHc1p6z46GUdxMPtpQw0NPCK4WIkHCjAh3pe9UWUCh8ACv+bDwqLkC3F7amX/lADwg2hlfKw7ixEBnheRLhGY1FUDAyonL/U/QhAJYFAEKzM1jNRgOx9mWMQMiK/WZkw8rFCBDsbwzBgAABmRfdnAgBKe7zZ2LwTIFtx7zPxiUiCTU98VqsFLP3n98VTBkopnmPJLpRHIxboCNK3dWYJ+m7jfqttBdYDBGGaer1eRSdTkq+QzMPmtBs0x6lz+W4ZnjNSivS4/bq72adx9+Pf/s38ODLfw3PvONv4OIf+GPAjde4DhLuAYAdmCFIM5AIFGXL8WwPKeR5zfkGa9GEMwQMDNLMIYTj61d49cF7Po2Lt70DiGx0eI/rWEGBeKlFuPb15mII5n0xivHV38mTYKcmgo9Ay+CtKU0NChxWQIMBa1jXYo/cNb3nUOLtUr9Roh7QJ+vxhfX6paZpunSg2JMGCgIKUtoECrSCkM+sAOW41G8GMPOYZZRNMZ/e32BqNSWmU/NEKgoJkdsZUfqsjGiYemMGNFT1FoZgCAj02I0S6BTYQ5iA/b6GS0xbKKC0gwCOIesyUuQi0/LZKk1gWc5T5jBT5OtSQkUjE0Tu/eea44d41vn6+tttfUjibU9AmmMFCGKYVLP5zWQscywnAraWZcNlO+S8aksXRnPmA99v25zgzwk8H9DPhwhOZpuzzEwcSuDQ6Q5pf4PbKfNYQm3zdp2QP5T2J8+xGfRLvY+v18p5J5fIhgh7YCMgYd/qcr55AY7jxrWsl9XlUExYEgZU2vfa7xbZPv7t38BD3/xbePptfw0X/sAfQ3rt1dYx2R3le+eda/Uz8hwrIYUApKTs0UL225kBBtIhNZ4TOXzwpbwk8fa3syGVa6GFLvkCKCV7l5p2K6ht3tckxA4xOzF6wFe6QiNZlCkCtL9Rwx0KFLhG9QClmPTkVQlJSXubim7uJlf+XUOpjeh1TZk6E792hU2UCsDcK3IOEzA4sArvpss8szFQBnXYLmD9vC1LVLoqnYc7Mmj6nBglXZ8Ua1ts0ceojquXY9PItJeDIm0bNkjkXNHJKSK/T5nrGXas7GKud9HHIf9c2jn1/WHBVembue2jUcnecL2d/zr0riy12X3OSnzBmcs3U+ycKNS6MAZeMb9JcebcAv7SAksgz+vKOLohFJsQOgIAA9ZnFeRZcAyMc4lk1YU4ffm75NtQXq5JhhnbrMeljQYISBsLGLBg6LVXgRTx/L/+TTz8638bT731J3HhD/zHOe+jOiYU2GlKlHfXSRFFaSGwI5kya5DnGIcWckhwgTw7M8CAtAClhJOrL+Ch5z6Gp+//bAsKlooWSsCgUqoxRKBFboaichO5RMBKhY1QNYos9bS7FiLFbnSAwMbjNRpfMUSLCUlLiXrqWv5YAUNXrAftKGoKU5FxmmeVZZ9RN9VJRZwUjTTPxegls3FE83prBXh0KKGyI7WtUG3tVi947fQMiOoDOVtqFzM+UH1T2o7afgDcB3GuytozcA7IU51Q25K/F/lVLJCOW0KSyor3pPJpLDOm4q/DvkgJ4pomTEVpkgIFBSAAJYEKceb67PfZ65E+mCtNLICBqPahlvdT9F8BBAOQzN9XZGLUF3Je2iCARMuElgc7F7JXX2LLEaCdgM251HV1LvDB7XPBlswEdgyJ1bcDdqQc25JDIb8DtumyAnBDm0u0O8qhtQyepx0Qcj7atCv5NoX50SHUlFfsSL/p4uhwAGM97jC9fP0ex//v38TDv/F38NT3/mVc/Pf+qMNQrQBR41AURiFv0Xf8W1fwHcUYALx3NO8I9XO4ePvdPTrP3hqF0KJaTVeahCWJLxbvaN7XuHOIilJTSWkGJABqMqbYeNl67a1kXvNl6yxByZM4IP7odiHGymJTot4sXl2elDKxlkq+jhVPFVXKNHMiRr6YeY0uJ9YohCxtm6ricP07HXPUzM9pAYHNx/CeZY2PZGNLO4R2pjzG5bsYx2yUU25TfltLSmmRnbAGTSfQyTK0AgKyIpT8GXI2tRGAIICgsAc5wRaStS39qdralIYJQ8nOTjoxSwxDBgQlNyelHEeVXITat4R99lyzx7e/kds8KflhpV88zkH/NX2X29MkIDpguAEDNykTXLvAMmHkYTgXUuR0kDzfi1e/MhdKQqDST2tzgetqrrfF6NutoZLVhMpiOJfZEzePSPImdkeg/Y2aRwQU2eG/eQkhdtD5NmkfG0cPAJIyrFZ/AwxeurwYPtEBAu2QHv/r38DDv/l3cfl7fhwX/r0/Wn9j55g824R1a0Uy2Jbkw8wkHF89xsNf/Cjuwp3DPjw7wEA69doLePhLj+Pp+38eF8+9M08e5eUgsccp36dd43lQCgWt2zgWhQnH/+ZflOeVuLMFCOKdZERasr35RvzXDGKqD8p/qjHXQtVR71JHPamAbkKtJSRJ0cl6NMkabgMSph2QKVadqAeiJn7dxFulaA/aeHEpMwANnSttn/IyHEOtSptJrl0rSom7BtSCgfwbm8Ff7rWUY9Ew+ZFlzXg+ZcOnrChK+7OyoCyfSAmYjiDLj/JN+2dqzxZwwU1pi9nABrsdr8suLMHUtNkyB8OXUclzbcmyKjRrUvHQAhIECKkchAQCJ2txrBRHbwDt9xl477l9CjRTvkcDsnTfLfSb13dlfEfgUMtDMwYHyET+3MkDmWvyXMDrPQ9U+xdZwXyPpizk1ayyAznR2AMDq7qr1GmQRzQxq5Ekd2QXWrAaxSOvOSQCDqT5AhBsKflTun5bAYH0Wa6LgIKLf/CPqcapsdBMX567qWHyZE62c5BSwvPXTvDwlx7HU+9/An/5mb/h9iNwhoBBu3f0z+PiuXtRebn8lxKQ1B7gO1ZSacevvWDPY1c8EKHypBz/T/89Hv7Nv1sfquO4NuamqEIAaBKw4cpWXxwF1ibUoQ7+xMlmpThxeTuxlneBCyiUpE1Mkg1vZIJNQJfBrurLt5taQZX+UUlvpBRKMSIykQD2gAz9SOraxeVoI8Wf67IKBCwIMAzQ2mZHBLTUYq6jViKkKUbddn0NnHFz8htcr1a3RyVb9WEDBxCECUAo+TSFPWiAwUpKE6F4aDCbeFWQEEAxMosgHjIRkCYeXzm24zsVEDDlNfQ6XmtB9KH9BrR9B9wyeQCAcIg86PYg6y1ldOhWzQG5zgIgfW2+vivy7Ow4NGESr3gswYJDs6S35DmSUKlDJlxfo3Anp01Q42/rqRX2IIzhJVM2DqYTNpDjujz1vX8ZF//gd6tKteC+MH27I/4sO1LKHFRAXdf7+NoLePi5j/G7gc7d2zi+tpwZYMDvPtCgoJayBErF54qRO3oDyoYV2fNIQEPlIc4FFDz1fT+BP/X1n+D7HJKMZpKamuIIXUm0Stp75nvSDkgSb0VWtvOeM+kFG0SAph0S9vkzUF9M43jyN1MaheN4pjexkgGoHmZR7BoQKCalTPElwBNMXYGeAnaUfrdMs3jI2RhapCeAtG0Q/52OqqfMJ5oNtoqBkPPG66DcB4tlYMja9lQj34QMSg7JLisZtRpFzRXAe++GtNWpk3Qx8ReiSenlbEhS5M1a4gyiCSnNQNjxEuPsyXFsOB87moAUEVJEmndt3zlAa3OfyXclsxYANPuaSD9Kaw6Rh1AT+uQO3d4NyPPAggETu6cl2Qe2yb++7pC5ahmxzNYUMFj8NB2jPzypclQXCgF6WXLRQ8KISZhklzdnymG0Mid2u6G+cje74ob7lfRA6KgIQ5HH4OIf+p7mXKNTd0fcz9OO699sUz2181eDgusv5py7n8fFc/eM65LLmQEGI1AgcVA2oLEOfGQFR5GAHSFME9KePaIS09vfAFHE8b/9l3j4N/4unn7bXyuDRjv1ck3rXQCOhxz6SaYNaoldTWoHxlAVw5QFO084ClOpXxHglPie+xslBlk8KtmtLJo1s84bnrrJJW2T0IuOQ+6O2PvQy330BJO1tsogAWiVqumLcogrqI5UpQm0npbcyd3UqW2c+miU38KbGCMF9dvWGNQbLnuJJb5tYu1AyrHhWM4zGDJGQv1mme1R7fMMWaYeCxgQpUk6f2Cq7EBhEjJIQAUD/f7xqp1enYjKHkAB/HjesZS3FecbsOFnFi7TzZxhynKXjyWacn8kIO7Lb2gEtLb0F38wfYbaT/l7lYdBbsUaY5BUhLpkrgOFTxF5ECCY2zl8r8Zp5N60FQC6PUrMb9s21OeQrZuOr+9voORQ7Pecx5KBfNqBY/7FidqxcxYAm1DpJVNy9XowwN8NIND6yubb7I5aQOCwQbqfSjvlXBK2IqOfgDomct8uWVbdWgM2sS1a90r9dke8RF62pQ55x0lS9TZJwQwKPtHax4U5yqNwRooLCkqGNDKan3iixT13WuQXe/DnGTiiYvzEkB7/q2/ioV/7W3jm7r+Bi3/4e6uC0RS4BgNAi743Uo6pVBSZJs23EsQtm35MqB6RxE8lm1Y2ApLPiqoTUFDjmSsehkwuZTRsYhJNdTKVpT9KgAWVa6MkCsBbG1wbbZSqEeKSkXykjpeENuuVDSaAeoYg64qwW1DQJf3QYMnSBvq4T8CL5Vil0/U12YDs2p3ghrHyUpfWUDWGrBh5HT5gMMBMgcMYgGV0NkBAv3WuaZ7T7yKvlF1IGX37xrxJMwlBqPQ9kDgGn0JCs+lXnPMSx1T7zAKtA/srCTMCwJWHJVk4RA5UvexS5STXNcfy510r7zqk1retrY8nu8mbh15bnHqjhIW0tUsIGZxJ3ghyUqg4NMzMZqM536hJlSHUFyTphMotyZQDXdUxBOLIeA5MboMXFqrvRkg5BIcMiNihq84cWmdOdHRKaPbB8MIiR2/I7WrZiyYXaMQSyJzlG/igYEM5M8Dg4rl31i/dRKC6bhoJacoGVbyPOCPRDKSZvZY4I+yO8M9f+QYefOk/wTPn/w4uvvmtjWCWwZOiwwar9CMqIBnUudQdyHUVmq5OON6+tKVNKS9lpFhXLZSlORooqCRFfpASUF0vb4IZxF0YAieZTWjoErvWRkmjW/3cUxhYgJVksueb31iYrooNCXR1CeqagZdY7uXcX/evUf7dltjqvAYDSX0ettFpk2vIivfb5g1UMBAgb8OzYCCm1AEDYCWMICdUOGFGwewFGAQiRA8kBGYHkgD7pJP2BFBFH2gd2leA6h8NYlH6bJMsrMmBrtdIFsw1AFqZGN1/QcZLm2w9bwbY5rFhXRULcEPYsX7Y7xuHi/NBduUFaSDqkipLQuWIDVEeNf9dWFmk9yyQlThueE2xamt9InJGuV7ZoaOU6r4TifcZ6FalSNK7F40+emPHepTQn80DChO8VUIA8Pz1F2tOwZ2WKaiMpFfODDCAWT/dvBkQQJJklEbpJqTskchLfXg98YznX3kZDx7/OJ75wf8S77rtbd1WnfTG72rQ3jo9pw2g53EOFIql0kWBCM2oKEbZRUtikZSzfAUoCL0nCVrN5iFeKUyHmmA23uWyA44Aj7xTBRD0M/sYLbnKXTMEzdpm17NZKCOQZutjDYEatxXTU29pfqENvn2ldsqf3b3Xy23WPEUtd8bw6fCByhnQYCBG/S76CgZqGKEaqKbb1ZeS+Z74sXPGCAEEUEIgvr8GCQQgBsoggRRI2KF4qWV5o5ob2su2xnXYR20/AUaHqM/JkVcZ1cNlQP1KswRAKwvlUh2CuHm5Bpy5JteaPuvqk8Fq6W/L4gibaRwu/Y4UmvJW9DrMKU7OrdBRHhAwLKbMhdjo5I36uBybCkhKJL9JLeNrfj+yH/SGN3X1iyrEJ85alwOkZFVW5z31gc/hwp331XEq42raaMoZAga12GVUw/ehC/0VJabJ7MHJtRfw4D/9c3jm3Z/Gxdvfzmov7mvSGAAcvbFuw9yUqjA04nSpc89LKbcZUHgSYy6TsaVOSR2TF0TpEIJdDgXAZw10fXTuxHCSBSXABgyMktgcgzT0PkcaV5Mbei7D/+x9H93ee3Spl+qqRlVsdEwByCu2IG5DIZpUHRXP1ShkoAVFfHzA+sAzZtS0LyEznjDGP3+PKQ2BQMoMwmL7G5Ag7QcicRsrUOBGB2L2gGIqKmxWIYcQcvJiBt/FLCvjenD/6MqpuWr7Cejl4PQyUGtv5UCdcWV2ixynwec4OuEdk/Ey8im9k5Laf0L0SzIOl+icNIPCDNod5RVhb2Bn5ugNhXKnvDlScWj4i2l8yxaMVhF1K280CPBCa/reW8IoCrBXx6SyVwXoFca3dm57d/XtDW9iWRvlAGUd2yUE53J89QoeefYjuPzBS8wUpAggIEE5GCvl7AADi/INLWonOZXPredx/FvHePhX/k94+v2fxYXb766eW9w1gpF2b6zPHiqcAQhYpNAHIKYpSigVfdrEqFMEdup8nCEIVpgPZsBiW39N23mZyZZyC1OeZB4YUDFrteytoagXjBE3UfW50xMNAFATWRsf79rmHiI7g0nTKFR9fOAdj+pq67BUX1Ln+Q8hYGpQg1bUa6UxaIoNkHbYvh+xAh4QKGMlz1ioR8gXEoQhSC5QiKkFCYSquwMAiii5CeW+RAAmBoh0in5RB2ORP/mbuvaNxn/L2HN1+/EHXj+ZPaS+npxqJqfmhTDgn/Kr6gns5RedI3ozzT5ImDKoMLqprDIS3eTpJaAaf/lsdJN9Z40bxtT6eav+XQip1FCWAvO6l1fsRZreWAGO1NEDA8qxkiecXD3BI1/4MC5/6ClcvOt8tgmB+17AwYYWnhlgYClSL3GqKLqmEAJNCNPEnfqlj+HyBy/hwrl3Kiosq0w90AIMViiuzXFKl55Ex3a07x7X7IF5z7cCDfrlGrxBTBXq/t0My22pSVkOktUhgib+NRWjEyMDgpQSZlRDU6nraoBK27BsbKQUo+ooXGquM1ODFlC0c0plMvQe2jZAnuumvGhbN9aKhQUpnmMDFrYZEVtPayi6PIHBOFTQUO9ZxkXrvYV6NOOY0XlwgIKABAk9WDaBP6bXrT/0NZa9kvFvsMRB497WYlE27QX1ic0fKZ5seoDgNPOpjgMBlDBRBQhTvm4iQgg7BhAT2PjLZm8KJCCzDM2eBUglyZYO1EklzINqUH3jL8duTudC69zSr60ubd6XsLEtAJDe8F1tXXVioVomHNHasytXT/DYLz+CJz90GefvusBhNZoAcHKuBgdr5cwAg0ottUurMqne0aTNT4nwwrUT/PAXHsGlH7qM83/kAispS5MpYJB2b8q/XutkBwDwQ1EoIH3cULyrzUbVG4VuFhpVgYJF0AC0Au8+yAAvPdlcJNv2/ZyYji6gIH/n863RKQYn1T6Y9auVG1q61ZpTYGvSHO288dQ0yTZxVLyusccOsA/1uU0d2rr1ynnBmGxxBUwlrZHT86NlbpShVD/Q7R2NkRQ9VlMgZWjlAgYJwmbovggA5gwEWrCXWmN6YB8sGVJrRHVf2N8cUnrmoK/YobI4ZG2cus7GOxrNp0mWAWaKp7IFFbyFwG+ACUSYiMdnivIdCIHDPUGDBCfk0DGfXiNrJfNfBxyMjH+na6uXvVXP1r9VZyala1tQIPlBDXweFAMMdm+sdRadqlnW1NuyF66d4CPPPorPP3AZ95y7gDlmnw1A0OAgxVqdBSE7Q8DADj4bJjZAqHFS9ODgxWvH+NiXHsMTDzyJu+84j1f3CSEQAqgIttBkYkDjG76rCoQULci603UMSAmmFsoENGvC9e3W3iFfjUammwEgKAoWSqAVcEgG7PQJbqZ/gQVmphXeGGufCwiI6u+cUj5fhXyOKTMJiROT8zWyNYi3hrmrZlZmkuwr1Z2kjyRVArXPgKoEm3s59x/VYEnZrtbZBTfm+dlI6lAD/7bWakRHj8o4LJL/2uvM8SUgMDs9NalD+zn1Rii1IAGphh6qLtNGrFbs9Wyv/vh6jLPtqq1yJ3WROkifi0qaizebv8u9DpxHRDx/KPC8ISJMIYcSIrNaRInZAkoI6q+ABCI2UE3IQXRR3DeA4DR6yDP8ol+1bpUw0eG6lb9LyIrbzKE9T78m4HT2AUA6elPRpQnVqfWSgAHgxWsnbL8+8CTeeed51qNEvKderlwBBwg5L3IZrJwZYJBkc5RMtWhQEGOlrW0S1UvXT/Ajzz2Gn73/Et5x23m8OmdBjxkcaMHO8TQAiGHXeOu5FqZWDb5sgIDdGGYUX2+ut48xlKQAAQAqBigonypwKMLSAoekb+4JjmE1mklnhHcVDCT+7X6O2MdUztXPecxyw+cEV090VSxAQKpsgQIpkOCDCP79VtcTuX595Q6hl6XYx1pAAyyDGo8p0cUaUFc9pPHXQ8HA6PwEan6vd+922YSFejKbUMtam7z2WOMK9Aa2HL8F4wrcnIyNjL3MHf9c+9u1+uo5pEMFuwwK5PNuYs5mphWQQMAeKE4XaMqRyF1jXBNX2qlUC4s7HZQPiuH38mZgr0f9nbo1AAlZpS7fp8+zQNan3CYJuaTmdr5tsO0AgBuRuqXBBRQo2wWg2K/P3v8k3nGHAgVIDMRVLDLQBBA7Xd3KC1PODDDQSNEDBTH29PXL16/gR7/8GD79vkt42+3n8WqMPOgSN+N4QhHsIgQAXpvRfOc+djraKLiRwHbCjTbZS99D7mObD6zFBXUiV9ueKvxC1U69F5Y2tiNy/WM+VsYiX6fBwD6x4trHiBnsSabc/hhT6ROeILXRdl8QnY8k1HIg+cvKSM5p8OABBz7XjmW397r0w8D76o3JdmvSeZbqO+VQydRc317ngQjvvqcph7TjtPedl52Zw++3YOx1czyP+vUaRwC4IUzPRtmqde4Nvzb6Mk/s/AFw0Byy80fmzm5ioLDLWxDv5tiCBMq+KaGEFwKlzMCy0+U7LgCQl6ead6+U0KLnNMHRo0aH6n6zjKwUj3GTepGErETP5mPjdhxmG7SefzWmCgpyW2akco3U/+XrV/CpLz+Gn3nfJbzt9vsw5+uDgLpAKAdyFYLkYHynMAY2yVCESECA9mDnlPDytSv41Fcew0+/9xK+//bzuJH7SUgoImBPScXRWsF4bY6uEOjh13LnGlRHgG2SV1K/RRp7b4D1HFOpkG4ToFBvEfqaUAQ0cjQsZVI6bRDvv4QBEjMB+8Q7gu1jUsAgZWBQwcA+pvwZhXHgPhSF59dpWbG1xwMBN1AV3g2IYuCkqhtoFfa00VjZHdF75b7lLvUiqYsUC2SAHszoa26ocdfX8HXtU6fFEV8vN/t7KVuMr2UntJ7b6lnbZ42o9m1jVstUuqE1/Pv8rG5Pm4FsaVkaef0yZ4A6V/Q82Qqspeh5AqDMlRD42C4Q5kT588zAQDEJu5h4dSAF7AJhT8CUZZAoYUq9odWe+ag0+iYfGOlN6Z+iO1N7D2BNd6ICgMIYcP17vQnjcPVOmrpd2xb5rmwCANzYp9K2Uf7VN165gj/9lcfwD9/LTi2Hb1Oe34mfmFnv0ieUc3dAOQzyHcAYSCdLckaT7IYWFLwkoOA93Kn7uRcSyn3LS6NSps5qXPRGTA0iHID+Wr8NAm2FOcGPuwM92p3RKxztSUp8kI/nGCHSInCQa0fFJql59d/PETNSYQI0IKjHYlFq+5gaBcfKTRRfVYBumes4VFDAXgqigILMAIkCjBUoAMgTKfeTNjAH2jwtUnasIvoGRKdRIVBnlEIW9ApkWC4BYC+AIF+zdwDDPlW2QcL1cn6PNGQbgG2Mg5erYYtVyl5ZClN4IGArANhCrx8yPt0xEPbqUqL6EAEM++5XfvFkaAsQ6I9tnD+o8mXnD88Tnr+7wHNmH5k12FPCbmLAsI+UAcGcmQVCmkLRp7NjaLmftusZ6Y+RrgRwy/Rl0ZXaHhSgk4GDAxS478b9DKi8B1SbAAA3YiyhU6+Nv/rKFfy5r34YP/Xuf1RAAZBzeEKuKJjl5nMCFPjPRMqBHpQzAwzY+KOAAjG4MbFxYUNcQcF//Z5LeOtt9xXDpZWNTOxGMPL9xRNnYMCf+0zpvm5AS2mNjOl+IQEvqfg7sJxM1Hi7aL3KGjNs6Wfd3tIWlew1jE8rRD5KILQMwZxBmmUIWrYgVTot3zvaGW6KKLRJDD8lhKSZA/4eEuXd9lI9Royy+RjPHvGcLBMwOQNtjfjI2Ggjs6Sko+uqpkbhCHgI2UMgYsPDYRKpR5bTxJ7dHiwfkgwoRnciQoo9kzBK0HT13gYAtXM0pm2phBO6/SEWEuvW4uyetw1sHw9bZHya5hjWSp5F1MrHIfJj62mBs3dMgwE9f+TYUglE3fzRcwfQnyPPlRlIlLDPjMJE3PY98fx2DS3QeOa6NIRGqvJh9Qv3W68juf+Sy7roMspBYh2Zio7U9Rf5DQmLS2xl0y5btD3wQM+NWL/r9gLAr16/gr/4zz6C/+IHP4/vu/1e7DNzPQXCzMOBEGryIUIGLilhBuv9Geug5cwAA7scUfIKOLbNgvPi9Sv4scwUvPW2+wq1LYJVb8Z/9jmJZp96o3kj6riSAIR6i9DeCsA2tKu9ay3o2qDKPZYUmzUeXCdhDGp8XScVeZnIQDUUS1SxzogegRn5PgobjECBVWiLHqft+CDUmhwnntEJACpTENEfGxVPqctxrdyJ6pjzi7+XFfKawq7XVQAU56QATgUzMwDMHBaZZVzzGDAASJjz+OtimSUABRjzwXrt1j0DupIvdqZcObELeYVLPlFATGhBPOCDgi3xd6AFAlv7H6j9Pzzf5Ha057bKj1csI2CP6bkj5w6ZP1Mgvs6bP3nu7COwg/4MIAApEXZI2IesgwWIBmaxtD4FtusTwF+hZPWKTlA+jW7UoUQdItH1302ENLcOI9CCBOD0tgAAXpt95+rXvv0CfvxrH8F//gOfx/e+5T62EwTwmys5dFPCBQmIlIDEu4ciUJ7/uc4JWFJHZwYY1MmvaHqgGN6Xrp3gU1/mnAIOH8T8u9aI6UKBM6dz/4JCQpoz7TqnVjDyswDWezaEJ3e2+QIWAe9jGgICUWwF8BjKUIpuRjsJvNhhTSqyyXg9YKg3tspO69SRopZ22MRCW2dbpkCb6Oelwu0dKyKde6CPVXqxHh8liwE8oZrkNf0MiUdkTRFjKvlBUsft4CCp3+Q8oxxTjGBvTuqeYurqTAYUePQpUOOt/jJJP55anqH628sb0DHWAKUsqZ4XgACwDEyJwcEUqXiGExj82Pmrvy+BgkMAAdCCAh2CAlrwLWWr7ADA5MhOSBVUBhkQ1Z5R/Q5tV1eXhbrGpAxeqgfmRJgEdAoQjSgsVVKgY282FrM6BOgBQD3m60MA0ExKqV8uVh/WEGPVhymxhy0MyC7xnggUCGmfkEK1BTJXIiAMfrEF8qgGEOg+zAfEDkixDusM4NdeuYKf+Ocfxd/5E5/DH3/zvdyviQHUDpTtHpV+TGDWANl2pfyXO5kdhyXpODPAYLQ0BeAlHT/65cfw6fdewvffcb4qoAiXgiplTlUgMkDY5+7cp9gIhrwpDsDYfUr1z9KafZ2lbz3s3jtwPAgnsUhPAqBPKtITAxCjmBpmgePadblfaa6jlEd0ntybebdkUDwLMsNvKsdjyog3G4cw9YpPK2tRaDrfoMRKm7b2gMACJWmfl+w3LFNdiqevrGCJyrMiapyVgQJ1IMlT8GvGSVfRY4YW16fnircrVvrcEy985u7cBzSWUmR1wjKtKoBcjjFIYA8ohfxymjx3GDDwunlWmqkBC0TVwIZADShbMqQWTNq+tv1tl8lKO6XP1kpSCIvbxxZmTlTkBUVOCDGILmi/y/wBeL7EhFPNny7XwAPQysHQpYyD3De2+RUjvaHDQBYEAFjUgwBcZ6nUVYd6Sn6RCiemHD7JAEEzIDsEZt9i7yw2IWcZPK+k5o8bGrGM8Te/dQV/5fn/I/6zd/0C/vhb7mV5j1neU2ZVxNmoH9vHCltAQEgFQgwqeYaAQUGTquNjYlDwyed4Scfb7zhfls0RESjwAOv+6ZOeWsQrkzxF9lyQqmAA7Rrsxijoe8bUPUee1cTIFA2qJ4NN0OPzbVyxK1Emeap/VVJRScgzCHoOvJfDvjAKKAlsRdGt8J8S1wIyXYrsPQcWQE/BYdITvyqyXdBedT/7tCIDWuPPx30wZFmTLfseLBVp704xJwIYduo8G7Zc93x/m4+wyHQYA8V1XmZ/LCjYUegAgQUD/QoWlP7MPyv9tVaSUY4x5RukCpQEKFiQYAHCPkRmDzAGB8LiFLnjzip/hEEY9fOoj4FtS15PIzcpZjmh1MhMisCc4/fVKK7NH+nn35v5I8W213O8pGxlBEaJlrqN+m/XvqTaFzjkUnOOEnaSPZjZtzllFidQY5Ct4UwpYY6KZUnL+r+2WXR97R9Zwg2ggIL/9F1P4Hvfci87qEpOKetkIqrJj6YIo0jIfVqrOCxnBhhoORCj+9K1E3z8ucfwmfsv4e7bz5fYiy6cJMMKBHk3tg4cpFQm+ayPRVasopSIKuU9FQq0LZoyWtsQZmvx4or2eZNSCEGWJdqkogwedlOCxNuFIgyJMkjKhi5ThUBrDCybUAwkqjdUPCGQUXL8rDrh+Wq77GqtrC1THAGBUYKmbtfWfIuj/DnlWaiT5WYAR7kPZuJrWy9J3auh49vneLFrCwa89ki2uGUI5LwGA4EqEBDl4m32AqBTWLbU+tc5llAZArlGQL0AhahAgqSHzGCAMGHCTAm7rGRpTkiBQGBvtOxlH2o92fAomZ1osW9tKMADADY/R47zb9S9NsgNe4BsiI5yAuMMBplaXubcxrER5b6+1fMHWGfWFnWCqoNlB/j8GBTs5z5ksEX31aIuiNQwCKctM1IZ1zmm8jzb03ZzMC8vSxcNCr7vLfcBUHMv93XH3sKfe/JMol7WvXKGgIHqdPA2kR9/7jF89v5LuPscMwUhcbJGQM6MjgH7EEEpJzdNoXgWuiztUKazuC2a84rEy4moxIjKObQeDwU2xDOy15KNaZBMF4d6H038FiAoih6AJBWJNxS1BvbAgVaUA+9Iys4oQu0JAcCuKAbqFAMwjgtLian3qJeo9VHSpUetS/u6LYrNEGeipRQBBUgApp425PamBjRIe21+hgYNa2XJULmZ1cqwsxc4BgNsCCow0GEFuc7pGrdkOJmNPx/zVunEyDLpgQT29PlamgiSZDVNrHB3EZgJ2GWZBbJhzczN7hR9Cljj16/qkd+syQwwkJtVmVlK7qWGZbT0O3D6+VPbC6cflnWALbKcdk69I7YECpq8gQVQsJaPtBhaVM6DdhysrmjaA8MYr5TGVjnhDgD49W+/UMIH33fbffVZRl/pPSOCAfW2JokfDoDt43dE8qGUAgq+xKDgnjsvZO3BL5wkFdMVcICJkz0otd6GFI8K3KwQnKITqmy8VNcBc6yezszCuo8ROwTEwCEFDxw0Rn+DoG4tnoe95i11RdXntGvQ9TVdHRdoXU+pb42vQ/1tsvFNO/Wk18l1ALAjKglCOvEI8A1A2wdtO9fWYXNfjNsm7WrfkFfBgN7ps9n1U4EF+Zdb2Wo5z/DqvqLA9cziK/9i4gMR4CVXKpwQU2USZqScVFX3KgkTIYJwlIA0OfHbQLekP/nccoLm6y0zoxwlaYtdtifHgfW5A/jzR18zehfJUin1CQzWdgJkUMM+4gjdDJlqjXMBOoUJ6UGB7M3An4Ni1rgPdlNYzMsB1kGxXDdHHpMJVFbZTETFSfzNb7+An3j+o/i7P/h5fN+b7y2/H4UBBRQIMLDgXpcEZtI/9qXHcA53Dut6ZoCByFEBBe+/hHvvvAAgJ2QkAvIGRXoJy24iTGnCHrExzjD0osj9b3z7Bf6+0UMYMVWiACReKl60pkSnKTBzMEdMU2YR5lAQdZhTUZwxEiL5cflR8eKIXgxRJopdxrPmaQPbEDTQxt3KMUW1lWMbPT2P+h8pc1Hk2gvWMXWgn2TjZkm/56+G+Uio8XM+zt93xGZWvGUvSQlYZg9sny+1cQkMTOp8UTio/8ob8QD3zXKlPtozMqAgV5g/U/ZJiTDRhKTAQkyUWYIahkkp4+AMEgLa7WLl2skAhZvqR9OXpfprMqNusFlmgAyOFmQmcHKclhcLMkcvVVor3u6Y3tbah87tyg5mRif1YZ99kHAlFUeodX5gvlNZzTOqzhY9t5v6LZ8tILAhuFyTzbq+bCgWeCbJKhtIODrr7B//2kfw937g83irYgo0ABM9K9tPM3hn/VzedqnmtS7aPv7Np39yNGRnBxgAtdE/r0ABwB3GSzVYoCjwIM0p1fWnu4Bdao2zLd/8Fm8uAdSELcAXDq08vCLXiQIEUCjRHYBdTjrax4QdMRjgz3Jc8iPyzoEFFFSQALTAQCe1+e8T8GPxu1DRquyPzp8P87iX+iMVCea/WofpkRgwb11ZWl4nhlG+exOJUBW93E9XfS1kpGPoXO/aLr20SOh0b1937T0eBQGqfu6Kbmtpl667MlrS1pYt6MFAbTOzAfb1482rvKWigMuPVuOqQEGuHVH7ilxSr5udqK7Njjlr3IKEKeU8thxu0IZU92FKrNy5Rf74efKi+7D0LbbJTHvPbTLD9Tu9zKRseMrxfM9D546uO9e/vW44l1P7eZJVENmgWydoJ8ndYMagOmlsNbckWQLk6jdgWcdpp2enQgb2RVFb9ZtXBBDoFTZAdQoBFOYYAP7Ld38eb7v9fHef7s2WqCzBRH4IUBfPaR6VMwMMLCiwk5DAncXxyfpKSolZCh0pxvkIrUH6xitX8Be++hH89Hsu4dFfvr8omBF9uEQd2iITeoLv7ejdEPVSlv0c8/GpW9YI1CQ+QEBBW4/Ry4ZsYp5mCHbeZFFGxPNKyTxvlXMDOirRW7PtGUh7a89r04q68ficY1bxb62+NuBFMReFbhU+H9MKPoEa1kF7j7rIbzqlbdpevH5qx0tyBwprIO3LrAC/v33ugUABB/VzUyH72UOH8upuCiB5ZS5CFSIKgAIIwiYISNglyUXIq1sA9qKlT4V6QDWiW/sOqEoWaPtPfmNzLvg3eF1khg1fKzP2mPx+xFahPey2WZdu6ekB89ZjOzw2pzhjOTQ6E0BhKomWlKl2m6TcOj60qt903oR1dpaYAZuL4wHuxe5IqVRL2l9W3ajju+K9Avfd+a7uPqQ+VJYWTdgghFZGdZFXM4t9HDkXUs4MMBBQcN9dFwH0xrkU4skkoED+yWCFVF8ABLBgf/2VK/izv8IvrHjnHYzkdgoEAL5Hwce1QhxUPguEVl56Mk3EyDICwKSAwo7cXRLthkJyPym8JKxWptJU7WSxYKBZ2mYQa4BPY3nGVnfDtuVt/aRfE2zvGdaT67y95nfK2MVsIMuphVh6Z2X4aUnF1NG8L55KezRw0NSx9R7ro/1e8NrbjYMeH6lNNvIU99Xwx4ghEJCJkiLKiDShhPpZv2++pumz8ScNBCxQmAPyu3mzoO0ySAhIlPswkOo7Aw5KX0n/LfeZ129y3us7QMvNQGYsg7IkM6qfPJnhp9TXnudHKR3SPkLPnUPmTK3a1vmJ5hlahjVI814ONJECCVFetkbQb19dSrBEeeZhem0LENBJuQXPYoOTI/UitTkV2nGRsQqp/YndGbPKoAACxf4JMLAOjSovXDvB4198FE984EkGBWn5hVXAGQIGAgpGHqAunHCf95MGKw3ZfneXahJISpyo8ae/wvsgvPNcpXeEapLnAL6HAfSKelREoQFoJ1No6dGdji/mDGZvS2Wgz3IfFS+JsFvWZsCACKjEqYfUdO4kq0z1X9MTq1etKbn+V9qiVu+WolLeigqnZDWP5xEPyNnGCOaxF6mkSpuTnKdQjUBYMwD11nFhemvwA7QgoBzWrEABBZI/0IOEDghooND0Rz86ZD+R7o9QDZ8DFJB2EDaBaJ/DDDnkkK8RNkGebhkX7q+2Jm5/5UtWAWNMbn5FZVXqE2+FzABw5YaPB1RlZAMB20CBXD0udk7qM+0zRiCXQz95eTixbpPXCUvy5ESEHSY+NmBKgQoWRmW0isRbqlsdmFaHFQBgdZdjU7o+UQBg0kwO5X7JOjKZ6wHgyFh3/bxRgvDI1p381gk++uyj+NwDT+K+Oy+UObC20unMAAMNCnQnWWpPSpP9nOeyjlkmAC9cPcGPfuXD+Oz7n8Q959qYTHmRhlYghmb0jgMDJgNQg8bfLc2sKeaYWrAwa7CwA+sv9NnuS2VrBrulroSOLoIq/2A8Ka1MIZX3k9W2lLQ2O737Ns/oY+OU+mPt99Mpea24RR6KQSRiI2ANgAINAIz3CHjQZwihlAFrDJoNEcR5wArMaICAYgkoJaR5Xu4Tp39omnhnUWmLYgz4cwYAcW7OUZrQhRwUUBDDOXX5DBv7CSh8vNtXQAOeOrl5HWSGvxq5yddkTdT/HnWOrM2Um5l7ZICJBbmiw4SdLY4YeOkiMwjtctQ51RUZwpRaB2ip9G+SxVCfWe/b2o/DdXg79BomuscVYACAI725ggX2K46XLse/dYKPPPsIfvGDl3FfZgoCZPEaLQrFmQEGZX0tsjBksNxlVKvrbfazjlleuXaCTzz3GH4hIy07b47cHAMDDMygqkPdZymtk6OQZnIAAhQwKNeY5KspA4bU3t8rMmF0G9ay1wsoEJAgTxEDsxafBtAo09IRCzXdqOyGxQMK1tvN3zug0Fy7oRjjpJU6KWNogUMLGgAxhEldV5+x0B/Jtsd4tIshAh8MWCAQNesCwN2LVoooPQpI8x4UVH8UoCD9su9BAhHLFJmQA6rx7PrIAIPmmzOublLlKJ/CgKRFcLmlOONawjBabkbtW2rrlrJh3pX7KkDggdwCcMMEooApMz1ZijihMPAqsTkowJA4t0rYBJ2fcKQcoGE1VXWtHmNJ4Q8aGKyF25r7et3mfBa7Il+GwMDcS/LXLAjZatsA4PjqCT78y4/gyQ8xKGB2hoAMDlZwwdkBBnZQO0NlPSYA2iuT7OcE4Pj6FXz02Udx6YOXcf6uC93AASyggEKOCrH1gzV4/obs7UScFFOpQ2oo5rr5C8ftLGAo1+Rq6EQk+aQFZClhzWauT0Uws+KMcwUCKjbd0dFxrjW4lYr00NI9p3rAAKrxM9emJcNnCjXoPyBMTGyy8VIKXoMBLIMG/n1rLPizM9Vdw2fo/6UQgQJMAghSitwHFgjIuC4ZF8kaC7kfkmpHitzGaWJ4mwgEYQsUSMh904QccvsrY3DKfpFzW4y/0z9AlptbJC9AnmON3MhRdNeeqtwCoFvASjb+BeCGXQELKYPAKUxIYcIUAuuywFsPC5sgYYddyRWxm2Ft0GHGKVtLum11tgbIQMNyjrpF6WuS/hHmBGicUMAmxrblDSrJwNqXUT1FdhMFHF+9gke+8Bguf+gyLmb7ld+QjUht/tKonBlgEBS1Lx0YAJQ4qjZOuTQKJFORz197EY994TE8lTtVD6YuEgdqp2mrbD3avKMipXgDZWnCjMKLl6RirJzBXQGDLN8CYIBCFTorkJag9tBzEx6Ic+nXEptO9RjUsVaZzp0STYd4naXCjjI8rYIcKfKowYEZI33OK0G9j1IURDaI0J5y/tyBBs8j418URbTkLXbFUtuaJXEZnJYdaADBCAys9QnAxmPec33jnEFCZJCgAAKAFiRQAqgqQJ2QZ+n2zX3R9cfA81f9Afhym5YA0gZZQX16W38tRw5wOLh4Ru6Q+abkljKQA7LcZmanAIb8HWFSQEE+12MhAwrWiuSypECB7kPdxWeN7soHqyOT71TyakahNcDNEyn3bOGI6Gu99Lb0l3JCARRHtLapFp1jMAQBbm4PcHLtBTzypcfx1AOfx4Vz9wJpRqCJE+6D9KnMk/EcOTvAQCHEoi7S3Hqxls4G1OAGHF9/CY889zie+sDncPH2dwDza2iSffTzUjv5hyBgiZIE2t8sFUHh+XPSwqaoOwENkwUNqGtmNdXlPkr9bYRS5wjEbNw18BIgEPmlnhWM8bVpf6MYlhRjc31VqmM2xa+sUY4GLJAHHlZK5+Hp74N66d+4z5R6htBeI0o/K0qtfDXNHjLN3hhCJZMug+BXtFbJZWoqayLsQGmfBm0rxs/zkkubBQx4YCIAaY6l/WlvWQSAgVECMLfAqT5oUx+0K018dkSuL3Kr22/qv0WGV+XE1v/1lme+YNuNjAxzyn6WS5HdPG5hd9QChbADQKCwL0ABwibQvtyDcnjI5iisvQ/Q8ijbjb8+5jBpi/3RgtI+vAV4uTDlNxbMyi9GtmXJrgA4fuUlPPzcJ/D0/U/gwh3vBOYbPD4BCCR6pu8zr5wpYFBpltyBGRTQyHOVQoTj6y/joa98Ek/f/1lcvP3twP5GHVCgUza0f40/6Lgk0Akbn1Pf1+KS5R6mGAVIilKVeopADkGDtMNpEyd1OQoj6Tqmvh8XwAD2+6JU05yBw7zPoMABCABSrMp4c1HtIPHIc59VT2vLu+2c4nl6KwojjZzDEjTkSUrGA0MILWAQBQoUGhbyO+VRN/deo5gH4RMAjTec8rWnYnJ02w4tMbJBSLE8kkLtU25vQlnYK0BBb5i9sc1dyEi1N1lWRIXAivyqey+yBYPiyonHdCjZXb+rKgO2KzXHD59nMseKMRd5zPKbph3SjdeAaVfO0Y6ZAsQMEuZ9CxJULkmz4RXQeNql2lZfOWGg1X03nBAa/24bS2BZvC68JboYQLdPh77XFtvitUPV8fiVl/HQV34UT7/vZ3HxjnewfFIqibYpcB1kjxoLEmw5O8AALUpsQYEJJ5jJe/zK1/HgV38Mz7zn07j4lrcC+9da9Ad0E5bm19oKbAUCXXyy9dD4XuPJSsYgWNq51tkHDfwbJdD5mCsjDShQgliAVqZXR8yAAwYKQEiJFVSKwDzXmFecN3noI6YgKSVakPikwUJvsGgBNCQPGCzVa61Q4H4BqsJXgKGhazW7oK7V4Yg0GwZCg6Rp2lTPEiooB4yx80qYShIgX5wOB1+GLh/WL8bSLGtMBSiYH/T3cPJFmqRJCwLy54YF0P1idMipjW3TGB9MLa0YcOVTP7+EgRxmZytzYBgMkT8iAqaJdY6EAsIEmnYNSODQEYME2h0Bcd+DBNFPshQVvp4qVWrqPdBTG/WvBxCHRetfDcaNU7YGFHS+UPcIsS22XZrVUuGOYr/+1KeZ6Y77/CZbAkV04IAyC/MdwRg0G7QI1V0MmE91A8Dxt76BB7/6p/HMu38aF9/y/Tn2meNFDe1jgcGN+sULC2wQRh2n7DwUfd+usdkTNvFqCjXxijT1bJmEAR3dfF6KwZr2UIqlLXHeczsyABCAUP8aMCBAQLVf6OtV71R5pQXA6ax3+W4McTMhp4mp69OUeQAaln/UP1eUTUps+BRQoBDyMQcoqGubMES+Z8n612WJZjcKkUKohrH0r7oghOUxakIEC6BhpY5NG1Jbn+SNgWlHx3oAfjjkNEDAMcKnKgsgblU+1XMbgC1lwPok2x9LRWRPzbNEIW9AlY3kNFXgm8EBYuTxm/fMJMx7Bg6Sm2CWoiIqo8oPrHVY0k8qDLSmb7krqs5Jur8WdC63u2XwCiPSrajpQUJnU2yb5FFiWzwgoJlmGPt129sKWGcgwOvWKc4VHFCqjuIC/3RmgEFdf2299dkHBSky0vpnfw7P/MA/wLve/H2MZIF+QBH7AdSeBeAaTwCVbjfCGbUxtLF2R5ENY5PaICrP0ov7ERnAoH/vxKvd8IYz0Zq2eIAgzswUWEAgIAKowEgm7kYlW5kTcSmNAtNZ79IUzSro50wHerzTtGgMbOZvA0ia5+brVXdTTtBLUcIJEZTbkmIAQpavAhL2aDP+Y2YUYssieLhgIT+hP2QOTK1stufW1cumkMMBYIYPOYzTQrLkEAzI9UuswGD8F8felkPlzjx3yAaMgIANgxww15iCDkDKzOQMlrNpxw4YEdKUddq0q6GhaceOWpi4bzPbpQ0rtCMDHKiTAM/hKsbfY4JOqWfLnFN5QbYtBSQEZlSQ5mWb0uiGvdO+PoTw/Ld+FQ9+7c/jmR/8KQYFOSFc2DxhCfIkzY9kfZAoLeGCswMMKKrOTHq5nKKTVBz8+Ve+wZ36A38f77rtrfm6fC8v+9kGZWTwSlmgs5CA/R5dVvdSzD3/3lNGZXJK2y0YAPq4nxHkfJEbrx6psCYWC/RtyWGEpi0eS7C/USdsaidoobXNq2GTeB5dmXPd89cInqgpsuISxQS0n9EzB6pDBz3glF1wjRPmedkYeCVVI57izOOqY9u6etJlQPXKBPA0nnow91X9aD1z10CtG60DW3lLSpHFAgCNQVfHNrED+fpRrsChoQJ37L3+PUTW9LMVKCUyO9k1q2ocUGAAgfcaZn++qbkWgZTBJ6tKBvlpd8RgewKzAykhYQfCvgBaAhjEhgnNShTVH1YWbW96iaFSb9fRsmO7Nq7COAIlTAKgcbYKSMjhkm5VzW7HBjp76UgxAxyxFYOZoxht354Az3/7V/Hg1/4C26+3vDUzBZFZAp3cmxiAUBSQwMqDDPNgy5kBBm7GZvKXdhSk9QN/H++67W05xpQFhYjRXcp0Vsod3k1gzRYodkDVZQnBNp71KPYO9PF3r+mWIrdxP8BNEioJbvl8GhiM9mGOBzaahLq+mik4RfFAgWzhXBPwBqEFRcPLsikAVVGLMjKJiwcV4yH28iInHKNyYN5Duc+hRgXoQIHOVakt0J7M4BmH9s+WMqJxmz7L1+x2bWw4K+Qm/HHQs5d/Q2GqRkT3iTdMo1wBO6ankrE8XnFuQKkwX5oNY5AMBo4UKjiQvpompHkGBerAwemTR2fOM5hz/eQYKqBN+xt8/5QqQMjMg9QvdY6X9MEA7C3pH5PPxIeWdaoOO6ap9hulVFg8wi5T9DnHCjteVSNTc79vlttyeCQqgABfVnIiTb96TcLfv4oH//lfxD/+E/9FZQpSZMRW/oo9pAxYIgM4Sk1i56icGWBADcKPpWNaZZM4JvPP/pwBBaxcaJrqd4JSFgIQdIxLUU6W3hopGe1dA40gDxPyPGrQllmFQACO+wFlnXCJ/QnydZLcbIIbN3tgnGy28xZjP7GyKIorK62KZPmYeP40YZHiJOvh6xioTeAzyVINEDBtHXnTq8WOTbdEzIuFO4ZwISvdW8XQHNe/156NXOOCAT+kNF7+t7Lq4TSlk2sLunP7NfBG9lgBYEd53mZDN4VsHPI9inzlOZx/X0BECIWBKQQE1G9yIQmNNONmV4WoYubPQfkeXlFeMU271vBpqn3ODEJJEGXdQXnCMUBgNu2WzTc118o8WyuK9m6+e1vKjRifAVuwqEdVX47rxnKR8jO5TWL0c52z3DDoUe0onrkp0u9EdUMi59Gj949QSnj+29/E/+Gf/0U8c/E/x7tue1see5UATOauxR5VZcuXmFC4KWcGGNRG1k61y++Ov/UNPPhP/yye+cGfYvrFDIuAA/5tykwPVQiYUlEA3dK+LUVzwIeUUXKevX0BBiiJPwnwE4QUSKAUmObDvoIEfpDc2K+XnG8mBU+WEn0RShxTXWI0E797PCstFtSIEr4RL0hilLqsJBx2zIAHBIQ9Mb/RSwUX273UF9JmOezQuvU3G2VBG5Clui5sntSBAQUEWu9lkHviLa1yYsCHlOESXQMAChBQSlIAgyjRAuQVSEBh6gBMoU2WzABBK3keKzGYRrZWG2Ou03Kq73FaVkoZRG6q8pxTrMfizC970cmR2jhOqGv6JzQ6pQCfrXOuyW9q5xsZudRsZdcXSq92ANp49jY3YBUMaCCg2rWqQ0VVn4Y5OWAuuHZkCyh4y/c7N1OrJMwzUrFf2hMbl7MDDJwX4uiiszff9Za3FiSViEGAjWm5L+hZUIR1m2VC4ZPAu1sxZSfYotJrJIg0oiaMhAOy5M0ElpgfIDa29YY0+k0TW++GGlMIGMjipTeisX1ilptZJUsUOf6WJzCJtzYd5cnsU3vktK0rxjA2XooKo3ggQCcPlXtYiv0Q5sDIms6CLz1kY9dAByYWy4iGPhQMdEtWTbIXtd8b0FDG24AGac4Bho5jz/rHCtjLdx2i0/O6hAtFwVfalI+lBiQQUMN5GXx2IAEAhaZGh42Jt/TSSV47FWtg8ydErgxdTsqLJv2bmJcRL4QoTz3ngHUwkH97EPA2c8MCoc3sgOdQee3cCAC0Xmn0iWJibRJiA7a32hDjzf/zf/VrJXzwrtvepu5hEzb9udk6twpgD8rZAQYOLZJyTOf4lW/gwX/yKTzz7k/j4u1vR5K199mA+zkgh1GsSSXQkHDiGnwIOJh2rJyK58cJewRe3pP2N0DTEdd9f6Oj3oEM+lK9hyfobfKd/H4jkjVJerr9q2vVM8VJE1q0P/Jw8nm9Vrtbmz2Ip1tFxMdaz6QBAWaVhqbW11ZqAAsTuVyQmSqpMx/M16p4uDrvJsutFZ1L4eQMSFuSHLdLp9TxAgT0NQJucx8kAxb089JIEbmxU+WxAbW/CkPQg3tK9e2gOkTo7RViGQVKUYGEACAhSM6Pet7rMQaABkrLbAtfuy5bVq683RlJhys1WAAKU1C/D5LwyjP7edfl4XhhLjv3dHu37HVhQj71+AFs60aWtWNDnDAkEQG7owIKaHdUQcHuiOuaj7mgwAJyub8uyeSxqTn4/Le+XhINL972NmPO1fzNu0vWua3mdfOsMSCQcnaAwaA8/8rX8dBXfhTPvPdnePMHSVQJu+plWJbBDJD/whovw1gUQlD3m6qCIlZOJDsCUsibe3Ad0v4GJwnJMfHA9zcq9R4jMIWqAACmSaHAgC4atW8t1qt2vFLujvF9rbJtPJIoyTUOTQisey8eTTuo63CiAmVsk2cw83n1oL6NuW31QOo/Z4+2XLvThjAZA9CCiLXiGZ6k2lXrrJXFAhDQICD/bQy/6qdUatv6HdE7WAqPTVDV1i1QvlQx+Alwt4RNJpdoDBQ0yMjzMM99eRbhJvoc6BR/AWSlkQ54Oq1sKSYlIQG72vYKHMy6fWMkCyBQ865jtrwymne6PZYtOY2+0GxOzjsg6ZNDwAEF6OTL8qyAZT05AATQmzcJIAhTs8Mjdmyck55jTT8MWBJlT8Q2gfLmRV/7C7yk/va39zbKgn6p6wgUbCxnFxgQv/vgoS//SN4m8m4+rpdyyACWCTjVeIy6T/7QTmCPRrTKpTAGE7QXg6MJSHP2XnZljT9NeRewuEPK4IGBAB9v6DJpi5oorsiNsvEt3a52Kxt62Lk/eo+of3qj5OWYZgW8Xca2eG4bveVy3Bp/ayCB3pBqj7l8XykeFa7aoenw1FDjLYCA/K/a78UhhzKq2l3qfigQUCBAAEACzBvudBNUvVXRtbbqqZhXofFJXUOEQBNXo7a4AQcHAwWpp2FyqpE9pJ9zCzoWxZEp/buVUIxbGp0SG3kp+RXSDgmxpNiABjLttkxDw97JvbwyMvKGKQGWdYN6UPkUNKOWYt0fQ0ACESg7RxKmZKCQw6/sg+WlkrvqSEnS3VJtBgnKlJchNts6ZyBQ9OK0q3rHvEDKnY+lEo7NKCHbCQDh+Ft5n50/+V/h4u3vyDJKjkwavRaUg/r/BwaqEOH4+ot46Msfx9Pv/ywunru3GlMK/pbFgDLiKNfK/Zq/QBujHhUnLsqTeAZSTnENO1BeepXmWYGEGyhU4H4P2h0txgvrI7PXqevmxd5zG1YBgRZ8vkk1HhgJn+0Xx3DsesNXfrXmvXlxOgpthv3IOy6/dwyj3Kcb95XJ5Xh2DT2uaO7Ri7Xa3/RGa9gnnudpjdJGINC/zrsCgPIKb9VMDQia1W5Lw6enEAn0YHktYpkva9/u2YIFOhAolP617KDq267ag74F4PQv2j7W5/K1bthlq2xpuRK5UO2Fbm/+XRNKaUADgKO2X04z95o2mc/qB7ZBXftI6qRYVQAl7JPmvQsQSoK1MKwAMLFu5Lm0Y91Ychw36kYPDOhwgQEDqQHdikExQHGx6H4PAcfXv4EHv/pnOPx9x911zKnWt3uOq+f85xxff2mxOmcLGOSOOL7+Ih7Kb5m6eOd9+WQq1yTU+P9QWZR7OsoWQJqOTMfbSWEUT6OcckJemaz8Fjua8jse8oZIJW6Y9zggCYPE2MYL9ZIqO7ENxeftZeCxAyRvR7O0mIdQTT/povtstJJj+G5wfb2ndLYqaU9BO14xYIyceMcrujLoxezS3aGqxMphtN6dfSNnDx6ATo68YuWw9ItRTA4jIDzFCAjMGhSofonSFnW9lCWyt/G5FRggJAQVWxDQQET1dbkDoBCkUh5QACpYKH14ij7Nxzqjnz8nI2Nb5Ao4QLYW5CqptmtA6gIHabPSR0PguTb/gKHBW5z3Fvgq0CLjqMOvxXFSb2dFjCUkQnGHdum3CkPs6vNKTY2Dt6gTnTABwoSogYBnnKXPDrERuTz/7W/ioX/yKQ5/3/HO3C/mtx7bOWKktC4Rp/m5j+MunMOonB1gUEDBywUUXLjrPhPvg5oQ4sEvAAOHAjy+/mL+uGuVhK1LF1ZwlJNM3Oz9a6CADBLKDlUaKIjwK7DAjxir5C4WL59dZiDT75J5K0AghFb4HUMMoKURNZga1q720Wrx+lodT0ZhawVdpocYQelu7flCiYMOfSxVqakOlWopMjEbNcpGbWL9EqpyF+DAz60y2SXmAb1sOf3t5QboflhiBGJM5hr+Lte1faT6VdfJ6zCtj/NFpKpPlLjPMgDIPAf2VIFCCLSRUciVUHT7retL+P2ZO0H3qXTF6y9XVabktx1wyO1twIPXB6eZhwtFJ/51b6GVzyb8U3czzHWPM3AUGCwadrWABGnfafShl5i8q8l8RRcaIFCPV1YOMKC8PNS3C02/APyW3/d/Fhc0071omxymSt+f6hLe42sv4KHnmEn/S8/8zWH/nB1gkI32Q899DE+9/wlcuOuCmchScvzF8ShGRQb5+NoLePi5j/Gx3Ru2UYNqQJNW9m44Q4x9VmaSJJQiMM0mXtjvqkjmeV3JdRsl5FVKTAl7njQNEKCp88RHyhPYAAgOLPZ+1uAXg1f0jk+Da+MH9F6w/FaK17PWAwaS8oKhvOBUXw2uDFr7XX44dZ6h/tu3nNyjLhiy7Xf6IyYfCAgIiPnmcu85CqDhv/PCiE8QA8d/p0BAyi0ofZKq6GXWoACFBNWPGPRjvleWVfltOmUfyt+ohGhLP8oPXze5AgDq5Up+T+W3BKKp3jPUBFDbH77/f/pS7zcAvZ0erDpPv9Y9Bbkmh1DDjhMAHT3ID4rjtqzoQXh6MKj8gTBhU7KueV7tCgeYKvbwqQ98DhfuvI9Z5A12qTzPBWscsk4p4fjqCR5+7mPMpJ+7F0ujfWaAwfH1lxgUfOBzuHjX+TpII69+EM8d0WfPX3sRj3zxo7j8Q5fwnmfuR5rewJfL7czfeo9KM5OZjGI2ZbLIREnCBBiwUNboCqpGqglG0pYOGBh1qASp0GFOUl6DhDUQKCBhwRONrRHmv23P3AqwoFtqn+MpZzmuDR7K91qvcl9VybX6FghkFLMo9GLAiECUGtAQKFWZMKBBH5P7Nk+Vi0wFLQjSbfOAkWUF5gEQmGNdPjgjIUVglvurOng7AEwAbuSKSnsmIlBoAcMUCJTFVgOFiYAoIgoNAHygsNx3chGdqu/KsUH/aTClf1++YKtM5SsX5KocVwCKjxG4I2sfSZ/w3/qs7f7/lvp6z6lsmcuUZf1WwYADFDpmlR0kCcMi2SWd+d62ZsouuInJSyAg60VPB8rT9F+3n7JNEPGjcj3/f+E//IHSF+U+jl1azYUqNi3g5LeO8fCXHmfQce6edrI65cwAg4eee7wFBYoG155TKTQNB8ZciOOrJ3jkCx/G5Q89hfN3XQDAim+UpQ30hrDczUxKnrz9ZBH6r50M+uVQFVU3CUbA6qA3GsULDQyBwFT6clYAYMlzAtDH7E8JC0a/8vp9i9GXn3nGDqgGzz7DFq1gJxlfz9gBgNDlAAMEsko9KW+wT9Czz/MU+giUWUMmx1Lito5YAekfDQTk3jMA2We/7a++w/aq4tJP+wBMkQEDG3/CPnHfNf2W667ZBMk9EPmyQEH31RAcYCPAVLI7AgBbQFTfT06FTN11f3lylZtdQYB8d8CD/M57RvPscbUWi5ZdZJZMvi4yZTRhEh04AZx3tQIUdOgBaCf7qGiBcMOiobxSuXOSTukE1Ue3/V5kkYTBEbAymQHw7ZI+416RbdzxtWM88uwP46kHfhEX77wXSHmn0NHg4wwBg6ce+EVcvOu8a8Ts5G6KTBo1MKQuPbl6gkd/6RFc+qHLuOfcBcimhDdm35sAWsHoH5maMW/oT9QJM+U2hGlSgMFhF8okkUlhmBCvFIEwsSkHDTeZ6tHEoPVnZXCs4tTV0UbaKxuim35J/ccRxW29XPFurYGrv1svwgsVirwocsIEMWStcj8EMAA9aNhUjEGTNm81ah4rIGBA95PuI+9tfVIZefHVPiXul0TYQ5gDysYA2CMtgoSQZU2zBDEDgjlB0e3p97yvgAqePOB0q+RKzlv2hY9T+zsNIKR1p7T+I3ZBgxGoeq0xZZI3slefQ1D5IlNCwyhksJCAm9d7Ngzg6b18e52IC/QMErBoYnLfVHA/Yrb2sZ4Y2SUAbjKrfmgg4MrVEzz6S4/i8oeewoU77619uFLODDC48B+8i/snI7uctlKMmR1AXYgIM3oAdeXqCX74C4/g8w9cxjvvOI99rEKxLx6WH1PMH/nvQFYpo+oqru2E8RD2NEhc05nJAPxEq/Jgh4ryELHqu6iUY0zJjUWP6Geg956kbMlzWiqz07kepb2mnOv59h6W3YjG6IVQhSaoqSseLaDBARUDOCkPuQMK8gjKMXZoRye5nh/UNbZLNrEoC4DAsgN8LDXHdN9BPcfWTS6aiA0Bt53nE6/RptJf+wwS+MV0CWmufRSlfxI7yhEVUPFYsJzx5mGH9ZEGsbp/5M+oj2z/2L7Rz9RytSRT3BaWqwoAclsUOOC2jcGDbb8uk3fwgKIZ7CU2QwPfiap+o5gQiBBkPAkIkXNEhFFogAIAoIIFvv0t0Hf5wxyVXgd6PX+wjofS8fVLkxuilOU+ig7x7ZJ+3pJNO77G9uvJD10uTDcoIZWtfr8DGIOy62D+N8sgp36Qu5LypEsVDb9w7QQfffZRPPHAk3jnneexNwNxY06dgHhesn7eMppsPUX+rrOzCSGMAYPOTAbaZU36WboOBfkqwO0BATH60t7oAYEVw7JmoG9V8bwwz3BZwy/KWevoqOo2DH8oPSTefCD5m48HQgBVhdh4exosVGZhGSzIxwpC1oplapaMHQDX4AFojB7Q9+1oOC1ImBMbuBQTZumPfN8pCmggTIkN/BQJc2CB6wCC9EuWUYIAhYUKnaJf1kAAyrE8h5A6uRKZWgyn5UYFNYMD6c/5b/4QijGu97TgQZcNmxIfVNbYDE+e99AOEDARg7hAddmqHOuBAgAVgs3fan3Q69pi1PMXvSLJBQCxZz/RXNfeu+uTpn80e1Lb5IXAABRbI/O7e4WHPHcBjLx09RgfffZRfP6By7jv3AWenwQQOBG1q6QpZwYYlM4CsjfbGjihvIFW+UuRQYkAXrx2go996TH8/Psv4Z13nMccU0fb1JhsLzDFzhoFI8UaQ+35abq0o5djn2QkCUZexjuwOPa6OZuS0nQces4/TgD2cyyKcx/TqtIExh450HpQ1ns6TbEemWf8Ww/Ov1Zfr0tV2nkiS6w3D0KIVDyiG6hAYZ+V3z4QG8MMEiTuviem02/M6BQrMB5bTfV2/pMxhDrcMqLCpWim4PUqIi9iZGagAQcAMIfEx3JdZWWDBgl8M/5zaH+shQUsIzACAqx7BHiiHNdlmzypz0G1K5K5vuqxG/L9Fs8f734VmPD3NfC7CxUo7KbAoSNCYdEoy/1MqOEz8plU4NbquKWwkfw5WJdnoxzQMltSfwmByW2FMdA2yW3PwI69ePUEH//SY3jigSdx753nS2hNztcafgcwBsUbTG3sWxgD7fF6JSYW8Jeun+Djzz2Gz95/CXffcaEgx3zratDi6eKOUPfwio4XAr1B8Gi5wh7kOB6gQIN8QRU03eZSUt9OPUliqu3bq7bt51Zp7mPcpDCBsTEeldFW62FVNaB7nvfMg9kC59oAqp5AEsWdEELKjBTlJXe8Jj8ldh0WQQLgxt0B5a2pwZ3RT3nbgqUlhl54BmCZnPNvJnlOIKSYioe6KW6uKufR4SNqe05JAQYGB9yWfF/pg3xc7qKdhuZ+Th8s5VN4YIDvz/LtgQHLFMj1S2UoT0T17eak75Pq3Ej1d/lmy88a6cMD56Nmy5aYMgYGoYbT5ojdRJhA2BNhly3XjOzsUOtlk7QXjYUb6zY1RuXQCgC4VfrbA/TFQKfKahFSDqFQeY4AhZFuG43bC9dO8CPPfRifvf8S7rnjfLaLHJJDBtByy6V2nBlgIIKgEaEHCrRxTyk1CO/F68f45Jcfw2fuv4R3nDtfaVV1vcyXUTxde88ejb6VQtf03BZqzgIGvkcbc7X399ZSJ92PK23bp1Zp7h0woJWl9Zo20aqqiIdUvhvPio9tulVTj/aYAQ8uo+HULbTXs0KX31Lt4AwQYkIDEEJig08p5Zh6lk8VdwfkGCBhQnlVcJpboKDLFKjxctbKRMTKiVCNr8hg1mZzSnlTudR0+pbut1T2WmzcguVRsW0ctdtLRgXG83UrIKjH8vmERUCw5e2/ETXhVO4ViHrZtSB3YU6dVu77H2lAgPw31zG0TNkcCCEyM7CPM3aBKvhNwI5a0LubAtdTnB7YpMbU6DSrz3L1AFTjz79aBgC3SmfvJQlUMX9N24AS/ip5NAVQVruUqH+mrYbYsZevn+CTX34MP3v/JU6UT8zCzODdQ2NMrEvkdwttOTPAoKGI1PeSa4Ce+i8/BPDy9Sv41Jcfw8+87xLuvv18Tz+3lzcetCdgh1LqTovcZKOluDRf4yPUwhqo+3vtk1PSJq7jcru2AIIxtbrdYGmjH0LvWfH9Wmp19PvxM4wSVqauGP2VRd9ebDgq+9kqeznJvxOlNOXYu8TdMWd6Ncfd92K4Fb0OVEOuAcISKBCFKgyE3GcHYoMpsifKMTdiB3JXIhyiUHT820uQ00DAS3CT+o+KbfcaIOBzvTFYAwVSGgYsf2QjrsCiBrELcuR5iiO59spoyLcCle3zMuV6pRLmYFaMClMWUs+SMfuTkAIV8CshtBSAGXPRYwCavS0qK9LWo7arPeTpZ2B70iiwlSXgVTf7lDDFPLeorrbZIefJzHGYRFvqnxj4jJ5rMeXL106q/brjQmbUeI4KOCACQnYsluYNcIaAgeQSAD0oiAoUeIkbX79+BZ/6ymP49HuZKfBYtGQE5DSgYC0xaVQsNSegQdNzNuMdaNkFoKefvTJa2reFVo05tGABAbfRsgap+XtwkfjqmpF2mlpi//aWOpHQ9NESrbf1+fW55J4fFcts8WTPn1Na9aSBNtTQlwwKIiuv8lyV9cRAoH5P0SiPQMMQhFsfp84j4w/0MrvcHi5bWBJdZxsG2ZJPEUiBgGz4gwSTS1HjfYAMLTVxJPciw/q3a93ggfRTzUth9DIrBnDowwKE3cRhtjlXzrJjJQE1e9oUgP188/qLj29ngnL1FovWzSnx1sNTZkh0Mu2cBUXAgeTICOCJVPVRqcegmXZoiv16H9sv7gNCpFT6eYIcy/VN2TgOypkBBhXZ1ViSDh/ooqmlr79yBT/2lcfwD997CXefO990OlE7CGtzRccnuU7LoMCLt1t6cZRsZBPZbBzPhiJk17nNtGzxmPJ3rIMCy7JIObXxV6Vm+islawxtvxqgPW/v1ZVBuvZS/Zco16XnepnlXvIW0MfddTXb463i1Maza7HTBTsd/E/9RUdQqsT01RwTjvpbLpaRcR9K5ppNNUPR5F1kelbYETEcEjYBUPImynFUQ0OBmMXJ50NSmjymQu8XJsikkh8iJ825A1iC0XMDtF6pjJgGMjG2bJlmOk5b5D7dcWHIxGgZdkwMaWHGRLeXW/n1WtJZfHxbwihg2B9PJ6PmfwSiyvoN2iSAe045iXbBoUmptT3WDuk2NvbrjvPtNbkdIRES1TCkLINcGt0zAwy6ncqcYpmApU7lex5WBwpM7e5F0VD9XOswTlSCU/fYuDGykxhP5gAqSsnG8aY8m2wyG9eTygYza8WLta2VQMQDIRk2Od4ehDMDxnB4dD9sAwJLSwbL/TpvdPzspSafZgdH/Wz9XBsu4vMtuAMwDB011zshJH52LaMmlzo5Fyz1RQMqbkE5zZgk9RsVBQESZ8Izu8eHpgQgqNUOlJV2avMpWKmj5FIQMs1MwJwfOE3U1CnqDK+NZQkcHNIX/f4IOR9hsnkQAgJaICNhLgEKW8DB0vwMVOflVpAzZyq+LmPl41aXNr/Jf71ltXYviS25IYtF5wsBFURncGDZrga8KwZ3axk1e2S/Inofp4a21A2X5vNhVfx3t0gbdTaqVdya4fuG6VQts1vkt+y0liTJKS+rCozKRMFQyqgxEDCLcZSKUlNPL4PZFkGoQtEBcON4c/ZebDIbAK4TUVkrrkuhqJ1nd96V8qImEEIghcC5bSEsKyKYuGvbx9rDHrMB+tzIA4dqaxvPPp1BE7pOly0Z+bp49diSgKdDQ0tgoE1Ebf/Keft8W07ZPbe0DIFAEy9vr9fx2UQVKOwkiRNUQicpcBhQ8ip0PoXOpUiREy7nPO92wzDEeqd5oPzmZFHqkduXk1p0jFzv2SG4vDonlA2jBRJ5vp5yjvYggbq5qcEwH/Pn1Ug3WTAg11hmoLRdOWWjhOhRKcxQmWPUtQ1omVudC+YnjfdzdSQKUr0l+9WFSDNrULDLBjE7M8CgQXkLYxsAvPhKzSm4+xwjraWOLYUUWKSaTRohHgnlwFFObIoJmAILbozARHlJXw8OxLsuhr9RehkAmOxkgAUyRpi4XmYWHGoLQKXsiOuztgmKLEsDnKx0tZEST0QqHpQGCVoRcZvajm3i+2YAPBZgiYL3lr61x+V6wxxsQPJ2Y7W1nReba/PfEVPTMgiKWRgAAQBFsQBj5WJ3SiwgAXLeHJDj5vuhZksbukP3P7BXd15cAkCk5oY8BzylUmrna1LnkOfsRDlPiNjgxwRMbPB1jFqUZF74gSPQLRljKbdSDi2NLucEyGqDKddbowlUUFABQdY9B85Tvs4HAwBgw57cfuVhr/ZDzxDIcY8hkLYtrR4Bep1r26VZEA0KdqHqmros09m7IXdpXXZen71EvkXKifID+7UoOln+pR+WZuSZAQZeKyWGJoNARHhJZW++81wfPvBeVFMekaqgypveUmoBwtEUsAOwRwQFYBdlF7cJNEc2pDmZpnrY7EnHUAW2WZLktW0AKUdxvbWytG1qIyTZ6GtPaieTkzjOXCcobd5qeFS2bp4CAGurNAD0mwNtmVBOsas4nI8A/AS4pW1MdXFzBKyxR8sG2F0z5RpvTwtS1wK+ctE1Gm3BvK0sX9zHUOsBfcpbVivXCM7WoEEDBr0iSbPAxfSJRk5U8ynyxLA5FFvHEOhzKWjw5dQyaBT9lk2r1gwq/16ec/q5CmwDARa0j4rWwYcydMCYEbDO2Fq40mNAeD8GbuNuCpsBgczbOg9paH9+9fq6/RoCf2AZDahydoCBLsWFVV42El7K6zw/835e57n0Uzfmp1FdoEJXymoHcdoTANoF7BIbSwoRuwjsKPCa3ZgwQTYCAvZoAYLQeBXJj2fLOK7XovRSb1Rjar1obUzb39Sb6Mz0o1x/mTWe1wK0SkjKFg9yy1arUt/REk2LyK3xBFpltMXWNeSUBgUOtZ2UUain1bHBM2w9vFDXFhAQ1HH7Apel4xpAeHUafT60jIg+/aoxC5BlOTLyXzndHjdLQoFVsDDJ76mel7KbyIyVA9r02YXx4mPkX+vcyxZP/podWCVckqiwIxo8rC3dA/o5e8h8BbaFxMrvNuobrletL+eJ1FUMvEtm+0IvItRE0ZJzYfOd+I/nUC2FLEcMwY5qyG9HgXXSAhggasd9N0BHL7xyvNl+SZ27E9+RwKDTpvXj169dyTtCPTns1DVlqMsuUKdwEqqyya9SwDQRdmlCmniDoB0SUiTsIwv0fo7YBV5nOsyWzZVY2j7V0na5WgtLGrd51rYPuszzPON1zeyOckA7wbe+OGnpTXEaeUsdRxOuf40xmvttouFMsTFt/pvbra/rjJrvCev76GJ1hDvxldzqtul2uQDA+13zjFQHq+SnqJY3nzdqnKYxGsSpHTbMe+an/MIbCQPoLpWcFc7V6QGDNviyIqFlFzJ4eJ3HxV7ngdLTyJ8ne1F1krvdb6ACGsq9UgUN+n52z4e1srbfxOJKGadoAyWhnpQYNkqdKOX9ELIO5e95eHMOVEjUMLSAl+9Uy1I4xOrUJXZA5lb53YJeKv1mBCGBt+n/pNivOy8sG/iljt0IEM4OMFiYhC9eO8EnnnsMT7z/Eu6980LXJ433s9FYTFkwOkWUACTeSyCqyRgjMO1CUV5zTNiniBQmdxdBDRSAVjk1m6Q0ky/XzQguH9vwoh7dj9BKzPSXI3i6fkfKYynnnT70dI3tb1uHnnZTk4z0RPRfZ229Yu1R22eOiuflsj6lgVerAQMNlfroGV6dPI+zHbMWGHTsQblWKhGBpF7Jqt7UCXmtt640ovmO+ru1Yl3E0pbQtK28FleuobyLh3o97pSFNhnQEPOBdm8TlBb3+5n0O+etjYGua8fiOOdHbMwtkb0CcCoI0PKmjwlw8HaCjWWFBTX9pcuWedu1y2nYmh7R3xPQhHokCVSDBA8gYKKcMyL6lQarSKQd6/pUdOkaM8BhB/5NIF9H6edIH+5C2zEn1/K7Dz7wJNuvnD8j/dL0ad+l3XUFFC+UMwMM7CSTTn/xOr8Q6YkHnsR9dw6YAixPaK9U44fyVxSOKKHi0SReraDZhB0RdpgKUh9tpextyTmbUR1lt28FAl7SmjW+um8O2aTFi09uBbuWCluixTvDrydgc99UjV4CKMbOK66XKhXoZYSVjuFzxWA111OhxVvDVb9EXbtOMbYHRnF+z+iQOjQEAaUvuK3N++0bYGD6qPs+GNUU/b7Tlbd9pr83xwQQ6PNUriMFJCaqYEFartkFwAcH/H1bnwNjkKn7vun3/MDuNcFe/y3Jn+oD73XCgPdKYQsSqAOwnnwCy4xKUy3n2Gn0RRk3DVqk7vncNBEiCEcJSJMNkVCjR3cxNTlQUiQXSspoqbCnR3XOQD1f9dC04Kx4wKDUQQGDK1dP8Li8JfFOzilIVEHgliI4QtrtAVJbzgww0AZdOvqla9KpT+L8XReXf+8o2KUOPFLztCDzgsTZW9ZbNO/y9zmxIOstmhkohDKBE8ZxQGCsg3U7tABLv3hgQKPa14d6H4tfXLnH4nNt3cr11fAXo58NX+v1toYPQG/8hhVThrwo5aAMSAUIiQhkAEOiwCEcglLkrRHTveDVxvYqdcersW9AgLRP+kP3he6HVPtR/8YaucU+2wisdB+WVuj+LMafkEiUsfSpAgsWmDX3CCVsZvCB7rH6/O5T+93t57jQz/Id7TkAJjyzTfYauSvHB3In4EEBh6TkygIHXY3Kdp1+Hm8p9bktONN14+v60EiMqIyCYhNkOakGCrptXtH6E4DrTI3CAwIE9LElpyV/7IrYlpOrJ/jIFx7Bkx+6zE6tAbROBKQppQ+Jf8MbfK10QC5nBxgY6vTFayf46LOP4hc/eBkX71JMgTMSHdvQ/DVoX36TTE6s0JpUfyWTTwDDrMBC/UzNu7/LWxtVHNAmDwHj7V69rPu1pJclGl5/B9rYtO47c/imCrmfW2+38fYt5W2M3bKhy/dW40tmrL0iipmMMcsHixIWpZ3E+yUCoV6n6XLt8SZtSEe0VamMMmmeJzoCRbZvyvUKBFi2QIGDst5/VnPhwFACTQxdE6lZR9rACWugwIJhDBIFBb40OHDAgvpbDepCGfWtK3OA27f6Nw0YHYCspeo0dVdcH7XtJtU3FSDJMapyq4GD+o7ylBaYrpuUbUXfxy41z73Rhj+gRBMayHA+gQYKKTEjK4zIUQIyOQvg5nVnEIAAQghVR07Ufl5kLYHhXAlpxvHVEzz6yx/G5Q89iYt35fA38S8n1X9uU+QYVUaIpB8zaHnh6smiwj4zwKAsjQHwwrUr+Mizj+LSBy/j/B+50HmWtoyMzxLdRzd+V92gV+KkUPoEAGHidf8KLMSUhV+SD/NnBgg9OgbqBLGhDPXo5rMGAdJWAQJbkW0r2L2BBoxS25qppMuKx7nkdXWGyzFupY6OcSuGTT0vLbx3loIiRymAJiYg2bj1RosBgjVS1dBV44di3LTRSs3AOr7ZsJ/g95VnrCwQGPVTflaKEal4yqqv4oZFZKEGv1Koxpr052lSb4Kjrj/1MQ2oemMIALpv1d/8rK6s9acNsSyxKx74QmpB1SnlDkDpJ3j9BGQAoGTPMFhuXxm5I/W8tkKncAWcJNNJ7kVBHqbCIMtMbCpMLCpQkPsrsCDfAdadS3qzwCLjPGkQwAyB0peerkwzRIF3TFLphJ5VO/l/fg2PfPGjeOqBz+PiHe8E5teUrGvQ1jpmtV/z3/IfEDNbQCnhyrUTPP7FR3En7ux/nMuZAQaSRfrCtRN85NlHcOmHLuPiH7kwNmpSrEew4mkdX3+Rr51v+BVRUkYKoesYaKGSA0/aOAALOgbYJA6ppngx/FG2uo3LhzUQkOZletQzPqVf18HBooc0ulfndQGdAi7nUmPQitJNMSOvnHMtv8lGbUk5A0D7Ivr8yioiNnjG0JEyciMD13pvvZFzFbNVyk5/uXkBDRBw+m7Qb8n0W3PfUb8t7IZTQICABMqeae7DNId6XQMUBiBB+sgBCgDQUe9rfWj7cc3z39CHNeQQkeLc9Z/bh7o6Tr8VUCCyR36/FdmTvtaAVfpO+mQECBaAQLLy6RVzr2zaVJ0U2MvXV105LYIFeYmeDkWwvtRW8nB96TpNSlcGuXmKra7s9KRvc9r+4Qo8/MWP4On7n8DF2+8G9q8Nw5GlygboyuilfCpRbf/xtSv4aM5Z+GvP/IRfD5whYDARJ2r8cI7JXLzrQjtoHgMAdAp1RLkCwMm1F/HQVz4JAKD9q20F5HeON9J4hUIlq4kwCWAohqXGAOsbIqk8pnlRFAieKmlxZSvkQyZA91Oce8Fe8jp1H3QGfzAR3GtV9y2Mk9zXel7W+CdtxOLM59U15Xpr7JoqziDt5ZYKVqWsvV0EBgUpTDzOSmmLwg4jsCD3EOpcP8dS4qPSAQJjxPLnoSEDkLQxEzCgDFntt3ZODQ2bIhIoBKRZ2rEvoIFC5oRnFKCVZEewFOt8GoEEJBTO1QADulV9N2CfPBCQdL9p2dN9dYDcAdI8BQak36RdVvbkOgMaBLACQMd0SVG6bNHwa7DVn+yube6tQJzVk4AwankuhKmysDLuGIMFu3w4OvXRrdLGv5wj1BCB3C1FkDA9HgAY6Ukpnq1QffPMez6DC7e/vTAFOgwEoA0F2fvk4yn/ThgjIuD4GtvHSx+8jPN3XVgM/Z4ZYPDC9RN8+AuP4HIGBZXKWci+lrJGu6aI4+sv48F/8ik88+5/iD/57MPAvIdWsm5RtE9B38orHAKGMBWvJ5TfCGBA+VtqPbC7Xu6ECwI8AU9z1wdD2ln1GeT+8kxP4c0b6GbTp43B0Z4X0CpX7b0uKOEKGCJQjKGqq6HEm1YoZV2W1k2TUrzZ6AtguGVgAXAVuFea/nMMWrmmGjVhBVxAYPtVX+v0l+sVSV/JtWLYsvFPMQIhVpCVEgdFFUAooAIZICABid8uCsy1z9KcAYVVmksslZHfWwQC9LlbLXeYpuo9GjDggVUAPWgAGqardpf6bM6NioCNpg2NAWzZHT5v2DPNnJW/AaCpAQpCrY/AQtWX23Sk1pONjlS5TFQAsgMARowcsMlGSLl429uA/WtNHzR2ggcu/0yDNwLPgQAiBtKJX6eIK9dexGO/xPbxvOTcLSCDMwMMpNGcaMiTUxs9apC58QoAXwnk88fXX8aDX/0Unnn3T+PibW/l6+bXmt+ZKWxKT9e5sWcHMLhJQwplJwo1RmdLqm3sEqfWUG6KyrA654yC5FsbRsYa7q5+S32milWW1lvNz/S8/6EiluNOnTeXELIHF+Qd2KxojdImBQYKxbvblWuTos976hwoQAFolakUq7CNEvKXGopcjOnuISiw4z3wepMaNwpTe56IfxcmpBhrEyIqZZ7Pl/sHIM2xAQjcT0CmIAAEUGKAgBTbfhKwMOirrf0UB8Ap7fc+EPBk71bLHVAdCmARMED6V/eFZm2ka9T1q4WKOTV19YGHBi1DQKzBgtS3AQo9qyBgAVBzhNqXNNUGMsN0Kt04cpI6huAwuwAAiHvVF1n+LEiAAN/a/wUw5N+wPEQcX3sRjzz7w8U+pvbpbvl9AQZE9L8H8DcA/G8AvD2l9Gv5+H8A4H8A8H/Pl349pfTJLfe8/KEncfHOe1FYgrg3gzx3g+ZS1WbQj7/1dTz41T+NZ37wp/CuN38fILkFaR4bxlG7dfwZWIw9rwGGGgfskaNbNCui2uoLu8MWCBDY36heEjA2IHKuPL/vm9VYvlcGnmkDAIDeG9NAwChjN4luUGcAdSLPqAqVQvmeRJFpylAYhTAB047vrT25FZDARk4ChsgestRnyQvuwa+MJYAu+a30wwiMHVAsDX5IYbCgDFcBB9rQxwIOgNw1pS15XAQkqN+0D9rWP0MwYJmBed+AgUb+9D0wkLtVzxJDucO8r7I375v8DQAsg/leDL7MeRwIBprqOWzCAvAQ1gJh6hJQaXeU66T0YmxZA+tNN3k6ZqOsYdnAFC8CAOsgAaezBWIHRF3MNzpwVEACUZ37at5XEJQgyQUUgeevv4iHv/Q4nnrg87hwju1jZX/GoOX3izH4lwD+dwB+1jn3P6aU/vihN7x47l7FCsTMGCygPrlOio4p5uMNKHjLWwsYAADceI2VRXnesvcEoIsP8iSsyH1LsloHGPiH+e/GiaAFvvSD6p84A3AS0LYoRqcPkjXm5Zm3oKiwhEvJDjyzNaW8HO7I26JME79NkwISYlbIkZVUCnzvHD6geS5v3WT7lUMNKbDSEwrdoc8LiIACk8VDRlYECzSvDSusFVFaivLnOuc2Rr4nBaH/p/b6LUXT4obyLse0gdKJnYslAaDaZlIArbu075etYZXFUIEGBQYQdHInhmQ1vDa3NL383ModIsueekYxvKofiAgJ2clx6P9DS2EwTLHgI1nGwgHGad4XkFCAsejDkmdQDSbJ/RWTepAulO9bAUBqc5m25Ck1dQoTT9/cvjTvWwYnzUCiSgiIDpB5XoABqVtXsMAgIeD4+gkefu4TePr+J3DhjncyE1HYlLSoCn5fgEFK6X8ANiC6A0phCAB0LEE3yDCegvUaEo5feRkP/tM/i2d+4B8wKLDJWVvjsEvFKMGkJ8+G+DOAdqmbJocaCtUxDJo1GQj/Zi/Ji58Cm2Koy/2zUWHZe448MAsKdNkMCpwihlsMpzwr62kAPTjIFHmx6TLvgZ4+jwAQG/ocgAqfU9vO4dirw0SglBhozFCGHuW5pWRKX+oMAQQAxzLlOcZwa69p0aO0NLY2JpqKdmjoNoHOa2hc6Y8KCAAsrmC5WVBQigWrG0uaFTiw7QLLNWk5LCBEPVc8dvlRmID9fv3hp523DltRrpHxXQAJPXsWezaBQnvvA/Wgt0/HYh6JXOsl4S72S26HsDmxtl8PJ6XEczvObPDF2AtAkMmZUu3fEj5jkHD8CifKP33/Z3Hx3N1VPktoIgL/DjIGS+U/JKLfBPA/A/irKaUrm36VB+ygDW50UTTi8be+waDgB38K77rtbS2qT2pSH+I9j0pmDtxEoRWwUJE0ABuvAnrU7AAg6bMGDOj2zftFZciXDqhTYBtNaou0wZtsa16j/Q0pBRlYaZYYpDZm2q7uwuF1tXXb5N2uFAUO9HMaA7FUH++7BkAiMtMEYMr3rconkeo3APmFH6yw9ItCBmEHcqrYAQRLZ2+ISdc6a0AwMAZ9BcZgyQJCPcdvtmTZaz4XDzGOx1MX2y6dHDiSQ/sbDV4Bv21LRm7LvJiVZyr1UaENIkKauP2se7AMEqZdYc+0dz3MwwE26L5Su8NAwFKOzaioJFtmB8OCA5DlcDcyzcq5HbBhx6+8iAd/5VN45r2fwYU77s7XzDnJN9WwxUKO1+sGDIjonwJ4i3Pqr6SUfnnws28DuCul9D8R0VsB/BIR/dGU0v/s3P8TAD4BAPhfgjNHAazGi+ScFCPox9/6BocP/uR/lUGBEh5biQ1eBD9CCY6dWBpBc8P4MpPRzpcqsCCxOa04jdJcKjomloCWAdnYtmFiFeB7RVtZlJH3u6W4BlkrDOXF6udMff1cZsEUVyEbTxiSFKZWLmwqFgzKM+2mNvqZXKmFCovHWaW5xOZ3AVpZck5fL7tdkinQ9N1mHtAxYBYAcJV7EOBS11sYyJQag8y1ZWXJ7Mlc6qGds83ggAIwATTPhTYHMhCYQpU5HR8+VM6AXs5HwOBWFFu/TfNYX29uJ+wZ5goQlhg0oHrXQBtiA3BqvacZRM/Jk7Z6QAFKr490OsDtmveQ5M8hO2jGjFkDC24MS2Taj5Sd2n/yKTzznk/j4h13F/3OSYn8YErr8vG6AYOU0p88xW9eBfBq/vzrRPQ/AviPAPyac+3PAfg5AKDbKcGuNPDCB15RAlZyCt7907xkpPGuqNKuUmTQl9okxhNo4uG1ZKOKGy3dJgjbo1M1mgYa2g1A2RyGb+ULwWj53xJKtoCnuV8TLrgJUFAr7h834YVhOEorCd3vRne475kX9qk5tpG9UF5RUw9Rfsoz7uKs8nvjPVnQ564737JawStljMSrycoS4HEOOzN/6q75dinqISEYq8TdJW0Ae4lAY/S7NfWHtJM0IAzNuURo5rhlbktIBSjhFKKoPgutmzghLkUAO5a/SclanBs5JOBg+WqavwSIRvNAFXcOSDHMRqnX2nzWzIQTaksp9fUWnYMFAyoloKxQqbp/wAQt6To1JkPGdyFM6pe59HsJGy4Vq9Ma4K9POPKgjh2/8nU8+NUfwzN/6tO8Y2IWXoICB4ACpf8/Ekogon8fwL9NKc1E9L8C8L8G8P/Y9GMDALbsO14vDrlT/wyDgtvvBnsRrBk4HBOL4pDfcAyZirJIACejrVZ1MCA51lfXxscCEihMaBLWZqlDNhwp1Y1hgBZdm7YOPYCV5BkKoS4/LBn3ykNKqRVy/Zgl3b3kDY0MrfpdN/HspFqQ8nKlB3ZOQSE3dTFjMVpX3oGBfE0TMgIbz+TtYCf31C1ylYijbKxxLx+id7Q5J0fLXJuO+vsPivx23chvCBFsaBdoUgCgNXSUqHqBiMCOMuiZ2ACkyGEVHVKJMygYhkzRzM2c2nF9pJY3LVdL7V4quR66pJiz1PV4z1n/yHwu9VWAYDJgwa90/WzyR1wGLc+BkTNTYuoGIJTTAhS26rdReMADA16+lNfkLWNiciksK6hzyXj1xvrcbkHB3X29CnMArCYr4/dvueL/FsBPA/j3AXyJiP67lNJ7AFwA8H8hohtgs/LJlNK/3XTTMsk1OlTGK0UeBJ2wkYvbqQI0KIBDM/xb2Xmb8pIzNpR8b8IeCTsQ1fX7bNBz/abJR5oq+5sfnencTLdhmqrDk1IFCAHAHNFktS9liFuavjuflYD0kwY8WSlQUAYhX8eOkaJJRbHY13+5dRowACZZqVta5Rlb3U6534G0apMw19T9AKA5qsNSPF2HC1T8FEAFA6OEU8MadKtVSkUOMCSbMvnzoaXrl8qoPocAgFFxc4iCYhRTAQsaKJB+u19ikAAAQcWhOYE0GxPJpzBGR7433qpcu7UNA9m9GZm2OT+kz0sbJpM7or1kzXyUe97kvFas6Ch8mm/i3ts0tm/n6wEIjM5uiscSholthoCCaVccAZp2jQMAmiropwCY+W9LZ7/s/M82r7CBYg8Xpuzv16qE/xbAf+sc/28A/DenuafLEIiBA5SyUddR4NUH/+RTeOa9P8P0S6lMRH0bReI4VJqL4sDuiJ+ZE2JkcxvKyXplXbFQjCJwU1YMWuC2ZN/r+FJDuVUvpqHdHAA0LDZpTiuKaVc9I6FMJR5vPKhSN/EcrcJYyR1YNP4mbr+oMDywAGxSqI2itOUQFqp9sF+HLjwgNRDj72yTLJ81CBiFExbzDg4zLm05ZT9sKrewXk4u0fD9EflYjfen5jdJ5ybkcMqWF3AdxFzqMgBHW0FBB3A9JgOoTIaTYU+WUlfMx+oS5G7u+UzacKlqPj+c24cWDxSUapuwgm2LV/Ry26WwobRx2lWdlQECg4Fdnv/1/QfMEkzjea5KsV/v+XS1XysOAWkZH5R/p0IJr0uxClGlSh9ffxkPfuVH8fT7fhYXzt1jumnql7GkqUyINB1l7zovJRTKUTLfBSxoZKpRqRiFpZiVoc0bw2nBRAjG6FBzTu5RbzeeYCUbfdp1CsV6RshUpLd3vjVFJVvde7Zn+IFeOThUO4CD9n3n68btHyrz4UqWA4pTp6Trs8YELIURiACz4dXwzYxbY5j/rhav7weKXb//hD9pMKAAwwgslOva3zB7cNQ9+1RgYCO7s1VuydQJyhh0r8vOAID0XNervCz7EWMffiiPOeUcBzYB/EW9pQF98QOFaq1OYg2J6uunops7lhfI7K0pC+yHu0+DMAaKIRBA0IQMpjfUue7lDuVyfP0lPPSVH8Uz7/0MLp57Z3f+ZsqZBAbDyaMTNa6/hId+5ZN4+v2fxcVz98LzVJK3wqEI7RESr9kC4j4nac0IuyPeIGXm7VFpf6N4+C5IkPtPRy4K34q0Ow/aM6KqD5YyeDuDvuQViWL1qLtycervaxWfowxG1Docaj0tedRybPRsUxrzvwYGPAp9C63eAVbHgI8YgBEA6IBEML8fxesNaFqp+imCBpvLGsfVnx94eWU1kXozpDrO83kjYDC/q4+wsnGKcfeOnTIE1IV0urqzHpNsCuwqGKCSo5XKd7urawMcAHeedzUdzXOgM/wjNm1zafSUbL4F5aDNlYWUlmoiVrWJ9a4kEM55xY6uXg9mRps2YXeElh0ggPhvCjsUfSbtn3bdDrfWPh1ffxEPfeVH8PT7P4sL5+51ZNEHp1tB65kCBm5GdiOYfOz4+ot46MufwNP3/wIu3HXfcNcuvmmdONz5eaIcvTG/UCPy4MYZGiTQbse/2b2hggTFGJCltGy8z5Qulm4pdWf1gp/NDsBb7jVSPpKZbvuDv9Sf6+Vvp8hOd5ffeYZfj6tC1Y3xXPKcDwAI/MPxhPNfxAUsUtpecevRy2/XBgUGOuOvgFJJ9DN/AXSv8R60YLEJawlZS2UpWauzKd0F/F95Jz1NdcVEd9saGuiMvGULUMe2NbYrLMWWRsCXvyGz497DKW7YJJlzsYADPh9rLkVKTR+kIXAANHgATjffAdPmQZIp66kF3VQ+x265rQYKJXlUMQgSIm0261Isad2PozWTni7u9HAXKnDAgFxvGIM0vTHrNEdnATi+9gIefu5jeOr9v4ALd97H/UCmP3Sf6XG1TPWgnBlg0CRkWZSljEPp1A98DhfuOp/PB9+rAlqFoRXI9IacXMfGXqjIFHd5ks1IKYLCzHt/I/Gqg1Q30SDLGkiGsDzao+U82m2Qyd4AAUtLacpaylYquRMqs8fDbiOVSkFDi1oHS53beovhp/blKeWe8t200bOBaybNqiQNXxQUyn98j7UpW2lmMxa+4a81Ghl/eRUtUM1atRepvVY/r8E8bS+N+mz0BjuvFGNu7tb1t5qPTXTMXC/XlWmhbhgygCCammtqsUCB5fl1G7vmGgUOzF97fPg453OVzzY8Ul4sBxRQMNwUTl1jWQe+cz52hM1907O5K2zJ8EaNtJZjTb2pAgWdPFq29gY4LOKER6Vmw9CIF+LUztiuMgFRjD4p4CDOTJjQ6DIAKTMMntwcXz3BI1/8KC5/8FJ+N5AwYhbYUm0TAUyNTOU6h8NtypkBBh3tYhGXgIIvPY6nHvhFXPgjF1v6VbzoXOSz3CKhVcHx6E11kkniSgYERUCj2hoVCZhYCClTdmtvJxwOm6XeFoCAm8G+kLgGeJN3uWyOyTvI36fJgdEY9oZfv5O99YCjdoJSWjWAw/atGiR5h/vUGCSgzZUynEj7DPPdq9YI0DRtlo+O8V8FBsYyxda0NL+t30/PFkixrEFjP8uaGJED/tO9UpzavwF2jAxoaO4j5wV0W+DXPkvK0hgtsTIxfz4UoI3KSD61bAbJes5AiVD/JWFTFHBICgA0wEFXyoZbmo5Yn/vATeoaJyek1rsFCxooUG7T4pth5XldBZZ0r/X+FSsQ1DseyqoDOR9ax233JneuX7l6gkd++cO4/KGn8lsSHdCX91wvYEEDhZRQ9zAIS7jgLAGD6kl38VYKeP7ai3jk2Y/g8ocu48JdF2ANCuB7VwCMsufejGFXJhbA2cwSWig5BSOgUGJ9qdJ1ZWBRKmE3kAHg028UmKJqjKwCAaY/enZlQLuXey2XppZrnsOIJvUocUOHN8ZfGX4x+hHtd+hrjZFb8ojb6vkea4Uv5BqrkQc7Mla62B73etSrvzUsLihqZLz+zhqp/h7qeSt1W3Rx3fYm93Rr7FPtQ9V3CkaWGwTKW3rItSnxPRJfJ+f0vfTz2rq1ZWksDh6HfPEaADtUPq1sWrlkoKC/UwEOIbSggetVgQOgjLP2zpsOOVAHrBUXDKheVgDBSyJtwIKuu9q8S7uFy3q3dVS6MKfoWw8IuKGD3g7dcOzQlasn+OEvPIJLH7yMe+68IK9xy2M4gQg+UJBl9iT9IwBBtcUpZwgYGFpZobDjay/gkS8w0rpw1wUInqxGoxoUwEfuQKskXsvL/UN+dqAJNE15Qsnyxvr65yShA0vXRXmTWwUMyLXg484k8xLHtFCq70P6vfnNUpzaPOcmi51y1sNa8/it8deGP6lzWimnZMZ0YOSWalxa3xn6fF51fUBVwPKT3uNNxgguGyi3ZkvgwHRsrGqjkXXpT30/6Wf9e/ntnG9sjcG8oSdtmYxMSR9M2ZKVN0t3LEBqQEPIzydiRTkn3edJ9bkDClIdvy1NGBnsIovyBX6fW7lUlw/7fKVGbS9SK5sjuSz9Q6kyCA5oQP5bGAeoyCXavxh8P33Rnd2CkAacaL05CouYa5LRtfo5Xb/bsI/VtQ0I4J4t29VTzikQm6SAwMj+3JhTY3teuHaCx599FJ974Em889x57OfUjFVlgSpQgAAFAxKa8NFCOTPAoEFjyuAdX72CR37pUTz5ocu4764L2DuD4RkcwI+ZCvp+bY7QnoueTBMRQFMOO+3yRO1ZhbQS3zuER3RZAAcs1Wvle8XKGrUWz9Ig19Lfh9uB/Lv2h1YpLnm5nvHXhl8r1gQ2YiklzEhFB8yFcszfN9Zb50YTgccY3IVi4IgIU6BivNDIRTVAoqDlO1BHIWi1ulXDqi71mBGPBRiBAA0A5Hdi9FOs/Sf3kTLqxxQTKPgNmbSXpi6ZiIoo274F2m4JqdafqAIGCxaAOp9rH6db1sf8N3VAq/Sv6m/bt1o2t/SpLSKb0nYrm41ckshZCxyWQANggQLKPeScLqd9a679WQumJ66LgJjm2dUAdgmlTWKlwy4Ap9OzCzpWswGNExoZtFqbw1Wo+u7VudbnpWsn+Phzj+Hn338J77jjPG5EkelUZJ+IeAPe3CE1VJTzaighJQJSaAHCwjidGWBQUVs1eMdXT/DILz2CSx+6jHvPXcC+yMg27xMYy8xrMbXKvAxIi+YIQAiUr5kQpqnxfFKD6CpgAOBkFZuiBtYLn+i41TAen8YCqo/LufLZr9G2BDRzzWkVrWf8teGfwYZpTloR8zGAJ2lTLafurcHKx7K2nKC8XCJQoHxsTTlzAypo0M/qKXNbD1u8MRqFAtaAgAcCNIiqfacBQttxnUFTqFIDrBto2yh9tg/AlH+zz4ZsImJQj7Y/CwOQPf6QbT0rTK68NWRxI/ba2q+HgFIrl3Jv6TNPNtfkEmhlswIF6uSSr6UhaFgGsoqpyc8ItgezDC+VAU5cZND63Al1XBjbBjRUkJCMfuWD0Q7woFICCJQedRxQ0a0RFQREADGu25pc21L2WQZevn6CTz73GD7z/kt4+x3nsY+pAWwCCkYgoYaFBgBhoZwZYJDKPu08QMdXT/DoLz2Cf/TBy7jn3IUOqZUBM2DAeqOALzOv7TWd2SpzyjRxAQwxITg0XcMsQAs1UEDDqAK6dNR/BQCADwJudVx+JaroNqG5n72XMmSavt4CAOr3+oyIhJjvExMQ1cMd8rArogADyd98PIM+BgM9aPAAg1XKxXElGwNPjQFYc8Q8PadBnhzYCgY8IKABFprzy3WTol85NmUjIn1WaWLCHhpcUVbu+ZkEUEjYz6YfpZ0yNvl6/TtpXOnLQb1X+3LQj9KHI7kEWtm0csl/BYRsl0uAZdPKJYBGNkdyCfSyCcBlwKSU4/Blc01ebaZBCwaSeobNl0jD0EcHFvJNGsAAoMvmHxXjWMkd5N8SExBTGtoXqOtsNW7MCV9/5Qp+7CuP4dPvvYS33SaggEEt5edakBACIeR5koigt3GwAOH5qy9+ZzAGSQ2agIJfzIkaAgKawTMDFuOAmjZKQYqEJErXFq9EhLYCBhcsyPdATkwPhWEA/Mz22u5aRtnph8Tn+Te+pw4YAGA6ZWmKzdE/qwGGNlBAb6T4nM8AjBRtTCkfa/vIU75rL7jzwEHZvA1UFHMFC2lBIScXKABaGetno3TwaDrbHvZi1hZkAeuAwAMDI4/2EGO2T1k35ZslAVYpYQKwT8y+TZEBhfTjDK7kRIQ58MCnue+/BiSYetKgmh1AVQeXQOoWgCqyqeUSYLm71XIp12i55GM9kPUYBgAdaACq76HzQ0ahAwFrXunOKKahZQfkGVWXIh8nJKyxtTrXRwAD91G7AkUXV6dGn1k9xMn02CX7PAB48foV/JlfeQw//Z5L+P7bz2OfoBiBVNrH7/BBnjgsa8IaTDAAofQRcCWH1+/CXXYUSjkzwEAonCtXT/DYLz+Czz9wGfeeO58n4PLgycTWA5eSr0S/+a0rAIDfvSE7ZNU6DOOhhqKTgQ2BQDF1YEHuYSm0tWKNtwcC+PwGwV0xJkBvwLv6rNgISzknBR48D1V7p5YJ0CyAAAHvGKCOlb/rlSyKtSiW/LcoXx6/EIAQqzKeAyFEYE/ARAn7kA1dVsJCm+8pFZCQ5l6OGttwiDCojzcLCiw7oMdASlzszFy0UUtV0c8JmOQZ2WCJgZ2I2MgGwpTk+oQpcp/NSPk4YY6pS2DUIGGxDPrN5gTcDBjQsvm6ymUkAEkBVjmGDsTyGKRhSAKo4EDO67K2P+EeQIpLAACJaklEQVTIObWAg+81AMlkHa/8nXRuhLrWgAXg9PpUe/qeHVlyLEdyVJ5jgN+f+ZXH8Pff/Xl831vuLWGFKWR7kJANPj9zIkKi1ACEAJZNyyCAgCvX2D4++aHL+Ml//BPDtp8dYBCZKfjhL2RQcOd5nqTaOMaxUYypHcj9HLv44De/9QJ+/GsfAQC8WiB+rYNNplqLM4eYOrBQhVzQc4W0CwC89oNUIbWhANtewBfgpdgo4FHISsAHSGBLElUyWtAaH2BMu1pGoPfGqsKV32kGIw7qbUvIcVY2OkzhsWHLyreAAh8kpBB4/TSxTDA9zslLmi6X854nDIy9Xa94oGCtUKBuPLaWEGgRHIQtQjwooyRGXVJmGQQcJCgjsKFJW0DUFkCwj3EVDCzJ5WEyyTUfyyVP7iqbfL2WT74X532EDGZv5DsLrT+Z7t8yHiPA4OWWVKYi5WsWdGgeWJ1EKXW1iZRQzIK0c61s1aOn0aHAsk78v/7A5/G9b74vg4JUnIXdFArADUnCjpxfQAYg8CaPiXULASElXLl6BR95lpn0+85dWJwPZwYYPP9bx/hIXtJx753nD6J4lkCBDOivfvsF/OV/9hH8nT/xOfzoVz6I/Rw7JVsnUouwhTrmc0vJPy0i5uu3J6Pp6iwtjVpL3tMg4JAkKVuHUdlCN8fGcKvjhmq1NOxWQCDnvBCHPqbp0Dkr3zizwkUgxFQztznrR1wEyoG/+n0fI0IgpEScWDoRJvGII4o3PIM9YQCYQ+8JS+22mFg2D9yOObLRTClhAnHWfgCmSJhTDm1ko0eZ2ZDxnhJ764Dy6vO5kExNbPC4OdVeW6nq/H0hsbMehzqePyu2DoC7gmGpaCnwwgXAMijYz+kghoBlspXLkUyO5BGoMgmgOilGLuOcAUTgZ4TEYICPJwYOqOPIX5Pcqj7LWlRn7tjxvdFdIeOn2rRh7KcI3IAffmuAguhy2KWa6WD9Cdjwwen0J9AzoH2f1Ar98Tffi30SkJvnZ0jAHLGbAiixqkEGBzEbfomLROQLxJ4k4IVXrpQlj/ee45yFJS18ZoDBR559FE888CRv/pAUS7ACChphgFIKDij4z971C/ieN98DgOOfLfVd7zNlhH0DWdgj3DiexJn5NwOwABRkDNi1720f2LYAbTxLjq0lS3F7Wk9I2ijPqAbYN+A3UzxvaRRzHYGCrUUMpj02ulYXTVmXeg/AQZe9DShqfAAO1ACLJyyluduS9Uv19K6ACm7zDgwS5pA/R74XiXILvDI6xQQQ1b3xzTr2nu0ZV2jkdY5o65bS9mPdq2BgpX/kEt2KbqXFClPglTWmoNzbkb8lMGCv3VpkJ+CYlUEgKslzUt8Qav0CqJ3TMSmGYlS2KQEPcIRcDwm7AehCb3ysDb/tU5+EqlmimtBb67ZZd6Z67DQroAAs2In6WYdSOETmOAqh6pwy90T/OeAgJF4u+fL1E3z8S4/hiQ88qTZHApagwZkBBk984Encc8f5liUAFkFBUwZ99GsKFHzvW+51E7CssZTjAVQqsi8AgOPMAhYEKOwD8rEWFQMLym9lDlogAGCTB8TXtdSotG+UQa3b//tRAlFtcFCzpZznuoaJld2EqnxDnqFr9K1WilNQCV2k6FiT8CXfvbKUJa6BIuSaJm9F1Wup0uq6iCo3u3yvBMIOWUZCan6SIgqzkRKwAzXyv0NWjrn/RHa2KJVlKlmuqee37mugkw23lGbpYtLsCiqzElgxSx7DrNiUwqQ4yzJC48Jm+QypfSiqTDaJJCsNsAZa+mIp56DUyfm9V7z5vDXEsVY0yyTTNcgeC5lVA9CF3viYH34DUgEJQJURvalVKU4ztL4ElsNIfGy84oR/X+9t+zKAavItKgtXn5uX7I6AX2obVX2SFhx8/doVfPy5x/DZ+y/hnefOF/29ttfEmQEG7zx3vgMEQAsKFgu1sVsKwK9d45yCv/uDn8f3vfle7JUrqgff857l2bqEQJhTj4w1UNh3QKEiYwDNWu6m+sa7LPVUddiSaKYBgW7fliVVh3jq3B8r500b5VltRnZ9rnhA8rsYpI5SqEn0EuNYld0272uUgCjHNCDoVyqsLx1zlzRK7Yzhkxpv0PM1Mz9fqz2j3USiXYtSmCnx+3EUqCzvywkcfjiSKK6ZX14M1cabdZ1HWe9DNmDQD/a+SyUlrlPpB6rAiZ+XwzcJq+AAU06OTMR5FoUdIMQgbAGV7wByuLPK4C6HAPoWjYs28BYEcP/0QEDbmtEcHM29UTl07hc0ACgjnZ8ZU2YvCCFlMDqnsmpFM2yyWkVod8xocnRkSWt5wkBPAmhAAD9yOxBYYlL99ks3UE28NfNB53BoRsE6Bl5JCXjpFd4H4bP3P4l7zl2oG3xRbvtCFc8MMPAAgfraFSLuPCI0sZpdIOwj8OuvXMGPf+0j+Hs/8Hm89bb7GHUmFkqAB3G0dnskExybRhF6JDAyjizUItD7TCVxQkkGBCEr1sh13huqmUL/UJ3turZZjRb28vth+27Oa/CUkVVEnkcz8ry1xWnrRlkOsvKNKBPSy1vQxUP4o/oJEJDr2uWMNwcILBjQQEB30dKacEApQcMgANk45m4q8VQA00T5GpbV/I7Q0t+z8XIOLQ1roJWg+TACQUvtt/f36qgd95TQ7HcgfcPhF149lAKH8oRJSSrUwt9zSGIAEDBJSCE/P1JTByuHazIIbDP4W+bWWuGwwrh+6mWFpy6cF9HXrejpXApTg361Cp9na16cqZxLU24mH0+pH9fAwBImGBEAeg8UMnphN1HRCbtAJbRM8OcCkDdH+vJj+Mz9l3D3ufMltMD1ze0YV/PsAAPdyiV0S0SZbukYvQIOfv3bL+DPf/WH8Q/e/Yt46+33sQDFBEyhCAsnxLCwcCwnlYxspq3955fjBb7lPylTS4mXDM0M62pseU6FPkP2dPap5ijoiaDLaAtbr/Aa8fyZUFZ1hCyJ0rZiaFONnUt8cq3YDVnqZ3WNmT1DKt4cHgIZIw9reRFLwMcqrkLdCpVbPN12sh8MBoBOAcij7XpudXmtl+0zquMkRfCUbm0BBk2IqD0nn3cSQuie3v5O6uwV/aumLQYU8LHlNtdn+fLSAAF17wqKqtHO2yMUkLDLDMIOzKTsMs28i8BMwC57truY1Hmu4aGG5BD542Pq8+s6d8jMHVKfK5u3NaTo6QI9n+xckrK2NFKAhOg+Bg/tNZ5etMwAn7s1gMC2DcBwP4ndFDCBZU50w24KxUkY6QSA54jeMfGecxfK8yOyvt4wPGcGGIyEkZDDMY3SoUIjApyEIUrjV6/lzSXeewnvuD1nbwZGnDSzlwAAO6LOQ0ha26wIS0w57h1T8SwCqFBXzXruDA4kKYVy3Ew8PWmbnghSpvw7qD6Qttcs8zb7HHL/mADKNBdI1TO3oUkCXPdCRgpMKwCuc73Oxp3L8VN4PZ5Xu7SXwlrRVN8hyXPD9ysMPAG9/CpfVpWqKIYVY1nr6Xhk5jt3AXUn9RyzvzkNiWSHcAhstNws/GatTGhfrd4ZZGrBggCFoNiUBIBy2EWDBEDyd2hzZrqWvdPKHbfLueYWzA8NbMqxfF9rHFvQQKtGEljWB1YXjPZVGOWlSLHOkt1zZQso2JJ8vYUlWAIERGz8AWBHY5YgDBwEaftLwhRkUGClYCtoOzPAYKl4U0TLSyLeffClayf41Fcew6ffdwl333GeveWJ44I7ABNiiUO9cQqNh0BJVinkPAKJX6/EnDxPs9TxgIxjwGcM3OMZTe+AJvscqJOgJp3lJLP80xp/Y6oZ2GYU2niyOu5kpY+S0fR92jDK+vNt8bYKH23UBPRt9Lraq9MwcVQZIY3+NfIn6kFAIP2bVmYa73rYknHxWq+7KTngby2EkDbUxTNiq2GClXuO6iIlQoVQwO0o7Fexb9QlLstvUzYSPAc45HKUgGTCLDaBTYona1tkTIo31292Hlg6Hegz6/usewUccg5Vufcp9cKWlSrteTTnl4pcszTXuzqG2kaiyqJaltiWEYso99w5DCLAtkUYgowJGv1gnQQpL2Wm4Gfvb5mC05QzCwz8l3uMy0vXTvDJL38Yn33/k7hbEhlj9RhiAmgXsMvy9KZdwD6mQiPuUNHmLukkvizEObN7VDyEDFQKms9t266Uzy83WJ9NUSec8d9drrcohp0ohhyI1UlpW8rWLPSDlqM5BtG2ba2sGcNNP/CUgjllY4EWAMi1diMWcq5rcw3G4GDUEVaJrXp3znmd0On+xPnNmt527drab7a2pWE+bF4F027JnJNVCQmKRUk1oRnoAYMcOwoy3ytL4crVRnmScrMgqWOHzAk53wMbwq3a8Mwrh65SKb87QOfpROyyT0g+1u3fkVlayWWQ0DH/hkvZ98FBBRbwrOUW7ULVc0e70LADIT/DOgm6vHTtBD/y3Ic50fDODAosI3bAqrEzBwy2UKzWQ3nx2gk+8dxj+IUPPIl7zp0HgKwkqsegt7wEePB2qS4F1CBBMwltVnctmp47LSXNx/sMbgDd+mYrus3AT1URWINfvZ16hxEdZ8uI1ltdeuYaQJ9C85DzZlA4qLedQKdgyd1Hj2TTM/Itm+AY/9E9Bs/m4rfE7i3QFYvENpQD7d3GO6yXaVBXTXYk87eEwZLNp2DGTBt2fbzeQ1HnHcV+6+QHGOcMbJVxr16aNSntN8BmaT8UwM/ob6pxoI4Aej3B12/XcfaRpJNl1dLc0+7fARymx0dgoGx3nH9zFHowIC9Csn0AjO2X/KAQOiX5cF0izwww8JTumsIFgBeuXcHjX3wUn3/gMs7fdaGZ8DEwfRpTQiT2FmQCHAUW9h0RZhDTiODJsk+xxBoBlLwBXuolo8R/5pQ6Cmzreu7RfuLq9k1bl8rUWAcqiuBIOiNXxhMpb/fA4aQ19WPDlz9Tb/QLSh6Ma+eBq3O2eB7rSGFZb2foaR1QRmMy9Pod498DANHquUaq3s2rVb3YyWkSAzYWN8b9Oj7PHdxmjVdQl1JzTOafwgcuaAA0cMi/0R6yenQ8RbBjjfka5Q1s7eqUKRLLYFTWQ7MjaNgRCx4qK+IwIoZ9kLJFTwCm3erLaZhB/USa9Fjm7bJ5UxNO8pbfbNi/Q8pMy/rb6u4dhSanSK7Rzt4bMwoIxEmZRS/K/dXzrlw9yfbrSdybmYJ2t0ZOZtfgrtup1ClnBhgsGY6a0NUChCvXTvCRZx/BpR+6jIt/JHeq8R4kligdK++Jf2MILpswEWGHqSDrfnOhOih6HbiUJcS81bPWf72s7i2lW9rlXsN/jwa5EF6c2PP0PcPvvUSqOeaNt/nbqIXR+8cbC71M1OoesSrutCaPVj/7hr8Yfe/V3PkYNcdas9WV19Nov17FlefgnzdgwD2W/2rgMMlEo9H40y0Ze/u9PWc5DozlWd+ALBSl7k4VCJDRecKUkDlGLnAAWmME2PyUsZ4A/KFcW4K7VLxVNbKiW7e9ggVq9u/QCaUAsCs3oYYNEf1t9fZScvEoRCCruo527Rt3PZ0HAMe/Ve3X+TsvlHEKAJLYJsrMjwMQlsqZAQYlNm+Mv9e5gRhpffiXH8FTH7qMi3ddgIiLVQT8j8rWypKV+8Yd5ZUIvFJBv6BJA4Ud8TpoAN0Wm1KWtj8l9cGLWzfGk/qkFC8mtS2nMdd5pPXWtOHgeRa0cR19MCBjRaj/GHHFaiBjbL4jxWoQ14zhsPjGJTWGJnTnLbBIB2aDkVX2jcefumN9O5dAgnMv7zneeVt+rz3/pfO2j5vxqgafuvPt+CXSrJs/tvrJv2djq88DuFk55rfsqfZRUP0k51hnif4T3deDhMqM6FwLvXF7oz8O0BlNK7bjAfW8iuTsnjZuIqmqIu/fwb+3e3cAY3090tVaT3vhAc47qL9/o6xC6HRfbcXx1RN8+JcfxVMfuowLd11AAt9fxkoAgewcWb6jwsOlbj0zwGBkaHLScNPJJ1dP8OgvPYLLP/SPcPHcPaD5NReBk0waIkz5r3TY0cThgwjOSk1Bf65AwtuOGdDouq4H75/f/l1MWENtpxy/1Vnrpynei0s8j78ABgDa+DeGX4x+yri3AQP1Gj5mjOaah9VVvFeqloLW55LxOqENjZcytmQAXcO7ZkjGoKDrC/PbNU+0M2qL9TxlGfSHa4A7T9g5Lv2/ZczAc72vx3jc3NoeOG43N2Ybim1T6RPWZazbguonOU7cHxo4NKAB5W9lHAAk6vQbf/69YaM8+OTmiiQAKt4+Agz19217rL729HS3qkjZIN6jof0Mqs7tUcitER2YYjP2x9dewCPPfgRPPfCLuHDuHiDNWX7zW1xJgwReKVIAQq5bjhwNy5kBBhpxFc+zQ10Jx7/1PB755cfw1AOfx8Xb7wb2rw48zHovoCJtCgwdjzADgd+FmEKOzUUg5qQdjag7Gm6AZL0y8vzXMta3xq31M25locHnIvBANfzAsvG3hr/Ed2Jzz3KN3E9UmI4Dz+svgaapugeVJTAGxhgXmZjeNUNW4QBqlB828PQ979Iz/GvswQAskHnulj50jdgGL1v3PR8YSNKgHzuA5v1GG3cPOHTP3VBG/TgEqKkfD+8a3EL5Ve1PFHqZza4tWeCwABr4GfIdBTyU58uzgUU9d9rSgIHUHhyBgza3ov5Eh1T496fTz1441H4vbAEyxEqxNIBu/E7Vg7UBAIDj6y/i4ec+hqfvfwIXb38H0v7VCvTCLj+D7RJlkIDEIXJhEAA+/h3BGOgkjZ6G4Y4/+X89j0ee/WE89YHP4V233w3Me3TGB2gnuVYcMgEAhNd+BylMPGkyqp7CJE8rVByHGNAkK1XhJKU3fPEbetxoha0ct6yBujY/KRtX+aoNwGlS6jD2ZJbi3pr+VkZ+DAAcgIC8f8Q8AykiaZBRXoowt30b1xVrAoAMANtEiQklECiAIIT6eZoWFbH+XbmmfDyMmuaKLnv9LgDYYnTU71JUOcx2nNf2v9V9HabxdUDp17TP37U3r7fUVH1dqqX6nDDna+YeMKR8rjTo96D/B0BV93Ujt0BWGrXvivxukF0ASLqviWrfa1nNn6vMUjUwhUmoxwQIkJVlIpATlpHzpOvhldP0O9AySfnek6oToFRc+ayZ3HwuGTahHOfr+fOybl5yynoQkGsU58oEpASKc5EV2r/q2qPjV17GQ1/5UTzz3s/gwu1vA+K+AW1s+fOYhakBCIEqgxDRts8rZwYY5E2j+rhM4g4/+a3n8fCzH8lI624g7mvny6CMaGdNt8mg73+3AQtEU2UVhmAB5XPLHABVCM2jtV0qB9u2yqHe+IsiUlRUY4Q9pmRB2R9CCZo+HHpFa97/CABk459k7OJcFWw+nxQ48OqfjKIla7zKjJ/yeaUQy8sReBKCqAIKRwGHbMhc4KCedWjsGhgAAG6gfFDXZo8hG6aUr3GNkxTdTysgK23YLJ/cl2UYY6aOp7JwvPZ/mvfteKCChaLmlfKj8oY0BzAAqA9ZL+MQQAu2RoA1aXl05JYvfR1k15HbJMBBgEJgJmEVMPBNG51okzc7WT5EthcZmx5waDBSQ3+sg6vuDpwkSBosoEuoBJY39hrpZF8f9yCgsqKz+q709Y3f7fTk8be+jge/+qfxzLs/jYtv+X5g/1oBfWVcMjBIYQKlqBiEiRMtFYOA7xTGoCQfoh+Q4986xsNf/Cievv/ncfHc3UC80QKCOKMxSMDQQhdhv/GqotQMmqYA0ITyEiQDFkB5T4IQdG1XSzuQyphKHApohA9AS8tLu1YpTKf9zaM31LZjEdpn+M9Ovjclfy0IcM5VpRuBYvxSa8QGxqtpVVGgWbGK4Z8mZfSrYrWggYziTXLPrHzlWjKAQWcmHKRIB/1tGYGkrh2CAWuM8vfG6Ns+PIBtKja4ad++9jkUeJA6EHE9wgQgchhvjvkeERQC0j7CggSA3zHCDyU5VJ5PWwGBww5YAMCXRQZbui8dmZVrV+VW7gEsMjRllJu3J03VSC/JrZLZJCDXgAVxjjrAAPS6UZ5jTc8WeV4CBF5IzoaCFDAAQtXB+lxuS1LXThICMSKyWNXySbGwOifAhkbT3IIAjxUF2OkEiuw//61fxYNf+/N45gd/Cu96y/cixRsoIZuU36UTJiSKPKZzQgoZKIQJiRIoTKDslNroqFfODDDIRGTpbBmc4986xsNfepxBwR13g+YbFQzowYpzGRzrVQFK0XSMgQIDoBWwkGk3R1hJPq+VgTEfAYDGADfUpvHS9b0BdGGFNTCwaBjGHmujJK2XvwQCcp2SjNs8VwDghBTEM140aLb/g3hAoTHscq6EkqZs+CnnoAhqN0rXYxbS7IckuNdsny9h/PbapRDBJkCgqexorjOhhq4sUd6NZ5tlV4xZMfTKdtvf2nsLfsjVsAQATVMTV23KRpkFfLmNVs48JmAEXuXZv99yq2UWWAQLSwCXf2pZMWBRZtf0nQEJbfhAPld2oMnpab6H9rtQ7x2oqN+XZhpXxnO0lA62TIAHAhxmFADbKLCsPf+vfg0PPv9/xj/+gb+Pd735e5HivoR4UpiYeqaJbV6YWNbCDhQBUEJKsR7P9khyS74jGANEDk5qtHZ89SSDgp/DxdvfDor7jiWguC8AQbzVpOnqXLrY541Xmwkxot08sCBAoF6DVmjXG5sr5Xjh2uAv0fTlfAsOholOKx7hIoWs+xFY9EwXwwEbgUDjsaVYaXO989lCfanxXDPKLsAwK8z8N6ljYvgLq6AUKixISAn81p6sTFNVtr7n6xi2Uf1X8gaaskpZbwQFG+PfXt5BilExBLntMbMC+pzUT7MH+bqu5PukeS59yJnZuhPHYHeVadnACrhgwMqtMvxFbuU5t0puFSgocksBuIEOKFi5TXJfFYYobBhQ9ZYGDW2lNtV3cEH9ONX9D92EYM1aiDedz5EFAVk/N87YTepf1wFbAQGjXJP46u8AAI5/+9fx4JWfxDPn/zYu/MHvRtozU0DThISUjX/kHRlFORTbktmDJDoqgSjPifJ+8bH8nxlgQBkYSMefXH0BDz/3OJ5+38/h4u13u6GDAhSQgP2+BQTWAygPygL46u9UxeYg6DWwwLcaIFf1nK54MeQRC5D8TP0RBcq3cDyUkdLfmnNgfu9S0tabVck4nVKV8zrXQNfbgIHSNvXcNC/Vfa4bn4AVmGynSoHyxEQLEhIbs5SNPhEh8asw8wu2CLQ7YmcCyo8SQwcAoQ4fi8maYfNLorqNK00T90VxpTPtHrMB1now5vh7piAR53ptyMaaaiXLOcD35peKYg685MLGCOnrdezcSwTV94AFVtuQ1QhYdcmtjT5BCwr2N9YBwQAMCBBIMR4gswBN9VrpC4oJshMfZYZA9vb15DafKCCBJMdDAC7Q9bsbApKykHTatGY0NqJjned6ITk4YY4hWNDPvRndu6R3LQhYCjlJufEajv/1b+LBl/8anrnnb+HiH/xuYN4X56GA5x2AlIFA2CmwPNdpSqmyB0QFIBxfewFYAMZnBhhIx1JKOL72Ah567nE8/b7PcvhAg4KUIIMnn3mP7DwJZ8sq+Cg93XitU1pJTZ4GLIjg2sSzDuXmc1JGaLvx/gasQTnWI9OOApX+szFN2/YNiWV9/UaXDICHBQPAGBDkOnkMgVauzV+lXNNSPefGYirPNaC8Vy6Hu1MAT76A6kyECTTPOVVae8ao4GDB0/XKZooWaNmF3U6BwfxrQ8GX9GktD5JfIfcsN6zXNVXVQGGhdO1eMjAaEAwM0zAJUejxeteVmknIgRpw0F7it69jCvT1G0CBBbKHyitRKNfSREVe+T7Sr3ORWcDI7RwrSxMmYL+v4BYoAJfrqPoxTOUlMBTCeg7nlnApMAYbSh6SkodRSI43rnKAQlOX0+vcnn2tzKtOPnXzTuQzWn34/Le/iYe+8Tfx9Dv+egEFmmFM2LH5SCknE6aqN9R9Keg5S6AUAEp4/vqLePhLj+OudM5vK84QMJD18MfXr+ChL30cT98/AAU51KAz9Tu61dKC6tzxv/kX/H3eZyQdAGS2wlBtOm7nsQpVcAGNZvnvRt7YhBPgMAIJGNOf+fNBceRDPMO1eutD0YzDKAlrQ5FXpZIxVjRRVaASz1ZtJaMYhDlYpT3HFTn4+sUlkKe5Z4r1HrvcHulTm39AwgDkZ8RYZJGCGrMBaCYysuIV05cdCOAb1WMHAAEASFsU/aiUfkC5tzAuxQvbAHzb5x8+X0RuTyuv+j78d6M+AQooTCkxuAX4fQIqB6Ssgpj35WcNYDhtKfdFfo4CG6JrrZ5VjEaaUcN2in7TMkK3UNcCWGYDllaglPu28vTQN/4mnn7bX8XFP/jdSHtuL+m+SYzm0jwDu122Z3o/Q5m7GhywHB5ffwkPP/cJPH3/z+MvPf03hk09M8AAiRv90HMfx9Pv+1m86/a3K1agufAU9+aBe/5f/yYe/vW/nQ/JYKpJmim6FAcCfEji2ap3qLCgSYxqlqBpin1NSG1MvzzKxp0PU3TdUqqt95kmYJ7Zc1H0Nnu2+RrxeqaQJyNAO1YKNHGfUJjymCivHXUMCX39yPFYNCXbxFzls8o1kJitJHZ5SV1eDoJdX54GSmx1KVijbCQUUYFvYRTK7xhQEgxQnpSsa1maWHXQgpwsFi3fWj4OAgFOX4z6R92j1teAmhQhiVxyPhEBO8rKX9kbuVU2ooQ8I8Vxy+coTEhTBM1z9XDz7ymzTYCEoyIAkdcKam+JvOr2S/grf+4YG7USBwDPQ1Pc5ZJLYH9j6TbTsuwEsp4lCXEJc5W9ZZV/kpKSnSZGl/tExrc88jA9W9iA/HmrjuU/Y3D51Ft/Ehf/0PdUFuCgYsYgJRDYDjz/ytfx0Jd/pDjNS7bwzACD42sv4KEv/wieee9nuNEa3XVxoFwoAIljuDXhK1ZlrQbv+N/8Czz8638bT731J/GnXv5Lrvea9HIqGKBgBVgE3giwtz67VFfHS1ViYJcYpb57wlqu84R1ROv3je2POXW2v9cgwQMMKc7tPcTIA+zdZoMkr01FzHR9zNfYHIlMhZLtF7R96DZFK0Qb+7ZgAGgAAXJ8FtPu9IDgZsJN5PTtSgiK5SgBYdd4RQRfKZY7jZScft6gnkubF3lMCed2GON/SL80D9ceGJBoAhqaON8vJSTEAhAgTBxFlXtBzKhkY0D5XMpymSgw8JiJZTfLbdnym9jLpGnXyzKk38fx+lVZVd9XgQBQwYDTjyOgX+q6BgYWdMdwfwYx+EAOw+ULZFpDjb44EKKPAxaXtpZnDfSr1Lkwr7oNa2DAAwIr/XPxD31Pf1B0BtDMk6S+67oq5AogtJsj3XF3tovjOpwZYDAEBVIoUywZhbEgRQATEmZODNvfQHmpZuRjaX8Dx7/93+Ghb/6nePptfxUX/sAfW65INAYbedzm+lxIvDpMOaehpU21EPPvM8DYj1FmF9fMz6nHlr25TrmveX8OCvcvc5TIAnVH04JITj2VDaDWwwk9lCc5wID0ca94SXFyXCtTDQQUBU4hVGCwAQwAkihFPRBQIMFdz12+m771xlGHzMplPk1ar5UQgxofARX6+oOK3460Zvg9CthLIrPnygOsV+XMk5x31AKFyP1EAUBCMHFk8RrLLnaSUyDgIc7ADjkZtU+oJb3CxtSLtsqp7gMnNt+xAKq/mrm6lV63JaVuzndAQY/RBh0i99V16larLF0vYyC638uPga9fh3p1w4qq5vdbmBO9GZVUMAS2TaI7sj6h3VFOJpyMvjC6AryN8oO/8ik8896fyfbRzHGnnBlgUBrtFcm0XgQHkQdAliaFAOxv4OTf/ks89M2/VRNBioIUWnB9wIsQZ6obcBCuQsR8TOpuYuSnjnW32eaQNuZM80JXyvUDL78tC56DVSzmHsMEtI3F7rPQ9JGzmiR5QELKiDno9u0XirbPkB8t6TodGFCT2yyzAhyDeagSd7KrF7dPTqmyCeZ3+cvCs9bo0AHIGWWNa+PQAQecvi9sP2iGMTOOBSgcChJ2RyBjOEiuE/Bq2bmblFH+OMjZKOdvbg7aOo5ykQrYd52NZfbBCzl1jOogOfG0pXeSjKN1CAO7pYwAgdYhu6OsN3Z1dcmS3gAAIhy/8rICBe+s9V4pZwYYlEbrUoNzxruQeFjuVEneSNypYZqQ9jfw/G//Jh58+T/BM/f9bVz8Q9/dGssp063izS8IwSZjbhDxofchXY8yYfJJJ9ucVLy+ZC7HCJrCeructeiLdXUmsJtpDPh94D3DUo5AL/Cj1RWAzxR4FKdVltrw67rZrPkBCJB7piUgoCe81EFArTwLQJOHchNKnZLaSqn04QJQQA/MDsovKDdpx9rdwKa5xgMHt64PAMWcrLzDI+ks9JSBQA7BlBRIAxYoxWYekvI2uTrRZ7G2yCUwTuoE+jl0ynnG9Rkwd3YF07Rr6j7UkSv65ODlrHb1iv6dvZ9TbMKyWzbIe7ec1znf1E/atNsVdqDZqjqzBGmNJch/XVCwsZwZYDAsQrdIsV5P5w2wwXz+3/waHnzhJ/DMu/4e3vXm70Oa9w2FRG94Y0MfHZqNfarlWOrYsLny0ggVZ0oxqpdJ5L8ycWNewjRYqtiEqkvc3ojNFoXkeCxb29m9cW9DsTHCJYZBHRzf0MYjNyTFyWSNjWd7AAjQAKCZ+HlclzzlkZw4bdQviWmZgz7kULyhcr0XqlhQmo5B6vYXcJmDoK5fYQi2tt1dl94yBi1gGIOF1OQx5WMA7y2Q1Jbl3s6qXujPq+9KG9cYgJueR56RV3WvYbueZgd4VrgA3QsfnkanOLlZh+jOUlINUxRHS5ylGCvrqhhYCsFfzjstPNOyi1KO3siMwbRTjgVvSsW7UA70SGkn4fi6gILPjJn0hXI2gcGWzO3EIYSGOsyD/fy3v4kHv/YXeG/q294GpBlhd9QsScHRGypVKL8H1kMLS/SeEuylzVq2btQCoGSaA/4k7yY4UIUa2BYbk3JgdvnWVxr3CTYb2n9UftkWR9me5q2S1rtNul7W2zWGXdOA2gv2AEDS9zKxdmmdbuWqH2OSEklV23x0enkMEPjLAazBqkE3bMLgs/d9/MypvTMttFGYAQgA6AGDxy7YUETN3aj3TfKcIyyzMBuKlsPSD64BtHs6DO+oPsZiIey7TTTQ4e+pAzqbwMOW4ukVYKhbgBYEHaovgam0pTCokFUQ5gcxJ0Tr3ApZObXWHq3/9cq0N7yp6sf8cj7WGVPVDyqfyYbUjq+/hIe+8kk8/b6fxYVz91QZBjbP0bMDDJZij+WcFqpsZAGUiR0mnFx7EQ9+9c/gmff833DxtndUJiHum2zt8Mbv8t+eBiwntEkZoVvXAwWaiX1ATLWJDYeW2oNa8b34zvc1hbU6Iakbi0gBjaFrDOAyULDPbOuSf7s0ASx7gNMp5fL7Ub203FnvX85r779pN3WGPwErb4Fz6rbQD+TIjssw17PqTwswgiJKtpZoq5bQWXlvVG51O0v7MkoKBBBNBTjUfKDKFiTNAghzAPDKggYMjFkXoJW7Q4Mxm5ZkNudW5oZXL9Wu8r0JLal2Tx54qMChPOaArdaXWI/hkm5S3vsWUKDrJuO1C+jqrpcpCkOswyUjRlUXz2Fqwo0Ajt7IYCDsUHSj2AYdQtCORS7H11/EQ1/+BJ5+/2dx8dy9dfykO5benqvKmQEGPhgw3lh3Ho1QHF89wcO/8qN4+v1P4MIdd+dJzuGDFHbFCwCANB25MUUA7pKuriwyAdR6yaOks3ztYcUoBeX5FWUwHeVzG1WVmnyxq2NLq5frSY9N6MdtZEibe4+Uo+mTJaNhPd/RmI3useL5pmHde6+/+J/qYPHLiq1pJ/oSMFgylvWppuoDRboMGG6+bAUAfPxgE9q1ywMGZfblk8WhKycJQVgHxTYolyTXb+RdwwWlB8kW0BnLxVd1b5gLXv30KpUO6DTnWlCkV3Xo7Pek7q23bj5Ux5SpMXQSDtWH+q6odVRjyLsLxgYsBKvr1W/dshJyLKwAgLR7U3WQgk40lNyCPnQAAMdXr+Dh5z6Gp97/BC7ceV/u21SvSank1OWDw+qeGWDQGB79BkPjnTWemSrHV0/wyJcex1MP/CIu3HlvTS5KCd17s5EHz4spShKSlA3CkqR+8Iz/Aj1t7tPff7uHUOqxNWbsGsQFT98x/N7rUHvqvCoE60Fb7xnIBmaDrmm9ROczshc8Pr1YbBWautr6Yt3wD8/rBzX3Xe4EfXZM2rdlwzvntnXShvFZq793qzXurKm/BgbUHrJTbBNwkCsUm9KF/BfrZ2vbFpddsddsuREMoMv2qesD9TmVBEsFdEoSphhSYRIc4AA0x26ZnmmO3QIdqEBNas6l5tpuzw97n4W6aQDg5goASEdvVMcmNCHGgQ07vnqCR774UVz+4CVcvPNetGEs2ZMjj48k3y8I5BkCBjVuM+xIRdFqMTm5eoJHf/nDePJDT+H8XRdqDDB3bt1WWaHjN3xXzixuE5JKEhJghMUTTGt5BsZ/jTlohORQP+5AD9kW79na2wda0DYYD6DazJg/RN2VebIeZCg3Fg0A7FzRnubNes22pxt2uaEz8/Uu8En20PA+3jO93yyVts2n6NxbUE5X11pkjGahhokaR0peR0Dmen2wNZzqPoNn34xcAMtjqr/djLwvAR1qjhMCpgIgNGiQ/hC2wOZjNMChadjpWQOnRdt+X4oHBnqmQB8fg4ZyI+c5jvNmQ6UCFMJU2pF2GRgUtmBsu4Bqvy5/6ClcvOtCA+R4PKg4upRIgYLvAMYg6dhNBwim0qGN4QFw5eoJfvgLj+DzD1zG3XdcwGuSX0JMGwplyLgjb0yCPHhZ8L33cAOOkC2V1RyJBaPr3GNzBq5XtsbbR5SmQ5tbjz/m7orIXaYMv/4O6PGqx+QawAML26rfNIX8zw1JaRRoOW7utcmzhu8VbzH4o/BB89l93kK5WZt/kwzuLXmmup8et7LVvrpwabxHAKJ/bFK/O92Yd0SAAbz8m/68/by1DNkQonoOuT2UGhAh14TuO+vYEDzGIQPZpfAKcGqdc1Dx2AF1vCzb1eBlBBzsPUbFc+Ysa9oYfyC94X8BrTOtzdKPfeEa269LH7yMe+68wK+NAIGy7UppBlLoAMJaOTPAAGUZB1xAMKuOjZEF4OTaCR5/9lE88YEncfe587ghr9Z1kPPEHxAydRR3b+Kh06yCyUg+eBY34MBnCzq6XV/jqiwsHmsev17Dxft1nm5sjXZj7M0xa/zlXL0ODWOA/H2Wtyjm/p1VrU6ra+TN7wIEpkIpqNfzdDTy2PDwvbbVZQvD2niMoxP919JX7b23W5d5VYJuXZkOkEYvL2IycaCluzXvhmrO9OPIz/Pu4vXt+Ls7hqk/tyTfwOlk3JNvQmpkmnLbCxAgAlFSoAEI+bsGDEALGgDKjlYbXumYuW3NKOU0uqw91jMCCej1uLrG3+9jqRJGny8wp0kxpzcSA9LZOEz2sS9eO8HjX3wUTzzA9ms/J4Qg/Q1MBB8g8AtQFpXSmQEGKexcykUAgXTyDO7cF64e42Nfegw/d/8lvON27tSuFOosFUGXnFhmFlqk7KLkEUIetcNjAIznXZ/gGOP8d4m2vhVlkd5Uz9RUf5tUtwEAKOPPY5eQIjCnVNozA5BXLM/yrKJEtxedY18UpjAE2chM5TzMebnHCFA4iuoWaELPgJRzZnzWDMq8ICC3Wna2lBvFax9fM5WT+Vo1dfZzDy4sgJDxYS/LKQS334dWaQGYjQy8jEOVXZQ68XlftvU1W0qV3V62tVxPRKDAfUdEGTSA2QOI0U+LgAGooAHlmnxc1akDB1vR88YyDO8IYCEANBVWXYZb63ALHIBT6HLFBrhMQGyZgFfn5DpNurx0/QQff+4xfPb+S3jnHeexj9lGxYQJuS+zQWoAQgkz1Pwtr5wZYCBJIE2nx5YhEHBw5Sp36mfu/0d4+x3ncSMvM9HDbSk2Aicv3ch9+eo+FnTWImSNqOuSJ2CMkkeet/6shcOLs2tPHM55W26Frh8lRNk4uK7HFgZgn2IDAMT4a+VYj9X7y3NjrpitXzT9EIzm0E5myF8CSAEB/tsqU3IBxAg88HkFGG5CGXrjusSYWOOvv3pGJi0EsJeAxGnLtNAX3muDm42ZCE0jJiLsVV9QQGmw9L92Bm7VOHj9r42+Z/A1kK3HUX5j5Ro4vWxruQYEDFSZ1sChHquAYUdBgQUAGAMGub9m2krIxRrtfN3NFJclcMJ/ltnQ5yt46IGD/NG1XNPhVn97DKq2UQBwY586xwmov335+hV86suP4Wfedynbr8QyTQkTMTsxgXVnCMw+VOeVwwy58U6PcTkzwCCvDehiMTEbI+n4F66e/H/be/dwu4rrTvC39rngTM/08+vESAja6W+6eyad7hiQZEBIVwZjYzC2u/+YEQhhHAyGCLD92W3jOPF0Ho7Bj9hGYLAxNo4QUrqTjGNsASaxda6evExnutOZnsnX6eiFJ5k/eqZ7ug06Z6/5ox57VdWq2vuce6SLrvbv+0D37Eftqtqr1vrVWqtq45bdN+Cht2/H2pWGaTkj5coBpPA0MxeyrBkAXq0ZVHNnt5orA1H5cRsctPiyNKQqKYikUcYzA7LRYfxN4mLOeSf8v9AJgJw95YiARgJqMOqaUfv36xRn0wdSUXbJcHcKSyrUihrXZwXy58zxkDh0IQ3u+IisgaJGzWih05I7PefWL5GBEhGQJCAlEPqzJvugbhkDACPlOd5gj2Wf2UOSLHDo9ZH9MyAK+mVcKe1hnri/gzKVGX8X4x8bfim/mly7c8Bkcg0gkt/mfGWNoJnokEoWGqIwTjwLcNdkCIM57/6NCExrCxp0IW9pLgj7doqDIneCVXIgvR1a2V3196ThUwB4pa795Klpgbn/2WN7cddTW7Dtqu1Ys9J5CgB2E1AmVFVDCrhmMBH83kzUJJCWpGfZEIOxfStx5/sQAgP7jgzx/t1b8KDt1Lq2yk0YqxjO3UgM06n2+KsjDshC7FmQAtiQi5QgaMiRAiB1x8tzGrsMyxJ/F57fMWyZLcz91NynchbV1RugEQGnLFMFav/2/3Zvhp9VeQVq/61C4uAUa3qMUNXACbhZFmPkCEENjCKiMGJuSIStpzR8I3DnXCvNu9mFFHQlBJN6FCbFWPEIDEQd3NhxdTTG3pEq8nV0/cncjLMxc9Cvrq/ivp2UbJXc/jERiD1bUp6bY1CPAbOXa50Y5IgCd/YqAEgIAyD0aFy5iRw1XCQSsqiAIAii4urkiYovN9TZ8lzbyqSghoIQmLu7h1AdTthZbqxHnzu2Fx985kZ88crfwpqV671XacAA23fkdmOuiABLCgAGsfEkBI6ZggwtH2LA0ctA4yWoa2Df0SHe/93G/VJzQyLiGLbECTvqpbADwIhDsuCSdYCQLJjfkQA6aEIWvaySWx4IY1Oa58OHymRftcSj21BKQmubRZlruhEBAEUy4IiAVJZjfx+rbZW/4wQ1eawSCq2qU6Va1VZ5OqXjjzVKlZlMwpL3Jpj2EhEwNsbKGcSB76PG8Mn+bCMI7rzs/wGR72cnm8yN8RzDKHtn0OT1bciRAi1VJ8ZAkXuuWQ0X6PcLRR3lfwCp8o5DFNOSrUnl2dxTJreALtPmOHeS6fhYLNc5mXbHUplO5fkEUqIwqggDS3iBhiigNt+rBdKQGlDu/0mSTk35elvBQr0KT4D5KXV1sxIlnri5+5PqtuhtqbOlvtZ0dZxLBQAnRnVii54/vhcf+cOb8LkrHsWFK9dhNK69l2ZMIoG2du3gxHtQsSEIte230lBdNsTAhQyAyF2TIwX2ZYzGterKjjGoTUKUE5ofnxinSTqupwVZAFwYwpUUst6ce0xbrtTmonf/tCU6AeV4c1eUXMnaTDROCmybScWuVI0QuMeMa04U51iQi6DemePynFMwNRvl6L9BRYyKnfF3fxtliMoxM8PWUQEVk2/voGaMK8KAGyXUZhBzKyayqELiNofIjS5d7O7PStZRXiueG3kPZJ3lu9aMfglx27UkUFNudzJQMkJdDI9frqiQrRiy7THJNce6ebxKMt0mz/LcoCLUgp1VZM4NKsIY7M9XVmfVzLpMM1mCYGS6AqFiRxoM6UXNGFnPmPeA2b6SZAGwutO9a6U/3bs8UTRXITQCKCdyptzUc+FnzdZ+NqGDkDAAyvJVd69AaWlpoqsjPa1NSH88Mj/c+Hvx5X24+/s34Z7LH8UF51yG0dhMNqhi8JgwN6iC5w7AqG3OAWqy334y3gPfLua0IQLLhhiM5ABFE7+JSQFzk48wrpuXMqrloE47bIRQ4bxSs2XLzWwrG3NzQggnaJJ0tg8ETwajA+54HK8H4IkOkM5wgLILOXn+NKwBKXHQEquAsodAOya9BECeFEgF2jbLAqxS5UiZWgOPujnve76miAwgSw5yLkgtK9wct3WKFFtcX609c5HxYxFTl2dkWGfOX2uuGItyuGZAeBP8te4diHu6Iv7OXzLzEyGX+B6NBEya3JnrO0C028mU7b9B7drJ3vMyFh6XGCeTFGjyHB9v5NXc6OTZEQIn0+Z0QaaBRK5r2KVxTNZ7anWN8I4BTd6IC6flcCJzvC2vIAi9SRnwBCXUz3K1ip9xI3UCuCETVrlFTyK6RyEBQJhTBSCZjI6EDfrhy/vwiT0/j09t/Dp+7qcuNTkFldmPYA6EcWVy3QaV+QoCsQupheTATFbZT16l90LDsiEGngy4H7CkYLchBW9atb5xtXvD1LygOOENUAyb6MnRuPZkQbLlEbl4JmPEi4i1xazU/ttl2ZPm6nTn4naUktBmhdi9LAkBgIQUlFBVMIKO5tqBdV/WHCo/iHK1sIFavnN9VuH7kucD92unGG0pmascn80ueVSaMxdP131Xkv/pBvy4NpbNKxghcFyb6zxhHBCYYa+LHhEYVFt24R1qKw9yXoOuBGAWy0PnBhSMORY3kVPq1hszsIp2zIwBG3IwsPlKA7ZeITvRqNi6D2s7O2AYq1Mj+k3+yXIISnke12bDoVnIcxXJlpTpEsyYM/JdO9Ls2gmhT3yyqPk56lRbDaksSXmReTqoG1ngiFiyJUJEkrxwQhQAZMnCYnS0llsFpGEoABjVNcYAXvrRPvzynpvxaxsfwQXnrPMeqUFtZI7YyKIh/uSiByC4OYpNPITxAjkRBMIVWBqWDTGQHzSswdh/dAG37d6Ch96+HRefu76VIcWIjSpghNsJmhPIEbPdh9oIo0wqIzYJZI4oaIy1K7QlUV2XQ8n7tXaV4sLusRNUtQj5DiqQn3nUtdtlTbjrAtpujGzNDFQmV6OuyZMDozCd0nOEIZxtScQhAwk1JktKnkGBFMxVVWdCIENS7nm+VhTGOGVtWxOhCr/JkwijUM4S58e2/+YUmYu+FIxxzEU49VjEKOUAtM38E2ItyiqvkS9WCSyUpuynGsBcRXCBQRKEimD6gpjBFYFgvAfmt/VU14wxE6qKMKprdCEHLnelZksGBhSS3qjuk8py84xQnk0fxqtv9OMAkqWPrh9lX3fJOckhq3tEoQNqSAdVBEi9LHJ5vO5m++4IwBh+WatbzjoA+WWs0+jnrrq5tOfKqGa89KP9+OTCzfjVDY/ggnMuCxJr2+vS9JmT6zHsXhNWR+47snCG5BiIZsakIIGx0yY7tbKsyzL9Nkl2BpVtvBho4q9jIBVElAXRoS322bY+2j8fZcHLEYI20pQ7PylhiK93glpVliTY47VVmjUDtTXw9jNVlhAAGGQyuN31MvY3gYLyiq+gOKfxEMxVpJIBjQh4s01hvLPpx8k6Po6Dyux9UPh+B5Y01MJSngX/p795XHNzfIpwU5BD0OIZSff2bPpFdsUs+wgQHnTYiKD3LJDxqhBjzsaJ52pDlOaYjMs3IghEVRheoCY3qq5J/HZynsryNHJs/g4JQryqxl0v5dydi4mAPA8sftKwGN3jM/NJTIY0vQwz0ybrDUiSfV2YSITdBi31ym0attiJWkgK1qXttnrF6REv58p7YDvAyYptDcaBowt4/+4tWIXzs21bNsTAdesBQQouWbUhGOx24u5CxhhUBB7bOA1bQRhUGI3rRqiccbcv7qUf7U+e7dhc/LK7CCJgZkldt5uNhW9WIGofoPH1MbTEs66Z5iz8XGNujJMadqgaBRmv6ZZ5CfK8Q9va70nWfQNlMjA3qLKeAbnmu9kgBj5LGraMeMy3rmrJgiCbru1x4bqqS+Ir0LwjWZkuS101b3XJ4GvGXtOF0/cNrNV3bTCzTtkv8SogF5Y0REEjCRS4jUfj2p4zY3hMFK7AqcK8A6BJugUikjuBDANQZvrhdW2egDCk0/w92dgOISclwey2I2blwWzqw36/C+e16qKTp1mF5a/PlOlIwYUrLwMgQ2uhTnH6xOkSqUeSelovgp80X70dv77zE9l2LRtiAKSkADCdyWxmoiKMBxuWxtygsu5Bs9ueDeD5lzkH+zIrwgs/2odPLtxsylWSotzzJFzWeUMeHNNt1lZLYWyD+5y2u3dsvRHMNhnK1s0l33iSEhEdABhE3oMug63N+HdNKkvgBiM3s1AHx3q15EktkdFhMTvFmWP2X6Eo3WWThgjaiECzDwb8s0BNHRoPQqqszfU6NEPN3JSo75PReApMGSmJaMpxfy/OYyDboxr7YPYbHpr2C5hx38TkJ+gTpoAwOLIgicJgQKhBOIsBHkRZ59Wg0wZe7vltu3iaY2UZDjwGymxfyjKgj2M16bWgKLQJCyf7RYgEVtkwcmVki4/qIW4t1L3r1ubmmm7PBrqTAglnj1w9/Ae+RP1/beMjWH3OZUkbnH6Zq6JdKFsmFw4HjizgticNKbjU2scclg0xKDe6EQiGceExw2764BT8ACPUpsOFS9AN2hdf3odP7rkZv7Hx69j69LswVxgcOUEEUmHMxVRLCNxdju0QBJEB3BK0ORv3dA/22cOW7IQ91I6S4S9lkptro7I60v6uW/nG7jpzbTtD19D27YRu+8tPTgJInDfnyqRgkp3jgHQ5VY4gyGuZSdyjkwSApnZzN7WwfyuGXssfmGRXuhK0PtF3GzXHGSFZcNfFZGGOCAxK1qpP890Pd74r2pZ9nqwxepaXJ3FOXMc1K4mtsSR1Q04XlZa2tpGBzjrYhYPRTNTiSRogwhrC0yvfh2aA16xYn+iaWM/MDQy9a3RGqF+aPjH/Hjga2kfn8cph2RCDmBSEiqb5m4n8xkRuR0SCdQvOVZjjNG747Mv78PE978VnrngUF6wwTG5uUFY/k5KBLsvRAKNYguSuQaM85NVBZrlwy8eZ5U4RTSIIpSVk2oDT2ljMIO8IKdgywXDSDwjl0CUDXhIA9ztHAjQvgPYpW0CwfmcYxT3y3/jvNnD276aUeHUPII0ndfYUyDOlOnYy8FE/xGXOsj/caJL90CQvC3KAkCwANgHWzvhLZAH2fkcYgPyKI2A6mQVSQ3eqx+AYHCasVmS9ghQQIn9fzRMbpcUsaZ1E70rMCfLoJmp+zxBqiE+8V0hbYi4AvM7aluykI5pwmI8kNXojfsIBET7w9lG5LmzfMsFDV2/HOuEpyH2sQ6K2HcxsXqRLNBoMCLWNGx48voCPfv8mfPHKb+IiG/MBgNdZ4dFcZ7md1kpZ1klVM/UOlqT5R5tj8mV2ziwvtEOirU1tWeNx8pgrrjTTa0seC4yST5gLnp7ELadNzcjVVxuQ0xKA4PqwFfafOmjExN+0pzD6zLEV8TOuqqlIWovob/18G7Q3mzfukomEfeCvn6Iv5HPY9Y1/0ZUVKfMuGc1/njRwQw5MlXKEQV4fehjcfTUgCHxD5KdBPGy6hJ0mGmvQvCzwXlTfZtikVYaZwNjri0uuhT6cVicBE+pa9UCIuUqTSDuGZXvk0EEohoGnRCBux1lzpO6HE3sHBvGkQ2nHgSMLeP/uG/Hw1ebTzGH9C+1tOX/aYN2qDYUPZUTwxqqZFVSEgOGP2XzF6kPP3IhtV23Hm85dH7D7syyr07KySyxUM/5TL7MixfC55xUyywETA5XIbZgCpAw61wbN6HdKpCu8r2m+ehcrMa1l02zXkJ2txkYe7bN/afi9OhVGzxs68elud55k+7LK0zO/bHuSfBj5W/n8N8XHoZCLKVA06kFbhd9iIsbX0hfUrAphQQzcOUeo2KSAA7DkicLPoHPiZaDA+EuDKY8LCWhqvEj59E2Lf089nsI6xl6ltu+2yPyNuQFZAmXKPMsrrXSsTq2XxIFkwhH/bukS0XzzW5wzeWrWeIg+mgP8XiEScwU74drzE4NBolelZ0DzQGpN2H9kAbfu3oKvXdPk3Gl5QxqWDTGIM2udYtZilBJx/HDMAJjw7NEh7njauF/kPgjuvZ4tZu7xLDWpm/g7NCjhv8m1UwxiIDSMuQSr+HeyOY6CwG5E/Robfmn0S++ExP1aubLDOiZB+5tUnaIc6+ilbeqhPy7r7g9UKjep5sR1OAOODT+7+ScQkwN/v8QkU8yEFES57BlvgkFzjtTzHaHWN0MKgMQLMOv2AxH58ddUnjwExMERCqr8/QNLJJy3pbGfOcMa1mPRsugbEV034esJxg67SYb9GZAaBEbeEYLYU2Lu5yxxkOXKvyfVS9rvk6lTXbqX5k0zyagxwudrtmGuaiYTGhGQBCEuw2HfkQW877tb8Mi1j2GdXZ3nuItZBFPuh2VDDHxmbTRLayMIUihNDB5YOLKA23bfiEeueQyXnrchcAO6l352lDTTNqBzwhnPos21malpZ9ibIj2pscSuqlStn2IQtf4HmnfQaeYMJC7jwAh0DbgGDUiHjzrbnVRxyCweztQzN+MvGH7K3KtqT3l+UkQudfOjRdPmvAST9F3OiLcZe+2+RbYdiAyG5jWAIE8KcYh33PP9GpdlywkmktPIXNyUQO7cv4sbJxy305/zX4Hx4RXNUxIncMYhF08cRJ1zK2CKVVaOJYZvOj7QVIQcOWo2mJIkx18a2YTSpkQ5m3D2gLIJyV1s2vDwAt77xA149NoduOy89ab/qfFeNW8vj2VDDJolZWlHJrPW6F7J6od/voCbn7gB33znDlx2XsO0ZNwQMC/P7bQXJmnp4pwz9pp7urT8ahLHrcZWS5OqJDtYUViJp0BcG5xT+twbfuced4bU/xaGc1azZaUNrBrD0gx5CmRd4MqMOCEBaXsTwqSVFz+3Kwp9lF6bk8Bp+qsjOXBP0I7PoL1prkUs3eYYyXsz5IHi8nIhmWnQ4kEBJuyjUj/E7bVeE7/VsAurWG/JwF1bGQ1sCEPjMZE5GU4PJUtlxbm0Ce26KdMsV/vOKHlZ/dCU1yveFVNOd1sg7cDZg0olAVKvhpOpBsPDC7jp25vx2LuN/XJ9bvLn2JME9WaBZUMM5NpVrRONYHCqhAE/AIaHF3Dj79+Ix9+9AxvO3xDGDTlkvWdX5AmDg8taboMWOqDgPGWvCyvdAQW92ebmanu2RrZC4w/4eLkw/oHhF0aQJEGQRtIru8ys2re1mwJMlDcQGbwOM+QuxKHT7LaFQBS9ApGynDZbLS5VxNxTdBQ8rc86z2Dz7ZhlG32Z4v/NwdSgZw1+dC8tgbwAiycFgTfEewWbvAoQeUJg8i2kV0QQB7IeFBdiIbu8V5AG05KIOETNA8LVMCUUDf+UulJLsAyGY+G6tgfHul1eaTwGCD0G/hrdhgHA8Mhe3PD7W7Dj3Tswf/4GY6+oqajzGrgxdMZ4DGRcekBRZ9bj0EABwcAaHtmHzd+9GY+/4xuYX7kWGL/aCLVINHKv4uy5MH4GlASkhcUqHoSAKGT+hmtbCckNi5kJR0pJGn5/TBr6iABoxj94F8o1zu3FDB6PxbUAyw9kTOI2jRQ3VbrCp4Ghm5y8PFKvnwpqvQVpjRMpx9FqdnF/MHGpJ+iPDKjtizrNhdM/pON7c+1ZDC3ItieWh8Eg7Ndgpq+Mn5nKgE72gvcu61ZH73/KceD7xhn7wUDIPXmC4AgBuWOA9Rw0hMCTBiJQRBjM5c3fAXHwuRn2mVNjUikJlW4wjpK/o/06fAhEqUXBBgQSQ5HHwHdDoydLtgsAhkcPYPN33ovHr30U8+deAq5HqKoBALNLqyQIIPLemRyWDTGYczIq/gsIQT3OzlaHRw/g+t23YOfVDxtSMHoFLm7oBdqyZZ9kxGNPGOyTPNrEMm/olZfeJca+2FlUbjbTMoPJxsqdcRe/PQkoEABv/Lk2Co9rv8cx12NzbT22v235MyAHXCkKshrYZB05mq1fKlKiwX1K+VOBU0OoGvtSmxdJDrjrjjpdCYSGGRCYTqgq8DjzrHilxXikk4gZk0CV2Hq5tnIONOMwln15vVJ+EbItVWXC6FL2iUJ5pwpUVSCqBGlICQPQeBUgdScQEAZbcJqbEdfNH5tAR00CJX/GHUmWsNrzfhmr/Wfxuj/0klbjV4XOrPOhVYvh0QO4/slbsPPqRzC/8mLw+IR5B1ybNlQDEOz3Emx9K6XpEsuGGDTuGIVl1WPbueNk5jo8uh/XPb0Vu972AObPuQg48eOICUthbgSZ3HUy5mbRdQlXMVFNHAsNsIMSqz6ZyLkttTh57P7vSAK4dgTOEADzW5wDmvOwZEE+O55Nt2EgUoPcbMYpQ58wMbDHIzJQVcFxju8T98rrVcTy0lXB54xq3aEfZikzE3b7SUNJ07k+qbR0sDp5P11JRGdiJt9JztC7f2OPmJfzjNwD08u+kHtPhKXMUwVUhhRwNTAkoBoEZAFUoWohC6ZoQRjEswPiIDGDpbCtCGQmDB+ZOkV1lfeIXCU1b0l7XE7nOwIAgE68AtXLKu+xGB47aOzXVQ9hw8q1QD2yHpkBmGujzxxBcKEgG8I5I0IJxM3gSQmBIQVUj4IZ7PDoQWx6Zit2XbkN8+esBsYnXGn2n4YISPYLADR6JWG/7rpAoLL1jRSz/50xuFCMcXT+pBKEYuJTQwQST4EkAfY+rmuw8wZIIjAahQTBlsPW24PxuElCqseJIo0NJcdkRlM0kcFnMWsy91i6ORiE5MEdj5WoKDOYdWrEwR+fway5RAa6yEUXMvFaQ2zktXbG/S3bKe/v7LlQrov7LjL8gG78NcMfGH07Zjgur0XuzT0tsq/JvZT5wcDPNl0YFdXAyznNzQWeBY6Igrk8JQuAIACCNKh1XGwCcAk5D0VUxyDvQtH1/jZMoPM1XS/1/OjHgQdBzbMCMDz+LDY9cyd2XfkA5lesMfaLHIFjEA2MTBHZ91iBib2NKumF5UMMnFHX3C9BOMEYoeHxQ6ZTr/giNr7+QkEKZKFCSJwQO4EZvRoyR02g3DkNbTNweU0sUIqQdM5QngVUYtLkAgAIvAE+9tnmEbDnEiLgSIBQhCyVopthRbMm7atuACC3UKXYa+CMulSYVAEnYM7ZmZVTng0ZaMiCU6A8hjhviVNEPDAeZcjChHD3aAbelb9I4jiLvIWu6JzfkC2goKSn6V+HHAkQ54oegAIJyJJeF05TZB4I5b6LzANC7mOZt2EFd5yrQSjrjii4cIvwKAQhiKoCjwVBtiEI+yBXi6YOvmInhwzoS5PlscgLICeE8bVaMioyEw+grOtjTysAjF4FpKfVnRfYc/w5bPr+h7Drii9ifsVFQD0ybagG5p5qAKba/q4aghBMZs8AYuAGnJrw5gyNHVDDYwew6Q8+gF2Xfx4bz7nQeBMyStNkaKcEAONXQ8YbC5T50V7vwMhq7qLwWNaFL691jz9JHoRcAlxAAoCQCADTkQFXnvQyCCLgFKFPTou9BtGn2ijaMIUqKzd+lm/PO0U4hlF84m/DyivfLiICm29sG2Y+HoFrO0idrqjNvVzX5ln1WDdQ6rEJjGRVTR5iYE6fq1wrjfXJIAmtZEDrm9wYyxn/SQlH3M5qoPejRgpiQiBDATkyEBMB6WFwHoRFy3wFjMaBzNNgEBACE6M2ssQAMG5IQUwSHBGISYIpvAk98AjC6xaF5nxdF0HaSuiSQKx6NPLXNpO/xer5yGPgcgzsuViP73n5eWz6wUca+zU+YYlAZfSMt0VuUyMTOCCuAOLGs3FGeAz84FBiM25Q8hjDYwcbUrBiDTAaATCDUFN20jUmhdZ7KLIusqCUTK1193xxrbs2Mw/Ou5/KKtouHoRSXC93vxZLjcmAuy7OJYCiKCMlmSMEsWJ0CjF2owZNGImmUuX7jwbhjJ7s57JpMADGJkaHqgLVMJ7E2rbHzarGY7Ola40mT6Eeh0ld9voAUS6D+VvOTtL3kTOgvp+1D3yx8mzNuLv3N7CqIaM8KGcgp0VpFt/FozJhsmBrHzq4vpRkV3pniNI+kisFpJcABVl3z5hC3oHJZZ4G5Ekq11Le0cg7V96L4L3emqxLIswVuK5CDxmz/R58I+s+sTXO7XB1nUYPSXQZN5Feb1afpDkQ/tGz1vFKmIDqUaDXayFPwx+9iE1778au+Xux8ScvMPZrbs54xYntJ67ZEgS73ybZ9ykJQguWDTHwkiY7PiAFtU4KRPIbACVO3QhCkK08OuGXNWUFCpiCTYZCYtqSkgCGUGK5WGNphhhjWhdeohQ7ulPbSEHucZkkq3iWVDpO/mM1VlaoAo85UJambgxgnM5i6tpmu7bPcKW7FUAzm5KEIF4q1rJ8MgftDar95eRl4H6K/h7MpYo3RyAGM1YfXQx+PMPssBpkklmod7ImywOtTJCQZ+edcX3BRhF7iTNOJPO7jURJ74A8LLxi5rIyCS7Je3ydflyRdwf3bI0cuPAg7CsYm8ZzXadhNKkzJGGYFJPoMUlGgGDMBXpdkgWn2/1jCISxa2D5ebl6toReAaB+9ZXwWtvvw//rh9i0/xPYtf43MP+Tb2zq7cgB1yCW+xo2nkn/gXCGJQhNHTQsG2JAtaDFIsHQk4Ljhywp+AI2rlgNksZVDsp4AEfZzI4o8HgUxtkgFJAgC9aX01L7lASYZ7jko8hlGBMBLeNZa4ssI4PFxnbVpVRx/XPPdm5Hea+dpbOdpZPXWWOEq4FF2WNxfKA/kwKDa4ldlDToPQbCsLs6+WNRDJaEe1WLwQauV/OQQCnpeyf49TbtZCRGpRj6SCFUisw1B+pgX9fGKzEXXjMtJpjZ+Z/CcBX3mLD3ThxQ47pZ/xyNTUKoxE0cV3iEiEBVYyRjckDVwNw6AGg8FvIO64lq5L6Rd0MOwn6x1wlZZ65VY6/JOmD6OZF186MJJ8Ry7+S92H2WXLNtvPOs2bYtNotgKh0m9SJRVq97WXMyHeh2a8SD3K7F6/ZEr4+tLRP6e/gXP8SmA5/Erkt/FfM/eYGfnHAN0KAyCd7Oe0X2uY7QWs8W2cRD/5wzIZSgLpuzCBINV6zJK7LYDa6dc/+OR/BrozPCBIQC1dqEsfgCu0wuaiMEpSxoX/9uypulYpoVyEiwn4kDTU5INQBg42Nc25nIHLgS3oNBZTxAdrbGXJv11KbCdpANmthrJds6CNuFVHHE+QVFBdmFEAzmupEBkmvCAYCS/eknd13G0F2ZvhQWISdmQySU6yDmIdl8nAmWzOVmpRohan4K+dHcu9F12XKyKLt9fT4AVf7aSi67jUgCyb03bG4Nj+1yMmaT9c81MKZG3kkknFHdyLoIK5iq2b6IZL0k5/JYIOs+QbZqSES8ykYSAinzQOD9ynm7wm6dkkguRocJT6A5l0nWzahyHtVI9TrQRbf7dyYKLnp7I70+/IuXsOngL2PXJb+G+Z+6MJ+f5OrGdTomfN4dsu9FYhkRA9GxIot/eMySgiu3mSUdztXt3P82xuYnnyX3e7L0ySoCFm4zhP2ubRLjZ4U5RarNsKclBaXBxHVeSFoIQifPgpgl+A6ua9CgMbS+z7zSEe2xIQYATZgBaPJJrKeH5ixZkPV259sg2x9lZ/vzylKunHfALeOKk7JSIkDNbJbCLWbdOfvA8F+t3m2I+0HOXHJLX0srXnLlVd3VSeOdDdvVeavqXLjuJPQTB5OO5m/i2pyzXhkqbNLlSALVc35ZbhOG4EDeZUIixWFOlwszKzl3v+XfQGciYIqOyGuGeLchSxjaCEFOj0U6TIYJvXF1et39zur0KnhOrLuLOl2b5MnjBc9vSAousO2J9ApCQq2SAu9NoE7ysoyIQcrcDCm4w5CClW+yL9bGXrg2SnrsJ7Q6OfCzhJZZR044leNdZ1YUGFYLF9d0yV/uhYtksOC+UpZ6SWm2De6OyWI0QNEDQxCDRRj88HizaiS3zptEmUFZEnJAxG0XCtE0oaAUO4QJaDCnEwFKN36BS2CllBAkHoNEDktKV+sD3eCrCa/RNd5Qxtdo17ZBk72cgc+QgLaPFYWYoJ8i76O6EojZGHAZM2ZrxHNEYTyyxNjmI4hVOsRNUm4g6yU5j2W8JN9AFBbQ5dzcGsq6OaYbf3UWLhG/i4L7On5rycx/kXqsuK9IvKtpUHw7sWmb5HU+7h9KJqfg4C9j16XWU+DqqU02or0i8hXt5r1ePsQggicFb31AkALhsrZeFcyRGYijEzb2V5WNqUMuQazgSuuSBMXjcVSePS6rE8upzCR3S+iiunZ137USACl48bkpww/B7Ed4Qig6xnUdegvcMeVeCc55gSCUoD+guEVzyYNdPAIxEYhJQOwtcMTVE4MOCa05xagpnxajn/yd2YZVC0lMitYQgG9njhh08CDkyvaF5L0pSVhSLoO25+NvfzRkQSEKZ52deBRIeA7IJfC5WapcmeC8DnEdMb18N9ekRj/ZbyNz79QhRy1ROloJI1f3yGctWpdpOizuAyj94o531ePyXkcynS6Wx5V6GlLwSexa9xuYf70jBY2eoKoCzZ1l9IvTNZUjd9GEQ8Hw2KFi/ZcfMSAyqw++Z7c5PvcS60IZiAFFMEs6bawPNXDW2Wm80EFj4h3IQJwk1YmrzZlX0sRxbRnBskTnPlLc/W7QRAJBVccUrNLSsNLgiY8rg0fbGUyLV2sfjAkIgHDBJZ6CluTLImIlqCiNeLOWeGe3uisJEARAfmBGeg1YDuycSx0oypWuFlLjJ70A2e9yZMMOSIxVFyTykA0PNMfjHIzkvgk/M1a8WuQW5D4U5hOcMx8KY5EADbBNEDTnyR5LMtOVVVI+X8FhsbIdH+9oGGc9rs2ftSAFkfcwri9moMsUnaXpsbitrp1d9XiYbJgUb47LYWSXxg7/8qVm9cE5a8R94eRDegmMrhD5IrkQJGB2/P3eVpyPc/PV79LG0wK2A4bHDmLT01ux66oHDSkAmtHvBzcBXNls/2YwM7PtWPaZ2rGLaM+PXjBFiqzsXJY0x2pngnin+RqWkJo5Uy9flMyQ9jfVdglRvbis8RbDn2bOp0q6GPOSil57/lw6G21IQXtMvPWLhBno7zFl30zuY1qZUMAEBMAbf1Eei9a4f9s/S5tHInXi3VQ00A7nP/YiIZW7/6OD2uw0o0+VcO5v7Wt3/lx7baL+Id9g1zdexyLqC6sz4i/fMZe/LMqSULBL/mRgrm5kPQ5TANCy2tuQM25Ni9yfOa9M2Dut34GRRBMRyVQTWzlIaNX2ZZn6K6pAIlclshP3TSBKk+SqAHZyKcqbE+HVYLlm+h437f1F7Nr4WWw8Z7VSP/I6I+uJLNR3eHS/nzR/dOdvZKu/jIhBJUjBQ5hfdYkixIPsV/8SkuCuEWXsefk5bBp+1PyYO8sfbxKp8u7MiT8QYuIa4S1ysMiEr1iBYDrjmFcioQJhYSDdseC6EmttjQWnUGPauZksRN9o17Y9B9DrKAecbH/grosIgDb7twM5Nv7yE961aJJriycIcZ2nnTzJc8F1QoFRfC2FF2OAKn6FFPwT1jVTt/jz5fJvL9lBpIO1S2feN1767QVEoi+IUNFAIQ0RYQC8nkkIg6ukIw2ubWoIw40De65RP0WkeieerOQ8L8q1xQfphJ2jNsS6CrKPAKTLa/kk6DNA6qpm3BcmM/baIsLpfxpec+9RTvrm0jJ3XfGb2LhibViXWOdkPJK5uhPXxlMQT5ozWDbEYHjsEDY9dTt2vv0r2HDeZcmSJu9+Vz5SlI8TmjtM+Qex6fsfxq63fAlv+c6N4EE0MlWhme2ga2Zl+kw5NJaRUHbIGI9dgp0Mf85gRsegfHik9VOrxcrGXoWYDMRWYdIYeEoOkni/+J26/Skw/N74O08pHCdt3m2REGQ9Bu3Wr9LMtPQmB54C4XlRSIIpL1vUxCi9pU5koOAtMOVN0T/eY+B+WsIdEQRJHBrSQAANTDhYXmMryMLoe0+DbJ9PeIwMqm3NZCjrnGIopwvEGKTwZdk/okkYkE7M7N9x+7PEwWEx+gyI2noSyBEAzulpf0h/n/Or1kV7lnTwTAZ1dfc15e85cgjXPXWbsY+rLm2dLC0bYrDpqduw8+qHMX/eOkgjpBqfaPBxLkZorzU7Jn4Qu976Zcyfe7E5Pjg7X5m2AacIW85FpyZ1dZg9T+TaTR5K6d8k+zRj+KUBVeLjesw866huVenakNWHcaak3OBoIXmxjQ6Mt/0vNvJSVSaEICqoFluo5IzjZCYibH+gEuP4bYYkBMYznmEvghkk3/wJvAU6GUg54LT9Epae65fGK8BNPwjyIImD9CqQKMccJ5idjURIH/q/0SgWVZ1EZmWJogjlqknHWmRyQ29G7C2xx8L8jNRrAkD/xLAYP4vWZ/HvU6mbtfManG3J6d4oRGnqGF0rctEWDu/DdU/eip3XPIL5VSbnLtDFCpYNMTCNXteQgVwyl0ScUGRdfUbIjeAOj+7HdU9vxc6rH8aGcy9uFPZcgRgAHQROc1UVBrEqUC1Cl1EiQUiizWWmzPgDIdRmzcLoSy9xYATr1E0cz5Il2hR+i79GNEceTROpiEwFcuO3zY2ttaWL4XfXS0IRnFeeFVZMr29uOh9neYThBO1vTj0FBcWinSrpxDj8k/cciL+jMrIyMkHfyH5pPALiFmr6QRp9U5xGHGQ54fXt4RyNtA1abUvYlydnTIVtIXOtzclw3hJ3vfOWmLqJxMuM90D1OPjmpLVu1WfAzPRx8yBW+nQ6fZwUPXe27gXQJl4Z2+b6ZHh4AdfvvhmPv+Mb1lMgNfIZQAw2nL8+zFguJHalaBiqTCYaHtmH63ffisevfRQbVl0KRuNlKHoMgLKbThO4Sd14tr7h724zjFbOqsbYq6R/7Y9svLyLuzyZbSM/UwT0JgZVL3vNPVT3eu7ioALpIa2+2kw/JQ/hPf5NKcZvHE2tkzyKCRAb9EH8SV5ZdOwdCGuhXTIROhmsAhk4Ff2i9YcLTMbkgSj0PoSeh7S/9DBPm+XXD2thE9U7MOEYUutLLEItsWckJBDeW0LI52UAXv+a4iPi4Cu/SH2WbeQM9C9QrGvXr93y3OvCOrVMvoCwD+zcBsPDQ2x+4ibseOd240kX/XvGhBJgY/65JK/AAMEe8LJBxiVIxs1HABYOL2DzE+/FjnfvaHIWpCE76yeya7fbE35yc4Wmam3IK+MJ3I9qwWndoglvYPyBkADkjH9DFsxveT68PnyWZiwLh/L9khg4Lp1ujrdYPZWLRb9r5aS8Rho3N3DHUSnxKxwvwgACwEA07MQ41YuDZLaadkRMKBaD2MADqZE/lX0CAKOoONcnsi98H3CgTjza5YynkjH7yAAlQtV2WK1C5Nlw2qAhQZYEWDcbWd3ZkACjgTXSkOZlpKQhrFeZHGTRlvslML3eLdQt8jB2sRk897qADLgnqxOwoHDzZ0XGfm351g3Y8e7HMX/+BqN1pbcGg6JyWzbEIN7coUZ59qrBCevewwt4z7c3Y/u7duDS8zZgbAVeSgVXc50EKZnxir9L8VV/TaHsTOQr+MegcZlrulxN4BLH5Ey3bfavEQBp/KXhr+0fjMYwMLM3AFyHyl6+uq6ruLWtSOR4kMagzTiae7sZw5yclYxbbNjiIkptZsWwOpDy0kei8AGQCNoomrXGRhPQCcU0yOn5tv4A2uVgkn4p9QkRMBLvzvVHF1LVlDG97HSVm2n6yEGOlXiMuDYOQL4dg8p+T9aGVxovAQekgWw4pQthABo9DORWxEyhz+K/43vyp3Q9S+EPXw8axKeC311sRm0TKwPjz7oe1rDvyF7c9MRmbH+ntV8ACGYVDQgNSTgTQglMA3VGOy7MZGMQAfuPLODm79yAb1z7GC4+dz1G41CAHUYdSGt8SS6RrJX5T4nF6Gwtxlty/edIgEYAnPF3ym3MDGajwLhmr+ia2XOo4OMvy+bGiKaH5cfnpGEIlaJVfHFiXqSFun/MtwxNcecM2lSz4swnqQHTxpFyPGc0kzZHwtrF9rU1IWfIZton5kb1cJc+Cfoj6oMTndR/VJXod9zWeEzE95TGh7lPf278vuLxMfDXNWPCHSdqxghVDWloCIPxmJC91uVnOMMf52TIVSCazp2VPlsMSitycqt4tPs0uDqeqDM6F232iwL7dcmq9Rg7+08Ae5JC1jt+BhAD7/aLvAOMiBxEBk3i0NEF3LJ7Cx6+ejvWrFyPE7WLIaaC+oodiKUXPg0x6OJF6IzFeHoDjwEHhyRj7eIJkERAIwHmvPvdlFWDUdsOcf1S++eGHaPtkqrt1ipjupV9mdIGVvaHuy6cOZl/c4TCXJ/vdG3WDUxu2BYTR0+enT1hDV/sXlcuDUjEhFUrzeibqpSvmWV/EFH+eYrF1Xb9M5fm65SGSMQ52x/yUXI8APBjAkjHhbxOosv4cDIfj4uqomA8DAiCHJAgC+zJQkIUGJYoOKd4SBaA0FPgrimthpkIM9SjVaZKetKuvadQfPxqXh2HJDD2HJh7wgZVIBw4OsQtu7fga9dsN5NaBgYw+paIULvQja1badgsG2JQc2iwNEJQcxrfdjh4dC9uf3ILHnz7dqw9dz1GdRMzA+TGJuafE/bltYVZuyzHkpckiazK25s8vJAiNmA55ap7Dpo6SxIATE8EHAlw78mUzWYb+Qwp0JSdVJCVorSD76hkFWHb+YY8nEBMHprnxwRi1MGAdX0vs4IsXyM1bUZ5QNTJuJcwzYz/ZPVLbiaWu6b0TrvM6HNGv03mS0R5MeNCk/mqCo+bY4YsEPFURAFAQBYA5ymwf/sqpqthHKYdK5Pqz/KSXncfwWtBhUiUwjnx8BmNw3eas1kS+48t4LbdW/DQNdvxpnPXY8zGW+NIwQAMYtN2R8BKvbVsiIGLUSeJcAwTUrCdpMW7Dx3bizue2oL7r7KegnEqoC6JyAmJU2YFL22AnLcgZ3iDe6OyJnGL5YnCFIOIg3+SvACZEzANGZBEICUHTmGGilNLWivBKaXAS0BC6VWm0W6gG0VoSaBTnjXZezghC8zC0zBm72UYoexpyKFrXHqpII16ziPSdt80eC32SxsR6EICNAKgkYN4PMhyphoTY0F8o/FQ1eFYcMccSRhXhKoGRmSI8agiDGpgZEnCqIL9beoVEwVfB6BZkNEphLB4IhBDDxVI8hz+6+4ZS/vA4TWT2ogT9uXnVjHFcPbrgbdvx8Ur15tJMtjHDiRBIILZ7p/DdsVYPsSAFdcLN/HvutZj388e3Ys7n96CbW8zpCB+ie57He4luSVHkjy0IfUCiL8Rlh8cDP9sHfDMPLHC1DLL2zLEZXIg0HgFzLmQDITHuhECjQzULMifK1cq35a+ce08MQ5/OwNvfjOqGsLoE6rKCEsFQsVGEdZgVJWJj9Zsr3fPt7MrMxCNXA4KryRZItfh/WnJhBoWO5M/mZiERMwCs+yzJB+g5DVQSIEkBDEBlsdzRCAmANp40H7HkGNCGw8VMSoOx0LFbhyw+A37m8BsvlbLZPIO2Ax4TxKoInGMvRy4ULBM3eu6EmZSItRFT8rnqFdyQyKk7qYoaXcaG+Hy1xLboDTz2WPGft1/1Xa8ydovAhu7VVmiJQiC9BqUem3ZEAM5YCQhkF6Cug5j4IeO7cVdT2/BF6/8LVy04jKMIgEbVGQzOrXnYeK4VZvx15atAWFGctcMboc2BewMpZZdrj1Ly4Ae++u7EQJAIwEpKZDegXHNiQIci7LaUEehn3HN9v1aUuDaWgmXIADUbhrlWLYlB7U5lt0PwSLOS3Du1uY8BefdNTGmsqMFRnKyIhRdMuBnlbQZYyZcQ/SZ1kdjAHNuHlk3cg4YZeo8Y1SZEMuA2meMOVLQHEvHgjsejwV3TRfUY0ZiZ6W108aCGAewY6D5zZY0xwSBMGDGiBnEZoM0qszMutm3wDxiBBY6q3m2008j/QONRUyqG6lqnpMjKqptEN3l1ekE48w1Jwg1uWPKS33u2F588Jkbcd/btmPtyvUYw7wOsqQAtSFATBwQBFM/LtZt+RADN4iiuEwQOkBKCr5w5TexZuVlGGkbZ4ydWzgUCACWmU2G3OxfW7suZ+PNdWF5XZSwFgNVFfME/jY9Wcr2e0QK1EeJmVIJFVESG9Xuq6i7MsyhZvYuVOMFEOVrSYzWMyDjtU3cNSQEMrM7JgLNOfPvpMsnp0G89C2HST9kB5w8hdJ1WeTJ6iP/zRsIWXfub0kUyBpENkI0BjCojbIYM8wMvGrCCJVzPVec5AZUVZgvIGW8y/hpQ86JEucgaDkJMUx9SC2Ta8bYjgNHnEyYjZoQm/cYZPI2upKAbpcBUJanime5JkuiIj/3XLINE1S3KU/Wq8U2AMDzx/fiw8/chC9c+U2sXmH22SGGTzBk97d9L7CeTuf9rMu8YPkQg8Cosvs3zScAQlJgmBarSnAENgqJ2SsclxgiDXvbRi+l3dlKZEDWORe/lNe3wbvtlHNdXa25OmhwsyaHymUa1VZYAa8UzUhqlGQFEkqRUQ3If4TI/G1n/Lb8Abq5FOW7ku5TmXuQxFjRxFNlklaaiBVmbANxMpY7Fi7zMvWnrJJJ3sw0tk90jfZhPrXnbIW79Os0yYBdwl6lsdV69yL66azwJzBo+uEsOANnfruvhI/JjNk5kCfGxAx2G9jBedAIVUXCgyblHqhrQs2WBFfsvVaGABvCMYCVfY0wLWIcuN8y38Dn3iAdC5Icy7Eg0aZbnI4gS6S6oqSDptWJvq7ckIUR27DxuLl+7EhCZBvadhVN6lkI28YhW4fnj+/FR/7wJnz+ykexduV6I4u12FcCInQlyAHDhjc7TAyWDTEI4vY+bpfi2aNN+GDNystaO8kb6yq8LjDuHelhV0JgrnXXybqI+zlfnxxygy6XWV4a0LHRH5BZ5uWTiZxwV2RcrDakMECoFI0XgFBXLqZKVjECdWV/i/DCXEV+dlIzcJZU2HYk614FfcBmE66SLOxuZEASAcCFDsKEK7jrxEBuXHzBz8Q9vlh3eU5MtOPu0CAKSejiPpuZejYDXTuWeeSs+8j9dv1Q2xqd5WyDlcE5odDnakMU5rgxdnM1Y0zmOkMcgDFRmndTuVBCMzaacdB4zYyInfpxAIRjAQjHA9CMCQCI90Iw56cjDzFyBKCLPoyXpko9KEmK8XS4nCE7EakbYu90+QDUhEYcgeiQF5a0KUMIgIYUfO6KR01OHDj0kok5l4OJ+LjnUHBpDsuIGLQLwqFjTaLh2nPXmzgzmxdB1XSuU/fsttlPl/oNqLCGepFly+u6zNRyAzVwubmwiv05J4XODTDvdrVuWH/eKEVTrzBT24X1mwQs6rxssc3FGrr+7b+R8nPnYiLgri2RAY0IpJu+GJC91lUp3l/fZ0DHbZjSCHfZS18lCMrBWW0YI9FlqZg5Fv1Wy5pNH7lf0gtprmuOMzvSQKgBnMUNWQhW7Aii0EYSACgJukZ4cisVUCAEQd+0jAHzb0oCmuPhWADKREDLo3HQwpraBCaeiOQwiedqmmRtoFvi7KxsgoQkBWtXrTd1UfRL52cWHr9siEEbnj22F3faJYlrz11vXEUVGReMm51X7S+qyx7yGuR1LNxPjpQARjk0M2/7POE5cAPLJDXZ+6cUbiCOZ0cziaT++XJUIbKzqzlubhzDumGtCzTMS2jcrwB8wiKQKkogWqalEIQS2vYymDUJcAQA9t9w9zdRDz9rs9fC3RPLXLd2xmBOb4yVk/zV6GHSL3DXTZqFK6B/REi5jvKnu5CHroj7KP7iZ+3OCwPOSPOZAGCOCGzJgsv1Wsw+H4A+Btr2MNAQEwIgPwYA+HEApKEyIDX+ue2Vu8DpkySnKtJRsR6U9ZkEOT0Y5//Ia2XOS9tKihzctTmCIJ8hwwdrVq7XPY+KvpkWy5oYuH49eGwvtj61BQ9cZTZ/AAAmw/rnKjN4xzVjTriCNIRbXlJr/CiPJjYePNO6peZAjffCuq9MxrNrGCWz9EmWppVIgDYIzD3y+ny7ifJ92IRPCNpe7zJ8UkpqBEIC4TAJAQ/baY9FKwekspuUBGgEIN7+lRB6B0JPgpQ182/7nu0RMv0R2g7K7rERF6HZHHP54rSQpsRKnxmbauvZCfup8QiEhJ7FeXeu2TOFfFm5bcKnIQsAAsIAnHz5N9ekRr9rsmzpexHF5Z2ZFVhOBwKnVg9q7SutVJgOFIQc5qgK+ug5kWi4duX6YO+HWPe4ukt9Mg2WDTHIMbWDRxdw+5Nb8NDVZptI6QIkFgRh4AZ1eX2ne8pcaQrTAcH94pnkBGQgXP/iSi072lxPnRRD3E1tA1sT/KAIte2UCGUNJelN8WqVVmt0Tc401+ZYeFkZ5JICY1ddJxIQEQB/HRRC4MptuIP6r22d2ra0sdkfaQhBXKd7DfTHziKk0EZ4ckMtIQxJIYvrJ07+NefCZdEKQbDHYsLQiSzYcmPCAITyfzJkH9Dl35yfXA9oOkC9WFTXGaRxzb6CzGwTOlPSEOtB7micZ64H1QMd4LxLSTKo+f3ssb340DPvwX1vM5Naf1VGD/ljXVwGhUvoZG+5eipAK4nx/qWuRY8ePXr06HF64B9/6wL80Us/VOnBsvEYHPvAfwbQuGu/+tL9+OcLd+Ofb7gHt154R3ixEh8E0sSiHN6w7a9i5z/Zja322wqX2ESQGBpr6/pNgkNHTfjj/quMpyPr0s3wuvhwXBO3Y9Y2G17JZcNriXFxUpz2oZODRxdwy3e34OFrtmPdeRs6kem4zsnER/zed3SI9+/egq9cvR2XrNrQXDIFz6W0+jh0dK//oNal523Iuv5zs39/PRDOQe030Rf+fIjrn3gPHr/2UcyvWuenQNQIo+sFvWEtmbLDowdw3ZPvx86rH8b8qkvzDZbzOpkHE3RKes3wyD5c/8RNePzab2L+fF3+O0P5hv3wyD5c/5334vFrHsH8eZeZR4cxD3FDnTmeKR+if97+laZ/4s0SqJFw+dv3DVV2qlbZ483v4eG92Pytzdjx7h1Yf/4G6zmA8DK46upffvXXA0F4wv08cNTujf/2UP67QJN308rm1wHxQTkn/0EZHZ6THc/clP+Vq38Ll67aECR5agmewb+uvMLDnH5zM+1inaV3Sjkc9JfXb3uz+n8SvQ+E7XjDtr+K/3Dnf8IjLz2AX9/3cfzSZZ/GzRdsTeondU6gh0svRsjRwy/dj9/71m9nL102xEAmznzlRUMKfnXDPbht9Z3JtS75zwuYHbAgsyTIudhKNmbrU1vwlWu249JoULZmQytCI58JAPuPLmDr0+aDGJecK40ei3tke8qPjHHILtn8shDqVoEDvMGzf/rrY/Kw/4ghBY9ea5SiKy9GLgaWM3munXsPL+C23Tfim9fuwGXnmfITJZQpQ0Ncj/1HFnDr7i34pq1/EuOP+qEx/k5zmSUUxHVjlNiQAmLG8Mh+XL/7Zuy8+quYX7EGGL0iiEFEBOLfEpkXPzx2CNc9cwd2vfUBzL/+QuDEj5uTLQYfMAaOCtcMj+7H9btvwc6rH8H8irXAiVf08nNQ6928seHR/bj+u7cYUrPyTaZ/XPGcIQG548ox3z9X3o/5119k6q/VWxIA9XcVkAc3dvYc3Y/N373ZkL6Va4Hxq2CqjLu6aii4UTuKHhJhhXDSYq7dd2QBt+++EY9euwOXntedFOjfAZDNNUf2Hjby/1vvbMZvrgyHfP6JrD98/W/dvQVfv/YxrFu1QU/wVMhQaQInn+X121XbcXFm0uYgX3tACjJ5LARDapz+v2zVfLF8rZA4WTdO9P7GHxlS8MkNn8YtF9zhn9vUs6yPY/j+tae++uID+JWFj+Mf4Y3Z6i4bYjAg0ykPvbgN/8vC3fjVjfdg60WGFKSxIApYp1unCvlv5jlfefF+AMDXrnnMG6W0+EmDTU1Czn476L92zWNYZ8sPssOjiuWWV+Vw0JIOR2q02b6b7cq25JLjgiQXMoP+vU/cgB3v3oEN5zeegrBHJpv9mgLMU4aHTfmPv/sxzJ8fDkqt7ZNwJgKw4MvfgXmvFBUjzdKI14EhD2f94bmFI/tx3VO3YddVD1qj9GNF+LxZEA2JUgYzfbbn+HPY9IcfxK4rvoj5cy4Exq9Gjcyk9YnjgfxGsjw8/iyue+p2U/8Vq4HxK8iBZZlt79i2fXjsoCn/bQ9g/pyLLGnKeAqCMvN9JZ8v+2fjORcE9efEY6D0lSNNsUvNkoTh0YO4/qnbDKk5Z7UlNQ1pYOFhoMjbMIjLE891+Q0Lhxfw82J8dYGmjRTzAYDD8XXeOrTuJZhsSRn4x8S/Rue68bXdkg4tRwOQHhQKiENtj8nXLXXgATGpiidtubYnkzlJFiL1uP+ImZQ8cs1juCzSP10gV6QFx8Xfv7rwcfzKhnvw/otCT7emk039k2qH5VJDNB98YZufNP/Ot3Zl67lsiMFcRXjwxW345PBufGrjPfiF1XcBiAyXgpy7CkiNyoOWdADAxr9jhCJHAaYhB3sPL+B9392CR699rNNMWMsIL7msnFB//R07POkwdTX/puEE3fgHxEFcPzw8xJZvbcaOd27H/KpLQc4oZWbBFNe1xfUxPLofm3e/z8xUX38R8Mp/bm4lSt9F1310XflH9mHzd96Lx9/xDTOTf/X/a4pSjVOuXaGXwP09PHYIm763FbuuvM/Uf3xCv08pU5zI1n/Py89j0/c/jF2Xfx4bPSnQZsJ5T4F6jS1jePwQNj1zJ3Zd+QDmz1kDjE6o5YR3FRC97+GxQ9j0zFbsunKbKX88Qs7gd/IcNAcBKP1Th3uAkvKX+Rl5DCD6Shhw0z93NZ4a5+mQnoX4PjfGoD1DzlqrVD5P/FelrRmoZCnq/yP7sXn3zdh59dcw/1MXAq/8l3x50fvmpH0uWbchPsPDe7Hl21sC0u2dJDDkR36DJkcO3DmI80BotNcVPClty1tzVGffEaefU09KFzCzOk5cm/YdWQAA/PrGe3D7RaGnW96VIwYaXB9WAL78wn345PBuX/7v0hlADB568X780p678ak334s7XfggMmL2kAfDegvEMblOWeKBF7bhl/bcjU+/+V7c/YOP4qyWVQmTrlwxTHozHrMzATWxOPEWRKcVouDQkA7dvS8Nv6y/NPyJu5zZK+jh4QVsfuImExNeuRYkZ8LRrBmYVLHbmeTTW81M8vVvDIy2aYsyPArkLJ4dDo8ewPUu5nzORcCJ/6rPctvc1+p5xvD4s8aovuVL2HjORUB9Inutr75ClHisz+D2/OgFbNrzz7Br42ex8ScvAEbG6NEg3UKGA2MrzeFYGLpx0H9+pv2WL2F+ha2/7MOgqiXhz4U/DmLTH3zAlr8GVJ+IbivJSHuf5fpHQ9JnXBvDJzx3TV+Z97Hn5eeb/glIR2gwVW+DOC+Py17cc+xQIp9JvUtemZbci3B8XQCcKJECva4JWSKC0ywLRw9g8+6bjX5YsRYY/dh7X2RuhtknigRZaDwOac5Fg4U/X8DN37mhs9GOVUOiPaLze62n47EJPDUxamUfEdcWVz4A3LXmLr2ASDfLasZ2zaGC8bI88Pw2fMLax62r72xdZrxsiMEn9nwMn37zvbhzbRM+kEZNm3mkxoQgVi16bHt+Gz7+g4/h3svvxZ1r7sTdP/go5jQ7lK1dy0z48AK2fOsG7Hj3YzaRi+3a4tTFNYm73B2XpGNeEWqdOIUEAIAaMzfl72ti5udebGbCXGdnzuG/7W7z4fFnG6MhZ2I5ZD0F+pxwePy5NOasIat4y7Nab1Qv/wI2nrMaqMfqvbFRy5GAuB57fvQCNg0/hl3z92Lj6y8slkGDQfCcRFcFZVdN/b//IeN+X7FGvMs6db9PgeGxQ41735VfTLYMz7X1W0AKXn9h4T0CoErt95AssCUKghR4T4R7vyGxavPGaPVwUHMifMEdZDI4rCRiTjK+MuEVQMzElfDKdS68cu7F1pNlzrH0LAT3peEWt6RQymwTntjswytay7Oe3Q7HhocXcOPvbw70c3c0pQ2iervTC4cXcOO3N+Pxd+/A23a+TbUtaWmZ8SHzg+xfD7xg7Nc9l9+LO9bc6T9VU6Lvy4YY3GONdkIGhEFKGXWjAIJ4qBjQ9z1/Pz72g4/j3jd/Gnet/gWvDIgVpZ1xhZeY/PDwXmx+4j0mu/vcSxr3soIgKayprP6CRUw+Jh2msDQTPKmvEgKI4+fDo/tx3Xdvwc63fwUbV6y1pCAiAvJ39Pxssp3Fnpefa5Tu6y9IY+YqWtzn/hhlY85dDF6XGZo3qpd/ARtXrC4auKR8a4wSQyXq5knBxs8ao1QoJ6ie6k1JjwX1X7k2vWOR5CAgBUr5Ogiy71xbnGzK9v7g2LMNKcj0T/FJSt9JBKRgxdpAQYdjShAF7UuucR9a/SKNdpwTEV5f9giIg/n6Z8dXObSihVXc7+HxZ0347G0PmPCH1W9sjX+jaytRHjWTtihEESbGEhYOL+CG39+CHe/a3m60c+NV6bvGE9pdP6tFZ8aGa/fw8AI2u/qft84+u5DXoenpAOPmuUSN/br8Xty15k6XY28LyD9m2RCDD6xxMZkOM10FFDF8UIX7XngAH9vzCXxm46dw10W3BzM9qqOX1+Z2Vq4dHtmH679r3Wtupp0DVVC/PVZwlw+P7M8LdUc3eWn53PDIPlz35Pux66qHTPa4IwDyHkkIWslAOHve8/Lz2PSDj2DX/Gda3b8xsu5ggT3H5UzvIvN+3UwlNzgnyLovGlWuEY/Mpn6R3Mzpw3TPy89j0/Cj2HXFb5qZdq46puL6yUIIJvAUrFxbJgBTkIPhsYON+33lm4JWUxwoI7/bTVqQlZ/k/b78XKf+KUExGWH5khS09UEmxgxADa0F8nPO6ix51mraJQzVJbxixpGN73tiM4Z3X1LVkB5JEqnC8NiBJidl5cV2fJG4R15vp7EAiCtAfPvYPHcc6kAik3PxxE1m9Ueb0Z40zAJjtDvrZ4dIBmR9k/Klfl61ztuXxLZobSjlYxGBuMZ9Lz6Ij+35RDOptSmsbip5RngMIBV5iRAUBQT+xd73wv346PCX8Jn5X8ddF96W3pd7eZlnaIk+xv3+iBWKTL2CwZh5nCp0lnS84xtmnbZW30xfdEm0C0jBqksiEoCy4HbAnpefx//8g4/gt9/8OWxcsSbvVs+Ax+OEHBCz76tkpudvrIvGMvu8wFVsZ3rf/1Bi9LwBoPJMtA3GaHx4wpl2iydEuq+PP2uM9pXbbP07EKIJyMHw2AEzE75ymzEaST3j7J/4Aim78Xuup+6friiV39WDkvU4OU+WRipPBimYwNPUFT6n5sptxqhKUiTGmPc4cY1mvwina0RehmmEH7/Dw3JS5fRPt9Bb0MYcKZD6+dxLdf2s6QRRh0AvewVgPblunw6pn2OPUsvEMo8K9734oLFfdlLLrn+COuXH1zIiBvHgmNJIWab10YVPGlJw0e3tZXQxsALDI/tw3e73mezfVZe2lN1e9yQmFgj1JUA9zriO83UsPVduDrNh1aV2oNtEI66jAWMHO9fWGHKjCMi+JxoEz9tz/JDxFFz+ecyvWGPGlDJrTmLLcaZ0eHVTvp/pRUo3ybTWCEJ7/sLw+KGs0eNFEgJfvjfaF5cDEl2NtWi7WR1wF3a99ctGqWeumxbDYwdN+W/7spHPGF3Ha+a6PccOYdMffsjMVM+9GNyqSCfDRP1fQI5AaJ6UhFAWcjBUz5MYP87T9NuXf957UjT9IPx3zUEl8TAOJTSkwPR/bmmndq8K11aiaB8QM6niqP2+el3kSLlmeHR/qJ8nuFeOjzCXpzH6gafYbt4VlNc5LKTDkwJhvxoC1k1alw8xEEiYOJEQrqrYySopaH2gXmYc+wSM0b7OLbkrCd0kEGzclG8HjY1ZyTokxtMOtgRekN05O+iP7cV1T96Cndc8bJg0hAIhgDi6z7Pf6DgNrOsxnBkOjx00RtsqxVLIQ0vyTeofwXsK5Ey+sBYb6vlM+UTtRi9XXkf48q96KF9+CS2GfXj0oIkJO0+QxSySDE35B3Dd01s9qSypqda9D9TyD2LTM3eYfRam6Z+28tv6v2udVeVvl7T+wQcbo+rDJJlt1/zzGsKZ7TfmxtNx+Rcwv3Jt2v8aQYjffSEBMSCVTn5yOQnKOYP4eYK0RvpNIzSdCAHQgRSsU27KIDOu4voFpGCS1Q0tdsvhvh9+pSEFq38hLGKCfKBlSQyYKsGwo1ksc1Yxq6Qgp0hjw5nrcBHbHh4x2fuPX/MINpx32aLd7THcoHHldy09500A4Fl6U/6t2Hn110NSI7OvWS5hq+EUVpJX4M/7Gxul+NYvG1IAoQYXO+ujKlW6Ud31391IAVOVN3rTGtVYqRzdj+uevh07r3kYG9xMaYYw5d9my19/csp/6jbsvOaRTko3fX5ZBsL6z7h/mM37fep27Lz64VZS09xXcmNHq29ypKaQKKz9zo2Z4fFDJjzR1dOREILCWKHKkqY7PKnMrsTIkQB1XElSEOrPXP1L+iwgDYnRFpO28yYgBS3PDMIHLrwrPQVtOqjNxljE4YP4nknI/bIkBkDTCZTEjPWBqnYqkH8ZnumibLQs0x8e3psIRTDjXaTh84MmFroZgCksP1WKVTPgvBeTRRw92vRZcYMOjx7Add8zRjVxX2v3TIhYaSXFdwkbZAd/pRu9Dm73okIRMP1/Kx6/5usTkb6uCMrPLPlafPnvM/KzmPpnxskp6Z8nb8Xj70jLn9ZlbY7YROSY1BSXa7rndk+om8rTNIH3wJOmhJTlDb8q+7nwSs6oTgivc2PS1JF0dKlrcz4iNd95r0mUDEhBl3BlB0+BtF+rtwbnpvH2LR9ikHPlJG4pQRgs7nvhAXzUrT5YvbVTR+bYsDZYh4f3WqEQH5xJwh2LSPRxgyYWuhhTzlyD+meYtInzdUuW0RIxpVENzy4+PqzPtEtJeN3cgk35B/JGb5KYfE4pOlI5iw8WLVX5sfxPjXScLEn/yJh+WwEt+UkaqemS+9PVo7J4T1NuciTCl0/eip3XfB0bzo/lv3yvfi68Zyr5aZtoCX3bWX+2oTR+xQfH2uxLKXSj2Rdnv+5986dx5+qtncJDbVhGxCDX2bqAuJjdfc/f75d03Lnmju6DJms8QsU1/PMFbH7iPdjxzu3YcP76Tu7lSWKrkwrdpHDreJP6Z6DX3SVM2XipLF96IkpMfUqPijqT7NJHHRWXprQmegdtMf/DVn7etf3kzOT78qcsv+ABKyAJHxw+EJIOZYxM9Bx1JjylJ6XDjNiP32sfNeHRDvdOMj4m1T9dni/fQaw/2zCpfvX1n0Q+Czohti/Sft215g79piw5y1dh+RCDHAov8r7nt9nNH+7BXX4fhMWX6zA8vIDN374RO9yOgx2NW9fM9ZkrxYSpR/UPKlkmXDrC1Qdm0NyUDPoiuZgAk84kOw96uTlJqf8XSdLM5ida/086A8isjsmWPxu8Nsqfvq8617/LI5Ikwoz8cDeZyU8eZPmz96RwRIq18Tv1ypUZ6J8uYB/ePYmkkqpy/Vvu7YJF2a8WLCNiMJkw3vf8Nnzs+3f7HaFm/bzh4QVs/tYNQijySY/T4OQpXWH0gvrHl8WGuuOwst2mDhpFeU6LRQ/6lnc1O6NdKL/U/xNB2edipuWnOH3L7yj/ExcbbqmclZ+S+ARLEstj5KR7Uo7sm87oBcg3ttz/i9elp4S0Ti0/7Tpk8far/JxlRAy6w3TqxxbZqXkYodgcCcVsDEZT/slWunH929C9fdn6q8pucpU23aCfQf0VTKOQF0T/nwyl3pd/astP9hk5vHe68duRjM/e6EXZ+0uufxanS09f0mpwsu0XsIyIQdfBu0106p127+hZPm9apdJV1Kcz2ilmXf+u0Movt30yJXCyBqXrh1PdP7NGX/6pL1/KyCTy0y75S+UJavTPa4mUddEUs9KfgK5DZ6EfSvfMyn61YdkQgy6IO3XWWIxS6fKCl6tRmlU7lmv/9OWfmeVPKr+nmyelrfxJ0Vaf13r923Cy7ZfEbFPYX8N4LZOCvvy+/L78vvy+/L78HE4lKQDOEGLQk4K+/L78vvy+/L7807H8U00KgCUiBkT0WSL634nofyOi/5WI/oY493Ei+lMi+ndE9LbFPqsnBX35ffl9+X35ffmnY/lLQQqApfMYPAPgZ5n5HwP4PwB8HACI6GcAbALwDwFcBeDLRNOvXetJQV9+X35ffl9+X/7pWP5SkQJgiYgBM3+PmUf25yEAq+zf7wKwi5lfYeY/A/CnAKb6mHpPCvry+/L78vvy+/JPx/JPtv3a9vy24vnXQo7BzwN40v59LoAj4txRe2winAqmdToLXV9+X35ffl9+X/5rs/xTQQo+9v2PFa+h5CteMwIR/QGAc5RTn2Dm37fXfALAagD/lJmZiO4HcIiZH7PnHwHwJDP/jlL+rQButT//AYB/N0U1/zaA/3uK+17rWK7tApZv2/p2nX5Yrm3r23X6YZq2/R1m/kntxEnbx4CZ31I6T0Q3AXgHgCu4YSfHAJwnLltlj2nlfxXAVxdTRyJ6gZlXL6aM1yKWa7uA5du2vl2nH5Zr2/p2nX6YdduWalXCVQA+CuCdzPxfxKlvA9hERK8jop8G8PcAPLcUdezRo0ePHj3ORCzVzof3A3gdgGfIfInrEDPfxsx/TET/AsC/BTACsJWZx0tUxx49evTo0eOMw5IQA2b+7wvnPgXgU6eoKosKRbyGsVzbBSzftvXtOv2wXNvWt+v0w0zbdtKSD3v06NGjR48epx9eC8sVe/To0aNHjx6vEZzxxICI3khEh4joXxHRC0Q01YZKr0UQ0Z126+k/JqLPLHV9Zgki+jARMRH97aWuy6xQ2ir8dAQRXWW3Nv9TIrp7qeszCxDReUT0AyL6t3ZcfWCp6zRLENGAiF4iou8sdV1mCSL6G0T0O3Z8/QkRXbLUdZoFiOhDVg7/DRHtJKKfmEW5ZzwxAPAZAL/CzG8E8En7+7QHEb0ZZifJn2Pmfwjgc0tcpZmBiM4D8FYAh5e6LjOGulX46Qi7lfkDAN4O4GcAXGe3PD/dMQLwYWb+GQAXA9i6TNrl8AEAf7LUlTgJ+BKAp5j5fwDwc1gGbSSicwHcBWA1M/8sgAHMJwUWjZ4YmM94/zX7918HcHwJ6zJL3A7gHmZ+BQCY+S+WuD6zxBdglrsuqwSZwlbhpyPWAvhTZv73zPwqgF0wRPW0BjO/zMw/tH//JxgDM/HurK9FENEqANcA+NpS12WWIKK/DmADgEcAgJlfZeb/uKSVmh3mAPw3RDQH4K9gRvarJwbABwF8loiOwMyqT9tZWoS/D2A9ET1LREMiWrPUFZoFiOhdAI4x8x8tdV1OMuRW4acjZrK9+WsZRPQGABcAeHaJqzIrfBGGcNdLXI9Z46cB/CWAb9gwydeI6L9d6kotFsx8DMZmHQbwMoD/h5m/N4uyl2ofg1OK0vbMAK4A8CFm/l0i+p9gWGVx18bXClraNQfgb8G4O9cA+BdE9Hf5NFiG0tKuX4QJI5yWmGCr8BGAHaeybj26g4j+OwC/C+CDzPz/LnV9FgsiegeAv2DmF4lo4xJXZ9aYA3AhgDuZ+Vki+hKAuwH88tJWa3Egor8J44X7aQD/EcC/JKIb3CcFFoMzghiUtmcmot+CiasBwL/EaeRGa2nX7QB+zxKB54iohtlP+y9PVf2mRa5dRPSPYAbBH9mNsVYB+CERrWXmH53CKk6NKbcKPx3ReXvz0w1EdBYMKdjBzL+31PWZEdYBeCcRXQ3gJwD8NSJ6jJlvWOJ6zQJHARxlZufZ+R0YYnC64y0A/oyZ/xIAiOj3AFwKYNHEoA8lmJjMvP37cgD/5xLWZZb4FoA3AwAR/X0AZ+M0/4AIM/9rZv4pZn4DM78BZsBfeLqQgjYUtgo/HfE8gL9HRD9NRGfDJEV9e4nrtGiQYaSPAPgTZv7Npa7PrMDMH2fmVXZcbQLw/WVCCmD1wxEi+gf20BUwu+ue7jgM4GIi+itWLq/AjJIqzwiPQQtuAfAlm7zxYzRfbDzd8XUAXyeifwPgVQDvOc1noGcC1K3Cl7ZK04GZR0R0B4CnYbKlv87Mf7zE1ZoF1gHYAuBfE9G/ssd+kZl3L12VenTAnQB2WJL67wG8d4nrs2jYsMjvAPghTOjxJcxoB8R+58MePXr06NGjh0cfSujRo0ePHj16ePTEoEePHj169Ojh0RODHj169OjRo4dHTwx69OjRo0ePHh49MejRo0ePHj16ePTEoEePHjOF/QLhnxHR37K//6b9/YYlrlqPHj06oCcGPXr0mCmY+QiABwHcYw/dA+CrzPwflqxSPXr06Ix+H4MePXrMHHbb4BdhNtq6BcAbmfnE0taqR48eXdDvfNijR4+Zg5lPENE/A/AUgLf2pKBHj9MHfSihR48eJwtvh/kc7M8udUV69OjRHT0x6NGjx8xBRG8EcCXMZ78/REQrlrZGPXr06IqeGPTo0WOmsF96exDAB5n5MIDPAvjc0taqR48eXdETgx49eswatwA4zMzP2N9fBvA/EtF84Z4ePXq8RtCvSujRo0ePHj16ePQegx49evTo0aOHR08MevTo0aNHjx4ePTHo0aNHjx49enj0xKBHjx49evTo4dETgx49evTo0aOHR08MevTo0aNHjx4ePTHo0aNHjx49enj0xKBHjx49evTo4fH/A1grZl4Sbz6sAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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R/IGveCX+xa0xF6E1j+xgYGp7jHsqxCd+/Tr+46fehr98/0/hq7/8Ffhnzx8jeqVA9eJo33VXJMrVP4Ky0YtBTZka+V/8lEDNe0yJmDhDQwA5COAKDsIMFGpIsPpz+MkDeX9f8z5cuuueUqgkyg8Dc/cVkOukfpntt9889em/izf8t9+DD/6xv4H7v/Lrs5KVT9OoBfohf8/qgXM/GTAQBRx86hk8+NF34AOvegSXvuoVUv8bNUHKpYAoOUAImv0S90Fg5myCrzsouO/CtvYBaFQCFOjL52ZpK566ITEv73/tVVz0MQtebMMeOMDyM8qBpLfTPs8vUn+ltD+QtM1TBQzl/6VMiP36vtp+9c67lGdNpwgMWkO8IOOspHd9/Efwzr/z/fihf///hu9+2XdUpNgaC7LhSB0fpBw/J9VuINpaavLOKCAwk56BGgpeer/7XfmbjYdu6AFBBoc6+3j6pvicf/LVj+Geuy4uGoGlGALfjLXSL0galTd9+Aqe8IFiHga2gkD+WzfGB596Fm/4f/0paXRf9HXAdNQ8t32tWC31k/MvMoCDX/t46ekZFOixuTcXImZAEKKDgZif05SNPJAS45oqNT/56sfwDV91H26NXAHAxDwz/sxi+JkZExicynETipGfmPFLv3YNf+6pt+EvXXoE//aXvQL/7Hmp4+yUhuSgwIsFW1xJ/91vPo3/7Jm348/e8178W7/nG/HPny9D+nzDHvRLADkwsKIkREj9MaCwbb/0a9fxZ37hLfhr3/QovubfvBf/32NGDKWOkSoORAoaDhhC4Fwvo/ADQiCtm4RIEdduSqDq1dc9hksXLgKcwBqsSqwGiEg4MLuIFt7xjrrw1Kc/hjf8wp/GB//oX8P9X/l13SGPXR3Uu5ng4NMDAxEOPv0xdU/8GC695OUFClx9pAZQPSTY6BC71QIIcp3rNw6y++CeJlA43/ZKPTHjD/RB4Zqe/9EHdHQDJA8BTtWwW4aHAy2jVXdADQX3e/eQZe6kqQGDp5572o1OcoGk1NoW2b448mQhzezXkjtrQzo1YLDYy/PPdkVFeNffew/e+Xe+H//5/X8Z3/31f3IWnT4DgDTVxr8q/HmcQQ4kmgWitTfiKgDXCgExl+C0Bg7MJ1ZDQeltWlG0gWhrQOBv6fqNA3zrz4o8e+/CS790Ky0QeBnYenDXNDr66uuvik8vjQswMG0Agbli4Bvdf/9FXys+/6nzDFzvvrqXGAFo+YOq45iCg4L3iLzsVAAOQ26AOYQaBkKEmRGGsKKBgMEBM+PajUO8/aMP48de9dN42Usu4jNjEoNtcMfA6ABgnGrjLzDAGBPrOe1ZM1Ji/PKvP42//My34vvueS/+91/2jfhnz4/Z8JvRL3BQb7e0Bge/+tvP4m9+/Nvxp17+Y7j7X/8G/ItbtdELWlmCFr3FE+TtVO8LRLqNEED4+7/xNP7iNYGa3/9lr8Q/vzWqymAgAQyRstowqN0UtYBBqcBCVGUhJs6g8JQG0j322qu476WXSu85cmkrDBIQBOKZpQOR6yiVz1SrC1X9/MqXAcy3UT8hxl22VMDw1K99XKD4m96Ny1/5shKz4KCVPJwuQkIsKoLrjF+7YYGGV3HfhYulLdE2YKttqsGhtIWHz4kSITEL1imhfI3g2rT5aAaDNkWIXqethYJZplfU4MWbQWmfn3saD3707Tp6ZT46iX273wOGauTJ/Pmb/TIooC3ujy9KV0InLcHDu37pR/HOp/6cQMHXfftsHHsPAMjT/B6/YfFp/1gORMuHzx6yh4FUtnUUAg8FDxoUuCFrS1DQ+qDhjuspCE/fPNwLBUsR5EvqgAWOEVx08Wt/GvebvGnGP6l/N3+f9oOAbrPn9nc+9Yt4w1Pfiw9e/iFc/rI/hHR0q2S8K9vpczdL1ANK16AKFHw3PvjH/5ZCQQCHqMdEhYPYhQFTBSYWw2ogYN8ZEnPxJ//2w/iRb3kMX/uVF/H8yBkCRjX6IxcAGJn1sxTHmJIABLOCAKrv/+C3nsFf+9i34Xte/mP4ff/6N+BfKBSYi8EMvo8nmBaUg176H//Jx/DeX/kOvP1r3oOv+t++HP/8M32lILov9tnAIAbKxwoUUAaFX/2tZ/FXP/YOvPOVBWoMHIYQVCEQZWFQpUE+CwwMoYaFqMpCVPB49rlDfNtH34RHXv0YXv5VF3FrZAQFBhAhhqELCZQmMEWFWV93Kb9gDI0pMKXgxS/vtidcqYtpVm95Gt1XF2cQo0DB3/mP5Pwv+jpgulWrChaPYBAQYoEChdkCCeIPJ63DgYCDm4d4k/n8tSdvt0Dm4nKuh3xrewys7X/muWsyOsYpEQx3EVA+v8VBWPtSjnDfiMAo8VkHzz1d3AcX7itti5V1dS3M2vg11yNTKKOT/sSPS/tvnUIHDsIIGvTqjHoZcVKu3yoMtf36tq7SdNJ0esBgiZD2xBq865d+FH/m4D/Gf37pL+G7v/YdpWcKzCuH37biZvCV6OBTz+INFp3+Va+Y0Sa5c7OXuxbcBW06uPm0QMFrFQqcb9CDwRIUrKkE+6BgDQZs/xIMmABvMw4+8cCjUj7TcQUDdY9LG94VEOCpKD0pJRz8+i/hDde+Dx+8+J/h8pf/YTcksUmhKft8Y9K4ZqXAuwUo4uDTf1eg4Ft+FJfvuqeoAxQVDlQxCBGsTztZTz+xjCZQdSAlYNIevBn1Zz51Dd/1cw/jr3/T+/GHXnQP/pX6/McGAsZUev9j6gOAfGf9J8b9V3/7GbznE/8Bvv2PvAd3/Wsvx790SkELBskZp3a+gSW14P/zz34Rj/2j78bD/+678JL/zdfjXz1ffOahqdvRFXtQ4+bBQL4jf4+B8D/8r8/ib33iP8B3vezH8G/9nm/Av7o1ZWgQZSFltSFAIMBUhiHUsCCAUJSFIQb83ZuH+O6fexjv+ZbH8PUvvg+3poToXA9BFYkQDFYEEigyOE1afweBhDDVdTqRxLz8t9+DD/4f/2aGAo0IkXonkk8pGP85P4/6YfCELK889alflvp/+Ydw/1d8DXB8S9SFbn2Wt5e03ua4F64VL6IgxocCDp57Gg/p6BibxjrAtTdE0owZCJHP7X5IsPbHxzQl19bojyXfem4PBz6R3a8L2D68ca0ONOylVi1G0+avAM7hc9fd6KRv1HasafPLSXWjty2h2scW8KHpXZ/4cbFfl/9T6dT28rKopn8RKAbdiWgALEr2AN71iR/Dnzn8Afzwpb+E7/7ab5u/aCsVYibVdAr/4FPP4g3/9Xfgg9/8nlIpqkwHh9ZUTVxj2/qf5YWtoOCl26BgzXXQpmeeu1ZBQRs7sNdVsAADJZDrKQnEefUjuP/F36g+/wYGkr1IDgYUDjIIAAAnMVzZ1ZNw8BufwBue/nP44D1/SaCgB48h1t8pSI/LAQH7BjQMemNRo8e/Ex/4Ez+OS3dfBK/BQKpVATPO2UgzKxggxwU8+6kD/Ic//xb8lT/6KP7AV9yDf/n8iAnIbgJTAkZOMwgYpxoApB4wpiT/Epee/Fv/0Ltx9+/+BnzmSKDJG31v8NsRCPviCv7nf/6LeOJ/+B48+Pv/Bu7+3S/HcW/2InsMRDh2jycGrvaV7botBPxP//RjeN9//114+9e8BxcUagwYWqUhEGGI1LghgIFCpSwMkRCP5fsnfuMQ3/cLb8F/8U2P4o98pYyeoMSIYIQgakNIoh7Y36jXigYJYQDBQwIDaQB4wsGNa3jjz38nPvjNfwuXX/IKiVdAqf+MBAziQoQCr7wKLZV16nWacPCbn8AbnvkBfPDev4zLX/6Hs6rAU6njAGpQCBGco+RVTQhTFxKe+tQzuPKzb8MTDzyqgZhTHY9AHhIotz+mImABEoC6U/LIax7vKpXmzinqQe1aMDjwtTTDARIOPnnYDEnkUrZUVJ3qMxwU7FGKu+1/lRmNU8rfre3y24rmIV+LfXjXL/+4s1/vWO4c30Y6NWBw0gCLd/3yj+N7c6G+HUswsHc88sJ1Dz71MTdkrVEK9gWSVCpHyMf76PUMBa9bVgraIEMPBb3kN1ugoX8p22CgrjIAVEAQLAM+iDBNOLhxqJML/YS8NOm4dhO0ykALAywebzYYSPVLJY3if4IPvvIHcfn3fm39kuebiMVx3QMCcwNkl0CQ4yngqU//It7433wHPvDqn8Kll16S47fAQKMKmHowJsaoDf+YGL/4qWv4/r/zVvyl+x/Bv/Nlr8C/Opq6IFA+1yBgEAAgg4AHhP/pn34MP/Xffyfe8gffjd/3u78Bx2PK+3xaGo54Eij43/0b37h6rJ3PA0B1Xfc6pEkM7z/+J8/ip3/1u/CWP/huvPR3vyxDRwsEpjTEQAgjVaAgPfykcJAEFEYBhX/wW9fxF6+9DT94v8Qs/MtbI4agqoICxRA09oE4ux1MTQhEiGkZEg4/+RQe/Llvl/pz1ysUHLReU9T3YJLOgiqHNSCkqlwqo8CMg9/8ZbzhWa3/X6FQbMeEWFSFRqWkmGpIoACJdZLvlKZc/x/8298mky+95BXg8RZ87IwPWmzjEUA1JACtkgA8ffPaIhQsCanMEhNiqoLBgdSLchyBZHSABmpbIKmhBkNVYyK9d6/iLsNtzhz2tP/52Caw0Jqo5LZbZ5H1+jonhdivv1A6tSe0f/vS6QGDE6R3/fKPaqH+BYGCLTCw4i5oH8rBpz6mQ+J+pI5OB/o12owPUMgcQBW97vZVUPDS++H64jUUaLZ6UNBTC+wy1w0KdMiR5kSOycfWwwy76oAGCuYgQg3YzDMO/okfx/0v/gZgvIUqZkAbxJ6bgK1R7MCAPYuqUfw3v7a+OVMIDAIaGADkWVQxAnqMxQkcPPcxvPHnvh1PPPB+XHzp/eAVGBgTr6oCIyeMk8QIjJMYgY9/+jp+4PBt+E8u/iS++ktfic8cT6oCrLsFvCIAqwOYuwT+8f/6LB79h2JU/+0v/cZKIWjl/RA79XVP+n//s1/EB/7x9+DNf/Dd+D/8nv1QcDvn/+lf/S686Q9I/gHn5jDQ03Juy8ArCjEQQgJGYud+YPzD33oWP/yLMnriq7/0lfj/jQkBjCGgxCNExqAKw0BBhk22aoIpFaGGhOvPPYOHn3yrTE51173gNGpsAss74yEhJX2PohxD0kcOHpApyX1rGRz8+sfxhmd/oK7/vo2y9yVNfUiw9yFx6WETqZIg7jNROt6Dyy/+BvB0pDEIlkeBCAtaDOpaMxVBeviaBev0OEi41ml/lpJXDQh9OMjHaVUu02Q/IWsfsExqRe6MNgJM2rEAH+9V+vBpbpBJ3UMGBR33cXv8Yqczz8Q5VZDwrk+8F997/S/ihy/+hbpT635zp+kUgcEektNUSOsv4Lv/yDv2Q8GiYrCiFBgU+MlPgGKcFocX2bbyuR3j/tTNp5vRB8uuA2AdCoB6qA9QxiG/z72UJ4IBrw44l4DFDRzcfBpv/Ojbxef24peLz3+DOpAb/hUgAFBDwYu+TjN7GzCgjZvBgPlXDz71LB786Nt0xsHLGiNQYGBMtaughYFxSlXA4DilKk7gE79xHT947Vvx/ff8BL76S1+B58e0FwYAdIHAtudnTYT/8Z98DO//h9+Nt/2h9+Crv0ygdWi8KW3yQYE+9Tb/43/yMTz2q9+Nb/sjfwv/zpe9Yn7AQloKYGwVi/9Rz+/zv5amxIiByvm1nGQb6X0USPhHv/0s/sbHvx1/+hU/jq/+slfi+TEhTAIE3h0xTEniE8YCCRanMMSgoxuAEBhjKpDwsedEHs9rTwTM3Q1p0jqeCiSkCRwUHOwd6UDCU5/+eHEfvOjryjvS68gQNfJzArNC8DTO3hMC4alf/5jERHzTu2VGxmnUwMUADgoVREBKVdAiB5s3YsHVAIGEazdsxkqdB6GtJ813bwYX4UB3JtQzJuZpiNW1wND7dQGijFTgAIDNV1GuVxvxg+eerqHAynnR1d1RkvM2r1SIgvGuT/wEvvfpv4Qfvu/Pa0xcq4Rus4OlxPrpFIHB/jSDAksnhYKFlKGgrRSW7gAIAAgUfOTh7tz4WSXQDVY99rkPUK6aoeBRDwULMKAfl9UBZu31MKyBy6Mzvvk9uVHJQJCHh3bUAWAZCHLZkrgPnv0BfPCeH5TzAzUMAKBhtw0GdBsHlUfDgIPnnsaVJ9+Kx17/BO656xJuTduUgX0wYDEBf/83ruM/e+bt+N5vfC+++steiaM0dxMANRDYd0AM+JS4nhPA9fj/8T8R98G3f+178Pu/vECrN/y1P98MZ1NfqH/8P/ytZ/DeX/kO/Olv/HH8wd+7EMjVSf5e5kMg5e+UGP/ot5/F+/7Bd+I7vv5v5fz3XB1rszEC5b4KIDDSxPjH/+vH8J5P/El818t+DF/9pa/A0ZSykiDGnTCwBTUKJBx7SCCBgmFKXUj4xU8f4jv+9sP48VfLNNPPT7zqbqggwY9sWICEp37t7+IN179fAg2/8mXqbgjr700u6BoQEII2fymrCAe/+YlqdAPzhDxPsRlUF8yoYZnFMBpAZFdDPfTx+g1Ze+Wx117FvTbNMddt19b+cAsHAPD0J5dmTJS8+CmxSTYDLBM4leHSQRs8VRZUZSEkUQpsGuuXfKPPTG0Htsr+2Z0h5fo3f/m9+N6nfxA/fO8PqNKdiv1Y8q/cZjpFYLBeZWr3wQl9Mj3iax5CBQVeKejBgG1vXAayfw4EeW7zj7wJV1//RI7+7QEBUKsEQP1i9e6a4OeWfxwXdcGlpbiBrjrQuAs8EBAnHDz3jIzO+KYfEaPdDuFqgABAcRkApXHrpRAl0PCZH5DRBy/6+lVloA0ihA8YbGDAYgaeunGIhz4k47S/8SUX8bwF9+2JGWjdBKYoCDyUEQS/8uvP4IeefTv+o1f8BP7d3/sKBxp9t0AgyvvaYX7zaH/Cr/72s3jvr3wHvucbfgx/4CtemX3sQFHf980dkIvb7bf03/3WM/gbH/92/PlLj+CPvOje5WflUm+ypAoStLamBPzKbzyDv/WJP4nvfcVP4N/7vfd051SoAUP+bg2YNCj4jq/7UfyBr3il/l7OGdiCFRlptN6/PFc/0mEkQpySczcIJAyR8IlPP43v+fk34Ue+5TG87MUXcTwlTLw9JqGFBBvtIDPnTW7I41/H/S9+GVolIUOCLzRgGRZcDAMn4OA3/x7ecPBOfPD+H8a//5UvA/MEmlJxsYElalXfLwoxS98IETRx7vBQdjMUFeHgxrU8j8nFu93S7YQ8skEyUz+/9mmaaiCHFjiQeRYEOtoZE0s1LoDAqhgsAgL0nnWq7KduinvxA3/ix92Mp03P37ZtNeDuuL/5938yQ8Gf+tpvXenr+7QPo5bzcYrAYDnVgRpvl41r8o6lKjCnKUT3/eBT6lP65vfINJfNMRUM5O0ddcD2kZsWF7pKYjMkaAkIgHWVoFcVyipoV3HppS6mwJSCfUCQZ39TIDCjr8cc3FR57ZveJQsWrcQQ5EYMKGXfJj+SIAQc/MYvyeiDi38F97/k5dtgIKsDA2ZzDYQIUMzzDBx8Unoyj7zm8Tzj4JYAQvmXuiMJvHvgV37jGfzwx96O//AVP47f/+WvxDgZDMyfXwUBmBvoYrSL0f/V33oWP/L3vh1/5hXvxde86J7FiYJsG9F8pkFA1CM/26DkAfh7v3Yd/+n1b8Vf/WOP4mUvua//zJrkb81Mk5+F0eruBOATn76Ov/qL78BfvPRT+JoX3VvNwujLUrYhbwOQyxhw2xqA+NXffjYrBX/gKxr3n//dhAwIgUmBQa4dUilLGeHAGFiUgl957hl8/1NvxV/7Y4/iD7/oHjw/ssBEYglcTPPAxUG3iZtDIMHiD+TfkN+1wxvXyjwaL345WEc2SNDiQkyCFHitKCykg1//ON5w7c/K+/WirwNPk75fULgHQDaiirMMPwMECmJ4mcsESiHiqZuHTgm9VKkIiYt5S0A29FKHeMW0lWN8+3avrrLpYw+APYAAIK+ZYYCg5UcsgYxv/Nvv0FUw79EYiiRCCgs4FMkfy+1avnx9V3/zV35KoeA/wZ/62m/d8Js7jzOgfZNM/E5I9BJifNvnOxdn6SydpbN0ls7S74z0dR/6GvzSL//9LludGsXg1v/1t2fb/IJF9/uefAVDC3EFcDEHTdr9yAUcf+fN+ayFjdvAqwIAusqAbK9dB0BZ4exO1AHb0nvyfjbCnNOtCkEvhiAHGZ5sZkI/D4H8WafpdlY3APPJh0wZcMML2yDC1lWQUAcRbh1eWJSAuTrQm3AIwKyXC+zxlOyR+ntTBFuvv53tLwJ5yF2ZHlgeeLBnbr/V+BKya7f1o1eHNqZe/W3rebsQlE36xPZ7rqeBNqXGTwPdzgJpz6RVbVpXRuuqWHsu9mzsGdi+dkIlIps8SVQFP6GSDH80NwWa4Y/mYnB/gz0v1gDFUf82QYsWm2PvnJ8jBGV2UD8x2EneQQArE4CFaltWQ3MMjyqnpta1aumGIdhtHWrTidtAd7BvB8tmZyesPXTf7fPiJHkdOwMUW2O2BYDELOiMuZcueCV6wc5U26xdXE6v/NAfW9x3asAAjZHOUPCa9+Hyhfuy3xpoKkkVbQr4Qq5mImyuI8YHfQnHg4D9xn3mDhz0YADAbIQBsPwibNV+ZlMUy+U7QAD0ggrX3AaL8xBUL0RpkGZ5CwvmZdYIATxriFyD0wsitHHWjasgMapZCE86qqBdg6CaYKgDBL3UGpn8uWNsehBgBsd82+36AEOUs+b5JdSwhEC6ndw+qqazzvVkqY6gaezWVEj/TgD5LO0qktbwGxQYDORZIrUsGSSGHmr/dB9Dnk27boQ9rx7A1bAg+UrUj3voJaszgUhsM2o3wxAJPAIjAWOaSiwCBww0VQGLI8laDSEworosBApYgGGfqyEYEDRBi705EjgBg7YJzDJXgn+eG5Ks42K/sQXGGvdCiAAa1wMxgKjTRxNADCKNXSB1R5A4zbKLQetCdgdkzwV14QCYt42E0n7abyc9P9R9YXGVdbC1tZ3qaoCVp7ZP5m6wE+fAdntbzKXg7AZz1cnM0yhrzMKlC417bqHjKb915207ri4d3Lze2K46nR4wcH7ngxvXZO7rBx6Vua8BeD9PqcDeAFohA1WwCC2M5bLtvYeypgrk/YU/PRBsDSTcCgGzbNN8lAFwm4GFGRIszmBFJQDK9m7G5pXYFAEAqOIF5EZqZUBhoAQREvK0xG6NgttVB8z4W4+0BwRt/ACAqjfaprWAvn1KgBmVdirfPKZejbn1PsnOoz3O2SqC2gBWNVMDR8XANEDonj8A7J37wz/fXuyN7z2Se6ZUFB6B5/m00oyy2JSHOo5Bvg/lPfJzR/BQppbuwV1PWQDK0Me1SZ7K8EgAQSFxLM+TQ8DEjEh6bQW6MTGGJDEIIxEGJkzE+hwZk0LBFMQwRgKmrCJEhBhBvTUbZtMxTwUKnJLAZM+qhvfugk7dpOfUIXZkbwIF5CGWZvCDbw1IZvUjXagsqPFGgEzqE7JRBqSu5q4cY7YM9IZc1t+5Xt7Z7pb8wU1baZggkCAHSkxCLO2mgoOPSwA8KMxzc/Dpj+GNP6eTR93VBvL23qMWtsu+Kul+s49348L8GE2nBgzY3fQVnSb40t2yyldutBwQIAfB6DYi9yIsD+w+uPm0XK9dYKd5OC+0KtBW5H0s32PFzUMPPRT4uQgaozBbx6AHBZaa3gdreXvj7/dJLlyZrsmT1eJEjatAAeGk6oDJ1K27YG2RImAOBbPn0gAAUCDA9m9RA+oZ+KhSAvzkOh4CujK09SYn61k6KXoL8DnY22dIWtCzxrV9vrTn+Qav/kSNcAchgWbA18LCxEBKUQL6eR4wulVVAJAB09ISAOZRg2AtKtKhkDUgjMwYiDCmOewJKJxQRQCAIACQRzVQ7D7frCS451vAAbWi0Ekz2doNpSN2cKCKghTMBCIGKOm7Kj1vaZInmHogHYDUhQN7DjbLoWSjDwdb20z7rYGC3VvpROrfnpqg6saimmCgADQdppLe+NF34AOv+imZpnkhdTuhlhr1wSdvH7//g39u8fynBgwQIg5uHMqQvmqVQUilBxQHwxwUPCSspIOb1/HgR9+Wr9eFAGARBOzvFj+Zr9Zthe69m0uqEOX97oXSHT4Slyy3nViCGRSkZlRBazhWU5HU6sakU7G3wMCiq8CMxbo6wNx3F3gJegkI+sZiDgVeBSjPgFDHCpwcBIIWRQsC9cI+6q8GIMPcJuDY/M9ulj03814BvqkMIZ1GZH+19TbTVPzRnZEk3ldduYjs+drcEqYImOqTh5kOZXIdKmqQfa4moKKI0B1VQntgEGCOdezIQAUUhj2gYMbJAWH7/O17DQdS58eUEAKB2ZZVZ4yBZCgjSQT+SFN2CXEMIHVTCAQCE2FdRSBIGRkIpAlE+gyDcwU6COQF1Y+r1qh91/c0oPYbDwc6HwClUeEPBQ7cvtq1UHQtoKgHfgpkcytQJ5c5J82O7EpwKcCBgrXRDhTyqIkFNQEU9ZwOFFDUaw8Llp54zftw6cJ9+1XhjlIws0UuZfuooz/WHtepAYODG9d0msurFRRI0iJWEOiCQqXszCVQk1+uvvYxfPN/+Spw3DXH5L727O+ae2BtiuIeAHgS9sbeNncVpDamwEFBVyU4KRTMMtlh8xV/l894PbSzgYE2cKmzaBFAXXWAGyCo3AWYA8HaMsZmFABUUODT0vj/Agkndw1E9BUBDwKDVwPSCJosKM3+Oj80lwA14gQej8HTKAZ9GsHjMSxwjUdZCptNop4KHEhBzMGgpqNlMECIUke192+r+9GwgwWQ0rAD4iCf46ATVcnMegYC5MEgRFAYEE05GsyNRDKp1AoopBTraasHKpAY1l0PuU64RaCW4hKk3hCgwalrgDAyMJAEWXpAmCBuhkQyLU8EkIK836MCQ1YRSFwN6K38yBMQBvQCiD0kAHF5/Zh9yakIZZu2KwjidjBmaOAAOgMh22dVDywHFRwQVmMO1tpV+52l7FLwCl8PFBg5PkF2lnzNQCHvd8MhHSxcfunleblZasqvCwLVMbL94MZhHvJe7OMyGZwaMLii01xe1HGq9S07KSjHDPAcFIAMC/IDV6hPvrkuVP2Nt/nZuN8GCPjKunUIaSbZBbmg6o+/EFCwlC8vOQIwyZAzdPXzV68k6XoB+2AgxxOERXXAenhtw1+kZVRBaj6g0ICgbfyBuQEAUE025JUA2YfuHAFlhEDQkQPNlLr2W+pHqA+mMmRFQEFgXAYBSscZ7DIEjMfANMqU0/rd1AHWc8m01UkMhIHCNIETg1OZd4KnUj+4A4fknjfpRAkUgv4jXeVPQUD/CgxE0DAUFUEhgWz/sKthIURw2M1AgcOAECKigkICVYrC2IPHQEgckeJyXZHRKDSDR2j9S06K9BBp9UQCFuVYBCCwgIABAgcCscReGCCMGocwxCD5JzFYiRjk3Aw2JXCEVxFkAqVKRXAxRBaP0IOE0l7W7/zSCC6ghX3bqAa/1zYwo3IrLMDBUtyBhwMAe1SDHjgsw0QVf2DG34FC0mPWQMG+Vq4HOwEgnc6qPN08CP7C+XtRqHN+3W5ZG6K2j+0xbTo1YPDY6+tpLv1Nk9tGbmsNCnpEVebkSKvMOAiosbfPdwAC+2YlXEv5vthmEJPvFS9SHVU+TyeAgplv2V/I/If6NpibpT2mdwcNCFQuBCLkuAFzFWR/c60OmCrg3QUJdfwAo0jGxfDXLgODCl6BAqCJE/AwoGqAlH8NAr1RA1tVgaGNEeBi+Gkakd0Atv4ET6DpuAIBZCAY5fs0gqdjgYDxWI2+bRfjn8YRPEnPkaVLDU5JAv+MxFDDAHeWWKbowMDqQiRQCDI6IigURJGLKRLCMBRoiAMoCiBg2IGGARR3eTsNQxcUEHdFUYhDntSKoikKETxEBUuSKa6ZO2oCY6DQWQRLJP86PkFBgaipP7NiqVKGA5RzIrHG8nEGBA6SHwMEG3aaSNQiczMkAoK5Gew7a5vgVQQNNvWgQCkJVPXefQcKAGa+7OVeLnW22fGqGgAQaNgGB7OGjeACEotqoL8AkWX/5G1vG38g917edeD2QaF009R1Vt1Tm8P6nnnh88ENWRuitY/70qkBg/vuulS/dB0jGKh+LnN4qH90cOMQDylp3aekZXbB2r0lQ78PBPy1l4KWllJo34MOdRNQwYL/ba0WYDMU7E0VHNi2toK38RjLMCAugs5IA8mVGv5aFmbd3sYPrAFBG3BWZuCroQAovf82rYGAzSHgVQFryDeNWw9NnMB4PFcFPBikEVDDz9MIPr61DAJ6DDghHY8ZAtIo0+9O45SNv4CBLmiVkigG+iJYme0bAy9VwGILtMyiKAYIElhIURUDhYY4SNxBGGKGhbAbRB2IDgZ6oLA7X4BiGEQud2DAIarCIGoCxx12oagJU5rDZk9NyPUqUFd5knqFGSgsJZux3+pnTIxRXx9RvQQw5TNrPEKpV6xQbIBACdnNQOq2kO+Uh60GG4JtioHGmth8JbzUQSiNm/5N8/e+rgB760hRFPbAAdWtdqC6PbVgRMl/k42Oq+F22uLFGIQNoJBjFDSbQLEZ/r6oY9DarLY25dqNQzz8YVkb4r4Ll+ZQunKvpwYMZs2R3rSvgqmWDGQ/VYfndKhQ8NjrruJeV6h2nSm/C3cIAlsrYt8eld1OLTAosLTXhbA1C0vHVvQRlve1wTJ7YKB1FTC03LkEE2YwsO0WFAYDBNnuJ8Lx49rnjXeBAp9C8wD8bS2BQHcuATXwazAQZy4CUQRMFZDv6v9PxyWQ8PgoqwKYRvDx0UwpyCAwjTMQ4HEUuDqeCgSMYwaA4jrg4j7Q71Jo8/phatYsxQIHBRRC/i4qgfydhiHDAu2iqAvDMAcFA4AMAkU5wO5cpSZgdw4YNXAx7EAhgKO6HxQaQhiwiwYJQGJZpGpiQgo1JAxBVmvcAbN4leW6VtezNQ/ixFIfWIEkMgvMal0zQBgigacCCJMDBIONCIEXczMEiIoQtWGIFjDXuhr2QQJQDDqv3NiCazHv5jRXIJYS73Ep5EsKHLSqwfq591++tSknBYVqv52zQwYL/ZGShybf127oglGvVfvVuZe12zs1YLD0kGfBIw0w9ArMCvX9rlBbQ++HLPnrz0BhBQLWegySR1cbOlADzAGgokzaAAX71IKllF/c1qXQyhm1QtBdUZKojhtoXAWmAhgEGCCYxJvYlIIi/feAIEeaozTSAHJD3b3NBgDkluTDEgi0qgCo4yKAKAH9EQRzGBAQmFZhgI+P6jiBFRhI4yTugXHENE7AJC4DUwPS8TQDgQwBphIwIzWKQft5Vp6uhbPPIYZSj6OHA/s8ggIh7CJwJKpCGAYgEuIQVSkghCHOIIE7kMBxAO3O1ZBABRIQNE7BXBAKCQgRKRRIEKVK/zYqlbkcEJdVqhYUfNm1YNqmCUC0301cAGFkWQAwcAYEKCBEAMQShxCCxFYELrEsSdsIryJQ62pwsGDtRIYEuYHlVWqXK8X6fryw8QYGB0BfNZA8tzlYeSBc2upc9b1Rx8lAYXJ5XcpPL9k9Xbsha7s8+sBV3HPh0n4A6qRTBAb77r42WL2hKUBdqCK/9BWBJcUAmMccyO/m+ZtJW813twBo3ln7pPQz1VCwf7a6ZShYS7NgQub+S+0KdmkSG9nXwIBzFbTqQO5lGRC4+AE/Xa4BAaP02qRnNm+Q5RZqKKAgvmFTASyZMGoGbGmK4d4Mg/uGEw4k8u6qMmBwwJO4CcajogyM6i64TWVgOh6za0CAoA8DGYqnohLkv86SpRUw8K4YC0C0c1AgWezHyrmBhDRO0rPfRaRxBEIA7wYQjZuUhBkk7M6LeyG7Is7JdxIooPFYH+BcSZBgRgkGTKGBBFMUFBIiycRMu1m9pAyqg9XJKIZqAKp66cvIksEBkSgAMmRR9GligKkAwhBDgQ8WQMgxCNpWJFWtKLFCBJXhkEQgczW4EQwVJOjJvZoAPU+5kYW60WszlhLfHhwAZQhjwBwY2H7DbgO2td2zY1pYcO33PkUhNft7AY+z/Oixtoqk2K+Le+zi8r7TAwZ7D+g83GZTWYXrcdx74eKiKiCfi8+qpwq0FWULtHVgs5t6AHBHUODPndWC9qJlpMEsI71jJTO6wakCGQDqOdGrKXE76oAZf3MXWM/MsrsFCIA5FACd6azM9+0gQG6rVgVsXoEtMw2+IDAwjcDxrQIDKzEDPB4LBKQkf9Xg99SBVSAAMhS06kAPCk6SeGJQFBAzIEv6toUYQJOgMacJFJICguSPQkDYRUw6QUAYA3gYgHESFSEEpN2EMI5y7BDLkMc4gIcdaDyqYhIyJOzOl+8Uc/BiCwkcB+xOAAlWhyOFHKBsAYycaAavA1Dq6oqtNHdNBQd6PQOECVMG1woQiEtcjKoKMPWgqyIA5Mbmg1MNCR3VkX1U/VbXZQ8O1KhX85/wncOB37avJi/t722vbICCwpLrgVCKSFeolv1wIOGO6SVvv+67cHGzl7qXTg0YrFW3gP0P/PrNQ7z1yYfwvgcexysvXJrBQKsMVG1hBwYWK5Db0aM/v8mrBdUwF0eYbUzBiaDAZ2rthTWH3BrJt3EGXhVYgAE1jSWos6MOnAQIpsZt0IOCOsvzB9BbVtiDQLvwUNmPat2BLTCwHkA4Vb3/PK/AHlcBTxPSOImh55SBYBY7kBJ4nKrYgbVERAgaF5Cat83Kyso3xnm5zs7XOE29W2Hp2fjEKSEdQ2MRGGkAMI4gDpgmFgUhyf3TLiIdTwi7SdWECIwKCKYm+HiE4yPAQYSpDAgRmI4VFgZgKoGLFOImSIiN22sgwgAZC1m7vcTQD0wdN8N6a2agJYqCvDcxEabAwJTE/RXKeSMDrIDA2mbIB5n0jToqAhR6g0ICN20Lt5BgVq0y6nta5bU60Ax3lNkVt8OBXd/DQd5mPXegGHRzB6Dftvfa9UVVwasJbn+Od9N2q1q/wY5ZKA5vv+69UI+eW0prpX9qwGDN4O5j1Os3D/G2Jx/CIw88PvPJLMYMuGPWgOAk/p3Sv6ZqQwsFvUDDDAR64BoUlMzV3+fTnTpv3OJL6hUEmi8ctQADrTqQXQT+M5f4gZMCAYBFKIide9kHAjEbfWQXgflhLXiQsBUGXJzAyhwDlatgw9DCSiHQYYVrowrqew/aE5fPFEIJMMwtpRr+QTWWTrChlPtypV80+LFu8vKIBT87YrO9gouUsvcZEcAxMEUCBTFoHPT+UwKFCWGYgDhm44/jI4EDHdlgwECDwIO5H4INgZwsHsHgIILCDqzxCfsggQNVwYsJyC6HxADHTr02Prc3esVl04MDTKjcC1sAIXGJQZBprMtCUTl4MUieaAMkAKGoCfnx7Wuhm7rBbtZUVSVyp8V9X4IDkF2RGjgo29j9BgACmyHfryy01b8FBUKxGcHPcYH6uHIid0zn9Wnt1yoMbLRHpwYMfFp6ML2UC/U1j+Peuy7lXmo+l/59IYFgFp/nPvegwPafxHVgp+hBwcyFMJuTgNwNLDBq5ROk8mJ6GPDfvaugUQcY9WeLH7CelcHCGhDYbXgoqLLbCestcQP6vQcDrry3wkAbQJhhoAoi9DMPTtXwwkV1gLnAAqcZEPTmGuilEIJOApbEqCXW0WWxGX5YO1m2DEUsx85fgN4zWEq9VTb98MbZOXWoYz8vRW5OkPn5e4BA0wgeB/AwSlCaVxEUDtipCDSMsNk3iSIQjnLQIuk03R4ScuCiQUKQmQkzJIR6RE2MMlPjwMBEjEHr+mD1PJahj0vBs7kMtLMu7gZVD3g/IAQAHJCH1cnohqKSJbQqgocEndmvWnmwXlBIOiNuJtq11Dac7IFAnzHZiIg5HEQFHgu6zEoBKzyi3gbMYw5MPbCzZtPtOaVT9tUx9hs4QADlHfbq5NelOmm5H6IaCu67cGnx2idNpxIM2tQ+GEvXbxzibT+jUKCFunVUwRa3gU8ncRsAy0Bgh2xTCTR3S1DQ1Bpb2Ggxw3pMuYHa+Mv+bepA6y7YBwT6qi+qBEAfCtr4AQ8BdgutKgDrCZFTDwjVsEL53sIAUE9DrDBgroF2RIFTAvwUxF4dgK1TYDMTOiCo4gFWoIBCACOBEGETC7VDT3sTEn2hJ2pVBjdpElDDhYeDvLAAEtIIUGLQNAFRIu5pGgUAJgEG1liEHLwYQsfVMBRImCQwMrsZFBJiiBq4OHQhYfDDIEPtNoskkLBbUBOqeISFBqmFA0CuE1nEnxgIxKJMBEaOT0iJCyAkaatCKHI3oXxeUhGAAgll1AJVkFDcAust6nzBJr2xFTjwvfyZekAKWKoetNsiufYHc/UA7twn8ZQYb9i52h0tIBCQ40mA2n61UHC7QGDpiwIMgPkDe/rmfiio0tLLtuF61Xb3ed+IgzUgkN9jBgTl91wBQHkZGyhoDcSavDKbnGgDDOiHnrvAxw+cFAgAdKHAZ88nDwJAHwbMjdCDgWVlAKoE+AWJxhoGHBwsuQrgt7ObjtgDwcJ0xD74jyKB1RWAlLJCz6ZB7nnjez11AECvB78hlmBr6gYwLigUW5WL6l7MFeHyLNfMzT2AyRkWmY9f3DABHEZxK9ikSpMs7NN3NRwDDgoqSAgyqZJAQqyVBNbHbUoCIDMxdmJrSLu/A0RNsAA2KR/5uxaLkEdLpAIH48T5nUiAtC17AAGkhlMBIbB8lv3oqAhwroYaEuQvlxVvtyRTDRo4AFBiDqQyZPVA2tG5ekAKA7wHEMxoV738TkdxT5hMPrZ7WGdHFi6Y8cxz1yr71fbnKtH3NtKpAYMtD8HS08/NoaCXVlx45br6t3doL0vV3AQdIJgFEbrtS0CQj/E5aVUC2wZsrzHVCIQFGMjbloEgz0iI2wcCYA4FPvXiBvwteBAA1NAbUL0QMNCuVLjFVeDVAQs2ZF4EArlsgYJ8j2bsJjV++a2ezz7XGvPWhy+ftYz8ktg0N7KA66V39i2mxrBX6yr4fW67LeNcz5eQqr/l2P31uy0HOYfCQYSMxHCAgDjIMwoxB31SiOBJFQMNWKxdDUVFkCmZR/m9LfKk+7qQ0LobUENCcJAQo0yuZI99CRTWUl6ciTmrB/b+rgECMWCBigYISQEhQd4bs2ilA2OrZbpFhAwSDAqoWR9gy004OIAOreYyY43Cgd6QJnMvWMO1DxDMMFcBimhjBeb1b6t96CWL87D09M1DfOvPPlzZr64ifQfcfmrAYGtqfTJrNjL7lwC0ESc+UtV2z37fbm1AwDZ5GJDfLUPCZx0I2obeGf82bsBy0AOCKmYAy0Bg282vajlsVQLJ/hwKegMlbFmSPNywcREswQDM8HsYQIktyEGEeSTB2oqFAgM5QNDUATP4tlqhuQs8ECgM9KBA7plgNaLqFbu3uZpIyAy8FZbBAIViyP3nEEv5Nb+lYN+pOnaWlz2pMua2QqMpdvm7dX2nIjn71Rxtv5WVbXe/tTKTTev1P4+umCYpMxbfFwfocFLpKnNwPdRowxkpux5kFkU3XwKF2tVAsQIE+SdxCMGmZiZSOJhDwsTQ0QpFSQgOqCs1ITFktGAfqH1q4QAo7gVzEbACgnTGRWFg4i4giI1mjUnIVDAHBIjFFVcDZ6Oeza5ucw9y+SFy1YLU6gFPue2y69vRLSBUagGgAY/I95K3aztmF2qVBEsnmszOZxy3BwV3mk4NGGxpkgwK3qfRm0ApVPORteNZZ3BQXXPhibTHzUWCRRiwfT11wH7voWATENgNtskH71T576gDtp1KTk8CBNLj6QNBTyWwPy0UAMXwz7NdQCCXzwlgQOIKmoWKZjDQiRvouQoUAGbqQJqyu2AVCIBi5Kzn6nrw1VT0PYNv3/3nZnlj+alOPx1k7L8BAunx+bmHWEEEeaAAanVBk3dL9fzG2WibEW+MPXT6XfmuoyvMneL2cxIXQK9M83ZTFxYAopus7FKS+LlJ1YMWEDQ+gaexrL/gVAQZ2jjofAq7PLNn19XgghbzstEokDARZXfcKiTAQwKyN5w671Mv5bkREhc4APL7lJJygKoDLSCEYHMoqDuUSxxCFxAAqdTE8nwRCiCYiiAZm7dZvXbMqwfYBgjWRnA5WB4/CgxYPe5BAjfuBsDFCdiGDX2zJSh4poGCFgi2Y/nsMrN0asBgHzTZOM9HH5i7D+QBFzgAUHovqB/O0gV7x/hNPRDwx/RgwM67CgOa100wMBt9sKAO6L6TAIEZ+DaGoD6mTPRix1g2e1BQZ02v7YJvLFUgoB+WYIBQjD8ZBFjjRY2rIN2Gq4DTXB3If6dlIADmPvWqB96R7xvj3DP+PcNPgy656w2++s4p6rLW0GdObj0L2wY1+l5NsnqiqTd73WzuDKBu8KttYiD8Yj0Elp7b5ODKA0SaSrxGAw5W7vDl7q/fgsNaMgf6lCpAECOhRkrVg1ZFYItN2ORqGPMiT6QuhwjkpaJZIWFiHZLIKFDQgYQEmaUTKKAwNSrK0toWZjhNbg+2ETIiwgNClhgICJgDAgwE9KR10DTNASFfO5U65ts2Becq5QDE/NBkc45JAJg4ny8H5ToVIUOCO03K7VANCe0+K0vzWOVTuKJdErBaW0KYKwWtHemc/o7S6QGDFT1Fpol8CI8+oEtPugrDKIWbXJ1rAaFNa3FXPi9rIGD7WzeBHbcZBprts21b0h3EEFQQAAcIDgiWVAK7s6WcxkBV4zX4oWruQx2P0YeB4CDAxw2EDBJ3rg5UxsncBUtA4J+RjxlY8OefCADUGEGXGp4b/1AMfxjk7lXCBpXFq6QOGCAUWJiBo8/favPUAQJgcb2OarGeJBM0ZeMOlmeRf5MyNJBBgz0TD2kdYLDnQj0loRfsmJKUqbobZoAQ9Toak4BhJxMwTWL8eYOKkF0M7nvlagCQmBC5sAoz5XdvDRKAFhRyJavet9jtFaHMGVEZTTH8Ca17QQAhQEYtSNulsGDn6AJCgLUMbnL4Aghrqses7fMm1KkIVh1XIMHCJJiyB0WLiqoOzhooWN4r5XPhNWk3P/PctQwF9919aXY3vQ7otrR87KkBA1/IvkrY3NHvf61AgSR3sHtQLSAAqKQin3rGvz2Pv1JXMbDPW2BgS9zAiUDAGxzX8+uoBHaFFgpW3Qa3AwW+E+C+Dj2ERut+KZCVZyBEHUTYugrU5OGk6gAxy5TDFjug37PMvQYEQF/GXgIA4GQqgEIAxUHOY9P6KgAwxbxGRTH+tl96qn4RK3vmJTbEPWd9QLX6401PP5HChodiGUop80jYuxHJvxOcYaw8JwbzVKDBlAODN/0sMyKKK4c8LDh1gVZgQdYnXnE9WC/UVAcpiAIITdS83OSxLOhElF0QNOwg4ydPoCJQlDn/zdXA8u4pq2QXnhk3H+RLapDbd9C/b9kGVvcrGwwOyD1/gwOom04CESmb91Y9sO9R81C/6i4GAQvqQbbKHdWgfUZV/tWlAABERUmAQoJtX1MSXFmIklIrBi0oSPnUbdk+25ID5TUmrofea7aoTUtA0aZTAwaVX0b/2oJIj73uKi569wGVF6Ey/PrBqmLv3JZa2us9qJncsxcEIFfu9aaAOQw0L8La+N/FYYgboGCrSrAVCtydNnlBtXMJuLwqYNs9DGSjYi4DFHWgGJtt6oAZna67wAGBxQ90jQtQoKC6344y0Ab+tSBghl9VAYOBSgnQnqcZ/AwBNse/AYBb0toMifyV4DNmnik/a64goFF+/APuvJ/2HIECdXmNCX2OtcqjPWabdNCeoz0jB3bswW7HmCk905RVHpuOWgChPEva9yzXhk2mScp3HPuAEGJWHQw0u24GjkAKe1UE+UuVq0EdMnnGxImhQxHrkUAASvCi9Xbbx8bAkiXxu5jX4SBkiiDnkpAZIcmdrA6wtvZIOkhsowv2KAer7SEWQMFDAjeQADkuzzyJBTXBejVE+SotLNjv12yLj4m778KlVRsDzN0Kd+JXODVg0HYqD28c4k0fvoLHX38Vl+4uUFCCQVDhcEINCaWi9ytXaKxW9wF1QMD+zph8iyrgQKBb6Su/W5M/dtOIdiTgO3UdbIGCNpv2/nSDZmj+0asCFpPhYaB1FeQYgi3qgPZEu+4C82mb8c+S9AIQALUhaVMPAoD9boE9IMDOWBgYsP+ugWyj9SonmWQqpZR7ljZF777lq9dWq9yXeitUZtdPKENLl9ajmA8nDQghIgzNFNQN9CE5t8JWUHDuhxYUfLR/F/w0MdsESpCJpCiUc3lAsAmVplHcDHFA8M+YByDJjI3yO3MpFRWBQ0RoVAQz+uJq0GffQEIL73nlv/zQVp5nvs/ShCScHA7yd56rB4uuBQ8HTjXY1z7ObocnaR+zTdC2mPR93qMmWFllD3QulPoyXjFYelUIdUzcxbsv1/vbDmdzQ1smGD28cbj6TE8PGLjPhzcO8fCHruCqQoEv/yoYhDqg0BLzQk+7Ugw6D6VVAu4EBKpKvqHhrY7bqhR8lqBg6dItgC1mz70ESzCQFQEUMFhVB0xi7rkLTKpWY5EWgGA1wK2XehCg5b3VNUDmi9YYALZhb3GYg4CuCJggPcUpAWkUA27LVo+s3xPyLJS2MuU4MUYWoz+mhAkyAQ6bcpDKWhbyXW7HB1QlVwE8SLtZjMXdYypBIATI8xuiLWktayIMeZsubU0WJyJAMVCZlVICSgfEOEjxAfDTUVegYEGhg4FiyrEj0BknybkeqHnuyDA45Ge/CEdWN3QkJIUo5wVAHMApqOEPsNErec0GF7PASdw/5JQf4gikUo96KoIBQnE1qN/f2IYKPHiY98rQWlpqaowPXjA4UBjowsFiBvZvq2Z9VTdD6Uz1QGGqVFYyJaOjKJjLpo5RQAUOVt5PP3cNb3nyocb9vR8EesXf22b28W7c3dkr6dSAgXmADm4c4sqHHsLV11/FZS1UXzjtM8nDS3RjBQqddO3Goez3LuHmb/2ZZwZ/MwhUlXdFtuym+g5maoF+5ub7CwUF+brNC5jhvlNje6rLGgzMXAVYUAeyX9r1FK1Rr+IHilFIzhc9iyFogQAoz7K6nwYCtIwzCADiV/ZDBFdUgeQkZI67yjVg370iME2sPmfGqIZ8YnETJFUBRk4ZAsYpYWSBgkn/+t+OnMTd4FwMU5LPFrBmf3sR19aAWUBbDAIFMZTA0KAGfqAgi1DpMUMkAYAoEDDEkGFhoKDHAVFBwf82EmQSoRgl5MJAcTquVCPoctfMEyjuQI1ilKe6doCYIQIAog6d9HVhCRQ55Soj6oGCoQeEYVcDQgilvtCkboYCCAIFgyoQk6oJoVYRAKSkriMgx4/IZD4LkKBvtI+9WoP+6ja5jHKQduMFhAOgdisAeV81vXtOG9vQ/LPgYIH1eicABd3uFQXAwQJQxR/Yxuz+dlDgU3f4O+bbeungxoHax8fx/f/l9y8ed2rAAJwECj78MK6+7jFcvnBvpj5vDKn534kEGRSWkrkngNrsdtUA4A5AoKnAW1WCfZEn/rjPQkxB/1KEPIMYzGVT9rcA5odtLsGAdxWYOlCNLJj5nBmYxQ8YJHSAwMUQzIbAAXMo8KkzW2A1KVA7X8Cgk+Nog9/Ou59VgeBjAwb3dxBVQMWKkTnHCExQo66KgABAwvEo28eU9C8wpoSjJIZftsn+Y4WDozFVIJBSUsDgvA2QXigAjJ36MGSlQD4UKCDEAIQQKlA4N8j3XSQMQYz/EAQczoWg26DbZf9uUFCIhAgWyFD3g1xDwCLSgLgbpP7pFNacfB2Z1O3g1YQSZ0K+jsSh1Klm/oX8jq0ExnGaKvUASAoISZQjq4fDTrZZveFUAYJAp7oZ1O2Q4xmCzq5IASEoGBCV0QssdSQsQIL1du2999anvbOqbVyUEXDncNDGHNgIli1p03FTaawyH1ibuQUU0I1RyPdQTmuHAEBWutfSMgg09+XqXW0f71tss4FTBAaH/8tTuPLkm3H1tY/h8oWL9YN30adVaqQfDwiz8984xEPqnvjmD3xzVijqaNc7BIG2sm4dZZAbn4WuuD9uVga3DwWLl9FzFHWgXHM+DQ4qEACWYaByFaBRB7y7gHklfqADBOYyaIGg5zawcoYY/AwHPQgA5iDQDCFsRw9YNHoNA7uZi0BiA4BxlDiBKdXuAVMFjicx/rcSY5ySfkeGgqPJqQaJcTwlTIlxPMnn4zEhsXxOSZSFlCwOobgUslKwYR7xUCkGGidAJKqPug1CIOyigsEQsIsBuyjQsIsKCaoWnIvk4ACyPwacV5CQ83BWE8ztEIO5KwaEYZCgY3U5sClMuugVGyzE+cyW5nKw+ARfb9CpNy1Q5jpjKhZQ4uHGEQjqXtD3OwOCKkoZEEIEOMLHITCF4mZwqkIMURZmcipCFYOADiQwkHRYX+7xcv+dru5vReq2xHcIB/XFiqKwcDH3eU8ba4fmzqU9O7UZdwAK+SwNLNyvUNB7k+Zl2LFDwMyWHNy4hisfeRhXX/sY7r/rXthw36V0asDgwSffjCceeBSX73ql+A4teancyjAbUkeE7ti28LN74nU/LUoEkF/g1YmFTgICVWDhfiCoXABuqE2VTMLLxt+2u7iCjVDQzcNK/jwcLO23ZCAA9GHAvtvSrlXsQB414NwFsM+2fQMQtBPltFDQKVcAYtjzjWgcQTPjYFYFdDKhEi9AKzCwogykWhnYCgNHU1II0M+qCBxPqQsCR5Ncw6AgMXA8pgoIAFENbLrhao4QBwnVFM3WKKpaYM/dAGE3hPx9F6WHey72QcH+DiHgXAwYIhQWAj6zERJiYKckRMQhipFMIyjtKiWhBwnkhrDmIZFWl8wFJQWSDfpasGIpv1Rsn70zNi9CiJDVNMMeQNB4BYtDQMhuBlEPiorA0JELLOs1MDjPl9BTEjIolK+LaRUKFvbzCeCgijcApI2DzoexFH8wG9W1BhPFdVEyLe4Df4ebQQHAEiyIXVqcV3eed3dvvXs4uHGIK0++pdjHDfbl1IDBE695Hy7fdW+n190YfzSVkBtQaFKftCANBdA17ntBYA0CNkb4EKfu7HKSyvZ6mWTnQliDgk7yakE/X3KQr87t/OBLC0jZ1h4MeFfBTB0wJYBTCSirQGG+LfuMzeC7OIIMBECBieoG5n2jdmrgShXwLgIFBAseTCeFgTU3wR5l4CgJDIyccDT2YeB4ShpjAIz6vQcC05REPcogUGAgb1tRDfICTfbXu46CLnQ1hhkofIbUyA8Bw5FMdLWLoQsJ54aAYRRIMJfDEiRE2uNuGAYHCeMiJDBPoDCAnFuKUwLtzlUqFAw441CMzdqwRzkgw0E29swaCBBlu3UOhgE0eUDQQEV9V1jjGGxCJkoFEihEBCq99sTO1dCBBBBmoGBp9d0HFkkh39bCIYk3wIFTEepYA6WsTurOytk9zrsXHCxkZSF/0K9LoBCyvWlVBUpTgYU9aW4/6u8HN67hwZ9567J9XEinBgwu33XPMgllurPvNSwskdnBjWu44pUIN5JAWsSTKwKbKiDXFaXex/NueDv8UD9XlXABCvrXP5nLIBDK+F2X2sagDZppZ39slYJaHYAaeW7cBbwOBAYPYPEPGxAsqQRA6eHl4m2eg4OAvN+NLFiaZXAJBkAyE55BQYKusJdEop8YGgAo/vwx9WMGbqWUAwePxpRjBY4UGo6nAgbjNGUw8MrAOLG6ElIXBrJbyUFADk7zxdZ7FcXa+KIDgSpYICKkKRVIiAHHU1L1gBHHhCHK5xASzo1ONYgxg4G5G85FxhCSfB7k989HwvkQMITkYhKACMagLoZjEniIyVweBglc4GAa4Ue3LELCcC5P4UydOkf7JlDKZZqAoLEHgNQdcz2w1rkWEEIEU6oBgQI4SBwDSM8RIpBGcBhExaEoQXJkk1s5KHCQwGqombkJpFvt70odOKmLYUlaWIIDuEBEoLS32cfZcTlsaZObY1pYyKCwVVGwY61jau2WXW8trdiTgxuHePBn34YnXv1I3z6utO2nBgzWJrMAAJ61VMugAAAHN6/jwSffUpOW7+k3fsNNqsBJYwi2Vg7A5T9shAK9hPvnXQhVNtYuiwIHa8dIFgsIaE67MFACCfXqrbsgw4CbEjdDgroMHBDY/Ppgm3fAAMANOfMpBA3cqgFAitO2dUDAXAjDoDcSkSgsw4BtizvYJENjQvHlc380wVZ1wMcNPH8ssQMnAYLEjDSlCggAhYEeEOxTKNP8YwhyLvOvwlzpDJAuHxxiECk5TQIIKWCcGEMkpBRwNLG6GhhjJBxPETEkfMkuYEyc4xGOJhYVYQo4DgIBuylgiIzzk3M1JEYMjDHVoxss9mEIA0IYQAPnIZA2uqULCVHrr8IpHJy2oCDl6wvKfW4BNRt0q78JzBq3Mo4CpVa2GRBUHchKgcCAGDcHCVRGNQSivGhQ6kECMJ/1T+vGvv7pSVwMds25S8E+bIEDVQ2W3AuzTKz3yEtybYTZE6/qelAwGLBRD0So1Oo0lbanY5tKXpbtycHN63jwZ9+GD7zqkRMpBZZODRgs3niW3st+1odWp/JgD25eF/nFkdYMPNjEdXftfTCwqGi4Y9aCB93+MqIg3CYU9OMKfJZ6akEbUOjXfa+Po+o3mruu26AKNLRjzPC37gKVCVeBQMEhL7jTqgRA+Z3Ps294q8/zgEIPAzlewBrgUMBgfdbBEkQ48dxVkDrqwDhJYGAOHkzs9s2BoFUIJoWJKn6A0YWCvcsUQxadtaLa77nsFy+tmgcBkWQVJTGO82S8Agu7GGQIXmJMKSBOjF0kTCliF5MoCClgCIyBCSMBQ0yiFCTGuSnhWIMXdzGVIZBRAusmjUEYIfEIx9nVQAjxHGKEAIAbzdCDBIBBQd0QBqy21oZTsGQWYC37DbEI+T3QyZIYY+kNUyiAQBEgNVYU7UXOxomDDi1s3AxMQWN8ioqQxR+euxd6oCCf+/Vp35S+phrkfV040C7KSeEg7zO1ZQMw9NrrBXvi1YT8qwVIyKfU9iu3463inc8zf+OIGQc3n8aDH1UouHByKABOExgspbZQKp8TXOFLIRtpPfHqRySmYMlN4KNh97kJThpHUOXXt6AOCmbuAwcFzTDEnvtgBgXu7jaGOVSAUGWl5Ei2o1YLFmGgVQe8u8ACCs3wLwJBwkwl6EFBldFOXLWHAGCmCngXAROdCAYSShChrY5nroKUP9fqgP3z7oJ9QGC/XxpZMDUPOtrDUb9QsvYzkroP9LkT1bP+QYx326Hdl7oBiVT2tUGKPu7Ap4nFUoyBgCkhsTT4U5qQOCAxcDTOAWEgYGBgtOGNxBgS4fykxzQqQiD5XUhcuRqCBS3GKCMFVyFhyJCANALe5dCrrz5Qcd+L6eAAhQuk0xkjGBPABCJeBwQKkPEawSkIVKkIAC1CAjN0hENxLyTUbUWnWd6bfPwBsBBvsAYHgAQkAnM4gNrfFg567oY2U70b6bgeyKFz+ZW11XPb4Ytkrni7S7o8HNx8Gm/86NvwgVf9pHZqTw4FwKkCgyWnZpO8nwnzQs2kNZNfOsb9hQaCPbEDlUqQtwe3n2oF4SRQwPt7fPmFbLLblnLPdeBhwI6Rf2bcVR2o3AV7gAD16noeCGz53NzI9tKMZBwEAB0Q0FzneAFtWIm2wQBrp9C5CmwaYj/fADNwa5pWJx5qgUAMf5lzwD6nlKoJiPKtBirtpWthZQU8gwbKww8reGyAYCl5cFhb9c0DQn+GRPlgQxqB/lBHn6Yk1imlhON83qT7CIkJI9WAMFDAZNv9REqTqAjnY8TIrDMuMia26ZlZXA0JuibHCSDBeoesQwy5Xl66AgVgGW67DyAVOBideiAR1wUQwiAW3gAhxNyLRoigyYChrCNgKgKRrLVBM0gQ1SA3t+aGyg9db6PJ8ha2NIVCBaQOHGABDuQKGQ5gUKRtC4USQ+jV4BYO1lSFxr50YxOsHNEBBJ9XLts2cBMObl7HGz/6rQoF9zZ7e23g8rt7isCglwopzpL5djQV0nqkQ1rLboHNLoM1KLhdINDPqypB/t3CKom6wXLcy6ZyeE6h2Vduo+71BXfAkjpQuQXMZaONZQ7C6QBBta+jEgAdKLDYgRYAXNn6oYYZBnRFwrJUcXElZBhAWF6XQGFA3AaiEFikf14Nj+uZCM1dYG6EFghK/IH8NVfA5IYR5tvOSoA8lCExRp0zICQGIjIAmIrQzkewZP9b4LidtLS0b7vZA4L/bn8tBsBPnOSTKQdyvQBbwTtRUlBIDSAQBgWBYQoYIxc3A8vyxIFYVIMgx8fEeRpig4QhRlB0QYs2OqEZVmtrOMAAOQwATwhWr4FynBvVIDfR1HNLGQ5UMjf1gKC/DzLaggKgwNAFBNKFnJh1dURVKMOQAcHaGIOEHCTAc9eCQWPEsnnq1QprttfgQH5rbWKSScJYXQX6Etj9Z/WAAFTKgp2nAIWVp+xwsmkvNfZlDRC4BY/8W2e/WuBo0smhYD2dYjDYrmvOoKBKjeHvxRFsSXtjBxZcBn5fDwhsv4ODNZXAPveGGK1mr3tLXgqWv5thwBq/Cggm9/1kQABgDgW+7OxZxbrKd0EABlcGA6oObISBhBI30HMV9NYn8O4CPyNhQgECm5XQ1iYYUz0bYZsECKRxjCiGMjHjPOZGvXeOL9TUGn2DC9tuRt9mU2xT4kLDgRhAQAiMNBJSkGGbBggBhCFMGBNhMDeDDnkcYsBIUCBgUS+SKggso2omi0ewoEVshQQHCmHI9b0PCjUMAKjaFJsx0cMBLHSwdS/0AIGSBC+qUmCwnIPnGhUB5mqAgwQre0jbkW0dCiicOO2FA0MPpx44KGJ1J0j7cwJAsHLe4gPZk7pDz3twUG0vSezXEhT4PG+3V6cIDE7q4JTCraBgFqhxctKSc+/xS9kxnfwALxQQAD2VAChSH1Df4dxdRvmF7UnBM1dC11WgVz8pEHgwaIEA6EOBz5v2mnKD6OvHDAS0rMzoG1DdIQx4V8GaOmDuAmaeAYEfmeAXLOoZ8dzzTowQDQKoMhprPfytPfel4/bt25KHOayc/BxtHvzXGKibx5Qd2FL+gSkrCYEICYQxAUOYMGj8g3czZBWBWBfwmrsaSjzCbUKCvUc5al3KoAUFX9dL/a8TTxMoRlHZvHrA0xwQADGeVWwCV4Ag383NoEBBQQNUUWLsDBLMoOcczZ/JUtvjVQO5f32GmNdVObO+ywSwAk8BBPl+ckAAZjbnpG3+5rSsfK93atu05PSdp1MEBgtphehmUHCn1/GN9b5K0AMB/7u8vxNDYPtvBwhQNqxBQcnmXBXQXNml5LtTB8rmOwCCVZVAzt2FgqbcyRnFHghYsNUiDKhiYDBQtru4AZ67CraoA+ImkPgB7gBBgYBlKBBVXAzQpECwW5mjdslolhkIy/48MhOdfb4udKp6b962dtIboFbAe3EM9pv6OG7+1ufc4t4oCkNzXhswr1ZsVCUgjS0gMEZmiUPggIhpWUVgQiCFA1JXQ/JuD4GEvJiTGf+FSbvYesEWtGh+cC0rcqDg63+b+nAAaKSJFkEoIJIN/u0DAoDsatAwkA4kmPFfbrt9xzmbzSICZbXCtV4KA3sAQctB2paiNiC7UVynj9Coxicw/C+A0rDZft2GGnN6wOAEBc1EMvrgozL64NKF+0rhVb4jc8yiGH4vz1nPs/U77b3+GggAMxiw49znXgwBUIPAnQCBT0tA0FcH9EKrQGBQsBEILKMOCqr8xWVLWIEAkMuxgoEcdd2BATJ1oB9EuAQDvKAOsH1P/aWMvasAqKEgP4OmntlzsPH21b4F4y6Be/V2H+xnyx8DOsmU+fetNE2yt7iSDUpBmyxw0eIarIc4uf2TCUSujIBSLgAqgJJ9y1Dh9/s0i0dAgQMosBkgDEEAISRoACPnJaKHQKAFFYGodjX04hFijGrkfMBi6kMC20JeNTwzEjDUoLDxicj9mnoAeXcr9WAVECJAvAoIZqBbFSFx3fPf58L3+5bUA3muBRBYry4woHk1RcRiEFgggK29IQLlyZocJGgG1kFhQ4n3bIHdVE5zG2FQYPYrQ1evwG4DQk4NGPAJbt4PSbx84b7q9/Wa3AYHAPLQFk3Od708NXGTNj74VRiw4xbUAfvbiyE4CRC0xbkGBMh/NwAB/LY7gwI/3en8+c/LcAsMSECVjigwKDiBq4B5rg5MLO4CXgGCVhUA6p5wNuBVVfM9+LoHbEa/XYvADL4Z+wgx8pHIfYd+ByJ0BsLgZhugUh/y86f671rK3iD7q9uT+8KQnj8zY4IMlTTgmmDQYPsLRLRlKuXYL9etMRXmv04JztUgsQkhEHgERgImJFUTgsYoTLIeg6oIYsBYy5cxqXIwBTlfiUcAQpBloilypSJQmsDBgfUaJGghl6FuDVB3350aDnKAHIDiIvU+a6cuyFl1RsVlQACVpYjNYAdtTJYAQW+lm1YBwckG9m6UTSQwA3YqguvsKRB0IYEZ2d3QggLg2qsNadGt3LcXTz33dO7Umv2ydBI7uJZODRhs9d9kKHjN+2aFCsC9RK7ZbQEBcC1hXLeyswc1f9gVCFTnPhkMAHeuDrRZfmFcBluBoJxrcbbIfGVr1NqXysdiNC4ColKOpHMPeBnUxQ1AYWCLq6CnDnBC5S6w+IGe8Wp7vrnsraF0vfm8zykB+4y/TcZjhn8ItdEf9KEGe7762SahItR5ILdvDgj7G6Y8g6J+T7BqwFU9Tvrd9tlxiQtEjA08WJlzBjLeBA1AT1kozyFvUzgAFyPWAsKYxK0wkroVEmNIDAoS0DgEm+KXMdJGVwMAVCMbZNY8DjoRWBql3hok9CA7k5jcaDEiub/Ze1h9OGD9vKAebAEEUxDsygYIS73/3FdbSH5f8m1Yc56eiiDnj8hTFa9BgivDOSjolcipmCeyD8Cajdhnv06UVt7V0wMGvUlqmpQXlHjg/Ss+Ga8ElEqbUx5b6h/engs3D2ANBGR/6yaQi/RgYE0Z6NXHfWPLZ1DQ0Da5fxkI9GJ3rBIAy6TdBnRS87x7qsAaDLggwjZugLFfHcgKgpbzOCWZj8C5C7YaJqDI3q2/PmDJDbDc+x9iyD3/IUr8wRCDGvhi/KMadwmQUwAIlGeotM+wa7TPXfOen7vcUP/5dZ5VH3zLjJyTfpCyLst+58/6PLgDaIz5M/GQtkVl8M+lTRbjLscR0sQFEJgwMSNSiUMYEyksTPUzIXNTAAH7VAQCLcUjyIILBRKad4wrSHDGzKfq/VroaHEq+1bVgwDoGg1igO3YpN+Lm9YDAlDaGg8I++Cgbdf8MZP+DowyzwLKJa2zswoJOudB6bAsgQKjWoi6AoaNyTXCXgE4eO4ZgYIHHu1DwW0HN87TqQGDfXK+LYh09bWP4dLdF51PpjVEpggUJaCadWqjZjqTdHpuBA8Ctt2BgFy7BgFgWRWYved7JAJmXg8ubF4c23RHKgHQgQKU7WieiW8JPDDZT6oRHAswoC4Bk0Z7rgIzREvqAFffa+PTugvWgADoS9nzBadqCLBjDAQsMt6k6hkEOAXAjEoEZSNf+7bh1qdg5AmlpvlU1K18rRWphjv3LKtUvaNUP8+ZW4cwGLzZc8rPKwggLDwvsAJcDPK8hqJKLAFcT9GBnnOf6yGx9PaTkroYegEECeyTeSMmluclbgbO6zhwDDKpoKoIiQQ6CEsqAhApSuCgPq/K1WCQkKYuiHtIANz75iTx8phc+2Bu031w4Ec27FUPFBCce8GSKWagdTho27l+uyfntviVGSh0OkCGCWTlQgyGqTJOTQDmoGBlU5Xfnva4VZCBXM4Hzz2tndpHcfnuS83vbhcIvtgUg6ZROrhxKEsnv+6xTqHWroC6qOaQkH+3T6FYAIMKAtx2DwL29yQgMHs51rK2nvPFuQiqXL4QULCQ215Q56LLYA0GfNyAugq2qAPMc3dBUvXU3AXFkMzjB1ogANCdQXApiLAHAq1LYOiAgBh6pwS4nqaHgKgQAJuVb7KhcWVWvjJ/xNRMQLUwdW9v2l4/O59/X+y+OwtQBTfVdFF45rNLxmoRKnuetAh3OT4hBnnewxwUumCnvc+k/cnec/SpxCOo1WHMACEmgEPAGCRfBgjm1kkEUGJ9lsgDJZLY1L6KAAChVhEoB9GtB/rO/OLWNLl2tG98nEXNz12hgfnk6sEJ4GApLbV77Zou4iGhZu1Dd4IOKNRqghycQQHFRrSw4POwKTU2Ii/o98D7cfnui03A4gnliOq3y4edHjDwd+lk5oMbh7jy4Tfh6uufaKCA659mv5uDAArOOLWXm1Ndm2YQUB07BwH7exIQWKpqWYbr5QvLt7MEBU7cOjkU5J+m5iaa17xpoKo1IcpB9YvThQECFnubc3WgfOeZPG3uAvNnt/EDS0Agz6CGAp/q4X59EGhdAzYczgyI9fS9IhCCjtVHAYGBTAkYQeNxDQHTMSzi3cbSEydZ3GcagfFYfP+jHCerUwo0sBmhaZL66EGgddjLjbrPUdSqGBUMivGnOAC6OJVIITtZwnrYlecYBmmgQwDHHRCiRPXrapUYbOZJqkBhSk3AaJC1FFKsga+ND5lAHVCon237mJOOOkhgBBalwIYGTkgVIBgc2MRKQyBZSwMFECaFvxTUYJLY230qAjiAUhKD3MK6hwQyI8fl/XPvo7xznRalUhD0/Xdw/9mAg55L4aRtYZ4nAScDhdJJcqAgN6vntSMaWOgZ4p6yFrz9uoYHn3xLUbpX7c4JIeGLQTFoi5cAHN44xJUPXcHV11/FpbsvNRWH3P9AmSELy6Dgkiya01506aHVEGCftyoC1WI17hz76Lk9Zp/g5Pe/EFBQftb5DqDEDbTqShs/0KosyzCQXQWVNB2df9r+lh5lVg0W4gemBSBYioqXcu9DwRDLvfaGBLaqQBvV7lUBAQPKrgX7HIM+yzSCJvkHnsoSwfZdA9kMAng8Bh8fAVPZBk7gUQAB7JatVuUgjdKUckrgyRrG5ZpJZjAiwSbeCYMYNIE4XTaYggDBIJ8NDBAH0O5cDQthyM9ZoELhgCJCHORdHQYJJk2UIdBcB0UZAlKielRJoAU3kRwfYwGFLKh7WPBwAMqxDjFJPAGS+PxZjZLAQpl5kQOBHCDYeQwQtNbrd4kJCUEmUJLCtZEM+t4a1C1CQqMAcJq/j7OH6qDAKrS5GGbft8EBGjzIcJB/sZ56+9d+EzAHBQDzOLMNigIoOrtiHTiDLjuPawObZECW3d+mdHdsis/SvtQet/a7UwMG7U0e3DjEwx+6gsdefxX33X2pqhTU/I7cnkVQaFPXldB/WK0qsBUEliBgqyKVc0XlHFXVskpPbjvN3Qc5s1uhwE7VKgQ+Qysvxizj3u3i3Qo+bsBgwakDAHXdBRkMlM56ww1bhaA1Di0QAHMoAGo1wN/K0nDBrTAwBJWSiRCDuQekp0jHxzUM+M88AcdHAgLHR0geBqZjgYDjWwIA05iNfzrW5auPJ3BKmMYJmMRfzZPUzpRSpRT4BZaqeQ5CQAgGCEFgIRLiEEEhgHaygl/YxQINcRCjvzsPGgZQLJBAww7YndP95+TdVTCgOGRIoDggxB120dwONF/hMhT30UBhdehpXQ+Q64E98zZwsYUDTJynBmDmChAGBpgKIEAVhAhRHSwOIULAxVYdlP3mZpDebNDpj8vqi1P13jLMFRTnE4ktBSr2Uk/WNpdCBQ/b4QAo7VAescD6Cyo9fd95up22soo9sOvBwcJtgIL8ddbFAxatZCxEdX+/CVdff1WV7nXbsvR9KR3eOFzdf2rAwCuX124e4uEPX8Fjr7uK+y5cWvQHzse1zkEB1XefaPWhVEb9hCCwVLFPMp94b8RB7cSw4/yXWiorx+6Bgjat9BgzFKz5xtr4C68M5P3OVdAErrXuAuZ+/IAAQx8IfAyB7VuDAp9C1RjI35hvqYYBCxw8sTLQwsDYgYF0nFWBdKTG/vgW+PhIFIHxaAYC6XhEGkdMt0QZmMYJfDwhjSNSSuBxAk8JnBhpFEiwdSk4sawlbdXAQ6J/3jFkUKAg8QVhiKBAAgpDlDUOhgG0i4iDuAzi+QFhGBB2wxwUBgEE2p0DdueBOCCcO1+Gn4bdHkiwVRftudMmSGCtL5NOdGT1AgCmHJC2XNUnLupB1N/HJEtICywIIDDrMtShnDMDQqA8dDKR1DmJS9AhpWTzUgxSwUMUFcG/x2qQ2UZd9WKD9t2Mr/C95F0OdriByYpbwdteDwd2udzH0M1LisKW9jOfy7sVPCzcJihUx6DVQ+p9Bzeu4cqHHuoq3XttzL7EwLUbh3j4I1dwN9+9eNjpAQP9e+3GId78kSt4/2uv4l4HBb1+qY9GBfqgYN8tGWmlhf2p8+RuBwROEkzoUwYc5i4c5OPcPosraCvxKhRUmWP0X0U4EHD+yl6+OgE1PRiYBRJmMHDugjsEgnaImwcCAFXjH6n0FCvb14CAlbkfSljBADCPGXABZsOamyCNxU2gn7OL4PgI3MLA0S1RA3RbGidMR8fg46kGgXFEOp4UABhpHMGJweOEpHDAicFTGXGBDAkrj9hcCIHUpSD/gkIBBRIoCIQwRFUOhgoUaBcRz+0EKAwI4gA6dz5DQlJIMFWBwyBKQhhA5m4IAhghDNjFQSBB3Q0jm5JEmEJRlSIF7FjeyRYSfFzC0NSVHkRaPWEHB6NTD0gBYWJZ2ZGnPiBEAjggBz8yWR0T45UIGTADRYnt0CnJiZO4Gvxy50QNJHSUg6UOwKbgOFENqv3ajvTgAChtoMFBPVKBVg3/kjt27fjSlpZr5KGPlpcTgoLlv93s83TlQ1fw+Ouv4uIKFPTsTJt6T8fbx//4g9/f/yFOERiwktBbnryCRx8QpcDXE7/ETlVXuUBDW5ChqdOH6p4AOi+5+36nINCeeh8N+ny2UNOmVi0IzUZy/2ZQ4FNPLVh6Mck1KrPRBU4i80MP8/bGVZAj1SnLjd5dYG4CUwx6QGAxBAzkYYc+jmBJJQCc5BioggPbBtTTBvdmFNwSQBiI8jLCg8IA8QSaJkANfw4enEZQOgbSCIyjGP9jAQBxGQgYwIFBDwbGoyMx+goDHgSm46lAwDhpXJuCgcHAhNX4gvLIQ3Zbi2JACFEmXRLXgZbZLlagYJBAQ8Rw7hzG3dEcEhQMoLBAu/MSi3DuvLgbhnPAMABhAIK4IxB3AgphQIw7hBAxhIgdZP6BlFiCGMN8pkuDBK8yDRRy/RlUTfCgABRYyGWidWfS+mOqlMxpQIiss2USQIGBFBCDGKqgr1ZQc7oECAnIc1NkNwMBbO95GKQOeTXQIMEC6fwQx97wxvKQlytAoxhm1cC1DRK02A9IzFejopK0qsHipZvvJ2pbFRbuBBSCv2ZTRHatxzpQMMtn0+kseVy+l9Y+rqVTAwbXbhzgLU8+hEcfeBz3Xbi4Rzaqn0ieJctBAlArCtduqHvitVfxqv/ym7sPZs3IL8GCPyZf06el23C3YI3AWmpjC6q4AvSCDUsjUPKyEFhYTlyMvx8GBWtInBJQ3UujCuhnDwM9dcCMP9hBAIpSkNaAQI/bMu2uT1VkSVPoBgJySwUGbFihhwHCCeMGJh1NsKAOJFUBZq4Cpw6ko6PsJuBxxDROSLeOK2XA/pkqkMaE6VhiBzgB03HSQENofAFnI5cs+HCltRUDOOXVH8lUA4UFCglxF0ABGG9NiLuAMASEOIqLQf+lW8cIu4hpGBDO75BuHYOGAfH8sUDEuXPA7hw4fkZg4fhWjkWoXA3TXEUgDWakuEMMESnIEspTEjiYmHTGwuJqiERgUAUJojxB1kvQchmgdWxfJLCmibmCgwmMmAhjSFk9iOqO4mRxRHNASFqvKHEVhzCRDW+Ncg59h2VxptZlyLCJlLyaUHUs1oY49kZpeUiwAMUGEswF4eFgFoxIAFBUg00Bihva2lZVtmtL1tdBIXSOyaDgCCZ3TPX7fXddWlUE1mIoluze9ZuHHfu4/J6eGjB4y5MP4X0PPI57L7SjDzppVnjlYc0qEzfuCR3yaIalnHK7KnBSSah/D5hVWLhNvcBCQoEC/4P+CISS+Wpmu32pdR3YaVaUAh+9vAwDog6YUU8bgYC5mRUP8rueSgA7Hp0e3UIPqAcCfp0B7ybowYCfZ2AIqhagVQeSxAvY0EL7bKMJTBEw98DR8xpPIEGGtwsEXh1YAoLkwMBX5u4QTaeyTKmUW0gECiKfi4GTpYLjDphSAk8RKSaESWIckgHCGBGGCWEckRQQeBwFEMYRYTgSQDg+AmtwIp37EtDuCIgD0vEtcTXEARh2AgXTqA9JPnMIRUXQeISUbG0Eyq4GH49gkJAY4FjmSuAkgYIeFADM4LOXiqtB4AATZupBAgAFBCZ5BpIXzoAQ1PgTOAfu2Ysv+xUQGrWwhgT9TdPzB/P8PW/TmpJQ33BxKXh1wR3SizeQAuASa2DNkboaFhWFE8gMiy5oBwFdNaFzDIDZcf6eFrPU6WAu/ez6zUO89ST2EacIDB554HHcc+HSKiValfUFIw+pr+0QFSho3RO+4asMvW27TRhYmoJV8r/tpdoCBT6uYA4EdaNQNpe8zYMOHaOvvvy1/3G2YJQ2LH5kgbEto44f6AEBuMxDYG4E5vJbAwKvEnRuL7sKYnMvPrreg4DdwkAhLzjUwgARKleBxAz4SYdUHUhjJ5BwUnfBlF0C8CMKOkDA4zGmI4khMJdBOjrGdDxWLgNOaVUlEOPfVwkWn3Kg6h0JYb3u5vPZfMBISJNM+5zGCUil3vjeYI5zSCzBkbsB4RwDKSHtBBriOTX4cQBPI+hIAAFaVrDhj+qCQIiyLxxJwGIaQRTBcQCFATFEkfEZSEGWu56YdB4MiUcwIE0JiEPIkGAwOiSpIB4UZmWxUO/aTnarHmS/OwNMEn9ACghgkoDFDiCUOASA4FQEq5ecAMQKEoDgXA6Wwy2diE4QYuVy1HeqcSnkTpydhep4gy1wsCXtbYfZH+v2LUACUJTpbIe4jmOw41oRtt1mv82fu/mXdP3mId725ENd+7hWEqcGDO6969IMYNtUPUD96+EvD03Rkxx+svhkLt59aVn+z78v22YKwm3CwGLqEKsZf7mHk0MBWebWJiLK8zvoyoamEJQzL+TXNWwtDOjnnjrAqOfMzxDgP3eAoHUbeJWghYJFd8HMVaD7HQi0qw96GBBfLs1goCgFjTqQpvmcA2nUeIISPzADgs4ww+nWUTH8qhJIrMDoRhaUfzaawIySGHZ370Eb5ymAhgII0akAaVoox07yrgQA2Z0gn+167ngLtkssJ54SOG+ToDzA+/ATos6twBMj7CbE8+ek/sZBGuXpWIY9KjRgd5wBgYYRCAM4DaoiRCDtMiDEEBF0JsakZWXKQQsJ3o0VI2HgmCFh0Lo4+LoYaWYIeimrEwmYAouakAQ2rVcb1GaHwPJZ62cPEGQ/5aGPIFsjQ0Y0FBXBlig2Q8a5XajWCthyE14plAdX1MaFiZNspMLtwkHeTo2/f2MzXA1H3gAJpRzmCkFVy7NqyV1IsH3V9ypfdfJQ4GMKtjyWUwMGlpZuugUG82YBNRzIOXjBJ3NyIABc5bsDIKjUAqtUVH2dQYHfXsUU7IMC27bUnW7THnlw7+qRHRiwHr4BgV9EpwWCpFKpX4EvqwR2W6ihwKdI0nPr3YaHAKCzDLGWa6BS7h4GKH9HZybCWh2YzUTYzDuAzgREFlQocw6MeZRBCwWYWOYbSDUAlBsNoCkh7iKmYzGvMQ7giUGJVT2QQhAFoTRp7AiCJ+xN1XBuBwAWhJjLO4Q8csH2x53NmNi4qhKr+1vmVSAKmDBBVw3QdIQwTMAg6z/wOICHUYZ0xmEOCDaZEsXazZBEVWhVhAEy02KU4sIUbMIiBQcHCWL/CUnjElpQkHuSXC+5Glo1a0INB8TIRj4lg4ICCAGYAULS/RaoaI1jV0WoRis5SLDMW+fhdlKrGjSwsAYHQIk5sHarBwf2O8C5B/T37WLSS6nM62hgozuoxHfks7iOJ1f5hM2e7W5//7U3QcFrHs+d5pOkUwcGS6mnJizBQQ0Fl/L+9rd2Xvk7VxNeCJWgBwTAdpWg2k7N76pTOijwLoQ2tiDPVugM/lJqhy15N0EFC30gsPgBDwTmHjAgYN1ucQQtENgfg4KFW5kBACAQIFkXELByy1ClMODdBNbLWl2foFUHdITB0oyE2fBbUKFBQp6EqD8ZEVLKcn4eLRCkyZOeekAYAE5BlAOFHRqi9MjZlIAaJvLfRiHoxRUspda1QK2CoH+DAoDBQDv/gQx1tL8qw+j9EqL42ZPMA5CO9dpyE7Cx+pym4mZQQKDdObB9jgPCMOYZFs0tISrC4AAhYqfrNyTnapCpl9VLIlZ1tioks6gJiSk3ymU0A1Uw23slfWLmGg6ArB5QVg2kxWOSuRIMEGx4o8R6sPbASy+idCgEEkCMxdUHmctyAZtjlNqbURAwl4JkLMOBT0HzmBh5tEJu4ZhzG6jaRt5eqQc5v6Xt3QoIVVvNJS+Wt8weDSAQ1Ya91hf6aTMU7Bl9sJS+aMCgZ8N6wvcWKLDUo7AtULAvzWIJFoAAKGqA/G6bSmC/zyoBkOkfwDIU5HOF/a1ToxKsLa/bqgMGAFuAYE0lsD9T8i8iZ4OfC63KtuzzIGCqgMGA+WyXYIBaV4GpA1OzToEtVuTiB2ydguSBYPTG/3g+RbGDgjJ0UHsygZAQgAES+W8zFYZQqQc8M/59ZcGrA2vbtqbWZdDb5mGg+u5AwWZT9DMpkioOgJQHDdB4ivI8c2OdnN+cJaaCorgR2EFDsPUaUgSHHSiYmyEWFSFEBA1YZKLiatD6l4chso1Y6MfEkFhpMKShtnrMoX4ms/KzoLbERdly6oG9G5OqA0Q1IKABhMBNHAIwVxGAAgnM2ZhLhhN6C9HtbUP8cR4OAJSYA3QBIasHHgQa9QDtdt22BAhy1RM05u48Xj1oTdGSC7x3bC/5mBvvPrj3rtuDAuAUgcHWYFdLbRUk9KGgTb0mcK/sYy1Qdf19vW13bAcG5K+daz8Q2O8J/tS1SgAsQEHv/tZGGSzCgOTipEDAgM5R0AcCy2KGgnJ3AMTIezhoUwUB+sGDQHbNkI4m6LgJFmHA5htoFipaWrioCwRpqhY0mq1b0EkUSSaxmYL0mBMBQ+nVB/ud6/VvmYPgCyVRJe3U4BA8SISQ1YhuSgkMVQrGY6kz0yRBiFruPUAg3d8u6CSzLA55RAOFWLkabFRDCwlSxwlxxS0moADYOwQUWOglq9fewGSVNMMBsnuhBYQQbObFAgj5uztxT0UAMIcEITCXw5WYJKC2mB4OZA5p5Ldd1QPfzLbqAQhIJuGzDNlkQGeK1J47pNxzMaNp73lDu+3TnkP3TUK379T+yQcAhwsxBbeTTg0YbE09IADWoaCqcJ1z9qJdq8AWf6Hee9xTM2i+e7PLQHe0QFDO5VQCYL9SUAUYum3V9w4M5O3lqicGgg0qgZ0755rLvXo4qLLbfKjhqpRlsKDCBgYkrsDNSQ/nKkhOEeioA/kYniSgcBrFKDsggIICmMHTMTBN1QJGPSAIQxS3QEgAovSCB1SSvw8I9CBAS701oGtYez39ffv3KQtdNaI3XaDt25B/aqDB3A6zlJI8wPFYDLqen+IOeRXJcawAAWSLO8nS0HkhJ1MRgqkIo7gg9F8MUQ1TgYTEjKQijkGC1XmLSwhc6rPV98FZsaWSynfretCh+UFSww9VwJJJDAQE3AYgSOELJHAqi9F5JaHtZu1rY3pw4FwLQFEP7N1fAwQCdHKlUi65tBoVoZPbUqZt2tOel9tbh4J9yOBNiikF79PRB3eaTg0Y7GHPKrUFfv3mNYUCGX3gU7XiVnseZy/9OFnAVUpNS7NdAfNK47/uUwds35rLoPzdAwR+X5t6ldg1xktA4BuyFwIIUB1bzl3nq2ykdrumGqxqVcBcqmswkN0HQAUD4LRNHTBDP5X4ggwEKcn3dlnj2SMJMlFOAmjQhnFfr781oGYw/XYz6rbNvruFw+q1DxZWw+ylNn9TiVas7tGWcDZYsN/5QMfOtu412tTcaw8qZNSCevbNCMWhAgSLS8Ck0ypHt7x3R0WAfacgy0M7SJhYDHwXEkBdMLYRB+bHb3u38/tu7hHWS9bfU+02qN0LtwMIclHKC9KpewpmxH1mUr+NmT2YAgf+XLV6UM5uTYF6ZWpAsPxBlQK9VYur8CoC7BxNdtZq2pZ2XT7r8RuObRMz42mdp+DRB04WU7BW2qcGDDZUqXKsK2ibJvL9r+1BQTm2nRzD/Do9OGjz00LCWp57IOCP3aIO2N9y/jsAgl5aAoLGXWBXvh0gAPoqAfL36s4A1L42rLxsmV16MEB+ZsICA6RlHHOjx11XgYBBTx1ItbtAjT/SVAOBLYvbQkGa3I2EfGOE0B8X2DP0atglkC/PSQyyZar1GLLFqfS3ZMbNF16I5bzN9dbUhEoV8HXO7h1wlkpjJhwccHNcLiegBqgWLNrr9ZIvszRpoOEEWzGXAHHJhFgAYZAARY6DKA3ezUChryJQKJAQIkIYcjyCQUJSgzuDBFbjhvq9YLcNKLAAzI2Xfzo9OADm6kFgNwTyhICghVsAgfRZGiC0QxT3JTtmIyD4dtsAId+8/apREQAHXNkG1KBg52tOVaW1Nl4+y99+OzU/rk3XbtSd2pMstLeWTg8YnMBXY4caFDzWgYLkjvOA4OWuXBWXuqWLW5bz5M+7DwaAuTrwWYMBYBkI8r79QCBZ2aYSwO33UFBlCZjBgW3Px3RAwL5b8OAaDAT7ncGAzPozdxWYxO/VAjCghj/HD6ihXwUCoOpNZ8PcXe7b1c8tBt8beyLxp1PI+ylGHXGisEd+dUv3vH3B2jZ7Ht1oKvekmnqZA//sDApSlLezyL7TVMrM3C9WngYRGwBCNm9sRA0OJuutcg0IFECDxhvYaBHvZgDNVQSbwKtxNTCFErQIILWQgBoS4IIX7Z7qd61w4wYRIavzokgUOJggHaPAkq+QacIFJOr3yc67BAiA1pVGQbhdQPD3wdo28VTVSypXzm1TpSK403pIyEqCXsTUhHy7C+W4lnptvT/HEhC0uN3v1J4oJ4t7Tg0YeDfoqryjf21Gw8deN4cCAPCBvz3D7zfVlWpbftvf+XMuqQafFWVgqXF8IWIIdOMmlSAfW1QCyV4/f5VSs5Dtyv3SuAgqyVPdAxUYBOe2YW/4O4GE1qNv1QFzFzgDxuPx3HhZD8Ubr+qeGqPvngf5nnzP+Nuwumz0BzX8oRj9IMaLFRSYyhLWUoidJa4XlCKrB0upW1d9PbXv5pKB/z6V/Qpd5Bb9Ca7MySDN3DQdaCBf7pYXSx7KfOKkLBD1vDUgIIQ88RdzysMdKaaOihDzc6tcDSGCFOwixSxvS9yBvj8ow3kDqEA1NWqC79ygfJ49l4V2iw0O9OFl9UD3d9UDPVhcE6U9rjsxJPfbczFYWZ6krboDSMh9Piq/s5QaCMh1prrQus1p0z6XwSIMuO22dPJjr7uKi959QNvsX3vtNp0aMMBSYXaSFerjut51L/nC7UFCpVB4ack2rWexbFuTley7gwGqd5Ur3e7ogi1pwygDy0lPJfCTPm1xHZTssm5fyFYHAoC5KrAEA6IYFDAAubgB7ypg3q8OMMOCCbM6sMcw6U3KH/vu5ezBy/M1AMg2NfbDUHr/IcrywyHIUsMkDbAZeqaYjTxnaTvmpazLypX9ZayZy7oT5Tnqc1oM+vAPTZ9RrtO6gBIBQQeSUuipNUBeJjjHajDYPRN2z6SAhB43HufYDXIwx+OocxloLbN9FnS4kDhN8kyyWiGKAbHMCYEQqjgEczPk50URSAplKYHCBK8iCCTYMy3TMCeUuRAsxmCCfXaQgBoSQMhqQkf87KYE+72biQ8FDvIxVC7i1QM2YEBx88397r4tEejjPIqhAwfdh9HciAOBcp5tkAAtnx4kAHNQgJZPq+O9IO1/8yMru8Mbh3jTh2v75QPd99m/LenUgEEvMKSXrFCvrkBBe74eJNgypgByoIpPazS2jwh7qkD5ewcgsPaSLQWMnWDoIbAeS4B22wIUtNn2sQNtz6Yi4I0wsOwqAFYDCa2nmg2OGXjOQMBt/EALBEClEgCoDFAGAH+zW9wAXglQA89UYKGCAP1sPdcE6NLUkMl4AKSUysqVmE/GM0H22V34CaTamSV9Wpo0ChnS5F9/0qigk0YNwj/ZCPVHgtg/ZgZ2/eBPUjWHOu6IbtyDe14VHOhzNZQVQAhq9PWZGDCYsRoGyW+Q0SRZRXBAIIBQoCGEiKAqQoC4GgwQbJGwLiSAdRpzu50iISy1Cm2LYG6GDAeg/K63cJB/74HBHet0COQoAHMvbIWDVWhwuVcYyG1kbtNMBrA6GXJ+PCTIsXau+ZWSa5R6KuZS2qIMtLbg8MYhHv7QvFO7L9Zhdu09mTw1YOB700vJCtWgoHd8r2C7kEC1krAkK3XNbefB+81dVeCkINCTpH3j1lpY799rfcYboGCL60Cy2LgPgMVJQ3ww58zt0iHtfTBAqF0FlM+7oA643mk2Fmvugi1AAKAln0oNAAoI2DPQ3n9fESi9f7Zepi4fDFMHTAkIQ4aAKQFpkmWlEzPGVFYHTAl5cSpb9GecBArGJL8ZWRYusgl57BnaPAm94fVlLiLSZ6rGn0QlGEiWrR5UMRgilUWpFBz86pSyhDUhUkCgiDg4WMhrTLjAUF1/gtMEDPOgUE5JevkGbs7tA6C4IOLQ725nFwfmgBCSgwABhMrNQAGgCcw6WRIFURFCBFhVnNRREUiMLsO5GhLnGIU1SLAsZ5Vz0W3Xb1n9bH82gU+WJ4BZ7EHPtZCvsQUOvP9wX/s2GwqJ0nZtAAXS7T01QY5tzr2S9toDYBUG7PPhjUM8pPbrchsoPz/N3rR27CkCg/WiObhxiCsfuoKrr38cl+++iKWnSc3/7VFGZjY+Fpg3gr2HvzR0pQsBwH4QWICArXOTl8jb9ar0WYWCDVltG6XKfaB/PQzYdh83kOGgow600vSSu8D7uA0I0h4gADCHgvYmgJlrYKsqkBq/dIEB/RxkmeAMAiOrYTcAYIwOAm5NE8aJMbIsFTwyY1R4kO1JlhxOjAQBiKRgkRQikqt/fvIdP49EULVAnocYd4npoLz09EABQ1RQiAINQwwYFBjOxwgi1uWqBTaiPnMBhgExDhJiYeDnZp6k6VgAISUwT6C4k6movZqwO1cFN2IN9trkIIACZIrpBhBkMacEnsqzFQMoIyLApjiU2ARidTO4yZWCun5CUNcPUR6WxwoBE8tbK0qDj0nQdsC9ayf2OCoLeDgwFXUJDqrjNa3CwUrqtXnddlAX8mCTPQCFDtlOtAUUJKd227q7fPcdRD1gTdpfG63m/+6zX7TybZ72N8CnBgzWZKWDG4e48uGHcfV1j+Hyhfv6xhVorI7JSvJ/Cwm+6NcmVqtO3/18+yBQVf6Tvs3ZB8d9OPCuhTuAgpI93sIBPmvz7fo3zz7WgYGZq0B3VsMMOXXdBfV2RtdXvQ8IgNpwAHPj4ecDWIKBnjKwBgNxqFWBBIyjLMgzpaIIjMkMvxj6Y4WFW0lUgeMpyTHVv4Rj3TclxvEkAHA8JiTmDASmFowrD3pwqkEBBMJuCPI3yrZdDNhFwhAChkDVv10MGOKI87pvN1AGiSGDgfyNOjPlEAbE3VCpCX4lyxoSxgIJcejGJmRICGH5WXuFAM7epbQBECaFvCSAQLEoDhqcKNevYxNEPSEw2aqkRTlI7h3tQQIgKoC3m7eTGMhwIOe8MzjIhbdHJdjaBubfEM1hYSMoyJ/iesj37cpgq13I+XJ/vX0QKHgIV1/308V+rdmurWntPT352b4wE5mE1/jKD25cw5WPPIyrr30Mly9czMd1E7vfN0EqQK0mhPKT5Tz1LpA/3gEIzF6AdaIuaYVdWzcCUC92JDtnMQVy4BwK8i7dtp4rmcBlqWp7EACWYaCnDhAad4HFBWwGggWFwAewLakE+QYaEJAPG2BAeoLJgtF0hIEBwEwZSBJMP3KBgcmpAmb4byXG8ThhTIyjxDgak34WOLg1JhxPBQaO9fs4JRxNAgDHUyoqgboXem6ExCw9WisKBwaRSP6qId9FWdvgXBR1YBcDdkPIkLCLAecHAYBzCgXnhoBzBgxDxPkMDjK98BBErTBIEHfFgDgMCEOjJCgwsAWM8gQKgyyLzcsBjF5JmIGCS1k9AMTmwbUROrqhBgSGufg4mKTODhDEVQGS32XQ0BENgUowYGKALB4BxdXgIUEy6bu/y24+wL2XC8lGNFQ96g4cuMsBaLphROKwyLI/aab3BSZuaBNZ7gLwsKAKyh2AQrmH5lILqS1FckcffPIp6dSq/QJzX0Hx5mIpXszOn3//RaQY+EI7uHkdV558C5544FFcvuuVQBr3nsY/pNmkGYDrOfPs+H15k88LIOD2rYPAQkzBWsoNc2b3cooFtaCqXDoksQWCpdEHdqW92XPvf7eRcZuqoU5LMKC/WXQX5PiBxvjvAQL2QYUtEABFKcjF1XkxrTx7MOAi1mHT6xJlAPDBg2zf4w6sgYNjggMBAQOvDCzBwNHEOJoSxpRwa0w4GuXv8Zhwa5xwNDHGBhKSXocTI02pwKBuA4A1bShPWRtIRx/os9MFkcSVQBUMDEPAuUg4P0TsBoGDc/p3CAHnouw/N6QFSAAGYlEciBBDUROGMCCY8VflgNIIRB3poMAgKy/JcW2gKTV1IhuQfRH1Sc2uBShm7V0MvcQWBOdi2A8IplIgjbWbgeTUNvdBjj/oQALkEB3f37Hc84e6miqXAgowtAGJ/jJ5tILBtsn4TZlKQOVKR2lvI2SjgXwGwn5Q2BDM6PedqD+v581K92sfw/0X7nWdxn2ula0dxeV0isCgrgAHN6/jwZ95K554zftw+a57+xXEB9zl5Pz1dlgb4Wq/PUF+7hgElvK/lPx9Ma9LTd04Av+dqnaBUUMBmn37UrWORCdbS+tEkGtcZq6DUCQ4cxfsBYIcQyDKQTvKgM0lsKQSADMokMw1daqdPTAOyPMKNK4CUWkadUBdBQYEWR1IC+oAQ2BADfqtJC6DoynJvyQQcDwxnj+eZjBw61hiDbJSoBCQJhl5kEYZQyJFK26EvAqjBi8uPvuA0sNSNwIRSQA+CMdDkO+REGLIroPdoO6DXZxBwpfsInbR1AMFhZHx/EA4HwQwzqsCEWlJRYiIQ5R4BA1SLMtgFxUBqiIgTbLSoqkIWkdo0s5H616y1L6zzh1BgBp1C2Aci3G0UQxT0nqSYCsWUgMNNo+CvfccWGNuipsh6KXzZD6sqkEPEtyLuLJmU9df3qZKJQGKmwM1/KvWgK5Lwb7vUw3aNnPrKAYPC2ugYPlZBIWiNuTzbUnMWel+4oH347KHgqX72qMStOng5vXV/acIDGql4MGffRueePUjuHzXPbMKUfulpqbX7IykGWx/mexq6L8hM1pbAYEqL/l8CxWg619r6dnDwP7KUt13z4VAud8t21FDgc9mqxb0khMI9k4RvbZgVKsOVO4Cb/RPAgR6THd2vQYKquSWL0Ysr1NWDRploIYB7ypQIAghuwfa2AEZRujiBGyEgCoFE0sA4fHICgVpUR14/jjh1vEo4HA84fnjogzcOp4qGEhTQkpJ4zAVBCb9zlwUA60DW3iVyCsGhHAMUAwIxwkUNRgxBIwx4MhBwvldUiVhwpfsAs7vIj5zNOL8bpDvXkUYCbfU1fB8DAIHgwQuxsSIQVQEGwkxJI1PiOcQI8BRRzYoECCaqpCAIErSzNVg6yk0SoKUi1PsrM606lLu8Ws9c1yAcZTg08SQVQxtMSF1H+wBBItFIH2vKRQ3g6kIS5AAyLPeMixuyfx5vbJ1M1gnYx6RX+Cg61LAkmrQpJkNaNrO7o8st86t7EFBYxS6wYwdUMjhZHva5cNPHuDKk2+eQ8Gq/XCxEnuS2ce7+a7FY04NGFgBHdx8Gg9+9G34wKseWVYKAEegpVKxg4EeIADA4Y1rer2en6eFgrlh7wfLdFSB6uFvk4byvOH76LGVuhz91lCgWXH/6mGGTZYXLyfn94tM9fb7rPVgIG8nH0yoV94HBDm2wBrq5jfg2o9sZd7MTliKzZVx9bmZiMjBQHYVQGYZtBkHF4cYdtwF020AwcjFTdACgSkEq0CgysDkVAMPA5mrMhisuBJs7gICiFirHyEFAk2c1YIYAxIlhCEhTAEhMlIkjIkVEKLc58Q4F0nzTxkQxiFg5JD3nxvEFbKbAo4jZ0CYFBAmJoxEGFiWQa7cDAoEYrR3sIBFpAlMWpeSToOsalMPLMnGN6dpDgRt8hAakKdjBieBSkwAE4hYFtICi4QUotT1rCq4mRhtn45wCAoIgSAgYNlzoxMyJLTbczYXOkgrBsqrBq2bYR6MaB8cHABKS9ol6MDCUlpqS5fac/ZGnRpQaCEBqEGhF69m11ppox988s3i/r5wr/5+BQj8/g2A5O3jOz/w5xePOzVgAGYcPPc03vjRb8UHXvWTqhRsELbXAEF9ebb/4OZ1PPjkW/R3vYrUl65eEBhYu5d9lJj3FwjgmdvAQUHZuBpsuJRU7CvvSi7CeT79phYGbH+rDlRAYAGFLRAgNcGGPAcF5vw5xxJUEebuszU6fgKi9iZsXwMDfgpim1TIgg/bYMIMBCCMLFnIrgJGFwgkjqDvMrBgwued8e+5DG6NSQ2/xBF4d8GUksYUFChIU6qCow0UpNiW62qoPpC2mQJlIcpesYdJIGIMwJAUQoL8jUEi7Af5exylrM5Fc7FEHI0JX7KLOD8wRhagGGPAOXW/nE8BY+Q89NEDQiRCTIwpB0ZGDEOUtyEDwgA/V4LMIaEKFElMgE3RPIOEOBSjv/Re+xfD5j2Aqg7jcalXACihqAeJkV0MCqdMCgxTBxA0UJG0914mTtJLU5m8bb5EMfKwxKXUe797yVwKm4MRpTB0WwAjuV68tkAbQGFf5n07XB/ZhwSUW0CtRi+7rNskUHDfCTqWC9+bNLOPK+nUgEF90/d2jlh6EM410BouR3YHN67lmIU//n9/7YoSsYfqVoDgRDCwkMq8A07xALAIBfq5goI9wYaza2o+K1cBfOMx/41/TSgfN4cBoAMEFlC4DwjU8JcRAwsqwTRVz20GfSbtVq2cAwS3GmGrDIBC7SqwxtrPRJjdBc28AzwHgpTqGIKRgeNxwi031LDnNjiekioDqRtHkEwFYDgDv6dH6GerQen2xUBd1YBcV9CrQKYa9BIzIyUJGGSduMfiGo4nr+5NkKl6Jhk6uYs6lTPjPAdZoZCtTAljZJxPhB1HjNOYYxBSYgR1MUwMRFUoJtKhj/EcQuQSpGhuBpv7Ik3gIAAqrgYF0VZJcC6HnFaGtUphuJ4mJxvRKMGelXoAsfBWJ4lL77cFBEp5nzwThQ2vIujfFhKgu3P8QfcJlieUn717trPnzhuCET0cuHiDVTjQ326ZF6HkxdXhJUioOo/Wtgf3Gysn38kIWGvbZ0r33k7oHtuGLfaxTqcGDN740bfhA696r4w+ONGSFt7zJakd218FMl64r3+azxcQtLEBwDYo6LkP8m9rKGDMXQhbsrnUQ6g0iU4swaI6YBCwBARcL6qzphIYFPQzqA3QkrvAQ4B+b1ckzIsQeVeBAwTvLsjTEiebmnhhIiIWN4KNMhgnCw6EgAAnHOm8BEe6L8cOjCkPLRxHUQWmnvGmYsC1qUVMpBHXKVsCH18AGEyQA4llFSs/8+xW0DiDLBmJK4G0UghrWUzC/HxTYgQCxjFhoICJBHh8SmpjExPODbpiIQeMacQuBowMnE8soxiYkBJjCKIaTAwJWmQFBCJEdTOwdzN0hr9mVwOnrCRQB0grWFgsuP4bleV+TrV6YFaWxFhmQFAX1gwQKICJ9Xk4NwMcJFh5Ur0CIYCyCuEJk9ly3xKbS8FfdhUOAOQaexI42KIqWCbteJQ2+8SA4F3TJwwY7LdXa7ZO9gkUvL1jH5fv+9SAgdz0fhLakk4EBUuBjT2i6/qKfE/1hK/VXigon6soWqcYzGMKVqBgUfUsBmGPU6MCAWAOA3aOkwLBLI7AAUAPCnK5NavokY1Db17aav4BoAYBQKckNuWAqqBDG1lgjbB3FyQgqwAj25wA85kJ2wmJxpRwy81KeOQmJjIoOMowkPIMhSNDJhdiQtRiCEwYAIxQwxDELUCJEHQYIgOIuygdXKckpAoMrFBX6rH3tRqA5DpRQEDqAWUgCH54oxvWmOdB0H8jA4FtwqWEiQhHFSQEYARSkG2i0CScZwaHgDFxnjBpAqliUANCCISBSddviAiNm4GcauBX4PSQUAcHM4KBghSm/pk3+u1QWJ4mGc6o55Hf2swgttk6AwoIsPdeYG4JEKCAYGsHZBVBL2WpjT3Yupz1YhyCqhSrcCBXdnAg3w0O5Fg441yUhduGA7tJ+w2k/WY/OsIAgSjnp3VX57b4hICwKciySTUUbLePpwYMbh8Klh/KwXPP7FcKXFolus6+2x5versqge0nN09B3pfN8CoULOXYw4Hf1su2hwGgVgfK5j4QSK8oW6e50d8LBU2+Y5QG2b2cFMPsGAA1CChQleWLFQQqGCBsX7RI4wZQ4GDUWxmd28BPVbw0S2FPCbCph89F4AjADjJ073hKiEF87Ds3UZHJ7nluguoz8jZLawGHS6l1Lcjfss8rC5J/rE6MZJMj2b22aUqMkap+KRILZAABExImJkwIGGjCeQ7gGDAlYGBGIo07SEAKAggxocziGM/J0gZ+Ia40YLbuhiktoShZbEZLShPEEqjbguusDGOcb2zhIBsuPX+aRD0wZcEBgqy8yauAYM+pzHWAYsxhbUGTpT1tg6Wcy31wYPelcCDbqzOo3mUZCKUnX8Ywye9yICNO3jmDgwOfWjiojl+YbXbxAqHbdknyTts63S4UAKcIDPaHtqz8xhtQTSeFgirdRuXK+ej9tqlE88mH/DG3rxIANRhshYKSzT4IuFxVMGBfK3UAWAYCM/q9OIIKCPRcHgo6yQi8bVzrl5YqiKpUAStHVQfMhztbxjjEmTqQmPPftAIErdEfVUWwzwlqxDtyTgy2cE5ECIwYgOOJEahMYfwlu6BBjmXmwt4shh422uDC6XbrO2RBJJ+CgwVbX6GdLdE+h0DdKZZ3kRBCyN/9Og35HsBILKZTBIUkwY1BYy1CAHPCoDEGHANGcoAAgYIpSKhFJKibQVWEGEE+FsECFi0egRPAEoTIHZDNhtRcApp6nY9FI8M8hwNGrR54QDDbSWq4FgABQK0iyIYuJJS03RAuwUGbsnpAEBXA1AP9vqoeAGgBQY7b4NY9iVFvz3W7v81Kh8KyP2cHDgoUPLICBct5OUVgsJZWoKGFAgrFfWDRoWupR3MbpSkzOJVysFBxujBQHR+699IFgnyOvkoA3A4UzLdVpU6YjVGugMDKYB8Q7FUJyvmWFRmvuDQZb0CrCwJUPlfKQEctWFQHDAq4XtEQbPMUpNmCRuygwGBgNvsyEXZRfOmBNNI+2XoGAUM0g69/9QSTO0/q1N2eCvHZTj2D7pUAE3aCyuu2z5SEmFWFetEmn2RqAYGEMc8zwJV6MLGsOTFQAAJhJAlKDIERtZxllceOiqCxCBKMWKsG7aycVpd7oABA9mWjtz0ROzgA1eoBnLqgUAAQZCbFZUAAUKkIDLcKrRlzV2VSp31YayKX4KD9yUw9sBEZmg+wtMWssQYCVrpUdgMIdjlfbvsM+d6h4VVme3TTacsXr6nHKiBU81TMAg3fsQcK1tPpAYPbJLHKiEIKNUNBs7Sl/KDjl3I0Vy/Q4WjOS1UusljysKFyLcGA+95zGeTz3yYQAGvug/m2FgaAuTpQdjnDDpwcCIBFKJhltJJq+zcyc8t0YKAEENYw4Iciao776sBGIGCeL3E8pqIMtMY7gMrqeomQWHq6GQCaFnWroe8Z53zN2+z89NJadm43r1lpMJWByiqO8+uLcRcFgTCAMQbIqogqjY805aWgEWRoZALrUtDIU/xOtKAiECrVABYTE4q7oRo1o3WbfVwMJwCxgoXF1L6gOa6hAweMvnthERAC8kQ+Pg5Bcxr0ZbdHV0HCQvZmzwQODtzv/GO2a4p6YOGPK4CgYKUIiRwY4QIY/bk3p2777M5XuX9p/Te9dqqClOIqAep+/8FNG33wSJkH4TbSqQGDfT6bxWWGnSG1eQqeeOD9uPTSy3M6rQKGMDf2XuqZ0Vzq/2ZLWqloM3VAPy8tlwxsBwJgGQradFtAAGxTCIC+SqC/r6DApVmAkdteJQdLXVUgxxEswUCoXAXyt6gDrNu9u4C5LHc8KRBwUhiAqATM8plNYXD3F0zDDazT25YS7vXO2tQa9TxRo2tmikG1Y9w+d1z7Sq2tKjc19cvbNX9/rSvDg1CGo6ZybuEHf9/+nr2SkKDzGSSBA2b5PsQAHhkcJN5joAAOBFJAICrBiRFACgChURHgXA32HjhIIGZwcJCwoIyxGcFZ8PP8jZ13PNScVnAgd14HLgZILV0BBNe2kXuPKkCAa0e41ir2pSqHJwUEAphLpyC3MxSLKjNTEdwVFC42p5kNWnZVz13Abhv6bVR5tq0rRL4f3LxeJve7cO/J4hiadGrAYJ+R5V4ZeffBjWt48Mm34OprH8Oll3aUAgDVmgm+19/24FupB0AZYAxUCsJq6hPlEgzIvvk2NDEE/u9JFII9OXxhgED3zVQC295CQS+Rfw6tCtBktiqvHgxY/MAKDHBxFTCwqA6wHYdlIGBVCVooYC5NdtDFJsSYaRyBn1ZhZvTVwFujTcV4kxoxiw+JRHlbOZZy8UWUoEDroVeX21Ktef7RVAFW2R6QR23lBqAqn3IscuCjlRPg4aGuIy081KBQysnALU90xDI8lIl0O+aAAJPQGSOZm2GuIpBzNQTaAwnZ5bANjGfToltaax+rTkyBg/L+BMk028RCU19B+CwBgt+/VUGAORl8DAJFuQdORUUA+pCQy6WXi7W0oc1u9lVtt/3t2AdTQTIk6H2Botivn30bnnjN+3Dpwn3b1I4VG3R6wKA3Gchacg/h4MY1XHnyzbj6usdw+aX3r/yonDPHB/jrLUg91XEAMp0u5q1ToVyeP98wAOwHAvtbNArkBi0DAVBDwZpK4H6ffzdLdmXdR02dmJXfgioA29aHATP+YAcBqGMHWL9nl4JmiyFqwAReBQKgGDzLqsEBUIyYrx7e2APIBt8be2/ozcjHYI0o8jFE8oxtIiIpFaqeNaEAhc/HGqL7elYepZaXq5cy76J1kjnDlBlsK8spcYYJDxL7ytQrF72q5O+HFQ7M/09MXUCIEFVBp3TIboagMEewWAQpowDk7yBZyIm6kBAKJKzF2VSFmpr6bze5YAw6cCBHz9UDq4Wcj0vVTIrS8ckYWl3mhVAQ8rkMEAoN5DgEA5K8mmerIgDLkABU7fkMFpZSY2i3KgS81C51rwEHOQIJh5882BYTd4J4iFMDBn15GDNprQ7iI1na8iNvwtXXX8Xluy+X4+yQ6pxuCg+Th9ppMAH0/VVNtd+nGHSDUpYq0gsDA4sd8LYHWu3UbcVmVEoBtsQR5Iu7hi4f617UpaRunPK9BQJq/m6EAQoKnLqYDErcgBkb7yqwEQJL6gAz57iB1nhJERQD5lP0BYva8Mtt7Tf+gz6gYM/KjiE5BqoikBoqQvkMKs/XkDc/k2ygXPlvbTxDb20OV9YOvgwectnbPmhZNmVuMRtboAFAF8Z6aQIQ7TlNXAHCSJzXciCi7GYIEBXBlJpEQEjQFUGL66HEI8gMhCFqe5NjDoqSkAPqepAAZEM3exabVYSk75Ub2SAPDX31QAEB2iba+gAaf2CGOp+FnEHnfOYTtUkvNCQAzl648itzN2xVei2Ty224XGKhHW/ar5ktcvk/+ORT0ql97WO4dPdFuc81m1dncDHrpwYMsmIwK5TOOF8A4j44xJUPCxRcuvtS5XvPv7fD89/2IaImOPjibozVPn9VW+laELBjOjEDvb9rIwtO4jrzHpEWCl4YlUBztw8KfKZz78a+94i8fVYrMKAQUAyVCyI0g7PgKmjVATNqrTqwBgQAZlDQAwAAqxAwRGmwsyEPNQCEAF3qWM4bLIKfnNHPQ+xWVqe0Z9cExnVn8Zz1pEJdh5u4jtBMIc02HFSHfgKhO9qD7bkkAwbKAZ6bnwmtPxPbFv1+BQQKpNDCiEQzFSG0KoK2JtnVoNBQXA3QgEltptkCFYuasAoJ+kzycwCW26FKItFn1sJBBvACB/oE++pBDry240rLYHUUKCAH1HCw1ka1+yZ/C6zn2QwJAPJz13al7Vi2CsydtuUdUFhqz9F815zj0Nmvy3dfys960eY1eVlz+50eMMgGuyefzY87uHGIKx96CI+//iou3n2pMpo5kta/K6jgFlwQwT1cfTA56KSZl3sDbM4gYHaNUnnuBAT2zU7WW/GwPuA2YwmABgpqWADmxFtlpIWD7uf65VuFAfJDDve7CgQCltWBrT1VueX6GbSv80lBwCsBZlSsB+ohYCDIXbox9qTT+ebhdO13cJnrfxrRLkXNKQFJe5b71B0ACLrIVLMsNcVBfNS21oSfOdItOhUoIlaLUMlcESPT3qGhuxAy7NWgQLNnRQvPylIFEA0gEEvHc8JUKTdTR0UICmZJXeJzVwPE1UBayYIaeDfUsQ8JjXLge71bJULbzB04AGDqQYEDoFIPcjBf8K0m9E4UmDBrrtvXff+MiuXcGRTaRtKBgv+FqRoCo6WsspF1MWLZ/bAxddv0hc6dfW7v1NskBnB44xAPf+gKHned2gw5K3Zv/zZJpwYMfFNEnU8+Hdw4xEMfuoLHFAqmtvLYORcqU3vokooAoFISFtMCXfr8t1msYMCdvkfb7Qu1pdr0FjgJ7uDbDTAEsA0KTtJoNS9eFXC4AANrrgIPAV66XlQHNLve321DDoH9QFDf3joMDBQkJoCKW6AY/6IIDDQHAZqOgMlm5RvLIkBpgi0lTJxk6elpBI6PkMZjMfiTHMvTMTBNBQ5YgUD/yg1uBAMKyAtQKRQgRlDcAXGQ7yGAhh2wOyfQMOzAFMBxJw1hiEBehGoQtSFE8LCbgcLYKAoGDZHCLCB0SNKLH5g2Pb81QIC6GYZI4KnEdSRA16HqqwhRQS+p+pPyqAZ1NRBETdEJk7h10TWQAEBAIasHe55VfYOQ4LwWDgii9nk4ALJ6AIUDp0D4oY3AHA76LoVOmXeOoep7eZ88KGQ1QcvAqwmyqVETgD4s2EXWUrdzh3y1nulhn7+OTbp24xAPf+QKHntd6dQWJaScu5fa6yylUwMGvRumzrZDg4LXXcV9d13KhiAfZ++M+3H2Xy1cr6SeioC151T9tnfuLgi4DS0IVNPUbrlsc+zerFL9sQsF5lJZhYIm47Pr+AZsxRfqVQE71mBA9/ddBX11IEe6u17mPneBH13ge5xAbVDkduf3ezvKQAsD0akCQ9DAQS4L/FAay2I/0whK+nk8Bo9HwHiMdHwkxv/4CDwdg8dRlvidRvA0gsdjpHFCOtYlqo9FKUiJRTGYzHCWN4WnVE0xbTPnIRIoBF0DIYB2EUQBYadrDww7BYUBGHagYQDFHWh3TqBhd063698QwUF/Y2tRhAHn7PMgk02NiVRNIF1HooUEwsARHIuScBJIMKCemCtAAAM86mgFdTNkyIPyFQwOpIOa3Q+q/lBiqRu6TsOU68gA/+4RJ3B2NajJoAhAZnesIHzrkLwl37p3FalhLgsFNXAAwFwLPTiQ6/gvC5c8wb5q5VdrI8kMv/5gr5rg/m8V6RMoB0vqQLvNd/Za23TtxiHe8uQVvP+1V3HvhUtlyG6n89qzf1vTqQGDtoB66dqNQzz84St4zAoVfYMKrNMmUF5aoKbNklrRrJ+WCO5OQWBjPwBAEzegqbvYkat8/t5mSgEwdx+ghYINOVwJ9Nm3RsSaOmC9/Da4bSmY0BSDbPDvEAi8EmNT/LZAYAFsFjQoRr92EwxBDET0ygBPoGkCpuMCA9MoCoFu4/EYfHwEHN8SCDg+Ah89n1UCnkakoyOkcRQQuHWMaZzA4wSekmw/nsAKBGlUV4LedzWNYptiyLETFIJAQJBtAgUDKAbQEBGHiHB+hzDI9nBOVIOsHpz7EtDunEDC7rx8HgQIEHdiMOOQISHEHWKI4CGKehAEDiYmHV5YTzxFQ8COtbfPElgo8wyI8MxJjGL7nLMxcoAQiXRkA5ybIc0AYUo6MoS5BCqaOhQUHoJrn5TObegjEcBgmBuzVhEMEvSNNXl8nwtvU1IQ8HAgF0GGA3LXc3AAuYUqINFW9Lb+QW8tFrvq1mTtnCkLM1CwA1dAod61r39e0olgILft7jfMuH7zEG958iE8+sDjuPeui7m9mSkh+zLUy0CTTg8Y2Ae92dbYHdw4xJs/IqR1z4VLGjvF/iezVEtT8ilPHsN1pWzHjvvz7iO3WQVxG3tqRpvvuezWuUgn2bvrPYO95Pf1Rx84KGjdBwCq0Qc+xz15sDOaZGnEySYYsG2oo90TO1UA+4GgVQj8CAO7lXpMfX1vW2DgpOqArDCosQlpBI3HoGkE0nEGAlEG1OAfH4FbGDg+Ao9H4KNbmI6OCwjcGhUMRv08IY2jqATMmBQMpmN1JSQgmWJgcxK4GYhsVcC8omLUuRFCQNwJGMRdBBGBFBDCEBHPD/p5kM8KCvHcDnTuPGgQMOhBAnbnJFhPlQSOO1DYgeOAXdwBYcAUZAnpkeBUBMIUiooQiGSxpY6KwIwCCegHK1p9iFocMi+CKBWYBBCYi4thkhqtBtEBQoLMsWDe/KwqUAEIkvyEMAggeBVhNhpIEVlVg2qVQHtua+9h3mGwUVoSMjeGBi+uuRWA0rnKcIC+S8GnXtu4lqoARZRreFAgok3xCX5Te+mt7X01AMbZk147f/3mId765EN43wOP494LGii/J9/t05rZiU7eLJ0aMGgrhn9BTX559IGrSlo8M6r+95UbwV3Arxpm/kH5QR0cMsvbQp7bipG3N3nqAUwv3/7YpeQNlFcB+8f6L3OZaiaMcTP6AGg+r+StaZQWJyZqYECOXQYCcxecFAj2xRAsqQTAHAqAerGg24kdiEHdBGQqgcjO2VVgQDAeVepAOrolysCt5wUKFAKgQDDdOhIgUBgYj47A44Tp1nGlFqQxYTpOYPubEtKYwJNM15z0L1JZgKmXglovCqRwQKBICIMEI8ZdAA3yNwyhUg3i+R1oiBjOnVNIeB7x3A7x/DlxKezOOVg4Dzr/JcDuPMK58+BJVASEARjOgaYRHAcM6mqIGhQ4EWEMYsQnEIJzMyQWFWFgYCKT7JFVBBu2aKmtBzZaocyLIFA3qVWgwFUMAmnPmRkIQYNcFwAhsLwHBgisgEAUQSQjNPK4fdbetw9ObCCheg97SsLqUEfXsNjIBv1cwYFel5qWpA1GbIMQe5ervy8f7JVgoIYFgwQAOaSgNbhtfIKlpfizXuq1+72YCjvs8OYh3vbkQ3jkgcfxyrtKTFybbw8Jdm+3m04RGPQfRyW/XLiYG3r5zdLvtUdHhYG5+YG9pAC684Dvz3D/N1tUgSX3R3PanCqC5XlQ4ex4t9+7EOwj1ZuqXsksk0CjFlQXaois09h4EHDfKxjQ37ZDDTMQpGLcZ0DA8xgCViNgjROjHuIGLEMBMF8xcD7ssHYXmBEosIC8kqB97gLBirsgHd0SReDoVgGC5z9TqQPTrVGg4OhYAMG+ZzBIsu2YVTFISMdJVQLG1IKByuYA8uJMPtliR4hy3x4M4hCAQAIECgVhiIg7Qjx/LJCggDCdO0I8PyCeP4dwbodo372K8CW/C3R8C7Q7j3TuPHDuSwQQwiAKShiyioDpGDHuxNUAQkxUAwIRArvprAHJM0dMHTcDIPVicEpCmzgxyGBC1YOYCCM4A8IQJSgS2sEWYy/1joPU66Cg0QUE3VYAAWCeakAAnIrgIQEorc6anojF3kW1vLCDBVnUSfWAPJTRlU0+QXEpyGX67oSqXJv93aPbY9oet6ZVNUFP7l3Lm+MNVjqBdg8zKHjN47j3rktdO9WDm21ZWS7L0wMGnW2t/LLFP0/Vdilee8naZ291vXp99uEiliWwJRhYApgNl6qOy/W5gYP5GAjknms+hjpA4KTKsmlBLaiSEwlXIaUDA8Cqu2AfEOTPelA7Qc6S28CrBMC8l0I9qQhzGDB3wb74gSUgEHWg4zKYjgoQmLvg1mcqhSDd+swmIJhuTfL5mN1nVQsUBqZjWcshHU9VAK8tGNVPk9zXSPnphgCEXcRIAgXTkUCCqAYTpiEiHjPijjDdmhDPR8nPUUS8NSKeH5DOn8N0tEM8NyKeP0Y8t0OYRvDuHOjckQDCeIx0fKsoCGEAp3MyQiPuYMMvQ9whhFgDAhdAsDgEAQUBBLAsXzy4ekJan6DGf19irYsA5LqBgSlVIxgA5CGQKan6ADZikJgP0mmzDRCSBwQUQGjcDF5FkNEHjP4iQ2tpydVQqwbls6kU8u4CpX2ZuRRcH8LU8tbVsLVtXIOFtse9pVfeA4V9aUtn0EPBPap0V/nvQEJy+b2TdGrAoK221538cs9CoGGvgrRG1J+/V9heNatiDhbyZb8pn+d52QIw+diVN2DBVs0CC4FyvzYFbnA7WijIwl8v2NBnfDVt64WsLhK1AQj8KAM/Y17rNuipBC0U9NLSVCKW1aXph9sJiNaBAEUNWIkhSC6gMN36TBVgmI6ORAU4OsZoroOjYwGBo2OkoxHT8YTx1gQeE8ZbY6UQ+M8tELSrR1qaXG2NWmtGwC1TDIRECLcmDaiTGAPeMUISJSImWV0wjQHDeVV6EiPuIjgl/ceIUypzKUzyu2BDLHcjaBoRzv8u0DQiTaPGI0xAGkRticdA2mUXgwDCgAhUgGCDLwJrh0G3hahxCEBWETwkABtfC02cgCmwqAlJ4kp8YF7Q8g3KBdIpISSNSTBAiEuAoCoCOm4GyesCJNxJssaS14MRKzgAssEVo0fFQOtxJjyuKQr72slq9waD24s/88dV57d9nWsvFWmlFFy4VKkI+bfdE9X5XUrXbx5+ccQY+OSh4D4NNLTUFnBbYSpfUUd2584+Dwf5vE2e2gpwElfBSWCgPa4demOJmoou26gbV9BsQgUFrQsh30TPL1kV7nyb7fIwYMcYDLjvMyDgEkPQTqFb9tdz8FfBhXZrKFCwlJaEjhJYuL4WgU0/vAQEVVBhGhUI3JDDNo6gdRvsiSNIGkPQg4LpOMnKwM5twPoPALwksK4QrCf5nS0GpRsGKtey4EYARAHTcUKEwEWbeEoIarWjzqsQU0LkBEoyfC9xkgDN0eZvSAjnzgPMYOhkRmECeAdKkwBC0H9YBwRfl0xF6EGC3ObJCoyZMxyQXg9c1IMIRgKBidU3LrERpFAQ3WdxP3jYnwMCgAIJrFMCV5AwyyD0Ia3chFMKNGWXgsLC4kgFIDeC5OAAKKrBzCtZfrK3vfT7lyFhbnBDc4yPQSt5XdZalpRgb7/m7oO+zfIqgs9H75HY+e/ChYWcnSIwsIJ4+uYh3vYz3idj+zs98z0VpueLf/rm4W3lq3y/PYVgKwz41FMMvPH3LoTZ9gYKimJQ1Wb3eVkt4F6D0ioCeXsz4sCrA3n/fiDouQ1sYR4fS9CDgl6yGKw2fsBn2XrFLQyYUBqC9drqyYhiBQcKBHvnIJirBKxzEixCgY0sGGVEQbK/U+r6wkmDBM0fTjrBTkJAGAEgYUhUprblihkwLHg67X4B504I4j7IcQd5SCN13TSWb9L7oBDA44QUxtm6zxG1odATgNJUqwdhkLkQOAFhBHgnoBBGRB3q6AFBoIh00qQSqBiAHLjaQgIzYwBVK0j20uIAACDDgbkuZWkjUQ9CkPyYeyFoH5xU3ciAQMuAkCfxySpCCUrMkODz2s/q/N0GKtWgcik4Pb47UgGohjACCi2o4cBDgyXvltiXepBA8G2yveNN/JndB4rd2MJLvfTWplO7L7aiHTq/BCpAbR///Ae/f/GcpwYMgAYKLlxaPbZXUXq9a28sLWZBtq8/7T5Yb4eCvQDjt/XkK5rvbo1/u73nPrDfV1DQTmLUS7NRBgvltTBdaE8dsBx4IIA2FjmYEA0g8Fwl6EJBL2v6QprB73k/uksQd2DAqwOVStADgmTzECRxGygQ2OesEkxjPRfBdFygYBqRjnW+gUlHCkzcDwqMAZxkMZ8UoHMKlJvlSKBJ9yeV+adQBx0CtZKwFnwIZOPdBiFSIJn0yEFCGCKCDmkkHZ0hoxrmDySlhDAxUmDQJC4PCiMChRoOrP7aNq8e8ASEXVEQwk6gTOHAACElEmPFyMsqJwUGUxGkbtaQwJCGd0qsCv261Vpqa7J7U3+eCEDSHqNTD+AUAw8I3vLNAAFYUBEKJOSi3DQnSZ90ykyK+iaSmNo1OMj3iiIT9ODAP+9FOFhpR83geiQwpdhYxruYuTkmX2IDIHjj/z6FglYJsfvp3YuHg6V0Evt4asAg37TKLydNLRS0RvRphYJHH3gcD/xX35J/t5UG1ybn6CkFe9PCdZeAQP7qMSgqgW33ow8WYwqAPhTs6/bY/rVRB7pvyV1gOWiBYM1t4FWCHhSspaiT3/Qa5QoC9EMp01KeVof86oXtugV58SIDAht6aOsUTLaWQZmpMCkQ5FkKx+MyU+E0glmnNV4wzimkbHABKceBCFOYdOiggATvEtIUBQCORZ5nN1dBHqII5JZqbahiyYOraEA1dBEQAyqzIIayb9BZEnVoYxwiEINOfhSLohHmdYyTlAdCkBiC0Qyfe45pkoY4TjIFs477pxB1beVBLLzOrGiAEAFMLPMS2PLaeZhrEmWAaV73CihIDqzUpoXyi3tafavPFvcArXOtepDdC6QwTWqCZ4Ag+apUBATYxElipax+dd7rtXfeH2MTMa3AgaXsVkBx4eS3kHkGB75gzKACjVE9YW9eTrkMB+0xa4nqLObjL6rRXoOdk6ggwMnt46kBA7vp+xwJrTX+XZnd/jY96KfzkEeZm1qOWc9P6/PKeVr/2eZj2nQSILB9PdeB/f6OocCnnirgtvdXHNsOBD2VYAkKfJqVMyGPXwZkAqH5vcifuixrGAgEBAcDtnyxrWq4CAR+DQNbyMgUA56yEpCnKc5QMALT5H7TSL2REIeYJ86RGwggNazmUgg6iyEmDTBUFwMnLqoDAJvQCECe1AjAzB3Rm+CofC9lG0w9ML8CkCEgG/wYpD7HZrbEIeaZEkMIMnPiTmZNpMalIMMIkrpkCEwlWsGOZNbVAXfndGSB9I4pTJCFi3SFQ12TIYSIQBGJbFijGN7ELFMWs025TbOA18Co6ngCMGRYWEnNbXGzSc7t4ACMwLo2g3RpMXXcCz1AsAvO3Qy8AAnI+zalDXDg7y8bd3g40EJhmTKa0Wy3PGEFELZkFbOi33zMulrQfi8QkAMXF+BgS/JQcN8epcDSqQGD3k17IvOFDfQVpLYHTfDzIFzFpZeW869Ve3tFKhmI5vLWUmqP6VXkfWBj1/d5JczvsVUJ8nH5rFxe+n3uAzvxmnbmGoy5OiBXNxDQq28GAmAZCvI1XRnmF8xlb2m4j4cA++tVAXmJ5zBAUHUAzq0AzrEDW1Y2rBY2MigYRzG8rI01a5c1BIADKMYicSYxCjEEYIiYxpgD9UQFSCL9a4Q/u5gDi/q3z/6vfHYFujYVcpv8+gmuMpcZEkPeV322KZVDkLiEqIsx6doLcbBVGikDhMCDrc4Y5D6DDdPTdR7GsWobCMjKgZSvKAakMQdoVnwMuoCTjAYQtSBCHskA5MBEDwkttJpM7uu+T0v9c/+WeXc9r8ABc9+9sAQI8tXcDJxhwNZAqN/0Tj3YoKfP4ACAnx3Rp+xa0Lz21ANgGyDkXG9sX9dS2wbLZ3feLedo7sV+194j0Ldj/vpPP3dyKAA+z2BARD8F4DUAfpOZ/z3d9hcAvB3Ab+lhf5aZP7rvXJcWbtr7aUpQCHcL0QMBAFy/eQ1vefIhvP+1ut51lfmFjLCjPHftteTzl2feKqfLqa2sbRZOBAS6YxUIgHUoWHQhtBldgIFq334gkGysqwRADQW9rFnjyTwvQz/vQHs7SyCQ5VcHA1klCHYspLfJvAwEzMgrHZpKMMrwOnCqoEDnIHZ5JLBOpk9xkIacAmKM4GlCGIY8vC+4xY7MNQDdJx+9CuCe8crwA96iHDWJ1nqVrrfv1QZzQ5AZeSC7HvJnXdJZQCJCAxNknQX77B9wmoAQwA0ccErym8EmL0qAKQjJFAR1MZiiEAaZmCrIok0cdCSAgoGHhFJv+6AANCC70o54YG2T8UGGA2f0bWcJZpTvEtCIChDKVwusDQUQXNxBlYXsUlgxqy4QsYIDonqfXteKwcSVDFVkOKHHbQCEnAUs98D3tbNrqW2LuydsknXYEswmSH4jIHNY+JM09+Hr9dPPyeiD9y1AwVo2Pt+KwaMAfgTATzfb/zoz/9WTnGjpJucVoz56SWYXpUAWXLqoSoGvOL1KVM1+xbWPqFUtfE91CV7W7svy2bsP/7utQFD+3gEQzDJ4Z0AALKsEkrVlKFjN1kojCrgydGVHeV8NA6YG+M+mGgS7I+31Z3dBAwhzIBA5m21546kc14sb0LGRUq+CXi+qwdIx43lpZEvteW7DsOdr32l6oa7tACIv6WzH2ecQ1SURu3nnlECBBcJCLOpBZDVSk7gXqAcII4hGWQo5RARduMvWtphYLJkpCVafZSQD5R5rImRJvIo/ykbh9pJcT65lwO3hwCbrCbnjpMDAqGECHUCQnOs+F6zYxhktZs4pBFyfx6sHgJn9kjSmUyeeQhcQrDxnBjdff1+rsd7e6qmr42bH7LEfeV/QewEymKUFQFgaImlDEg0KNjBMlT6vYMDMh0T0+16Icy0Gejgpphzc/Bb1w3xaF1x67HVXcVmhYEaNnUv5h+2DZIA9khZ8kEkNCEupJ0+tVkz73ABBOc0GIPD7VzO3HQjsytnA64aum8Btg/+Ng4JudhTI1kxYW2ZLIODjT4L/HEp5krpfLHYAOpYe8HCQsuEGF3AAGBg1gDClCgq8QiCZiyXvzDqDnztm47Cy2fjy4Neht4oT3f6mJJfgwB/XA5pOHivwsXvxrqC2DG4DUvP9EVX3VV03xHx9tutQAIYBNCUgJDBPAgjmYqAADhHEEZgURshcDQIKTArCqiQkIMcklPpdvw8J0nu2tqKChbVbXmhDluEAyDCg56/dDa5dazpaLyggOPXAn6d2L5TrVgoCOaOK2nguQQLcMfvSidtdd0AvDqx7DRQ1BO39uHshaxubkxkUPGpQsGhLlnPx+VYMltJ3EtGbAPw9AH+amf9pewARvQPAOwAA/0Y9dLnbHC489TYI79rNQ7zpI1dw9fVXcenuS5UR9Sn7bxduwAfJAKjG4PZiHnp+7u552/y7+wD6lXI5qNDdwedYIbArZ6OvX5bcBpa1XjwB0IeCNsaj3Zc/619rbpZAYAYGSzBg6gBzrQ5kZcC2u+OhcrWNJjBwaA0h9MIJIPPTxyE/o7q9DvVv4Ix6zziaIbPjSGIVAOhw0+ZZmrpVBZduUBBcfaKm3pV9XGYMnKSMMjRY2QClPO0zHFykPXV4RXHIyc7JWh6cZGnnpFHzNIlCwAGgWBQEIlERiIAUczmTwgNIRllkKNY4kAoM2u92MNWwkG9xoTHq2YUuHOSTUB1r07gXrCPjx/nrlV4YQPD7NioIBgik95sNZlVmK5Bgx6O1H3XqKQTAQvvbAEF1/MJnf42MambCHAX5e2knR7LRcxfvvlz9fi3/bfpCBIMfBfCDkCL4QQB/DcD/pT2ImX8CwE8AAL2EqqCBLMEAVYWQ33UKSr9fv3mIN31YoKDEFCyZfrbTV/+3R+dhJbSiHljGNFWVFf0HuCpr0bwyeih4QWEgX3MfFFC+sgeDJbdB3l+yN4eCpaxg/lLZdkseAoBGSVmCAQcM2U2wAgP1vlQAwCDBA8E0FZXAGz6fQpTervrEbVu5Qaq3LRj6bOR14iiZQErvVo2aTSqVbB/pXWcYILcd/effeza9OmZlVR40gOS2l89FOensc2W5ChL5Gqi3teXpk84pzAniZrCyBIuCQAGgURdpEvWgUhEoAGkEqA8Jth6CvBOUZXEG54DG4N4N73rI748LQui9HmuGoBqzb0EJQBN7IGdZci+U67g6wSkTOjsjn/ftSx4i3c1JHMJU1T9rhT0PmIpgv1uCBLlFuUdfA9ba4X1tMLDmsu1dod5Wji9tJ9n9+OqrF5dVhCUmzkbPrSnla+kLDgyY+TfsMxG9F8DPbPmdKQae/vwiHB4U/F97cNduHOLhDxkUXJRKJxnqXzC5SglU9NqmnnoAtMpA/bulZ9ilvwUyrf/eBgxs7YKsDjuUHLQqQc9tIJecuw5m2zvZCqBKNehCgNtRqSgOBGybjxdYhQHzM/dUgJ4R0++VAQMKEABzI+bLFAB25/Umig+d/Gdv/EME24RRpNupbBdDGCRwUb+X7VT84Fw/q1kQqI1cmD+azpOyWyrAJdlyz8FADK7+ulgNc8+wuWd0O+cu9rRa5thS5paya0HhwEDA+8W1zE0xEMMluSdTECpISPk52L5gkEBifA0SrNxt3gFouRsoFMPXNoLL7rXQaWFaODDDmdy7MY9HWHIvaFu4Tz04SZtzQkjwQx27kODvHcXQVz77fm5yliyttcVzIGja4vaibR3MakSZMtqek0HCNXN/OyioOsu9DtLKzX3BgQERvZiZf02//p8A/INNv2v+5orQHtA5/vDGIa586Aquvu6ncfmue0pDAyxSrfRUfO9oLm+1aQkQLCNbJazeA/f3s1gBWxhYqpgnSXvmIrBcnFQl8FnqLSDSS7PGbq2cVlwEHgbkX+mtWm8/w0BjmNAzTGvqADA3TjnTru/iA+mAvgKQIaBE4psxqgAgROnZhggGZcOfGHlp4TTK+gEyac/yCpT2zPzaEtPK0+pOGw3vupF/kcrkUHlkR4iIiAjxXFZvgj2fdvinPhc4xYY5AYMLBO0pDM4t01VtbJsHBChg2PPIbgZVZHjSz+pqcGBGLM/FQwKHQaaMpoBAlBdJSlQgoVUTpOer75C1M+zeiRO+3j040Nt2bUVRD4DGJZGPcO1BTz3YrBw0N+BAoJxnDgnUtMeVkiA7Kh++941sbo9RfvNCtMeUmtVAchurx2vdMEi4pkr34+r+XsjaXPFYvLvP/3DFDwC4H8BXENFzAP48gPuJ6I9ASvF/AfBtm87VSAJGif7m/Xf7e3DjQKDgtY/h/rvuFbkPcFapqR5mAPUFn1XKEwCCVzWATmVb+l3nPk4MA7MXbc/LuSIPb4GCqrfPtfHfBwXr+epv7oGAZfF2YaAKIuzGDSTkQMIFeTvtg4HqJhpFoOMKSNrLzGPqPQhQzJ85RDBF7XkiG/8xpWplxIllwZ7EwKjGfkzl38SMkWXSIxt2lyCTH9n000C/p2pGKk8LHQgBCgAEULBFpGRJavtH+j2QTH/QrkYp+wbEMMjcQwBIR3iwHwminzm7duQzBYW4FhSooyhYyt8TEEKGBJ7G2bMqkDBm5QZJllT2MECsU1AFAwkCwpANQQUJ2l1cggRYHUZtAE+almb5I9i76eIReAkO9P8110L34vuAwbVJvNAeLygJQGmTsg+/ZLaGhX252Nsuz9vk1fZ4phgYEBTbwiQP5PDmdTz0oYdyTJxdbR0CuPk7T5/vUQlv7Gx+5PZO5ipRNsrl//qTNNoHNw5x5cMP44kH3o/Ld72ySJT+IVW6EqG4GJI+gQIDZKduAKEHB5a2zF5Fnc/dB20GCf9/8v7t+bLkuu/Evitzn/Or5vjJTxb7wvkD/DgScemuhid0oQg2gCcZ6ELjEqRoMjiiPDERlhQTE+FHOxwOXyTOSKYgAuyuBsZhR5BoEoLEmRGqqgHwIv0PRnUDVPjFT0LX75ydufyw1spcmTv3PvtX1U0pfsqIqt85+5yzd97XJ9dauXIAAr4sq1oQW3HsyBTQwsJfIBRcqrNLMOCvtQ6EmhPTADR+A04L8GGZCoaFG5sHME0AxPbPPQyYBoBipxGYkCFxh1KGngoogj9BBL1BwpwZ55QxZ8Z1ypiZMScFgcSYOWNO0L9cQOKcMlJmpFyPXPZhfTO70xNRQ/uKkBetwSGGIuinSJgoYIrQvwoKUaDhKgZM+psp1FgRU5CDqWKAnlIZESkiHnSC14iSPIgfwfre2lv6MMspjGZ2eApIGJoaQEDI1a9DTQ+U/a4GbVuiasbQ75OaeAKhQkJG8VaXnKuZhzpHO8KGKFhPe+CgXHdw4J+1BgdWP3t9mpo5amGGsHs5SLD7A2XFbaYbyxO5n9vfhWl6K0+D172skZcrQGCagjJ3cyd36msxBTKIMh68933ce+fLuP/ZN/Hqi5+0ChhIGy/P8vByn/6DMyU8baKc3PneKrzXBBwzHv7oAe6982W8/drXnfnAGmSlkzJgHFk6KMaA0HjQbmgPTLMxur7+/im0At4+d2EV7j+/dPjR0iP9w4GCvWlRT53gt+/8RWsHFqaChTBJWDi62UrTdhBMh4WZQGCgagG8ZoDjAQgRGYTEEiV5ZhHaswruOTNmBuYkIHCdGXPKOKeMUxYIOKWMUxYAMGB4cpbv5Jxx1t/mzDhpqOTEVWsAjGP+eygIQQR+CIRjlL+HGHCIct7BIQbcOYQCAFMkHEPAMQZ9rXAQA67Ka8JUNAgKC6aFCBPiNCGABQ7SGcgTvCYBOYEp1XadqLQp5jOAIG1q0njUng4SbCfDPlODCP+6q0EcGYlIzCAhFAdGu04UwNCDpeDGmAlx53/QQ4JPNxlyHyocALtMCqP5am0OK6GhypwMjLUJDhKAVVDw88vN5umVxdoGENQVUb8l192fA0CM773/fbz+h7+s8uuT2nd5U+Y1+dT5ai3dGjAoKzbAdYhWcNnnDx4/xOvvfAVv/9Lv4NXnP97ai12DLRIFyDEpqI0NT6+q/uOs10MLGiuAcHl9PljpX4KBvtN1v9tMWxoD99meExCBy1AwSv3ktZaj3jN4FQb0YolMaE/nFUfCG/oOYJ41325nAYBNpzZLg90DmCbwBSAofgLxAJCchTBnFBAwLcEIBs5zwrW+P+WM0yxAcD3nAgLX5ySv54xZrxsUFK2BnaegbVrDJw8m8xK1kErb2DkIpi0wODjEgGkKuDMJHFwdYgGFq0kA4TgJLBxiwFUkHKbYQQILVJBoEgooBDE7sDvaGsU/QQHBdhZwlteHo4RNNvkVwxL4hm080CLYuJkmgAlEDJDTIqj09Y6MevRWXWnbmDNnRqdF8DpyMy2MIKGkHXPC2qFAJjyfCg50vm13K9B2fvYsaoptQLeaqm/GQpuwZnIAGlBo834pbSza1qCglGn5+aCAePD+D/D6d34V3/zFr+Hu8x9fmrUxlntNvi6kWwQGvsP41XFbEQ8eP8Lrf/BVvP3pr+FTz39cB3ad5Bf7qr0gbO5k3wuOMJdwINfrNbnnTuvVFgj4z7dgYKVelsnb6zbIE2jzX6BAf+r+AfugYKQtWDuDfCsGuTchrGsH9MneXMCDnQU7gMCiEy683C8JC7+dUOuztUs7X4FnAIJzEj+Ac8q4VgFvmoHTLO8NBp6cE65ngYHrcyoagmv9e54zsmoJ8izmA2Qg2VkKLGYJZh62m28/InEoBMkBSVEPkYhBTk4MCguHAgVVg3B1iPJvCrijf49RAOI45aJJuDMFXEX5XSTGIRJSBmJgpAIIctgSOAGXACEnYNLJtzgrYmmI9u29aPuqRaAQgPOpafMKCDrBW3RFnTusX5jwE7ODfJfVzEAqnNkEsjaFwUDZu0913PXBfS6Nu15r8HRwgAYOyvtNn4M+X5c1u83zOF2EBGAdFHalbs5eMx0stub68nk55K8T4cF738cXvvtr+OYv/jZefeFjMtcQ1+3DvRllkb99+qFbBAad7WTQkA8eP8Trf/jL+OYvfg2vPv8JNPvNncpn7V6ErrGIIB07OEpt4aCsOqyjjmxjw/LUBtzjL7BQRwHYBoE+jWY5y0CvGUDZ517ruQ1tDKDZfbDI2ugx3WJhtELpYUCeXL/71ECwqiHIuORQeCMNQRdjoHdUY3MY7H0IFAI4TsV/YFNDkJdA8GSu2oEnc1IgyPjp9YzrORUtwfVZYCGljKRAMM9yrkJKGTkxcs6qLaiwx1lPYDTwc3Xgow2GQJ3WAKI1CAEhEmKUQ5DOU0CIAT+dAqJqCq4OVXtwNUX8zNWEO4eAJ1PAnSkWLcI5BTxxgJA4YAqMiTcAIc9AjnI0c1BtAhGQdbtn8UEgGW++/c284CImlmTvB1sey1weIZ4BjQYhw0wMBRBClOshgpIDBPWB8IDANqacFmEYOQ/Y3Drdp0suSM26wsEBajZQog0oHDCcScFgYRMULs1t3RzuTcDqKzaCBPlOp00AlqBwqQLsPsACCtrvjbQIY1n04P0f4vP/4jfwrV/47/Dqz/4VhVaFMTUxNGVa5Kurs40J+daAwcIu3msK3ntXoeCfCmkNVcJYqaxcBucQAFweik2+dEAHB8W0sKc8HwIM7HzWWodv/At6E0Lzm33nHUiWWm3BzqyU5GFAvj8+PvqpgIC59SHAjmBEAC5qCfpthytAYN7pQ6fCWLUEGSLcionAdgtkxjlXk8GTOeM6i5D3GoIPzgYBM356WgLBPCekOSPNCXkG5jkVGEjnrNVkYJCQ0wxmceIzGBgdqlS2jgU9NpkiQpxAIQoYxAAiIB4EEuZImKaIMAFxipingOs54uqccX0IuDpkXM8JP3OMuDpMuD5kPHeIOEapi+PERXNyZ2I1MQCHwJjUD2NiaToxM0wlloC0YQCCOSkGsfVTAGXVZvWA4B0UR32h90Moci/XeUKDJjWAEMTkUAAhxAIBTw0IqJqCPgLgVuTVteS1Bov4Bw4OGkuHf0dU4QCWYZ0rrJ6HD947x/l58QIkACj+Yl49v3ufwgoUlM+6PA+BAWWuAoAHP/5jfP5f/ga+9dd/C6/+7M/LnNWYoDwgEJaHzK88eyXdGjDw6pa+8A/e+z5e/45BwcdLQywiqvn7WCodNGNIYf75F6RaCe05uM/qmQQDclwCwQYMjIj7klpsTYWmKsv2sy4sK9Bsi7qJzmKUfE49DFg2vf/AEAg6H4LeqXD5Oa9A40ZUvUWmB06FwBgIbuBHYNsNey3BOWe5xsCTlHCeeagpeHIWk8FPT6mYDP7dSYHgnHCeRUOQzglpZiQFgVz+MtKcwUnOcsjpjDyfKyS5Exq5d6ACIEf2AnYyIqlDXZgOCPEAIjkBMWdGCBnxEJATI+Ygi/kpIx8YKYlGQPwdAnJmfR2RMuPOISKXXReMOQo4XWXGgQmIETkzpsAAB7BqkTN0W2Q8QqIWirageLFTArK2GScQk4DQtAMQ1pKZAnJ22oOoQlwWH5R1m6P4y1etBVmsfBUKiTVQFVdAUB+EISCoNF/sXkAL7s8yhr0e0gIoOWVCdcJ0cyNZvQCt1uCS/4H/nU/9gmwECSVPNq6xhIT+UXvm8dV8LsuxMBMbFLz/Q3z+j/4LfOuv/SOBgmbe96HdgpSNq7lp77P7dHvAwFIPBe9/H1/4zq9choJeAO+1KVnaggJHncBG59nQDsjvVoCgMRDu6Jhdfpqk5VhoCzwUdM6GQJ1n+oiGTVFWOuRWTTd6iYG5AFgHgmLj89tQR4K/0xJcgoJaaPc6BLcS7OEJNwaC4U6DFS1BMig4p+JY+ESdCU1LcEoZ/+56Ln4E/67AwTYUpKItqFCQ04yczmCNCcDzLNoCc/YyOOhMCYwMCgGcEiirVmSaBC5yRogHBAAJEzAF4JzBUfvMAcAsn47S7LqW7IiYkKP0xzvW7MxIiOCccOcQRdGDvKI9iAiTag6SQAGHbgdD76DYA0LO1UnR0qiP+PHIeQwHPjKEaQ8sErxbNVIGqs1ZT3JVb/URIPQHDpVB5cbqDWdCDLc3GoRwNSs4XSsWzoiWo15rsAUHe+bVvkQGCT0gNFrntobkixdiMGzlB9gHOVBNQYGCv1L6Q00rcID92ulRun1g4FKFgv/HzaDAXm/BwUKd3gnT/rtbac32s2oyeEogGOWn5LleK+VY+BFgAQXs/tUb7M/KMHv+tYOBksu9QDAyG6xqCXZAwSKjdTIHIFHzmo9DBYPeZGBAQGFpNggBiIcmKNHe3QZPZtEYGAzYvyfnhHPioimY51x+N2eupgFw8RfYdCIMoQr+oBN5BpiTfpZBneakbMOkWLQG/WejVPKjK+ScMoiAORBCyhIPgTKuS78gPDnX9qpxLwISEjBF4JzEtAAxC7AuyBfag2AHIYmDIkEd/YiKRoG9ubA3MWj/KBqm2PWZlbmBU1qHg7KKDq32wKnPis0ZEO0BhXVAQLeyB1qHRG+mXW2lHUnhoL8P2f8jZ0TLGefOpKCfeQF7yXmx5MNrI+y3FwBhaErGavtt5WdhGiECEBZ1W8wHBgU+/40WZA0O9PVTpFsLBi0UfHJMT1uCdSQMbUJvkl2n9nf9+7U00hK4608LBcWnYSv1UNCXY9XZcAwFvV9Bn83m0ZvZcmuJZwCCRkvQ+xLshIJl5mSwt4JsoCHoTiW0+uMggYpWgcCZD0o8gtwGJ9ragiixB3KNRZDrNsM5JYELtgiHbfkokM61zjkwyCRGTIhTQMIknugsPgLet6DxuxgeAlW1J2Q2cPU1KNfihDgFfTbafBgkdhGuUonICExZynkOhBgyAgEnW6QTQCngbHBgQie6pXMWPXsGwEwaaVF8IMT3QP0NKDr/g1QBAex8EIQ2iHXl7kDhoqNvkxibcCAVrtoC8z0AwEHju6j24CaAwLh42Nue1GgN7L5Y26lQ4WDhjOjhoORjAAcraTgfNpoaE/puta0CfAEHQAsIe+DA8kh9Oax8/jeEBz/+k9anwJ61mvfuuX15uvTg/e+v5xm3FAwW5gNLi4kf4w7VqMxp3PBKeUNh2t/npulD0BQMoWBLU7ACBTeKVdDm8kbpJieVVSDQnKxBwWgr6iUoWMufCfqUFu1qnwEodcg2yRXtgGkItE8VICA0oYvjAXZ+wZwhNnIGMl8OUuTNC1nV4qMwuBMBM4lHfs4koXajTlBTBJBEbX8AQibkEJCjbEsMgcAc0e9CMDAAUOFgVI9Fa6BR/Gi5SyFEeW+7FGxLozkh2nZGCYlMqjGQ4EZ9KvVAUm8hyI4NSjZhLuEgZ5kYWefyHJz2IERQOoMpCQAQAWGqgMAsWiFnrmLrZ5rBxlF6oIny/WlYh7wOBwUeMuAd0mQhLOUV9DGhKnAg18aAAPJj2o28G0JCk1PeBwf1+k446LV4lte1ufhp4OCmycMBMCiHrfDl3g/e/yE+/91f190HP1/nqpEGophXBuC4kkw+voQXVr9z68CghYJP4qKY6juUv95rCijgwY9/oF9wULAQnpLWOmMjgPaqv5419ZoMtO9H5RhBQdEuYgAF3TzRzxuj7Yf1s/p6Cwjq5Z1agotQsJah2i7ssz0th0w589uBYXOUsWkHvNag7EJwvgVqNjAtQWYu4YqzrvATiz2dWcMTZxHQokUQkPDnFARXhxJRUO47TcFdJ5wTYU4BKWWEQMgTY8pBtiXOIgSywUBSc4PZfzODedLXrl56bamrWrlGZfVf4hsYFNj7SfItoCBAEDWAUR8MyaInhhCacluyMx1mECgwYmbMAChkRI6YASBzCYcbSP6LmZ32QJwTof4G5fwFr0FglsneDm7qwJN93xvQTKfHaSttlNbgQEoBUF76HnTaAwMEe6IHBAC7ToRdzR637V9gYwAHctcKBwufAy0no8aJKTaUYg5YEaIfReraZXXOb94NysGhfOnz3/01fPNv/hO8+vzH6jwGrMupGyQvH//eN//3q9+7PWBAGvzhO78icQpeNPPBQFXjflOJq+u9K1Dwhe/8KgDUY2r9dzHoGEXl5FYJpm7+qDvuaifa0BLo67U4BcA2FNy0RGtA4B2UNoEAuDkUNKltF+rVfjsyXwHKgcAILLsTDkFhAQSywmUkoJx0aOGGGyjQ6IMJeurhQDUQAhBYhGW5RiLgDolxjoRTCjjMGYmD3C/XcMcGHdXnAPV1toj8zmpQ5q/1lWSBw1DzCIgwoEV8g2pGmEINmxw1RHIkwjQFHB0kTDHiEAlHDY4UiDByX+DMSCShiSkxIjKmGFDOtctctLxMkk9Ooj0IZOaFWAGBczUxmNkqtP2uAkEFg0VAta3U9zd5h0bsFEGy4q0OoARiK0IJjXkBA0AogtyN88HUdjEtNBEdHBTNRRlHkNU1PBxImWwHwUXtAfCRQ8JYQ1srqIlEaP8p0JkfiWmSvvnp38arz38CzFl1hwMZ5uXVJhzUuX4hHzfSrQGD770vWxLf/vTXVFOA2kGcp6Z0uqqWaqCgE5JVBUx48P4P8YXv/Cre/vTX8Nf/X59D2ZLWr6z7tFAjoYykRZzwHbaytlzYLbwkPZ2WAGhhoIECl13fdbeKMYQBYAEE9rfmANtagvLgART41LzXqajY/kK9jDpxL4GvAtPQ9NTBQNEOFI0BNUAARjnlsJx2mOWwo3IUsn0vczkK2TsJkq7+Jw5AzECSv+U0QyIcJ8ZpJlwxmrMPkmoliukiL89AMJ8Eu26vLY3MFmvJr+SDvrGzEwCNgqjlsetyOJJcC2QHMNWzFQIBx0lg4BAJUwiy8YHqoUwhkJuvFXACIYFBWUIol35sfgdMsNMLY7ZVN5A8IHACwgTYsdyUtF3yum8LUDUKwEJ4bW1vbvqfpX5xw7wKB2XvvgEB0JkXuntbm3WAALRahLVx77PWwIG9X9MclLKqacHmzL3ag/LwHSaAvatvLyfQtU9/j+Ij4bKjES1LWTgDFMs4fvXFu6XCmIPOY1T7z1peOpm1hAKRj3dffPmiE+mtAYPX//CX5eyDF1+GD65SadIudHBQvmSfLyv3wfs/wBe+8yty/5deqffpVtnuJm3mFp2yrhKGh4h4BxWv0fDkuIbri87ddtqhk6Tv4E+hJXAlGqbReBuZC9zbbT8CYEVLoDnxUFBu4c03bkUhxli93u/gtkHvNUkdBA40S2VQOhio2gE5+IZRNQTcA4H7m5j1czRHIdeuQiAWRz1TeYubQECIjAkBc5IteMdJYOO5Qz0d0YDATkgE0EJAAQB5YHLVmAdSYHR4Up9iWHaI9gRGvRZC85nBgr2ODhAMfExDEEB6/oJ/Lcc82xHP3pTBGWD1P5hc/nKWMwZClj6ayf+l8j5S1GaPqj1w5294SNjSYpW/BgTL/ihpZX5poCCX68UnwcGB3WVLe9D7H9S2QgEEYKxFKNlY6Q7e+GEOieXei9KuaA8Gq27zCXBbNFpIGGVqbc70Cyigncd7KBjKAc0keZ8RLpdljo8Ca5xLs7L2IbKAEyNA6LPs55/uerNo1qOZx+Wu6daAwduvfR2vvviyvquV09pwchEIFQ5qGk3spVJf+zpefelu6QgcD/qrZj27kqgVcp2GoIED1Dyuw4GlvN6py6NXOvcICMrnLRDY35sAwVqf6zUEQPUhsEsLIACWWgKggwKnkl0zdA8zuLGS8IUY1N8QBBwoeBiw3sZQZzio0IEAAisYgFG0AUm1BlY6K0lC1RCAZaUNaBfXrhVJo8+BkUMV+EXQd2aAxnl0UGf5Qg//KFJYzH4tQBQ/gtB+3zQk8pnAABEKEEQARFTqzW6ZIBERS29iiP+ebsK3iISZBBbsnhlqYtDnkWkRrP/2AbaKf8aKZmtkXtirguuTX6qbL4LbmtfAgcXZB6r2ANgEhCxVA3Dr5bAna9UoUG+8Dgg6h5r2wM6RgD2/1SAUQNBytrcazVpPOW/6QnZanHGL1YJZwC/ZVhpru4cIMBXT0xgQVmRXyY+TX27RvFcrcmvAoCUhOOEQBg4eRpmjzlAn+Afv/0CPtvwG7v7cq43qnQeNv1SFLVOrHtO83AQOysN6SPBl0K+MOrV7f0lD4P8+qy/BGhDYW2o+chPkipYAwBgK7D4LlZuDseb6ykDp6qzxI3D9RD6j6jPQ1KmDAacdkEA7KJqCYiJAteMn+y6qkLIUQUiBEXVrHQcq/WpS9bep+nt7/8YZj3rvQVUM6ihuTDD9dkKfRr4QltJA+I38FW5aBst/AQEzV8CEu2pcGrU8SkjfnGUVHQkCBIC+4KI5oCwRA4l1Z4Vd7yGhaAnMxODbqe/fXb++iZ28m9ua7XplLqmmBW7EtNceVPMC0C5xgDqGS85WAGErLQCBqu/BoGAqUGXVzLqCbgBBoWGhRSh5XFkMdH16NxSsAMEl2cDltQGq7nwKTouwBQjD/Nd8NUczv/jyok9spVsDBr2Qr02sEOAp0nmA1h+4SZ7EfPD6H3wV9z/7Ju7+3KdgQpPdXYHLdlV/eIh/ZG8/k+/0e3eBhoLttz0kaGqjFW7AgF2/AAR7NQSNpWYwmJ/WbACgagnsQQt/gi4Taza4S3bGBqgcBJTPwqKPNH4ZIcL3Ea8Z6GGAmd3n8tqu1++3UNB3M4ODqQCBfjCYTftixzIRqYC0nQHlB3ord81bTuwzf4+uCi+mtulMi9F+Vv6iFTzuTzFd2D1SV1OXNp5Yit2EwADIwYGAm7Rw4AoI5oYQQlWlE6FoJYjQaBJqHbHTJHRmMS08I6Pt17Ezj12YfEYNUsyW3IwN+6Y3LQAmwKo2wbQHfVoDhJvMF6UL2+LDTZqbGgQHCMVuvwYJ+tC1MwN2z6GdhlWLXP76u++VEaUNtJ9wZ0LlEsFVO9+oDLao1bOBTNNd8t1+eTVPtwYMmvj0PFI1UR1stnJgxmjv/vfe+z7uvfNl3P/c27j70t1m1WfJJv3NLGG7c5fp2K1mGzhoVrjOPOI7ueW9ufEGDJTr6535EhBcmo8sfahaAn29eU6Ez6Ozr8qLfjXQ11lXR2sxKgZaARNctvLfhIGBdsALRAOCUjKudcQwIa4w4PMf6/f8i164m6Aqnv9QFTxVk44XaPW37VHWwNIE1Dx/I12CUF9/5XOM63ELrPxv/QP3dN9Gfmu5mQUWZF7mKvwIoKxwQQyNE9VCArf1J74QU31Ir01Y0YyJynkMwWu7nLYDnZkZgYvArHAgn1do2NYeMPaZF5rfdI1BdNnE0C6yDBCiNk6d+9cgQerEMrBjHnWfbUHB1qJqS1ZkbufKmW1sVZhkduG3B/3DA43XdL/60ivrvnAbg/X2gIEvZXHaWQEEQCAhtJXFIeLBe+/i3re/hPufexuvvHRXYrC7xrWOPG9goK2iEnQidr/fdK5xcNCUaKgCXwmCssP+NZqYgTrZWropDPgs9lCwS0ugDxppCQDsC/hUHAr1ozUAaDJc62ovCNg/DwPmMzASYplbAYbme6UWfKkWEqwX+pbzvYI/Ur3erGjtn91LjyMfnjOx5kDn22Grs/SqBzIAC7XOe3+NcqYEARQbEBuZZ7y/xo3AYVDnds087z0gABUSgj6rqXetVw8JVsdBfRIMEhbaBL9SXAMF2xqpdV5NBTvVJN6kMIQDK3GnPeh8D/rUaA+4vwtU0I2z56/73Q69FsEDQgWF1sywCQmlPraDSV1aXPk5dDSPepkh5RsXXPoILX5fx22rRaCcAPVLYKC03YP33sXrf/DV1vzt8tsVbrXYtwYMSqcrf2kICL6D9Gr1B48f4d7vfRFvfu4+PvHiXQkyg3aSt7Rm55SJg12VUwMIvca3dmxCHbTVtODLtJWGW2YGtq8hDLgLl2Cgbo1bz9WW6aBdWbqVz4bpQK6vQMEojVZITX7bQd6YBvT3mzAw6Bf23vsMjIRSKRYPQMCSr/cBbBG2ISCEVvhIhECUSUZWYnYYkAh/ylmv1Qh+8jdVQAAD8yyTUdZ2yrl+H4AdMzyKfljCSJetviSvS4hkhYNJw0b3h0zp9zlMiC6ENCiAo48HIVKj7PKAQRtdNt94oebbw4rXdfvgwJAUEhpNQmkXFIFmPgk2VbcmB7QrxQ4SGkFQ7M6+0+Qq7Paq9kqJOzggQnVYlNcVDuxZF7QH7sKaaaF8pZtbtiCh18S2kOC0CCuQAGBXHJm9CyxbANgFDwT9UfOrT9XvpcwFFMh+SFWLEChKpFLOqEdIZzx4/Aivv/MV3P/Mm7j7c3ddfpvZdvF3lG4NGPjCblIkIPaoroEfPH6IL/7ePfzuZ+/jEy8IFPSTvvxWXuSBxoCoQgBgE3BFv0xj7UEDB8ASEJwKbPDQ7v0SBuzvJRjYQ7b+8z325Wc1HQAjKOjSReP2AAQAoBH+WIWBuvpcwkDREmzAwI1XqKVcNfebMKCCn/SvvZdte1qSnEBprhCQz+WUQLl2hu235/kMTjMwn4E0I8+zvE+z2Dnns/SPnIA0w7Y2sIECgOGq1fqmgoC9RtRQw0TAdACFCMQJFCfQNMnn00HfH4pPB4cD2lMpD6AQEBUeDjHCwkvnLOdO5MBI6tBl20VNC+Dbi9i1lQ3QQXuVz/WzoNOMTOxc2itrWxW1cZnotZ0MLjqTQ6QNSEAcaBPKJKf5uiz8FmkNDrTEtbjetCDBopy8rnAw6OO91sDPN6O5R7Ikdy5zrMHGCiSU+R9YQAKg86rdfJQGpoS1eXWkJehNYf5RW/NrYnsS135BdWssUwuPAON7j838fd854q9rhy+lWwMGZYEy6CD+A+skvqIMCr7xGYMCXggBoO3gqavhQCiaAuvExXEJehHttZHnbc0lo135dgN8w5Hkw4KBvhNdEr9Aqy24GL1QM/BMULCdi6eGARvsXlX9NDDQrBJ8XQ9yTd2bNSAQtb/u1zcoADQ6YIUBSucKBJzKe+RZNQMzcD6B0ww+nwQIzicBgNOTCgP6N58FEvKcwInB8yx1kTOQGMwZrIEO8kBjYHEJKCoYRAIFDTo0TaBICFNEmCaEw1TgoEDC8Y68PhxB0wE4HOXzwxEUJnCYQHqeAceDjPM4Iej7CYSsWoM5y+SXmEowqUwGBLLboAEEDwd9+5WJ3I1SWwSUiX0NEgiJeQwJThgsbM4FEkKBBLA7l4HMwa5fVOwEhREcmNOiwoDER1iaFoZwoG8uaQ2G9av3a+enfZBg31xCgntKyey+OdbPq2Ue0DSUG+6aL9uaNXrO0h/g+kF1eq0yxDSDj1TTff9zb8uWenevLRmwRQm3Bwz075YtClg26KMfPcQb376Hr792H5944RXMbAFluFMNtbVoe71LoE73PPnNEgTYX9PvGxx0885CNdd20vazUTs3HXYFBhpKx+Vk3+kBodGQ6Bc+eigYmAu6DO0L93wzn4E9tmv7TSmuy9ooANAi4M8ACmLxDWiBwGsHIlAAoIGBJBBA6QzKM/LpGny+rhBwPgkInE/gWa6l6xPS6Qw+J6Q5IZ/OSOcZPCfks5yomGc9WfEsAXyyQoFtSfRbE8uZCPo3RD1B8SAnK4ZJ/x4iaIqIhwnheECcIugQEY8HxKujgMF0FDg43gEdjgUWcLhCOF5JZMl4AIIABccDaD6B4wSKB8QgBzHlXNXSScdizvKa2O1w4CrQ+4Gy2p5suxhQBncV+NuQUHxB3Iqx9Um4AAmqNVhCArC6VW+UbgIH7vO9cDDyNVibh/rr3lxrC7FLkOCbrwEFS4uAUtvzbD/H9gAArICCl9YYyxbrD7bSFE2BxLEsgJCB7z8W+fXW5+7jFXWU7/O75ju2NeffGjCwtmhU+dx2jvYHoin48rfv4euvvYWPv/hKE3nOT/7+/payjBkJpQp7HhUTAZcfVTjw2ciocKDZcSaFETHTsCF7GLC82YW9MLBF8TeYSj58KNj93AEM2PUPWTuQt2CgyhIAy+10bZYHHdP1FVv9h0CNyUBCA28AgUKAAMFZXs9nFf4CBAUGFAjy6YR0fUY+nTGfzsjXZ+R5RroWDUE6ncFzQjon5LNAQTrroUrnrNqCemZD7lVqAEKscEBR4vKHgxyQFA+qLTgERIOD4wFhiohXk2gRrg6YjgcBhqsDwtEBgkJCVkCg4x1gOggUhIOYI/IBlGZwnBANECBmhsDiDS4+AlQcCXMW7UHO63DQt++sZe+3g/oxtgYJYn4UMjFhGgJJPWOkRaiQAJLIl00kPduNRRhAwv4x9mHDwd60e16y1fgaJNhvuiG3FA9U/t+ab7m72OfT5oM1KDAYGMkXm79Z1VUkb5CpBYQfPn6Ir7zzRXzjM/fx8gt3F1ps/4CRhngr3SIwWJJedr2ytdoDD3/0EF955x5+57W38LEX7pZ49KPVoP9d+zwnAQGluQoH6/ms8dqBqjUYQYEfWAtqdq/3agf2woBP3sLYp0Zb4Mr0kUNBJ1TXgGDthMgCBHk5gJN7PdpRsAYEHgZ8f+z31vd75ksdrUBBryWwEMATAWROhKoNMAjogQDna+TrCga4/gA8nzB/8ATpekY6KRR8cI10fS7agvk0Y75O4Dljvp6R54x8zkjnjDznAgZ24iISD80IlkJQEwLJSYoGBmEKiAcBhTAFTFcTaAqYriKm4+S0BgdMz10JHBwPiFcTpufugKYjcPWcgMLhCuHqugUEhQJMB4APYlIp2oMoEzCrQOGqPShLz9DCAfFyPC7amhkR1ICCaRPMNyEwmi2QzJ0WgVooiGD9/lKlvAQELoJ7ERkQWPbCC+Puw4ADe3AFo/XHXpqfGtAalsPJAcZQi2D5Wpmu23x373tlUa8tWOS3ky8+q818YdpovUREYJJ2N0D44/cf4lf+8A38zmtv4ZMvyKK2LFDteV3e+udsEdrtAYPmTdsxgHYXwaPHD/CVd76Ir732Fj72/CvVxggLPMMXCUtW/PU5l/zfbFguB6Nc9HDgc/5RwsAl+buyoNXP6oceCuoqxn9/PxTszggcDAAfCRCIOph3AcGlQDtrKQZqoCAQiunAPNhNSxBR/QgiUM0DXjOQZlA6icng+gPxIbj+QLQC1x8A52vw6QnS9QnzB2IuSB9cI53O9f31Gek6IZ0S5utZXp8z0ikVKOA5Y54zMoBzdmcvoJrgRhN3oCSgo2U7qIp8mgLIwUE8RsRDQLyKmK4meX91Qrw6YP7ghOm5o4DBc1dI17O8n0/A8Q74IOYQ0r84HBGungOHBLatXvEgnSCK2eEQJiQS9S1lRlJVPvQcCguNzDa5KxzEQJvnQ/h+EFm0CV6TsAYIgVUgoFxE0L64AIS8DggoAnsdEACsBk1bTU8JBz7Ogd+lRUTVR2v0uAvDqdm1UH7T+nzt8UcALmsLgOXc239vlx+FA4nRvGwyy+904QD88L2H+N9850v4p59+Ex97/hUkiCwiV87ls5ayYCvdGjDwq1pr7xG9PXrvIb76zhfxtV96C594/pVFfPpiR4aTWVgKdCP7vWr2G6njsd6AzRy0Ewi2YKCvIy/w1+YK+w515f8woGCPtuCZgMDa20OAlrU3I10yGVwCghJbyVWSjzp4Eygw00EMIiAEApyWwEwI8zUwn5G9luD0AfgkQJCfPBHB/+Qa6XrG/ORJAYJ8mnH+6bkBgvk6Ic9Z4OA6YZ4zZmacM+Os9TXrmMlav+XkRdeUNvFK2cRlLRIwaVkPKeNwJkxP5CjleBURryLCdUK6SgIIpwnxOuHwMwl5nhGPB0zzDL5zRxwhzwnxaka4MyPnBEpXoJwRckLOWcwNnMG4kn4Ws7QmZyBmxHhQHwgCMTA30kP+JnAJvNPCgfSBqN8Z9QG7Hm3i188uAQLbOHOAYIsREwhMpEvwsYlhCxBkK5/2y71aAyvcs8ABqgbGhPfajoT2/XLOalTxA0gYahGAZpdY+c0N+Ggt7XWytGf7/Pdyx+bhBOCHjx/i1//5G/jHn34TH39ezQdZ2qT0h5XnNGDTve/TrQEDwNmRMC70ux4KXlhCgZ1kZwKgvyewrXK6lAit4F3kn9c75bMAQdvptgd+vw3RJ3+1+YqbjNrvbkDBDRP3eXoKHwJT4RYAdK8ZuLzHHetQMArF2zs1r0FBjdmPuuuACFNwDobBYhCsQEE6yWvzIVAtAZ8+AD8Rs0F+8gTzk2vk6zPmJ6diNpifnIqW4PyBaghOFQxEWzBjzsB1yjizHM18Vs2aaAz00Cct6+ggpqDLNYECBQI1mcwBODPjQIQrAFMW80Q8BJn4MoNT9V2IKTdOjsxX2hYZE3S8qkkjA7JFEwDZXvzYqtNtEg7xUFV3mZBVWzDn+i1TBBNXR8U1OPAxh+y1BwQi0TjEQIsQzIC+ZzntxauTESoQEAGRGQmjfe9jQLC+twYIUr7LkN4kvgwH9tje38CbFNZW7MB4/lpeq2Xwgan0y813ekAw7S2wcy7un6wFuWHNNVkbyR5m4E9+/Aj/xXffwG/9TYUCMKKav6I2+qXF6t6Z91aBgaWRTfzhew/xyzeAgr4C7Z57G9wgwFbVvVD1HvyWRh1x0Qk3oKDvUP3nN03UDyrUMpXrHRRUjcEFKNhhQljAALANBOXzp9cSeChYHGRkxcK2U6HPJrCtJSDcwJ/gEhTYdkODA9MUzCfwSSAgX5/Fp+D6hHyuToV5VlPBnNSxMCPNzmyQKwCY34VpCDwU2PHRo2Qe91UKUFmQZxV+BhoAIcxZ1L9CR6CQQBFIJ3FeDFHyTyEghRMsNkKiUHY/4FQbgoKutSk0wmcEB+Q9AiHE7nctQJ0SkUVOZ4iZh6GCXm/eA0KfDMR7OABa7YFN/CDpp+aIEFTMp057YDERWxW5jZnt0wnrPv8dkOC1Bh4EVuCgqWcyk2z1NwCq1sBW3Vt+CGt1ClRNgilT7J76pcXnBci02GuA0MRmsO6Bqvmwy6X74IYFQDuHZwgU/OZ338A/+gU1HxiA0hgOmrIO7nkp3UowANpKeLeDAgaqIFiDAt+ONHy5DDSjA3eNNJt4893NnhYKnkZLsFK0kkf5q/ly3xv5FfS2uTr0a0ZWoWCQNmHAZ+wGZoPelyADF2MRGBS0mVsHw5FDoWRNrhcggFvJkWkKlrEJJhJQuAgFeRab+XwuWw3LNsR5VnCYi8DPmSVgkcYf4CzbDHlNmlue+/dE4mNT+gAV4RBoS2OARmMQyMwobf1dMr1xknyTL8c8y/ZHLWuMGodhjqAo9UIhgueT1n8A0wzNvdzXXscDJh2sxOJ3MBfD+NLvwAR45hYQJozV46vlgt63606Jl3Bgjs6AahJsS+Me7YGOhDa66kJ8DiFhPfMbcGD57GaIHg6AdtXdw8HI3LA2n7ET/kALAGz51V+twQEw1uRuwQFW2ryWVXYVGJUF1V4Oq5gFCv7Ov3gD//AX3sTPP/+K80EQOMgkfUF2nnBTnqdNtxYMLI2gIDeC4AIUrKQinwZQIKtAcnvRq7DtoWC3PWsABd1HF6GgL9bTQEEpg4MCcv/KU7wPgb1fZO5C190I87x2ZPQWFKwdaLQWuthKs5XLLdNQc2ohwfWHWo+jrYhRr0/BftNCAZqIhRKvwCIV8nwWQVmCGWVwSiVEcR+qmAIhxIAcswi7EEAxFxU+7gDzE50o5ix5zDpZBqmbA2n4YdUAJB5INquTAkEoZpIA4FBMC+JPYc6I052IqE6JpAGRJEqy5Ls/4tmXk5NFeUxSH3EC5rN6eQcJqpRFiFtf0jm+gYNMwAzC1DklAu2WRuWk0mcEEKTxtxwU15IXVEVwWd12cBBIYdeZFmz1WGIhNBCvY4eAxrxQngwtUKtFGCZvJ+ngoFyDva9woEVr4cABwF44sDqH+1tQxJlGe+0AN99b1xysJatP74bi4YAA9ckAsvaTERyUKht0kQIFf+NN/PzPvtLmDwCDy/ZFMGlgpAoHT5tuHRj4lUYxH7z2Fj75wt3amewPt5P+sOs7IWmTOlADzngo8IfUeCi4BAQN5fqyeCodpD3+AmvpplCw23wANFDQnH2wN10CArveaQksBz0UmNp7LxT01eYdiazMi6BEPvvdi0tA0DgaUhfOuNmOmBUG9IwDVkDQ8wuksPKdJj8xqhqeEKdYthQGtdNzZoSUIdPBDAoHJEoSb+CcEaeANGfEc8Y0Z9xJXHYkmOnA70qAAkKfigMilmUOkJ0JiIQ46c4Ev1NhEmfEeCCNaxBAFiVxiuV1nGIFiNgdkFPqjaW+5lnVMQHIAUSW+wDGWZovRASKmIKADzGK34GZUUjbqgEEZQfrN1NoRsh2v7H66t6zCs4tODChYKaFsAIHKjoUWtW80GgPgBsDQp/Mn6NzUOzhAFjCQamRp4CDRTZ43W9qz+f9UO/n6wII9n1tnuLFwlqzQU0XYN1JoAGL1IRUzE5a3h96KHj+lfLMjHKYqtSJgsUWzPi+tKcFbw0Y9IPoXQcFL794t0z2JXhE933f+XzltgJShD/0tZ/YTYBG97oHAr/CXkuFYDe+M5L3I23B2r0X1wdQQO6zDw0KNjPXtaAfqEOzgTz9kpbAVm8jKFjNiqN3G291JVCvj1LoPvcw0J9vYMKRCEVLEGh00JE/5EhhwA43ggq5UewAPaAIFEp0QU4MyowJR8yAhCcOElwoX58lnsCcka4iWH0MLICRbVVEZg2FzCWoUYljACzjhftkQY5I4hiUYEeBENS2YFsWLQBSPHhACIhTRLiy4EcH0BQxHY9iRjBQsAOZ7KClPtkhULATDJOYCCCCjDLAlBQOgEjRZFoxLSSiEkTNA4KHzMBFVi80e1up70dSZxs/8ILfaQvg/A5Iv+fnInmGjilC0R6UZ2tQJHkGt+N0DRJsCazfuQQHJs/WfA5sMO6Bg0tpoRlYyfYojT7y16K1MalGzQDBfBlUe5Ag201F6SQhuUHeFwn44/erT8FfcVBg9dAzQOlbXGWU/37//t33Hm7W160BA19I233wOwoFewZiIwCwBAI/uQN29vpSSxDca68d8IL0UnKaqYtag2dJW1oC+3wEN4S+PIzeVLALCkYeWR0QALh4BvoWFGwlH2jEkp98at7lTxdhfaB1af9u+aB4ICiHH4W6WmigwMIbZ9UM6FHIMul3ZVBBSPEApCRhgHNGOKKGJY4EnhLoEMHnhHg8yBkId47VF2FOQMpIc6pBjNT7ziIeFrV9Aths0QoFPOi0/vlST6GErRcTgURAhJ6fUKIiThGIQc9R0H8H0xZo8CMNnRw0YiLpoUuIk7RBtAOX2j5HrEBj9UoEyjM4AJTcWAxAoFjaz7QHjCUgACje4b15irle9+kmfar/XW8I6BPbg1e0B/JcP77Mc8HdtQeE8t21h16AA/39luYAaG32ZWyU6/KZPO6jmST7xdyl+dvm7UgoPic1z+bvwBUWrIcxkPUs7z+13QfmU2C/xxgYN/M/+L7Jxxfx4urvbg8YaCd8pGEiv26agu57Jmg9CNRAEpJMAIxWez98/6HcJ7QQQMAgnnl737Yxx1OBhwJy3/JvRvaonqZ9nQBjdZl/22sJ7PNtfwLN8aUtiXsH7UWzgeTAL/jXoMDSUFvgH+nuZSuQfvLp58ORFaHkzmlYehggVAjwrw0wg5Uo1+OPySZS08Ss+WiEICtePa2QAYn6l87gEEHzuZ5OOJ/BmRHtQCQ9+yDNScwM8yzRC82pTz+vDotmx+eyGb8EakmXMTzEUOoKAOB8BQQQ5H05OyEYLASEaVJzg2gFDAbCNMlvHBBgOujhTId6EFNQyPSAwE45WxyOskKCBkXiDA4sURJBRXsAroDARGXnSwAtIKHeXiagUU2xG+NNnbnXzamm3e+Lo5wHgXLvdTiw+abuXACG5gUDhD1jegAH8ixtg263QimrjglbdRfPfrsnRgDfzaMr5oE1wPKf9bfbnselRP4TP3+X4jfzt2iczPdAzFIMO+Pi17/7Bv7xL8rugzp/VbnkUy/4R3OTn9erfLyP/+Zb/2BwR0m3CAwMCuSUxJdfvCsf6ETfeItqI1WV1Ph+/Yrvj99/hF/7zpcA6DYyrAPBQngCWFW9OfLuHXMIrdagsXfT2Pt1NG7XjkjufQl82e2DG5kO+nLumUCeUksArEOBrUu2Hh+gMfGtYGheLAdi10+abOv97E1TZysmphqpTnHHovKx+RJkKYX5FZS6VgCjIDoPCnJYUKnCKIEBFBIozuDDUUIlM6vPwoyYxUSAnAssmOBnPS0RUC2AAwI7crw4MjrTAa/18abe3LRczk+Qa8EBAkLotAvUgsN0QDnCeev4ZquH6SCNZvBAQbztzYPeVrScxZQAQHYlhDIWwRkcIgLJIVAWfChnakwHjLo9NirMVi92ciLX6s11pIv11351U1vAFUyEF7Y1B3JfP+6c9qBsTRxoD7Yy4OCg2a0AoJoW2rkP6LQHBaTqc90dlsqMDShYXq8f+DYZz3uAW66tzunUlYn1+QYIIUh/MUAwWACA3/7Ft/AxdZTvT/sclYd8JtHOMX5ef/e9Kh9fKUczj9OtAYPvP5YDkd78TH/KFJVOVE47gxMKVLdd+dSrgH/444f41e+8ga+99hY+9//8BUydyaBM/ABGQrPct2z/8SuWtBCCla41vzYwBloDg9HRQBmlkcnA7lk+p/1aAl+uG0HBGhD4z5yDoT65nWR5CQAGBTUb9cMCWTZAtVR2qMlWvZV7OACwe9rbHq56GLB6bVDHA4EJbmbpF7pa7fsSUwCx9uhpKvvMWSduilPVOGTdlaB2dTYHRfuNPTultr7MidH7L/QT4ca5CLtTp95vVNT2mfoJVC1DFCFj/gNEAgXOt4DMv0A/8/4WNB10C562QjMepQcRkxSXGKSdjEOsAjJEFQBUdmhk1SL0kODV46ZRALDQYPUn7W1W22BZ64W8XzWbGrrMKwYM7JzmRoDgQGBVe1DqbCWtwcGKacHms5In6rUHJXcKXm6hcGH8NpoX/5raD8bbsIHR3Fe+5+d2dg7AJAeG6cwg8bM6QAgMZM3cK3p0shwFrjtNFBD6JKdw1r5Q5Vs79zzq5GNf/j7dGjD4kh49aZqC3kYFZm188TmWxiCAaohP33+tskMAfvDeQ/xtPbDi7kuvAtDQtA4G/CS/qlJ3qXxuArFTr6FraN8lRlqDZjBdqKtLQAC0ZauXPyQtwQAIgJGWQJ7sNQRbUND7Faxlo4EDu3ZhqTbaRTKCALveg8BmPzENQTEVmNnAoKAviEzYHACy0/OQgcNRnsEMViFf2iXXdipFGQn1bkfDf5Bp5Ezo4cJWa3bN3sdYx7oJPSLtd74FNTHr8qHcGJRYf0NVa6Pag0gBUbUIFWCp6aesk8wIFoC2H97Ut2hto0wjl61cRA0cFOG7BgeSI72H0x4A+wDB20i4Wftvag/Kooecjb0DBH//S3Nf96tVbYF9p62OFgrW5njq53ySZanNb2QApIAQoOZtNSVMpk1QLw8PCFbUVXOlW9Da67Jo/qxAQWnjjSnv1oDBW5+7j7tKQgwMKFM0A6JWk+5W4MCSqzCbzH/w40f4lT98A19/7T7u/lyt1Ik2JvkiqTZWU91gkxy1A2QrjKiZR6ST7KB2u09nUvAmA+AjBgJ7aHm9rSWwJy+gwKDPPXIt8qPctsKTpa34Ee1Koq0vYAwBdv0SCABo+wnny0CwBQejbWSc5ZCglTI1q2OggbPmm6Nto4vXg6l4S+0y6iNrfWjNn2JDlbuYlLtfNWVfK3d5togi4gSQrnA1+hCxrHZZJ3pQbQ+blHEBFCLGsGCPL/Z/rylaLd1ynu/V6SZYV+HAvkNVIMt93bjstQfevCAP3Z4HLgGCaU+pXQOT/QQtIID8DIqiRbiUtuZBb0Kod14Zw75Mq30vaXlS20+s76gWwWAnBioahAYQrBMtGhqNQ7M3gX//8UO8oYvmuy/dvbD8qenWgMGnXrrbCNGGMq3zq+bAXhMY3NWUJ60fvCdbHt/8zH288nN3i7kAgEwWPQzYJA9sDw4igJMeYQqYLY+AxbaetUhhHg7scVt7cf2jgeVAKGV39dAAgT7kQ3EuBMZQ0GkJLAdF4G9Awdrj1myOazXlvzYyuewzt+wFAS0J15XIAghGGdRwtsNMw8DYQ1Z9X1fHds1Wze77bvKqz2xhbfT3JivcNW/vWpLtOpRrHVABMEdBfaN/x6rfxbXR+Clt0AGCTfZWh/Z7q6udoACswIJ+am3ZYNENNQlr04KPDOjPaDA4WNyn1x4wq0nrhnDgC7GqQfDmhfqpBwS/+KuA4O+0nbbmwvIszdGuvujLtUhVqzKESdU4AXJ+SGJIqGMHCEEhcrBEaLSUBggeCl7toMDP9aN0a8AA5kTWFXcLDvpTubxK+N33HuGr73wRb372Pj71c3e1Il1HSOfBRL8xAfmHMOCVhlwY2MGB/0lTSnfR4ADrg7+pi+6Gu4DgWTUE5ZmtoLqxlsBlawQFazm5Ub3A1cMAAoANjUqvNQL2g4B9FxjDgNcd+oKtCf1OWDGR2tpDEzGSUc00ttXOVrDMKIcCyefyYR8QSkvUFuFC9/d/C3w5FWhVjZKqXEniCADidEi2QvJANmiDxjzT1v2N6718HyAyoZZrvQOXQUG/U77nVotwsJBN6rGz/7v69ouZDZ3kZrKF5xYcACumBSuHMy1ojvXmeR8cAOuAwKi2+hUNgi3+CiC4gu2tl35OtJdrgnN7IXhh/mcpJ+n8X0xSbkzKJ9omWkgDhBrlcMyzMASo/AABAABJREFUl6Cgge7m7zLdHjDwA9soXSc+ImikqQoH5AZZLwy+//ghvvrOPdz/3H186qW7kJGiAWXUBit7y91qpZ/oR1mkgKIhoAxwHVALODCR39k+CUsbedgx/kYwsPy7Awj6Tj9c1faq6g8HCpYCaFxw74Phry2yWbJHTXbWQKAZWCPz0cZKdlMYXZpAB6t/L4wMBNgc70zwGwzoOEgm/DM0YiGXiIX2186JyFwBLGXGzFn8E1lOVuTMesYIl+2+FrtgR3yjsj2xxnGQIEcTaVTRAEwU5IyJshJankDZRlCM8t3gnYF1Ave7Ojirf4Vvt7jU1qwJt96xTLUHQHUiNkij0n5OEPh2czBnamULlOPtz7piWIyBMo8N6nrPGJB7tXAALP0OTBADTjQ708JQezCqv7X5ovnezUwMjEFwobVKWTy7vuxNCPb0tbG+C/gHaahxclo7SmeYY22guuW02fniNEoj+fXGt0V+VU3BAIA36ufWgAGpwF5Spk5AhGYvae5WYCYM3n3vIb5klfriJyGH1AgEkPPkhgabuaj6LRmUfRAoWVA42OUuM44SBiyd6EZp6DjX/OyGQLBVzj5tQcENTQc+G5fsrn2t+ipaC+xkqsRlHd0QBHoIWADBnhnLZRAowqPXCngY4CDb9RoQyEDOIrxzFqGfmMtr0xTMmXFOGXNm/WevgQzGnAQOcpbvZrC+zgXeUuZyeNLobAALIy3HSNtrYApBQ0DLUdNBoWCKpNfEIWsKQf8SDlFem+agnDNByzMnAkXEGCso5KTj16BBIIE5uLYLbT9fXQU6weDajHy/d7BAru3WQGFkfzahV0DBnmr3HoyLSzOLnzo8HBC1jpHPZFrYZVZoF3V9nV4CBA8t8Hkl7DJvjRZLjTC9mC6M+y71IFkg0vrOfBINX57BYUIgAjTyZml/piH4PfLyy0PBaG7/j0lj0HSizk4PVNWTrxMTBo8eP8Qbv3cP9z/7u3j1hU8IuRV1ZGoaW0wJ1iE2OlCZ3DNMW1A9navmQLK0pTVAKR+7v1tOdP43o7+lwwBtp3lWGFhk4KODgtVH9u93agXq5T0rhY0JwUNAU3dbeW8hqmR4AQVRQSAKCIQIUERG1QrkLCv7zCrAVUOQsnx+ThnXKWNm4DwnzJlxygIApw4QrueM05zL785zxjllvbc+JxtscIlzMEomvEOQf/UUSRH2hyngEAMCAccp4GoKDRAcowDD0QBhipgIuIr2O0YMonWYiBACoz2tkhDDhBAmWHRJJo0T0azC6lgcj4M1UHDtaG3pYWEPKGxAgo1581Pw2cj6jJLdbpxcGjVDONAfGhx4rYHUk76iWldDv4O9qfm+jgOFgRq7wwECUOb4fm4E9s2PVh7/t8nPTeaA3QuBCpJ9v0Ce1Z8lCsSGKMG1FBCs/XtIfPe9h/jS7/c+Bd08ZmW6kG4RGCwps4UDFNMC4PqUvn/4+AHu/d493P/Mm/jU8x8H5usKBGZG8I2e5suTBKjAQHmorkYIGb0vwaXUd/wKQe13+t+01zvtxqjDDDQgq+ex211pMALdtRtFMXTZ3AMFa2P/w9QMXJwIPCByxqKegVX1YmMWsN90cMAUZBVhWoIQGw3BrECQVBMw6+o96ao/ORg4J8Z1zjjPIvhPOeM0CxBczxlPzkm+c044p4wnc8asMHBOqllIaoawg5hY2sYOZVpLEtGQqk9BkFMSAxGmqHCgf6cp4I6CwtUh4hAJdw4RV1PAMQYcJ8IxJEwh4DARroL81iAhEmMKoqmYiTCxaBZysOOsJdRymZRzEiUeSfwCMpgvDbVs14ttyqnp734u8AKhXTVuQ4LfsWR9u2hDdWLoIaHflbOWLsEBsOZ3sAIHWtaRcLw4pzRr4qqZqNBh9WzPWWoRem0Cuvc0eL2TJZo0hIJmUbAGkkvNkpgSSIA1qKOxAwQDRXbapIe2qFUoaPopD3yfLqRbBAal2+pf3a/NANvxU13Hscp78KPv4d7vv4G3X/s6Xv3Zv1Ji0zd+BX1D57PcaTAxDCd675DU5Dns7IkeBdoOvwYD7eu2ozR5XwGCSwN3mAb+A1ve7WtQ0AOBvN6Xnz1AcFE7cCmugIeBTmAsfAn0+rCegAqJHgiIGoGwgIJ4QNESqHo/Qf0B8hgInqhwP2XGaZb3Hgau54zrc8L1OeHJuV6fE2OeE3ISEMg5I89Ayhmcsh5YyBUMzO6dl8Ul8mAgZgOKATEEhAkS9jgGhEiYpojJwcCdgwDCVXlfIeHOFHA9BRwD4Ym+L4CQGVNgZKayRzwCQBCTRghTUekzUCx8ognX9gdcZ3VQsNLGVV0MjHwPimah+BfsgITiQMown4QCCNplSnx+A2uqOZIueXkMeTjw99j2O/DaLgcHVi8GBzfQIvj5p0KCM11YqQwQnJmhyZMvm/2mf9bwmzvSyGSwMi8AG/LCA2Saa5/gvAIIU+kzDx8/xBd/74sKBa9gMZ/Z8/sF30ZfuDVgUJ1wfAdSbQHnpfoJAJjx8EcPcO+dL+PtX/odfOpnP1Zsj+Xce2sMc2KyBrZAMIPZr/EluKQVGK20t74OrAIBhtd3AsGadmCt8zTagH4JsR8Kip7HvXlaKHhmIMgWaXADCHLq6qv7HGg/W0sWtVBtqo0Gyf8t9baEggSID4Cq82dV5Z+TvtfV/ZM549qZBAwIPlAA+On1jOtZXn9wSnhyTjjNGemckGYBgXRmpDkhGRwkNR3MKgT0TAHOclIhD4InUQgg0jMO1LkKpMcsBzltMcSAqGchxAMhhIAPpoB4iDgqDDx3jAIJU8TPXE24cwg4HSKOswDBcWLMWg93JsYhBiQQJq7nZbB68GaID0OkCI5o4YDYUYJO3Op4uGzvQVt7J0W2vslobMsi1Zr2lnq5EDchxAYQ/KhvDvB5SkDwURMb7YEbo0vNgf5/CQ7WHzq+TrSc3xn1Oail7/0Q5LP9c+yNtQVElxlitFjw863tcjE33nyuc6W1A+eiLSjgQYQH730f9975Mu5/9k3xiVub592ctWfBd2vAwBLp4Kmr9Lbj+EZ88PghXn/nK3j7019zUJDr0bYeCHoQ8IO+3NtBidrENuHghqaE8jMs++Iq8V4yGYw6y56Vuc0WTSZC8/ovCgrWghAZEOjLbZPBloZgoTFqIbFfHfQDz6IQUqwR+wSm9HdNP7AMi8aAwwRQbM0HCgXmWGgQMLP6BTBwnquWwDQFTxwU/LvrGR+cZgGDk2gJfnoSIJhPSaFANAVpzsgpI80CBel8Rk4zcjqDc0JOM5AzchItmpy3sBwfZCGLAYQo5xyEOIn3dTwgxAnxcBA4mJL+DZimiDglzIeI01G0GD9zjLg6SLmuDwGn44T/5GrCnBl31N9hjgJKd5hxxQGYApBYNgMxgEiYFA5gcBC4gQMOkEVCEWpdn19p92Wbm2CzcUkiEJotjwoJbAKfFAA6QCA5x2JLgwBUf6oS6A1u4V6E/vrYWoUDLY7Bgb/DRTiQjiCCzgn8vcDga5+d39YQEJyJwX7d/r5NN4aC8sM1LcjaPNE9vZMlImuS+A6ZdznrSaohAiSvv/fjH+D1P/xl0XS/8EksopZemuc30u0BAyuwdrYWDoAqciQ9ePwIr//BV/HNX/waXn3+YyjH2uYkM67FqbdtTt3gH1WwgD/V/HQCsaSyWvDvqyqxCNUNbcL6J2N10VqkrnUgGHX0Ns9thkY+BFjUwU2g4MPUEqwCQQEAhncwbUwGOWMBBN1qsaxoUlpdQIwAoXxmKmZXZzXmgL5Xn4IMlN0BRVOAfVDwgQLA9XnGT08JP71OBQxOqiE4n+RvOsuJi2lmzOeEPJ+Q0xl5FjDgWeEgnZFVU4B0WWOAKH9DCKB4QIhyCmKIE9Is78N0xHSIRTsRs5gEmBk5BeQsGoGcGZmlPjMDP3McnUHn0hSAzHLcs0m2zJggWyRDiCJrmIswFtu+vldBJN9Zwk8JRb3yvrY/CySwtG0bPKlel+7AWGgQEAW+iEGUVwGBrYhYag8MEC7BgeRhn1MiMIYDueTe98J0oU1YmX+6eV6ysZznBe66ud/7epT/dySy2IN2Lwmqb2YMAAo/uaMNquVsysbbAtq0Uknb1QDB5q0Q8b2f/ACv//O/jW/+4j/D3ec/DjsyvL1PKwt2QZim2wMGXWrggEJTGQ8eP8Trf/jL+OYv/lO8+sLHUE6vc1qCAgnaiDbA7S48n+EF4GKyHwj/dsI3wR/GUFB+13bm9eQF/IY6yV5jraPss/+VR/nO6KDAB9LZCwUfiunAXu+EgroF1UFCs+d9oCUYaAgaAbBmQ11oiFy79+8bE4LsPjBHQ9lyKIpHi0cgzoaqOVChKc6C6lyYRVPw5FyhwPwIrs8J5zkPoWA+y/U8n5DO1wIGp1MBgpTOBQbyPAscABiewxBM0MkxyUwRiGfEeAClAzgeijkCAGYcwTzWqp21zqLubiiPILsGBM1CJOg2RgYlRoiEWTe7BwYSadAZFZRizxVtn5yNoCt7chO9FAR1W2PXXzf6gAdEcRq0493cjiV7TjFpiIZBqiMASFV7EIRyqjCUOa/fjVX0mQPtwV7Tgn1tFClxCw7a8xXqXNRoDS4mp5HtF4IDH4RWe6C/N/ByuRwn1Ru59i6aDw8HolLS9xj6pFRoaMu5OmfMs+QgRjBYACFEafuU8eD9H+IL//I38M2/+U9UfmXpB/2izN33pv5itxYMmuQq/cF771YoeH4ABZxaLcE8S/uOBvpCDdwlE4peSPq/WF5f896/UMA2Txeg4JmBoDhSmdq71W5cgoIPw8nwmaBgxZeA3N72PVDgtQTyYqMObfW3BpD2T+MSWB1KnALdmqjeKyUgUWZ1/FOzAgMzZxeHQLUKqfobCDCwrrhl62FmyMpcghaAM4uDYVJnwjQX80D1IUjIKsAXGgLTHvRVkFHggHNWv9uAnDMCJXAIcn/S56QZHA7IiUEhI2SRbnnOyIGQY8B5zrJ9MRLOiTFpOadAmEmma6mHjDkTKGRMHMVMQDqngpEDQCzBZCKonKLIIYByrlqDoqFmdWRrV4kUo/SHNfWyU6dzSqU/CCCIgFnAgRd2WSpSTEoZ4KBbLgkgVr/GDTiQTD8THMjr1imxiGW/TrD/ezgAGqgqcHDJB6GkJSBUCPALQa0D7k5yLPMqu1+OkoOD3ixiWgpiwMFBjU2Ta/utmhosG3n4vgyhqZ6U+r1/+6/x+f/h7+Jbf/2/xas/+/NFayS9FuCbzOMb6T8OMND08PG7eP07Dgp6fwIHBcRZBrhbwaylempbFfK8AgUyoFVT0Eeq8wK2M0H0w2XYnb1Dy24oeLqONISCRv39Fw8Fu/wJPiQouFFyUFA1LL32KNZ+YBoCUhtymAAKxYTAWncMIEEjEEIggHPdoWDRMiSqITdBh+ZBESQaIZc6lqzJCprUpk2cxT9AU06QWWQ+6wXIkciDlT6RHp0c6tHHRBEhTgiTmhRCKL4I8lfz4f1IBpvTZwau9LUFWspsopuRIFqCKRNmEq1BYjlx1UKPBQUsCkDQeheBFUUGAdUpESrQAQEGztWH8BIcrCbrX9twAASQBr9ZxEExObQVx8Xu8iHAQeOUqOBxEQ4AFGfEHg5ulDpA8NqDNTgoz172z/Ec6+djFdjOrMCqXfLARnlGgYZSvtLLSl8p/cSeb6Bd5E0Wn5x5BsWIf/Xnf4rP/0//Fb71V/9vePUv/WWBBfihFisgbZih96TbAwZdRfQV8/Dxu/iCQcELH0eNTWCCs6qYd0MBhXUo0Em9rADVUUgm/7juU9AJ1BunRsreEAr8bxcdKyyvf4hQ8ExbEfdCQWnfFgqq78UGFOzK3Fh7VFeFo37ioCBEV38eEAgWzVA0Bq22gF19ppV6bKMNSsCfmQiHKeCcMkIMYAbiVIWe3lHeq+06zQFZ+3WIB3VAzOBJxkv22oLk6i06M4KOCQMA74gYpgPidAWKE+IUECfdoVCcEDXugQZBOkxBgxlJueRRVF73KTHjgCrMTNtStAZEEGWzc/4DWpNC0MAzBgeqbbB+Y5M+gKGP8t7UOC/3cECxXuvhgG3r5RgOGK3fwdPAAWDf/fDgQG+qY7MKYvdgd1PfviZwt+Gg3GOn0PS1IFEeo17NWPgcWB9AEmDjXMuY6/3GcIBVgOScQQH4Vz/+1/j8g/8dvvWf/5/xqb/0l9U/xeYndTgl0s7o/OzsPh68dpT/9oAB1inpwXtOU/DCx52gcELAAcIzQUGY5H2ok73fh17UwjuAYDQ8yX3WlHakLRh8djEN67CFggZqgH/vUFA+d//GUODBKLuB5T7blxMAXAbbyJnQEpN5ibd1VvqJXTMA8LsQHGRaHXoIGKVIhLM+MYD0PAEXWTBmZA7FYU/ShJ9ihh1QRCFLVw2EGAlpkq2KOUXEdNCgRrIbQXwLztU3YLHqcbWmZgSYxoACgtMayO4Ei2MAAYJIEudgkoBHUbcuToHwM1dTE99AoiaSRkB0ZXedJG5MikVQquwgki2BRGZSQN2l0MCBOijmGXbyJZtOZ5ouroQbLdLexIxRBNUqWLd9DkqO6OngQLKwEkb5KeFAbPa8DgdWT3vHaqcZ2NIa+Dtu3b0AAjGYRRDL/KFHu2e9g457MjUSSwWVfjKAgzWr9Pd+8mf4/KO/j2996v+ET/2lv+wWLgTbxULQkN6+H/TV4bVuF+rw1oBB6wRXG7/6FHwNr77wCTQdbagtWB5r6ZNNbj0UVI1Au/orpgPTEgxOuQNoAQNrUMDu9XaFtBPzLhPChwQFvgx/EVBw490HN0lkHuhudQOU+i3bDusPFr+3jNedB64Oadl/lpEN4cCArYRFLPRpCoTEBA7iO31kl++rCTGkKjRDktDEkXB9zrgOhPnAiOckWok5I2UGpwmpxC+QaIc5tREPs5ELrIrqcpmCAxESh0EfATFEeS8BjggxBlDUAEQa58CAQIIcSfAii2dwdZDYBncOUaIixoBjlHJNAZhIghuNkl//MUsgJHOmI9MYaJ2Xs8/8ilAnZzZTjK7iioPdYqyt9Bfqx1rAqrRYSdURT4TqFhw8q88BcDM4kNI+Ixy4+mnTQGvgv3fJHwzbcND0ASvJABCYSLXRpNmxMtb5t/SfjAUc9OnBv/03AgWv/h/xqf/Ff1bal5iq3wSrBmkr0WjeWk+3BgxGjf7gfd3n+emvyT5Pp1Yfawv6WwZV5diAdYRZ1MCXtQTFl6DsSW6FqP0DnBDtUh+nHPCddCXd2GbXPLG+pHryV+MzAeyDAr34HwQUjEwIw4eaepMLHMhjTeUZx/XbqO/6frMEgi14hIKnhytGfaxZokkn9ikQmMV2PsVQ63eSyTWQHFYkhxBl3DkEPDlH/MxRdijMx4xTmiQ2wDkvQh8LCAgsMNgdUCjfKaGQt9rV2lLV/aKuV4sbBAKIarAjHyrZYEAEvkDBFAUIDlHPVYiEo56tcNRIiFOQ7xOhnLkQ4PuTQQGKn0DRGsC1VUBdBXKqq78yj5gwo3Zlaic3lo7xlP1mLa2ZFP59wwFQdixoKXTOGsBB/XYBqSpQnQnladIF88EaEGzNxZY/GgECBYFiJnFcjQoLCg2iPUjgWPsPIxfZTlFMEd/7t/+6gYJFmTqtwRAQ+n5D7by+lm4PGNiKRAfgg/fexet/8FW8/Uu/g1dffBl809WiCgYyoQ60zmNFyA98CTaBII5h4ELWbEiMAMHnd38akHi5bvfstAR2zbQE7r2HHODDgYKtwEXAChQ8a/LqSl+nHoQs+fpeTNzUXu9hwIFlb2LyoJCtVO1ivMluUIGWAUxqy4+ZEaeI65RBgRFJgOGYAk4h4xgD5sz4mWPGWWHgnDSUckplu2POjJNFOWQ5ZCl1BybZBGonK146KwGo/gCB0BysFFV7Ye+PUf7aGQpTjIiBisngEOupi8eJyqmMUYFiIjk7YXKAYD4pHg58Kn3UhBzpVjETuk5lzLFOysNw2eoLwMU+7R+01X+A1T70zIlvBAfA5fG65wCmtmQtHMg1wP0SJVaAPADbgHAzzUpf38PF2UqRy+fkIUEAQepNj1Rm2WEj2qVQ/B7IYg7kLFomFg1DmWpiVJ+Cv1eh4Iaao8Wc3ZX9weNHm7+/NWDgKfvhjx5IRMPXvqFhIrUliVYb2z5jImczbiPVsSN3swUXDUGvCvZAoFvNPBCY0OwF5ihbwJjAAUfge4ViUdEBq4PJAwEwhoLmsxXNx18oFNhDVvwKhg/xgp/QTDxlZlzRDABLOveZHdRdCwNSouHOlK6/ZO0nvt5I/yOdvKNWdgZAUbbWTYFl215mzEEF+8S4SuJoaEcry7HK6tQIrloClnMWkp69YIc0GQDknIt/4dZxy32KHRzEAHFIRHWStHgEMVA5bdG0B+Y7MUVZ7duRzEQoMBCJKgyoyaI/ppmIaj26/DGzHGfMFcTLqhCoKmMDgiz9hRGXkADUPtab8y6pf4HN/lS/s6VNEA1ZrzXQYpe0BQfyuJvFOrgpHEDzV+CATVPntAelPBvlvRE40WLWvBSFtf21mkv0TZmTDBBU27gABFt0ku2GCwUmAOBf/VtxNPzv1dHQJ+77wzBj1L7uNN0PHj/CvXe+jJfw0uotbg0YWGU8ePxQYkd/5k3cfekVHZRmf0rytw9YYupnbTBu6rwOSA4RD/78z+S92n/rCq87CtdN8Lbq8zBgtllLfd+j7lUJKILWdrc5DNzIXnilXlRnLrUEcr01HVjeG+BBLSfQPuppoKB06wEUuDu3UNB8tOJPUSYfu33RyzTPu2hy8Pe0n3h1sAOBNbOMr1fuoKDXFljWg0k1mCOkeN0HFie7BMKB5T4pM2bO4CzfYZb9Bqwrf2Z276FOjmONQDYzAuy6f325ff2OAS8sQqhq/V6jECC+CJEgTpFQICJy76HvgYkCYqiC3z4v39E+JL4Wi+Yr9Z3199LfZOvmwunMAaaZq2pMf3afrZgT9vYvl8nluRqDmWDkfNfAgfym9M4ODgCZd54WDuz3ADZiHRCG3v6k+ffaA28S2DN/Da4P8wy0cxfqHG1lsu81j3N9hIjqHF0gQQ/o6gCBtFLLaZnMTUV//n/6r/Ct//z/glf/0n+G5ZP9vOwXFXa9m3c67e6Dxw9x79tv4P5n38Q/+NZ/vVontwgMIh48foB7v/8lOWXqxZcBOArlNN4rS1QGdz2Dvf3cVnoPfvIn+Pwf/aZcj9MYCMy5UCf3NICBsqp2j/Ey21LwH0A7ng7cy/XRqcE5L+GgSwsHTifA1kwHwD4o+DBORgSWUFDQ5EY7MWxyrHBg5fDPqmljZTcEg3ayflZTzKgkNqHYStrq3la4zMABuvoF1DEuSh/Ue5T7umtAXfX7NvOBnteqdm2r5Cit7Q7w1RldQ1i/iE6qFAGC2k9kxWa/MXNB7VMEhQ/qbMWD/Jit3K922auMHYwCKng7TYF3+uVyV+wHg7W01e9GqXe+4+pv4KcUDwfQ1fBNQijL97Q+eu2B3nePUyJKniqseyfOrSlwoensX29mfrlwY+yYq3Vg9ZCQCqxOcifbzZIFEJgiwKkCAoBv/bV/KMGL1nZMmUwCbc/jnXb3wXvvVvn40qubwHRrwODB44e4V46evKtXHYWWyFTSO0skM6da8+QPwE3msUDBt37hH+Ov/t7fAserVSDIUPUvtzAwolDLZVmccp2kvJbAnJoMDjJk9TRKzVnoi88uUMVFLQHghVgRMu7NRwEFfZwClL8OCnptwdqqwqsjybf3DtXuwAcDGExGm1DV5H4JA/pmpHEhywFB1N2o2gNmLsKUm19I2qHl35VWnPs/lPRR5tFfWutr/ju2kCuN1MEpHCQACgpAAwvFudluaNeAdpHS9NWbgsK4Tzb3tvKa1oBoEw5KLgwO4Dh6BxzIYy87JfrnFjggyEJm4JgoN85PFcCHN+BgpM31AD2ayzLXvBsQSDnYmWN0zibTTkW1nY0BAYDILw3VXmKCLOSS1ZVbaHjNQVnQyvsHjx8V+Xj3pbsXDc+3Bgzu/d49vPW5+3jFFbqxYRmNUj2hDA0sKBQU11CnfvnzP8Xn/+Vv4Ju/+Nu4++IrAACOkwOCWAR/zva3hYECB32LdBoAm+CtizVwMPippaasI70z1UG1SANhBgBrvgT27D2C7Jn8CaRgu6GgPnQdihp1JIBNbYD7Xc3guo13WV9NTsvfBqbgPsB4ZSKvl6I+9heKsGsnzYUWyv2mN0n1f+GFXZepFsKebdW7OFek+XxZh/5v7yy2Vofyfh95DFXi/YO7caujEOKPANSfLOuwBC8agay7dvNogBvw77UGNg5W4IC1TFtwILe5rD24iWnB6rDO1+K3odVb5+hBXdVbbIDSQEO3VYIhKPTj1q/qUOtNfFgqJATdDROCAUIEQmoAAQA4HpYmhhLQypXRQYCZEbiDgwoF93C/k49b5b41YPC7n72PT754t4f7IjCZrHMxitbAwlcGF0XMCw4K+N5P/gRf+O6v45uf/prsbtDdDzwd4b3Gc4baZtVW28MAi+7CUjO36BszZgA6WLyWAPJ+pPJcTaYBcWqqoUptBAR2fcWXAK5cwLNrCXw2bEUM+NVZCwYjKFiNUOg0Qbs1Jj5D7joPIerpBNdNhdaoroBtiBoBlISGVoBc2cY59LC37ZtA/Y4vyE3goKvHds++n9j6FZDBl11bWTmVZ/QCgIb91hdDXnOr1XOfldWhvzYAWqAFBlBszB965/V+zG7GeBrTw8oKuTk7oIODPhU4wLM5Jdbv4gbag9b3APBVV00M6+XvAXNf6jW8i0VdP5dbXVjO2MxV3EJCZvF1YUIk8amR4F4OEAAgTFWDEKI7u2cpn0RrbbuYbCzUUOsGBW997j5eVii4tOsCuEVg8MqLd6XBtHctHVyomBSY5NjSJnxl63Eo3ps//mN84bu/hrd/6Z+J+sU0BAAyxeI/kBjq0e00BQ4GGMtJB9CBYnkk7WxcAcELxfpbLC8OUjEneDhYS6tAILXnYWA5QFy+XNn2pL3+BHZpFxRswcF6RhbfGwOA5MTXxUj4b9VJXzuj3PY59S3Xw9NaHAd/QmRzgqSFArdr/jRRZsjWKQbPEuoYnOUcBP1uiW7IEv643AeoAY12ONOVgEdEMvmVnTwSFbHEBJkOIrSCREkUEKhOvwTCWvwQM/WRQYOa+yKRbP3Weixjldp222wv96XgvmjnJNb2GvTxHhocMIiWoQOtRR93T7ipRsEgYAUOCsSgCuo1p0R7/E1MC5KoucdIe2DPHpkX6h2Ai2aXfjxfzOPgGpaLu605XXIlC9ERJISg1xUQxA9BAQGQRafuVrADvBoQt2d6UC5h9qsJwUPBKy/d3bUt3tKtAQNfZb3DEACQNykEmcQEDmRglONdzVHj/R/iC//8VyUOws+9WkwG9pyzLqTk2FvpGMl1nhrrhYerElKNQO2uVCYGNN9nDD+4Sep9J+yaPWNlFbwqAB3pr6m7+/7XZ/+p/QmA/VBwA+3AHj+AS3Uwcix9GrNKwrJerL7GQNDBQE6yhY5Tvb44KCzJyaFpBpL85fksr+ezXOcMnmcFAzntsEDB2U5czODEYNPIJKuYwYRdooZa20t0QztUKRymCgdRX08H0CSvKU6g6QC4v4iTfHdSR2A7fModVS2TZ5Tn2/sQQeoTRKSy1kAfrbC7BL8j0VS0O8w61tUZzdrMtfOy71eTRNUwMGQ7ZIadmiPgb5m7gbbmIhx4PJD0NH4Ha3PA8AjnTnvQ/1LqwZ6KASBgc457mmTagsX17rOmf1h76xcDqhlKYmdIPJGoC3zxP6h+CADA8QiEpKdmOkDoHVd7zZkLjPbgvXdx7/e+uICCRhZtlP3WgEGj6qMlHBSTQkBxLeCiNq2Cl4kkYuJ3fgVvv/YN3P1PPwVWIEimkYB4bhdNgf61DmPe2UVDa3mEHxzSrmVycADQu93cOOlobZwQu0GybtPdJwytDPJ3HQjG2fsIoGBP2gFD9iQPRaOYE17d6K+5nA59CMb5kj+9f8nIPGrf0ymhCno7NTLP6I8Sp3wumoJ8uhYBf74GzzP4rO/nM/h8KpCQTyfkeUaeE/ickGY9CnlOSGcBhHxO4MwKCPIXALbOGCkhxUOAnJwof8NBBHc8TKBJNAhxiqBDRJgiwjQhHI8CAXECHY4CDXECHa4EHg5XQJwQjldVcxAOLSRoqGnKqbwnHd+2Mvaq8lGbN2272q7cSK1ArTbBNL8mPAwWGpOEA0APCwUUShx+LoK+Pn7fuFiHAwmC1MxZVkeo+b4EB12NaEnqWDFAaLQHXMvun9+bF+SezdJq/NyRX9BTJK/9Lbt9yhzovueaXupHYCGxbrfV74QCBnK9LDrZaRAMEEJn+itlI1StgW6p34CCvQqmWwQGvsS0gANhgQ4OII4s5bdEePDe9/H6H3wV9z/7Ju7+3P+qAQIzEwDAWU+3M+2A7yjMaITGKJn6cbWvuhHRaLpv2Lc3VWh7haK+eVog8FneE97YLtXf8TYQXJoEd2gGtmBgIRS6a0sfghYQXLZrlnyblt9ThSMFxUzVydDqagQFlGZ9LxqACgQzcD6JRuB8Ap+uBQbmk2gDztfg8wn5dEK6PiPPCel0Bp8T5tNJQECv5zkpGAgQpLOYFThDXgOqQbAy13YhW4nGCgfxENRnKuhrOQ+BJoOBiHh1AE0R0/EIOkTE46FcD8ejAIKCAU1H0OEK+XgFHI4KDUex2YaDmC/ipFLMnYEQgGDaQJ03fNj6RVvzzjZ2r8t20jKo5cLIUQ2o15Z74+3eGorXAMSr2Rsnw5Wx4RYMNpaqkHWfDcTtlt+BhwPq6qc82tXP05sXWkCwhdCzptZ/wrXpxgR3cb5nKQ8B4utGVZNgEThzQIndMSdGtiBfBggsJrvFllg3h++BglZOrhfq9oCB/q2dbQkHcrXCQX9m/IP3HuHeO1/B/c+9jVdeuitn2ufqQ2B/AQED0w4sYIDbPPlE+p8qLaowoc4/vkBBHZj+9Wrkv/rlMR4O7OkmGC07zarITVQ9EOyEz0X+t6DgRlqCDxEIehiwx206InFdRbjsLVSMw8StmcB8TAJX/xLw0tm0CgdeQkE6KxSc5fV8Bs8nAQMPBKdr8OkJ+HSNdDojXc9IpzPy6Yx0farvr89Ic0K6FiiYrzPyOQs8nPUgpVn/nuUshVL+NOgdkapJJBKCwkCYDAqCAMEhYLoKCgARcYoIVwfE4wHxakK8OiIc3fvjAXS8Ah3vgI4n0PkadLgCjldAmgUYpgSOB6nTyOAIUKrChqMIwWJP90210uYjdXLfxqXd9K8JvrKi5KUNuhwsxQxiqE+UfRcNJJBXs38IgHAT08Izn7HQvFkCwrZ5oc7l9Vv9vfyXlzfwV3y7N3BjrzvKWV0E8nheHJm6TZMQSRaqNv/PLCHNRRsAPSwxqgxjNNthXfkMCu6ro+EaFOyZt28NGJigtYEOoKioLLBG7XpKnFSr6MHjR7j3+18q3ptpAASmIQCAWcHAwtX6DrEWFjYGO4oT6MZZ43Bmqwpyn/nXYxJYGQw2yJvrYxu6vX4aICh032ehZKVCwZbp4MPWEmz5T4yA4CYw0AuGkVqxryvfEkyoviY6oYIqHPg4FaSVU+ooO/+BLSi4/kA0AvoXZ4GCdH3C/MFJwOADA4Qz5ieiOUjXCemUMF/P8vqckU4J+ZwFCuaMec6YdXzMmZHgoiQOmsTaPZAEL5qCOF9NRJimAJpEcxAOAfEYEQ8B8SpiupoQjyeBhKsDpjtHRAOF564QjzOm5xKi+UscZlCaEbR+bLj5uV1gALqHnASwwlTAnbp29G3vNYTW3pfa3L5LZAKnhYUq+AeQoJoEWXGSWRLWAaG3w3sn5LXk/A78b7e0B97voI+UaAK1k6f+js1cUb7jAKGaWVvzgk8lTx6A/Hw3mv/KExc3K9oiQusTIvmlEujLPutvuUcGNPdkFC2CWYZEY+BMDeqkyAEKr9WcZMnHKShbErmdl/wvMpbt4tOtAQOghYN+teWjbdUuIf8/fPwQ937vHt783H188oW7mLOYDURj0PoSmP9Ayi0QpFy3N6Wuyi3oTMpt5DZLRWbZatAGPNXQrfYa2KEtGN0caFbQJhytPkqHcUIS2IaCS1bMPVBA7rsVU9BQ8Y1tpzt3WaxBwdZ2Uw8EftXgct6sHoYVgqotsnnLNEYCJOs+JlZHdWdBKn4FxbSwBgXXHyBff6AwIGAwP3mCdD1jfnKNfJpx/um5AYLZAOFJQjrNmDNwnTLOzJh1XJx14j5reOWmDlyyFohEOKiq9ECiQp3mhAPZgUdAPE6Y7kTE64h0lQQQThPitZozTmdMd66Q5xl8546aNDJiSgg5F7AzqC5wYFu5smgvWCtfHBHbw4X65LVDo7ZvyrzS9iZAiesquwh+IhESDBSzEpzWgIC4og2tj+lW0ex8jfbAAbAOCFvaAy2zqcgvwYHVYbmHqzaBsuqg2JsXVrUHYCw0JBdsr/5T72MiK0kury3uoi3qilJlULAtWcDJ5kI5BySjghUrNM7MCFm13AQEVeIESBwEDhUGgSq/fByfNa0XcHneBm4ZGIyS1xp4m5VV0sPHD/HF37uHNz9zH5944e5QS5DABRCsI+QOCKwDjBxjE7gJ71o+g4cCUkoldxgHSkjXIlT9qrGWEsOH9xkpT63lbwSkXtjSEuzpVM1jbwoFe8wGO1WFy+tjKLBdJTa5swPBS0DQCIQOEBbJhALQOpmVSUZmnbWilAmRM5r4A/pPthym6kw4i18B5rM6G55aKDidChTMH5yQrhPOH8z6VwAhXcu/05wLEJyywMA5s/jaQH1ubNxoNv3kFFx5o8LAQQO+HIK8PhDjzCyAkGfwnJGvMvKcEc/iu2Dmiphcfwinpp4o2vZHKlsUKZ5FQE6TizSn9ca6q0KD54zMCaUJ2Y2JQftfavvyXSfsRPBfBoQAQqLWxBCxAgels61ILytAn8yEAGBhXhhoD0yQX4KDS2kECWvmhd73oIJfGSDbEOQcLNslIorWIBsQMItGh828Z0edO3BYKd5IJsxgfTSDEy0AARDtm2kIwASGmBXMvJBzlQXvvvcQb+ii9pWX7lYLXjeP3zTdajAw6gScussN+kePH+KN37+Hb3zmPj7x4t1NLUHOrQCdHRBYw/tY8aux4PU/EfhQG5MJfiqCv0TJ6qDAvFrLvZ4SCjwY7Nl+Z5/tTY04XoGCRTn6IDzAAhQuP9hpC3aYD3ooGMWhuOhc6gSCVyH2e5uBdXMSd+9Xi9dfMIFmMQtMyNl2wyygwGlGPssugzQn2XGgToV8Tsizmgpm0QzwnBdQcJ0ZMzOus0IBcwEC0xokHpsTGjMCEQLJ7wK5EOIy8+qkLL8+2u8CIZ1mUATSiUCRJN9TAl2fkUNAmiNoTsjnGSE6J0yrj+mg9cNtvW3V70padMcb9gFwB4grgOBNS1DtAXRuADNs0/Wq5kBV64y9WoPliK/iNlRwWDEtjODA7rFXRjWaX82T1x5s+R5UMb8fDvzui0hiIjA4CKYeCISs2w0CC7hFb1rQ79NGIYt8SDIGBBJaQABQQukHsGgMOu1BVCh89/FDfPnb9/DmZ+/j5RfvDh3FpfpuTge3Cgwu+aR6W9ijxw/xxrfv4euv3ccnX3jlopbA1Ms26P1JdZdShGt4gwK0UBCVCCQi1hIKQlEfbQBBGYXr0xt3r2+yJ//SY1r86DQd+sHYDGITtU6ifsIe1a93qrqUngEKEsyxdAUKBsJguO/ZXTNzksGBdzrNWA/Q3JTWawrqRfk/JcBvG+Qs71XVvshbzlIH5jioq/K8smQe+RTatTUosPcBBtYCBImXE3tiYBp035wZlLjueEgi4NfKZGUWOJJjh5Gz1I8daFN+4OrTaTbWxGfuX3f9YE8fAJaA0DsjB2ZkqBByTqlZ7aI3goM9qR9XJc8VEHrTwh44AFqTwqhu+zmlaAWsSno40GdYmZ2uYBvu/IO8ZqSUUOZgJslEDwfQxWNkQnJwQK68MZCMc16aEix5SGBCAQT5TOshQ9p+oD344XsP8NV3vijyS4P7WbrU2lt929KtAYPWeq5/tZeZPQiQCnmkpPX11+7j5RdfKUJ/BAWZW7PBbLY3BwVrgpJCCwXFNAAlPwcFJvhD8EfKYkWY8gXB6Qq8sfMgd52pUZO66/62a+npoMDBzZaWYDSxbcBBoy3w1+E1Ae4anMAvn1dB/zRQ4CeEkRnJUlh5PUp7V7MWSEh+FOS9RRT094tyjYg00BBAUbYR8pxlBR8JYVbVPwHZpHvdAwxAxk4IdfU/AmbRFCzNCQf9OxHK+0Aq+KIcqxx01wKVeEUyrigEUOzKFWqZmz4SLtWw/v5S9aI6nl2aZEf9wDSZKfMCEkdwQCTqa79jxQtIlSGNDX5RBq81GKXVIGGhziecW+3Bit/B08KB1E3JrtQBWjjQTzePoeeSH27ByPfJgWakB53QqC30nBWFA1aaMzhA4KLiX8BB4EW1+nImZsQsGgQApV8kuDkh18r8/o8f4G//4Rv42mtv4eUXXykL137Kq89Y+npYv1lLtwYM+kKunSnw8EcP8ZV37uHrr72FT94ACuaUh2aDkbC0M+FHUCBOhW6SdH/NTFAP27gMBMMwmfb5inDk7kJvi7opFFi6MRT0poM9QNDkvYODHhQ6bYGlBQS51/L5Moz1TdPQp8S1B5X/7LP279rv1pOueWIEp4ASZljDeBNJZMEwz4hTBF8dkHNG0NeT1UWj/6cigMNpxpwJIWVMJFuqJgIOTEih+hqY5m1t2jEgMN+CSBUIJsLYAfEYMV3FukPhasJ0NSFcSUwDsu2MFv/gMIEsuqGrB/E1iNoXLtepPwq3XnNyssopMQO431l/2oLCvcnMTN4p1UdEbbZn229K1mipNejf73FI7HYpiJBx4r0Xqjvg4FKZR3BA7vP+GHrXHOMb2uf9nNmXjdyhUo32QKAgBNF2qcegLiIkM2wLiwEcxEwLYLZy+uszZ3AiTDG0vi6Z8YOfPMSvfedL+KeffhOffOGumj1qUDRgfQ6pwdMup9sDBms2fXfZQ8HLL95toUBXiQ0UYAwFlnq6Nb8Cg4IphiIAIgwITCsgg7o3HZiWwCChEaIjGHDqMPtsGehjWTf9ylnKMzYfjASkr1dPnzeCgi3TgQ+O0w+mLVPJmhYBrdmkXHPf6T+/lPoJbuRotbAto0KBNyn57xYHVFs1Dx+uhEmh/OOgZ7uHIKd/xgnIGXQ4QkKAA/G42GRVIxDStbyeAsKUEKaEdJgRjxHpFHE4ZxznjPk6LbYp2g6FEhF5kGUri8HBRINti1cRceq2LB4n3aoogDAdJ92mKHAwPXeF6XhAfO4oMQ2mowQ4OhxL2GTESerFOSTWfzSeTTXPTOKVDla1ced4ZoKPuLax943c6hPaFfYnHbO2ldWEJnWfD7UGwOaR7MDGWCv+Ca32YMu0cAkOvNZgwGCrcGBmhXLdlbkrDfoBPZo7fd2Jo2UCiFpA0IcXKNB+QLqPMmgZzARHLGcgTIHAICDlAgeAc0zk5RqOs4AEUhY40Lz98fuP8BvffQP/5NNv4uMv3BUtHWoQNL+Tw3dprzlY1s843SIw2P780WODAjEfWHdhg4ICBwoL+rffj+rJbg0ILmoJ1HRgIFB8CewflgK0EZ5AO4p6e9kADrZk3nLFPH5MX26gM+FQ51F/EyjYAQSraWHDoVVtgZXpUlz3rVQmOZYytVtQqfSZfmsqlf9aKCgmJqrf2ezOZiIjDRjUQEIETQexqx+Ozc84RHmOeu3TQYIHpeOM+XQSAXyaMWlMAwlsNC0DG80ZaZYzEtJZfBSQGDnncuw4gBIMDECJ6maasCC2NBkrBzkzIfpYBqNARxa7wIDgeERUzYEEOzoKFFw9BzpIFERygEDTQXYjeBggqsJvYxIp7VIXh8VxsIEDfX+jPqEvdnrN2CObPjI6U2WhNUAezBvbPd/GIPs5xsEAwYSpYvYGHNgjDQ7sTjeFA1/eUZOtaQ5WdziZ4wpRE0KeiRtAIHLjnkn6QJA+kVWFQfo+MZctqRmQRWJmcBCQ7gFhLZlZ4Yc/eYTf/O4b+K2/+SY+9vxdHVuCLXYY31rv3aVw7NKtAYOtQfXA+RS88tLd1m4MwOzFctRqvQa95rUFBgF+18ECCFAFYgg2GdLQl8CbDXogIH9a3aZ6XUtvg3fDMc+vnNdMCFtpBAUi4FycBb24BQXNIO3Kth8INswIK+mS083qo5wQAGoZM6owsDS1HmAltfXlgaDGqzBtgT3Tvruc5XQGQpDY/51vCF0R7NAgCgEcRKPA0wScrjFNJ8SrE9LxhHgnYTodMZ/OGgL5hHyW3Ql5nstZCXmuwY1yYtnRkABmPUgpc41+OHBeJC1YUL8BMVOI30CYolwvYODPSpgEBg5RhX/EdDwgHA/ummoJjlegeKhAoHCAwxFMUaIfhgl2ZgLgNAdNZrXubYHMaLarhUBqPuC6Nc/mDur6GXd9ousboX27pcC4mLzw3EzenLDDQXEBCA4G6tCofgcjOPAREiE2kIW/wQ5WWTUpbGlK6o83zJVl8IjWgMjm0lTMcVHHZyYgZxStQSINRAXS66I9IAIoQx0MRXswAgSgXXSa1iAy8Cc/fhe/+S/ewD/6hTfxsedfETlFpBqOWgcjx+WtvrBVT7cGDNZK+ehHAgXf+IxBQW3/0Slppi0AWm3ByEwHLIFgZDYQUCBnRlgBAhcPuwjMHcKS4QbqU8woa9oCn/rbjqBgr/lgYTpYK2Nvq7lUjh1Oh2vJYE4mMLUbaoOami5TnbhIs2eDkVey2KwbncD3QADUXSk1hsXSjGBFIF3NEGVwCDLxBD9Bk5yTcCCE6YBsBw3ZwUnTEZxm0HwGXdWDkw6jg5POurVxnjX0sRymlO1QpSwqT2ZGVv35CApKHRQ4EKdHxHqYUtDDk8IhaqjkCWGaimbjWQ9UYnegkp2WyhbvwMwxg25i5gQwq0OaTMqJVJ3cAQKAZtvaTfuGPdPHADFo9H1kNW0JSbrghLi4VzsGK4B67cHA72ADDoBqVliDA/9on3qtwT7/m5UycR7Pq6pBEE1SQgn4pMGxxDE8yvHJEDSy/EtUQ5HYFKxcYmLTuxXzggcEAA0kmHz50588wn/5R1/GP/wbb+KvPP9K43Rai9NqDZoFGnChs4zTrQGDSMsB/VC3JL6pUDAaCuZbsEWpZUtKaK9tAQFR61TY+xEMgSDPRVCWDmsqukWqmZEBua0puJTWir8FBPL5RwAFW/rESxkEsGVG2JWohwO5aIBg1/LOAbdmbvGTPdBCQXSvhxk000GwFdShTFogtaPnBGYJ9SvHLN+RswMsdHB3zHKYz1I+u677/zmlAgKiJWDY9kezqbIdKgLRIKxWbSFqKrskgoKBHLdMFRCmCIpy8BFREF+B6SBlNBC4cAxz9scwE1VNgUGBO6p2NIOaP4Hsb1c4UCEXIZNOJlY/BFQzlfWRy91D6qB/JrDaT26SqlqdxpqBrSX6aEw6QKjmBa2/xu9gv8+B3dt5K/hHrZbrKarD3cAFCCuprRuBO9PMQU13CpKBh4BALOaFAJRzdky5x2peYAwAgbkqfvXvv/nJu/gv/+jL+L/+td/Fzz//Spt97XfQcMl96ufjPlH5b5xuDRj4Yc1QKNAwkS93+zz3dijzKgWAqfvVmmNhDwQ2ya8BAWnMewGD1ILASN1VhH/SkSOaggIHCKsF3DIj7En/XqCgL0OZLVszwtOerNZPUDaIpCYrbWY3OfjfjrK8cHlonkfNRQ8D9tvqf9DVL9yESPUgMIJqS1h8DBAzSmCfnGTSyQmYPHwmBGZw0kiJnAUI9Lsi6GeNCSD9kq2vplS1TLnGTZAM7hCF/RbCELXsBETdPeAEOYVQnAeLgJ8O8r04yU4DIik7kZoIqIUBsucE1RKE+l2DLLQg2feNSOKZbmYFUemiqgS0cTwwLrQ+F/qKPk1/u+wr9nbUN26S2Pbud+YEJto25TlI/zDhYM9Wxq0s7TYn+IVXKWfne9HMt2xqOgcKVOuB5PhuAwQBAnFQNP8DdcGRcMsbgGBpooA//fGjAgUff/HurnpYlJmWdWGvHz5++B+HKcEX0sIc+wMlio1rA5AtWYf1Xsb9c+qKbgkEjaZgAQTaMfVkvNpJUycsOweZol/zjoUBcn50Nz0M/Az2MsBoUDarGfvrJ6ZeyJVLN4CCrUbZqy0oE/w2+g1tnHqNIAO3aKAKbXb39Nml7q9LK+4Gi1Vhb5qxz0L/w/JoAulBYKbmJs713PZyfrv2fq1zhvYttjDA7NrDJk01S/jrqG3Fyfm+AG2QoRuCQYmtYLtqokBCBTBCXdGjqPztOhM1Z9LX8zHsN3bd3cdA0vWXYe8jNOFxCSYM5eOseQqlC6tKuLtfHvWV0QO7dh71nVGIcQ8K28nK2wnCNa3B2nWnPXgmOEBbvz0cANtz0VYaahSGfbM32fbzbn3P6m8gpoUEkAAp5VRMVJFC0Q7nTOUAKNK/mceA4JaD+Nd//i7+t3/0Jfzf/8ab+PnnXykL0D1l93OGn4/h/j54LGcrvISXVu9za8DA7EL+6MlX7ZQpYBH/3OyGtg2JiW1bakOzvnMFAH/840cAgGiC/iZA4FZxjXbAro80BKV8+pcCaoBUzdWFCWELCkZR2kYdcHVi6qh0Ewq68t0UChptgXs90hbYYBuVraoqWzgAmXMUFT+C8jvq6orcPbazXZ7b/XTx+4Vt0H3ZhMSyTARQlPnYf4Nz/S6bYNd+s7a7ZW1HyKJ9Rv3zhuonQAT24mJYfKc8wldqozFaXl+cqtn8pvTQeu/ub5kvFA6KacAAhZ3GyH7VlcdqqfihrFTRaAyO+tWof1D33dHYZffd5rrXGhTtY6c1uGRqGMEBgLJjYQUODLz9gq3kkGtPviQMt/wMhnDQpAoFNFi0lJtoIoUC09YyJdFsIcgiL5jJKiCGOAQEHx/HA0JGZZC/+y/ewD/8hTcb80HVIprcQXFUXvgjdVDgRlCBgvuffRP/4Fv/9WrN3CIwYDl68ttv4P5n38SrL70CoDstTQc50/hgDzviVO639PL8kx8/wt/57hsARP1D2GMyUCDIFre9age8TwEuCcnS+R2ZlyQWz/rdNTH0dGkPFLRU+hFDQfk8YHmCYlve5u5UL1RtYNUSABecCb0QX80yde/r6+Ekt3KjkV3QUi/ERjfMTX/o/m5U+dZ6/2lk/9OmrdXvprB42t+Nvt/VmwWfWzxn0Fn8iCz1VuReV5FURs1m6p8ygsr+i0vhaONEtUZ7TAo3hYNux8IaHABVe+C1uZdOZbxZ6sDQtiauleXiXJxLZRORAIGapIgTkKv5CmHaBARvYpAakuf9VgcFJftk/9YWIxUQWiiQ+z740fdw7/ffwP3PvIlXX1ze36dbAwYPf/QA9975Mt5+7Ru4++IrTUc3cW+rACKUbUi2L7nfn9zX/Z9ocIl//Ok38bf+37+IeFMgyOKe4rUGrRoXWB8GitdUaVx4cRTMyP+s/WzLM380CEdAAIyhoJna/oI0Bc133ftLk0mpRT9nqJagvF9mY/H7xZexFOhDvriQv1H+m5VoRwal95RqXR4PvLiufaGEfu6+t7xnm48GILoMb9X/ouwDcOrr3ve73lNfrlPpj/b7LZV7f0//0DXTD9AeYLZWBiu7X1Qs9C08+IF9Nrj9mpbfpz3Q00BCb1Kw93a3jxQO4HOygAP4z532oCn/RiEz0DrkXQCCoq1dmNZGT7ZL7Zxk5oSiRcgZCOkiIHgfBDNbv+y21PfJtAWkWSD3nro+7BdpDx4/xL3ffwNvv/YNvPriJ+FNhKN0a8Dg9Xe+jLdf+3opdBuRywUmVQlIJEdqenOC35/s0w9//BC//t038NuffgufUEeQQ1zbZTAAAlZfAnPessNtUDvg2vahalOFDri1Y3asfN1sCizU6murwvEieTnBrkKBU8d9GFCwUB2PzAcNFLTnQWwWkrcFfF8ZQ3vv6td7SXlDuzuWbWfgNjr0qj8ymlkjEKrgN6GfmOu8p/ditEeHz0m+kwBwZv0Nl/dAXe0YVNjOhI1diotk9Rls+yKo+OtIdfgzQ8i9B6ZI3a4gdk6c0Pu0sSFA5gukfZm1TvV7/Yl9vo1X23bQrkvw6XtZ9SL38sXu6r9d6tP12c20QZy+DzXnCKDOlQRgZFYAdNx+WHAAYM92xl57cCl5Q0ZfdcXWVm1u68kLTW9m6L9mwGGaypzFZyZr3AMOuwFB3sszJ6rhrUfKJevT3oxQtAjU9VnOePCjh7j37Q4KLqj/bg0YvP3pr+HVFz7ZdkSq3cQ6YdAxYc6FpjUI5lQAFA90QMJQSmxqCaNsE9m0EwiKuaBoCFLtbFs+BeaMZdNvgQNeLhe6fS68Y/VcFwLrZhX5657giHQIBd6WvQUFa2nLdPCMUND7mGwJfv/xEgIGbdbZ8BftuWfJN7KNu8lzseoE6mmQ/nWup0L2x4XbUeFzrv8Sy1HKBgRzzpgzI4MxJ0hEUAWGcgy5HrWcMutryVMqgLDezhYB0VZHgeR1DHJwkvyV702RdAIkTFEmwykQphAKIEwKDnJd/hERplD7aQiMSIRE0BgiKkSCLA6ao4x9k2CjfwPP3M7kPif3Gfxchdr2vcViC8RGpqgtOFiYFQwO9H3RHlh5Ntr4MhwAI9PC2Cmx1sfWPDWcFl2ZAdRw0EWDoP4CzQ9a8Nuap5c+BwG2M6g4JNIM8HQREGwXA2D+a+ROKq15Mn1LOUfHAXFAb9pVn4IGClxZNtrw1oDBqy++7BpOOntDqUp1MgABrzVAkLO2QzB6k2r94fsP8avfeQP/7Jfewssv3S0aAgA4xB1AYDCQE4DOpwCQa13jNLHJtSxrJoMGABaCtLW37z0HYNUZjjpVLbCYNHdHM1yxsa6XpQJAE8TIlXEEBX1x1+z2NHjd3LXXhHTXnmbbZX2gPqmAQCrlZWIgRPR22fJY1KOjy5HhDgjsLIOZM+Yk5xmc56RQUCHglNx3MuOcMk5zxllh4DxnnFNGZvlu1u/k7EChaBNaLYJPBtUWNTQEBwKBcIji0X2MAgOHGHCYgr4mHKeAQwwCAJEwUcAxVliYgvj+HKaIqYBDwMSETDK+6/n2QoqsgM9Ogiz6d3EObvv1Lu3XoK2b1wPzmPVxgwVDh76Pj2K3jFIjHNHDgT036+fBzxoqcVdMC0AnSKt03oIDe84aHOiTm0iJpj2Qx1woNffmBKowoH99Wdh2GXSpMTGU2uvHuWpVmCoo0CzbYBUOCiTsAARA+nApP3MDCVZHArxjbYHl1XwKGijYo73ELQKDZUzvDg5cpfVag6AXlRHAzPjB+4/K0Zavvni3+hIY1e0FAk5dBxMYsC1ffRev27WAxbIWcJOI++tee8HJ7p+lUbdYc5gbmQ4sV3uhYDWaoX+QffQUWgIr5+jvVqKVvw0MjMrlIaCUZbyq2FRBli+pEGAAzdY6rvFoFA7c04oN0kNB0gPAZs3adUqYE+OcGHPOuE4i0A0GTinL9VlA4HrOOM8Z13MSWHDXBAyAlDJyytq9JfIhQ0BEqqHW/uisBED7W7BVj5gJJBIiEGJAjAGBUMDgagqYJoGAqymWa0f7LAQcY4WEQ2JcRcKUAg6RMUXCVYwamla2ohrtRjCyColMrfNWAwXN1mLT02Ax2e5tcxneblVu7Q7AgujYarSAQtEi1n5vQh7Y7vf2WYUCDwx+THWmhSbCIWCW71VA6OCg/MbBAXTlvtcpEajaAw8IozQyJ5SykEZ9JFWjkc5k9tr/XaS6iCvbdbskJ3eyQILWA+u2RjDvAgRAThpNDDAk1LKXS+N4J0ttwdKnYGUBs5JuDRgsbFyLz5dag8Ao2oIIE8bAu+8/wi//wRfx9dfewisvvdqceFgmjnRG3XaY6usSn8BNKB4GdBtZs/cbKPu5pR+1fgTN3mz9W30Pgtuj3WoLDApucmrgFhQsVtO9kyGw3QFXoGDLl6CUf4eW4JmBYM0csiEQhtucLgy8KhRqeaWMuo5jDV5U5lMqk2dvEmH9z2+FMig4z4zrJEL9lBmnORcgOKWMJ+eEJ+eM63PC9VxfnxPL31kgIM1ZDkiagZQzOGWkJGYJgQNUXwczJ2xoDEjV/dZtLTxyjASKATEEOc4gBDlp0QDhEHGIhKtDxJ1DwNUUy+s7h6hwEHDMjMQBxyB/r1gE7FWMdauqLgh69XwFYNe+BgWVfupnO9u9Ll6XmgIby1XgeUBUQWYaJLvHQJg2fWIlrX22S3tgeet9DyxPAziw79w0hDLgTAtcf+Ufs5m49xfptAYkeSK21wARo9UqLDUJNocDyzm8cXjXoFsEAkJuAYFZQ5hHGzjyWw30FYPkXwBDAIGtjq1OrY7MBOYWbg8eP5AtiZ950zka1raQvG5X4u0BA6DtkDu0BoVIQ923/ujxQ3z1nS/iG5+ROAgNEFikQgA0n8ZAYCaDDgiYJYTsmj8Bm+PK4rNOhJmGoKwq6usa3W0csKUoxFb6RG86sHoyKGjE8dNCwU21BE8BBCaT9vkNrJRnBAQjgeAH2ponc4HSLjc2CZOekmgR+oj1/AP9GADH+nubJO3REtJbfAQyyzHhc2qh4MmccJoFCj4oQDDjpycHB2f5LJ1TgYF0ZqQ5ISX5K91ctQY5IacZzAl5Ptc+DgD9pAkUdZsc8RwQpgOIIkKcQCEixKD/gDhFxEjy90A4h4DrKSAeIp47ZFwfAq4OGXfmhDlFnBPjziEgHyIyB7HLThFFCFFARAZpxFI7+yKAihlh0UI+CJl3Ii6w4LRG3mHtUrtLJZS+Tf0YdoBYAljp2Rig0JlHUbRJvTbAp35MbAICRf1GBpswK+UhDH0PpCB6ncdw4LW5+noJB66OMPA7wDoc2CO91sCXVYR01GJYPqzGAkDZ3dzNt6NnWf928zlbmFxT9RkgZK6AYFood2YHcQRyLGAQOCFQLCc5imP8ssymKfCLt4ePH0ocn8+8iU95R8OBT9RWul1goOmS1kA5rsKBfmxHM7/52ft49efuOoHIjdkAAJDn+r53NOQ0BoK1yXJDcBTNAJnu1Udts8NgXGhXpynw2oIPDQp6e/seJ8M9ULDDbLAGBGtOWGuAUIs40HpsAYELX70AgV57sEhu5dGtFMG1nEQsQABUOGCdRP3uGldG02NYm4tfAcSpMAOnOWNOaKDgp9czrueEn14n/PSUioZg1sOTDAhmPVExmfbgfI2czsjpLAcy5SyvtW5y1tVRGvT1GAQEtB8LDBwEFKYJIR4Q4gHxcIUQM+IUEA8Z0ywnLWY9m4Ez45yC+EBkmUzlcXU6CxRxQtZzF6Q+YmZMsdZX0Prrd5wUIaj/9uwsWm/7Tu3ct30HCgYJMq4vAELxnq5bsvvUj43+/brfjR97qvY3572tbY1Wxj1woPdu4EDrYuGUaPB2EzhwWoO2XBp3IM8imDNgJhsyVZKqKqomYQX8gMG8nsFsY7sDBDMxgJt5h0M1VVA6gwMjhAgiEr8YDDS/3TwtUHAP9z/7uxKnoFuE7j61FrcNDMqgW/b2YtOycajXzYHn4XsP8aVvy9kKr750twKBTQbebAAUU0IxG+S6mlgFgtxNEmG59bDGfXd2Ro0BXyYMOxmOaicvoID/gKDgkulgp5bAg8AQBkblWp3wXHns2hoU9DtIyiqxEwgrq8Z1x1JbKTJA5lhqM54GdglSL8S6XSsn1RpIw1gwrkDl7CIAdWcAqwDN0F0FLL4FSR0HzymLpkD9BzwUnE8GAwlpZsznhDyfChTwPCOdr8HpLCAwn1VrINoDaaANjQHJCYlEETQdEEIAKRDkSaAjHq6Q81GrW3wYshFTuR3heiZRpxLhnDIOkYrD5QTRHEjzCgGkzIhuk7ts+YIK5NJSJTIpFTj0YcwHfaPTFq23PQCQtL33ZlfbtKm5Cah9gxh6ZBO4Io28L9uX686r1el/8IFvpaBzoRPlmo+BeaHTHgx9D7xGxZkeNncsuM8+MjigCDlKWeGASNT6Qcuv47BEo1DtAoNlfp4Hfbuf27V2l4Cguz/UnABuzQsA5JAzZ14gCtK6VKYO10KSHhkUfO4tvPrCJ4e+Ls3Okv9oTAkbDinNinzQ6R49lgOX7n/uPu4qFDTRCXvnQgBIM4ZagvncAsGww/R5D2JGIPUXcFRrr8tRsR4KyklxFQoyWrIcqaBG1ean3KfZjnhjf4KBVqCPX69PHgNBvxLqytDNcOM0MAX051YMg1ENHEqlXrhxTFqcMmhaAXMwNUclpyUA5FREsruaRslpDazarA5seh5OTd0SUXYQyA6DrKAwq78AZ0aeTRBn5KQ+BGlutAQGBSmdwfO5AoFqCzg7k0JTfOvnGYmTAAIn8HRA1Pyb8t/MDTmI0x2FjJAJeQZC0G2XiZEi6+4IyXPKLTzkXiWgya3Va33aNactMCAcQoFpD7B0TFtrewDVSQ0o7V8BUXOnEfUoNKgJdZeU7xCKYF1qHLu0Nl64vQYsAWFhXrjJzgU/vjq/hN4psWoOUMo0hANc9jkoZjYDCYMKXX03cACAc2rhAND2rtoFsKzeZb62ph9Agv3W/obYAAJNB4GMDGl3rz0ARBvNcpaJnH8yqU9ObBZJVjMPPRS8+HIzL6+mLXmJ2wQGABZObKtfrJ2uxI7+3P0SRnnhidzvOAC69wzM81JLUL47EJIhFtOAQQGmeiqcHQ1bzAceCsw+5XwKTDytnZ7ovcVXvXrphlBwSUvgy4t1KFjTEvhBsBbtz6dm9WPf3er/3PpIVE3BGhSMVonOjwSQyXG0WtYcUgiy4igOStA+hhYOOKu2IBWtAZhBpNuwuE6OsslRVpkxEGaGOPgFWU0PK8uepNsF24rb6COb1Zk2yq7fyVll2gVB1iUaEKzFQNhKQetB/qEekw6pN4OqABfZ0Oq6AL/v82Mo2HRKA1B8HUIoQfh8+8O2JcsdUFarOTk4EM2CaArkd3WlvVEJK2Omfx+673l/BJkPDNo734ObwIFdA4ZOiVK2CgtDOHBlWhS1m+eYBTjJaRpU6YZgh5ABsirPqZxJV6YOIgFeq+KcZJ6e5woHhWVW5nu7bt+fzzrfQ8AQKP5EANTEIYoK1nqS+Z7FN8HaAnDyS6FgLbm2sbrfSrcGDMax9AfXiioluwOX3moiQpkWYHXHAeDOPsh1t8EWFPiJtoMCmg5YHBtrUNCfH092TQ7rYBWiNdJdfezaft/+4JHegWUt3RgK/DMvQcGKlsADgS/bVuodkJaLxpr/csPy3spl5fSagnUoaIBgg9TtIwooO1DKJM9mNlGdYXluBRXWlba0FyEEiR0gETsVDnRdORFhJt3LT8AUGMcp4JyibOObJMDRIQbMmREiI2YWkwUzciQwB+Q8IWj5MoCgf8sqn6JoEygWzYE4XFcdhmk6EMTPgEJAiAcgBsR4AKl/gfc1oDipM6LsWIgxCMiok+IhBolVYPENYsRx0jgHFDBRkOBHJFETo6pjiQQYoH+DmhFMU7jo58M+8CztnwsgrMOBIQtUAJtGQWveJBK5G3ufBbTmtj1jxwSoh05gZKMfaQ824GBRCa32YLjFfGVhR3C+YW5OkNu2z2vfk3PmQ1n5yHbZCDYBDaiwlrmpaIgV3As4Tjr3zeclHIycMDs4oACBi6jOkADYTlFKc7tzQTUFRBbsTqGgkV8v12eWItvDqLYN0ADCWro1YDAEgrXEjAfvPZIDJT77ZlG/lAHvDzsabEMEsA8K+rwYEACgOLlVo9MKkGoGnPAvUGCQcEFLYANiNCxH6tPRd3ptwSYUrE0Avc/HyMnQQYEHAnTl8o/ZCnBSTkYsE2ZX6D71gOCvNX+5+81ypbi4n185hjoomwFahIMNXs10gQGCbKsiUU+z7venqiaNuoTMpCGPY8ABjISAiVmEO7N46zOrH8Lksill+6CrR1FlJ5nXpjsI8SB+BvFQzAohnZHnMyJfNY6HHgrqPePCATFMSyCIhyvEg+zrjoeA6RAxTRFxIsRDxHSMeO4QcXWQLYz/yTHiueOkWxhrTIOjgUMMOEzyN0LqK4LKbiOrz6otMNNYbvs92v7+4ba/fVZ9Bsr7xV/Xwf39RmnA7dsBglTM91opauRpqz3wcCCFbeFgpO93C6ZVOBhsZRzOad3th6Vj1mqjMm4SrN0JgaIAscIAA1WLRwFECcgk+hpOupoXswDKNvRZ2tlkQD/BDuCg7GL0zWiLTyuLaQ+o+nx8773v1wMDR1AwqiT7Tu/bNUi3CAw81m6rKcspjHrKFJXV/QUogLMDgcdQMEqhrpZ6LQGPTAfeTOBNB6HSY8YYCIpQ7bOwUhe9tuDDhoL6fj8UrPlIbAGPFqNoQ5hR4o3vSYvybK12PsJEPIhyaUIpJ1F56namQDJZhAAwGFHXmzEDVzGCc9Ite2a9lyShh1N5PRFwiFmc+QLhPAWkOSOeCdMUMR90++IhIM1T2a5Y/A7KzgQZA16VXjQcVr4QRGugGgP7K/9EExAn0r+hAYI41XgGP3OU+AXPHSeBgyuBhWMMuDMF3JkiDhPhzhRxFSTAkQUoi0HqzLQFZnduYo/Yam/QPh9pKnC4cl3/ijDdd8ubjKFa5goIvY0e5RsDOLAVatGb25cHcFDKdTM4ANa1Bn25fQpafmInZLVc4vlPoDDJZxTAlEBZQEBW7KpuylWbwBnA5KIoqqmsZmLg+aPf8XCAyWkVdKxLHTs4gIzpBz9+F/fe+Yo7MHALCPoZcF//vUVgUCtg68TBBgpeesVtM+qgoBeIcCGNoQNz7SF+t4GtFJwvQdESlH3rnZbAA4GZFkJcmA0sHG4PA30/8ar1SwvoDwUKXCo7LMoD9kFBP5n5J426ti/b1hntuxPR+vKkrIh0tZcgq8wQ6lzIubZ98/u2Loq2oE+c0ZgTiDqtgcT/h823ja1EKvDOIYJSQkTEE4iAngLhySwhhQ8x4MlZBO31OeHOYcaTc8T1LMGPTnPUAEdRYhh0AY5yOiAnO5hJnRctwhIgYFKKah7eVO3+pAcYRQWBLsBRnGIJcHScJIjR1RQaILDgRncOAccgUHCIAVeRChwYFMQgUeUmDcVctAekvbDTFizMCH0TETXtX9rXXvftb86nfk4o7U8tPC/6zTP2ZyyhYG0c2XdV2b+w0e+CA0DGOgaOycuM3QgOtu62NRcWUwkgofC1PLYjJVtfUM1WaQ+OIEqiBeu1B6RRHHNqtQeU6xxwwe8GaEFPfAwm1x/FpAckPHzvj/H6d35ZDgx86RV91nKe2TLF7Em3Bgx4sPWvF1ybUMDmbCT/mtDGA+G4UCEC2xOBAwIuZgODgoEvgTMbbAHBaDWwTLSwu7uPFpENre4Wuw+wEwq8CeGCT8EWFPiJrB9aThMJYD/4lEL392vUnvY10/WFZVv735lwQC3u6OtNACvtD8v7LQe0+BZo6WzHAon601Q+chBQ/U0kiWdAFDERS6jgzDjPCYcYcE4Zd6aM0yHilLLCwITr2aIf5iYS4lmDJllI5JwZeRaPauFlLiGQ2cFBWxABAkA0FUSEMIlwCZOck2AhkQ1cfKTDQxSIuXKQcIwBx4lwDBUIDqolKD4IJPHng/6NgcohaHJgjTRYO+Z5qB0YRcOz9q9NF7C2K8PavvxuVeD7sbM0k45/d7nnj6DAjyV/PfgPUVXwu+DA+RwUOFjTGthzPByUZy8dLO3Ra46IHgqWfgf613wcSCDBzAw2EcqJnmq2NbNAdr4HXntgjZ9TAQV4bXJcnz8kM1m1BpPLeEZRZeiY/95P/hRf+Oe/im/+4tdw9wX1iTMwuaAlr/3Gzzn/UZgSRhVTG8NOmTIoKKkT+MuVgmoLxjP9cqWI9RXBjYBAnREzlkCQsBScpcTcD1otJreLDurU7AsTQp843xwK/LVLUKCP7M0ia1oQdNdH4FPMCVu0UHR5qFBAQb2BvZ1Q7iy3crZe1RxgmrR+IjglUBwPVA8DZaVo+RiZwxq7s60eNK5BQIUDBpBVRU6ylz+QBvUhwhQjDokxT4Q7Gip5ZsZZoyHOnHGaJz1HITcHKc1JgiLZIUrznOVUxsTlACU5uKmetDg6YdHOS7BDk6JqD6IeihSJypkIgQh3DuJQ6A9QOuohSsfJDlFS/wEiXJXXFQhE+OsJjUH9CogQwyCiqS0OvHhsNAGunzShgVnbVbeh7mh/MSMCvaagaBDLWPHXB7bhC/4FfuFgaW1MLb5nq+vyITWmhYZzt+AAPXjvgIPRFkwThJ2/QdC8+ts2r32ZdH60uZOc1oAwBoSg5gWECcgzkG3+tp1pohVgW0Bko0QW6FjZsbIAxWYBZveTaw/e/wG+8N1fxzc//dt49cVPiuawi4BZfzyY7Eb9ZIMjbw0Y1PAeLmll2IES9z/7poQ5HtoOnbagdyYaCEpZJdRlgp8I2sGvQrEHAlrZgqjvDQhs8SXH33ZC04rRD2gVhOYwhE613uxIoK5/rJgQbmw+ANzktg4FfjdFbzroJ7C9x66OypzZn7aGMRBIjQDNISvB5UThwCYAAojrjFS2Xk0mqQd142vbTfTteRgrgGDb1XKqZhOqEdJCRDnjPRKQmJADl6OY5wDkHJEm4H/GEjpZAEHDKJuw179y4iIwc0bOchqjnbgogZK4HMEMQOMIWH2vg0EMEOdDmJCW0xNNgB+inJYYAlTIowj7SFSOW56i7La4igGTHsJkxypPgRBRQakAAWF5VLqZEft4FX0/sbFsL0dtD4i9eJicGnyr7T0UNL451h/h3nf3g9V/++TeHNcK0fG4sjFVgFvhoKjkdVFr42rdIbFC1I3MCqjCr2gNDMrl0U1sg/4I+cbMahNOXzcFfrzvAStAKAiR7Lwxnx7vf1DPs0gAt4Ag4zQ3PgjYOoCpF+iqNXjwkz/D5//lb+Bbv/CPcff5T8jcBox9TFb7RPtlHlzz6RaBQZ2Kqfy1OAVf1DgFd8er4ZJ6IFjvxKZC7q9JXuqAriGNnw0IcnYwwCibmqzsvtwBVITt2nkB/k2jLQCGfgXy8YVB3XfKHZqCLdPBZRMJbuRPsFAckJ62pisR1gkI/mAVNzkJQJgwcCs5QOsp7nZOW5ywKC8agTG2K6uPgcFBsWVGRBJ1fIRAwYEdHIB0ZS8VkcDIMeA5DmBG0UIld0wzs5gjEmtAIf09a/8zDYH0UYODLre87IO2UApwzn+BxPlLBc2kPghRV/pE9RjlqO+jVlM0DQSoCCo7XMZgwK7LSy4TdwFhg4KR5b3TGimSVUAE8FRt79vd3vt27+cQ2PvaT5oYIHb/XbnQ766MrXpdxTt5OJDrpj3wAuoSHMiXNrQG9oxC+R0c6FbJRmtAKIUejhgPBahzDADYuVSZdd4kro6NwcrXAUIUbYE5pRMlcKivF4DAWeqTc4HG1ciYXXrwkz/G5//oN/Gtv/FbePWFj9sog3cmXqS+X7nyNnC4fofbBQZ9qhGhFArApdO2yDxQHfrkJwBX6YvGXAxkHSQ7dhp4IDAIKKYDBwOlgQcFtkVuH5vMUhi8brrOCgy1Rwxb6hT33oSwEwpyBwXs7jyCgj4HK2JzcydCD1HyxrQCDg6gjp9sg9smKoOl4DNZVjGLiHdbaWA7bl73k749lyocAAqpFilP6zwQASFKDBWt98wCjCMfFdnOCO1nsipmhkKBFluzYXZde58yN+2UNqYcf9oDqbYA0Pagtl+ajIyk8Qdgk7eaI/R6KN+vELDYZcMJyJc0YQMY9lojE2hm2mle585+u5FG7T5o87qo0Dx4KHBwYMkvjtbSWu9cG1uru3ycaQEd/G3Bwbq/QTdyy6q4X0VrO+l84pEtDV63ZefSr0flZjnKsEBCYlZnRIHKDCCrr8pFQAi5mhg4w+ZXi4PCqy1R04M//zN8/o/+Dr71134Lrz7/cc1kbvocfJ9r+oRf6o3BYCvdGjDIqtay9O57Eub4LQ1zLN3YoeWe5FTMTEFXj/aZW9nBDWIA9WwDWvgMXAICW7nZRJ25dmabfEddyhkyAIMDpqH0tLG2cDrcMiE0aSx+FzsQmroaOxr6ctmdeyi4yQpoCwq6TG1PXgCEyrkCAoChZmBIaRcG/pZtuF8BLmytDPWxlq+zE1z+96Tl6laYB4I2fjVbGDzI69ZMNTLp9FM6uu9sJV+8Xtnpz5svn3f9tXy3PrWBWvKBhkqm5drSJJa7964sZNokamFgLT1Nm7vKWB6vXoFAPt+G7j3JEPhS8gC9BgfsNGe9drLAQR/PgtZ2KlQ4b76+ZlJwfcHMCauKCG6hYHUe5fr0rPc3jVc2EM0sYbsvAQInwEIpOxNVie9hkADUTLvx+70//zN8/n/4u/jWX/uHePWFjw8KtZXGB855E8qjHz3cnFhvDxgApWEfPX6IN/RApFcKFKDCgRcIqkZu3Mq5AwBTNbvPis+AfKl+16ITkotgSLQLCIq5IKuqVjvxcmJ22dNHX9qzX/vc8vzuMrECG6uppqYXN990NkQFAg8FjUDBs0NBefyNf0AtHFCsqj+SbYI6QxYBMST+PVLxaZNvn3LNXnRe8hu3EVBY6SlDdea2MGuyuNOkA8a6rXkVRPd8D5eF802S9eGPIl2qaw8DmhcPfCMTwsVH4ubjqcydAziwp2dgZbcClmPLxjVZHIE+Rw4OVvwN/Jxsyz1QVftLXreHYzvXuPJaFatKLADFjBAg07vl5CIg5CRaJHNSZIaZGQAqZhF5cO233/u3/6ZCwfOfaDM+MEGOtLJAnW+B1pzy4PFDfPnb9/AiXlqtn1sDBta4D96TQr/5mft4+cW7jY1zOIwaqnVKqKJe1uvacA/+/M/089gNVqrmAvUpWIYwXgcCb/vtVbjAxoB2H4jNz9ncHQzY26G2wDscrlWsPMHVk9WfnwkumxDWgjLdFArKyrFbYcJd35o3G0hUAGiOliWUWYIX9WA3eXYhtHn4VCMQvcYCy9XG4n0/4fQr5xWNx6JMy3Lf5PjWm6Zd2/BWTDCbtnv/XRpfH6/YLa30+dU83zBd0CY0QsB/1gmD4a1VcrbnCJjcVUGK9fG2BQfepNDDwc39DWzOXcJBUx7Wk3JRDxXzWgPzGyjOiK5wttBam1tLVvRnGQBRPaOEspjDEnOpg0uAID5LEeWcjZCrFsHVoSUxHygUmOa56R9LOBi1WaMl0LZ5pFDw9dfu47/51j8Y/ha4VWDAePe9h/jKO1/E11+7j0++dLf06OUWvqWay2+nqWYDBwcU8eDH4ggCAIhadd6vIEQAoWw7rLsRtoHAzAU5t53WBOZwxJqAB8bA035NvtdpC4rYWzMh7NAUWN09KxSUdtghdNagYFQXo2HTDkM/6FQbVMLOfkSrRXsyOxfSgaBv/Bood+2B0la1wyxtmXbNTv6UW6vdM7sJKusBSE4NX7ZWuUmsBC3yALHiab3r4C7vwKv17YMhWbCwctAYAIucCA0AtjiQDKjnj6D2zYXvD5nQGay+19T45TM0/eMj0yws8mSvO/jBeJroTQfkrt0EDsoz3MKjiO8VOGjg+0b+Bs3dUfwNhiYFcUTstQaB2RnbNsrj62dlng0M3bEggJAhZ5Nk1vJeAATZWiw+B5TlIPSyACljNJQ6+NZf/2/x6vMf0+f3/df1xyaf7Vw7goKHP3qIr7xzD19/7S28/OIrKwWWdGvA4NF7D/HVd76Ir7/2lpgPrMFaKdAm8yFY/NV97NYRCXjw4x/qlpH/Dn/19/7XnXaA4I9CfhogMG9vDwPWbMkZh4qzll7K1Pqn2qAVj2+UlXPRFpC31VqnxBIKmjRYuWpqoABYhQJ7XA8F5aMdi1DfjFtQsNAWrLQ/l49tondx6oe/WV7cWmVd/nX37UZzE0V4W5tkoB795iYUFeqAi8zZHwGeZnASWyfPswj9+Qwwi6CfzyLE57Pc04MDy3kgnBl2nDInORIWADhZR6x9ZOuExSbIUzk3xNoygCLBjmemoDt/1ERH3lQ3HaQPTAcBCSKFgQCaJhl/cQLiBAtBXnwFzMzHBh51nHtnPw5+fI/UtrVV+/a+SfuPr6/1LNr1rfLVsnqWCwYAW3Bw8b6w37jvdnAA6sbXwnyrdbzwN/Ao0/ocbJkUSOe7rMUuzAcSV1rWE0g7CPELr7X51gOGgQ+xOCZywD5AYA1nrtoCsgPHypxbx8yrL36yrewGauHAtPs7SkMouHuxfW8NGHz1nS/id157C5988e6Sah3FWirbXYqDFmsnDWUCZvkiHr73fXz+u7+Ob/7Nf4K7L4jNh+MBtqJoQhfr5HNTIEhAgQHrmKPVs+0T98fHWmqmKgcDXltQziMHWkG06lewAgW9XwHgOu4GvXZpSxk/6u5tPIauzGihILgP+i1znheXWVtfhfn3q3EkBvm21GBVyRtpW0VlUxP6OonKywoHHmJZarg57XM+I6dZBH2awbOCQZrBevhR+SzN8v1ZNAZ5TuCzvE5zAhIjzzNyOQeBkc/1eGUPDPb+UrLohx4A7H04RHkfAkIICNMEREKc5ERGOkQEfR2mCJrkFEbESSBhmkDxIEAQJ4GEOIHTrO8PZcyXsLOmrgWWUFAWAEvY7VdlN27/pg/0l6j/ymbqa73s73er+BEcACjBgWpMEN71TKAX3e46b2hpOz+upb/BAA4G/ga9SaGMd6c1yKSLKP3LaAzGNQ8Yz7uzgq/VjZ93mZ8OEEqgJI5lmyx7/wvTknFuzVvWN8tizPvArEeRffS4QsEnd0ABcIvA4GtKQpbMY9bLroW/gfkRLLQGOgtTxIP3vo8vfPfXJOLUC5+EDQOBAAODdSDg8r4GmtkCAolsWJuu+Nhoim4AA04wunnNawsEBuo2LrKy9yYEn4ZQ4FKv0uxNCMBqJx2ZEHrfgvEjWyCwslP3uYeFNSgoxVx51pbwX8tz85uV+1pagxmb2GR7nkKCxWd3J77JDOd9BGxSUS1BmoHzSV6fTxUITtfyWj9Lp3MBgfl0Bs8J6TyD54R8TshzQp5nAQVmpLNoDtLZIAFyVoLCAYBinRtt2yQzFZilQKEgRJI+HgLiQa7FgwRtEhCYEKYo0DBFxMMEmiKm46GAQjweRCtwOILjBDpeAfEAThPocGzPNjkcq8aFelNGNQU2u4m0jfuAXHvOHxi1fd/+TRz/7gujSKZ7Ug8HADSaoS31uTwvu0seEEbJ+xoQXTYpNHmnzhlx1d/ApxYOakbWTQolBBJLiGXTGjBxuQ3z0tSyOv9aOyeNr6GahFJnDhDABIY8N6p5uAEEEnBpMuIXZcG2PncaAm/y8n9Nm4XlIqz6FLSaAjU6rqZbAwYvv3i3dFC/GgSq1sBfVGyQ11R3J/itag/ee1diU3/6a+Voy7JKHoQuFi1ABYEREGT7jmaCIVHnrDPaGEnaSSLRMoSCpQYGIIfT6HXTFthfETgOCizi26pfQT/FtZ30WfwK1lIfuay3S/cTJ7nvXNISXJpQ12DAg8ClAEzc/34leTi1UKx1VeeDqUABgWuf1slXVr3dbgRmWdnPZzEVmJbg9ERh4AQ+XSOdzkjXoklI1zPm00mg4Fpggc8JaU5I1wIH6cwKCWJGSOcMnuW8BM4spoXMeqCSFj4NKqGYDAwGSE0HerDSpGAQ9eyEKSIeSP5eRdEaHAQC4tUB8xQxHY+IVxPS9Yx4dUY8ngUKcgYOCXS8AwQ1DapKNvDyZMLaf6mO7e6cEgs61jvMLiLs7Wx7qwuw2K4Dy3XrByWLbu7a06fLitZ93/qxrWibbzI32gO91Iy/Xf4i5Xs3d0Zc+hv0IrvCgcBA52+QAYQIgiyCTP1vJgWvNQhKTE2ssq7dPNea1iASYQaDAmNO4oQ4xdAAgpRESicaA12YZT0DwtadHSBQnmsde5NmrXS50pu1tGYX3Y7r7oOvv3YfL7/4ym54BW4RGKylHjIXybQGZReCNO2D997F69/5Fbz96a/h7osvL2yLHA9lJWHCXv4uzQaZobHkq7Bk1ChzPQyU/kHQIBttAUydZStmmc/0GlnYV9LwsFSFDLQL3RgKujrdAQXAEgrK7zeesAcG/Pe2NAR7VlcLIHCagTUYGIWk7iOqbflMkMufgYz5ffhoayB9DwIF6WsyiUBnPN93HSQwy4p+PoumIM0LKJifXCMrCBgQ5NOM+XpGOmekaw8GGemUkM/ZQUHGnCsAz1oZCdwIx17VHKgGOfJnF0wBCCEUOAiHgHiM8trA4EreT1cz0umMeDyIdmM+IFwdyjOKDiAEIJ0BksBPi0Zp1LQBFmfEQ0EBeRvLqJpAH4V0b7snrv3SIu1ZvRQgIMjuIgNPquN9Ee4clwHB7g/41b18Ies3CdKvywY6qmO0h4RRsvsWKFEAMTiI5BZjl+Dg0lM24EAcV6mGPze1iVOfZNV4EKSObeaPgQoEWPJz8qwAh1QhISGVaJx2yKaVhgkIWdtKx7LAg2hy7awTcoAAABzryYqL5OdaJ4/65HcfiM/dBWLt0q0Cg154LFJHsP67xdcAGQ/e/yFe/8Nfxtu/9Dv1wKVeVa5QsNds4KP8eZMB57o1cZFdBzUUZEItKwknSCJRiQT3bFCwJq6rtqAPYrQZ7nhRno2jql3yc9BFHwL9cAQDW1NZn48+tsLWytDiTNh97LOlmWG9bLUMMkmJ74eoIDO6SYSAiQQOwOIYSBTBIdeT3IaFZPkszYD6D+Q5iVZA/xoUzB+cBAZOSeDgOmHW91lBYZ714KXMODNjZgcGBr9a5jQovAGu9Ml6yFHQ1wfKOJwJ0xM5TClexQII0xyR5wi+msCJEVPbVykGpNO5OC/GKavGZBL721oK1VG4OA8rFMxubD9tnJH1dkcJokNAibYXgovZb9DrVuHAWE0P7ANhE9AweekAwcwD3sQAeA3DNvgAqPDagYT/2dAZEUCjsR1qDVDfc3gmOChkpGeJGKMRESIDKfBio5B/bZAQM2EOuTExEOn0StvmBRvbXjMoRZt0jqb2oU4Y+F0wfs7NLMGL3vj2PXxDt+zXelct0qBW+3RrwGDMTXtS20kfPn6E19/5Ct5+7RsFCkYRxtZWEpe0BGY26IHAT+224iGy4z8rFEy6tLTDYp4aCnK6kZZgoXbxgLDmlOVgaNuEsLx2EQicdoC67/avfRrlolf/Wn79KZZWpj4SZS1fvXdee5Bd1xFqA3S0T9pPIgiEGQIHCLE6i7qVLmh2wXC6v0BZfYjaP4svQarOhnmW1z0UzE8S0mnGnIHrlAsQnHP9m7QezlqRBgt9MggAgIOeZRCJcAgSQn4OwJkZByJcAZhyRkzLKUqKTJJvdUJM5xk0xbpLoglS1tYHm8mgxCLR8a3xRjwUpLw0CTIvQ0WXp11o92Lrd+0eoEKZ5BmBxCbuASEqHPgm7R38SnEHjzeh4F9HXclmrlqETBuAAAzH6rC4GyYFyYPOH+W86txKYLeCHqcscMAMkKrpA54aDpCBRKLFmjMKHMRMpc2Bbp5mBQSWeqTA4CTmBQ+LY+0BiRnOaQaL8ShEMBOK83FX51sRLy2435ufkS37a1W4/GWbbg0YAJ1g6GzPl38c8ODx93DvnS/j/mfexN2X1CbTrYatwWftxyNfAps4vC/BvKElWIMCzdYzQUFxS3GnyK1qCfqBCcAj16oJYScUXEKQNcesHggaG333fTRP6hP56aD9Vpdf7l7bKlFer8ebYGzvKrEyAa2Hc2S3wnCTiJ/QBA5i6STEXLQGzHLsM5kH/iR/EZ3wcykEP3lpfm2FJASk2xKlH5+zwG4PBWeW0xXPJkDRgpY3hc0MBFI/cmYcggYZN6mh5YyQeweQHlur+cldPl392mmNtZK1zFE0ADSJeYCiHXE+tdqCOIHjAUxxAQVz5gL7dnYJ8/6dRMM2V3lUhArEz0T2x8vFUJzeSBaPnbD1nX45Dpb5oO5/uwVR1SLYIN4CBGuyPtnavYyvXXCwdEZsTQpufbuYnzrtQU4AsdvFGPUZdqungwOkyikRdb5O0KDpLPN+zKTfz8187Z3cZMdRBX97fs61bhjifAxiBYSuLVdMCA8fP8Qbvy9Q8MpLdwvIieb5ZttRbw0YeEFSrm1Aga8YOYXxAe79/pfq0cxr20C0Ly7ONXCmg6JN0Ac9DRSMNAXeDr0bCnSvbN3jnmss+Z7SVyt3eXiLp9YtKBjVd5+eFggWk+ClSITmFY/tSHEm6BtNQQcFRROkPzDh4NvZklerR50MZLEkJVg4MJVZnzF3QjOTeDZzYCCqFdxiD4QsAjBNAoJxEs/8aQalWTz7J3Hi4zmBom4XnCLonFSW6vZBcwwkEVCRgRnYpZkr824pb71+8belnnQVRZIPBHVWDEHzSZLvEKQcMSBq+cKkADAdQNFtZ4wTYDEOKFazQZxK+HIDmxEU9LDPEA0ggKbNl+2Nps1N5ZyBBhDMiSAEElMVy1gGMzLRxVUe6XPkz+WxYIjgeECedwEQLKvAOuR7bmG9zxAOtvwN7MfDB3hIqNqDemxILgu7QPFGcMAdHEAseGXRtwcOIsvicQqE5OAglHaudJch37eKUcsGyAGClLmaXPRFabcHj+VsoDc/ex+vOPOBVkHjJ7JnO+rtAYMNDcGlyawezfyWnsII+GHGqNuUTFPZ2xxvAgU+tcGJ9JqDginKgNG5ujli9iZnzS+0BI3U7jvcSs0NoMB3zhEUbB24swYE9qhtIHCDxZVlFNa52uNqOX0UCC/IyupZ31uZbF4smgLU1yOfEX8PD392f2JghqymGwcmQrFPTmEAB5CKiWECVFMg96v1EY5XyKW99Vx47QsxS3CikMVOz0liEEzHSVfkMzjHUng+MJgjjlaKJA0XmUXlnYHMhEDcaM9A7cYEU+n6/npQuD0EwoHEVHIgwlUkHCMhHMTZME4BkzofTlcB8WrCdJx0m+KEeDwgHMUBMV4dZFfC4SjbFA9HeT8dEI5XqimQOAccD8XREGFCgkzmIyiYVWPi29wch3e1N8kHXuVcAEH7uglQ86C3sRRt0Ug6qTtfjcWY6Azjq+OhnA1DBRLIjeURIJRRugEI2V1j/W4TRrmDg1VnxGaaHFjF/ZzFDFvWm2NuiWQbIpgYIcj210xABoEYyEHqWvxIpCoyqhlsCgQGASk3ZgVg3W0l8SU4QLXdaIWQVbiZ4pQMoq8jK2tJdf56aFDwOfUp4GWttXKRFvfo0y0Cgx2rmUE9LI9mlsTunwkHO64W2IYC1htY39k6htbyDnw4/gQjLUFz1rwTIIvUB03QzA3Pf1coyF0dPSsUeCAALk9+9cyA9RVSXbNp3ks5dyxhgepoyNWubJqCNUfSogHoHBhmyGoXZeXBjQOTt0/OeQkHlGUtRQEI8VBNIlE9tbUewpWuPHJqJoiAdtDbansOAYimLQigMIvGYJIdAuk6IMwZ03VqHRBV9X1mqqtqV+R+V405HgYCDtqXxfGQcAiEiUggQJ0P4zFiuiMAMD03YbqKmJ47Ih4PmJ47YnruCsFe37lCuHMHdLwDunoO4eo50OEIHI4IV88pCHRQEA/geChjN29BAVctwbO2twmOpIKjAAEAhgjTtePTx2n/uKBGqAqE2iRECs0eELywl18uQyj7PnYTOJDvdnDQbx3vtQZ9DI+SAmyjogSq022+qj2QY7mjxiUAiGU8JdISs4wr0n5MusA7xADKDA5a5lzn9C2FawJj0ro0k1EewEEI0u+MlhJXrUQ7/7V9gSHy64sKBXdfutsqQbAiE2ld02Pp1oDBHjVln6xS77ujmS2tQUFynxe782glCSzOqQdkUkzMDQwAaIDAPFsDPgQtQc4oQKCDaHtVDZ0kQgsF/d8PUVOwpiUYAoFfCXXagtXtPayrCTg4KPfVMaSDqY+lUG7tH9P9tUevCYmFlshUziowmGUCMftkgQO230rmMgMzvOaAWjiwbyap4XAFZAqgEHV7VEQOEZECwkFW3OkDCRpEU0S8lmvT8Yz5KmK+njDdSbpbYW63Lc5ZgiBlccBqnG7RwoGlQNLHPdxOaqoIB4EBv00xHqeyTXG6iqIluDpgunOUv89dYToeEJ87YnruDmg6Ag4I6Oo54HhHNAXxAI5XFQriwUEBIWUBgLmUgxvQsTHtoWDOfLmtSZzMSLezRa5wYAPBz+WlT1ZZ3aTFAWnuN/bDxdjYGhcQwVk0gTZWHCCYD4Jqwxfag2eFAyl3hYN+67jAQUB/XshiHrNIgSaJQaDE4BCeWntAms8pElhBwgOCaMZqnvqt5cyMlFFMR71mKMt/ZRdS+Q00DgLWZZstau0U4bZ9x7+xdGlZdOvBwE9Q/juPFAreUiiw5Cf9VuBVhzS7bz3juz0JsVcQRGicbnWKMc2AfQZgCATma7BXS0AKA3XHwSAW9whxidrwm4PPqwmhQkFe1BGqKt7qv7vVs0JBv9USwObe57qZTCabAgdlAqocbusNy09jxnTvb8Kgo217too2gVGu6UqSMhcVM9lsDFINwggOjlXTQ6Qe7hoEiUKxr9P5WgTj4Qhcf4DDdEQ8foB4OmO6c4X5yTUm3b44udgG6Sy7FHjOCgcz8jlLQCMNdJTPuQQ5AjCEq6ICt6BGh1ACG4VICgWTBDky88EhIqi5IF4dxGxwdcB05wrxSs0IV8+J6eDqOdDhCuHqDnC4kuBG0wE5HoB4bDQFPF0BYUIGFlBgGgLzKfDmg5FJcA0KfFvvSf5b/U/8+2phLjm4+fhQKGCT+sQVENz4WJjcUOGAFaS34MCuGRxYzj0cqHWswAGT2cB7k4Krj5X5jNg8BXU+I5bFEcnZI7blFyHeSHtQApB1gODbobfIxn6m0CFbAABi8jB/EvN8srrKRKsRJD0UmPxi9701uddW1sp13CIwWCvjqFJuDgVOE1BbtYDCmlomBlmJSOAQzUizMKfWS1nLYWYDshXWQEsQg92KBQRKrPwBEKytHNZAYKEtGO9AeNrdB1rcVSioNLs+4W2f6VDLUU7MrAa+Bg62jm2163YAi+XVA4IFRaGAYoOUVaEkEwojoWHJryQnVdOairmqH2VWsdcGBxkSHCiGCXwgDegTgTBLUKRwAOJZnBLPR4TDFXC+Bl89J+GSrz9AOF9jOp9wOJ2Qrs8l1kE+nZGuTyU8so+KaFEQ5YwFNKGR3aGlMhm7fiY7BNtQyOIsKNEOfXRDC4Mcr44Ix0O5Fq8OCMejmgmuqoZA35v5gOOx8SdAmMruA1AsPgXZawoyFluNvcnI2tJrhtbSIjAZKuwX4RFoFQhsn3v5zH3u4XlzjOwcH+Ck490BgpoYvPbA1NBbcNCnslLW/I52K1RFCKHZqWBn2ACiNVir9ca8AIEaTgA5QLB82kIoiPYgqPYgbWgPmKt5gUkAQXYXQCCBeai3tzl+rV68P4mFLeg1Cx4OCOITZ5purynQ20jLdv3mpunWgIFvk/UpuJoP1qCgT43A27qxTypcGNBOQWU7Uzkd0X231xBsAcHCbHAJCHp1e3muUHXrO1DV7N6EUP0KtmPGXwKC5UpnJxT4HRUNEGxoQQBdbgOrwVCgEwYcnDHKASxsZgVtTyrX68TYB0XxcIBQgxbHQaWYtsAEBqDzm1Mx+0mCwbIzIKNoDsSZkJCD7FYIU5AAKekMhAmIMyhNCghH0FH6TD5dg87XoDs/Azs/IZyeINo5C2lGPp3kACWNdZBKzINZzlDI9UClPYcpjQ5P8gcnyZkIk4CBAoLsMpgEBOIkmoE4iR/BQbQABRDUuZBDXAJBiPo6iumARVPgHSZNU1A0gKgmBEtlVUgoAEhBtD89CPj2Lb/p/IiaRUH5W69D3wf3Fx4KxBZ1OWjZcIzY6hqACeIOEDhM8NoDe34PB5ZdRqs1sOS1BzU/FQ4SnLMdmTdGEEhBFzIZGuK696No7H3VuZKIh4CAnGDhr0PQPgggZ12ps2xfDB4QqNUgZED9gOpOp36uL015QUJXB2ioZkFfuN/18qtv1dEjaMfrPt0aMPDDt3fSsNT7FKzdxa+ELWX3ubxfpwT1KdGBJtcm71qPKgBlstBBT7pK2AsEbI6Fyy2JaARol2xgDFWcFQ56v4JngYJlFugiFBTTyGiyG2hBbKIoJhFdOZTtTGjhoG54Fn1OrzUgsulScJ7Nbqn6BujEaN7LCwclZkzWBBtOMN7fZKHEcQAiW6Nk/TRndsFSpAvIQS2EGCaEMIE5gdIZHA+iTs1nICeJb3B4DpRn6TN2+uJZD1nS93HlNEZwRj7P6I9hlngDtT388cv9ccuyFbI9Zjkc5GCyS6clIk6iGdDthyL4J2SSo5YNDMphSKohyMDmiad9nIJsfci3lWmGmAscSJnG7du3beNHBCwWBnVcVBMiGQhQfVR5mheOa+Nkzxgppwu044TyLGM/YBMOgLFvTp+89mAbDjp/A9YKWNMarM13zDAVIPnFTnCAwAzkGQgTYohipsU6INg8UfoKKkQCbq4vlS5/1vSzUi+to2lGBUqr5wePH+JLv98uavtex4NrtPjG6HWbbg8Y5NQKOrdnHbAtidtQsJas+tY6voGA/3LpBD5L9pfq6sAOPvpQgaCbDJbdJWAIBQttgQWACfCHyYygoIDTCof4OhhBQbsCGkCBd6IE0PhNdA8mhydcyruEg3Js64bWwIRy0EZOxCUyIYDqbYwlIEyoJogd28oBVMHRJ9b/PBxYsJTk8hkJSGSnacrKWzQOqW5b1L5TX2eZ8TgpLOjxzSmV45qRJbQy2/HLdo1ZAstYIV145tWDdyzoEiA2XyKU4EMKCYi6jdCAIOrxx+XwsliORmaNR2BnHVgEQw8DyCgAUByJ2eqUS182KBiNdK8ZmkDF+WxPm5bfD0yHfnHgtYX22T5twToUjMZJscVruwkoVEAgVhgwDVtGAwcFpNHCQW9SGM0HJbRz+cIIDgQc63Hj3GoNRg20Nu/Zd81pgKiaGANrXcTdgGAmBvMftR1pgUtxxv3H/rr5f5TK6ZXuNxbRsNd0rz2jfb1cRK12ck23BgxkcDgSNuFAwcUpWG5JvJTWYUCdRrQTFyFii/E+f15T4CYAuElhz06DAgTN7gMPA4NOoPWgL5ZlLE6FVTtQz6EvuSjC6SZQUOtrkBwUFH+JDgoWWy1HjpQLqRtKpYvw12seDnRZTwBYNpeD0J7MFnSiQyDkzAUOctknXe3NDSA4++MEVS1Gy/K4krzgXJibNHk4sEh5rPVnu59mAqKqS6WPWX+LYk+NnWZmYxdLA5w5a33J+7DwXTGB05aPUwLF2F4rZaXaL4vJKtTrJviJkO0kU4QCBKWPavhb64tla2FWYY/aXyvYXg5tjZrL0iYpA5PVXrhhW/o5AFVAmPnQLxAIKILJtAZlrrC2KybDXOv+JmOF4RZQuQUEylUgB/GP93AQFL8NDvpKG5kTymNVtherWQcHZmtvnBHthsSSCduKyAPtwdocCGtcW/iQzC8hgimBOFZAUDPrFiAw69bCQA1c7lkkeTlQ5pphXTHefe8RvvxtCV60BgUrv27roUBhr/9eplsDBmVw2FsVDg8ef08iGn7uLT37oHJY/S5MYbyZ1raykY56iYC1pAIfxW9r8D8VEOQE3wFGWxGtjENnw8ZsYA6H1Ey4Nu17D+3+gCFY2V2ZF49ydeExxU90rebDhE830Q0nOa7lKNfDGA4slJlNXxouzTsiAjZxWaAUPX/eZi4GMtUtWDYhWBQ0qwkGEKPXYawsJ/qKcmlhXdDfGyCAba0n+UlkYlInHOKqlSFbvVo/iyCaxF9Rn70rdsSGwNncJTLQ6tU+2B5U1kMpA0uH1yy7BGzbsGSJu++ylaK0FbAE2j7bXhNouW7VxIPp2F3qUdxAwD5bCAbqNQduvHTtMhwrVqoRFOwZK4Atd+BBejRWFjsWSl2JxBz1gHbulN+WXZsODpBlHrTxhxBFTlMuJ02WcbSqPRjMhY0ZxUECm1khV0DIqoW6AAimMQigRotQqn0oL+oYHCU/d7773kN85Z0vljDHbe2NemD3PDdf7on5YunfKxgQ0T8D8EsA/r/M/L/Ua/9zAP89gP8UwP8HwN9i5v/fxZuVBqiA8L33vo97335Dwhy/+HJp5Fp5FRDWprIeBsi9CIq2GVz6ZxOSWf/aoAfGA383EDRqXxXXW8Kyn3gXhQuNtqCcLud2IIygILuJda3z+whtfRqaEBrPai1XF4OB+klwJIAWk97AbMLu2FaKC5OC5c9SAiGyOB1m1RQw62Sgj8vaniZ8rA6snmJ9dHPvLS3LVuqFWW11LsUfv29Bo5+kmj7rLoRyg1JDbV/fMqBeKANcH/LVIf2sUtba0da+/zWK5H6O3FnXvrfErQ/t0mIhUF8XOHD13M8LHtoqsNW5wb7TaHpsrABY7kBwaWu8NO8rIPRwUDQF3vzW7+hBNQWMUj9H1Pc+8qOOqREcFE0mxATWBDBiYKg5cGX04EoMc7xkCihbf3MWM1ZO4BB3AUJiqRsLRmRahFKr2th9y4zGWe+a8O57D/HVd76Ib3xmEKcAfVd0TxiajgYwv5H+fWsMvg7gHwH4XXft7wP4H5n5/0BEf1/f/72Ld+rI98F7D3FPT0m8++IrKPor+94AENYSoSNiOBUaTOU8+BHWB33RFPRA0PsQjICA01JIjtCmgFCfN3L/QvfaHSyDMRSMtARrNdlPdNTXi/t19azudh9cggIPAzdNqpEgoDEp/P/J+7tf2ZIrTwz7rYi9M89lw/CLAXlmqjj6H2zZPU2yqj2GPEJ3F7sBA3Y3i+wPcDSSIcCGIQG29GC9+sGCHgwbskZUk0MW2dKD0U1OU5ZsaPreKrZGgKy/QayiRuN5n657Tu6I5Yf1EStix87Me0lhBkcBnJOZO3fuveNz/eK3vpJejnWRqsoMyKgx/2N4trzIErhBPrfnGYXa+Ojyfi/gZtWcLefduhA+l9q+uGYcNovOufPDvlGOgOCsvGl++Dd9vhmdDwzj08bi/lBvFzM7L4KiERjAzqfdwWiDvGdv9sfc1gBhrkQVws+LLN3OIK6PIzgAiDUhOGugMBIY3NkbsF1OJKStmTs8EprELO+LPcoEHIjwz529QfPz04Nej1lda/+FgQgmuXP0vmLZGNnGbAcQjmwQIMwBEynI2W8eDtEB0BmUEgF/rqDg2x/MQcG+Na2e9n7CEgSQ9PKzT66OnX+iwICZXxHRPzsc/k0Av6rvvwPgz3AHMCAXUBUvf/bn+Nrf/QN8/4Nvi/rAdGfjbjL6V4drHenN7Kd2vuc2Hy7QOni/AzBacJcO+SYgsHwHUUDyIW3rlC3Lrrg9XPLXzg2xM9pKTV97AApmOzz7fE08xN24nzfQotPMj91NJtLS3l8TTs4gyBZIFrjxunua1GJGODWt97BcR6z3dWAwfdSeedqDCADULcfOLkS9pZ3rraPvY3a/WbjeGISni+Dpx9uzRo+bGoDFLJphvbK43FvSpM/i7imFD576h4IFe+f2SSGaKO1iB0TjTjMErN0chV/f6H77PNvlxWeNtTgCDnv1AnXnjGyanRtBwbigX/NCmJajOXM4f8J88fuYSiE3xpWuswYAuvXC3w/swQgOYJsnM0bU34o3ctFAXgxP4dwF0rim4rI1jAUkRLdGTjA2sQMIB0aKDDH6rax2EsPmwVTMs9kyjoVPPnuFb/7o6/j2B99roCCMCUI/1rp6Rnb7ABS8+vQTfO3H38QX8c7kaaT8k2YMZuWfYeb/Rt//QwD/zOwkIvpbAP4WAOC/345bpb//G3+I99/5kgOAzk0nomNnDiaLE3q9WYJ0GiCLlfb18Fzttx0Y0Mu8NSCwYwEMcNF9VMeCoDf26hgDwo4tMBUCNeMvzdnnoWA9LwQaKOh2rbynwcZylS0YVAh6Vf+8/+7wJsPndP0z0MCCsQbBbIoCQGC7PLXFHTBhud+tjE/K4bxZIKiOIEETwhXN0FH3bOF6UmK+hq0IGNgqOwjYarO232pV4zxGBWMr7b0E92nhgO3aldmBh73W4f6tPW4DhQgE4m7e3tqx7O6X5N8nkmPCtMn7Jbf3iYAlJQcOSyIHC0sibMRYsoQXtyBSVmxaHOn/j+w0gP3ibt939Y4fIliYbSq6U8LYZ+7ZgtmcMDfcw8+0AxddmbIGqhCz73jv0TOyBrdYIVs3eoDQwIEnF9LlOUE9FSzGArWcCD1rEBu0rzub18xszWSzYZC1kVO9GyCQhlmWOAfyDA4KAkjwag4ljpUGCt7vxti+NGEfL9x7oexBwe/8+Jv4wa99C/+HH/ybh1f+pxEYeGFmJqLp6GLmfxfAvwsA9JeJwYyXP/tz/M6Pv4nv//q38P47vxJObuyAh/7dgYPjYgP9k09f4g9+9HUA/c7Xz/Pz5+54cpefDxBwKbBEOTs/8YO6tOBFyhCMLl/2OeUeFPB1UBCFwwgORrZkbDM/dTCIOdSVHpXZDmfGA3efZwBBakmArMPUAIL8LBLDQbBFTHAAjvb7FDnRGAggti+69rZ+AMQSWuIZyKIZn3AEBVut+orwXvMtcMVWFDBUxqVUfRUQcNmqg4HKjKfCzhxUBRy19uDBvru3pCD8ARX6SQS4fZeSZllUcJCIsC5JXrMcW7NkpVwSYcmEhRISVfmcCEtKWBKDk/T7opEqkZt3QWQLYoAxm7uJDpg/qwzNWYPZ59vfxd18Tw1Pd4B9q2LMJ9CBgPH9mxbmxrLZWsPHrIHNtFmcAwe++uoAIYADo+dj4yaQRPTU6xtzYEyCuK2bwg9zr4Vu/ezXH4mjkcFgUKlvBRAskmJjGe9jDqxEUHCz3AIF4f3Lz2TT/INfG+TjpPzTCAz+f0T0l5j5vyGivwTgH93zo5c/+wl+58d/Ez/4tW/hvXe/3L7odGeAsQcdOJATO/q489FFsw799gcf4YP/8G9M6c/ZYtEZDF2LQ1DL3oZAPQ4iIHAwMCBeNsOZ7oFMVaBPoe9NhfC2oOAN1v+ubaxd35gtmF1sXBg7MJC6Y83A8uD8CKgMoOjj+e+PjJvscsD0WbkDFP29s/WDnQugmMcDms7Ss3qy7eFk9bX0rLFImF8BAk9VwMLGFU8bO2C4FMbrS8GlMLYir5dS8VQY21ZxKcIqXLaKwnKNyhLyuJbaxoKpLyrD4GIdm8AImVBsmBIoREOkJnA1y6OwAQIWBBBIprtlSThlAQVrJiw5Y82EhzVjzVXBAOG0kLJe5A9DKfeLnoH2tM9LYsZ/8n0zSm0ug3Ec76lcv8XBuLinHC704+dxTlCIDtiB4R5s9DebrGn89qzBm+D7CBDeDhzUplIw32GyZ97P37ipAkv68RZMqe4BQspgquhsELhO4yBYJEVAxlN11CRtHBnCWYmgYNxcdmunP/8ACia2Bi8/+wRf+9P7QAHwTycw+CGA3wPwf9bXP7nnRwYK3n/3S3s01unRmmqBozA4mqgE/OSnr/B7P/ywsw7tOk7PAyYdaMJfBb375XMDCQIKFAS473jZA4KZkJw8OxvnbeoBCEvgNgRqXCho+DooMKHUsQRjAx8g29Gg6udmCyIteE1tEAGBfefnp/A99b/tdlaBLZhMtPY5njcRCpNqdPclo0cTJPpl8sWjkIAEc42y+O2bWlEzSSyDnAhcCBuasC7ahxEUPJXqgODxsuH1pWIrFa8vAgbkeBFmoUiSpFok3HGpLLkRmMGlCoZVVQWHPAJHTTXHbRQAgbrwZmEESBkByamgSZayCPzzKkBgzQkPa8KSKx7WhMpQgKD9vAFYKlJNKEQShbIyoKqEnJqRqYGCxdQXASAYGHDGz21h9pT+NVfNfmBMGmT23VgG4e/0t2+ATBhKG7g8CqbSrRyo5q6BljtZA7DOe1XBHsLqobkqlD1QVQJrH1wFB8wwf1sDC3IxtT9Qo0meTsY98HKQEAFCZRBlwMJEFxsDrJssDn3AMLs2S/VsUUrZ6hzAwtgL4wbzsMzWqCugQJj0L3XfHZV/0u6KPwDwqwD+B0T0MwD/JgQQ/IdE9E0APwXwv7rnWgYK3rh0rIE+l74mAl7+VCJOffc3P8JX3n3Pz+mMo4YdxAwQiFpAqSsDCAEkRLWBR50bAcG4HQsMAWnQF0G5kRnIU1Dg7+8EBYeWtXYsCP1oTwC0gX7IFgBz0APdcduuh/kKgJuAgXDcp9oICqY3jWj8QPD7+zo9/yieRP9sBlISLCOiBe6R5C7JrZy32rK/AdBMixIauQKuR9+CfsEjtMGYgtqBgtdbxeOl4PFSHRCUSxF1wVZQN6BU2U2V0sCCYFnJqgiWyIc2Vtmy9u2oAzijRZRBSTI/WuTDpABAGAN5nzOBckJOSdI+LBklkdf9vGYUZpxXu8MGYEFOVVUTCbVSi0oKiEEiBXbCwYC85tSyly7JwC23+elz1EDsCAyO+z2OTX+kuJsPY3Ia7+FK2YEDQACC7mTZBV18xivXHefT7oYKBg5YAzHS7bE2IAd8thysJf50yh4UXAcHUFbDyQFEcKCfSfsw52ajGMfobm2dAQTW9UiDPun4h7Y9J2EWSEOEk85l9oBjudkqQYCCxUKI7TGqqft1c2yv6wL+5WefiCH+r39LXfa7Djks/6S9En7n4Kv/+Ztey0DB1QXfbzxhDeQLeBYxSBjKGJs6jstbVsO+mxgSHLnawJiCA7UBIiiYLLJeKPWgYHA5PAIFllDGQUFt9HWMFOegwNFtG4ijdXYskS2wgb5rJ+uLt9llhfJWYODWYjvuAEcgEAVBMO7B7vx2rRZlLjyjgYEAClCSgL6UIZbPCygJg2CLHyDgINvimyQd7FYZG8nKHMFrZU0brLYEW6nYtuqg4LJVlK2iXIqcpymWa62opaJscgzMqOWCWi7gWv0VMYHSBBxEUOCfLUdCXv015RUgQl4S8iIAoaSEXBNqVXaEAV5a/2UibARcEmHJZhvR6u6yhETom/1BhjIBARQsRMgpMASWT6KWHthHpuCOPrf7d32u7/fMVsh6CDST09mYDcfaeQoSoPQ2QZ77Kkh4i2LAKArPsCZYaPFD1qDbNYd1xePD9OCACL3FYtI1KK9tbh2BAyVSGNoMpT32dFNiY7dVDa4xGNULNqZdvbDsUz270aQwhAo3G0iw9rC2i0sVxvf9enm0uXJQ8Bt/6OoDB5A3yj+NqoS3Km+KsG+VMTUzoxmaAQ3ldR0Vdv3NjmDPCBzZEmDb9nYER6DAwMAvCBR4/HgoZmHuWIJrSaOAgGoDW+Dvw0D3JoxsATAfrE6VHrEEe/XBIRjYgYZJiQLenmkCCHZCobOPCOeM1x2etRcIYtVsxqHgRTomFSBtIjxJQhpTpWGvJ+6JS05YKqMgYWFGYbXer336XgDYuKWCrgYCWWwFuJi6wMCBMgkKCOp2QS0bEIAB69iuAzjoq60BcXzsCnPAeQNSQsoXpEXBAa8AssyFVZqBiSQQnz6rGz+ypE0+h3sl3VEK/lDDRFVFLDkpGIAbLq4GDIwl0ARUDghM9deFIC9hrNicnfe197ep9rzf1X1S9eGWATDu/AkmYOv1cWxuycyB6r8DJABz4ejX3c8/snto3dniHUBlNolgJ62BBDzT2jC7LcKsuFUDRwWIuDAWBQdM5PYrUa0AvCU4SJivtbU6OIj2B1gWGRt6H6lsam2TMkCqftDPns46AcYa2UY0YCR3g98DgvtLBwo6m7t61+b52QCDOZo+aIAbwOHVpy3hktsU2E+7136n2KkNRpbAGYIJUFBQgHtAQWrCZAcKPL/BHhS4LcFoU1BbchljDCJLEAGBT7wrbWdqAwrv7TeHbMFsUb1TdXA3ELiHIYj37BbKARTM8lOMgAG9W+nu8XNuCz4aa8BJAAGl0gMEZiCvyGnZc43QfqpinFe4YCHCRoRTSqhJDPLWLGxBIsJCUGah3524y2JFb1xYLb1yBevYdFBQLqimRijHwMB03yUnATlcAayouCBhBSe9PkloWq5JmJAqz0PMoEoejdDdG7U+5r2w5tQMEFPS7+WcNSdkktgUM1CQAWEJygVUth4QRIDwBv0M7WeK/UxJx40dU0raBI0b+0Xwe+eYtv5Uye3eAA5i/ATrdQcKhyWupa6ycIna5kxkDexbQvOioeu3iWtMBQMMWF4GaWsASXLTAAfggAio1AwT7eIGDphME7IHB8B+3VVwAEoQI2/Iem3ggFgxHCtAUL8jqnpcmD5PZw2ADfy5Wqmfz28FCrQiLz/7BF/70e9LHB8DBa5CmG2m9uUZAYM3xVSY7kRvZWEM+L8HBEEovCkooKh3A5pQSilQWgMgAEDLKrsoNS40F0SjoecuiYsYtnFTkcWMcyMouEk6Uc8WWBtFFUKnI4ttdvPasz7tB/YUCOh7vmcS6IRp3wZqOJ4zBQXHLqW+ez6wD+EDgOc54msC0toAgu1WckXOqxjl7ZYUyfp35n5cu+0IW0yCjKq77LVU1JqwVTEizKKcB2cGcwJzEQPBvIC4gjiBqiQyoip5HkHJhypnAEXC5UZwYGwBDBRYIiT7zm0O9C8vct8ENz7MSVwTzR7BvRKWhPOacV4zHtaE85JwyvK35oSHRf7OOYmXQwJWBQQNGMiulMoFKBfJMqmMATRltRsQqw2QAXk+6GOkBN72fdz7ylsgbW2fuAvVXXhXZuP7cGwHNQZXmEfBFCg4mwDszeFul1uhkk2lII8bWAMAs0UmHqpgZw8Swb11TOxbKmRxdV16bwU0w14BDDqmrfqLMirbBYAI/h1AsLkaGRugsSYcw0YDQJvHpF4KGlhd5ktFYw86gDCDCKGND467HRYwBwXS6Ae/npdnBAx+fvVBzMI4goIOEADHoGCWnc6FSW0CRq8VBUlXFx4GpS2i11QHlHpQkBYcpaE1I8Od6iCwBNdAwejV38UtGFQIjTzlaZvNrj4tM1YgvN8tlD4mDqcUbIVgpNtgxQHBHhR0BqNKs8PTEXN731Wz7SaRMjgvIhSX1ccAK2NAzEBuLIXo5cUlqq8dC0c+PLb0bzoIQLQBAD73zwV9loACoowNJxHelEHpgpoSqC7gbZPF1lQKWdqxBkGZOiBkxocZtCydfUHSNMvLmjXrckZeEpYlIy+E5ZSR14wXa8Z5TXhxXvBLp4wXpwW/dM4BFBBOAyhYl4Q1gAIDBkuCqw6obM4YYGAMiCt4E3YEIe20VPagf23MJgXsvLT5C+wFinXYjXW8RS29MsYFiesl9XtTT3DtGYXOpuYGe9A9SGMNRBgnmCGibQyiSsGy0AI9dRDDKe1ugcYeQNOJJgMFYGTTXehlU1oEfFECUwHVPoWxMAcqjGsBo8omq5SeLByXoShjuKrNQR5UKvoQSHNwMKaztjZDAx0jn3NvefnpxwoKvqMJA9++PBtgcKSHvulDrBP3KDXz8Mvw9oApiMZp4ytmPvsHJSLTuLvSHQeTLDrOEphwifYEeXkr1YHV8oq6VA9Ewy6o5wGF94EpGFUI8TJOc014xmsqghEMBP2t12HauG2hkjVK9IHMatxn/GTc8XY2AwHgRVBQRB2E7eJgwFVDY/yJWBfdJaNs0mdlkx3zsso18truk4NFfF6xeDpiY2kYCRVpSc3YTZvHowZS6YIGCcVekRLhUXfjZatIF6BuGdsmn/NCKFtGLStqOQ8GiEOcjStqsOiR0BseJvFGUKNDAwRpAZZ1QV5EuJ/XjC+chB14cVrwhfOCL5wELBgYOC8NFDwsWeIeJGBNSQMhwV8pqg7qpQcF5dIAgQE+BQVmdHncr9XrK+NBDZt9LuuulqkJlGDI1q9TDQhY2PL7xnob4yaPARnrO5AwtU3A4ZyUHXNYFGxHHVQKBDMghMYlkPuYSuGINdjhEr1NZA8QVAtsLIJWK1EWfZHZcQDu9UNk3iXKJtQNzKnVOURHnHnXTIsCJILFyLE1ZHilqv09AVQtNiL2gP+g6Jr5Z5/9BB/+6PemoOA4ZsbxHZ4NMLDd1yh0+Gj3CTRQ8Nkn94OCaGy0Ux9w+By+90vMB5m50AAD8TGAAYB8p9DZEjgoCKqDvB6yBIwADgZAcGujIFiYHBQQ4MFfCAEgJMfNcFAQVQhH4GhAHzt2INKoAQzExdHeXwvEFI0h5c8WXTg4iC5gQtcNu0IOd7P+NnuAKDg6gTlcI2XZRRQAeWn54VWfT3mR2qUFbPrsbKxUBeWKNZ+QFgIVbb4igV4IwJIWLLmI4V0uaoQHpdklguB5zfj8acPDmvD6kvEXT0U8FZaEWiqWLWHbKrgwisU3qBW1LLpp1uBHwX1Rmqf1Lznj1dwTLYaBYNmElFRlkBNIVQR5EbAwAoLzKizBw5rk2JJxWggPS1ZQkPGwEB5yRiaJb7Akqe9ChDVrboXy1KsOjDGo8optE0Bgf9avZdNur9M+BQAJ12t7Wz1uVLVR+8qijWt0TD9tIctHUGCj783HuwAQGkBCm5sKEiw0+PFl9XwTepbivO2AzT4g6aFob4BEkrKYTOVAh+pLxyiAWPcH9oCDaoFZ1qdOtWDrRLn0qoVaAFZVLBcR2G7XxDuQ4CWsy4ftEQxBgRrAn14bOAAHfpErLa9Iy20KPpYswl/9Lt774lf27Tfu6HwzdXB5PCdgYLUk09lZiTs93jXSG4OCieScJv3ZnTcb7i3oRhtkbbCZULS4BB0gGNUG18IbH7EEdQ8IxvjmsyQyo12BgYKYMfIQFAyMyc0kUHcAAvsbQwxPu0Hr4v7DEeAYOHDjIZa/G2CJS+kF/wwURNo5lrKBzdG+Vmk8Awi6CyeuoEWFrrcld6qFnFdQzsjqsWBueKUyEmUsxDgnwutUcUqEp4Vx2kSQPpWKzy8Zry91GuPgUiqettoFPapVgxyFqIgwo0Uzxgv1beNIPAXE0I48mJF4D6QumNFpERuBswKBhyUpKBBAYAxBBATnJCzBquqENYmR4c6e4Eh1UC8gjVDK20VYoO2i/VTba1QTTfoUpPS1CkRf5onE9qBWETrLjSXYxrqxYzruhX+MTN/x43TjHejnL8S/3hgzsI197AHCwdrnHhMH9PgIDhwQAM2dMTAHdv+jNcnnfQQISUHBLfagbkC1TZWqidz2oDpA8HovjR0ZS2NLZhJ2QoH4aaI/sibe/9TUCrfBwcvPPsGHf/INfPSb323ya5Rz/W7z4H1fng0waHosAJGGsUZRegzh+JH64Fp3tJOOVQJzXTWF3YNNIvjvpy4kHRhQ8RsZArHMwj474m1bgsrXAcFRiQZvs5CxPy8ouKoymACCMlkYjzI/erNanZVi9A4nXShT1uFykKjFJq35Ph2VCSjgmS4akJ10LfooG7g2Kw67BS16Tb32qFpIeUVKi+QcqISkcQtyZeQEnHLGw5rx+pLwWBlf2BJebxIR8QtrxlOteNoWD5n8uFVctorHrXThki2EctX8Cxb5UF6tHyZCJIJMi3ioAjulFuI4hj0+L1nYgiVpyGPCaUk4pWZg+LAQ1iXjnCQssnkdGBhowGDidXBFddCBgug1pP15tS+NNYIN5TScM6gzbY4HdgAYEpwp2B/H/c0xrzKpah8QemCcbdzPAEJYq44Aws8DDjKJWZ4NnGu2Bl4dv38DCGKA2EBBvcIedLYHpl5I1q9Z+5V9jkVwNC1hrdq5pHbtNHG9Htrsar2Hq7789ONefpncObCxiq3KOBgrWp4NMLCKjo3bAm70HTI26mx/fxMcvE3pwAHQuQkFcNANsLhgvAEgMCAwqg0K+gV8NuQ7jNntqHEYR34GCmJcBzlc34gluAcQ2MIYOZt7GJDEIewq1OROF0mEWOxyMVUnxFjscqWWmU1d9ZwypqRs33EiF6sv19KDgwTwtvn9r6oWUgF4FUOqvCDnFTlljYxIKJWxVkapwMaMNWW8YGArGZdS8VgZl614foWnrSVdeiry3liDMdGSAwQFoEB7LaHtLRVyTJC0EBwQjAmSjC1YkhgRWlAiAQW6+1cwsOZgNxCCFHnQIgMEM5agltuqg2ugYGZbEMdbsA9ydad9Z1FKdRztxzx1DKCNfxv7ZRj394x5sITRlt1qD4znAKHlBTBd+SxITkx7b/Y5o9/+LXCQIRsYUylE1gDAfp1iI99tXZB5WYk1BDEhG1BQliRTRsrCFhi7R1TA6rfYRVk1hi6ChHDv1qh6iNJ0DPwiCg+v0aXeN7W7e9P0t/dsAZ8NMJhFOoslmnKMLon37ZXvLx6fuzMoSQHRBaM5rrpbHeiecfdAZmhIPSAIO4lRbeBBi1yIsgezAfZCFOjGeXya3q7gHlDg0eHuUx20mx2DgkifGvAZF8aj7I9WLHq8AQIDCN0IIauvPkfKzaXcFg6SPBPiasptx7eswHYBJb1ThQIFdV9NuiM5WkCGnSZvmzAF9rX2BxbdqXEFqmZ9SxtQF1DZwCkrQFhQE6FUSca0sUYGrMCWJQhSqUDlBZfSAMFRhsYxVbMlWLL2bmmZ941vicfE6LEdWzJhTKHcMiMivCf/WzVIkUQpFDCQVG2w6PgUF0Q0hqADASV4HUgEUmybsAQ3QMFuvHYfVfiTsXui8hOAQDI+XDXQxtEI/n2Ou60BTUH/XeNeqXpjDuK493ngfRTnu6xf7DxhAwdHhbrdte6Eq+zqkfJNcJAMWtxhbwDAUzQnawfSPleAwNqkWYHCVL2g89MBQlr6WBUjSPAHaet5aIF2bFy/f0Gg4dVEflG8tz3e5LUbG1eW4mcFDKzZdbxNy6xRx2I//YWwBqZkBDANIkJhF+E75mEwuV0B3QQEFr1Q0uNiBwj2O+r+1t2jUch5oKDAvA5GUODkWcwRsfPYwKT+imqHBzgEBRW9nQTaIglEnevBqHejDgEJLec7h0AqVt/s6VxF169gwOrB1EDDoueVTRZ/AJQYvF1AWJpeuRZQXvbtMLRH98hVaUZrK7c7WBWo2I4ng9IGC24lACEhp4yUFqy6Q92YOhWTjZWqx4vZCmh7bqoq2KqkXGYWdoX9M/tnQHe0ByX7uiksjeV4aJ9V0KuqwfIYyHjbZ0BMqeU2SCQqYTJguoVQxvXidhseijy6mprqwABA9CgZ+0cB3mHfqTumg4Jl7UAB5UVsC0z4Gwg1dsBUCDeYwG4e6LgH5mNf1kfqxv0IECzUcEoGAQZwEABBxxqM7IGdMxrXiS5D5hX8KwcHZIDcoAVbk/Ypm/0ZtC2INM2zggRmeGZGQQYCEMw5hLiNnZQWvVEACLWI1xe38dGBBLs5TcZAHAe+fv18Jdb85actIu9XAiiYAqjhdbTBurZFezbAwAYDAE+VDDQEzBBQMDbq9FoYAcFB05Ngazb9kJu0626RoJPJpMtg59Bda6KnMnbABlhqu4hrgKAJ0B4QHMlmO+brGhooIEBd3BooiAtxi1PQJk5E29NcA36X4caDoeEtUNBRqbUtiHxQR0B3LkBn1ewhVnEADkh3+kDL/c5thnm3L0DKWYQM5KEIcCFDVGEue9e8FKTqkwVF2QSp49ZUC6rKEICQgZqUDn6S505ZvALUQPVkYynbWErSrge70SPvlWtA81qJ6yYwGW+BnerdX1vio3HcUS1izMfVQxj78RvJyqq5mV4xEnUG6J4+iyyBBGOQz+qCypH166KUDkbE2mdH6sGRDXRBcGXsW4ZOCRd9kMGwTsCBhQgcWYPuZnbMCH4AXOAR/ywzIQpIVQtqpA+LkVwCOABIAL+CgzQZb/F9BxKwBwjGZVwFCMbuqTujsHItDs0uN8pRGddzO6YyQ45TODa9SPcpgoL3Bpu4o+KgUT/M2m9WnhEwaOK8koADEDznxqtPX+Ebf/whvnsDFAAIw3ICEqIQGwcGUQcOoL93gDBBmPMEQBJZbqpCAN4KEPhYPqjv7lhYpI9AgakTusV55sI5tWJobdTfOKgQDkDBUVCmmcAaS2IRQoILGJlJ9Jqhx404tTSp+9zvkLTsRhVW84cmUROsEggIXKUfq7UNN5CQtVJZp+CRrjoCBHVlbIJJou5xaZH1UnBtNeFiSVtA6qalBqwRgObZYkU2Dg2sNRsPhNc6Hgg97NXY9bMep+6jjie9mLJOLSHZZFyNC3YMJhZ1w5MkZR6ZMoK0AL68/Z3w0zk/BB2L/eRgwNpZAYGomRa8WZTS3Np7AAVvajMEtLFPpCC6NgYBGJIU7cABtZkxC3fd3dUAAuzXzh6IF4C6BSs4cLk4AwcH9gZWxkOGI0yt0AGEKte9ChAoA2aDwAzUDeDs85dtzAE+Z48Cox2t7XN6NqiXJ/UzUHCP/IplBAVxnFyDFM8HGABh5FADB5BG/cafSOrkL7/73qG/ry1Sx6BgAAMBQXc+v8Y/o4EBHsLU9oPjYFE2emsQkG8KCHwBH+s7PI7tzMxy2ZPQHIAChz5mTzBLSQvMZ7SsQu0pInK2+lud+RgURO+KW8xI3E3YnqaYy9MMDurjxTjsHThIYXyYP7QZMC0KMpZVGIQQHplm7m4HFu5d8Vgdg/CCRGFDSuASBBQAjx/ggEHrSqlXTyhIIPteLtL3iR2LDerVf3PK9DDR1LgbdUEP+GiOxmDdQs16bW65C8zzpzaPji4A09gXRikbAJixN3Ye0NrBohsC0paqRhiB2tX4IzfcjcsvYuwbOB7GfkxS5GoFA8e21rHVNwWAcG2ey2/Ms2fMF0Apg5QhkjYcwEESqV4hmQlb5MT+drO1TYT+2wAEgChL3Q2IJ2XlJpsejhErx0bvnghtndcxMlU3DJ8jKDCm4FrMiq7Y8oI2TsLhw/JsgAHQC3IbBD/59BW+8cMP8d2vfoSvvPvevEX0R9bYPhm665pI0GNuwRroNSgydv3bJMRpt5juB0jUrY+uSW8DCHzCjPWm+NThsFO3d4CCIR+E74onqLq/ic1EE0ozAUMNFIT6HIEC031bc08HflxICC0Ri3Zt1buWa+CAJu5OZqxEBHd3SgtsxysCd0i2YwZtsY12yVtugAVmD7QDoMtr61W1Nr5+pVZmunP/7lj401sAg6tU7LWIc65OuXLp7sMdtTdkbCUPS+PYLldClQNwMMAOBpS12QECwt67aK8qjOoDYwpmoOCIGYxLkAGErGO/Otsq4MBSJ1gPCHvYAKQBsqmdwdinbG0kd3WAAIhqQW+QKN8EB6IG3FMHszWuAn699JYAoRDU/kXVDMH1mgNIAKCB0EJjWzla771hk7drDyRk/Xs1gIK9YSlulggY7/zJ8wEGJuRkRyDo8tVPX+H3f/QhvvPVj/Clw+BFaC01YXi4O0xdp7pBTjQwNIAQr+XnhDIbFIGureiF4lFwoggI3GBsqFccCCOGJXsUmwQE5DtBQUsjXULkx7ZLOwxHPWsPILRtYwuMQm2L335hNFAQF8WZWPE7GooO4KAEtUIGo5ItQAIOKpnhW3b3K+FkqflxKxiIKhVmlhUIYtUs3gMy7eiOHW78LA8daG8Ki+SV8Ms7ewYDKBF4xHuELIHT+Bb3sBtvWybAhDqBHY11U3g72b0Dvd4f2FP+k990ACgK/vh5wsAAsvgfuhpTiFxogGACEir6zUANY9+Pg4d50QPiw/EfurMEcBCBcXWBb2AAjTXwdITHLri7ee/++wYQshr4Wbjm1DwFboCDQvLMJSxucfXtqqiy2kECw+3PUroOEIQ0aW6OSec+EcADSADQgMLYHtN1rgcIM1AANEP574Usv/tKzkvXHo7duBsf1y7xbIDBWD7+9CV+/0dfx7c/aI16ZQ/SJg11TNrAGgyFIiltx+wH4W5p7X7WDYRBd9uE/lsCgoMdw7XSVAgNFJB/vgIKTHUQEkftDHSGG+1jwNPkNbAFaAPaFz6WNuiosRkoGBBRDx7lsIEDIqErE8uOyQ2xdIGM/UuUQRTAgOohjWJk00+OdDdrZruRGmfZOVHumZYpy0BNJeEC+kqkRTaQUorc27IFKp1uKZShhngt1XJjNLiYq5a89xJ29XfHlI9dEgVweE/qukCU2nsz5gMg2R6DKyAFmxwV+EQEaKIxCrv0aZhie7Vd/8HuP24Kaqc/Hpm/AAgAHBoTU7Qf6hnCow2BjX3gDlBwMP5JfyO4UlqiUhv7rLtrsAFkNNaAHCX0r3abqVEiQFwAinNf2AOWL+WZroADYh0iCg4UJ9wkg45AAtf7AIJlhjQjRlkbSdeAABI08VQPinIvBwaQcJz0rbnUR1Aw2vLcM+MiKIjlk89eXf3dswIGNvA/+exVBwqOBk8EaU6rTxCAHepEhHei0WqDK+KBLjYuJB0Y0DdxEZhZ3d8DCMpEAZWj/ya1BcJcDcXtS3YPpJ/NH7y5I4pg2akORrceoJ8QVu+gPpgVzxbZquMlsgXN0HDQq+JgUbTPYXHc3du/l1UxDeDAvRX0PGFXdHEY9I/9ew7jYhD8Q3v1oGG4FgmkScoscNmkY7aLCDwE4VxbQifems++ueOJzQOjbpuENN4ELHCVYEV8sSyRaqBXK7gyuNQBOLT33o53AIQICDph3wn+BEoy+FJK8pkSaM2aZ0FAQFqyhFReNE3zssLCSos3QFbXQLWfmBkJBjfC47wkcwZALhbPAVwH79/F390ftGvmkngrp8m949/AgQFjixYIJmcN8vBzwQEaMvyaGuhoDUDqAQKx2GmbaoErODGSArtKQAWBGNiSzkcCsgUrUB91Sz9RbZHWcrQOEr8ZQCDdFEnbjenkdR2w6zlQsLaYrTaYyAS9AHpQsHOpPwAF92jLrLz67BW++aOv4x28e3jOswIGBgr+4Edfx7c/+B6+/O5Xrp5vjTmTVTPWAJBO01/pSwQAR3dqQMBeRzBgu997AAFz200fAQLWXfDuKQIoMKYggoJMUN/xK6DAVQe9jcHchUlvNKXVwq5rRrmFnVDcEc1ucbNQd8frRdvWCAMLotIFg9GBEReH1v+DsAdC9Dh4W8liaMBhwrh0YKGpbdz7wNwit4tWLgEl/HYLIX2LxP6vW0G9FNRNgveUrYC3gnIR0ODfGXCoDN4KqoICAQiaMKnWQF4YSLjdMWTRD50NgAIAyZsgIEHyJdCS5b0K/rRkpFV293ldQEtGXiR9s3xXkJYMLMpk5UXaeQmxI1IQ4Msa3AhFaDe6P+8YgG73H42GrSLh/T7zJ2BAwEbJtXXAP4dzbhWn1QcheatEzO7zLe7Ix7VNBbtavaIzcAHmawGb+gsgDTHOKmUlCFKz5CINhhQrVBUMFPTggPVHBg5m6U1sPbQ1cgYQjmwQDDQQSSRFAQuNRWjrgG4cDSgA8PjrV0vfuBZnJ3ofHE2re9bByBZ8oqDgWx98D//mH/3rh795NsBgDwreu/mbNykM4GNFcvZZl7WrvxlfZ+4j1wCBvD8wKuQeEIx0kU2GyBZEUNACxDSbAkm+cwMUmOrAoxty29kC89F6hSnoz9mrETp0fGMidAvj5LuDrw5LjYBAd1e2oNjiEK9t6CsR0DLYtXu2e1ufVTgF6XYJdQK8FmECSKP01QKsZghp6qxN1unY1mooZaCgPF3Al4Lt6SKA4PHJwYD9CVAoqJeqIAEoF2UUNMsiV0bVV1T2MMgRGES1g6kEgAAMNCCBgAAFBJkkZXRKyKuElBaBn5DXDFqyfF4yqoIEPp9AS8FyWl3lkeQG/c6WDIgoo7CePdgQT+IK9FR/vrHzb707m/cylvoDrkziNnc7QBB0wvcUE6r3zIHDEnZCspm/ok64eS2erwV+dU15TLLQmGrhGjhYFI9Ul95wsODgQO+QkwCBWVIvi17uAEFvOhopsjMSA0DAfKMANJAgT3xLSmhThfcfzWwKfgHF5OMfqny89kzPBhjMQEHcMV9L6nKD4QbQB0f6F37wN+R3B+fy8P6aL6m53zkg0EHP4Xg5AAQAuoE/q5+BAkJAtW8CCoI9wU510MUtsEod69SGh5PTdUd2y93tniRPcWE8+j7c2hmT+Q0DayAPAIDQyV1md4ktmE/+HWvjQMIeIjebDr+G2Aa4C2hdvN0tvr8BBMt5761jtgaxPc1WoGrqZBP+j08oj5sDhvJ4Qd0qyqWiPBpQqKgXOYbKKJZ++aJshqVgNubAbjnpLwuJbE9mGRWRJcphXsWmIC8JSPI5rQlpEZVBPmc5tiTk84p8Wh2IZJxQUkK2CJPQfBXGh5OqDkzF4KBgBee1AwT+mfaRRg242l/1SdkL+limUfvC+wh849rQAYqD4e8U+nDha7PpHmA8Yxy77ymhJRnj22DBGQSgqRXld6SqBFctRJVaymJ3sKNCCG5oMIIDkjZjGDiYb5oAAQhZJ7ldPnoxoEo7xERNCQ0gEFiSnAbwNAMJdvd72v5eUNCRMVfkGxEFmzuRj7dW02cDDAwUfOWL77/xb2+BgpefSnCkGHHqFiiY7Q6mNGEABDI4b7ge8nWWoNVJQIFuYA9tCmagwGPM70DBRHXQ6cVjCyg/ZwKKeUJHDgcm3ghHhWxnzjxdGOfn9+8T4uSZT9qoUmJm183698ePePhAccGl0De9q6iEXpZEfcHYU0O1NoAguveUs1qVAy2CXxXjw+jSqLYE7KqBKioDBQHlccP2WJ0t2B57cMCb2B1sVYT/pcp4NStxV3EdlAR4vJAMQqKKVVVXy5MAhW0RtqBuCemSsJyzYlGG8MkLUi6oyjY0+4eKaBDpKayXRe0HFtB6AtYz0ukMTouAAIsboICA1TYhhiNma1bM57H39hvruPalAw2Ty1H/bz8H9JujRxmBcRf6fPY8BlZ0Hrh3QrzgeK+uUYa1wT8bfKlqazCoFqyGSZIf0R3goJLu8Kmtl0fgQB6Tp+xB0t+OsR9kYSJdUyNrwLqRgrMIZgYRQUL4eLPcGkliPHr7Oh9/Otrc8c1neDbAIHofzApRH2/7GhqO5dUEFMQy9ss1QGCHd4tLUBvY8XtAwVE9ARyCgplLooCBY88DBwVdIKMjQBBbZiKNgX432+ls931yzzIbd03jZIm3dcI3gAGEu6buedvbm2rzDijw7PD0B919CUhqnS26TIQQwBlLzqDMQLo0gFAvoGKBlQjp1OhfuQ0DucgOeamgrcBD9canqSw2BIXV3lFAQblUBwXbk9gmREBwYXYg4MF3rrSZJ06CjjNd1y/MWIlQmZBqxVIrmLMvTsVYrwxwTWLjUCpouIkZJFpgIQcDeQWtZ9Dp3EBBXsFpFRuEvAJpbYAgr5CcEjI3mVv9ZkD+el+j62vv79kXty7A7bcS8McENbkq23gGUXXpsRvz4VqZCzJCs7vgBhLMFdlu1EmtyXs26Z8aewA01UIAB+6xkOTnxNSkbgAHkvOBO4NECuBA2mPeW6XyFBxAryMhmUNgKJY11NjE9htVQ7j6Ua+h9xnz+Nw7BK6VI/kmoODDTj422Xd852cDDN77q8egwNprBgbi7lEO6GfqwyiPTMEhqh8Agdz/NktwLyjYP39fJwcEWhdLTCPqg+sJaBwUhAx0nT3BqDoIfvj3qhCayqDlmu++x41dJ6FLy2odlhEmfIdH+t15/PraAh0n71FQkVl8+rfJH0BkCWRs98YK3Nj7qpAkG8r5hGQAoQi9TekC3jQsM5ELC39cZlCtSscvwhosC+q2qRugGPrVPG/10aDQ3OlmoKByn255/F02aoa1IVW6Vf0+tvuRISNlNUxU7wQzTsxaP/FUUHXB6UH+zg/y+fyAmk9AWl2NwGkBsryvEAGyizRYj2MHXO/b/nUEsTaMm2qp/303DuNx9Lv0qsjADCGjzdvMUmGcF/4MB7LC1qAds0ak6gQxQJQASFfiXMzWCRZ7A7lRaaqFBFBp8T/EY2EJE5hAlVF0x+7hkxUsmLbC3B2XpO6gN2g+Bq6CA2g7jWGlO1skwAGDqR9HgKBVuG/zE3CQAMQG/o7km4GC73z1zW0Wng0wSL7YtGIdcES53AsK3r+iPnhbg6I3AgVDMaOa+Dk8/o4liKoDGAAIoGC5ZmTIzT1xF4ce6Cf6tRINtQwQdAE+5m6K/vOIiAM4iCzm1AtDy7jw3ovaGWFS6oExWdM1MGcv11ierv+IFcyxsAau8pHdzMIBIKQMlItspSmBNrHVIBBiXABbROwIF0auFcwn+az0eyoV+dQHGDJPBABAYaRaxXWLgBWEi+mXDbkGsDYrlvzIVFcJwEo9ME0pIS/NviCvCfmcsZwX5FMWm4PTgnxakc8L8vkkgOAkdgfp/AI4v0A6v3BQQOcXwHoGL2cgLeDlBOQTWBmDStkBQUxNXcCefZIZrse23rzVry6gKS7m7IKCKAgZyLgex6i1Zh6aNQZRS6B+LQrXBNHxvAq/PwLU3T3t8tFt0Xb8RqBbrphrAAEI7IKey+TXIv2e09IzB0DnzriBmr5PJacAbIjRNgGocmlh0xpAADBdR2PxeXMFHMhaZDJIvovsgbV0TPKnh/zRxzamUF8r94ADKyMo2G3VjjEggOcGDLSm1tcRoR1pDq6BglF9MHbULVBgX78pKDgq8f4zMGD1IRhDIBNcFuO3MzI8BAWTKGe9AeEAArrvA1sQ1AhedROoQ2MQ2iSSCXsAt+OCSbtDO1XCrPit9U0HDtBAQYy6GAGgGYUWCD1vu+hrel8JvypqniUnbABSZSRiLExIlVESNYCQMtYlg9MGS45E2xMY4q1AZjCXEpLu7FJKWHNWt0BLsARXL6S89R4E6i1QUpEd+qVi2eRvY3muTYPQRKbnlvFhIq1nknG4EGFZEkiBQFoT8iljeRAgsJwXLC8WLOcF6bRgeTgjnxcsDw9YXpyQX5ywvHgIDMGL9ncStoDzCVhO4OUk9gUKEi4swR4NEGxqN2GvZsi2ldr15y0dfuxPt/kxQMBw6/cYP6S1Vft873gV91oOawFpX8QTZw/c7hk+hmurIaIKJXmJ4L7OWQN/4IRuM4Fh3fCDdl6C5SegurW8dJGpVKPEJcFjHVTFJ+axYHglJQF21uZuizOAhKE5Dtv5jcEB2jGiAWxMwEG3VsV74zo4sBJBgW1qpxuiKxV9NsBgnFRA0OkMghuYAwJAGvWaTYGVcaMwYwrs+AgU3qR09jZAtwOJ57RFpAECsyWYsQRvb08gr1cDnMTSsQQJzTdcWYLAFgDX9fkUoLEP8KMJNSymaXJOPA/o+80XECK5JfMeHARhb4AvCpCtsO8yubIKk3YB20+54byqCygR8laxJBGcS04ihKnpQUsirEyoibCmBemUge0RgKoTVK2QckZVvXtNmjmOEhaytMAaNGh5RFozts+fQMtFduqnjPJUkE8V5WkTA8THgrJVLOaZoMaInv1S6fejEpkC80owMGAeCfms7omnRdiCk3okqCfC8uIkbMGLc/v8oIDg4QXo9ALp/OCsAecVnM+qOjiDlxW8nFFBuFRp04sCgqLswKZDfisVm3036cexD2M/bgnIFdiIsWQCF+nLJkBst9d7ADTvIerG72x8+zgMC0DckABt/YvnXcPT1+yvKksfdqwBIAsTcWsJzzBbr+920EACA3tw4HYHVYFCIKaiUSIDxHvVQkJTJZiHl9tjRJAQG2VYW9+0MDAFBzwDB1pmzMFYHGZdkW8ff/oKv2e5gb743k3Qd1SeDTDQsdEfm6ArL5MGM5fEj94g33X8Mo5/2z2OZTRaGkunM7J6TJ47vrXNd9NR94CAjliCmepgtCfwrIBzlsDr68FcdAkbVAfNH5zQuSgqWxCbcmROol7Wzt0L+L6tjnZbR22pK113fzKGR1a+trAOfe5MAZogKcwiXCpjqzUIlf06efFnlS8syBSRCJKlVCyZcM4ZGwEL6062AjkxqgGI5QGUFtCWQGkBtiRULCUgL0h5Aa+6Y348gdYT8vk1yuMTttOK8nRBPj2662J5uqA+bSiXgu1xAasrY4tvwO7GWEPwo2tBjixWASXyOAYSr4BCvAIBC8s5I685qA3WPSA4n/YswXpy1UFdzmI/sKjB4SLeCFsVRuBSxSp9YxH+BggeS8FW5PNWxDVzqw30HPchkImxJTEgZbWBQNFAPKgNHMDlx25MNg+VHiTMxy91Y3IHCEz1Fcb3rA5+tclc8Z0ygmBW1oARXBf1OFtisWYRCPdS0rDo4zpCPJjummoBuGF3sI+UaOyBBAiTp4oAoc1xv9tUah6tM2PZsQbddz2wc+2b3fiKtB6/iqePAOEnPz0GBeM+inD1ts8HGADsgzKu3WPjYThujfPxAAriNWZ9OO4eD5/q4BwJsdu+9JSi2A++ow6MC8UICKKRIeE6SwDLd/CGqgOv4xEosCAxDhJa0pgudjzajnvES9YPM8rsGgiIjNDY17P2NH2t34vlYFGAMOaIL+ODsuw8C/agYAuAoKrO2sZOpNyTqn0uAJaUQMRYWGwOVs7YyuYAoTKjJMYCecYVQGHCmkUPj3IB0gKUJ3nNqwjMy5OAg/UMfnoNfnoNOr1GfnhCfXpC+cID6tMF29MF1cDBReIcxOBHDRRUlAu76yOAlkcrjJWW/tmGigjMvApSNXAQgxjl04q06ut5xXJaxY7gvCKdBNg4KOhsCU6iNsinZkuwnMB5RQHhomqDS9G+UnBQBkBw2QoK4MzPpvWzWCOx3+RV5hmzJdYCgIoFCZtSBASAKmOJq7VdB804WOZyA/U3x3EYvxkBZHMb3CYMBZBwhyd26+PsHnqDOA+JMgBNrJQg8Tcgx8TeRXb6ktfjTnBA/XlmHEAAdnYHQbUwYw9ExaH9NQIESBvcUuH6swXANLIq6WCV7kDAlXLL5snWwdmlXP39ww/x3d9shoZxvPRAgIfXfXk+wCAassDgQStHDU9oWaw+CoaG8Zw7xsxhmRmGRNShm1EdQKS6K/iA7erQXbcJQQLceCgyBA4IcMASMP/cqoOboGCMHGdpZT29LPzPFp1dG6IJ7NiN14BAnAjyaluDeT0o1MHIAwu1WtU1iq2z1B3KApwcRT0tLPRzYXlvgEBi4PMks7BcKCXZzSYiFCYkELa6YUnkAOGcE9acUCtjSYzKol6oLLrsVY0TqSxAWoF6ArYnID+J6975EXx5Ai6PqI+vwZdH0OUJ+SLHT9tFmYNtFzqZtyJ5FJQ1sIRLXNlNvq8FXEHnUdDYAtIQyGOo43wWxoCW1eMQ0HoCrWdRGehnsSNYGyBIq6gN1LhwpjbYFBBcSsVjqQ4INv2uqkrouM+k3yThDiExkIiFDdFViFgA3GzMEdk4QmcDFMF8HM8+lm+OY/lktlc94JUbR4FIob/GDVEsFdDEQrFfU7MDiOAgAcwS3fBNwYE/xxXVAiH7xuKIPaiAAwRzN3WgRAYutI0GFXAs19bevvH7cg8ouFbo4H0s12zi7HW6Bl4RbM8GGJAGe/F43GadPTs3vG+g4Ht4/4tfwb61QvzuybedkB9AgNsHhFOjeiM5ku99kgG5Tq+33D9/83+3ReYaIMAhS7CLYlgL4iB6I1BgWe4OQMEsm5yzBXbdyYD1QX4HGOgWTw4JX45yOdgFqbn8kdbJdwmJ1CVPgEAiSbISF8mcCFvMZMy6k3Z2QEDBNmEMqhCyetAYHxH4iUjc6BgoKFjU7mGrjDUnFNBOvVASYU0iXNNyBm2PQD4BdRNbhOUBdL4AdUO+PDlI4MuT5GDYnpC2Desk14LFOeAiN/TkS1WlrNd/zxhIQwkoiMmQJBRyluNL9jgE0OiEtCyg5bQDB1hPQFpaTALzOtD35n54KY0ZiGqDrTAuqv4RYCBAoHADBFsAdFZifyUisZUjxpJkQJqagCvv8uhkHaxN/acsQXhvLIEzfON4jiHIx3EMNHCgc9HGc1LAa0Dc62O2NMDh9jnufnudODk4ACuvFpkDUytYsrGKm+AAQFMtRK8FBRhdtERmUdkM7EFKavcyAARTJ0RPjqoGH1ZzV/NMmlZqbOdRd2BCBO3sFDqwMLnm+HokwQmElwPTzd338ZW7MRNTvs/KswEG4Bp2+CqSD8CBHXn56Ut8+Mdfx0e/+Xfw/rtfnqPwwECMVE5UJ8xAgCNv3WlaxKm98Uj/jAYU/D7D6OkooqB7TPH9AAiEHRgSIMVcB2+gOgBwNePcmISGZ+yBto9llLP2sMl4FIzKAcE9YCDkcWjJilorW+uGTvI6WBjcuAMxK9DNtl6ayKVW9jzvGZK6dlODMrGM5lDHIFyGXai7rCX5LjHpAiwAYUnCIGwkY2lJScABq/0BMxb1YCgV2JJQ1lntD/ICoG6g5QwqG7heBBCuG6DviQtweZJMjAYKNsvM2LI2HqVxlr6bjxlpXm3vgzTJIMlyiJQ1uVEAB3kRIKB5DRwMpAwLUGSRDAsgbECdAwKzI7hseqxWPCogqJXd4DAyPAA6xqAqUwBu6oQIGGQokY/fTOJ6SgiAQN1RzTj4MKOps3m1D2A1G8t6czZAcAB6ZyBBZ71fJuYZMENcscpvG5ys65upFVijdkr8DLm4gIbabBI9Q5OslLbr342XaHdAxhqIAYHctvbsQbA9kI3SHiDYszv7wa3POnUm7W3BdiqDAAhiy43RJK+BhZnRoa9nwCE79PLTV/jwT35XN7Xv7b7vgMXRJumgPCNgwIjDOoIDOxbFtaW2/Og3v4v33/3KMfrm/hqRZmsDtn3YgQA9xQzY2rM26944aio3XWP3KP5Ie0vlUQ85BQRRbWDC8ihg0duoDowRMBc5JLAv/n2secOqzRCzb/4ZKIh13tUT+/q4y6VZRd8JdOR+SRYYozF1B9IZWSdNy1plwWcS+5AlJxQUMTxTAR4CEu9sS2bFhU9iHyBbFeG0cAIy8HoDllSUMUiuXuCcZJerAOFSGzjIAJa0IK0L8go4g1QuoFrBZmNyDsKeC5K1KxjYNJ3zAAYcWHolJj7sKXBgplYCHCSQjZdlgY0tT2xEOp6cdcryOa9+TtG22zZV4UxcDwuLMaGpDbZSlTFoaoON1csigII7skl7/8aSYYBAq5aTj98cVQjaPwYKMkL/RIYvjOdbc9RBAXr1nrUr6cbJ1iAXmmGZivFDIjiIQXsKGkhPlIGYRhlogEB35BK10MZHUy342vqmqoXIHhhtq/TGFCCwZGcsLJCEVa0CNKBg94v96ZvAg7VZ2rytV2n4fhd6+gA4dJc/YDlffvoxPvzhN1R+fdlaZyfn/BpxszS53lieETAwKAoINgzgYCiCtL6Bj776XfzqO1+aoidRS/AADvb8w4w1GMEB25ORfd9ou9n1YumZgwYEXFDuds4BHXaAQFkCW1AObAmAG8IT6EHB3aqDPUtwxBCMJYKCPuHQnXXldp7ebHdxMqaALEiQghvbgWQ4OFhAanMgwaG2ygLydJFcKIGTtGFhwqL2WciARCVmZx9kcMwFT/UtjZ5bgS1V1E3YAyChcMXCjIUkhG/nwcBiuHhJfBD1UhiRtJ56ynq2Qx1AFnXtOVlsrtHc8b2PKUKlNpZMmHlGwwm49AiMhbtARJUNBFiQIkw9DUxtIGqZ6uDrXlAgu3pyo1GzEViyuCsumhzK3U5JxIwDAAopzqHvIyjQfBhvlM2ULBR6P55ByZMdccq6q15gaoYEanY1ta1jYzf285Ra8jDb/JOskpQWMMvunurWAEGC2B3Yc7+NagF6wxgQSQE8+2duFegAgsX00PVaWblsjzMAhdi8N90J4+bFW8i+o+n3R/EL4nrc0/8KCn70e/j+B9/B++98Se0mYtyIoczUT0eqKC3PBhiIa1nb3ZtqgQcNjyGt73/wHbz/7hwUAK2T2NC2foqYzI4kFfjgPTiw/zah3GL4xiCLp+xQ6A4M6M2Pds2jkDyIYPhWLMGgOpixBJ0tQd0Dgtnw3AEmPTiCgrtsJiZ17W/W14sUDBpAsF7nxMhp0SEm4GAzSjYBi6EdtUwHgIKkKdzg4AAloSbWHaqAA9nNHE/UarSrgoWtAkAVI7cCMIlv/UaEhYVByNTiIBDY7RbIdqu+y6O240uEJOQ2KJ2mTE3sn8Odzo3Ck/f2GpkkwLpQBLjkSarOOMVMpBZ0aAxKFN1HR9fDqDoYQYGVIbWEtofUVoBAAwgRFCyk7qapgQNxRbXcJPobBQievKwLSb7hlmFwV4LhYTeeDawqQHCdfMpiOGhzQH+aQvvP+kz6pXmCRYDg4J2y7+aJEriKnQDVepdqAdhvUvbsQYiYyAxKGaZeiGDIN3lkqqu9OsU8OAyPWx0Nv99TuvkwqECBAAqGubQDBf62p/9ffvYJvvaj38f3P/i2y69OVs2eKTIFep1obDorzwYYAAEcmDAPjQZEpKWNOqM7Af9tAxm6W6J9w8swDuAAbVDFjGfXWILZ7YF+EAHHYMAHz7jDO9o1/7wsQaAl72EJjL6rdutA1c3uOrbyHgz1oGCX+dEWUDOiPNrhevv2OytZCWSRcSio7caAxGsP4MA6X5pKV9Qs7oZARUZCrowMoaxNnbAVRsoyYkUgSa+OUQMTzUfMZrYNJAt9YRYferawyiKY8lYbGEhDJD7p/I729M38MA5nC13XjNOnnJfZsjRTJ9XhO+YAIPRCjHmkSWNwDAyMsSS2WncgwIq3Oc+PO0MQAEGCMEgCAhogWBUcjKBAWIQJKCiX3lvIwO9sPB8Bg3E8K0vgQKBWn58CetEJSxOO4wo5na/GjDJkt10bk5musQesLo3RRfoOrwUAe9uDiXphBhCYSNpTNy4zmwthEvTawdUT6MfotTLOj9laHj7u3u929BEU/MYf4v13vhQmhdY5yqzxWvbwd4AC4JkBAy82SEzAAz3SskadCcNBhUAIwnGwNwDQJhaCWiEuqLEPbiKC9naGJqdgICwQblQ40r1vAQhg9X5TlsDdEMkBARh9Zjq7/mR8ki1KVvcJKJqCgl1QpoE5APuE4NKWO8pZFyASRZGBBHDXxpy46/OUMtwXTMFB86xQF0aSsMWPpYAKQCmDSgUlEV4GECymex2E4Uhhz3au0DbeanXPiVSBTbdvuVSQDqYYYRHQhEZ2bOBJ+4wJ/3SUUUjFQEot5DR35xoQkO8aKxNjScRiRn/W9g4EtO0jU2BxCyIg8KBUBFfpEAELwbOYmvqgBwUiGB0UBNbAxnOX7vzaeAa68cwpyyDRIBLCGIhabMxgSJSVRdKfUANjR/PW7A0ABQkKEECyORKQoOyBCuIpeyAdhjcxTATiEsuI6oUpQLANC1fcMsyUdoSrguPafjeFgPAbLTPWbQcKYh13oOBXJrLLFoeD9fxI3h2U5wMMVJhLYBHqju2RlgnFvnc5MgUGDobrj8VAgX2z00MRdgvQsRXq/jUK8WMwoNP2FwkIgDkoiLrekSUY9L8zQBAZAwxtZzU/UpU1YGR1a/XzdtktouKFwaWAWY3mYl2LZBj0+i6LLFiALCac5fr2rIHtiOCAWMLoArpLYlm4ZWOWsZEYtW2UdLdq9DZ3u9gouJD2Y+eomK5ddnvtR5crv5ldo//cDnTeFHU879o1+ZDxAPZzIYKfaPg1XuOWvvda2c9H2t0vIiO7V0rtPFJBT+5JQAoMoGqExhJYuvOF1IYgeB/sQEHZ2vgtxn4FVQK4GYACx+OZEihnMNQzgFQA256JCSwSOQhj1c/jOOnS0fz1zStZPgjNbhgAgti5ZHBOc/bA3r+BYWI8dh9A0DoSwTaPR94bADoVi4MFyKFZG73JWj++764Y2ngHCjCRXUdeVsO19h4t8/J8gMHB4vPy04/xtb/7Bx3S6hrV+f0AKo4omfGW6AXbXshJGbOi2W9n76dAAHg7MGC/OwIEoe5+6C4Dw+CG6CxBUBtUe72SbnooxgiMceOtgTq2wBkTbovouLMKi6gDAgMOQ2FudSauoGUFVwZRbospdKeVAWrbUaS8ym6bZVFLkMhqqGhp4lmM/xZkcL6dSyFS3rGbgFFAh/c8vnJ3vjsPTL4XNQb7cTunVPHjtwx0pfbfddcZ2nWW2jYPUypZ4iZjMFJbgO290e7xu0bnk9P58TpNkOvr8L3fP3xM4UMECPYTAwEtGiGFzwIGLPkVETQnSZ/q3ICC1ElYBAI3I0MDBfrZQYGDA5YYEx6mHMfjmZpQxYIGduN4hmEBNZTmCmZy1sAXuO7aDRhP5zGG9MMKEJLOEU5BvRDYA7E1SGAuQXWnC8k11UK3fl8BCKiI9hadUeYVkGARHJtRZ+8CH5uAMV/rZ+V4/e/Ly08/PgYFg+ySZxgAwuEDXH/Q5wMMtHCo8MvPPulBAa4gphnjoMXDdAJtQioMiHPn1pg4RodBB4RGH/mxDgwA97EDeo9rgADYg4JbLAEGt7FgXGhW4swqYBDAgd3SBJO+cf9hRHBwpTF3bMFQb9fNce9aV9Rp0HYjERSpztE2KNguAg4gC9ZuMVVwwEZ/poyVsodNlnS6smYXbgBhYRH2mSRWPufrunEDC/K4rPQ4OWjImZxlSGj0+Oh3H495FD/uBf+lsH+utWpQoKqfxXCvVrP8Zwcldh2gByoljjEtUXWROsHfjmWSHApJ9e9JgcGak+yuNVOkRHekDji4AFZ9f2K9dksa0KkAbPyNwh9o4YjtuSMIOMyaqGO4y2pK8xgFnTuiuou+ESiodT6WvVQw1FB225QJs/WA4HR9N4+MNdh1XXcbn8+Tuex0Oxt7JXVn9eIx+wNpoyxo0dYaBQic9b3HKaewKAyqBdd13AAItsW/wiII89dAhHnEENm9JGlUbOsYuOsIMFwr137x6qcv8TWziVOmeyq/3mBzywFEXCvPChi8NSi4ed3r6Os2SJyAgPAcO1ZA398EA/E3twDBrM7aXm9kYHiFJXBXMQyAQIXXrNVjHvoIDnaPGtvR2AJU0AAGzKaAmBtTYJRsbAf7bL70thgv633gIElQFVukxGMhe8TJXAmFSAQvkwpjQjbWpGoiF/VgsHa8x5huzy4oAEmk8fxlEXQffha/fDO2u5SKp629v2wCAC6l4qmI0L+UKhEAa/vsAKNI+GNngRQQ+OuVZdHT7KT2aq5clAgpJxfea5boiDmJsLfPJ3tPhHVJ+h446fvKEvwpJcaCBNTmFigugqlTBRyxAMfGmr0NjIMC/d7BhwGBAAjkd4w+eZm9lvtAwXa5PZa1GDgww2wRvCmsJSTHucUrALgTcrGMc9qOAWj5Q9jYPZnMrGNdwkbD7Q+M7enYA9J6J6BzaxwME30zxrWBg9gmAMzp0I8YizIDCCgdizAHCQzS80zojkDB7hM4p2k73iqdS+K7X8JdMmvc3F4BB68+/eTqpZ4NMOhAwc/+/A5QECm41ngjW7ArXUMfbW2HTpwxAuPxaPcwAQP+uwgA3oYh8HoEUBBdnIYgKJ2B4eBxUBF8ySu7v7jT0QEQtOYPE5daXoIuJLDU3NW8kfKlse6x/UI7uEHW2A6jJ0pcVG2bb8xBKaqnrXNwALSFy56BUgcQBAwAbOAAustP8zbyzG8DYGDegwVLBbwViWWwVQZR0nC+QErs1bM+MVBwKYytFLy+VGwKCAwMXLbqYYJrqahF7ls3eVIx3xB2gUt1Npu5pSOeZVh0MGCULlRLpUKeiJAWAQ9pSXhNhJQFLJh1/7okPCowOGXCpSQ8rAlLzt45Bg5chZAak5DJvAOaoaDFEDhiAUYA4EAGynhRU3E05kDf2x8CIFB7gUPPAwcFtdnHjKqwe8ayj/8k82GJx5Q1sGuhrU2sM8+NqdGvlvL59rxmUiPcABAkRoCqFxASu5naztmDK4aJFv1Qn3oHDqy4oLwPIIDRswiWHGoGEvS7yCYAup7GmAI7wXxbXrz89JUEL/rqd3cu9fsd/yDHroEDrffLzz7B1378TXwR70yeRcqzAQZW+Y4pOApzfM+1TDgG9x9AOq0vB0juiBkYd/A7emhUIQzgYRSG9gxvAwisrm9oYOgeB4ElMCPDFryo6aNN2I0l8WBPoJJ2amcwtum1Ovrpb9H3w/1YIhbBd1eUfAG2xYqTMhimkqgbkBYVQkkiIALSbt4+1HbcoGCkSYeAoShYKNpWEt63glP2EL4S2hceLwFZAiKJTJa2sp2/gAMBBY+Xgou+GiAoW0XdqgCSwigRJKgLoHiGCmtQaxzH1oRFfMutOCAgZwtEjgkAIAUDOUv+hJwEJNRFwj9fSsV5zXovAQOXRAKCQmjxROotkCXglIGCc+5dCSnBDQVBCGzBMQAQoNDyknQpkeM5UDBgNjGTIFydq57axbR1QgHu26xh3TCuoNHAw7+czB+u8DSY3amNERiP1/4AAKhKjVzVBSZU0mBgRMgKFEACulOMewAT+oSdYWLKHRB39gDD2hrf3wUQyIU6OVgIqgYDD9zO9QBcg8tgcwAOLUNp33iT8uGffMMj8ratpwC8n3csvPzZn+Nrf/pNfP83/hD/xz/6Px2e94yAATVQ8MG3FRRw6/zD0gS/d/IolAwUfPYJPvzjr19/jnsAwTV2QK8xVRcMv/dBdg8oCHXqbAmAjiVoqoO5geFVlqA2YWZUeXwUBbNezDAPZl0IuW404JkuZyPjclAoJXC0gjP1CLCnX69dx9G3LO6clvZ7Q+cePEYoT6LNc0SQAUwSmlqs4hpYYBg92zLeOUhAaN/wKrkSCJWzMwECDAhEsuNPIKAANVVZnGujuWMx24TIEpStomxFci5tBbWI7YEwCJDXypJcSVU2zMXBGE9ihBhAoJRAlPVVciOkREiZxdSjJvGwyAQsGRwcFTckpFKRk4KDMFhs156UprZog6ecsCRhEs65jy9gO/rRLmBkAG4DAMABuqK5qOaaheieJS5zoGDj7trAPBrLw/pFR36uVgwIcOTp9JLo96SO/dDGqY1Rv5zOcwl6xH6MTZXPAuRElI/sASHda5jo7IGEjevYAxw8lJ7TpXa2NjCAALig9znttgjKJBhcZAbI1gf7fd0DBNu9I9zjoHz0Wx9pmOOhDiT3FN7Rvht7aChhY7uTj1fKswEGLz/7icYp+I5kSTyil7yEzpntTsNiDkqaW+Hr+Oi3PsLf+MHfgEuymf3ATGVwjSH4RQKCK3XaqQ3e0MDQWAJ5ZY1N31iCAt4tFuMmg7m1HCDg4L8Nn3nKGbzpxI/z0BbRPAz9IW7/1WILSkzoYsdMTwmAavLJTEADnwBcDzljphQ4yJ7RdvjkAMFtOaq0uYRkTp4+eEmEx5xw2UpnHCWzveJSWtS9NKIEQCjfqtevMgZ3oKBU1CJ6b3kd3EFnbI0KKAcEKYtXB1dI8gYgLnQpJbl/TUj6TONgMePEaJB4WhJOSQDBeUk4Z8K6ZJwDIDCd/5LEmyCmPB7VAAQbQm33D4YaDA7zFQeAf6YOtPOUHbi5G/w5xrKoxH7x5dY8N3ksScg01wIxUBt74DqL6Llwj2HijD1Qgd8BhEmZsgfAMUDAABBgqgahO5o00J29tk47PgCEbo3pn7NPiBSUrOP4cPl2Y72i4LJv8vFGeTbA4Gs/+j189NXv4r0vBvolIDQxsGHswIJT61FQHoOCWRarWbltUHhDZeDv+0VnBAW3Sq8yAHaAoGMJCJ2BoQKFI9XBbBcbF4qwNHoxMi1OhSPVQRdpb1Y5R+/hM8lOggmgZQW2i26EdIeQ0l5opb6NaFxQxx0Yx3SwcSGRxcgFftfuaN9h3y+trgYuRA2xzxGg6Z+ZUKoYNmZi1CSGggYQllLxGgBR9bj8yWQ226vYCSyLGDPmTVM8K82fksyLXfS7ys4IMNurggJnDPbjs4PSCWK8yQUE2RlWVSt0XUOk8k2fiULIYTU2XBexM3hYM05LwoMCgwf9/pzb+1n0wehCuMsZYdb/R3NXKuvjwku3Wx3Pn8zpuAbYUSIZfwWiCuD6VmOZllXHcJjzXcfsBUsc8TSumV7F/tjhfJdNtZoatFTl+oRue+CqBZWtYpioBr7XDBONPZgABOBg47SrjAlrXZ04CvhYKwlL5iqGGCVKBbiH5rexQqn3bLtZwkzRdSXkf4SzBvfIM7Q4CFE+9vfZl2cDDFxoOxIMDakN54YbHa0eJkoUkICCgo8dFIz5rvUkIB4dF4T/NkDBUK4OuFuAYMISNMPD26qDyBJYkJ7dAsHtEcbpNpZklO6Vc/r6hfro5JO5VBo4GA0RswiuHcUawAAAtQ2YCHivV6Qi7R7Hk6271eS+bdw1X2rWnbX1jxg2CotTk4CEUgkbKzCo8pooSQS+S8FjTqBUkCQcotO2Y4nuhlwlzHLNFcTJQQXXirwkFCxA2ZDyiooLElYwFXASgDBthZQ6toBSEsZA1Ql5SUpeJaSUQGpnkJJ4IyxLFgZgTTivGb90yjivGV84yd/DknFaCA963oOyBA9rRiZgTS3yoAcZSgYGFAiUrQEC1GYsGHf5wGRuvkkZ5vGU8bPxTMBiBmd5OpatX7rxvAO2gRUc14I7SoLMhxRsh2ZlnPMWFn4ECGrSKwoAJjFIuKZaUDUCK/1AtfbswQFACI/yBiWAAxfwaLt9Be8tLLMxgxXGHszAgVyahzan7q7xlbq1p4Ipy/XrBiDJUsP7udYb4v/EQcH7f/W9cD3gCi54PsDg/S++L298Ad+jLJ9osRywBAC11My/9RG+MoCCIOtul6g+8GM3QMGtMrOd2AmvHvTsAAEldLYEBowiSzCAgpnqwFzpDBTsAEH8PGmwqaFhX4t9PdXvWKKU6YqjfIRv4FlzZyy2E4D3Q8dYDBPV2YD2gOHcoY2HPtjtTg4FyNg44RkiKLCFPGXZPekrKKtufsGaFxQQtioswkUBwqVKkKZlqxJ+13bbVDv6PSbzSam4eoFSceYgEaEUMRasRcIv15pQNgnyZGoFZxBmjIGpEqiBAhAhL0ltDMQAMS8JOSfklZCXjLwkLEvGi1PGeU34pVPGi9Mi4OCc8WIVUPCwJAUE8ndWNmFNwGpqBAMFSWVq3UBl8+BCb5ab4EofWj9KhfuePwDyroJy15ccNjWQe4exDNwaz2Esd3N+tubtmYRba9sRkzBbKCNAkLVDni6DUK6pFrTurlqoBhCSsgdieyB9NAEIXpMbYOye3bzv0K+BA72crT13XHe2TA4PByJLvydZMZvdWsLUtJvSHhTETWL3ui/PBhgcoqwRHIzlBij43m99hC8HUBDp0DAMMOvO/UOq2JyBhLtKR8YOg24QLEC3A90vDoTocRBZAhPus9gE9pnRDAx3oCCSJrg2/FqPEPYn2kbnepMolZeyLuRCuTcqMXfCuqM/by3YtyZ1uO4U4IHd3czOd4EZFyYPzN/v+LrQtsbsUALSqrvvDM4rOC1Y8oqcV5REyJVwqYzF/kho9CUXYRFyESo+w+MD/ONHiQmw5A2v14o1Ex4vCY9bxbYllMWMETNKreBSUUp0Z1yw806w9gjtHL0RZOgpGMgEygk5JaQFHSAwluDFecHDkvCF84LzkvBL50VUCMYO6OvDQnjIWQ0OGxhYHRBotMFyAWnSIo+cWS/NnZCrewV00Qav9Jm8pPY+7titX82/nva/3R3vxlrTs+x05Afj+laMkptjnPTJeTim8z1hn8Oie2z0v6/UwAEgm4ukF5KQAA0UmIrBjDy6mAdVPQKcMShgThOAwB3AaurlDEzX7PsYFLnWsPtnhvs138XE7NmC+H5cOz2Xyx1ybQ4KaHf9o/JsgEFFE88yDWfofY8SR0DAEJfEr//xh/jub32EL737XkedRQp2yhocoeixvAkFGScvW02H43bv+J7aAJ0CgqkOex/BcLQneBNQ0D9ftPDucYsNc//uanto6FJJEye7Byg4sN0C0HZb9xh1HbVjLKOayOnBERQ0QLALsmTXqM2C/8hQT5iPBM6ahyIvInTWE8wWhNIKygs4r6C0gJYTcl6xJGEQctWMi5WRKeOcgXMivE4Vp0Q4pYpTqnhYE15fKs6LgIG/WBJebxWPl4LHS25BkQY3RlQxUGQdMGKraO0edrbapqQ2AyANJqRRf8wtMS8JKScPVmRqgxkgMDuCBwcEYmewkACCRY0RR0BA2xMsi6H0S8hmeHny/uJa9PV6P0H7yQwkHGznxceJA4Sw2+zH3iCkb4zBBgjuHNuHmwSRunxDreDzUm+cIEaEnWxkNR0d9kmdgOM9ODDDxJQIll7cQYH+OkZM9HwLVYxrPSET8x4gWJtFkBDasT1kaMe7bQGG39/zu3Ez0pqhqWCHR1ObTJdrRBkgBmukyjGtcjQ0fM8NDWl6r6PyfICBDdhONjZ0BfTCprMCJ8kG2IGC3/wIX37nvV0LRm1/zx7ohLtX4MdzDUzYMf88magT/+LdIjMDA8GorQMEBhTQWAJnB9DAwZuCAgADYOpxLRlIoBBNjuD2BUd2Bh4r3dQJEnx9vhh0P7wyce9B9+OOY9RlduyBgoIiIZmxXQCW6HXQnSe7UVsz5Ns9lhpegUhsJShJaNu8AMsKWk7yuSzCIuQFKE+gfFKd/Vld9pRB0JTMC2U8rBmvLyL8X28FT5Xxein4wprxeit4fFjw+lLxeNnw+lLxeqvYNPDR46VqECWJbSDxDPrgRkcBjmKQo5Q1mFAOcQZWZTdM4K8J51WAgDECZkdwSjQHBKSREhNhzQoItkdRF5QnBwRUL1B/TPD2JP1UNrCF0tZ+kzod95Gp4pwpUIM/1AK2fgP2hqxH+v4DFcS0HG1CZuP9yrrg+vjJHEkQsJOYm50BoK79cryG/VAEB9Ma3AkOHBSAkRm914KxB5bOmesAEJTKd5XtsC6MatvZujqWa/3zJmAibEJDk+zCxlvxMNNoXjICEIJ80zaw3ApmaNjkWwMhR/eJ5dkAAx9gaOAgUi9yyoieWwcxgD/76St8408+xHe/KkxBCQ1nP7Gw60EF9ibkU7uY6w5N0AVw4DW5do397qKjDW3S6/edymAABIw+G+IurHHFLj7B1KZg+pzyMjIFLvypGRqZm5idB7SJsKu7BhchAB7fnURreZWx6RbbyWS277tr9IxAp2+ugBs9ogZIrihe3f3YhE7cjW4XEaYenrBvSQZkR5qy7LhzBmUBAJQXAQXrGcgL0ukMLguQT0C+AHkV3bkyCGsmXJIChCQMwkKin39dEl5vjC9sCY+FcSlZgEBlPJUVjwoIJEqiBEK6aBwDC6Fs+RMqt9wJUiXuXCI9p0FqeRFOWV7XnESw54wHBQjnxeIQUPAyEPdDUxnkwBCYHYEDAmMIIiAoT6C6oT49CmNweQRvmwC5soHLBSil9c0VpoCtb5a1sTqsbM+ywiJpdqoA263b+hSTlHVCexij07E5jNHD74drjGsDsBNuhKbuN7agsXss4JjEiDCJxaCvCw4OgEMB5CT4EThQo0RSUOAsQoVma4TYqxB6gOCqIEMnuQMJAOYbiDdhFsc1t2vb+0snrIMMGx/NVnMDCVl3U8YegCv+7LOf4EP1zusNDWl6n2vl2QADa8zEDRx0O3lgt4WNKOqlgoLvfPUj/Mq77/V6UtgGnrrBHkHI7vKzQkL9+NPqTrMDB35uEHCTAbfTT8YJ74M27kauA4IIBLpUyVdAwb5++0OBKO1AgceVh7qj6fcWtz7pj9olRzpCfu1GWQiTfXiO/S4NQ1sfoH0eAUG/ExHrYMgCpphAIreFZ3VDtqZS4HKRXalS1ai1qTuOhJAKEFMr0LKA8gpan4C8oF4egfWMdNoUIKwCEAYG4ZQJFzNSzAIQ1pzxhQUovOCxFFw2xmOxsMjAU6mSMrpAX+V3RSMRlgEQzDIuzjIpJoLGHkjuLeApizVi4RicaF0IZwUDOe2NCtck5lh7huAiIEEBAV8ewZcn6Y/L03GfXFMfxD5hBpZFReYCImWEiBqI6OZhbnr+GGp8ZPYOhPZujNo4HY4dJczZgYHdGuOrJyDyWVwKmVEUJCST/hbgp3K3PvoW5w2Ijw4cKLUgXplNtcAMz9ZoQrIDCCxGuxwAPGlYZrPz8g1E11aTB71nDe76Jk3P3f02/I3CmnX9HR+jZawUu47IHrz67Cf48E9+d+JSvwcFkfU+Ks8GGMSBpZ4voeK9xUFkUgoDH//0Fb7xww/x7Q8+wpfe+UpISSvFu90prqPSGIpDv1O7IvVXn3ZSvNWhHvwA+e/AQI8cm9BHUxegDcoY2piBORAIT1CHz7EKI0tgrMAYYS6H7021MDYDoCwL0O6qNODuEUd6dLfI0vx33T118XA9pe1G2BdvagkJYHEU3PrbL1PVrqCIy5npr1UIQQ3dhIbvhRGlJDR8NuPDBOQFnBfQ0wI6PYAvT6D1KQCEs+x88wqkJ2A5gbaLeDLkFbwsWJlQKtzVcauMh5yxLXA10mMpAgRYcjJsamC4KWA8yuhoJTJrQLPVE5aosQcJYgdAyVQBSTwpsgABGx8Wg2B0OVwIYkh4uajLYW9LMAICBwVPrwNT8GZ9IQauIcNHMU+SBgZ8vDlDYCxBiCg6hB6fzdu7xqmNM2APEu7YDY9qBN1ve5ZE0qOZpVOZYYhBclMkUqPEplow9sDK0ToxFltqDRyAhAEqkPXB2APSqrfNhQKEMG+dRXAgoBELlU1wRpmHJ+J6Y7kfQVX7/bW4OEcXbUxB21pEKSGpsdHVGwT85DNRf4+ggIdXAwX3mMA9I2BgtZVBNC5Ifl54rSxMwe/98EN8+4Pv4UvvfsUXxFh6V5sGGiI7cQcoDh3KcHHqwuqWjmtGY+2BgBy/AgYCajQQsGMJJqBgFvoUCItH+BwecQ4ItI8sDG2MOZ8DKCAct2sDB+3z2D7johqB+Yy6i8WvRqQx3AOEq0V005VASN2zMGSXRlkMITll2YGG5xOKuvqfZM4TIcS1glWHZSyCRS+kTCqYEtKqtgZ5AZdNDBAvK2g9C0B4eg2sJ7VDWIHyBARDRaQFyd5TVndHuG1J0ciHX1hFABQzLNS2Y+yTOgH7dMszUxlLv0xpn7nQIw0mzW5I8MiEOUlI6RTdDTVVsYCBwaCwXERdswkQ4O2ioOBRjkdAUDbUy3ZHH0i8AKosAGFZBQyS5szwcWMISEM/K5AQd2B1Cda2H6OMAn3Uy/vHKQDKDYz7T8POOBqERm+dA0bCRV7ow6LggJVOIJNQDFRlD0aAYLcefQF2oJ9tc6fXVHCQNEdIQhOOCO9NY9PWDjoECXKfCVBwRqG16f1lskG7UWbrsR8P7QE0zw/StrF6f/KpyK/vqUt9A3B7WWfXi1k0j8rzAQZoDTIKbfveinXGS23UP/zge/hralPgBlThelGNkMKx7rO+HntDmNognnG/PnxK/Y1AwL/fW6AeAYLd5yugYHw0Z9n6R232AbgOCFytEN7795js2ocb7+sNr7vV+1bdgXkXUOjbLmteIg8wJLv+C6hSGy+seRPM+Mus1VOV17LBss/FEgUScxNMUg9N2sQEwCyxKyhtHUBA2UTgGUB4egROZwEIeRFvhi0ri7CAfMe6glLC0oXATl0cixmrNANZAJw5mJUOzlHPCk3HhP1Zz1qa4s2iEl5C3AEFBuZdUDZ5fXqUnA43AEG9FAcCsf2tDyiTJKbSkdMlh9Lnl0rm0O/Jd4zmDuygIC1gf78PO25tDhzv9Lq5NrThKCTb+QZ6bHGcCEPzptAVjfUrcwiupIwBGNWAgNkFzAAC2tSN6+p4Wyuyhu/Bga/p3NiDKCjjTrpt2CYgQdfeHVCwugO9auFt1umwRoeTMUyXbk7NPocqdSqFP//0Jf7gR1/Hd74qhvKF4Sr0ri3D+hevd608G2AAaGV1MJlKYYey9dirn77C7/9IQcE777l+HWjoCghIOVx3d9PxWFQnRIHme87wVNfQ5QEFPgMC9iidMAz1PRKIM0Dg1xoGZ3wU2wnGx3fRHMCAHI92BMeAILIErUbAjgbtUMhB/eu83rt+1g9RmLnw8mdiZzMS62siZPWpRrm0xbM1u9RDPRAsGIsIhSoChpNvBSilfVCg0RixAuLGKAIsLRlcK9JSgLztAcKygi6PoPXcezMYSHA9tyQ18udT6jtb8KuRFk3zcbmfCNfK0Lc6ztz1qsoC7tFCPYiN6u5rbceiq6HW3bwL+PLox0ZAwKWgbuI2WrfSwMAVuwIrXZTBZOodCT5lrE4zEl1V+K+qAlJQ0DE2mGYrjXMTuD5O5S17yu/dfLM5aNuXEDRn3kXV+9a2M5IZsT2PeCWoWikAhATZxVe1WEzaxTb7DCiErp8uhTyAA5ng8jmyB6RS09b8biNn4CmAJNLjc6CgbF98uDfxNhsqw4cgobVjX+cJ8EarAzPjzz99hb/5p9/AH37wPXzpna+gGOCayaOJDLhVnhUwmJahFSIo+JaCgtkEjOdPVRLc4vub2mK2LDrN3BkXXtGuzUDA7ngvCO39SDvuBlg4Nv9NO3cEyLsnjrLZqOGw+3uTNLVXGYKd7mIvjI4Awc67Qo9F0NOhaF+g2Otsz5oB1KT6VgUINQGZMtKiOuRi+xO7YJXdujWxqg4IALIIJLIEONtFwIEFQxqKh7y1XARIqCggYgUHDCoFWKqEi1Xh56oGs5g3bwbzs08SjhjUfO3dYJdi9MVQs3stseN313Zc8TtfgNuC3EcdDEGjWOoZ40R03gXm+WFtoe1iqpu6bT1DEwDBNGS2VcvsDAxkkbSheYwgi7cIrScBYMrKGCjgvAL6arkvSm2AYBZufGS34s5/x8yF9SgluOGg0e0NIKD1dWz7rj+K9yMpgwCogJoABIYwBYUBSgoQbNNhRoqAx0PgSCFcKYYPgMAe6DEDCEADCUlQiIMECs86ZRP0+AgU5FkbWNi1z1iGTcthlEv0Av9IYO/YOAb+/Gev8Ld+/A38e7/+Xfy1d94TFmfYEP+85dkCA6P6x2Mff9pAwZfeec999ONuOZaRvorhe+NgBeK4s8kmC1qnDx+9D4BuoX1jIAD4BOAw2W6CgfDAkSUA+nFPRB1rMIYvHpmBEQwYAJBFiq6zA5HOA+Y60E5p3UDBkbvlXeGbZxNJb5HBIAI2YmQmJGJNvqNjzAzh8toAgP7ch8eqr5Z4CJCdezwHQS1VLXgM+S62a/VBYHFhVGxIy6IxE5QGNoBweRL2gAisBowOFDSQkqk9XBgepPD9J1F8/Gn7VbPPMBo4uoByba6G1hbWDsGo0EBBVwLwImMBALHtoOQ2HmnJDRTkRQDAsvp7+xNQsIDzeQoKol3HxpCU17oexXHqUPFgnJqaxRi8bK8gSY5FPUAwRbRlrKY4cmfzj3X+WT4QIgcJpAAhwYSYrJekxoGm1nUWIYAEQJ5nlLNH641NfZm3CooCQAAaSIiAYAQJYBywCdoaASgAHFQuqW8XAFMmYbapm6h4Y73G+vs6PcilP//ZK/xLP/4G/vavfQ+/rPIrgVC0ngliizFO22tYZlaeLTCIxRrl409f4vd/9PVDUDC23ZQBeFM0poq1Fgc9XOvQYE7OjPLbBb0/b3ueGRCQ726DgXDIr7evQs8IxHrcAgNzKnMPBrpslJNi94ttdgQK4q6roi2014zn7NaxSzIIG4ngNypTjOL00ZMJc2mAnE/tAhR0s0ADB0o1c1KdMwA2gZwSco4Ut6aoBdriNGsbXeFFFZEgoqS0CqUEKltQAQR1QNCTX8tZMaqUEAP+zKj3KwtmVzpK/kBnf+t5YhyI4Pbp7oYKCOSxmqcB5QC8TErm2B4BGBggyIF50Vc6PQQwcJ6DguUETgt4OQNpQQGwVeBSNQ5EVe8QZwsAKFiIY9Sa1pqyM95kZbR0nHoGQybfyduIzFB7AQcHod0nc7EThiRjzDwwLJ+HsQiyJQqgAHeChNDvR+tsBAdAWMcY7ZoHIEHqyR47IYKHyr1K09pENnkGFIa5yOwdcWjI+QsC1gwBBf/yj7+Bf+fXvotffucrQQ5oe9rm1Z/+7W//rIDB2AZxcI2ggCegoN8pN2SazAf2jR8ooWMIDCAcMgYHhnOAD4KRFRgBgv0WuB8M+LGh/t0j6utcr9kmWHQ5jBOt4z0iGJgY/fQ3trYZpHZgUY5AQQGj5XeQP1toNw3K0+3I9DZWjC1YMiGDwDmBFGxwksAuNoFYVQtLWmRWFV0wqCWysWhlrHkOXG8f9OL2l5cm0KIwm7nPjYXVYp5LETVyCWmir5QYOnomSP1V6Xd7L+02f75ZaaxEE7zSVqm9D6GGPVpiENq36oKDuozPIFN0YMGGe0c30QgIzF6D1jPIjDzVC8RtCvQ955PntdgYOzfRoi6g5hW1lYoCiS65G6OAj1Mbo1kBLOkrV4A1KZGETiG4H3cAB21pG7eY1+alBhNC8d3wDiQkuPGqsQgGEooKfgIrkGggQarGnfD3R+L26ktq1yTXQQKAHZtgdgkECHC6wiaQC1u1zaDWNjyGWt7ZQb1FCbLpz3/2Cv/ynwoo+Gt/5b1ONgkBJJVtESX5EGAFfuiwPBtgYN0w22XMQMEtP/2BsW73OXh//GCpTawdK9AuzuEP6JmBGRiYqg1+DiAwq88MDADX2YGZuyEdgQEOsR3mVAUswqHtQzq1i9Vb28FfuQcFG+8X241FUAg4kCvFxTcD2Eii820MCSdcWdiDnIShRqP8WFULyCTgINqTUAJtTV+fcka1hXRZ3EjOAUKgwWkMtOMC+Vrqmlb683oBGQX+Ttirl0Q1YFCEfjca369R6gAe+mt33ekCtwl/EbzheJKUy2lZANupJ3JGZQQRUYjHa89Ku+/oGqwW+wY8qAEXC2IESgIEiHpAsDSbgnQ6CytgxoXLCUirGBkuJzBljQfRVAfFQGqVcSRBpfT4MEbH8QmEMZqEFVsygYvEgfAesO0wi2uhGAnKa4LBhFDump9tw8MGCAaQkCkhEe1UDeapYCxCBAlVhfq4hjH69Wm2dsVDBWyaAP3BsIaBm2wf2AR3hzQZoA00MgnCIowOmMO4vwMUKKk8+UIu3YGCd95r9acGDsRbZA4O+nuJaviW6Ho+wOBAShso+MMPvucRDZtA6UGBCYh4reheFY9350wJhYDLJi4rQBiz4f09rnVHrmKjrQDCb66Ve9mBIzAQ2YHekJDR8tkPiw2HWh0+IPlTsLp8EteGzhHAkLUN1MCQ+2RQmy649icLL+vCu3+MjSDJhxIhV1mAl5xQlNY95+wJg1hXV4+Pkgg5ZaT1AaBHkAW+KRcwZXC9iGA7nVGfJEIfnc4tbLLlVtDQvGLMUDpwQEBvLBd3dQcgIsZKkOOMupUeBKjLXrlsDgDqJVju16rnSGZFy4vAJWRVdJAQetLbJukLqWCXv5QVKK250fYpIa3ZAUNeF2m3NXdgQSj+ZhswZRmCsPdnivMyqFn88xiKmjRuQV781cNRU0I98jxYzmJkWAUMXILqYFP7AuZ9MCkDAzZGx/EJtDG6MLm+XsZh7cAB6SYo2S5Sf1+5qRPkvIN5OpujKq2FYRdgMIIEt2cJqgYoQLimanCQoAJ9XNsCCdu9Th4PAFwVY0aKEShENqHzcIjH/IIYkhrJRbwJD4EA7T5ZuGmpT0MH0a7r7//sY1cfmKF8OwcNHBjbzKMd3BwczJ4plmcEDPrPzGZo2ECBDZ6Z98HMLc8FZHwfvo90k729jsRCh4W/+EwzQHALDMQn3zMfY73GQbJ/OrMbAO4ABNfYgbodLzJmbT4+uO9CEmARIjW6nKvZmbuGNkBgbAGjGW9VFluCAg7AoIqOV2naFrGvb6uSCKnKIrxkgLcitgEMAAULqcGB7cg0c4zUUo0SlwdwUv96ykC6gEoCp1UAQlpgrnZQIzoKFvbgg4RLamRnfUxX9OzkrFVphtUBFJStgC/FGQHeCupF7BzqtoEvRVMuV/9dubADDTvOVZIpyePtV2oT1skAQZaMiibI80oq5C3TYhYQsCzimrkV0JKRtgReFtCakRegbkBCDmqIgf63Xb982d6b4AfEtsD0xSFUsae+jp4cQ5bLal4HKbkKgQM4MHuCpjboQYGAgYrLdjw+x7Fp+SfYkgZlm+tNReJBmHQ+EAm9TiqII0DwiWR9dW2+Tuhyc3ElY/ciSKgbOC2uaqCBRaisqgUDBIOqIbIIM4Bwbc0T5rdfd4kGoDCCBG5MAhGuxkmITUjh/357hvm3JGCoDvUgAP/Zz17hX/zxN/D/cFDQ17Fby1V2ZL2OsQaxe9/E3uDZAIOI0yqATz4T7wOJaPhe23HfuM6OLaB2vAMLQL+T3l1pvNOcJXgbQDDGGvA73qIG0A8oavNhV983URckexr3Ka+wsMHTxUXf6wMNzWTPxrCUysJ3qnCzcMN7AtQb1ncR3kaaCRDQHf9+0fXNrv44EaEWFi+ERGLLJ9t0qfEGYKliS6DggDWhTOu/YHeQBBSgJBEi9QKqiwj6WoB8cn98Yu4yMVIABp6uOWRplMcKwMEs88smv1W3PqIki/GGwCJoYCWuqLWiPl4UEBTUx4sDgvK4oVxYv6uolyoZFTdhGuql9sBgMhZt3BkwSKuqCZaElPXzIgxAXgn1vCBtBXkp4PMqbpmlAudV4xkQuPbqBPMYiG6YzTYgeGSo4LffdV4Z8XsDBpoxUWI65C5YUQttvIpRZ14lcJGqDq7ZE8xAwaYMQUxMNR+bMhQTE7bConphFpq7MHJmWNbYbqpNBYRNmmj3Y/P49pwltTsQQLAHCagFFjNDAJmsKLa5MDVDOQIIPvP2ACGCg9m4m+UdsLEotw1qB4uRQLpWGiC4ARC65vD/3Zata/i46XSmJKzLP/nsY/yLf/oN/O1f+y5+5Z33GpAJbMKsHKyKvtabnPz401dXN7HPBhjEWv5Ewxx/56uS++C2uOwBgQMA3xH3hnUI30VQ0Iv+/cO9LSiYeU7MJsKR2Vdn0TABBfeqDA4BQVQXOCAoczDgYAFtoemayowzbRmoYBbmgHhwnQw/G6/E3I51Ft2qPpDf9LH+7ZhVy+8STZZrW6JGcCCX0Osw3O7AXBpTPolQqgWoiwpzAQZUNeqe2RSsPeMCNP99C5o0BQ0hZTCpOoI3CZmMsnXjQe6X4NYVtakG6lY6ULA9KhjYCrbHBg64MsomUQMthoS1ZfQGNLo6qV47JSCtGXlJDSRcEpZz1rhFCdrAegERzly5U6GQ2h2kNTc3wvWkMRpasilXA8Td/0T4I8RzAKgJu7SI1jrYHHR5DgJAYMqowNQV8W1AwT1jM0OzD4bJXsBYbLQOEqOGcdAJCGa1K4g2QcPcPTQSFrsDotBuESSwRUMUWyGmhKRqBiJ0aoabACGoGCI4GAXn4ZrYCVfqmARnEUzoK7USdff+WdeZaH/QmnoGDoAQRFqeRcGBff7PPnuFb/7dr+Nbv/E9/LV3viIsj/5gZEDuKR0oIAEF3/jhh/givnj4m2cDDKxjXmmlv/vVj/ClLyr9cmMnPdOxHyX4+fPPXvn9DBS0n9+xY8f9oMAMJIE2AUZAcDTw5/VsoGBkCd5EZXA3ILDda7eg3AAFXBorQCwLL9syphOLK5hl52Yl3dEWM3o7lmisaXo7zw1fhTko1i5hZwaLZJhlhlcwFpW3NWlwpCr0b6KMnLNUi4u3EUeGha3d+kWZYxvG9jXQYCyDqiN4u/irRwT0YDxPANRlrVaUwrpj7ieDqAjgrEK5VAcF5VLBW8W2VVSo6x23HW4EZOrw5XEgVhDSYwEKg5YmzYqqGOSeSQwhwypl9gZETdWQTyvyWT0CQlwBCrYA0Sag2/2rsGeL+AgTZgRMBBxiNEhLeKTgoELtWhQQxJwTpsoyV0QDBSOTZbYEh6AgjM2r4zwffw/0mwX9Ua8qiGCgtjl9OH817bF83drNQIK0EfcAQef2EUC4S8XA1NrmABzMSnz6BhKadB+DKM0M+66Bg+tF1o5Y4rT7/R99Hd/WMP1ts6Fr0RtgAlu/gQYKPvnsFX73TyS3wr/xH/zrh799PsAAkvvgG38slf7yu+8BvHfzGdu1R3i9oJyBgm/88EO/X7veDWF3o1wDBSNLMAMEB2x8/5wDKIj1BFpdZ0aFFn/gusrgDkAw01PGh9ZFonlyJP2N5a8HXJ2gdtkWiXDEZKQTVqx1CRtU38qkbthsfERoq7YLsfeJ9jPRdmbyajRuReYswQ8rsOnilarqUUlwTiagONjKSDl7m3vUNd5nhJupZRDamWtpLIMCNgMEFBIIYZHsgpQSsu6SSxijm9WvVqAICJAumUMvS60sgKB5hMix2CE2R6SmlZUSrxV5L6bU7gCgTMiLsAFpyUjnFcvphHxekB/OyOcV6dTHECB7XVbPEyFAIMGSFTFMsEfBZWxV05H7MZpnKq1az2oylFs4Y3ed5RZHw90RFRRIV3LHZMWSiDyIzREgsGOk7ooG7hyMYb/5gR7rcKCBUe+yAZzenMNVu1hBfAD4zhJcAwgpI40qhhoAwQAQ0IwBfK0cwcE4amfLjn3fAQRqng1vYvU/LVfkgrtA+j/gu1/9SG3iJMV1ilLK6tmO9Jed3D6CApOP73/xvf9uqBJefdpST37li+/5rtwopkCYBr3ObLbss/6lRPjJp6/wuz+U6/8LP/gbDRDc0JXDzhu7IbAFwJ45GMsMFMxA8REouIclGGMQ9AGJgtBSipt8JzHEsR92vv1OpJumrV0Yspu1YW/ggKn9jkSws14r1tXRcdAXUjiedaFgf5XxsKSkKoUmyMbFVzap+7FSlDVYII+3qSV4gT26+ZhLtGRxgRRL8jkzQ0jI/uDXokM6cBiBmQIDLhuwCCNBZ0k5TJdH8OWkGQZPwOPnyHkBpc/VQ+BpV8dFBxnXDVwT8toEGFcGrxm4FKRKWJOwBs2Ya8YYAGvSlMuqToj2BTm85vOC5bwgnVdhBV6csJxOWF6ckF+ckE8r0vmFCP/ziwYIFCS4+6DS+6ZeYHMnvZHqWLF5Y/i0C8zT5RqgHyNtVvsd1ONAp4GxBWNJqng3ADWOSxuTSYVo1vGVifyVVBduv3KChPr9qswX1i4z8HkF2B/NY9u4q30QgIFpUYDg+TgYRBJ6mFOWMa19kyjJvroRhR1AMIHJzN163sdA2IMDkwf+5NyOOy+pbMAo89l/IOAg68cuRMT+F/ubARhDTQPk6/X7/+x7DojEdknqQhBW6UhuxSWqrf3yaqBgTM18VJ4NMPjwjz/ER7/1PbyvoECEi7wxdEoGy8JgiqXFF1c9qC7eP/msgQJvVFuIgd66Xgt719Qda9C72EVGYG5TcMQUxPKmLEEEBCacxpDFdtzq2gS/6rZH46SRctwtJHHxaS3lGcmUJWjgIPtvxb5AkYr/XgSzTXzfZSgQtPmTE4FZwsMuZrEFMdoTVzBCSj17EIvvyAw02o5Mn9u0DQUMFHUT46bbNYAgi5aELzWTsGbwyZ33S+y7yE5Zlrhk8syOm2rHGAN9RVHWIK/A+UHYg8sT+PFzsZzfnpDyAlpeyy59zaDPM5LFGEgJRI/6Hq7T314XGS9bxUaExOLWuBC5nUEJC1gm7OwLiAjLg9oZLEnenxYs54TlxYrltCC/OMuxF2fkF2cBBacV+eFBIg4+vAAtCgzOL7qog7VzHQyZIz2DZBP+nmOj2jxs882G21Ek0dEYOKYot3GAAAo80iZb2xA2FkYr17b43xqTcTxmCMiVIEcQjxgdUN0cp7ZW+Px2xrO9zoKPXZ/LWiciRHVgVwNjEJjVALSCSYACFZbAX8wKHAw8y3iPrGAUmKN6YWQPrIYprJ++3EyadgQHoKBW0PYzpqBtUOSCUw1OaMOdnGAFTgoSzH022T+WGxo70tV7ePZxTbf3APCxqg+a/Or7bFaeDTD46Kt/B++/+2WYsLBJAEV0xhrUAA4yOdYFsN85ZwI++Vls1K/A4oU3/VX+VhYAAQAASURBVO6kkPnb31YjWJkN0ltlRLRvCwjeytOgy2wXgMEdgCAG3aGclSIk/61QucB04IZJxrYPJalDYo92AE6ip80su/VFg+hsOqMoZVCpcKyhysQxfL6HjHV6tjEQtiCP/VD0HjmpkERTaxggaL/pmY84YmYGsdeSU6W0IOUFyyL3oXIBl4uAhKKRFZcNtD6C1hP4/EIAwnICP8pOOz+9RloyyiJugtvTkxj1vX5EXjPy44bymJBPFeVpQ75U5FNWu4PsnglHXgmdR4IxBGtyQOAswWnB8nDesQTLCwUEp4cGBhwQnD3ksOcmSIvnJmBIsKrKQN1E+M+yGM6Cnh0ZtM12nxzPU0BQJmoCIsj4TIQFIhRtPAL3jUkbj0tOCg7E0HXJKaxlgV2AqRbI5J5c64j5jDW6cz4DCsoVIHQeRqZi0AYgUghGLCQDSaAtcvsN2VFn9PYHKV0xUIwAwa5/BSC8TTHccG85Cvnesu8mWPhz0pUtqlMAdUtNrZ7AXH5FtsCY7u9FUOBr8/HzPhtg8Kvvfkknb1WkaoMPzRXEaKWAJGVxlVMjS5BImIJvGBPx7pfa7hiAx2ePHe1AoPq1ncgKhnJWDsFAWJzGMurMZoKkEyLWBi48JilZo6CBDrpBbdCxBMPx5js/BwWzBcSrWsoADu4oXGHZzoiy2xlIhldCUsdgJsAsgxo4UJ15YWSl/RelGAozVruFAZDwTMYQ2IJsbZ2H5zaX0AgQdjW/CwjGJc3ecOi/pvYymxhLCb0QIdMJ+XRCUpBA25OoGvIKLGcBCKeHBhD0bz29xvLiNfLnr7F8/oTt9IT84oTy+RPy0wX18YLtaUO9rChPRQwSL+ryaG6MB3EMTG1ApIBgTcinjLQmLKdjtcEhIDg9SNjh5SwAQIEBL6eWvbBKyIeNY4hsjXnBLXiQrh5d/7wFXu9KBAQjWMpEKBDwiiTbGa7iSVC4bT+XO8YiUQvdHUGB5PaQ4ymhqRfCnMeB0LpWjua0fe4BwsgEJlk/1RVZAEFVjwoCcWpgYaZeCABhZqBYa69eAI4BAnC8nnqd0FiDGRg4BAlB7bdTOY9ygwuIgmyZqFOqRo2sxh6EywHYbfZ+oob4H01AAUVD0kl5NsAAXDtBTJSNiQmuIA0cxI4cd9EpiUvH1//4Q3z0m38H77/zJQlQE3RrHlCme4YYvhfo1QhvADGp6cus66Kl7V27SrvOZGd5NbFRVBscsQRvoDq4BgreqNg1PSZ5hW2vCNCoauQGQkiMpEg7pZBMZUlYWEIcW3Iai5Vvut4RsEW5H0FAJITy0LdRCGxhyzcmwzm676y1RjBCCU0QAGrxr/pm3TlmAwnphOV8AtUNtJxB5QnYzsD2pLH+jwHC8viE7fMnlF+6oD5dUB6fJK7Bk8Q84K2gXIqAArXEO7S30hDHAgqyBCxazLNgQT6fkE4CDpYXJ/E2OAIE6xmcT8ByUiAw5CMo+/gBMW8Go1H7XJs9wK32tz4AooCO512f5xky+cSdUBf4qotxIgWo+4X/6L5dIiWb1yNoTA042jF/yrCu+cO8DYUZSg/4MQcHrPob9T6iujXbA2MPyEIPp069wGjgZjRQdDYBkwiKA0AAMLguWjuHeY6o8ru7BcLbUSUz3E9ZaI9jUC4AUQgKlX0tr1X7T0HtaG5g6/snn4nL/vd+6yO8Z4aGM6ByUJ4RMBBcR4CGzG0uIVGlMEss4fSLTiYHBV/9O/jVd74kNGxnGQ7vzL6E8L1mdcpiS2zjysEKY2cIc1TsvFtRC68FKJolN7orfHFgCeYGhkeg4L7Sdhb6JG4ZbrUaK1ubAaI+N0EWOtcrKDhYkkykQrL2FGYNwQrkTKggrAxwWPFnlO+s5Ikx4vj7loNBBf9E+Mxi4ccwzXKdoQnQ65aNSl5017hQwpLFinpJDSSsiZDTgnVdkNYH0PYI2i5AeQQWEb7pxS+hPn4OPH4OvjyhPn4Oevwcyy89gbcnAQXKGpTHTaIlbpuGTbaQy33oZQCIoYrtNXlUwwU5Ghma66EaFSZTFeh7SUp01gRFZ7AyBhWESwXKJmGHj4IJXQs7fE+bAxOVUm3newwuNJDQ7fb1Gt3im9vYWXG9xLFnQz6ySA4cCc4UOJvkr2He27WMcfWKdHfRxhCBziTnU873g/4jcIDku2T3eNT7duwB1QYQUobHA5gZKGKfrKmLoEjjpmv/uPcSmM44t/2Y1jdkjdX678LBx+tYWudaNN4Du71FUpuYWV3HZ/n40wYKmveBwqHRduSgPCNgYGYjupNU1sBKsgadbdypGdu9+vQlPvzjD/H9D76D9//KLwPl0qy+gdagkTGwVSCG70V4HpsAE3WCX2ICEjrTnQkyjOehm+zHDMEUEIy2AuMAHqOfTUDBUbEB3mXGG773Go0+49YwXumwLWOzzFcDJaubt450NCV19arwEKasaLu2S/nrcsP/u3v28N6utaQ2DUtt4CCCgm0AA7MojJXZ+36WsDAl6XePt5FESAkwEKBwToQ1C0hYSAwvFz8HWPID1uUBVM4yzssFtD0C6wvQiwvo8gh6eg1cHtWT4Qnp6TWWi4AElA31YuCgSMyDbfPwvSMwkOcmDTpE6oK4IK0aX2ARO4ddGmNnB1ZUVRdwXtV24IQLA9smuS9E6Pfhhi+l4rGy5MvQc8YAQuaZMmtnYN/WYvwnSYssl4ZlOCxg5EphCOtu3hvDrtkK4c3Gnk2LqAaNhmfJVAdoa4GBAsT5HwF966w2pw0gUNIZpeuaggMAh3MbwHX1IIe10VqjFoxRTyN74ACBxb2RyLKWogVIAjRRFGB5GIxBkGfSf9wMRzscZM0Aa9t9HW6Chqiema2pM5WCbQ24aECjjC6kudWXaOqiDcxBgfVzv35fBwfPBxgALnxbop0+kETbVbZC4e/lT/8ePvyTb+D7H3wb7/+VX/YQtd1u2X5nwIBIJ0mCj+IIDrrnaSLL3kQVgXlPuMcEt0FpA7gbpAfswM6GQM+dAYKdHcE4eEcmILImR8Unu1Tyuu0Atd90oKABhd6rY9ACCn/o4Ej8jbXuNSRnUeDFMIvethKMlt+3OINYfQMY5rpk2LNCbQuqGJhtMPYADgo23c1WiLCqemwUVh7kJtzYXdiIHCS44VkiLCnh8wQFBgISHtaMTIxcBcCs1ViEE1azRVAWgesFdLoADwIYUhWXR2gWSNbYCEkDJzGzZ4T0JEpxvlgfHmUqjBELh0yF9oe0HrIDjSGQIEGvL8XBgGQshEcWtL83bV8DBou6Wy6ZwJvsQ5ZkxqUtFfeM4o+7eiAYAno7XR9749f3Zj6NHkfZ1gTM5nKCCyiPZmgbmz04ALCjsw+f+pYxtj9LYBZM1aDsQQcQgv3BNYDQWIN9Nkd/+NAMR2ttbFs/TH37x6aIBod35Z6w35VLcOekBhC0vgL0sqpP26O/GgwNu7V+eJ5b5dkAAzde88ZvgzoGkXD6qHtlvPyv/gwf/vAb+P5v/CF+9S//shrUBct7oG9Qpb4MTVMYTgYOmg7AfjSjK/ZHCM02wsrMt97rcAUQ7GwI7mEIOn3YAcI8Glxe53R8bvxOJ1bHEhgoMCHigGFoKe4XLYLsiKLlcgXcx9gENzASefoMNxBBdFcDzIpdfmt+6wYUSFUYSyJsFSBzS9BiUe4MFAjFLYFvqgqxyho5j1U/PlFzZLUjSCqAxCK9qRMWSjjlhNcL4fVWsWbCOSesOeGJRNWwpoScFCykB+SHh85gkbmI3vNsrpAXiY/ALYgSZvkchjLLQ7APPiSuhdXcDCnvDAklhHCV91WEfmFJWfxYKi6F8Vgl1PBTqV3bbqWpGN6kXQUQJKQEVE5YxJ0ASwYKywqzdHWVebjoRDVDwLizz0GajDT0rcA5s6/D7PHfx41BNDD2jQEwmZ+aDdQAgC6lDg50zpEL1SvrwggGjuplc9mvN6gdHLD0AIFExwoLnjQChIS9DcLoxQCgcwGcBpEK661XZV4TdCgj0pFHm614HtBYE5QQEMpct9nVC1Gd8urTPnhRe745S7CzcxjKswEGY3Hd/gQcSGnC79V/9Wf48Ee/h+//+rcEFIT49Ye7ZDtGhOkgpjYI/fxh8gds3nlLWL7smRp7BAP22w4QDIuAD5B7UyCPaPYqwmw7eg6HgIBQBxVK58Y5ggGpnE/2nWoB6BiI2M+2KDDgaNp+5SxB96D3lxoqZgDAWo1Z3c4UKBTIbtQMHpdEQE1gEp32ZvVgKJ3NU8F1KVVfue1qJwI3pdQJsNMigl9AQsWyJZw2wueqWjhnwrrkoGqoolpwQ0Xzgz8hnU5YSBZQKpcwL0obTxObEwL6cTP2s/Yxp4xKBhhCzoHBvbAEMHCkKrhsBY/6eauMp2hToO35tPWA66g9AWEL1iztuuaEyhULk4fATpk91LWVFmRI82NQMwRMSY47e3cA6P053miEtub134f1ZtwI9dbpUlzQan917EACPNiYXqmLRmpHD+iDnet2HA9HJdgk7FlZTBkEAy6jDUKCGZ3uAQKAHiRMHmW2CbMyrUmQF96m9zKwZtdF1OqUVJVQgjunqlNeffYJPvzjr0+CF837+Z7ybIGBlyj0huPEFS8/fYWv/ej38YNf+5bYFHAIDjMsdv3vLWq+ix44ODAKzuyYAxqm4UqkYAA8d6UcyyEY0B8cuh1eAwQjQ+DthjmiBQK6N5UBAgAwFD4ubUONRjuCqDaIgMBXywlrgAEEAk5bx1bs1Lf9DN5d77j4sgoLjuOJgyrUzQzqISmLbGFBB0sKQZZyAm8F26SDbRd70Z1vqYytFJTavrPzrCSqbpC2ZsLjJsBgBAmnnLCkisecsGwVJwcGwR4hmcujggMIlZ4TIWMFpROy5R1CpKSHBWi2EBmA7FRESu92bckoG1Bq3ecZYLiQj6qCpyrMy5ODgroDA5ciGSQvamVo4KC1o46VxAq0ACArqJT5ncL89GopI2Bugou2ZSIgJwEKC/VGgIRG7+9ZPRtl95br7EK/g23zeSosiABNWiYUdmAH4poQ2QLKfl0e1oC7nnWmYrBNV7A/6AACzRmEzgZB9fEU3BzN8Bvc2ESz7p+5AM6a5wjI7WrXta+uTTNQMMoXt2czEN3Wb06LZkyVaJGvPn2FD3/0+/joq98Vl/rRKP4WDXpQni8wYPNQSLvGsUZ++enH+Nrf/QMBBe/8cs8SzHzzh+vvDGcsXoHaOrTz9sY5PkDH4zBwMP9uilwDIJgu1NcAAYcBC+wBwazE2XDFoPL2b2+oFCgAL6LGFIxqCkpaT+vvfnJM+w7ox0W362nHefK8MTFMTrLo1CQJVyw3guVMkJgl6n6Vk6gcNlZjNUbRgWCeM2MRMCCvItjkpK07l7HoY14KIVHFmkU9cCkZOYkKYc0Jaxb7g1NuqoYlE07BNqGzVRh84kWQWaAcCmNP9bzDeGhwrdVPhqMEGKqq3zHX3FrZgZbnFSi9bYDZChgYMFWBgAER/DNgdSlVWYJZG4qBprS5tBWquLrO3A9tN55ScwEUcNCDgiU1o0Q3AEwIkUV1XkZ20hrJht2N8SjvJ3MDM3A+zIeB1WHfqZJto2XuWdrGe1QIbymMDotfewIQGDi0QYheDGSRBa/YIZiqkKZbyfAEk+NH2GzW1jNQEPu7bvocSb9rGyZSQIAEvPrpT/C1H38T3/+NP8T77/w1UeEd9fd/VxmDbsI4xX8gFJjx8rNPFBT8e3j/r/xPGyiwvwgIJgNddqlo4OBNBSQgiyqji8YI7AffCAb8HIoLDPyZHRBY+OJ71AWxfabPemtXgjnyH343VyPE83oWYQoGZrYG9tVsgXLwE2m7/SI8aw/qnsWeTXa8pLH2KWVQWpBTRs4kIZar7i6qXKOI9MM5Z4jIS7DgOkvSBDmVXHBcBuAf9eCbqiBisSwHiUXAVdZdb2GsmXApCWtmZxGeBlWDGdUZOCCygDmiW29hd1v8BNNjzyzurxZuL1avFlNCvTYAsIKA6LkRDQgr41BVENmBpjbgadt5uyZygFUqIw1eAmZzYBb/Ueifc8K6EM45IxM6N9EWS8IM/xhd+GpmRCNnYD4WAXibd2NRvsAMRBP18+mwDPOvxR6AAAHSZ6EMxvV1Y7/mTtr7DQVV/xsTkmbnZSyCAQQ9R9uBzH5FIqDBEjUlwj7UslXTQMLwCEcbs7cqRxtPtwMo6CJGFlUnAPizn/05fuc/+pfwg1//Ft7/K39N7J8sSNKRkecRwJyUZwMMAGBHTR8M0JefvsLX/vSbCgp+uVnlGyjgwT3vnhLAyPSZ7nl8f27uPwPdIJzaDxgguOFhAGAPdu6t4xXhfFvgAx3kCcdv7oJm3/mP7wECB/XfIXeg29YO99alFp1hpOrEiTbJDpdXpLQgZyBXwgXG5CiFUAEoOChoIKEyUFNFhWTTW3NCqcWBQtVtjFuxJInwNhNyT4gAgVAqISfRp6eUcCmMNVc3sLMdrlna96552LnqQesTu2f0tBvTN0uT9s86xg1o7pkzz4z22Tw4DACYAaHZYtwCBGObpUSdnYDZa2Sl/hPB22jJCp4odfYaBgoWBQW9QadeCwDqJkad0U5jxlJKg42Nam8UsJpKhvy9zUNyFVwTkN01Duecd5Zvdtg6COE7O52HTvSnP2AQ/Lf5OoC4tiZ130WQEMLQjywCV2+T6MmQFSC4oeLIIvij7dfku8qEwdzVdTzWrWlA575ZgZf/9d/H7/wn/wr+6F/4d/DeX/llXfdDcL2QoKl/FmOB0vX2xXMCBteQcWjwV59+gq/9+Jv4wa/9u3eCggPMyBzsCXA80WafhxLVB4IvJkZIPycg6MDANdrvSBh3gny2W/En3e/2D65zD+15rZhhzkjBTuu/SwU9RHEMfT1Ga+yDMEEWTF2EiQQYSOa+RSZoXpDyKgZC1ELQAkkmeWEHBwjgwMsCJKW1c6p4DSAlxmWTfksm7FSgzQQdIMyChXv2dboI2NhKNFqsSn2T2yqYcJT34drD+JilpX7TUocxGD9b1aI9gAGB0YjQVC7xfAMFQANTHQjQ94uCHjE4TFgXaZuHVQHAYioXUcOclwYKHgIoWD2OBIWAUyK4qFxAZRNwUDfJXbFbc3TM3RqDwYgzZjCkABTMor0JSIIHX7ONTLT7iOXGXDSh7j11BaDH+kTGjpH7cymwDZSn1+mvgV4ImhAFmiCdqBnMk2F0dWRgr2aw56cWTKhjba820gAKokAm8nvsN6HDmqzqakLFn/2D/xy//f/53+GP/hf/d7GJq1sXStpVKTiQhxEcXJm7zwgYHFRyAAW/46DgV/agAHw4QQGblBOk588Qh0oaBOPB8+klbe3eDTrqrYt1GjdAcKQyGAHBATV5tYTBM9LoV3X/4XPPJAzndY1wT+kXCkba6Uun4ZuDGx0QQZTQuFw0TbFd11zv+rvCXOxAEsUvRTe7uoA0eQ94lZ1gXrHmFWlpqgV3Ea8MUlCQAzhIJOqHJ3DYuRKetursgQnAxYRl3oMDf2QdPFW25HpQqpfUIj+neP794Gxm0Q/I8x2VfHD5t72v3SsCgvg5JRJw5A+wBwXNo0M9MnLGmmlnuHlaBBgYU/CgTMFDzlhSU8UYOFg1+iSViwSQUrYAERxEt09jDGoFDyHXGWjjD5AxmBcBDEQKVjcRdiBhsbgipjg2B8kYmRVA7wp8j7cA4io4E2Lo1hlPER6Pj6weZQDVjR3lCcKAvcogTIDCuNM+AghBxWAAoQIt5g23Kkbbr7g+T0sQ/HKvhC4+xDRybvh5J3ekXn/2D/6/+O3/9H+PP/rn/694/y/9czqJNZR0WrCPoRMA4PhsN8ozAgbDwjIIjBEURIM8GVhDYiD0nWOfmxCkibAMk2x2njvEzEtXAwMIM5Yguh3GXAYI7AEw1A/XQcGMKaDBVXCkJocFZb/A+BN3r+P7W6XtkZTM1+BVsutQir+LTFl7UDDGpOACbAIEqvng6+5NQIEtYhMqV0P7csrgrBH78gJahDHguso98urUcFb2IMHSedsQkQUxo4pOPzFeE5AqYSGxrDdd9pqT0uZpoMnrTjjeW9wFUptOhGrdXSsa6410vOV5iMDkAKMAGNgH/WBx/11QB13/SPFbOQpJbcc7g8GJ+U/zQIjMSe+eaDEhTm6sKUaaD0tu8SCWhDWoDOwvJyAZS7BdgHpxUED1AtQN2DZwkb/d+DtSI+j4g44/1lgQoAQsSzNOqyZsVT4AKqwO5qzO13Gu3h5R1L+ze4bNDNuVAjhwK3v93Iwa2Xe1Hi9hCKjk5Wi8mzD0dMYCEAA4cOpUDJbJUXM2JLR03IkCnr5jein8kPeUROdvVASUZTTwogDBY0Y4g7CXO3/vH/4X+O3/9F/FH/31fxu/+j/8H4mprsuq1F7FylnGwQAA30St/WyAQdyZdhQzIIaGP1abArXeHK31O4tR9Ml/dvH8/UYq/KKQpARnCzqhemenXAMEM5Zglstg1J97vWb3O9jR0+guOAEEBwtLANld3nqgt/a9B6eMzEkHEkh5FucAhwsH+4Hd7iwAAt4usksrzU113LG15hKDQ6TsgIDzIhH8lhNoKWBepV9ykeerBWk5C6NgqgX9S2CkRVwGH0vBkkiC9KQqgqgkbEvF08aoazoM0BMpd2n3vnHv3WXP9PExfHCpvDtWuWVTbCGg951rdgcefCf1tguZRDVi8RjiMaABhhRW66j6kM/tfiML0c6x3xwHiEognBbqvDaEKeiNDNck1zBA4KqDukkUybpJVsuyNXCwXSSk9HbR8bfdPfaEApexh3SR4FASTAHEFbSsMtdTBpvy3JTlUWkeNi6zuVvjTll/cu++YtxNS3O30OUyVYf1jDUCbAQICghE2AYBaHS4ldnaxgg7ZvZ6xkRNBhAENHETGTP1QmiHu3Y1xgrovRjVwYgIb31vHgcmswDEeBJcCl7+o/8Sv/33/jX80V//t/Crf+l/HOqs/YuQP8ZUMAcq4V5ldMwcPBtg0OnAA8X88rNP1NDwW3j/nV8Zdpb2eryj3uuXYwNHIdl0VjtjIEOLGCae36S/5z22BGT0OGoAOgMg6CbMWLeDQRGfO2XsAIFTk9cXlNo1cUtYcoUQ7NsgLEqWKlZXiOCaaeBk75PdA7/i/exqg8mi7OqEUmSRmizQEl2NxEYgr+CijEHZZIEuG9KpglMB80meYzkLgl9OrlqgIq5wl8L+SpQ9p8GWEx5LxbYwtpLwsKhr3mKREfuQvmaMB/SC3t6XmvTYXi+P1Hs91CD8t9LiKlT1BuDKqKXqMJPPXNX90Lph1tFt0wuCJB+iRG5Tk7J4PyxqOyEulyJsPatlUAVE+n8M8mTfx/PiMTO2HENKp9RcOGPuibMyCKsyB2ZPkGmuOrAU17RJCGkqTyALK102yT+xNbaAy+XquPOIkVl2t0Yjo1bQUoC8NC+phcK4Ty5sRzuCGH5ctkP7+Xv33A3LS1RGEEnKcaLRVkqBwgQksKtGO77hmDmYPQQQNntwAR0ZhF0uBgBEGmZ5CIwHoLmYT5bOyj0bRrphZChrYEAAmjWxbm0N5dpYA3t0BQcv/9F/if/13/vX8B/8z/4voj4AAsDjBgJm4G9snRt2BbE8I2Bg9DJgFPOrT1/ha3/3D/D9X/8W3n+nBX+YuqVd0WGNRmftvVDLvbDU6G0dUMjHoGAobwQKjtIgA5gbTh613QTMKCjgGJb4qD51v5DEXcYYShgHbRAnlgECQKBH0sUqJYHvKdl+Ij677g7itQMTRMxNdcA8X5j9+6BS6B5SWZKSwGkTFYIBhCo7tspV2AOuYAgoENsD6aO8nJByRq5iZbCxpIvemLERcMoZhYEHDfG7LZoJcGFsNaEwDpMAuVU1z4FDzzQkt+S/bBIoCElAmYMDBQVbYZRSHRBUdQcULQ031sBVC9Om8/FmbAFlFcwptTUuJ+SsbJDaXeRM7j0wGgga/T+GMB4Ff8t/gMMkVOJqKGDFMlaeNQiUZSXcJaNKksmTuIC2J1DZQOWx2RJsj8B2Qd2eAM0xwZcnH3e8bUHldXvcUV6EAs+rD3VSkMBlQ8rmUmiBhwZWsGMC5wGmbA7PwL2Mr8kj6vy10G9+O5Xvplzch2wPIEF32VS3xiDoLl7sE60++zV8X0ZWITAIKaPPxZDFcDjq7K+Bg0nZgQMS2yEDAnJtaSGJzmg/HJ6WMsBF1AcKCn71L/1zXfsTB8N3BQPtmPZ9bIluMwfA1s2D8myAgRJD/vnlpx/jwx/9vgZ/+JUbAwhN9+OfY6MNQhPYg4IY0vUeUDDZwO/VpneCgqMojTOwM+qcRlBgz22gwPRv9qoMQQk7i3EhcXAQ6mquaNd6oQSwKwJf/O6FcVNPjap2HsKeKsq3ICZa32HAuwW1pYd1D5Sqet3Sg4KyibHUaGtASW0EE0j7n2sRlcGy+uJu+xxifZ9tN8QATtJvZpiYxDAxE7AweebFWhklifV7ZWgIYFnIop9/zNJoqYNZ23sGHIxpGEMvX3LC42XTqICyiBZmZN7HVHBQsElfC2CQ5zAGAWiAULpEd47OEBA4EzISaiVgkTbOS7+rzQoGliXhFII0ndfFgzaNKgDb+c8zIu5TVnvqZLQ4DQuleUhjasGKsgKCnYHhqDq4PMrYujyBnx7B9tlUWjruDsccALCOOSxgbDKuSCL7CUpWvXVeZJwvCTF5Wzcvgpowgvuic9cCS3Xz+I45bO6n4xwGmq5e2L8A9NEndxKAoMaBtQBKkZPL9aQ2VAEcjCzC0boXo9VO0zwfgwPMwMFkDb8bHPTag56OIVJDw38Vf/TX/y28P4ACAG/mwTVRH7z89NXVnzwrYGAsz6vPPpGESB98R8JEOjWX4GhtZhVquvQ4sILhnVv8Ag0URABgcd59x32stzsqPVsAp9d6Q8L6dqDg8KYTRkD16PHY0c6iHCwijH7HAVynI9uGksQQiG2iERIk9wASIbH2tE6wHFgDswAWak76OL7vPA+0fcFVKdyq72t73+Wab65jYhiVGghA37VUC3g9a1+yX5e4NsPEWpDzipwWlCSpoTcCCkipfPhOnxkonOVxrR31fakGDiRA0Cy986bXM/XAtsTEQgmPmmDp9aViLQXpUr3Oa5VFuDIBRYSqdWS1sad9zNXAQd+3DHaTENFOCf1ZmV1N4DYIKuSNtj+vGQ9LwsOasOS8cyGMCaNMBRB3/ibMBRT0AZo8OBPFHW3LZWDMQ8t1AAceGYDHJYhAIBoYarpqVx1MQEFkqfrxJu1POcuUTwpyK8AprAs6noU0jXR0Gt5H26fGFhgoEAAZgL4ec3zPbzaPLXgQmcAMYN9AQq68YwIJhERZ3PDiHGNCFLIwn/17ikthWyf1KWvZG2fq9x5qnSIsuLKE6xcVE3Bgqgo1uOyAjv9e3v3ZP1Tvg7/+b+NX//L/ZC+PDnb6rb8HhsheFQi+/PQVvv7HH+JdfPGoJs8LGACSZerrmlDivXe+JJOTwgASpVbbKQMwAxBZ4CtGA47oJ/zyH/zn8hsXmrnR7Snvdta9sJyXQ+wXDSKVCod5HnS2BHNQ4DvlHboMi4MxHFEVEkFByh1LYEKqMveAoPZgwLIMNmDQ99NYCLZYyMlGNRKR6iB1diokJ2aJVKbdlJw1MJVCan9GT+YMLkNbjJNsQuNGQzpKpAap2hW2e8MEHITjtEjDMZQ5SAXg1XcoAhCyRkEEaqW7dnDS3tS1t4MHfd1KlXwDxRIPVWzMnqXwqVaccsJWGQ9rxT9+JKRUkVPBosL0URM7PAHApciYXyr4QijKFXtfAdH21z+3Jm+LVSJCWtRGIBHymnFaEh7WjLOCgfOa5W9J+KXzojkfyN0HlyQGgUug/yVqo4Z0RlDR6bS3sUVke0h5086zDIjGKpj6QYFeLS1QkbkhhvdUt6YyeENQMI633fg88vnUhnbgag0fVQeUfD2rOkZk3DRQYGzgmDH03rls3W4xBay9a5zCEBsbWWYFIDAJOE66M3L2ADqXaoG5+LpOPmXd+NmA65+qWwOPwAFrTBrNESGGfNXlgvxuHtl2uqaHXWpTDUuEVNZMnCwPBapVAF9o2N/+f/9vm0ui3Xsmj3xDFGRZXM+iPZyeb6Dgu7/1Ef6NP/rXD3rwGQGDWoGPP3uF3/2TDzXL1FfCQKiykwS0w8IgMctR/ezDihBWMxGUL//B38dv/yf/CgC0lLCTnbXt6EZAMNNmHKt5dFKFXUEHAhwMjD/rQUF/MxtMqRtQh6oQBQVxV2GCyncVARBYnPuwiWy721aleTEiRs9xg11IrPpCjMy67ze+Tl9ls6EhepPmLo8ThjLE2IhgroaUEri2xZOIRNhx0uRtrf0o0dTK3kutcv2wi+OyebUYsqNLJ+kfzhWoWd0aNTBS2cT1jBKSAsw1SX85UxOSr7d23tt0uCGiLe5LVvVEyz/wWIpmJmwpil9vBU9FXPM+vxS8XhI+zxI2OT0VpdQJj0SgVFA22cERZZRUwaWCKDeVArU2iyqElADKCTklSOiHjLwkLIsI/7OCgS+chB14cVrwsGa8WLPEEwgxBM5J8z9k8RQwYZ6SsgNkjBJ8929zbqbztk5rBq46cLmCSlNBkbrA7gIVlQuI69zIcGSnDuJAxDbrPrv6UlUIQdibCyMlYwVs3Md5HjYCCvYrKxjAHBQY+2Rg08aeNcvxw8v3CTKXBfQ3wM9Jqh8BQkqSpZQVrZk6iFJu4IC5p+JRwyIa1/W+bV3/3vH3Vo9wQShwUEarqWPsCfZltq57qOUAEJLaUnAC3I4CUMNQcmb7j/7G/w3v/2XJ3TPLYNmt4/45vlIYA41hMFDwd37zI3z5nfemdbHybIDBy09f4fd++CG++5sf4Ssh9aSoDQIIUDPUKeIE0HV+EKAv/8F/jt/+j/8V/ODX/jb++f/n/1KBwJwliPr3SL+NxaJpVVKBOI67qKPt3kcafGALQr37m01AwTX7iAkoiCyBfTaGYFxAjOoOL9O891bMWrxCHjOFHxaIThIJHThISduvWrVannIwq6DWnboZEVESYWz2AMuqTRkAGDYQ1MBLFxOxB+qj0O1KLUCxiIikNghVaGYA9QniwQCW9uasVKZYKJMbsWpERZ3gOUz4bhdggiPoQGXo0c4GJC78hRmnnL1fX5eCy8Z4LAmvt4rHreJhSXi9ZjysGX/xVPCwbviLp4S/eCxYt4rXF8JTrhJNcGHkjVBqmhoi2rNHg8OcCGmZswRfOAsoOK8LvnASQPCwtIiDD0tzG3xQt8GYuyDS/pbB0HXYiNMsgGtj4QCYcaocvyNoFodU1NsmBq7GCLhBYbNlmXod6LjiUgRE2RgjAwIKCrLMTWRzl13bcf3MCK7GgdlsRtLZQb3P1wEUjCA/zuk3ns/a/okb4K9VVEhMmrpa9Q1MYoiLoCrswMGRnj6qiwM4ONbFD6wBBfAAwNIy29jYr80YWJRh/bXjYUNaaWJHQWqnxGTWFnjvi+/BbUZ8l7VnAqaMr9cnrBNQmzsFBV959z3sJUZfng0w+L0ffojvfFVAgQM1U2ruWIPUI87U0ob6aKC26L78r/8+fvv/9b/BD379b+P9dxV0BKrddnUzQLBD2Qg7E+ZGq0LQ+xhzvke+9eD4jfILBAVVd54VpvtuBm9Hi8c4aYp+G4PQbIVd5+ssiy4mTkMynDlgMmMnnTSGzEOecgn2Yl4bGUgVWGznEYwEYzttF3k60t9Ri9pGcZFRE3tfsAEdC9Y/LKCAJG6CrzFA8DcvsmDXtpDT0E/k/YewMxqAX6d3lPfZF40EztLHVQFDqcDGmnmwMs4547IAT5qV8PVW8XoreK0A4cWa8fkl47wWnJcNj1vBXzyKXcLrRWMrbAXZDBCD+6I/Inr3xJQJy5KxZOoAwXnJ+MK5BwTtTz6vyhpYDIF5oiIEgS2C+SiZmD9jZ8DWgEE7N4BwDt5AFkHTohcaKLD4GBFo2Dix2AMVGtI2KVC1sTSMLwMIFjPDQEFeQOvJj8U5jWRGw23zgpR9rerAvg5OYwpsDTNVw5vO6U0tEY/mtAAEdlsOqLow+Q7pFwcO7iqduqDCrfonDMOIh66t88zcjKfRqpVJ7ShyauwBtXWE9Vgbc6mNoSCbOsa3cy1v68jLzz7Bh3/8dXz3tz7Cl999DyNmn5VnAwy+/YFUupcT0jhMLJ2SoHoqNHAQ840PhRUU/M5//C/jB7/+7+O9L35ZJhjgqgSneWucTNLqEVHG0vn1Ghqcs1T6ID8PIADeDBQsvnBEmtEp6npMM9qEYfSLR0HfCDbX4vEM0ZOXKou9CNAGCOw6GYSq2w6C2BkQS7IgUylYBjIR/g30OQgwcEBq+DMkRMLgU0626xuJAgqLeIxvALTJa5M6hlhmZRIsvLKFtbW+6vou3q8Nkh1+7OhEzSgXdxRpkeROKcvYzQsKCFsFLuadUBmXQnixZrwuCa9VvfD5U3GA8N97WPCPHzd84bTh9UVAxOOlYCuLuzyOQY/ksZqHgLkajoaFL06L2xAYGHhxygoIWujhNVt0QQ0qlIIh4Bb0/jHk+SDUgQEIjGU2cf18FXoscTHA4qUBM1o11UL8jfVdzqBSwObFQlXXo4PxNYwt0r7zaJt5AdazAwbZsKyNWUjZvYs42AsZqJc1Sr1XJvP6GiiYzesyhAfM3IN+Uysklf3iJyAzk23CKziw5xHvEhWmaXlzcND13Y0SAcIV2wKg3/zN1np7EiKIwfQAEMS2QOtk7AAgxpXM7ZiB0PH+0XYkggKP5JgcFHzvt2TTPGLUo/JsgMGX3/0KgDaQUwcONNAEoGDAGltHJ4V841aI8PJn/xl+5z/6W/j+r//7eO+vvi+NbcBAQcGR7+/Mdx+ICzrJHNBjQbt1FSMcF6tLGo7hNijwY28HCopW1AABgN3isbNSr+3R7LwRHPi5+k/miU0YdPYGxKqyTUBWPZ6nxg5tynpTAsTfOyXw5QmUMqgu4lNeFt9hWrAVmGEYt52Ql0D7WnQ60/fuBLy5SqbsY25nEDkrsQGPdNMxjn4AHazPxUnzOSg4SGnBspxwXlYUJFwKcMlipHjOwMMCXLaE10tWBkGAwhfWjNfbIozBpbqb4+OliHHjVj1UciyZ5m6HD6upCHJgBqgxBEvCOUnGwkVBQQsm9AR61GBC0QAwRrpU4e20vrbhdH2M0RIPaWjtktgvQ36NeA1KYV6bWosrgMXHlXw1jC0NZtSNKQ+/nZ0tIM3ZYZsVAwWc18YAJmONmgphtCvg8AzRpsDKESg42rcY+I/zGpC5bYGC8gD6SwAHCYRC8DkuF0UDB6OF/xE4sIfpO2bfV3eUkRm4a81ns7HQ9OoGjlR5wAlIGsdBGmgVlrJSU4ViItFHQBBsTgQUfIwP/+R38dFvfYQvf/G9xv7AnvUYITwbYAAobUONVvbJaKoEAwfaLayGK32+cTnjz372E3ztx38T3//g23j/i+91dgSAxo03VM3cDY64e+7aXjs+2b2UKRif+65iNhM2OLjufxwBgdFOM/fKsGj8PKAg9gOw31HMqmDFKMhraU2tyZiBSoykKgXW3Q6UeiSdaJwdLvkNiZLQvJRgHitpWT1MMuWL08KoVSOx2eJfjrsnWVz6sHM3OjgKGxtjnrhrVkn4d2Puhvgs3fleRxtkDZj4zlLpZqwnUFrASenn5QzKK/JyxnldsGXCpRLOlfGUCQ9LwsYLXl8KXm8GEjKeqoRqfiorHpUtGPM4ePMM+QjM3TAmJ4rqgoe1ZSs85RByOMlukZ4eNcLg49490Gj8sqFqTowuMdaVNvP+CmwQjaqiK2O0swtKZsys0e/sOrEPwwrcXXV8lpREdUDUqRJYNytuR5CXMK8bQIigwO2DbN4GtmDWPH1TiZDPIJ/ftxj7CA5sfotgB8rPAw64NKWDs+3JAZgYabdd974yQQV3AwR2xdspgKh71n0EtQJB1G3UPDGsepwlrDVRgts+uVcaOjnVReQ9AAWmXh+f+1p5NsDAZKoJDxtHjTVIKlQI4OTxuYF9Q7382U/wtT/9Jj766nfx3l99Tyaf6dxtENcGCKLfrzX+dD/CghKNSjOmIIfnnhYDAf4+0JM+SPaMh7++BShgzEEBD+qDWEZjpHHxmJVoZ2CLxlGCHCC0l31muEqhdb40h4MDsrgGWzMMrEmsjVNxgEDryZMrWdAZ100DvTpg2LV3wh/o7Q2OBMkRCIiCbGAtPGTusPvdlSBQkDJoWRoNvZ6krusZ6XTWneUKLCfwckLKJ6zrQ2MRqiR0OuWMFzXjsmU8qi3CpVQ8VXOFlNgIMaBSfBwLOBRjDlgOgmZQKEmJ1iQZDTt24PI56OlJAghtTyCNG1AtaJDFDHjDiILagXuVkDEtIwN0DSz4bn/Ry4Y5of3s7NW1MRSvm3KzL6AELBJPpQMEXVwVTfA1zu/aPIuiCgHY74RnJSdCqdyBA0DWhmtyNQ8LW6k8ZQTlRebwCA7MYLEDB8YMIoCDMc6B1SimcfYGH/oM6BeqK5XyqRvqMAaC6isIlzcxlkNKojlhIs/eWCD2B8i61rN6R3QG0mMdWt6al5994qDgvS++18ktHvr7qDwbYBCL0fJAbIBgyBUAwlgsYqK4PL7vVFENDAEgxlsjIBiD+sRi/SdUkoCDplu7QhREUGB1IocX8nQBQXY3jMYplGDuS0egAEHgj4aGBgqcGcF+EYkLB4Bu8ZhWLTxzXCgovEnYt43tPKwt46JBzCgKDoyiQw66uKrtkMztrICreimAxUOAzUKdOz0y0BZzf6Y3sfmIgsBAxpBRzwCB5W1w4WbR8ex4KWrkJ7/hsh90lMU9k1JCWlsmSOQFdDqDlpMEYTo/iJ76/ADkEyifwJfPkfIJeX1QFiGLDUJlXBbCVhMuFQ4IJCaCxEWIURi9fwlo0QU170BIUjTLUGjsQHp8DRQFBOUJVJ7Aj6/Bl0dUfeVNogp6bICyoV62u9vHPQEoKRWffPdNyxKOZwmgVRUgAE2IRzB4RxrpNs77cyPrJKogkrMjwIcBhmBYaDFVhhgkkSkY3Y2jIBs3SLbdiHY+bZ6SbwQWZQOu1vVgrgNhvXYsIOtiHWwOMrW1tegDzsEBa3AkVRcfMQbyYP06ufv+CjhAYwtGGQDs5UCFuGsSA7boswd4grtqb84gkKsYYj6Ja/V4+ekrfGhxfIwp8PvfX54NMNiJH4bvzPvNuAGE3Ea7lpefvsKHP/xdfPRb38dXvvieTKAACIwhAOAZ7aLLHtAmV+yEBJPdpBtaRcGz5441MvVCd6WBfqaAiuOxDhDIFN8HYupBwc77oAM7fZCTydOCMU76K5Tr7NtwIA2H3EQCgLl5OtjSjYSBA19YdIJJtDvVy0Yr9VrAiRFTMjOvNttFAIM9XK+FOG6dPU61YWE1qtH0yDs9d4FbqANu0c617IPglA1cCupWUC8FzBV8EVBRFSBgIvygwi8vsqtMS0ZaM/L5BFpOolY4nUGnF8IknF+ATg+g9QzOJ+TL541FWE6oS8alwj0axMMhoZxanITR3gRAF2EwxhkwTwLzLFgTJF3x9tSzA0UDBT29Rn38XNiBp88FDFyewNsTyuMT6kXaCLWibOVmu6Sk4GDNIEpIa0ZasoAEBVJci7xqnzIWkAUZCvp/Y2miKmKXhK2NjuGjgQGjiFN4bcLL6eIABjqwb/Mb86BkO4bTRq6uT4nZd67MDRwYEdcAsfRZ+3h9ro/l6GwO4IAV/MOTKbVXCXOEKTiQuSxt2RmXzwDCVPDP2Nfj+jW2pQ/wJsf6q1o7V20xWbLIAQKgthzKIJiKIeaTmO/3qQMFX1GmAOiZ7Hb29fJsgMG0cOuYCBCsjWJuhVfaqN9TQ404oWL0OfM42Go/CJpRx/4xbKKloGMbUQFzH8lvt0smNUQxIQ+gZeOaLyptMWl0aFtUjkGBWynzMQNid61o9diN2YPRN5uKu2WT9u/T8L2wFo2BMXAgi3DT50HntSTU0SGfhaL3LIwxtLSxMNF6ndkXHSA0SmycaLVuhlFcgbTALOKTMhBcNhEk20V00ZFNYA2CM+x+y+MFdSvgbUPZClAYddtEl1+rMAhlWPgAkGYtTIsItHRakZfXoDUjn1YBCSdhDdK5AQScHjoWAXlBymfkZRX1w5Ld1qbUPsbFwdrl7mmWayCRJAMkCw70dGkJiCI78PQarICgPn7uIKE8PqE8XcCXgrIV1KcLuFTUbbvZHkhJbB+WBXgi5CWjbgvSkpHPa2cMLO6vpsjS6ySdX8uqu/tlt8PnuBM1Ie/vw2DuXtt83s3lA+vzGILdqWNuLIGBhKPoma6JQ2AJqIEDYNhHjXsWLYe70ivrwtGevKmF5Q1B2cAb4ACWX0FDKNt6CbJjaJWNa2awOdgDOXi7mRrmqH5H8sC9FPTkpCCBwQ4QAF2LwR7sqQcIQC+5pFjwIpNfHfA7WL+vlWcDDEajtUhP2eexGOB9+dNX+MafSHCkL73zHjaTCwMg6GwIAiBweaKlAY/4fPrdAAjs2Y6dYtCkIBGgYTt9sO9uFEGCUY99jAKDFEchjnsr5X6gj7fcTeoJqIn1H99buUa8jn3r62c8yI0hgqoTSN/bpDMvEAMJlBbDDAC40+HFtM0RENAOEAR6bwASLuCZYayEuKgl8YiwmAkAxNAxgQ1FuqqhKlOwdaCAt4Ly+CQ7DWMOtmCrAIAro+UfUJWCMga0ZOR1cZCQTp9jeXECq4qBHl70LILaJCCvDhI4r+IBkpcwtpK78O760fuZlbHZJPiTgoIGBjTxkIIAZwdef+4qg+3zJ9Sni4OBctnAyqZUZQqO2gGAsAIpgdeMum3I5xMKZB5WQNUM6vaZrT/ERoc0+mA06HRDwMlufkpXj/M0DOwxxXs0KoufTcTYXxP2PUNgw3UEBbFfbHmx2WDrEY8nhjJdUzsBGb4I+6GxzNaLiqZSsAeU/unBAYWL7PIrMLtqwVBOi344PkQPvOTY9X31jC/kof6jTp/CORbZNQIEQDadiXQzOAKEocESWRoAlV8ap6C76fCs1wy8rTwbYGAl1jnaGsxGZGXg409f4RsaHOlX3nlvGp++BS4KOe/5SqhQLeO9K+0BQLTUffOKzigvvbPtKoxavEExjqDgwJmrvw2u7/RdHRAO7kwhbtf0/sKBIVJkQ5BJVbiFY7X7WihcOZ90IY/IfP/qC+UMRACy+PAkWp56IbjqolyAVSOfoS3OHsYxZRGUQBcPoFYWUHCRHXF5vAg42DZRLTCjltr9zoRiygm0ZGcPiqkVTqt8fv2IdH4tLMLTa9D6j3sWYT0B5xeigliW5tkw6LrTuMBqu0QmxtpDAIJ6FGybCH4DA5EduDyhvH6N8nRBfbygPIrHQXm6OBhwlmAr19uACFwr0rKAa0U+ryiXTeR/Sk0o1kCQhwBBXVAhbQumZv1vqoUd3R8Agj5UL+z1WIOq2L3WsNjMhL5+1ak2/fiVHaSP82sTeigNPPSlF47szzT7/t5izOAIDszaSp4V6PIrmLtydE1HxVVDxDcsM9uKuFm8JhuY0YV+N1AlicnExiJPAILhHAD4+Kciv777VYloOJVzQzXbIx/X+dkAg6N+3aG60HAff/oKv/+jD/HtD76HL73zlUO3wzH0L6C2YuM9BoTcAZPD56ar58SQzgzTcdtiMmqwsF+Awg4melZE74NIMUZ6MbaVqQ3Gdu7pfbJHsCZwB9ER5d5bxihjN2kxn1z2UPCKJIp0npw8PjMws2+IwEYFDLKfkJTCHNmHGHUPMba+eUTUi+gOYf3McH94rqLqqBWULvOqqvBzgciMclGjxIleveYKukjI3ZQ30CL69PJ4ESbhacXyeEI9X7B9/oR8XrC8eBDhvJ5kZ3x6aCDBdszLqSXume2MJ0yKxRfg7cldCw0M8NPrlqZ4e8L2+WuUx03YkscN29MTqqpV7M/AgEddPDA25MrIawYUTOXTKjkelj1nJwaJFmpYgwpZxMEQVIjzCiQNTRw9AyZpy6e7fBkysM3tTLDOBEwU/vEcYJg3V+ZMmgmHcY6/6XxtKBpQfbrNKSMBokCdMg/hvasU0IyNjRUEWtZVe1aPksgWTEiNI6OtAY13nqyfwJWQyirc6bbh5Uw+uHoZPUAAdMNGWi+0zLIGEBLk/E8+e4Xf/9HX8e0PPsKvvPteS3t9o7/uwUDPBhiM3XeEUK0TrVH/8IPv4Uvvvnc1g92oNgAce7YPY4kTZCizwwYQ7pqEDvuuUJDGEpiNgT7vGJDpKEPirLwJCOh23cO5t8ASEHb+4+IXJo8XAwOTJ+90fOF9a+b+N/OFsp0TvKW6eqVwTNL1ZqScYaKAa0i449S55VUQ1UIN15QHFkqclhXJhF0Vo8OUkoRT3YraKOxjItSqQMPKBtWrk4OEvGZUBQhpK6iPF6SnFXldUJ5WlMet6dxPJ9Drv3CQsAvN64KQ0FnoW0uH/ALVDC3NyNJeNQ5BfXpym4rydHF1wQwQRDC0qzNanXfdanEmstgaEImRZlqy2GMsMcrgCRQ8ONLpDE6LgoLFVSsYVCsdGK/DGoMbO3vgYAc4n6FH83a2pFwD7L+IeRvrlc32YRCMjt2HOX5UpM0YqJKIy8BBJjRXRl0Wu4RF5p5egRa7hgEMzMEdEtMiNs4AgbN+Rw8/+RwBgh0r3NQLejNZUwNA+MnPXuGbLr++MjzLnKEd++9abZ8NMOhqyW2HO0PVn3z2Cn/wo6/jWx98z5FW/N4NdzBx03MhBO/Isc+PGjwNJx2dtzvungcalGmsbgQEUR8Z7Ama4L8OCvya3Xzpn4jCsx+lqz2m7K9UNLRtjO3g0Q7R+rRp5Pq+iG/Hvvdz4y13M3k+ta+pSKw9LFOc1TmT2DJYKNecFnBWUEAZSBKtjymD6wWWeGlXuMrOooovc64ybWutSE6Z64Knq7vvmFPqBWWtqEhAZeQ1oVwKqFSgqFuf7bi3ArpsqE8XpNOK+nQBrY8uNDv3R0oCEojc/x+AqBi8CiFEsMVj2C7CjAQDS7elCMaEo/2AqU3E60Cet1yq168roT1jrgZRp6gHgtYpnxfQKuqVdDo5MyI2FgIK6PwCWE+o+SQsQW7ggAM4KPooo1HvCMDHMfo2lPstFo/DOd2ptAe6s7kb75HCb7sShX54/pFOt/U0gdrmiwz002Gdmdsz3AQHStUSxO4AxPCoiMoY3LuODs01XR1o+DJh5zs2ayb5bQAIkVUxz4wKCRWdg4H1Tz59ib/5p9/At35D5NeOmTYVJzXvrcN+OyjPBhjYAHcqa8ISjKDgS++8J4MJPSAY3U5mOqOboKDbRdqfTTxqE5LaOX2nHcwQpWavGijZ+2BPAMZN+4mju442BdfAAAFdRrtu4RnbaHcjOMVqzyJ9Qg0QsGQ9qzromUOwEKbgkz1heYZFKlZ4rPdR5jhzxyT7VdfPYuyUtA2yAoVEhFzNCj9jWTKoAwgX0KbsgaoW3P0NcGGbIbvcLTzPBrjrXE0JyEmEeiLQpv77Kfs4jyXS7WUrYuUdYiPQpTSAsC5qnyC76ujet4sDEBMA2b1iMKYhDkN0vzQXQ7ejMPdMNa6sWwFK7UBBpzYIQMBYAsqayVFtLAwQ5PMKWjKW0wn5vCCdVywPZwEFpwcxwFwUGAQDTF7OAgQWAQdsXhqkXhq1eWnEAGHXMhbGMXhr7Fmh4Y3VPKr8jHoGRnua4/nbAYRwnt1qOn+pr0ObxwEAAO41JPN1DhJi7UegYMu7gQNQszkgAixDo/tWEmROqWqhC26na0nnwnioOrA7///J+79m25LjPhD7ZVatvc/tJj1vdoSIbn4EvzhmCAJ9W34YDUUQIPw2RKNJKiBpOKGxnq1w2A/+ChqFpZEQAqfRIB2eiAEBEqJmXnhvAyRnwh/AT45hNyBHOPwmNfqcvVdV+iEzq7JqrbXPuSDHpu9UxL17n/1n7bVqVWX+8pf//Jiq1J28VSbB+j2AQOGmMtDaMM/goh1VMKSJAhhqOhRzLfzpJx/h7/3gffyLL32AX/7cO6hWN2ZwTdgxN/Fr/plHBfHrBAygE8K045fGBAp+TUHBHkvwGCiYLc54o+eN2v70TecbLGxIDo/+1UdB3W6qE29cB38ZULCznnYFSpqe+/tDCFWo3qUv7XgVg0+a/ASIrTUrQgqWFYequqmLUgq79/1ICAu2dd/9lDyG5CnWGpHm4vvpJxBysj4MVcAkSLWDhMSCzITKhMwJKVv/glXZHVovEBNinBdUyySoD13hppRBTCg5gfIKWpJa80zBoi6gtSBlzYqopaorYmdjuBWt1yygUiFMVgzSRHtRRU05gZlRWxBjrwOglflsLloOf19ErrwdIDS3SKjDIGvRVMPwXAMrQ5ZBqe2+UVCUaWpL6tflwYZI3ABBrOXAp8VSNrMGYRoj4CwBn++A8zPw+dmmQqRwbiChQAvTxPoODgZaRVFbe2upmzX31PUGbNecvkdDrQiWvm/3gp739vAM6Pf2MICb+9jvfTRQhj1swXPa0VEsMFgBjJhyjTJpTxhGcBADElWBAslZBFgmg00Kkb4ylsYHnuYkGWWzxxd4vQWPWzBcoP0QaJIjO+BgT9YL+gciOPjTn7zE7/zgffzzX/02Pv+55wMA8CM58yAHx273/eB9H68NMIgX6RZk1HQzU7AHCv4qftstSH9dS8EaCkdnC0alig0y15MLyhT+wfnHR5YAGHOZj2oxRCDk83B0bbcAgQuTdp0ANBJfxhoBQGh7CxwF/qhssGshTbHU39No9xZczE47SgMHuiHJXED9inw+RDogcMHsQrlX63MFJhs6cI5wB1TgkrkLiAlprSACcrKqfkzgKkisdKA2iBKUVtAng08JWB+g7EECVgMMxFY7IEMejKp++AycMuh0Aed7cE6op6XR/XtWNpuLwLvYbHyjcV2lUUhWZx+qgQavAXDlBhL0GNx6hm/KQw9z2LRhAAl1U4uhV3WcVmZiUKlasOnWtdh5eoqmVn+c0jRPS4+fuLuzOII918EZNbIE6aT3Ip9RQTsFn3Qdadl0TT/TctHbtTavs701FtcZgL7WKrCS2N8CKdQAQmzQ5vOkBggNhom7u8j2d5NJEdCH9r+76bptRPbSfrllZihASJQgdm61RpB/DBD2ZNMeOGD03goODpoi9/lw10JkD46rL1hmA7eJJHT9QlAgkMxA0UwoK15koAHmyWtsTgAHrzL+7Mcv8Tv/6n38s1/9AL/8ueeNgXGZHNmBjWvBTvypoAB4jYAB1NYaXnFfTwQFX3zr+RBAGMde5ap53PLVjL45BKaAWrQsc9+Ig2KleMNk36qOYweZH4GCmG3xlwEFft5dGUZAYILD09CkarvimKL2WElPu57WMhgMcEFLE2Olr506Ux2jO803ZpUu9BAtsQAK1iaUEZ7LUMq30Xk2O3tUPOD3j9ol5GRlfwsjG0OQWZmERIKVBDkAhOrv5zt1L6x2nRZ7QJwahU2Xe8hy1hLAD5+B8gl8p3n9dT0NhX7k6rUP1kNlO9fq70tr9Mv70O9V7d2VBAWlvz9Q+MfAIDZW6hUf7XymFEP/fa1DwGiFoHYzCLh9vrs2emEnzho/4G4QBwVeGnoABKdzL+7EGZLOLftAkrEFlLBWXT/XAAjWqmuwiDIDa5XwT8tFO0CAzubNtQXoHrwCIEtfWxsoUECqnURVl2ZW/JdNoLiP2Q0TAINhwj/rPgZu72XPUPGeBVNr80QWBAugooOCQh3wR/fgU8CBrZ4GDli26Yx6jS4vY2M93Ja5FqTgrj4PQgST0fkmK50qII0PcHDgNpEzMo9I92H82Y8/wn/6r97HP/3VD/D5zz2fz/qQHfAxs9FNx9ywhl8fYBBuHNBdCj2lQ7MP5htysCfhjUL63xjm8SjCM4LlFAGAg4NJubq1vQEFt6j3/x+BgoElMOuCAWganuekl84YuKXh0ejtWuZfdThutK/HSFhdelCCeIobZ6XafZcHcICwMY8itwu2oGAVaYCgiPmCbb6ACA62x2u9dKANgq7FXAckxhooKMilIifCOSUNRmNBNoGYBViEkDhhWZSypvVBmx6tWUsCs/Y3qJcH0PUBcn5m6X2a48/XByQL4vPSwLPf3q30aKHrLRkV81NG/6xNQMiKKIehV48dS0cvRnRc9svjKiIwYWYFA8Ze7MVDpPOpB00uZwswPONmY6nklR6VNbgKUKy5VFtD7j4Q4KEU6yFh4EC0ydS8th5dVxVNAbj8KEyWz97EAGBAoEADX/OBmoiGySEo8OwZS5Ud6nC0dNNouOzs5egO9CwVsQZuNTVwoAAhWVAgUIUsRsCO85cEB2qdPxEceHRilLWuU5w1wKRj7CUhUoYiggOjNrVirTWaoj59sRHcPNywBBQU/IMACubh8Zbjfe7GUSNu9gzPY1zwOgED0UVskfgE4KNPXuK3vvcevvXlD/HFt97ZnYfom9YFJA1p62s9KCSOCAQQnjd0FlgC0FgKdmAKAiigvyQoEHS3yBwjMYOCOBd+rT1gaf+8bwkSr2DXwYEpHhcsIYd9d8T8dysKQ9ZLQLiCPLXIj2FCJYKDCuk+PrsuCfcyjgI0pkCqYK3deltr7w64BxCG0YS3dPDH2j3wWqxhUKnaIEgS1rJqF8HEqFWQWVCFUIU08pgJmTPSKQPlokAon4BV0/iQz9Zu+ApZL5brf+1dBS/3oGehx0KtkPXa/fRFWi8B9+/rMqstn9PBA7Bv4evnx7l4FVDRbrkr91nJA7sMRFP6QFP8+v7Y78CbI2nGgdYXaPUIrBZD6zKZF40pyKfGClTPNrA4AhhT4HEEyhA4GNC1UwS4loqHUrEKcF2LsQTAWqu6FKQ+bU0B4NoNHF1TqvgyYBYoIUObBRWoxNsT5i6T2ISMK524lxOwD+5rCcyB/Gx72ZqWkTF/CvaTAoSUISxg62hZqbMHrvK1DPIWHESZ5a8dgQOKPnhEOfezgQMKRagcHFQaQU3rhCOwrAszNKnHHvhq38hfs40cFPyf/7aCgmZkBuD3tPT2yChIAPH/E2AMSGord0mU8OLjl/jNP9CKUF9429wHIkHR690xNqjFJERw0G5+u3MhXCVQBhEMDDePOlLrLoPuz2vvwX78KUF6O0PCP/c9zYGGe6AgKsw9UHCTcoyCxHPza2xzG/7GtkvhePNc8HsJ2QTQqkJZpAOEtOhxRAs9CWMAB2I3U6oiAmF1AVCXC5sRbdtmzU2goP9twnxCGl7jXHsxqFBfqSInZSXUncBY64olMVYBzlWwJEYRQqrKICSmEHtAyOmElE6QuoLSSee5XrVqYu71EFgq6sX7C6y9YFCoEcAiY3pgyA6IoEFBgQzAYY4L2ICGWjXqO4CFox4F/ZZ3S18fJuUf4hXmAMeN8g/ZEJv0ybnWgj0iZWUGiFtGQU89XHrqYQgsPAIEaxUFBVWwFm1F7YDAQebcjvrxtWRTI0ATPAYiUaW5GaxQ83ZEEGDuLrbnCdgHBeXaWAIq1wEQ9DiD0vcycLif216GsgYiWvTJ446EVSYo/aFAnyegj+gixAgOovUN/xsjOHDl7DEHlagb832aHh8H4ICMiWj3KcQ9DUBBqOkdAdTt42wRRuMSdm9mpmADCqiDgsgOzGxBA4V+rXa/Xv7Fi5uX/NoAA7iyAOPFJy/w9e9+HR989UN80cpEzuQmHYED9AXoi623Ee7RvS0yOE46toDgKHK/B/kAG1Cw58MLq2dmC+wIocEHhliJiKjbMW7QSNF94AtyFxS4IKlX+3sECK26XVk10KwpkuluWGqelNCZLmdrGrUqpWtXquzBAsG1gQMXKAzfjKRCtMIiorVgSGKCFDIWQVqRkhX7YxbkXZhvPwcATCqU3MKrq7YO9iJZag1VZBEIM9YqrfVwASGLtF71DhAyERJlDZhbRGMPAhhDNWZgeaMJeJKi5YWtRTMaUPBWzldd9+UKlAJ2oBDuk6zXbTphAAxlLQpoD8DCHDynE3QbBCQLEowAYEiHdOvfgjI7E5DU7UL2GWJtlWxFmFRRZSB7RUJrIsZutdrr1lZZ0qL3qqrLYJUREBSBuQnGltMPtTaXgb9XLQ5hXkdHLszBivOWw9IBwzxUyVN41HXDQMuaIZt6ts8xU9vLG1DgezmWq3a2wNZP9Q6hXqVzuMcWFOqdJlMGJfucA4RaQUlsL6MBfYS97EC/+e4NHBwZuf5ZoNn/ZiiZtW5AwoP/eqC3y9HAGsSD9pvhnx7cCq1L7nB7tIAS80FApeU5CjqzGcef/+Qj/Kd//D7+6Zc00HDWLcA+KJiHxzP0B0VSL//iBb72/d/C23hrfzLxWgEDxYsvPvkR3vve+611sivMCASaa85e08Vpdalh4NxndNingj/98UsA6G4CO6y/doTenpTKdys4L678+DI6KJhdCJjea58/+AlHpP060AQJ2nnLVpCUqwmJa6ch1xU1KiaLNdgLeIsV88RAAkltjWl0M+rF2b4ewAESt/MGqwUuJkwF0vq4a3Aga9CcqPIiIWU+bSPX4j5JdQ04y7I3NvnmTKgiA0AADGhSRRUNNBRhCBdkJqyVsSQNSDynhJXQGASNVdC1k5mMtTlpXZ0FXagH+lc8tiMwN+4jJrf2nEkI1Qe13bP9Xa4KIgzs7bkk0o5LQmoFh5oCEoRsrGkwtzxGs/yPXQDgZGWJQ5XFyADYZ3XNUAAAFqMylCsO74cW5K0okVn36xR3slY0Fs7jCK5FGjMQWQIPQvTjzIBgXjtzjYK2pECdRWj0tQF27sWa3E7IiQcw726DDu4pyKCdvXwE8OOaCRUsH93PUhXwpwxkYw7ZlKzUw71c7VxR1XdPpsxxK97AmEG1jDFE7Vfghkvh6GA7f0sBkYICMQXv7AFRBx6QCAoO6jbYeSoh1H/vd/6VpiT+sscUhBPsgKYbrIMLGyNQGGILpOLFxx/hve//Fr7za/8S/7vf/z/uXTmA1woYVLz45E/x3h/+HXz46x/g3bdDTIEB7+hCADqi0kVCze9D4SZF2unPPvkIv/OD3wTQe5HHm9RuBvYBAflv3gIEt/x22xf9aAAwgAEHCP7CrVLHs/vDr4f9kZzhkGBJ7IMCqiOFDVNETl3rJQaFwQwpdn3MKuhrvwOUF2XyoOwAaugtUAGhYgJlaSyOkFrqVnTc2ASVFIXU/49kPHBiDeNuSc/AWjS6e626a/V5nDEV8C7M3cqPo7fn1mMBZMcDqtTmSy2oKELIwljL2tIci1CvhUBOAY/BYwQGcwJnDMJ+CBazeyXw590aBBRE0Cz8Y8zCVLKYJ6DQGjhFNwTQ2aE4GlPQ3QGc88gGhFiAjRvAGxeFboZN2UNBpf/zTo97/Qo2PUOKQLBfkCjWIIhph3uBhWuRAAS2oCCOxLRZN87S6XNqYJADMMxJweIQ2Eo9PbaD+b5mIrCMe9ndBc190OIJDkDBz7KfDZF78DDlxXYQACwgXDs4II1DIE7DXvZaAe6jb4XMuigfhhp3Cs71M501cJDg7P8oxecDHbly+RAgJAMILa3aAYL0wmxABwkwYzTGDfyLL2mgfJvT8MtHlShnPeS6rYECCF58/BLvfe99fOfX/iXe/dwv36SNXxtg8OKTH+Jrf/QNfOfL38Lzt94B0CfL3QTs6CyAAK3ljebKA/rN8yEC/OiTl/h7Vobyq//XX2kb+rEb8ySGQBxB79+ovd7g2w/5ue6wBdO1xDEfOroQevSyXY8HGjbhUW6DgkhfR9oxnERrM2wZCAK0oLJdcOBbmSxNy0EEqZJsKMeUqHF3gFH6qAASIYkhgR1wwImwmrBTCrhbGbXqvERhz2l7fyL1WyFtgWnMgYIPJEBWQFgzIzIpQEhQNoEsiDGx1hFgiMV97Ge8EMwiBIMt2ts7BLc1OLEMMgeZ1dXu8drdEdeH3uRo7m1QVqRam9vCKxrqvR0Vhj72Colklv4eEGhNmlqzogyw0v76LwCAHevfmT8vAy4GAPS12l1vMjZIEwcC/pqBATGXgKYb1pbJ4kGre6AgWoG+Zob4yrBu9B6aTDG3WG6UP7U4FWogYAQF55QaKMgRFLCzBLqXNZMoAvwRCAxZCTMoiKzSU/dzrerWGfYzwt8LiIoBfV3cAoA5q3K1vdxT9EY3jMcatN+XUaZ51L4DAhLL9++o4ObYlclSWpdDkGwAApuLl2lkECJrAOpyfYYf77z97nB9cUTzMOoc/yyHNyNb8OLjl3jvD97Hd778Lbz7uS/cAD06Xhtg8LU/+oYiobe+aL4lCxhB97dZ3E4HCBiZAh/q9+kL4oc/folv/KGmPH7RkFwOd+zWDXkMEIxFf4C+TLgdnMRre/NmMcdvemwBpvM/osLDKTe2gOx6kj26C0GDjpxGDILEX5dyDArc0thp9ANA09Jqsb26QpBBLKp8/EPLCc3XaTygMgekTSaDQIkBTKuDA2MOMjQYsQK4SwlXVKQqSGCkKsikSpoKG1NQjdasWpCFXOjr8Q9KAbThgj0OBwprAdjynTUFTX97LJokJuypkUZqlcRKdxJYqr4emUNRLXbgwEhgcDpp758G+taWXSKtydPaXkddQdbgyDMhIqNA69XcGO7C2JmYhlTScWCgN2haTgMQ2G9SlFHRW4jXKuoGcMVfO1N2qxyxYCx8BWAAAnMxoqPU1nmfDXntGMHivEb883Nmi7IFIyBI5EBB/y2JWzBzY5cc1Lc4FcW/+wC/gwJPNQZkyxTMoODWnhaBVGWCZF3bDuj7Wd14Ckx1PYtx8UQMkQKm5FWPkQTNpRBZg1uSLeb7ewEgB4F+Qrv4IMpm/9uO2O6Uv0YW07QDEJxBYGrip1Vv9aOJdN3hMjsfAJa5Z42+5mfkL2z1z4uPX+C9776HD7/yAd793OdvMgU+Xhtg8J0vfRPvvvVFADjMOwXQ0nZquCG7w2ZcWzN/Xftdv93pHXcR+DEbemsvPwIGAHhcRH8ef99f5xE2ekpNGMeBTOhMwvElthP3LITmQkAPknSfIrlQaBHL9vfqAiMIjxkUHCgLqaWBg9b0pxa1GP1jBo5ILA3KJXz7TmlAYY5urqwChcV6C5hQpwogM7L0YjRa1wBYSUvvrpWt1gBtlEA1rObzP9PFwKgMWqT5fO9sMRYBuAKrTrjOvZmYHvQaq9/p9M3ve2U8/bsxDgjuLNbPODuUWSvSJc5qmAMaj1CuChSqNnvCsjb3kWdCUMyEcOZgx5LsEiw1puAwQ+CgOZGQdqD00sOlRKreFXVnylzhr43yHssPt6Qtu4Gx6uX8fglbNha92oSZhHUHqLKvfLwuYnLGCAzG+BIKDEEDBml0Hfg97fEonfVroGAA9RL2sYIBl1Ukk9J1wOfPIyjY2dcqphJkvRrjV0FYldWhClpX1YC+l6u5EcDdpWBAR3F9dym0VnKumyfWwN+i4cl2VGlxr+OJD3/vyegSfhhWa8XWjgECD/oQq81CgUVw93QDCu089Q13Uz9phI8OAYnYgoK/+dbjTIGP1wYYOCjYH70qoq2ztiDEX9wZLz9+id/83nv49lc/xPO3nw8fS8F476/vgAFghx0YX98fzhhUQIxEu7Fe4lF8/e4Bhhl0NrYAweL0zzXAE68n5jEbW9CuO9CLXtv+LzNE9DhUNSIsU5+zNqdqdVD1vcVDpoJuXKXii7EMRbohx6ZMUmYs0v3Iwmmgjt1a9Kp1e8ohAoNd6zH+HV0Nrsmm7xULvnrVESsxOrhwFiInbgxEtDYT62tOPy/MyHyncX3ujy4eU+KZEM86m+DsUAxQ25yYBZa2lMLuHqjOCngrYysq5NkBaxFca209CLygUBVYeqDs3q+jSpavOvaqE8aXPFAV6OCv/1YHCcPngQYC/H1nBvbul7uWMtMhyOvxKO5COCpEVgPbV8d9BTGXkAH9J1iYt4bUqta0AXgwmyxRuSJSACEz6ByYKB9AkckUk08+zxjTF4+GA4DIFHicwdEYZHUz5GbQ0D7cDuwgYWARgF2QAHRlHmV74n1D7vBcd553UPB1jbl7650ngwLgNQIGsxU9sAb2vqu/OJFHgPLFxy/x/nffw4df/RDvBqbAbxm7MgTawnlVMEA3bpTsXNNj41Y/cwdE82tzzYLIFjQmxEujNh5WEAVJsy5MiBwCgrl4iNcv8NgAr3TnkpV2TOx2sRUtB0mqUndCyj5I3S2c4quB7VSdCSjcO98lIpyQWrCZiKBk6nXusyubLbWs96Cf4qyEjkrftvent1eLeJwr5Dlrsf8b4xTOlfP2gtjOzFgSNV91JvVTJwcPTEh8wnI6jSChrpY6eQUWVTQu1DegNxS9EcsUALOxAikUElIwcK1AWRUMxLoBq4wZAXOK4Ozn35u/eUnNc3Q0f8N3qAv1/lpQ/NObERT4x2bQ1nog2GcUIBhbMIEBdxUl278bQBA+N1Q0tP0KREBww0Bhbq4QsMXiGLuntSvK4b6eh9SqLsJagWQs4AD0pcsTD5zlbNkI+6zBkUvhls296Tr4lOHr+uA98Wv27ooCAL7m90EC0BnAmLXzKlJ/X39ZoOF332ugYHPuzmocjNcHGACbC+3gABhs6nATlG4aJ80DNdQn88vQet/TT5WrPpmAgP5uMFFQsQEDUXvsbShy9Azo4qrQSLWqAvUVRyvaFM8/vh9ZAn/csAURDIRzPopMrlUfgSZE4uW2creRYvZANI8ktyhyYi2YIvMWiKxB7VMlHn8AF7hJ5RsQipxQixVIFdpRsAKVxcpJi6UWWqnphI0vOlLTDhCAXlERoN0GOTOIaPdmR6HNhZZc+VXRc/HPFlOedphhuI5KbMWTEhsVbY2eEnBircZ4TiuWnHBmwt2SkEiwGEhYDChEkAADBrFa3ibLJqxrzyTwgEEHBhEMrLW2YkJXA3D314KHKriuBQ9FCwpdasVa0DMFqr7+KnOhr1nuv1ndORG4Oq0vodBQBw/Ryo/L2JnIudEW2lqk9r4DAHf9zLEjcV96BVINBI4xI/uF02KwKURCQKG0+3ScIm0BohZ0Cdaqo7qnssUBQY+Rsn0/NdfCsLd9XwOIPTjUtWDrw2doYC9klzVAYA0iobZn+Mws3ZNHk2+RmZzcCZPcJnH3wh5IcLe2gQSU7m6w7w+Vdnb0jX5uez00gzCpo/7aBQW0c8/H8foAgwP045MiEYdJ2f0MYKDg+79t0Zuf15u0N4kODIDjmIE9MDAvsGH4edxGc08dno2BsHmG97v278g1fHcATeZGaGNabMQMkcm6MJ3QrtQFCdDYgVbQCNDo5cgeGEhouel6otsLFU1AE9EN1rIbxKWIFldJpB3evAJZtYIj2rjFysuKPQcNwWsipvAjWLC5jYABwAAa4N9t07ZlGwDSdMjmdKSWNlcFoVBObdT5Ze3Pq1HsVaSBhZmd4EkJKkOQsCTCKTPOmXHKjBMzTqnglBh3ecVdTlgy4S4ZSGgR8WTMQgYvucUlNCW0t76JWh0BjxeoFVhXaWl/qygTUAS4LwX3q4KB+1VwKVX/Vb3+h1Ufr0WwlqLfC2Bpbw46CPCIfW7ulFO2UtUiyMxgFmRNZ2lpog4KNFPA1/7W6gdCAC9G5Q//bAse7YqcKIAFZwgCELjV6pzg1qYpswjSZGyGdNjQzFwiQpZCul4hriZIg2HBrMWOUjbwUNs19cme9vfEAm46cDqy1zvXAYNoJVsHAf4LrZcNddZgV2LuMDs/84jMymDcTTEH7utoIMFYlYFJMNlgQEE/b3Iw6JZdeRfAgNB4D198/JHW8XkspuAR/fIaAYMdAiZMSquDPb2uf+vkvvj4pWY3fOmb+Jt/45c0KntzTP0sNQt5H0kOCHN3MU2b0lEcUdskhIA+/b0bY278FEdkDeaymi6QohvB5M1wTcN1tRe4W4VuDQLdqgB3Ci1Nyy1aEhEQBD80pdQK1jSLs/2bGYROG2imQt98VAs8IIjtu8keMwABhUh2zVQRNoBg07/Xb0KMXajJAIMBhQjIjpiGVTpQINbXV3iqJGn1RA/ZsN92UBCV4bVUXIqCAQUKnVGYR7OMjTk4JfVj3y2M85L10UDCXWacuOAuM+5yxZIIdznhlLS7X+YeIa+BbkkVVcpDQK7Pgc9dAzESGllZzMClCO5Xva77teB+VSBwv3YwcH+teLiuuL9WrHbtr3rdzIRTIixJUGtFTsnvWgMHvaBQdMVotgpxzxDQ58eWv22I3cyRozLqjQFo3wklbh0kwOfYmbwnBDrvuQ3IIYX561k/LygKDorGBbT4EaKeSVSrkpgaqLOZ92GPJwf+un9be/V4KlIhkUEwKNXO0NwJNbIGUHAAYCvjdsaT3AhBjm5cY8Oc7n23jCDBZPrAJAh0Hh0o2NkDUFm1d9h23qXNW5Ot0JR9NWp/17IP9tjopzkqXhtg0Hw2g4U7f2ifxicRvPjkR/jaD76B3/vVf4F3f+GXNqyCfn4HJe4pzQgS9sDArnINn4sL92dwH2yYIuPZhs0ygAIahFZABB0IbYAMtzkQCtRjWFKU0E/EF3vsmBeAgLsMBjAABwIBEID2QQFcqMA23c7mi6xDdNs0v7dbYO6ZHZtTVVf8MgIGLzt9CBqAxjQUn1ZokFyBDAVziDVVsogAhVGpmtAzNsKUn4OCh2tpinEtguuqtfpbjv6OkqQpzuC8qKV8t6w4L0n/Zcab52zMgQMDfbzL3BpBJZIhb96VaPMQofNMDVzVXkBobkB0v+q/a3h+KRWfPqx4WCsergUP14L7q37m4VqH+IJb18ukSn/N2vmyVjZWQQtYJM4oVbB4qAsMDBDfLCyUYBkC0OXpTMFjyn9W+MPf8TOYAQC2IADYBwLtObZGyzBJZPPgWT8e2CuOFMB5af1OPOuoZSlF+TDv9bYYAguY0kj9bwCCxQ2IoAX3+XxIBwkQGZjRWcZtLvFVx54ReeQKbqgqfr4bJNQAg/f1aRc7fqeuB/INaCBiYByAFz/+U3ztD/+Opex/Yf867NwaQLkBnl4bYOATuemSNSu0GAcAACJ48eOP8Bs/+Hv4vb/9zxUUHObmBhAQ/UB7C2UPVd4IXnnVQMPHBsVNY/sqjlkw+Xc4vNfPr1+DligO1JfFP2ysi6Ncdv8eMAABPfYjYEBPMij47ZzNwLBvgn5PB79cc6F04OCWDAUA0fpTsIMGBNBABgiC8sMEGoJbwivqaY8EQJKCggdCS5W8eu+BZgmpeyEx4Vo6SFhNqa5FcH/VoLxiUfm19O6ROiWC3tKYcCECJ8JnV405UFBQcF4S7jLjs8uKZ6eMN88Z96sChGvh9vycGEu24jpVkCAtSv7A49NSCmNp4esqDRTsAYLPLsoO3DdgoI9rFdRSH71OsuskopbWeAdlCJgTclUXCdCZhZxCrYBEOHlshgGCsz2nAAQ8ANABQKT/m7Ufng8AAbPyl85K3mIA2nP0z07jVpCz7mH7HhFaQK+1Ovfz0eyBABLcTRaBwsHv++9sgD95QbJIjUeXb7DagSEIUQMI7Z0IDqYR5dxf+TgyPvWXp9c7izDGJNDmOFTXcR6GNxuasD9ZQcEPlOnerWi4J8ceGa8RMOh0zKYZBrALCICKFz/+EX7jj/4efu9v/zP8zb/x7x/HFMxjj5I7chUMn5uPTf0zt8DBIzfTw1f2PuU+tir9uX9utFamA8hBJC6Zj0zUZ6wgKWmccNaCTH4Y78Lmyh/oAMCPJa8CAl6ZBjyon7B3Tf60AQRur5PTrUS7ucp9801MQ7Xyu1VZhiI9wNHdA2sVJFJF4wAhEYFqBYNwcQaANQjyslYtVBTy40v1YjvmslgrpCpN7q5lmxx9MLcvXQmcGYUJ17Xi4co4LxUPC+N+TeaqENwtjPWUcKkdIFwzYymM+yQ4c8ith8Cpcx8xHsNrRsRuhLPL4KcXZQU+u6z49FIGQHBdqwKCKqirwq3xGu3H7BqZGSKElLkzFlVZA8DAAHNjFDIzMjFOSf8tuWduOCDwiqCeLdDiD+ix1uo+EUH5z4o/0v7AuA+fYmgcrGtbyAfPA5M3M5vS42dgYMFLHIM1zsCvKzKLR3tfAuvn5axngNBOSypEqJ/fxBrEFsZ7cm5zPHt81AxzJX40HpPp/v7MBgMdJMSvToCI9hhrAF7fxmXTi5+YUfur37SKhl4MD5v5HOUU9pWFjb+2wICI/gcA/xaq71YR+V898o3jt45AwSc/wm/84O/h93/ln5r7YFL2ewCh3dAdNmL+zmbj3lhphwjxFV9H3zCOpv2XIyiICNr/jgTT4Ww6S+BuANGgveYPbHS+KyALNrOvR2WLICBmer/747bWPeLxgXB/R2G6YW3a/bhxjyJACOCl/b6DgnCePcLYhV23hFoUPlOo0qfFkgr0MRO13HwHCA+lIK+Ee49gDqMsySr9VZyMEl+LtKYz0XJ2hVnMovP3iJX3ScwotahyzKoA9NjS/pUqWAujVMHdkjRg0FpKn7LgVCpWV5hrbTn3Kawij62ItQY0s0BwWTVO4FIqPrsW3F9LYwl+GkHBWrGuBWWtqKtmHxxdGwAksBbJhDIIUgVI5vYIMRZLInOlpAYG7iwg82xxFc6MeLGgWPPhsG5Ay9royp8a7R7BwM66/RnXLDZrVsLaNSX1V7S/xM/TmQWgAwZAO325SyAcf/jtxhocgwO/qqjU3Y2gh3WZLBs55+83luZoBNfo9vUD4+gQmAWKdg8gbHy9wIBq9xjrCCRII8ZefPIj/MYf/w5+71f/ea9oSFYp15mI3fmk6XE7/toCAxv/axH5fz/lgw14kXk1j3TwESiYq7UdgYJb7z/KNFA80/3j++aImxc7m/iJwwMSaXrNfy76QIEdN4INIU+7sbrmjC4UQj0B2YuHiPTV3vVNitV/7+Z1ReFp90W34wQIJjp0fB3w+7FXE308hyBwg4BrxUoCwHGgIJwb0yDMSJysZHPS9Egoo6BFlQiJtKuiAoSECyldmll6z4ppWko1JZ65sQaSWAV0zOeCKc5GAasQKdAYhgLMnaJ2RnefKdvBmlLJrM2iSu01/Tmu9f77cydCzTBQUKCBhU8HBRLjCqKlWjs40Fvm7gRuRZ3OWUHBOSecl4w3TgnPzIXS/6Ux4JLHgMvsjAE76+ZAwFIDYxaAM5FR8T9xbQKPrc/9tanKiDb7rwfw2p5rrz/Cxs1ujHCuMUBPNvtvZ8wGwSTv9s7FWYEqfjk9EFF14lbh/kwuhAYSgrzrFVDGuWhnBtyU7XGI9PsRX3vs7/adihc//jP8x//6H+D3f+Wf4rkbtVYM75BVdWPl9tkB+OsPDJ481DLoVp63691DgS9+/COLKfhnePdv/AePWJfoCvkpLoY42vfi5qD9z9y6mQhCIPrjdj7aDHVg3DDbQ/ZcZ3SfZ/+kjF8Q/6IpOpgSprSds8kKOQIAEoTCk3xgbiFIVf9ns7gISiwFFN/uVRC83lFQZOMbbS2Cj4ozcbgGQAOo7O8YLBmFHAWLiKzBj78mKYPttZwTVgFKpZ6ux2JUdcZ9KarcuKhVuosP15YeGUeqmgmRwCiox0ZOhWZhVI1NKFRxJbOCueJ+tTQ/Aw/qixfFCtlcHeYqWaueb4SIc48BDbhUUOBVC++vBQ+WceHxBp51UTyWoGonxI1DOTJgTEimrRMrIEiZsWS2OArNwnh2ynjj3EHBGycFA2cDBXeWppkZWCz40NM1E1tFXymgUjogCG2ue7nhvcJP4zoEsPXTh7U43NmdtTivQ2L7TpRdDUf4nkubPSg35JEzgUMczwx2nlq3ZfqdAaxEI2L49ZDeKd112hlSe3MaM1uw0cnxWwYKxCzvQ5dCBFVtU+0Ze69gzO0ZncOG1WO9+Mmf4z/+b/4Bfv8/+id49xd+6XFFH+/rFB91NP46AwMB8N+QJoX+MxH5L+KbRPT3Afx9AMC/1/1Kqr8CLeQ30CZ4iClwpNV+8QDhPjU48AgFPiV2ICr+iOp3P3OMSB01C+YN08fgMnC2IFiju0cnrTWmJT/1HLRuwK3r2gcAW598X6RHi9V+0jiXLoyorsqIVKCh+706FQYKsK7Q9sA3esvfKuXcSuL5Y+qgbSfDAtl8qC1NSws3kRX3IQMJS1qwpIwCVTyrCBJ19uCcgDMTPqWiJXCtA+OSqJU01vr66ha454ILG4VqvngNUgDmlFYyyUkgs673L714zIIwrqVa+h9jLWRtrFlTz8XpzPBd0xtepEmLEtnfNWRS1Lqbbgi//0zahY8FVM39FQre+7Woa0TTEtOSNP3Ssi3eOCcFA6eMn7/LBgK4MQZvLgl3i1aAXBK1ec5EWJKV46naOMrBgLcwHhqLhTUXI/l93bmidbbyVdZfTPEVX3cSSk3XYoTBuEclgoL2/by7H3fvQjAe3EyAxRw00HALMByNoGj30hj99wQ9RZH8VQll7J4g726eg+w9BkNoyLOZf+HG+FmYi504uRf/5s/xH/+3/xC//7f+Cd79hc/roaUnaY7U8GRk2XUIegbV0fjrDAy+KCI/IaL/OYD/loj+7yLy0t80oPBfAAD9DXIeueNKok0QoroP/q76ZH7hl0br8qlswHyDH11oj20Gfx4pvZHqixvlKakm/q6Dg7339KdG3xthOuoefek91nuz6uFaBmYjAgG2VCj7VjFZUcPPuMLaI+nGPG6CFz3xGgUC9NoFHgMxIHDpbYSl7vaW32slO3ePaxUcwzXvFnOJtRnyomAhZbPq7D3Wqn/kTYI4I6cFKS0NIFyrdny8Vi30kplwTlWj5i+lUfenzMipYEkarJcTKQVvQYWVCWzxB83qbnNsgMAod2JCslx/rw54NGoFIjVQq8Y6iAy13NqtmIsOPdZOQwMD9XxEtDMmZwa16wj3JlwHG1MQWYLzkvCmAYI3zwlvnnJzGzw7JbxpAGHJjDNbv4ikFR8VEIzloHsHygKqCgxIrPX0tM4a8IxrDdi6MHG85ob1FtYa5QUI7ccJUHAg0/5sAH3bptqjBGKb6sf2ox9WUzBT6x/jYKFXwZxZhB3LeJYfcQS2I77bDZ++149U9ONquTMXntXWDSEFBCM4ABpAeBU5vzeiS/TGUFDwv8Xv/4f/xJhuecqFBRa2x3o9RjP8tQUGIvITe/x/EdF/DeDfB/Dy1nd2WQOLOXjx8Q97nYLPff4wJXEzjoJzDlDt9vtP+Y1Ap/lvBot783s3ftd9b3NcwfiZuKn1HEf5PzEnHrfRQqrChtiwGjxaJQhAQNxylPaaPwcGd3gbfl5M/XxZNPK7NUtK3ICQWqoVSjQdsD97veWL1v2HVE25RLCsw1pppxjqMbQshpTafMT+D724C0NSbu2GOS/wjoJEBhDyGcQZlDJSPmNJhCsTUlG3wNUq9Xmk/KfXglOqOCXGOa/46cI4L+qb12h+xoPl/MdUxjnfP4IC98MvWRXjeUkWpMetWqJG4XvdAoRiQH1tAWrMF2dkRd+vRaxGgKY3suixlkQoNWGpsOqFWl/gDglXUoZiTQYIblxDSqbQEw+A4LwoU/DGKeGNJbcAwzeXhGcnxl1KWBIUCDBpRoIDgtU6SdYVtD4oIPAOlFJbi2LZW1t73QjnNTats74Bkskzn1BbY14TwKt8OoOVl+0xdHJaj4oGCtICr8fhvUNiqm2rILm7L/VFt8ITWedS8loghMS5AwWviuluQCAA9x237SxfG2tr68xYg97VmsZj7k0BHbNh7beDzuiswQgOQDDwENyoPwMh0H8X0xzYZgmu6A4K/rEatfsXuP076IyBLcBtbPDXEhgQ0ZsAWET+rT3/WwD+T7e+44skOIvgrMHLj3tFw3d/4Zf109TdC21C/WbMroM95RwUw2HO6Xxd4eZvvjMo2MgeOMoPCjeMeHMjydWUlfvlw/cGnxv1gMMNY4CAoNu5AUNq04FroAKWkteFjAMBBwl7hYDiYPTNbPxPU0SV0HqdZ1fAgAbdtb4SNE5QTL+aQUG5AqXofHmL2cfiDvyw0elvVpwAek5EDTCQC/OUlXmxtsO0nLTsc8laapszkE+gsjaAkJixFMG1Ci4syJxwTmrhfnotOK+aoXCXE944Ffz0UvDsZHn/ay8ItHrZYKs4GEciaiyBK9ZYGTGnhDsrhnQ20HBibvn+sVVwDP5zIaMxBgSkOKUj6GznwgBfKzJpNcScCEvhVzp3reKogOBuYbxxSodug4XVbXAyUJATYaEACMoFtF4UGHgDqfUKuV46IPC20xPIbGvK11JUAo9RJhyYQmZVrkSQVA0Me++CDOIdRQugsQWcdK0aUyAgrLZHi8V/tFLcP8PeBAGJpIGD4iwfKYDvS6K7GciDlw/GLCcHxoBGuV+nT+zJPj/3cCajzKN2NQBZq3fAwAGa0pamJ8JXb8n3g9FlawQZ4aAiePFv/nt1H/yH/9j0F/o9bQfqv7dhmGmS7LJrMg3jryUwAPC/APBf2w3NAL4jIn986wviyNGsE72xjBd/8Se998EvWEpHjK6P/ugZqc2AINyI3RiAR4Yg3Pzd7+0BAjpUvoNopP5CZLZmQNB+JVriiKDgAEfu+Sr9oMQYc/c7G1CAVtCnyljgRwQNROwH+ChG9/2YSLdsgt7CCgcFQLY2yx7sJFTs2mkLEOKoRYV4FOAefwAFGoPwvhVl7cP9wMQdNNhzdRskUFpaPXoxcIC8gLI+olyAdFKAsF5xyguWfIcshKUoOFA3gyq2Ty8r7jJrEaA14Y2lDul/XiVRwUFVhbBjCrJH2yceeio4IFCfu6b05TRWBXRAkG2hpbD2igiQNPYgVVUazAIuyhqshMYaLEnrJORUhx4ITz1vZQoso2DRwMKYgujugzdPGZmAUzaGoAECgNZ70HoF6lUBQbmo62C9QtZLAwVwUCDSwaWvnwAsd4Nbn7KWxDNfqsktaBBrMflF1ONdNsfp6793tDRQQB7wCqziaam9IuXT9qeEGiiaOVOYFCiQZtl4KqeDBa3poK5AIoEIteu6WYipT4iHIALo4ACI7AFuBl3rH/GI/pLLV6uFwwC8+qN4jQn341fMtR9uBW4eXo3HaUWQYWngkKqBhv/tf4bf/w//884UzMZhWy+3GWZnC6L79mj8tQQGIvL/APC//Nm+rABUALwMDSXefesLFvBTx2jTZqFXX016nECNt43lfwPqH/bxCgvhFuW0yTzYBQW3h9tfe6c0swR+OhtQMEUSb9mNeJ40+CcdAETrIwqbuDDn/gGAKpDega7Xnc+szY80PUlxfG4XrOBgoQRhQevmaEpdM1QIjwI5Zwr8uh0UeNR4CS6FowC56JPxwi5tzXBnFKxuPOWsIOF60f4QOQPLWYHCcgLKCeAFVBZIAwhnZNHaBScWXKpg4Yy19sZD92uxJkOp1Qi4eO0Aa8AUGw758NbBS+q5/t5+2bsxnlpVQK3852WC57bBgIKDIoIFGneQ4cyBtADLkzBWVsbjUhhrrlgL424RlJpbx0Sv+Lh3zl6x0Bshndq/3iRqL9NgyxDcW1Dhg7qc6hVULsD1okDg+gDxWBVjmmRdB0A5gMmfae2UXhTI8ECrZeHH3etLMO3LZry464A80HAEBaVKa23d9qiEtuNP2J+JScFdFavlIChMLSbVWT53AzpAYErQYmmW6nyksTyAUbX1BhzYVMFJk70gROBJYYIjOOhHVmKjxUlMP/AKKmAcbsH1Xh1634CXn8Tsg89vjcporDpDvMswB0PyEUDg468lMPjLjirAR5+8xNe/+x4+/OqHePetL6rP3fxEuhD1Uakkf306EG0DAX1I6Aron705nhLcuEMNbUHB4+kmh4t/Ygn8pQ0oeOwcJ9aiCZO6ZQj2hI23yfUANVcU84gNalqLYCZjHsPnWTdqISB5u2ZnDZgBSQCpsJVi1gnzNpOVB457GEeCXabPD9O3FsQucsQ0gAVKGXK1DZyXBhJouXQWYTmDzndAyUC6bgDCCYxTAa5Jo/3PCXgjA5eSWjOih1pxtVoBLRtAVBnMShZAjx2A91TQ5w4GYhMhLxG810BoHruNpLJVQhSyOgdotQ40g8H93t5tcv98tQIhNRZjrljotQgWPggqnAABlQegrpCHe8j1obMD10sHA+u1g08PNrS1cmuNzMPXSCzlLCWAg80Fb1+nwEy1AFfSIMXIFoCT7lHZggLvWxH3aGz0NY8ZCMY9GkG8u/yEmjek9aFQD4QyCI2fuCUrZR8cAFv24KkjfrQ1bJ/BgYMCYwmOmtUN41V0Qku7BsCLlun/49/RlPpWp2B7/M5cBz3RXnc2t7MFPj76+OXNU3utgIGYw+ijj1/iN//gPXz7qx/i3befoy04v9nkS0qVfQcHdpyDIMAhjSYt42eB/YWws4BuUWa7xwtBN7cAwe4IpzSzBP1t6RrtsejagziCARQYS7Ba5b09YaPKQFpu+/zT6joICkgYmQrOidUBHfhNglcxUD+zV1wjCayBMQiUckutQspAVQZJUlV61tpFg9HqhUTWUKymQK+yxzvgoO4+J2ZgLe058VWFPzGwXoCUFRBcLEBxOYPSPXC5B5YT+PwMsmYgPYDyCZLuQWlBymecl4w1Ea5VMxmuVfBsIb0X0nsSrKHIkNcUAEZ6vrVoBjWB73RwBAKZO5ujbh80tw8wbgcRDeqbW1avVSCZe5dFWxvuhnIA6QlZe+fpbowOXGi3UuES/mWGZhZcHjRmoFyB9QIqF1BdUR8+U5bg8qAxBFcFDJ0p6O4CBwJH9z2Oud3wBjgCHRAYu0QeX9D+VoBJDoK9a6EFtQqxZRsEtiAGG9ZeQnsGBd7Mam+P7nlB9/boDOIrtNEWvBkYE2LdYm5rp1P5u3KoveZ+ed4FB+ET9r3dW7E9vF+bH7PpC2MyKLg8DnT+bmzBI3pB9YH3pgD+5Cd/iq/94O/j9770Tbz7uc83V0b/bPitBgBcPwUmYdIbPl7+xUv89vffw1t463AuXitgAGjr5N/6noKC528/N6VvyMmEvZY1KO6t1kmVHYW8U+L2Tz75EQBAkrsS+k2f1x+Nb7dPjG1F5y/NC2u8sfPjwEoe0Wc0vn3IEniwznRem1xoBH/VDVCg7XWtyU/ttfFXkSbw11rbNbg16HQ2k7a5FbYodHZHCQZw4AqM7cQWt46quhaIi7IGXIFskdHVgopqbQFIXhTL3REgK0pD3UdMUTjAgUJCL0NcgwU4zmO9eu14au8RFwMJBKRV779nMCwXZREu96DlpMqquRnOQFpA6QTJDwAvYGMRKhhXE/5+H55lwrrIhrlxyz3cfrtOm+bABNzqIOjNgwC0QLTNCApebL0soV31XufJp56fKyINdDOQQKFaIStbwM4OXHbiB4wZkPYY2IHrw8AMRDAwAAJDuZTGCRgBgLNWExDQF4cYlQgIPJgVeRkCWVsQq7uiLAURzM114K+tAgOE6krYAwXXsq1Q+dgeXUmQRRpAOLP1OEhs4kW6228HHNAko9p07Mmi5gquBg6232wAQTZvDWPIZMMOe+BMxgBydw54ILfbd+Z34/EmOfy1P/oGvvPlb+H5576AVgvDXQUbPRWMVgODDRTOsWii+vG3v/8evvXlD/F/+P1/tDsnwGsGDD6yi/7gKx/ii28937nJDCFL49uLOG0f3gn+46T9rr/3mwCAOinsW6l2+vvz/2hm6IEM3f07vn7gqjz8/cdYgqcF/4TrNivChXyRkSmoMlog17UMwsbpYv9svFomlYUak0B2bEN1Dg4yWzMVAYt2JiQxGtMsJRLprgVotTnKS1fMJwCrCd71aoCiqHXYHJalN7tJGAITyVkHoCkMfSqNTdgwCkXC8wL1KROAq4EEBi8ZuHYWQRmEk7EIGXQ6dxbhqnUQKC2QtIDTCclrI+SdIDOBBYJq2+E5GGkv1IaBEIWO1jSIwuvd2eXfDaDZlXvw2nrEeweZIRI+cbP6/Nziefm5RVDiDYw4ggKCVSe8gq5rBwHGEjg7IBM7INdLT2s1MFCv63A/4308Gn4/9XkAAjHuBB1wDlUNvRaGz6UDAqIOBuzRnwslBQPcwYAYY+AM3yrqQirwPTuCgodq2SvSi1Hd2qNrFWUHpIN45NQA3ZIYVIHVwYEoKPB6F7CX9hT8Zj4dkG9qCWzZA8EEEA5GBAcHvzqeWQg6PJLT8/Nb+qHx13bcD7/6IZ6/9UWNiSOCByJq/YfpQIOu4uBamDvAqn78re8pKHjn7eeHVwu8RsDAQcHvfsUuWowRHpSjoqrupqKBpukfdKagVwR78fFHeO+7X8cHX/0Qf/v3/iNYV9yN8IojBsA0nOeCdgMajsd86McAQTz+liWwI4YAw7lV8TBI0xVjFsaw+C2VSRefmLC3zldmsUZQcKmjsHFwAKi+bQUFhcBCqFRRxehqMBIqiBNSFVARcCJTbhoNXWER76TUqV7T0norCOs18gmQ1TIFyCRcyj0jIWV1MdzIPacYrOgBZ8kAx0AzszEJyiK4QpEw39LaEGjTpLquI0i46PnJcjJgcGd/f9oYBD6dIWwggbOBikX7M6QFkhIkp5a3ro/SqX3sr+MBHGACAmE9j2tsfC7j6gs+T2p7dQAKr3A+sYshw4BALcB1LkTUUw3r5QFyfUD1oMKyQi73CgIsBXEGA1JkuGfjOZmCTxSAgDJBDQjsMQHAFgAAvV4BgL2aGK02hrkPKGuBLGlsgbkOWOtkgJICwuBC0G6c6q7xfboaKHCWb62hE+i8TwXgqm6ZCmkgHoC6zLLtUxLLSEFzCzGp85YC2mvGPTFa9UhgVy75ahoAwg448OM6QXE0/L0IEGayYe/ru8rf7Yn4uRv6YdYN77z9rh4icZPTXuH1SFc1UDAVkmug4C9e4v3vdf34/5dZCT/LGECBjyNwQCFXdU47mbIABIQXH7/Ee99VJuKXP6fHX2sozHNwTm5wkn1GyxXroatsF8Tw3Scg3aPxVFCw2Xjzagl0XfuczVWVUXhXYw4kgINVVLiIuRrWqoJpBgUxqMz1sFojggxr1CMa3b5WgEpFAiOzv26sAbQZkKctOWsAEVWWfr5+PYs2PGrBf1bTgEJ55MYUAOG10uZqLoqkk2GAoaxAqluQwGZ1Fm12NFietSpTYUCBEoEerqBE4JzB60VdHpf7TiU3kHDqAYumNGDFk9wPLWlB8hr5nDblcG2FbMZGrTeBdbCGJgHWBL6vq/B322+Nbn/ieQShqVUHBUN54liu2AsQHYEBCyx0MFDXtQGB+f7E9EBKuoYcEOyBAQruoeYOAJryHyobxiqaPkdzGWQ/lgWwImdlCgwAOCCMAYfOFpR+B1tQsFggqrfDLuJK3oBDYwz6PNRi7gTWPZlBWLmCK2OFuXeqaNEjrkiSunwgUvlnmtc9Dc37EtfOYexTHdaDOJNILVqhrSHC08ABcNuy3/3uBAI6uyXx7X0dYTrAez74uihVX0gU0jqNOWglp+M4yECYQcEHX/kQXzD9+Fhc5GsDDD4IoMCnjYF9cGApMjGHNrzZAIEA+JO/eIn3/0Dpl89/7p1Wx32V7c0fDoMABuywEMv79cXj78XztfFkQDDd4FcCBY8Ic/dfDeDgkeHsQRzFApiaVRFAwVp6ZHxwOdrBCJUFtT0qG5AtMK1ANP3NLSEovV2rWHEVgvaLt4Y1yeaiWk94Kbrxct40VmqgKZSupThXBh7IP+Pf8yp3ltOuCMmi18sKqlYlrzIqVusvYMrHKeq1dzGUtSuJwqsBhATie33MC+j+p61QUrMglzPIUh+7/1mpaO362K3QW9k3+3fYT25cN3QoxMNw6zAE+FJ4Xccjv9+o1dqBmIMCAwlDoKCnGF7VVYBYg8DqENRV72ddy3gvJjcQMTdwEEEB56yAIC/2nvctWHqwYFpGy39P6ce58L9jxsHcMKn1POggwEGB2L0Wc8XVaoGctT8X6D4qUHnWWAXpgL09Tvu0ioBFM0EcHFRSQCEVKKTll9pyAXZKWd0YcT3tySZfD3Dl/zOCg58BLNwCAo+BhL3jEBG8YPFatUiUC+8BINzQWY8xBV94+/nYTOrGNb82wOCdX3y+mfm2CA+ZA96ZnZ6X/+J/0En91pe/jc+/9c4ABlY3kYFGsflwLgIOBmxVEhQMtH0v4h8BkVq7e0ju5kaa+a7hSp4ICppg79fR+nkbONBGHQyQpgk9hr5LNeGALnCAyeoIe70HN433ydmCunOhLnxSsrKuBjzcIqkEJM7t7pDVkyePmpMEIbWhNF5gYlLCfR3mxtgBD2J0N0JjEmpR5S69VG6rmb9eNXK8rC27oa4rNNSO26R49L0qpp7JAJhP3foxcL50NmHJHQREMOBxCTk3+jlam0BoFR1cRrdFxzg/G4Zgh/6NQawDg/AqvxUBgdhODT0wWklie203m6CsAytQV2V4pNZp3u3MwrzbkwbQIiDweW9gIND8INb5D26BTcMtYAhEvNX2uwZfcjymxhfkloUAzo0tUGVhAN1m1PfpPLprYNzke/s0kii6vxWIOIDPYZ8ejZ5CLeMePJJP0fwzWfUYONgdj4GCva88AgZ2y7w/qiukzecqquJdL8DItKHuw2Y87j6YPn5zvDbAoOWwGj3jN6otnx1wMC+cOKkv/kIDNb755W/jlz733IrBdMVWjDIH+gIB9EZWiDEENv8exUpKeZPd8Ohjkgkk9ONtF+cRUJjZgj52QEGw9oZe7+4/b990eLVlDeI8zyOxlltNFVhJq6AVMfrRNgfz6DrYvSYDIXxjJVf0lk5VlCJl0efKUKcODgBNP+JtK9yhj3wcMm3nZVJS/h0pnXkwBUSxfn4ECVbQyAECMaNeC+qq58ioKJbaCKe0DSAoE8Xqt2WegEICL6nHJsxAwS3X4KfuvRzS1jq154+OJwSubjH404/bAJg/N1fBYWnrHSAgtdocj0BALKd2jh/wOZZakXLazHFjbHx+I2OzFxzYmmippT+wJy1+Z5qleY5iVKh/J3butCwqBQXdhSASXAd2qFt3rPWyaKDcXt8zXMIperZCIpVjyS7hVjOu3VUQ9uWm6RJRe63LJ72iPXDg4ykuhVtAQJ+P1v8eIKjSP+ClpeNxor6oQWa78bRW6Q3iDCC4HNQgTRqMvog9boGC6LrmeICd8doAA8IIDuZRMYIDtz/jpPoN/RPL8/zmr30bn/+Fd1ohGEG/uWuVjf7QA3VQwNDN4ZkQusl0HAEEBQemnIniXhiv5Qnz0RD40fBNt3chASD45tMGKEZr2ZtOgbXsIzs/gqa4Xe33iQlc0RrnZJDSjwnqKpjOoTfoiY+EG0bHMKoISGwzgZRCB8y6EEC0S6TO9Q49/pQRQZY9ykRpxwY7dO2ldOmk/m0HCJQuIHMV1LUAV4B4yo0vFXWitplZmabEA1hIOTUlNjAKzhQEMEDZXp+C3JqFv5Nq9z/mGOj7EMOwaUgkdVt1MAQNzoxAMXdBA1rF2ZntnFIar5mYQQYGfC4pn3qvC4vz8L4X8CBRa5Tl1rw4IAhZTyNL84Qxu12mLCrPSnB3aDRoHhte1pgFWrJatHT1XinBWAhLe2WggfhYATQFReYyjn0KzCJu8moC67vyKRpiMPl0OH+9ENJjMzCDgj1A4Mc5BAT2gaNeE5vb0GR9l+ke4+Gpt0KEVC3OwCYqFqmL1/YUpmD+7t54rYABsEWDUbFGcLAZ9vrLv3iB3/7+1xUUfO6doaRvDcebsxKAfmPJLFUNKtFUugYQDP019sKot3Z+4Xq0WiNtrsN/95V8dTNbsNl0B/aDcN987t+K7gQKgMt8jOSd10gFRgFD1mL9DTTPeeWqPkmx+IFhHvUiYyU7r8dPVsQmWiLDPNhGhIGqahOlwir39J82LwfXHQTNUUOU1gc9zmmtgBR1TZSrKqLF0uXOvfEOrg+Q5WRpcvfANYPTBVxWlIeLUsXMkHVFQWnBiHqrKrxuQgnNjZswZkYx5UZm6TKTVVZUt0FkFbxBzxAkB6AFyk2lnVuw3DAZT0RscewoqyELBNhvQlRWxHLVkQ3wYEFZ18YGyFo6qGqxA9vfJs+/85FImYKcG0uQzgYG8qkBAopBn94Qy7JDxOsJpKVZ9a1qarT6b6yxMDn780gBxHncAdCyTwZLNVw2Y2T2hBkFFUlgjS01/gWFUS1ScGAP4F02ddoy6TrLrF/PiVsRLAQg4LkDbKc7uxF8X3b5dMRtsMo1ROZgjzV4fMRf2OKQp7MEe4AgEh570ja6uwFjDKCiVstFdjNWxFxa/tI0Iij44lvPjYEOvxVA2K0d+9oBA7E/ZgDgE+TgIA6/eR99rKDgX3zpA/wHjSnoboMYiR8pOf/9Gk6EpZ+Tlwdt0HgaEsDB3ntzM5Bb40mfnN0HqNvd0A5YOzgQS5sBQNZS1REtMyAQcFWrQ0TfFMsm0AglFe4MZQ9WeFbCdpm6JaLgQEGB1+T3bn7ZAtdcyMQJqABIBDUgLgIs6G5ki9q07DzOPsJxbvrv9eptMMZAmzNpSuMKFHMj5CtouQLrHXC5BxlA0Ep7C3B9QCIGscYOFHjzYb9VI4NwVHe/MikwMKntVrCzCpEa7wGN26h6ccAQ3AriN6jdrJ0SvTtrdreMbGw1PGc41DoAgLmoUAwUFGMEIhvgQECq3JwrsrlqfxugclCQzllZgtMIAPj8rP99ugPyYimCS88M8BRC7kGCTWnfWlvArnU4Pw7zGx7FprNitFh94UdmLxO3NF7vftkXHoOTNJBxtE9juWytPJm0lLnt1QSVCw4imDuoaNuoGtMmMoKCW7LJdj6JGHNwUEZ6vPwnj6PSx674N6+H92p7PrIMLVzG/vSsBJ/aYjZYraK4oLqcNVBg7MEg96Ap+xEUxMHh+I+BAuA1AgZOGd264AgO5tcjKPilzz23KFyjdWpXEn4z1x0hkwzJEeB9ffoXwolVSHMrzLSOL/UDQPiksfneXmyBX3jceLsWCXdwUIt2dpMKkaK1AmyViRhAMFKBoUFNiwvcon4DKhXEAirc858NgLVfbIYv7Za7zazNfVqlO4a5NKbLtkusjtqOJtR+u2/knksfBUCk6+J5KuDouf1MCYkSeFHZ6ilz3q0P5WoA4awV9S73SMsJ9eEecj0BD58hJU1NJL6gXNQ9sMJk9XVV260Z1/tWcCuow7wBCnUCCtH9cDMFTw/YgAFFiyyAhUeFbwQ4MQPE4wgmEDCkDh64BfaAwFPmpv3NjLRkUE7IpwW0JKTTgnQ+qdtgOYHOz0DLGXy+64BgOSsIsAJT4AViIAFkvQkqrBKobNbZ0ZrS+e3ral5nwwfnIWH9Tm/5cZh1j3qNEJc+qQoSKtbam2BV8xPO+1RrSPTS2bF/xtk6XW7BwAQU9IY3F0JPBw5uunkQm0BX2dSsKvteYw3C/LwqKJimE8CO7sDIFgzvzaAg3ItyAFKBPsckGNgCgOA+hmTGpHt4vHjRHiiIo4MCh4/74/UBBpZj71Zsm1y3Xv1jMq0hPA4Kin3Wm8AAmtsbRwI190JiLeZRDXwDaAFyj43oSvjLjE7NHdFw05hTzoDeczw2DhA1QwiAJFba36yG1PKY/EJEQX3SIkJXqz+QqiCTN9TRyPyYKu71170+fwosAXFwLcBc5U5nugUSrFWJG2jGRNiCAL/3EHUh7aH/zVxTOO9wLg5oEifknJDyHVBX0PrQqu8hnRpA4OUMefgMkjJkvQD3BhDyA8rlqi6CyxVeAAmAlVke+zV0ZeivhLLL2AcKacmQa9kNaDwCCnrRASwUjEzC0ZiYgVgt8ggI7AUKlut6Ewjs9SuYSxMTkwUSKivQwUBGOi2g01ndBnfP9PH8TP81QKCtscVAgeQzwLnV7ShWPKiEtRWzdI7WUwMDBgSS0VxMWhxoBgv+vThmq1Zp/EnRsO9RBQfEFbkS1kqtpoH3rXiVferA3TspZu7ve3VKZRsAlRPF4kfcUBlBwa5cemrwqhU++llBAcKMOXx6bNT5uYECBwTeTCwOi9zCWkV1iBs0j4CDWNxvBgV7bgQ9gXoTJb1GwEDgvm+nZY7iCeJmdFAQYwpugQK/mVHfqkwUJNmnUHdHoAh984e3+vMpQ+GvbNzafB4QY7Onfyk4aI2FhODtjVunNO6L1sFBtSBLkCpIr4E/d1ecx9y1Ldbq96AcFzD6WlfIUWjq5YyBojMbIHDadd91NN//5v6W4xbRbOlHc83+hTPSKYOlgNYLKF+sgdKiEe7LSWMPHu4haUF9+MyC2C4gvtc+C7lgvRj9zav50Mly8Xsp5uF212oyktXFwQSqYkyAoFjOGTODc0ZJBGIBcwWqlrolIRAJKNUGEKRaVoWj3vKIwI4CPnQhfCogqOt6yAzEY+2NyA5oUKa5DPZYgru7XlHSgACd70DnZwoI8hnIZ0g+QdIJkk+olFAqWrMqL0Pdqwe+eitjIjEWSoOaFQRrMS9u7jEDErJvUDQ+gACOCgbSs6gE4ETIkiAJf/l9SrBmW535c1DgLbJ1j0oABBajE+TSpjYGUa/EOhstf0UCMhqTGgDu8rDrUnIlI3Iz6H1vHOmR1V8TXTuJtWNsBAcuekWAQsCfmf761pePmQJ3IwxswZF7xsbrAwyAxhoAQUcBjW6Z56KBgi9/G7/8C++0Wv+uSCIo8IYzPrwOeCI6BK/N+g/Un+Hy9jyCgqjMHgMEj2FlXUb7G4V2FsVucZoAEDbgAAHDckImp7VU4cAimys0tYgMaKXMajWlLdjy4ci5CUd0EOVWxi1AAOwLyBkQlMAM9B4CXYDPrWfXWgcB6daTZ0q4kNy4PBIN/tYlERZOWJZn4HzSjASjopF7kyS6nHvzpIfPkFNGOl+wfnYPWhLqwxWU9bHWCkqa8igecYmtktw2eeoUMqGqgFtXMDIEFRUMjtSsBaM55G7HutUmeO9eDG2s//KgIB5rHhEQaMBlAuWkIOi8IOUEPi/gnJCf3Q1uAzaGAAYMJN+pyyCf7Z8CgmsFrkWsAVEoMTyVF36VNbTXcrwY4EwGDpRJEI3r2QHFbQ4QFARRS+etorKLgRZgfbRPAezu1aN9mkhBsWcVZQMJmUdQ0DJ49kDBLKuM8m3gIF6jSMsMn6/+MZ09MwGRVe4B4NJBVvtMRxFskrIxC/bWEcsQgWEcaxUQC1C5gQMSQCAKFET5jx/9+CN84/tfx7e+/O1N9kHUKUB3UQ0/fGO8NsBgLsCjL2JAcREJKv2ioOAL5j5wIFXR/UZxUwDbm7h7Lvafo3mnBNkRO3XaeVZmR7Sgjw0g2PmcxJeJ0ZzR/j7RLjg4jjVgkGi3QTEp0sCBMw6cwJQsbsBcKYZqWYBaCYXH7I5MVoEQfeOMc7GlVCMYUAFEh4CqX5aE5x0URJeRd4Jc6yjIvaGMyLbx0zzG1C3CyYFATjgbSDglwlKAhRUwLJxwcoBwXdTFwBlIJ/DpjLqcNP5gOUMunwGXB+SUIZcHlJxADyskJ/W5rwUlry0KX5VPumlBz4F3c6qef+7R2IO91MaY2SHhDtcKym7xlXDXa68E6fA5BCdSYlNgtf3toEYBD20yJgZQMDEEMbiwuQ1Od/b4TOMIDBzUrG4DWe7UZbDcoVLCpQDXqpb1tVZcK3AxMPBQZegR0noUPGH9ZJZmhZ+Llv+OFL26KS04Ta8QyXd+ZB+jgeGPotSCEPXMBQCVNG1YuO+TCkC4t8u2u+R3sh037lVmBSwOCBKFRwcF3s/C2IIGCmoZAcEsi1pswc4+3xOacQ1u3x10RFiFYf7mD0NZAv+sCfsEsXLsqsUrCOxAxW/LExmFdr4mlzJTAyJ+jB/++CX+3h+9j29++ds33Qd+2jNbQF4c7GC8NsCgme1SQRb52xhtu36fXO9HrRUNn7fgtxrYAqBP29z+dR4z/YemzPxfBwWd/rvtJ3yC9+zpg6w3xOD/oCdTXwhCnWDMCZlvkqQDEQYSJd2LAq0jEABCEi1p7IE6/vPztDoA8F+c3S6Jxjm0n7Yv+0H1wVTIEJkt2IKCIkqfxrazDxtrz2lh6bEI01D3QWcMMhNOueJkQv0uswZlZeBcgZUZ10o4pYTT6U1QXbXpUbmgsnZMRD5Z/MEJkj+DnM6Q+89ApwvSSeMPeC2Qa2mPs3UNoOXtA2jKP6Y4xswFWtImzuAoc6GlOt4oiNRklVR4h0qRqr/VMg68jwTBK0GmDP2bBbgCSAAjt2vrLa/T8fUFFwktBgisJsGjcQSnO9R0hixnIJ1QlzsIZ1wqcFk7IHiowHXV9XO/VlxLxaUKLuu8furhuvHAPF03VR8LYU2Cc9Lg2yKCc2Jkbzse/M9MahJVnabjQEXpYDxBFaq71Ir051X6XvV9s7dX/dCz0eOAgCi4/nQhNlDQAg1lW3DslcakDXvvgFcfR7K3l3LQeRYDCBEcCNkL1Bu7AWj9WxJrLFoSoPB+1ckigmSGVA5shxhr8MOfvMR/8oPfxDe/9MEhKIj3fAhobczw7fl9fYABjDWw/tnOGkT/D2PsR/3Ft95BtKXjDWhK5AZDEHvVd5+goeawQWZQEK3cm8rtZxgDWzAPMgurXRIDKPuf3b3uTjn3d0nZA9vUwmICLimVBmULvFtxhtP5hl/33Bo77pW5o98mejtcP4BOKdZO6fnveXaJSG/5HEHBQ624roJLqfrPWANnEy6rCvdSe/14AK3q25J0LSyJsSTCKTNOzDglxn1mnBPhLidcM+OcgaUCa2VcE+HMGfn8c+ByAXgBika5k0XAy/mZBijmE2TVVsF8vUDWC+p1BT9cDwv6AAfgwBX5FHhIS8KmF0AEA94PYK574CO6FqLrQIxChi0Kqa2PhKclcs5DIyO6FghTcy3w1KHy8Lp2Cj6l89ILFC2nDgz2AguXM8SAQU0nrBV4WMXcBsoQPKwKBu7XgocieFj7urmstX22VP0egM26cb/7KXNjBXIinNgofWEsObp+9DqpQrMLSMuAE2QwMtSCn/aKG77DntHvL6KM3xB/47amHO/X+HtRxjWZpzdJrdWp6dXGdbDnQognvhk8fuZGjMsha/CEwX4AciOTLLRNAUElXZ/saEGDq7oJdAAOUqVDo7MFT0MP92c/+Qj/6R+/j3/+q9/GF54ICga2ANgtVz6P1woYAEBLVwn5rA4O5pSOW0p/b3jqjj9/DBTMfeL3rF1XdH6et8aNDJfN8I+2nhAREBBDUEPLU49VPtiQ7aDSjkMkaK4FEp1vE/hau71od0NSP5lTku0aGvgaL3oGR+PCjnRYWNyBWiS7PjjS9ksnp/Y6SnAyLfqE90DBZa0q7E3Ir6XgWmQDDPR8XcgTFhPySyLcLQmnxDityho8FMFDYdwVxl1OONeKtRJKIixMWNIJy90JtC6gdAbW+5bB4AAB1wdLcXwArhek9Qq+u7QSwGnuENgi9oMSbUr9EVdByp0V8IqJRBg6BDbJlIbjj5UMezOqoTOlrx1vNCUVXNaetnjaj0N46vUMvSSylS5eTgeph5ZpkO807TDf4SoaVHitYq4CwUMR3K/FQEFtAMHXzv21dACxVlS5vWaWRLiWhCVVnDLjLDxJaAZyaDtO1nY87BvNPOiPjbrf2Tex5TEAJK+c6NsKvXKif92V1DxmOdbAgP0m+b1ugOAGS/CYXKa56Rd65cfwmSgHXpHFP5bFswUCBQQOEJi1TwSxwP0IQtQn8QY48JEOANCf/+Qj/Gd//D7+6Zc+wC8/Emjo1+Cys7EFT9R5rxcwEAuSmlgDAPjhJy/xm3/wHj749Q/xzlvPm597HkdYyoNg5+hhj5QHoaXP7fnZiBDSerCxeo/WYTzFuFj3QIInBByxBkIe9ugBAgRNMThgDYAbC2kGCOrKcYAgRCoMbIOybeIUK7TduuKW2z61h77le/TjWiU4z5ho4ODG/S6ibWc9yHCVERTcXysermsT9Jcirbd9RPu+PnJm5ItWf1sS436puFsYd0vCxQDBtf0TXLO+t1ZRlqESViac8h1SPitAWB8sg8EAQl2Bh8+AqzIH3j1QDCQkrxAYFKxO3Th3ox9+xz0QgYB1aFRUm1p5ZQqswXxvh78cAPh5VC1j3LpSWmdE/xx5qeOdQkdPuhYHNK2h1Kl3nzydAStUpJUKHRCcWnBhAeHigMAs/msF7q+lsQT3jTHogEDXS1FXVKkaiLjWttbiemGPSUmEJQnWRKgSRLN3bK5Aroy1VGRiLOjugHm+B3++B/nNtUwO9hAFJSvEXVG1+JEn7F2Zaqd4UbWnAIIjf8XuCC6DsPdvnN2hgRXla/zFvV93OesxHtX+KMbaFOpxVj3GV+NGqKprNbPFWJWKMlXdmy/hv/vJR/iHf/w+/snf/gC//LktKDhiU3fZgv9JMgZAt2wtf/Xlxy/x/nffw7e/qrWjPbCQzIqM09SJOh1EpKgO0sAAoLn0M0tANObUt1QdP04ABONNA/YxLcXYmOGTnnUxjwgOhqMYa9BiDW6xBrc0ady8RDgGCN1maMEWwKjA52MCbdGOjVNmwXEDFCBU6wtBkdUZnCqbuRuDS921oBv6WsSYgjqAgtVec5fCPNJarWJjRc6swWjXDhDWU8Klsv0TPFTGQxXcZcJdTViTYGGy+APG0gDCgxZKKg/anCmfW/EkWS8dJHgTIW9B7Aq5HgiGPZfALRAQmy5R7xB41B1w050yKHqqFUNTJAMLsS8ChWsg4PHrmK/BmxgZGHDWQNKCyos9PxtDoIDgWoBLqbhWBQbXAtyXgvtVmYKHAAwua8VPLyMguF8r1rUa6JT9dWIuBC0E5tKHwVybHMms6zGTrs9lWLedKXMZxDHO6SjI70l7CaCgeGXew+FzdkLDMTcl1/dcBv69J7AEzhDcYgtabMHEFjwVFIzM5G35HHswEKEHGgqFLAKLMxBlmL1oERljWqHGA4Ug9+bZMzb6v/vJR/iH//p9/JNf+QBf+Nw743kD3X0XdIuf+8AUvQJD/noCA6AplBef/BDvfffr+PCrH+L528+bUnVKxwECpPc1I0KrgeCUT+wrDli1sEdYghiN24Jy8MSbFTYhTctzDyzE8Rg4aI+WxaE5wHX/oIF6H4SIRwc7QBBlBwhl2rSTHzteG3AACqLAeKI14dYCJ5AwQEl90wCQzOoRi3uDxXxoHsvoFx/mUexRI+GLR5YHUHA1wQ+gtexlVqWyeLqR6PdOSenktRCuRfDGKWnsQoGCjyy4ZsI1C+4yY8mMM2tw5LWai8EBQjmD1gtQV2B9APIKqldVnKHNsHd1bNa2K+G9Yc2TGmNgCtaBQevESNzaNc/Cuu2iyeSRcH+x7CkIAda1tbKmAAxQSwMMr3wNU3dDZO062MoWW0EisSJFDghipoEHFnaGQIHBpXSA8NNLaYzSw7W0zAQHBNfWsKmvEf1bXU7+fiZGIUGtFVV4WIfzaO4CW88Ek0dwWWOFg8q19fAYaP2n7Cm/l2RGSgMO/yPs51m+zMfenGMAAfPjK4CCdsjwOEjcR1wc6hbW3+QdgFBNzDp7QKTrYJc9gK4BH/+3/+cP8Q//9fv4z3/lA3zeQAGF3/WpaMbp0fm/otv8tQQGnuP64i9e4r3vvY8Pv/oh3n37nelDUFAAtKjZGkCCf4YErWVoXKKpMQToAYaBJcgeQ0AhGjdSesD+DQubUTzV0BZ7RKjAxBpMh6nhM/2SXXnqggSqKtJa4M2RnsQa+Lm35+hAAUDMy3m04NMgGMImDBtybCTjvxGWfrMg7Fo4QbiCsEBwVWsqLRAmAwRK53pnyMReetnBHNr9nOcwjmgBxrLOtSgrcUXVNM1K5nZgrALcZUapqyqMhbFWURahCtaqjx5/sOaEU6oaoMiMSwEWJuR0Qj6f1CJcz9re2Uovo6yIHR7Z6PemVN1an4crVGAXAAgnUwzUAcOBFReiRcKYFMQcbJZLu+9NeU2Aoa2HG+cf2YtNZ0PvYUBJK0zmE4SSUf0KBBSI9dRDZwWcBbhf1c10f926DZwlcIbJXU1z2W9UaeCgVI0V2FwO+SO1NZkIfa1aDJM3KXKXhBoq0qprKiNzDcyBYKDzfRztLV0QaDbyHPH/qnu8PX+c0m7Hn9eZgQIJ67C1n55AwZGMjL74+NKe4TZQ8HsyO8wTcQJTr7bq2VmrYMMesLEHgu7QVZCAxhT841/5AL/0CwYKgkyiwA5gAwqCMRev4Ynj9QEGk4Xy8i9e4L3v/xY+/MoHePdzX4CnM8ZsBb1HnS3weHsyQMCw4FI7polEfc7HbgM2ejCm57Ro3Ohv8zEFAYmdAJFT/t3Sn9mD8UkYrqTwBHBAftTUvjqAg3aOPG3meA3BgtuvMrLznRsWBaT3XC/71mGv3W/bwRSA+zf1OlITJolzK1NdCeZe0HnQjxAoCleGBYQRckpYqlpvzAQWdTEBWvYWGIFadYlkU3jVVwEA94hshFqUpSasWVothVMWrKI+7TsLYjylisQGDKoFrHFCXp4hLXoPqFzV2pbexEmaMjBFOyuEeHei5d8ssEnwEqlinay0IUVsz88b7rP225j9z+E8o1W7/GznLuRrIhmQMTAQehis5i4qFoB6rcrQuMV/v1Y81B58GlmC+6s+XteKB2MQngIK4n5ktmDV8E+LYqUWkOhrkW196j8zaDgABjt2AsxtFIoHFWeN+mtP2WNse0x8j/m9nRjB8X4/cf8PIwqpyQW5AZ/J/t5Zm3ug4OgnJVxS+BdBwW6cRPv+Vm4TMVBXCOchO6sYQGhStXa3JYkZKnZoJs0+cFDw+V94pxmwQGeHdIpowxbsgpphqhkvPv7oYFJ0vD7AIIwXH7/E177/2/jOl38X776loGBWrop8dSJb6eTwt9VhGQJ7oqs8H7kNeCdfd47G3bPEBe3g5Na7cUSKYfjmfjvCgl5HIPqfeAYHDFUiAF4dHPj5H6PRXavkCAAEy7AFnLWLNOFlUe8S8uaVJlbqVNICSGouF7KexUKERAlgsmIkeq8qBOeUILVgJcJKWmnuxEoDLsmjybkpdC3zWo0CZlRSJbBnJ7fTNwHAVVAJjT7u72vE+irGHhTBNWn65JktuyEnXBOQSQveXKmXW2ZKSCmp+x8A6mrWdcgXt7UnR/drtvpnq8ws8CiAvYKkX+MoO2N30DQESYF33GvGqDloGM5bD/iEcw/n7SwHZ1Q7V29mNJct9kqF92s5TFuNGSoOCq6lDoGoM2XtqXtxeNDhkgwIZCt+ldiyWbyYkaa6ZgsCzEQ4M+GckjUissZE3EsODwCxTixSucJZGPHgVKDvs3mP6QU0Zb2JJZkBQ3vNbtdBIODw+b3XngIK2DuAdlBg8HEABIey8fjMRlAwy257Xx/DqUf3xpSd1QACaXr3kXsBAP57DzQMTEELJA3THf8+BDXtemqb3xcff4Svff+38DbeOrz81wYY+AJ8+RcvDBR8q4ECICjXAA5arAEceSnN08JMp5XDAP783yjSarQzewnT3hSEnMaLAT9zJO7eaBtA1RlBlVUDB0JtA+zOwc5hPQvGwY+nbhIU3Q7gwAXxY+Bg88N9AT4GAvQz0gPQoP77Rm83RYDuWz4Y0Qfu0feULBqEazMIFHMRUK7gzBAmZCHr+QBI6DJ3TgwtcqU334V8YkLiokL4WsBUsdr9X9JoGc5BZu6KipbiKgCLDOAgsg019bLMuTCuSbBkjU3QaoqmHEJPhv7oQDXr49L0MJrgOLqXg8U/WV9VV4EU7HYJHIXx9tjcFEiwcNzlBlj2TlYWLh1YcU8876Yg3DJbe7VL713Q26oDD6W0HgcREKxStZBR1b+vRV0InoKosSYKKtp1kv6XmMA7a4FdblDPRvDslfOSWvbKKfHw75x0fToAyIzehpxGF4K6Da4NFHj8CTz+ROqQ/fGkfQYEFia4bWze1XVDtgbMyHEzeA8sGIu7ey9vgYId94GEterrsEmdG8tlOwIrNYCCnXoLu5Ol56vMQdVAaAMJnBbVM0ljS6hugxMB4B9Y9sHnQ6Ahw3VNbxTne7oHHO4wHdNwUPDhVz7AP/r9//3RJLw+wABEGlPw/d8yUPDFfvOI0FIZTckRpaZe3dds+th8PzSUsCQC/uzHL/E7P/hNAGib0ze6sgQy0neRuou+1KOVSto0BVSVEkdRcCDiyGCLCWwD9Fie8djKdoXugoJQtbaDA22rvPaYA9gmrDvgAGFjo7MIm34L/j4CGIAyAbIHBqYgMz3EZMkATUg1S4ZZN2/KnR3MEkABQ2jV+S1XpHRCZbsVDFRzIS05CqnSAiZVkBtNy55v3usZuMW4CprluDfcp6wxKON7tVYUItxfq7kWNG1xrYxTQgMID0mrKOaVcJ+l1dI/J7XGE2TXzTXW0ND52xWOEpX9vvKv9txf75/pBtSemmFfC+iyX62e/XLX2+qg6VCg96Wn513s3ObS17X2HigPpbReGLF0sb5WNSi0VKy1Z6S0+hWTIs0ErExYwEDCzTXg99/dBksyV5UVwzpnHopinUNRLA9KTexg1ZoVsaVDu0FSLQW0gYK1sQSHQanA8T4D+l6LgDxkpkRF30FCQYSloNDnoIECwk2mgC2kMoAC4dzBQQQFdQQERwWZRMyV2ARGXExNmA6g4KlyvKV70gpIRguElgpwRuIEsiABFl2Pzh4AwD/70gf4/OeeD4f3/dHYAdvT9pK+twcKgg588ckP8bXv/7a613/x+fbcw3htgMGLT36ogYZf+QDvfu7z45vSxVUDB95TwQWSKBp21sBjDXz8+Y8/wu9YGcr/zX/1t1tTHLYN2dwGIQK4+fKaogtWTxzEylS0iJRkSjr370jfPK7wZtkT+7vPr1MECEoZtIqMqigKhHMDB8p0wJgE7n/PyZ1B+QuFsssDiyD7wW+PRZ2L5bXPo6wgTiZzCJQXjWTP6lft4MDmrKy2qdz6WJE5AzxHLouaYfCNV9UyS6qIMzFOWSPNH9aKKqkVPaoilrnQ4waAHksQhwc1OpPgo4qiPH25ogqhJotrKFo/PxfCxWjjvIpajUz4zEvo+j/rcJfgXfkmBRydq8NisXPBsfIv9jnBtuukZ2jcstK2xcG60PPz3QUNr3jOJZyviFa3nMsT6yPC87H0tVe7dLA2FyhKZsUVEq3q+cj9dncjM2sRLAMDTIRz5tYw6dQqZs6ltBkLY7zXDjSbQbIO7gNlCgwUXB8MFChrIM4aANu9Vla7gJCyKgypDLA142oaNqmcMBZBSgAHevAmv0b3QgcBbZHM8SyWgtzAQgMFWvq+YmQJIpDdHYN7S7/roLl/pg4AoIGCnXidzXBWlxjE5lKQrPKNC5CW5l4oLoONPQCA52+/q+Jxaq7gXWx7ESs0/eWIflOrwsYGFBBv91EYrw0weO+7X8eHv/4B3n3rnX5Tgf5Itog9XU8qQGlyJzhbAFXEpmH+9JOX+Ps/eB/f/LVv47l1sdIc9cASNPpunQDBIzXASbl83SwCNTd8MVVD2GTKvUKmciYbi+5gfhgdIPRgQ3RgRAktK0EUdmuoA8Eb3bS/h+sIlkKba+qvh3EIClxIzYDAv78TGCWpwiOAZb0264YAFVKABk7VonNbyfyr3KySZL7yMBUABJy1Fn1OBdeV8VDUSl+rWZC54i4nrFJRTam4C8GVCIDBrXCUcjbcIxMMLXBR/0KxQMO1ah8GZk1pYupggLmqDzpZm2fqSsOVcCzGZfA4XLfNa/tV3FT+IkrF63NpEdVSpXUM3BuJAO/P0Ot72HnSbdAQGYfd8w3n7G2DpfbKlkU0oHMt3fXTQQB272cPDu2xA0xKIXHUbzfup7OK/nxJ3F6L9zMnGChgZNbaBgoG3G2EHnzKlr5qrAGJA4I1uDHVWCGpqANTsG7qROiN3QHhyXpQkLJyUkeAQBm691psgovWaXKG2BUMyvPYdWDK39/jNMS4NHeRWdszILglC1UXkLWink61Wd4mUV12P7WvQ7wOUZfCABBEjT9Jy8AeUO3rpldORNNDnUlzoBzck1HfTS7cFz/+U3ztD/8OvvPl38XzITvvGBm8NsCgpSRKxS5UjDSXaE+FRsE4CpNeMpftzQYKvvxtvPvW80YHZ0KPJdhLCaqrWdljWlCM/qWkyl+jWZMCBAYAs9iFDIWP1yGGlIfLw7FLgUiD7dwKa5vCvsgc4g44QcSAgwMS/yBRZw+EAu3YwUEDFjtDLYnwXvBt7oKC5nrYoQNLAVAgqWqjoVoAK79LZdXn6xVYCFQtR5gIoCuo6IQqQ5nsyrtVupIqC6KEcwKWUrAWtuC0uqtcKrQegfrevQfD2GwpWpsOGPaK3vhwhZSYtKENgCuJKZXalErv6AjklZvCcaZAlS6Z0vWCW/obe+VXo9Vf4Mq+AwBX/kpw9NgKv3YAh9mEgFs+3deurFUHDREw/GXPdw3n6gq/A4B+r2YwEOe/nT8hpBX283Dmp8eS0BBX4s2R/F4xEXKK940GUJcTb1xErW23gSUHB4kBHoIN3ZXgLsxirEDtBaQk7LVyfcI+g4skkIEAgSp/qV4rpKsSuoGYugthAgWcRkBwIx1xz3UwG0dHchBAAwMzwNyVXbMVPriGy25WRw/MZDtvs7IMbKhbYdFjpqXFHjhCyYRWOVGkt5ImRLaAmlHXVuROsOGLT36Ir/3RN9S9/vY7oRjUDboArxEwcEv+5hDjxgl6Y0OLZmcLEqlnnwD86JOX+Lt/9D7+5Ze/jedvv9vKjAKG0A2ZN+rOfHo9G0EXjqzXjU8SgJaCtSIsKnJt94E6WgT6KgcauNHX+4aIm8PeCl+XdsmtZ7uxByEa0XrCK0hpcQdiot7BgAMEx1nN50YNcIjHHFQYkNHNIqt9idFZkRtBT37uADa+zxjtLrUooKkVRLUHWREr2KtFf7cyiAqE1rYtVNao8CEirCxIVYO6ClSxZEooBvTdEu2tmPeVUFSYroii8pyBQ2QWojKKFHZiPadrUSXUQUJUPtUUTp0UUVfAwNZyb78X5/4RAHB0TX4d8+h1IUYl2WI4HgEMPvbONzIVfr6ru3ia8j+e+715b78Xf9vcAf35vuL3a1JmZ3s9Eaw5W5ITdWaHevE0z4BSF0QHB5m6HFJQYHEFzlRa4FytVQMN7T0FB6Ob7in77HDMMT9AcCOMSv0mSxBjCWaWILgOBD0TpkLjOSIg2JOBbWHo2VmPIzPF0R/0HMv0eQQ5FwCCyfY5cFPMBeOy3fwq0CwrqyAqFcDSwUhakC32J1t8W4W0Pgw+epxQPG2JSGgLCn7tX2rMXRu0nZtpvDbAoN1yYgjJodWqH+7sAYBNTYNEwMtPXuIbf/h1fOvL38Y7ARS4UIr54s2fV0uvPmf+u03EffvRZBYugPVq4MBYgha1+9jtG689bohNylSjnEYKrVYZXAtOVzXXwsQeNIDAWlJZr5X2AUJwPQiKbpL1avhMLQ9iZxmMKJ78nBooJF34vMoQK+yTLeBJiroUTPBQIzwEC2ekBO10Rtrmtwhh4aAMBZDEqizzyNLMdPtau3W91hqej0prprF7Gt3oy56tV38erdXYpW+jrNZRAeu97hT3PPyaAdwEAX8Z5foUxfr/jXM9Ym0S04YF8KyPqPT9NQdfmTvzka0RlWcujW6SObhyWzAtBpD6YzJs7W5LmgpaqdJSoj1m/zxpTPtsAwrMum/tuJnbax5fQHkxxe6phWzPXwEQTCyBM5wu22bXwcwa+LoYLo3cJlSwE2DB7dH2YWcPBlBwIN9FjFFpAKHCs6UgvdJuO36M9awAQGidGxHXSa9dMJ5nv88NFHzpmw0U9DofnSE5Gq8fMEBYeD6dT9wYZBbzDz9+gW98X0HB819811JFepAhgIDQzXfXYgs0+rfupASNJyxoQTvISudlwoD85mv04B3Y5sDIFsT35rtewy5ghFgDAtLkWnB+ipw9SNzAziFAkDlqV1dzZA8EVTdIKcPpEVZo5/G1b1aR/t2D4itzZ79dCtM3sZ9XLfpZAMLSfgtSNSCIGRmkmQoCVJlb0AYLpVktovSmjPej36ORadhzSairQiPhZ1+3pzTG2IV2X81Mvlq1RaArtFn5AqO17n/7YJqOvVG2+8rV/e9PiaeYre2jc/3Lnqe/vneu8xyO5zfO4ZK6a2aOBdA4o30XQLT8XYg3EEBTQCUFQM7bluzs/2C2nrsovT7FENdUmlXrtPU8tOOluQ0rNAHpiXusgQIvj+09M7wXReougYElmCtlegDhESAIsQR7LIHvtRKeDwbRzv2tQAAHaKxBJYIRofoeMWhuLDfIZbFGXrUZhBsZX9Z2zeLB0V5rRY+g9wJBDvmUA/AkMG/l3AxS6qzf0RhAwdvPh3mP83lrvDbAYB5DhLyPnZxZ04F2h7QL429//+v41pc/xPNffN5AgfrmvUAIOkLfAQUxJWjIEa61+SII6Og8BkvujWEl2HNHzH69slVW82BzT/gGccu+UHctCKl1U9A3kcYeZF3MTejUFgvgLgZJnTlonRVtw0iydEjhppjBrHPFqcUFYLX4gFrh5amPiqSQz1+wWLyBzmZIYGO8tC4MB0kBahB8loecyMxWzBtq2452tlg26XI5HabLXYuyCg+FUbLgspIFxekjkyr+xKaEy2jptqJLtk70s13xApOVbkvNFfDReEzBzj55z8qI342jgwLA+wBEQLABCXUENLfOM/a12GMEInDZm7M4T61tdiJk5iH6/5R1XZztPU01jOmieEK6aGcKGlinwGb3lwHfc3Wv4E5Io3MXQrBqtzcgAWxO+cbYJcCrg+6MDSAgBuUOBsBWXIwYyFZ22hX/BAjmTINjl0GDQD2WwJ6UsMc2sQVB4x3Jvyj7nDUIHoZh5mGAYXcMAe6yL+dryNwYUqm77PFf1NbMLtuLMrbNtu3uBAqLZFwnOvaYAgD79/cGOnhtgEEVveECQDvrlZE18NEW3nb86OOX+K3vvYff/Yp2YYygoDUkqc4YRPdBAAXrpbEELU8Y6IvIInzbImnKB402aucZh1Nw2N5PBwgzKJg/56/PGwTBtQCRFmPhJzkAhOBigIGvbWlbUSHgLIMreNsOVEnZEQi8C6K0jZS6kJvr+s+si2cizE1ziLRZTgt0ipNlARV2SO8uKWR1C6r5B0Pedfvq5p5MnyFPqyKoP5SMYcBQXKeIBsydUkKpvcDO3SotA+JStaujAwSmaiBBRWKxBb+n7Oxmo/iaADZBjJ42B3SmYR5z0OSeop3TNP39vVFg51AArlreuUW+BhqgQFCFwjnCrvvxc/TnMYvgMSYjAhPPGMhMlh3QAYGnDu5lCmTuvTaGIlNNfqAr8rimmyU6KvJN6V29gv73HBXve2U3Qp51P7jLcr2CrJdz6z8xr+29veWgewcQOAjYAIIhk+AGO/AIINiAgPnvR2QfQd+Lsk+aEMb4nSBrEV9z5RzcFc7SzPVXmpyv6j4g+2xr7NbAAbXzaz9VC5AYsWV8DR/Y117Ai09+pNkHMaYg6rvgRjhizHy8NsDAh95rvbHSiEjbQDtWh8/PRx+/xPvfew8fGCgAdFM3W7FRd0FJmT9vyBP2wJ694iHAIxG7+2CgbyQ+cCO4cHFaOxwzIE3fB/MGgchuYGKLPQgAAegAQY85sQgRJFBSv77VR3ArRSgBbIBqScq+OEBI2YqyBHouIvE4HFCFgitDK+BGbc5g0IUp4FWsBsHYYjxuCOu9seM7TdRr9UteINDsBm3U09PoFk5YE3BXKh6sRn8ECExkVfgU6CbW11vwKEbFV0XARAEQoEkD/Vva9w6RAbZKfs/6/qsYDnRg5xtfb+d/4/xeFRTMLg2vJeB1BW7WEmjphD140NMGe7bSBSgFdO0yYlDkT1lLw987azLS2+Ie+P55gbQIeSmrgoPAxP1Me2vusBkZAgqfi+6CW301dsCA3i88CgiA2wbRLPuALvtAJj+JBneCunxM1pIXKxrvxUZOT6PLe2MMKhSM2fyL11XxOisVowK3YGniBAKFmIOd34LK9Bcff7Qt7jfP9ROCDn28PsBAuuHRWANdBh0cANNE6Xj58Uu8/wfv4YNf/xDvvPW8wTcjvFTJWfrhgNqNRWgK8VZFMT/NWu3+RxaD2wbxG9iUC7ifs99Y6SzBwBbE39n5+yY4aF/q7MFc96DKHv3pLEK4ES746gqI5zklnTsXkJUVIEhVYMBZ34OoO8HnTuoIxuYxRUSPnfU8TTEo6z4j2E9tHS24TWtgYKjiOA//3d6sKun8ePMeXsD2/Lzk5i64sjbwuSbGUhnnZA18itUn4IpLhQW3qeuBiXAttRnbCbRR2HvlmPVvuvk+YMc0VoIb961vcHKwYXOPar8Pe28rgqIrAUAr8jPn+c+R/089v/ibbU7i+e/MibME0W1wyoQT8wAGvOLgwmj1AzxlMDM0CHDtKYNjl8vS1s9T1o79tV23bf3uKaZ+7eojr9ByURXIBE5pt8iYW7Lbm7W/rwZ2IAYVcnAHGDuwX5QoyDFMzIC94BIzAgJgCxLg37sBCuLzqM8bwyzo8dLQ54RQ1hhsz7tc7sXotmzkobx3cNDml1sfE0ncDCs/DtVVC+5R8jjEPmHT7X/x8cte3M/bAOwAsGGu/6fGGACRNZjBATZI9eXHL/H17xooCCmP3efnWlisVoFtopYnrJHvrWCIvx8XSSv+EXrF29+NsrOgnZ7L2zdVYw7s2h5jC46MkRkc9O91QNXiDoDmB3WAoPOnHyQKQAFojMLAJniRpgYSBERFz5ktPqEGYeWvtQ6J5raJwvQgbqRZRo6MA82/3wHOLj4ccwYBQ+TxVBBmr3RzrBvfqNeUgbyA8klp3LSAeAHyAklncF6w5DOyMJai4OBSBZlUMS2lICfG/VqQV0amamV6FSB4eWan+vcU6Bxn0J/r+0d+fFe02RiGKoQlja4FVcAEJNa/ObAWQUhyYMrmPP85tsBfHwMOt+emvz2CFE7UYjESsNvS+FYcgfclWLKXICbcpdQYgpMDgkRYSEDrPehyBZUHSwk0ULBem1tRvH7AU9bLjnXOHuV/Eyxg+FtgH/esIPL3BOzW9hP2VAMqQ4aBAQKTZT12wJmCmTE4dhEAIxDQx1HJ1+m9GRT42AMFcbjr4BZrwHDDssvdKI8VJCgT4/FRKt+qAShAop7wNGo3Bi32Ssrajkl1RauzAoCqFbwTgafUN09bWMoCZbrf++7XtY7PW1+Ep6YDW133VLYAeN2AgSm51JRfAAeUuma0iXrx8Uu899338MFXP8QX33o+HCqKkh7ME1u+GmBwyzOODS0X/HVAFwJEzVfXW4haGiMnwGho3WDmRnDdhf74s7K6A58iMzjQWXCA0IZYxbxpU2+KKJlgb/XtOQOioEABVdZOiCxolcQcJDjNaXMrAChNPtk4KFggA5KftKS5K8LFYPbVUgB6EhkgfxQtCiPrXO4aiMGl7tclItDpToX8ctJ/52fAcgbyGZQWyHKHUz43gJCL4MGKLDEl7aRIwJUF96tGvcf4g8w99RF4vIlTjPoHsBv57yNG/PslVt4P9ou/rY+j0joqBPRXf17d9XA0Fy3FcAosPLECAu9keUrW5Ig14HCJgGB9AF3vtcDZ+gBcHyAPn6FeL5DrRdfM5V7XsxcZ8pOdQAE4gXLWYl2kZb4dVErKm6h/7ali697ZRh/hbyEPxA0g2M8jLYf7CQDqAD6og5YngYG0CwRmJR/OzO7nOA7BQvjSXqDhU8Yea2D6HdpDxg0UgbDLCStER6RyWzQAwNOuUWp/vvnBcN+BZoB0PRKgkMsiy0fY44hefvwS73/3PSvu9xxuNopXdwU2oOApbAHwugEDG/G6OzhAmN0DUDAhMgLaRmptN6eN1AJRXGPH92JwzxzE45Zk2+wGAJxF4BQC2ZQSfwpbcGu0tWJ/8+5y69PQNuN04D0fmy+91hQEChZGoGAlqK0HhDS3glGt1Vw1rqQj+xL9qbcubn6+uTjqx5HawYJvRO8+t167cL9eFAxcrM68AYZ6VUuwXr1yXBdrRAxelCHiJYPyCVhOoNMZdHoGPt8B52eguzdUseQLJF9xWs7I+YTEhEsRMGmQXk4Z11VLILt7oYjGGexVXZzHXtVBf/2WAgYOlLCMrz9W3KidR2An9uoUPAUUzOdUNeyindNQfXFKUNmrPuiZBkduA2URCCcGuFxA1wfQeg+s+ij3P1VA8HAPuXym6+R6gayXYY3M6wPAsEbElD9S1nWSFmA5NVCp8TvZap4AjcYPTIIdHDMoayNY3I/upRAXMFclbG6CqUyxy6ijGAFgyxCMp3e8do7eUcdJbJp2fGl+nMdZgx5fIMwgdPZTjE2kJIDkdl6edg1jCAbZP16kLs6EMeA8yLiWauqxWtMFfPSJur+//VVlugdDeNgy2/iNp4zXDxhIp8b7oGGuDkGBP97QK/13AvJuPxOkbQ9p6MBkDxTkBcK5MQQtstc2vT+v+NnZgkFn2uNhQxq/vFuXLrL7u0y9Eh2hM3H6e84gOFAgJNYWybrBamATpG1C3SwTW7CXirVxE8wnveMuqIDXpegfqw0U6L8HBQj2WC8X1HVFedCa82UtQBHUVbMZnD5nZlBiUE5IWa3BdM7Iz+5ApzvI6Q50fgZ+9iZw96ayCPkC1GfgfMZ5uUNeEjIxLlWQiiDBejhc1b1wXUvr4bBy3SjG4d5sFDC114m86qVPZf9Dwo32KoiAK+S+Dh4rh9zOYwIoek5olQEBvPL59N+VDWDZmwefA3UhwNpYJ5yZcLckZDK3gbkOTpmQpIAuxhJcPlNA8PAZ5P5T1M8+1eeXe8jlHutn98P6kLVASh3WBgBwzkCiYX1wzuDTSQHoctYYpuWs8wBY/MA0nHV4zM0wj1v7iLqkmDMJ9jobzjUGvC03sAUGwL7cuhEDezwMDeyBA//tW+OINQDDAoetZ0J8bgwCAO0VAUzgAECF9otw4wMILNF0oa5LaEKxe0NCoPyvq/7aGML9o+0xsjZPGa8fMLDhN9yH6/sXHlPgoKAJugOM/djK8sHmP+LUFqe9AQCj/3APFPCydSGkBeDUELg+7rMFcewZWbcAwWMbctjEQTHc/BzCfJLHH0hjERKsHji5skomazK8iIs3jtLfveFKuDUaILANHYsx6YVAqCo1O+QlW1zBem2goNzfo1yuqA9XlIcV6+UCWQvqtaCuGncQrQSyqnecE3hJSOcT8t090t0Z+dkJ/OxNyMNnoGf34Dd+DnT3JrxWhtQKWs7gfAJXApMgk+ChViRKGo/AXu+g1zyIZYHb9Nv9nXsR6Gu9H0H87GYa7XhewREA9hoo6evj9+IxXeHvNVLy11/1XHqvhLGXw615UPdBjy1YjBVIDJxZixdtWILrT5UlePipAoKf/jvIZ/8OcrlH/exTrJ9dUO4fsN5fUB4uYV3I7trwdbHmhHw6QdYMPi9Iteq8+El7iuBwMR6PlJorYrdokH32lcb0vTGAjQZFU6Ur/t1iQzfkxWbI02WTvx797r1Oi60HHK8h/9zMGmgcFAG2bpm7tT64FKycMbADDljQ6xr0uW86ABhdCreGyDAfDgp+9ysWKD8Zwj4ds8h+VXfz6wMM4mwEZQ/0SXthPpn/8tc7KIiY+ZXBAWlKUAtO8Rx8TmjFd9pn+wbXCljZ3AcBFFjUuqTuUqggy4E3iwgYK+89MiU+ZkDA2z04XrLPyWZOO/Kf/X/zKBg3WSM4CSgBKGwLvxBAKaSLzuf6xFUeFb2jctFMkqHDZPAbxu9K7bEG9XJBuVyxfnZBvVyxfvaA8nBFuVwh16KWYRX9VwRkGpAzgxODTxnpdMF6XpCfXVDuT8iXK/Ibb2gke1nB9kh3b8Lr3XMuOC93moFAAFfGtYq5FxJEvJ0wtzLMevp9jmZlu+1gOHbbPJ7PPvNeAhrApuWyT/k8RoDQz0Hf64GH9ITz8Iejzo96Xtt5ANDKE8f21N7KuGUceCzBdWIJ7j9toKB+9u8g9z/F+tOfYv30Huv9ZVgX9bKiloq6WsChrQtiA2M5gZaEdFqUVZAz2M6XmJVZ8Fopwbqf45G8U58bFJuIdD3gjQkd7lKc4tHadJItKP+h4FBjDcYg6FvuAQeoRCYv0BU8aDTw9paEx5MBQd5PACFez95ombL2hIYfJbAFUUelS7giggMspHEJZPeMrEPi9oLRirER76RShzHN2wAK3n4ernfUdfFbs/zu53FjQvA6AQNgHypBJ8d9Mh8Y0qpt0Y6H2GvDuW+CM6zOb++SaEVDdnOCKdYRjwg/pLJRaqBA0rIBBQ2RY9yE8fKHn90BA03J3oDSremSfafRxWHD+2Yfg4l2j9amUJW+/22UNoB1h1Hw8KkoOPSa9q40jMHaSP23ocf3qGCtGMIAXS1OxFJPeWezmrUn16Ig4OGC8nA1y/CKulaljitQruO9JyakRVmDdL4gnRdVHM9OKKtalIsHN9YCtlRNunMgo/+W5Q4pJ3BR9iCRWNqiZjAUm2pXlnHMCrfPLfpcUy/TezQiIFQKn4a/42f2vAlzSOjsaorlXh87D1+DNRHEy1b7704AJo4GgmhqUkS9HsEpQTsWzq6D+08hn/5b1PtPIT/9t6iffYrrT+9x/fQe66efYf3s0tZEedB7W64ygBMASItGr9dzBhugBHStEDEkF8ipjnLElDxZJU6PR2rGRGQNOA2WPbDP+h2NOQbgKUWF6o5MuEXu9fsslvlke508fM6A9bQe9uRWNxyMKXBAga4ljxiLvaJHhTAhEtIOiAgWOXGrgivErWgbpwRZeUgJHUbUBcnYiCNwEK7VU+p/9yvmPgiXU+3EGCODcnifHwEFwGsEDEYFNo6PPu4VDb9gSCsqNx9t0UlntTaggPoy1IcEgRekAACGUD+DTV59Q/WpMQi6mfPkPtgHBa2N741N5/XXfV7mTTUq2XFU+5yY9D36mbj5B2Vw9AVbsF05iCkGmRSTTPXjO5CI535rbBWQshIe18Ccgbpq0B8AWMaDpAoWLUCC6wUUaESngkVqp4jXgrpWrJ+tKNeKeq2NOvZBTCg5gZeKdClID/49+1xRhmGx9LoqtZ//nV27u1XyCeekgYnaU4GQSFDZBLUxSpjS82bFP9fnn8FYnG9g3CdN+WJUEsDWhxmF8eAZp1HgR0AwxdfvnsdjSmoGDvNgaEwDAT0zgWA9D8x1sF40wPD6AFo/0wDDT/8t6mf/FvLZpyif/jsDBPe4/vQzXD+9R7m/4PrTK8ql3FwP9ZrAC0Pqivwsg5Ouh3otkHMd3A76HWMG8gLKC2paAM5WgVAZRrHXKrq/3615nzNgKxv3xmzp35xfvKIcAPT+2/tkstbdiwcf36yReUS51YwAbEHCoUtDfG5U/hyBg5bKWa0JExFQQr2UWoCFtKtrrBvh1xKyUVqjKb/KXQOUuvvb9NesuxxUteqIN3WDPeokHY7XBhjsDREFBb/9/ffwrS93pHW7LWfXpAP4IhrRqgUGarAcATm3ynhxvmNe/ZDikzy2II3o3wINvVSuxxTMjXuA7SKPDEEU+gBuCv5++aqcBIZC1fk2gC7/nE/XbKW5hbbb29392m61BbDQgYKyCvG8qd2NScHcGoPyEe15D6CyB555ABX5xxUIcTFXT3TpeLxAF0lSK2qpqgCKoF4rymVFLaLtpeOp5Iq0Muo1tT4Hu6fMGhFdiQM4ADwKGiJgqVjyGSkTcgWK1RgoAgjvA7koWFN4rgrSBWlct0fIkzbvCuhmTvrw7Vmwh3XaznM4h/i4fw4C0owIdGU1+7r3rsLB4tixUJQhWK/brANnCj7TYMM9UPDw7y4oDyWwBXVnLbABhayg8VrBuYIn5eHdC5v70eKSVFYsxjKetZpmWiCUsE6GhMsM4NUCz2YKPgKFGQhEEPAq+7/ajWD7orr3xxbPTwWuAFq9FScJvF2xgwQ/1z03Q2MoAjjwlvRSxRS4/s1sqeXl2uUHsWZQldXqD5hLeaobEUevS+HBnGNWibs1X3z8Eu/9wW9qTNznng+um/GA0lmXnfEUgyqO1wYY2H3rSmoABd/GF996Z7Rudo7hC28Ta0AMays0FM6BNeUh48K0l9CW/tutEBZYAgcFXlt/rX1TN3BwAxDoud+2AmeA0CYN/bggahvf02iKgQNF+YQy/fYMCqK/Fxj9zh5gttpL0ccNqPUQgYKfb3x8qnRritUVYgUKq6WSSAFC5oSUQ3CQCEQW0KKbnC4PoOUEXB/A2QACWbbBABI0tchBQa0jfUz2d9oJCvEARSQCJcIpeTZKKO2MCFIrUAtSWsBpQVJIZcrRlKZbY36vw7pgn0NPB1173EX/8pFd2df+4G6JPu3298EhHDw7zdq0TkgJ9us8OgdbDIM/3ecLsT1vF/ZxTvxKHBQxxCoVXq2C4WUEBZ99qhkH9nj96T3W+wes9/cWcGhMgYGCcjEGaa0btoDXigpWZqqOgWmUrNqeBSZSqH0B+ydpAgX5jALNTCk1NO2qFvsRDKGnsAU7t2pwE+25anzP223U13f2PbG06piJCSRqgMxMgcuyCOBmmdYnzk8Wg/xSlSwTSJDOIAhtwAFgr9tma6DA5rFCq1wyJSCrK0FrShQIrSpjq7qStW5OaWzfRi8AXTc4W9DcCfr44pMf4b3v/xY+/Op38MvR/Y1j/RULHLWf8+s3LMI3tqeP1wYYzCOCgi+ElI4ji6Z5EQy1NhbJQYEVShqEISdoVDuHG2/LPBbZiQ1FLK5AmIG0NLeCIvwOBGJ3vkiBxdP2m3sECmbr8HBR+LWHH2jtPg0cMBScNJAx0csuILz2v78dkwFXYyT8JqwkSETHQMEP7prx1tiZGFeCTPZbov55JkIWQIRQmbDku+GrIhV8FlQrcSvLBVRWpPMV5ZJAOWnMQE4oXJpvmFNFqdToFYnsQNWuiLQWUALqSihXBq8rykMC5yvktKA8XJBSRn34DJwy6HJvU2DUpVQtTGPV1RKrayrxTMLbpLSAS/08vMT0VFSqm4GuVveGozQrEevCzBW1B+EC+ybKAALmwND4+/MNnc7B/e0BFJCBb2FGivn1TeDuz01rXzyUMtaCRfJwr/UIrhfUh88gq2YbyLW0zJS6rijmWmr/zAfogagAWjAqmMBJ1wsxKZNl64k8GPGsdQywnEHLGbScwOdnqOkE5DMk3xkouMNVxr4brROm7cGNe3VPgNwaMjzsAoG434FxzwMdmLjbIDM6OIinQ/1vphEUeDvqx2RY87NLAAQi6hqIDChGcNCuD9LljhAqibk6CKlq8zhtjkVI6QSwVbstpMaf94CpBZAE8UJtPlnDOdu6bAC3X9WLH/8pvvaHfwcffvVDPH/7eZORswshjhrmIE7LzMQAGrNwa7w2wCBe9o8mUADsB9TMR3D06h6FppPI+nM75AIQMxB09e1kIQTBOQCCwBi4T3CPAlQrcJsFsJG50SK0DRRrBmzp4mOhywRjPvRYpaEFUnO70i5jAaALC8EmOr6llIWTJyYFC1WvacUWKPTpHC96vn8zk+Hfj1H3hVQoJNLyviKELDp5HRzU1vuCzs+UBqwFtRbkZ6v6jK89TiCtpTEEIoxa1G0RRUDMw2/nX9GzGKrmuJe1gK8FqRVYetCiN0RKJ5N15RRRCkQKYmna+Qe6Ba6goBWT2gEGXvHRhddc8z36Rnup3A4U+lqfIzyGkwrn5mCgA4FW+vrJ52C/F1x0nhKmTJ7W7N8wHHGObC6GFupW0hjXB8jlAXK1csdlRbUMlOoxJ+0ebq823ncHAcQEXhi8MNI5IZ2TpiieF+TTCfnZqdW64Ls3wM/eBN29qUxBPkPyqYOCAAiuVQJjoP8iYD/aH8P53thjMxDQ97f7HOjvO1NQPBvExEiBIB+gEoYVvppcPSB05kB/cff7UYYxTIaSsROQZvBEdnm85kY+KEgwgMCs4IfZGQnTF5TAxvKhrsoYRIAQy8If+dfcaLT1+eLHf4qv/eAb2vsgZB/4149i5HxmIkAQ9J3oq/8ji1l4G28fzOFrCAx++ImmdGhMwTsD7XIUXxBRVZvI9sSFnwqXl5/8mX4/uBBu0kSxocgUNbznE4wMgeB4AXRKtFvXERTEzRTpY8hObYCQr6wMg9YUqOSTp0jBAVO7bMcK4cyGlLUpxxxQ1kCnh4BiQABoKXWrxL4M8ZonILCDnGdLxUMHidCiznNirATkZlnp+VcQTvkOHJSHCyG2tEWUFfluhRR1HUjZNk0BgHolUDF3gqdAuaWYyNwHW8DgwY1STIlbFUYq2s6bvR+EXTSxAdM6AYNZ8W6AQO8G6j0+hnr+dozh3EKRlqNulgBasO3hsGAs8d9oTMbUg+Ip5+AxICEF2FP2nEFo7pjm0tuZpwCWyNwJdb30KpdWzliKVi/cAhay+8mgVMGV1F2wc/95YaRTRn6WsTzLCgTuTsjPzshv3iHfnUGh+BXdvQGc7iDLM8iijzWdcDFQcC3Sa1mY7tH01WPmDlAD6AoZjIw0AYM4Ihjw421A/was23FrX+tFOiggmNvQDRjbcAQMoECrVbpcDq2r7R7aTWhrRGWYfoNcO/ovit6PaufuhZH82uZqr7pu1DXD0Gqjnrm2iVlqLeMDQIigPDBmbUR3s6W4/8YPvoHvfPlbeP72O37VG+ZmN06uzb/qh41LHKYfrYzyP/q//CMcjdcGGAC9dvQHv/4hvvDW83bzgWNQ4H8TOlMQXwfQrKEXP/4IX/vB39XXOEHEXQgzW+AWlFlWEyDQYEK0vPMICKJfMAKa6BsdqCFnC2gEBX0zSViYXVnMLYTFNxORVvgiQqJksFt/iJ2nE0XLhK7EfajFPx17sDyclZH2N6DCwwECBA0kHI1oqRwJpqvfDqJWyCeXinNiSGJUsVx8YUjSazylU99MYkF/VYPDhjs9BYtxuijDsnCLSFclb+DE6GNeGPnMSOeMtCiFzDlppUT2jm5hmLKkquWasZBGRDOUj3WfYveFBbAqjdacyz1r74fQD8JdCqX0eQyNYHweYdYRBWAQy30LgmUfa3nYsXpjsdpAQgQGr3wOERTMFUVjWjD6+h7mqv2GV90srTvq0AynrSUrTuQFinJCWpIpyRVABpFnt2zvfVoY6ZSwvLEg3SkoWH7uGZY37rC8eYf05s+B3vh58Bs/D9y9CZyeqetgAgWXIliL4ForruaCXK1l9yp9XxzVEFhtDnxf6HQc7zfgBgu4s7/99ZmFmMEHwW+JVr50mj7KsQR0QBAqo96SYR7zwpzQIvbdyBGNHWDIGDxpxxvkrgVUMGAyD4BsAQKMEeINQCho7efbvjzQFyZ5vvNr/xLvvv28sasbfTWBhPY6upEbjUfYHH/0yUv85h/E3grH47UBBi8+foH3vvt1fPurBgoO1ri/HN8/qq7lnycAf/LJj/DeH/4dfOdL38Tf+q++CqUoK0YXQmALYlORCRDEOIIYKOTpZnusk4ODvToLewjbN5P7UBttigm5uqXnC9PjBzhBWKkyD68Q0ckSCNgxhs1dYoIULQhq8TqbUcLjLDyKKHsgVTRa295P03f1tLtgmivv7Qf9S6v6l81V4QChJkaRigWWD59YwQExGN2X7cquAkjmH/ZgRK9eRzkNOexqjJplksx6YVUO+ZzBp9yq33HOWvAmWQMdYlWAPgwgsClQglnQ80KPVjCkMwPXhw4Grg+9H0RZ4VUepbELu7y43lK75lYr3xpFubLWjyaAdjaVK387zwYAvMGQMSZHjAH8mv0cktYNoQgIlrMWD0tZffQpgzatgvfcLm7ZiZYujoDAgUheQOmKlBMkZ/CygtcEOeUuSLkC57p777WWRVK3wZ25De7ukN+8w+nnn4Hf+HnQszfBb/w8+Of+PeD8hjEFb6CenqFyxqUAl1JxaWwBcA2AYC0Vq8iNveAVMP2WCEhoYO7a5+zxaO/59zF9Lu7vOHxJJFgckbPoQOuT4dlDPVMEgxwjA5F7cqwDZF2f5LFfnDZGDiAbGQtgI38jfU8kZomruxCislBMbnGIP9gABEnh3P2IcfRYnXfffqexY7eh2laPTbZtG56yPzZcOj76awMMvPXkc/fJiCLIx1DwU8aLT36I9773Pr7z5d/Fu5/7ZQDQhRe47qH8aAAEMEBQRA28W4CgVtyMOG33MbgR2BZiAwcRFJSrbaZAJW98u37Mft4kBaieUimaosNQCtAidZkAYWU7ksA6K1q1r2Ilj5k0GwAqQGZhEa2JWBK3vbYzD3ugwIVg38QysEWwc0EVrERIpWJJjFWAcxXcLQkiAkmAoEKEcMoZOD3Tev6mTNiUQzWFdM6LWv6fZi1edFANsU9xr3bHOSOdtASu+5b5tCCdlqbomjKKpVRhC0EMasb72Cyp0ArcAcH1oozBetHOkN4WuKyQEko6m5tkHpqN4W4QBUaUEnBxSn9kDcbztdFaltcOBEpoXV576eCnnIdG7mtRMDLfO62X1uKayjoChFTbvdwAF5kEpTMhedFztOOl0wIpEoCAnkfJK/i0Pnrf08niCZ6dFRy8eafVL+/eUEDw5v8M9OznIOc3gOWZsgTLHQpnXFZtyX0pBgoEuL8WPFTBWiqupaqccct32gPej2IVgMQCi5mQbB9FYB5HQlf6ZCl83sTL9/DR/t6U3Ga0IEMNju4MARO1qpTOeKKumilS1l3WYPzBTsuTKJUvvh8SBnAgor8rxoDG+CiRAzks3XWqbIOmOapLQRVzJShAYJ1P9swzL6zWzpsHxmOoutrc0Tdccq8wfvjJR/jt72vDpceYAh+vDTD48Nf/S0Va4TUibKjuVx0vP7Z+11/5AO++9YW+GIlHH3igr9yN4IBgE0dwBAgOKCJCZwpoesMJKDYE3hB2AwVrQNqzRRiQhkd6W0ClsEfnaqxD4gywgpFUPTWT9JE1+Coz6Q5JFWsxwGDgACZI4ngMDETGt1kvvpkPxlrrbmMftogcb6CzVmUN1qSlhO8yo4CVImStJHhOGcv5TaU5TeBw6nnl9XTGspyQTp8ifXZBvjtteih4gJpfo3bSU3YhLbmBgdZgKZ+06+JyUuvXuu01yrytCIy0krsORLrLwMGANYNqzaEMEMR6/k6dV1PO81D63JiTppRHBe3AoLlDYj14O2ZkJWZA8rOeh/ahWIE1A/kELAqKqFadRxHIckLvTGhU10bwqiVJyTrmWeyBNzES6P5aUsL6mTFGS0JdMvi6tnue/Vrtvsd7nk8nBZOhXwY9exN896a6EJ69ibo8A5Y71OUZ5PQGrkJ4WAXXVXtlXCtwXSvuV2UKrqVqI63aG2kB6Gsfun5rEe25caNOf1TiQLDyw1LzYMKMvhc3MQxhbzsA0AJSbC2vzYghUgaBEKxt2+8WCEpmdfdofwkKdrKWyFvas/Y1sEgjAiAJYEoqS02OBAKhjQYKZCuLm+Ss+wBBdFlCSo8/aADBgIG7df8qjNbHxo8+eYnf/v7X8cFX1GhuVyj1Jh3x2gADR0Jk//xmE2kVqliwxxjxTaOl2Yjo/a6/jXff+iKGHNHIEAADIPCFNbsN5nSiata0L8BYf77/DLWc34gKKLzv4CARQBMo8OfYYw3GH2q+WPetC9BQbQQHIrp53aUAaIAOMIIDqbDGI+oa2ItETv3n9e89CtpeKjKCjQIgiWDdfmNoQayyy4QkCZj1XmS2zoRmMZ2rQBa1MgoIIoyaCKfTm2ZBZlTOIO755TWfNIL8We+st6yazlarQK6TyDRlGi1Izknb7GY9Jp3uQv76eeyvEUCc3hi/j7dBwVM6RHpAZZ0UsneKFOJ2/t5bnnPSGAgu3YIH9BwLMJQI92yDA0BQ7LXHzqV6vr91JqxrhtSKdFo68J33Eboc1PiD+Kb/YWxHpvZ5MuZArMGZEAHXjJwy0ukBdT1NDJFt+nj4JanCO+u99qyDIcjwjZ8HTs8UDCwaU1DzXYsnUAAguNbOEtyvReMMLNhwXO92RQbcIdhtYe0pzdGy92DdzQT60+ZG0B+4tbf9WMQYQYErzOBC0CqUEygwpqDLsRgPsiPDiFU7U+pxVX4v7aQSJUDcPWDybj75p8hkWNoldOUI2bYQQNgNKDXYhNEaxWm8AW8DKP2n9xit8TLbpc96jOz8iIA//eQl/s73v669FX7xecjoeHy8NsAAALqvyfrNO8V98PF5MvvzOVDjnX3aChEQqD3/FJZgTilaSx2KAsWfSOaCyDH1idz6HVE2A8EXZ6CgOMouLSK9beyiSsvTzxpb0KhqGOoGPCuDKSF5JI4ZHqvVO/Dh4MBZB6m60PaKnoSpHBrqzENEo5kLBLlaoCIIa6nIFhS5Gj1YyygkBwtKYN0KHRwIKhhrEay5swdVGLUWrNXAQb7TokJeijapRc93bwL3n6I+3EMun2F59qBKuayqmHcs32Zhu8JfzqCcOwW+Awi0QFZoqetz13ytQfW5j9wCC7FeWzyBrNcRFFgufrVsAM297+dLiVFQWkxFWjJARj1bZWlWMtWe+/fsScUACAAcg4KrRv2X69rORb/Xz0dSj+/QJkMyUN0aCMY9CM2zJhygNHPYN39sU2yrUCqwaCtkrOqOoOUMuj5AltPgkuHrg6aXBpfMcH/c7WIxEFhOCgJPz8Dnu9ZyW/IdsGiNgnp6hkIJl1Vwb4r/WoGHwBLcrxVFBJfVQIG7JwNL0C6qAmCxVEB93TtMEmmmjlP4xE/fh0Df28BRUaOeNjyCAlg0vyrZTDTFFKy9toSvZQk1AhwIBxnm91MzU6pSBEHpC6AxQUnjp5z99NIjc32WvYJtfqRV9LpyYnNnAkIqBzUwOLAH0t2+et0JIK2JsAtKpuE2LaEz4H6ubuTO44efvMQ3vv91fOvL6j4wQhmNLXhkvD7AwKPzMLIG/srMGsQxVAkkwo8+fonf/F70yURf0AQIAKWt0PuSR1BQao8lWGtnCURGQDAXCSECUiUUVit5Xj4Kf4IrATD+SmMKOihYN5HpdQowk9LpN2Rp7UV9LrVhiJv0hrhtGj3eoFcW0ZGtuQ1V9QO4QJnnHcDTuvuZiZeh9QiIK3IFElSZmwbCai2K9yqbtYB4C7xR5kDbFZ+SuhFW6elfd0tCkYoqhFUYp5RwOv8cOJ9A6QFYteAMnZ8hXR8gD5+Zhe6++2sPsAvR9R6sR2kBiDoY8CC6fBrBgDM5O0FzXlyLggIeP2K/L1Xz8L1PgzEFdVVg4IAgAhli1kBHu8HEqqS1rG+F1B6U6Z8HopAOvlKpCkCLNaoKYCn2BnAQEM/HPwMA5PR8ElRbo0jm53U2Jq0aH2PzvrukKJQon+ZW4HMpIF6tHkQB1jtgvYA8q8NYGYi0e8237nWoZEjnZwYE/N8dZDn3rINVNMgwsAT3a8VDrXhY1VV3mZiCYbAEcBDikaBW+dx22i36x/ciAQIsNk+FnD14ZH9TDzLUwMIxrsBdCc54NqbAQYEDBEu1hdRBjolGTsIb1WnQYa8CY6YUhArABUypxSLa6t4pBb3ftRNAK9ZUUBpAqHAQIPA0BoHsmvUET6n0Kon7vL7DfTd0ZYcBn+f8Rz/uoOD5Lz7voCJ+8BE3xusDDHxIVRoJ/T5U6I10Q3de8Q0UQJHWJnpzN0rbb3Jq8Z2vCgpirvHGRyf6vTSlIRDZTxtTQERWwMi6A9YC1E67KShYgXVFdQtyiI4FWhYFK73lG6tRr3Z9GqGvbYTBhGqIG0bJg3Wei4g2niT0im9jUYJ2C2LcRLs29PfizLtrkRIhS8JqAIEKNBJ+tUj88M1mTVWzqOJ1V6VXKwwcCCk1mwRrrY09WIVRjD24JsKZT1juTqCiViTKA7BcgbufA5eLWpnrxaLtVVFEZasXl1rQ3lF63VArAB2UNgrS72MtaqV4lkkIVmyNoKIiLhacGSjvGRQAMBfBxFAkbteheftam4Fz7gGJFgsRUy89kFBvbwHnjIpVA3grg1itP0qMWcvN5+T3t7U2L9pIioZKkz7fqc+3+54bKEiPzrGEOUY+SP+0rI45JXOo9xAAn6QThBfrx3GGLGdIOhkroIGF11LxEGIJ7teCewMLq1RcVgUEa5nWNMxlUL1QUGgxzdxAwZJTaBylAAHU6f+n7EMASMmLeVG0nxBudtvjHlOgrAFZV0uVY61XhbsLBsZTK1Ju5vxAjkmtxrR5l1i7ILoqWKkFSBarYm7RYpY7BeJN7yX2ZbTAMjpctFVlD0gnaw3gADvgIJkeIiudPPgH4o+Tm4AYWIMjXdZBwbfx/BefN4aG4jGfMF4/YABAlwJ1cGCQq1ZpZTV9ehr1CeBHP/4IvxWYgkcRVkgp2XMfPAYKvI7BnAKUiCxaGAhrov+sXZfHFShb4BvFwUEF1WsDBbuKyvzEeimsVpYLtbzo54iB6uVnqfmPmbNS+OgLXvvFWElpiDOYbY6AgKei0KCx5e9eo6RY8ClbzIaWNlaAcC0VKSc8lKqZEStrp0JW9oBZelC8nUypgsQKGBwgZAJWqVirMhEPhXFXGGtOONWKUyVcmXBOhIU1D53qM22+Yx0bB/eN+btp3pAhC0TQ01tbSt1ctS8Ol8xeXY3I/PnVGB+reeABpUSaXug/nZRSjoKOkrMDExBg0hr+FixJxENsBC9ezndpIKdZ3/E3XbmnDHhgoAXmVQMzBQCuQFpyODcHAtP0pfE1Zuog1H57Ltvc3DIOCrzFOUdXjR6jrdemBX1/6aOIAMvk7x7AT7jHpD5v8W6qadH7ns8QzppyuAoerFjRpeg/BQMaXHi/Vlxqta6atQGCKr6OzSamdvlqkbMrYcaSlbI/JwUCS5p9/uNePNqHPj9iytFXtotIf4x7nf0eYQQFia3/AGDuL+tXUUcXaAMF10tzkw01Mbzolcs3MRdoRmc9qUC8hDEndYsGfR1jM+KVOyiINVNiemePQTgGB3QADsjXySwfJga83dsbuuxPP4mg4N3G0HS24DZLEMfrBQx8Mg1psfn82SEvU1vMY+QtNaZgBAW22Q9pl9Dz3OSHbxitqKeWWVtYB6AgFiLxnP69YB7drKMboaHIxhb4P2MNIiiwPHbE6m0FRu2qpaZR2/Z7tjqcLVD0ykBdW7wBO0qxBe/WOdlm34kPCteiJx8RbWyvuplru3dFNMXKgzhV0CRcSwURcC0CZFMoBUCqqCvZaRoI80BQzzUnwloJmQVZCCsp23DK5uMtlrmwJGQS+6z+WzhjOeUeNNUyQaoFGpnCcCs0UuzuEw0KTFrnx61C9GjmVqugJAUIxOoSAkBZesVES7VDUXqdeIUX6aHF/OlVdwovZjWX2pkBK77EOSsgaOmWmnLJp5Mq3FhTIBYgaqdt81CcGq7GlKwgvjTmQbIG8RExKmvsg5jffjgvizOgZNdhcQWtIqKnfLLFCljLYmFtQOQKGpRaF80xXiisfZv3OVXOXQyxSc7+Pd6WQi8ArgW4XrvrKqYgOktwqRpHcCnSAMFapa3fNioCONC0xJzUPXBKCgrOzFiSAoMlsYKByYqP4GBviHRFKEBoVLUt3e4jAg0HKt2d0N2gCghqkGFrq0TZQIHFyQwFsoAe6EpsdSvQAw6zsgUwtoDIjp9Sl59+fhBMKrqNWLthFSsCV3sqtN8IBwdkhiIxAKHGLjQARX6Ok7XUJ1tvrLEGzrrs6bIfWqChuw/YpwMOqwPz/YgbAXidgIFTMRM48En3Ca228COq/dHHHRT8zQgKzMTcWHsHo7clVYRQRe9hNfRQqvqsiuyDAj/GHPRDtrOI+nMNOLTSxxZbMBQCseyDulfpbqLgpKqlSsJtM7UCNo5ma1XETayKhBhIrAWDAIA1nqCQomNG71IWx170bArPm0ieBZNb+SaYCsiCOjUOY62idQaIkKiCKiFZR0wNm66wE8Vaay/G0tKtBNeiwmqpDKaKVRiXIjglwqUKHirjfq24yypYT0kp0IUJuXgQVUbiDEvgGAHmcFM7V+XgUsJa8cdgOJiFkHqgaRKAtekPqEfSAwCfgWoVE1UIqrLk0wleerlnhPCQBeBgwLv93QQEy2ksMOTFmWbmAejCfKrAmFIGl1WzJfIKWosBhAS+FsgyZigcntuS2nm1fxbIyednaqk7KMgnVdJpGTqberhKtIJ1/tUnjZQHK2ywxHbvsd7fapdfBFhXVey9xwGsNkEd4ggiILismop4LbWtWR+RLfC4gVOmBgrusroOzsYS+LrNbKmCjFZYyBsX9ZsWhv1sjSAdXew21bijd3zO/HfYQEEmNDfozHjC0/q8JoeDgla+e1+OxVNvKcZSlRGrkTUoSJQ0rRBodRmIupE2W9mzrDa7A4UJ5O4Iy3iqMJktbiSJ9W0Im3o8uP5ujP8Cq3znBLa1pAezuSbCy7/QlMTf/cqHeOftERRs5M9M5xyM1wcYxBGoHa+ZDZiscgls8/LRJ9pb4cPIFMR63IcC3X4KHQD4T6sgkPZ39Z8VaQTEDAoKtgV9ZqPRmVFH2e3K3GIJ6TyopW0iiGxAQatAB6hlV4tF7dpx1mvfVMGlAE5qCatjH8SpZxgYcvYmQjwJiBjLEYFAXMTHilTnfoFS/kWAymQCViOEV2uSlDmBSkGyeHUiBlagctWcYytA4mxBsTgEf35ZK06ZcS2CJXWAcFkrHjLjflUBe5e5WV/J0iBd2Lpl1K95a/37OukUrBwK16aMKDSXYUJOJ4uvuABgED2oP18EdLLGUFX/9nTbBGWJCl0BZohRk1Jrd615vYCcLYZA3Qcxa2KTSRF7F+zkyov54lvv+lB0CeuKtDyA7bVyuSpAOOnarVXAIdYgnp9WFFwUFPi5ne40k+D8DHS6U6Yg+vUdHFDCKhiKj/l92LsHbSk6zRvW9OzyiPc3FjRrrkXpVQvdZTAzBJdV2aoq+npcG0wdFLRgPluDmRjnzDgnwl1OWDLhLqUeeEgBHHCImPcz3zOGuAMd/eftvjuT4CBojzXosQb997xkey/ENrpEB+PGAMOmjHZbE12Otb/LClnNwOHcZaSVbqUgS5m6jPWDEMMaKY3X4/I6ggNhUvnO0oAmm67xWgfS/tZjRhdqXzhhnZuBK4YyyIIm3XvoDQM/MFAQGVjyu3WT9d4frxcwmAM4jIohYw7EP2J34uXHmpL4nSnQcGhFuxlDGE6ngeInoqAPCieyBfPo1hsNqXxePjQCgaZAfQEYBdf82XXVDWUbSFrJWdtM5Tr8tq49q85lEePE0tB6Yw1Ekb0Qg5R6aS4Fn4YIClpE8CREffF2YFPaeQ80bbuHaL8F4lYwBJwNHJBZX+4rFTAlXKyEKZFZmgRjDWqLK4hd50rt96ZKBRNwLdQAwqUwTsYgZK64X1XweiDXOTFyYhDJEMTlcRTz2KvNHh99xONwAx7aRroQYUkJabkD6AE2vQ37qqWuV6WMpoNkTaOra0G9JvCkCLzCIOcMXqzAUqizECPsvbLgJq0yotpYP8Mjy8uqKYAtk+MZ5HIPuV5AJ6WN69WyJnYqIXo56lYHIuVeH+B0BzrrIyy4Tx/P1p3wjALCtVgdixATdKs0+V6MDG7c33ic6E70EsbXteAh1CK4tGyD2oIQHbhGpsABAYBWtCsnwom5MVp32f8lnBJhYWPE7F9uQX8w2n52k4zWpe8/Cu6uxKFuS3MrxLXcZUDc/zE2SuVXYAvc0HH5Y+tGBuAQemv4fJeq2R8ux1YNNtRS4tVcoO4+07/BuctR++eyNpZ5Byz2y65pryprAUbWIHVmBUSbujk2FXDUMbQiHxZSURlGrCUaTJ95Sv23v9qLF+0aWEeL+MZ4vYCBjQ0VM/lpBLF4kdcpCCxBWHDbspUmCkS6MqR9+uxoJGC3KE9ToOwpPNQ3FOn7ahVMbgT3RUlfCHFDNZQdEXYJ8CQZOLDgw6H9g6dtidWxb/+kAwVGAwfeslknuptYG/o1ggG3FizA66hsc2vyZEyFcEbiBE6L9rSvhGz0LBWzjjkj0QTFtNcN0ALbamMPWs2DEHugwICwWLbCpXiaV8VDYuS1IpEGJHpeeKZe2MXzuOdx1NZ2uObw/cwErlq9LplPuTKhign7fIcWaAdYu3Cb7xaVT+pbvTyoIi2r5uEDY064uQM2dRaWM+h0VhdCPpkFvmjK1eCr51EAiSr2tr9yL1xDzhpcL5DLnRZksjoB6foA9ih0VxR+jkCv9ZByLxJ1ftaYAq8NAGtVLPnUMgButSxeD3PYO9651Rbc729Md/NeBiIaaFhEhrRD/dcBwbXsxBLYmgRMySeNGzhl3oCCN5eEu0VB65LIwAEZOABYiqZexgqpB3uvAQILjCVcW9wEOGsFQ0rNb94YlyAD9Ny78h2BgATZYutkh7loaa0xZsVHShZAa5kJ1K9FSlEQ0BRlB6nRnWCd5cEkrQBSInNfMgHFuvDuGI17YOHWGEDCYNDWrd4RAGRZFiR48clHeO8PfrO1AfDRD9l1waAP9+KWdsbrAwz2IjthIEF0Ml1Qvfzkh9Zb4dta5rhtCFdM0VRw5eGWWG3HFc3HA+Dq5XgkJqwFrfsgca/e1z5DvfpYboieGnV0043QNnPYYE65/axjKAizedN+h6AwVpoPrMnIIBMagvW59mDJVnshBE46czOjXQcFFtBFSeuQEyvFzfmMwgQuBIJa/FQEZEF1iQj3fm4BHFShkS0IlLVaQoxSFSAwAScTvpkJl1qRV0ZOwGfXnhZGTpUyDWyQj73mM3sjVqZrwY6JNc1MdlpHp5Na0kDTYEQETgnVIrflasq8XNVy9ywW/1ET+OpGWEZmYDkBywm4FcTX6gP43XfzsfYYGK99b1kcVB7UanX2IDAJUq4aNNsA5CPneX42xBTIcgbSCXVRl8JRy+JYRbCnEu8zfPH++Ni7vz2WyNiC2msPxAwDDT7ssQQtOHan8iPQ3QgzKHCG4C4zni3qOnBAcEqMJQEJolk06xVzAaFb+67FbHEa/hGtlu4cg06tn8qOhdyt2SCrbknPTZGGVxhTvMJgRbviDDI1uhNYjDXwi6gCJNb7GX4idqhMAbTdGoN6bnMejNHoDhcPZFVw/fInf4b3vv/bvUx/9HME1uFmbBxxRBGb8foAg3nM1iYAiHVJ/N77+PArH+Bv/sLnOztg1u+AJtsoulIkJPBIhdsMcX5deVdx+kzafUvo3QeHPgL+3RYA5AZXZwu8pjibNdjor0b7TeDgqWOKIB98wx6AOA2SCkHSQki2AKXCLAlM7pUJuc6AoHiE8Wi1OOPR6EM9OfRCTBlYE5AW8x2eQGUF5RNSOiERq3UNwdW6YG4upYEDHxXXoiIqFNtDMcGkQVo99sAttsTV0r68ulvtpV7tnh2NaA1uq9YFmnjKQT8XDySzaGlrHS1COKUMLHddUTVARWrVXy9K4ce4EwAt0o5T70pojAGfzhbRv7SAPaST+uqNMVjN2pYamBfj7PRakvpIvUdUKGYj5axrIV9BJ41Er5cHqxOxEzjbuPxwrgG41HTS88unVkTIuxNeq1rqD7UOgX8PVXBdiwGDbe+BW/fo1v2dq3B6aXTPLnB3gccSVDuveSSLW3EwcM68CwqenfTxzMDCnpGgLAGVC2i9AP7o9QJC4aC9PaeupZ5KK5xBpCyUpAUkCagGDqS29RZjvGyR2cTEVN6JSo/DA1mL/+lptclF8Mga3ByjnGxyE2jy1AsuCakx6awBsdYseExuAzOT1AGjg472XpyZCMY2Oqi0b7z85M/wtR/8XW3N/DnTXygDE3BUank3y+lgvEbAwLQpateLAz1T8OLHP8J73/9tfOfL3wqTCrNkDLW2RTNRWC0KJfVjixi9o6sq1t32SlqOPOMCS1V920kw8EkU6GcvOjL4qtkfoxshKN1HUSKNGwrooMAUQle+hGj13aSgLEBmN2Bpcs9olPwVQyGTUIRJPMXOlECrt+CH83zloUrgot9P1/a4LHcalAaAK4OKgJGUlu/BBrvgwKRNC0hr7xSAa7XaB9zcDN16456C1YQBzQH6eqxpqo7oYra1wFQt2pyRiuBqQWWraFCZSMUJbt0wlpSRFkeYTgO7Yl60qVBZ1WqcrMMWQGhzLGlB5aVH87vCpaQKtPSKkZ5bH/PbgR5n4XPjAXKZ7zQ0QYp2RiwXYL1YmqWBhdVZhf3iXDEjQsHLyc7VqwqeUCjhWrRl8dV8+WsF7kvBddWaAU7vxzLDq9ReMfPgHrW/D+5zA0nSYwXm2IEICPZ+JzGQU7oJCp6dMt7098x1cLIMhSQFdLk3182D1gtYH3qXzSHaf2ZlDAyEZl6UNcZHJKuCNaaIZFGXkQOIwNRuxg1ZJSGjrLnHPK3a4qFuyrKDoUyvtEeVn92dQFWDiKmiGXBq6THm/i/DcYPsjv1reixKN5dalpleadclTaGXXR30Jz/5c/zGv/pP8Htf+ud492/8krpTWgbFNJe3gmMaWDuep9cIGBwMm9wXn/wQX/ujb+A7X/om3jWmoAe4lREQzD42CgUzfYLts+5OYDLflMCCdAwgiKLPiv0eAnFs/MkGBpwtIMAadkQ3wh7DEQYngGtLRfQN1XaTxzW0Nr89B1zbzuamXPzfHkhQFoE3r41szMQShIpm9fLQWwSvHRzMAUZx81NelNZezqDTVfsLlBW0nNXHuJjP2VwLNiH2mEFcNF5jE/DRwQEqjeBAZAAIa1EWYQYJwJhXPhx9uldz+pkPjzR3RiInaumTRawDZGVIBu6WBLRwT28d7UGJZIzJg1WGW5V6N7fNxp9pxXg8bkDSAvCyCdpzH32k40X2e3/42vbgzEyxIh9h4YRleYa03A00N8q1xyO0olHT+Rq9XS0gVc95DDK8rBpLcDHLfK/M8K16AbfuURxH97qlxe6AAX19uy78eEegwBmCsz1GUHBO6jo4JSAVZYio6B6jYq6ay3132Xhp56P9FgCBBBYJtUBSBWQxGQAQtICUuhXrrszYM2CUZRBTXNKYQZIKyYt+r02MBRemvAtsKQCa3THEGXR3QoLv/v3OsU+V3UOAtS1Rou4WbssmuBGam2xHD734yZ/hN/71P8Dv/8o/xfO/8Uu2H5QJ1bm74Rd4BabAx2sGDAyDEQ8L7+XHP8TXfvAN/N6vfhPvfu4Lo0IFtkE3kdIhgpesjEYxWTAVRBczgXrtbb/x1paYjB0oJgzXai1Lp3y+2LWLgwuh0dIcSyCPinc7FbYxrJ69lkxaN06GRs3Plngr02s+YwTKgtQjd2sxHoIC9/kZKMB6RW2BZw8aeGbpSRKCzmbWIFq0tJzB54fmW65SQLkAqEhScZfvlAolAVEFweodYPQRO+3vyvjKhKvlz2/YAxFYTVIAtcUiOEBIrI9HwxXFnjJo50M9leyUGUtlbfok3Itn5fB9Awfw/1eNVPegRGEGJaWOJfhee5Ik9XXDqVXoU9fBCVfRHPxrVav7apkg19b+t5eSXn1rodfsjzX6W6GdzFhYa0EsxaLl0x2WfAcqGl/gDcH8nGVwlz3hnIsBgh1Q4NUFL7W2/gRH/v5b9wl43K8c73kEBHvrwAEBM7cAwyW54mecsjJGb7j7YEkKBrLO6ykRzgzQeq9A63oPrA/g9QH14TPg4TNt/OXtuBtLN7EFbd1kiMuF5QwSQZWqwLvdjQVEpeXa/3/I+9dnS9LsvA/7rfeSmftyzqmq7uqe6e4ZkLBN0bIoyiFecZlRyKRNAwKFj8AMAIJBSHIEbZoOhz/wb3A4bFlmOGSJEhnAANQ3kCCHsmErUD0YEBTFkBwi6aAdBInunp7pW1Wd2947M9+LP6z3zcy9z6nuHoY/lTOi6tz3zst7edaznvUsSZS1I01i2PuOLKJ5dMn6R8ZATMpApBK0HD/xIi40c+pjcdR1bGI+X5ASPWWHjWGySH5R59iMsgr3rd31dxAWTG9NAZevmQM7WegsZLEfzQyrvseT9/4uP/Xr/3P++v/4L/PVN/6I/kxKn5RJfmDuZUqWa/R8/z99nMJLBAwUqVIo7Tnn9OTdCgr+Y7761g8dA4LlwDhVxx69cLmhKc7jK2f9Wgw5F8/rQvPnpO02k9QYes4nZNHFmgzLxkj1s0m1a44dyZbNRmYK6li9CjMFpw1rZsSsoMApwKwTaWmHXL37ly1+Kyh4gX3spDx/AYugJ7RA5dN9XtQon4KCoJ+zSCnkGMmLiK168udyrtlra2EzDqRxQLo1NBWA6HNtfad0XmF/RLRiYUn1GeICGCg4qCWLxtxdzE+PtLCYTi8ABZ+2IdxXjmZE8AtPhcaZkrc2pPtu+QIcUD8zC7+DOGp+uOg67oT1lTGwrtT+OxXmnUTclYY/VBq+RNxLBqFen1ksmvVfjXxVKFdr7LX6o0lCKOfsbDNZ5E6CxXsYvSN3wap5SCwAzItBwQwMZuag5vvHkO5E/cvnU5+Rfq98fV9O4Z4xsDzue42lnkD1JPWfTPdt5S2bxuGNigxbZ4qeICPjARn3yhaEAwx70mFH7vek0vCLCsQ/ZZ5NTpIxQNPOPy/j69TrRFX7JerPc1S7PGrFDFXcXdK02Sy2rQhVd4fXuTtVRSxSjS9c06qvRtVH3JMSnQK8UnpupQyvsvaCmhVN/V/KfSnb+tG9QOZNf7l+U9dwdH+Y0gj37T1w9PWT7/wWP/Xr/wv++p/8D/jqF/+wXmdJz0yBqlQUBkeyxvsCt3L9L4a5erw0wODOIYYn7zzha3+7MgU/xL2AYMkSVEU8LKiBevfN/LeAqkVlwRpQ8v46qBK1FXGdPgoOiizhjlq3/mZ9ndN87Gl75TuD6eTaM1k3TZiiKVmgUP29uivOpjR3mvkUgdHy68kVsZamLV/r6B4tjiVASLrA59oOOIzHoGAcyGGYathTiOSTjVZbF5c6+2ZQwVnQ0jcJI7IKSDfn6lJOeN+p2BKDobAHjStloVHT1WYWDlZBYeNyiR4NMWXMqXPTZxzLjWAJCO5jDdL0vRkYpKwb5kx0lsOhUc1JKkROwAFozOaMugTOTpnpznhQy16rJlJpBgTz5qqAYD8cb6yHkDR/H4831ul53bPJKRUey8ekorlkCTYrg5Bqvb3DOodxBYxXv4vFeWtkqyA2AiGVXP4CFIQXMQX3gIIQ451ywfpsamvvel2R0qtgwSBN130PSFh+7/THpyyREd3s7/Mo2HhlCrxF7Y5d6eGB6gnMuNfUQdiTDzvy/lY7gPb7yS8iDcPkE3GnZXQxtqpeFuKa41FV+lFoakGQJHOnwBcAguU6kSn6l5KCUDqdY3CQzOyb4rS6Jgej71nNsuDedMJkkWzdbDl+etQ1NEWMcUftmDFz/xfQ4CDmeUW/c2lUluA4qLNSWWQFB1Nwd8oW5HR0ThMo+BP/voICcgHGMussCssylwifpkzryX1/6YSXBhjMNJM+hSe/+y2+9rf+rGoKavoA7gcFkwr3mFrS308LlGmOHx5zJJxZiBBZilYql1HrX7kjzGJ+9akSYemeV53uKpsA+c456sXLPDiyaH5uuoakCHoJeGBKJVTF8Z3Oc7X8rHwvL0SK93nL65Hunzl5HvxS6dScJ3Ygh2p7OhD7gTTOLYKrM189ls53NgRso+r2+s8UEZVZ5eJ2qBUUvlnpYgcQDQZdwMSAs4ZDUN2BC8IgalISYsZbM4nG4NgtcXncm4uuLELRJdzHOKSTv5u+rvkpfXUqONDxkBnIGKeRvCS1gpYY6ZjBQcqlTW4W1cEYi7FL7XR5POWtUuLIrndMc6e/27Kp7gfdWHdjZD9GDmOiHwOHMRFKu+DlNRmjYjhnDZ03tN7ReS2pW3tL5xJ9snQus3GaYmhNJhqDNXky5DHGYczdTiLLc49ZTYvGIiZcMhx90mqEobAaQ8ilTDG/EBS86NmYe1IHp6zBfRUL96UcltqUKmKtuhK1Nr6HZamVB1aNttoiMjTDHhn3mjoY96T9LXl/SzrczqBg6InDSOzDp86vHLP+S2lyzMyhROGlD4caCOVpcz9WBC6O07VCcwKabDJALuXOeTYz074GSuEjRgGtN5icyFHXs+m8X7C24Wrb8gXLWX+/gJflGq5llmX7N3P/l9r/4POs38Ji7V5WLZyu4RNLvVjTl6Dgf/R/4N/44h+e7usEAFIxnjP6znPT6HtSCkvhYWE6nrzz9qeyBi8NMFgeT979Tb7+a39Gqw/e/OMcC00+Byg4iXiFue/9FOrnpDe6fi9FxLjJZ7z2zT4CBxmSaJrhPvym+ScdOZU1mOxv5dgtbLnJ6kSo9TwLOi5nNSMvG+PpMQm3RPUCtQvcVKNcI8gFTTtHZpU0O8YAUs4nF/GlUoQv4N4LjbZ0NMsTpZlIIRKHkTxqt7RTv/xkDeIKcAgRGyK2AIIEmJxmC6OcpvwevqMtoMnIXLHgqihOVM2t9rRCMDM93lYqP9+vE1gCg/nnep+iVCFjmkpPVatQT+wuQKivb0sEb0wmZZnK3YyUjzFjJRMkEcQwVlOvlPW1Cy2rAtljFzrKKeTMZAt8X2Of2xJt78fILkRu+8huiOz6wH5QUNCHxBgSsWyoJfAqlTSaFmmdofOBVeNYt46+SWxaq+/pMyEaNsmSvSWkhLdqge0y2KTz4fOcf8yzKHKMRVA4fTwWTtZnqkzOfN/vOyogmKN7uZMCOO12uAQD96chjtOGp2mXxsw23NXJ8E7lQQqYkj5g3KueYH9L3l1r6mB/Sx4OxMOBOIykfiSGSA7x3l4UuY4flD3IpQGW2HlDnlxiX3QsBctlzThaL6oxWl1/Rft2aEdCM5fZVrtjKXqlnKiGRXIioK3Hcn1brmVH5wbzGp7TZNQ25QYKWyBVRU6ZQy+6XI7XcGNkAgxGFmzBxNglJragagrugIK8eP0qgi/70bR2nIKDY3C2dCJ9+523+Zlf/Tpf4ssvfGwvFTDI6EV//Vd/ZjZ/qMI3FpsnnNCo9wCGeiwHj1ievPd39dv5nlr+BeKsA0sKOKhIc2oudHLUtULpKJkGkJkWjMU2fG/6QBZIUcFBRe4zVZfn34WjyToJuOrGX9r+vggQFL7kDijIlIlRSomk1v0sKxtedEyuZhqlVMfGnBNxDOVrfcdI1FzfaMjeYdIc2bgy4VJW7/+UImZzrsJQwOR8p2KhCkadcTgbGYOhj2kCCLXNbS1jW9anwz3CRDjqv1BTB/r5nJJIKc0AAYhyDDZeVCMfU6b4Ns0bWs5TpGwlF8BTIx+0yMIwCaGWT0Kj7er+V+rrE1P7XwUDiV1hCm6HwPUhsOsDN4dAHxK7PuiGPMaJGctJzbwmJsxbnBHWreMwpuLw5/R+NnVDt+RyPZ1TdXvOCogUJOdpwV2efwUGy54EMbEwENL7E/MMBJb38/SY7r05/toeAYNjMFAj/xkwHAOE5fNclqTOwtc5heXKpq+MgDnqd+ArIDitPAiHGRTcXpEPO2UKCigI+wOxDwoMhvHOvAJIJkEyWO90F0ypzK0C9j/NcOiogkkH2txGfK4NOl03BNVpZWtmgFDOo4gImD1Qll4L+ThIum+Nm87LzuvkffOq5HmlsLVkZS+kaMaWazjwwnW8ruHK/C4DvLtswZGHQ7mGmj6YQMER8DLH4GC65tPUzfK655ny5J23+fqvqo3yX/rP/tLdCyjHSwUMKhL6xk9+YwYFIuWh5hLhn/xReSDHoGBOTEz6AoEn7/82P/Xrf2HxtzWdMCPO2uSiUsBJlDHIUj6HO82FYB6ns3hFjnzFJxLoHmMQoNgEM1/Dor/nqcf8EZ1W0Pico6pMwYIxKJN6oms1D2wLAAEAAElEQVQXeOruhVDosuKZUO6T1v6mqdfCBBZKaVGuwOTo0RSgENO0eKVxBncqjsrEpGzCsga7DuyJMQCk2xSqUlG6BdrqFJgyFjVDsmIJFnxUgFCtaqvavlYE1A1Z32cJEurH45+lxYY017Kbe2vZT4/lBrSknev7TAtuysTCIrjCLkg67g2fSrOp5SI9gZ3KFmRNHdR8fAUFuzFy0wd2Q+TmMHJ9COx7BQ5xjMSgkafKABaRjgjGQQyJ6PSerrydNmko96A9XpIUYBiSM7iccRPrsVxo52sgHzcqSmUxn5wM0+KZLM7PGnW/rGLPCtg+7TksgcCph0X1r6ibyOxroa9z+rPKqhjkqHpDDa2E1tpS4jmbFnkjNAZMHIrIUCsPpC9g4D5QsB8IhS1IIRQQfgwMjLe6+UTttHmq+p9y98ugYWIcZUpF5qnNdBH+oWmeF64b072wRQuk610ZTFrxYPLEGCyFe1Pgc99apwNwEaAcixBPjdqkdDKc1mRTOiVWUXm9Dd/HOl61YQLHjG9NJSyOWWi4ZGPK75T9aGYHhDtajgkULQCQCE/e+dYECpY2yvcdLw0wOAIFpSFSloLMxMDCgXA6TiPvO5SY0kgAT77z2/zU/+Mv8tf/5F/mT/za1+4/iZzRbn52UqZqCD0DAm2ocfxnL2oyVImHiSlYWjcfsRpKYOnZFuT4Ahr0DltQ/vYOGBAdcHUe1wldI7P7vMKrsQd5XvikOqHlhFCYiKQLh1g3m6ZUgxQxZcPXMssly5NjZRTqR52s1Y5UrCmdlQ1wwJVrTGJm9z1rJ+ZAn0mide1c9ZEMY8q4lHFiibbQ0MlMfvfLzpjV8pZyj+ptqeABFpsuaoM7sw12ctc7BQpwN4o9FaUto1BzOqjQDdJRm7uUNEWZCkv1ay1YrIxBtQfup85/yhoMpd6/pg92fZxAQRgqMMiEoIu1SkjyVGJLEJwrjFCGfb0umd0hq92zCWX8SJrKvrAKsq2gaZHM0XUvryPnYkfM3IdieZjK7JGnzbxxCiFTaU7lFuvtMvI/BQLOmKOo39ljBkCH5PyM6qWAgts5qlTzrdpno9pf2wIIjhogWcFLtTY+KUesoKDfkw9afVCZgjCM5BBV1DtqFcKptiDHRHVCrd/T+VicDI2lehtMVUxyqksy8+eFKUjps9ePiaaXahhUvCly1LGUAmSnAUbOmltapojh+1z7ylGCKSmfirGFxdCpophajtZA5FPWwWluHq/lc2+bFx9ffeOP6nXOJ3f8C6fCzhr0nl5rrUAQo+n1X/0ZvvGTpQvjZxwvDTD4mYKEfvTLX6lZocXNSce1skux3qc/IwB+4/3/kp/6L/5XiuTe/GPzD4rOQErL0cnkp/QNSMwDY1rj76OwFoNoBgILQDAxBMe5qPkoOSWxpUzonmN634qYZ/R8LCS8HwyktGgJzN1TENFvTgZPJVSvaYXKcuRiYZsrnRbDZFQkcSQH9U+QEBFvVTMQNZIWq5zFse5S3yinTByD2hINI2KFeDhgjYHelGdhSMZiVoVBqGg9Z7xr7jRisoUVcEY363rdMR032IkTM6PndAwWZgARYiY7CkCoqQmzcNizR1a5cDfPPbVbrnTzIko9PXLS1ETt8pYESEUEKwu2qSzUtYlQzgUM1Y8pESIMIXEY48Qk9OVfHI9BQSrP65ieFowxhElAqVUgvQg+JFxIeKuv74vgLhi1qHbWMMaENXZWq3zKdSzHZ0x3myDV+2hES9HAzFoNkXvvOXB032dHymPqv0aazh5v+DCDAJin42mjLWsW7cin1y5W2OakAdI4zKAgDrOmoD8oKOj3KuQ9HGa9TojEsTAFJ6CgnqMoLYJxDuss4u1UsUQxORLfTF9Xd0xK75IKEPIJKKhpqikGPl1D0PUjomtfbWykIMGWGGYBEo6i7mX0/fkAwh2lQPnbCg6QWt4sU3w3gYT60veVl5VzXqaHyxa9WMcr43sS5E0va5BF2f2nHsu5Lwaqx0xZ25cNlyoo+Kxt76UBBr+4oEfmPbi0qdRdoIRK1Te+UFLAJOSYShPrIfzGd/8rBQV/4t/nq28sQEH9u+WxfA0x1KZCei7cYQrmd1n+aAFcloO/fD29z/TH5S+nRhscDZTJ4OKkguA+IFDp2Lo5nPZXr1/D3fxaBTW1VDPnOSpzYmejxfIakjPZJkyjWgBJpdObV/rM5ppGyEjK2Owm7kDlE8cnoFGOTCmFPEZlCoa+jINaXVFAwoo5xVLf3zUY2xQvdFl03Jvz1jlXZkCmR7IETNMjLJ8uQUSI+veTMC4vv3fcXOdUw3B0r0+iUycaZU9NuKaN6PjvapRTPxe5u5mmcs7a+S9NQr5ZnFfAQkhT+kPvgaZ0KihI8RjAzWkvQzRJC0bzrGcIITG6NL3HBJCyOik6UV2GWM31ajqhhAByvAfc2XBKLsmK+iPYTBmPBmMSJioYcPbz3W+l++dmWVo5MDe6moHBvNlTb30NAuq5ccT2ltLZu1VJCkpKzvqk34EEtTpO+9ujUsTqD5LGqALeknKjfgSWvVEqKBBnsd4pIHAO23hs20xNqmr7bXyDaVpS6VcyOWTe0z8jpTy1nZ7WlXvWkDjdk2JPXL6OMveNMQUk3InCF/N5+bW8aHCcgumaNq5tjsvgmQSQ6J4Cxe+AF+OCxaM+DvCm91n+4t19Zz6/+rN8/PvLj4vzPE6ZGH7jvW/fAQWf53hpgMFXJqZgedRoWGvW7zhs1RuY44zQFg/pCBS8+cfLS56OhOPVd7YGVnBQl4R74rny4XjT/ywg8EL17RIA1PO8AwJ0UJ8CAabN7n4gUH+nZrXuQ5sTrVvy11m0FjiX8OcIHAQmCjDbhGlLBH+yKp8WPIkYkglHudHpZ3bxHIoIMWetbBCnPQGyyOTXkIzBVK+WKeLQ+n5rPdY4kpECDKQwJnJ0n+p1L2/K8h7VACZRylRdSYzkmscvCvmsQOdUx1BZhXTPHT8Wqx1vUFNnTuRIi3AUWLxoQSvXd3okZvHk9L371Ff/AsfydSaB5j3XrN72ci/AXoKD5efWCDnKhJtjlsnt2ljBJC3c+az7XNmAZf6/0v61pPCu6x13Stjqa+qJlq8XX94nPJ6qkYoDpMRhcg2V2Cso6PezvfFwmNwMUygVPTVkr/fLVuXN/LWY4gviFBTYxuNWjYKCptN/paX1soMlpY/GZJu9AAXVMXQC2Iu15PRuV48AvUdMUXcFT9P9XNwn7gMKJyxrhhks1M/rdU/rbv1ZZGpzrD9AahM9mDfg+lzL944e6NGxeM8a6B0di3Tp6UYPd/akozW9fr0EAwu2QBsGzq2ZT5aqTz1eGmAgzJvd8feXCOoe1qC6DUmlFgDMnD6YQIFQS/hgsdm+6FhSWne+X87tU0DA50G5R2xA/Zmc+gvcBQKfxQicTt7p1Mon9w1t0ChOyt9PtJuZf2CNPbIur88MmMFBiejrddkicjLOkoaRMOjilUKYjYOK42Ats5oSuKAAoXiqZ2N08TS6NKfyvmR1P1u2BM7GYK3HFJYhG1ncwxp2c+d+nN6ren9rKmYW+QkxS2Ei9O9CPHYODCndadu7PGquWjeo49bMWgNfjLdYiJ9ONqkaxpqcp+in0t9W5nJZJwZv02TN64rXQB+KBgDBGkNWAw+99gXNLyWVYKz+Xs3fVrGfc0vrXyk0fS3TLWxIOfEXbbjLa6nYPIqmIuanZLAp40QZG5fMZ95bC5MQ8EX39tildLGxM5/fUkvEdDbLcz/VFukGJ1HbU08NyKZW1YN2oSwWx9qmeuFkGMbjVIHVwMjgNDVn6gRFn41zYAXXeEzjsa3HNAUU+AZpV5jV5k5ba00ltBMoSGKJafaTWFa7zID5s9cSMtPzvQ8ohBOgMDMKWuEw3+djoHAkzObuensUoCxTVSJoCmyx3sIRWLjzgKc//pTtWMp+VO9GKc2kMt6VyTh5TxZ72ylb8OTdb/P1X/szfOMnf3kCBcsz+JSdC3iJgMHpcXQTFrX9GRaphcIa1BrZUhf75Lt/7y4oOKHiKzqb8vXL98vV9+AEBMAxEFiCgOl3lwn0RQg0fe+eAXJncHy6TmAJBOafn0a6d3OBLxrbqZ5OzhMdqnXJ+jEXkJDQZjnGmeP7WSdG+WiMJTuvC54xWOsQsyd5zXfmMRKDVROjU+vhIlo8ah8NZeNPGklZbQwlZijgYFV+pzQmMSNYr4tx8XCQo0qOE9S+PBYR+jEwK1UdaWZnQgUJJU0RrLkXKFTRI3AnhaJvUzev486c8wY1U9S6yB6P1yQZlYZqWgCjXRqnOnprCDnhojbv8c7QBCE4w+gMKdsyNlQ/EE3CFEfq6bYYEGuwxmCdYL3Fem0AVBv/+NIcyBmDs9DYk83YLGzB60Z8z7XU0rKlDbmU5k1iEi6JVm18yv0EjkSAp0CgMjKV7ndSARVzLxPqAjzP9TLQ7r7nqUlPWR+W1tVHzceKA6QyBYM2HitOonM/ieU1GcRkcCALMS4wzZmqJ7CNVxOwdqXanwIEpF1B0yFtd9TWempvbT0JYUy61pyOb03D5U9dT+pZy9Q1sIwf8gkQzCeeLwvAyAzKkDr2bWGS1NBjDsoMd4Kz5Rpcx0cdU+Vs6veOwMJnrA93O/ZWsbYGrdPLT0y2ss65XMf8QuUqa6A6raOaLn3y3m/xtU8BBZ/neGmBAZyCg5OUwtIIKMUJHDz57t/np/7v/0sVGr7xxxYbmD6ImcKx88A4BQ2cDILTyX4KBPLxwnFvy2dgZjVO0GkFBSdlQZUZqIKf06j1RSBgmrjzGd5/U2EesCVIUwJGAUIWMGlmDxyQcxFQ2ebIvESCdvOT2pfBOkypWshhwFiHhAHbBmI/YKrpUSlfrJFR7aq2tHMVuzD6yFk9342FoO+fQPOmrhi3ZKeubrWL4wK8zNe92JJeVCJVn88SQNYSLluFWWq7ulxIq1dCPAEKS73C8jgSrsERIDDoIqnflyPaenqcWRfRKOAqirSGSCZiiUSaZEgOUrZK9deeD2Ww7KlDMWESpGCOUhK1XNEYg3UGW+x8V61l3VrWjf5beUtX2gl7J3hn9eNCnW/KpryMIOu1VJ+QVDaSVGJCSZo6cNmS7ee8l/cwApPQUPQ8XGViyEwtxENczPEFhf1ZY+V0DZhSXJlq175sUZ5Ku+Rc0gY5huN7bq3aSFMfqaF6g+hbF0rcazWQ8RbbNkVg2CDdSj8WUCBNB77VttaugAGj7a0xjjHPqYOQjtMHFRCoHuVzrCnle7UkMJVfrEsxzGAhit7/I7am/pNS6TABCIuIClKlvvLR+5/qE+5bi2fqX+r8Ji32g1yARDp+vkfPPJUrSOU8FlRqpVUnZmNxQyZqyc57UjWhE+HJe9/ma7/280fpg+8XFMBLBgzq7VsGAXMAN9fVL5mDJTh48t2/z0/9+l/gr//Jv6zVB0dobFHGB3O/gHtQ4h1QcAcQLL93f+5rvqiqfahCRjONjSNk+gIFcI1Ol1ReSnnBGtwDAE6AwfK400hm8UupPASTZ4BgRWnBVH7V1LFuBGscprEQeqZSp6Afq/I5+4bcH3QRKr0UnBuKhfKo3gZBS3tyVHGa3hpT/kmZwCcTNMUCEMx0rZIT4nSBRywkOQJl99JvnxfQUUu9zNFkNsbgbMnNIoQsn7K4KrBaPrPlIcxDdilim6OqRRnV8lagz0QDbNHIOmU6LJZ7xmQ5qu1vdTTcGSH4YnDkuQMMRI4NjlpnWLeWbedZN5Zt64o9smHV2KmVsLdmzu3LHKXXCHF5HUApC9b7ZY32Msiim1P9ma0J6k+5f5WWtgtAYI0yCULW9sXDOEX1umnPXSuXTMELx8Y0Pu45ln+bM7XldAUCBE0pUBoKHV1LFc0V3YAYs/AFmeeJ/kxbmIt1yhA0xfzLt0irugJ8o4BgyRJYBQVTX417wG3OcxpsAgT3jN8XNaiqV3X0pPKcmZcFWDDkGQTnmUmwQtmwK1gue0Fi3ryrEP0z1mU50gNUnxwzz/Ea/U+B4jE4mPaH6We2aGfSlMnO1fJ7AobCEbhcBqoLZ9on3/m7C1Dw1aP0cT2WQPrTjpcKGNx31Hy3Hi8GB2+/93f5qf/bn+ev/6n/M19944/OD7M++OIEOK+slqONeXHcAQdwDArua/N8GlVM75+nqHOyZlb8X35eQMsJKDjN7d07SRcAYDkx7xOf3XeEQuPX3CnopF5eec4gBSDkctbJFNI5lcXedYiNSLBgPBIdOXiwVf3cIlVY1WgelXHQKoacMMv2zNMNL/fPmFJnbbRF80naJ6eElC5EuX696B8xRaMix4ZSsS4kcwQ20benHHo5H7Fu6kkhZVJLNYApym5zpOiWyeNg+RyXbM/yOEoVLKLpZa77FBzUkaTMkrIG1TzISsaUqMiV6LnW7LuiNdBWwGqJ3BbNwViNi9KnWSKbyRJ53Vg2rQKCtXNT18Bq/evNcalg1T286Do0fVXaoKfZbdRluaObuXPvTgDB5FUgIDkW8V+YhIDVgKeCAslJLb1jmMdjWsgaF+NBP5j58yWzdd94y8W2OKcJFNwpNxQhlzbFE3t2mqao7386N3wBA34x79pOAYHxs5bAOgXqpYNlKBUHyiRp2qCuPTnPgtv64bPWmnCaHjy5vnpUhowyxhK6zi6DkSOtE1ID9VJWKsegYNIbvGBtPmIA0rRBHzUyMkAufqMnO3A+3S8mkKC/vGSjs5UynuT+958C1c8PCr6f4+UFBstItiCB2u3wFBy8/c63+en/6/+MX/mx/4ivvPnHFrmeCgQqY7AQHy7YgzuIv1Qm3NEVLEHBnTbP+XjiTjOpDF4DUz6smltMaNUe5a8nUHAySV9Ui390ivccLwpoQCPS5eQOMU+TV6O1cuUZTM4EydgsuASxAISQwBmL8yvERSQ4xAYk9uSo0YnEcQIHjL3mVIvQSnLSBkw1ajrdmI3Vc7Kl+ctk+Vx+1xi9KWHUTTrq3+WTroXTIlyfXe3zUBZr/fzF3d4mwOFKG1vflhbXXjUNxhfTpxOQ4DRuD+ludcRipByJ3F6Ub12K26broi4iWsqXkm6MQdS2WSl0p2AgZNwAjdESwrrJH0Z31EQp5MJMpWJfPCn47zZR6pxl7U1hCtyiDbNa/055/gIwzOe4jklXU7/HyRp/zz1bVgNMKYL7wEAMTCLASuOP2io8hXA8Fk7HARyNhVz1MGbRoKyM2ztr+un4W37PFHt2V6Pg4q1i3f1AtbByE2D1rX5dgIGmDHxhCdz9gCAeA4JqN50Xa01lByoYuA8I3GdANT3TO+vSYq0pn56m0hSMzkZYukNnwgIc6NJup/Ty0RvWNXnJ/Ew/W5a4VzZZg7cqC9DJZo/W6hc1nMuTkHneL7JVAycdA8vUxnEwCOaFoOBeh8nPQxWU46UBBnWTXx7LMVWNM6pgqYKDt9/9Tb72zV/gV37sPymtmZcveo8R0DRx9dbde+9rVL/8YVmVjkBBbfN8Sl3BjEDraDsp05orEmZNwVxzP4OCMKF4JkHbi4x5Fpc9HVbktFLm6LgvR1sn/ylIqAAtkYlGAYIppjIugysAwfoVxmckeF2Q4wBxBNcizaiiq6EvPyuiqxo9VYvUBbU6ubgtN+h6QUuRYtUfiNzd4E9BQBjn9wmjXvOSRn6BjewUpRXqVnyLNK1SuK5RkFBythUkZOMw1mOtJ5uli9zcrXN+djJfKkyAeN5EF5EQzELZ8qwxlmT0ecQkhFyaM6WMFUtroTXCISQ6FzkEw8Hb0nbZ/Qu0XZ4bA3XO0Hm1/vW2Ov+ViguzKNvLcQqFludfI0ArQsEGRJlBgp6O3Llf9V7NtrW5AAAdazMYCEjoS15/KKWB/VwJUCn+zzEGJrDqvI5PUTp/GiPVObA+zOUYPX3NGkiapgyMCFb7GtyZB8s5YN1sUmQdpmlVM2AVqOrHe1IGpcR26W+hDNHMDnwaGPg0IDBd5uLWxcXfH71UCdoDWT0qsq4/zpo5PVbzDqXMIdVJkWvAX2aFLMWF3AnYXrg+kwsloRo11ZyX7y+f2wQKjptIUVxy9ZTKTypIvJOC5uj1VGj47SNQkGBRVTYfp0v3Kag+PV4aYPBZR6LmUNHICeHt977N13/t5/nln/hrfOVLPzzleoB5F5z6CBRgMEtWXpznLfnkyZJ5edSHvBQTLQdhfZXMTFEZdxc11nOsqtWyQVSAsGwik/JcN58X1B9wpMquamxbviWik7K6s9XjbsPeebLfmfQTSJiRfTaCRBglF3FXJqS6AagbnS0pButA4kAuizRB9QW4dlJm15yrlM162sTh/mgJZnCwrJ9cnnNaiBqXjEAMMxiY8rvpSPOQa/Ono7rxuVpiyun6IvSqOd12NZWF1ZyuLFgEMSrGFOOwC6FpPnkesvgoCyAwg9I009S5dsGcQbA1FmMs3loi6rg3Ju29ELNaRXfechgNfVK75DFkhpgY4mzUdIe4Ya5yaGpTIGdpjUyAoHEl5WCL9a8pIz5HLdtbnn8FN4vFnZr2KykQI8XHvz7aozs0L5BTH5IUXsAOFMOgWhZYXAXzoExBHofv+/lr7b/RZ2qdjuFqNbwEki8aq9WaeDFWJ6fcKjCsN7+O+QoOKjB1CjyzsaTiR1BFhRTGoLJ6MS0aUxV24D4wcJ+o877j09aRGg/d5yqq13q8boWc1RrFQCSqHbqpQU0BB1k3fymbeRaKsNgsXpkFcD5dn8trUV5ruT5XAfs9jEie1um55Pn0kMX/2fqjOTuBu+XeJMKTdz8fKPgXOV4qYHAfAlo+p7j4nbfffZuf/dWf0ZKOAgpksfFmWU7G+YHWuR7T3Zu/jNBY5rBq6+Gjspg8i5U4bRtaxCtH5TRKHdWujsurLq9OpOahS353AQrGmO54/eupLKL6cnrRzNatS1BQJ/Jpnh5mMHFfaqK+Vyg/WzrzOTHYMskseS4FW9DHzjS4plFa1820LrFszj7eyfFK3cjvy/vfdyzFW/XeFFCQi7jrKCosKnBiII1KIacwO8xN/e2X+dKTsjDjtDRMnNda8X6v4KA/HIu+UqPXbB2kAhBKxDn3tVgyH/M4Wy5smmueqcnTKGjaYI2jlmk6Y7HW450lFoDgSxvqxlrGBCFaDiGWxktxEk2esuhVxOedgoDOWU1PGAoImHsBWIPa/oaxbNhK404e8icvnqvAVERzVEtxsHHzmD29T4t7JFUv8CJA0B+UHajgYDiQw6idCsuzjyG+8LkbU8CBtxg3qpmQd1PETn3GE2iwiNaJLFiuxdyrQMHqMn5nVi71LQvdzCRWNo5cqoDy5F640LgkCGFmB2ob69NKmRpwwHF0r5e+AGHf5zoiBnUgzTXomAHB0fpVXUhFU5cuQy7VW85oE7FUXk91NDJVLZzetaP07zKtcN8aXQCGwBy81b8VOwVuVQM27R/3bNzLiF4FwKW8kjzduGWQ+RsnPgUVFKSTeaf3efH5nSu+/3ipgEE9jByLLo6pLOHJO2/zZ/7mSW+FJZVTfq+Cgczdmz6eqDqEwkrUqFuYc1inaLR+LACBELi3A2Ipn5MsOoGPFsPZVCNlpsYxFQwoa7Bw2KtoP8/e/fWwi4k3bdoyg4J7rV0XR43CYsrYPE/qivgnARI6sccpKhAg3q3Bt2DJU+OYiUEQi7UW75gW8LqYK1Ao5VplA5ETJqZO7KVwUD8kXVwr/TuVry5zj/patdwxx6B2s9WDvmwKaRi1t/097ozAseWss9qfofW4blAzmXHQMrGx189rmZjz5NyW3KNVTYJYRMKc3jqlnKfIOh6DhEnfMlOly3GlpmCL3HdhKxQgNESEMTK1eI4us/JSTG3cnc1iGs6ijYdU1DcDwJou8MUcc1b7LwV+6YXnC3NJ6RQR57kRWDWLuY+Sl+lexGMxYU0ZDIcCCgpDcFDL4TQMhENP6kcFA7UHQYj3P/NaHWCNmgc5S/IRmxLGRWgoG4y9S1vDfF3FcOtIvDjdByYB4+ytMqdCJyAgomCgNCjKC/FrLTkMOS8YAnXjrP0+PjfziK4Fp8HFp68l5fuVeTBZwUGlJGBaw6CyFccgIYs6XUpt21xstCVX/UzRvxRdyQQqT8dGXoy5T12j8wwGTljdpQZs0nnVFMvipZZB61jOUXU02khKEyH6F0/eebs4Gv4yP1pBQVrqaO6CfeDIYuWzjpcGGOiN+/QjA7/5zhN+/td+hr/2p7/Bj7z1FWJmUcIh0+tUMDCJmBbmQKCT4uj9S64yp9kGeMph1X/TiaRp0GnLz5PoVkwhGmY3uuOrm6OHXH6U0QGhfQryBGIm292K+HPm1NRlyRBUs5zZ932u6V688537ndCFPqMe52ExiWb3vmNQkkM+WkREBGczLixsZi240uym5prHUurovNPzXoKEYms8tSytlHMuevScELtYeHPSnHIuZWCSdOENI8sSr/lCyyaV5jbQeYxHrWzTGElB/91tUqMbbnQW4y3BWVzfkMeIaUbcasTEQG5X6uBXgYpvFaoaRzZex0+tbFC15DzGpoUhTSzKka4lL75Xrz3OUFGjyhJbmFIpYubIUmyjegfrCFaONpJZlT6n3OqYqf4DpgI+WZT/pYCMC7vfwtJIGj/3eWYxkEq5awFQWWLRmJxE3LmyeAvmLobyfoHcH1Tk2h8UCBR3wdTvCftBXTj3A2EYtFvhOD/v+565empEjLfaz6P1OCAVACYpoaFt5HRZ1mhf6f8K2MT5+doXSvU0UeMzIDjSRy1bIhcwMKUKwnGqoJYfVjAwaQtiOp7H6WQeZwV80cjRlVRgWHPqyyh5uZ4kKI2/dC3J1agqZ6KRiY1ZMggAgQUhlEBK6ZPNtmyaWcumcwFOGvAvNs/lGeXp42eu0ZTgLS9G+2Ldr6CgliHft4Hrn+h5xFJOflc0LHzrnbf5+q/+DL/0k9/gR778lXu73mY+mxX4rJ+/NMDgznGyc2XgN999mz/7az/Df/oTv8QPv/Wjszf36Z0sf7cEBKce+bHQU0BxX1MaXCdrhiQa2IktI5TFqK3UQ55tS5dipZo9iHFGpC84lmClAppYBkrtLBcyR6DghOVcfD476C3d3gxlYJ6Ag3qPYNZvVIBA0u54dfWooGBJMycyKegrVKbFjOmF/eit5KNIc041OKx3s598UuZgAgon1Hk+VRzXTdIbTBEW1ltUb8+U56sRWyh55LJgLFvZxqH0cxi1C1xa9LsXI5jCGtRUQi5+9qZ8dCljciKliKSIqQASME2r11DGkOTMXJ1yTJNPkecElOJ8T6p6/kS0WcfRMjqt9e2IMhViNA+N8VPlBNYRkSNnx+WoFZiU/6bqBpLqQqTS9mGYqfwclcKvYr7aZnd6QZmaYtWSO1XZW3IuHf5yZmr7PQl5X3R/KrhUYSuFKZi6FA4H0uFAOPQLYNCX511SCSHe/6xFCmBYRHLWkJ093nBOBIdSOhnW9MKkCShsYZ5KpgsIWKRPjloLFyAwlbgZV9aH+1MF1c64j6odqfbcyz4eMEe+xmjPCRHdaJ3Rae9quFWZwBKBfdZ6MpXPAlIAwrTSlMArLMbC0XqWavVBASgJoqhvQ8xFarB4r0/dRBcM4wvX6DDq3Kjsb73g+lHsdK+PekbUy1nuIUX9GJKmd+0CIJDhW++8zc/+za/zi//2N/jhL33lDkuwnG93ruv7YAvgJQMGp9e+lJRUUPBXfuKX+JEvlYYSheaerClZBJL5GBBUZFZz6OMiKoiUnvOJWsaKM1pepvO1qI5PzzcXB7l7DEr0hdIJa8Ci1nURKcCUY5rmy3Tepb3qAhTU3zkWzR5bz3prJjpLF3OpKoej01zWhU9GSszgIItGF6E8i1hByqJB0GlQPrexTVgR2iDsjfr0t6V23kpeKNZzKYcTrGgTGF0u81HEnD+FWs+mfD8FEINxXqMvUzKidROJcVp0T22XgTlqrKmFoCK05SKabELGiPWWnBI2ad25XVDQDrCy6AopC5fGVpAk0xjG1L4fd1MfEwCqZXcV+CwMcnIIk47ieAyqej3XTde3s3p9dCqOnPLTfk49GFdW/rJpVfBV73mIL/b9H4rCv5SkTqLP+86tbJy5bpw5qagvJ7JlXjU/8/4U7UKOCgrGvlQbDFOXwiUoiPueOIzEvugLhqCmTinf+6yNM7rZGUMKyhosj8nLoDxjsUV3cnrPp/GgAGiZOpm1JvZIhHmv+Vk8LmmeUkJZO2f2UatKQkr0MR/N11Om1BgwaW40VVlXBzPzKLNFd11PlvbGL1pTpjJruNP0KZ6Y/tRPlSTIZKNsQhRwuVIDZbrnGYCUBzAthtqzYPFsaiCxTDPClG7MuKM+I9P5FOCaUDZmbqdeAqITxqDuIaAgQk2SmADC2+++zc//2s/wV3+igIJKhC7BPMcM3fSM6rUs/n3W8VIBg+VRdQYiirSWoGCKcusv54V/d/nWUbfBQo/mvBDSpfn3NRqqfQKglsYIReiyRO/Te6bJtOROvXNVGUu6/yEuXmfelOeTz7kwB4tzrpvyfeSDhYkdqKCg2s9aM3fxq33pj1O1sgBOgkEngGp/FO3XyoaQqw6iUpOa6khl4szPrvS7DwpOemtwNtEY4WCNggMntPkui1BBjFY7CEYc1pR1lEWzpKL2PXatM5qDNVFL1VrRqBWYSh6BKlQ1OWEKM1BzlJp+GCcgNm0USfOyyQABjLtbDSFGMKMju0hyARMGfT3nIC5smkMAV0VPMq8GeqIcCS4rKKjCzDDqZlc0ErOQcpy9IKaoqOat1cJZbaM9qZrflNa7IrMOgQWQuVfzUNmLU2agNgCq6v4wKAiLYb6Wek61Bt96clRFv+Q8UfjifAF75b0/4/5MqYRQqkyqhiSOBaAMMwM0KjN0CgrSqJ08QV9eDGhzacimAvOEmGI+JHNzMOPV5ZMqQq2VKdZN9ziXe6wOoQsAVkSitRKj3vsja/TE1Pa4brYvYgcUFCSGpGBgiNoK+1PnqRiczZhkFhormdYTZ2amqPaYqCLtpeFWHSagbbCTCDbPGgcKOIg546whh8iJ1cikZxJ0U3dZ9QmOxdqX5zXzaDm8Rwypz3MBCk7XaWN0HV+ydRWYiSn3/241x/L9ZfqzsrfkorTIgjGZ33znbX7hb/+sMt1f+tGjbqRLQMDi8wkMVFbm/58ZgzuHwLd+tyKtX1KktfhxuudPlrRMqrTPMhquyPRkUEmmuGxltKtcoeErI2HsUoHHFEWlmc6cXPfuK6GDkxC/RmOn53/Pzg9HNGaxXyovU3QEMEXjM5V/bAdb0wrLQ4MymWgyk/WskpSbVDz3c1YwNdb7mk70D+lu3bs1CWeEISVc0EY7ziQO1tBFw2jzlGYYU560CNUbwVZKroAaLV9TAaMi51zAQdkQij5B4qgAIWkTJeO8+iaU+15jF0kJ22huWYLFBEcao+aAl88qzUK8adNIajucYtI8atErJBeIwSIhanVFUvMmsaF8dPP40VesD75wuWn+uvxcCjiYHPkqKCibXg4lQl/U4Ndc/qxiL5R9Ka2bNrAFSKg0d3V1LAN1OsejypEKUMY5Ms/D4bji455zmShz68jVlMc1+vOGI6tyWbrJfcr9mT5WZimEiUnRqhR9xnGRLsr1mZXUwSQoWz5n0jSXRWq5okxtjWvDInF6D00RmkrTQtPNvgJF56GljX7WCUwCQnsHCOQ8U9dLS/RapRTKxrQEBIegoCCkzLDUFbxwfgouC9iESYYiK5nTkXYOMrw97jWxdK/8tDUlpPp7Ol/qb+eQCFXUWHIJy3Utp3ycJ61PuY6Do+OYfX0RQLh3nX6RKLayNJmp/fSY0ixAPN0/Fp+rZ4iSHL/9ztv8e9/8Of7jH/9F/tibJf39GZt83RnmSpxP//37jpcOGCzvwbdK9cFf/Ylv8CNf+tE74sT79tAlS1Apn9N6XVDK7bRWt1JmSgEpX2UFklHVtCymgORMWhr0lGhnQpBFOXznyqTUZS8qEu6qUF8ADvKc06pHZQtqo5rqNjeXjS0bxSxuwOK8EkrtHbnmLRBUa61uwGWC1/NOSSfBYYzEpHTm0bkV+nFpitNYQ1PyoWNM+AIQKtPhTE19FDW05BKh5IlN0BxnYRiMwxhHtgtVuvGF5jYlNzwiiw0Joy2BKzJ306aQMCkplZkStrAGpGJnfR8S/ZTjvs5/LzxkHh8vfKMFHTo13amgYBxIQ4mMYxFbVctrq+PXlBJLcR4ZDsceDAvnPKyb6fHFe08+EEvnynu8AFIR8913Dtocy2kFB2U01pRPcouF+kX3qdwfkfsXgHtv2/fxHBZvU5t52QUIMK3+c6sG1ykYoF1huvXcrKiArVRSNDMg8BM7UNmDmr+eNn5mVqCmPvNyDctz+XJIxwzBEBJDnP0oqllVyhzZGENxpCz+E7VRETAJrp2puqDZrOrUvXIyrLpnTclAQrQiIeWFJkHBgTb4ussaJDLmxAzu6JWXbGvW9ue1MgGRRXXCyQsUpmsqYU4JTCqsnaYeF7CJLGbSeSkQK5UdEzA7HqO1WgOYQNvfe/9t/vzf+Vn+wx//Rf74m3P6+76LexFY+BdJI8BLCAzq8fY7b/Nzf2MWaijVnqdo7/RY5mpeBAryYoL0MR3ntYAgEWd1QhA1tRAS+KSbUH16tdZ+oqeO6u2ZKNzpWFBTS23BneWqrJSi++BnHpXKsxTFcJnETQEFjZNZJDZFewsaq5xLNcTBaK27KYBooq9iVk/9siDV76dCE8aU6YP6wC/BQW1aM0aLt4nRGYIzhGx0cllDU+g5J0Kb8olwMhcQMLeDNkmjCrX6VWtmtcAtm16taojmGCCImskY50l14ysOZZXfmQxtFsp0B4iJpFGOKECtaS/itPK5WKPlbBVI1pKuOnYWlDHMdthHNfv66kyj/DT5SaVGSxqrRO5pGIh9FdEFYogv9GAwjcf4g3biGwc16PGNllXaQ3F2LEvL0oCnahliUHagpjTCQOwHZUyG8YVeANZpCqHeZ4NS8jg3R3J3rlbqbrWYP6X0t9wmTdnJBDAwdZOQkn0owMgIqTwjWTy/nFTjkjATFjJGMF67SJrGYRuPbR1u1eI6/WdXa6RbI6stpu2g2yhLYD25mg0tAYH1qjGQRe46Hbc3Xq5by3x6znMb7woIQs5HxlRDUiDQh9m9MsQ4Cefq4a2Qstphx5RJpQVi3ewn5rGuJ/Y4yHihYVUdn4VhVSMvRzSChHlTyznrekUkiKgz52esdxpBL75Yzof6reX6epoKq8fpWl3W7xwjYtMs3BWZAp9YNByhMDQhziXcde0IkotGQ5/Vb73/Lf7Cf/6z/OX/6S/yx978igZ0SVM3deO5zwdiyRZIQQL3pRGevPM2Lwog4aUCBjOSevudt/mZX519ClIq1HZRn0xU8EnQkKkTqQrq8pyHyrOZB3BkFJRzZqRsslEINtM5NaiwaPTslznlKnw6FX4ZWwKa2cFsYgcmYDF/Xs9reUy/Vt/7BCFMm5Nhul+V9nNGjWY0MgdT6rmnevJ63kAVSlVxkxTls7UeaxyjHIstI+BiaYRj5AgcjDFPoGCM6VjtLIKNmWCFMZaWvxmSnVM9ISlbkHOeKhlcKgYpBYk7I5PTooi6pFkjBKn2v5WhsDhrEeshjpCcAgQXIfRaKmgWbaGNIVmLNfNGPj0LY7SESoRoImapSjeyMDpyC7Mj3fyMtwu72hKl16+dm+jkKd98UpUwGxvlUtKYjyN4UBVaEVZVgybNm4/EsdDoC4U9xpC8Q4YR6yxxGLHNgG2186WMg3bEXNj8TsAgL+yjx6FoCLSFdiylnks/gPveO3uHaWYGwfiSVonxeCWrtePGzZt9FehNczBNxjhkCyapM04sNsEFsFTbYOMjKTjERa0kWVYffMbzdV2roGCzwnUtzdkKs9ogqw2m2yg46DaFJSi9CVw7pQsqQ6DdN3Vvqo21qi1x/bpuOJSfVRfC6jtQKwz6mI/SBhUcHMZY+l1EFSSm2TCtzkkweBJjFBo3gwIzsXY1jaCgoKlfC9xXWnzUtIgC9kQmO3DrWlrnkChkEslkIoKLQl+1gJyeY3kOZR1c4sOjIzOXLJ6uryfgoFYmTMC6polK5cvRZiJWe0kkGGOmj8UhNM57BlCAjYLMCoT/7rtv8xd//ef4P/0pTR/krJUYqZysOSFEJh0Bx6DAlOdyyhY8eedtvv6rX+fLfIkXHS8RMADIPHnnW3z9V79+1I8a0dz35wEHy5RtWn5EgcJvv/stoKDvfKr0z1M1AoA1DisJjxpcTB3TcppFVtVWF3Qxws09kkx51HWiGLfIoZ4omwtar1afiUy2puSrRHP7JZqAmWKqZX8W8MbQGKG1YMJBQcF4mPLvslSGS3WbK7XR5Z/EQLYObz22TOYqGMre0qeMjXlqnVsPnUBKZU7gJdZqA4jJ4O9liM2UHsnZ4F0qTZl0o7bkaeJMJY7WEETVy8ZkTYMUTYIzQiz5U2sbdTl1RXdg/WTLjNWcOr6dS/bE0MDU0hbTI0aIQ0CK4dF0+4xM4jPb+olmdiWytG0z5fDFNZM7nqrT7UlEWer064XmPHdnA4iQTVKqv2x8eRzmIR+jpg+KwC6OgdTXltbx6HxTP2otftuoDmJU2t+2HtOkCfBWQDM/4IW+YclQDCNhGBUQFNZgthUuC72zC/bEkI1oqV+M8wJp7BFwyhU42Zqjv+celSYDNSUhOWOaVlN81pXcv4InO20IiUBhgqwh9kIqboanz9c4h21cSR20+E2HX3eY9Rlmc66AYH2GtCuy68A1sx3xabogzyVvteT3PifCygosrYRrvX9IiQilBFEZghC5Awr6MSpoWDTDmm5zBkgFQJfvSfGmEClNsizeQFOqiOYgY1GaOolQF74aS9agBBtiHTkFxLW0rlPtgTGElCbB8Vj+pp5mKkGiLZuus2aqkLDI1I756Chl5dkYyKWJUWHmZNHQqqbDdEwXL9dgtNurL6XEJWiLqK5gTHAIkf0Yp5JPHcso2zFpNXRV/ou//nP8H/8nv8gfefNHdf8pDPCUJV6c+6eBAj4FFHzjJ3+Jv/Sf/SVedLxUwOBJMX/4xk/+El/98lem70/C7ReAg888Clvw2++/zf/mv/h5/VYBBdp2NE3RK2hOvG6htnXFsKIoDHLWMrFqrVrU18BUq6wnvbTUnI1Jquo7M/sViEgpmSkUeSq1xKk6CuqwSAsBUYhLl0NlC5wVWicKCoYdMuyRcJibxiyV4WJmyriwBQoShtnb/2Qyp5S0AY8VzChHGZMlVblciFLK08VYIxrJGBUmmsquJ1PslhMEU/zSE7aIHes1QgUH6agsM6XSB8BorbMVzVNOlQ1omsHAbMscRggN2FLL7zyV7m+sRURp9+AHTD+QRjdteJNffvEzcE2DbXUDUcq5KSK0bs47F2GaNrlpdBOpNrbTxlfHqy6yIpEsYV5Hcpo2PqnnPA6TW15KWTUG4dig6agu39XryNjGT5tkPeoClctYFylNi4qB0H2gIFWF/2QMFY7eM6ek4MAakgmar4dZjFjnzaIREEfAqQKFOQ2nFxzLPVLnuzrqpOm479C0kIKA5JTpEWcnt8PT52u8xbYNrmuwXYvfrjRtUIHBaktu12SvwCA7P9kRq1aganHuVhSkNOesx0WK4BgUzPNoBgdMngSTADipnqCmDlLOEygI4XiFrNH4kRDRoH0wrNAWIN/eF2QsjKtOPTWmNtWLNWZqKhYDNBEDtK4jZG24NpX3ZaZySsp9o6x7atKmS0hdL0RKG/KiE6rpV6rrZEqzsLCuwcuqpMJ8AQp+kwqOCR34cg7onjEmuB0CN2OcUjTAVOmVjcExl1cC/O/+5F/lD33xR+6AgBcdLwIFS7YA4Mk7T+7dH+87XhpgoEzB8qL1rhZySJmBacWCJNqZTrLWulawsBBRz4fAf/mdb/G//vWf53/7b/5V/p1v/sRU/rdE3iEpt7APQucMKWal0izEqPQ2SUVX8bAnH/a6WOao6BiUOsy5CK0sqeQ8c/UxLznGWFIZymToaVtRkQ5ZF9SYM8loCWEuC8xYtBHOqvlHzmaaMN6ACT1yuEGGWzjckA67uawtV3W4nxbhyXhFFj7sJVLDjUiT6Jo1icyYC9VYIneDTOrmyh5UUFAXNyslN2+rCEkmXUIVMCaZo4SEMgDEfCRKyll/w4hOysYaXNE9dN4qiMo6QZMosxwNSNLNTVKl5qotc6fdH4vRjzhNLWh3Qou3NR3gMI0vZkdpssudrJGLJfKUSqhivm41N1aqtsi+nUGB0yZL2ToQt7DALQvBpJUobpQ13MhJxW0xaBvrU5EgTJucigDnuygyd3Oc1N01HaM3FnFWgW4RcuUyr3KKyhqkwkiEQBi1z0QcQzGFKmBkDEebmvGq6Ti1Gq4VClJFkL4pjn6azqisSs3NV2va+c8zOQckzj4jdS2+sx4XEGKtw7henSvbiO3dlAI5erZiJsGhWzXYtaYLzPoMs71A1luy3ygwcC3ZdZMd8ak7XuZY97SsKjiMkT7pRq6lhfo7daM4PSZzIhbzpwQ++m/WUX3aXFzO29ocS22tDc4KjYPOSQkwdkg4KFuQ534mFIMtwqj+EbVEVG/i/Fy7ddE4JUwneNPQl41eTdMU3ISi6zKYIiHJxWGzljMrQCgzYtaVZdT9VUpKyWY9TxPJxmLKupaX6bBQ1u1QgKEYbNNBGyBlYknPHELk6hC4GQOHUCzVKeZtk5eVslZ1M/7Db/zoYiKfHMIRCLCLz+8DBC8GBfeO8ul4aYBBpUeOkVCJOhY34D72wC7AgSl5nCy5lBrC33vvW/zFX/85/vd/8q/xB1//YaCIDZnV9Us6LtbNZqXswdrbYqKUkTiQDjs43EK/m+u0rSNbiylRFsVYBlOiiPrPuCI2gslXvFBjtVTHGe3lUOv2vTGIRAKZMWt9siubcsyltE+0HNAcDpjhhry7It9ckg63E4DRG2i0TMw3Oklq0xeRRUtqU5TqI5IjSYTGrRhimujFvTEYSVMZ0/R8jC4+VubmKcvDGtUC1IoFUyOBcu2nZik532V0FFyAz1k3RsCXvr65pjmYm7NM46hMuBodNbaj7TrEOIzRtIoplsepbIzG7YmD0w2v1pKZqq63UyMdpa2bGRg4PzVRquVs2daPrW58rp3dBhf0pAridUFTEddItYPLlHRWDKXlsxr4GG8ncd10TFHwHL1blyftwqRuKx91HIpG8q4pToQnVQCxiKLSyd8v/p26B7IABFWcafxi4/Dathrfkt2qNCFS0HonAmdmrK0IxqgmpuaXJ8Fh+Ue1H+73CsZKFYUpOonULRpo3fN8NUW0Qrq1sgTbC2R1Rm7WpHZLbjb0CYaxmAgxg/3jcZyna4hohDyWEsM+pUlEuBzjRlRzs8QHpT6mgPIy1tM8p1Tse2faTeziMn9vpJYh6kanaQOZdEpm3GP6a2TYTX4QpAJNqu6kmmyVxlSM/cQaZOuQboUZDuoASgbnaboWG3T9qMK+Q1Rg4OycPvTG4I3Q1aorM9eFLcfBBMIAZ7w++aK9kZTIMuozLWM45azpxJJKUOdNyE2LWW113RUhJLjqI5d94Nl+5DDGqZKjsWDkuMRztpVe9JKQObDVZ/b9goL8Qib9046XBhh84ye/wVe//KPzNxYiubIczhHBCXsQF+BAyqqgOR3ht997m7/wn/8s/8Gf+kX+9S/8yCQ+1Pr4XIyUMkMIfPj8lu988AnPrm+xknll2/LWgxU/+HDF6xvHZrjEPHuP+NF3yFdPSfsBay2u9dimgW6F3Zxjtxf4i4zfCn7TYGyLYEhZ6A8Dt/s9lze3DINONFMQsZP5ugOGPiZuh8z1EPj4duTj/cBNHziMuiGfd45X1g1XZx2POstzO2KvP4bLD4mXn5Cun5J2O8IwTgk8cZbubMP549c5f/wF/GpLjoEkhjgM7G9v2O9uGUOAdo3ZXpA3j3APvkBqt2CbQueDs2CCTgLvTBE5RXBK70+bfREsWiMYY6ao38jsqjb/KxRhSsSUGWJkGAO3h4Fd3zOGhHeG882K8/UaGlcmZAIUZeRULSdmWpCycISYNAedIp13nK8aNu0GJwYjs3+BEUNyHuMb3URqWSozBV6thsU6xLfksqHmakFsPTQN2Xiy0dRBwpIwxGTo9/1RzrLEQQr0asFXUuvfFAYYD9g00uRIYxy27Yqla1/KEB3ICEZIOTHEwNUnz7m+vCakiDiwXjUQWplQGY8G1zY02xV2vcauz3Qz9M0EDPI4kPo9cXdN3O0YbvaEfiD1g/oD1IqEPhDHkRzAGcvZxZbzVx/SZq+LpYiyMM4WMNCR245gHEMfGYY90QTwGeN0UVfwZxRE5TzdJ2Dy7GitwWI0bWS9zn+HIk+K4+A4QFQgIjGSxx6JAVutm5eeCzW9UcyKTLdBNmfI+pzcbEjtluBX3PaRq/3AYVTzKmOdVtSg69CJXPSoW2pfqgt2Q+B6t+fydjeN73XbsukaGu9orJ3mTo1Y59czBMmziZkxWKNlfKXY4GgeaqWBwTszMQbOFjM0EUiRfrdnfHZDeP495PYp6eYS+h3eOVbrDavNFts0mKx0xbi/4eqjD7j66Hscrm/JodqPC67xmPUac/YIe/EKXLxGPHuV6+h5eoi8f33gk93A1SEwxkznDdvW8eqq4dWN56xxbBp9vo55T6g20CnrM26ahovths1qRet1JOQEKQTG3YHx5orx8hPizSXx9goOe+IwEPqRGCNm1SDnj7CP3yQ9vOS2ueCD28DvPNvz3vM9n9z0xCw8PNvw5uuv8NqDDY31R06QNVlxvPGXr8t4kPK9CgqK2eS9LMEMCr7ON/7tX/zcoABeImAwpw+YQcHp52LuBwhJUwukzNyaUfit957w5/+Oloz80TfUXEKt63QxSVmmbnEpJfaHA9/9+Bnf+eBjyPDaRYv0W7ZxS9s3DIeP4MP3Cd97l/HyOYyRpm1oNivseoUkwWSPsx2N29PaNV2TaRqDZMNwGNj3A8+vrnn6/Dm73W7qTtdaQcaDdqbLWSntZk0f4dAndjcDVzc9zw8jt70i137lsduWdWjxK0cI15jrD0gff5d4+THp9oZw6OeJCpjG0w4H3OaMbUbjmww5DIR+x+H6OVcff8TtzY1ujGcP8a+8QROge/wWtBZnbAEy6mjorcHbzBgSzhpMzhOSBvVZsEZovcUXcVPrqq+B0petrR0Y9Q9DYX9CzIQQ6YeBm92B/TDQeI3w26ahcYYQy7mUTaNKHDIlT7io+R5CZLfbkcLI+brT3K20rP2KpuYuy1izzpGs00gphFm8WZz7xPrJ2U7pbkOqTnaVSjVFXGgcSRzROEI27Mpm0gc9txpgKjBAez6kpI6GKWHJWBI2R9ZO2Z3Od8oYDHoOxvUlynWEYWRMgV08cBN3JMlY3+I6wXUW441q+UzG5EG98geHcQnrEtbpBp5yoXbjSBwScUjagTINpDyQbLGTtVrdEQqzFUOPiYIJnlUKdNaokM9ZjDMFFDTQtCTf0SfLLgm7EIkCcR+J7DTfb31hcYxWm1VtTZnHrTN03rFurPbdyCpK1ZDSkL2m8nANkpK2g05Jc+bVnKk4R955xq7BrDbQbVRT0GxI3RmD7dgNief7gQ8vb7naHTDOs16vaZzVjaxoYKbGQ2VMVqtzFRom+hC43h+4vN0xjIFVo+C7dXYSApoCnEEtxF1SpTwkUp7hx0LegxF9j+nrmjooKYOaNpjBuebfw3Dg8PH3GD56l/GT90nXzzBhZLPdIq8+xjsF99laKCmN3e6WTz75hP5mRxrG6T3FaVWH2Vxhr28wh4G0G9m7M57vA8+vej666Xm+H4kps2ktqfOsx5ZDamhag7UwDDtlKUQ01eS7Upmh93K9XitTlSG2ui7kbBjGzGE/0t/uGfY94WZP2h/It1fE3Z7hds/QD+At/uIWFwSiZ9clPrwe+M7HN/zTD2/48LIHgTdff5VHFxtSWpUuo7WMsz6f6g7JJOisgdAdV9fyO/cBAnJSUPA3fnYGBdVd7XMcLw0w0GNBA8A9fFyk8mpSQEIGJrtUM4ODb//uE/69b/4c/+GPax1prOUJ5b52ZXCTtZSuUuTr1rFddYQY6Apab50idiloxDYN9myjKtluhV1tVZ28OUdWG93Q3YaEIwYYh0ge9wwpcehH9sNISEwueMaIWvSGgwoG46BCxLBn255juoZMq1SZNax8xBrYeMdZ51h5Q+ME04+64IlgfIdsBbve6gUboZbHddst7aPXsdtz3dgAGQ5Ya2m9p2s80RnGUprkcizxmjb/0W6NMqUAfCltar0tVGZeGJkXgxRr8VYmUNAVYLD8vClCI2WfhTFox7PUOEJqiTnjvdfFzVrIxwvhNEzKx6rwPoTE7RjpQ+T57sDzqxvCOPKwD2BccVJ0mmNs5qZHGIMxVg18ykZdjX+qGZA4T3KegGUsznMpG9UyiMWI0wi2AIMknsMY2A0jt4eR236gHwMhxiKozWoTTcml5jRHGUbovAdn9VmkTONaxLUkY8nFpXLMmcFk4srTfvERr7z1CNlqtCubM8S101zJcURyLiV9LbQrUrMmuW6qmEjFTdKEA27YaZfCsS/dLLU3wPR6oSffXpdU1jU2ZGLjGUzWNBtqh5uMBdeSXcuQLAdp2CHcxshhHLSBWGF5ctE6qC5ISCjz4Kyl9Y5N25Cy0upr70GKGAwh5ZEkEDGkHDFoRO6dwRExYVQm6KS3w/ScS46cZkX2em+i69gPies+8OF1zzvPbnh2c8B5z4MAD9YdrbNsSk+FnFVZf9+Rsg5YYy3rtqNrMq23rLuWdePonGHl7BTh18zNkDLWJA4yT+96HEbdiKrQd3ks56K3ckRvW036Kzgl4nIswDTgnaFrPK0r88XaSeRpjaV95XW6fU9e3VD7V8wlVIK4tmh9EiaONJ2w8oazzrEPCVe6fG4ayysrz+Nty4POsM4DZncFw15ts21DbhLJNRhj0MIUS0iwH0bc/kBIkcYYJEeGAAOO0W3Iq6Rzca/aENNdY1Y3+MNe94emKekGnWutszTW0nlH10acdaxbR1vLOK3QWTlay6AatH0GKJCaQp1TBktXzyfvfIuv/82f5Rt/+hf56pd+lOND+KzjJQMGi6OAAjl1Qcu6UFSQIGIwLPy6jfCbv/uEX/jbP8t/8m/9En/8rR8tbn6zKA3QCFWMahVSordw1givnTVIWGFIPFo7Hm88XaHacopI02HOHiKbc4xvkPUW2ZyT/JrkO5JtyK7FuIZDMNjbHtNHxOrg7cfAbt/Th0CKxTozZ0i6ODHckHc35BiQtsOsdqw3ryDdBkvDprHshkgis20cZ41l7QQzHrQWXUTPrVsXKrQtGoJSs0um3WxoH7+KPT/XBSUGMAabM77r6dZbwtCriKzQiykoRZzMqIpu5ijGGWUMQFsqOzsrjE1hRI7dDzVv2Bb9Rld+5m3BeEVEOIrgRBsqpZyAjsYFBQjGUNstK50nRxRuRQchZfqU2I+R5/uB73x8zfc+fsY4jnzh4RbnPV3rcSXCWzfrUu+cy4ZcottqvlPFciX/HcUTMOzHxC6NDDGSipjLIzRicKnQwBjGMbA7DNzsep7f7rm8PXDbD8Sooq6VM/gqJC0Li4rRtEe9twP7oWFYd6wxtEGwIZGHQNjv6Ps9/bAn2kx8sMWefRn78AuE1UNid86h1GYbA40xeJMnj4tQrJC1ZjvRDypSFHG01tN1W9qNqC/GFL15xqS21ymphqSzgj1c4ffP4Nn3iNdPOYSRcdjR2sy4F1y7RkIiBqHHsEO42um9GGNEkrJZ9fpTVoX4mGAftNuitZZN2zBsOnJaKaiCMjYMKQkhGYagIDMkpZgbsazxdF7wPmOzNoOq5cdyqqz3rVYe+DW5WXMImV1IPNuPvH91y+9+dMX3nt3gvecLQya9aniwajQaT0VZvxiXrpQhm0opo+M5t16ZAu/YtuVf42icziGlqFWJ7yPqL1BYrgo7qqhQXQ8FF4/nojUVyJvJM6RGryklQhgxo7YfJ0W1M/ae1ju69Ra/WmOL06Mp/SBshta0tG5LuL2dtFNasTBOdt2ayxdyGDDjgbVreNQ5vBFuBotBWDeW88Zy0RlW4RZz+wnsr7SNtnWw3mrU7EeyaUoZaCZJQHYHUs4ceqfrdSzrVjDgNqTOI26DaS8wm0eYcYe5vSLvbpQ5MhaajpwizkBn4fHGM8SOdetIGB6fNZw1hrWFtTOsnQY6tfQSZvtoNWJjso5eggI7pQ4KIFjsd0/e+RZf/7U/wy//xF/jq1/64alC6G6N5ouPlxcYsAAFJ+BAKqWSIYu2ZpWSwPnWO8suVj9KqXwptqLzBGqMTJGZxcAgxM6Qzhse+Q02Z85ay6POcd5aFeAISLdB2g6xntysiO2W0G7pE/Slhj8PIMOANQZjA9basjHUCRu0tIyM7qcFqecIw4G0uyYNB+TGYDY7TAqs1o9oNw/ZNIZD1GjZGUWrLUGZhpzUWMWvwLdEvyG3m6nDGkBrLZsH5/BgS+48OQ6Y0KvzIZkmZ2KMWjN9OBCMB6clm4dhIOPJ2aEE80yhtSWisSZNquiaw6xUW1OZBassQecsa6+iJ1UeUyow9J8r98e2FosumreitcVSxoWVjK/vL3PklIFsDVKEcH2IfHy14/2PL3n3w0tiiAwxc75d83Cz4rzz9IVebZyHpK1YpeTEpZSTgm6GyXqSbQg4DhGe7fdc7vb0w0jO0HpL1wpbgUYylghGOAyB/WHgdrfjugCDXT9gyHTWqD2rlHptIGWNYPqYGVMmS2Q3RHbDyEXXsAoRNyTibU+43RMOe+1Zv3mIPH6TcP4Gz3PL+1cD739wyfUhgAhrb3mw8jxaezbO6kKaYYwj+5C46kd2o4rhjAhrbzhvvQIXaxBxhAS3YeTpbuT5fmQ3Kutx1jne2Ha8cf5lHpy9jly9z+Gj7yC3zzkc9lrV4nvsOhEOkb3LXB6uue0DfVCA5Mt4AXDUJmKlv0FIHGIiIYQQlAWQjDf6u7lRJ8UYM0OA2z6z70f6MSICbeO5MB0PuxVYyARMHLSMNY5QAGcWAeOKWFQrJcYs9FHdBZ/tej662vPOx9d88PQG61Q/0jaelbdsk5qkudJvoAwfUobWaS3/ARXJOQHrLd4YNq3nvHVsW0tjoLFajltFygnBoQDImsomgQmU/iSGoaQxqsHRck5OJkZmri4CGMcAw4AMAznlYtTV4OwZXdfRnV/QnF1gV2sV6fmO5FRUK+4CzEMOz6/oi07DSkmR9reY8VYBQlbNjIx7Vq3FdI7OCQ87CwKdNWy8weyeYXZPyZcfk26vyTlhmk5LTJtN2Uh13UyFOhzCiOwzYdCgIaVEjJEUIzHVuqcWYzva5oLWgGxuoL/BDHtlR8SqYdKw47xdkXNL5yyvbSJRhM2q4bXO8NALF42hVQJP2WNbdS8y6Q4mMfFCT3BkJV0Nosp69uTd3+Rrv/bz/PJP/FW++qUfnva7/DlTCPV4qYEBcISmpq+XjEGebSy/9e63+bm/oY6JP/IlbW1pah5ddDLF0rq1NTpZnMkYk8g+Qyd05y3jymAl0RhYOaMDO+z1OTZrpUC7M3oct2NidzWyD5ExKI0/UeM20bhMU6jJTHFcjFoWCaqRSBmtahCtn88pwdCT4kiOIyZGbIpIGNi2WzZNq/cgRUxQrwIJaniTXUdqt4x+w2UfuHze83QfOARF/w/XLT/YwdqsWa+2SDzAuNO6/UKfNinTYhlvrkkYklGqPBwGYjBYLMm2SGl61DgBtENbzbMBk3GKQRd5K1I2cb2vm0bzp62FxoIlY1IkxoGUEl6K6IpM9hoFSrbsBrVflhjIwWCTxWDLe+gESmQkZppC4YYQudkd+Phyx+VtT86Z1W7g+W3Prg8cxsDaG2KS0mnQgy3dNIwj13EnttSrNyTX0gd4envDR1c7Lm8O9CFgyLTOsRkjZNjkBmsCyEgfEn3fczgc6PuBGLSVsoI8Ye2ElVPNBUlTHbugG3QfEvvC4mwOlsMw8sqqwScHriXaltSArLbIoy8wPvwy791G/uH3LvkH7zzjOx/dMhwCzhm25x2/97UtP/h4w6OVZ+M1PdGHxNVh5HoIkyCsCl2vmsB552mdgtzbMfJ0P/I7H93yzz684ebqQAiJpnO8+XjDv/7lh/wrX9jy1qPfixNLetoQ9jcE22JdC8kx5oZPrm653AduR7XsXnmD9bqZrp2Oc4yjNxkjqTABpalNGOl7w8EJvTc0JiNphJyICW4PA89v9wV0BBJC6yJ90v4Fr1xsMQ4k9BAHjXBzPHre2l+jMETFaOgwBnZ94Pltz9Vu4PowIhJYXe54/OBAuFgD0NjCANl5A45Z3UFtiuRiGuTQlOK6sZy3hrU3dEZ9QxoyEqMKC11DMpZghDEpk2TFUvsxGixOMo1VR8TaFn3pW+DKuTSu+gToJnqIPXG3x4YBh8UZj1lvaUi02zOa83Pc5gy7PivrzJrk10TbcT3e8sHhht951vNs15Oyusc+Wjku2paL9QY/3mL6GyQFzLhHcmTlOlrXqg6nAAZzdYM5XBKff0y6vST3ew3EnIphc3kmKS9F6JkcE0GCpheAnIpVdEhz1VkpOfQlRbP2KzbrDe06IIdrCFpVYcYdXVZ29aw1DAliNnjv2XbCuc+sTKIxpnSIzVPJdmtnTckEDk5TB8vW8aDpg3d/k6/9rT/LL/9b/ylffeuHy773+XUFy+PlBwb1qNUEOVPr8avXv6TAk3e/zdd/7c+U6oavTEwBlKirHLWle2sySXTiOJOwNmF9omsiMe3JYw+HXsWAlTZ1HbnZEJotV0Pish/46Hbg+WFkKCKflbect45HK48rhhuaa0pA0SkYyLGGEIUqtR7xaohjVhtiGJBDUvOQw450c6mDKg5a9mYEYpx6IAAqgGtWhGbDJ7vAu1c971zuefeTW64Pgc5b3nz1DLM+55XHsBoT5+16KuNBBLMGh1EK3HhsTIymJRnHYRgYguCtx3VOF6Fyk40ri9BiDE/GKUWgU6PAzkLnLI3JtGUT9CTi7pJwe8m4v1Wlu3VIt8V354QETYZWIkESQypuhEMkugw2412j6QijFSAGNYJqRFX9YRzUhU2KetuqOEUp1DTVgmNVQJirYUoGUAGhlqUqKBgi3PQ9V4fA0/3IRzcHDkOgMcLFSrUY+2EokWzUkrVsSONIjhFL1L4W3tI6YeMN543F9VfIza1GPX7Fev1QUxQpc90H9iFxaQ1DbCBlHjXnmHaPnEckBFhvCWdf4INd5B99cM2T//fHvPe7z7j6+CkpDHTbh4Qx80HreeWs46xxJFt7g2iN/X5M7IZIP8ZJNLry+nOfNd0zxszlIfLBZc/zT/ZcfnTF4eYZxjVcP33EzSEgkvFfOOP1sy9gc0LaNThHaM9JzTlPb0c+2o18fDswxFRAkccbBQVm9wwz7skIq3aDb891Hgv0oXTfRDtPpnEgmcwwKEAPWPbDwGGMXPcjl/uRIWW6xhGtp10H2j5gbUvbrDGhGE3FoKAMdCOa+i/YqVomlN4gkLFWx5LWyQfCOJDCQCMtjdGNoi30ckqJMRQdxXBAxh4JIyZlGutoJdJkg0+DmjbeXnE43GBiwDqLX21wmwtW6wtcrfAJCWlsaaAGQ2GXGlucFQs4uG9eOqPdRmOKDH3PcOhp0sDaOKJr8ZtznDU0Zxvc9hyz3pS0iq6F0a247gNXQ+afX/b8N9+75jsfX3MYI2ed40uvbPjyxYovncOr6w0+x7IBD0gYELMvFuXqkS5xQMY96eaSfNip6FcsNK2KQFt972Q8KSgHUGl7Z8CTtboLXd8lJ1JWP4Kn+5GrPrAv9ZyNNTzoPI83DRet5bx7gBtukP4W6W+xhxvYPWVrawfSFtsYVj6xtonWJLzJGJunfi6gIHC61+VTDZ30vKjtyxdW0m+/85t87Zt/jl/5sb/CV976oe9LaHjf8dIAg+MinPsPqbXS9WuAHPmN977N1/72n+OXf+Kv8ZW3fhhyxMgs65ifUyYnVcyaqnIdB6TfIbfXyNVz7M1z0s01uS/GQKCb9fqM3Kj46GqIfHQbeP+658Pbnue7kZQyrTc83rZsvcUZzaWvXIlickCMxTuHVVaZIcrkgNbHzKo7I+WEAZx16kEQQrFWRsGKiEZEUBBnaVhjbVHGOw4hcT1EPrg58P/53jXf+XhHfxhx3nKIhjdfH/lkN3C27mhcYuM6jb4Li2HW2mnRJEPaj2RpCOLox8TAQLID4jpsY4u+Q7siwmzAUnUfWpdcSqSEotTWPLQ3en9cDqTdc9LlxwzPPmT//CnDfqd6ju0FnD3GnL1KHjOMGRsiNgZsFvIQiCaSTSCbFhmyWtn6BsQyEmiJNDmy9vBg7YmxwxjDo23H+aqZulEWOx/1d8haFKeRjAJQJvOdjhHDPgQOCfYRBYpD5tArMGicZdPqxI/9TkV5cSRisbbDScSXfLyyTKKgYPeJUqjXz5U+3VxATqy6R1wbVbHfDCMgxJyKda1j052r2j6OpHZLbxqeHwbeeXbg6bMdt8+u6a8+VjHl+gzfOl45a3n9rOULZx1bpz72Q6hpBX2OvbO03vBw5Xm0cryyamicIBjWbWbM8MFZy0etWt/Gwy1juuLWep6et7zz7MDvebDmQdfQtWdah289qTvnNsLzw8gHNwcuDwHIeONxZVyY208w1x+Rby9BDObsAS6OnK9fKfNaBYregJOITYFwc0MmItaTXAvZMqbEfkxc9oEhZXos7ZB4EOGQoAkZYx2N6+Z1RWSxOMtsjlOqZXzJ1Z+vGh5tO1JUoPBg7Vl7aHLUcUegFfApFhv1QM5C7nvioSf2I7akIK1kkERmxGTh8PRjuP5IN8nhQLNas3rwCBN6LIl2/UBty6G0KNdnF6w+F+0gy9T0Zzk36yWq/iETh8jhMDAOA5mklT+uUyHtymM3W8x6S/Zd0S11ZNdxiIndmPhkN/DB7cg//eCW7354TRgjbec5jInWCg86x7YxuNrvIo0a2NTGYqFGcVpGCqgw2jpNaXQb2F6QVg+VrY26bmY0PVJ7OjRWMDmQk5obiTPErH4QKWV2Q+Sjm55+VIbmwdpzO0TeOGsBx4NmjR13SOhJu2tyv9dzaVqkXWO3Z8jwAIkPkHiGadda0mwKswR4uW83m7UEUjwWalO7J+9+m5/+5p/jV37sP+arb/0Q1Zb50457NNdHx0sDDOajLMKk+eqrSrxSK/XIhifvfZuvffMX+OUf/yt6U1ModNPxrZPyIOhv9U/7G22yM/SYwy1y/Zz89APi04+Il8+Jw0HFhr7BnD1Cug1ZrJaajYHnh5HLw8jTm4HdEPDWsO1URXzWau3teWNx4y320GtezTpMs8K1WwiqXh+yKtljzmQLq/VDsm0wfqN0WywpAimb1NLiY9EQ5MgPPDPVx/djYhwCYVCUvB8S14dx9v2OmWi1JKwagpCBJiNNgtiTsy/R9JzXzTmVch2DM3eHaW0sUl3LnKj5iiv/WlOZggiHG/L+inj1Mfv3f5fbj77HsL8huxZ38Sru9R6GQF5dwJDVXjklJBczkZgIVzfsLnuiZJxX/wGaNU17jk8DFz7zqLWMD1dsVx4Ry+OLNY+3HRedV7W2aIlXTgFJRXhXS97Eoja9niilAiFDyMI+wiEbDkkYSt8GtWYWHJm0v4b+hhxGoliSW9H6rSrGndKOK2uwwy1m95T4wTvEZx8ppXn+EG8MbXeuG7IoiOxHZT6uDoGz1rBuV+ThVs/V+KlnvPaMMPjVmi4/xrcbHr1+wQ++dcEf+oFH/EuvrHnYCS4cyDkzdA3rRjhvHWeNY0hKl7668Vx0lovG0eQBkUxwHQ86T2OttvcNCbGesb9VkZqdlfQpo34OtlUH0GbFvk9cHQJXfeDqMNJ6zck3TmgJ2NtPGL/7z0hXz1Qc+/Ax9nWwrmNl15MbZmvUyjfdPiOGfbG99qR2i2svpucREYYMJOGQDfuoz0830Yx1Fme9gqKMsntZK3FIOi6MuKnr4EXnebztCI/PWDeWnCMXneNRa7nwGZ8Gmgjx2UfEYUcaB8I40mchSIsVj40BiUVrFCJkhaeHq0u4+h7hg/cIlx8joWdcbWH3BdXBNI2CmdU54i19zIhRoBh0imgn05QJjqlh3H3zNKao1sohKiNCIiZTGkFpZQbNBlynwYdtdR4gU+XPfoy6rgyJMEbCEDFG6MdUbJzzUUZYCtCqaWAVRxulMzo7BwVQyhNXaijVbthXjUnW7pg1CFs7A/0NMuzVd0QMjWs5bzbEnDlrHVe9rtU3h8A4aEqntZq62TSWrbcYseSUSLfXpOun5HFAjMU2HebiAfnRDpGIkVgATjtZdwPan6Y2RjvdgxYsgZT0wU9/8xf4lR/7v/DVt/54Wddr/480icYRmUDq5zleQmDw+Y8n732Ln/7mv8Ov/Nhf4atv/bDmBVVfuxB05AWw0LJBABn2EEdk7NXBcHcFt9fk60vS7aXWaoto3j0XZGu95jXz3Ca00qzbzvHK2vPGWcvjtedh63CHZ7jDldKFZJ1gSd222mZbPM6r7kBzgoeQ6dyK1flm9ievtFM9subXJQUwvaLsMgglJ7wTto3lQef4woOOfoxc70astzzceDaNmQZppiza1iLJqsgKUdlzu8ZEh2DJYypGHXNf9taqLqGC2zoPakdEaxZq3CK6qR0gfQEFMhxg3JP6W+LlJwxPP2D3wQf0t3vEW5pxJHqPMS3ZduQkSNKF1Au0xsLuknC4gv6Gcdxjc9Yy0rMHxPacZv2Ijcl8Ye3w0vHKOmOc5Xy14vFZy0XnWDm1vnY5TtG95Fj0KZUE1AVs6tEelc4espCM1ZK4AtyqwEvGPfn2GeH6E9I4klwD64cYLN6qqlqV6oL0pb762UccPviEOIy0uz3SrLCbR6z8I1ZO+wykBGNQO2/VetWIS8sKu9U5D1aOLz9c8eEXz7WbYjhjtWn5V770gD/0pQt+3ysrtrcfkn/3HeLVc3Ce7pUv0py9znq7ZuMtY9Lo+Lw1bOIO8/QD0iffhTDSnj/gi698mbM3XuO8NfxXZy3/8EHH/rbHOsObjzd8+eGKBytHVzz3SWOJrIQYYUiJMZSKBmDlLCtnsIenhI/eZ/je+/TPrrGNpwPM9gGyfoRrNlgN4DV/P+5hf03cPVPVu/fI2SuIaWisV2dM0VLHiJZLDlnoQ2YVNcqOKautLtUrZY7ycgEIroyTlRMuOkc8a7E5cd4IKURWTnhl5diYSDPu2H/3PWx/Rbx+TjzsiSIkvyK0W+jOad2GGBWMq0ufkIfMePOM9PQDwsffZfjkE/IYCZtbvBXi+TlydqFVAb6haVa6cUVhNHMPhpSP283XTo1lCZmWxyhqDues+sEIomWovsNIA616TlQBpjJntri3lvUoJzaN4eHGszt0xDFytvZ84UFX2AItkZTi5pWN1QlsLNh2sow/os8nlk5dQvchcThEhpTJWQOPltngyvQ3mqYYbgrbK+qq2Q087B6WR5mn+XlzCJMTZMgqbh1Toim+GWTtD5IOBy3pHXuyEWg78moFbaP22STEhqn7p6afF709ltH/HVDw5/iVH/uPVFPw/8PjpQIGpwRMFrOoQFj4fObMk/d+S0HBj5/c1AkccMw0FJqVQlMRStfBmuMDrLXq6NV2YFScZNZbzPpc82q2KTYJik4frjzb1iECZ43j9a3mqi4awV5/gL19qvXcY4+xVn3WjSGNFud0weqDLkB9aV3cG2EfhJ1NeONw1mPd3DREygZLHLUvQtA8JUlrsCUcaF3Lw87ygw/XtM7w+nnH5X5ERHh0tuL1jWfr1ETHFnOHXJkasWSTwXW4BnyyhCTYPNKgbnZN61k3ZQG36tBRG5tAsUyoalxTa6Vn33NvBEsq+dUeklqp5uFAOhzUqWx30K52QZv2SBxJccDQYNCuja0z+DwihxvS9Sf0zz4k315pzb3zmLMHmAeP4eKabvuYC2fxK8OIwTnPZtWw6RxnXlg7aEjk/oDE2luiqJiM0+QtSvHFRFl8M32qfR/0osWonezKGzoL7J4Tnn1A/Pi7pBhhtcU4j3ilIFNRT6W0ACDDwLjbE/bKFvmHz3D9Dd3qVdbe0XlLH9PsJWGA2mL7cANmjzWG17avER9v6bzjoy+eI8DjTcN/99GaN7qEvPMP2P+T/ydXv/s9xl1Pe7Fm+wNv0Pzg/4DNq78Hv7pgTGrJ3fSXmI//OcPv/CNufvd9+ssdft1y/gNfYPMv/UH+tS/9AV5bv8rve7zmo9uBDDxeN/yeBx2vbRz25kNk97zodQykMJVuiQjeqcfF2ivrZm5uCNfP6J9dM1zvcKuGdigdJcWUqh6gaEkkDDDcEJ59BPsbjLXYEBDb0qwfq9eHtexSKv4G2rOjL5F03UxzodZzzhBUa5CzrkXYQI6JxnesHSQv5M7R5IYHTkv9PIm1S3Rxz/jRR4yXH5Kefk+jz1A6fG7OkYevIRj8pqF1rjCIauOe4oCJI2lQrUI4DNoVU4R0OJCHA4w9Jg2llbih8SusM9iiEaljsrYzTlbIeTb/Uhth3dljzNBox02bAiZFtNLZ403GNY2mDowKpJEp6aY+njmxdfD6xvPff+OM188cOWcuVp7Xtw1vnXc87KxWT4WDzivjVNTrtc9EcoWFqMt1zqUrpbKa4xjoozIPMWU1ODNa8tlYwaVByw/DjnR7pZVdMYJvcesdshl49ewx3rZsW8cHNwPXQyipKF3PjRSfiMJQmPU5jAMguqb4Rj1sbGnIVkox9VZEvTdlLGarrCSSJy3cvD9R0ge/oKDgzR+aU+RLEFHXg9NeKHd2zrvHSwUM7j1OQQHMoOAEaU0lRqdCxZynzmCptKvVmmVVjeM8drXGhYAXQ+q2xBi1L323hs0DUncOvoMx0xjhlXXDeecmxf3KG7be0Iw32GdP4eYZ6eophEHLU3yDjB4Ze0yzLqYhjUaVWVXD++JQqE2CpJQjyVzzOqlbBW8srdvgfYexu8kYSYY9NkbOmhWb83Medpbf+2A1lbt1jedi69mYiM8ByU4rO8p9yig4MBZ8J3SuQUIGF2lSBmNxvqVtnE7I4vtZa3WFuf5+/jxjxJQ2qlqWKWHQpixxLM8oYawr3Qk9joxbNVoeuj5TWtN4ctL0hbWWxhlsGDBpIPU74u0lw9OnxP2AWMFdPsfd3mDGEYaRbvsK1rbaUdAlVjaydZmtAz/uoR8hlU6UOU4mR2KzpmqMnyZwQkVoKRbRYjEo0jr6rOVzeSTdPiV8+B3C04/IOWMfJOT8IWwg5Nm5zUjCt1v8+gyz2mCcA0pOumwErRXWjWHlLSElOmeLiFMw4w7ZXxGffqBpq8MtTYx86eKLnLcb9mGNFbhoDd3194j/5L/hk7//X/PRP3yf6+/eICJsv7jhcYg83F7gNg/wrsX5DhkPmP1zwofvcflP3+Wj//Y73Hz3lpwzZ1/8Do//+Xd55Q9/wBs/+K/x6I0vcNknYi5RtRfM5XfJz9RBL4vBPgKzuqAxGzpn2TRqjLXylnVTKjIKUMxBRYDGOb0v6zPGdss+Jg5BfUCCK1FwvyfdXhGff4KI4FLCrbb41QO86HOpvgExxqPnl7TPKbXr6uxrUPoDVLrb6WbgjWfrPNllxEasS4SccCR87ImXnxCvPiR9+B7h2ceE3YEcM3bV0JBx6y12MyAkGueUvo+FoTIaIcv6DOk2NGc7wrK9dzFG0yZbIxI9SI91DcZaXBaiyaUhkyGZPDGDainN1OaCnAkINA6z6lg5gaSi2HWjoljv1OpZW0ibKQ0gpSrM58DGRL609Zx/4YzDo06rj6wKareNwRyukP0iNeq6SbM1Zk2FjGOcmkgVzDL5FIyptHQv82/ldd10RdRsQlCNzait5s3YwzgQDzvy/hYZe1w48GD9iPVmy1lj2Y9pqtyoLeuVgupI3TnmYcY2LeawQ8KozdfWa9z2DLtaqz9MphhjZbLM7ZwFyBZK3Qh5kQJ/+53fXGgK/viRbu7e/e9f4HhJgYHcAQRZtHnMk/e+zU//nQIKvnRMvxx1qJuYAi0JySloQ6DxAEDs+2JPZcFaxDY4v8FtXyEPoyJTDKnQWPgVQyEvOm/oPBgpdcYkpL/GXN0g+0vS7RXsrlRJDLimwXUdpuk09ymQc5xq8J0VXNTNNaQ6oTNDKQecqytmYODKxFt7o8IzMcghwrhH9le6KfuGB67jolmRWvU2sN7RNJkVo6q9+0yMRt3JpptnESc0xiIeGgzNGDXlIUbZD+uwpcMhFE1BjWBFS6BMNV+pYC1HJJbFLKUiolQwYpzHdmvs2UNWrwXsMEK3wTx6HTl7lby6YMhqBWyNo/GW1lucCeAbgikisRhVQFVo0+SvsM0nWOtJMWG6LV3TYcXjx4DZHUijJVsVHUoOkz1uBu2L4GNhbDzZBVK2SFLTpVorrQZFESeZzhgaAfY3xOcfE66ecnh+DcbQrbdYMWTnOQS4GiKHkBiTxXaWs+1j7KtfZH17DTB1JtQNYKCxhm1jyVkrOraNo3UGubklXT8jPvtQmyrdXOGGAy72PFw/5IHzEAbMxx8x/s4/5pP/+h/y3X/wDt9974bnY2RjDbY1xH1pgmMc2TVarugajfBSIu579k/3fPD8wG1MPLgeGHcDOSZe6XtWP/gv024fq/1wHDHXz0gff4f4yQek/Y32HvANsnlEuz1j2zg2TcAZTX81VnTziCNiDHbVIc6xfvwA++oXidvH3CbLZR+56uNkjrVxXpmEoae/3kEBTvL8Y9i8RiNbOiN4ScQcp2eWkqrWJZnyeURKt8A07GE8kGNUYO4cBE8W7ZKaIpgx4sdICgMujqThQDzUcrtPiDdXyv7sep0fjUITYwzON9A0GN8STWQYIykFhhxpVhdIGHExYJzHH25pvMeePcR2a0y5XiGXPLdRxtB6xKj4GTEKBig9JrIsNl5tAQ2la6F4Oiuk2Gj5rGia1JLwgrbPrhFsSuQ0EFNPDgkzHtgwYtrMA3HEMSqjEfbI7R4uDyXyBpoO/Ircar+J2zGxG2MRE+bp/JZr3vL7wOzBUETD2mY9qkbIeXLTkVIgpAjDQBz2pGcfQL/HHm7oVhc07Za83jJgGdJs0ihGdULt+oE2m1tdYOOIJak+qmlwTYM0HpzqunLS6rFpt8qx9B2Tkl4o/IqIliSW6oOvvvVDaDniXBV2315WTozPqy+AlxAYHKUTRCtzRZTufvLeb/LT3/x3T5gCzeFMN3LKB5c0AhQlKEgKjL0Cg3EcEa/NbZL1BO+JnVXtR1QhzhgiMSZCzuRYxGhorbmXhPQ7BQCDahTS7fXk9GVEcE1L03U0my1utcV0a2g6BvEkI6Qc1XLTCskK0tjSYW2+H5PzWyq912NRFhdzo0q9bdstEg6Y/oZ4ozoJKvXqnLaM3ZxhVlvy5pxxtaE/e4DZnsNqg28aVTiLKcyE1Q5z6OBuWnXMqw4MRkrP+oVXgSngwgp6wkUYWTud1fxaTe3kUgqYjYVujTl/RDNGmm6rG+/qjLjSJk77ZIlisb6haRo672m80HaemA6Y/iFpd6PtX50hxYi0Hdm3GguGQB72+p7DrdqoOkNsW3LnwWszpjQO5DAq7VsMbkwXi9DJQ+zUa6KkqUwRgdpismMFOgfeZvLulry7YbjZMe57XFesiJuW6Dpu94HvXvfcDJG1N6SLFe3ZQ7rXfgA/HNhYQ05ZG/hYj6SEM5a11/yu+mWIGrWMpcHR7S3h9oDdqY2zSxlzsSf7Boae8PH77L/7Pjffe87hec8+ahOvrRM2r65Zv/4K7vW3iGePuRphHyIrZ7g4e4x7/S3Wr7/D5tVP2H6yZ5/QyP15z833nrP+7vvIaoN7dYSmRcaBdPkJ4en3GJ4+I/YDbtNhdte4cU9rlAbeNg5nEmuv5a+SdIMz3Ybu4UFFrK+9hXntBzh0D/n4euR3L/fsxsS2UdbkQddhG72/OSbCocfc7LC7G6S/xm82+lyKwNSW52bKOqHP02gTsNCT+gO5vyUd9pqmyVlTQK70x0iZPAbiYST0PSkkUlIzHRl77dgIZN9iN2uMU0DtN2e4swc05w9x23Ps2QN6cSSXwY1aEjwORLNi9fANaDr82UPM/lorUM7OMeePoFvrvNGdScFBtipErnltMVgxWKP0P9YSs9rI106VKYETBdzJCYLXtKopYD8FqsNoXZ9zTsQQGIeBfn9Lf3PFeP2cvL9VJ8H9Den2mni41XQMKADaXGAfeJJrSO2WmyFxMyZuCjiubaJr/4baebX2iKjrYVN6q3RW03Y2a+8Y17Q03kDbkA5rwuqG4faG4XBgHAbS1VPi/hbcx5jSEMs0a7r2nNyuGbP6FWhaSRC3wfmtindLi3WsIRoIRXiY4oikA3kcCaWDraSg7NKCCcgiPHnvt7R67sf/Cl/50o+UdbAI7ScgMD873deONQqfJ40ALyEwmI/jXMuTd56cIK36M5lzOEcoy6is+OROHnYqPjz0B4wYsjMkt6E3DTdFrX8YMyEKKVty0lyaouhMI5nGJNg9x9w+JV1+zHhzCQfddMRYTUustzRnF6wuHrI6O8c0rQrXsi5cKWZiijSiLnRGMj5oj4Bl2/qiTStVBsKOyFBalIZiQwtKfmz8GrFXmjK5uWR4+pywH8gp4boGf7bGXTzCP3yMefiYvpRGamtfjTCsK+yBaH1zHZjWOGpv+Vyej6YKypPKqq7W+tyogCDp5i+pNKPNtda3tMYV1C8AT3YrZGswNNj1KwQs2a05JOEQhGgs3nb4tsN1Ld4Xq1jJOCLEiM+QfEve3egi4zyy2iDtimRbjXpjVMUykWyFHBtSaslto6ZIKZJDIIVRIytXF0oHoUPCiDSO2uzIFkOrzllGZ/Epq8e6MUhQA6Ockt7PVgFabjbsxsxHu4Hv3fR8dNWr5WpWRf4bD97ApUHZrH6PdFstETMGk4TOWULKrL3aSKu+JEJpHxxDRGyEMJBDTxo1Ws3FFMw0nu7hhrM3D3y5tWCEsy9uefyv/gDbP/AHSV/8/XzYW75zfeBmiGwby5tnLa9/8fez/QO3vD6MGG84/+4NpMzq1TXdww2mKe27a9Q99uTQQxim8zJBz1PHSMAX1gvU28JIEU34FrN9gHcO2hX29S8THrzBR7eBf/Z8z+8+27PrA4/PNWf8sLP4ZqMAuHVIr/c9lai7MYbWWVYuMxpb0jBm8rMXMiKZPGoKIY0H4mFP6veqQRJU8BvLdSGkfiAfevIwaOBQExXGaY764WuaBtvfQhjVQny9xT98jHvwOu3FY/15FqLLBKdVD2My9OlAHzLd6hXWm0dI2GGJmFWHbLdkV5XwRcEeA9pIvqj/q4jQGAUKRj0arFiyKfdZlClL2RTaW19L05ZS9CB2MpHLWV1EY0yEEBj6A/3NFf3VU2XGnn3E+OwjwuVTxusd4TAgxuBWDc2jB5huQ7aO5NfsgoKCq0NkF8LEEjTWTACxWkkvV/Xatr0tc64RbYrlnMMXkakVDTzS0LO/voLLZ+TrS8bdDXF/S05RK126FXZ7gbl4lZQe0awfEjEMWQhZyEmjdMkGEwUXMp0XdbXsVrRpwMQrJERSf2Ac+hdsZQUU/K0/WxwNf6SkbauDqzn63dMgt3xx/2u/4HgpgcHEGpSyxd9459t8vThCfeWtH9Kf5xNRx/JzADQvKFKUcGU7G8rD60OEUe1TxyA873ue9yOXh8B+1LI8a8yMTk3GScKaiIx75HBDfPoB6dlHpMOt0njNCnP2QMuqzl/BXryCO3+AWW00Gs8ZiYHU98S+J/Q9MfQ0UeuoV6WfQs7HaCZlGIvnf2MNu1FtcUPO7ELAjRqlt6s1pt3qZmg9aYyMuz3pMDJ6SxwCHSBN6RjX90h/QIZBKyacAqliMK2bSK3byoHZoqMyMUnroioISFE3hRSUfi3fS1XzsVigNGdfxI7WkltHtiswa1gl9rcHrvrAbszsE9jGsm49xjTFgtWSrZAk47YPcBkGLOI3mJJKwDhNBZmiNM8J4oANvdZQZ228Y6PBxiKejFE3gJy0ZHFUgKH5XE0X1L4MPsG69WzHhnHT4XLEpYHWMTeacl6ZAqA522DPHkB7xj5kLvvI9y4PfHDZ05amXmedY+1XvLJ9TbtF7q4xxasf65AokxW2N7qxCSroM91K38MXrcbZQ8z5I+TsIdF1+mzbDV3T8fj8jO0bHzFc32Lbhs0br9L8d/5V0pu/n+/sLf/4oxt+5+Mdl/uRi5Xnk1fX/MuPt7z53/sjPFxt2Lzxj7h9/2NiP9CcbVh98TH28VvYV75IbjZkAdsedKyMA22MuKHBdi2mWxUaPGHF4Y2hc6WmXkSrFvwK9+BV0niGrM9I29d4PsD7NwPvPt/z7ic7+qAVQo+3HfuQuWjPsGcPaM42ANrVz/mpQqZ1hgfrhmAatpuObdewbn3pMqj3kqxmSfq8g/qc5IQY0XEBSLG+tWnEhEFFaFnPW0V0TaHdFWiaOKrYUgRpGtie4x6+ht0+IIhXhT2ZnAyjyexNZBdH4hBYGWHvDeftQ9pNB96Qu4bsyqaPbtikuXQ5F0C+nHPZaOOjbFQrMM9DOzmeQoX7KHDTFy+6Le1cqhVZ2g69Hwb6vmfse8JhR7i9JFw+5fDJFcP1LXmMmM5rS3CrID23W4Jfc7sPuo4VUOBENQ1rb2mdljP7RRq1HiKlNTsZFw8YRMtMmw7Xtri2xVqn5xwCbnOL2zwnPP+EdPUJ+dlHxMunpOEWDrca0MWItQ25WWP9BpKWpR+S0JdqsZgihszKBy46x4Mx8aA1+OSQMUOIDIfD8S5W9p6lz85Xv1wbIhXRt+g6Oluuc5ctmFjwz3+8lMBgPjQn8/W/+bNTQwmgDNb6K3rjTikXsopkEEuWYp8MhBJi98MALpOz5fntgU/2ke/djjw9BPYh45xnu2o4sx5rDeLAm4TLA2bMSOyJ/Y407MkhYLoO8+AV7Gtvkc9fZzx7zOg7gl/T2pbWuWK+kTDmBhsTpj+oknwcMCmTxdIsL3/hvtbZhm27ondCa7VU8HoIDMVgxBnD9RB5sH6Eu+ix+1u6QdmR0e0n5G66ThuFtCuCWHK2EEECiAOxKuqpQsJqoqRH6VWfUkkD5LIYFfV2GBUMxIEUgzIXQZW/YrX5kNKxLUijVjHWkaWUKXlhjDuu97dcjvDhLnA9ZJJxdEZbV7ssHGLW5jTWYMjEbBndirB+hUHWHIaBMcTZHyJRKH5D61fk4QYJKkZru4a2bScKW4kmjbYiVVSpaRIrajurJXKGJgmdh4eblTZg8gbGAZ8GnE3asne9xV08xK72mO0D7PkjQrOmv03sQ2Q3aIOnEDOX+5Hn+8DtNnFxdoZPI8a1+jCaDoxDJBY/iFomKuqv4FfI2UNszricMKst9uFrpO0j4uoRhyzEmLEb8A/fwr35+7j4/Vea+3WetH7IfvWI71wH/vFHN/y337nkg6cH4hh531ue7kb6CP3jDW9+6X9I9+oP8Gj3TFNnviF156TVBXtUWW6t0EnGdlv8aovZnJP2N1ppdPZQ+3kYP01ht/D9xzi93nyuLbzbDaE74/Za78/lXluPjynp/QuRPmXyao07f0R68CqtcwrU11vENTirwNKvtPRus15ztuqmDqra+Eb5MJE8ldaIEUzWczNG02ZAafvdEhIMYhkjZLcmN1v6bDgEFWCKgPWlzbGzdE2DXa0Y3Ernnmjf0j4lDjHTZ2GfDVfBcOjBpMBZoyyEDUKzXsNqDUbTHzkHNSfLSZmw0GsaLGoFhWbD1LHTWKegxVjE+akyQNlCKSDiRAFf2IcqXoxZGCP0QY29+myIYsntCpoO03W41UHnfZvw6xXd44fYh4+Ri9cI60dcD5F9yOxGXYs7ZzhrHGsvbL2ltVnF1MMwCcmrR8C0M+SoYM03GLfCOl/aY28ZxTDETJ8DvfccugZeOSc3W2zTkUVIzz4kHQ6kYY/pd5jYazmqdXjX0CdDCLCLietx4GYfCGFk5YRHnWPcBFKwPPAWiRmGgbBob1+D1ifv/TZf++af4xt/+hf5yg98Zd7c81wyPgGEOhnKmv/9ehcsj5cWGGTg7Xfe5uu/+jN84ye/wVe+9CPFcCYd3af8ohsoix73Wc0zsnFTMxGNzIVxjNwcIh/eDLx/NfDx7UjA0bZCMp6mNZzZBtMI3iZczJhhNhMSY5DVBnP+APval4gP3uKKlqvnPQeJrNaZ83Xm4aZj4w2dzTjTkEqemgoMYpiZgqybrRQvdXJCStRounPa1QXORETUsGOIias+IDgMhvPzL+DE4NsV5uxD8kFNnaTbYM4eYh69TlxdQHfBaDr6Q2BgzybBFsF0LWZ5LuVz9YxKU14zp1AqC4otcxjIcVBXt7EnDj0pqhWwOIdrdVEz1mswYtStEduQsAz9wIClx/JJn/nkkLkeItkIW5twY8b7TOMyPmijnWxBEvTJcBuFyz5xs4/c9CP7IRBKG+q1t5y36u2wXZ3ho2Pd6vcbJ5ioC02Omk+VKriyDvHaahZTG32n4kGgznIUBmHtHePYw6h+87I6Qx68So6JPOy07LVZE0VLH60YVo1l03rNkVqLds8TxiQ07YaaXs1+RRSDNbpxWZPm5mAYTLvBXryqSnYRZHVG2jziYNc8OwQFHMNITpm1t2yaB7TbR/hiQHQImQ8+PvDO9cA/+d4tn1wGYg8xCiT45DLw/7K3JBGu+obXNw/ozh9ipLBZIXF7mdiNe8QIm8bzYOV4uHmdVXeGW52R99eajvItsd0Q0TmgjbY0uq3Rq2lWukFnyM2GMel9EdH7tGodTcqsGlv6Y4hqUJq13gffIM0a++BVfQ6+YbNqwK/wvqVtG5rSGa92/jR5ADLZaFvv6B05tUgMU2WNGKORtxikUd+P2EbGPjPaFftgeH4IXPWR3ahj3xnDqnFsW9jmRGxAktGpJGqu1IdUulpmbsfM1ZC4OSjrNiSL9ZnN1qpYLgvetRgiRK2koICDFEbifkfod1NFh3VO9Re+RdoWsSow1K5LChrEuHK/I0cblSYMi5230I8DN7sDt/s9h0Mgmw7pLpSNes0UMHxGu1hz7MPXkEdvEM5e42oUrntdr0JhQNVMy3DRWsz+EnN9pf4fY6/Mkiusn5l9DkSkdGvM5NySxBBNQz8qwLodE89uD1ztdux3O7ocOPdnnD9osNQ0yfMjgbsKQj3etphoIGf6PHDVZ57eBvp+wBE4bDwmN7TS0GLxWZDEtLeo4NDw5P3f5qe/+e9q6+Tf82/c3Z+yzABhufedMAV1P/x+jpcWGCgo+PrU+4CyINdaUWACAzXvPW3WKCUmpTEHSRvfyBCm30lZ63tDDAwhsetHbg4jV/uAPi/PIWheP4ouBs6DkYhxXn38i1UyzQr3+A3C+Rf5JDj+2eWO964Gnh8S2+2GN1654K2HW75w1vKwNXQStUQxaj6bOECcTYyk2IKmw4502JNLgyTTdJjzh8j2VS7OX0cnrOPZfizinZGMJ2bDxcWbuHaLe/gF9WwA3YDbLbHdMkjDECHsRoyNrJNqZ401OGcx3mKzSg2nOzyJB1NpvVuoyxwhDkgaiYOi8LjfE/oDYRw1JdF2iHHYrit0rNFFyTWIbbRsDEsyngHLiOWQ4GZIZImIC6yGSNckuqhpFV8qOXLKDClziHA5JD6+HXi267k6jIxRAcRZ5xhTi7eW7WpNt1mzbjOtF1wYJtGoGEdCMEVomstGYEydrMqYWK9RcZPnhcoaIXhD7A0MgmweEMYDNqMlh74luVapUwPnnefNRxvWBRi8ctbycN3SNU7FTlkXIEDHm8wCLFmM4zGBazckUSEWTQerc25GeLqPvHs18P7lno+vDwwh0lrD+dpx1njW3mCMYUiZqyHzwXVkxNN1gjOaUxYjuMYw4vjedSQTeNZrSVtKylhdDyNXu0AfE42zvHrW8cbFipAtj1Zbzl/Zwv5Ku4eKJTcrxsRUflb1KvVeinXTteM8Xixdk3m4bnnzldLvIGUebhrOO48zRaTrWkxR7kvTwfohdvOA3Px/ufvzWMu2/L4P+6xxD2e4U1W99/q9brKFKLLiJHASRxIpdVOxQFiObMn/BKBFNqGYlmCEifKHozgykQSxgkAWYEWB4QGmZItoSqERINAQ02KcCOzXoi2KkhAYkSCClMTufnNV3ekMe1hT/vitfe6t16+bbEZBhLeBQlXduvfUOXuv9Vu/4Ts0kjQ0DdY6rBEGhDcaYyQx0SGL+vWCczAy5hJhr1I7RyJcI5Wqg96TQmGaFMcZDsPAyyHy0WFiN0YRiDKabeu46It4NCRFkws6S5I31fU8V7vrYU4cpsh+qk6TVvbDXPdHwogQkfF1SirgQxXkzynM4rI5jdLVcw5iQHXChNKNkuaf8tU7piYEy8Grqhx43fkUGa2GGDmOE/thZHccOY4TOWWssvj2Ct9sUN05dtpLTAOwQv0L7Rl3U2I3Z26rZ4XXio2vSYFXmLv3UfsX5Psb8iwxS1mPbjtU20txpBclQEkqixagcSoQ58hYCjdT5oPdxDs3e957ecd+f+C81by19Xz+rOFq+wY2J6IyMA+opjt1M4t1WOtRQFKJmBVjLIwhc5xkTNoaODaKuTPEJM9RPx4FoPjZD36B3/9X/lWR6f/u3/k4itbvUCglSp8nIa3H16NC95OSgre//vYnfPXh+lQmBo+Tgi9+7ov1qwKWeXw9Tghy/cNJ3qgCw3SV+iWn6mBY5zWlVHW7Uo2OOLWKF+lQpaogSM3eVK0ktJcAr7uVZNttT15dctAt79yO/J0Pdvzdd++ZxkTbd7z5bGT8XIC8wawdWNH7jzGSs7h9yf8hj7MoI3BhpaUSGPaUaSRrjR4P2Cg0yLOzN4i5MCXDPEb2QVTBxmCZUqG3a7rt9mTqEVINQiEzJnHOU0qqpVxUbY86cpchK2mrL0Gj1DZrjlUqccEWxBPgMKdYD9ggic08kua5qptVVTAt80ZpY0oVkBawj9ZoY6X16z3KTBQiMRfmmBlDYk7yq0uKYjQ5IyCznIkpnz7jfk7cHANjEAnUDKy9IyqNdp5u2+FtwhYRtyolAZ4S6t8rkqIYKwj5JTGo/2LqrDNqMEXXJEqjsJSUmGZNKhrTX4qIkTlKFaYtRhUao3hj43HW8MZZj9FCQ3x93XDWGlZeY6YRVc15qAeWWSigeungiB5C0g7dOYpWFOMZsuI+ZN49zHzjbuIffHjgxf3IHALWaFaN4XLdcL6SObs2lqwcq5VDNYpxCzE9SgyMUHR7W4gKbudIToHjFLg9zFzvJw5TIqaMd46bY5JOhjFY26ONpuvOwc+QhSoXK8pW1c+jeZX2uuA0isroRqSotbY03vHapidl0eF/0hkaI6Oeoi3JdSjbiXJnf8FQNAZNY614lZjFIlwLIFCrE75AL7PhqnRabHzAFiBfV86BkerYugbvPV2j0XcDUU0cQ+ZujFwfZkLKtM6idfWWSIWYssiL5yyAx6qnsKztMSTmKFLDtiZJ1ns5JI2laE0sClsU+tE+WqiKADlG0iyqqLpI1406UsDW9Zwjrxw7JQqrQ1tYBLuUke/IVYBtDhzGif0wc5giIclY0RlFawyNu6BpL1+JOUPMHI+R+zFxX8ef3uiKKVCcNUaSgtsPiNcfkPd3kLPY2/dbiYNGHE0fxsUSj8QALaJiIk0z91HxwT7wy893/J1vXPPuh7eMx4GmNfw33tziFLSXLZvVJSZMlPEowHDboH0jiaAWYSKUMMSWhDznLFbwlX2kSkYVJfTmR4nBVz74BX7/z/wof/73/gRf/Pw/I2qbn3A+LQn+kiR80vWtkoIf/As/yOf43Cf+DHwKE4PlQ//kv/jn+MLnvviJN+aTkoFFLatQk2EgL5veNnUunvgb939PXiML19akic46LhvLvk2ErBiTEnmDVyplSRgKSmbkvkX1G7T1lZO7YT9lnh9mvv7iwO3NkekY8bsZBVz0hied4YnrCEUAWaWOD7KWOXuxXjZ5EQyDcl4OqRQkMRhHyFkEepxHu5a1P2dKcJwTc4LjgjuIleNeg2BB6IZjyBxjZK6azs4qNt5hrT5ppVPKiaKkKq1LnWiGIlC0UA6XaqNULIIqRYBQKZFiFOEfJapiytj6S3QjqBay+aSjXildztE2Das+cJwLU0ySsKUoyVTSkKVTIGBsoRcaMisnAkCdM3ReXlufDgGNsw7XNPi2wyKHlEJGNyWXOmMtD4FxqdSXxKAUWUs5oZXBKEg1CU01kQxJzFrmKWJyoW23svFTEExSnNj4Bmct204LAMzoqtkuojBquK8KjIGEBpXQOtcxAqKtr05viZhBWxGgidUcaB8yh5C5PQb2Y2SaIzll+X6ryaVU1UHLum9puw5lpCKdq0lNLqXqZgi10CA6FMMwsD+OqDmSF3W6mMkpM5XIfozcHgNPNoI+b50WQRosRWViSifq9jLWlnFCQZ+SThGZVhRMtFyttrQ+s+kappRJSZ6DV5lWZ1ScZB3ZVmR0mxWHWEg54nVCNwVbqEJb0m1afhdDmxpI6ps6ASEXTZQlg1HiLSIJgsVqh7ceN2acHcW5s/5SKDpvTmty5TSaXJkPuuJ0anJb13dKQhGUUYdh1be0TYNzDm0WvxRRNSzaoK2XQsLMpz1WlAAFSQkdIzYlOWwXzNCyj8XqlcW/uBQj/64qVRwQ0rIsNDmIE1NKHOfIbg6EGku8VfTW0rqKF6GqS6bMfo4cgyTv1oiYUO+qN8HxBnW4lqTg/oY8z2jvpSO7PkOdXVGatXg0LM8qzqgSpVJPkRRmApZpVtzuB957ccd7H95y/eEd8xxoesvXG8Ob24bXV55Vs8F0B7Q21QuiRdmmAiyXcanEf0PBGzhrNa1xXLaGy0Zk1E2aRIxrUfQEfuBnflSA8p//XeInkZEYV8+vTC061SclCd981j2+vvqoaP6j/8kf/Zbn6KcqMXicFDx0Cl4dE8CrCcFyw1NNDJZLKVWNcBQYSQ7e/vpX+SO/+CfqiyRUHCnDHdvVE3JnUKqh8ZF9EsvW1iuhJpZETIqUFDFnCcpLBut1ddxriKNUt7nqkueSyWRiElCezRFVIgZFno4C0EFRbEN2PcX3TLFaQfstvj1D2w7rPBFgfycdhGkg727Q7ZqmWbNymtg5YSkUqUj2pTDHIt7rSu7Z4k0eFr6wfshUl6GM0UqAPbVhQUknkMxj0ShKPjXHilq05QV0k1nolYUodaCgfp04ExbXVI97U5O6+tNKxJB6b9n0bfVOh/ujtCUNWZKAAEFn6TSohM4JmwKdjiJTuxLlxM5qdm0EBStn2Xae1lu8tQ+Om48SyuVAOFXoWlflw3pI1JafaGQkjDJV6pkTm2SOifvjyN1+YB5nPInkFb3r5P6lGTUd8C7RdSuR4LYWZx3eKDH5Gu/I85ESBnLKgBFxGuXQaIwSGddC9Z9YAgyir5brHlkkb1un2bQGhSfnzKoxPNk0PNu0XHSebd+wXvWsVj3Ot2QjM9JUhbaWAxQyOkXCPHI4aPaN4r7RnLeWTWt5sZOugdaadWtoXaW8VnGa5b2put5sTarKI/0LDeQk6pPECUVCZ1EI1CrRNGsuVyuxFo6hVsYDaThSwii4nSppe4yFwxyZKfg0kI3HOIe10sI1etE0UPWQXDxWanJgNGRdLYGFsrcA9aTFLvcXJT4P3lpaL+tsCAlvNRTYNJaLznO1ciK/rSM2TeRZ7MBTMcRQyEE6boYsduTWs+09F6ueTd/Se1tphnXNFumEqdqRK64RuqXzZOORvmISb5FcsHV/Up8BSj3s4aXjlTOLjK9ShVLthFVG1nzF2SzT75hEsTXmAjPc64i3wuZa4k5MhTnnU5LeWs1551g5RaMierwXVcxpgJIFyL0+wz75DGX7jLS6ZFaeWCRVaaxCzUd0OFIqwFlNR4xrUaVInM2JmAIZEbAqy+i4SkYL3dOjfBaPBuPqe82kRS+mJLzOrLzC9Abbe9am8HpnuWoNWwsc7kTmOScp6oD/8+/5j/hCTQrEC6ecCteH80nWe0ZArcs6eszC+HiS8PbX3+aHvqmT/snXpyYxeDw++EL90N+UDNQvLh2CXMopIXho1Tx8o1ZCZytF8V++83P88E//CH/iv/W/5Q/97X9NvmMeJTtOgYv1E5qNZ91YbqbMIWuKKTiV5DDShaBAk7ClSNtf15Y/QAo0VnHROt687MS4qA00jeVzVx1vbh3PesvKgB735GkvOudKi42v7bkbI8e5MIRcs2/Dxfp1GtdiXUN68S7lsJcVk6JIf0471t2lJCSdwxtBRD++b6IeJknBYv6klCClWyuVTGM0rTM0umCLhBRdP9vjbgFL4Px4Lqt1VVirYLQCkzJEowUbYRuCbnC2wdgWbENRRkYBdcMsYiads5y1klwZVUQjIAQJYnEmjwGFBFNnCioO6HmiyRlnHF3v6K3isjPsZ7Eqbp1h03pWXvjrJQaKShXZXTtBqlaDi4e0rpWhsacWZqmVEznLhESralADKWUOw8TL3Z7re3FTXDuNVhrnDQ6FjgGdAzocsfGI7zqatsVmK6qQcSRPg3CukwgrZePIGDDplBgsSZSplbzVSKWtpQp1ClZOcdFo0saz9ooYk9g7N5bLznHeWjpv6bxj1bd0q46m7U8jN/EUkH0kHaQMKRImzcFnDk1mWBuG2XN70Z787sdYsNawaRxnXrNyi95EwQq4RkbZ8qqY2plYEgNyEh2JMJFTAFWwZhA9/DRBOOCMIcbINI7MwyDYhZzJ2pFNQyiaISQOIbMPATUnojZ472mcpVS9e7NQ4vICsn2g6ipjZckr9bDPgZNXQKnMgJQoJdBacei76j1eF8aQsFokgVfesvGGhgk97FFTdS20HTEpVFbkURzVWl1oOotzjk3nuVh3nLWeztlXlFDlsBPcg7KNVPQ2EHRDsA3Rt1KcyCAbk8V8LCNjw1evpTu6jMQAHva/QezRG10kThjpzA1BjLZS3UexIFkywjSRbaRo66HZWhmZrZwoXerhTtwIk9jSq26DWq0xT94kb19jas65GRPHGJhjoXOa3ivOfI8vmZIljjIptPWs7IpnveXNreP6qqPkxDQ5Nr3E5YvW0ViJ18uzRBtQglkJJRHSTA5AjDQqoV3hXGtW2nHRaLYe+jLD/QvUfBQ2lhEzKIAv/IbfRcAQY6n35gFLw3J3VTnhWUx+KK6WBGGJh8v11e8gKYBPUWLwGFPw8fbJJ40MEtItWG76UiEt3wOCGUha8dWvv80f+ukv8eXf9+d57W4r/6gNjEfKcQf6FjPtWfUXrDZPWXvLMWkOCQIJk6GkQgqKpDJZ5RosldCFYkFPe87aC946E0/3J+uG4xzpveW1dcvnr3qetJo+3KOPNzDspX1oe4r1HELiZki8ey/e9EbDWet4a9vytD9jc+EwxpBvnkMYpUpIQTj5cWTtG7SSbFwkRJd7V07VuyQG5QR+651l3RjWTrPxmk5nbBwxCRGiWRYr8EngmEKdc6tCLomsDVOGYyoMRRNsKwh80xJ1i9YNznaSGChzqiJhmZ+LV1HBnDJqr6GzmmGEMM/YEvG5UKaCdUoqjeOtWDeniHYNTbvBdWecb9Yco9i+KqUEGe4NOo6SVKqEppollap7YSpgtYJXldZ15qhrtSXdAvGWSFhliKqglLSCD8PA7jBwGGdyShhE2TKVgkOCrI6jyAWHAzZ32NCIqlqMxHkiHA/EKKjt7FpyY2qFWFDVOEZUKWt7vwSYZgF5WkfT9GitKVmh156LRjNHj9dCdV05Ta8jerjFHCfMpOnylnXzlJW1uFUnKO8iIyjFIjubCYcjx/0NZv8cd7hnkzLJNnzm4ozjWcMhZKaEgMusoXWW3ip6W3BkynQkxwClYKynMZZZejDSSq0z97K0recJHUZJeIYDrl9hfSPz8phI80QcBlSMaOMoTg6gVGSeP4TEfkroWHCHgW0/cL5qUMqglcZWbIF0xqRr8EDdM6AzlDpnp4DS9aAV3YBcZhKZXAw6yWFXOktnSmUkIAY90x59d4cad6QwSbLZrin9Oba7oISCz4oO8SZw3tG1Ldu+ZdM2rFtHY4VWueAwkHdLLKCVAdsSbGTUDUfdEk0Ar0klkbOCVFAZtDY14dMIA+FBx+C016U9UgcWoEvBpIiNI53ORK8pWaOUo7FiRR9zTe6Vwlt9EinSj0Czj6XcXRyrpkhAaYNebcC16Iun5O0b7HTP833gnfuRuzGQMpy1lje3LVYpnPWoCXQc0XnGkOn7xJN2xT/xpKdRhc9etBznyMpbXl83vHXWcuYterwRAzcSCsEu5Fzlsoso3Zoc6Uk4Dytj6U1mYzLsnqOPN6TDvYiXOQ9uI51QICjLFDMhyzpczimJc4hl3TJKUDLF0XU8uCQIuj5braRo/tIndNK/3fWpSQw+ngmd5jG/SlLw+MYv1fHysxH4+W+8zR/+K1/i3//nvsx/743fwS/f/V15Xdeh9IF8vKcMR9Lz9zCbc+yTW862r9P3F7QRpgJzyegcEXmwfJo1KgolRlSa0GR8yXxmfcXKdbx11pJywRnFxhvOPPj9e6jjbbV1nmUhbRwowxQLL44z792PvH87ElLmYuVFyOiyR2161ts3MMZRhr2wAoyj5AjhSOsVrmmqq5p0TpYxWVrAXo8WqEbsajunWTmDS0fsBMZ0aA9KN9I2hVeDhnzhdHhmpchFEzMcp8h+DBxCIaiGaA3FtuBaou3I0aBCJoeMtY94+FQZZb14LSB0S+3prWJlCjuVGAgQM45EC7C/p+w/Ir58n3z3UsRoqp68PX+C2VyxbbfgZXZuTMaGo8hLGwhGeqSLkZWqXQOlzQlXoBZhmOWTF+SzV0aGNmIApEshhUgME/M8C1aDcjKtWSoBVRIlzOh5QBuNLhHCKC54KRKnmRiFTZGMA6/IypC1JT+i22oUTgvPXc1HcdksRSJMGHC+58x1dFqRvMUoI0H4eIu+fU58/h7x/iVhGLDOYZ+9Dp/9PCZ+FyY8wTUd2EaefaXV5mkg372gvPs1wjf+IdNHHxBDQHUdenvF9ulnWG+eQn9Osg2pyCHmTUHNR5iPMI8ieawUpBbte5y2hEzFwRSMXuhzwgDIKQmrw2hxEm0EhAcKHQJ6HFEpk32Hsi3LXKuwVGqSZMzzTAwTMQR08XWUAColeU/Lgbg86yVJVKCyPiVnBVUhNomYI6FEYirkBDbB2kKnNCUFQb0f7mH3knj7grS7oQTRftBnV5irNyAV2mYr2ChtUM7SdY7NumHdtTTeVcOyh4N2GYWVGgNjgRiFIXIfDbNdi8R7GFFxlEo4FNI4k21Db5yQm2uBIxiKj+3zZYQIqJzRYcLMR+w00CYwbSsW1EYRkjkl+aYmAEYtANJlfPQAKpXZ/FHAy8ZJUlCTpbR6wt70vL8L/IPrI79yfeTmMOOM5o3zlsZqtt5IFZED5XAv6p7+BrW6p+vPebO7ZP2s57vPG0KSEemqAh314SV6vENNo/jkuPIAdKlrQOdEqzLeQ6PkmbrhDvXyA7HC3t3KGul60QtxHbnZAHAMmSmW03Ph0ZlUw5yMsBRV9LDOXLTC1K5VVqALfOXrb/PDf/EH+fJ3kBTAP8aJgVLqdwP/J2T89qdLKX/8233/r/ahv1VSIDN9TgdfLFkE+Urhb7731/hf/tU/wL/9/X+Wf+r138F+ztxPsnxzd4EJIgCUD3eE3RH98iPc3Uv8Gzv8s+/mfPs6hwRjWZCpRfaOXtrOSkR8hj0MO3Q3oKc9F82ac1/V3dKMPh4w13ek2+fk406c24yj9Bv06gzqIT7HzG6M3BxmppCZQsZV9G7nNKZbIUSI5uRShjLiHDntsXqsFdDSEqyXcSIk1Hgy+tStMiWh4xEzj+gcMN6iTJSdIO5PD2A8+c/qb0uFIWptKQeOc+Z+mDnMmUm1JO/IthHlsLlQwoQPimAbroynV0IT01Q8CJx4+U4rklH4lPHKYLJBzwobRAbaqYKNIxxvSLfPidcfEa5fkocZbQ3N2R4dJnyOGJWgBJEUzkaAmLEQjRL3X6PJ1uCMkTn6Qk9bZrHGfLNtak6gqxmUUlhlBaiaAzpJuzUYRSmiTqhq618pJcZAFeVvtUJnaR+nlIgpEuZATIlkxAwG15FcdaHL6tQNMrWjouZR5q1xkLGY1kJPnUeU9WKElcVevOxfEj96h+O777B/5yOGl/eUkGk3HeGNW9p5oo0j5ukOfSaUv8W8LI1H8t0N8/P3GL72K9z+vX/I7fvPGXcDymm6qy3rt57RvPkW9tlb2PUVuIairbyvOFerczHQ0tpUVL7GNGsCy8irJmi+l5FDVSBMYxK2SxaMhzVWGEJZEruMnPJpAQ0q2Z6iqiiHeaMLOkVUjhgq4j+HB4fPxdejrvFCESGgpGU/1MSAJC6AIUZCFCZGrF1Fg4BibRJwM/OedPeC+eWHxJcfMd3tyTGhO49LqWoIOKxxdLbFloy1hZVXbBpD3xi8e1BmVHBamxIXBZOUUuE4zbzcDdzuBuZpQhXwpqPpelKciDmQ50IeZrANK1vBtY+8CB7ixuPZbZbnFwbUtMOMR/QcMdqxMi2db0nKnuKKJgs4MEcI4SEOLTGzlMpmqp4CvkX5lmI8qdkw2BU3Q+SD/cS7dyMf3I7sx0jjNOvWMsdlYFzQOVKOO/JxJyDtGlfN+VPO2jM2zYrivSQ4YUAf9tJdHPbSCenWdb9rTkZyqdAaRdHQKlgZhb7/kPLRrzC9/zXCzUtySLhNLy6L7YrcXXBABLtkfCm4iuUsgsq40VL0WK2kU0B5QCB+LDn46jfe5kt/6TvrFCzXt0wMlFI/DfxPSim/8h294j+CSwmi7d8Fvh94B/gFpdRfKqX83e/kdR53C4BTBbxUArkmBbHAlEQ9brGx/Vvvf5Uf+9kf4Y/9zj/Db7z8Hl4cZ5zWvBxkoR6UZ91fofuXJN5n3h1JU8DvBlSKeOsx1tOvriAJ4lxrLWwFDc43JNdQtCKnKGYrwx7lGkHTOuFgl2kkH3ek/a1o+FdwlusblHWYkkU2VVm8lTl/5ysgUMvnnGJmCJmjFWtetBHWwsmgqFoZz9KWI80wjw8GJrYqyVnZfCyVUE6YNGNKEg342ZBUJjlLrtx9VTc81Ye9PuCTTGrWhbkUDiFxiDAqT/KNKJsNsdo9Qy6JJirwI03bYZ3DK1s3CgLgUkBRD7FDK+YC2hSiTmSVSaYIJXI+SrUQJbDnEIlzQMdE0ydsmvE54kgomym6BpMKwA4ZkhKOuRRLVhTirH3IUBaQ2SswIMFaqPQQ8LSuBko54lRi6zW2WAoFb4QOarVGYzDW47sVtvE4CppyAmiFrIjakWwrDnF+RW435HbDkLR4ZOQH0JwEE0lQSphFEKZktGtRPkAUT3iVZ6mqXrzP/O477L/+Ifv3bjk+HyBD2hYadc9x85K+7XFKKvzSresizORhT7y/YfrwOcd3XzJ8cM/h3SPD/RE0pGOEVI2lattZrbagvRy6KVDmWdxNlZZETVl5/9RuQUHYELqQjKXrzoReiszR01xBXlmAgVY7sQf3LaDQ2jFXpovVGmcKvRdTHoVYVjuV6nMqAmrM4n+gUmBBoT+KZALWt/a0HsiSZMQQmUNiDtLdydWcyNSkTeks625OhBzJaWYOiThKN8laLfckBklO5iNOG5z1GJXpdGJtCisL3lWDMqUWGMQJUxWKqJLPKbEfZ24PIzeHiWkWfwenqa17T+cbSpogggkJV0rd45JFiVHS48pZgMcli119Go6k4568F38AYxyoPRhf9R10lRSfT8npN8Ug40+FCkpRrKMo0ScopiHajuOUGEJmillGHhoaJ2JgrRO7da3E4EmXLNop+3vCcSLnjG5eoG6eo9bnwhxrZLxbwkyeqnBSLui2E8aSa0QAyggo2RbIZExR9KZgDteU6/cI7/x9ju9+wLwfMI3DbXp0vyX3VxyU53qQtfzyGAg5PzJalrxyoRs7I6Dwxhhslu6u5CW1E6Xgr339bf7AX/5Bvvx7/xy/47PfWVIA375j8B8D/3el1E8Af6KUEr7N9/6jvn4L8MullH8AoJT6KeD3Ad9RYvD4esAPSDIg8pyc+NAhCi0mpMzffP/n+N999Uf4N773x/mvX3wPuzEu2FvevRfhjI8OEbfe0p0/Q7/8AG1vmO/2TClh+xvMxQvc9gq/viQaJQHKGJn/WYX3hhgOqHlNGY+UaZQkYBrhuDshVJkn8nigjAPxKPQ46yymOIxSlCxjitY6LlvPm2cJZxTHOeFMRe96UxG+8hmV6zGuk4MxZ0kG4oQqARUn8v5ORiST0BtZ5GG7FartwXlQGl2ybJYs3gDFWXLjSWEix5biHEpX4N1JjevhzwVFTplQNFNWzNoRjOIQqtRvFJrSlEQgpUsF101s58AqirOkXjjrCglEMch4JAnos4wDaX+A3R49z0K3OzlHOXS/oWwu8Tlj2yNGGbrzDauzc7qzM9xqDV1PUpZYlDhm5sXJrRAR4LkqBqNri/qEAuIBsKLEa6EofTrMFAhNCU0ZAzYHWlWIVmGVkba4UjRGqJJaWUzbYYrF6YKtFNocAtE4ohWcRlaGbFuSX5G9UO7GmAh1DuTKw9z29B5Tqu9F1ZY3lW6aHihqZCxC2fSuIXdALnjnMRhKiIT9kbDbSxALQn8rKZJG+XrYHykhYjB45ymdDJa9a3BW2tOGLJ2QUiiVylpqVi+/V/vtR4CsVITmGXI5YRRi0azaM7S2GOvRfkSVRMwJZQ3aOWEZKCNskqwwypG1RStNY6SK8KZ6JVSZZpsDZdhBcZyYCJUeCRVsq5ZJLyfg6ZIopKyZiwhqTQlSktNaa3Vy4rOqYIrw4UOaMWGQvVYKqSR036M3lyKQVqWhCZNgT+IAeSCVgZLW0Hbi6mhEx6FYh1aGRAWolUKIiWEO7MeJ62FimAOaQmOEthwzcj9di9aFKStC0Xhl0abaNy+dxiVBqG31UsGgqWqTlGmghCjqrdpinCi5UrLQsMcjeThQ5kESA61RTYvut+j1mZhMKSMAZOOqb4ojoRhjqvgHWHnD03WDM5qQMr03PFs3XLae1irpFmR5VrlkUgjEEGFW2JREUXY4gJfZvzzjOhrx4sbp+jW2X2HXW8ASYyGTJGzmjFeFMu6Idy+Ybm6k4xMibtWiV2eo82eMfstHFQsB8NFxog5oTvvUVqdaqyEVTVNknygjNF5VGzNKF95+56v8yF/+IX7i9z4A8b/T61smBqWU/4tS6j8D/tfA31RKfZmH4ptSyp/8df2Pv7brTeAbj/7+DvBbH3+DUuoPAX8IgLNf+wsX6vy8guhi5ckuScHfeP+v8W9+9Uf413/bj/Obrr6HfYjkDKGi8n/l+gjA378+4s2aNzevYa5ep7l5Thon4bLDSfefmlUbYzDGyaHuNE432LMnzEkMg0pKJK1OMqSnS2sBpyB0JkrBeI/qN9CsKhUq0jeaq97ibc/TvlJzlDjOrVwVcNEiCzunUtuJHq0L1jYobQUQF6UbUMJMGQ8CjjFO6FZa/AoETv/KsxBkszYnNHpBugKvOFdW1TcBK4kxESWJGpt2JJWZYjg9j4W7PKVc+fII172Ku9RprbTw0yRBJE0wT5Qwkqql67TbE6ZALlqQv8ZRXEc2DuUaTLcWRcjxgFOw3mxZXz2hO7vErDZC3SpSZsU5kKNwsXPJ6KJJ2UBWGGUxxlWA0AJKe0BmlyX4LclBTigUOQTm3Z75fo+LhY1vaJKszcLDvFUZiyotSjdywNTZdzCR2QbBgGDIRmidU4HjlBkrkGnhgEPBZdGtt/W5FFed9rSmWPl5rBOAWXWfs1rhtaJtGtbrNfPtQIkZYw3dWYfvxFJaFDmDqM8pA6X+PYmlr+9ats82uMaKk6PV+POO9ukZ7tklXL5O3l4RTSdJThGaqaoKoiVnijGn9ZSKjAdDLqfPGbImpERImt71NK5HxZGUZgyLYI/DVfGgQsWJZAXKYrIkBMsMffnsdt4z3xyY05our7HO8UrbvD5nVfIrKnsoIziQAnOEIc8csyEWyAuQ0YpqqPWOxiisKuiuIzW1Im1WlO09oUBpV6j1OaXdUFxfRYWkZZ+niTBkptHhxzV2vZWk3rViZ509xTqMaYiI4Nayp0IF205RWAIpI2qtCkwSYKC1sl+zfiQ0hiRxqiztOuHlFyWg3CUmUDE42jwAnOXWZUnmwyyJw3iQYimFE+2Xek+LdWK05Vrh+KOgspMKEufWzuA2Leetq8mCvPdtK+yO3mkxbDAGmhVqfYbTGj3Pp06EcssodInF1TjKWky7wp1d4TdX+LMnqHZFymBVJhFJSUYi5MVxVepqbQ3GW5rzFebqddLmNZ4Pib9/feTrNzKafr6fRWZbi3W0VRqdJA54u8AKlzqrqreWQlbwX37jq/zB//RL/Nl/4Sd/3UkB/OoYgxk4AA2wgVe6G/9/vUop/yHwHwKoz6jy8X9X3/QTrxQYJ070QpGJpZySgj/yW3+c3/TktzFWfm1IDzKj717Lw/ul53tW3rB9uuL86k3s7oY2ice6X/fothenNCWbv6Cx1qGcBWtQBpqzK9QCMDMOddyRQ0DQK3VBZgGqlerYV4r4umvfiGKi7QBwZeai9ay95rWVtO8NqrbY5W4URK88I6IZQpPLVUmvoW3PRPO/W6NGydbV0jFoV1KdrLYyUlDCgdY+CQUTsL5KFrsGrKNoJ5m9sSilq155FVipCULRBlxG2YlUphrkpa26gKSE0yzUyN47rK6I5frZSgriUBcnmI6U6cC8u+N4/Zzd7Q2HYSRZD90WTCN6D6mQlUfbFc36GXqzx8QBbzXddk23XuOaTtTitOgA2JjROpCnmaBlrg9gtYHiMFljcZLFk+SQyYkFA/KwEB+ShZwy4bBnursm7faQheLoXYezTU2i6mgVQ7KGYDS56u6nlAi2CgSlTEiyZudZJHIXd7f5kRe3UfoEti21glSlBYuIZblWDiHbEBBaqsozbnVOc/kM+9oLuLsm3B+Jx1GSA2fxq04qIWelAlzU2JQhK412Frdu6dUFtm9Zvx5RVmP7Frft4eySuHnC1GwIdkXUnpAE1aLiBNMBFUaZLYMcENqeun8Lj3xJLL3Rdf+qKl/cYH2LM0awIUaRtMLUZCOVKnqUQKmC0+Bqp0fFERUGSIGkM1MZCDrhVmu00SdF1NMjPuFoauKlNRlDiJkhJ47FMWqIRtaQMQZtFLrx2MZhrMaq6q7YrGB1Rto+Zbrfk2Im2o7UrGUdV0poYxRquIMwkoZ7DnGGroXzC/rLpzSbM1ReUZrThB2jLLaC/azW9N4JvddIQnYCKSL7cumyKusFA+J8xZFkKJpCrgkvD66K2omFuRNnTIsih1hBzoZcpeBUyZKMwqmrp6KVhNV3qG5NcT25PWNUnmlOpCLzfaMlybUKvNOsnbzxVCwUSaKXCtxphS2Cscq2Q1+9hlptUPMktug1NkkRpF+NxSi0c9h+g1uf01y+RnN2xUw1k0OSo1wFuHLtbOi2x697MaZyFvvaW6irN9kVz9fvDvzS872cLRZe7CZWjaXzpo4RZXSQS00KrEbpqqdQhJGTgZ9/523+1Z/+Yf7MP/+T3zQ++KTz8Ntd3w5j8LuBPwn8JeC/W0o5foev/f/N9S7w2Ud/f6t+7dd0VVDxSSDj8bWwDpZxYMxCb/rb7/01/thX/xX+V9/74/wTl99T57ESbMeQGebIYU7cDQE0vHs98Gzb8uamZb1+in3tc6gsvtp6tUVfPCO3G6aUmQEMqCI2RUUZslZENHb7BK8s0fbQ7iunmVoNVb5z/VDS2lUnxsDjBavmEVeOuDRXY6IKiKLSpnwvCot+xZAgp8KYRMRIWoaJ7B396rLysw2lWwnoxnro1xS/ltdwnWTOtSIuJFAZ4221xRXQG8aDc/I5ql1r0Q8Bs1QdB70oNqoHidnGaJRXNFaChjGaTdey6Ty9t6JRr9UJqEScUfNAng/Mdy85fPQBu4/e57C7E8T66gzdrinGcYyZ3SSjilyKAMxcx1m/pdmuYN2iulYSIi1gS41GpUIuA9OcOYSRaRblSWMSXSpgPbYVLIDSBq1ECnepnk4KkMgCLDmRwsy0u2W4fkEYZX6uXSOUPN+Ba0U8JSMUQDQzcg9zLMQEU0iEmEk5VQZJeYVJMqdKVVQPcsEnjnOdzUp1Zyi2IbmeY8iMs2j1ay0J2cXqNezF67TPPoufd6jdSxgO5GkQ3IQWLriyTkB3zte5dpH7YT1N1+MuZnrRikU3HXQryuaK2W+YdMtuKtwcJo5zIOeE04rWWPruAmOP4ihakhw4tSpfPtMCrCtlSYYUuSjmVNHsSmF0xmiZNXtnsaZS74oAAEW9sz6rFCQZmQdp0YeJUDJDGOmtpvEWja+U1Pp/fzwpMJaspB0/FNjNhdthZpgDqXbDGg/Yltb3qL5DG0WthaVr10bwI9mP7O8P3I2B8V5m0VopOqvZNJqVcaiSiYcd6XBH0ZD3d5R5QuWIP6+z6Nqs1k72kUgMWzadJ65bGlPkvSFgXr8wGnTtAC6KiVoklkteVEwzaqEw5giuAoBcQXdFumpugjlWgSVDqmuPnNBhkFFnv4bjXoSqlEb1G0p3Rl5dcsSxnxJTkuLOW+i0OFm2BvR8QE2HiiNaKKTmAZtg/EMXpzsjcwarBZBY42vVbpCvlBOmSinAGHS7xp9fYjfnRO0ElJ0LiUIoilCoiUKmaTfoi2foccA39+imxbz2OeL6KTeHxHu7iXevB27uJ7iEw5xeWcvLSEqb8iDKV3/FXHBFOgU/+jNf4sf/hz/Jb/9YUqAfjpBf8/XtOgY/BvyPSil/5zt4vX9U1y8Av1Ep9XkkIfgB4Pf//+w/e++r/G/eFkzBb37yPaLC9eiSoKOqaVIGDcOcuDsG7qbIs77FrC8xzwI6zJjVlrS6Ipie+zExqwQkepPFftkqYllm7kVmwasiwiIx1sovk3JNXGoyg6Lq3Et2LwFTY0tAjffo4Z58/4J0f0M+7sS10FjMaou5fIY5fx3anqg9U8mMaeZ+kjlbZ6SKLI2jXz9BW4/utgAU68muJ7meIUqiRJHg2jrPpmtovBabdm/RjRff+OpqtuAMlo7BaZSgFJAoZYbaArRa0VqFM4au4kHQCmcdXScmQZvW0TmD1wqVZ05OkimSpolpd8/x5Uccr58zHfaUpdpQhuJ79ofIh/uZF0NgCBGrxaTmtTUkl3CrBu/W6MaxCP6UlEl5JiCS14dQmKr4kVZJDl91xDiHcytp/WNAl1eSg1NlhHRr8jwTxoE4HsnTKJWKqoqSych7rtz3TCJH0cFIRZGQ0cqc0snrQbpipa5bCf6tWcBn0k51y2hC1UNMW0ncjCXohv2c2E1ZXP7mSC6K1hmejIk3zte8fv6Ey/PX8JdvYOajdGnCJCIzNfl4oGwuCqK1pVrBhRgrnaWmJ/meYFfcjokPbg+8f3vgxW5gDAmtCltvOW8tsRTWrsdpQ0m1a6D0SbNiccs02lQNqQfK7by0Faj3wRR01PhQcFZj4KS1oJUo8+mKWVFproC4mTwd5eAlE8ZBaMPWohbtz2/qFMgzzEozA7th5GZ/5H6YGeZYNUE0URWMV/SIy2OxHkx97jkzTYHdsfDRcOD9+8CH+4H7MRBzpnOWJ51D4elXvayZnMjjQJ4HVJixxuC6HtOtUbaVZ5EjKge89uAMqXWkvoE402pRhiQLeG/p3lkt+5RKuURbMYvS9gFsqGq3TBnB+ziNUhqtDFo3KBshQZwz+2FiDILbQSlaI/4sJhzR/VGSfhDzsHbLsRjup8z9mBhSRmnNVolbpnMazwzTjnz7Aen6I9Lh/hQHdb9Bby/Q2yfkbit23MoxpkwowljKWQ5fCpXBsXxmcE5X+2uL6XqSXxGUBQyxKGLJjAnxdpgFQDKVxNb2uNUV9vWCOtyjnKesL5mK5W4auTsGhjmduiQ5lxN26uOXFAicWHUAP//uV/nDP/Ml/oPf82W+99cBNPyk69thDL7wj+R/+HVcpZSolPqfAj+DTLP/o193grK0D77F9QvvfZUf+9l/mX/zi3+G3/zke5hzRhdVpYDVKSlIubBpC9vOQYTOG7zR5IpRaN0Ks74SKc1mRekvuZsidzMMFBosuIxJBZ8yRuvKQdUk7ZiV45AMhykyhcxhjgwhncYZSydYKZl9rpyVeVljOGssjTboHIj318QP3mG82YtmeOPpL7dSFWzPQRWydwwlkQgMMRNCZKqrUAM0hn51VVXjFBHLMSSOx8h+TqKNUAqdM2xbT9IJ4xr6foX2RkYlzp5mg6VWGKffa1DJFTAmFDqxAfZa4es8kaWqMRrnLH3v2fSe3husypU/Hmv1XW1jQ2AejszTSAwzizmJGJx4plQ4xsyLIfDO7cDdccZoxXnfMGcwTcNqHfFtBG2wVu5LLtLhKdqSlSFkxaFiDcRLImFMoBsmNn2H1zI3VhVnoeqs/tW1qavvg6myybVqV5+c26daIYSSJRBlYdNMQdbIgrfwRuGMtFa1UqfALuh8Eadyj7jsJ+yHbcRsKhbupsjz48zL48xuTGituDzMXI+JfYDPXKy4XF+w6a8wYUSlUTpV1VhHhFYeNt5J8REliYixZNOSXMtuzlzvR967OfC1l3e8e7vnejeSc2HTGqZeXACdcXiTBRMDp9mgUVSApuh+5IrUziegsbRdQx2ppFIoQWiKjcs0yWA1WCWKj1ZL8v3xS/AyRg4M8/DsvlmnA06CVrUtnYpijon9MHKYAsdZkPMpFyGyONGbKNoS0YSi6p4vxJjZzZHbMfLRfuYb9wPv3hy5PU6kXDjrBYO0bcX8zBiP8k0VUirEMDNPI/NwpK2UTXKE4uSZ1fn7yhtU77HJc1SREGTUdRqE1YRHOrFLR7VIUxIlBQDl1FGg0l9VzijtQHm0EbGG8f7A7XTkdkjcjzNDSFil6L1h7Q29bei7Hovsb2qnb1+TgttRHBads6xQGGdpG4OfJ8gj4+1z0ofvcLy+J0/indBerEU9c31G0YYZy90U2U2J+zFyCLF21+TjGk0tVKpPhbc0TrNSBqMEZyEgS+pezKf4sp8S03GiI1I8XPSXMvKxLWhDdisBVWdhHnXeEJIcx5vW0jhhUDgjReBpD1ftDFPHqL/w3lf5X/w//wD/3u/+Mt/71ickBd/pDKFe/9jqGJRSfhr46V/r9/8q5798T203LiI9P/+u3NQ//rv+LP/k0+8hRAHEaFPQWZPrfDvbQmM1q8by1mUHH8Hnn615fduwbS3WKChGWuxKk5sV+zmxnwvXx0jQmc54XJNoXGKyWixppUFNVAIYC9pxiBO3Q2Q/zhxDYgiJqSLKRbxFgv7KW85nx+vrRlp9tpFgHANxDIT9kTQGbBNJbYvNEadAWUOyFq3yibK5tFzHmJiSxlSPAa0MKcGUIocgAkq3Y2QIUq11zjImMNbRtYU+FTrboLwDI26Hi2nMMhbJ8KAyWQohJaYwCxo4Z3SeYBoFRJiqtLK1uK7D24gLGhsUpgh/WihS+QH0aAzZWLL10ilQVkY77YrkO8JiTRsixylKZo+MKnZT5H6c2Y8T67nFOUujpNrOUAOew3qPdo48zMw5oUvGaqEDhpiEfqYEKV6UFt/7ZZXWroGowiW0b2jWW5opEO2BkGUmW1xTqaH24WeKoBVEdIpTYE61o2QQ5crGiB68Nwqn1IluJ0Hi0YGdHtzxitL1uSz7T17/OCduDzOxFO6GyH7KHCLch8RngxxK66ah8514NeSIToKTeRyTTm3a6ow5V9e8/W7m7jjzjZsd71zveeflPc/vjkwhYpXCaF/lrR/el9C1tVBsEaCbMxa3ANzkRlGo2Isi45RJFbQSyfDIA5PBKNFNNLpULyB1ekYYSylecDCA6o0kFKsVzXorWJ/aGWD5mfrJFTL+AcGEhBQIMck6SVJQ5KLQaJlbey8MAyUrZtHnn2Kqtu4T9+PMborspsBulH3ojOzJMcrrFt9h2hWsttIxcY5sPdmIlPOSqIr5V4YSRFY3Rlw44Od7wnBHGQbxkgBZh6aBpoVsiCEyhRmXGpR5EDqSw6smCCfbevmllCMTGIcDx1jYTZmPqsX545hy3lqe9J6QSzVxgxwyYz14x5iYq820MTI600rMvHw2FAUxR4bjTLw/EqeAaSO2a3AxCBjSNhxjPukd3A6Bwxy/Kc42NSnonWFIcK4sXjsBJCsDRUZVoeJa5iiJ+n4KDOPMkCMGi1WFdbutYNwsRUcFQ76+bZjSmtvDzN+a4NlWRJh8va/ASfVRDMnEqfdvf/hz/NG/+gf4d/7ZL/Pb3vrC6fu+RV3x6ln4q/z7P7aJwa/nepwcPP7gmgcgzPJvf/3dr/I/+5kv8X/8/p/gv/3stzOlDBaUEvMX/8rPq6rlDb/xtQ18BP/k61teX3suWsPKatQUJYAbR8QyxVSr/sisCtoHQownqlsqBVsKidp2tQ7jGpSbKTqQlSaXVOdM8t5TxTzMUT6d1Yp7J9rh675FuxV6tcWvW+KhEyaEdzSbHt+vcG0nfGAQO1yEudBYjVOCeF1gCymLGFQohSEW7qfIborsp8hhjrVK1rQ1eZmTbNSQC1kbsK724nRlgnBSlpR2ncwwxzlwHCbmMFMqKpl5j56P1RQliW1s2uIagw0enQzoBkX1f0dAjEU7khaQod5cYIsSWlu3ovRn4LuTxLk3mk3nTgvlvPO0VmMpJ2fHXJ/VA/9bOhdt29JNkljEXDUgtAAkFzUi9XhBKo1IHi6shHqIVPpYs9F0yTCoW4iSVJRlHqrrFi21UgDRvqhrsxgtdt9FVetaRecUvdWo6SCqmkXGGGKW4ySJUvoRGHfBjOfqf6HYttISHWJmjoW7ITCGxPV+EsVPpZhj4XLdctU3tYMm2BCjHiqc5Vqq9zQnphQkMZgTL48T1/uRD3ZH3r85cL2X1rJRik3nuFw1nLeObSugOGeU0LQqgVjmbGIuph7hAkrV6DDK4E1D36w4mowNEvRDkiRARnLSJRD2R3lo4SotZjla3DyVDZgS0FbTnZ3TbM7EcvfjHYMTNXehK9bHX1GkugJHjZb2etM0dG1H20oySn02AvgTzEOKwl6ylGoi5Fk8QjateJxALXp8B/0ZJgVoWgE2bs6h25K0x+o64quz62WsoNOEDUfcvMcergm7+zoeMqhGRLLQmmIa5nnmOExYLxWwMfo05ly6rFqZqj4p49dcAiGHExh2qLHjGKT1nmpn9AFwbEFlXHlYR1oj2JCoUfVZacqJDWathbYj9SuaTc+8P6KtQTcev27Rqy3FrYim5ThF7qdwimmHKfGq6ZeMxXKhUisNynmJ01YsqlM1cDsp6OZMiJEpBI5zwJfEYYbOWFpjccaJ7kWOrJqWy86irlasG8fdMPN//Yfwxqap4zH9yLunhoya6P9XH/wcP/aVf5k/9f0/wW996wsndcjTElTL061h+PG/8atfn6rE4JOuxwDEhejx19/5Kj/6n32Jf/d3f5l/+jNfYEoC0jNZTFoWoJZRqjoIPqB2xyciW/mbn65YWYVLI+qwE9EcKwF9AX8tsramZFSKJwqjKpnqayaoeqVonGO1aolZZk1OiyZ9TEmoWClXjnZ17qrXVGf+x5Cx3Rb75A1UDNimIU8B3zb480v85TN0uyYZR0pZQF0qs3aaYqXS7J2hq5WmrtibnB6AXaK4p04uba2VNpu3sjkWvvkS1JbDJ/GQECzKk6L8ljlOM4dxYp6j6H7lLMqO40Eki1OAxovG/2aFyWvZWNWoRyktqoxKk70mt5myCugErt+iYxQ6pF+hXYvJiU3jeLrKNNYSq7xu7wznneO8syJqlGMFG9m6jmob3hrapmG7lpmkNZoUg3DcvaP1DmskaC1ysK+oSH6TnoPGuhWt6WhMxzTMpJirTz3oVG2fFRijMEXhCtWRUShtqRg0BacVvVX4PKHv79DznjINkgBah257iu2km6I9WS0VNlVRMNFah2u9JBlOs24c553n5TFwmJN0J3Jhd5wwwBQju2Fk0zh6q6sWf1UFBGpmREGYP6nIoSwAUAnK98PM7ihU33VrOetsNRNyPO09V71l7TS905g8y15baJ/11XUuqDyj44yKA3kUTwWNkkNtWrPqznC+wcZCMILmFsGYagt9OtDqLTklsQImda7DWE3TetqLM2y/yCdnTvz9x9SnklEYSpE1Zo0+rZFYpBNmrGPVd2zXK9qmwVpTD1fZU2oxjapiW5edRZeWjTc82zTVT0GzbcR0yhjpPqhuKwnBNmCsRbcryupcBK98L/untvlLFUlTKWDyjJ6PqMMdHO4o0yzJZMlVQl2OqjlEyjhh2xllnRh8VQyU4SFBEGEeTamF1SILDQVf40drFaVYchH0vXmUXCwHoQJMUVRiKVYrxlhpzEriWcqZYhymXeMvn9FPI7bxzOOEbhz6/Bn6yRvEbivg2iqCBNT3YapwkOC3nKlgaGNovGPddZyte1arlsY5we0gs35d9SwWaqJKEVOxRDnnEyjYwUmQSsWR82bDatNy0TmOcwv/ED571hFzPuHLYv3Z5Vz6f9Wk4E9+/5/lez/7fSxG2tXcmsdp6ifhFH4t16cuMfjEkULNplSB/+Kdr/IHf1qAGv/9N75ALNAYgyG/AjpUWr6ulKh/uYrcPWykl/CEA9zfwnwU7XJrKWWFsm1F9UPnjAiDoGh0plMJnwOuWHSWdpDSMk5QzqB1S6MVK6sYjpowz5X6I4siZlF2O4TEMYhSozWKKUli4LuefvO66CVcPMGECes87uwCs70it1sZWYwj5EirFb61tWsgdKdm0d+vcz2jaqsei1WSPMwpY5SYCp11nk1jxdREV9nOuhiFjvSQFMTCK6ZMIUSOU2CcRf0NBW4J9ilSSkLniE1aKFVFhG9UbU1ipUVbjCcbRdaZXCwliQU27YiuIDVlG7Ky9ErkiBtnKlhPbI+dlraht5reFDxZ5pFUjIlACSna4B2sugaF4CziPKMpIj3diC69yhnFg+jNY6GjRdOh1La6SNp2lAmOh2sOcySlirkwikYpqYrjLFK82uJdS8gQDKSs0KrQaIUJR8xwizrcku5eita9Uuh2JUFjZUF5klZgHEvmpnIUS+f5gNWaxnWcbzZcdI7XNy03Y+B6lOou1AAVU+I4ToQQOA6jJJVayfvQPOAAlCQGKcOUC3OWLtRUKZYxZXqn6LYepxp6p7lsDRetY+1lP6hxB8dB9Cu0eQSYrCDWFNAFTAmU8Qj3NyIMVgq4FnN2hSoJ351jfM+UhbprKt7CaSCMApZDiZYDlrmyOnKR/2qlHZv+DLu5RC1a+1X98JVnXT/3yQmySMLdNY5116CNJaOw3tN3HX3XCENCmwoMlfWWAYusx7Up6FazsQ3PeivOe5mHpMMZwd9Yg7aXqLiSQ8JIxV/WW3K3IXtp/0NBlVnwLzmL1HRO2JIFa5EiOUeRiU7xNF4JGaaSyXPATQHrE1lVnAbSnbVVnhcF+tHSN/Ve904zN5a89ngDwxylU2s0a2dZNVrWRB2NkQJoRWctrZX1Mi2jXy0jrBAi2beodot58iatbzHnN7gwi8psf0bqrxhtz3FIJ0rrReuk4HGi+bK4jaolcdYG5z1dv2K1WuGbBlvF1Qql3reMKxLfO5UYdUY7jUXRueqHggCPVZhQ8wEOEeVusL6n68/J65WcLb2VcVPV5ShFsEQlCybu3/jZ/zF/6vt/gu/97Pc9hJN6rxd5+KXR/Enn46/l+tQlBo+vj48T3v7GV/mR/9sP8ad/z5f5rW9+QToJFYWqjMaZhx80VJCW1jgrwc6XmXZ4Lt/y3t8j7e/IYaZoi16foc58RetLRttZjTcCPvLW0OpIU2ZMVIJj0KJAZmpw89rQ6YaVzsw2EYdIOuxEASxnCVZ9z6ga9iExhHKySB5TZjcpsl+xvlxjt0e0ymjrwLdk35Fty2GWOacqhd7JfLMxGksUms84itBIrcRb1+F8jzfSnj5PMsMzSlzcOu/oG8eqcay8xetyku9c5uJLUrBkvjHL7HdK5QTYiRkBjzUtOrSYrkeXhC2NtO77Fc77Gmsqqh8oxhIxzCkxzIlhEpxEMD20tSV3QlKDS5lViKQUhBo6DzK+yGKtq6PG5hZtM7pzGNVQpQRObUPQaG8xqqGxijQ7oSVZTd94MSaiSAu1Ut9euWpSUKpmQEIzTpmxaA5JcTcJy6AxMgtuDKg4o0NFaStNcS3etTjXker7U2FEz3vUtCftrimHO/E88MIS0Uiyh20oridluYvaGlSK6DCiwhHGAaUVtu05b7ec9Wc8W6+5m2XN3U0CjJ1SIecEqaoqRkU2inICPj585FykYzDV5x6ydBJsga3TNEaYJmeNpXOKM29wYY863sF4TxqPlCwytMX15GYrbBkqi8M7CAiDgELOQdgS8wx+Er0Ca6sypaZzLYXKzggDjKPY95ZcXxewcvgOUdao1gWcYiyaMSu8ETVBwvgQb9IjgbKcT0FIlYLOir7x5AJNIxgj4x3e+0qb1Fi90CrrM81F3P/Ge9xwy2oe6XKu+iAOWo/yLeqRgJpdxgq5nCy9ozaEpJmnREeiaxTeGKyxwuqpoyStwHlP26/EbXQcBQPV9STXkpq2YjYUuQKppypuUIc7J0Mzm8srB5YBvC6svKVEJ0ZDGrZei6JoKahCne0L48qEI2ocKkgyY4zB2pbO90RnT6qoqqo3HubAqtmgjQO/Rm9fQ8dAKppke/axcJgEr6AVbBtLazWdE1GktsxiKhbmk36CWZ1huxbfddimkWdeS9BUFUN1daptysxaR2xrmK1QR5fOs9FKYkEKoi67vxNZZucx6zPs5gKArQ7MxjPlQoiFkDNWGf6Lion7d/5ZOb+WTq7VD14xSn37McKv9fpUJgYf7xpoJS5Tf+Av/yD/8b/wk3zPW1+s3YGC1WJpWz1OTlmtrS0sbxSNBjXt0eMOXr4DwPy1XyQMk8iBrreotq9zZX2q9KxesADSYm1cwaogeugJaY9rjy4aY6vkpTWgDPv7e4brr5Pe+xrp7iUlTCjboM+vWD15i3bzjGPXspsUYxI/hJxhzjB6Q++2dM2i7GbkQB4CUxCsg1GC4LYk9HiHnvaoMMhcP2fhoPtWpJLjxMo29N5LdVHvqVUJqzM+R5qYcRFMUujiUKXKltZnIVKu5QEwlyWpmaIiIoyFhJZZoMo0rcOfneFzxDuL8w2ubdF1bIAS45uiBM+xnyKHaeY4BqY5V+UxScg6K1WUMxqdI+FwyzzcEnY3hPtrSb5qIChdjz5/gjIFugb6Dm0dpY5WxABLhFSclhFMaSy6FKzK2BzRqYpS1fknJ4lc9YAvQIs+AUbGQTFzDIXrIXBzFDnas8bS2GqTHCf0tBf9eED5jpJllq6tdLGW8QdJJKEXt0RjHdo16G5F9j2lWYvkdJBuVGvF776UTNnfkW+fw3AgUjD9Bru9oL16Hd9fkteXnHnFPmimKDiBVEcmQmdb9k/5JoyBLlWwSqsHwKQSRHZjJTBvvEYfr9Evr8kvPyDe35COO6ncuhX6/Cmct5Rq0T1Gka7pnGHTrKEkdLdCH+4x1omSaCmS7KZ4asuTg+zXOKPCETUdYR7qvW0pyqBdTyrlxNLIKJIJnNeEvImSVJmqDHq6aguZUkQJMsvi0UXj0Ww7TyxCRVTGYozFVNXJJdjriktIaYZxjzreoHfPKbcvKMPxZNdrVhvc9hK3ucBvLnH9hqytdGIqEE7orGBKpimBuShigXUj4wxVXRJRGm0svm1R2zNc29KGyKwtwbaMds3ke9KYKRoihilKS9/YRyJeWroEj9k1qmR0iZg04eJAF4/oPNHoxMYXohMQHyBjizih5gkdBgiDrPu6R3XToV2HbtbYdkPEEhBTqv2oCAmsdRjvyXZNCJFhChznyDBn0f3I0i1ojWLTaPo8Ym4/Ir94h3z7khInlGuwZ1e4z3wX3bPPsb44Ay/UxAykKGqPOc2YNNOUCasCrStMKII1xKxO8VJYnhI/Swxi3LS/J6eM6xr0+ly+d/8S325wzZpJK+akePtrX+EP/5Uv8e/9c1/mt735hVNX1qjHmI7FVE5CzcfHCN9JgvCpTAzgEe6L6kf9F8VQ4ns/+0WxUtUVVJJfvYELpctpVX8V9HSAaYc63hBfvA/A4f2XlFxwZz3eWPRqQ2lE7TBmof8YI/agajpi4igcX+vJbY9enYvud+376OIEVKNgnva44zX7d36Z4y//Ert3PiIOM6axdE8vWH3XS+x3/Sb6i89Smo48ZeYExxCFQxsyBw9dEhlho0WaOZdMjoJx8AocCT3cooY7yv6WdKjaB1pVbwTJ0nVOsjkBKOicamKhcc7hmhZv1jRJYYuvFtPigBeXcUJeZGuF2jOEJG3lAgGDNh7jFLZxbLZrujzSllEO2pwkcFmHck5qmwpknGNkP0buh8DLw8jdMDFU4aGVt5xpTaM92ls6r9FjwJYZNdwxf/g14kfvMt3ckXPCNh67PcPmDH0PSZTQdCtSM8vmFv62phSFshqNpcQJQkARxQCITPViPjECSgUSLolN0YZYFiBW5uY4cTsGdpO49zVGcYZGk9FhECOj8SDrtA8SZLR/EGzRFqyYcJl+DWR0yZi2w51fUbozUrdlN8PdlGS+WkQZ0LQtXltUiuT9HdNH16RhQluD3/S0T97DXL6OfvoG6+6Svt0yWMVoNTFXESMeqFRLoFquXEpVKFQnkTGlHnQrOgN6vEHfXVOev898/QHji5fMuyM5JkzX0Dy7RG8uKPWg2o2JIUo2P2dAac66LTpH3PkswNVFh79foxYraG05AUFThHmkDPfk417ubbtCG4/uzgCYU2Y/RQEKu8DNceIiZLqUcabKgVdaYj3aKo0WCvK7KgqDPCOrFXgr4y10ZQlINF9umdzLIkJXaYL5QLr+iPjRO8T7O+I0o7WhuTjDPnuTxmra9RpnCrltSHMm58iQJ+6myGEOKBXofOBsTlytJGHVrcXXUl9Gmg6TWxGAKoVOG6K2jLrFqZY0ZUwJ5CDYi7nIeEiFROfEJ0T0DxVZ11GIojKOIrZEmjSQ4x6mPXoaCSGgcyZlJcDl5QjLScyUHnsn5CKaBKsNej0L8Le7ILO03yNjKicgcMqSMAxTZJwDc0yUIiBbbxTrRtOnAXPzDeLXfpHD177O8PyGNEVs59m89QyXA3azQU8X+K5nLoo5ZlKOlDij40Q63JEPt5RRunqN0ljbUnxPMk2lGiNU26ZHrzbku5eEORDujsx6j9sM4EEdb05gwqZZ8XNf/yp/6Ke/xJ/+53+S73nzi68AEpfzatGZ0N+iW/Cddg0+tYkByM34ytff5of+glhP/o7PflFmUo/SBqXF6nS5tJaWlzXq5BKnwoCaj+T9HbH6aMdpFinX9Qpz+YyyfkJaPRFVwWrl6tOEPlyj9iI6RAzotsOdPyHFWdpr63MxvjFy0JQSUWEk7+9IN8/Zv/uCu6+9ZNrNGG9Y7waU1qy6FaZZ0V98liHK/3cMhZATQyrsY6ENGWeTgHeK8LStKniVMVah5gE1HSj7W/L9jXQLQAKo3AwedP0FUCOSwwMlRYo1gnrebHG64DqPTbMgxHOSVifLGFs2Rqx0o7GCJkNRYlqjNa51+N7TrTt6ZlwaMHFClXgyU1mkmHPKBJ04hondceb5buL9+yM3x4ljSFhjOO882jpWnar67AanFYrINNzD7XPGD15wvL6jlIJbday9gzhL5n2ScK63Y1lXtWugK1VOxQgkii7CgkCwEaqkE5cdXVfdQleqKzQXMdGZY2SYA+McmWLGL8QFpcSCNo6kQexeMVZoY8goJWlHLAVjO5x1FK2xxqC6FZqMaVpUf0ZstwzZcDfOfLSf2U0BoxQXncUZhWvWaC+vm+bAeLeHlJlu98z3R7rdPeZ4j7l6A7W6oG03NK6jWEs5meYU4chXkO1yLRoWmAdTLUVB5YCaBsEQHG6YX75Pev4Bw/M7prs9aQ5gNG1tjyvfkps1h5C5GSM3g8ymN40DFN56dLvFlSha89Moh2/bE5stpdkQsKSSZZ5vaghMUdZ/iuRSMP1axje4qoMgWINxjgzzzBwjqWIPTqf54lSao2AVsmBYVCloJbgIgxIraYRNUao1eUadwHmvXNWDRANEMRgaXtwRDoMkI1Og9R51eYXnNaxWBGNROhHI7Baa8TATU6J3hn0/i29FabB4lM64qnKolRY3Qecx1lKUxdgGbTrAc9wPOGYogVz1FsaQQWWUSrQYDEo8WGoHQIHEkBKxaabkiTjuSPcvCTsxaysxUYyV2NP0wsipCp8PCq/lFKNyinWPWpRtMX5DTpm5QAyi81FUdR2NkTFEUhUCcxo6p3AGeqsxu2vSR+9w/MY73P2D99h/eCDNiWYjHdLV1Tlld4u6EgxKwZJzElfcGEiHe9LdR5T758TbF+KUax16e0FZP4HVJUF58fBQhm71BJMS5vKIG/bE40Q8jlKwPhGlSl0tx99+5+f44f/0X+HLv+/P8T1vfqGCtz92zj1KCh53C369SQF8yhODr3z9bX7wL/wgf+5fFJepV7ec8LlfaXdBVRUEg+izqxTES3yexFiommHYvqW72mJe+yw8/W6m9WvcVJtgqxVrp2lJqHlPfPE+6cUH5BBwqxWEGe9b0iTqW6m20rVSWKHGVuMRZOaZCyUtMpiZNIcaxGZUGGmNZzKCrC0xn2Qzh5gE/Zy0oNZVobMC/tGqnCyWyzxRSuabXMz8imLlcFeVUZGnEXW4p4SxIix7GYm0DTpsMBXRXxYgFgsSWdgJIUnb/JQcJKFsam3BVA8I57C+RU/SXlVxkrn6ya0tE/PMnDRDUtzvR27uRz66PfLB/chhirSNY972tN5z0cs8WVehEKPBJPF6z1OgxCya/Y0X98LVBt2u0X6Fco1UuKqKyJ42IXL4pemhNZ0DlAi5Ui9zqvdV6KhCeTRQzGktllJI5BPVKVYlK42AXa1Soro3jZR5FGnbpvLrXQN+xWEWkySlpGrbbJ6i2h67GgUtrQ2pKlfuDjM3Q+DFUfjwWpWTWNVmvcZsrjDbS/x2RxomwmGUJGya0PuBxl9Lmz1FeS6LAVcFsIGA2F5B6j9iYBQt+Gm0rq6E8jrleE+6uybfXTPtB8I0kQVZh+sa/LbHbC9hc0Xya3b7wPNj4GaYyUUxJelgtc7QrnpR4Lde1iyaaFtwK44VH1EKOF3Y+hVlPogufs6UeZLkZRpRacZpL9S5WkzElCvjSJ7bcpAXOH2eUkcWJVbapNIonbFUzEHWtVNY97Vq0NpKe5rqbokkHco1aL9Ct2vMaoNpO2zjTx4VeQoQAiZFAbhpqimU8Orvx8BHu5EX90fGKbBqLGEbaMm0KtKqhDMCQtQLCfZkliYdKO06bLMWDI7PMIldesiFnAoqZtDpgcllzKMk56H6F+yNyIDrcS/Mh/0tDEdKylBxH7pdUUzzEHtsi3GNzOW1kkSiyLNSaRbgqVoQ/JkhFkKBjBZjqJQe6Ix1/OdrsqnCKDFwGiSu5lfj7ZLEiyHcIsqWmENknoO4RE4DZdwRnr9HfPkB4XAQ5855FDpnd0agsA8CcG+M4mLzBo3SmBjoUmSAk/JhCXLefOWjt/n9//kf5s//3p/gi9/1BRKqmka9OiqXpnMVLPtHkBTApzgxeJwUfN+jpEAv/ZUClAc+uEa+blRFj1ZjHuGchpME6+J02L92hXn6JnzmN3LsnvH+bua93URImbPW8uamYeMNzCP5/prh+S15nsnnEdVvyFlmo3EOlGwE4IMCpzG2Q63OMGdX9K9dEWOiO4xoa2ivtvSvXaLXZyeOuwAdFSBiHHMSrjuLnSwy331Mp1Ko06JX1kml2K3ldftzUndG9j0JLUCo6VDR7QMMO3Su9sVmFrvXOFdkdnyFSQEVkV4eNu5cuwWLzkOOGZwhokQ+tvpJYL0ctKXahCShVpWciaEwlchUHNMwcBgG9seB292R+zEJLsFojutOZqxlOdiFP26bBt93tGdrsAbdONqnl7jXPod5+lnYiGzqVDS6SpQaI0mFrij45TOrNMufs/CTc5grl74eHFqLPbZv5P4kC6aClpSpc0KRge69peREb5XoSxiFCrOMKlKURMl5aHqyX7MLmfs5s58TzhgZyzjN+fqJjExSkLGLtlUsK59YLfdjFXVQio23bBrL1eYpzWdGQcJ3LfP9kZQS2hls34hxjhbJXTWP0hlScsgrkISo5IfEcEGnQ5VJrg6J8gVZg/UAFUOtBrduUVqRNwljDH7b466eYT7zecLmKbtQuBkiN2PgdpB9abTYdU8xM8SE9T3GetnPxhGV5zBM3AfYj3JYrL2wjjZ+jWl2cl+rtHOJcug4J8+h9wZiofcWVxHpy3y35CT3IaXT+s/zdFoDIhGtUdkJkj2JHPYDXE9RrLzPhX2U0jJ+0eRuC5snmKcjbp7pSxGjqinQrjp832GaRnAKSkhruRTmKox0dxh5fntgDpHQGta2cGhg8oXJwaQCRkWcUxTNSWehGDE/wvq6LwtFyT6dMhyD4AZyTfqMEsBsKYJpOlFhi+hkSOcx1tgqcYN5pgSp/ovWqLan+BWpPaM0K1JBbLjnI9qvMG1P3t/JXqiJw4kWTJ1RFcGuCM1cpNOxCZU13kiM72zFgGWggsf71y5J04zxjhwTdtVKnD+7Qq3OSLZjjoVjSNLZm+RzWJT4ngwHpps7ptsd2nt67+HJG6ANw5z5YD9xN0ac0Xxm0/DG6hn9Z8CWQm/fZd4fTufXz7738/zAz/0YP/W7/wO++MZvpaQgipYakhLMysfPrmUkrh79+nbn47e7PjWJwaPc9GNJgShCvZIPIIujqI9xPpfXyI9MiPKD77syFtX0AJinb6Le+A0c+2f8w9uJX3xx4N2bI1opPnvZcd46Sldb6fNMHEZyTNKyM5aiHdm27CdpSQ1lpi+KmBSd7oj9Je6t/xorpWleeyJa9Mait5fY86ewfUJutxRjRUrUarxZnLZkfq2NJWGIOYsTYEoY0oMUblXyU22PalpoV+T2jLy64m5KDMdMzEk6IL7jbOMFVJak0jRhOmnjywfNVYnw0XMpi9LhY+e7xHGOHKbAMMmhoCiM0QljoQIVrTGUqB8WeBF1thITaQ6EAEV3qJxxJWNKqlankRQK8zQTwiwtv1xOc3CMw/Zr/PkTVgXcHGB1JhoQl5+hXHyGg+2Jc8GpjCsGZ6TytVoqQ0kYZ+naLJ2lMAlLJQWhsOZcu1IaZRNFUQ8JW3XqkwDPlKL1lvO+5b5vMCXRGUFlO61OBwwArkF1K4pfE2zP/THwwT6wDwlvLUFbut4SlAgx6RLqe4JSAoUHBTVVZ7Axw5BhKprUntE91TSrNenZS8LulhyjyOu6pho7VUXG03ORkyynUJ+/emWMcBISKQCRnJRYeZu6Bl0j9tftGraXMA/4MKFLEdXLzTnm7KpSzTZM+4mhYlVyofLeJdCXuuaKtuAs2nmScoQpMpG4mSeuj4E5RtazQeFo+x7t13Jfw1wTG0GPL34kGy9mS6u+4bxvab08N6OqiFaOlfIZZK/Oo/DZq7R30RpcImfBPSi3RCJpqJRUvUQQKeSQCyGJF0aYC5Ndoy4/h1aWZrXFbN+Dwx3eO/z5E1y/RhknMa6K7eQUCWFmnmZSEDqlRmFKwpUsnb3xQMgDyUEpDqwR47Pl0SoNpoKXK3j4pBw6R1QU9UaltIC1k6JNGacfKWk+OqFUeajCl9hhXO1+ba7I2yvS+im7ZNgfREBMCp+Os/M12rVivDUeTvFrAfSZWth1zoARtpfVGoPEWJUTGjGNs8t6UZbcnaNf1zT9BvPkdfL9tSQersFePMG9+RuI/SWj7hjGwDEWjuNMnCM6Jja2RWmHMpacMnEK6JSFEQMU4ziEyLv3I9+4Hsil8PyiZ3yy4jecP6N/QxRevb+GHbx9/f/mX/qb/3t+6nf9Kb7vjd9CKUnOI6XR2goL9mNn1+MOwasJQfmmryzn4+f4HN/q+tQkBsv1SUnB48ZLhfic/l4efZ1lZpzFbviE8i65AvIa/pb6CAD97C3i9g0+3EV+8cWBv/21G27uJ5wXgZLPbjvixmON2PBqZ9HO4jcdentB7i64GyM3c2IkYX1iGzLbxtKojDVruPwulN/i3pS5WjGW4nqia0nKiiofAjZpdKIrchBp34p/u3UkDHMuTHNgmiZIBa2WlmGtypUCbcnthtlvuT5EPqgmQyFlOmd4Y9Oitg1X29dwVgsae9hhVJaKgsVzXTB3nGavD6JHizpYqIFlN0zsh0nudSlMna/OYctzOaVqkhTkTA4TcZyIw1GqKjXS+zO2XnHZGsaVq0mfYu3B5IitinhCIdRgPW59hn/2JrHfopQldeekzRP22THuI2qa6aJjlTWbTtMp4SKXomDBUKR46hiUaTwdBiXMp3VzAhouoxptwPiTr4BxrVg9e8v5uuN47PEl41WmdQpTonDIcxY+umtQqy2pWbMPiesh8vwws5sSXQeuLYxFPOsxRtTTqmiUVdAaxXljGToBioVUaJzFWEs2nmwb1GaFPX+CPt/BdCSEIMq2qCrxS2U/LF2TGaUjytpHOAypIhd3xdNhS610qi9AMb4aBj1ySixitaw1Fdzao/sNWVnyEMmmYOxM513ttCguWst5Y2mNMEYUBW1ETTQVEfEaS2QX4eUYGYaZoTE0RokGR7PGrLaoaZCDPecqUhNpnWFbHG3RbNc95+uOzlu8FWdCFeKDo2l9/ouplGAMRIypxCBJVRL8gWpkPYpQl3SKihJMw1xHgbshcjjODEeprFt3yfr1DXZ1iRlu8SXi1xvc+qzu5YrdKBlbMiZH1h7G3qCK5rJ3XLaGrVf0BvLhhlRGooHY9eKOasyjBF9RuTVV3fhBovn+OFO0WFsbI93K7MqpOygvUUdJNSbIb1UfwHpM31OKRnUb8sUz5v4p98nx/n7i/d3IEBLOaJ50jtfXnsvuCm8c2tTu2+N1o0QxESNqko13eK0wJIiBNA/keRLwstJkLBFL8Vt0e4Y5ewP9+igUyYV26jvC+oJg1sTDzITmforcHyfiPNGSSL5w1l2gtxf4TUc4VKyWEeZRLJr7MfLu7cgvfbgnzIkP7ycAVs7wue0bgr1QCnbwL/3NP8ZPfeGP8zvf+KdrNypK9zeLloTSguN49ez6+In28a/Jdz0+H//of/JHP+F75fpUJQYfHx+cbtLHeeSPLrW0M+EEHJKg/tApkO8z/OyL/4p//cOfACC3Z+wivL+f+JUXe15cHxmPAd9a7o8tYxSzFoxB+Q6/6VHWYC+eoi5eZ3Rrro+Jb+wDM4amyRxCZmgtvS6YHKRatucUsz1VEPNUmI9CE2ytpreKbWPQww497TElYp2n2VzQnD/FrNaMqbAfNAeVmcZ0EhwpJxc3S7EtsdlyN0pS8Cu3A+/cHBlDZtOJUlvfejbnW/rG4htPuXuBjpPIgzqhkJUlZ11Gi49msLkKG80pMs4zh2FifxjRqtA58TDPWdD8UmkstK86QggTJUzkYU86HMhzkPlnypy3W3JusEZz1orF7aqxnDdaqn6VBQxYZOauuw0Og+3OmVXDpHs+OgTePwzczBnrJ863idfOC68V0KsWr6UtLBlJOIECyzTCeKgHgnQMSI8wFlqjrJegqI1oSxhHSTMqTTTWE5xh3TZcbXpsCaR5whsZaUFBWStMkaYDJxba4yFyOwZuB2EyZG1IRQ5kbSzGGlGFGyJmPuJz4axxKAxGt2waESzCGDZ9i/cNyrcEbUh4YmsZ6DnmkTEHUpTnIaZFiqZRuCaKVW6aUTmTkCofI5Kxpe6vkhMlBXKsAkK6ClO5joCY/0yxVB8ThbGa1jj6pqVrG6zxwlf3Fu8Lm14OXZKIa501hm1j2DYanyfMPGPsCmctqWh0EpxDKorDlLgfZlK2rL3mSe8oqxW4Ht2uyEqjrOj9qxTwRrQV1r7hbNOzbhtaZ2isRqVJuka1Y5CDzKsJMyXKPTlFEWMkKXCCfXgI14sFuUNZS8qZKRZuh8CH9wMf3u64vd8T55ELr3lj5Xi6fYPV+hxLwDUW3fVyv4us82XNnzeaubP0usVqOGsdz3rHeWuwww16uiOHgeQdmUwxiJ+CtZxol2RKMaBExS+nxBwCh3GSbgHQesvcGGK2J1fLh/0v83mW4kEblGtRbY+2Dmsb3NkT5v6KPT23t0e+cT/y95/v2Q2B3hv25wKINhou2i0WUHFksQsHSQycVjStY9U1rLuG1ijS4ZZp94Jpd0MMM0lZaNbQX4gpXJQuSMwFbxq8aXGNvJZSmjJrVA4kDcesxHV0mJimiYZEWTuafk178Tr24jnNFCgxoXx36raMMXN/DBz3E/MYSTHxKyvHG5uGi67nrD1Db44A/NRv/z/wO9/4LQ+HVCm1e/0Am0ctsuv6255vj+W6H5+PX/zcF7/1z/ApSgze/qak4BOujyN+gcXxTj0CS70CnqpL/Gc/+tv8wM/9GH/i8z/KH/zlf4viOuFxh8xhSqTqyqW1wlnRQAAEeNa0+LM1qu0xz94ibV7j5Rj5+u3I+4fIEGHVi0NeyZZBF1wK5BSZguh4347Vq6Aap5z3ljc2LZ/dSuasx3vKy/dIww7TNNinb9B1BtMYfLvF6JaSIzkESsq1BWvEqKcUim2YkgB3bsfA8/3Eh/cThzFyHjLbdc8bUZOMx282GCuCMWU8yJy17cA1J4bBx731oBYOpUi7LUbGaWYOM96KoYuqKoQaaXcTl+5NEjGiGGCeYRpQ4wE9jBR1wK4CbU48W13QWMWhc1WpTrPtLCsrwirMg1QZtX1N1hSrGYPig7uBX35x4JdfHnm+m7HO88aTkSmBM5pN48iuVj45QnzUKRgPYpkcBKB6kuyFB9nmUkdZdhL0tbGoaMEE8bOoc+yp88TJE0g48skqGGPRTYfqVmTXENKDHkTO+VSYaaXwztJYQ+MsNg7ENGHmPWYccUpz7lZ0q5a1t6IAqCzWN7SNQ2lLVIYQE/dj4eX9zPVu4O4wEqMwXNZOc9E5tt6wbgxts8WUUF30dJVcthW0KR0krZDWdZwFe6E1qVre7qfE/Zy4GYIAtFLBWsPZquVyo7nCs9UaZw1KK9rGcZkatgZ0iaIW6qBJI+ZwjSkZ07aYxmKINK6nSeCdfUBuF05Stcu9NFXrQdQCLcvw0WlN5zWu8aw7cfdsrK4A5QWLNAmIN0ySFIQZwkxe/BtADjEr61nlLHN1pOgo0YKJUJoqF564Pox8/eWOX3r3Be+/uCeGmacbz+6qJ7PiM2dbOlfAZGn/Ky1rcx7wWrOy8KSz+NKQkkMrWDnFWSNJgR1uKIc7sZfOrYhQeCfdppRQugKJU8Jaf1J0VUXEkHJOgheaNDE24sRYyiMTnweMkarJYNZWcCptJ/u8gGpXpP4cv35G2k3cx5HnQ+bD+4nb/cSqtTirOW8tl72jTxljmwdgqzYVyqIwxtA3jrNVy8opzHhPmq5J1+8wPn+fNE2UboO++gy53TAleG8n3Ynbo8TWdSt4m/PWVkdFizaFYBJjVtwMkef3A4fjQGchx4DXLa9vXsM8u8GHSWiLTXvqZlgj54LWqspqFA6TOGxOsVBcd2KDfd9nfssDE6MWRhQB9RalawehutZ+3LF1udSrfYRvhbn7VtenJjH4lknB427A6c/fIsMq4hlP1b2mtkB/9v2/wQ/87B/hp77wx3l6LXQuhZixeKM47x3P1x7rDGdrzxvnHeetFSnPUgRZvLkQDvbZ6xxwfLSfeX838sH9TFYWYwwxOnJUYDKpOnXdDoEXx4lvXA+8fzdyPwSc1rx+3mK15vV1I+/5cEd6/i7x5hbaFnRBr3p815NcQ3Ydk3OMRhGSSHkWbVBZs8BYNOokr7lw0EXBUPjX2rdo35Gtpd0+EQWwthPMgZeWcFFV2EjpE2Jb1fslePSaeKWEIdMY6J3McTtvTvoRth4kp+5NDbikGZ0CLifmOOGSmJa4JK5+l+2Ws7YjFmEhdI1j4zU2HBE/l2rTrBT4lpIdu8OO93cTf//lgV98757dbsJYy36K9I3j6cozrhqyV9LEqJ0CZhFeydNImQdBtIf5BFQ9LStjTjgKpYRRYHyD0jMlWpQ2WN3gjDARGmcgasyCidAWfCuAReMFn4LsfWfE9dOYwrZKU6+dObnClXHEpZE43qPublA5Y5se1axp+0tC0xGUEXChMxijmKLw9j86BN6/n3jn+YHr3YEQEq3TPNtKEsm6obIIcdqijbSV41yYcxQqJkIH1kqSM6tEfjYX8YMYQuZ+Tnywn3h+mPnofmIMGecMl5vEW8WSTAPGsUb2W+8NPhuyMbgCLg6wv8ZMe5iOYlDEBa5vsXHCdRvGWFg7Q+8120aU91oreviqYtCKdpIoN9JFQVsUgmFwSp6LN/pkaa3iJAJgaRaaY5jIo4wiyjRWzEF6FUGe4sngKQNaC0BY1oVDmYacEuMcuD8MvPvijl9695oXL+5JMXK9ESnuVWPZdJ7z9QZ0oKha1MRZGBAJNl5TOkens1CoVcGEAbN/KbLZ+ztcHDFG47ITV8wYZJ+FSYCHtZW9CHo5rei8oTWalRMatEH2syQ7pQ4Ay2lkW8oyMjESH6xH9xuMbYT94Hr06oKgPdprtJ9BCwV2wS+JV0mNT0s7srJdpFMpkcYaRedERdOnARMOTLfP4eV7zO+8wziO2ItzbLtCbV8nZMPzw8wvfrDng9uRkDPbzvHGWctnLzue9A3nSiFpUSQnfSpqbg4zdyVC9my9YXPhWZ+9jhkO5N0Nykny0hjFeWt547zjo7sJpTWuMZz3Ip9u9Ktl1DedX/XsKlQvBm0ezqiPX4uAErpWYpmvfOOv8YN/4Ye+fdH8setTkxj8mjsFjxKFb/6+JRl4+PWV936eH/ir/xo/9cV/i+979k/xd1/+XQBUmul9z+ubhs8/WeOMYQyRJ5uG7z7vuWjtg1KdERCUvnhKXF1yN2ZeHGeuDzOHKdE1ovy2cYqLRrEympwKszGEaLgdNVMU+9vpGBgVNE4zRkE2qyqxOb24Zn5xR+k65m5FPr+G8yu0X2FNgzYarQ1FBXIu1UGMysWfaNqGtdM86xsOZ2IffDdE1n3L+bqjbTzGCsjGtl5AMZMRkRFjwAn/mWpDu1QKSpVTteGU2Nw2OrN20CrDurFcdpaLVjZ0aw0qjBXtX+e2KVaQV8Ygsqmt1QIoyjNp2GGVopDIaQDfYZSTeX2I2GIwWd5LKQVVRXkWx8ohJO6HicMwMx5njE3c74/c3e84Hlek0KGLIY8jOhwhjEJTGvaUoY4RYqV+Vlqp0lWP3lqwhTJXEZzZk90oHOwkn01jTuCpRb0sl4pUt04qOh1kplpBb15LwMkVyb7pHFedZeWl+2DTBGkiz0f04Q5un5OHgWwsarXFXBww6yv67VPwmmKW1m9hDOIOen2YebGfeHE3kXJm0xo2rSV1VJGpWgEViCUzRxhS5jBnjrU9a7WAY1de0xmNt9Ucuf58LuKjMMyJuyGwGxNGa/G26Ge2q8gYEuvGoIui1aC83Nt8/xL2L0k3z4mHe/HG6Dq01dj1GlfvQe89K6+5ai1j72hVxmo4b63MoRe9CeOADLpWzuYhCdM8ANx0jieMCSmQK5U0hwkWRkIMoryIOq0HpSsgsSbHmVIBqUaQ/0WjiyOFieNR1t/9/sh4nEgxoa3ifpgYguzPlLNQaXMBgoD5UsSmgo+JdQl4FUglwDSI7PW4Rw07TJ5xqpzYL6bGQJWS7LcYUDqAqbRo6+mceFjsO0cKVjwVLDQ6Y1SW/a0f1vDpyFs0QZyH2KGKEl8ZZaBZofotKhSMVbSN53zd8WTb0lo46yyfOWt51kt8aqxCjZMUDaaqoOZC0SI2pY0ADnWcYNiT76+Zn98wfHTLOAz4lNEXwmwoSBy9OczsdqOsyVy4XDms1qy8jKi81WijaZJijoprK0XfYUqyRzrHk97SrS6xF/fy2jnDPKKnPRdtx3ef96RceLGbaJ3lrYuO1zfiDaLm+QHXsKyP0yj80Zm0dBBegR7WqyYCp/FBKXzlG1/lB//iD39HSQF8ihKDX21m8nhU8InXJyDqv/LuX+cH/h//c37qn/m3+b7L/yaEGRUENOKmPSu/4tnKo1A8WYmAxVlredo7LhqFvr0RQRo4IcknLPfTzH5OhCj0nsuV5bW14zMry5NOszKFPM+kxtJVoZvdFLk7Rj4Kkti4RVe9UudKGIn7gbAf0FNiPN+RjjvSNKCyuOLlKoUasywrp2WOqGJVEEszZ+0Zam3xdsXKG+7nhDZy4PTukeuiUhjfgVaiTGgU2jqUESCZUrriDSrVT4sZjFOFlkSnEsUqtDNsWqnKt61l0xgcERXHEwVQZvaRUkTJrxiNMwbaFltlR5NWpBIoZUIpocXpKujUFGEWmCTdi4WeXLJsblfpS9vWsWotMSS0Vqy9ZuXAlwBxREVLCjtMOpCGPQwHwRbMIyXMQkeNkXJyv0woowTTIXKJKD0Lla3at5YYyHoip0JOqjIocvXxAGM8uK4G7IrhKCK/vPKagqN3so27xvOkd2wbQ6cTdj6iykyOEz7NzHEiHHdCKZwHSk44RNbXNY5iRQdfwqXMqilygDbOoJXmvLec947z1nJWOxTi6aDE1Aehot5NgftJ/O2dUYTGSqJmBC4rXSEJdLkIlfIQHPspCjisCAOEOjPX1dTKK3BWoVIgHK/J+xeElx9S7l5QjgeUMTin8WnGxQlXZko80tUA/7R3qNAyNBI8O6tYeY2tFbcwSNoK9O3AeFKWOZDM16OwNEoQA6JKac7zJM81zJIMhBliIqf0zeshF9EDWMZNAsARjZKQUW4DccSXwMrB2muGRmOcYtVatq0TPRJFZaxMIq6lHzjsvoAqCaMijQpkNVNU3R8lyChQe6wuWOuEgmm07K9S3WArVZs4o5Tc101jmFrL05WD6Gi1mFF1KtGScKpgKQ+0Xol+Eg+MQ5mE9gmtNSYtFu0tSUkXUyvpIF51lvJkRU5Sjb+1bQU0aTPmcIOKs3QgqqFWTGLrHU/JplAI8zSQjjvGuz3z7Z4QZnTrKbXwMKoRTxyjMVaC7dW64c2Lnje3LW9tPRetFcaTdxySwmAZZ8cwWl7EQIhCF76fEhetxywMl8OOMuzRxxsuzns+f9Gybgx3FxFrNE97z9OVY0XATntiFW8iBoqrZkjl48bLMsopPEoATmfYCdmBAn72Gz/HD/6lL33HSQF8ihKD7/j6loAN2bBfeefn+IH//Ef5qd/1p/gfvPbfETWrUk5ufXrc4azjcv2U9rzjqhd/dG+gUxFz/wHcvyAviYHzZNdWXXwB2bXOcNYbPnve8BvOGt5caZr9h+Sb92B/j+nWXJ09o7t4KhQzrfgH3nCcE8/OGraNyCjrWDN8pShVKGeaJsZxRE0zKiRGWzjO4WQIYwGvQZciJjLTQZDM7T3n3QWr7QWbRnMMhWMsNL7g4kyYjgTTErQTK18lYlDGGDHjsQKgWmZkMvcuGFXkoIkTNk2sSHSNmFT1jeLcK84bTZtnzBzRcayt2nCa26sq9qS1FgDX/4e9fwu2NMnOw7Bvrcz///c+p6qrr3PvIShfw3aEZUIAAcx0D4MwKBDDAfDgB3IaDQyDpEkFRAYZlB2G+WL7wZYlWZJJ0aQCQmjAngZBPxEY48JAkEJXzwAESFoybYsPkkWie2YAAjPTl6o6Z+/9Z+byw1orM/9/732qenixo4CMOHUudc7e+edlrW9968aMOIxGA0e18OIAjMHqQRA4EhhanZAss0FytngegZQRl3GL918O+KantmACvnpvj8CE99/Z4vnbI54eCFtkzG/9Oibskedr4HAN2u+00NNhh7w/oMwZJeVWmowZZNXfvAMccqzWZJlVuGUh7JGwz4zr6x32hwNmex1hYBov1CfOVkxJCmje4XK8QLTsCyHtdvnUNuJCDgiHA2LaAemgld7GCGy3oP2M2VLyKO0wzNcYcQDna1C4xIG0FfWtCFwG7fSGvMHT26DR/9uID1yOePZixJMbxiRWAIwDwrDFXJTOLUVwSAWHLBAhlEE7gQ5WAjnM10DJmOKI6WJA4I3FWUS8da2AYjOpFXYZgFsRGEmwHRijFEjWeR/ma3DSgjcYJwzDiMutZg0MDIR0ANIOGYwLAZ7aRmCOuI5akTCSdhalw5XKhBBQOILCBIwX2Bdt/gXJIDrg+nqHbWTEKJiQwNlSVNNsBcO8loEG6ZVkwKA7E3ruCoIJfe3QnZHnGWXYYcZ9bOkCTw+E52+PmD94C09dBOQiePb2hI8+qef1MhLkwdsQPkCCBvhJCOpKIe29MkRNX87EAAIgA8AbUArawAdaEImDxgahmMvV7h0ZyGcAQQQbaEDvYSSUibAFYy4CRlaGKo0gGRAogElaKjNbQKeMFguQzc1CKGHAnLKWB97vMaQDnh0Ft++MuIgTLgbNpBmu3wLff6umKtJ0qU3IBDhkIEEgqeDqMOt5nzNkf8But8N+v1N2peicOGtl0jEynpgGvO/OBAC4GAP+lfffwv/guVv43XcmXOx+C/TV3wSu74NuPYEnnvoQplvvA8qEnDQFdD+rPN8lraOxHTbgYQQko1zfR3j3q4gc8L7b78PtaYtDVhftxRhwgQPC/a+DdvcQHBhYjrcCALR4NykWOxBO67BFoOHreOnzP4RXv++V9wwKgN/OwMDHiajO1770S/gjf+uH8ZPf9Zfw+z7wzXrhAaBkuDcr7B9owZr9gHG6hScuNkpn7a8g97+O/PZvIr/zdW1+NG1Aw6RR2AAgwBQZz14OeGIz4Hc9MeEDW8bwtX+Mw3/9/8CDf/JPsHv7PuLFBpff9BFc/nf+Vfy3nvzdGAPjyc2Adw8Zt8aID9y5wK1pxCABZXuJ6fYF8tVereLAuMq1mjHePdzH168PePd6RkkFUxDNk7fKevmdr6Fc3QOYEZ54GtOTz+G5i2eQbt3B1VyQRBDnaxweJOyQMOACcTshDKPGJDArUAiK4jXbA61PuBTkwx7p6j7ocI0tZYQomAJjpAPGXQLuHcDbSSsxF3MhHHbWglnNfLLoZg7BOraJ+uBD0DKqw6iFT2IEhQAvYSVmkclhjzwfVAgdZoAihuE2nrt4AvTsBZ7aDrj/dAIz4anNgOduTXhmQ6B3fh3AHinvEUUDspD2QDog7w9aNW3OmlJpefvMBUCEkADRkzmNEjRgkMHYS8aDRLhfAq6vd9gdZqScodXvCBwYw7QFHQBJMIClPuxtGNRVxYQBBdNBEMOICAYXVVYDEXizwXDnDjbjiPkwgyID0wX48gJhCKAI5LJHHCLKQJgPwHPbAE4D7sQtcplwGdVivDMyLrAH339b3SpSIHED2t7BxXAb16lgCtq1k6DNaiarTX8RGXF/D3z9To0q5+EC08WTuD1u8PTFiHv7jAepIDDjchrwzDbgMmjg3AUXxHmPEAEZAvLlBYo8BVxMkFQwjAPG7VaDD4nUb55nRLlCPBRsDgfcwgFTtH3KCdhpYS6SouWK4wQZt5iFcciibaaLViIc+Ar3qQCcdU8pIdSA05aMJ2LV87LW3ujPRClqlQMq4itY3e+Q6B4QJhAmPLN5AuWpDZ4YCG89qfLl1qTW+rMXEcP114D5HmZJmMcBPA5aI2GcNADYwLnEoGeQBMICzATKsYEYWAEmeGCm+bVz0vsHtVw5J3ABcL3HuLvGLSoYB2CfNQOLDtdIV4Q8DaApgtGlrxJrF9pBU6OFrfhT0UC8/W6P3dUVDg+uEecZT0ZBHJSRirt3wF//GuTt30J69+tAKRqj8Iy61VLxLq2EXZkhdA3JGU+QbtMuqzzkzYBxM2C6fYG4vQSHgFvTiA/cUQD4gScTnhgDvunJLZ6/PWD79j/G/F/9F3jwT76EdLXD5slbuPymb8Lw3/4f4wN3PorDExNK0kJhU1R5VwBIGDW+AIDsd8jlawjQ1tnT5dOg7YXKzHSlcTGHByj7B5C8r3rmpJ56VP315hfw6c9/Bq9+7zcGCoDfzsCgX+hucV/70i/jj/zcn8RPfvdfwSc+8C2QMi8iPINdtihZA3nyBBINLsulIKd72F+/o0r2sNMLFgZVWiEiJ7WcLseAyzHg/ZcTntsGDPd+HYf/6r/A1//BP8RX/9GvY/fOHuNlxJO/8TU8Nx9w8T9k/K6nfzdubUc8yIztZsKzlxs8cxExPZgxP/d+0IO3NGe+FMSnb2G+uIOECft9wVev7uOrVwfcP2iL1aeniIsg2FBQZXZ1D/ntryHt9ohf/y2EJ34T4bkPYbzzPsTtHZTpFjIKkA7YXyv9Pg0B07ABD4N2mQzBcte5pmIxk1pGaUa6fgDMO2woQ3AAHXbgdA3kGXlgSL4DyBPaKMkCt7QKnfnrQJr+CQBFYxu81C6CpgRSVEWpwWMt+ImkmGV3QNldY77/Lg5X1zgkAV3cxrh9gPdfPoM74waHXMBE2AyMTd4hvvWbwOE+5rzHZmCI9cSVWQFBSRkyZ+SUWyEiaEnWUIr2wCiiQVNkXlcRSMmY5wOudte4NwPXGLE7JMxmaROhFUWKEcO4AYtoc6ecgHwAZgVegRnjEDDyBsN4iZCiVaLMSi8PI8J2i2EcFNSECIwTaHMJ2oxAZCTK2OVrbArjdhSUQTBeBsybCSQFm0iY8h589XXg/tdQ3vk68u6B1oW/dQdBMsZbEbeGCfttRBZtEDUGxpPbiFsDa0DY1dchb/8m8v13VKFuLhHuPI3bt57B5cXT2G8n7JLS+kNgbAfB7SjYFA023VBGjAxsRki+hEQAh436/EPQjI9hVB930cDVEAqGnDAcHmDc7YBZ72sjajX/ncIIGTdIpCmUh6yFfcQ+dvuCQez8DkDYBHDJFqlvUfIwn7f5iovFnAAaS0HWmjxDQUEYvXw2gKx1PsAT4vgOnp2ewK2nNvjgrPUNxsDYUEJ499dB12/jcHUPIRKGiy2GW0/U88LjCHVduewK0NQVZZckaTQ/xNISSTM+YC2FpRQgJY19OOy0ZgVHYJ4h776L/PY7wFwQwoBN3EKGDYhGYN4hXT+AbEeEcbAMJctPYG15TRw01ikX0Dwj7XfYXV9hf30FTjO2QRCCgPf3wW+/A3nnN5F+6yvI776l8mmjSpefeh+EAuYsuJ4FX9/PEMx4sJ8x70fki4AJE+TiDuLTt7CBxnlcfuBZbJ97P4Ynn8R4+RR4m3D79g7Xuz0uQ8EzE2Hz9X+M/X/5q/itf/CP8Pb/56s4PEjY3Jnw7FffwdMiGP57A57bPofDbXVFAJp5kEWAGC3zaICUK219fnUPcZowTRFhEAQwSA6QfI08XyNLxlFFAiKrg8KdzlrprZUue+3NL+DT/7c/ip/41I/jxVrH572P337AQHPmTvyc8Ytv3sWnf/aP469/8kfxiQ9+q1oTpDG2FCMkBYRRSyIPrMVNgmREKuAgKCQ4WCH9EhglKL3NF5dA3AJxBOWCKTLuTAPGoGlftziB3voKrt98E2//49/CW//4Hbx9SLgVA0CE6c4beOap9+HWrafwxPu+CTRdYJg22I4RlwODLxh72WsP8ekSKSXQ7adQnvog8vZpfO3BHv/0/ozfenDALmn9gwHA7XEEDUr9Ewh5f0C6f410/xrD/oAxzQjzAeHpAzjvwcOFptVkwnzYY543wMVFZQqIWmytfyYAEEE+qH8b1/dA805z3w9XwP4B8mGPvWTs9k/jFmWU7VaVoZdd7veOGNDO1ABc0FgxnaBMgTZEaM2PCLAOdgCVgpIOSFf3Mb/zNtKckO+/A7p1H8PhAcJ4gYuoipCvdgj7+5D7byPNO+2Id3mhZ4GAkvIxKHB/clhe8n4uWuRIuac8z9jtDtgnIA2aA16sEJQiK0IQraE/DRuleQ+tBC9JBqEgCiPygJgZnKK6T7wWhxWwwjBqFLhki4CPtvcAJCOQpoXO+wS+3mObBLFk9WfmA+jqAej6HeSv/1OUd76GfP+elo4dBw2yIgbHCbdufwBz0dRADz68NQbcGhh87x3IO19F+fo/RX7760iHWYtlvf1b4DvPIDz9fmy3d7CZLvW+FMYwB/CDB4hbFaxTMNYE0AZOw6iVBfMMLwNOHPUMiWgt/VLASZv4xLxHyVqZkr38dmAF7+Ok9HRSliwJNMZAoN0CiyCVgv28wy4DF2G089gabp1NBbOzISjISVW1n24uRZtxpgQ8uEI6JMiwQbz1JLbTLUzDRuMf9nvw4Qpy7y3M999GSHvMQ0TCkyibDahMes69IqkbNcGBAWlAIBEQ7X71FqcV5PIh1iNFsmYZ5etr7P7pb2D/1teRKYDGCWG6hIwqGzBsMAdCvnWhrquVPHDDgSlAKAMpY55nzAcFuxEZNF+D5yvQ7l2Ur/8m8ld/HYe3vo753pWJAUI0mUxxwGHWGKx7u4RdKrgfZ+ScQTLg2e3TCE9dgXPG5vItxBhx8ez78cSHfhem5z6Acvk0LueC5w4J834H2V+h/NY/weEr/w3e/a/fwNv/zdfwtV+7h/sp48mv7xA3Edv3vYnb7/swbn34KTxlqdEHk+tEpIWm4lblvqWzxsCYmLANhDEImDXNOEmujGutOMnB6mhQ24/uQ1wOrkYDBZ/FJz76wkNTEm8ajz8wIE3HE09yqX6a5XjtzS/g0z/zx/ATn/wxfOLD3wER62cvokEzUjS9zSgijOpHijEiRi0mI8JAHjFvNtoQ5IKUUrp8EmXYaMcvEYzMoFHdCRcDgN195PvvYr7ShjXE2qpURJAPBelqh/TuW7hMV3jm9gbDE0+jWM3/kQGOzyhdup+B8RIogjJcIN96Fu9aB7rfurfDW7uEORfMA2PDwCFHpGkExy3o4hbixQZ5d1CFNyeU/RVwdQ80bgBS8CAC5GHCrhRsNhukomWYCZq6JN3Ser44SsbuwX1cvfsWMO+0ccl8Bbm+j3J1D2V3hZRmTJIwX24xBU3pE0B9s6JI31mIxeDQgEGMqB38uBNwVnCFYgHCjFwEh8OM3fU19tfXur/zAby/Bk1brZmfEtL+Gofr++DdlWY73LoNjAPArMrb/McL8HLqCDp4sY/atZEDDnuNPt9jQOGMYhUCi0VH51KAQmBEpSN5AFFEwd7y4bXqXiazUIcAyhMQPanLLENriYs0A4i1UQ6x1VArGUgHxALQ7j7KvXtI13uUbOmi+yvkB/dQ7r2Fcu8tHN65j/nBToX0dsIYAsJ0iXDrSWC4h4t4CRm0vG9grbKH3T3I/gHS/XeR772Dw7sPkK73EBEMD64x7q7AV/c1tffytnbZo4AUAsp2AtFtxHhLFWjJerh8v+tdh7bnjv5sfmI02h6WOZIPWpxHQoQw9AyQlio/pIz9XLA/JByK1Sex+B3thgnsDzPCYda2vcNU91TqHjtzdibPHDAgySj+O0Ta2OvqAeb793BAQLn3Dnh7S2tYxAiZD0j7a5Sre5DrK6Q8A9stxu2lApgQK2tGcWwF9MXWKmuXUiRu0e9r6trvlJa7bPcwJ8z33sHV138T12+9pUzR5gJs/TGUHcm4KhkXlxd44vZtPWsWsFyNBVGHS4G5AfZ77K6vtUz4vAela+3iev9d5Kt7KPsrlDkBROAYEC82oItbkLhF4hGHrOWZ7+32uJ4LrgNr/AoLgkQ8cet9aqHfeRZgQnz6OUzPfAibO8+ghAk8ABebApYLzO8WfO3Xr3D17ltIVzvkg9ZmiKaLSy6Yr3bI998FdvdxMd7BfmLskxqLIoIkBBo24MsnQVkLs2GYEDcbjOOIcVDQmiUquzCMyDlD2PZh3ABx0F4V5OdKu01WULCuU7AABS/WiqtnXRAPGY8NMDhtny1H65e+HK+98To+/fnP6KJ+5GMalVsACe11JSpSfeJ9HwQAPPHBDyPEAWGcEDcXprgI4+VtjJe3sbnzDOacUTiijBco2zvYYQTPBZdJu6dtAuOJkTHtCHT4MG5fvY1LGvDs+9/C4d09whhx+f5beOKbPoDbz38Ut595FpebETwOmEWDujIEFDcYn/ogbvEIfvIDWkM+Trh/KBgOGZEPuE1b0EGbCV0Mmh5454kJt7cR24sAvhiQ7zyh7ZetmyFvLsG37oCfeArYPgGZLlGc+RgmbLYXCFELq3g5/F57e4Q0h4CLiw2eefopyEFTpjCPwBRRNgNkf6H+8stbiEO04JtSFambVeTt4/ywux+VlVmBzaUxCGbBZW3GU8KAQIyN5ZbHaYNkCgKD+WeDgYsSgQjIQMAmYoBgM06IMbYyv6eGMQXEGuVMQ9BI9CFaAyFtO0whIAwjthRxh0ZseYM8XmKTrDuctfQl0tz9aQjYRMY2MuhwDczXtTMcl4RACianccAwamoqivrBW5MZqMUhFhTWsSraoIkQRN+jTAO2JMg5aR8riShlhMglChekaQukAgqssSa3boOfej/46aeRt7dQtk/iUEhjU4gwsoCvCWFMKHJAGQn5iScUGOQCREbcjOCLS9B2BI8RNGkwawgRwzhgGxnBCr2grxVh7iXh9lxksRcwsMgcMEwBF0QI01aZAIEW3AkjZNgAwxYybnGdCjapYD9nC57UaPkhaLOyi8gIhwlj2WG7HRCiFp0RIttfLYFeUgINQd0GSZmCmwYRqUV7cQGOATMIGLcKzMeNnv0xQDYD5GIDzNpVMI4Ttk88ic3tOwgXCiLYnru/A5CirIgUUNCYCCrGdDm47dPd7I4BMICufv/bd55EjFHjMaYNeHMJXNwChguUYQMat7i82CqLaCJhIR/qAzNCHHB56zZCCKpA0wGcrkH7CdiqfCi3LlGee6DzjhP4iacQnv0Qyp0P4np8Avsp4ZmwB10kXM0JTMpQxYsBw+WIcQy49dz7wWmPyMDFrdsY7zwDiZPGRhBhiIyBAsbNCDzzLMbnP4rN1Q63p0s8eP4+8iFhfGLC7Q89haf+u89j/OCHIU89icvN09geCnZZzaMpWmfdJy/B17fBzz4HLglDCNheXmLcbM29ogZFmibkwx45qcGCfwTQtNF6Gqxn2AGCuOxbKfymv35cmYJHAAMP05ePDTA4PfxYoroQFotGpBWhLHrzxedfsMAbyzcvZBQ0aZU8CnjqQx8FADzz/L8CL66hmxUqirsA8KRV5JIwoYxbzBSxSxokk+yWTMy4GBmb9H6EDz2L8uEPIv33v4T9195Guj6AhoDpzqV2lnv/88Cd9yNfPok9SFNz7HUSCThO2D7zIWyf8fbGgm0q2Owzpt2MJw/a5hgANlZF7LnLCXcmxhMjITz4COj6HcjVPWDea2DQMII2l8aUbCHDBSQOkDhppLNY6h+6i2+r7mWTmIAhxvAW/AABAABJREFURjzzzLN49s5t7S0w76xA0A5ipYSroCduGQ3cAMBiOBCw4jAwhUshNJ+cuTWEGBRUqPFYMI4ThotbuHz62ZZr7PP2bm2ARZlbS1UvcZs1I6DMlpkSYdZe1FiC0q6bMwQcg5UIViofcdB+B8OEMG1w69YWt57boIyXyFZNc5e1dHQ28BFIC+oMkTCxts0my9qgZGtXlPKFZCBZ69+c6pkUwJSmVAuRuJ1ZQC25SILbFxe4faHNwryJmBz21vbZPqzeP5hB4wZ860nwE0+BLp7Q5l7jBSRu4BnXlHagw1NaofMDz6G8+xGU+2+r/9pfJ0TQuOk+prbPPkrW+M3uDlMYKuin7mzQ0GJOKERMm4jpzpMmZEMTvHGExEnTNQuwL4I5tWZedQ+YsAkq/MN8DT5ozAzP1yilW/dhBKTovkOD61QfH58PCmoFk4Hc6fIC061bVvhnBE1WBtuZgP589q8VtKIghVgBMkh7ApAUrc9gsTYkRZ/ZzjM5ayByzB74+9n7bG/dwfbytv7QYzlcRsQNZNhAgsoIcESGyQYPzOvlBBHGzQab7cbqd1ir+zSD5ivNlupkBDHrM17chmzvIF8+g3cPglv7gssHe7xtrgRAZdytMeDJzYDbk4LqWqsBwAygZC/Bre4uYcLm9pO4fP53431PXCA//zzmr/0m9u88gMwZcTtieuZJxPd9BPy+55FvPYddvMTVoWBvciQyYQqsxbMkgQ/XWja7a8anC6qZLnjiKbu3DZBJ3BrrGTpQECogkI4xqPrre1/Bi737YMEoPIrZvByPOTCw4cGF/WIR66Kuij8Igl2WrLRNIVX6koGQqz+ujLfMJDaLFZ1Css0T62m+t4CifSmYjQEFWUGYDIzjJbCdwc8Bw/YWwnP3VFmy9ibnJ55GmW6jTLeRKeKQNKgrl6UNQvCiMwoarg4ZVylhnwuKSC06EggQiHU7BK5TwGZzB0wBHLeqZMw3LTyY4Bz1wocRRai+dw8I6jzEfPr2vTIHZBURszEBUX1xG2i3yTzri9nvLvaqVwwOBDpWQGwPSgVonV/OlrsWCIkFkFyrKnoK42KkGRQHFUbu5wtRFW1O4JCs0p0CBCFlb5AbgHGBzzGCx9GAS1RQEFWgCkctBMWDMkuiTq8a3W7CtIggBwFlMmqcEePGHDgB4BmUWEOws1qpFKOCGW7V41CKprV2nRGpZ2CKBiVKyZpVAj0ntVJkHPSQhVirrlEYFBhsL4FpC+FRz75AmTdidXGoZoHwCExb8GVWwXfY6d77PbU+C+KxQIRaX0Sc+XDlRaz7VMrymcyVRA7EHJRRsHiC2PqEWCEjiRNSAQ5FSzJnaF68wMK9SKpyK6IdMsEDiGe1nK1lMw2TumtCtABAW9qUj86HMkikZ2SIFcDUz8NkbMGkr0+se4Am6sldZkY3y8POvxR1kwaVcSSla5VdmnuhTtxAZNDXEEdlbB0yh8nWMWr/C/LeBVQNBu+yKJ5+58O+EXM1BCZwGNVVhK3WI+FRWZMaKzMgj1uU6RZ2STMiUtZCb158SrP91Ai7d1BL/GIM1lUUtRaLD7Y91XsVgek2+HZBHDfgO89g2D3Q9MFBKzbi1lPI2ydRxksc5oIkpdYemjWCFBBGDgHT5jZQtqCcKijzfQCKBZ0W9O3JZdya8RP0HpjxWY1QG0f664b0xfc6fhsAA2MNFotkTIGViXxxVTuaKEA8WMfR3GJDAdncWtA6vmmelJYtDi0ZLZysvW3qnW0FOGTBNQm22yeBMIA3t8DzXoEIqQIt4xZlvMSMqLW1vU6+AQARFWQAkKxnwz4X7JKWnT0UjXIHNIJdoL7fXS6YMmPHAh4GTBcaC6EVuGpYlPphw6BzQWt3m9FAcC1/bMIomGIDEwppXwY19TJg/Rn89ykErRJ3hs6szEEFAqT72oOB6uPtBKOXBUU/UfeH5m5PfV+Ndh/U8qZo1e+GATJPWt3wsFsABDaA4OmQ/ZxrXYdhrELev8YwWsGXofZOUP1J9cgKeYkomNsI4ALMhixjnFSJJC3yIkUr1aklmC39zLr3mX+ZSlxSxz07YwCMOiBGFA3zMiCjCjL7W7dUadqagthYfYlVjAdZOpwFQAICumSEcVI2pmNuFq6hHhDavURRQNG4QAZRe5b6972C7f219Sx3bAEFpKKtjv3eitBi/WFgVSzOpirwMiiQGrLucSlA3sBbfDMAYn3+9fkg5pOAwM9JPTfD2IBNVQ5uPXpQ2oq9tHVvZ7/JL3UfeCaEggR4yW6RChT0KnbKxoBenX8crEOmVohEGLsYn1ANB2+qlM/IigLLmRBom+RhY1lcAxAnHMmiYYNDIZNvKsey3R2NXdBsEpAq7ENOKuus/LX3sQlQNjgyIRTR+B4hbOMFhtsjMF2ANk8gWO0QUNB+MIPK4+tkWStruZ5hsQb680hR26uzXm1C24e1XgGgQZw9O2DtuNG9xd1Of9WURArdb9SThm9k/DYABkC/OILWcOlz3/8qXvjoiwuruwNuap1ZvfTlKwAyXtaAmmxf5GLWnSHWVKT2u88CzJ0bQQMM2RB1QSqEMVxiHC+1yWnJdjhCR2+qL2suwG7O2Be1+hUYAGLvVSBIWVmFZB0NfURWpiIVxpwF+yxVsJZAiHFrhqF0T6zVADzovn/Go9gC88eKwIr6WHAiQ+vCG6UpoZjQNcHGUSPsV757qcxBE4QaiENLMOAI2392Lvimqz3u4ICc1hPtrqlCswBhBA0HjYewqpcyjCcBggdJtiPHTdgPY6PH7WuwskkuVMVdM5aVoOlxHZ4hqYkOJuaRhTAELSwEPqhFygHEVinSAAGVtHAv0JrW9DV214wf0H70Fjk6psGVbm2f3LFLaA10tNRwWxvYWXALm2SVgXJqiMWdFGX/3IpdPIcpJqrxBeoL11oXut7CxkqE0dxuULdBaXc2d2tPxrCpkgOyR8UHTaulnPT8jhsrHGINkuz8afZHOjof7j55GCCo/mYDPcKxyoYajPZezry7nEpegAQ1hPRZHCgTcPpO2ntL7we3da2goFgALRo4WMoLg3akrxmKFcMigDmCOYKGpRxKZmztzPBxI0mVc6lyF4D27GBB5IJ9YsSgRZ8iK1gLUHDiGQObIdj+A0MMmMY7GDZ3qmzQ5yIcMnA4lJrOqimtpW5vZO3BEUg74gYmYytIXSZECGbMWObzQn3LsK1L1C8XbP2+8OZdvPw3X8IrK/1F9u83BgWW47cJMGgL+9obd/ED3aLmbvV9gU/R437VPIxrlxs1VpVkgVGQBgagFvzcKejklhGR1hYPhE0MOATCwKUeIrIe6JrSYodfFBDsrHpiKkAqxTID9P2L2AUpWFyS/kk4Ew5UsEt6iANHIBUUIQylHeLe4HDwUS+4AZBT1KB3OqsoqxhqsFrmCNFSCC1yu4QmrE5UpGy5vA0MwAGC0fG1PwOFbk9OvVxQ/UEAIrqLKfCGMf6Z8gxJwxFAwP5aXQHeYjkli/ZHe0NTmhRjpdtpnNS6jgMkKAXrFldaAK42/9Ktq4iueRH1iBRWQRqZEMKoa1tT9mz+2WtLmHshqYI6maaGTuEDLXCtB2f2u9U67RWugwLqFIPdHXaamIMxHAGUo4KV6L7ufv8NQLsbwdtYS9GGQSEezb+BldiBxKXicvarUEC2OB1nCjymoNjddbDrbcpJ2l1PQhjCoOsdlQGjmHV/e0UaAiRp34HF2QCW52MYK/tyBAgMfOl5D+2zs5MmwwpOXh8QBa1Q6p42sXTX+pFac7ESG0jweITV68mapVuvrTGSqQMFvm5n5QWAwnoPmQmhoKVc+tk3eTYXVcZzAa7njF3KagRlWFv3tgiHrDJulwrYjDEmw1jQuxOZcc3AxlK550GPpDfLYrYUT6j8zkXf+5AFu5SNvZXKhERmRC4VIMSgDGpkvQcBpO9vQIHgz6pztqr3J0NVX3/jLn7op1/CK9/7Kj7+EdVffAYJ/LMAhMcaGMjq67sGCv7a972Kjz//YhVcPR3e/816Y/r7vrfWxW419wIlGYKckx7kvfstPT/dBlvzlilkRagGCmJoiN+b/ChjkK1vfbGGNfr6Ogebh32f7X38c2Dt4qXvaxcmEHYpgwi4HCNEBCUopcpENaugIn1pYMDBwnqNNI6BzBdpnxfgIFSGWMEBVctFqc0TqMzp0moteVpYqOCgwNw3JuCl2xufd5sjOU5RutesB+YIDlH1imTt1RCSttTtAAINI6QHB2mugVxtzkbfO2NQXQiTBmaFaP7taSFIiysnabEi9fyxdncsEGQWRGMZBgDR2YMYIAYK0AfOunuBNIaGnSrO7kM+BgFugS6AmVul3BRv72rKMFdTWSbqhaLrHK10soRBA1G7wElxJqNz61SqlRjVebACC23u3IGWRmlX0NLN0VkCF+qptPsr0u4NYC3AoXecxXqNECEEAkdzH4QMkcliIIquO7MyTSFBLCBxcT4soLAHBceAYNL1dZeHA8Yk1QhZ389T59yBfjRrNQS9h2KgQKwVdB9/g07ZHd3HNVN3BhTk0gymm2RGIYCK3ccCzAQtqYwmf/w+u5H04JAUFBgY0JitYs2lZCH7/LP2baG6JmyuhDESrmatRTAdUpXHQ+D69wCQzMjr5fGxXC8arBoIE7MChEiI1EACswa0tnlIVeS73Leubmv0hTfv4o9+/gfw2U99Dt/xvIICAhpbi39+IOGxAwYnjvERKHjh+RcbwpYGCPrv/e/WaTZ+8fapNHrd/k6D+QqSAHPKil6LVKCQsh1u1KoKiEy4ZkLkjMhcgwO9YpnHDNSDnxrzMBsbkUsDBQCsQ95yMBGGoBchZQFDa9kHInMlJGxi0Bh7gvY2WJ1Ov9QLinv1PgUAOe3tN97AAfkicwBHUzZ5VnaElTVYtxLtswwAboDAfMauiJz+9Wh+t1JE0BSUT9YpTOhFCtSQfGBnUQKGYQuOGZQHUJgrQKAabHZQwZ+TRomvrfA+p9yAgYICD+g0ReXWqj+HuWxcCK0j45NR2aUIIgsgjGLNbAZjgLj6noNahMQKvKijjEXU4u/38RwQOAfMwlAVVjZl62vfH46ZgFAEM6sVFjgiDlGt7DzfrJj6+A8pTVn1cz/lYjrDEszVZQDMpSzYPXEXYLfmIgbWC6ytMCGxIBQCe48ONOuagEr7Uxy0SFCfXuluhMXZsAC+MHWM0lDnPRfNbvB5OyhYMEuLM67fKF3enfHqU3eKOypjKFnZj2JsQGi+75N30gKuhdncMg0UZLtzPSjo7+MpmQFIO2Y4LXu0g6fu0y7l6ko4WBfPlBUMHFKxdN/TchBABQfBlP+QlDmIs4KESBnjKiZBumdwprZ31bpcZyLEAMTE2IeCkQkxEYYYEBMwBUYMrHISALNUVg12Dtseqox6/c27+OM/8zJ+7A99Dt/+/Iso4kDCXC9VP2Fh0Pkq9ivxKCDhsQIGp0ABcAwKbgIED7OO/aztSwMD/QFxdsDdB4cKCpr/ElDlvUav7gPj7lI4fVZptFwWB7+U0mIbVm6DyhCQ5ukWIczZ3BWhvZ5G8xKAjEkYEhjJL+iJ9V3Q293/k/3DdnkYAiG1AHQivn7mVgiWVuX0PYVjC4Vo6Uc1C6UHBC4sk61RkuO9cXAgRSroClAQQAxMIYBIC5lEY26GQBg4YIgBHAZQiqBwAFLUjnMOEFKq8QaLdC9jDHicNFq7goLJaPfJnsP829LiUfY5G6DUOQMadEhMOkchTCFYDZpS2YOCc+xBQEtpLBo5vqLte1dB9de7wnW/clW4oVmHNs92VmHU6mIb1UIqgswELvYcrPNkaIloLeNsispiPmoanVgRp3Nz5o4xOMFknGIJ8g3rDWgdoCxqnU8hqFVLAi5AginbOFW03AtkjgPKYa9xBCfOxTIOYgBq9o9+dkAw5z6AuZ3vfc6Qzjo/d76dhaSi3UZjBWe6F8GZnGjZVzUOQeNvzt7Jzq1xiinoz4JneLjsOKeo2EDFMsapyWg3vtwocjlb0OSZswtzKhUcnJKN3vWVmavhNEa2zrWNTVjLY4/hWrO0PejQNS+ISXuERCYMWcvhz1mqq6HujQFnANhbjIq7F77w5l38yZ/9Qfzo97yCb//IC8oS2f+xSMsCM9nbg4NTowcM58ZjAwzOgYLXHhEUrGnnsgID1fK0N5pTUzrKEshJQOBKfLbD7IeoHz1A6GkroB3oHgj0r+MUVlotQCRoAx6F4Aog7PAD5v8lp6thebgMoCBDEEkptFPAwOe0FhiO8qOfygKDstQo8c6tIGbdigUfLqhjfUF9n04xZZu7C0i37pxe9D3wwMw5F7VgVnQf0CyoQMAQ8iIIKZBgyNqO1QHCOGzVH8zqF0baW2GguVbiq1HdQE2l9JREjYKfjNpWC2vOzYJNZpHsc65uqFTKESWfiBCFkXLCFFSQlSIYPPEj6BLGoBZhNcUKV6agKtwTcQaVNehTpsJwBMrKag9uouMDU7WQZpbGfIjGS3BnvYr7wPNs5yI3NqlSVat5dwCm+uA7NiN156OBAw0IdpavD+Tt11uY7W2z9mIoUgUvZdKUwzDUOSk4IAViE7eUwHomCM5sHJ2LOEE44tABgtkCjrMsg44f/WxzPdvu844kGAxMRtE7FS0GKIQAsNc1MKajv5N9Lv3qTp4DBS4700Nkh8u/tdxR4OxGlxlgxV0MWGRYKlNRqrHkcvKUjPS1chk8BFoAhX5Oi9fvAMFi3bvXUdlRkIQRiTEWQcyEFBQgRNKaB7G0+wBojIEyPYK/++XX8cM/9zL+6ve8gm/7yAvwljHOXBUV77Y/jT1wcLBey35NbxqPDTA4NZwpeOX7NaZgvRo9SHBQ4L4wt3h6QOCpgQCwq8CgVD+TZwJ4LvTe0gXr5U7Lw+qD6fQB9NH/vh/wUo6FQX09JiQmxCIAa1rOueEIWAqQSbRNcBYIqbA89beeGnmcNms/z4qCM9nvnAEHrCe8BkeJ+5WXLwpYVkSWlvnRgsbkSGjOKWOXdG90L9rfLNepD0BSa2FkwjBnbCJjEwOmohkjORAKE2KIGMYIpJ0qrXwAcjQ3guYkU9BnKNUN0tLkeuE/r1wIyRSVs0vOPok0FxZDFWwUrSgodmanELQ8M2sWiLDZXOYW4aiuBGRGTb1Cp2jrepsPiOgoqLPg4SzNKSsWOGZn5odYrxyCxmiY9Urk8Sfn5+yV4daMhgOv9XlxANYD+/VaJ4I2DwODMjCjAMQIpbkUqBBGjgtpSsSQzADfcCaYW0ZHiFpgSTTq/mDGhWcOOXWu6ccdfV5a4HE/qsUbgJEZkRlzVDp7ygp+iwgGc0H5eWEhFIa1TYaegRvupGAZU7IGBbksrf1HkR3rkV0+ifWuENHy1DjtLuhHDwrWMrNz7li/AmWxnNJ3JX9qnHLbNgPPKikGwWAMwRQ1YHVktrVRICAiFqxINZYlWUThr3z5dfyZn38Zf/m7X8G3fvgFNVZJEETX3nN+CqEWbvrnNR5bYHCUklhwxBYA3dcnQEGR9jc9YgVwkiXQmILGEuxmjVhNOVdwcO6Q8pkD6MN/N3cAoR/+96GjvRwJMykKHkLnuuAWlORD7MIJa6nlUAiZ2/v4Zc7dZVgY91nfP7Nq8BhYD3AHDopxiUIKXBLI0pUAzbtfHnFBF6HfCZ8m7Jc+x752wy41gFZEhWm/bAsfY3BgwBqZnDWdc46M7RDU8mFW6yoQhrgBh2wR9gMgRoP3PnC3rqqC9XSuWJvz9C4EMfDpFtFs4MapUZ2zrlcvWDK0u+cQXGBLXTsAKAZ+2CxqKhnCpdLbNfLfXTb9vN8DS+PsWfPTt7UO5qIhIsxBFhHbj2S9rnK+F3MG0BeBWYOYJTCAKdxyMoDs1FqLsNIa0BS0KPra7lJgKIszcARCqTEP4NBcN+sz4RXtLLDQ3QaHrDLFi6Fdd1lIfr57GdPHGPXneoxq9U6REYNG26fCGLIgRUYWwSYqeBrcZQrNXJAMFGcw6fSdFPTWenMleWZU7r4WNFDgCv6U7ND9tJ91GZf+N7r1S9eGB+4xq8vSrfUirI2zjDHtD+NJGZpVXu7RZOijymT/3R5YNDdv6EAEVY1bxIEAI6MgCyFb7luSgl9984v4s7/wg/hLBgrcAAggZAMHQrqItXndijVwm677so67b9y98dkeS2BwBArOgEo/X6WzEjx//BwocDrKQYFTqPWjp/9WoOCQpQKCnv5bX+r1ODf/9d8EonpARws0HAJjiIwpMkb7HFmprdhffhPatJrAGtmvL3Uf2BdsXUig3xg4yEB1KxABfnITLHeZnJddPk9lcQzMrWnrU6Bgb593KevXs34+xdb0CH8aAoZAuJwidknBwcUo2BdGKoLtEFBCwQgNRlP2QAMUJZgfn5tvvI4j33w0cIPOfdBcCHtjoZQ1WNahKIJOCKpgEWFkZAiz7U1ArJ5uW0cAIupPDaTWOCRDa0cs09H6girF5qlBX8s8/1NWt4KDFpil770MtI3MmHPBEBhTEQwWK9Fbr4W1mFOUVhyLu4JjJ+ds69wzS/2c59V5WYOvdcqvnkEx4V1AmRFQsC8EyupSYAN0ZGeJAxCCVSjMrCwHP/w8OEswF2MKkrIE13PGAzvHV1bS/JALHuyTzn/ON55pv/ebISBlwSYCm5Us2cCL57QzJsY29vdycSfR7uWipklR5dW7D3pQ4IzMQm4ACB1AyCIa8LmSO3X5mECizFMQoBjrVIRRGCc12pwLxgAc/AcdOOllazHwkPrMjofIY/9/FkIoenbVlnLXbAYQEFi/ZxJLRLe/86AKk5cA8HfffB3/y7/zGfyH3/XX8K0feqHGZWhejgIBrZBKtdLjKVLYz/EpUPDS33wJH8VHj//IxmMHDPyhXzVQ8BCduhgiXXQvoIffEKobK3//y6/rfxUDEJ3Q7qmtli7oPq+GUnvrZB0Uk/Hw0YIK9fsabRxZA7osxWYIqrw2Ay+s4khdQEzg6u+Kgbso5vNo+dTl7r8PZkUuwAFBS/wWO+GFQCzIXbhWfcduH2QtfGztPOhtzuUkKLieMx7sM/Zzxj5pM5yUysmU0SEwxkPCNATsU8HFGJBGLXZyMQSNRoZajgJWH77NGhYnUWsILHLxsfR/d+l8qT5TBxKyLDIResBZVaEoKGBqsTBAb9GaAFqBg5YTBggDTKEW7lmO5rbprcE1SzCnFgTmKWPuSivSamm0dfYc8oLR/KpzLtiUgBQEU2CUyIjSAoCF1YIsaACWOZ6cswCLIlxrUNCflx4UeLZPnzFU17kTqcwGAnJBItb+JObD9/cjc/gGjo01kHB8HgwU9K4Oz80/GMP1IBVcHxKu5oIry9W/OujHfs61wZOzkP15dlkwJsIcg8YnTafJ5mBuGNbcII0FFiho7+5lvbb2d/29lA4UFJOLfaZWDwpOyTfXiyI1rOjkPDNEQURgvTQBWM6Ka30uB0g7CzCcs4DJ7heT5ZyianlfwvcqjzNMHncLxKwZQ9nOSAz6ukNoZ6wYNVKK1m1QplZf83/xdz6D/9N3fhbf8qGPIxdpMVv2pHTDOj1s9PrxR/7Gj5z9vccKGPQPvS5z/F7GObfV3//1L+BH/rM/+kivEVh9S4GVNirmu79pnHMP9K8JmAvAvh4CVwprDQiGoL7EIailNhp16z7HyM2KU5p3WS7Ux9ovSLRcoz6Ku2ccxECzH+5if0uG9CEqfIq9oB54ahHWriTQhE+RprSSWJChOCOjHz0ouD4k7OZlrMcpQTpExjQXbIaMOUUVphtXvi5U/broDXZWaUArMuRBW57itbZmSwcUa80CWDVKZ0ZMia19yM0qdEuDgVDABUuLlguCaAR9XoMDEFAUI+hWN0DmYKevcOkugQZSUMHYUdntVSqtrlGL1h5CQWRlYEYhFGZkATbC7TwFs4tMSDr76tarsLmcujkDaNVHT7AbDSyUY0bGQEFfFEzX2FgtO/OcxeJRDChJQUSotQ3cQmZ4lc9Y+z2cOgsOEOfSMg7WoOD+IeMqZezmgnu7hKt9qufZWbBzQHfIBcmCUvvh7pEYtPPlvgiIC6IEzYu3ZyF+9HuZzShyw6oyfWUpN3yckxe9snNXQi+H/CvKghA0ziOgIBAjFSBywaEoAD1wqQGAu5kQnb01NiQRqkt3zpoFUBpVdAQQHiab14MtXsZL0pyLVTg1/p3f/1l8y4deOPr5TcDpUcZaP940Hhtg8F4e2sdawXm6h3Q/D6x5/3/vK0rv/Nvf+Vn8qZ/9lFFaivZYjOIVVbJFrIpgIJjZjIZsGVwEhVQpONj09zo11szA2p81BO4CXrheCPUv0o0MQTQh4XXD+6hgXxryC86WjmUKJ5gFsHY/ZBg9SPp7sbvcImqlqtBsa72MrrXfRRcXAhc+Ur/XJAZPHUJVSodUkLIyBS5Ed7Omo+Xc+XuB6j7R4FBGllD9pTpil0KaAMSjHGt7JVO2XSBlfY5WoS6b8GwWeXWbV2bCmSdnoTw1ykcRQQQjcQEya4TyyqINKKDIKtRd2EOUbsUy26Q/gz1F7KCgshdyDAr2Rm+v/d4ODHKReqb0XGpAVoFSvyfdZGa2kgEXFK0a56Cmdzn53xdpVmwpSxCpNQrKaUamAwWpoziK2H0ODuwVoGUYzV7IGEM9u1k6V7adfSJNgZUaGrZkY9ag4JBlwRTsUqmg4P5uxgNjC673ucZ0rH3uRBr1PgfCxqpQM2cEBg6JMEUNpk1ZEEiDVaVQzWQREbuL9Mj3En4fZZmiCnRGRccWrOVFz1KSBUufylJwZRWp9YdJmWpGSRLCkBkzF4yBcMiMFLVokQaC81EguLoZaBH35XLZz1U/j/VwI23txnU57AaaF5iLbOwMGUgzg8xduQDwLR/6eFubFXBiHIODRwEL71U/PjbAYP3QR/LGlH5xYWMnmMiKDwvVQ+xWLezz3/vKF/Bv/W2ld/4nH9BNi0RN5xulxZ7cDK4bH7hgZgJzMbRezkbK5tWt6oNgHKH2zMApMLBmBxQUKDvgaUsOBjzOAH7gTqyrAKBgShwEKoLERQMTndrDyme4fg0ReL8F6oUN9OIRWkETOTGPmjVyYjRfoVN0jZ4/WAzIbEGIOReUFWMA6BkogZqSKVrPwK0r7r6OnLEjpYuTKS8PQBO7uTWQslvDCgTcwpNmefXWiVtTx4Vom5BShagLWdgYh5VFG6FBcpVtMWGfTZKz7UfHpi5SdyuIkRZlnnJrG+455D0o2Jsiy0Wts9xZisr+Kq29GWz3Y3ser6Wxz1o1kINmsZADGddA8P6S5+fdg0gHDBlyxMhUANYxBf0aL9a+smZSD28ughA0ABSi9HFhaGYPGiPjMxe0s+mxAbWegkBjH7p4mV3KeLBXpkABQcLegG5KGSUf1xholixjpoIYCIcsmArq3Vi4PM/Qo+t76aP/7fW9rPvgz7uWZzgtJ3pQsEiTPiOXYqAqTxDMIIgKdlJETeVMxQrDxaJl53NBXxjuVLZYstRV4L3JZjfS+riuU3LZqyxqFUSqFRLdUPPX9kBdXwfCEgA4uPD90f+nCh76RfPsvPdiND82wOBRH9pdrQCBTWD2IIBIfy6kVu8vfeV1/Nlf+EH8h9/119TnY1cjdlSWuqtIFUR0OlWFtKerzJkwB14IzXXg0KkshWWlLqvMF8IqV/b40K3BQF9u2TGNpuu1A9UPvwfSCVsBrPlIQEJp7IE0gOBjQQuegLRyCgGc/lEFb0X8tdqbLbIwur/u1zR7bIKBArGP+voWHZxMbDETdnMBc8aQGGMqiGaF7LNWG9xnLcgUiqYHJqhV60pBWQOfCwwItGdaK/614DmKEehGceVuz14KqpTNQLVoM6ni6gEAmeuGbANKt+DVYoQJdekt8ebu8BzymonTgYJTablM6krLJWOIXbg5AI5UXRT7UkAclKaXoEDe9LCCm0ebd6ngwNakyIKRqevYAZci58Fn2482sgi6tlAokEp7+1ZmLAFGVcwOtgqqgbC3IOV9ZRB0TfUj29rKAhSUXI7OcbHZENMqwLmTLd2zrGOJ/K72Smfx/zh9Kk/hC+ooWf8yLP5/yRLEwBbI2RRh6JXfCRklYgxUCcgRNa7ByxYfgYQz1WO9NsxaNvdrd04+P6psXn4sXbleuAxo2TtutDmjUmW1bQStf94ZXD7uvqENlz73HkAB8BgBg5seulKOdqo9VtQtD3HWwH6hWODNFy2P9D+ylBFIo9U3kZCyVm7zQ5YKN+o1eyaD0lkps1FX6rP21LmHlTEGjst3tsparf62xw1oTW4rnNG5DhwILOtzN6TZj2UFSF0bseci86UOVklt4V7onqG/8OjWek2HrVEvsPyeRGrgIpP2XmAGqHhevK1DpiML31H8WvhJkWUAW4FRqqj04pwJpTDmpNawUrBsFl6BFLY1QSs2AixKk54aLUyqMVTB5uiVDW/UUGjP9yijwwyn6Rj/L2m/8jDXjdTz3uWsF8tWsJiDvviWB1v5nJmopti12IWCWNhe/zy1LXZmb/S3rqzYRxlsgPMhS9+qCnZuN6ApWwdh9f0XJvbSotYI/uZKSqV0Qafmmkml+sF9Xd19cOoc9zS909un2C+XH8RunaJ2ymZTPm5A9PLB20kDa8De3LBeYAfQ+5+tfHQf77J2G/RK0OWUvr/+/FFkVa1EGBilAFscp9ImkZqJ4jVOlpVp43sqMw80+ewpol56vpfPPUPgqbvr2C6XlYP1Z3a2pDfgFrKbaMGo1HNnP/AujO8VFACPETB42Kg+I/teaS2FsUS9haafv2gVp/7KH3wFv/cjL1ThyBactwkBEvqoW6p56KkU5AikzMhRFBRUJmEZTNZbKutKcT7vVosAJw+aN+tQn5amfwW7TJ6S2Lf91MNGR5QToEtQXAiLWljBgrfcFcNGvyJo6l4PEPqxiFvo/vNhqBdoERkAIYg0dGf5uwUCsZS3RHbJQkHMmpY5WC2CQMWq6hEkMESKKV+NA7ipSem6UloRHFGL0lnULPqaxS7sqdHHUOs6WGtbNjbG2SfSstUlqZ875eZCqAqWUc9FP2ihsPr/OPuoi3ibFSnz0OGgYBEHUWSxfrFIvTuVRi8rE/IhY8lC3fSLqPPvn9/33QczwEJgFpTcwIGvb23TG2ydYa16WZUa4VhZndt3oLl1/Lz42QGWufVrcLI+h8tHpXqO3U/NXtKXXT7ofRg9VTlYB0AiZRItGymQ9QtxQI2lJVrsexGcZVvbIVIZlosyr970y0cvG3oGszdczs1jeY7JZJaunWDlAguEVKjGm3jp61o90VPOqyG3DER9r/K5L5jm4MBltBb5OgYDzpT4cylLciwXCT3Aaz8jWyv/e6bGFLzyDYAC4LcBMFjLjx4gODgIZpWyXbNf/tLr+FM/+4P40U9+Dt/2kReqjHRlCQCDl50ly9O3FDYFCqzpObEDCmgNkfqiNU4pryuXAagHrR4GPvZJuYtA+3/r30T/PdYDUxkHv1gQtFr0XcQ0gFqABYQCLUiUiaw5jvpHyRQz2YUkphp/sA6Ecn8ho/mSHYj1qNcPOneHu/qRRX3MGjin6MQtfM+HVyo56MWOjENklMIKzqSlh+r82P7eaE7353XW4HsZ/us9qPFn8OExcyS6jwJlX9gyryIxImk9gshZHTWZUVhp0Bj0jCxAga2hKq8mYFV5YbH2BCzW+xT1WAW9/36l7Bt4+ZcxXOkCPt8G33og2c8bcBdgs2JdmQXW4LJQrNaGCeWSqAUNn1lfrcxoAbvsClX3C9RZ2Qxrz2vngFb7381nfV4edfi51MBngKMyLD0oCJERrEz2NDA29jF5LZPgRbyoltNWC9Y6L/LSiFg8R8eGsRlVlW0V0qp83e/62js4qPvbywVqcqFvZKZMB1WZpmfYqBV3T6yzPaL+lgZ4UmOypKXbjiHULJV55XLoa9P0xa5OVZb0MwIsZbQ3wntUIOBugv4sDKEZSH4P/f0cCPRsznqfXv8G3Qf9eOyBwXr4+rvxEABNyykaV3D3S6/jT/zMy/ixT76Cjz2/THlUPaKvMDEvKsxVC9vqqktE9XWeyuV1OhZo3y/m6UoKvvkrH5V3/qIlKxD8QrEDAQFKsva21sEOXSi8v9+qgyFxAFnt+gAgFEJCowwTYP3pm3shstu/x2vtArNnC6pV0AmADvjWvxUHCaUTvNDuZKirhNr7vT/WfQAcM2FPQAqn/bO9pTUEzeQI3OI72sXX/ajPhAZk2B62PrOdD+4WQ4oK1AB9NlE/iQZViVGJi9zsgpRVEqyr8lXrxMBBFUzEzeqw+ROwWO/ewnDGw78RUku6By/BFCWxR2CblcdLP6un58aVT9bTt9wvG7rXYG60dqWPO6VL5O6wxnTVeS9OnN4xJtLURqO+IzESaQfTjAJJrZ0yYHPmtrYAFszcaGeiuvCYrAupzpcd4HKj7Nf7X+z/pbRsJDIwptS5Ra1T6dYIGjUf2JrvEJJBDE0HbmDBz+80BEwD43IM2I4R2zHi9ibiYoi4GBjbMWATA7ZDwBg0ij5Wg8OrTp65i/4sBd3TEQq1tEYyo8Gf8aRcoGOWoFrZVZbZ3lpr6HPyS1+vyS9iRvBKo1bdMQsdFb0qUd3AW1nGJvSFx/reGadkdE1ktjgBV/RrENDLQI+bcNmht7ytj8vzxXphCebpxF0gtEDDfxZQADzGwKAd2fOjXlY77Xd/7S7+mPW7/tjzNy/qFMnC75aBeu7vgqD1IA8aeyAR9f8FrXgSsMo3t7Gg23ACXVPzV2ncgalIAwIKBtqlQm0LvAQFumB23Fhr1AsHkHWo4zCAOepBdNYAp9mDNbBe+8mcBqvMwUOsAsAoSmJTupoeSoQa4twgVD/a0XYLJFDW+hJJM0X6e+6MzGYIiIHMygqYhthSPG2NY+2+5uCrKz0NVHeNnQ7joTqrwJWvRcIKKSidgnJYuao6XU0umpLYNfU9slKG2NxJHl/i/QmcHu4twbXgJwNgLvm9XLWDF3fdxKI9GhKZJU2wIFs2qrWl52ZqC7yur1GVLGnBrUCmnPiY2u7jRdZK15WVkC+X7lF1OzEgWbXUVAEXa0GopN9rcKQszq6zBcwdKAi2zuaqC4QFCO+ZubP7LxbTZAI+kFLy/twxlwo+1OrXttweRBuYsJszyonz21c5vZgCLgwYrEHBpYGCKdiesHcStfNEp+8hiEDGJjafmFbhaxkYlrLIzsA0tqyt7WmW4LQcs5bV3hnUP26QYeSdNg0cMEeEEK0keUQG1XLZfUpuMTntQaK9jD4nn3VZljL6FAA4FyuxZhkBYAr9anWPV7dh6Vbx13z9TPbBw/TgqfHYAgNguRinFsY3UAh4/dfu4jOffwmvfO+r+Fi3qOd8tFNs37QLSpVCcwZBsPR/OWgo5hns3RNLwWSv2B0sF+ZVwHesAOUDcJhBdpm0YU6C1/D3Dm/ifeFX1djI+sPXkrg8AGEGyqC19UNEiJNaJnUhpDPnpQUgLWVJPcRsf+sCwanCahl4V72uJr7Pz4XSEAYTrKq0Zr8YpCmETrE1JWLVHkPGEDTtazeX6lfshwvjMbjVpVUjt0PAyO6jbcWkmFCtukg4epY1uCFStqGmzNoaFheopYGD2U4Asc4zsgXx2WHztXTrZGJeKBSnh9nmtD4zqri6+dm/TErN13LVDl7MdRNNwaqvnM0H298Szd9l1lrx9Tx3Hes2Q2i0diCMgTH1gIaW1HY9LxU82tnr5i5Qv79eMgMHRgHH5lOoYN3BAWW2gMDza+vBvD0oiNztfafQbgK3RKEqVCFAGGbNygIUjFGLQJXB4zFCZYoOWTvznTq7gQzQDgoILicFABeDgoQL6/txOUYMxkQMrB/OFhCk9vw4dQe9cmOgoOtt4CCtwJhTOSIA3SAPeuNmqCyBgNJeAUE+6HxyAhWVayQFkmZINXTQ5giAQqydTWE9KSi0jpvEETEMkDiggI+aswlQKzmK0CPJZ4JZ/Z2idoueut9zZV5fp/va9chg6eH15yfed81K9nUKPrFK2X+voAB4zIGBj7UgaT/Tf19/4y5+8KeO6RfqPtfXsIMYrB0pdQdz6adHRbCKBageuDVwAI4tbQDHh8pRNtAUvrMCDgYcZZeEctC2wJITSpqBUhowKFkBAABihoQIitoCloYRNIxAsa6BJQMyAiLgOMJr6RPs0heTG+LBd6tnWIEbDyhaKNJzdGHdDL16yDOIA4YwIMSg5YGLljsNuYCmqELuUKr7ZTRf6mZgXB0ythY53+cs98WihkCYhoiLURXYxj4m67i4iaHSnf2HCzV9hnQCIS2F6qLkLtTkpQJQCFqgiDTwLJH6P3vqWPdtWahq7TMOLvSZqjVbrUFvYwzombZWuuomWc9PwUuBYIiqXDMCMrIWKoKZiIBV/CyWddMAAxMWve7HwBgjWaCodv3Tz1zdN650m8JAi43xFt31zpn7y6Svp422oYh9Y2u7z5Ya+Q2u7cB8dn4LcNvtv7Bo6WxusKwyiKJNjdzV2NIMQ3UrtNTnsgjudKDqjMHFGLAxQLBxMDAEbEfrGMp9O3HCwABLBqX5ofdP2NpIs4A5NqBjTJNYDBAeIg8ctFeg7WsoGZQOoHwApYMCgrwHSgLmA2Q+QHKCJAUL4uCzl2chAmboIChLwONUm5hR15a7sgkGIBSo+Z4sjbv1qOAAS8Xfs3FNb3iXKKUiTukNjyEZrOaxnw/rErIY7bV7UPA5fOKjL9TfdqbqGxmPPTBYgIIT1JMu6g8s6Jd2jhvyX/ev58M11pdnIYoab6yb7IrBPqtlThU8dLM8nrsLwz5OwKk1p9nyDKQZkvTylDRD5oMBgxmS1u4ENKEaB1CMoDAoIBhGYJhA4wYUM0SmJRXYgQO1DKiWHvfUwn4obUo1ir5a9D0oyE4VJkDKsXC1dSPSvgOUEyhEcJxQW/cSwEUQEGrxpikQrlLBGAm7QYXmPrUqfafapnpvic2ggVoOEC6jCtqxp2B7xeBKIc+aby+9SLG9t06LHIaqIKgeF42boCL1WZNYbX457eNcF6s6pbgW9KzP0daXzoCX9fzaECAsKnvp3iKDbb4eDLuO4u5TbMdA2BgYULBF2ISAgVHXtIEaZ8Xmk/MWY2MgBeCAgQLygpVpwMuZg/e8tp3/fe2PX89Pz+6JvS8ZCIOBg1Dn528tAKQHUwZUYmjn0gtIrc+t90HxZmneNvzCPrvrYIzqGlGwDITeOndwLnLy7gkHkLBrTIWDHLX3hskCcdqwaNxBAS2cfD2t/lBQkPagtFe5dtgB816BwXxoMi3NtnidTDM3gss0hAgZRgUKwwiKIxAHZRN4AIVoHVCHZXxCJ59Py2bbNdcRDlaztDPanwNZv4qvCbUzAoDmncmKWN2oax3h7//aG6+p/vq+v4ZPPP/xlWyXbxgcPNbAYCHQ5HhzXnvjF/HST71s9Ishre73SMryootZggBovloq2bPL3xQaOnCgkbTtMPQHYznPRun1Pjan1XowIAYGxC6Q/x+KU2/SELbPjhk8RGUMQgSGCTxtQYOCCowbo2pxEhwUo5zB6qd1gdCPBSigRgs/1I8o+RhZExslqzQh5QSKI0IYEZgRrdRr5IIYIjaJMaSCKTE2Vk0uZdQqaOvUsFPKa2PKaxGsZaBjsGehU+BmJVyFGCQBKNpYhzmq9UKkraltu4kAKlbJUAISaSqVlMWJrpXiPN6BO8UVTMGeV1wZPdDVKboCO57fcghAankHADtosSevVZ8Ii4ybegaIas2NwdwHGwNbQ+RjUOD0tiksMtfYjfM2pRvCUN1eTaQ24MX03ta2giyiBUtwcn7Oxqz3nkypGvAijqtbZStl6x1s3XYpa/CkFGyH1m2z/gVpnEl1zXTs1qWtrbMEHlcwEDqr3Oj6Umx9y4l7p7FH4KApvzLUVQ3GHLj7SXluYw4eQRbcBApkfw0cdpD9tRo8+2sFCDkBOaHM6aRMIyYFAswVCCgTOunnFUjQuKqhPaO18a5Mpe1jW5SVwu/ve/3s5/QG3eCHjbkaapT2ut6UWst2d6f67wN4zZmC73sFn3j+hSWQ69g0l9/vZTzWwEDHUtn7eO2N1/HST7+MV7/3FXziIx9D601fOpYgt4u+CHwBaL6uQTAkAsmtq56UAuqTy4nN52UEkFu+1A6D/3w59QY6GpLPQEqq8CsQUDBQv08HyGGPkjLKnFFShqRU6cl+fiEG0BDAMSCMqmhLmkGbbfPnsld5oIZurRVuq2HfCQShBbTtA3Jq6iVQgZau7Vx9ie4mAQRILSaC+nWkYIj/AOQRFEaMcUSMIwYm7ANhSII9A0Nk7BJhlwQXibHvip70qUjLPOQW0Lex1rWR1P9XfbMmYFGSWl29S2chGNDAoQd2SgY4g0LBEAZwsEBTAmYL7gykaVZMhGj5sAuLwalLWgaktjgIU6y926k/zyfo7mYZruYXCZQJZG1jGQUcuVKokUXXvNbuOLZqT9Xd2MRg66ksR6xra9ZsnpWBqW6mh827A17BBH0f6OUBsxZ09ihr22f9DAsG5sz8Sj6990QAJz23UoAIDB3wck8GQZWtnsOCIdLJ9tDrM1sVfwdmB9YMKj3LFgfRA4J0aK5IyUtZ5oolKoCRMABF/fUC1GfSmB/rEUJUg5GZcVIWOChYxGWUbAHTM5AOqhznfQUFZfcAsruusi0fZpVrc0ZO+UiuMRMoRnAMYJNvNE6gaG7SODSQ4N/HEYjRlLJ1RO2Vdz96hb8GBSd0wnp+RzrBlT+MMahyIjTAW9lmWeqv51cNl0RW4GANPh8+HltgcLQUXe7ra2+8jpc+/0P4iU/9OD7x/McWm4fKEpTGEKwC+QAA+ytFrWnW4JSSFbl6XYD+ve0wkFFcjVI2C7hHi6ERb5LdL1X0tQ0lV/+aswWHvfrf0gFlTsj7GeUwI9vFKSlpIFhpKXrEBDAjM4NjBA0BYUyI24yw0WcoMAXugXMcIDkAHIE8QzMF4rFAoI5ENVDQCpX4z6QDW2kVYGT+xJwsCii1NQHUb9hZAsgHIIxAVuaAhg1CjNpJLRMOuWDggE0EUg7KGghq7vI6bbGvEeEZCWtrdoy0pGFXVtdRjAGg6L8Y4xGiJnebMAmmxNzCZSIDBbDiLMcNahZBXFjmf6tixWlGJidTrh0YxmlGxmNMQhxBIVQggGzWN4DIEbucMWTCzK3A13pdPa/bC3FtLJBvHRnffN6HNu8zFm2L6wlqkRstXGncMGDgaMBG1ywUrV/CFnDoMT/rte2L7ZykvdcxPpXRMHZv9YLCGrND5lIgEUiIGOKk80t2V0hdYkRBXVq5YB/loec1EjT+pTuvziIMDHBJ4P2uAQI/sz2z2N01+F1LrAF9EMBaSlM9L+bGCRpcWshqFrD2jjglC44MhJJUnkhuMiDNyhL0oOCwQ97tkK4PyIf5obKNe9kWA3g8IEwDeIigOKp7wcFCHBZxVgjx0eWyf3ZAWHILvF2xGQKo7GI2VoIUlIQIjlZkOx8qAIPFc5AIxHyvd2/SX74vDg78nADviTV4bIGBjhXlaKDg076oH+2QVof4FqCgu/hUTCkDkKt7kJQqpSVuyeS8KPIDoFKDCAEeJOXggNjjDeywAPAgGmcnKuDwaFwHJB5DkA7I+3ZZ0mGGpIy8P6DMGVJE0fXqkHIM5kpQtqD/f+0LxWolhFjjEKoQpKAXmkMVCNwJBA+sJXv+RdCRr3fJlYZVgamgQPbqT9RnszXuBS3bpYl6gTFMoGkD5BEIo1pEcQIPGwxDwMABc1ErduaCKcTWSljKUWBzzT+mVifivOJqwrUCx7XlZS/MHintdGzJOn+ZAJGaFuquBWUM1GUZhSyTpZsrltSsB1Cesmar4nI3lLvGjgLMzCoJg96DkAFRC5fjhJGt2yTc+tMugUwBOXguOB8BGVe0NR3R6Hl3d7SYAqPm094U74pF6iwyX4PKIBFZZcug2h/G/gUFXggEKq39t5buPb+uMCamz+ioDJGBAsr75bp61Pxq7ynYfaHQ1laKra0gxBFTH0xrLjEtxhSwkVYPZX1eT8WXOPMyBjurh52uaQUEB8h+p0zjQ+4ZRXU1ohTQtKmggEpCdYtyWLAGXmwKIhUQqEhxWdAbCAlUioE/lbfVPTqr4dODgrTbo+xnZQ1mlWslLYEYMZt8I/AQUKYRdJgRD4MZQQeEaVQDxAKuXUFTHIyB4mrhy00yuQMFvR4AcFoX9HrAXbgxQoZJ1yjtIRz1VIqVnGeACvCLb34Bn/6ZP4af+NRnl/qrfw8pDTB/g6zBYwkMzrEFN4IC/1vzHbXc/1WU/2EHACh+qVxRm3Ur0g5MfXnfJFY/ozgSZUXiflAA6GEBGtpbHTYRUSVkgGDNEJTDXAFBSe0Dq3bDAFDs4oQyQHJBBJCZQYE0CCdESNjppUmz0vrBn60DUhRaWjP08HtN9T6dpwkDLClhcyU4U7AIMpr3BoiW2RTUIW0aJt2XcVMBAiUNUKIwYDNsMIaAORDmQq3AiWjhkxojhLpNi3SqnppfuA7yrJRnz3SYgD0lZMX2m+KgDFNQpaBnbqxKbAgDQiDkQjXHvtbUX51zF7RH6at9QFkFBXNLXbV0LzkRkEshqiuBAkSGulcLC5cJnK1yIAky65rWSqAn5gpqRW3W/vqjQLgTNLevLWTVgqpLtz1a156VsXTbbMCrCLT1czmeq7t1Gu3dxWk42LKo+fW6Fme6uv2XTgmomyY2cNixRhzGGqCbmGqhnVI0z77H9u6d6AsEBV6B18NO5zbvloDgsFOF6/76jvms0f0xKgBwyt0vx6BrjsLKHlaQG2q6K1WDlRpb6y4doLkpq1vIgVJROeMy7rAD5gPKQQ2ffJhR9jPStbpK88Fip9bAgAg5KDjgFFDmDB4CJGXwOCjbMGeEKYGHqPsYIiQOx8q7k8U65cYYwFPAXeb3OsB/pzunYjoAngnm7zlMIJNx5bAHj/bnttEE4Bd//Zfw6Z/9n+Mn/tB/2gIN1y6OfvwzsAaPJTA4Ne7+2munQYGPo6ARMStWrRUHBXLY669f39cD0aXPuFUuWRYClyodRRYYY8ExIejh6IJPpNbZXCLUik4dgNj79SxB2c8oKSHvU/0/5KIuhXJMQVIuCDHUyl75MIMCQ2JAiQncuS4ot2wBWfnP/eh5Z8qMBg760Y5wY2YaW2CC3wHBvvkTURVtB7YcZHl65bgBDdfAYdsBBGUOJM/KIMQRYwzaj75ITQsT4KhYjCvdRa0FS+miNDdLcU3H9mlUvRR3RihEiGVUeL67ODUep7quXF0LQClU60OcVWAezNlT3Ec54Apcis+zd4GhubrEQVccqxKrAM7SVkMcwSEgECERkIS0WA+30tM95FgwRitqXrtS3pCm1sXU3DRnsUDU9bpKKM1iDYNatkHXNYtaYzetKwPLOA0DhmvXV8npkfa/rm3MCrws68lpaU/FjQXI7OXIbz6nXsyqruV8MIZgr4F8PSCwQD457BrrKMfzdUrd2T1bQHUrUlCfubMg7D53WhgJdU09jqL7uqqsTu5Wg8FdpTXdOqkyd3DQyb6z8o0JyMomKCNqij0XyDQgAhApCKWAc1ZgmQ76zK687ZkBHMniCgJy0hoxFtztOgD2+m0NmtHFMTS3hRte01Z/8bBTVmtShkso4LWv/Ar+yM//G/jrn/wxvPiR7zhS+uuxYA2+gfHYAYO2VE1xLWIKToECr9Th56oGIJoPvFL2s1qwwCMH+bW3YCv76kwBVSrfMwNqdCoamKhP0zERZdaL4sE37jooKXVMQaqgoCQ9qOu2oWQVtgKAYodV7G84d24Yac9D/n3/fyfO5xoUHO9P/cW63tUt42BrFTvhl26xpnbB6LBTl8L+GjxtgcMWtLkABrPw0h4SJ0gYMIYBQxxqqVTPWa6vW333vVLISws2HUBmKXq9CMz7JSW7elFxajanSs3yqGCg+m1DWfjHA4fKCpQ+kMsW010ztTZBH09wKm6jO8tNgRntaa4tEOsc06xZKj5HGWqQFZUMiaMGJjJpIUFS370qsePKfGslxp3Lo65t2ut+9SxdR3cvqNt+ziEqo1EKSEZgaBZS/expdv+81jUfTs7xkfbfYmd4VOUiZbD55ZppwxwQOWAAoZgL5Oic0okiZ5XJmmsQn+yu1F9voKCycp1B098rl0kUR7NaGZgP6ovPSZXaOgj01P2u11yaS3X5H/a5LOTM+v8kiwKAXCCm7B0UPJJ8M2vcS81TYCSoAsz2nlxEM7RKqezucqpd3IDJYZdJJWUFA/79KgPsdFBkVvklBYhje580K5M8H4CB8Npv/BL+8C/8Gfz1P/gf4xMf+VhlEW5kCxYTb+6ER2UNHjtgsB6vvXG3i978GAAcIak+NaeOLsJ0EfhnwTl9dGzeq0DIKUPs8PZV3wCAmVGCHTan6qMGeXEMKClpoIwzCWcyFOphzKIfHuxoTQEqKi6tNeti2KEvRRDOlN48+v1TowZ9tTzZtZXoP3OBUIBFTnOzKwQO5KrCmg8VFNT4iSzNpQBUkMXxAB526jOMI/J8AM8H0GGvPtFpC4xbFZRhgMQJxFEt8jCYhO39cLb3Rbpo856STzW/uiSb56GlUEmeFz7Gnpr0PHwYyCrAgjbslZn+sMCLIqn/ttsC/72uNsEpUFDBiysvUwgLN5W/XQ0yjVWJlZwsetutcDFLUpVmCIP21GCtR18szgSrZ3FFvChqtfbVO8sx71FcgZ2K5enPIYdqqdMw6u846PoXta4rUNADLnd3uOtvEWMUBl3blEDjhCIFFLOyBx6QG6IaJXZGiaMWv6ETZ1REGSx3e3bACodrC+BTpqDsrwFj4np3I9aAO0ZQIISi6ajq47ZS6WlW12Itv9Pf4+PvjtyXMIBGXiaa2j6eGufkz9GvSfv9LiPM5SA6uQiovKQYTLZrpL+wKnt1LZywuC12RGO2koIRW7/s4MrY4ht1QMwIMaCkiDDFJhN93n7WQ8Rrv/6r+MN/58/jJ//A/0WZgtU4p8v+WdgC4DEHBq+9cVfrFHzvK/jER19YIqUOvYrXePMyfuthVJqkuRXUyKn676XS9xoh6/4uV9YAIIGPomVTUSq/AGAEFCRF6gDIT8t645kUAAbS6m6FAWRQUJ+fU2jE6oB0hCxZwEwo4Fo4pbbnDS3IBujQdnVx+O9zc2z63IhrZTCB6Yp1NL40wXuEVo0tcGtPhVSusRsOCspeYyj6i0cd0AoxIB9mDSrKCWXeq3th3iqtOh1A4wYYpupPJKt+BnQuHCuUU632VWbKkfXdWV7VV9u7PZzSy6bAalxFc8NUcODCEp0yczqcBV5Webl2PXgpJ5kCB1s9KDjyK3cCVaw4TFVibqGXApoMwFULt5XM1n4aAQKCMK2NcPuQDmh1MRDGwPTBp9IzHH2RLqDNt4vs7t8LHCCJq3/2n/e6rkHBTcXEqpDOrMGcFgtRXXNF51Kj/q0MOVmhHfLASgcH7sIrGuvTl0CnbGfSXQb+YW6DvNs9/D4lVVw+AjEozRrFb3eUxNylK6NKus9r15eISlrtvWHgsE/ZJpdZ3KxiZ7CC9q1wt6eyRDpXb+oF4Ei+9Z1Tq1xkVnlpr0OhsQt9y/LFeQAgOS/cxVX22zquMyR6+U+BkZFBMYCzAogAoNR4rlSzrxx8vfYbfw9/+At/AT/5P/0/4xMf/ra2li53F3VwdMH/WQGBj8cKGPSqvoKC71vleZ5EqEXBAZVG0axoL/X/taZHihyL+r2KXhIHBWKd+3o/qJRsbICgREV2lBmFBaEUiBBoZU/XOTID1pXEy62L9iBGgW5igqentcNY7PKIXR4psuj9EAYN4uMYunzfWKnEGhzpgVNxQHELjVr1tiKoaV+1f7mfYlJlUEj95EXQWT+8FAy60IsPX+OcssZQdMCrLlMMkCGCPD1zzohbt0T1w+MllL5NkKxlUCnN5v9dsjSLvGRXDKbQFq6DU5XYev9jPXZKd0vJQMgKUmyNj5QYMQizKbGh0eA1j5mX5YwX6bWukMwN1lvbqUtxXc0VAKrUtfO6UGKuDIDO/dFbuAOIUo2ZOSW0vEaI11NYMDCrAN+jwLguwLeObHP19CxfE2c9nA6Odt4KgyhbRH3HIJCC67qu3d4vQEGfOdOBrQq6DrtFiu3R/gtXOdJT55Xe7V1LkqGV+HKTR+vzWdPjTqyfuw0cFBz2SNcHlMPcspbmdPIucZdeLTEc3cn2y7y8x2huOXV7HMsCwIBYUZkQPHWbWiEfttRBz4rw7AAyg4qH1OTrKqia69S02yS5bOtkXBgiKAbEcag1XHq2tnfpopQFGGvHWe9N0Vzik/Jfr6eDOKmgw+V/KQKaMyQGSPZS1HpXfvGf/t/xR371f4ef/M5/H5/40O9d6iXPBDlyJ6xcMEdDTzxBjeabxmMFDHxoRagfUFCwbj1ZF5JWPysmdK2Iz1phnRg1wKQo4oZ9XlcYPCp45MNQsPuzqs88mCLuDyhgzrICBAvcYwLHiEwzIjMkJRAxSkw1CNEvN1lmQnstZxcYYRz0wkwjwhTB44AwdVXBLGDKK6CJCV3hYGAANZCvGGNQZYHYWhdBYYDEUhupK/oCWKEPLNZcmz5ZQE/S3OCyP67gSLO6cCha1HHKGlSUMoIh+OLBjhY1rtR4rs9T8+AXG2zK1oWwR5z3lLyDAgcLpcsF7xSVt0oCIoRF87btnC2U2BBUqQupNWlKDCQ6T8kKYIGl8qq1E5yBMTeYK9YukAs5V1CwDurU12WQ08u9woUxHFIsyM9kvniedajZDEdCq7IwXYGlHiDM+6VbZt4vAcypADmfa4j6O0QQSuZaaEXEKEZlB3xtSqrpX+KHlGhRrXBZ7bR9DZHTYCs7+1IWLsdqbRIDGS03vWSguZX1bJSsVHvI1YcvhW09+84a3dn0ImzzAcWDducD5HC9yP3Ph7kCAwcEnupX58AMygWYBgXZMZjLMrf3tj1t97UpKpcFxYIl8xlZQFYp1Ysjstd04aBBjcRV7ogFaoZJ46fUiBoXLlIp6hZYy7eajh2DyrWoH2GK1c8fJq2QWasknpG5IqVmU2iWpli/iIIcSC2z1VjrAPtrNdKyu29C1SM+XvvaP8Sn//P/I37yxf8Dft8Hv2VRJXddWv9YjwEnWe/+9d/QiokfxUfP/s5jBwwqKPj+z2lKRz+6hVwCWe18pv3bTfh4TjdceCtouPvW/1v/JgSlnxzNOpUfnFjnBWOgf8NGezUg4Ae3VudyRWxFfGpqXj/EA6KSWRkJ5XDQOIUpV5owTPmsEPB5ERN4GhAGBQQ8DXpZrEIYDVMtAuLMgZedBQXkAq3dXkS/hrWaXqQoGVIVRc7ChL7sKJF5Mns6vxu9/7CvydAAl37PuSmOhAbcNFq/qEIs2yq84VHsVtmtRh/362wKtiqDlZ++UcelgQLzRbaRNW7ErWVmIJOl0jWavgl7619PpEoskCnrbAK5o5MXqV5mrnn+c6V7TWEZfS32XH2aVT9fj3HpvNlLa7XuKCzqX5UYhaguN4tcXzMGTp3rXnSgwDM6elDgwYYOCk6sq9YGAASa5SHOStT1lApIpFuXuq+k7jjxwOO1G8nTlisrkwwclMV6QsTOVm4A5twZyAmECFCXgruK0q9MRtRa97Uy0+JS5HYu12u3ch14QaBTqcx6NYrJAgHHUO9QKbLkMBcMJimoJm5le+1vtI1xYw97WaC5+XqCMmB1OFopa4QIlKDyZpz0vtgZCJOuLRfRoMGYKhN6utR7AMXGEIQYwNNQGQIex9pwyeXaWta6q42cOaC0YF1LKSAWgERjCNhlfzamuOkAZSScie1ZWWMqTP59+j//t/GT3/a/xSc+8C1+y2z/uZObYaXDnAto5/jUqGWUv/9V/Mjf+JGTvwM8RsDA6ZHWZerFhcXmv+WLufB92d8zBbXmOJu/3nNZ9fC/9vX/F176f/779TU5RpSUwUwQ8x2VuqJWaMXf2Wkto8PWqLUeUi+2EYI2ADGqrWccFgU1stYXCOMMtjSvsrHaBikj3hAUyX5AjVZTpmAAbzbAtLUUwBEYN8oYBI3qF1YaPgNVCDgocMHQ9sWAgpMywRouMYGqQOBKGyLGSut7tTGNjcCCnhP345mLRuX3cbBlH5l8FK9hr0Uy6j6H0iwh/x2R1uK1txIdBLhCkOY6cIWwqMQGGNvD9hnwILmarrkeNSpbFobBAhSsacNFlseJ1wSan95eywOq2sigAFNivhDUUrc6a5xDAPIMCVDayOn4M8BgAYAs97+cYjU8nmcFCk7NsxLIJeOUSJOcm7A8sV7qvqFjYdqv/4k1bOewi3voP2N5BiRnUOQa36BAomc5UouPgIIuioMp0/bWffGsGtvQgwJjspwp6EGBpzL3d8gmbZ9D/Vn11Xc5/RqYGBf3FhZXkooegUXthX75RAufqeFs3QhFvw3BXFZWbZNi1rTjNAN5o/EJorVWlBUNoMMMGeKNci3EsJSzg8lYZ0O90dIZOUu9nM3KzGgtkgTig7+hGju+kqWouzef1wH6mSw7LS4Y6p/45r+AT3zgX0OtvMgOwFirZ1KoMV11rqgQwsAk+0PU32lG86vHTPpqPDbAoEdCNSVxoQiWoGDh+9L/NqO1Q6+WYsRxwC9+9R/i0//gf4+f+D3/K/yBX/lfg4YRLJoD2w9KGcJ8MiK1L89JVm0wTGM9qJ6PXwuheGU/P1zOPHTAQIMiraiOFVyi+YCwbQWQamqjpdOsJtZ8b+4+mLbaSGlzAdpegqYNJE6W1zxC4oRCAalo1TutIOjCoLmqAVh1OSBCQKwAIgBIRBg41Mhr4dh8i+a6QIqt9gNxVbA9Egdc+JpSLmpB5Dmpa2HOKMTgOFd/JXJHN/sx8HztDpBIzpZKZqCgL2xySpH7GQjmM1wHMp1yJ9X/C+f/772Mzg1GIUBOUJyPMtZKbGGNO+thsREUB3WNcLGyv3DeePWiTs1bUJ+UY1/9mtVYga1vaEk8y+dE7NA3PG7aL+ZKa/dnoC+rezRK7vzaBizhLpIbzmSXCdFcRVZXZdaYGylFmcM+z34BCmx+NbaAGqMZqN1HN1zigOKti1lLUCcxtsA6P5YiSEbE9BJHBGCIkgZut5EaCRwnBbvFO7o2N5bZFQjE4OGAvA/gcahZAev193RwjUkwINBXOBymJmPXZeoBeNZQZYFMxtLYii6xpXRqgLTKqZwCeM4onE7qgB6w8DSYG7fNCwB+3/u/uVvnfm76UaDG2KkU2/atSTbTge8FFACPETBooOBFLFcMOAIFBUski2aQaaObqKyBde/7z37j7+MPf/Ev4Ce/7X+D7/z9f85e/Qf+RT/S/9+P7b+A1/znE1P7O8PH76zn74x/EWN9rqb/n8zi8RsCIP3PFIDVmC5naTlCQOamOaG/ZGl7ODhYuNcfARQAjxEwOI+EjpkCBwWyDoxhawLCEWC1Kl/7jb+neaS/79/FJ57+H/1LeprfGb8zfmf8zvid8dtyGJtR+9RUtiC2AM/S6S77txhJ0DMHd994/T2DAuAxAgbnQIGPCgoMaRVZ+r9Iw62RQObWGfDar72GT/+tH8ZPftdfwu97378K2V8j/ZevY/i/voDdd/+U9grvivD0BUMWo2YbkLY27tp/0rjRj2lTq8wpQhya/84PxhFO96Ao83N3jVz6Er3niq/U4T5E6ztAMVZ3ggSLLRgmIIwowwaFI2ZzI8zZXAlFkAx99f3iAytu9QYvXstdu74BIR9A8zXocA2aryG7B5CreyhX9yBX95Af3Mf8YIe02yNd75E9kOpE/wctJMS1hbQ3h+JpQNxMCFNE3G6UVvTOakH7tMOi1yutWA+OVH9yrRTY56v3EfN5WRq1zqsGHnVNU0JcvD8NowqEQecGjsdn4BQV3gf1Lc7Aca2Fsr9etOZGTovqbX109FH5bg+KHcaapbKgY/1nVk72FG1e/eJdQNnaDbYuM36qzOx6fr3f2ClZnrb/UtZ0nZXSilw9wjmwFNaFr9u7+vnnc+fRq0D272/7mq53yPt01GzoUe9N2I6I2wlxM2G43CBc3gJd3AZf3AZd3AZtLiHDFjJuIcMWOYw4ZOCQC2ZzJ3jzL4EGJ/uIVgLzbHOyksDe12Heg7L2JGnFmVJLY71JjpnrY9lSebB9/8Zla18srPWc2C0q4Xoxtpt0wcJ1O4wgk7fjT/0BzO668RROi+kq0KZqxcq597qLrUaEvxsT8Pop9/ojjscGGByPZfaBxxT0oGCBukQDkGIRZBBe/9IX8dLP/DH89U/+KD7x/m+GpD14tEApoAp3r2/OsR1QkaWgIeKTPcF7UEDTFhgmlDAAQf1hEvRw1KjfXoh1eeHS5bHDc8NjPiorSx7p7T7KVZOfKuQdsfb5ssBRYF57PtRAogJYBbwTfpr6+4/u56WutOl6iAhKl6LEQdOthAnFajeUUsBWMVFi0MpmHlCaWsMqioOuDR+nlrbCS8VAQj4OOiwF63rpy6HZExqtX1Cj0okXAYhSinamq+lxFilfLIDTo+d1Yjr9PjreUupIxIqt5MV83Hff5tuUbp93L5aSRaFocJQV9uozEgRokfml1EBNxAGSTzgxpNR+B4vW4X2tghUo8ApzR82esnudLR7Cggu9eZf8S1pTcSVdSv141HPQsqA8CNGCOgEQlWWlxtVZhMVnVIBqoKTMtl4padGd0pUQLqWCgqN7c7xbBr7ew12tGUj+g+W3wV6L0WTGqSFWz6PP16eg3Qfrn3DQdYvt+1rOu+u6yuNUFSus3XmTqw68+LRsdVBggbKSLSh6ndK4GlqC2eJEbtAFNWajMxYAgEfXA84a6HyzgYJ0hu2G1FJ9eO2Nu/jBn3ppxaSfiPs5Mx5TYLAEBX3craxAQX9vmZUx+MKv3cUf/fxLePX7XsGLH/xWSNprXjdnKwcKyzOWFpQVNRLVA+OWaSrHVg1N2/ZhFfnEAYHV9EcYtGaApaZUg6kLQQ3kX4od3tlamFrrzpJAHIFpU3s+IM0tyKdPqeTusMYBQp1F5YLzpsYd9g9JEwLr3z4tgrhKiprC02Um1BoPzCjslmIBSIuY1Pc3y0ef5/h9atCWD49iF6uxzzqXvu5EL4jtRc4+/2ItVgGSbRItJ3oxjy69zqPoSchS3zRSi8QEZV9XoJ9X/Xr1vn1q3ZmgyRbkZ1H0wd47szXI4VoLgHJqSoutmQ7sZ73A7AP0/H171sWfuStgVFP9XLF2oGA9t6OCYP4+fcriau0tHUTXlNCazazXVNp+VACeO7CwBta+hmfSxE7WMTk1aiaEBkL1a9p6CHQ1Jvq1td85AlHo7oOB5/W9oXqvLJ3aA377jAQvfFa1+vEzMQi5U0CElSygY5mwnGiXclcDaTWNu8mqACrxWHZZBUxntkBBFSybERcmBQNhUCbU3Mwn5aq+rfZJKVkrUPJs2TjeSMqzMo5ZpxASiJf9XU4ycHFQ4zAOVbcIR4CHLrYg1Ayw5IbtQm+pPgMTSASvvfk6fuinX8Lnvv9VvPDRFzuA+egg7zEFBsvRswWC9lmDELte7IXwxS+/hj/xMy/jx7/3Vbzwu15UxVoKEKwT2qCXsAKEkjshybXPuuS8qKK1oI/HbQMH4wYYN5AwtZTAqPX8C7T2fMqNOlpTgESwVrYE5hExjNb0Z6gd9pxFII5avc+tjeJpUwW19CizgQKu+bL10p0o+MQE5EfTlbrElr4Ij/2ohY64Q+NdmduDRhiHGCBd1TN9Ldtct368ToRXbwwKJrDIbDihuByQkIOKTmiylWctsIw3F7ChKohq/QXP2VuCi1bHwus2rNaxj4Z+yHikrmnrPObOMgKHmlVAIWhdCX8GKWfZmXNDSj5h5aZm0dVf7IDomn15xNHPjbq96nPAF8+5XtOHrRs6oPWwwd1adnNSMAsD5Kv02N6V4MV0fK7hxFlkPj6Llk+/OIvdPBbnPKiSl8CgIpZenbTK64k701c/Deb6BLHR2rSyrrla9FWviqC8B6sUWPrDuwdt71eKAlPRqpCSrEOlWGGvE3KruguqxT1qV9Ogxk5yXFceLlcjB4RgPVXSXkEK76vcWkyfAxCugYNmPnFY1gk50gfezrpzJQLoYguGRQZYEqmgwMGXZk5oQzIR4PUv3cUf/fwP4JXvfRUff/7RYwrW47EFBjexBV6qs2BZgOOXvnwXf+rnXsZ/8snP4Ts+8gJyAZi1+hZgtGSwcmXTtlXCAvQge8oVsDi0fhC8LoC6DUbQ5sJYghEybPTgDhsUCpgLzFcnixoBvdhSSq61sg3kPdkDhhjAUUAp1rKzDhCkzAoSJFuBnG7dyIrrVFBggqBa9cfC1ddBOnfCw9SL7oGWRBXPIaYARK/lMGnHvWFEGNU3SikgiAEyq3u+Lj3tlk+NL3AhN5jQGzofv/t1vV7FmRgDMiUnybrKAZq6FGOj83IrB0wnaggsSq06K2JpWlX4eyXEECpoai9gVmOX7wwDCYTS7Y0ApPUYvP4G1ffU9XXXk1BTCgUJtKrC1urHW6roDYr1kS3i7vfPgoKamqq1Jc7Ny8vYVrDle9pbt/a1uJXbpy06Vd2vL9DqMEiua061QFGzDntLVZjNRcFAgXbpy9rvoM27Uwyr+AL0vvH+LAKL80jcxRiw1ToB6p7yUCprEEo5Qu3n7szC7z20dGoatAAYDZauHDWPXplMXcOyUqxH29l97W4EXv2833tnI8QBjgSAi2LdYQSfkFmgoKl9PSDgqIDAjKy5ANliotYFmNZyVbuAEgKLyVTCEDfaDn1mZWFn6vbKgAmzyvHDrpXw7kEM0OKM+liYaVubfoEjJI6qdzjWInKuD9ZFo8wPjl968zX88Z95GZ/91OfwMXMf9Cv1XiD/YwsMfJxlC1aH4pe/9Dp++Odexl/9nlfwez/8AlJRXyUzteIbcayKlKdtVYAMKIr1oKp+eKEiC4RpwGDS2gBxskMwQeIGswBzklofYC6GELtAnv5QBLtHAQJmBQYDE2b7HOMGIYp2XeMIhATKVrO/ZIiX/BWpAsjTZKr1tXInPNQmoNO/VHyt/L8FNQior6ZIw6gxEXGEDNrrIDpLYDnWXva5b1bilSXBvKiHXgs3jYbMnbmJYwtS8jxtXiqKanmXUgHCwvJ2y8nZIqPDydoq62Ex69aVgVsM/p5ktQC8DGxnnR0psd4XSspa9C5yFECogHr61Xsd1DOZG0XNrP0kXGFwR6nbvPuW4BVUhahnm7tz0injavHVzc+qyHzvi+49iWiraTQmV6AArLfe1/M6GcwZYgsmdZ9t5xZr59qsyvV66tPaPFZgi7Xwj1ZY7Jp+uXL3+RO/5zNA3J6jAoJHOYuJFzS272kwhZrtczpoxb0crWT3iTvTlwv2YF0aJ7Vqo4ODscmF7r66y8M/znIuD9FMVWQs3Ai2V/ZzEuMkTF4tZBWHI0CQrfCSB0W6XPV6KyKnZCqBsiCQytTBgiQzE4YQMIyXQNoBRKBkLs8N2V2mdu7ystNqHXbPq/ugC/QGoPqgKyJXg7sNFOQOGLFosfVf+tJr+FM/+4P4sT/0OXzs+Reblwnqbn6v47EEBo/CFqD72S+9+Tr+zZ9/Gf/Rd7+Cb/3wC5WdJgK4AIviG+7vDAN4MuvYL3ZOGr3vlpBba654Bo9CtXiCQcGAxBEljDgU4JAFKQv2pSDb93Mu9WBkWR1iohrdqxG+jJkFQyDM1EX8xg04WAlajkBJtdCMB1DW0VF54hZRFZ6nTxkRNZ/nQ4b1HdEgHSgrQ5QsmNPcNdNWLSARlJLBsN7pLvRSAM/xyJ9KxIsiUh5pvQ76rECtL9wSYisk41ZhX2XOwJ+3I/agr0qbp1lru/f+bbc+gaqQFhHoBlKaFeEWWVwqMY7VkloqMhOQ0kdvDPXs89gYnOZrhFYrTF66mOH9N47Ogc17YeF2vtwjC3ddKMbdGuoIVWFqVm9dE9LqfwS0csqmXHmtFP01T1jdPShwarYGn7k1+Y2sZ4EFCgoQlVGQUkAjgIMvdLA1NTfNTfvv67cGBKGBghpIF8LiLAKwHiJ6Dr1TqLMHctgBxBYnQMiBQIMW+mJrMnbTnemrn9K4UYt22gLGclaa285o0eWpufWPOs4HILtxoj1rxMCk/pCXc68MnO2nA4IwVNbVM6eyeAYVFvL0YTJ1CIwcgMCCwqzPGQhj3OjZpGCuNGpxB3GwYGZt5LYI9D6hE/rsLwBHReSS6SMHBYvQGQZ+5c27+Dd+/mX86Pco013srBSiaogtV/fh47EEBotx6rBK+/EvGyj4i9/9Cr7lw+o+EJLqI1Dai0BG77jy802sSHE+oPYv8HEiIKa6DpwlGDbIFHBIgkMRAwKCfRbsUtavS8GcBKmU2qQEUPcBExCZERkYAmMKjCESNiUgsiCLIt3MWmkwxgBia3WbA8BeA16OhG9L5+p8i91goNY7LxA4q3VuG8SYG48zKAXIpLXS1W9oLXylgCePANegzsKhVj0r84DQV3Pshlc8I+ajqpI1xW7ovq8WZbNM+ucUABSUUSGvAmjBcjRYIJ4LgBDNksxtLd1a6IK4YFkQC8DYBUwtIqi7dCrp4iCWi2sWrLsiqFHggIKDGoE97/U1ZrfIaAlm1j72Tvj2gGCRxdJbuE7/nplnBaGnGBhXdN/gnGqqre9xbOW7j9aTQl2vk+tZivao8PXMymhThu4XdE0JWFjuFIa2//3e27xP7n/HINSzaHtT1mdRChCGRawQrCmYzAcDSntQUhlDowYc5/0BoZQb78yiSqBXPx03NZWuhLEF7tkZ0GKqYgaX1Dt+DiMQGg5z6LXegRprpNF/CspsX+pvV6bOQIrHEHA05a/sQEupBnY5Y06CfS4GDnCjTB0iYWLGEAibGFBCwSCEIoTChDGOCHYGtV11qMC/nuUu0LsOvz+WRqtndKqVD50xyGVZWdYZA78NDNVfP/zzL+OvfvIVfMdHTsQULKyBRx+PHTDoD2RjBo7/XwT44pd0Uf/id7+C3/uhF4Bu0bMISJQxmAGACEMYm5CKQ6O3QFrO12vq26gCMhqadf9XnCoomIXssCozsC/ALmXskgKDfSo4pKJMghQNmumpJEO3YyREYkyRMSXCHAWbyCiirZ0LM0oECgiRIwIHgGeNOyCz3dc+X7uc1T97Ypz6KQO12MZibwzxEmn1LgaswxppcI/VSvfmP7y91P1gBhNBYgQOe4SxtVE+aqqzprzXtQL6NCZTSMWt894PXSft5XnFvs76euuSvp11UANQV1Hxi6CyFVNxlGMdYhN69e+61tAdQ1M7BxZrBkZJFWZheDMm98NLiJofHmMr85rTeTDjZ7y3ZLvPcMbA2Ba1bqn+Xb+Oel6ksjCei06hWztj3d7TnHqL21JtES2jh1uRmGpVrgPpTq0n6XuT+7qJNKUQpDUNBuqCDTvL3QJ6b9z7bu3WwLR0QPzUeaxNsUSOzmI57EHzXnsLrGosxDHd2DTrqL5GbJlTvL3sgvga2Cqg2lm1ujpFThOHdAwA0E7Kif8gYw2MwWMAfRZKD5YNrPRuA3XHFswF2KeCXSoqT7NgnwqSFBySK9ulPGUGIjHGRNhHlalJgCSEDYKCoKAuJ3UtXMDdHjVrgaMaXH16eHcWaoB3GJTJio0xkDAiSXN/ZLFy8ytW5pe/8jr+zM+/jL/8B1/Bt3/4OKZADBQUae6ER8UJjx0wAJZuhMXP+kX90l388M+9jL/03a/gWz/0QvXfB9bDrhpJPHtFYwkiIUTfvEkPQtKDIJL1MJyg5F0g1ZSZOLR4gizYJ2MFMnA9ZzyYM67nYgCh4JAL9klRbi6yOBxMwGiHNzJjkxlzZKPJAlIRbIeAgoKSFOlK0LS/IWhhH3jt+hZK6a/eBNORL7YbZOsjgJzyKIgCBcaSNXA/nzIyATB3TQVvxKALan7iw17RdE6gpG1uF82H3IJcx3X0bhwDaeVckZP1M4odBk9d80h6j8/ogjgrSDDqcB3YqWtFC2XaB3M51S01ILKrYeHsjWVz+LovFznDesKqcmHLly9BXUccVFiFWAFBZTw6y3zdFXQBZlbz7/sQtLoXvJ5cm6On9HGscQR1T63Oga/lI89pAbAchNud48bAOGsi7IF9tJQTdo7FYgq0m6V1u3RwlWd7xlmp4xhr500axlqn4ejpOxdLAwNd0FzvJ+cOGNx0JrviO1IUJGDaqDJKB5B3ADXWwP3dZ+9Nz7yMkzYxmjbGFCgD4TS3rDqrrtkCE58njz/b57Maqsoc0Q+sAoIdEHRgbza63YssKVOg8tRBwa4ChIJUSjW61vI0mAthiowxMTaRMUdBGtgCIFWeGloBAmEYtvXeEe8h2e9cqmxjez5e7LnEEZqBoIxBhmaiLdwd5kpwPfX3f/0L+DN/S93f3/bhZfEiMTm8cpDpW59Z8vV4LIFBP04FwvzSl+7iT/3sy/jLFlOQuwCUlNWijUygotkADE1foYwKvSQOIC9KkrXaoLDT8R0uWwfFRHUnzAXYG7J1UHD/kHA9Z1ylgquDHuTrOWM3K3Mwp4IirVEJW6DMEAgxBGwGRhoDDoUNFLRAFTGk66syCAFB2QOQWz+50tI+zhU1ApwWpCNBePIvbEmcNYClYjr4AulcyrCBpy96Bz8yGhjjxiyilgu/nNAJi6wDAwuA1lvjvc95TS1LcyPA3BxeTIqKW2F5Yb0BosLZ/76fH9CUaQ2AC8dW7UJJtG5qHky7Xmy1dOz5pCkMDVC0Ai0lVmG1YDw6el8BYjc8XqB3EXRtqgvb107v9xY4DNytakB4QSiIJgtTSMt16/7/Uea0TFM7AQjq2hrYAmpp2Xo2u/Vks1Q5KLiqle8o6/OUpGyMt47u04DjeHbfF+vWB83dxA7ddCZLwqK4WbGYl+jxQwnkbZm9oNS6LoePngVy1mXo6qlYbRWJGrORBEdKa8EWnAIFJ74+SvlbjVrsCF3sT93rUNMPU+c6OGTBXIDdysi6ShkHl61zQcrqql3LUybCYMbWZgjVwComQ7MIbo1RT1FiYzU1jqvFZ6XuzjkIMyHYx3CtgiUBYM4triBboGSx9RYR/OpXXsef+4Ufwl/8149BwXp0zpf3NB4rYPAosS9ffPMu/sTPavbBt3zohUUgoueGBtHSkwhkoFVqvAEVBwbKGCAb3Ve66H4fN/jAHgYKruas7ME+Yz9n7FNWd0Iqi6jUITCYCZtYkDJjzoKLMdQaDf0W0xBAXRAAGXyPFNQwASzC+Jj16IfZMRrkQoqyi/glV+Fwyp3gh7QUAKwxCRBdY/UtGFCJ7X2986I2tJqrRXQjXWsC2POWF8LXL6ErD40/rwp3HUDFCCrLzQ1OsFbPLpRDBxJ6n3jIRwrRo6jFLep18NQJJdYHdnm+9fqcO0DzvWBTasSaCbAACN08K5ip8SVyBhR28Qg91W3rfcrC7QFltVbcpFwzMDIcrVs/p/q3i3m1OVVX0A1rKbaW5T2uZTgCCKHF5tjaSikAW0WUWJZyoANIi3Xr57liMh75TMaIyiHZmSRPTa4goVXuAwR0gto+ujvUFJan/knnRlDfvFbgy6LGkwfFVbZgtaiunNQ7YrEFnYfyJMdk5+lIHq1Agfvi/SOtQMFVSrg6qJF1dcjYzwm7WZmDUtSF6yMQIUZ1I8wxYM5WO0DCenYLcKBilrSOjBuL3Z070gs9kO1cIQBqPIF2rEXNTBAR/N2v3MWf/4XP4D/4rh/H730IKDg3HsWd8FgBg6PR7wWAL37pLv7Ez7yMH/0eZwpk8TsuxxMEkQERQoIKJY838JFhaYzEQNYAJZH1tegoLy+uUVqkrPvAGt0lC1Bwb5dwtU/YpYL9nBUVd8AgEGHPBUMgpMRImdGdcZU/yCDS31V9FMDGgiQWkFjjKApdkM8pf0C/qMpTEZGly6g7QEjOuhMEAK1cClqtS5esVQ4lBI61/bVwBAVrx5vNuo2mMFZWT1W4KxeBrCzIDAt8zBqvkVdKwgWxlXWviiIAWkiKIoIJZbHsjoXlVhXeChgAzQLshMOxy6CbozhoPa/MdNVE5wa3eLU4C4cACgIJHZgxQVVBQh9D0e9xffHO3+2W7Dog0p7rrJULVP94X1mwMjBem17K0Zx8Rot5redkjE/d65WSTaarT5VDX65je9kADY4NBDBroRv0YKs0dqaCAY9J6ffb5tsH866p8AoAC44K71SmyM5km6fNjQiBLN0wogEB3+vO5YBozMy5u2PAxd1ZveKqBddMAWfxKHk5ciGcUj69G0Ejs/z7nvc+RTV0LFRdxwb0kki1rueiQYa7Lq7AmderQ8bVPuH6oMBgzkUVv/nxAZWTQ2TsA+EwKGgoK2GmDHJBoGzytACZDfw0FhYW51NjuLpX0LMa6jp7fASARVxB6kDsL3/pLv6tv/0Z/Hvf+Vl864caKOjttnp+F5t7YjMeMh47YOBM6xplf+HNu/hjn/8B/CeffAXf9pEXj0CBj2qNF/0nBlW2DFGQYL+XCgBWJSbGGOAcY8ChXqqGavUQz6lgX6Qe4oP5vpwp2KWC633C3g6yR9BKEZDRXnMgDEEWTAKgvjKG/k4MhJgIkQQBBGZBKEapwhpHEUNo5Q8DOiuPHRMoS0Cq7L3Ihn+tAWbLPfHPDg5gB55h4ED0hxlAFKX0YhjVb2pCDsGyQiQfCbbeYj1lgSWzvHLuipuUZeGocyEBoQIDXTcvJhVOgQTpfL9HQNFHZ1WfshQL6hz7QiwaHCuLeBnqFAUX/SKQBXgWVxoOtpZuBpGhKWV/wRvWtZ/zQtHZvOu98w+sqczGvlD9uYMDSwn12IwVs3A0p/rwHThZMy2rdRT73K8jVmtZ50VKBvo5ZwPQ3IOtXvH2cz633w7+iI/AQC7H59LdbufOJQO1EA8zunOpc4xxySbAwetN62pBuHWuVjrYLfNkQMCLrnlNgDUosC+by8DX110D3dfOzjRsIMey1P/LYlj8nhSLcdC5mEvBmNgadDiXBSh4cMhVniZjBBbyNBcMgdVwQUTgbNkKKkd3SY2tGBgxFXBkEElXxVGLzHFk1LifE8ZBbwQkOwMAbE11jUWAlAt++Ut38ef/9mfw7/7+z+JbPnTMFNwYswG9lz2ofNh4bIDBOfuWAbz25l185vM/gP/0U5/Dt3/kxVa8CErTnFqobH5zsgOTzJXgFrluot5OpqDR0lYpDSKd1UTV+qupJzV/Fpo6kzLmrJkHh1Q0pqD7mC1jYU5qjXhJYGSgMKFYEExgwm4uYM4YIuvBD4wkBXNiJNbMhoigB4/MwgQq2tVP3J6lGxr9rv5XBwdiQCAQAFmyBiwNPPTComZ/EJSJg0BI14hF8UcRt9YIIYwqr9AExhq89JbqQsHmY2FWjG50ReY0nS6pWQ62FqeKSAUiRGogIVLHJJgxLbXxTlkKYFNqbb6dz/sUIOiURFVsOJYz+ln0TLN2EUgGEpgJofgcvcSrbkRVykCbp5/f9Xy7gL1Ky7uOccsW7fuj8+MUPZrFq5U7VZH5z8WVWZ3PDfNagZO11V1KAwOnQOCpdXRg5axB4SVACL7fXUyH9Gdynfbb7fV6jn3t+ySNkj9VfOfU2YwMMKmsityAQrCvY8cmVLfDI94hwTG48ij+Pqc+61E66ZZxN8KaLQjUgEJl5mztzpalrixUO4OeEeH7nURrFMxJ2teWnrhPHlugPzukgpLLkTytrg4m8Jz1fpPVhAma/RVZ5fbeAEIqsgQ3IIgbjhwa0Knnt917BwXelVZlksnAYu4DAwX/2oc+vlxfLN0wZDId3ZqfGuf0pY/HBhicHAS8/mt38UM//RI++6lX8fHnX9AoeFNi2QPfVsP3TwogLO2SiNR9zdU8IvNFoipMUDOaBCoEHAjUgBJDgqleNj3Us9UxKCK12JEX5NCuaMtiHCiCgoJEhJkKYiD1m1nE7RQLSmEU8wO629bR/clxSsAZ5+8d6ZiCGvlkisAUpdawOWYNgIaIAQUxJGqVSYEyFbaONRCoU2pViXRWp4/eF1uyQNAUa18tzIOk1H+nAiIJIGVZ5KQeH2rFTtRCMKEQFCS4IE5ECMXmiqaA3TLuwXwPkPwIlU6wVbq7AzHZjNF65NZbJyshXERBCwwkKNMJFl3f5HOEKqtgKYAn70I3TzjDZDR874LxmJa1Fb5cT6nvw8a+OEDwZmBtXnGxfqfmBfg9PJ7XKUbonGVb52c/KKSqhwgopG63HiAkQge09C709G2/14t9PgFYnIpPpYtGt7Mp4nLj/NkkVmBaz6YBhWAAVnPyqbIJusYKwsDorNzlPapAtJtrDwjEz223luv7XeeKJVugJR90TYOxBQwA1rCoKtE1oJaCUy6qXp5JsTWFga2icnC2eIIsKhtTlgoKeiBLovg3FQHngsCEQxZMIlU2p1hQhGucRcoFgRi5dIHqRRDtQhLMmKLlufDYoVzPgp9pqb/zK1/WmIJ/9/d/Ft/8wY8fPXoDB1RjNvxOHf3SexiPNTB4/Y27ePmnX8Ir3/sqPvbRF1GKdL0NXNk0JUssBn8NrWPJGpAJPwDmdyKQczT67cmL5gEkRcQCdTpL1Q5FX6PAMw/q69jX0v1M52hz8Nd2ISPd+6MpHo+oXb7GmcVbZSdAstHIgJWB08JGZj0Fu2BMBMGKNVi/tClpQAWIkZv2Nw0gZBPAMzWryC259fx7mrhXqO6jS7lg70AsFw3wMeFbXHl0j8vUlFQFBYkQg2DKrSoaF0FgUQCxUsD9fHtx1lvVrqhOWbZ+Tvz33XrMkJMscAChlXQ1etZAQhAC254x+8/VuvDcjnU1OheWPscKWuyz+LzkRKqa/2GdoK0rfF0EVNxNYwweEbhIjZFYW0I3za3vluo9UBYBcZ3yeuQ1tL9jkQVAgLMHi+d5+D73gMXZAQerDv73Rb7x80mk9DYBU2DEwPp/JJXlCCDAvvc1Xd+nRVyDHLs1zgECETnapyO2wMCBzwd1zfwAFZxK9RUUgI4DANfyy0FUv17+OQkqOPC4gVPslhQBQpO7gBtz0u1fQRaGmKU/mJxnk32ZLKi64Kxe6O996ebkWQi//OZd/Llf+CH8OytQQAas0LEu1cXrv3MDW/Ao47ECBv32fuHNu3j5p17CK9/3Kl54/kWLoFfzVi1PqQiLRRWUZIJQuwhAYw16+grQzSNWOh5Fq84FVBKhbrwLU79Q9XXtddY+RPdl9YMt7J88/B9UL2Gj49SCWPwdrV4DOPqdymzZrKjSjD5Zf2KtUyaWOiYAiEN9PWUM7FIJWR6tsQb+B+gEun329FAAtYYEE2olxWT0eAMEp4HNWmiJAPucKyuQVtXONP1IkCwAcd0VzmMzrPUCYmLEQBhz0T4UMSDmopUmAyOTIPcKuFKkgjWY8Xn3AtgV7SlAkHJRH2puZ+XI0VOUxVBQpTEjkVgVnFnOet51jm7V+FnjE2sL9EKszW9tfZ8DLT0I9XNypHx9f4HKcjB7YBp1QvXM3HAapKwBVZJSrUkF/ccjFGC2NYxBlWgMXN0mFSD4OZXmGyfCI+3xmr2aO8A6p4xUtAKqMweuJG46ozEAPHsZX2UNdgYMnE2YrJNmL/fqnpywKH3uDdwcAyz9vfV97MDBYm0sFsIBDXVMq7uOPA7inOwRsV4JAk9iXM/dn4k74ccnfofJCyidkKd8/Br6OkvZ7OcNaKfTzx6LG5E205UcXAYVNwDrv/orX34df/YXfhD/3nd+Fr/ngx+vLHYgdxVRB7bsHJLHbHTzdkDegz+o0XzTeKyAgY+7b9zFy39T+1F760n3dwOEAE+pU3qtoLNGjTU4uuT+2YURVAFaYytABNnQe1OC/eWShWzzl/fANrfkAlMtsDGGooGFRQNhElgLaxQ7xOYPI9agGK5/pz7G0F1Ahv8uGkpfWT1efIZqMFUvPjUag4QXZ5w5Vr5XRAFCMWEvsmQNAhOS8WV9z3ZfVMnNv+euht6iWMuvKjo6YdW7CNzy8nLSfXyH+0o1D9uQus2Duzk4DRtZEDMhBQMIRTAErYg22ddRCNmCkNwNUlH8CeG7VrjuMuj9yz2tnIGzbg8A6tIgQmJVcoly5wrRz6AeJKiFAxxb5LotJxTDam69snUf6bn59fR3QBN0FcQYYOHi91E6a/P0/NDNqbdk3fXWqPhHXz9igiRBIgU6DrD8fUjU4vZYGDYr/FH2t0hjC7XSqVTQ6oDg0NXyf29nlBC5dJ+1rG9MhF2QhctBGRGp9+vU3fK1PcUMLO6vjTXb0rMFbDKCOxdC8Lt9LjiyjqyvXoy1FG0SVmOjOnnm1rRT616bIDAwBr2/cxZ1uQqBwQreDIMQEzhwZQRjIIxBv3bZrNletARXq8UT0V4FynzQInW7B4wKLuyc2P//3S+/jj/9t17Gf/BdP47f84GPL1zY/ny2nApy0FiYBXN1AhQAqh9/4G++hI/io0d76OOxAwYOCl75/lfxorWeBBpV7cFwzEp5U4FZukvWwEfv0ioAfvVLrwPogUJrVmH44CQC1xfrDhCp78+VesyEkTVAcDMw5qwf09D+nLPGEhyVRLaDOw2MzcD2dewOtloRakFw9YN7qpP6cq0aWgcKqCwLCIlVI6OCI3AgdllCEQNKpIKzLFkDImqswYrKTeZZIwZSXgZZrUcfkOXKae0myCKdzxYmcEu11LzqWe+28RHMf+wgbQhqjRUBoqA2VIkiEGZkkWqZrRVIoWPBCyyBY09590otFakKLZVSvwaaT7LOmQTJYgp6kJCKto5NgmYF+57AFcPK8sPSJeBz6pkL9YmXZvl04GZt3er7uDBFBw50PgEe6U1VwTiQ0b+9ARjaN4LGrojH9dR5ydE8l2unL+LrJ9aESJJA2INu1TRz0Ktpvw/f3zXgcybL+6DUYLiyPKcedOwxSTed0zHqvdeYAtY7HwpiVhkQUlm4w2IHyh71ngHHd9bHkeu/Ywvc5VHBQFWuQN/MbZHuuw6MZED5fa3mCavfosGgYrE0YjFAgpgLxsA4lFLl4ZwFm2HpTvWsBK+2vJan0xAQA2OIKlsjq5yOJreJl+eUXGOfGT1jXA1G+96//jd//mX8xX/9FXzzBz6GDEEo7QUrW0Aeq6Gf3QjpQcEaEAANFLz6/a/iR/7Gj5yd52MFDPyhP7cCBUBDrJoz3/LvAxQI1CZApJG+PSL2C/MrX9ba1HWYcJX6+djH5rn+Yu6LIno5RAAJjKkIEulBdKVQRJBHPamBc73EcybMJtHc/6Usgf79xg7ydoz6dWQ9xH7Q3brwwCQHBwBqdbdSgDK3tDugoh2C5nALFeNdTN+TBdsRmksBSo8FapkKa9YAWNLN7Yf6KXV7EIgWv3vKAlRFCnMRLC2vXtB6tbOUs1G6J5QYqZURQzDBzChBf3cUBmIx1xBbXQW2S58RqUV16dI1xF9jXNCxT2csXVdsvfL1tTulfEXUQkloICGKUqYZziLo664p/aMtWLkGeqvb55M6ILBuRnMOGPjaugIjEoRcGqshfOQS+Ubn6WcjiSzAwNm1IyjaCgTteUBA4OqziKzR4q4INGhWX/Dc/vpcfG81Q0gsnkC/PuRWu9/PrAPXOT/aOZ1zQLCaJkPQ8x4zV6ZrDIwshBmE2K33msHxsb5vN43ePblmCwK1gMPehdCCDR0UuBvhWO4ALmcKCINWaCUGc2yxE6KuqCiEiY3ZK4IxM1L0uK24mLPL016WAjiSp9MQcDEGladBP4bAiKTvpcC2xRU5rf8wpqsu72qZvUx/Nt+zgy6/B0SNLQhY1oNgW2tfs37cpB/X47EBBq/d8ND9AnEXfegKjDvWILKmJgbbLBdIv/rl1/Fn/paWUf4jf/N79DV7lIjeD45qSejXblvrpVDrWusIDIGx6S5gTxUurNVQahaD5+7q8yjVFYMzBQGXU8B2CNjEgI2V9nRfeDCmIhA0lYmhtf5r1bR5xRysI4NFqxGiMQZECv+D5hRWl4L7hr2BRwFa3QNZC+b29Sn/b6osg1vLSwuw75TmDEF1KxTBbs5aAMUEbV8KtQ8y8jVlUqoxl6ygoP4Kg0k0Yi8Wbcud/VBx/TmKUo+ZlKlyptnZqLXlcIr+PgUKeqW2nreasnreFAgIEhtgNYBAbP5U8bm0YNW6F12MwDkl2wMw93/3/nDdb+mmpqdFFYOmfSaLcYiBTOHq68cjl4iYID+2TG+a601Mxk1rlwwcBFGaH4Fr9dMeHDg93DOFzgr0eyvSXC89KNCzuWzo46CgFuBZlUE/d1ZD1lbruQQ754TNABQheLOjKOqy0fkraFgzTICyOWl1P3304X9rndfXKPD4Aafz1y4E8gyEhRvh0eWOB3UEDoii7svImpE1uMElx0GbgLMCKk8PK2CgKZ7KriowUCNrOwSMgas83diHy9Ql8Gls7Bowki2QGJh0o7L3RH37h7Uib2ACClcg4SyaK39eva/Pw7fF5TNwDAqk+79T47EBBqdAQX9uF2fYEZcrMJgVT24Z0IK4/FXz+fzl734F3/786TKUCxrHfqCoGBbBbNvEpIoC+p/awLVdN1fcu2Q1u4NaAZNX4SplYT30lu1m0NrefoA3kbEdG0AYLQ93YP0IBlAoa3c7dJcVXrt+QemZNhEPAurAAQAObimTpgH5JTCXQhDLNDCXTWalydwy6QFBn4GxZAqk/t4pBbWMI1iyBL2g1bXsMjh6qyELIgkABTylFGQiHFIBEyFRATOjFFUOWRTkZdKgKMqiGq1wVSD1XMji01HqXKVsj5T18ge9gih6rNrPGGBp4CCzKrnKGgAVIADH7vFq5eK01d1S7I4DOL3S7rF164GOCgq4GGsQBCWpwCsgu5NLtsMVVTgzV12386CxBwWn1s3Xsw82k+4/pQiki8+I5njpK3naI/af6t72sSLZ51e6jnl+hou6DQ7J3AelgQI3Cvq9789qEbUbHcgOgQFkYyIJhQ0kWAxCkYICAhc1VIjRrXHHAHTan5jquQjd/zurE2yNnH3oFVY1SFzmWCXTCgpy17PjnNyBMwcMysY+RQXgg+ieSHBanhfyxAHKEBi7mWuvhFOylJmrQbaWpw4G9OtQ47mGLtajBwULX7/vHx2fY6CxCw6uyMEB2s+5gq5WvK6yPabX1nrvvTAFPh4bYHAOFBwTOagWrGYSiCkuZwvc2gEg6j7409bF6ts/8kIVAi1vlBY0jqPl+r52OIrohWPRtBmy10i1OElEDBm7xIiczU+onRU3A1fL132O9VnYAQRV3+IYqHZcvOxYg4H1Gf0QD4QGCoo2/UAFCOk0cier5lgAwuw8yILek85vqXy7xW2w0r0FisiRSwMHMOCw2qsa9duBh7WwX4MCTVdraXW5yJGg7cdCWXQKooi05zgzPG21n2M/1grkJpT+XgabdQSc9iXqfHAaHJACt2x/6OzYGpidYmXWLMEaFNSUrhMP6tkaVilIWZKsPu9UTHAGpfUjlmxHINL52uu6YuoBDHAeFJxcw27d1hHo/aAb/q8f/dusAd/i904whDeN/nfOnVXAYhA6IKsxMgoavBS0l92NFscTA5AK1XgYPyeAyrQscpTJtA4c9bgVlWmorspgcQUKCPq4gt5t2YOCh8gdjepbGCMg1i6xTDVGR0T0AdHOCZvMO2SVkRdjwZzjkTxwltZjCEZu7oNN1GykTSRsQkAgWMGjZUEpoqbA+5UTQs1YcHe2u5i9OBxsbft4OP0hahBnZN/bFnDoDM0aGLz2xl28ZDEFLzwiKAAeI2BwHhQsL55jr1BX0PpvQ6lNBw0k1u/amYKPvLDI/e1LkPpnnNkcAOZ/19mEQlZkBurGIL3UTAFTAHZMGKJoZa2sgW2HVGqxnqPa3aQpSxp4QxiZsIlBEa+h2jHoxZiCAwOA8gFabrgDBe5KkFO5xHqACWplLMGB/c6ZeAM2940f/iKPDg58nPq5C8qeti7lvMBloqOI6rVSaJUM7YNbRLK/xsJfjpYhAjShCNQj1vKLu/epsyB0Co+Q3MduAiRDZWOwmAN2i24d8OXPU4PJloF+58Z6XR0ULL/Xr/v1rkDgDChYC1z9tjObDBwU+5pBVdkp6dKsdmdlXEmfOyP1/YAakOb0PvcbsRqn1sx98L3/fQEUT38J45oWgt1jmZJ4HJJUi2/9ta9VEQYXZanAQCzKpNw01qnOPtQNQdX/XLBc735k4Ox5uREU9NYs3RBXUI2R+RgUGFO5rKFSlDkSDQT19RYAyCqPYhi7XWBLQ+Tq3p2YseNsmR9c3Y2nZKkr3zEyApnMjAETK4MQCBi4ZYQEu18ev+Xyf70TguZuaiXFTHpyY9t6HeRXndDOZgUHK0ZG37M9j4KCH8CrndH8qIbJYwMMfJwEBasIV7KcPY836H3iYub9F7/8Ov70zzf3QWUFVhtF8EhbZwHsfVe1vsn+Xq1qMotPC2EEVtQazLKNFLAVYI68yHHu/cxAu9x+YDyPeQhUYwoiY+E+qKCgJKXy8gxK+8YUZA08lKxNgLwLm3dfoxC0rrgIxHqDtHa0pEAjPDzeALgZHIBbwBxgl6lzI/jwGg8spOtBWlGPzUryWA1VSsFeidVSIAG7P7wTkOv2q04rtngPTw2zyGTSV/a8cU8p8iA17s7LYoj+nUCViMe3RDtLKavf1KPZvMmVK+oAOrKGqwLzc8ot+l9/TlW5nhL+2f+mNLaGjLI+NTQW4/T/BSY91zcos3OKrL6vv9bD5u3/VxpoCNKi1WGP8LA169drXfVynS3h+9q/BuxnCkSW+0osiAIgMLzYmKfNAbBYAJciFrNi41HPq7sVa2odtd9xYwZojOcppqR/ft8HX/OHg4KHxBXkzvCo3UiNNTBj5LzsUbmq+LJlLqjCZW1eVAE7quUeOVr/Aw021NRlXrCOPTDUvYbWKiGs4rOWgCB2vn09D8ey31+YyBILyRS+/RoZgPS9IWOyC7WXcbfEIo4BS91T3xsGCn7qZbz6/a/iEx99oa0THg0cPHbAAFghtZPNOAqIuFoQizQ7EH75K3fxwz/3Mv7KH1SmoI8ErReLu/K3q0NxtqwnoAecA4hYA2dACEIoJkS9v3mGB9FYtsIQjvLG60vaZVVF5dXODCiQKTI78Oo+OIDSQZkCAwVUrBubFEiatdc8AIexYv0YIFE1XwSokAEpFZhiNDERL+IN3JmbTNaRoParyNAATCoCsRK5/owRgPQAoVoNvYLUPfSDXCBA1qg1jwcAGIGVdQkmJEopFqx1fH7cz+guGk8F6+nFUxbFsjStFziCx0kdGasenCrSrAOPb6EiQND9VsFBDZDZ895kNT8MDJzTx9HmU0spQxWsmKUTmTXiHYzE6hfivjEGLN7B/bYdEjxSUmiWZaSWaudAt1dQpyLnT80bTPDiN75eCBbs9ZD1wur9+pS+lvuPo30FTu/tel9RLJoyi3L4yNhnTTWslyOqy0UVe6lKVi3+cva8As03zqSv2bsYfY1j0HV3izOYYlsDIgBHa77OEnGA5L/jZ77WVKATcQUVFKTl15KBlBQQ3CB7KFpTNW4xTgD0XsRR3QpEVVYHCBKL1XTQzKFkmR/n5Kjvd0/ZR/bCQp1rxORsLdDkGRXnGpFZ4yS2XhSue1qQkN0ZgjEkxtKiGaU9Q3BK90AEr73xOl766Zfx6ve+gk88v+yt8NuSMVhe/LZQRzmxhsgdwfW09xd/7TX8yZ/9QfzHn3wF3/GRF1tgnSE9l3OtJn63MUfVu7q0m8VESf3yrCBh8A6MTMhCNSDG08GUntX/6ytk9TaFC6qavkPNv7cINEx7pfHyoboPFBQkICWUbH3ca3dAfzOCdgBMoBCBlIBobgVQPfin4g0ceFVwALWEs8J1FZyBNGWsqBkdpaWgRWjQoghAWCpIggfUKVOgZa8FXNjKNXskueYzu+WQi67caV948zW6ddD3SVBgYMVjSMHYxEtgpucDlWly4epn1OlmsctfFYn9nwrloAGNUiy4UTtPOiCIpybv56EDAmvB7mMdP9Hnquv6UltvsjUvOnMHYIUtwyATCktXGfRYDTuNvajYV78mU1gwpXy+GNJNc9d1MbYj0De8Xo8CCB5pb823TwXQTIeAgKL3IQYQad0NBiEVzXJJpBk1XmQn19giPnteASyKoznIWoKuBgj6tT4HwE5Vq/T3qc9v+xpoCQqO6xUs4wra9yZrUlI2ICfzBXbyJ+uEpCiLQCECcRlrIKHo+kvBECeEqCXKZ1L54PKmFEFmtnunYy1H18/jctQZGU3xNnmfbyrMhHb5KJiyL9pKnkMFxhUUdAwQBCgk1qQO9ed1XakzPDpdd/fXXsNLn/8h/MSnfhyfeP5jFs9SUCvbocmYm8ZjBQyANTjoRgcOyJpxeDMgdwi9/sbr+OM/8zJ+7JOv4Duef3FxCX0TKn3naLJHjJ7y5531+kCaNTQlNitbwQEoaInhrtVpAdUGTAIsGpnoMy0f3A+yMgaGLOvFTObbO3TxBKlSeJgP7VI6MAD0IrpEF7UKBQCFaN0WWf++8NKlYAyCxxv0h98jsYnMVw0VnEIABbLnIgME+pAOFLRkAB3l1UfWypNKv/epix5zoH7FMXD1hQPHwMCF7FJZ9R/unmklZ/u69A4IvKJlDyrXrG19b6KjksMs+v8UCFECJBzn7Ps69GMZRd4UaC/U7bgcDRcGmWSx3pmUsQhF6WyyIFIG1WBEDl6oSV/5BC5oa1vZgpsBwboIT1/LYP3yA9p1yCT6va1Nv17Ace2Ms+Wa7Y086Kunc+u++u+c2dsadErQ2ikFoMiaOpgL9qRnNFLBnIFYlIlJhbQbas384Ec6s87GOBg4xcY4Vd6KSz0CEPA3WwECX4/j1s/H9QpOuRBqhpODgjSbnDkjfwCIuxEGA7AekyAFkAEQAYdB6xwwIReyvileLttk6Ep++lr2Dac8bsCNP7IW8P4slSm4Qc5rwLbFlvgzAIuS8j1joBlydmi6PXb9U2V7XV9lqV974y4+/fnP4Cc+9Vl84vmP4dR4FFAAPIbAQMeKLajdAvslWXYKfO1Ld/GZz7+Ez37qc/j251806639ft+GFUDzpwFHoMAPTzvgsvCJAah9xdXnxAAzyKgmBwocIoQjhqjtbgug1LlUt/NiMFBZDUeS9SD3c+uCDKmk5jroLqVoKzidayk6PwDgAJQCoQLkrOyHEMSbn6xcCrBCIMkXrRjGty5LVgLBsiCPS/CGQPBwo0GgcQ1YKUkOtfKhsFOEZIwLV0tB6xGs0+pWa9gpr1MCNZLGb7QYA65+VUfzioN6V8Iyn1lPn8UXmFXJoJq7vAYJvh66Fst18LUAmtK3Y7D4glc/X1Py/dVYv08rf6xrGrEEY4GWzX5OretybanGxpxSUloRdGmt9wqpPtqZZwhG65kNiUF0sfspnV2z7n16ZuBU/4ZH21vUaPnaUtxjREgBZcoFibUoj8YUUddd0QBuLI90Zpc9PpbM4Tk3yUnQdeLcnGJK+rO+BgVOs592IZg8lKwxBS5vHiZ/gGacAOpagIIFlfe66JQTJEQEDuAwIILw/2Xv/552O67zQOxZ3fsDnatcZUYiATB/QmLZEkUS59COZVmEQDJJpUISAEWF+uWS7ZopO6nIU6lK1VxNbNdMjaOybIoSKRAgVMlUkYQISVZGxncAkJTs3OU+wwOQmqpc5Fr49u6Vi/WjV/fu3u/+DkBJ9U266pzvfffe7969+8daz3rW6tWPgXSjMrooP+W9Wd2tweBzUGD1LTiS7zJYJJ0zkgB8MiIZgKeUDx2ZEh3qnxjTYEDLQcHP/jbuP66ggIsCAWHII2twqdwZYHDxdU1q2NadRrFwwvVb13j+68/hhU++iI88IbswMgGFyINDgNZvu0PDPSiw757Jiz2wxupLut0tEwGUddZpAI0CBE5ZlDFlnbyy/7zvnd6/I0Prws2eB16vso3jCVYBDHFChgTiAgYgKBdlg2WEIta887yJ1LOJWYq7FHpkTEU2nzKruLeu/FXMQuq7kNX1oGAJKnwjWBjtL2Br8QFMl7FV2r21YBvWYMAONJZGsDhGiqN/J1ceu+8VJERaOrYDIG0xeof+uaPd9GKp990DkgVZ2tNAQso15bCDsbpM0NrYyigYMlLXOzAwUdC3eYe+nc+22U7xIUSbU1+3S/0r8Q0MAfXMMvYz1w2VFkq+cuKvbe2eHhsnHbepydzYF6K2jUeumBhAOQNcR2PHz6EFSMZSRvdqZFJrumMO/4wtYFXqJicxlkHFAEJlEJxwW/Q6ZR5MCYOvRG6u74gcJRJFHF/QihmRtoTS6hgTL5kcdya4rtwyuQ6oXCeCyXHmDbYzJAEtOFBWVRgCOZahLF3QP5WlIe8zEXzcgYIP10EymyQww3Fe7gwwAMKLjtgCOx7/IrlP5sVPvYSnnrwnk88QG7OzOSYEcpwwMSEQh8HTR/ivN+IfCwMPCBZMyqJhKIFSAi1XFXEqKLBzFsBCDTCItgpgtFYNhuGA2LWufTxBBAV98IyBKUCuzcv+PLJPTiorOJM8i0iRsQCbQqLwLLCGiXYKETCwQI2ZZ0sS7dBOkSWJU2AAZcEQMIyCN/ukLUar9oFWlc7bgwHbwjguXa0+wHHx91C2AFwBCwMoqVUsxdmith36UkcFNQdS+3Van/icCEo2FhfVouY3o92b4GjnQitHy91A8fxeOUfK/tQ7hAO3arPYdwMg0PfvpfpY/8rWvJZTgJQ50NiMABKMYVgPxm1s46NxOwIAR0BrN3bCwdj2s3ZpguFKcCGgqEKNDO7Abi/hzUayW2OslIPcxx0wgzkDJbv8gRpT5r61N/QbAlVm9mwAV4YgynK7hks32lMGd7IclIThs0uNJlVW1V3ayfpRgKOJwKa9HXRJWz343rWAgqe/FEBBkNc71iCfcifcKWAgZfDKQQvE9bGvvfUAn/3WF/DSM1/BPfPJ6Lgx+joi6RTOk6K1iIb75TfEBeUdXQqoCpg1yAZA6z8DVOnbYCJAB5UM+sok2CCHuSGsgvFde4orIFwf3GdAwclCXMCUW6vAhAPgbIOs+dcDSuklBQkGIqz0NeG4uxWqVeiKFRgqtKKBjd40/b3d9JA/0YKKAYQGBBpAECZsovhbbgFq/y5kylD6UPaUUAp6AhSsTawdZjsY1s8jFwJ11zfTI7SpADhm88mKa8G2JK9AIdX2DECsr9/OX90pqggE8sAaba3a9h37Zojv0LfZpXbr22wEBCrYq30MYNrPIELWv4u2a2GgpNq+MX7I/op7hqrxjDBuuzEL7C3/GfsRFf3RuIglsiHxN1EuNmvpY2BhwxTM2+pU0QxpERwA+p2LyE6z0AvtXbbx5Way0ty/6wreZKUE1hsB6OvNTk42cpwSaFnUbbCAS0F67H3AdqPLu2MsVmBVqW4lnamy1d7mARSYUSqg4Ofw0tNfwsce/4i/D8f327EG0nKXyh0EBpPSDcTrt97EZ1/9gtAvFr2pnZNUk5Vu0tngt/tVRqJ0y2+2CgpuBBiwDjJRwnVJi1NQ6lYw6h1EjjahAw15EV9bXuA5BYyy0t/u3ze4MFiW6Tki7lcezIrFQCRlMNIs/Qm3k40s/kDoNwMHBBEoHIRwb+GdKSUM8MgeuIsBFRyYFRafMdroBzi2GiMQaAQhF6C0AadUK7V7hsdiALAAJQouoqz0Yg8UpN5227kw95FAeyt7JhZUZNe24qpUezqcVWENXR2pPqmXTWeo+t4NY3PvrIVu74DwHrHdrF6x7NwBIyAQguX65cgUlF57X4UQCgBl6/NFXGtJlq0xSIFBAGIYjF2+MGZDw8xiIOxYfOfmvc+U8Az72oAlAwVsiYoiSxA6ZPcSYliQXVFw2VAxNhQAc6pKWuUjbikf+wBsM+RY46+i7EaQ3az3p5TB2yLB2YswyAUQcFAEEPjy7kyuR5jFpWANGlb/StMgjn/G9fceyOoDcx/s0HFp3/eW5c4Ag4sYiJJ36PVbb+Izr34BX/v4l3BPG9U6h5RqSW7V1gc0kcdR+Df/hDFg9dk7KLj5c/flM7MG+o0HPZsizosABmMNUgblxVkFNposJR8EDQOhpUR/XXBl2KSyRCLeTrGkcN9kcRD6PEou8LibdAIIpBGpbOL+UCqRTUCGYBgDXExHfXmEGmzKUHOlCNUKOnqf8/RuA4W0UxAOrmwcbGiVRRSG3c0Bb0P5nqqZNwQK1XV0CTsFuKTjsdah1mkw9rQPWRkM6XtyOtwUlCkw1uDDqsBoB75GFeuVVg6f06i9I/A6WX/5rol5UsPjHJZ92+2BQAUB8diJvta/kuhMl6+puzBZ/6bsbe1j1wHahbpHcNO1dXy35h1PtEQsY3tz3FYU2ILerctqJQtTaG5TwJZFOzhAqvEG/VOLmgYW92QBfrr5A2/rUB5yJw9Z62lsABso2JTd3bYKGGZAhZIyvCR/S40lQ8rgNQFXBNnMgUO7WIxFavrMmFS7SZwT1997Dc9+83kHBX2GWq/PQbmkL+8MMJAyGeiBb6yg4DeFKRjcg4LFEVuQ0DVoAwaKCwhiSUZiSpe3tYKCm3daZMplH7gCwN0KlAADA3kB5wzKFRSQsQiquN0KHVn1jf+uvnkfzBOP+b06EEJ5EcYCFEyP+NkfKoOeC2D5IxzNbi2gAJBGfsVY3wsWBEnl9UsrlKPCkM6cTJ4g5KmcUAoxoZXVNVgkgxpWBeH1SeFvggejdqyCXH4w6XsFNWQuBkos0vOoz5JNjGrAa3bl1YIFcwsZSJiVGQvQgK4SFEnZuvoPxkRT/+Ci0frH8dCMj1GJ7dezARyj6flWfe7PDX1uq5DqWJDz3I0HDzQ+OWYB6Lr6/fFTrsJ+/oRjBOzm7A6sNW3TPc+owvDZg7C3VZWqso1J2QCTTwEg7BS+UVMBIMg9wzUjgygAATCDt5sWCJgrIYCCfaChGnJm9EBHgMpL0mclbRcPtvRx1KJpb/VOlBKA64fXePYbz0uegsc/3LZ5L3t3stgba3/zrtwxYID9wG+YgjfwWWUKdus8O9RGQFxaulunPH9+mIDMgbIvjkANHJSb1YNnAIA3lnX8kIEv/6gOPgUBbOzBsshAzIsuc0x1kHo2maMs+e15itdGgBH9Z4GtkOxdWQWXxT7QUHiZRV3pc5sENllLd21QYo2SmyiGUXHlUIXaUOjNSm/9R4Hn5wYW4yXhG63/Ll6EOtagZRWAhvTtfaUA+mCqpu5Aq8ym9at9aLk2xIqrisss3Z5dALBjbeBnojjqWICGeVGFEteIP0Ld28+P3oY/zL6n0PcGBC2w2MdtU/eDMlD+UyZjV6/QMz4m7Xj9HBPu7OZ5BFLDZ4hh4EAdQTwvhJSzxD9RqTInMJyULrxDfO5W4w92qx1YZW5gBXit8VYOFtRo48IHcnoDpYR0JfEEdPWYMBXZWIzSjotL/aCl1z0E2SXx2a8/JxkNH//JKbBvyqiPTrgY7hAwOG7w67fekEDDp79U13m+F+XShDVhsm3NgCs3K8q6gjehktjWIoXxTFmEBGVCWhZQutGUoJJ9kH1ZTgnAIYM4gYsCBLuZKf3GsTgADQEEyJ8ACjSmgVWQmbXjgs1dDNRZFQq6EBTDLivkgTD240EgW9sCuBjIFOvin88gvcFzGuFbN3sxH6X/ckB/jtqSrC6x3WZAAZiPt14pRHAVlJi/R/xNU8k9YKGgtKxOHtRlzAbQ+NGnJTIv/n1iiR8p4KN6axt73e38jtY/0ZazenRjMfqp5acH/a91SHG5Mto+rsGpjz5epV7Hc6NtL3lO0992zuo0Alh9O/ZzWp9DlmMw1VM+jsoGRgGuHpNdB60tc2hLe5eRa2HEht4SEBhLIMHZPJbPVlaTzwzSDV3S1SKggJLI+3wGKJ6zOG3r5Bc/9VXRX2UdXscn73dJX94hYLAvTELvXL/1Jj77ez/vPhnGAaqdFMtYWdFyP3E6y2434UK9NnMx6KC72YaCBCWBsYGusqPUVLii01LEzQC17PS9GIuDc1hsgNXZPoe4BKlmBwgGQosHSiNaOjvlNhqkEyAAoBW4o+QhQRifEcSx7KjH+O55D5AiXagPaJ+jdWRg6qJpn0cNvcir1KHZnIokBfEeJAA+8i5RyT0AmCmw5jexnnL/odKKgGDQ9w4YZvW8DfvS9TWA0/WuwrGrt193i7bU70fjz5ciA/sYnnhbr2tgVAbzkVIPCvRnl8Zp1y5s43NS4nOaQOYZSN0B/61tz1kIo7IOQmAHcCBZzQAQOCdvZ2mXxds6Tfq/mfMpVcBgsQ0pWu0KCiz4O8Z6GSgYGWw32/5ZVkqSlJtbAidhF6hfyt3Ph4YF7EFrHRcxidWDhw/wvG6dfP/Jp1p5E0ofzOzMzJQhmI+OuwsMSAbda2+9qbmjvywbSpifZ+ePaRWZd461nVI7VeYkV5bVqguCM6XQUSRBhDf6NWfZDB1okKhljfPI2rJJQosbgBMjIaMopZCuFnhq0PUGvFypdRCER1SG6obwCa7vQL0g1eN7EKDneyCg77enH4GxoDhD0wYLMiaHCmuIDfmbv9DaTRty91TugEEECnzJ3RImYiMg+uf0CitOyFEQpz07JfB2ABKAen2vLHoremLFertZvWdgJtbL6h2UQjpUIGjrPCod43NJ4fpfYKxwOwaM49jeRaej1vsR2vRwdY9dH4N54/g4GhtAO1f7+B67BQZlNjb753f3ppQqMCHycWjt5kumo7uQEyz2xXaKBZVgCNjzUvcXco40MFDbY+Qm4hEraG3nYEFag9COcWcXDCSYOyElNLtPcUEMADeffwQFZVWGt5S9XAY8NT5vLAmW0AG3OOaIVB/EeBHTFxUouMlkHxh4/eEDPP/NZ/FV3zqZq+xV1rXRZb0eu8SOTcrdBQYArt+Ku0x9RAZP9JlZaajvEHhojBRqhGgIOxG/vg3opFG4JGmtaLkSWsnQKCVAlbcNMMoavT+Yv7scB4AAAWRlGzal9UrdXq15JxWMuS51bBVPQJO9daC/jwN7BwL6a0Z+27by7edeKcSEIgEQ9GuJwe1a4gYgAGgSjlwQxsOYirPlDONk90xZjStz8ajlnJIqE2qiqc01RDlXEMuAqN3eWqggAMDeiu2XpXbpZuVHk3aKSmSiQFqwIHVs7tFUtbXEpyAgAL6dkr0Avhx4AR6gy119WzYMCF7zw3blUE8c1fMSmJmVyCScKeGeu0Q7B+1EyZbXSfs0sUkWP6Qyq25YZAChwOOJEgDfMUM+N/OomVM217TOlMEn42G4uwaBWZJl5qn+1saTgRQiEFaZd6NEyB6AWPuQBwJ5xkhSJvkXgrIt/4zFgWG5CknrbB4t8tmy2w70zvXDB/i5bz6LFz4hoEC4lQpmSXeObSsUjbtHAwXAHQYGvh/1J1/A/Sfvod12dTJpFLUZW+DDkH2DLwcIBZBUlkmsXGIG5yul8q9k4OYFnBfQ1WM1AFETIqUrHYQbCx11AyR9YkGLSr2akWrMKqCTTegFlK8EAORFBqQOUst7gEUGIw9owqE7wNpm4lNsqKtZsQlvDTk674zBIMtY2MOBY9rmmA9i20RAGG3YPbttxMAUSCPXcwPm4NBfPirRanCBJSjfYkLIllMB7XIrAEhW7eIAATBrpKOmA7XKwLHSCorVlS8wV1gB1DDQKo+4AsaUzIUls17nvh7AuK63Ua4RgEUXWQQKAdQ4OyOVlT8Tmp77uh6176BtXWHHsdi7qS6U4SqPCAQuMVdAO+61TUiZTN/ILS9iZeeljlmdi7RcqV3FFi4EWV2gQGH3PLqdfDAAdtbV2LMLxiykReTIumpfC2tgs5jj57JBVkyJPCUNMiQSF66VlGgnkykl0JVkoE3LgnS16PLyRWWxyn2Vx0wJnK9kvmRJ1cwpO0AoqCnEC4AH35O9e77yiRfxkSfvuSuboXqAzY0z1mXDlPmhXD98MO8T3CVgYEKUkoKCz6lP5p4zBYwUlsqFn3ojtsldhNkOqJyrMtx0hCUTjID4z/IigzZfyd1sra1NXkWmkbco6wqmJPQVgNz5zvogxHS1+IBzIBBBwWPvk3rYNQYIPGtiWH64iwlI+0k9mOBzv5Ve6nQsKeq3bVETnHeJAWioS9SGezj0CaL6fBC9n3ckHJsKjqlb6o43Inn0zj2wWFUQEbmRJENO27xsHo1dM7hhBw7q7/TzGr/Ud7u1whopq5GiCstm/d0CEJiugKFg+USg1bsuZoFhdu2j1HNbm3rWINwyZ2cg53gt9T6jNo5j6zagINS9UfBHir17fn9NY9FeAsKxaH8BMs6NSWnW3+smaShFjAtUOErLlcYxqcxQ8CB/7SFBfrjBEZTVpMh9B+8SmQIPqK0AwcFC0fblDeAMoiwAwSxn25zunTDn8uafGUEeY0VCBjMBWwJKqaSsuRKW3MhjWh4LRtljoKv3CTC4eh9w9ZiDAWNwOS8OFAwUmAvhwfeu8flXnsOXn/kqPvL4U5XsgzanLgvnNnF1bbqJzLZy/fABnv36s3iSn5z2x90BBlrkpZ+T6M0n78lBql3uaXtjIRLFCZ0EbKloNetYuPTbD6/lTlx/m9KyX5q1Qgbu+/7aftVozuB3/lyXRMqSRC51CUyMO4jLF2Uw5hYUPPa+CgquHhPf4NVjcv7qsQAINGNh2gOE+bppa7c29mJW4lXseycoGGOxmI28Vejb3lABQrPbWg8KtpvDpUUAYMuLYhm5ZuJ3MnamUTR6cgQc7Ni2B5quELetZRAeobTbzk4AAXA7UBBZFmAczGRKFtDA1puabCtlsKVla6z0tW2rGMcxis04UqbWl0d11N9YHZFzBWG3BGBeX96Pm3ddIqCxZ49cIyMqe6D44zif1XM23ptxbtQ3F1kCzQwsS12Hj9pmvpvqQmP5aX9NnvSbvXWyZCRHmmf2R9VtsMs62cclaR4Dpk2BgdL5N+9ILhgAWJWte+yvSarid1pwQIlQ1k3G+DKWx7JKTLLTmqylx95XQcH7/hpgf9MCpCtguQLnK3BWoJCWBhRYLMPnX3kOX7JdfgGAdRdTqJ0FCPDBBj5wJYyK68dPvoBfe/k/G14D3DFg0IKC+91ZRgQIXpRONwBgLgTfKS6wBm++9QC/8K3nAejGPALfwAnIA3DAen96H6rAdGozCXq8eUeoJi5I27qPhgfaJBoNE3CllNX7ZPBfCVOQHntfpa4MEOgWzgIUzNrLjqatDRwcAcMsdtZyu2FnekIRLUEHL8kWyOYO47IplR4sgVHhqDCKKrtNl31eXm8cJ3MVtLoe2sfB1gAvACB9aROglHOj/I1GlfcZMQh9AFJqYxlG+Sb6BFJSgUNKXuIVLiits6Bgdh8N5GKr97YB2MC5tgcxiwA1EMG1zjzT5UfMQAQEvRKdlZRFaWyb7+W1AwcXkv7O2tqTfxX9z9fXq1sIOj20SdjOAQqgoNvlGRjJY5DTM17+0D0QcF+4/R2OdQDYGgaMssY2JWHtxKheITs4aKDeZssSqXvX4/aTRk4tKAjLmaNsmWXHHMoWVXSJMiDi1v8BrPLExriBYU3JvK0VIICQlisUBUPCEq3AjYKWkKkW24q8lMbYaKpkxpnKcLp6TGSxsgR09Rjoff8jZQoem4KCDWr7MDwNNgD85tMv4MMfeEp3+dXsoMwoZkiRAhiVr40cbQBBC7GuH15X/fjERw+7884Ag+o+qKCgVdQxKKZtvDhgDbkZKLANTb791gP84qvP44sffwH/6//mZ7AWhrGTCbJr4JIWR81CqSXQamuSdX2wBaLkBXzz50Lf2aAsFonfuhK8zsuVTFgDBMuV+7Fw9T4HC+K7umoBQVp8C2djECIYggOiypQAOw/WrkQ7wHZPTHpANhWSNNOcVJkSyz+YANLvIwYBxxabRROPQAHzXmh6yeGe2x4sitLlal11pXE3RAYh+G3tHAWlf2tAcME3K/XEHGBREmU1s7bPFI/y3nylgilgUBLBnLKM3xBHMb9f60bqQcGOIXiXFru3+fyCU+1t4EKi3gGPfKeaoU/80wqmFJBFcECAvKO1TzE0Ie9IlIaBb0ALCnpAEH/Tj3cO4FfGelS/Cg64gCbASXYu7BsjMgR9pH0KoOCHI2PIZYywtQli/yDucFsWIK0BIGSg3IDeJyCA8wK6+XNJEnfzjjAmIZ7JXJSNGxio7JgZadE4e0zcBrQIUODlfSJ3FwEHbOCAMtZO15RS2+NDj9/zeIOkDoONtBHNYLEu2K0CGrO7nhzJmPQLbqc7AwyOQMH+OzV0GUNjBoyVQgAFBXjj7Wv88qvP4zc+/gI+9PhTAMSIZI2rTZDtaEFAThlp6UDBdgOGpJb1oEB1A/A7socChaQb8gAVjFHJhP0SmuBCHYjoAYEFuTg4kBTGBRWp9szIEaLvmhAA2rz3YNn8Q7+DxKUruxBmTyzJgDMI0PgPBwqQTI+8JVemElE8q4cIuOnpTEMqEKgKvs82KZ9rqlYXBBEEmL820O0NQ3AGDIR7RQXWR83v3okZQJaVKaawLFvcerOzZM1KbZSUKeEmDmAgLGL8gL2nlUG8jtxnAg6m7oAAyohqvcxSPVuvQfsP2952LY1g7FSbo85Li3y3+uWl7g1gLMwgGNRyq2AjCVy2dqGiVDiArABBQZ/UbVOwagxG8bEdAcXReB+/2AnQlLK3EcMCkw0IWDvnneFhNPnINfvo8oYbgJD1s2z5nJFyBmVlEhQUIMtut7zVeUBlExm5CmNLwWV5a1msMV0SU/CYGmFXwFLZAdbPBYRNZe/K7ICgFPjaji0GyjMBScCBp32ASLzata0+68sDjSnwmDtjzw+Gxp0BBjGm4Mw4i9fELW6bLVANFHxLQcH7n/ItZVdmWSkIAoPBqgQLCEsi5PyYgwKn8zdBpZR0EN28IwhzsAyvKVG4heWH5jJwQOCBLVcVCCg46GkrBwWO5Ntd8oAxknf0bku9FBgYS+Db50K25/WVFSNwsDE4aQR0ytIRlMAQAcisQinZdqosNDazsCt5AWOVc+tNqJ0u61QBSZNRfggIukCt9wwMTIBA3CWzCdJqFLFaiAQZIws1CmuUa57XG6GK1YolDRarCkoFZA8S+rayenRBie4miSsD4u+iy8Mt5Q0WtOZgBfBgTTLq3MHTuF5NnY76wILrYvvrXh/SnjWb56zdfSNLv4Zrhj4A7rZRIHARJGSu9HcPEow5pOpeIO0veV4K7WrKK7gMBuN9mGp9uaqWr7kUc1YD5qqO9RTyslCq11ISOYM0BQU9AzuTOdLM5+SOyBxRljcEZGIBBwW6HbowCTktYAMFaQHlVT4Xk8PynrYTLikoONyGvh9TIcbAAYEHFl6FIENxHaxF3NBRx9hW5lZK4Tq2iUWxJPUhqC9BoGI1zI5AwXMKCu5ZzN2JcmeAgYCCPY1yBA6AnuLi+pcrKPhXP/MCfvwDTyGydGthCTLXAZoBlKSToQBLioNzBa028TYHCMiPqQDZgHX1HAcuQIAdxey7KlJG8RiCsBQmLUBWd4JO0LW0lFXc+50dJNS28qkQGy/ISfMausEAGbsEiNYiFjSrQZUc0EMDDtRC4mQrFlg+F8jEg1pGVhVb4eHfNZhR1yxXwZnluWlMl/VBh0NmINLLvZvgXQCB0VbZvnzUntc3uBeToGL2+zpuBQkp5zYvwLY6EECRMdYABAAw68rvOykXgECf7rf5acyzEfz0Bhi8Lg589Fln6xPq4u1sW5VbP+zyeAQw8Ejtb+9Sk+4QxgmaKDAGtqGOKX93OYxiQHRpM7gIaCoFyKUeA9yNZpJ8ut4+BB5SDDwMxkZc8gx1VdZIe6HAYyCzuwtSwg4UBCNEGMoKCEwRVnlb5U9T+4H8sXVCwhqInFmJkIgVFDBSUpCQCEvKErStbgZOizAJJodLAfMGsmWOIwMtyuII6pvVXoGlVTlsrgQzytbC6kKogKCU2gZWNjbgIyaOuFrF2CokxkAhcnZ2to+PZUz86qdexFMNKDiCElLuDDCwomPPP1sps3ZwUFBdCAzgjbcEFPy6goJS2oEbAYV0VAcQIK7sJRFyWpAey8B2o/ToFZBvVEioT2xhUFl1E5kBSlXh5UkyfDLq4IxsAQgra5yeKv61sIOBjVldJfXdjQkR18K4sTLIhXhOZKECule4AAXfNpmNSako17OhGjjg4viXYYrgqkYnm7BDENPJ/JlJhO+q9KBGV5MGcVEUkLPgnB+Ge+AICEQQ4HSsKSRq//Z1bZZyiSStvmUdM8XWNps1u1RKVBmWXjk1IBSY0/3Bat/55J1tOaClu+K+a6XhXdFZPW9Tl0t9YTuBBpdVs8+HvYvHB6EeqxUOn+tcqee0DwJYeM+BAqBR94GZWADu+/DSeO/GOi1LbScDA/ZXVzqxxygJk1AZgmNQMLKMe/kDlbmPIoNWErCQyICCpo0nwsJSl0SEnDIW3Zm2kcMaqMj5ykEClvft92qJ7UmaoAg2zrK3TZTD5jZY1SjbSgsIZgZZYccCGgEnMjSRbnFuWF+JhBEUfP3hA3zuG8/ihQAKXK4OW7YtdwgYtK/rjT0aX5G2ATr0ynjj4QP88quVKegHMFCTUdhTCwMpAIRShN1rAEJ+DClDGIRyBXaLQAdnXILTvFqwLk2gNUhdggvjpDT/1dYNRAuwZADrVmSyljpZgbHHPgO4UZYgkwAPm6RLkriFzAA7W4AhBdaAg9yc0b83AK6qBXel29PqxlGegGVbhSbPVzsBSpcC1w7809LM7wYEpBbIab85CGiCtDrXQQ8SYmmUEIappFkisKqCUsv5jHKSWx9Y6OgU/wUXwjCKPdyfgKr4Q8ChM0Jn63ISlAmDlg774pH7wY47g2MKRcHbItcZ6HdmS/vDA44VEHggY1huaq6JUbBm05YnxvwI8PaAIOZAYcphhVPHTgZ35SVQEJXhBgBqsBgQOCeH2LsmE4GSyKEly1LEjQQkZGJsTMgg5MRITNjUzdDK4bWRwzI3tgq6R7I4goMDOXwECEY6xUoxg4tFjJqBVZRJMNYAGvTebDpJmkb5G8/ihU++iI8+UUFBO5ppdySWOwQMJg08OoGKsmIQDDPwhq4++I3AFESr2pD7n28bsjYsESEn6biVgFwYKTEyExYFCCsDi9NbC1JaxA+vy21MubVbDodCId+AI/Tk7IAYHS1VtTJj84kpt123IgNWJ6B9lraQvzYho6hfdVIBwJqAxRBtYvAmE3OLg7TwI4ADXV9MG1CE6qVyE7ZklS2nDSDQ1WMiNN0naJS4vs/A4tylQY5U+COyAA2bc6R0AhhASB97KkOcvJBcxnUcok/2YgqEGZJ6mYGyTq1Yua2Chfis0cqY2oi1zbpj/vUWG1N5HeI7nqzHeN+PwM6YZRf7JOWuPyoQuG1fBAc5XKrE/hgABoYIgwgW0PfLJF6hz0/hoCDIjMNx37tdzMWi7hb77il8LX6pt4xNGd4SFESWsjdMVgZs9cXWjPG9THLWQA0T2op/XnKayGFlElQOJ1qwLCqHOeRAuI0ctr8a1G1u2yM5HBmSKnPZ9claWPSJehHsL1DZAokzquAAkFH8+vdkb4UXPvEinlJQEBYzQB9/kTW4U8AAQONK8ANaeizN3bk3Hl7jF771PP71x38HH/rAPUGzeo8tIFsAWDfGqp8zEW6UxV0ogZOs2y9gbDowE7Eo18K4SYSFxNpOtCCrd0AUpAkKrWAjqOrSSotlmMUOiOsA/nnlgnVrwYCvSDD/X2gToroDtMQtMdYkrhJi0shplgGbAWwFS04ySB8FHFAC0yZR6aS+cVOoqfoBKfgBo7XVBHsBVWh2hTq2oFH+2s63AgApKhjMs0hGUOD3QNe/ByWMCYYxBhEUqJDJ8Vhxv2pvxZJdZ/eSm7dtxYxxlP6Ebg/XDkQqkK/C+4QrGlCy/+W4Hl27EaHMGIEREOj75RH7hMO7eJ/osVbBdP3CJluCor8NYAAa1gdAM/ZH5RLL0sQvmd88uBxmy57fDSgwmbRBjkWZBFRm1pr5xrtH4wsSYS1qnAWQsGTCQgkLEzZirMRYOjmcEmGFgYSMnPMjy2HmwNIGOdwbZpvKYrv1FuaBBbgyC9O7pLASgUSfxFgDA0lWPdtb4SufCDEFAQXYk/4H5UroxUnvQpiBAmML3tDkRV/UJYnm71Kby0HB6muHK4pdmaWTCrDS5gN0JfKB6fSWBsZsSSjAlEh99LY+l+CLnp1XlX82qWzyxKUu/QTcusm3cp14a6D0bIlM6RtM6ybvqkuDitafGetWgJyQWUASMkATpLtNImtRAFZwIOgoWtoZSJXaEwXHACui130pdsIT2Fu/sZi1O9pWGGise+4VzND6P1AyA2Fi3Rn/WrzKUUlkbg+9JWx4sFukF7PCBTcVx/PaZm2thip62JZNxW5TOnDQnbxcj6jIadyH57J7VkEfnzeNS/KSxRKj2jLt3xAHEvrHXIYz0MBd/zEX4Ip3fRjHPgAf/6eCNgegt3RtFpnJPWW+X/o8iykwuRWZ10ug4Ix82hIBhTVXigCFJal7kxMW2rBkcldD4ZEcFrmVzOi5hRyOgZUzOWyGWZW7Y1dJhugROc7iloVvUi1joptjBkqJqNlb4akn7/nvrIxYg6NyZ4ABcPll/bruwjfeeoAvvPIcfvPpF/Chx++hjCgk1LkKSGcDFfFV65qwprpqIQ5Mo7eI2KNmSQNl4vpcoB0DPYgpOihlMLZ+O0Pja2H3ca1bwcrsE25TZAtU4de/cyJC2WQiIUF8dGjBAevnxdrGFL8AW62TUGQlRNZuHTiQXAeaWjpJMCHKCrCsDTdA0FhbjeJTa+sKAwUTSlBk3IGBoeL3jrig/CfKxRV+ECbWjwjXnSmucFRwAahJXihLNfy6MVjYWbCIlTI3xUH7Aft897vvJwBCP7+6Z56uwy4W4Hb9FPvIYRHX/rlt30hV6vyNyiWlFjQ0VukZ0GDtNIotsXNXR0ArVjLE9h8CXwVVyf7qSg9VWDFHQVwCXVShM9eYAQ4NyszVYoYAgRkoKMp8AgMZpV+T8uxbImwsin9lxqIAIYOxFN65Gf4i5HAPgDi4bq1kEubCVo1wr9UvlNcfCij48jP96oNxUcl7WO4MMOjR/vS6ASj4ec1N/eGwJNGXeU1K9IFF5McKjc0nvzKwkChpczV4NG2p0bQJJvR9yu7fzSaef26DeIyiclDA7IBgLe1Es0lWBi2WQIqu58NntuvgphPQ6ivNqFSw1jcHcMCszARDlzESyATQGeW2s3j3iqXNC3CgTOz8CYUCBKXC2AsNe3YHBprfNpWMjdueikhflolG5QMXZPZ9ChYAB1Q1aK4FCByP9WVErffj4BL9Prp/N89O16Gj/Ed5+WcgwOaS9VXff1Zu30/cgLhYzdhXdj4F4GC37AHeCDgAuAjy5Jqzc+EyqOLQnhZ7ZdZynxeFEcZ8aLNt16ChrQ7k7Uhe2bGkHHxioCgbkNRQMYCwFo0/CHJ4UTmciy29fjQ53K/uMjkc4yZm+sKYAiLCcqCuZ/L2jbce6IZLAgoGxEJTDBRc0pN3BhicLXH8CdKSXax+8glp1KTmboEMltlyECvRHwZU5oCSuhgCSJCBuHk0rYEEAkQ4BKFidQXCfEercGLcQx8/MGIIRoDgKLZr2H6p+rbss70PIEuIRoV10jrzoOCASNqvACB1WSQy14oIzZ0lrDcc7saGyaCPygPolEpVnb0SiYp/pPSb1K4BNVj7RmFS2+LStAzVDrPcFD2g+6qZwqEKGCJYsN+79Upocs4D+7+hln1NLl4xO7Z7p5PHLtWhNwZ2gA21j+z8DhSEH8c+e2/6q1UuvheeuhbtXM8C1WMKHPRECnOhCvcKFKzSEfQd1jwyaBPXV2xbX9E0AFW7xEWTB+dEWDdRzrlIlsstEbCJO9Y+XypNGIVRB1q/xMIiFJCvlFoTYeMKENbgZlhJgsdrH0Rwp7eO40Hf2wBQDwaiHDYmBGj1hJUtyNP+vMK1UC9SQ1IOvNmBgljfM+Wole8UMDg7fYla+uWjTzwlnU3wtfmEFhQQSSY5mjzEaCR/hgkjtSJikMySkwKGrVlyY88BVMEOkHYfANlTVBEQ9HSc+MQC4taXa1ahaT2EVrMYCApukfAOqAZaXDLk8q8bpDKhKjhIYF2vq5OSu5TKQCMw5Uve1Xk2F2JXNXOO63cxyPaKw9p6pDx6ywEYMQT7OsTrLpXO6QEgyG0dU9W60eMXAEO878yqreWyhBkJ/zNKdGT9jAXacR2mbEw4f7YPz/Tfub7b7XcX2loVd98XTR8aiJNrY8px++0e+OV6v4qiO6ZpVttaYhsZsAIG8yEcb9q3a1t/thlj+lpLTsBWJM6KZZkzcsK6FWRmIAt4SKwvW+THhXUpOLju71RQt/NA7DSCBT8zkwawVoBQl1xXly+wz5Mwk8EAdkssLwVQNvqBYkpjPWYJqFLobwRw4GNB9u75+QAKbgsILl1+Z4DBJXGUUAf06130JquJkCBKylgDpkov5UTgjTxyVAbW+KlFoWSCDD4AOjCl03ndsNrgI1LLm71zbZVDX+qywhYMHPnnADRMQdMmFqQcwID8bQFBDOrxyF8KS4UMcQMAoQIGGrvKDBxYfyRtLwp9FAf6aNDPyI2R8D6k87nWaabwo7LolU8ji9BSpbexNPsSlaexMA5KG+uyKpseMPhxvaZarPp7qjW/hVxB/dW4HAXtxWf25d3UoVFqGFv/PQAYMXF/of3pJ61/9GuQBXEODQGETpzUdu0B4Hu0uSOfQ1vYbzrANXqWbawmgcgVHFBh5KxuVmbknMJns7oJKZHKM9P1VOVZGvdJMSRSWIEE7QBCZHMFJACr9sVtZbC1Ty+Ha9t2c7ArmSpzILkXjB2QvsyBLYig4N4HxzEFl8jfSyP5zgCDadG5A0hjGSh4ISzpKKbRWJPzgDRRUUV1ZsWSDkRKBGKN0J88Ok6UxDL4SK/PJLv39TEJgOQJGJVINY1oqj6SF6gTN1FF3LEcgQFzFfQMwSVAAATLZiKgDBzEOnZvO26ErvTCqP9VDwpGluFI2feKITI10YPRLDcaVHmSu6/JEWGlbyMfD+G+I2bJdW1jiVoQVbx3q4DifR61vBtl+cOoQ2mO179HgG5kCfplFiTXved72dc5fHGXXG+5QgHexT5ugUT/LLlPW2bK6jaln4dkQgHQxG9yfIG2OXQ5HsSfvzDXgG1lFGkrWFzZAhtRMHoGICG+E1XZYqApspUWRA2Vo8QhzgiXZXAfMzBa8n3BkQMALl8puDLc/ady2Fa+JCJ85+0WFFz0/j5i195ZYKArWaRo47z+UJM/fLKN3rTGLUQoRcAB68BmpbKYIb2km5UsBKe/Fr3GolGN7orFEaMqww0ANnYGglIQcGUuLPsAlkhXjYRVpBP7YEJbjjgDAz07MIqLiOyAtWUvkGZjM7ZRrPrMSpkJ/f4ejeUzsQB7JRAVgN2zCSrtVqH0dYulVxAjxSDrsduWaRVEZaRsl2giYAXrdXLOQMPQFQUg6JJdR5hyeZRyilaPtz4UUHvActuyq0/X78Dlvgdai1CuCbfsgHnzuHcxFoCOUbD5EyZvTewTz9u5cd/7AtkoA7A7tHNxxOv6ujVM3uxdwrWsvzcXLStIkCV48o9U4C65+uuXojlhWJkDBQ8jkADQcDnjrJi1n21OB4Dg7zuIc+gTLs1cBTNAkLyfWhZ2yQkLVXeGuWyzGWsku0caKPjKJ3SXxFso/dl+CrNyZ4EBEJAit2ki74/oF5W1SZe8mLIXqAYs5nQLnSdrYyo4IMDzHCTegwNAUStoJ3hy4WbJil0QBUkvZHbBKlSBCVAV/+5V9bABAXvOohutTJkBYwHQugrqjmdSzlggo+C8U/T9QNEfKXm7h5zX33ZKfpRhrVUCtd6PYiDXpCxSqm+RG+Ef60GKGG0pE1D7yvNmAAoi0STbAuAJt2LJj2o+TMosn/2jlB923c6wPJdAwGic7K8NxwfNczOtcej74Vhp52q9to6T0TXRPSnn9XsAE3vXRgsgInAw5p7ChD81700WqzHBqHM+goScCQuyKO+yX/9vCnktRftANmrb9P3OzM/e5pLtrKn2aZxfmMvdUezAESiw5/axWgYKFu0ckcUiv7OCgu9+/3X8/CvPCdP9wXtTRd/jo9sCAit3GhhYeeMt2VDiq7r1pCPa7joypqDI2SWJPwzFJhS7afK+nJFRkAsjo/rGkJVWgky+2yoSG6RWZvQkIELABmmevVR3PYCGEeiBQM8K1OAn1C2V9dg+0O3gvfSv+fUjq2ICIi7D1GZ3Wj+uwOit+9ad0luF9Zz8Bv5MoHO7hFnVT7BZbotYdoxMtMAaABAYla32XwSGmY0RCFuwblzdC3ov6gKkuJVSTbl5jxT50Zh89LIHwj+scglgz1ih2ViS6+TvX8Z4ipYoUGXBHiRUENHKghY4mBvDmUHUGAE2g0B9+JUprCihj3eIxQAJw9qEEIMWTRZsDCwkroarIAMk+l8CCHs//6UVALNCA+05G+OX7jsCR70htgQ21v/lVIFDgsYYyDljCl74xIu4/z+9F+NLa73s+e8Rvr6TwCC2Tdx6cgwKAlJX8o2SHCaWxBdI5BO76eScQMSgVECbXJ93tNdeiMQyi0z185de9sJIGFkUPRDoVxTU8xUIZAUBMUraGAP92a40lj6UNoS4T2LgH8J5V/QqDEYbPR0FXtp9+oxp1n9RII9WaPj3k3KlbX5uV3jY+moKyaIAb7QUAIIkZmmtl5ry1Cw2apRWJtoJqlm8y6j0dPionI0heBSwMHOx9OVMDEI+cc1RueQaOAswZ+Pt0cZae6Kf6qPVRHIdNdf3LkNg7DYkD8jjZqO0JSezlWQFgX5uykA2yDPHl8e3E4NA4q0EMAgYiNszG1BYkAeygXayAWnM9DxKiWN7ZIxNY0sCQOvbObIEiwYaWn9lBQuJCN81UPDJF/GxD0ZQEN+JPJSOsBOrTTnbEn/lgAER/Z8B/CKA/48e+qfM/Oqj3OvBwwd47uvP4kUFBUAHCGIqw/p8ECUQSWrfAvGPrSy/3XRS5wSQuhYyC3uwgLFuNXhmUZDAzFhwWXj2gnImEI+E4JFfcmgNBCDg1wUg0IKDwaSfVcUFptCPkuSoxnEkAixfhLMD9rNu4o/2eOjTOvcZ0vo8DZeAQSyXNqjz76AmuNNfGHX51KVkUZesPLlm/Nszih0YC8bRWDyyhk4sLb91uQRiosulLz2Abtwr8bpHBAz2O2u7DGkzUwzmSTT33ajMcoacGYunxmDMQXIIDOo142Djmk7YgqDZ2NKwD0rdCli9qqjMoYECS/E+Mh6OZIWmbKpAACJHBRS0QMFAwEJpuCnRpZ0aj8f5/tyCbg5FtwPGSjSyND0zG7PhmlxNCZ4Y7irJSozvvPU6Pv/Kc/jqp17Ex568J2brQGeJ8E6gQQM/6rT9KwcMtPyXzPzPH+WH1jSvd6BgBAh22d/8JglIGZmSZ+UjXV5ncuYq2ZaXEiFLS8LCmuVQg2c2qiABEKBgZZQWc/cuJ9iD1h9prIBdvwcBMUZgxAjYsdxNdD/XtPNs2NVlnQxVKtZ+xsAUta4LgUnyF8TJ1udTX4Nf0Y4VPW9AIAreuVDe13lG6/bKvGzdMXW4RuUfo6DjfaL11gchAdWq8BYMzzlrDR9ZvWd84vKb8b3PRFi/18VcLn2JLhigtmETowP1ZceAslBOt2n8jTVOqpHtdo2xP0iowBf7iPkZkxCBwOEY7URVHY+8O+aAoAQwYEsjkxxPRFiyLOXbmLFkazPNDBj2QVlC29ra+gTydM+238CtZYa2lZ1hUEiWRCG7YgUKmDAKtr/ACCwAKupTy7zF5rbsg5fcTQuOZfjIVdO4aQMgyCofIjB48+3X8flXnsVLn3oR9598CuBNN4ob6CoGmHgHEIxB8HdAZRQePHyw64b+/e5cef3hAzyroOC+rz7QTuxSjMrIKiEhEQHQTtBdxK4ooxAkU5/e5kqDFCUNZwUIORMWlh0Dd37xMCCvBvEHI1nVC7AYUDaPRsYOBBhyPwICvpGT/UOYzL5neZtlzdu0yyhImk6VUhaFaSPSlosoOACJZXKUEnWDTMIICtZSHADUnSSrsO2BQGOdndBxUs29sPXvgxwQ0UIz66wHBDMwYJaFHbdyyZXUKK4uwI6IdmzBkX8cOGJR/uKBwWyZQgqAwdwwQOuKiaV3pwHn23gBDUEVMcuKJaiw9Xat6+5RDCwqQGjSqO4T9uxdDGNQcXRNHLfVlaXnEof0wbIvwJKAdYN+TgoEZL0/gaYKgqgaGztQoGPcV73o1vI72RHlBgJ4o4SkxhlA4KSbNbG4GVjn+jZhFGK80g4saGdfWqIa33tj3vl8mdGkMJ4ZaL1xVpmVCghs1YEBg++8/To+/00FBU98BLTdiFUSU2FDdZUCAhGvmq3HNsrqoFgEBc9//Vk8gSf3Havlryow+AdE9DkA/wHAP2bm/29/ARH9EoBfAgD8j/UYWvdBAwr6Xc7KVgEBM5zM8vGR4Ht0p4yUMihlvKFI6zHdrWstIpgWJmxJKW3AQUKx4JnJgJyVnYUTBBl1H9oI4j0IINRAwSEj0AABBUq+zet2cW+CWrGkMySFishubCnlvXvGVn6wsAY2zvtim6yY8C1oMzk2nwMguC0YsNIEeYV+SERTQNDTtS1Vu18BEinGeqw+dwYIdwZDtHwCxWkgwb8rsOqp8EvlLwUQdM+era6JZeSKOYrYv01bm5CMCqJnAs1laHcar7snlKQJdwCUQm2sjYX8l9b95IqeLo/j9nwFxYlI7mtxMAxAQUn8nCHjg5OCnU4htjKGfEndYsZFCnKkbKCyjmXITH4AquhEhjCRGhdJt3tOqujJgcKMUegDGmNwc9b3sn1Ybf8pBhwpmqy2zcLPyuyZgWZtIyCqymOLJ7Cm/tw3n8WLn/wdfOwDHwLWd0Qel4KeLiL19xhAEPIg1es0tbWPSVRQ8MKnXsQ//d1fm77LXwowIKL/B4AfGZz6zwD8KwD/OeQ9/nMA/wLA/66/kJn/DYB/AwD0flnctQcFLUsAQAaqsQTW2M4a1IZnStrw2QHCg7e/jedf+TwA4CrLxLbEQZU9kO+c4P4wqW87IGEodlJ6Y8l/Re3fPitaBAExGKh3DdQJDKGpIhCwdnGkz4FdGQCDwS6FnDKI1AxKyr7kK2TKcrl1SSKwCs5MAhAAaE51u70kk6KEnTab+WJ37XkgVEfUf/N36qMdAYR5kqje1wi0sR9ACwiH+05kNNn4lmDxmKWzMYNdOJGvkjFf6WYKMlDhgPRH9FVbxri/CqWP9q7tp98ngKBlZfbWHHDQ5oFK2ArvFIT7sqkygB4Ie7Du3lOUJwO6cOAAQMGDlFLgMkaf3rTD2TE9K5ZOOBZJtkMtq6LCxGlx6PhPdc19Vpki8uQmsIxh+/RbyhEi3e0xZRASOKUOKOTKKJSx64GhbTsAC9b+QKiSMVAdKDrYzL3N+xBeAahxW4QgJwJLGwEVALz0zJdx//0fArabtv06/WTJIYiT5tsBiIrsgmlvFjYUM/341RBzNyt/KcCAmf/OmeuI6IsAfu/MtadAQXQdzJSfPRu6EJxk+9/r738Xn331F/DSz/42/u7/7RPIvCGnLLE5LJt1lIIOuYpl0afb1TY481qN0HJQoN9HywVjwE+Kn80PKE8foPmttkXZpKJlg7Et+33gYyXrhJZBKSjfGAPgyoUB5yuktMCdkEUQr4RyaHIpFbALJTAV2O6MK9t5Ul8pITk9S+qqqFZXK1DnwrJhBQaMgP323QKBaQCoVr8XLrNSrZ363gvgoGEJ7itSK9csXLZltQA4oQIGdesAgZo/rMVfTpmt6x+BgRH42q3b7wH25LlLpqCSRRFdsTyc0bsN91HydQUNjfcyoegKC2xCtuO2vJcuxiP0ZQZwfUzbZ1Sr1VL0ZpLIedJ2zqrw5Deka+6pgoKygrYbBQFboMGrPHHjDDiWJbr9M0HliLIHDhQoO6NAaUG2LaGT9JUBBXM9CGPQgQXAAYN+lL+hWW8rq6OcvsTUusvFYggA3P/Ah0DbqseqIds+iwEq2hYAFahrK4FYjxtDQwnXD6/x7Nefw4ufOrc18185VwIR/Sgz/5l+/V8C+H+d+V2/+gBAq+g9psDiCcpeEfYdoNT4g7fewGf+8FfxtY9/Efff/+Nyan3HXQyJskTcJ5nMmesAlGyIMlLanfiCCOrH3c6CbQ+PNlSpghEtWwBU90DZqgtFkagPPDsXJy4LaGBjEwDwwESnpBM4Z6CsyrJkgDLEdSOAwBUSgJQWsCraXDQDpO5PkZIotZxqUBQlUW5rEmW3pIS1FCxITs+KMFUaVuXLjFA4G8Vt5yMIsLa/BAQs+vgoADT2Y9+3femFVbR0WBlhzyIHYCOxck1hzQJijSI3AXhmBU1fHiX/wLt5xqU1+REIAJi2f2TeZhn+rPSgvoRjlwLfRhuejYCC3LffDbUDBakyBWdW2fRjPYJc+76kpEa6uAUsRa8o/foby9ufTaklqtaugQJTagYKDCTY3yBTpvIEAKlMEao8u+HhQEHdk5wWAQkqh3LK4JSRU9wqurpttk4eG2CIfVvlNNAI5ANZHeV0nMdxNdcufsvapVRgIMBqvaCXWIGSnkqQtM4oAGvMgYKs6++9hme/8blGP16COn/lgAGA/wsR/c8gdf/vAPzymR+1qw8aCdoEGrqvi3lqHdffbrj+s3+PT//RP8TLP/XruP8jPwZsstKatneAEqzitGiWKgEJ/WCMaLWr4cUyAwR2sgUB8Pdw6i6yAsx10FkwYYh4JS7g9UYmrE3cpo26krK6KAm8XClAWEDLUgECX8l981Xj78qRObCjGkHNJIpvyUkCgzTLJLYCSyIFpJ3/FmjBQCl15o6WGwIjYDAGAMA+PuBSToijANBoUcTnz6zWnbAK1Chgw7oqGYlxQQMUjE24ApwGX2Kabi1y/niUjhLDWBkBhX70XIXPt33WaCmuHD+/EkdPP0If1GWqR77sBiwos3AuLwd5m4wAA7AHBgYcrMy2U5+B3SVVUNBk49MldYmMPRB2wADDkrQvIigoNwoKinxWQIB1BW8rmAuw3oQXa0cGA84QWFwBKIFSQlqunEngJMYH0Q2gLgZOiyjMwCbYtVkNPduvIcpnacM5QDhTopx2GT2Sz50cjowtAND651U29zrJasWqd8om4ICTvpHVXRwfrz18E89+83ll0p/yd6q6Ylz+ygEDZn7+UX7XLklEAAIVFNQGjsds5rErRhuwr/3gT/HpP/5P8fL/4r/C/R/9MWBbfcbR+o5K+kUmBa2CbNW3bvub22Cs4486AX9cojyk8LcBQP27xmCf3r+38/kpgl9vZNKWou+5KSgIAAFoJ3HKju6RFxEKKYPzAmwL0mPvk98CAAsxHcWXMQeJIO6EBDAYqWiEbpE00X2WSUsiRcl8vDaZ51ZT06ZB6QPHqwQuWaNnFFBUPiOXTw/8Yt2s9NZqtHTarW9lp86oqFqgUDPJmethunnQgeLv0y3L7Y5ETVv26ZRpGI82e+Y0pe972A+zPpDPsR/q+vvWGBhl80vedyNmoQcMQAsapO0qkHqUjXtGiY6OsvEthJqEJxkNTm75DkFBYA2IC8o7fy7HomzZVn2/spMrAJyJFKUvyp3zIt/zoiCBxABRAEBUgQT0mIAHOpDPMnACRgCHsXwbOT2U0VP5HABCNLys7SY5C8QuSAhCQe5LBLWaACRcv/UAz77yebz4iRdw/4mP1luE95yVv3LA4FFL7cb5K+/YgiawLihPMF77s3+PT//xP8bLf/tf4GM/8mPSaebTASRaNGX5TRNstwJIdSDqAPTBCLjEycBYwvqrlDBSFVbE3AvBT9dE/ca4CaejToKBda1AYFvBNljtuZt+zzJ5bUki1hvAGINF0G8BhD0AZFVGSSDa5DeF3IxeKIuEsd1VIHkLDBzIfWqWyaWQnEcVmtJEAjt6y/RsbvkqLG9HRe9YAKKp4plZE/q11tO6PQ4HPeMgQA/GgKsIFix/RA8Uin+uYAGokdnWIPHrVOV3Jw5Gs5eCli3wB3U0w+HzzcLHhb5QIBABgv0mrrcHbtsXFZpXIBABwhgwFB70hbrMRoABwA40QH+/z8YY54GU2VwYxWQsqa61j9n47LOzBMYaEEDqjnS54kykuhLWG5RtBW4EGLh8UcaA49r8gWwBIExAXqrLclnOgwRjdE0Wp4ydfJYGEfkdnvuu5XS/HN7ksx2LwZjGWAOgclP1Uq/PWBJSsQ/cpPenhjV47e038Nnf+3m89MxXcE+ZAr0BgFEqpLbcGWAgJVjQu1OBLRj8rq6zZVz/4E8FFHzsn+Fj/5P/uZ9jIm9SKjdg1gBFhGA7YwgoyUBzsyP5ZIzbe14ccHZZRIf2Hg0wiCAnDLQwAIkLeNsEDCgQQAAGDRjgAmybCBkDB5Gf3MwRr++9aIBhliEVhamhZ6s5heOigZMo6qh59XGUBSRQAVLIERHToQJtshH7GJv50iYyfunOejxWOGcAgAV+7qwIACiDRFvhXWgwVpisvQIb1QRcCTjoA65qoqm9+wFoZ8bAUNmVJlhydMGFMts580wdYkzGyC3QA4FRIO6uP0Z9ESpCsRK7vrBjFPzabQCcAwQgfMeUYSjZfhdAiH1ABQ+XNhGLbTmbE9EFZmPasvFlCJvnoMDaFwwqmzIFa2UKjDVQwyOCAlbWgMsm50fy5QYh0EflS85uhPCmIEHBAucFfPOOfM8Lkl7r7oYyltHQa2rDWGzDeyWnw3y+JKOjoVfWGo+1VWgn8RaoQGBYp4Lrt76Lz776BVnd8ORTFVpw0fdw6TstdwgY9MiKx58vlOsf/Cn+t3/8j/Hy/f8CH/uP/7qiW+1gC7ADpPMQhHMACbbMRn5D3eBDO8hODjgAQWgFxeIsSPVHtassGFCUXswV0rMDht63mxYMmJ9rtCZwAywrCyeltZZlBwCgPkKL52BKQKGK2LcbEUL5SiRS0V8mtY11mWIhtLT4knZCsi/UfUnheOweU/r2PSoY4Jzyb0CCP7tTODMLwvpx1O/NWPGaKfjUsWaR2kRNwFVdzz0GCtZuMTr7VIkKmoaHL5agaluq9pZ1aKz8CRBwoAAbU4Ha3eUzARrQDRz2hzw7tf2hfxsA1wMGB2kRJExAg7ZLBA5ABA862rg2X5wbs2YdgeHofmmW1UVQkIAEViBwAw9iDqwBcQFzcfdBAwq2G5E9RzLG9GFSpnVLDUhAvqos5bpKTNO2CkhQFsHYBZPJkmU1eX+44RblsoOCboHsaWDQzeeG2T2W0w4qVo3D6B+zoQZjTsr122/iM6/+El56+ku4//hHwo+5Q/c2EsflDgEDLX0nXXJchvLan/0pPv3v/okwBf/xXx9EzIb7rauAhMgk9EttgAoW/HNneZx6p5GQmrAFgDMD7hKIwT4jQNCj9zBZfYAOoplIXFnV3NuMKSGZ0KWAttWjhUEJVFYhu0yIIoFJQFVKGbpuEaSR86Ty2qzdBG4F46TJokVpzU1+rlr71h+jTaFGud6nyh9oAcAkqcsO3O2YoNDHO1AAb7fG4iFJCMMepCVAISmNaiKAQc4ooKO9EbrxqD2tbWq1avvOrrfS3z/2ox87uH50/9hPdnzHCPBW+yauyun75V32CQWlUr9X0OCBcJdYBh3r1gYROOyPaVsF1sHasW/fo7nSRtFTBVMOBuLyOpMTmqfAVh7YEujATDoTaYZB2cRlcFbObEUUOHdGyLZVlpIIZg3TIjlT2EDDtrarpqJcpggUbDRB5VLXx2fKTE4HudwAhgAGeNu8DXjb7yLisVwH5frtb+Mzv/+L+NrHfwv3LKaAxVHo+shZg+Nyd4DBLQDAqLym7oPf/Vv/HPf/o79e79fdl7fQeZ2wIPeNucNhLEiACQ00LjGxRTuwALe9uC4BKhGFhxUFjcugbPUdjCVYb+YTNb6rHuMUwAES2KwwZgEFFO5hrgz1g4GFhvR4A32TmCExgXQDJnkEsy4F1TdvlnyGMls2lCbHosL334W2dQsztkW0/L1fDhRN7zPs6cOj8TsBnHF9dxtsVaOyd8FWyRRRBQvSlvPHj9p19BexzQ5LmAf6nZvvt6/PDqjxPNhr6t+dCvDZa3T94sA/grcKGkaAAVB5EcEe4P0VlVa08Zzp4QoUgAoWRseO2nUEkusKHOwzGsZcBSZjbF4bW1Dq/BdwIG3MsW1Ld21sc5tuCXuAsFyB1htwDoouaeCdxSMoaPCMiVtqQEIjp7v+9Kq8Wznt71PlNAB3ETgYGL2/3/gMKPgOPvMHv4KvPf1F3H/iI8ez0HXb/JK7AwxOlwTnqSgBxLj+/p/g0//tf4KX//Z/KasP1sm+b13nGUhARGNWOrDgP6NmKN6qNOlvzfekz3QwYPXsAUG3/HAHCkZLEZuHd4MzItgTA5eLhrh1lLr82wRgkG17nSXwT4VRKeTAQCzd+YgeKX3gwNKfKHwAc3p5pNTjtVO6UJ7rIE6vHa3l3r2XCTSgXdtdosI5GWxF1IAFABKdPSuBEZF26cDiRIk247W3vGx+DHz2Up9OWB/Viy+4ao6Ccf0323vXL1HRREDQsYqVNcMeXASftzMO4Z47AOGAr2Me5KIdUJi+Tzd/ZonRfAniaD4Dp9pO3kGvi5tFnFHGZZOA722TQNltdTcmYQWXBKRSAUJR9kw3uHCQEJ4XZfV7JaflWWFM2bHe4GrerbTyNd4ruKmgzOv1D/4En/6Dvy95dh5/avi725a7Dwx88CUwVGDoJCQUXP/gu/j0H/0jvPxT/9JXH1DOsJABkfGTzrNiuGALx2ziKi10hgq6VEo/2Xqr3lcQVEU/AgU4EAwAJCagKJqd1JsooS4LEgUkuQtU8aQ0/a3XnclZAxc6ZlVo2lPAljHqz+Tp+/qEo1PL8ci/P7Pgdwq/1mLoGtDzQyUThWbsK+AYmIVcEZIC1lwG8vdMsJWvmgkKx8GCN2LXX2HcT4OqwnU7/2ooPTXbutrqceos6dN16wHcASvQB+Na/7jlGufI2X4B6pp7re8Z0OBt4fT1wHqNoECPtewD0Mc5ICqQJs4B/ncmBYbMmbVbl9bY2YIjZiVJenkuCoSWBey216IunuKbUg3rRBfkCeD9zNZUxYBCPG+Wgsj42F+Rwn838nqn/P3EBT0yKtSxHMENC0qiv/7wV/G1n/nXuP+ExBT0Y+BRyp0GBrLfQU/LEGyz8dfe/jY+84e/ipf/7q/j/o/+TTBvDgYoowUHwNhxCkw6tz3GR6P+TJkNoCi4OmHmoGBQKGV5rwzQJuhbiiL3PGi79gYVEFhAEGnQjy0XStWaOZpoVNbqliChPUl9YRYENI1652DFzpT/bel9u2/z/mMqcGchzZT/qE9OWleNZWNtmaqrIAZbRZCAElJUd9R2Bceo927aNSreQZtcAEwHLyN/JgrQj1Gbve1UHSd92+yPEsCAB+NO2DV5xC36BmjGOhsws8+9hRoy/AFoXJF6YfvuAUiQt0XrtqDQt8M4hx40WNuPymBuUWBcqEyYVX03ZhmDlJJa8JrLZLNgQQInnRtULssbbV/528mdlMe/K5r6KSV5jl3nKyHsWCevI2h41HJ2I5e+mMEFdK4Pm/cC8q5/8Cf49L81UPBRRPdULLdxh1i5O8DAhNRokPesAYDrt7+Lz/z+L0ujvv8nNBgGLiy5AFhIFIFbFeGeo31SrYysjEtU/aOW3jrrn2P1pCQgJ1wnkykHALXIu2a0dKPdM0w+n5RAAwjiMiLkBViuNBPiFXiEYB2FFQdtwiIQgC0IwP69zUq9AAC6DGKjSOBhyufY2aGfeQTEunavyaAGCqZjCtr79SDWCdxq/UOUTFzT7cFW3XpvLIvn2Wip6pbCnpaR4h+CpwloGpSh8htYz6YU6ZLy2tVz3M++MiesynEwEHN3hP65Vd+EOhrI7gFdcyxcZ7/10RSPD1x28b4NExEBX+hnzxwYxwC1OxjG++/bdzTXBvNE78Fg0HLl7QNob6Ykba8Z+5jWRu6ckjlAK3di2/eJkfr2s/umPJ27u2vf6zIDMF0/97EQDDMCBOhf/9m/b0GBpYw+wxZcYoxxl4BBX4hkcpvSC+Dgtbce4LOv/gK+9vSXcP/xnwSXrQpJo7RIFRTKDiDI/ZOs2+np/KNyokPedQmI2AWIrhAQX3PxY4AoLMpLnQS5kvU8mBi9EERQUm4tGWugSooGyUeGg5YZEv8hO4bpi3QXTej/EQgICasQhX+gjF0YmbXojzlBA/qp7twAPMwSRZ3ZoIWIwLip1hFr/gYLtrK2X67c9+pLRINwoZESnj2zdwv0QEqP7Vwk/ft3xZXhgHIHoIxHUHTSALer7wD0cccO8HozdLHFHB5yuxNzdqCw9GXa8/H97adHVmn/vjZn7XediymmDjZgaO3oSX86sBBp6VrPKTU6l2GUALAzf4ytggO1dGULezVUShG5YG2/BGB5JHfsfYEWCMT2GoCweP2u/DDk8hGQtTJjkPLSAoLQf6CM6z8LTMGTT2HmRgJwmRWalLsLDIAGHACi7K7feoDPfusLeOlnfxv3Hv+wDIqkTIFRY0wiYHNyGrIBCJDOEwGkSnc0l26DOHeW/mQQx3c7KrntWsoYomQBAGOFOH1CP/HiToqOdJc92nUBZL7vOoCJYzLXhF3OtpGlesF37AGWR2mejyz4Ry09iAgWdAQiXo5AZZIUukhZlsgSeSR2BAjiwy3CIKgvlTWIaRSNDaDS+fsXkPMDEDDzw+/cViMgFRVddIkAPn76+AkA4wjyS3Xmg9U5PUPQAYJdH53pH8Dbw5ctGpibtYHX+naFutiGHpybiw+aGbCdj6lVNF0syp656UofT+IGF2B5FcVTS9KmV48hMYO3RebjclWXOJprAhFUHm1urCUo/Eb5j8DDbcolgHBbGT26dsQM2GcbK8vigEBWVSjAS7nGFHxcVh/AVh9FYBfdRMAjtcXdAgZDN0I99trDN/HsK5/HS898RZAW69IZJlAp4KwUq9PatnY4+Cdt8CxLDcjqqWhjEqK7oWxt/fpBeFsg0F8/sjwOJnZzGXA7f9gI6XZKB5BlQj19PfIlA5012AufaLUegQHd/Gma1TGuxgDGSqBvi1G7Dvqqj4EYWppHYGCmSLvrmkjsopZUCLjida3R2F5fSGAsBYbryO+o1wwVq77HowCsEeW+Y5tScDX0y8xO1JuBhsXwuvsYOGYJppk+T/YPgArkgGaFE6l7zK+7NEb8WDsee5eDgEX9nK/qMtXg1pPEP5XFazYj4goSfHtjUvZuwtZwlJOWzIRIjCpmDVxW48mYq0WzokYmp3Rz/VHkUF9Oyr3ds/q2f69k9IQZiGDADSgASEsrN7VPrn/wpwIKnpbVBzU/zC0ZAtVtR+VuAYNYusFx/fABnv3m5/DiJ1/AvSfvyeRiHdyWXpILSOMQHCCo4PXBbtMyLQoqhEkA0Lkb0hwkALdjE876HLv3ni2VPCr9Upv57/bBY4XiBOioYOq+4wQ1PAEEu22hLR+7JWxab/YpnicJnABglGWsqUtn6WJb67sEele+z4KgLvR3VNr2/VLRuBcDBztB6e+rftXmES142K2jjoB2EJj3KPQ7IwAoSzYzYJscJFgUeYwgj+P7Uv37lR+x9HW/VG7bPzHQzao1AqFAbeuBcjwzNgWIGBPw5/DtiJcrNPsMhFgf3laP/dGUemKdWhy/x7QY21qG4EBPgjx7ZjdnWXMoJJWdCt4oy3mK7z3gT87Lo+PfNXEv9rwcxo3JaOC9ldMTMGDv0DADptzTVVX42g+vff+7+Mwf/gq+9vSXcO/Jp5rVJnHZcXzmrpwZ51ruDjCYNgwJKPj6c7r15L1wisFmerEm5kgiUIiyKv5NqdrWauJ0hcgkiMvCni3+TQLawDYKHbMLiuk6rT8/GGAAumCuVlnHCXQY3R1KjTA4KCOaqg9g6pX/JWprF+iG+t0Uue04VjZ3FxTbsW1bKyCwHO1caupV9TFzvB9wMlAugABLtGKvY9W9FMF8inY8uEewDnfBVyHwqimmnKy9O5dXlcdREN4CEGgKbQAt6No9AHVMAtJWzE3sBOWrzi2S0KxD9zFTBbizILN3kIqNhbuCKV+dw0Xq0azQ2f1ocjzc8zZlAAhmicVm41SUbIhtSBJ4yApgWZcRs7oTsK0CEFiSkFmMEWWugdeUZFzbOO/lxcAqZdueKSrkA+aPm+/738T327/xQbH2is8FhL1o7t/J6PhbCm0f5/VJGS0fx6wAQI1Vv2NUAY/RsnPX3/8uPvMHv4KXnv4t3Pvg/RYM+DOPZDbX+jljeCzj7w4wsNI1lICCZ/Hip17EvSfvdaFstj44C7tia9+NAisbhFVgMG/1OAAsjzmgcOagdzlwAZYBULASB+6RTDkAAREt9hHdrd+pU9zxmP32wvP3x+Pgmiv+GcJv1saLPwOdWRuEBregwJS/g4E2FztCSlaYe8FiD2zXugPaUpZYraBEEp/h1Ru0RaDAG5dC71+OZbul368LbOujsXeUPNBa41bPWHoQdhIUDJNjRUU2bNdS61DkWaztRptu/2OKyV6zoK5Dd1Yko0kAM7KCjJ1rfiPBuKxVoaTiMq7OKRuwhGW6j9pHoxLjTCKAGLVVB1wvjte1BhpTohqkajEmZQPlRUAHM2rwn7BJclzZBGsbB1yMRue4TOkNgPkcb1R5Y8mP5nlXDqzc40yDqAyNy2egkdF2zuXbHCwAOCWjgb2ctrE9BAKRTbW2XN6nxzJee/tNfPbVX5BdEj94P6zs2mcL9efvPlO9sme+JuVuAYMJKPjqp17EUwEUjNKCSkpQSSUr/cPwZS0OEmoKXF4eE6snrJFuqLMeKAC7QdhUPQzs/SS7AAJGlH0zCHE8mS9Z80cMwxnKu7+dT9zSWBFCR26AZGMH8WByglVYmsJSy6ffoEXTO1s0usQfsAtX/7vVdqes00hXcexcNGqN7RI79fQ4guL233ftmh8h7OwoGvsMIDjqqzM0Y4wzmN3jwD0j25KXsRXq9+CxW2RUjt6nP6V+VQEZ2r8U2Id+dQ6zbNRzm9L3cWyrfAWLuyBzu+SlruPXdqECWRVlLIbd6sK4tfHKBaDCsO3Ewbb8ugAs72PgS8aMAAUmrdMS3oELDBXHZY5NwJu9d+/jtlucabajsTc6ZzI4nL/t7rNNWua/bDmdlkY+cxZXwvXb38Znv/UFcX9/8GOIWS2BuR5zML2rmfb8CXBwd4BBNyB7UMBA2GikFnf7mcwFAILuzpbrXM/s4AAAOD8WJk4ddLsUuD2jAITBHCb+5LWmmzHNQIANMDpYvjKgoOLz+7o8ggoLd24/u1BCnaQe51E2yM7wG1xKWl15oJTMf26WbNnqBi3uZzZrt1KnQCtYh3VPSdkC84UrKLBI71GClaCMTwXLjcpswsb7zFaF2OeZf7O/T6N40C2/VRcatL+2aoHL1reTeqq17nvbc/0c6z5+xT2g8YDEPg7h4nvZeA9MBeBjxVfp9IGGlwIO+2edKb17oGxAlvgGAQldop9tdXAgelnB8oQxqGM5nrcdTSHzKOlz40Zn5hYialcx2TwbWbRhD449pd3KkiOZcraMFVz/ORhjJk/C92i0TWV1I6e1xo8sp7V2sf3s3EBWN0GEAHh5H67flkD5Fz/1Ep568h427PWXd1NsFEUF/a6nPVi4fuuNfeOGcneAAQDMmIIAswpsjPQUVz0PAPstdQkp1eYyxkAGlQ5IXxd/gFaBinhnvrWRBX9hcAnyrMuORqlQ7V/RKvYDLG7FG8slW3JnnMXPWu+2LQEK7AxDhCJpHUSW6wZLxLUGROOZORHgdbeySuM6G5D3s4JUEV0EBJr6mUKU99Rqt8+XynsZ5NS5noCRdaM/s86PQbNmSWvSJBEsCwhrpd/zlQibUdbMOHz70WFMi76HgSvKV678myRZ1o7LlbZ5fd99sqT9uzmJOtoDIShsv8Oj9sOsBAaiWZpX1ELVAFkDs7KqRN0A2+rAypfgFgMI4zHfBGRuG2hJco9RfMQU+EStEmVJdhnDSXOVjGQLVzkrrx6s7L55xjXwMpMt7a6e+naU9ztrWsCjGyATWf3DltN+7pKsll0Sn/36c/idT76IDz9+DzeOa8byGazvS7r5HGs/BIBQe7TG3D2JJ8aNjjsFDI5BQQ8IZgMXUFAABhjNjnxEwJsPHwAASmQTJoPPXA8jsABg7iMb+f47316/VKWZvAgTtczffbRtKwDfujW83aS1a2l2L28GJDcgK7ZlgupaECgtslbdlt4VAMRq7SRgKxocRbqXhWZQsxUC9tnrYEFYpiA37MWMXWpKtQMEmmYYy5UIIo3yjpkdR8rL7zlQ0JfKUTDkrt5DxVgFk/fbLNDIAma7WBjP9GmU43ojlHNKsvzOqXgSJm27gWevM8t3ViLTAuwBAdVsjjtAQG2mP9b3nL2j08JAEx8UcgyeytbYl0fqzwBISNvUFVRRlqAUUA6raHQHQXOJ9QBBbtuq1t1YNmDbsxyexwCV5dJc/L5BTwoKy0BBMnCQXZY8iowB3qWcIZ5um95+J92xNYAF3oayurJFLUC4tZz2c2Pm9shoA4DPfv1ZfPmZF/GTH3gKN4UbA26kq+xxiRnEIg/IAAJa9uDBwxpz92u/+2uDFpdyh4BBG2ho7gMr/aAtZT5gQfIlUWQQgDfffoAvvPIcAOCdTfYmH7odAk3ujIJXIsQpHPlrvS6Kew8GV7OjWpikhbn97CBJ3t3apAVJoc0uVa2pJnt1nUzTAUpgmdBke7zLX7FPwz7vlEWR67OJNZMas9LY8pcL12CqvKj1JYraKDPGCmARgZsXF6rjFzEB2bIDlJfGmqXlapfuWZTVgllqWum7kd0zaF0uzYw89L0CdXlofAe//+D94nPqFz/mQbMkIEES02wVIJQCXxqpVq8oLIL75C8t/zOmBaiAwNrbQNYwMY+CgZEl1oDSo3cN76vn7Bvl47aOfdgAkvblsOvXpSoXcmVTszLytjpQcHBgY5YZvGiacsvcuK1ALu2OoKMyYLsopihPytyEc9FF4K6CtAxBQQGwqWVqsnRTebJN5AzQG2TDXhm/TrgyGucJJmeEHTI5k4hVLgOpoH4GOqAwkNXvlZzWfrjI3nJtOwD40jNfxYceF1CAeF3fUAQY/CcGkJQ5YZWCnRiIoKBZnTcodwYYjEDBzieDFhT0itKLftkYPti+/fY1fvHV5/GbT7+A/9X//WdwUxjrxAoGERJ1KDUMQFF6FyY24MKHu4EG6Daq2A+sDXsgsIF9csp1tS2UGMFmg1AH53bCK5gtpEXrlpOt8rB2YXexJTBSItHtJDEdiQi5sE4mgJOAAxg44KLBWLZlM4OpCAjLDPAignS5anxoDg7UyqUub0FT+gC+GRhYrtyCbfd+yDWRE0ywhr5ygTFmK4C5RSIW7yWyNd6op+3nTEH9XEFrv7pGYmjEgrd8EawZP43qprIgrlwQAiLUuVvPP9sQqgEDKaTRxslEWbd55/je9nX8q/39u2ecTdPM4bMkpGJgsQRdNR8HdKWNfSddfitA4qYFCX3sQiyjANkwdn1MKzAT8GXt3DEDeg/ph+wswaZyZgvyppjxVTiwBj9secNBDrMaHwIW8gAo5CLnxbCrslqlrberswrAeTktFTwEAUfsCgD8xPufwlp4rJviYxkA2/tJmxs4IL2vsQZvvPUAz4fVeZda+84Ag+g+6EtvGVsn2SDuEW0sCcAbbz/A3//95/EbT7+An/jAUwCAVVFDMuXvCnAEFEQJIoCFqMjGYomaM7PBNWIF+slZSv2tTUoBEQwuwMYCHIxMteVR2wQpZ6OvlKMysS9MAIGSTGIiwqINlKBUl7MD8o8JtqwdCYSlBwe4AeEqgKmrauXZ0uQUlrmZteOJqcp+gxavr/ZADCDsksEgL92eD3L/opZUTSyTAxDoKERrnFEJVGsUPk2CljMWy6zE5+7u0zNZ0XJSoJAKJPCzAGkBmRILmevIXDiW5tY+96VjCgA0MRrODGiAmyupCASGMTbAzk10+N63KA0ImDATk76Nke/Dzb5sqWApYJa4DQrLcY0hcHAQGYUTSaV2wbF5qezMcgVcPVZBbtLMiOlKXEX6mVOSFRUBFKxFZMsG/Rtkj8mSwhUIrBN5A7w7mTOSNzkRVggQIAApsbIE4h5ORNhU9phhl2hg1On9H1VOA+eAQJTpVqx9jnSS9S8TK8EtzCuzGF0UWIPXHz7A577RBuJPpJGXOwMMvqpICNgrUDsGKEDQcxEURHQby7e//zr+wR88j1//ey/gx99f6Z1VBaBRPBUAjIEC0IEFhBPaqVaaZSj6eTa4zoKBdSvYwFi3CgK4sA5C9u+AWALAWJ5GGSgZpONEJZ2ohAzGkggry6RdcnIEy4rqOQFgzbaWCCiM9RI4wA0iOMCVbCXbJHBZVbGbX1cDG/2F7CXiCoLoIohCdKlUKqsl5daUWVSBgjXgYP0aBcao0PBzXPJqh+odLrkY/CcW4zC6vguwmkZuW1CcWrqAWL+UrV33GyvJn/0zR8m53P2SsrhGeublNqBrYL0ftsFBmzX3be7dKoL+c/O+4a/DP1uJwEVXJbAAr1L/ST6FzdkErDWJF41SfQMe2Dga401wbAS7Or7ZXQUKCvLVKVCwFRbsAg4AocomkzlcBByMZA78WGjPA7mTvatpIHPkuiWTyxzaVCYX1ngmbkCCGShToy525C3ltB2eAQFzZPXGqQCpeVuw1Te6DLTdLabLlP/1wwf4uW8+ixc+2RrNHRbZlTsDDCIo6It3Egc0F+j1qEjlnJQ/+f7r+Id/8Dz+5d97AX/jA08hrtBadVTYACraOcYIRKCgHxv2c+AddQt2FMU7G1zmJuiROrNM3JWLg4G1m5R23AZoKeyDvUyQe9I6JgJuIEg8gbASkImxJkIuwEqEtQhjsJJYCwslLIm8fgncNB4TYTkDDmhT5bCCCgELya58ulELXT1Wg7uAc9brgBXgyAqE7xy/Jw3YAqpvteyFw1GJwNH+yvjIjXbplyDdpvTXN1ZQT53uArImQEGPOS0ewIYAsTBhOiXOBqJ618AACEwDbf2+e2U9+n6pxOt3Qj/MK2urM8/YGwULkBRUQ6Fj2UBFWABxe9XvKEVBwuqgomETuj0sdiWO811MTBzHSwC8x6BgNYNEQcHqAAHOEBzJHQMCJnuKNvZtZE/SyZBAAQwoUNhE7tBWsGTCQkncDmXPXBIp4zkx6mIfWj/G/o9jwI3QIKf9vbiNubB7tGNK/m5lzxJ4G+h1ElNQwUEhiePy+xPw+sNrfP6V5/DCJyooMMBwqdwZYDCb1D5nJheX0TUMfPf7r+Mf/eHz+K9/WpgCs7KtUVf9jflwAG4G1IYajW+/iWAh3gtADeDyercDLwafFIgC70HNjB1YC2MtxSflWuqENLZBvu/bb19qNHBKQCrkyHtLhFSEoluy1G9jQe7YJD6A9ftG0uArJDJ+0XU2Kyo4SAQslIFsqVo3pc804I1IrfgNKKtQ3eYPt8yU2Fuvowh3U0hTViAci37WEiyoI6vA+hFdv9co67ZtzXKRsWHX7n+PcN2lEi9LzcHsVhP5/dkBQE0H3rEKZv1CXr5NWHVg9gWXAIDAsnRsgMcWGDsw9tlGa83KWe9BT8GPhLxd1wj6cPGpPtYo+miRZhiwXpCWRSxW3nSlQmAMpmxCASJjE5diWj265Z2Nq2YEfN2lkMH5CgxSILAHBSuLwjdQYCyBAYFVv48MkfdE9iDIHiZ1RYpxshZgSUnqRFvDInicE1gTcYqsTlT7CqhAwdvygoz2+od5b4q6D770cYVW7wDALMUKAZ5vx9NwGLCx3+v5B29JoPyXn6mgwN77zNS4M8BgVxhT1DW53D8YKPivfup38GM/8hGsgSowIbxujJxop+BTABBE3Aj1nlWQ4/Zp3F2jARYHVwQEM4ZgZd4BgrXUCRmRehRys9TgSTdMSUwS1JNYPjP0uyAfJlkFt25Fkud4p5QGHCBpO3XgAOovXIgk4r2xLAUQiCWl0dM91Q1U67V2oByOFmtIoDNjBQqoUqVb618dMTdx4te+rGPBq6NX9GySjDMejxW7zl+sPuUIIkQAMQIbhwG0WZ7DgTUwMNBmnRu7P1o/fAQFkTFotwMegYCeOUN4YnzcUST56MxM2bsFhjr37FmP2sfZgYEooawWsACFjJQyclZG7YhNUPdOZGyobwh7MDB21VxgwmagYMN5ULDqPJHr94DAvsd2n25LwAoIZJ2tyhsxUISNFOOEE7ChYGNxNQhAqMylBS6yWucmq1Mw6Kqb+HYyejZWmvHajZ0tIKK1Sx7WBFuq8g8YYFfefOsBvvB7z+HLz3zVM/6mox8Myp0BBsdIsyuqoEflTwIo+Jvv/yiALmLW3RJikQNtx42AAlAHn1xfBYcxC0dlNsiiy6BOQJmUHFiCChTKdFK2qH0yATah8wxpS7OQK/f6XcCBtUU2YJJU2QGgIjEIukurRtRiCg4ySYIpWaObdLMrDU4zf3egwDn0lZdIP1MMYLOkLYMlWUWTKXIIspr4VY1GjZHWl1ZIxkjrrJqeAMRIa6u6jZ9G6QyU/fB5AOKgH4GMmHPCnkMwqxYVLOTcMAtNxD1w6M/fbQt7AQT4CiL7fQAFR5Z7+7aDekxAxEzxW/9GQd5H1B/1deznxXSzWsAVGJCAbCIN0CVkWpCVTeAdm8CwwNBmf5b+BeXBtTI29pWlMUBQgdl+OWIPCor6wVnbZSvsYMHkjzEF7jY4AAWX5I95Sar80SXQQf6sBW6cALLHDbL1ryQui8ylzTnx10NzAOho5ArQ+3EVSz92IkY+AwTi720cRX2TQcLanWEFSUDBL37reXzpma/io0/c210yiosYlTsDDC4Vofd7aqi18P/k+6/jH6r74G/86EdhkbRW4sT3GANz+gDgrU5+oAMKCkYS0LAZYhjyVKj3zGwvpIwlsGjfHhRwCfTdLSZlPznNv1eY/XO9TiZnzFBbIEuFWJX7xpI7z9qRE4Mh8QbE0EhaYLMvCg765YxEGSlnEYhxs6vU09i9lK4KaWylijBkiLWydqyABXmuJtywt5KY90Gds+JjhGgaaS3HggDzH8c34nioe8bRMW6im+2cWbd1PbiAuKMEMhYL4az19K2DsAxIoGcBdt/9OvZ7RMsMuOxGGB4Ln3s61+ZWD/JGK3gskv5Sf/dBcpnEyl1ykuA4BQpLsqA4atiEJcnY19AbZQ9izEdc/dC3CjBkahp3TTsHdssRS2XGrI/seuYWBHubWPxAp4V6psCPR0U7kEEmf8rOGGm/FwiLyYWxyVo+LEhunKAAsk+EsAdFb2MAQdoIt5bNTat3QBIAejApbdTdt1TDYVM52pfKItY5/e23HuCXXn0eX/rZr+LeABTcpvwPBhgA0oDMSsEyuW+JwPju26/jH2mgoa8jhQjmI8uvAQvgKVAwa9AvN+Gu44PaOdA+Q//2yNME124JUESh+rGUgwcMShrMBg/+Se2xRN0xDUgEUJXedHbBWQObBMWguxNm8m4JYrkaQPBbZq7CEWOLtQ9Y65NCbeUEK8BtYNUIhMWgKmDf7hY0BcADp44jratgGgEGKzkNhMesy8OlBi5aZkKOmcVkgCGCBftNzzB0t9+VXnhGAAC0IIDD+SMKv+ntyTuPDkf6NhoBNp/seZdW8MT+BuZ9HvubXNknUCpYCB4oJ4G3YvkeswkKFKBgrWNvpvMAcFC8Y2tKy9Q0OQrQucsO2jsTycottdKTUCQoKpBSAkwbGwBI7m/dGyC3LcVYS65KzowTlzVF9qgQT6fQ9A4QpAmh2GBYRkAgHh+xSsAeCAzvXVqmSavldXOPKonB8N23X8cvaZ6dj15IXnSm3GlgUMeZaJ4ERcra44llHeifvv06ftWWJH7gKRSotV9EYGQQtjRGxH3JpXbkplo/s1igkUmADjobiLHOsYyEXgQFlxKDZJJAyaTLAWEOqhRmAcODcEyhRx9fo/T1fAQEMQjIQAFRGylsghCoaLh5NTbgJgyBMxEGCpKgf2EY5jkiRmWndEyYM7sQjLECEQxsXFkB+2dxG0cuGn9eKFF3x3azc7XtuFEeBhRWFaYZ4i+tfUy42eZt69eNLI84JhHAhLWvAwCAqGUZDEAQ9q6Jw2JgFRUQGACQ4xUAzEAxMKZjrRzNi9k8HjEBR5H0fWxOk9dp0vf9fFk0vmbJhGUlZCrCIqTKJuQivvRcKkiIsQkJ0LX4hHSSvdkDsT1rExPB1ZwvFTD1hZSWzZBAZGJCZgYyYd3Egl+ysQWEkqzdSL4zv6dyCKige1Y2sGT1NnCgjVPiXO1+M5PJwJwZOKU7oltw534in5MWKGyxKt99+3X84qvP40tPv4CPPnl/vuzyFuVOAwMrFMCBfc6QCf8nb72OX/n95/EbHxdQwDZRSCg9BmErOnhS7VDrxL6TN2Y/Z4MhAgQAvuxxJ4zRDsimdAPQ66GgJRdhNsBStw3i298SYYGsDhCkJAo3oZ2YgEzWcOOmnFdqMhmXRDv6dEltMhLrlt2rKiYozLDMXg4KlOGJLiCgKrj9vdjbLQq/UdDgKJBqBAY2/WsKYd2qcgAw9ZU2jEvpBRmmKzziMlBAloLGhC+rPicDe/bY2we2BdKujIBFZCQikO0Bg90baANtZyVa/s3feJ7HgvY2Qjbee1Tiwr6YXKcK9DkYmLnjgJN9j9r3Sy7yeSUB0kTKHOjnVBqQQIWRIQo0E+n3ChR6lw9wbm4Axy4cnrWnPFrmOkN8+kV/nBO4yKqJnC0YmiQDqrcfATmCdHjNi95vVEZAy46nJG03AtfSHm1ipKZN7On+YTCleP/xDCAAxuO1Z1P7hE1x3vWg4E86UCDsXsvkPUq5u8BAjE0YY+WAgGV5XCHCm9+7xi+9+jy++HHJTV0YYBVwVEQnFVSAQIEaoiSdPqXIb1HCGLz4PvrHGQ0ADlqy5QNIBFL/vu83AInR24gC9Uki7MLDR4E/cdIBx9QoUCfhkmg3CUeD3SzSpk0MHEAsDZtfSbVGnG9xGdGlJUQm4EZ5H0ZR1REMlAAUTCHIZw6xGS1FHYsBQcvdLkLMVnTMV3gwk/SjGeWpvicFMDfbZfCoRGABwMHHzYELw9scHWgAbsUYnBGqkdK30q/W50l7H5WWKWjBgJw/DwhKgY8Bq8rRGPB5RISka+7ls8iaZSNXbgYSlpywbKWNS1AGJxEHZdHvGQBEEAe0QGE6X/TADBRQlK/UycgkLtWlABsBCxPWwiBiLN7WdOh6i2xMX6KLIbIDI3k0i+2I8sj6BbjdOL79qJvri8j0xVUTUUYKCJJ3SlTdB196+gU89cE9UwAEkH5L0XB3gQHgjeErAwJz8J23HuAXviWBGh9+/ClsYZATi8DeGB6pGgECoAIy7SlLQ4g9pTuicbtq7jpvR2FRuIytPnLEJ6TWgZTCW1jYg6UwtsHEhPrhmnfgvaIG9r5wYD/x5NgBGNAX2Efcj9tGLP1gQvLoQhUmaIVctUrbJZ4xEUu/miMusVpLdRNIXEFVBs4YKBAwAXakEMqmftNkQVQKAhQgLEi7ICpZfCxuL1OI2QKqIKyQlVtsGFwjnbvOt/0powCLLoweMMxcGKPxPpsrwB4A9Kly4zWH73Oy8KDdYga+S6Bg5TIFBEdj4WYLAFHnxapAccmSFEw+i4K42SQOYdmKs3A9SDCq+YbYlZ+Bhjaw1N8+LMHbl1Fehr54U7OuqQ8yEiAsgMubDYyl0C4VchukS8Psq7GM5NIZmdQD3CiTgLH8PXQfdEbaqFhsWpwXl3SD9VV0G5jRZGxQIgFA3367Bhp+9Ml7zbs37oNHtFvvNjCwEgECAddvvY7Pa/KHjz7xlFDSrMKCxWe3UT3WAwQAuMqyrxX1fs4JjzoKFOsHZLMMbXCPyKpF/TiakFeo9OtSAFsZcCR4gSp89/SWASJ7H/h1vT9sBALsnex3I9p5SnnG+pk1uQMzBg5amprR0tOXVnH07EB0FUR2IAKCrfAUEOyKKdlBsOCjBFxtqH1yahc4BABxQdHWiknf9KDhBuzjwoCDlZsL9tSIygfGlvzoN5NKz5jnU/U4W2bLeeN4sO+z6wAFi0wKEGr/S/Q9YaWCRSn4JQUWQd9TAhcJSwDie2uTd/NOQgH2QOrM0CMiTa6jmjHIRJOvNu9ylrwCVwxxMWC+0gM4BoXA7WRTdbXtmYGRbNKv05LRyiE30vSgzeeYgiD33d/phmGdgCYDY3QP2YZQ3zVQ8MxXcf+Je+8JQ9CXOwUMBrK2LSRbT3ru6Cfu+XI5hgS9GRhIkCUxxiCY1WnsQzKEnEmVzsBCKrxTADMwEK/qledRYa5BeDaxZELKwTgprU7yuzo5j0oTFTui3cJ7RAYg/u2XxZ19NytRDh+tGwbGQKDfwMXAwKXkT5cYgl4BzALOrL1yIrcSE8GVwM5P2sQe1NgNoNKkQKuIL/mRrYwU4YiKX8NnStR0whoVsFlBATicLY9at1iom18RKPk1B21j1256r6ysAZGwNDEux6j1RPJZAniD6WxCIdW5fzQ2fPyAG4CQSL4vmVBWqln6NIlPzfK3KTCoFvJC25it01foE7BJ+8Cj8Pv2msmieI3N79E87WNKFhI0weHcLKbEylkZdcktMJJRo3cbFcNBLmMRSD27L0cWtz54pAf87KBuuXMZJIiMIADffvt1/MK3ntfkRfcrO2D3OSlXL9kxdwYY9A0yap/XH7ZbTwJh3qsfO3H1OycyX6IzunVgJGV8q8wA0FJOyyAkdoRQ3wtFejQp4/nFb0QXptsxWOnr6Odh5+PMu/Agr6T8ievVm9N8OZvYDAwwH+8V0WeDbDNDjt0GfenHYA8I6ucKCty/TPvI6iWlHSCIlKEcP27cxjLvFKEVSnSogG8LHG5THiU+oHluKDOgNG0jPS7L2HTeJMuIxwoyxFJfi4j/Jdl4IY+sX6OGU3CQMrXj5MR7Fg6CR4GpuZnKWp/dZ/lbiSVwsdTAVNujRJYNTuJ7DAgwfGMeALqXwNigOVx9QoMvDfHUzuvo6pPzADLpcWpl2oW2u2RYvWuZau5KqucMKESQ4O8RSq8HemPQXKl2fNE+qps8iWHw7bcko+FXPvEi7j95T/qQunf3+g7eJ9aBcCiX7wwwiIW6v4AwBc8qKLj/5FNACPlLJBPDQEJWkFCKsAgWIMOo83tJNFxiNULPXq8BSh0p2DPKtVdCrdyZT8pY+iOjcTISBKk71IMD+d28jACLUZMMSGdEkNDpnQgKZoDgaBfJWc72GFTYg4KmTUiEflT8I6vA2AFrsxFDIABgzBBMo6oHjMGsGEnJhceKENVa9usGZeTzNXn3bpT7mfv3JQKCI6B0tp0WUENft/5xOUaU2jFDNmbqePAxkwxIEq6yWYwdOzIZL7H4uLOEXwUepAqgDVBd93uU2BLXHMADbxUg2AKABPh862UUoZUBURHdft7XC1tQUI2UZih1hgaAOuMAAQAASURBVAL2p2pdB8+7ZJwcy9DuXJDXbgyqzOpZkWhANnXs2rZf+munrxQYyJyX42++/Tp+/pXn8MInX8T9D94zL05s0eZFCW0b8eDYUbkzwKDv9wgOrh9e49mvP4cXP/k7uP/ER9DvQlaXziXJq62dXZQyNGaAUQXYVaLANPAOEMz8vRctbBuAFyag/OZyqUr4Ajye3POUELgNaPHJrl8VXDHVXRcJunKEoSsSeDege1AQt3idMQSXlp8N20NfWPy/Zgnug+uucvvudSXHHgjMmIFL0dRA6zu9VLjIBN+YgyXWgQXA9zowi3kU9X9xPXhovqNrZ9edAQHAHAgcxcDI+XmdAEhMTojFuQJNgGUbTX8mzXhRUBBXrlxN0Ep8XQOSo1IDcm2iyFxZN5k7tnKFWDJ9LimeY/CmKxtYcQfGMrTJgImqxM5GwF9UvhosNTUWgJ2bdrIid1rOyskmhqurQwLt6l669+6NnL6MZH/fvsAeGLz+1gN8/pvP4qVPKVMAxpnU1wYRIig4pwXuEDCwQt1nAQXP4sVPvID7TzwVdiUb/Vg21pEOEZBgk0YScFT67SpVRdawCd4DNuBrx40Q7Mj6jhNtpph3g/1sj3flyAUz/hxN99COM96PlP4OGdc4XCbtVtvWYj02BQgb4ImodtSBFvdLBlAgOdofDRQ0PmSoUJgmmNDfhPXpdg8DAXbcIs9njMCS02FaZGDgPx0Ufxu1VutKFPm7lBYsmMU8BAryMhf9/5eAw6XregCg1a/nB0BgFggrvyX/XbTMD9tNb7jPnUC7sVV3Dcy7sRVzXAA4vVFQX/qEPjtGYQIONtSVK1nHP0HyDFjGP4uDWEa+b1f+oq0sAM63irbzETA07WsasugkD+OkfVD44d7JymOps4Pwj0xY9YreDjeMQI2HsB0NI1gYPp/olNwfAS0guhvF/f25bzyLFz/1VTVq17o1+qjY5nC8afsmUAAHg9celjsDDEZKLIKCj2mjSs9qaN6M7wl59JNuLmIgwX5xldTFoIoM6IPh5MNsmeJI0ffgICLwHYAYvPeuy6d5nFsl3qROHSn7cKzdSe9EIarr67VdKQAwUEJSlmZTBsZBAYnAk2RNhI3Y472OUkhHJXepOAgAgCTMTylm8Qcr4ULCJ6AV3mfcAmfzPOhHa87YtF56Vwsgr2WR4QBwZXokt24XoK45B+BLXIHajs48+DN4hypvE+E/MphncQH9XhJ2frSfBABvO+DR2s/icEwxDd1VC83ZqWUORAHsQIOV2ZCdjTU5dw6R2Z4BM4EvSkrlENUI+LhXg4EDBwtQe1Q3L3OF1cvYW8gKAC4vpB9Tcw7xGACmtAcbw3sPuFC9NBoq8W9pXJpmALZgAajGIjcAaAy4rIrtSq3argBwpcGH1w+v8dzXn8VLz3wF99//E8D6Dnw/jK5UGcsgKgIQbAmUggN7t7HuaMudAQZWpqDABq/tSgYAKLtBK52XXKFRqcgr+ZawNXLUBs8OUVLtiFH96rMwBAGx8/Yo3Cy7Uo8BnXYYKPP+GmuDyW/pwn2nEz4CAch71p0MpX2JdDdDIiAtoJTFV0vyqsV9CRUcWIa1xEKJWlbnrDRpTMl6FCFvedsFBOimLByQ/YFTuhXSYU8I2q+pPto45yjRUxXQLXiMYydaIbNdBaN1E9ktoF1KNgILRq0bWDBmAZgvJ4s56WdlnuClKn8/dgsQMMqL0ftwAeyE4awdYxvKX82FkSThlCuGDjAcbag1SqBkz437K5y1gN1tZXEsF2jDflh7/AvaMWdt2m4HLVZs9nZl2JbQrVytwICCrJpS31YiEIxK3MGCAYT2XH2D8b2apF+D+xrlzka9m2EIuFuZwxNGYCGyC9oybXWaqnWulyDv7RmJNzz4717Ds6/8HF762d/G/Q98CLStbft270ts7cMiV9UciOAACg8YEnN3VO4MMIiNfxkUTAYv0AxECoqMU6ogAdJ5MSYhok1PLXw0wW1wBIEVQKP8OCJx4AIaH0xCXFDuvfXfMAztseF9pu8WJrMNSGdhCLajG7Fs+4q0wfaBTymDFCAUp0ip8dMUJcfMu2CAAcyaDpp16Rm61NDVkjNEnzqrd/AaAFqlD6BR/HZ8llltltbUBbELBmrGRVV0LUAY1FTau7Nc+rE42pgoDoFm3TkAziNaXdgFq2QPAiKAmNa2O93niAcurz03EGX3e2/aMsy+XVsG9mjUllkAg61o2jEMXfxLCxIqcEBzvNbsaNr143Q0RqljpyIAqIB0tqsj1ax7ERCUDVQKgALf9ty3gzY5FeTspRfpXiiCgQAD/Vjz8p0xYsdM6Y9/Fz7H3wdwgMBsAtIue7CgdTsp7ykccijBRfQLgAf/7z/GZ3/v5/G1j/8m7r//x4HtZt6GbnQpMGHRU0fgwALxn8ST0+reGWAghXH98IEGGkpMQeM+MFBgg7g53hYfDNrwJFv64frt78r59Z06gJrBo0gT2Jsn/TNCvaOyPqbk+uPt90bSu6K/oOS769zHvG3deYAvOEYp1QmVstgokS3gtADbCkpZwUAGIbfCJGUk3Vt2gazBXm3JiGNecspgI7V+SsKayjQ1NABwCpZtqPds7futt0UOiiumMO0V1lEwF1Dl1CzAc1eCFQO0K2bMumWWvd0dMAwUHPzaChbkWAsYAFF6V/b4s3RxKJeSfrV0awUA9tuzbRqvB27bpjUddwQLtmW4tZu0KQZtKm1XSu2HEWgAsAMOcmy+rfNs/PZjtx+3lgQJBGUD5FwPChb/rG1p2zwX2+45fLZ/YGcRojLjrcZ2nZUhMp/0s8uSqFbr+TmzUAPLhyDC5HtzTWCM4/HgAq11U7AQqjR9L6khtBGmsv6zv/d5fO1nvoj77/9x0HZzqKdAkgYbVOTdE0TISSQtKiCQ4oH4n3oRv/a7vzat6x0CBhEU/I4EGgZLm4JSdWXq6LZToGgHjin96+9/F5/5g1+RU9s7sAFUAUKLNOXvYLSEDr7MBgzYjYaia5G533Og9EcK3yepXVeUULWVG853VjE0mtg2mRkA1EXAKYtUoQRKCbRcCVCjDOYMlNwCBL4C23tmINvG80X65BI4WBQc9KmhOVGzJM3KVfh8lNFxZ7nSOcvfBG2v+F3B0QXGqOm/g0Kp0ajVsKVOYXXgoP/eLROdAQbo3yWHZWYXtey+zPz/hwBAf3ApSr6naVt1Ek3xg/b1H7U1jVt2A607sQEK+t1TJk9Ag1yzBw0AdmwDUPvgKKbjKD35cktQkAGAN1FSxhKUmxYQcAULxAxeb8ClgLlo9OVWZa43/ViOeO+k7C/B8XtqZSyl5J9TAyD24KEFDiKvKQ68CBZOAAV5VnDiHMj7kazfyXkAL//0b+D+j/5NZQqCwSQ/tgfV9yAGUgahgDkBVGQ1CgpAGcSybu764Wt49hufk0BGX7I/LncGGFxbngJjCqw0CjQo1QYUtOjWfxqo8Osf/Ck+/Uf/AC//9K/j73zjM6Dtxs/HuAT5HbV+MqAOmAGl3wb0dUDA6166QTIAC3KxTMxe+bMpfAMAIdaibHWSdkCB4/V9mzZtZeaYTEyfrAoImAi8rdJeeQEtSwUIfBUiba/8/nwLcGB7qS/K9fW52oEWCFgZZU2Lyr+3VpOeP6X8O8XkSsn7DCJkB0DuOC4EjQBqgjvtXSw6GUrVJyeCB4ChMgxnAUOsVm/JHkGZblbsGJL3AgA07ewrLd67dqYI+kM7Y9fO8PYrNnVPgIZSzDWh/YA5aACOMwOe2aXvLCgQP3cFCIgAgTdgXcHbKmBgFaXG641cF42Q3ugIpYkJiLJEPujxAAhSVmud6udwfQQNlDWmqTP8opzvjcHIRPRAwerrYMHKSNYDFQj4uYHRBygoWM/pJmMGirhjK1PAUg9mgIDrh6/j2W8+r/rxo7t278udAQYOCp68h4aiQTv5m6Ue2vBOffkxvZYYwIbX/uzf49P/7X+Cl3/qX+L+j/xNObmtO3dDI5gB7MVgX/aU/tA1cAgGAhDQycfB+j8EAFH5h8nKpYIKedeA8ieUsb9z1gBNDyzMMsjzIoIlL84K0HJVAYvy06QfyaowAAclgAOx1kWYkmAEF8ikIGEECPxhGAOAPj95DwCaz6lTSixt3ij93jrwvh2wPLHtp3WvY4uC8IrWEQVLp18RApIluWaNGWCYsQtAPSZVD8fjxh1zHRXqXj82jEkACa0b4SQIGFCzI4ts6q+90OZ9UBz7fA809aDNHTwkEjCMABoKtSAhftaLbGvwNgAS7s640iY4au8ZyI2rDyzR1ilQEL4TF2EItlUAwbaKTLG/KoPYloqrPLkoSwAgm+I3MJC9rTnlFjR0gAFQoKHMJa9owALlrIp9JMdj/5HwjT9MWe/jFOr+ngRv6rsSbwAVcc2yMAPymzAuWYK0rx8+wLOvfF6W7GvGX3nmvLZ3BhhEUNAUb9DSNi7rrGtAAtC0Fiso+ON/jJf/9n+Jj/3Ij4FZNZYpzx5hAkFoTLaei+CjZwX883kwUEwBdQregUBgAQ5BQD9hzSoY1LsvbjVtARREkFCKTNwiwtoACi1XwHYjuMCaC4oTCvnnTFmaUcHBogxaYQZYqNLkTRf2tSDsipMbqEpnxgKcWqa1Bp+qBWP1lkA/+a1vZ+PhQhn6UhtLNoABu96/B+WV8hQsABi6I4AWIKC+CQCcZgysa4ZxFR0AqNcPQEDZ5q43qeSp9r9t27eWYjv/dzFKwdrkVPuC0wJjGqyt47JdT6o0YRfieLdXHZUe8BLQpNzN4a+tuGqAwCVQcPOOAIEdMKhggI2p7d2XffvGdr2BBjlUtwIAly2MFiwguC/le6pAIQAKpATebgsSeAcS6ng4Kevj+Ju4CahZVj/oULtewYAwB1zbgIt+zrh++ACf/dYX8NIzX8G9J59qrzsodwYY3H/iKW/0HW14qfh13LALr/3Zv8en/90/we/+rX+O+z/6YyKAbOORoq4EiGJyrdUjTABTb2BvtfSD5AAQcHAR9D68U2AgsAFDIBBZB6CyEFZM0YdClABOIhAoAZu4DThrdKwpesD/AgoOygZOgNABgEUi+7UZHpAIBogJVCTXge9vYa+hCn3U+w4A9IsvFUrUMgH2T39Tfail+ln7QNaZe6qb5EfBnRfL0Jdqb4ahVeOgoQMMFjg7Yhcad4QDBrlXnS0CHKzss9p1DR9KXAwyVP7AHgDMWIAZAOuU/8jtBty+P5pXiTQ1uv7o2zn6tC0Ox4CzAYaUkSmBU8ZVys4KyPgOAAEkKdsxd/WM6jwDv7vliE1MwVpjCUrxuTAFBeZG2G5Evmxrw1AisppWZvIEcJliQAApVVlPBM6mKJMaHRUkECdwSQDWChKYRUikDPHJJ1ewlLO0IHdBfSbnrc8tkcqjyPoenHLnKrAgzon+Yp2H5iaYleu33sRnX/2CLHnU1XlMte2Oyp0BBqeK+2SOC28bXvvv/wM+/dr/Hi9/7J/h/n/0152WAVcF63SODRwggATAgcLhw8YWzK0BAdCCgrOAwM53E1d+0n6v9dSy1QkJyPzFVmRCK05irFXB5ytwKSDSepHGHQDAFYEKpC0LQGQ559QqoAROCg5cysmKBUYFCLJR0H6DqGiduvUUvzdAQCfr1kVgM1ch6W23j8I+dOsALYVaJoIkFrOW5AW0rfeBWP2KEA/A6q0bVVKXwAIs3wRFgVh9qo1vHWgzXMZG70ukMXswb8fcir8lCBgxBR3DJlUo7Wdg7Ps+2T/+i95y1TZpLdPQxmmRhtJlurZsV4CZBOUmDc41VqGoy6eYS4H3sSHAHhj3cyCHzztQ4KsPSp0DARSAt+o+CKBB2nUCCvr5YGPBSi9PoGDBZEr4bkCCtb9IX4g3VfApqzwU3zulpPJI28FcnLcFCJRUFlBrqfPBONm5qwZyvmmLLk4s9mE+sUMKJVy//SY+8+ov4aWnv3QqpqAvdw4YzOnAQPcA2pkUPtdTDgru/xf42I/8DQACFppO8eckwfTcDRybhm4Wzymm3UABpkItCrSmjECB3/6AJQBaxX8WFPTvQqlF/aUIJwkRXMIYbCr8kgCExCIsqEjSIhRQWcGJlJ0hMG8KGDa5h4IDIuhGNpDJqzNefK9tGYECt5rsPG8OBNwlwNFaKioQyjD6GiogDWzFvpAm6pRP037V+tiVPgBLvrT+VL0uBoAaXR3BQhN8dQtmgYBWmclDFCAY4IgUQDfWm3etY2ifRbMd+xeDb/V+R242uaSLjrd7GlAbxN20x99ln5HmQUkpJPXKEncTgnMNCEgfZLVwDShInxKlABSyAoJxbAhqK9U2B9x1Y+6xITNmoIBjfoKiIFhYm+JjnIOxosoYPQDey5iL8sSuTQnMCg56GZOCjNk2cTFw0VsE90EpTuo24KB7rjPzGxTEWT0P5PwpGR96Yyjn6/kdKIjtEUvDTtVxeP32t/GZ3/8lfO3jX8K9DhSQ1f9CuXPA4LC4v0/xtVNENhoKXvuzP8Wnr/8PePlj/8xBgZUWHJgmkkEDAG2GbLSdORsk4dwIFExLFHInCqWst9RnURUe5vt3Rz0gypi7SXhpQDXJ3S8Pvlh424DFBjdXwW3KqWw+oZkYlLJGXAu+S6ggoX1xrY66DCptzdUNcLQuO1hIWEUoWpAVcxd1bRTqUdyGFbumF06oghuACDqg+rU7fyoDiJHXTpfaeVNCQAsWFChIew7AgjzMjzsI6NiDWuk0/tyXBhi04PUS3eq/6UAAgD1L06++iSCgd7PZdfrsWwffTvpyF5CL2n+0XFXAsFxJWy5XoLzItbZqJ2UQZbV6KyAgO6dAIptrQlqrBpICQ3QQgz7NPt/NB4vLclay3nBkzR4WkzHx8y3kCQUwHMd6Pd+ORRrMq2nx/pvVx8abyiIOcv6WMh7AIUsAYN+2nbvK56qdC2zfa9//Lj7zB7+Mr338S7j/xEdOxQKPyp0CBju2wIJ9bJW1SFHRelA/EUsAEJUVr/3gP+DT/+6f4OW//S/wt37kb+w6aE/jBHCgtJKlQ+FAS9VrB+XQtWEkfH+4KvAIDnwNcHgsh2scHCxSNwKATQGCUm9Acmvf27Ox/vf1baK1+yhho0htsl6asC70ReMbIIB2m7gVdGISIZGT/yLsxrgANrk9ToD5NBjgbRVGwPynpfgxY2J4DcwCAATlNGqzo/Zjaz+gBnNCwRxuqqLRRFFiuCgA2BCCr9YdUJCAtwFQ0GdWVkFbL7gLRgGP/ju95nQZCMb4fS88O2tKr2sCb4HbAQETxGcDcAcs2lHfmgsMgPcJ2+eUdcmuzou8SDyOAYW8+LERSIj/DCSAdNVJ2N8F2GNlKw6QfT6s43nhjE3ok1lJWf38Ol5tvpvIDfJl1n5TeRK/hzlh4LkHX16fTiZRDwD6gMVJSw3LLWT8pcRy/rSch7qnd+mxuaFUvlqena89/UXcf+Kj7cqYeK8TrMGdAgaSoVAbO8YTkIIDTf5gQMCVVNnw2n///8Sn//g/ldUHP/o3xL+0LLsAkIYyjQFfwO0Eo98i1HNynIl0sASFX4mK+myl7I2qRymgXJE2lyKCJgpFE+qCGEQw5k4wmi4vF9LYzCZtFH4p1b/qVthN1FFRi8XSfVLRYCQLBh21vQWu2XuwBa518QKd24C4oLzz5y0Y6IKqHAioD9WtVOszUzCTxPfNboJRsKlibwKvDGBYdPW6+vtyroLJLY5SWQLipMAhAUkFlN1HAQVbn/VLurRODohHgY7Nca3ygdDZgfeJkOyZALlkHLOxi7Gx8yM3zqOAgQjwOpAQBfilvqbYzyHPB+fFgQJlcSs4OAggIT32Pg9WNPeCR+zb6gbKrihqJP9eOcR2i8GdFQhctjUpJfHNF1KjZAFh1b8AL3rvjcCpkye2Kml284Z9DPIEGAMBuWDoxrH79QC5SZg0GPOn3GMnaPn9b9Tn6TJeDMD4vGiEss3RyOqF2BSkjOvvfxefdlBwr4KCQT3//64EdypJJ3ACiAnA1oCD6+9/F5/+o38keQp+9G9WZMwldJY7ktq/nXAc5uWuB7rv3FxXn2BCsqIAJjhQEZdG0uMdPaeKnzJ2ApNsQubF7ysutCA4F1SAgU6Ani2RytMJ2Cc8MurUqDAJ9gm+bW8iy+Kl97Jc4ESA7SQ2KkHheABbjBE4YAd4W4GbPwcHtmAIBjZzLWzgIn5Wa0veJkoi7DvsqV8Lq/JQV1URPCDnENih8HrGxGwVuFUcnFEt5k0FoAJjALsobWsvYx70/gAasBDrvKc3K2g+A48j4O5XA3Cx3BadhT4AAnb92cRcMzfBUSBuA0a0v+UWddltjOm51Pek7U6JlKHR1Ts3A5CgrAEtC4rlA4kJwlIGKIPSCs8gajEKOk+ok1G74jEdBggK3A8e572xrVwNFXF7XOlpdkbP5VHZgFxjjG4rR+TPPvh2CgCANv6mX+bYgQG/LoznnSIOvx9u5mTXNiWMATJZbQC+TMCB/rIH4HaPGBNEFdwcgoJenp4sdw4YNI0PVHAAoLoVZDEbJ+D6rTfwmX/7q3j5p38d93/0J2oHqkKSmw4mekeJjwdMz2sPOqiZeFovd0PYIMrV0kERV4D6pAhjwdrcO1r6g8hrKtHqaYVxZROwv2ZQDoPkIlJPqfGnsk7CXTs2IIFVMBnLsXUMjv6ksXhK4zt1mnnmLpgBAmMNuOjxDWUVwMUbu/KYKYVat5gVs4ilVUT7NyzCvmHr5zj2OuupbSu9n4IDzx0RgIb3ZWQT7PdAAxZgbovQx7y1zz3D/rjirwfq5xMrBHZZOsM9mrHZgYKuovKbnGsMQQBUUybPH9eCAut35jIfA7oAxwECJVAWazstWUBCXsQdsC7gZRUAkK/A2wJa5TtfvQ/YOoAQU4zb6gYH6FVJTOeLt1HnLqAEpyp58xgtBoCFXD6Iy7LANjNrWBcFVNXYOCk/7PlW+oBboJ0LvfIPv7kEbC3OpnHjTcHALeV6d83OACRD9tx+7+ugbtPIOL32gz9R98GXxu6DWzIFVu4cMLDSW+6kihYWscsFDx5+WwM1voh7H/iQ081GpXHwbe6Uo3dULBPq6cjFEAdBMyE1upbCinwuALKDnho8uIT6VWt/aJWN3iVMVBoJaaCNZejrunun8L4jus7aZjEf2QQNT9utmtT7tb7Bj2dWTw8ILMCK5+uweX2nAgIOMQUDQMAbV7q5E3qtEFOLnQuiH7W3HhtXQmBaLvpVR+0f+0CLRGh3iq/UczFQrKn/CCx09z9atTUsM+U9AqvAEAwMr7N6uaI3az3U1S7POqa3zYP7RLHBY1sAHXLFpqzE4zRMwWTu7OqluTo4sQJF1j7ZkJbNI+t9fBpAWB6DuTZoUTaBH/MMokgFLOH0AOsSPjN0VKmQBewMy0Rhe19bbIoAXDBrwDAH11ddOUUT1mYqDY/GbW9w+Mc5kBixWXrGr61BhBdAQC/rL7mNe2UP1AEXDcB4zuzQ1M7tKCONAWBlCj7zB7+Mrz39W7j35Ec954gz0I/i5tByd4DBhY5is7oVJFw/1OQPT/8W7j/+EUfDTjWbE9+sT2qFK9Ig0W7jkzqBKqeVDa6EZmAZaxCv8SPdb+WoK/rc1TcyBoPf9vfdxVqcjEreT84w0Wi/XK6fAE3Qm9WbCEMh1rgPSgADnRtBWYJyliUwsFAYZV1PAYJdSWloKTooWK4qIDgCAyO/KjAM6jwVu+FtV2o/lHYMeTFA0bxXljbcPXzu096VbnXNXpGO+np/bPi+Ib5mX8WWEWzibXq/eNExRaUCu/UGjZ9n02V1mcAWZDcoztzoo3qAkBZderdoTBBCP7COuVJAiwTmeuzQwhqICKBwBQgpo1mHj20uL6MxQbpEr+Yjh2Y/qHPS5tpV9j6hEGEPPJqskOcPlLl/HfR1H+sS3mP+2xPG3JlYg1Exl4F/j597BiuCoCCru5VAFlh6/X1ZkvjS07+Fe0/eq4DhktvoZLk7wGBUdgNC/rz2vddr7ugnPqJBReQUNAdLY5qv+ojyApqOmSK3HjF6qYNkGqzVHDugPE8EEZ25f5jm8ueqnju8XfzST8h+EHe+saF75kjhREAQgQFCoCH4dq4DiyOwGIIICg5KDXRSEODUJSEty5ghyEuNWO8DN2dR1rFcAgKXwMvsN9F1ENmllHZKva1PEHYXltZOgdXs+CUB3bVFj+ubWBupQI230VgbspgEDVC0OAQq4k5CSr4fhsWZGHCkBWAm+XzpHQEZT1nBRVLXkjJV/nt7l+6znSNA6r0VjTdAVUbUZ/KbKJDo+7bnUGAItK3qZw6GSpASUS5c0jLxWc1bYdzPt1V6h4ztCYBxa/ltJbfyu5etM9kZWdUoB9UtdP32tyV50TNfxv0n71WmYPY+R8+alLsDDIYNEpSz/n3w8IFuPfkS7j/5lC/nIxK07xS0om3GGBhwb4HP6tEMsgNhtpNcbb2lPIJgfy/KwYA+HPiXygw8nXXDxGeOQIGvZFj98275oS9308QsIclRjRsIbpRMsoQwKzgY+T31uggGjB1IV8scDMTla6OgzW7Vx2kB2ffLJQv9vSq3yLMxLRfAzpDBaC64XRtZvI2v6jFqvGwNUOB1rdkAtxXIQp2nqwXlRsYVF9JVM1ljU+bvYuMlBqfytoEWZSHMJbZZLhby1UekjE0FBwts1Y1sshPibTRRj/vSYyzKqM1MDgaGwAyXH/rc7+vyF1puScPvkScAgJEP5OdE5uerzmgy5pRw/fa3hel+5iu498H71e1g8RGxSs2XEQCav87dAQZNUX+RfrO/1w8f4PmvP4sXPvUiPvrkPRS9ktIiyxNVoZApBVMywD5L28iV4I/vEedJKuoEvXfr306eeVv/UysEol917Ma4uJ3txQdeoqIvKDMHCO48F8HOFQj0aVz3VdBg1ZxhPmV/9pZAg9lzKfLcrYHlSoT7coU+6c1u45dunfXUrzqwCnaBqMAuwK9ZweLXPIJSf5SkMlpOuT2OlH6MZak3nd970mZO86sv3wCBr+wxhiAms9LPFo+Sl0dfsbIbN0ATj1IrqnEJIb04NG6EzKCxTXZQlctxmVHp9ZlRAZ6a1Q0AOEPbB3bq3cgov8mBnHg3cvXot+aGhsrBeIuRW7ov+TG5NLIARLL3we/9PF785Au498GPwcCA/WuqjQAUAdCOdzoudwgY7P1LERhcf+8Bnv/Gs3jhEy/io4/fqwwlQbOAEYgyEqEBCQAqUIj3zwdNdyG+YO/zugUqPpwse4bk6DOA/cY3F24dM6b1T41Pb+IgYjzDBGA0ZYawiQJAM0Q9UOqXvseSkm600imZUmRuc/KAM8pQQZ+GMycKc1fweWk/5wzKVw0QoEWWqCEAh37XN2/dIx/iAKT5wivzo59YwQLAl73+0MrR/BmVR440B3ZtB0zbz2MM5ECbUMnZJbXSyyZupwgUdI8AW9Jq7oA+x8VRzoNdvgNnjdoA04tgaqiU+gcqaAhtcmjIzGTVTKHvKO6x0RY/n5ZJWvrFPLPIhHEEwqDchnbvZAsFRkVOn3AF97e0uRFiBq7fUvf3p17CU0/e8+T+JSo6r4T8GbfLQdxGKHcIGEiJA86ZAgUFX/nEi/jwE/ewdaPRN+MCsBF01zHJxy/I6xauBOACMLg8PGdD56JS1+9xKFaig5t7jK6p37vB3r0PhfYCAjCwyG8iPSfh3i63++v7ik+BRHagZrnLya9L8JSIsYLNCg/qzquQtUAu/U7MkpRlvQHrjo+kAam2np3yMpxOu5StxghcAgKDfPkxKNOWcTYNfxEUxPbUfjc6Oi3NOQLa3OxxjB+sAPgLKWHe7JbBAl0SmtpOPAtqBTrr9ChepbYbrtS96Cxi3SfDXAlHQIEsPkEZBQLa/AijVx8EoTYMU8rNOf/e3uTE9+jL7trJ2/P2ir5R8vqvNM07lkePKouAI3m0v6Zvi0aJUp6qzN1x6rBX+F8+3gIYGDCLrgQQrtX9/cKnVH+VeXtZHRPUcTR4kTNm6J0BBj3y7EHBl595ER95/CnYTmT9jwnSkEQkG24BaNgEoBkwnMYK4pKib8jwiWL38/3pRvbXL7PJFSdfnze9dEP4NhOyZxnrNsbcMArNpkWTY1AgJvfV9qUqbthBQaoJVyjDlpxKwGgCdG01+S4pNj2U/aEEBotyXm/AnADL0LbegKDW+npTl6vFoDOgyffg5VKWRwMCuYIBT3MLgq03ZwqCnsynWD/rw2KHDKRCAFamyMJ3triLGChmbdoBBu+vnmkYlXcDGg4YsL3lr7W6BAC6VS1y7UFbAoP23LeljTlX6kvIj6HxKxa7QpoIy4GCAogYzOjPu8246l1OlhnRXBAOMANIbYLTYptV33V7Td1uO7btyOiKFqsp/ihjh8ea37TyeCbjvC1iiGLHXsbt1AEgUVi63YGEHkTEa+J1VnajtD/fyMR4n979NVbMzcjT3zAkJu65rwvT/eHHBRTMNsiyajHL3PB2CXYRTX7XlzsDDIA9Wo2g4KNPSKDhCLU2g4YZiVuQALSKDABWHqOxIQ0Wjs0s9ZGiP3N9M9n0QFT68V0jcPDj3TPmoyZa4PInioxozCYYAOAKHBRkybXjrY/9nn4t6fksip+pAgQNpPJ9MFAnEyVUYRv2wgAyGJsIU8CzRlpSFl5vGoZAGnYb5EqwF6cqwCMIsNiAyaY4InSzW3uW5tYFtf2eJsuPRv5xDumfjV1x4KS9HAFDCNL0sWegwONqLAMhV+DQP/e9LpGSBlrF7ud7y7YqthYADBRftIJPtCmAJubIUwZr4qwaryJAAdwuifWVBRaHEFmFQarmfXvsx1iTPTSA0B3r1OTSryAUNv4GbdMCgtaHHXdwtOZpt3wO3/1adjlkMiiSMr1Rc6y19GTHAFTlz40ciXLdYE0Kmrl3i87IpIS6L+8MSJTu+nrR/i0u6Q3bffr64QP83DdFf/3k409hKzw1/uK7Jr9AjhlzEJv2EmtwZ4DBDBR85RPKFOAIvaqly2iYA4KABKAOXOuArbSDwesxGNjvVul7PcPBkcVvv6vv2U08/b7pzZgZm91HK7M5hbp/D6BOGNughRJgPAoRISdjVzgofkamCgBklbdM0BzlFsasTTaQMAIITOCyVXBAZAQBLOMfmHXpVlGvQwGuHgOtq0d2g9t9JAhwcLFbomdlEBxIeVGKW10BmrLW6F5bhwzdRtcoYDvOQRg3fc574ZnMl0nyLyX/WBmXEFDrcTNRufVgwQaDzZEGNISH9+VRwMKULaD2fESd4dgQBPSg6kjJhdepglnatDEEHKjaP3YAwAoMbIviJrB1iRk2Ld5A4w9YAxm7TZ+kKQfjrd8PoE8YFrOHhjG2AwURgNq9DtpqC2NvBgTqNS0I2IIM6uWPyaoN/K5kj/QYuVyu8kfMBu9ClzFVke4ZBzQHTIFv4ZqGVQhzIYU6x5kw2sPlkt5YC+ONtx7g8688h99+5qv4ySeewhrke28MJpJz2ipVdjKjELmha+CAIKDjCIPdOWAAtKDgo0/cQwlI1Qey/sgUbOGWiko6iiMgMEZBrmcctewZN8BQ8YcTM7o/AgA7fgQCDABwaSffBoALh2M2WUNVQsViyl5fJEOETARKkmiaCPq9TticCKu2KUENZWUOdgBAAVlmVDqMw/kOINheAu5a8O+WrIZV0ZFYa7rHREzKYglZ+g17/L1De4w3WqkC2d0CKQADBQfcgIPsArMU6X8u2I3V2P99icKqsZQApEQKvrIDBrWVg/W7hvbhChZiuwH7XeGkgcaVmkn1puITe2Xgy26YgugG6Kxd9k2EpAXMIVL0laSdq1LzV7xlGyfUGKREWXS0XacggEu7jbcvfw3goSZR24+9We6Go7HXughM4Q/aB8l31XQA5fEJx4Bgi58DGCh6zMZyBA4j+dPLnkeVOwB2sqcea+UPqQzpWc44bzZYtkg5vnEEDaYbAhgIw7g3HGH1CfPBR/dksNk9rt96gC+88hx+8+kX8KEPPOWGXO+C8cLkFdWN2F2uMgsQieDAVuc9gSfHFcEdAgZW4uqDDz9xr078gHZ7ut2KWc/Ec6Dw5lsPAAiqO1MuKX/g2N8/AwF2fbQiZ0BgNBHtOwBH9g6SQkX717TJkPRDAikYQJigFSwsASgslGSSFhOwXH8L6AAXdoFHAAHRcrMtZgFnDwrCRlkMztQqQHUZGM3uLW7WdL7yxh2qrqisYmBgBAEdG8BJXQuqrDYdW2WrgrawCNfCrcXV93tXjSrknIkJbpvCqsCiMpPzKWlgbVrc+uWeVdB2oZ456CHvGSBwqeyAQssUjILiooXbsCylKqUIsqyNR5ZtfKvT7UyERLVdM4CUMlLKyLmCMGET1jGrEOMVQoAj9RUZtBcDOBNfEQFBA6bcrTBoQx2jR4Bgg8oVlS8lgIitMFYuLn9WHsuemdyZyRxgLHeAsezJMPkyAAp2Q65MpTStuiO47XerowNFdDrCDmq/GQMdf3OWU/vCK8/hi0+/gA9pTIH8dg5giwr/BNLgbEVBZONbBy0LKPjcN2TJ/j/93V+b1uHOAIPCwOsPKyj4yJP3fG5FEjQOul44ADIIXEHblr468L/79uv4xVefBwBcSHxXHxDrOOjcngVo6svd33DOgABwezAQJ2QpVSlZHd31OXnHCg70L6gKyiQT04DCohN1SYSVNiyZkEFYchIEy9LORIILhAIjpMQCzhMhcf0c2QP07gVKktTF2IMOIDQWcPjutHnslF5Z9TT2yAILdC0rEChFcyapUI0goATBav3V92s/NuTxai1N3DYuDC3GgwiJKlDIJVrAASjAhFgFBu7DjPELsZ3eqxLbO8YB2LkuCG4EBKISG4Gt2NY+l7gCanmleVtDGS8QXOlYWxtIsL/Z/y5IywK3x22PDnMvlAJz6fjYBVqAZmU2Lq3NjDUI38+wK9aWkSWI7Ip/LpUhGAGCdSvYwFg3Obeq7FmZXe5s3MqcCgqk2mdkjinjKHNSAVYCMjHWRMgFWFX2rAn6neU7tyDBXQUGCBjBpSwv5yCxAY4CGLxLOBgU3PfT+L1G5d98/AX8xAee8rELtEOh11em9wEFK1TrbwxLAfDmwwd4/pvP4oVPCpN+xHjfGWDQgwIAlSFAr3jnFgOYHSnKPJSJ853vP8CvvPo8/vXHX8D/5r/5uCvSS2UEAmJ9yuB8BAJt3eqfkZ/uEiBYbWJ3E9OobGA8WUclKcwWUCDWf0pAKgISlmyTlbFkwsYySZkBTsCGDQslLEkUaGYAif08io1u2ac8kwqoKXugAEFZgWmSKh8TA3q8LwfUtVutAzaAeyBQpI9K4coYYGxdxf4DquvHSvWvzl03SyLcEIfzQpEmktiOm3BsDxTkR4kANEt2R7LtPQQG3d05/PV5EBTXkTUb6W0DwQa61gsgGrhdmwsLBvdfJwWtKQkATqkFCkQLUoKzChzBQhivTdAocH6c6vc+Oc6ZmIvIEhhQ6FmCUqrLILapjeG1cAMIDCQUBQy9zLmVvIHJF/3cyZxEwKYggRWrCxMm7UGJ9LvobUoM3rRvA5NQAB+OBgTMgxAyTHuf+DUDo9JvdYup8hMaE3dkNALmKtCZSdLGBpqYudaHGW+89Tp+7puiH59S/XhU7gwwMFDwlGY0nM6jwfF+PDaNz4zvvP06/v7vP4/f+PgL+NAH7s1uUx8xOHnECMTP8aczdsDudyluICL1niFYuewmZ0Xt3ACfIbVXoMsMxRKtExRYMqOsoqBSImCTicJJLJgNwMIQjVQEHGw2IxOMo0NmQfO6lkAEsk4Es/ZAE4DABGg60rob5QlrNwpU4FiYFlt1JgrKFH8Uog4EBgJ0g1hZIzZH+n9fR5v4RqNGCnXJEo64pNZ1Y/EdiYRRyCTtTdp39nm0WiQ2SYoVcRNrXE6v2IklnN+zZvPo9xErMANff1HtviRVXB1QQPNdFRItEhKA+q/PxCqNcAEkzFiXZkzPVxocga1LoGDdqstgLUXBQWUI1sIKDnqDREGB/+3a3U3hyhAACgiY5B8xUmIsSC47Vt37hgPZJFtRELK+j8glOZ+5rgbIykwSsFuVxno8kjebNf3AqNRmGnZRX6JhOAMDsZi4bG+C3bS0QMavGCgYXNOXOwMMziIhN38GpZ9vRMCbb7+OX/395/GvfkYCQWLMwqz0dGTfsT0zgK5KI4YAmEfz2r1GAT12bug24PkktQm6DaT5Bpk8hTlMXFK/ALBuMlkBRdMZEEgron1BAidZPbCmCg6Y1GpIgtA34gYcAKLEEgsyjkE1QAUIUIBguot3LTwrdSLbP2+ngkYhRWvK2qt+b4FApFbXUlQx2bFyilY95b7ZirtvRFltO5BAJGNHQIICBrWULAi0LuPiHTgABsKoab1zZdYbYwZtvwbeWD9TVmfBwKPS2kftv6QEIqibTL4v2tZLpiFQoM7tQPYXxt6QBDcaCRD+HbdgLO3VHD6X8GUUkzGKJzBQwPq7CHR5AgqMJViHLCWfljW7dw7yBiC1J1TjKThIISjPAE8EBzamuQBbkngmqYc8k1GZgggQvBaDLmhWHASgUMt8llTdouOwvVV7l9ltBqDg5195Dl9+ptOPF4bPnQEG9tIzdEVE3uAJ5IGGR+U7Cgp+/WdewE88/lRz7syuhTNAsPs8+g3vzz1qoQkYStQKv9uUrbCCA9Sbh8ma1P+WQChFlA9I0TWzWPfMWEAq+Ml9YsxiMZkPL3EbVFP0s1CCVamVgIT3Fut4JkUBCQws0v77zM+qMmrditOpPRDoraeiSisqpJap0fGqUsD/ph4YiOI3JbUSY2FulNQuvkPbKkGAloGEdu23NEyfOMbKiBV41LIbh94fFdZFoNCDAYT27/3cMzB2iTWLf1OQxMaQAdIPSyrSJzfGkhWJq8mEfFP7wP/lpGwNQgCu9iU0vgate6f/bvSx1OeWbXxyrBsAszbn+DtUGbip0rW/sL4ppuBqW94GFJwts/c/ss7PFhcphjkQ2APUKWEMgrWRPduNFr/u3HseeFWmJQZCEhHe0NUNX37mq41+nAH7WO4MMDhTCKoMAV++AkhwG9CivQgKfrIDBWfKESg4XVneExwGcDIIWxLWIJMAncyKhpXqy8z6PWgB+8wAkijmtchnUeJeYxSuaL2fuHZ8NCnTwYy8xTY7DV1nQTURHCS9iBUgAHWiHsmZqGAAuFC0cz0IMKU0C7iqPtXWKl1Lq4Qijer+1iAYTQnFts6hgZP6QZPTqtWFU1Q5WTsxJ6wEbChNfMdKAkzEoqUK7lwBjZdxAZUa7cu7EcCzeTFzvTGH/tLPxqRZPxgg6P3cPSCIAC0qqtgXwLg/rC/kM2pcDSzGBlhWA2uMJaFhc2y1jgMFDSBNxKDSBY9qrEJ09wAVMEgf7JmdS23ej/9m7E+MmHdTEtEuNsuMk5xoKmPsujjmAQNlNADKbQD0frUCHIwB2OViiYV2H86VKLuA8wp5Vvp+Ha2UiW7VN996gC/8nuRB+OgTJ5j0rtxpYGAZ7SyWTRSiJTPSJSh2HHCA8J3gPngUUACgWQIjdZG/EVH6Z72GUTs8BsAYnbUk3xJHLHb9wQZG1mA9YvHlEwhcdI100lttMmmqMCSUJP66UkRBlGR+RGqsppT3MyNasiMr1gMTlVa19ciZqAEIRHTbeedt5JPNabjxtXZ4BADkHeEWkrkFzlDUvQIa+VSrZarXq+LZ9LM/H8cALCfCzSZ/ExGusvlY5drEBOQif1F28R0bixISmpgVLAyis7XBBBTs6ev3whKblb7/+n4zMACMAUGMH7jk514LOyC42Wqf2L3P9Il9z4mQVvm7JELahDlYKGHJJSgxQl6LuhhsxU4ZuHwMKEA+F3M/1ORg1eWDCuY4KI1Lbd237S2KMRjWYRkCzlmNEWJlCYswfubzW5B2MkYuezcyJqxOQHXh3BYQWL82tQhf0viw/n78efTbM6W/vgcWPbNnspYAfPvt1x0UPPXEvSmgPyp3DhiYQrYxOwUHCQCL5VyIXeF+5+0HHmj4k4/Pkdalxmbe+5esXtN7ogpGU5xsJ+z3elBAgg6OwhpnJ0e4jAFCzhaHAGxqQXrsAVUfYynk3+W5c5Nhv3Tosv+VUpy8OiHNYiX7R4dtZYi8j9K1dorXyd9gHQUQYGxABAKmYEb0dAywmrkIesUzAgMjy7R0YdkpJZRNAwQRWBN92aLLMf2dWfoOieWcunA2yI+XpEpTLdVNHcyUpM59BrkWFMRGnffLD6NEIAAAfcbOniWIoMAo7eJjPVDagSUY9Q0Q5kDom3WTvrHiTA4FkEAGEhhpU1CgTEICYdnILdt2bjCWMDfE7QCA4hJJXZ4arMUYH2I1PYoPuQ2jSVoBTyvMIktzIonqzzIPKJEGABYJpFBDBChuhEgfkH8HLEfAeLJfki8RDJxhCOLqHQB7MGAgO77/sD2Oj42AwFkFPbquZ1ojIIhMwQwU0OC9jsqdAwbALcABGRITgPDG96/xK7//PH7j6Rfw4QNQAFy2mkgVbyziPw8Hwz3qZK7HmNtBmeBMupy33xjSZsICCdq7QhWgXOhgKSOdym3g9QwSJVJ6AKaT1WjrWdS8+U1rQNx44PdlRNfF5j0KWuvBQPRV25K26CLowcARNR3dBDdbmYIBUzZbL6UB5CTnU0pqVZ0TKhp35b8pYLeKzL0EBRqZgZUlViOTuKbAcv0aEnWcAcGPWm577xEgkOMIY7qW6N+Wv+frdtRPmx7LScZN0W3ARyDhKies6v5ZmBoKfKGEm80A9FZBwiYAetlKyAMifz02hNjnS1jdW+eMmTtc29kj6Js23r+7y8bArBogEBeefF5yUsOkSK4AAJQyaCvIWeYEURoaIREUjOQKcFm2PAoQMBUg95cSm+SM4o+/3V+7v/g2Rvsl41GeTfUd3mNQANwlYGAtpoPckXGAggT4EpaiittQ8BtvP8Avv/o5fPHjX70ICoBKQcXSW9aNkvdj+98xT3zvDUjg3TGbSxawp3gHWYFCAUlCtdwnQToGCvBjVWicYQ0uTVYDBNESskHtv22soBYNj9pvRz0HZgCYA4ImjztXX/VsWdttaOkZIJD68CEgAETZANUqnVmkjd81WlH2WYVoXzbmGp3dHJNrV3C7jcFE8b83eymp8johteLzYq6BmIOgL0m1aNJxkCy4KLIvUODlQoNdMxZmAWelIKd9n9Xv+iEJUjCGx8aAAQQDbGsxZV+wqAJdUs33sZatgoGc/PzKFVCHaiLuR8KkCcG4zp+aSx84cjJE90APDlKS1MIcAAf7fM9YUQTwFyBDlyUfGCHSareTKwBOA4F234T65iZCRkzKri0ulDNK/OjdRmW+6XO9aS8T33xbYgok0PB+U7fdu50AKXcHGFjpXtozVfkEUkCg5jgT4cFbD/CL33oev/WzX8WHNabgkmGRutZ2pD4rRzc8gybJlH29UeNusM9sf7nxpy8krofeV3uUNRGooKHef/8iMW85gMPJGi0eglGD5wHBaCLuwAGOGYJRPvdHBQR1dUFpGIEICqz0gVd5II16MCCfRxR1CD5EG4RlvuwRa2P9RAEUjMqR0u8TAI364Exp+lIHXD4hjPuS0QbdLoAmyZK+TCwUvq84UDpbPov7JWlQZiJGTtJ34vtWS5vUH572Lp++RIan+GCUp0n/GUBJSElyfogSZiQFDFsibCyJqJgZKxE2JOQCcCoNQCC15pmBlGTjtxlA6Ft33Nx6kC09OVq3KylA0PlEOp9oSbhiTWqW5kYIEHOvzOUJUMeq9fMlAABU2SJul3BsIENGr38ox29TLtxmxIICe93S367fSfFNX33wIu59UIzaNPrhLcrdAwahJII3ilGsXkiU5IPv1SUdH3miLumIZUy1Uff9AqVqc+1CnS/14RGajPc2gWbK0c5HBQlA8gkwDSO9gdafC8wVRtzpDMAQscfJegkMnKXBosto2i4jUMD7+IHop44rC6J7oF+PHd0GfcwAgCbq2o3KvFfJfQS2AQH5XhmDJaVBHMfe52pR8NG6kn5qN5wBzivjHhDErxtuX3L4vVXBnnGpTnbexqaAgRpTA5IAvoUtiRGwQpYCGp29JO1Pkv5c8hjkAWiBniK6oyV2s1U7RTh1IBHWUhy0gMf5P1jjSCgB0PgQQKx15ALeQnpxgucBSUohHlq0FyzKohcwV/mWQI5zfKMeat1yORohnQES++ySLAHmboAj5W8yRY7VL7Ev3o3qf7cyHDhmI6Ju6S/rXR9vvPU6Pv/KczWPT7g+vu9tXGjAHQIGI/QVD8WAV9OZr3/vAT7/Sp9GuTb+iL4f3a8+sD34KEzr1G91y/s0IAE13sFyBgB7q1rO6yTP9pvwW7/fcb0rsq0gQL6fAwL94N+7D+rbRZdRDw7ie3lgoX2GCvtbgAJbsdCvebdiyr8wN4o+67Ni1HVzPgAA+964CQ6isW2J3CU3jjVhv2Wt1K9t3z7HhwnwTDTcGrcHBTNaX55bn7UhsF68r2Nfz1Fd4zcu0Bgb+b5ocqylMDa3YvcxNftUvan73oI/AA1g8Pfp3nvWx7kTVqLMdXBC/e4sQY7iZiAsYKwhcnBJGgeSAVJXhUWuM2twIGy8884KHs0zOR4AKnSukVVNn+GyJLJztGPntKrOWMIdt/4au7Kf+219++2Sxys09rKkf88zZTaK30u5HovVbxnpsgFYePOh6q9Pvoj7yhTM3nHIThw0yJ0BBsCAJpt8ZgAPdJepFz/1Iu49ea/1SdtFB4q+n9zAXjHddonKDB3Kye7cyVHeyCoXPNgBhUvL+ICWeTgqDVrXA/3EbVC+/Y5aC2fKEgRLxv2h2IODPpEVx76Fvqe+eAxeM8UBoN1pspMIqUaz1psmcuV1lauy+GtXbYfF/jMAYPc8syRrxAicXaM9GruxUGCLtCG9HS0Owa0+VuATf59oCA6oe27dQle/d6AlgoBZ3RsBpu1tgbdWT7Y68ySuZsGpvURumzrc+r6vs33t1+X7fUqlky052MZALqz7jAAECTTmArBG98P+MLp5NAYFo3m3Zw/UiKhNXBU/BaYRwm5U2UF/YbJjCASa+XXhoVp2QzZ8b2Q/Hs11FssRkyNzfPQjPU+qv74p+ut+yGjY67lZuVT9OwMMRiCgHgvNwAUPHj7As9/4HF781Fdx/8mnmqtsEPWbYzHaSbOELz6gbgtJQ5nRXPt3sdpgz8UNRmtDyRKCCabKNaD4iP4BDAGElTOoeWb574RQGPDdIS/cf1EhaEGkturENlEyCj+WSy4Hvy4RDBmkRCj+GYBGlxcOPmet8dD1rNqvj7Su67PruTPLsGy558jfKu/YbSs7E/iDYtVfspuIzqxYhkoDCT1AiG6Bzdpw3hxaVz12AcT073L0PtlZGQG7V0CdLthT2qONlGz1ydFqnTZbpbZfOG7nZmW2oieek88BHJ3UcCOlE5m5qFB7ZTqbg8Y+WImuSqAFB/K3BQLlhHDsrfuZwm9ij5p6TuTiiMHqG4mS79jqdwuvOIrnui09PyqjLjXd0p+y7w8ePsDzXzdQ8BR2760yngZ3Oauq7jQwEAeXNpYO1OuHr+PZbz6Pl575Cu49/hGgqK1DUQDtMPbueXGgnEWkZ+o+HNx8sJFKuI5Owti485rMhfp9vtWtAYmmlk2xiXLJrXMIdoalnfpEAbhxVcxg2wLVzCZ4hsvEIVkP1ceJ77YmaJH8BWzLsJvAtbr22iwgAwNz7Xu0pNOOn3ED2JK1keKP/laEv42gHWiLPrtgtISCsSfLYRmIeTM4mV2o1xvzwnxRqMyYgV2SmaAQ3ov3A6AbeVHjVhoBhz5p0qXtywFMl/leiFU8vezX24hq4Ch12tRaIWk7UTgeXXkOCgIYoPAvtOKwzjneGCO5QI8sI9q/Qc5pp1EpUzl4axkI1EYMctD/dnoh1szx87ssfVO0EUjtE66/9xqe/cbzePGTL+D+4x92/eUbxVECuOo0q/cohdyR2rozwACYIMegVK8fPsCzr3weL/3sb0ujbjf6w36QVGKUeyenn9i6Z44q1P12Fm2jg5kaMFDGg94H/uh8N1lm1fIPOvXCRKhAoU4W3+p4NoH0Hj6gJ4+n6fsPjnf3tueSgzYyo9bLBpI00Cq0Ehjm+5Tsimr5K5hYdP0Zk1iPSwgQtGyRogDIFXlUAnKzfdWj0tdqN4pfjlXlb+lxj5ZamWLsfa1VEQTpjstgtVjjDajeGKxp7WVAQdpNkBkD1e2gQGHB8T4kM/eAAzbcMrDs8J3bA42bcPbOWYNxO+AQQUNkGizHRdy47GjHxnJgZl4aNzZmbIMmAQhh6W9oM8sm6hnxtO3qfaR5UhhfOyWMgUzaNXG0TsOlE7kZZcTw3j8kmVfrO1D+fvyCzBvIpGkQ4VnZ31xjg2Vtb+X663U8+8rP4aVnvoz7H/hJua57b6LAGrDVs4R+anp6Wu4MMDgEBVxw/fB1fPb3fh4vPf0l3H/8w6Cyj6MWFBmOk3ip2wfppDVQsatIHBCTWO2ZC6BHveyiSSVXaSdCAx4COzKj1CaFOiTcToYwQQZAwq6r7X/byIpdZfyjR2B3k9O2kiVKblFpFmCJPygCDpJRBYVg1MEGWcJlroYly8QREGCtULNFIpNHtttZtgXkdqQLNBwtC+xp/6j8K807VvyRZm6sPdRjCM89KnuK14ZQ9Q/PgsmawDP9F5Nr2fFLQoW6DyPGY9cGap0+SrDqvB1o1w59fE3c0lnYAfLP1lYz0NCzDfbcKBX6WIwzY+lyPpC4IZOMHwMF5r9OCnzJWqGTl4eW+AklfNwDLRvaPqeTb/65l2m3k3G9tU/NdtS0k2/NecRzA5k0fM7BOp0LdR7pluuHD/DZb31B9NcHfhK0rbtrxI2qdeDN5bKm9tO6ybi/NEPuDDDYDZQBKPjax7+E+49/JHB7bQfJSqGo2ML5bgDEjmloqZMLt8YTbY+aqe5pWs/59wlYsPAfE0bb5TqRLqGr77IHCRUlp+H5erPZZMH8GnRWBqUg4JOjeBALKqYk67SJQCSbMtv4N63PZoZ24ACp3SnuSrO3cWJP0LIRcKVKYIP6qgdlFEB3ZPmnTvmbAIdZh0Hp70AA7WlfhL+zws1nahSfuWIaZdd/PwAKdTfM/bNmpSrw2m5ngMBt2+W4bVrLif0zDdplv/vg5tfY3hoVNBS+zDQAdZofzc5+RcksJ4iNqxEosO2cIyjwGczbHhBwCZb6QFEDY/ZvBBiO4qBGyj4ChAkYeNdyrTdyTJ65wRFkm8m9HUOwl3s7w/JEGbk+3Gg19/fbb+Kzr/4Cvvbx38T9D3x4xyhYfSQ3DwG8aV2C0ePCEUEez2frHQIGOAEKPgz3vwx89FK2sSKzNrSBwMGGbNr3ksV8iS3oJsQACDTX6XFilonCBaw+OAYqCIoMST8YqfMHpi5ijhIomZshzSeb3ssf0zMLdq1/jEDAFGg7Kf0+DhR0sqZcAUKCswcwUJxsmaUi5MSArQkn2QqaDDOg0uML4NHt0UftQXYXqPAR7Z+MGQC5pWZCOn52cOCtxK3gLkFIA+espege0vY2xiWb4IMpxqoUm8yQOAYKTZXcv74vbrMFax/WBieAQKS/q7Vr7dS6DUdW77B5Qju1lVJmKlm71XbaLNkPxDVgbeSfC7xtSrnsnojtBszH2ij+IgJNH2cGBvSvMQYWT+Bvw0WUkLVd2SoYmDGU9jn+3bXx/njrNoj9MlD25i+PsQRBjjWyaiDLvCQzGFo5ZjJM3AUVELTyqgMKOwNJ9QTXZ1Y9cEsdsDtdFf/129/GZ179RXzt41/E/Q98CCgDptrrQzALSSRdwQ4cEEmbzlzkWu4OMDgDCsKABwao1xHXCYE7cEVI6V0PaXq/3WSZUmdhkkaQsK4OBNgUh09uqSOXqFDq83gQEUUxHNoGTgp/UwAEE/AQf5tOsBByvpuQ9lm1KxEDVFShqaLfGJzkOwHCHqSs1lTzVgDYXQyJJGCQ1GqK1m9Uflm1kLXSFbpgN7TKv7d2naq9ZLGBgbJK36pQbKy1kYCWDtz1X63coH0daAnAYoqCLwWwkMFkUeikSq4DBt13oLIP9sxRqtsmpLe38g8YgZb2Bk5ZuT+EdjPWKpPMBaYkiQZgy/ZqexlI2HR89YDBx10218R8jf+jjDsfawFQZbJ7MVA2BZs65krZt5vJmsY40d/PfPyXrPuJ0hcjZiC79Hzfd7eRXzSQXWwAQf9iWQJIUFkDNSbcT6/yyEABhfrMXMgH8n9WSIHB9dvfwWf+4Ffw8t/7Ddx//49XwGB6qjaGBh2i1otPgIM5YXCXgIENROmE67feqDEFH2hBwaGwOGgseYBSSlNggF2n9XU8PNe5A/pJSlx2zIADgm2tQMDOAz7huGEN9oO1qZ29Z+qUe8p1MgF14gHglOu72+TTe/WgIbIO7vFqkLtpHZ2YKe0AAhUAujMmkQoXIiTKIBLvgcxnAul68AIR46MMbm03tH11ycpNUZEFC60yAGL9U9mAm04Ym5BmrsJxxhDN+q9jX9qA0goCjDkguyZlOFjQ/iNK+llS7rpFHCzlBruiKrJaRWracJSgJcDAoWugAU9H1m1svzMs28k2bAPUartB2ym2H6eEZIAhZWBJ7rbZmKq7IYBQS5pkLgmr2aOOwQgIpkDUQJUGrnk7soHS0I6uvMeySA8eKn+OFn+kyPVzb7hwfx1Q5dYFBRuVM5ksUtbAZZbJq7zIOVaAoFa0MAmdrPH3D/NIffi1bybMxZErZVbKhuvvfwef/sNfxcs//esKCjp9Y7cxeaozUA5XcNBZSaI/kHD98PXDKtwdYBDK9cMH+Owrn8dLT38JH3v8I+POGFFjZ4q5EGJHdY1/ytE69bXNJmHnLtjWhiHg9aYFBBEMqDXqgsbqfmKhNSswcMGuEweok8+vCZPPv/csg913a8EC5SyikZUhUGrMJ6aCAGMQ2GUAyRxwV0TrWrCfGyhIDJSEYQY3AGFtcu3TU5btjKaNFhlvLpBh51CFdM8EyTDQPj3ZXyO3j4A60rbJruhceOpxKgYe6nEYQAiuh6ASGn+8tWUt3bywowEYRKXmx3rXgLWhK7A9ULC2bI53cya2J27ZnvYZy+JtKWBqqW3p82EBqFql2cBCErAgIEuAljEHdrx+l8YZLWI4yvIXAWrrmurapmzH7TkCC9KAu3blUbseWf9HRosxZ8AjyStnMomAnLX/MriI/CFO4JKAUkDLlVoFAhAoL6LvM1pZ5MZI0jbRuREt9w4onNIB8pK7Q9dvfwef/re/ipf/7q/j/vs/dEE/WZvsQQAA6afAGoCSL9l/Ek9O73pngEFc0vHZuCSxsxD2VgO3fy+U6+9/d3+9fR50zLBMmYOBdTOaiIayZ6BgW/eAoGwtJRcmmaP6UOpKBahAg0yGLUzAABaqEqHWzRBR+rY21zAHgRsRu8YFiD0USGmbkMbiUVHBvMmEl4arrgWSwEQi+WmCCOTMrRA2P/uoHFm0SjBLv5VNAlLdChPFX8GBMQZrVWLrCt7W6gba1qH7Z0SbNnWMiivZvwDaAm2alit1I2SgWGBVlv7W6yR+QwVNCSBB/6bwORM1iWDi38P2bP52QGDnHhgoL1NEHNkCUXa83lRAFZVUbNsIFC60rVuikZbOS23XvFTAkDKgwMDYBU5ZQZh8TgYekrghirkiHGRVwHDUnpfGpv1r41T2roMGmEaW4MA44chYAi0Y6NmAGRDoQUAEAAPwdiinNukfu56TKG4iAmeVLcxgLO4Z4PVGwYHWgYrrfgHU0Hp0cqhxDwQ52VP8R+VA33z63/4qXv6p/yvuv/8njvUSUQdOuNaTxluk1SWPX8H/8eX/0/TWdwYYAAYKfk5AwRMfDZ2H04pfrh0Li+sffBef/qN/uL/GBudtnrF7Tv3tdL0/OmQONMxFS8MNQIFOfB5Muv6dHQgAPumIUjMBGfo5TkD93RCl6+9If2NoHRUXHE9Km5AGgJFAZVULrexcCxJA1q5cSB1IcPHad51Ztb2QNSVW2JW8K68DIEBcRGFtK7DeNICOFRBcZHj6EtkcA2eAKKG8tPSpAgK+ecfP0XLVMATMwfqNTELnZ++BggCxuIrE2jAKzW5Mm6Kycw0QYDSuvwEzUJVZCwaatpwBZnvmI7ZxpKkpL+DYlssVKC/a3qkBCiBp2wYoECmjsEjip+Sck7si+vwLtX1DNSmO0QEgGLlgHhEUeHvab3rgBRyzl8BFg4XD5+H4AWR8msWcErCVCnDtlimDtk2XD2VY8CLZ1qZlE5Cnz+ASzvWP4xLcFWq8WL3eEx0g5eWf+q/FfTB553P35WZ8AKhL9p/5SpsxcVDuDDBwUPDMl2VJYl9UsEl3llt3oIGCl3/qX+LvvPJce7LvwDOdd8FfNiuUEjhuCp8yDim2WekCesZLfzaPBUCBTMLZBAQcICCbog8TUVE6UgIXTZCq5+U9bgEO9FmGEKisKoQBcAKVbbq00RQZQJ5NzvBBO48CdRqFa0fBOgMQXQUDIMCmqLZV/jKDtxsRkOYWisBtEHDlJViw/j2AAFHmBF6uqhLbVnn3ZZE65AW8rWLtWvBV2ZTJseCqLMcCEGjXeAf60v+eH/u7fB08UEgjWjswLuCCYu1njIsF5Vo7l8374CJIniogODjmCLjyAuQMygIMaLkCKzC4CBSSAui06NjM7nJrA0Ktn+sINel1asyOQNcoLsPu8cMEBREQhGuP+mK2LNGNYlKmYLZf8VFJrWVNj3IPoAqvM9ddKPff/6FHq4O5Xik1YwUIMXfPfLnZBmBW7gwwcFDwxEfPK33nlLjSMnY8dGAEBac67cwgcQVnzyEA7EJIgmFU+LJk56OcJcTBlCAgEzzZb1KN7qUkO69tm8cAQAEFqR8+Agq590FApb1Tu8OLu64uTUoupQEH76qwUwZ2QwEOyhgABOIk7EEECIC3exOoZH+5FUg7gWrKqaGzx2CgggBxF2BbwduNKC0NFHW2gCWglAuL1WtW0zYex5TJBRjp3gnk7hz1lZK6blRx1TFeFCAoCND+tOArLOzBipxsHFdgQAEkeDsa2BrSqNbpfR9WJTZM2hWUUtPmARDwJUAwAF7t59rmUpVy2Oa1vWubs7oSOAS00bIIUMiLuxk4Lw4SUgAJAgpyAHa5gjJqV4/E8TuSLxfH7+lVB++i9K6DE9c2hs0RQAuF+q3LjdEM36EA2dk0A13qbhNGzVhLU6j7uJx+rGsNQmXOWvHvsn3jcyIgv/D867fekORIkUm/8Js7AwxeeuYruP/EgCkAVCAmCNccWAMAQ8HF7A13/f3v4tN/9I8ug4KLQGAgNMNz5HtVXqzgxDdTDeDAruWwXIZSoNBN8TKL4FpvKjigooIWGsBX/XITt9TOQqX43f3asx/bLYKVFXzgcVI+Mlq3Z7D5AbWv2ZJ+qK+WCLaUyN8hTNaLFmwfMHgbMGDnuOjxDWXdXBkxq1IKSmrajoACAE0RnQmUBCCmq6Uq+6yf+cp+rL+1GBBlbgo5G0MsQVkMBm2lYQoakCCNWN0HI6E1Kl3Mjx8LQOHQag3xAzOXjIMta/cOEJSb1QHYe9P2CWnJMjeVgXEgcFuQ4HEKMbbjgKGJbT8dy8BuPL+LQil12FxAppo2sCR7h+AgVSYSaqS4wWLH9Z12IEAqofcJMqmTR2SMja22Wa6qDDJGp3O7jUDBEBD08n4m30d19vMX+mGkU0YMXQ9cwli5futNfPbVAApOljsDDO4/+VRQrBaUIT6omhGqBQcAquIF4KNRfy+g4B9IIMgHOlAwAwJHQnHng61BI/I9t99BDhCALEpI38vXvSs1j22ViaCCkigJVWyDf0Dl+WYkGa1vb1ftDonb+4dI4KkPdrS0cQYKqE+eNJucXTEpRdSCgybpB4RF8FK6v5hbUwO/tgcOGgAIbgJ3Hzg4WBswUG62RhlxKbLuvRTf1XFYsgAbSgkpFaAkydao0dPpCig3K9LVIq9Fxuhs4BWgRd0HgILjpQZgrTeinLDIZxOSXXS29I32RYzROBJi1rbDfgMQKPBRFs9dwNsgjmAICiKz04GCwz4ALvaD9AFV61OBQrrKFSSsC5AFCFBexLVjbgYFB5wXBw20qIuhdKsdaD8P2niO0bzoxvW0fQeFovHUGyXCLLpYOgIHOVXXocmmILvcWHEgX0BmqKif/1Ae7YJuD+SQBoseA4LqvhkmO5KH95UZt+FRIGJkpiftP7z/CAz2de1AwWciKDgbGIk7BAwAoI5W7MABoCLMdscJA9UnDGXvtOsf6JKRn/512bACaIXbqJFnDX+RTei+++QNwIFLqCvB1t8CjMQMXpNbUpTFj0xlCYK0aKAN70EC4D4/mqH8aM0fBb31gGAEBux+HSBgstUBMwpvj5KHzengAKgBDkCTeKSxrkprsU782pZDwhT9WUAQlVFZtyEYECU1tyAoJ2AVocgoKEhIIWkJLVB3TRLBvSRwFLQpC5CM+4WbZFehbseMabRYD4AdIEi/Mxx0Wf+ETKDVgh28SCfojxLk9NnwmsDb6KfulYe5xDqwa66yxlUz6Qs5P+mPVfqDKe1AQlk3pCUjXWVQUpCmyt9cOx7bod8tLgGbAoScvU9ivEfjypGOqFn4YrtP2vuQKQhuS7uXgwOVi5qBQeJRmD0hFxtLWYq8Txd3YCzmDiD8/8j7l2fPkuNMEPs84tzC7LSSmaZRWVxJZpLW6gbqkcXFTE+zwAKgkcmsgawiQQNB9hhntBatR2bazFYLtdqm2SCaQGclgBmTbAAUWeSoZ8F7swrktM1CCz02MpOYWZi/YVD3nHAt3D3C43XO+d3MAlt3wizz/s47jkeE++efe8Sx+m1b/giX6Cjk9huO8mZq4q5TYsa/nUniEnKzAzZixu6SQwMUnT6SuQ9bD6+lAQDpwULlLFn76e+//LRhCkagYqfcL2AAOKWUnDAUferqVaJwROEptEWeOsdJlqH8i/9AVpx69csO4bnnHFDnI8M1/VJjV7TDj9A961K9jnLNcWPvXannRAoIyC1+RKZkLZEOwKlFRFz98wAELmIG5NqWtgP4TDxvNFCbeuVdptwdWJwvy2oyHse1DRCkFhCYpzoKGeg5FUNwu00BQdqhXYMmnA6zpdVbzXIFMutCJsMQsvIk329buU0SQcs9NWdDEl0KE9Mu7nLJevFVf1NY6AGBntMlvPlCAeYhU4jgmDS3JsjwNuYwRnBaC3iKstTVKHnXQMG0XVJCCCHnsmxRlt0OQdqSWanxlBCWDVhkLMp7eDZK3yncZoBgDAPpNDvQON8D5FbhAzBbiW8KCGxsVGMplvpRWbPEHBL5rSBhke3QJibGJbeVBwn5XbVOOTlxkTY/WoCtqjdQ+vLMGdGcDz+t1IcKOMQxEJjR9BeULHMfn60cy52LqzFaThx9vE4vcPWkOqfAJxpe8A73DxhYqRAWckcXtIo8IHJnV6V0/Yuf4xt/8fv40TvfxcNXX9ds+wnqc2XYcXbjUMeNxAOaG3Aerv5lZiCoQUsrEJSubOKy5BQrNZnEmSlw75qPtbH/hsqrQAAwzhugJn4HZJRefg+AgJfbDLUfsAeVDP07zmLbTaIbW4zajL6yMFOWIKWLQMGshGzo9a/zUOMSMygIiyhFH+eWc5eca+Az5ytqdQfAZbmQGdJYjLYXOV8ABHaK/6BMu4qenBCm1DVhyYm3FK/yShiMFfIxaGEOgrs3J0LCBmBBTAnbKmOOtpC91vy4CUDgLWmSb83k4BbAFfLS9lmiBjTTBsQN0Hn0kteBPN2VOQHsZoxovofQ78re5PFhoVIUo3m0Hn/jNOlFBSh4tlIPMpkzVd5DTmH33PmiUuRBHcvUST+TITMIXr4jHbSnfxrd41kBUBBWMpRFqjIQGIUQLtTXPnzD3JzPqQMDLWCzZ/MMvLdt5sCAnZtBwbvfx9uvPTxR53G5N8DgENFZx21oenaG4fr5x/jmR7+HH73zLySRMTMFA+8/Xu0elzJqVNeQrl67JQMCdnWu2QTKIOGqZPtCjA/FK81Mln3VwPX0bB6kGlaY1ceHA/QdWgBQ3rMoMB4l8syMv5fNHdiXISAAiqdm+z0gyIBr65gXuMRCiWE3oMC8IQUFVbb7TrzajH4Y7JNXD9OY9ksHBCrzdvnqsXx3qNCXWbJ3KJs5rm3UNekHstKW9+XcmsiAzUwIQQwTBSAmxLhqrFy8+rRuiHa9A3BEEpKJFvN2QG42390Kb5xDPJwCYPkclABKYJR8D8DscvmiRE4IBXK+R17YS8OIcMtsSTGqp9dNY+fF/TTnqXqJNsRT9nELqu03JyCUtQEMLIhMasBAE90zqJ6Ume5pHQ817KllBPLsg9HUvtrztvsfliybUMYFFTnJ9kCHIQ7bqWajex3Y1U23r59/gm/+6e/gyVcf4+Frb+2sUpArNT1yb4ABgF3P0Qrnjl8ahTjh+tnPM9J6+OobDVMQekVIcU+uY+M/2NcN1tE72KCCBzapHrQ7U5K4ipvrquyLHC+AwZ5wTukPP4yk+9IZ49/saz+53JWjRJ5qXypJTs3+6rohKEhjUMCcPZ+KDvbPnNCfRlmLChevUpICyzvR0s+nDvY1vQEYsBkIRpGimS4Hi7f6BKtl2Q/vyA+t80Go7KCMpr5ecs/+XE16i6EYFhrT1pSWDOBybF9ZIF5XOW9bQSmBFl0Lwc1USOsGcvkHAApQWMbvYPJrwdtQNtnbdmhH+5ew+0nAznorCYr6iXcBB3Jdt+iXn7FgcSD/zJaGripv4bwJKKj2OWDkmTf/Hu0+cNanFMs19BL1Tk4c3GMCso6yfY0eGunkHX3cy8HnNrWAamBD3GvU+7xjZftaHVrX9fq5rGj45GuPzzEFB7K+P8Bg6oXvWG+Njf7ls0+yUB8+eMsZYJeQZdeYkZ7lGEySRnjSsL5+bVPlbULtReSrSudjB3JKiGF/DjP7ge06siN0x+84msM7ovtboz+KjeX7tKGZvt3GisPvK4PQHzm1oJUHCkAZ0CNPZrRKHtniN2bUNoRlAYcETgQmBjMBWyjqt6Wnc+hAAQGV6XB57vxyhTxne8IMTKdlNQmf8qyBEsJsieh+76hNZuthWDii2z8EfYN9i3nJUsJOLgJdlZAZr7d5Bk67xoEl5salLE3tGR+b0hgMjBj7M2k7AN1UxmqtCWu7kYE21sN+x6Vk6Gd9ZP007Ko2qUibXY9TYw84N/7qjzwNQAIa8GC6xTN27lx2/w9exv30xrwHCFMAQBZO6MHAqM/77UoaVG9zqzO8LrbV3vz9DnQRR2eWXT+Z2ZDrZzd49NPfwpOv/3ACChrG1N9jUu4RMBiDAW7+Nhfh6bMbPPrJe41QuaDn3LjNoIhXlUe66/k3IMDXyX7Plj0d+aBOBekto+h89xR7krEFFap1nnF5r/HA3i0Xxr/q85rrT5TKI2/CAzmhlFlfXpWnsQdu+07FVpgkpeDdIlKlLkueEUHKWIjRilVoQarcx07LokXNgkWeAl2uSiJhAwQqRsD2jWhWeYgoTS9f1x7DT5I7eZdrym4zHGeYgRaIOCG43y1YrOuXa2RhPU653w/DZettya0ZLIZEOkuHdO2DahVKLgsiiQiO25KM8WkWn8qUdojd4jslfv4CjI03km57ODZn8h6MzV1zNhqbNhZaINDqmvb6WZmBm4HxB1BymJRJ8PqWgfKpcFeNfX1r9dD9NpRgH3CL+leewBkUtNPQ9bqJ/WALwwzDGbUNuVH79cHXn+Ct1x5W9S/2YDa+ulfN5f4AAwAjw+v/tl8qe/rsBu//9BEef+0J3nztYc6lNkH2je3uFmJPvTX1mNXFg4BMpnFB4CMfeFZGcKh8VIUgmVHIwMGDB87eBxqUi/MGdIRopRLd8bYn7imagQ9T16tiSKh4Ihnw2AsTjN4sa1Y4ZiGn3aviNNaAgoZqiweXV5jURDeyZyqda3PnyTw9TjmpcxZmyO9rxgKoDEcLAoZzsh0IaJOtOprV7l8e3IjZ9cGG6i1eIFXnFmp/8F7M+4zALKTUMmpNnau+MzAw43BZScbN7bP33QoHFqp1P4ACGnbKcCEwoA/3jPI/HMtTzfhpp9GZbI6oc5MbNde3HvRI9ibavXft9jjesQ05oAGeF+qacX2p0rGt4U94+TrWIoHBHShfCm3AQj7H87ET+xEWd874LwBc/02xX288eFgtvREILgF3BBL2WYt7BAwOvHFjAHXf02c3+O2fPcIPvipCXVODDBtUWHaR3oe6wTCjovaAgMfQBVhzf+3oAU1dS325AwkAqk8F27FMbNs5odo8XaqBdjDqZgNxJH/A3om07jYVT57KrODHWBGmhhEJ6Fe8dOBAs7v9MQbn5adleWEXV20S3bJHatnwzUd6ioAab8Er9tlaEG2ioA8dZBagGAAOC6ppn40R2M3jaGPHbf6KvQOnkp3ugcKUUWh6UmvgKyBQsxl2jgcPF70DkPuChcuYE3BVfzwoMzs8WGbZEkrbD13ZM1wb813aeG+9j9GKfK3hB3VtfZpOPzCswFz/HI9hrziLjun16fkyY1cTo9Odew7X3XRqozvB+bPtCUXPJu6BQn07arb7aqR2e4AKrhv75d8p2DUDeReQ4N3EvtwbYDD1zBtjDABPn13jWx++h++/+wHeePUtJJWqNbDdoGpoK3p4b2E0X5FZBx0BgRrAFHDgz7PSDsyW7irGn3O1c74zcddh/XfdR/fz5zav2JWR0tgLq3n92aJzIsKm5wR3QJC5flYZACyGz2EAEGxIoAIAQBIFzLK3HNMkr4XKQi6hLCDVzs8mIBsQwSsDmhmoqOZCGZuxaIzCAQAA9JPK3iCEpRgDFewuXTww5sTs+pkZ1Mna+hRhoRq7xzCzvSm7M1POer1ehoP3ym82Cpkxy7ReAzrajsAJwDD7aJCTofz0YHLc7uQYhFkeSFn4q5WLC0EMZRb3AUFDr/f6Z+yo2DEn6mkZEUUjvQKc0y2tHh8a/JP6099vVDevDzf2+pOz/vH2woACYQIURi85KX4Gc/vOwNx+Wb1NX+aGbQDCmXJvgYEHBd4oP31+g9/58D18790P8OUHD/ulWJjdnOO6of0D2rBEcwv3e58VKPWUbX+8Ph/VfY5LeY9i/BUkNMaftBdlfdvcKZzt0bl+R6hpcm+HskudVWeyLJtjYCEZUNBOHxQkzAACJYn1g3TVSAMM2djJNZxXO1Qv20IF4LKQC4Cc6Ka/CegT0jDQBSenWZXlWcsX97LiDxGVJ6ix6Vrpl6cfO0XWycRIjr7Kl6fItl/lQyhAAc1c972neqPmtofvNzVs596xMh8+54btfaz+sr5ATs51n8/uwJHvDy7pEUABiHulnQXSJIS2K/IB6Iz+IUvggOMIECSM9WSrryqHZuC93nm8nyjtvWfGvgcN9fkngxVOZzp9qLrGQgcEM/gEc7KIkQGr6CvO9iMDWR6DpbZszbt5Cdw8v8G3J/Yr6DWk75/1pLZvCw72Wu3eAANgHxQwnFB/8wO88erDCmlV93Gtl41oizZ3kMEIXXeDbgAIPBgwIJDcDRnAZslPrj7bThO7CFcGN9GSovKBcv0sunimQ19Syv24ASiqv9AjdAMFBEbUgSgeugzaPYDAgDP4fAAQ3IwNOO8RQD0/u7zDzpu6n7WSt7g/eyPpV2PzXp83/iFi5u0ls1voPaQi+yJnmJwJCBSz7EWuW5GLrQJJsQcJ8rACJrJY2D+0/t0avBEYyLIgAUij920edfzOUfpbcH0qtxI74NOABpZFh/Jnnw0sWU6KHfOyKIIY1klr5n4GlUcDBEZg6kzYoJLfMSDY3G8vY2DsuADn2YNzsmjObE4dGvkGALws/Wh6Bc6wEnF2UuTLlyyqk3X86HMMJIxsR7dI6Oj9JmDnk+c3+Pafvoc//spjvP7FHaYAqscVrLfgQF9rt9wbYNCi2T1Q8PqrbxXhDxoqoG/INlZ6tNbbiO6q63cOEBgYkIHLWd9szNXAmdennOQJXnud6EGQ49JGA2ZW4gU8FQ02WpReOjcrECgIPQQuA0/puxYgREIOMxhAkOOD8IIDCPahqkqxW/sNaPf5SzYU98grPqXMY6fINwZ4lW8XtH18NrOlrx83rAypzJAp0KBAITdtOAAJPuTg5FY/t/V8L3t/iyXb+248GF/Td9cD1rfa94VtL/KtqiAYMJtlBUnk398xDQYmh/3nRfpO9df1H7c9ZVfyLIcCIrcGECQe9KM01k/+dToj7V9x/w2rsu1Rr5gbeC/GLY9Rt+/EsyOAWzPmVHThyqL/iAgx6BqSBgjYcABPAQKguquxHWedqxFj8MnzG3znz97HP3/nMb706iDRkC2vTP7PjOoAHNg1e+XeAIMzoOCPv/IYr7/61qESzcecEsnrBNg5yTpU41s3CnEGCODquJ0EBAYGNiBPm9oyzSvl7ICw4utfg4T6vabzLw46WGxOMJ1mwGOK0lGj9KgDMKVC4UWSQZcGDEKZhSEAgXUuuOQGjGYx2CBsvV4XKx4o9WEyXH5n7xU3Snvg+XulnTYx/mb8rB8lHgNK+L+DdiggAMUQqlG05ZdEZowQSBYXdCAhKpsgwo2ynG0TbgAbXEEtQ7iHO7nMDBmrTDYHBsyApbRvvLwdPpKD/a1lIe/tgYOAJllwKhCABQVI2ZPsI1utTLIsGrncpV/lijf5Fk0i4QgQFJk5veN/575XZGr65ix7aYa7fZ1t1Bf8u04Ot7qMHYjw97yr/ruFta8CgiDHsvEPDN72AUI0hUXy/sSATY4G1AA3wOXIZiS3nRj4+fMbfOej9/Hddx7j9Vcfdh179KwMBvKDCzgAHYHoewQMAAxf9OOGKciGWs/di48FjTNbhufoUR4wtHfqsmcHoCAPVPSgYOU0BAQGBmwQyL5y36oOTa0CCLfNm/h+2i7Wdh4g9AghEmVEnu+VClK/DKXXAEHQOBVkHgiBC5sgqMLTxTVAyN4fYlHmIsxS3wHIA1CzRy0YoNECKhOPbas9tsTcGf9OObv+AZiCHyvktopeviQvqABAAFhkMY4xCbAKCiIigGRgIRAC6fx750Vz4ykPFz5qPN869u3YgYQMAsx4JRZQuIEruSSeg+lLZAJwlksgVlBQPMkWNGTQBPu9iGIOA9Dg+lcOUwDOYMz7XCc7X/ncv3AqqbACmXAg66jPTeQ7Yi+9TvJlBA5mYZ89QCD38vforx/pPF9W3YwqrpVZAYEALQqal+QAAlKQ/sHeG9ffaniJTF5cbIfVwVUh5wEM3762Gz//VEDBH73zL/GlV98a2iv/LM8ETsvAnrXl/gCDbHyL0c2Jhi0ogJ3L/tKu+EYIzSTtanoI1fcYTYep1hNsQEH7Gjb4gH1QYICA3TPyDIsp3hkfsI5roMGW47Ub2eCy4z0O4A5UrG40RKkgiAgrMEXpAGNlYKHQAYQAMQxG4Ym20pGQOMf1IjM2NAxEAxBAQFnHoV/Ayi+gM128ysV/R4rY4v0t9c2oPd6UOBtEU8TMpR+sud3rfgB4b21cDMiZZ0SBRN4ELJEQQVhiAKnHEwhIJAxCVNmbIpQsAAMIkHi9l6MzfsM5/hPQlExuDSBIqXiym/ZtYxFMRuuWsIGxbuNxclY2ADr5RCIswYEpUopY39+AEplcjKXqQjOxJMpmJsvr5gKouimP1dTPO/ZDFJ0zYwlSo5NGgMscFWDOXvp38HLfM+75nGZfawTbvK6DKIS/M4Ci44QRI6wsQCwSsAVSvSHAWLal3y0QoLAlx2y6OrfgwOoeQFn/WF2tDlb14Tvovo8/vcbvf/Q+/uidx/jyqw+nDFh+lt6vMAcT1sBXYFLuDzCAi3dBmAKbffDGqw/rATf+6aie+pgJH3Do06Gu8x30xcsIFFjdUuJcl5LAcrJyHuj4ngzxiPwJbXzKQIQN9uAEY7IUMFCcnbKEc4PSCYiJsIaUabwl6GIiDGUHZCAmDSkElsQbo/U2HaglFwEVQAAKSEC1LkIvjgHGK0o3lT43Urx2nnljBgC84jUA4NmhlYtxW5kP21pE3bZXaStThGLshLlZiBA3NXxbwhIJC4Us68AsACEQJPRiq/TXAGHLHnV046YYuqoMDFgVPnGAwIBCBwgYWFVeBgbWHVldKicAY1lpH13sfYOBV2TAYAxLBRiasIQxMPX6ItIP7XpkKe07d23frIxNAwg84NwDBR6cQmW7F84cATDPYAK1wb/U2Pv2Gl1/phTGwCls1UNZdyTO4OB0MsAdytRWNDL6+ac3+P2P3sc/U1CQTxvYKPL3OFH1BOCTZze759wbYOAV9NNnN/jWh+/hT979AK8/eNjTTc7OjSidPeFXHfNkQ4wKEanybZ4JQaWrjqxIJLQol+siy8dkffEDTOhpt+109KxjemNfMSWhUaaghi0hJKcFJFqgIEqpFBuYGwBsBaEDmKL0mAhb0NwB1BSeR+mWY2ADPCh6bpGyTXWs0N7g3TsZGdBCaaMjRetBQFa02oSth7s65bo6o7amlEGFGLgCMnwbzZbsN5ln6juoMSfCEgm3ICwBWAJhCUGeSVsFEFgNHhNASWh3hsg86vuRceyOlYGxMk3pgBXXLMpm79uEDPQr1UNAsKakf0Um68afi8wCAUsIGTBkJiGzCg0Ds1mCJw/ZBcuR6ZMfVYKUbddL7asWrvJ9dS+keQYU7IU0PZAd1hnHxn/yxetTuiwEdz8uYDAlLqyoFgNsVnyoE+jZAgs1hea6i0vzHh4UvK6goLNhVofpxuRRLE7zb//sER7gtel59wYYWBFQ8EgWf3jwUJUXINyxUqUqZPeNMgAOCOyAxr/+9GnZONMXzCvIcSC2qlTgwMesiIElBmBLYhyTUlmBxIBCnK+oHlYQDZ4HX9Bg1yXouh1kOZLgBmU1yGAgwT0TjspCGXxGdZlcN5a6+/yFjbkCB162W+JqUOZBQtnuZ3AgqQUaIzWly2Vub84Qnrx7RSzxeE63GbY2EdCM+gZU3lbxaAUYeBAghk+U9cqpMmZm6KSOcs5WKdi60UKjxCwWHoiykVsC5VBNAkkdkLAm4GqJ1jhAkiQDmcHJOVM/JQ2dqVGLKutqbYlWwFmgY1luTn6eXZmxBCsDt+uGDcC6qWw3zvJbkwcG/EKyA8TgC0BIGTAsFDLLEDV0YGBhiSEzC3mfKh4xPiLPlUiSa+GZBa7AgonSDI+fktaJ14EBE7eXtbw3GlBQrq36PvoZAy0oqI7dARRUSXYnwMCLMLMG8PL2gEVrw23GDGWwBwgARsm/8aAg56FY5z+LFRrv1IcP8inZZozfLf+m0l9G55p9/MFXn+A//vEfTqt0r4BBAQVP8KZ+JdGy263IICyo2IODbG8agdrmX3/6FN/56P16J3oUPyo5CcUAApnRqcGBxdqTggNKjDUkLJBV+DhXHvlzADIoKRthGYhUXigUT6liBgYDbfYufvE2oO6Ms+KpWaDI1QaiL21890zR7ICuGK5gRQ1+TfMSwuivsXsCtTI1IAAunmxHvXJhBDwQYOfVeiBgxmvNCYjO21UjlpixJc4KujVubbFpo4Hktxg4/ZvE401gLByAmJBWMVjM8lVIrBtY2QMOEjhYXDulxOpBiVyDyiK4xE9jEYa4oJUpXNjAwBA3iYUKCm43YwZqlsB+mywLOEhDOZr4juRor93JUQHDEpNjYAroCoEQ15SBwqIfU/JAYYkBK5CTHAW0FUBguR0+kcy8UyAP507G/o1m/XhUJg55VyR/B8VBcYadgrBIGxcjZro1BNpd9wXonTRbcbw6xxyOE3przgL1gMCYAs/+1OEiynaD0DMFM0Bwyi44J8de/7vvfIAvvfoWgLrNvF60n/6Zvr9kObjfHhS8dfBp5nsDDOzbB99/1720eh8BUONAeb+zr7U3PygBlLNDv/vOY/yv/i+/IfsvAIbR7DRZiIuUIdCkOQYYAhCY5Z4bRLlERGyk2d9JOyYTOOlHYaYAQZ6d7P1CeT/x4s+VFgQEarfdb7cxAgRAGYxyrE+K80i9yqLv6jUuxlp4cAAgA4S2DD2tgWfVTS2dgAHPCniKu/VoPRC43VJlvMygAUBKqVomVd6Ru3aIAQiqEc2QXUVCYvOWkvR1oWawREZi6SubrvVAzCAFoTJnv4ADS/7MWmyQ+AmgWvUty9jq7WQ+inWPQMHKCZtS1uVvucaDApPp7VYAwe3GL02eHnRdRWUNVsqsgoADMSiyj3PIJrMJW6pAAm3WXyXHJhH3BsetPZFDOLiQPZjYZmnXuhDkPbdkYEDDe+AaHEDCgYDt0992b0vaDuWPZzarsKRjIIGeofT1Hb+H95wLSJZ77QOCcf6Izd5B1k1TQNCAgZO+TQ129PfrjinYs0n+QZ4pAOpQFAH4+PnT7DQfgQLgHgED+6DEW6+VnAIy6AoPDgDjoM1YtwChLR4UvKlC9Rn4bVxqVnK9jKEgGwyy38fQ83QZqH8WCZEFICzMWFQZLhpbXxLnY0L3AZtWhtk6mEfuaix3gPycPWiAAcbv74EA0GfI2zHPEkxBgQ1I1N7TXvHMARF1cTqfkFopTgcIziQMmidrCXBGbxszsDahAvNmWzBwuyUkRXOb0uB79DdQFGAMBCTClhKiTRtwDFGIlAFTSgISUyL9K23KSUCm/NUwQmAwyDFsCmDNWLncDmJp2xnd7Y1VC76S9vlKznauTnhgDc3IX20bdiGDDIShMvRMAVeAwMt2JtfbDQCSYwz0WAi4igGframAhBRyHoIAAyBsxhpQDjkszDmZcQmEJXFniKSdOCt3DhgvoONkOopxHzEFvuTLuehK4jk4WABwkA8O24wDc1KAYhjLjAVntD0LdRhCuFxPtUmkwBwMtDN06hkoRd/4fBAPBlogcATYgDpkYxvZcfQ3w9wmtYDAnp3BubuNBwUPf+0YFAD3CBh4esSEY7FP27BV3JM2Mqsr6L15oO6Ef/XpDX7vo/fxx195jDcf+JhP3QE8ip32ifwc7exax0AyiGcgYWNd4Q9AjIQEwsLAQj5jmA4yhnUAa0/emy6016nbKYlAHRYYLZ7kQYA/x9gBOebnlGMXEAR3/47GwxjQjOZMZy+Wy8YIEJhMLW/gEkDQsgPmyX621lS3Ga2RwZrRsCFQ5em2Xu9sRUpZo+CsTyN1XFQDMWOY+JlQ+rE3WG1pZT5KgLMTj1bFq96JZFXM1Lq+EDn4BNl6AZmxjH0Cbdo4AwUJywiACyFkJuGzNeGVJeCWGFeRsBJjiYQ1UQYHSyBsTFhJGIaNy3EOKed+LEE/5cUaVkxcAYSsJ5xhmgq8klHRbZm610EWlKbMjKU9GwJuGARKDESo8yGAbYHMTGEGFlBmChYDBBqzW1z96mmMfSX3PlC3p6MqZ+0urORA18RWx9C+dw7sswWVnYGzQ+7lWpBQsSr+Hd37jQABEV0UPvDl3gCDt5uXLkmHVij/T+qxyECQ/d6bsQ5my1D+8Vce483X3pbrDSF6A2V/TzIH9jzzvgCokhWjacbIvCoDBcYkmAKNJCDhigGO7eIjVC0V2i48crQa2V7pVzOst88uvTwEAkAFBuwensLL+zCP6x0OTowZAi/jdl0BP6OgzR+weLcxA5Y7YDHxliEwgOABAdCGEOZtlL0iVWrlN7DEWNHeV1G80UU9X4mNo2yrUrQErK49JwCD1Th5cODbYFRmMh8VPzsH0PqxKHRiXaGRgSUISAts75WlBECyOa9iUAZtA5IAqqjxxMTzOHgqllR3yO+0AUHZmcQhh2pikNDNVQxIzFg0FGEhj4WDhA85CBsTpBdvABYGEGWa7hKDTrt1bGdS5styEwYyH4k+Ywc9b5jvpAZKnCeuAII5NOQ8KAEExejbglKLq9DGjCvUTN0G4MrVbbQQ0hXuVkYOyFkmMjQ6JusX6qebAr0BhnvWrDC7tnCOKqgND5ELgw4WnZs4pR6sADL7wIOCNgSzV917Awza0sWhJijNVhozxsaAwsfPb/C7f/Y+vvfuB3jzQS9UP7WlYw2cxGdKsoyHAg+zx0QOGABgXeNfPFkaGjFmYCGqaF97RLVgkgGRBqnvAYUWCOSaN0KJTVerllt2gqDmR92x7dpyzA9Iu/ZMXM8/f7bKmhkoOaeWpwcFW+JqmmELCowOb0GBTyZsE+DaYh6yxF776VR2DEAHBrz32gKCmuIOVbKcTcGTaYqaYQ+NiQcnw6ZdZrI0D2fU7z0omBWiMgbA2vZBFpmxXAYRAIBNQiSr8s8hAGEjrJwQiLBSQgxRgVhSuSwVS2OyBoqhG4UWvOxtlszw/XIbihCkHyh42QiICQskCXS1xTkg+9iAUARIn2GnGDjwC+nkFfYmbdL2f9vay3cCxEnxIc0CJFDpJmEv5UZX5lg5r8AzPu34K989kFrNVqi8RC+NlluvPhjnHI7W2fCOhl+oqmYRir6/k6635kbvkPoVLw0IpMlgmzmkXmdazt3jC5kCK/cHGEwapUruQA8YRg1kyyh/X0FBfoRrKFtP3j+7izftVK+lixgoCBKQhEEuhikbqtAAhXxeDRYAVIABoHJvX5G7EgcDeR8hUt/P/W8fGvDHKhCgO2ZU2ui+VnxIabYcabftjJgBK4t1t0lwQB3vBgrYKB/r2lFwqvUlD0C9hEZ6dYgA55LiiNxUu0H2vB4XINBnz+dpdk45AjVzMystODiKCtjUXVtvwgyhTPMT71rOA4glyS0iyflbkDBCYkn2Q5D8m0A5jHOlORZ1XkfYTUo8Agix8z7qayNKqMem7Jb8DtEhm3qRlvQZGTm3Y0uoQjhe5IadKhk6WRa59vu859rmO0ndywMS1XrJM6tArUu8uJgFbJb61rUd4oCXoIt6o1kb8lmewAwI5GvpMv3uXyc4XZ9TBpp39SAgEGVGYfi6Bw7p02c3eP9nj/DB15/g4WsPD8feqNwfYIAesfnNlo6ZNdD186f41ofvDWMyXg+ESUfxQ0H+uCHQKJqqY+URHLS+lAGD/KMmFk4ZBHC3TZV31k7HrAfwXUdjU+9q3+TcZrua7TBA3iP5tPc+M7Oino1A+Z2NVpUEOu/d7N+vpT+r+hjNDDUGJDAkeO2UWEEA5QTRJc6T4KycmUZnaxaUefb7c+7bKVr9EsBzDwtwCtY2cr3b9+g9pzrOLQdiQ2MziYFhljn/C7NMiyNgCRFr4pxrU00JDS7hMyD/fkUp/vG00EL7n0363GsXaxM4GbXTfn3hFk21xxs00CbAAXXbyDmDUg+szkjb5CVWcAMMwm+O6QT6JMLZCBmNrZepg46YxUMAoBfnc+HF1ej0vXqTD43m+Vn5LlbPkR2KNGcL5J1KPe29bPPm2Q1+66eP8ERBgd2vrekRWLg3wGBoTNDvs9KhOQKun93gWz97hMdfG4CC5iaR2vu7TqMdpnyC9kRH8sWBBALyB1IioJqFKsAA+GSuAhBKrahO7IIfjDQdxLtVPPUa87NGh44ZhzNPrUsVy4OBBLE8/r3d7jJo9QQf6yaCxDA3vS/JB6As3g1WGhmysqDkKhAC9cbIyijJzlPVPnzQAgC/Mt/eSn17S/seJWABPeUq+44BgS8Ve2f3dVOFidBN3ZVzPHUtibejL4+Ol5LWVR1TQmIFBahXRdxbDOnSdjpsI/ttcXByybihNupV+M21hW+DfBy9gcv66WDcjMKu3FAUnWHp7tLP+vFl3/jX4OOSUmOcWl4jOcwYgAIAjLIBwPxSdDiwVd8IIQcUuDkdsHE8Nt57rMXNsxu89xMBBZZzN6ot2312usW9AQatoIZd2n/FzAXICYTrZzd4/ycFabUCbWUY/L1HX09jdwxoPsEKTMi06u5aUQUHrqMZGnVfp2tBgwcPwCi+23sKVkZ9/w42+ZQ3Py13eF4u3pMhytSpzPyQe9t+gDNrIMapLDhliVc2DW/RXDZmBmLAuiVNdJN1ABChi0iV2HJi0vUj3NoSE3bASr1Cm/M2JwDA5mgvAYfzs89MA21pV2uOIyBwtslGWdftN0j8Koqz8FibT7MPFgL8d0ZyXojmfOwto1wvKX7cbqM2y4meNGZulhjyanu2z9rH2iaDM2sLQgYhrSGcZcyfH1eDE1/AsU93GNCX6KHRO7aGvxwaGH9v+Kc6Gzilt7WSorM3dfA28yrAtrqaggSpF7V36d6Vhr/lXtfPbvDoJ4/w5GuP8faDN/J+AyCEy5rv3gKDkdGuCpdJM9fPn+LRT99XpPUWxA9ve2BzD/3kLDAGA/Z9eikjoDAPMcgJ7k3Ike7VZ35tmU6qgEMblmAKupqaczXazME7W+JLutuLWPvjJw5ZzgFIMICQ1IMjyPcADA0k6PQtAwKqwC2JDFClEYMsMkWy0FRMsnYEs2agO6MDuLyDgW7xFPNstTYAmQHwoYDRNKx2gZYjFkBP2zX+o9a76xrxtZLj7kAV77aX0NOmny9XNyjn4wAVaDj6jLn/VoU9J03aENhvxzMr7o0YHJ/bYXS2eZDkWYedBLkRLd7L/LhdLjvYll+FXmh1cqNTGWOP3xv+HR0t51ymp2WfGnvV0UAqOpp4ChLyLbNR37FB7n2unz3Fo5+9jydffYy3H7yl77iV+w7Ah2zPy70DBlIMETrDDdQdR8v184/x6MNv4Yfv/gAPX30DeSJ0q/Da/IC0HXS2Fig0Ha/N1Dnzbr5OjfGXaUx1x7RzyOPpqiO/HEN9WDoQ4o9N6jC4hnMHH1w7iOPZKW0cL7MILIAgEel3HaAnl9UoEcq35qexbpbpW/WHZcrCL+0a8jMRtCtD+nUgbJ/NFrhk2pVJZRRvhXtOK1IrL8T8nCk7/XDkn7Fb9MVWtmTUrFgPHoCEKH+5Bw2eaVgTV6DBnlN/Yrjce++VRm167vPOpS2rufTkAQOdSpTbdZqKIOv6j6YJzKYO7MYQ9jzsl1doqk+bEIB3zFogcIFOLg/2Y8eM8KZAIKCsvU4gXYiCSUEuMYj02V7fnbBB1j7Xz27Ufn0fb7/6ZSCt1f0YQUBCrtt4Fdm2/K0AAyL6XwP43wP4nwL4u8z837hjfwjg2xBA/79h5v/y3F0bNDWiiOyYluvnH+Obf/Zt/PA3/wRvv/p6ESowNma+k1vjGTuQn5EyUKAWOHiEyqnUecQi7L2pr1v+XcIL5RjVf5vjQwP7kkv3DfmmDv2z5yCm1LcGPsM4nu4nUA8SBgABwUIOmkWekJemTUlXoVODIvO5Jct/tHaENfHe9+jbRaGKITlH+e+txDbyHG1fK/KzRn9soEf7jhXq2YTVc3WrQ2JlKO5/AMuDB5vlUy13DUyBw2h9EOC4je09L5lT77/I2LIE/vPNHhD0YKA4Lof5T5W3fGBsR8cGOuysXrtTGXbCAeAZ1vGAIdh7bOuEwQxvgtFyRAyo4S8gwQACAbwpi+CMNwBKq+7bf6/r5x/jm3/6O/jhV76Ht7/4ZQcoyv2IE0DyzVmp27lR9bfFGPzfAfz7AP7I7ySi/xmAfwjgfw7g7wD4r4jof8LMW3+LQXGdfErva7l+/gm++dG38aN3ilCrOP7RI3cAAeAQqQcEBgb2UKvrDTRRsoSRtz82/q3GHRrq9pqXUrz/vlOnhvnw+/zgo+q6USwPEq9z92NJDuhAwgggWMKbhQIolJUoSancirbWJrWEuCsGEEvrHa3aN5pfbdtG949WXZt9qvfYOADoNM2s9Fe0f30CYRuuOSqzmPcssarpwYf391f5evuZPR1YcNvtJ7SZC+Az4ADU4AG4sM31xyi/42iBnSNAMAID5+PoOx52dR7QgYrZ8bJjVz4Xl4kR74z7tE4tSzKvX61vScMBDTvAuo/V2Kv+Ed2jQShlCtyoLgAhd1ae2x+t/82zj4v9evWN5h3NltlIkwyPDA4Arfv8ff9WgAEz/7+AoefwNQA/ZuZfAvj/ENH/G8DfBfBXxzdtQgfcGurirV9/+hTf+Og7+NE738Xbr37JXccZyQEj41s6D+UAYz3YOkDgjtlgrc5BeTZvhYUAkNfNP1toNBeqMfgEgOJshuyLlUvByhB123Y26A1waEMmRvmj0HY5ludougwSTgIEW4kyeC+SAXC9wBRQwEKWAwNXTXp+66UPV3BsPP7ofhNZopk3AKWfHSp+2z+pFA/k7GfFFJpU2slej2VzmNMxK/sAwBkkbrLCO2N1h3ergGNQgcqXBzx4SNbuXH8auvQDDPrBfF7/oFqn+8FZEFgBgkmf6JwZd0xkukOxu/OqdrLr2pe+oxF+kZJ1aLWz16M80K17Ncq61cKZMZZgFRs7oOwkBX1msEqhMAjGYAaAkj40FNszqW/t1H6Mb3z0u/jRO3+s9su/cwm3uqWpUIMDQBzZ+fv+m5Zj8EUAf+22P9V9XSGi3wPwewCA/wFOgoKE608/wTf+TEHBF1/X/ZtrlAHV7ktLqVWMRGoGDw5BgQEC5lQ6q12fV8sZdfZeEe4OtVDAwOczJIFukrYNosGgkh/OJDThAaHeTB2iUurTWJ5ae+IJSOgAgjw7KEAI8MZfQYEDCX65aqDIMbmYdyXciTfcZowTSrzYlH15cwbSql86TEDa+jyWUd9r+4dXPLl+RbZdjop5HObK2j41qNYGJkNubunFMYOLUyDA/r36dwVw0fsWD73uS7mPuXdlIhkrBhxizO+XGsYhfzLaPb4KYfgqnegXUsV+2d1TYOBIhllGE53lrs/nehDQhkbtfOfYAOicm06n2R0vdHruVEZ6s6rEHEzmonqTudZhUIqeYlTnQoBAZgY8OKgMvv/AtN1Tz+NalqO6Xn/6Cb7x0bfVfn1p8A4mbwP15EBKykB/iFpd+dyAARH9VwD+R4ND/5iZf/qi92fmfw7gnwMA/R3iWYLhHBR8qTnXN5I9ZI9eHwywuoKnQQEMFJjyB6RT2/1dBx8OqNkg21tN5Wy58N4Va2GDKms63XZAwYMGGWSmzIsxqmi6C2J5BYnzhEUIDiTIczuQoO5wVpG6r42lZ8PYyqMBAXMvb5OE1qTK3dqft9J38r69fuVr4xR2Q4WaDLyB9DIX40gKAgJAtk0lZNPIMLfbRB7Oh9c/B2DAvzuze/+X8+7kwYG+O2VOPzrgIO8eVCagAA5Rp6j4NUXQrVgK4E59BSj9BRj1GWQZ7LIDPkTgQaQ7r0a7RbYiwx120xl/njkzZ3WYv7Ytv2I9VumwXH8dE0hACIYLRC2dAQdABTyI/TlFf1XPrF9AQcF38KPfMKe2ead8L5J6enCQ7536cTgonxswYOZ/5w6X/QLAA7f9qu47+dD5wOhAwezaqhE/L1R7EhToYOKU6o7tOsQu8h4B5iMUfddiRl83W5bABiCFUNgLIlGweow3BQoGEjSAWwxWUq9WDT/r/X0sj7dynu1n1BnBQAEJPnFRwxekhsKvKd87f0M+6Rw9vq1NHy0GL892sT7BWwEFbb/ZVr1tqoFkV1erjANoJucghl+UWyhGL4lBpAwSVnBYYFOwOIRKhtW0q8wkDIrzSjtAkBLMgFFaGzCwVfIhTuPx4wzSrk9k72X9Mi4NOCVQBgexgFiVTwFHsSSQEYHDkmVgPYAbsLRXrxGQ6oCAvl8XZhkwpHbulB2YMQM7YCCZvIEicwcAsk5q2IJOV33OuuhU2RogQKHUM7h+HKLUN9hiJRiDg5dSep2+79RqyRUycADpJ4CGFbTP2nk75d+0UMLPAPyQiP4PkOTD/zGAf33qyhmliCanwIEC7+VnGnUk3JdeSPJTYgSv+4OlAgXtAPPn7lBDfHYAjjrb2eI6GoUoYS9viHQAcgoAVhl0QeVs54YolB2VUVehcUZmB7I3Z4hcmQGjhQmivIrXlxxAIJQEHV2ApJo2ZDkmRTHsztd3sh8p6yH1bwAgA0BnDFnCBbzeije2rXLOtopSVsXMrSGctDM5IIYQs4GT9ZKvxKAtVyrvBcFAQohi6FQhWhyUwyJGPBtFB7IYw7nZFRgYymfrAYGXS9oyGOBtFQ91vZX3Xm97mRhbsCcT1z+9TCgEIC7Sb+Mi4Gm50ncVsMRhAaDydCxCSYAtoQnPsFi/KhW5sF+Z7Oy4BwP5mgEg8OdPGIIR+9KFOj0Aa8GA01Odjhq1x0vSN13ZVnfaDkiwsKUZfH9bCkBK6nu4Z7lz820cmPw8ysyp3bVfUll0c3vMrh3I/m9ruuL/EsA/AfA/BPBnRPR/Y+Z/j5n/H0T0nwP4fwJYAfzB6RkJvriXzvTLOxP65UUKkQo6aLJpKkrBJ35oY4mfa4asTC1pGacOSYeQB90eKOiU4OxdRwk6+Va7ftawkBlYu0f0RrYoLU7OMG1JQEJIhUWw+jdoHHCDb0rX6YUA/ICos4HN6Ctlm+OGnnIu70F7ysfJ9lAptzR4wwJgXZEZgJSAbQWnDbyuBRQwg7dbYNukjSzs4OsyWXEnKw2VMy3qHYcIVsOHuIjxi4vsiwuwcAEDUGMS3DLdwYBWCdUY6wKVOVPojdoIMCUnk6QyckAJ64q0raL0t1XAwLbKeFB5gRN4XXvZjBLNRoyWGfkYQfGqAKm4iMwUKCCEzDBgKQChhB1KOKYKV+QwWfld0bwn+xvQ9jlgdzbBHViCDAj0uiEo8IDAOy0jYGbPa3TPy9A3VWkSq9m+itXIlkIsRhLIBp9TKkBgFMJo2Lbqvm3uig/N+TwdwJ2DXXBY2a9XX9B+nWAKrPxtzUr4LwD8F5Nj/wmA/+SON642r59/otmb35PFH9SI5048EtJQcLN9qQMHgDP+u+AggpFAEWWWSwsOgnzChOVTbLBpqLIdOwBBIe6zAwNAMByYF1J8DFQomuw5uovz2JQ6ZxTOKSfHkt1jAA5mnbmK05k5ahWlsQdWT/thIIFKnIAqUVj7jSi7C5SvgQDPBKjXC8cEZANnXrCBAA8WXIiBtw02b34WTjIFR4Ek4dNYAjVqvFyJwYtXqvATaBFPndVYiKesYMB16aq/Wiyz6/faKhUwuAAUGEuw3oocDAwYYNpW8HYrYGC9rcHVHWXExhpQqEGAAwsGoDpmIS4FwOacjADyYMGHGarfe0bCA95BafojgJ4lyL99J28YheG97wYKZoCg0jd3CSWE2Omsis3LumfOFAxZhMwYODbNto1dGoACH4I7Awpmi9DVNkZ+d06tXXfafk1AwAk2/N+0UMLdSwcK/DzP19Ea8SHyGg7OPdrPzofeX1Ao6z5icgZCf8OS6VKZrbKQKEqLmZIBAakzBc4GVR4j1+c3dnSddXpOW3kfe9cYO3BA1CfSjUDHyy4VMv9cHlDH2QDAL4XD3Y+2OOagDVO1irb1vvKqmFyFBLJnOwEC2dtV48abMQkb0rppWIGF1i3fe57LIARQ1OTKSKAQEJYIxFUMGSepT9wkpODyG8iJhpYrCfFwAkDSl9OmEbcA6cihj2V6WWV59aGVOrEyZXDVggK+/SzLLO/LAMH+TmR1ICcAQ1mZ4qe4CMugQICWpQIKBiK4CUEEC0GghBs8kCVvPIBOB7ELbR2V3eV77+CZX1J+JbMMBgZ9GOLzoKAJcboLq3tW3r+Fkw4BgeaPtO16BAiGYMDVichNSVSn1sql9msCAK6fPR3ut3J/gIEr15/+XFY0/Iou/gAgzzDI2Z8jtDUABG6fp/2MiixTS4yaQgYgGQDMAEI0gLCJQj0DEICM1nNc3JL3XJiB4uKUgWMSbLF/712MmIRLEnjsPl5+eUqiIW83yFAj87ODEKg7eUXHzYqnz0weRHMvySvQDhAcxWW5ZgTM21JmwIcG8vHW222MW1q3bNg4JZkil1JZkxcA+7a0d9W1eCkEWT1vWUCRFJBtCMsGLJLwxy0QoJDjtExB+iMRiA3UetClgHgmRwPje+1TXWMZ9pt6/CkzBUgToKBgoZUZr2uRF5BlNpSXyizLK4QMrMISK6CAuABxEVkY66LgwYceWEM1uT/n/I0CFIZepskNbkzdIS+hk++o+DBo2VlyoI7YTGDIZGb20i5Wp8Teh5kvSxLEZSBANg+AgL/G6yE7/2UBgqpN9+0KAPzlp5/gmwYKHvjFi9IcVFT3bMFl3YdsxcTXqjz/utwfYKAKKC8TqcscS6f3iqwFCL70g28kVL9dZaJmapnEhll8NntbsTYkSTywDBA4yYChAIAReDJzIS5lOhtQgEJEPl7Re4AorVmyYrzqQhB3XsbUAwFgjsxtINqxM6BgGKNz7eMQd1ufqowM1cjbaoGA/h6CAUsUtDZyYADq+Y6YAXYGbwQGNt2H7P1KPXMSYltUpiEEYZaIsUX9+mYK4s8vAKeg/a30O/9euT8NPP4MBAwI299LSxcnR2YLqm2g9H+rX/6rYQMFAGndwLcis23dKrnlBcMmcgsquxQtlCAAIS5xDBKWK2D9rAAFy9+IV+BbZRIo5PwENtZB+7clNHZTcymg0I6mrzzQ0jqfiRebYR6xnSXGV0KcynqKY3IOHMj0PXVUHJOZDbnlGzgG8y7dZdfwywPd67nQ5skZUvn3jg7izuBT1mdzdsDrrQHQa4ot0//Q7Jcd8NPn9+zXCHQ0oOCH734f/9sf/++mdbg/wAASk8mg4MGbeT+3MU8AJQDex1uGq8ChCBXAGO0qSmC7r44kT50yysCj4AxMjssVT9SDBAL6KY5ABgsSzWimrKWt5CgAtUJsDb8/NgsjzDy/VunMBuUsbgccIvOXAghmZcQMtOGBCTMAVoPjkwQ1S/6u7ID3dHndilHLCaiqaLe6DSkG9dZKlgsiQFtACoyABGwytZCDhCgoqgpIG06pg5HnOvLkRnRm23/MyHXG7gR17ufFby50sHFmVjpQkNKO7BgbRHZYkYECxZDDXiEQeOPCvKxbHZqJiwDEZRXGYFtln/5FuFWwINN0czjN9fcOJLRTcyvZpXP93IcUq3YpukhmmKiDUoVFD5hMdThyuNM7KA2T2TknwHmdYmWkW9r3BGpd01w30jv53AoIAFMw4Na0qI55r53qr96e1U8/fPf7xX4xl1lZRDVDrcen9qt5jgcF3j6Oyr0BBtfPP8Y37StT7UvbAIDz8DNabjpgG6fR7b98/gkeffgtPPnqY/x7/9k7E6o9Vh3dvG72XpCLsXK76EiIBSQ4I1QMVzJ3D/nTnGag9PhopbG8fPOMMVAAIZcNkOgl8UM3WLtB6dmD3QHplhZtB6TbX91zFF/bK6N8Aft7wBBglUTA7MUqG3CGIfC5A+l2RVpX8MZI65bDBUNPd8eo5VfakoCDtriwAkXKMXTy4R7fRuTDO7amBNUy916RV4JV27i6ZKMUJOkWEA+IzDDZb70/azLgJvXIU119H8oGJ4ISg6KG7ZIAJEQ18gM5zfdpn0SqJ3s1zMu2McIixj0sstCsGMVFx+GWxzMpI0fLlZyzLKW/KKuQZbAY29fMvDEZ5rno3IODo77fMT0pyzrvB4GFKtK+Tw4gTJhMYyiT5tfke6F3ToA765P8Gn7fKFzg9zdrFOTr7XcDAkBBHJKqfx+ECRwrwNXYKL9PT1EF8PDXft3NckMGbhVIGN2rBRvu2CWgALhHwOCb+unkt197a3xCBwj25rfWjXn9XL93/fUnePu1h/vXu3Zi9MYnZ9I7Y0MGBA5WJ+OB0ZL43VUeiOSO+6U126VK5WfPIOTqDwHCCU/OD9RmUA7XG3e5Az0y1xq1QKBRhF12brvdsCN5WuleIo9eNwIFWSE6ULDLEkxAgU+Q8zkE+fETAEAxVMc8GKBAlbdLSn1nOtxo7EDZy0UIOZmOlqsyc0Ez8k3ZyeI95in5xXwaUDBQhmVYiHHL4AC2LsKq10bFCwwGy/PZPFOZJZFBvubSMADaNnkvYnBgxEXTJFTDBX0y1eZ+KseqG2wJpLk5KQnzIhEUEgYmSHuGK2UHsMAQCRswyTS2GSx9l/UWWK6KfV/XfXDQdYY7jIXcGHGcWOtAMg9AcmEyBcKPvvOSx4dV4wwj6ctw5sD83dpFigBU34Rpjb/81FUrZ/qmAbq7QKDt9x04viB4ooCSzaGl0IOEvdK0+fWzp8f2sSn3BhjYS0+/HmiljVNZRx5SpEG+d/3T38KTr/8QD197mBlPRzTPH5U9XI3FlWCR/vGhhB4I5Mxtt7+dGsduEOd76jPYbVN0A9NARHV+/zbtmt3DD5SM3ns4IPMTC+IGxsgcKAPTnY/ufAzQd9v+biCZQcm1SQUc+CWuh0UVYN5Ual89v7zPPSfPALEQgPW1kRzdWhVWzFBlo2avlhJoAMAyGKBQJ9Jp4mFYIsKyIFwtBRTEBfTKFwQUXL2S1zPAcqVrGSxqrKPIUZmErPz8yn95CezQtYspuTwOEhQcRNjKk+X9AA4yawcLMpg2+ec7vwLwZ7r9ChDjCgorKBLSuiGGIGEZjYGHQb4BNR+78sYn52mMysCT5W0DLUHyNqDtGXQm0BKaMRpynN5W0stshXmGe6UByPvjwuu2CD/tkT3LyQkdWKh+e53SMJnmnGQHwzOnAwfl6PXa9Qi6cT7xlN15qZUJGmM9cTZ2wwKN0S/joDzD3tZrk307URcGgUxn2RormdVxYGhku5py/ewpHn3423jy1cd4+Npbg0TTcbk3wODhr71dhH8mlpOFOstqJQEFP3kPH3z9Cd567WE1i9g+zdqWmpCsbpdLIPtyYGz0p2MHgHrJWKtzBQT6ATycsmQshd/X/e4BArfHBr1ltPpWJZZGptW+UTigyyVAPUCr7ZHCaOvnj6vMumml5kW61uvW1aL2zc6XEIAtgUg95SjGEAgC2LYARLGCMSVwIqTAIGgcmkLVnwxgjDLq5XEaKriSlfjClSbMtYAgLhkA0FKAgWTav1JAQbyqQYFjDth/P6BTko3cSBUcs5zCMlVWviETwLy5YaLrJ2wArl5R2UES2oLrBxTKrIVtRYgrwrZmRibdRgROOSHRGJkwmdnRLWo1kOtoGmheK8Lf44Wm4w769YzO9sdOjZHWOap1gmz1TMIMMHBznv5wt3S6ZbFn9nrDl6LL22PHgGCo/yt5tDIDugTBJhzAzbbVw5sBBuYfzsKObWheK7lNQrEVAJcDxihU92iZghs8+tlv4cnXHhemm9nJe6477w0wGIOBHaMxoWOsLa+f3eC9nzzCYwUFLRCwz7ICwGiRoBkmI6K8AjDgY5jyX3BgwZHs+qCUB2H30SjzhjNAmAAADICDv49Vp0WVI2/aA4627MS7anbGD9yWFm3pOEzaeVAM+Pm6W8zUjmg4QbzQBhzoN9XzMZb65GWsjWUgKl4f2SqWus5+TKBtc/koQjMzi7EnTqBAsIx6H1oIwOm1CgCALI/AGas8vY5CBgN5wR4HCBAXAQVuH1NwgCAqINDf+VsJLXsgCrPvE6XNZLEfVoAQqtAaJYCj3IdoA5Imn/IGAslUP5/Md/sZaFtlSqBbAAkpIS4y4yN+YbwOBF6CfCtAQCGHakooxq2kqACqni8fa1AM1DHvNr/mRIxbum5j2LLwx+PFM4uyo9cL1AKA5iNy+RGz+zTndX1kVLeOdWp1ykQvTNaemSWVH4UApsbfuo9t7tgDX3xvyx/O8scVGQRCxZAZA12AwryYU1vC3y3XvV/uETDoPdMRpXOmXD+7wfs/eYR/+bUnePPVh7Lgl90r6xLuRH3QH6qTWmCQG986SnWctKPE6hg1105R+iAhUvbPjX+nLIAutDC6rr6gB2bTJWAHIIBHyq29blgfoUR93YR9cYs5ZZAgAIE5SJzbwEEoFCvbI9MmeEFjw2zDRxPLLPkwJ5npOxAnYFPvOm0Sb7VQREwl/sopU63scw0GhsvHVCvjBBQDZXPo/TLIHgxohnwHCDwr0LAER4BgQqSVukK9IA8QVEZMktBG8J92JiCpx5ZWgHTRIAU3WG/zdNFq8SNOEq9XeQe/cqST9aVyBjCWtZuG6wGBgMWYFzwClemLGVS232PQrPf2644VKLD8jp2kt+H4OTV2/HZv/LuQmdtnIZ/+vud1BDBgOXYcjfb4NNHvhI3IOt115Jnhv5P+b6oVsn4qx5Ld3PY1IOHoMTfq1H7w9Sc5/F3C2l4283vcH2DQmMdhQ58oT5/d4P2fPsLjrz7BGw8eTr+1vmGno1zQQXyI07rqGBz4c3oAIef5mxVE2eXs4LJte7MutDAq7eiYUf07FGErvkuBncXnKP+nBkjjqRVIcACBA8p2zh2IMKMn9DeDoUBg24QCt2V4SZgCJFnbP+cNpA2Idj95hn3zgAD4tf3Jjvvipn7VL+ooazNGQDFI5rWqBztfwleuT+7DQJkhUMPUAYJQ5GL/Wk9q3DhaZSoAgQiSEVMBBDWIQcFC2sBJprtJUuIVyH1YyUIJ1K4sqfLnbVWAVpbmJSf3YZ8eyd33WzcP3gMBOa0Y/ikgMPaG3Iwct5Qymu8u1CyBbRewUIVzcl0vdJIaT9Ru5WyUK2NGoHvGWZ0A7OqF0b2PtitdnIY/p3rcn7d7zh30vQEBHujxlLhmlm2A7YjNSrZfXyvhb7uFgfJS5je8N8CAm98j5GfFSOO2PH12g/d/9gg/+OoTvPHaQ823KR3Bd6aKMeiopb7zzIoLc1amsTby5WfuWD65pxlo7bgbveuoS8zvQ819fF7G/MZtXtde/G0vX+NMqd6xMj6yg3ICaE1jdwCBN9iCVNlI6/lsn/61a0jnFUv8oGRi52lrrAZN9+n98rzug69jsq4g15WMGJtFowB0K0naIjt2jvvoD1OQOKXzeD0Y8DkE/nPC7ADBnmc1KrlLNN4QgUBhkQPG1qQVYFnMiwMD7tPLzPJ9B+YELP1HqUjlTJzq6aR+YSjXFu1XAC9qB22L3dX0HBjw7ZHX66Cx4c8yDw4kNJ96rgBBw96YqEeg7c7ja/T+ALxeqMY+HRjPgV5o69ZFJUbtNLj1JfcZOnaVvPpzz5bS72u6176wI98fk50bhEkgrS8RITmqYMb5XD+7wW//TJzatx48rEISl5Z7CwyOFFabWvb02Q2+9eEjfP/dJ3jzwcOcpMTN9RVQqJ5RgEKLLi8ZgFZC0/X2jb92ruF99tB5u4OHnaiz/QdARJ57eblUTv4Z5ZuJpX6J9Jw9L9U+4JMBAuknhf03LTYHEMoULkoGEAIAzrRguwgVeQrbrRlxmntsDJD8LUbIG6TRSpGcaWVnTLwHqnkD2QNVI1TNPEDDDPB8XOwWluZIkHYivZ+1U6DSPrJmhzIrloibv6mgI9NYA2N4mIFF2mc03dTPwQdQT6s7Wu9jr8zaKDMIBQgAyG2Up+gaW5NlPgAEPrfDA4ID9qZlPXV3VV5k7J0to2ccGfyhsR/t3GmmUSbU8BYHdbmbHpeSl4KmAgQCSN5FjXduGxbdla9nzgABGC//5e3X6w9KonxgVM+o3m+n3vcSGOwprlH5+PkNvvXhe/j+ux/gzQdvIblzZ6GErEMUEPhzevppXM+9Mvh6wS6TNGfn+ifuMXnAhGFwF1H9GcKuXlMwcgfk2hbfuT0YkHoZP8A6oJwBcnyaMQnmpbIZkbQJQDBAwOKllm1nZJh1Cl4xQgwFAUGGVcnnKPKa5mnYmV3jeFoYufF8ZjW7c7iJRedzmyS1sx5nCwSGY8HqfoEbReoO5c9/ABV9WoCCgATKklBg5sBCYXwcYMjt5OLh+Vwu+1F+Tz+W1YL0i9qwbr87LZ4zYgjcGv17bdW20yhBrgMJl7rDQyGMd7dGuj2tzpnub3JknC/pg3vnvogIfOsXgkDuGNTAMwQkEIDABSBIvcpGdm50/0iFtvaLHZDIAEP7xFn24N4AA2AfJc8a+uPnN/idD9/Dn7z7Ad548LALGQxZAnueAwWeHcj6CeiUp6/Iyxh/L+tm0/7Scb/jnAfAAw5utvvrqkccIBV/eOP6mRmEmKFRAFADAs4DwwZLMTxyD6KYv1lhRkeS1Wi4UiWAxhuFnoOBYSll2EzDJMwdQFDtK7+75VezMaq3vTGxOiXXUWce5ggA7BmYvWLY0trEbmwArkTHHeDTthKaXR3y/EYFOMgfBx7cts+yLy+SXNulOUDIHp0XwEk/sk0EbNpqtr7+mMEpgCBhDgZaxyi/rW947FPkR4Z25nAdU/ju9851w0rt7/5bKTTY0JQkFxJQQ59fXhBvstCxtxnunORu3urQkf3KpQUYXOR7BBDuDTBoQwedQR9c8/HzG3z7w/fwvXc/qOgXYNCRcRkoqABBAxI2nwntHrQddPWzOmhWtoNRHncMdJsTFN1Q8IY9uh43GizACCD0SLitCk2ut0FjhkFoaaqAAkE8NdJBSazb8mgZNIQ+zGDev3me3jOFtV1jRID9hhpmTLfG3xnxkYHP9ymmcGjk/XjI1ZSzR8r4rMGfepQXIQN/nQOdun+rTjOg6fucnj+4pZwn7RgaEZe8Ew8m9BkOAHSAQn9ntoCrEf7i7d+2/R2YHA8Iqm3XH3z+04g92NWBA53YljQ5wZ97Vv/tifFFdNmZcjR5Y1f/KRhIgABYO2jjkQw0qOH24ACqw1w/tVclquW7Z7+AEobQqzM4AGq7NCr3Bhi0oMDvHim+T57f4Nt/+h6+95sf4I1XH9a3mihI/5y2tM+1fR4U2ICwe9pA8AOg7fCz/j9bP4x3eMCjwTQq7QArK8XJvSzdyJ/mr7EBdjGQ4HqHdeg84OyZ6mXm40QSx9PBCf3SZYADBBOA4ONwQ5DQeZADozArDQPQL5giT/WKHxjQ+JUBsP5Ug+B8nZel7d/RBntv8WJq9vzDfL37U8qevfocJ+7qX2OUYAxFaNgJOeCvmzETAKZri+yWw35x3Cd2AQH3+U+tI9NWtYE89c7B5pGhn+k3+9nqslaHvQy95Uu32qVd4zdcndtbRSKsBlaVFjD9Zitsx9A4Hu56HuybFeYyS81YB8CBArVfrYgMRHjmrXrugUjvDTDo4/q1VzQFBQ8edsd3y45GIhrfp65HDwouHSw9eOgfejTYyr2GuwHIN2hum31+UEWU45WxN+UapHfKeTwED6t7vg2uEXColJQpcy40nY/ldXE8+ZEBQhVqwDzMABQmoV+lEjhrStn97ZR7xhzybYAjb4/dcZPLnnI/W/yr+fecMTWj8M8lTtpwnJzwXM+94/jI6B0zAEABCgTHOsHlpBiohN+OJSwVynM8K3Gmxg3cmDJAR2AA6AGB14mj/mL3aatoP0eG33v3Zvj39Jjpn/ocDyLccwe66khPteUWPQDIhr8y+uWcFa1To7+5vt6/e0wECrIvgmpDjrG5eBEyg3lsv0bnka++1svAgXutYbk3wACoB4CVll4xof7xVx5PheqLCFPuF6j2aMj9F5i7Z9lJ9tG4rr7NBbPBBJQBdXYw+YHUMxnjLpHae4yQdSqU1y1K54suIXHV6+zzDKue5AHDyqxfqnMdOD+OsxPNWw0WZlSdxfIAS0rkvDjUCCAAYxbBgwZggOxpulGKimKU53Lk1dkxOy/xmHli5s4jO+tZlXbQbfQyzm/m3l/kYrt1TNhpqnR88QpwDwj0oL7xWpt399e+TBkQkXt3zu8eqACIEXjYAw4mB+uLp6gX9wqzXKmzANKuawHBTL7AZczmEQAo96iPyzmlPuV9z+mm4HST13UVW5RqZ6T9/gSF4snltUmdYfefl6EgibERtdGVd8Iw9FDsQ1/avlQdq4B5fXQGCkzO7fnGGtg5Nk6PwMm9AQZnGP89pDUTlAixdCBrtBwPypy1goPM26AssKvgIAbCpt5hBGELDE6ipPYU2h4ouGSg+cE1IhBSWwdbV74TTj2tMQTCxkUmOQZvRsSMfKZcVfUS6eCUAbdyGchRBblpjCCaAnJUndwLzdQeV32lE7xS3hQMyHfr7DrZ1yUs6v3a2Q+jUila1F59CwBSY/xZn6FkBtYtYQNnNsmOc2Ld5oEi3q9fzG2hSk/f1YM12Sf9kcLAWFLN0lgyFVASqrxisnO6sTkABJ33yj0IGslD3v3ly2QmjyWGLIuoLJgHDZbMKoCh5M60YQu9xW7x1d/tV7oxChnsylg3zgKCPTBwBARa3WR66WKdpCUMdgtBqTrLefuml1ZnECOVunmjb+9AqpP3QhIeXHpw7ccL4IHiDsB05/s+A5Q6f2z2690P8OaDOnxwlMDNqPvbEX6+N8DAyixL9iwoqBLbqmPW0azjaZIIUwcOoAqTubAFyYEDgLAlRmRn+BLlgRjNGOk6+gYcPHvRlpES3Bt87YDbiz37c+sEGf29cR6UQQ19AJWBuDEiFQYiOuVhqLwc020UuYyoui1xyU/QXu9jalblHGtjNzRMgRhA0EN5LjE7RgLzQdcm6O3R/gmQjzGiGL5Vjd6aDT7y75VZFa4yB2CkJKAiMbu2tTbq65dBrCXegRDMeAXS2RiiJM0QLiFgDdIuKzGWSOCtAARjUDL+dcp2lBzof5c2Kf3Yd0OTDXMNCNatBgNrSg4o/erksyhoWAJlwCC/5S+IYVkBIbQGgIfhiSKjc33MSruGih1vWQKT8QwUWNkDBWfYgREgOAIDVTK3e889XeTP76dFy3ag8owQqErsy+/L4xAE4JgzHxIFMlC0YyO2KdeCxoDA7j9indrn+y7xc80p+BNNNLTjJwmyXHyoY6/cO2DQlgDgpkFao3N8MbHp96zyTk8xE7QxCeIaM5CIZZ4yYwwQqBiFJcgHYkgVIHTqFScowyDKEIpmFwhIsIGXjWum7Qsytw5jX3MPgXoqjmiKxvdKRuQo18u9yoD0g9EbDk7cofM9VH7XzGKPjrNnC004NMqbmvexucQAOn0zktNEKbdMQGvsVk7gpAAAwg4UcCDySEm2xdghG7s1K1TOsvfNWoElOMNHavBCa/yAhQICKSAgYENCTADrB4JEgxpgs35eK70692AsQr/TxBlQxocHTHJOAQUr14DAgMKaEhIDK6dKTkVud5MTIOPTyylAjH8IAg4ioYCCGBRUibFYKCCyhRXYGQceUshitPiYQnDyAeq+5+U6WkulufSissdo+v0jFvMIFFwKCGalZzULa5CZzGzU5a83+HK8AIL+2AmGALgzICD3/HZMfaJTEr//bu/UpoM+cybBcVTuLTDQoZazNz3SAhpl5n83HawYGK6AQgglpkrQ76crQMiAAMYYcAUQVL8ioQAEAAISlEHIIAE1SMgRUPUsF61bARA6MFWnB+Pzc6DYDz7SXT2iHg3S0cJFNiCL9zUejECLvM8PQjlWD8RS+/LjDEXbxtnsLav33dFPMyXcAoE2LFCMWWEERgZu3QoIMMN2u8nfLXH27BLXSWFtMRkFkt9RDd1VpGwAl0BIIWX2a4mkNA4BKWFBAAeSfhjkeUsre5P50NjVJcuY5D/vDfuzs/eqCf8tKNjYAIMAgjXty8vEdCQv68N78gpEWGINqGhNWKJnFCTEsEQq4Qcd98YoeKPhp9yOZDsr/m18f5wV04k+xGl4xPpLFeoE22rfAg4g/dU+Gk6BEJVRICJE5orpBOZOiuVreQfF95m76h9grIM+DzBgIvRgwK47Chn4vBS7xvSXr5usaPgevv+ufPsAqNu4ZbhH+3M9Lyj3Dhh4mtIjrTfd4g8jpSZ/6/15ALlzrE2i3ogBJGMJSBqNgQ4kWIKcR/NBjUcGCZEAlrSuBcBGwiYsOkg56X5mIBIWpgql2zF22dF2bFOA4JN2WroVKLhhdyllq//BYAQKDWsymw1ET9EB/UAEcBoQjGjsEXKu2Iyd96y8LvNAMQcDR8zAmkT2Ztw8GDCQcLulbNjM0KWUco6FKdPW2BVAYDIDQgi4ikGVccgGDwhIYCyssYMtIERV6oPE03xvFOUXB16QPH8kyCJsY9eIC21ts0NiIKyD2BgnBVJObgUcpCynz9YiwxeRmcnJgILJcNkClsAKEhIWCtiYcAvCEhyTwAERjCVxYRKcd1lmw/RTbq2cIc1GWMB0lxnmfB/H1gAOlJhec6HOxcCbhfISqcNhDokwlp7NNEdF9qNiOWdOijGOdf3nL76nd4C57pFjdBgikGP7QMBeg1DGQt53IRgoeqvW0bbM8WP9dg/cebnNXeN7x3UmvTNhBOAeAgNgjrRGIukMxwAcjIo1no4bsCX+ECltXYxIjmNDBqI1oAcKlu2a2xvQb8ALwFggJ22Jsbg44GIxeq23BwuLhRfIX6P318D6xkCMNeixMqP22kHr+5pH5keo3A/GF2UGMoXtlUZT7zODYhg1aMCAnfcyAEFr2AwQeDBwu5nny5mKXcdNAwBYSGPkREhMCEkM5BIjgKRyFdOxICAFztJMEBDpFYOssUM5fBYaUBAdLdoquUr+Rn0TMnDeqHin3l4QEcgv1QZhwKpkWuap7NZtw6bhBC+7I7kBJjuR0xrJgYSkAIFxFUnCLyzXLSyGP4GwJmAJmj9ChA1BwzNJzxeAkGfVMHKo0eRU+u5AjjvduDgwXOm04uAoUACy95/bBOU8Y4ZmbCYgTEJONG7YzLJfnRnA5SSoDoj9TJYX1TnA5wsC7NkzIJDrOhgPVVgBvUNqxwIJKPjtn5WvJFbFHE40IAGj3jIuT5/d7B6/V8DABGxI6wdfHQjVlRYM+HjP/CHyZ9GLcxIQUcMECEw342HeQDYopos1edEDwM4QUbkuRoJ+pw9XDLCOAkvWAnzikMQGr1A6Tx6gAXqspqbq9Q/KQM97Rp6kv8KBADu/XQRplLgDYDog3eYpNuDMR57OlFn2vEljL1FuFjIw2tvT32sSo9Z6ui0gWFlitCkrWOctuJdMgRAYWIiRWDxdJEIIst1+J9DHZwMkfk4K4pYgdHgMRdnZcQEJNJ2u18lTx4gYphJ+069M5FFgrAFvBA5KWzMh2pcxjWGg8ju/OxsYgIYV0lB2I/mtUFDgZLclQgwCBBIL87KlDYkDUhQgnighsYRlEmsiIoJ41hHAlsrndRlATMOETjPYAHZne7RldGjIcEL7dB5Mdd8GSj2MDbX26NjMJKhi4TJtdkk1mwkUfWMMKIDspNjxpuafu76x6470jXc22jwBO2e23oW/rgUH/jk2pnz55PkNfuunj/Dk60/wsLFfrNeoCq9Awtli9vEBXpuec2+AgQcF+dOTI1DQNEILBtq/bblWpGVef6CCtmuGTEZWxQYwZwUxnVfMyDecfbGxHcycvX5RvFe2fwoaaDoFSc6py2jBkeniIThG5ACmc+VbADAy/ntU6x0xQKHgaMYa1NujRDmLiW9ANbOgBQVJPd8zoGDd0hQQVAmlpvguQEE5IZFEwS1RFNeiCXYLSdstMch+BQVBwYJ9q0Ay+Htw0JbMpIEzSKAklHxKynoZIwJ57oYNCwuo4EBqbAlpJQTS7H8HFs6UPfkhqawdQLCDV0i4BXAVAz5by2TXJci7JaacIAokLCFIfkQO7REii6e9BR4mdFqp6H9XKmB84l27exhgcK9tQECeW4CDd3T8NcVBAcAkzgXE8HtHIzOaoXdQ5Dhw5aqW9YwTxKV6Rs6ZGH/9MTP+9ujWk88AwK4nf30NAvz+Snc58ODrM7I37/1EQMHbA1BgPT0acPMgwZ84KAnFPv7gq0/wH//4D8cn4h4Bg4AGFPxaLdQWlc0bxkaBw2Dam6+fPcWjn72v+zZnxGqzZs9iquM+yfIO0AMDe3Jt9KnkLKAGHHKPc8BhoZLg2E5Vsuvb6UpWNjitMSixOXZJPsDM+I8GppXZcrd3Ku5VKwqTTDGXfJURYGDU8eoMsjwQyyDAPYtL1rwvdq9tAv/b2SWhVZokWfMhUBVOCES4WsTbvYqU/wYiLBSwGDNAAg6WIMeXSPhCjAIGyDL1BVAUgKAKWHDpLutWgLP04Y1RGAEwwOLZEQNI0o+/EC3VTe6wIQFboboTByRKOWcCedIqcLsmYRWChNVs4S2oZ9vKbyZXK1sCgiZhBjfXTdqTkDTvQPbVYRlWkGhGclEJ2QyJif3W32U8fC5jweMqojwWvO7y+gWoHR47biFJz2gintUx85d42TrmyPADc+Of79HUpQLDg3Fwys5oefLVf4m3X30dSGvVEczOuCW4qnplqVo3b/TVJ39TO817XebeAIOnz27wvovJzBQUDX+7RjJDUAGDhOvnH+PRh9/CD9/9Pv7+f/4uaNPFgCnA+9hMtgCKLXJRuqmycf6J+tcl3/AYHPhrynHtIBV4qB8wAw/1fYCrQOV5qMsRTTWf4VH/HRl+O78dmMDcyJxNoGlLNyfcDSD/dbOyLoWcNFvVkjBPlKsWxYJ4t9khIqH1ZYqkGJMYtA8kSX4rLmTIC2dZfHyWmNUCAomHU04+9KDglSVgoYBXooCCqxjwhUi4WiIWAr4QS8JiDDpnPzMMCgwyQFAFmkXKQ4Vna/8byxYApCQJiEkBQWJNhtVQFxLwb8WIW/HJQSHiFhsKCFAZBaCAggIOYoCwL8RFjoEQdmYojOQof5G3bV9pzyYkQ3U7zfrsCBTke6D2PPcMmNu1+7yjYjMS8rb+LeJqxomes6db7O9iuUzu/gk1a9CWWXb9JU7FpUa/fs7g+OA+o7+d8R/ZFn9cy6+/+mUBBYCzH5BvwFhlKKd5Z1xnz7WmMuzKAG4a+3hEsN0bYPD+zx7hAxeTmTcWcNhgzQdzrp9/jG/+2bfxw698D29/8cuyPxkYaKLyw6+noTSwob7cc1WBkdayjDv3V5F4c2AEEPx1Sd+8HbTcABE5tzGa+d44XWY0597AlOvqwSfXjO/lz5+V1iMpOo0qA2+n+aQ4DxIOV7UkMWYyVUuulyleMo2LNd66buKd2mBbk4CCpQr2i0ETQ7zkkIJN59pI4rTTleCcF2VGbIkxgwMDBEsQhuCVhRQYCBUuwCBkkCBT7SSXxu4prEINCLL3xBvA+lXC0UeEfH8n/VgRxXyPlIS6T2whBaGwAyRpE4tcc7slxCWCKCESsCYgkIRbZK0ByQeQZETC7RawxDoZ0ZJyL5Gln9lhMxSiMi1LJAV/yOxLCBqWUTla4i2R/LbcGhv2bYb7MLvdjZ/Pb+w02+53P3YGTsvAMRnd6/PSK7sGHcfHLzb6wL4dyecMzp+V5G3K5mxGkt/cgAR4u1OApj3x5tkN3v9JbR+PnL17Aww+cDGZQ+QGHIIBOca4fv4JvvnRt/Gjd/4Yb3/x9YzkyBBdVwbR1ZoXdL/DcF9u2OZ4tGtMm9RvlwFEh/LzIDUAoburQU0dyvfnnC2zxL+RsjpSaiNXasTy9s4fVePWD4Lg3j1nYlORS06BI6BduKpdk8Jiw0uwTHSt8sZgTUAj5jzxWyjqpB5mEmMIVsNVDFpi+b2lkNcryAv1pH5IB126r3i3ZS7+lRp7mWcf8hz8JRJeUSM2YwlK4qF50S0gYCBtMobShvxZ4txnKslr01D2eBAiQAExxJynYACBSJiDAGFX1sRq+CNut6RGN+B23bCEgM8SYSGb7RGwLvo32WyFkNc0eBF5+rUNDBAIEGjWNlBQ0K6WaGsblNUSRTLhABC0Xu7I4L2c8dRPHQQwHU8zp6Sc1888eBGd8vkZeqBmjnNl5byGQS6/e9tCtQIdvNG+WSbfJ4kA3iQ3jQGbV1qDhCDAfGBHrp89zTkLD18rU/YDYdgfrNwbYPC2i5lUJnOPxmk9m+Z769efPsU3PvoOfvQb38XbX/ySekV6TerzaaVM9uf4kN9X4180xwpAGIAJdz01+zmzEDYq6mE0AxJADybsmkuyXjto5BUVdbumv5uKTApVS5sOGM86KZT0JCoZ8pKAJUDB1qSQW9QLV7UAAUCeZrZEArOsZBmjGDIiyVqPidVoMZYUBERAmIQlyEp9K4kH+8pSr9pnhkzqP5eFN2J+pcNirNS7DYUhWGLYDRtMAQFvIAUCAgw4MwYyhhrF6fovUxDvhghIUa1hBFEAhYgQ5GuFMRE2IqyBEZPEmVeSmR6BpL63W8IvSUIsV1vCbUhYU8AaEtYk1yb2KyPWMj2SJ4ChTG31yCX63wYScLh8cgztx5bKTI8RIGiNvk/09Pus30v9q82Lxli/VDBVF1Wevzunc0j6W2ewcKbM9MgofDLU/YDT1ycNfLN/38hPrh/d/5LC3oZoG3OoQQIKGCAuIAEoQOH6+VM8+un7w0TGo3JvgEENgGpAsEvtOHbAezrXzz9RUPBHePuLXyoMgQEMAwZ3afzRp7iABijo7WkQUW6vbwAGNftbYEH+muY78NGO0aie5wa01rzZTGV3M4iOYm67pQFE/t1rwFRWtrMc0ACbBVJYg5w1jwISdgGC5h/kxWo0kWSJZdXDJUm2el7whQUkvBLKWv9rSN2SvtD6Ze92AJLsjf0KcKMlfEtooCQWWuKhGf09hiACgAGCZMDAAEEqQKHNqK3aikrfowBWQICUCkDQf3ECELYkHx9bEyMqQFgTY10I6xZkDYNE1SqJKYVuiWmR7Tm5Wr9pl5Q2ZqD9lkL57sT440tnGQJyxzwYqLxiatgElN+dcXRtMjWQR6XRRzzQRdGd23hC558z1R+cD3V6fc97r/rjjkEfyKLTT6Prdq4/LI0MzdawGfucx6Y9hBlQHZSPO8aAWOzXow9/G0+++hhvP3hTjwf4jJa91rhXwKCmghxCnHScvtPIfg8Kfv2LXyrsgG90zxi8KFqcAQUMGm8WI2z3u3t6Kq6EJ2rmQdVgdz8ePW+nvlUZDrKOV9y5wYCK7m5Y6llAQRgCn+JhZSK8ToQzg9GCBKoXrWoXrAr6Gu3yyLSEvNbE6CuB9g0A+QiNPLP9OJBIYZ81aZd+LV8GrNfx9x/88eyAecRtDkFUhqAHBP53Ki/MCR6Ue4Va9zsBBpS2GiRwFBYhyL9o/1AAQgqS6R+JkZQR2BjYUkSK9Yep2kWmXqZsofI1ILBQmS8/+iqlLazzMsHAEAjsOUTMODaavhyPv8rJ0PK56YyRcR94+h1jNbv31LgfsZT+3BfU88oC5N+A2BYKIDX2pTYBIHtvAwiORVCQcP38Y3zzT38HP3z3+3j7wRsudJNyHeYpr1LuDTCo6JdREtTugCjnXX/6c3zjo+/gx//gn2n4oB5s/hl1pzhGnuX5d6SYdkqH3IGhx0/tsYpNqJkFuQKDgX9ykLdPn7332YE4q0fLgrh4dl5YxsCCou8MFByKHoEECznMQEJUgGHrT/ivKi4OKNiU0QQU/IoeMADzr9mNymwxlzaOXT4hLGwAwScR7uQPpFX/JgUIq1SsZQzKhw2sdeqmc/8bcGNNRARFAQnBQgsLQFErExHCghAiFsjiXinJUrwry9oOGwtgYAj5sLEwPfYp63UAyl5EvvYKHQDQV/QhgjNAAO54CwZ8iMCDAQf1Mzjr8qUmbGj18iNj6o8flQEIILxEHTEz7COK/0J9/CvTw+VoW4Oyi4J7D4afcWPZX6zbcn4BCHonAMBfPr8pifKvvlHGJgVwNZNnVJ9S7hEwaGiyJplwHCuqO1oFCl79cg8Kuk46oq4uo6n0wPS1Og97dt7orkN2oTf+8nvAKrTn6PU9i3FCCbyMQThiORpmIBsc3VfYEjNGBgact6qGShSyqGYDCQwfcqBmwarScp4pAKBgoQx8DxgAZHYBmpsA1NfaPY9Kn4Q1MUaOGSAFCpbwNwQDzGN2INnY2howIMvg8rYTYjOwGaMyPArUaJU2SJKMSEEzsZU5IFo17ECgsCAqSLgCyfoBgAAElaHlESQ2mVPH5rwMOfu/Ju/cBnBBvR0QYPc8EyKYsgIjXeeNSwUS6nPzcf/XH7tjkbF5V70w15vn2IBeZ57Vo6My1qP5zs2Wef+DcK/WcahfR++Vr1H9xMpvGYggQmnjgOtPP8E3P/pd/OgdAQVs1wKw1HO/2PYeWXZ/gAHQI2b9PWUI3D4DBZZTMESkdm/5UW1TN8jGHbXtoFmRdvfXmg6ypu88aAcDlULfSQmivPPjuoHxAlThXcohaCF4YFBNBc1AYXNAwYEEbBWbwDnrlzJIAAqbYIu+9ItNWdPP1p1op41SfY7vFpfoMDMm3ojseZ/KGoihEYNBm0skTElqXM02UIYAXBiDFgxwKn11r39SAG+r9DsKDUhQ6pQXAAQKJTnRgALhVj8JHRE0BCGhCGFuNgfgLLxjJtJvVzK/g7xbmefDDhwA+9nzng2w+5HbNWIFgMbx6ViBdn/D5FTI6A7OzcssJ0CB15eVrvRhqhM68vRbHOnIwTleVwKqLzODTaUCWb86gGCJgyg6odyIS0eqfqcCDgCAqCTKv/M9WRwJIjuGPzflJxwxOvcGGMxyCS4FBb/+xS+54wcD5BAU2EAedO5Zx67yGPzvFkAM6jOdKdGU4Iy+/fDGP8Sy/2hQtMcn511ShgPNjnk62rx+oNDSZrApAM7Iy3bS88TVLyCBe5Cgz7ABVOacy3bMz9U6+vo2A7yb/sWNAsDLmfUx9TA93ZzM2DeeZXIMQA4TqPE/AgPWZ3PG5E4/1L7HSoHyFnqQoDFWJAKHBdjW0i7KHHAGCjUTFEPMDJCfjcNZEiKVGSh4oXaQR9Xn0PwwdfvGnvwsLNqBAb22AgNe/zVA4Ehv5We40jkyl5QJYOwMu9eNwFwPviQdmEvrAFlf3VDrQZ+/FQLYlsd2jFiuYr4l1QbdGIAOHLh3a4EAMLhGc+L+/Dv40TvfFabbXT8CB1qb+nlNuTfA4GJQoKViCl59XXAbu4bOv+kcep6Agl1AsKdY7ZluXzWQdgbV6dIa8VB3/PrY3GiPjg/LDExoXXhb3eaA0YgR4FQSbkAgYhhQYBtkLUigwXkkSF0wRAETFVAAKrBQ3j0MlLs7V0snkREzeaJv7c7e6GLGXPf7PcPhjUZ7HFz34REYyMm5E0Xt+4S1bf5KUJyCBDlvRWnDkpdAbh+M3dHjdZv5EFPThoOcmizOM8B2l2K2G7Xb43Y+mkYt53h9VgOFLkTQtqvu70MJ+3qKsW+4uzLy3rtzDpyc5vjno+/cOjQzfecpfatTkC+U2jXFVBRbkXMJYxTDTELiV+BgXDH3c9w3236Z7ddXvis5BYDUDx6ANODAhTVm5R4BA4+2zlFh179Qob7z3Uy/QBEZmTIlTdjICK6hr+38DkxYZ5hU9wgUNIAgnz+ga/lSZqEtIwVnaNlOmSBmOfcki2AGogX1O+CiQuttPC4bEJtgKECBiB07EOQmXAOE7jwgMwkymABYsk4VyvDsxbwMY5rdvp1+OqN223OnMdeJFzhiudw5LQgAUIcI9sBru3+2L8S6fzula+3Nm4JA1/YUoya2OaLdMTxlOfKGwHesTy4zUND0xd18m+rcvWN2rxNAAk3faZ2aDiykug2PAMGkrdmM2kwvWTmrXyaeewcy9tiCo2fvPd+XHf1C8vEL25BnhhJSrc43gBCi1NvAgF431Xv+biMj70KWVX2N6dTf7YyyMShoyl4IYqfcH2BgpUXXk3L96Se9UA0IeHAAAF02JwoA0EYSs+TOV3CQv7EeoyhbvY6CdkLPSrTlABTwDEwA4G4An0DWXvGpZ0eeTpsNsK0GA3LuYJCEvg5DRsK8Snte9jqTGg2RGwUIjecBgkvYkek+HiAk5FifJe4YQADKuRkNmWJFrg/5bnUmvOTP6zzBk8Z7eC7qa3KVD6jfNvbahLHy1XtK+UUoXH+uBwiAtHvjnVWTjXboXMCp1jMx4PqMZrMo4y4mOwEdXTbiHisxGhttnY6A4qhPNYZ/xhCMwB8fhYR2wN8wxn8m7n/Soel02eBew9KFPU1flY8TVY6P6XM1+tnYt6zX4BnUOC8WFtOd5Vgb+rTjI3Danu/uf/2Ln0v4YA8U+Gqy040n2LD7AwxOUUqGtD7BNz76vZy9KcV5hiQKy2I5hJT3ZYDg2QNDjaPzbeARgGXR+KzeIqCAA99WNr87RIn9GogYlQYUVIPIy+RUbNCdEw0QOCbEFWoMOFdUG5BjWV6Zb32nbE1MpR4PkLqIXX9vcNQdKnScvxdXMQ6OVvNTg/yc4qM+NfLcPYW/F9NtaNxOSQPZiztU1O7YCf/pfDkTFrrLuXulBQpWdoDH0TtPj7d1bvJs5G+j8BsGo1L+B4qf7BmDsNS5sMQd+tuLgoKjUKa/pq3b9PxxPz50Zi7Nb6i+0Rzy/ckAqdcrg5Lb2pJg7feoTzSAQEd2Oe8IELT9YnS+7stObWW/LiitgzIo9wcYzIrP6oQJ9Xfl2wcP3nQnxuo8aVMx8MyGJgFi10Fs4HkQQLGgO07lfD3ORMBCOkCjdHY1VEJjQTqexW7josbNKbik9w7B7ZOEMQpxjLAnxa9bXlGtWwMS/CAdDTK5QT3oQ8xKIQ8yH3IBiqH/PEqLjv32iFbz643vWZs9j38CALzxz4pY25iZ5a9nhuw84BwjdFDIK7+BARyGihzDUx3fM6iXlp348jC2PKKazxqagzKUkZcPhcxaULAZEVSMBZUciZBBwwwwHDAOe2U3ZNT8Hm2/zDJLnMYdGYGTYGCqtw4KeWej7fsUShhB19CYAYIWIAJQQHACDDTHq5ABIP2qPadzav8F3n7gQMHIyL/AuLy3wICJalqVSFc0lHmeD197c6z7VZjZcDND53zJfS1RKsTO6GcvND+XpiAhf8h3cfX0KJ5SAQhaLwqsCFdBREqgqB6z1T9tIAUTgAMJXpe7wXY4qGJrBE4a8TaON7t+lPizo6C7fIPm9/S7ElWRwE8FDhoAOb5sDAQAzOf0cwJvq7ZXmfrHKQHNfra1AratKL609UbxTIJXI3O2faYMiUrbUq8Ec+zVKUs7pjeot9tnHpWR0WDOLFl1nvds3blZXsCxzNpnDkqWEeDA0UBmJNMl8yyJEIC4VGCB1ZBQXCp2wSdNlo/fBJSY4yAEMa3wJBTVFheurPSiD2Pab9M51g6WdG3t3DKYlY6qwX99XuzBwUFdAYi8B+Bgqre8vsoe/bjPek8fIaBjCLzOeVEwcAkzkMdU3Rf+Utcp+OFXvoeHDxr71dq7ppzNb7Fyb4EBUAvj+vnH+OZHsiLUw/aDEqNEoY62syUodV+IYAMOek7vKTqgQLFQw41BYTt/CTCjQkDtYVqnVOVIoShBTgkUUXnf2VNvDB6nTZ/j321SBoZ16nmqTMp5EzAwywA+AwbcvVr6DgC6pDS912F8GKjBgd9XbU+o2gEYSGr4s9FfbwsTsN4Ww7beZsYgG8GUpK/Y+Zvlmlgb7hu4HHMPVOLrauBJ/zIFYBOjR0TgmIrBS5umYrQgrvGQ2/ZoZTornARze+MCFJZsYER2AdSO7AAZR3eRHaB9y8CR+8vByW65EqaOCLRcFVkvV9LWIYIVOIxBQpMoCxemzBXb6ZvZmCqvqNtdErULa+Yxs6E4FyYae7Q3+k2okHwbtaHOmlwsVfb1d+1bHBk9r22iJQx2ujLLJRgBATu/ZQbs/BEz0AAB6PTXCuDlegwA3skQQQECLkRlOQXPP5YVDd/9fs10e7lZCPUllPsDDHYUUhbqb/4J3n7trek1PgO5TvxyiSpmYMNSgIIZ/tlKY80+zyrwwNiwR/RghCYu2MYE8wdtgB4oxKXyuCgu57N7RyizoZFfCgBo9w+MzCi5BzDEDmCG2qv7DABBW49RaT2yGShYb0vbmPFXQMAKEnhbFRwoaNjWYsy2NRsxTtLOvDGY5S+AY7bAFF6UKXwUCaTeEIXi8WZvNiVZMAhQVqnxuMyTisuQMt8FZQey7Pq1Z8mIQFh1Hnv+r9xjU1lm8LRWQOrzlCHFKAyBAYRNV2ZcFlm8yWSlQJ2WK2l7Aw2cBDRkRR6aPBinK2aybBmuxlu3bbmn6iEX8sxT6DTvKYc13T04JflghpNJJTs3brKjAkzDQUTuWp+s3LTHkA24UD91QPVFgUDraFzKBjT7WjbAO7EcnVmmgOtnT/HNP/0dPPnqYzx87a1mSr1dZNtxLKtR2dF79wcYtMUSNfIHJX6QQUE9BWlMPQsbYB22yT8AgHhVGICG0vPzjflompHty0ChGJtyTI8HMfKEfvlZDxbyIjb+uWkTDNPOcMgvPFGWI8W0Nz3xEsPf3H+6ZoFV0SV6sb/WD6ozYKAdELMwgtGont7cYQqGoMDCBdsK3m6LQTNAoEAh3cp56XYrRkwNW2q93W0w8PVbuSbDEEiVYMgGLlxFUGKEq0XsLCXVTz0zkK9drkQOatQoLi7pzpTkQFlOy6BfOwDMJp+syEWOxBoO8QmJdp9UGIJfmRzDKnKkIOMtSN4NxQXMCRSvVIFLvWm5kn6xXEm32TaVIybgAMP+nctUqXsXXcEASiJ1lScFSD+1OihjCSCzlgDqtQ1MFZrTYfut+P1pk8frc8o5NXAg72v46/17jMokLNmFJPfyBJye8TkhrNcNQcAoN2DETHoQ0AKA9hxve8JS7NezG/lKYvPpZG7aML+HtrHsb3X8ScCA+wQMOmWvSOvDb1VIq28IZ1S6e8b6DCpncbyqGQAUFCfMQg8IasAAYMYwNICBK1DBRanGq3wtuQHdAQZXHzqT4T4rO6ED2R4Y9p38gvmyywND754/XRa53VeBvr5/dKWl4iy80NC2ovAnGdRJ20/p7ZJLsA1BgbAMjLSuSOtWGbJt1Wdkj7fUzdYWCCbfFaAYwLoo0xYlJyUuACMAMYGTGDneNtDilFiIQIygKCAgx8yXK1HScXGAQMIQthRxpSwbRdmVUb/2fVpXPAw6tVfCHWtOvCWs8g5qdEXOCTaOSsglVaBgWy2ngzOYn8rSJtrEkOUIbIhLBFIAB7lHWMwzVSCBBVCGg4C8pgJjEU/Zz6qJBjTbaZSh75ejpMSq//sbpPq8zniIs8PFWpfrtC5Zh7WhinKj/NjpcsX5nvYzdfvMMck1vMv6K8DY+TgxawD2dAO28PIfsAAzALDHAOwZ/51jCGKWr5/d4NFPfwtPvv5DPHztYWWfyKMpKu3InjGgUDPfF6zreX+AAVAJ9y/996h/7WFz3IUGBn93H2FLqoJK4xDcrAF3lwmjMNtPHgSMVquzayzcMAAMDIBiuZYc+9CtbubrkjfHnWe4eIf39k8Y+T50MwEA3bkzpTgx/CdCBdOwUUXFeVmEIud8zwC0AGFWrM38VEROhfLeuAcFDSDICw25NkouryRADFr92H7hFYrqOZknTEVB0tUXQIuAASxXAgiWqwwG2L58aDSs96pMJlk+rQyavu3Zl7SBA4s801oAwip0PRGV+P7tL0XpEQmVz/KPYgSnmlEY9Wfe0ilZMhKIFSBgQ1xsnMs3NTiop88CRAhBQzP2pcOTUzgrAwN0YMvOGci17scnKPgW+Da/uT1npM9G22Hu5fsPB5/59kHZdVIXDfRQZ/wBgAip8uonAGCaB+DBwb7nv2v4BwsS+98bgJtnN3j/J4/w+OtP8OZrDzuTTtVfW65d2CHv4PiPJvnecf3sBnvl/gAD1yDXz57i0c/ex5OvPcbbv/brdoIcRw8ERuumtw3RmsUtoZY0oOuiW1MBmXEYsn4tWOASOxoBB8c2DEGD22Z/rovpmrdEcaIc7PmzMvSynVzvYtzdPebb++fvP3dyT3+9TyIFHFCzUILus22YByv7mOSZFADmgLzcL0SJcaLakG07Mp6UkSE7KkaDUySEJaLkGQgDkJmAV/4t9/sLmSUIr3yhBgRGcTYAgb1CpdArRaAGqj4UYwDXPumcgtw7rQUgLFei0ENETiwLUf599t8VLx0AbZI1HxYgYQNSQEA6L3IFBymlwsZcUlqgVTFmVHI2KDjquv4E9aGX6u/nCrdAZOR1N+P78HPLO8e52e7P78eU7SFjPHfqOR2xO/qgM/z2e4/+b43/gdc/N/o9+zwz/v33U8rP6//vDd7/2SP84KtP8PqrD2GfYqjeWh/VfsiLToCEm7+5xjc//BZewwPMyr0BBtZY188VFHz9Cd5+7W05Zue438ltZNNZAei65eycj58L0loTd0kyw8UB/YZnvXKnijA8USjTXAv9U4z96IMqAiiMQZiEJ9wLdrkR7e+zZc+g7wze4Tr0Fxnzywy/PLM+3k3tIRQZmGLQ9StIY8ClF0SYH0QBQoMuC7CKIcNyVW65rmLQmCXzX1kDUgUTrhak2xUUEwKi2EJAYutIoC0AEYhRV3xUQ9MaLqO+oYlyIRBoWTIoCMsiMXEDBcoMULwCXb0i+69ekf3KFggguMpGmM0YU/ltHzMyefgxBsg4C2r/TCZlU2YY5K84pgXlM88RnDRRNt2CvhBkBse2yri7/axQxtsteNVx9AoQ44p0K8xBAoAtIAJIgYGkCYXGxOzIU7pCkanlGoQl5oTEnGdgBsbCMllWoWJgYOct8gXJLEOKDlw5irtiY4A9Ruawj3cXMPozJgjqBMiQZ86djiq80B17Qf0z8eCntH/r9c8M/wUs8+wLqa2UhqJ0O9//2SN8/90P8Marb+W8GHkX53C0zyEZZ2Y/hkwCUslZ+Opj/OGP/3FfES33BhiACNfPP84xmbc1JuMb0DMDHgy0n8kFxsPj4+c3+PaH7wHQNQKHLVybLU80t0PZA4sqVNbsDBQzcKgpJH2gggMwOxBQWARu8hu6tzw56OXBZ4zykYc/MOKj+07vf1D27gUUeVX7PIOjU1PByLNNPPWdNmUPAljnoVPagAVKf9/68Sq3bX6b58jbKiGAQEiaU8BMiJqdb0lznBJoiTlhLr+hS5ZrE+WMJQhLBGlYAFevZMNPCgLolS+U/VevAGEpDEGIkk9DMuUuAwSKMBiaEoafNR42DaTfE0m6XQiLhkAA5k3kaLM1eANtt+AUgbSCQhRWISj4uv1M2npbAPql9JNtBW4/QyCdQaBT6VIkUJOI6JMQqx6zI1NLPhRwECv2BTozgeJVYWCGYRkq4KplCTJQ6EMzux+BygIubzJshz1GcHj+TmuO7nVo8A8Yitl9gQPd0zMpncfvaf5Klr3Rn7HJFR+y40jOpHZG+t979wN8+cHD7pMywT0jpxXoWMqVdkyCdzQJso6P2McnzeJ+fbk3wOD6+cd49JP38OTrT/DQxWSOAEHuAG6//K2b9uPnN/jOn72P737lMf79//NvYOu4oFLaBt0jsVvWYQQQMhjQg3ZOsKMUs1c2Aw8CHFLVmysQ0VVtRyGcNOw9xX8hIPi8yoQezXFhB7QEDOgQIxIWwZJ6DCCkDRwDKOnS0FevZEYg099rEAOxLaAoMxR4jeqRyqeFY1wRli0nI9o0uwDk/INhcUbLT62rGAL7awbKcgmuvgC6ekUMVlgEBHhAEBZhDqIwB7LAtFRlYyAx6zZ3Y60rWWlJxnsIhJCEPYsEhBARYgRFBkcBBdCwggAE/U0RIa0CVOICuv0lOC6Se7CtWZ6k/7CtebaC5XMEl5y4J1MAWa5hWVBNV9TFi7BcdYAgz+ioZL+AjSUIhTGYAoI2TAPUxk0q18j4b3c85WY/E6J8iXrniN6fGn2L0DWPb53FGSDw547KHXgQfPmLb2FL7cfbi0Pl9T8z63fhSCKYXHObFmq4fnaD937yyM1u4D3DdH+AgQcF1vgtIPCN7ht82hn0x8+f3+A7H72P777zGF/+4kM5NujTAz/0sEyHcRV2sF98ABKKR1YfK2ELo5yaRwz7yKzfzN5ytH+KnPcMyAWl/d79qHSnWPjGNsmq4lkDZAaGXUzcQjb2GVPiBFuQhykCQWPli1D5WNcyt31bQWmR8IIBhKtXZBqbzlQQintFaObkS7XmyVjZYJF6twY4BkbL8gg8IEgjQBALc5AgqREGBjIoSNLvGci0px9TVT2p9NMQSKZOghACCzjIIIEQw4IQFnBalSVwAMH2hwVhuRIZ3n4GeuUL4M9+KfK26aHrCqy3iMuKdtEo9qzBQKbyt5GrgQH7rYsbVQyBbntAAA8IRsmbIWIIBgxgHhi89vfevrbs+Dgni+Q2hIlCuUS3XKJzRu+evGLH2OAfOYMVp3okYNxV788VV+6SjQ1g5hzxJKIcum4Bgl8iKwF4+uwGv/XTR/jA2cfahezLvQEGH3z9Cd5yTIHlEOwxBMPO0hisjz+9xu9/9D7+6J3H+NKrb2HTA5vrDDPAuIckrWwHnWrGKPhM32L4dZvYb+6CiNlz/Pmjcuk7v7DuwY4yOVn3eb6H7TI5laRR1lDNECRQ1NkhMhVNFpoKChCSUOGkND7Lioe8raCrJIZrvQXptEVyix+RW9GPmsWshsWvyqer8Q0NVgYKr4ixCguShQwcCOC45PDBpo9dE2MDsCUBAxsYKXFmD7LzxaWtPasW87Q++S8kYw1YWINAiAoSYhCQsAQJNcSw9AxCWgtAsDyJdRW2xtaPUFBm00XJrTKZ5bonU6CXqy2JbKGDlh1QwGAzOUAhh17yvSh0YYQpGFCqu8XRnb7yO4EhZX0U2672j6VyUblkvMr++b2O6n7W2Pehgf4elzACZ0v7zvbc0TvnOumfAJJ6uzCBgQRgABB0fyLg6d/c4Ld/9giPv1bsoz1y763uDTB4S5HQEUswyy0YdZiff3qD3//offyzdx7jy68+rAahTwo5l1wy2HfqzXpKCag7lH02OFTHPRgo96iQamUcG+rvVN323+HFPZExI9DuGuUglUHl6lNdUy6ySQQlt6M8m6DTUh1rU2XVKxggZnDYCmhI9SqVzBsoLCA2VsDi6EkMmRosXtdyXftdgEnp1u03j3ZkrDQ0AIo5Ls7xSkGB/qaIBGBNYtw3FmCQmJEY2NjAgYhjTTLNdgODkxyfMQaRCBSACAIRYWGJf8XEAgqYEJIwCAsDMQmoWILF9S3vYAPFNf+WEMImIZK05sWlyNaR8EtSt8srH8kVqGSbWQEFYAYUbJ0HUEDaBQME2LK3yhbsAYGxfspdI5eRofTnlvPG17TX5etf+jh+ObpmDopqMDAKD4/ld/zMWTnS0Tlc2ZzoF4w0nZS4Ps/eJ+h01CpMkCtNtR5jxtNnT/GtD2V2w1sPiv06w7LeG2AwGjid8ccYSY5AwSef3uAfffQ+/tlvSPjA38vuD8w71nRgtmj05Pt1RS+s2tgbekcY5cMGEqrz6nLJl8p2q/cCKHsv7wKo38mWkWkPWOefgQFTvTKI6nPzAKUaLHigkNmEUJaZzcyCzcmHGCCbTspmjHgDFpfIiLKkshmvaiEqk2VrxGYru7WGKtjCRA4MKKXNDhwkEDaWlXETs7AEGQhY+ED+rYmxsqyvb2wCJ1ZQIPX1tbWJdKShAgqECGEFKAALBSxM2IgRCIjE2FjDComxBZIwQ4iIS5T+vd2C4yuSR7BICIeNeVnKbAcDYpmRmawMOpXt4GM6OYnQzSxgxwBMwwQGKCasQAUEGl0m1a2Nnu+3RyDhyJC2923v2d730vJ56ZZR/H+ki2d6+EVxTx+qLDcOzW6rl4mimslmbcuOHnD3S5DQG/Swzfih5ppEEv7+nQ/fw/ff/QBvPhCmILj+ZPWblXsDDEZI+kwuwYwpyKDg1Ye7gOBsB7Rjnl719x2FFM4kELe5RrHppn4wVnRudVJd4ZeZvnT0CqNnUSOLESNg120oCZz+NTI6rgaYggGHJVowQDp6DTD44xUiJ4mHUztjhDeN9xWgwNnQ+1yFlIGCnYOrciyDDQB+BOdFqiola7GQMsUtAwFLbBtNNdTtNlyQEks+AQQcGDvADKxbElCg565bUrbAgIGztYNwFxEjEjIwICIsMSBiwxJI/sWA1bEISyBsCYiBEbgAihBfkS+CLzKbgZPOatApj/Yvh3tepmzhqf8CDnzyIAe3XQGBmAGA1WAW+vQ1vCgmPjD8FS5o7jG6f/u7vUd73awczJO4qHTPmtRvpmeBXtfeRc8Cta6d6dlkP7Tk2wxE4btdnp5OpDkFsv8IHBARPn52g2//6Xv4k3c/wBsPzH45/XWiGe4NMDhKMtkbTL5U4QNNNGyfYSXfc3TOBBAUb0q39YLNI/ymcrtr6zWVijtGFRAqNx87ABX1ffbR/l09ifa+seG5lOToUPjmdlkopcuhIFl93ocXCDqYWkTOvA8YNHjun+3BRAEL0eWBWB055yYYYJD5+86KOtBQL2hVEiFNFI0A9W9JUCtrrddeag4X+JkFCTmZcBgu4MIOrJuECAQYJAUGsi9Bcw5MfoP+YGt3BJKcggACEWNhVmMfhEHYEpZImUVIO2EGSVaU2QwhajslZQ+UvRHZqUxtZUQPDs7IVrd3pxJqqGDGBNgzPAiY6Ss0x9r63RUAzBybM06NlXZG1ssa+23Zu++ecd+4rV977fmSl4zS+1fG23cP5qw/bTGtDBDs+SS3CSgyNRaByAPqscPk1eDs2MfPrvG7f/Y+vvebH+D1Bw/zMTSMQt4/KfcGGMxotBNgMBcDBX+kOQV2X0FtXE0DAerGrJ41AAVHgMDuY52W3eDrO/q8SW8xHnAGCG5dd6DGCLegwpdLWUBfxTPX5vptNWCJ2dOUvzFQBQoskmAevqF08/zJH3f3YZdTYT9ngAGYhSbK39HU0rJfvMQQPFgooMEDgZzgCDSdetKTB9O0WsOUoAYp2T/ODIDNLLBkwlG4YN0YKxd2oA0xJLuP9p8cBXHVD4rwgmrFkCQ0EAg5ZLAmYQ0k9yBgoa0CCF2Ywc1ooCRKXADHIlMLYfIvMp6uLLon25F8bd9eToDJAXMGYOS55+OVhS8/Z9T/nUHAQF/5+1SJ1jsGeFQu1QOza9vSGndO/cmj+h2BmFZ3VosK6QpCBhZMLlEt7aaueDRdrvkx2STrj9bwez0C9LrKOxp75ZPnNxkUvKGgwD/LGIUzOO7eAANgPLhmxZzDoHTMzz+1KYkf4EuvvpXvYcbfgwNU++VERpke4sPew3oOQEELCMoxBxCqe+y9YX9sHWScxEYfzkDFi5a4c08DJ6u+ZwSAVAbDCtbrGRQA3nqgoJfkkaMAvkLjOZZnhr5SWKV+HjAY0AAaI+fpC08TVgOYq3vPwAM0ZyErilDfy58/K50hUcMutrAk3ea1BpJ6+GqsMhgAqnDBxucBQUrYZQtyaIeNNRAWIDBBFiSUPIKNxfAzM1YHECLVYQZhDhgxiRwDIU99JBhAKMxOlnEj37OybeVr2wVjvHyjP6oD3H3Ldg0E/Dl7YGAPCOwxmWeMspUz4OHSMjLuIxZgX0f2RQy/00MQvZR1jclEO0803cKiozj1LGz9APlTTy3X301HnIGCfgaa/P25goI//srjDAp8MXDiQxR75d4Ag72pK76YgQeK/f6rT2/we7pOwesPHpaBbz8q91RKgFAyHhzk/Xoq6eCLGiNlpZu2wKfiWm0SV80ijM6f32u0PKp9biZWfaQ+r2UV7lLWZrBVJbWGmarnRtjgBKDrCXnqbt1k4EZF88YYwBni7BFxGWjJv5YDfCMZzqYa+XuXc9ur61kl05wH27Xz3L0ynJbFyGsMmMdavNcyq4Ahxn8D59CAzx8wQGAhg3XjChDY8wwQ7M7rzofEdUqoAYJ8toJU2TqAEIMsQ65hhgjdp3KUfAXOX5I2xoZM5tSDgbvIt3oF9GChff+RJC6xlXse7jS0OeieyW14QDBiMo9YzBosjOt3CV1/VPaBx851e/pw0PTR61enf8zTPio220buT90UXQAZrPo6eKfBrj0LCIAaFLypnwHwxYcvskvDqiAm5d4AAyuzV/W0v2/kT55d4zsOaTGAnNRhSR5AjlUzyj2CuqXJ35+1M0GMj1FIBg4AQZpb4EJBgWUfxCDaQPAgZvq+g8NDxex2tYtrrHvGbW/kDUrcGT/+u3cecMQBKzJC53KcK+ouQmTk43om83YaoiMMupJ26o1KEe6cd+IcovnBme7Zc0KOjAOj9xLNILTTC9uZBS0gGIUNgMb7vaBY4pS8h1Ru3YxNoCFAiERYWcJetKVu+mNufzeFN3+SfOKdXSJfK3ttfKlBuss5o+dMAYHbYe0v5xdQ4BmCI0BwNxazlAtVSlWOxt/RYkOm+y4JcVSzmRxgICpsqO+DgNNDE0AwChvkffnYGAz46o7CB3vv5MHBXrlXwKDtEqOcAF8+HgjV6BYzcPlLpvbDmAFy3m0edFwSO7gwBgYelpBVFCgxOHAxdAT97LIYw2wYUQynGUlOUr+Na8BztswGz2jQzMqs4+0Neg8aMvjRnAGgRueFnuN8To7tMecBuYF3kybPlksUfeUxTg74c2bLZ59J2rprgtYoLjwLXbVTDEezC3xiYVssHJc/DLbzWsG9z4yMkvg8A0FCDysjA4RVx4aFvWzqI8BZWQO1wrYy6ycvI7F2dg+fTGuAtWxImU1pk/sePvqFiu8n28Dgz0DBESC4i/G/RI/dZbXBUSn9Rbed4Zfj5PoS7QKCbhEvuwd6MDAKFZj+9Um6+X6uPlYyKNApibPSAt8z4ODeAINZN5l5A/ZBpD9phCrefzGQGeUZ1ZbRW1GCiewY7YOE7B0ZSCgAQZ4hSlwWy7FORll5L9CBaYbSBiSVgcjcMwLyzPlA2luec1TuoqxGTELFGuR70/AcH4bwOQte2VczGgZsQT50B4/sUg8MGBtkOe7uezJp6kVKP11r/Hzfh4DSZ1JTx+CosxA0HOCOpx36pf2Sse97oWmYpOAAKACBmLHC+hMLQBj0IysvO2dmlC8z7sf1+QZQRsm0uU/tMFxnmQYuKmGa9yTsJef65CQ6BeT2Hi2DafWWkGjpR+05cq/LwcElTo71m7votRYMAMXoy3FtG6Jmf71Al53r2YGzYMAebWEuoACBDFI8U9EAyY+flymJe6BgVo7EfG+AwSXD/+O8+IMsE1ndp4LrPiRKlXe4BKX5CWXJSldscRxWeObju8Tl3hQJYM1cTQxEYHE0r4GEBZS9Oasnay/cUBpylofwMrxqX/ZCBqMyylXwStwPRn/+SNF6JdtRd0A3QNHcY7Y9U0oVKGgAQUvHtlSsgTq5Pw88sfrZM0XXGecLcj8uAX6j59uzgqtHbcTL78SYJJO4+x1UZ/RufpnYAKrDX042t9115+clnZVxWZNBtkfeZmdctBorse7rc2Ty7BjTHZjrtVnfzf4Ke+NT7mvPGIU2Acmwz0DB+rHqn9xnYdc5XTNMbr4MGRgDekk5q9daEFCu130DZkD296t1AujAALnzLWegBQKj0EC5zgFkTZzN9W3e5ePnT4f262Us3Wzl3gCDURl5Cx8/v8G3PnxPlol87WHPKJAh0bowm6E3dI0KLISs4FVxwq1vzahW0fOJYIFFATCAGPV+TFgAbDrHGxDDsyRAk/Jl+pbeb/F0Xyx5CQtebhLQrBzYgVy6NQsGsTt/vxkYsHudTe5pf89YJO/oMs89GA8KjuL0R9R8m7A3mup3tnjd7D3zmVden1+3zQxMxEsR4R3KNA8ErazKiV5eflZElQuRMDz/bMmxX2MxHPXr12aQMIeE+yz0YfkRpA6FMYgU1DCnBty6MsqN6PWWvW85t2IPWEN1pKwoBqFNVXL2GOvXi15vTOViIUfPJDSNZszmUfH66fMyRmcdkFbfXMIK+IRXO9ayAS0I6PIM9O/SHLcSIB9E+taHj/D4qwUUlDbXdhnprAtBw70CBkefMH76rP6gRD426L8ZZZs882gpiBGwuDgyKwCinPBlx2w2Alf3o2HGeMj3MqAg1MKiD/MG6Qo6eB1YAJrB9oJU9d5Uw72yN5uhp3v75+1Rr3vMQEvflWccv4flOADtrAXPHGGXh2tBwWgBoHaKX7sGQGXYTrRVbezVWDnjlQ0aASEU4Ips2KCLCNEpbziLwod07tBPZtntM1alrBXP5UuOBrY6GY732z4rd5FvLVvSZElUazNY8iQpWI/aH2LCvsGk+ueoP+/1Zd8+7EGRXpIZS/ShTcAxlwA4sDKVup3U2TCZxTIn3sDCniMyCpudMUAvqq8ucTpmuQLkrmmBQAYBdn4DAqpwgtvv62HHASC24TY95+bZDX7rZ+UriUBOxSnF0Uy1c1vL5Qgo3BtgsCdokICC951QvZj2VJofw+xOvgqOYvZWI3uFHjCU6+v5zShggalaucyDBX++gYUr2xePFiQpL2AD7OolhxWA84bh7BLO3fLNw8FV/p5dFOQo/8InlDJLnoh5X6SMAMFRsSOD5pgCAwXtFL81eXBQVhwEpD29wZolL3o5hUZuIXlPljODFViBglJVBUjYSoToliyeUazAZUl+IiPfN3XfIAeDVeNtzBpaK8xLYGnwAg56oAVgV7523Vn5moxjIGBTkEWUgUJIhCWgmnq5RKkrJ1mjIarj4EN/MBk7wGveaxufHvXp3f5sHiT6sKac55hQaxNAwptARg5W100RxcK1vHybmrMyLA0gOmvw76KvZiu8nnE0jkAAUNgAAwHteiUjAODtkZVRgmEkdG988+wG7//kEZ44UGD3tWL2Ka8ZkneO2NDiGIzKPQIG5XfbAB8/l+9RP/n6E7z92kN/CJ37t7PAALkeFhw0i+Tuor9tyiIAJDejwa9GZsAAsEEqNGNhFkivKcyCXFsDhoUKJZgggzPfONZvuKcEX6S0Sxnvle7MUdvBD6767wgEVAO2vedB1aockTyQOCeTGkAAhPZlHXwxEHiTdSmwSZuvVi/m7OlmAzYBBWviCgxsug3UlPfoM8a3G5ekpUBILMaqXt+ZAAUH/p0NOCwhVIBgcbS3z8IeTwus26QRfVd879ufRkkKCtSQJsZG+rGpJCsfhkBYkxLj3IQNBqDAZPyi8gUMJACLLWgSGGtSz9mAmLIwG4uizcbI5KTyNFkWI3PslQLzfu3beTTc/TRer1OsTWoWNDdRDiMl38Ks+qZsno4t/ip0USWiVn9gDABsO8vcydtAQOWMuH35/k0bEeq/Xd1c3w3gyg5dP7vBo5++jydfe4y3H7xRnUvuf9tr9TBHFED1AaUzPty9AQaVI0qlQZ46pPV2RlpO8A610uGqQ4Uoo03SnGx51II6PQKVY4Ze/YzHbMh1owCGAg6AHjAACjScImxBg13XvB6AEr8aFX/q5Rh9XvY64iiOVo5Rd06L2IEx6h5du1d8jgijBguWH5IS50WtAKVgY9AAsYQNLNGMmcEka0SEQEiDFG0zWlZMUW6pGDI7r7t240JxmwC8q6CUdZVHkIGAgIIlCkuwBJSliGPovnp4ZLhMzh04G5RZn00AWOl3A8YGFuQrjoQ1Sbx73RIWBVRAUEZGjPSqiT11uKAHBTP2wMvXZLZBjY0zZMGMpF43emfL24gkttLAFhGqRZpMlsHJNZjMnWcK9N7pUclGHkU3WFjTZCMHhbH0Dg0G1wLj/KtZ2Tu2XJivclY/tXI57VAM5HwGAFTXdPUzATqpNUJp7Y7ZFgC4fvYUjz78bfzw3R/g7VffgHwFdCvLcrsXJF2em8pTi9Oq3bda8RXzcm+AwchIPH12g/cyKHgLGRBow1TrpgNuFJzIZNbGa0FAqY+3ZqH6m2OEFHISo3dYufFgDRxIFdkhQcohB7sWGHi/nr49frOXXs4M/06xVgatv89RAo8/p2yX363CMtjFRIViJZWtAjEKBSAQAaT5HVcx6IySBGwy137J1pmxJo1DM4k3yWK4AtUGzE8ju7S0ACAqKDCqe6GQQYH8C9lALQoIloHBKoyByDPQmEZtFeis2DqQZQVGLv1dwW7S/RsLkF0QwexWZ6QgUxe3BAoCCkI09gAKIALWkACVvSRSFNq7sfMXlZYdK/PONayAIicDXUsIysagfP9BBSjfeBgDApM74MCYyfls/7Z+7EIL1gY1cHDX2zl5H3XK48xaAr9qfdP2vaNFgkY5AKOPo9m1vfFvbIa3LX6/O1a2J3ZGbcv184/xzT/7Nn74le/h7S9+SfZnG+Kute938KZoxzKIynca2N79ZL+/N8CgbfCnz27w6CeP8OTrHwj94j+iAlQAwRqIRiMEQI+RAUprt8/VpjmZct3YgYLqmG7bB3CinTMADQCyIpXaUYfs/eDPXXjQIV50isvLmic+us2Mnj7KJ6nucVC9VvG1SaNMlMGAsQgZIECyy4U5IESOiEiIiXWVxwIO7HdiUgAhhmtBKOAguRdJ7JiAcd1LfoH8jsoERDX+gdzfKOBgiRImGAECM1ZBjX4kMVTysSIRbXC5B15xjpVmXep+7HNrirw3peAZ1H3cKVKQjzuRftxJAcJKSZbN3oLE+Dc5ji0gBWUVcj8PiEFDCS9Jxga8TMYWnvGgoACyniUwORMKq2Oy9sZqRFVb3fZKDks2DeFfmfWc6dLPg/uas/Ki5fPQQSPd0AKqNu4/9/zNoURvQ+QFqu1qhsYJW9LVPa24/vQTfPOj7+BH73wPb3/x9ZI569bMkW0BBERJf0O+99KwCL4EkpyFPQR/b4CBb8ybChS8WRrTN6RvxNx4fXhhhuo83bNXuGESfJ5C6b0FJHTgwUITzVfdItm8ooJbjTHo0T7Qo/1ciVPv8XmVbtqVLwdGnk78ljJTPFRWuNQLR0YrMMBBAkmBLcFNm8d5twkAxQAiBoUE2gAKMXu2tAVszAgpIED+rtDZKERYKWmOABfjNXhvK3lmjDNWZpS8sRLPlSpjdbXEGhBEyp7r4sCAfNbY/TWAYFJLGyilfaAN6dPWj+UglT4dIgBJqmUAKUlcPgXJJUihfAFyTbIOwILYAYRbbFiTeuxJYrUJQNgIK6fPRcZFtgoUwmXAK7Mw7m9hai4BX8d9PJ/l+3x3tXM8ztweZ0zdUXk5OqjTJQPw5GVXDrt+O2KTW3th+wCcsRnHIeq6XD97im/8+e/jR7/xR3j77/xdYQKalyJbxEZzoYQaEEBA+jUnsRepYxAsvP4Ar03rcO+AwbWBgq/9S7z96hvyPXagNDqnpnFnqG/Q4ACuP/1EfiTXWDuIt+vyXQAs9Pt96MGBB9LzRqyDV7oGIGIFMHZr9W9wGdEczUA7iNkdPoGKbMnJjj1QSJSTtZKCgCSRgzxlyDzbQISFe8Nlnu26yYeDliSGbtXchSVQnsEAWA6C1LENMXhPtsqOR80QeO/1KobKUH0hRgkneHZAAYB5wwYGIiCeybaJ/A0QwI+rVCnRqk3yD2XBKIjCIoEZHAJiiKLQQsRViMoeIIODNUm7bEFYhIUIK0dwBH65bVgo5q9B3m7AmoA1JSwE3V9kvDdbYSZjADkvw+dr3AUQeIbAtgMKMJCwQtFrBf6jN2CXFOeYWJ8fHa/D//T/RypjQtenCcU/ArMjo9/ahcG13f4zZcB2fOPPfx8//gf/Kd7+O39P7IyGDVgZAWkjDRuwggQigDexDQoICKgZBAq4efZUneYn+MP/7A+n1bpHwIAdKHiMtx+8VVDfkCHYafS2wXX7+hd/jX/4f/0D2ZeaDpbLpbRYDwiqATsBAezBgz/PAYhcoxHo+NsoLxp2GMZCBkbogoE582Sh+0i3Q6AKJKhJVLBAOmWOxEs9ARDsI0VrQj+dMZRpd6MFetrFi8q0xOLBzgzVFxQcRDVSZrByyEGN1KLhEjIwkDYB2ex+p62MH97q8TMyWB7oUgAoFlYsRHCIQIggkt9LKH8TCDFJmGHNQIGxMJRFiNiigIJfkiSFrlvCyrK64OKuma0hcYmM95I3z4ZmPDuQgUEFBjjrr1Mh0DNlNAZHOuHzGKu/quL63pjWP+kMdtszVuAFdD7Qyf/Hf/+f4u1/++8qKDA2IIAsh4BTDRJIwxyq+xmpZhAAMAKun/2lzG7wOXeTcm+AwfXf6Et/dQwKOpbANf6ZDnH9i7/GP/xXf4Af/7v/BP/Oh+8VeqfqhDuJJmdKMxg74y8bemoDCFoA4fZVQKJ75gsAhVb5X3qvl5SfsJ8WPTFQ9rOqR5EpO/YA6sm2IGEjwsKFNUhB0wdI5qwnLh98WiCe7bolbIsYq5UBTow1JWzLeDEkYDwrAfBJb2ZYxEjZlMOZkYpBPO9FWYYYbIqiHAsWIthuFQCsklOTNrGcvJVtMLCuoqwspAAUC1tV2AGuIIwBlkVaIUQgLAIWQlCgsIDCAg4RFK8QQ0QKJCuAMuVQgDAHrLIOuNJPNK8m5wUKEljl/WIyJkIOvbRJhW3iZlBZG+vQsgNRUYDNXe/AgGM8D8Oeel5X2v5eHfsVjMHh+S+oO/buZbec6eOZwc/n9+d2zMwL6vdaH9f3evvf/l8M7m/PN+cxuSttXCWASxo3Z9clCVPw4W+rfXwTOWdiUu4NMDBQ8OsP3gAsMdCDAj+gZqBg0mGuf/Fz/MN/9R/hx//uPxF6Z3StlVHnwoWoHs6TLXdwP/vwwx6IyL/+NtiCPcWze+yOdb2EWh2AqDz9NHu3oSB3826V7g4UwCQGYmNJTGQS9mAzFiEJ9c0KEhYKMuk1iqcrU/HEkG0LupUT5ZXGfaddmz8SqedawEC07b1wQZDjBJbcme1WwgRJgcG2OjCwFiCwrcB6K/0/bQIMHDhgBw7IgQJxlzW3gAhYrkBxEZkvCxAWAQUUgbgIi7CtEm6IVwjxCgsIkQkbCRMQFSzkMEMoYYbNgQRWhuFFZGzrO/gpne0Mg5jZgD5XgxyD4MGAMDJpoLda1vM4rr1bPo+xtQvO/xbYgz0m93RY4MV1eLNDb6TeP1AzAP65XAOBUtx+zSdgvSeYMzgAkNmD6+c/xzf/9Hfww3e/j7cfvHEq2fPeAIMfvvuDavYBgMtAgZUjUOA71QRpWgfibXO3LR2OR97UTqEQ2h3lZ5TVRIQV8B1xn676lZWJ8e8YjGH9PufAZlcHKgyCA19MoQYKRPIvRAEJFBBCBJMyCUyyEJLS1xkkWCIdA8ysH+KKYpwYed5++w0GoNetJtbhwkMWox7Q1zFQpq4Xnzuw3oIMAGy3AgbSBkq38pcT0me/lP3bCl5vgaTgQEMKbIZt24bKh4iAqOyLgYIQQXEBhwBargQIxAXhlS9IH9kiEK6A8Jmcl1bQ+hk4LriKV7iKkotgYYYNMuWzmtEQCIkj0iJyfCkyBjIQIDPyjhmoGIIZGFC55pyNARDo9MyI1rZjn2s5DuNN8x3+tsIKO+CgSozdfL5YfY3X1WfeotLVnZ62REFbPxVoGYC6rgIaitGn8hfIv4fgAPL75vkn+OZH38YPf/NPZB0E9stT/fcglHApKDhTJHzQg4KqOFDQAYLWc/LXj8CBT2j0jwiTpcRCAPtZk+0MiBZQDM65c7mQCjQAk7dxIZA5oDynoZLdi5y8LWbn61LNCimMQQYKaQWg3q8aOwMJIJ2Gpwl0CcVwMZfEwsTN9zKiLvKjQKGSMtfVA1At4WqAYGagLKM+0iRcsN1quGCt2AFePxMgsK3gdRUwsK3g7VZAwLYWpkD7NY/GSg7N2GyEIMY+SqiA4yLMwbIg3f5SmITlFdCyCouQFgEvIQLpSlmEiOjCDDKbQcDB5oCYyftlypjUyI9mFLQ5A8FubmBAQdRuAqcHATs0913LYeLiIfgYG9p8dBvrs+n9Pi/dhN4Z6/rnzFmb6GSMdHIIYPtkpSUAqg7OBIHqQTZkaYa/rf+d9JkHEQnXn/4VvvHR7+JH73wPD199Xc/pevyw3BtgsJ+QcwnNJqjs+r/9r/EP/9V/qKDgSwBYhX7yXiNQUH3ezcBDM6BGHXEbrJkQ4viLJTTAgzNg8aLF6nri/jxB0rK5h7L1+g4YtIGWk9+RnA44R+3lZ/rwjKMBzUIYi5A0Xk4RpPkJCBFR2YRo0/GCTcdDXntCkt8KMGDYVEl5nC2HPXwV/TtacGjkqQYgswN5VkEbLthuRYvdfpZDBXz7WWYJ8j4DA9sqSlaBAW9bRctzSlX7UiBpVyqACnEBh18KSLCwgrEHV7cl1HD1CkARHAUUdGGGEBHiFeBBAhc5bwMZ+5UYZzIefSDHTyUczSTogEDLCvA2BgItCNijvWflzoBhTp3PvGrTbzw4NjW2vlygQ+5URvp0Jr8ZCGhLiLVOHjktAQCF0v8vMfZ7yaAnc0KuP/0E37B1EB68ATBrzoFVbr/cI2Dg4pkzuq29hILO+TSKRhrw+r+V2Qc//vv/tMopyOAgP0hVgIEAonEcqkOnDShwHXIYZhgOMO2YI1bAFQphDCwuKC8c+gD6ge8NsB1rAAJv6ynQUNX1aODwkRcDmMklbKimBmEAFkZAgUL+TToVDxRkCmRYZBqpXs9KItq6CQAqoKCk87SYkQLQGaeKss6hgYQqedCDg7OAQEEBb5JrkG5XcJLkQ96Mlu/7jCVzUiRQkATEcLUA2yqgwO6tvykueRtXr+Tz6GoTgJAiKFwB4bYkK663BSQEAQ6schZQVhZUknrOgVfpCTUQsAPB2APrOJyAxCJbYwCOGIEMBAYg4ETc+y5lFitvjT/nnzuM59607YGhfSm65MJy+My944PnVxomRGR6P221npuAgW6WWKtb7ClHCebD64DrT3+Ob/z5d/Cjd76r4QMLOVgoYxBCb8r9AQaYd/hyggMAAHzSh8V4rn/xr/EP/8s/KPNI8z1rtqC+zoMDMVosn4MDhaDjPkkn2+mEuQO35+x5ANt+p5dw1snB9ZLilPV6HPbsHsjkQZ8BkudvlXJjb4hTvo5XT9nVZQYazpears0LiADIKaFNDkLZRyUfgQpwaOft+5kPQc9jDyz0aU1t+nf1dVZjQ1sqv/OUQgcGNFnQGALiVBn/HDK4/eWYIZgAAmYFBpM+ztgkfMAEIgbFlD2qcCVKlPR+HUBISZiBWIBEWK7AdAuOV8LWhBVl6qMmLxIJ8GxlHHKA4Lx8tT+IXIuMZ4mCp0HAJFdJjr84EPCFt61/31n+Ezf6aAQCdF913SX667C+kwO/Kp3WXh4cCzAr7ewb+x2jgoI6VNknjjsnA6h1wojRdNd2oKApOdx94EDdG2DQDaBquxiVmoopgmUKuH7+Mb7xF/9IVpz64pfKfTKrQOW+pMkkgLSjHeMkjt+yCELbtjoCYY/Mc1RZY6ZuBJwFEKfLHQfHWWptVkKsni3sRTHonApYygPIEHf7bDcGON+iCe2QxfNesN6De7alAh+5T9UD2A948oPdAIM/L4MGICc4Av3sFAdq866KIZsYpmzIynoExAxeb5FaQOByCHhVMGGAICmISIy0rkNAsNc/SavOgSuAIK9G+d0oLjK2nGHlTXMQFCAYaAgG3nT9Awnp3Jbcjwkw6+Q7kO1Qvq5ew+TAUwCA63tj3G+5rc/nFIc/xQrMwMDg2q4PvBRdUt3wxe43KacMft7WOtm4DbGEx+xeDhBoi08AgdMdBmTt2IgZGORgXX/6SQMKvNLkyvYdAc57Awy6Qs6IAxjGVQiwDnb96Sf4xl/8Pn70le/i7S++XgakGX3ADXgUCgmu0Tg1AIGARcMLumIcJ+hkcTX+LsZGChCywQTknCa3ISe0XAwQmnJ2sN7Va2nub3epDHpqjtnJNjiMnvMysfsdIfdLyjRhaeL9uvDMLNwRsocAtF4CgGKs7PiAOuyM1l7du2S1FiAUMMB+RoECAnjmgJPsW2/ldwMKXrjvda+gzNt6C1quwFh1eLrlh/KiSglY1wIQbj8D4iLMw3KVQUIFukZeGAA6I9dWtvlY+T1e3KkY/1mMPp2UY2HXXj5I2A1felBwCSBodctddYi115GuesEchd1E7ZblvBgQDBiCJm8pOwx2v2Ho0j2zKkFzCn5PcgoGTMHZ3AQr9xcYACeFEXH93IT6L3KihlwvHi8jugFv8XD9670EOBToFDIjZYBAUM+AE5gaAJA2VXAOmSt1BfQswkwvnFbaM+/8cy7HVNw44xdAPfisnMg9GNbDK+sGfA0pVStthvNIfJMZI1UiXo65W6byAESUk6fvIRf3dHQ1S4aV8ufUrz2wre7vVkCBGeET/YkigfPUq50SQj7/sKSkZN8GXgFaNA+BWfIOtG6WR8NEctwoXAoIlWzHMdpp8cZsYOwB14dGfcfCi/59jspkanI3ZtoE3kv7vddT3tGwcTBiLVtms73vSwaLp8qFgGCqd1plGprxOgMDeq0HAwAKIOjAAIABe+jvzaGsCKoPc/Xs+6zYr98tiYYvodxrYLAbR7GYzPOP8zzPhw/eHGTXBlEChIpK5HiVgcIwlkhRE7CK18Z2ziL1CuZJGEhwyk7qqF6NBwq5egoWgB4wxAHvvienvYMW8niR4gYvNQZ+dwD6pMTWsDZGFYCj6/LTmoo0b6rhHmCsLHs6dCfWms/ZAVkh1jUwRaDHynk7lCF65dbV0wySr6+jgf1CRGyAlOt1COTcvu+IvDeJ46cADgmczLuxt7O+31xbATjr20H/kbt/U1LSTPANiJnIk+cak0TCwtl0RgnxUZ/Y2hrUI1nKzrou0DZ7gX5QV2IwvsKkngMQ3Pd7YLfvu34PxAxuukiKr4L1oRDdynp2rK4aW3vZdcCL6ZAd43+KMdxjVkY5T/6ZHgjY+RNmAMAEDDTGn5pzrI75WVeOMXByG7xHZb9efX0+u+ZCxubeAIMpCNjxuq6ff6IrQv1A1472pTACOdap2wCAsOR9PFqZrAEKGSToPTJQoADtToVy9IrdJwBFba60aYrDsTI6k5FbAYn64umsh6P77i3KdCc07u7RDcTRM/I+FwbqX0Lvww1IKLervKjq2oN4q97/4jLxXMrhcCovfZoMZmAAqABDBwrs2Rr6ktk7wjjkZEDrp34hrxMfe8+5BHBAQA07oADCKeBKBh4c6LQxTgoiN9TXNdeOjMiotlNAWE44esW+VHVKdX3a/t9cQ94YdWBgwC7l6+d9n32ITB2VnBMVrb8HcVg8g6CsJphz6LNlNbM+afTHxTpjIIthOTMzq7tmAs5GAH2aRAjAWOIzrMAQCBTAkHWa6fl2FkJTrp89LfarZQoatutwtlZT7g0wqMoIDLh9TAHXtnb01x7j4WsPy8pRvvjQARuFaIzBUvZnQKAgYLTC4hAoAB2jEJQ1aMFCrk8zwEbJQnlbAURb9tDjbJGljpWY36It3cC8AI3Ls2o6uGqrM4k5bckyi1VsOCtLDfvwhqwk2Qgaz+Zko+pYntbw6r3Loy8L21CjwMwT7soRpboT8811opC7O22bri/gAFCETr/LF8q5A4V/xBiUeg88+cZg5qRMDyK03mS5PpvMSKjk26z10fX6k3PcX6jNmj5OBra0PsXguDY1T7QZA6N4NfBy+r7UJwGLOiomB6d7LmU15bLSDy7RGXLPybvs9fWZAWyvmYUid5wQM/aZjdrLFRiBBZf4Wn0ht7FRHK/6d2na9fpv/rK2X419ylm+8rLlwpOg9v4Agx0wwI0RuX7+MR797Lfw5Os/xNuvPZRzhvf0A1xlrZu8fKEImbksS+lAQPZAZ9ni7nx2AKMCC4CABTsX2TSeXtZTdk06xKyjtEAg779DPsJsUO6EBzpEDvSI29fsjvF41tkltt3mhQDIyaO8rfWsku6+vTeejYrzxrvlgvfitsBQEZJ/x5Z+7+jyfY1cHzcQpm3RVWjSX5qM+lP+ySwuPjFuR+/RGXXLmajOaepfLVs+0AIXJr0RERi3eelnAM6zjporgPE7uiQ2issJihro+v8FeROVcwI4BycVWSijCbjQpzu3m83Q6o2XoS/y/glQmPaXEYviQMBwAbXWdtSJwlMQANRtM5j9Uq5xdmnECoRllxW8eXaDRz+t7RfIbI1jiciYPoxBwk5fuUfAoEdXI8FfP3uKRz95D0++/gRvvfYQjuAHAOwxoY4Bla9dU8xrpktpjD0w/PRzSVbcCUFAFdUkC7owk8WnAADv0ElEQVT6CxTwkKvSGvbxi51a7GRyz4vLJD6qe/I5FSLP140Q9rFSzItYdQfqtrJ9bV6IBwkhRsnKt6TRZEBNWyZHHQbUs30/wBTl3vSwWfG0rFck276HzVuqjZSc1Ny7Vsbh6pX9uhx5pncpB/3rcAqcD4u092tBWdsO7fln20KTHW2fATnSkEcP0tRwaG4FQtQVHAtLQDqzYjde7cfAi/b/artnMwEMGE05bqwmcOycXFQG/evyRc3GoZnRddO1BIACAOz4LCQwYgIo1I6pu392Mpu/G+Z26OmzG/zWTx/h8def4E21X3I3dWLM0VEwwCSMWnnT/x4zBj0gUKbg2Q0e/aQIdWtaJHf3SaNsKLe9TahWQQOQP9FKFJsx2hghAwv2MGUXiscyCEX4inng0Fa4G5CTl7F7uF2VAjE6q73/4B6nymRQ7jIAIwDQIXh3HAAG01L72vfy3M0LYfnyHSMBV6+AbGofFTqVsGZwcDj4LHfEEtkm3lZXLIYOe6z+ZpWFJuJZwp0YJ5/4WSd2jhJBd+PeoxUqy8336z4qo/6zk+BJPp/Dcm6856osjSRGuuv9R51stUd7ftsGvg67dVegRQGAAUUcxrsrUKBLOttUS1AAliUDgpydvhev7sbABf1fXjzLYu9Lsx2jqddUfw+ck90y6z/eePtbj86f9sEdgHDgZAxDAfq7YgH02tEiZd7MZA0/sT0AcDshWZ4+u8G3PnyEH3z1Cd589aF002x7ypuSwYTMho4Awn65N8CgDRfYgDL5Xz+7wXs/eYR/+bUnePPBQ12v3gNli7OVMurWFkrbknyy1QvaL00LaBdSSiGQojmyxnNPUzROfqBNPNqqZi3qB3rv4AKjzrPzXwYwsDKj/GfGv9k3Sta5KIwAdHIroGwQ7lHjIQNL171fhCOgdRVDrJnwpMsHE8QD5FWX6t1ZnbIDBUcydd5/XhQyqVgMHAA1A0DN1wxHXqsem2VdAwbm5izNJR+y2v/k7XiqpZ1DDgTYLAtSFscAgoAD1PIcgIKL5W/vbiCM0zh3Ip8qsqXlqsjcvgMRJGRggACQNhDjEvP5HRCYeajAZf3fvW/nmNh5ns1s9sHrzF3n5EQ5AQx2911wj2FOxkC/yLktc9Cw0Q0A8PbeG//W6fRhq1a7rgN9+/PnN/idD9/Dn7z7Ad549S2sicXesFaVUYGEMlKpsAgOIFidZ+XeAIMRQ2ANZaDg8Vef4K0HD/Ura9I4zVA4bDgDc2vD9ZBLRquAgdZmDBoEMEAZhppUd6BBK1YBB19Zt0/CFxcY95l3ewEYOPxKm7+8HbxnBqadd5A7UiuGUacfKC/P4HTsjRmZ5IzQBoRFlhc2gEAGDnRlPV0XQNg8/UtB1/G3aVzyqpQab7Wt8bbtzlHvjJIaevvEMcWrGhCYd2pgQA2W91jRUNg88lqbNjmdAFf104mx8WzNIiCN8jkMGGOjzAu5dRnyss0p5THJAGjbnOwdkKbw4vJ3QCvLfrkSUGDydn+zzONSA4JQflv7yFcoIyog0NLUL9r/q+0RKODG+M8dEzn/BfQBcBlIADBiSWbnT6f/jXRLJ9cd739i/Gf2xY6PyuZsCwH45PkNfvfP3sf3fvMDfPnVh9kGhZxwiLyCZ7D6UAEIch+CZxCOyj0CBj0gYADXf3OD938qoOANDR/kL6zppQYS7NrK5k/sY+8E6omeeSW/i9F+7Mafbh9pAQpokHtEuM0GPDQVHIQHRgZ+d+DOjg3BxuB53bP41HoS8nvH8DehhoaJO5knYjIFfH5IxeAYe2MfwglGNytIsAWAKAJBvz9wFUHLIp8oVkVOOo0LyipgvdUs+VtgI8n2TxtAqZoKWI1ZTs3Sy4XyF7GEOpHTPFBNfJsaJref3EqBFXXtWISppzqiUdu29MX1IYlZ10Zo9r0B9t93AANRQjtkKznGBbZAU5a7BwhAibVvBMaagRmg4MyqnpWCGsVmJoTJfwoIHBgzloCWpZa7AYL8LQf9rfdjAwbuA1zlPMpt1HqowH6OFJS51Ber+r9zOMvdco4NTjklzdXymKPxn08cAYQLmQHMgMaIdehB1EynzDz/lmXeY6DP2BQrPpL1809v8J2P3sd333mMv/fFt5CYLc9QwIDZDN1v+4Krq2cQKoCw0yz3BhjwoHENFPzgq0/w+oOHSIm7RqxQnRtp7edYW7s4onvsAmssy2FofOG841LgYMesBH8GAZZR7pMkq2n+4xofHh/vP8co7OupdhCfH6DAPsszenYlllbWBP18ruaHhEXukFf/2wpIyB8kCqKs7dsD5ACChRfsC4EU5D5EQOQ5QIgYx72Bmh2YGKVdQKCfL87MAEUk/baAv1cxQJoVr4DBAwcPvutxo7If4MsuxBZapaWtpjkdUFAggEHzB/RDUMYWMG+gsIB4K0zCtgqLYADBAwULjfhkUJN9SrJc+Z7sGyB2V0Bg33SoAFiwY9a20TEGY5mPjBGw3/flfQYMZj6xZjL99UOHBJg6FMPxfzYXYHb9zv6j45UOcSdVeHxm3PeOdbqpPt7ee6+YbfmrX9zgH330Pv7oncf40qtvSXs7NoAVDBARkuox9gAByF8Jyp8hgUm5jLhRuUfAoPxNLIkaBgrefPCwYgmGDAHXjTkabL7zbMyHjdwCVVuaxk81qwceV99+Bzx4cN7WACTI/Zvn71evq8v4eL+vAiTdw3raNTSntoMol8FA3Ruk/rg/Z3TrrpoDIEYA/Od1QyBQWOR9YwMS0iLGyliEtIGpBgj5uwTbCoq35RPC/q9bWKhNkBu2jEsO9LMPqpCBxa+bWLawA6EyQjUYUKNk28E+Wawgl4UpY5b8Gg+sZ4qxqvqgb3uZR/0d1WDGDBpY5SzTD9tPR7NtB9kOy1X5yJICAqy3tcwdOJMVzxl+psJU9n7mx0kg1rMyMYcLMjtA5bcHAxo8qXKiWl0GXNr363E9Cn3mdxwc724AYOiUYMJgzMY/xiT3kZ6dUfKjR86esyfHI6M/0kGz+8+KF+fGjP/606f4D/78ffynv/EYX/qihr9J3V8GwJQBAtQWsdNd8kAFDFyzB4BMedyr0r0CBtaAT//mBu//7BG+/+4cFLTKLIGrhvYNXDe8/N372nFmKRvJl2RFb+TrvwIObN8cQABO0eaT5nVqB2wp4+4xZQ8GSGEOHi4ve8gdmA9WufZYOQI1OAjGpcIMFatxAigh/wYRogMJnNxnjCeGCkmYgrBcgZdXxFCpYSKjue07BeoZD6fadS+g0KxNKByFC5yXmlqjpJ8rrsHAggQBApL8X0BAYlYDxZqnw25MoY9e+kagsqOAMAfKNN8mgMT+OrAQAiHQgriI/DmtPUjYbsFJ2JuWRSifbi5yz+CsClO8XLnLOgTCtJwFYgYGMhBz+qoKgza6ayTyrvrudxgcqBlMuducqby8nDX6M0N/wtkuzzo6uTk+kuGRwS/6affW7hk7suMi9796/hR/8Bfv45/+g8f4e6++5eRRdFUCIzDVBt+DAZRtszvGHtiUxwd4bVab+wMMLC7z9JkHBRKTAS4HBXUsKZ+Kv/7F03y/vLP/6Rdc60plR1l36MXBXVuzBM09nKK1686UI4Zgfp39cnVqzrkEqByWA4P/Iii9VnKs+ygngAYAq6LxQPI7AthakBAKazBiESpDlePiKX/dkDjJ1wTVMHHSjHs/Ba+te5tT0M4qqHIHJuxABgT6W5mB1YGB1QGBDSwAQceCAYUtsbIHLKSHY9FG/d+4JCIgEoECEEECvjRdIYL1uACCCEIIjECEhQpIWJYFBAVo262EfhQgtCxCJ3cDZy9L7i6Z0MAAQkSy2QV77IBuj8BABcj0mOmq1nm5pM+3/d4GVRs2kPP8Tcb3PlV2DPHgsOy7BAlU15278EhfVDpmdt1JUGCl6HZXGrn+wV+8j//TP5CcAmYgGXBTQ29NZuAAJPZsBA6YLcgu+z5Rp/nxV5/gH//4D6f1vD/AAAIKftuBAnbH2nwCAD3a5rpTtaDgr37xFP/RX7yfd1RAIPnrLu/RrcGOjYtP7S332IHdJ91ttM0ZBP+7vveoHmeBSSvDmfLz7TM6d3zzUj/P4hQPlh1QEFZhC/o7UAUSQogIMYKMRcihhq0BCRpuAIPilc7J5+ozyDnODch17csCjtd1q+S5aYX7RumqeKgKDjbII1dlBlJibA4MrKkAgXVLch4LcFi3VAyYAw7SBn0/yx+YVsNPKksiwhIDFgUMS5BtSowIxhIEEKwgxMD6G1gCIYQFMSwCvrZbcFh0OuPtAJxdFZDgpkFmuStr0M3q8TIHKrnnMIEmbKZmZsFeqICVHUipBgOWC2XsjAcCnqFxXfmiPg/0/b59VTt3G4QwDx810H97RnimLu+IC3bLrpwOjPzW0BAvquejC/x7yf4fFRTIM+w6/8ySdOjBQa5zCw5039NnT/M6CG/YZwAm5d4Ag5u/kcUf9kCBlRPfeenKXyso+Cf/4DHe+8k7+f7WWez+WyPu8zN36uvWQR3jAWS/hA0406n37uc7NTAADlPqkcfn75S2piPEfjSIZ8XeIxMwRm23QIGV6k6oQUION4iRCkABCRZqYK5BAl8hf93QDBYzbDrecCU5V7p1BYyu9jMKZkYpXlXswKYGfU2MxFy2uQYDa2L8UoHAqoAhgbFucl2ChhkMGAz6ly0AFkjJC5AwAZEQbhOWIMaeiPCFGAQgOJCwECMmabNAhIXhtiOWJQrLtt0Ke7MDzrAUBueuMgdRAWBulkIld59YeAEY2A3VNH3+kr7u3240BoO7VbtOy5GpHh29xLvee48X1VeX3q/V48AlurwuVa4lc9bjFo6OgTJxDCCDgktLZg1QAISBg6e6DkIJr+/f694AAwEFHwxBwV4JoOzdSL+iqtMQAX/1aQEFo0ZrQYHvQFtTgdMg093D+vv6uWBoKWcpSF9ic9APAA9iDtmQM/VrtmcMzaXAzAMwq7NQ2yQsTQMUIu2DhBBI4+QSaiDInPycoGgL9LB4rNV0PAUHbG98NX6BMiWrGKic2W6Jazt09ZbEcK8KAjau2YF1Y6yccLsaCEj45cbuvPKvAALOlPeeghcjLiAhkOYT3FIBAYEQAuG/WxO+EAlLCFhCwtVCWChgiYSYGEtgbCyATLZJWJxAiPEVhAgBXTnM40CCsjpcyVzrvAw6DGXiPW93MleAUM0osNkcDgzI330wYMyLBwKMEroB6n5+1Metu6yKCmZj8wxwaMslXvVenVs9Wd9jvP+OUdGLyl3CGW29YnLyDiKHqHbm0uRvP60dGIRvm8IM3Hx6g2/r4khvPvA5C/OXuzfA4PvvfoA3HjysQMGsBI3JWGzfwAGjgIPAjKSg4D90MR+7r14qv0kaOYKqzu87u+9gF39W5AXwwNFncPcG5Kh4MHBr6tIMfSpx5NuGovTXjYzxpWUPAFwExvQ6IgFeUk+hykdAIZFnExiRNUnOQEIaMQkRFKMy0XXIgXfm7w9fQOU4Wqt9NM3NG6VRuGBNnL3XNTFulSH45ZawsjACt441yIDAgIX+/myV+npg4FkDYwsMGADAK0tAUNkuxgI4gHC7Ea4iY4mEZSN8ITKWjXAVg4CCxAhBQUGChBlYwhMCMMZhngzOnLwB9EuPO3lnmc/Wc9iZTTBjBjZIToblZmQwMAECVj3r32eGLhEqhRMpL94tgMHrqO3Fx+JsHM7030g/vYhOGhUKd3uvu5SInv7fmKs6er1ihfJ/fXJu3udAgfRAt4Fxgvknz2/w7T99D9979wNhClBdMi33Bhi88eBh/r0HCsygO1sGAJrhyeUkEP71pzf4gz9/H//0Nx7jS5oIEt2NiIv3a9O4IghbkEEciQ47+cs23N39J9efASe+bja42vUbIlAJfPUDwDKaA+Xr7HzPgowG9onF86pyCQhrZU6BMvhamXMdV82ZiEQZKLRswgbWBMUaJNwau0AKElCzCQRloCGeLZjLB7fg2m24IICBguLB+jUGWqOU1Au1ZMKVzfvvAcGaGL9MaQoI1pRwuxUwkBi43RJSSpkaNUDgvUcbJwUgAL9cA65iQKACEq4ySyDgYE0FDKzM+EIIsi/IvoUJSQFCZMKiAG1laYfgwjwEFHDm1kqwTsOXyDuzBlFyAJzMjRVgOFCgJ7RgIPGYFfAJnVo9kWlTtWFfrk6oJxB7I+49WUDG4ssYdyPDX59X13mmi0b6Z1Qq3TIoxEdmcL+8KPAoerB3NoACCvx00RLGLADCkqMBdLOp4K610oKChPKMI6tyb4ABcAAIzNPi+kMSHiAEFAPx8S+u8Y/+/H38s688xpe/KFMeE5XOHwCAyqCWRiZsiWWKtAbrbPDlMIMO1JydGujOqHkvTnZmsJVnHT5qetIKSSSz4hVL+Ua4GgOSpDE5T/ehDOxq/G3HA1KqNWcHTnsm7t1GdZQ57mr8iXC7lUFO6uUaSBAAwDqzgRCoTImMur45pTJnXwa/IIQQVCnY8w/evTJISjhY+CxpfNpmE2SavwEEKxsIEKO/JsZniXM4Yd2AlVMFAj5bCzhIChSMOYA+E6iZocWUmAMIMcj1IQTcboyrSEgckCKwJgELiUOm2TeWBaEENARsrGwCKUAgxqYAIRArKBAGJyhIEHmLQQ8xZnmfkbWXeZ4lYJ4+12s7GBBo8wVSqkMEawMEgH52R9t3R7rBxsrImG4oRpO59mT9tXL9+P3PjDP/7AJouDqnP2/8vEEFhru97mnHbVvZ+cLWtffeP7qXU77OPcvPuvHnm070jgUwBgSA5d+cZwjaWhFRAQW/KaDASnaEp28r5d4Agxko8G0pg6IWY1HC2pFJkNbvf/Rb+O47H+D1Vx9KmIHES7RYZAx6P5ZrbKAvQT6XSkYDWkypAQjRD6zgB2bfCS9lDY68Crlnc84LEBPV8zbOg9XeKw6UQ0zNOa4OMwpuVFpWhhx42wNgM5lW+zfrE6UOa0DFKAzZBM3aCsomEBWgQCSs0i0BQRcs8YsqAcj7vCzakgkFU8CoPdWkYIDhwYGcU4x+mV2wFzYwluB2S9iSgohty7kKBgpsmiNQwIGVz6CgYGMBCTapGkBISRccjADkGVdRTkgBSExIIelfDSUgYWPCwgELbVgi4QsxglJhcSIxtiBjMShIK8yNG/sXyNreyic1t/K2/QWkHbMCMyBwduy3Y2VksEbveCn43gMEI1agP6e+94voHaL6fqZ3ZkyDB0hny1kwYPXx14wYgnylsQGoQwZ2vbEDHgy0QGAGZlqmYFSMVZyVewMMjop5+KOSUIT88fMbfOfP3sf3vvK45CywMgmEPKc0EoFRWASbFmIKelEGgQFQ4ry6mlXBewcL3ABpEoCAYkR92bhPXLHBaJ3VG0V55mWjcDTlzIpPeukNugxSTgzS94lav0uSbfaSG/OzU39eRZc6hevlYNfsUZxWCqMhmmh1dVuDhg5ycmI7P5+cZ2BAgCuakKj0i0IR8tQjkKNScpPyeGpb9lCdd9oafZtlUH7Xx83oCQAooYKzoMBK0vyLlYElcQEHKi9hNQhILGwCE9bkCdB8JzAHcJD34RA0VWDLsxqWIBnZkvPRTEFN7cJWfLGc7VgLAphrRuASIHCmL1qpkgYPAMHemJJrBu/tdI4H3x54z8oZUDC6x1l9c6a0rEELCEZ66AgEtPc5YgYA7IIBu0fLDiyOJgilm3aJh0A/Mj5WUPAn736A1yeg4Ey5N8BgjxqZdSlrPFsI4uPnkr1piYwl/6B4BtZBF/KLSOhgUOYgcAEJAECRVEkQFmjsVYHC4hIWbTBSpSBQBZFssC0YGPvGe15QKxjPTMSWnmxkI2Dn3GDsvRDd79gAOU+3Xe6BHW8HmRx395zVpWn4Dexkg9z4eTqaytPeNzqA5sNNo+L3Z7DA1AEFScIsQMHeZQQWAG6URUnazAzCDpgardCZjVNjmEZrEHhQkLhQ315Jz2YZBNNqarsXCyHsxFx9SMESD8Pk/bbECNqZpF6U/xbAIGZ4Y8KGkD1n5prBEQXLTiGPVxjdk6/J1v6y2z8DAUABAjMP+5L58CNA0I+z+ricU+5xKuE3zPMRPOtpgNvAto0jybdqjxdwYFXzr36kb2Z6prxjD4rkusa4dw7HEYjor/UgwD/jbN6AXVMxA3lsFNbQ64JWOv69zH79jtovYwr2etaetO8NMGiLf+nR0sJAbVOePn+Kb334Xv4KI1Dm9DLKF8JMEUSdh511JonS8kChAhZogAIAsDAKI7AAjLN9FzT0YqRqcAnQgLvO0WoecLiX31A6Qj7fyWkvDnhmcI4AwUiJzQYbgG6K46xkWWoFcp6DHm/BFytIWFBCDZX8PLAaPbBV7jobwcDCLYQ18WBh9L4zD0MfAmAMfisyiUsfm3mpBg4KUCggoJpFAO3PJKsSImkbKM2/kaxImFJCIPH2EeW5S3OvfM9Gedq9QwgOJCCDBQ8avNFILPVKkCmLSLII0rolMBE4FGq9DfOA4UCZvWtf146k4/LHr13iQb1PFDwDAvYSgGeG7QyofhnjCFCm094j2NhgHSv2uwbbGWjb84w90OcuaJ2Zy/QLcA4AnAmryDXt847B1AwEAGMgYPs7IKAnGitg971y9939XoUr12q/7NtAuXidP7l2VO4NMJihqaFAbZ/+vXl2g9/62SN88PUnePiaS9TI9GEBBQYWrgJlj8GAgDW2JCaVKZA+RplQ4un59gBitHWvKR/wjbO5VQWXRum2AMKu25jzyGKGAxxw5/Pk/Jp5KM8qZRSvO+vRzOJwQDPg3I+jhJn83EjOkysKCShyXJxSX8xw5uf2IMF7QL7Yeavbl+eNW2hHK2MzNirAAAEMwFzByznHCr1lnkaUtYGCDahWKgSQDXxQTRcCA4lk5UECYog5dLAlCyOEbnpiGiwpnJ+hSwu30xdjoAYQNGsdUJnm6BkGM8IEMUIbIOAs0DRp9FJ5tjK155pcgTkAOJPvM/NYjwD1HrgE7j5+AA2HxqKL8sK6BopCYTs92PZjyL/vzCkBFMy77ensggsN/yyEsjeuZiBqJkvbPwIBeT8KI+DDgz63yNciag5M9byd7nqjK/4+/toTvNUkGgp7ozsuYKbuDTAAxsyAzy2oGkXPu3l2g/d/8ghPvv4EbztQAGicXC9g1ANrCVQbeUctdvscwNj7gmOmg8n2I58bM6XqXshASvmZvba8v/JQ3P5MZ5Tww9JM6xl5NMsFig0YG7u9KTvAeND5v/6ctmRGB0WWrRwl/ksVWDCgYMBq0+sySFCadJRI6cvG6FygMnND9t8CjkWQbZMHMFZ6cYD3vTzaMb9nsAwkRJLzAqt2SowFASnIwkUhBSSyGQ66kFEoIMC6QgsMjhY4AkbsQdmXwQAoA4J+W6Z+ioFU0BOogIOtsHw2XdZPrW3X2RjpzDPZ9rPztApjGUyUvAcELRgYgekzGe53GTuAZ0vrv0O2k7gKi+YxpI7GzCkBisy8IRpN/5t5+pd4+MDE2DcbYbC7lWMbhjIAIMfKhmcD2rBAu8+uWS6YnXSt9uuHX3+Ct9wyx5bCAy55cf5OR+GrewMM+hWh0DeOnQsAYFw/u8GjnzzCk689xtuvvg4k7/eV++ZrqGC7JdSDJmmCoo9FelbAJyeVa+xkM/oFNNg9kH8X8OBLfY78XWL1+aV8zZU/1420dllnQEDElX+OAxJ7ZTQ4j6i3Geq2a327+qePYpKzj7MYqyPn1O0jOSAkgMnocAUKS+pBQkkY5UyRtkahHXfdEtfGNLhXiNQAB63sCDzI+fttAYyz2iPcbJDEmhxKeSqhgADRLFWyoYIDV/3qWPV6A9IgNBbJ2s8vlWzn+WNlP+X9xTD0jFT77pmx6avUnQvM6f7ZNLuzjljbXHU4oIDCFgwM81OADAQIZeyMxk3bS2bjJk9pdPvbcTNiOwUsiP5b9IW2xJVjMnRKgF190iZF7tH53Xs2eqQ9XumUpgqj3JNWlrNPiNt1VVsAQxBQXePuHcG1kCYd7Pr5Uzz66fvq1Oq3FVx9zVHtAUL7xL7cI2DgOsEAEDishP8fe/8atNlxnAeCT9Y5Hzj/9hazJoEGtJfYjd1Ye2yLN9y6m5JFiQQEkrsTu4Nb82KQFDgUKXktifLaa3sdnpA1Go9FQpRIgRBJNRrA/HAMRZAgadoSvq/RuJCSZY8ndmJ+zEawuwF5Ymf9YyI2bOJ7T+X+yMyqrDpV55y3uznyflJFdH/nPdc6WVWZTz6ZVQcccfi9Izz01XN46r4v4+wtdwgo6I3wohfJUyjupOPo74HI7HvKJ4huRwYMecllazjZLxX3xwAHHlyIASgVcT2Q8za78+fnzIEEpXMTKLBbDPNntcoSCm+h754iW5qmY+cv1aD9qWYDb+2vaVoGvwEF84ZqkECcWQQyar6RYGVlKdvaH7LrAig1PFG9VkQOQSQveFUm83NSTklwYZZ8VtMIcqf/9WYhbCnBKfZQKH75W66R4UCRu8fae7fA0T7Gv/feW0pthH1irgECzw7Yh6T6M1t0fCzNdd97zMiZ7TGDwnFZAtqenStCo+g7Je2a9HesGfoeM9LLM+s6Gv4+lSxbYeoaFKTnN2xR3m0dLBadjHbfzw/vgYKrl/DQsx9M9ovjJPpInVfSu7cAAlCDhHk5McAgVA3ZAwRgxtH3DvHQsx/AUz/5RZy95XakT7AC28ABtPHS8qjQbQUNChgGHc01YABc0iL3wQFQ2I3KuLs3653vQwMN5bYGKJbYibXSGqBbaTc7d8YeVPepn1PWtZRPUnTI4EzAG5DyQYK8o1+idhgIsQIJQ+QZiyDxVZ0rrV64PLus1xYjau0TQgaD9j5EQGutCCAnem2JxbbKweLRsuw79XVLuZFL1/bWAlhacKcGAtcKfkL1HjW74QFBzQ7YdEsDA5mKzue04taEvqda16MuWTwNx8SdM2c7M9C2/a3w6Pw562UfL15rouctG/dqd/d+fQAyBwVef9nPNQDgVzkV4WZJ0bTL+xvl8OqLePC5R/DUvU9k+0URsiIna2cJoA5AsHovjbYTBwx8g6RG0QYhjji8fIQHn/0gnrr3CbzjljuU83QNBFRclyuO26IUdtB9pI3gWITcQADrX9tnoMETAflvbrJYH7T9bpur3IB1YEGzAVyeSwvP3UNBNgZpnXwDoKvMahao5QW1ftfVNnlGRWYzcOB+Tyy5Fq2P2iSQMEgGvLEIO8wBAkEMTorhQ2QXAiUjs2RrAmVjFELOTbF7TcixdW9Qa0oaKJM+7RiATTFZYHsm+1azvg+k2PejPCkHIGQ52YwTP5UuX5dBwRogWGsvIIOCpU9Me0AwhswOjEOYgYGB5JpAGQjYPhsfM1DQGStr40R+l2NF5FE6LSXIbux3oCHJ7lr1hqv4mnHfx7Dn3+1r1p5b/+0a/2of7NsogDP8lf2Ju64NOnz1ZTzw3Efw9L2PKyiIAGk+DXEXIFgZfLMsDNgTAwyaKM0BAnDE4eWLePBrH8LT9zyBs7fcCbB91U5Jr4rSmTWOBwa711Ov8h+0ScYfQBM02H3ctp3NHlBoGdK+/Gb6dkUpf9PseKHU3IBP0mowBj1QUZ/fK03WIB1bN/5d1F1VgDqDqJbn4OQuek9nlsQMDmwp4bTNktAm52WQQGPAAUvG+xh06h8JqNi1AAJDv6jmhLHR6NSljGxl4++NTt5XUtOw8xw9DfRDPXbMrmvVIe3b/gpNM9FkrYayvxkI9t4PGEVuCICUBEfK/kyBMkBAnsHgV8Qk2s+rLVhKBwiWGAJhBEp2wJbUzu0l9xOQkGdo2Fgptl2iWqEdqnGyOkZMAEB2XCjrkYwTaBE0IB9adF5apccSyN3tnHYfrMPIwDx9oa7B8u+OHWjJs+H5rxr/hQ930XTctD2Hr76EB775MTzzrs/hzM3CFNhHW4jDIkAgBQis77nWzU8MMMidqERuTVBw6k4AUQQLBwgKZNcSnWusaZd6ZgkGAFQGiVOIYW74u+DB3yf9doOidRzVQHfX5I8/BTcC5kCD3f4CGDSAxV6lQt9t5N1oO/sNtAee7e89MrVBCcrIfQhnCGIWIwQs5A/hSI0mAwkkICGyTndk6Hx6wshimHYcMUZKXzIcI7s8BQMJAhAKJgBzuroVfzfDIa+yTEvfiDi1V8hbQjtO5M1SN9UydZ2PZbannx8icW1xiZr5ISzT6Qp2R/NDbMGvFsMDSNu0gAAwb5s1dmCkIO2RDH3FDLi/A1lbUQEEgkmm0nPLxmleikVyGo6L6az80ag2aJC/Kqv6wD6lY9ipccqqIfcdxV/fAkn1vllHda5Rz+j7fT3D30sq9NvTPE328NUXcf+3fxrP/Phncfbmtyko0DYwZ4OVnU4ZBVF/y3Z2QqkCkvNyYoBBDxCAZfbBg19/BE/f8wWcPXW7MgVxDgiKxltgDgAgHjcMc9uop8G3AB4A5HBDdf3s2qKU5+Xrq2FTX195CvVza0+iDSyKJy6UPvpeNf5+8PUGnV3XKl7Zpfqr+SP32VwiDPqVwiHI4sUcSDx9zgxCAgmRMKD8MNEwEEYewC7UsJuEReiBBIBy0mGDsvfNVmfgLwGCZHzQpqaJlEFQAOC9UEI+p86gXvNMU70bY4brvlO0TYaG9s/P9DEQYJ+RZkZidMCQL5rqObPQDwQkEMfMItQAIeSFvjzDY6+2lDcgr9AHaeNAzVBBCNJeFiaw9RoG3x7WZoD4e/6rkDpWWp/tLtpgbWzA6Z5CL/mQ6AbQAJNJm+Xsl44R5rLuM12B8nitp6l+7y5A6oSOF8FBHwyU+3r1698b8ThvU8Dha6/g/m9/As+88zGcvfntKXwgDo78lVVet7EHWff3kdvJAQatxI4ZKLjDDaYGKDBAsMHwUJwwm5U7M8beuHaiXh5ANO6x6H4tfSO1uta25oxCg0oEQGgBl3a9V+vZGWBN5F3E4uZgYTbglgabvkn6vwjhyAARcBAg3+PV32HQvyNCGAASYzQxYWAZl1PIYCFG+dR2jNl79aGGtMogzUECgAQUlko9x32JHWhR0z5ObQaIyAMMEVJthMAMisquFUbItddWoFYZIhRtkvtbAmthyEbHwBqQP2FseSHQvJAKsHmQQArado1ZJkWbKEioF93ptcnSNMNWqMBYmhBIp4iWbSFLRWs4QSQlyWU6a0pAgYKAOPXZzo3jwtqg0D0NEOCTq+v2IqBkG4rH7K8XgMqoL3nm1XVybYsRWKEvenllzXouGPWOEzSr0xKLo0w2iPD8qy/j/n/ys3jmxz6Ns296K8C2nFeKRwJpnVLIfopYZA9Y2IYlRufEAIMkTGdArhUULNI/iZloqI1C0ITZ55BWBs3MaPeu21TaA7LwClr16Bn/pAB6YKdxvS9dcNCRdY/B2ULRtUorvGNTe5LxGTKiDgOIdqLwwoDgQYIyCdGBhCECE4lx4pDzEQYiTCABCcUyxVSsoS/zuxeUxULeQAoVUKam0zmEVQOUgECcJCPaGyBj1wovtTFetraDtoUZIt8OOcwj2wLQAkAid1b5hzACYZCcAczbwgO2AJFxUFkXoZ8FkAC0F/Pa2h5reQP2exUM2IwpnkogkNpnq95qtwO0HYAKrMlG2qaaNaiuW9UrvrSMYsuI9ox/j4Kvz5s/ZOFY75L2/VYNffO6jc/Xa59/7Tu4/3f/Kp750X+Ad7zprQCr8aeg51wDOOCo1y/X58QAg9qj3AsUNO/nUbens7j4y9OyX0GDm7RbdZaa3l/6dUPLPgDFKwn7v8MyNH+3Smtg17L2AKABEJry36gIaRhcPbNB4jAiAYUQEkBAGEA4BodQgARWkBAdKIiQWQcTzZMW5YuckrxlyXJ+ARhemFxsIvdL35rxqXMGzBuFM0hNA8QTaFKDEydYMq4ZJEmyqIABGNhJ1jTHmP8mYK5tFRvjIuhYMNmHIcmZTN7jmNskDNo2ch6FARxGNVKDAIUwCGALQx+wsRn8ktWxnARjdmxBnry09HJ7tJI567BNCwyE4re1XwUGbLZUapuo7SFtYAxCPSbSeNgKCuB0VD0mdF8TMLjfRY7JvuO//t10wvbwvLcA02suC0BxgQHYy0boc57/o+8qKPj7eMeb3iLv2ZJtwaZsAQdy7uGVFxbrdIKAQe5Eh1deKEHB6rXOI50fBDBv+K0DkHfX0FHXGIIb1fkbz6EhT1wrgUuDKmxRwwv3zid3qL8OSGgpvLQev53rl9prGSUgGSYOWdGZQaJhkGlCyN6qhBqGOUigXQo3UBgxqPcqoCCDhER1O5DAnNdJGA0oQNcQ4H4vJLeR8wCQWYJGFvtAWwzQLhsf+8cOKOx24GkH5gjsjgFm8O44GakECpjBNWvXfBE1JsbOhAEUNBE0BNB4IPvHA22XUcCCAgFpj/rfuArYJgbk0w8lqxO1TQS0IQEFAN2FeMg1SN0WxToDKnva0haJpZkaYGAOztKY8AANyONgZQzItvITk7VJCQBoEFk6l6DPMlTt2yxLlH8THMwdMqlvi639wenELc+5Bi4iX1u9zuFr38H9v/dzCgre2rtK6rLKJtfgADi88iIe/NqHcBtu7V51goCBvPTR5Rfw4HMGCu5CHQedFYuXdcEBFft/79VXZs+UzVZc6weJXlEaw+bxlShpmGs+rtet9Z5FpTiAlreRjiw/Oz8xX9Ea/Bz7ICDFW5F/o9MW9gx7BwMJapy48lzDMCSaexNI0H9DGGTZWAMJXDIJDEmqiw4o+C9xLoVCDQTIdmmAfAJhlx1Qwy/hggwKkH6XYADH3wemnchz2gkgYAZPx8A0yTnmxcYo4MG3T6sE8zI9K6MAYBhAgwADGg+AYVSAQMDBG9ogYRgdkzCA9bdviyI3BMihBpq3A+Cn4PXbAUAxo8O3A5n8UYIB1TRlW3gwwLHL1BDHAgiwgQY/Bvbt/6YebAxMuX1AATztMpMDHevaxuk7FO7/9VIKdYn1K96Bq1UQWu93DbquPH4t4do9SssJs2dWBv4/+L2fw3/2I/+J5BR0ZZuZzmaxUIMdZ5kzcnjlUloc6Ref/lvd6p4YYEDMOLxyCQ/UoKB5sg1RR1Nb3IaCzvVUwXIEIKsXHr72Hdx/+Auz280GYgsQrBnx2fkLHb2ntTrXLCkKK1QPjHogqUdVexy8Qxs8AM3B0C1u8PMSEAD6irC+rvWYpAxNQTpvSb3NAigMYwMkDNmwmcdKlrAoBsn+jkBiEmJw4ACUMuuBem54u317n2odqu1FeponmSedDNNxMkrxdQECCQRMO/Dx6yUQUDDAk4YTpglxN6U24IkzQGgUopA+xEMhIIyDeqYiaw5BjL0DCnRwE3D8uoCEYQSGEeGmN0ibxAEcDkBhJ23BB7DQQ5dFsDQJ5ARGA2z7tgGQAVkO6eSphbZvDga4BGcGsHiagYHE2HggEKe9+r0VjtbvY2oDOWAv7sI8ANg+heiBQmrM/cZ33tS+4ve3GL9az1X6bS+9Vk8BrPWbB0m9sgYuZuc7+dh7OplxjHO9CwgouPltKFiaVj6OFVoBCWrLDq+8gAee+7BO2b9rseonBhjkl+6AAjX8TEETMBw4SKUBDmgAmPH8ay8neufHnvvLosxkzllq3NRRU3KIll4H3tf4N86fDY4tIKX1uPrWvuO5zlt05EbMON3nWhRIT2YNQNBTity6rlfUa6IQNNlXvVcz+ESilM2jpQCyGHgYRMnSkI0SUQIJOXZu3ms5BTJPwytXYITua5XCU0X2Vs0IeY/U5wrIrILjIlxggAC7Y3lHZQjYQgfH30/HYMenXQEE4m4CT+KKc4wCekz+rU8LekAQSOUsQCGMQwkUFBzwMIJeF+aAlDWgcUScdglA0DgpmDuQd0whhgMg2O8RA9GM0RluYBu0ZnUkmXPMwMzyA3zoxtgCl8MRjZGpwUDKNfCgeKvHrPpqUj0Xo3QeM1J23ux+ch5P3sBt0C17jOl8Seeaa2Bg07j2Rd/XSss4N0HAkoyb58euHqwBls8zSKDAGftWkq5c2zivehYAHF69iAee+6jaxzuXKTGcIGBQgoKqEIkgVFgmEm/4M6LT31CExhGHr73sskPfIvcg0sYcEhVmbcEx+h+5c9RGOwztzrYPUl4AAnPQsDYBq6qblSl35GKg1V7HtHOKxSnWetD0aLul+raS2nqgoOdxACkOTmFIXoS80zwhzoME3h0rkBhBPhZOAxA1cZEktJAMkybOzaZAkrr1yB+aEY8VBdtaQdZcPGMA8ytW4tVrDMHr38/G//V/MwME8XiHuNslMMC7nQCB4wnMETy5kE/0fbBcstnaPoQAGoIwCAeDAIVxTCAhjCPCwZgYAkw7yXG46d8BTyNotwPd9AawHgs3vUHe0QBCGBwoyCEfP6thCOTWTaBrkn8+pjokMmZTC31YoGYLbFtzNyxs0wMDzUTPVt+ui10ThuypGoXtDZgBK0t2c9cV47OH9fccw0usxw3TX6g881plum3ycqjv00sAbMl7DRD48AxQTQXPydHXBQj02OHVS7KM8gamwMofCzAgov8zgL8N4H8P4G3M/Pu6/38B4L8C8F/rqS8z86Nb7jkHBRWiLRpUh3IKHwDElDsuR0B/H772iswjfedjOo80AwhNXQLGUeNlAhJo8IxByB282A8dkK5TufmrUg+tTw9A1KU3qOprV9AigEy9WcczhSE3bHsdyMoy1bt+fq1grqcY8td2pBDK96bcplzJoP4NZLDAkXIMtgYJ0w48jGIwlf6WxMUJKdQQNR9BwxKZSaA8u0EH9UBuBUZSt381ZqtGiOcr3plXWhiiOqHw+HXxRnfH4OPXU+iAd6+Dd2qAFRTE11+fAYJpNwETy341WhwZPMnfLOO2V0aBwEMQwBACwi6AxxHYTRjGATwx4m5C2O0EINx0EyhabJ2FtRlvkvceRtDBTYgchdU5uElXNByAOChA0JwEZXgQpiR/msl/g+zl5fry1xBlwQzUAKEXKlBQMAMDQL5Pp/9muVcAoZ79VOcKXUt8fWkMXyMY6OqsLfrKir1rZdgL2n5T0h5K3Vz3iyVnZyW06sGAplcX11hOE/wxy25dnC0yr+e1gALgj48x+C8B/J8AfL5x7L9h5r+w7w3bL91p/CQ7ZQyAHEvjmEDC4asv4f5vfxLP/Pivy4pTHMF2fhjkPLk4ZXxiVLrUJdbQ4DqkgZIYBSik/ZhRXDODWlWfC6SfDSSA0khuBRatknIwSu/B7p36pI1xq2MIc+/D3mkLg9ADTbovySAdt+f6fVMCB5QUxEaFqkteGyAiDhI6CDHT1Y1QwywfIQrap7DLAzuxCUL+pxUY7QXWjJMDsMWqdz6BLRmkRlJhJ4/AQAHvXp+zBMdirKbdBN5NwhDsdgUgSLHjaMbLAwNVgUFWaSNlEmhgRGOeOMh3DAAghtJjPxAjSb7NNaGKIePNjKmFG0BDCRAMqEWjzNsKd1X2Nsa87BMwmDJQsNBAJ4lwLVTQYgeW+m+WdcOg2bhxyYRFfo07p74mld44LX73QcEPFBC0iteZLcPdCpH2wEBjNkdxr94zMGcF0kfymqECKGOwAAaafXXeNgIKPro3KAD+mIABM/9XAMr5rze6dO7tKRvycURCMmCMQRIZ9YMVfh2E7MUfaJKVV9LW4Vk+tcwRGEM6p5jy2Jl/z+TpukYIIk5CrTpjJ8bWgEAGFx5IzoxlOrBh8HVk2fM8NimatRyEwVe+EY6xrOgwVz5kMvTgqEGz9pTszOOK0xwgYFeGGhSk8eSmQPo5+RZugFtQydOBHv3P5F2hL6AyUNkwFUlsnMEC++RBDwp2xwUoyMmFYqR4YvmnOQSWOxBrg5BkNwcFqV1CmIcW7M1ixDAEYSICIyACkyzjykHqQxRy+McuJPdpWXuWbtOgMgkRzJMoXPMcdRwUi/M0FW3NO3fkbiBBdcJsnQHLG7D2abEDIqhrAgT7gIHV8blPfpA/7MG8A+XmmCRnZY3p8+9xLfrJv3f9Hj0d5e/T0lN7AADA2xnKixK5ZxQGvg4TqJMxOzfdc64jSrtmifiaaHirgoI9gNa/jTkG/0si+kMA/z2Av8HMFzdd1TBe3SV8E3KzE53AWNdB+MZH8fS9T+DMqbuEDTCPwIz4MJZAQZOXUIEFAOma9LnXBBiKWoDMA6pBw+AUrT0X2GQk7bzEWrhOvTmGV3kOrWmLeykcqgZnfa9WSXI3tqWSwzCK8pFssvwupnT1fA8UaNDuvzZgXLxVzWIKpTRZBMtTcNO9inUSwlDRgA4seLktycSB0MIAWR+tY9fmnc5AwU5A6qrhUWM6kGIi9egTiBL2IxvSed0NEFAIqPMOgvUFl6C4WOIETAQmAQozcMAR4BE0HggYoQGgqOFDkTlI2Jq95W1/ncwBFAmErXUG9k4kdP2ymTcANL3aRSBww8dkxYDWCYJLTJ/LxUopWfUz9tVJdb23OitLAGDB+4d7qy4AKO7RYKfqfIHhwP32bEGjn9Z2jxnPX30hTUk8c+vdiVFbD5Pl8gMDBkT0jwG8sXHorzPz73Qu+yMAtzHz/4eI3gzgK0T0f2Dm/75x/48C+CgA4H+U9zfBQCXQ7tLDAI6+d4gHv/4InrrvSyLUBAgUudvf4UAfuL6mfwYMQA0agAZwCOPsfALm835rhVIBBqoVTAUYaMjnAiiNrS8teW0ZcD2ls5R4sxhf3ygLJ4e8tjyQpoXpsa5CLh5ZDSiTodU2oAQICghqkFAwCUN098xKobvC3Io8SjCgHqsaJkkMzKCgkIEvVe4HDYPQ/UNEwIC4yzmAGCFJgxzBIRR5BpJn02YT5L7WFygBAktCxCAzFYZRZCYzFGTfbHW4XhZ4asMRDMuTCaCBdbGeSt7XIGsAM3nLvgwGYgcMyC1WAEESFs37X/X+xZjyY9KPx2os3qhxaO8MIDOg9k6OtZN3i3O2s9BHoXRg0ns2dE+tj5ZCAluB0BZZUCiYKjmvBUgqA94C/M0QgjEGN+U6FA7DRvv1tQ/hqfu+hLO33p10Hzvma2mVRis/MGDAzD92Ddd8H8D3dfsPiOi/AfC/BfD7jXN/E8BvAgDdTDxDUy1hzhBX2fkPLx/hoWc/iAvvu4Azt51Rr8MnG+XfPBxUYGEODha/A5BfpAIOQA88sF1bAYd9DGV661Zm85Y8hCXqrTXomgOuRtO5HZY6fVFSXsZY/Cbvrel+LwNqKOqaVZDLtrEpRYZ3AgghU9bMksRYgQQvn+aStHJCflDDM5jJQt8dQOmtFgazLBQG8BBB06SLW40aVYsyJS8Q4m4CDSRTE/Xd/PTEMNk4UNltAQb2bmr4/fRFDwhs+iKGUcHDKF4wkaxz0POkzehQFMZ6sj7IYDDA+i2Ta5AxgKac0zj2fUzrUsTXfejQ97M6F2iNqWuxAS0Q4MagH39ZNzaM3ELxKxYmZyZdy3Pmk6MAhTWWz8r16iFgTyBUGf4tRt/vK5zRth2afzQM8CGBBAIAmWlDodSPRdu0QBur/foALrz3PM7celpkTdC/QxMk9Mq/VaEEIvp3AfwrZp6I6H8F4H8D4P+17eI5IJgLNlM+HjMxgKPLRzj3lYdw/n0XcPq2M4iA0o9wCU/Z6PP4BmfoSwBRswsFaLDzrdpNsDDfx7Nra7CxbCiBDmion+lLobT6yH154Hlk3ImvuWtle53ySiCpqHs2UDRkj85avUfvJqDUAgt2/4UQzCzjObrz/JROBxL8SnNLS9ImOS6A/NmKkS1Q6EsIQITQ7Ltj0HCgCwwNkmsQggAoCsAQMYxyH8k7EDCQ8g9Y/iaZNeRT1DUZMntHCaMkZkDDDPI9i6AgIAMChEETC2WNCJCukug85qKwfmo5QJYnT0DMRrQfd33ZLi7J3Zg1MAMDrj55cy4nGg9qgSV5NUGAyZI8I9UAAESIS+NuK83MTmINXVQ4MA4shK1spy8b9Y9sruugLhDaIpMFGr8JILzxL86tQgkNx5XHN6S6mKzrv+lx+vfo8kU8/JWHceF9T+HsbWcAMNhm2im7NwvzLbT5H9d0xf8jgMcA/LsAvk5E/4yZfwLAGQB/h4iOIW/xKDP/q403LYVc7KMCDDBQzFm+ePkI5776EL78ngu489QZHEcXITUwSvKD0qwEL7oyhCAxnarDV0YsKRx/bOWzoksMxKqh5AgMByWAcef4OqXHNtYkryndJdRdx9HTdh1bs230UHqnLMiFi9i7vKcHDFRR7jOwsKDkqZ5N0qkXKGRjadcmAGH3zt5Od0naFXkUrdYDAvoesoQwp3ciCtm4xUlAgmXWTw4sRe1D0S3H655nfWXpC5H5NeSdM1PijJ8ZePur6z9AgUL9jYUEHow+b4WyKvm0PozaOg8Q2V73Spz+2kIOnbZdCwn0QMAMAFRjbmm8bRxrQEcPeWfI65/aeVpgO9Oj9tI7KN9T343t3fah993v9amAc+O6ibWe7a+1MDDpvjSUrHuhLFYzs19PeqfWbBVJGzBCBgjGIiyUP65ZCf85gP+8sf8fAviH13RPb6gbgKAGAxHSTy9ePsQHn30YX7zvSdx56jQmbY0JGY0RUXL0rOxiPkGWRB3So6ua6Z/S6BfgoXF8cf8KE9E1lI1zff3srjnBsvJeUHbg1fhaBwBsotVm9+0UrTcV7+fen8v4ewkYFCwEnUq2BhTsnksGoFc8QNDf5br1Op0xTeeMaK00t5qU54pke+uPwXnUMQJERaIm2fvEnDPDFUAywEBAiilbvyJP/y7Jw9ff4uQptpqBAlUGsY6ldxPq1rLI5UX69UMDCPh3WgMD/lxfev24latTv88CEIhpznsGBj6ZbSmjvZnM1hZIfo3CCSnHmRzPIGERMLj7znThgt7ZO6a/ZvCXPPvZc/z55f5WWLSuX8v7r40/ILYlqzOuD6cSAbxw5QgffPZhfPk9F3D3qTOY1C7lBdAaACHV7d8yxuAHUjoozIOCaDZBhX3xyhE+9OzDeOInn8Qdp85g1/B2SBOAKMcTAADHkdOSqB40FENMWYZAACh/tbAMT9TNU9WhEc9rAgoPAgA3xxrYB0CkGmxIUEllCXUX+zw4WKDUWvdcKg7UZDYASTGxhXdMmXE5nSyDhaliFaZ+djnQZxWstAyETddCNiap/VsLQqVj+ZVELJVcOnKaJXNx1IUC7GeWFVV1Xktevdblt5v13Zo97s6dhbJ69+6Vqr5dZgDYBgjqa3yp6thkA+y8FhDQBbMSG2ALaHkQoItomfEvktcalLVWZFFEhTxabGgVQi3Cp8WxPtsJtA3frLTqvYfBX/Poy/ssycixBG5va7tl+BM8csbfX3tsU34rcFDUgAiXrhzhka+JU3vXqdPYRQYRpdr5iT+SdJsBwlo5QcCgDwjAmSGQ760DL1wWoX7h3vO4Q4Waim8Hyj+8oO38mpHyXaggu/SEmlGYDUt3ggGKxqHMZhQXV95/bSR1nz+n9LSBbjhjqXQotuaiPRQwTwQt2829CWJv5BXPFxkFc6Rst7uzJZLalDLmgwwWbEW6dKwBFOK0yCgUHjiAcm2JjaVeEKq1GNTs3Z1n3MyoNkl0Li9+tQW8heqVA9cBDGz3ImVcnLnxQWXdi5wBq8NavTugoFkajM7qzJ0eIxCGNhAIg2MChhIEpGNz1tRLIzaGe1lp90oVG0rF4Wxo2MaX1zXF9o3XMQC2UfgzEDA37kuqZkkP1SJsGXR/SQsY+HOmyP3nabVfvHyIjz53Tu3XGexUZQzMAHSpddmslk9XgABeHEInBhhwZVxagMDILwMFj997HrefOiPhVZSN426cyuRkmROvuRpEebsEBuVtVwEC5u3mF4Ty1zvToBcNC0ZS6wwUAALAHESk01cGbwNdtzNqqRiEvp2A3FbyyLI9tpicesjnD9zIYAhB5TBITRJYWFm6llP4gIG4S3HSNaAg4vCuwkpweyMI8LR6ubzqAnPj9xdSmt+/LntwR9ddNj+racx5drz2VP0KpRkkyPlpoR3Lu+gVN3ugG9rpZc2vhAaY8kJYKaxA+dPfCQik6bBjPseBgKjKzus/L6FrHU/2OumtiRCUDSVKtig/yVg7dPTLProFaOsXt3/VyfAHUMqhiODWYLhRtR4gaN2jAAYdo2826HgpT4eBl64e4aeeO4fH73lS7BfbyCdMBARmIQaMPWgAhNoi1OUEAYN5B6hBwcTAC5cP8eGvn8Nv3nMed9xypvgeO5Abs270evgfx5gNNef+aQnaJb4lzLpWp00ai8K5SyrvZwUotAZ0rg+QPO1QgZhuFbsu++yM7qBEW1nVoGDm2aRr23UIvrb2npTDPabM7PdAGSyEYFP0pgZQKPdxHAvwAFZDAsm8LkACkIECMFdyXpPMkOJ8CmgNCGZrrdf5G7bf7t9gcPoUrEi1V7++J3/tZTa/uvjdMCYt4195qJzOs2NDioPX3zrhaSpJhHrQe2Cn8/Fz5RfazwO6Cgxww/vHzOgPDSAwpHFjIGCqHKDCIWq4xVs+LQ1knUTgGTiYg/AsjxRCLW+3iaXqnVXb1Jr5WPPGrwcIWOna7cZ+L+P6cO/5MfZt0EtXL+Jj3ziHz91zHm8/dVo+285i8QMyIBjIHkCIBAS1h8GBt6V3PDHAYHI9x3cOCx1wBQruPHUGERkUMHNhnFBt1400cXlCS0/aIJmqJiCD1itlTffWQKFtHAFkX7IBEorTmyGPgpHwm513WIqhtQBACRqWgVrfweD2exGBFCAEpVAMMIRA8sliU2oFUNDchDjl0EOMmLEJNiXRgEKHTQCA5vTBpSlZlUEB4ABBBgMA2l9h8/HnIgmtEYPW5y3Rrj3lXLzOhn5dg99lUFqCgBQO8nHvRg5JnXSaqGy21Q/j/FsnyiL4D6EVK/r59llptxaIs7bh1B55SmbNCqQPbYUh7WeliCPL45mBqHorxnIs1boN2DKGtMoaPvVjSP6KzvHAIXmhtotKndLTN+5p+U/TsOayZuy7xzeAorVuuzXi0coH8HXccs9d42Qi4NLVi/j4N8/hs+8+j9tvOW12HxGMwJQMP5gxgcrQgsnXUAGw+NInBhi0AIFHzj1QECNX16XbpdJq1JkCrH5T57rmyRvKKkhInJGUgjQuBmcGCW3PYA4itjzflxYSrr0WH7rxwAzI7TBD+Y22aRUqNtgxBZwUVwgEiqLkQpgDhYEIIcjiOmEA7BPG0DwEMQyae+BzE9yHczxQADpzuXvRAwcEAGQP0455Y29gIIT5sToGXdHPdk/7V7Bt0cZRNiw95Qs0xsRCaYXcWlR1ZnwG2UeZ4aJ0bSMsVIABl0Nix2zaJQxEkAMIwgC1FupJpW63TnuVQEDbyk25ZDX+QBAwYCAgDAANxmnIwpUtIBAx02V+/Fzf2LF24GT8yY8pBdTWBga8AbjPUvMN1yNAn0lMu6t+Kuf077/2/BtR1m5n9fMBRxsHL129iE988xx+7V0CCqJWMLDorRY4QBVaIMoAoRMAS+XEAIOp6kRbQEHNDiwZo7rsowRvSNn6vGrg2cD2hzK673sGcr4HGg1NvlLPmkbzg3URBLjfU8yZzsa8mH6eOiN3SPF9/Q2S8EHQmBsBIRoYYIQo7x4iIxAhkPwdYskmDIMaJxYQQNEtMWwJigYcWOvtaWwzTMkTX1fTCQgAbTBg2rqXkFYYmioO7WLQZnDSdgIIc1bNt5t/iy1xaytzpqr0Kr3RyUyPaw/4bQVxBJCyPQnAcQRiyBnzBhJCbidjHTJI0FADQa53H0JbaisAiXFZbacKDPgQQYSCAMcKTK5d7K+1R4wOBOjYsXFzPWMG2g6DrTuh3maATefmqt3KUINnfW6E/gDmBn/OFsxuM++X/0Pr7n1LIQPgldcyKHi7MQXIcl9iDowtCP5ESPstieHEAIM60cOU3KUrR/jw18/hcc3erKVRhw9sX7oR2gKs903XiRR6FBQwD0WslaEaeR6xDxZkQss76AEIwC5qIU27f+sdlgaul7NXZoC8M8esyJgzkvaL6KwrOoHIg77HQAQKDiywGRcWL4dY2ALKbELQmN3kQYKGHGYgIRmfufdaAAV5EaljJ9FtcYEo9fgz/SxGpQcGEgXNpbFh/QtWmbPIlPXcnrGxtjHxt9qmV2yBI3O6m+1ieS9F2+h5pH2cGAOp8SpAnCTEhWFYAAlTDvv4TyUnkGAvvk8baYVboRyfG2AAYSsYUFYgNtqGud8udZssjZVjG/upbTiNf9820l4OZKuYAmw8c6U3DEiUz96iM4C+wV/S0V4X1/e/Xl3qy/V+HXjoJJT5Gn7n1Yv4xLfO4bF3ncfbbjldnsdApD7pWJ+XwUHrSWU5OcDAbVunsXmeCRQ0SoAIbSttlBZAaijAJeOeru80RkvvtAbylnoezwZh3h7cj5Z3AKD0ENLJ8qcGBkYXtkoTtbuBbDKsPZuWYssAgTcZoWNfx0DOCJEqOkpgYRwoGaQxECbKbMLAfZAwEJKn2gQJoUxULD+8g2yIum+BEp118gXYhxDCqN4oJWMDzjR0z9gkr5NlKi4zY6dy33Fug5SzEw1Y6H0sYWpD/zR9KKEbNfQE1ya5jUYFA2Ow9jGamhECuiDO/oIIg4IEoUAmIO4AHhQURAFQ2i6zvARtp9U2Su20EMppgAFGGSbYp30mMHZTHiu+jYC8b60cwwECZc92QGqDHTOGCOwUNFNg7KYM4gCUQMHa2YHGuQnkxpaUFltrm15nyL2z3rDi9eZWve6L15c7vb/Xm+m80L75EpjIz6BVZ/KVVy/ik986h8d+QpiCVj17IYHImTWwNopYDyFYOTHAoEaZBgq+cO953Hnrmc3Uv6havZ84m0VeTAvltQDBVgBQG3//s57c1hrkPS+gLtaxC89AQ9xpH5Xn9oADMJdDayjUNVsa1LX32QID2fPR++vfXnZ1KppLYMzHzAhNHSPEAhISk8AuzEAshi1mI9QFCew+0c0MnyAHVN5ozRMC7YWiKE9ZW2IHYhTZxVjS0WZsLDF3N8VkaHb628DBLmbjv4us28i0tv4WUee28NvBvY9th6BhAJIMdvs9BkqgYQzI7TEEjA7IjUOQdkEF4lz7DABioBSGCAaclCngYLkJGv6BBwRxW9sABVDr53Qs5wzU7ZOYgT3BmlV1dVwghwjT1E0dIxRI+q8maLKCBMmjkddhysaRp7lz4fXXPvpB6t82/J5BTNfZNZV+3Fc3+kKVfvM6Pp0+ta+fNgCGtFhdh3UoQMEpAQWON9yryLMUHOgNXrhy9CcjlGClBgV33XoGDEFP1m8CJFnDuMrAnAQmN2mDAz00K0S0iS3ooVqrN1CCgR5lPqfH1svOXTMACUntvLHX7Z0BgnROiZopADsbFHt0VQ+W/ECvAUHePwcEHgz0vNS2QeLkqU5q0I9hoQMxPjv1VMUQ1QZI6h8IGIgxBaGvob+D/k4JjB4kmKfq8wwskx51e1oncdiesnLONHVwsWqaGRyTXRcQqLHZcSzAwI4FHMh5Bgzk2h1HxAjsYpR7RRYvN3JS6tYWLW/IwKS1wxCEkg5kf4ExBIQAjBT0dwYKwy5iHAjjTkHCpL8pYGRCJGMRkKdoqVeVmQhNPK1ZBAULP+i2SSDAtY2FcXJb5TCBB2ysbWZjY+dYG6lde0z0AJr8lmMGxNJCOVE+Wi0hBBmbQ4TuIwwsOoVY9MIOrLN2S6CwpfT0AjDXi62wiG+jLbrQlwGlbkyGeuLC4E8OKAwVPrS62PlpBvNKqUGBjY/vLIACTyA2c3G09Ka9MwOXrh7hkWcfxinc2q3biQEG1rYGCp74Sfn2AZA9fnWS1fCX4MA+yGpDnyBUOusP2/+dqxcBiMLyKsN/aZaZC4NpHd+vmzIQLSLaFijoDYA5Uu7eVs4vOg0ndMxZwxR0otRdKcV0vHWv/oDoMSVLoKB+pyUFaMqv9pK8UrR2R0oyFIUoRhIYA2MXCDt9/5EDBjDGyI7KlmQeiixeKmlyTwSmIMZoIIAKkECaFOdCL7pwUndRqYZQeyva1eGCngdqDIEAgTkgON4xdjHOwMBuUiMUGcdTTCuzHU8RMcbU9w0kzOSuxYySgQHZBkIIOBgECAyBcDAEDCEKKFDDn0DCRBhDwAEzRm2jkaYMEHRcNwFCUIqVLRxkdLmpQS7AwfW2Sw0GrK32CeXsFEQYg2PjxFgc6L2NvQGWmYLIOREwECXKOUbxhgIohSYGN7bt2QORMBPKxjBn/TY47yoBhUbZoguAOQho6b99dd9Q6avC4LtnkOrnbOx5xiKU983H/Huv5nvZfneO5RTccUu2Xy1AYL8LUEAZFLRq6+3j33rmr3Xf58QAA6B86btulZyC1NB6jq0IGM1IMBBJV+YiUjAgypXIsYcMvKRIzm6orBuAGiiU8aPBdVajmTjmzjQxp2cZOqdAs07fYiZKAFHKo4c7zCvwFw4k3gD02VAQsGONUSmC3qXjcMf9vdrUnC89QOTfuX7XgTLtDegUnYX4UG8lYjMakSWDN4MERoQwCTtlAZgZOyLsYskijBQkpqrKmYw1AEnsWz1TciDBvFUZuOKxkohxMU/DSSedURsd3ggIzAM1g/P9iiEwwPD6zoxSTADg9V3E8SQMwfEuJhAQjVlgZLBWgVrfH4KxBhq6CYEQKCawcDAGBIo4GAg3jQEHUX6PIWAMhJtGUo9aGJ5RAcIbOIBDBDOBh5DapgYIgwcIlDO2ZQqkfXAGTtJ7tokDaX5hNUvytAXVrJ3MCDMLy2JAbIkdMGDcAgNbv181P+aMFM0NaDHlsGMg67bekje1FQy0E4/Ley+Rto4IAJDfr2XwW/R+K2FW7jMHBB4MbAECdf7Wryko8GDAjhtDYJfVU0Rn93Z1aNnHXjkxwKD30nUjE9Sjp8wGJLfXQAKJp8cKEgDg0qsyZeSz7zqPB75yT+og3mP2STOjeeHpqTLwR6jB014wQfcpLT0xY9RnQ+N0cpwVQJAyEnMAMdA6Yk5VdOcRlQPGBsuEjKI9gp4fz/cB5gqiLmuAyEIYE2SBDkP1g3pPgd1IUQUp3g8nJiiEBXCQGGGtuHaEOLHEqAOB2d6XwYEwsYQZmIGdeqgD1AjBPFFOSnUiBU76m0i3UQ7uII8GQIurXiZ9yBkM+Gm5fl77jJbmdtjgeIrYaWjAwMIuMl5X8GDswPEk2wYIhCnQ++xiUuJmrID1UEIIhO9rXxjHkEBCZNa2DClcMQRCHIDIogpjiIgsYI05gIMZj4CJJSlvJMlS5Aiwhos4AGBKwD8owxMBUJqdAqdo+w1Stwdcm0x+24AC2jM+9gkXeEAgdeBNgADIoMCzBb4tUq6B9VdnCNNKodhmIHullxS4Lygop6avPnZWPOiZv4/+1pyK8hjSMblWfzeAwFYQ4I/b/e+69XRxzj5goJezsA8oAE4QMLCXvvu29Zf2wmMW4TLLvOUAyivxqadx6eoRPv5NWYbSZjeMoVQIABKYgIEOLXbemDpL9jRIO7spNzvDEvI8SLC8XwMJcowTgADEeFppxdtqI75WanDQKz0FsRRr84AIQI7rVoAo71cclxQEpcU9AKSEOGlDBQd1aEEbo1jEDuwajOwmCKxyjUKLiqKX+DbvGDsSI2R5CImWRX62LSpiBmciN5ipTCjqfWjL2qy1hHQCBxU1nYxPBxTsmJugYBfLvAEPCo4nAQavT2qgYs5FMCNeswVWBsoMT9AYdRAtB+yiJAiytEZpkKWhgiaBzr+AKrnWFFjieUMQyzFEIAY35hQ8kYR9AjJAkC/ScQIJ1hbTnm0hXaVcAyJqG0VtI5buBMYcELRCBltYApFpu2+LBNtgwI55hsAn5Vq7lSABab+VtXi6D58CpUPQvaYCBb3i22dJt90IMLA6i6uomPxZAgB+259nU3Ltml6YoKcv6vKCgYL7nsTdt57ZBKZODDCwlwZKIQPtWIs/KA4j5dQizh3zpSsX8ehz78fj9zyJO92Ux0Hjx0DZwVsLb6Tzqr8RQk2bx8qQBpmiuNAjl9P4vOEcQUVc3htPAGpY9Xlema50Ik+rtQaOP6elKHrxtR6oYJfBK+8kv0cFRhh0doCdExmT0vwjLDdBbIEpuzJLXvexGSWkc4AyYcdKNO0dADDArEvlkr7fFHOW9o7BAZgw5URFqI0CkFdbZPWIM0gIhDSVq/d570JWzihZFc0ACTiYG6ClBEMzXOXfbHwMHMQYszGqDL8HBX5/XeoErSmhKE6xbbt/GEhDFBETkeYeMCJTDpkwaX3l784Q7AI4IAYQxAizGlLxvjjlHyVla43QaIu6HVKfabQFcw4XGP40QMARKYfAwP6OeQYITC41JkoyszFTUU4eCAB9ZqA1lReYj++Wh2zXtUoKBYYywdByEVJuApfs50DO0XEAIU+p7AyQRjW26rM1INAFAZVh32L8WytB+qqPRAUj4B2H2b3mr1yUi1ck0fCLah8tRL5WTgwwOFPRI2teWKtkZlnCCS9cOcJHvn4Ov3Xfk7jrVJmzYIMwsj7MPAbrWHnXzMsARAklGt6hZwZAKQBGONB9E7FsO7AwRvFAARTGk6MaTbvpsG3WhJUiLraBQlxD0sBSBy6PWIf07wsgKVIEKtY5MKCQZaNAQeuQlWuZkxDrrElXepS+2BrGTtkD4vxpafl4SQkQrD0yXZ1BwjybmGf0YF08fQ1kI2TMQcsrZeZiSv6+ZQhC6wexokLhR8br0L6h4Rxf6YB2/ocBtzqkYH3MwgmWbxBC6C4Cs1aYpR9wYDB8kh1SnYkITJJZn2KClqV/HW1gYICBYoEoDwisD/uwQT3lcCY/w1GVMsuMgNtXMQJACQRqL9kvAAZgtqAR0PGMFwq7MxMzqp6SD6HKdmZGPVsIDWEmJ4e53zhVWdNjW0BACwDUxr/l9c9Xkc2l/5EqKeNQXutBwD727Oh7R/jQsw/jS8YU1HVcuPbEAAP/kt0Pd2xWvISLl0WoX37PhcREMHO66UGg0rjrcRtMhSdB+b6AKJMl4JCoSQcYhsEYDcIBt8HCAVAYzwPQLLnHSm/hkzoRZym+1lIca0g6HW60gTdew2CyUnkzwFqZcrlXmi2KNF8DQZVMoL3XQGgxCoDLf5CbuHnfJUCwmQwgAQh2nNRaBGQwSfqv5xAlOTl59YwRox3nn70fScgkEOtfoe0PBmm513cxbUe1qhPJjAyfdGhhhXFQUFBnr9nzTDGTrlVQJCGSzkqgYqbCTWPeDkS69gHS37WS8hSg8o65DQIjAQQg4YLFNjAgAGT5A21A4Bcj6vVTv2jXTF4pZ8YMffscAAUAALaBgNay4QAs7aV4ZqljG3KpZJZ0IObMqA+hGlNoIQfPjMoV5cNuhO6S4w0QsAIAaq+/Nvy10Qfmhr91vW/Xka7fhl383hE++OxD+PJ7LuD0bRI+IJTO6VI5OcCgome8IIu4jL+muoedd3TlCO//6kO48D4RKmACp9Tbh0CFxw84ZeHuWechtM7zgESu4XR8SQGNA6kSErAAzI0ngAQaAORMYX3Zmvbt5Qgseg8bB5O/3p/jSyssA5Rx3JHk64KmfDFkQ7ikhO19y5Xh8vOWEjc7di7dc4gy9UloUcYQCVNgme8dkRZ8SQrEBqqMVpETz2XUKzWILJqx8R4WopAESjXcE4BQKgHx1GVKY6CQkzoJiBwwDuXMhFHlarMTAJn10iuj9ZXgpyz6GQl5TYOb9PfBIMdnUxcVWAx6bIBT/i0RNsaqzFaQfkJUf6+0cYsavKPNDgDr/bG/RLFeb78b4LSOmdfhAL/fg4DZd0MgMunNk4e7b95uycVvl+O2yPHFnBVlaAhVUdno9BYgzKgv9RIJS3lNzW8+pBPnOssb4tZ3H9a8/Zac2nkG/fOMMZjdu3EfKybbQMDhZbFfT75PnVrt93H1LrmcIGAgf2tA4MGA/LVeqr3NdWICcHjlIh76nXO48N7zOHvbaZhUi1kMAA70QQUIcLMb0j53PD8qU/tcncc8P+5BSeurhHa9V1SDzo2LgICCCjgAorgO0C5LWbWteNq1DqjlQl3qFph7zDVgaIEFoA8YfOgC6Cc+La1NLkxCBgcyn5vBk/PIGGnxrAQGGJnNrsxT7SW2+5TrdiT3GwJhigAF1rguqxcm3wvAAF20RpYTHjX5cETAjsTYj0GAwBvGkKYuTpEx6W+ojHMOQq6dX9tjcC8RgiUUUhFWsAWPCnZAGRcBBtKP6pURZdXKcnEqM4T1x7NMTtb9mDNjA+ZFWXt5GxhI5ywAAqDsc61ifcrPqx8bx4GSAQDmAED2NUBABQDM+Dez3u0HNjP3TQfHmNHawTGnyg/vxIrq9RZGhXN2bH9dmvqqsgk94FN+JdLyTnTb7e958UvG3p9X1Ilmu3IowT1zZq+AOTVD1r4Bh5eP8PBXHsaF913AmdskfKCzskXX0LxPt8qJAQblNKMOILBPs9aLl6igD6+8gIe+9iE8dd+XcPaWO4Dd90v46bYDT7N9gxtdPQfOw5Da6AEeILTBA9sSpWwJaFnbbWEa4LbHYdb9XO2q31T+XQQAnUG1hKCXSgmeqABEFj+29/eAKULoSGZjVtAFDK2PNY0VZbnErrTmdufpnUjsgRnLIciaGQkg6LPJ/rrbxY6QZvpBZWXhCQMHiDKljyBdnkhyUkZI3HsMOdySlj8OOQnRFjuKzIgHQWd/KBDg+RTF1uJGVjxLIL/zPmMr/DLJBgosH8GDAaPJxyGse8YNQOvlaPLuAQG/XQMC+1N/+2Ppi4Z+Wq7F0YGskOdesMoNua5r79oDAMn40/oY3Wd8JpmgZLGycwOAaVFPlaHZ8jlDRdv1dJTf3kdP+fNaRr+lv5YM/dJ2IbVqwQeKu2op7mVQIH8DDi8/j4ee/YBzavNziRxA0Mcvte3JAQbaWh4MNAFBtbKZxbMOr1zCg889gqfv+QLO3nw7MO3SvVOCGZCAAO1eL3uiO1Zvy3Xzc4fc66ym+ZlASt7xAKIHHGqjmZRXAzjMntXX4756RUdaAwC9hJmlWFmroxZKpwBS1JjP79+/DRjMc1tiFwBLdpQaJaW9kLDoSy8SMEHAgX1AxX+syox/kg/ne/n2aSm/AkjouQNUEcDiu5ToWpvtcgCAw7AY9+5NmQPQnUe/FJ8vE+TsnQmtefVi6EsQsDVObgbSZFoo6IYMfanHQxMQ6A7PEsi5OTm4LkQoFjvzwhir3u+NP4BZTs8SCLDpbi0A4Mdn/Rt6v6pqe45Lz15SqYuwDTD4+2/VTXU9e3P9awPfNfyta/1zZzXFzMDXle8a++pc2n3fXdPpjO7FmQiHVw7x4NcfEaf21F3gOAFEICp7f8F8L6izEwMM/HuuAQJKvVMa5/DKJTzw3Efw9D2P4+wttwNxN7t3rUFoKs+ZHUcGFOWBiqws6Kd8LH+cReOH6SMtbtCAqsHV86rL/UA+5u+3VErF2vYsWoOrHlizAdUI6fSKyME0XO7wJockCy5Bw1QDhjhnF6YKLBwwZrkLQI57Lq2UW5feNK+isLyOJcjpzxl70Lx/x9Ax5/Yx6foQExbescWirC1AA3fOWlmaS27He96xnd+Kk+vmTBZrMuyVJVDQKwMkmc6WQB961qsqrXyepVwA++S0BwGygmPe7xfU8mOyOR6Lr37265krbG0Wit/s/qEA6Hb7OWDw+12N1qtQVMfkJr+3Gv657dCncyxojc1efEM5zAx86zrT0mZ/lnSiHSLC0ZVLePC5j+Lpe57AmVvuAOIEogj5ZgcngECqJwnrsj0xwMCZ1G2AQPcdXbmEB775KJ5512/g7JveCkzH/Yd4Iz4JquPa0KcTevns5X1ab5BiRnDgwg0+0t/27IFIgQMA+GVa83bMI3UGEoASKPSrPR94eqtlANAK4SSNWzI3W10ELxdy237denn/DJ4iK0AIJViYWJI3W8wCc5WvYTpzKL1EX9L87EYPWEss7Bk3f1lrPnSzNA7XiWFpf5B3XDOEtYdc39NKy2tek8e+c8RbMqnvubX4XJ2aiUkhHn3OEibMK5OWle/1iV4ybx0X7zEBHhwUIGAGANw4jLECAdc/BtN+1Xt+HA5a6R5gAEpwIH+36yNXpdWk8wxdIO8dndGvnZRaLv5YUWt0jtfH2j2Hqv00HW9ylECEw6sv4YFvPipO7anbhbrT1XtB3AQIAK0RBicHGMBi/lw1tIGCGOEBAXHE4dUXcf83P4ZnfuKzOPumtwGxvEcqLUWjjAH1jm8pLVDRC09UqDylCPlBamEOt20DdACUuy2GhlPf6xnZ1NieDbSO/GVfBcz0GqoHpN2rW5H63QOMUSHIX7ZBYCAhDOn9o4GFmMMPQou7bRZNZkvZtqYF1slSVns/qMyAtubj72MA16ZJyfXuVx+V9g403Yh6OqfvL8A2/bVWZl2+Ot6E2Ncy5Fxda9paDLrFuBUkGChogQOTv96mbF/Z9m1fK1rf9i0Q4HMCBgUBlo9hx4vtYIBB34gjaJqw5hjNxt6Gj0bJNjkA4HVTyC9h1yTHxY3bCjBYOyBtLzdwXxfl9zeU33NI5kCoBwyWQwT+3L1L616ejV6wRYevvYz7v/VxcWpvfpuGD4QTIA662J1AWem7QeykR76dcoKAQQcQdAZDAgU//lmcfdPbCwM205At+cVefvENKmbk0++Gm+TCCwWA8OChARw86yDHygG/WHwsbGnA+cHmlZAfdEXAMaIAGF4U7ndZV5NRrYQyKLDP3ybAEAZJCAyjKqYh5ScIq4BEaU6QvAIPFJYWtEF+g+RLjEvzHNFOxOwBAg8GWpnj5M719+k9py5bPe19Fsvat1xvHXr+XB1CS0ufu4v8x9SYgaBooOiNeq73TCPaMq3bvgX8ahCQ2rYCAi02IMF8noDIQkEzg+KUdV6c8rjzY67WdW6Mdccb4MABVYa+1C9wYN3enBSw9xiGma7rlZ4O8sdmhr/FkMz3lfeo7cA1Gv/NxTribuEUkc3ha9/B/d/+abFfN4v9ylkaAaAIikCTPWiuhlGWEwMMKFYoqwcK4oTDq5cEab3z11SobaM0Q4rF8xZCDq1yrcq0GiTsAUA+CVSgdswGahvdAzXzIM9YQetNaq3BArRAgMnaD86GokqLnEwlACs8hWGo6ktJGXHyUPI+hEGUUxiVVRl0aeMBIbEKbaBgS/GmbQYsYcoDBZUEQktEPXnSfHstm3pLwmdBrzoA4eU497aAOTBeUIg/CMqgOFYrsVzj2tNMPdHtyBS1tI/1FWsjVuPPgChdBX9E0q8Si4Dyi6vk7rs0fbVu2xro9YBAwQg0gEBiA+Kkem5SUCDgAAYO3Hgrx9ryOPPSbo4zoBxr9tsAQwMsMIVKT1WOSvGMjjx7usf/7jEhtcH3ej/9buj9G6S7N1/WAAYeoB3+0Xdx/7c/gWfe+ZjYLw0fiEPEmSmgCLCtq2rZS042CxzxiQEGzbhQiyl49aUMCm65vW2o/P16ZZUxaF/fTELZo4iBbxBpdQKQBwEwA+MZhv51RXf2innBky+Me4sJmCkmAJAPJLEpOQCs8U8Gcqp7R9YMAEHVcsjKiYIoIBoGlZUoKA5DBgthkIEUBnAYdVANYAUPZIxCGHQlQUtaRF522G87oNCau22lZWJb8fEeGABKwx8avz1wsH9JYr18j26ceTnMswSet5YWCyTbWQL+d5ol5Glquw+RJsNRMvj2L7M+CgogQMFYH8L8Q2pRYwVMlMIG7MZ2ayrpPu1JsFkY5XYRGmgyAg4IGAiIE9JYizamyjHGPr8g2jhb0GUKlOvxlbaHQdulARYcUEiyqEMMWqwPZNZgg96pwx++7zYYkX7YxDMQ1w90k36e3WoFKNg7N+pAGgJ4/rXv4P5/8rN45sc+nZ1aCkgxL4h8PHNQgAMG5mB7Xk4QMGgptoXwgWcKau+1umf1oOovyuf6M2sUzmXElnvfSu0p20aD+kGa9g2DAxBzADBnHfI5m+m8VNfOIGuxBg0lxU5JlQrOyaeWkw0GX1QOFEICDKyGXbRsAA1jBgskCk3YA1VuyhiIxzOAg4QhBgUVQ5CExhiy4ZgoZ1jbtL7Cc2WRpRuq3VJxONcMBnycOYEA2/Y081qIx7fpEnCe9dcl5Vr1q0afm3mgtu3DRHp+M79EQV+Ka+v2QPlLl372ylaQIGEDUcD2hq323NKOA5XbiTGAPLsw7nHShMEp70+MwJRZAp7S+OJpp9dGiT3fgHElv8txxTtksODAODlAUAIFJ6E6tLlWmn2udghrnSPnNJkR74yUd9tLB8vuXshFfw5+EXx9RK1jU55c49kU8Pyrr+D+3/0reOYv/WoFChw4UGagDisYOJB3XHd+TwwwaHo6TrkdXn0pJxrefPuiUOQ+y8puFQj4OJ0fgP6+9cDsIXf/rDq0YBvOc2Zjoip0D6yDhnTfnhdXPLzhOTZoOg8GonksBgTsmjiJnJxHw/6cWg6tYgpbZWHKCkHkwGEAD2MBFMIwpDwEVkBgAMHCDiAHLsIAMrBAktQ5qScqiWsZKJiRYeZkJNZm87WWWN3ODDgwkIxFmXjr92dZtxidtqeZmJw6Ubfquy3QO1Oe1mdTzGPAzDNtMD/NUJG1NRGgDBBQ7ucwynoJJEst8x4gwdiHIjdhQxuutV8CCB4MxCm3mzf86ZjmDWj+gAcCHKMkr1XjiWv2bZ/4VtUuNp6MdQOQknt5arQbYwYUgOvQMb7+S8bf6RFu6d1r0LephDKAxFy/iwIVlRvvStBFwzCzIRko2P787Odfexn3/+5fxTM/+g/wjpvftgi9y5sKUDBwUN61X04MMFiiPo+uXML9NiVREzWKYqgLQA4ihqJTLj66wQzInwYgKFaCaQzSDQq2VQql6xSuN5Qe4RfXONAAQBE/0PLuqspVtchAAEA2KvYeHhB4MACUCix5tVBvR89xcq6Tz4qktfQeYsjNiyyAwnggMeQwAMPYBAmgARR2iyCBNT8BZkhQUtbJ+QZmK7v1is8NaAGE0qCo3GM2FgUYKPZnunnNy8S0A7eMi+uzHJ0CXmibxXZKfXGABwgUdOqpN0IVqGuxPwBJm5lXG/VaBCBM2v5Daj9AVlWUb1bI4yNJbgFZ/gHlT7IzZ8O/pe22sDrXAwZ4yqEE3h03x9H1jCFQ0P6BJFPiAI4BCM74MUtcJQxiJAudqiEHjgkoCFRwuu5adIs7jy0sUuvZBQB7TboVkP5jMwdawCERIyE9Q2SUwYHV33TuUnn+j76TQcEtb/cVW643xwy+mAUcAAkgLJWTAww6s4sPr76o6xR8LucU+NileUwkFDTZcQMHgJ5TojmJOWpnHYZu8k6+vq5uu8MWnbVF9fUeIR8s11/aaRs0oOxwKN+dl9C+lQowrAElkwG7wZquW2EIuoAg3TNToTO5ACWCPtZ30vgoEQFq9BNQOH4dpIaGQgAPI3g8yEZnFCODlIMwgGiXww1hAKaQngMKCPp3UG80InujQAYKoG3G0xuZZt6AxZlrQOAp58QMlJQzduphTrvkZZphKYzNdAxMU5a/A3hFGMgzZAtjoVCE5ukDEh5wwM3AGIYBNBxko2TgYDxI7A8No7TbqMAtKkiIUfpznAQYsoA95ijPU8VJml9CChBqFgFA8Un2Le2WRlERIpA7NMMEBuJaYQLLHdgJEGCOwO5YwVuct5kbO+z1y4ohFCciGxJ5nwk8xAykph04ZoCQ9CagQEzr7wECXP+odUp6eLtuMwAA52QAqwBgMWyyVJzR5RizXOxdAXlfi+3rO8+esWC8vQyaeWMAnv+j35+BgllYxoXZisd3n51Zll45QcDAFe0ch1dfxAPf+Ck8/e7PCygAkMBAndikjcgpZgNHXTkBp32UPnoDOG97mhJapqCIcQ/2IRVvVNOj1+5RHidFhhwdYPBgYVJDl9bjHQRgVEwCT7s5arbjjffiuu5LoKBRVhVbi32Z3SQkVM8U1IirV2qe6qSe5XgA7HaymqWFG3YGEA7k2ZpngDhk7zYMAI9A3CWv1UACKGCwqZHqJFmUsf5S51yu8qe9aNRKuMCMSw0IKuMC/VeAAWUJsDtWQxMTeMjbE+IuK12eGMzyd7VNrFgfHHRFtoFS/wrjoAltAaxGPzEHCt5I28iDBAwjMOm+cRQZhAHMCupsfIcICQCFDBCI5FsSKX8hswjW6+sPBC21GbldM0Ynef2cgV2dQMi5DYkj2LeHhQkM2O2O22DAgzgrS21j49udYo4DTZOskBmV2amLeusJHJihBAr1Cjhfa+cetKZH/A1aQGAJBLTY2w2lsKkU5J4tHbjhJkvMrDt5dt3zr30n5xTccrsSLRUgAEpQYCChVVyC4lo5mcAACgpsmeNTdybvhhBFiStNaEo27WM9DnY0jF6bjqFiE6yRWZQSixngaQIN1ukVJNR9yxSCou0EJoJ2xh6o2LCOQrY7MXfOSRWkewY5L6FEv3kw8DRHv3P2b/vgS0mCK9fYSnKz4hOGWvLRrxVR8lgECDRBgr43Tzs1NscSalCFXHukzENSFhQyaCBTIInG3iWQkBedKr/UuYINsmEB1gGBMQdmEJKBmdqAwP4qM8C7XfZEWVkEBQIco/ydWAxBjIiRlW3goh146rcpDY4hAAAFBSEoOAgCFMIo4YQEFLQNYOBgHAFjEqadhIOMRUgAQfMkQgS0zcw4y+wbBQhBvzaZwkW2hKy0AEMMvrXZcltpe1lbGRjwiZ8dRifvy4xOnLRNXKhgE6MDpDHSHB9WZ2uHjuHbtK5EqJjGhVJQ63nnXHcs5QQsgYGeI7Vl3ZkW6DH5LLGvnZlR/noPBpRnnj8jGXfC83/0XQEFNiXRX+ONP9Hs+vaU9rIcXr3UPQacUGBw+OqLeOAbH8HT9z6Os6fugg8LzBgBAwRAMuD5d5kEwpQzSxOa9h6E0eAGu/Xj8xbDNZDAMcp3aLnquHZPux1QgIMEGoBM2y2VhNqHdqxLlQE7yjV18vreVYxMxNUY5L6YrP3x6j3lHfVUPU5hENGM+t7TlMMeJo+ATd5pAoSe/JlEGbPFRqcpUdZs9VPZkCnk8SB5nYmypl32SO062slMB6FeHItARR/MtPOCQrX+URkY8SQtecEZlAYFXXicCQxkkFAAAs7MQTzeIe524ImFIYgRkwIEJJZAk0ljBEcuAEHrmwn+C5Q0BPkdAkIIiIPOcVegMIySyyHAgBDGEeFgLL3ocSdtwaysVASNIgfW82g80D4YNbyg8rRpeMTFQjAGEBJ7aPk2CShsaCfbx/NZIPN1Bvohnh8kIJgVC+PYtgvlzPJA6nwdC6cBXUPZF12jjtcaJmixA1sAAVCAgtlMLw98eu/p2QEHBowVYG/U7Xp73cqQP/8v/wD3/+OfEVBwy53l+dS+TxcMNIDd4VX5NtBtONWWBU4gMLCXFqbgtOxMsjE+S9GbJWEkRmBu5GuwcPjqy7I/HKjnUU1D4QhgKKbNMKIYOc2cJSDP3R9QMgQALMeB1PhLR1cP1QMKDxTSK9YGvZPc0gMHe5T6/Nb1STdZve094yReXsihBSJXF8vzgOYGcAQmoXzlHYNYe44yTW1FGSba2GTNMXUHDoOAD3kJAQOAJlnFZHwE0I3J4GRDslOjI+Ag0djGFjiQUCzVrM9bLM7oFICgXtXOgAH42kCBJrLx7rhkCI5le9pNwMSIu90MDLAyBwYGlpgj6yOkDAEFAlcgIYwjdjFiGAfEGEEHQ7pvGCdgjMIIII88qraLY+OB+QKSd8WUz6MIpuhi5tVKcamNNuQQtdoo7YtluKCVALo7RppVUIcMLA9kLWSwD0tQzTSQfSUgoMoQNgFBZ1ZJet6CXklZ+3tS/UtMQVFaDlSlE1t5VQVDsAAGZms6AGkGTUH920wav09+zPbd/+1P4pkf/3WcPXVHeWwBTMwBQC1zkY+3j596+v+BXjlBwCDoS8tXps6euqshrMpIEpDCCHCefgEKjBmIMuXxH31cbzWWCgHuXjVgcKGIRItTGyiY8UkUI5FGNjJa9oPCqFkPFlZLrRjQGABAHxHX9wH62bWtecNUveMwZs/KFJ3FtA0kAPLNWq8YE0hAogSoUpCz0pKPJaf5WCoAjqQhhR0QZL1xxAk8Hki9hhE0sAIX1hyElF2YwwfkptcRwYOEZHBaACH1G89gdYwNMwpjA51pkGSpwEav4ThJ26g82Sh2NfI1KODdlBgC2/aAwPpk/ttmDAyACi5lAQhR/w6MOELyPThggnQTHItaCyPAMeg4cu8/UWZhEsjVkAdJX8FIyWPnMOa+RpSjdW6debiFjYBpsX1SG7Xax8KTDhAkhsfYj2mSkAHHGUsAXskjaPXvGNtMVMMTlr8NMFDNEGmCgSX9sEE3+CRVX90kVmMFzbibsxB0yrUPf8R88XwWwQKjWZ3T1YMNVmAGBBZBQIcxaDgIz7zrczh76o72dM46N67Yt1C4YR8XJi6eGGAgL/1heelb7wKAROOsemVAZgbgFDGQDPLzVy/igW89iqfvfRzv/If/Png4cNc5g8QlGPCgIaP4rDjYzjGgoM9vrQY4M6JAoRz2jfGnsjKfvDjfDQgAc6qsLqOXvdQ5GJPiMo59wiIZo6BUbLG+QWIbFFD1Zi9YFbckXPVYFUCNDETf7yT3ALtjpBXhjD0Ag6aYmYKoINEtVSreqRodRgYJUtH5AC9yK2LRt7LRsX8xGyD1Ws3Y39CyIM8lUGD7PTjo3n/fJK+lojIQFsjYvwi4SYPgap15TOl40gVUvdOmtvFtlIFAYgxqlsDCBPW0w14xgxkCirzjoZJfNdYz0N6TFegYSADoLpsMoN0bUIRabcDyNCV1LX5Zg2m09/BsQGJPt/Wdrv5bYQT8+6UvSALlSo96/rXS/2d+6Ey5HFrBFDRyE5aKjv+jK84+nrpz9bITAwzspc/cdveMwoH/DaA1jaP4cAhQKNTDyxfx4HMfxVM/+SWcMaEGJzq2SUyx2NcEDW6bCxBRgYUwpv2FIa2MqDxWn+M9iFbCXvHCroNtTJ7ZK162VBK7Mabf6c4OFBkgIntHy+Z2YIHsPAMScGABWJ1f35y7vVRSDoICEYwCFExxRDgQEBUchBIgYHIsgq9TB1zVRsf2se8HnfY2VLOlUMgUP7Fk6kM8exADAxAwIu52QPoAcf5rOTDigc+fSd4rA4pwguUchHGUbe2DKSEx/aZtCrF4/1ZR+SnDB10tTti+gAQQrH/U4i2ch5XVI3kqwEJah8CFCMoww0oZlN0CJBlTvejitVv9+lpAQLoue8mtxdG4NnYLpV6oSNaQ0H0eLNRTFckzBDG/h903XJ/Okz9tNgAUync0EOB1YGNmQGH8C+DfN/o83NSucyXbNTtm/Vvs1yN46t4ncObWu9dtA04QMHjq3idw5rYz8sM1ENfGayZMM0kNYXHE4eUjPPS1D+HCe87jzG2n01k83pTOyefzbB95AND7wpfzLrg4Ln/ZzlOwkPJazYi6Z3ZXWaxLLwbYAAKoaDLADw45Xt9zid5K71jUUcEPR9AgsvAf+6gBAzn2oAsWgCJxKYGGhUI1e7JWOCotLk4hDUhoP3uhDLCFFQQgCGMgAAJQeaageItXbQDMRF93XwawmSWR1HDL4k5aqxSbt3i71FwvHwhxN8k0y0AyC+FYkmh7iYdLeQYeEMj9y9yCBAgOhjRLgQ6GcobCeJCnKdqURd1Htg5FUCW8IfkNxdiU6/Ja8yacje2Rtsvx2wIFHvhuKkTaNwfpw46Jq3vqrA9bsu8GJmARABAhLo37tTGTcq4wG/c16GWy+2ZdVwCFwTavQ991dJ2BgNbHoWazAerzaqo/hQyreiwZfGOjV9kBO551AFssFAA44PDyRTz0tQ/hqfu+hLMKCgog1iknBhic/aGzspEQrG9Eadhk1Jt3mHfqoyuX8PDvvB8X3vcUztx2prguo7rSc8jbdUihBQoq0OBjk/7cDrsgA+SgeJbBHLv/ltL6apqwLh0Q0EPJ9TUbSutDTBkcZQU7BwwbwAJQAIb6eVuS5NJ77UVvW70dOEiB7CDvRQS4j5oQSgXn5ddcCrasbO5fGp5gIM2s5Wknc/2BFJtNvT0MwHQM3g3A7jh56MMosW0KATwO4IlBuwkYZYZLjIxQzVAAIGDBit928gsGEKqZCH7Kos1G8IDAL2YEnbJoix+RrmVAChjgF6saciIoKgMwbzpO4AAwwDbvO+ncGhBo+7VAQU/zNIuGB2gISXZpFtAwV9u9BLpm8twSCJgBAGcQGx5xktGGshhuTU6Rk5PThcKKuLyuKiy5VppfhlSZsL1DjwVohQx8GNAb/5ndwdw59c/3dbK6OjZ629tRuUWSg3F45RAPffUcLrz3PM7celpkZ6DA1l3plBMDDGbsgDNkDKR/APJHUapixCgAXLx8hHNffQjn33MBd546g11E0X7Hen3wVBYNblv+cnFZOSDYe8UVc6AnYAkwsD+vgcAT07ChLCbL1ChZt9eQstx32YtITnLxzkDrvbnylJk1Q50tE3wFMACZsgWK0EsPJKzGIuv3mSZVtFgAB5Wc2Pc8FIZpsaROxoUSks+2KrgbSZZ63h2n5Z7J4tLDmNcAGHbgcRSvTBfToYObZNEn9Wzjsay011vYyMswNvIMgp+uGLLBqxc6ohBkaqJ5t0O1bashOiCQAEEY0u+07LUzbrZschPctkTcAgNpu8USAD1QUDB8M+FYvoCLnc8AQVW3feLkHQDApjfNUGw0hHJsG6uWljHewpzKC6dt7ug2u2aL4dxLt/Xe279zAko9JzQ/o3AmO9v1UNl17FM6H51MCkJaFO3i5SM8/JWHceF9T+HsbTI7j5mSrljTLycGGKR57h1AkD6jauez76i6D5J29MKVI3zo2YfxpfuexB2nTmPXUHK7SbLO62Fej5XUgALlpOEU0YEyaEjdyHnMxdx1QLzm5JmUA6lJa6J6xx66LpDsAgNQA4DrRspWL0/dGtuRaVZOAKF6d6NmPWCIk4SFlgCDPdMnPAKlh7s0xamXmAlUtOT8PZNX4t41gQOTTWIUGqUGYJa0yJz2cdAAgcknTsDBTQjM4GlMDAJpfNsSPEmX2KWYv5tALgN+uElAQr0UcloKe8/piiYvM/ryeiGDljpb3vZ5MKBAoPUNBQblZNCeNyiVqWTbKUugIB8oxl9xOVGxfLrNzpgl2AHyTtoHEyBY64MzarwBAFQmMyq8MoKFAdTn7DOevcwKB2WROS2PrSV0sz9Wl5ZOs/q2mNDWu9t+Cg3jP2egC8fT7ah7Q6vKXk/vpvkJ9Z76nkSy3kZk4JI6tU++7wJOK9NN6d0hY5YG29ssJwYYeDjdAwQGBircWjTUpStHeORrD+ML957H2285jWNt5XoFMEF18wZs6ZYWadReD58ADLKsag80dFgGGXxeSXUG1VLxg7wCAAD6ymKfAdMcx07hOdRLKGXAKYSSQVMJGiY0AYMZMAcYWjkMAGY5G1tl1szKrpXmRsq17kTNJCO/JgdzGvB6ge6T7wIkCla3JccgAyWedolV8dNG89z5WORqLH7HAtg2EwToZ8inQdEAATUd7j+mVMeH9wQDq8lcCYCtlIIZ8r85gYPWyqgih5X7ou5rJTAvAICBrBYAoAG198tuux7HM6MHd3BWhjyG3VjOfx14SqC+BgUu36r1meB99FmDzZw5NLX335FB0mE8tx9m3As2oFGttS60q+5XvFKlG9JQYQZY7NcHn30YX37PBdx96gwmhvvGii3WtZ5HdWKAge+8S4DA/wbKDv/ilSN85LlzePye87hdhVpmIeWSUN02Nq34IpsNDt/IRC7pCyVwKEBDyAbTqbRsJIHC2957MAHzAdUM02SqrBg0utPkLFWoZL3y+BpIZVkQAgYBD5UcGAy/Hn0CDDGiYBh0VbxWDgOAlMdQymvdGHCqLWbyKw1Ox5Np3bMAaqF/LNU1r7kh+9x78DxPxfI2alYFEIBkSZw+dyPdd8OStN1ida9mKTQXy6mocHn30huexcOBDAKW2mINqHHO9VikXgvAoGyNPst9hsndU88Zt6jfeZ+KBTXujbsa+PS9DvGC2YGDNJaDeIxNJ8r5F96RAraP3ZYDFNIBQlDWtBwCGfiLo9PQY/vqMHu47tusxyr7IVUoncqSjG2Agg0YcnYRGoyBl5F7qBGDpPV58epFPPK1h/HF+57EXadOY9JjRhnU3/FYKicGGFh32QIIaiMGAC9cPcRPPXcOn7/nPN5+6jSmFVi3S71lW/1qG5C7o0Sh3U4FEZyMomcVtoIGd7tcydag6tHWjVhZD3jZbWpZy343mCqZL0UIiwVoiTd8454QMIptQf7H1RcIi3CMn1tuzEsrXuxkd+05G1r7GeiqPJfWtUX7dIxZxZjNps623oHd2hr63mznHeRr2mEq7btunCx+YXRW3flaGIURdLIweXAtvxslu1mp2Bjkvl8YenZhHKOiAfiwUHMJ9pWyGhNvhgEGFDR4CqEoOwBn+IxEW9CPwJzh2zReCx0m0KPWVy29JoZLvnLpQ6wqkSzXmbC26y/7u8X4t/QasKzDaulsIZdaZarv5H6WOCrL/MUrR/joc+fwhXvP445TZzBBxiaxjK8aIFRNOysnBxj48DA2gAKWRmUAL149wqNfP4fP3XMet99yZpY41TIFBhyutfEB35+59JLdoDEyK+ho2Rs0uAeFSilR+i+XJuJNg6A9gGoGppYvqnPmaBuNwrMwYRrq+t6U5MKZLiP5SFHezgyDgYZFsLC0ch3yO8yVlH+JGgW2vJd9jFrt9XrBbDV2uc7JyOXGdCfNPTRGBYiqY7P9W6oyQ8oL1H5PHm5f936bKpPfLS22Y+9SfGSjAggJONR9wT3bgYHZ8um9fuL7yJY8gAYIiMjjcIp5vE4LenD/MVrqsNYYlbfkQn9ROn8O8O2eWXfJf5abNZMa2dPLslV32bu1AJGbbFycl57hnndj7IAUn9NW92Iqxoec99KVI/zUc+/H4/ecx523ntE6EqLJ0f/muWxa5eQAA/07a2RIhnTPYL145QiPPncOv/Hu83jbLae7Br/+3fqAXNeXbDUAzfdnjJsPZMdIwIMfPGugwc6r6T2g7GDNKntPUP82B1djEFkbZNBQ3iM5ALpv2sC5DZasBsAWBbIooMkoQDLfPWAYqAQLIeRwxGCOFmyVwPLLhanyvZkhAGZUZ6+0jHkLEKzFQ/29vFfaeo6/plFSjXuemL9N85wF9qQlj6U+16jnLFyy6BkulLqerm5kIRgDTTN2xRgBAA4MNAEC6XVWT2YsMgSddl1LBrQvQIICWLWAhT0jZ31nICAdU70X43xsJgm5sbr3uNQNx++4sZmBQNHDHVjweOgHqbOAvt6y80p74p7h7juvQGNfp8pFrzUMavc2worKL3RIBCrrPmbg5Vcv4tFvqFN76rRca/JmAIH0XMcedMCULycHGMSyU6yxBMzAS1cFFHz23RI+aDlQMyTq/y6c1xpUzc8Ha6k7fRpwWQMB0AZP12wHDUCZ1OdrXD+7NVsDqHMI5mxAU9notsmDmTGBk65uAbGakPaq1ao6kKxxP0DCCIMCAkROgCGEOVgIgZWylPsKaBBPS2bQjUq7TUiJnjVz4MECgGsOMwCVUWgwA61kTztngS71f4uu2OyCw/raiJWCC41911tW69AC0ivKuJgFZIeccUq92LFC0uYq0wQIGYlJMCvGXIQK5NptQmmzIXMQUCYFVmxAdMYfUrXILKGCCgTE2B6Tfjxez1i0V7HxCIgO089PNEF81HawWLjvU6WuSlK7Zl1lx9aMfw2S/D1rnb6PPgeyTgfyotv5gvk7JNLK/bbbRgJeunoRH1dQcMctZ7TujMAS8AqkINExM2B27duv/4kBBjWSq5mDunhQcEcDFPjrCoXVAA++w1hn8XGilr1o5TAMrjMdV9/WMdU2G3RAE6UDGan766gz4OqPytbxxBoEyDmdQbWgeJizsuHIThmZ3NaLKScz6hRIjfwcMIyBgMh6nDFEuS5QBgrGJGSggAwUkqwY6QNOXIICC0NkqThhtUqhNDpgwPZ1Eqa88U/A16rpHr00C6flYQGYJ00tuRZYjj2vlYWPGUupAQm1DzVn+cBseD5WGx+fqW0gW+Z7yx0MJHDd3nBhAU/vsgN9xXu027zb3i45sAcEEhOqQCDaMR2Tk7wOdgvj0MbgtY4/AGkMIgKDGv+BCDtuAHconS3SKoDC5FiFlDhXSI3gO+OSjpK/pZ6yY8V4cLrLOy9A1uHXor/txt4H4KnU4YADC+59/eOCey8jDIiAl65cxMe/Kfbr9ltOzyvoa+FCCXbPtXJigMGS0pvlFLjwwdtPZaEugoKG7ut1JAAzj9jfX86dl12rs+l9jpFj7gMRjt0H35ooHXAsQzYlHjRYsUHZK61YWo+CXAIDLSUk+8p7+wHvmSC/QI4piSwTOIAggGEMlJSUbCtQIAEKITBCVEYhCpMwEDdCDyKwQdmEBBQKJqHFIKwbzKUwQTl1CrBWLMAAe4C2Le/D9tUgz7dBPRZmupEXf24qNXPVZCXc7rrvlvHszKRtjWenGKw7l2AJcDa2BSQk5oiGdrih8w5Wmu3cmSLIcKEBtEMDkeeMQA0Edmm8wW3zdY+749QGEqrbBUp6aqdjbxdEd+2I+yBB7bwBBaPKa91kpaWjerpJRdH0/mv2Epjr6zVd3Sre5qR3mDJgmEKuoDl6FpIeggsgO/0dkWUg5l2Ygp/+ZnZq8/PtmpI1yHWTHwYQlsbsiQEGBf1fK0RXXrx6lGMyt5wuOpayg63wf2dnpy4rtJz/+tzq7Af969HoznpKFPS+UyXmAYNnGIo4oPUPzh1uRmu13slt9+KRWwCBV0rsDFWMnNoqJmNVyaayTuZtBmUAADPkYgTGoQ0UZD9hHIIYiyjJiwMxdkQIxOmeQRH6AGBKbELOoM7NkoFCXqlxhT3wGrDOF2gYi61gwM4z42H7l0I9dRsC7X68tQ9vmYMNlP3aFj3KHmk+pwbBdc5JNvjCDFlojQzgqfGPth/sDFILJJDeT0DCjEUAkMINXkDlC7tt174rbdsMD7i/GRzkdt9NERMYu6kNBGx/BCejb/cEVlifWI41mDwDyTz5COzIwDk0BCdsCAVCjm8DFBg8lW0YrSFNJ2lVCLMhX5SeTvLHrE8D6IYwveHfRzdbsT68g+vDhd5X/RypYBAWS0MhEySn4Ke/eQ6ffVcJCoCKlNISOYNeZgHIW17rBAGD8m1b7/6ihg8sJgPk6VbRQAGVN3A2eAYOhkCbEnSAuUKtKfT6vLrsmHN8L2Ylu4PrmEGVaizBgi2Y0QpH2Gul0kMIPN9cyhvYCgoMEERmBxJKQBe7PZlVUbEqKiQlFhQkhSCgwBiFkQjDpCBhigVI2JGEXgwkkAMJkxqMHeXcBPNAZZpoBgoJlm5NTAQKpsAaYisgqJNrJ3A6x2TtwcO1gjigBHIAZmDOF29sWiEDb2SA7JmG1E/bTNBS6GgInhngdI9IBvAINosl6jOXpnV1WQQgAYXFUrRrBhKpXWPZpjcCDOyYU7tNvD7G+uMrt1EeY3JuYEJQo4MgvycAQ2RMgTDouwwR+lv65RAJO3ABEACUICHJrlOpFV0EYBXc1nrYX2vn9EqdTmrnDkSwT4tPjfO692skcAJzxuwVDwpuPV0wZtWls6JdurQ3C2rpxACDXtEQM168KlM6Pn/Pedx5yn8QSZWRUgU9yjQBBPJXGTjQbe38FAQdTkqLMavHuVDPHmr1xdgDCpR6+YAcgiCmBBQMucqqdMqGJB2lA2Up5rVU18YgvBGgIK2V0/Bk+ovpSZKhWT4BBRCDHoAQKXv/gZTqnIOEMUq7jRQwBlIAYAmMnHIQ4IyMMQnELrnHe5w2vcqzCt1CxRlbAIE3IB4QWBt4I2JgIBuP3D7ekFjb7CZuGpL5vvxOtYHx3bjuVv4bI4EoLYhooC4BPbdvHDIbVLSh9veRyrCRUdbiNXEaA2L8ZYN9ctYGgCDtaSvH3Zj2nPy2a8temGDH8kXPXeTNYKDVftaGvRKCnJ9BnVOQQdEmVMHuAw6IxGAHTp61p9RT6Yh3Kaer1kNyTM9t5DT543bOUjGjDywb/i2sF4Ai96IHCIiAl6/mnIK7lCno5Y21Es73LScWGHjn/qWreUXDO289kw5E9SgYSBSLNWhKJTPnQK8xXZBoSQBjig9JrJqZMQXGCB0ARiWxDBBAB0uidngVOPjiO29JX8n+nVJ5A1E+zkYlIcW9dgpifMzLvgGxWodqQP4gSg0KWkYoEBWr8dqUzQwK5iBhHEjpT8Y4EHYRGIMBggnjQBgpgAOBohl/RlQWwWhn/QCe3Jv7tLRuVv+Xhau/ZkBEDm0jwsCmxDNvSIRWjsmQ7Ox6NTICCmqDwgmATFG2TTlPLgRUs2e+vULVp8wABMrbQ5D2kb8WIiIFZ4SwI4yDbI+BEALhGIQxZJAwDgG7yNKWnIFehIw/1imsbG3EBIRyKpeMo2re96wtM1BotWOrLRfZnoV2NGC341iwA7sYZ2ECD+hqMNAaR/53DdZitD4+T06+UWWC6B6judcY2NKgz8GAnNNmaFtgYA0I9Eo3ARNIbFbeb9e0wmByLPNJrp/p31cUFHzuHlm8yB8rwIDfgRIU7Nt6JxYYACKMS1eO8JGvy4pQd916Jh0QkGtWX/5E5wFYspDtqoGC5rDpuRlIC0gQgAAArFnvrCiCzKMOpMCBMwI1Y2ve/sZO6+krQPOl2d3XBo6idqCMe8kx/Y2cgb0EEDKgocSScJSBMIETCBo0q4iYwUEBW2RMpplN6zKAIMor2IjhduxzyUvNB+SbhiGw0p4aFgiMuMsAgXcSHpgQE0BgxhwgIHueRk0HZSgmzusldGPX+kpWaiRfNHUDEHD123uWPZbAU807/W2sgQECORYRFTgUxoWB4yliipwAgAGDGCMmxxiYQl+ipfO7ZyBgv+XrwqEABkOQ7YMhJKAwMjlgIIY/QoAeB2VFiDAhYGQAQwRPAhgmNfTSFNLvmBg2b27QfVPNHlQA4Ua3YQ0IaqbnRgOCXhtF5hmAA0oWB0AGao6J8+G6YhowMAv9yD65Vx3e7JWtgECOz0MFrVkX+4CCef5LHxAsTt8EmmDAGEi5Z37GS1eP8LFvnsPn7j2PuxQUyLXLQKA6tHc5kcDAGuLSlSN8+Ovn8MRPPplBAaSTDE5qnBqK5oOb1DiRqBJT8qMm1RTMmrNxNKiXbiBhUI8d0qHTmvQhTZhKVNeodUSYf72x1Zmp7hELZWvsq7euQnEvffnRwESQQWtMiYABoxdRAATTsiGQU2aEGJTq1KSOAL9vXoc1JRcjEu0ZGQkgjAHYTcYmEDBZP4iYWAwJ7xgc4szzZAbIAwRAqWnzRktqOie8udLDM3aYszJbMijJ6LCFAfqgwLMEuxgLhsCMT2TGsf59fRcTGNhNUwICGRxwyl9JOQeuf/r4rU8yTLkE+ndU4xIoOtYAGIcBQyC8vou4aRTQEFmORxBGltT3uNPZJjq5iwKkcw5BMvkG+T0OQTBAAfYJkTjT4GmftavKVtsU7MKKVRt6Vn5r++3L9ORw0DxksHPbUp8+S+BLzRRI22TjL/vbgMCHdlIIpwMIlsBAW7/kOpsjsla2L8ot9duiT/dlB3pgoAcEAkpZ2Lorkij/fjx+z5PCdNvxBSZgH4C1VE4MMKgFcumqfCXxifuexN1OqK1zragtdr9FiIbOfB8a1Ru02cxeEThbBAYwDLrCOhNGABMxRs7Z36MlgCmL4BGvn/LGdQU3yMIPUqAEBS303hqsvSeOri42WcwzJaOyCWMEJlKDqiwJJRkIUJo0lJNyD8h7PGIQMNg+KcIuWNuUHb72fGo6NEIbyMVIbdsMS8Eaec8TSCuPhaCGI+h5ChBSqKkCCdY+SwrMGxSpa+l1JlCg56bjeizlgETdr+GtInSQPM05KDCWwADC8S4WgOD1iQsgYAZK2qF8lykyhiBhq9w2+lc9zRgImASg3TRQAgBTnHCggACI2h8DDgYBdRq8xjiwgEowdpEwQsAnRxlTHKXBJg0xsBr4qO3CRGmKlxyrwIE0igLC7W03Yws8oFPZWQ6BHWdcX1Khlfq3yJTTtv8rbaF/9wQDWxNDARTGElj3aHOIVtouXc+Ys5T6bhaSNaNvx/K1VQft6NOeDvW5A70wgQcC9p4GBMopto1QgD5P1tl5P55Qprv3VcV9ivVfQJzmpQY4QcAgb79w5QiPPCtfmTq9GRTwTFB+/fUIo+jl96AdPg14AqDKKc075ay4PVAgnfN2oPuaQEEfPTKt0l9CZc/fa2n6lxzX3xXCBZwoHKrdUsioGKVhDAgdwMIbDNbETPOQyiRFWklSBKIyBwYerKK9KVf1VKtesfoMUQzLBABRFNSu9jwVo8Wor6qKi8kAgFKvlccZkJmjbj30b80epL7m9vviPdYJXHjsxXnJ46/DMp5uRgoZtEBBCxB4Dy82to0hmKD9TBmLwNnLfB3ATYO+TACiBroji1KWEEemtaWOyirF/AxfJpX3aAAfJUAWhZllYO1m4AAoAUKvtICc7Td5eieimDGyAgoM2PkZIr0SiHJlQj7ZG59ieyVMcC1AYEty3VqxHC5wDRIyS5lCoBoOyowrEuNqIVupQ37vNb0JbMsXMCBg71YDgfK7EZglB9rTTGavvHYRH/n6OXzRObUtfbFoy/xv/Wt9+YUr4jSfwq3N64ETBAyssxko+NJ9T+L0bWcBlJRNt3SEbMrWQMHFK0cAgAPXUU0RAM5zg3kI5Ly70sODetHCKJRAwRvSAyDH01w17Zljx8z4V1oDA73Bmzvtopjyc6p3A+T9AOR3HOopc9cGFETGPkt+HUZ7z2ipTFDvQj3EgZEyqUm94GxEUNDQMMCime/e0EzY1h89KNhaAnx/Fe+3XjSLTNkzUv5F9iTFSBhAFcq+3DeSLGjjjb1tb52+62njUG2Prlmanq0zYul3sL/U7J8DUfJaTU5bi4EGxSm9CNCsvXoswRIomGKeQmpjwe61RpEHEBDEKfFMWs8t7DME1w4Eet4ysF1/eBn6HC4gy9iYyjqXy4cxPUiQ4/MHe725t640o1+922z9DDgdkM6n4nzbb+WRZx/Gl99zAadvE1CwT3+1G/v+KOpHEtwvOqf5bz7z17q3ODHAgCCg4EPPPowv3XcBZ37ICdWhM/dzdr0VP/gHt+/o8hE+9OxDAGSRHJ9gBNigz+dHty+DBWoqCn/uONhSqJS/fqvG1ACDlbVZAUPR+XPnTu9cIV05L/+t58rWA7slS18jryiBLJORJEkz6gWmGK9nfn2Pzm4VT2fL+7eVoS+iaNRTiSJHMxiemk7gANUKZI7dAMqUCT/4vee/VMgakPO0WJAZaIA0254CYTTYEGWxnqBx9VGyRIEhYjcJE1ZDDGjwJDLjGBFjZGAMCMocIOgVkREGAUy9aa+13Ac16AYKQko2LPMMDgbZbzkIlhhqsxQCyV+LdY8hgBQoyJpCmg1eGSjvza2V1Nbut5W6ny+BAh/6iSj7PoAZ00OkU/8CyVRAAEGTdw0IAKpbgMUkIi//614vApgZyDWdsaYvWrI0eVn4DpRlN7pcrlHlaPlcZvivVUemvTTXj/uAAJpdn/tcKzH5yfddwJnbzuxlp2b73EELaR59z+zjPLxelxMDDF64chEfVKR19rY8+6CgauAFW4nVf3q1vjkFHF4+wrmvPIwL73sSP/H0u3BAGcnanVivNoUAWKem7EGgASAWwALc+R4w2MUHVVVL01O/h/ypY19wf1vUl+zPglxKfqlLAYh0R/dLjIFEVkAXMBjFWmcee8Ag+1wdOnqh9BJ034pybL6gvdoGcGDo3abHJgp7RY7Qx1juQlAmCoQ0M8QDFKHqg8bhWT0mM/gMooAQWEMkEqsPAyMQY0cRQxgwBM01UCYgJXQyY3QJiACwY6CVgFgXn3ho7EBICYfy1/IKDBD4mQk2rVTAgSaFDjkr3kCBX+VypFAsfBTIGbuGUl/q0612uhZQkC+e3681s8cYLAMH4hGrERv6q9lt6ePlvrwy6BYA0IyVV3piq46AyY2Q+rHcU8+r9GJaYdjdow5lAllHbtWNNQBI+1f6S2vZbQ+UatDQ+gsAP3Lb6VzbhQ+z0WwZRUrvSV4MDFz83hE++OxDiYmQ/tJvmRMDDD747EM4/54LOP1DZ0rEBi8uYPYVNSsLDXB45QU89OwH8dR9X8LZm98u106vS/8lxd3JkgZdNpeKDssOMBhYAFABBgcgHFiw2vu4ZY9qrn/PPPwaHFQgwE+D6dFfxfXFvecdzbMbCSRYi7C9Py0rUg8YRiTAYPHY3uJKtq+uR6vUyUatKUg9j2mf4nDEppKrRSk3oQcObEptXn6WMGDAAJmBIN7zgDGyzkqQ9h5B2JFkwY+RsNOpnmOgYpbCFBnTGDZNW1wqW6cpBhJg4Bc38tMU876SJfALHY1DSDIzo5amlm5Q8vuW9bevCkl7GcsjYEBWCPTgwOLkxJws/LixT69NG0zLg2t91gBALa+WB3xteqHM45qFZeVmSUekc5xO9KFMuHNKubR/LyYGNt7XAwC7jzHUbYe0MvYtOwSAdt/HpuKAgV9KnShxQWBI+OD9X30IT77vgjAF2W/plhMDDGr6pWiMag37TR+80d+HVy/hwec+jKfv+QLO3nw7MMn6g7R7XR/kxFt9CIeqL+MN9j11ewTagKEGC1IdBxjsYpTAYUvxnaGH7uuB3uz87mbLDELeMxvkilrL7O36d+l92fSuCOBA1xxYYhgAdL9d0ZWRVrkXayxoxhtYerHECFPSlGKGBFZvQAEk6Qp+Tj6k2e40COW+myJGyEyEMQxFtvtNgbGLAX5O/C5o0udoU+GWFzoCyjCOBwlFXgGV+3oLGxkzIExBDRBQrHzY/A4GsoJOX8pcMXBAOaSX2uRGFsIcHFi8fIT230HWcVi9l+u/a1nzLQBgYZk1Y7ikC5Zqye6MUhe087W8wWfOs4vqpO+hvNW6nPRvvSZAqz8039vLp7hntjkA8ge47KWAwhGtbQ9Nu7aiqhCN/zAXYUKKm2FK3+Q4uvICHv7Kw7jwvpyzwGVlm+XEAIOzFSiYAQL/JTwgfSpXStVoAMARh1dfxAPf+Ck8/e7P4+zNbwOm1/Nx3S7onKLhgu6i8stqiugApMbzgMHAAoAuYHA1TvtTtVfkVCP5FtKf7Sd0WBj3xD2+CyBLC9uIyuQdI8sgJ27OQUP9bYB6+pdnGIASNABuOt+Gui7N1tgSd5zdr7i3/A2d41YKhUc5Zsgs7WLKMiVkaj1Yj1kuxqDevuWr1PPkWZmEabTtMtlzaXndpdUpAXTnysv7l8sgt7LiDQgMQGIGZJvSUtZm9PZhB7YwYXVhd56BNntVwnwMkvsvMOfcElaDZ23KeYG0Xry8V3r5Q3XY0PqozZWv5WFfE10zgsX492N/i1Wu2VWtQ9Z/UjJr6m7vWYRqv6vR8uOLqizrwGX9t+B0LtkZq3TarmCmtzNFxUtbI8mZGh5Mdka+okcUcXjlEh569gO48N7zOHvbaatxs4/W5cQAAzW18qMDCHIj+Y5sDVgatsOrl/DAtz6OZ37iszj7prcC03HxPKp+5wMNoEABJZtQgQYDDHpuDzAAVBhEpDfJMcatC1j0FOIiDeZkNEPBNfOyh2ue1gtwX56bf4XOgwZRGPYVujXAUMd3D4LJi2ZKZqksZVpvpSH9Oe179g1AOsdZIdYKSGhB3rOYRsuEoQZOLMmfN2FIvzPLEmZ5HNfyUaVFOdq7W0gBuS+uJcHN4t8OoG0xfF7+W2nvWTH2CTlrg9TA9cI9NTgAIX+4DQIKUnvCJdbNaujkWB2t++MSIKoBQP5q6ILxix0Hy8lksVTj3fRe3g5Z/hTkmwpOJn7sm4zyo7ctgARch+7r6L2Z8XesQG1XbP9i/dZsS5KjsAOmIxNQIMbzVy/hwa8/IuHvU3eBozAK5pTmZfXa5cQAA7BO6FkDBDUYcL8zKHgJ93/7p/HMO38NZ9/41hQ+KMqs8RpipgwAdKPYnwxfBRq2AQZ5ph808jfXo6ekW9T/khfQlKceu+6BUOVocK0gEDIaDkMhh4MQZoAhmldRhSX8x4aAEjRIddWw9WtalMLLd4qmBgTAMiioAcEWGttO8nUNZlgqj8rrLA4LuRyDsiwM8NgPzax9pKYu+6yx0VpKdp/kt60gYC9ZO1m62usLc7p2ERyoxU/fZqH8XZbEAVL5jKXVST0Itffw7w99dqAaOOnvGQBoGP84dR2p2RhfGd95uwQBeV/Wc17v9cCCZxfg/gK0t87LfztOZfHelR3R85Md8bKoAVNTRp3K1vYmyTAW6I8MFFB0IWvG4dUX8eBzH8XT9zyBM7fcIW1JUXUn53ssAKkTAwzIvqSz1pi+IYsGld+Hr72M+7/9CTzzzsdw9k1vToBjliDSQ3XphFIZ5oYrcX7yku2awlOGQ9UOMLj9RVjCPzfRdAulSL7s52B0ZajXZdDgB8F8QNQyLOqtdSaTTQGeKMmJKej2kAEDBSAMmWUIQwkYoiQ8ZsDQ+N3J3XC7ZsWLtxWn9OdsAQSFcaru0yq1MYvsLiy8qvyudigW+0omap7wKveo47/uMaXhXHLcGsY4yyL/vRYGppZryyNMz14bG67ojMz8XpTlCgBgTiugypc3lZFiXSLbTvV9jFTOBojRBidrfaww+Jj/DsEDAIg+MwAQp3J8s/2u9OMNGNep4t4R8PpOryGn74pz0jVZLxbsju5f1Hm9pL8u/b/gSFZ2Q38Uz5nJZY3RMF0Qj5M8ZGPK750/kSvnszEB0peet/D3vY/j7Kk7AWZwmmirbZva408AMNhszLoNyzh87RUBBT/2abzjjW+e3bN8Xl5yZJa4WFeNPG1TdebKU7Zz9gEMckoFGtIzFkZK5dXPacHtsmsjZxuAamim+TItde1oGFQelI/WoMCAQ8iKhCmAwoDEMAQFDmFUL0OuizMjWAMGe7VMTa4M5+77LC1ksmS46ilzve26DNWJvt7aEi5+awfkojJeW75361hxvCWg1r5G5VvJamuhliW5FecuP7pbarn5GXD2rsbQRFRAwK4nIFJOFvXfZ/HfZlkrS8CylwOQwqocgciguFMwMGWdGKdyTBtTUI9lZ/SWxnFdQjGOtYLOwGd9l3VbBgy1E5DfaqYTk6C26znAG39gPwDgbcw8lKo7crU22IbZZerkUl5DHeBYOpesH3dx4OHwtZcl/P2uz+HMzberjSKQrteenCgFE0vl5AADE2aL5t6Agg9ffQn3/+OfwTN/6Vfxjje+pYkEi0be7dL1ddNz4yPnFEK9Q/4M9m33tQEkx3IcvkKOabs3WFr+SMkY2PsAmCuG5FX0lIcojqQ0OIocTD4pO22aP9PVN+0JKpegdGIwTyOAhiHLpcEiIAwKlDJYYL3P4MFCEMVkHrTPkBbveA4OrinUkF4vA4V0TsOozQ1bBU7ZK55+jVpD35RLoqobXpe/ugUu6v3AttyCutRe+zoYYsxkAbi+q+evxHDrwtWYsX4mz87vK8bYGX7HJHimIAEBZBAAor37Tg0K6v6SGZI5CABHYQVqEGDgoMEOEEcZvyo/TmEF+708dvMLyBqgKbtUx2+h8wz4uzE8AwqVjvOGkepnbtVv8iJto17rOH/uFoen6ncc57ZhZgfs/UwukP7LxgYYwqcAUoPO/joWNuDw1Zcl/P0Tn8XZW253dklCDeCQGCO2TvwnIpRQAwLb7nm7QBsUvOmt7hjQRcpm+Ip7aWkAA26B7BDAO+SO4QYS4AcQ4AcMgHIg6TXdXAa/r1VqmfRosiUwwFHWtTdFkrwTAHESWZlcGgMoVdMPHKt/CDo2LGQgxj+BhmFUsKDKRsMKRJKTYMwCa44CEQE0JARNYQCl+GVIg0/+zResqkUmv52xrmRd6My0s+3hFkbQKayC9vQVKChd347tQb+P59zqM7zUj6ilnDtlCdC06r5Gw24pru7+PdLMomLc5Clf+RyNg+u/MkyAhB6MRairHbCtn9R9xPrFDARwHQ4wox+VHXBMAHuGYErjlqddOo9tzNp4BZpjtjtek+wgY07Ha5K3jlmeMlCwcUs9kGD3rBmFWmit0mAKVnVbcSwDgNr4J6PPlZxWCnMrxBJBIYB3eu9pcswpIOBAQwAsmVNsoIACDl99Efd/+5N45sc/K1PqFSzkr8cYOAC28VQnCBgUgMB++8aenSvl8LVXBBT82KcFFBQKKG+3OkazU6Rchw2KzMCCizN6T9lAgwcMeSABCSzoPXp5DIsDaWbhysECYH8w4IGAl5cDC6X34ZSOr4urNxkgUBklDyTIPx7GpHhoGEEVs8Bq/GesQhhV6WRWQbyVDCTqBasSe1B0lSWDmTeXVz5zxt7HQVthMqAJgtM1qV4LHnZdNvTZvYDFD7L0DEKLASkAgYXeHNOGvC1gUWWfErYc/areKinTZJhAPrdtD9lY706/KEAAUBr6mg1gxxLUbEDMrICNV552GQRMu2KcpjEKAMzdMeoLV2M0vaMBdzlQAgUL9W0FCSnG3tBntUy17vN6l+NgiQWoWROu9fwSc7JUiORakxNikpHY+PxeHhwkxNkoKfz9zs+mxfeKRxbMAckzFSAslZMDDOq1CDYowjLR8K3aOuTOt2EP6bRLsbUWINB9rdBCq1AIkpEahgI0sAcLk2MXHLPQAwsA0E4EqkrhebZkmQEBe7Zk2nUBgQcDSclwBKYpe05+kLXkZAMHSMYcAHgYkrdv3giFAAyjgoVBwUIGChhHkU9KVBxAdJyYB5v1AGUUZrkdiW0QJeU+l1Hgghl92PldUuRIir+VFLUYIrP9K2wP0Gvbec1bXvuW2PKNKlkpuucXBqAPAEqAvOB11uwb5Xhseh5xAghg5Kxuo7Mdi+BBQq5z9V6N7RlLxL2ZVRsYgTjJfXa7BAQ4xjRObXs2NnVcApCxuXFcyrYDBDoukx6zsckBHAMQxDsWfaFjESOkT8cMEETYLs4ec1sZ/Zri61XdNugyOTYPfSYWwIOA69XpJqNa54V5H0/XFf3f9/W8ffhH383265bb5zdhzucri5DvkeXQKicHGDRQYjfWSKRTEj8pQlWkleGACpEjAJlLS8wKDvx9Qu6EYagGU9WBNnSk3Nl2aeDNwIIONJ6QDBpPu3kMD6ZIdVqL1dd/p61IfOkzB0UohXPuwBJL0FM8SenUFKXFMTttlmZfVF4IG1hQdqAACgYSxgN51/EAFnoIFZuAFHogp9BGpMQny+twQIFd+CYvdFVaAm7udUd64YItgMCDgcX8j9yOrRwQL//UbxvArVCGvf68ECJIpQdOnbGhnuHxvzsx7ODDSva8Thxb2CFGmvJlCwqgiskqIJB9DiDY82WjaGe70+a2nwGB3OZbWYEUGtgdy9jbHQtD4AB8dzzuMxZN//jz1eCn8RghLEKcLEyeHFaydkxGMmS2fDIdxgIQ6jh7qsiC81cB3lY+QPQ6qAUErkGHFyUEcFQgVDAFKLer8bCWgA3UoOBOPafNKkjOQg49bGE5Tg4wAPpKyVgA7ZmHr+o6BT/u6BcTGpDihsSUBqo0EucEkWFU5QqdCRJzR2eeA4Ut9fRlUhrTOmNQsBNcw8asKFPsyoOVGigUCNG5u/MK5rNaoGClbAIFbjCyH5hyg36NXAwTgFL+OazggQIGAQR8/Lq01/HroPFA5pE7kEA1kxCNjZjygAyjggAboD4RVADXbMooPGBovJsPF9ixOmSwFRBwlWVehXwSJdoDc/rMLp1sdXSsQbGgTK+vLxWnHIt4e0rINXDscm18GAlIFDU779T6w3oc2xgiXgUIacqXTQ8DFCC4MEN6Ga13EtSGNl8CAyvMAE87adslMDAdd8fgjCpf0k8UkD5jxDZLKMp4TLsH0DTJqo01OBiWKWwxpBkcELML0Uxeqg391dFbyCBnBoZrpreVXyEXLtY7P7gHeqswC1AkVvvrlgABKJThg1tudwC1CrW0gEJlG3rl5ACDljGxAWohAgCHr76C+21Fw1vu0BNZlEQVQjCURWn+MadBYewBDUGBQyzroAhxFnaMhtrK+jYTeor7ufsYCgUyAEmINApI0AGbklpmbEJXkrkeNcreWEiTBbcYi8IrscHrjU+V7k4ulV0oRyRQQAYOKABTAIedGJzxQFgXIv0bZD3yYRTlOY2gEEDGLNgaCZwBA6l8ywWYMkgQrzHHoVP9aaa9pO6N9y7zBBZAQfIaHUNgAMwAwe64zP/wVPI+rI5Toj0DUveP1kJHrfazNhQ5zQFfCiPZtuWFuDCShdp8zklKUlXGyOLYNB4UFLVgafmdAEIYGklbHhzo/nIlgwwSRCDLbb0F/DnwBniQMKX29SECTDuwhQ8MIMQJ2B33wYDPAdo85tJOOZfjHPzWxYf69igWZxdwMNOk88c4fZVCAnC6tWYG5AX6zEDLpmxMvCyOO9CajleAICdPI+kfALNZaYevfUcTDX+9BAX++Y2cmsaLLInyBAGDhZLol6sv4oFvfgzPvOtzOHvqDndCTe2R209g0tnJDiRI0lqAKWKYZ2YAIQQFAcI6eNQMAD5PEhzXB40xGjH2KSrbjjGFk+SyTCP5wSavuo4QUn4FBcU0WhersgNBrO+mIVnZN+2AAaBpyvkS6oBRdOCgAkwtA+P3cdyp0ppyoiE7wzKMid5MoQTb5gjsdsIWTDvwMCaZGqtAem6mMznnGBiK1/gp/Hrlbv5xYRTINUrxUpWhSPuWmII5KEh0Mucks15sOVHOBgR2x02jwdMEjpwUIscI1m9aJy/MvnG9D9VqynMoQzE0UBoLpN9EIItb+4RTCuDxoAwjuQRUDGMCcxjGglEzQKk23/W7/LcHDgADCKnz5+Z1rONqGycnRNpj1r4LgCBau1rbbgEExipYO2nbSpWWqWWOXAI6Z2yobhfP2Lk2aQG3IkmRcpvXTsxa8fkwm5LEt4QLemGUHhgASkCwkIhZM1liY5wzYblOFWtQTEk8dVe+vytdILBnOTnAYEUg6YNI9zyOM6fubLBQETa7e2mOa1KGYcggIYUpGMHTt+QAAiCGIrQ6ZNhPqS6VOpa1UNqgwKNNtzcN0iGBICCU72jvp4aI7BggsqMgsp0IrOeBbDUuM0jOK8G64jKlJYAHOQQToF8pk7olgDCOua8E5TrNiMQJPB5I3YcRNFidWQarfc5NlRqRhZlCGsjFYiIaF82VXQ4tbQYFcpI7JiChzjzvGg6jle08fw1HxGMBEAYCBGiwyFH7aXRgARVQyK/rjGZFIScv04BBCOnbCaQzTWjQT8gqWKAQEA7GbGBM9pMYIOYIGg7AURMCYwAlDTeCsZPzJgAjJaCfWCcv+w44gALGkj0oXnq5fdfa1kIEDcAXff6ASyqswR7vdpsZgjVQYICAfHjHsXNdQGBjREN2S4CgnnWVxNZLNq20dwLxW8rWkNcWA9tiBoBFdmAGBlrMQDDmMgODw1dfxv3/6ON4+t2fz/brekHAgk94coABMENPUoJ8EOkbH8XT9z6Rkdbs2jyFJH3As6B2Y/Ebw00CCLznxrEPEqIYoERnWdJaBRQAh157r7mWmOUT9PJF+pqNFcmqc1qFPcUxuoFoIGFw6DyaUhMPh8wjM+p6UAW4O54DhAFJkSUvskNZrxXmKPlJmfkF7wAaJYQgCn4EYZf+mgyNijT6mSKQ4s9RZdWlnqEAgZCS13oy7nmWiy/mrkkxa/32gfeKjCmw/tcCBeZJTjsxPrspAYK4mxIYSEAgAQUDCKWntWZoRATWV4P+kb9xUIOjQCAESiAhjANokLrItnr9FJCMPhSLDQrulN0iQJW1Mm1DyPQ0cx4CXKDSjuy51DOeJWi0z6b1VXxoqLXeQCts4EHBtIOFgrqgYK0t6v1VnkcBBmy/BwTjQQIDM4bAQnQrYIBJPpbmKtetdzluOIEDz26mc2w7JUtqyM/lgll91nrvJt1bsR8tMNBMhLUyjPkcAIevvowHvvWoLnN8F2ZgdLU02n9Fz5wcYGAx32rf4ZVLeOC5D+Ope5/AmVvv3oa0Gh6cbDmPbRhnXlwNFKQzChigRjJYepaBBgUHErnYYCAWsrTlT3vwFRSVvw+wQEWVbEpKPEvnN4AQi+dtio8cpW3vTJ34NjnFJs+ze3mj6LZblF7vXTQxCnBJURQ1YJA/YMIYgd2xgoMJ8mkfv5pZSEqmTFxzHmaWthqiDkW51eOxd01TtoSZWA0JeeDpaG3zJH3IoAYF024qAEF0CYscOYGEMswzf0+vVHMIKCAGAxcBIYTEukwDiY4EEHdAgObQxCDXGjNk7BU7sxKnPB4aJcdzNxqi+l183LvRpnstL34toMDYm2oacLOuFKSdXYIgjWH7+NnADPjpwovMQGUoAadzqPrdlb0DcL4vVwnixScGPMHjWdw4ZV0OgJbYVt9XKkZArm28HwUBPD0gUIcmAfB4E8yWCdP9UTx172+J/dpou7KsGPrt7updlp3PkwMM3CIlgAz8wysv4MHnHsFTP/nF9D1qPwCWhJwXhkBh/BMVN7wBBVBoDPgtQAHAfBlS9xwA7TBDJ9bVR+JA6pRA0RFLhbisKEvvo5TJKhDimD7eQgoAeLcTWbv9TaBgcqjjhb1SsyYNpVa+mFELKJVNBKB5GewpZh+Xlgo5ZaX7ja5QL7NMouoU82RsG7rqo/sQiuW+JE9IgQINEhrhKSLNm075IEP6ahul/rBe4koYh6eYAIEHA23mwLeZkxtC13ONkbGSyJ4K1Yrbh4xUkVPIOQYo+n9HUdfe3JacnAVQkEoNCsCF/khrhvg+75V7DUYotPN4NDmaLFy5JMyaDQDWgQAFydPZCgS8oUyyB9b0jn/fQm8nJy6PG7Y5+uM4ZzahM72GikUA1vXsFodrCQi0+haQxnkCRsNNAIDDq5fEft33ZZy97fSqvZIyFP2FSWVUh1vY9flGOTHAgKsGPEzfo3ZCradzLAiG661qcPJ4kH/bQF6JHzaBApCMKZRI6y3BPCtVR6nDBOyZgU6W64w12MeDqiht74kCrF6+vDsl5VeCBeIoXrkBhRgTW8A+JBEzeCoWSAKW44ZmEOy9FhTceqkU8wxcuOVKc+WwhfqzNrOER7iExhk40EPgKNMo4w7AIKwGabwdOyBKjJd3x5AFZkhodq0rNJdCYu8DAKNULfYeEEJEhExRo8lNVQPUmFtlkN5TaPwORZ3isha7noOCOpyQ8g1SroEmJAZNbjPgMwwgnaKa8hQSjZ3PlQRRm9GgM0/MGHZBgR9D6+Agl9qDc85EKhVo2ALbSBOBAaXCZd0AAweSeFuFKVvOcD1l1IUQuqEBzwjYtN8OCAARYm0k5WHXpWvSX1Iw7KiB1iwyjHl8ETAPS1oY0xdXp2YI1voSsB0IOBBQfGLaPY/Hm3B4+QgPPfshXHjfBZy57exqj+DCTg1O5wLwuXC67/DqpcX7nRhg4AV8eOUSHnz2g7jwnvM480NnnKLOnlL9t/8db2s0ybpP7OH47yAbeM1bN2Nf04QdsMCVxyA3rgCD7dtQupRcDwj4Dtm7ZosSrNgUn7zJlWfEYGAUOZCTSWJNfJ6Cy6gnx6QkwOCe3Vy+tQZOjZhgV+Glaza6q9dSGp5PAggAkDLp1dNBBQ6YRD4OHGB0wJICMLnYO+liWOn5lH9TAGgnt42qoEjU6hAjOBJiYISBEKqwghn2xBxM/T5jSYg+zyCEIAChAgRlfgEhjKOAgvEgL141jLJtoMB+GygYxkRv22yTGSiwNu8pcZNVPRYa7XejC4WQkzjNSMMAAUpwYJT4cAA2lq2unzd2S+MBKMeEyXsGAkowxQvGEbjx+iWxc6ZjaFBWs33NPAS6UsxYLzlW+4KAJCfk65xtev7KJTz8O+/H+fddwN23ncFx1zalWgC2uJbaKRBLrhxHgDXpmwCwMulffwS30a3d1z45wEA7+fNXLuGhZz+AC+89j7M/9A6YwD0QWPsoTu9DJ0TAxctHAKCNRQg0SNvmq/WPGP59wULKZ6gGw8xb9WXWyStmZAkE1HRW2nbo2N+/NZArilNAkqdT5+/qAQNzBA5kahY5oGTsggCHXUpeJLuvW4yEWiEYmPdayccpPvnpAMGGmGi+xxbPMT+7Kc+6TnYuPP2n02WJwByyV2TsQozgQJC4NAlIOrgJtJMVNDnougvMwC4kgypT3o6BaUyrZ5Ku7RAORsTjHcLBAI4RcTeB3MwES0YMs6mLWf7RUbPBtUECB/bODTAgQGBAPRthERDYfrfSZWIUxlEYNLcc9jJL0GAIloyJ81jLLHnPqHQvVgsf028muLVSxuXZP5qwa8/XO+rPPfs/kMaAya7FAnBQeW40jF05btAnQKlPZMsxL6ZX7Npiu+yfqH3vRV3a0KP2d18QkACA3NdMULJHrmoPfeUhfOm+C7jjljM4nvr2KK1v56qbP9lO6sgOAhJUXxxeuYSHnv0gnrrvy/jFZ/4GeuXEAAMOg9AvX30/LrzvAs4q/VIIn5WBTX0srwXYAgj1jktXj/DIsw8DAI4npf3NwyNtICJBcg4w6LDYDhaAeWfXfb26zQZYiz3ogAD2ndZtt8Mv6QGYFxeAcXX3q7xxAghzgGRfiisAwzgV7IIHDAXDAJQJRfbsRi1nMmtNLdJ3bipF7/UsKsX8nJlSbLWXb1/NV0j0H5FSpA2AYNdGSzJTBuFgAHhCGA9knYZJputh2kki6E4Wd7KkNrBktZP+HsaYp8D55MTGNEZpXg8KeMbMBr9QTopht6cl+vULPBhAGCSmnfYPefVK59mSAoT9wwZh3l4t41W3lzRW2p++XbKWLyJCgMxeCfDLsRfgIMh5BUCwabb+OXU100ajr+t7LIUBhAUYZn09zbVfMIjAkg5Z1h/ys2Xsax3J28CCv1+6/3Xqz1UQ0Pnw2oIdAoAn7nsSt586jeMWjc2cm9I5twL8BSyIHYL7MieBaMDhlUOxj+89jzO3nUG7DaScGGBweOUSHvqd92tM5kz6RETdENYI/jdQNZzfoeXFq0f4yHPn8Pg95/Hv/8N34zhyMvg1OOgDho1gAagAA1BMddpSUkdvsAAVCGjRWiaCWhw9+egN3KYooKR/3FExbxkU2bszc8opWAUMBw22pU7oBAplUGfKtz7xXCcTsR3zBmSTp6SybwEv9zx5VpjtA5DpP6UEuakQHRsTdDtOosw5wtbUBwXQwU3Fx3XowIVrOovk2P38LJHWAki1fNdnJZi3Wi5g1J0f76bD2dz4FhgwdgCQFRHLpY8pecaLnp61U6uNyhcqF7Cqcy0QXUgoroCDQepMDvBZ+44B1rcJN65v1/22xQA0AYALvbQMobx7Q2esEicyNhJFToPfne8805MNsICs22+E7uzqzQ4IWAIA9T59q1TefvNp7CLPdawBAireCkTyrc8AgBgOlCIBhIuXj/DwVx5Wp/lM9Z7zcmKAwUNfeQhPvu8CTiso2AIIygYsG8j3oZeuHuHR587hc/ecx9tukdkNhubMhKZ10RJS0+NEM8CwDBa0Fh4wACV6TqctjLSOIVqKbdWdGugj2/rJ9ZjzfS4TaHaMQEQy/YzUDmAOmGyBl8QyxIg6LJE8ag8aNI8BQJHsRRtkll6j5SmsTTXyClSPdxOMWsxBZXy4+GRjyO+DIYdi9J3q5Fe/zn7+Wh4Dg8iVuMrt0FCNsQXF0skeLHCU5DYgA7neanHeWC3R2Wb8gfUFcqrPa2dPN5SGrJU7UH0Ua5EpW2mbnBMylP1LXtxeugIHQDNfJM0htHs5Vsp5vsnQpWRlV72WbPUduHivLaC2/AR5LdemAeSsVwE09cSSjgBKPTFzrPRAIPF+V8FC8bfDGCzIzO+rAYC9Xw8ELDmfpW6dgwIAAgpaVdS/djhADEcAZ0BAQGAuAMLF7x3h3FdL+9iHBFJODDA4/74LuOvWM7II256AgFE2oA8rvHz1Ij7+zXP47LvO462G5CChBCD3ITMpvkM7E1EAhh5YANqAQS+3Wukf18nXRpwzbE0QgLbMTBazff4myB18qXjgBHCmuZqyUNAQBgR3rGAZ4m4xLDHLZfBymikIq39juMwAVmnoN7MENTPj7109N4OT/K0N2+C6/RU8su0zoBTGuUwKoMVgnrqJoGkKqQGytQ8vWdmyspzPhO8lu5l3vzXhLX0me8HTrWjf3H4b28MfSQshsXbOmEFCYhIUBOhxkXEQcEBDGQ7StpQ2dL9TMl1dOn12ob+uxcMTcHIAoHCynPHv6lRXtf31gk+oK/WknVI6VnZNBywA87G+pCur9u85TN4+rNkXoLQxct3czvjSAgYkCjDLgghMAgAk5tQGCJcuH+GDzz6ML7/nAu4+dcYWKUWgOSDx5cQAg7tPnbkuQJDPk/sxgJdfvYif/uY5PPau83jLLaexc5JM2wm9SclMgf4mpXgKwMAZ7W1gF9L9Sf5LOQxID10sLYO+1rntupa85Dpuduw+W8eFrrJ3mgMpLxeJS3t5DKQswzgm4DULS0T5XsBsTfoUcsA8/rhUnPJoZ1VvoBzdb/2hUkHxt96eVSUpUss+9kfz++2T+OpZl9ZqnnD7Nq+9sVaM3l6ZE14Yf/OkV7zdFgiYA4BrlX+9nbPBC5CAkAEC1wCB1OhD6mpsgrJB5UeXtvfPWe5Lq1+m8EoGANraaaxPsdQJMfZ1p1RxrguWql3YYv2uSFsXoNAF6VilI4skPAcW5Pwq06WjK3+QOnLml9iz2lUp7Ex6L+0u0WQARuBlgPDC1Yt45GsP44v3PYm7Tp3GxFmuHri0yokBBpNrDABdUBC5Dwh8g71y9SI+8a1z+MxPnMfbbj6N3VRKsf4NAINPrrLDCfFasqLudoBhC7sA5MEA5EEElAOtVWqk6t9zSyfvAafobmb3nCq0a/emqpImK7Ien96Py8Gv7z+AAOImYJDtHJYIA+YMQ2UkGWgvRLNa5pRjM1FtARDUxqillPYqXhk6wED5EIDMnqwmgwIOTJQULXuZJQNWVXgrZQsbFVB5VV6ubq+yMi0GoEH7+ppev6zlUnXkKpAQ+gCBBge+oMmjgLEDUqU9+mGvD3Zi4DUDkIw+5gBgAnf1ZKqh05v7j/0s01xDb/y50o9ZB9jzva6Y3D09yFgqpVNT2g47vsYCtABAoVEqHQnMZeWL2RaTU5K1viczlBnoAAQQXrp6hI98/Rye+MknceetZ+TZrO1JeVz0yokBBkv09xZj5w3dK68KKPjVd/423vKmu7HTKVju24jYcSyTyAOwq1hU8+mtkw6BZoChHAwlWIAeNy8a8OiZN3d+YD4ACgXpZGPnLsmHIR2bmZNMOAKTDSz3rB6xbDje6j7YuwSRGxGJvBQ4BCgocIBBwgyiLAbyIMK2BTD0chhssCzmcSShxZlRK16gF5/eYqCSspk/nlt1WSgtwKi+uRgeyjkdujcZN5OLVaIIUVjFkkErKulqcG3gCugDLDnW9/i9TOtEtxsu03STvGMOEhyLkD60NaDOE/F9cFb26W+VXEwmDIn4+BDABLftxn6sxnzk/ni37lCP97Wx7qs/EKXXK8e7lMDmHXMa81F1X/Z8M1AwUJDaijnR70ul5TSVrMGyXkzX2AayvPx9p0ZFavthZccRAwi7iWe2IwLJdiS9QRI2MG/ghSuH+Knn3o/H7zmPO06dFsDn2Oi8dHhfOCcGGFin9A1dNPLG4kHBW2++G7tEl+pz9AG7qRwUtT4kAnYqeDN6RhF5wJAGg7ar95rtPoac7bfzm/Sk/NzWgnOFDBzitZ8t6msNDBgQYBbZW2b6ZHQ0SkXRWiLXr3iXgQKp0iA18FmJFArEQBRYWAIsgIXowYIdHxLQ8EYyj+49Qg3ATGFXd2sbL54brl7yVl2NvnHL+2tPzf+so+mk9L1Rr4mWbfQtf9e6u23AqDN11Oie7T47a5q2rORY9YwN7diTVzDPV+9LRGKIzPAYKkCWUd6VZWpeQUoq5Ybhb1dM/7ZZp9SnYukUbQEBMeZxzrw+xm/U+D4GpzG+I8ZAhB1vBwlE8tUSQu4XwaDZD0AfwmSJNgioAUBtL4B5n0xyIhTC5Ci2g5QeGECYIgqZ9MqLmij/+D3CFERWGShwSP02v0KznBhgsBX5LXnDPnzwljfdne/VaeQaIdeDwwbGTq8b9D47yoDheCq9ZMAxC9rP22BBOqHNX7VjFuGoO1AxFipZ9cIpHhAYQ9JTFAmYRVFC9gwbcEtr7gNIc9wtGUkAARxAmAOGcaAktzGo9wAgOAUSdDAFItmPFrPgQRYpYCinSm0xeDM5V3+X4pd23ipQqG+KpSSvFZkXb8Wzl/RKdQYAOjTVvuxVub884H/Nuk/1e0uiW6sUMrB4NxU/E5PhgYIHCdGNU2u/XqhB7mNAYbluXVCZxlYbBNh5FjaNsc0E7BwI2E39cX0jxvQxbEwzdrp/iMBOx/Uu2O8MEkYKGAJlLzmBGwcSTN8ZUHAsgQcNXo7pd0cP2jE/Hr0utGv2YU96tsHu7VmViUUGLdKIIf0yrVdgbULAK1eO8LFvnMPn7j2P20+dTh2vBAcmmeVygoBBqUDzgWWlYR3pO8oUPPYTMiWxFwOqG943+FRrvCoPwS+pSoGwY10Exg0IAAk5S/0arIIzLJa80/IGW6XlVS0Ngpb3sFtQHBGcFIYpJ3muKZN+3fyMtgBKCUSyXD4hgArAMO4yWBgds2AKhQgI0UIQ7ICChGeOVVFtS/pcNpS+NA2aU+ZJ5h1AsARu/f1big3VMSzuWzemWwz9lnN6ZQshs5W08aVVp9YYscQ10wNmTIrwHRpAwO6hY2IGEDIaKDLAnd2av6fb3icJzrMBPSBgbIAH+DvOIGDH3BzHVo/IXOjRLeM4RBu/lMbOFAghirzT2lzMsmKnAvwJjCESdiGCp8wgGEAARAdanF1222C4Nv1n8l9jAzwQ2MsWVGVwFaFA+u3W9UL6X9ZVsv+lqxfx8W/IlPo7T53Rd2UEzv041ci/cKecHGBQ/V4Cs0SEwIxIIqOXX72IT3zzHH7tXbpOAYvXPkVgYGAKuUP0lGDJJrQfbswBESXQkLwRBxSIhUoSQ8fYTSXFlpJsnCLyDl9cqKfUz227awwQANcHCgwQyG+9fwMkzIobLObJmVJJykYBQyBgDKEECup9jIFTCGIMnlEAhmjJjSLfiZCAQspFIM/KZLAwW4LUSkvW7hXbymieE+P399guO6c0FvmRHtxV1SjAbt1HWzHQtbLPJIS10gqnr5WhErxnMYpE4GrDe/bmeSZmTu9hiVy2FiFQUrKA89ow3y/7HEgoKuJKJfYWCLD9BTCIZb+w8ZmYTM6MQB6zefzWQGAXYzFuYc/YMm5dCSAgZoYOAEIQAxXI3FcgsBrDyJgCYTDWMaIAB1Ng0cFKpSfwRi4RD3kMEPVzHbp6T3e0dB+AWf5UDQS26H4r1r+MFVg8d5ZvhQIUWHn51QwK7lBQUJfEGmCLO3CCgIEvNSjQD+VmYwrZCMx4SackfvZd5/H2W07LoCMxzjU4GEFJGRq6mwKBoxqZRqdoddLBnbdjTkBhIMIOmWYSJC3nCbouQQKAjKTdu9mA8cXpqlJWmB9YijO2yhIoqBVLBgrO+2jILbMF+jfkGGIGCxEBYvRDIByDMIYcfhh1aV1jE8ZA2DmQMMEnNAo4IGSWoFhrAVwsQQrkQb7mMBcKaStL4ACBP2dL2Mees5QYulW5tdq8/UnlG1NaX2Usk9ec8Xeg2p9np7QS3ACUSlZlTUDO8oYe1D7iAUJNydbsge23Ev3rNMRWsz5dIFD1ibXwQAYA0l67GBOQ38U8Xndx+1hdGqe2HSE5P2AzRgIUEKzDluCgNXgmqM7tgQN91xZQN6DQEXcTOG8ZM73xMgfY89JiAnzfrfvtOFh/7oMCc15e8UzBrQIK7P1DJVwPDtbKiQQGRUnegKJ56xEEvPTqRXzsG+fwG+/W7E1ox2JREhGCXhkEMmOp1mEMAgQENGhDGs2kj2bO68X7DuO3B+TOZfvTfRQhywdxpXcOJAMFnJNSgApN23tr0XE6K7XDV58zQAdlVARv71qFSCTWR4seRa1osrLpnJ8GnBhv80IA9UaCUJWBRBkacDCQINtTxSRQkZswDiFRvsLUcGYg4CnlPEWqCDMYA1SDMKcse3HzLaAA1Tk3KjG0zgexZ9SxY982BZCr2nmJVl4r9YKIXpmVRsfOp3ReBgAlOFhKcKOAJj3dy/K2Zxgta+BA8EE+J6JmD/LmtfSHFhjoMQMMYDfFIlegZgbWwED+XYKANQwYmQtjE4ikPwSeGSZffPv50ks6rmXm9xSyR6nHiq7J5fEbBQi2zcbQ/tkAs/5dZd86IAiQ8IHlFNx56kwbEGwEAnU5kcCgRkW+cwSIAZXszffj8ypUhgICta4e5DIAGghgSgIbtdE4Ii/6YrGiCiAAJUvQK6zsQU4+4Rx/YqXJWAw1BSRjTUSYImMIlAeNGz2JCl16OGUDZ0wJG/iowAGGIAMlRqEBoxibMQTsYpSbMVwyRP5tRiQQFcZmaV7vpHUyJRTZqElHU7JQlxGsgIEQIfFMDpLwtItaRwZGEgVpoQbrNIEhNLIOvpzkQzpA2XmYVS6Cb8e6bd12nWBYH/f7toACMwpryaFrYR8g08fAfL/tS+9RbDebbrHUBqXersNHdk0rrGRgcEc5wU3AnsSuKVBi4Ix940nAYS+Zy08Ba7Vn8kx1bJqBMlHk4xn4+dINGzhAEKv2v1Z2YDdxk8nzgKAGA0tjEijDNUX7hbptlkOAnt3rgYJ6psKawZth1R8QKAD6uQE9dsve0fbXyecHQ+iCAbvvS68e4WPfPIfP33sed5866x5a1mErQ1CXEwkMrJhMBicchnwl8aPPncMX7j2Pu3RKh5VoNgylUvYgAZDGGwFMxBidp0bMwEBKacmDOYrmWEtIsbIWe5J75risAYql0gIF9b6UiMWZKZmiggGNv9s7ciAAQbftGIMoYGJTNoQYTPmQ++29TJWJ9uCeMkr7U0/nRYAQAiPuhCWIIAxqECZEjGosOMhgHwdKRiJClIqdb1BrtroYIRmaYvoa+gzNDS2cmQIAM1Cwm+IMEEi79MM9PSNhz4jMaTvvK6u1ZkyA0qAAuUlt/xAyEJBtO48yWwRtd2LHHsk+iVljltyGIRQUNQISoE6zgG5gaUmiBoVLa4pcD0Pg23uNIbhWQNBvlzYgCEGYnNZsI2PztgKCtZQUn0fSK97Ab8mXsXDxmq61c4E5M2DHevkDcrwEA+W6NrJM/6PPvR+P3/Mk7rYPIrnSqt2++ujEAINaGL3Ge9FWhLrvSdyliRopbMWZ/kpeE81BAiCNxyiZBA8SAPmOz9QACgAW47Tt+b/2XOtw8wWUCjlUA2gun/I3c36WZfpGaLyL1TxGBgfOAIGAUZOIxsiYSJbMHSIwESXPdDfxAkAweauhH6hrbGqDYiWyuXyqNR3ls4vayQPATBjB2AVTCuKlYGJ955gSFSe9nU0L7S4/SnCuf6aVvey3FMK1gwmfOFiEDtAGBTuOMyORPEoFAgYCPACw/QAQFdlZKKsVf/YGptV22fjrb6UCgipLDxgMLAya1R6IMA40A4MjQophA5SS20aTRcis3wTJG7qWsu9VdQLqPquN3ugcgjpsUBdhDNtjrgYDwDJDMA6N2US0ca0SJ+iWHmvpsKKuXvY6wPw4I6KCFeXojb84B0AOFQPlbIK61Hq7BgKyfzlMAMjHNOcrPorsL10Rp/a3fvJJ3HVrBgVeFq1mFTW1XcOcHGBQ9ZIWxXvpyhEeefZhfOm+J3H3rXOkZTMEIiz276hbNQZGrR4MlAavLRIyDOKdHpg3p0ABUCVkIQYFC0C7Eet3AEpAAHSSqYBiINm+hDargVQYLpojbEu2M6aEBgLs/YY6rk1CzaOksHc62Dx9vYsZFBhLMZ/FkL3UMFQVR5t67pVoDRiAibOxMBAjISDRvmOAhhyq6WdUUsutmDOQaWVgHvv0cpd8hSx8Cz9YLkPK2VDtYbNo7BlF6EcVGybpt7vqedbHiuQy3d5FTqDMjP/xFBUEZDDggYBnDjJQKDtyvd47AIx1/0uep/VjVuMvq4iGEDTklD17IBgak3NIv0oM0hkAnLZNTr4koO2BNfJ5LU+tVs55XJVx8h4QrEFB2sb1gQLmPNY8CKynGvaKhfPy+MkX+DHXSwSuwzt1jo+BAQvXeTAwhnUg4I3lou5CPZ6QkhDN4WG0GNEMbC3BHICwSciAe4RrtwbA7enpJutBJcjxi7PZfQ50epT1NTv2whVxar9435M43bBfuUKlkxH9geq8XjlBwCBv1+iSiHDRfWXqbIN+8cUGJcgBA2SgAEjHMqDAASlLODmtyEABrIJWYzo67bB1mlhreWX/jnWH8wPKx6bS6Y1OUQ82T3cCdUy0AkIOKHjFlb2YiAk0i3ObpwpQ4dkAZYzbSu9LvvX0xi2F1YpbDgcoe5EMJHrZEQKr9/MyXmIMvDdDXiMyr4IDi4VHKmfOsNbfWBADCWQAh5ZzATwj4H/HGBMgKPfLbwMBNTjwv0MgvO7fX+s4GshKLw3EyYxQlEaO2VAJO4QmWEz3roxJBgOZrvYGycZO2AgK6qXI96W1t3huXtdY+ADI+iKFFjbeDzC5iD4SsTqG1Cf3NhJCWyBAfrdZgV7eQAIJC4l18kwpW5d9r2diWYDGpqQToDlkSE6QBwiAJJgDAAfbs6345MjUp4DZ+9l7WB9Li6+5/gUIS0uFXMR+PfLswzj/Hvl0cq+0QlUGjFrOX6+cGGDghQ+UCEw+PfkQzr/3As7+kAg1NVR1H0ZGmIDEydWZLAbrgYYHEnAgO295hcUEFvRhB+5ZVno0HjV+tDpd2l8NqtQJGzecJWwW3laGoEtT6EaSvIQD/V0yCkEAAypGISwvjjRbGCm4+jXq3U9Yo+LdLb6Z3nCDFjAmqVd6oGDp1vNBK8qCoOEN63+k652zhjYgim0iUW47ZTuSezkE7Kao+7QxA9L0vKCoJwRBGJHVI4/Z+AzJWAdMK1MPPAhorYhn+4wliJHTdq8YY1AnueUYt8a1jcI2OtvFstM6FkMopq0Wa1sgj51hIyDohejqkhxNZKVsFHavbFHeBvbsfgNzYoqCWSNt94Cc6xOQw3dW+6hKb20spTDCtTICDRDQiqHXn2JO9WnpKCtOX0VwXh8mgfuScUv7keU9hiwXRmkcV3VyDWg679jrV75PHThZgYQp+NCzD+HC+y7gzG1nZn3O4Uff7Ko3KL2znyVXqPdGOTnAoIXiCbh4+Qjv/6oI9WwhVINU5TAsjW8QaogynW755m8I+rlSppRAZAaOUYYeWoY0/86P80xDr3gQMHtflB1Q9ucfyZPCulFsGUBWC1bGRxcAUSCRj8qDscwqrC3DWhifjl7149aDgdqbqRUZgPnUqFoxtR9ZlJbRWEtWIjhkb8qNLC9lDhCISBgqAhDl0DiIrIn0o1ATYxiCfttdoegknvZOjXwEA1NADKJSMlsQMATG67sIoy8OxpCYg2Tl9EXHyMBAiTkIgZrgwAMBCyv4cELOIyhBQSDgpjH/HvW89HfINLZlvMuUVPFa/TTVYlVMlb3RuS0PrqW4t7YrgNSBk9Kmcsy3iuS1uOJAQB02kllB0FlBempkTEwZhKUQnf7uVPtax06PDcjy22Ygge0Mgcii0p+qhwPKPCVh3XJ4yc88A1CETP3f4lmVTva/Wg7ZDOhU/WmgRt/Sa27Sr8MSgMPLRzj3lYdw4b2/jbO33gXwfHIkVd9nCWavXL8BSpBAs7eo7rlPQsK/rYVuJsZP/XHX4k/Ln5Y/LX9a/rT8afn/j/IXf+eH8U//6R800cGJYQz+v5/612mbAPzadx/Dp373U/jlH/17+ORbPg4w52+iS9ZO/nRsAkez9Du9oSItIoAC3vCf/k/xj/4vX8ODz34AF957Hmd/6B0w+sn+5aSi7EV72srvA5wHrH9fvHqEjzz3MD7v1r6eLRjSYAKkunMPp6ZBL145wrnfeQhPVvRUgYQxLzV7Z399/SOAo+9J+OaL9+n3wPV963XdmcspWUlGFcNQz9P//T96Ab/wux/E3/vRL+HNb7q7O8ujN8Oj/5lnefHv6DLZn33Xedx16+nNVGBL1i1hvnBFPIHffm8VM0wMif6s+k7xW2U5gQv5MgMvav1/9Z2/jR+++S75YpuGcmwq4y6257nvJmBttsL/8797Cb/5zz6Oj/z5z+J//T9+e7mmwULYIbgAdhkWKGcf/Nf/6mU89vuP4mff+jn8+Tfe5bLc4bZtIasyZOCp7JHCLGQwkCwj++g38lfoltqyZuV6FLfvghcvy/g6/54LuOu2M0XMd6kta+Zt6oyLl64c4We//X78/Xd+CW9542kAeWwAJeNwLWPj9//lRfzVb38Qv/rO38Ydt55J46IOBbRCL/4T6LUcCya3oX8a4oW+9my7pTtNrhcvZ/1z961nCnn3rvXF5za19O6LOjvg8XvP4+5bz8z6SeudUx9L78jJHpltesPf/5/g+3/lv8Njf/Ab+Pmjv4lfOfN38Mm/+FPYapugf9lt17YJEPv4zFefmQtay4kBBp7mfcxAwY/8Ej755v8QFKc5IGAdaroPgH4nfV5YqZpM2QAPPvsBPH3PEzjzprcBx/8GIAK5Bhn0b24QSoMfsA5MJTjQvy9ctikpF9KUlN5aBbNQAvqGyTqmDcqn3ncBZ287jdRB5UFzWbjtVAMnCyZRxvLAgMPLR/jQsw/jqfeJ0cuKj9IAjUX+RXv9d2ZGVIrcK8dXrl7Ep373g3is+gom0E/mrBOEAHSThF4xUPDu87jrVAYFwwaKef61xjlwO1J68EkNbxU1Jkc7s16kcpvNlIHG6lPfIgQwXrxyhE9+8xx+XZf5nq2dH2g+1W1EmhMfRwcIxpwQap8g/xf/7Yt4/J9/HD/7ts/hf/c/u0PkXs1SWCqhkn8gyxOQnIH/8v/9In7t9x/FL97xOP78G++cAYE6VFAve72WP/DK1Yt49BvvxxfuFaMhtHkHCHTAt2uuHAKk3L5m9O6+9QwsoGs5JGBO9+6FjCYNgg/I33QJ+qyXXr2In/32+9MH30z+ktvTGgtlrdeS5b7z6kX83Lc/iF9/13nccevpZgggf1ck9/ewxRACOPze83j/75zDhfecxztuuR2YNC21pX9N98LrYSqODapnbewcWk6ZJupZUriKvtC3dTjCCjO6+vYFBQU2O6A35tdAQNMeAXjs9z+Ln7/4t/Erd/8t/My/90Fg+v68fqZ/1c4QB0DzkEAMoqgAgZNtIgUIj333Mfzi734KfwF/cf7iWk4MMDDhf+a7j+FTv/uL+OUf+SX8zJs/BsTdvAF40t8x9wq/DeRt8lMBCZ/+578FAHjmJ34DZ25+K7D7fkZqCJC57hm99cCC1NYBBt1xePkIj3xNZk+UUypbODoDA6BvjHLnBA4vH+KhrzzkBuVx+e5NJsVKCziFQj6HVy7hoecewVM/+UWcfdNbgd2/6SBYSnkZHjD0PiFr3tSlq0f45LfO4XPvPo+3nzqt1aRc/YUa1zkCVu+kZ9RofPybee1x7wn1ktBa3gFQxU7179HlIzz8lZzzAvhVEss2tjZkclOunLwCAxwySGAGXr58hI994/14/N4ncfsp/faHzpgZg6whUSeH7qIsSrUbkXM9RmMX2CWGBvzhv7yEX/3uo/jrdz2OP/s/v7P4SJb83Q4McjJbZgH++X97Cf/glUfxN888gR/+M3cXIMDHtmsg4PMG/OwCnzcQQvb0fuu+J3FG27fn2RV9ZPYW+T39+iSHrn2NCWJCXloBGQywAoRI+S4FQNBjKTbO8m2XTyjou/3U6dTvcwxcr4MkNgPL/b6Ojb/06hE+8a1zePzeNpMy+1Q5IcfLTS5mAGOc6Zajyy/goecewdP3fAFn3/gW4Pj76OmVstK60mzSIfYCpYE8uvICzn1FnJIzCrqT3NnJFW7dlmaXLVvc5JRmByjTF4p33xME1E6pVuTnL/5t/MqdfwM/8+c+AEy7whbl+ti7T2JTKAAUQDQsAoTHfv+zYh9/9Jf/ZDAG4Amf+e6v4VO/99fwH7/jP8Inf/inZN5TpzHINVZ2wxKB6+6bNz/9X3wRP//SLwGAdOrd686qhNRA5IDCrEM7sJDXzxbzcnjlCB/8as4+rR7fLdTY9gjd3vHoe4d46NkP4Kn7voSzCgqybJDlY9cAmTmoR0/63GQewIevvoQHvvkxPPOuz+HMG98MHP/r/M5AkgerDAwscRhmgEGMIOmSsKIUL165iI8993781n0XcKcZPQeqep8lXpWfCu2lq7LM6OP3Pinhm9pIVPRova8GBLVREVD2cJlI1KukA6Q+uUjXm3K0YGaiDi8f4cNfP4cv3fck7nDhGw6UZnl4NmbyyaEowUJrieXff/UifunFj+A/escT+OE33l19hyFXfUnuHsgOaeiI4f+n//IF/NKLH8Hf+5Ev4i23nF5e/KYAZSV9TTAGojRqL10R+aQpywsAuhg7ay+mL3V4+QgP/c77ceF9T6Yp0Ub6EGUAVwMEEspAjjsGQVis7Di8+OrFtAyufUVv3z5u71cnJROAF6/K+PrSfRdKT5jQ9YAX2ViroMrw6MolPPDNR/HMu34DZ9/4ZnEavIzrT2xqpb13TGl/BgemTw6vXsJDz34QT933ZZw5dSfAUzF2glae4UDBFtnp36PLop+fcqDeM63LoeqO7UkyYhy++jIA4Ffu+Gv4mT/3fnFqfTFaxGSlxl57SQlVGwzCZ/7gN/Cp5/+6MOlv+Q/xDD/dfecTAwwe+85n8Knn/7qAgjd/bBGhEUcFDQ1A0Akr/Oq/+DJ+/uVfxq/c/ov4uZd/SZU6UDYU5Y7rgMKsQxtUd8j38MolPPTsB3DhPeez0Zh9i3but5QgphMO4IjDyxfx4Nc+hKfufQLvuPl2QaIVEOgyKBVYmD0XwOFrr+D+b38Cz7zzMZz9Mz8MvP6vK2Sf5UROLiKzOWBILEMYABAOL1/EX3724YKerUMRQGYepNrcqGm7XLoi38544l5ZUaw1V70GA7Z/i4E5/N7zeEjp07O3nhaFmk7px+STVwAUVCqlY2I4DVQW9DVkPkIO3ZCTF3Xi2nDnZWP2ytWL+L89/yE89q4yfOPXmbeytPS3X+7bL9b1ndcu4v9++Age0/AHmVxNntqF1jLbe9S2rWPypJudNAPPwHzsbGkfBg4vX8zj91Qev9qLxDlABghpB6pscQMJ5D73zHnFO+ufG/FA0WeBLEM7ZvK6pExlUz5xKr3/Lc6WvZDK8/DVF0v9sJvT44V+c7qj0Km6nfQHyzscXn0JDz73YTx17xPJ6fHXsNPBfuzMnptKpd++9zwersMfLabVyyTtr5zQJJtsd0x/AsDP/tlzc1CAEiDJvVAABBUHxMZEgO17tBGf+YPP4xcO/0a2j3GavaMvJwYY/IKBgh9+dFvnnZUKFLhzfvVffBk//8p/jF95+y/gZ//sOfzcy78E0oZLjWVZQbDOGKWxOh3aA4XDqy/iweceyZ26ZiJ8LWdgoaPEXMc8vPICHvy60ne33InkqZo87HzPnqR7lYqzxSA8/9p3cP/v/hU886P/AO/4Mz+cY4bOCKSBLAcykncgqQcYnr96CQ99/ZHsCWAS/jmxC1nFl3kcLsTg6lu3/tKKYt3FbCowYH8LQKCyOvzeER766jk8dd+XBfTZoO+xMEVRAEEBlLZLBZqM0nsVVKqisLubjDIQKOXCoBnI8kzMC1cP8cl/pJ7qLWfSdQVM5PZ2XYqq6/ZLVy/i//rtD+Dz+j157922PNvVZM/Kwz3SKctPve/JgqnxwLn9ImuADQBPMr6+9iFh4m69C2BdMYCnEhA7gMBAYn9SPom+hAcEAHDxyiEe+VpesXUJFHgTt5ZrZOcn+bz3t3H21J0gM3px2s62JnlVBhGqH/7Jzzr9cNypvGMIzDFq6FTRqyzGD8Dzr76MB77xU3j63sdFv8WI5C3DAFrZFjXYLqpRtfvh5SNhIn7yiyXTCiwDAZPJik49fPVlkc9f+lX82Nc/0B1AhKiyiTCGtSjMjjnI4OCxf/o5/MLR38R/fPbvqn1c6NdaTgwwSC8NZFCwbzGhAwrtuQQFf+4D+VxWlKbnc3GPCTDOyhCd3pOI4eNCh1cvuk6tnnwaINqx7b5ANg694hMFmXF45RIetJjeqTtg+RVyvIPwKwZlBgbc8ef/6Lu4/3f/Kp750b+Pd7zpzSjm2bomSMS4BwTwgCGDg3ScAg5ffRkPfvNRPH3P4zj7prcoE5HzOAx41Xkc9vhs32RfvSjKRU2U7K4o5saen+dNnb8eEIBZjLYHBbXSsOvX+mtj/jIT4fDKIR78uuZ03Hx7VroKPr2nJLKR2mbZUAUEsmFiBi5ePsSjSi9bdrcdk79lvbeMOq/OLl05wk9/U8JD9kGYUJ23lBA4B2WlQi7kf/PbNafmGuRfF55U/i9k+Z+6K48tOy0HDvRBJUCwvwYS7B0t8dSYjrUV71r9dBGwKjBKRu/eJ/CON75VxtcSCACcjrhO/VC/gOpLMkDF0TEDCS5lebIwlQ9882N4+t2fl/6frg3KvlBqq9Q53XfSF/Vp4VQ9gbO33JGZ1kIOaIOB6pwaEBDHEjS96a39ulTOwNbymT/8fJrd8Ikf/qlcp5VyYoDBJ9/8MdnooSH1zmW8WaTPBuuQG9CMDEd8+l98SUDB7Z8qQUG6X6MkNKcjm+1ZdnxKdTm6+kLVqRU0GAHJzoiyIdzOc61artEPr7yAB577MJ6+5zcVFLgBsQE1rhUb9P/Zj/wneMcb37LS4Vj/t8Fh3kwJlGSn5ly89h3c/+2fxjM/8VlJZFSj18rj8OGbdI8CaGTa3WpzdCUvfnVmZZlsYIbPZ+GC2vOcgQLdX59bGKWuDOv2Cji68gIefO7DAvpuvl3pQXermjYtvFc4eesiTw44AEizS1o5LxlgUSGCfcpFjfn7RC53x2K7KWtGktfM+2fG4ZUX8FAKn71dlTrS8XmZy7hZVI5Hl1/ITN+pO/N91aiJkTMvz+0HUhugAgn+ETa7Yal/tmQ164/s4t9WR46p/k/f85sKml7vM4gOCAANQ+iea/35+T/6Lu7/vZ9b1A9r+mypHL76Eu7/1sfxzLs+h7O33J67IGc2AWwOlXvmytqSVv/SqVLQUb3z9vDrfNwnUPCXfhXvuPltrgKlTEq9qG9T6Lr82zOuAgr+tkx5/OFHpQbJPi2XEwMMUlGjLsvH2mA0wYqRNsNPTA4QKGTXBv30f/FF/PzLf08SQf69D2WhaplRUJvyAXI5vHpp3qmt0azeKDv3+l3d/a9cUlDwBfFkjIZjFvAD7dRmXNM2kBRiBZQcyanhA/EEzr7prQ6ML1sIYqP46oGSPQWG5iz845+RmOSb3p6ZFEcjslGCCQA4tsDnKwCpfazdjq5clJi/n7K5WbrI9S/eAan/NEHBNZW2Eju8ehEPPPcRYVJO3QHzoHxJwDIBYGc6iPLb+r6r5xxeeSElcp295XZwFfOcfRqcsEnhpPp/T6asPfXe8zh7210AporOqcDrBrq/BsUJNFn4bO9Se6mu/lcu4QEDBbfe3b68BQ6AGUAAkiuQLp3PXpndvHwOsAiS6mS4w6sX8cDXP5KcEnKzt5rU9wZAIMczKPgPDBRUnnAJBtaYRD2nCj0WoODUHeX5tR6ryjo7xzi8eql0qnS/f+/NeSjpPTnV8fC1VwQU/NincfbmtxdDt3x3uxYzGaR9HhDo8c/84eMlKOiEpnvl5AEDIBuBPHNYCptHFAEawGwU1VB0+E//s8fx8y/9kkwZ+fN/2Q1ot1Cw366eW+6j2fbha68UnfpGF1NaT9/zhNLXjJzFm5VdYfCNNSFk5WD75MJkVA9ffUlyCgzpFvH73gDpGN6WUfKg4Oa3l4AJgOV2eyChSEefYTQiuwGhSpNiSvSUnIW7xNOeGboNA6ijDApQcNvp8lwHzmx/ArFOBvldHbNl9796KYOCW+7seL8AUL2XO03EUQEHfX72lJ4QpTUdtyHTHkCgqL958hb+2L3elWVTia94+4V8LHy2VBa91hZoqkBBow/L9h59KLGMGtP+ysM6u+F02j+TUQ0E7JyZAeNi7Ej9BRS84xbHNC3Fw31p9OH0eCI8/0ffwf2/93PqNLxtfoeGvLpesTsnhxczKDhz610F8yWl0x4bSwYFX9imn80pATAD8oUO1fu/+nJOxLz5dneu3iMcuH1U/c3vWq9loBeX4YM3f0zTNPYbqycHGDQ7gHmJKDxismAe1PghgiEN+Jk//E38/KW/K4tL/IVH9LTG4Bg2iq5qkMNXX8b9NqXv1rvmjVa8R3v/Ev0mnpIorTO33t0wxw4AGECCC6XAyadQEGKYD199Gff/45/FM+/8LM7ebIO+58FtC1d49uXwj77rQIEOmtnAX7qZ0YhS39zSAi7S7AyXKCbH64FzbaEWAwUX3nMeZ247PfMMC2bGKdcaUBVv6hgjUVofVaPXYSI2hSbgQKKr/xVvVIU+LUGEL/vL6PDqpTL8Mc2zr6/p/sbUXH1xGTRtBQGN81NOgYEC78kvAIJWgtu8/vKuMuXxnCaS3t1nStBgBvw5XE+XA2pQcPbUnW1A5g1/1X9z8purg3OSEpNY0eOFDNa84eJvuf/5V1+W8Os9j+NMwcS1dcS+oYqjyxZ+deEhAIlxdc9iJx+qw9JF7MKB1ldf0vDor0vOQqt429IDTjUI0t+ftkTDM38Hn3jLx2f32VpOEDBoDWq4jq2dlxkZF3gam/GZP/ycLC5x5u/gk3/xI2XMqio8HMz2rZXDV1/GA5pId+bUnX0QACwDgY6iKRKhHL3JdSedASQggyRnpCrP4+jKJdz/jz4u85CtU9eJR267kNoaSNBEw/u//UkBHR6pt5Tu6oCZl8PLR035AMh0r3+HPQfT4WWZfXDhvefFaDAX96RZfM976qVnwUXfzTHtpLQSE9QoDYO/qf6Fp12Djmu7Z//+d9yQe6ZCJPf/xkckkbcHmlbyBqy0xlwxvowJcsfL6/vjuVcKUFCHD3qgoHVOj8ky+dzzeAIFkpzqQiaVE1Uoj5o55Qh2oODwNc2uV1DfBEQNBnUTg2Dt+81H8fS9T0j79nTkdTBZM6cqyd2Nz/SoDIqYq2cWbSDnHF59EQ986+N4+t2fx5lTd3bTcgrb0rIRLlyazwv4zB/8hoACm5Loju1bTgww6KJyi+ElJVtclDY/bUI9+3eFfllLDgv7AQMZlB/NnRrYpIzK3x3lUnjCX54rLWAOkKCGuxmvNSre9mvM7VuP4ul7H8eZU3eV2ehLLMHGbO+EpD3o6CXhAB0gMN9nMj28+uJcqS8NmDVFXjNBLaVe138G/kIpLw8GgALMJqX1k1/MSsXdv6TcXZhrq/y3MBHXUZZBx76lEfNP9X9ifv9OWza9yc6YK6YkVkZ71QC2ypb+U9+vCwRdjlCDhSIYE+Tl0zBqqe8Ne49jGb+f1JypyhPeMo5n55Vj2eqfjHb3Ov/cPZiaYsrp3dm58U7kbLz5cHLNTJWh5hmoWSohm+Uu4Kne+TN/8Bt5nQJlCtaZqn4fPTHAYG0gemSbkZ8I7jO//9m8OFKiXwZHM2M2MNjong10uSVCeSTa7bRblFV6J435u8VVztx2uo1EG/cgRwlKqd4ZcEbpw3jq3t9qIOl8XnFdyxvsKJfDqy9metAj6YXwEDrntAbSbNA7WWz27BYGWY4J9xLFMFMs+flVDLIGsgAOr1ws61/coFJg/nkL71Mm6l0qmYgtZSPgAFaMdlGp/ePBQK5/Mb6a97+2MXd4+SIefPaD7fG1VOeNnlq3/9S6xYWg+kyUv0b2PX+19ITRM2pJR2po1ZeF9m6O3xs0duX+kvPSGr/52mtjCcypSom2yWmwG88dqoJ1LirRYWpa+r+qQ3GboM/aGJ4y+/XLP/JL+MRbf/paJgjNygkCBnt0DCfvz3znM/jU7/21uVB9co1d5hveGg+ZVmuVRD+2lPpKnRc7uyHpmr7eo3CdQNl456PvHc6ZCI5VmKCVINYHTDOj9I2P4ul7f6trlBbjhCuGPYOCXP8bAQbS/ZtKvZWwtXSvuaysbQ6/d9QEfU0GLF1cxn3LYyWQKJiIltLqteNGI76qFLeUJVDm6p/G1zUa69Z4KxaPanry+xqkqn9ePnSJhtX9i9BWmSCa91cgoXrU4eWjGais156Yt7EHHTV9XpZ6/O41Vut3aZzTBWXXCCLr4vXnmVsbTlUDqO8z5q5J/4dhMwtlnwH45R/9e/jkWz/Rvl+vLIjw5ACDxdJsSfngUk+oszyE0pByaHt6vhgSXfTkm9Xd0OkpuLXZFzzVfUr1zrL4iShF+yBJCzwAjRBCQWeXx0wOSanf96UfiNF4/sqltvyvGQjUSr3HFGxVWiVzVRdRWu9vGqUmA5YevwBoXdWa4aeZl9oBGBvKolK8nuKYssXwGTZ4kis5KQ99dev42t9QLTNNy23aY4R8uMrrnwJ0z9ilft2XxuSm8bsnEJNrnNNTg7JrZQYaZaY/F5wZdmG9dp2vU//78Geoxlznnef2q23nrqWcIGCw38D8zHd/LX1lahFpLeUudDJPgevw5PemH83TuDEI2urQBR2dd56xD+lAG0hcM2haK1uYlE0yXpanyH9pnvmmyq7cv+NJtm6zRan5+3eYiOtRvEX4Qz29ntG+ZurX3X9W/329yLXwUPFBpBs4vrCl/6wo+W7dHah3/X+R4buGtrhWo9c/p5Vz0QJlN5ApqMfXFjmsjLXi/mv6fyUvrnxoWdJXhNfs16K8/iTkGOxRRKif2iBUoC+85F7rT4/Ub7Anv9lTvTHl8PLFbfffOpDq+3/v+WsOfyw/yzzJfeV/DbMPrhsU3Ij7bzEUnfubJ3wDPfkU/mgxTTewzJTuDfEicx/4wY+va+0/GzzCLUziRuPWK9cPulvl31b57xsWvBH6Z1kf7We/rq38iQIGDOAxJ9RPvPUTTaW1n5n4H9poX59R6ilpe4v97r8veucfAGgqyw+USfn/tXe/sXJUZRzHv79QAdHwL2g0tLE1AlqqVFIalChiEUEJJL4wNcH4JymRYKUNUShEE1+QNJSoFYSkoTUxNBIoVYkRpAajr1r+FBBKRRtq+kcM9QVqYqRUHl/M3OVS9+7O7pzdM7P390kIuzt3nz7nzJyZZ2d2zzBz/wxz8OuW2Vj6v+qZiCGMZf1Wij/kFxmHHF9V1//vkxeVVfY/gx/cZtKk/u+3L6sbv3+0meKPbv//wwrHL6i/15vowuDoTju6KKj6PqjW0c35JDn4gSp4806r2ye9uhtb5TMRQ8cfX1GW4pPw0e9Pf9B4s8no/7xFd511PvrxNVP+qU+/N3t8zfS+0Y+vtPGHPX51e++gW8DEFAb9NqJBOnWY+E0oClLutAaJ37aiqW78KjdcGlS/gwbU271PUv+PI37qyx+Djq9B13Vb+3+qzVX6p45Rx0/RP3HU/6cb9fHraBNTGPRSt1O7md7RuSrRVDuvuoOmVx6inTutmdZvzp3WsIVZG/t/XPGrnCmra5jtZ5DCsOn9368/c4yvlGdrhumfQbaxURy/+kn3248BSFon6Y+S/iDpZ5JOnrZsjaQ9kl6Q9Om6/9aoO7XbTqXKf/1M/V2302tVYwyTf2q98k/Rjjo7rSrrpylFQS+92tH0g8ao4lcdh21Yv1OauH7r7uua0v/DtqPK/q3OPi9HUQCZCgNgG7AoIj4E/AlYAyBpIbAcOBu4FLhTGv6H1OMuCgZRZcNpyqAZdfyqg6nXTnGQQVl1cDalf1LFH7afR1l0jGr9DtM/qU3S+h1FUd+2/u/XP6nlKgogU2EQEY9ExNRt1bYDc8vHVwL3RsSrEbEX2AMs7RajnyYXBY7v+JMWf9iDT1Pyd3zHb1L8UR+/bn/89p7Lc50xmO6rwEPl49OB/dOWHShfG8g4Kq02b3SO7/iO7/iO38z44ygKbnj0hp5/o/+bNzsRSb8B3tVl0c0R8Yvyb24GlgCfi4iQdAewPSLuKZdvBB6KiC1d4l8NXF0+PQt4YYg0TwP+PsT7mm5S2wWT2za3q30mtW1uV/sM07b3RMQ7ui0Y2a8SIuLiXsslfRm4HFgWb1QnB4F50/5sbvlat/gbgA11cpT0REQsqROjiSa1XTC5bXO72mdS2+Z2tU/qtuX6VcKlwLeAKyLi39MWPQgsl3ScpAXAGcBjOXI0MzObjXLNY3AHcBywTcUNNrZHxNciYpek+4DngSPAtRHx30w5mpmZzTpZCoOIeF+PZbcAt4wplVqXIhpsUtsFk9s2t6t9JrVtblf7JG3byL58aGZmZu3ThJ8rmpmZWUPM+sJA0mJJ2yU9LekJSUNNqNREklaWU0/vknRr7nxSknS9pJB0Wu5cUuk1VXgbSbq0nNp8j6Qbc+eTgqR5kn4r6flyXF2XO6eUJB0j6SlJv8ydS0qSTpa0pRxfuyV9JHdOKUhaXW6Hz0n6qaTjU8Sd9YUBcCvw3YhYDHynfN56ki6imEnynIg4G7gtc0rJSJoHXALsy51LYl2nCm+jcirzHwGXAQuBL5RTnrfdEeD6iFgInA9cOyHtmnIdsDt3EiOwHng4It4PnMMEtFHS6cA3gCURsQg4huKWArW5MChmZj2xfHwS8NeMuaR0DbA2Il4FiIiXM+eT0vcpfu46UV+Q6TFVeBstBfZExIsRcRi4l6JQbbWIeCkidpaP/0VxgBl4dtYmkjQX+Cxwd+5cUpJ0EvBxYCNARByOiFeyJpXOHOCtkuYAJ5Do+OXCAFYB6yTtp/hU3dpPaUc5E/iYpB2SfifpvNwJpSDpSuBgRDyTO5cRmz5VeBslmd68ySTNBz4M7MicSio/oCi4X8+cR2oLgEPAj8vLJHdLelvupOqKiIMUx6x9wEvAPyLikRSxc81jMFa9pmcGlgGrI+IBSZ+nqCp7ztrYFH3aNQc4leJ053nAfZLeGy34GUqfdt1EcRmhlQaYKvwIsHmcuVl1kt4OPACsioh/5s6nLkmXAy9HxJOSPpE5ndTmAOcCKyNih6T1wI3At/OmVY+kUyjOwi0AXgHul3TV1C0F6pgVhUGv6Zkl/YTiuhrA/bToNFqfdl0DbC0LgcckvU4xn/ahceU3rJnaJemDFIPgmXJirLnATklLI+JvY0xxaENOFd5Glac3bxtJb6EoCjZHxNbc+SRyAXCFpM8AxwMnSronIq7KnFcKB4ADETF1ZmcLRWHQdhcDeyPiEICkrcBHgdqFgS8lFNdkLiwffxL4c8ZcUvo5cBGApDOBY2n5DUQi4tmIeGdEzI+I+RQD/ty2FAX99JgqvI0eB86QtEDSsRRfinowc061qahINwK7I+J7ufNJJSLWRMTcclwtBx6dkKKAcv+wX9JZ5UvLKGbXbbt9wPmSTii3y2Uk+lLlrDhj0McKYH355Y3/8MYdG9tuE7BJ0nPAYeBLLf8EOht0nSo8b0rDiYgjkr4O/Jri29KbImJX5rRSuAD4IvCspKfL126KiF/lS8kqWAlsLovUF4GvZM6ntvKyyBZgJ8Wlx6dINAOiZz40MzOzDl9KMDMzsw4XBmZmZtbhwsDMzMw6XBiYmZlZhwsDMzMz63BhYGZJlXcg3Cvp1PL5KeXz+ZlTM7MKXBiYWVIRsR+4C1hbvrQW2BARf8mWlJlV5nkMzCy5ctrgJykm2loBLI6I1/JmZWZVeOZDM0suIl6T9E3gYeASFwVm7eFLCWY2KpdR3A52Ue5EzKw6FwZmlpykxcCnKG77vVrSu/NmZGZVuTAws6TKO73dBayKiH3AOuC2vFmZWVUuDMwstRXAvojYVj6/E/iApAt7vMfMGsK/SjAzM7MOnzEwMzOzDhcGZmZm1uHCwMzMzDpcGJiZmVmHCwMzMzPrcGFgZmZmHS4MzMzMrMOFgZmZmXX8D1fcW/zDLBwiAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/05-Minimax.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb similarity index 99% rename from python/examples/adjoint_optimization/05-Minimax.ipynb rename to python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb index 4331dffd3..15f9cbf0a 100644 --- a/python/examples/adjoint_optimization/05-Minimax.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb @@ -189,8 +189,6 @@ " objective_arguments = ob_list,\n", " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " decay_by = 1e-4,\n", - " decay_fields=[mp.Ez]\n", ")" ] }, @@ -2071,7 +2069,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2085,7 +2083,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/python/examples/adjoint_optimization/01-Introduction.ipynb b/python/examples/adjoint_optimization/01-Introduction.ipynb index 48668958e..d40973f54 100644 --- a/python/examples/adjoint_optimization/01-Introduction.ipynb +++ b/python/examples/adjoint_optimization/01-Introduction.ipynb @@ -34,7 +34,7 @@ "import numpy as np\n", "from autograd import numpy as npa\n", "from matplotlib import pyplot as plt\n", - "mp.quiet(quietval=True)\n", + "mp.verbosity(0)\n", "seed = 240\n", "np.random.seed(seed)\n", "Si = mp.Medium(index=3.4)\n", @@ -54,7 +54,7 @@ "metadata": {}, "outputs": [], "source": [ - "resolution = 30\n", + "resolution = 20\n", "\n", "Sx = 6\n", "Sy = 5\n", @@ -77,7 +77,7 @@ "outputs": [], "source": [ "fcen = 1/1.55\n", - "width = 0.2\n", + "width = 0.1\n", "fwidth = width * fcen\n", "source_center = [-1,0,0]\n", "source_size = mp.Vector3(0,2,0)\n", @@ -118,8 +118,12 @@ " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]" ] }, @@ -201,7 +205,6 @@ " fcen=fcen,\n", " df = 0,\n", " nf = 1,\n", - " decay_fields=[mp.Ez]\n", ")" ] }, @@ -234,9 +237,17 @@ "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/site-packages/meep/visualization.py:357: UserWarning: The frequency parameter of plot2D has been deprecated. Use the frequency key of the eps_parameters dictionary instead.\n", + " warnings.warn(\"The frequency parameter of plot2D has been deprecated. \"\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -295,7 +306,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -304,7 +315,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -370,7 +381,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -432,7 +443,7 @@ } ], "source": [ - "resolution = 20\n", + "resolution = 30\n", "opt.sim.resolution = resolution\n", "f0, dJ_du = opt()\n", "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" @@ -445,7 +456,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -494,7 +505,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -508,7 +519,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb index c1bdea868..6ffc6cacd 100644 --- a/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/02-Waveguide_Bend.ipynb @@ -15,7 +15,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -75,8 +83,13 @@ " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "\n", "]\n", "\n", "sim = mp.Simulation(cell_size=cell_size,\n", @@ -117,7 +130,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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+XK31DK21w/P4YWCV1noSsMrzWAhLcbs1BSVOFk5KC9iLoosmD+fmOZlU1LVysLEdgE6Xm7AwFXBr+fpSZHgYf7xxJloz5DbWJUea+fe3djB7XPDV9fsyo9RzLbDM8/0yYLEJMQhhqF0Hj3G0pZMFfrxb1xv/2nXklG1dLs1jK/3X5sAMmcPi+K/FU9lUWc8fV5cM6j1tnT11/djIcP4YhHX9vpSRrVuVUuVAPaCBZ7TWS5VSDVrrlD6vqddanzLXTSm1BFgCkJ6enr18+XKvYmhubiYhwZofWb0h43GckWPxXlknf9/XxR9yYkmJCdwEcfuHLad97qWr/NvV0gxLt3XwWU03D8+OYUrqiTOvTv75eGFHB/lV3fzIEc3UtOC44J2bm1vYp9ryBaOjn6+1rlFKjQA+UkrtGewbtdZLgaUADodD5+TkeBVAXl4e3r7XimQ8jjNyLJ4t+Zwp6Z0svmqRIfv3lYzPV/c7rTMjJTYkfk4c87q5+sl8lu1188ED80mOi/ziub4/H28VVbG2aiv35U7kviunmBSt7xh6KqK1rvF8PQK8BcwGDiulRgF4vp76WVOIINbW6WJjuXltGobioSunEHvSPQaxkeE8ZIHkNhgJ0RE8eeNMjjR18PCb2/pdvKZvXf9BA9Y3MINhiV8pFa+USuz9HrgC2AG8A9zmedltwNtGxSCEGTZUHKXT5WZhEMz4WDwzg99edwEZKT0thDNSYvntdReweGaGyZH5z/SxKfz4yil8sOMQyzee2MLaSnX9vows9aQDb3lmNEQA/6e1/lAptRH4m1LqLmA/8A0DYxDC7/L31RIVHsbsIOliuXhmBotnZoR0GXDJwvEUFDv59T93cmGWjYkjeqbg/uqdnew70sSyO2aTnhR88/VPx7DEr7UuA6b3s70OuNSo4wphtoISJxeOswXk6lSif2Fhisevn85VT+Rz6/MbAKhpbAdauOK8ESwKgk9vQ2GNzy1CBIgjx9rZc6iJBROtlShCwYikGK6blUFNY7sn6fdYW+xkRZF5C7cbQRK/ED5UUNLbpiHwL+yKU32w/dAp29q73Ja7r0ESvxA+lF/sZFh8FOeNSjI7FOGF092xbLU7mSXxh4gVRdXMf3Q1t3/YwvxHV1vuo2sg0FqTX+xk/sQ0woK8jXGoGp3S/wLpp9serCTxh4AVRdU88ub2L27UqW5o45E3t0vy97E9h5pwNnewQMo8QStU7muQxB8CHlu5l7Yu1wnb2rpclqtbmq2gWOr7wS5U7msIjoYT4qyESt3SbGuLa5k4IoFRydYqC4SaULivQc74Q8DolP5vPLFa3dJM7V0uNpQflbN9ERQk8YeAr0wffco2K9YtzbSpop6ObrckfhEUJPGHgM37G0iKiWCEZxm9lNhIS9YtzZRfUktkuGLOuGFmhyLEgCTxW9yG8qNsKD/KDy6fzPqfXUpiFFxy7ghJ+j6Wv8/JrEwb8dFy2UwEPkn8FvfUmhKGxUdx44WZKKWYlBJOYaUscexLzuYOdh08Zrl+LsK6JPFb2LaqBtbuq+WuheO+aBg2yRZOZV0rtU0dJkdnHes8bRoCfZlFIXpJ4rewp9eUkBQTwS1z7V9sm5TS879czvp9J7/YSUpcJFMzks0ORYhBkcRvUfsON7Fy52FuvyiLxJjjy8nZk8OIigijsPKoidFZR0+bhlrmT0gjXNo0iCAhid+inl5TQlxUOHfMH3fC9sgwxfQxyWySM36fKDnSzOFjHTKNUwQVSfwWVOFs4Z9ba7h5rh1bfNQpz2fbU9lR3Uj7SW0cxNCt9bRpkP48IphI4regv3xSSkR4GHcvGNfv8w67jS6XZltVo58js56C4lrGp8UzxhZndihCDJokfoupaWjjjc1V3OAYy4jTrBE6y24DYJPU+c9KR7eLz8uOytm+CDqS+C1m6doytIZ7Lh5/2tekxkcxfng8hRVS5z8bmysbaOtysXCSzN8XwUUSv4XUNnXw2ob9/NvMjAFLDw67jcL99bjd2k/RWU9+cS3hYYq541PNDkWIIZHEbyHPF5TT5XLz3ZwJA77WYU+lobWLMmezHyKzpoISJ7MyU06YLitEMJDEbxENrZ28/FkFX75gFOOHJwz4+uwsT51fyj1eqW/pZHt1IwsmSplHBB9J/Bbx0qcVtHS6uDd34qBePz4tntT4KLmD10vrSp1oDQsny4VdEXwMT/xKqXClVJFS6l3P41Sl1EdKqWLPV5vRMVhdc0c3L66r4LJz0zl3VNKg3qOUYlamTRK/l/L3OUmMiWCatGkQQcgfZ/wPALv7PH4YWKW1ngSs8jwWZ+HVzytpbOvivksGd7bfy5Flo8zZQl2zNGwbCq01BSVO5k9IIyJcPjSL4GPoT61SagxwNfBcn83XAss83y8DFhsZg9W1d7l4Nr+cBRPTmDE2ZUjvdXjm88tZ/9CUOVuobmiT+fsiaBm9asQfgJ8AiX22pWutDwJorQ8qpUb090al1BJgCUB6ejp5eXleBdDc3Oz1e4PBx5VdOJs7WZCqBvXv7DsenS5NhIK3CrYRVbvH2EADkLc/Gx9XdgEQdbSUvLxyH0dlHqv/rgyVlcfDsMSvlLoGOKK1LlRK5Qz1/VrrpcBSAIfDob1d7T4vLw9v3xvoOrvd/OyzNTjsNu75t3koNXB3yJPHY9redRxxK3JyLjIw0sDk7c/GK8s2kpnazPVfzvV9UCay8u+KN6w8HkaWeuYDX1VKVQDLgUuUUq8Ah5VSowA8X48YGIOlrSiqpqaxnXsvmTiopN8fR1Yq26ukYdtgdbncfFZaJ904RVAzLPFrrR/RWo/RWmcBNwKrtdY3A+8At3ledhvwtlExWJnLrfnzJ6VMzUgi5yyW/Mu22+h0udlRLQ3bBqNofwMtndKmQQQ3M6YkPApcrpQqBi73PBZD9N72g5Q7W7gv1/uzfehJ/ID05x+k/OJawhTMmzDM7FCE8JrRF3cB0FrnAXme7+uAS/1xXKtyuzVPry5h0ogErjhv5FntKy0hmnFp8TKzZ5Dyi53MGJtCcqy0aRDBSyYhB6GPdx9m7+Emvpc7gTAfLPeXbbexubIeraVh25k0tnaxraqBBVLmEUFOEn+Q0Vrz9JoSMlPj+Mq00T7Zp8Nuo66lk3Jni0/2Z1Wfljpxa1gkF3ZFkJPEH2QKSpxsrWrkuzkTfHbXqCNL6vyDsbbYSUJ0BNOHeKOcEIFGEn+Q+ePqEkYmxXDdrAyf7XN8WgLJsZGyMMsZaK3JL65l3oRhREqbBhHk5Cc4iGysOMqG8qMsWTSe6Ihwn+03LEyRbbfJUoxnUFnXSlV9m8zfF5YgiT+IPLW6hGHxUdw0O9Pn+8622yitbaG+pdPn+7aC/BIngMzfF5YgiT9IbK9q5JN9tdy1cByxUb472+8lDdvOLH9fLRkpsWQNO/OSlkIEA0n8QeLpNSUkxURwy1y7IfufPjaFyHAlF3j70e1p07BoctpZ3SwnRKCQxB8E9h1u4sOdh7j9oizD1neNiQzn/NHJFEqd/xRbqxpo6uiWZRaFZUjiDwJ/WlNCXFQ4d8wfZ+hxHHYbW6sa6ex2G3qcYJNf7EQpmD9R2jQIa5DEH+Aq61p4Z2sNN8+1Y4uPMvRYjiwbnd1udtRIw7a+8oudTMtIJiXO2PEXwl8k8Qe4v3xSSkR4GHcvMPZsH2BW7wVemc//hWPtXWw50CCzeYSlSOIPYDUNbfyjsIobHGMZkRRj+PFGJMaQmRon8/n7+Ky0DpdbyzKLwlLO2J1TKTXrTM9rrTf7NhzR19K1ZWgN91w83m/HdNhtrC2uRWstM1iAgmIncVHhzMq0mR2KED4zUFvmTcBOoNbzuG8m0MAlRgQlwNncwfKN+/m3mRmMsflv7nh2lo03i6qprGslKy3eb8cNVPnFtcwdP4yoCPlwLKxjoJ/mHwGNQBvwIvAVrXWu5z9J+gZ6vqCczm43382Z4NfjOuypgDRsAzhwtJWKulZp0yAs54yJX2v9e631AuA+YCywSin1N6XUDH8EF6oaW7t4+bNKvnzBKMYPT/DrsSeNSCApJkLm89MzmweQxC8sZ1CfX7XW5fSsjfsvYDYw2cigQt1Ln1bQ3NHNvbkT/X7ssDDFLLuNTTKzh4KSWkYlxzDBz398hTDaGRO/Umq8UupnSqn1wK+BrcA5Wuu/+SW6ENTS0c2Ln5Zz2bkjOHdUkikxOOw2io8009Aaug3bXG7NupI6FkyUNg3Cega6uFsCbKPnbP8YkAl8r/cXQWv9uKHRhaBX11fS0Nplytl+r2xPnb9ofwO554wwLQ4zba9upLGti4WTZf6+sJ6BEv9v6Jm9AyCfdw3W3uXi2fxyFkxMY6aJ0wdnjE0hPEyxqfJoyCb+/H09E9nmT5A2DcJ6zpj4tda/8lMcAvjbpgPUNnXw5I0zTY0jNiqc80cnhXSdP7/EydSMJIYlRJsdihA+N+DFXaXUl5RSa5VSTqVUrVLqE6XUl/0RXCjpcrl55pMysu025o5PNTscsu02tlY10OUKvYZtzR3dbK6sl26cwrIGurj7beA/gV8B44EJ9Fzk/ZVSaskA741RSm1QSm1VSu1USv3asz1VKfWRUqrY81VuiQTeKqqmuqGN+y6ZGBAXEx32VNq73OysOWZ2KH63vqyObrdmkUzjFBY10Bn/D4ArtNartdbHPP+tBr7kee5MOoBLtNbTgRnAVUqpucDDwCqt9SRgledxSHO5NX/OK2VqRhI5AXIx0ZHV8/d4U0XozefPL3YSExlGdpackwhrGijxK631Kb/5Wuu6gXasezR7HkZ6/tPAtcAyz/ZlwOJBR2tR720/SLmzhXtzAuNsHyA9KYYxttiQXIoxv7iWOeOG+XRBeyECyUCzeo4ppaZrrbf23aiUmg40DbRzpVQ4UAhMBJ7WWq9XSqVrrQ8CaK0PKqX6nTbiKSUtAUhPTycvL2/Af0x/mpubvX6vP7i15n/WtTE6XhHt3ENe3l5DjzeU8Rgb08mnxYdZs2ZNwPxB8qX+xqKuzU1pbRsXpnYF9M+NEQL9d8XfLD0eWuvT/gcsACrpqfF/BbiGnhp/BbDgTO89aT8pwBpgKtBw0nP1A70/Oztbe2vNmjVev9cf/rXzkLb/9F395uYDfjneUMbjr59VaPtP39WVzhbjAjJRf2Px+ob92v7Td/Weg8f8H5DJAv13xd+sMB7AJt1PTh2oV08BPS0awoDbgTs938/1PDfYPy4NQB5wFXBYKTUKwPP1yGD3YzVaa55aU0JmahxfmTba7HBO4fAszBJK/fnXFtcyIjGayely24qwroFm9WRqrQ9rrf9Da/01rfV1WutfaK0PDbRjpdRwpVSK5/tY4DJgD/AOcJvnZbfRc1dwSCoocbL1QAPfuXgCEeGB1/Z3cnoiidERIVPnd7s160qcLJgkbRqEtQ2UbVb0fqOUemOI+x4FrFFKbQM2Ah9prd8FHgUuV0oVA5d7Hoekp1aXMDIphq9lZ5gdSr/CwxQzMlNCJvHvrDlGfWsXi2SZRWFxA13c7XvaM6RloLTW24BTbkHVPTOCLh3KvqxoU8VR1pcf5T+uOS+gZ4847Kn8YdU+Gtu6SI6NNDscQ+WXeNo0TJT5+8LaBjrj16f5Xpylp9aUMCw+iptmZ5odyhk5smxoDUX7rX/Wn7/PybmjkhieKG0ahLUNlPinK6WOKaWagGme748ppZqUUqF3S6eP7KhuJG9vLXcuGEdsVOCe7cPxhm1WL/e0dnZTWFkvi66IkDBQk7bAzkpB6qnVJSTGRHDLPLvZoQwoPjqCc0clWr5h2/ryo3S63JL4RUgIvKkkFld8uIkPdx7ijouySIoJjpq5w57KlgPWbthWUOwkKiKMC7PMb5AnhNEk8fvZn/JKiYsK547548wOZdCy7TbaulzsPmjd6l5Pm4ZUYiLlQ66wPkn8flRZ18I7W2v41pxMbPFRZoczaMcbtlmz3HP4WDv7DjezQGbziBAhid+P/vJJKeFhim8vHNLMWNONSo4lI8W6Ddvyi50ALJT5+yJESOL3k4ONbfyjsIobHGMZkRRjdjhDNstuY1Pl0d7+SpZSUFxLWkIU54xMNDsUIfxioBu4xFlaUVTNYyv3Ut3QBsC4tDiTI/KOw27jn1trqG5oY4wtOP8N/XG7NQUlThZMTCMsTNo0iNAgZ/wGWlFUzSNvbv8i6QM8tnIfK4qqTYzKO9mehm1WK/fsOdSEs7mTBVLmESFEEr+BHlu5l7Yu1wnb2rpcPLbS2J77RjhnZCLxUeGWu8CbX9zTpkHm74tQIonfQDV9zvQHsz2QRYSHMTPTxiaLnfEXlDiZnJ5AehBedxHCW5L4DTQ6JXZI2wNdtt3G3kPHaGrvMjsUn+h0adaXH5XZPCLkSOI30A8um3TKttjIcB66cooJ0Zw9R5YNt4ai/Q1mh+IT++pddHa7WSBlHhFiZFaPgXoX8xgWH8XRlk5Gp8Ty0JVTWDwzMPvvD2Rmpo0wBZsq61k0OXjPko/PtOoAoPZYu8kRCeFfkvgNorXm2fwypqQn8uGDCy2xolNCdATnjEyiMIiXYuydadX3ovsv39lFVER40P5BFmKopNRjkE/21bLnUBNLFo23RNLvlW23UbS/ge4gbdhmpZlWQnhLEr9BnvmkjJFJMXxleuAton42HFk2Wjtd7DnUZHYoXrHSTCshvCWJ3wDbqxr5rKyOuxaMIyrCWkMc7DdyWW2mlRDesFZWChDPrC0lMTqCG2ePNTsUn8tIiWVkUkzQzud/6MopRIafWHoL5plWQnhDEr+P7a9r5f3tB/nm3EwSg2ShlaFQSpGdZaOwIjgv8F4zbRTJMRFfJP+MlFh+e90FcmFXhBSZ1eNjzxeUER6muDOIFloZKofdxnvbDlLT0BZ0JZL3th/E2dLFX26eRYxzLzk5OWaHJITfyRm/Dx1t6eT1TQdYPCPD0i0AHPae5QmDrdzjdmv+tKaUiSMSuOK8kWaHI4RpJPH70MufVdLe5WbJouBaaGWozh2VSFxUeNCVe1btOcLew018L2eCtGAWIc2wxK+UGquUWqOU2q2U2qmUesCzPVUp9ZFSqtjz1WZUDP7U3uXir59VcMk5I5iUbu0FPSLCw5gxNiWozvi11jy1poSxqbF81WJTbIUYKiPP+LuBH2mtzwXmAvcqpc4DHgZWaa0nAas8j4PePwqrqGvp5B6Ln+33yrbb2H3wGM0d3WaHMijrSurYeqCB71w8gYhw+aArQpthvwFa64Na682e75uA3UAGcC2wzPOyZcBio2LwF5db81x+GdPHpjB7XKrZ4fhFtr2nYduWIGnY9tSaYtKTovl69hizQxHCdMofa6gqpbKAtcBUYL/WOqXPc/Va61PKPUqpJcASgPT09Ozly5d7dezm5mYSEhK8eu9gbTzUzdNbOrh3RjQXjgzsiVK+Go/WLs29q1pZPDGSaydG+SAy4xTXu/jv9e3cdE4UV2Ydn2Lrj5+NYCLjcSIrjEdubm6h1tpx8nbDs5RSKgF4A3hQa31ssH1rtNZLgaUADodDezvtLi8vz9Ape1pr/vCnT7EPC+eH1+cQHuAXDX05HlN2rsWposnJmeOT/Rnlry9txBbn4hffzCUu6viPvNE/G8FGxuNEVh4PQ4udSqlIepL+q1rrNz2bDyulRnmeHwUcMTIGo22sqGfLgQbuXjg+4JO+r/U2bHO5jf/U6K2dNY2s3nOEO+ePOyHpCxHKjJzVo4Dngd1a68f7PPUOcJvn+9uAt42KwR+e+aSU1PgovhGCtWNHlo3mjm72BnDDtj+t6WmfcetFWWaHIkTAMPKMfz5wC3CJUmqL578vA48ClyulioHLPY+DUvHhJlbtOcKt8+zERIabHY7f9d7IFaj9+UuONPP+joPcMs9Ocqz12mcI4S3DPvtqrQuA09U+LjXquP70bH4ZMZFh3Dovy+xQTDHGFsuIxGg2VdZzSwCOwZ/zSomOCOOuBdZtnyGEN2RCs5cOH2vnraJqrneMJTU+sGe1GEUphSPLxqaKwLuR68DRVlZsqeam2ZkMS4g2OxwhAookfi+9uK4Cl1tz94LQuGHrdGZl2qhuaONQY2CtW7t0bRlhCsu3zxDCG5L4vdDc0c2r6yv50gWjyBwWZ3Y4pnJk9TZsC5w6/5Fj7by+6QBfmzWGUcnB1T1UCH+QxO+F5Rv209TeHTLtGc7k/NFJxESGBdSKXM8VlNPtcvOdiyeYHYoQAUkS/xB1udw8X1DO3PGpTBuTYnY4posMD2P6mJSASfz1LZ288nklX5k+mqy0eLPDESIgSeIfon9ureFgYzv3LJKzyV6OLBs7a47R2ml+w7YXP62gtdPF93Immh2KEAFLEv8QaK1ZuraMKemJ5EwZbnY4AcNhT8Xl1mw50GBqHM0d3by0rpzLz0tnykhrt8YW4mxI4h+CT/bVsudQE99eNJ7B9hwKBbMye3rsFZo8rfOVzys51t7Nfblyti/EmUjiH4Kla8sYmRQjC3mcJDkuksnpCaYuzNLe5eK5/HIWTkpj+tgU0+IQIhhI4h+k7VWNfFpax50LsoiKkGE7WbY9lc3763Gb1LDt9Y0HcDZ3cK+c7QsxIMlgg/TM2p5mXzfNzjQ7lICUbbfR1N7NviP+b9jW2e3mmU9KcdhtzAmRhXCEOBuS+AfhwNFW3t9+kG/OySQxRpp99cdh76nzm9G+YcWWamoa27n3koly7UWIQZDEPwjPF5QTHqa4Y740+zod+7A40hKi/D6f3+XW/DmvlPNHJ5EzWWZaCTEYkvgHUN/SyesbD3DtjAxGJseYHU7AUkqRbbf5PfG/v/0g5c4W7s2Vs30hBksS/wBe/rySti6XNPsaBIc9lf1HWznS5J+GbVprnl5TwoTh8Vx1/ki/HFMIK5DEfwbtXS6WfVpB7pThTE6XG4IGkp3l3/n8q3YfYc+hJr6XM5GwEFv2UoizIYn/DN7YXEVdSyf3SLOvQZk6OpnoiDC/zOfXWvPUmhLG2GL56gy5r0KIoZDEfxout+bZtWVMH5MsUwQHKSqip2GbPxL/Z6V1bDnQwHcunkBkuPwYCzEU8htzGh/tOkRFXStLFk2Qi4ZDkJ1lY2d1I22dLkOP89SaEkYkRvP1EFzkXoizJYm/H1pr/vJJGZmpcVw1VS4aDkV2po1ut2ZrVYNhx9i8v55PS+v49sLxIbnIvRBnSxJ/PzZW1LPlQAPfXjiOcLloOCTZnhu5jJzW+fTqElLiIvnmHLmLWghvSOLvx9K1paTGR/H17LFmhxJ0bPFRTBgez6YKY5Zi3FVzjFV7jnDn/HHER0cYcgwhrE4S/0lKjjTx8e4j3DLXTmyUlBG84bCnUlhpTMO2p/NKSIiO4LZ5WT7ftxChwrDEr5R6QSl1RCm1o8+2VKXUR0qpYs9Xm1HH99aza8uJiQzj1nl2s0MJWtlZNo61d1Na2+zT/ZbVNvP+9oPcPNdOcpz0TBLCW0ae8b8EXHXStoeBVVrrScAqz+OAceRYO28VVfON7LEMS4g2O5yg9UXDNh/X+f+cV0pUeBh3LZCeSUKcDcMSv9Z6LXByofdaYJnn+2XAYqOO740XP62g2+3m7oWSWM7GuLR4hsVH+bRTZ1V9K28VVXPT7EyGJ8ofZSHOhtLauIUzlFJZwLta66mexw1a65Q+z9drrfst9yillgBLANLT07OXL1/uVQzNzc0kJCQM+Lq2bs0P81qZmhbOvTOs24xtsONxtp7Y3E5Ns5vfLYrzyf5e3tVB3oFu/mdRLMNifXO+4q+xCBYyHieywnjk5uYWaq0dJ28P2GkRWuulwFIAh8Ohc3JyvNpPXl4eg3nvc/lltHXv5udfm2vppfsGOx5na68q5bcf7OH87HlnfYZ+pKmd/I/X8PXssXztS9N8FKH/xiJYyHicyMrj4e9ZPYeVUqMAPF+P+Pn4/epyuXmhoJw541ItnfT9yZfz+Z8vKKfb5eY7OdIzSQhf8Hfifwe4zfP9bcDbfj5+v97dVkNNYzv3XCytl31lakYyUeFhFFae3Xz+htZOXvmskqunjWZcWryPohMitBk5nfM14DNgilKqSil1F/AocLlSqhi43PPYVFprnvmkjMnpCeRMHmF2OJYRExnOBWOSz3pmz0ufVtDS6eLeXDnbF8JXDKvxa61vOs1Tlxp1TG+sLXay51ATj319mvR09zGH3cYL68pp73J51VOnuaObF9dVcNm56ZwzMsmACIUITSF/5+7StaWkJ0Vz7YwMs0OxnGy7jS6XZltVo1fvf/XzShrburjvkok+jkyI0BbSiX9HdSPrSuq4c/44oiJCeigMcTYXeNu7XDybX86CiWnMkAvuQvhUSGe7Z9aWkRAdwU3S5dEQwxKiGZ8W79UF3r9vOoCzuYN7c+VsXwhfC9nEf+BoK+9vP8g352SSFCN9X4ySbbdRWFnPUG4U7HK5+csnZczKTGHueFn9TAhfC9nE/3xBOQq4Y36W2aFYmiPLRn1rF6W1LYN+z4qiaqob2rjvkomy+pkQBgjJxF/f0snrGw9w7YwMRiXHmh2OpR2v8w+u3ONya/6cV8p5o5LInSLTa4UwQkgm/lc+r6Sty8WSRXLDltHGpyWQEhc56IZtH+w4SJmzhXtz5WxfCKOEXOJv73Kx7LMKcqYMZ8rIRLPDsbywMEV2pm1QM3u01jy9ppTxw+NlrWMhDBRyif+NzVU4mzu5Z5HcCeov2Vk2ypwt1DV3nPF1a/YeYffBY3z34gmy1rEQBgqpxO9ya57LL2famGSZLeJHDnvPWJ/prF9rzVOrS8hIiWXxTLmZTggjhVTi/2jXYcqdLSxZNF7qx340bUwykeHqjIn/s7I6Nu9v4DsXjycyPKR+LIXwu5D5DdNa88zaUjJT47jqfKkf+1NMZDhTM5LPmPifXlPC8MRovuEY68fIhAhNIZP4N1XWU7S/gbsXjiNCzij9zmG3sa26kY5u1ynPFe2vZ11JHd9eOM6rZm5CiKEJmQz4zCdl2OIi+Ua2nFGaIdueSme3mx3VpzZse3pNKcmxkXxrjt2EyIQIPSGR+EuONPPx7sPcMi+L2Cg5ozRD741cJ8/n33PoGB/vPswd87OIjw7YlUCFsJSQSPzPri0jOiKM2+bJGaVZhidGYx8Wd8rCLE+vKSU+KpzbL8oyJzAhQpDlE39Du5u3iqr5hmMMwxLObtFvcXay7TY292nYVu5s4b1tNdw8z05KXJTJ0QkROiyb+FcUVTP/0dU8mNdGp8uNPVXWazWbw55KXUsn5c6ehm1/zishMjyMuxdI6wwh/MmSRdUVRdU88uZ22rqOzyB5/KN9DE+MlpuDTOTI8tT5K+uJjgznzc3VfGtOJsMT5ZOYEP5kyTP+x1buPSHpA7R1uXhs5V6TIhIAE4cnkBQTQWFFPc+uLQNgycXSOkMIf7PkGX9NQ9uQtgv/CAtTZKTE8vfCA7g1xEWFs7H8KBnyKUwIv7LkGf/olP577J9uu/CPFUXVFB9pxu1ZjKu108Ujb25nRVG1uYEJEWIsmfgfunIKsSfdARobGc5DV04xKSIBPSW4bveJSzBKCU4I/7Nkqaf3Au5jK/dS3dBGRkosD105RS7smkxKcEIEBlMSv1LqKuAJIBx4Tmv9qK+PsXhmBotnZpCXl0dOTo6vdy+8MDollup+kryU4ITwL7+XepRS4cDTwJeA84CblFLn+TsO4X9SghMiMJhxxj8bKNFalwEopZYD1wK7TIhF+FHfElxNQxujpQQnhClU7+3zfjugUl8HrtJa3+15fAswR2t930mvWwIsAUhPT89evny5V8drbm4mISHh7IK2EBmP42QsTiTjcSIrjEdubm6h1tpx8nYzzvj7W/rqlL8+WuulwFIAh8Ohva3TS43/RDIex8lYnEjG40RWHg8zpnNWAX2b4o8BakyIQwghQpIZiX8jMEkpNU4pFQXcCLxjQhxCCBGS/F7q0Vp3K6XuA1bSM53zBa31Tn/HIYQQocqUefxa6/eB9804thBChDq/z+rxhlKqFqj08u1pgNOH4QQ7GY/jZCxOJONxIiuMh11rPfzkjUGR+M+GUmpTf9OZQpWMx3EyFieS8TiRlcfDkk3ahBBCnJ4kfiGECDGhkPiXmh1AgJHxOE7G4kQyHiey7HhYvsYvhBDiRKFwxi+EEKIPSfxCCBFiLJ34lVJXKaX2KqVKlFIPmx2PWZRSY5VSa5RSu5VSO5VSD5gdUyBQSoUrpYqUUu+aHYvZlFIpSql/KKX2eH5O5pkdk1mUUj/w/J7sUEq9ppSKMTsmX7Ns4pcFX07QDfxIa30uMBe4N4THoq8HgN1mBxEgngA+1FqfA0wnRMdFKZUB3A84tNZT6Wkrc6O5UfmeZRM/fRZ80Vp3Ar0LvoQcrfVBrfVmz/dN9PxSh/TqJ0qpMcDVwHNmx2I2pVQSsAh4HkBr3am1bjA1KHNFALFKqQggDgt2D7Zy4s8ADvR5XEWIJzsApVQWMBNYb3IoZvsD8BPAbXIcgWA8UAu86Cl9PaeUijc7KDNorauB/wX2AweBRq31v8yNyvesnPgHteBLKFFKJQBvAA9qrY+ZHY9ZlFLXAEe01oVmxxIgIoBZwJ+11jOBFiAkr4kppWz0VAbGAaOBeKXUzeZG5XtWTvyy4EsfSqlIepL+q1rrN82Ox2Tzga8qpSroKQFeopR6xdyQTFUFVGmtez8F/oOePwSh6DKgXGtdq7XuAt4ELjI5Jp+zcuKXBV88lFKKnvrtbq3142bHYzat9SNa6zFa6yx6fi5Wa60td1Y3WFrrQ8ABpdQUz6ZLgV0mhmSm/cBcpVSc5/fmUix4oduUfvz+IAu+nGA+cAuwXSm1xbPtZ551EYQA+D7wquckqQy4w+R4TKG1Xq+U+gewmZ7ZcEVYsHWDtGwQQogQY+VSjxBCiH5I4hdCiBAjiV8IIUKMJH4hhAgxkviFECLESOIXIUUp1ez5mqWU+qaP9/2zkx5/6sv9C+ErkvhFqMoChpT4PR1fz+SExK+1ttwdn8IaJPGLUPUosFAptcXTfz1cKfWYUmqjUmqbUuoeAKVUjmctg/8Dtnu2rVBKFXp6ti/xbHuUno6OW5RSr3q29X66UJ5971BKbVdK3dBn33l9+uC/6rlbVAhDWfbOXSEG8DDwY631NQCeBN6otb5QKRUNrFNK9XZlnA1M1VqXex7fqbU+qpSKBTYqpd7QWj+slLpPaz2jn2NdB8ygp899muc9az3PzQTOp6eP1Dp67rIu8PU/Voi+5IxfiB5XALd6WlqsB4YBkzzPbeiT9AHuV0ptBT6npxHgJM5sAfCa1tqltT4MfAJc2GffVVprN7CFnhKUEIaSM34heijg+1rrlSdsVCqHnjbFfR9fBszTWrcqpfKAgZbmO1P5pqPP9y7kd1L4gZzxi1DVBCT2ebwS+K6nfTVKqcmnWYwkGaj3JP1z6FnKsldX7/tPsha4wXMdYTg9q11t8Mm/QggvyNmFCFXbgG5PyeYletaczQI2ey6w1gKL+3nfh8B3lFLbgL30lHt6LQW2KaU2a62/1Wf7W8A8YCs9iwH9RGt9yPOHQwi/k+6cQggRYqTUI4QQIUYSvxBChBhJ/EIIEWIk8QshRIiRxC+EECFGEr8QQoQYSfxCCBFi/n9SlA5CmakQnAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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+ "image/png": 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\n", 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\n", 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\n", 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" ] @@ -437,7 +450,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Achieved an improvement of 25.7x\n" + "Achieved an improvement of 28.0x\n" ] } ], @@ -460,7 +473,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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L6iJoflZVVyWdTiZMotCkfV1aRsmplRxSpssxojXkBOIwSsoGcpXSHRdAVdJocS6tVBcN1sgZRFKSZKvVqqykoDaxSkYnr94jCp8Q2o9IndR1pFrbyPs6n8/L2Cv7rmEt9pnntM1cHUQ1nP0kOalJqCdar6v7jfm8D13CtoacrEuj9VP7/X6pqkWqU5Qlo04yDtrNZlOqdxysXM+olQachB7SUJVvOByW54CHxcyqmqsdpvffN0SifdM+RZoA3+P/WvIlUm21TbqQQJeURUWimYDBivndbrfMetJF8HU4dFISteRk7dWmInJEMBmcO15tNhvc3Nzg3bt3uLy8xGQyKTNbuPIDQIWcavMQbrOp55RtIDE5aElWQtVnVTd5f0oUXXWii5R1MTP76GpenUc5ZRO6JhCR0ZeuqTqrGgPv59/PZ6wHpaKu1Vyv1+VSM/5Nf4EuHWsDasn57bffvlQ7noTIrqMU5I+9Wq0wnU5xeXmJn376CR8+fCjtPto7ALZsULctXfq4OsjBS2KyvCNVXABbqqcSiSooE9sp2ZnOpitQxuNxOVjZRp2oUp5kfW7aDz1PKV9XfV41BFVpdZJUO5khIXUAufQk8fgd1Dy0njAdR8ciGXehlpx//vOfK//Xzb6p6/b9zKfAXfg6Y1Oi3d/f4/b2tty7o9P5WOZfcz7r7Bl3qLh3Uu0wkpMqn275oPagk1/JSClJspGcXIEyGo3K+yiBoglLn1Hqt/BnqOdVampdIs1Y0nKV+juoQ4jhHl3Y7vYkTQQ+CyXnvmrtsaCWnH/6059eqh2fBB+AhObZRqrecDjce/BG6iFVTg5OShfW12E5R6q26hBi2qDGGDnQ6dXVbCBfgTIajUpJQuLUhTWiVEdHymOrpFR11gmq91T7V1P9NIzCV7U7/VmenJxsSdes1gL429/+9lLteDJ8FlXvItWpbreL0WhUVkI/OzvD2dlZORPr9R4GiVTEyPMJPDiIOIDpNNHtAGlXknS8nyZD6LpOvqfqIJeGcQMlSu1Op1NRMZVgaicriVJEdAmphzq9lKBeGymK0fJVVVz2Sz22XH1DcvqO4Y8dF4eIWnJ+9913L9WOZwc3w1mv1+WekvzBKWFZQjJFTHUUcaBxiwRge6Cr1xZAhUBRvBSokt29uJ5sQHJSFSY81qrSVdup10Vqq5KPMc2IoE7UiKTsm8Z0PR5LJ56m8ymRo7BQhGMgJtCiUMrt7W25MxYlGYAK4SLJyWuAqmqs6w4jicrrFbxWB6eq2iwupmqexgajVSZqA3PCIJSEes4lZKrWrRLOX6PD39M0xuhZ8tX7o5OIvl/n5DpGtIacACp2k3ofAWxJEODBblIVFNjOHdX0OI81drvd0iGkGTy6VQOvo/ODhzpLNJvIHTYOtkNVWl6rEj0iHM+7RzZ1PpK4KmXVA6zStM6JR2h8uG3EBFpGTh2kGoOkh1BJS1tOP6fJ6TpoSEaPYWqSN/BATg2+U21jaOf09BRnZ2fJHcLYXvZBHUneXgAlSbWf9CjrK21jJWFKQnpGkE50SmaSkZqGpzeyTf4buSbSRmICLSInwxNU+ziAdKCrM0VtwjrpmSKoJhOQyEpMbjuvnkuWHOEem+PxuJJsoJOEqtUq7bV9DvZRNxzi6/39fcW7HKm8OoGpVHSHkk5y2n/dLiK1mmVXH9qE1pBTQw9ORgCVAUfoUqYoDqikVG+kOi80dY0k9FKWTtqU9ORkQdXUyeqeWG0nJZaSczKZVGK/jM0qKfVzmhwQfZ86mXQLCmoGvpdLyr53x5tPim1BLTkPIZ6kP56GRPwatdlUrWUoRSUBBwgTFCLVi/f1nFaXop5+R3U2si1ZpIvndW/NlFoLbO82llIBlZzc8JaH7kLt8dKUAyl6JvobUKXls2JanktdvYfay3ouq7UG9fw1FdGsqrM2sL3hj9o+tDvdFmLwXG05d0xw8EUETYVhNDuGyQRK0uiIyBk5eqIBrdc5Oe/u7nB9fY2bmxvc3Nzg7u6usuW7Hu78cVVUzQD1DnPyo+SMiOlOIfXwRuRsC0kPusAXCaKDNILHH9WRoelvSmoloH+nksTtzoiUACphEaqwmu3jqq7ukO27UvO7PfFe2+3hH+BB/aV9OZ1OMZlMcH19jevr63KHao3NqpRWc0AnMZ+UCLXV3bubUmkjNTZ7awPoDNhUuNrp6haALUKqPQVUS4sADzZanUqbCqVEazSBh9Q9XZWh1dp5RIW6ouwYH9jeRg5oTVBQhw3JeXd3h5ubm5Kc0+m0Egfmdymp3GmmNrZ7sdl3VXUjVVY/H002+yQfHBsOnpyu3qWu8cHFQarxQB5U5xkCoGoWfYcTkaordwgDHjzFugqDUpLkHI/HlS0Io4oGLj2JuoQJ9zKz74xH0lt7d3dXOod8otLP+XOKEiRINgBbhE7FZSPVtc0qLXAk3tp91G8dJAyicxC5EyPKHlI1mPBUPuaK0qGiEoEJBrtsTE06iFax1Kl3LmUiiaPeVIaTdBUN453qGOMzdm+2hnCUPPqM1EHmiQZ1amqbSJjCUZBzX3CAUXJQ7XMHhTojKGHV+6oZLxxgtCkHg0EprShxi6KokJMEpRrr2/d5cWsN1UQedLfRUlJHn0Okqiph/VmkpF7K3tUkDC+ZEmUDuQMqCtO0Da0ipy7pms/nlfei5APgwV7iEQ129ewC1bABPbqsmTMej8tDvbFRIWiXOJHjiVD1NeUp5nvaZ/18ZBPyvnX2rduedHyl6um6FpBqg5oSUduOHa0hp3pkNb2MA09jhOoIWiwWW/myVInVvlPHj+5Izfd9uZcmGHDgunTZh6Apx4nbnJHXM/Iu817+OSD2QXi6ope8VFvbN4vyGHXkUY+ccm1BK8lJlVYHsA5yVUeVFIyJMtPID3UcEbwfk901jOJxzMjr6WSL7DaFvu/t93hsdB++p9k5PFxyqdrtlQFVTfdNlmgC7Cs92yg1gZaRc7VaYT6foygeQiWESjldWwhsF5Tmqxbp0nCKnqdE1LCI59TWbZ8QSVBXvaN2atwxRdA6dTn6Hldv9Rp1XmmMVicjLxCdWmkT9Utf24LWkFNzOvm/q1TAg1qqXsi6Q3NonXAR8SIvrL9GRHdvp3tS3RGj10aEj9Tbp0C/wxeDqwT186mJyNtVZ2cfO1pDTkpOAKVTSFU1DYksFgsMBoMtqaAzOM/zfw48xi7VC+v1cCM11dXZ1P/RQFWVNLq+jgT6fPQ1Ol+nVvqzUmJGzyLy4Oqz4W/iE1Gb0Cpy0nuqK+09x5POHEpDH0jquVXvLJ0h6uyhR5Y2pdpu/H4ffL5etE4N1QGbUmHZPie2QjOlAFQKjenzc3JGOcdU5XcdSkzXRtzJpX1sE1pDTuCBoHXv0yal44gqri+hipxIWh2PRcQoNUg69UamUtkiormaF0mTXSp4anCnnC/qFIo8tU5KqrV89VCKS0uX3JSU0YLs7BDKCGNsEZQsWqBKQybD4RBnZ2dlSRLez6VvKpAfqb/7EE37ocu+fGlZ6lzUd6qafo5ag9rNdRLTNQhNpSR0EiSZfQF3G9A6crojxcnnzhtVwfxw1dcHJgt2aQlOzQUGsJcE5bkI+jneW21rr5vkZSydnP6sSEBNRiBpVDqqQ0zPew0klcS0/Xl/NTtctR4Oh60jaGvIqY4R/g1sV273OJ1vuKPZLqlNapW8UTxxX+lHMuyjjvo5rS/ErSG8TlBdvVmguhyMIDE0XVH3PPHn5pv5KsG1TIw66HTdJ1MmB4NBuRB8l3rr/ThUtIqcnN0560cSlO95VQINpPvA89CA3terBmgSfcoT6iES/1s/o+opoW3g8jCWISFBXXr6JOUrTvxZqndavbEa0+ShSQf+DLj4m+3V6n7Ax0lgtfq4HeJsNquUljl2tIac6t7nBkYcdBzgvI6B9Cgsont8pEIlQHWJlc72JKzbfPtCJbCTk31Qb/RyuawU81KC6kZLSnaV7L4UTJ+Rh46ilTYkZ0RMBZ8DyarZWOv1Gqenp1vbOB47WkNO2odMPidBOcg5QBkKcKngNhQJquot1UCgmi6o4YHIQaNIhQ6iEIrbrk4yqo5KTlZB0C0K3RtNgrpdrYkRmo5I7UInLVVt+TlNAPHq8Px+XbYGAIPBAOv1GqPRqCTnLslZZwocElpDzqIoKjM990rpdKrV3jn4qNqqPRVlvZCgdBxFKq07VNw7q23UhIJUvNOlpzuAdMBzQbUTM9pKwdVjElSdXfp/VJiMfzOpn+TUyUlXBqkNzImE0h346AgCgIuLi7LGUZ3kdE/zIZO0NeRUdXU8HpchDrV1ImeHSoAoNS+K4REaKgDSdVkjJ1FdBpG32aW1emiVkJRKXvFd1W9tO9um6zJps2tCe6Tys4AZbXtOgJR8i8WilIRsGyU8zxdFgdPTU5ycnODNmzeYTqfZ5jxGaP4rs3iYHOCOGw0TaF3ZKG7n+bPu3UxJyoiY/O4oEcGD9yqJSCpVpUlMSs6ourtvteDrOzXOquszVaVNVQzU3aiLoijVZxKVtiWLipGULJdCtXY8HqPX6+Hm5iartYcKHbARPMWOFQm4PEqrylFtUzvTA+ke03RvbeRZ1bbyNZKWqUQE/u33URtW9ythCRLdaVulp1bEi2xfHh67dAnpr75jNeHhEi1uTVKy0DUl52KxwOnpaVn4el+H0DEQ9CjIuc+PoKEUdWJoUWmtXKDkjCoVpApwOZkIj0VqDJOHLjB2Imo/1dbU1TYkJQ9VFXW7BSVmKi2OEtvT8aJwiYdSfHE1Jw/2h23W+kUk6M3NDSaTSSk5V6sVLi4uyj5ERcPrcMgkPQpy7guVnhpSUXJq0rvbl05KdY545pDHUFMkIFL2p6q07g1W6aMeWbXjdD8Uvkc7U58Lv4+aQ1QtUKsDcvPeiJCqVdBs8IlG7WLfu4XEBFCqu5xYdoWeoknwUNEacqpjQ6UA7UMOftozKZsyWqniqysc6lGN8lhTKiXb5TYn7xORk3Vn1QmkA5zkZD+pUbAwGfuusUvfHsIrG2iISUlJ6asTgfZdPcYqSR3uTd4nQ+iQSUm0ipzqYFF7UQcqQx8qFetImUrPA7Y3r9Wq6RpXVIeOOo5S3lpeS3JG4RJVbVW9JTn5PRo24r3piT07OytX2GhdXS9MpmqvJ7cTPinpZOUxVoeGunZpIMeE1pAztYKCai2hqmRUvSC1cFmJo1DPotqGHs7wXbdS5PQ4qktJtzkjO5TkZN8YS2RtJErO0WiE8/Pzyo5nvjuaPhv938M7UZpg6jXjI1pDTmCbeEpOndWpAtcdkTRLZe8A1b0xPfZIEmkqnZNTJwK2Ve9JEuoem+6V9T02O52PVQGBhw2HSE4uFudeoV6VXtPyPJvIJbwnR6RCIcegiv6SaA05IweLS86InL5AuE5yemjEQx2UchrT499M6laVk/dym1bJqdI4yvxxlVGlF8MkzJJSjzZDTZSaqtpqQrsTigTl+Uhqtk09fSpaQ07gQXJ6ISoOTA54z4ohQX1tJ6VDtBDa7S0NvOvGtQyuu0eSiJxNbrtprqoTM9oOUe/HCYjPRkNNSk7utK17huqyO1VXU+11x5i+1jl62krioyHnriQEl4bqwFDvnmYJOTHd9owSBlLkJIlITt3dSzevZZDdk899sOsqDk/Z87Q84EGiaYjINQhN+KfkpNTkoXWRmDPsi7i133xVx1jkrdb3Hb4ovC04eHKmwhfRdWpvpshJaaiDllLCVVz/7ihGqaociUNy3t7elntjXl9fVwLtu+6nA9olKF81+K9eWfYzyoTS9DtVZ8/Pz0vpSXIycULtXlWho/KjfijxUpk/rg63BQdPzn2hA9xT73SGj65zz60noEffo3By6sa1JOjV1VWZoqaSM/qeusGtMUO1XUk82pVqW+vyON1kKZKcPM+cWWoDfCZu56ba6xUY6ojnkrMtBD0qcj5GtVUJGs3c7jjS18hzGkEHHgmjIQ06hSaTSUnU6XQaemwJlzw8p04Xt1vVNtZd0OiZJXHp8HGC6sFrmDOr61WdbNreiGBuN6fUWv42bSElcfDk1MG7S+2JQhMk2Wr1sAW9X+ve2Eil1YHlf3uBLQ17aAmRfSRnRE59LxrEfC4a9tAUvUhiamaQ582qOQCgfHYeh1XiqWdZF4Xz2jq0jZRELTmjoHrToAucaQM5VApGDht+Vs971o++EpENxfOaFeQLirVEiEpTJS+/T9vsUkjbVfdb+dI33WDI0/M00UAXmXsxM6Y6RpOHktLjq7sSEjIecNDkdMnCweIE9VBHCj5QUq/+GVfZPDQTrfrXAezeS7+3B/T1GvfoatyW/dV6SL7NPYkY7bgdLSTfx/7V5AjNhHIvcpvsx6eglpxNz9iIpN8+1wHbbv4o7ublNRjXo8oZDUyfGFKD04kZtTVSoTVhQFVzzdbRa32BOb2veqijR1eeeIpepLa66uqSUiclV3H39b7u0gyOFbXkbHqVs1T8z+EztZNhnwFSNzh0kEbODpeaurDb26L9iRxCAMpJQpP41WHlji8mFZCIDIk4UZlgoKtNVHKyTXzWdQSN8oddcvrk1HRh8NKoJWfKe9YU7Ns+dcrwM5F96ef0Oo83+kCKbCm3KT3JPaXaccKIvofvsS2UaBoa4v9aC4kqK4nJhHZNz/OdtjUOyr9derrWEBHU+66/w2McQ23DwXtr9wUD/JRY6rXcbDaVEImrjZrAoOodr4s8lDohqBNIFzurFzcKNajTh/enNqMkUdVWC3GRrAyT0NZUYuqqE637o5JXEzY0bTFlf3rWUtRXvWaX7ZnSiI4drSGnVwvXAc1BTw+k23NKXF1x4c6nyFPpkjNFSh3EaudyIuH99T0lh8ZkvWogHT6RKqtqLpPaveSKr2dl2p472NRhFdmgHkpR6amvEVLho7rrD11Nbg05oxUfKindfo3UWbcBU5LN81w9hBI5hlIqbt2AdHtNpZyXF4mSCdzeZKU7Zv9of71Okg/+yNZ255BOTqph7CM5H4tDJybQInLqCn0iSh/zUEYUx9QZP7K1UsR0u8vtz6c64HytqaqzHkbRcAklpZYgYR6xe42jSgz+nCKvra4rVc3AkyXqyLQrBHasaA05X79+jfF4jMFggE6nU4nFqX3IJVaqunoSgkpOJ2PkqXRiRgniT7GrfH2pF8L2Ld/9vajsp244BGxLLZ2goswft6+1RGeUd+v9SfWzLp/5WFFLzi+//PKl2vFkuENGCUCP53g8xm9+8xv89re/xXg8RlF8rId6d3dXXqekAR7sSR+EtLkAlHmsOhA12ydKPiARozjfrgQKvZYJ6xHpvAq7E1Jtx9TaS/ZP7UFNiND6RVF5FOYOa6UHnaD4PeqYY7jGsSu761hRS86vvvrqpdrxSfCB5T8+g/B0fnS7Xcxms5K8Hg5QR5CXNHFvrQ5SJaIG4VWt0+QDbT+/T/NqfYsHleC+msR3Q3OCRvV9+MxUA9DvUXI6MZbLh60FNT9YqwD6Bkp8PuroUpt2MBhU0hcJOqk8lHPsqCXnN99881Lt+MWhhOWr2pdcERIlAbinNlqVQnK6KpdKclfS+soNlV7dbrc8pwNSJYe+R2JG6XdK0NQ2hWwjz2vlBLW1lZyU/Pf397i7uwsPFonWKg+q2vK5Ux3XiVXJqckQqRTC1G9/6Kgl59dff/1S7XgyXA30SgXr9brck+Pq6gqXl5f48OFDZXkW8LAhrKbCRQRVcnKQui2phxPXvbNOOCJa6O3tURU25exxdValn0p+9kdTFN0OVnKyTylS6t8qNT0JQe1ldSrd399js9lsSf9dklPV/0MnaS05//CHP7xUO54E96Kqusf1hpvNBpPJBP/973/xww8/YD6f4+eff8bNzQ2urq7KgamBe1WfIoIqOaNgu6rJqRxT9w57jiz7QVKxLAjboNtE+AZCrMYerSxRh49Kfk5knmTv5OR7VGt1PaquS9XSKyox3TPNbCYSX00LAJVKfy7598EhE7SWnGdnZy/VjmdDURRlP66urtDpdDCfz3F1dYX3799jNpuVUlMHgqbH8dULewFx6ccopY0eS5Ugej3b6tk+vh+oZgFpgnq0ozQJ6janO7XYvtXqYS/RyA4nOOm4WqvEdHK6H4D9BR6KjtGLTuJuNpvKlhiefH/saE0ohSGU5XKJu7u7UsWdTqcVp5HueK22oBJzV36pQge/1pV1NVgTGiLVVQkaeWHrCOr2WqSa83vZZu2TTh48r+VWXI2dTCa4ubkpz+vOYO5s81CVhlk2m01JzKdIzUNHa8jJ1SFalpJ1e4ri45pHEshd9wAqKmcUj0tluagdpesbVXIqNLSg2T5ejYCSPtpMSLeA14Gt7fZkDD4j/u9J/O5YY6EyOnwoJZWkt7e3ZbiK0lftS7aHbYu+SzdHeozkPAYSt4acHEhaL5YV74qiqOy4rDYQ8CDJODCcuMD2Nuc++GmTUop4OEXzVd1DrLVkUzFMPTzpQNVYz3iixFZiuJruK0ioCURhE1dzdTdqTjjsL1B14PG58Tms1+vSzHBfQBvQGnK6a18HFYDKgFgul1srL3zrgSgg7udU9dVBrqtZeD9KEjpInHSRyqoE1ZBDtKmvxy59BYyr56puKzl5brFYVKrWswYS/9ZDJbJ6pVWS8pyuVaXkpKmxr2p7LOStJedTEo5fEj7IPByh1+kuXBqPpKdyPp+j1+uVjgsnIO9PImkIRMno5wgONlXrtEgW78v367Zy9/CCV6Z3b7JOEko69RrrqyanO4n5vu5sxkP3bPF8Wj47Pif+VppCSJ8An8Fqtar0n0vWjoV8u3DQlRBcIqn0UbiK5ilq6qGsm5DUQRRJUD0fpcWRGO4B5kAEqjVm65ILohIi7Oty+VCuUrUAT0VUKZmSmrre0smr+75w01sPFfG5qSOITi3VCEi81WqFfr9fmhn9fr9SyNpt52PGwUtObWOdUwao1uTRkAXjnJEnEag6gzQOCaBCUFV79XvYBo199vv9rZUpKjkjgkb7YuoSLiWZZv14TDZSW/WzqclM1VrGbj13VkMhOoF1Op2tyvK6pI0bGTNphFK33+9XqjR4Yv4x46ALfAHVZPGUJ1XJozZOr9cr1VrdL8QzhfR9Ja5LSCWmEhXYXoisqzhU+ru6F21Yq04eJV40OamG4IcnTEQx2sg25WSSUmPZD6rqAMq4rCZKcC0pydnv98vsIxK93++Xi8F128EU3AdwyDjo0phANX0v5cnTuKHvkzIYDACgYsNFKXNUu6KdxlwaqyR21ZakUPVQ0+R0Aok8sTp5qMT0CguRVPTYpRNQpWdkMrhqy8lFM6B4PZ8BnwvtaC3N6ZsjaRV59qPb7VbImSXn/+PQHkKqvaqq6tZ/w+GwnNk92K9SUl357niJJKW+5x5dJYtnFQHpbQrVE6tLq5SUvmzN145GZIvsSicoERFUQytaaYJSkwTVLR+0EgMJypRDJiIoObkV4Wg0KtMY24CjCKVQguy6RolGInLwqXTyinNKFCWqxz6VrFGqHxFl4LhqToLqd6vUJin4GQAVJw0909y1zJ00bkdGYRW9TtvN8/q+J/PrxHRy8rE8J4lJop2fn+Pi4qJiUzKcxft1Op2yaiD3aNmHnIeu0gJHQk5g94+hNqeqtqvVx23qPE5I1VaJ4aGLlP3p70XpfhEJNCTjzimX2GozzufzkiBcW8kEgOl0WpFqkQPNycn39Dq93p+rOp3oMQeq5oTXMmLVP24tSKcXPeea0jgYDCrSVTWHp4yFQ8HRkHMfqP2jGTgAKmorJZR6RX3plWasOEl9BYvantGg1/b5q0pRzaSh1PQk9uVyWZKTmTr0ppI80USRKh3ibVX1Wyc8t691cvGwiW8vSKIOh8NK/0jOXq9X2d1sl0NI23jIaA05Pdyh3lh9X9XflEobSc/ocNXXB0tExqjdSnqC0o7eZrX9KD2ZfM4YpMatdRC7Q8i/P5pQ3I7X/kaTi6+wSe3dwslS7WNKXk9FPHa0hpxEneRKnfcwSYqAXjFByRul9vmRaoPHTpUg/BylJolJqcnkcw1zuBqqXlqHSlkFScd4rS8M52ep5rqkdY80ycrPuh1OT21O3ztCqIfRS1cCKFPHmD6mg0hDEh5PrFNN9fB4qKuATlD9Dg/TuAeVfWJKnSf4c2XIc2A2m5VqK0mqZkFRFJWd1aJ+uVdcNQEmzKvpcCzk24WjIafaUqn3STBV/Tzx3eOShDtoXAoCD7s8q8fRkyT0u1IDTcMVCpV2SkrN1onIWWdLfio2m01ZTZ+ScD6fo9/vl2SkWqt5zZq/61oC+8ln1FYcDTl3QRMAvJwl4bMzZ32gSs6UpOt2u6UNyMM9oe7d1Fdvr0oYVUk9B9br5mqt2OckpkKJ1u12K6SjFNU28npNsOehktOfR+QxPla0hpxAvCdnlAGjQfjValWpR6SqmcYoKSH5Gg02/h2pti6xI/XPHThRooDipaWOEyeVl6smAq/T93xS83u1Ba0hp3phNRuIgyEq0EzvoldET3llPbaph6podY6glMfWvcm0xbQwmRb1Go/H5cJuqu7PCc2s0iJkvvjbn2PU57q4aptwNOTc5STQLJVXr17h7du3WK/XuL+/B4CtrQu06p1XTNdYZ10IJYp1els1Wd/VZO2b3oN23GazqajpWuWeNZEuLi4wnU636vgQUZZS6plGNrjGgtVb695YxjMvLi7KuKZWCvQCXvrdkbnRBhw8OfWHqvvRGMh++/YtvvjiCwDAq1evcH9/X0ojzvK+gDnKDkpJSyeqJyFEsc19BpuHUhaLRSWLSavac/LRurFaK9YnAVXhHZrgELVH45wa6lCC6mZJ4/EYFxcXlUP3BvVQymbzsIyubUW+Dp6c+6LX6+H8/Byff/45gI9lPyeTSenw0VS9KE6phHSJGK1Mid4D4slkn8GmEhZ4WLmhOapv3rypZAax8p1XWed3KgGAx63fVRU7NRl5qqRnCPHwTXu1bey7aiqZnAeCfSXnYDDAq1evAACj0QifffZZmRQOPAwAlVBOQP0fqC60TsUzXWVN2ZT79DFKHohWo+jGQlHSu+Optp1PRt5nfW6+ysZLfJJ4Gm7iq04CbcoQKnb8KEdjjXsIxaVJ5EVNHXo9/97nNYV9JYGro5Qu6u2MSozUEVPv91j4s0ip7DppeYLHrvBU6j5HJj3DzrSGnBkZDUZIzoNXa1Nw+yoj49BwlMr7UxwcGRlNw1GS8zFe0IyMpuIoyQlkgmYcPo6WnEAmZsZh42gdQik8NeD+XPiUe0d92Pfcc+ApIaHHfK5taFUoxVd7pBZKp/CYQfSSAy7qj/ftOQm6T19TySL7JpEcOdoVSqkDs2z4ynN11z/m3r8WvD9NCSe1mHSfhFaR8ympc/te81T8Empt6h48/xJq7adMYJm8MVql1rYRLx3rzUR7ErJa20ZkshwujjqUkpFxyMjkzMhoKDI5MzIaikzOjIyGIpMzI6OhyOTMyGgoMjkzMhqKTM6MjIYikzMjo6HI5MzIaCgyOTMyGopMzoyMhiKTMyOjocjkzMhoKDI5MzIaikzOjIyGIpMzI6OhyOTMyGgoMjkzMhqKTM6MjIYikzMjo6HI5MzIaCgyOTMyGopMzoyMhiKTMyOjocjkzMhoKDI5MzIaikzOjIyGIpMzI6OhyOTMyGgoMjkzMhqKTM6MjIYikzMjo6HI5MzIaCgyOTMyGopMzoyMhiKTMyOjocjkzMhoKDI5MzIaiu6O94sXaUVGRsYWsuTMyGgoMjkzMhqKTM6MjIYikzMjo6HI5MzIaCgyOTMyGor/A0F3Hz40s3OyAAAAAElFTkSuQmCC\n", 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" ] @@ -491,7 +504,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -513,9 +526,9 @@ "lastKernelId": null }, "kernelspec": { - "display_name": "Python 3.7 + Meep", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "pythonmeep" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -527,7 +540,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.11" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb index 6a68a85a4..b7f61e4f7 100644 --- a/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb +++ b/python/examples/adjoint_optimization/03-Filtered_Waveguide_Bend.ipynb @@ -38,7 +38,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", - "mp.quiet(quietval=True)\n", + "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" ] @@ -64,7 +64,7 @@ "\n", "pml_size = 1.0\n", "\n", - "resolution = 30\n", + "resolution = 20\n", "\n", "frequencies = 1/np.linspace(1.5,1.6,10)" ] @@ -121,7 +121,7 @@ "pml_layers = [mp.PML(pml_size)]\n", "\n", "fcen = 1/1.55\n", - "width = 0.2\n", + "width = 0.1\n", "fwidth = width * fcen\n", "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", "source_size = mp.Vector3(0,Sy,0)\n", @@ -199,12 +199,15 @@ "source": [ "def mapping(x,eta,beta):\n", " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + " \n", " # filter\n", " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", " \n", " # projection\n", " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", " \n", + " projected_field = (npa.rot90(projected_field.T, 2) + projected_field)/2 # 90-degree symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -228,10 +231,13 @@ " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", - "\n", "sim = mp.Simulation(cell_size=cell_size,\n", " boundary_layers=pml_layers,\n", " geometry=geometry,\n", @@ -244,7 +250,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's formulate our broadband objective function. To keep it simple we'll just minimize the sum of the error, where the error is the L2 norm between the transmission and 1 (i.e. the desired profile)." + "Let's formulate our broadband objective function. To keep it simple we'll just maximize the average of the transmission." ] }, { @@ -259,7 +265,7 @@ "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", "ob_list = [TE0,TE_top]\n", "def J(source,top):\n", - " power = npa.abs(top/source)\n", + " power = npa.abs(top/source)**2\n", " return npa.mean(power)" ] }, @@ -275,8 +281,6 @@ " objective_arguments = ob_list,\n", " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " decay_by = 1e-4,\n", - " decay_fields=[mp.Ez]\n", ")" ] }, @@ -294,7 +298,7 @@ "outputs": [ { "data": { - "image/png": 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1v/tdgfXrNX7+8xTLliUYGhphYGCAffv28dJLL/HSSy9x4MABBgYGlFUsNya/319COnV1ddTV1SkZ22MRoztSVElnmij/QjOZjJq2KX62EImxG1liBcaMg9w5jb1UxcK/K1m58ofA0youUw4hNF0vZjlEYtNms5VMaZD3krhOR8dp/OpXH+ajH70buz3GwYML1THFzZtOVmSy6zPbMLoky5YlGBgoBlsHBgZUM6zJZCKTyRAOh4lEImSzWfWZynunjD9DsXn2/vvXsnbtNpYsyaPrRT0kiZvU1NRQW1uruv3lOU3TFOlIIkD0j6Sr3xj03rRphLPOCtPdHWFoaIienh7+/Oc/s2/fPg4cOEBXV1cJSRplayORiOp8l1YNuZGd6IQDVdKZFsq/UBFgj0SKd9pEIgFQUidjlBU1tijA6GI19lI98MDVXHbZT7HZ9tLdHSnRRhYYtV8k6KhpGolEQsVzjGNp5Pi9vW9j9+7rufDCW9H1N3jtNZea9CB/I1IJMymtP1aEs3VrgXe9K00wODpbfWBgQD0kBZ5MJjGZTPh8Purr66mtrS1xc8q1hqR59qMfvZdFi7rRtKJSrnyXot4n5GZ02TRNU+UQQjiapinlxdGgt4Wf/GSYJUsG6O4OMTQ0RF9fH11dXbS1tdHf36/WUTnEfbPb7djtdjUkUd7/aPeCzRaqpDNFjJ3aMDpDyThyV9KmUEoqMH4HtrGX6i//8k7c7pfo7g4TCoXURE1R+DO6USaTSY0DjkajOJ1O1XUspCPvGYks4803v8DSpV8jk/kDBw8WdXXC4XDJ4DhZ0Ed6fWYbpYVzBWKxorh6OFwskJTNe/DgQdra2hgcHCSXy+Hz+Zg3b57q1pd+NChVBjx4cD7bt69mzZptLFjQidPpLlFVFAuwvr5epaxTqRSJREJllYwSGRaLhVQqpbKHv/tdgU98wsx//mcfCxZ0c+jQEJFIRI0KlthTOBwumRhitFgTiQSDg4NA8XuS6bGapvHMMxauuWbqek3HE1XSmQIqbSgp9otGo4p0ZNSrMVU61TEx27at5qqrtuDxvEZvb9HkHh4eJp1Oq74pj8ejVOuMgdCRkREVkzHGGwTp9ArC4W/g8+LTE/wAACAASURBVH2SwcE9DA6iJh3IQhcJB0nhH+n1mU2MdWmL114G0EnWSDaxsSwhGAzi8XgYGRlRwvHS/lEuD3LVVVuYP78dh8OJ3+9Xky2EfIxZwVAoRG9vr6rREZS7cbqus2uXiS9+0cmtt3bS2nqIAwd6GR4eVqSUTCbJZDKk02lFZGJBGeeGSdBb13VcLpcineefd/LFL9pOCsKBKulMiok2lGgYi3sVj8dVwyVUnrxZTkKy4Nevv4/m5gMEg8UaHKktsVqt1NXV0dLSoqQpJQslJv54c5qKWAn8BFhNKPRUyTNWq5VgMEgoFMJqtTJ//nxOO+006urqxqRfZ3J9ZgPjHV96ppLJpJKhyOVyY+JRcmOQWJtkeiQeVtSUXs3q1VuZP78Nm21UJbCpqUmlpS0Wi4rPSLpcrF2JI0m2UoLSuq6zZ4+db32rke9858/MmdNFR0c/AwMDRCIRAJVplPiefPeC8vUin1kI65VXAtx2WyObN2dZtWpsL92JiCrpTILxNpTRlK4UEJ6qhbN9+1olwJVOm1TncF1dHVCUVJD5WX6/H5vNRjKZRNM05c6Nj5XAdmAt8NSYZ8WlGhgY4NChQ3R1dTE4OEh9fb2SKZ0Ix7NbXK69pLDFrZWZ40ZrQVQXY7EYPp9P1esUXaq1XHnlPcyd247dXrRwWlpa1LRTmVQKpXPIxCU1rgEoDSq/+KKbn/707fzv//00Pl8/HR0JVU1uTA4YidNoNclnNcLhcKhU+cDAWdxzz4X85CcDvPe9/tn/Ao4SqqQzCcbbUBKILM+CTJdwiiLqRQEu2Tg+n0+lR0VASpoJpZ4nkUhMIlU5MeGUIxQKqWI0n8+nAq7jWTzHu1u8vEfKbrermqV4PE46nSYYDKpZ31I/I8WTQ0Nns3PnOlav3sK8ee0qG9Ta2qoIR4bpQZFwpNu7v7+f4eFhlRUrF6bPZrPs29fKli2X8vGP78Tt7uTQoVEFR7GYRI85Go0SDodLAtPl0DRN1SAFAgF0fSW//OUn+epXX+SCC+aoLNvJgCrpTILJNlR5Grb8OYExg1U+l0okRjVtdFifzWZTqnUiUyEbQFw4MefL31PXVwLbmIhwjEFKgFgsxsGDB3nllVdKJDAqjaaZSnNruYJgubjXRPozUyE04zlJOlvaEpLJZEnhXzKZZHBwUN0Q+vrO5Lnn1nP11TtZujSEzdZMTU1xYGBzczP19fVKC1mkRWRMtPRBhUIhstmsirHJ58hkMhw6tIj777+Kj370Hurr24hGR6+1sVBQXMRIJKKq1Y0z1I3QdV1ZwBbL+/j97/+ea665n//xP3zAnBIr+0RHlXSOEOUbazKMN3lTFox0J4sFY1QAFKKYSKZyKoRT6VwTiQQHDhxQOjLz5s2jsbFR6TILJiIEUdyTlHGljnf5jFLQWE46M5GngOJmttvt6LqOz+fD6/UyNDSksn/xeJze3l6i0fMIh7/IFVf8hLPPNjFnzjz1eumbcrlcaJqm4kSJRHH6p1Sc9/f3MzIygq7rqjxCanP275/Lo49u4IorNtHY2E6hYC+J8RibgLPZLIlEQgW/I5GIIp1KMUC73U6h8F5efvlrfPCDd3DWWTkslvoxBH+io0o6s4CpfOnlFk5xLlXpwhJLxziaV2IWuq6ruJHxUfq+UyMc43safx4aGmL//v0sXbqUeDw+5s45mSKijEGWoK1xA8m5S+uGbHCxEkePP31NZhgd2eNwOFT3vsfjUZ34xXN4D8Hg7Zx22s3MnWslEFjBwoULaWhoKBFeF/JMJpNEo1GVFRsaGmJwcFAN5RP3U24G+/a18uijH+Oii/4Dp/M1wmG76sMSi0iuhyQh5PgjIyMkk8lxv5/iTWgVb775T1xyye0sXjyExTKvqqdTxfgYSzijMN6tRSLTYrEoV0E2Dkwkezq9GI5AFq2Y/+FwWBUMGjNB5RKaqZRWMvtK4hNSLzM4OEgsFlOp+VwuR6FQwO1209Iy2nYh1s5sxYiM8Z2mpiasVuvhdPQK+vv/k8bGG2lp6cbvfw8tLS3MmTOHuro61bMmFos0i/b39zM4OMjw8DDRaFSlqcuv4cGD83nwwatYufI2rNbn6OuLY7Va1ZwxY0GnZKkkqxaPx8eN5QCHlSA/QG/vD3jPe37AokV9mEweYGrW9YmGKukcRciCKJ9LVW4hSLxDhuvJ6NpCoUA4HFZypYKxpvfMCKcSxD3K5XIq8FmcXKlz991pli2LMjycVlaZpJMzmYzqders7FTFkkY5iXw+j9/vx2q1Kpcmn8/zzDOWGRGOMZhszCDZ7faSYHwweA5/+tNXecc7/pGamh6amopxG5lj5XQ6FfmKJpFYI/F4XEmSSCGgcaYZwIED89i69So++ME7sFj+RE9PRMWQJO4jGtaaVhR2HxkZIRwuFoCWZ7OMKM62+gDR6H9x3nn/zOLFw1itLoCSxtOTCVXSOYqoFDSW30Pp5E3Z4BJTkakEQj7SqV5e3ZxOr6BYhzMzwil3ocS6kmzKCy+4uO46B3fdleScc0L09gaJxWIqm1JTU4PZbCaZTNLf309nZyd9fX1jMjGSXk6lUkpyI5fL8eSTcM01owJWM4W4WpIBlBaEjo7TePnlT3HFFZuor4+QySxQ89clkJvL5Ur0os1ms4o7idi90Y0R0pHm0C1brjpc2Pk6XV0JlSmT71TiczJNQiqLe3t7GRwcLOmvM9b7FIsSL2N4+Ce8853f5ayzBvF4/CXtNZVkYU90VEnnKGIiwjHCuHiMmsay8OSOFolESnqlIpFlhMPfAFZzJBZOeTA8Go3S19fHI4/E+MY3bGzcOMzSpSP09hZ1XsLhMFDcREbSEX0asXCMwlKySYxuxrPP2vjsZ21s3TrzMS7lVp+4V1arlba2Bfz2txtYs2YLCxcOk8s1ks/n1QSFUCiE2+0GUBlDo8UjdT8ivC6WjlimQjhr1mwjEHiNgYERVawppKvrupIr8fl8qr5K+sWkelqC7NJU6na70bSL6er6d971rn9lyZIh/P463G73GPfuZMNxJR1N064AfgCYgU26rv9z2fOfAL4PdB/+1W26rm86pic5CcprRgRFwlkzJmgskGClLGBZbDJ/SfqERCRKLIRoNEoul6O39228+eYX8Pk+OabSeDqolGaNRCLs3m3l8cdP5x/+4Q80NaVpa8soTeZIJKKqpWWjioWUSCRUqt94R5ZCPkmT//GPtfz939exeXOWiy+e2ZTK8sI8GNW3aWtbwM6dV7Fhw04WL+4HnIowLBYL6XSagYEB1dYggwdFQkJkSKVCOJVKqXYFi8VCV9fp3H//VXzkI7+gtvY1hobCJdo9YpXGYjH6+/uV+2kymYjH40pcTIhMPo/Eo+z293PgwD+zatVtnHHGMG53oCTWdzK6VYLjRjqappmBHwKXAV3Ai5qmPaTr+utlL92q6/rnj/kJzgCyeYuTN9eUBI3LN7akTAE16rexsVEJNYkoU6FQwO/3E41GCYVC6LpOR8dp7N59PUuXfo3BwT1HfN5GS6dQKNDfv5RXX72Oq676ORZLgtdfd6PruiIV0eZJJBLkcrmSmVsSHBayMWbhZNO//nojP/jBIu64I8rFF7uOyDUwEr7x+m/ZUiScRYsOYdyfkkkTfR3pdYrH49TV1SnLLZVKMTAwoETR+/v7iUQi5PN5BgbO4sknN3DppbfjcLxGb29MWS4SQJdrms1mVce7kLC4dSI3YkSxf+4SDh78Z973vp9w5plBHI7RYs1yBYGTEcfT0lkO7Nd1/SCApmlbgI8A5aRzQqM8ld3evpCdO9ewbt0OFi48pBZf+d3YKPIlmZbW1lbq6+vVHU26m2tqapS2cXf3GfzqVx/mwgtvJZP5A4ebjmcNhcJ76e39Aeec83VsthBdXcVCOekRyuVyJaLmsVgMi8WC3++nsbFRicwDypKTz2w2m+nsXMzdd7+LH/6wj4sv9s5IQkOKC6W8wOl0Khd1//65bN9edHnmzm0nk9FL/k7I0Djvyli1bLfbyWazavSLqPdJ4d7w8Lm8/vqnOeecb5DJvEJ7e7FMQOI4laqKpW5pMgjhdHT8K5dccjtvf3sQt9uranyAMf+ejDiepNMKdBp+7gIuqPC61ZqmvRf4M/AlXdc7K7wGTdNuAG4AmD9//iyf6lhU6r1qb1/II49sULPFhWeMEpfit0uQsqamhkAgQFNTkyq9N6rMiRXh9Xrp7FzM5s1n8tGP3o2uv8HBg9kxdz1jV/L0Uazzqav7DLW1AxQK89VnLHcHxdWLRCIq9iGfz6iWKC6V3W6np2cJ99zzIb7znX1cfLF/2mNaBELYbreburo6Fdh+/fVGduxYw5VX3kNLy0HS6dLNKWRlhGSoMpmM0rKR2JTU5IimUTz+FwwN3UJ9/acJBp8nGESl2FOpFKlUakrkUgnF8cYfZGjoR6xYcStnnBFSFo5cx0rX4WQKIAtO9EDyw8BmXdfTmqZ9GrgLuKTSC3Vd3whsBDj//POPyW3AWM7f0XEaDz+8mtWrt7BwYTey1mUTyh1WSESkJgOBAA0NDUpkqnzypmQzXnrJzze/OY+vfvVF7PYYb7xRoywlqQcBjsD0lrT7Olyug5jNi1XMRtwlSX0bg7fyECvIqGIn56NpGt3dZ3DvvR/h619/jcsuq1E9UDPZNGLleDweAoEAyWSS115r4L77Lj8swNWB2Tyq2miE8f1k04ouUigUoru7m7a2Njo6OohGo6o5VNdXouu3AWsYHCyNoR2p1VG02D7AyMgmzjvvn3nb24ZxOv1jWlXKMZ2bS3XuVRHdwDzDz3MZDRgDoOt60PDjJuBfjsF5TQlCJg6Hg0OHFrF9+zI2bNjBggVdFAq6ujNJLYuIY8nPbrdbNe+JqHql2eKaprF7t5VPfcrOpk0hTjvNycGDC4nH4wwPDyvdnUoo19aptEDLe7U0bReFQuuYqmchHZFElakLEjiWQGsqlSqp7C0UCnR0nMbOnR/hq1/9A5ddVnQVx5ujPhUYXSu3283+/XO5445zuP76R1i8OIbD0arS1EataHGpJECczWZVb9XISFGnuLu7m87OTkKhkHo/XX8vsBUpS5htz8ZkuoRY7Ge84x3fZunSID5fg6odKrfUKmEy4qnOvRrFi8AZmqYtokg2G4CPGV+gaVqLruu9h3/8MPDGsT3F8SEL/5VXAvzrvy7mppueprFxhJERq5KRNBb71dXV4fV61R1e3APp9RlPIvTJJ2HDBjNbtxZYsaIoog6l9TShUIiBgYFxz9UY1B3b/lDaOqHrqCZJkXAQ/RhjLMXlcuHxeErG8obDYZUBkk3f3r6QBx9czw03PM6KFV58vrmq5eBIXAOJi734optvf/t0vvzlPSxZksXhWEhtba1KhRtJR4rypNhPZmIZq4PFuhnF7BVeVsZK0um7Wbz4/3DaaWFqa+eoNWLsVyt3paZ67apzrwzQdT2nadrngf+PYsr8Z7quv6Zp2j8Be3Vdfwj4oqZpHwZywDDwieN1vuXQNO1wnUmA227rpbXVxKFDHpXSlgBxQ0ODmksld3hxyaRJsVwaQVDaGmACnCXyGRIEtdlsHDp0SMmbJhKJKWY5KvdqGStm3W63Ik+jSJXT6aS2tlZlf6SzOxqNqr85dGgRDzywltWrt3DmmTms1oCqoTkSwpE2gt/+Ns+XvtTKN77xKmefncVma8Xr9ariP8kUSUNmPB5XdU/xeBxANV1KIFi+v6Jr8x7E5dS0XSWV5DN1qYwZLDm+y/UJPJ4eYLF6nTFmKDCWHxgzduNdyxNVpP24xnR0XX8MeKzsd/9g+P9XgK8c6/OaCsQC2bw5y7Jldnp66tSAe13XcbvdNDc3M3fuXFpaWlQq3LjhxHKotGjGWzBSx9Hc3KyqghcsWEBXVxcdHR0cPHiQgwcPKi3d8TH+HTyVSqlxxNImINaOwGq1UlNTo3SKk8lkiXU0OPh2du5cezjG1U7Rez5ySNf3r36V4jOfqeMf//EN3vGOEZzOWnw+n7JyjJKkQtBSD2XUPpLjhcNhBgcH1eA9WAVsQ9PWjyGcmUIsQJPJRDq9gkTiTorXfy/xeCuRSISamholAm+sOjY294oGs0yFrbSGTlTCgRM/kHxCovQLtZJMukvmGxUKBVVV3NzcTGNjIx6PR8mYTu/4Y5+32WxqakNDQwOLFy9mYGCAtrY2Xn75ZRwOBwcOHFDax+ULtlB4L0ULp3gHh1K3S9LHDodDxZpEw0csMrF2pKBRanai0Sj7989l9+71rFu3nXnz2kusgyPpF5JM06OPxrnxxga++tU/csYZQcClsoBSVGmExG2EYIxC9EaJCVHvM1qAuj61GM5E8RapNK+pqcHhcBzWrP4RNtvHyGSeIpk0MTQ0NGYwn1x3sdTkfTStKH0iiQe5kZU2556YhANV0pk2Kn2hsgFl1InL5VLBYRHfmi3CAVQKWsYK+/1+6urqqK2tVZtP5CmMAubF2pql9Pb+gLq6z+ByHUTX56o6FXkAKl4kYl7GHiSpuZGN5PF4VIFdb+/bOHjw87z//RuZO3cYXS+dijGRFtBEkPnjQjhf/OLTNDR0MDLiUpuzfMqlQNd1NdgwGAwSDAaVCyqfZdTFnXoMx1gnJIRsbGmR99Y0TQXgU6l3EQz+G4HAp0mlniWTKZKiJAMk1ldTU4PX68Xr9arPJecprR4+n0/pZs9UHuR4oEo608B4X6jceQKBgIqHSK2KMUsymfbJVBeMZM4ExgXpdruZN2+e6tHK5XKEw2H6+vrYvdvKq69exznnfJ3a2gHM5sXouq5EqmSigui6JBIJgsGgSpsL6RjnZ8tcp0QiQX//Utravs2SJV/B4RgiGm1R9UgTuZKToZxwbrzxKXy+VxgczCuylYxUJeIR7RqZjDk0NKTiNsZsosXyPjTt39H1NUxGOBaLBa/Xq0Y6S0GnuNdGy1J+Hh4+l66ubzFnzt+g68+RyZTK28owPSF+mc4hFo3Ew8S1EktTWjdOBsKBKulMGRN9oWIKBwIBoLggh4eHGRwcVLU5uq6rhVkpC3GkC0ayXy6Xi8bGxhJJiZGRER55JMbjj5/OVVf9HJstRKEwX2VIJC4jFk04HCaRSJDNZlXq2GhJSG2QxWJRGzYWO5/29pvw+28gmdxLT08rZrMZr9c7RjainHzL+7+MG1aEwR57LMEXvtDEjTc+RV3dy4yMRNW1DgaDauPJdZa7v1hXUkkt7pV8JvkM6fQK+vr+hrlzv0g2+xaZTN2YKalGK8bpdFJfX68e4j6Lu5bP51UnfSaToadnCfv2fZWFC/8W2EMsllNusricLpdLxZxEmlSmgBhbY4wWj3wPu3aZWL/+xCccqJLOlDAZIYjPDsW7qmSRRCsFUHEeIR4x6zVt9mZPy0IUy0Luoi++6OYb37DyD//wB6zWJJ2d9aoLXFLvYtJLh7OMppEZT6LIZ6w1gqLr1Nv7Nl544Vrmz7+JYPDXDA6OTp1saGhQxYtChOXWjvFnsc5EQygej/P44xm+9KVWbrzxKfz+l4hGR7uypc/JOBkiEAgoF3S8hlxjV/rAwFk899w1vPvd/4bd3k8icXrJ2GCBpLBNJhMulwufz4ff71e6PEI6UqEsgfWeniX86U83s3jxV4BniMVSihzFNTS2vHg8HiUy39jYSF1dXYkLJw/5XE89pbF+/ckxaA+qpDMpJtMELl/MMqYkHA7T399PPB5XFkc8HlcynWIu79plmlWTuHyDPfWUxrXX2tm4cZimpjRvvFEMeufzeSWhKUFiiU/IHRdQ87VisZgKdopr5XQ6OXBgHo8+uoF3vvM7hMPPqo5sgSjnRSIRZT2NF0g2jngRYfUnn4SvfW0JN974JIHAq0QixToasRiloVJIB0ZjbFIrZIxDGZUOAQ4dWsTDD6/lf/7Pu6ivHyCValUWSvl5iqVjrFVyu90qKyUaO9IukslkaG9fyAsv3Mz8+TeTyz2llActFosqqZDZWlKOIO6Uw+FQZC83q3KcLKOEjThlSWdssHK69RXF10829UB8dmkSlLSryFDm83n6+vrIZDIMDw8TCARobm6moaGB3butfPzjR3uMi85ddyVZunSEtrbMmGIzCXJKmtkoXCUWjVhr0WhUFSEWN/syfv3rDaxY8e/k88+SSqVK4hNSj2IymWhsbGRgYIDm5uaShlYjpJFUtIh37TJx663v4pOffIxA4CCJRHIM0UtmZ2RkRKkvSnDd7XaPsQwEui6zy9eydu025s3rIZ8v3hCMo2LktUbINZPPIGQpLpUUTO7fP5e9e79MU9PniUZ/TSqVUuluKQwV90wqtPP5vHINxWKSWqPK3+/JRThwCpPOeAHL6QYyx/tCpfRfdG6kLaG/v5+hoSGlaSxkJKNGpE3g1Vfr+dznrGzbNnMBq4kghHP33Wml+Cf1NOLCGDejMUYgPVd2ux2Xy6WmKmSzWaWI1919Bm+++VcsW/Ytcrk9DA0NlQiLQ3GsjRRKHjhwgLq6OlyuotRmoVBQ6W3RtJG53j09PTzxhM7tt1/MmjVb8fnaiMVG40JGiIsqAXyZqpBIJFQcRNw1I5kcOrSIBx9ce1gNoAOwjskwViKdchkNiRNFo1HVBpLJZOjuPoNXX/07AoHPEo3+UgmbaZqGx+MpyUBKXEwITCzjcDis4lWiny2f/2QlHDiFSWe2sGrVaEDTmIHKZDIlJfWS+RFxJqBkPInc5Wpqanj+eSff+lYd99yTZtWqiadozgRGEfVly6JK8W9oaEjV00jXuMSX5HMZLQOpmLbb7UrlrljDs4xI5Os0N3+OcHivIl0hNCNSqRTBYJD9+/cjmjyxWIxMJkNrays1NTVqPHJ/fz89PT08+ST85CeX8tGPbqalZT/ZrDYmCC1EYHQnjYLnkUgEp9NZ8jtxbYqV0htYt2500KFgvArfSsFuyYqFQiEGBwcVqQ8Pn0t39y3U13+WbPbXJRagsZJcYj4ulwuTyaSsHSHRYDBY0iws1tvu3VbWr9fYto2jcsM62qiSziQwqsZJh7WmaWqx9ff309fXZ6hkLUICulCqn3PgwDx+/OO3s3FjkJUrPbN+vnIH3LYNLrggx/BwWvVFiQWQTqdLZDbKq40lLS7BTbEAcrkckcgyQqGfAGsYGHiGYHB0ZMt4Xc+xWIyOjg41ykWEyFOpFPX19UAxdtTb28tTT2ls3HjZ4UrmQ2haMbY0XvuBvKdszkKhQDQaZWhoCE3TcLvdpFIpJVPx+uuN7Nz5ocPH7wRKmyqNZDbR70TeQ1oohoaGDisHnk0i8W+4XNeRyTyrXCNjalzaMEKhECaTiWw2q/SSJBYlyoVGrSZd13n2WRvXX29j2za4+OKTj3DgFCadscFK0zi/HwtjSndgYECNZTEq9os5L3UfIyMjKsAp2Qjx0XW9KJHZ07OEO++8mO9/v52VK2tnXLcyHowm98qVkEqVZm6k3iUSiSj3z26343a7VXak3M0yynd2d59BJPJ1YA3wJLkcU9LrFfdpYGBACaHL1M158+bhcrlIpVI8/7yTn/70fVx11Wbmzj2Apo3qKY9HDMYslMSQIpEIZrOZeDyO0+lUru2LL7q5++7LWLt2G/PnH8JIOOVkZkyXVyIdyZRlMhk1Jmho6Gzi8TuANSQSu8jnR5taZQyPfA+JRAIoWswiAubz+VQmTLKEQjYmk4lXX63nlluchyVebZysOCVJJ5fLEQwGy37bAFDh96WQIjaoA2Dfvn0MDg4q09x4tzJqF8uil3/lrisLra/vTDZv/gDf/OabXHppTYnC3myg3MeXSmBJw3q9XqxW6+EivlHxdKfTqYbfSZ+VEI2cn9PpJBJZxptv/hXNzZ9jYOAZZqoNHg6HefPNNwkGg3R1dbF48WKam5vp7X0b99zzPtat20Frazu6XupGlf+/HHLNs9msmhMm7kmhUOCNN5q4667LWL/+PubPP6SOP95xy4PV5UQn7yc3l1TqXSQS36dIyMXWiUwmo2qHyqedihVjrBYXwTBjGl7Or719IXfeeTq3397PRRf5gCrpnFCIxWLs2VOuHfxhgAq/L8WoZfAhAP70pz8RiURUnYm8xhiEFaGtchNazOKeniXcd9+V3HLLy1x+uQefz6c29mygUlBRgsPGVgWbzVZi7Ug1sriPHo9H1epIoaDJZOLAgXn8+tcbWLbsW4TDewkGzSUWjvFzT3ZtdV1XUhjy2LevlT17/ifr1m3jtNO6KBSsUzreeBaIWFJy/YsxnA8etqA6kNnxlc6/kltV/ntj1s9utxOP/wVdXf+bhobPMDLyPOJlTySgLs9JbEf0p43a0/K6gwfn88tfXs6Xv/wMb397gGTSXuISVzr3ExmnJOkMDw9z9913l/22SDpjf19EeVOikM7evXuVyyHkIpkd+X95RsNo4fT0LGHHjnX87d++wKWX1pSUzM8GJquUlnOX8zeW0Ettiyz0SCSC2+1Wc73dbje9vW/j0UeLafFcbk/J7C3jZ54MlV4XjUbp6jqd/v4vcPnlP2bOnGHApQjdOCWhPIU92fvKd9DWtoCHH76aD33oLlpa2slmR7/LmWxSOQe5lkNDZ/PCCx/nggu+RybTRm9vPcFgcEwmbzJI1kssZ4m5dXYu5umn/xcf/vBdzJljYnj4bUqzWm4KxvM5GYjnlCSdUCjEAw88UPG58X5fySUCeOWVV6ivr1f9RlIQZhyKBozZFABdXaezY8c6brrpOS67zKHK5Y0NekeCydKmRhdQ7vzSKCjD5qSmRF4jbQLFu+75vPDCtbzznd8hn3+WoaGhMWN1p1r3VPl1q+jp+X+cddYteDxDxOON6rqWE40RE3V0y/MyJuaRR4p1OAsW9KFpzgn/frro7FzMww+v4corf4HXO0h//xygSAChUIh4PD6tjvpCoUAsFlOxwXj8L9i//0aWL/8eLlcXfX0LFeEkWYe3KwAAIABJREFUk0mlNCBp95MFpyTpwPgBzukOKuvq6iIWi6ksT01N0VqRTJYx9mG8CxYJZzU33fQcV1zhoKWlBb/fX3J3MqJSAdpEmKxSWuQagsGgEhlPJpM4HA4aGxsVuUo3eSwWUxm6eDxOf/9S2ttvYv78mwiHn1WklEwmZ9QlPharSKV+Tk3NJ8hm96kNm81mSwrljJXEYrUZdWaM/VBGN6mj4zR27FjD9dc/xtlnp7FaF5S0KEgjrvE7k2tXjkqZM5lrtm7dDhYs6CWZ9JV0lLtcLlUaINXNUuksQWgY248mgeNIZBldXd9i4cKbSadfoqPDU9LA29jYSGNjI01NTQQCgSNWYjyWOGVJZ7YwMjKiMg1GNUBZsJKWlSI3KN4Bt25dw5e//Bzvf7+D1tZW1T9TKY4z3gYazxqazMIR4pDRtTJbPJFIYLVaCQQCKtOTSqXweDyqjiUWi9HTs4S2tm/j999AMPjrEpeqXHh9ZhjVq0kknqa/36uCq1LUJ6Qj7RaSYZO+Mrlula5lW9sC7rtvNddf/xgXXJCkvn4BHo/n8GYuKgRKkaMQ23RgnE1frPMxqcmsUirh8/nUlAnJcIkFmU6nFREJmYqbXgwuX0Ak8lN8vr8mHt9DPF4UAJO/l2biaDSKyWRSfXMnC05Z0ilfSGLlTmWBFStC5e8KJWNFZMojoFLKkiIHOHhwPvffv5ovfGEXl1zioLm5WQ1xm+hOJE2L8v+ZEo6crxTbyWxx0f6VAKRkVYwVsalUin37Wjl48PMsWfIVksm9DA7mSuqPjhylejWFQjGjJQWUkUikpGpZNIMCgYDqyDaqLxoD+qOEs54NG3Zy7rlJ/P45ysrUNI1wOIzJZFK1QpJ5lFokY4lBJWH08lHRuj6axRIrViZViDUlVmI0GlWurBCPfNdiwcXjf0E8/iM8nk9SKDxFOFx8nVQly78AbrebhoYGYrGYShTMZkb0aOHEP8MZYry78VTu0pP54eFwWKnqSVGXw+E4PMp2Pddd9zDLl9sIBFpV895UTd+JgqRTKX0XFyIajTI4OEh/fz8jIyMlBXRAiXkvQfHBwbeze/d63v/+jTgcQ/T2jkqMiqbwkWF8gSyZuCl3b8nuiISEVO8ax/RYrVYVH9M0jYMH53PffWu58sp7Wbx4EKu1pUQQS9yYkZERlclLp9OKcCqVPRhdNqNLtWjR2CyYXEtxr8QdEgvO6/WSTCaVGLxYP+J2xWLnMzR0Gz7fp9D1XSSTKVVPJSQl51cuS2LUsp6NeOHRRJV0pvia8rSwZHwk09DevlAt+DPOiOJwLFUdyFNJjZdLMJQTz1R7bST7JgFi2cxyF5Tq4fLPeujQInbuXMu6dduZO3eYaLSl5Lw1TVONnzPD+IRjjNeIFo1xFrhYJCMjI9TW1ipFPendslqth12eq7jqqi0sWHAIcKm4lbiEsimNfy+Ba/ms8tryG097+0J27FjDmjXbmD+/nUKhNLVebnlJB79cb3ERpQVCCDYUCh2e6PEOBgZuw+v9K2CXep3xPcqnmorFNjg4qALK5RlVQXXu1UmIctKRhanrOp2di/nlL9eyevVW5s5tQ9Maxu1uHu/YUOr6Gf9mps19RmnQ8jt3+Shkmdowb147uo5yayR+JcHP6aaCi6hMOOKeipyGuE1iGUgVuNStDA8P43a7CQQCtLS0KAmLogVyFevW7WDevEPoejELKYLrXq9Xxd1MJhM1NTW0tLSodhaJpRjrZKR4T9M0OjpO44EH1pZMDjXW6oynESTXWlLZQvpijVqtVtU8OzDwPez2a0inn1TfXSWLW45jsVhUUFnXdRwOhypzKJ+2UZ17dZKi0vhes9nM0NDZ7N37Cdat2878+e3kcqOENB094PEaDWdKOGKGV0rpy/nLhnrwwfVqaoMEsCU+Ie0QYokEg0ElnDU1jG/h2Gw2fD6fGjgoAWRp0BQhManWhWINllhuNTU1RCLL+NWv1rJ+/X0sWFC0QKT0QfqbpOJaXB+3201rays+n09ZQslkklAopHq25PsudqN/jDVrtrJgQScw2olu/M4q9YQZ/xUrRW5EQuT798+lvf1LuFzXEo//quT7KUe5BSaFhdlsFq/XW9LMK6jOvTqFUDRtz+eNN27mwx++i4ULBzHyUqWM1HQxE8KRhVmuwGe0cERWobv7DHbu/Aif/vSvOfPMPLpejOEYrSIRyWpsbMTr9bJ//346OjpUrEFeX5mESgmn/HVGSc6mpiYVdxE9Y6ezWFczOqWhCGkbaG9fyMsvf4Irr7yb+fP7gFJLzjhMT1QCRNLV6XTi9/vVtYrH48oddrlcRKNRXn+9kYceupzPfOY3vO1tOYwDaY19VxIYhtGbkbFa3WhtSopc0zT6+s7khReuYdGiL9Pfv2vM9zgejDVl4oYK+RhvdCeq/EWVdGaIbPbdtLd/n4suupV580YAb8nzR1ozcSQulfFhFAoXl8psNtPTs4R77y2O+r3wQi82W/0Yd0ziHIlEgvnz5xMIBFTaeaKJokWMJZxyGDNTIrAu4lUWi4V0Os3w8PCYjIyu6wSD53DgwFdYseL71NUNkcn4VCzDWC9j7KKXn8UlMl4XUf9zu93U19eze7eVO+54G9/+9pu8851+wA+ggtwiOCYNv+l0WhX1VXK5yl3ztrYFPPLIR7nwwlsJh3/PwMDk62W8WGN5PBBOXMKBKunMECtJJu9i4cIv4/eHyOXmzXi0SiUc6YLRNE2NMvH7/aTT6ZKN1tm5mHvu+RBf//prara4SHsKSRmzONlslqamJlwul4qTZDIZwuEwUGkzjHWpKl0bo4rheBmXSvGSXO4igsHbaWn5PBZLL4nE/DEawlarVekMl2e8ygP7QhRyzZ54Qucb3/CwaVOICy+sB+rVa6XUQNwxiUXZ7XZldUghYHkgWL6b7u4z2L79Ki67bCNm858YHi5M6SZV3qoj7SxSJS+lG089pbFhw4lJOFAlnRmguKHs9mux2dpIpRapvplKd5zpYrYIx+Px0NLSgtVqJZPJqM34+uuN3H33u/jOd/Zx2WXF6moRFYfS4LNseJlUAKNZu3w+zxtvvFEhozX1uVGiqhgOh5VlIzGdcDisKqVLq8hXksvdC6wlk3mVSGQ+8Xgcr9erMjwSt2loaFDSsFLHMh65SZ3Ns8/a+NSnTGzenC3ROxIilr9Pp9MqCF5bW0s4HFbz0IPBoIpDZbPZEpLr6zuT3/xmDZdfvhGv9w/098dUBnSqkNdK3Y7f71cTZPfssXPddaYTlnCgSjrThHFDPU8y2ah0UKQ3S0z8YxXDKYfEa8RV8fl86o77xz/W8oMfLOKHP+zj4ov9KrYxlRJ6i8WiCg9lSsPQ0FAZ6ayi0mz08SCTJiQdLz1t2WyWWCym0smjxZmlhBaLOVSxpgRQJS3u9/tpampSxZmVsjrlKFoI5sPXf2LpCCFk6WWTSuGuri7lcnV3d5PL5ZQFNjR0Ni+99NcsX/49NO1V+vsThEIhJVg/GYw1VuLK+f1+mpubqa+v56WX/HzqUw62b9dOWMKB40w6mqZdAfwAMAObdF3/57Ln7cDPgfOAILBe1/X2Y32eRZQu+FzOouosvF6vGnwm8Yfpks5s+eDGiliJmeTzeXbvtvL3f1/HHXdEueSSWuVOGAsGJ4LMsGptbVVKfNKXVpRnLfZSVUqLS22JuHAS3C0UCgaJz2FFflKnI8VzRdKp7LKVZwpFmL2hoYH6+voSS26iornpXn+xBiW7J+lvu91OKpWiq6uLt956i2w2i8vlIpt9Nz09X2HOnC8QifyRWMys2lVk2qgRxmAxlNYyweg8c/msb73Vwuc+V3tYwnTy8z+eGJd0NE17DPjc0drkmqaZgR8ClwFdwIuapj2k6/rrhpf9FRDSdf10TdM2AN8D1h+N85kYYxe8qL+Fw2FVsCZ1J9O1dGYz6Gcct2Kz2cjn8zz5JHz2s7bDinOukorb6cBisVBTU0N9fT3z5s1j8eLFxGIxurvPoLv73/F4ricef7oki2e329XYYynGE21p6cSORCJjmjklCF7cdJVdNmP7gBCPDLALBAKqm342CUeCyEKIopstRCmtHH19fXR2dgJgNl9KPv9vWK0bGBx8mnC4VBFRaoKMmCgFL9dHMnFvvNHEl74UYNs2nYsvPrGrkWFiS+cO4HFN0+4C/kXX9cntv+lhObBf1/WDAJqmbQE+AhhJ5yPANw7/fwdwm6Zpmj5bEdspYfwYhQiSx+NxUqlUSROoYLJTPVpZBiGfZ56xcM01+uE74MTuRTkqfRbZHC6Xi+bmZvbta6Wv74ucddbXyGb/TH+/VwWYpR1Aivnq6+vRNE1NDU0mk0QiEQCV3h+L8a+/ceCcXGeJ50hB4ERxHJj4+pdn86Sex1hJLOqAUGzl6O/vJxwOG0oKVpLPbwbWks0+RTbLlHrZyteNVGSXa1y//noj3/nOHO69N8sll8y+yP/RwLiko+v6dk3TfgncAuzVNO1uoGB4/tYjfO9WoNPwcxdwwXiv0XU9p2laBAgAQ+UH0zTtBuCGIzynCpg4KCpdxBJTmE5A8GinNUePPzMfv5ygZOH//+y9eXRb1bk2/mzJtmTJmuUxHpNAgTBcIBDCZKBAW1aZMofSgba38BFKS8sttP24vfwuXKD3K6wLKb2k0JS2QCYgpJAWypCBQoCUEjKVJI7l2dZoa57374/jd3tLljwkjmOnetby8nR0tM/ROc95x+f1+XyIRqPo6fkc3n//Wlx99f/CYHCjr69GWByBQEBUDFN8xWq1CuuEYjZUWZsbYw9KE8jCkwfg5cNo558PSoRQRzi1Lng8ynQNl8uFYDAoAswkkRqNRqHX66HTXYNw+LfjWv9IiMViiMfjImDe2tqA1avPwFNPuXHJJRMv8n+sMFpMJw4gBEADwACJdKYaOOerAKwCAMbYBFpCI18w9DTMrvgdDZNHOEe/f3IpaGppT08PPvigdFDTeB1qarwIhSoAKCRMNwdjyrQJGixnMpkAKDcPWSHZpDNktYyPcMgVoxtytBaU8TTPkuSH3+8X2TaXywWn04lQKCTek1ovlOLAKxCL/RQWy7cRiXyAZHJo1M9I1wr9nTSEyNrU6/WoqalBTU0NKisrEQicizVrvoCHHz6Eiy4yitcebX3YZGCkmM4XATwKYBOAczjn4Ql+7y7IJZ5A7eDfcm3TyRgrAmCCElCeRIx8wcvVp2P9wKcT4VDzKBUEdnd3Y+tWhl//WhFRnzmzEyQxSn1SdHNSRobS2OTmZAtXEYbO39gJRz7nY209GU/zLPU3kSYRNaBSgDvbBVMmos7F3r234NxzHwTQBb+/Ubhh+UiH4lJUPU5Fk2azWcQMaXZ6b+8pWL36cjz44AF8/vMleYXhpipGsnR+CmAx53zvMXrvjwCcxBhrgkIuywDclLXNJgBfB/A+FJn9tyc3njM66EIaq+budCIcuukoPU6D8FatugoLFryAGTMcSKeLRTGeLLZFwVXqqKZaJmDITcjuExqvhSPf8GQZUND4aCwcGaQh3dPTA4fDgWg0mjFiiFo1AAiX5403FuHLX14Nk8mNQKABwWAwoxBypPMdj8eh0WhQWVmJxsZG1NfXo6qqCiaTCSUlJfj4YyNWrZqJlSt70NxsEgL6Y81CTgWMFNO55Fi+8WCM5g4Ar0NJmf+Gc76XMfb/AdjJOd8E4BkAv2eMHQLghUJMUwpyrw39ng/TiXAIND3C5XLhnXc4nnrq81i4cA1qa1vAuUq4FnxQr0dO6VKgnQrlqJyAiv7IUhjC+FwqIkUAInZkNpvz1uQcaS8bTfKkIK7cQyVnlRyORqxbtxBLlmxAbW0fYjGbkLOQ52gRcvWsqVQqGAwG1NbWoqmpCY2NjSgvL4dOp8O77xbhJz/R4He/C6O52SyuO/m8Twcc1zodzvlmAJuz/vbv0s9RKFfglMVYYznTkXDI3A+FQti2TYVf/epy3HjjC1AmbxZnbEeZLbnXi3NlxA1NQqWMjqIf48saQzz+oDGAjJgHaQbnGvFzNOdHnqJB5EqEQV+KvMbCwdnoDnCuFrVJlGDI5U7S/6jGyGAwoKKiAnV1dairq0N5ebkYJfy1rwHr13M0N+vE6+V9TRcUKpKPEiSNMFL2assWYMmS6UU4AAThbNkCPProBVi0aC2qqw+BsZKMpzwwJDJFTZWlpaUIBAJIJBIiRe71eoXwVH9/P8Lh8Ih1OGOBTDo2mw12ux1GozEjVX4054esGho7RCRJFhbnHO3tM4WEaUODokdEGa2xjIWhILhOp0NFRQXq6+sF4RgMBmzbppLWzyDPXp+OKJDOUYLkKHMJZgHA9u3qwSfU9CIciuW88UYc//f/noxvfnMzzOZWJBLDL3g5rqLVakUQlMS4aIY63YB0zvJVGo8VFJym1g8lTa0TujkqlWrCY1yUnaPPt7v7ZGzapBDOzJmKhOnwOFVu0D6oEJJE1i0WC0wmJV6TSThHt/6pggLpHCVIrzZX8HLHDi3uvFM7LQknGAxi8+Yw7rprBlas2AKb7TCCQVWGa0HILsyz2WxCCziZTMLv9+cpiDtywiHpiuw4kpzBeucdjiVL2FGdH7lrPBwOC/lXzjn6+k7F1q034dprnx3U8ynKeN1objfFwijmQ1XGJPa/bZsqo1t8uqTER0OBdMYJ+UMvLi4W6UySTqBg6a5dFjz0UCX+8IcoLrts4gehHWvCee21EL773UqsWLEVNtsehMOKTKkcJ5GDqABEMaDJZBLSn16vN887HTnh0Drk/jHSt+nv70dJSclgDKToqM+PXKvj9XrR29s72Hs2B3v33oZ5834Ok6kXsZg1r0RHPh2cXMRNVvJf/1qMr39dlUE4tN10J54C6YwTcqOfXq+H1WoVmi3UsPjZZ9X4wx/OxJNP9uHSS00TvoZjRTikoPfaayGsWFGOFSu2wmLZhYGBgAigZo9lkUFP6VQqBZPJBIPBAL1en4N4jo5wCHSTkzSpy+VCWVkZPv3Uih/8QIM1a1K47LKju8Q556LSuLOzE11dXfD5zkJ3932YMeN7SKUOwuutFtXPsii6TCT51p9rmw8+KMUPfqDFunV8MIYzvQLFo+GEJR36kIayDJl/H+11o3UzJBIJIWtgNptFh/nhw/XYvPka/PSnf8O8eRVQ6hknDkQ4a9emcfHFacTjQ7OyxnphysJZ9GTORThW66cIBIJCoFxG9lNa1hKixlc5tkNjcJLJi4UeztEQDsmw0hiZgYGBwc732Vi5shqrV/vR3Kwf8/7yEQQRWnd3Nw4dOoTOztmIRh+FTvc1RCIfwu02i3FEFEvKnvhK+83+Pdf77dljx6OP1hwzC3kq4IQnHfp5rKQDkCTl6O9BrgQF/dramvDqq1/BzTdvxBlnGAFUHOHqc4MIZ82aFC64IIZgMC4EvuUbfzRQMR9JeVLf0Ouvx7BiRTnuvHM7zObd8PsDQkKBkM9VkP9H7Q9GoxHV1dUoKSkZlPY8Ax7Pr8DYEnA+/hgOuXaUOZKHE8ZiMezfX4lNmy7EQw99hnnzbOBcN673yHdcJOXhcDQikXgWwCIkEu8jGFTiSaThTBnMXPU3Y8Hhw/VYs+Y8PPFENy65xHJCWTcyTljSydd4OZaGTHkbukGzYzk2mw319fWiLsTlmoNNmxbgpptexrnnhqDTVY0pXToWUFB06VKG3/8+hrPPDsPjUbrb5XL8XN3tuf4m90PRIMC33krh1lst+OlPP4Hd3g6XKyVen+sYsomGfpbdT5pLZTAY0NragJaWH6O6+g7E47sRCpWK2pXh01gzWxnIeqDzSYFXUsyz2+0IBufi1Vdvwh13bMGZZ+oRi5UNk2kdiZhz/U2uNO/rOxWJxF0gC43z4owK6NEwmqvV3j4TmzZdj3vu+RDnnWdHOm0SgeWJQGHu1TQC9bwQ+RiNRtjtdpSXl8NiscBoNKKv71SsXXsDbr/9bZx7bhFstpkoLy8XPTFHQzycc7z1VgrLl6vxm98EccYZfjidASHnSXIHMlFmZ06yCUKr1cJut8Nms6G0tBR/+5sBd901A/ffvx8nneSG36+D1WpFUVGR6DUiS0Muisu1VmBIvZCkLQYGzsbu3d/A/Pn/jaKiHgQCjaIwMFcPFhUayg2csuA5WXY2mw2nnHIKOG/G5s3X4Y47tuDccwOIxYrQ398PtVotLDUiHBLcGstnQi7ogQM1+PjjL8Bk+hoGBhQLrbS0NKM/SqfTCVIcj5XDuaJZvXHjUtx880acckoxYjGDkEOdCNIpzL2aBFA6VQapQZIWcK7XyE13hLq6OlitVpSUlECn06Gurg4NDQ2w2WwoLi7Gvn0VeOGFS/HDH76P+fPVsNtnC7U66kE6GrzzDsfy5Wo880wAZ57pg9vdD6/XC7fbLeZCkWZxvhtYrgdhjKG0tBThcBjhcBgtLXV48MGT8R//sQdnneUHoEiYms1muN2KggjNGs/WrqHzJr8XMGQhFBcXo7W1AX/+82LccMMfYLN5EQzWZ2jCjHR+6KaX+9s454hGo+Ccw263g/NmPP/8DfjZz3bj9NMZYjGNSNEHg0Hx2cnTMUnYK5fbKBN0PB7H1q0MP//5XFx99S9x8OBnSCb1opDParWioqICNpsNZWVlIpEwVhcUwOAo6sW48cbn0dTkRixWh3A4jEgkgtLS0gy1wCNBYe7VJMFsNuOqq67K+Nu6dcr3G2+8Me/r5Kf4+vXK304//XRxsdrtdjQ1NaGpqQkWiwUffaTHr37ViAcf/AfmztXCYCiH1WoV8phH2xOzZQuwdCnDH/4Qx9lnR+HxKC0FTqcTTqcTLpdLCEaRdSGPWAEypTcI9PPeveVYvfoM/PCH7+P00xMoLTWhrKwMJSUlovGQCtf8fr9oY5BnN2WfP/rOGBOjfpcufRH19T2Ix41Cv1muTaE10VqpkbKsrEwU+wHI6OWKxWIIBudi8+br8LOf7cZFFyUQi2nh9/tFWrusrEwIlms0GiGvQTE4co3kdgb5vL35ZhK33mrBHXe8A7f7M7S1KTGwdDotihGpC5z6vUbK7mWDZqPTKGTOdYPHFUQoFIJOpxOEeSTW8lQdQ3NCko7NZsNXv/rVjL8R6WT/PRdUKpUgnXPOOUeIMlVXV6OhoQEzZszArl0W/PjHejz9tA/nn28GYxZxIWo0mqN2q+iCWbcOuPBCIBBQiafvwMCA0LUJBAJIJpMig6JWq8VMa2B4fISIdf/+Srz44tW45ZZXcfLJCZSUzBDTNmmcSlFRkeinSqfT8Pv9IpOVHRuRLSrOOQ4frhejfhsaHACYGAEj3+SyW0gkVFRUBKvVivLyckEa9P6kY7N/f6WI4cyZA8RiStuF0+lES0sLvF6veFBQLZXVahWFi7JlQmuWg8Dbt6tx551V+MEP3gdjH8DtdouiQNpOo9GI/ioi+9FihrR/IpzFi9ejvn5oFDIpEwYCAaFvfSS1OVOVcIATlHT0ej0uuOCCnP/L93dg6MaRze6zzjoLgUAAWq0W5eXlqKysxL59FfjmNzVYt47joovKkE4rWRIqWDtalyrzgmFIpYoEqeh0OqTT6cFsikNIg9KMJyIZOR6Q3Ql/6FAtNmxYhBtvfB6zZgWh1TbCaDSKm7OoqEg0KlKmqLi4WIz0JSKizJl8/uiGevHFxViwYA3q6trEqF86R/K4YrpZqZcrnU6juLgYFosFFRUVsNvt4pj7+/tRVlaGzs7Z2LRpvojhxOMaQTgHDx7EgQMHxEwqv98vBNpJwzgYDGbMycq2AnfuLMMDD5yJr399E3y+v6K1tVWca6qsHtJvzrT8qG4IyN1FzjkftACV1ommpqHzk06nEY1GEQgExDz17ATB+K+fcb10UnBCkg49KXMh398J2U/wk08+WajB6XQ6fPKJGbfcohmUAGXgvCTjSXS02apcFwy5G5wr0zWVcSZuHD58WDRRmkwmkc2iwKZGo4FGoxHSnaT3sn79Atxww3NoamqHVjsDJpNJSEIQ4cr6xhRAHxgYEE9h+plISY5RvPjiUtxww/ODLkNmHIhzLvZNREfWIW2nUqlQVlYmSJDcPI1Gg08/teKJJ6rx0EOf4cwz9YjHi4UQektLCw4cOIC2tjaEw2HodLoMi4oEyUgmVW5aJct0374KPPPMBfjSl55BV9dbaG1tRW9vL0KhEBKJBKLRqJhCmi1Vm0048nd6n2zCyd6GSJHejx4iY72upjrhACco6QD5dW3Ga4VUVlYKwfW//rUYt9yizRjzMdbamLFgpAuGrCi1Wi20euW5UxScDQaDMBqN4qY1m80AFBLp6JiFNWsWYNGidaiuPgyVqkQEyCl+IIOIigLL1LzpdrtF6wFNRCgqKkJ7+0y8+OJCLFv2EmbNcgHQZYw3puwRNTWS+2Q0GjPU7+QCQ7KE6Pz/4AcarF7txwUX2BGNlokRNtFoFF6vVwTW6XxQJowmbobD4YyqYdla6eiYhZdfvgqXXbYSodAWHD58GA6HI2NuO72GChPJ4kmn0xm/03HI10guwpG3I8uSzivV/1A9FVlD8j7la286EA5wApPORIEu/Hfe4fja11RYu/bYjPkY7YKRZ2fTjSZrC8fjcXg8HoRCITidThiNRlRVVQkLzeFoxEsvLcCyZS+httaBWCz3hSuDgtMUqyJVQLVajVAoBI/HI262zs7Z2LBhEW65ZTPOPDOC4uJqoRlMT22q2SE31W63i2ru7KkNlKKnrJVy/ouwZk0Kzc16cK4TM8+DwaBwPemL+qWIgKLRKKLRKEwmkxgTBAxZIH19p+Kdd5ahuXklSkp2oLfXmxE8z4Zc80PERqRBn4us1dzRMQsbNizG0qUvoqmpA5xnvr8cSKeHR39/P/R6paqaLEHK+Mm1SxPdTX+sUSCdMSCz23fiq0RHu2ASiYSIWXR3d2NgYABqtRpmsxkej0ek+OVHky8LAAAgAElEQVT52aFQCCUlJbBYLGhvn4m33lqGZcteQlNTG+JxPsztyKUtLKd3iYBSqRRKS0sF+aTTabS2NuDVVxfjlls2Y968CCyWGuj1eqRSKfT3Kyn+WCyGkpISWK1WVFVVoaqqChaLRVhYI8ltKnpE1C0+dMlS4JYCxDabTYy04ZwjFAqJ9HkikUAsFkM4HBapaDp+j+cMfPLJrZg792GoVH+H1xsUEqOUycsGWTtkwckkkB1MdjgasWHD4sGgsQPpdO7iRCIVahtxOp0AINZMn1lJSQnKysrERNTpJn9xwpLO8LTl8ArakaFsf7wV/0j2k9wpt9uNRCIBk8mE+vp6GAwGBINB8SSPRJRucNIh9njOwKef/isWLVqDWbP6hrV3yNaO/LSlsSvAUPCXTH/SO06n02hra8If/7gcixevw+mnx2C3N6C6uhplZWVIpVLC1SPSMZlMKC/PLC04UolXeaigTqeD2WyG3W4HoAR6ycWKx+Po7+8XMhty1ioUOg/d3fegpua78Pl2wuvlGecy3/USiUREDA1QAvlmszkjkZBKpbB7tw0bNnwey5e/hOrqFkQicbF2subkQYNEOj6fT3T8UzMxBe1NJhOqq6tRVFSE99/X4Kabpg/hACcw6eR7ao43/nK8JUaJACKRCPx+P4LBIFQqFex2O0pLS2Gz2TKCuxTnYYwhFpuPTz75Cb74xafR2OhFOp1/GJscpyAhdXITiHRoxrjT6cTAwAD276/Exo3X4MtffhYNDb0oLm5AWVmZqNTmnIvBd+Re6XQ68ZQ+2smbcm0NZfcog0cTGxKJhKhlikajolIZAFKpS5BIDE3edLmGzoX8lQv0mYTDYahUKlgsFtTW1gqCAID339fgmWdm4e6734Pd3g+Pp0hYTWQ9yu4tlUSQ6+z1ekXnvEajQSKRgFqtRlVVFTQaDXbvtuH224syutGnA05Y0pkoHCnhyLUo2U9Lxhi2bVPlFJjKl6mgJyDNk6IiN4vFIuaJ+3w+MMYGA5qXoK/vCZx11v2w2fqRTFaKm0G+0GkcbigUEvGZQEBpsyBXjZ7CpCbo9/vx0Ud6PPusMhWiutoBxkoz3DESN6P0OwWbKSg6EaN+iXDI3bBaraLSWR4TQ1krzrnkgjYDeA7y5M2RIFsjdBzUTiGPW66oqBCaxvfdp8Mvf9mDWbO06O2thUajQSgUEoRDliNNzAiFQuIBQlYWtVuUlpYKd0+j0eDdd4vw8MMWPPtsEBdfrAWQu9J+KqJAOqPgSAmHpBfI76cnpkqlwo4dWnz966WDei/DRbFk5Ar0kklOloNOpxNVtsp0zXPh8fwKFRUrYDD0IJGoH9aJTjc8xQ/cbjdKSkoQDofh8XjgdruF60BpdCKl/fsr8fvfX4WlS19EbW07Eokisa+BgQH4fD6oVCrodDoR9CQ3aLQRMcDYXVq64cm1CoVCGbU4csatqKhIam8Zn54PZfDkJlkqwjQYDCImRQS7c2cZbrmlCC+8kMT555fB75+B0tJSQYrUP0bn3eVyCWkO6qcj0ikrK0MkEhHvwznHnj12rFt3Jh555CD+5V/KEItZhKs5HVAgnWMAeqKSBRIKhYSF8umnVtx9twm/+Y0fF15YAs6HMin5LBy5KhgYimXQk7a4uBipVArFxcXo7T0F/f13YubMu1Fd3YWKiiqYzWYRPCXyofej+AF1NEcikWGkQxcz5xxtbU146aUvY/HidaKSliwASlsDEBW15EpRQ+RoN8Z4Ymh07GQNkJyoz+cTo4WpXkmtVksWznoASzBWPR8infLycpFxo+MgHaVIJIJgMIi33krhjjvUeOGFJC6/nCGdLhXiZuT6Ka5vDC6XC9FoFN3d3ejt7UV7e7soQCSCDAaDiEQiMBqNMBqN6O4+Gdu23YBvfes1zJhRDJ+vUWRYC6TzTwwinUAggK6uLlFcduBADZ588nTcf/9unHRSCQIBu8gK5QMFkQOBgKiGzbYU6KZ3u0/Hjh1L8cUvPoXa2mJYLJeIil55X3IVcSwWE7UtFLeR0/FyOritrQkbNy7DwoVrBgknc9ZXLBaD1+tFMBhEb2+vSNvPmDFDSGiMhCMJ2hPxUKxIp9NlZMIys2JDhMPYVuTLKeRqaqW5WlVVVbBarSguLs6QnvD7/XjzzSQee8yGxx/vwhlnFCMUGpKw1ev10Gq1IlhPcq5FRUUIBAJob2/HoUOHEAqFMkieXF2lcPNKtLXdhosvfhQqlQcOxyxh5eab9TUVUSCdYwTOOSKRCFwuF1pbW7FrlwWvvHIxbr75JRiNEbS1VYt0K01vzH49jfN1uVxwuVwIBoMijiE3p1Kv00svLcHy5S/h9NNVsNnmo7q6GmazWaSu+/r6pCkMys0kp9nlgCbFSggORyNeeWUxlixZj8bGDgCZNTUARCCavhsMBgCAxWLJILpcOJosIcVYqOcsl45zKnUJKIaj6OHk31+2m0uWCjWMms1m0SZChPzxx0asXXse7rprOyork2hvNwtLj9wxZR2pDD1nn88Hl8uF7u5u0dUPDIm201o8njPA+UOorb0TgcB+7N9vQTqdht1uR21t7Zh0oqYKCqRzDCALUyUSCezZY8fLLy/B1Vevgk7XjkOHSkWnNF1YVCDHGBOBz0AggN7eXnR1daGvr08Ee+UnIWNssLVhMRYuXINTT/WhpqYOjY2NqKmpQVlZGeLxOFwul+jtIVKgFC0FM+Vyfrknqq2tCevXL8aSJRvQ1NQOIHcnNd2EsVhMxC4oCzbSjPGJKEuQs0G0FrIqQqHzkEj8AkcqkUquGhVKUt8YnX+ll+1GLF/+Imw2D9rb9XA6nSLQT20pAIQL2tfXh66uLrS0tKCjo0P00BGyJ5+m02tQXHwTgsGdCIWYsOhmz56ds45oKqNAOscIdBPs21eBl16ahyuueBIWy1643UpJPrlcdKPabDYReI3H42KSQk9PD7q7u0URoBwEBqiXZyFuuOE51NU5UFKixHCoFqa0tFS4SlReT0P0ZI1hqtyltKxWq4VWq4XTeRr++MfFWLx4HRob2wFk9lFlm/NyvInMfRpGmEqlhukcTfRcquxsocdzBrq770Fx8TIkEkdGODqdTkz7IKKmc9rSUicV/rUjEFAhEAiIOBApG5aVlYExhoGBAXR3d8PhcMDhcKCzsxPt7e15K5/loHcqtR2BgEqk061WK/x+f4bW0XRAgXSOAagxcPt2NR57bD6+9KVnoFbvhMcTFnowRUVFgnSi0SgqKipQVlYmAp6hUAj9/f2i7YHiObL8piIfsRALF65Fba0DGo0GZWVlMBqNMBgMop+J9IptNluGxGkwGITf7xc1PjR1k2IQsdh87NhxM6699lnU1XVDTsvKxEMgMiRZDbVaLYr05EpgstSOlHDkQDj9LltSZGn29Z2KTz65FTU134XLtX3UtHg2iouLUVZWBrPZLNLWciWzcv6Heqk4R0bGkkg8FouJdoaBgQF0dHSgpaUFhw8fRmdnJ/r7+/NYK5lZNs7ZsNYXzjNnyE8HHBfSYYxZAawF0AjAAWAJ59yXY7sUgN2Dv7Zzzq+brDUeKShrtXlzGD/6USOWLFmHYHA7urq64Pf7xRdle9LpNILBICorK4WsBDUoUvo3kUgI14FubLJwFixYg/r6Vmg0SqCT9GOoWZNAN5DVahV6PCQI5vf7hRphKBQaTHNfid7e7+Gii34Bu92JVMqUV3UxF+gmiEQi8Pl8opOcGjm3bmUTXnhJlhf1Or3zzjLMnfswfL6dovBvPCCyJmkMmvigUqkyCKex0QHZApSFwCj1TQWb1FMVCATg9/szHiiZGJ7Wz3ZPqd9sIuRUJhPHy9K5F8BbnPOHGWP3Dv5+T47tIpzzf5ncpR056Om2aZMf3/teNRYuXItI5M9wOByiipdK84Ghm4QySBaLJSN4K7coyBcyEc6iRetQX+9ASYkGFosFM2bMQFVVlRDzkuM/VLWr1+tRUlKCSCSC7u5udHZ2iuyYx+MZvAGawdhjqK29ExpNH6LRGSIonCuzQ5AtEHKrgsEg3G63UALU6/V4770SLFumPmLCyXYj5POUTqexb18FXn75SjQ3rxzspRr/ZAZgKFVO66bUu9wtrhBO5jpkAqBMFY0iJm1oquXKHWAfuY6IMSY0rrVaZUxNMpksZK9GwfUALhv8+VkAW5CbdKYNqGL4z3+O4nvfq8Y11/wWDsdadHZ2imIviqcQwuEwnE5nRo0FNfHJJfKyAFdrawNeflnR1G1o6IBWq1g41dXVqK2tFSlyKqmXTW8qYEskEnC73WhtbUV7e7uIuRDhAOvB+SIkEp8hHJ4tYj/A2NpIaM2cc4TDYXFcNpsN77zD8a//qprw1hIiw507y/D00xfg8st/iZKSD+DxBIVFmWv7kSC3gdA5VLKEiwclRtvB2JAcR7bbR5DlKLLlY4evYfTCRZ1OJ+ad0wgc0nwa63SK44njRTqVnPOewZ97AVTm2U7LGNsJIAngYc75xnw7ZIx9B8B3AKC+vn4i1zomJJNJvPFGHLfdZsVXv/oK9u1bi/fffz8jFpP9VCN9F7/fD4vFgmQyiaKiomFTAIh8WlsbsHHjIixevA5NTV0oLdWL2pHa2lpUVVWJgCUFiKmOhWJF1MNFxWhUzKdcqJcBWAe64ONxa4ZIlbye0RTt6CaktoRAIID33ivBf/yHAS+8kMBll5Xkfe14QcH47dvVeOCBs/ClLz2NUOgd9PR4RTNsNumMpfGX0vAkhNbT8zm89NIyLFq0Dg0NbaCm4Oz95Uq5yxanHPTNPIdjq5QmV7m0tBTxeBxerxcGg0GIn011i+eYkQ5j7E0AVTn+9VP5F845Z4zluwIaOOddjLGZAN5mjO3mnLfk2pBzvgrAKgCYO3fukdnTRwgS8b7lljLcffcOOJ3b4XA4Mnx1cnXkimDy+WWXi6YMkK8OKBfm/v2V2LDhcqxY8TZOPjkFxuqg0Whgs9lQWVkppCKKi4tFhzQp+9FNE4/H4XQ60dvbC5fLhUAgIK3vEsiEQ+ujr1w9ZEDum1euIyKi3bu3HL/97efw61970dxsHHb+8vWp5YOcIo/H43jzzSTuvLMK3/jGH9HZ+RYOHz4sMjskNDYSSDqVeqJKSkoEmdfX1yMavQAbN16H2257E42NQUQiSikCqfvlcq3kthMiAZl0Mos8x96aUVxcLApK/X4/+vr6UFpamiF6lk06/xRzrzjnV+b7H2OsjzFWzTnvYYxVA3Dm2UfX4PfDjLEtAM4GkJN0jicUgSktHnjgHwB2YffuvmHBQXrSZZv1cjEdoJAOaQPr9Xqo1Wr87W8GrFp1Eh555CDOPtsCzhU1QHriUcGaVqvNaNqk2h6K5cRiMbS3twuXbygTolzwjC3FWCdvjvQklVPpjDG0tTVh06Yv4MEH/4GLLrLn3J6aHinOkStDJdfeAENZqq1bGW691YIf/nAHvN53haYxyWnIr8kFahilDBWgiGbV1taKuVrPP38VHnzwM5x5pgGBQKNQKQwEAkJZUj7mkSyf4eQ6vl4wIiyKBZL0q9VqhcFgGPZ+hblXCjYB+DqAhwe/v5K9AWPMAiDMOY8xxuwALgLw80ld5RiwZQuwbJkKv/qVGyZTJz74wINgMChMXLoAEln5WsaYsEDkQW3FxcVCXc9isWDnzjLcd58Zq1Z5MX++EYyZxOup6ZBqagCFxKjJ0eVyiT4kIp2Ojg44nU6RTeH8Ugy1BmzLqNTNDhLLcYhcNTq5gsrt7TPxyis34bbb3sQ551gADCcdWjeVCQSDQSSTyYwmRoqZUUd8NBoFYwwHDtTg5z+fi+9+dwtUqg/hcDjQ29srLMfsVLQcL8ulxUNlCxqNBvX19eC8GU8/fTWefNKFyy+3I5k0o7+/X/R0abVaBIPBDKuHzgV9/iMHvi8FMP7Z7tSTRz1f1N8nEzZQmHsl42EA6xhj3wLQBqX7DoyxuQBu45x/G8CpAJ5ijKWh1Nw/zDnfd5zWmxP0gT73XAInnxzCnj1D0hM0E8nnG1YJAECZzUXBQIPBAIPBIGpAAMUy+ugjPW67zYLVq4OYPx9Ip4vFTSPLK8hd4BSzIcU+Em7XaDSIRqPo7e0V+jKcN0OpXFg8jHDyIfsJPpLF43A0YuNGpVL6c59LCQst+0lMMaJgMIiOjg709PQgGo2KYwQgapc8Hg+6u7vhcrnQ13cqPv74C7j66l/C5foMbrcbDocDoVAo53qKioqE6iG5I1qtVjSMWiwWYV1qtVpEoxfg+eevwpNPunDttUrdUyKREGRjNBrh8/nEtNX+/n6EQqGMwYfy+cl2raLRC5BMDlVKy1bdRGCqSpgeF9LhnHsAfD7H33cC+Pbgz+8BOGOSlzZmDM2l4pg3L4menpTopSKFPHJfBgYGxBNQq9WiqqoK1dXVqKqqEn08arVaBAGTySS2bVPhvvvMePzxbpx6agQeT0rEDjQajahepnJ8pdxfuSldLhfcbrcYZUJZJCIkRfXuPHC+EnIvUjaBZBfdASPrKssuUWtrAzZsWIQbbngOjY0d4Lw2L0FR6wTFJ4g4SHsHUDJ9Xq8XnZ2dOHToEByORiQSd8Fo/CoOHjyAtrYSoRCYbVUS1Go19Hq9sCKp7oaGKdLvANDT8zls3HgdHnzwM1x+uV3ESygor9VqYTKZYLVa4fP50NvbKyQr6JzLLh0RK6kFOJ2noavrHqjVS5FK5a7DGQ1ydjOb5KYq4QCFiuQjgvyBNjcDodBQsJWsCqPRKHSMgaG4gdlshtVqhd1uF5KdVFMDKLGEXbssePTR2fjRjz6AyeTFP/4xZE5ThoL2wTkXwWOPx4Oenh50dXXB5XKJXi0AQhEwmUzC4zkDbvd9ABZhJJM+m3TyuVPZNwsNkqOpE8BQNiW7epasM6qOpmwTEUcqlRKaP9Sv1Nk5G4nEswAWw+/filRKLwoXSWo0FxhjMBgMsNvtqKqqEkWUjDGhhaNWqwfT4stw221v4swzDUgmzUgkEsJlpmAtjSimFgk6LrfbPWxulSz/0dragL///XaYTN+Ez/fWCFdabsgBa3mCBmHrViZpeo9798ccBdIZJ7KfIHS/0YVL8QGVSiWmU1qtVlRWVqKiokJo21A6ljIm1Hj57rtFePTR0/Gd77yJ0tLPsHfvUEc4kQ5JRlBjJSnS9fX1obu7G319fSJQTDc558q8KafzNOzbdyvs9lvhco3cbU0Xdz7RrVzuFRXOLVmyAfX1ytSJ7NfIILkNavdIJBKix4ncTWpIVWZWnYVo9FHIhJmdXZPnT8kgoiDiN5vNQqKCjleZmrEYixatQ2NjEH5/A3w+nyAk+rzoZi8qKhIurhxIJlUAmXQADOrhfAVz5twHj+cj5PG+jxjvvluEb3974uugJhIF0hkH8pmsFAA2mUyoqakRYljFxcUwm82oqqpCTU0NKioqRD2FbD3Q72+9lcKDDxpx332fwGjsQkeHT2gi0wxtAOIJSgJcJCLl8/ng9XoRCASGzR1Pp9Po7JyNLVuW4cwz74fH8+Gw45MJgfqvaFoD3VRk/cjjheXCxaFepHbIkz3leU7Ugc4H5T/IPXG5XIhEIhkTM6kbnnMOl2sOurvvg073NSQS74PzYmFtyCNaiKRorWQ5kTVCRXTUlkFd8NTaQLPFI5EyeL1eMXpHo9EMazmgSm/5M6VANBE/BcQPHKjBq69eheXL10OrjeHw4dOg1+vh8XhEHxUwZNXS55wN+r/swnHO8dFHetxzjwFr16YzFCmnGgqkM0aMNpWAlOXUajXKy8uFkh8FKEmsnGIG2Q2KW7YAt94KPP20D42NKnz2mUGY81RJDAy5PDRahS522X2iJkACjYl5+eUF+Pznf4V4/FN4PPmPlXqOyBqg7Jqc2s4evdLRMUvM5m5sdIDmOhExyVKm8typSCSC/v5+OJ1OOJ3OjCpqmQT7+k7F3r23oa7uLoRCHyIYVM6JnCqm+FYsFhP1QTRRgR4C2eef3kPupWpoULrpY7EY+vv7M7R0SKIi3zmjn00mE8LhsLBOd+2yYPXqObj//t2YM6cS/f3NmD17NlpbWzMyilQyQCqINHlDRq5q5n37KvD44w1YvTqA5mZ9/g93CqBAOmPASIRDNx7JGJSVlYkbn6qL6UtumJSflspcJ2X/552ngdNpEZkt6limQCFdaPF4XMQNKFVL5j89kQmtrQ1Ys2YBrr/+D9Bq96KtLV/PjwI6Fgq4UkaHpkTQ+zKmKOq53afjj39cJPR2iHAACAuJYk6UOSIrg0TUQ6GQiEHJM5445+juPhlbt96EefN+jlTqM7jdZnGsVqsVFRUVsFgsomxAthCo94naBPR6vYijEXG2tNRl9FJRawPVO2k0GvT39wv5kXwNlhqNRsSJzGazsF527NDigQdsWLXKgwsvrARjVYhGo5g5cyYaGxvR0tKC1tZW9PUp9V3yyKH+/n4wxjKkL0g2hNahWJgX4LHH2nDxxeaRL+YpgALpjIKxZAEom0EukGzByJmFsew/Hi8WvTU2m02MEKanndxbk62fLNeFUNyhpaVOjBI2mfaipyco6kryZUso7kF6wFR0GA6HRYqYrKxw+Hx8+OFXcP31v0dDQy+ATCEtWms0GoXb7RZuKMVT5KxLdtEc5xzt7TOxadNiXHvtszCb++DxVEOr1Qp5VVonWZFqtTpjZhcwZG3JNyvtXxHgGt68SedXlhnxer0oLS0VnfLZoCQCxfbS6TS2bmVYsaIEzz8fx6WXGsSxplIpmEwmIbFKnznJi/T394uYn8fjEdasXq8Xr9NqtejtPQV/+tMC3H33+zjvPBuU8rapjQLpjIKxpB3lwCIwXO8lH3IRGl241MRJFkkgEBhWeJatW0ProNaFw4frsXbtAixYsAY221643Yo+z8DAwIgZHllzmGYukZKhLJ0aDp+P7u7vY968R2AyuRCJmDP6xuQCOTnoPTAwgFAoJOIp+c6hHCOqr+9FLKZ04RsMBlEcKHeByxkkImMaZWyxWISFQ27T3/9uwoYNN0izxTMriuk7peL7+voy4lu5tK3J8qXP9+ab6fMdvq1sMdH+SHJEll8lF5tzLlL+Sg3YWXj99W9g+fKXcOqpxeDcOuL1NlVQIJ1RcCRZgHxkIz/F8+nJUGCSdIXJipLnl1P8hvZHcRVCPB7HwYMz8MorC3DNNathMOyD0+kXcROfz5eXdGj9lJUha4SqX/v7+wfrgE5HOPwLlJffhni8FX19NeCcC8sol6gUybCSfg/V51Dgm6w1lUol5DsWL16PmTPbARRlVGFTIJVS3XIGiW5k+r/JZBId+MXFxQgEAnjzzSTWrDkPy5e/iPr6dnCODKKh88AYEwP7iBjJzSXrKpcVOxYLmdpYyKIlDSX6bGXBM1IOMBgMqKysRCQyD6+//i1ce+3vUVfXh2Ry9oiSsFMJBdIZBRMtvwCMHpSmJ6hc89Pf34+BgQHxJesZkxtHT+VDh2rx2ms3obl5JYqKPkFnp9IWQYWDAwMDI+rqUqOm3OtEKetIJAK3+3SEQqsBLILf/wF6euzi+Gg92TcjVVFTELyrq0uMuXE6nXC5XAiHw9BoNGhtbcC6dQuxZMkGNDQMBaVl7eZsYiALjwr0SAiNyNNkMqGiogIajQZ795bjscdsuOuu7bDZPAgEVMPE6en9AIjCS3l2GL2/XOtDGE9hHsUDSe2RRNzlanO53kun0yEUOg+vv/5tLFy4BjU1DgCl04JsCAXSmWSM5YIkU5tuXpqT7fF4RPBVLjyjJ3wymcTBgzPw2mvLcPHFj6O4eAe6uvozFAspnjMaspswqejO6z0T4fAvQHUy0Sjg8XgEqZCAeXawVc7AUXuG3LIRjUahVqsRDM7FG28oQWklC5ZpIcrHS+uUY0FyAJnIhwr69Ho9du4swx13qPH4412DUxv0omlTrknKdpEpo+TxeATJyUF72aUay2RSuaaHYkTZrilZmeQiqlQq9Paegtdf/waWLFmPpqZOpNNaUdpA24/m1h9vFEhnEnEkg+TIfaCiNKrfoKc4BWKTyeTg1Ikb8cUvPo3S0r3o7Q3B7XbD6/XC7/eL6RNjAVk7NI43GAyip+dz6OpS6mTC4aFeLaq1ocJIktGkIj/ZIkskEmLmemdnp2jOVMTLrsDevbfgy19ejdraPnCuziCX7PWNdO7kn+nrr38tFpM3zzijGO3tZrjdbnFe850HOaYSDofhdrtFhkoeZHikc7vkEcyya0gPH3Kx29qa8Kc/3YRFi9aisbEdarVCRjabTWROp7qWDlAgnUnDkV6Q9FTV6/XC4qB0tVyn89FHerzwwudx443PoaLCgf7+oSBkdgxoJMjbyPPADx2qxZ49P4Ld/n8Qj7+HVEqTkZoOhUKi2zkWiwn3T36iU3yE3KvPPvsMHR0dAACd7hrEYj/Fuec+CJPJjWjUKvrL5HUNl4UYfs7oO702lUrhvfdK8O//rhOTN0OhTJeGFA5znQu5gDMWiyEYDIpgOBUhjveBIkNuBM3ValJcXIzOztmDAm7rUVfXilQKgviqq6tF4Sk9nKYyCqQzCZiI5juyeEgvRafTiQv+/fc1+PWv5+ArX3kJdnsr0mmNqPGhbQAIN2I0UByH5CYcjkbs3PlD2Gz/B4nEX4TLIveMkctHX3JMSE6dAxDBcHm2eDj8W1gs3wbQhUCgQUzElOM3YwERnUw6n35qxW9+Mxu//GUPzj+/DOl0qeiFo9IEItjsVLu8TzovcuMm5xxbtzIpSzXmpQ5DLuKh5tl16xZi2bKX0NDQjnhcWQcVR9K4IWUK6NS/paf+Cqc5JrLbl+o/KDCaSqWwbZsKDzxQge9/fzv0+g4EApmyFzQoTplz3jtqEBkYShnH43F0d5+MDz+8G5WVdyAQ+JNIVQPDXRxqdaAMDMmvZm9L7oQyMO7zSKVeALAYkcgH8PsbEZio9rcAAB8vSURBVAwGhwlj0T7G4zpQFmzDhitx993vYdYsLfz+GSJOItcjkesZDodFoaWcOpfPDRGuSqXCjh1arFhRMqG9TjLxHD5cj3XrFg7WEXUgnR5qwpUnj1Jpw1R3rYAC6RxTTLS8AN0oNBP77bfTuPPOUvzXfx2A2dyHtjYuMjiyDAMw1GWeq6xeBhW/USXwJ5/cjfr6uxEI/AWBQCAjxpEdB6E+J3nOVXYKW7Z8EomLkEoN6ckkk0XCZaOvXMgmvewbjdbY0TELGzYsxvLlL8Fu70dvb604f1RlTfVIer0ewWAwo2ctl6tDcRe1Wo1duyx44AEbnn8+nrMO52jhcDTixRfl1pJMtym74HE6EA5QIJ1jhmOhZyL3JL31Vgrf+IYajz7agZqaTrS3h0WhHd3Y9CQ0Go0i+zXSUDYa1ldSUgKv90wcPPgTzJr1YySTW0VAmfYvD30jyMLvJpNJmPsUUJahTN68N2PyJt3MtG2+2MRY3C3FwlEKC6urW+DxKO6p1WqF1WrNqCjOjjvlunmzO9cPHKjB6tVz8NRTblx6qXHY9keLtrYmvPLK0sEsXqYIPEF29wqzzP/JcawElOhmeOcdjuXLi/DEE72YMaMN7e19wgrJfuLJ8Y18EhUAMvrEotEL0Nn5ABob/w3Au6InSk4l5wJJpdIXBa9l2VG1Wj04efNbwyZvymscy1M7l8XDORfyGkuXvoiGhjaEw0qciYLc5J5Qdo5qn6gxlKxFmfSodSEajaKr6yS89tpVuP/+3bjoospxB25HcxOVqR8LsGjRWjQ1dUIe5JdrX7J4/nSwdgqkM8E41optb7+dxtKlDE895UVDQxdaWnpEh7JMOnST0JxyUgzMlxomYorF5sPj+QVqar4H4H0Eg1ERmzEYDKIBNbu/CRhSACT5CrmAkSy0jo5Z+MtfFuH88x/BwMDf0d9fLKqjR8tMjQaZcJTWhg5kGwB0bkgCw+v1oru7Gx0dHejv788oAJRjOvSa1tYGbNv2FSxfvh6nnVYBoDKne0fvlWuN+f7GOce+fRVYu/aiQcXFTpyIt+iJd0THEceacLZsAZYuZXj6aT9OPtmJtjalBmdgYEAo29HNTUFdv98Pl8sleq7yxXMUwpiP/v4nYbPdCs53IBgc6panIYDUACkr48n7oNgRBZOppoiyMOvXL8DVV68CY3sQDGaqCI4nHZ7r75l6Pm0Zlcxy1TIRMY1XdjgcaGlpEdZiLtJRqVRwOk/D3/9+O+bM+XdoNFH09zdnyGhkr2ekY8j1t507y7By5RwsXrwGVVWHAYxtNthYLcOpggLpTBAmg3AWL+b43e+iOO20fnR1+YY1bsrl/9Sg2d/fj54exRoKBAJ5LZ10+hKEw79FSclNiEbfQzyuEpMSSDheo9GIQkB6v2xrh1LtNHeruLgYqVQKvb2n4NVXb8QXvvA0yso+htMZEQJduSDfSHKdTL7tSSJ1qHkzc19UQEkkTDPFu7q64HA4xJwsOVYlN2NGoxegq+semEzfhNv9IVpb52D27Nlwu90wmUyi3eJIwBjDe++V4N57Z2LFireh0TgQiWRWWh+t6/RPMffqnwmTQzjACy+kcPbZYXR1DQitFSrfByCsDxKA8vv98Pl8YhRNKBTKGQCW5y7F41sRjys3gslkEuJkdrtdyKKqVCrE4/FhY5IpFc05H5QW9SGZTOLQoVp8+OHNuPDCR8HYJ+jrC8Ln84n0dC7kkrmg79lp9HyEQ6+h4HQ8Hofb7RaTJnw+H1paWtDZ2YnOzs6c7hUApNOXIpn8BdTqpfD53oLPp4xJdjgcaGxszJiuKbu34/l8v/nNMvz3fx+GXu9FS0vRhLYzFOZenWCYLMJZsyaF88+PwO32w+12o7e3F06nU8RpyLIYGBgQzaFUJRwIBIRerwylevUKxGK/h073DQA7EYmoMgKtpIJnt9vFjC5ysShORCDSIVJSLImT4HDchaamH6K//2/weofcGyLJfJCL8PIVNXZ0zBKzxevr24REqnzDEhEkEgm4XC50d3cLmY6Ojg60t7eL/q/haAbNpaKpDYDSP9be3o6Wlhbo9YpSn81mEyUHI2koyaRGc9OeecaPWbOicDiUtgaaOZ9d1T3eLFVh7tUJhslyqdasSeP888NC1rO7uxudnZ1C3pMEtvr7Fb0c6rWieIo8BI6gjF35EoLB32DmzB/BaOxGKDQDXq8XbrcbwNAoZJoNRXEds9ksMj6RSETcrFQfRDU8fX2nwul8BDrdV9HXtw1O51BAlm6okeIhlJmRq3/l/7e3z8TGjUtx443Po6FBieHkKiakuBFJptIc9+7ubjEDLDf55Z+8GY/H4XK50NraKkgnHo/DaDSKYszRLJX33ivBN79Zhmee8ePccwMIBNRCcVClUsHn84m6IVlSY6yYqmNoCqRzhJgsC2f9eob585Pw+ULo6enBwYMHceDAAbS2tsLtdotAJunUkJUjWyBA5qhbZW7WNfD7n8ZZZz2IxkYfVKpZ8Pv9QlYjEAiIpk1ZAZHmQ5nNZvj9/mEBZXqCB4Nz4XSuhEZzM0KhP4/5uGVNGKpazjXd4fDhemzadD1uvnkjmprc4FyXkTomwpIDwjQXrL29HYcOHRLkmhuZhJNdLsA5RyAQgNPpRFdXFzQaDRKJhCCdkdwszjl27izDvffOxH//92HMmhVFIKAQtU6ng8lkEvElUkiUkwTyec5HbFOVcIAC6RwRJo9wlP1HIooGTWtrK3bt2oWDBw+ir69PuAREPFRxnO0myDIMimTGFXC7n8S55z6MU0/1wGSqBQAYDAbRohAMBkV/UlFRkbA2iHjMZjOCwaCYU0XuWyqVQih0HtzulTAav4VYbMuYj1uW01CpVDAYDKioqBDVw3SD7dljx5o15+Geez7EKacUIxarE6l6On5K1xMRkeyr1+sVkzjzY/TZ4tl1Pn19fYjFYkIPZ6R6o337KrBy5RysWPE29HovHA6DEMCnNdOccgra0wOAZrOPJPM6lQkHKJDOuDHZhEOtBR6PBy0tLdi1axdaWloQDodFbEQW7c4FshhMJhNUqivQ3v7/MH/+ozjpJDfMZkUSAYBwjfR6vdC3oSkIpO/DGBOVyxaLRbw/BWAjkXkIhZ6E2fyvALaN+/hp/0Q49fX1QnSdMYYPPijFo4/W4IknunHeeXbEYgaEw2ERu6L4ldwDRkFv+j8dW27kJpxc0hpUlxQKheDz+cQki+zjkYmntbUBa9dehMWL10CjcaClpUgoB5pMJqTTabjdbjE00el0gjGGsrIyGAwGIctK4vXZmOqEAxRIZ1yYzBjOxRdzJBJDWaDe3l44HA4cOnQIXV1d4jW5YhHZrgBZDRrNF3D48MO44opf4aSTfCgttWX0WlGvFJEQuVI6nQ6MMVH7Q/O8zWazKBIsLi7GwMDZ6O9fBYPhm+B8m4gl5YtFZD+l1Wo1jEYjKisrUVtbi7q6OtTV1QnSe/fdIvzgB1r84Q9RXHKJBem0SWTrZGmNUCg0ODo5JMbeUBo/HA6LoHRRUVFWNm90C4dAro/f78fAwAA0Gg045xktH9lFgm1tTdi4cQFuuOE5VFUdRiTCRfCdMSbGCvX29uLAgQM4ePAg3G43GGPQ6XSwWq1iHLLVas2QD6GgNE0VmaqEAxwn0mGMLQbwHwBOBXA+V2aY59ruiwD+B4AawNOc84cnbZFZmCzCee65BObNiyMYTAqXgCpmOzs74XK5BNGMtfxe6T6+HC0tD+PKK5/CnDkeaLXmDCF3unhJOpNGF1OfEgVhPR6PGFxH5MQYQyh0Hjo6HoDF8q9IpbYiEomOquGT7XoUFSlP/cbGRjQ1NaGurg4VFRUoLS3Ftm0qfP3rKqxbx3H55aUAhmqCKKtmNBqFe0Z6zn19fUgmk/B6vcPqXjKD2GMnHEDpM6M6JFktkRows4/N4WjEK68sHRTg6gRQIs45aTAnk0n4fD50dXXh4MGDaG1tFe6VXq+H3+9HIBCAzWaDSqXKqDDfvl2Nr36VTXnCAY6fpbMHwAIAT+XbgDGmBvBLAFcB6ATwEWNsE+d83+QscQjjJRw5GJqd8hxJxHvdOmDevCSCwbCosXG73eju7kZrayt6e3sRDAYz3oesiFw1LQDNSLoKnZ2P4bLLVuKUUzzQ641Crzi7d4emLVRUVAhxKK1Wi1gsJlT2aCAcraGjYxYOHVqBxsa7EQq9h/7+2KguH71WBueKsHtDQwMaGxtRXl6OsrIybNumkmZzD93MFNymplJ56mU0GhWD8UIhRUGRJkeQKzZ0nsZHOLT2SCQirBy5+5zE48kCOXy4Hi++SHPBhlob5GuEXGWfzwen0wmPxyMIh46BKr6p+ry6uhqxWAzvv6/B97+vmRaEAxwn0uGc7wdGHdFyPoBDnPPDg9uuAXA9gEklnfEQDrUfUJpaLjSjmyP7SSjv/5JL0giFFOEsOa3b19eH9vZ2DAwM5HzPfFCpVNDproHX+xQuuOD/4aSTvMLCkZHtDmi1WphMJmHp6HQ6pFIpEVeRyeLw4Xps3/51nH/+I4jFdiEUQp4CxNFBBYmVlZUoLy+HXq/Htm2qMc0dy65YpoBrJBKByWQSsalIJCKsIAXjJxxCIpHI0E1mbGjiKQXkSYBLkafI3S2e3VWfL94Ui8UwMDAgXOBAIIBduyxYubIKa9YkcNllY2ubON6YyjGdGQA6pN87Acyb7EWMx8IhP5+yOmT+0pNPr9dnzMXetk016IMzNDdzRKNxBINBeDwedHd3C9Kh+eSUOaJ0eHbsRv5d6Rj/IgKBZ3DOOQ/h5JPd0OttomJYdi2GKm/ToteK6nJoVrg8zYEsrNbWBvzpT1fjuuuehU7XifZ2g+hUH4seM62FrCK9Xg+j0SjG9/71r8UZ53+kVoDsgC2NEKYhiOQGGQwGGI3GwRniZyCdXoMjIRwCHaec1SMJ1M7O2RLhOJDdLZ4dd6OAP00h1ev1GVk2qvQmq6ev71Q8//xFWLXKhebmqT9kj3DMSIcx9iaAqhz/+inn/JVj8H7fAfAdAKivr5+w/Y6HcOS53JTSpg5tk8kkpDGNRiP+/ncTbrlFj+eeS+DCC4FYLC0Ih6qNSUw9nU6jpKQENpsNarUaoVBIiGXJF2VZWZkgNOAy9PT8D8477+c45RQXLBbroFJf7uyKTFhEkvI8KQBiCiXnHHv22PHss7Pxwx++i5oaFXp7G0VlNAVos/WNs9+XAth0nDNmzIDZbEZJSQnefbcIX/taJuGMB/IxkVtC7qPRaERR0ZXg/CEUF9+EVGo7OB9ZtmMkZBdfqlSqwaDxIixb9hIaGzuQLcBFa6Tv5JpRrIxE3ylDlv1wYexyvPHGt/Gf/7kXzc3VU14XWcYxIx3O+ZVHuYsuAHXS77WDf8v3fqsArAKAuXPnTtgQoLHGcKij2+l0orOzE11dXfB6vYhGo2LGtd1uRzgchsPRiB//uAq//rUP55+vQjxeJG7YgYEB0Zip1WrFTajVakWamoTBXS6XCJzSgD6r1QrOm7F///245JL/waxZbhgMFjGDO18nt5xpyTWdAIBIp7/3Xgnuu68UTz7ZhzlzbPD5ThEz3KPRqHDD5GkQ2e8px7eowLC6uhoWiwUff2zET36iwfr1XMRwsq26sXwm8vuSJaHX69HdfTLa2m5Dbe2dCAZ3IhBQHbFLCEBMQjUYDNKo35uwePF6NDS0I50eed3yWmlUDn3OqVRqWIuGwXAt9u79Gf7t3z7A1VdX5HyYTGVMZffqIwAnMcaaoJDNMgA3Hd8l5Qa5VW63G11dXejq6hINjSTzQMVrH39sxDPPNOChhz7DWWdpkEgYM0YBk4wEjY4lc5oaOA0GgwheAhBxC5oUWVR0JT799P/iiit+haamXhQX60RxH1lNuUDvL8efcvUPKWNcSvDCCwlcfLEZkYhSs8MYE/O+6alNo1mAzLgRFRHS7/Qkr6ysRG/vKVi1aiZ+97swmpt1kGMg422ApJofmphAg/a2bbseF1/8KAKB/QiFWAbhUHqaYm/5LAgiCtKrLi8vlyZvfgOLFq1FXV2rEFEfK+Rgvs1mE2sLhUJgjMFovA4u1y9x++3v4LrrakSgv2DpjALG2I0AngBQDuA1xtgnnPMvMMZqoKTGr+GcJxljdwB4HUrK/Dec873HY70jIbsylbq/k8mkiCWQ5MTu3Ta88srnsWLFWzj55BLE4zMADD31qbmSSAIY6heiTJbH4xGxCqWdQYdYLDZYzdqMv/3tXlxzzWrMmuWGWm3MiIPQxU9/y86s0cQJ6h3KtnRIz2fdOuDyy0sAlIg2iUgkgsrKStFaUFxcPCrp0LmjGFEwOBerV1+OlSt70NxsPqrPhY5Hr9ejqqoKGo0G27ersXbtmfjWt16DSuXB/v0W6PV6eL1e8Tq73Q6r1QqDwSBIJ9t1AiA+HxoNbDabBwnn21iyZD0aG9uRSmVmMseyZiJrSotTnEeZwHE59u79GW6//R0sXlwuaphKSqZHAJlwvLJXLwN4OcffuwFcI/2+GcDmSVzaEUHu9ZFHlJCPXlRUhMOH6/HKKzfglls24+yzEygpmSGepjS1k8xkpXJYJWo4/H4/PB6PCDBaLBZR/EZyFU7nadi8+RbcfPPLmDMnhaKi+ow1ychuiqS/UeyJBsjJrQdyWv/yyzM7uCnbVVFRgUAgAL1ej1QqJdLrcsyDrCg5y8OYIvD1wgtfwIMPHkBzs+mopR0oZkQ6zbt32/DwwxY88shBzJhRDIdjlggAWywWxONxGAwG2O12mEwmoaEsi3TJcSL6ouP3+c7C669/CwsXrkFTUyfU6iKRSpePJTs2k/0ZUBYumUyisrJSyKj29p6CN974Nv7t3z7AddfVoK6uDlar9Yg1fI4nprJ7NS1ATycq3Sdf3Ov1IpFIQKvVwuk8DWvWXI177vkQF1xgFEFl0quhgCpdwMCQ2l0ymcyYYGC1WkVvEQl27dljx3PPzcdPfvIRzjnHCrW6fNjNkV0dS8gmndLSUlit1oxpkZllA7lFyzUaDSoqKsAYE7ErEhEj+VIqPqT4E2WR9u4tx+rVZ+Dhhw/hyis1GUP2jvZz0Wq12LFDi9tvV+N3vwvhrLPK4PM1Qq/Xw263Y/bs2fD7/aIim96bYnQ+n09kC+W4CZGoPOr32mt/j5oaB9JprWiKNRqN4mFCD4Fcn4O8bgKpMH7yiRnPP38R/vM/9+LqqytQUVEBk8k0LQkHKJDOhIBuVpvNhmg0Kp7sSkn7Kfjtb7+Ahx46hCuuUIrdyDWiL0KuYCA9KWnmlc1my3jy7tihxRNPVOB//9eJefNqANQc8XGQOU8uVlFREbZuZSOW1hNpaTQa2Gw2kebt7++Hy+USTZzkylEGrKqqCuXl5fjHP6rwX/9VjaeecuOii4xCRmMiAqMqlQrbtqmwfDmwbh3HxRdrEItZhGVWW1srBgNS8JxzLor0ent7odcrs86BoXog2XJUygYWYPnyl1BX1wdAKTWw2Wyorq5GeXm5cNVkq3OsVtyOHVqsXFmJp55yobm5GjqdTpQBTFcUSGcCINdo0EVVWlqKjz824re/vRg//7kDX/qSUh8iuy1jeZqTm0DWhFxw+O67RbjzTiWoe+mlFnBuHhZDGI+LQmsiK2HbNhWWLh29tJ4sCrL2qF+LSNVoNCISiYjxyFarFXa7HZ99Vo3vf9+G559P4JJLDACGxtBMhHLecAutWMTO9Hq9sADpeGmmeSgUEhaK0WgUI4flAXwAsHdvOdavn4+7734Pp55ajGRyNgDls7fb7WLyJnXqj5d0tm9X43vf02DNmgSamy3i3EynoHEuFEhngkABRbqoDx2qxS9/WYsnnujG1VebRA3NkTzB5UpXuuC3bAFuvpkIYeipd6S1JvJ70f6XLh29ToluWJkkKE5FhENNlpRG1+v12LXLgttvN2HdOo4rrhjKxMlrOBrkqySntcquSXaMi3rSKPOVq9fto4/0ePzxBjz2WBvOO88Gzq2CUOhaoK5wefLmWD+fLVuAr35VKRydLpXGY0a2338ifJ177rk8G4DyNVaMd3tCIpHgf/pThNtsKf7KKwN8YGCARyIRnkqlxr+zPHjnHc7tduX7scBE7D+VSvFoNMr9fj/3er3c7XZzt9vNfT4f37w5zO32NH/77fRELTkDR7v+VCrFY7EYDwQC3Ov1cpfLxV0ulziGjRv7uc2W4hs39nO32809Hg/3eDwZxxgMBnk8Hufp9PiP8Vh9vkd6TefC4D12RPfncSeIY/F1PElHuWDS/M03kzyZTPJUKnVEF97I+5/ahENIp9M8lUrxZDLJE4kETyQS/M03k9xuT0/59edau7z+N9/M/Lv8RZ/78Vx/LhRI5wQknelECIX9//Ptv0A6JxjpTPcLsrD/E3//BdI5gUjnRLggC/s/8fdfIJ0ThHROlAuysP8Tf/8F0jkBSOdEuiAL+z/x918gnWlOOifaBVnY/4m//6lCOozzCZOemTKYO3cu37kzU+ud6r/GergTUJ9WQAFTEhNxy8+dOxc7d+48ortketdTF1BAAdMOhTaIPDgBDcACCpgSKFg6BRRQwKSiQDoFFFDApKJAOgUUUMCkokA6BRRQwKSiQDoFFFDApKJAOgUUUMCkokA6BRRQwKSiQDoFFFDApKJAOgUUUMCkokA6BRRQwKSiQDoFFFDApKJAOgUUUMCkokA6BRRQwKTiuJAOY2wxY2wvYyzNGJs7wnYOxthuxtgnjLGd+bYroIACpg+Ol7TFHgALADw1hm0v55y7j/F6CiiggEnCcSEdzvl+YGLGxxZQQAHTC1NdxIsDeIMxxgE8xTlflW9Dxth3AHxn8NcYY2xP7u0mfpFjgB3AVLLWCusZHVNtTVNtPZ870hceM9JhjL0JoCrHv37KOX9ljLu5mHPexRirAPAXxtg/OOfbcm04SEirBt97J+c8b6xoslFYz8iYausBpt6apuJ6jvS1x4x0OOdXTsA+uga/OxljLwM4H0BO0imggAKmB6ZsypwxpmeMGehnAFdDCUAXUEAB0xjHK2V+I2OsE8B8AK8xxl4f/HsNY2zz4GaVAN5ljO0C8CGA1zjnfx7jW+SN/RwnFNYzMqbaeoCpt6YTZj0n5NyrAgooYOpiyrpXBRRQwImJAukUUEABk4ppTzpTsaViHGv6ImPsM8bYIcbYvcdwPVbG2F8YYwcHv1vybJcaPD+fMMY2HYN1jHi8jDENY2zt4P8/YIw1TvQaxrmebzDGXNI5+fYxXs9vGGPO/DVmjDHGHh9c76eMsXOO83ouY4wNSOfn38e04yMdgj5VvgCcCqVQaQuAuSNs5wBgnyprAqAG0AJgJoASALsAnHaM1vNzAPcO/nwvgEfybBc8hudk1OMFcDuA/x38eRmAtcd5Pd8AsHIyrpnB97sUwDkA9uT5/zUA/gSAAbgAwAfHeT2XAXh1vPud9pYO53w/5/yz470OGWNc0/kADnHOD3PO4wDWALj+GC3pegDPDv78LIAbjtH7jISxHK+8zg0APs+OXa/MZJ7/MYErha/eETa5HsDvuIIdAMyMserjuJ4jwrQnnXGAWir+NtgycbwxA0CH9Hvn4N+OBSo55z2DP/dCKUfIBS1jbCdjbAdjbKKJaSzHK7bhnCcBDACwTfA6xrMeAFg46MpsYIzVHaO1jBWTec2MFfMZY7sYY39ijM0Zywumeu8VgMlvqZjENU0YRlqP/AvnnA/2suVCw+A5mgngbcbYbs55y0SvdRrhjwBe4JzHGGO3QrHCrjjOa5pK+BjKNRNkjF0DYCOAk0Z70bQgHT4FWyomYE1dAOQnZ+3g3yZ8PYyxPsZYNee8Z9Acd+bZB52jw4yxLQDOhhL3mAiM5Xhpm07GWBEAEwDPBL3/uNfDOZff+2kosbHjiQm9Zo4WnHO/9PNmxtiTjDE7///buX/XKIIwjOPfp/BHm2AhgqCBgIJFChGJhZWCTUCwNkUIpLDQwn/AxoC2GhCxtbBLIYq/2oBYhCMKKiixEEtLueK1mBEPA94V2XdO7/lUs3twPLu3vLczs7NDXkUzEd2rMV1S8RqYlXRU0l7KwOmuzxhV68BibS8CO+7EJE1J2lfbB4AzwNtdzDDK8Q7mvAS8jDpi2YGhef4YL1kA3nWUZVTrwOU6i3Ua+D7QbU4n6eCvMTdJpyj1ZPifRNbIfIcj7BcpfdsfwDfgad1/CHhc2zOU2YlNYIvSBWqaKX7PRryn3E10lokyLvIC+AA8B6br/pPA/dqeB3r1HPWApQ5y7Dhe4AawUNv7gUfAR8rSl5mOf6dheW7W62UTeAUc6zjPQ+Ar0K/XzxKwAqzUzwXcqXl7/GW2NinPlYHzswHMj/K9XgZhZqkmontlZuPDRcfMUrnomFkqFx0zS+WiY2apXHSsCUmHJX2SNF23p+r2kbbJrGsuOtZERHwB1oDVumsVuBcRn5uFshR+TseakbQHeAM8AJaBuYjot01lXfsn1l7Z/yki+pKuA0+A8y44k8HdK2vtAuVR+xOtg1gOFx1rRtIccI7yFrxrXb6QysaHi441UVcnrwFXI2IbuAXcbpvKMrjoWCvLwHZEPKvbd4Hjks42zGQJPHtlZql8p2NmqVx0zCyVi46ZpXLRMbNULjpmlspFx8xSueiYWaqfaf9w5Qc5EGAAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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8stCn0/IR3vW9dX/XFfNCoz/H9GFQ+oHIY3if01rF+wD7FJxp/G4TPuSsuaGiq22RH1VcsvvgO2PAND8cSu+JbmwOuK9Uo54eq9uWDlOxziW87/5NPyZsc+RTSC62s82OhLyB0nsQEqFNq9tM71fRyS4/9x3D+4zfdQVEU3tsKb2YfrN9bxtDUntdJ8c03w9K70CVRER23ZjYNG3nI4TuABaPrvG7rmCniqiT3NYBuWTXia5+3qcj0SE+N0Zgym+G0nsgBE/TtF93r0ov44pyshC2sapu6s0kvE58XSR3yS7vQ/fc9D18vrfp+46Rn9E9HErvIEmS/jRacUptnudXpBfvC8FVmLLJLr/WtVd+HiK7z35CxvG2zo2i7g9K70AIX9c16rpGVVV9ByCn+iG4hLdNj9kiu/zclMq72uW7P19cslP+3eOUXj6gi6KI6tTatm1R1zVOTk5wcnKCw8NDHB4e9uKLi2oMSWvl1z5TYz7y+YzZ1Q7KdBKObUbAhZoN7GNajdV+M7yIhoOmaXD//n288847ODg4wGw2w2w2Q1VVKIpCW7xzRXLAfw7c9AiERXdXNuKaDbAV5mS55ffsIqr7ZjHkLUEX0ajrGpvNZuuN2jdpmmK9XqPrOnzwwQf46KOP8ODBgz66iwgvinoytgN7qOiu7Zqm4cR3kV/L27MVB22ZhW8dY9epOzsAP6zSHxwc4KuvvsKjR49QFAWyLItqDNY0DebzOZbLZZ/G2yK77uq4tnO+XcWy0FTeFN19MxEbvjINqc4PwSY4xbeT2A6A+XzexXgJbODtgfPrr7/i8ePHePnyZR/95XvZibG/fP17E7YxekjBzCS8/DfbFW98o7z4u20xkmsmwPe72JC3I2ZNqqrCbDbD8fExTk5O8ODBA9y7dw8nJyc4Pj7G0dERZrNZ7B2A9ss7I33sPHz4EF3X4cmTJ7i4uEDbtv3NLsQ97XxvSukjve5zgF42NdqFCu/DEOGnDhK6xUamNhI3nLJzkKYp6rrGvXv3kGUZ1ut1L5WI9r7Fq6Fjd5Pw8t99x9e66UCfSv1Q4V1Ti0MwrY+g/H7wBpYGxAHUti3yPMfR0RGSJMHl5WV/6+r1et3fB8B0zr1rrG4T3hZlxXOb8Or2TMLrvrsusoYIb+u8hmCbfvRdeETewBtYOhAXw6zruo/sYqpOLer5pOq+kut+b4r0Km3bOguJIYL6RFPfmQbXdnyg5ONgeu8gTVPked7f3We1WvVTdaYxs+657rULU6T3OeB9Zw3U/akLa0zt0W3XhCljCMWWeXC6zh9K70BM04k19zbhBb7TVrJk4rXuPba/i237tsc1SxAiu247ruxlrJim2QriD6X3QJxdNzSi+BTIbL8ziS/LbttHaCT2ZWjRzmfdgC3jULczdcdy16H0DmTRfc6qm3LhS+gctg9TFWVtwocWAF3b1kGxh0PpJ0ZN2XW/84leoYypF4Ru21d43+k03b9ZKOwE/KH0O2Iq0V1y+HQuPumwaT+hwg9N68n2oPR7xiXmmLG6blzsm2q7om/ImgLbd9zWcIOYofQjMRXihhbPhozjfRfC+K5i843yOmzC+3RkU8AOwE7YJV+IN0PS+dD017Z01jUlN2Ze23dazrSvKcbfFHs4lH6L6MQKnU8P+burLT5t8Gmbz3aHzqMPqW+wAwiD6f0OmKJQN1TyqWX3ySB2CYUPh5E+ArYR2XWr4nwLeL71DtvndZ0ROwA/KP0tZchKNR9uysIYU4ExZEkx0cP0fiS7OuBMi36mxHRCTsh59ur2bMVJnylH1+zC2JOaYoTS7wHf1XkqIVNeNgmH1AeGnh8/tEah63DU8w7U6wMQP5jeD2Sfi0pCT/zxja4h42LfWQifv4f8G3AV33gY6feAGjldp8a6tuWDvDrPR5KxQ4kxItqygyRJGOFHQun3hCyfz5JYHT7LZF0LfkJW5NkY0nGNidqUfTiUPhCf6SbfsbOPJD5Velu67BqLTynP2G25OgGeSTcNlH4LyBXtMSJs6yC/iSL5jvMZ4cdD6QMZsqx0nweqa5GN7xBj6H6nOoGITAelD2RotX1XB3JIpX5bopt+51M/CJ09UGcybkrmcpPhlF0guzyodnkK6jbG4679DRGeUo+H0t9w7lKqO9X59OKa/q7bbxM9TO+3wDZS512wi/UCtn0P2R9T+3DYVU6MvHxU/plimyZc57SH7mvbZ6wNaZc8dp/qtOFYYaQPZF/pdkgUVhf+DG2zz4k2UzDFegbiD6UPJEQ8laHyhSziUafKtjHUCLkakIxayde1VbdNXaVeF/mJH5R+IOJgEzfAEEWlkOg4NMLZluXa5J+CIUMJU1vULES3DVV4ceNQ9cYj7AT8ofQByAeVfPBlWTZIKt1NJmV01WnXAa22w7UP22dd+5RvFR2y7ZA2qfsR/97ipyiK/rnoCCi+HUrvgRpFxEGW5zmKokDbtv3vtzXmt0VBwL3IZcwyV1OqrXt0bVN3nrwJdb+y9GVZoixLFEURdHNRQum9kYUXspdl2Qu/2WzQtq33xSZ2uRDFtggnpJNyia5mJqaIHnJCkLoP8e9fliXqukZZlqiqqpdfjfjkOpTeAznCiKhSVVUvfJ7naJpmK1NdQ8b1ptdDo78ruoek9z6rAG11AznS13WNg4MD1HWNoij6VF8e75PrUHoHqvBlWWI2m/VFsyzL0DRNH+WnkN51wIaO60NPvXXtU42iuufb2Cfw9rbhRVGgrmvUdY3ZbIbZbNZHe0pvh9J7IOQuigJN06Bpmj7C13V9Rfpt7HsMQ6O7rR0hi2NcsvtW78Vz+f+iKApUVYW6rlFVFcqy7FN8Sm+G0juQI31RFH2EF1HfNpbfVntchBTpxkg/pj2hMwVyRyNSeFHIk4t6TO/dUHoHQvo8z3vhxWsR4UOnoPaB6zRbG64IbHrP0AthuMSXp+1EjUWu4FN6O5TeA3GQyc+F7EL4bUf60IM4tD2hkTe0TVOuRpQXRMlz9OKR0ttJHP8Zd+e8zoF0XXdFcPFaLtrdpdNfbzq6VXhyJ8CVeVfQ/gNQek+mvOAEmZbQGkNEUHpCIkMrPc+nJyQyKD0hkUHpCYkMSk9IZFB6QiKD0hMSGZSekMig9IREBqUnJDIoPSGRQekJiQxKT0hkUHpCIoPSExIZlJ6QyKD0hEQGpSckMig9IZFB6QmJDEpPSGRQekIig9ITEhmUnpDIoPSERAalJyQyKD0hkUHpCYkMSk9IZFB6QiKD0hMSGZSekMig9IREBqUnJDIoPSGRQekJiQxKT0hkUHpCIoPSExIZlJ6QyKD0hEQGpSckMig9IZFB6QmJDEpPSGRQekIig9ITEhmUnpDIoPSERAalJyQyKD0hkUHpCYkMSk9IZOSOvyc7aQUhZGcw0hMSGZSekMig9IREBqUnJDIoPSGRQekJiYz/B/sQrbLIz2qXAAAAAElFTkSuQmCC\n", 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LYJ34nGvmndjHR0RfLlhaKyBW4qZ5u1T28vKSrq6u2v78xcUFrddryrKs7d+rUm9Fhoqum4O3GZlXbddW+L7tgeXiVyjvQa6weZ7T1dUVPXv2jIjeDsKlaUrr9brt38vNe9u0WZPXZEyEd50+1DFm095kfEH3GdcpUgDptVxeXtLTp0/p+fPnVFVVe2LMKIqOmvU2FdhV9C5che+L6iaSuowTyK+7SAvRh6OV/ssvv5yrHIuAB5D4FNhPnz6lzz77jD788MP2bLjiqa/lU2CbRvkhsqsq/dAobNqyMN0eWC5a6b/++uu5yrEo4jimpmno7u6OXr9+Tdvttj2Zxn6/P7q0FV/eyqQfbiK7yZy7eN81Ag/JoTddoDMWtt8R6NFK/8UXX8xVjsXyww8/0C+//HJ0Waubm5u24vG17PgKvy7ydz2nq9gu/XabuX+Tfau2bYrpgKfu8zaLnMA70Kfv4eLigh49ekRlWdLhcKCmaTovX03kLrxIX/O9L/Ly9lVz3UNG56cWvu8zOsEhvjmYsuuAM+z4hJiXl5d0OBwoDEM6HA6U5znled5m5rmO3ovYRld5uzaLZmwZGpVNtmWzPUT3YWil9+2HleWJooiSJGnPdc9XuBGvbmPaxBxDEBNhTA5AXc15mya+bts6xq5PkN8NrfR9V22573BCDo/Ux3HsfCkrG4Zsc4qWhs12hm57jG3hQKAHfXoNvMqObzw/Ly+1HaOS9W1j7hFzFacSfuyBTZ+B9AaIYruOGA+pkEupzHMLz92OpXz/+wKkt8B0znrK/TEmAg4dHBxC33jEkDwB3fOgH0i/IMYY2ZY/43qgkgf3XLMGddIie+80jHv2h3vOVJXUNn/fBttR+aHYrPAz3U4XOGC4g0hvwSlG6m1H401Sd1WPVZF9yJp+G1yj/9DlwL6BSD8RYxwgdPPspnPwKqaYM1dtW9WSmarVAfn1QPoJWdJg01wDjkMXxyDhZnog/QSMkegy5TnibOb8h5xYYyx5xZaN3O1AVLcHffoF4DL/PeREF/Ko/lh5+lMMRo59UASQfnS6xJuigg6NomOUaegIu07oqWYzAJr3VpxzZTMt+6kGArvKYfN7n/P/Zm4g/Qy4CjK2WK6RV2bqmQndZyD3cNC8XyB98902Uds1/VXef9fSXZP9qd5n87zqNRwA3ID0M2Hat7cdfTeJ3nOsAlSVRz5o2GJysAF2oHk/Ii7LY21ed/0MN4tP0Tw23adtuRDl3YH0M7OEk24srW/s0txH5HcH0p+AuZfoqliC/Kq++qnLdZ9Bn/5EiH38U0ctl37/kEw93eddl/ACcyD9CZmj0ooSTTnyrnrfmCm/OnAAMAfSe4RNptsUmYSuJ+0UgdzDgfT3BNN8fFOmiLpDDyQ4AeY4QHoLll7ZTnkKqiG/jarMJmv0l/4/WSKQ3oKx0lhN3utSmW36ylOk+NoukjGdv4fY4wLpB9K1LHWMFWJjVvauFoBLi8B17TwG6JYFpLegr4lpkjNvU4ldzjqjaypPsZzW5cy6RP2/hWo/fJ3Brt8dfX4zIH0PYkXim3i1G77EVRzHZ3PBzynPyjNGObr2I//W4pWHdAcA8D6QXkOX8GLFS5KE0jRtr00/9bX/xk54GZII09eqUe3DdWqOf3v+zdM0ba8tKMoP+oH0PcjRPY7jttKtVqs2us8Z6bmJK1LXtfHnp4j0Q8vUB//+SZLQarWiLMsoy7L2CsJy5AdqIH0PLLws/Wq1ait1kiRUFAXVdT3baLPpOvkxs9/6ptD69jG0ZREEQfvbr9drWq1W7aXDubuFabx+IL0GMcqHYdgpfBRFlGUZlWXZSs+fJTr9ddzHPPmEaZPetBym++LH8v9gvV53RnuIrwfS9yA27eu6bpuXRO+EL4qivYb9EueVh6a/mo7YmxzoXE8kwiJzP56b91mWtf17MdoDNZC+hzAM3zvXOh8IkiShsiypKIpRT1IxVqUd6+QVYywFHhrp+TkeSE2SpL3J0kN8PZBeg9i8Z9I0bSN/mqZHzfqlrwFf0gGJyP5UWPL4Co+xsPBRFKFpbwCk76FLfH6eK53Ylz8nzm1lmy5XQuzLL6W8SwXSGyBng/FzYoSfMtJP3ZR2xaRcY6fgyuKrknSAGkivQc70EvvzYnQ/h6b9faArWUq+ia+DboKeyoqa/BfkwTzVa2B6XLMDPaTzx4D0ANxfOqXH2XAB8AxID4BnQHoAPAPSA+AZkB4Az4D0AHgGpAfAMyA9AJ4B6QHwDEgPgGdAegA8A9ID4BmQHgDPgPQAeAakB8AzID0AngHpAfAMSA+AZ0B6ADwD0gPgGZAeAM+A9AB4BqQHwDMgPQCeAekB8AxID4BnQHoAPAPSA+AZkB4Az4D0AHgGpAfAMyA9AJ4B6QHwDEgPgGdAegA8A9ID4BmQHgDPgPQAeAakB8AzID0AngHpAfAMSA+AZ0B6ADwD0gPgGZAeAM+A9AB4BqQHwDMgPQCeAekB8AxID4BnQHoAPAPSA+AZcc/rwSylAADMBiI9AJ4B6QHwDEgPgGdAegA8A9ID4BmQHgDP+H9yTdihFcHVtQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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s1XjGHAudBY/+el1vTu++izvwQvRoyNFwgvv2J5PJ3n3uI74WJv8m2Yq52B7/ZjwI1DUuYR+l/gBb+XZY9A3ACnP9mrPabW6gqJlsFjqLhJNeWt5iq6YJO4j+9PR0T/Qcy2stHtZ9NpvF9fV1JXpYeV4VB5ZeBxp269ValyYFRcSeld9sNjEcDvcSfCXhZvG7xX48Fn0DECNEgviXG1e0lg2y+ju785qZ16RXxK2VRXacM/Xj8Tim02n1rAtk8NRU7B/LX7Hg8VArD7HznHxNFCJPwFN8+Xfj/gKer4/zHg6He2GEnjfgqgO/l/1t6mkl+lKc9ZjBRbTZbGI+n8fl5WV89dVXcX19vecGc8JLL8qIQ9eeBaIDgW6DLTyENxgMDpJ2ED673GzhISocLwR/dXUVV1dXe+fEq+Jg/1kYgWOJ2L91NJ8zny9+U/2NNGmJwUabgfj7/Nua42kl+i6Por1eL5bLZTx79iwuLi7i5uYmrq6uUldYL0JOnOF1Ngjo/rTTjpttWPBI3uE9TdzxoLJer6ukHSw8RD+bzapBDOLLBI9EIY4HxwmPhbP32SCoYtfkKP9mOAb8JpoHyAZX0w679w1st9u4vr6OP/7xj3FxcRGbzWbPDc4mojCZi68uKse6XJbT+fCcnc8EzwtjcK2cBT+fzyvB41kHMOxfKwPY32g0qmJ5TTrWlTE1445/Z2+HDQyvlstlRw2Fmsp+Zp9a0c9ms9oL+rEC1zYi4ve//338+te/jo8//jjm83lExN5U06bfpu5i5FITtsWi0Mab0WhUiQ/C5zXvMsGjsw4uPbv1yNyjA49dao7hsU+InmN5ZOyz6bTYFk+Z5RIbfx55Dc5DcHjD2xsMBukEpK5dp3elVvQXFxfxy1/+Mn7zm9/EaDTa65vuAtvtNp4+fRp/+tOfDmrw6oLj/YjmvntYLkUtobbXamkua7PlJCEabzhpBwuPbD0vgRURe0k75Aum02mcnp5WyUIcO4SnguffQn8nnZzDWXv85tgGr4PPeQ4uBepcf4u/mUZL//7778f7778fERHj8ThWq9VzObAXCS6s3W4Xb731Vrz99ttxfn5eDXwoVQ2Hw73v1Fn1UqkJ72ndGWLOeuq1zZabb5rid7bwPJOO941zY8Gfnp5Wx8ETYJC8y84b24LnlE3Q4eReFv5E5GGPzjwsJUPNIbWi3+12cX19Xb1eLBbf+AG9DLBVf/r0aTx79iw2m00lNlhZ7QLT7DKj3WPcaMPLVfOUVYhal6+GB6D97rvdrhK8ZuhReYCLj5wEnyv66NXC11l5tdBskXl5bL15Jv4dsHC1V19LmBjY+NHUK2FuqRV9r9eLs7Oz6vV4PK4SPY8dXHij0Sjm8/neBQxhRuxbabzO6sk6JTW7gyz/G09o0XvQlRpv+DZUEPxXX31VlRu5AWe5XFbHyHPiucU2i+X1/CBS/I3z5Ro//63z77msx+VM/q2z0EhvoslNQLby9TRm73nk1FsZdQEICNYJljBiP07VxJQKgAWvAubnkvDxWueuc/abBX99fR2Xl5dxeXkZX375ZWXlNUuP4+ekIbr7IHbO2EfcrpzL5Ta2zhq78z6yGX88sUjbkHlQ4ey9Ltip1t7CL+OSXQOcBR+PxwfWnktlEfnSUDwjrrQGPecJVPzsDquF5Dies/RXV1d7DUVowOHmGw4xWPA6eYerA1xP107CiH0rj/PX89DZfjqAZULH53q93oHY69bxM4dY9A1A9BpnsoB50kkWk6rg9TZT8B6ye8dnS1HxsbGF1zieM/Wz2WxvvTvOqmubLVcJdFIN9qsTarjOzjX1LHnHjTYqcPyG/IwKAVtyXt5LF/C0i1+PRd+AZoPZGrN41dLXiV4Tc3Xr2WGfDMe/PCcejTd48Mw5FkbE/rp6OnEnq//zuWkiTVf04ePP1tqPiGoeAFcNQJYQxfmWEnnq4lv0ZSz6I+ALmsXCN4LUQaKN6PnecqVlqrFttXQlwWsMz5l6tsCatGMLX5csVAubJfGy5be5l4CTeEiUctNPhg46WULPgq/Hom8Ju6sak3NbahvRQ2y8nWzZKb7w2aXWxhuIHO48z5jjfno+B43fUZ7THv6I28GGO/z0DrZcmuNBBQOaJh9xTujm41Imi58pWftstqOFX8aib4F2yuk0VxaJxqOc4dfQQOP3rHGHE15widt02uGmk2qBsU+IHVl67qvnxCTH0dgvGn+01s9Wnn8bDAA4Jw5P0GiD32i321Vr6HGliEXMcwqyZJ4FX49FfwRskXSNebaMTFayUpc360XnB1s1LGCJrjpN2nGGvlRBgEt/fn4eZ2dncX5+Xll5Ttqx4JEs1NVyOcnJbr2WJtlrwGf5uLSZB4k77dTjwUJFz0t8WfhlLPoj0YuV3fasQy/zEDQrH3Frybj5iTPkvGotBM/x++Xl5d5sORU8z9hjK8+C13vWc387RA8Lr9OKsS/eD58rJ/C0BZmPk1fK5d58bQDi38Wu/XFY9A1ozzz/nTWfZJ/TzwIuV3EZrZSw4+mxKMPBvYfbzbPl+Jh4Es14PI6zs7MD155DFA4n4GHgGPTuN9iXluk4Y88Z+VLHIjwMLutxtUGTf9qN5xl37bDoW5BZb33NHkBpUo3G6rhI1bXn9eV0XXpdyBLWHZaXS4vsbsPCZ+vpQfDccacWno+BRYbfJ2u9rTtn7dPX1zoIZklA7RWwpW+HRd8CXMzcX1/6HFt1dWEj9hfT4NfaYQdLqoJna89i5xo8RMcTaHSZbF3CGsfLSTK27HhmV1qFra237J7zuWlDD+DvqOB14IjIuwJt5Zux6BtQV5Wz0JlgtbFEy3gqAn6whVW3HmLXZFpWJ+cEY9Zaq512HMOzdef94v1sX5mng9+HBcolR55Rp9l/DQN0e/o7cvutVk/MIRZ9A6hpq1Bg9dlq8QWfld1K7asQAYud7yDL2XLOmmclOV0tl115Fbw23nCWnm9vhfIfz52H4CMOJ9jAY8hKdGyZS73yGEB40AAc02uCz0JvR6PoOZ4dDoedmlq73W6rpBcSX5rwYgsZcbvUVtZJl7WPcoMLP3PzC0SumWqOgXu920UsdV07tvDZ8thZhSBb6rsUw/PUYpxvRFSDUhZ7Z9eRxu2MWvFSiGWa8SIaDWy323jy5Em89tprcX5+XmW62dKz4NFNpi4pC4tFrd1t6KDTgSCbRcaxM4udp8bybDkuo+HYcY7a1suxPO+XO+fYnefZc5lLj9+J3XI9B43fQRuXPUucmpyjFtGYTCZ7Lt5jpd/vV5nw73znO/Hmm2/GkydPKuHA2kdEJVBc3BrXculN21hZWJnVLy0Owd11HH5oSy279TobkIWJ/WnOQO9cW+o70DwHf4fDl8zC8xRcDYfwd/bM/1c6AFnw9dSK/vT0NH7605/GO++8U1mJLsVNm80mrq6uYrlc7rnE7M5yLTvrrtN+eRY8Z9/ZymtGmuNa7m1H11tJ8Nxaq9N/IUh26RHPszvPgteGJM1vcHxdqqOrhecmnCyJxyU6fA/P2huQNT2ZQ2pF/+TJk/jJT34S77777vM6npcGXDQffvhh/Pa3v40vvviisv6lJZrUtdSuNhY8prtqYo4z21oF4O411N7Zpedeel0tV5N2WaKQByDtqefJRrqwR2mQ4/PhEEg9FY3VNVmXJfq41TebrGTKNFr6rvPWW29VF//V1VVst9uqRq6uq16cpVheRZZNGMnq1+xO81p2uvCF1t8j9q07z9LjshzPylOLjIEG4QQExwk8FjyHKVxD5/BAS5kseC7B6eCnlt3CPw6X7Bro9XoxHo/jW9/6VvT7/VgsFnsNNHDvtVbMNWT8e5a4w0Nr9hpKsKWF4PmGkjxDTtehY9HhOLThh627WniUAlnwOsFIBc/npYOILgoC2JXnnIB+FwMQchm6AIlFX49vYFmALeRwOIzz8/PKGkE0Eftr6Km4NLbVxSd4vbqsBMeC1/vZ8QWfrYGv21QrD0vPg1BW99eZgTyLDmBfWorMknfcX5+16Wr2H+/jd8F3dekxFb0p4xtYNgDrdnp6Wl3Q8/l8rzzFMawKH2Lgz9Rl5IFO4eXFNyB6XsCDY3bEz5pY0zCDBc8WlYXJomcXH+jApi69Cp69ltK0Yv4uCx4eEHs82e25benrsXvfAISXXVzoPGOLxpYbgtcecXVXddINC4Iz5XqRqyvLQmHBa24B1p5DD87Q49hK04FBJvjSOnW6ok5p2m0W0wOduputL8htwSbHom8AwszuKsPwhc4i4wEA29Nyk+5PraHe+EIFqIML97Czx6FtvzwQRRzeGjqzyiALXUrdgvzQ7D+37eJcsjq9VhAy0Tueb4dF30CpDlzXCJKVmFCT5pg7K8ll7a0cS2cttPg7a3DhQUc9Ac6aa+NNqcUW++KwBd4Cr6ITkVcdNNnGbjsLXT0FHoA4ns8GQgu/Hou+BWqt1AKyZYSw0bLKYuQLm8lExw8WILvCEV8LZLVa7XkNOglFS1+aR1Ch82ucP1ti9hj4wfvgY+cbeWSZ/16vd9DpWRoQdQDJBG/qsegbyHq5VfiDwWDPyq3X6z3xaDYa28jEjn2pJ8Euu3bpZWRtq7w9XmQDeQsVvm6rrvEmc+lZnCpQbB+eSslr4oFHuxGP8b7MLRZ9C/jCY6utFyHH7ex6a5Yezyzupgs2s9pa0qoTOlvJuqQaDzw8wHAMzyVHdcVxLlp14LwEVxpg5fH7wVvi34F/Kwv9/lj0LdELDyLii5tbcXFB1sXuuPjZLS3F5Jwg1Dhd6/HZ8XK7KyfE+DmL3fGsjTeatOP98SIeukx4ZuVxPvrbsPizkMeCvxsW/ZFooo0tjy47pdaYL2C19OpOa7ItK//xpJwseciutCbVtMyltX4dVLKlprN2XfwOep8+btuFF8H5Cc2NcEiRWXQdHE17LPoj4AuO41a1pBG3iz2qC65xfJYzYDjrzuIriZ4HpVJ3HVtfLXXpMfMAU+q000pDqXFGy4zYB7bFLn4p2QiyUEcTlibHor8jejGqyCCYOhcff0fsZ8gjygtm6kO73tRSqsutIYm63OzS84CjLj32yRZak3bZHYD0vNmSszei4tfQI/NGSl6P2ceiP4K6ZBwe2+12r8uOs9OlbSIcwGt9cMuuXtx1JcAs2Zi59OzW41lLc+xt6ADGCcFSp5xWM/iZH4PBIHa7XVUR0d+tNADqb2Lhl7Ho70BTUw0sPAYACLfN9krCZrcb+8Wzlvq0nMUir8t6l3IHeiw4Du2l5zvv6j7Zm9D+BT5utvD4LQFn99n74JDDlr4Zi74FWWOLou4+WzP8u4q7bl/ZwIIBRJ9LyTRe6IKfM6GzBS0JHvs4Obld4kr3lXkRWn7Tc1Nrz+elJUFYf+0R0GqChV/Goj8CFn6TWx0RB25sqa9ct49t4VmThDywZPF8VlHQJBhn3zmBx2IqufNanuPWWB5YspAmqzJk55x9D8e6Wq3i5OTkoEmI5zuYMhb9kWi8zWiCDsJUAbSx+uq2w7px7kBd/oh94bP1xH60B5+Pi5N2OkCxtwHYjeeyIJ8Xzr/ud9RzVpeejxn71RZgnjhkS1+PRd+AJrdK/w6ybLy6/SWR69/4DnICiHlZtKVQAALlLDmaXXAubJFLiULeJpJsOD/1Krh2jm3we1kvQt3gx8fEC3zA0vPago7p22PRH0mdYNWiwcIisQeywSMTPe9zMBjsWV8Vfcnz0MGKBxD2AvjY8MA22FOAeDkW10YZtsq73e1a+XX7ygadLF6PiMq1589Y8O2x6O9JybKrCOuEnwleXeWI/aQbrG4p068PfAZC5Wm+Klw8a4hR8ij4nLLyolYL1LNgK61JRF7/n88R59BUvjSHWPQPQNZsgwubL3SO79UKa/yeiSrisCcfIlPh8GeAtrrqg89Hv1MKJXBMLEpOXmJw4X3o4Jd1/HFugbP0/Nu6Ln83LPp7ojE0X3ylJBa+B4GUtlfKgkNkEBLPRa8LAfRYs4Qfd8HpMZXicI79eWDTOj4nIfm7PF0XYueFOdS9123wMZpmLPojYUueWXhGBwH+HifVsu1nDTQsPAistB+1/CwYtsS73a7KyLN3wsfCwsJrFnt2DmzluelGwxZ14/XGH6+WY28AAAYBSURBVFyOa/p/KHlHZh+LvoFM1Jkrr4Jh1J3VElr2+dKFzK5taeCBCDPR47sQOryENqLRPEG2XgBviwcYTh5qHkDX2dPpuzqbD89Z41Fdj4D5Gov+CNQVj6jPupe+z24/u6qlfUQclrzaijPLxutgxUnGkufC2yr9zefAoucOO96/uvelO/WWqgbcgMQtxhZ8PRb9kdS58xHle6dndXt8nv9NPQHebymWbiJzw9VbwN98jLqvrFrA1h7f05VveBvq9WSWvk7wpVl8tvTtsejviQqp9J5mngFEwnD83cbtztz8LCRQOK7Xunp2niz0LK7XEEcHC/2NODOvSbtM8GzZs2W4bOnbYdE/AHVloybrnL3GRctWvwl4Czytl7PxeM72x4LkqgEPWCzWUvlO3XeFQwC18iz+kuDVwvP9+1T0Fn4Zi/6O6MWvFk1jdf0e/i5tk4UfcXurqYh8Wi0LFhNhODNfamBRgR1zvDiG7HhKVpfjeF2Vp43g+caVpTn7Fnw9Fv0DoZl4FbwK7hiLz681p9DGE0AIoWVCzfKXjjcrybEg2TsoTalltKqQrQBUErzex49v8WX3vh0W/XMmSwSWvAIWdtYyC7jdtdfrVTe/wKy80uy8Ove91MfOwtIselY6A5wE1IGHk4B1Fn48Hle35sYzFu5wIq89Fv0RsIXL2ljrsulcpuOMPWe16ywqX8x6YXNyDULhllZMUNGFMXSSStbDr8fCf/MSXFw6Yw8A22BrvtvdLofF7bkseoQoerPKyWQSp6enMZlMKuGz+LWl2Bxi0bcgi1t5xRheJFKFArKEnuYB+HP8XXahM2vG2+EYmZes1jvSYKoqXvPxacmtLnuOGJuXuNbltDmGX61W0e/3K28EvxsGG7Xy7NLDwqvwNZnXVO3oOhZ9SzKxI4MMlzqzjhlZJpzf5++rdVfRwzqy6JEVLwm/dFsqCBNJQBZhViOH2NjF1lVvcVzYBx/HarWK0WiUJu/gPXDCjkU/nU7j/Pw8ptNpTCaTNINvciz6Fqjgh8NhTCaTymWHta+LyxUVemnAyAQfcWv9eXuwqFwK4/vQa9cb3s9WlWXLCyGV1rTHMz7DJT+28qVBh9tsufqQ3TQDj+l0GtPptLL44/HYom+JRd+AlsEgeAhCb16ZuedthY+/swx9FtfzZzj5ljW9QOw6CGRr4nGcz+evFh7WV+9Tpx5ISfRaosO5lm7KwfuHxefYPltf3xxi0bcA4sZFzhZ+PB4frM+mF1zTBVhKAHJcqi493mO0F57jaBZdNm1VLT0fg3o5nE1XwWcTajTXUNpfljvQhzbncAlPF+Y0ORZ9A3zR800q4Xry4hERdxN90/41a15KUmlykMWmolPxqWsPuCyn4svW0y8lGDmu59yDhkRaBswedXfRseibsegb4PKRlpNKF262jfvsX5+z0KFUZ88aYHQw0Blz2E5WnsyEXrcariYZ+ZiyDkTdX7bIBw9CejOPpsYgY9G3AhcZ/53NU2ce4qIr5QXqtq3VAG3GKU2PReydkZUMtUmnjfehA1IW1rTpTSgNAvzalOnVNZREROcXHmNxqIBKpTbmoS1Om+3VVQayMmHTedSFGPpvbY4le+bv1z1nx6H5DtfpK9IfwKJviWbYXyWajrft+ZREdMxAdCxN227rAXUUi96YjpGK3sGPMR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjjFo+PfeczkKY8xzw5bemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHeP/AYxiU3BuDiO3AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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glVPomfAgPciOOJ4JDxEPfffNLDWf1Dp8XTSjFXe5ZpN6/njN3o2GCBxC8Pc5pudYnhfSYBLlscHY8vnwOUQ8fx6C9A1Q0vKFDusOIufIDijhPRXbi+GZ6IjhcUstvpceSKXpMN4f3GjExnpDTu6ooy46xsDMiiGLN2Gw4MnLb7k4xwNvh4uT2MLnPJmAjyD9Gci59uzC5ghv5qvibHnZdeXUHFT6m5ubJN7Bwnv30fOUeCZWVVW1kIGLfPSml9ienuO57bywb3X11cLDzdffQ/hjbYK3ETgdQfoTodbOI726+0ApdmfLBSvr5eJvbm7s+vq6Rniut8dx8aTE+8Nkgv1x6g+6AJfzsmueC2E4nsd+cCw6wXG8ztYdFh+/0yYZ6vLnSB4i3ukI0p+JnEXPpbD0YmTCMwm5Uy2sr7r1bOX1pplaWsuFQyDYZrMxszeu/Wq1SttDrh/hgsb0GtJoKMPnxeet4YEKePgeu+eekMfWX8c1cD6C9A0oCUS4+NX15d/ys9nxffG0Fz3KauHWM+nxOVt59jJ0YQ9IBlEQZN5sNrZcLpP3gH1gu7gvH87Rc+tzuXkdMxX2crUApTi8lEnIvQ/kUSR9Tlz5UMHkrarKXdyhFtCz9NiWbpvjUrjbvFqO3XkvLafCHY7X668PK4/zwPHsdjtbr9e2WCzs+vrarq+va1V9fE8+Pi9NR8Ly8zl5gh6eORTyPALv93wMPKZeJkG/G8ijSPpv60JpaiqXsnqb+/D+bnb6ubA7CsLA8nl3gsE++IECGE85z7nziK2hzqtgByvMx8Butt4SGufAPerg4sO1f/36td3c3NSyATjXqqqOVuVhQsEkgG12Op1aqJEbz6ZxV89IU4E65l45c6AZRdLzstE2otfrpVw4W2pOaWn+HlCXXlNyTHhYeI7bF4tFTWDbbDa1WFsJr+W/IKKZpYliu92mCQaxPPYDjUCzEdgH9wfg0KHX6yXvgFNwHkqpTI3nOWzKbSu33ZgIymg3q0/Aer22m5ubI9XZc4Fzlsmro+dYHtZcV85x/M6WlN1tvq88SAlvw8zSRIFCn1xFH1J/KgxiIsHEMhgMap4QE57HgseGP2+qY2Cln881F9cHwc9HkfS//vWv39VxvFdg1xjxMz7Xi5eLTnIXsVdLz8UxiO3xQIyPv/MkwzG115KLV6qxhWeREMRnl169F1j3fr9vo9HIRqNRes/jgwnGIzKvrmMLzuEBTyB47eXqvUq9UPAfhiLpP//883d1HO8FcGEtl0vr9Xr2ox/9yH7605/a97///USk4XCYCKA3ZWAo4Zn0pfJX7k3HBDS7j2Nzd87B91g7wCTCWQEWBTls4H2gCcdwOLTxeGzj8diGw2GadOA9VFVVEw95gjSrd+spdf3NxfSYVPTBmQBv7AN5FEn/y1/+8l0dx3uJL7/80i4vL200GpnZG1f54uLC5vO5mdXjzibis/rvvdacOMitrrN25+HmHNgnJhKuuOMuO3ybaiY8u/SY3CaTiU0mExuPx2lywT7MLLWxYhJ7Fp+9I83563hp6ITyYh03nlTD8p+OiOkLWCwW9vLlS3vy5Em64A+HQ3Kn2a1lK2vmW/qclWJLiG2PRqNEQI+U3KQDohqsOwt2nnVHAQ4eZnWLDNJPp9P0AOlBQKj8d3d3R3E6u+L4TBuKKOmVwAC7/Jz5YM8piH8egvQEziWbmY3HY/vmm2/sq6++sslkYpeXlzYcDm02m2Xr3eH6eq69l+fnNfi8/h2qOMfz7AVw8w7ePvLwEAM5ducGmrpdHPtgMEju/HQ6tYuLC7u4uEiuvtl9GnCz2RzF5p6SrlWCfPxm9X77ZnURlNO8XOMQjTQejkjZEUB6rE3f7Xb26tUrm0wmNpvNzMzs8vKyVsDiWSx9qHXX34Jo+Kzf76eLmvPsOEZ25UEUdulh4b3lt7DQ7D2ohR+Px4nss9nMptNpaswB7wZ3p/FSZapDcLjCxAehzeqFPHhgG3yHHO6cw0Iou/tB+jKKrNYLrm1Aum46nVpVVTYej2vClyr5QC6WV0vPcTusGYjHF7JXxcYWD9ZO195r0Q1bRrP6hAXXG3E8CD+bzZKIh9y/1sd7qTQQlvP8THxvkvTidR5XXXbMy4FVAA3i59EuU34m9vt9cpUHg0Gt5h1g66YxuyrynqVn9xhKvE4UXLyC44JVZ6vHKj2X7rJFVNeZ9zsej20ymaQ4/uLiIgl50BZYYc8p7ipwgvDIeOB7WnzjWWo+b177zylOTm16FZOBOoL0BWBxCh5ajcdgErBFaqrdZ4sIkdAjOk8im80mEQafax98Lt3Fw5tw4G7DrQfZ5/N5eo2CHHh+apX1nDjVCMvOKU6u2vPGkccT28QY4VxRzah3+OHQJeAjSF+AikYcD6tVh9CEB8fkLEABSr6qqmqLZPDM+9psNmk/mBCg1HNVH8jAdQCelsB6Atx6EB3WHpV4SNPhPLwW1GZWm8T0Jh9cS8BVdzwm+uDxgFs/HA5r6Ui9rVe492UE6QvwLJpab1SlmVmt+o0nCiUGoGkuQC0h9tvv948aYXAMX8rDeyk0FOBAuEMMP5vNbDKZ2Gg0SrE8LzritlqeRsDiILv2HMtz7UFu7Hm88H14Okp6zkqEmFdGkL4BSnwVkQaDQbrAcEFqvzm1Pl4pr4qB3CwSx6G987AaT7vfcIMN1R+U8IjZmfDT6dQmk4kNh8NUZYcKPJwXXuu5seKuN/3AuUKZ18U5uQInfAdpzF6vV1uw5JE+kEeQvgFcEMLdaZfLpY3H4+S+mtnRhJCz8kx076HuPW9Xm2twgw30zGMNQfephJ9OpzafzxPhId6B8BxG6PoAXfXHpbdw7Vm1VyVeU314rRaeJ1VoCzgO1S5ymkvgHkH6BoB42qgSiv5+v0+kV0uvXVvN6lZeC1bU4ivhkIZD8wv0zENqThts8P6YhCD8bDYrEh7nxJOdNvLQEmE+DyW8Kvaa49eHqvhmVsvX8wQU7v3pCNIXANLBaqP5xKtXr2w4HFpVVXZ3d5duIwWC4PuacmMVumTtccHDpedGG1gHz6T3rDz2o/G1Wvj5fG5XV1c2n89T5R1PYioUYj9Mei3G0Tv1qgfDZMd7dvvZQ+EcPs6Jx1nV+3DvmxGkLwCkA/FXq5Xd3NzYZDJJ8SUUbrN6KKC94838G2ZoTM9WjuNoJj3fw47vcKOWV+veUV47mUyShWfCo74eqUBPKFTNwBMI9fZefN6qyOu4aJUfvsuVe1yNp4+w9M0I0hfgWdvFYpFIvtlsbDqdJqLAMzjnwvNcXLP7clR1rXkt/HK5PAolzKx2S2os4smJdiD8ZDI5WlDDTTc0FYgQIheueMuO1WrniO/9H1g7gDegqVENpQI+gvQngN385XKZFPvNZpNie15lV8oTe8UnCmyDL2omPhel8AVvdlxL3+/3axaeCQ+lXi08CA9PghftcOii+4Ol97IRXpmtl8LkZ6/6j6sdc/0IIk9fRpC+AL1osYINaSwUimgOugS2Wiy4aTEKV+Dp7ae0yk6tIMfvXHQznU5d0Q7Hr4TnGn4W8JhQTHBYeHXPucDIWwd/6njxZxg/r1w5CF9GkL4AkAgXMgtbmAS0z7yZ1eJzhl6oujKML2Z+zW2vQEwlA4t1WDjDtfSw6pqHh7LOLj0Ij3Qgp8VUN1ALz+ftFTeppfcq7wCP7GZWEyt1vLztBOqIpbUEXKyosoO1hOuLvDXKWpE+4nRVTowyq9fP8+sc8fGbkoX0rDsaYGC1HJR6iHWalvMsvEd4bc6paToWIbnGQLUOs3zJLf9N3yuZw6o/DLG0lqDqcqfTsfF4bLPZLF24LNghXcWxKLu36uKqtePSXrVUXhyrFzhXubE7z6vk4MrjM5TWch4eqS+O4XOEZ/HOqybU0ATnpy44wKp87txLxPZCqlPCrDajXaa8AXpxLRYL+/TTT+358+e23W5tuVzWYkhedcbVaLmUE1t4JoLG9nosbNXxzKTDGnxucYWVcmiGAevuVdpxVoBvcVXqlKsTHI6TSc7nWVp/wMTPjQPGUPP7OW8jkEeQvoD5fG7Pnz+3Fy9e2Hq9tlevXh2lrdiCoUTUIz1f+N5SW45RFUxwJTxq23U9PKw7VtBp/K6FN0z6JgtvZtnuN6o/5Fa+eXqHxucYN288eCy8isYgfh5F0v/85z9/V8fxXmG73Vq327XLy0t79uyZDQYDWy6XNhwOa7eCQk5b++UpSjFukwur6SvVENitB8G5ZTVX2FVVlcQ4XUcAsustq70CnCYLr70HdOGPR8xTwhkNKTDZcTEQ994L+CiS/he/+MW7Oo73CqPRyKqqspcvX9qXX35pf/jDH1IbaCY6W2fPKrHazN9hV7dEenVl8ax5eHbv+cYXfLcbzudrwQ8vyfUIz96FFt0w4TGZ8HOuLJaXEvN2zOxobHRM2MvhDsLq5gd8FEn/k5/85F0dx3uJTz/9NBEWt3ZGDT4Iwhe/kl/jUU1XlQp5SrErP+Oi19tUm1nN+jL5dT06im68lYHnEp6LZbzmHTwu3hg1jYdaer1xZ1j6ZkRM34CnT5/aixcvbLFY2GAwSKvcuK20pqm8C9mLVUsuvWYAPMU8V/kGRd7sPv1YVfflxLpCDRoF37EW++T18bqvHOG1Oy225Z0fjpnHKwc+fxCd77GXK/8N1BEpOwdoD7VarWw6ndrHH3+cetJDEb+5uam50SrEeZYrp1zzMz73BDvv2SMheuhxilEXqnBln7c6TS28l4LMWfhcDwEzOzp2b2xK8TxbdBB+NBq5d/sJ+CiSvm2zpRK23++nevWqqmy9Xh+VsPKFxhacL2iAP9Ox9QhvdkwS/h4fK6y32X2xDcOLt9myc0rsbbj0fF68LZ5AUPrrpeN0XMysNuGxgKk38Az1vowi6VHA0UbAqkAN3+12tRp2ToVx2yi0it7v97W14bpO3ItvS3E8p6nwu/1+n5b4skW/u7ur/e9wbLwohbMH2B/vx5tozo3hYXF5Xb2WLGvKE/vHex1DvpcfVyCqex/II2L6Atiq4P5ySIcx6blXPVs2EN/s/vbLnNP2ik2wX7W4ar3gunMdPq9wY3gionocTDjWDVSvOEe0w8TJROW1DPBQMHZ8Bx3ehi4VZgHPI3xY+jKC9A04hfi4IaSZpYsYpPHc/aqqitZIXXgGE5AJr16Buv/6ukRytcZm5RieXXr8hsnOKTWs6MNxo8klhym6eEm9ALX23F47CN+MIH0BqpbjIuNCGDxY9ITFwgXsKfaa1svtn+HF3kw2TleV4mPPgnJp7beN4UF4nigRe/MxIsvA3gSHSRhTnki0NoGJHzn60xCkbwCTnl1LJT4IDiUfK/C4Ig3W3VP0lfyqZvMzg60blwB7+oCSQonOeW5V/UtpOZ6EsA2MC3tEENs6nfuaAXb1YelZSNVlvDwBc3ttL6wJ+AjSN0DdSo4jUesOIQ/fQW92rrPnPval1WZMoly6TycEJbhXo6/iWu7uMyXCc7car57e68WHG2mgOw+aiCJ9CG9Dx0YnOJ18S1mNcPHLCNKfAM/FZyFvPB6nmB4XIq8y6/f7ifzs7qvbr2T3vAHvc73gWfnG/thKsujFr73sALSDUmmtWnhuzVW6RdZ6va55P1yvr+Ohk1hO4Aw0I0jfAL3YWJwC4VHlZnbflx0kwWu40Zw+M6ur+rB0fKF7hPcsYc615ePnxTpcvpuL43VNfC4th21yey605bq8vEzEx7JebI9FPXgQqB3AOHFcnxMqFSWdJBCkL8Jzm72FHrifvNm9KIbcOQjPMTcTnpVs9gKY8AwmPFt6KOF4r8q83j2W03slpZ4X6uRKa1lcg2t/cXFhl5eXdnl5Wbs3HrygzWZTIzzKgVH5yDUEvEIP582TnxcyBfHzCNKfAHafObbXlBSstBKfweIVTwAgA5f0qvuqBSu8bS3gYZdd43a+84wuUGES8fp/JhaPC48HXHt26fmGmMPh0Lrdbi3MQSUhhD8UFvV6vTRJamETeyIe6YPwZQTpG6Dxstlx7h6vYdWbwCq7V7WncSyIoiTnY2TCcxEMV8JxntzLxbN4x8TncIT3h31xKhPERyMP9NqfTCZpn9jWfr+vdfPRlXI8NpzS89pee5NSwEeQ/kywBWVLjxhV4cXkUPKZuEx8/ozf8wIaLbLBseUsfK6AhUMJJk7JuutY8ITCng8Izfl0PVbeBocaqi/gmDB2TH6vi1EgjyD9idC0kKeGe2sVVKFnN5Wrz0A8XNiI01kTYHIyNDbXe8mpOs+KuR6jkidHoJInAYutuoLnBZUyFDxmHN9zGlFXCYaL34wg/QlQQU/VfFz8SkZV3HEh8go4uM+eYg+icnGPd0F7x8NuPrYFsBDHnoO68rwfnig4E5DLl+O3nLoE1ELrramYwCoe6u/D0p+PIP0Z0Lg+Z+lPyR3DgoPUuFhBbC7u4Xy7EtKzpkpCeA38XMoM6MTCk4ruM6cJIP222WzSuPCkwrfm8m5CybE61zjgHLR24BTvJPAGQfoHgEmma7zN6h1nFJ1OJ6n6bMXVI+AKP1aoEdPqNnm/OavLajj/Bt9hy5rbh77Gb3EeIO56va7l4TUe1z77Oglg0sAEYnY/UXopRH4EygjSnwh169nKQ7mH68tWSa0ip+pQ6AJis6Xlz5iI7L561hiv8XcNGWAp+XcMnXy8vzPYfUfDTZ54OC0Hi49qPNwFWO+KizZePEbwUNT1D5f+fATpz4QXP7NrD8WdXWHNtyOH71l6Jr+6/BzXmzUv1uHPPQVejwvfY0ufCwPwDO8BPfk05w/CQ9wzs1SMA7IvFovUghttvGDhcxNdyRMJlBGkb0BOtWeyIx1lVl+mCnLrtqDa51bjmVmN/JgUMElo7M1inKd+A57lzinrLJ55YiRed7vd2np4HDu7++v1OpEekxc+57vjrlarWu8+FvUwtnrc+F/w+0AZQfoTUXLvQXyze3e32+3WFOtcBoBTc6zWmx2X2SIuZjFO1X/8TiePnOKfE/UA/r2n6kM45EkGwhuEPBTfsHsPDwB99+HaYx2Dqvg8hjhu9bS8kuLAMYL0Z6CJ+LB8WjYK5MilHgCAiQAk58U7/Fu15ixwlciaU+H5uyrseRMIx9cgPCv43CoLx4UJgdtw5/LufLxa+qw39wjiNyNIfwLUxfeIz2k1gNVqFubUVYZHwKT23HB1z9UT8MjJC1c8V59r+FXt52fdfq50WBV1hC/serPwp624PcJjPygC4jv66L36dC1B4BhB+jOgbrlaSiYAP9QL0LjfE9ROBbQB77dMHA0ZcD7ea504SlkDnniQdeAJB2lG/g4EQE7LaQyvkydbeCzfxcIeLObhtllB/DyC9A/AOSTNTRQIC5gope3l0nQ5Epsdt91W1zyX0/YEQE/RV4GTF+XArTezWtjCIQd348nF8DxJoiU535k3dw+CQB5B+hPRRHB1gb0HtuOl8bzt4VmttR4Xu/7ew4vBvdy2t09V8XXfXh0/vB2k3HhCgJXn/D57BZ5Lzw06RqNRIvt8PrfZbJbuQ6A3vAj4CNI3oMn6emky71HatoYIOcGPU3as/HONAPecA2F4dR6+57nwJTc+B9Ql5PQGPX+29JyHz7n03W73qCPPfD63q6sru7y8tPl8btPptNZtN0hfRpD+DKhlZuErZ9kZIAfIiN9C7IKrz+QFsE0V20BmLvRhd5szA2b3JFWFPycEemPAzzwuJS9GJxa2/BqicHkzEx7W/erqyp48eWJXV1c2m81sPB6He38GgvQF5C5wtiSees+iHmJRJiTHqhq3cpstTv+VSk3Vy+A8ea93f9srVM+Z1VfaeTF7Ln7HawiU7IJzoQx3B9Lj8yYlFvtYpdf2W0+fPk2Pq6urZOlB+lwKMnCPIP2JYLKzkoyLU9tesdXW0loWrbQZhD5y7q+C94GYGdtAwQuUcpwPW9ucB+GlK5XsunafSY9t8rlz5yC4+tg+9x9UC//kyRP76KOP7NmzZ/bs2TO7urqqCXl8LIE8gvQN8BRkvuEFUk0gkVdH78XMapVza8R5clDwxQ3LrQtgOBe+2WzSA3/niYyJ6bncHtG5VyC36eIFNzwZqVrPFYWdTqdWeMNdda+uruzp06f27Nkze/78eSI93HuN58PS5xGkbwC742yBQBxYdHTEzSn4ZnX1Xa2+1/7J6wiDY+JjMzvufsOr3lDthtd46PJVnpB0DHjS06o4nD+35mLSeR4NC3kAJhEU3UynU5vP58nKP3nyJFn5p0+fpvbauD+9FgEFfATpC9D4m4tD2J0fDAa1Zg4aD+dSZmbmEl8J79XPq9uNbbEyrmWuWM7qkZ/3x14Fu/OY9LgHnt45VknPx+N1yWFtIefWg/RXV1dJtQfhOVUXyv1pCNI3gK18VVWpL7vZPeF5kYgngvG2+POcAMduvUd6z9Lz9pj0WNjCpOcHW3zP+rKGoSWwIChes4vPrj3Gxqu+w1giZFC1nokPVx+VeHyPPK9bUMBHkL4BmjfHaxABN6/U7jcK7yLUvHkux6/bVQvf5OJ7rj03q8DkwDE2nz+79Ux4PLObz6o9zo2Ph595Xxo+4dZYKLPlB4p0oNirlQ/ClxGkL4Dde/2cO+Z4cbd+v4SS0Ie/8zNvsykXzi41yK8Cn7fQhVOPENi820Qz6ZR4JU+GJzTOCvDEAmLjmSeZ0n6D9GUE6RvQRHwucjE7raOLV7nm5clLnkNu+zni63LXU7rJqojJMbv20s9ZWp3IdGLhfXmNSZjgTHJvDb2ncwSOEaQ/AVyIw59xCWpTHO9t0xP4ciLguSGDFy6oZuBZXk0NaqpOn5nsnohWSlny8bJgqsT2SO6RPaz8aQjSF6CWg13eJtHuIfC0g6Zte14DXjfVCGjhkJ4Ln7sS21sarMfjHQu/986Fye+91n2GhT8fnYaL9dtfyR8IHkLIt7nPt7GNU7yIkqfiESv3OncMpc9K+3rovlsOd1CC9IHAhwuX9FG6FAi0DEH6QKBlCNIHAi1DkD4QaBmC9IFAyxCkDwRahiB9INAyBOkDgZYhSB8ItAxB+kCgZQjSBwItQ5A+EGgZgvSBQMsQpA8EWoYgfSDQMgTpA4GWIUgfCLQMQfpAoGUI0gcCLUOQPhBoGYL0gUDLEKQPBFqGIH0g0DIE6QOBliFIHwi0DEH6QKBlCNIHAi1DkD4QaBmC9IFAyxCkDwRahiB9INAyBOkDgZYhSB8ItAxB+kCgZQjSBwItQ5A+EGgZgvSBQMsQpA8EWoYgfSDQMgTpA4GWIUgfCLQMQfpAoGUI0gcCLUOQPhBoGYL0gUDLEKQPBFqGIH0g0DIE6QOBliFIHwi0DEH6QKBlCNIHAi1DkD4QaBn6DX/vvJOjCAQC7wxh6QOBliFIHwi0DEH6QKBlCNIHAi1DkD4QaBmC9IFAy/D/1UGZkagtO2kAAAAASUVORK5CYII=\n", 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4bx6IcnJycrCjDW9YqaY39qbbbrc1AaP79DFZoc31+NghlzWxCjOOO6Dz7kX+//C7UYZJ3wEl7cLEZbOVvwctzNChlVpeq/XuIP/d3d1Bdxv8XJjb2fZVCLSxFuV+eZj12J+P9+hjsoLU2BsPx59Op5WW1/vDAo0JiRbaYzU1a339/zG6waRvAR4oNoVVS4NAHGjioBdHrTMycOOMtrBy8I4DayxQcB6QHZtUMulh2kPYAJvNptLyNzc3cXNzE7e3t5WJD38e50ChD44/m80qKwLHw/HVtOeoPd+rY5Dl9F16exxM+g7IiKrz6PB3BMrYlObSUXy21DwDknPpK4ieFeFEROWzMxlBfDbtI/b71XFPPsz66+vrGulB+IioLAkQ/fT0NM7Ozip/ns16jlvw+dga6mralwRDlikxusGkb0GJ8Lyzqza6aPqpTcNzwY3W1LNw0dZZLntlLX9+fl6RH6Y9R+lhtWAN8/k8bm9v4/r6Oq6urg524kWAEIQ/Pz+Ps7OzastrCB+sUwOYbCEp2buQlUt5tYLP5D8eJn0LmBxNZbB4qLNOuaYIfUZ61uow5dll0PLX8XhcJCSPteYCm81mU211zaRHpgA9+hFREyoXFxfVOWDaQ8sPBvtZd7h3GoEHsnoGvW+q4bMiKBP+eJj0LeBAF6fOspRWFrnXPDVbCCC49sZzSk41O1f7ZYRXfx5anqP1PCADkXoE8DhDwOdhK+Li4iLOzs6qAF5EVGtmtyZLqYGwTN6sbDcz6bs2LZn8zehE+j5GR/HgrNfruLu7i6urq7i6uorr6+tadJvr39mE5xw1CKeRevbpdQRWFhCEeczVdrPZrNLs8LPVrIdfrTl5TtGB8LwPXkTUYgVPnjyJi4uLuLi4iNPT08qKgGbmibZ6L5mc2o2nNfoRdZeILRzW8m21/kaOTqTvs+QcDAaxXC7jiy++iOfPn9dy2Pf395XWzDQ7CMTvGgjMRlezhmdScHMLzG0mPAJ36sfrGuDHs/UCAcYmPaL1OA+0/Pn5ecxmsxiN/v/xYV8+I7wW9WSz9vHdLqk47XGwiX8cbN63YLfbxe3tbTx79iyeP38em82m0ohqhmcBPxAJ7/D/OfIPocGprYh6eSo0GhffcJSeTXpoYA7cYQ1a9APhxWW9ICV8eZj18OURtWctn03gYe3Mvf3ZyK5SpWLmGmRmvs367mgk/Xw+P7jxfcBut9/d5Ve/+lX84he/iE8//TTm83lERI3g/JCCyJnpnmlzNrmzklQdXY2iGJC85MOD8Fgj1sJFPlzsg7/jnJoRgJYH6bGWwWBQSyFmQTtodxCfp/FyvX7EYWMOuzeZIMxe/BkjRyPpLy8v48MPP4yPPvqokux9EgDb7TY+//zz+MMf/lCRIqKeNsLn2IzWsVZapMJ181rNp2O0uDWWc+QcpWcfngnEjTus3WHOcx0/BB23zULLswvBFXhM8EwQgoggvRIezxTXMUTsW305iq/+ehPJ+/SMPgStmv6DDz6IDz74ICIiptNp7eF/rEAV3W63i3fffTe+853vxGuvvVaZzaqhIg6j9dzvrhtRqCZklJpnQDZodxARvwexIvaVcEx2FNxwTT3WiPOOx+OIiNqIraZ4AQs7dVlA+kzLwwXRuAPAQVGsLcsE8L3nd6MZjaSHPwssFouvfUEvA5iUl5eX8eWXX0ZEVBo1oh6RBtgvjdjnxUsk19RVRNS64Xg+PbQ8a3clIc4JwqO0FqTnICRnCUBOCDL25dl9KFkTnJHQ6jv15bkrr9Txx9YQMhZwJVj7ayfeQ8p6+4hG0g8Ggzg/P6/+PZ1OaxL5MQMP3GQyqYpYIvadbCArtCMeOk1NcRWamqE6Glsj22wKo9iGG114oi2CdjDpQfjb29u4ubmpEZ43xwAxsT725fVcWdOOFi7pLD0uEeaOPE0nAlq9GBG1tt6s/4EFhonfjtboPWsprsXuC+CXQ0tF7INTbFbjAdWHMGLfkqoRbXYXYP5ylZ1uQ6VbUvGGEHAfWMujnp7rCmDWa1krm+CcGYCG5158jl/olB0+tm60ocIKQke1N7+wxqzJSWMlGgg0cjhl1wIQiTU5P8goQ0UFm5r7HK2P2E+34Yo6nm6jP+tkmqwvHpqVCc8tsii+4epBWCAcOIQfn7XlIiePa4FmR8xAA4MRh6TnHXa4SpDdohLpUQegcZIsK2LCN8OkbwFrDSY8NCGnrpbL5UGZLPuhWc28BreY8Nl20lqjzqW1TEIQHaTn1BxnC0D4LPevHXqcmkSBD6b5sNvAVYTqy89ms1rHH7Q9/s2BQZ20s16vYzgcplWN2phk4pdh0ncETHQmPHLWID1r48lkUhGMzXpOwTHhm7aQ5oAfjocHP2JfS49CG07LcSERm956TRy4y4KE7EIgZsB5/yz9p5tmshDB8UrpNq3M04ae0WhUcy10OrBJX4ZJ3wJoLH6AWSOC9GjMwWc0Oq5NMiA5fGbV7k1kZ+uBm2cw/YYbg0obVWiHHoQYXuy64Dw4P1sTeKFJB2Y4WzQci4BLgeBcWyEN7h8+D9Ivl8tKeHJdhEnfDpO+A/ghVh91Op1WUWU82DoeKvt+5rtn2j2LZrMZzFNzeYotk5CDr5pGU7Jzyyy35OLF7bhs3rMvrzUGvC02sh04bheA+Nk91SCizg80DmHSt0Cj2xpcg/aKiCrFx0TjRhPWrvwzj5TOtLsWvDDh2Y/ngFo2S4/jDVpTz400MOtxTcjaaDsuxwtASL7ebCstrerUSLtW2eF6NcKPa9Jx3VwnYOQw6TuAic/kgWZmE19r6JnQ+kKgi3P1Gqhjrc6pMpjzrHF1rh1rRS6JHY/HtXp6tMxCy7MfjziAVvhpgBA+twq5UiddU16d6xu0rp8FH0p7remPg0n/QGj3WJauY8Iz8bUoR9tD9eHm0t6MeGxma0Uc6gNwHgQiYco/ffo0njx5UpX3aqCNO/TUokBlH/x4vDKXhTMATHgdPsKCQwUDm/e4vtJWX0YZJn0H8MOnVWAAkzersGPzXZtqSufk1tssHaeltSA9E15TiJPJpKjleRKOTvnRkdwgGRfYMMmZ7FzUoyk5TYlqIw9reBYSOC9reWv6bjDpO4C1U1YZxloqGxKhk2JKUWs+h1a8oeDm5uYmrq+va5NrkSNnX3c8Hsd2ux9qyV1z3CYLDc9tudxAA2HDL54LyOdj4cKFPyArrrGUU8f3MXaL6wm4RgDXlZFeBYlxCJP+CLD25Qd3s9lUgS82T5nsGeFZg7Ew4Rr6bHgliM8z7bgKDtBmF43UZ2W2WIO6Ehos0wpDFi5M+IioWQNMet2eC/dPXRIcgwUtjj2bzYpTh4wcJn0LMpJmI6+48YW/p9+PyDfOUGGipOPdZ9BAgzy8Zgo4Wp61yGrXHAja5r9DuOj5OH6h8Q0ck68Z16nmOAQmhIRaQ3yP8FlX4h0Pk74FqKLjvDU0PhN+tVodpMUQ4OJos5rwXEsOEvB03FLQjodyRkSNcNyhx91yOlJLW3K5sk+zAhylj6gTXgOaEH7cJRdxqK35pYE8jdxnmt6NNg9DK+m5/xu15H0AHiTWkijEwd9BVCY7v9S85SAWm/BMdJ52w3vaablrVgzDFXAZ2XkXW9bu3KGnm2ZqVgDn4xSmpuTYTSkR9xiiqgsE0rP1EOHx113hIRot2Gw28eTJk3j69Gmcn5/XRkWB9NnDhgeUo9es3UubZ+DfHMTT33GjCtcKMNl14k2m3bMOPd0eW4dm8rVq7IItISY8R9yZ6OoKMUrBU47qAyqEWFEZhzhqiAaCJo8dJycnVVHKW2+9FW+//Xa8/vrrFeF5VxceH8YPKirzOD/NhNeNKTkKzdFo7iDjh5479kBqDtTxBpPsu7PpzZN5saZshp72yDPRdWwYa3j24ZnAgJIU/nwpU6LXr8fQugcjRyPpz87O4sc//nH84Ac/qBVt9AWbzabaF56j0RH78lCeec+BJpj9nPdWwoNY2CwShGdfXyPSrN2Z7Kenp1W+nefn8YgrdTGgkVUI6Qw99eO10IhTcwiysZDTIhyOA7B7kFUf8lpVu/M6tNTXpC+jkfSvv/56/OhHP4r33nvvj7WelwJIF0VEfPLJJ/HRRx/Fs2fPajXorHnZRwfh+eHLKuq0OUZ7wtlXjagHuIbDYc2M5/3lsPsMb1HNvQFazpsJId21ByTNqu1UmETUJ+NqsC1iH3Tc7XbV2nRIJn8nsxCU8FoBaNKX0arp+45vf/vbFQGur69js9kc7AgDDQQScWCLi0qa9pxngkTsy0wj6rPoR6P9dtEgO7aaKuXecTyOKfA6SnvzIW6QNdFoeW1E1Mp2IfxY0ETsR1+pMMHkIa4TyKL7OB8EkE7lsaZvh1N2LRgMBjGbzeLp06dVBRhH7jn6zQ+xBu9gvmc71JZaYDVAxVNxlfC8EQVX12ENGeGzPLyOu4LVkw3rzFwGtnb0unAd+BnHB6FBbs4mKOm5h0Bn72Fd1vTN8AaWBXBBzWg0iouLi0o7oXglImpaibUZvqtEyHan1QEXIAT8dw7W8XbUXE6LgR4Z4dm1ANGzZp2MXBBk2lKcuQ1cr6+uCvvwuEaN2mu/QSYIcU9Aep30Y9K3wxtYtgABs/Pz84qo/GDhQV0ul8XmEPX3mRCaDVF/FYTXvHs21or9Ytbu2nePRh28c/MMrlnz8DDnucZeffBsXp0KMy3XZVOcXRC2oFQQZfcEgz9s3rfD5n0LoFkyvzGiXnTCkXztJss2ruRCJ9aEXErLE3o4BcejsNnM5lw4B+u4wKc0AIOvN+IwlqA19Ryhh0BjX5xdHhYaPE2H72PE3hXRSD4+ByEMLa+1CA7ktcOkbwHIqBNg2FSNqI+wYrKz6csPM46Lc4C4PFZKA1U8TJOLbDhyDstBU4S8aSUEAA+eiKhP19HSWpyPhUsT4TlQl03+1Uk6bMrzfcL9wfq4eSjbrTebbWDUYdK3gB9+nWHHD5dqWjavNSbCwkJLd5n07LuWTHiY79vtNi0RzlpjuegG2jgiDq6PsxBAF8LzfeOmHwgt1sgR+9r8Uok3Cw8WgtkOPNby7TDpO0BNb9aA2jKrBTxZ5Rj/LRMq3JPOufYsOIdAY6kZSHeg4a2zuX0V16CTfdSayDrlMsJzAZFukcXCCwIqIqoKRm3kwTpxX7goSQN4pf8LYw+TvgM0FZeRH/+GVucAHXfYsSmvFW7QrmpVaOEKjrler4utrNzQo5V+HLDDe2bN4JiZu8Jlwkp4Nud5h10e981mPYKg2rykLhBbC0p41vImezNM+hawpsY7Ex7aR1N2g8F+l1U179ly0D507VjjtFjEXhuCIPyQs3uhhTJc1cZCRzMFTHiuCsR1QICwlufr5i4/TOnJttXGMXnDjvF4HMvlsnYvuLFINwdhwlvDd4dJfwTY5M3qz9GIw5ZB1lFWCpRpMU5EfUQ0N/dgPQzuQOPmHxY66rPr9llKeE69cdZBR1OBmCggQkkwCoeg6XG/2FLhTMN4PI71el3Nx9dj6/72KqhM+HaY9B3ARFYtjYdRu7+Gw+FB+on/pm4CP7TsuyuBS+2lul5AtXpWs541qrCLwH48V9rBkmF/mwmPKbtPnjypioeg5WE1sF+/XC6r7cCg4XF8aHr47ib7w2HSH4nMvB+NRml9ORNeO8Qi6uRnwjOps8YV1rDqUqhQgpbMmlOyxpmIuiuBd+78Y2GgkXUlPTQ9NwDhmLAooNlBeGwJznEQXbtmT3Af+lg9eixM+iOgGl+1J/u/TV1iJf9TA3Xsk/O7ChPNAuAcrCF5Ky3dO4+j9GzSYx1Z5xvfEzXrQXouE+byYL7O7XZ7IIQ42Mg1DVkFX+lllGHSPxBKfBCM/4becvXvGdkDqgE51q5MPl4LpwM5xsBTc7iajwkfsR9ggTXrOkpVhBAqvKkniJ719cMKwjqz7MVoNKqyH1lpMJBZQUp+m/2HMOkfAI7kazqPW03x8LLJHnG4RZM+mFlOPLMeWAvyGpiI2oWmhSwgEo7ZlAng62fXAcIlm8eXCRgcgwUmpytZgCqY6NrYU8qWGHWY9A9AloLDOzQ6PjccDmv+K6DmPkPTZHpODvZpJoHTWkx43RJby4iV7OpG8FpRLKO5c63KNcsAAAjFSURBVC4X1jr4Jo1bCpTib+xiwIJaLpdVio8bfUz8dpj0R6DJX4Q252AcCMIPNOe+Af03zqXHZ1NcG2SyPDb78Fl6i8+f9QmUmoKYnNkQi+wcfO9ASj4vX69+FmvAfeXgXraPXXY/jT1M+g5QspcCRqqtNptN0ZfHcfT7/LNaADguuwbsB3OdPpM9q9rjNXArK5NHNfxoNKqsl4g46JjToKC6CLgeNs0zv5zjCFlhEfv50+k03bHWmr4Mk/4INAXi1ERlU1//zVYBfx/gPD0Hu0qFPl1mxWnEnNevtfTZxBvUHWgajS0ItmRwPA4uatEPl/Lq8I2mMl+QW0lvv74bTPoHoCk1lGlo/h7/jQNnpUacpvNxMIyJr2ktJrtG59XMZuLhfNxToAHEUt3/crms5eG5FZiLcXh8GM/+R3cgB+pwzWxpcBMRt/UaZZj0R6LJpGcNjr8pNI1XOlbXVJPW8HMZbYnsfC1ZxR8TR7MSHE/gNGFGdpxXR1jhHPDFQXgd0qljuCFsINgmk8nBIM/s/8iow6Q/AhogKqXuADXd8RklVna8LuWlpYg3p/xYQzbVCGQmP8cNcAxdNwfaOPUHIZBlC7ikF2Sfz+cHff+s6dmnR1zBJv3DYNIfgSzFlfnw+CxH80EYfAd/b6rUa7IYsjUxWRnsGqibkF1HBr5GJv5ut6tpY5Qkwx/noSBqheg+ACA+BnXyHnq4T1zgo9fR5X4ZJv3R0Ag73tW019/zv7lRJbMelHylGAFDyay5/iznjvVwUw4Lnkx4lI6HAFtWM5AF+1jT8wivkmmPc+g5tcBHU4XGIUz6B0C1e8lHz7QqCwr0xjel7toIr0KDCanVe5r6UqJn47H4mBzs0+IdvgcgOZv4EAYc3EMMIDPrMZIb58gaiEpjuU38Zpj0LwAlszjTlvp3NfEVbYUmarZzvCCrrNPcuwbqEB/QCD/n0ktlryA9iM4CAiXJrOmx8Ua2zRf+XkpPZgNDOWBo0pdh0h8B9bfbfOAsh49/ZxV8QFtAqlQopBpdSc/VdVgHtCM+w/n4iMMpv22kx/czoFiJA3lKeB7rxSlDbiBCjT+aerjEGJ816csw6b8mKDGP+R5r77ZjaP48EwL4mb/DGhf1Axzt589CY2dVcvg7uwXsurDQ4OClFuBwPl4FCTQ7b9jJW3mhyYdnAxhlmPQd0aY5StV6+upyjIz4TWZ+U5EQp92Y0PwdaG2+Rj4vSMoCgNcHkqs7o5YQ/pZ1yXFqjstsuYsPbbsY0IHJPOjXV/Pe2j6HSd8BTQ+PatbS31TzMrFVC+vnu66PCZ6Rj9OGJYHE59ZinezzGgzUqbpsbnP0XWvueV3Q1Oy78zSe1157rTaVR817B/KaYdJ/BShRVJsrsUsaX9N6Xd0BLZzh88LkhmkNkiG1hs9x0C/T6kxSBZOZt63S7r7Mxy7dMy6+weAPmPQgPJMePj0T3qRvhkl/JJqKQDLSl8xyDeDpz+x36zFAdC3mUag5DhM6Yj9Zl4N0GqDrktvPiI5e+ul0WhvhhbVkvfwswBClh4bHcE2QXQmfzb43yjDpj4BG3zWKz8RlUzti31yjGplbb/nF5i6/6xo4387HZzMdRAbZmPicN0dEnb+j18+TbUBM7eOH5s1298X1ogmHh4qy1cLbcoPwFxcX8fTp04Ox2rqRp/35Zpj0HcHE5tw2HtqIqD3UQFvUnX9u8/2xjojD4prMrNU6+NVqFcPhMJbLZXU+/L0p1YbzsgmvJOdtq3QTCpwLxTh8rbg2nj8AX77Jj8fmlarlbdq3w6TvANXmIPtkMqkIj4dWNXPpeIpSfCCLAbBmz0gPzQqiwaxGG2obQdAHrwU7uO5sb3glvZraWp3Ha8YEXGj7bH4+0nScm9eiHM/B7waTviOU7LPZrJZa0s65jKhN7/hOW3AQ31HCq4nPVgOXvKJ3nYdtjMfjqtYdxTGaOmPfHRNuYV5nJMw21cSwC3YHZrNZ1S2HcynpeZS2Cheu7XcQrxtM+hYwuUB4HuqA8tK29k4meVswEO8qBPQ4SnZ94CGEeBoN72LLZa86jIJr6rmJhrW67g+f7S2HtWeCB+84HwcG+fgZ2bMpQfbpu8Gk7wBoeWxUyW2e0FJZbTsjI3tT1F3JXsq7a9SboT59iXT4HXe1cSaBLRydsJvN0temHY0rQPjoPD7NBsCiyM7BnXtcE2BN3w6TvgUgFXaw4YcTPj2TJCO9Ej37mdFE+Ox4bSk7NvFZ4/P46GyEdBbDAPFKG2eUdqHh2ILOq8+sCh66ySY8a3U+n9tru8OkbwEIBQ0Usc8j80y2NoLysfj3TQ9nKQvQdkx8PkvbsdbXYZRZmo5JryY1fgftnpXA6hp0LaX+eHYplOjZDAANZBplmPQdwD3vTPimKjuGkrT0OyA7VpswyaDE1847JV8Wk+D0ZPbKCKfCJ+JwmEdW6VdKi5aClW3/NnIMWko+ez90LCtH5Xd8pg3ZQ9j1wWwLDrZ9NwsOZrUA/DkVTkymrDBJiYYMgq5D18LXV3JbsnO0/WzSR0REehNM+o7oUnDzsqOLBdEEJdJXIVbbeY8VkiZ5CpPeMHqGlPSeNmAYPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPYNJbxg9g0lvGD2DSW8YPcOo5e+DP8oqDMP4o8Ga3jB6BpPeMHoGk94wegaT3jB6BpPeMHoGk94weob/AxDF8fIwTIv1AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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LKGMCZ957Bh8DEnDjC5aKrHKzcw2Egteds+6wVdXZ2VlN/JOTk4bqjXlgXGxcgY0wb25u6hc2tIBqD2KyNoHFBTvk8n1i1R7jZbF4SHcQme+XSvs24pvw/WDSd4Alc0SzE6zWtqv0yqS8EgIvSHDtPwebWqvoIN1BeGxQeXZ2Fufn541NKtXWxhywqNze3sb19XVcXV3V215DveeaedTkM+nPzs4a21lnkQfeHoulN98vldq6eHLxjmoGWa89owyTvgfYC63JMiVPenYOlrBMCJCe31nN58UC54earYR/8uRJTXq250EMSG0sKIvFoiHdr66uatLzXnVsQkC6P3nypJbyOD93wFGbPqJZcYfz6iuL56s2oITnvxvtMOk7AMKrJx2hLA6pKenZvtXwVYnsWh9fctyxl55JqKRnKc817VDrZ7NZXF1dxdXVVbx69Squr69rW56bZMCO57HOz8/j5OSkdu5xVqAulFoM1CfGnjnrmPSaDFRKbTaaMOk7ADWYQ1mIXyvx286hSTclsvOWy5l0R3huMpnUKvb5+XnjBVse8XMsPmzHL5fLmM/ntVp/eXkZr169itls1nDgRUTDZ3B2dhYXFxf1wjKdTmM0GtXnRW18ph2xx75P1mK2iDLBVerbru8Hk74Du92ulopoF8Vto3ib6gwsqVmCq83Oaj2HtyL289zZplbCQ8LDsZYRfrVaxXw+r9X5y8vLuLq6itlsFrPZrHbgwWEIjYLNh4uLizg9Pa2bdbCvAfeNHZ/4DhahzGOP49pCoBnx7cE/DL1I30cVe2zAg7Ner+P29rah/t7c3NQhrSx2znaphuVKXnr2D7A6zPFrtuHbJPx0Oq1DdBGxR/jb29u4ubmJ6+vrePXq1Z4DD3PhhB84CJ88eRJPnz6N8/PzWsrvdrta68mSkIBMOuN+8WKRhUL1e6UKPRO+G71IP+QbWVVVLJfL+OMf/xgfffRR7em+vb1tbNWcSXp+gDVXXomuNjzGzpx2CJUp4eFUQ6ptxH20ASr97e1tzGazuL6+rknPITr21kOrAOEvLi7i4uIinjx5EmdnZ3VcnsNxAF8HwIsXt9/WMF3mCyj9b0pEH/Iz2wWr9x3Ybrcxm83i+fPn8dFHH9WqPuLnqtYCGsfXMBZLftYEVKVH5hsn3rCanRGenXac4MOEh3S/urqq/RTL5bK+FozNpAfx2YEXEbFarRqSGtedpQlnO+rosdk9cYus14dW0s/n87Qk8rEDtmxExIcffhg//elP47e//W3MZrNaTeYHW+PLmceeic8/q2ce50CqK4inhFcvvUp4Ta+FDQ87Hjb8fD6vFzFIa1broVFAymNMhAHhoMN9Y5VcIw5Zz30tw+UCIF4EoX10PYuZWWE00Ur6y8vL+MlPfhI/+9nP6ljsUG4mpOWLFy/ixYsXsVgsivFlTSzJUnW59l0XUtxXVeeZ8GzDQ71uIzzb78i0g0qPFyQ8e92hKSA6wOE5aBXwGajtrYU1uE+8iMBE0ao/Ppf6N3BP2MFZeuH+G2V0Svr3338/3n///YiImE6ne9sPPUbgAdvtdvGVr3wl3n333Tg/P6/VWS4hxcPPcWL2VmusXckAsIMLZGAbHiTnbDsmPBYcDcmxOq8SnjPuIu43noA5gQVGIwO4fhyHCEdGVr5nXJzDCxVrR+rzADjXQB2euhAY7Wgl/W63i9lsVv++XC7/7BN6E8DOI8SvN5tNHQbjOnJ+yPi47AEspYxCwrKDC4TnhJiskAZSlEOCrM4j6gDCs4SHSh9x33xDY/JMeEh4hOl4TDgLOZrBWgsWErx4seJIh0Y32F/CTkPNamQNw8RvRyvpq6qK8/Pz+vfpdFqv7o8deIAmk0ksl8taooNovJkESzSoqazu89/4GCZa1sRSC1u0Np5VY0g/VukRg7+8vKxVesThWcJrea4SXrUKaEIgunbZ4dRdJbyW+kbsFzJlffxx70aj0V5zEa1YNPHb0em9Z+nFvdWHAjjD8LAjTMWedW0cASIiz5yh/em41ZT2tGPJyGTnMdl/gFx6ZNqB8JeXl3thOS2AUR8CzAlO59VFhqv/SrX3WufPDTrZeYd7xzkFnLoL8rN0z1qIaWKTsQ+H7DrAmWURsWdzq5qK7+PYiObmExqn1kYUSnree0772yn5NLX21atXdRz++vq6Dsup+q2Zfuw74GQfHVM3w8hU+6yxB7QlvVcqrTkcenR0FOv1up5rlgZtwveDSd8BfojY5oWtDTLy91lyMbFYhc7aTem2UyzVNbyloUDE4TnTDll2kPCLxaJWyyOi4UfIJLzG/nF92loLiwlLZyxwWesubbbBDtDMSYfzRdznHYzH43TR0eInYx8mfQ+w3c4ZcXCoseRlScWqMwiQdanNyK6NJtkByISA444JjxfSheG4Y3ub58R2PPIAuBY/i/1zLQInKyGeXlVV417hZ64JQJgQ4JCbZjmyn4C1iIz0lvbtMOk7wLa35r1zlVlEs1gkIt/yKZPwkKa6+wxvBInzs9NLM+1AetTEI/GGN8TgeWEe3G0HOQBZ0Q5UaSW8SnmE/diBB62B8z00dTcDk1fNrMViEdPptEF8k74bJn0H2nLfmfSamKPeeXXY6T7xSvYsUy0jvNrwIDy89KjiY4Jxam+Wccftr2BK7Ha7hlZRkvIRUQzRaZpwKXdeP+OFVIuRptNpvfBo81Ajh0nfAU6JZWnPdiqXi2appyrlOdbPf9emEBHRUHMh6aFeoxSWO99w4g0Ij0VIvemc0suFNGp3g9AcHcCLw3+73a4R1ci0GdaK+Br5XnPfAC1Njri372FCsLZh0nfDpO8Ak0WTZ9jDztKeE26U9JqEUyo+AfiBZ3Ue2XacaYc4fKZuY37shESZ7LNnzxqdcJTwqlkw4Xn7ag1lgviqwZQy6LT0Nkt2YmcfVPxM4zDpyzDpewBEzKQ3S2yVUlpCyl54JgE76thppuExPOBMdjTAyNpc8ZzZEcmttZ49exZPnz5N+93BLGC1nglfSvJh0mdhRiYvq+tsEvHCwBl4OAb37vj42I68A2HS9wR7jbMceVbPVTPg3yFxtc0ToOW4SH7hNtVKeOTTsw0f0SS8ludCnQfh1T+hhAfJuWuQJvmolNdtrkDurFkIwPeXbX4uXMJxuC5NDjLp22HSHwglPz/sSnBVa9VejyhvpKESvlQ8g9Cc5tLzAgNThBNv4LTj8BwkcrbgQIVG8xCMpbkImTOSE21gMmQZdJqezBEHLcbBPVQpz63L2G9g3MOk7wBL4uwBUju0Tbqrh19fWkjCW03BYQeig+ycSw/pB8ciJ96gao4r57Q0l+149h9g0cE4mnmXLXa4Lxx1iIialFn+fLagqkmg1Xeo/GTV3pK+HSZ9D7AjTIlfqrHHw6uqO971xeo0HmDebgreeTjruAGGOtI0Ts6bYOhGGLDhQdAs+Ybj8Vr9hvF0gcN1MuGZuNoyDH/X3gJ8nJbcHh0dpSm4Jnw7TPoOwFmkzrqIchNHzSjjGDOnzmbNMVnKwo6G8wxkz0gYcZ9Wy1l2IDlIz9lxWS89DcupDc+qOPswdJErEV67B5WIqlqRltxG3BeAuaz2MHSSntM/J5PJIEprkSK62+0aPeIQzmLHFBM4U+E1m46JzQUjuokG7xHP0pZj0iAVmxWw3ZnwrMpn6nybl342mzVsZlbpI5oee1bHmfCZRqMqeClUCcLrPCOitvuz/59RhptodGC73cazZ8/qWDYkZETUJM6Sadgc0NAbF4rAEcVk5+/xxhp44eGPuG9+UdrPLtviilNr2cbW3Wu19l6z6Dj8CPLzOXH/uDZePfasObH9nhFeq+h00chMKmMfBzXRODk5ad3J5bEAtuJut4t33nknvvSlL8UXvvCFvSIUPJQR9w83h6k01KSVaSy5VerzJhjq6d5ut6l0Z5v94uJir48e5s32O3fbAeE52YUdZLg3WiKsdnxENDzpGCOLybPHH2CtQAnPC4Y6/jSj0eTP0Ur6s7Oz+N73vhff+ta3agkxJJtps9nUG1rg+vmhQqYa17RrVRw75zTWreo6O6VKrbEj8hJfeOW1px23pmJfBIe/dMsuXoiU8LDlNQ6P2D4TtS00x1V+ABfi4Fwlu1/DpVzDYMK3o5X0b731VnznO9+J995779OazxsDPDS/+tWv4oMPPog//elPdVNQJmSmqqodzzY6S1LOamNyqIqL+bBk1Lg759DD/8D1/iAUO9HUYYiX2vA8vtb4Zx18tLONJsxAKoOgnHLLCwYvfqrKs8NS+w9w/b+xj05JP3S8++67sd1u4xe/+EXc3NzEdrttZL4xUaF2R9yH8jK1nr3v6GSDcyk0XIgHnLemBtm5So776LHqzXn0mAsvRNohl1VoJhWq8Ph62W/BZgovjqwFQXOEr4DPw84+Jj3MADZttJ0YmxvGPhyy6wAquZ4+fRpV9fEWVyDOdrutVWANG7HNnNny7LBTrYETfkA65M1zd1yQHKm02rWWC2eyHH4unkFYTgtWNOOQia/mAhOdia9qPWsGOJ6dfJiDOu5wfEQ07gNeWUtwYx/ewLIAVtEnk0mcn5/XEmm1WjXSVbmMVcN4sJlZxdfWTkqy7fZ+d1eQjUt5eVsrzp3n9lYgB6vbnOGXVctlmXasSsOPoAU0WATht+DQoza+YE+9ZjqyVNfcB8xnt9s1FkBu7cWSnkPNRhPewLIDeNBPT09r8rCNzOE13W4axNZQHNvvmVRlm1cJzxtXamZdlmwDQjLhs2o5bbaRZdrpVlSs0TDR+V6AuFpolGXwae0BL0A8p/F4XMxDsHrfDav3HcBDhrZMGqJie1z3mud3ECMjOpNBS1PhoOJuPVBl1VEXEQ0TQ3P4tXAGn7EKDjs7qyZkCaqhSFXpNbyoVYmZ040lfOa4i7jvlsP5CLgf2h7cyGHSd0A9xUr6iGa/dl4E2PZXokNaItGHVfmsUQe0DU2y4TAXl7qqPwFSXhN+2IZn1VulsYYhM8Jz9R38Exya4xZa0Ey4fRYTnTMaWQPCAsx9/RCt4EpBk74Mk74DbQTgB0uTU7K4Mtu2pVAczAnYrboBRpZRh595Dqxya/YfS2OuvY+IxjUq2XFutuF1dxv8ncnKPfl4wwu+DsT5eSw+B+YG1V61Hl0ITfoyTPoeYFWXXxyr5s9YTR6PxzUxQfY2wmvfPG2cCYB40DI05MWkz7adYntZr4mJk1W6lbz0aoOjWEl7CmIh43OvVqsYjUZ7NQxZBIM3zjDhD4dJ3wH1MKvU53CWZs7xw5/ZqUo0rkvXphxMEK4uy4ipFXuaEssxbx2fJTzAlWycXZjtLoN7pNIdEpnDapgz5+WjlgHn4OIaNg+yff2yxcrYh0nfA+o9ZinPG1hwQsvR0cebPKISTKvB2ETg/nmsNagZAXJlUljDXFgYuCpNbWT1IbCUx/fZIaeRCryzSs+E52gDbw7ClYoIeWIsXojYZAHpSx12M7Kb+DlM+gOg6jir4FrmqgUyJbWeSc8OK3UU8jnwe/Z3zg7Mxqyqam+nHfyshGdTgNX6bO86jAEHpHrX4XTT3vfsk8B2VZPJpL4+LKTZnEumiNEOk74n8FCpWq4PKRxSo9FoL1mHz6WSXB9eEImlN0cIskSWiP1y04jmTrm6fRZIqH0CWF3HAqA9AEoqPacIoy6Ai38gxbGQ4PyTyWSvmw5nJ5bIbhwGk/5AZKRnex6k5+QYlNyW7H0mO6eoagiQz1tqRoFzc4iLQ46ap65hLraxIyJV67kYJqK5JTWr9KgLgMTPmm+yxFf/A+5lRNT3WU0eLU7SlxeFfZj0B6KUTMNdZfjBRHIKS2UgU+OzPHQtUYV0VF8Bj43zIwTIXnTeV05DgCy9ORKQLTg8BrQGrvzjJpy6Nx78DpvNphGhQBkznx/vSvZMA1LfibEPk/4TQKV+VqPAHnZ+IDlbjSWS2udKevYVsMmgoS32nutW0Rzm0io8zEU1DS1zVW2C23RpTz4O1YHEWCThD1GnIv4ObYIJnxGdNZ9M+zHuYdIfiNLDxLYnS2zY9hz6Y7KqQw6fMeH4dz43j60+hiw+ziGzrDCHx89MlFJGoYbmuOpNFxhOTML94flzCBE5+/z9LF8g64hrlHlAt88AAAkUSURBVGHSHwB++Bgcw2cVH5KcSc7E0nPx+ZngrOoiQsDEY0eX2u4q4bNQF4/L0rLkhGSNAnY8JD3CcppBqI43Po9qTHxtfK94gRyPx635/iZ+GSZ9T6i0YWmbLQIs9ZlYGnpTO1/Baaj4XRcCtquRm67NJbiNNxNQbeM2JyEX42BsTZYp7e2n9zK7h7oA8v3R7y2Xy3pLq6yOwCjDpD8A2UOphFUJhs/4XY9ntZnPE9EMt/F40CLYttb2USzVtUU1Lz7qPde8fIzB9j6r9jqOxvt53uytzxYZdRxiPuyNh2efSc8+B0v6dpj0PVCSQCUo8VUl5oQUfB9QFVi1CfX+Z3UAbQTk0CI+Uxs5S9flakCMzckymi7M0QeMz9fJhM4clZwXoJ10OYzHG2myTW/Sl2HS90TJ9gZYpWevPFRzde7xcUxCzS7jHHg+Bu9d6bwRzd1mtJKNSa/NOTE+S3kmv9YLcNgPpOXPOIyJsl8tCspeTGg2mY6Pj/di+5by3TDpPwHUKcWEh+cZyB5ETsRhoqszS80DTebJcgeYbHhvMzO0HwA7JLmBBc9Xyc5FPhpe00YfiNOjvh8tu/SdpTjIjozHrL22Cd8Nk/4BUEmsxMPnXQ66UnINv2e55awul/wGOnYXGTLHGWsrCDWqdoOxIMlXq1VDe4A01qw/mBPaqFO7BbNXnrUkbReuczLKMOk/ATKyR+x3fVFyVlVV27pKTiV7lqbL5+MxeWxWdTNveZuTUcHXBntaNQR2COLFGXalZpq6H4BuuAFTga+PMxz1/1C6P8Y9TPoeyEirKrVKUia+ngvv6jDD51l5bUnVZyJrbF0z1ZT0fA36c5tmoItJRDRsduTqay88dlJC0ut+ALylFtqNs+ef588LJJsaJnw7TPoDkNnwSh5+KNuIw+m5JTJ2zUNVckjELF++RHp2ApYWryxDULMDed7oGAxiI+ee7X+16VXCQ8pr2m9Es5EJLyyc1Wfil2HSH4iSGplJ4Ih71VhVbOSXl2x/jUmX8vTZftY4N79npGfCs+2uc1YnX5aay+fUmPl2u00r+TabTboJCNqFcxET9xtArr822dQ24EYOk74nVPoyydmrrceU1Gm2TXWhYHUdDjQlPKAqvZbicmcbVpXZbADhMR7PJ9MgtBYAgJTFGKvVqj4Prhe/tzXuVDse8y3l+XNBjyV9N0z6A1FSvbPY/SHhI5baEfse+ozwgNrxei5W0fV3EL9EeNUi1HGncwThsFBxqJBDdlyjr6E5Le7hDkXcCRfdcFEqbNL3g0n/CcEhLpb2WQistDDo9yLuveSsGWT2vo7LyDSTbJ5ZeE8JD6LyOXBeVr9L4PRh2POc8qsJNjg3CA/pXqrVR4MOduaZ+DlM+gcgk+ZKWrXhsxCaSt5sUYiIWlqq41CRRRY0QSgL40XEnoaghM8KWdhzjt9L1XKs9vMiop10I/Z78EN95/Zb2LwT21lp5aBRhkn/QChxukiv8e0u1V8dZDi/pvDiPQsrsmqt52YiMwkz212TiLjuHeOB7NpskxcAzCm7d6w5cHMOqPS6JTd+1225NbRp7MOkfw3IHt7SZxF52i7+DqJm8f2s1pylGrzk6tRjjzy+h4VEHX/q8ddcdpgak8mk/p1VcCYgSI+5cqafXpsuItyNByo9CK879epmF7bp22HSPwBZ2K5N5YY9DqmqpGdyobgl8/qzRC2p+izF8UKe+tHRUd1jnh2DHOrTSjclPKvr3KEHKjaIXyr6wc8IvbHkxznxjn5+THiW9Kza24nXHyb9AcjsZS07zSR7m4OvlCrbh/QZ8VVd55JZaAogvqrt2fh87SAVE11DZ9m22RH323Cxww9jQFuAVsJNNuG0gw0PKY/+ezyee+D3g0nfAxnZuYFERNS98NgxpigtBiXnGsaOaPbg6yI91HYQDfHwrG982zVz1ADSFxtIcgNMDp2pqo17AQ1itVo1GmBibqx9YFGBlOfe+Ux49NLPOgIZZZj0PaBkx0MJYkNKtXngGaWQXVvoraTeq2TjhYSlPCfB6C4x+JmbViA8h/F5kWMJzMTH37LiGt1Qk3fJgf8A91Lj8eyl151qLekPh0nfASYbP/jInUc6bVbm2QeZ9Od39shnhTGZZGPSZ5lvp6enjSKX0s62nLzD9jtL4CxBhttqs7OwbRdd2PTcbJM3voQWwc09tXc/O/KMMkz6HmApP51OazUURFAvNzvJGG3Sp01DyEJzeLA5cw/HQ2qycy5LeeVcd+5iowkyIKKmv7LUBQEzs4EjBNAkuOMNO/J4LGgO3HCTt+HSXvlZNaKxD5O+Ayzlmdh4ONUZlx2f/VxCSVPI4vBt59N8fCU+b0TJUpeLcyLyHWy0tbZ2wG2LJmSbduhY3HuPTRHdXRffZaeqSd8Nk74DbM9PJpPGIgAp36XWH0r8iHwjSv5Z3/VY9s7DoafNKNne5xAfO9XUlwGCazy+lBiTZfhl5b6Zo1QJnm2Mkb1M+HaY9D2gaaWoSNMiFkXp4fukD2WWuAO0hQa1/BZSPauRx3mh3rNkVUK2EU4jFKwZ8b3LnJWabJM5L7PPLOnbUXU4ngbfZTB7ULu87X1RktKHfL8Nmh+g+fVt+QE8ZkYwJR7PT+Pw2UJUulYlrpK4zcwx6feQ3gSTvic0zPZ5g865dA0Z6RUPMVe6xm0bo89YJnkKk94wBoaU9A5oGsbAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAYNIbxsBg0hvGwGDSG8bAMO74e/WpzMIwjE8NlvSGMTCY9IYxMJj0hjEwmPSGMTCY9IYxMJj0hjEw/DddA1taYCNInAAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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fTXjiK16fkt0WVUKM7WHSb4mUe56aYMNWil14kBxERz2cG2pQIoMQRkmfsvAgEW9q0el0cm49ynScsde6PGvsdZ4/z86Hld/f3888B7wv7oW202pLrQ4Fxf3F61Al4AUt5TEY28Gk3xBq1VNjo9ia4W+0PVaz8lySw8Hx/O3t7dLEWwUr/VA+Y8IfHBxkM+2hoItYuNGqr4eXgQQePBjt0oN2H2OzU59bOwBZVcfjuDXUYKj2wJb+cTDpt0QqAQfLzYm8iHxGnLvl4Dpz55zW47VnXie/RuTdeh6OAUIeHBxkhGe3Htf28PCQXZcq73QDD4h8kCfg1lwo+2DlU54NJwIj8qU6ljGr+i7VQOSY/nEw6beAqubYTec95NiSpggPwQsTX8dJM2FSwyfY2jHhsQstu/WIuZXwUN5hbzzeRAOkR0KNd7vlhh14D5hJD4GPjtXich/3+HPDDgQ9Wrrjz2y3/vEw6bdAqgOOLTU/oJwg45o7q9uU8Nwqy/PoOWfAABnZCqfc+na7nRGe422uFlxcXGQHduxB6yzcd+3QY9cen5lbg7EogvRcgkvF9RHfWXPOj3BCsEjZaGwHk34NUjX11GAJtJoCbElBLrxeXXomBtzgVWSPWJTmOLnG9fjDw8Oc8g61c2Tikbi7vr6Oy8vLOD8/j/Pz87i8vMzyCKyvL7LyCBnwedW1B/lT2fsi9x5fMfkH/wcu270amPQbQHXpmvS6vLzMXGENAbQ9lhtnuHlGya7xO4OJgsRat9vNVHf9fj+rybNYhs+FPvnBYBDn5+dxcXERl5eX2aIEkiIxqKRPafcj8gpD9VZS/QlKek72YTFBGdSEfzVYSfpUprgM0EYPkBg7x8I6wh3WbaJZ7MK7yaAEhqy4Ns9wdj7lykekCQ+ZLXag5Ww9utq4Mw+JO8Tx5+fnud13EcejDAfCcwKPBT5YILRMxzPx8BlUnaekB1iQA2uf0t2b/NtjJemLLM2mYAvAP+u/v8pzpH4fsd1nUbLDYoMoZ2dn8fLly3j58mWcnZ3F9fV1jvSateeYvWiYpbbIpq6Ha/EpwqsIhwmPc2Hxubq6ykgPwt/d3WX7xmEQBtR3GLoBRR831ujUHz5w3VyiQx6Ap+wwkVGfV/eew4NUb4OxGVaSPrVxQdmAfeiHw2FcXl7GN998E8+fP4/T09N48eJFXFxc5EQsILC607D62iWnop4i4Y0m7UBEuPVHR0eZW7+/v5+1t/L5UoSHEIjLc1DdaSyPHAFn7COWJ91waAIyQ77LBxNf1XfsFWhIYO3942BWr8HFxUV88803cXt7GxcXF/H111/HH/7wh3jx4kWcnZ3F1dVVbtdWLlvB2mtGvsi6pwiPB1wJDwt/cHAQx8fHScLz1lOI4RHHDwaD3Lw7WHh2uzFTD4uLqu+4W07JDtLC22BhD94DxNfFQwkPa180mMTYDitJ/9vf/vZ1XcdbBbiWw+Ewi3dvb2/j/Pw8/vSnP8Xz588zwqO0pdn2othWh1gWufNFwhuQkGP4fr+fk9lCJIOcAU/A0Vl3HJZgUeG6PCx8akY+oNJkfAZuvqlWq5lOn4mPxYNDA83wp/T3/BpjO6wk/Zdffvm6ruOtAB6g+/v7qFQqcXJyEh9++GG02+24vr6Os7OzrKyFOB6EZ7KrVBffsyfApOfzp+J3LctxDM+Z+larlVlFVA1QJsQ1o4OPZ92h573RaEREXmO/anIuE5132YlYzM3jfoBVu+qwldewBiGCTtlR997y3M2wkvS//vWvX9d1vJXodDrx+eefx9OnT+Pu7i6L37nVlTP2WtOPyG/LlCrD8QPKGXpkrZksEN5w0g4uPWJwJO5g3WHVB4NBjvCcgwDpQFJuy00p71RmzF4MD8fAZ0oN3OD34nvGYQ2sPsAdeWrpTfTN4Zh+BW5vb+N3v/tdvHz5MouLtb2Viawuuy4EqZidf+aHmodNwsWG6AbWHQMxEMOD8FxWZGkt5L/csafnRXY9JcJhV1zltvyeEXmCMuF5ug432WiZFN/zkXLvje1h0hO0/Fev1zO3WF/HXzV7rYm5IqFN6kEG2bk9FmOr2bpzhxtieGgAIBg6OzvLSnJI2KGKwCo3uPWYuMNtszobXwkPqS1rDvBe3PUHwqfceh6YUXSfitx5x/XbwyW7FUBsHLEgKJeO+OFblZjDayIWJSwlPFt29KuD7FDaQXTDM+sjYmk3GmgJWGWnFl6vG1aeS3RFs/RYYpzaQTciP7KLrTxKdHyPtWdhlWdkd/7xWMlq/AeWGZpFrlQWnWCrLA1eG5GPb3nx4Bhayd7tdjM3Hjp61Mk1foeLjRq8uvWqI9BsOMfdPCBDh2OohddWYO6k04UMbj0Sk7hHqbIlJzvVayoKofSeG8UotynfEamyEavGUjEql67w4LMlBNkRq7Mrj9idXWOW1k6n01zzDA7dXZZ3i+U58yAnkoVanouInLaAh3hyDwEWQ5Ab740DP+OeqDApNWmHE6Kq+CsSNZn8q2HSrwHH5Vo64mkxeK1Ot+EFgsmu20Yz2VOxO6w7stqoHkBaC7ceZTmeuqOEB1gay9cCKw8Lz915vJMub6TJfff43HDlU3Jbts5c8kt1GILEEBzxLr2rGpOMNEz6DcFuMOrYsGggIpettGGE/45LcNwdh/q7xu468YaTZhiCgem5vOccD+HU+Jivi2W2EOJoO25ELDUPwbVHGMik5kVOPaNUeROuvoqYmNS4p0gcsjbALv7mMOk3BMtKOeZFXK4z8/A3sHBIZkGVhrgdUlpYeijrdMJsRGSuNb7nMVdch0cMnxqkGZHPrHMHHQiPOF5zBqlpubyvXUR+K+lUeY0JzyEDW3q25CxwwjmgHGTi43Oa8Oth0m8APMBwyVEzBzn39vZyCS6QTQnP1hQHyA/LDsUaJ9AiIhfDQjOAejwEOBDecJONWnjudONr4u2uUMJjq8vTclH+47mAulNtSkDDhAdZ2V3n79lLYU0BJvAg6YjX2cXfHCb9GnC2vdFoZFn1k5OT6Pf70el0cnE2Z7FBLmTEmewcP8NrgGXnufQR+SQal+aQqUcszzvhcBmMR1Gp4o5DC2j3sYjB/cYcAR4EgvMg5EHsz/cN0Cw9T9fBIqn7AfAsfrwmImIymUREZAvw/v5+oUdjpGHSbwC46fV6PTqdThwdHcWzZ8/i6dOn0e12o16vZ+TgLaC4DAaS6xRZWHXE/KwB4PIVT+3hiT0gPGfp1cXmygH3x7OkFz34aDZCT4HuYMuhAxa2+XwejUYjZ+UBfAaO5Znc/JXdep6xx12M2A8PYRYqB5r0M4ph0q8BJ72azWZ0u9148uRJvPfee/H+++/H4eFhRnpWqEVErhRW1GHG5S2NfdW6c/wO4Q3aY5nwrCvgkVS6bfXR0VEcHR1loQqGYoBgPBoMB4+zxj3RPecB1ebjc3HLMVtyJj1+x1qA+/v7bOfbWq2WVT00lDHxV8Ok3wAgEXTwcO+fPXsW/X5/qX8dVgd/w+IUHhzBRNe4lyf2cPPMxcVFNtNOJ96gbMbSWrjfmrSDlceknVarlbn1qArAuuMcbHFxDm59ZXBNnyfgcGaedQYa12PwCHsZ9/f32TXW6/Xo9Xq5kqEt/WYw6ddAFWu8Pxx62dGDziU7ZJuRzGOia2sol6TgMTDhb29vl8ZUQ3yj/fCc5WZVHOcVWATEkl6U5+BV6LhuTMhFuMNuPC9cTHi+h0x6ThIWWXn2cBBW4D43m80srHEybzuY9GvA8TxiYS6tgTTcIqoZbJ34gvdNqdE0lgXhMbWWx1TrDjTcvcYiINYEaKUAXgrieFhVjNHCwgIXHLmBiPykG17AmPA6757FNyrEYWvPlh4LH0g/n8+j3W7nNP9O5G0Ok34FtMSls+I4667CE/w9J9MY/MCzS8uadlhbjLfiOJ5FMYit8T0vUtwXrxJbkJe1+9yDD7cexIqIpdBBpcj4PLg+JXyqIYn/DjkD3kwE9wOkr1QquTjf2fvtYNKvAMiExBfXsyFiYbddW3MjlktX6uJy8o/3swMBWWmHFlkmIQ+WxOLEjTOw6Nysw/PtEA+rV5Eap8UyW05wcgMNwhIQvkgfz+Id/C3H+rzNF+4JJhpVq9XcBiEm/HZway0BBMUD22q1Mmns8fFxNlOe42AWoaiuHEiRnafkFu1ai5iaY2uU0niCDXfq8QaW0ACw4IfVfdiKC+O9WejDAhx8HiY85ydwHUgAMuFZZou/5cWCZ+1hEeJFEOTH/apUKrm8if7fGevh1lqCZtDr9Xo8ffo0Pvjgg+j3+/H06dM4Pj6OXq+3JKDhh07769mNV9eVBS+Qt/IBNxbbVaMqgDwCZ+ZBdCU8PBJujwXhIbzBwsKJO3gBEfmpvMgVaNstD9HQPgR4CrhmrWLA0mMRBNlZ+zCdTrPyIO6zh2Ruj3KZ8jVQC/3w8BA/+tGP4pNPPsmy9icnJ9HtdrM4nq34OldeG1bYksO6pza1hNsbkU/YafNOKm5H7M7dciAi1+GZ8EjcISMOQqESwWEN7gEIi0VFO+ci8tUE5BzgtURELoEHwuM6uM1WuxV511tjPUz6FTg6OopPPvkkPvvss4w4/X4/U+FxtlpJr1lpVbYhXlfryg87P+jQm3OSDtWEFOGxMMAlB+FBRK4OKNm5K4+VfAgh1MpjAeHMPdffIyJXUeAuRC41wgPhXYFU96ATeXRCr4m/HitJ/4tf/OJ1XcdbASS3hsNh7O3txaeffho/+9nP4qOPPoqI72JWPPgcG6esvJbgeBw1MuP4irIYW3Uu+/GgC1QReDNJJBYxaAOWlGNt7odnl5431URyjM+NngN4DTzNFqTFIsHhi47mwmcAeff29nIdg7g29nB4X3sWHWmSclWoZSxjJel/9atfva7reCvAc+AiIkdwzUJzrMoSU1g7PMQsJdUNJ+Dac/yMBYTjZyabkp2HXnCPf6pUxvp97pjjBSciv1GFVgRwHvUg4D0wWdlC4/VwzeH+g8z897zfHy9APHgEMwd0wIgJvx4rSf/Tn/70dV3HWwvUr0Fo7vzikpdOfVGBCQiOEhw3yXAXGVs0nnnPZE916PEWUSwU4lZVDi+QQ0AoweQCQbk1GKRPjezSWJx34tXSHKA5D+QEEN7oDH0sPjyDAAIpXeiM1XBMvwZImLEFh0WE5VTrpnG81t2RmecHPGJBOJBdLTsfqS49kIsJpVNrcS0sugE5eZcbnePH8TwPzOB7UERYHRfGpbqiBhxOIiI0gF5COwO5FGmsh0t2GwDuKFzViMUDzwMitSdcS3M6cYYz8oiduTGGy29Mdt0aisVBEQuRDAt/9PxMeLbuEXnpMWfbWW/PCTsmPBY+fC5eyFJTcbXBCC49i4EgktKeBzQKcfbe1n49VpLeN3BRc9d6sDanwFVWSS2ID0uLsCBiMbZKG2N4DDVn5Jns6s5H5OWsbOG57s/lOFwDd8ux9BjEZ2sNKw6ys5hGCZ8aq81bcEUs6vkq02URT71ezzoDdUtu3ojDibzNsJL0dpnyU1tVY44HnxNiIByr7thljVgkpVSsAlLDouuwDbbs7HHgIecEomrXtTklYhG6cLzN/fda/+awhhc1JTznAtRTQTjCpT4o7Zi0vPBARgxlJLv2HNYYm8Ex/YbQkhzH7SAXJ7G0ZZSz2LBMOq+O4/iUVVeNP8jLllLLhKzo4/IXBmawwo07AZXsnBDEZ1bC8zguHe/N/Qpcl8ccgslkki2CuCbufeAdenkfAG3xNdbDpN8AKqtNEYy/sjAHf6/TZXgjCC7JsZJOyR6xGJBZpAtQYoKcHFKg/59HXHHvAPQKEZGLsbWspoTnbkSd9AvvBYsNtP84x2Qyya4HnxUlOszx41l+vPMtt/Ya62HS7wAmAZOtqBGEh1qw+8xZcd7gsYjsICJfg+oGtKEn1SEH11tbgAGdesM1frbySnjuSNT5exi6iRl8GH0VEblzwfJj6tD+/v5SiU530TXZt4NJvwHUyivZ+DUcl+r3ID2PzWKiI2ZnC8bvr0MnIvKjsfE9Fh9157mcx+CsP4ctXKJcl6XH9tYHBwdxdHSUdSVCtgwrjy65yWSSG6HFcl1UFCAzZpde24Nt4beHSb8jikieii85TkZMz7vkMNG1v1xFNupVsPVPgYde6L+rao+Tk7yocOjCOYuIyLn0GLb55MmTODk5yQ3d5A00UFnAosafA9r8arWam8tfRHhje5j0O4LjSHbbGTwai9163e5JXWuQQevXLGDhUEKvQc+VStAVufNwr/HvGiqklHI8Uvv4+DhOTk7iyZMncXR0lLPyrOLT3gV8buQfQHqdB6AW3sTfHib9I6BkS0lB8bOSOxWLsw5da+6wtNyUwzJX7qjjkdc6kJMTX7gGEDtiMUBEz58ivGrhIZzBgVp6q9XKhStagWDhE8IK3ihEd/xhDyUVrhirYdLviE0sfcTy7qwRi3o3vwbE51l7HF+nXOyIhXQXCUPOGyB8AGFSs/Vns1munx2/4wQlj6Ti8/BGHiA9xmqzSw7xD0ivvQF4f+Qe+HveJDRFdhN+e5j0O0DjeBAfX/EgcpKNy3UgN0jFenRV17HAhxt9tJ2X6/7cBgviF70/CMek4jib+/m1GQcbcaIOz/MDuTmHa+lY0Or1etaqzElB3BdUG1IVDJ3IowIqYzVM+h1RlKUH2HVWq8QE0iYUnRmn8Tx7DCA699tzzT+l4mPBjbrbfN0qh4VykD0JyGpBeN1tN+Vd8PtxWFKv13MWn++nip3YO1DprrEeJv0rgD5sGq+zqwxUq9WYTqc50qcaZ9Taar2dxT26dZZq9Pl99auW/fR8qADgvVBD1512t5lio+ERDm1RTi2WzWYzptNpNpjDbv7mMOlfAfiBY9eeH1wtsbGohV1WzU6ztY3I70DLrnxqrzxtyuHkXUrYo4TnRqD5fJ5dK/5Nd9stWrDYeqfOn0pq8gLJ9ykicu2+KoYy1sOk3xBK7NSDlpLqqlIOv4drzu+TKkXhNez2c286k76oAy9FOFUSqpqQCY+Yn+PulBeBcIYPrl5ELCcI1W1nPT+77bVaLasw8FBQvm681q7+apj0j8CmbiW/jsU0iKuRQeffqXxXXWGeTKvyXU7Y6TXwAsS5Ap5nh/eHRY34bvFh0rPWgGW64/E4i/25LMfZex6akdroI7VVFYgeEVlHovew2w0m/SsEW2k9OCutrwe4ArBKXAMrDGIWufIRi7JcxKIbjwdgaFIM14XkIC8EHF+rbn48HicrBFydYG8Af6Pz+nQgCZ+v3W5HpfLdVGB4A5rBN9bDpN8Sqx6uohIeXPRarbY0BopVdHzAkir5WdHHme8U4UAw/KwNOay2Y+ENn19Ldrh+Xjw0fNDYnLfeilj04/MYMT50zh5fS61Wy/QKqQYnYz1M+kegiOSpzDW/jv9tlTVX2S7/rKU+rb9HLEIJjeF1yAdbe47pcZ6UaAiv0/fg/AC+R3cdi4Awwov368PwUFh6XoQw7MNu/eNh0m+JVWRmYjDxeSHQDPk6jbySX1+fsrJMfCa8xvN6sOXk69RFg0lXqVSWtPmphKHOycfEoSLS8/sjd6ALk7EbTPpHIEV4JjoP0cDrNNPM7v0mtW2Fkl0TeCmtgBI9lUlPeQVI1KUSf41GI0dU9noiFiVKlDB5dx0+MGuQNf5YLPle8yK5y30rM0z6HZFK1mkJLvUaTpilXpcSzHAGnK04ALLrw5+Kr1Mk53/TOfY8KQdDNXn3Gl7MIhaNOMjKs4XH63Q6L5J4vOc87gs+F8IZVBRSeQy+n0YxTPodoBn4iEXP/DrXU8nN75FKhDGR8Xe8uLBlLSJ9asIPj+tOWXTdbYYHaHA5DeU9eDYs8lEvA191IeFJwTqHnyXGqfHfHpe1PUz6R0CFJ6l4H19htVg4khLzoG6v7wGo1WQXl18TEUvkS8XzusnEOsJz7M/5BtUQcB1fVXrcPcg703I4hLIk5L7dbjc7eFcfzW0Y62HSb4hNHipVhW0KJT0OLrdFRFa+Auk14cchBL93kQpuFckxQVfjeCZmxEKLrwM+tcOP8xmpTjm8pw7FRDNPak5ekRDJWA2T/hEo0q6zEk0PrnEr1DUHAfA3UO2xhS8iP7+PWnxWz7F7rSOz8RqEA0xOJjtvv5XaVJMlxGr1+T05Scf71mHIJo/Stnu/O0z6RyBVmtLfqVXj1/EDn0rUpR5kvC/IodZf/y4VQqgijrerZtKD8Lhmdd+Z8LDG3FMPYrLno/0FHCJgNDdktjxVF+OvMX5LXXsOpYzVMOkfASUWvmd3POXmw9VN1fHVcqvVZhSFEiwC4rCBf4a1h3WHBBbER+1dvRfenINn27MlVvc7Ir87Di8aSNIhl8GjtHne/eHhYW6Ti21aeI08TPotoBYKMShbXdbXc32ZE3mrynrqqq9y47lWrZLeiMXiAi8AwhhcH1t9jut5Gy5O2HFijd1uWOLUXHrcA5wX37dardzUHuQveBsrWPper7c0pCM1oMPYDCb9BlD3NNXWGhFLnXIct6aaQtTSr/uq/6aS3ZTQhy07FgPE6LxBZdF5IhbDOnjnGt49Vnee4U0lIxaLC/fb43OD5FgcUZpDfgBkx3vDg9DKgIm/OUz6NWDCgwzcz93pdGI+/27mG5JdKYFN6n1TnoN+H7EcB6cWABBqFeknk8nS5pew/pxHQGMQzs1708G6M+HZCuuYaiQtIcXl7kCECDh/KjGI2jwWWN0jwPH89jDpNwRbV7i5nU4nIr6zhLxBZMTySCp9ryICR0Thw8xfUyEByKwTbOCm39/f58Za8QG3HfE8whQk2HS7Kk6sad08NUFnOp1mLcDwjnDPsPiwB5Uq++ksQbv2u8Gk3xAgGD+YEQtLqEMfIooJz+9XZM35tUXfFy0e/BouESJ+591xQeZ+vx/D4TCL6Vltx6/FeGveZop7+dWF11Jhs9nMdeVhoWQvCslCeAUcv6cSnU7mbYfKGhGJ25n+D1qCU0Xbpr3dRaTXB3fVQ7xuAUhdu/bS67Qars2jcQYLHObhtdvtpR1ndAvtVMVhlSSXBU1YVHV+QBHJ7dqvRfKm2NJvCLbI+BmWSdtJV/09f1/0wG77AK978FOa/pTWXnexwefjEAAltiI3W68hpRPQZiL+HByurCL4pguksQxb+i2QStBp3/k6PJbgm7xnCko07b5LqQo5ScgimlVaArXy+n3R16K8hb6nyb4VkjfIpN8BmyTrdsGqh3gTD2KTv00tXFpx4Pfe1KXe9to3/Typ9zXZN4ZJ/yqxzYMMvA0Pa1GicR0J36SFfRvu2zsKk/7/C6/D0j8Wq65x18XqMdf7GM/F2BgmvWGUDEnSuxHZMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSgaT3jBKBpPeMEoGk94wSobamt9XXstVGIbx2mBLbxglg0lvGCWDSW8YJYNJbxglg0lvGCWDSW8YJcP/Avv8q63t9MN5AAAAAElFTkSuQmCC\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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2ngm8TqcT5vGp3JaIs/I7OzvY29vD/v4+ms0mNjY2UCgUgvhF72M0GkXEP91uN5TnrPhIe+I5xUYrATzIeFDwfVKPiJ6CVgGsZbeI8xYci8FJ/wRY99xaJSX+Q+IbuvRWgEOyk/CqhLPksDF2oVBArVbDzs4OXr58iWaziXq9HnHr7b0wnu92u+h2u0H4w+fja2FyjhNvSHqVzrL6YAdn8ho8OCgN1g47/h6z+XxdjxU8OR6Gk35J2HhTyRdHfP07O2aLzSxM3DFT32q10G63w7RbWl1m6+OktmydrVQqkfn1m5ubKJVKsXV5HfR5eXkZNP3UALDhhfE3hT6craekB+7WbXFarvbf8z1SwltxEJ8PuOsB4GFjQwHH4nDSPwFKeP2gqqhk3u+S7Noi2+/3g0vPclmr1Qqz6Gh1H3J9aeW1RKdts6p800OLVp6uvcp7SVQSj6W5YrGIYrGI9fX1cG0dq3Vzc4Obm5tQWuR923l5OgVoXv+CTiKKa991PB5O+iVgLbwOdtDkmLqk+rs6zprxu3Xp2+02Wq1WGHNNwmsdm9DpPLTy7JVn26xN3GleQWN5nXRL5Z0+B6fWlkollMtllEql0LBDFR7zEsw/MAdBd93G9HYmn3btcQNv3DhtJ/5ycNIvCeum63poK7gB7kQ3tO4kBa271uE1jmcyLa4mD0QJzwEZnIjTaDSwsbER4ng9hHhP7N5jlUDlttoGSy8ibswWlX0U99DK63APegxq6XUkNgU9HHNNMQ69pnmkdywOJ/0SUCtPwrPDTeWj2rXG+J3JOsbutO6W7FwmEeceA3f95ySP9sk3Gg1sb28Ht1775NXCk6AquT09PcX5+XnYZkNlHGW2ugaLvfjFYjHIZ/kaSXzeu1ps3jevq3E93zf1Bvi+2rmDjuXgpF8Cceo57oTX8c+Mm3XUlMpqVXTDOjyz9LSSPFQ0WQhEtf86DYdx/Pb2NjY2NiLTcOy92932XIRJBZ4usOChUi6Xwxqser2OarWKXC4XvBiN6XV+Hw8PDUvsWG+t3/P10UNwt/7zwUm/AObJZRm3Av/4IHMLC4BAeJ0+Y8txbKBRV14319g6NxCNsfP5fCB8s9kMUttKpRKx8iqYIeFbrRYODw/x9u1bfPjwAa1WK7IXj6TM5/Mol8thueX29jbq9TpKpRJWVlbC62eCUrfv6OgtQrPxutWG/2bfcztz0LE8nPQLQglPy01durqhdEs1hmdmXON3WndOv2HX3EPTYfjhZxad2vrd3V3s7e2FmjytPHCXSGSm/vLyEq1WCx8+fMB3332Hd+/e4ejoKLj2WqZjpr5er6PRaGBnZwdbW1sRKx83LIMej/VQ7OAN+57p/WrvvZP98+BB0msZKknQuNm6kpr8Yk1b98LzA5xKpYILbV16Plga46GhK69JlDhNPQmvFv7ly5fY29vDy5cvIw01JBK9Btbi2+02Dg8P8ebNG3z77bd49+4dTk5O0Ov1IlaemXpWA1j3bzQakVh+MBhEwpmHwhElul3cwb/X18vPYZwuwuW5i+NB0j/1DbVKNO06+1z/sz51rbhM+qd+l1B3GkCwkuxvZ2nt/Pwcw+EQwN0CR+AfK5hs0o5E54BJLWnZ+XFx92cJTwu/v7+P/f197OzsBKktNfC8NpN27XYbR0dHePv2Lb777ju8f/8ep6enoX2WzTJ065kcZJce8wVc1MnXPa+dmATWpB1XaPG/eZjppB2+57Y86oR/Gh4kPePSpENnxvV6PbRaLRwfH+Pjx49otVrodru4vb0FcJecYtysjTOUtl5dXeHq6iqypCLOuhNq9Vg2s4SnW88SHd161sdvbm7Q6XQC4d+/f4+3b9/i6OgIp6enQfHH5yZBmbjb2dlBs9kMYp/19fXIoEormNFmGr4v2nOvXXm0+gwNrDVXLYQ+fJDGcnBWfwLT6TS43/1+H6enpzg6OgpkabfbuL6+jsx6Y5Zam2cowGHcruOqbXusQuvw2Ww21sJrHF8qlSKE1/74k5MTHB4e4sOHD/jw4QOOj4+DxFfltiQhu/R2dnawu7uLZrMZEoTZbDbcoy2jsfZOtR1jdzbnrK+vRzT73KijiUYlN+W7Kue1OQ+P9x+PB0n/zTfffF/38YMjLgwg2cbjMS4vL3F2dobj42McHR3h+Pg49JqzVKUTXVm6sltoqJ23Zbh502CU8CzLxRGePfIUubCqQHf++Pg4kP3w8DAyHEOXVpCs2WwWlUoFm5ubaDab2N7ejkzcoXpuNBrda4TRuXz8eSaTQT6fR7FYRKlUQqlUCssyuVHHKhytZp/vmx6UbukXx4Ok/8tf/vJ93cePAkx6DQYDrKys4ODgAL/85S+xsbGBbrcbBkRqmyuTXvx7YjabRdx3bTx5KC7VspVV2mnS7uXLl6Fdlt1zJJnmHeiZvH//PmLdrbZenzOdTqNUKkX0+4zjqe7Te9dSppbi1tbWQqzPOj9Jzy259Ep0LLbdjMt/0/15D/UgOB7Gg6T/29/+9n3dx48S1WoVv//979FsNkPGW2vptDaamSbUMs1zSbU8FTdjjzp3JXyz2cT+/j52d3dD0o7dc8A/XPqrq6swxfb9+/d49+4dPnz4gJOTE5yfn+Py8jK485asLNGxJs/yXK1Wi3TpaUadLriW1/Rg4NgubdDR+XyaoFPlohKfSVS7qNNJvzg8pn8AnU4HX3/9Nb799tvgLls5rFpkTWTpB1nj0jjrZMdnM1lXKBSC5JX18RcvXoTYWpN2dOlJeApu3rx5EwhP74Svwarj6IarnJfbbHW5JcuRKkMmEXktim3UU6Gl1yEeJLMeHjwkeV2Smx6T7tHzLP7icNLHQOP74+NjHB8fh5+rJdYkmw584N+quGQe4a1lZ586yc4Fk7S4tLrlcjniHtOlJ+G/++67SP2dnXpaFrTQunytVkOj0UC9XkelUkE+nw+uOg8MCpRYdlS5Lb0UHiKW8MzW62YeVTnqVx4GmUwmKP3cxV8eXrKLAUmvnXNAVBxCK8Y8gHXvaUWtK69ZZm0v5Uy7SqWCWq0WVkjrKumNjY3YZZP0QjqdDk5OTvD27Vt8++23ePv2LY6Pj9HtdiNdenGvFfjH/296F7VaLWjrebhoA5FO+mGvAEMGrclz0AZbcHXYhi7btIS3wzdUBGS1DU76xfAgq+muOe7DHgDApxWMSjAeGox3mSlX1Rtr4oynddEkLS6J0ev1cHp6ivfv3+PNmzd49+5dILwO34h7HbwXrRDUarV7bblAtJfg6uoqaBDYEcgqhq7RKpVK99x6nayrYUKce6+JUtuyq6PDvGz3OCTTlC+IpyoIrZRXLaEm6XZ3d0NmfmdnJ1hatZA6BINxtMpqNWmnk2zn3b9KY3n4sG22VCqFiTjAnSKRhO92u6FRSJOD2vlHt75YLEauRdj2ZBJd43b1uK6urnB5eRnmBdLa03twfBpO+kfgUzLfuFZP2zFmtec68ILDKymlZRmOW2V1hhxwR5TpdIqbmxtcXFzg48ePoSxHwrN551MHFj0OTRzanAFzAVQYcsIOW4JJeuBOzcdkpIpwOF2HXpISXmN4rdVrXV4Hj+jyTrf0j4eTfkloAi5uZptqzrWjjPG71sFp4W3dPW42vQpYNI4/OjrC4eEhjo+Pg4Y+LoaPex2M5cvlMqrVahiMoYQnKS8vL2Nn+N3c3IQmHa6/1ln4PLiAu3KmJTx/xocOD+X3Oq2Xswf4OvV9csyHk34J6KgndoxZVZpOe1XNOVVpLImxDPfixYuQmWfszusyvNC6vg7AODo6wocPH/Dx40dcXFzg+vr6UTvfSHgt01UqlVCP58gqkv76+jqsribpaeWZbNNhl3zt2oSkJUx143U5pS11asKPwzt1wpAKpByfhpN+CaiunA874kldeI1rOVCS02dYC+cAS012WctFl/j29jYMwPjw4QPevXuHw8NDnJ2dhdl2j0kq6kQczrzjPbAMyQOEK7bOzs5wenoaITy32LKVlwcJya4tsxTe6IhsFdtow41qAXQqr67s1lKk43Fw0i8IzbrTerPurDJWWnQdIElS2Z+Vy+XQhKLxuyYQ40Zc0cIfHh6i1WpFJth+Kg+hE3FKpVLYW18sFsO++KurK8xmM9zc3KDX6+H8/DwsteSwDe6Tpzej3o9153n/HI3NDLytuet0oriuun6/f2/rj5P+8XDSLwhaYCa+CoUC8vl8cIdp5RizU1xTq9UiBGcJjk0n3DoTF5faqbWquGNpjoM85nXrEbzHdDodLHy9Xg+JQ26cZfsvh4Vwiy235jJxN51OIwcVvSC18My+69gwHfppp/xqOVQlvvQCrq6uwsgxkt7Ly4+Hk34BqEyWopNqtRrRpGvdXcU1mhHnIaFTY6w7rwo+JXyr1Qq1+Ddv3uDw8DDo6bV5hverWW116an6Y4jBIZqshdMK68Sf8/PzMKVXS2Wa0OQDuKvpk7S265CCHruxh3/Lr6ps1Am+HERyfX19T0jlmA8n/QJQBR3LW4zFKVO1G2a2traCFS0UCiFBp0TXzL+SnQk0fshbrRaOjo6CxJYTb/r9fqjFx3XtkZAaw+vMu62trdAjr9Jau72W8wB4uGgZUUmvFl475Ojak+y2eYbvsZYmbfsxv3IikZftFoeTfgGQ8FSa0X3XUdMcFMHsPA8FLoTQ5BZhM/Oa8Lq5uQkJtI8fP+Ldu3fBrT85Obk3AIMuNqGlwkKhgGKxiHK5HAQ4lPYWCgVMJpNAJM7+o/iG5OLhwl4BVfSpLFnnBegQUTvplw+FjeuV+PxdzQ1ox6CT/tNw0i8Akp5uPV3jer0esu4kPfXr1sLrhJm42FUXWrImfX5+HoZ3cPIN23w1c63dfmrZOaWmXC4H8Q2TijysdByYxsuU2GoXXSqVCpNzdKKtzgbka6OnoosvbBgCxPcqKNH153Edea7Bfzyc9I8EicT577TyjIepXqN7r4sdmaSzZNd6NZtIGOsyXmVd/OTkBCcnJyGZxiEYJIvOolOPgwsqtGJQLpfD0A3ui+M4Lx32qZtr1X0m4TVxyQONoiEAIYYn4QeDQUQ/oGutgGj8rl/5ntmYX4U73m33eDjpHwF+0NXCb29vBzENl0ropFdbflPrFdeldnl5GeJmDtHUkdmckc/EFYkI3AmBtMmFAiCW4vg9p9ZwhRTdbc7R4+JMWnhm6G0nIROaupJKe+M1aadZenbh0WviA0CkNKdk1iSfhkGO5eCttTFQi0wLz8RXs9kMQyI3NzdD6ynjdbrUdt86P6xaq9bllXYWvh4AHKyp8Sslr1oxoFVnvM72WE7XoXXn5BvG6awMcKcen1MtM4mqYQPDGb5WletyCQifQwVDbDhiToDX17+303gsdJaBb75ZDN5aK7DddBwbRXd4e3sbBwcHYRglJbNKcj0w+CGmVaLryzqzrqSmJSfZtaSl++DUNabqj8k5kpxlQnbpaeMOpbVKSGbpNYZnRlwHZirpVZjEwZb0GphZjzuoSFSN01n2Y1mPIU/caDH9f6U78OZpHBz3kUxTPgdxyaUvvvgCr1+/DkIW9rrrznd13zUTTXeX31PoQmUb43R2qjGGZllMia5k5xw76gE45YYhB8OOarUaadxJpVJBQ0+SWsLrMAy+Fp1hz0Yaiop4kOi47X6/H5ntrwcfSU8vijPy2H2nXsFDyTn1qnjwWA/NEQ8n/QPY3t7G7373O/z6178OsSez3rRu2v6pgyG1Zk0LT2Xb6elpWAvNzTK0rtptplBhDbv0qBPY3t4Oh5Edp0UvBEDEZdbNO/QwNIbXwRV24IcKjEhW6vQZkjCBN2/FNvX6wF18TtLzoNCYXt8H3QHA+0hqKLoMHnyn/vznP39f9/GjAKfRXF9fY2VlBV9++SX++Mc/4vXr1+Hf+AHjB5QSUH7Abc2ZHXEcSU13npNp2R4aJ0fV9lxbGaAbzxn4HJbJ4ReaSKSSjW2srMNrSMH595qlV5eeOQM+eH1eWzf5cMCFWmy+v0p+uvMktbbZxll5HqjaCszX6qR/PB58p/76179+X/fxo4COvUqlUqHensvl7slitV+dVpxyUC050Z22+nWSzca86sbT/bXTcbl1Zt50XHW7gbt2VrrfrP3zXrhTzxLett5Sycc8Bt8reg1KeF3GyWvyfeU9cSsQ31f1mOYNvdQR3axKrK+vO+kXwIPv1K9+9avv6z6eDSg2YazOwRJsM+U8OhI+bmstLSsPDp0kY7PSTJixY69er2N7exvNZjOyWy4ufgfuBmBo/M4EIkuAvA8AIRTQTTcU95DwdOs1bOGhp4S3pNXch4p3eC3e60OjrVlJqdVqoZGJnYH6PI758ONxQWh5iB96imjY8WWnurJ8RQEM5ah2so2Wn3SarE7J3d7eDlZ+e3s7tg9fNQHcnKtCn1arhXa7HXIJPMBsRpx1fxKeykKGOqzv87XRpVfC26SarXAACFZdx13bPAAz/9lsFsViMSQu6/V6WKbpeBy8ZPcA7Fhr/bm6+RofM2PNshObV1SVRrdXY3btxWdJjL3uuvCCcTz3yuk4bK0e6NRaJuyYODw9PQ2deXovXCSpIQVJrzPuGNKQ7DYJGUd49V7iGoz4dzrdVqG5BQ4SZdKSYiPH4/Ag6ZPqJj1U8tEPK5N5jGd1ZpvOfdNuMs3u26GZlLNSQsv6O0dSc9qOnX1vBUB2TDV78Nvt9r2JN5PJJOIaax2eoQXbgIE7cRFLc7qJ186h18NM227tVF+rtdf3Wg9HWnl2B25ubkZGezkehwdJ76fnfCixdB21ro2im6sdYCo/JbTmzBo4SU9LTxmtjakpe2VMzsPI5hI0jifhOQCDlp2kpKSYEluGDCousqFKnHrOei+6tlq9Jd43/9Zew7Yzc8SYJvGc9I+Hx/RLQrPzrC3beW/6QdZsuE3WkWDqTrNZRh9Kdo6pGo1GEa+BpTldRkHxDT0RHhRqcXW2HT0OegCsw2sPu06+0cGVQNTC81oq2aXGQddZ2bhfv5L0pVIpNDnRyusiDsfj4KRfAqq+sx1hVkxiB2XQmpJgJANdafvQ2XO0tCSglZ9qaY4eiMbdrCzQstPb0Ll22u+vk280YadlOUt4IJoQ1NelIhoeXPze7rnX76lP0IGiOs/PrfxicNIviYdcUVoy/RmtnrrPJDuz9LT2OklWV0ANh8OI9VOLqs09tl2XHW8sCVJ3oPJaflW3m3+rgzVUJhwXwwMI3gJHijFU4YYb1TbwudLpdGgispJjndhbr9djt+84Hg8n/YKIyyozMUfiqAKN1p1EmGfZtUVVZbO6256EttlttYy8R1p9Hhhqze392u05TDxqSa7b7d7rC4jLsjNUKRQKYdy3zhFcWVkJIiGdsqPNPTpKnLE825o5a5DDP9y1XxxO+iVhW01J6NlsFlxYjWv5sO6uJTzr/3R/9WGXQhBa37ez9zRrbl14zS/olBsSX6W17PqzdXj7Oq1V1tZettJyBBhwl4Ogx6DbalSrwEEgnAegW3sdi8FJvyTswEklvXXt9XeUaFq20pwART3MwOssOJXs8jnsPdCbsEk0Hi72PvicvG/2w9Otty59XGlN5/BpIxAFNDpPv9/vRxKSVvOvugFNbGqDjdb8HYvBSb8ENCmn7Z3ZbDaWEGpN7dYWWlXGsozddfCjyltZW7eDLRgyMFanB6HiGuYN7JopJTItvCbtGNvHKe20LEcxkZUK63jt29vboNunWlB1+hT/2FHj9I7sMs841Z/jYTjpl4SSXktdOq9NZ7fbjD/jfH6ANWGnSTidMUcrrwMq+dz8nuGFbtmhhFZJAyAST6tkl1l/e9CokMaW5SgoYoadcmGO187lcgAQMvbMFxSLxXCw8CBjQk89Fm0RjquSOB4PJ/2SsK47rad+MAHcq9eTKJpMs5JUde8p41VLq73uwJ2IinEwd+hR0VcsFiN957Sm2uyjU2vtyikVGOlr0NIcW2+ZbOO4LsbhbMNNpVK4vb0NQzj4yOfzIU8xmUwiSUab2OTDJ+AuByf9EtAPvd3sQosN3A16VLLGkV9dUxX9kOh86DXiQgzq9Vki40P735kxp7fAuj7DCXoYcVNmrYBGn1sHcqp3obkFkjkuH6IlSn1v9B6pPVCNgFv7xeGkfwL4wdTEnMbvtKC2T9zG/Hb5hQp+VMJrG3RU/MLY1z50rJV6IaompEuvDUGWUDzMeJ+qo1e9Ab+nR6HPZx9x/fJ07/k+MA9ANSDXauk8A4/pF4OTfkkoCW29Wz/smrTTcVV6HavaU9Ay0z1msstmt3UVti7HZOKO19HwgSTXEEIn3ei98b/1vtTDidPU04PgNSj4UY9Ct9aqZwQgDNCkV5LNZtHtdsM8At3s43g8nPRPwLxaOC2iWnyd5W7jY15Hy2hWshtXE2eyjvG7kl2tLUmoZNSqgO6WYzJNnz+TyWA8Hodr2XuwlQCW+yjzZXVCF3Hq8Eydp6ftufr+TqdTpNPpsGaL8/w8rl8cTvolocSw2vXxeDx3HrseAgAiROJ145J81q3Wibg61YbWXZNfOqzCLtmI22DDcpkdjjlvfh9fD6W1fH6GN5TL2gGhnU4n4q6T+HYZJf82lUqhVCpFFn446ReHk34J2CQeRS/ZbDYyeISuqWrLGaeSLFbGywy8hgzqCaj11VZcPvi3Wn/Xe7ErtHRGH3C3uSbOG4mTANODYV8Aiap99/l8Pljr4XAYtvnQatPq6xRc+9wsc+pYLjt5yPE4OOmXhDbXqLyWbikQXTutH+I4Ga2SWd1m/V5r+7pCi+RnTK2jp4A7wrMiwFiaMbdO8eVrWltbi7xWftW+feoOCB1wqVtutDGG03i53YeEV8WfFdzwgOShYNuXHYvBSb8E4uS1JD1wR+J5f2ddZduFp2IfldKqEEi/8t8ARPbBqxhIp/nQnbfNPLx3Jg1t6zA9EzvDzh4qPEiYtGP1ALhT4elSDNUE2JHZ6mXwPvhexiU+HZ+Gk35B2NhbR0vl8/lAXmatbZafB4Jq7bV2rV1wcVtclPxaMYibQmPJaMuHmlSM65jTzD1wV3qLm2VHL0BfF+vrOm5LM/hx8mL1HOKGa2goxVDGib8YnPRLwja65HK5kOFmDG8bazR2jxs+YcUpmiyzCjWr4iN51TKrK2wJP0/GqqGB5iRUBTdPbEQPRkMaHhB2eIb2+uvzWZkvv583mdeHaCwOJ/0SUEKqy80BjbZHPQ70BOKGUChhJpNJpEZPUqVSqQhRVABDq6nWXXUCWj3Q5wXuSG+n+WpIECcyAhCJ29V7Ae4mK2u/vh4kVq6s12RDEddvb2xshLHfTvrF4aRfEkp8u2edLruS1BKUiSmb7FNC0zOgB2Fdbf3eWuQ4wjNJFqeEA+6GapKYOsk3bn201Q7QHedr19fPn+k17HXilIoMn9bX18Mqr62tLWxsbDjpl4ST/gnQWJ0E5QebKrrxeBw5FDjIkg/biRdngW3SSjPbD8Xu1poq6dW9J9lUQcjf52Fitfhx8tk4WbLVG1j3315Dr6Xtuo1GA81mE7u7u9je3ka1Wg2lQMdicNI/ATbJpN/HxeUkAF1/687ze22dZfxv5b1KIiX1p0hvvQ61/Brra7Zck3bWwutXvj4dC6b9+ypasu+ZvQ4JXy6X0Wg08PLlSxwcHGBvbw9bW1sol8th/JZjMTjpn4A4910TYEoi68brY14tnxlxWk8tw2nZj5ZY4267cMNabI3tNREY999xVpmvX4mvPfBxa6SpIbAZdz0AmSexhH/16hUODg6wu7uLer3urv0T4KR/ArQ0RleY1vQht9i68Ix37c/mPZ+GEGqNrb5ff6Yxv2bl9Vq2fGfvlVBLzQPJtvZyCq6uwuJXbcKxMmTKi+nSv3z5Eq9fv8br16+xv7+Pra2tyLx7J/3icNJ/RsQlydSCaSuq6twZ22upKk6hp7Jcq4izZKUrzX/Xw8GW5Pjvlujz1G6qLaArr8s5OLhDRTnU+zPhpzkFFQbpgsrd3V28evUKr1+/xqtXr9BsNkMs71Z+eTjpl4ASU8t1dLtJKI3r7XIL1vXnqdCU9Jbw2uTC0pw204xGI2Sz2WBRgbuEn/1bIJ7wcbAWmSOySqVSWLvFSTnFYjHE3KwCUHPP9211dTV4SPQWyuUyarUams0mDg4OcHBwgP39fezs7IRllS7KeRqc9EuApKQop1gsYjKZhA47TYbZpJqOsdbEmu1os6o8bcgBopl2+zw6ZkuHXLLlNZVKYTAYBI/AuvZxr5dKw5WVlchG3Wq1GkZd67hrNv8Ad9Jbrtgql8vo9XqhyYZts4VCIWyk3d3dxcuXL7G7uxv21jFc+JQGwvEwnPQLQq08BSMsz3G4o2bDNWuuMb7q2lWFBtwnviU8cEd6q5rTRRW62qrX64XNut1uN7KtRkkfBz3gdIEFib61tYXNzc0wE0/JyeqCbve1nXUAQohQq9XC7nmuouY2G/YmuJV/Gpz0S0A197PZDKurqygUCvdGRNuEm308VAKz9e64Ep8lflzjCy09CX9+fh6213KDra7X1twC70En3W5uboaZ9pubm9ja2rpn4dlVx2w9Kwu68Va33QL/WIXFfABDBS624AFi3wfHckjNO93/P7xvcQ6se62dZ1pzt8Sep0Aj5gly7AfdEl/r7Ep+ko1W9uLiAu12G+fn57i4uAijp3RcFp+PhKeF39jYCIS3ZKcW3rrfceIhhh88ZJjz0FKfHfXlZF8KsW+Yk/4J0Gx9nLR1Xm07DnEf6Ic+5PbgsGU2m1fQffXq5tPNpr6er0Mz85yfX61Ww2hrJftD5LT3pQeT9Sq0ocgt+2eBk/6fhU+8h4/Cpz7Yj3kO/R0bYjC3oLG+nY+nk2jsgBBaX3a42Tl8j73vOE/HKhgdnw1O+iTCegJxCUAV8ijpdTCnXXrpVvhZwEnvmI+4pKLr2p89nPQOR8IQS3ov2SUYtlT40O8o3KV/3nBL73D8dBF7OnvQ5nAkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwpD+xL+nvpe7cDgc3xvc0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhOH/AYAaJInnIF5hAAAAAElFTkSuQmCC\n", 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5eXc/WjasnxXmYbBwRz0OLd/V/zZMDiP9JyBMPfbJ77v7YS++lxKebm673Xb5eLr0LMR5zK1n5V0ul8Pa2hqePn2KJ0+eYGVlBblc7l4sr9ehxThnZ2c4Pz9Ht9sNNPPovbGOXzvsUqmU81bCDkB+Z7xWXoP2C6iIx/vTg8cKdCaHkf5PhFEEH5WL99Nz6taTeBrHa3rOHyShh838/DyWlpbw5MkTPH/+HFtbWygWi4G8vA+Srd1uu775Vqt1j3z8jHg8jvn5eTdrj007FOT0M3jvGgIxXedbfX4X/JX/ztpqPw1G+inhV8wRvqj3WOzOX7VTzid8rVZDrVbD+fl5YDTXqMYTWtJkMol8Po+NjQ08e/YMOzs7WFxcDBXv9Do0VXd+fo5Go+GsvA96E+l0OuDaJxIJR1D//fVQIuH9rkTtrNNuwVEZCsP4MNJPgTDCP+Tqh5F9FOHp0quFPzk5wenpKc7Pz53FHeXWA3fjp+bn57G8vIzd3V3s7u5ieXk5MBwjTPHWeP7i4gLn5+e4uroKVN7x/piT52w97cXXcly64xqH83thbl6HW87MzARcfD3cRhXlmHo/Poz0U0It0biVYf4h4cfwtPDsVT89PcXx8TFOTk6clVe3fpSXwRRdoVDAxsYGdnd3A2q9H8f710TX/vz83I3D0lZdJSy34XBu/sLCgvsMTcXxoFKrTYKT8NrdR49Av7dRpcWGyWCknwJKdj9VFnYIPER2jeE5aPP8/NylyY6Pj1GtVl0VnMbVfnspBTGW2i4vL2N7e9vl5Ofn510FnCrpvivd6/VweXnpZuB1Op1AXz0AZ6F1CAc79bSSjnsBtNdfr1kr8dhP72cGwtR+Xxzlzxseh5F+QvjkHmXtw/6dH6PSCoa59EdHR470tVoNl5eXI628nypMJpMoFovY2NjA1tYWVlZW7lXe+ffD3w8Gg8ASjfPzc/eZPCAYg8/PzyOXy6FYLAZIPzs764aG6l4AvX4dHsJyXZ3Ww5/Re3xoNoFhfBjpp4Bv5Wm1tbhFf84nPPPMtIKtVsvFz6y4Oz4+DhTh6FLKUYRnCWw2m8Xq6ip2dnawsbHh1PqH3HrWBXBVFsOKZrMZsM4kfCqVciuxdAgHh2Iw5ccR4drrz+vm+2m9vpbe0nPhn+nn+4q/EX98GOknQJiV98U0/xBQ919deZJdR09Vq1Wcnp6iWq0615pWUsU7fhazBKrWZ7NZLC8vY2dnx4l37KIbZRV9xf7k5AQfP37E6ekprq6uAh1x1AsWFhZQKpWwvLyM5eVlLC4uIpPJYG5uDt1u15UOX1xc4PLy0o3zYu+9ZjhUwWetvj99yJ+RrxN2DJPBSD8hlOz+CKeZmZmA9QfuRk1pdxnTcRTsdDCFzpLX9tWw3LSmvnTT7O7uLp48eYLNzU0Xyz+k1lNIbLfbqFareP/+PX766SecnZ2h0+kE0mycrVcqlbCysoK1tTXXoptMJt33wUpC3ke73Uav13PfH+N+7QAkkQG4ll3+Pa9fu/e0tNfIPz6M9BMijPTaAaZ5ehJfB2BwkizLas/OztyvzInTKqri7Yt2owi/t7eHr776Cvv7+y5FN6rUltdIV7xWq+H9+/f4/vvv8eHDB1xcXAT23M/OzjrhbmVlBRsbG9jY2HCfE4vFnOinnkyr1XLxPK+fUAFSc/R+pyJ/r2QPqzUwPA4j/YTQnLpOdPHdfM0r08Jz7522xzJubzQaLnZX665eA6GFLfF4HLlczjXT/OpXv8Lz58+xubmJYrHo1HStitODS8ttDw8P8e7dO3z33XeuhZbFNdQL8vk8lpeXsbW1hZ2dHaytrbl11tQpqA1wYCf37bGmXjMBvkDnq/H8exUQNb1npJ8cRvox4Vt4XVOtCyTj8bh7uAG4Qhd/4o0SvtlsOqFLp8o85M5TTGNqbnd3F8+fP8dXX32FnZ0dlMtlZDIZZ+X1PoA7C0/h7uDgAG/evMHr169xcHCAZrMZEN10eu7W1hZ2d3exubmJSqWCdDrtrDyr8JimY3yvM+11bp9a+FHk9XsY/JjeSD8ZjPQTQONfbm3VajUVo2KxmNtuSwuvhPen3nAnnnoMauFVtCPhc7kclpeXsb+/j+fPn+PZs2fY3t7G0tJSaOWdT/herxcg/IsXL/D999+7WJ6fz6q7xcVFbG5uYn9/Hzs7O1hdXXWfQ++H980DUfsD9F6U8L6F90ttR8X+auWN+OPDSD8m6Nbr2CoKbrSIGmtqZZsSnvE7O+VYRz+qRVahnXNMy+3v7+Orr77Cs2fPsLW1hXK5jGw26/rkRxGeZbYk/LfffovXr1/j+Pj43hbaZDKJQqGAtbU17O3tuQq/UqmEVCoF4I9z7FSEYyEOLTyh5bu63IKTdbSOQbUMZkNGDSExjI+xSB/lxgZ16bnDrVaruXp4rnTS1BNVfG6DqdfrgdZYte6cEqMPd1geXltYaeEp2rF7rlwuuzJYn/B+DN9sNnF0dIQ3b97gX//1X/Hy5UscHBzg4uLCXRM/jyHE3t4e9vb2sLGxEUjR+SlK1Tv8slkSntttdfmmr5f4h4YvZBqmw1ikj/IXPBwOHdkbjQZOT0/x4cMHHB4eol6vo91uO4vIBxqAy8VTqdc+eC1JfahrTF1XzcMzLffs2TNH+Eql4lztsFHW1CHYLnt0dIS3b9/i22+/xatXr1wcz/HU/Ey69VtbW9jb2wt4ExxiyXQlcJep0MpBkp1eSiKRQCqVQiqVcgcUr9Nf/83WWk2H8v8Lfx2nItJwB3PvH0Gv18Pp6Sl+/PFHHB4e4vj42A2IbLVa7oFUN5VxP1NWzMlrKarm3R9qntFKu0wmg3K5jJ2dHTx//typ9GrhNdZV666z6z9+/IjXr1/jxYsXePfuHQ4PD92OeSV8IpFwgzT39vbcZ3H9lZbMAnDhj9bYK+G1I48tuOzrB4JaAwVA1UtYpKO1EUb4yfEg6dvtdiT7likcAcC7d+/w+9//Hu/evXO16GxCoVvP1lB9ePngttttR/Yw687PU/hFKZqH39/fx9dff33PpdcUFq9B3fnLy0scHx/jxx9/xJs3b/Dq1Sv88MMPOD09dfl4pucYpnCu3ubmJra3t7G6uopCoeCISneen+cvs+D3yAwC74ODNnRZJr8zWni+jw7EnJ2dddepL7/pyfAwHiR9o9HA73//e7x8+dKVcUbhi+XDent7iw8fPuC7775z8bsOsNCuNgp4ANzDqA+uro8ex7pryyljeE3L7e7uuhHW/joqLavtdDo4Pz/H4eEhvvvuO7x8+RKvX7/GTz/9hHq97oqA/HiZrbnr6+tOuGPen/cblg1g6MIDkYehbsnlWmySHoAb0sHDki8eRCQ+v1N+r/6QTO0ENITjUUv/29/+Fr/97W8BAMlk0lVVfcnQ+HFhYQGZTAaxWMwJTEoQPtCqQPNBZEGKxre+KMX3Ae6WZFDV5pJJjq1+/vw5njx5EqrShxG+1Wq5Kjsq9G/evHHxu86843XQHad4t7W1hc3NTSfchaUBKXJyg2632w0IgbOzs64jr1QqoVQquSUbOl2HYYgSn2TmIcI/1xqAUfMFDOF4kPTD4RCtVsv9N2eXf+lQxZmxbtgsOXZ/cZkDDwvtlfd3qj/myicSCUeQSqWC9fV11zxDF7tUKgUWTpKsPuGr1Sp++OEHfPvtt47wx8fHrrZA8+eqH3B67vr6OjY3N7G8vIxcLhcYwMHviYebdtRR/adLTm+lWCyiVCqhWCwim806b6HdbgO4Iz2Jreo/vx+t9Gu1WoFyZb8d1xCOB0kfi8WQyWTcf7OhIkpgrAoE3W998Jm2UuKpGx+We/eFOs6Mz2azWFpawurqKra3t7G9vY3NzU2srq5icXHR9azTgvoWXhtnfvjhB/zhD3/AN998g7dv3+Lk5CR0jLVe09zcXCCWZxyv5byM5fl5TE2yspDGgY0xmUwGxWIRlUrF3QPderrqw+HQqf4Mhyh4AnChUzwed+KoNvNks9lAtsAwGo+q9/qw6sikqIJuK3DnotPSa655VCqJD6Qv1GWzWSwuLmJjY8NZdg7AKJVK9/bH+y42wwrOqn///j2+/fZbfPPNN3j58iVOT09dLX3Y9Fz+mkwmUSqVsL6+jvX1dVQqlXvlvJoV0Ln8TEly5HUqlXIhytLSUmBhpm6epXXmIaKk11idXgErHLVtt9frIZ1Om4s/BixlNwEeeqBGKfGEPyGX8S5Jsba2ht3dXTx9+hS7u7tYX18PWHaf7D7hKYI1m00cHh7i1atX+MMf/oA3b97g5OTEZRtGWXim6DKZDJaXl51b78/V0wNG123V63Wcn5+7jA/d+nw+j0qlgqWlJVQqFWflY7GYE+OohejwTN14Q+vNA5ekbzabgb6FbDZrpB8DRvop4be3Agh14f1f6ULH43Fn3be3t/H06VM8e/bMDb4oFApulDTd6sdGVl9eXuLk5MSJdnTpxyE8C3HK5bKz8tyCozvjgbvYm4s4OAeAo7KHw6Gr5CsWiyiXyyiXyygUCshms64piYq8HiZ+daIeplruzNoHkn9xcfFeQZAhHEb6CeELbyrwaUxP8O9VmZ+fn0c2m8XKyopLwz19+vSeKk+XWr0EQknix/EvXrwIiHajCK/XmEwmkcvlsLq6GuiRZ6qWn8lsBKf2siSZe+76/T5isZhrCFLxLp/PY35+3in219fXgYNs1PWpAEpRVUlPN5/1AayKNITDSD8BtPdbx0/pw0oyqgvOlB7HRS8uLmJ1ddVNuNEGFlr3UV1kfvmprpP+8OEDXr58ibdv3+Lw8BBXV1eB2oBR90OrXC6Xsbm5ifX1dXctdOtJPCU8d+uxgYhTdijecYYeLTy1AV6/306rnpN6UEBw/HUsFguIeZeXly5VyHs1Sz8aRvoJoCktJaZuX+HPqRvPvnfWza+urmJrawtbW1vY2NjA0tKSi3W1AWXUg+sLd2yPff36NV69euWm3rAg6CHCU61nff3m5qZrzSVBeX/anVetVnF8fIyjoyOcnZ3h4uLCWXlqA4VCAblczq25YqjCTAfvRb8z9YgokPopT07ZbbVaTthj+s7fqGu4DyP9BODUFqrSyWQSQLD/W6v0dPNLqVTC0tISVlZW3KtSqSCfzwdy7o+1i6pyTsIfHh7izZs3ePnyJX788UfU6/V7hTcKJfz8/DyKxSLW19exvb3tJuEkk8mAhadwxy7Do6Mjt7OeVv729tZ9N1puq0Kkkt1PZ+pkHC2x1fQnf880Icucw0ZsG8JhpB8TdDcZq5KoGsfrz9DSLS4uolwuO/V6cXHRWcBMJnPPa/DdXT908Pvh2R774sULvH37FtVq1RFwlLCohF9cXHQFQGyoSafTzsKyQIYkq9Vqruno6OgI1WoVl5eXbioOt9eyqYaEH9U+q6uuaOGV9KzEAxAodGJVHsmuY8bM0j8MI/2YYD6dK5zYeKIiGwmvqvXS0hIWFxexuLgYsHwU6h4a7ujH776FZ7ecX23nP/iqCzDtR8JvbGzg6dOn2N/fx+rqqlPXqZbrHAHuqme3Ya1WcyOyKQamUim3slqLiIA70mpjDqvpADjdg2TXhZ78HnhAaOWe1uPrbgCL68NhpB8Tmscm6VmlxhcJTwtPK08XnlZdq+nUnR9FfLWOHGLJiTccgPHx48fAjHqNlbXyj6uoSPjd3V3s7+9jY2PDTcIh4fl5qtIfHR25ZZo8YKjWU79Ip9OO8NqPANzpAozJOReQwp5P+l6vF9hvpyk5nVHIl3oOhnAY6ceET3qKU8lkEvPz886lzefzKBQKAdVaLbs/E84vtFFoHMu8+Pn5+b0RV/4ADO1j10IgHcLByj/G8VTrZ2ZmArv1uISDG2/YistZAhTu+P1wMAYJzxCBZGRBDwtsWq2Wq9XXcmSmBlUHABDQKbST0R/cYRgNI/0YIDEpzinJM5mMI7u+GLNrTKuqfBjRw9x5HZ9dq9Xw4cMHvH37Fi9fvsT333+Po6MjR3iNjXnddJlZ/76ysoLNzU1sbW25Mlu/4416AffZ8cVRXxQJSXh+NyS9TtTRhZu6xotpNsbkusnWX1tNaN+8bgvSbbhm5R+HkX4MqGvMuHV+fj7g6vPFdc105/24PYzs+q4hr8MAABRZSURBVDCrK69VbxzTxbn079+/x+npacBSAnAHC683k8kgn89jaWkJa2tr2NzcdMU3xWLRpdIAuOYZbs09PDx0Cn29XnfNNCQ8U3r8LN4vVX/1PHSVlz8P3++F970hv1KP3x0PFS0+Miv/OIz0Y4CqMl15Wnef8Izd6faHjWpWa+634erwDbaq0tpy1dT79+9xcnISWF2thNf2XNa9r62tuTbZlZUVLC4uugEWbG9lTNxsNnFycoIPHz7g/fv3ODo6ctV2SngdX02dQl16Kul07/2NN3wv/X7ZSafuvNbh+01MauFVwzA8jEdJr+4Va6ajBLbOkkQcAsHUG135h2J3fVA1Tqc15+CITqfjXN/z83O3BYdrq7n6ioIdlWp+jpKdqTiunlpdXXWioopstMjX19e4urpCtVrFwcEBfvrpJ3z8+NGtyaabDtyN01JXXNdMk9AU4rSAhh1xFAAp3vHFEEPDG51NEPZd0kMYlQUxBGFDNB5BMpnEzr+tb9rf38fu7i5WVlaQz+fdcEeq1aMKUAim3HSvHfvQm82ma0/lyupareYWXPrba/nga7deNptFuVzG6uoqNjc3XUltpVJBoVBwtQXaykoyc6zW8fExPn786IZ/Xl5eBmJuGgFaZh1lDcC9n2720VJZP0XHbALvh8MvNZ0Xpsgr8YFgOtKI/zAmGqKRSqXc/7AvGVSwh8Mhnjx5gr/92791wyz8uXR8aXqKcTktkApPLHJhOyqtObfWNhoNNJtN1yeua6GU7L5IVyqVXHkvr5Vxuw6g1I5AWtFut4tGo4GzszMcHR3h5OQE9XrdEd5f0KmVifq+6tYzPGHrK/vs1Tvhe/B+gDvSs+BGdwWqwMn7oHVndsKWWj6OB0mfTqfxD//wD/jrv/7re8MQv3Qwt5xIJNwedo6polg1ypKry63DJtrttiPX4eGhK3JRt53W0E9BqWXnsItUKuUWSm5sbGBvbw/b29uuLVZ78RluqJbgF94wLacxPAmvqT+GEXxRCOR1c/pus9l0o791lh3wx1BRvyt6H5x4qzPw/K29Cqb5+H3w/41hNB4kfaFQwN///d/j17/+9Z/rev5iQMLyoQ1Tp3UTC2N0/luNSf0CF7rPrGrjtlq6xtpPDtwRncU8zLeXSiWsra1hZ2fHEZ6TdsLq+YG7TIFOvWGl3cnJCarVamDOHXDXFkxyMaThZ9Azurm5cSu/Go1GoPtNN9Xw++N7Ux9grp7rwHRHgIp1vB/VFObn5+95M4ZwPGrpo45MJuMeWpJO3XctOtEx18whc1stC1wODg5weHiIarXq1ltp2onwy0ipcKfTaTdHj9Z9f3/fhR4U6lRf8NNezMWzeYbdcmGE5z2z4pCZCy2zZcst1XlaeN1Lr+GOuua01JyTB8AdlNpA48O/Jl6PX8xjuA9L2T0Cqst8uAEEFGOKYf5SC7q4jUbDWXjtPb+6urpHiLB6ee08S6fTKBQKbjQ1hUWKdWHtuYQ/AMMn/NnZWaDwBggSnulKLqngOGwAruhGXXqOsPLn0ivhtUVYi3jo3o+qsuNBxu+EPQ3pdNrc+zFgCyzHgFp4fQBpNem+U4Djq9FooFqtOgWeW25p3bVOPuyh1pp+1suvrKy4vXLMKrCZh5tn/KGZfikv++GpK5ycnLitPVzT5YuUbJelVWWHIcXJMMJrWpGgCOen3egx8RDV1d0aFvD/h04P1jn6pt4/DltgOQa0xVXJxNQSh0NSkNPUmy6upCegVs1/Xy18oY6wsLDgpu1sbm5iZ2cHW1tbbga+v8eO16eEopBIwrMfnm59q9UKbN/l4UFysSiJoYOW7LKOnjG8T/iw5h8tFeZ3qbUL/r4//X9BIZPty5wWTH0h6s/rYzD3fkwoKfVBVdJzQKQSn7Gtrnsa1faqU2P4UHOM1fLysquqW19fD0zb0ThWS1rpzpPw1BbYLfcQ4dXL0KEhdJ81/cg5dSrahRFexUi+v163jsBWy6/flfZAsAiJB5+JeOPBSD8FwiyoP5aZrr5uYPEtXliumcUu2WzWrYGqVCpYWVnB8vJyYMON7hf0ww6N3ymu1Wo1nJ6eOm2B9fQ65ooCIMtq2TWnn8WyWr3nx7wYLZ7h5/AzNBuio6/9uXjAXYqODUSsjNSpvYaHYaSfEn6nly5d1IffLxNlagq4v7tOxbJ8Pu8myZbLZWfR/L3w1AW0dFWbdVgIRC+EnghTkdfX14E6ehKRRNeaeK0m1Gm07XY7kHL0Y3AeaFpyq3P8/XZZ30sgKGpycm+lUglMD7Z4fjwY6aeAim8kv3aBEUxJ6VpnPQBYlaY9+blcDrlc7l6bLmv7OYiTabVerxdwkdWdJ+G5Xltz5yQoMwMkE6+HB5FmKDQPT09Gu+X8tCOAgHVnAY12HzLdx3n5/vcL3HlGauU5qITlxebajw8j/SdChTdVu5PJZMCiKxn8KjIWujD1xHHRFM4Yt7OCjnPufVGR5NHGHS2D9TfB0s3mddAKa/UlxTp+pqr0mof3Ca/uvHYnsuVYvQd6CWFWWkMgeiK68tqs/OQw0n8idJgjH27m3vng+w0yvnXnEElt4NEZejqAwi+j1SGTuuK50+m4AhdNDwIIxOssbtHP1O47phe1tLbZbAZGTqtLDwRXeOv473w+71KLVP9brVZAJ1D4IqdmM/hezM0/NkXYcAcj/SdA69FpsbXnPJFIBLrK/Akzmvsm2Wl9abloRUlq5rD5e75YyKJlwZrj1oUbOhNAra9qBdoe6+fhw9Jy+p34U3EpuLH5h15Qt9t1Hkyn03Gegd/jobqHVgWS8GblJ4OR/hNAC6RkYjkpXXglvf4sSa+DJBlbA3CE0mo//6XDIHVclA7CpFWnW0wrSZ1Ay2mpF2jaTdV/tfB+Hl4VehKeKTWO/2bWYXZ21pUoD4dD14LrDx1R194XO63OfnoY6SeEryj7rabsDVfSUZ1W8c5XxinC8Wd9QY5quc6VCxPPVFdghRpz2oyDOfyDc+nDWmPpcpPw2uo7SmGnlfZ7+1dXV90GXLbSdrtdV3N/dXXl9IRut+ssPasDtRRZJwob4aeDkX4KhJXNkuBc1KAxPMkM3O1iZyxOz4APuBb9MI4m6dl6q+KZ5rFJXFas0eJy0g/TfoVCIbCIQvPvPuH1sNGmIl9dJzHpfrMdmaO6OKIrHo/j9vYW7XYbg8HAXQevJZlMBg5JfW9qHLo4I2yclrn6D8NIPwW0xDWM/Lpa2m9r1Tx6v98PxLF8b6rwOnlGG3pGWXitBdCtsZVKxeW0OZZbF2VSpafYR6WeXXOsJvRr4XnPeujRq2AV4crKCsrlMorFYmBj7XA4dGGNEp4HJ78v4C4s0jJjrTYMK9c1jIaRfkIo4RlH+8MZFf7Qy7C+cI1ftTnGX900ivBKOpbvkvBLS0tYWlpCuVx2xT1U6mkxWYJLwjOO9/Pwo+rgfaVeZwlyQjAPGS2l9WfsUXugN+FvD+L3Tw9JF12M+v4N92GknwB+QY6SWQ8BHYLhD8AM2yTru6O6IIIWVkMEPShUU/AXZpbLZUd2uvQ6qZfhAAB3jczxs8qOLr1/vXSjtfiGWQGWENN1HzW7jvfhi46sWqRHoPUItO48DHXQphY/GUbDSD8FfOutVtx/MYWmm1h8iw/cEQC4PzGXf68jounOh1lZlu8Wi8WAYEd3np+hE2t11ZROrSXhta2V0Ek+2pijBwvBNCaJq6lFVf/9Tjz+vVYb8mDyr9MWXYwHI/2E8ItjHiM+LRZjZlp8v5lEXwoSh7ltfbC1BZfDJOjWayGMzvSjt6DXxuYZvkgkFR9pibmsktet7jnLeH2dgEU3OmOfYYMO/fRJ69fkc5yW7qX3V1Sbi/84jPQT4rGY/qGHTuvzfeVdy3n5ex4AiUQi0EFHC6txPF17LuFg4QpwtwlG43e+l8bwHIBBEgEIxN7A3cQb/l5ThFpQxJZjqvUsxCHpWdKrbcfqBWk/gz91Jx6P3wtBwg4NQziM9FNArX2Ymq1kUOWZ1WeDwSDgZivhVf3XLABwf0CmL4Spa03XmEo8ye3vifdn8LPwBgiuq+Lna5eg1sP7bbfNZhMA3OezZgCAG3zZarUCFX4qzPlhEFN4LObR0WSjRo4ZwmGknxJ+maiKUfx7tez+GGd9oFXRJtFoYTVVpeutSUB+JsU8fr7OmdPW2TDdQOv22b1HnUDLeDV2DqsP6Pf77oDSabvpdDow7EPr+lkHoFZbFXl/DgEPFoZK/vdqeBxG+inhN4P4LrCfo+ef82d9FZ+k11cY+ZW8egj45Ov3+2Pdh4YrjMEBONKrJ0Lia05fRTYVBhkyUOBjJR2tNbUECnJcrBG23EMPRhVISXb9Dky5fxxG+imhPd60toy3NfXkt93ycPAHTqjl1jJdbXXVbrIwq824dlQmwS+ZVW2A70cSsUCG9xqWXdBUJd+71+thbm4OnU4n0Lqr9wTcFdcw9abLLbQIyPeoNG73B3haWe54MNJPAV940yoyxrwcUKEv9Qj8bjhfnFN3nX+mQp9fhqrZAc0S6OGiBwzfS1t4aYn1Z9g7wDw+oaGBHip6GCrxdee8pu60HZiE1/fzRTx+/9qpqM03Zukfh5F+Qmgu2Y+r2V3Hh1UtkLr28Xg8UD/PXDwQtMBAcFGjptz4Z2G1ACSQvwSS76Wpvm6369zv29vbe/vutHTYL0jSoiFfZeeL15dIJJy+oEKi3yoc5ploKEXvR1OUnBdIodDwMIz0U2AU8VXZ1hZXRVglHsUtTcvxIFBrzoeapNFcuBJeX/x7klItMV1vv+7AF+PoevNXVdkfm3yrPQrMsxN67X5I4gtzWgikQzHZo08vxSz94zDSTwmNcf1qMsbFfMj9mF4FOR0MqcRnTl1/Lsy1p7dA0vvuspLed7+V7AwJ6Jmo+02lXQmv22fCavJ5/3x/33PRHgMeNmFxvIZRJHy5XMbKygpWVlawuLgYmJFnpH8cRvoJ4YtaGmtqDOq75frvFVoZp3E3f/YhwS2stt8vcvFjbh5IPGz4bxOJhMvj87q08Ue1ApLdd+v1+xhVA8/70zTmqBy737KbzWZRqVSwvr6O7e1trK6uolAo2A67CWGk/0T48+r0AQ4r0/Wbc9TKKgmAYP7f/z0QVNHDxC+F3yxEL4GWvdvthqrz9BT8a/c9EyAYrmhcr9evoYmm/MIOR5/w5XLZLe3c2trC8vKyGwluI7PGh5F+SoTlt7XFM0xVf2imnZJKXXElOl1yP30GBInGyj/9N/xZ3zJfX18HyKKHgqblwlxv35r7JNfUpVpiHmqjwgK+1+zsrCP84uIitra28OTJE+zv72N9fd1ZeXPtJ4ORfkL4Fsq31trrrqT3ya315Q+V9Spp1Vr6Fp8CnBKHoAio1YF6D76lDSP5KPdbr1PJrilHba1lt50KnWEvxvA+4Z8+fYrNzU2Uy2UT8KaEkX5K+OTXAyCsC09J5BOXjSgkhar0KuL5YiDfQ8ms4hvHYANw8+41u0DV3S8X9u8vrMRVvQ3tL2D+3N9kQ4Tl+3lo8T2ZluMev83NTUf4nZ0dLC8vu4WVVpQzOYz0U8Ang+bg/bSVlo/6jTgsXFHhzVfwlezqKrNYx8+pM3xglRvn6ulATd6DpgrV+vv35xfIaEWg9vNzPh5HetP11lZbZhdY0jszMxPwjpiWW1hYQKlUcmu59/f3XRwf5tabpR8fRvopoVaJU2NmZ2cDMbD/UvEuTOBTsoVZea3L1248IHxLLWvauUaawzX9Edq6D28UNNbWDj8O7+AAD87g0002TD/qOG8eRCxSIuk5G4Crube3t7Gzs4ONjQ2Uy2XkcrnAYWKu/eQw0k8Iv7iFTSmcgqtFNaNU6lGHQpio5Rf/aJ5fFWslPcmlq624qII982yjZe5ddYeHrkfvm5trdNouZ+L5o7W1zp4Hj5KeqcRUKuUGa3KS7vLysivCYcmtEX56GOkngB9zptNpxGKxwNhmnyxh8TD/XEUyP9ftV/35qS8t1OF7aG6ePee09CS9jtLWPnbtTQ9rn2WpMWf7Z7NZFAqFwCw+EpNDMLVAh/l+38rrbj1/SQbn+/EQ0RjeCD89jPQTgpY3lUq5xg/G4sBokusD+tjDOqrSz9cJFH5tAAt1aFlp5Ul8Ei9sRZbfoKOE54hrlsFyFh9r4NUS87rUA9FyXp3BR9Wfq7Cy2WxgkKdv3Y3w0yM26iH9N9hkAg9+Dl4J/xDGIb3/50r+x94jLJVIoqlbzdherbxaeL9jLha76/MnIblCmxN21e321XT/MPI7AbVKkKO/wnbYaxOSEX5shH5RRvop4Mfo/DMf45J7FB76Ob+gxr+2sDJdKudM5YXF9Ep46gi6UpsvtrTS5SbZR3khmiXQEl4/U6Ev3403sk8MI/3nxiPf3WfDuA+7fz2jDgDf4obNo2NIoVkD7Y1XCzwq5Ai7Nl/L8O/Tfy8j+ifBSB9V+IQblUnQn/P1BP/1qaScxDMyTA0jveEO42QYCLO6v1gY6Q2GiCGU9Fa0bDBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxDD3yN/H/ixXYTAY/mwwS28wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIob/DyJg5nL3B2W6AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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XKpWJZ3Dq0FteXhZTcNxut4imU+6dou7UGgtABOqmrdjTzuvkJaDu+2dmg0X/DSB/6Pv9vvCypxX+4OAAZ2dnKJfLd07PGQwGuN1urK6uIhaLYXV1FW63+9rqqI7ay3UAZIU1SYB6vV48//r6OpaXl+FwOMTzU/lso9FAvV5Hu90WNfi0TZfbcOWtvVyyK79HdFwgow0W/f1g0f+BqFd4qnMnwf/66694+/Ytzs/PUS6XZ2qqMZvN8Hq9WF5eRigUgtfrVazC06yxms0mcrkcMpkMcrnc1J2FyWSCz+dDOBzGysoKlpaWYLfbodFoREdepVJBPp9HqVRCvV4XBTVyg45awMPhEFqtVrEzAKCw4aKVns/094NF/wei7levVCoKwb958wZnZ2coFAoz1dcbDAZ4vV6EQiEheHlbPy0o1+l0UKlUUCgUUCgUFIKXI/YUIKSIfSAQEF1xw+EQ7XYbpVIJmUwGqVQKuVxONOlQQY3slUdmnHTD0Ol06Ha7GAwG4qG29JKLcri1djZY9A/EXT946i0rADGEUg7avX379l6CJ0Gurq4iEolgZWUFLpdratBLvoZisYhcLodcLodKpaJICcrip7N8JBLB6uoqvF6vqOyjUuFsNotEIoFkMolsNotGo6FoqyXTDLL+IiNM2iW0Wi20222xO6AAHp/n54dF/4DcRfjqfx8MBiiXy0gkEjg4OMAvv/yCg4MDnJ+fC8HPkqPW6XSivj4WiwknnJuuYTQaiZLb8/NzpNNp1Gq1iRVvZrMZwWAQsVgMGxsbWF1dhdPpFDeVbrcrnisejyOZTKJQKAhHW/l9Ipss8vozGo3CZpvO+eSvR6aZLPr5YdHPiTrgNE34k/6OSmtPT09xeHgoVngaUXXXFV7eeut0OlEht7a2hmAwKM7a8jXK19Tr9ZDP53F2dobj42Mkk0lUKpVrotdqtXC5XFhbW8PW1hY2NzdFAI/q5MkmO5lMIplM4urqStF/L+fkjUYj7HY7vF4vfD4fzGYzOp0OzGazED2d8dWC5+38/WHRz4naGIKCVDd9KOUpNVRpR4U3yWRy5qCdOndOAbZgMAiPx6OIjMt23QSV3H769EnU2auLfzSaz/PuQqEQNjY2sLm5ifX1dfh8PphMJvE7lctlcZbPZrPXjgn0XCRkEn0wGITVahXtuPR7UemvxWJRBP1Y9PeHRT8jk4JgVBNOqahpH0g5jVUoFJBIJPDhwwe8ffsWHz58EHn4+xpEmEwmBINBrK2tYWVlBR6PRzH+SR1PoBr7ZDKJjx8/4uPHj7i4uJgYsTcajfB6vYhGo9je3kYsFoPf71f04rdaLRQKBWQyGVxeXk6NR5B/ntlshtPphMfjwdLSEmw2G7rdLjQaDbrdLjqdDlqtlhh0wYJ/GFj094A+dKPRSPR4k1BNJpOoMydh0dBHKm2lINfx8TGOj49xcnKCy8vLOxfeTMJkMmF5eRlbW1vY3t7G2toanE7nVHdecrVJJBI4PDzEu3fvcHJygkKhMHGX4XQ6sb6+ju3tbWxtbWF1dVVxbCB3nGKxiGw2K7b16hsYHX+oB9/lcontvdVqFQYZjUZD5Pipn57Lbx+GG0W/qOaD6rpwebsuf+BoxS4Wi2i329BqtbDZbLDb7WKLOhgMRIUdOd7E43HxyGQyKBQKNzrL3IbBYEAwGMTe3p6ii44sq+RzNN2oSPDv37/HL7/8grdv3yKVSqHZbF57fofDIbr0nj59KlZ5uRd/PP48TbdaraJcLqNSqSiCd4S8ytOQDb/fD7fbDZPJBJ1OJ/zvJ23n1TEUZnZuFP28b67aRkputHio/3G3PZf8YZnludQBuUmVa+l0Gqenp7i4uECj0RBtpk6nEyaTSXi1yw0sqVQKFxcXYvvbbrdnsrtSYzAY4Pf7sb29jR9++AE//vgjdnd3EQwGxdaboO65XC6HeDyODx8+4ODgAO/fvxfZAnXwjkw0aW79zs4OlpeXYbPZrh1xKN3WaDTECGv1+075dvLS8/l8YrKOPORCLfTRaKR4sPDvz42iZ1cSJWTgWK1WkU6n8enTJ3z48AHxeFzYNNvtdpF+GgwGYkJLrVZDqVRCoVAQAyPmmTQL/L7Ck+C///577O7uXquxH4/HopEmmUzi9PQUHz9+xNHREU5OTpBKpVAqlSZG671eLzY2NvDs2TM8ffoUkUgELpdrYtqv3++LszgV16ifz2Qywel0wu/3IxgMIhAIwOPxwGaziV0B1eyTYUav1xPDLMkxhw007g+r+o7QmTyfzyOZTOLk5EQhmna7LfLOZrNZ5Jg7nY4oNiExkPPLLKh3NPIK//r1a7x+/RpPnz5FKBS6tsI3Gg1ks1mcnJzg/fv3eP/+Pc7OzkQOvdFoTBQRbet3dnawt7cntvWTynlJpGRlpd7hUV7ebrcjGAwKY85QKASXywWTySSOA91uF+12G+12W7xvGo1GeOOxa8583Cj6g4ODr3UdfzjqkliNRiOGRAJAsVjExcUFzs/PcXJyIvLZmUwGlUpFTF6lgBN5uHe7XeHcOg+yiEwmkzDG+PHHH/Hjjz/i2bNnim03ZRQajQZSqRSOjo7w9u1bvHv3Dqenp8hms2I01STMZjOWlpYQi8Wwu7uLJ0+eYGlpSdGWK4ueBE+ltvLvS9V3LpdLOOzEYjGsra1haWkJVqtVvO/k6U8PWfTkjEurPXM/bhT9X/7yl691Hd8ElL9uNpvQ6XTY29vD69ev4ff7cXV1hZOTE5ycnOD8/Fz0mrdaLYWNFKWcgC8TdKIo/d7eHr7//nv88MMPePr0KcLhMKxWqxA8BdXkWv53797h7OwM+Xwe3W73xhuRw+HA6uqqKMKZ1JYrr/KUXqPSWfp3qqaTc/w7Ozuims/j8cBgMIguvHq9jlqthkqlgnq9jmaziXa7Lerx6SbKwy7uz42i/9d//devdR3fJL/99htyuRxWVlZQKpWQSCSQTqdxdXU1Nb320EKn0dc0oprO8N999x1evXolzvC0wlPDS7FYRDwex+Hh4bXS3knIxwe9Xg+v14v19XVsbGxgbW0NHo9HEeORb2zynDvypQcgcusulwuBQACxWAxPnz7F7u4u1tfXsbS0BIvFIppqaHRWoVBAuVwWLbndbldhgU1BQrbAvh98pr+BfD6Pf/u3f4PD4RDbTprj9jWgnngyl/T5fFhbW1NE0ukMTys8edWfnp7izZs3+PXXX/H+/XskEglUKpWpryXfqOx2OwKBANbX18UWnFZ52h3IVX7tdhuVSkUEKDudDrRarWimoWk6Ozs72N3dRSwWQyAQgM1mU8Q+aBQ2pfwajYYICFJhE235aUchpw2Zu8Gin4C8iqVSqa/+2jQTzuPxKAJe1Lu+trYmptPIYux0OigWizg5OcHf//53/PWvf8XBwQHS6fSd6wDMZjP8fr8wx6Da/WmQB0A+n0cul0OpVEKn0xG5+GAwiI2NDezt7QnB03OS4GmVL5VKKBaLKBaLqFQqaDabInA3GAzQarVQq9VQLpdRq9Xg8/lESy5zdzhlNwE62991csxDvabdbofb7Ybb7Rb98NFoVNhKU2+80+lUDIkEfm/Pjcfj+O233/DXv/4V7969m1pwMwlqm11bW8Pm5iai0ejESbbyTbHVaqFYLOLy8hKZTAbFYlFU0Pl8PkQiEbHC04ANh8Mheu9pVl2tVkOxWEQ+nxeCp6AgAHFjKBQKuLq6QqlUEjcP6rjjvvq7caOqeUjg18FoNMLtdosedbKSDoVCWFlZEYJ3u91iZVPHDRqNBi4uLnB4eIhff/0VBwcHMwke+N1Xjyy2VlZWrlls0X9T2W2pVEI6nUYymRSlxKPRCE6nE8vLy6I5JxaLiY48KlEm0dO2nlx2aDKtHKWXqxppem6r1eIinXuwmEv5DExqSX1IaAtMjSw7OzuIRqMIBoPwer2i15y2w+rrGo/HQvDv3r3DL7/8gsPDwzsLXt2Wa7Va4ff7EQqFFOk02eYK+CxCcvo5PT3F2dkZstksms2m8M9bXl4WcYFAICCKlug1h8OhqFakKbylUkms8oPBQDH5ho4ANK2XJuES0xyBGCUs+lv4kiuJzWZDOBwWgblnz55hc3NTuNFQkQ8w/YPc6XSQzWZxdHQkTDSTyeSdV3h1/p8aYDwezzXzDYLKedPpNI6Pj/Hx40fE43EUCgUMBgNRU09xCPLPMxqN4uhEZbuNRkOs8mTR1e12FS47dJMg01C5tp+LdGaHRf8HQGYU6+vr2N3dFZNnNjc3EQwG4Xa7JzrDUM059aOTxdXJyQnevHkj0nI3Remnodfr4fP5EAgEEAgEYLfbFTEduvmQ4Kng5927d/j06ZMIFprNZthsNtEu6/f7RS+CvDuhEmXy5Mvn84q6B7m+Xr7hUZS/Wq2KTAq1D9/mY8B8hkX/laH58Ovr63j27BlevnyJ/f19xGIxLC0tKVZ3NeohENQLf3h4qGiamRWNRiPO4Ovr6wiFQtfacoHfg4WpVAofPnzAmzdvcHh4KNKB4/EYdrsddrtdNNO4XC4xxw5QCp6Cd1dXVygWiyJFJ6/yamSn3WKxiGq1Ko4+LPi7waL/CpjNZpjNZtjtdvj9fmE39eLFC9HEQpVphNqNZzweK0TfbreRy+VEPf3p6Sny+fydr0k+y1MdfzQaFR75LpdL7DYoF0+C//jxI968eSNKeiliT7+nw+GAx+MRgp/kLVCv11EoFJDL5UTEnqrybiqxpYEcl5eXSCaTWFtbg81mm+jpz0yGRf+FIaGHQiExDYZMJScZSxK32W2Vy2XE43EcHR3h9PQUuVxuJk89GYvFgqWlJayvryMajSIUConz/HA4FFHzZDKJo6MjvH//HkdHRwrzTr1eL8pt3W43nE6nOMfLvQC9Xg+1Wg1XV1dIp9O4vLxEPp9HvV5XtOKqfQdlaIdzcnKClZUVeL1e8Vry9/PKPxkW/ReCnGFoltzm5qYoaQ2HwwgEAvD5fIrCF/WHe5LrDTneJJNJfPjwAUdHR7i4uJg6iWYS8vcZjUYsLS1hbW0N0WhUpNXG4zHq9TrK5TKy2Szi8Tg+ffqEjx8/is5COoOPx2PhGORwOEStgdVqhU6nw3A4VFTdkeApzUcR+7umiOUpPBsbGyK9KFuDMdNh0X8BTCYT3G43wuGwCNQ9ffoU0WhUEcme9iGd5I1Pf1+pVERN/cHBgWJ7PY1pRiNGo1HUxFPWwOFwYDAYiAq7ZDKJs7MznJyc4PT0VDjc1mo1MaaKRlOZzWa4XC64XC7YbDbo9XpRVEMNQPl8HplMBslkEolEQnQpdjqdO5tjjEYjlEol5PN5EQuQo/hcpHMzLPo5oFZaClKRMabL5RKCf/nyJZ4/fy4i8+pa8UmTYycF0CgvnkwmcXBwIGrqU6nUrSW2k2bCUT4+Fothf38fW1tb8Pv9GA6HSKfTCkvs09NThUBbrZaiBp8m1TgcDlEtqNPp0Ol0UCqVhPEIFdak02lks1lRtkteelQLMA355iUPxVD37zM3w6K/J3q9XgSQHA6HEL7JZILX68WTJ0/w/Plz4Sm3tLR0bWWfNKRRDQ3DyOfzuLi4EGmyd+/eifScvMrdZB9GRTM0T359fR1PnjzB1tYW1tfXYTQaxUp8cnKCT58+4fT0FOl0GqVSCa1W69qOgm58VqsVdrtdrPCdTke08FLnXC6XEzPvK5UKarWa6KKbNLTyNmT7cRb93WHR3wPavsvFJ5SHpsGOZD4RjUavDY8Epvvjk4Bld9mLiwucnZ0pztTpdBqFQuHah119EyEHWboZUQNPJBJBNBrF6uqqmFlfr9eFd97R0RHOzs5Eae2087ZOpxMRe6fTCbPZjF6vh6urK7E7yefzovhGbpmls77sfaf+HW6C6hU4Pz8bLPpbUK+cFPiKxWLY2toSbaIkeuofp7p5teDpgz2p+IZWRTKPqFQqIoj28eNHYcJZKpXQaDRuFIfZbIbb7Rbna6fTiWAwiPX1dcRiMUQiERG0Gw6HyOfzolmHJuUWi0XU6/Wpr0OrPFlZW61Wcd6mQReUh6dW2VarpZhIqzYcmWXF5hTd/WDR34Ja8OQM+/z5c+zv7yMSicDr9YocO5ldkEGm2nhCLXYqVqFBEZeXl7i8vBTbYDoDJ5NJMXnmpu4/vV4Pu92OpaUlsar7/X74fD4sLS1heXlZNPDY7XYMBgNkMhlkMhkxeOPTp09iaz4N8ryTjzg6nU7Ux5fLZWEPXq1WxdGAVnRaoeWiGgrk3VX47Ip7P7i1dgKTWmtNJhPC4TD29/fx3Xff4cWLF9jc3LxWRUfCNhgM11pSZajuvFariR7xy8tLJBIJJBIJkb+mf69Wq6jValOfj6bF0IiotbU1kc4KhUKilp7y6Ha7HePxWNxYyOjz/Pz8VsEDECaXlJ4jBxwywaDIOkXu1TcqCtqpra5n8b7jrf394NbaCajz43a7HcvLy3j69Clev36NH374QQieTCzUfvk3lYW2Wi2USiVcXl7i4uICmUxGBNBSqRQymQyy2Syq1apoL70pOEdONysrK4hEIuLMTm26S0tLil0H7Tbq9TpyuZyIF5ydnaFYLN4qeBox7fF44Pf74XK5oNfrRb87rfDTvO8JEvl9PQVnjQEwn1nMpfwW5A9RJBLBxsYGIpGIMKPc29uD3++/NiduUvqNoOIUqka7uLgQLampVEoEuSiqfdOqTlgsFvj9fqysrIgYAxUABQIB0exC7bEyw+FQjMimKP3l5eW1wZVq9Ho9rFar2FHQTofy8NQ8MynSP4l5Iu9U5cfb/Nlg0d+A3+/Hzz//jB9++EFYVdF4ZrWo1ekjtXEkWUql02nE43Gcnp7i9PQU8XhcFLt0Op07D3KQTSpkZxrytKMuuUnBLqpfz2azOD8/Fzee22bpabVaIfhwOIzl5WXY7XbhX0dn+bsKfl5oR8UBvdm4UfT//M///LWu45uAZr3RXLqtrS38/PPP2NnZEWYWXq/32naUastl73d5Ogtte6leXJ5jR04xaiaN46KUIBX/7Ozs4NmzZ9jb2xNWVB6P59qwC7WZZbvdxtXVFc7Pz3F8fIxEIoFyuXzNVlp+bZrTRx2C4XAYbrcb4/Hnybck+Eaj8cUEL1+Pw+EQM/AsFosiQMpn/Ju5UfT/8i//8rWu45tA7hnXaDSwWq1ie0zz1SZ9uIbDoZhkU61WFWm3RqMhUm+JRALxeFyk3cj4cRLqFVer1cLhcAjR7e7u4vnz54rJM+oPv/o6gc/HDKrso219JpNBq9Wa+vpqwdPraTQacSwhA4yvscJbLBaEw2FEo1Gsr6/D6/VO/L2Zydwo+v39/a91HY8KeftOlk9k40RjmsnRlZxeqCItl8tNnGM3rZKObj5utxvBYBCRSAS7u7vY29vDzs4OIpEIfD6fItMiHw/kgKJsgEHz6KkXfppY6QxPgqemHKPRiHK5LObzfU0nG5fLhUgkgu3tbWxsbCgm7wC80t8Gn+nvgbz1JtvpRCKBs7MzsW2nUlNyeGk0GlPTV9MET1NwqaiGzu9bW1uiBdbpdE5NrcoffjLAkNtjT05OkM/np1prUY0+Db4gc0uLxYJGo4GrqyuRWqSimy+B+v1xuVxYXV0Vhps+n49X+hnglN0NTCuVlb9utVrIZDL4+PGjMJWQy00pbXXTtlf+QOv1etGxRuaSGxsb2NraEhWAVOknj4uWMwdyYEv2lkulUjg8PMRvv/2Go6MjpFKpiRV3VHhjsVhE0C4cDouMRaPRQCaTwcXFhUgtzjNuexYMBoO4ES4vLysGaqrNO5nJ3Ch6fvNuZjQaCQF8+vQJh4eHYmy1PHftrukku90Ol8sFt9sNn88nhj1ubGyIyDz14Ks95ya1k5IvPbnWHh0d4bfffhNeetQBJ0OltXa7HR6PR5h/uN1uAEC5XBYpR2rE+dITf+T3j0p+qcCIe+hn50bR85bpOvIHkGyZ5VRcJpMROyQ6307avlM1mk6ng8lkEq2uVCa7vLyM1dVVrK6uiq/JIVeNvLpRfX+/3xeDKFKpFI6PjxXdefl8Hp1OR/TD08w8uha32y2MLW02G8bjMYrFoigqymQyChONr4HZbEYgEBD23PJOh94H5nb4TD8HFLUnd9Z6vS5WPSrlpRVY7pU3GAwwm82wWq3CdIIso6mibmVlBYFAQFE+e9vctsFgIGa7k+lkOp3G6ekpPn78KNJzpVJJbMdpZadrot0Gzc+jrrlKpaJwrq1Wq+Km8TWQB3HQrmeSLx4L/3ZY9HMwGo2E0GjQIkE5f+D3c7ZOpxNNKmQ3HQqFEAqFEAwGRasurWROp1N4xQMQ5bj0oIo0MtnodDoiRZjP53F5eSlqAmi8NkXqKW5A3naUniRvO3KwpVSkOko/yxmenHVkgVJb7V1vGgaDAR6PB5FIBE+ePMH6+vo1q3AW/N1g0c8BiW5SKSmlysbjMXQ6nahXdzqdCAQCwpNufX0dKysr8Pv9wkGW3GfkqHyv1xNjmuWprVQA1O/30Ww2xYioq6sr0T1HM+aazSbG47FwrTWbzcLXTi12ej6aRkvtvO12+84BXqrgczgccLlcMJvNovipXq+Lvvq7pPnIkYhaltV9Dyz4u8OifyDUZ0v5QbluisZHo1FhlkmpNxKFvCLSKk6W0bTayu2qlAKkm0Kj0UC1WhXdbtVqVRhW0PadhE5beZfLBbvdDoPBIOIUZFFNqUcKTt51ZdZqtXA6nQiFQsIMlEp25RZiKlK6DapE9Pl816bvsOhng0U/J7Rt1+v1wt+dhEsPk8kEm80m0l+xWEwUlqysrEwsnZXP5hQxpwBaNpsVKUG55JduEN1uF51OR4iU0lzyFt7pdIq2WNpVdLtdEayjgiIaFDlLpZ3BYBDn793dXezs7GB9fR0OhwOdTgeXl5c4OjqCRqNR7FRugvwJHA6HYngGMzv8zs2BLHh6GAwGYRZJZ3kKktE2l3zhZYvoXq8njgPUa18qlUS7bTKZRCqVEsUw1WpVlPGqW29ls0oSt9frhc/nE3PqaIWna+h0Osjlcuj1egoDjFkFr9Vq4Xa7EY1GhffA3t4ewuEwrFYrer0eUqkUDAaDiEHcVI4MQIzKol2KTqfj1X0OWPRzIBtmGI1GmEwmMdyBtuhyvb7s/V4oFGAymdDr9URtP41vbrVaKJfLwts9Ho8jmUyKFZ4MJfv9/rUmGSqqkVOAFBwMBAJivr06WCd72pHgaXrsXaEt/draGvb39/Hq1Sv86U9/QiwWg8vlEjPpjUajSCeS2cakSkV6TvLUt1gsiiGY09yDmZth0c+BWvQGg0FsO+XprBThr1QqoqiG7LE8Hg9sNpsoMun3+2g0Gsjn80ilUkgkEkilUri6uhJpMhrwqIayA1SxFo1GsbGxIbri5P56g8EAnU6Hfr8vUnjzCB4ArFYrQqEQdnZ28N133+FPf/oTNjc3FT6BFIUPhUJYXV1FIpFAOp1Gs9m81lYsG3tSwxPPrJsfFv0c0Ioub+/1er2wfKKoPp2taTwzdd3J23zKwctbe2rQKRQKN7askjho2EQ4HMaTJ0+wvb2Nzc1N4ZPndDoVs+VoEAUZcpJr7X1y8BqNBg6HA6urq9je3sbe3t41J2BCDsqR806xWFRE8uXaBio2op0Nj6eeDxb9HMgmDiR4ys/LeXQAIo9ORTPZbFYU6JAQAQiTTPLPowj9bRNsZNssst8m4ZGphslkEp50JCS6JhI8DbO4LbCmRq/Xw+VyiXl9lJGgo416O051ApQ2pFiIGgpONptN1Go1UQA1HA7ZPOOesOjnRBa+nJsn/zcqQFFX58k3CtpqA7877cgR+ZvMIunMSznsJ0+eYHd3F7u7u9jY2BDDKGl1p9egasJKpYKrqytcXV3duqO4CdkOm4ZeUHyD3ieCjjy0ck/6HeVVnlKWVH9ANQMej+faczO3w6J/AGTByx9WOqNOK+CRz6zqnyVhyt1zauQIPXXj7e3tYXd3V/jxU/6dVkX19Nh8Po9sNiuChPLIqlmg3QOlCyn2MMkRWK47IOegbrc7dR7deDxGs9kUgy9TqRSWl5fFLoGZDRb9HMjltXKeXhboTUMcyPOenks9sWVSLpr+TU4D0pZ+Z2cHOzs7iEajCAQCihVePm7IW/pMJoPLy0sx2GLWbb38u1BEPp1O4+LiAkajUTQJ0U2Bpt9QGlIeYCmv9ur3i2oIEomE6E1wOp0wmUzihsZpvLvBop8Tit6bTCbxkEc03ZVJZbz0/LI/PL2WLHhyw93c3EQkEhEdaJQGlDvvqOCHZuOR8Mrl8lzON9RmTIU3ZIkdDodFum4wGKBeryOdTuP9+/c4Pj5GOp1GpVK5ttKr3xNKJ6bTaSQSCcRiMYTDYfh8Pj7bzwiLfg5IjFRxRxVjwO+rON0AbouEy2W76t0DNcVQvpq68zwejwicUanr0tISrFYrtFqtiCnQdr7VailW+Hg8jkQigVwuh3q9rhg1Nc3NZxok6EwmA41GIxyFaFaexWJBv99HuVzGxcUFjo+PcXx8jFwud+cjBc22z2azwvFHvsabjkLM77Do54BWXhrt5Pf70W63FRFydWfctOcBoMgE0PZdrkajm4rb7RaDJgKBgPCfJ2dYKm+lktxWq4V6vS5my+VyObGtJ0deaqSRbzo3DdlQQ6nJUqmEfr8vLL+TySR8Ph+sVqsw5aTXzuVyqFarMwUO6XehOgL2u58dFv0cUAWc3W4X1W+j0Qhms1kRwGq32wAwNaBHzyWv9CR6uYxWLqWlB/W903aeatkHg4HYypPYKS1HE2QLhQLq9ToajQY6nc61HPmsqz110FGHHlUVUn0AHQGoEeg+/viTZuAxs8GinxH1B81oNIqzdafTgU6ng81mg9lsFiW4JCAa4HjbvDZaaQ0Gg2jHpYkyVEpLnWZWqxVGoxGj0Ujk12U7brXYS6WSwqK70+lgMBiIo8hDrJz0fOT5XywWRVBSbgaaNX5AE4GplJgChMxssOjngLb3tNIPh0ORM5dLXeWIPkXHZYFNW7nkKD4ZXVAxD+XA5Tw3ib1Wq4k23Hw+r7Dkph72Xq+nGAlFwT75z3lvAJSH73Q611KGsz63RvN5piCV71L9gRzEY1PMu8GinxOdTid65WlrLxtXykU6ajFNi1bLufRer6fIfbfbbTElt9vtCuGTMQWt7lRDT331tI2nop+b0ojqGoF5kNOSsyIfLzQaDVwuF1ZWVsS8PrVdFgv+brDo54Ci90ajEVarFaPRSDTfaLVaxRZXdrohQcmWWjSznVJrtMUn9xrq4AM+1+dTh5xcsSa73MhuNxT0okYduTeAfg/5628xOGY0GuH3+xGJRBCNRkWenoU+Oyz6OZGFT9VhFD2v1+vCsKJcLovtPvWDy0KXC0xodaSacyrXBSCGYVLMQB2wo0etVhMBuknlvJNSXV/yPZJvLLNkBOjnl5aWxMCP1dVVeDweLsq5Jyz6OVFbY5Go1S23lG+nM74aEr66bp/Ow3KZa71eF6W1VHBTr9eF0JvNpoiM07n6Ic7o87w/8td3qVmg7zEajfD5fNjc3MSzZ8+wtbWFQCBwqzMwMx0W/ZzIPfO9Xk+cu+U/6UHDL0iM6g+/+owvi5Ycb1utFsxmM4xGo2jO6XQ6wjCTXGhodVcHDL+m8KnrkG5id43Wy4IPBALY3t7Gq1ev8PLlS2xsbMDtdl+7kTB3h0U/J2rB06TaarWKWq2GWq0mzvQk+kmCn/bcZKNFhTt05tfpdIr2WLrBkBW3Ongmd7vNK3x1DGASFosFJpMJer1eYRN+V+FrNBr4fD5sb2/jp59+wk8//YSnT58iHA7DZrPNdf2LDov+AVB70MuRetnrnqLtJpNJ0YFHqNt0qV2Vau3NZrOiJ55WejrzG41GEe0n4dPjIVf4SX0CRqNR9MjLfnZ6vV6UAFNBDgU1p3UdGgwGLC0tYXNzE69evcLr16/x4sULhMNhxbaez/L3g0U/J/IHVS1mipbT9Fl5Co76rC2X4NKNgmIBVHdPwqKgnlwEQ6s8rfpUrkpCu2k4xV2EM+2mYTab4fP54PP5hBMQ2WuTiSUFNalQiMZ112o1sfIbDAZRYkxBu729Pbx8+RLPnj27Jvi7XjdzHc0tK8C3l7v5xqDtNYmNtrFycI3E3mg0xEpH23xa6enGQYU4RqNR8ZADgbL/Hp3f5Vw+Tbmh4pxCoXDNTHMeyyla2a1WKwKBACKRCCKRiJi3RzbVcuqSfP+oS458/2q1msjBh0IhrK+viyg9jaJeXl5WbOl5hb8zE98kFv2c0Koub6Xpa7mwRhYl2V+R6OXdAq3sJHbZe4++D/j9SCG70MgDL8gRJ5fLifJbuvFUq1XhqjuL+KmxiFZ0t9uNUCgkpuqShz+t8OqsQ7lcFoM+E4kEMpkM6vU6NJrPc+rI2y8ajWJ1dRVLS0uiupHgTrqZYNF/adTFLbIDDm335QetuLJ9FglfvbJPaoKRhS/faGiXUSqVFJNuqDz38vISyWQS6XRa2E/fBhluRqNRRCIRBINBuFwu+P1+rK6uYm1tTTj1TPO6I5MN6rIjey6NRqMY97W8vAyPx8Npuflh0X8LyOkrOZ0mB+/mGREuW27T2V7e9heLRVxcXODk5ATxeBxXV1di3PSkYpfBYACtVguv1ysm82xubiIUCsFutysm704ao62G6gwajYYoCwY+O+TSmC11Tb36mpg7w6JfNOStO6X/qtUqLi8vxbmarKdvEr1Op4PT6cT6+jpisRii0Sj8fr9o+pm10WWavwDteJgHg0X/RzDPGfQ+nWh3eU7a5lcqFVHMIzv1yqKn44fFYoHb7Rb9/JN2I2ohT+oavCt8dn8QWPTMZ+Q4w1176OnYQTEG5lHAol801IHFh+w3l1125D/vc13EfZ6LuREWPfOwPERwjbfxXxQWPfM79+md55X40cGiZ5Tcpx6fBf+omPg/i2vvFxgW8GLCYViGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALBoueYRYMFj3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gFg0XPMAsGi55hFgwWPcMsGCx6hlkwWPQMs2Cw6BlmwWDRM8yCwaJnmAWDRc8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbBYNEzzILBomeYBYNFzzALBoueYRYMFj3DLBgseoZZMFj0DLNgsOgZZsFg0TPMgsGiZ5gFg0XPMAsGi55hFgwWPcMsGCx6hlkwWPQMs2Cw6BlmwWDRM8yCwaJnmAWDRc8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbBYNEzzIKhv+XfNV/lKhiG+WrwSs8wCwaLnmEWDBY9wywYLHqGWTBY9AyzYLDoGWbB+P9xVOVxq/qEcwAAAABJRU5ErkJggg==\n", 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\n", + "image/png": 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\n", 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Iy4/pJ1UoSjPypOY+lS5Lq/xY9NXDon9LIcGHw2HMzs7iq6++wvfff4+tra2K9rE2mw3j4+P44IMP0NnZeaS3nigWi0gkEpiZmcGLFy9EW+2DRGa32zEwMIDW1lYYDIY93XykK7e0KImgakF6UM49vZa89FLh0/FY9JXDoj8mP4fXnkQSiUQwOzuLO3fu4LvvvsPq6mpZgpeazv39/bh16xZOnz5dtllPJJNJLC4uYnZ29tD3lcvlaGlpwdmzZ+F0OsXeXZqaSym56XR6z2pPVYpqtRo6nU70BaB9PFHaY48deNXDon/LKBaLogx1aWkJ3333Hb788kssLi6W3d6aBK9WqzEwMIDR0VHU19dXdB4ULdjd3UUoFDr0uXa7Hb29vejq6oLZbBarPFkrkUgEbrcbgUBAhN2An6oUlUol9Ho9TCaTqJ6j/Pujkp5+zirGdxUW/VsEiSQajWJxcRFff/01vvzyS8zMzFTkuANe5bGPjIxgZGQEDoejbLMe+Cntlkp2if0SkZRKJfr6+jA8PAyn0ynadtGqTL35l5eX4fF49uTM0z6e2mw1NDRAp9OJFT6ZTL4maGmDEu6CWx0s+mMgjTGTIKq5CMl0lebU37lzB//1X/9VVqhMCp2HXq/H+Pg4hoeHodPpKjqvYrEIr9eLiYkJbG1t7fl7KTabDSMjIxgYGIDFYhGxf2nUYWlpCXNzc/D5fHv63FNrMZ1OB6vVCofDAa1WKzreUvpuaR2DtGMQi75yWPTH5CQcSSSQQCCAhYUFfPPNN/j73/+OmZmZigQvPZ+mpiYMDQ2hs7MTarW6omNkMhlsbm7i/v37WF1dPfB5JpMJQ0NDGB0dRUdHx56bC9XBLy8v4/nz51hdXUUkEhGOOOq7R6Y9iV6n04nkm2AwCIVCgbq6OmHy19XVQa1Wi+m6POiiclj0x6DS1tb7vZ5KT30+H168eIGvvvoK3333HRYWFqqeWONwODA+Po7Tp0/DaDRWJIxcLge/34+FhQVMTU0dGh7s6OjAtWvXMDAwsGfopfQzTU9PY2pqSrTRkna5BV5l8RkMBjQ0NMBms4kknmg0Cq1WK0J40t6DGo0GWq1WNN1kKoNFXyXSnuullHMhUvgpkUhgZ2cHExMT+O6773D//n0sLS1VPJOOkMvlGB0dxY0bN9Da2lpxL/x0Oo25uTl8//33cLvd4vOUfk6Xy4XLly/j8uXLaGtr2+OxLxQKiEajWF1dxfT0NFZWVoRPojT9llZ6i8WChoYGUQ9gNBr3FT21/ibR80pfOSz6YyBdsaQZZYft7ek1mUwG0WgU6+vrePr0Kb7++ms8fPgQOzs7FRfREEqlEv39/bh+/TrOnz+PhoaGikSRzWbh8Xjw4MED3L9//zWhEgaDAZcuXcJHH32Enp6e16yJXC6HQCCA2dlZLC4uIhAI7BtiK3XkGY1GaDQaZLNZ6HQ60RlX2nW4rq4OGo0GGo1mzw2BKR8WfZVIs8WAVwkm0g64B72GQmHBYBDz8/P4xz/+gXv37omuspV2wCFkMhm6u7vxxz/+EdeuXUNjY2PZmXfAK2EHAgH88MMPuHfvHhYWFvZ9nkajEZbE+fPnUV9fv6d7LTkkqcvO5ubmgTF1uVwOlUoFg8EAg8Eg2m6Tk462C9KZAtRoQyp6pjJY9FVA4SjqCENmJw1qKJ3sKp3HFg6Hsb6+jqmpKTx8+BATExNYXFysev8OvFrhz549i9u3b+PmzZs4ffr0oV1t9yMUCmFiYgJ/+ctf8OOPPx74vFOnTuHWrVt47733RD2+9H2kXvujevfRqq3X66HT6YS5XlqJB/zktaebAnvvq4dFXwW0msXjcUSjUcjlcjFMki5GMkfJqUV93paWlvD8+XM8evQI09PT8Hq9x4oAqNVqnD17Fn/4wx/w8ccf49SpUyINttzPEolEMDExgc8++wzffPPNgY05XC4Xrl69imvXrqGjo2PflZZi89QPL51OH/je1CrbYDBAq9WK9FuqrEun03saZVD2Hk2qZdO+Olj0VUDNHrxer2gQST3h9Hr9ni4xiUQCgUAAy8vLolf8/Pw8vF5vxQk3pWg0GgwODuJf/uVfcOPGjbKbXBIk+KmpKXz22Wf4/PPPsbu7u+9zVSoVxsbGcPPmTXR1dYnJNaXQMAy/33+o55/GaZlMJhgMBhFWlN5MqXyWcuzJxGfBH4+yRF/LRQ2lFxYNfNja2sLLly+xtraGdDothknSRQwA0WgUbrcba2trWF5exvLyMra3tyuOve+HUqnE4OAg/vSnPwmTXqfTVbTHDYfDmJycxKeffoovvvgCa2trB77X8PAwfvOb34iU3oMsCSrH3d3dPXCVJ1PdaDTCarXCZDKJLjk0+ioQCIhcfYp07LdtYiqnLNHzHfUnJ1wkEsHy8jIePXqEiYkJbG9vI5VKCS80rfQ0+GF7extbW1vw+XyHmrqVoFAo0N/fj48//hi/+93v0N3dXdHU2nw+j0AggKdPn+I//uM/8Ne//vXQJJzm5mbcunULV65cOTSll7Y9fr8f29vbB0YhqHmmzWaDw+GA2WyGQqFAJpNBJBKB1+uFz+dDOBwW2XnSrEepE5WFXzls3h8BpYNGo1Ekk0msr6/jwYMHuHv3LhYWFpBIJES2GDnygJ8m0NDe9KQuTrlcjlOnTuGf//mfcfv2bXR0dECv15cleBKLz+fDo0eP8Mknn+Bvf/vbgSY9AJjNZoyOjmJ8fBxdXV2HZvdJPfcHhR6pSabNZkN7eztcLhfMZjNkMhkSiQR8Ph/cbrfY/tBQCwBC6NRFR7rn54WpfA4VPV3QtXY3pSm0ALC0tISpqSns7Oxgd3cXy8vLIhRV6nHfz+t8XKSdamQyGYaGhvD73/9+T7lsOYKn6MH6+jru3buHzz77DP/4xz/2ddpJk3FOnz6N3/zmN+K9DhMX3eh8Ph98Pt++4Ue1Wo2Ghgb09vair68PLpcLRqMRmUwGsVgMu7u78Hg8CAaDSCaTQvS0nyfBx+NxbqRRJYeKPhQK4cmTJ3j58qUIp9TCF0yiLxaLWF1dxdzcHDweD7xeL7xe74HZcj/Hd0OCr6+vx+DgIG7cuIFbt25V5KXP5/MIBoNYWFjA3bt38cUXX+DRo0dHfg6TyYQLFy7g8uXLZSX60ARdv9+PUCgkchgAiBg7TckdHR1Ff38/GhsbAbyqqKM5eF6vF7FYDJlMZs80YOCV5ZVKpRCJRMRI7Fq4Jk+SI1f6P//5z/jzn/8M4NVdutpssV8T0tVVpVJBqVQil8v9YqOU2tvbcf36ddy8eROjo6NwOp1lCz6TyYi8/v/8z//E3//+dywsLBz5OdRqNS5cuIBLly6hra0Ner3+0FWemnf6fD74/X4kEok9FhMJfmhoCFevXsXw8DCam5uhUqmQSCTEbD4ae5VOp19bxWn7QDeIUCiEZDIphl0y5XHoN0V11cRJOaLedqSCSKVSVefBV0JpfrtKpcLw8DAGBwcxODiIoaEh9Pb2orGxcU+DycOQptV+8sknePDgwZFddOk8jEYjrly5gnPnzomBFYdRKBQQiUSwtrYGt9sthlNSslJjY6PwDZw/fx4ulwt6vX7PHj0cDiMYDCIejx/Y3jqTyYje+MFgEOFwGFarlbPzKuBQ0ctkMuj1evE7zRuvJd6Uh5jew2KxoLW1FWfOnMHly5dx8eJFdHV1wWg0isSfcshms9jZ2cGdO3fwySef4Ntvvz2yAw6dh0wmw6lTp3Du3Dm0traWlc6bzWYRCAQwNzeHnZ0dFItFKBQKKBQK2O12nDt3DtevX8d7772H1tZW4XxMp9PiZygUEib7QZYIdeIJhULw+/3Y3d0VzTdY9OVx5BVUup/i3mQ/H3a7HdevX8eNGzcwPDwMh8MBi8UCnU5XdoYd8Gpv7fF48P333+Pf//3f8dVXX1U07669vR2XL19GZ2dnWZEBiq/v7Oxgfn4eu7u7InmmoaEBAwMDuH79Oi5duoT29nYYjUaRsUiLCPXRI+fdQTdaajQSjUYRCASwvb2NpqYmsdozR8MboV8Iqd9Ao9GI5JcPP/xQOLiqqRen5JjJyUn827/9G+7fv1+W4KXbi87OTly9ehUtLS1HWhY0X8/j8WBhYQEbGxuIx+Mixbavrw9Xr17FxYsX0dnZCaPRKMRZKBTEDSWVSiEWix3pN8lkMgiFQojFYmJufTgcFgMvOXR3NCz6N0xpEwmXy4ULFy7gt7/9rYiFHxUaOwhyps3MzODzzz/H/fv3Dy14KX0tABiNRvT19aG3t1fEzw9D2iFncnISu7u7KBQK0Ov1aGtrw4ULF/Dee++hs7MTJpNJ+CPIyUffRzqdFk65o0gmkyJkl0gkxN7eYDBU1AuwVmHRv2GkZuupU6fwT//0T7h9+zaGhoZgtVrLGip5EKlUChsbG/jmm2/wt7/9DV6vt6LXKxQKDA8PY2RkBA0NDWVNtE0mk9jZ2cGLFy8wMzODWCwGpVIJq9WK4eFhnD9/XtQElObMSwdYUrJNOdvHTCYjZttls1kR25d272EOhkX/C2A0GjE2NoZbt25hfHwcPT09IhW1GqiDj9frxbfffosvv/wSy8vLZb9e2kyTxlgf5bGXTt95/vw5JicnRRae0WhEZ2cnRkZG0Nvbi/r6etFZZ79aBunoqnJEXyy+Gp8dj8cRj8fh8XhEhp/JZGLRHwGL/g0hk8nQ0tKC7u5unDlzBleuXMHY2Bja29srbly5H16vFw8fPsRf/vIXTExMVPRaMrV7enowMjJy5DmRAy4YDGJ2dhYPHz7Ey5cvkUgkoFAo4HQ60d/fj76+vj3DL0rFKO0iRLPqgP3bc5VClXx2ux1KpRLRaJQTdcqERf8zIpfLoVarYTab0draivfeew9XrlxBf38/mpqaxPjm41AsFhEKhfD48WN8+umn+P7775FKpcoSjpT29nZ88MEH6O3tFd71g94vm80iGAxienoad+/exZMnT7Czs4NCoQCTyYTOzk4MDAyIWPxBzS5osAfVKJTu8w+DCnPoPegGwqI/GhZ9FVBKKTmkaK4aQbXiVqsVvb29GB0dxeDgIHp6euByuYS5e1wztFgsivLYzz77DF9++aUo263k4jcYDDh//jyuX7+Otra2A0NflL8fCAQwMzODb7/9Ft9++y1WV1eRTqeh1WrhcDjQ09OD7u7uQ/fY0u5D1PJamsxzUNNRgnL1aWIObTc4O+9o+NupELlcDpPJJNo1U309FX9QDze73Y4zZ85gbGwM586dQ3t7O8xm84lmjkUiETx//hyffPIJvvzyS/h8PgCHm8el/2cymXDp0iXcuHEDZ8+eFR57aeUalRUnEglsb2/j2bNnuH//Pn744QesrKwIwWq1WnR0dODUqVNwOp0H5hdIzfp4PI5gMIhEIvGa6I9KjJJuCfL5vCjDPYnt0rsMi75C9Ho9XC4XOjo6YLFYRKNLCjep1WpYLBZ0d3fjwoUL6O/vh8PhEKObTwIy6ScnJ/ethz9MKNLWUw6HA6Ojo/j973+Py5cvw2az7VmZpW26qefd5OSk6CWws7Mj0m01Gg3q6+vR1dWFtra2PfH40vcnwcdiMXi9Xuzu7iIajYpBFtIptYd9FroZAT+t/BSv5+y8g2HRV4BKpUJDQwM6OjrQ0dEBg8EgklMoV1ylUqG1tRVnz55FX18f7HZ7xU0qD0LqQJuYmMCnn36Kv/71r0fm05dC8+euXbuGa9euob+/X6zMtMrTyhmJRLC1tYXnz5/jwYMHePr0KdbX1xGPx/cMrNBqtXA6nWhraxPz6aXCI3Odtgh03KWlJWxsbCAWiwkfSOlM+oOgkdV0TDL3pUk/zOuw6CvAYDCgqakJTqdzjxOOOuXI5XKYzWb09PSgp6dHXPzVTL4pfQ1d4G63G48ePcKnn36Ku3fv7pk1J2W/2n6j0Yje3l4MDg5iZGQEw8PD6OnpgcViEdsO8lHE43FsbW3h2bNnePToEZ49e4bFxUUxqYbeg9pTm81muFwuOByOPWm20kculxPtv9fX1zE7O4uXL19iZWUFkUgExWJRdBXOZrPIZrOHblXIYqAKyGQyyR78MmDRV4DRaBT58CR4EmhdXR30ej2am5vR2tqKhvojFDkAABfzSURBVIaGqp11pS2lKR6+srKCH374Ad988w3u3bt3aAENXfgKhQL19fVwOp04e/YsLl26hLGxMXR0dMBkMkGtVgvhkjCDwSAWFxfx8OFD3Lt3TwyyLF116T2oMUZzc7O4gdCqTo9UKoVQKISdnR0sLy9jfn4ei4uL2N7eRiQSEZYStbmmEVaHQVsr2suTdcCiPxwWfQVotVoYDAaxelNHF+CV6U8VcjR99Th7eHKcRSIR7O7uYn5+Ht9++y3u3LmDpaWlsvoaqFQq9PX1YXx8HO+//z56enrgcDhgtVqFk01qEVAn2/n5eXz99df44osvMDs7i1gsdqCQqN+d3W6HzWYT8+UzmYzIsqPPsLKyIlb3tbU1UTdP50r+hHJnzlOnHjLpadgIczhHil76JVLH0ncd6Z6QQnEKhUIkmUirwGhOutFohNPpRGNjI4xGY8VhI2ldudS8fvr0KX744QfMzMxgbW2trD75NE12ZGQEg4ODOHPmjAihHTT/jRx2m5ubuHv3Lj7//HNMT08fWqxDoUur1YrGxkaYzWYAQDweRzKZRCKRgNvtxuLiImZmZrC4uIiNjQ0EAgHE43FxLSmVSjGJtq6uTqz6R63aFPLLZrOoq6vj+XZlwk00DkBqyjY1NaG3txdDQ0Po6OgQqxl1dlGpVLDb7WhubhZhuXJXKnJCBYNBbG1tYXV1FTs7O6Kj7MzMDF6+fIlwOHzk8YxGI1paWnD+/HmMj49jbGxMdL1RqVSHJtxIO+Tev38fL168OLJ5CA2fdDgcaGhogEKhQCwWE00uNjY2MDc3h7m5OSwvL8Pj8ey5idCKLv2uaeDFQU009iOfz0OpVMJkMlXUFbhWqaiJhkajqXrW2q8J6Wp++vRpXL9+HVevXsWZM2dgMpmQzWaRSCSQSCREjFo6dVVqNu8H1Z8HAgHRXmprawsvXrzA06dPMTc3B7/fX1FrMgq/3bx5ExcvXhQhRdqzHwYVzqytreH+/ft4/vz5kYKXy+XQ6XSw2+1wOBwwmUxIp9PY3t4Wwz2mp6extLQkeuDv5xMgPwL5FGgWvTQGfxC0/ycrjLZeLPrDOVT0Op0O//qv/4rR0VHR3rkWnCQymUysNvX19eju7kZnZ6cYCklmOKWP0kqj1WqFWV/qgSeHHO1x19bW8OOPP+Lhw4dYWlqCz+dDPB4Xvd7LhQZRXL9+HVeuXMHZs2fR1NR0ZE87KZlMBm63G8+fP8fExIQYUX3Y96NWq1FfX4+WlhbYbDbI5XLRBXdhYQGLi4vY3NwUTrrDkMbbyXo6rJEGQSa9Wq0W2wNupHE0h4reYrHg2rVr+PDDD9/U+bwVSGPVwE97ThK0QqGARqOBwWAQKbj5fH5PUgshnWfn9XqxsLCAqakpTE9PY3p6GnNzc/uOfyLT96B0VJlMBofDgZGREdy4cQPj4+N7OuSWK3iyOubm5vDw4UOsr68fKTa1Wg2r1YrW1la0tLTAaDSKBJ7FxUUsLCzA4/FUNLaLPO/UXKQc055mCJITkB155XHkSs/sD4XpaHY6iV+6F6VQVTgcxs7ODl6+fIkHDx7gwYMHWF1dPXQFPCz3XK1WiwKZW7duYWRkBE6ns2LTlsx6chg+ffr0yJFblHHY2tqK9vZ22Gw2FItFMZyTBF9pM9HSmH45yOVycfMtZxvDvIJDdsdEJpMJoZFTLpfLIZvNIhaLwe12Y2ZmBj/++COePXuG1dVVhMPhqrdJBoNhz+p++vRp2Gy2ijvGkCPS7/djcnIST548wdbW1oF+BPLU19fXo729Hd3d3WhqaoJCoUAwGMTq6iqWlpbEXIBqP1+lr6M8BKvVWlV7sVqEB1iWQTkXEu1Do9Go6N22urqK6elpTE5O4uXLl3v2ytVMw2lpacHY2Bhu3LiBK1euiMaVla5wUm/9zMwM7t27hxcvXiCRSOz7fNrDNzQ0oLW1Fd3d3WhpaYFGo0E4HMbGxgZWV1fh9XpFv/s3ATn+nE4nmpubuUdemfAAyxOC9u3BYBCbm5uYn5/HxMQEnj9/jrW1tddGYFUiDCryGR8fx82bN3HhwgU4HI6qTFqqFaB6+Dt37uDx48f7Ou9oLLRWq0V9fT1aW1vR2dmJpqYmaDQaRKNRrK+vY3l5GW63G/F4/I0uEOS8owgCi7482Lw/Iai5YyAQwMrKCqampjA1NSWKU6qlsbERg4ODooV0X1+fMOer6ZRL5/jy5Ut89913uHPnDjY2Nl4rUpEmHTU0NMDlcsHlcsFut0OlUiEajWJtbQ1LS0vY2tpCLBZ7o+3RDQYDHA4H7Ha7yDDkOvry4G/phKB4czQaxc7ODtbX17G7u1vVdByZTIbGxkZ0dXVheHgYY2NjGBkZQUdHh8j2q0TwFPeOx+PY3t7G9PQ0vv/+e1EPn0wmhW+irq4OKpUKer0eFosFdrsdTqcTdrtd1NoHg0FsbGxgeXlZDPJ80/kbBoMBHR0d4ibIobryYdGfECRCaX19ObFmgpJdzGYzmpqa0NfXhwsXLmB4eBhdXV2or6+HRqOpSPDkrIvH4/D5fFhbW8P09DSePHmC58+fY3t7G+l0WghdrVZDp9Ohvr4ejY2NcDgcsNlsIsswnU5jd3cXa2trWFlZgdvt/kUED7wSfVtb255RX0x58Dd1QsjlciiVSmg0Guh0Omg0GqjVaiSTyQNzyCnsp9VqYbPZ0N3djf7+fpEr73K5YLPZYDAYyuq4I81wo5lvFC6cm5vDxMQEpqen96zO1OmHKgibmprQ0tKCxsZGmEwmqFQq0RPP6/ViaWkJKysr2N3dFYMn3zSUKUo3JrPZzOG6CmDRnxDUAKK+vh7Nzc1oaWlBIBAQKy0ln5AZrdFoYLFY4HQ6RQjs1KlT6OrqEn30aJ8qbRYpTdihxB8qXyWxS1f2xcVFLC4uYnV1FZubmwgGg2J+HO3X29ra0N7eDpfLBafTifr6epHRF4vFsLOzg93dXRGHp+zBap12lHgkl8urmhWo0WhEkY/JZOJe9xXCoj8BpEKmzjqhUAipVApKpVIIjYpz9Ho97HY72tvb0dPTg76+PrS3t8Nut8NoNIp0UmmJKQlc2ic+lUohHo8jGo2KKa700+12Y319Haurq9je3kYsFkM+nxelsDabDW1tbeju7kZPTw/a29vhcDjElJhCoSCKZ/x+P5aXl/ck3lQjeJVKJdJmKYpAQysqOZ60YQfH5iuHRX9CSNtdu1wuZDIZyOVyWCwWuN1uUZhjMpngcDjQ3t4u+smRiUq5+6VTYCjph+rraTY7Fey43W5sbm5iZ2cHHo9HNJqkugBqLU3ZdG1tbejv78fAwAC6u7tFYxB6fxqPlUgkRPHM3NyccExWk0BDNfdOpxN6vR6pVEpMpqFJteVALcuam5vLmsLDvA6L/gSgFVmhUECn06GhoUFUjhkMBjidTiSTSSgUCpjNZjidTrhcLjQ1NYk9O5W+0r5dWnZLee0U/5+fn8fm5iZ8Ph+i0Sii0SgSiYToIiMVkEKhgFKphMFgQGtrK/r7+zE8PIy+vj60traisbEROp1uj8+AQnvxeByrq6sisaia0KNKpRLDL/r7++FyuaBSqeD3+zE7O4tnz56JhhvlUFdXB5PJJLY/nGtfOSz6E0IqfEpmyeVyqKurg9FoRDKZhFwuh8FggNVqhdVqFYMgAOzJ1ydnHIl9dXUV8/PzePnyJebm5kT220HhQDoX8spbrVacOnUK58+fx/nz58XkGenNprRYiGbUzc/PY2lp6bXkonJQq9ViiCWVJlOuvs/ng06nE5Nqyo3zUw0/m/XVw6I/AcjcLRWbVqsVpqx0T06z2KlhhFarFXt4ajUVjUZFEcuzZ88wMTGBlZUVhEKhI/fA0r59DQ0N6O/vxwcffICxsTF0dXXBarWK8N9+KyV13F1YWMDCwgL8fn/FXnqlUgmn0yl66g8PD4vEnkKhAIPBgEQigY2NDSwtLSEQCJTVpIVyCcgKqsYRWOuw6E8IaZ92qUc9m80ilUohGo2K8B01fDAajTAajdDpdGLlymQyCIfD2N7extLSEmZnZ0XXmWg0WtYFTsUxdrsd58+fx4cffoixsTF0dnbCbDbvO1uOjkvDO2gS7cbGRtmmtxSLxYJz587hypUrOHfuHFpaWsREXvqeGhsb0dTUJDzw5Tj0KHpBDkxykDLlw6I/JtLQGWW+SZ1utBff3d1FOBwWFynV6FNMn8x86hrr8XiwubkJt9uNcDhcdqIPCZ5q7a9fv473339fdL8tdRSWfhbqvEsda/1+f8XfiUajQXNzM86dO4eBgQE4nc7XOgrRNshkMonBGFSifNT3nc1mEQ6HhcMym81yck4F8Dd1DKRiJ3MzlUohFosJ4VJBys7ODgKBgLhIyQSXy+VCiBTGoqaSlNVH3WSOgqrh7HY7hoaGcO3aNbz33nt70ncPEjzwUwfejY0NzM7OYnt7u6o0YoPBAJfLhc7OTthstj1ttqXfHZ0z5fmX21cwmUwiHA4jEAggFAqJduPs1CsPFv0xkJrypYLf3d3F5uYmVlZWsLq6CrfbjUgkgmQy+VrTx1IxSKe7lLNnJV8CZfYNDg5ifHwco6OjaG1t3ZPRd9gwyVQqhUAggMXFxQM7+hyFQqEQefvUZpu+J3pvaRIRjaIq10Sn7Q+VL+/u7sJutwvnHnM0LPoqoQuZGmak02kkEglEo1EEAgF4vV6Ryebz+YQpKnU+SYVPqxQJg35S+yh6z1LkcrlwGjocDpw+fVoMzXS5XK+1z5Jm9RHkewiHw1hbWxMhwWq6H9PnyGaziEQiiEQiYpQ0Nb2kEmS3242trS3RR68c4edyOYRCIZGv4PV6EYlEqmokUquw6KtEKnpa5ZPJpFi9otEoYrEYYrGYGLeUzWYPXL1J2NLVWJquSr/TTzKJ1Wq1yPDr7OxEf38/hoaGRHNMeq30/UrTeGlc9ObmpnAchkKhqktlU6kU3G435ubmoNPpkEqlxOQbyvTb2trC9PQ0FhcX4fP59u2Wux9UyUjfcygUQiKRqIl5DCcFi/4YkOhLH9L9uvQBHNw8g15Dr6OYPyXs0L+p7bNGo4FWqxWtt5uamtDW1oaOjg4xNJMci9Jj03vRuVMSjsfjwdzcHGZnZ+HxeIRD7bBZcgd9J/F4HOvr65DL5YhEItjY2BBNLvL5PEKhENbW1jA7O4vFxUWEQqGyV3oAYm5dOp1GOp1GJpN5o7X8v3ZY9FVSGqKjVFdqhW0ymWC1WmGxWPaY9tIVVnqRS2ezKxQK0XFXq9VCp9OJh1arFeO1DAYDzGYzLBaLSPixWCyQy+VidHY8Ht9zw6HCnGw2i2QyiVgshmAwiO3tbeF/iEaj4nzK9SsQ5Bvwer1IJpPweDyYnZ0VjS4AiIIgj8cDn88n/Bzlvgf1xKcHUxks+mMg3XdLBy4AP4WWUqmUcNwVi0WR2Ua/l67wtIrr9XoYjUbU19eLh9lshtFohMFgEDcAjUYjCnTq6uqQyWTg9/uFwGn6K4US6ac0ShCNRhEMBhEIBBAMBlEoFMTnofMj5+JRUDYhjZwKhULY3NyERqMR2X/0f5QyXEnfAen7cHy+Olj0VSLNvqMYsTQEp1KphEkuNc0VCgWi0ehroTjpHl2n04nV2+FwiLFRFosFJpNJJPPQtoFMdaqKo+ackUhE/JtCgFJzuNRBSCsnhf7o7+TQq0RoJH7yeVAacukWo9qMOroZ0YQbDteVD4v+GJDoybQn4VJjCpq1Lh29tLGxAZ/PJ0JV0r0zda+h9F2TyQSz2SxMeIvFAr1eL2LS0rh+OByG3++H1+uFx+MRqzaFCUnsUpHRTUqlUomBEZQOTIU6dCOgkFu1ApUm3VR7nNJjUikzRSiY8mDRV4nUUadQKMSoZHK0kdBLHXBKpVJUmUWjUSFEAHssAwAiD58cVyqVSjTgpO1DPB4XeQFutxtutxterxehUGjPjeUwHwKFHMkEL630O2lT+rjHImtBr9ejubkZ9fX13COvAlj0x6A0nCaNgUu73UidZ2TW02vi8fge7zOJOh6PCyuCuu8Eg0GR8EIOM0r19Xq98Hq9CAQCiEajSKVSe1bpUkjI9P90Y8hkMqKmvvSc3xYPuUajESXKzc3NsFgsnIZbAfxNHZPSUJjUuScNr+l0OphMJlgsFpjNZkQiEcRiMZHmSjcCEhrF/iORCAKBAPR6vdjLS0VPXXPC4bDY09OUnaNWaPp/mu9ObbBLG3iU68R7E8jlctjtdgwMDGBgYECEJ7nMtnxY9CdEaSiOHGVShxndIKSdc8nDTk4vgqyAuro6qNVqse+mHHVpuylqniFdlatxukmTf+i1b5uH3Gw24/z583tGh7MTrzJY9MektJRWmjcvDYtRBlksFkM8HhftrOhBYS5p3Jn23eT5Li2Ykc5xp5/SUGA1n0X6821Do9FgcHAQt27dwsWLF8XQTl7lK4NFfwxK8+/pQXt42puHQiF4vV7s7u7C7/cjGAwKU5wETym6UuFR7TmtwpRDL13Z6AZT6qSjY7wrKBQKjIyM4A9/+AMuXboEl8vFgq8SFv0xkCbA0IpNZjat8pFIRHjXfT4fAoHAnv18adz8IAHTPp5WeroB7Pfct2X/fVJotVoMDw/jj3/8Iz766CN0dHSIhhxM5bDoq4RWeanTTdqYklZ5etDNAIBooKHRaMSKLs3blyLtvSdN0aXf9zP1yZFHW4ZKUlxPCnJk0k1K2q+fzukoqLHo8PAw/vSnP+Gjjz5Cd3c3C/6YsOirpHSVp3i7dLWmfbhGo4HJZALwqlkkDZmgFZ/CdqUpqZSWS4kzWq1WJP6o1eo9mWjS1FeK6yeTSZGRJ40U/JyoVCpRE2AymfY0t8jlcsL6oSxBuhHudxy73Y4LFy7g5s2buHr1Kjo7O3ky7QnAoj8BaLUmTzutyNI8eovFskeMiURiz4NMfRJvaXYfFdpQrj158ikzT+pDiEajotFEOBxGOBwWbbLJIqGS3+NC50UlvtST3ul0ihl4UrHTuYXDYcTjcfGZpc5HmUwGq9WK4eFhXLt2De+//z5cLhev8CcEi75KpF1vC4UClErla+2zSv8tHT8lLYJJp9Ovhe3ISqCtAD1o1afQnTT3nuriqZVUMBgUU2/IaUgpuz6fD36/X8yjk3r9Sxt8SMt9pX/T6/VwOBxobW2Fw+GA1WoVCTM2mw1arRYA9rS3ovOh9GBpajClImu1WrS0tGB0dBQDAwNoamqCWq1mwZ8QsiP2eu+O+/dnoDRUBuxtUCH1xO/3KI3ll652JDZKl91vn0x7ZWlDD2nJ7M7ODtxut2idTdl9ZGJLqwDpBiQNI0qFSPUEZK7rdDq4XC6cPXsWp06dEmO56LmU8ENlvNRVyO1270lDzuVyYgKPzWYTswCbmppEu24WfFXs+6Wx6I9JqcBL/++o1x5F6epa+rf9zoNuRrFYDH6/XzSQpFFYfr8f8Xhc3LDo9dIuQNJJOdS5VrpVsdlsYohkS0vLHnO+NEtRemy6GYVCIVFHT1EJGgDa2NgoxmyVNtRkKoJFX0tIhUZ7fZ/Ph62tLTGTTpo9KE3/ld4MqEhIrVaL6TJ2ux2tra2w2+0iNZi2HYcJVHpOpanCtF2Svhdn2h0bFn2tQqs/1dpTtEBamivtTJvP56FUKqHT6UTTDsr5p3l9JpMJer3+WNVt+4UnmROFRV/rlDaukDoAKapANwMKNer1+tem6Zb2/WPeWlj0zP5IIwvS4iBpQ07mVwmLnmFqjH1Fz/YZw9QYLHqGqTFY9AxTY7DoGabGYNEzTI3BomeYGoNFzzA1BoueYWoMFj3D1BgseoapMVj0DFNjsOgZpsZg0TNMjcGiZ5gag0XPMDUGi55hagwWPcPUGCx6hqkxWPQMU2Ow6BmmxmDRM0yNwaJnmBqDRc8wNQaLnmFqDBY9w9QYLHqGqTFY9AxTY7DoGabGYNEzTI3BomeYGoNFzzA1BoueYWoMFj3D1BgseoapMVj0DFNjsOgZpsZg0TNMjcGiZ5gag0XPMDUGi55hagwWPcPUGCx6hqkxWPQMU2Ow6BmmxmDRM0yNwaJnmBqDRc8wNQaLnmFqDBY9w9QYLHqGqTFY9AxTY7DoGabGYNEzTI3BomeYGkNxxP/L3shZMAzzxuCVnmFqDBY9w9QYLHqGqTFY9AxTY7DoGabGYNEzTI3x/wGUFBY+1sJMrgAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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QTCYRi8WQyWSudTzK5XJeVgt89imwL/FzijvsEveDRP8NYNZS+MFttVo85fXjx4/Y3d3FwcEBT8C5a1IL29bPzc1hfn6en7UniURoZXu9HtLpNOLxONLp9NgiI9zi6/V6Hpf3er0wGAwj1221Wnyg5vn5OVKpFFqt1tjzM+++RqOBUqnk9fStVgvdbpfH44X3K2ywSdwPEv03QCzARqOBRCIx4qU/OTlBIpFApVK5tYpOiEwmg9VqxcLCAj/Lm0ymiT3sGcxjH4lEcHp6ing8jmq1OrEjDkv0WVlZwfLy8kgyDqNSqeDi4gInJyc4Pz9HNpuduLVngrdarTAYDFCpVPxYUK1WRyoIhS20SfAPg0T/AK7bJl/3/Ukf2Fqthmg0ik+fPmF3dxcfP37E+fk5MpnMnWfNS6XSEYtoNBrh9/tHLL2wNJedm9mWejgcolgsIhKJ4Pj4mEcJJr0OVrCzsrKC1dVVBAKBEUtfr9eRyWT4FN1kMnltxEEmk8FoNMLtdsPtdkOv16PZbPJwYaFQQLvdHumHT5b+4ZDoH8B1H7zrvi8WUaVSQTQaxe7uLv7+97/j06dPvHKOfdjvgvBxKpUKFosFHo8HXq8XDodjrMOM+P5arRYymQwikQjOzs4Qj8e501Bo7Vn5bCgU4nF/4by7VqvFp9+cn5/j8vLyWsFLJBKo1WqYzWb4/X4sLCzAbDajXq9DrVZjOBzyjrntdpt77knwD4dEPyUP6W/P+rgzR1c0GsXe3h7evn2L9+/fIxKJTL2dF94TADgcDszNzcHn88FisYx5xtl9CJNx0uk0IpEIzs/PkU6nR87fwmuz4RQsRCfO7GNFOmdnZ7wCcNJZHvhzoo7L5eJhP5vNhkqlwgdrNBoNlMtldDqdsfFYxP0h0d+DSWfiu4xd6nQ6KBaLvB5+f38fHz58wP7+/lSZdtfdk8vlwvLyMlZWViaetcX31ev1kEwmcXh4yEddC1Nuheh0Ovh8PiwtLWFxcREej2ekgObq6gqNRgOpVArRaBTxePzasdnMyrMdydzcHM8jqFQqaDQayOVyyGaz0Gg0vIsOWfnH4UbR39aR5a+K2Ok16e8AuCWqVqvo9Xp8HJNKpeLVa+x63W4X1WoV6XQa0WgUx8fHODo64ufeu+bST0IikfAqutevX+PFixcIBoPQ6XQjTTikUikvT202m0gmkzg+Psbbt2/x4cOHiXPpgM/Ou7m5OTx79gzb29tYWlqC3W4fm2rbarVQLBaRyWSQz+f5MAxxuy420cZut/OGHh6PB3q9HhKJBGazmefyP6SvPzGZG0X/0EwnsfdX7Ex6DG67lvgDd9dr3dZtlU2NZaJttVqQyWTQ6XS8dnw4HPLGD51OB9VqdcTJFY/HUS6Xp0q6mXTPLpcLa2trePPmDX7++WdeK8+GUDIGgwEqlQry+TwSiQROTk6wt7eHjx8/4vj4GKlUaswyC8tyf/jhBzx//pxHBMSwxaTRaIwtHsL3VqlUwmQywePxwO/3w+v18mxBlm0nPr8/NHef+JMbRU8zw8ZhPerY9pxtixuNxkgZqUwmw3A4RLfbRafTQafT4ckv+XweuVzuQdt54LOQ3G43NjY28NNPP+H169e8Px3zzAsbXLKxVOfn5zg9PcXx8THOzs4Qi8WQz+cnCspisWBpaQnPnz/H8+fPsbi4CKvVOvF+mPNN3NmG/YxhMpng9XoRCoUQCARgt9uh0+nQ7/d5nz7WSJP9yZphCot3yPrfD1L1HRkMBqjVasjn84hGo9jf3+cJNIlEAq1WC3K5HGq1Gmq1mlu2TqczIgTW7mnaPHQ2gEK4RXa5XNjc3MTPP/+MN2/eYH19HS6Xa6x8td1uI5VK4ejoCB8+fMDBwQEikQguLy9RLBavDQ3qdDp4PB6srq5ic3OTO9sYwqODUIxiiyzOFPR6vVheXsbS0hL8fj/vcc/ep0ajgVqthlqthmazyQXP/pzUP4+4OzeK/uPHj1/rPr45QqvBfBl6vZ5ns1WrVSSTSUSjURwdHWF/fx9HR0cTw1Ks1pt9UB8DoYikUik8Hg/W1tbw+vVr/PTTT9jc3ITL5RrbnbXbbVxeXuLTp0/4448/sLu7i9PTU54HcF2kQKlUwm63Y35+HisrKwiHwyOCZwMshDn5QjFOygA0Go0IBALY2NjAxsYGFhcX4XQ6odFo+AJZrVZRKpVQLpd5i2/2fOx5SPAP40bR/8u//MvXuo/vAmZN2Zil1dVVvH79Gk6nkyevnJ6e4vz8HNFoFLlcbqLj66FCv80PwSz8mzdv8Pr1a2xsbIwJnk2NSSaTeP/+PX755Re8e/cOx8fHKBaLt+40dDodD8/Nz8+PtMoW3iNDuJsRHxNYcY7P58Pm5iZevnyJzc1NBAIBmM1myOVytFotVKtVFAoFpNNp5HI5VKtVfp9s90CW/uHcKPp/+7d/+1r38V3y/v17ZDIZ+P1+VCoVxONxJBIJZDKZsZLTx3ROip2fbLvOmmKwxejnn3/GxsYGnE4nFzw7E7OY+cePH/H3v/8dv/76K46Ojq7tqye8f4lEApvNhmAwiIWFBfh8vrGyWbElr9VqqFQqPH1WGPkxmUzw+XzY2NjAy5cv8fz5c8zPz8Nms0GtVmMwGKDdbqNUKiGVSo206GL3xHres6NRr9fjr5nO9tNBZ/obyOVy+OWXX2AwGNDr9VCr1VCv19FsNr+apTGZTLBarTAajbBYLJibm+OtppmXXniGHwwGvCb+3bt3+PXXX7mFv6mRpvD16HQ6OBwOBAIBBAIBuN1uXjY7qViIDZlMJpPIZDKoVqv8cQaDAXNzc9jY2MDOzg6eP3+OhYUF2Gw2HudnyTjZbJYvrCwFV3h/7Lxfr9fRaDR4qTCJfjpI9BMQbq/j8fidfucxFwGJRAKFQgGz2YxAIIBwOAy/3w+PxwOfz8eTWcSCHw6HqNVquLi44Fv63377jWf63QWVSgW73Y5AIIBgMAiPxzNSlitmMBigXC4jHo/zpByWt88SetbW1vDy5Uu8ePECi4uLfCKtsMKvWq3yhSOdTqNSqYz02GevLZ/P8zwAFikhpoNCdhNgZ/tp02Efikqlgtlshslk4oUo8/PzWFpawvz8PLxeLywWC3Q6HXQ63VjLqEajgXg8jt3dXfzyyy94+/btRMFfdxQR9rBfWlpCOByG3W4fex7h77IeALFYDJFIhFcGymQy3p9vY2MDz549w9LSEhwOx0h0QxgVYYIvlUp8ii2D5RgkEglcXFxwrz9LhGL3RVb/dm5U9WN5nombkUgk0Gq1fKgky1Dzer3cqs/NzcFut480tRR+yNvtNpLJJD/D32Thb9qVsPx6cYUeQ7itHw6HXIjMuZnNZtHv93khDevCu7CwAKfTOWLh2X0XCgUkEgnE43FkMpkxwbPnKpfLuLy8RCQS4T32hQU/xN2YTVM+BZMKVh5yLfE1WD25x+PB/Pw8lpeXeaNJh8PBU1LF/eSF4mu324jFYrxa7927d3fe0gvvaTgcQqVSwWazwe12w+l08u0zc8wJjxP1eh3JZBJnZ2c4OzsbyVdgTTNZNZ7dbp/YlJOV4sZiMcRiMRQKhWsjC61WC9lsFul0Gvl8fmxxeEgx1CxBor+FL+mwYzPgmNhXVlawsLCAUCgEt9sNg8HAjxrX0el0kEwm8enTJ/z7v/87fv31V5ycnNwr20+hUECr1cJkMsFisYyd5cUWOpVK4fj4GAcHBzg7O+PDLe12OzweD0KhEEKh0Fj/PAZzOrK2Wqz2XnysEi5M9Xqd1ztMU35M/AmJ/isi7jEXDof51JmVlRWEQiE4nU5YrdaRgY/CxhfCGXeDwQC5XA5HR0d4+/Yt/vjjjxvDcrfdExOry+WCXq8fserCv/f7faRSKezv7+Pdu3c8K7FWq0Eul4+MwmZ+CLlcPmaJ2+02isUiD9OxIp2bhNztdtFut/ms+l6vN1b4Q9wMif4bYDAYMD8/j52dHfz444/Y3NzkgyiEjikh4go/5tiKRqO8TfbJyclUgmdIpVJYrVbuNGRn+UkwwR8cHOD333/H77//jpOTExSLRQCfnb96vR4Oh4MvYDqdbqyYqd/vo1wuc499Lpcbictfh0QiQa/XQ6PR4HkBrISYmmXeDRL9V4CV2zIr6Pf7sb6+jh9++AE7OzsIh8Mwm80TW0EDo9WJwjBXLpfD+fk59vf3cXp6ikKhMPK813np2XVYLF0mk8Fms/GUW7YACUXU7/fRbDaRyWRweHiIt2/f4u3btzg6OuLOOwB8rp3NZoPZbIZGoxnrzccWrFQqhVgshkQicWOWoPg1NBoNZDIZXF5eYm5uDlqtFkajkc7yd4RE/4VRq9W8VTRzkLEBFOvr65ifn58YBxd/gMXhKNZMk6UFZzKZsdnxN/XqE/bVk8lkI9NqAoEAt55XV1e8OjCZTPKind3dXRwdHSGTyXDBM6ek0+mEzWaD0WgcC/cNh0PU63Wk02mcnZ3xRpyVSuVO/Ruurq5QLpcRjUZ5bz22uAjHbk96D4nPkOi/EFKpFFqtFk6nEysrK9yCer1euFwuuFwuuN3uEcELB0kw2N+FW/5ut4tUKsVnxycSiYk1ANchHvNss9ng8/l4GIzdU7fbRbFY5CW5Z2dnODg4wP7+Ps7Pz5HL5bjgJRIJdDod7HY73G43PB4PLBYLz7obDodotVrcW390dIS9vT2cnp4ilUqhXq/fuWkL62VwdnaGUCjEk4jobH83SPRfADZdxuPxYGlpCS9evMDW1hbC4TAcDgef7ir+kAoddsLvCen3+0gkEjg8PMSnT59GvObXcVMBj9lsRjgc5mFCFhrsdru8weXZ2RlvZx2JRCZ62YWiZ85Ao9EImUzGU5jZ6GtWqXhwcIBoNMq39nf1xA+HQxQKBWQyGWSzWdTr9bFSXrLy10OifyBszBL7u1KphNlshs/nw+rqKp49e8a98x6PZyzLUSzwSR9W1oOv2WwinU5zJ9qnT58Qi8VubbUlfA7huGer1YpwOIzt7W2sr6/D4/FAIpHwqbUHBwfY29vD4eEhIpEI0uk0r78Xo1QqYTQa4XA4+ARbNhCzVqvxWDwbgMFSdrPZLK+Zvw2hj4J16Wk2m+h2u9RRZwpI9A+AOeaMRiPUajXkcjm0Wi1sNhsWFxextbWF9fV13ip6kuCFW/pJgu92u3weXDab5Y679+/f4+joCLlcbqpqP5vNBqvVCrvdznvjr6+vY2VlBQaDgVe6HRwc4MOHD9jb2+ODLIXDJ4RIpVIYjUbYbDa+k+l0OkilUqhUKnxyTjQa5WLP5/Oo1Wpot9v3TnemGP39INHfA6lUCp1OxzPpWCKNSqXiW9z5+Xmsra0hGAzCbDaPdJgB/hS5ODzHuuoCf46HikQiI1/RaBQXFxfIZDJjFk5s1aVSKfr9Ph8tvbCwgPn5eZ44w4ZbGgwGtNttRCIRHgI8PDzkffivg9XKMwceq0hk8/eSySQuLy+RSCSQSqX4jLx2u83bYz3k/4G65E4Pif4WxNNjWBUac3wJK9FUKhUPH7HhkcLe86zAZJLYgT/771WrVTQaDRSLRcTjcd7PjnnpWZvom7a0bOiFyWSCWq2GRqOBx+PB8vIyT/X1+/08VMj68L99+xa//fYbjo+PkU6nrx1WIXwetvjZbDbI5XLeACOZTCIejyOVSqFYLPJa+4eMrBbCFlIS/HSQ6G9BKCylUsnnuK2trWFlZQVzc3OwWq3Q6/VQqVRQKBRc/CwphSGRSK6tXOx0Osjn87x3XTqd5nnmsVgM0WgUyWTy1s65zPKyEFwgEIDVaoXZbIbT6RwZd8XGUbEz/MePH/Hbb79hd3cX6XR6LAQohvkv2LhqvV7P02RjsRh/HZVK5YukzLL/G9rmTweV1k6AWRDhh14qlcLn82F7exuvXr3Cs2fPEA6HYbVaoVaredtm1qOdzVAXps4KFwDm0a5Wq2g2m6jVakgmkzg9PcXJyQlisRj3TNdqtVtbZSuVSuj1ethsNjidTgQCAV6Sy2LZBoMBRqMRVquV58Kz8B87wzMLLxa82FegVCphsVjg9/sRDAZht9sxGAy4dWcVd7Va7Ys52Wiu3f2g0toJiItc5HI5/H4/tra2eF+65eVl2Gy2sd7y130AJ5WTxuNxXF5eIpfLcQfaxcUFotEoLi8v+Yin22CWXViGGw6H+ZmdnbWFUQbgz7AcE/zBwcG1Fl7sK2ANPhYXF+H3+6FSqZDL5XiYjy1YXxJhS2zi7symKb8FoWVyu90IhUIIh8N4/vw5fvjhB6yvr8Nut/PH3GZp2M87nQ73Zl9cXOD09BRnZ2dIJpMoFosol8solUrXhsXE1latVsNms8Hr9fJYO5sz53a7YbfbYTKZRop3GFdXVygWizg/P8enT594/37xGV78nMJU4o2NDSwsLECr1aJUKvGF7EsJXnwvrGce68xL3A0S/Q2YzWa8efMGOzs73IouLy+PCP6uCEc4n56e4ujoiA+aSKfTqNVq/AN83XZYLHi/34+lpSWsrq7yNtV+v58nxrApMZNgtelnZ2c4OjpCJBIZ8dJPShRiFn5ubg6bm5vY2tqC2+1GtVrF5eUlstnsyDirx0YsbJZOTNv76bhR9P/8z//8te7ju4B56tmIqnA4zLfyFosFZrN5bNiD8EwpnGjD2kGzTq/FYhHRaBSHh4dc8KlUCqVSaaJIhP3kxRgMBgQCAWxubuL58+fY2Njg2X5sBpwQcQMMFiWIxWI4Pj5GJBIZK9aZdD+sldazZ8/w4sULhMNhAEA6nUY6nUYmk3nQTL7bEIqeVfLZbDbodLqRaAgtAjdzo+j/9V//9Wvdx3cBExoLq2k0Gi4iuVzOM9nEjwfAM+ZYrnqxWOQTWtgZng2ujEQiPBPtpm2p+GfMW84E//LlS2xvb/PustdZdvH32PBKVqyTTqfHnITXneGfPXuGn376CcvLy5DL5dwvweYAPFY47ib0ej3fdc3Pz09MfCKu58Z3anNz82vdx5NCPNySndWFbZ9SqRQKhQLq9Tra7TYfXplOp8dy5SdZdeHf1Wo1DAYDd9atrKxga2uLt5MWz5YTT8MRLk69Xg/pdHos9/26rDi5XD5i4X/88Uesr6/DYDDwhePw8BDxePyLWnkhFouFN9xcWVnhwzoZZOlvhpbHeyD8UDWbTV6FxsJtLKbOHHJsu8+6vkyKilxn8Y1GI2+QGQ6HeaOLxcVFzM3NwWKxXHuf4mv2ej2kUikcHh7i/fv32N/f55lzk2BOu7m5OWxtbfEBmWazGfl8HgcHB/jjjz9wcnKCXC73xboHix14JpNpJCTJFj0qtLkbFLK7AXZeF3+QhFa+VqshFovh/fv3fE4cs+a1Wu1OQhBaZoVCwa0zm/22uLiItbU1LC0tjXjmhWW57EgituwMltK7v7+Pt2/f4v379zg7O7u2l57wKLGxscHn0uv1euTzeXz69Alv377lC8fX2NYDnxcinU7H8/yFuxyqo78bN4qe3rxxxNaEiZ4NiIzFYmi1Wveq/NJoNHA4HLwpJcuqY955lsfPMv8YN812Y0MikskkF/wff/yBw8PDa0tyWeJNMBjE+vo6LwtWKBRIJpO8yu/jx4+8A+5tPKSrsPDxbMCFVqsda6dN3I0bRT8pP3zWEbdcZtv7WCyGi4uLW3PVGWwHwRyEOp0OLpdrpO89y9/3+/2Ym5sbiRwImWTd+/0+Op0OyuUy73jz7t073jxzUrGORCLhOft+vx8LCwtYWFiAy+UCAFxeXuL09BTv3r3D3t4eksnkncNzjxFHVyqVsNlscLlcsFgsUCqVGA6H/HNKC8DdoDP9AxkMBuh0Omi1WmMWT9jbTvihVygU0Ov1MJvNMJvN0Ov1fKorE5rP54PNZoNer4dOp+N58rfBesmXy2Xk83kkEgmcnZ3h8PAQ+/v7/PwtFrxUKuXVch6PB8FgEH6/H1qtFtVqldcAHB8f4+TkBPF4/NY6gMdEIpHAarWOtPQSVi8KH0fcDIn+gYgbWDLE3V8ZzFqxZB+fzwer1QqDwQCn04m5uTmeYDOpV7w4YUbYGpvl87MogrDzDRs5VSgURmr4ZTIZFAoFz8n3+XzweDyw2+1QKpUoFos8GnFxccFr4ac9w0/q+TcNwigCG6BhtVopPn8PSPQP4KYPrrhFlVwuh1qtht1uRzgcxurqKtbW1hAKhWCxWLhFZ005JqXOssKcer3OG1qw8zxLDGLb+Wg0ymvv2UQY4VZco9Hw52STcZ1OJ+x2O7RaLe+pn8/nkcvlkMlkkMvlUK/Xp/LSsx2NyWSCQqFAv99HvV7nhUY3tckSj88W9tMXLorktZ8OEv0XQlyEw5x0i4uLeP78Oba3t7G6uspnvysUimsrxliUIJfLIZFI8MEQrOsMEz2z9Ol0euRxzWYTw+EQcrkccrkcBoOBt6l2OBxwuVyw2+3Q6/WQSqW8+cXFxQUSiQRyuRwajcbU1t1gMMDn8/HwosFgQKPR4JV48XicN9S4DalUCo1GA7PZPDZ9h0Q/HST6ByDs5z4prCe08mxs8/r6OnZ2drC9vY1gMDjRojM6nQ4ajQbK5fJIjzlWtsqsLtuusxRiVopbq9V41xx2hGAjq1jXWq/Xy617t9tFNpsdGRSZzWan6rTL0Ol0CAaDfIFbXFyE0WhEvV7H+fk5Pnz4wF/jdaIX1xpoNBre6poGW9wfEv0DEcbyxdEO4ffVajUsFgsXmsPhuFHwrVaLN7e4uLgYaZfFztWsKaQQYd89mUwGq9UKh8PBowF2u51/sVbcrKcd6z/PIhEPEXwgEMD29jb+4R/+AS9evEAoFIJOp0Or1YLL5eI7ikKhgHK5fGt4U61W8z6ExMOgd/CBCCu9hNZnUmotG8dUKBSQTCbRbDZHatzZ1BkWaovH4zg/P8fJyQl3xOVyOZTL5VvFyApSAoEA74fHRjuzhhpsmzwYDEZ62sViMT4yeloMBgNCoRC2t7fx008/4dWrV1haWuJjsvR6PQCgWCzybX6hULhx4KZEIoFCoYBCoeDv13A4JGt/T0j0D4SJnXXNEcO+1263kc/ncXJygn6/j3Q6DYvFwrerzIJ1Oh3U63Xk83keF49EIkilUiiXy3fqDy/sub+6uoqlpSWEQiF4PB4+HFMul0OlUkEqlaJUKvE582xG/H3KY1nYcWtrCz///DNevXqFxcXFsbl4RqMRHo8Hfr8fPp8PqVSKJzTd9B6zPyl/5GGQ6B+I8IMoFj2z8oPBAI1GA6lUCp1Oh094MZlMMBgMfPgFS/ap1WrI5/PIZrNIpVLI5/OoVqtjzyuueZdIJDCbzQgGg1hbW8Pm5ibvyOtyuWA2m/m4Kkav1+Nx+MvLS7643KfFlclkwsLCAra3t/HixYsRCy8sQ5bJZDAajXA6nXznUSwWrxX9cDhEv9/nkYrrUo2Ju0GifyA3nemFMXQWYqtUKkgmk1Cr1TydlFl74egn9nVT91hxzJ51tNnc3MTOzg5Pn7XZbNy6CxkOh7z6Lx6PI5lMolQq3dlLL046YsU5CwsLCAaDI4JnYmXI5fKRsKFSqRwL0bHXCIAnP7FGI8T9IdE/AszyCD344jFL/X4f/X4f7Xabl6CyLTY7rw4GA/R6vTsNgBBv8YUdbXZ2dvDixQssLi7C4XDweXLCJp3AZyGxFGJWGVitVu+dMqtUKqHVarmQGZP8Hew96fV66Pf7twq50+mgVquhVCqhXC6j2WzyOD2F66aDRP9AmJWftOW8TTxsIZi0VZ/m+U0mE693Z+HAhYUF2O12LngxwkIcFgbMZDJoNptTPb8QFmKs1WpoNBo8dXhSNl6r1UK5XOa9AVut1limobh7MJsDcHFxAZfLxRtykuing0T/QNi2Xnymn0a804qdPSdLTZ2bm8PGxgZ2dnbw/PlzzM/PTxQ8W1hYVlw6nUYkEsHZ2Rni8TjK5fKtve7F9y2k2WwilUrh/PwcTqcTwOdzPjtWMAtfKBSQSCQQi8V434FJsXrh9Xu9HvL5PM7Pz+H1euF0OqHVann4jz2eFoDbIdE/AGHuukql4lt1Zr0fu0MrG5Cp0Wig0+n4oMylpSVsbm5ifX2d9+JnZ2QxzK+QSqVwdHSEo6MjRKNRFAqFBxfQ1Go1XF5e8sSbYrEIr9fLMw6HwyHa7TYSiQQ+fvw4Vcedq6srlEolnJ+fw+FwwO/38xoB8WJLwr8ZEv0DUSgUXIBms5nnlN/U1fauCMtvFQoFd3yxhBuv14tQKMQ76fj9/hHBs0WHeb/b7TbPzT87O8Pe3h4ODg6QSCR49t5DYI06WNpwMplEIBDgVrnf7/Nsv+PjYxweHiKbzd55sWFDMdmoLLH/gQR/N0j0UyL+ULFMO4/Hg0AggF6vx3vjPUT4TOxC5xjLlff5fPD5fLzO3ufzwel0wmQyjWzphY7DSqXCJ9/GYjGcnZ3h9PSUN7QUn6nvA0sqarfbKJVKSCaTiEQicDgc0Ov16Pf7PCmHDbO8Sz6AcJ4gSzOu1+vodrvU7/4ekOgfgEQiGamcYxl2LMFFIpGg1Wrdy4Kyo4NGo+GLytzc3MgUG7a9ZaOyZTIZnwTb7XZ5vD+dTnOhpVIpnoTDBlQ2m81H6W93dXWFdruNdrvNF5lUKgWz2cwr9yqVCp9Zf9fQoHjYhlKppPz7B0Cin5JJlp6JHvjczslkMkGn0yEWi6FYLKLRaNxLVMJad7fbjXA4jKWlJQSDQbjdblgsFuh0On5ebjabaLVaaDabqFQqvCqP5dKzRB+WgcdKW79EL0TWzINZf7lcPrIoTHst4PN773A4eI9AVhVITAeJ/oGwBpLA57HNRqORZ76xXPGrqys0Go17iYt56dliwoZuaLVaSCQSXqXW6XRQrVZRLBb5tFvhbHhWZsu2xWxH8KW3x71e71F2ERKJBDabDcFgEIuLi7wPgVD0NMzybpDoHwjbgkulUj6i2mAwQK1WQyKRjCWg3FVkbJY9S9ZhjSfK5TL0ej2vj2db+Wq1inw+z7fviUSCW3ZW0NJoNJ7UGVjcfchoNPIGJCwsSZ1zpodE/0BYsQ07g7OQGvDZ6cTOsNVqlY+6ugtM9CyJJZVKQa1W86QaYSeaZrOJQqGAdDrNRZ/NZlEqldBoNKZ63u8JoehZT4BAIID5+XnMzc3xrD/y2k8Hif4RYFtM5nGXSqVoNpu8fp151acJ5bEPPLPi6XQag8EA9Xod2WwWer0eCoUC3W4XjUYDxWKRt7Zilv0uram/FuJc+rsgfJ9YTkIwGITH4xmp3CPRTweJ/pERltqypB2WW89+Jv7gX/ehZfF1NvOO1eNns1l+pGDfq1arPP213W5PlVn3WNxF2LclLbFzuVjwCwsLWF5e5r3/Jz0vcTdI9I8A61bDculZeSyLjwur5e4yhnrSddvtNvfKq9VqPuyCnflvqsb7WkwzjPOmxwkfa7Vasby8jFevXvFJucL+eACJflpI9A9EKMxut8vP8SwRhrWDqtfrD87QYzPxms0mP1KwheY2HjJh5rpr3XYdVoR00/2xnc+ka5nNZiwvL+PNmzd48+YNnj17NnKWJ+4Hif4RYMIfDAZ8SKWwE4xarR7JP7/Oi8/EJA5DMfGIO/QIFxxhK2zmNxgMBlxwj+m1F19LKpXy4wtLntFoNPx7g8GAx+dZuJDdl3jCLvDZaWc2mxEOh/Hq1Su8efMGOzs7mJubg1KpHLkPsvLTQ6J/JIQltjKZDFqtls+iUyqV8Pl8aLVaXIzCnvUAeKUe+2LXlMlkI+2iWDUfs45sARkOhyNHAdZFt1Qq3ZrqepNw2M+u26WoVCpYrVbYbDYYDAbo9XpoNBqoVCo+souJvlaroVAocIejMEmHxeHNZjOsViu8Xi8fyc0svFDwt903cT0k+gcidNwNh0NoNBoMBgMMBgPejXZhYYGnurKvTqfDLbHQ8cf65TFxCxcTcTMKAFzs7Kvb7fIee6y3fDabRaVSGbGu4iYf1yH+GbsfVgDkcDgQDAYRDofhdrthNpu5lWcLFvDZ91AqlRCPx0f6/lWrVSgUCjidTn6dYDCIQCCAYDDIU47Jwj8eJPoHIrbGbHvLutGyLjgsQYed/VnsnI2YZr/HLCSL/Ys78kwSPbP6TPRstFU0GuXpt5VKhW+v6/U6b14xjZ+Bjb5i8/d0Oh0cDgf3rPv9flgslpGiH/YahsMhb6/t9XrhdrtxeXmJSqUCuVwOt9uNpaUlrKysYGFhAW63GyaTCXq9fmS811NKLvpekdzyJtI7/EgIz9/CRYA1jGR59gqFYsTSTwObcsOSdYTpt7Vajfffy2QyI/3z75ILbzQa+fDIUCjEB2SYTCZeBOR2u28ctNntdpFOp3FxccEXo3q9DrlcDpvNNpJ4c9eBncSNTNwOkei/IcKedY+9XW21Wjx2z8J5rJ4+kUjg6OgIkUgE6XSa97dnvgTh9rnT6UAqlcLhcGBpaQkbGxtYXl6G0+nkxxGj0cjbed9Gr9dDuVzmE3i63S6kUilfQKxW60TvPG3p7wWJftYQn9vZrLtUKoVIJIJYLMbn1AHgba1Y51r2O8DnePn8/DyWlpZ4h13mmZ/ka7gJYZdgYQXdtNchboVE/zURd3RhCOffXfc7Dzm33mXXwLzowtx8ACOhQHYN1qVWq9XCZrPB6XSOZcQJ7/8u2Xa38SV3QDMGiZ74DAv1sXj+pF7z4gQcFopkDkbiSUCinzUm7RweY/ss3pYL/7zvfbFr0ILyqEx8Mylk9xdGLKDHEpTwOve55qTfIbF/PcjSzyj38R+QJX5y0PaeGOU+03SIJwVt74lRSMSzCQVFCWLGINETxIxBoieIGYNETxAzBomeIGYMEj1BzBgkeoKYMUj0BDFjkOgJYsYg0RPEjEGiJ4gZg0RPEDMGiZ4gZgwSPUHMGCR6gpgxSPQEMWOQ6AlixiDRE8SMQaIniBmDRE8QMwaJniBmDBI9QcwYJHqCmDFI9AQxY5DoCWLGINETxIxBoieIGYNETxAzBomeIGYMEj1BzBgkeoKYMUj0BDFjkOgJYsYg0RPEjEGiJ4gZg0RPEDMGiZ4gZgwSPUHMGCR6gpgxSPQEMWOQ6AlixiDRE8SMQaIniBmDRE8QMwaJniBmDBI9QcwYJHqCmDFI9AQxY5DoCWLGINETxIxBoieIGYNETxAzhvyWn0u+yl0QBPHVIEtPEDMGiZ4gZgwSPUHMGCR6gpgxSPQEMWOQ6Alixvj/HasWpnaCGuMAAAAASUVORK5CYII=\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -2116,7 +2120,7 @@ } ], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta//2)],need_gradient = False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", @@ -2130,7 +2134,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n"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\n", 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\n", 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" ] @@ -2159,7 +2163,7 @@ "plt.grid(True)\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel('Transmission (%)')\n", - "plt.ylim(98,100)\n", + "#plt.ylim(98,100)\n", "plt.show()\n", "\n", "plt.figure()\n", @@ -2167,7 +2171,7 @@ "plt.grid(True)\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel('Insertion Loss (dB)')\n", - "plt.ylim(-0.1,0)\n", + "#plt.ylim(-0.1,0)\n", "plt.show()" ] }, @@ -2181,7 +2185,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2195,7 +2199,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/python/examples/adjoint_optimization/04-Splitter.ipynb b/python/examples/adjoint_optimization/04-Splitter.ipynb index 5bcf768ae..4518401a7 100644 --- a/python/examples/adjoint_optimization/04-Splitter.ipynb +++ b/python/examples/adjoint_optimization/04-Splitter.ipynb @@ -22,6 +22,14 @@ "text": [ "Using MPI version 3.1, 1 processes\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/site-packages/meep/simulation.py:5441: RuntimeWarning: quiet has been deprecated; use the Verbosity class instead\n", + " warnings.warn(\"quiet has been deprecated; use the Verbosity class instead\", RuntimeWarning)\n" + ] } ], "source": [ @@ -139,13 +147,16 @@ "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta): \n", + "def mapping(x,eta,beta):\n", + " \n", " # filter\n", " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", " \n", " # projection\n", " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", " \n", + " projected_field = (npa.fliplr(projected_field) + projected_field)/2 # up-down symmetry\n", + " \n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -201,7 +212,11 @@ " mp.Block(center=mp.Vector3(x=Sx/4, y=arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # top right waveguide\n", " mp.Block(center=mp.Vector3(x=Sx/4, y=-arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # bottom right waveguide\n", " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + " # \n", + " # The commented line above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "\n", "sim = mp.Simulation(cell_size=cell_size,\n", @@ -243,8 +258,7 @@ " objective_arguments = ob_list,\n", " design_regions = [design_region],\n", " frequencies=frequencies,\n", - " decay_by = 1e-4,\n", - " decay_fields=[mp.Ez]\n", + " decay_by = 1e-5,\n", ")" ] }, @@ -262,7 +276,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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SjpxOJ/3796e4uJhYLKb8UhKF0slHh66xvPji6bz77mgmT/6UK6/8AMMoUQTndrvxer1EIhG1Nlm/rhlGIhHy8vLIzs4mLy8Pm82Gw+EgPz+fDz74Ll99dQqjRr3D1KmvEosNVCQeDoeVMz47O1s5+0OhEH6/X31HJBLB5/PR2NhIXV0d1dXV1NXVqfNpL5WiK5Dpz39KScg0zaWGYVQeiIxMvwEdob08obq6Oux2e0K0Kdl0E5J57rmTWb78aCZOXM1ppy2kttahojlCCEJEoumIBiGbTkhHNCXdz/L229+iqmoKw4YtZNy4xzFNEsy70tJSDjroIIqKiohEIng8HpXjJN8bi8WUxiF5TBAnorgJNpYTT/yYyy9fRV5evjIPxSHu9/sJhUJKG4FEXw9AKBRSmpCYmU6nk1WrrmTt2lMYM2YFZ575KjZbvrqWQjaiGcZiMaLRKMFgEJ/Pl/B7OBymsbERn8+Hx+OhtraWrVu3qnMJBAJ73NuuQG94/lOtCR0QMvYGCFl08kHctWuX0oT0erBkzJlzKitWjOb449dw7rlv0NxsV2FxITFAEYLuT5K/8LrPKCsrK8F8Wrz4m6xbN5nhwxcxZsy/gPgxom0UFhZSXl5OaWkpLpdLkZ4kM+qbW2SKCQlxJ/Tq1eOZOHE1F174DqbpVOahONLz8/MVCcg1EGKTkHkkEsHv9yecR25uLu+/fznr1sU1oGnTFgCtOUKiUWZlZSmzTF+vyJUaslAohMfjUZ+N1/Tdo+5FW5rQAT4GAJn5/Cch7UnIMIxrgGsAKGz9/4wloP1Ae38tGxoaCAaDKuKjh7MFc+eexnvvjeG44z7k/PPfJBKJb3SbzaZC4bLZkrUhfTOLKaJrLtFolKVLL+bzzydz2GFvMHr0vwmH4/JFg5K8IXHeimNXNB7Z5LKZRa4Qx6uvnsPHH09k7Nh3OfPMN4hEHOo8xY8jUTkpFAVU2F2IIhwO4/f7gXhOlcPhIBaLsXr1VaxbN4WjjnqbqVPnYRjZ6prrZmIoFFKFwhLtysvLU2uRz7e0tChzNi8vD4/nj8AV6n50l0+oNzz/aU9Cpmk+AjwCu0f+0DcIqCPoIXTxzeiYN28a778/jmOP/YBzznmdSKRV20nOucnOzlabVddI5P9EywCUVvTJJ9eyYcPJHHLIa4we/RiBQGCPULdsRvHdSIRO5OjlI7r/xTRN3nnnEtauPZGRI5dy0kkv0twcz+TOysoiFAop/4w4y+Vc5Pt1ec3NzeTm5hKJRFRI/Z13LmHdulMZOXIpU6bMVQSky5C1hUIhmpub8fv9SpsRc060UbmmYuJt2PBz/P5pxEtJfpzwma5Gb3j+056EktHXCQigsbGR2tpaamtricVi5OTkKDNj/vzprFx5DBMnrubss19VGpBEsZKJSG/rIU5Y+ayuAZhm3Nm8adON1NScQUXFXIYOfZDq6qgiKdGApHRBwt+6M1v+T5zFfr9fbfaWlhaWL/82X399CsOHL+Koo56gsTFOYPIdkhGtk5serbLZbAkEGovFlAPZZrOxZMmFfPHFiRxxxBImTXqWSKQ11UBIWULxyb42PeNcT14Ubc5ut7N06cWsX38clZULgP+hqipOQsllG+naDC0Vz3+qQ/SzgZOBMsMwtgK/M03znx0d09cJCOImzLZt2ygrK8M0TUpKSsjKyuKVV85i5crxnHDCR3zzm0sJh1vrpSAxDC4QB3ZLSwvBYFBlVktpg/g7otEoTU13EghchNP5ODk5v6eqKkutRzSfUCik6ql0IpKwuZCS3W4nGAzS0NCgykreeecSNm6cyqBBLzFkyP3s2mWQn5+f4E8SWUICQmiQmFcVjUYJhUKq8DUQCDBnzql8/PF4jjhiCSee+BTRKOraiNNdL/IVLVHqyyQT2uFwUFxcTFFRES6XS2mRL700lQ8/jJd6TJr0Gh9/PIyqqvh1bktjTTek6vlPdXRs5v4e09cJSNDc3Exzc7PamG+8cT4rVx7DySd/zlVXfUIsVkJLS4vSfsSUEBNOdwALCXm9Xvx+P83NzQQCAdXYLN5A7V5ise9isz1MNPpzdu0yEhIW9YTGnJwcRRq61iDmXHZ23Pzxer1qw69YcSkbN06lomIuFRV/oq4uvt78/HylJekal2g9uj9LfGShUEhFqRobG/F6vTz55LGsXHk0o0a9w6RJzxOLtTrY9SJcl8ul1tfS0qKI0+FwKJNQCmSLi4txOByEw2GeffYkVq48gnHj3uOssxZhmvEiYIEe9dObyckaOouujLJd/NzFPHPhM5w09KQeLTPJOHPMIqA43G43xcXFlJaWsnz5t3n//bGcdtqX/PSnG3E4KlR5grz8fr9KntOzh2UTSzfFpqYm/H4/LS0tKoIVJ6AfYLM9jN3+U0KhVke4EItsZvG/iLYlzm/dbyPOcck/Wrr0Yr7+Op7JPWjQvXi9cR+MXmIhUSeJimVlZam8o0AgoOrlsrOzCQQCNDU1qazlf/xjNMuWjWT8+JWcdtpCYjGX0gqj0aiqY3O73apERbQfMdEKCwtVcqR0J5CC3bvvHsySJf05+eTPmTLldcLhxKTLZLRqp3Gy07Op9xcHcqzgrU1vMXPOTGZfMJsTB53YJTKT5XdEahlHQl2NTCSg3Nxc+vfvz8CBA3n//ct5//2xzJhRxY037sDlGqCyk8W/EwgEaGxsVGQjVfGyGaTItampicbGRlW8GierPxOLXUtW1iPk5d1ENJqVUJypZzfrviUxhUSbEhNKyEo0qBdemMLHH4/isMPe4OCDH6G+ngS/VFZWltJI5Nwl01p8SlIrJ1X4wWBQOe8fe2wsS5eO3O0je51YLD/hWsZiMUVsUv4i3y+anBCTEJDereCOOwbw/PNuLrmkniuu2MKmTeU0NTUlRNMApV3BnqaZ/t7+4kCOhfjzP/OFmTx7cfdZGDNfmMlQY2i7n+nTJJSJBARQUVFBv379WL36Kj766BimT9/ILbdsx+0uVqUFspmk/3EwGFR5PrKhw+EwPp+PhoYGamtraWhoUP2rAUKhvxCLXUt29qMUFf0Gw8hXWk5ymYjegEySEqurq5XWIj12xByLRqM89NCRvPPOMMaMWcExxzxDbW2OKsKVSncxuyBOGJIoKPk7knmdm5urilel7cisWcexbFmcgM499zVM01DXQDav/NU3TVNpf3q2uWht4nSXYlu3280ddwzgySedfO97LdxxR5iGhqHk5ORQU1PD5s2btcZupLy/dFuw5o6lGJlAQO2p84MGDWLTphtZv/4ETjvtS266aQcFBSUqi1g2rkS0IpGI8qPo5pOUHjQ2NtLQ0EBTU5PWL+h/icV+QHb2o5SV3YbD4VZZ0npekh5t053EgUBAVZVLnZm018jNzeX++w9h4cLBnHDCR5xyykKamuIlGHqioZRaSJdFQGk/hmEojU00JdFQHA4HCxeeyfvvH82xx37AmWe+QizW2otIHNDSsD4SiWAYRkJWtJiBUmcmDnWJxP3hD/148kknV18d5i9/iRCNOhIyzuvr6xO0lF27dqmfRcuCXHUNO4vOHrukagmXzLmEpy54ikmDJx3QGvZFfkfokySUznPHkrNo2yKiLVt+SVXVNCZOXM0Pf7gJp3OwauUh4Xo9nByNRlUdl/hhkpt86aUUpvk3oJWAJOnQ6XQqMpANrWc+y4MsZlcgEFDOYafTCcQ3zYMPHsH8+YOZNm0955yznMZGh+p4qCciivNcNCn5DjEn5fu9Xi/hcFhVt3/xxY9Zu/YYxo17j+nTF2GaNqXV6I5mcdDrPqzkQlppPSLdKnNzc/njHyuYPdvNVVcF+fOfw5imkXBNRFMsLS1V92zBggWsXbsWm82mlXD8GoC77rqr089LZ47daG7kqdhTXGK7hBWzV7CCFZ3+/n2Vv2PHjnY/3+dIKJPmjrWnCVVVzeCII5Zw/vkrMc3h6uEXLUc3jaDVj6L32JHMYr1bIoBp/i9wHTbbwxQW/ha3uySh9YYQnJh1ejhfEhtFVjQapbm5GZ/Ph9frJTs7myeemMDrrw9lxowqrrzyQxobW7US0ZKEhESWmGGShCghd9M0aW5upqmpiZaWFnJycvjssx9SXX0KI0cuZerURZhmov9FL8yVPkA6gernlpx0mZubyz33DOGpp4q57DIPd94ZIBKJyxetsra2lpqaGrxeL263W33v/Pnz2bFjh0q0jN+fOAndeeednXtAbt7/Y6NDooTPC5M9N5v/bv5v5763E/J1/1gy+hQJZfrcMcGIEYs56aTnCIUq8Pl8NDU17dEQDFpbesjG00PlQEJGdNzMuh/4IYbxELm5vyA3t4SioiLKy8spLy/H6XQq00o3j8RPI0Ska0fBYBCv14vX6+Xll89g+fJDmTZtPdde+zmhkKHWJOuF1plhQpDiWxFNTU9uFJKLh+X/RCBwIUOGvMyJJy7ANIuUXJ08xWwSjVCvlpfv1vtnS6Lln/40iGeeKeGSS+r59a9rCYXifp5YLIbP56O2tpbt27ezdetWqqurVYsRgC1btihyTo4+HUiF/X4dWwmcCzwD4aowYcJ7OWA/0Un5fYaEelOm9amnzsEw4s7m+vp65YgWU0BMMilSDYVCyuSQ7F4pRwgEArs38z2Y5rXAg9jt12OzxbUS6VYo/hx9KCEkZl2L1iJ5SWI2eTweVq26ks8+k3Ycn2GaDrXhJZon/iYxjcSfJeclUTeRLxpYKBSiqelOQqHvUlw8myOP/A+x2GC1PiFgfSBi8mBEvU4uuT1KVlYWd989mGeeKeXii2u58cYtBIPx6yDXsr6+nq1bt1JVVcW2bduoqamhvr5eyRFHespQCVwEPAtUpZf8PkFCvYmAoDWTNxqNKseyRHBisZjK/hXCEK1BTCePx0N9fb1qPVFf/wei0e9hGA9ht18PkJDAJxoQJBZ4ikYkGxlaN6WQREtLC19/fT3bt5/E6NHLOf/8lUQiZarWTApbs7KylImlN7gXB7uQkNSpSfvUeDfDuwiFrsLpfJwhQ/4KVOzRhlbXBEWzEaKWVAVd45JrYLPZ+N//PZTnnivlkkvqdxNQa+KnzWbD6/Wyc+dONm7cSFVVFTt27KC+vj6hn5AQoN6aRTips2H2MOF9OjY2JEb0gij2OXZs22ySntRl2Bf5HeUe9XoS6m0EBK0ag5RI6M28WlpalG9Fz3sRrcfj8VBXV6dC8lu33kxLy1XYbA+TlfUzTLO1d7TeFVHMsPZasApB+P1+1eI0GAyyc+evaWo6j8rKBUye/CqRyOAEp680TZNMbtn4QjB6oiOQoMFlZWVRU/M7gsErcDofZ+DAO7HZ3Al+HvHr6L2QHA6Hqj2T0LyYleLAljU+8sgoFi7sx8UX1/KrX+0kEIgoE1Y0tdraWrZs2cKmTZvYvHkzu3btoqGhIaG9qx6uT0Z3JiuaQ024AHgWYptixOjaXtf7Kr/PJiv2RgICEhyxOTk5Krrl9/tV1q+89A6Ifr9fvQKBAFu2/JJg8DKysx8lN/cmolG70oAKCwspKSmhtLSU0tJS1f4DSDCNZLOLX8jj8SiS2LHjVny+SyktfZoRI54gGv2G0ghyc3PJz89PaBMrRKrnEulZ33r6gd1uZ+vWm/H7L6ag4D/07/9H7PYslQ0u+U4SCXM6nep6yXCA3NxctXZxesumjkQiPPXUiSxdOoyzztrMz362g5aWLEWCzc3N6rt27drFtm3b2Llzp9Iwm5qaaG5uVveso03YbWUblSSYSCZdXIrRRfJ7LQn1BgJq7wHbuXMngUBAmRkOh4O6ujoKCwtxu92q4buMAMrKylLmkfxF3rDh53g85+FyPYHL9TtisXyVPexyuSgvL2fw4MHqlZ+fr0wjqTNrampSrVWFhKTlRZyALsfleoJBg+4DKlTWsZCCy+VSCYzJyYyinegh9HA4TFFREQUFBXz88TXU1p5C//7PU1FxH+FwrjLRPB4POTk5uwckGirJUMwwSVfIyckhFAol5FWJT+q//z2eZctGctJJn3HZZZ/R0FCgqvmlsFdKXXbt2kVdXZ0aEim5Rz01YaNNVJK2PqBk9EoS6g0EJGirvevOnTsJBoPKfJEpqY2NjbhcLuVMlkpvmQYh+TTvv38527dPYcCAOQwY8FfC4VKlAcjmPeigg6isrGTIkCEcdNBBuFwutVEDgQANDQ2qWFTMPRkrVF39W/z+y3E6H6dfv9sxjIIEDUh6O0vKgOQVyfnokTHdUS21YgsXnsnatUcwYsRiDjnkSRoaXKraXzSz7OxsvF6v8iEJ+eitYaUxvV4mEgqFeOaZySxbdjTHHfch5577Fg0NDkKhkCrQFU1IzFuv17u7k2IgoS9TylBJxhAQ9EIS6k0E1B4aGxsBVPRLnLB6D6DGxkbq6+spLi7G5XKpGqz42J8TOOSQ1xg5chbNzf3VRjR2j7YpKytj0KBBDB48mAEDBlBeXq76BAkRiBPc6/WqPJ5AIMDOnb8mELgCh+NfFBf/DsjZIzSuT7kQfxO0dluU5mV69rS8d9ddB7FkSQWTJ3/Kcce9ytatbqWZCIlIh0NxDpeWllJSUpIQHdPPRUYcNTc38/TTk3j33Xim9YwZrxAM2hOiiZJlLc5+yVmSrgF6TZ2eq9VjqCSjCAh6GQn1BQICVM8bSbaTSBO0ljVII3v5Ny8vj0WLzuajj+Kz5086aT6hUH/VR0ifr1VeXs7AgQMpKytTJp1EsaC1ADMYDKrImc1mY9eu3xAIzCQv77HdtWZZbW5E2aB6YzD5f2gNZ4tGIZ/97W9LmDu3gLPP3sIFF3zApk2OhCmrYrIBKomxtrZW5TnJmCEgoWxFil3jBDSW8eNXMmPGK8RiJPilgsFgwnXWNSjdCS4EG6/Zi5+zjFLqVlSScQQEvYiEeisBtRWN+sY3vqEeeL32Sd/wkr8jG+WVV85izZrjGDfuPc45501stvKEZmayQaRXjji49cZhek8c0WYcjvj0ji+//H80NEyjsHAWpaV/JBbLTVi/pBTovhI98qVX44uPSHKPbDYbt95ayH/+k8fMmQ1873tfsWULCbVaeodICeGLn0o3l6Q/tF7l39zczKxZx/Huu6MZP34lZ565ENNs9cklR9v04mA921rIWJrxA0iqkEQAuw2VZCQBQS8hob5EQADl5eVqs8pf6eTPyvtZWVm89dZFfPLJ8Ywd+y7nnfcGTqdbmUDSa0jS6qWcQU9M1E0MPUwvFeavv34e69ePprJyAUOGPEpzc4nSwsQUAxJMRyEe/SW1Y0JC8tmbbsrnscfyuPLKZn72s61s3hxQTnYhAZEthCBakW5u+Xw+1VJEL//417/GsXz5kYqA2oKexCjaUTAYVC99pr2ch8Ph4Isv4sc7HA48Hk+nnoO9opKMJSDoBSTUWwkI2o+ONTU1qdYS0DrOOHmaqWEYLF/+bb74YtLusTYLsdvdyjksBaCSFQyo0Lk4iXV5ba3tH/8YzfLllYwb9x7HHDOPhoYBqjF8IBBQUS+9uFavcUsuMxHnt7xuvNGhCOg3v9nF9u1xB7w4wYVIpABX5EArEYlG5PP5lOknEbgHHzyCt94azsSJq5k+/RVisfY7HsqaRMvSuw/4fD6lAUkOlF471m3tXSvJaAKCDCeh3kxAgraiY1u3bsXlclFaWqo2sl5/JabJihWXsm7dqYwYsZgTTniOlhaHyo0R4pGolGw4nTB0DUaXLT/fe+9QFiwoY8qUdUyfvoy6uhKys7MVCekTQcS8S85W1jUivbF8LBbjF7/I47HH8vjOd7zccstO6uvjmd76xpcolTiyoXWah5hpYjo1Nzerc7Xb7Tz44BEsWjSMSZM+Yfr0VwkEIipZMRm6uafnRTU0NNDY2Kic9YAqkF279kfq+LYa3XdJB9UMJyDIYBLqCwTUHurq6jBNUzmF9WxmIYnly7/NunVTOOywN5gw4T8EAoYaryPHSMRLIl2Aaiqmm2Ni6ujtMH772xKeecbJ+efv4Ior1lFTU6gynPUQvITLi4uLlZM7ud0IJGodpmlyww25PPZYHpde2sQvfrGZ2lof9fX1qibL6/Uq7UcnSX26h06m4tcRJ/zjj4/n9deHcfLJn3POOW/Q1NSikhZbq9wTiVcf+SxEpHcPEKLKzs5m/fqfsWXLNHVOuj+tS5HhBAQZSkJ9mYCAhFYTydqEzWZj2bKZrFt3Cocd9gbjxz9OKBT3mUgeC7RuKD0TOnnaql7YqXdTvPFGB48/ns1ll3n4xS9qaGgoVCF6IR3Jx5EESOlIKFnPuiM6uZfP9ddn8dhjOVx6aRM///kG6us9eDwepXXI94jvxel0qjHQYv5J4qYQn5CqaZr861/jePPNQ5k8+VMuuGAJfn9rOxLRwnQi0p3nktCoZ3JLZFGu3YYNP2fr1rMZNeodPv443tCrPf/eAaOqG2RW0mMEBBlIQm+sf6NHO8J1R9c7e4fvJ4aA9UGBgoEDB6pex/rEC4B33rmEL76IE9Cxxz5JNNra20fyXeR7ZZPpnQT19q26+SFE8bOfZfPoo1lcdVWQ22/309zsTOgbJD4mvfhUH/MjMiW0nZzrFNeAcpg5s4Ef//hLamqa1BQQv99PNBpV5p2utTkcDlVYKpNZXS4XbrebkpISSkpKcLlcPPnksbz55qGcfPLnXHTR24RCrdclEAgk+KmELHWiFEe3JELqofns7Gw2b76J7dvP4bDD3mDGjDe6n4S6GpX0KAFBBpKQEERnxpLIA5X8u/z71qa3Dkh+8ne0dXx7Jkh7stqChOilAFOII64BnaoIKLklhXy2qakJQOWyQHxziR9F5ofJJpRo0s9/nsM//pHF1VeHueeeFkKhLJWNLZ0HpRJeNqfu52lLi9Cr72++2cXjj+fxrW/Vcc01n7JtW50yu+RcxK8kJCm5Pn6/XxGglIWIf0t6Qv/rX+N4/fVKpk1bz8yZq2hubr2+QtLQqmnq90nIWDLD6+vr1eBGuUbbtt1CTc15DB++iFNPfYmcnPIO72/aoZIeJyDIQBLqThPskjmXdKn8vfkBOnq/o/dk/IzkCbW0tLBkyYXKBBMC0ltZQOJ4ZwlbS/KeXkMluUHi94hEIrtbmuZw1VVB7rmnRYXZxQkrEy/ELBJIyYS8dO1CyjIkDP/kk04uvLCa733vYzZv3kFtba3KUhb50uEwuZm/NFOT79OLYXNzc3nkkVEsWDCYc8/dxve+t5aGhsSZ9dLYDEgwcaUfk6QDSHZ2c3Oz6pcEsH37r2houIjBg+dx3HHPkp1dknAPk3O44ukVe763P4jRthO9MzCHmpgXmhjPGRibDejiZO+OSlkyjoT6og8oGXovH5vNxttvf4u1ayfvQUDQGvaWDSlheWlDISaMbEipSZMJHYZh8Ne/HsyiRcVcckk9v/mNl0AgNyF8L/VrTqdTmVhiRgopiE9Ir9uSTX7LLW5FQN///hq2bdvBli1bEhzwot2UlpZSUFCgnOritxIi0sP0ch3uv/8Q5s2r4Jvf3MX111fh8djbNDV1E1R+l4kkMmhSCElPpty589d4PDPp3/95Ro36NzZbqboGbUHvvwSpJ6HY0BjmBSa2OTZsW2xdT0BDY7C+/fczjoQ++eSTLpW3smYlv3jvF/z52D9T6i3tUvntyTqqw/ePUu8J2eTl5e3eGIcBqPnnAG++eQGffno8I0cu5fjjnyUctieYeUIG4p8R7Un3NcmmEHNMzJxQKMR//3s8b789nGnT1vP972+ipqYgwdEr0SLJjZFqe9mE4igWH41Ex2Tz3HKLm8cfd3LJJfVcc83nbNtWw86dO6mursbv9yu/T2FhIWVlZfTr14/CwsKENiCAqqCX5EUx+26/vT9z55bt7ge0i0gkXoyqt3DVi2ulBCaZ4JJTACTVYdu2W/B4ZtKv33McddQjOBxFisR1gtDbeiRnTqd0+GElrf2Aqrq+35CSf2/7H8k4Errlllu6TFadu47Vw1czdv1Y5iybwxzmdJlsxre/1pd3/9v2+y+r91o78ElGdDyb1+12Ew6HefnlM1iz5nhGj17O6afPJxzOV2UYuvNZD+OL6SG1Z7KZhTiknstmszFr1nG8/fZIjj9+Deee+y47drjx+XyKTJInocoGFl+VkIG079Cbo5mmya23FvLkk04uvbSJ//f/1rNtWyN1dXXU1NTg9/vVufbr14+KigoGDBhAaWlpApHpUTaHw6Ec+tFolJtuyueZZ5xcdpmH227zAC71vsvlUr4mGQEk10ZMXT3PSKJ8cn4tLS1s2PBzGhrOo6JiLscc8x+czvKEzgA6Cen9ptMGlfRcnlEHyDgSmj9/ftcIqiR+gWbBu1Xvdo1MHeP3vtaO3u/oPZvNxoIFM1izZiKjRy/nrLNewWZzJCQBSl9pvVG7aDmiEYnDVtdqRIt6/PHxLF06guOO+5Dp0xfQ1JSlWlc4nc6E2jLd4SzQc4uABD+QaZrceedAnn7azcyZDVx//Qaqq2vZtWuXagwWDAZVb6T+/fvTv39/ZYolE5BAz3n66U/tPP54NlddFeTOOwPEYvGm9KIxybUSTUi0IIkS6v4g0bCk22RWVhaffHIt27dPY8iQl5k48SkKC/ur90S2Hv087LDD+Prrr4lGo+Tn52MYBlu2xN876KCDOnxO2sM2tnX62JaBLdRPqafkjRJyw7nQOTH7LL86u7rdz2YcCXUJKun+vwDdiOeeO5mPPprIkUe+xWmnzcdudyoNRl5S0ySbQo+EQTwyJqFrKeGQTffQQ0eyePHBTJr0CTNmvEowGEmYnCFzxMQvI8SlO7Xj0y98aiMKEQL8+c+VzJlTynnnbeeaa75i27YmduzYwaZNm9i2bRuNjY3YbDbl+5GRy1L3lWzqCIQ8rr8+i0cftXH11WHuvjtIJGJP0Jh0QtRTEvSUBdGKmpqaqK+vV+kSTqeTlSuvYP36KRxyyGtMmPA0BQWlFBUVJfSQlha1gssuu0zNXpPv+9734u/dfvvtnXoOrtp8VaeO/SL4BQ/WPsgvyn7B4dcd3qnv3l/5v//979v9fN8joUp6RgXtAuh1VDo++ugEhg9fxOjR/yEcLlOflVCxno8jTmPJHpZweX5+PqWlpZSXl6sRxZFIhD/9aRCvvlrBtGnr+eY336OhwZawYaUzo8/nw+FwqPfETMnPz8fhcCQMKdTbXtx992BefHEA06at51vfWsWWLWEaGhoSRuUEg0HVRE0yn/WomiA5jG4YBj/+MTz8sI1rr43y5z+HCIcTs571z8uakievihM6GAxSV1dHQ0NDAgF9/fUURo5cyqRJc8nOjvcqKi4uVk3eJAdK7zE9Y8YMKisrE9YgJPTd7363U8/HVb+/ar+PXVK1hJuevYl535nXbUGetuQ/8MAD7R6TcSR0IOnvsaExYhfE4lGArbYuP3tdfuw7sfbXutuZ2Nb74mfUs4pbx97E3ysunk1p6f3U15eqNqlCPOJM1clL2qpKq1fDMHA6narzopBJvF9PMRdeWM11122hrq5YtVlNbrcqOUeSIiARMPGvSOhe8nby8/N58MEjePHFCs44YwPnn/8m27fHo04NDQ1UV1dTXV2tGra5XK49wvp6UmMyDMPgRz8yefhhGz/4QYz7748RCu1ZfKtnNicnI0plvSRHxieR1NPc3IzD4WDdup+wfftURo16hxkzXsFuj4/e7tevH2VlZdjtdpW02dTUpPKxAJUKkUqka6VBxpFQp6MBlfRMFGC3fNj7Wjt6P/k9fSOFQtewfXu8f7I06pIkQb1jYCgUIicnRzm2xUEso3b0aNWttxYye7aTyy/3cdttzfj95Sq6BXHNR6JmgIqKyQBCaVSvF8eKgzsvL4+HHjqSefMqOOuszVxwwVK2bNmpasCkELShoSEhd0nvYdRW+YiOH/8YHnrI4Lrr4G9/M4hGW7Wf5GPk/PPz8/F6vQn9pVtaWmhqaqKhoUG1bo3FYjQ13UlT03kcdtgbnHHGazidbpxOJ0VFRar7pIwtamxsVP2rBR6PhwEDBqjf476ynuu8mK4EBBlIQp1CJb2i2ljQ3NxMbm4uPp+Puro6ZQ6Jw1kc00JCkqvjdrtV0WtOTo7y3/z610U8/ng+V17ZzD33BAFXQhW93W6nrq5uj2id5BWJGSPfGQwGsdlsKgdp1qzjWLx48G4T7x02b97Cxo0bqaurU0Wg0nxMyFfvOSS/i8aityqBOAE9+CBcdx38/e8AicW8ukNciFrKXqQliJxPS0uLmhri8Xh2r+8eQqFLqaiYy4QJL2C3D8LtdlNcXExJSXxMdnFxsZptX1hYqLRMgTQ5k+sXJ8Uc9XtnsS/HvrXpLWbOmcnsC2ZzwkEndHlztX2R31F1QO8noUp6FQEBqh2FzD4XLQVaHcBCDPLXGeKNtcR5KvlCv/pVAf/5Tz5XXOHnT3/yY7dnq5yhtjofynGiFUlKALQm90no2+fzMW/eNFavHrHbyb2EjRvrqKqqYtOmTTQ2NiqTThzpEuYXTU46R+pN73Ui+slPDB58EH70I2M3AcWhdxVIromT61RQUKBML3EiSw2Z9Cjyeu8iGv0+TufjHHTQgwQCQ1WRbnKBrDj/i4uL6d+/PxUVFWo9xcXF6udkM7yzww/35dglVUuY+cJMnr24+zSgfZHfUe1c7yahSnodAQmkjkm0IAmFi4Yjm9Rms6lEOam5crvdhEIhHnzwCObNK+Db327kd79rJBLJSUjUE4e1EFosFp/uqs8a0yvJRXuSUPeLL57OmjUTGTv2XU477TV27Aiwa9cutm7dyo4dO/B6vQmlJKIFZWdnK9+MmGtOp1NVzQuZxE0wXQNqRXK9mjiM9XKT/Px8iouLFemIJiQdAeIEdC1ZWY/gcv0On69ElWwIEet1ZhIEEDPU5XKp9UgDOllbTyCdTTAdKSUhwzDOAO4nXjr+D9M0/9Rlwivp1QQkGbwtLS1qs8kGS+4vJJqKaBhFRUU8+eSxrFgRr6W64YZqQiGnIhCRI5nB4vswTVMVt8rEVZ2EpLwiJyeHOXNOZc2aMRx11NtMmfIywWAWXq9X+X5kSCK01rSJA16v0ZJBghKil8jb//t/Nh5+OO4DeuCBtje1bpLpOUCSTyT9tIPBoHIiywSNxsY7iEavwWZ7mOzsG2hpyVXN82XempCwPhtNh67xtFfC0V3IFAKCDkjIMIwFwHWmaVYd0De0L98OPACcDmwFVhqG8ZJpmp8fsPBKei0BAQkV6roDW/wkMhVCzChxkEruyuuvn8dXX43hhBM+4sorqwiFBihNQ47RTTCZ6iGDFmXzSeV6S0u8oFVaaDz99CRWrDicCRNWMWXKK+TlxTUCn8+nyEb8RzpkE+vz7OWlb/IbbsjlH/+IR8H+/ve2nbtyDlKOIQ72aDS6h7kZDAZVlnc4HGbXrt8QiVyFYTyE3X49sZgtgRibmprUhA5JZBSTsT3fR0+28sgkAoKONaF/Aa8ahvE4cI9pml09KmAC8LVpmhsADMN4CjgXODASqqRXE5BADz3rmpDeRkR3zErUZsOGn1NdfTqHHvo6p5++nGAwXo8mzmo9GTDZHySbVyrIAZUXFIvFyM/P59lnT+LNNw/ltNO+5Jxz3iUYLFY+rEAgkODwbut82srnke/Ozs7m1lsL+fe/c/jBD2LtakByTcR8038Xk0w3OyUPyTRNvvrqpwSDF2KzPYzN9lNME0WcEgmUlrLit9KjdnpzuVT0EMo0AoIOSMg0zWcNw1gI/AZYZRjGk9Aa1zZN8y8H+N0HAVu037cCxyZ/yDCMa4BrACjci8RK+gQBdRY7dtxKU9OFDBr0Esce+zyGcfA+Haf/de8oyhGf3X4o06at57LLVtLQcMBLTsDvf1/O7Nn5XH11mPvvh672Jrz99reorj6ZvLzHMM2f0cX98rodmUhAsPe7GAL8QC7ghq5Ortk7TNN8BHgEwKgwTJra+WAlfYqAdJ+P/K7/q5tWOTk5+Hx/wue7lCFDXmbSpGcpK4tXo0tOjoTYk6NKkjsjTmKZNS+N40UreOmlqaxcGZ/dfsklq/B6AzQ1Nal0AsMwlPnWVkfJtrKa9f+7777hzJ1bzBVX+Pnzn2NAXpuak0B3SMsaJZ1AtCp5PxAI8MQTE/j885H07/88hvFrGhuNPTRAmUQi2p9ek6e3fxUfV/z4nvEFZSoBQcc+oTOAvwAvAWNN02xu77OdxDZgsPb7oN3/t/+opE8RUHIvaEg0W/QKbpvNhtd7Fz7f5QwbtpAZMxZRUDCE0tJS1RZDTBbdrBD/i/h+pL+z+EEku9jj8TBnzql8+OF4xo9fydlnv43Xm6PqzMSfI9X2sm7JqZG16+ZMcrP6WbOO47XXKpg5s4E//jGexwQd+1n0qJv4l8R3pfcy8nq93H33YJYuHcaRR75FefkDbNyYq0LfurNffF6FhYUUFLS2NJF8Kz1S1pZZ2V3mWSYTEHSsCd0KXGSa5mdd/q1xrAQOMQxjGHHyuQT49n5LqaRPERCgfB3Sa6etED3EfRm7dv0Gn+/bVFYu4Lzz3mDgwKFqI/Xr14/i4mJVDZ/8F12ib36/n8bGeJsNGeMjIfqnnjqRDz+MF9OefPICAoEC5TDXSUUcv0VFRRQXF6um8jpZCHGKxuF2u3nttXNZtuwQLr64lttv95CV5W6z/WpbG1z3kUlETEhFfFS3396fhQuHMHHiasaMeYYNG3JV2xE5D1m/y+WiqKhINdGXvCshIb2oVjQx0YSi0Wi3TNzIdAKCjn1Ck7rlG1vlRwzD+DGwiPidemy/Ca+SPkdAAsn6LSoqUhsGEpMVP//8Ohobv0ll5QLOPHMhFRWV9OvXT2ULy190aY2hNz+TbGjppywvn8+nCGbOnFNZvXr87rFC/yUUKkgwd6QftLT9kDwjKcGQ/tTJyYrSHG3Vqiv55JOjOf/8Hdx+u4/c3NY+PXvTKnRHtz4uWj+322/vz8svV3DCCR8xZcpL1NW1Fszm5eUltHuVNUmpi97CRP4A6A3QotEoP/95DnFPRjzLvaCgQK0tjgPXjDKdgCDFeUKmaS4AFnTq4Er6LAGFQiGcTidOp1OVDOidE+12O8uXf5tdu6YyfPgipk6dT//+AykuLla1YnrbD92kk8zkUCiEx+OhpqaG6upq6urqlDkWDod59dVzWL16PEce+RZjxjyOaWapZmCSOSwaWUFBAQUFBarWTaJudXV1atSOFIyGQiHy8vL46qufsmXLJKZMWcdNNzVit5cnRO2SfUjJDnM9WijamPze0tLC739fzty5/Tj55M+ZOnUhXm+ccGR8s8yOlxIUSW50OBzYbDbV9lXMRkkEFTP1t78t4fnnS9V6GhoaFAkJiXdF2cbs81NXirE/6H1lG5X0WQIC1GBBt9utWp7KJrPb7bz22rl8/fUJjBixmFNPfZmiohLcbrdq+CV+EslvEQID1Eby+/3U1dWxc+dOampqVJ9ln8/Ha6+dy6efTmTUqHc44YRnaG62qxC/mIl6Ho7b7VYal/TUEULy+/1qplhWVhY+n4/q6t9SV3c2Rx+9jMsv3wAcukfoe2/+oOQUBn2Q429/W8JzzxUzderXnHXWmzQ2tmZS5+bm4nK5ErSgSCSiCFuKeqWwVa6fEHAgEODOOwcyf35/jj32A9577xiAhN5CokXpv3cWpx18WqePbQ/dUerRq8o2bMNtPdaO40DkR4h0WyuPYcOGqZIDaSAvBZIvv3wGH310DGPGrGDSpBex21vLOMRJLFnJ4vvQE/nEievz+aitraWmpgaPx6NyYuIENHn3bPt5hMPx79UnXAiRyUYOBoNKA8vJyaGwsJBwOIzT6VStPGT9mzbdSF1dfGrFSSe9QSRyNNA6oaIjU0wnUp2EdJK9+WYXs2c7OfvsLVxwwTLq6kIJwwuln7UkZkqlP7R2i5Qs9WAwiN/vV2aY3W7nb387jNdeG8rxx6/hxBOfVSRUWJiYX5KKHKJ9QSqGPmQcCcUuiPVYO44DlX+grTz0pmb6X87DDz8cwzBUGYNEZl56aSrvvz+OCRNWceqp8wgEWklM/C3y8EuFvWw2QPUGEo2nsbFRJSZGo1FeffUcPv30OEaNeoczz1yIw+FWJpw0NZO/6pLcJz2cm5ubVTTP4XCoVq2hUEhF6N599zJqak5nwIA5jBz5T0KhoXvUfLVHQu0RkJ5uEJ9r5uBb36rj8ss/Zfv2RLmyNj37WzQffZS0XA9xNkciEZqbm3nuuZN5++1DmDhxNWeeuQivt9VRLUXE6YxUTZ3JOBLqSyZYe7OaKisrlTklm/7FF0/n/ffHMX78SqZMmUsgEFSzxGQWmG7Xy2geqQez2WyqBkqKOIVEDMNg0aKz+fDDCYwfv5Lzznsbl2uAau8q60xu7yqlDEKSEmWS6JecY2FhIc89dzJffTWSQw99ncMOexybLSehoFSPtgk68gMlZ4//8pdO/v1vB1dc4eeGG3awdWticzOZQJKdnY3T6VSN33QyFW2tpaVFaYZyLePdAo5i3Lj3mDp1IeGwLaF9x8svv6x6S7dW838fgH/+85+dfkYO5FgdXwS/4OH6h/lByQ9Y/8Z61nc0o6cTqK2tbfe9zCOhqm6QWUnaEVBHKCwsVKFzm822ewMcw9ix73LSSXPweoMJGoTeF0g2p96OI7n/tESQxARauPBMVq06huOPX8Oll67C6TxI9SYSk1I3vwC1QaWxu5h+MhJI1gPwwAMjWLq0H5Mnf8rxx7/Jrl1lBINBTNNUE0+9Xq+KtomGmKwR6TlOoqnI1I1//zveruQPf6insbE1aqZH2/SRPkJIcq2le6Rcs2g0qsZAv/vuZaxfP5GRI5cyefKLtLTkqOigYNasWXz55ZfEYjHVWE5I6De/+U3nHoRrD+BYDS0DW2g8vZGi14qYtWPWActrS753u7fd9zOOhM4555wulVfrqmXVsFUcs/EYykaVwaiukfsSL7W/1pdeAto+l91vJbwnm3thfOKP2mzZ2dm8+uo5rFkzkaOPXsbkyc/j9yeO/IHWQYDyr+S+CHG0NfJHCCKuYR3N5MmfcvXVn+F2txKQkJdoLIFAQG3QthzeyeHyrKys3U5iN9/85i4uu2w9mzYNIBKJUFdXRyQSobGxkV27dimzE2hz5I8Qj957KBwO7x6smM+llzbxm9/UEQhElJYnRbH6+sVnJucmvi655tKRQDS0zz+/jl27pjF06HxGjpxFU1M8jK87wwG+/PJL6uvrAVQLW8GOHTs6fpg6wIEcC8T/AE8Bnob6qvoDk9WR/NXtfyTjSOjOO+/sMlkra1Zyw7s38PDEhxlfPr7L5AK89PxL7a91N9O09b6QkP6eFIgKCUnbicWLv8maNXEfzaRJz9Dc3Brxks0v0TCp8JbWG7KhZbOIxqTPCHv66UksWxYvxfjRj76gpGSgGjyYPPwQUL4S6cejk6GeXClO99/9rpQnn3Txne94uemmWmpqCiktLVXFodJtsbq6WpGOOIH12V56eYleUHrbbWU89ZSbiy+u5cYbt+PzoRrQ+/1+1RdI2pJ4vV7VsEzXGsUhLtX9El384osfU1v7TUpLn2bgwPuprXWpFAR9QCXEe2YLCaUNKrHmjnUGI0eO7BI5S6qWcNPCm5gzc073OOGe3/taO3q/o/cikQhLllzIxx8fx9FHL2PSpGfU5tDbmAIJ7SX0FhqSfCefkY0rWtEzz0xm6dIRTJmyjh/+cB3l5f0pKYmH+vUJqvrIZKnT8vv9+P1+1ShM2sGK/8Vms3HHHQOYNcvFFVf4ueMOD83N8faz5eXlyjela1mSswRxJ6+uGcl3S1sNgL/+9WDmzi3hvPO286Mfrcfvz1JEIp/TZ6OJw1lIR5IuJWyvTxuJRqOsW/cTamsvoqDgP5SW3oHfn6uCBbo2KNBNM7nGErXvbCZ1hxHYvaAnhz7YttqI0H4QJuNIqCvQE6nu3Yk4AZ3I6NHLOfnk52lpac3alVwavQWqbp6IGaI7igHlxM7Ly+OFF6awZMkIZsyo4ic/qcLlig8eLCoqSvDJCMGJdiI5P6Jl6BnQgOrt89e/Hszzzxfyne94ufvuAKaZpfJzZONLRnhzc7MivEAgoFIGZCPraQWhUAjTNHnqqRNZvLiC00//issu+wSPJ1dF7sRsk+ukzzDTc5H04l/JkJaeQp9++gNqas6huHg2/frdiWkm9sCW72gvQS+5gLe9AMS+1Jvtz7Hyf+ZQE/MCE+M5AzbR5VHm/ZWfcSR0oLO339r0FpfMuYSnLniKEwedeOCzvNuRD+2vVS562++3OnrFj5OczPbxxydy2GFvcNxxcwgGW/snQ2JOjO6oFf+P+JJkVJDL5cI0TaWlzJs3jaVLj+DMMzdx442byc0twuVyqUGEUoiqDwjUw/pStCqNz2RdooH97/8eyvz5/Zk5s4G77gpgt7dGkMQclHwip9OpcpR0Ek0mP/nuUCjEW29dxKefjmD8+JWcdtoStm3LU5Nm9Y4Bupkl4Xm5znpfJX1d0WiUDz74Llu3nkJFxVwqKu7D77clrEe/1vr91ckiefpHe0QCHWcad+ZYc6gJFwLPglllYtKx/P1GJfstP+NI6ECSvJZULVEE1F0akMg/7T+n7XWtHb0vuS1tTRodMGAOw4b9g8bGYuWXSZ5Kqk8Thda/gjk5ObhcLqXZOJ1OlQfz/POn8M47I5k2bT3XX19FTo4zwY+jZyvLZpNpGaL96KZOcu7OE09M4M03h3LRRTXccYeP7GyX0mgkoVI0J2mdIRMxxMQTwpHv0VuufvTR1WzfPpnBg+cxfPhs1q2LT2+V4YQyF01MJX2T6pEyac8hvit5LVp0NmvXTuSII5Zw5JGzqa52JsxjE40sEAioNrhphUrSstIg40ios716hSC60wRLlr+3tXb0vrzXFgm5XDdTV1eszBvZLPJXHVpbwArR6YmCxcXFlJeXq35C4XCYp546kXfeOYpTT/2Ca6/9AsMoaNNkgdbSDskYlhC6TkLykpKHhQvP5L33Dufss7fwq1/Vk5VVotakXwdp96F3RpQXoCJwXq+XQCCgxjR/8sm11Nefh8v1BC7X3WzcGK9XKyoqUhEvQAuPJ7ZEkZox0TyFHOUeLFp0NqtXT2DcuPc45ZQF1NW58Hq9CUmMWVlZKsqWm5ubkKC4N42m21FJWhIQZCAJdQa9od2BjmR/j55ZravnspGFRLKysnC5XJSVlanJqxCfC/b220cwadInXHbZB0QiLqXNtFUuIY5cveeyHnGS8LUMRVy58grWrh3HCSd8xPe/v5lIpCIho1knONEAJSwuDm0hW6lrk0kjoVCIzz+/jvr6i8jLewyH4xY8nhzlB5OpF2LG6fLENxaJRFSmtJirup9o4cIz+eCDeKLm9OkLaWmxJxQAiw9ONCzppSR1cpBiEqokbQkI+gAJ9TYCAigtLcXlcqlMZ72jn0B+F/KRfJfCwsKEYx97bCxLlozghBM+4vzzFxMM5qlNKqZccr9pIT9peObxeFTzd52MfD4fH310NVu3nsJhh73BtGkr8PsPVomUyYSZTEw5OTlKM5EpHjLqSPxOn332Q2pqZuB2P0l+/q2EQq11eeKsFvKRCn9p6i+FqMFgUJGeEK9gwYIZfPDBsRxzzPucccZ8otFYQjpDfn6+isyJRiohf7/fr+R0te9xn1FJWhMQ9HIS6g0E1JbfaNCgQcp8EAexfC65hYX4gYSE9ByYf/1rHIsXH84JJ3zEWWe9khAyFtnQahImk5xoIn6/n6ampoR2H83NzXz22Q+prj6HAQPmcNRRT9HcfLDK5dETBTsiT11zkZFF0jZ2/vzpVFWdSEXFXAoK7qKpqdUU1adfJPcDko4CshbdLNOJcf786XzwwQSOOeZ9ZsxYALSap3l5cYe3OLl9Pp8qgAVUPZkgOSLWI5pRJWlPQNCLSai3EhDEyzZko4pTVH4WyObTSxDkr39LSwtPPXUib799OMcfv4YzzpiP39+ifD12ux2n05kQ9m+r/zOgzKOGhgaqq6upr6+nubmZ9et/RkPDhRQV/ZdBg/5KMDggoZ6srbnyeoRJb/cqBAHgdrspKiri0UeP5uOPR3L44W8ybNij7NiRpzQPvXpetB8hH72tiDjUxQeln+f8+dNZtSresnbGjIXIaGkgwUcmZGwYhnLwS3RMqu+BnndSV5IRBAS9lIR6AwFB+38td+3apcwVcQDrTmlo7TWtDzQUzSXelP4oxo9fyUknzaW+PqiS/EQzcLvditTa0rKAhO6LtbW17Nq1i4aGBrZuvRmfbyYu1xOUl99JNOpSa9LrtJLlyfok6VInQSGh7OxsHnlkFEuXDmbChFWMGfMs27a1htrlfPWZY9J7yeVyJTSpB9TPeuuU+fOns3KlENACtQZZuxB7LBZTXRj9fn9C8a/03xboWlG3o5KMISDohSTUWwhI0FbS2caNG1Wuj2xmXdsRgtKr18UEmzdvGmvWjOeoo95m3LjZ7NzZWmtms7WOfs7Pz6ekpCRhwqhe/Cp+ECl3EJ/Q5s030dx8OQ7Hvyguvg3TjG92vTZNz7aWF6A6GUoNl35uEtH63e9KmTevjFNP/YLJkxeweXNrlrgeIZTES9F+xA8m10j3DzmdTtXAP07QcQI666xXME0jYe2yjkgkojpUyv+JL0gawOkFvVJz1u2oJKMICHoZCfU2AmoPej9m2Xj61AeHw6ESDIuKiigoKFB5QGvWjOeII5YwatQ/2bnTn1B0ahgGDodDaUMiQ3pYSxROxiY3NTWpdq+xWIytW2+muflS8vIeo6joN5imPcHHIkhOoNTPSxzbegmKnOM99wzh+efLmD59I2efvYRNmwJqFLWsT0ZgS//t0tJSSkpKVDN/fRqGJES63W78fj///e/xrFx5NBMnruacc95AWtaKT0qyxYVs9FYgeqhen+4h6Or2q22ikowjIOhFJNRXCAhQ89PFhJDNJBnQhYWFFBcXU1xcTGFhIQ6Hg3/9axwrV45gzJgVjB79JLW1HhobG/F6vapfjoTG/X4/0WhUOXOzsrLUphMTrK6ujpqaGlVWsW7dT2hqmoHL9QTFxb/HNO0JZpxeOiLZ08FgUBXAxmIxlfcjyYh6ZvbDD49k4cL+nH76V5x33hJ27aqlvr5eaTCAanGbnZ2Ny+WiuLiY0tLSPVrbCsGJI9npdDJ79gksWzacE074iAsvfJtoNF6AqpO6hPF9Pp86H+lHrRf0yjl3lM3c5agkIwkIegkJ9WYCass5PXDgQAKBQILjVdd65OfCwkKcTif/939HsXjxEE4++XMmTXqFqqqI6pwoIWo9kiT+DWk+Zpqm6gMkxaISlm9paWHJkgtVlKq09H8IBvMSsqZlQ4oGIeFr0SokKiWjhTwej3JiRyIRnnhiAkuWDOPEEz9mypRXqKpqoqamRvmgRHOy2WyqC4BcC0lAlJasUl8maQsQH6y4aNEATjllLRdf/C6xWI4yPx0Oh5pKIomdQqpi+spwSIkK7ss0kC5FJRlLQNALSKivERDAgAHxSJMkyzmdTqX5SK8fh8OB0+nkb387jJdfHsjZZ2/h4os/pKqqdcKDmHXSuVD8QlJNLhqAaZqq7gpIMDvmzZvGxx9P4LDD3uDgg/9JXZ1TRZnErNMjaVLrJY5hMZEkyiaJj0KMTz89iRUrjmD8+JVMmjSXTZsaqa+vV+kAopUASrMRIhYzUghbzweSMPvtt/dnzpwyzjhjAxdf/B6RiD1hzXovbz33COI+LafTqb5D/FkSrm8r273LUUlGExBkOAn1ZgLqCAUFBUpTcTgcFBQUUF5erpIYpXfPvfcO5YUXyrj44lp+8pPN1NQ4lSNWkv8kSVBMIiEjKYeoq6tT/YUgMVy/YMEMVq2awJgxK5g4cQ719S5VIqFXpCcPaZQoksjTC1FFowiFQrzwwhTef38MY8as4MQTn6OuLp4KUFtbS0NDQ4LZJn4xaUgmle8ygkfalMj35uTkcPfdg3nuuTLOO287V1zxCV5v64QQ8beJU1uyn2OxWMKwSOk/lGxumqaJ33+3umdt98Vu/719gYmpCMLYZHTFGLNE+UO7Tn5HeVEZS0J9lYCgtRxDyMbtdlNcXKz6/eTm5nLHHQN45pl4u4xf/7oBr9ehCKu0tJSmpibVPEzfOEJIeqQqGAyqv+zigF2wYIaqpZo69WX8/mzl1E7OfBZflT4KSMw6ISSZCZ842/4YRo9ezuTJz9LcHExoESKmmrz0wlpxJuvOYmjtm5Sdnc299w7l5Zf7ccEFO/nJT77C52ttayK+JZvNpro46jV4EpaXvkpSj6bnEW3bdgstLd9U90yanOnRTvFVd7YnUJgw9jl2bNu6oR/QkBjRC6JdJr+jjPGMJKG+TEBAQpMwp9NJUVGRejmdTn7/+3JmzXJy1VVB7rorSEtLnBxEcyosjHcw9Pl8+Hw+lbCX3HpCb1ImGdfAbgKayLhx73HGGS8TDrfm9IivRTZ0dnZ2Qphcr8iXPs1iEkrY/8UXT2fVqvEcffSy3f2SWtMBJDFTIlaiWekNx4A9SkzknEzT5B//GM2bbw7hvPO2c/31G4hEWrOq5brqDc30khIx4/TpInrkzWazsWzZTHbtmsTgwfPYsuUc9d3tRcgOJHIWXR8lSnTvH9wfVKKmzkSrukF+EjKOhPo6AUFrA7Lc3FxKSkro16+figLddlsZjz+ez/e/H+J//idMNJqtojx6Qp/UY0lkx+fzKfmiUbS0tNDY2Jgw9jjeUnYiY8e+y9Sp8wiFIsrRrIf7ZZPqTnOn05mQGCgbU3oPhcNh5s49jVWrxjNq1DuceOLTBIPRhK6JumzRfiTJMbkpGaBISpzjzz9/CsuXH860aeu57rqNhEI2ZYLKuesyRDPUC3n16yeEK7lCjz02lk8+Gc7RRy9jwoSXefTROAmlrHZsf1FJjw99yDgS6usEBPF2FuL7cbvdKgp2221l/PvfcQL6y19CSKmBbmLpzbYksiZ/xWWjyuYTEhIN4a23LmLduuMZMWIxEyY8S0MDCWaUkIloDRLeFkexPoJa3+DSrGzOnFNZuXI8I0cuZdy4x2lsbHUAi2yRK7IlwiXTMMTfpWdAizb04oun8957o5k8+VO++93PCQbjLVf1nCSJ2AkR6aUmYoLp02bl+202G7//fTmLFhUybdp6TjllGbt2udQ9GzJkCDt27MA0TXU9hff1avv9gR9/p49tC9HBUYLnBMl7KQ97jR26TnSHc9cyjoT6EgHphag6fD5fwihh0zT57W9LmDUrn6uuCnLPPUGi0VbyEXNHfCritxETJDm/Rf6Veet2u52vvvop1dVTGDToJQYNeoCNG+Oahj5SWhIeJUplt9tVlE5KJoT0RFORSvwXXpjC6tXjGTFiMUcc8X/s3OlV4Xtp4SG+H92pLjJldJFECZOJaN68abz//jgmTlzNRRetoKWlIIEEkxuwiXkpaQputzuh06WeqGgYBr/8pZNZs3K49NImvve9Laxd6+bLL79U9+zss89m2LBhGIahxhndemv8vV//+tedej5uabml08cmY0NsA/8N/5ers6/mG1d8o0tk6vj73//e7nsZR0K9nYD2pbi6rq6OnJwc1Vb1j3+sYO7cQi69tInbbvPS0tLa40Y0FSEg6fejh6kl+iW5NLIhJbLU0HA7weCFFBbOIj//djZubE3C03spS4tUGQckWpAkTUqTfAmdS2g+TkATOfTQ1zn00AfYtauRpqYmpX04HA5FWvK7aFXRaJTi4mIAVZyqO8ABNZn22GM/4Lzz3iASyU1wyou5JddML1PJyclRmqPeBlaPFt5wQy7/+Ec2V10V5Oaba9i+Pe6PamhoUNdp2rRpnHXWWeqaRaNRRUK//OUvO/Ws3PKHWzp9rI4lVUv4n+f+h3kz53Xb/nruuefafT/jSKirkU4ElIz2wppbt24lOzsbr9fLo48ezdKlFZx//g5uvLGGQMCRMGNMspNls+mN3iUjuri4WKnLes1TLBbD5/sTkchV5Ob+k9zcW6mtba3pkjXq2o/LFTdBHA4H5eXlVFRUMHDgQMrLy1UPIwlpZ2dn8+yzJ7F69ShGjFjM4Yc/RGOjR5WESMhbsrfFn5Sfn6/MOz1rXPxOYkqGQiGefnoS7703ZncpxmuYprHHJFpAlb1INwK9qZtoTOKbys3NVabtL3/p5LHHsrnyymZuuGETW7bspLq6Gp/Pl+AH6t+/v/pZr4WDA2tZfKBJkUuqlnDxcxd3q4Vx8XMXM5Sh7X6mT5NQOhNQR9i+fTv5+fnMmzeNdetGcsopa7nyyq9paCggHA6rv9KyiZMTEoVApLxBH5Uj5kK8edg9RKPXYLf/Hzk5N9HcbO7RgkPvWSSN4rOyslTu0kEHHUT//v0pKipKCH2HQiEeeWQU77xTyejRyzn66CeorW2tudLD73oTf8kBkoxoqYqXFh85OTmqBu3BB49gxYp4v6RzznmDSMRUvYYkX0ic9pLLJBFAIT9xWEsnSWgtyfjd70r573/z+M53vNxww2Y2b95KVVUVHo9HmcyCdJxF35NBnl88/Yt2P9dnSShTCQjiTtivv76elpYpjBy5lNNPX87OnaX4/f6EqRLiVBXfjsfjUeaYzKcXv43L5VKZwH6/n5qa3xGNXoXd/n9kZ/+MUKi1fYfeoEv+qutRJXHcSudBvbZKSPDee4eyYEF/jj9+Dcce+zzV1a1ko1esSzsOWaOQTUlJCSUlJRQUFKj/lyheIBDgoYeO5M03KzjttC+58ML38PnsKsyf3B5XtCCHw6GKgMXnJtqPnhDpdDqZNes4Fiwo4sILq7nuuo1s3drItm3bqK2tVT4mfRZ98ojs+DV07PHe/qKzx7616S1mzpnJ7Atmc8JBJ3R5gW2y/F6ZrHggyPS5Y/A3Wlq+R79+z3HUUS9QUxMfnSx5P5I/o9dfyex6j8ej8m30hu+yuQzDoLr6twSDl+Bw/Ivc3FsIh1t7Eumko4/MSZ6w2la7Dgmn//GPFTz/fH9OO+1LTj31FbZvDydscvFXSc2amF6SlFlaWkpZWZlKS9Cd3jZbfLDi3LkFnH/+Dr7//fXU1eUr7U7ykfShjdKMX4hHNBi5ZuJPE63p9dfP4733KjnttC+59NK1bN4coqmpiYaGBkXu4l9qC7rDXH7vLDpz7JKqJcx8YSbPXPRMtz3/yfI7Mhv7HAn1lAravfgROTn/4MgjZ2O3H6SS/sSpmpOTo6JbYlJIlEx8G7rWISTkdrv56qufUl9/Nv36PceAAffj9RYnfF7PNdI3gB4Jk3wgIRJ9Dtdddx3ECy/0Z8aMKr75zWVs29baE1oSA2WKqS4zPz+fwsJCRT4lJSUUFRWp6nbRZn71qwL+8x8Hl13m4Ze/rKexsUA55z0ej1qP/pdZT8yUtQCqMFV6JYXDYd5//3LWrRvH2LHvMmPGu1RXO9Wx+j3w+/1UV1er79D9Q8mdKnvSJ9RTPqD9kZ8SEjIM4yLgNmAEMME0zVU98b09aQOf8vgpXS6/FQ9y+OGPMGjQ0ZSUlKhcFWndKoShNwyDxEQ8vWuhkNAHH3yXLVumMmzYQo466j8EAv1xu90J5og+IkfMK1HlJXtYMqOTa8X+8pdvMHfuQKZP38jMmctpbGxO8MlIk3jd3yQJj06nk8LCwgTySQ7333yzi8cfd3DFFX7+8IcmgsGchOhfcj8hiXYJ6eh9gsS57fV68Xg8eDwe3n//cjZvnsLw4YuYMOFFGhv7Kf+QEHUkEsHj8bBjxw42bNigrn06zCBL10TfVGlCnxJPDP+/nvrCdL0BnUFJyW8pLT2asrIyVeWu105JyFlviwoktJ+A1jIGwzB4773v8PXXUzj00Nc59tinMM0CtYGFZPR+1dLgS7QMMaEkiqUTUCgU4oEHRjBvXgXTpq3nkkuW4/M1q3wZITVxPMOe4XBxPuvJiJLEGAqFuOuug3jqKSczZzZw0007aWpqdcrrc+eFNMXnpA85lAxu0Vr0bpKrV1/F5s1nMGTIy4wZ8wTRaLHKkZJrKeZeXV0dmzZtYtOmTQnXXkePNLrXkM7Pf0pIyDTNtXDg4cV9RTrfgM5AfCT6PPZk6KUHeisNPTNaNtiyZTP58sspHHzwq0yY8B8k01rPbNZ7K+tTJXQtQi9wFQJobm7mqadOZNGiwUybtp5vfettPB6/itjps8CSSybET6NrM5KkqEfP/vrXg5k3r4Szz97ClVd+webNraUaYhpJy1Uxm4T05PyABJNT1xBXrryCDRsmM3z4IsaP/y95ea3FuHKt5fpK0/+6uro97keqkO7Pf6/3CaX7DegI7T24xcXFynxpz/mZTEK69hMMBvH5fMRiMT766Gqqqk5j8OB5HHLIwzQ2tg5L1IlHzBZ5ifNWiE3f0Pp45ldeOYv33hvO1Klfc+ml79LU1Jq1LT4raN34egGpfI9ErGTTS3QpHA7z978fzmuvDebkkz/nnHNWsGWLkWCG6kQkBCQamH6OyYQia3nttXP59NNjGTlyKccf/wI2W5HqXCBamRCm5A7pJSYCPVzf0b3tamTC899tJGQYxuvAgDbeutU0zRf3Q841wDUAFO7fGjLhBnQG/fv3Jz8/XvekT6tIfrBlI4nmIw5smRH25Zf/j5qaGZSWPk1R0R/ZsaO1Wb4QgOTOiLNb11okxK0PDRQSikQivPPOJXz1VbxU4sILV+P1xks09JylZJ+VXmjbFomK3ysajfLwwyNZvPgQjj32A04+eT47dthUxE/WI8cJcYn2BK0Z3snJl3J8fO5YvJr/pJPmYJq5CV0BhISk46IkYMqkW4fDgaQH6U3hegqZ8vx3GwmZpnlaF8l5BHgEwKgw9tmQTusbsI/+gOQoiqC0tFT9BdYbiOnQnch6U3lJ5Pv88+toaLho9+z23+HxkDDWWAhBr7SX0cn6RtY7Dcqao9EoH3zwXTZvPn23CbOALVuKVJN8cUbr5pdoIsnkE79crY3xxYz797+PYenSEYwevZwxY/7Dtm1RVVAqsvXzkPOXDHJ9Pr3I10ckvf76eXz8cXwowIQJs2luNpVpKJnhesMzKXGRgmLpny3lYzLFRL9O3ekWSuvnPwm90hzLpBvQGUgFPST2zYE9B//l5uYmdE4MhUKsW/cTGhrOo7BwFgMG3IVhOFRoXDcrxM8DKAKQTa63ZZU8G9n4a9Z8n82bp1NRMZdBg+7nq68cahPKYEXp/6wTqZhIevKjnJM+k2z27BNYvnwkRx75Focc8iBVVV4Mw1DN08TfozvSxWksRKy3AJF2s5IntGLFpXz99YkMHTqfoUMfZOtWVE2cnKdMwJVkzEgkgsvlUomUEC8FERLqSU0o057/VIXozwf+BpQD8w3DWGOa5rSukJ1pN6AzaGhoUJ369JonSMxgFtNGTJ9IJMLKlVewfftU+vd/nmHDHsBuL1Mak0S1pNezmDDSYRFatTORLQ7s/Px8cnJyWLXqSjZunMagQS9x0EF3U1vrU/4RiX5JD6SioiIV6dLNrmTtTkg0Go0yZ86pvPvuaI44Ygnf+MZ9bNq0C6/XS3Z2NkVFRYRCoYTMZylyTZ70IWZjIBBQ556VlcVHH13Nli1TqaiYS0XFvdTXx1Tlvt1u36MWT6J/ujnmdrtV1E+g+4Ta03C7Apn4/KcqOvYC8EJXy83EG9AR2mvl8eGHH5KXl0d5ebmKlOnlEXqoXnJdfD4fr712Ll9/PZmDD36V0aOfJhodCLR2QxTtSgiuqalJbTZ9fjy05hdJuUM0GuWzz37Ili3TGDLkZYYO/R9qappobGxMmJGWk5NDY2O8Wb1eeqGXm+jQfU9z557G+++P5eijlzFixCNs3FjNzp07VW6P1J1JwmR+fn5CrVvyBBA5H8l32rDh52zdejaDB89j5MhHMc1Cdf0lI1xq6xobGwmFQni9XjWRpKGhAY/HQyQSUZ8VHEhW9L4iU5//XmOOZeoN6Ajt5ZJ89dVXAPTr10+1ySgpKVHNzVwul4qCSabwm29ewNq1kxk5cimnnDKfWKy/CmPrkSjZoHq/Z4ksiYmmm3ziH9m69WYaGs6momIulZV/xePx4vXGX6J5CIE1NjZSW1uL2+2mX79+DBw4kIEDB+6hBembOE5AxzB27LuccMIzbN4cUNX2wWBQTQlpaWmhoKBAJQ8m51CJ5pb8f+vX/4xt287hG994hWOPfYrc3P4JDni9Ji4ajdLQ0EBDQ4PSkgzDwOPxqPYdojkJunvyRiY//72ChDL5BnSEvSW0VVdXJ/SBbmxsTKizkr49S5dezLp1pzBq1DtMn74Qmy3ebkNvFKY3hpeIj57zI45dKUAVDQIgELiXlpaZlJY+zaBB9xEImMr/olftyznpHR6FBJLzgPRWqvF+QGM55pj3Of30efj9iRE/PWvZ6/Wq4yVxUo/qSY2ZmLEAK1dewbZtUxkxYjEnnzyP3Nz+ypzT74NOiqJlClFLN0YpfxHtSNCdIflMf/4znoQy/QZ0hL09uIZhqLaqHo9HVa1LeNjpdLJx4w1UVcWr7adPX6ictmKmSDRIh2xuPRolYXH9s/Fo158xze+Rl/cYbvcdeDxxk0qq9cWXo5+LhPIlWVGmWgg5CBHZbDZeemkq7747lgkTVjF16stEIqbS1pKJTO8BJKUg0oVRCEXkS4j9jTfO5+uvj9tdC7YYwyhr0yeVTERS1lFTU8OWLVtobm6mvLyc4cOHqz8A+hjo7sqQ7g3Pf0aTUG+4AZ1BWVmZ6gEUDAYB1Hx4Qdy/83dCoRlUVi5g0qT5hMOOhK6JunmlV7A3NTWxa9cutm7dSnV1dbsJkbHY/cAPgQcIBn9CVVVrhXhysl6yNiQIh8Ps2LFDEZYQiGmaLFp0NitXjmX8+JVMmTIXny+QkJVcU1Ojzl+H5EJJ98SCggJV66aXa7z55gV8+OHE3QS0QPme9DUmt30Vp7/f72fHjh2sXbtWmceRSITKyko1uWT+/OlqTW1ltXcFesXzLxc3E14MxBQs3rjYLLunzFy8cbHZHThQ+dzG3j+0F0QikUSZWK9MfX322WcJ9zIajR7w88FtpO3zn4xx48aZZjv7ugfm1HY9+ooG1CNjhC30CKTtraCrfES94fnPOHOsrxAQ7PmgBoPxcLtEqiRyJbVQ+ufFp5Kfn09+fr4K3SdnCZummfB5aG3mJSaS/MXqTkjTffFn6ZElICHUHovFaG5uVmOg28sYlwxwMaH0mWF6blV75mZbEDnSrdLn86neR5ItLRE0uXYOh4Pi4vI95KQjUvH8ZxwJ9RUCagt6D2QLFroaqXr+M07f76sEZMFCdyKVz3/GaUJ9nYB0s2hfTaTOqP7dbX61h31da2fW1x0mUPI62vuOdDW/IPU91zOOhLoamURAAnnw98VX09mHvyf8QMnYn7V25tyT/WYHiuQ1WATUvnxr7lg7yEQC6s7iRwt9C+kydyzjfEJdhUwkIAsWugrpFGXukySUahvYgoVUIp0ICPogCfWOuWMWLHQO6UZA0MdIqCdvgAUL6YZ0JCAAI1Wh2M7AqDBMrk31KixYsLC/GDdvHKtWrWo7otJeUVk6vvQC1v3BfSvuM43bDPO+Ffd16vjOyO+KAlZBuhfrWvJTI39fn7FUPP/J6KiANeXEsj+vzpBQqm5AV5FQum4AS37q5e/LM5YOBGSafZiEUnkDuoKE0nkDWPJTL39vz1i6EJBp9lESSvUNOFASSvcNYMlPvfyOnrFUP//J6HMklA434EBIKBM2gCU/9fLbe8bS4flPRp8ioXS5AZ0loUzZAJb81Mtv6xlLl+c/GX2GhNLpBnSGhDJpA1jyUy8/+RlLp+c/GX2ChNLtBuwvCWXaBrDkp16+/oyl2/OfjF5PQul4A/aHhDJxA1jyUy9fnrF0fP6T0atJKF1vwL6SUKZuAEt+6uVzG2n7/Cej15JQOt+AfSGhTN4AlvzUy+c20vb5T0avJKF0JiDT3DsJZfoGsOSnXr5oQt2Brt5fvY6E0p2ATLNjEuoNG8CSn3r5XVmfqKM79levIqFMICDTbJ+EessGsOSnXn53kFB37a9eRUKZQECm2TYJ9aYNYMlPvfyuJqHu/APfq0goEwjINPd8QHrbBrDkp15+V5JQd1sYvYqEugPdcQP0B6Q3bgBLfurldxUJ9YSLwyKhDtBdN0AekN66ASz5qZffFSTUUz7WtCMh4F7gC+Bj4AWgaJ+O62IS6s4bwG306g1gyU+9/AMloZ4M8qQjCU0Fsnb/fDdw9z4d14Uk1N03gNvo1RvAkp96+QdCQj0dZU47EkpYAJwPzNqnz3YRCfXEDRBNqDuQDhvAkp96+Z0loVSkuaQ7Cc0DLuvg/WuAVcAqCg+chHrqBnRXIlm6bABLfurld+YZS1WeXUpICHgd+LSN17naZ27d7RMy9knmAWpCPXkDuoOE0mkDWPJTL39/n7FUJvqmpSYEXAmsAPL3+ZgDIKGevgFdTULptgEs+amXvz/PWKorDdKOhIAzgM+B8v06zpo71mUyLfmZL9+aO3ZgJPQ1sAVYs/v18D4dZ80d63JY8jNXvjV3LBXkZc0d61JY8jNbvjV3LM1JKNU3wJo7ZsnvbvnW3LE0JqF0uAHW3DFLfnfLt+aOpSkJpcsNsOaOWfK7W741dywNSSidboA1d8yS393yrbljaUZC6XYDrLljlvzulm/NHUsjEkrHG2DNHbPkd7d8a+5YmpBQut4Aa+6YJb+75Vtzx9KAhNL5Blhzxyz53S3fmjuWYhJKZwIyzb2TUKZvAEt+6uVbc8dSSELpTkCm2TEJ9YYNYMlPvXxr7liKSCgTCMg02yeh3rIBLPmpl2/NHUsRCWUCAZlm2yTUmzaAJT/18q25YynUhLoDXX0DrLljlvzulm/NHUsRCXUHuuMGWHPHLPndLd+aO9ZLSKi7boA1d8yS393yrbljvYCEuvMGWHPHLPndLb+3zB0zTNMkU2AYRg2wKcXLKANqU7yGfUUmrRUya72ZtFZI/XqHmqZZ3tYbGUVC6QDDMFaZpnlMqtexL8iktUJmrTeT1grpvV5bqhdgwYKFvg2LhCxYsJBSWCS0/3gk1QvYD2TSWiGz1ptJa4U0Xq/lE7JgwUJKYWlCFixYSCksErJgwUJKYZHQfsIwjHsNw/jCMIyPDcN4wTCMolSvqSMYhnGRYRifGYYRMwwjLUO0hmGcYRjGOsMwvjYM4+ZUr6cjGIbxmGEY1YZhfJrqtewNhmEMNgxjsWEYn+9+Bn6a6jW1BYuE9h+vASNN0xwFfAnckuL17A2fAhcAS1O9kLZgGIYdeACYDhwBzDQM44jUrqpD/Bs4I9WL2EdEgBtM0zwCmAj8KB2vrUVC+wnTNF81TTOy+9d3gUGpXM/eYJrmWtM016V6HR1gAvC1aZobTNMMAU8B56Z4Te3CNM2lQH2q17EvME1zh2maq3f/7AXWAgeldlV7wiKhA8NVwMJULyLDcRCwRft9K2m4UTIdhmFUAmOA91K8lD2QleoFpCMMw3gdGNDGW7eapvni7s/cSlzdndWTa2sL+7JeC30XhmG4gOeB603T9KR6PcmwSKgNmKZ5WkfvG4ZxJXAWMMVMg0Srva03zbENGKz9Pmj3/1noAhiGkU2cgGaZpjkn1etpC5Y5tp8wDOMM4CbgHNM0m1O9nl6AlcAhhmEMMwwjB7gEeCnFa+oVMAzDAP4JrDVN8y+pXk97sEho//F3wA28ZhjGGsMwHk71gjqCYRjnG4axFTgOmG8YxqJUr0nHbif/j4FFxB2nz5im+VlqV9U+DMOYDawADjMMY6thGN9L9Zo6wAnAd4BTdz+rawzDmJHqRSXDKtuwYMFCSmFpQhYsWEgpLBKyYMFCSmGRkAULFlIKi4QsWLCQUlgkZMGChZTCIiELKcPuKu+NhmGU7P69ePfvlSlemoUehEVCFlIG0zS3AA8Bf9r9X38CHjFNsypli7LQ47DyhCykFLvLCj4AHgOuBkabphlO7aos9CSs2jELKYVpmmHDMG4EXgGmWgTU92CZYxbSAdOBHcDIVC/EQs/DIiELKYVhGKOB04l3/vuZYRgDU7siCz0Ni4QspAy7q7wfIt7nZjNwL/Dn1K7KQk/DIiELqcTVwGbTNF/b/fuDwAjDME5K4Zos9DCs6JgFCxZSCksTsmDBQkphkZAFCxZSCouELFiwkFJYJGTBgoWUwiIhCxYspBQWCVmwYCGlsEjIggULKcX/B8uKmMJmCpX/AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -307,7 +321,6 @@ " circ = Circle((2,2),minimum_length/2)\n", " ax.add_patch(circ)\n", " ax.axis('off')\n", - " plt.savefig('media/splitter_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", " plt.show()\n", " \n", " if gradient.size > 0:\n", @@ -345,7 +358,7 @@ }, { "data": { - "image/png": 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QesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMIbQA8YQesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMCba8udBJ+8CQGdo6QFjCD1gDKEHjCH0gDGEHjCG0APG/B0HFjsW0aWLiAAAAABJRU5ErkJggg==\n", 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" ] @@ -367,7 +380,7 @@ }, { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -381,7 +394,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "current beta: 8\n", "Current iteration: 3\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -390,7 +402,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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J77AfixqeELv3CHs0nudtUS5uNYk4fqHm/f39UV+I2WzWVcyLxeKoqS/zHlrGlr7A/f19N5ILkzKgr/dqteqmayq5q3iYWfTZu+o1cYV9+G+uSNQ7AKWuvrVl1jTIIsa6xu3YlisjeAIQo7rbh8MhptNpMevPFQnKpyETW3utSNFDD/ePu0Vn03u1TFX0f/rTnz5XOb4KWLC73S5ub2/j1atX8fr161iv10cPF8eRsHAqUBYi9zDjPuX6oGfZZxW9Zrf7rgfi5W1LffVxLhU/fudysfC4+a1UUUQcJzS1Ess8Hs2TsPCzims6ncZyuYwnT57EcrmM+Xx+4kW0TlX0f/zjHz+pi5/FlV/i+Cw6/I131OFFlLCCEafzuONhREzLg21wPLZKfa49W7MsM67n5rH0SrZNJiYuJ3+yGF7Lloley873Oasgtbkus/Q6PoH7ReDV1VdXV/GLX/wifv7zn8eTJ0+6TlIW/Huqov/973//ucrx1YAHGvHmZPLuXeh4qPD+NG6i40/WXKeiV6ut8WtJ9FnMy9uXYnlsz3Pgq6C4nJoRr3kivH3W6w/HBXxva6LPrjEijirV+Xwey+Uyrq6u4vr6OpbLZfzkJz+JX/7yl/Htt9/G1dVVN9WYlqNlzmo34rvvvmv6Lk2n0zg/P48nT57E5eVlLJfLuLi4iPPz8zg/P++siL5QQl+dzDE9P+gsJq1AAISsguBzaR9/PicLVwWvk22qENlKZxWTnoetPX5TuCLjvvelkXJcyWAJ675cLuP6+jqePXsWz549i2+++SaePXsWP/3pT+Pp06cxm81+4BPwoyd1c6uW/s9//vOnKcqPhPl8HhcXF7FareL6+jqurq7i6uqqG7+Nd6NH5DEow4LIrCePvUclgm1YRGrxEKurMEriQRnVU8E+o9HoKBPPeYHa8F8VfhYSAM0HcFhQqjD4fsHKn5+fx9XVVSf2Z8+exfX1dSwWi5P7Zd7j7H0FfikiP9gR70S82+2ORtqx8DSJpy46W97SzDkR+Ww5HKNmTVvYj5e8rYYjOijo7OzspE1et+fKAvcki+dLrQWZ6DWxx9fA9wCZ+cViEefn552Lf3l5GRcXF93bbFt350sVnkVfAdZut9vFer3uLCSEjt9KzWIR+ew1vE02zZZaX90vezdeqQkOSxUf5x/0N61wOFlZmhFIwwlNUJaShjyEVi0+H4srXJRhOp3GfD7v8i0Y4ZhNU2be4845FWD98K606XQa6/U6ZrNZJzwWTQZbeM28oxLBA7zf77uHNjsOl4mPwa/QKlUWfF6NzTmc4CY6TjSyR5LlEWrXmYU96uJrq4aKn70AHeOgMwm3/tz24b73FRDjciIOFQCEqfE3o66uuvVnZ2ddh5XD4dDNxMNxeuba8zrH0VyO2v+Oha/ufUR0x8K1s5VnoeF7nC9z7bPzaC5AeyryrLY6HFkrkCzsyLwe8x679z2oi8m96fBwAm2aYteef+fkHVDXt/TQ8jG4bNoBJQsLMje+lo/gyidrksxaGrJKKhM9ysgdgLAcjUax3+9PxKviL4U1Fnwdi34gKmJ0xOEHXDuhZMKPeB/jarysVlwtNwtTY90seVajFMfrOn9X6iSD8pQ8k75+AaVcAj68r3oQej2mH4t+AJmIOXZV91630yYszoiztS5Z6WxfFVdpvfZdRp94St5CaduSSx5x/AJOiLtUqfSdywzH6c1PhB9O87ViSz+AknXTjjTaDZY7z2TuvbrM2XnYbS654VxGrGdZ+yH0eQQlr0e/w9+cCMW1cxik/fBL3z02fDFlLPqBaCaa+9+DWiIva1vn6bQ5K65t50yWuVYXeAhZmNAXVqgolSyRBxeemxWzRF42z0A2Cm9IeGPqWPQ9qMC4nRpi5YeY6WuyG41GRxNp6oSapcQYd5DhGLlP9CUrrULP+uzjw5NsQNDs6eh18rZcVj6XttOjVURfeoFycR5A70/2fzDHuHNOBXXlIUoeysnNbrxfxHEHFH1A9ZioQGqdc3DcbISfTieFfTKyVgUIN+syrKED/s5cdQ1zskQel4Gtulp5tvS8HpHP+JtVVuYUd87pAW44XmSBYbYs+iy+ZiutGXy2gtqjbGg3XA0FNC+AbXEMoK0Pmm9AWVU8mav/sbrh6odfeqExPkAXaHgGpZmIzCl27ysgETWdTmOxWHTDarHE2Hp2r7HUATcR+TvoPnTAjfaQy0TFCUC+JhYcjscuOo+Uw3nZpeYONbiumuizfENJ9FkCj8uNdYicJy3dbrcnU2S1bO1L127RV8Dwzevr66OhtVdXV53oeWhtlokH3KNPLTcn9WDJWbxsiUHWM06z+2qlWbSaUOPfs7n4cA7uNQfUGg8dWpvNuKMekQLPZDwex2azidVq1U1cWnvvgHlPVfTffffd5yrHV8lkMjkatolJNDD3WjaJhoow4odPosH987F9FiuXQgyUQcvCf/P5OEbGftolF+fIBD9kEg2Nwx87icZmszlqRUGy8HB4N9vR+fm5BV+gKvpf//rXn6scXw2wZniAxuPxURyP4ZtDp8tiYXDcmU2XlTW9Zdn0iHKCLEMFD/b7fVo5ZXE9nzNLxnFSThOBWg7eRl9amQmey8jTZ3NZN5tN3NzcxO3tbSd8TJfVKllCOKJH9L/97W8/SWG+ZmAxtttt99ntdt1vbIX5O21K+9CJMVXAtWSWipBjbIWTb1kyj7cptY1nxyy56VzBcIYff2dZd/Vmsj4J7BVtt9vYbDbx9u3bePXqVczn83j+/Hm8fPkynj9/HtfX10cTarSW0f/Vr36Vfl8V/W9+85tPUpivFX74dQrs1WpVnQK7lIXPLP2nmAJbKyPevyRAdtn1XFkYkmXw2cKXms40fEAiLjsfKPVYHDoFNibKnM/nR5VvS5REX50YMyLaukvE/f193N3dxYsXL+LFixdHL7u4u7vrssUQvXaw4c4zKvrayy70Qa5ZRaCCVxddk4c4Dy8j8nhbXXWtNHjbzNLXrHwt+cfWXa09XyePwUdljG2RaG1V9H/4wx8ePzFmyyCWf/LkSRwOh5jP5/H27dv4/vvvI+LYtdW2c+5Wy6JXoSETnVk4wGPNdd/aQ5wl8FT4fDxsw/F21nymFUEtA68ehq7rubi8mqzE/wTlxfng4nOz3WazOXr7kDnGnXMK4Npns1lcXl5232G+vMzN1ISeDqJh0fHDjQc+s778O5dNRQKyeJ7348y6Cp6PraLkCiGz8llSjs+lXoQm77KJRvge4GUiLHq8kGS1WsV6vY7NZhPr9bpbNzm29AW4/Xw2m8V8Pu+aiWr97bNjYJ3d06zpK8uOYx1JOI7DH1MpZ5Ze19WaZ4JnS58JPovpNctfq1iya4JHxNcN0UPkEDqsvCnjvvcVNCsPwUccz2MPS5Q97IC/U/FG5JNS8D4YpVbarg/9X2bi5zKzx6DXU1vPKO2ffUriV4/p4eGhc+fx4TfVmjK29D1kItPmt9Fo1C3H43FXEWD/iNMBKVlirZSR1t+y33EOrVCySiUTFHsVtePwsSLed8/NmjMhQByL701WvqzSwH3Kcg+acNQOT9m6seirZDE2Z4sx0EOHtqp4Io674apHUDs3H1fPkYmfBQtBQmwoS9atV0MMTTyyW83nx3Y4J86j8TpXInwMlEez8rUEpIYRmgishTHGon8ULFx02tntdul4dli20mAWFb0KrERN9Hoc9FHP9i+55CgXKgwsUXYOY7RprtQMh/fV4zs+Brwj3FeuMLiyyIRrV/7DsOgHoJlnWHjEkVkMnoleM9aZ+1nKXsOasjXG37wdr6uLzta4dq16zaUmu6z5bkglwJUnKgTMLsy5EoRN+n/g68sqP1v2Ohb9QDiOh/DxYUvOmeraMFl+eLP4uJTFLgkev4PMmsLDmEwmvcKoJdiGVAYlobOnoxl/Hk/Plj87Pv4npcFGFn4Zi34AbP3wMCKux0suVRC11yyp4LXpjoWfWfKsy20t869CrGXHS9fMx8ya4zLRs3eUDcrRZByLXqfO0g8632y325OeeqaORV9Bk0H8EPODqb+z65o9kBx3wwrz97rO2WtYbI3VtQ8AlwllLDUnZglLvn4VeOmjlj+z7lkCTkXPM+LgO94Pv0VEV/nW8hzmGIt+ICwgtkh4+DiuRTwP95RFj3XuvVeyvKWmORY8ewtZs1lJoNlxS4LhfVjUtePX4vrM7S9Zex2OzN1v0Xqw2Wy6fhS1fIV5h0X/CPjB5wdQH/b7+/sjK6/j5CFOWHkV4eFwKMaqpQSfjkbT46lo+Rh6bPUydJkdT8OBmvDZ/ddeenxf1dLzZ71eH4leu0Y7pi9j0Q9A3Xx+qHkEG1stZJ/VEvM64n4VjvbdVzFmlUdWwdSug8n6AWT7DRF76T6VRF/yEDihp4N5drtdrFarzkuC6DHSjt+ka06x6AeQCShrsoLl4R56EXEieG6bzkSA2V7U4meuPM/eoxWFVhZ9bn1fTFwSfc0byMTft28pJMD6druNu7u77j7xyDp4B1wBmWMs+h8IHlLulKOCK1l57a+P4+GD3zMxs8h1gkzOI3CM39eZZ2hsr4LWbXSZNe3pdtm5SoN8drtdXFxcxGw2i4g4if+32231f9Y6Fv0j0DZ1kCXHsL3uC/FyzzTNC6jwsT/OoeXILH0WHmiZ+O8h1j4Tbkm8mdtf2qfWepC5/vv9PtbrdTchJh+HK7vNZnPUuce8w6KvUBJLKVmmMS0vWUzc7JaJSD+TyeSoCZBj/yxnoDPyalPeEOHzUgUMoamHUqKUB+Dz15rceD/E95icFJXdbDaL8/PzuLy8jKurq7i5ueksvhN7x1j0j0ATZ9m86ipgFgb2h+CRxCt1duHvMQ02BqvwyLWIY9Fl8f8Q970kuqzyQw5jiIeTufxZWWvi1/vCcxIuFotYLpfxzTffxM9+9rO4ubmJ1WrV5USy3ostY9EPoJYtZyEDjUfxXcR765wl8LKEF++rXgPCBC5bRBxZY4iTKSX0VIy6T1YhcTmxH86plULmYZSSlCWLj/ONRu/ePISJMJ88edK580imYuoy7gtRS1S2gkU/kCyBxn3a+WEqWeuISN3jLLlVynj3wXkAPl4WkpSuk5ela+KMupK1OJTKqufNysLb8jEnk0nM5/Oj7cbjcSyXy7i8vIzFYnESEhmL/lFkSTMsI+oTPigQJx5GzKyrHkDN9dcPXF78jRAgc/FZBFlCrZSzUMGrpddOQuyB8Dn4PKVkYlaRcll5IlJ8FotF9+qxxWIx7B/bGBb9QNTS86y3SLQhG18SDcMVAQaPYDsIjLukcm+06XTa9frTufZ5Wi9txiuVKYuxsc7bandZLJmsJaGUSNTkZtZ7ke8JV6p8jXjn4Hw+7140Op1OB/9vW8OiH0AW03MiqZS1zxJ67AmwYEo9+3jJbdGZ2BHH6htwM9FrOMHXytfM22uZuKkR+2ReUEn4fE+5B2NWVs6NwLXnPg+YvHQ+nx+1irQcw5eu3aLvgW8cxMOCm06nJ+5xRN5jTSsHxPY8t9vDw0NXkcBzwKQd0+m0G+TDVl2tOwuexdFXPr5m/mSVEbv4uBauEGuVTpa8K41TyCrO0WgUs9msuz8RcfQyy1IfBfMOi34g6t6zpUcir9QTjLPs6g4j5ob4R6NRJ3IcG8nC3W53InRYOl3PrG3mLmvrQpZRx++Z98Hei94bFT7IehdyE6haes1voJvzeDw+Co1KZTfHWPQD0AcUVgWfiOimzcLYbt0X60iuReTj9bW5Cw83exkaN2svPIhHBafnzix9TTRs6bW5TvMdLHqURe+FWvmShdakIYYt41XiqEgt9mFY9I8gs2YQPVsozVzjw6PvMtFpfIxtsYw4TXj19crjeBpklh4eR8SpJQbaB14ri1rIgeOwFefyZZUYn5c/CH9ms9lR27yb5YZh0Q9ELT3H9BHRuZxqKXkiDZ38sdZGD2DFIAQ9B8rG5SxlwYFWOJmlL7XrZ/3uuaJRS8+eRymmL40U5PJySwH6ISwWC4v+A7DoH0EprtdEVvbBw43mN7jaPF9cRD52PeI4CdjnvmryMROQnoPDCrXEmgvQDjnqomuLgmbu+dgl955nB9KKEdtjDD2L3sLvx6J/JGqh0EBLMfoAAAQ+SURBVDwENzwTDbv32I9dfZ3DjtH4P8sHZH+jrErWpFi6vsyTKJ2HB7/gukotB0ArRk784Xe9DxERk8nk6L0DnFQ0/Vj0A1AhaLMURK+xNr/qClYeS17nUAAPLsfMmjTL3PMsE18js/J8rVjP3PwsW87t59zcWEusZa695iJw7Ijjlg7usGTBPw6L/hGo8Dmjzt1dWfCZtcfDDYvIwsd5GG3f13ZrnZEX60rJW1DRs3Xu6+DCrjgLHcdl1z7zKnAPONdRSuhFxNE8BBb6h2HRD4Rdek7iaVIOFQDa2NGphrvT6rzuvOQ2eTzcnADkhBaLkr/TpsAh14alJgAzwWfhC7exq9fDnsBQoWpyEqBCQEecvhDCnGLR95DF8OjyyQk5tcDac02ndtZutfwCDa4UdHJIWHZtxtJZZSPKlj8LA7LkHVtpFZQKHK59lrGH2LUs6jVxcjSrOLDtbDaLy8vLWC6XXZ/70otFzCkW/QDYwmNAB6zyfD4/cb+1PTvrS1+a310rAN62VJFkws/6APAyy8CrhVexlxKULFhO6mW5gFLikCsMXlfhs+ifPn0aV1dXcX5+3o2bt+j7seh74IcNQzeRhZ/P5ycj5LBU8WunlswDUNefvy+JvCR6TfzxUgUf8eGi52RcycrzPdEmv6ziyCoPrmTQE+/Jkyfx9OnTWC6XXV98u/r9WPQV4JbigYToz87OYjqdVqdbzjLtHAZwt9KSkJHNV4+h9NFz9Ln3IHPjS812WaWg4s+El5VHKxG17llFBNHP5/O4uLiI5XLZzYyrnYBMjkXfA8fz+BtxfZ/geV0f+Ky5LcvIZ2LO1vvE3pdAy8SdLXU9c7+zfbL7wPdX9y/lFVj4yK3MZrNubgNu2zc5Fv0A9OGDq1/q4KKUsulDKwT9PtsuO052zr7rzNZrf/d5Bn33gPetHUd/195/du2HY9H3oFYND9wQsUcMmxpa/+6rJIauDznnEIHUtumrHPrKUdu/7zfNLdjKD8OiH4Bam6Eu81AeK9Qfut3H4FNb077jlzwA089Zz4PiLk8FPqfA+viSZfnSQvvS5//KSW+ORW/M3y+p6B0EGdMYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjWHRG9MYFr0xjTHp+f3ss5TCGPPZsKU3pjEsemMaw6I3pjEsemMaw6I3pjEsemMa4/8DeIg6grIyJbsAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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" ] @@ -602,6 +614,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "current beta: 8\n", "Current iteration: 13\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -610,7 +623,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -646,7 +659,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "current beta: 16\n", "Current iteration: 15\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -655,7 +667,7 @@ }, { "data": { - "image/png": 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\n", 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twuZ91lltPvesxwLvs+5IhBa6Lw0FzD61on/bC6fxaSlu/SSPkf094rjfooJXqwdg9fG+blCLJsHUYvOxOdtfin/rLH0m2uz38/8j+/3cawFr2sSilrooe71eNUiIrTheMfEIe0Lc0LL41+t19Hq9nRAL5whPw+TUil5HjrWR9XqdPmVWraxaNBaIutpabQZRZp4AYl/u21YXGevydgoLmct7eViwutH8O7LGlc+VrS4P72Xrr+Lkv2ujpcfJ4vbNZlPNiMO5Eh5KmzVqbceqPsDt7W3c3NzsPNRCH0KpFhOC5IQchItpsvGoq5LoeVvt5876p7PGArAlxboQP+JjWFwee6/xfkRU1hjnx4OLcC2wD2yHce04Bhoa/B37VMFrQwVvAXCuYjgc7kxBjvwF78vW/45a0fOUQ593NJZGnD4YDGK73aa13rix2AXWhzGy6HlufBU9n0fmJWAfGhJASNnv4P3pYBz1EjTehzARYuA6YL3BYJBm7SHwUncfeylcBFSy8Bw2ZRWLWQj06tWr+Nvf/maRF+jUXZgf/vCHrbpqsHxXV1fR7/fjG9/4Rnz/+9+Pr33ta7FcLuPq6irNbLO100E2maXn59tp/KlZfI7tNQEXcS/yUimuZtqzkICPkeUEsmSeJhS5AdBEHrwEFrsKP7PyWOoeeMlDi8fjcfR6vfjrX/8a7733XuuH2G632zSuqbX0P//5z/8+Z/NPwh//+MdYr9fxrW99q7L2EDYLk29AnaRSRc8jzbTARkfjMZk1VCuO7xidcQeutMLiLGXp8Td8ZvGztc2SiJml1x4G7DcblcilytnoxPF4HGdnZzGdTuPLX/5ybLfbePr0aazX65hOpzs5i7bjmL6GFy9exG9/+9t4+fJljEaj6PV61QMneOppbQA0IccDSXhhUekNfihTzjX7pcQd+rDZIuJvGXXda9l62k+vFp73wdtkVX/aw8ANmgofMwnjUV+z2SxOT0/j/Pw8ZrNZfPOb34zvfe97cXV1VV0rc0+te9/pdFrl3ms30Xg8jvPz8zg7O4uTk5M4PT2N09PTODk5qSaxLD0mmbvd2D1XsWNdtWZ1U1/p02vgeWRz6fEr70P3ra5+tg3/roi8eCjrHuT12fPhJKUel8+PrwWGI49Go+p/8s4778Q777wTZ2dncXp6Gk+ePIkvfelLztpHHO/et63LTjO8m80mXr16FfP5PE5OTqr51U9PT6uHSepDFLXrTGNjfM83dfYYKr7Z+abnoqCI+zxE1q2VJfWyfTbpLlNPhi25Jux0e74e2iOR5RwU7WKEOw/r/oUvfCGePHkSjx49itPT03j8+LEFX0OtqtueCIm4f9zvdrvdycBfX19XolfRZCJnIDpYLdzI2TRbOjsOsugR94JH1xn2nYUGvI9siq0S2ew8JSFn3W11CUZ19esSjfw9wiz1vmazWUwmk2obCz+nXab8AXByi63UcrmM+XxeufeZ4NWyR+wm/ZCEyh4+qbEsElgseO4mi9h33fU73k/22CucK2+XnUfJOyj1QrDw+TpqD0ApCcjhAJKRpaf4ohG2wfLz6R8Mu5vL5TIi7i0Pnp+moq/rd2Z3/vb2tppNl/u9YYW1m4oFydvp/jOXHMLTR2KVEoZs4TXfoB6CuuQaQmQNhWb/S/kBDgVQF9Htdvee/qNzDGY9IOYOi74BLGat9b69vd3rnsti24jdjDpXpal3wH/n6jbdR0YmfG5stK+bk38sEvYMtPEphRDZtti3Tiiiws+681Tw3W63ut488ShPWFLKZ5h7LPoG4AZFAktrxUsxfcS9FYTlwY0MCw2BcGPCpa5coorPmjjLMt6ZW6+CL82vr9uxZ1CK7bEd/ybNS2BRr6gkeix8fnyNODeReTgmx6JviFpiWJ2I/UEgWYwLqw2wD76x1XrjO9zIpT5wPge8L2XdsWSi4f1wLF8SPdbT88hEnyX0VPTwYHCtuKHE37URtdiPx6I/ErXkcMUj9qvmNJvNLjqvi2Sc9pWze695A16vKXWuf5b4K3kNmpyr8xLUzef16s6deyVKE2VY5A/DpUp/R972pvRNbf4e2NIfiVpGTlDBKgPtKy5V3GUudmZdS1a5KVmXooYdWE8LjjLvA79JPZq6BJ+eS10yT79Tz+AYL8fcY9E3JIuHdZrriH0XX2NQ7ffWTHqpn14bi0yo2Xucu4YI6i7rexa+HgeNGYcsvI9PMpHHc/Zj0fBIwx5Tj0XfALW2XEGXPYYqq1DDfjgpxoN2uEBHRa5PwKnr/85ufC6XZbFjlhnO/utvRkMBMdZl77NtucsOfzsk+FKXHeYfQIKTK/ks/uZY9A1gN5ufLc+Pr4rYr0EviR5ChtB52mx9iKWKnhuFjMwFhnAxP51WGWbiPVScg3WyWvmI2FmXGwn2gJoU52AwjhbnoLgJ6+hMQqXfZSz6g7Do+HFWKPtkSw8BlKyOZrYfUoaL9TPh6/Fw40MQqAfgzLi63XquKvySd5E1cBqKZFa+blgur8uDc3DNeQYieAEYcVhqjNqEy3AfCCzscDis6r0xtLY04IbfZ4Jg8TxkwA03DNxdqEk1nEd2Pig2KsXtpfhc3fpDno0mH1Xw2Zj6rNuTqxIx1gAPGLm5uamGOnPoZEuf46G1hFo7iApjt2ezWfV67NDa7O8QQ93QWrXG+kjrTIT8yqBhgPXPrHzmHpeKcThXkAlW+/E5icjWm+PzDPak0PDxezw/ENdtPB5b8DV4aC2hoscUTJiRBUM5D02ikVlCvblV/CULn3XRlQbMQKilRob3kVngzMLy71EhleLwUsODv+lEltlY+lKXJRpifjwXjrVarWKxWERExBe/+MXUizGeOaeW4XAY3/nOd+Ldd9+N0WgU3W43ptNpTCaTKrbW2Wq05BQ3M2eh+abn7i+O4Q/1wXNmH6+Z1WfrmWXpmaznIdtGLTYn3EqlwqXGT6fzZm+k1MXJDxHlxvj8/DxOT08jIuL169dxdXUV2+22mPT8vPOTn/wk/Udb9DV89atfjR/96Ec7E2PiplPXN+ubjmg+MWZm1ZgsbtbjquXWWWhLFhifeX21/GxReZ+lp8/y8VnQOn5eM/fZNUVYw3kNJFbxWPDRaFRNjPnBBx/Ee++9F0+fPo3NZhOTyaR2ZOLnlWfPnh0v+h//+MetEj26oG5ubqLb7VZTYH/lK1+pZsvROJT7rjO3W0XP897z02oiyokvtaos4CzuxnbaLcYi1FccQ609i1H3q33pLHzeBzcm2ji87RTY/Jt5Cuxf/epXrQtPlW1hCuxa0f/hD39onegj7q0aXPjNZlN82AWuH1focZKNRc8Pu8DCYsR++EZmcWUPuwAcd7OASxlydfu1kShZbBY09qvWXo/FrxwGNHnYRWbttXFDyIRzvbi4iGfPnqXJzDbxINFHRLuvWkQsFot4/fp19Vir6+vruL6+jpubm0q0sEZZcU2dpS891qqUD+CcgBajZLH8drvdeX6eJs3YwqplLzUS+KyPo+ZjqIeRuffZ3zNrj1ct9Im4f6wVHhxSeqwVX882sVqtjp8N19xZe8SEcP/55sYNBdHzdFT4myan2G1t+gDL7MZlN19BqSqvl1W+qfDrquO4MdGwIZvSWoV96HMp56A9GHxtUJ+vzxfkHpKm3Xf4HzyEbNu32d/fE3fZHWC73VbPZ+OqORWodjGpG4oad+yHk1ZZVxXH2Vm3msbjh6wZr5+53irETLgR+910JcFn8b0KPov/8V5/C64r0JCHG2INf46x8G/jDWTbfha9i1rRfxZbqU8DLUMFLAZUw/GijQLWg5XnfADW0XieXVnMHIP3EXkmnsWjLrGuW9oH74tF2OQmLq2j+yk1PHosXEvtDUBjw332WgNh9qkVfVv7N5nM9cYNihgbZLXsnc7uNFdqvXgCzEz0OB4/DZYbjEywvB32zTGxhghodLhKD+vhvXom3IjhfPS95hw45MB32e/QxlOTlhqO6HuLvh7H9EfCcSSy+Phe43Htq88ScBGx08XHDUbE/sMxeWJMXi87RxXOYDCoGikWMzcMPBIPr1iPQw7kLbAeBvPg/NnLqAsT+TpFxE6jwF4APvNvtNAfhkV/BCp4LBGRZqm5247jXI5lAVtYfI6IPcGza8/70Lg/8xzY2qr3gc8cQrBXwa44nrLDsT83Dvw7+XuMhefGBdeOwefMO+DfqI2Bxd8Mi74hfJOjy+3m5ibm83lERNWfv9lsqioxnetdk2IArrAOQcV7Tg7y9NiaAAQQr26H2FeTdnpumszL3G1t4LRbkI+l361Wq6oBw0QeeK+NAn4jhyxZxaJpjkXfkDrRw/pxnK9ZfhVOROxZ9iz5p70CauW1K4vPtd/v75wPN0yZlS9l7jMXmxsLFT2LG/vjZTgc7oxBwPZ4ag83BOrCsxfFyUq+hqYei74BesPBvZ/P53F9fR3r9d3DKyAwtvQ6hx5gEfNUUvzKNzBb7MwTyESP8xoOh2kXGq/L2xzqP+fGi917Fn3plYuMSo0E74snz+BEpmbyLfzmWPQN4bgcxSAQPr7DjbxcLncmuVRhcpzOT6GNiHTOvYj7TD+7vOoF4Du14Fl/OO8/s/YlC59tp1V5ECwqFnWOOx5wxOtnjUbWJYd1s/oFDZ3MPhb9EZSsPWJUWPtsFhx10dEoZMmnzDvg/nZ4BtwNl9WkZzE4/qbraWyPpXQdIvbnuVMLzVY9G2HIlp+tuoYDXHqM7zDiEY0cdzfa0tdj0R8BiwI3H2q9O53OTvycjQbDZ57rjgXJi1agqQuv4+mzzH9J6FhHLX4W5ze9Hpqxz4Sr3pBW0mmxDXsF7PYjn3J1dbXjFS2Xy728idnHon8AuNH5Bo6ISvDsvrPYVfCItTWxhky+JvbUsusEGqWx+FlloOYNjhF7tv6x4lfBayjC26uln8/ncXl5GW/evInRaBQXFxfV/0VDGLOPRf9AcGPhZkYWeblc7sTfLFSd715vehZR1njofthz0PVY/NlnRb2CzDPgdfGaiR7u9qEGIOtJyPIRWtu/WCzizZs3cXFxUc1iBK6vryurb3Is+rdArRujgoMgMQpvNBrtxLNZEg1hQkTsxPEQft3suTh+Vu1XynQf4xZr8qwuIVgab59V05U8D25kl8tlXF5exsXFRcxms2qCUjR+l5eX1XH5f2HusOgbUhIObkx0XeE7FQ8EuVwuKyufjQxTqzkcDqvjc9eeVupxH36WPCxV/IFMeNl6vE6WBGShlnoR8F6vVRZ6ZB7AcrmM+Xwe5+fn8ejRo50pyYfD4U5jttlsPIZEsOiPoCR8tVzaF45tuZuO3XssKhAInrfXG1jFzLPL6ECb7NxBnYXP1i0Jsu57fVXBq+izZCNb+9lsVk2MeXJyEo8fP44nT57E+++/H3/6058qS98kR9EmLPoG8E1YcqPZ1VWrzftBOayKPkuEaVcbtudBNwxbcT3P7JyVuji+lPhjYZdEis+Z2LNrXNco8TWeTCYxmUxiOp3G2dlZvPvuu3F5eRknJyfx9OnTwn/TWPQN0USaZs0j9kfTce067wPlplnmWvu7s4ZDRa1WPBNUZkUZ7SnQderiYt4Wx9cGIGL3sdalczzUKG2398OM8aCL6XQajx8/juFwGJvN3YjBp0+fxi9+8YuIiJjNZunAnbZi0R9BJvxSYQxnsSFcrKeizjLduqh1rBOhilUtrnIoeZcJuOTSZ/ur8xia9jDo/tFrMRqNYjKZRMTdcwq63W5897vfjdFoFD/4wQ9iu93GaDRKG8O2YtE3JOsy46fIZsLXbiy4wBGxU42n7n1Wn66eQZYpx/lst9tqNB6/z/r+ca51v1mpi+NL+6n7rkmXop5rqWah2+3G17/+9fj2t79dPc8OnpKz+HdY9EfAws+eMotYm+vCgbr57Ppzo8DlqdkAFX6/Wq2q57ghOaiPvObzw4JzwHnxq/5WXld/S5aYq9s+s/h8Hhye1GXwsR7CK32S78nJyc6xuLLRWPRHw4UxEBcq6yBgTrbByrBQYHHxGdaZLb0O7MHCn/G4bAhfn3XPT7gtPSBCexkYzRXw72BPo5SJ54Ubm8ylr0vg8bH4/8DC54bY3XT1WPQN4ZuSp7uGyHTCCQiY3VAkklRk/Mx4Fj7Xq0Pw8/m8emQ2BA/Rq8Vny1964CUfl+F11VJmoQW+1+t0qNuQj5FZfz0WjoEqvKx2Ad16bY/h/Xz6TwCO6VX0LJossbVarSrxsEgQ5+u49H6/X1l6HGexWMRwOIzxeBzX19c7Ysd5sPBh/blajUWsBTRMqWsS5611BXqNuDw4S3pmjQF7A1kDimN2Op0YjUYRETvTiWcNiNnHom8AW6BM9KPRaMfKlfbBE0dqLM1WjL0EiGexWOyEE+rOZzE9u/o6tj+zooyKNduOX/X6aJmwhhjaEGjooeeHfEZEVDMCdTp3Zc3j8bi2x8DsYtEfCcePEP14PI6I/fLXzH1FvJ55Ayx6dZN5cI32HKgLj/p+fdU5+7SCUMXLAs3CAhU8n6vmFLQB4MaBG4Es38C1CxF3op9Op9HpdGI4HFa1/aYZFn1DVMAqesSZpTiWRZT1v7PFV2DVsC+1kmpBtYHIknhZrJwJuM7FV7Th4R6OzOpnDQGOpZaeBzVB6IPBIKbTaTX+vu0xfFMs+iNhFxaihyjZ+qpo+MaGq9rt3k8ZXer7Lp2DNigcgpQm78is6KHsu3ovGXocnjOAPQ1uFFnwpVmGNKaHJzQcDmO73cZ4PI7ZbLbz1FoL/zAW/RGw2Di2x6y37ELze41bkdSDhep0OlXhTZZZP6b4Jev+0iw8vAntTsx+K4tfs+v8Xt10tvQqchU+1s96GPj80B06Ho+j2+3GdDqtnv5rS98ci/6BsMWHtdebNhO8Wn+2+nj6DGfT2dLhsyYBDzUGJdc8i83192mDwfvV/IVm7SFk9nTqchQ6pkHzCBF3jQtGIKIbEzG9Rd8Mi/5Isgz+dntf6gprNBgMqow7r4suODxLnSvs0AjwnHtZdVoWh/MNz256CRV+9jvxql4DXwf1CDCYCOfPQtcsfZ31Z+9C949nDMA7Msdh0TdEu6OQwEP1F24+3Iir1SrG43Gxog6vXHzDE0Hq5JGI/5E/QMzLsa6Kt87yHRJ96Rrwumjg8DfeH/dCcGES/g7PhkMXdMVF3JfOqvC1GpJzKKYZFv0Bsqz9eDyukkccmwPuV2YBs+BZ9HWfte6e54zTAT11FXYg8wj0c5Yj4MIZXbRrjwchZT0Adb0NmtTTz4PBIGazWZyensZsNqsqEy3+5lj0DcHNijHccGEx1x1q6jlJlo2Yw5TZPAc8L+wRqFeQNQClIbileD/LB2QNRKneAH/TnoNSZp5d+oh7y62i57ies/2a5R8OhzGdTuP8/DweP34cp6enMR6Pdx4SYuHXY9E3gC0UKsA6nU716GeupovIHxVVmhGWa+xV/KVGoIn150Qgv+L9MaJnC6qiZZcbwsxic24ksB/tBVG3XYWPeH46ncajR4/i7Owszs7OYjKZVKPsLPjDWPQN6XbvBnJgEAPiek4mZd1MWQNQmiknezAEewZ4X2fxteAG58Kv2iCUYMFnjYC68Fp8k3kEpZ4N3TbzGrCMx+OYTqfVwpbeoj+MRX8AvonYfYTVr0uEabIsW3RCDJ1Nhy05htuWZtspVdjVZfgPFQDVLaU4XbP02mXI11EbkNKS9ZrwoKImRUTmjs6B1t4dn1E/GST/PesLz/aT7VP3rwNa1HVXgWevdb+l7jz5t5TE2qQh4HV1v3qMY5YsSchW3qKvSC+ERd+QUkysfzsk/NI+645Rahjq1mty/Lp1VDh1n1VsJZE3PUaTfZeSjBb8DunFsHvfkEMiaMIxFWPHNhifVT5JETZpeMxhbOmN+fyStoSeMdCYlmHRG9MyLHpjWoZFb0zLsOiNaRkWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMix6Y1qGRW9My7DojWkZFr0xLcOiN6ZlWPTGtAyL3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3DojemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zLsOiNaRkWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMix6Y1qGRW9My7DojWkZFr0xLcOiN6ZlWPTGtAyL3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3DojemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zLsOiNaRn9A3/v/EPOwhjzD8OW3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3j/wOnTGnFK9OuyQAAAABJRU5ErkJggg==\n", 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\n", + "image/png": 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\n", 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\n", 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/lEP7ehWXLxvAojcy59sjdqd+5n2ArO8My8jCLfXNs8o1de/5epT67Jre0gZNo/q6TvY5n2NXlxwWnEXN94Za/6xrBCvPk2PAo7Loy7SKfji09397e7v3lNkskMZi0py5us2aq9YbHLn9TPRcecYxAH6NiD2ht6H9Y8Qp8KqFP5ox4JQmjjkrIdb6A3hK6lHq8XJDgf2isVyv1zEYDHYmJ+WhtOoFGffpD7Jer5uBHPqYKjQAEfuDVriPDleU58bnfbD3oBaxFCVnMZfy9G3uNFxp7jOzxWXhcrcDy3q93vtN/gzHjIaAr8t2u20msITFVi9CzwuFUdqYrNfrZqosfQ6BekK2/i9pFf177733RR3Hl44KptfrNSWyEdE8qoobABZ9v9+P1Wq106fHjcYWXh9ZrZZex+Vr/5oFX2oosJ1aerXKWC8r8FHh6nFwAA0NCAuZj7EU/c/W4/8HL9ncfxxbWC6XEfGy4Xny5Ek8evSoGCCsnVbR//SnP/2ijuMrAazS1dVVDAaD+O53vxs/+tGP4q233mombtCoNG4sHT7L7r2KngefcOEJC5K3V9Fkx60ueKl/rf1nnLO67Pg9tsJZnCBLy2Wxg0PBQRwTroNeUx7FiEaAjz8imhTqX/7yl/joo48s+gKtov/FL37xRR3HV5IPPvggbm9v4+23346IiMVisTcKLiJ/DJTm6LnWHA+0ZE8B+8nKcbGfzBpyv1uzDuric9844q6vjf47p9baAnfYrpRSzI4Xf2v0ny0/nxcHSln8PHyZH201Go3i8vIyptNpfP3rX4/NZhMffvhhrNfrmM1mO7GE2nGfvoXHjx/H73//+3jy5Elzc2HySjw4URsAjU5H3FXjcX26ut7cWHD3IINFwYLQmzoTvB4jr4fjYeueweuyxc+Erttl7n2WiWDRs/fEMw9NJpOYTqdxdnYWZ2dncXl5Gefn5/G9730vfvCDH8TV1VVzPu7T39FruxC9Xq+qq6TpvclkEpeXl3F5edncWOfn5zGbzZrny8HN5O0YFodWt+E3WcClhz7yughqYV2daYe7BSXxZinF0vp6DLy+uvzqiWTblKy9dnMiYqcBxHXCk3tns1mcn5/H/fv34/79+3FxcRHn5+fxxhtvxJtvvumofUR6AZyyI9QabDabePr0aczn85jNZnF2dhbPnj2L8/PzmE6nTZ09R6jZVVXXVgXF4s5m29H+PTcOepzapeDf1XNs8yLa1s8m6lCrz+dW+i3uy2vAUbMG2s2BtT89PW2s+xtvvBEPHjyIi4uLmM1mcXl5acG30KrqrOKpNvhxv+v1uom+w80fjUY7jUUWjVYhaXAKT1/hSTRUNLwN5uSL2E2n4X3Jlc32l1l83k82/BfnwCLPYg36O6XApHoM2fFwowLRz2azpjE+OzuL2WwW0+m0Wd/Cz6nLlL8CHJjiGxVPV1H3PltAJl6eMms4HDbFJVxbztux4DVo2NbN0K5BKWBYOlYtL24TVNZQ8DaZ8LWLoAvX7Q8Gg/QBIdzdssHy8+lfGbZmyAXj5lytVo2lx+dZ1BxwX3w4HMbt7W2MRqOdNCBPvtHr9XZy4dzdQvQfuXFOXamlz2IDmRiz2AFP46Wz/Op2um+dHVizEZmlV/FrYRLOFfMXZJOKHgqE1o5F3wEW82q1am4m3IwaBc+CWhGxIwK2XADPVceNrS4yQDEMnuDSFrBTSlY4Yjeqrw2UPgK7Lciogs9m91Vrr4Ln/j4Ev1wum2Kh7Am9WfDT7GPRdwA3IiwojwTTviMH8dTaqkXG57hZeeAIV8qp6GHdOa+OzzTIp1ZYxZl1EVj0nBfXJ+6ol6AehFr6Nhc/W9AwYjtcaz42/l2LvRsWfUe0f9nv95t+Y+a2ZqKHu8w16rD6EAcaDTQO2qBo0EuDhYcCWCr8UoYgs9alab2zbXk7XV+vlZ4XrDw3Etqg6e+a7lj0R6KuNKxsxP4gF90O68DqlwJ/WDjanTUmXVz6UiNQcs2PaRTaRK8NQEn0+Cyrlttu2x/XZV6N/uFVzFcN3/DmdbClPxJ1KbkMF+5nm3ufFbqUFv09jUh3cW1L36s3wbA3gnx8NoimdMwRd3n87Fz4GNrc+yzIx8N1zath0XeEb+ys/BXAJW0TvfaNNcKtgTJ1cdvc3UONQNaN4ACjChMi5BgGu91Z44TzYiFrMBL76RLIK03meUzWwtxh0XdALTOCWtmst11TdtiW88uIkGu0mwtkshSVpsQOWXe12EgBYju1pBpDKDU6WSBQh8fqsbRZdBU+BizheHkCkrZ4itnFou8A38yonuMn2bBlP6Y4h0WPUXtanMOv+mDMtm5C6eaHsNQ7wfFhnV6vt1cLAIutxTm8bSn6z42SZkJKxTm8aHEOT9iRjeM/lMWoGYv+AFwIwg+5QOknvitF4CN2I+hstXlceFZRFpGX4XLDoRa+5OZrH57rCUrRffTLYVlZyNn+eTt287UAqM2tL11HLcPFvHg8Icl6vW6uSZYNqA2X4b4iuMHwIAsM9OBRdhBAW/oNqAvM3YTMdc/69dlTbrOhtYpa10zwWR58MBjszNmfoeeIa6d9f6ybBe20odT984CbXu/lyMSbm5uYz+cxn8+bJwijMbWlz/HQWkLFAjGNx+NmNBfGcOvQ2oi7PD3+jihPH83i17Hxmeh1fY4naKCPz0X75hqRV7IuiR6Lrs8uOV+7LLLPXaBsPD3HGTKvBdcI54HZc9A9ioidx42ZfTy0llDRYxKNi4uL1kk0uK/KqCB4Eg22tOwOd51EoyT4zNLr3xy0489L0fAs5ZYF4krb6jaHJtGIuJvVJ6v+Ozk52ZtFeLvdNq5+RMSbb75Z9EpqxzPntDAej+Odd96Jb37zmzGZTGIwGMTp6enOk2nbLFLE7oQRh6bL0r5wyVqp1c9c6IjDQ311va6pMG3MuL996Hfa5sjLYh+6wHXn6bLgfWHmnM1mE8+ePduZLqtGfvKTn6Q3kEXfwltvvRXvvvtuvP3227HZvJxmGS4l0CCaChA3OASPoJPOm499Zekt7CeLD/C6+rulWv1SF4TTZaWAZLZfnRgz65vzfjkin3k+2bXQ2gjOeiCLcnFxEdPpNB49ehS//vWv4w9/+ENsNps4PT1txjjUxKNHj44X/bvvvluV6NHXvb6+jsFgEN/5znfihz/8YXzjG9+I1WoVV1dX6ZTPEbs5eBYibnbkmTFn/nK5bNJQWT+f3XQVWHbcHMXH72qhC2+fRcd1G63CywTfZQps/p2s4q7rFNha0ITzxnbwxv785z/Hb37zm51RizWy3W6PF/37779fnegj7iwgrMl2u+30sAu2RHxDZvPe6xNuInb7v3osENWxD7vIpqkueQAlQWbuPJ8X779N+Lq/z+thF/Ck8N4Pu3jJK4k+IqoSfcZisYhnz541Qr2+vo6rq6umEWDRa9qNc9LHPNZKLXZWjloSPlDXW7dlC6vWHetnll9dc943PwBEXf2S6DPPA6jwuX4h4q4hRJeJnxzEj7XiLk9NrFar42fDNXeTMCIPrgIC3M/kijnc4Bpo4/x3luqKiD1x6PbaD84Cedl32G/E7nBgdeuzYFvmPajFLzUY2T544eNjtJ8fsfsASwien/XHsxJ1Td/x9TqWbNvX2d//T5yyO8B2u23ywpklBxrIy3Lm2IeKvGSxWfTaP9WqM90Or11uPA28ZVH8kvXO1snWz95zg5f1/RkOmEbsPl2Hg6LcoPD5deV1vIFs26+id9Eq+q9iK/VlkKXG+EaOiJ0bPmK/lecGAXXjWKfUN8c+NaAHC6f7Byp6boiwP5TWZrUAaIiy+oCsoSu9Z7IGAsep3oWeQ0TseFXYJosnsIU3Oa2irzW/yWh/G5/BwiCAhHXUPYdF1kaB+6lAt4coM9cbx5F1HViw2+3dY6EhfBYFz/zD22P9DGyjngXcet4fjpcbEm0Q+Xzwvs3qa4xABW/Rt+M+/ZGw4FEBxjcmbmqt1NM+rPY3taoOwuBpriE2/i0ILOsWgCxAxgNoYP25YYAngJp7ds8xfTevv1qtdl77/X7jjWj8A+fGNf1ZKlK7Gvp/0O6G6YZFfwQs+MVi0UTwI2Ln5sNc9twV4Ci8iiBiP0ePbTAbDws0m1I6E30p5lBK3WWWEzEIFZiuj+IXTPzJn6Hh4u4Ed0/wPa4jzqFLVwHv+XPTjkXfEQ4+IeWG0V0QOvqYGOKpLnBmmbQ/DbK+PoSfxQsy0bPFxbaZx6HReu0raz9cRc8pO13QSGK/g8GgeYQXhM9eAVv9rD9vUb8+Fn1HVPTz+Txubm7i5uYmNpvNjuhRFsrDXbWfGhF7Qs7EDNjtRsEQ9+c5us0NDDwDdslVvCriLKXGXYbM0vO4Ao6o64KnAmnDgC6ENlD8Hf8uxyYccD4Oi74DGjiCe39zcxPX19dNKg43sYo+C4hxJD8if0Kr9sM5i4DvdASaRvpvb3cfnZWl41TEbPWzdbm7wtdExY6/EfDUhafAwjEeKvaJuHuyEK4Rdxv0upl9LPqO8I3ONfTz+bxx59mS8bRXmrNnwWPwjn7PcEAQosa6HAfg7TSVqALmc+JzyzIFWf9ZGwi15iXBc8VcyRvgRkTz7/w/gPA1DWnaseiPQK09l9P2er3mRm6by44Hjuiz6ECWDouIHVHj+2z8PR9v1q3Q/WZu/qHttAtRcuV5wbXCtWNvQMWur2z1eZos7Tpxo2hyLPoj0NwwCx9WZrlcNrPpZBNdcI0+i55/AzevWnxuPNTdVzef99flvNTVL0XCS6Jn17xN+LD0eOVAH155P9woYJ3VahXz+Tyurq6a7lHmvZgci/5I2BXmm3K73TZBKIy7xw2p89tjIsyTk5Nif1mj9CWx64yzmfAj2ifN5PMqufPqeeAaRMSe8Dlaj8CnNgB87bIAou6Da+oXi0VcXV3F8+fP4+nTp3tZC21IzS4W/SuQBfY4kq7uPCw2W3hM+VTKmeskmRGx1z3gRWv+sxLaiP0adj6nrPKttK4GAEt5/lJ/n7/P0oe6L25YF4tFvHjxIp4+fRrT6XRnnkKAabPMPhb9kZQi5AgqZf1ubgAg+vF4XBQ9XrEuYGGz15CJPovqa2qPUcteOnd1oznHry5/JlwNzqllzvbPmQS29E+fPo2zs7OYTCZ7Dx6Bl5Cda+1Y9B3JBBSxHx3nYaUAAmShssuaubfs+uP3s6g9NwIl4WcNQcYhwfN6WQyAr4NmArQLoAFD/a22DAP69M+fP28mLZ1Op81kpbw96hTMHRb9EZT6zLiBOYWU9Ss5Taf91CxdhoV/H319Pp4sWKjizyw/01XwWLdN+NvtdqeAqC0diP1pvCGLIbDwl8tl3Lt3L+7du9fMUvzgwYP42te+Fg8fPowPPvhg5/9h7rDoO5Dl2DX/ru4ou6S8H1h7jkiX3GF8DpAh4Ao+Dbxl7vzriL4U9MMrzhHWVa8HFk1PZvl0rWdo+831et3MhDubzeLy8jK+9a1vxdXVVcxms/jwww9L/87qseg7okLSarssAAVXXwWJktjMtedtMxdYLTdKVPm1TcAl977k8h/qCqjbr+WxLFZ8x54A7+OYDAOeJ4hpsB88eBCj0aj5nT/+8Y/xy1/+MiIizs7OXLhDWPRHoOkyfqQU14fDkqmbDqGqqLOId1YDfygNhb4rHvuEY1ZvoIR6JW3rZa59FgxkD0AbAV4nE352bPgdnCseNRYRcXJyEv1+P955552YTCbx4x//OLbbbYzHYxftEBZ9RzgSD8FryoxvWHX38Te+5z6vRr6zIhcNgOmC+fl4v+gG6ICeNlHp+WZ97EOiP+Z64u82K8/dB10/q1n49re/Hd///veb59mhi+Qo/kss+iPAjcZiZ/FDtKU+M9+07PpzkQsXuGQVbZrzxnPcUO/P4m9L6fFx8auea5voNXiXeQrZtVDB87F0CRry/4HToPg/zGaznf1kA55qxqI/Ak27sdggYgTa0AjAyrBQuG8LK41tWfSoV+cFZb94XPZkMomTk5PmuXo4ptLjrzPRl6LbWhjE58GR+EPBRPaS+Pu2dfn4tGHh9CP/X7i02Wm6MhZ9R/im5FJaCA0uurr1HHCDRVeRLZfLPXc/G4qKcfyTySROT0+bx2eztVerz8JvE70ekw4UwjVQwWvDptepS7EQNwrZsWkDA+uOoCiODee33d7VS9SMn0//OcB9SIh+PB43c66DzCXF3HERuxNAop/PRT0YqqvWHoKeTCZxfX3diB1LSfQY5lvKOGTWvlTWi+PWvLteoyzQyQ1ARBQbBf4dDXZC9OPxOCJiZ0rxrCEx+1j0HWBLl4l+PB43AbNSH7bX6zVDcLMoNhexYNYYDN4ZDoexWCwa8Y7H4/QBjvg+c/V5+q6SxWZKI/hK/Xm9PhrvyMYIlH4DZMHNiGim3IqI5n9QCkSafSz6I8GNzSPlJpNJROTPUlfrg0i89oO5vwrrr4FD/O7Nzc1Orbn23SF+feU+ulpr7ZOXqvv4WFXwGvPANdIGgBsGjbyzWLlB4us2Go0azwkpO1fddcei74gKWEWP/mXmyqqItPSWBZfdvDxpJPeVM2upDUQpiKdWPgvEHbLCSiZ4HlmYjQ4sHaN6IrheEdGMUBwOhzGdTpuah9r78F2x6I+EXViIHqJk68sNhIoRKTcU9GRBwLa8t3oP3E/OxKp9+Yj9seel6HtWg6BoY6RWnmMKuD4sdhV+1pVAjUKv12vc+clkEvP5vBmqe2ytQK1Y9EeQCRl95yxaXerH8oMhcCPzTR2RBwPbGgH9WxsCjpBnFXSHUm7abdHfUzdduxyZe8+ekQYb1avgeMdkMol+vx/T6bSZgsuWvjsW/Suiwo+IPWuVufbqlsPqo78fsZs7Z0uH9xoEPNQY6KuKPRNMKc3W9r1G7TmWwILOuiDcALDw9ZgR5Dw5OWnqFuzeH4dFfyRZBB+Re7ZGWZ8WEXae2FHngcN7TuNlAbRSvXvXKLYKPztPvGaCZ8+BxY9hw+jGsNA1KJiVMnOWQcXPXSgdhWjBd8ei74i67MiXo/oLNx/P8cYTZ2pFHc+ky89X55lil8tlk6qCWw6vAH1e9GXxntOBEWUxZA1G1+vA63OmQfeL7xC85O/RveGuBRqMiLvqPRU+xww4hsLxCtOORX+A7IabTCZN8Ij75gDi5EkhYdkheBZ92/tsmmgeocc1/BoMzCg1CPx3m1XP3H214izELAPQlm3Qvr++H41GzaQZZ2dnTWVilvIzORZ9R3CzohgEA2sw1x2PaY+4s/ilqaDZE+Al8wjaGgBYy1IKUK141h3IGohSvQFfC+7Xc/Udu+hqiXk7Fb1G+7URQNB0Op3G5eVl3L9/P87Pz2MymexMjmnht2PRd4AtFNz6Xq/XFInw7DER+zXjpZlhtUF4HfHrI6C0r9v22kX0EFRWg5Dl4LVvrvvLrL3m9LMcP0R/7969uLi4iIuLizg9PW1Eb8EfxqLvSL/fb6rBIqK5ATmY1BYd5zLbbPKM0gMi9OEQOme8zrNXKrjh4yoFv7jhwvloLUDJpVfRs2tfSvu1bZ+591gwuhATYmI2XIu+Gxb9Afgm0gIYRO7bgmV4LS06ikynztJpo/kpMNlMO10j+8ek+9py9lqYoxH6UmFPVsCkDUG2X86MoDiK033Zb5ldegcit86DRFm8ajG1oKS0n2yfun8d0FKaMSdbr2sgr3ScfC6lRq9tYYubiVD3fWh/2f41SKi/aSIiIr0QFn1HVLCHIt9d0mBtAswaA/677bvS/g/9PpMJR11//TsTXZsADzUGbftuCzKahvRi2L3vSOkGPYZj8uGvI9ivEp+nCNv+BxZ7d2zpjfnHJW0JXcZkTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGVY9MZUhkVvTGUMD3zf+0KOwhjzhWFLb0xlWPTGVIZFb0xlWPTGVIZFb0xlWPTGVMb/AwDGTIq6kbZzAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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jovKaOp1O9WRbfI99mJxa0b/qhdNWF39/lK3xoW1l5a8m28Q6esOzO47E3na73XPls1q7iiwTrybkWOicBOOYnI+Z96fZ+joLnx0ju/TZMln4obmI0nXnRlCHJPN56f8F1r/X61WNEvoroCHg2XTMPrWi7/ft/d/e3u51GWULpxltvvHYBVV3WxNrau0j8ti8LoMPMtGXXOnMkwFqjTPr3KTBjYgdS8wlRhwjhiMjVMq2Vbo2q9Wq+m29Xu+sz16beYFVfYDb29udp9fgs/YHRwYeNyLce9zIuDmzLqiZeNnFV7eeLSvQkp9a7AwVko7I4z79KlrAXg6WR9VCPSW+JnydtNFhT4lLmkikYtu4DsvlMobD4c5gHiQs+Vxt/V9QK/rf/va3n9RxfOqwNYCIkZHvdDo7gzz4wZQQLfetzzrlQPQq/MxiZ1Y7S64xmsiqS5CxyDMx9/v9ymKiMdOGDMegv/NEoHxefDw4B4RGmTvPDRJfV32aDcIWrvM/ffo0/v73v1vkBWpF/8Mf/vCTOo7XAtzQV1dX0ev14utf/3p85zvfiS9/+cuxWq3i6uqqOPKLt8EDbPAbLDVbolJCTrvuwqKXLLda7Ih84AyWhbixDNbVxov3h1loIKxSDoDzALzfLAzKwgttkND41nVbxn7G43H0er3429/+Fo8fP27dRJhNqRX9T37yk0/qOF5Lfv/738d2u41vfvObsd1uY7FYVC5mXcIqGyXHLroKnm92HTgDIPQ6cfA+sU4WOmB5iBzbQ3JMLXWWu9DluGFSsfNx8zZZ/AqLmuci4BGOGO8Aj+z8/Dwmk0l88YtfjM1mE3/4wx9ivV7HbDarztc4pq/lgw8+iF//+tfx9OnTqmstprZSF15v9lLdXLPdTJM++Lo8LHLWWGhMy54H/s5EjePl48e7uul87tww1V2HrLHQddAIsVXHO08qOpvNYjabxenpaZyfn8fp6Wl84xvfiHfeeSeurq6qxKBj+js6B7KvrbpKWt6bTCZxfn4e9+7di7Ozs+rmwkSWg8Fgr7yGz1mX2kxUOtaek1V1STadmENH6PF5ZLG9LpMJto6S5QalGXy0scisPa6Bnie+x1Rjs9kszs7O4v79+3H//v24d+9enJ6exhtvvBFvvvmms/YR6QWoFf1gMGid6NVN7/V61Q12cnISJycnMZ1OqyfIojykMa6SWT+9sXkobjbNdWYBs2fVZeei56mfs0QaX4fM88jEDvj4S5WFLIzQRo49HxxLv9+PyWRSWfc33ngjHjx4EPfu3YuTk5M4Pz+P0WiUnnvLSEVf696j62ibGQwGMZ/PqyzxfD6vnimHbrecwa6zqplwu927p9bqvHr6roLXRxGj3s2CL3kKaoW1IdD9a94A8Lnq9jVc4XW0kdS8iB4TNwoQPVx7NMaz2Swmk8ne8ZhdHNMfAHXq+Xy+11EGySStPWeJLLXuPGvOer3em+5aLaUm2oBaRO38ojd/ljdQIfMxaPigAmayEht3R+bOSniv6ziEd762OD59dDfPZdDpdGywws+nf2mQgOv1erFcLiPiLi7F/HgsxtLNy4LQabKwD96edorBPrSvPvdBhxizzi0sxsybyJbhB3RkU39h+yATfDYOAdflUKWAv+eeiPCOsicA8T5t6XMs+gbwTYjaL/cyQ1xfsvKA3XPtfotWGR1WVIQ4BrbU3AOO3V8cq3ZnVaFrApC3y2LXR1+XwgNeH254XZIxK//pO5c62ZPhZ/qV5iI0ORZ9A3ADQlCwrhF3FhjLaXKL3WvuraYegN6oEBD2xwLmjjOaQef9ZXE2C740U2+n09nJMzQRV+YlcA+6rCddSej8YuvO1yvzWiz2Zlj0DeGbFIk7zXxrt1NeF+8QL5erOETodrtVOLHd3j21hmN1toLclRWNRN2NnyXmVDws8Lq5/DMrn4UGuj+9XtrTD5/5esBzwbqlioI5jEX/EqhFL7n0mnBjccJ6a507S2Tx+rrdumNUsgy9vrJSWxanq2Vlj6EuAaheCN65MdWOQaUkogX/cuw/qMy8FJ+XG/BlGxTz2cGW/iXIst0RsWehtCHQmLpUmsteWJ/fDx2jol5I9oInAosLbyTrNszHA4tcdxx6TCX3vjSQp86rMs2x6BtSSiBxH3zOspcSebp+Fvf2+/2dRiAiqmU0e581GCU0L8EhB0AMzees6zdJ5HEM3jSRlyXzdHJNJFFLHXrMYSz6BqiouEccl+xgrYDG9BGx16MuK4upmLPOLnW1dt1nROxYcCQGcYxorLAMW2z1APSpPXXJvEMlu0zw2TBinTmIPZFS5x5TxqJvAN/Q2imEM++lzjma7GKBY1go9+7LhMKZcxZTaXCOCleTZUgmquXHPrkfApKO8AJKjUupbJd5IaUafalzDg9FxrZ1+rJS6dLsYtEfgK0bP84KXT5fphuu9rnXjialWF/LaCVh1YkMLr02BLwsBMVCL2XQmTrvBNcy4nA33Cz3wNcW10mfj4fuzDivtuNuuC8JRAWxY8QdHl+Fvt6wRmzh1dXU0lg24IbFrlYc30Hw3PVUE4S8TwD3GY0U/87LwZpz+JCFEFkJsZT7KFl5tvS8LT0uvp64mQeDQVxfX8d0Oo3FYrHzBGEe9mx28Wy4BMQLYKl0aO1sNqseYQWR8U0ckc8cq6iL/nEOrS01Ripg/R7XIUsSatKSqWso1HKrhdfzhYeAvweDwU6OAQ0wlhuNRhZ8DR5aS6hQJpNJPHjwIM7OzuL09DSdRINdVi1pReyKTTviROzPqQdRl5JlvE6W4Mtuds0xZMenlvYQmn3PqgA45szbyKw94Gug3gKsOLL6vF1MNhoR8eabb6YlROOZc2oZjUbxzjvvxFtvvRWj0aiy+vjMWXvtT8/wDV43hXVdJj4jEzvf6Ox1aGKMj61Ur+dlsmU1094ke67H0mS6LPaCIHqEW9PptPLAMF3WZrOJZ8+e7UyX1UZ+8IMfHD9zTttF/5WvfCW+//3vVxNjzufzSmQqkJJrGrH/sAt+ZW5txO5EF1nMC7KGgq23lr6y4y59zhJsWdY927Yea2nbpYkxVej8QiKTKymDwaCaGPO9996Ln//85/Hw4cPYbDYxmUyqEYlt4tGjR8fPnPO9733v4zma1xTUf6+vr6PX68XXvva1+Pa3vx1f+tKXYr1ex/Pnz3eeDadTWGv2XF1/1Jo52/wyU2BncXnmRuvnkmgzy5vVwev+zn4reRTaSGRJvKxqweVKDoFQiUAi769//Wv84he/qMLTp0+fvsJd8fmj1tK/++67rbL0WlKCBYmI6mEXeODFYrGoxItrqPE1gEXTh12UnnBTynTXPexCE3gsLn4WHn+XiZC9ksySZ41E3e9ZaKA1dW0g+Dqw+CF4Pt/tdvdhF+v1Oi4uLuLRo0etL9ttt9vjLf3bb7/98RzNZ4jlchnPnj2L5XIZ4/G4ehAGJ9FQ61Y3lN17Fj3q8h/3Y61gAXm76n6XBF8nfj4nXhd/8/55Xf6+lL3nBCeOW60+4MdacWO6Wq2qGYn03I3r9AdB8g4iZqvJPcTQACDeVPeel8ONrKLPBMqhAd/82F6WPFS3Gr3qlFISD79xkq3k+pe8gZKgMw+D3Xvev/Y30NIfN6b6fEGdVqwJus9jyNZ9le19nLhkd4DtdltNVKG95jhxBkHqRBMRd/PsYTt8MyKPgL/5HTc2esR1Op2qJIWBJyXRZ+U9FU72N8ONhR4rqLupS4IvhQx17n52jrg27AVpQ4Jlm/Iq3kC27uvoXdSK/nVspT4NMuHwTRdRf60gGFhdvOARaDyvWf/MNYe114QeluFtcEysFh1wTz3+Hetn5wRvBS43MuR8vjhuJRO6fldaVxsODkdK1QBzR63o21rfZNj1zmJ0TkplHWS4geCbkUXB3/G6HAurMODulyy9ZsT1HY0Ghx5aheBhrNwosMAyb4AH5cBLARymqNeQhRqZ1effOOegzx0wOY7pjyCLIxeLxY7I2NpqRxlNfGF5blC0oYXoEDawCFg0meg1BgYQqsbXsNqwnv1+f6dawCLU+B3L6jZ4XzgWHsCjx6nVh0zE2gBqlcDUY9EfAW721Wq1U8LjuH+73cZgMKiGo2YltKxTTkSktWeICMJnz0MbDj5Oduv5BUGyhwIB4Rw0NuYknrrjfE5aZci+Yw8gYjdvxOFBFi6xsLnh1N9NPRZ9QyAGPGN+sVjEzc1N9eQbDALBTc4JP6yfWSYIPBsXH3HXYUhzAbqNUmabB/Wo5VbRs4usXknJ7eaYGtlzhD38N0qUvV4vVqvVzpBdrk5wWMGhBecpXtes+GcFi74h7AIvl8uqk87NzU1lJSEU1OJ5LDm2oYk3LcHxb7wMhMsiBToKDvvhBgif1cJnItYse5bcy5JpLHK882dtAJbL5U5YAeGzN6KJU4RN8HxcYToei74BGvfC0s/n87i+vq5Ej5saz7LXqaUYFnLEXexeqrdD/JmVz/reqyjrSmQafpSW4WvB29euyRA7v9Bphj9jtBzX1/ldjz1idxxDxF0ORD0BU8aib0iWxENMDzcaN+9yuUz7iEfciRS/ZS56ViLjHmr6mb/TrsTqyteJuGTdWUTZspml1x5y+LxYLHZ+1/U2m031rg2Bhi38iDFcg6wTktnFoj+CUvYeMSq+yx49rS9+eGWWnWZ3neN67Vijw2vVU8hErvvipJi6+9k14GuRiR5iRsKTrxXEjxeXPln02vNRuwZjW4vFYue4uYxpciz6I9AMPFswZKRXq9XO1FfZhBcYE47kn96kSOqpxWdPgLfHoi9NHFGX/CpZ/ZJ4spCgZO35GqnwOekHwXPuAeGCCh+h1c3NzV4Hp0PHbiz6oykJH2U7ntiSXftsPPhoNEqTadgOC5UtOYtdp9xSbwCoh5CdlwqH0Xg5y/qXxJ8NiOEkn3ay4RFzOvcAPInr6+u4vLyMi4uLqqHT8MTkWPQviYqfe6hldXcdkINavsbQ2DbKeNyHX/MBpTnysldEFBsEPqemYuFj1ro+W+Ysm89zCWgnoSxfwNcJrv3V1VVcXFzEdDqN4XC4dz4Yo2D2seiPIIuZ+ebFMkwplh8Oh8XebtxZhicnVbeek4WlabAPNQSMJuzqauHZ8WZC1QZAR8LV9abT7aOBWS6XlehPT09jMpnEcDiMDz74IC4uLuL58+c7GX7X9Hex6I+EBcM3pZa6dB222sPhsFiaKmXbB4PBXqY/i+vZzT8kfCWz8nUeQZbxzzr5aNzP56tlSd1ntq/1eh03Nzfx/PnzOD8/j7Ozszg5OYnJZBKPHz/eifFRzzd3WPQNyUTDsWSpQwuvD1GqxcvWZxHx/rO++ZrcU8Fn4se6OP5D585kQtR3VCbYQmeNWt015v0Brp7c3NzEvXv34vT0NM7OzuLBgwfxhS98If70pz/F7373u8rSc0cmY9EfhQqs1PVV3XZdl2fN0eU5zsX3gAevINHHjYsm7kriVxHXub9NLD2+g4XlEiPnJ7hRwzq8j1KjxMtyA4KZcGezWZyfn8dbb70Vl5eXcXJyEg8fPjz4/2wrFn0D1LrrC2TurFp89DlXsWcZ8FLjoWJmIWXWu4nQS9l6/ayxfin2Zy8IDUGpodBrfCj8QOMxGo1iMpnEZDKJ+/fvx3A4rBqbhw8fxk9/+tOIiDg5OXHHHcKiPwLNnnMMzWj3UXbTdZRbZt3591LjkQEPgo+X0Qaqzr0viVmFpy5+tj0WdilvcEjwum8Oc8bjcURE9ZSbb33rWzEcDuO73/1ubDabGI/HxX23EYv+CFT0/KhqDHtlWPScWOp07max0YahLtOdNQZoOFAC5O0iicWhwKGYWc+X33n5LJY/Jvuv2z8mycjhQJbM/OpXvxpvv/323sMsncV/gUXfEHXvud6O0hlibRU/CwPb4TH1cDvVvddBK9lItvV6vfPAB34Cbl1JT48vO18+b162lLmvy8Rn4UWThiHbp8b9+hTf2Wy2s91SL8W2YtEfAVt6FhVGi7GIIX7E3BG7WWRObKEclwkeXVf5NZlMqs94eCOEz8+65wYpewRWxJ3gtaHSMh+TVRk0oZhZbu5kdOjFx6beBFciuAckztVlunos+iPgshk/U20+n1fC5RuUe+hx0i0idpJKGJWnLr6OTuOHbeCx2Sx6FX5m9essN46tVN/n5TPRZ9cp25Z+xj65UciODWEQPK2I3enFsI3tdvex4W3Fz6d/Rfgm5QEzw+EwxuPxXjyrQsAw0IjYEz67+xB8t9utRIuRexD+aDTaEzuLXoWPUX86kEetdFZeVO+AvZksnuflS4OOsioINwZ8HbW6ASs+HA4jInYeaoFj1+2YXSz6Bmh8CEsDYWHgzKHs82q1qn7XTDfH/Dy5JOcPuAsvC1rjeUzigXd8zwLLLDaTiRVk3WNxnTixqXkFrnZoyJE9S4D3gcYQx4aMPRKYdSVHs4tFfwS4ISGkwWAQ4/G4stKcRS7Fqlmf84h8TnuePkrFVHqve/Fx1VlrtsRZAlC9A46z2RNi0fNnPpdSolHdei5dDgaDajgzHlnddlf+GCz6I1C3FC71aDSqGoPlclnsCaeTQmbiididzTbibq54jZXZarJAMzGx6Nm7yOJy3U+W9c8sq1p49jKwf22oEMZkiUa29KhURESMRqNYr19M0T2ZTGI2m6XW3uRY9A3hEhFbMgyeUbFpJ55ut1s1CBA+buas7p253HwsWaINx6iuOWe52YpyubCUfa8r9QENAyBifS8JPqsw8DGxi4/GdrvdVklUTMCheRSTY9EfCQuOY2yNU1Uk6i7DcrHLz4KM2C9VNTk2PkY+hlLWnj/XldxKJTxtaLJwA+LWhjFbjq8fHyuuQ7fbjfF4HN1uN6bTaTUTjy19cyz6I2ABaSediNizViXrD2vPI+7wXcR+Vp1v6CwUwDqM1sSVrGHRcy15EqXf+ZqoRefwQr/XZTPhA1yzwWAQ8/m8Enx2DibHoj8SLdth2iseSqqxLCf+FotFDIfDvXnieO539gQi9ofuZh6AvusxK9qI4LMKuyR4/K0xP+bYZ3FrfkDzD1kjoHkR7BfXU5N7pjkWfUO0HIUEHmZy4RicZ4XlHnX6meeO04kkddJIHqgDcXI8zgk6Rb+vayjqtqE5AVQZ2ANCg4SecbhumjuAq87HwB1tuO7PjRE3GmhMs05HpoxFfwB1X2HZx+NxlVjCbDARd1Y561WHF5JP2bzw+tJ+99gu7ydLBDKZkEsNAZ+3XoOmiUPuE5BVALQ+r1n8kuVHYzubzeL09DRms1k1VRYG11j4h7HoG8JZ+/F4XJXUeK47wIIsDZ7RJ76UvAJtANgDQEOgPdZwDCULXsoHqEXFebPo+TuOvUt1d+0fwL0CS4k9Fr928BkOhzGdTuP8/DwePHgQp6enMR6Pq/3g+EwZi74BfMNC9IgvJ5PJ3uQMPMAmmxxDXy8r/tJEk+xK87t+d0j0dVl8tt46mi9LXmaeQl0mP+vUg0rJZDKJs7OzuHfvXpydncVkMqmqAxb8YSz6hiC2xCAGWB22rtypRF9ZH3IdJ595BFnMXxK91v3rsvx17r269tpFVi21uulauiytr3mSkqeg34/H45hOp9WLLb1FfxiL/gBaa8d3sPp1NfSszKYNgTYIpcZA3fnMutcNd61z7/Vzqd5fEm2WnS95CnwttQHhz6XtcgIPA4s47ndcf5jOgXKHayFRFm9mNdnaZ9vQ7fF28VmnoSo1DtlvemylY8j+Vlj8oCTmupduQ787dntZkpCtvEVfkV4Ii74hKtpDVrJJ7bhpKU0bhezvQ8fVZP+KiicTLn+uE/gx26/bNj43aVxMLnq79w2pE0BTjulE8qqCfZ34qITYtBEy9djSG/P5JW0JPWOgMS3DojemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zLsOiNaRkWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMix6Y1qGRW9My7DojWkZFr0xLcOiN6ZlWPTGtAyL3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3DojemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zLsOiNaRkWvTEtw6I3pmVY9Ma0DIvemJZh0RvTMix6Y1qGRW9My7DojWkZFr0xLcOiN6ZlWPTGtAyL3piWYdEb0zIsemNahkVvTMuw6I1pGRa9MS3DojemZVj0xrQMi96YlmHRG9My+gd+73wiR2GM+cSwpTemZVj0xrQMi96YlmHRG9MyLHpjWoZFb0zL+G/Y3nEsm28C5QAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -867,6 +879,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "current beta: 16\n", "Current iteration: 25\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -875,7 +888,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -911,7 +924,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "current beta: 32\n", "Current iteration: 27\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -920,7 +932,7 @@ }, { "data": { - "image/png": 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\n", 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Ym1+tLVttRMbzhgVeLl0f0emwV8CxiaygYYj95ROiLwimxWIZK4i+VqslkXhetQYgzcaWHRc6xKDLTLOoIFa9ay23C8dGR6FFMXpjTF3OyxOvpss0xuB1HBxP4MAg1xl49fch9C+HEP0lgLVvt9tmZkm+3JvfDrgOHzl2s/TYFVbeEz270Cx6Hiawh4AAG0TPgTQWPY/ds6w2exyetc57T0VvtiruiMZ/ueSKnks9y4SXEtPXkbrDBZ+VL+fP8fHVWiIoh+PjfbjaELCOzflzfHyM/7MCa14VHI/TuVDGE7aX4vPcdg3QZdULKFm/Xd58gGAzckX/oj+uN5UTF+lV/ePWHSvr4snb10OtFE+mYeHpxc6RexYPu+wc0NI8e5Z4OciG9qil5+EAj+U1wMZt8h513T/2UvLSeir4rP8bMg7eVN+sEuIY+1+eXNHHiqLPyZp7zhe4ToLx7gjDhSy6cq5Z2tXltJtaeh3TK/P5PLWv3mEna5oru+RsxflvtJM3/S2yxMidGv+mm9ylxvNo8irz+JieR1RWQtUboMLW2V/sZvONKlj0mkLTaDqn4ACsqpcS44i4FvTw93EgznPR11nkInii4owC2qbTftcJ0psxiGNhWTKvA+T1DYLn5Ir+448//rLa8crhi2k2m1mlUrHt7W27ceOGtdvtZHwNq4sqMwiY02+6SCWLECk0XiffE72O+7mD0YkmHHvg72MXPcsicrqPxYfjbW1trXRGiGHw78ZtUdcbXo++rmk6r2280Kd3RxzuwNAW/MYxn96nktcLfvDBB6XqInGBDwYDq1ar9v7779sHH3xg3/72t20+n9twOEzG2Dw3HG66FstwNR5SaLp0tt4YQ+H0F1tqT/AcdOTPcPTfC+BxII4j+fq9XmluVo28nos3Bs8al7Nl53ULeONJTOicUP78+PFj++STT17sYngDWC6XrvuUa+l/9atfvZzWvCbcv3/f2u22HR0dmZnZeDxOLkZ11SFgoOvYcVCOy2LZ9QZaQ68uOLvIPOFGrb1mBwBb9lqtlnw/ByfhNqMjZO9Co/U6js8zJN5QQoN2EDE6T71tNm73hYlNrVbLer2eXbt2zXq9nj1+/Nj6/b799a9/tfl8ntRTRGrwGbmWvlKplMrSK/V63b773e/aO++8kxTdYM15FgPEjzSXWTovb7Y6cUbH1Zrmywoc4j2t2/cm62gcQsf1mnLjDAPa7BXZZH0O36nnrueSl7Pn80JaFI+YnIR5Dru7u3bjxo3U1uv1bDKZ2BdffGFHR0dJx1bGVN+HH37oWvoQvYOOU2FVsFQ1JtUgUMRpMAT19FgKT5BhEWfdc45f0/n5EIXWCvDYWSPxeVbac9NZrFmTZLid3licj89DA7b08Fp0mTH+u9vt2u7urt28edPefvtte+edd+zg4MD29vaSMujAzMyKi75er5dS9HB9NRCE20XzKjdwG/PudINj4pFFwSvfsEvLn/X251tZ88o8GFaoqw9UyHnBPW438NJz/LuhjRhuoE18LE0LQvw8RVjH79whdDod29vbs1u3btnXvvY1u3Xrll2/ft263W6xf/SbT/ExfV4euIxgfbbJZLIyocZzcxl233Xs7N3LXd18z+2t1+sr1hppLLO0e+15D1nt07vdanzCg9uOtvE4XCcfeUU9aAeLX2MUlUrF2u227e7u2vXr1213d9e63W4y3Nok3192Ik+/Br1Qp9OpVSrPJtnoBBq1fF6ACs9ZVLVaLTme5+J7Y3itxMsStlp+FnDW8blTYfc6r21oO/bH+FtXDmIBa1wg63fn7IfZs+EWhllYoQgdndYflJm4P/0l8S6c5fJZpRwuNB0/Z8GWFxdyrVZLPYc141w0nvNYHK/jPUTfvSAaR8FZuPrIQwZexkuX8tKOg70WiJ47DL4FF34/jgl4QlX3HzUOi8UiCajqugVh5TcjRP8CQGhgnWXhtBjEjcg4avhZVFyPruN//M15dbi4OBbwYgGaFvMW7fRW9+XcOHcaKvo8D0EDjJqO1EwBSorr9XpynllLkQXrCdFfErZIGu1fB8QPcbKV5gIfDeRhPwwFuGgGXoK6tVnpPR6v63p9EDtW60VeXK22J3ruPLxbaOtvmCV4/C7o0DgQ6nktIfzNCdFfAUXGjtpJ4G9c7Fr2yhFveAjILnib4o3nPeHDMrNrjw1utLrSmm7UjsXbV3+zPOHDk8F5I3C5aXAx8AnRvyK8i57r3/GYFZH2cuRmfkxBhwgaIc9LlanV5s7CzFaGCF7kP8sas8hxzhylxz6aztwkGxFkE5GP4Moo6vEEr4aw9K8IL+Wlr5tZppVkt53J8grwyO4yf1fWpBtsGn3H37DSsO7eOWJ/L72ZtWn1IE/24eFNUJwQ/RVQJJDHF77muzVYpSk4FnrWpmhgDAFAM0vFELwCGT4mj7PV1dbgGgcW1wXydCyfFcjjdftwDiH8yxGivyRqkfF83UXoCZfHvSg59Sy/V72nKbQsi8oZAvzNgbKs6bMQ3XQ6XYnea2xA79CrEXz+fk/k+rem7HDDDBQzcclziH9zQvQvABfnmPnzyBktmOFIOkTD4mABa7CNJ9hofTq3Ce3imgK49lw3wG3hZbe9++ypJ6Kiz6rmU9FrDf4mxTnz+TxpjzdtOFhPiH4NnuuuUWmAsSv/zcdgUWoZbl60OyvXrgUzurAE2oCOCCvb6nBAjz8ej5P0HE9tzZsXoKk/3rRiDu3R+nv9zfl3REdk9qwMd2try7rdbrIICd8WLC+FWSaiDPeSqNh5KiuLyxubAhUXRKIz0bzOxHPv8f0sTC+lxoE3iD3LE9HhBuf0+Vherp2tvVeKm9URqaXX34c9Co5J4H4DrVYruauQ3uI7yCZWw3XAhcMr4ZhZMq1zk6m13jH1olZRaVmpWtI8a79uai13REWm1vLv4X1WOzceclSr1WQYgvfYyrP4gXo/WsZbq9Ws1Wol5bhcPRiC34yYWruGSqWSzKPnRTRarVYyWYbXoIPw8Vmz7OWdNV2XVciincCmi2iY+ZNbshbR4PE0HnURDc9Cq/DVa9DzgdXO8oi0KIg7NEy2wdr/fG4XFxdJ2XCQTTlN+YbU63X73ve+Z9/4xjes2Wwmlp4Fz3efgbUHKj6OhvOFD9al3nS/LGvoCZ6XyNLnZqv3qefP6fJYaL8XjOM2Zg0JdLksHiawNwNPgWMLsOwnJyd2cnJiR0dH9ujRI9vf308tl/X555+Xfrmsn/3sZ+7rsVxWDu+++6799Kc/te9///tmtrowJt97HUtZwzKqxeX18nlxTBWVegXeTDSgY2l16SFQXavfS895qTrtoDZZGDPveuLhBe/ndWh8TmrxMTcAC2O2223rdrt27do16/f79vnnn9tHH31kd+7cscVikSxtxh1yGXj48GHxlXN+9KMfvZzWfEXBmHMwGFitVrMf/OAH9sMf/tC+9a1v2Xw+t9FolFoFl28KyVZeRahpJ749Fd+4wrsoWXB5S2CrlYfgcQzP2nrCZ7Hz31k5/CzR8zg9S+CbxBSyavoxnOFOD17YkydPUktgHx8fF7sQ3nByLf0nn3xSGkvPFhY3u+j3+3b9+nXrdDrJyi18w4rRaOSugquTUjzRY918vtmFioDd6U1udqHfp23mpbfV4vMGj0UfVeycR8+KCeQ9rsuxc0CSz0/Tfxz9N7OkQy07y8use/+d73zn5bTmNQMXFFfDAUTQkaP3ylQhFF0rX+9wo5YeMQPEDS5zWyuIk1Nm+h1eUI4FnSV43vhYXgfA+/B362uKrgjkeSzs1TDIU5dtLL+OCORtAI+TOQ8NF5gLbnThCLPnF+dsNksFpur1ujWbTbeyDH8XuYElWz8O0KEDQIekk25gSa9CHJ6br8fN8wry8Ip2NBjK6BLb3jH0eHnv5+F99kWO9zKJlF0O7F5qlJn30aizV3bqWTpE4Nk91mg/171rFR+7+NwuCJ2PxQE9b5INPqcTe7TzQp2+F3DU38YTowreiwFkfdY7dl5n4dVLrOtcXqTjyzrfrxq5ov8q9lJfBl4PDfF6KTiOumsBDn8e7v9isUhcT1hcPrZG33lIwR1M1g0aIXI+DrvpmiLj4QE+x8euVJ7X6cPbwTHRfg3sQXB8ftpG4HWI3n5fRQG9juSKXudHlx1vzIwgnpbR5qWvWGzI9+u4Vcfi6jbzZ/mYZs8j9/j/6WfRafCNLDmtp2vwZ0X6MX0W7/G+iGWom7/OE9jkveDFiDH9JYDgcddaCMAse+EItbZAg4I6JOCUH1tTCJePwzUB6Ii0Qg6dBWICEDuXEk+nU2s0Gqk57OhIuBgHwwt0HjgGhix4H9/LHQKLmstzg5dPiL4guHCRpx8MBjabzazRaKQi3t6EHLWYwJuuis+YrZbAmlmq2IRddG4jT3hB8LDZbCbR/zzR86o5evNLHjKgY8Dn+FGPj3NjrwDnh/MIC//yCdEXABc7rDxKQWezWRKFn8/nKzdh4M9qThtusAbQAMbPEC0+x3e54Q4D34V555iQg/JV5OlZjCxgXaUmL0eP4yC7wPUAk8kkVV/Ax8I5o63wBjgWwL95cLWE6AuAi3IymdhwOLTj42M7OjpKFpvAxd7tdpN14iFIzWFzQE5nM/LYngOAPP7nMTJ7Cmar6T4Wu84G5Dy3J37dT3PjyC5wpSGqFDnFOB6PrV6vp9oAq8/BPz2HsP5XT4i+IHBfR6ORHR8f2+HhYTIG5mo7tfaa5uOacsBegY7DeXWcra3nd7BhwXN6UYWpZbXYR4cOXtmtdljwJDiYiSpFxDnOzs5sNBqlHlGByB0Qhik81vci+0p0BJcnRF8ACASiPzk5sadPn9pkMrFarZYIvt1uJ1M89QaO2FCcg3upI//NqT4WP8+356AhzzPXqjwvXaf5ed5POwBvX/4d0JFA9JPJJBH8cDi04XBop6enNhqNkkcVP4//NWBpli7u0Zx+CP9yhOgLwKk6BPFOTk5sPB5btVpNgnt8HziIXmeK4X2vqEXr9rVICPvrDDv1KDwLjdf1vHjTVFvebwHBQsBs7QeDQSJ8bHgN+3hzCniIoQFEtMubnBRsRoi+IBgrY1yPC9jMEhcXUz95SSstvYU34JWRogCGBZxV6ONNq82qscdns84Lj1mdA3c67BWwSHl8D9d+MBgkHSTED6uPcb9mAbj8mDsA9i7YQwirvzkh+oJoYQ5m2y2Xy0T0mISjy0FzFL3T6Viv18scN6MwR8Ws00l16KATUzSW4D3HeXnPvf094a8TPzpIWHoWvaYH8fvq2gOcOcHwajgc2tnZWYi+ACH6S8CRcYxlYX2w0IauaoOOgBd/gBXTEtnFYpHM3GNrzhbeWwRTRZ/VAeSVV68TPO/jdVQ4FxY/zyzEmB+/Gyy2nj/XAPBiI9Pp1IbDoT158sQePnxon332mT158sSGw2FqqBSdQDYh+kvCY1q4o1r9ZmYpcfJYHss3c8UbW0wIn5eQ1nG8N43Xa8M6q++d26b7qPBRP1Cv122xWCSLWLLlxiN3eN7xWfwcPxgOh3Z4eGgPHjywe/fu2f379+3hw4d2eHho4/E41f7oAFYJ0ReEBcUi5QkqWnzDEXYUzPBceg1e6So5OA7Kbj1X3xM974vnei6MJ/ZNYgB8zsvlMlWXgA6g0Wi46UAWvBeTYOuvdRLf/OY37d1337U7d+7Yn//8Z/vLX/5in376afK/qNfrqUKg4Bkh+gKw2DS1phFmvdCwP4J5PFblCLaKnoUIy+61i9u3iaXXv5ElyHo/7zfhIhrtBNABoLbASwV6befvV0uNDnI8HtvXv/51u3Xrlu3v79v+/r7dvXvX7t+/b48ePVpZwjx4Roi+IJomgyVhy8ULSJql026YG68LVeoClhyx1nw9xM8CyxLopuP5TQSu+3OHlNVpcBGRlzr02reurThGp9OxbrdrnU7Hrl+/bu+9954dHh7a73//e/vlL39pT548KXROZSFEXwBckLxCjrcWnuaWGUxpZcuuQSv1AtjNV5eeZ+Llif9l/R6bCl/Tfd6x+FGfq1eAYyJAur+/b51Ox5bLpR0cHNjdu3ft17/+tZmZ9Xq95HcPQvSF4Yo6rIWP19i911JXLnbRCjmNVvPSWBzw0lhBFjz0wN8qvE3YNMq/Lt2Xd7y8+AG791nvVSqVlRmN7733nn344Yf2/vvv22KxsE6nE9N3iRB9QXCRITjFy1ihjNZstXgFr8E66Yw1rWzjCSy6Lh68iHa7nQTKdDkttJWfF/UE1nUQnuXepFPZtHZg3et4Tzuz/f19+8lPfmI//vGP136+jIToC8Bjar6HWrPZtPF47Ipf68SR42eXXEXPy2yjnh8163htMplYr9dL3VdPbxiJcbRXtFPknBUvcs+PmxwDr+vwwNt/0+PyWgN531t2QvQFQeqt2WwmN1dotVpJfhiBPATa4PabrV68vLClip9nrY1Go6SiDdvOzo5tb29br9dL7vLC95Jny591jzxG27ZpME2Dc/z5dXUC64J/2jYv2s/H1cU6A58QfUHgnkP0sLaTySTZh8fxnKIC/Jxnk+l0VVh51K+fnp4m03l3d3eTm2kigt3tdlPeBzoBlAN7i23gUeMFmp5k9Lz4/LzPepWCeX/r76SdCw9b0Lnq57wgatmI+9NfARy9bzQa1u12rdfrWb/fT1x2s/RFCpdT02/YD+/BFecSVozlz8/Pk7r14+Pj5Hvx3fgb4scG1x+Tf/i20fh+Fa9ZOkOQdRdcDkjyjDevTFirBvXRy9F7vyPaqdOLdXISXgt8QvQFwQWFUtp+v2/b29s2n88Ti6oXMbwACN8sbcWQ6+dAH9aRu7i4SOIFZ2dnSf1+q9VKZuphg9D1kcf8vLouexcsXi4b1nX82YryZCGz54LnIKfGGrw5Cd7EIhU7ZzAwvOLv9P5PgU+I/hLA0nc6Hev3+7azs2PL5TK1Uo4Gky4uLqxSqSQi8+AL3XORx+NxIppGo5E84nvhzjcajZSLj40tva6so9V/PENQb+fFHYZ+hmcS6lADbUQngHbz/IKs1X9Q17BcLq3ZbFqv1zOz5/ey/7JrFF5nQvQFgQAbjYa1223r9/t2fn5ulUolJUC2bPgMLDdyxllz6fmRMwE8vDg7O0sEwpbYu60zu/a8Fr7WwquAcVzvs161Id9GGp4Iix/v4Xfi5/jdOOXopTIrlYp1Oh27du1a4nGhfcFmhOgvAS5wuPjb29tWrVZTq+Vo+gypNr7JBMSftyyVB7wGLzrOHYwutMH7sleBDoDPzzsOfw+3jScUIcgJUfNzXp2XVxdiT6TRaKSsPQc30WFiOAWL32q1rvLf+8YTor8EHMyD8CuVSuriZavFc+Gr1apNJhOrVJ7fQgpuv0b4tZJvU7ggB49ZuW8v3eZ91jsG78PBOgwJ0PlxJ8jjfB1+6JqCaB97I7VazXZ3d61SeX4rcQ2QBvmE6C8BLnKMUdvttplZKkXGgsc2HA4T8cBqwX1FJ8DiVsFnDQX0b40ZrCt2WVf8opFx3YeDcN6qPvx7wBXncT17ALD0utAnXP1ms2mTycTa7bYdHBzYZDJZSYkG+YToC6JpO6xzh/niWGceY1ovoo6iG74xBKL1OjffLNsqb1L+6hW7rPsMv6+f90SPwCP+5uwD/14cg0DHoEuKcacJD4mHJrPZzFqtVlKWHIIvToi+ICz4TqeTuJy8hDMX1iC/juIaXSNOl4+aTCbJFFwOmClZFXSbtD+r3DXvM+vgDoIrEDlbwDMDq9VqaobhfD5POgv8vvAIlsv0smGcnfDuKxDkE6IvAFutZrOZXLyY+GK2un4eV9VxGe1gMEheY/GPRqPURBtEriEODvxxHGBd8A9savWzRJTl8mvwT8f5GhjkwJ833uesA96v1WrW6/Xs4ODADg4ObG9vz9rt9kpxTpBPiL4gbOnNLFkQgy2d5sB1cUieSMN3hIHosfH68DzRRu8T51XHedkAsC5WgPPU6bm8eQL2Nq3I0zUDeVlwjvjr34ifdLtdu3Hjhr311lt28+ZN63Q6UX1XkBB9QTgnDVeVa8J1/J21RDQvmoGqu/F4bKPRKDUswBrx8BC8DoDXiefvyhv/bxK9Z4GbPa9+Y0vO1pqnHHPhDe/ndRCcttN5A8jvY0NtxO7urvX7fWu32+HeF6Syxh2MCEkGeZbU25c/ox2CrpzDNfe6qdj5ZhC6tp6m/fjRyxIwEBGnGzUo51ltfvQsvWfx1Y3n0l0+Flf86Q1CAxe3JwzRXwFedLtIR4BH9Qy4Nj5v0yW0Nyn40QChF9zzJsR46TktBPIm1+DYXiFRVkwga2IO7xMWPpcQ/ZsMV9dleSF5+XlP9Hlizfo7+EoRog+CkuGKPgJ5X0EuW2zyKqztyy6MCQ/i6glL/xpRtKgmKD1h6V93QuzBVRC5jiAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGSH6ICgZIfogKBkh+iAoGbU171e+lFYEQfClEZY+CEpGiD4ISkaIPghKRog+CEpGiD4ISkaIPghKxv8DRF1z57Zl7qIAAAAASUVORK5CYII=\n", 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w2e32ATXekw+bUPhde9K26PLzdP7M4uaQ6HMA975ardr+/r7duXPHzD646ZjLY/krAnO+uSR+9Ii2Q3Rp1W+x4hQIzjen5GW1ZnZpro0bhBgTmw/MxaLqPv/Ou/twMxG/RRcXNcVWJErwnweJPieI3rfbbbt161ZwpzG/54g9UnpsQX3ALNYK2wvB5785Lcb15Tw92GwuduHBTjw8n4YgeVDBX57/x3bFxaDDUwdYcr4uPx3wsYE08YubRaLPARfkNBoN6/V6wYo3Go1EJxtU5bEIORrvg2k+cMYuus9TsxXmijaO3MMrQMMP3gqbq+G84Dn1BivOYuZr8OL383RfYx8T/OdAgcAkEn1OOILfarXCjxc5+9iOLvyj8+68j1bj5kXvS1FjUXxfd8/ijd0gcP8+sNzsuvP7sbvO1+UtO9hmvn6TwpTgk2SKXl/WB3wtOkfx8WOPNYH0ZKW+WOg+GMdW3QfDOJjnpw0QtZ+Ps7XmAKF/DQ8OHIjzr+WU27aCx3QE1+49IXFzyNLnxAsftfcx153TdLGoOFttTuHx+SBEHyDjuTO/lq/Dr2bzeXD+PLHP6efdsRQeBB5z2fncaaW9u7u7W7n6VxUMie3JFP22i/KLAgsxVkwCEbP4fKEMB764/BTn8J4Cz525Lx/m2rw7Da9F98E/vs5YX/tSqZTIKqB+AI9hIMLr0gpnYL3Zcqd9jzyVybLysQVBscpCvgYEUv1SXSFLvxVs1bj6DH8xL/apKbj8OAdHxHnDiDTR+2g6XoPBAtV1EI6PIbB1Z5FyKTBECs/Eey1cFmtmoUEFwGCB+2luetp0hwN9/JrY0l/vycRKdmu1mnW7Xbt9+3aolYA3Ia/gA5mif/r06ee6jh8EseAa18LD1fbVZ4ig+2W1bGk4ms7WGqL3LjqLnvPyfnEKrjNW0Yf3ZFFzPh+wq8zTFV/ei/fgaD28Ag7ixVx1XwDE7+uFz6+BlfbrA9CYBIuWEERtNpuhz2C325XoI2SK/g9/+MONuvhZLuDnPL+3LhDLzs5OSMWhsg4iRCoMbrbZRbUer6NHnt5XsMHaw1oDTrux6DmqznN0XDdbbnbx/XyZu/zE3F54JOVy2ebzechGlMvlRFSfV8L5NF1sKuFLffmzxeoTcKwfzDDtqNfr1ul07O7du/bw4cOwdTUvVUYT0aI2xkwjU/S/+93vPtd1/GCA2DA3x04zvDwWP0AWpNnFCjzeSw4iZtGze455KbvcLI5Yasy76wgq+uW1ZsnGGzh+b28vYZH9e0L4GNxqtVqidp9r5326zs/T/VQCsBfC22PFPheLHhWRvV7PHjx4YL/4xS/sm2++scePH9vh4WHie/fxEQn/A6WsL+LBgweF+5Z4Pmxml/LvbMnL5XLCXcVUAC4+ltOaffiRs1vuXXpuuRULyLFVBNwxh/eTjy3Nhbj8PDrmJfh4Qqzyjj8H1/H7c/rNOHBNaef0MQoInQeRRqNh9+/ft2+++ca+/fZbe/Tokd25c8darZaCdkmibm6mpX/58uXNXMqPFIi+0WgE95G3gPY7zvJzMYuIHzRvXMlr4s0sMQAxLHQMFhAYIu4snJjr7L2EtKIitsZe7LGpBncW4upELknmoKZfV88DiG8ksru7a81m0x4+fGhff/21PXr0yI6OjqxWq93cf/xPDEXvc8BLVKfTaUL0LDAI0buV7FLz/JRFwaW7wAsWIvU7xC6Xy8Q5fI89Poc/FwYpv700xxfgacQaXuB8vkeA30U3TfQ+sMkDCTwOFPLs7+8nGpU0Go3E8mXxgbR4lkR/DVarVWjo6OfDsSBZrHCF22RvI3o/x+UOu3j9arWycrkcKgTxnnyN/jrZW4BIvVBjGYWYB4LPw4JPW4cAISMjwTUIXLQE74IDpugtyIG6WC5fxFFxTk58ftgL1Be9mF0spkmbR6OzDmIA7CXwedmLQISd5/s+bbZer61cLptZMuXF/8Z9bt+FOAa38PJWnz8Prs9beXbvY228ObiJ+gdOS/r6hNlsZuv1OqRE8X3pd5oP1d5fA44y+3Qfovp8LP/lY1DAg7kqLDQXyOB43GD5KpVKYu6blef2YuVz8hzez8W98NPWFfC58Hr2FDgCz98L1/GnLQrirrrlcjl8dj+QSPjbI/f+I8APl62nrw1gocVy1xzFx/HeU+DUl09rIa2GY7iwBoUzfD/t/D7iDuGzq++nLbjP1+fjDH4hEn9O9lIwUEDovMqPC4nm83li2iHyI9F/BN66Zi0aiYkflWIYODAI+OAbu8QgJnAUFHEREd4jdr0+io/z+owCbj6z4F/vYwRpvf9wLajk8+fxc3SuG8D35mMpYnsk+k9IWgWgFxuO40KWtMo5Ph4i4bJXPO9vsJ6x9zfLrm1ny+1LcX2FHV+rP45vXqCY4rD3wgMWBg/c9+eU4K+P/CPx0Sj28+NClv4TkmZ5YkE0/OX7vhSXj/eur7d4MVc767pi3geX0fpKQC6S4bm82UUsw18vXHcc49/Pvw+vUOTSY18XwF6SyI9E/xH43LAXg9nluTwf4+fCXELr38dH2HEs7nMZr693T8th+ykBrtE3+cDr+PqBH4SQjeAb4gpZgTzuDIRgHlJ3voEIahIk/Osh0X8EnJP3VhtwEA3WlwXvt7hOS43545Hb52o6X5bLgwm/d8zK+9w5RIfnY4VI/to4ZYdo/DYpOxZ77Ia+BdPpNKzo42XFIh8qzskJV8fFUlE++swBOk61sUXm4hwfmWYLCdFzWgzFL/4+R9tjYuWAH4JnbN0heIgyJtrY9+Fr7rG8mAezWHFOzKLzYIBlzPAOuCkoB0TF1ag45xpwasoLykeY2c31Zbg+NZZWhgsgel+GC4vK6TVv8b3oMTdGJoCLZDhNlrWZJg9mOBeLnmvv+bvCYMNNRbjqLtZjH4OA2YcORdPpNLG1l9geuffXYHd3N6zbxvJaM0vMa3mVGzeW2GbBDQfhzC5X/aUtuOFzpBXF8Dl9YRGAJfWr/XywjZf7Yr7PpbgsfHwuFr2vu/c9A/l7g1tfKpVssVhYp9MJAwSvWtQAcEGa5yPR5wBLa7Hgo16v297exVfIP3heVWaWDJDlXVrrrZnPifultRzci52Hz4UGIPP5PBwzn8/DIMCLa9j95+h6bDDjeT1X9LEXwU1FYoL3HobZh8FpNptZs9m09+/fW7fbtXK5bM1mU1V6W5Ip+gcPHnyu6/hBwVaQm2hgL3k00YDYUCnmV6pVq9VE8YnvXc8uNnsIbOlZdFlNNNj6c+AOnydWzGOWrPbj1JyZJYTNPfC5/XaW8LkyL+bec09934wjlqLEYDcej21v78MuQ/C0dnd3rdVq3dyP4idEpuh/9atffa7r+EGAHzwEViqVQs813y7LLNlWarP50FQSgwIfb3bROcevImOrltbAgteTe/HHsgDeQnJLLr8TjdmHwcN3wWWxcz8/L352vf2gxJ/Fr7Lj9/B9+dPSmWYXLck4rYdbr9ezRqNxaYFQUfHTRJAp+l//+tc3cjE/ZFBUAhFwY0z88PxyTyyaQTdceALoxIpzxppGcE25mSV+4GaX95i/TmNM3De7EDQ/x++P52GBuV23F73v28e589jiIs5IcPbA5/W96H0hUrlcttFoZIPBwE5OTuz7779PNMbc39+3g4ODwjfG/OUvfxl9PLNH3j/+8Y/CfVNsYc0u+t7hcQh3PB7bcDi81AIbAT5Yeoy2GEhiLbDZ1efcP7+OG074zSTNsltg+6aW3KDC7HJnHwxqHGjjVJpvlRXrPx/7Xj2x4iCfovQDAH9HmE41m80w3Wq1WnZ0dGR37tyxTqdj9Xo9FAcVTfi//e1vo25OpujNrFjfUgo+rzybzWwwGNhgMLDhcBiCYZVKJVh5BK/8VMBvdgEBe3cdP3yIylvdbTe74C2xkObiTrwcIedrjA1O3tPA69lzSFvjH6sGNLvc8z7tNTyvZw/FDza1Ws06nY42uzCz3//+9/kbY4oP+LnlZrOxer2e2M0VRTaw9LH5P1a+cXDrqm2t2EOIldlmbWuFaQWfz6feODXm5/EceIwJ3lv5mPCz8Bae//rvn//i88W8itlsZm/fvrV+vx+mZEibig+oOGcLYmkjH1n2JaicOsNr/ZwW5/LufUz0sQIZvD42YHCunWsGcE4WPw8ALGKOzPsiozSRZj0HYkJnUW4zPcj6ba7X61C2Ky4jS58TH+GGleYa/NgKOP6B81pxs8sddzi/vl6vE/+OrTiLDRgcuPNRfY4ZpEW3+Vz8Opxvs9kkvA0+FrEC/7lBTOxpLn7WY+J6qPY+B17wWAiyXq9D1Rm7uWnnYIF4AcaCcTxHx3ZT6KXHx3hvAOLHa7igB8Ll13Jgb71eW6VSuZTTR5wA9Qn4LLgW4IXPAwC/B+7zX3GzyNLnBOKbzWY2HA7t7OwsiMGveGPrz+60FxKO47p9tv5srTGFQDdcs4t8rF+og3OsViurVquX+slDuOzuo97eL/f1GQOui/d/eR7t59JXufzi5pHoc8BFJZPJxE5PT+39+/e2s7MTFqr4DSY4+Ofr1v3cPG0NPIuey3ZhpXGcj97HqvJw7N7e3iXBm12squNMQ6wPPQf9YttTceERPrdHAbYvg0SfE1TWDQYDOz4+tjdv3tju7q6dn58H68rBOg7mcf09V9al5aXxGA8IbO3NLqw83tfvIuNr8bECjlN2gL0RPM871PquNpwO5JQgUop4LU8HeIEOrjs2rxc3h0SfA7jK5+fnNhwO7c2bN/by5Uvb29sLa735uMViEXZQ5ce94NmdN0s25IAofH2+3zyT3XH2LBaLRWITC18CDHxmwAvcR/i5ag+R8ul0apPJxCaTiY3H45Dj9yk/jkUwft4vbgaJPidYy93v9+3du3f2+vXr4N6zUObzedh6CZaWU14cQIN19ivEOAjGlr5SqZiZhdZU/JxfWbdcLq1er1/aQCJWv+9Tir7wxT/GpboQPKoU8Rc3DArwCnZ2dhJFSTwISvg3i0SfA47ao+77+Pg4RL55BdpsNrN2ux0WgMQWP3DqDgVAsOr84/elpxA6Dxy+PRaul6vmcG0cT/BFPb4EGOfxNwgfxUXIZIzH41AXf3Z2FioX+/2+DQYDm0wmQfxI96EOwccXrkrjiesh0ecAop9OpzYajazf74fova9zH4/HdnBwYM1mM1TocaANc20s3WU3n6P+AIMCfvxYvcfVgrEuObEAog+qxeoK/Ofm+zwF4IpBb/Eh/NPTUzs9PbWzs7MwELD4Y6W93sMQnw6JPieY0/PcFRYTlm82m9l4PLazs7Owjz03iUTVXq1Ws0ajkbDYPCjEcvj8eJqV96lCrqLjG8cNYgFED+fTvcVHDAPix3eDtObp6amdnJyE22AwsNFolHD5OWDIwU6UGuMzpC3qEdsh0eeAg2O8Ag3uNkezR6OR7e/vhxVgKN5BMw6suW+324lCFhYbW20uvuGy2pjo06y1L4KJZQ348azvAedgl9wH+LDx5OHhYfCKvOgnk0kI9rHYs7r0IHiI12sAyIdEnxP+cXP3GMxJ8W/8KFnw6KYDC7+/vx9cW7PL4kOuPTbvhvhZ9H5NQAwWiBf5NqL3A4e3+mz5G42GNZtN29/ft3a7bd1u146OjmwwGNh4PA6C51V63oPw9QGLxcLG47G9fv3anj9/bm/fvrXRaBSuj+MZIo5Efw34x+kbSeCHOZvNLnWr5b3fm81m+NFzkYw/Pxfm+Hk/i52f95Z+m3LqbY+PBddiQT6U/6LFWKPRsHa7bdPpNCzZja0w9HUO3s1fLBY2HA7tX//6l3W7Xfv73/9uL168sLOzs0QQUKQj0V8DtsRmFyvh8COFm8/luNypFt11JpNJWFMf6yCDmn68J1v82Dx+27l52ufJesxPCTgm4MXPC3O4JTYGOy7p9RWJ/Bn8IALrP51O7dGjR/bw4UO7c+eO/eUvf7EnT57Y6elpvv/IgiLR54QFxzX1ELyZhVVn3voikAfr562dv/lcOgfovNX3K+hi83qwzUCQ5zg+ngcAFv96vQ7pSe/Z+M+SFZPA93zv3j27f/++HR4ehlqIp0+f2snJSdioQzn/OBJ9DjiCjoKamNXHsbG5+t7eXqJMNT/YrKMAAAidSURBVNZ6Ki215tN2sevj9/OPb/P5sp7z8QAWrb/P1ppFzeLla/VeStqghde2Wi1rNpshE3L79m17/vy5vXr1yt68eWP9fj/sw+f/j4qORJ8TFj1vdGF2OajFrzG7GAi4mMcvYOEBwNfH+4j9Nj/ibVz3bdnWcvrzs/jNLgcTY0K/Kq5QKpWsXq/bvXv3rNls2s9+9jN7//69vXr1yv75z3/ay5cvbTKZXCpxFhJ9LmBpEZDjfu6A3X3AFputta/D5241vkV2zBX2IvE/bC82vsZtP2/s+FgEf9tzpd3Psu6xc3GcpNFo2OHhoT148MAePXpkjx8/tuPjY5vNZpmZjKIi0eeERc8bXvj5LP6mCXG1Wl3a1pmj1LxklS1+rMjGz6N9DT+/tyd2fWnPpUXr094nZvFjx+ZJGaZdJ6obMQBgnzsJ/jISfU6wwg197Wu1WmLFGne6Af4+LBUCf35aEOuay1s/cX0AerujrZZP77G138Yix/L4fjCLDTz8mm2FHMsEXAfOaJTLZWs0Gtc+VxGQ6HOAHxcq6prNpjWbzcSOL9ythvGCw+DAK83wOq7sQ0kvylpR/ttut63Valmj0UgUAPGUY5tpQJal9tfPHglnGdKi8FkpxKzMQiw9mHasLHl+JPqcwL1HRV2r1Qolp2aXo/ixH6x/jPvL8QKWxWIRVq6Nx2MbDAZ2enpqh4eHdnBwkBA++u1jcQ/X+rMA+Rpi1jptbs1pRO6Ow0tiYWnTds5Nu6VNR2LfF3sFHxuz+KmTNiBK9DngAFKj0bBOp2Pdbjfs8oofMbrocHkuiFnZUqmUWFTC0XvUr2PFWrvdtuPj47B1E+r7cavX6wnrH+vZxwUvbLXN0otksObA7x+HGgOOdaD3P6YePPBwPUHsFht0YtOI2FJg/n8S6Uj0OUGuvdFoWLfbtVu3biWaSfrINFz+rPJQuPrebeadZkajkQ2HQ2s0GuGGXHWz2Uw8jpsXPrvLPlXoK+N8ARIGISyf5b34EEhDxR1fl9/th5tu+h1tY0uD8f3wVALnqVQqcvGvgUSfEy/627dv22KxSLi0+BGfn58HK26W7XbycxAi+sqh5TXWqUNEWMDD22Oj2o9vvNuOF7yvBWBh8kAR64kHj6ZUKoXgZrPZDFMPTH9g+XlZMa845ClJWiMQvD8q/PBeaRV8Ih2JPgf4ce3u7lq9Xg+WfrVaJX685XL5UiAN0fqsHDdcWFhX7/6XSiWbzWbhGmAVfY0/iwr3OZXnVwn6cl+/173Z5VgDd+DBQFGtVoOlh/Bh8TEoYWDkwQnLjOGZcO0DpklYfrvZbKxSqVi73Q6DTawrkUhHos8JBFetVq3dbtvR0VH4IeJHzIU74/E4CJ77zEMwafl9/muWjPbztcTq8f1AwJYT54p1qDG7WMOfthWWb+GN98bnxXeA6Qdv6AlLz/N/TEU4JoFmoqXSxfZcKF3ebDbWaDTsq6++CtYebr7YDon+GiBt12q1rNvt2maT3JseFo3dfb/TrO+Iu02VW1auO/bYVUKIvVfe1+JYDBRYUozpB0fz4W1gQOJlt/AQeJDA98Jttc3M2u22LZdL6/V6dnh4qGh9TiT6a8Bz2FarFVaQwXrhB+7Xu+M+XHUE+HhhTp4SVx/RjgULP0YQMeH7aDkPEpgCYEccDAA87/b9ADjiD8FD9IhB8LTCzKzT6Vij0bCf//znYRBVue32SPQ5YauGOSyi17BccGU5QIVuMWgggcg83HYWfyxFlUfQN2n5YjvV8DRgZ2cnxAp2d3cTK918ZoOnIfie2EPgeT1/P+fn53b37l0bj8fql3cNJPprgB8r5qSlUinhqsLNb7Va4YZW0OgEiwYas9ks4e5zsO9Ls+11QJQeFnpaPp1LaHkA8MuXuQCoUqmEAfOH8l39mJDorwHmpbVaLbj29Xo9uKEonOl0Otbr9ezo6Cg0hkRraDSGREdY3w6a20j9GNm22s4sWSyEKQ8HCTmoiGrIRqORCPiJ7ZHoc8JVefV63XZ2dqxer1/aVIK7wU4mkyBwf+MdYNAzD+LnJhu44T18E0oIh/+a5Xf1fRYAj6X99bvbssWOlfL6VCReyzl7biQKNx9xlGq1akdHR/b1119br9cLc38Jf3sk+pyw6OHW84/Zb/vEQSi0zebFNLj5/eBQc8/H8Otx3+8Th1w2p9W88GPRejNLpPdYkH5OzoFJnotzEC5tTs7XxZV1PDXy1YRcvVer1azdbtudO3fs7t27Vq1WJficSPTXgINWXlRsYXkg8AOCXzvPffR5EID1500hWPS8Px0X28QabfL1MWzZEa/IqonnvDxvjslCjYmee9fjfVn0vkgH5+HrwfvgWOXo81O6wv1TlOQKrnKfY1aWV+KxVfauPK9mu6q1lt8SKubiX7VElW++vDWrKaevoec0HYhNPXBe3onXn8tXNca6AEv0qUS/GIn+C5DlbntPwT+W9jfr+LT392LxKbXYMVnHxR5P+5yxc6bFDvJcm0gg0QtRMKKivzqfIoT4SaFA3o+A6xSgXMf1/dSFLv4a/Pnlnn8Z5N4L8dNF7r0QQqIXonBI9EIUDIleiIIh0QtRMCR6IQqGRC9EwZDohSgYEr0QBUOiF6JgSPRCFAyJXoiCIdELUTAkeiEKhkQvRMGQ6IUoGBK9EAVDoheiYEj0QhQMiV6IgiHRC1EwJHohCoZEL0TBkOiFKBgSvRAFQ6IXomBI9EIUDIleiIIh0QtRMCR6IQqGRC9EwZDohSgYEr0QBUOiF6JgSPRCFAyJXoiCIdELUTAkeiEKhkQvRMGQ6IUoGBK9EAVDoheiYEj0QhQMiV6IgiHRC1EwJHohCoZEL0TBkOiFKBgSvRAFQ6IXomBI9EIUDIleiIIh0QtRMPaueL70Wa5CCPHZkKUXomBI9EIUDIleiIIh0QtRMCR6IQqGRC9Ewfj/+NPwx9UfnUcAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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ris6BOPYo+Jm/j8hVbMDF/vnioi+Jjt6jSAbltbqwhi14bOJHEdmw3ICFy6veIgiGfXRBDrv2EDoHz3iEIIsYDQFEi3OwhUZDo/v/umugGzeOBfB339V3d64fF/0eIILfaDTCDX1wcLARvOOgHPdXUUxj3fwMtrEEj2AYz66rU3UseuyjZ/jhiT8sS49jWCLWXQO25Poz/v6xdCDj4n+15Io+1cIHrkTTEXG8z5NgLJdLMyCnS2GtNBxH1XEO7v+ze47oN0SPeeDh4jNa9FZAjYXODYV+ZlHrGICO6Fv59CK1AowOZjrXS67oP+sPbg3lxA16Xf/MXcfSgt11rBi6r8lpN7ju+FwH5HT+m6PqXHuuxcKBOIgdD+yPc8REn9enZ+ttRe6tFXm0h6KDcVZ6bxc8WEj/r6xyYuv/4RQnV/Q+o+gV1rBPBjctjxjjhsAagKLdbd3HZtFD+Nwvt2bswfnQN+foPffn9aw/7MJrD0RH27Xg+Zy6n279jrqR4t+q6P+Af9eY8PmYVsOYKq7qAlguOUeqkdKC26/XZ+f9uI89Ho83rK8OgulAnt6WhaVTfzogx16FXsrK6q/rlJrluue57Wy9uWFirAbAwhoxiPNiWjJr/jue38C5Ilf0v/nNbz6v63jt8M20WCykUqlIt9uVk5MTaTabslqtgni4j83DQ0VkQ/Ba9NpV55VwYsE1Hk+uq9is2IMuzuH92Y23gm1Fu0B8Xku0eF9PCspBx1hDoI/D5ct6dRwcE9ePY+E3Tm1OvKJU8v7R77//flJNJCLrg8FADg4O5L333pP3339fvva1r8lyuZThcGgOLOG8ucjmwpM6b85uuuWuW6kwbf2t/LxuaCAGrprTUXkOGFqVeTq/blXP4VxWo1H2b/x23HghbqIfnCHBeyh//vTTT+XBgwfXc1O8wazXa7NFzbX0P/3pT1/N1bwhfPzxx9JsNuXs7ExERCaTSRAVi5+FC2tmrf9mTQyhK+CsyDgLXffhWRQQALDcdJHN/i3vg9RjrVYL3k6lUpHFYhEaFMu9xzEtURcdu88iF5GNbhIGIuGhhyc3m01pt9tyfHws7XZbPv30U+l0OvLw4UNZLpeSZVmoX3B2WPpKpZKUpdfU63V599135Utf+lLIwfNEFyIS+vS6YEbkSvgc2GJxc7/a6i9buW0R2RI7W768QTf63Fa/PK/BsRoQEBOx/jvWCOjxAzzFGC++ydOG93o9OTk5kdu3b8vJyYmcnJxIu92W6XQqz549k7OzM1mv12Gocmp9+w8++MD8sV30Bjp1hHHvPLVVs9kM2Q0E6HTBTBGsqPWuPrZe8NJa451rAjhgh9da8MCKwnMDoT0G/s20tbbm4uffV2/LrjrPNoTXaASyLJNeryenp6dy//59uX//vty9e1du3rwZJiJxRESkvHufasoOU1gjEMSlrBcXFxuDakQkbMtWFO+LbLquOugWE4YWFn9Wq9VkudxeSEN/B37NrrvlnnNwDBSpotOFSNot59f6++vfhmcNRoOmG7KDgwPJskyOj4/l7t27cv/+fbl3757cunVLWq1WsX9w4uSq2kqDpMxisQhpNr4hdQS8iDhYIHBndW2+3l/kSsCwemwpIWwtZp5KyxK7iGxsww1MHjg2BKtnB2IvxFp6i9HzAujqRjRUlUpFms2m9Ho9uXXrlvR6PcmybGOuQCefNE15CXT/eDabbQS3YkU7sT4uC55vcpTyxvq+bOlwbIzw4/3Z+vPxsJ/lknMfWltWaxUd7aGwVcZsQLE5AvXx9O8Tm1eAh/geHR3JjRs3NoYyczAy1vCmhq9PvyfWjYObEDcaB4ny+vOwQpzfhsD4b0sQOL4WNSxcTMyW28774xhW4AxiZstrBdu4/82iZ/GzpeffIgZ+R3hRyHisVqvQr+c5CPV8Ak4cF/1nYLlcblmkMttzIY3IlTW2hAHx4hhw1fmht7Vm5NF9aGwDcbJwOYjGltrKnfP+iHfoY+TFLvi1DiIi1Vmv12U+n4f5BXVD4hTDRb8nVs67jDsJd1zkqj/OryuVqzp+FjmEzxY+5sqyu89eCbvnPDW3nqjTstQQr46ycz+el9DWVj4WL9BdI65XQBcGDaIOEhaNQTj/j4v+GigjdjQSllVjt18fk4N0vE/Mtbfcce7js2hZqHrufrbgut9vTe2NY+UF8LRA9W+BB8c5uFJQ1yM45XDRvwYgbNzAeM/yHvC3dYzYZzpoaKUJ9Zz52mpbIoaVt5bs4nX3dHcgT/T6e3CDxttwd8QKBDrF8c6Q4ySGW/rXgK5cw3uxdJ1l1WLbitjuMueweUYkuNDoO1er1fDMLjSOg5Fz3LWIFQnpgB08mCLuvZ64g//GNaSektsXF/01UCaQp8Vqud/8OV7z5zoYZ9UK6P4wAn9c7MI19Ro90m8+n29V2uUF8rBfkUCe5eLrQB5P/GFVFDrFcdHvSZ6lLbKfroDTATcRO2XHuXzdEGjyxq/jfGgQuDafx+7rlB3y8zq/b6XsrHhALACnhc/i5hGNSNnpKca8ASiOi/4z8FmKc1jAHKCKFedwY6Gr4XRhClvvvNJUDo4tFotgrafT6UZUPnZOnQFgS88LfuicOjc8uwYnwdpjLoLlchkaFD2pqFMMF/0OLNddF74ALqzR++h+vBaSrr3X1lA3Enm17Dp3ryev5GvCY58yXL4eLsPFmHe2/rzEF571b2Odh7srXIZbrVal1WptzN+v4wepW38vw90TLXZ2Z3UZbF6xjHVDs8u7K/fMjYaVMuO6dlw3W8DY5Bfa3S9T7MLdklgfn4f96vUA9Pl1OpGvRw+4ERFpNBrSarW2ynGdfHxorQFuHMwrD2q1mmRZFh1aqyec0MeMWcxY7lnn7fGZFj3q460+PgfFOOKu1zTQngiuXxfMxCr/cE6d92fPRMcsrGAmN2o6loDjZ1kWuiONRiMsDe6CL4YPrd1BpVIJM+boSTTQp+dAkxb9PjeiFpzGEkZsEg0cj4Ni2isBenuufy8ydBjP/NBBR709vg83gJbHwA1clmUymUw2FuBYLpdhEo2jo6PSv3lKpGnKC1Kv1+Ub3/iGfPnLXw5LT8Oq8HRZvFSznqUWWGkozj+zMLXoWGhaULocVufWEcjT03JZkW8WEG/H21rTZcX6zjGRW9voLAaP+uPRf4eHh9JsNuXi4kIuLi7k5cuX8pe//EVu374tt27dkk6nI9PpVJ4+fZr8dFk//OEPzfd9uqwcvvrVr8oPfvAD+eY3vyki8YkxY2vLsZXnfDMaCFgrflgWGX+LbBe5WNF/Pif21Q9r4QrtEfC5dYNkTbeV1xBY3RaNbtB0N4GFjynMMDFmq9UKE2M+e/ZMfvWrX8lHH30kq9UqeGW6S/O288knn5SfLuu73/3uq7maLyhIIQ0GA6nVavLtb39bvvOd74QpsEejURAupr/GGnE8Ey5bLJH4gpKYPhuz4lrTXcdWg4XwrUE1IlepOhTV8LBejornCV5PpAnRaE8E3zFP9Lv+BlY/38pYcBwD22CM/fPnzzemwD4/P9/rfnhbybX0Dx48SMbSs1XG9M+dTkdu3bolWZYF0Vpry2EBCh4BxsEnnWvW69LxqjVIS3GhTN5iFzooCFi0aFQw/Tauhbsb3M2wZs7V3QPdNdB9fcv660E0u+BYAAcLuUHla0GDgYY4ddb7zHv/9a9//dVczRsGbiidFhO5WrYa23AKjfvWLMBdy1qx8Lih4W3Z8orEl7Xiviwi3uv1VQ39wcHVcN0iaTrLmsci/OwF7DpGDHgoVv2C1f1hEHdJrS+/Cw/kFYDFpCPmXBRiDSUV2RS9jkpbC1jqmnO9Pj0Ln7sUOoBXrVY3Vra1RMgTdei4ARoGNHio59dj+/m8wDpXzAsoKkrdKHFg0ULPSmwdQx+vSMNXdN/PcrxXiafscmC3ki2YVbzC6SarUEaLEsdBoY/uV8N6wcoje4B+LYveurkwGo0bHt3vxvfDuVj41m/Bx0Vjx6LjstpYia32DkAR1z9WwxAj7/wxPotXYO37RfQyckX/RWylPg8sEUH42pqytdV9bPYQ8DlHpLlLoN1jjgNA7NywcLaArwtiw766So37xBwYxFz6eB/bzefzkPJDMI29EVh8/I0Yhk4D5hHrBuj3vogCehPJFX1snHSq6D46rDDcXY4065tVp9ogKlhufUNDRLqrgH0x0izm3uP6uKKNGw0MWOHI/HK5lHq9LsvlMgT80BhY49q1V4IugA4yctcC15eHi/vV4n36kuAGRj/78vIyiIO3gVXnm157BiLbw2cZWFJdGisiQWBws7VnAeGyl4AcNy+2yWLmOAKi/TrSzzn7IqlF7MfXBW8Av5UmxUKazxMXfUnW63WIwg+HQxkMBsFCcp8cVlpbXxaDiL2sE7bnvyEeAAsMOKXFln42m4UJLnWmgBshFi+Pqef6gVgjgXPpyS54X5wPcQOr/+9i/3xw0ZeArfxoNAqloIvFQo6OjjYEgCGmHEjTbrHI9qw47B3AInKmAKLA33yMWAUgL43NqUEE7SyrrQWvRY/Gj4UO7weeBM6rqwG1pcd31d0AfO5cLy76EmjRn5+fy8uXL2U+n8vR0VG42THoA1VjbL05aq2Hjmo3XURCkK9er29FyXkfPWSVg3n80KlB7nrowhwteO3JsOghdhQbjcdjM82IxgBuPtfyIzbCVt+t//Xjoi8J+srD4VDOzs7kxYsXwYVG4Q3KQdnac3+dg2r1en2jck8XovCYfWyLCLrI9sKPVnbB6mez0ESuug/adedGgffhbVDpx+XFo9FIxuOxDAaD8DfKllGbwEVGaATyrD7jDcH+uOhLwAEyuPcQfb1el8lkIuPxOAwCwcwxelIIpOwODw9NF12nDLEPXut9YjPScPGKFq5VIMMWn626zq1btQRw5fEbjEYjGQ6H0u/3ZTAYyGAwkH6/L+PxOHgBuiwY18vdIH29ZUt5nW1c9CVg9348Hku/35d+vy+TyUSq1WqwaFmWydHR0UZBjV4J5vDwMEwAwcU6ImLm1iF6TqNynb+eRAPXi2ddDKO/F29n1dUD7otzHz8mfIj94uJCzs/PQ/BzNBptWH2IH8/snbDHESv6cYrjoi8JrNt0Og3WbDweS6VSCe7tYDAIgufZYK1hobqMVBfyAJ2r13UBnIvHNnnkVbftqnazioiswCEaQVh8BD77/b4Mh8Mt4euBQegCcCyCA4gcVHSrXxwXfUnYxeeBM6vVKgSweHZYPUU01orLsizc5Lpqbb1eb00TxdF9LXbdp48NULHes75fkd8Az7qPb40ZgEsPN384HIbGkt18iBtBP511wO/ODe5wOJTpdLrX/zJVXPR7AOHzRBp4jVlarMUetZXHfhwdX61W0mg0QqoORTW6oo4H9vCMOVYGIPa86zvGttWegA7yafFjNhtrSDHKifUwXh0g5AZhNBrJixcv5M9//rM8e/ZMzs7OZDweb1yzW/44Lvo9gZuPmxM16pbwuD+Pvn673Q43Nc9SgwaAp5AWuRommmftr1P0u2Dho4wYotf1+Zbrj7+5XkB3G7gGgC39cDiUFy9eyJMnT+Tjjz+WJ0+eBPFPp9MN8XsDsI2LviQsJhHZyGtbfWEuuIHb32g0zLHxuggGFvTw8HCjPp+PqWfNsQSf5+pr9LXHiPX/ecitzgjokt28/rj2HPgY0+lUBoNBEP4f/vAH+f3vfy8PHz6Ux48fh0AfUpwe+NvERV8CbWUBC1Zku2RW5CoQV6vVQsGKFayySl9xbraoeE+Lu4il5+/D6FRhEY9A59bxrBsB7QFww7DrOq0YAgqBvvKVr8i9e/fk9u3bcnp6Ko8ePZLHjx/LJ598sjWFufP/uOhLooWPG5StEU8txVYM+x0eHm54CLqG3YpY8/68wMYu17WMa1/G7We3mQWvP9fPuuHa5zoh/Ha7Le12Wzqdjty5c0e+9a1vydnZmXz44Yfyk5/8RF6+fFn4+6SEi74EHEzjNBzQxSWw/CwIjI7T4uaRbfxg4XODww/LaurrflW/hxU8y2sE+LeyrnHXteK4+C0wLfndu3clyzJZrVZy9+5defTokfzsZz8TEZF2uy3VajX5SWGAi74kcLN5fTZE10WuXFCuhOMbHKPvuM/ONe88Fx67/7y9vh7rGjnHz++XCWoVTe3liZnPu6t7UdbTwDOWGMN3fvfdd+WDDz6Q9957T1arlWRZ5kU9hIu+JDwuHRF2pM9QOw9Y/GwFMf5eB6jYA9CDVTiPrQfBcAPE1Xy6WCfmCcTY1UDkib1st0O/jp1Pb28VGZ2ensr3v/99+d73vlfouKnhoi8BbjLk3BuNxsbAGrbijBXpxqw2s9lso8oM1l3Pmjsejzfm2u92u9JutzcG93AxEM/gwwNxygqgSJ4+VsG361x53YO88+lt8BqWHEFWF7uNi74kbOmxxBVScLDomDoKgmO3kr0BPZc8u/pczIIadtSxD4dD6fV6G8LHcF5eHloX8WjLyJTtY3McQQfmitQH5Ln8lgfBzzFrb3VpnG1c9CWBpccyya1WS7Is20gP6RFqWvh8U3MZLtx2tvZcvw7Rn52dBdF3Op0gfIi/0WiEIiCu/beG38ZEy8Eyy/LqkXh4X+9XpHYgVkugU3UcrOS0qW4IuFuVMr4+/TWAmxNLJLdarZA2wsIS2lJNp9OtiS91Hx/WnYfV6vr+4XAoWZbJ+fl5OCc/0AChEdINAKy/NeYeMQWghwFbDYUeew+sJaj0KECrQbAGCllFPmhEkTVBI6ytv0/qGsdFXxLcUEdHR0H0nU4nBOf4ZgSz2UwqlcrGPHHaXWWrCc/g4OBAptNpcNXH4/FGLAHdC+5mtFqtUNvPbr+eyYeFy0FBkc1FO6zVcGFFdUYBYkQDw10NLkXW4wd0o4DfhcXOqUuUM+OclsC9Px/HRb8HLPobN27IeDwWEQk3ORfu4AFPgPP3QFeycRUeGouDgwOZTCbmCq4QFtx5NAwQOx7YF+e0ZtRhb0avDc+z8rLlxbVyVgMDizCqENfFjQAHH7lxATwAB1mN9Xodxi6ISNi3bGYiZVz0JdFFIZ1OJ4geQtM3MT8wMIdrz9m6MfhbNwJ6KC2vqMNFQ7pRYKtoTYvFAmarzCvEspeAa+TfBGML2BvRMQY8+D0OPnI0nkczYomuLMvk+Pg4xFZwfU4xXPR7wKKHta9WqzKZTDb6zywaLFaJyjCeBhoPtvZWBNtKU+mIvG5k8mbVYfdZW22rwYoJiwcUwQvSombB83toHDjoCOHrKbmwYk+325XlcimNRkPa7bY0m83r/Qe/5bjo94Cr8iD8SqWyYbV0IIst9HQ63XD5RbbXduP3ylaS6aCYlarTkfHY99SP2DbcSMBio/HjRpD7+tz10LUGXF4MrwQLgN68eVMqlYp0u105Pj4O8RR374vhot8DdmfRd2XRa9eeg1iwirxGHITDo+pEdpe3xroDOmYQE3zsuNZ+sby9FYVnq68j+VZMgvv7XOHIDQ08kXq9HqYZPz09DcuKpZ6eK4OLviQc6EKBznK5DCvP4obEI8uykEbr9/uSZVmYJopr7PWCENyft6xymbLXogU51uc63mCJnguOYPU5KKhTf+wBccAQvylH+HVKDt2ByWRiTjXm7MZFXxLchCx4LCYJV5TnhuMZYa054lBmi0IczC6DYh8cs8h17fNdrmtbzkDgerWlxm+3XF6t0cfDivEZfl94U+v1emvaMa4/sAqInDgu+hJw/xVpo2q1Ks1mM7jliDhjRdvJZBIWfkADANHzbLqj0SgsCIH543hVGJ5xhiv4dtW/M7HPrPct1z72WhfcaIuOGIMOKuppwa3cPlx/eATtdlvu3Lkjd+7ckV6vJ41GY6s4x8nHRV8StvQiEpZ8ZkvHUWeePJMH0fADFp8bB24QeKANjsMRbSv1ltcI6FRgnug5KMiWW7veLGDdb+dgJk/2GYv266g/0qCojTg5OZF33nlHTk9PJcsyr74riYu+JLBmuHljM9jolBgXwfAacHpUHYQPbwADbOAZ8AoxPF88vAs+366BK/o9VAHie+oSWZ2J0C53zFrrkX9s3XmyUCuFp3P5zWZTut2u9Ho96XQ60mw23b0vSWVHEMQjJBF2WVO9Le+j69djq8Tw0FpYej2/nl4VlheA0EFAfrayBBy40xadI/QsXi7XZSHD0ltltlaZr2409NoBfEzdn/fCnChmS+iivwb0CDX9Xt4+ukHgYhndKMQe1hTauoHR59W5f+s76BFyOi1nFe9YxUBWrt86lo4JWKlAvY1b+Fxc9G8zbNktL2RXiq9o1Z/12sX3hcVF7ziJYYreA3lfQPYtNnkd1vZVF8a4B3H9uKV/g4hVxTlOBLf0bzouduc68FyH4ySGi95xEsNF7ziJ4aJ3nMRw0TtOYrjoHScxXPSOkxguesdJDBe94ySGi95xEsNF7ziJ4aJ3nMRw0TtOYrjoHScxXPSOkxguesdJDBe94ySGi95xEsNF7ziJ4aJ3nMRw0TtOYrjoHScxXPSOkxguesdJDBe94ySGi95xEsNF7ziJ4aJ3nMRw0TtOYrjoHScxXPSOkxguesdJDBe94ySGi95xEsNF7ziJ4aJ3nMRw0TtOYrjoHScxXPSOkxguesdJDBe94ySGi95xEsNF7ziJ4aJ3nMRw0TtOYrjoHScxXPSOkxguesdJDBe94ySGi95xEsNF7ziJ4aJ3nMSo7fi88rlcheM4nxtu6R0nMVz0jpMYLnrHSQwXveMkhovecRLDRe84ifF/kV8BJiR+2z4AAAAASUVORK5CYII=\n", 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" ] @@ -1132,6 +1144,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "current beta: 32\n", "Current iteration: 37\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -1140,7 +1153,7 @@ }, { "data": { - "image/png": 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4l+cGi8D94h4Vfmy9e+w1Om+2PfNXq1XI5zNnz8CX7n+nngI/31p4K3DbaUfPQ3v+c7sutgxjsI0Lk+wmmy7494+LPgd0pWu1WthEkV1jWErLBTI6l4+1qapUKvfc7FjBTSznreLlsXabaOCuqaRunElLq25+zMLbgho+1udo5WnpOZ+n2HVHXjvYqeCJC/794KLPAS1otVpFp9PByclJeK7dbmMwGIR6eUbi6bbzQrcW3QbXNDIfS4Wp4Gg1S6USbm5uUhH8UqkUXHu7FJiLhex+eerKU+jMtdtqOn6+HUzs5pt2WsDvbFOQLvj3h4s+J6XS686vzWYTg8EgiJM5d9ubTvvN26o6XY1nha+iVwFa4VH0DAqu1+tUr3quL9dKOBbExFp6caCgqHVLbP1cHRC0dx69ER3MbMMNO8C9j/8zH1TucNHnRCP4nU4nXLzaytlG0fWii7nT6mZTtMDdQGAtq93GWufw6t7rdIC5c4qforWutoqer2Menc9p+2wdfHS7rtg8fZvw3qUwXfBptoref6zX2LQR59BsgLFer1OLa/IUt2jeXb0BPUbd6NicHkCqcw+f0zJWFaluWqH9+Hk+OlBQ9Lrppp3fW+u+j+B1oIp5Qs67wy39A9CI+cHBwT0hxyrnYvltnaOr+Hjx87nY3JmipWj0M9RTiJW1xgpheMtaDWetuub8bXAuJvSslX96rlnsKhZy8rFV9LtWaBUNG2G3N11tp8td+TpNdVnX2a7P1yi6Rsp5b7e51pJduxWV3dRCrXHWvJvvZb+jPmePj/1eWc/xvHcV4WStBbCpPqVcLqPRaISYSlar7KLiln4P9ELXNBXnx9zlhcKlyLTCTgVMq80cNqPjsYCXuui2TZctzolZej02qxRXsYtvYrX81jXXAY5xBWu5Y8LjMbusdVYdQ2wQq9Vqoalot9tFvV4Pv417Ba/ZKvqvvvrqfZ3HB4GNrusCG17QuqZ7MpmEVWK3t7epZbVcZMOAHi9MtfB2Rxx18W2+3EbsNQagn2EX6ui8Wz0J+51ZN8D7w8PDe+lCTmX4HlpPwAGIAxyDifo5WW56TJB2AVBsFxz7ezOrwiKpwWDgoo+wVfR//OMf36mLb+d3P9T7WxeT4i6Xy2GJbL1eD2JiGkzz3re3t6mtmtnE0g4Y1lPQfLt17/kanWurxdZtoQ4ODkKhkIrezr9jde3WUuvvkhWA4+9jo/X2NVa8avFj0xn7Gl1ApCsKG40G+v0+Hj9+jE8++QSPHz9Gr9dDq9UKS5W5wWVRG2NmsVX0f/jDH97XeXww0FLxoubCmlarFRbF8CLSOTpFr3uuZ4le5+WaNiMaN+DnqGA17ccpxOHhYbDGOqWgi6+DBb+n3hNdA2Bbax8cHKTSdlrDbwt9eH4Una4J0O8ZGzCs4PUcWBF5dHSEp0+f4he/+AW++OILfPrppxgMBqkFTrH1BA5Q2vZDPH36tHC/ks6/S6VSWDOv6+dZgMNtknmxslqPx9lj1FW3uexYd1udf2u5KoDUHvK6Qk8Lguznqrdwc3MT3ss2pOSgx+mIDlZaH2BX7el0w56b3TkXQKq0WAcQ/T00Dcr3brVa+Pjjj/HFF18EwT969AitVsuDdmmibu5WS//s2bN3cyo/UrgHOzeW5HyR0PLq9tI2eh9LzcV2qAHur3SzFpifoXNuzst5PhobUE+B4tBgXaw1F4WvA4CdLqj3wN9Ai5V0F1t+lr63Vvmpx6BzeJ53pVJBp9PB06dP8dlnn+Ff/uVfMBwOUavV3tt18GPHo/c50HLTJEnCghog3ts+S8B6PC00XVKukrNVfHyNWlFaSU4jKJrYLrUa6eZ7AUi57lkNPJiHt3EFu2BIG4MwuMaOQDHRaykvA5z8DJ3va0akUqmg3W6n9gbwuXucrHiWi/4BrNfrsFRUraoKyTbN0HsdILgG3rbJis1DrdvM8l+KmcE8nUMrNshmBa9bZWsWQF+rHoPO4e2+fM1mM7QB0+ag/L0oZCt6Fb4GMZmqBJDytOzg6rUlu/HinJzoha858VgknMRy1rSMy+US1Wo1DCAqDIXH08LH0ns8J9sk04rfRtPVO9E98mw3Xn5/67FoB6BarRbETsHb3Xn0d4xZ+9iiHt42m01qP4BdZc/Ofbz2/gHoRZ+V/ooVnnAez0U1rNnPEqstV4251HrTc+O8XV1eOwDEpiSMjtPziHkf+noAqc7Amje3grc1BGrttQ5B5/na+ZYLhdhROBahd3bj7v0boFF+4G7RS2w+rq/hPfPdAILFBxAVGQeGg4MD3NzcBLHSWsYq1miBgfRgFCsTpvA1EMnsQ2xLLTtgqOg1w6HTlljDThYC0apXq9V7nX7U81ksFvd2/HHy4aJ/A6ygty0asQUrWsZK604x83grMH4Gg3Wc77KKjsfpY5sCU1Tw6ubrPF936LEluera00PQdKW+Nqvhp3623uyxWrHoFv7NcNG/BdS6xy7CmLWnxdUiHD7WgYDHa0FQ7HW2ss2+n0Wf08FFq95iNxWbipQuvIrcijhr4NEBgIOZnfbE3s8F/zC8kqEAvA9xZE1nnA8Pt/RvgV3pIp3j89+2OEatp7WK6uqqm2zdYHtsLHJPbIBR03C63l7bb9Ea2/fnnJuPWSikq/rovdi4g126a/sBxgqBdHrg5MdF/wZkpenshZ31WitwzptjgTybXtMcu42+6+ttia1i8+1WdDanbgcUG8iLLeaxFYU6ZdEqxVjjTe07wJTecrlMfZYLPz8u+jfAWuzYfSxlB6Rry7UiL6vxgxW63Rrb/tvOge35arWbTZ9pB19mC3al7HR/vmazidVqFU3ZaXBuW8pO6/zZYpsFUQBSffudfHhxTk7sKrCY6K1gY8U5arU1raXFOTY4qALXKj4tptFefVnFOfpdVHwUN1cN8t/q3uvr9PvwfGq12r1qvF3FOXbloe3HZ4tz2MzENh7x63U/vDjnAViLum3uTWJVbKVS6V4Zrtbe83X6Gi3DZZGKrmKzLr4Si/JT8Lb0l4LX19q5t/1NWJHHDT61Kk836OT8PtaEU3fg0ZV8fB54XQZtuw05++Pu/QNgEwdeyCp6zXPHaujVKmmOWxfcxGre9f21iEZz6eop2Pmz7YkHpD0QPU8tONK18rbnnj23Uql0bwmyWntbYkwrr/P1WEMRDhAcnG5ubtDv94NnoF2EfAC4I8vzcdHngO2YdGML5syB126uutt6kVNwKhYKZdvSWmvJdGCx7r2K3gbSWL0H3K24A+6ET6uprrxaY7u0VvsBWldfByNW6GWJ3u6gY8uMbQyC6xXa7TbOz88xGAxQrVZTgVBnO1tF//Tp0/d1Hh8Uau1shZmWmFJcFCaFyItcj+F7aktpG+22BSfqUqs7rfEACmyfJhparKPvp9F1/ntXEw1NpdkeATZAqYU7Knr+Hry3v4fNZPDf0+kU5XI59CEEgEePHqHdbr+jK+KnxVbR//rXv35f5/FBwIte89Ptdhv9fj9cYLpuW0XMxTO64ES3qaaQrDtrG2ISdcnt+nUVl7XwtnEH22UB9wOK/Dw7P9fvFZtv23ZZtpmG/p6aUrRxBh3QNHWoA6DGTfgetVoteAdsMjqfzzEcDu/9PxU5uBdbrQnsEP1vf/vbd3IyHzIaZCqXy6m12xQ7LzbdipnpLaasYqK3W0zprjVZS2WzGmMSDe7ZXXbUQ9Ay16wmlvqZuo2W9ttXNzzWUMO+57ZioVgZsX2NTT1S+OPxGOPxGOfn53j+/DkeP36Mbrcbehqyman2NCwav/rVr6LPbxX9b37zm3dyMh8yaiF50VlrPZ/PMZ1OMZlMcH19nWqMaZeU8qLVnLR2w9XNJK0INHhmd5uhyFQYdhmsxhBYs28X9eiN76kuPS28NvLk1tNs6hlr3Bn7XWNWNyZ6PX5bJeLFxQW+++47/O1vfwsCb7VaGA6HoTuuxl2KJvwHif7zzz9/JyfzY0NFsVqtMJvNQp/1g4OD4D6zDzvn8xQ934Ovr9frKWuv+9LpxWmLV+xmFxpLsEU5fD3vswpsrOAp3tggw8HHuvQaL4h11SFZ9Qu7xKhC1/PWaQGLdmq1GgaDAU5OTgq/2cXvf//76PMevd8Da2HU4rM/G9NV2g+OIgTuXGebSz88PLxn6YlaerrZh4eHYaCw1t7Om+nax1aq7bK8es52MFJRq1fC42yMgNjg4bZ7/e31Xl9vpzoAMJ/P8fLlS4xGI9RqtfB9ty17LhpenLMH6g7bmnm9actnO7+21s+muWzqi8fQ0vM9Z7NZECwHChvU01JfAGEBjI2C00tg5F7fi9/bfkd9TgOKWQPItuesV7PtdTHRZ7HZbEK8xbmPW/oHQmtvhWdFYktYNRjFfnea9lPBabccu1CFx2qTDOvecyDRjTDYMlvrC3jeVngaV1AvhHEBFeJqtUoNHDy/mAXP8hjs3+3v7bwdvPY+B7ZMlb3bNptNKJTZVRZqrSjbWemcVefktMb8XBUhgDBo2PSYfR/2luMgxXPgElgN9tlmGTyOras0pcdYQ6lUwmq1ilb22QFAvwsf673zbnFLnxMV/PX1NUajEQCEKLEunlGBZ82POR/XqYMVOR9zfk9rzQHBlvRS9Kw1AO5y4uoZ2ACXBubYr45ltVo1p4FFXRnH7ACj+vq9dwnbBf/+cNHnhKJPkgQXFxc4Pz9HqVRCp9MJFlHr8SlMtdIa+dZ5cWxKoBF4uukUPXBn6e16er5GU3TA3SCjNe6KRu9jZbK68o2C5yaevNfjbO2+nZJ4gO3946LPAUW4XC4xmUxwenqKFy9eoFKphAIdK1gWhwD3m1Ro8M4GyayXoAU4nEZQQDZlp+XBnMvb1Xk8D6IDg2YN7M269aw1mM1mSJIESZLcKzzia1XkWYt9nHePiz4HdLEXiwWurq7w6tUrPH/+PIheL+T1ep2q0efrVVDq4tuqNRWhBv/Uyuu0QCvyNDCn8/nFYoF6vX7Pysci8FoiG7vZLb6SJMF0OsX19XW4acUiq/noedALYIyA5+HCf/e46HPC+TxF//3334fGE2oduRJMt16yOW0NksVW0/FeLfl6vU6tKgPuuurYdtM6yOhcXKcY6mFY4eu52hvfkwVDFP1kMgm38XgcqhY5CHAKwMCfCj8W8HPePi76HHA+P5vNMJlMcHFxgdPT05BnV2HN53P0er3Ufun2gqaQtcDGpuTU+tPS87VaKmyLfiheFanWylsrb6cV+p1jN52m6HoCCn88HuPy8hKj0SjccwBg6ysG/tT1t4E/noPz9nDR58CK/urqKkTvNepNyzeZTNBqtUJJrl04orvCWPFZq6/RdhsM0/l8bHWZtdI2e2AtvVr8WDGNzeFTuBR+kiS4vr4Ovw8Dnufn57i8vAzip+Wnl2SDnLGCJefNcdHnhHN6ncdSiFx/vlgsMJ1OMRqNwmovbYelTSQbjUaw2Lp+HEjXqtOi857PxeruNe0HxK21vm/sloWm3tT912mNtfoU/tnZGc7Pz3FxcYHRaJSy+tomK7aKLzZgOQ/DRZ8DLYvlxT2fz4NotYnj9fU1Op0OWq1WagGO7SPXbre3BtRiJbGa09eae9twwp67fazvHXuc9Rvw3lr9mPiTJMFgMMDR0RGGw2FK9JPJZKvoYwt++BnMEszn8zf9by0cLvqcaJGMLjOl683n6eKq4HnP5bfdbheLxSIVxLKBNF1Sauf4tjmFraQDttes22Nig03Wa7eJf71ehywBO+N2Oh30ej0Mh8MQ4KNwNZvAW1aLrtVqhel0itPTU3z33Xc4OztLCV8Lm5w4LvqcxCLamoLT7ZWn02mqj502jWy1WkiSJNrV1QrKuu1ZYo+toNtVSr1L6Nt+h9i58ny5ZoDfudFohC5EGsWPtcnSYKHWBtCDmEwmePbsGQaDAb7++mu8ePECV1dXALZvIuq8xkWfExvk4iCg3Vo3mw0Wi0VqpZ32suNcXtfTZ3XOqdVquL29ja6Si4neThG2CTnrb1nPWy+B3on+FsDdppRW/LoRhqYOiXo1AFLBPK0UnM1mOD8/x6effoonT57gr3/9K/7xj39gPB7v+99YaFz0OWEgTSvveEFqFVypVAr16DxWg3iNRiNYO1r6mAdxe/oh6gcAAAksSURBVHsb8vIawMsq3aV7mzU339eax6YF+3gNmmu30xB+d7tqD0hXJMayB3aAnc1m+OSTT/Do0aNQAPXNN9/g8vISy+UydT5OGhd9Dmy0XFNjWZFxXYZK4dO6U/AarNK0lVp/FbdNt+nnbXt+n++362+7RGQ/V8VP6x9LGcbShjEofPbDY5fiJ0+e4J///CdevHiB09NTjEaje0uAfQB4jYs+J8yvZ+1GE6stB+4smbZ9jjW9VPFn9YGn27xP+sqKJ8+8/U2IiR9AatDS47JShjHBcvBrt9t4+vQput0uPv/8c5yfn+PFixf45ptv8Pz5cyRJEq17KDou+pyUy+UQnNIeeXphx9bUa325Rvo1CKixgazdXnbl1W1u37rleS7+LAsZC+Lt816xDEVW2jDrXPRYxkra7TZOTk6QJAlGoxE+++wznJ6eYrFYRKsMi46LPgcULEXPba1sAMrOQ+17MA7AEtys0lZds24LVfS96TbrZ9g8PZ+PYfP29nn7bzuViX3H2PtliXlb6jALHWQ53arVamEAYCrVBX8fF31OKHrdromBI7VkWaLQOS47uNqIvZbz6mYO2jmXOfD1ep2abjBwaOfS+wS1ssRrv4cOOrE4xr4ZBH3/fcWZFc/Q0uZms7nXexUVF30O1NI3Gg20Wi00Go2wrNZabUbzgfuCohVilxkeY7vfzmazUK7Kst/BYBA2dGg2m6HMN7ZzrQbI+B1iRTb2e1rsd7OlsXxdrD9gHmu+b8Q9NlVw9sNFnxPOI5vNJjqdDrrdbrDKAEIzDS2tzXKTCS0+cLdwhy2ouIBlOp2GBSyDwQC9Xi9EsHVHnawBwM5traWOxQwUu6RWy2UpQBvktJ+ddYtNRXgu+nvFPIqYl+BBu9dkDYgu+hxo8KjZbKLX62EwGIR8PH9kuvu2VRWQXQOvwTyN3rN+nUtVT09P0ev10Ov10Ol0Qn0/xc+bDgDas49CURHb3nnWSvN4bZxhd+bRWAeXE3PXGY172JqCXZ4Bfyc7jYgtStL/JycbF31OVPSDwQDHx8dYLpepjjV60amYs+audPVj9ee09lyqymlFq9UKde2cZujzVvjaRntX2y6t+NPAIwWvZbT8ftzHjwMQz4VrD7Q6UVuAq0egtQ/6O9mpRLlcDoU+7uLnx0Wfk1LpdYFNs9nE0dERTk5OQrPKSqUScsOlUil0iGG6blteXZ/Xkl5drkvxj0aj1AIe3igwip3tutjEg6lCrQ3QtCAFpCKkoHTNvFp6DmTcx6/VaoWpR6fTQbvdTp2H9vnT76ApUF0arHEODjCVSiUMMLFBwtmOiz4HvLgqlQoajQb6/T4ePXoUGmDWajWMx+PU+nYuKqH4t6W6+DeN/jMgyM0ktKRXrbJWCjJ9RZGxgQeLeuzc3BYAWcsLpGMNdPNjlrfdbqPdbqdiDq1W656wmfLkikMODNpejL+Jxjhub1+XJXe7XZRKd7v6OPvjos8JXfh6vY5er4eTk5NwIerFOxqNgnC0O4yKTgNVsbm+PqeBQT0XAPfmwhwMKB6tHORn2RiC5rw5sGmQjQLXMmE7UNDF58IaWnmdYlCkKnpOBThNYS09sx9ctchgabPZxJMnT4K1p5vv7IeL/gGwKq/dbmMwGKRErxf4wcEBJpNJsLK6G4xa/pj4s6YBu3Le+u9dQsgqHtr2Ofpa+1mVSgXz+TyIWy07W4FzEKLwOQ2hh8CBU7cH53SCuwN3Oh3c3NyExhwerc+Hi/4B8MLlxarRa01XacqKHWLK5XKqWozWE0ivJbfpKktsahBbS/4mgogJ30bLdZDgFIClyRwA1BOxPQG00KnRaITIP6ckAIJ7z0xBv99Hs9nEZ599FqYZXm67Py76nGiwi+kpRq/psvKmwarxeIzpdIokScL21AyiacqOWCtsBb3vYPC20fXvRKcB5XI5xAro3cTScDZoqFtoMdevc3VOK8rlMhaLBR4/fozZbObNMx+Ai/4B0JWt1Wohgsz5KQtl+JgpLPaEY5uo6XQaIvKcHzNC/aGwr5iyvAwVelY+nQOGBj91emSLcPi8Nh5x8uGifwDM1dfr9RC513p59oPr9/sYDoehku7q6gpXV1ehR9xkMgkVd1pbr8tuf6zsqrZTtFiInYfo6XBA4HtVq9VQh6D7CTj746LPiVblNRqNEMmPVdLN5/Ng1bnlk+74wpr66XR6T/y6M6wW0nDerFkAWrzYZhF5LaGKNateXu+1bZem+mJijPXc17m9rT2gq8/3Ynbg+PgYn3/+OY6OjsLc34W/Py76nKjomSe2q+S0WaaWrurKOV09x4U1vOlGkHoMI9jaeccODgxsbesTnxWx1wCbTdtZV1331tO23sxeaHGPFgTpHnqa32cgj9Mi7SKs9Qf1eh3dbhcfffQRHj9+jFqt5oLPiYv+AWjQiqInamFj1ldbYWlVnPbR1wFAha8DBo9n0YpuD63W39atZ62qU3Gr9baRdxuAo+Apeu3vr30E7bbVtqhHI/ixdQM8H03zNRoNz9E/gNIO98+jJDvY5T5nVd3FPAM7ENjH+nfbWstuCRVrtJGFuusxkRNbBKRuvXXtrXtvBz8G5jSIl5XutAt0Yn9zokR/GBf9D8A2d9t6Cva5rPttx2d9VqygZ9dj+5ze73rNroEnK3aQ9djFvhMXveMUjKjod+dTHMf5SeGBvB8BDylAeYjr+7YLXew5ZGUNnPeLu/eO89PF3XvHcVz0jlM4XPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwXPSOUzBc9I5TMFz0jlMwDnb8vfRezsJxnPeGW3rHKRguescpGC56xykYLnrHKRguescpGC56xykY/x9E9HkYLAAMQAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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1hWg0ik6ng0QiYUpm6a67LK/tYtvnSKYFyCgmWQAkB4/xeGxEOi1NRuHKQwqeBwcC2SBEWnqm4njIaLv0KOz3pdwfKvoVYKFNJpNBJBJBv9/HxsaGmdvPmpu75skSe74t3Wm7WEXW/UtrPx6Pb82z2Zu/1WpNWGHpyksRS6/A7gjEQYjTBzkQTIsJKA8HFf2KcDlsOBzGcDicKMRxueoUI5tS2uK2o9ByTk7BU8QULvfQs6cJzGezSSdLVrlLbrvdviV6WRQj3XrZhdbuCgTglrDtFt/Kw0NFvwLSssZiMTNXnVUyaz9e5t9td9+OkMtUGEXMKjWuEbdF3+v1jJWv1+uo1WpoNpsTvfgpeDuIJw855wduT1OAyYYVKvaHz0zRB7W6SQbIZgk4HA475+Z8jmnpMzsib1tLO//N6LhtsaXoZeqOZa+09NwdV+65J6vcpOtuR+nniV1+Xsp6MFP0b/rPdDWRsGvF35R5z7VMtHjagpZ5j5k2MLhe03bbZVTczpMzcHd9fW1SYRQ9U2CyG4y9kQW9A57farUmat3pttu976ULb6f2XO9LRb9ezBS9bg74JdKay9+JqyR21vkUlUyl2WkxOY9vtVpG9NwoU87pgUmvxA7kMWJvl7q6BL6M4B+62O10qVbufYmqegFcls0ubOH9bN3k8hrsJaNMpVGU0u3mgEDX3vd9+L6PZrNpBCzXidui56DCaD/n8HKuLktfXRV48rrtz+GhCx543RhyHa71XTJT9HZTgvcZKdLBYIBQKGTWvNstlRksowvO6LeMBbhEaOfNWcFG4cs8t8yZc4CQ59ivFwqFTOZAvp6derPTacBk0ZCr/p0Zh4cOt86SVYXKbUKzRsEf/vCHgRoio9EohsMhGo0GotEovvvd7+LHP/4xnj17NnEeG2PSCsuloUzLyUi+7W43m000Gg0TYOP8nLl3+5CipXDtMlxW/8lNJuUUQnoQshLP9lo4YPA5lgncue6f5VIvY4HllEm67cyaxONxFAoFZDIZ+L6P09PThZ/7fWU8Hjs//JmW/re//e3buZo14erqCru7u+h0OgCA6+trY70pYlmCysAaLb2rWIZBOUbSmXqTeXGZKrPTZna2gGLnoh47HSgfP60XPAVFix6JRDAcDk12ggf/Pi+28abTAClwvie5WEm2HONS5mw2a9Y91Ot1/OMf/8Dp6SlGoxESiYTxzhSd08/k008/xa9+9Sv84Q9/MPexKQaDnJw3U/S0krZF4lRABujsbaJdwTRX5Z6cw7MlNguE5PbNUvT2YEFBuf4mBW5be5mm5GtIZg0K87Is8n1JgbPoKRqNmkrIzc1NHB8f48mTJ9jf30exWEQ+n0cymUSn08HV1RXq9TrG4/HEtEeZ496HQqFAfkqz0nzsfpNKpcwWyrJuXVpj13POu49MK3aRopVbSfM2d72R78HuR8c0nz2Y2ME7GeRzDT6y9t91ffYhm4fan4G98lCKnIuX2K2oUCjg6OgIX//61/HBBx/g8ePHyOfzGp2/zfLufVBTdvwSu7q3MBpOK0u3cdYuNy6kZbM3nZTY6ULeZl996bIyR29vZyTPl9aczz/rteUc3xa9fB/SOrNSUfYQdPUMkIOT7DcgH8uD/5N4PG6s/LNnz3BycoJ8Pr/w567MEX3QtgFahvF4/EY7plA8tL7A68HGruST99miJdI1tisJ5f2uRhz2Kr15/fns56WYaZVlezAesnuQjHfweew9/aS15+uz+WihUMDe3h729vaQSCRW/h8ElWCa8iWQ/eruetWYbW25eGbWtci8PM+ntyGvTYqXryEj9PIcV+deOWVwDQrSDWdrb4o9lUqZNmBS9PZ22vbABGAiC2EXG3W7XYzHYzOnT6fTiEajzqnHXf6f1hXdn35F5Nz2beJyt13XArwWP0VM19223rbFtq21FDv791G47ORL68x5NQ/purPvH3v/UfB8Htn/b9EFSTJNyAVEDH7yGqetaFRmo6J/gMxKhdGqUxiuUlnpskvLKp9PWniKnYL1PM+0+2I3X7ufn+zxJwcLOWhwYJDewjRx2oFLGUyk6Dc2NjAYDMzrr/O+fveJin4NkBacwrdLZYkMjMnNN+wqQWnhKW7P85DJZMwhu/VKq2/PuSlw+++u150nevv9ymrA8Xg8EfBbt62+Hgoq+jXC5fq7ymflPFmKT4pQCpb9+FnkksvlTA9/z/Mm5uT2fF+K3zXI2MKcN8+eFjDk9bOGwBX0VBZDRf8e4CrckRF76ebbUXKXm+6a28tAnBS3aysvO/A3LSXows5MyOnMvPSmshjqH72nBD1yrUxHLf17gJ1Pt4N7oVAIg8HgVpQf+NJ1ZpCMq/pktF6m/WRgLRqNOtOErmtbJrruSrvJ98Kly/a5yuKo6NeIWaW8rv76slJQLpbhICCFK+FjmRu3A3mueniZ2uPfeF2uslsbO1hpvw97ow8Z3FM3fzlU9GvArFp1++8ysCdr7O3n4NxedtJhBx9265GR+0Qicas0loHAaSk7DgDLpuxs6y73yuNgxQFLWR4V/QNjXtEKMNkay74NTOa42UjCFSBj4I1Cj8fjZltpW7gUr7T0tOyy7NauxnPl6xcNxEnhczXjcDg0hTnxeFxd/BVQ0c/hbZbhShYRgkxT2UK3K+9oIRctw+UGHtJ15/329tn8fZEyXBb3LFKGa1+PnLKwHwHw5Zbg3HfA9grs9xtktAx3Rd5mGa4t9GkLbmzXXBbYTEuPuVbHyW67rgU3XD1ob7g57dr5WHvBDV1+2+JLdx943cNuWipRBhI5XYlEIigUChgOh2bAYTmuFusshi6tdcAo96xVdKHQ/S+tlbXz8ne71p4Dl9x2yhV1d722vZ20vUZADjQyZy/n/jK451oTID0IKXpb/AAQi8VQKpXQarUQCoVM8ZCyOLq01sGsTT5Yl84mGnIFWLfbndqSSjIrCu+qQbfP4+GqinMtsqF4pfjl34ir7h1wN9GwvR/X1MM+pg1u0wYOeg5y2sEmGrVaDf1+3yw+yufzaukXJJimfA78YicSCXz729/G4eGhCYQxcEXBy3bWXAXGFBnwuu/ceDy+tfOsbJdlb/4o3XN7IJHRd7r4tPZye2yXwO2e9rbVd+XFbcG7BgoiB4NZ59nINQPSk5Edgeg1pNNpXF5eolwu4/PPPzftsnK53NR2WUHkpz/9qfN+bZc1gw8++AA/+9nP8J3vfAcAzJwXgGlnzeaW3D2GwSRXN9xut2u64boaY8p94+VP2TdPrqSTc3oZHScUPbvgyr3rZB8+4Pa8X8YD7L7+ZNp35y4CaDJuIb0Evk/GDLLZrGmOub29jUKhgHq9jk8++QSnp6cm2i8X7gSFV69eLd8u68MPP3w7V/NAYaPIer2OSCSC733ve/j+97+Pp0+fTpwnW2CzHz33fGfFmJxfU3w839UCmxtY2CJl7z3entYCm/EFuwU2ANNCS0a3JdMEv0wL7DnGw3n/rMfIa5LP0e12EQ6H4fu+OTcSiSCRSCCfzyOTyaDVamkL7BnMtPSffPJJYCy9tJDc7CKfz2N3d9eZ+mAOnEKUu83IAB0w2ZySe9PJzS5835/onc/nZFksBxV6BfQIZFMNzvGlJ8LX4wYZ0zrw2oJ3ufh8H+QhpsPszS6CznhK3/uZogfw8P6z94BtbexqMVt8rsozWRYr95rnQWHaFl5ua0WvgPvL87XtIh2KV25rRU+CG1/YnXHtWMI6it6Gg/U6XOvboN/vL+/eK18yLaVlC3xa+k264tIT4NzU8zwjdlpguecd4wD1et1MCbg/nRyQpOhZzMJroMCloIHXu/rI4J+8bl6r7NbD13voYpJxCTKr/sCuiVgG12Pf5PneJpqym8E0q21/4fnPlW7xLFFQ8LFYzMzH5Qo2ezrQbreRyWRMpVsqlbq1VbWcz8utqhnNt3PuoVDIDDDhcHjiefgcMvglhS/fM28/RFyBu3nX+ibvZdlYx30xU/QPcZR6F7gKYmadO63Ixa6s431SoLztSpuxCaRsLsnUVTKZxM3NjRGrXS7c6/XQbrdNgI9w7i+DgnILK5bustuuvH7ZkJOvM+2zUx4uM0Vvb5qgTCJTX/OWkLqKUWQuX0JhuQpuOEjE4/GJbbRcomdzS7vNldy62k4P2gf3tbPz9bIWYZGCJOXhoHP6FZBBPG46KS23XZ4qH2cXusjzbM+AwnJNHTY2NsxryxgBH8t4gFzwwsUwMlMgo/lyZ1tmD+xaAVdaT05l1jXoFyRU9Csi95rv9/vY2NhAKpUCgFtFMsBkHbwUPTC9u4x092OxmBFdKBRCLBYzYpQr1ChAXh+bW6bTaWSz2YmttSlyBg3lfvaM9HNQoOWn4OV21/bOuvYAoTwsVPQrQPe52WyiVquh0+kgmUya4hDX2nHbyk+LG9i/y9VnsVjMWH8G/+Q50iugF8IOt4VCYULwsr6AgmYqkV4CU4g8WJsgK/04QMhaAFlZ6FrHoAPB/aKiX4HRaIR2u41yuYyzszPTZYYiSaVSiMfjEzuwALcXuMginmnWnsEzbt8EwMzpZUpNPo+cftBdpwW3y31lvl7WB8jz7RJePl4WD7VaLbRaLVNoZBcbSevPz0K5H1T0KzAYDNBsNvHy5Us8f/4c9XodqVQKzWYTu7u7yOVySKfTpnGEnbuXATlacTsHzp8yas7zGO0HJguCXH3yZG85uV21veBGnifdfh6yEEnWEdgFRCwvrtfrqFQqE6XGDDwuKvhVyneV+ajoF0TmpQeDAer1Ol68eIHPPvsM5XIZiUQCtVoNtVoNm5ubyOfzE9FzuY7c7jcPTJ/Xy8If/m4HDad5CvZ0Qs6x7UCbXYHnWoUn4xK2+Flh6Ps+qtUqyuUyzs/PcXZ2hrOzM1xeXqJer5vKQPkc9vXI61LuHhX9ClD0r169whdffIFKpYJoNArf91Gr1VAoFMwuMbJrjGwrJdflA9PXl8v7bGtui95+HmB54SwadbdLkTmNuLm5ge/7xtJfXFwY8V9eXuLy8hK1Ws2UE9Or4NRhFdahOvAhoaJfgeFwCN/3cXV1hXK5jEqlAgBot9uoVqsTEXPu3srGkZ7nIZvNIp/Po1gsIp/P35rru7ZtslN7tmWfJnoX80pRl8H2EDKZDAqFAnZ3d81cv1ar4erqCmdnZ3j58iVevnyJy8tLVKtVEwNgDEEOAotcS1ALyN4EFf0C2F++4XCI6+trNBoNNJtNtNttDIdD00nW3iJK9ozLZDLI5/MolUrwfX+i8QaR7ju5K8HPY9nnsKcfnK6MRiMUCgWzBqDZbKJSqeD8/HzC4jebTSN6mSlgvEC26GbWhH0MWq3Wrf/NQy8Nfgio6BfErjWXK9jkktVOpzPRC072eqPwc7kctra20Gg0zGPl3Fmm5myrP0v09u13yaysA7sNZbNZ7OzsmJWFjPDb1YGyD7+dCWi1Wjg/P8fp6akJoko4YAZ93cgsVPQrwGi3dEUZ+bYFaHe3YcCv0WhMNM/gczAAyMg/cWUAeFv+vG9cMYFIJGKmOblcbqKQR1b7yamCDBDaoj87O8Pz58/x73//G8+fP8fLly9NvYQUu+xn8FBXvN0HKvoVkBbfLkuVeXLg9RdPtpmm9XLls3u9HjY3N5FOpzEajSZaPNtf3IcmeGDys1nk+ux4hswu2KlGDrTtdtukBl+8eIGPPvoIf/zjH/G3v/1t4rm52Eg2OFFU9Cthu9cy9URcK9FCodCtAhi6t/J3Cj+bzTpr9NcB17XOygy4PJdFGlqyNiIajSKbzeL8/ByVSgW1Wg3dbvdN3sJ7i4p+BeTuLtKFJHZem4RCr/vlyRw3q9tY5CJdfrsfnitvv45W7K7iENlsFt/85jfheR5+8IMfoNPp4C9/+Qt++ctf4vz8/C4u9b1DRb8CnJ9z3m1X00l33y675eN5DuezsksOD4q/VCohm80CAOLx+Nr2d7+rgckeTLe3t7G7u2t+/+pXv4rPPvsMv/71rwEA6XQakUhEg3v/HxX9CoTDYROJj8Vizio43pbYFW3cPFIWuNDis3Em03r8UtvtuWjlXQt41s3620xLu8nP1y5YAoCvfOUr+MlPfoJvfOMbGA6HSKfTgWyBPQ0V/QqEw2Ekk0nTwkp2obWZVl4qA0w8mK5ivp817LKAZXNzE5lMxuzcau9os+5Cl0x7L/PeYywWw4cffogf/ehHC50fNFT0KxCJRExlned5t0S/yJfM1XSCEWoG9GRDzEqlgkqlgkePHmFvbw/FYtGIf1aQb5rX4WLVctZlRPU2BCgXF7HOYd0Cn+8SFf0CuApP0uk0SqUScrmcWVQjm1wsgr3QhavnuLKNFl8uXjk5OcHJyQkODg6wtbVltnKKxWK3tnhexdV/W0J5mwKUaxC0Em8+KvoViEQiyGQyKJVKxuJy8QitDXvLAYt3YJX7zMlddGjxq9UqLi8v8eLFC+zv72NnZwelUgn5fB7ZbHaiYy5Lfx9y0O9Nl9hOO1d+hkFG96e/Q8LhMDzPQ7FYxNbWFgqFglkv3u12J4J5qwjf/uKyvNf3fVxeXuL09BSFQgGbm5solUrY2trC9vY2SqUSNjc3nav85Pzffl1ZTATcbtw565rt9l+uMuFZj19mKuTqN+DCtSJReY2KfkHkFywcDiOVSmFzcxM7OzvY3d1Fr9czm1Jyn7V+vw9geeHzMQz2sS/9zc0NGo2GWb/veR5yuRwKhYI5uHsrW3dxTT+nILJ5piyHlc0tZIsuuXW0LEaSG2BSvFxrIBcccUmx3GdeTkMWteLSci8yIOl8fjoq+hUIhUKIx+PI5XLY3t7Go0ePMBqNUK1WTRCp3W4jFArdEj6wuPinBeFYzMNlq69evTLi4sq+ZDKJZDKJVCpl6t658AeY3NHWXsoqe/KxFwAbgfB6ZBcdAKYnP5cU53I5c3DwkdMOue+87O8ng4nS25GNOTmwyOIoZXFU9CvAElHP87C9vY3j42Mzz2ebrGq1OrH9FKvx7JbRi8xrbbeWlpkFPa7rmydcWRsgBUXh0crLx7q8BNvKS9Hn83nkcrmJeIMciGSvARY6SSG7WnOFQiHkcjkUi0Wk0+lbm3ko81HRr0g4HEYikUCxWMTBwQE2NjZQKBSQz+dNr/mNjQ00Gg2Ew+GJnWZd8+hlxL/IeRQy97Nzdee1m1VK7BWCdKntBUVS9CxPpschDylw/s5mI9xjPp1OmykBX0eutru5uUE8Hsf+/j6ePn1qpgq6KctyqOhXhKW4TN1tbGyYhpguy+X7vnNV3rtIMVE8b/sxEteyYu7fx0FBdhJKp9PGG2DWQS6zpceUTCbx9OlTRKNR8xzSA1GrPx8V/YrQBU4mk8jlcmYAkJtM0mrRXW02mxNbW/N55E/g9WaRZNbqtIeKPWjQ45DBPM7NpYvPTIMseOJzdbtdpNNpDAYDeJ6HUqmE7e1t061HWQwV/Qpw3huJRJBIJJDJZBCPxzEYDMzmEtls1sxrC4UCzs/PUS6XTXsoNnyQK+5cy3HfB+SAxvcLwMzROXVgHILTBOm+c0rU6XRQLBbNmoT37bN6F6joVyQUCpmtrFiIQ4vE/P3e3h4eP36Mq6sr0xeuXC6jXC6jWq2iXq+j0WiYFXWruNPTPIVZ574ptrdhb2AxbaGR/fs0wfKzld6AbB+ezWbNGgSdzy+Pin5F6KqGQiHT0w7AxDyUq+bY6aVSqeDy8tK0hWY3XbbPovjtXWFkPtzGJbSHZP3sQcleJSjn/hS5zBrQ/WeAcGdnB1/72tfw7NkzbG1tTa06U6YTmjNHXI8J5D1iV6PxPv7kAMAmj77vo9lsmk66vu/D932zhr7VapnW0c1m05zL7q+yvdZDxbbUdNuluy7TiVymzDQec/oyrcfzCoUCjo+P8eTJE5RKJSSTSQ3eTcf5wajo3wH2whppwWUHWDkwsFc8PYOLiwszLWDlHzeOlFtPSc/grpHzb1nqSkFLEdNS8ydFLi05c/ae503EQljM43nehOhl1D+ZTM5c0qwAUNGvD+z6ymW1tVrNzP+5Mwzr/OVmlK4toqfNs+fVCNguuKumXqbkGHGX4uYhy3n50y7XdRXt2CW8ytKo6NcJub5eHnIjSXvBi+uwn3Pa77M678if9u1ph2t/Pft3u+jH3n1X18S/MSr6+2Ze6e372P3mLpGfm35GC6Gif0jMcqkV5Y5wfqF0onRPLLLW3L49z11f5HnvEvv137Rtln2fDoJvB7X0D5xFhL5OLCJkFfudoe69ogQMp+i1A4GiBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBAwVvaIEDBW9ogQMFb2iBIzonL+H3slVKIryzlBLrygBQ0WvKAFDRa8oAUNFrygBQ0WvKAFDRa8oAeP/AXHUpUUDZBj7AAAAAElFTkSuQmCC\n", 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" ] @@ -1176,7 +1189,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "current beta: 64\n", "Current iteration: 39\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -1185,7 +1197,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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Z2tpeLw8GA3S7XROz5gxsbw7Bv6XYe72eObjbrT2bU5h0wDF0Jtt+MeVYnj8YDIzweVDoNOFtj7oK+n5R0W8Bm1yk02kEg0GMRiOEQiHjtLPbTRO5Fpezvmvmc5nRnFFl3JrFJrbomeRC05uHFCX/ngMABS5DZdyyy57tOXPLbrq8lWt1Cl6Fvjuo6LeE5bDsDstusLbgXYJmY0rpAbehcKTg6STjdtX9fh+TycQkpVD4wDei5wzMBBcpejmDy8w4vp5cFki/gPQxyLW+dP4BD39XnjcZFf0WyHV0OByeawO9bjGMXOfLAcBOU5UzfLVaNS26ms0mut2uET1ney4pKHpujmnvaW9vuilnc2nOyz3vXdfpWrKoyHebpaL3anYTv7TLQmh8fFX6q0sALk+4nEWlU41hMHrDy+Wy2Rev2+3OefGl6Lmm7nQ66Ha7c3vbcwbn4CJnbSl2ezZ3hfxeZvMO5X5YKvqX/Qe6mkgsWsPe1Wu4Hgc26xFH1slwWzcxxp4h5VpaetXl+rrX66HT6aBerxvBs3SUaascmGXRD/CNGOlko9htJ57LVHf9LsWu5vvDZ6nodXPAb7Bnc9eXXJrztmlvD3TSA05R0uymMGXBCePe3D2H4TA7bCd9CdJ7z+eVIThptvOapOD5HHKQst/7rotdRk/WGcC9gqp6DVyedSkGCk9aMnZmHIBb6aoUdaPRMN51aY53Oh3jrechU1elA1Am6PC1ZBjOjrkzQcZO0lkk+IckdiL33lNesFT0f/nLX17Xddw7chYYj8fw+XzIZrM4PDxEJBK5da6MbzNMRaHLGnoi1+lsqtloNMz+d0xqodjZdFM24LQTXeQAY4t+NpvN1afbGXbAbcHL2dy+j+yygJjeLP8vym18y/6JP/nJT3b3P/wK4Bem0WggGAziBz/4AX72s5/hgw8+ADBvAsukFM68k8lkLrWWjjUKUBaj1Go145xjCI4hNZlEw4PrcLlzi517Lx15AJZ65G3nnB1TXxSCuy/RyyWTnfQUCAQQjUbNDrmtVgtPnz69h6vcLWazmXM9s3Sm/+1vf/tqruaBUCqVsLe3h16vBwDm1ufzmWw0irTX65mZhc0zlomeOeqsJ+fzyBRY25NuO0XtFNzpdGp2crWFK51xi3A142BOgeucV4mrmEgmIXFfP27nlclksL+/j2w2i3q9jr/+9a94+vQpptMpotEofD7fLavFq+iafglffvklfvWrX+EPf/iD+bLLvd6l+OU6W4qR4qHTjOfyoKNNrrdlyqsd/7ZLaoPB4JxQx+PxnIkvHVgcgOT5dmagK0xrC39T1h0kXMsjipyViNytt1gs4smTJ3jnnXdwfHxsKhtjsRj6/T5ubm5Qr9cxm83mBkJlhXnv8/k8+Skt89LLZhjcTopipgkun8flMbZNZXm7qKzVvj65hqcgZImu3I5axt8XvYYrd8C1BFj1mdlOTFfSknzc3oabA5m0ljjIBoNB027syZMn+Oijj/Dtb38bb731FvL5vHrnb+P8QJaKPhQKeVL0nClXbY7A3ngyyWVdKAZbIKu85PYsT9Ne1uXLtb28NjkILBI8z+XP9lrfdT28tcuFKVhaR6FQaK76kO+fg4L8O7Yfs2sKIpEICoUCzs7O8MEHH+C9995DoVBY+3P3GJuv6b22DdCmvIx3WIpJznrENYDQPKfA6DiUYpQWgAzfSSfkopRZu5iGP4dCoTmHnmuJIZuAsjsQOwPF43HTOow9BKWYZXMRKXTZi2A6nWI4HJoORcfHx3j06BHi8fjW/wOvomv6FbjEc1drQ3utvirtmcKTa3Nb8FI4dkMNvpbr+qVZzxi/nSJMZIGPLfJUKoV0Oo10Om16CLJnIBuFSienfcjZXy6zWPILAOl0Gnt7e6bKkZ8N/SnL3qeX0P3pt0SGyF4l9hdUJvrIc2x/gy1GCp8Vf7Z45LnyliKxS2ZlXJ9Ip1osFjPe82w2i0KhgEKhYPoEyi5CcoZ31TXY71UORMxSnEwmpsegXMYsakSi3EZFv6MsEjWFYG9DTMFz3czefHb3HtuhZqequjahkHkBNLsZMuPMXigUkM/njegZM4/H46ZtmLwWV50Db6XgpejD4bDpXUDTX9kcFf0DwjXzExmvp/DlHnhynW8fEq7jZWIPX4eDihR8JpNBLpdDPp9HPp83Ypezu9yHb1nVonxP0tkol1dye29XoxJlNSr6B8qiRB25rnd125WOM9vhR6FJE1/G8GXYLJlMIp1OI5fLmYNreNn229VFyE4Ast+PfF8A5hKPgNUlz8pyVPRvEDL8JUNf/Fk+ZgtfLh0o+lAodKt0Vzrt2NefJrx00tmOumXrd/6+yK9hVxLqDP9y6KJIWQuXM9Pr3vGHis70bxAyuYaOOBneYy8/3m5q3rNyjbO/9B/QhyCXE3bikV1uvMy8d2UDykiKDjjbo6J/oCwKb1G0o9HICH40Gt0SuAznyeey++VJR57P5zPiloVGsl6fNQTJZBLRaNTkvsvQocs0d4UV5WAlIwqBQEDj8C+Biv4B4VoHE5lIQ5Fztl4WspOilyEyO2RHLzpn90gkYjLtarUacrkcisWiCd3lcjmkUqm5nX4WDTTAeiE7FiYxZLesPkFZjIp+R1nkqLJj64QioeCB+dmc57heQzrMXDO9nZxDR51MzqEnn7F6O3y3aJPOZV54V3LOeDxGLBYzoUNlc1T0K3iVabiSdbzRLu+3bebTvOdj66Th2gU3dvvrddNwGbtn/D6TyZgBIZFIIBKJ3Jr1XWm4toeeSxT2M0in0wiHw4jH45hOp2YZo2m482ga7pa8yjRcW7jrFNzwfNcsScEzj3+TghtpUtsFN3aev71UcJXCMqzHohvO9hQ9E3bswhs7zMgBgMuWYDCIfD4PAGbAYbWjZuith3bDdUBH0Tqltaxb37S01hW/lrOUXT1HpNhd62NZCy9vNy2ttWvxeZ7reuTnJmdrl1efCTv2Tr2LRC+tAuCb0tpisYhOpwO/329yBZT10dJaB8uq3ZiCGo/HzQwjm0/aTTQA9wxum58uB9Yi81Qm4fBnCV9j3SYartl+myYa3G2Hv3e73bnBSZrsdu6/qyUWxc+BIhQKIRaLIZfLoV6vz9Ug5HI5nenXxJtT+Qr45Y5Go/jud7+Lk5MT8xhnLrln3LJ2WdLElptY0DEld4aVHnNpVtsWhKuW3d4lVwpamun2Gt0W9SKxy2vYdK287vnSguEgYHcFikajuL6+RqVSQbVaxbNnz7Rd1gL+7d/+zXm/tstawre+9S384he/wPe//30AMDMXANMbj22qKXrghXdbNsbkxhOy9XW9Xjc977nNMwcANsZkgoztW5AzJc1iDgLSGy8TdSh4OSDYwl52P3kd4rHrCWwrgJED1uun02ns7+8bK+CLL77A06dPMZlMzEajXmuMeXFxsXnnnI8//vjVXM2Owth2vV5HIBDAD3/4Q/zoRz/C+++/D2A+WUTu+8521Zzl7Vz32WxmBgnZApuzVa1WQ7PZnOuKK60HuYusFL+r0EbO9py11y1QcS03bKG8rtnSfn25LPD7/Wi328Zzz5oA5ga0221tgb2EpTP9F1984ZmZXopFbnZxcHCAcDh863y5oQK93XweV9kqZ1xaB9zZhltPNxoNNBoNs9kFN5zkjjeyzTbFz1RWubaX7aXszS7kjO+a2e0lgRSd7e3fRTjY2r4VrzJb0Pd+qegB7O5/+DVizza2w0uu46Vzyk6K4fncA55bSMv94+W2Vtzqqtlsmu2tdFur9WGc+qFc710zGo02N++Vb1iUMy6FLk1tVwiOv9sHM9tSqZRZJrAnPgcFewNLLgPonAPmQ4B8TQ4utlebry2jFHJWZ4xfWi92Y0z5vnYVV6hx2RJHvrdNcf3tyzzfq0RDdkuwRSzvd2W1yfvtc1wZbQz5Ufj2VtX9ft84/pjdFovFzFbVNNsBzJn2wIte/HYvfJ/PZ5Yv9GHQApGDkUv4fF75fu33tktsUw78Mu/F9be7+NksFf0ujlKvA36h13n/nCEp8mU58/J56Xjjzywgkamvw+HQZLSxSQVTWVutFvr9/lzKrXTkMZTY6XRMVRzXvNIvYGfe0fRnCa4dwpOlugDm3js/D2W3WSp6O+lDmUeKZlEaqMsqsJcDwIuZVK6jmcUmIwHMdIvH43Oil+Es4EVIkX6CVquFaDQ6lxsgBxj6AFybXcosPTu+L8ODtrWj7Ca6pt8C6cRjPJ1is8NmPJ+3dnbbosFiNps5i0+YsMIkFJrltBZs0dMhSGehzCmQve0pdFdtvF1bLy0Sl/hts18Hgd1CRb8l3IW22+2aGm/mgNt958gib7jt7ee5cokh4/NsTjkcDs15MhkIwFwugYwESNHbGYBMCmLtOi0JOfPLJYDLOpDLBp6rs/9uoaLfAjrJms0marUa+v2+yQmfzWZzXWAli0QP4JbDUP5sN4wIBoOIx+NGhLIRpp32S/HKfe85k8t6eYqbgqf4OfPbSwF5Pp+fuQUynwDAXE4+Pwfl/lDRb8F0OkW328XNzQ0uLy/RbreRSCQwGAyQz+cRj8fnNmq0HV22ab9I7LK1VSgUMrN6KBQyM62sUFvU706m9kqx2448KWR5vuygI8+XachMNmo0GiangFaFTGBaVrGnvB5U9FswHo/RaDTw/Plz/POf/0S9XkcikUCz2cTh4aEJr3GXGdm9BnDv8Arcdpzann4Ac55+nmOv/TnI2Otv2yFnZ+PZvejsdlny7+T6n7N8s9lEo9EwOQWVSgWVSgW1Ws0kFjEleRWLoiA6WLw8Kvo1keG48XiMer2OZ8+e4auvvkKlUkE0GkWtVkOtVjMVX3Z/OIrc7iILYC585xogiAyZ2VmAizIBXQ5E+bg8xyVwV06CtCRoHXS7XbTbbdTrdZTLZVxdXeHi4gIXFxe4urpCuVxGu902ba/s4h/7NZRXg4p+Cyj6i4sLPHv2DJVKBcFg0Mxy+XwemUwGqVQKsVjMNJFgWW40Gp3rKmM77RYlBEk/gZ3Z53IGLsOVPbbsd9fzylnfTi9uNBqoVCoolUq4vLzE5eUlrq6ucH19jWq1ahKM7EjCpux6gtAuoqLfgvF4jFarhXK5jHK5jGq1CgDodDqo1WqmC6xMrGEpaCKRQDqdRjabNTu7yi8szXg5CEiTXf5uH3xsFZtU261KW+U1z2azuQ65+Xweh4eHODs7MwPA1dUVzs/PcXFxgcvLS1PzztmfA4BdV6DcLSr6NbBnETry6LRimSer5yqVismeC4fDZranILLZLIrFIg4ODjAYDG41tZCtpIDbHXheRvDrsKm1wAGJ1xcOhzGbzZBOp40jsd1u4/T0FGdnZ2bGl6KXJcWuQ1oTMlS6yCLRmX8xKvo1kWv66XQ6V0/Penfmy8tGj1zPcxCIx+PIZDIoFotoNptmfUsHGzd/5Oy5TOgusd9X6vSi16WjMhwOI5FIIJ/P4+TkxOQO2BtmyBwBZhIyq5CDx83NDZ4/f47Ly0sMBoO5a2A9gYp+MSr6LZH19DIMZgtQetbZq5318/zSM0Q2Ho+Ry+UwnU5Nl1fAHcO3Bb9LdRK2g5Cpw6FQCMlkEoVC4VZ/fTtJiOFAlhbzc2q327i+vsb5+blZKlxfX6NWq80VIAG3U5t36TO6T1T0WyBNSBkS42O8n8imGvwisz0WzVqZ/looFOY8/7IFlmtW37Uvs/x8XNdIC4jnLAofyopDDgqj0ciEB+v1Os7Pz/HZZ5/h008/xd///ve565CNS1X0L1DRb4FtXtODLbEr0fh3MlmGdfNy7UpnVqFQMA5BVyjvIbAsQuDKSFz0d4uYTqcolUo4ODhAKBRCNps1foJarTZn+isvUNFvgcx1Z0KNK+btcjKxTt3Od6c/gJ1zut0uDg4Obq1PXU69hzKL2REIef82+P1+HB4e4nvf+x5yuRx+/OMfo9/v409/+hM++eQTXF1d3cl1v2mo6LeA63Om2toFJbI4xiVYGd/mudy2SbbMohVQLBaRTqcRj8dv9d57aLzs4OQaSI+Pj+falH/44Yf46quv8Otf/xoAkEwmEQgEPN8Uhqjot4AtmLk/27opo9Lc50YN7KJLRx4dWExpbbVa6PV6ePToEYB5px6z8+56Br1vFnneXfe7SpPff/99/PznP8dHH32EyWSCZDLpyRbYi1DRb4Hf7ze7tcZiMTPbu3B9USlUOz5fr9fnuuXKvvj0XnOtb+f176pTbxsWvYd131s4HMbHH3+Mn/70pxv9nVdQ0W9BIBBAIpFANptFMpm8Jfp1vmS2k89OZ5V18MxlL5fLODk5waNHj5DP5434l5n7mzSy2LbufRNRvQoByuUSS5vlQKjMo6JfA/vLw40TC4UCMpmMCa3J1lnrYIepuHEmQ1X9fh+NRsMUr1xdXaFUKuGtt97C8fEx9vb2kMlkzN7vdnntNqb+qxLKqxSgrD/QpJzVqOi3IBAIIJlMolgsIp/PI5lMot1uz20kwcaSwPodWO12VDT1GZeuVqsolUo4Pz/H0dERDg4OUCwWkcvlzPZOckvocDi8006/dQW6qSUhP0Mvo/vT3yFypqfwWTE2GAzmwnXbCN/+4jJDrdVqoVQq4enTp8jlcigUCtjb2zNHsVhEoVAwVX70OTCt167r5+vayUR25d6ya5alsXaB0LqFPeueZ5+/bO3/0KMcrxIV/ZrIL5jf70c8Hkc+n8fBwQEODw9N+yx2kQEw1y4K2KznOsXPuP54PEav1zPmPqMH2WzWVOzlcjnk83kz86fTaTPzMw1W5hXIxhnSqcj0YXsjTjsT0W6MyUw71hnwCIVCc119XQ0/V30ucoBZZ28+Xc8vRkW/BT6fD+FwGOl0GgcHB3j8+DGm06npEuPz+dDpdODz+W4JH1hf/IuccLK9da1WM3X6sl6fpbzxeNyY+xQf8GLbK9n1VoqegwRFay8VmGPA9xUMBhGJRBCLxZBKpZDJZMyRTCaN1cFlBw/Z3tsuIZbWDv0crEKk9aKz+eao6LfA5/umT10qlcL+/j5OT08RCASQSqVMm6xKpWJi7IPBAD6fb87Rt4lX3TZr5WYYvV7PeX0UkxwQQqHQXONMRgqkoOT7o7joJJSJSDxfzvLsxy9Fn81mkUqlTEpxNBo1AxEHATkQ2JtwMnORFXg+nw/pdBqFQgHJZNLk1yvro6LfEr/fj2g0ilwuh+PjY4TDYWSzWWQyGVMeWy6XUa/X4ff755pDutbRm4h/nfM4g/f7/blqP1ex0CKHl9z+2l7j238jy4nD4bARs7Q67N/peLSdkByc5N4C/X4fvV4P4XAYx8fHePfdd+deU1kfFf2WMBU3lUphb28P4XB4rkUWZ69gMGgaQ8p16evsCsuZeVPYN2AbXGXFHBB4sLsQLYFUKmU6CUuLhLN8r9dDIpFAvV43+//FYrE5P4XO+qtR0W8JTehoNIpMJmMSdqTpynUnv4jNZtOsheXzyFtgfpdYYHl12q5iDzS0OGg5BINB4yuQfgh+ZvJzo+UyHA6RTCYxmUyQSCRQLBaxv79vmosq66Gi3wI6nAKBAGKxGCaTCSKRCMbjsfGac01Lj/rl5SWq1arx8HP3GNk8ws7Se1OQAxp3zGUYUoYHpbkuW4axb+BsNkO/30c+nzcbeL5pn9XrQEW/JXR2xeNxk4jD2S2fz2Nvbw+PHj3C2dkZyuUySqUSSqXSXDNN5tZzZ5htTPBFlsKyc18WV89Aef+iQiP790WCtR2RNPc5ANCRl0qldD2/BSr6LeEXk+EtO8wk2z7ZLaGvr69xdXWFm5sblMtl0z6L4pcdYe14uI1LaLs0+9mDkl0lyJmeImfUQLYM58+hUAgHBwf48MMP8d5772Fvb29h1pmyGN+KNeLDWEDeI1KMcg3KW7kFFBN3WDbbbDZNN91Op2MKbLrdrrmf57o2oNxlGH6jA09mBEqnHtfz9IPE43HjxZcHxZ/L5XB2doa3334bxWIRsVhMnXeLcX4wKvrXgF1YY281Ze8S02q1UKvVcHNzg1KpZAptuCxgh1i226JvwLYM7hq5/paprrYpzrbfcqam446HFHsymUQqlTL+EOY72E5RhgJ537KSZgWAiv7hwFbPbP5Yq9XM+l9uDCm76NobTK7K6luVI+Aywe2cehmSkym79Mrb6bfyVprwMnNQbg4ik4rozFM2QkX/kJD19fYhhe3ajFIe9nMu+n1Z5x27kEb+vOiwB4tFf29bDfJ3rYl/aVT0982q1Ns3qfvNq0B+bvoZrYWKfpdYZlIryh3h/ELpQumeWLeGXP68ylxf53nvEvv1X7Zt1rr3KS+HzvQ7zjpCf0isI2IV+p2h5r2ieAyn6LUDgaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4DBW9ongMFb2ieAwVvaJ4jOCKx32v5SoURXlt6EyvKB5DRa8oHkNFrygeQ0WvKB5DRa8oHkNFryge4/8DI36WHqzP/BsAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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xGAzQ6/WMEO298GTTDwqez5Hvw2Nlvz8ez7m8jLpzgJAehf25lDeHin4N2OQil8shHo9jOBxia2trJuUW1JrKnh/byMcoeAqO22UxZSVdZbnqbjqdmgGi0+nc2QSz0+kYwcqqN5eIKXi5K649HZDnKeMAm7gHXxhQ0a8Jl8NGo1GMx2MTnJMW3jUnlbvXBKXi+Lt0vWVk3Pd9ExBjaktOEWQ+mzvfcm7dbreN6y2tt0v8clCwg3wuzyVoVx7lYaGiXwPZ/MKOzAeVzNrPtyPnEhlgo+A7nQ7a7TZarZZp3sGlvRww5K4wg8HARNMpeuni2wE6Wmk5CNA9p+hdJbUv08dfeTPMFX1Yq5tkgGyegO0Amm2pF6XP5llOubsLLTZTYcxz2+v5ZRCPouc+9xwk7K2wbXddptJssdvCVsFvJnNF/7L/RFcTCYrhvi6QRa+1SqFK0IKWRc8JGhhcn9XOc9uRdBm4YySdVW2NRgP1et2IngKWQUG+PnfUlZF1262X52HPxWVjzyCrvuz3qjws5opeNwf8Eikq+TtxlcXKx12vJ0VtB8zo0ktrTdE3m020Wq2Z7bHt86Ol52tT/LLE1ZWv530peArdjsJvgtjtdOmbXoz0UFBVL0HQnFtaRj7O1k1BkXta2XmpNFn0IuflvHFxit2QUyLfhzl3O9dui52Cl58R2EzBA181htyU831dzBX93/72t9d1Hm8cKdLRaIRIJGK62tgtlRkhZ+CLwS9aE1l0A8wW18gKNopZzrcpUFn4QmsvBwa7N58M5FHIjMTLfLycw9uCd7nvmwS3zpKZCOUukXn/4B/96Eeb+d9fk3g8jvF4jFarhXg8jg8++AA/+clP8M1vftMcI11zCrLT6ZiiFApfLo+lhWd+nRa71WqZNJodZLNdf1kkY69Ak806ZOceWT0n3XpXgE4OAACWGgxWHRxc3s+qz5WDG72qaDSKZDJpdvppt9v47LPPVjq3t5HpdOqcz8y19L/5zW9ezdlsCLVaDZVKxTRq6HQ6xpKziIVWmCvGJpOJOYY5ewqQxTW08vJ5smRVtoaat02WrMKzW2pJa28H6gB3vIE1BNPpdKaeQIqMr+16jUUiXlXkrjJlWnM2JOE22LlczjQSbbVa+Pjjj/H8+XNMJhMkk0nzf1B0Tj+XTz/9FL/85S/x+9//3lhQNsRgtxxp8RlYA+5uPGEvQaXVZupMClu64PZKN762LL2Nx+PG6tG7CCqWiUajZuCQ1tP++3g8npkuyONd4uVgZ79f0PE28lxkizEuSGI9BMVeLpfx5MkTfP3rX8fR0ZFZ2ZhKpdDr9XB5eYlWq4XpdGoG302dttw3c937SCQSym9p3sXNTSm4fxyAmYUqrtJT2yLO+91VAGO/lrSC9nbSdixBxhPs2gCJnWt3zfeXFY1c7msvJpINRO2sB/DVBiBsIhqPx83mHuxMVCgU8PjxY3znO9/Bd7/7XTx9+hTFYlG3rr7L6u59WFN2vHhcmyMwwAYAiURixn1fx321V8ctqiuQ4nANBrTU/J2uOr0B+fnmYWcn5rnG8rPIXXtkb0DZN8AuauLAYPcWpCsvpy3b29sol8s4OTnBu+++i6dPn6JcLi/xjStkrqrDtg3QqrzMjikULDMFwHJitKcNkUjE1P5L958WNRaLYTwez4jK9XpyAJLnKC28PSWQnoV0v3ljhyD+ZOcgWbosPQJ70JADA6dGsVgMpVIJh4eHODw8RDqdXvdfEFrCacpXQLrL971qzBYTxbnofHguAGasu33Otlcgz11aWbuDrxww5LEylkBR0oqzCSgFznZgmUzG9AdkpyC78adt8e3yZ7mACPiyF2GlUjHLmHmMjCvc5/9pU9H96ddkkWt7X7isadD52JVm9gVOSzmvdTbFRatLQco5dFBLbx6TSCSQTCaRTCaN0D3Pm2kKmkqlZjbtWGZRkkwbMrDJTMloNDLvx11/gNnBWZmPiv4B4rJQdiqO1ttVoGPPj+3pgBQ8019s1S1bdnMebt9ki2+68bLbry12252Xn0d+XjuIaIt+a2sLw+HQvKaKfD1U9BuCnRsPcl/tNJdrJaC02hQtrSfdc1pwCtd1k8KXNzlYuFx5+TnkZ5P37ZoBOdi5AnzK8qjo3wJc+Xs7lecS/NbWlrHQsiW37NcvhS8FLX/aHoGdPrQFHpQztx+TAU5+lul0OreRqLIYFf0GElT4YgfFZM7bLniR83LbVafYeV+KndF3Pn+e2F1deucJVQ4GvG9XH6rYXx6dFCn3Qtgj5ZuEWvoNxA7MEXvxjEwByoVAdj7fzo27avhlk4/xeIxEIhGYBrQj87JgaF52wvZgXAVCdrmvsjoq+rcAW6CsEJQ1+BTiaDRCLBZzdriVffK53p9puUXBPG7oKQtwmCd2ufdBqUSX8O0uQ8DdNf7K8qjoNwRXvb7L4lHwTGdR4HadO1N2st11t9u9M4e35+1ybi8LcmTKjvc5WMiUncsTWJSy49oGLl9m/ECFvx4q+gfIomCXzHW7XHEusuFP/k2+hhR+t9u9I265ss1eBCNr6mWenkU5dnEO9/OTW3wtyrFLKy87Ao/HYzOY6FLZ9VDRL+BVluFKlolu83wWucsUetB8n3/jTc7lZf7bHlRk9Nyu5Asqw+V9WYZrv4dd4gt8VQLMz8PWX8CXZbgcbJi71zLcu2gZ7pq8yjJcW7CLrJ88fl6fffvil5/B/ixB9e/yuUHLe4PSgBwA7IIf14IbKXa75FfuBUjhc0twAGapLYuQtEJvOXRprQNGuOetoqO1i0Te3NJaKRb5GtKtd7W2Josi6fZUYZnPIgcAWeZLC2+7+EGlw/YUgySTSZTLZdze3iIajcLzPF1ptyK6tNbBvE0+ZH0555WsDZebSrpwCcx+zNVAI2g+Ll1zuZedPXDIQel1N9Gw79sxCfmZ7OmGnELwd24U2mw2MR6PjWHSJhrLE05TvgBe3MlkEt/73vdwdHRkasEZ3ebFJtNbcm856bJLi8l2WXLPOLsPni1SW3TSJZYlt652Wa6IuByY5kXOXQOQPFYSNIgEHe/C9hhk7ED2xkulUqjX66jX6zg7O8Ph4aFpl5VOp9Hr9VCr1WbaZYWRn/3sZ87HtV3WHN577z38/Oc/x/e//30Adxtj2r3re73eTMMKXrjT6XRm11m7MabciIKDwaLGmC6Lb5eo8jVkg0y+FhC81Zb0EKTYF7XIvs/AmR3ck16AFH82m4Xnecjn89jd3TWNMf/+97/js88+M9F+Dr5h4vz8fPV2WR9++OGrOZsHCqPAzWYTsVgMH3zwAX7wgx/caYE9mUxmNqNga2sKyg6y2aJnv3u7Bbbsiivz53YzzWVaYEsBDodDc9FLz2NZwd9XC2ye6zLPs9+bg9lgMEA0GkW73Z5pgb29vT3TAvv58+crn1tYmGvpP/7449BYejkflJtdVCqVO5tdADDWU7rotMD2whBevDye3gE3uuC+dHKzC+alXd6E3MBCnn/Q+/EnPQjZ9941b583GPD1HyKc5vAzhp1pQN/7uaIH8DD/u68Z29rY5aGy4WRQVxhZaGJPDbhhhm3le72e8Qx83zeegdzpxv7/0ZLK95Eeg9z0wiV2O+q/SaK3YZ56U873vhkOh6u798qXBBXOzCtekcfzonPlpROJBNLp9IwgKVgZA2i1Wmg0Gmg0GittYCkDe3ZxDgcsu0mFvVBHTgn43E0QUlA1YhDzUpiLcD33ZV7vVaIpuznYopaP25bPNZcOmgNzDp5IJExpq2uralrqdrtt8tEseb25uTG16HYFHl17xhzsVJadWeB7s+YA+EowHBBkIExezA9Z/K7A3aLzfZnP43ruQ/x+5or+IY5SrwPbci46Nuh4uwyWj8mKOg4Arnk0m0DKllWsYU8mk+h0OiY6L6cUcq893/fvpL0Gg4EJWtq76chgpL3nnSv/LwfAh3iBK3eZK3q7R7oyi7SStkcwbwAIKlIhFBlFapfbsvV0t9ud2TTTdu87nY5peyUbWto738pUnkzt0bq7xC+tP3/fFLc/7Oicfg1kEI8iiUajZscbebOfZ08BXCW4PFbGCGRAjUUrDM4BdxcGDYdDdDodZLNZs+qN2QLu0sPYgRS73B1X1gvIoh0Z5KP7z7+73H4dCB4WKvo14dbTdLHZSQbATEksCSp4sUtRJbb1lqJPJBJm80s7TUjrzOh/Pp9Hq9UyBUGyGIiBQxk8lFtk9/v9mSIfafk5NZDegmtwUB4WKvo1YHGO7/uo1+vo9XpIpVLY2dmB53mmVtzOnds3wL1NFZHLTLe2toyIWIxCkdlNIylIGcxzpQYpdgrWThcy3Se3z5Z73svn8FjpQfC7stGB4M2iol+DyWSCTqeDy8tLnJ+fo91uI5PJoN/vo1gsmsU4XIVnC5+4ykxta0/3mcIHYOb0tpW3pwKy/ZW03q7yXgqV7r0tYlnYw2mALCJi8RCrC2UtgYwdLCrlVV49KvolkdHr0WiEVquFs7Mz/Otf/0Kz2UQmk4Hv+6hUKsjn8/A8b2attz13Z0COi0FkEEwex/eV+8rZzS1dRUHSzZZitVOD9Ar4U7r60v13ufIUfrfbNYJneXGz2TQ3Ti2CCoqCCMqe6IDxcqjo12A0GqHRaOCLL77Ap59+iuvraySTSTSbTdTrdRSLRRQKBdPhxd7pRfabsz2CoAIg+btcNSZrCeznu6YUQbUEMlAoB4V5pbkcEOgZsHqw2Wzi+voa1WoVFxcXuLi4wOXlJRqNBnzfn7H+riYd9rkp94uKfg1GoxGazSbOz8/xxRdfoF6vIxaLodVqoV6vY2dnx1h7pswobnaUkX3kAHdKTyItukzRubIFL1M4s0zUXQYl7WlEt9s1FYT1eh21Wg21Wg3VahW1Ws2Iv9VqmbUFnFKsswpuE4qEHhoq+jUYjUbwfR+Xl5fmIgaA29tbNJtNeJ5ndm+VnWHZgCOXy6FQKBiPwLZ0Mv3miu7bjwWl/YJYVIq6LLaXQOtfKpVMPIALiq6vr3FxcYHz83Ocn5+jWq3i6uoKrVYL7XZ7JnAoA4HK/aOiXwJbCAzkca56e3uLyWRiLnLZJ072g89kMvA8D4VCAeVyGZVKxQTK5HvI+TtxCV3+bh+/Lqu8hivTwBWJcgrAUuJms4larYaLiwvUajVcX18b0csVhrT+Mm0o6wlkFsL+36jlX4yKfklkIM+ubedFGY/H0ev1nL3e2C46nU4jn8+jXC6b+nlG0WnxmfOXUXngrmV3if1NlU673ld+B8lkEtlsFuVyGU+ePDEpRNYM2EuAZVbA933zPbfbbVSrVZydneGLL75At9udOQcOmGFfNzIPFf2ayGo8/pQVacBscwsurEkmk2ZOKy0cX6NQKCCTyZgAILFX77lc/YeAy8KyriCRSCCbzTqzCjKl5woOdjodDAYD+L6ParWK58+f4/PPP8fZ2Rmq1SoajYaZGsj35Tk91BVvbwIV/ZrIKDYtv50nJ9JiM9BFF1Xms3krlUrwPA+TycR0kZXpO/LQBA/cda9dQUm5pkPGBYIChLIHwHA4NKnBZrOJ09NTPHv2DH/4wx/wj3/8Y+Z9mBWRDU4UFf1auDrjMM/N3+VP2YRjMBhga2vLLJuVTS7kfLZUKiGXy80Vz0PGda7zMgPy87mWMwdxeXmJSqWCra0tFAoFXFxcoNlsGsuv3EVFvwbMlbPwBgjecdV+HoAZ15ZWTBa50AvY39+f6VxrC4OPbaIVc6Uk12F3dxfvv/8+CoUCfvjDH6LX6+HZs2f46KOPUK1W7+NU3zpU9GvA+Tlz78Cs6Ome2o+7PAMeK0VP4fd6Pezv76NcLiOXywEAtre3N7a/+30MTK6B9ODgAEdHR+axd999F59++il+9atfAQA8zzMbeSoq+rVgND6dTs9Yexv7ArXnrLFYDP1+38QE5EaNLGf1fR/dbhePHj0CMOv6ykIdu4TXvr9pLCoMInbFIgB861vfwk9/+lO89957GI/H8DwvlC2wg1DRr0E0GkU6nTZr1V1zUHteb/9NBpj4WLPZNBZftsmWy2FLpRKy2SySyaRpsHHfufqHQNDnWObzJRIJfPjhh/jxj3+89HPChIp+DWKxGDKZDAqFgllK2+l0zN8pwnkFInaQT65PZw0A22Szlv3q6grHx8d49OgRisWiEf88d3/e4GOz6JznPe9VHLsscrrEOodNC3y+TlT0S+Ba7up5HkqlEvL5vFlNR7d92YvNXsDC1XNcyNLr9XBzc4OrqytUq1W8ePECtVoNJycnODo6wu7uLvL5vKn4kzUB9nkve06vSiivUoAUOKc7ynxU9GsQi8XgeR7K5TKKxSI8z0O73Z7pJ89W0sDyHVhlg0oZ3GNemgtYTk9PcXBwgEqlgnK5jEKhYBb4sN6fJcAPOej3sktsg46V32GY0f3p7xFp6Sl87j7DenApZGA14dsXLttY+b6PWq2Gzz77DDs7O+b9d3d3sbe3h3K5jFKpZFb5ZbNZpFIps+Em5//2+9rFRNJyLjpnV/uvoGXC62JPURbFL+SKROUuKvolsReXpFIpFItFVCoV7O/vo9/v4+bmBp1OB77vA4BZKbaq8PkcBvvYjrrb7aLVauHq6sosz83n89jZ2TG3YrGIfD5v1vNnMhmznFfu9S6768jaf35WDhLsyCuX9crnyhJX1tknEgnTupvlxNyrXm6rvWwRjnxPrklYNKjofD4YFf0aRCIRbG9vI5fLoVKp4PDwEKPRCOl0Go1GA5FIBO12G5FI5I7wgeXFHxSEY2qv3W6j0Wjg/PzcNOSgwLmUlxtkUPR0+ehByDJXvg/FLpt8cKpAL0TWywMwxUqZTAbZbBb5fN7cuNSYXofs4+9q8R20ZJfnyF1rORgpq6GiX4NI5MvuN9lsFnt7e3jy5AlisRiur6/heZ6xbsyx9/t9RCKRmUDfKlF1261llJ9NK1znJ4VLkcmaAlnbLgUlPx/FxSChXAMge+Hz/bhFVzabNT0D8vk8crkcstmsGYDkTj0coDgIyAHA1ZorEokgl8uZ9Qmsr1eWR0W/JtFoFMlkEjs7Ozg8PEQikTBzaXbLubq6QrPZRDQaNeKyN4Ykq4h/meNkBkCu9pNVgbIlVtBnlPveBy0oAjCznNj2OOh12L8z8JjL5WaCkBycKHquuOt2u0gkEjg8PMQ777wz857K8qjo14SluNlsFru7u2bZKINntF68KH3fn5nbLzvPvw84CKwKd8pZB9eyYg4IvLFlWDabhed5yOVyppMwPRK5p1+v10Mmk0Gz2UQ8HjeDh4xTqNVfjIp+TRjsSiaTyOVypmCHc1cZNZeLcnq93p1IufwJfLWxJJm3Ou2hYg809Dj4fTAGILsMMfjH4KH0Sthgw/M8jEYjZDIZlEol7O3tmW49ynKo6NeAAadYLIZUKoXxeGw2n+D8NZfLmSh6sVjEixcvUK/XTYSfHXPkZhN2ld7bghzQRqORCXByjs6pg3TXZcaA3X85aBaLRfi+b9YtKKuhol8TBrvS6bQpxKF1KxaL2Nvbw8HBAU5OTkwDzVqthqurK1xdXaFer5ue8Le3t+h2u2u54EGewrxjXxZXz0D5eNBCI/v3IMHKlCGnAnIAYCAvm83qfH4NVPRrwgszEonM7GPHIJfcG+729hatVgvX19emMWS1WsXl5SWurq5M+yyKX7aEtvPhNi6hPSTrZw9K9ipBWnqKnFkDOfeX2YdKpYJvf/vbeOedd7C7uxtYdaYEE1kwR9yMCeQbxK5G42P8yQGAS2bl6rmbmxvc3Nyg3W6bFlr8ycflSjv+jd11HjIMYjKAJ911GdTjfJ5xkHQ6bW58jPERZkhOTk7w9OlTlMtlpFIpDd4F4/xiVPSvAXthjbTgsoMO01K+76PRaJgpATeK4LSAu8/KLaflvnRyye59IuffstTVdsVl22+KlYE73qTYPc8zcRDGRBjZt0XPPgapVGom2Kc4UdFvCoPBwFj5RqNher5xf3lae7ndtGuPOiB4nr2oRsDlgts19TIlx4i7jMrb5bd2aa8cIGzLzki+HDCUlVHRbxJyfb19k8IO2mvOjgHMiwfI+67luPKnfT/o5tpfz3Xf9hrsQiC15C+Fiv5Ns6j09m3rfnPfyO9Nv6OlUNE/JOa51IpyTzgvKE3ZvSEWCdzlei/Kfy/zuveJ/f4v2zZr2ceUl0Mt/QNnGaFvEsuIWIV+b6h7ryghwyl67UCgKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCFDRa8oIUNFryghQ0WvKCEjvuDvkddyFoqivDbU0itKyFDRK0rIUNErSshQ0StKyFDRK0rIUNErSsj4/6QdvrwRM+prAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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YNI0g0+m0w6S3TW2vjjXXhb7c4t0UPX8mRc8MNtnthiXBFKUdKpPTcmm206vOBUD2ymMYjjP52BT0LkqOlftBRb8EoVAI2WwW+/v7KJVKCAQCpp1VKpUyJr0MuxF5bpYxZa/sNQCOMzXNcnvqiy16vgYFLwXKhBeKW07AZTxcjsTmAiFFz9dlN105HnsymahXfYVR0S9BMBg0hTLJZBKz2cy0gLZ7w7lhe9bnmfpuZ3mKXYqerxEKhRyLBBt+MLGFiTE8a9sC56w9JsVwR58XN7/L3oD3gS5ATlT0S8D0Ws5Tm81mjllsXmWvMm1W3meLxl4s5LmcN3v+vA13eZrr0uHGW6fTMTs+xc7HUuzc1Xn+l4sNb6uOCt7JXNHrH+sSN/FGIhEAMLutV892+9+82W2oZbWZ7X2Xj5WONz7ePirIltXcyTudDlqtlhE9d3tp5nN3l+a8fG/+vvbv9Zi76X1nHn5q6E5/DW4muoyN89xum+v2872KT+j04u4tzXziFkqz34MVflwAeObmzs1dvd1umxvNe2nGS7HzmqTTUB433H5fZfWZK3qNk3rjlfwiFwSJHQe3y0ZpRsswnNvzpcBkQo48OsizvCxUYZELC12k4GW+OxcVL9Odv9+qmPZuiw5Tnr2m5vgZ3emXQIqXOyo91nJaq+1Jd8tPl0407vpuu72s7JPTYO3hlNJjL7PkbMHz/O4mdjfT3W0RsoW/Kjt+uVzGxsYGcrkcYrEYQqGQWiSCuaJ/+fLlQ13HymB7wpljH41GHfFv6SRjLHs6nRox2tVyUvAMj3EHlhlrDH3ZKbZ8XZbtyhvbYPN9pMeeefDMkOt0OsZRJ814twgCxQJclq2yL+BjC55/i1QqhXQ6bdKdk8kkNjY2sLW1hXw+r6J3Ya7o//jHP96riX/fqZbLvj535VgsZnaNTCZjwnGBQADj8Rjn5+cmI63T6eDi4gKBQMAx3okJOrPZ7IrJTQHak27tsz077rAJB0t07VlxAMx0WVa58frouOPIZ5r0bsM2WT0ozXh67BkSnEwmjom2dtLRstj/X/w3LZnJZIJQKIRUKoWNjQ0cHBzghz/8IXZ3d017cFYvxuPxKzUPyjWi/8Mf/vBQ17EyyHNxPB43u0Y2m3VUwwHAcDhEr9dDs9lEu93G+fk5gO9mtFP0cs663OVpIdDElgkuXlNkuZNxd5Mz67gY0TkoHXh02tl169Ijz/fijs7vgaupwFyU7Bx+/lzmGsjjiO0HuUm83xZsMBhENBpFpVLBj370I/ziF7/Az372MxwcHCCTycydfqt8x1zR//nPf36o61g5ptOpicWzcIbjppiEEwwGcXFxgV6vh16vZxo7UKSy0QaPBDLs5jY1lmKQMDzIkl5Ziy/Ldnnd0kEoM+yYTecVLbC7As0bk2X7NWQYj78/E5Z4o+/BXgB5ffLa3JyEfN9kMolnz57hpz/9KX75y1/i4OAA5XL5fj4InyBzRX94ePhQ1/HRwJTbRCKBtbU1AJez2+2zOB9P3HYxe2iFWx08RSiFz2vgTi9FT+Ew/GYL3Tbn+R6yxJc9/ThDnkcbCpeil4lCwKXgY7GYOYLQ1KbvgQsTrQ/b38Cjzmg0MoulrDNgheMPfvADHBwcoFAoGOuEfzc16b1R7/2CzGYzs7PTdOUHcxFkSa3smOu2SMhdOBKJoNvtmiOEtDrk0USazdJRZ4td9vZjgxC5sPAIQYvC7g0gd3jbIuGixPAZFybgcvyUW2kufRAy95+pwMFg0LQay+fzSKfTVxx16rD7Dq+FT0V/C26ThkoznjsUz/1eBTq8fzweIxQKmcgBu+16WQn2wsHvZQ0/z9zy+MAmIfl8HrlczhQT2YM3CBdA2Q2Ii4RMT+Y10SKRoUW21arVaqadFiMP9JkEg0HE43GHdSP/Pvb3ylU0OecGuCXi3AVy96Vgr/uby0ViNBq5zrST2GdzmvDydaRYE4mE2UnL5TLK5TKKxSKy2azntB3bWqDQ5w3pkBYJd/1isYhSqYR8Pu94v0AgYPII3Cwivh6gn9mboLn3N+C+/w62+Ofhlm5rh7UAZ7We7MHPx0vBc5em6DOZjBH9+vq6o1uv2wBNt8QhKXZely162fFH5jdw0eD5n+HH8/Nzh19CvqaK/eaoeb9iyB3LbbGRoTz7Ay+PGrxPJthwp5SPYWhODuNgi+5cLodcLodsNmti4LJ02K0Zp737e1kg8vrsxQn47hgzGAzQbrdRr9cRi8XMgmcXKCmLoaJfIeRObwteHgEAp/jdCnwoJLcMOq/8fTrhpOddOuLkLu8meLebvCYb/j7SQmFiE52HyWTySrjPjhgoi6EZDCuG2+7u5pW2q/bcHisf4+a9tx17MvtP3mR+v9cOf9vfVb6WfQ3yOGEnCCmLo6JXHg09hz8Oat6vGF7nX9vJ5+a8k/fLHdjrfC2tAplaKzviykGTTNGVzrl5170IdpafbM8lswelNaCLxnKo6FcI+wzsZYoD7o48t0XBDq3Zj5NNPdhpx26QyTO1TP5ZxJHnhTx+yNl3soknawVYl0DH401mBSjuqOhXjOuSTLwcaPyZfK4dQ2dojNixclmo02q1TAWf9JoznGbnB9jhN+nh9wrZ2Ts7p9Q2m00zAqvZbKLX65k0Z/4uWlSzPJqccwPuKzlHvr4U6Dyk6K+bXy9/RkHajT1YqjqZTByz6GSxEMuIZSmv7cGX1YDM1acD8LrkHFkK3Gg0cHJygmq1ig8fPuDk5AStVguDwcDkGDCmz9eRX/Uzez2anHMD7uvv4GUSu70375c7tsx6k6LyykGXxwBb+KPRyGGyU5DD4RDdbhfZbNaEz+hRl9d60zRcHhFozsuF5uzsDPV6HcfHxzg6OsLp6alJw2WVYDAYdO3wo2K/OWre3wJZcLNoCEmavl4FN4BzJDVj6UykkcJatuAGuEzaGQ6HAOAQfK/XQ6PRMIKX03X5XjJnQBbcMI9fFtzwGi8uLkzbbQqexTbNZtOMsJaTdti4g/fJEmQ7nKmLgBbc3BnBYNCRsBIIBIwDTIrKNrm9CmC8dnm3GLbMmpPdc6SgWMhij6qSvevt0lqe1yliPq/X6+Hs7Mw0A6G5LhcX2aEXgDG9eY1MsLFLa7nDs7OPVysve+EKBAJotVo4PT1Fo9EwRxH7jK+i92au6Le3tx/qOlYKfqCj0eiVJhrcxWQtPb3MrP9m/za7iYbdt17WicsEGruvvMxLl1VwrFWXFWez2ezKrDl26JFdb+0mGrJunbu+7aEHnD0E7c430qtPi0T+vewmGhQ+IwXMrXer9+c1UviHh4f4+9//jkKhgO9///solUrmcSr4+cwV/c9//vOHuo6VQZ4Z3dpl8cM7m81MIwiaozSPb9ouy54TZ8+D4/W4NaZgqavc6QFnjzzWqst2WaFQyLT1CgQCV/rZ2wsQ4CwI4rXJY4Tbc+zIgb0Ty6k9cnKOm9jdQoyvX7/GV199ZSoG3dpl+V38MlIjmSv63/zmN/dyMasOP8SxWAyVSgXr6+vIZDIOBxY92rK9tFdjTNktRk6coRClSSuFD1yKnlZGLBZDMpl05MXTmw4422V1u11Te+42Y4+LFCfMSjPf7n8nxS0FOm+xWAZbqHY4ktdUq9Xwt7/9DYPBALVaDU+fPkUmk0EsFjP9A2V+gR/58Y9/7Hr/XNH/+te/vpeLWWWk+SpbYMs20xTHcDhEKpVCLpczcWSatrzZz3FrgW33iJPmvYx/89jARYXms1cLbHkWl74F+fr0RcjOPzTb7ZFbbmK/C6FL3AqNAGcF4Xg8Rq/Xw5s3b1Cv1/HixQukUilEIhGk02lsbGxgc3MThULBVOe5vfanzlKif/78+b1czMcOFwRmh62trTl6xNn15HyOTHO1M9/kedvGjsXbgy5kdhrfw06ikaKVwqUQZNmtvFZ7p5eCl3Bheyh49h8MBqjX646flUolHXYB4He/+53r/eq9XwKZjELHEr/KbDTbe0+xcMeW4S2GoNw64crdzs37L8+wfA9eg3S4yVx6e+otv5dNOYgUuxTPXe/yd0W9Xkev13OMtdIy3Es0OecGuJ0J3RJDvEpP570OFwpaC/br2qa0W8hNih+4bKnF723Bz2sxLXd2OjW9HrcquIU66SRVrqI7/Q2wkz1sT7ZsaOmWE8/7gatVcbwvEok4QlJuprbdM99uuS1j/ra5L3d7W/S2t9vezYm0AqRVIf8uyuqjufdLIB1mjM17hYjsxcIuUpFxfPs5Ulh2Pzz5moDTypDvBTir2Ch46Rvg5B6ZMiuPKFws+HvzNd2u4zHQxWYxdKdfAgqQZZ+z2czs1HaGnZc3mlB89lnd9rbLVle2Q80uqOH7yu/ljQ5IJvvY03MZTaCDkaWtcrqtjDK4HQGU1UVFvwTT6RT9fh+np6dot9sAYFpH26WkN/UHeB0L5M/ss73dVNItYmD7F3iUYLyfmXoMI7KOneKXCURMImI+gZx665ZYpDvwaqKiX4LJZIJWq4VvvvkGtVoNoVAIxWIRm5ubRmTRaBTAVeHbSSxuO7y909MLT2FPJhOTgSeLdexstNlsZkx12ZM+Ho8jnU47ctx5k6FEJhJR8HJMlr1ISEuBi4Kymqjol2A8HuP09BT//Oc/8fLlS0SjUTx58sQMsJxOp1cKYdzM7HmCt3d7W/gyVGg3sJDPt0OE3OG5Q9MpKM/8chd329kpek7FZWWcPY2Gk3HvI4lHWR4V/RJcXFzg9PQUL1++xF//+leEw2E0Gg1jBp+fn6NcLptUUBkzB672p5c7s1caqjTPZUUd4Mza88oPYCafrIqTYpRfpeNPfpXneTu/n6WxUvytVgutVsvM/mO130247likLI+KfkFYFnp2dob379/j1atXAGA+2Lz1+32USiWkUimsra05ztr8Kie7UNBe8OfSOrDDdV4jrmzHn52K6/bV7fHSccfFgHn+NPNbrRaazSZOTk5Qq9VwdHSEarWKer1uhlIybKg8Dir6BZlMJjg/PzetnY6Pj83P2Bii0+mg2Wxic3MT+XzeCF+mzcrSU9bl27s0kYuF26go+7luryFNa7lj3mT3vG5RkKnF8vc/OTkxoq9Wqzg5OXGIn5YRnYBu76ncPSr6a7BNbvaSoxOLH9Zer4darWY+9CcnJ9jY2ECpVEIulzPC53hpVsqlUimkUikAV8N1XuKXZ/p5vgC3TLW7+pvI7yn+8XiMTCaDfD6PcrmMzc1N7O3tmTZYtVrNLABsh0Xzf17twTzcwqLKfFT0C8LS1dFo5IhPs8vLYDBAt9tFo9HA0dERCoUCcrmcoxyWs+I4pZWvI0Ura6Htc73kulCf/P6uxCH9BTLTkD3yWN5aKBSM+S8tI7nzN5tNtNttR5MP6Ti0O/5In4KdtqzcDBX9gtjNHwgr13he5Qf9+PgYiUQCsVjM0fWGlWC9Xs9xvpVCtbP83L6ft7vb3HWG5TxLYjabXZlLl8vlUC6XsbOz49keS7b5svvvsxS52Wzi6OgItVrN8Z4cxuF2PcolKvoFsZ1bhJlxFL9sssEdkCJIJpMoFApoNBrodrvGarA/qGzAAVwVvH0EuGtBL4q9AEiLhb6ItbU1pFIplMtlRx2A7Jwju+QyOtDpdNDtdk2U4Pj4GG/evMHLly9RrVbR6XTMQqxcj4p+Qbyy7IDL862cnc6cddm2mi2s5E4n01pns5mZBS+Tbvia8lrs+1YB+3r4O3D3d8tVmOcclD3/GCI8OTnBu3fv8OLFC3z55Zf45ptvHuNX/ShR0S/IPO+42xmTzSmYNsuuNrJXHhtCygSZfD6P6XRqGnACl+f8m5rzj43Xtdn+BbcqRn61LSt5pm80Gtjf30c4HMZkMkGtVrsy907N/Kuo6BdExsSvG6vkFQfnIiCz4hjzpiOLH950Ou14Tem552uusvAlXte87HiqfD5vjg9Pnz5FrVYzVkCj0XA0AFXxX6KiXxAWrMjz9k1xM2U7nY4RvZ3jTvGn02kkEglEo1FPS+Nj4q6uPRwOY3t7G+l0Gl988QU6nQ5qtRpevXqFN2/emGIomdSkqOgXRja+9GoxfBOY2SbP8bJxJjvl9vt9bGxsYDqdIpVKeTr05H2fCtcJNRgMmlDoxndxliMAAAeOSURBVMaGyZTc3t5GtVpFv98H8Gn9Te4CFf01uDmlmE0XiURu9doUOxNSZCdbdsm122OnUikzLUZOof0UP9iL/E7BYBDxeBzhcBipVAq7u7ta5++Bin5B7Hly5Daio/DpqGJCiyxmaTabePLkCSqVikntjcVic9N3/QitMNsXolyiol+QQCBgatK54962dtwOYdFD3e/3cXZ2ZgpX9vb2sLu7i62tLRSLRWQyGTPsgr3vvZpy3jVuefzA/DCiLkqrgYp+CcLhsJlxl06n0Ww2Adw+C0ye85nj3+12zU5fr9dxdHSEra0tVCoVk+KbyWTMkEg5WUeOh74L7NJb2bJb5iK4lfg+FOqwu8Trb6+iX4JwOIxkMolKpYJKpWKSSGRG2LIfPnvHl7t+vV7HmzdvUCgUUCqVzK1cLqNUKplFgMMt2fDSFqFtWbj107cFK2vs7Wm47NBjz/CTi84iTke3++Xf8yFTjT9FVPRLEAqFkMlksLW1he3tbQwGA5yenrqKaFn4fKaqDgYDtFot1Go1xONxpFIpFAoFFAoFFItFlMtlFItF5PN5ZLNZM9zSnqnH15Ypr/bsPLcmmzKXQCYV8WgTiUSQSCTMHDlaH3KmvWzn5db04zoxu+U7KIujol+CQCCAdDqNp0+f4vnz5yZ/nmm3cmzUbZAfcuaWM5Ov3W6j0Wg4JtlyZr2cXU/fg5xsK1tiyfx3AI50YTmLXjoYKXjWDAQCAUQiESSTSeRyOZRKJWMFFQoF43ug5SFn/dkLwrxeAPaMv5skSClXUdEvQTAYRCqVwpMnT/D8+XO0222Mx2MzTmkwGCAQCHhOkrkt3HWZcQZcmuUUkKzdp+Dk1BvWrlP4cvdkOJDOQQBG9MyD5zXwOeFwGLFYDNls1lQQbm5uolwum34CUvjy2uiLiMViVxKf5IJDq4KOVB5hlMVQ0S9BIBBAPB5HuVzG7u4uer0eACCZTKJer6PRaJiecDIBB7g/RxN3Qzbn5EgnOeiSO6hd0ebmgbfbadPSmLeIdTodtNttnJ6e4ujoCN9++63xL8izPjsG8ZiSzWaRy+WMU5IRiUDguz6ALFPu9/sIh8PI5XJmUZELk3IzVPRLwLAdz/Wj0QjhcBjpdBqpVArRaBT1eh2dTscMpnQT10Mgd+SbIs/wi8IU4m63i2q16jDd5feyzJh19nRGZjIZ00KMMwaazSY6nQ7W1tawubmJzz//3PgQVPSLoaJfApqziUQChUIBFxcXJiMsmUyac6r80HoJ/rry2NtEAe4Lr2uWEQGa4sPh0PU1aH2sra0hkUjg8PAQmUzGOCAZdZjNZiZD8fz8HKFQCOvr6wgEAnjy5Am2trbu7ff8VFHRLwkbY6RSKTN8gh1ieD7l+fXs7AyDwcA4zezCG37/sXAX10zn4Wg0Mjs5FwKZYix9I2xU0mg0sL6+jk6n81H93VYFFf0SMPWVYSqa+8lkEplMBplMBoVCAevr66hWqzg+PsbZ2ZnpAMMZeDx/P9Q12zy2YKRXfpGsxnA4jH6/r51ylkRFvyT0cgMw3nKKvlAoYHNzE/v7+6jX6zg+PjY31npzEgwXgPvmsQV+FzBUWi6XUSgU1HO/JCr6JZHDJXg2nUwmjgaQbIfdarXQaDRwenqK09NTMwGG7bJ6vZ6jMaT0rMtuOvbEGft2H+FBIs/x9mRemuOyr79bBqAcmEH4PIYIGbJzCxsyKWpnZwdffPEFisWiOvGWIHDNDvDxbw/3yLw+bzKFVvZ4s2/8mWygwaaQnBzDr/1+3zFN1n4dOUqavgM73VZeO+AuYnsYphywwZ9TmAy90YnJ7+mIA5wpvNKvQVHTi8+kIsbz5SLA60gkEsjlcqhUKtja2kIul1Phe+PqHVbR3wN2/zf7q9vCIFs/cyGg4OWoLHnjAmC3kJYdZu3OvW7/3xS0zHJzK56xB2Uy7BaPx40Tk6JlBAO4jPHbomfojs+n8G3Ry2vi5F0uBrdpZOIDVPSrjGz8aPfNk4sCv+fP5OOlme9mhcj3ItJslzdZnec2MkuKX6bUSjHK3H3+bnIhotXglpbL3f0hy4U/QVT0HxtejTXd/s/m/T9e58RbRkjX1cu7xfCve73rchaUhVHRK4rPcBW9ekAUxWdoyG7FuM+CHJ6v5/WBXyXTepWu5VNCzXtF+XRR815RFBW9ovgOFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9ovgMFb2i+AwVvaL4DBW9oviM8DU/DzzIVSiK8mDoTq8oPkNFryg+Q0WvKD5DRa8oPkNFryg+Q0WvKD7j/wO6egU1DwbhHwAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1397,6 +1409,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "current beta: 64\n", "Current iteration: 49\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -1405,7 +1418,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1441,7 +1454,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "current beta: 128\n", "Current iteration: 51\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -1450,7 +1462,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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r1VAqlUT32729Pezv74tAF3n2JkHLy3i5C0+nmnjDNOFgLgcW/QUwioGt1znkf1ORiFKphHw+j0wmg1wuh0qlIlx9JHoKXaXAFrmfHkWx0exMwpar246KPiCHB4XLpZvo2ZA3Rs46y3cTCC3rK5UKcrkcEomEaIiZz+dFnjhFB9ZqNRSLRRwdHYnXkousVCqJmV027I1jD34ZJbqY4ekp+nFWhLlK0E1LS+ZBXqun0wDQbRbv57qjVN5MJoP9/X3EYjHs7u4ikUggn8+f2k+T6OV9PDXfkG0BQLvba5g6dMzVpafox1FbTb/kpf3vuG6efueSl5qDnGvc798N+ZhusyzN8JRZtre3h1evXmFjYwO7u7siCYXsCnTeer0uLPHFYlHk/VN463m21mImn56i15chMiq04ukVpjqse07/etpTy8EulUoFhUJBGONisRi2trZE66xCoSCMefLMTft/ssyT1f06CryX//461B84D1jVA0AzsRzrPa7QWUosoWAYcpeRsS6VSuHg4EBY3+PxuMg4o/ZZncpHkWtPnt31K4Krjuyq7NSlR27Dxbyhp/X+X//6l2G+LVm4tVoNJpMJU1NTiEQiA4u6W6FK4M0MrI96o0i2QqFwKrKNotqSyaSIW6e4gE5Ve+StjCxy+Sf9/bqiT3U2MiO57D7//HNDfXPUHpni6T/77DN88cUXCAaDPY+jQpnlchn1er2tJDPNQuRGIzGnUikh5OPjY5FfTuehiDdyr5FfnazuQO8Vhz5GfhL86ufl3SBsNhump6dFMxKjM5LL7s9//vP5XM0VIZ/P4/bt2/je976HZrOJfD7ftpyU990UsiqLnhJtaAlfKpWQzWaRSqXass6y2axYrtMenPbhg4i1W8GNSUs5HeeAQ339XC4X7HY77HY7ZmdnEQ6HUavVsL6+jv39fTSbTVE2a1K+h8um50xvMpkMNdPrcblc+OSTTzA/Py9ELkfe0ZJZHyHXKSyWjqe9O1nVOwXJDONpkAtvdIu/B3BuFvtOA815YzKZEAqF8OGHH+Kjjz7C3NwcFEWBw+GA2+1Gs9kUq6dWq9X2/2Ukfv7znw+/vDeq6Ad1803KjaQfZOg5WeQ0y3W73vP8HN2uT/8a/fMkVnlAs9ls0DQNd+/exeeff44f/vCHCIfD53btV5zhl/dGddlZLBa0Wq2+SSSTIHiiUwiwXkC9YgP0A904PpuqqnA4HG396hVFgaIobT3v6adsB6H6giR2SvSx2+2igvBHH33Egh8BnumvCSQOWSj9ctD75bDL2wN99KC+GIhcBJTETuXDfD6fKB9Ge3Cr1SpsHvSg56iQKA0AAHBycoJCoQBVVbG4uIg7d+4gFApdmbbclwTH3o+C1WrtKp5RDUPn5SuXZ04Abb/Tv+mnfobVd83RGwBl46TNZoPdbhdFP10uF9xutzCsdXq43W4x65Oo5WW7XvxyCDSFIReLRVgsFoRCIYTDYbF60YeL09ZmklZilwH3px+Rq9r0UC8cWdxms1nMyiRAm812yhhIoiGxk7GMZnC/34+pqSlMTU0hEAggEAjA6/WKuoCqqp6atbt5GohOAywlEZGh1G63t608jLoNHRX+tq4ZJAISPYmZhEcCV1VVFOukvbaqqqeW1XQuWrJrmtZW4dfv98Pn88Hj8Yjlu6IoY/9cdL3yrK5vXMkMBov+GiEvlUmsiqKInyRsmq01TYPb7YbH42kTP83O8lKejqGlutPphMPhgMPhEOe22Wzn2klX3pJc5Saelw2L/ppBgqAOPKqqQlXVtqU5leH2+XxiltbvuUnEiqK0rQjoNbR0lw2HwGkDoHxdw3wGPfpz9toiML1h0V9TZPHLFnWn0ylmd4/HI6zqJHoaIGi/T7/rxd5ppj1rElK/Y4bJaGS6wxsiA3BVrNhX5TqvOjzTX1PkTDuqXKt3kdHv5PaqVqtipifjHS3v5TBeOf9ANqTJoj3L8r7b52E33Hhg0V8z5Dh/EhoJnwprUOlqytvPZrNiv6435OmNf+SLpwg7WvLLhrzzWH7rfe+dSo0xg8Giv0boU2mr1aoIJ65Wq0LI+XxeZKbRXt1ut4sZXvapk01AURQhdLlJJ7nsvF4vNE2Dy+U6tyg5fcAQW/BHg0V/zaAaeQDEEhx4E5wjx7jLIa+dZmnK16dEF9kDQJb/QCCA6elpBAIB+P1+If5Bg3MGES3VI6By3xTLzz760WDR9+EqheHKUF7/KGG4+gIcAMRryKqvqqoIwdUv++VwXPpddgl2igCUIwXlyEFa1lMYrtlsxuzsLFwul7jWblWbjb7/5zDcEblKYbjU9IJ+AuhZImvQhJtuRjn9wKGP3iPXICXdyAk3tLKQY+7lcF95dSCLnhJums0mQqGQGESYweHU2g4Mmlo7KciFNIhORi+5oEY3xmkhl0N95Yg/RVGEyPWrDho49LM9FflUVRWRSATb29v46KOPcP/+/bFcq5HoqeqrNMuNk0GbfExSEcZOe/HLLqJBvfJyudyp7L9u167f79PgQP9WFAVutxuvX79Go9FAIBDA3Nzc2K/9OmPMqbwPJAC3241PP/0U0WhUWMPlGQqAqJNH3V3lhpKyP5uOr1arolQWPc6rXJb8WeTfL7pc1rhr9aVSKZTLZZhMJqRSKUQiEVitVpFP0Gg0cHx8jFwuBwDnmg8wyfziF7/o+DwX0ejB+++/j1/96lf43gCFMXO5HIrFovCP6/ekVBiTatknEgnRQTabzaJYLKJSqZzqHz+Owpjyvn4SViXjwGw2C2OhXBjzxo0bqNVq2NjYwN7eHhqNBlRVbfNkGIX9/f3ha+T96Ec/uh53yIBYLBZRVNFiseCzzz7DL3/5S0xPT/c8Ti6BTYUxyQpNy2waIOQS2KlUCkdHR6IDbbcS2FRAs1KptJXA1qNf4hu1BHa9XkcymTzTe10HupXA7in6J0+eGEb0shGMZmvaLw56s+pDVeXj9O2m5GYXcpOLYrGIfD4vBodkMilWBXJnG2520ZlJsrNcNiOJHtyfHsC3gwDQua1Vv0ow/SB3FA0CVB67UCh0bWtFjSvl2vgyRm1rpS/vRd6n6/BZR6FWq7HoR6VfA8txhILKDSzl1UCxWGxrYPn69Wtsb28jmUyK9tOyt4Ei8jo1sCTRX6eutfrSXjLyIK1/fSfkwXxYOh17lvONg0ajMXxhTKO67Ag5aKQXgwa+dMtCo9/l4BaXyyUEPDMzg5mZGUxNTYkw116tqsl+QHaBYrGIarXasdnlVafX5+lkuOv3+c/y/XTzXEwaPUVv1GSGYf6jho10k19Dx3YKlSXIIEieAIvFAlVV4fF4xGxPiTV0znq9LrYHx8fHsFqtwn1F4bndAnXOUxTMZNBT9Eb1bw7DWcpAdXOx6Z8zmUxQVRU+nw/AtysCt9uN6elp0T8PeGPEqtVqKBQKSKfTSCQSiMfjSKVSbW5FMgTK+2E2gBkDDs4ZI4PuHbshz8AytBKw2+3wer0wmUyw2+3w+/0i80wOZaUOuZlMBolEAvv7+9jf38fh4SHS6TTy+XxbMBEZ+8hgOSr6gCEeRCYTNuSdAdkdJqeunsf70HtR/XcSrZxNR6KXu+RmMhkcHh5ib28Pe3t7SCaTOD4+bgsGoi65ctfcarUqjIr6iDrZGMiinlzYZXcONBoN4Te32+3weDynXtMvkm4Q5P03WeFla3yn8lc0c1N1nHQ6jePjY2QyGdHznvrdk5VfNv5RiDANLnKUoOwZIDfjyclJz8/QqWIuc76w6MdMo9FALpfD3t4eMpkMNE1DJBKB1+vtmljSiVGE3ymOXe86lGfner3eJm4SMLnyaKYnFyEFCOXzeTFAyCHC1Jq7UCiIQKJsNot8Po9yuSxsBszlwqIfI61WC9lsFq9fv8ajR48Qi8WgaRqWl5exuLiI6elpaJoGu93eVdTybDeor7+Xb73b8frn5UFDfpD4ZVHTaoBWBCRmKrRJg0SpVBIDQKFQQDabxdHRkTAiHh0d9Yx7H+Tzd3N3Mt3pJno25A2IHGjRaDSQSCTw9OlTfPnll1hbW4PD4UAsFsP9+/extLSEcDgMr9crijzoS0Xpm00OQrfgoGGg9yPPjF5M8n5d3rd3esjVdml7kMvlkEwmsb29jefPn+Pp06dYX1/H4eEhKpVKx2ui8/UaIJnxwaIfAP1NWa/Xsbe3h8ePH+PJkyfY2NgQvvBkMonNzU3cuHEDgUBAtICiYhJutxuapsHj8cBut5+KGhvnlkD/GTodq89lB87mqqVV0OLiIqLRKG7fvo3t7W3hNqQko1QqhWw227YCGFbc8nfHA8PgsOgHRL6pSPQvXrzA/v4+isWi8I+nUik8e/asrXsMPfx+P0KhECKRCMLhMGZmZqBpmqgXR5yHB+CiAq1MJhO8Xi8cDgdmZ2exsrKCUqmEXC6HeDyOjY0NPHv2DGtra3j16hUSiUTXFQBzPrDoB0A/izQaDaTTaezv7yOTyQhLeiaTETn3VDmWykZT77iZmRlEIhEsLCzgrbfeQjQaRTAYhMvlOrXkvuiIyFHCSPXXStsW6qHn9XrFayuVChYWFjA/P4+33noLsVgMqVQKxWJReAjI8EjNN8jASMbHk5MTkXOgtxPwzD8YLPoBkW+iZrMpimeUSqW2m4+s1uT3zufzogw0LfF9Ph9u3ryJ+/fv47333sPdu3cRDofhdrvF+fWBLhfBqAFFg2K32xGNRjE1NYV79+6JWAEyEJJ9gL67QqEg0osprTiZTGJjYwPr6+unRE9xEhRbwHSGRT8iNBPJdeXpJ91wsqWboFXAwcEBstksSqUSisUi7t27h5s3b4rlPp3nsjO1zpJ1phee2WwWK4B+hUnq9TqKxaIIJabkong8jvX1dSwuLuLo6AjFYhGpVArxePxURKF+5jdqLokeFv2I6G/qQV1KlD+fTqfx7Nkz5HI5pNNpZDIZPHz4ELdv30YgEBBVfICz5+xfFvIAOOy1W61WeL1euFwuTE9Pi+V/pVJpq393fHyMv/zlL/jjH/94qloODZ5yEBPDoh8ZCrvVM8iy0mQyoVariSw58nVTgc2lpSUEAgGoqnqq2cZVodf2pF8uv3ys1WqFpmnQNK3re7ndbhweHuIf//iHsK8cHx/3jRI0Kiz6ETCZTKLTyygFF+WbvVKpYGdnB7VaDblcDolEAg8ePMD9+/cRjUbbupSQ3/wq93CTBa8XvRymO8znW11dxRdffIGf/vSnsFqt+Oabb/Db3/4Wu7u747vwawSLfkD0/mwqdKEoypldTuVyGbu7u8jn80gkEkilUiiVSjg5OUEkEhHRfdehd9s4DJTywGEymTAzM4Pvf//74u/vvvsuXr58id///vcAvl0JWK3WM2cRXhdY9AOgT3k1m83CDed0OsfiZyYfP0W3ZTIZ7O7uYmVlBe+88w7m5+fhcDjE6/Xx9/Ke/6quAjrRyVYiRwXKtfCIxcVF/OxnP8Pbb7+Ner0u0pGNVgK7Gyz6ETCbzfD5fAiFQvB4PMhkMm1Gt7O4i3K5HCqVCuLxOHZ2dnB4eCj80tFoFJqmjSVy7qrQKWJwkGM+//xz/OAHPxjqOKPAoh8Q+caxWCyYnp7GrVu38OLFC+zt7Y1tFqEYAEpXJUv/5uYm7ty5g+XlZSwsLMDv9/c9Tz+D2TAMKxz9quM8hSdnE7ZaLZHoxGLvDIt+QOQbyGq1YmZmBgsLC5iZmYHFYhH7xbPO9DKFQgEvX74Ucf63b9/GJ598gk8//RTLy8uYmprqOtvr89fPyqgCugjhkSflOtg8LgIW/QDohWyxWBAKhYTo5T3luJot0HvKsz1FpR0eHmJhYQHBYBB+v7/t4fV6oarqSMvi86ZbbEO/2oGdXtsv47BTPwCjwf3pz4heRH6/H+FwGKFQCG63G4VC4dTrzkKnQSOZTOLrr7/G8+fPRSz/7Ows5ufnsbS0hNu3b2N+fh6zs7Pw+XwTI3aZfgE7veoCDDOQUvkw5jQs+iEhi7HJZEIwGMTNmzcRjUZF0sh5xH3LJbCoos3BwQEAwOv1IhwOY319HWtra4hEIggGgyLTjeL+5d569DnkYhr6op5y3T19p17gTZsugl5js9lgt9tFopHT6YTD4Wg7/iK2CpM44E0KLPohkQNHXC4XotEoVlZWRLptLpdDuVwe69Ky1yCSzWZxcnKCg4MDPH78WMS2K4oiBE+BRIqiCBsAGb4o0YUKYgDttfYVRYGqquJc8rGy29BiscDhcEDTNASDQdy4cUOkEAeDQfh8vrZMQubyYNEPiTyDKIqCmzdv4v3334eiKNjc3MT29jYSiQRyudy5hYHqDXRk7c9kMkMfOwjygEH+brmNNvCt6Kk4aC/RO51OKIoiBiU6Nw1U+hbfcimvWq0Gi8UiahUwo8E18s4Alc3a3t7Gzs4Otra2sLa2hmfPnmFra6st/puWydepj1wnKIWYlvYul0usPuQVA/WU1zQNPp8PgUAAU1NT8Pl8cDqdsFqtopR3pVIR2xq3242VlRWsrq5CVVUAl98zboLhGnnjxmw2w+PxYH5+Hn6/H7Ozs5iamoLH44Gmadjc3EQymUShUDi1Bz4POu2ZB01u6cawAxO11CLDZi9UVYWmaQgEAggGg5iZmYHf7xfpxZS+XCqVkM1mkclk4PP5UC6X4fV6sbCwAEVRWPBDwqI/I4qiQNM0qKoKp9MJt9uNQCCAUCiE6elpPH36FJubmygWi13PIWfs6a3UZ20qQecbxPo9Tr/+IJA7kopjbG9vw+FwnLI91Go1lMtl5PN5sRLw+/2wWCyIRqNtLal5AOgPi/4MkJWbqt3abDbRY466zPp8Pvj9fsRiMeRyORFbT0Y04E176bMyyrZhEkpLk03i+Pi472tLpRL29/fFCmoQvz/TDov+jOhdWSaTSbipfD4fFhcX8fHHH4tqsPF4HHt7eyKufhDjmx7ZpSY/N0noVyv03FkHllarhenpaSwsLCAUCrV5Azj0djDYkHdO0PdKlu5isYhkMolYLIbnz5/j2bNnePnyJfb395FOp1EqlUQjSXKdXUdDXy86xQeQyxH49vtYWlrCT37yE/z4xz9GNBq93AuefLjDzSSQSqVwcHCAvb09xONxEVorL/mpB10ul0MqlUIymRT14q9quWjaBlEAj81mEzUJXC6XqI5D6crk2iNXHg0CwWAQDx8+ZOv9YLDoJwE5Ck5+0NK3Xq8Ln/vBwQE2Nzexvr6OjY0NxGIxJBIJZDKZU1V4Jwlyy6mqCrvdLoRLvnly6WmaBr/fLwyfoVAIwWBQeECcTqc4lnz3FDCkz6FnOsKiv0jk/SvNRMPGglO4rb7NNK0MKJKukzvwvLYG+hlVbtYph+yS8K1WqxApzfA0y5MfnxqB+Hw+eL1eaJoGRVH6XgvP8H1h0V9F9I0j5Ui4UZpTjJNeCTNyBV99NV8aIOinHParN4wyZ4JFPwnIpZ7o33pkURiVXt8TZ9ANDIt+kujmH7+Ode5Gpde9yd/PQLDorxqdykVfZTdev/x5HvDGDov+KtIvYm5SBoFRhDpMEQ1mJFj0DGMwOoqerSEMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzCsff5uupCrYBjmwuCZnmEMBoueYQwGi55hDAaLnmEMBoueYQwGi55hDMb/B4V+6NiRSptbAAAAAElFTkSuQmCC\n", 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\n", 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EQm4sEhv5twF01bGTreeUDUjtrpPJJLLZrKjWS4KnmZ4GgVqtJgYOrXVc3hqweC8vLPpzoF8RiUGP1z5Gy3TK7qO9c7VaFUk4NOjI0WzpdBoHBwfY399HPB5HJpMRy3AaNGipT5Vxh0UbkMODwsVykujZkDdGRpnlTxI83Wipns/nkUwmkUgkcHR0hFKphFarJZb4wH8q+xSLRWQyGcTjcdE8M5/Po1wui1mcYvvlzxmFiyjRxQxOX9GPsyLMVYIuWioBree1Mv0s9r3+fZLrjpbqtKSnYpzb29s4ODgQsecUDUjH1ut1MdMfHR0hk8mIfbw2RFXbcVcPLOqrTV/Rj6O2mnbJS/vfcV04p72XvNTU817j/vyT0BaB6PU8zfCUWba/v4+3b9/izZs3iMViyGazQsjy+TebzS53WrlcRrVaPdbYote5MNefvqLXliEyKrTi6ReiOqh7TpuNR/tq2luTdbxYLCKbzSIej2N3dxfb29vY3d0VjTFrtZoYNGgAok6vtVpNGOdkg5zMVRd8v0H9OtQfOAtY1TogUcmx3uMKnaV88XK53OUuU1VVGOLi8TgODg5weHiIVCqFXC4nqvbIszadBxn9SPy0fycPAH2Hqw5taagxqHarIrfhYr6nr/X+2bNnhvm1ZOE2Gg2YTCZMTk4iEonoFrVcgVUO0NG63ORkFAqCyefzIrKNWl9ns1mk02mkUikcHR2J9NJ6vd7zIpc/Tw6TpftGcLOZzeZr/x31MpTL7osvvjDUL2e1WkWpJavVih//+Mf41a9+hXA43Pe4drstcsObzaZwn5HvnGZcsr5TiGoymRTJJ4VCQey/KSiGBga6UVQdcLIRUNsOm+7TTH+RYhg1huG0c1cUBeFwGI1GA4lEYqTPug4M5bL7y1/+cjZnc0VQVRV3797FD3/4Q7Tbbaiq2iUssqrT7EzCNJvNXU0j2+22sKhns1kkk0nE43HhN5ddaeROazQaXUEw/ZATeHolz1yWWW+c52G1WuF2u+HxeKAoClwuF6ampjA/P496vY7NzU3s7++j0+mIHAhO0PkPfWd6k8l0Oa6WC8LtduPTTz/FwsKCELk28o4KO1Bsuuwzl2c2Ej7N4DRIUHSdHP2mB60BURuGS8jRdGfhR+81e5/1IGOz2TA7O4tHjx7hk08+wfz8PKxWK1wuFzwej1itqaqKTqcj/r8uy+B3XvziF78YfHlvVNHrdfNdlgtJNmhpY9zprxxCS5zXuWvPjx7Tvkb7V26VTY8pioJAIID3338fn3/+OT7//HNMT0+fy/e4ggy+vDeqy85isYj49X5cBsEDJ5+HLCwyLPYLxhn3gGC32+F0OsXN5XLBbrfDbreLGgPyQEW1BC0WS9d9Wpo3Gg04HA5EIhGsrKzgo48+YsEPAc/01wQSDonkpBn/pGO1yLYA7XHa2RiAEKjNZoPNZoPT6YTX6xWlwwKBALxeLzweD5xOJ2w2W5fw5WPtdrt4nmZ5Kn9tt9uxsLCAO3fuIBQKwW63j+HXu7Zw7P0wWK3WY4E0xLCGobOOUdeKvpeo5Rm237Jbfk8Sod1uF+W/nE4nPB5P140MbNq/VDbMbrcL74a8jJcNoHK1IeD70GKz2YxwOIzp6WmxvdL29AM42Qfg/vRDc5WKQchCJs9Br4Ie9LxcuZeEphUi3bdarWIG93g88Pl8CAQCmJiYwOTkJEKhECYmJkRhUJfLJQQuezL6DUb9chbIYNrp/KdRSLvdFudq1G3osPCvdc2QBS0LmwQnP04ztqIo4i8trWkgoH/TzO7xeLqW7cFgEIFAQIidZvRxQ7YAbU4/h9oODov+mkEzs7xHJpHTjKsoihC62+3uWn673W44nU5hhCMfOLnD5CU7Le9psKBl+1l/N3lbwgwOi/4aQkterWGM7stCptr8NEvTIKC1urtcrmNCl41x2piEQVKO9bzmJGMiMzgs+muEHKhDs71sVacZnoRMYpebcsiipyW9/Jdq9dO2odfna+8Pev7jeh3TG14fMZcGo1vbzwue6a8RcpKNXMvebDaLarXaeHw5HVfOAqzVanA6naJoJmXr0TGym037+dr78ueM8t16nTczOCz6awi5uCiSjazcVFHHbreLmH9KGHK73XC5XGLpT4Y+WtqTIY/2/bTHp9fIATVnYWAzmUxd/nhtTQNGPyz6awa5skggdJ9me63LTnbXaV12ZAy0Wq3HAnGoQafssvP7/WfmspO/G/B9WDFb8AeHRX/N0FbHkZfkRK/gHDlIR+saI4Mg+et7BefQjcJtaRVwUnDOIAE6cnBOu90WxkgW/HCw6E/hKoXhUqtrEn6/89MKsFfknrx/lgcK2QugDcOV/f70V94SkPW/V/Sf3jDcqakpuN1ucY5ywxEOw/0eDsMdkqsUhkucZFCT7/eKV9e+rt/gJA8Y8oBgtVpFYI824cbn83WtAOTVhnwsxRPICTdUi8BmsyEajaLVaiEcDovXMfrh1Noe6E2tvWxoLenaKjpA/9XJWaTWOhwOMcPL0Xu9Em7kgYNmevpLnghFUTA/P4/V1VV88sknuHfv3sjnaTT6qvoqznLjQG+Tj8tShLFXxRzg4otokLuPyowBOBa9Bxy3N8j7fXlwAACHw4FAIIB3796h1WohEAhgZmbmTL/HdcOYU/kpkBg8Hg8+++wzRKNRUe5K7hkHQNSXp86tsnVZbihJnV5rtVpXP3cqs0U178dZLktb2eeiymXpXWnohdp5AUAikcDc3BxsNhtcLhe8Xi9arZbo2QfgTPMBLjO//OUvez7ORTT68OGHH+LXv/41fvSjH+kqjFkqlYR/nJaoJLx6vY5SqYRsNotEIoF4PI5kMilKW9OgIQ8AevvMaffXwPHCmNet/DXVxJO3DjMzM5ibmxOFMWOxGFqtFhRFEf9nRuLg4GDwGnk/+9nPrscVohOLxXKsBPZXX32FUCjU97h2uy1KV5NPXF6WdjqdniWwqQkllcCmUtdUPJN6xNNjNCDIM7Z2ZpcvblnsRiiBbbfbRQnsZDI50mddB04qgd1X9M+fPzeM6GUjGFW9nZiYwPz8vO6LlQSm3ZeS2MgY1avZBd1OanaRTqdFZxvaRmjr3WlnebkzLTe7MB5DiR7cnx4ARIOJXm2ttMvqQaG2VtrGFnJbq8PDQ9HaKpVKIZvNHmtrpU1tpdZWRm5rRd6n6/Bdh6HRaLDoh2VcDSxPgiLO5Bs1sCyVSqLP/O7uLt69e4e9vT3RpppKSGnPV25gSeLXzvj02VcZbfFPGXmQ1r6+F6PE8/c69qLzA1qt1uCFMY3qsiO0dddPQruc1FMIQnvfbDaL6jPyezabTUxPT2N6ehqhUEiEuR4cHCCbzaJcLh9zMVLIKnkJSqWSsCv0cpddZeH3O/eTWoAP+37DnMtl/G37it6oWUyD/EcN+58qi022J2h/c20deKvVCofDgWAwiHQ6DVVVhejpfShkNZfL4ejoCEdHR+J95ew74PvQ3UG+z2W8kBn99BW9Uf2bgzDIwNgvKKXXcptme7PZLDq7AP+JqfZ6vTg6OkKxWBTCpf1to9FAqVRCOp1GMpnE4eEh0um06JlXr9fRbDaFkU82/LGgrz8cnDNG9O4de9FrmS3bCyi9lQYBmu1pTy8HA7VaLZTLZRQKBaRSKcRiMezv7yORSIiGmZVKRez1yY4AjLal09o32Ip+OWFD3ghoM7zkSL1xfw59FhnoejXMlCMAqSNMLpdDMplELBbDwcEBkskkstmsiAakUFl6X/o3vbds+b/OwT7XEXbZnQGtVkv4zR0OB7xe77HXnCYKPYOEHMIqC1EecLSlqyhikDrk5nI5ZLNZ5HI5qKqKSqUixC17CygoiAKDKpUKqtVq16qASmrJ1Xeq1eqp35NXAOcLi37MtFotFAoFxGIx5HI5+Hw+zM/Pw+/398xNPwm9KwNZ+HICjfw+2veSa+WRUGlWpyW9HDBEOQQUIEShxdRSm1YC8uBQKBSQz+eRy+VQLBa7IgeZi4VFP0Y6nQ7y+Ty2t7fx3XffYWdnB16vF/fu3UM0GkUoFILX64XD4ejrvqPfXm9wTz/f+knH9/Idy0tzbRFNEj8JW94C0PPaqEJKHqKBQg41jsfjSKfTunIH9Hz3k74/c5yTRM+GPJ3IgRatVgupVArPnz/H3/72N6yvr8PtdmNnZwcrKytYWlrC3Nwc/H4/HA5HVxUY2couV6zRQ7/goEHeQ+uVkQcT7Z5du3c/abCQtxKpVAq7u7vY2NjA8+fPsbW1hXg8fuIW4Cx958xxWPQ6oAtd9nXv7+/j2bNnePbsGTY3N2G1WpHP55FKpfDmzRvMzs4iGAzC4/F0ZYLJveAcDsexqLFxbgm03+GkY8cRVSiTz+exuLgoWkrv7OwgkUggnU6LXALyIowSGSj/djww6IdFrxP5omo2m4jFYnj16hVisRjK5bIwnKVSKayvr4tqsdpmj1NTU4hEIpibm8PU1BS8Xq8oTEmchQfgPAOtaECbmZnB6uoqSqUSVFXF4eEhtra2sL6+jo2NDbx9+xZHR0enGgGZ8cKi10Gv2PZsNouDgwPkcjlhFGs0GlBVVVSWpXrxbrdbVI8Nh8OIRCJYXFzE0tISotEowuEw3G63WHYPMuuf9Xc96THtc9oEJCqe6ff7xWuXl5cRjUZx8+ZNLC0tYXt7G5lMBuVyGbVaTeQH0JZBtjGQC7FWq6FQKKBarR4zZvLMrw8WvU60hqRqtQpVVVGtVrsy18hqTRerqqqi+ivVi/P7/bhx4wZWVlbw6NEjvPfee5ibm4PH4wHQHRp73sIfNGmkX5ShFpfLhVu3biEcDosVAIld9iRQ7oAcSkyDQzKZxObmJl6/fn2shiHFSQxSgciIsOiHQJsVB3TPdHTByRcwYTKZYLfbcXh4KMJiyR9+48YNsdyX8/AvMgdilKwzrfAoktDhcCAcDvc9ngqT5HI5kU5cLBaRTCaxsbGBhYUFkWl4dHSEeDwuUqDlz5PPxai5JFpY9EMi56bTv3vd73VcrVZDJpPBixcvUCgUkMlkkMlk8PDhQ9y5cwcTExOiig8wes7+RSEPgIOeu9lsFkZQCjcmD8GjR4+Qz+cBANlsFl9//TX++Mc/IpVKdb0HDZ69ag4YGRb9kJyUcqtnWWkymYTRTxsAU61Wcfv2bUxMTEBRlGPNNq4K/bYnp+255WPNZrNooEFEo9Gu13s8HhweHuKf//yn2CZks9muFRbzPSz6IaAEGNqrDxp9Jl/w1WoVe3t7aDQaKBQKSCaTePDgAVZWVhCNRru6lJDPvFcZ6atCvxWRHKY7yPdbW1vDV199hS+//BJWqxVPnjzB7373O+zv74/npK8ZLHqdaAtQKooCt9sNm802cshppVLB/v4+VFUVFXIpnDUSiYjovuvQu21clYbor8lkQjgcxk9+8hPx/AcffIDXr1/jD3/4A4D/rASsVuuxPb9RYdHrQJv2SkvOQCAAp9OJSqUy8mc0Gg2k02nhpspms9jb28Pq6iru37+PhYWFrm6w2vh7bTHO60KvlYEcOSjXwiNu3bqFn//857h79y6azSb8fn9X4RCjw6IfArPZDL/fj3A4DJ/Ph1wu12V0G8VdRD7oRCKBvb09xONxqKqKWq2GaDQKr9crRG2EIifaFZbeY7744gv89Kc/Heg4o8Ci14l84VgsFoRCIdy8eVNE5Y1rFmm328KgR1ltmUwGb968wb1797C8vIzFxUUEg8FT32eUEFctw8T49/p7FtCqh/zzlOjEYu8Ni14n8gVktVoxPT2NW7duYXp6Gi9evBD7xXEWmiwWi3j16hUODg7w9OlT3L17F48fP8Znn32G5eVlTE5Onjjba/PXR2VYAZ2H8CiJ6DrYPM4DFr0OtEK2WCyYmpoSIbTynnJczRboMykENZ1OI5vNQlVVxONxLC4uIhwOIxgMdt38fj8URRlqWXzWaH+Xk3z4J7n55OdOyzik+n9GhvvTj4hWRMFgEJFIBNPT0/B4PCgWi8deNwq9Bo1UKoV//etfePnypYjln5mZwcLCAm7fvo07d+5gYWEBMzMzCAQCl0bsMqcF7PSrCzDIQKqtJMR8D4t+QMhibDKZMDk5iRs3biAajXYljYw77psueGqiSRlrABAIBDA7O4vNzU1sbGwgEokgHA7D7/fD6XSKWAJty2dtXrx8znLOv1z7XxaktqMMvcZms4kcA7o5nc6u489jq3AZB7zLAot+QOTAEbfbjWg0irW1NbRaLZF1V6lUxrq07DeI5HI5VKtVHB4e4unTpyK7zW63C8HbbDbYbDbY7XZhAyDDl7YUNtDdF177XnQ+NLjR97RYLHA6nfB6vQiHw5idncX8/Dzm5+cRDocRCAS6MgmZi4NFPyDyDGK323Hjxg08evQIdrsdW1tb2N3dRTKZRKFQOLMwUK2Bjqz9uVxu4GP1QKKn2Vq2lMuidzgc8Pl8mJycFILXit7lcolOPvJgRIMLrUrINiLX+Gs0GrBYLKI+ATMcXCNvBFqtFpLJJHZ3d7G3t4ednR28ePECL168wPb2dlf8Ny2Tr1MfuV5QCrHT6RQx87T6UBSlK82YKggHAgFMTk5iYmICgUAALpcLVqsV7XZbFOKkjr5utxurq6tYW1uDoigALr5n3CWGa+SNG7PZDJ/Ph4WFBQSDQczMzGBiYgI+nw9erxfv3r1DIpEQXWjO2prca8982gAzSEFKPTSbTVEoU5v1pkVRFHi9XkxMTCAcDmNqagrBYFCkF9MMTym2hUIBgUAA1WoVfr8fi4uLsNvtLPgBYdGPiN1uh9frhaIocLlc8Hg8mJycxMzMDJ48eYL19XW8fftWWPd7IRer1FqpR20qQe+nx/o9Tr++HsgdWSwWkU6nsbu7C6fTCUVRhOWdthJUmptKcQUCAVgsFkSj0S5bAw8Ap8OiHwGyclO1W5vNBo/Hg1AohHA4LJarwWAQOzs7KBQKXWWkKVFHrrgzCsNsGy5DaWmySWQymVNfWywWcXBwIApo6PH7M92w6EdE68oymUzCTRUIBHDr1i08fvxYVINNJBKIxWIirl6P8U2L7FKTH7tMaFcr9NioA0un00E4HMbi4iKmp6e7vAEceqsPNuSdEfS70vK0VCohlUphZ2cHL1++xIsXL/D69WscHByI+m9yYUj5PYxCr/gAcjkSS0tL+PLLL/Hll19iYWHhAs/2SsAdbi4D6XQah4eHiMViSCQSovabvOQn41WhUBB14tPp9JUuF03bIArgsdlsoiYBVQv2eDzw+XwiqEd25dEgEA6H8fDhQ7be64NFfxmQo+DkGy19m82m8LkfHh7izZs32NzcxNbWFnZ2dpBMJpHP51EqlS5tbDm55RRFgcPhEMKlGxk9KZQ4FAphampK3MgDQk1CKEaAov7In8+cCov+PNG2gtLuwfVA4baxWAyxWKyrKmy9XheRdL3cgWe1NdDOqHKzTjlkl4RPAtVGBiqKIjr+kPjpRi670+AZ/lRY9FcRikSTS273c+Odpx2gX8KMXMFXW82XBgg5JZbCfrWGUWYkWPSXAbnUE/1biywKo9Lvd+IMOt2w6C8TJ/nHr2Odu2HRUyab6QuL/qqhLQQpP3YVOS1/nge8scOiv4qcFjF3WQaBYYQ6SBENZihY9AxjMHqKnq0hDGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwWPQMYzBY9AxjMFj0DGMwrKc8bzqXs2AY5tzgmZ5hDAaLnmEMBoueYQwGi55hDAaLnmEMBoueYQzG/wfBc8/34qQ2LAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1662,6 +1674,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "current beta: 128\n", "Current iteration: 61\n", "Starting forward run...\n", "Starting adjoint run...\n", @@ -1670,7 +1683,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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/PanTjzhdkKU/5cgFxBwcHCAcDmNrawu7u7tCOCpzih0cHCAQCAhi39raQiQSQTqdVlx6qZJpytI3k0V2HmBCO4pAGbn1cuncXzxSkI4YIpEIlpeXsbS0hPX1dYTDYUH0rEFEJpNBOBzG5uYmfD4fEolE2T6kI5Fqfe8ojPX8UVX0rf4nS5NNxEEg7bqBau1LrvhCrW1PArHwKznrkskkZmdncffuXTx69AhbW1uIxWLCw5kVlOB5XvC0y+WSS4fdtN6tLGh4XwcsvFPc4YUhtsjtfmjwPC8sjbF17/v372NmZgYrKyuIRqPIZDJlCSty8+hGHnxnjUoFRYHK3XiVQqFQIEdesxQKhYqCFk8FAPlGDc08DDKZDEKhEFZXVzE3N4e5uTksLS1hY2MDkUgEyWQS2Wy2LiGfR7ED31QhEj/UxKMkcY8C4huqWvonT54o5mqJhZnP56FSqdDZ2Qmv19vW4+Tz+bLINpY5lsvlkM/nkcvlhCAZ5oxjot/c3EQ2m6143lLO683ezunheaapdfr33ntPUVdWq9WiWCwiEomA4zi8++67+NWvfgWTyVT3PlguN3MCsoo6+Xwe6XQa0WgUwWAQW1tb8Pv9CIVC2NvbQzqdRiaTEQJgWJBMPB7H3t4e9vb2zrTnXS6Lr17Ezk+V6ln3HxZhKMVut0OlUiEWi7V0vueBprz3H3300dGczRkhl8vh8uXLeOONN5DNZoW6a9L66izefH9/Xxhys+ElEz3LA2dLbevr69jY2IDf7xeW06RW/DzRrlWAUumb7j8GgwEOh0N4iLjdbjz33HNQqVTY3NzEzs4OSqUSdDqdbKlxpVLV0qtUKkVZeik2mw03b97EwMCAUNABKHcQsUAUFp7KnGniQBoW4ZbL5XBwcIBEIoFYLIZYLIZUKiXcxGcB9psqOcmOMyhnamoKH3zwAQYGBpDJZKDRaISHQCKRwP7+PkqlkjDvV9qU4Kc//Wnjw3uliv48e7tPCmnUXi2kIcXsPdYrsLu7Gz//+c/x61//Gg6H4yhO+TzQ+PCeFWZUGqwiy3HPoeWW/c5yKKtKpYLVai17GQwGcBxXZn3Fv09aX0/sD4nH4+A4DoODg3jttdfwne98hwTfBGTpiZZgAmW95FiXXbPZDLvdDpfLha6uLng8HrhcLthsNhgMBqGstzRxiPlB2PShUCggm80iGo0iEAjAaDTi5s2bePPNN4VU5lbrEpxjaJ2+GdjNWYt6rbG44utZsuBqtRoOhwNutxs9PT1wu92HLDcTPSsRZjQaYbVaYbPZYLPZhGrAUksvFb3U0rMgpWg0Cr1ej4sXL5bVLshms7Ih02fp+h4FlfrTk6UnyhBHGDIhGwwGuFwuDAwMYHx8HJcvX8bo6Cg8Hg9sNhv0ej20Wm3ZvF3s8GPvief0lebscrAHA4uM1Ol0ZVPPZmoMKARy5BHysMqyGo0GOp0ORqMRNpsNTqcTHR0dcLvd6O7uhtfrxeDgIJ5//nl4vd5DpbuPE5ZvoLS23A1Cw3uiHLE11+l0MJlMsNlscLvd6O/vx9DQEEZHRzE4OAiPxwOHwwGz2Qyj0XjiXXOVHlffCiR6hSIWvF6vh9lsRmdnJ3p6ejA4OIixsTFcuHABIyMj8Hq9sFqth/ZRz5p8K5F41fZJQ/nmIdErEPGcmzndrFYrOjo60NfXh+HhYYyMjKC/vx8ul6uyQ0gUfFTp82rnANRXQPS0FRk969AYiZBFLn24lX3IfSb3ufR9pXvgjwKy9ApEnMDC87zQLJIlGrElNa1WKzj29Hq97H6OanhfS+xk7ZuHRK9QxBVvWZIQC4TZ399HPB5HNBpFKBTC0NAQ3G43nE6n4MjT6/Un6kyjZbrmoSU7QnbJzmq1wul0wuVyCUt2zKNfzbl3XNCSXV3QOj1RG3HwDHsYGI1GOJ1ODA4OYnx8HJcuXToUnCMOnxUH4si9V491ljoIWSceAEIwEIOsfkVI9M1AYbjPEIfhdnd3o6urCxaLBQaDATqdDmq1WgjBZb4A1qbbZrPBbrfDYrEIa/zsISBX104uDJelI+t0Oly8eBF9fX3CdzKZDIXhysBxHImeOBoqJdxYrVZ0dnaiq6sLbrdbSLgxGo1lBUaAb5JmxBWHxHUI5BJuKMOuJo2LnuM4RYr+tKXWiv88azBLb7VahZEBWx1giK29NO+eWfpUKoVEIgGtVouBgQG89tpreOeddzA5OXnsv+kM0XgYLptDKY2TavJx1ob89cAaYUor7jSScMO2UalU4HkeT58+xf3795HNZjE0NASn03m0P+KcQUt2MrCb0Gaz4datWxgaGhI6ugKHb85isYhsNiv0aBfnd4vTR3O5nFAMIhaLIR6PY39/XxHlsth77apTFwwG8c9//hNms/lQuSy1Wi1cW1YuS4n87Gc/k32f5vRVmJqawm9/+1uhMGY0Gj1UT50VahQXxmTVcMXzVrb+HYlE4PP5ZAtjUo/3xjAYDEL1WwDo6urCc889B7VajY2NDaEwJsdxsk7D847f7298Tv/+++8rSvQajQbFYhG7u7vgOA7f+9738Itf/KLhEtjiarjM0jMrv7e3h2AwiO3tbfj9fgQCAcRiMRwcHJSVwGYPCVZAc29v70xXyz2uEtg2mw0qlQrxeLy1Ez4HNFX3/unTp4oRvXi4Km520dvb29bjsIYWrOEFE3kulwPP80LN+0QigVAohOXlZczOzmJ2dpaaXfx/lFjZthmaEj2olx2AZ2Gq1eaxwOH1ZUaz9duy2azQ1mp+fh5zc3NYXFzE5uYmQqFQQ22tzivStlZslMU+YwE8Sr1G+XyeRN8sPM/XjCQ7ihxvnufL4uDX19fx1VdfYWZmBsvLy0IDS/Ewt1gsHhr2nueS3tJAHjHVGo7K0UqBTbnvnnTBzqYaWCp1yY7B1ozbWQpcuu5e6YZklsrhcMDhcGBgYACjo6Po6OiAwWCA0+mEz+dDPB4/1KqarTSkUimkUqlz7cCqtswp97trPfha7b7Tzv0dFVXvZqXGMh/nf5TcNa40arBYLJiYmIBOp8PAwADW19cRDoeFeT6zbAcHBwiFQvD5fMKDodr+pdMWsVgot/38QcP7U47cejdrY82W+5jo2VJiOp1GMBjE8vIyFhYWsLW1pYh+eUQ55Mg7AqRWUNrDrl37Fzun2H5Z4Yt0Oi34HJjFzufzQlEMn8+H5eVlzM/PY2lpCZubm0gkEi2fmxhp6CwLwjmPEYZnCRL9OUJcsUYawipuFVUsFpFIJLC2toYnT57gwYMH+Prrr7G9vS1EAvI8L6x5s2hC9l0mXHZM4mxBoj9mmICAww66VkYCzeSO7+/vY3t7Gz6fD9vb24LXn4mevViMAIsuTCQSSKVSwrRA/MpkMjg4OKjr+OK0VxoBHB8k+iOgVCohkUggnU7DaDSWhYSetsIOzJLLvXieFzz+LAowHA4jFAohGo1ib28P+/v7wgMgmUyWRQsmk8m6HwDE8UGibzPFYhGBQABffvkl5ubmoNPpMDY2homJCQwMDNRc5pP6AJql0v+fnB+gHgqFgpDGGovFBLGzMOFMJiOEFLMlwYODg7JRQyqVQjgcxsbGBlZWVqomFNW6TuIpRrXfSxymkugpy65OxIEWpVIJu7u7ePToET788EN8/PHHUKlUuH79Om7fvo0bN25gdHS0ptjaMQqotI9m963RaGCz2WCxWODxeATRiYfl0nm/WJiZTAaRSAQLCwv417/+hVKphIWFhYrHqyeNmYTeXkj0dSC2ygCQy+UwNzeHjz/+GJ9//jk2NjYAQIij9/v9GBgYgNlsFppJmEwmWCwWoTec0Wg8dAzgaKYDUqefFLETkP1drvxUvfT398Pj8cBiscDtdmNjY0MYKcRiMQQCAcGZ2Kygpe2s6cFQPzS8rwNmydg6eCKRwJ/+9Cf88Y9/xOPHj4UKO2q1WqgHZzQaYTAYyspGeb1ejI+P4+rVqxgfHy/rB8dEeZRlpWukUbf1WIVCAfv7+0gmk8jn8ygWi4jFYlheXsbMzAw+++wzPHz4kER/hNDwvkXENxXP89je3sbS0hJyuZyQ+MHzvODcEqPT6eByudDb24vV1VWsr6/j4sWLuHDhAvr7++FwOA7FkB9FLH+9+6u0TFfr3wxWL89ut5f1kQeAwcFBuFwuOBwODA8PY3d3V9iX+LgsnPjg4EBwFPI8Lzgb5XIMmH+AHgLVIdE3QalUQjabFTzWtW6wXC6HUCiEVCqFra0tTE9Po6+vD2+88QbeeecdXL16tayGPCvCcVKe/3r8BNWSSaqdd0dHB15++WUMDw8jkUgIoySx6AuFAvL5PBKJBPx+P9bW1rC1tYVEIoFwOIzFxcVDD1bgm9qGUucfUQ6Jvo2I58YADlkuVi8OAHw+H1KpFIBnYbMTExPo7e0Fx3FlKaFHPeSvRj0NKCshF0DEhuQsiagWxWIRfr8fKysr8Pl8wlLiwMAAlpeXhVHW7u4udnZ2DoUYi7PsTjrj7TRBom8BueGutMBDNYszNzeHbDaLtbU1fOtb38LNmzdx+fJl4XOW5XgUQ/2jph3nq1ar0dvbC4vFgpGREWGpcHd3F8lkElarFdlsFh999BF+//vfHyo3xnEcVCrVobqFSodE3wLim0hcv70WrNwTz/NYXFxEKBRCLBYTqudMTEzAaDQeahF91m7cSg8r6fy92nfrGRlotVrMzs7i888/F5pvRKNRSi6qAIm+SbRabVNVVuVEEIvF8ODBA8RiMczPz+PmzZu4ffs23G63sI3YWp3kfL9dVBM9Gy3V+xsvXbqEX/7yl/jRj34Eo9GI2dlZ/OEPf4DP52vrOZ8XSPRNoFKpoNfrYTab63bmMUqlkmxxklAohFAohLm5Oezs7ECv1+Ptt9+Gw+FAsVg8V2WcxRWFG0Vu6tTR0YHvf//7wvusxNhf/vIXAM/qEGi12jNVavwoIdE3gVqthtVqRUdHByKRCIDmosbYEFa89BSPxzEzMwOe57GwsICpqSlcvny5zOoDEJxYcq/zRKVKQ6XSs5ZXarX60DRoeHgYP/zhDzE2Ngae54WciPNcQagRSPR1IBWTRqOB2+3G4OAgdnd3BeE3ilyUnEqlQjgcxj/+8Q88evQIPp8PuVwOk5OT6O7uFrZjTqrzjnRFREy1uP333nsP7777bsXvKhkSfZ2IbxyO4zAwMIBLly4hGAwiFouVedpbsfrAN9ZsZ2cHn332GdLpNP7zn/9gaGgIk5OTuHLlStUb+aSDU6SjjqMWHVsSZdVwDQbDuRz1tAsSfR1Ibx6O4zA8PIzJyUksLS3h66+/Ltu2GcFVio1fWVnBxsYGtFothoeH8YMf/AB2ux2Dg4MV98U6vp4UxyV28fFYNyGiNiT6OpEO7wcGBhCLxdDV1VXmIOI4DrlcriXRsUq44gIXAPD48WNYrVaUSiUMDg4KyTwulwsejwe9vb3C/PU0WblKCT9SD32lh4V4Hl/NdyF+r1IHHCUh9XUwSPQNwIaRzPPc39+Pjo4OQaDtPE4lT/ODBw+wsrICnU4HrVaLzs5OjIyM4OWXX8bU1BQuXrwIm83WtnM5SuQEX+/2tWglS/C8Q6JvELG1slqt6O3thdfrxfr6OgAIASHS+XkrsBuY53kcHByUValZXV3F5uYmdnd3EQwGMTs7i87OTuj1+rKbXs46VoocrFTei73kLDeLH9BqtbBYLLDZbHA4HLDb7WVdaJqh0nebjf9XOiT6BpEO84eGhjA1NYVMJoNAIHBou3aIvlYCSTAYxL179zA/Pw+TyQS9Xn/IkSZXsVZcDENuFYF9hy2Lse+zDDexw1Cj0UCn08Fms8Hr9WJ0dBQvvPACLly4gL6+vrbHGVA8ffOQ6BtAzloODQ3hrbfegsViwePHj7G6uopIJNL21lLS8lfSXHK5lN6TgOM49PX1YWxsTCiX5fV6YbPZoNfrBasv/h0ajQYGgwEGgwF6vV54wADfZNyxxp8cx8HpdMJsNpddUxJ//VARjRYolUpIJpMIh8PY2dnB/Pw8Pv30U9y5c+eQ1RdnzrW6pCbNvT9taLVaYXjPSm9xHCcreDY68Hg88Hq96O7uhsPhgMFgQKFQQDqdRiwWQzQaRTQahd1ux40bN/DSSy8Jx2NBOiT8Q1ARjXbCrIvNZoPNZsPIyAief/55wYk2MzODUCgkFHxoZwhopeW9o/La16q4I/UN8DwviLQeDAYDent7MTg4iP7+frhcLhgMBhSLRaRSKaHEVjAYRGdnpxAn0dXVBQAk+AYhS99GSqUSAoEA5ufnce/ePXzxxRf46quvsL29XfE7bHhbaX+N1IkXi17sxKtnNFCtnx07F7njSbdpdgSj1+tht9ths9lgNBqFa8KG9fF4HHt7e+jo6MDt27fxwQcf4OWXX4bH4ynLmyfxl0GW/qhg4lSr1ejp6UFPTw+GhoYEi/Xo0SPs7Owgn88LZbVYb3m5sk+tnAcTXaP7bOYcagm8mgCl381ms0LSUTUCgQD8fj+CwSASiQS6urrKHlgk/NqQ6Fuk0lLXwMAAbt26hb6+Pvj9fsRiMRwcHGB3dxeLi4uYm5sTlvkaQeqFP41UC7Jhr1b8Gk6nE+Pj4+jv7z8Uf3+ar8tpgUTfIuwmkw7RVSoVxsbGMDY2BuDZDZ9KpbC2tobp6Wk4HA6YzWb4fD4kk8lDFqqSINo5MjjNiK+rWq0Waul5PB5MTk7i2rVrMJlMAJ5VGGo1FkBJkOjbjLRcNkOlUgn95TmOg8fjwdTUlNBXjm3DLCHrPLuzswOfzye0pT4vGI1GmM1mmM1m2Gw2OJ1OuFwuOJ1OWK1WGAwG4RqyKVEmk4Hb7cbrr78uCB4g694o5Mg7AVhGmLQ7DKNUetaDfmdnB0+fPsX9+/fx5MkTLC0tIRAIHKoFd9rR6XTQ6XTgOA4GgwEWi0WoR9DZ2Ynu7m709/cLr+7ublitViF9WCxqFihUqx0WAaCCI49Ef4RIW0FV89RXwufzYWlpCaurq/D7/YhGo0LvOKD5rL6jQrx6wIbmWq0WWq0WHMdBp9MJVYesVivsdjucTie6urqEV6VEESmFQuFcVRQ6Akj0J0ElR1+9FAoFoV1WPp8vyx6Tq75biaN8OFT7XdJwYGl4L2uMwR4MlCTTVkj0pwFpPfhq1WCV6pwSOyur5QQQNSHRnybqLQGtROjatA0S/VmknlHBWUQufVf6PtEyFJF3FpErNHFWxN9oYQwS/PFAlp4gzi+yT1HyhhCEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwiDRE4TCINEThMIg0ROEwtDW+Fx1LGdBEMSxQZaeIBQGiZ4gFAaJniAUBomeIBQGiZ4gFAaJniAUxv8DRr8rWxYZSJMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" ] @@ -1730,7 +1963,6 @@ " solver.set_upper_bounds(ub)\n", " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", " solver.set_maxeval(update_factor)\n", - " solver.set_xtol_rel(1e-4)\n", " x[:] = solver.optimize(x)\n", " cur_beta = cur_beta*beta_scale" ] @@ -1749,7 +1981,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1800,12 +2032,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1824,7 +2056,7 @@ "plt.grid(True)\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel('Splitting Ratio (%)')\n", - "plt.ylim(48.5,50)\n", + "#plt.ylim(46.5,50)\n", "plt.show()" ] }, @@ -1842,7 +2074,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1863,18 +2095,11 @@ "ax.axis('off')\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1888,7 +2113,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/python/examples/adjoint_optimization/05-Near2Far.ipynb b/python/examples/adjoint_optimization/05-Near2Far.ipynb new file mode 100644 index 000000000..b1396e9b6 --- /dev/null +++ b/python/examples/adjoint_optimization/05-Near2Far.ipynb @@ -0,0 +1,1990 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Metalens Optimization with Near2Far\n", + "\n", + "The adjoint solver in meep now supports the adjoint simulation for near-to-far fields transformation. We present a simple optimization to illustrate this feature." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "from scipy import special, signal\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "Air = mp.Medium(index=1.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "design_region_width = 15\n", + "design_region_height = 2\n", + "\n", + "pml_size = 1.0\n", + "\n", + "resolution = 30\n", + "\n", + "Sx = 2*pml_size + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "nf = 3\n", + "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "design_region_resolution = int(resolution)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [0,-(design_region_height/2 + 1.5),0]\n", + "source_size = mp.Vector3(design_region_width,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.Source(src,\n", + " component=mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "\n", + "def mapping(x,eta,beta):\n", + "\n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()\n", + "\n", + "geometry = [\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "]\n", + "kpoint = mp.Vector3()\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use the `mpa.Near2FarFields` objective, we need to specify the far point(s) of interest; and provide near2far field monitor(s) `mp.Near2FarRegion`, from which the field at far point will be calculated. The monitor(s) has to capture all outgoing fields.\n", + "\n", + "When evaluated, `mpa.Near2FarFields` will return a numpy array with shape (num_of_points, nfreq, 6), where the third axis corresponds to the field components $E_x, E_y, E_z, H_x, H_y, H_z$, in that order. In general, we specify the `mpa.Near2FarFields` objectives functions of the field components at frequencies of interest at points of interest. In this case, we would like to optimize $|E_z|^2$, and focus the fields of different frequency at the same point (0,15,0).\n", + "\n", + "In this example, we try to focus the light at three different frequencies. Our objective is simply the mean of the fields at those frequencies." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "far_x = [mp.Vector3(0,15,0)]\n", + "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "ob_list = [FarFields]\n", + "def J1(FF):\n", + " return npa.mean(npa.abs(FF[0,:,2])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = [J1],\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + " maximum_run_time = 2000\n", + ")\n", + "opt.plot2D(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(v, gradient, cur_beta):\n", + " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", + " \n", + " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", + " \n", + " if gradient.size > 0:\n", + " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", + " \n", + " \n", + " evaluation_history.append(np.real(f0))\n", + " \n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.show()\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1\n", + " \n", + " return np.real(f0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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cZI3FYpHuS9jW6XSSUcqTdXg+XI8JznO7d+sZZa8SwfN0zxIvKDcmkgrjAxlhbCEHJ2r3QEmyQe6bzSaF7MhgzAtCVowvhscrWhgzoiYIn6hnPp+r2+2q2+0m75W2Inc4kbfbbXW73WSAuK6PJYTN/VkvGBV3eHhu92wPRRuMG2PshIuxwMMdjUZ68+aNXr9+rYuLi+TpuuRxyLiynrkvsgfjm8+NQ46KJyxxguAX+pHnIbHLPcvAByddOuX4+Fh/9md/liy0pGSZ6EC8vjzMrNfrhTIV9yiYgJ6UcA+HCQQx5mUtTL7ZbKbRaKROp5Ou1+12C1bZyTS/Bs/g5VO014mE9nc6nYLF5bpcixA3r1JggfX7fQ0Gg6RXo5V5uRxhG6EzSSLCN7RwrxrwsJLnwHuB3CBv9F60Sa5Jf47H45Sww4g6sdIn3g8YKNfu6G9fbG6MXWZwHRYCy5NiuSfONXMjnj+PJ6iYe51OJ/WhVyMgfzh50l/0qcs3rqN60hTDnctoR0dHiXR5Np8red81m800JyFYSJR56VKK941HIL6uXUphXmJwb29vdXV1pbdv3+rm5iblBw5dIy/vdLJHx+92u5rP50kPxrnwxDNr3yNhnpPxYZ5ixLfbrR49epT0ZF/XHwqlke7R0ZF++tOfqtFoJItIIo2OxKtwkdwHhesxAenATqeTBmA+nycCIvmBJunEi7WGAAj7b25uNBwO9ejRo0IomJOmezI+UE4m7ilyLUmJ4Jik3MND1jxz70TqxAA5kDhDHnAC8+TKbrdL0gHEAel6hQDkwTVyT9eTeEgv3W43jQPleMDDUZ8b7lkTYUBQLFLegyfn40dbaBfvd8I9pHd64suv3ev1dHR0pNPT06QJ4ul2u90UpXgyyz1dr6yAWKkGcGeDZ/ZEJNEM4+xz1ce83++r0+mk93oy1CMs1pHLED6PIEqfd/SrGyeXaPLXMCi+ljBSt7e3qUIAidC9WneIiIZwwpDPkM0gTtaR53ekYh01z+XaustTrBnGa71eJ1nx94J0QbPZ1OPHj1NCgiSLVCygdoLw5IgnfFqtlrrdrnq9XiJcNMPNZqPxeFzo0Nvb20KmPvdSufd8Ptd0On2vzMWTSUw096QkvUcYHvrm9be5hgrBuBXHOPmEdkOEV0bWP/dSMF7uodLfeeIHgvYMu3s49D3yB2RN25y8CWFZRJ7k89AvD/N5Dk+UsMA8gUi46SVynvjj/XzeQ2k0zbxQnz6A9Pr9fjIiXIv+wDnwccylGfcU/bkZO593ntBFxvIxgVyQYiB4l7T8fm7omae73S5JUbQ1n6sYc08SEjnRvtyJyCUoHyOqYCgBcz0cuLeMLCGp4JgxZsyzTqdTiCydXOESdHv6UlKal8x/OIQ6ZByRMlD62Qte5gPBkU2FbKfTqSaTSUqCMcAeNnpYV61WC5lln1x4E5Coe1q0SVLyCO/u7lJ7ptNp8lQgWMIaCIJFzQJh4vGMk8lEt7e3ms/n2u/3B+sz8wTPbrdLRNput9MEYrF7SOz96iTNszNx3WvxMNslCTc+LmW45un3428QkWe6nZi4Ds9KX1WrVc3n82Q8cu/ax4sw0TVg96w8K874516nE64bMK8CyCsS+CztyfVZ5BuPSvw1l5p4/txbZF5BMO7Ju0T1UP97khT5h3Dek6J4t3kEhCYLoXmC2COnfJ65U+Laf+7lUtKXEyTPTRljtVrVdDpNES/Xdqer3W4XHCU3ivwfOU1SWlvu9LgMw5zLc0cfEqWRLnV0u90uEdHV1ZUuLy9T6RQTALLKxXMPpSBeL7ZnceTlVxCga0q+EKR78kXbZXPA2dnZexqte5u5t0FbKUm5ublJz4fH0e/3U/0l7WPBQLp4H55JxxuR7mUM93C4t2vDeThPW52sPNmU/+SEmycICUnda/SEaB7yOeHTJu8zroXB8IX6XaFf/j76zLVV5ght8+oOH9e8j5ycXIOnDzyp48Sb/xwyJvkzeX97xOHhuxsb6b7OeDwepyiNKh2MDs/EZ7yee7FYqFqt6ujoKDkGeN152Rjz3mUHb+tisUhrezQapX7LE6PuNNDHHv16wgxpgGiL9nvCm/6E9OlvnAgvk/QohD7CYJeBD066LITVaqWLiwtJStrp5eWlrq+vNRqNUmmYh5y5p+DeiWuRZOR9MnuW3L0U14CceFl48/lct7e3ur6+TiVdEEtuIfG6pGIyB28Dr/n6+lp3d3cpaTQYDLRcLlN9b66reTSAVOIbJCCSPFTjNUiOBeIa8m63ezA85r5e88vEpO+87hc4MWAspGL5UZ4Z9zF1I5MvCF8MbmDc+3FDwN+kew0/71/e5xqnt9G9XCfKPFKCmPP+8b5l7BgzJA/6nDb6GHN/nsnHl3a5t+yymHuWjAvPgMeHZ0kSljbxL5GYV9O4ls7fXUelr+bzeXI07u7uUvKL1528vR89yciadWNBf3kFR67fcg/WIXJQXt0C8cIz+/0+kX3uiX8IlHrgzXg8VqVSSZMDaYH6Wd9L7pbQPYhms5mSHcfHx6mDZrPZewTKgnWy9qoI4Aktkm/oUSQ5qKf0hEmujeHdIo/wL8+6Xq9TvSD3Jfnkk9nrR/FIfNeWe2dOrGw2cW/TyZC/sRuK+mavpJjP56rVaoVscx56ub7ueq/3rcs8tIvPurd3KGqgbxzuITvpuhHxMNHJFTLk/xhR+tKTYa7P5vo5UZZHAQ7vq1qtluYLZYiQmifQPILxdrh36yTuROOeOHPNz7Rw0ofQSAr6Fu71ep3mXrfb1Xg8TvKXj4NLXF6eRnRWrVYToROpup6fz6F8PbKWGG+fH8gkvV4vvY8tvD63fL4wx1xWIkrmWfL7loHSSBdrLt0ncjysdN1Fuq9ScGvbbDY1GAzSoTbsWNntdkmE5/+uATsxOMF6R3PfPBHhcgYLyL1Of/9sNktkO51O06TLNd+80B6D0mq1UqYbb2WxWKjZbGo2m6nf7xfkg0NJNRamew0sKkiBJAKGiwNZttt3xeTT6bQwbu55uXfsi4YQEA2X9/NePJN6vZ5kBMaK5/c2e8juCRA3xrzu1R3uveZlSBBZ3me0O09Qud4LMbqn5BEB7eK6vV4v/bDra7t9t9WcccXL4tq0BW+O+zB3H5KzvJbVDxzCW3VJKt/gggFzfR4S93pvl2wwWBhySUlPZQ16dUiu5fra5oex9fXJ2oPkO52OhsNhmlN47fv9PiW6PUntZMw682jFx9eTyx8apVYvnJ+fJy9uNpslQj3U0R7KMgk7nY6ePHmijz76SC9fvkylUrPZTNJ9MoxOdavMpPbQ8VC4mGuR/sNgu/juGvEh/SjP9mN8nBB8YwRkjue82dxvy10sFoV9/J6Bd53RidK1UDdoaOK9Xk/9fj9NSOl+cTrZ8a8fTuOlTe6RuEziOrTrnQAPyfsDGcbD1lqtVtDs+ZwbVB8rNwYO2umepYf2kDxjy/hAmvSjJ778NZI9XN83PWBkmPtOoHn7fPw8MnP9HrJhbnhSjIiMe/NeXqfP6F9PeuIIzefzQl/53PL55snYXBfPIyW/NzXxXjLmCTT6E2Jst9vp5LV+v18w7pRDuoH1OejREUZxMBhoOBwWDNOHlhakEut0W62Wzs/PU+dMp9PCZgapSHquHTJIw+FQL1680B/+4R/q/Pw8WUe8Akq0XIB3knAvgokF0dBWD0dZsHmyhAnmBiKXLLgGIRELk0GnTtA9MsJ//nWjwH73TqeTiIEyqbwkC3hyjR8m43fJLS7nOGnl3iFj54vLPUCf6B4ZeGKGcc9JNCcar5DwJI57yV465xJBLjtJxXNgnXBdp8U40X6vxMg1aO+j3Li43JKH016BkFc0eCLNE4FokZANkhabVnweMY/pd5yYTqeT1iXv9XK0fD7kDog/m3vdeYTqY5CvC6QtSYXILk+IexVFt9vVycmJBoNBmlO1Wi0dq+me+6HNGEQAHBvw6NGjJAOVhdI8XRJItVotaZQ+QT0Z4loUFrvX6+ns7EzPnj3T2dmZ+v1+IWmGPOFhOwPgkxCr78XihyaFe1p4NIcKs1lMTvCuWe7378pUOC4y1zo9HHetkEWIt4tngHciqZAYOVQVAHHnyafc04RAIMtcT/ffIatcYvGssN9PKp6D4Pdz3dlJ2InTCRMD6SR9aMwOeSuu90KIrkvmJA1JsdjdMHJPwuu8j/K+JfTNjSLIk0BIL65P8xqeLe3gOFKqFjabTdLQPbSnbURdlUolnQd8SELLt767nuzz1RNXXifthiyfP+izRBqQY34d5oufGSGpIEfw3mazmUo8WauHjKyvVXIabIIpC6Xdyb0GOthLSXxBYwkZfNcfOZyCDvfDqV3PBV6PyuJ1rVcqngzlgwMR4U2w+809Kj5PmznIXLoPjTqdTiIln7juMfuic82ZrDmLBW0216ckFZ7dSYYQkWf0qgvuKem9XWB4SP48PJMX2ePh+HO5l+xaHfchXHW9ODc+h8jMS6b4P687KeYGhj7jnnh1/HjI7JUW/lx8lv9DuvQDr7vXTPIM5GEzr7kT4FGDPzufRz9FI767u0uJK65NH5EI9j7weeQGzOUj3wp/aDMGbfGSOnIYPld8XN1I5lGI1y979EBU6jsiV6tVipJ7vZ6Oj4+13b6rn+dMXz8jN6/i8MiQee7r+UOj9M0RZE/J6JMlZ+CxguiNEB2HLlcqlcJhIRzHlwv17iUwaJ5NPRR6uJzB5OdzbNRYrVaFEMzlCEI1l0RarVaBSHPNk9+ZBF7a5Fo3J3RhuDjv1TVHSJLnoS/pB9rsZwS4V3pIR/M+8h1weBsebTBxWQC0xwnSyY2KCff+vETNx9AjF/+M//2QfugykXTv8WAgXebh2Xl+fudzXlYGQXBgivfRoXJCJz6MIJsR9vv7ihLI1+cz98yjL2pi2cjjO8g85Hfi9eswDk66vruQDUj5fJfuDXy+ixSvnr5nLeS5E8ia8T60Y81Jer/fp0oeyi/pV6QK+oocj5M3xE7lhp84mM+bD43SSNc7+u7uLtXnUsuHwE9He6LHd3BtNpu024TaxLxERlJhITM5xuNx2t5ImYtUTFZAbL4DjfDGj8Xz8Na1J/8/5OReuC8G1/FyzdE9AUgbYvc6SZcqPATL6z2r1WpaEO655CGvSzEksEh0cB0+m3sxtJ/nyz1lD8er1WrBU/dEpGvCPnc8eUibvZIAr4jngPjcyPhrEAv9Qb/zzK49uxfMuLg85tJRrtPzDP6vG2lJBa2c5+JZXIriXu68UDFDmE17uZ9HPK7zuifqc9gPSMeI5J4u88a36voZvnyGOZBHMpzsV6/fH2ruDgNt4zrSuwhpPB6noyX5G2Ps89AlHS99rNXelfJdX1+nmubcAfvQKPXresiIYq2w0EwYiIZkEUkotEyIwwu83cIjuEvF4momL0K9e5bS+9qsa1k58eWemYfUTA5POLAAfW+662j++Tz5AQHS/ul0WjiImZrfnICl+22z3v+55ffncS0zNwrel57Ecc3OwzMPh/3vOQGhr/t4QLqcwcv7WZh5ZYQTLn/ze+E9ckaHj7VLDLyfNjtxOjzx5Zo6bXDCQAJwyYDPe/jseiqkmW8y8dwDkZrXg3vJ1EPEmstabiA8MvNSM66TG1mfUx45VirFmmOqNnIu8Pd7HsP7xJ0MDCpbjKlwoX8oSWSOcl3fAIFnzN4A6ny9VLUM/Cik62e7+jZftFrp/gQsr3N0sd1rYl1HwiuTVKiRhTQZGNd0+JuXsXDguhNF7pm6nOHEt9/vE6E4ueXJJveOPGwjlOLvvNd1LeSFPPnl3px7n06oOZk6geWePcXneL2QzGKxOJgMlYqJtVz2AU5mTkIuBXnyLzcaPq98fHPd2jXu38ZI5J6/34fnIh+Rlwe64ef58yL8vP8PJbuYE94OPkeExhj4iWlO+pC4H/riJJfPP9aOR3qHdNk8mnEnAqfBpRvyGez4csORj6Hr8HjdHrGx3Zm+QW8mooScl8tlYf3Rd07CXtdc9lf2lLoj7e7uLm0ayEmPwfWSjpOTE52enibti1CAI+A8geCVCh7OeNh+KIT3cBrP+vj4uPDFfUwatluSTMsJISdWv5cvVl9stMM9W+ldCY3rgnlSiEnLNV0zy8NeX3Selc8TNRg/npUJ6aFfvV5PO5byow3xiHIPOjceriPnhEc/IQk5XOLII4Tcm6JvvJ+5hofHTj7uibon6ITnR1u6JikVv8OLvnVPMtdD+dcNAT/5ONMf7gnnRM39IFzGplK535XINRkjoiGPirxvPOR3cvVNFH5mCHkEznvGKXJP0+dbPiZ5P3iyljMiGH/3hL0iY7d7d75Lp9PRZDIp8IuPM44bW6HLQmlnL/B1zJyz4BsFeF+r1dLx8bGePHmi58+f6+nTpzo9PU06bKVSSYk3OswTXIc8obwdeRjh0gBZW87b9CoJvl3UN0dAyu69uPaYl7LlurB0753TBrxkkjEsILwmP8rRvVfpvkqBUBHCIFFDn7tn4GSfZ849cQUZ0o/cg80V/gWKeGJ53+eGh+fPQ948EZonLHndQ3ne6+Ps180ND2Pv0VFO3sg3LHgOiCGczz1rSUnDdKObEwV9j1Hxv7mh91yDVNTKfRyZH/zN54/3l3QfhdAu1+vdMGCcuabLDEStbCXmhEDWIFUFR0dH6fwS+jU3gId+d7CmkQhcE+92u0mO5Mthfas1a7Fer6ezGJjzyDNXV1fp+j7fPiRK83Qnk4k+//xzrVYrXV1dpfDZvaTBYKBHjx7p5cuXevHihc7Pz1NWEnKV7ndrEd65Xnpo8NzTfSjBgLQwGAxS0TYeoX99tV/LiRkycU8n95D8u7xcRsk9Fl/MkJukFK6hlfnC5H0QgGfkvcCfZJYnkCBbJwfXqCWl5/dkCdfjd+7nuqv3v5fr+MJ3T8STWBjanIzz/jkUsrqu6Z/PvTe0WC9vo13cB42Z0/Emk0nBaGBs8oPMcw3VvTM2BUCiXqHjf2fO5dqsOwk+L11WYgwIq32O8owu6blW7ePiJO7a6uXlpd6+favr62vNZrOCI9Fut9NXaOWHzbgmn6/FnPBYWzgG9EX+tVfIGZJSzoO2d7vdVPFA/y0WC11dXanRaOji4kJ/9Vd/9R5vfCiU5umOx2N99tlnajQahXINJiuE+/TpUz179kyPHz/WYDAo7EZxLYuBZCI9ZCnz0EUqVjawGEm2DAaD9BXuTDAsJvdhYmBd8yyukz/Em5/nymKg/Xg+lUolJfx8wdFPrqNKKiwM90x844VvNMkLzJ3E6BevxHBS5vl5Rg9N/fu6eG4ng3zhoxH7YvMEFM/mz5+TuuvxnkD1cD3XzN2L96SYn/HBfICkISKqZZDHMF4Ybd8e7Vl3lxF4Hgw2/UeFDkbYDQ5erBsmT/p6WSL9vFgskofn8yyvi3fJgHlHn9J33IMIjyqCq6urdAQqRA0x430eHR2l9e4JLZBr6w85TV4i5vIXz8O93ejwnLSfb87ebDaFb61BK3a++pAozdOdz+f6+uuv1ev1CskZSAQdt9frpQWAjnXovND8BCPvrLzT8mSFh6z87iHRYDBIoQsEwWJGx6JEhdCOxMqh3TAuP3jywUM9SSkU9HDWtbL8ekgY7p3lGWnX0CSl55GKyUT6yRelV2P4e7yQneSan5p1aJH7JhX3jl179yQl4wRy8nS4wfDPuS6JtOI1oZLSxhNIKvfw8YaZhxAa9/QyQffU/Zlcc3fSgcScsJ0wHkqmMW75M5N7gEA4Me5QFOBGzE+1y0mQg2VwPkhU+aFOvM5zI0dsNpt0jOvt7W1ab7muzr3ciObzNo+WSIbd3d0V5ECcNMiWQ4fYJu3JR/9mFzaWlIHSE2kQlxfTsxWQzuT0eKzncrnU9fV1On/XSz3ygmpPKOQZ8px43aPCE/SvauE1vBMvwwF4XX5dT5R4iJdrWK755tlcL0ViAjJRICA+DzHzd/rBdVo3PLTNvSna5EkwJ0GICxL1hBw/XjrkROUGIq/8wHuGMPLSMJAnVwDX82egD2kDhIpk4e3kd15n4boBP1RtwMLOjV+eeHLD7vOFZ3LJBMPufenasZctolv6bkz6Eo/Va5Ol4tkZ+drgOVxe8kgkJ0nWmOc4cJ5cIkEy8V2cufSUO0t5stTbwHNOp9NCbTPrAScIHsGzdX5xrXe/3//+JdLAIe8Maw7BcI4tISzJieXy3beLXl5e6ubmpnDguYcRecLFvbGc9Bz+GdfcPLSCZCgn83BQuj9SrtFovHcOhLfPw2cPiTkdLc/wQyg8i29vZBJ5iAWx5NUPEDneJs/t3mxOXk5K3CMnjFxrBE7ueZVEHurnUUA+Lk6GvpuKz3t2PNf18ySgP0PeHu/33JB7FHHIUPEMjG/eT/QvferGgTb6tyZAsPluxuXy/vvkWBu+uSDPJbihcvmDuerkS3vzxJ7LWtL9BhrIi/wC64X/c/jOoYjr0Br0CqZDmzFwIvC42aXJHMfQsDbYPOLfIkzfsO58bpSFUnekeQIl72TXDSkBw5NiN5l/hQ+T61BncS1PUkn34bTLC/nidc0TCcEnvYcsTEyu0el0CmELulO+28Y9UEjtUBLQvaScPF3f5rMsMF/8nr32e3hpkW8v9RCS9rkHgoaNp5AfjOLHBeKBONm5J0073CChhzL+tNNL0/KkGoub58UThGDcKHhy8RCRuvF1UvIdWi5X5MTlhEa/uD7s3pbLLfQzfe7kye+0n+dmzAjp87nt0YFXNUCmeWWD9zUJW08OuibNnECPdqMvKX39jjtZedv8/+7w+DkU7lnDI/QHXDGZTBLR+/Z0HB/a5vIR65H2/V6SLqEQD8sk6Ha7qabPv90XS4p3l4fg7m14+E7n+e4jxHc8Rvem+DyeNm3Eq8WjcJLwguzc8/KyLha8C/7efn8OkiG5t+VJD0/O5JKG/3hmncXp9/XMN2cPQNYYDPc0tttt4eQq7tlo3H9lOYaICe0E7WVpLpv4tZxMKpVK4WAfIg/31FiQLC6vDvH793q9g0cWutFxLR0yddL2ZClRAtIMpOT/+rkFfm+uweYgnxfMDR//fGyBG2JPrvGljXkUQl9QoePzE7g84BsbcsJ1WYC5SSUG84XxJYLzr+zxeejPkFd/eHTqxiSXHqTihhpf+xgE2gu5UnbKfHZeKgOlVC9I95ruYDBIBNJqtXR0dKSzszOdnp6q3+8nkssTMfv9/Td5Mshoaq4BegY+L/j2Yvt88niFAZ4S3hXv94QVBOGeVJ5wch0zJ0gP7VmQrhd+VzgP0XibgHsHeQLRQ3V2++V97gkg2k6NL1s6aZeHnn4YONdhYeNdcQ9K/dzIeDmPb292j/FQ7alrnyRJCBcxZvnmDUgIYuT58Ywgbfd2fcuwH+qS18X6T74dlnu415uPcz6muReee6zMB5/7vB/ycgL2RB/vZS66F+wH9zDfuYdHVnkiUbr/0ku0VZfAfC56pJFvO3Z5ygmX8fcogvczjzCU9DmGxtctc3u1WhU03TI83lI93f1+nwRwBrTb7WowGOj4+LhQMeB1oSSKsFxMOhavdJ+BdZL3pJLrSfkkd+mDxe9JAq6Xa9L8jYF3yeCQ/OGfkYoei28ScZ2P9rtWyAThfS5DsBi4h4fyPhbuUUECucfoZwt4CJbrkbke69qqh+qMgyey+IwbAxa6k4sTbq7pOvEydxgDrnuo3MrJ0iUmX+j+PIcMKP8iFfjBTf4v4+KaPf13aCOJVPTY8rmC8+HShc8394L9OaR748o9uL4Tq2ufbohz/djHDicCp4V1w2cPecqH1qjnM9zRcenD69VxWtDE3eBg+Ei0ceA77yf5mK+RD4lSE2nS/XkIHKAs3Xc4nUwZx3Q6LexmqVQq6bzavMTGLakPrFT0+Hxi5sSbe5ZMXF8IPiF4HwPv5TlOWl6fyu/5hCWUdm/FNSh+p39YrL5geCZf0Hliyu+73++THurSAjILhi/fUecLolarpd16GDo3Xq7xkgQhgvFqBic0Xyz+hZH0ca7pekJkuVyqXq+nrL6/h/5hPFer+3NpCTNJ0kr3Gz62221hPrqE4TuZeLZGo6H5fK5Wq6XZbFbwtNGgvXwpT+TwjPSvy2S0ySMM5oVXl/i682d0icgNm4NnOSS9+Nz1NeJhv1cO+bpyGdCNJmuY6CZ/v0twLmdhcLy6JH8+lyF9DXsS2vmpDJR6nq5UPJkIfYsj3tDxnHR9ayGLEKKhGN+t8iHv0j2vnHD5PQ9Vc/HfPYY8NPTX3ePxMiI+41UNkt7zxIBrr14W45qivz/3nLx9fh9CKvrZJz+e7qFyLw/1IDK8ZO7Pd2r5xG+13n3RJV9ZxH1yI+kEjIGBfD3hlxsgFpqHh/QN7fQxp330EZsBtttt4RwB7zOezRO5jBH384ReLjm4Fk07PIHqzy8Vjwb13ACf96jAdycSER4qpcyjEum+hMy1bE9I0v48Gerty42CRwzfVQLoxOuGMSdJyByteTAY6OTkJEXF5Hy8DJVnZ65jYDk2wB2fMskWlE66gA6ZzWYajUapxs5DXbeo6Dgk22q1mvr9fprMeFxMPA9pci/2EPD02Oo5Ho81HA7TpPbw2DU5D7fcI/SsqSd+MDY5ofOceDckgZBgIC28SfemfZIdOm4QUocImOx+YIsbJz7ri9f1YArfPRGx3787ltG9IjTITqej+Xz+Xrbcnx24t+RhtHt2Xm3hHrgncvBi8zF3IqbmGeJ0rRlpxMujXO/3mm0nGjceLjvk5VBubHMdfbe7L8Pa7XZpXXi/+JeKYkw50ySvmnGd3+ed5xBYN9zT8y6uH/sWc5d+GFsMGQfJTCaTQq3xIeResFT8Jg1OLeM7zU5OTlKkTAmle+BIB8xJxpovTXBu+TFQ6tf1SPfnAEgq7J7ZbDaFjCl1sHROo9FIXyY3GAxS7ezjx491fX2t6+vrRJZkh9lq7Isg72y8jmq1qtlspqurK33zzTc6Pz/X+fm5qtVq2uopFc99qNfrac+5nxM8mUwSEXlZleuJ3i+uKbsn4RONygD6ySclHhgECekQqrfb7fRV6xSuszh8u7Fng5EauI57mx6NQNyQvVTUkWmzh6uerc69Vklp/DlIh7mAwfWDhvz0N6+6gKD8UBk+45sOMBB+ghjtcO8SMsC7pE0QDV4dkopLCnweI5Qn1xaLRfraHfRGPDTeQwUHa+D09FQnJyeJdJkXXoWSG/fc6DFWvrkCI16pVNIuUc4j4VAjnACvhOBaGOHLy0u9fv1aV1dXhXWYEy8ED+HjQXPEqn9VF4TLt1dz6A5rimjHy82oaiCiZpcn76PtnvP50CiNdJm8fsI7WhnAqpJU8wkPCZ2cnKSvYcbjm06nurm50fX1tS4uLvTtt9/q9evXhe2Lhwbdw04m+Wg00uvXr/X8+XP90R/9UaHsCO+AycnxkkyAzebdt1pcX19rOp0WqiDQoThMxycrEx7yow8gfr6xFH2Ra5FUYJulVEySYDBOT0/17NmzRN6Skn5J+Yx7xIwLheW73S4teMZvMpkUdgi6p+SyRm7kPFMO+XgSEdJiYec/h0gXAzudTgvf7YVX4wYFCYU+wMuDcP1sVe8TPK6joyM9evRIjx49Socx8W0oeNucO8Ac9j7xyIW+5LSrb775JvWhJ8Q4bvT4+FjD4VAnJyc6OzvT8fHxewczuYyB0YG88Ug974BXzLkSGB0iSUnJyB8dHSWnh/a7/ICRXiwWevPmjV6/fq3RaHTQu2Q8aEueABwOh3r27JmePn2a1oB/R+J2u9VkMtHNzU0aa4yFpDSHObAcwp3NZsn7xwFxfioDpX0Fe6PR0PHxsdrtdqEyAfLFCvf7fT158kTD4TCVhjFY7XY7TTq8XXQdOvjrr7/WZ599lk4VwiPLEwyH4CVSFxcXWq/XydqTsGCSrFarAgHM53NdXV0l63t7e6vVaqVarZa+XqRaraZDQFgweCZMfrTq09NTPXnyRGdnZ4Vn3e12iVTc+6PMajqdFjLTvV5P5+fn+uijj/TkyRP1ej1VKpVEVmxEgQjzCT2ZTLTdbtXtdlOb9/t3Wye5lmvBebLF63EJT2kbxgidHu8Ur87JyT1eEqh4Miw698J4Nj+AiPlAu6Tilyv6xhvpXu5xI/Ho0SN99NFH+oM/+AMNBgNVKpV0BsBsNlOtVkvj644BDgDzgc01+/0+ad54ifRlpVIplFWenZ0lb284HBZqrJkXXuHBWoNgqIEnMuPYxfl8rsvLy/TVWawBxr1ararX6+nRo0c6PT3V0dHRezW89BME+/btW71580aj0aiQsHoIGDjWYa/X0x//8R/rk08+0YsXLxIfYDRYZxhjxpU1ulgsdHNzo+12q+vr6xRN4EQQxXCcbJkeb2mebqfT0bNnz9TpdAoWFV2v2Sx+zfrp6WkiOte2sLaEyl5GdXJykhI3X375ZaFcKdcOD4FFT6iLd+1fF0So7UkGyO7y8rKg0dFuT4AR6vM18ixKSJfndG+GwzwIDT2T77oguqTfazgc6vz8PHkMJCDycjyeiQU8m810c3Oj8Xis7XabTmCjAP/u7i49t385KPCkC5o4Y+GkikfoNZpolv7FiIxDvpPKS8nq9Xr6TLfbLWyqgfjQSj2ZRru81M9rXZEE+v2+zs7O9Pz5c7148aJAukg8tVqtUALJ/HWN3TfQ7PfvivUrlUrKJ0C8ZOpPTk70+PFjPX78WKenp8njy/sC6Q5ttNlsFrxvjBtGVZLG47Gke5mBaMt1fGSuk5OTlMRCLkPKoc8gRgwvJO+S2kPwnMFwONTLly/18ccf69mzZ4VqBSIc5gy10xDpbveuwocdotfX12luu7bu0RtRRxmofFdySdIPVimMNyq9/yV9niTwgTxkdTyZ9V5jrQqBHxfnfxvkCQu8hkPguixUJ1q/L9dEa/Lymu/qj+97Vv51vTovy/ESnu+756G+zNvkr/szP1QZ4tc+9Fx+Xb/+ob8fel9+T//3ISnpoZ/fpu2H+jK/30Pjlve1j8WhvsznDb8f6oe8HYfG76F75vPGn8GJip+H7i0VjzLN58VvA+7rycrvW++Hrs/fH6peyvvDj9r8gfCgy1wa6QYCgcB/ITxIuqWWjH2fxfuh9ZTfxcLm+P9py0P3LTNDWjZ+277+j9gH/5nb/kPhQ8zZH2v9HULZ3POd9wpPNxAIBH5wPMji359dCgQCgcAPhiDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiPr3vF4ppRWBQCDwXwTh6QYCgUCJCNINBAKBEhGkGwgEAiUiSDcQCARKRJBuIBAIlIgg3UAgECgR/xdvVMT3z6v+yAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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gHA6j2+0uxBjOA8e3WCzi9u3b2N/fh9frRbvdRq/Xw3Q6FU6Yz+fSZm5UbpAt4foV7NxNObFIAjp4xElKi+JFYroW8+PxuBBjIBBAr9dDq9US94aTXmM6naLf7+Pg4ADz+RyFQgHr6+tCBnqCb2xsIB6PywR16owcPL/fj9FoJBvNyckJ7t+/j08//RRff/01yuWyuFpcFKFQSMiTruDW1hY+/PBDfPjhh7hw4QJ8Ph+q1SoePnyIg4MDPH78WCYm34X9yUU+Ho/FatSWo96ouPADgQBCodBCQI3u9Gg0EqtXkyEXJi0jj8cjv8dNrtlsyiKmBc9AJ61RShmDwUCepQl3ZWVFnuX1euU9+I7LiJeeD7+fsgH7h+/IIKmWcygVeb1e8Vh037G/SUgMCOkAmt6w9Lho7Zn9T7Kjjtput3F6eiqE7Pf7EY1GRU++fPky1tbWMJ1OcfnyZayuruL27dvI5/PS75TSBoPBglxVrVZxdnaG4+Nj3Lp1Czdv3sT29rbIZHwPHQBln1KiGA6HaLfbKBQKOD4+Rj6fRz6fx2g0Qq/XQ7lcxrfffovDw8MFr8q59mjlUwaJRCJIJpPi7XCTXxY01KC30Ww2RZPm3CBoBPn9/nP54MfGj066esBSqRSy2Syy2awsBFqS2mriBH1RJ/P3qT2FQiEh3WQyiXA4/FJaDQMUzWYTR0dHGA6H2NjYQCaTQSaTQSKRQCwWQzqdRiKRWIiQLiNcbe1QJ3306BHu3buHg4MDNJtNeTdnZJnvEQqFsLm5iffeew8fffQRrl27hkQigfl8jkQiIS7x0dERWq3WQmBQt4ebXL/fX9DrAIjmGQwGEY/HkUwmpd9IarSQms0mGo0G2u02BoOBfL8muEgkgmg0CgCy4egNdTAYyAZJstGyBElQbxicO1q60IEgHYgkgfIzvV5PiJ3Exd/h5rPMaufGEwgERH8HIMSr39s55+ixsP/6/b70Hd+R70mQ7BloJFnQGODzQqEQwuEwUqkULl26hMuXLyOdTsPj8WB9fV2MgS+++EIkMWrQfDcA8t7NZhMHBwcLFi5lDz23nXNKZ7JQt67VagCARqOBSqWCSqWCs7MzFAoFIcHnWZPMYKKH4vV6JajHuauDpc/7Hs4hGgBcq6FQCNFoFKlUCmtrazKPC4XCM3GXHxOuWbqrq6v4nd/5Hbz11ltYXV0F8ORlOXB0P2hFvezLc5Jqt0hbRM70m/O0Ye6S7XZ7wRJjJoNOP9MkeZ67y+/rdDoLwSkuYJ0eR2ssEokIcWWzWdy8eRO//du/jTfeeEMCFtTbGKB5/PgxKpWKLGSSmd/vlw1LL3S+u352PB7HxsYGtra2kMvlsLa2hmAwKN5GvV5HsVhEPp9HoVBAo9GQ4Br7l5tSLBYDAHS7XTSbTdlA2RZawV6vV9qrgzlsqzNtSVvT4XBYgnVckFp7Zv+TzLS1plOIdJoX+4TEk0wmZQOhG8oAGSUAau25XA7b29vI5XJIpVKIRCLwer3o9/vSd6enpygWiwubidYRSbR6bEges9lM5iSzIi5fvizeGAmF+qvP58PXX38twVBa7Ox/EhwAySA6PT1FNpuVINt5mUJ67rOdXHsAJEirJa7neavONaq/W69nBopflMGkoVP7aPTt7Ozg6tWryOVyAJ7EUCKRCFKp1Et95w8B1yzdCxcu4E//9E+xvr4On8+HwWAgL8rJRnfiZTvWmUbERViv1zGfzxEIBMQKAp5O5vMmAC2gbrcrQQ2S1XA4FClE5wE6czBJFtRm6cYxqMJ28TvoXmviTafT2NnZwY0bN3Dx4kUkEgnZSBjEy2QyuHTpEjY3N3F6eopOpwMAC32n20KycOZJ0l1MpVJYX1/H5uYmVldXEQgEMJ/P0ev1RBsNh8OIRqMoFoti8XITiMfj4glwk6LmSVLUmQA6wr8swOIkXbZX53WSlJyEqi1+vr/ze/nH2SZqzHyX+Xy+EExim2nh5nI5bGxsyGZFT4iadigUktgArTZuvE590zlGzvZFo1Fsbm7i0qVLyGQyCIVCC8ZEIpHAzs4OarUaWq2WuNW0yrke+BzKH8yIyOfzQrzRaFTmgHNs2M+UnbhutXXe6/VEHnyeC683XWaB0ENqNBoiS/AddX7/88B266DqxsYGrly5gtdffx07OzsyNu+++y4uXLggffVjwzXSXVtbw0cffQQA0qGdTkcmKaOdOuDwomilM0Gd0V3+1MShra3zQGuJmhSlDwZ2ms2muI4AlhZScFJSk6PeVSwWZSHQXaYGyl2e78JgIINA50kQ6XQaqVRqIeCj20HZRLtlyxYRFzzdOWp5DGqxHwKBAFZXV+Hz+dBut9Fut2VhB4NBaSdzUXUBh26Dtnr1eC4r3iC4wOkSkgD7/f6CFKUXuP4u50bu3Hz4k5sg+50SEKPoHPd4PC6STCAQEBd7MBjIuDFThBvPsgi8k2CZYqjT0/hMpogxIKktePYfZR5d6MPx4E9KMpwf7XYbxWIRjx8/lk2EmR26fc52UnpiUK3f74t2zDX3onUMQNYLA+rcsLrdrmj8tM51ZtB536s9Jn6OHlIikUA6nUYmk5Esi+vXr4v3/RtBuvKg/3ZBGdTp9XqoVCooFouoVqtotVrSyZyk51WkkHj0pNNBH5KH1gppQb+IeDnZe72eEAutlE6ng1gsJmTprPIiOJkbjQYeP36M4+NjlEoltNttIQUWUTh1No/Hg36/j3a7jXq9jmaziXg8LnIHrTetrfF5XEg6IPQ8105nV1QqFXg8HsnFJaHRfSfo1pME9AIk6a2srCx9rtPK5Fjy53mLiPNAa3UkRM4n/c7nvauTQLSFrduqJRF6PuxbHfSaTqdoNpuiZwIQeYK6/3A4RLPZRLVaFQJ+3nhoy51zmpsB/64zHfTm2uv1JKWMKYGatHRwlMYFAPFEwuEwcrkcdnd3kc1ml46Hlm5Iup1OB91uF71eT0j4ZaxcPS70mtrt9kJw1Jm5oAsXlhGvzo/XawV4kiZYLBaxubmJbDaLZDK5EMB0C65mL9AarVar2N/fx1dffYUHDx7g5OREJjYXj9a+lkFrSpxQemLRoqSVdV5ppxOcwP1+XxLV2Y5l7vmynZFkVq1WcXJyIjoo8wEZfHBaurQKO50OqtUqHj9+jHg8LhOCOZ3aG+C7knSBp5OR767bq99TFw3QWm61WpjP50K4JDS6p7RgAYh8QCIh2fLfdfDkPLceeLqQtNboJGYuQEaldV9rDXgZob7oufwdPf69Xg8ApJKNz6Xly9/XKXjMZKC1yWAxdU56DJosnHOI81d7KQCkbznuOnWPFnWxWMTh4SGOj4+l6Iakq4snOFfYDurJwWAQJycnqFar524OywJrfBfKKMzBflHwzDkPdd8u0/Z18O5538vNSW+cHKfZbCZzWafFvaxO/EPANdJlKlitVsPh4SHu3buHr776CsfHxxLdZCfrhbQMWqOilutMhQIWAxXfRYDnhOdkZcSYgSJqac5yQbab0kKxWESpVJLKLWeWBl3JZbmanU4Hp6enCyWgTGTXZKoJi59dNlHpjjstTE5wuu8ARF7RbabsQUuOgTqto+qNjm3SUsKLFotzDDQRPu/zTsIlvksw1tlm3Rdak+YiZp9peYPtDAaD8jtMHXTOae0q05J1BhX1OOp+1KTENhSLRezt7eHRo0fI5/MSP+B36ZiE1o91PzWbTZRKJfHKdIm5nuucT9S1R6MROp2O5JnrbIOXgZYszpOE+EwSJPvgvPmgDSXnRsMxYvHFfD5HMpl8qbb+EHC1OKLX66HZbKJQKEitOoXzl0kHIahpMrOAearanaDV9F0WOqEX2MrKCjKZDDY3N7G+vo5EIrGg+eln6pzDUqmEs7Mz0fl0tBx4GkXWcgPPaiCxsngkHA4jFotJuS+JmATIyDWtBcoezHbQLjzbwIgud3sGG7jIaB04+5R/+HxdMg08TUNzRpp1zusyctQLW7dZ/+7Lzg9Novy7c5Pi3/nvesNju/XGznfQ0obecJxkyg2Km+lkMkEgEFhIg9TjoK0vAOJdsM947gMLf/hOLMAoFos4Pj7G2dkZ2u02PB6PpExy06Bnoi1I9sN0+qR0vlarIZ/Po1QqScaMsy/ZHn7W6/ViPB7j9PQUKysrLyXjLcMyr0g/l+uD8+M8+VF/H0mWa83j8Ui2BvPuKZO5BddIl53GiaWjkHpBOz/jXGR+vx+RSAS5XA6ZTAaj0QilUmnBUgaWn/LFxfUy4j7wZEKlUikh3GQyudTK5eCyvPXs7AyHh4fI5/Ny7gGfDzxdbPws8DQ4k8lk5HAULhQGKbQ1pdO9GGnWhRXabdI5rZr0dV4mzwKgDqn7UOe+cgOg+8wFq605uudaHuIm4BwjtsU5Tsv+/iI4P/e879GBO6c2z8XJzYz/zj6lTNbr9SQ1jPEDn8+HSCSCWCwmmiFT43Tfa9ebJKYrwPT80hV2sVhsIWip5RBKdMwmYbUk5Qf2uV5zzuBlr9dDPp/H4eGhnP0AYCFwxz7T7ZxMJtjc3EQqlVp6uNN54+U8fW6Z18K5Go/Hkc1mEQgEUKlUUCgUFgqDnGPtlJI0n+hT2JYFxH9MuBpISyQSMji5XA7xeBzVanXpC3OS6o4i4e7s7ODNN99ENpuV0t5mswlgMWCiFxYHmETyMrXc4XBYUmg42Z0nNHGCcOKXy2UcHh5ib28PxWJRou06ss930jt4LBZDJpPBxsYGIpGIuPt0qZZZG3TxnBuB1t30s2j56kUej8eRSCQkL5WfoZYLQLwIkvzq6ipWV1cRj8dFZqCF3263UavV0O12n7HsdeR5mSXL31v2d93f/Kn75LwxPO8Zek7oMdHWK7M1WPZNjXYymaDVaskZCO12e+HMAgbSODa6rbRq9TGJekydY6i9FudYO8dZZynQwo7FYphOp2i323KQD99VkyifyzMj9vb2JDOGc1C3je/C7+L8XV9fl356HrSkQktUbwbONez1epFMJnH16lVkMhk5eOfx48cLlXZ87rLUUD4zFoshm81iY2NDxpebphtw7Uk8JWs2m0ki/urqKgqFguTiaTLTJMGO11Va77//PqLRKL755hscHBwAwDOuHv/Oic7JoyP754F5kbRu9QRl+/gMPrvf74t7VigUJEWM70KrUe/MrH5KJBILk5apanxvPen15GRb2EfLgkq6D0mesVgMsVgM8XhcTvhiQr6ziIILLx6PY21tDRsbG5JWROu13W6jWq1Kn1JD04tBW5K67/QCc3oozqCZ/n3nuzt/z/mMZYStiQ54mprG9wiHw1hbW5NNhvOn2WxK5Z7H45EjCLXGzoAwjQXKYuFwWNKstGa+rL3cJJ0WqdP9JtlzjfGwn2g0KulsPFdiOBxKO53lvuPxGK1WC4VCAfl8HplMBul0WgpFnHNfbw6hUEiq8pzxDid0vjXXxHlnLHA8w+EwNjc3cePGDezu7opnx+o7HdPRHh7bzDaura1he3tbDrBigNotuC4vMLJLF1W7b1p30wuPrkA6ncbVq1fx+uuv4+LFi5jPnxQKAIuJ8Rr6vxl917Xc51m72lp43oEbzoXAbAC6erQ+dC6tTm+iNp1OpyUHk0nsunyRsgg3J129p4l/GZFpS5kLkYspEonIZsAF2ev1JEGdLiiP7MtkMtja2kI2m0UkEgEASfHjGDoP1HFa3HqsnYSjyVCTqd5knISjf5//z/n7ej487xn8qTMSOAb0dgAI4TJ1ShcHjEajhYpDLnYezckqP1Zs6eIdtl23Rc9tEhTbxXmgz0UAIPIP0yfT6TRqtdpCsMqZNcO+YGHPsvz287wPtovZG88jXb1JUM7SBSzO79YeE88zXltbkxJrHpalU9SchomOYbDqk8bGb6y8QFAPYy6fdq/0wR5aM6Jrtba2hkwmI7XhTMR+mVPI9MEumojOI1Jqd8zX7ff7C5sEoS1Cap7cVKLRKCaTyYKlOp/PZZHOZk8OCGdmBKugePwhF5XH45HKH+Yz0iI7b+PgImTQhTs/3cBsNiv6Gw8u0YfikHBI8gxi8JzXeDwumwP1bLZPL1SnXsr26ioj9rnTzXZKEpw/TjLleCxLAVvmYuoxphXPz+gglyZUvicDXgweUWZotVoL7dSpfPP5XKQZaqwsF2Zql5bSdOGCE9TM2cf0iFhUQPmBcggPfeKGPpvNhBT1YUDcwDmHNSnpYJretHQ/MVWMstLzUrt037OAiTLceaBBw/nFTZBeCMdKn6Xh9Cr5njzalNax23CVdFkieHZ2hv39fZydnT0jhOuoLwlDT5D5fC6uLI+/e55MQHCxOiO3z8N4PJaDjjc2NpYGMQi67alUCtvb22g2m5jP56jX6zLRGSXliUr9fl90Qy4KnR0xn88XzljlmQORSATz+Xxhw9FWVSAQWDigRx/8wf+fyWTkAG+2xbnbawLkImGaDfVx5jTrohZgUZbQtfm0ArmhaFnpPI3WGWBxWl763/RnnS455wHdabr6bBtzgdlXTGNizrI+CY4WnTPYpp+p28i+5zGNqVQKsVgMq6urcvg5iYVnPdBr4Pfyv2mFejweKWyp1+siG0QiEcTjcYTDYSHlVCqF1dVVIXVWZ1EKYEqZ1+tFOp3G7u7uwqljzg1NexM8GpPnLeiDys8Dx+tFGQgcR45PvV5HqVRCNBqV0nduEhwzviMDwjo20e12USgUsL+/j+vXr8ttKm7C1ZQxVmkdHBzg22+/RblclutotAbDAA9dOL2YqDfRBa7X67JwdQ4foUV6uoBOt9wJPotaWD6fx/b2ttSkO5Op9caQTqdx+fJl+P1+rK2toV6vYzKZyGbC7yyVSnLyFCPN1FgZxWaAgNVw3Ol5slS1WkWj0ZDzd4GnGQZra2u4dOkScrmcSAj8d524z4INrUE6gxq6CINtoMegCyion9OCIQExAOT1ehdIw5mbzefq57Ofl2nVwOLidRKs9mg0IWo3kylY9MD0eRIcB+Dp2a/cnPTv66IFWqx6k6f1xX+jQcH5ol3iXq8nB0Fpd5mFB/V6XYLP3NTz+TwqlQrG47FkssRiMSmv7/V6Mre4KaZSKeRyObmJhFa53+9fODCf/+7cTHS/s7qUKZIk3fMsXX6Op85x7jgD53ptUVLjMamhUEiObAUgxgD7nqezOXOsZ7MZSqUSvv32W9y4cQNXr15FIpEQqcwNuEa6dCGq1arkE3KH16lFWjRn9JSLfTZ7cntBPp9HrVYTAmMAg2ToDNQAT8+Z1RU954ETjFHqUqmEcrmMzc1NJJPJpSWDtOwYbIlGo9ja2hLNjpJAu91GJBIRN5GVQJQl9EEjXq8XvV4PxWIRo9EI5XIZ9Xod6+vr8Pv9cgtHpVKRYKSWYi5evIiLFy9KcEVXj5FE2+22TF6WclLLo3yhZR++Oy8ZZW4yyZVExmAUNwFuJnRFnRWI+ihInb+s4ZQWuMAoSzg9Hp3/SleblrcmJhYwtNttWdxMp6OL7fF4pKSXY8oD+ZlvrsfASfz03vhv3JhIFiQ2lmF3Oh00Gg3xBPv9PiqVCg4ODhAOh6WgqFwuo1AooN1uIxAIYGNjQ8YiGo0unOPB5/l8PgkmbW5uShaKx+ORfkkkEhJkPU/zJKm1222USiUUi8WFs2yXZRDo9TgYDBZiFRraSOB3TSZPDmandEaZcjKZSB+SL+bzJ9WVevNjm3ng+vHxMWq1GnK5nHhAbsD1irRKpSJHzmnLSv8eAHHVGQxgEQXdbeqc/G8dpAGejWCfZ0E5we/gAHBx8S6lbDYri9AZ9CDxsmotmUwunPfKu6u63a64pWw3XXeSA4m62+2KRxAMBmUT8Pv9KJVKODg4QKlUEn2c78CgCg8UYl+TVGmlcXHzjz7/Qm+I1DV5EhSr87iYvV6v5PCyIomWDN0/Bj25ufA7NQlz03yRBOQkgWXaLQmQGSDUKXUxCANj9Cq4SFltxXcEnhxXWa/X0e/3RQKoVCribZD0dUqY7kOd48uxjkQikt3AtnL+cOPn5lQqlaSCan19HZPJRCzf0Wgkt5kAT7Ni+O664o1zLZlMIpPJYG1tTcZRl8c6U8WcAT4SZ6PRQLlcXjiwxpmK58Sy9ag3Kqd3Qk+VljQNE12izkN6mEMNPD3WVM8PFpSUSiUJZq6urrp2/oJrpDsYDOTwF7pCyyLQOvUkl8shGo1KEjrvN6IlRuLQJ8Dr4IwGLSJgMWDihLaO/H6/6EjHx8dYX19HKpWSRaIJXu/WOmNBB2S8Xi86nc6CHqqlFZKBTtgmKbVaLWkPNyweG9lutxduoaAVTSmAml273ZZrkqgHM1BIMqaMoYNAABY2Dl7JwqAaT2wKBoNywDqj5LS6+G5ckLRWqCMy8u3MBiH0HNGpQcuCZ/oz1FO5CenrlbR2TqLleESjUSQSCdHYx+OxXB/FSzl1qfSywhNa8bQg9X1tPPWK/aT7Q59KxvkDQCoUgSclu36/Xw6c4XM4D3TQWBfKUO7gnONmtOw2FEKvLb2edF768fGxSGn0JLRX9X3Wo17LNNo4/zmnuLnwgoRIJCIeG68BokSp58l4/OT+uaOjI6ytrcnFCm7AlYspqT/+67/+Kw4PD4V0uWgY7WQaSSaTwc7OjlwdzUvtaNnS0nAemfciK1bv0vonQdLX7h4t3f39/YXTiHK53IKG5CRQZ8qQPvGMOzHLg3UwjIRES9upddI95426nIjUtXVwq9FoIJFIiO7Ie8D0/WP6wBZaZ/q8AYLyBImXtyE0Gg0hztXVVbEQKTnMZrOFSiut3XGjmU6nsojOC0Rpz0L/dG6yus26kkx7Edr15fdwo+VcZB4zg7esNDw7O1u4BVlnj+ifekzZt8PhUEjeGXgEnhAlTwrTFYjsMxbgkHDpwTgtRr2xadJhGyhXMNqv5x37n4YEx0qXQNPqLBaL+Oqrr3Dnzh3s7+/LhZqMb1Aq0rKQ5oTz1qH+Pf3u9Bo4P+m1xGIxyf1PJBJyRREzQ3TmC9+PpPvll1+i0+nI/Yc6fvRjwTXSPT09xd/93d8BAMrl8kKUm4PNSpHd3V1cuXIFFy5cED1wMpmIG0PXVadMPW/g9GLX/19DTyxd+UOyooXNPzdv3pQbFjgRdF6uzr7gv1MmobXEVKFQKCQyCfuFE0unbJG8+T46GOgkIG5QvHiP6U26MonumQ4EnXcYCqHTcDgGwOLZETpflGlIXKi0oBn44gKNRqMLXovzcCC9QS8LtGji4f/XBQNsF1126s20zKizcj7y95liVCgUcHZ2thD81c/XfcV26IR/eh+UXugBUEvk8+lt6GCU0+LT5eDU2XUcQktafCaPX+x2u+I1MtWt3++L1qtJTp/MRauSKYbVahVff/01Pv30Uzx8+BCVSkXmBeMK2lpfFgBdth6dpOccU63P8r3JG9vb20gkElKCzjRIj8cja43fNR4/ubT2zp07uHPnDn72s5/hypUrvzmkCzxxh7788kuk02nRYLmL6tP6c7kccrmcVII5CcxJEs5F+Lw2vEgjJOmyhJPBE7pJdMm5c1++fFluF+AE9Xg8stD1tTI8Ya1QKEhqDa+zAYDT01Nsb29LCWm9XpcD2blDc+JzUugFSUuSFgstG95SwDMcGDjTh/BQ/tAntel+IdHT+uWi12Ws9A6Ap9o2vQKdW6qj/bRA6Y5SV2Slkc7IoHXqHEf2hTNgQjKidqvzQblZ6E2WFrCuyWfgtlqtolAooFKpLEg5lBH4U/cN20kCo8zDn5TRZrOZPI/joIsytPXpJGBdYclnUWOt1+sAnmjV+XxeAn4MFq6srKBQKMhtF9SbOV95cD+zALQ8wrvVPvvsM9y7d0+uoeIGylQ6Ejc3GSfpvmjdOseahKizaZzyDceZ8iT17vl8vqDbMwA4HA5lrb2II34ouKbpcgHRLdPBJ/6k60KRez5/UnHGYFK9Xl+4qE6nG/E7fpVdSlcORSIRmTQkDeDJ5sHcXRYXML2Hv6sj8iSrRqMhk59XVbdaLbHqqDv2ej0kEgm0222cnJxI/qV202jlag15NptJZF4vVK3v0vKt1+tiDbC/2GZtlWgLkwSgFzjwlMx51xSzFdhWEq3OdWVf6sg68FTnc5KLboO2Jp1/d35GEzL1bUo7ug0MaNHD8fl8Mr/6/T7K5bLIMrrwg/NVu/C6TXw23Xk+k59hFgs3HwDPbBocU8oAzK7gPNNeHAnk8ePH8Hq9iMfjaLVaePToEfb391GpVCRjwOfzyRzmyV1a/tDnKVPq4rxrNBqSrUBLeTQaySbLtnOT02XV3wXLNF/Odc5p5uqXy2WEw2HxGMvlsuTxOoOBeoPWKXluwdWUMf505sjS/Sa5UkrQ2Qu83I8RUq1zauiAy3fpSE5olimTdElaOurLf6dVSsuMOy/BnZVZG7yckKRHTU2XkDabTaRSKUynU6mXZ5I3XTtdfaMtBr6DjvhyMfK6IQaD9Hc6U2WcepsmkGXjSVJhOhzHhtkRWpbROibzlmklOQNIfIa2+pzjrNuj55VOi9OXI+rcWraBxMCDhvh9dEtZvXSelHVeVoxuo1NqoBcFQOYU3WfneGryIdkOBoMFQtZGwenpKfr9Pnw+n5AwUzS5OesLR3nQ0Xg8RiwWk/iDltkob1FXZgYKvQSOD9+FpEuj6XnpY8vgfG/d1+xbWvUnJyeYz5/kLJMvGo2G5LFzs9Q5+vyOZd7Tjw3Xy4CBZzuROxej8qyy0elTvAZcJ9XrQdSDpHU+5/OeB36eaV/U3WiRRCIRbG5u4vr167h8+fIzebs6B5gWHvM6a7UaqtWqkChJgIucuuFkMpEqILpFlA6cmt15cgDBjYBuss5cYKoNdVguXp/PJ33sDHjo79eWnbYkdGCQbaT0Qj2XpEoCoRZOq98ZeOEYvijgosHvoKbH72UcgFo530XLDWwPPQXKO043X/fHMu+AljyJjBuwzhenxctrx9n2ZRa7liu09UgJiG1gIIlB4EqlIsTD96XkwY1YGxEMIFLD1RszCyj0ZhYOh9Hr9eDz+STFjuPvnJMvgtO7Yp/q9aznBm/MoGHGdcPNVW+yywJ6rwKvhHSdncmO5kKku8IzQBl11QvmvF1TT1LqUy/buSQwivM8T5STmwUPV69exYULF7C6urqQXwk8tUbozrOmnO+g3UeSld54GExibTlddW31sV/4vjpLQl8IyeeORiMpgKBGTBkgl8tJgvxgMBC3US9SgmWW1Dv5/yjJ6LxdnQrFogg9dlomIeFxrPg5LZ/o1LBl4CbAdulsCS44Ep22JGnFUS+nfk3SBIBEIiFzEngqlbFdevxJTqyqXF9fl3QkpnwVi0XJq2ZwlcUjgUBA+pUaJeUnTcb8HUo6ei5QBtJXDWn3fFk2Dd+NaX8seWc/cn3oYhkGgXlTBcl/NptJZssyb/R50PGJZZYu4SRebgJOA0hnqDiDrj92wOw8uE667EAd+NFXZ+jFQ4tWJ5o7tR3nYtQdrN3RFw28XpR+vx+ZTAa7u7tIJpNCjsFgEMlkUo75o8ul023YBi7ewWAgVWY6P5XpPiQXWkWJRALZbBbZbFaq23Swi/enkaDpqjIYyXJPllCz0kpbuLTgt7e38fbbb+P1119HMplEr9fD4eGhXPnCE5w0QWpNke/C69tzuZxkLVALZICM+ZNsh85O0IEy/UePmTOirf+/tr4JkiY3N/1ZbvQcB5Zh69xPFk7M5/OF/GKmyPH9SWCczwx4JpNJbG1t4fr169jd3UUkEkGz2cT9+/dx7949nJyciGTB+/io63q9XmkXPQ6OHSUuGgU861kXQDByzwPUdWmzHhOSKgOZnH/LKtG4PubzuVRebm9vIxQK4cKFC+JJUCI7PDxEoVBYWpywDHrNOsdLjynbov+uJSGOiz49TR+VSi+DHPMqrN1XoukCT3c0ff8YD3JhaovWPZ0Wq5NUnRrksoX4ooHn52KxGLa2tnDt2jWsrq5KVF4vKrpPTqtdP0+f9r9sUQNYsDDS6TS2trZw+fJlOWyEaUu0iCqVipStcjFxo4jH40in00gmk6IR8mg/nbHAd9zZ2cHNmzdx8+ZNxGIx9Ho9ueeKVpU+3o9pVlpj5W0XW1tbkkJH95yuM2URBvKcFVzcNNiXTiuF/48WstbNnfnQ2qLhAub3Mj0MeBrE03ff6fObdTaF05rkrc6UfrjIOTd4s8nVq1fxxhtvCOl2Oh1J19LkzbFlSTI9B+d7cJMm4eZyOWxvbyOTyUg1FrXxer0umza1XWrImnT5hwUSnKf6ND3tidHjnM/n4uWtr6/L+tNVYwcHB8/IU+fBKVPptax/h2POf+dPzkmP52l1J1MFPR6PVBPqHGvnd7iFV2Lp8gW5Y25sbGBnZweRSATD4RClUkkWKReZM8DADtYBDB1E+T6ZDBywTCaDixcvYmdnB8lkUqwu6qkMJjAowSMP9f1V2k3mxNauKxeXPuGfRSEXLlyQQ95pbTE44PP50O12Ua1WpYKJeZRM6KeVS81O52OOx2NxwfTGpXONNelponP2P9O7dMoVNVpaaEyH4jvzOZQ42A7tojszNbSO7VwcWsKgZas3QKdFpOceLTtG8KkBUwvXhM4MA76Pc76R0HVkXOfLchx1lg6JjP/feUgQT4Gjl6cLD2is8EB5Bp2pX5NsuHZYvaY9Iy2jUMrQNyiw/1m52Gw2Je7Cz+l+5zrhBvPw4UOxlr8rqS2zRLWe7lz3WvqYzZ6c6re6uipl+71eDycnJ/J73DR/oy1dDf2yXq9X3CVaRQw6cKJpAdxp1eiqGWcKlTPI8SLQraOrzMUIQHRaBvxqtZocnJxMJrG5uSkVMVyUuopGL0Zd1MH3ZzCF+b06Q4KLEICcwaDf3WkR6sOB2E6eqcDf56Efjx49wnT65NAduoX7+/soFouSx7gsH5r9r9N2+E60jj2epznLJFxtHTr1aG2xaGtGW1z8dy0j6ZxcXUoMLGqEur+cz+SY6NxURu89Ho8UF+gMC50dw3fR51bwu1utFpLJJIbDIfb29uRuL521wj7lO8fjcZEKNLFwLlIeicfjSKVScvg9+2swGMgcYik9pQwSjy7A0ZuALuEej8doNps4OztDPp9Hq9WSCwVWV1fluTQe2Be5XA7r6+sLt2s8bw06SdT5znq+6HHT8x+AbHD0/mjxcp1xw3tVhAu8ItIFnh71SGusUqmIpasnt+4cvTNTn9IWkA5KOfPyXhZ656eL5XRrWGJ4dHQk17bw1t+NjQ2xMkajkZTL8noWffYsLUEuAN7aoM/J1ZrhZDIRl04vHC6aZrMpFhc9hVarJalaOlDFc43n8zmOjo7g9/vFgmaajSYP3f866Mn/PxqNJOhHF5TyEau7SGpsox5LblLAs4EqZ9aAdkG54PV46U2aGQkMUNFqZv8xKMkbDDhGzJ9lnEFXdemrYZwWGSUd5gNXq1UcHBwgFovJvOHRpJoweDYGLctOpyPjyGeyT7X1qgOYXA/8Dp1rq4PQmlRZBMHD2ClnkcC73S7Ozs6wt7eH4+Nj9Pt9JJNJzGYzOVxHSyyc07SaXxQAdXIC+1CvW44/vQFdPKM5gn+4lmgM8B1ZbKRjQ68Cr4x02ak8i3M6nS5cqa51Wa3P6iP3uNhJEDqo8n3aw8yAWq224IrxubR+6C4zK4BJ2LR0OBn1CUw8a5SRVp2UrYsXmNalswy0ppzJZOTGYLqC1HyZ+cHvpe5LAteWJa3dk5MTFIvFhc+QbDXhAk+tDe1BUCdkhQ8XA11Qapy0hPje2prXhKyzJkiYOgvBmWivXVz9e3wO865pbWti5abP3+emR+9KF5swG8BZnsxx18EcXbk3GDy51pwZH3wmjQR+hn3Jz3e73QXph+9McqOWzooyxgi4rqgTM2WRRowOavE5jUYDZ2dn8Pl8QqpM+2LGxfHxMcrlMmazmZwprav9uCZoueujKb9LBhGhvR3OWWrP3BB4khjfWfPFbDaTjIr5fC7xBG52r8rKBV4h6QJPyILExb/HYjHJHeVg6ZruZDKJ9fV1rK+vY2VlBe12eyE4Q8tPpwm9jCvB59RqNTx48AA3b97Ezs6OZClQ79W7NwNS/X5fTumvVquo1Wpy4AwrwLSITzLlonbe00S9VEsMlD54QAytZQBSMKKr4vQffh9JjM/m8zRhOANanMw6QZ6f0bmPuk5fu950xblZaT0ym82KC8qqPW54TE1jSba2nHRwjG2k1c/qNxa0MLjIs5m5yfMQeVpBOl3PSXBag3Va6MBTUtSSkc7O4dzm2PKdtOXJsWKfMvvDqSunUilcvXoV7777Lt566y1cuHBhIcuFG78eX85dnYVBa48Vd8zp1Xf1kXiHwyEikQg2NjYQDoexs7ODK1euYGNjA8lkUrRojken08He3h4ePHgg512/zPojJ+g+pl6vg57M6iiXyyiVSmi1WgscwcA1i29Y4MIc6Vdp5QIuX0zJn840FNajsxQzkUhgNpuh1WrJwuACunTpklxMGYlE5HDnYrGIUqmEQqEgdfI8ElJrb8/DdPrkvNm9vT3cvXsXb7/9Nra2thbIhqTB+5kuX74sqVXUzCqVCk5OTlAoFOT8XP67jq5y8XEB8zu5qXCC6dOddAJ7KpXC559/jkePHokGy42HxEELIZFIYHV1FZlMRk7Jp8vMjUrrorTeuEHwmcycoFZMK4rkp8keeDZbxe/3I5VK4eLFi7h69Sp2dnYkiMUS61arhdFoJO5zPB5HMplciK7TWmd2hi7+oIRBklpbW5OAaKfTwenpqWjkPBCGVqxzUWpJg5knHCfqmZRy9K3WWgIikZNA9Bm9PIKQwU59KBCteGq3uVwO169fxwcffID33nsPOzs7iMViQvAkrPl8jlwuJwcrjcdj8Sa4uWiZizew5PN5RKNRrK2tYXNzU25LyWaz2NraklQ4WtgkNhoh9JQqlQru3LmDb7/9VsrNXxbcYCKRCDKZjAQKs9kscrmcXDPV6/VwfHyMBw8e4PDwUG6Q4IaeSCTg9XrF2ten+mkZ4/t6xr8KXE8Z064GrQcueK/XK5kDvGaEFmwwGMTm5ibeeust3LhxA7lcTo7d4wny1WoV3377Lf7t3/4N//mf/7mwA75sG0ejEUqlEu7fv49KpfJMRY7Oa4xGo8hms3IId7PZRD6flz97e3uoVqtSIMFUHVoTvGSTrjcr3nZ3d+WKc+dVKT6fD8lkUhYircXPP/8ce3t7ciALI920Fnd3d/HOO+/gxo0bosk1m02USiW5jyscDiOZTC5UqNH6ZWaEz+cTWeLhw4c4PDxEtVqVQI1TZ9b5tiTzRCKBnZ0dXLt2TUgXgLjETG+jVchzbZl/7JQSWILMz+pAGD+rA5HM29QHgDsrl5zuLftxbW0Nu7u7eO2114T0aN3pc221HksPx+t9cv9YNpuVTaDRaODRo0e4c+cOHj16JFY6XWDOi93dXbz//vu4desWXnvtNSm40DEHElYikcDGxobk9pKkKKG02205P0RfnQQ8Kf2t1WqYzWbIZDISJOa8oGGkj+vU8Hq9qFQquH//Pkql0jMFNi9af1yviUQCP/nJT/DRRx/h+vXrC/nI1M2LxSKuXbuGr776CmdnZ5K1wbYOh0McHR3JyYTLrNzvsiH8UHCNdOmKRaPRZw6/8Pl8chDx9vY2rl69Kle+0EqMRCK4ePEirl+/LsEqfqeO2m9tbWE0Ggnh6Wc500+oczo1OhILrS1n+hl3SO36cEIMBgMpJdXJ2NoSiUajWF9fx8WLFyUtjPJBNpuVSb7sfFkuaBI0A0Q8jJx6OD9Horhx4wZ+67d+C6+99hri8bhoXpVKRVxAWgnOS/5o6VJb7vV6cmUQf0frxXxX9is3KnoJyWRSCkDW1tbEcp1MJiIH6JxSarLMY9X52dPpdOEWhkQisZD7THmGbWWaXblcFhdaX+OjPTJ6CWx3Op3GpUuX8NZbb+Htt9/G9va2eA0kbR0HIOnSW/P7/eJtMKLearWQSqXkcHjOm/l8LlVt2WwWr732Gm7duoW33noLmUxG5oyGtshJlhxXxgiYR8vYSafTWQgG6nkbCDy5NFWf+qcLQZYRLj2hbrf7TCaJXmdOuUH/3srKCrLZLH7yk5/g937v97C9vb1QHcf1Tos2nU7j+PgYvV5PjCEabdPpFGdnZ6hWqwvBX/2sdru9MKd+bHheoLX8YGozAy10k59piHI/NdE4I+c6kv1MY/9bu+Wu9n0EfIIbAUntRdCRbK3NLXNV+a7OYgrtyr5s1JcTUJfZOjcXLga9YLS3wT+6fcugx8QpJWjd/Lzx1e+my6CXvdOy5z7PFTzv2cs+w7ZTPtGZCOe9sx43rbU65+mydi3rX/05PX7LclO1/r/MujwP572bTl9clj7FMdIVl+f15TLQc/i+VqQ2aOjVPG+9O9PGnPN02brQz2Imxg98Xc+5neUa6RoMBsP/IJxLuq5mL7yM1flDCds/RErIr9KWFz3/xxDwn/fMHzNg8H3SgX5d8OvUdrfH78eco696/Wm4yTsvA7N0DQaD4YfHuSz+8uUiBoPBYPiVYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFyEka7BYDC4CCNdg8FgcBFGugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYX4X/Bv3tcaYXBYDD8D4FZugaDweAijHQNBoPBRRjpGgwGg4sw0jUYDAYXYaRrMBgMLsJI12AwGFzE/w/gtA6z1AeSNgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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DAUVFRXj66aexatUqaLVa/juZmZnYtGkTKisrcfz48aDvLViYr1QsFsPr9fI+1JmCdcaZJBTf4FSZ6efGfueCggJs2rQJGRkZAACFQoGlS5di3rx5WL58OT777DOcPXsWg4ODcLlcE7ahqakJ7733HuLi4vCjH/0IMpksaB+vL2x81NbWYt++fTh79iza29tDun+O4yCRSCAWixETE4OSkhIcPXoUjY2NQZ9jMpKSkvDoo49i1apVqK+vh9frxYkTJwRxKzBmXHTZDxQfH49nn30WP/nJT5CXlwe5XI6UlBR4vV786U9/mnT6ESxqtRoqlYr/8bxe76Qdbywcx6G9vR2VlZVYuHDhLTnXRSIRurq6cPDgwUkFl4W6ZWRk4OGHH8YTTzyB9PR0vk2+bcjPz8fjjz+OqqqqkPysk11fq9UiOjoaiYmJCAsL46febW1tGBgYmDSEx3eB5vtOsPciFosxa9YsJCYmIi4uDnK5HENDQ2hvb0dPTw+Gh4eDDn2aCI7jEBkZiSeffBL5+fnjPtNqtXjooYeQn5+Pzz//HLt27cKNGzcmddM0NTXh0KFDuPvuu5GUlDSltolEIni9XrS2tqKysnJKgsv89cxYmq5QTEZ0dDSee+45vPDCC4iOjsaCBQswd+5c/PnPf0Z8fDx/HzPNjIsuW5UuLCxEfn4+H5PHcRxiY2Px5JNP4tq1a9i7d2/QizoT0dTUhG3btsFisSAxMRFXrlxBZWVlyCLA/e/CUUtLCywWy5SF1+l04uzZszh37tyE/kIWmxkREYFVq1bhscceQ3p6+rhoB9Y2kUiEVatWYc+ePTh06FDI7Qp0/VmzZmHu3LmYM2cODAYDHA4H2tvboVQqcfPmTZhMpoCr+sxKF4lEfIzw9xX2PNhMJdC9KBQKhIeHIy0tDdnZ2UhISIBCocDg4CDvV3Q6nXA4HNPyPAoLC7Fq1SoAo1/Evi+I9PR0PPbYYzCZTBgaGoLL5Zrw+i6XC2fPnkV5eTliYmL8xrdPhtfr5RdB/UVyTAbHcaisrMTBgwcxf/58tLa2YufOnZMaKsGiUChQXFw8ar1GpVJhyZIlmD9/Pm+pCxGrK5h7gS1A+SISiaDX6/mV6+nAZDLhiy++wOnTp2EwGDA4OIj29vYpnctisaCurg5GoxFJSUl82E0otLS0YNeuXRNGJACj37BsmjrZd2NjY5Gbm4uvv/76lqfPHMfxiSgsHE0kEvGLETabDWKxmF9AdLvd8Hg8owYXezmwv++76PoK2tj7lEgkfLiaSCTiLdqrV69CKpWC4ziYTCbeyp2OZyGVSjFv3jxeNCYyAsYmAEz2exiNRuzatQsLFy5EZmZmSO3yer2w2+0wGo2oq6uDxWIJ6XhGTU0NfvnLX8JgMGBgYAAdHR3TtrApEokQHh6OsLCwcc/CX3LGTHLbai+wONzdu3fj2LFjIYWGTIbT6URTUxOamppu6TwOhwM3btxAVVUV5s2bh9jY2JBE1+12o7S0FMePH5+083D/m53X19eHsrIy5OTkYNasWaPiNMcikUig0Wim7YXldDoxMDAAk8nEizDrnExo2Mou+6+v2PtmVH2fBRf4/9Ndf/5m3+fAXkgs1AsAH8omEong8XimxbUA/H/300S/t0gkgtlsxrfffouysjL09fUF5Vt2Op04duwYvv76a6SmpoaURejxeDAwMICqqipcv3496Hh6f22YjnHrD7vdjmPHjiE/Px9PPPHEhONqprltotve3o4tW7Zg69atU7ZEhYCFGU3Fkuzo6MBXX301KlEjEEx0bTYbqqursXXrVrjdbjz44IOIjY0NeNx0WVG+5/MnEkyEXS4Xn1wQ6Hvfd8EFJr4Pdt+TPauZaNNk5+3q6sJXX32Fbdu2oaamxm96ciB6e3uxf/9+rF27NmTfrsvlQnd3d8A4+DuBxsZG/PrXv0Z3dzeef/55JCQk3JZ2CC66HMfBaDRi8+bN+OSTT2A2m4VuQkiIxWLEx8cjMjIy5Pi9iooKXL58OaRjmIV15coVvPPOO7hx4wY2bdqEOXPmjLNwmOUjkUgEjS+dCUH5vhFIcGcSiUQSsLaA1+tFfX09PvvsM+zfvx8tLS1T8qtXVlaioqIiJNGVSCSIiopCfHy8cPULpkhbWxvefvttDAwM4PXXX0dqaqrglccEfUJMUE6cOIGdO3fe8YILABEREVi0aBF0Ol1IP47JZEJpaWnATJxAMF+i0+nEzZs38fnnn2Pnzp1+IxQkEglSUlKmbYV37ILdRIjFYkil0lsqZvN9hN1zsOISyjOdDLVajeTkZL8v/4GBAezcuROff/45jEYj7+YJ9dpdXV0oLS0NaWyKRCLodDoUFhYiIiIipOvdDkwmE3bt2oWysrKQI5umA8FHDBust5r1IZPJEBERwWc3zRS5ubnIyckJ6RiO43DixAkcO3YspB9ULBZDr9dDo9Hw1ddGRkbQ3NwMi8WCyMjIccckJycjMjLylqd1LJuIWc2BrCTWxtTUVMTGxsLhcKCnp4dfSHK73TMe1ysUbEFTKpVCp9MhKiqKX93v6upCU1MTzGazX4uXRT/IZDJ4PB4+q/FWiIyMRHJyst/PzGYzmpqaMDIyAplMBq1Wy2d1BlM1jcFxHI4dO4YTJ05g7dq1IYl2Tk4OcnNzYTQagz4mVFgWYX9//y3N7lhY2u2wzAUVXRZStHTpUuTn5085KUKn0+GJJ55AcXExDh8+jO3bt0/ZeT8RYrEYixcv5sUu2A5YU1ODjz76CNevXw/penq9HgUFBUhOTkZvby9aW1shlUqRlpYWMEfeYDBg1qxZIV1nLFKpFCqVik/AYCX1xnZqmUyG2NhY3HfffSgpKUFKSgpcLheMRiPOnz+Ps2fP4ubNm7z4ft9hC5WZmZlYunQpCgsLkZaWBplMhqamJpSWluLo0aPo7Owcd7++hXgAYHh4GCMjI7f0XGbNmgWDweD3M41Gg7S0NCQmJsLj8SAxMRFRUVFobm7G5cuXgypQxGhsbMRHH32EjIyMoAwONi4iIyOxZMkSHDhwYEZeukqlEo888ghKSkpw7NgxbN++fcoG18KFC7F06dLbMlO7LXPDtLQ0lJSU4OTJkzCZTCEdq1Qq8fzzz+PNN99ETEwMVCoVjh49Oq1ZbQy1Wo3Zs2eH9DYcHh7GgQMHcPLkyZB8fnK5HAsWLMDjjz+O1NRUdHZ2oqGhAWKxGEVFRQFXW9ngnipyuRxqtRo6nQ4ymYyP5xw7aKRSKSIiIrB27Vo888wzyMrK4ouTpKWlQafT8XUT/lp8vl6vF3K5HGlpabj33ntRWFgIg8EAiUSC7OxspKenQ6FQYO/evXyUAIM9Q7FYzNdwlUgksNlsU45HZ+fxh16vx7Jly/g6CHPmzEFcXByMRiM8Hg/Ky8uDvq7X60VZWRkOHDiA5OTkUdmQE8EqeKnV6hmp/REVFYWHHnoIa9euxYoVK6BWq/GnP/0pZIMrPDwcJSUlSE1NnfY2BsNtEV2pVIrZs2cjLCwsZNFdsmQJXnjhBT5WcdasWTMWZyeTyXh/abDJEWazGTU1NSH7q2NjY7F27VqsXLkSYWFhmDNnDnJycnirJZi43VBgRWFiY2NhMBjAcRzMZjPMZjOcTue4gtNSqRTZ2dlYv349X7XKF5PJhJ6enpBWy+90vF4vbDYbXxENAB+fq9VqMX/+fKxfvx51dXU4e/bsKNH1eDxwOp1wOp188Z24uDgMDAygu7t7SsV8JvqdZTIZ5syZA7VaDalUiqioKKjVaqSnp2NoaAhNTU1oaWkJ+lqsH1sslqBEl40PlUrlN2V+OlCr1fysLiYmBi+88AIuXbqEsrKykM4TFhaGzMzM27YecVuuynEc2traQp4aSKVS5OXlIS4ujk8XtFgsM1ZRy+12o7e3N6RstImq1wdCpVKhqKgIK1euRGxsLKRSKW99Op1OKBQK3nIa246pClxERAQWLlyI+fPnQ6FQoKmpCbW1tXxc6VhBUCqVWLBgAXJyckZZ1h6PBy0tLTh58iQaGhqmNd76TsBut6O+vh6nT5/ms/SYqCgUCuTk5GDBggWorKwcZXGx8C632w2lUonMzEykpqbC4XDgypUruHTp0pTStycK01Or1UhJSYFcLodSqeRTaVeuXImKigr09PQE/fuwXT+CzU5jsdl9fX2T7igyVaxWK2/MeL1exMXFYf78+Th16lRI48BsNqOtrU2Q2rn+uC2i29HRgQMHDoS8+CMWi/nauzqdDm63m1/MmAmsVivOnz+P9evXIyoqKqhj2NSlvLwcVVVV8Hq9EIlECAsLQ1paGiIiImA2m9Ha2gqz2QyJRIKsrCysWbMGmZmZfIaTRCLhO7LD4eAtU7YAyTqM3W4PaXrFFsIKCwuxdu1aZGRk8GUWjUZjwE6o0WiQnp6O8PBwvl0cx/FxxZWVlRgaGpqxAXe78Hg8GBoawuXLl1FYWIjZs2fzv5FIJILBYEB6ejpUKpVfvymz/pKSklBQUACdToe0tDRIpVKUl5cHXIjzh8Ph4EXTVzCYRW632/nawiwdWyKRIDMzE2vWrMGNGzfQ0NAAj8cDvV6P5ORk6HQ69PX1oampiU+KYZlvq1atCqlWdG9vL86fPz9jRhBbLGRlVk0mE2w2G6RSaUgum8HBQRw4cAD3338/X3NBSAQVXdZRTp06hRMnToR8vNvtxqVLl3DkyBHcd999sFgsOH/+PEZGRmagtd915jNnzqCiogIlJSVBvRWlUilWrlwJAPjqq6/Q2tqKsLAw3HXXXbjrrrswa9YsDAwM4OzZszhz5gxsNhuWLl2KhQsXQq1W85lMzB84PDyMa9euYXh4GGlpacjMzOQtaYfDgZqaGr6Y9FgUCgU/3WShMUqlEqmpqcjPz0dCQgIkEglGRkZgMplgtVrHpfYyXC4Xv8DmK7omk4nffSKUam7fF9j9DA0NwWg0wmQy8S8eVuTFarX6XSDjOA4ejwc2m40vwh4eHo7ExETk5+djcHAQTU1NsNvt/CIzS5Dx9yLt7OxETU0N5s6dC7lcPipt22g0QqvVIjs7m+8frO1qtRoFBQVYuXIlNBoN1Go1ioqKUFRUBIPBgP7+flRUVODSpUswmUxITk7G2rVrUVxcHPQUnOM4XLx4EWfOnJkx99LIyAjOnz+PoqIiaDQafPPNN6ioqJjS4mRZWRlOnz6NRx99VHCLV/DoBZvNhhMnTgQ1tRpb5YkFgL///vuoqKiA3W5HWVnZjK6UG41GlJaWoqCgANHR0UEdo9PpsG7dOixbtgxmsxlyuRwGg4H3D7tcLiQnJyMmJgbNzc3Izc1FVFTUuC1kPB4Prl27hk8++QQ3btxAfHw8Vq5ciR/84AcwGAy4evUqtm3bho6OjnFtkMlkSElJ4bfKGRgYQE9PD1wuFwwGA8xmMz8lbmlpQU1NDdra2gJuODk0NITS0lLk5eVh2bJlkMvlsFqtMBqN6O7u5hf0JqrqdidWIJuoTSxkTKFQQCKRoLu7G0ajEQaDAWq1Gk6nE6dOnUJpaanftQmPx4ORkRG0traivLwcFosFqampkMlkMJlMiIyM5EP1oqOjMWvWLAwNDeHSpUswGo3jokfa29uxbds2aLVazJs3DwMDAzh9+jSOHz+Ojo4OZGRk4JlnnkFUVBTvAmHWbmRkJPLz8yGTyZCamooVK1YgJSWFF9WioiIMDg7C6XRCr9fDYDCE5CLr7e1FaWnpjKTwMlwuF44fPw6v1wuFQoHz58+joaFhlMgH28dYuv3atWuntZJZMAgmuuxt0tPTg8rKyqCOYUU7fAex3W5HdXU1GhsbwXHcjFm5DJvNhpMnT2LVqlUoKSkJuiOKxWJERESMChZn9yCRSGAwGBAXF4fh4WE+cmBsgRKbzYZz587h+PHjGBgYwNWrV1FTU4NTp04hLCwMlZWVuHLlit94RYlEgvj4eBQWFiI6Ohrt7e2oqalBS0sLuru7+aQNtr8Xqx4WyEph1dJ++ctf4t5770VKSgqsVivq6+thNBqhVCphMBj47YjGtonFRfpaycx3LKQIMwuVveBYe/zVSGC+9fDwcN7vvXv3blRXV0OtVqOlpQVHjx5FZWWl3+ktSwZyu91obGxEV1cXIiIiRi38qtVqJCUlITc3FwkJCeju7kZnZydaWlrGPUOXy4UTJ05gcHAQ+fn5MJlMuHTpEtrb23nfenZ2NhYtWjRKSFjMMFvQi4+PH1dkKtCuvMHg9Xpx8eJFnDx5EjabbUrnCJaWlhbs3LkTIpGI31+QwXIAWGW4yWDbI6WkpAhq7QomuuyGysvLg4pflclkmDVrFkSi7zYM9F0AYFuECAHHcWhoaMC2bduQkJCAvLy8Kf9AvlPv4eFhtLe3o7W1ld9qZywmkwnXrl3DwMAAn3TQ2tqKvr4+cBw3YTys1+vFwMAAmpqaYLPZ0NHRgdbWVrS2tsJqtfIRCixoP5iEBrvdjvLyclRWVo5aLGSWfGxsLLRaLdrb20dtKshEzld0fdvpdrsFiXhgg9JXbHxfAr4vABahkJCQAK1WC6vVioaGBlRXV/PtHh4eDmphivlcnU4nvy+cRCKBXC6HRqPh/fW+9QsCPQ+Hw4HLly+jsbERIpGI3zcO+M5Xee3aNZhMpnGx2x6PByaTCa2trdBoNMjJyeFjfqcqNmwcsBkXM4RmEubO8YdcLkd4eDhflnUyjWhoaEB5eTlSUlL+Ot0LAwMD+PLLL/Hhhx+it7d30u+npqaiqKgIHo8H586dQ1NT023bv2p4eBgHDx4EAPzsZz9DYWHhLZ3ParXiwoULOHLkCLq6uqBQKHD33XcjKipqVKWqwcFBdHd3j9r9lllPk2U4uVwutLW18VYx25TRYrGMqp7l9XoD+nEDYbfbR4mNXC7na1TExsZCJBKhpaUFNpsNHo+HF9yx+IqxEKLLMpD8+Z1Z+7xeLyQSCdRqNRITE5GRkYGRkRG+IPlUY2xZQSNmVTPBZKJpMpmg1+v5lfWJ+jrz+7Ldc9m9MNFmC3q+fclsNqO6uhrl5eVoamrik2p0Ot2U7oed98KFC/jtb3+LQ4cO3dZ9+WQyGZKTk7FkyRKIxWKcPn16UuOup6cH7777LkZGRrB+/fpbTjIKlhkXXRbaVV5ejmeffTYox3xycjI2btyIkpISOBwOhIeH8zuK3g5/ILNq9uzZg9bWVvzzP/8z1qxZM24zP99OPhb2mclkwjfffIOtW7fiwoUL/NbrbHrJwrFYZEZXVxd/LKvuFYyLg0UWtLW18WLhu5uub1jYrT5Tp9OJ/v5+mEwmxMXFITU1FXa7HT09PeMWhHwFnonuRFXLpgvf5+avDb41jBUKBaKjo5GamoqwsDB0dXVhYGDglovsM8Flosvu2ev1wuFwQC6Xw+l0wmazBfWbsHuSSCT8PTG/8/z580eVnzx79iyOHDkCo9GI+vp62Gw2SCQS3HvvvXzizUT9duxndrsdBw8exG9/+1tUVFTc1g09RSIRIiIiUFJSgg0bNkAulyMpKQmffvrppLHJ5eXlOHXqFA4dOoT777+f16uZRLAt2IP19cjlcsydOxdLlixBamoq3G438vLycOLEiVE1S28HrML+v/3bv8HlcmHNmjXQaDSjyh6ykBvmo/UN6xkcHMThw4fxwQcf4OLFixgZGYFIJEJNTQ22bNkCrVaLH/7wh1CpVLhx4wYOHz48KjLBN5RsMnwXFDweD+9CmKmpPBP4iIgIqFQqGAwGPgHAV+jHCh6zPoUQXV/RC9QGlqFnMBigUCjQ19eH1tbWafNV+r7o2AAXi8V8u0IJuWNt953xtLe34/Dhw5g9ezZvpZeVlWHLli2oqanhre2ysjKMjIzAbrejpKQE4eHho8IRmV/U7XbzkRXs5WS1WnHw4EH86le/4t0ttxORSITo6Gjk5eUhIyMDUqkUixcvxrlz59DV1TXhy5L1Ofb7CqEvgvt0gxlYra2tOHLkCL8qXlFRMaUtQG4FNkj9XbOxsRFff/017rrrLr4ACStQ43K5IJFI+BVvjvtuq+rm5mYcO3YMO3bsQFVVFW8Bss595swZ9Pb28gkS1dXVOHHiBMxmM98GJp7sehPB4kMjIyOhVCoxODgIu90+Y8+QBcZfv34dUVFR/LWYkPjzGbMptxD+tLFi668NUqmU/w7bG663txf9/f0z+txYlS6DwQC73c4LxURjhRWMZ/fG2mc2m7F//36YzWbMmzcPHR0d+Pbbb9HY2Diqz7DwK3a9lStXIjk5edTOIMzvzwwI363VS0tLA07ffcPphID5cCsqKiCTyeByuVBVVRVSaYC/Sp9usDidTjQ2NqKzsxMHDx6EXC7HwMAABgYGBBNdVlnKt2P7otfrkZmZCb1ez1sHbJrHct89Hg/sdjv6+/tRU1ODo0eP4vjx42hqavIbg+l2u3Ht2jU0NjZCqVT6rfTl257JOrRMJkNkZCQWLFgAtVqNmpqaGRUP4LvfjrkUmN+Rrd5PdF0hftfJrsHEF/jO6mHuEpPJNC179010XYlEgpiYGOTm5vKx0pPVaPCdsfj2Ba/Xi+7ubuzduxcHDhyA3W4P+IK22+24cuUKLBYLrl+/jlWrViEnJwcRERGQyWSQy+V82rPvDMu3//tbSGRWMZtZzTQcx6Grqwu7d+/Gt99+y++AcqcWXrrjRBf4rkMNDQ2FVBlpOpHJZEhMTIRSqURfXx8fv8iKvjz22GN49NFHERERMcr/w1akmYXHpm+1tbW4ePEiurq6Jp0+ut1uDA8P+33zBhuDKBKJ+NRTVkmpt7cXdXV1fr/PVvSnQ1yYj9rhcPDieyfF5U4EE16n04nh4WEoFIppy7BjyQz+REAsFiM2NhYFBQVwu92wWCx8TPVkLyvfsDdf2GaYk+HxeNDZ2YmKigp+4VCpVPJpxP76YWRkJH784x+jp6cH//M//4P+/n643W4+iiUyMpJffBRK9NxuN19i9E7njhTd241KpcKyZctQVFTE79vU3d3NZ/asXr0aKSkpAY9nUzGNRoPExETk5OTg0qVLIfkGJ0oumAyJRIK4uDjcddddyM3N5cOU/MWhzp8/H8uXL4darcalS5dw9uzZKadVsxRUqVQ6I6U2hYI9e9+djadaU0Kv16OoqAgLFy7kE4OuXLkyzjqVSCSIjY2FXq9HX18fbt68GTDTbSxjRTeUlxx7QSckJPC7GbPMyECIxWJkZGTg9ddfR3Z2Ni5duoSRkRHExMQgNTUVcrkcp0+fxpdffjnjcfTfR0h0/RAZGYn7778fJSUl/Ioyq3+g0WigVCrHdWx/gigWi2EwGHDPPfdALpcjKioK3377LZqbmycUJVYbNSMjA1qtll/dttls6O7uRltbGwYHBwNaTfHx8bjnnntQWFiI8PBwtLW1jVtQkMlkePDBB/HWW28hLy8PIpEI7e3teP/99/Hhhx+GXBdDo9EgISEBUVFR/BSZtS+QP/VOgi1KSSQSvrpcZGQkNBoNX9s4VAGZNWsWnnvuObz00kuIj4+H1+vFlStX8Jvf/AYHDx7kV/yZW8ZisSAlJQWFhYXo7e3FyMhIwFKZUqkUBoMBiYmJiImJgVqthkKh4ItA3bx5E0ajccIwLqVSieTkZBQXF2P9+vVYunTphDtzj/39kpKS8NRTT+Fv/uZv+BRzmUwGp9MJnU6H06dP39F7pt0u7hjRZdahr2/tdjF79mwsXryYL/bhm90zUViYP1hm2n333Yf09HQkJCRg+/btuHnzpt/pvMFgwP3334+NGzdi3rx5UKvV/MKOyWRCY2Mj9u3bh9LSUr4CGkMmkyE+Ph4PPPAA1q1bh6SkJAwODqKqqmrc9eLi4vDcc89hyZIl/L9lZGTgqaeewoULF0KqB6xQKBATE4OcnBxotVq0trZiaGgILpeLzypkLw5Wr/dOQSQSQaFQ8ILFok8UCgVfJyEqKgputxvt7e1BW/BisRh5eXl46qmnkJ6ezv/70qVL8eyzz/IzH+A70b1x4wauXLnCRx1s2LABYrEYhw4dGrcVOQuRWr16NdavX4/MzEyEhYXxhXisVitqamrw3//93zhy5Ihf4ZPL5UhNTcUTTzyBRx55BGlpaeNCIP09q7GoVKpxmWwqlQqLFy9GZmYmGhsbg3peMwXrf3eSm+uOEF0mFtHR0ejv70dHR8dtLRGYl5eHxMTEcf/OfjS3243+/n60tbXB4/EgOTmZTwoIhEKhwJw5c7BhwwY0NTWhp6cHQ0NDvLCxTSZXrFiBV155BYWFheOKk3s8HqSnp4PjONTX12NwcJAfjCyr6a677sLKlSuRlJQEq9WKU6dO4ZtvvhmXkMIsO3ZfrO2tra0hL1qyc42MjMBqtfJ1dT0eD797glQqhclkwtDQ0JQ2TJwJWHpsWFgYwsLC4Ha7+Ww9NqvwrdYVygo3W1FvaWlBXl4e/2++UQC+9PT04JtvvkFMTAyKi4uRnJyMlStXoru7G2azmS8oBHwnJCkpKVi3bh1Wr17Nb07qS2JiIiIiImC323H06NFRdY7FYjG0Wi2WLVuGDRs2YPbs2ZNun8VxHDo7O9Ha2gqJRIKkpCTMmjWLN5TGPpvExETk5eXh0KFDQT+z6UapVCI+Ph4RERHo6ekZ9/K6XdwRoiuRSBAdHY2MjAxIJBL09vbeNtHV6XTIzs7225mY76y7uxvbtm3jQ3Nmz56Np59+Gg8++OCE+etSqRSxsbFITk6GSqXifafMugoPD8eCBQswe/ZsPtvI9/pssMTHx/vdvocN5r6+PtTW1qKyshJfffUV6uvrx1mtnZ2d2LFjB9LS0pCamgqLxYLTp0/jnXfeQU1NTUii6HQ60dXVxZcptNlscLlcvIXB2uUbt+yL779Pdy2Gyc7NPmchfyxqgOM4WK1W9PX1QSwWh7zjA8dxqK2txe9//3t4PB4UFRVBp9PBaDRix44d6OrqGvV9r9eLuro6bN++HWazGfn5+XxRKH+CqNFoEBcXB61WO+6ZchwHuVyOzMxMLFiwABcvXuSzGFlSBqv5EBMTw/f1QFitVhw4cAB/+ctf0NjYCL1ejw0bNuBv//ZvA25jLpPJkJ2dDZ1ON6N7GE4EKySUmprKj1sS3f/F6XSiubkZw8PD/IC9XYjF4oCV75kItrS0YP/+/Th//jwAoK6uDlevXkVnZyc2bdqE8PDwccex/9psNn7qzWCDxuVy8bsUREZG+vWtsdA0FhngWzvAYrHg4sWLfNUx5sv1565xOp3YuXMnXySlp6cH586dC2l3AYbX6+WLyY8VNlYngy3k+UYDMKuPhSSxEKPp8v8yC5VdwzdJxDf2mbWdhemxFxSzeKcac+p2u3H8+HHcuHEDS5YsQXR0NGpra3HhwgW/fdzlcvG1Nr799ltwHIeOjo5Ru0z4xtCyqBN/LxKv18vv5uE7G/K91tDQEEZGRkb1z7ECPjg4iE8//RSbN28eVUFMJBKhuLgYCQkJ445j/z+2iJPQuFwudHZ2wmQyob+/f0ZD/0LhjhBdr9fLT7dZfN/twmQyoaamZsJ0QL1eP27PsqamJrz77rsQiUR48MEHER0dDaVSyfuUmOA2NjbyZfvGdtTh4WHU1tbi5s2bSElJGWfhsO/HxMQgOTkZ5eXlowac3W5HW1sbOjo6ghKu4eFhHDt2DMeOHQvpGQXCnzB5vV6+0LrvKjtzp6SkpPCWfVNTExoaGkLewmkitFot5syZg9TUVDidTly/fh3Nzc38C8JX5P09r+mwvFtaWoJ+mbGaGe3t7XxNCt/nygSNWamAf1+r2+2G0WhEXV0df68MFpFx8+ZNNDY2Ijo6GiqVij+P2+2G3W5Hd3c3Dhw4gHfffXdcyUa9Xh+wwDkT/erq6hnbYCAY3G43uru7A8bb3y7uCNFluFwuyOVyfkEgkMWr0WgQGRnJF/Lw9Y1OB3v37sW6deuwePHiUf/OOmVWVhZ+9rOfQSKR4OzZsxgeHgbw3bR+x44dqK+vR3R0NDQaDcLCwqDT6SCRSDAwMIDKykp0dXVBr9dDoVDw+2gxq9Vms00aVpaSkoJXXnkFTqeTL/XHBtXYQRoMwcb/3gpMvJh1GxYWhuXLl+PHP/4xcnNzIZVK0dzcjCNHjuDQoUMBFxoB8P2DLTD6Qy6XIz09HQ888ABKSkqQnJwMt9uN6upq7Nixg8/281fScSYI9Rn7W1AWiUR8NMwrr7wScDt2htVq5S11liUpl8v5MdbZ2Yk9e/bwBXCY1c8K2nd1deHKlSvo7+/nDRCdToelS5fijTfewOzZs0fdG2s3K4Szb9++oJ9PMIjFYoSHh0Ov18Pr9aK/vz9gdAZL7nC73XeES8EXwUV3shz7+Ph4Pm6UbS/CShlGR0fzC0ULFiyAXC5HZ2cnqqqqUF9fj66uLtTX1/OrwlOluroaH3/8MebPn++3wLFMJkNJSQmys7Nx+vRpXkgHBwfR2dmJffv28VvgsM7OsnSkUimio6P5tEuv14vW1lbU19fDYrEgJyeH3+Y7EBKJBMuXL0d6ejpOnjyJEydO4MyZM2hoaAgoQiKRCGlpaZg/fz70ej2GhobQ0NAwLkNOqVQiLCwMcrkcLpcLHo+H3+gwPj6eL3NYV1eH1tbWkGYlzH+qVCqxdOlSvPrqq/jBD37A+3znzp2LgoICFBUVYfv27bhw4QKfIBMREYGsrCxkZ2cjKSkJGo0GVqsVLS0tqKurQ319Pfr7+wF8FwGyePFibNy4EStWrEBkZCTfhqysLMTFxfEvrKnk3EulUiQnJyMrKwsajQbDw8Po6OhAb28v3G43H3bGqof5pl8rFAqkpqYiKysLYWFhMJvNuHr1KoxGY8A2sEXYoqIirFixAsuWLfO70OuLTCZDeno6srOzYbVaodPpkJWVhaSkJIjFYjQ3N6Ourg5HjhzBgQMH+NohDoeD77c6nQ4xMTH44Q9/iPDwcMTGxiI/Px8/+MEPAgo+q5728ccfo6amJuhnGgj2nGNiYpCVlYUFCxbwv19VVRWOHTvG7//GIoWysrKQnp4Om82GsrKyCQurz3TND38IWsQcmLhmgEKhwMaNG/GTn/yEL0doNBr5HOq0tDTk5uaOK8G2bt06OJ1OmM1mXLlyBe+99x727NlzS5bb8ePH0d7ejszMTL++LuC7DpGcnIxHHnkEJpMJ169fx4cffogvvvgi4BRZpVKhoKAAf/d3f4fc3FyIRN/VC66pqYHRaMScOXOC3vY9MTERjz/+OB588EFcvHgRv/vd7wKuFq9atQr/9E//hEWLFkGhUMBqtaK2thalpaX89twZGRm46667+PAh5uNUKpWIjY1FbGwslEolbDYbqqqqsG3bNhw6dCjohRJm7Wo0GhQXF6OgoGDcgmVUVBQ2bNiArKwsHD9+HHV1ddBoNCgsLERhYSHi4+NHRXU4HA50dHTgwoULuHDhAqxWK+bOnYuVK1ciKytr1MuL4zjIZDIUFBRgxYoVuHjxIj9LCRa9Xo81a9bgmWeewYIFC6BSqfj6BZ2dnXy1MJlMhpGRETQ1NaGiogI3btyAXC5HUVERVq1ahezsbGg0GjgcDly4cAG/+93vcPTo0YC/3T/+4z9i0aJFQZdiFIvFmD17Nh577DEUFBTwY4fVm62pqcHmzZtRVVUVMP6Y4zjcf//9ePbZZ/mwtImMAfY7trW14fjx40G1MxAikQiPPPIIXnrpJeTl5UGv14/bJLOoqAgbN25EdXU1L6yJiYlIT0/n9eP999/HH/7wh4AL80yPhIymEUx0mTWj0Wj8TpvcbjeSk5OxZs0afj96g8GAtLQ0v+cbGy8rl8sRGRmJlStXQiaT4fz58+jq6pp0ZXYszN/ldDrR19eHzMzMSY9hdQ4MBgOqqqrw1Vdf8ftejW0zm/bm5eXxPjG2i8Tg4CBUKpXfyISJ0Ov1fHhRZWXlqOmgx+NBREQEnn/+eaxevZo/RqPRIDo6GosWLUJdXR3sdjvS09OD2qgvPDwc8fHxfNlDtqDoe59j8X0WbDNHdp9jn5NMJkNubi5iYmLQ3d0NnU6HhIQEvwNeoVAgLS0NiYmJWLx4MSwWC2JjY8elaPteh21NbjAYRr0cA/UT3/YtXLgQr732Gu6+++5R32FW4FjYYpjRaIRKpUJWVtaoLc01Gg1KSkpgMplQXV2N/v7+UbV9o6Ki8NRTT6G4uNhv2yZCo9FgyZIlyMvLg8FgGBWHm5eXh/T0dD492V9fVavVWLRoEQoLCycNKfOlr6+PX+ibyvhzu92Ii4vDq6++imXLlo37ju/YnzVrFpYvX47ly5eP+57BYMADDzyAPXv2wGg0+m2LVCqF1WoVdDt20SQPZNrk3+VywWKxTLiaKZFIRsUchpoK67sKa7FYbmnawBZ6Qv0xWN3aQNdmwfismpMvgSzqYGFxpmOv7Xsv7BoTJXlM1CfYsWyxxHfngmBhW4NPZDUFakeg9o5NhZ3sObpcroD7wU0ES3VmUQP+6h4E0172GTtHML/dVPHXrziO4xc4A7VfLBZDpVIFvQ07I9C9hALbAp61O5hx7wv7vsfj4TMkJzqebZk1jQRssGCiSxAE8X+IgKIr+BbskzFdcX3TFec5E9eeydjFYKbIQlxvMm5n/CbjTmu70L/dRNe81WvfzvE3FiF1JxjI0iUIgph+Aqr4zG4GRBAEQYyCRJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkBIdAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBEQ6yeciQVpBEATxfwSydAmCIASERJcgCEJASHQJgiAEhESXIAhCQEh0CYIgBIRElyAIQkD+H3B0cBAM0Q9yAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 72\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", + "\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "ftol = 1e-5\n", + "for iters in range(num_betas):\n", + " solver = nlopt.opt(algorithm, n)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_maxeval(update_factor)\n", + " solver.set_ftol_rel(ftol)\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(evaluation_history,'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot our results and see the resulting geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the performance of our final structure, we plot $|E_z|^2$ at those three frequencies. Because our objective function only concerns the average of intensities, it turns out that the optimizer focused on maximizing the performance at one frequency, and the structure behaved poorly at other two frequencies. The better way is to use the epigraph formulation, which is illustrated in the next tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt([mapping(x,eta_i,cur_beta//2)],need_gradient = False)\n", + "frequencies = opt.frequencies\n", + "\n", + "\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "plt.figure()\n", + "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb new file mode 100644 index 000000000..3e552be49 --- /dev/null +++ b/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb @@ -0,0 +1,2042 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Near2Far Optimization with Epigraph Formulation\n", + "\n", + "This tutorial redoes the previous example with epigraph formulation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "from scipy import special, signal\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "Air = mp.Medium(index=1.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Basic setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "design_region_width = 15\n", + "design_region_height = 2\n", + "\n", + "pml_size = 1.0\n", + "\n", + "resolution = 30\n", + "\n", + "Sx = 2*pml_size + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "nf = 3\n", + "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "design_region_resolution = int(resolution)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [0,-(design_region_height/2 + 1.5),0]\n", + "source_size = mp.Vector3(design_region_width,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.Source(src,\n", + " component=mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "\n", + "def mapping(x,eta,beta):\n", + "\n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()\n", + "\n", + "geometry = [\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "]\n", + "kpoint = mp.Vector3()\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of the calculating the average of intensities, we change our objective function to be vector-valued intensities at the frequencies of interest.\n", + "\n", + "Note that because ``meep.adjoint`` looks at the fields in the frequency domain (i.e. Fourier transformed fields), the ``objective_functions`` from ``meep.adjoint`` can be vector-valued if and only if each component of the vector is at different frequencies.\n", + "\n", + "Also note that this objective function is different from the objective for `nlopt`, which has to be a scalar. In our case, this is a dummpy variable `t` from the epigraph formulation, introduced lated in the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "far_x = [mp.Vector3(0,15,0)]\n", + "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "ob_list = [FarFields]\n", + "def J1(FF):\n", + " return -npa.abs(FF[0,:,2])**2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = [J1],\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + " maximum_run_time = 2000\n", + ")\n", + "opt.plot2D(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our objective function that we pass to nlopt is rather simple. We'll introduce a dummy parameter `t`. The goal of the optimization problem will be to simply minimize the value of `t`. The gradient of this functional is rather straightforward." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(x, grad):\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", + " if grad.size > 0:\n", + " grad[0] = 1\n", + " grad[1:] = 0\n", + " return t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key to the epigraph formulation (and most nonlinear optimization problems) lies in the constraints. We'll define one constraint for every frequency point of interest ($f_i$). We'll define our constraint as \n", + "\n", + "$$f_i < t$$\n", + "\n", + "where $t$ is the previosuly defined dummy parameter. Each constraint function is then defined by\n", + "\n", + "$$ c_i = f_i-t $$\n", + "\n", + "within some tolerance.\n", + "\n", + "More detail about this formulation can be found in the nlopt [documentation](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#equivalent-formulations-of-optimization-problems)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def c(result,x,gradient,eta,beta):\n", + " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", + " \n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + "\n", + " # Backprop the gradients through our mapping function\n", + " my_grad = np.zeros(dJ_du.shape)\n", + " for k in range(opt.nf): \n", + " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k]) \n", + " #Note that we now backpropogate the gradients at individual frequencies\n", + "\n", + " # Assign gradients\n", + " if gradient.size > 0:\n", + " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + " \n", + " result[:] = np.real(f0) - t\n", + " \n", + " # store results\n", + " evaluation_history.append(np.real(f0))\n", + " \n", + " # visualize\n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.show()\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 0; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ehpLJTmUa5i2R9DLrXj282ygzt4pGyxtbszBrP1te9vN76xOdY+Z8ez4eHW/b0dbDxkTPzmh6PpvceILpxXDUx7z3e9fZZ7DtkvHtK5YYvsQlYzMn3nPWvR0sEtUoE8pkGl4d7QOdn0lUhvclzStHf83o+cHZGdEjligegVfPaI2xMh5632/o/S2R73UmOTsD6dHrZ1u049W8/JKxjCMyo6clkyVvzYoe/TQwyUL0+pI9R52l9B7qMdoeZT0jRtNAr/5RB5kRGW9AHHXs7AynN5MZ1XFrR7azm5k21Da8ba/+o/XtLQNuth/M9LOZ2e0WXagY2EaUX72QCeTRsa31n3SVYXbpwpu6eNPM7NRzS731k7G8zjqzjDKqkxVpTyAytmc7wj0DQq/8rQO893nkh4yIZm1rol9+kM+2+GskwL14jvrAiHuXDL16P0JPqkX46aI7G3xbRrjePtH0qLWWWh/NlGmvzx0tO9hraLPZZ2/t7p4sUjN7H32mrJFfqoI+KisjBFs79lZBzGZxmecwR7Oo0dPHZtauo/rZ40bP9u31W002Th+lJ4+kPNP1nOWNnN56U2ZKaffXx2WE2O436gCRYEeBNQoqzzfanmx7mUjPrrUZPaYys843g+24vZ9HyrSPjZXMQNcT28yAuZVH+Fnq7bW3jYne8dH7mXiL+uQo69TbmfbJCq7VAO8cvMvsvL5474x5Cy+5OUJv96YvGTuae0bkUV2zAaEbfKYj9DIp78YLT3gzeALg+a332itvtE7n1bXXIXoPGM+8P/q9MnvsKHZG55zxX8bPPazg9mJCE8VWL97kf7Y/zSZE2f167TKzRBIlJ3/L5QVLNDLZLNc7xopfFGzRiHbvtHkmcC0j0cra2HLs1uPssZFgjbA+2vqzNr3XWbzZiLC1LbPCPbK5VYAf4YutbWnfu2cZJsq6ZVsPNF6WHMVZL9ONzumZfI1rahSZUW30xKGeHY/sz+Vkp4OZ6yG3ZPK9/aPpcyZTyXaU2Wmg3mfUoex79hhvSSeb1UVZdqaeHqPlp+iYzL5Rm23JJEeZYK+81ratm/bibeaqH1u/0WymN/sdlaGpEOCX3JG2ZVT1OkW0XjSTFelRM6qnl814wSDZd2vjnxaPArp3t1tmDa13HrYcu+7cW//zyhbf6f9efW02klk6kP1mBlEpb5Q1Cd7txplz0f/t51FHtj6OfNDLJKN26NFLACIR7F2S6PUDe3739Lfe8V4Zs7PPrbOCR1Ka6Xpi1RvJWvu8nvqoy4nsmugjn+iUbfSt2a7nh5lfYfXszWZrnthHg+Q92UM2A33EVDEawLzz7E1rR7Z79LJO+3oUI5kyHjnj6pUV9bd7sXex9QS9lzzZ+j6T8kxXP43ey0hHvynmbfcyPm/9Z7fbfcpyvMu49CPhdN2ixo2yMluHng1ry35hIsfoh3p45UTZvjfA2KzBPmbPfq6z2+i8M9PM7ANpdKzYS6TkOtWZjNj6wOuQdn+v3bRvvA7tfdmpfSz1F3rZsmUUF17M2XK8c/DO28tOvdmL7TPafqa/ZfqvrqfNij290LNKry1ecdfiS34jzWvk6IlirX2eyp7P55uG6d3Npe3pANfC4jleC4IEsT7OdlotzLpMK+C96ZXYFxvLsny6rlFPT8UXgv35Iiu0I3HRSJ3F1z2h8YRH27H/9eCnO6xmt/u/n+r2/GE71+Vyaeu63tiwMRL5wfNHdA5SX11/a1d+Wnz0zb8WBStctkzrC72t69Xa/8fFuq43tjMZYC92xZZGC63XJ71lMi22MmDO9F9tU/s7GtxsnGn/zwzUj6RMdK/Xa3t/f2/rut5kunLCImzRN5Syjw6E8/n86X/UaGJbd2K9rYPABkRr7SbQRXiXZWnLsnyUo8Vb113blPO3t4Ha7Mt2XtmW42wniIJWkI7Ty0qjjmltW0GQulm/y38vS+p1NrHnPZ7P2tWvxabEgRYxHVO27TVetqQFzaur3ccTG7ufjW/vtuAoW+3FhRUp6RuRL/b7/cdg5q1x6/qJrXVd27qu7XQ63fQTaS/xre7TmX7WE13pf1JHeS3nLD637ecN6jbm3t/fS6/XfbroihMOh0P74Ycf2rdv39wvcaJRywam7lDn8/njTzId62AvqzkcDm5Gcr1eb4JUj976uP1+397e3trhcGhvb28f4isBrIPaGyC8ukpgii3502UKUj8RcAl+8YWciw14T9R1G1hfS/3EbiRk2q+9bFFsaft6oLSiIee/3+/b8Xi8EQjtC88P67relGvrqf1i407XWcdFb4Dw4sq7lNGzqdtU+1j3ncgP1he6P+g/nUDoOmsfy38bv3K82Hp/f78RailTJyA2M9cx5sVVL9O1ddX+0GVFuiFl6dfir/1+3759++aK9rPYDZT9YbJ/Op3aX3/99fA1lF52lnGgl9lk8DLF6LVn13tvZMejYmQelbUlULf62r72suitdjM80gdbmY2LmTqPYjdjd1TWvTH7aF9fLpf2448/tuPx+EizYSXLRBcA4B9EKLqlX6RVZmUAAFkqZyovuXoBAOCfype7DRgA4O8MogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUAiiCwBQCKILAFAIogsAUMgy+HxXUgsAgH8IZLoAAIUgugAAhSC6AACFILoAAIUgugAAhSC6AACF/C8T8q6hOkFX9wAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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5/r1JJys7m+R7387V6ycG/Y3XS7Kxnl9x7ujE1vN16flZAoHim20tjPGK14l5jKskimMgS+SwDh+Z7U4XXV6y4vvqefmgWkpwlsvH8eO+ain1yIV9/uFv3Gc7SnAQNZBGRV8Fafwene0zgawmRHX5Jttsr8qKAdm7NsnnRXz0Bg8KG2eoPVHJrnWqNsWYfuQ6JtrIqOpZnYsrQgbbPisDn4rMLmewL0p4cYLDR4xx3Kv/KBJ+qokR/a4meKxjXNf9qEx4quiOVjDLTHGQqaBXWYMSRdVBj4A+Zv95VGVnb81CsEwcDFwei/ySpS/XQWUMPaHhbFdldjhAl2SNS/3Hv/l1luUyLLhviRs+X13DzHzK/l9fr0w1ZlTmly3Z4x9UviVDVEK5Wq3u4jjKHB0zavJd4uds4f0Q0c0GMT/5w3dnVbCivSzTbU1/EXclJGw/K1tlvFm9+YZGr1z8XPkZMzZnB1VW18tUMiHI6q4yTexL/AIbFl+k2orVGzxKIOK1WlWp87B9MA6zjFZN8FiXJf/okGOCszMluFl2msVRFXe891XFR/Z+NoHyudl78ZvvzyjbmfCi/fV6ffefxrN7LCO234tPs3uhNS1gVbY7Yn/Jsi8TXPydZbIV2dKHj8HXmTBygOG1MZUZZKJYtV+cywOhl4lyHTLBH705w+eyXS4zWwVldnlCz0Q4bCuUL9V3bFTnZ+VEvbD9RuKD7Y3EXQ9lg7N/5UPPdu+Lg5hKF7itRtp4JtP+cwRno1UjqwERWe+jZaMPS85Bn6rssGe7yoCD3qZ1noSwbbAM9W1gmWBWdjMy0ePz0beqTvyeOq7y+ZFzMyohZnsj/d1rVxT1LONFMX+kv3orFq4X04vvXgZcoerdQ2XJSi+qMYeXimaL8If/Y8peZ77F7rPOHckGlgZOVd6jE0MPzi6zzIzLeHZQqicH1TagnpgosnPDfu+Sw1uolsDoAwv3o9+At/S4t8RmFt/V2HjmGKx8YnrJ0Ucz9fICbnzOZriswVT2xudm5z3yBEqW6eINs16QjXQwL4n572x5i8dn+0az47PlHp/Pu0vQJ/Qzy/7CBq9ysuMeXcmMkO01Zr9GHqZRv1u7X2mMTBiqXdg/fI12OU6UXS6D/e35xraijrx0z8bmI0nT6NOgWP8lT5Bim6kHMWYw/UYad+KjMw82dm9DvgoaRl3IV8dg0LEAZ0Lcm2GVcPGAy2yq9swEgcWtEt9q4KI9ZbcHD5x4PTJBVplkRiW4o+Wom6Cq7VnIexNT7+Yqt5Hqh8zvJUmASjL4dwhtbLeKvzPRQt+rcdKL416CsATVF4/aepRplxdYIFVlR68rZY1UDYpssKoAyPzLzuPvTKjqkNWFZ18sa4SwpbLTrC4xaEbsVu9j2dkugd6AY3+fQTZ5jPgV4PcW9GJjxPfqWqOqf7YvOINtZ9f3e2ODbWKsqPhXdWHbI5eM1Lk8NlhDeu1TJS1Lb+A9g3cX3ZEAUhfA1V7FzK46JguuJY1bdejSmxoq+LMvIslQ5YwImfKb69DbIP8IaJf7mOGBNTLhVQN4xF7m2zPrH7+5rfF95ffIBLG0v1TMZZN9VWbVh4+OMT6/yubRb3WM0pFqFbpkDD6D6dd0VQW5Q9WyvQK3TqlAxMbFZWVvxlX+Z5nhswZq2FJiHq97Aan86gl1BChvUo/jepNcoCZL7h8sO8sY2XZ1fPbZEuHOJh61fK7aQ8UfxnLUTfWtqveI7+gT2nh2TGZkX4ozMr5Q9FRcYx17qzJu89ivG9ees5XlX/qabia4+JuX6qOzXAwOtcEe38fA7C1JVJaY0RNy/qKULFtQfvCx6vsD1GOeSriUf2oiykSi8nMErE/1hd2t1eJbgf2W9cvoY8c9solxNGHgdq5Qy3sVF1Vcqz3EvVjsZa8q/kYFVy31VTuq/sriA8fX9Xq9+44SPn50on4mU0W3lyWqIOUBlGXJahP0W6kyTvZpSec9MwNprf4i8XidTV6YIcV7QXWTBM/H49UxjzyJh75x5qyO5ddY92opiuXw9xBgvbI6KLAd1DlZHLPfzFt3eHA7jggj+luNv8znt/qqViGVr8oOxtHMywgZ059IU5092vnVe2xvdKmmhKkXWKOdzwKjRCyrH4tODDi1BWvptbA4ppdp9urHwcwrjda+3YTey5q4POwTFvhs1wouHUfqw/6ysI3sXFiy/M5sKDtqWaz8rR6IYTgTV98ZgmOjZwf9zsYOC/7IKrMXm6OrVrRb1WEW0/8bcJaR8hInWwqMZhuZXXwffy+dAZVvqvOX2FXLLhZePn5pe6CvS87D5V9r/xK9KgMZ+YIY1WZot8qm47X6ZqpKKFTd0I8qo8TMHdtjaeJQvZ8di6sSLK+3Q+HRcipfHxEqFmm22VtZVX5WK2RMVtQqhn18b6bv082WwupuY2UnfsdP9Vhf3MTB13xTTQUqDj6+JswZssqUOfvijCnLljgL4b2r6v9SRd0yYcvathIhXJpx21d7asMmimIW8GoiUdntarVKv8mN20T5y+WqfosylH0G21ot26slN5+nMlU8NpsEwl/kfD7LhzOyrBrtZ+2uzsnGS/zgXnBVPp6D9cTP0A8er+ynahts2+z7PtQe7vdmmuher9d2Op3uAk4Fv1qOqKyPO1NlHwFeoF+v17evqMtuQqC9CGL+Z5T4msUsCzS+4aAGQQjLZrNp2+327lulws+4K3s6nW7v4SqChZy/mYrFRbV11L211o7H491NCUUlingu7rWsBgO27Xq9btvt9mYbhSbuTke/cn8qm6rf8OsS2f/VSv8DxgBtRZ9V9uI3TkYR01wO+hx2l8QGfwsd1gn954lCrQRVonC5XG79iuMbLzWpVRr2OdrgMiJ+2B76pvxGn6NtcXxgWRiXM5gmutHxr6+vt+CMBuEdC4iaSaORoqPjPew4nOkiYEM8OJMMcKZGEY8BvV6v2263kwODMw6VBXAdMCjwBwcuik3Y5X9RhO+rLOF8Pt/5x4N1BB4YEcholwdFtE0MOvziaPSXt+2EnfATJ+poD7QfAhO2MBaUXR6carLgfsR+U3HG7ZklEJi5bTabO9+r/gvhwdhAccf64+vwOQQR6x/1Rv+VrxwDp9OpHY/Hdjgcbq+xvXliix+0o8YGxkPUIexsNpub7e12e3sdMcVtr/ox/I82xfb+S4puNMrLy0tbrVZtt9ulWQxnjZXQbrfbWyCgWLams2n8mwMOhVEF6W63a9vttu12u/by8tJ2u93tBy8DqJtHOFCxw4MQ2LAX9jGjifbAwI/gPx6P7XQ63WV7UbcYnNHWlehyBoDlHI/HO7FBe1gGT6IoAChc4a8Sx7AdbRK/o5wgbEX9sR16dh/xGWMN2wFjAdu4185oT8VFa+2uHfb7vSxHxcbr62vb7Xa3NsGMF+MChY0nC7xEdLlcbnGA563X61tshG2cIHl8cZzF60jE1BjmWNtut3dlhP/chyzmGBfH4/FWzpIE5K2ssiXT//O0CxzH47H98ccfd0L3jTPivTtnhK/Vez17j5BlAllZVftmfqplf3U+l9Gr/2i7oN1OnCxu6549ZbcqY4mvI/Z6ZfTsPdLG6r1nxkZlV5VR+fVIey+hiuFna8flcmnff/992+12j7iakTo0TXSNMeZvRCq6n/J/pBljzEzeY1WcMX2frjHG/J358P8cYYwxfycsusYYMxGLrjHGTMSia4wxE7HoGmPMRCy6xhgzEYuuMcZMxKJrjDETsegaY8xELLrGGDMRi64xxkzEomuMMROx6BpjzEQsusYYMxGLrjHGTMSia4wxE7HoGmPMRCy6xhgzEYuuMcZMxKJrjDETsegaY8xELLrGGDMRi64xxkzEomuMMROx6BpjzEQsusYYMxGLrjHGTMSia4wxE7HoGmPMRCy6xhgzEYuuMcZMxKJrjDETsegaY8xELLrGGDMRi64xxkzEomuMMROx6BpjzEQsusYYMxGLrjHGTMSia4wxE7HoGmPMRCy6xhgzEYuuMcZMxKJrjDETsegaY8xELLrGGDMRi64xxkzEomuMMROx6BpjzEQsusYYMxGLrjHGTMSia4wxE7HoGmPMRCy6xhgzEYuuMcZMxKJrjDETsegaY8xELLrGGDMRi64xxkzEomuMMROx6BpjzEQsusYYMxGLrjHGTMSia4wxE7HoGmPMRLadz1dTvDDGmL8JznSNMWYiFl1jjJmIRdcYYyZi0TXGmIlYdI0xZiIWXWOMmcj/Af/w4qkCOGtZAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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cAAU3OM5Nv7eI2KMeGhVlpvmR4L2MwYjGXx6B8ngb++eJiKrai1A4XqXtkqdkznXzI35sjwTC9eZ1u91u2JhhCu19tUiX682on/JVFLZer4dSBMfH9dS6M5LleOgsGeHSKXOcjB6pQ3ScLX1praVKDB4Jy0kp2uW685ghN930+/39/ZMSl9ubxsbrKNNWicwdGvVFkbrmL7vknNienA2dfS90JV3VRKmQXpuT8KoeCYubEIwAuIhU2BbxMkX1NL01Vh6f8poUI2ctLu80IngczGu0TCt944oKX9UmsJZi08g1Ri8dMBpmCujRp0Bn4BEBx6q+aRyUByNR/vR+SA50kq1UuRXl+SaJxkzHS6fDOUmXNAfVJ1mDbKEVkfHFiNZr05qniFm67mTItdZPr3dyzRlEMGLl5hiDCA8aeLSNzsBLKLwzUXX+Qxu2tAV9XtfprLSXFFmOoWypK7pO41REzYDMT/KMRqPBxn+Vke5m8+NRJkUfVY9EwrOWEoQIQWgpNBVEwta19Oz0nDJGVyguMr24FFHpVisqIqFRIZkqs74nJWGUz7l6BuCRrn46yfE9jUOy1YubCx45qdamyN2jBEWNmq/mqFRR13rEQfLg2OlAq+oJAUiO0hV3zjQ4OusWkVCPnIwZqetvyUjjV2rMOXHteSZbY/Mxbrfb4XqtB4lCY5LD9HEyUDlE8Hq/dVOBZ5uUlebY0mW1zY1F2plvrvodbNQf2YLsjATIDKzlxESg7gBbgQDlTEemPQiWUXT0she6PXths/nxkLo2s7iJQqWnd/ZdzhacmBn5eBTj8BSf7bCWq2hW9+1TMfRZT/E9YvOUnIbDz0oudADPwb0//8fNCz0DgGlYixz9XncRhcbCSJNExfG0SNTnRrLknWfu/OigvW9FXSIu6pTm4neB8cyup/kelVbVHuG6A6HcmO20sinJXbrkN86QINm/O6pDdsDxMzrnOjqhterEraiaUTodMUmUt7G3arscN8smDtoMM5hWOz5vzZ3Zp3RLsmtF2rqBSP0/J+PPge7lBW1IUdkURQrufZV2SDAkVSq0iJJ1XEYsVbVngPJ4TqSeEougWMtqpfattJxoES2NtmWAkp0bDNsW8Xha6crHWhozBbXBSMfrYYfSeUblHA/lwtMJilRIkBoXSZlj5BzphDkeXuvRJ58gxahHRkd5+TrJcTEAOCQH1hGpqywTcZ0ofzkJXePlNEbLrgd0cpJR61kZGo/kRSdDtCJHjyxpD4fOZ0ue0hHPoqbT6V7UTVJ0W2N2xvKaPqd1YsTuGU4r66yqX1+kK2y326HIXrWv4NPpdDi+IuLRNe6t6Vl1S2uLFClUbqSQgHStFo238SqVlKJxoT1K8jS2Vcui4rIsIVLQuBRZeoRTVXvemfVOjoNj9r9ZK+eG5nORhPpT+qi7zvz0BYmXG6VOpFJ+kimdnBOq2mEU4+WF0Wg01Oxbn5OOOXmxtkmZuUNxwhURiLwUNIicvf4ocP4iHNcRz/hahNvK4Fi75w0Ksh05F+qwy7yls+4cqeMeNVLeKjlJLzQmPSlOm4+yZY6ntWnHs8q85ZolJT+y6OUPtstjm3qSXy90J11Fukp1q/a9P42X5DSZTIZFGI1GQ31Y1zspMhKVYfO4ikicx398s4NeueppnckjVY9k+Rmm8/o/jYzKwuI/a84kQJYGpOBuPPoMz15KcT2CcLITiel+fz25TE8Ck3Ogc9K4VWPXurCet91un2zoSRY+Rz2oxUsWhzIVnsuuery7kGvgJR85AEZl1D06M81HfUpf2Odut9s7DuXZkIxb0RXnciidZtt0FC3iZKlDpKv36STdmUt3W5mYfld5xOu6BNN5talgRw+M4llrBVt6+JDapB3Tnrlppw27Q/V71o79+Rj6X9VvgHQlZP30IylVtadQHi2JKCeTyd4zB3xzgZ9l7Y+bDKwd6lQCx0DPzdS9VRpgNK521V7rlkp9hkZLg2ilSZvNZu+mhKrHiNxPTchAKQu+x2zCHQzXQ89U0POHr6+vB6IiEWq+Pgaum16M9Bj1yiB4G6o26jw9F7Gzb0U0kgUjVIGpJXfOPc3mZ/ie77r7ccT1er33FC2ttaJxOiHpssuL+idwflx76ZP0zzfFeBrDnZqiR6/5ehYqnXCdbNkDT8S0HAFLHpqT+nG9olxoSzyaRkfimSnXhA6Nka946VdZXtDpBZYRCAmDxFO1v9HmisP74OldWzuw6oMpqUeujLyqalAglifUZkvpvK/NZjM8tpDHaOT5qx5reXyICiMnKhGVTmURteFgdOk1Ukb+3NAhsSjC1WMbr6+v92pfkovkTKV1ubZAAqaOiMSYVqs/j754EsMJinoymUwGcmOt/zm05iA9Y4AgvfYI8OTkZCAajYmkSaP3coiXKCgfbe4ycxKpUU60Fc8eGClKHzlWbmxK5rIt13fXm9Z+ipdFGJTwb7ZJ+3UnxSfzecDCfQuSrK+lykTb7fbXe2SMkS43zdwren1NkY4WgooqEtJ7WmQ/0kOyFZGyvsYNOEYeKoFQgVzRnMg5XzkGPe5Q6a9SNSeuqsedayqulJl3ACnVYyTjtUsSr2St/lRfU7qn/mXUSv34QHM6NT0DmU838/7UZ9VTxyADknz1U3PhRp6TkIiCUaqXOrwEoba9hkgjJ1lpTmxLzlLjYJlHNW/WK/VtCJSRP6uW9UrpPAlkNBrt6azGL52VfBT1etbIaE5HuvhwcmZeKhnpmJVAHfeSncbKfpWp0I50EwYjYRK01t0jdbXNkgCzC99wp1Nm4OLconlR33uh+6Md3bM9l8JXPRbw5/P5XlpY9XielrVYXzzWQ6sej6JJ0Um2LM5zwfxkBQlX4/GablUdVEQSAzcE9R7nphcJVxGn0jWSjqI/zoEEo2tVYzs+Pt6rse12uyHdYkREMmSqSPKjETFqY7SjNfAIlxmNiIoRqYiPu/D6SZLmGjkh+I679IubtSI2OmWeSvCyioiPhKqjhRo35STnprEwItV4KDv1p2tFtgo29BmeDGCEqPVXOs5vCeFzchUx++Yq22Dm1LIFtiudYTAxm82GDI3rIjtT0EAnSxuTLWjtuB/CjM0zXOeWVsD0qyRd4pDXEVij0Xusw/BzqgOxbqU2GBmrLR5XclKqeozYZHBsw8fttUq/hgut/hQttubGOUvBmA4y6tRc1C6je0Y8atM3aejovBamqJqkK+NwkuJnWDtj7VwvZgOKpDVWEQc/0zIKj6QFj3SZoXDjkCc/vM7PqJvy0tw4NmVWal+kK6exWCyaNWO+qEMiKLav/ui8XN6cO0mXei1ZtwjTycZ1V5/nizrE7IAEyfXiZjftrlXLdd1qrQPHr6xNJTptrpOwJWefI3mEv39pdCNdeTqvH3KhWnUiGYJSFJKiPq8ooOXdWudNaWDyvlRuRmFe22tF47pO7audVpmDUSnvYKraj95Y//MoRdEX5+le3I1bYyQhM/pTNMWNChGual7qg5+p2j+CRgfFcYhYFUl5vZaRGdN9jpMGyPGwRKDfvazA0gejK/Xn+sNaIOfHOVN+um48Hu89h0AnCFpjcBKjLFrOiuupz47H40E3/DvYNDfOV7rnwYjrK3XKbcADC7cVri1lq6jUI3GeHKFj8zZZhtH4SLayKSdsd7b6PO3wV0m6k8lkSGUlWEViXFQqBlPe9Xo9fJMwPZhAxdKLJxsE9+DyxLPZbK+OSqN270+iO0TCGouIn18tpJSTd0w9R5BUZBL6oY1CzpPzlZy4qUJnwNo2CUX/53qQ+KjQh9I0lxOj/5bC03mxP+oJZdyKCA+16X07eRCUv5yxp9LSG+kQiVWOU+3oc36WnHNppcCUI9Nv6rLrDD+j8oHfaCR7Yk20pcfP2YBfcyjAkJ7TKShj4FhFgh5BC15W8ShXeuqlQ56vZj8nJydPNva/JLqSrr7ckQJVSsYaWNU+uVTVUNP1M6dVjwRFZdXCt3Zi9RkeL6Ii6TMkN1dyoRXxOkFU7T+0wyMKL0OoHfahz4i86ZiqHh0IMwSVRri5pj48dWeE7KTO6M4NmgYmGdKI9J7aptExSmUEpLWlQVN2rajLyUKfV/suSy8vUC6Evyej5SkSyY/9cW2po76vwfFLJ3jMTevKurxSaq0tN50O6Q/12/c7JAvXjUOOjvC1oc22yFnX+hzdRtWGy83745lkkTvnz1q77zfIEZyenv46SVeT01dgKwqQMolAuHPqUen9/f3w+ar9eg8VhkYrL+/RTKsN9anfeQbXd9BJWu6NPd12A3O0SFqgcumbiH1XneUCGjzPAasfJ1Wm5ZyHf2U95aQImPI4lG5yzpqH6wUjfVd+j5y0Rr6OLcelPvW+yhssH3mJR+PWZ7zdQ+kq1+jk5KROTk6G79fjuVSVWfSis/Ez3+yPZRs/2cGHGfn8ufaH9Iuy9M1G6rtnOVwf2glPqrjT2Ww2e5uSusazS/bn0TsdteTLOwI5Z0W6/HYLyVpyPjk5aR67/FLoWtOVgHgkSYLYbrdDUd1rL1WP3yzLTRHWbKoej9t4qugel8TBdI87yIwI+F1pNB7WR6mMXrfTGDgOzdl3sdm+5q8IV0TLQ+0iS/bHqItH6fQZpnt+ZG00+rHMcHx8/GQjrRUtM7rmPGWM/J/6V+RNGTOT4O+eLTix+jVcb82Fa673mRm5c1YdtJV9tDaSSAA6EXJ8fNw8MiY9V5rPsdABknh4akDrx4jR03fqFoMD1twlT19TJ3nqjus9Zcgn2en/vpGnsXspQ/LU9Vwr2pjbuObNL45VUEIbV1ai00M8WqbPHyovfQl0I10aU9X+rXlVNZQCfLOIkZTXs0g28mpMPZzoaIgtRfSUVN/+KuPhg1N8N/dQSuYlC0+FSQpMqWgcimYoh5ZS+jxIvIom3LBbYNQlQ+JJAxqofrLmxnVqnRahUTGz0FxbZNfahaZu8bP8H2VFAlJWoGv1kpPyiJubOB5diwBYp/9UbVkyrnq8DbdV6miRoJcJ5JQ1Zm46Uc98E5F25AGBOxgvR1EG/KZkZRE6dsholScbPBNln8yo3FlynSUDz4gVpfP0D0lXtqaIm6WOHvgqX0zJ8gJTPRk37zQTuNCeguisZNXj4welnF4j1hg4Hr7ouXXzgG4g8JMXh0CDF2G2SgxuEF7H8jGxNkt5SJY8B0nn0HI8u91uuFYRFNvisR/NxYmaa+fpH08SeNTmm00s50gOrUhP6+zjUsrt/6McaYSMAKUXdNYua8+o2I/W0Use3Hykg1R2x76pD5ybrldbXoMl8XoWpzFQxzhuRYmtUsGhUo2D0aLsRXPb7XbDDTWtYIRBQdV+GUS65/rB6zk+2YKyZo9ute7S19FotPds7JYj/5LoenOEH+HxFw9A+866p3Vsl22zPnQouiSB8P8etShdVJTL1IaK2RqP2vOaoq5nhErP7jIj2BfTQp0hlZFX7X8bLhWc/Utmfv+/7q5qHdHjHFuOgfUy34V20uUcfW6e7vojO1vOSCRCQ/Y0kzVdXUtnXlV7mzEkAJWB3Pi9L93Jp7WhfrRSc+o3yZ0lMK4X5XMoK1BG5/pDh6gok0RNh8e1ZV/uxBVYcKwe5bL/lu25ozx07rvFA1onzYclQz5jgqUS6jCzmB74Kl9MyfS3pbStu4bc8FuK5oTXMihXGgfTFj+G4kTrqSAjMtammQZKqRm5ac4tJeccD0UNVDqv0yl98g0zkpkTamujyNeSNUOtEY2am4+M3rw9zsHX6lB0zDSb8qp6+oBsOofn+heYbbhj8dS8VaKh/OS4eA11TzJzknFbIHE892rJmbbGI1vcdKLNeb/UF46/RcAqcyhgOpQRehvuZBnlUh5eDuJcGbiQP9yu+GrpcQ981duAtWHhdUIqna4hPP2hgrHtQ4KkklLhOY5WvcjJTv8TeVXV4GVlcFIWkhJTeScVkoYrZsvx0Ft7hOe75KpJKxrwqMTBaEbykJxIEJQVd7FplLqOm2jMLDzS4cvJ3U8zUJaHshH1Q93w1JzX6xoSNiPU3W63V0JxWfF6ypPr4XfBtSJrRuce5bpO0nYYZZLguUksh8gSDKNkfd6fr9HqW/rISFrXSDe9v+fs0/VS8tHvzDYka9pjy1YYUHH8XOMe+OpfwV7102/Bc09FoTO609+HftIj8vuc9D6V029LdCVneiLy1VErtckH9lQ9PciuOraUhcpBI3YlUlsCywQeUalcIjIREbbSdbatyIXRliIQ1si8T8mQayt50rGRoL1tN7rRaPSkZKE50FBZ62SUv9vthjRY7TIic8JVO3KojHZ1vZ8i4A0vTtzb7ePjEfncDJYTxuPx3qF+/aTt+GYjI9FW2UrjrKq9FJ2fUT8MHrgWCn7YLonN68telxf58/km/LxH7bRrdzTOAz+HPwjKqie6nl4gebQiK71X1f72hVZbrfTOF8+9mKchivqYvkyn070jPy1i2Gw2w5Oy1JaiXBmKCIjpsf7m7qoX+nmDhuAEpLmobsj3uTHHcgkPxXt0TNmqPS+LsCzBjRrehOJZAuuX/L87NJIhd7pFGj4WRjWsP6s9EgAdHtNOj3DljLws4tG9Ptsan156n9czvddcNB63D+kX59YqPXGc1E3WKjV/jZOOyG3B68ebzeMNNn7HIkldG8YqrdB5S1e89HYILQLmvFulHYKRb6sfRreHyiBfCl1PL3ByFB4j1KqndT6ma/ScnwIVtEXOXjPSeHhyQQenneSlmNrAqnq8+4U75PwMo3Gv1zHa9tScMmiNX9GHnsxFovHUSWRHwlRbJNCWHL0cwxqnp4ye4rszpBPQHD2S5FzVj0dRJKKWcYkQRPZOUGyT16hdZkbKStSXShaHnCFTdidMka/PkzJWBiTd8DKBg/ZB3eB4SXpyMC09ZXnD5eAZCEsVioj5DRGUx3M11J8asWquXkKhDNQn18UdrPApAv/c6HpzBHe1W9GCkxPJ1csTriyt0gPLBFTy3e7xXnPVE3lg2o+LeQ1IoCJV1RNlZDTnD/o4lE55bZDExX5ZmvDoyzed3FDUD6NFfnedyJ8RkG9i+fr53Fnb86jD00XCU3yPbLyu6lG0rvUUvNUHdYRRt9dOW7roJEInyn0IZjcaM/v2Nrw9zypEMC1nKuKh3lA/WzrHLMgjUKbf7kz4ecpZQQif0+ybrH73mtpSdkad1xxaDo0OipkYdVSy4V4Cb9BgVtILXcsLmqQmr9qWBMSNFimdC5vK6IqjflobG1RKCVpHwpTaa4y6jptDVY9pkiuGxtYiUC4un7krZaQT0hxYA6ZxjsfjPYOkUQokxlbEQ0JTP/qcy3uz2QwHy5mqegTuDo/zYgSk+qhHkm4kJE/vU+tAaI1ItOpPa0Pi4Rj1Wa2NZLzb7YYoz515K/PQ/oCyGHfUvIGBT5hzcj60fjwTzajXNwL9pc+RbLjBqnkpU3IH7Y7SMwPaia7n+vE8MEt2/IJYRqt8sRTmfbDWLD3Se+QH2TlvPfZS0K/2KWNVj8Srn1XtZ3Xudru924MpWNaepLBUKPXjhXxGRVWPxKsNJvarb73VxpOu17jdcBQdsgbHeqjGqHGScKoe7zhjfZCkLuPjzjYjKzeIFgEqaj89Pa3pdDqURti2CFz9sPShfjQHRQ8aPyMXKTK/BLS1dq36o/pgdFK1f/qFBKR50nD4kxmSxsgIzKMdzVtOSZErgwG1zciYJSZ9dx+d7tHRUZ2entaLFy/q/Px8+B41zUW1fRIhX5KBjnGJsGU3mhujW6byWjeePdeceb6bDp3Bh9qvevxuNrWr/tbrdd3c3NRyuaybm5u9I3Pj8XiPbL0cwPVl+YTBFMdI0vW1pr7SMfHFNTyUEX0pdD8yVlVPHmeoBeMGlOpJ3A2W0jHdrWp/R5ggpZZierTESJJP0+du7vn5eZ2dnQ2KyhsnRKIiEz1oXM5CxquH1Xh0pdMB3ERiFEUSqHq8Y0yfYSTICEbjXCwW9eLFi3r58mVdXFwMpLvdbve+UVlyWC6XtVqthnnwmRQam4yHqTIjChm3bqHmUTVuNuprY5hWy5C9PMA6p673qOVQ/VBrIV3xZyMwNeXzg9XffD4f9JbEQDKU7mht9QyG+XxeJycn9fLly3r9+nW9fv26Li4uBn1Yr9fDvf+M4EQoImY6JWUkHqX78xdcz6m7dBiSkb7Pb7lcDjVffWW6PsPsqKoGG1mtVrVcLuvy8rLev38/fHO0f/s37Y+BFc/VVj0eDWxBNkDbVVlNtqEARmOWrbIEwVMvzFC/NLp+MeVyuRy+sI+R0G73+BUxt7e3dX19PXhICUyKqTTOv83AU31GhixdMBoQuUjRNIbRaFTz+bwuLy/r5uamvvnmmyf1oPl8Poxfi3d3d1cfPnyo5XJZ6/V6eLiPiPf4+HggLaWheogPFa5q/04sbuLwu640P0/JSOhnZ2f16tWrevPmTV1cXNTx8fHgDPTUNv+ONBnMcrnc+9oVETQNW/L09zXfs7OzOjk52fu+ubu7u1oul/Xhw4fBUfGInW+yuTwYdesaXiuHzGyI4zo7O6uXL1/W2dnZQH6Sx83NzRD9MRtSGxqHxsRTK9Qdrf98Pq+XL1/Wu3fv6t27d0O/PC4mYqvafyaJdIvzYqYjWWlcIhk5OZKvn6yQDtDuPn78WO/fv6+rq6th/ufn5zWbzYYNZa23xiDSvbq6qh9++KG+/fbboQ3p/mQyGXSeN2W0sjg6cTlD2rTvn9BpyDakH0dHR8MT38QhWlt95dXDw8Ngr73QNdKtqj3F4PdzScFYYxwGOZ3ueUrWsGigWkyleFxQeXVd36pTMarSwjByFWHySVLaOLi9vR3GzR1nvU/SppF5fZYRmry2ohsSnGql+luRJV9KZV+9elWvXr0ajF0kwyhXfdze3tbl5eVACH6iovVUJ60p1/X4+LhOT0/r7Oyszs/Phw1JkS6drb7Q0evOGl/V4ykXGhujN2VEVY8Pp2GNmo7g4uKiXr16tRdxingkC5ZZNBZGRpINv76bRCjyPTs7q9evX9fbt2/rzZs39eLFi2H9WUvVGPiMXtoOyYZZDW1Asm+tDzMC2RjnpzkrCFmv18OzUKpqL1qUXNWGCExlOWZIrbKXbFQE6GUSZk6SJctp+iy5gJmJdJZ1ZDlYRfR0mOSnHhh9orPPNhIptdepWhtR3JDy64aB7fZ3bp8MfLd7ct2hdlqbYLrWF7c1fq9J0WAOfb6F1lpwXD7Oloz891adtyUj1gFb8/B+Dsmz1X+rX5dZa+6H1ry19s/pB6/9lDxa+uBt8e9D17Q2t1p6/5w8WmM4tOYtHfC5t+B9e6rNeZA0W/LyfZmW7FvjO2Sjz9l16/2WjFr9tsavDPAz4mCRuBvpBkEQ/IZwkHS7nl5QOv9TriN+rsc71M6/g58ybu/7537ml4ifIufgR3gU+v8z/l/0t5e9PSfnn8Idrc/0XLNEukEQBJ8fB1m8703HQRAEv3GEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUeEdIMgCDoipBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6IiQbhAEQUdMP/H+qMsogiAIfiNIpBsEQdARId0gCIKOCOkGQRB0REg3CIKgI0K6QRAEHRHSDYIg6Ij/Cwfg5X58i64rAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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p2YPK214QBlc4PkvI4rkriJXxuSFAaCUlrxfyw6tyz8stOuNwMqJAg7fFeNxQOHEhRJ77lpRCYS9Q8j3GAgnzXO12W4PBIKVJKAwxRsgbwuW5vbjJtXyNWE+XAS+GeZ4epfa2KFdcN6woNnKC0jJfGA431C6TruSslaesGDs5YIqAGFTP1VJTGAwGhdQXJOvFYObCUxaAtJjnwpEpPLN8TYgYPdRGdlxuGBOerT8f98CQurFEByFTD/X5nOuNkxlz7BEGJOqG8hJXeMTFNfJuHdddlz8MDzyTp3nye5WB0kg3Vxjypf57FzInKgQETwwvFI/IK5CQiacSELzcymLZ+bvnw/gO16YjgAKX5w4pcHmXAAQLSefFN5TNnx8vhfFst1ttt9t0fVqvPIR1D9rTKUQSUjH8QuBd6SEjwjO83W63mxTVPTS8Ie884fn9noSHLuTAizhuQKWioc1bwUgfeGjr0Qbzz3NWq9VUqfbOilzJUHzSU260XXkhGsaKVwfZUGwi5eUerhMja+5k6x443q9X3klHueF2DxVZJr3Fc7ku8TyedsPw5KkQPFfW18eBPJD3594elblHnKf/eG5PeXjU4XOFDrsBwxnwNWXNXde5dqfT0Xg81ng8TlFIbrjLwk+yDRgrBBFghWjb8RDQe/cgKgiBfkM8Aem5TQyBci8pFwYspOeWpKKB4HeEdpAL9z2fz4kIKLB5xR4i8Pxk7ql42HUp9PJQ1711FBQhdaH1gpxbfSd15qlWq6Vr4SHlHqErNh4/ho0KP9dhjd2r8SiEOfbcKuPy1iZXbpSK4qTnnGkLIi+KTGGY3ZuGxJgfHxdjwhvKjRZFMW9BPB6PhTYqX7NcjjGEfBbi9lDdI52np6eCkfTIytfG78Vnc5lCNtxIE11C8DgJRD5eNGZecEBIgUC6HvHkRIZMe+sdc+my5t53/qzIcO5YIBsYHy/gYtSJPkajkXq9XiFV91Og1Jzufr8vhJ8k50nCk6/yRDmLACHQtTAYDFLTN4JAGOwdCt7fKRWrkwhCrpB8DsGHZChAoSx4VDyHEwrkhefg4bXndT1UdsWBuN175n5OrLTcIdjuLSFwjJnve2EhV2KezVM6eJv8QIYeUvKne5F5GOcGg/v4WniByftDfZ5QGJ7Vc7VuRNzoeIuXry/P7Qro4bivpdcKPMT2NBUK77LHXHqBEsL1Nid+h8yxfp6fdq8vD9O9PoDRcHnwvztpUbB2HT2dTskbdZlm3vgu/cnoLLLhLWzc1yOafA3c82duPeXHmrhxI43mnj9yRQSEU9btdpMB8WIb3/HoowyURrpUu6Xn8JWJZFIgXiqm5IpcWOmdHQ6HGg6HyaPk93muhu97egLvjHFJxcqqe3ar1SqlFbxdBkX2sB1vwolgvV5Lei5+8f85kbiH6dYaIfLUAESIgrrQMn5Jhe8Q+noRMg9TPeTOQy4neo86pGdP0J8buOeee2jcx/N5GIw8T0747ikLDDKKjvJA4J6vzouQ/qwU5bw+gHL6/zFGrw/4nHzX3Dk5exqD+3oagc6JS2uK7njOHBlET7y1z+sE7sB4FOT5Ul8TN3C5LnknyWq1SgRMtOrdCz4fHk3lzgcpLk+buYfc7XaTsYJ06TbJU2StVkuj0Si1saFLGED+PJ1OhRa3MlDq5oj5fJ7CREIBTy+4J0bPo1RUUPJM9CcibF6NdOV3gvHwC+ufk40rE60z3p+42WxSS5oLvAuvezperPLCF6TF8yAsLmQQhuc+3VvxfKdUJDgUhsjA+0b5vBOQE5qnYvI54Vn4Xe65XCJeX8PcyHm+3Qs83ifrhRCMHzJEodLnxzdl+NZl9/qcFCGvPK1ziXAhAydnb5nyXm4IxAm0Uqmk54BEkAsv0rnBySMEz2X7etE7i/zwTKwZxWCfW56DOXRHwgujwNNMTnqLxeKDVANynssHnjA64Wk2xspnkQueCUeC3m2e39eG6HMwGGg8HqeUgqcdiaQ9LVUWSt+Rhifnu8JQeEiT3TaQmecEHa64HoI5wbmXIX14yIxfy3NrKItv12U3k5Nsnrvkuk76WPTc60agURZPJyCIvoNIUhJSz4c5OTGeZrOZquj0GqPk3hfNvLEGrMPpdCo0qONhk86QnsM+z0/mrWCep/O5d4PjOT4Pmb3tzYnN88fb7TaRBLleimfdbrewEcTb2fI0CAbUvW4nJKlYL0BmarX3mzKGw6Emk4mGw2E6m4FaA5sLnDD9ud2Ies8o9Qx+75HapWIccsI2Yh8HROubUdyj9bXxohX/hxFgjO7d0lrmOxJ9fHmd4iVd9BSUOyuevskNZs4HrCWOGfICr6DPi8VC6/Vap9NJs9ms0Bn1sVHajrT9/v3BJpKS4vP3vKCCVfP96iwCVpWtrJxPgLfLxLpFl55DJ1cwlKJSqaTqa14EQGm8R9O9gFardTEfhJB4hwIhupO9e6MU5PLKKiTpIZnnLFE8Jx96bQeDQWHvf+7dIOi+BZPPYWDO5/fbqfk8ZIBnxFrhqeTj5f8lJaPLc+P14RVC+Dwb4ahHN0663uoGgTEmvEknHs9JMh7WIZdDzwdTN+D5eR4Oxul2uynt5b3Z7q0iFxgpJxLC58FgkCIJT5t4Dp3r7vf7QnGY0Bvj1O12k0FnXj1/7ekOb5W7VMxlrii2Odl6r7ZHMh4l5N0s7ih4VIQnjC77HHB9v7+nBngG5w6PGvwQn/l8ntISHBjkc/IxUZqnu9vtNJ1O0wL4oSAIAsUohANhYOJ8J5J7VpAmZOs7irxaTK6U4hLeD7vXvKCR57s8xws5MU73kqRicciJHWVxTzf34CBZ9zQQGi/q5WccoGyeK+ff3mojqeBt50rnxQ7CVr7v+bvj8VjY6kw3B2QMSXBf9y4huryFCCJxYnCFZZ48P+fV7Lwg4saFZyO9JRV3RXn47oY5Lz76WR58bjgcJoPh6Saelft7ask9Zkkp3cRzIh+0RrHxBm97u90WokBfdyIcwmquRR7UuyjyvLDLQi7TrovkdD2Hy5znpMo8onveBeI7Rd1o+85AyBdDNp/PdXd3p/v7+9Su5qkqTzVB2NvtNm1v97MyjsejptNpYS0+NkpNL7x79y5Z0zzPdTq9P42pWq0WCMXJihymhwKHwyE1YmMN/dAS92Tc+/H8JpaTBcYYuOeTpy88v8zWYPdu3Ftw5WZcwHOP7D13kvS8LTlm8lGQM8rup5u5J087GZ4exO8FJPcYGTP353AePITFYpHmFAPlOWCv7EMukDCkLBXDTM+3oXAefXiuzkkXY4As8B3IjvRHfj8+4xVsz2vmhIT3yVgpkFYq7w8nomjDKWye7vFCIzIJUSFb7skxnxhFlwUMpoffyJEXYIkISFnhqecFZGTEi1bIkPc7M25y6BCk10TcuLjjgZPiBS3mEJnxHWTIAroMaeJpc+zo/f192g7vbYTIAlvyqRE56ZJeOB6P6SS9slCqp/vw8FDos8y9SXos3WvJw3wvTPBdThXzxHyer+WzbMog14fn4/lRbz/JFdE9Wy/uQLrufUEynD7mYRgk5QU+HyOeJPnU/X6fzjqYz+fJ63dPlvmE1JgzLL6HezxHvu0WJYfkl8tlOlthPp9rvV4n4vTr5AbGW3+8oMncslZeAHVP2s8iQMHJV3ohzdMXEDb3Z3zScw+150m9KOjy4x6yG22XVeaIQ38eHx/TGcoeMSA/GEI8O5cN5O7SphaiKbx5jBHpH0+n5F6lp6n8Gszzfv98FGWv10sy5F4z4/din/+/p7c8xeNE7KmTfr+fIl3IHBnIu1s8RYWRWq1Wur+/1/39fUoL5Gk85pLt0vwdhyGXsYeHhz9fT3c+n2s0GiVBbzabhUKIFwkQABdcSclL9HMxpec+T89vQWReNOB35H3wcBBKJwY8cPcCGCseVP7DdRCo9Xqt6XSaSIvzFFAYqqx4SHjL3J8xcNbBdDrVfD5PxRnydnl120kfj5jxk6vNc6QeOuJh4+ESRkrPiuSnvLE2EIenUSQVCF963gQB6WAYIBWUhflg/t0Tc/nwSMRTTpvNppA39PCaOWZsUvFAGsjAUw4U6PDKuSatjmz/xTB46xaGjC3UpMPcOHkO2NNorMtqtUoFoFarlbzlXq9XCOm5hpMWER0eHz26FALx9rwflnkm/eGpB9bhUiSY65A7K3TqcH8fs+to7jycTu/PQKZDgnnD8XCu8LSWnyCXd11wst6fXSGNPxF49yzcA0JJ8Qy63W5BkfEKpee3T8zn82ThsVYuFHnbCoLMQmP9vQ2GBfA0grev8H1PF+Bx+HXZOsybIPz8YI479FObMESei/IWFzzO2WyWCKlSqWgwGKRxej7R55b/Y176/b6Gw2Hy+Pk9YR5/R8khbnb6oKgeLnorTh6yMhYPH0kLYEDc00TZMZDuWbm37rl/3yrNZ91L8uo9kVRuNCEJ/470vOGAVEKlUikclo03tVwuNRwO01wT2SEvfoAShJB7uO40MBcYcT/YH7mkd92NFVGCVDyH9+HhIXmJ7KprtVopT99ovN+J5obfydTzr64LnqPNdYiioEd1/uPFNo8KPPXCc3i0hcx7rz8pDD9/ItcFfrxgx9x7hPyxUOo2YC8M+Z/AJ9z7drG0kCAChRAimNJzozuL4SkKFjXPmzrhuvB7uxJC5ITLzik/YcqLQIRjeIuEQ3y/0WhoPp8n74jQjmfEOqMweCic24u3KT17n3ie3jLHnxAyLUW0FXmUwbNj1HxnmHs5FLSYY/fg3Ph5UcMF20NsvEHWh3QH0Q4etVeiXZGq1WrqbvCcIgrNGjsx+GeYE2TIPU5vr6OFDuJwQ+9eLKTAHPj85mF+7pVBQJAGY/HOEWoP/F+z2dRgMNBoNNJ6vS50d2D8SUtxWDxhNt+XlM4nuBTm+2aIfP6Ql0s6xBryu7yAm/+gt74BxiMC326Moe31eppMJrq6utJ4PE76yPrlBUv0wX/KRKkH3ngrjodz/HjOjkLCdrst9PBWq9XU6uFFFA/jIK9ckP2kIS8cef7Iq+xS8fhEXxwP2TabTYG88mf0Ni/yoSgTyrfZbAppAgpYy+Uy5QwJ8/3IR+bEW768guyVeRd4VxjWgTERLnNff62Sh3Fehfa3P2D4PG3hBol5cO/W85Detsb9XIkISykoSc87kDytkRdkvMjqhTYfH6SOoclzjTgC3qRPMZZn9oPv/VlcF5BJNx7uMHjXBK1hebjNs2OQ/Z4YG+n5oHGIizF7UdgLvHnKhXnmOnm3AkbEC3x4onjBnjL0eka73U5OFI5B3oJICgqDQ1EVp+Pq6kq3t7e6ubnRaDQqdAF5ROJOnusovMSzf2x8dNJ1L9Mrqr6gXpXFa0FwKEww6Qga+ScsOWEeE4m3LD1Xdmmn8YNyKCZ4vhHknrk/j5OUE1tebPO8FySJZ5V78u7FIWD+2hQ/bs/hITbeJYrkSub/znOmUjFlQxj67t279HJKlMKND10RPkaKgHmRzY2bp5byghypJcJqDDZjhHSZL+nZ6LJmhPsQrBOwh/G+tqwpc09O1w03YybtQuqF6ASZpVjrXSSM31MAns/FyHh47RFGHkW5B0qhdr1ef9BBkddIkD2KUNzLu19cjrify/ul9IN7kDkH5I6Bj9+L2V5z8DVmXXgOxsrrtK6urnR9fa3BYJDSLn6QueuBFwxJ9Xkh7mOjNE+32WymHTsokRdPPIfLhCAI0rPn58cJetEht9IUPVg490IgXUkpTHPC5js/BNzPQyT3FLzY416Vdz34wc8IMQSQeybc08MxFxjPB7qS5DlNPwqQsJrwjS4JWsS8kbxerydCcmUhrMQA4o1Alk6u7kV5Hpxn8+6QPIebG0bP0aNszJMTvqcSLhX3UG4P3z1X//T0lFoTD4dD2hhAuEtet9ls6vHxUZPJRKvVKim0z7EfDEP6IidEJx3g3TQQMvJ/iVh8PokgaCfzPna/bh6puWxfmn+f20t64dfwXl0fd6VSSZEFhJqnFXFGPPrBuJFaoSBNeg0Zzc8IRjbQPfinLJRGuq1WS5988olGo1EK1VFWhK7b7RaS2gjC8XhMiXJyhl5s8sXzKrcTEBaUXCZFF0kFwfJcYF6oyYXOBeAlz8DJEgLybaCMx1MeHprlIbZXlj3t4qkEPu+Ek/e8ehrlJc9Eeg4tIRfCNr7D2wQupWcgKu8kIVfLXHg3CB6WkywebG7AeA43ku49ufHF64MYPefsp8Z5L6wTkhcAJaWcqL8OiXG5F52nb/jT58jllXCZVBUynssQqTNPy+QtXC43Hj0SbRLWe+/uJZl9Scad1HNi9lDdHQT/PPd0zxW58lSUF774PhGkF888imYtve5CJJAX2yFbvlcGSiXd29tbdTqdlJfEm4AQut1uauvwMMAJs9PpJE/LK6QIc3qw+nN/pBMWYav07HX4fnEnHO7tu1yk4lsn8nYxxuPpAbw/FxhXAD+1LK/qIlyj0SiFu8wXVVo8WPea8rY2vEC/l6dA8uhjOBxqNBrp/v6+kO+ETHkGiFgqvnKJeWBtUDL3Np1E81YkV3L/yRUfJXfl5LvMDYbMf1hrJ6xLqQ83pDyLRwveD0y0NhqNNB6PU3eIv0ECuXNvnu/7KXakK/K1RR4YU6vVSmuFbDvxesrCj1LlbS2sFeua7+Zk7S+RrkcMuZy5TGCwuD4bk3hO5o5aBXLmqRiu6alC3+6cpyQg3Px8iDwfTPcOqcgyUFpOt15/v12Sii+eE29GkFQ4U3e73X7Qj0mF2nvsPC+E8vkBMvmuMYSckIPFcAGEPJ0gUQbPq13yghEWD1MRGMjOOwf8mnnBC+EaDodJuTnpzJUdonRD5J4+8wcheQjL37kOQntzc5NecEmrm5MV42TeXQndw/P8tpOoezj+O//TvcU85w0gx9zz8nSOp1jIpboS555zLrue23dDAqF5vWA8Huvm5kbX19dphxoyx/qShmD3GHLhZwYg194VwPg8HcXZ0ldXVxoMBhdPVPO18B1rhNp8zotV3Ju8uhNrrgNcs9PpfLCBCbmm5xuShOjcAahWq+neGDbkOq/ZwAd+6hzPjTNFN40fxnM+n9P8YcC8c+XPopAG3EpJxUPCge+4whrmrx3x/j2smb+jyUM8PusWsV6vF3I9vqXWSRdr2Ov10tm9fnCMV5ndS3VviTHR1iIp9ciyO8e9bsjDv0sPpis2OVYKOO5FeHUeAfV2Oi9YuTfihobDcnhu0jp5PzJrSMoB5fXoBOFuNBopX5+Hp3lHhLeZed7+pfYe9+r4LGNwr88Nhj+3h7uQAWkV5Mz7l91gMB/kF0ejUcHD9Zy7P7PnWtELxstaufFmXSuV54OI6JUeDocpnwnBe38yz8D6DgYDSe8Nsx9r6KG5txxivPM0EOPnFfX5OcAYSEgYfdtsNkmmuR7P7UbGzxjBQ+X5cWI8JYVBpLAIl5AGotOJVKanRsogW1Dqiyl3u11y4z0HR26XXNlsNiukAQgPvd3Juxn8TFnPFVJtp4Dm98Yi5runXBl4xcdkMklFQEIZvFXSA75wKCbeix8+4t9DIXl+/y4GgGZ1T3P451AQ30XG/ngPHd0rQwkJ3SA46dnLZi7znCvXQam4h1fg/SwJjy5YaydoX1MUhzF72ggvNV8rT5O4d+zGz78PgbE+eUtbPj6u4e8Ocy+ZP3NCchnHcOTdJMi2y433B+NQeBQD+bTbbQ2HQ11dXRU2uXBvfwa/Pp+BKOkFRw48FZIbRveS/RXwrKNHHd4R446Et1/i2HAPHCHmwVN/XKterxfkzqNLrgHh+uvqMUCeJuOHyKcslHa0I+1I0odvEMiJdzabFVrFIFAm239QQg9n+D/p+QWJhHMIiueOfDwooudSJ5NJqo4SzvgZtVhqD0G5D0IM4ebnwXoYlRdhnAzwBEjNLJfLQg4VoaYbxItQfg0nF4iePlAXfG/jyz007umHhHiO1oscXhTier5ebhhIfzAH/N5DbebJq+5OeD5XbhDy/D24RH6u0NwPwvfnzefEuwgwgFyb52e9PfeOvHhemnX2dciLWxg1Nqp4msSL1YyBzoF844HvOkQOXI49YmJdvQsgL0TmsuMG3XXXo9/T6VQoDCPPrJ3rtnvhni/mvnlfO/PNs/hakYbwiOpje72lebq02SBk0nN4x8IwCVgqCjt+HqovJJPu/2ZBXJm8Sp0TNmE9P066FN78QGi8XD80x3NopEw85UEumleHsKuJbcI8t1dWIROv/Hr4nVf53dvw/lKe23O3CDleCF4O/+/nEec5U+mZCP3/GA/eEJ6uz3veIeLrTvHUCQzwGcjIFc49Wd/6inzxfZT1Uoift0R5i5KkQkdDfm1HnhJga613V3gevNPpFIwKMuMbOXJd8fyqF4rzYiBGAjJxWUJueTZSHGxswcukYMqzeorLu4z4jkdxeaorj3L4NzrqBU1JhXWFoHN9Zz4xADgStDqSx/UaiRtJ5InUWVko9R1pfsKSh5XuYeAlrNfrZPmwwF7JlC73B3Idfu85GyclLBzChWfAwiAInhf0/N+lXNThcEhnJPBzOBwKJ0BRLPGw03s3Pb1BesTJg2d24+A5ZZ7f22zwUNzrdO/NPVaexUP43Fv2sJ8wXXrexo3B8mq3Fz8838c6+X3zYllesMoBqfqxnHzPFdlDf37nh117uoIT6DyS8uKUf96jCFdoyMtB5OVrhlfrxueSZ8vaufHi+d0Q+i485pJojzH4hiD+36MuPofeeATiHSY8u4/R022e063VagW9g4A9beXF00sh/yW+gFgZP557/iID5xtkhsjwzyq9AMid5KGSwy0Xi0MYBAldCncvEa//Ll98P5fW8z1ebIEgfXsxJJVbW4wBByXf39/r7u5Oi8VCp9MppRTcS2NO+L4fS1iv19M1vbKKAnlhwzso8oKAF7RIbTCPnjNzb9bzrN7C5cbHc3TMNX96axfhLkTtnpmHcb4uPLc/KwoPGaKkEIwf9AI5Mn5IyluKmHs3YMzX6XQqeI65MfPuEH6XF5lywnHj5a1bzI8XI9lt5/D1dY81j7AkFQjXi2/8zvXOw3PXiWr1+chRPpfXQ/I8LaG7pzDyNJ7vsKSATXoud8K+T6cZV+48OKm7l/0S13g3S1ko9cAbqfgK8jxk9eIOIboXJSQViA/4QrhH6vk9X6D8eD3Gg/JRRKNyT4iFcDabzTQ+JzEOSmbr7HK5lPTe66H4wKEsLrQIDF4OqRSa4CGqnHQhCZ8/N2ieY/UWHeYR0mKMCH7+ff9BwRg3845x5O/+44CQvP0IT4/w0OcmT63QEeF5adIzjIncMPfytj/6SC/JIARKKgTZcXLJjT7P5HOU55SddDEcEBPzfDgcCsbtpTXNIw/P3zJGqv+sFePjmX3t8PToHEJm6dxgHplTb4XM02h05nh/rD+HVDwzmeKk11TQZfegLxGxpwf5//xQHi9a4kw5F+AIlI1SW8Y8fHCSBO75YakJuZk0Lzo5ObgC5B7KJcvIouSVUFpxJpOJXr16pfF4nIjVPTKavEnSk1pgpxL9xlh/zwviLeVeL+Gl9PyCxzy3yTjd+zqfz2krI4pL3pjPX8r/eqU/r+56V0c+x24sGJeP10mVIpI/Ix6eHx7DNUgj5c/nzw15Oem7/PgzMyf523d5Ls8j+9x436eTtOf+IEcnCS9o8hm8Z/eofW5e8uB8gw292S8V8PJioR+IQzrL54nnRya9+4XPUotgOz1GN99EIemDA5e2263m83laT9bSDYMbsZxcMSwYQf6P67gDhecNX+RbrHPD6nADVhZKI916/f3mCN/5wWRcahXKW4QQ4jwvzKSRl/LeR/d2UCap2DPshoDwjSb38XisyWSSPM/cC2N8hIZ8Js9bu9ede+QewtOt4RVjFz6e1+fJm9id8FACb9O5FEJ58dLDLI4D9ENjvGDJD9dwcub+3qXhuVpaf3jOSuV9PzJFHd/kwbp4uO455e/KezIO3yqLkWEcPIOH7vR6O/HyDG5AfA4wVBwYRB6VOSFNRb+2w8nISYlrzWaz1F2QGzLPJV+SG18PT314Gin3LD1H7gQmFU/Mu9QhxHosFgvd3d2le+YRWp4K81QPaRr6oJF59+4ZH9ECcpiPz3WCa5Pe4Wc4HKaIugyUtiOt1Wrp+vo67ajitR/kVH3nlqSCorLgnvz2qqz0/AZdNkOw08S9Fan41gNCfs9J+cYB95pd2S8l/L1zwa04AkQ4iTHwsbA76Hw+p3nwMNRf+ui7iJzkIUwnXIjsUssRgi+p4KUjsKRg/LCbSx4IXiHPSU80vcl4R65sPoeMx/Ponl+XnqvmnlsHrBsE5yG7h/Gu5G6o8bi9dxqP/3A4pCM3+X+8cM+rsh54d5K0XC5T3rZer6e3MksqyI8bEjfc+Rt3/fQ27wDCm2fd3ZDzHGx24Rnd63Z58bbBvE/YQ3GXA+9VJvrwqArDxZ++nd/7ipElDFS3202yBYEiIy7/5MNxfPiszw+y4/pEh41zhfPVx0Rp9N7pdPT555+rXq+nRDc/5FchQC/0uHWDlDxX5fu4vS0KS8l3UJTz+VzI2dLaQlj/9PSkx8dHffXVV5Kk9XqtwWBQKKxQlGKHGp7QZrNJp/MvFotCYcoru34Ai/Tc48ohJAgb3gMERyM7Auf5O4QFgfUWLFcsv54k9ft9LRaL1CXCmbikR9brdRJuSSmv6gUu79/k3AG2pZJD9Vxgnkt1RYW4yXvn1XE+7yAPShiN8YZ8PLrg/3q9Xvo3YTGys9/vC219Xmj1tj68Z0kpLCdq8LbC4XCY5IbeZeYQgvBrkWbCOWHd0QFCdrxAN8yDwSDJs+daeUeZ78w8nZ7P4mAs5GMxemxD5xmIAtz79e/h4X711Vd6eHhIesU42ZThRV2eBYej2Wwm+UF28m3unsZx0vVebHSdlKGf0+AHAB0Oh7TztQyUTrrVarXwGmuqphSgeKVyXn30vkL3Ijk1nrM0IQ+OcjscDil0RgGr1eczTyEZSIowbrVa6eHhQbe3t3r9+rVevXqlyWSibrebUg/D4bAQDs9mM/3+97/X119/rdVqlcYyGAwSsXhLDZ4pROOHQ3sl3HOuzAFzlBOCF4jq9Xra4OFvqsWI4aV4imE6naa1WC6XBaXxjg5/KSLrcHV1pZubm/TDDr48JPScoBM7xJqfppbnVj1shAg8P52nDdyT9l5SvC0/VhPjmbe20QGB7HndALJl8w+e5eFwSERzc3Oj29tbXV1dJdLzlAA5SVoOvf7g3RzSe8Innwqx+LkL/j0+zzj8vA+IC0OzXq/18PCgx8dHHY/H9Cqs4XCo6+vrQhrMQQrk7u5O33zzjb799ltNp9OkS6wDuXUMD/dFp4nWIEd/Y/disdC7d+/08PCQ+mp3u12hmOwOHLrCjtDr62tNJpO0MxUnhF7pPyvSxctot9t6/fp18ti8aj+bzVStVhPRLZfL1LC/Wq3U6/XU7/fTIvmE3tzc6G/+5m/0+eefazweS3of2n3zzTf66quvUmhG3tA3M0jPubh8V5gf5M07qfBsh8OhxuNxerMpCnN3d6cvv/xSb9680Wq1UqPR0Gq1UrPZ1KeffpqU1dvA/OATT3N46Lrf77VYLFJu2cNOXlIICSC4tVotEeH19bWGw2Fhx49UDHP3+/fv0Xr79q2Wy2V6jxZk6MoC+TpJcugKP9fX1xqPxx+8g83bkx4fH3V3d1fYZYZ3Dpn7FmL/nOfqIAPW1dM7/kNd4ebmRuPx+IM3O7jHhifoLWiNRqNwAp4XVpEzSHA8HqvVaunnP/+5bm5u9Nlnn+nVq1dpS7cXbrwTZbPZ6OHhIRlbTzng2eLt4tUSJRFZINeklNzYO2l6j+tisUgOA2/H7fV6OhwOGo/H+vnPf56Mq59TjQP1zTff6Le//a2++uqrwgFJPJ/vCJWUWsj8ZZHn81mdTkc3Nzd6/fq1Xr9+rX6/L0l6fHzUf/zHf+i3v/1tKi7CH0RT3op2Op2S09Jut3V9fZ2OlvWaD+Tv25E/Nko9xPz6+rrQ3oOlxVucTqeFEAtFazabqYjgxal+v6/b21v99V//tb744guNRiOdTifN53OdTqfksXkBTPrwbaf8yX2l58nfbrepkFGpVFK4OBqN0ksa8TR5HxoW3lMCkFeeYsBjpxmfe3vODwH2UDnv3OAZ/AjLwWBQIJeXihf9fl/X19d6fHxMYaQX6SAAz7chtIRu/X5fk8mkEAX4Djz37J6engqHD2GgPKfuIT95Pd9owHj8iEw+12g0UtrI84Ttdluj0Sh54vmOQjxxJ1yMGGPyt9DSJoiSe2sUJD8YDPTq1StdX1+n9kOvB/g68BYEIgTkJN/UIT13aiBTfkyh54rd4887SvhupVJJ6+BHq3IOLU4Lc82cHg6H5A2v12vNZrPUTeBOQ97H7D28FAxz4/7pp5/q888/13A4VLVaTY4ZbzNmzvPCthexmTei4dvbW41GozQHpMq8EF8GKp5bu4Af7Y1tTnr82//uljmvsnt7jMOLOi7Ifj2vOPu1yI/lVfn8vvzpRTXu5ffzyinX8sqwh8GX2lO+Zx0uzpGPmXt5EfAPaYfxCr2ndfJ5y9fC58iNRT5Hl+7nHQjuAeXXzefoUgvQd33OC2ieI/4hY8tlJL//pW4Ov5fXAn6IF+Vr7B51/jzMcd6B81249HvmHifIn5X75J1A+Rx5NHFpjnzMuWzk85YXKV/S6UudOJd0+LvmyP/+Q9fnD8CLFyuNdAOBQOAvCC+Sbuk70r4P3+fxOX6oZco9nj/mXj/0fi9d88fOFX3s+3zX3JSR9/rvgjLm6b+DTP1XdeklHf0x7venJq/h6QYCgcCPjxeZvry9b4FAIBAI0g0EAoEyEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQIkI0g0EAoESEaQbCAQCJSJINxAIBEpEkG4gEAiUiCDdQCAQKBFBuoFAIFAignQDgUCgRATpBgKBQImof8/vK6WMIhAIBP5CEJ5uIBAIlIgg3UAgECgRQbqBQCBQIoJ0A4FAoEQE6QYCgUCJCNINBAKBEvH/AZFaxGU8MFY7AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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AvpKCR47EslwuNRgMAmkSnTDmEDbrIcondm2PRqPgVEj3ElgikdDTp09VqVS+1zX/sYjN0x0Oh3r16pXS6bT6/X5IeuCNSuuWbz6frwnsUc3XJp/QT2120obcfyzwuNBvpfXMLdUMGIXJZBLkD8T9bDa70dO1BsRu/tVqFbynZrMZCAsrjCQxn891d3cXvOXd3d1A/q1WSzc3N0GLSyaTYcG22+1gtPL5fPBCSU6x4KvVavDs2u12uO5+vx82JQkLvE9L1twPc8SGYKzsfDJv6M75fH5NVyUpGs1O47HZDYvHDzAyNoHK+yxx22u1ny8pkCTjhxS2vb0dogeum3GrVCohgWWjLYjd6t1EexiP0WikZrOpdDodPNbJZKJms6mzszNdXV1pMBgolUqFxCVrg/WCUYiG3vZ+o7Kb9cC73W5YXxgu1rxd96xt5i6XywVH4o+FNdjo4ZZM7VyyhrhP5mtTFRORJEY8kUiEtfurX/0qvObHrmaIjXTxdMvlclh4WGN+rAdjJzXqpUJU/NgSJDY45Ai+j47L65l86d5o9Hq9YBn5ruj1EM6h1eKFA7vIIXW+bzabKZlMBt3u+vpag8FAkkKoZb1RNgSbJJfLablchvALDTibzYakjPX4LJFhpCDdcrmsvb09TSYTJRKJkL0fDofBWyBBhmdpxxyyt/Nj5wCi5T2ZTCZEKVbPn06naxUJUQKxawT5A6+6WCyGa0DC2LQR+Vxey2a290KlBEaIecJwkWzd29sLUREGF9K1hDscDkNkgOaIbINeLilUDIzHYzWbTV1eXgYtM5VKqdPpBPkNXRoiPjg4CPNvKz6IvuwPY2ETcDbzb+UYSI99zb6QtOY9f9/9Buz+ZV7YL+wzDIt1xOxcYVStY8MPa6XT6axV4cSF2OQFdBnpgQAJeUulUtCdLOlCFFbzJQNL+FkqlYJ3YyshsIrS/STaDGb0+h4DE4iXcXFxETK2aE3S/SLf29tb018TiUQIUy0ZWS83Srp2gY3H4+AJocVh+e1G6fV6IQRPp9NBlx0MBprNZmt1iXbjTCaTNQ8sm80GwuHa+E7upd/vq9/vhxAPfdNmxa2naxd6NOqw0QI/JM1Wq5WGw2EgudFotFYdYQFxc31o7Xwn98z9E+4zhraqwN6/XXd8DhsZ4qVaoVQqqVwurxkP5hGPCz0erRkDYD+bKAdNtdvtBr0a+YpqAgwvXihGxUZdzCPjy5rB27URCHOHRLG1taVKpaKDgwPNZrNQAsfalBQqZC4uLnRzcxMivW/zFB/7u3VmbNQE2RKVWUPGb7uH4ANrnPP5fChpzGQyYayHw6EajUbIH/2sPF0miAwzhFMul4MORXgsPUxMNBS176vX60H3o4yGWuCoLhm1pHy29DjxkpTo9/u6vLwM2eTJZKJSqRSuZ2dnJxAf1QVoWzYZKK3rpnbTc432Om3ZmyUQ+xq8on6/HwgBvWtrayt4e7VaLSxevGq7WamDhsDsePZ6vbXyMIgDg2KJzRpIS7hWPrFRBCRlDexoNNJ4PA7kQgRE9GOzzGy0xWKhXq8XwnE8Zrw8yuVs6VD0M9C+8Ryj18r3U/bI/EKg6XRa1Wo1eKsYJRvWEhlYrxPCHQwGajabGgwGWq1WyuVyKhQK4dqtZssYWB2W+bBlkTYCsZ4tcw8xs94g8Vwup6OjIxWLRS0WC+XzeVUqFSWTyTA3/X5fV1dXevXqVZA8rBa+CdGIyO5Ru+9Y+5JCBJTNZoODIClUTuBgWccln8+H68BJ29nZCREI76cU0a6HHxuxkm6n01G1Wg0LKplMqlQqKZFIqFgshkmzi5aNZzcdNbNHR0eqVCphwd7d3YUFC1lb7Qpg/disjwFrSHlaoVAIVnJ3dzd4IbVaTZVKJXwWm54mAmQDm0QjIWTDbJsAsKUz0n0Wu1QqqVAohI1vx4nqDUgRL3dnZ0fFYjEkGfFoIYzhcBiSXcwJSR68rU6no263G8JIW2kAqVot3sonm7Lj1uPCEDAveB/D4TBECrwWrT6q89kyMsbfdrehwdokGrAblTlCxuHv3KMlC+6T9cX1VavVkOgh8rDlgdPpNEQR3DfJom63G3Ry8hTUxuZyuUD8yBl4r8PhcC3/gRFkvSFhMF5WisErh6wZt3w+r4ODA9VqtTCXvI5102w29fr167WkLVLQu2AJN7ovrbaO7AVh4uh0Oh1JColoSWv7xpI00lqhUFCxWAx7FjlmOByG+v64EGudbqfTUa/XCwPN4BQKBe3u7oaB6HQ6ajabYXPbZEw6nVapVNLBwYGePHmi7e1tLRYL3d3dBWJngW3SrcAmC2vBgoV07bVCXIlEQqVSKZQqRZN+kKtdZHgr1orjXdmKDbyzUqkUaoWpMSUpRmE4VQz8JrSEpBaLRfgsOq2sMZtOp8Hj4t4tGTGeVhqxGWPrKdlEl/XcITHrgfGbzyA6wQu3GwGiQlJi41DozoZlraG52hA1+r38JgS392/HgNdwH9aQR73KaBmW1XKZWyQfSg4xeDgPtowQrRgZjgYVwn2SQTSbWHmFucV7tY1GkkKkYZPOdryQTBgT9id74vr6WhcXF7q4uAjVSNaTfww2woy+zu5XIgycLCLKcrkcqjnQZZlzeKJcLocuTMYjujfZM51O5+fp6aJT9Xq9QKIspu3t7VA7Op1O1Wg0tFqtwoay5R+QUaVSUa1W0/b2diAyex6BzcxumthN/x0FnigER1YdooUwrRfCvRJSQiBcI4t2sViEZA9yBKBGEQ0tl8upXq+HtkUWHLovYaEtqZLuSSCXy2lnZ0cHBwfa3d1VuVyW9BCeQhDcC3qYJRnuh3mgmsBqsQBCslop14cWaD1Dxg8ShXT5DF6by+WCpERrK6Eh5E84b70dmgVs2ZetTrDlSTbhxA8kCphTQnnWxO7urp48eaLT01MdHh6G8inG2CZ90IGZ30QioW63G2pFrZ4MadpWVeaUcJ+OSEmhHNASDd9ty60gKWQYG0VYMrafwTru9/tqtVqhjpdGBcjv26LHx/bepv2KZ18qlYK8l06n1W6311p3rbaby+W0u7urZ8+eaX9/P/AKURv7hnr4wWDwzmv+oRFrGzCJGKu3sthrtZrK5bJWq/vaw+FwGM42wAuSvtm9YgcdYrOlQY/huwjmVg5goaGxoUPm8/m1zcNv6/Xa0B/dkSQMEgvhW7FYVKVS0c7OTigL4lwDNiohNddhqzhsFUKlUtHx8bGeP3+uk5MT1ev14FFZfXSTfmi/b1PlxGKxCNqZ7eaB0KxWOZlMQg0qEgQSDEaHENsaM0JVvB17FgcbaXt7O5Am8hTGBs+Q+mSiDnsdGDheS8WBDcuZV6o3bNlZPp/X3t6enj9/rg8//FCnp6eq1+uhmwqDg7e8tbUVQl26wtLptMbjsZ49exYqVrhG1gVzbI0JobKkoLOzVmjOIHEEqeCl0q1GQrpYLK6t4ej+YjyoLb68vNTFxYWur69DTgGD/23ygt3Hm8DcbJKO7N7aRNCslf39fT19+lT7+/va2toKdd7wUK/XCy37/X7/50u6EA9iOyRDaQqbezabBS8WzQkipU/9+vpapVIphE53d3dqt9uhRdK21kYRTV5t8oaB3aAQLwRVLpdVq9XWNGJLtnw+Gx1PzJYusfCpwiBkxKuAFC2p2ZCWsBvihxwpGXr27JlOT0+DFwW58Tl4VehfSCfSQ5naarVSt9vVxcWFJK1lxu3GxTvmukj+MB/D4XCtqqFQKISDbGg0oGg/k8kEvc7Wv+7t7YWDe2azWYg68vl8IBPes729vXbwzHA4DPdrtWK8XSIuqhTsRmT+mTuMz/b2tgqFgg4ODnRycqLDw8Mgf/A+5o21gKNhw/paraYnT57o8vIylIlR8sd6s1URNl8BcUPm9Xo9XAtdcRAi88l+wjjQWIIzgZdr67dtjuPy8jK0f/f7/TUN/TF8l33HvqBFmwTu3d1dcDBobqAs0u4F5rFWq4XEGWuQxiKcH+aTfRkXYikZ4zf1iTaJlMvlAlGWSqUQkpFEsod+kDVuNBohobW3tydJur291d3dnbrdbqgxtMRnNcZo3agV7qOweqYt5en1eiFZxXVyrXiBtpCc+7clOljlcrmscrkcDvngbAQy4nSCUTFAnaw9+QoPDXLO5XLa29tTvV5fayW1eiQEyLXYEi/unXtDb+x0Our3+2F8lstlCP1J+mGEqEQYDodrSSeMDWEj74XIol57sVgMEgk1sMgFhJi28wsPEc8QIuc9nNEBgdrxwJO05UaSgnG3FSLSvSaPZ2oJyxogQnabQGQ87GsoPdzb2wtkwHdES/darVYgTa4Tj/X4+FhPnjzR3t5eGB8qFTivgz2JtETEYgnXki5ri3GDEInabAT62D6yCclojsBGnnjVdF4i7WHAGo2GXr16FaICohuuEbkPzqDSgi7PTqejTqcTeIJ2YstXPyZi9XQlhbIqNh4TTqhVLBZDAT5WXHoQ8i1BLpfLcFoQSSSy6ywEuxiY+E299/azLaIShtWsbJID3VBS2PQ2003tJKENiQ6baWVjMj6Et81mM2hPdEVhYKLtqnxn9MCgwWAQiMxueAiCMbHjjbTCmRIU5vf7/bXaXDxte8iIrUvluulAjFaNIA2xORgjwmWMUC6XC96o1Wh7vV4gFuaf74FEmBd7b7YZh/FFU8Xrs80dGB++B8eBQ386nU6QcCAXK+HY+2ZN2BI0PhPJw65BvEy7hrrdbvDkMXy7u7vhjJBSqRTmFKONp8t3cXgR4xDVc1lPRJvWMWKeo8YkSlxIJNFWcAylTZjyfqQF1sTd3V2Qd0jgUeoFl7DPWfPtdjsYiKurK93c3IT1C8dEE7xxIDbSZSLs4S4QGgt7NpuFxJg9PIX322JoOnFWq1XQgje1/lnwfZTioHXRjWKTLNL6gdS2ZMiK+yxCG/rZjWX72F+9ehWOoKSTiVDdaocYn1qtFhYmB30Q1hFa2USQDVfZpLe3t+HIwMlkEvRXfjBC1gvh2qkBPjs706tXr8IBRbbUjbDU1rfaNldavm0JmH0dxsAmLO/u7kIbNeOKLoqRRl5ATphMJoGIeB+ktlqtQkssR0fa+k5It9vtriW9CL0J8fP5/JoxY+3c3Nzo1atXQcZhLKJaO5KRNUoQSavV0tXVVTjDgv0BICAIklbtw8PDUE9brVa1t7cXygRtswnzOp1OdXd3Fw4y6nQ6ofaX60RishUtGD2cDNuAYvcHP9HcCw4WUg/fyfhv0lTZ63ij7IHr6+uQSGaObWRB0uz6+lrpdDqc64wkAemy9yw/xYHYSRerRtlSKpVSr9cLoTkhFb9ZCNYDQxft9/uhthRLZxcL5GClBb4bsrET9lg2lfdwLbbrCCK3hIXFn06narVaevnypf7whz/oxYsXur6+1mQyCQeGHB8f6/j4eM0r4TMsoc3nczWbTb1580aXl5fhcB1Of+J62GSSwhmx6LyTyUR7e3uqVqshERMtGcJ4rVar4FW8efNGjUYjnOXA5tnZ2VG9Xg/SCB4bEgQeLmEc14UHTZiPRw8J21AeAuT9trbXhv82UkEK4vNYV9yTlbjsPaP5YuRKpVKIPNLpdKieIAPO6+m2PDs709HRUahGgWyiSVXbTk1tLt7b7e3tN1pqmTsMA4QFYabTae3t7YV5sUd3Ws8RA/H69Wu9fPlSvV5PuVxO19fXIdHE9/IZdj1aJwkSjUopmzxdG4nxY6NY9ldUArRSF4aStYWUZN9jrwXtWXo4yhE5hIgr6tTFhdhJFzeejYQFR/uzBfyUk2HlNm3cfr8fkgjSQ8sghGhDaWvVbIhJEsAmqIDV/CStETrF2SyAKHFxQPenn36q//qv/9LZ2Vk4LQmSPTk5CfXGfLZNkiBL4J28fftWt7e3ax1nbDTOurVnW3BSGYuzUCiEkiu0TltYb8upBoOBbm5u1Gg0giwEUddqNb333ns6OjrS9va2lsv7w3AI4SA2W29rkydUNFD7adt7CePn83nwbG3HGt6RTZTyOrxHSEFSKNODeOwZxlHvnutGbkCmqFar2t7e1tHRUbifVqsVmh1Go5Gur6/VaDR0dHSkarUa1qJdFxjGxWIRIoFGo6G3b9+q0WiEOnYrOWEgbCKISAYv/vDwUCcnJ2t1xDYypBro7OxMb9680fn5eUjYNRqNEFmSZyAyAOwT1rxNpD62V+yeY7/a8yiiRGcrJuw+lhSMjq2S4D02mUy1CtfF+2z7NQba7vWfJelaWAK2P9L95qEQnFAEzY+Btr/RfKzlRKeKtiRay8rGtXrupoGPXp9NrPA4m1qtFjRpS/T0db9+/To8sgZvcWtrKxSYNxqNUPiN/kdXGK/hMzgsm7Kr+XweSu5OTk6Uz+eDB9XtdtdOCctms9rd3Q0a2KYOLTCbzQLRN5vNQIx0XR0fH+v09FS1Wi3UmXLUIx1Vdrzs3JPUibYhk7QjObRa3VdtsB7wQiFMpJdE4v4ROrVaLSTkmAvbnIBht3W2tsaYayPK4nwJ6d77ozYcwkgmk2r/78c12fM53nvvvZDgjUZYrCm0VWvYeCoG+iyNEJBcs9kMRsxqoblcTrVaTbu7u9rZ2QmlZFYz7na7ajQaocSLjkPumaaD09NTPX36dC3BxVgPBgO1Wq0gbdn5je6T6JxbBwfv3e5LW25p5QhJa1EKNe68z1Ys7O/v6+joKCQQLbFv4horu8SJ2Ek3ugCZELwOGh94/hJH5DH5tOxFwyaSAnw2ei2LQ3o4uMRmg8EmywvsRNlKCqtX2vIu+5lseJuAsxlcEiNIJWiRtFmen5/rxYsXevXqlRqNxtoh3iTsKFl67733QiKy0WisJdFsGHpycrIWNUTHAN3v9evXevHihS4vL4OHjq5pNXEqStCbo80TNsEIaUC43C9GkOgGg8vmoyQNj8l+VjKZXDtkiLG2J0iRZLXHidqkjr0+S8qE+qwXtHPGAX2SZM2LFy90eHgYnrOGvGA3t42Ebm5uQpKHU8tKpZIODw/DI3HI0J+fn69FQrPZLMz1y5cvw75BlqD8cDqdBq/a5hAAY0q3WvTv9nrpKn3sjJRNYF2x3oisbOLaOjW2EgiP3nbWYfCQCTlc/fnz53ry5ElwYCgTY46jjtVPQbhSjKRrydaGEQwGnmwqlQrNARwgwiEzLD4IYGtrKwwqoSf6JO2v0oOOTBiCnmnLmr5tAmyG1p43EE3e2WQblQnRukw8M6QByIQFTzszeurr168D4UI4fBfNFXg6yDGSwnkUhMtbW1va2dnR0dGRdnd3Q6KITWa18vPzc3322Wf6+uuvdX19HTx0SrqQJvDEIA50RsrNbFme7c6LSj68frFYBK8Sj81WIdgkDRov3jAldxhivsee40GBvH1/NIridRggvp+qBh6ASMKPkjgaDnZ3d3V8fBwkBgg7mo+4vr7W+fm5Li8v1el0lEqlQgnd3t6eDg4OlM/n1ev1dHd3F0r/bHjO3KLV8ggprtd2+iUSD6dtFQqFtTNNkJtsUjjqlDC27AP7mm8Lz61XSiu79FC/zPhbrdhGKta7t/xBbuH09FTvv/++Tk9Pw6mFyeT9E2YsvzAO7LkoP8WB2EkXvc6GJeguhA4kLehYWq1W6nQ64ZEoeLA2dLCTgTdAMoCEGh1EHJANaUbrdh+7fjwvagFparBnMdj6Tpvdxzu090cBN5uEjWlrcZvN5toTNrgW7hVvkgoGNNf5fK5ut6vb29uQ+GGjX1xc6PDwMBgevDEIgSoJ+0QDkje21Xg6nYYGFXvgta2bhHSiJXwQfTSEtbKQrdVGdsJYMRc28cd7WRv28zCM9vv5zWdICnIH3vBqtQolSyQ3GUdOBGNsptP7h11CpO+9914YY2ukqVa4vLzU9fV18MgIk+v1uur1+loTCERkDTf3wGfyrLZ2u616vb728FSihZ2dHe3s7IT2ccjJOgfRqgSiAjsHkLPVxN+199mX7GkMQiKRCBUxNmlpcyfo2dbZYC6obT45OdHJyYkODg5CYw2dZshKtpGCvUMi9GdJuoSYtLFK6yEJtYcUW7NQUqn7k7IODw9DyQcbwia/sFosMhanrWqAACEmSq6+y8Kxi9AW9lMyZhMm0vrxdBgEro3FwqNP7GfYxIP1wJBFuFeytXwvixKSIyzjZLBoTajN4Eezz7bsx3oY0UoCSUG+IQQk5MeD5JqjCRLG1DaQQPy2/pOxtmd02JOiGDNL4va8WVtSZAvm0VVtBUo06rLRlK2P5XHpJJ8w3HhsvC/qlbMm7CluaNL5fF61Wk1HR0c6OjoKhxvZagzWMVEcJIVhYh3YdcO6pdzLtgdTKsm1YvgswfEZtvPQOho2N/AuRNe//SHJFfWirTG242rL+CqVSogKKJWjEgZjhHyHAWYcufZut/ut1/9D4kcnXciMttRKpbI2yJZ4afmDfKl3JDO/t7e3VitKqGizprbshTAJz9Q+lcDWv0YXWRQsFusRU8BO5YCtAMDLIizEG4IY7Hvt+62+KCks9Gq1GiQQsrfSw7PT0NtI+iBlSA+H71DVgJcfJRw8mmw2q0qlopOTEx0fH+vrr78OiTFIGO+AA9QpF7OJCgjTjh9jA9lCIPbgG3tQtd1glpztNdsyPtpmbRQAYVPry7qzhh2pxF67bcCxVTbMK+uGcSGPUC6Xw9jZR/ZAatbwcC400Q8hMtJEIpEIESARHmuZ8eSshZ2dndAoYcvUrJdJaG/P02BdovsSsdgktI1Y7B6yf7MR5iZEIxkqDQqFQqhe4m/WuNv5svdj230pgyT3Q5UH90OXqq1/Zm3wSCgaPuLweGPzdHO5nA4ODsIjZWwPu3S/GW3NYqVSCRoaSTKyx7bYPrpBouVdeKSWbGynC4v6scVis6l2028iAoBBoIWRU4yY7Ogp9lb3jFZyQIAsMowSHhJkwqlJeH+2/Ri9dDgcBm/HnoxmDcbW1v15xaenp/r444/18uXLcMxmNBvMpsA7tGNo5RgiFiv1YAhs04GVbOyDE5fLZQgBCRcxUFQl2EJ5O8asHTRdm0xjXPAUKVGjHM/eKwY66glaI1woFHR8fKyPPvooaIs2YWvHhPMkOKuhVCqF8kG62vguzgqgygDSTKfT4XyJer2u3d3dkANhP9gqBAxDsVgMXZRWY0eiQJcncuI+2U+QupXbohFDFMwVnaPsSSIW20xDGaaNnqyXy3jTQAThkjwdjUahUoOIhM9iX+H9YxhpYY8DsZEuorclA0swJARoAEin709e4sAONgRJABYWGx5AuoQfVstCx+12u2EBsFk3lYxFLXyhUFh7mKM9IMaGaHi6VqPmAGYWyvHxcTj+D0/DWmkSOJxpWqlUVK/X1Ww2Q12lDe3tAR7cP08yRkLo9/shWWObKKzGzoKmJvXw8FCvXr1aezgk3gbXSjiHd2RlA0iVzWtPotpkhOzpZmTemSNrUKPzZD1f1pqVW2iSwGtl89r5xbuy5BqdS+bIylKQd6VS0eHhoY6PjzdWLlgySiaTocyJ5Nf+/n5oXOHeSbrhXUsK647oj+MuGUPey9wQ2a1Wq3BM6Hg8ViKRUKvVCudIQIBWB2d8+Rzbtm7lOpyjqHbO+sLpsE82SSQSoRQQTxMesF4xXq4FtfkYNpLP/L68vNSbN290e3v7jQS0XX+2iSUuxNocgfZlrbBdiJwg9vbtW0n3HVX2wBaqG2xWnEmOWlfCFMIqWhcR0+lI2dQQIa0/boZHlfDo6e3t7fDflA1FS2wgPoR+ai7xSDjKkvNx0ddIovFvvJpqtRqSiyxiG/KjEULYWHMIf7FYrB0mBLnYUNUmtvCK7PGRNqkiPTzpwVZxSFrbnHhkVtO0oavNoNtEEVUEjAe6HPdHqI48YMnadqvZaASP1ZaEoQkzVlyzLWWiwoX3cJ+MI++1UkU0Sci92yd9UKWRSCSCwUEWQvvl3jedzre7u6vT01Pt7++Hk82YS050o1SQzywWizo9PdX29rYODg7UbDZDFxz7BG/VroVoIpr1T4KKiAeC3FSSaDVze2YCCW+egGFfEy215LqQGDAU3W5Xi8X9sY23t7e6uLjQ27dvQ228dfCscUDKsJUMPzZif1wPZytEM8lsCJ4ThVcyHA5VrVYDOUkPB2hI3+yC4TVMGmVktiaTVkw7oRZWS0QHK5fL4RjCnZ2dEMpFSdd68MlkUtVqVdK9d1Kv18Pjuakp5jrRfdmUEAv6L9Z4Nrt/xDpECNlBopQ7WS2RQ1xIzDC2dCQRgttGlOijkvAmGR/GOjqXhJt4QFwH92klFK7Danh4lEgyJLBo/7ReC6RLuExS0h5GvikJyL9tSZIlAFvSxN94T3QcoklIPHNbD4wsQgKNrLqt9UXX5rVEETb/YXMC9mBvtFzWNu/hAB8roeTz+VDXOplMQlKQLsdqtRoMUbQUkugROYPWYbxSKlusgY0SL/eGF28rfTCWUcK1Mo+NZBl7DDElks1mUzc3N6G6xFavWGPIWPV6vY3e9I+F2EmXcwDYdBa2pKbdbgevgg0DUUV1SGm9uJp/M7hRbc42K0QXF4iGnpCILbvhvF88QJud5jsymUwoAbL1sZKC7stRfVGtGjkFo2GvAysPubMAMSh4u4TalN7Z6oD5fB4WHB4tFQ62ddaOif1tN4SNDOzTCJAhLGkQmkd79yFl23UFeUEkVsaIyjl0CeJd243MusHztfdkE4RcF/JGNGFm15OVDli/1pu1koRtd6c8jHZVW65ovUH7vVY6sZGEPYSHdYfMRLTCucVEjfYYztFoFKSJ29vbYCAZ9+h5IMh1tVptrbPN1vja6gk7Nta7jJbzRV9n96+dP8ad8WDd0L15e3sbyuaix05G1zH5gZ/t43oIoUul0lppSHTR2gTNcDgMlhpvjQX4rjIv60EDFrP1Jq0FjMKWGdnaSLxfWyZmFwpEenNzo5ubG/V6vbAgOe8UiYDvHY1GQau1CRMOeIc8rAZqH0NjT/niXm0IjJeDLMD42vCd8bEkhddHyGxlATtuEEG0QsSWNkFq/J2EEJKAPfHNPnE4Suy8H9IFVjKwxf4kjGieyOfzwWOGGPC4IVFImXGPev28h81vk3d06rHmuXcMhvQgGdkD4/H+eJ2V3/BsSULb72JtIoFw7CeRAiQ5m81CWRpjw/UjMzH+rD17XCnfYec6euhTtPxv0760SUlbrcC4Rg3bJtjqFebeHguK5LGpcYl7ZnzhmZ+VvGA3afSwiU1aKosUjw2Li9WNDqKtGngMkKXNhDPpj1lC61FAaNbrtvoQkzubzcIZBK9fv9bZ2VmQR/b29tbCdN6PHnt3dxf670kylMtlLZfLkLiDQCEr7gHPjtDUXp/VKW01AJ6wDddttJDL5UJ9dK1WC2VjtgWX+2HzUfiOHMJc8lpbK41UY9t3bbkgiUI2uk3asLHxmOyBJvb1JI7sYUCLxSKUJnKQNeM1m80C0dooQnqIIKznabvl6Ao8PDwMT+mwUZdN6mHIog0xtiLDenuMMRES0gIRj/RwtOXt7W3I2LN2RqOR0um0arXamqGxHjbHJjIHVjPd3t4Oey+qddv6cGvc7WdH97aV0JBsbDnmu8q27H63uQ04AoljkzxhwbWz9jZx0Y+F2M9esBNnYcNztEoGklAEb8t6QO8aLDaEffqB1XUf83J5H+2YZJQJvfBGmGAWwng8VqvV0sXFhc7Pz3V1dRU2E5qtJUUm3pawkaWG5MmKs/Bt1pb2U6t/0/gA0UoPJ6+RTWccmA87jla7q1QqOjg40P7+fqiPtosZ0p3NZkGPJOxNJB7aa/l8vHLaZHmczHK5VKvV0mJx/1RnNpLVWiUFT80eJdnpdLRaPRzKbTcxui1JIw7n4VD2i4sL3dzchJDfZult3S7jbDcx64a1VS6XdXBwEGrR8fYtydlORqu528Sr9FByiP5LNt/KBbaKR3oIsTudjtrtdvB0Mba2IoNxsiTIezkCEW+XubTVBax9pBdeC5FHjW10n7P/mB/m17b/Pka8UYkCKY69yI+Vg/jeaBXJY47fj43YmiMkfcOS2WyktYo2o2jDLltCZEuPot9nN4/VQgnNrBa4iXSx7jzdlXNVmSCrMSIFsDlZuGhKfBc/NnMKadkGAMiQ19mNbr1vm+Wfz+dqt9vfqInl3u3CsuNvD6Phc2xhPWca2HMQpAfDaQkF4whJ2ooBq8mR+KrVaqFECqLsdrtrda3RBB3vJRHJ5qIULupZWS90d3dXu7u7wSMjiUSLs71u20CDF8p6Q/uzITQ1ulSjQIRWn7UEYxtTuE9eT8lUu90ONeucaUGtMqE8c231YIgumnS2lQ1WK+W19v2z2f0Jc61WS/V6PRAq82yPNJUU5JutrfvmHXRd22UGovKBbfYYDAZrpLvJ67V5E5wMe6qgNSqMgSXcaHRsPf64EJuni4dmH0MCbIhvPd4o8RIG2YOugc0o41UQzuIhrlarkMm1XU92o/LeWq2mZ8+e6f333w8hGtqnJR9LQPbszsXioVyNciZb0yutP0ivXq8HkqZukgSIfS9EjTeBVEKbY9QTIyy1XUTcJ4sNLxmv1Z4eRjLIVmdEN060KoDCdeYdjc3OCwTHv22nFHIFWXTbvWbDUEjMfiYbzCabbH011xDtiLPSDWczU2Vg7y2aL7BS12Lx8PBV7gGSWq1Wa95gNJdBtUK321Wz2QxnJ5+dnenq6iqclZDL5b6RlOTaqWYgUSc91IXzoFd7OLmNamhWwAiRZMK5sBKb3XNWN+c841QqFR4Sy761685WYBApMC+cs5FOp8OaiUqI1jsnorPVMdaTjxLupi5IzqiIC7GRbiaTCWVWdAWRzbVeErDEazOdWDd7RF20hMc2LuANQhx2g0Mu0Qy9TZjZ7h0ev41Hw8aSHkJCxHwK2QnT8RbxTjAAeGHz+TyQPTW6aJ8UzxOyY4Q4tpFNbUuUuAdCUYrIuUfpQeuWHh7hTWvvbDYL9Y48i4rxfUx3Q9srFAqq1+vhbFt7jdlsdu3MVztvjHepVFrT8Om8itbA2gTW9vb2mjZqa3ejbdqsARpHyuWytra21hJu6KpRb92uE+nB451O758ld3FxoVwup+FwGBKJtlohql1aJ4NE2M3NTThw6PLyMhz0BLEXi8U1PdRGN+l0Wjs7O+GkOdrIDw4OwnPTrPG30UOlUgkHs9uGG4w5XjPvoRySChmaUDBom5waq2uzBqzXbcvnLJFa4yY9yCnZbHZNqrOEy+utIwbR8kMjCnsjDsRGuvl8Xk+fPtX29naQC4bDYXiuFZMLATKI9J0DiMZ2P9nw3HbKMHHohHhdtpUyOql401dXV/rd736n2WymJ0+eBKKlweHg4CDU6XK9nU5H5+fn+vrrr9VoNEKGdbVahYVkS5asscjl7g+itmEb3gCeAMkOxiDaxoxXyBNPSd5tCmmtAapUKuGJCuiBnILFEw3wvgkHOecgKnvQ3npycqJarbZm2NhUeFZUMcxmDyddUZLH/UoPVRT2nF2Mhe08KxQKoUKAelL7PZASsgudWTb0xztttVqazWZqNpvB+yJJh1GzZ1zwlJDpdKrb29twaI09Hzh6XCXzYDX31WoVZCqSWhgt3mMfhUSFgj38xe4jIkwOC6Khx0Y9rLHl8v5Brzc3N0HqYL1Tv5tM3p9dzHxyohf3fXZ2ps8//1yXl5drJwHaNWe9S9sUxAFC3Auyga1ygKDxdJGBmIPoWSrWq6ZcjvngviuVSkhSxoHYNN1CoaAPPvggZOQJxdrtti4vL3VxcaG7u7u1bDLAi5MUSNjqh0ySXRx4x6PRSBcXF2o0GsHzxYvK5XIhiWLDERYQzwj76KOP9Od//uf64IMPtL+/Hw6Ytr3j3Menn36q3//+9+GJv6PRSAcHB0H7JbyxmjO6Y71ef7Sg3NaLkvTA42fR2fpbSaECwSYCrYeKd1mtVoORu7291atXr/Tll1/q7OxMd3d3IczjMxk7NgJGo1QqheTbs2fPwsEtVqdlPolg6LgajUahbXm5XIZzAfBCqEIgusCDWiwWgdCiTSUQOF4Y17jpMUXSg6babrd1cXGh0WgUHj9kz2OwpYQYHgzd1dWVzs7O9OTJE3344Yf68MMPwxkLnBEQDZfp9KLxBnnD1uvaaA1Stk8QoVsxGiZbqSVaXcB6TyTunxTMYeiXl5eaz+e6urrSYrHQwcGBjo6O1iQZPiORSOjt27d6+/atPvvsM3355Zehw83Khta4sIb4TkiU1yOJ0PFH/S26Nok4CBtDa49wtNo7Dg0nuNmmpkTi/qknyGFxaLuxero8C4yFQPaZcMyWJZGRJEuKHoeXjLYJsfzyl7/UX/zFX4Qn6L5+/Vqff/65ms1meO4UJJNOp4OkwaK2IEydzWbBm9za2tLp6amq1WqQSdhAJNRevHihTz75RG/evAmeIId7/Nmf/Zk+/PDD4KWx6FmINkyyQLYgRBqNRuGJuTwVgcw7GXvp3shhIDAOm4DHVqvVNJ/PdXFxoaurK/33f/93IB7pwdvEc7QlgFRZENLX63UdHx/ryZMnwWOxOiaeLxom3h0yAdcvrR9qbT11C5uw4ppYc3h2hPc7OzuhJRvPjTnnnriGRqMR7pc6cevV0/hha0N532AwUKVS0S9/+ctQRraJcKX18y4ODw91eXmpQqEQkrA4EGi/hPM4KUgjaLZR4rU15lbeIEJYLBa6vr7W73//e3355ZchMsxm7x+I+uzZM/3lX/7lN2q+qRr56quv9Pnnn+uTTz4JXnIUtqKBfWUrkzAo29vbOjk50ccff6ynT58qnU6r1Wrpd7/7nT799FNdXV19gx+oTLLlYrbULpG4f8bd8+fP9f777wfHDJLnAZ1xIVZNd29vL3RYkdiqVquaTCbBG4WsbM1oNNtoy79KpZKOj4/1q1/9Sn/7t3+rcrkcstkXFxdhMiBpkhHSgxW0SSL7PYBOp9VqFc5esIXjkCVVCyxmQuRGo6G7u7sQnloPi39vKl/Dk0PLpQTJ6nl4HHhL6JI7Ozva29sLBwa9C3iT1M1CgkQXADkCrwCSw7MkfKM11Z59G00cMUbMD6RFIo6nJdukl9VA7TzZxKJt/+Z6OQiGUB9dc1NCCzKx97C9vR0Ig7GysgIyCOCQmuVyGeqRo97+JnAWA1UdNkGFYeGa7TqwFTpR/dqO06Y6WCqI7u7udHNzEyokWJOdTkfNZjNo67ZZYj6fBy2daMBqo5v2E3oy826f0sF+LpfL+sUvfqG/+qu/UqlUCo5Zs9lUv9+X9PDEiei9WOmGfxNB8fw3HD/WCsnbuLC1qWTK4Ad7RCbhq6SNG3BTDWS4yHe4/DZ5ZjemzThHS6bsd9vf7/oOu9k2lZjgkdqzCCyxogt+l2aOKGyJj9VHNyV3rDdmPbnvCiKNaGLTfn703rnX6Hc/lnB77N6iVRH2e78r3nXN36X43l6TrTWNzmn0tZu+15Z1fR/YionHShtxRqxW/13ubRPw8G2TgJ1TPNzoWmLerPH9LnvJ/uZz7H1tqivHoD7W7LDps/geK21sMrSS1sj6B8KjExEb6TocDsf/IDxKuj/JI9g34dss5LfhMQv/f/q53+U7vsv3/dAC/bvu64f4rj923OJIRMSNn3Isfux5/i7f9X+y7v8Y/Fh7+U9lbbqn63A4HD88HmX4H1TEcDgcDse74aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4YkfqWv2/FchUOh8PxPwTu6TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgR/z97YW1s9ZXOSAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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y3wK02+0YGRnB5cuXUSwWsbW1hUajod4dNeHt7W3kcrlTXTnGd6RbIEZtkn9cLhd8Pp8KFFIQnQdmkOhBSfp2+wl5/X5Oi3xTCcjn8ypgRBeD1WpFJBKB0+nE/Pw8Ll++jJGRkZ5FrG9o3Ajq9TrK5TLy+bxSPhqNBjKZDLa3t9HpdJQrpFwuY3t7Gw8fPsSzZ89QKBQwPDyMsbEx5e5JpVI4OTlRPvDR0VHE43E1rhxnY7Cx37Pmcjmsr69jb28PhUIBHo8H3W5XZS3oY8cgq8fjeWsWJt0i1Mr1d2PcxAD0+HVPe4+MVSQSCVitVmSzWXQ6HQQCAfh8vlPH5W1hmqabyWTw6aefIhwOKxcDNR/+MQ7UaQ+vmx80Y/UAy9vCbrej0+ng+PgYlUpFabu6u0NP0+l2uyiVSiiVSkqrGAQnltGXzQW0ubmJUqkEp9OJYDAIr9eLZrOJQqGAWq0Gp9OJTCaD4+Nj5W+mrzKVSiGXy6nrckFXq1Ul4CmgdH8Y78vv92N2dhb1eh0ulwuHh4fI5XI4OTlBJpNR2q7R5DdiTPfT37G+4XAsmB5E3zyDImdZLfrzMZ2QQoD+8H5zTN8IjJt5v++o1+tKsNXrdUQiEWXxxGIxXLp0CTMzM/D7/T1CAIASto1GQ7kRqtUqGo2G+v52u62CQ+VyWfm+0+k0VldX8fXXXyOVSqHZbMLtdmN/fx8OhwOVSgUnJydoNpvw+/0qsEWNUReUZ/n12+02isUiisUiAPTMbz6PLuz43e12G3a7/a0qPJybXG9c62fNuX6yhH9arRZyuRx2dnZweHiIbDaLmzdvYmJi4q3c91mYqukuLy9jbm4ObrcbrVYLDodDmeZ60EV3+usDyEVBDZNmM31XNDlO2+m+CYVCAc+fP8eVK1eUycbvs9lsiMfjWFxcxPDwMPx+PyYnJ5U5fZoZx0ml/7/T6VQCh1FxACgWiyrFhUKEuZHUbqkVlUolFItFtFot+Hw+WCwWRCIRtFottRC5+F0uF1wuV48GA7zKSw4EAsqfzGyFdDqNfD6vckR17cn4fMbosPF9ctO02+1K6NKdwefUXTCDoKnOhU8/N1OmmPVi9Pfp90UzHuhfgcXnpAuIAdtoNIpAIAC/349AIACPx/Oa1kVXErNr+Flm79TrdeRyOeVisNvtGBoagt/vV+93b28PqVQKpVJJ+Xf5zMzwIVxfFJbGeTYI3qvD4cD09DQWFxdRKpUwPj6OeDyu5h/fY61WQyKRwPPnz5WQfhvo2rjD4VDKCa0DZg4Z54W+qejX4MbBVL9ms4lMJoNarYatrS3k83n1mT8YTbdarSKZTMLr9SIQCMDlcsHj8WB4eFj5w/QEbODVAtV9g9zpvF4vwuEwwuEwnE4narWaCtS9TaFLU2t5eRnj4+NwOByIxWLweDxot9twu91YXFyE1+vFyckJ3G630nT6LW5eU78/feHTn0kBC0BlfVBzpvbNqDd3bi6IarWKer2u0nwajQbC4TBisZgykxnA40T0er3KVKbWyKBMNpvF/v4+9vb2VHoYFzvwSmM1+ub5PLolo09qClwuKpvNpjZO3t95s1C4IBuNhroWNzA9ek8tzDjPdG130ELmc1LI6WPJoMzExERPZok+npVKBZVKRW18FOLNZlPlNK+trSkrkGvE4XCo96pvdNQ8KcDb7baaHx6PpycPdZBLrt+87HQ68Pv9uH37NsLhMGq1mkqZpMDqdDqoVqs4ODjAl19+ieXlZeRyube67vicHo8Hfr8fbrdbpU/yPejyYZC/mnPM7/djeHhYPUOpVEI+n0cymXxrAcDzYJrQZd4mTaehoSEAwNDQEDqdDlwul4pQc9EZtRJOEpfLhdHRUVy6dAlTU1PKN7O7u6sG7229fJpaa2tr+PTTT+FyuXDr1i0VNXa73ZiamkIsFlMmrNPphMfjUYE+Y9BGfy7jzgxApYWFQiEVoPJ6vQiFQvD7/T35k7VaDblcDsfHx69phjabDblcDk6nE5cuXVL3ZrVa0Wg0UC6X0el04PF4EAwGVS6vbjIeHBwgkUhgb28P6XQa5XJZZQYAr4oudMGroy94/Rn577qLiEKW2p8xIDJIiBurkigUdH+j7oY6zfTsp+kY3SCcFxSAnU5HLeqxsTFEo1GVTkUNmwu8Wq0qpYFuFOBlWhuj6sx5pkVHzZ2C1u12Y2hoCIFAAA6HA6VSSWVVWCwWBINBBINBuFyunjnH96PPu35zk/PtwoULmJ6eVmNIa4j53wcHB1heXsYnn3yCtbU1ZV29DfS1HolEMDk5iXA4jE6ng93dXTx79kzJCuNGwvdM1xmt0+HhYQwPDwN4mcVQLBaRy+WQzWbfmkvkPJjiXgBeaQfcxZvNpkpricViiMfjaDabqFQqOD4+RiaTeS2zgQvD4/FgdnYW3//+9zEzM4Nut4vd3V2llXInfFv3X61WkUql8OTJE+VCcLlcCIfD8Hq9ypzUBYEeMNGvRcGiZ1pwMutR+JGREYyNjandOxqNYnx8HENDQyiXy0gmk0gkEiiVSqhUKj0mq57QDwD1el25CrjZAa/KSIGXuah6ahHQGxHmPfdzAenPpws7XeByPPr5dSkU9WDXIIHrcDiUO4TmOoDXFh/HV89a0LVVXcDqwp+f7+fmIroLQk85M26i/F26D+i/9fl8cLvd8Pl8AF5agW63G/V6HaVSCdVqtafQhK4Xl8ulrJLJyUlMTk7C5/OpfOxMJoNO52V1JIs79E2J98lNSff3Gjcau92OQCDQsxkxQM0NeWNjA/fv31d+5retLVosFvh8PkxOTuLq1auYmpqCxWJBLBZDo9HAycnJa0oW8/6ZqTMyMgKv16vGggpFo9FAoVBAsVhU461f57vE1OIIAKpEjw7/oaEhRKNRTExMKFfD+vo6Hj16hP39/dcqSyh0JyYmsLCwgMnJSSVoUqmU8re+TZgylEqlsLq6itHRUQSDQdUDQU9A1wUPn5N/uPCY/kQNc3h4uKdc0+v1YmxsDBMTEyrpf3JyUuX/FgoFpcXSctBdELoW6nQ6MTExgcuXLyMej8Pv9wOA0pzoD6evi5own4dmMf1ounZBYcB7p4Dhu+ZipvZKDVb3s3GB0M8MQPnK9Q2LQicYDCISicDn86FcLiOTyahqMF3LohB1Op3K1Gd6oX6fur9PD4byu/lzugL0Ckr6G4eHhzE7O4sbN27gypUrqhCFqYscX36nz+dDIBBQOd9WqxWXL1/G+Pg41tbWXkuP43dRoAQCAczOzuLSpUtqPgwNDSGZTKJeryMej6tURt4330GhUEAul1M57XRDcCPT/+imOq/BAN/e3h6ePn2K1dVVJXDfVoqm/g4pPCcmJjA9Pa0Ca9vb23j69Gnftc5Uups3b+LSpUtwuVwoFArY29vD4eGhyhahmwx43UL7LjFN6PKh2JSGL5BqP00Zi8WCaDSqCgT6NdIwmhEAeszTt5mjS5rNJorFoiodZUkstV6g15TWtSaaovl8HtlsFicnJ+h0OqqwgZFuCqlgMIjJyUmVF2u1vqwoi8Vi8Pv98Hg8yOVySvBTKOjfTVfCxMQEfvCDH2BpaUmZo9SC7Xa7CpLpC4+fpU+RqVvFYlFtmgxYGceAaW9cuN1uVwUEC4WCGg9qrcPDw8q1QbdJLpdTgTSOKxff3Nwc5ubm4Pf7USwWsb29ja2tLZUrSyHHQCtNbbvdjnK5jFwuh3w+/9p9DA0NqXdK32+z2eyZY/V6HYVCQQlum80Gn8+H6elp/PCHP8RHH32E6elpDA0N9WTTUHhxbLlZc2Oz2Wy4cuUKfvjDH/YIXWPAka6FYDCIsbExjI2NYWhoSGW1cOynp6cxPT2tXAzcwBgDODg4UDnloVAI4XAYgUCgx09s9GNTmWC64Ndff43l5WVsb2+/VbcC4caoa+V6imO/3H7OfY/Hg7m5OfzoRz/C0tISOp0OdnZ2UCgUsLu7i1KppDKAzNRwiekNbziZORk5gb1eL0ZGRpSzfG5uDi9evECxWOzJSGi328jn81hbW8PU1JQyw3Z3d7G7u4tsNtvTeeltwoDV+vo6IpEIpqamMDk5CeB1gUstU1+0fMnsLzA8PKw0OGoW1GRmZmZQq9VUcQC1In1D4SJ0u91q8lBQ2e12hMNh3Lp1C3fu3FHmJjUffi8XFYWkUXgPDw9jenoaCwsLuHv3rvKZ8n04HA6Mjo4iGo2qHGB+v81mQ61Ww8HBgSrhpZChwKaVEwgEVKqc3W5X+cDMoojFYqrclP0FqtUqZmdnEYlE8Pz5cxweHqpNmlZEPB5X95bP57G3t6c2Zr43Bi+j0Sji8bjKrtEthkajoYpAmG1Dq2J4eBgLCwuYmppS71T3k9Jvq4+1vlE6HA6MjIzgvffew5MnT5SLjIKMLhW+a5av6nPB7XarysWLFy9iamoKgUCgJ3tDd8uUSiUV/NPdFnpQ05h1ArzcePb39/H06VOVj/62BS7HjVkiOzs7iMfjygp59uwZ1tfXkc/n+7YP8Pv9mJubw4ULFxCPx9V1ut2ustoYbH4XmO5e0CeKxWJBsVhENptVKUjUsOg71X1KAFRQYnV1FRaLBel0Gj6fD3t7e+dK1v82MKH95OQER0dHqFQqfQsMdF8gBYzFYkG9XlfFG/Td0qzXF6rb7UY4HMbCwgIA4ODgQGkpxWIRmUwGyWQShUJBpffQTUAB73K5EI/HcenSJUSj0Z7KPkLtC0DPM+jPwWeo1+s974k/t9vtiEajuHjxomoERO2kXq8jk8koK4R/qEG53W7lu6avulQqqYVPTTQQCGBhYQHvv/8+rl27pgpFGo0GRkdHVYMX+uZ0f7keJHS73ahUKqrXAt+p/hmfz4dIJKI2JG6GxWIRnU5HVVVywXL+sv+Brh3q42xMxyN8HzSJL168iNXVVVV5BUClfjkcDrTbbeRyOezu7sLpdCISiaj3xE1jYWFBPYP+LilcWTxCi8Tn86nx0QObxgwAzvVqtYrDw0OcnJz0BFXfJnRnsJDB7/erMvSvv/4aq6urKnOHcBx8Pp9y2dE1yI2M1hrdUcYgthmYrunqPkeaa3t7e3j27Bk8Hg+i0ShOTk5QLBZ7gkE6TLF5+vQpyuUywuEwyuWyStb/LiYB0SPBnKxGDVEPwugbRq1Ww/HxMQ4PD2G1WjExMdETyDAuEObWdrtdpFIpHB0d4fDwENvb20gmkyrPkONEUxJ4WZrJHF29LaEufPVsAKPA5e+02y8bhDx8+BAvXrzoSXnSg3wzMzMYGhpSvSMKhQJOTk5wfHyM4+NjlEolpSHrlgs1Z5ZRp9Nptbj0ZHtmrIyNjalqL/o6j46OVBoQrQl22NLdI7qGrvttqfkdHx8jFArB6/Wq9CKmN9IXurq62hMkLZVKWFtbw/LyMq5fv/5abwv+rbsbgN70Orqf+I78fr/KfgCghDmrqRqNhtp8p6enEYvFMDIyglgshpmZGZVGaRT+VACYScRKLBYFULD203CpsTM3nvf9XQorZjEdHx8jkUggk8moEnkWghihJVcsFnF4eKjaDjx//lxV1/UrIzaTd9LEnBPeYrGo1JMnT56g3W4jHo+jUqng8PDw1FZxrGqzWCyqyuo8SfRETx96U/OIuylNM6NWYFxcNJsfPnyIzz//HNvb2/D5fBgaGsKVK1cGfgc1QQaMdnd3sbKygq2tLeRyOTXpaHpSQwRelsO2Wi0cHh5ic3NTmZ/M+zRqNMaABN9RLpfDvXv38PnnnyOTyfRYEdRyufDtdrvSfo6OjpBMJrG7u6s2B5r1FotFCZqTkxO1EZycnKjKN7oJbDab8qPSB60H3ijk2AiFQpeLj89SKBTUd+jN0vn+2QOBfmiOq8PhUCWiJycniEQiqtkQP5tOp/HZZ5/hypUrqv2hUfDqebm09uizZNvKzc1NHB4eotVqKW2Un2f2j25pHRwcIJ1O4+bNm4hGoypar/cIMVKpVLC5uYlHjx6hXC7j8PAQzWYT77//PsbHx1/L8NDnNOcks2BOq7bsh77J0z1zFozTsBqSm/JZsuHw8FApcvv7+3j27BkODg5UTOK7cImcl3ei6QK9DV/Ys8BqtarKEPprOAn6vSBqSHqV2Hkngn5dYxrTIDjxaM7rLoFBz1qtVrG5uYnf/va3+M1vfoO1tTWUSiXlP7t27ZrKUuj3XRSotVoNGxsbqver7he3Wq0YGhrC0NAQnE6nEsatVgt7e3tYWVlBp9PBhQsXeiLrRg3beO/1eh3b29v47LPPsLW1pa5Lf/XY2BiuXLmCubk5DA8Pq0BVIpHA1taWCtj0S8nhu0un08pdUS6XUS6XX8uz5Tzhd+sCjALCmC6npzcdHR0pdwLNS13rBV6lNKbTaeVG6nQ6anMdHh7G/Pw8rl69ir29PXXPfJatrS188sknmJ+fV4n8RsGnp6npY7C/v4/19XU8efJE+Zx1H2uj0UA+n+85XYMWTKPRgN/vx5UrV1Qw1KgE6PeZSCTw5Zdf4sGDB6pT3Pb2NlKpFD7++GPMzc2d2jKULiVuSHp2w3ngvNYtxtN+V4+HMC4wKKeWa5qpo4lEAgBweHiI/f19FAqF1xQzM7MWyDs9rkfPW2WEWw8WcCEbj+Ug1CLpb6OPhulB5/l+feGdB04aLgT2ve13bQq9X/ziF/jXf/1XrK2tKQ2uVCphZWUFy8vLuHLlCqLRaN8GKQwo7OzsYH19HUdHR0og6AGvcDis/KrValUlyzPoSP9dMBhUbfkGaUP8fvrPvv76axSLRbUIKHC/973v4f3330csFlMpe6urq3jy5AkODw9RLBZVlZg+xpzorOqq1Wo99fQs0KBJywo9pk3p16LgZeoTj5ahcOh2u0pD1M14apu6+Uw3A4U1A390I42NjeH999/v0eb5XYVCQY3V7OysyuboN390DbhSqWB7exsrKyvY2NhQzWXC4TBCoRA8Ho8qzqEPn9ZGq9XC0dER1tfXsbOzg1u3bvXMG13wttsveyQ/fPgQjx49Ug3lc7mcMtsbjQb+9E//FHNzc30FN99VPp9/rVPbeXjT9UYhylS/crk8sA8H3zddQ6xSpTVF/+937RI5D+9c6AK9bfCcTidCoZDKxdR3rn4mBc0zfZKdd/c9z25rhIKqUCggmUzi5OREpXwZJ2Cz2cT29ja++OILbGxsqKwF4OUkyWazeP78Ofb39xEMBl/zC9NnuL6+jgcPHmB3d7enZy0nsNfrxeLiIj766COEw2Hkcjlsbm5ifX0dh4eHSKVSKkXtwoULA7VbnXa7jf39fXXUjl5txxML3nvvPUxMTKDb7eLw8BCPHj3C3bt3sbu7qyL8vE/9uSjwrFar6ouq9wlwuVzq3VHAR6NRFUii0OF/M4OCPnLOE5qx1GKZqqibtropTe2ex/Ow4o/ZDfF4HOPj43jvvfdwfHyszF5ed3d3F/fv38ft27cRCoVObRXI8SiVStje3sba2ppqHRmLxXDx4kXVHjSbzcJms6lG/brrql6vY3d3Fw8ePMDVq1fVET3GYB2bzj9//rynepHXWV9fxxdffIFbt25hcnLytTPEKOSPj4+RTCZVn4XTNu5+6BvdaVC5AV7GfihAB2m5TD+cmJjA7OwswuEw2u2XRzJxzr2L9LB+vPMj2HVzEAD8fj+mp6cRDofRbDYxOTmp2tnpfkyi++SoLRkDWEb0wNGbOtP5OfoAj4+PEYvFXmskTqrVqspjNN6PnsPK9DDdXC4Wi1hfX8fvfvc7rKysqLJdI8FgENevX8ft27cRDAZRLBbh9XpRKBSUiV8ul+H3+7G4uIjp6elTi0joA/3yyy9x9+7d18ok9YKDYrGIVCqF5eVlfPrpp6ozGjMP9AAS37P+33rqFQAVvGJXNa/Xq/zGTOWia4AC1OVyYXp6GvV6Hel0WvU4YE4uexYYeznomrPxnqrVqgpAMsXu5s2byoes5yZTqGUyGdy9exc3btzA1NSUyiwYNMYUmF9//TW2t7fRaDRUNse1a9dw6dIlDA0NKX/mw4cPsb+/33MdWk2PHj3C9PS0Erp6U24KZz1rw3gvnG+Dqsr4jOwyx6Dfm2i6hIrUWWvUYrH0FPwMykqi331xcRG3bt3C7OwsHA4HstmsivcY5cy75J0LXeCVadduv2zAEo/HMTc3B4fDgZmZGfh8PtTrddXJSB84PQpt1Hb7mTLMi6SQ5GQ6L6xyYnoNv7cfXJxDQ0M9AhWAStVh53p90TPN5eDgAF999RW++uorFcQyYrFYMDQ0hLGxMYTDYWVajY+PIxwOA4AKMq2uruLBgwdYWFhQWRf9TMhWq4X19XX89re/xcbGRk+1Ea2Ozc1N+P1+1eJxZWVFnZ7A++Tz6Mn5xneivz8KUVYcxuNx5U9lapjuD+T79nq9GB8fh91ux8TEBHK5nMoNpkaqlzDr3228B30T4OfW19dht9vVqRkHBwfqeB66M3j/Gxsb+O1vf4tbt24NPK6HY5xKpfDgwQOsrq4inU7DZrOpJk7xeLyn4XwsFsPQ0FBfIddqtZDJZPDVV19hdnYWwWCwp+k+x5wtQlnNp2v7elezfves3zszZb7Jydd6JzngVUaJjq7lUjb0Kw3nvXu9XszPz+PDDz/E+++/r9rHbm5uIpFI9GzUvw+YLnSNQTEuINadNxoN5dPiQYBWqxUnJyfKl9bPr6NPIP2l6ZosNS/6jfUsivO8EF5brwoKBAJ9A2p6tJftJ7nrUlBwcwmHwz2mKANw1IIODg4GThpjXiiDb0whYoMPljEvLy8rM5RVSMY0sXq9jo2NDayvr/e4RAAoX/ba2hrK5TIikYg6FaRQKLyWikNr4rTx1TMBWDxjt9sRCoXUfY6MjGBoaEgFMCkY2PyFpwt7PB4AQDqdVteihjToHvSAHf/Wn6FQKKhTZ91uNzKZDPb395WfUBfeDE6tr6/j2rVrPalf/J1Op4NisaiquljUwYwWpsXpQo0bTD8hR8HELKClpSXVVJ2/z/Gcm5vD2NiYskZ0Icpc3X7fQZ8p530oFFK/OyjQ3e8aTDujtai7gIgeb6AS0u/6VIDGxsbw3nvv4Qc/+AEWFhZUs39mQOlNmvoJbbO1X9OF7qBdH4CKfrMtISP3s7OzuH37NhKJBAqFgorkGh3zumtBT9MBXhUCGIXgmzbpoFALBoMYHR19rQJJhyYbe39S4+PEGx8fx/T0tCrP1bUSBjmYxdHPZcJ/Y8pRsVhUmsrw8LCqyuHkY87tzs4O8vk8hoaGBi4wRqeN8N663a7Ke7RYLGoR6xObi0XPkhj0DAyA6q4IClFmZvC96WecUVgw95JtFo2LWF+4/b6f74+/p8cb6EbhZ/UMCOPYAFBd5gZpge12G4VCATs7O+pwVh6hfunSJczPz2N4eFjdN3Ny2UVs0BiyWpPxD/4O5z7bM05MTChLQO+NW6lU1MZpDARS6NLq0NMl3wQqIgx6lstldR960ZQ+pkYLic/rdDoRCARw8eJF3L59W/Um0YOndK0NCqJ9E/fIt8U0oUtTXG8oomsIjFAeHBzg4OAA+XxeLTK/34+FhQXcunUL6XRaabpGLQN4pdnqJZYsl/V6vSqtir1i38Ts0AU5hfcgzaDTeXkYIs+C0/NHbTab0mh4kKHxGtxAqP3wyBpjeSoA1YMgk8kgHA6rwoj5+Xn8+Mc/Vs1NyuWy0gQH7e7UHq9du4Y7d+6oo3qMY0Q/Ot/pIH8bFyuj0Pws3xH/MA2P1URMu9JT2/jOmaXC/+dYMfmfFY1sBsOgHSvjdN8ezV29R3G/d8n5yec+zdVz584dXL169VS/Ob+H/SF8Ph9u376NDz74QKVtcUNNp9M9h7jq1+Qzszc1qziNz8E5G4lEVAUgN3Rmdezt7WF9fR137tzp6eesX4PrUddyzyu4qJE3m011knYgEFCKCYWv/o70fGp9nPnM09PTuHHjBhYWFtSz0xrb39/H4eGhem/Ga+jFIm+ac/xtME3ocuG53e6ewAmhWXt0dISnT59iYmJC7c4228tm3JcvX1ZHL+v9dvVr6LshFzPNUyaOMzLNPMfzYiwm0AWC8Vna7TZSqRSeP3+ObDbbIyi9Xq/qnESNRv8sn4u+zVwup06eZZqarhVUKhXs7OwgmUxidnZW9cUdGRnBtWvX0Gg04HA4kEwmEQwGVcVSv0AIJ+P09DR++tOf4uHDh8pPaxSqzJ/l32f50I1uIf6MgTn2o52enkY8HofP51OaHgNBFMQUuqxC01PT/H4/YrGY2mx42gJTjtjIhu+DHcYoEIzvksICQN+5qz/LwsICfvrTn2J6enqgFUGzOBwOY3JyEkNDQ5iamsIHH3yA69evqz4ZfDYeXa8fUc/5yGKFkZERTE5OYnx8XJ1npvu9+aysQItEIjg+Pu7xYafTaTx79gypVArhcLhv0Q87w+lz5U20XR6p1Gw2Vd9h9kY4Pj5WBS56kNUI13U4HMbly5exuLiIQCCAbrerKhvX19exurqqUiz7KQR0NbKE2yy+82/iS7Pb7fD5fHA6nQNN+larhZOTEzx58gQejweVSgULCwvqGJpQKISZmRmsr6+rI7/7oZu1dAVMT08jGo2i1Wphf39fLdTz+nO4UJgPqteqD3oWugcAqI5dwWAQMzMzuHPnDpaWlpTPlffB3GMmyM/NzcHj8WB+fr6noXihUFAbBoNuBwcHKJVKKv2MPX+vXr0KANja2oLVasXIyEiPK6OfpuJ2u7G0tISlpSU8e/ZMlWXr6K4hozDiAmW9P6v/9MIHLlqPx6P8t+wTGwqFYLFY1ELUm7Yb2zLSrxsMBpUlMzIyouaAw+FQaXN6FJzfr5/LxoWuW0BGv/8gjd7j8eDKlStYWloamKerjzkP/+x2u5ifn8eVK1eUL5ffx+IJlrTqc5EpUjMzM4jH48qP7/F4lKuF4875NTw8jKWlJeTzedjtdiQSCeRyOfVzuifoBjPC98XTpblOjae+DIJBYvZZYX663+9XipeeRz0IumNmZmYQCoXQbrdVB7+NjQ08ePBANQ8alLNPF4We6WGGu8HUhjdnNaLhoCeTSQAvzeZ0Oo35+XmEw2G0Wi1lbp/lAGfdPk2+SCSCUCiEYrGoDgU8b7qY/nIoHBhZH+TP5cIKBAIYHR1Fq9VSFVzXr1/HwsKCMvO4yIGXwpp+LrvdrhpSN5tNHB0dIRgMqt68NA3ZdvLo6Oi19DR2sLp48SJ8Pp86SoZa3WmRarbd1I/x0dH/36jBMmhJH6zuf9f/8HO0ANgDlilf6XQayWQS+/v7yGQyKihC6JKIRCIYHx/H5OSk6irGyLZuKhv9g/xv3UdMjVx3gRl91UaoMfM4mEFw4+Fcnp+fh8fjwfj4uGr1yd9jTCCVSiGfz/dUIbL5/82bN3H16lXVBIjukmq1qtwReuoYS8/D4TAWFxexsrKieuLSKuznnuB7ZRe8eDyOsbGxngyOQRqlEVb88aTqoaEhdLtdZLNZpFKpnmySftBdRD8/10Y2m8Xm5iaePHmCFy9eqP7CZ8mc824YbwvTTo5gEYPL5Tr1Aek7SyaTqkKoUChgYWEBXq9XCdOzfDD9/HYAVP7lm/RpYM9b5osycT0YDA68j273ZY/amZkZWCwWhMNhXLt2DUtLSxgbG1P+PvoL6fjnWW80hakdW60vT+gtlUqqxJbCkhOHZbSczDQPPR4PxsbG4PP5VMS4231VCcbUMf167XZbdf1iYQQ/pwtp4xjqApedsXg93V/H90MfLRtsHx0dqWAOA0hHR0eq8TY1US48Bn9YWXV4eIhIJKLS9Gq1mtpojaXCvH8KQY4Fu1Ppz6cLAj6/cTw4ZpVKRfnv9THle6ImF4lEEA6H4Xa7VZ9kXlPP6OH1dNcZi1RmZ2fVXNSLO9jilP5XpngxtjE6OqoCUKurq1hZWcHJyQlmZmaUe6IfNpsNoVAICwsLODg4QLVa7el7ex53HcecLRb1snTdLXIa9Md2u13Vf3ljYwOrq6uqrNmYeWOE98tn4Hv6rjG9iflZ6Tv8nVqthnQ63WMCjo2N9TS2Pgt9cXNCFgoFZZqfZ4D1NJeRkRGMj49jfn4eExMTqmJuUEZGt9tFJBJBIBDApUuXsLi4qPx1HBMK2aOjI9XMgz2GmZpGV0an87KqjIn55XK5p0sY8KpfMU1iXeMMBAI9+acck2731Tli+v1zURgDEPpzGseK30c/LgWr7s/lNZgfarFYVOc4luHWajW1QVIrMmZH6Lm/XGDFYhEHBwfweDzKZcAxoabG+aBnLNDvTZcEfbz9Ut4GjUen0+nZqHQrwjgOPDm4X+9a3Q3C+c6f83fZeD0UCmF4eFj5RrkB1Wo1pekHg0FEo1GMjo6qEyu4iQcCAZUb/Pz5c6UU6S4Y/Vmt1pfNniYmJjA/P49sNttThHKewDQzYKhQ8Zh53QI6C94XGwClUimsra0hkUiofh6nCVx+j+4eM4t3JnRPg1oF+ypks1lkMhnl9NZTYc4zWHqVF9PEzit0uaPqfX55wkA/5zsX8PHxsfK9hkIhlWdqFG7dblflvT579gxHR0eo1WrKHzszM9Nzqi1dHH6/X6UtUXsxJsMbU7H6LWr+t1F763a7SlAz+GhMVeIz8Nq0KPRG2Lo2C7wydenvpV+QWik1WpqsRg1vUMCDrikeL8RsCApfHvbpdrvV9Y35x9SeGaiz2Wx9c337BR+Z9cHTPIwuDG4YtDx0YavPBd0SoCuB7SnZ48N40gWzR3gqRCKRUD1+3W43RkdHsbi4qOavntvNoghmveT+/zHwo6OjqgeKMVDGoh/60Pl73HDP6ntCoctNXS+NP48ypZf5s2Iwk8kgk8modNLz9Fj4gxe6RDf7T/sdCgXm8hWLRaUFUfCeZ6AYoeSxNADO7VrQtWxqZMZsCePfDKC9ePECKysrSKfTmJubU1qTMc+SwcP19XWsrKyoaKvD4UA0GkW9XofL5VK+Uf3EWU5aPp/enFq//34FFNxM9PxFowBlU+1oNKoqAY0+UX2cdUHHFDHd9ATQYzWwDywAZLNZZa7qjU30dC/dR8wNl2evGd0Oem8BNqthhR57Gh8fHyvTnWPAd00Xg37wodEE5VgxsMVm7pFI5LXTIzi29Lv2s5D0YCr/Zg6vLnR5dBFjAgBUO9QXL17gxYsXqjiEfYhZrRePx1+7f66JbDaL7e1tFAoF1TeXPSQGCUM+F8dsUBGCEa5vAEqp6JemNghaaXQVMhtJ769x1j2cx43xXfDO+ume53eoJdG3RbOJwvcs9OosNgSn+XQeocvk63A4rMp1aRYx75aLHHg5keiPXllZwbNnz5RfTa9eMmpA7NzEpt9MdaLGOzo6qgKBTIeKxWI4OTlRQSCa55lMBhMTExgaGlJujH4LhsKin+ZIYU23yNzcHFKpVE/mgfFd8T4Y2LJarWpR8HfoqmHDnOvXr6v+yTwJgOl1um9Pz0ThmV5sd8mDKfVzxeg6oGY5NDSEixcvYmlpCV6vFwcHByqAZNSmmYbFeaJnMgzS8hkEnJ+fx+XLl1UaoL656nmhg3JbdUuEPk+a70xzY4oY54DeqD6VSiGRSGB3dxf5fF6lHTqdztfOBNPfG7XkdDqNvb09ZLNZFZzkhq4HIun+4+ZI/zLHj13IToNz3+12q7TQVCp1roILjg/ni9vtRqlUUtbLeYXpuxC4wO9J74V+6OZWvV5HsViEw+FQJ0Sc5TviLs4A2OjoKBqNhjLxz4INuhcXF1WDbl6TDVWoSRBO3r29PZVPTHPfaJrri5ELif0QGGjhhsPUNrau03OOgZdaCnMTR0ZGEAwGlQnLAMVpY0QN3jieHIPx8XHVQGcQupZJrYyCkMJaN8NHR0fVuWI0MZmlYFx0FFJM/+PR46yCY0MbFj/on3M6nRgdHVU+dZrqh4eH2NjY6NH2KNSGh4fh8XiUYOFCHoTF8qr/A7VcHX1TOsuEpvaez+eRSCTw9ddfY319XXUGo7uBc4A52dT8jM3iqSXzmc5KFWTP3mQyib29PcRiMZXCxvvTW18CQDgcxsjICFqtlmoezmbspz2n7trge+Im0s+VpY8nlYxUKgWfz6cEfb9iit83TBO6ejkuy2HP43PhJOQC00sVzxK6zOFkP1Sm9DC/9rSFxJzLP/qjP1LHATHnVg/GGLVW9rBlKTPTa/T8S94fBcnY2BiWlpZQqVTg9XqRTqfRarXg9/sxMTGBWCymfKt0lzByy4VG4cCf0xdHU/y0jYZjrP8+/03PDT3rXVHgeTwetckxIl0qlZQg0NPE6C4YGxtDLBbD5ubma+/XWLFGP3Cn01FnzDEIRvRgE0/OpT9Tb1TE90A/ZTQaVSlnR0dH6vfOmqsUZrVa7bX8WG46xnaJRqhgnJycYHNzE/fu3cO9e/ewtbWlTjBmQJW5xXz24eFhledMLZCb5oULF7C0tKTSynRNm4J5ZGQE8XgcyWRSFeDwqHI9NVIPTjN9jH98Ph+y2SwsFovKqx4E5+nw8LA6hYKbNdtzniZ09eyWSqWi4jTn1XJ11xvfn1mYWpFGzVM/hv004Us3AFNx6GbQTyIYBE05AGpXpuZD7eA0FwW1nlAohFgshnq9rnJqo9Eo/H5/T5cuToRCoaDyKgEgFAphcnKyp4GJrlnxLLRr164hGAzi0qVLqnWez+dDLBbD1NSUynrghK/X60rD0/2f6+vrqtxZP31gUD6u7tNrtVoqwNNsNlVF3cHBgTLVzwpe0he9uLgIp9OJTCaD7e1t5a+121+eUqyfTsCy6FgshtHRUVWIwfQjFtbo/RfoQ2QXN24Oup+QfQLi8bha2AxOsZkSU8k8Hg/i8bg6XZhxA2MrxX7zhK6Mw8NDPH/+XLVn1Pv/8n5P841ynu/t7eHx48dYXl7GxsaGavREwcD33u12VTks3RZ+vx8XL15EuVxWJxxPTk6qVpM8rFKfg6yOm5iYwNraGvL5PPL5vErfo7DW/f1+v1/ln1utVnWWHLNuzhJifKfMLqHywOufpRRRweG5iHonubOsEsoGbly0kszCtIo05qyGQiFVDUYznUK4n89MD2ZYLJaeUwD6wRfGbINyuawOVHS73Up7cblcpx7VXiwWcffuXTSbTdy6dQszMzMIBAIIhULKjDS2dyyVSnj8+DE+++wzrK+vA4CqtOGhkcYJzwnMKqqLFy8q35T+HPTbMqiYzWZV7whqhdyUDg4OsLu7i0uXLvWY9YPeD3NT2WSnVCphZ2cHy8vLuHv3Lp4/f642Lf4xTmx9Mc7NzeHOnTsYHR1Ft9tVB04WCgV0u12l/TNCTo01FothdnYWjUZDNSQCXvVLHR0dVWlP1Hyp2TgcDlVAwPk2OjqqumrxbDir9dWBoHNzc8jn8yrRPhqNYmRkBBaLRQU0Nzc31akKxrmij0e5XMbq6qoq/7516xampqbUIZMMFp32HmgF5PN5JJNJVWHI+c53WSgU1Mm21H6ZzTA5OakCi8YMEWYYGH3KnGfMAtjd3QXw0tUQiUQQi8VUZzC6NyKRiGo+z36/iUQCy8vLWF5ePlXLpYVnsVhweHiIbvdlg5pkMqmO32Kesx5w06FVoKd/6n79QfOcPmpaSLSEeXjpebInvi2mabrDw8O4deuWilZ3u111KGUikVDHhxizA4xNUowvgZOVEWo2jma2Qz6fx5MnT7C+vq58oJVKRQm8Qdpup9PBwcGBKiv84IMP8PHHH2N8fBzj4+PKN6z7nra3t/Fv//Zv+PTTT1EqlZTGeP36dfzoRz/qm2rERUBXSDAYVM/OvzkODFyk0+me5G+OGbUljiP7GZzlw6ZpB7wsA338+DF++ctf4t69e0ilUj0J71y4unuFz+Hz+RCPx3HlyhXcuHGjR0Oij7pfYUK5XIbP51MJ+9SGmabGMuGRkREVTGTmRTgcVoKBAbVu9+VpGmNjY5ifn1e+Qi42nszAd8Dxp4uAB4nyxOmjo6Meq4yf0bNCKDSOjo6wtraGFy9e4I//+I/x/vvvK8F7VmSe2h+LgLj56ilkLB5KpVKq+RN7M+txBz6b/oyDNl+r9eXxU8y42d/fV5uH3W7HhQsXEI1G1efpzuC1MpkMPv/8c3z22WfY3d3tOT9u0HNarVaUSiU8ffoUT58+VaXB7XZbFW+wUi2TySCVSqFcLve4epgCyvmpl3gbn48+49nZ2Z4AJPAym4Xl+GZgmqbr8/mUX4k+1nb75ZEwn3zyCb744gtkMpnXPn9WlQs1p5/97Gf4kz/5E4yPj6NYLOKzzz7Dr371K+zs7KhJoPss+zU36UetVsOLFy8AQJ3MYGwAbrG87HB/9+5dfPLJJ0in0+rzGxsb+N3vfoef/vSnmJycHDhGgxaEHvSwWq0oFovY2dnB/v6+agSjTzIK4FAopJrGnMd0otD0eDzY2NjAJ598gp2dnZ5NkBVNTKfS828ZsGHS/NjY2KkNsek6oklZq9V6mm+Pj4+rNo36keh0LzAuwJTCQqGgqrdarZbKPIlGo+oUCnYw4+f7jQutEovFgrm5OYyPj6u+woRNX5gWRxcPAOX7r9VquHDhAj766KNzvwO6B+LxuOo9wY1ULxMvFovY399HIpFAqVRS89oodHXO0uCSyST+67/+C5ubm+qdVqtV/O53v8OHH36I27dv97gY9Cb82WwWn3/+OdbW1s58RuBVdSr7YegphW63GzMzM/jZz36GDz/8EH6/H3t7e/j3f/93/PrXv0YulwPwal2cpwKOxSg3b97Ej3/8Y8TjcWU1l8tlHB0dqWPl/6A0XS6ksbGxnsMRx8fHkcvlsLa21vfU2LMGwWKx4PLly/jLv/xLfPzxxyr6b7PZ8PDhQ+zt7b3WxQtAT437IBeD/t08Bpw+UuN9tVot7OzsoFgs9hxU2e12sbu7i/39/YER437fp/8b/ZBMw2IKkd4Tgdqax+NR5ns4HH6jlnXMPKDrxxj4oT+cWgEzK+hbZO4oj3o/ra8DNS+3241mswmPx6P8rPRFAlD5m/xjzNOlP5BFL6yKAqDKa6lp8vOn5Z0aTWi99wXLoWmWdrtd5TIxClVqYOcVuMRms6mTIwKBgCrJpZXA98z4AX29/dwG54G/v7+/j2Qy2RMLAV5uIvoZefrn+G4Y3OZmBQxOx9KFNn+PY87nj0aj+Oijj/CTn/wEbrdbHWOUSCTw+PHjcwXg9Wez2WwqbnLnzh2EQiG0Wi3V3IdWpllYzniAt5Z3wQViNHM4oeiH+iZQG2KgBHh1pPZpjYvfJK2EAkKfWDr0KfXLT7Rarcqn9m3Qx6pfOTXHlQG603yIp30Hfe79xs347vi3vpjogniT7+z3h9+pf7d+L8YUPP1+jPd7mjUxCLpq9Dxdo+muu3Z0dOH8Td6B3tTIeH09oMVii2+robEHQb8gFP3Cg+Y9g2Bvup74eeO/c9PjHGKg8qxeCqdBv3W/ohVmg7zlYNrAF2Ka0BUEQfgfxECh+3tTHPFtk5kH7cJvk/NoE+dxVbwNznq2b/t933TszPCJmc27Govv+h2f9/u+zbz/pnwX6/n3ZW6KpisIgvD2GSjhzcsIFgRBEEToCoIgmIkIXUEQBBMRoSsIgmAiInQFQRBMRISuIAiCiYjQFQRBMBERuoIgCCYiQlcQBMFEROgKgiCYiAhdQRAEExGhKwiCYCIidAVBEExEhK4gCIKJiNAVBEEwERG6giAIJiJCVxAEwURE6AqCIJiICF1BEAQTEaErCIJgIiJ0BUEQTESEriAIgomI0BUEQTAREbqCIAgmIkJXEATBREToCoIgmIgIXUEQBBMRoSsIgmAiInQFQRBMRISuIAiCiYjQFQRBMBERuoIgCCYiQlcQBMFEROgKgiCYiAhdQRAEExGhKwiCYCIidAVBEExEhK4gCIKJiNAVBEEwERG6giAIJiJCVxAEwURE6AqCIJiICF1BEAQTEaErCIJgIiJ0BUEQTESEriAIgomI0BUEQTAREbqCIAgmIkJXEATBREToCoIgmIgIXUEQBBMRoSsIgmAiInQFQRBMRISuIAiCiYjQFQRBMBERuoIgCCYiQlcQBMFEROgKgiCYiAhdQRAEExGhKwiCYCIidAVBEExEhK4gCIKJiNAVBEEwERG6giAIJiJCVxAEwUTsZ/zcYspdCIIg/A9BNF1BEAQTEaErCIJgIiJ0BUEQTESEriAIgomI0BUEQTAREbqCIAgm8v8AFC4Lt2xTEyUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "ub = np.ones((Nx*Ny,))\n", + "\n", + "# insert dummy parameter bounds and variable\n", + "x = np.insert(x,0,-1) # our initial guess for the worst error\n", + "lb = np.insert(lb,0,-np.inf)\n", + "ub = np.insert(ub,0,0)\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "ftol = 1e-5\n", + "for iters in range(num_betas):\n", + " solver = nlopt.opt(algorithm, n+1)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_min_objective(f)\n", + " solver.set_maxeval(update_factor)\n", + " solver.set_ftol_rel(ftol)\n", + " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lb = -np.min(evaluation_history,axis=1)\n", + "ub = -np.max(evaluation_history,axis=1)\n", + "mean = -np.mean(evaluation_history,axis=1)\n", + "\n", + "num_iters = lb.size\n", + "\n", + "plt.figure()\n", + "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", + "plt.plot(mean,'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('FOM')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot our results and see the resulting geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the performance of our final structure, we plot $|𝐸_𝑧|_2 $ at those three frequencies again. The enhancement is universal across all frequencies now." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "frequencies = opt.frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2\n", + "\n", + "plt.figure()\n", + "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use a CW source at individual frequencies and plot the fields." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution)\n", + "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", + "source = [mp.Source(src, component = mp.Ez,\n", + " size = source_size,\n", + " center=source_center)]\n", + "opt.sim.change_sources(source)\n", + "\n", + "opt.sim.run(until=200)\n", + "plt.figure(figsize=(10,20))\n", + "opt.sim.plot2D(fields=mp.Ez)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/Bend Minimax.ipynb b/python/examples/adjoint_optimization/Bend Minimax.ipynb new file mode 100644 index 000000000..1253d83df --- /dev/null +++ b/python/examples/adjoint_optimization/Bend Minimax.ipynb @@ -0,0 +1,2107 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Minimax Optimization using the Epigraph Formulation\n", + "\n", + "Our previous tutorials have demonstrated a mean squared error approach to minimization. In some applications, it's beneficial to perform a minimax optimization, where we minimize the worst performing error over a set of objective functions.\n", + "\n", + "Because meep's adjoint solver can calculate the gradient at multiple frequencies points using the same forward and adjoint solves, it's relatively easy to perform a minimax optimization for our bent waveguide example.\n", + "\n", + "Most minimax problems are difficult to solve because the gradient breaks down with min/max functions. We can overcome this by using the popular epigraph formulation, which introduces a dummy parameter and transforms the various additional objective functions as constraints.\n", + "\n", + "In this case, we'll generate a vector-valued objective function -- each element of said vector corresponds to the performance at each frequency point." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using MPI version 3.1, 1 processes\n" + ] + } + ], + "source": [ + "import meep as mp\n", + "import meep.adjoint as mpa\n", + "import numpy as np\n", + "from autograd import numpy as npa\n", + "from autograd import tensor_jacobian_product, grad\n", + "import nlopt\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.patches import Circle\n", + "from scipy import special, signal\n", + "mp.verbosity(0)\n", + "Si = mp.Medium(index=3.4)\n", + "SiO2 = mp.Medium(index=1.44)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the same setup as our original bent waveguide example." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20124611797498096\n" + ] + } + ], + "source": [ + "waveguide_width = 0.5\n", + "design_region_width = 2.5\n", + "design_region_height = 2.5\n", + "\n", + "waveguide_length = 0.5\n", + "\n", + "pml_size = 1.0\n", + "\n", + "resolution = 20\n", + "\n", + "nf = 10\n", + "frequencies = 1/np.linspace(1.5,1.6,nf)\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "print(filter_radius)\n", + "design_region_resolution = int(1*resolution)\n", + "\n", + "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", + "Sy = 2*pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx,Sy)\n", + "\n", + "pml_layers = [mp.PML(pml_size)]\n", + "\n", + "fcen = 1/1.55\n", + "width = 0.2\n", + "fwidth = width * fcen\n", + "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", + "source_size = mp.Vector3(0,Sy,0)\n", + "kpoint = mp.Vector3(1,0,0)\n", + "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", + "source = [mp.EigenModeSource(src,\n", + " eig_band = 1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size = source_size,\n", + " center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution*design_region_width)\n", + "Ny = int(design_region_resolution*design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", + "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "\n", + "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", + "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", + "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "\n", + "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", + "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "Si_mask = left_wg_mask | top_wg_mask\n", + "\n", + "border_mask = ((X_g == -design_region_width/2) | \n", + " (X_g == design_region_width/2) | \n", + " (Y_g == -design_region_height/2) | \n", + " (Y_g == design_region_height/2))\n", + "SiO2_mask = border_mask.copy()\n", + "SiO2_mask[Si_mask] = False\n", + "\n", + "def mapping(x,eta,beta):\n", + " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + " \n", + " # filter\n", + " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", + " \n", + " # projection\n", + " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", + " \n", + " projected_field = (npa.rot90(projected_field.T, 2) + projected_field)/2 # 90-degree symmetry\n", + " \n", + " # interpolate to actual materials\n", + " return projected_field.flatten()\n", + "\n", + "geometry = [\n", + " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", + " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", + " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", + " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " # \n", + " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", + " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", + " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", + "]\n", + "\n", + "sim = mp.Simulation(cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll slightly modify our objective function. We'll remove the `mean` function which was previously required to ensure our objective function is scalar valued. Instead, our objective function will be vector valued. We'll also return the *error* of our bend (`1-power`) rather than the raw power itself. This way we can formulate a minimax problem rather than a maximin problem -- although we could setup our optimization problem that way too." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "mode = 1\n", + "\n", + "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", + "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", + "ob_list = [TE0,TE_top]\n", + "\n", + "def J(source,top):\n", + " power = npa.abs(top/source)**2\n", + " return 1-power" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "opt = mpa.OptimizationProblem(\n", + " simulation = sim,\n", + " objective_functions = J,\n", + " objective_arguments = ob_list,\n", + " design_regions = [design_region],\n", + " frequencies=frequencies,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our objective function that we pass to nlopt is rather simple. We'll introduce a dummy parameter `t`. The goal of the optimization problem will be to simply minimize the value of `t`. The gradient of this functional is rather straightforward." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_history = []\n", + "cur_iter = [0]\n", + "def f(x, grad):\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", + " if grad.size > 0:\n", + " grad[0] = 1\n", + " grad[1:] = 0\n", + " return t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key to the epigraph formulation (and most nonlinear optimization problems) lies in the constraints. We'll define one constraint for every frequency point of interest ($f_i$). We'll define our constraint as \n", + "\n", + "$$f_i < t$$\n", + "\n", + "where $t$ is the previosuly defined dummy parameter. Each constraint function is then defined by\n", + "\n", + "$$ c_i = f_i-t $$\n", + "\n", + "within some tolerance.\n", + "\n", + "More detail about this formulation can be found in the nlopt [documentation](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#equivalent-formulations-of-optimization-problems)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def c(result,x,gradient,eta,beta):\n", + " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", + " \n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " \n", + " # Backprop the gradients through our mapping function\n", + " my_grad = np.zeros(dJ_du.shape)\n", + " for k in range(opt.nf): \n", + " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + "\n", + " # Assign gradients\n", + " if gradient.size > 0:\n", + " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + " \n", + " result[:] = np.real(f0) - t\n", + " \n", + " # store results\n", + " evaluation_history.append(np.real(f0))\n", + " \n", + " # visualize\n", + " plt.figure()\n", + " ax = plt.gca()\n", + " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + " circ = Circle((2,2),minimum_length/2)\n", + " ax.add_patch(circ)\n", + " ax.axis('off')\n", + " plt.show()\n", + " \n", + " cur_iter[0] = cur_iter[0] + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 0; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ED3iG6AHPED3gGaIHPEP0gGeIHvAM0QOeIXrAM0QPeIboAc/Ee/4+GGQrAAyGkR7wDNEDniF6wDNED3iG6AHPED3gmf8H5Xh9SXsZLCkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 1; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 2; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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ic0nWiT9zrbwTx/iI6MsFS2sFxIO4ad4ulb28vKSrq6t2PH9xcUHr9ZqyLGvH96rSW5Ghouvm4G0y86rt2grftz2wXPwK5T3IB2ye53R1dUXPnj0jordJuDRNab1et+N7uXtvWzZr8jsZE+Fdpw91jNm1N8kv6N7jOkUKIL2Wy8tLevr0KT1//pyqqmovjBlF0VG33uYAdhW9C1fh+6K6iaQueQL59y7SQvThaKX/8ssv52rHIuAEEl8C++nTp/TZZ5/Rhx9+2F4NV7z0tXwJbNMoP0R21UE/NAqb9ixMtweWi1b6r7/+eq52LIo4jqlpGrq7u6PXr1/TdrttL6ax3++Pbm3Ft7cyGYebyG4y5y4+d43AQ2roTRfojIXt3wj0aKX/4osv5mrHYvnhhx/ol19+Obqt1c3NTXvg8b3s+A6/roUlukSdjMu43Wbu32Tfqm2bYprw1L3fZpETeAfG9D1cXFzQo0ePqCxLOhwO1DRN5+2ricxkdk2W6UTvSiSq5rqHZOenFr7vPTrBIb45mLLrgCvs+IKYl5eXdDgcKAxDOhwOlOc55XneVua5Zu9FbKOrvF2bRTO2DI3KJtuy2R6i+zC00vv2wcryRFFESZK017rnO9yId7cx7WKOIYiJMCYnoK7uvE0XX7dtHWMfT5DfDa30fXdtue9wQQ5n6uM4dr6VlQ1DtjlFT8NmO0O3Pca2cCLQgzG9Bl5lxw+en5eX2o5xkPVtY+6MuYpTCT92YtNnIL0BotiuGeMhB+RSDua5hedhx1L+/vsCpLfAdM56yv0xJgIOTQ4OoS8fMaROQPdz0A+kXxBjZLbl97ieqOTknmvVoE5aVO+dhnGv/nDPmeogta3ft8E2Kz8UmxV+ptvpAicMdxDpLThFpt42G29Suqv6XhXZh6zpt8E1+g9dDuwbiPQTMcYJQjfPbjoHr2KKOXPVtlU9mal6HZBfD6SfkCUlm+ZKOA5dHIOCm+mB9BMwRqHLlNeIs5nzH3JhjbHkFXs28rADUd0ejOkXgMv895ALXchZ/bHq9KdIRo59UgSQfnS6xJviAB0aRcdo09AMu07oqWYzALr3VpzzwWba9lMlArvaYfN5n/P/Zm4g/Qy4CjK2WK6RV2bqmQndeyD3cNC9XyB98902Udu1/FXef9fSXZP9qV5n83PV73ACcAPSz4Tp2N42+24SvedYBahqj3zSsMXkZAPsQPd+RFyWx9r83vU93C0+RffYdJ+27UKUdwfSz8wSLrqxtLGxS3cfkd8dSH8C5l6iq2IJ8qvG6qdu130GY/oTIY7xTx21XMb9Qyr1dO93XcILzIH0J2SOg1aUaMrMu+p1Y5b86sAJwBxI7xE2lW5TVBK6XrRTBHIPB9LfE0zr8U2ZIuoOPZHgApjjAOktWPrBdspLUA35bFRtNlmjv/T/yRKB9BaMVcZq8lqXg9lmrDxFia/tIhnT+XuIPS6QfiBdy1LHWCE25sHe1QNw6RG4rp1Hgm5ZQHoL+rqYJjXzNgexy1VndF3lKZbTulxZl6j/s1Dth+8z2PW5Y8xvBqTvQTyQ+CHe7YZvcRXH8dnc8HPKq/KM0Y6u/ciftXjnId0JALwPpNfQJbx44CVJQmmatvemn/ref2MXvAwphOnr1aj24To1x589f+Zpmrb3FhTlB/1A+h7k6B7HcXvQrVarNrrPGem5iytS17Xx+6eI9EPb1Ad//kmS0Gq1oizLKMuy9g7CcuQHaiB9Dyy8LP1qtWoP6iRJqCgKqut6tmyz6fLaMavf+qbQ+vYxtGcRBEH72a/Xa1qtVu2tw3m4hWm8fiC9BjHKh2HYKXwURZRlGZVl2UrP7yU6/X3cx7z4hGmX3rQdpvvi7+X/wXq97oz2EF8PpO9B7NrXdd12L4neCV8URXsP+yXOKw8tfzXN2Juc6FwvJMIi8zieu/dZlrXjezHaAzWQvocwDN+71jqfCJIkobIsqSiKUS9SMdZBO9bFK8ZYCjw00vPPOJGaJEn7kKWH+HogvQaxe8+kadpG/jRNj7r1S18DvqQTEpH9pbDk/ArnWFj4KIrQtTcA0vfQJT7/nA86cSx/TpzbyjZdrYQ4ll9Ke5cKpDdArgbjn4kRfspIP3VX2hWTdo1dgiuLryrSAWogvQa50kscz4vR/Ry69veBrmIp+SH+HnQT9BysOJL/gpzMU/0OTI9rdaCHdH4YkB6A+0un9LgaLgCeAekB8AxID4BnQHoAPAPSA+AZkB4Az4D0AHgGpAfAMyA9AJ4B6QHwDEgPgGdAegA8A9ID4BmQHgDPgPQAeAakB8AzID0AngHpAfAMSA+AZ0B6ADwD0gPgGZAeAM+A9AB4BqQHwDMgPQCeAekB8AxID4BnQHoAPAPSA+AZkB4Az4D0AHgGpAfAMyA9AJ4B6QHwDEgPgGdAegA8A9ID4BmQHgDPgPQAeAakB8AzID0AngHpAfAMSA+AZ0B6ADwD0gPgGZAeAM+A9AB4BqQHwDMgPQCeAekB8AxID4BnQHoAPCPu+X0wSysAALOBSA+AZ0B6ADwD0gPgGZAeAM+A9AB4BqQHwDP+H3JN2KHPCbPYAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 3; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 4; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 5; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 6; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 7; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 8; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 9; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 10; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 11; current eta: 0.5, current beta: 4\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 12; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 13; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 14; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 15; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 16; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 17; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 18; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 19; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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bcumRrde6vHbT4Vo4lkd9Hq49W3lezCDlZUlvr9d74UHgWUt8mvfIvi9+ZFUUYzOY9GuCCczSUm0YySwWYnJYdbjPbFm5XRbxPCwujoFR0CBMt9stxfAQ42R1eS2n8aYWIDxn7EF6rkToJpgcOuCzqwZeZNa9KgSqmv7DqAsPjBwm/QrICMwWGz9nNyDHy9yXzok6kIx71Vn5hsfT01MpHkcyDS2zBwcHMRwOS/vTZVaeFyauy8PDwDnprjn4HG7TBel5owxW3WXS2aqEZ9V3v24S0KiHSb8G+CZmAQ2y2+32hxZRzG7He5jwIDs3sYBonDiDhdUto3hgB9fk9/b2it1qqtR3OB+28mzhuWWX43kcF1YeoQRI3+12SxJiVd9pqY+fucKB9/P3nRGek4Am/Pow6VcErBWXtXhkFSw9iMG6ch2AwQ0sIL6W5XgYBj4X1hqJMyTSdnZ2it1oQXxYeU3ecYkQGnuc02g0itFoVLTsPjw8RESUcgfb29svtrdmwiIMyabwsoeSVTnqsCwsMPlXh0m/BGoZs/gXW1rB3Yari5sfi4P2w49Go8LS675wEeV6OEtskTTDXvNZUw2EOBnh4X1gX7rr6+u4urqKq6urYhGCGCciirLc9vZ26Xhs5XGdAHtDbLFVqptl7usIbLK/HrWk52EHTYLefGoZ2T2HVURijRNmcG+RrENGHu9jtx7xOye8uO8+4rkez2OvkLzj1lmU6HQ7KlwL3Hq0615eXsbFxcUL0sM6I28AsvN+9hAmcSigJUlexABVNLK7X7UhZaZ41M81lqOW9K8tkWiZBf/+mOWXZZ+lctBVPxPv0ek1qJ9fXV3F5eVlsWMtLD3rz3XqDYtu0JvOElfdeTYiXhA+09VzFx0GZPBigQULPyNxd3t7G1dXV3F+fh4XFxdxcXER19fXpXCFLTznDHZ2dqLb7UZEFOeNcIB7CODicymvKp4H6giflUSN9VBLep5s2lTAhZ9Op3F7exsXFxdxdnYWZ2dncXV1VbjmXEKDaw+CcbYeSTLeahoxPHsY7A6rGAZuvRIe1hfls4iXhJ/NZkVuYTQaFWQ/Pz+P0WhU0gTAwvMW1wcHB8XCsrW1Vbj1s9nsRe2fE5+84HNtPtPp41z1d/hudNEw1oNZvQQ3Nzfx/v37gvTv37+Pv/3tb4VVxCQZ3NC4EblMh2Qfy1tBFO1cY1IgPwDrDrdeRTggPJfnIsqEV28Fcfzl5WVcXV3FaDQqlH+YwIOk3WAwiKOjozg8PIzBYFBY+VarVSwQk8mkIKDunQdUlep4kct67CNy8huboZb0f/rTnz7VeXxWwA01nU4LN/z+/j6ur6/j/Py8ZBWz7jMlPavueFS1WveIeJGd1/gd7jWX5jiTzgIZLvshhkfiDpl6eCt3d3clLQAShYPBII6Pj+Pw8DAODw9jf3+/FMu3Wq14eHh40RCjHXW8mHErbqbg4wqDyqAtx309akn/29/+9lOdx2cBlNkeHh6i1WrFj370o/jmm29iMBjEeDyOy8vLUhOMEjfi2TWHlQXhsACwmAdWUPX6WbccynGIqzlpByksC2T42MgpwMJD04+kHUqFcOm3trZiZ2cnhsNhHB0dxfHxcRwdHRWlQLby8/m81AdQNQePk5JZ0w/eq3V+lgAr6ddt1TU+oJb0v//97z/VeXyWGA6H8d1338Xbt29jMpnEaDQqbvTMkgGcAOSNJXlMVES8iGvRtcbdcpyZZ8JjMdBNKyLihRiIS4QgPGbeseqO8wb7+/txeHhYkP7w8LDo2EPOAu9R+Sx7L2zReRIw3seLJBOeiQ/PiR+qkjRWh2P6GoxGo/jzn/8cZ2dnpfiY41EdLhnxUpXGW01p7A8ScP1du+VA9KqkHRYPltVypx5idxCf591xlh4PHPPo6ChOTk4KK68Z+/l8XpL1qtyWr1H30MMiEBGlpB8P0MTvsTjwBp3e+GJzmPQELf/1er0ifmdVGh6dTqdIenHyjF1cdlEjohTPMhlYz54Jbqrid2Tp4dLPZrOi/p6V41htx9Ya57SzsxP7+/txdHRUJO+QsUcXHa4N35fuVx9R3raaCc89+LDg+N64n4Hn+OP/RqW9THxjdbhkVwPOSoPwuqlE1Q3HLi7X2uHqslXXrDz3wyvZs/HVnD9AleHq6irev38fZ2dnBfGzjSp4zBWy9cPhsEjccbceW2Z1y6uUhJAlY2HjoZz4nqqsvBKav399jYm/OmpZzfrvpoKJiqSVxqwqhAEJGAgFELOj3s6qOhbYQPXGZNdxVzgeJ+3G43GMRqM4Pz+Ps7Ozoryo1QZcGz6n2+0WVn44HMZwOHwhxGFpseYsuLEG1xoRpRn8ID4rFrWer52L/P3xHAJtSDLxV0ezTfkKYEvEApOIcuKLxTCLxaLkJWVJOrXqqqpTy86jrlhLz3X4yWQSNzc3cXV1FRcXF/H+/fuC8BAR4fURH9zuiCh1zoHwcOlBeGxOydeIuQAY1snVCHgiUPThwc1IyAnge84Sefi+lfD42S7++jDplwAxKyw5XH1OuKGfPOJZmMKurg6eAOE5Xs/ceM7Ms7wXBIE3AbceNXjW0oPwKMtp5YDdei4JMuH1uBAVoRyICgB3GqqKUIVDPAlYS3Fs+ZnM8ChwfE7s2dKvDpN+DcCdh2sOVxi164hy5j6irJlndz6z7ojtOfblllqACa9aemTrIbrhXWlwXlwqhK6eZbbs0qsrDkvL6kKdo4dyHFt4tfKw3lruy8px+LfOMOCNO/k9VuvVw6RfEWyx+/1+DAaDoo49GAyi1+sVZSWuLyOmZc08j6ZG3M6WHe60zrRTUrC2n2vxvKMtb0OFxhfu1sMCxHH87u5uySrD26nbcQekxyLBoYyWFnm82DKwrgGWHepGhBV28deDSb8CWELKwpXT09N48+ZNDAaD2N7eLm5oWB6eDa9Zelj9KrKzdedaOB6qpefpuTz5hpNhPIQDu9keHBwUmfrhcFjq0uPaP8/k1xFfOA7rFpjw3PUHYnI8z8haZnG97Xa7JGuuIr2tfT1M+iVgTTzq2NCjv3nzJr7++usYDocF6bkuz3V95ACyCbJKdm43VcLDtWeXHjV5Ft5khMf5wMIPh8PiOlSAg2vhBYan9MKb4FwBVygQpmRNQFXzBLltVsuhrEPgPgYdPGLCL4dJvwJgfUBcqNVOT0/j9PQ0Dg4Oot/vlwiK94HMnIFnYQ278VWjo9i6w9LxiCsQHr36k8mklFNA6IHR1XDn37x5E6enp3FyclLIbDH+ChYeWXPo96HZ5wEgXPMH4dnKYxHRrjlOwCnhWeqLa4fen8eUceXA7v1qMOlXANfj2UpCtQZLr9YpazRR911bTDMNP1v3rDX28vKyGL2F1tiIZ6VdRLzI0h8fH8fp6Wl89dVXcXJyEgcHB7Gzs1NUIRCjZ9OCQPrJZFIsDLg+JjvH8hHPclu+Lm29zQZeclwPS4+qAVcOTPrVYNKvACY9iKOaeB4Qye/RNlJtsslucFWpMeG5LAcBztXVVeXYrogoTb/Z39+P4+PjOD4+jpOTk0Jbj2vgtmDE8Ux4uPc88SeTFGd9AXw9sM5ZWzLadgF+XavVKsjObcou260Ok34J+EbkjLQm4kB6TkKxFa+b68Y3K8fRIIgO1oSFh7wWAzBQxkOOQL0TlOVQdYCuHkMukWhD7MyER+jAZUB2y1GTVyERvB9W7yH5xs0z/J1p85LqEpDE4wy+3fvVYdIvAZNWp9hwEo618BqfZhY94qVV16YTLZGhLKdu/Xg8LvXD45ygDYBXgs0wVGLLLjhP+8kIz/vUa5cgNyJp4o4TcPzMLn/Ey23BeMGAMEhnE7jTbj2Y9EugAyk5OaV19Iz02U2cEZ2tIBNE97bjIRiY6gOrC6JDR881+Extx4M3tCrA47ozwkdESTyki19EuQ+BXXKdHqSxPb4n/r74Nbwwuka/Pkz6GrB2nOvs3DQS8Zxoyia+4u9VsTqIxllpxO+8Tz2Ih7l8vHc8t/fy0EzU3kF43cGWyQlrzGVALCwQ4Wg9ngVLTHhYZ1QAdGMQJn3WMJMRnvsfWPFosq8Pt9YSQFTEmEh+6Vx5WEgWrnD7LEtNGZrIgovLm1jqhpYgOO91B+KADDzTDpadBTdw5bONJtnD4EQhexOI4bkqgGsF0eHlKOF5R1629NpUA2QSXH3w/xU/uz6/GtxaS8BNC3S73WI+HLLeqMmzS4zONSWAJqQ4tlVLDoUbkxuuu7rFLGPVDSyRpNMBGLrFFc4d5OT97GDlr6+vSzLbiGeRD+sMdBFhQQ9CFFbRZWIatep1ZOfkKguajNXQLFO+BOoqzmazePv2bXzzzTeFi398fBx7e3vF1JzpdFq4nLo3m5aqdE87DKvkzS/YkqvUlBdhrouj9s5DLI+OjlLC4zpV7KNqO1h4rcVrFx1beCY8J+z4erRlVvMdfH5VZNcFR3MrRj1M+hqcnJzEz372s/j5z38eER+sNZJj2JlWt39SVzezejyZFgSDlVeLrsMkWOTDYhvMs8MQS+jo1cJnSjt4GrqhJgtfWGjEZOPPjIjStXIcz/X0iHIplBdIhVZCeC8Arp5kSkYjRy3pf/Ob33yq8/gsgBvv/v4+2u12fPvtt/GrX/0q3r59W9zMrIvHTTydTkulPS03ZaU3fvCutbCGKlphgQ+LbSAJhoVH4k43v+C+AN2Ek88F3kZGeFUV4jpBeO2AYwuv+/RxMjAbnaX/L/weCH903oAt/WqoJf3vfve7T3UenyU4KcfkZTUa3F9NMEVEiWDcjsplMLaqyMRrGyz089zai73lOEOP/nzU3jnW5mvQGrw20HBZjuNmWFZk/hHWsDcDsjPhWTHHC1iVFh9gUROuRcunOi/QWI5a0v/iF7/4VOfx2QKWEIS5u7uLiCjcVQyaBPH5JuasONx6kA0/g2TZ9lZPT08lK8uE1zn4WlngqbURzwsQPI46wvMEHI6hebCHzsvDNWAB4227sqYaBpc7s1geCwRbem3bNelXh2P6Jej1erG3t1dY7Pl8XtqT/eHhobSfO1tVdqN5E0t2oTMZqWans1FbOoijqvYOUnLVgON37b9npZ0q7tiNxvmyS69Ju8zCM+n550w7r7E8+vRZN5HF9CZ/PVyyW4Knp6di3vvj42PpxkciDyRmWajq5vWhAyU1ftdBmlDYYdIOz9JTsoNsXJpjt15DDN64MuK5Dq/dgTg3hB+sHMyEN7h/sHhwqzEr97SNVskPQmdNPZykxLGMetSS3l/gs5vNiSzWlXOSjktSOkCSraC2grKSjwnPY7ayJh8lO/QCIFtVIhHE133sEDPzwqNTfLBAseegMbwm7TTzrxJmgOW2nOXHsXl+vs4RtHu/OmpJj5u7ydCbj28uduHh3qqlB+GyTjC1UEwOJMxY/stNPrrwtFof5vOpUIZLaKr84xq81t+1Do/rZZeePw/XqKOwEZ7wQqblvmVWHjE/PicjvQm/OhzTL4HGn2qZmPisJYdbrRs2cFKKS1NaFuMbXHelxXvhsoPYSlA+t2zEFMIA7vVnDX2mtFNpLecmuA7PhNfRYExS/n4yFV62GHIVwVZ+fZj0r4BKR7PmEU7K4d86JIJv7Kw8ptYdJIebrTc+Lzravadz+bVLLkvYgZjqNWjSDq8DQXXrLrby+N6wmGj2nsGlS14QrcbbDCb9GlArj+fsRs2IDvd7mZacBTC4uZmAyAtAFMTguBgE53KiimNU3Zap97gbUPMT3C0X8Ux4HTYCl5wXE96dh7/bqkoGk53zDSb8ejDpPxKyMhQsGG5iZMUz0rM1q5L0gii8NRV/HnsdWY85l+CY7FlSLGueQR1ek3bsNYDwvPsufubEIzoTcRx10fX7zBZEE34zmPQbQjXhuDF59FNEFJl/oEpfjmcmvsp5uY+crTmTnD0Q/jwQBhaes+CqbMPxtG+A22M1aceE1336IAnm0doPDw8R8bxrDbvp+B5x/Zxz4Id+T8ZqMOnXhIpMMuKrAEWREZ8/X1/H7jmIjyQdE5TzBEwKSHg5GVa1GUVElAjPyT+N4bnkyBaet+3Cjjk8gwCEBvmxGPEipYq8OsKb9OvBpN8AfKPxzcfDM/Q1Cp38kj1nLjuPieJRUyrueXp6KtxpToCxmk23v8Z5cZa+KobnBBwSj6wYxDAPjObC7P2IKDyE2WxWnCPcdq4A8PWwJ2Cyvw4m/YrQG4xvvoiX2zHV3Zwc68PiaUJQf8dttjxFVlWTek5IgPHusbqVFmvpVWmn0lpuxuEEGyb3YFQXegG4vRcqxogokpqsC8D5whPAArqM8HWek/ESJv2aULc+uxEzC8TxOUNjc7xGLT2TnDPzDI57Va7KZM+ELaze495/lg1zmIHrzmYIqkwYWXs+z6oHcg681516TPr9GOvBpN8QSvxlYELD2nFTjCbjlinV1NPIMvNMeBbIcOabj1WnpWdFIcfZ3OrKykHtddfQR79LTjYi+Vm1QOp8PRN/PZj0a0AtT5V1V8Ud69bxd66f8w2ssT5+5qSZHi8iXiTqWBiTbZSJz82ktTztpsrCcxzOn1811CLTD6inUoXM69GF0MRfHSb9hshqylVQi83uPGfjq6wWyKPEx9+0LKdSVY7bcR7aBqwNNIjfs5l23DHHiwkP1+Brh6uu8/25DKnXrVoDvJ8TmTphyFgNJv2aULJXufjZTcwkU8uXET7zLNTScywMcmtzi5Kd34/jg/Q8BENn2vHCwcfJZMDwHFinoJ2HXAngBUD1BwCr/zinYWu/Hkz6DZAl8yI+EJAbSCLqJ7zqZ2b/zjQBmkRU4Qo3y+DYXBvn1lvW6IOIrNHnGJ6hqriIMtlZ1ae9/lwZUOKzF8C6f3yWvt5x/fow6TeAEpKf2TKBMJkFV1edrZoSna0oSMavUbFKRkS4x5p74KqAbjWFc+UkHP+Owwy8nwnOSjscj/v7WfjD23ll213BY1DPwIRfHyb9mlDCs2VdLBalrLOKTEAy7hHnhYFdd30o2bNnPj8Qpq6MyOGGCn+yIRYcX/MzFhW485DuquvPnoUSX+W9TGouKVa59sbqMOnXgFq6TBoa8UwmvtEBJXqWxOLjLSO75hM4bl8lw83JRc2O4xyY+FlFAq+FFZ5Opy+m7qjnkWn6dXMP/h7x+Vnm3lgPJv2aYNdWiQ9RCYAbFETPau+sM88saebmVyUNs9IWk1gXH74m9k6UVJwbYOUeA648nqfTaZFE5PIdFiW19llyD8fX+QP83Rjrw6RfE5nbjXo1y0fZCjGR2MIzmfiZj8XPAL9eCcvZb86GK1HVY+DjqHdQtWDp6zC2SxdEzXlobA/Cczyv3x9/J5xPMPnXh0m/AXDjcQYb5I8ou/eLxeKFK8wuK5MpI7fWvIEqwQ/H5pwM43q2eiuaE8hyEkr0qjCCF0SeCVCV5UfyTrPyOBc+1+z7rgpzjGqY9GsgK5Wxtec4VAkD8mdWUgmM9+A5i10zq8uTcjQpp9ZS8wPaCcjHz5JmvIBlXgSuFYRnz0ZFOkp4zSdkgz90rJexOkz6DcCkYWuW3eARUfy7LmHHf8tca/0bfzaTT1tvqySvy7rWli062WIAS1/lueA1qqxjSbJ+vzr/3+OvXw+TfkNUWX0l0Co3Y128vCzDn1l61bdrRj7ieSHKQoosXte4X/MR+t1k54njaiii1YKID4sSFIUguc7+9461m8GkXxOr3lyZtVYSLyM7x8z8rMdRN1td84zIjLr38HVnib9MW5D9W8+3Sk2H96iF1519sBB4Gu76MOn/zlDXXAm/zLLXkTUD3/zwQDTZp5+d9QBk4QDnMEA2CJJ4QeC/a7mu6nwRFnHjkBIeQzl44w/vY7c+TPoVoS579gDBMounP2efC3Bpq+pmziyoHh9KOd5TLqI8A4/Le1mLL84H2XjujY+IFwRnhSIvBhznY0yWnj/+zj36GMqBEVxMfG9TvRlM+jXBBOdW1m63WxAOpMLNjnHPGl/zDHy19BF5Eq3OouG9KNVxQo8f7Xa7SJxx3TyL/2GheXHj6Tw6o58Jz94BH6Pb7RYiHl5sMiuPTTuxNffBwUHs7+8XpM/cey8A9TDpV4De+LjBeUMH1tSrSIZd5zqXPqJeOqueRcTLch9bbi6LcXmMHw8PDyXPgMdU4ZhVwzK0fMYPrc0/PT2VFiOexsPKRXy3nLSDdR8MBnF4eFiasJv18Bv1MOmXgGNRrhljEOT+/n5hvXTohJKdS2cZ8ZeVyTSZpuGCLjZMeBbCcGurtrhqsw2sOs/I5wcWPyW+Wnkt1WV98fASdEHlmB4Wf3d31+79hjDpVwSX5mCJkF3vdDqlnWmZ3LwArELyqky95g4y0mtSTgUw2UhrHo2FB8fr2Z502dw9Jn1m5XkRVMLjeGzpedQXsvU6dBMDN63KWw8m/YpQ1x6kRmyrgyeqHpoVz0heF8fjZwy1yEQ16xKfe9q5nRWWNxuyiYWP95dToZJeZ5WGAGC5rW7eyeFEJs4xVodJvwI4psdNGVHe2SWrOy971p+zf+P4eM4SVhyCKPF1Rj6TH94Ja9+5asCk1xg+2zkW03rU3c4WvqxGD/Kyxl5n4+tedrby66O1pP7rhmVCZknZamWx+Cqkrvt9RD6EU3+nCwmHFHq+WUZfJ9VElCfs8iObI5AJd/TaqpKUWprUngB98N9M+FqkX4pJvwaymzcj+6pCmlVfv0ptP/usqvAic7X5bziGEo/r7kq4Vcplq3xHdV5N1fFM+EqkX4zd+zWQWddMJPOpzqEKVd5FncXV16xCtlXPZ1NULXaZ52OsDlv6DbGq2/73QpU4pw5151yXS6j7+e+NqmOZ7CvB7v3fC5+a8B8Dm5zzD0k0k3wjmPSG0TCkpHeB0zAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBoGk94wGgaT3jAaBpPeMBqGzpK/tz7JWRiG8clgS28YDYNJbxgNg0lvGA2DSW8YDYNJbxgNg0lvGA3D/weIBnhRrRVJAwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 20; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 21; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 22; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 23; current eta: 0.5, current beta: 8\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 24; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 25; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 26; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 27; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 28; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 29; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 30; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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Tf3E46Z8IS3r9mf6NwrqxWtJSl77dbqPT6QQLb0tyPA5Jt76+HpJ3JDytPIU4sU46vS4rub26ugrJOxu+0MLHBlpaz4UL1kOKvVioM6uk51gOTvonYFYCa1ZWXJVn2lXGmriKcS4vLwPh+DseW6fS8Nyce7e1tYVarXYveZfNZqNDNxTaWHN9fR06+Pr9/tSsOp6TMwM46sqq+9Sya8Iulg+wXpB+RjyW1+k/Dpz0T4StNc+L3W3MrnPz6dKzdZWZ86urK/T7/RA2rK2tBfeYsl+69aVSCdvb29jf38f+/v5Mue2s2vx4PA46e3bSdTqdKXkvj8G5dpxBv7a2FhaBVCo1Fa6wpk85rX5uekxeB4B74YDW8h1Pg5P+CbAP4UPxu2rpdRCFJupIeu5C22q1gmvNQZbMfFP+yj75arWKFy9e4MWLF9jb20O1Wp1K3s0iO3Dn2tPKc8R1r9cLpUHeF617NpsNlYJsNjs1iIOLiHo0tp6v4YCGRPxbO/bLrf3HgZN+Sdjacqxcp3+npOLwC1rT6+vroKHXSTi0/tyqKpPJIJVKhbHPTJ6plT84OMCLFy9CiU4HZMyL5VmbZwKPTTV07W0Mn8vlkM/nUSgUkM/nkc/nA+ntqG7tgbd5j5gUmdeki0bsuj2Jtxyc9AtCSa5WzIpI6I7aARitViv0wzcaDZyfn4cMuars7NirVCqFbDYLYHoqLsdflcvlYOlnWflZlj5Wpru8vJzyMmLnLBaLKBaLyOfz4Tz6ebC8SPeen43VMMQm9tjPWBOXNifgWAxO+iWgD6QKT+iyAtMbTjI5RkKxH54NLNSzs1U1ZlkZO6+vr4fvueEDd5zlRBzuR0eXe1ZikdepGn8uRtwbT9VzFP7k83mUSiWUSqVQCuQix0WC9815+NqcA9y39HTtY+W6WZ6UE345OOkXgD6AWlPn7i6MdzWZpXEyB2AcHR3h6Ojo3hAMrYOrVSUhKK3lXm6bm5vh+1KpFHaaVbc+1rqr98LkHQnP5homEEnWdPpu+g63s97a2gpJQhKeHooq8LQFV+N5FTZZ0tt4PiZycmu/HJz0C0KtmCbjSA5a5lQqNRUn2354tsdy1JXtPstkMuGYjMk1YcdtpEl+bku1ubkZCD+rRKdhR7/fx/X1NRqNBk5OTnB8fBwWIuYSuPMMt7bmltaU9aZSqWDh6YbzM2Kool6QKgi1zdjq7i0s4d3aL4e5pNcSS5Jgs8z6YKmAhY0wOhySQhUAU259vV4PU29OTk6CC637wOl5SSCdBKuWlqQj6Tc2NsIce+vWW7kvLfFgMAhtux8+fMD79+9xcnKCi4sLdDqdqYoBm3d2dnawvb2NWq2GQqEQZtFTT5BOp4MXxIWMpKc1V/JqBn/W/4N9z0O9DI75mEv6p5ZG7APHf9uff8xzxH4PPO5eYhZRf6YS1YuLC5yfn+Pi4gLtdhuj0Si45FSPDQaDsDCwH54uvQpvtD0WwL3ElW5Qsbm5OeXKFwqFUDLTfeAemsjDa6vX63j//j3evn2Ld+/e4ezsDK1WCzc3N6FSoD35Wv/f2NgAAPR6Pdze3oawht6QKvFUZ0+yq2x3ZWUlLHCzynKxjL8Tf3HMJb0djpBUaEmLrrASuN1uhwebFvb29jbEykzgcS953draEl4fYoYLutMsY3i+8vl8ILxuURUTB5GI9FIajQaOjo7wzTff4JtvvsHx8TEuLi7Q6/UAIFj4Wq2GFy9e4ODgAPv7+0HLn8lkwmLX6/XC86JKQ+0VsBl7XivfNyvZqHCSPx3O6gdwe3sb4tvr62ucnZ0FF50tp8xya4xq694U2jBLr4RXUgB3+9urnt6SnS79Q4SPVRHUpX/37h3evn2LDx8+oF6vo9PphD3lNjc3UavVcHBwgJcvX+Lw8BC7u7solUohwcjkHfMO/MxiY6pp0XmtfNluOztLjz93Yc7HwVzS/+1vf/uuruN7RyxMoGW6ubkJ9fXj42McHx/j7OwsjKxi1l1darsjDEU3JAldWZ6HX7V5RgUwzJhzK6oY4a3IhcRjDkKn2jKhyCGbnIozGo2QyWRCP/7e3h5evXqFV69eYX9/PzTvcABGOp1Gp9OZcu1jmXeSnS/djlrr89pzrwuHXQh8EVgec0n/hz/84bu6jh8EaHH6/T7S6TRevXqFX/ziF9ja2sLV1VVw6dkMw33cYg+ebkFFosf2YYsRntLWUqmEWq0WkmeavNvY2JhJeNu1R2+DYcnR0VHwVphf0Lh8fX0dW1tb2N/fx+vXr/H69eug8tvc3MTa2lqoTnCR0JKbWnjV6tvmHPUOVGtvB2/w9/blhF8Oc0n/pz/96bu6jh8kSqUSPv/8c+zt7QVLz/l0nBNnRSPANOlUkTZrZxaGBZTUcr+57e3tKS19rVZDuVzG5uYmstnsPWvJc9sdcUh2zss/Pj6+NzOfqjv24+/u7uLw8BCvXr3CixcvsLu7i2KxGJR3vAdrqVWZSEJrcw53y+WCxWw/Py+GPaxoqLegwzXtphce6z8eHtPPwdXVFf7yl7/gH//4R7DaKqABprvs1LWPSXVjJSgmtUgIDsCo1WrY39+/R/pSqRRkr7MIrxWG09NTvH//Hu/evQuufKPRCLJf5hXofufz+XDuly9f4uDgALu7u9ja2pqaoEvXHpiu+6vG3kp36cFQWcgEnu51T8KT3Ep6aiPYauzWfjk46SPQuPjk5AQnJyfh56oms98TVjs+j+yU1dKd5wCMvb298OIgDO4wS8LYGJ4a+na7HTLz3377Lb755hu8ffs21OCZmKTrrNdSKpWClT84OMDOzs7Uxhh6Plues1oDWnKtPDBPwdImcxsq2+XLkp7qx8FgcO/3jsfDS3YR8MG28+SVyLozjFo9/pt/r8ckSanaY2a+WCyGhhkOv+AwS8bx1qWfFcP3ej1cXFzg6OgIX331Fb766qtQjru8vAwz63WTS7ram5ubqFQqwcOIjczW+7LzAUhCxvDa66/yYS4gvGYmG3W+ADX7SmrbeRib2ed4GHNZrRlmxzT4cGsNfJZQyLq6dKNV1srxViR7rVYLSTsbA89qRaUu4PT0FN9++y2+/PJLfPnll4HwdOd1Cg6PxWGau7u79wZw2KadWP8BRUYAQsIOwFTJsVQqBYkwY3SGCjpwg8eypGc1pNvthpmB7uIvjmSa8iciljSyzR9KKhKernyxWJzqiuMsO7bElsvl4MrrcApVodnGGbbGNhoNvH//Hl9//TW+/fZbHB8fo9FohHJcjCDcGEN3wmFpjudWWMLT8tJ7oHXXxCB7A5gXUEIr4TW+Z46A4JyBXq+HXq8XXHxP5i0GJ/0S0AdM43wLWmUmrjjDjmQnwZikq1QqwRrSlbfTZex1AAiNM81mE6enp3j37h3evXsXFHYkvC0V6kAM5hJ2dnaC4k6Hb/B8VspLAnKr7FQqFcpxtPKqHmRfAi167Lh0+TUhygVBSW936nU8Dk76JWCTcbE58kxkkeyM2+nK7+/vBwtfrVaD6CaXy03F7dqkQnDR0fJcu93G2dkZjo6OQuPM5eXl1Kx8PY4uSMViMbophk3cWcLraC9O2Emn06G/Xt36fD4ffm6n6FjxDT0Jmwi1pOdCY4/nmA8n/RKwCrPYNlEsw7HmXqvVgnVnRp6uvG2HtRWBWYSnBex2u7i8vAwS4dPT09AIFNv6Crjb746turyuWq0WBDixwRaW8LrN1mQyCfE6753tv0wGMhRhR96sKofer8qI2c7M0eBM+Hlc/3g46ReEEp7lJyba1NXXGfRsSWWijkk6HTX1ENkV2h5Lt157AliH1zFVCnooKrWlFmBra2tqEZtFeE7MpVjp5uYmeA7METCWZ1MQrTyTnno/+vnSk9IqCBOnlBRzmKgO7nA8Dk76BcFYmPu+Uw/PxBXJy2GV1Wo1WHnG7YxvmZG3PeUPkT3WHku13enpKa6vr+/16NuQRJtpXr9+jYODg2DlOQTEEl5VfpeXl0GOrENESHgd7MH982jltdRpG464aHJh0BCAeQBaeoYVTvrF4KRfENSms+WU5S0OlKDGnGKbSqWCSqUSMvK0erGMPPBw6yhdXbb52gEYjOPtjHnmCNbW1rCxsYFqtYqXL1/ik08+Cdp6qu60603zBp1OJ8zj5ywB7nHHej9n9inhs9ksUqlUuKZZrbes69t5etptxwGjuvuPbU92zIeTfgHQCuVyOVSrVRweHuLNmzd48eJFyHYzzmepyva92waZx1h3frV9/UzcvXv3Dh8+fMD5+Xno09frJek58qpSqeDg4ACffvopfvKTn+D169ehmUbn+3GB0S2zz8/P7+1ky7ZiDs3k2C4NXbQXQcU8dqa+TZByodAyYb/fD5aepPcM/uPhpF8AnGBTLBZRq9VweHiITz75BAcHB9ja2golLib37PgqnQenJH/IutPS6ZguTrxRie3V1dXUrDrmGVQfwKTd69ev8ebNm9AyWyqVsL6+HmJpJTwtvI7uZrPOcDgMYhzmODTPwRIdr5/JOK3t64QgWnq19tomzEVP3Xu39IvBSb8AOBG2WCxiZ2cHBwcHYXMJTpLRHVx1q6d5c91mPbBq3WMDMN6+fYuvv/46KO7YHmsbXbRrb29vL1wz22WpDWD3HAnKxh269JzTTzkv5wKyPKmLnG2bJdlJVqrqmH3n56sl0JgISRcOr9UvByf9AqBrTwKxrs2933Wsc+wBnmXd7YOtnXnc544bUTQaDRwfH+P9+/d4//59mKrb7XZDTwCHa+Tz+SAGYvWAgiC26epGFcCdOk6HbtCd58YcDCFYmtMuOmbvuYCQpNymi7v5cLAIScvPZNb0Xs3gMyzQxhy39I+Hk34BMDPNMpwq6LTMpVb9IeuuCTNVpLGbjNtWk/BnZ2c4Pj4O47pUcceqAjejsJp+KwTSnWy1Fs74/eLiAvV6HWdnZ8Glp4Un4Zkc1E0srXBIS3ws8+nYMCuuien89WVluk74xeCkfyQYsxaLxZCR5+4uNhMfI/ys1lvd841WnRtZkigcrMl4mmTnNtLj8ThYW1YV2DhD5Z/t1tNkouYMdKeb09PTqWlBFPswT0DC88UkoN4Tt/LifXAoqApruEDM+5xi7cqxbkb+29V5s+GttRHYYRhad9cOOCtV1fcC8Vg91qGmsS4HaHJ/+GazGTaUbDabwVLSSgII8XSlUsHOzk6YXMv2WGoDKPFV4Y0q+3RKLmcBUt13fX09dT4uHKxW8Jg6LWge4W23n342+tnpz5ivsIuqE3wxeGutwLbGsqZNwu/v74dJMhwdpUITWk3V4evx1DVVZRvJfXFxEchBcl9dXaHdbqPdbk8lv0gWagbYB39wcIBXr16FARjceorhRyxBFpu2Q9Kfn5+H3Wsnk0lIUJLwmrDTkhwJr/dDz4TXz89ch2rys4x9doSOBrfTg3hMx2wk05TPgLXMo9EIn3zyCV69ehUELazLl0qloDLTWjJLXlZNZjvTSAgKXRqNxhTpVXyi5S2ei3E0y3AcU20JH9vTTsmlo7o5KZduPRN33W43eDwsWzJ+V8LrIA9m/Tn+WxN3hM681y7FmJUH7pqEtDKhU4Qcj4OTfg52d3fxm9/8Bp999lmwapTUFgqFsDXzcDi8V4NXgqmohEm58/PzkCTjphnqAts5cXz4VQfAraY4wJJyWh1xpa43r0evS0uBuoElXXrdn57n1km2jOGZWyDh6b1o0k4tPPMBdNNjbr1CxTtU/qkWwK374zGX9L///e+/q+v4QYBWutvtYmVlBT//+c/x29/+Fm/evAkPJq0Lya0z7PlgKsG0xt5qtYI1PTs7w+npKer1OhqNRiBHTLRCwtEN1oRdzMIr4WOhhm2eYR2+Xq+jXq+HXXh0N14uNGpdqZhTwnc6neDOWwuvLv3a2loQ4KiVn6XH531ou3I+n783ysvxMOaS/o9//ON3dR0/CPDB4YNOKS1HPzGxZ8dExcY2WWKxSeXs7GyqDMYsfLfbndrXzsa8wN0mltoOe3h4iMPDQ+zv76NaraJQKNzLzvN6GGZYC8/KAL0NZulV6EOFIevxukmnquQYtjAPwX5+XRi5Iy930rHuvO2608QddQi6aadb+sUwl/SfffbZd3UdzwJ05WnZOJeOclRtILEWnnV2LbkxuUVXPtYVx6/M0LNkyEEcLMmVy2UUCoV7vf2EzStYwnMXXRIeQLDmdoQ1PQ6dUU+pLq07JbKquKPHEiux6ecW209AZ+hTi8Dyo5N+MXhMvwBUZUfXnmWudrs9NZZZVW0kF9tRSS7G7rMedFp5agR0IIeOtuJkmlj8HmvHVcIzvKDSjsIbLctxowq69dTHa287lXbsr2fLq1aAdKNKGwYp8e1ATH72jOdJeh0H7qR/PLxkNwcaSwL3S0ij0SgQiFluJt8oPdWkllpCkkLjdj0n414Sni2r5XIZlUolTN3RkdLanGKVfrTIXKTU81DhDYmlhNcSHRN36sWotNaWFbWDLtZFp+2zqrCzyUvgrq2ZnwcTeTHPxjEbc0mf1A9ylqJLH14raGFbKy03ScHYlmOl2IpqVWhq+bRhRzfC4J70VNYp2WnJgbvchGbodcTV1dVV8DpUxkvCM3bWjSaZyKRLzzwFk3Y6ycZKa3m8WLlPd8xRhaJq7TWRySSexvNO+sUwl/SxCa9Jh1omrXHT4inpKafVFlBadsbp6sbSCpL03PeNQynsJBoA4Rr4vUprNeHIUEPVftzaitNsqKUn2ZVQXDxUaUePgaGKJjUpUrL3ozvtWmWg9h/YcIclPn4mehzP3C8Gj+mXhGbnSW61dPydyk2Bu7hW+9bVmqmeXa2aDuEAEPIJ3BqKLbwkEclHmS9jb1pknRtPK6zuPI/Hc3ExY9JOVXYkPO+b96fDQ0lWSoG1MYfv0Wm4MdJr2dBKit3SPx5O+iVht3TS7Zh0Zrs+sMCdCk1LU7rdFV1gWluNXTmBZjAYoNVqYTweo9vtTjW78No0t6AbUpC8LEuqfl7FLkp4O7yCXgIXESU871mz/jpAlApB4G7unc0/6HgsYHpGv2oF7Fx+x+PgpF8Cmm1W8quwRONQ4I7s9qGmC6xxr8a+XAA4SZYaeBKf74slxHhduoEEpcKaCdf6u+4mq+2r9BQ0GanaAiW8LiKcist97LjDDYCQG6BIh5+tfuV9qRpQd72d1brsmA0n/RMxyxW1ghK7mQOhbr0dvKENJbTwdOttyQu4U7NpIozg8ZU03EmWIYSWv2zzjO0A1PHTSnhaZIYkTDxqD7+WO4G7vMSsicAa0/PaYxt5Oh4HJ/1HgOrtVUjCejYRa7WNlepsNp/ks6o6623wHDZPQAuZy+VCZx5LgHZoJ5N2PCcTlUzaMfmnu8sAmBrRxcWEcwfYy8+FZTKZBJLTe7EzBFXEoyEPSe9Z++XhpF8Slpxqna1WXBHLNFshSix80OScuuxcDOwoaa3z83tm0HUjClYDdChmrB+eSTs7245Zet0AhDJhnfeve9yPRiN0Oh1MJpNQ2tTNPmzVyDb62MqCYzE46ReEWmB15fnSeN5abz2Gxq6W3PpvEpBfdXy0lsd0oixJwmugdl6tu243RYGL9hXYEVdWVKRWWGXCtPCc3sMRXZQIp1IpDAaDIONlIlLHh6tOQkMkJhhjuwE5Hg8n/ZKwlj6mOlPCW6tkXXJ1qTU5+BDprYUHMOV12NHU6tJzd1wm7nSIJVuAY+2xSnoSnh7E1tYWKpUKtre3sbOzg+3tbZTL5aDZZ1/+aDQKsbkSniGRflZKeDuazLE4nPRPRCwGt7BSXtWXk9ws9WnGXMtlsVdMr083mEo+Zs2LxSJKpVLIpGudG8A9FWFsAIbd/VYtMEVEnL7LwaGUCtu97JTESvi1tbWpZCTPM2uqsGNxOOmXROzB0yw6f88Hne9R0iuBab01aacLg7rw6lEQKt1Vrb6q+bRJRcUx7B5U0c3l5eUU4Wnh9f5JWt3ZplQqhRfDBxUVsWRodfhqxel52AVNP2fbgut4PJz0S2AW4VUvHtOPA5gi8Sx33Zb31JWme6yEVwtPQQ9JSMuuWnUdec3z26SdJbwuOExWap8943luZ6UlQK3B20XMljqt626FO6pBsOVPx+PgpH8CbCbeinOswEStvFp6tfKzavmaG7AWUJtjqICjpWc5TptmtK5OqS4baDiQU/vh9XwMB6wWnouKHaWlWnp6FHwxV2Hv17YEA5haoOz7nPiLwUn/RFirr6S30FhVSa8PsG21jc3OV/deXWPWsJWEOrySTTOaQGQtXrvmZs2008y6imVs15z2JVBYxAWAeQOO0NJeBf1MdLHgPVNSrAuGE35xOOmXxKysvUJdU/5bS3Ixa657Ddjj2h10tDxnJap2cCVddL4XuNvCiqSn6s5uCknCk9C0+Jp1p2eju8oCCM1AJL0OF+G0X5KfeQNadDtMQ/9Opb+OxeCkXxKx8pzW6olZKjx9v+7Jrn9jS3+qWFOtPglpRSu0rCS9TYbpDH7uJGtjeJbZdPw2r4UvHo+EpsjHCm94DC4yOmOAo77ZGEQvQ6XF6+vr4fe2euF4PJz0SyDmXqtghNDWWeCOaOqm0+rHYnW7mNATUOUaz6+afbrXsyYfxXIL7LtXt1mviYuMfgbaD0+hDa05Ca8bYvCcnDVAPQDzByQ+LbkltWr9nfTLw0m/JPjQa62ZNWbrxgN3RCMpSQDg/o4usyS+mt1Wkmvcb/MDqvSLvdSSasJNFx117S3JSHKNxzljQMMMnfCjgz1sjz+tvCY1eX8qWNJ7ciwGJ/0SsFZe+99jcbtmmTVG1b56+/DOU/lZNdoswY+1irE+df1qr9FWCwDcuydtEWY8r0Ib/UqvgC4+rTuHkNDK63w9XpcuOrF7cDweTvoloKTXUhm3bKIFVxIC93dh1Z8B9xtvbIxv/17jbP03y1p0k+kqz2sBtqEJz6XXpaSz5UUlv04AUrWdJhB1h17duov5BF1o+D5Ndsau1/E4OOkXhJbSYpnzyWQS+uepxmNiK1aPfuxXLQWq4o+/V8JrLZyusmbj9frprcQ8CHtuDRWsclAXIx57NBqFBKMq7TSHoGTntXIhU9ktj0k1oWrwHYvBSb8ENNa2bZ9KQP6dfq+u8Sz3mt8DmPIS9CvB9zDWpQVV8itBCTusQ2fNcZGy59X4n4uYbe21x2AIw5ZaAFOx/7wxY/xsdPSWHa7pjTeLw0m/JGLZe+0Qszr7hx5Ma9Wt62zjcRtnq5VXwlu1H3A3lHM8HodS2izyxEIRa931WjWrr9dtE4wMO7RaYEMPTZSygYj9BLG9+hyPg5N+CcxLsNGlj2nHNWMfs+qWULHse+zv+R7NovN7LcsRNv62DS322DHYON++N3Ysfm8lxxr2qBCJuQA29GgjTz6f940uloST/iNCH2A+1NoTH7OQsWPMSpbNe0+MRLpo6LHt+4jYYqLQxc7+XL9a6AKo12iTgHoOhh3crLJYLIZZe6VSyXerfQKc9E+EktGWzbSbznaGWVIp4ayl1+PPQ4x8JJE9r43Z+b2eRz0CKwlWL8eeWxOEdnhlbGGgdkErCXTp2SlYLpdRLpdRrVZRLBbDKG0n/eJw0j8BMamsJsFi+nz+PHYs4G5LLYYIPJ4t39nsvcboSlIbZtjYWhcq/p1dkGKyYUt+3TBDy3Ta8GPzG/o56eaWOgSkWCyiXC5jZ2cHe3t7qFarU/P2Yp6HYz6c9EtCS3bcgGFjYyOIVDKZDIbD4VSCz9bt52GWR6Dks24zwwg2prAOPhgMkMlkQoIvlUqFGD+2CKgnoIuZVempMIn7zJHwummGdvnZDThYXdAk4Pr6ehjtxV16t7e3sb+/j+3tbRSLxal2YcdicNIvARKeAysKhULYHkr168xOq3T0MYQn5hFerfks0lP0YptZ9BWbrDuZTKYSfFy4VJOgpTMuetpqa3fKARCacPQa7VTd1dXVMOaLm3ZyjHatVkOtVkOxWPQk3hPgpF8QSoRsNotCoYDb21usra2hUChMCUy0X37W0IdZLn8sfrfhBICpcELLdrqHnUpddd89/nxWj7rutkOS6xZV+tIFQLe31qYctfR2kAZjep5LZ/vpi5l7j+eXh5N+CWhmmcKTfD5/b9dWm1GPJeMeynzbv7P5A4LWngsNia9SV+1sY2srFwC62rx+El6HcnAEF8dhcVCHEl63t1bFnL0+OyJMP1c9J89hd6r13W2WR+qBjLC3MEWgtfFZI6ltjByrs9ss+yzowhBLDOo12YYba/lJcHa3xXaxpdVdWVkJuQqSnoSPkd22GNu8g16fqgS1Rq/JQLuDrvbwewLvUYh+QE76JWHLX9Z1n/U1hsda+Xnf22uy5UO7AGjMz1hft5pmCGP3hOcrRkaba9BrjGkA9KWejCryYjmMx3xmDgBO+n8eHkPsp2CZB1wXoJhgSD2UmFTX9hZoGS5mza3ltV7IrGucdZ+P9YIcc+GkTyqsAMe+YglG4H55cFZo4aT8wcJJ73AkDFHSe83D4UgYvGTnCJjl9bn7/uOCk94R4OROBty9dzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYXDSOxwJg5Pe4UgYnPQOR8LgpHc4EgYnvcORMDjpHY6EwUnvcCQMTnqHI2Fw0jscCYOT3uFIGJz0DkfC4KR3OBIGJ73DkTA46R2OhMFJ73AkDE56hyNhcNI7HAmDk97hSBic9A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYVh94Pep7+QqHA7Hdwa39A5HwuCkdzgSBie9w5EwOOkdjoTBSe9wJAxOeocjYfj/ra04easn3N0AAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 31; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 32; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 33; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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bQ6PRCG79xsZGmIozzcozl0AxDqfjnJ2dhT52jbv50LZcHcChr8u20Nr3y8KS376XjvnhpH8i1NKnfTBjyTL9XjPcrMvzYRN2sWQZCc9svVXePSTEIWiZKfft9/uhmYejta28l4RnX75ab3ot2iCjbbX2fbTvkYpzYtbfyT8fnPRPQCx5lRZv6ofYWj26v2xbZdaco6hGo1EgPImmVYJcLod8Po9isYharRay9Zubm4mmmjS5LcGsvY7CopW3Qy9ZFtSBlmrx9bVqmU7fO+C+pkHJbQ+Kx7j9jofhpP8EiJXj1HXXhyU5x02xL1472SixHQ6HmEwmYYgka9wEB11yrPX29ja2t7eDEEdHWk9LKKrOnp10TBxa4RGHWdoZezpFN9YgoweHdiZaS69iHj1AnPBPh5P+CYi5mjZTr1lpzcozXuckXfapM5ane396eorxeByIRoutPeYszzUaDWxtbWFnZydYeQpxtEQXew1WAcjyoG7B1YQh5+evrq6iWCyiUCiEg4XPZzP2vGe17jbcsB6R5j9cgvtp4KSfE9P04PwQW1dVm2dILGbGSTBm6XXg5NXVVXDhbWmQVp5S2+3tbezs7KDdbqNerz+YvAPuCKrjrXlfdO1J9oWFhXDNYrGIcrmMUqmEYrGIlZUVLCwsRMt0doaeVTLqvWlYwO+tlkDfa8dscNLPCJtk0uy0tUa2DKfxOjvWDg8P0ev1QtKOJNfnISFWV1cTHX2arec0HDbVaK/8tNZZW6bj1hzek+7GU2+Dar9yuRyuw5icyTst1dnwgN/byUJpSU4NZ/Q9ccwOJ/2c0PicFknjVH6AWYZL64dX0tuEGbPi3N1Gd5rfFwqFMNyyVquh2Wyi2WwGt54TcdIID9wNyVAxDjvp6NprWY5LMrjllknCxcXFsNUWQHjd6tpbt15Vi2mkT7PuTvr54aSfAbESG5NxJD0tcC6XSxCKCydtP/zh4WEidtbDY2VlJSTtuNGlUqmgXC4HC8s105VKBZVKJbGpJk1fr6/l5uYmIbk9ODjA4eEh+v0+hsNhYjAI+/K50pp776id1wUUlN3S0vOadsIQBUVa/Zgm0LE5Eyf/7HDSzwiNz5mQo4VmvZzuNONkOwDj22+/RafTwcHBQaiD61goWlaShG69bpttNBqB/KVSKRwAGl9P66LjwTUcDnF6ehoOpE6nE5p6rq6uACCxx56eRKPRQL1eR6FQQC6XCz97cXGREBpNW4Jht/MAd3MBeZ+KWLOSY3ZMJX2akOKnDm1gsaSxM+PY8UaRDDfO5HK5sGGWe+Q7nQ7evXuX6Ien+Ca2B45EBxDi90qlEkhfrVYD2dXtT4vjbelwOByGXn0eRh8+fMDR0REuLy8xmUzCqqlKpRKadzY2NoKsd3l5GdfX1yH+ZwONinLG4/G9wSFKen6vmnwLWxZ1Cz8/ppL+qeURm3Hln9MysZ/iGrF/Bx73WmIWUf9Ox0ednJzg6OgIvV4vMTaKpAeA0WgUBlmyJ5196dP64YHk5Fi2yjKBpm4898hzB1xs8426y9bCk/Bv377F/v4+ut1uYvadHbW1tbUVKgOrq6sAEMKAs7Oz8D6R+HxtfH+07KeekWbu7evn/40T/9NgKun54c06tKRFV5jZd5Kelp5u9e3tbZCzHh0d4ejoKLSo6tJHZuoJjVUB3CuRra2thUTa2toaisViILxdTQUkY3gS0RL+zZs3+Oabb/D+/XscHx/j8vISt7e3IZxYX1/H9vY2dnd3sb29HVp0aeU5o0+bjtIEOZr3sLvoY0m8tF4DJ/v8cFY/AFowxuZqsZnlZiLPJvFI+lg9nksYbWmOX0l2ZuwZt/Ohq5rpzsdaW9Wdt+O36NJz5l632w1z7LkJZ319HTs7O9jb28Pu7i7a7XbI2OdyuTBQw6rx7NINK92lbFdXUus983ns37k45+mYSvq//vWv39d9/OCIhQn8oDJmPTo6SiS7OAaaCTgl3O3tbeiHV9Udh1hal15LWbEd8tVqNWToGcPbtc62rdVa9/Pzc/R6PRwcHIT8Amfu9Xq9QHiGEo1GA5ubm9jb28Pe3h62t7fD5B3KgSkQirnoSnglunomekhqglEfPLTsvj7HfJhK+t///vff1338KEBxyXA4xMLCAvb29vDFF1+gVqvh9PQ0TIM9OjoKLm2s+00tq+6aY8LO9sMzcWcFN9VqFY1GI6ygajabYfccl06mufR2+o5OtH3//n1ibv7JyUlw6dlAU6/Xsbm5iZcvX+Lly5fY3t5Gq9UKbj0Pw+vr6wThbX2dSToeZPRMmH/QFltV8tltuEtLS8F7sGutHLNhKun/+Mc/fl/38aNEtVrFr3/9a2xubmIwGODw8DBYbS2xAcnssspIdXiEnQTL39OFl9oe22q1sLW1FVpk2THHhZO6aVatLDPmFxcXIadAsrN6wPwCN+NoebBcLqPdbmN3dzdY+M3NzcQQDlvhAO4OG1XfAQjWXUVFbNCxVp7vF99fVpD4Z91Nb3MGjsfBY/op6Pf7+POf/4x//OMfwUXWhRLTmkZiwyNiMS4tobrzJDx19GygaTabqFQqIXlnCa8eBl35jx8/JrLznU4nJBOZWyDhl5eXUSwW0Ww2sbW1hb29vaDjr1ariQm6fH0Mi/T18vXpvD4mIqkjYBOQ/i4JzQSnyneZP+C/2cUejsfDSR+BkohJO/49P8wkmxJP1WQ2A51GdlpBluIajUaw8HxwEMY0wvNQGY/HIf+wv7+Pb775JmTnGbuzkYb3xXvJ5/Oo1WrY3NzE7u4udnZ2wtQdXYzB94ZfbQstgODOU2tAsmuJEbgr79GVZxikoRDfN04U4r/T3XfMBi/ZRcAPNmvVhJJYVXdaO45lnvU5VYVGstO6r6+vh4k36tJznTTj+FgMP5ncbaTp9XrY39/H119/ja+//hpv3rxBp9MJ/flW/UdXm2q/ra2t0I9vh2nqa1I5ss7u03l5i4uLCakwXwcz9hoKUdKs0ma+j8PhEPl8PswL5M+5tZ8dU1lt2yEdd7DtntPUi+odkOysgbNRptVqod1uh0er1UK9Xk9Yd03aEXoQUUfQ6XTwzTff4O9//zu+/vprvHv3LmTn7Zx8Hh7chNNut7G1tZWYkW+bdvSa6o7bHn8A4WBj9YEz9wEE6a6Sns9nB4BS2z8cDkP7sR0B7ngcsmnKnwj74df6uk3SxeJ2jrWyZN/Y2ECr1QoS21KphEKhEEpbVhashKDk9+joCO/fv8c///lPvHnzBu/evQsJu9hiDFp6zsinh9FsNhNLMRTaf6AuN40EE3RLS0uJsiNn7lNyy5+3Azfs4A2+zzpd6PLyMhwOJL4n8x4HJ/0csAoz4I7gChu3F4tFVKvVkKjb2NgIMTvHVNdqtURJjmSf1g/PTrmTkxN8+PAhCG60/q6usIYjLBHWarV7SzHYIx+z8Iy/degHE3talltbWwudgfQagO+sPKsAsbn4WtrkocbrcbqQx/XzwUn/BKisNEZ4luCYwCKxdFLtxsZGiNvL5XJIcql+Pq1TDrgbc3V+fo7Dw8NEWY5rqFji4j3rPbKZxi7FYNxtr6uu+OXlZRAdkfQkNSsBGssXCoUwWUe76bTKYa27luU4LVh3AcRm8Tumw0k/B7QcRffXroli9rpcLie2xpLstKg2blcpr/aYEzEtPZN3bN3l3no2zsRIQZkv3Xo7cYeZd72ultYuLy/DHD8dkc2BmRQYsf2XyUAAoRtPwyEr6rESXFp7nS9I0nsybzY46eeACmnY0rqyspKw/MyGMytPonNzLEtwFNpYZd20DjLr1vf7fRwcHOD9+/dBIswsvda5CYYdxWIxNNOk1eOVdOrSs62YrcWsYlB1VywWE249QwWN5W1jDu9TRU76cwBCW7N6GFbh6JgOJ/2MULedLa6MwbVdlPE7y3Aatz+UpHuI7HSxtVuu0+mEphlOvVEyaI/+4uJionuOzTStVgtra2shEWdr8bTwbCLq9Xro9/thVz219XTrdX7eysoKJpNJyNhb1x1IelDMD1AIBCARVnCmoC7UdDwOTvoZQZeeNW3dIEMLv7KyglKphHq9HjL0JDsz2FZC+1DLqCWgTrzZ398PcTwHYNgYXjv3aOFfvHiB169f49WrV9ja2kKtVgtxd4zwlPUeHx+HWQJMEgJ38/dJeOYoGCrwENLEnbrmvMfYfH4SX118jgx3Sz8bnPQzgJaI2W5KVTc3N1GpVEITCUlfrVbD8Ei2w9ohF7OQ3Vr4g4MDvHv3Dm/fvsW7d+9weHiYIKHN0rM0Rwv/+vVr/OxnP8Pe3l7CytvrMW/Asd2x4Zl8X7QFmHkKCnGU8GkLLUl4zvbX7TY6pmwwGIS+gdgmXUc6nPQzIJf7bnZdqVQKlvKzzz7Dzs4OarVaSMTRxSUBtCMu5sY/lHXWJhp16d+9e4c3b97g7du3+PjxY3DrKa3N5/OhhEbxTaPRCC79q1evsLe3h62tLVSr1cQYa1phrrk6PT1Fr9cLXYYq9uFkH07nZa+/1vht1yHLfJp3YGi0vLycmJ6rsT8PPcb0NoxxPAwn/QxgTbtarWJjYyMMl9jZ2QmWntl3ZrBt3/i08puFrYnrAIz379/j7du3ePPmTWLiDWWw1AdwAAe1AZubm6GJZ2trC61WKzHGOtaWyxVbJDxHfXEJBwmuc/pUtqsJQM4X0HhcN9/YOfj6XjCDz8NDRUFu6R8PJ/0MWFhYwOrqKur1Otrtdmg5ZZ+5Tq+xk3HTrLsSXhN16lrTstG11gEYOvGGI7sojtF5+DFNP2W2dMEBBPebyTLOAiThj4+PQ3ce8xsMHehR8DUzcce5gsz2n56e4vz8PAh7aKlt16KC70sul0uEB1TluaV/PJz0jwTLUZTQcrBFvV4PCStVz+ljmnW33Xi63JIJK+6K51z6TqeDDx8+4ODg4N5gTt0+o5UDHk5W9acE1T58ju3mPMDDw8ME4Rk2qEuuY7d5eNgtuFx/TdLbsqI9HDW7b98nO5LL8Tg46R8JxulcBU0VHfXpdmSVJXzMVSXRSDZ2l9ENZi1cra0O5ORWHI644gAOnVxLma/er3W/lUiM33nA8EF1HwnPjDytfIzwzPhbwjMBR9ee1wfiG2xjD/03C1fnTYe31kag8lAAQciiMlpud4ktlZhm1flVG1Z0lh431XI3PIdq9no9nJycBOJol5l6IO12O8Tsuq5aVXFWeEOrSdENx2pxPj9bcrmyigcM3XsdiqmutxKeg0GV8LbtOAZLdPv/lOZJOdLhrbUC2yVHMlFOSjJx7juVa0Cy406lpLbWbJtVGONycaROzuXj7OwslKjYR85rUO7KDjmOuOIADFp3kjTW988s/fn5eZjR3+l0grqPVQHgbjiGjrCmx6CrrGKEV8/k9vb23iASImbR9f+IScrYjH/+jCMd2TTlKbDW5Pb2Fq9fv8bLly9RKpXQaDTw6tUr7O7uolqthr5w3cyiH2aNS7VRReN0xs2aGY+trtYRUSwdcrdds9nEzs4OXrx4cY/wukI61pKbNjyTQ0A5NJNru7RzUEtySni7jlsJTwuvYqHYFN8Y4YE70jNxyDKpE/3xcNJPQbvdxq9+9St8+eWXQXSzvr6eyNaTOCR7jPBWVMIYXWNm1r5p1WkR7Uw+nYfP+J2lw5cvX94bcaXuPKH5BBXecCFHt9vF8fFxkNiyFs96PHsFGP7xHjVEsEk7FdGQuCsrK4l6PO8tNnVINfm6D4DVAif94zGV9L/73e++r/v4UYBNHhcXF1hcXMQXX3yB3/zmN/jss8+CS8zWV35QdUyzTdppVxqtOwUutKacSssZ+qw966gokoSurK6aeojwtuVXCW+ltUwUMobnYk5N2FnrSkmwlvlI+LOzs2iWXuN/1drr/RGW8CoCYlLSdgQ6pmMq6f/whz98X/fxowA/OHShqSPP5/OJBhHNtvOrLR3FYvder4dut4uDg4PEDH0le2zrDS2qTqLRGH53dxdbW1th+IXWyq1Lb8MMK7yh0o5JO7r07Cjkocetugw7hsNhovOO03apvONr0nZhioFsS619/THSc0MvDze39I/HVNL//Oc//77u41lAt8XQErOWri2eaRae7rNq1zWbba07SUtXWJN2Gxsb2N7eTkzLZUfbNHGLEp4HEXftaR0eQDg46NKrOw0gZOlZYiTZqbijhacnpIeqVoasViFm7ekdqBaBlj42xMSRDo/pZ4DOuwOQ6GenddS6u7q7vV4vdKfRlScxpi17ZLsqY3idmqsLMOxOeiAu/NGDSJOIbJ7ROjy9DOYzdKKP5gNYZqSl1+SjHmI6RTgWx2v5UAd/qEpPd/vp/HzH4+EluymgtSWsZFZdZLqzKg8luViSS1Oj2dZSdWVpYamyI+mbzeY9NSDzDvqc2tmmAzCYuFOPQ5V2ABJKO63Fs8RHbQEtvA62UOGN9snr9zpwUxOi9JT4GqgY1J4GEt4edI6HMZX0WX0j0xRdaoFJJpKI46no5rMh5Pz8PBBfXd7Y+ideg9aUWnYO5KhUKqFVl7G7robiIc0pNtqdRzks3XAV/JyfnwctPZOUJLzW4nXtFBWDepDx4NDXp9UMlvr0eXVjjnb3aRZfE5mcysNOvlh1wjEdU0lvRx87ko0yug2W2WpaOSrtOM9NlzPwebj5lX9WC6/juDhckn35JDtwN1SC39sd8TpFVi2zehzMouuBowIc4G5qjbr09HB0Tl5s7RfLbDb7z+qCNhlNWwGmz8F9eB7Pzw4PhuaEHRJJUrHMRTKq5aMFZRbbxvD8dy2PsTSlSatcLhdyBouLi2GfvMprVf9Or0OnzejceLXs+mB2nW63luX04NDXrR1zusnHLq9U74EHoU7DjZGeh5H262vLsuNxcNLPCNWs20YZ3cyiMSk/rMBdz7jNTut8PV3rbEUoAIJkluGF9usDd2U5e2/8qjVzjdt5TSU8Y3MdXkG3XglvLTwJT6Lz8NLxWTxQ6KmQ6Br68P1RS2/v0wk/G5z0M0ITZGqZaJ3UfacLD9ztsLN1af6c9uKTxEygcQIOCTKZfLfthR96m7HXZRH61YYWVtmm3XLAnbRWlXa08NoLYFdw8/Dic3NApuYhAITXw8OT92/fH+DucNJFGnbdluNxcNLPiWktn8DdXHlq1m1WHUjG8XZqjC6ApHfBmPni4uKe1Be4s5S6LEKvo+U3Hell3WUAgexaemSWnknJ2M45eirUFHDJhyYfOQr78vISwF0uQoU7+trspqDYYFHH4+GkfyL4gdMMNf9sBSf292y/vSak9Hu1hLakZZNfCo2D2aCjuQKSUmfv6/Vs84wm/9LKciQ8l3zU6/V7u/mA77bQMr8xGo3ujcnSEEjLl5oIdNd+PjjpZ4Qlua2tK+mJmAWzXzVs0MNCQwjNF5Bwdg2ULY+xBEdSM75mRUClrLqMwrbHaqbeboxNI3yz2USz2QwDPIrFYpiMSyt/dXWViM9t+696KZp78Pr8/HDSPxHWRY912ynp05RoVttP8mlSjjG2TRiqhVfrrmU4Zr1JSMbZquRjHT5t4g219Cw9ascck3bcUEupMMeKVSqVYOVZMWCFQ4msAh2Cr0eHjMZm4zseByf9nLDuuf07K0pRzwC4r+5LS7yR5CwP8gDQWFrdYD6/dsbZBRRKeDsU0/b724k3KpFVV5xJu7W1tbDkg3JhThlikw6Tf0p4WnJ6DEp6KxRywj8NTvo5oB826+I/BHXfVacfc9n1ELA/EyO8zZpz7n6lUgkP7pazc+l1IywJH4vjbRsxQwe69bVaLUiFOThU9+PxwFA9gFYqdDAJ3Xub5HTCPw1O+jlhBSFaY6abnyYa0XKfWnLGybYMSOvKkhuABIF05DbnzzNJp2RX6866vo7LYucdCa8dc3amHb0X1cLTtddsvfa8s+yoxFVBkmoV+D7F3utYtcTxeDjpPyFsAo4fbHVVaeEZP9Ol1hjeZuht44ltVmF5UOvurItzN7x2pDFJxuvFknYxl57X4nx9VdvRq6B6UMludQRWZgsgcQDo+DH73lrFnhN/djjpPwGsYAe4P2STUEJrLdwKfNLq+Up2FQCpLt3uk9PpMiQOh2TYTTbsIUibWksrTCuvElsdsmElwXztOnxEcxc2x2GtuU1mWtWj4/Fw0n8CqBXTA0Ch2XlbhktT9AHJ5JwmA5XwTKSpbJcqO1paWna62Llc7t7sPtXT280z2sgDIFESVIWcJgV14AgPHO1VoP7f5jG0aqGHKLflqJTYrf3scNI/AdYFjbmkdsijWnrbVGLFPTEdgMbDqtNXyS4PANv/rhaeFlg36XDuvtbheV0SmgTWzTYqMdauPtb9qQHQyUPsvWeCUPsCaNVtHoOdi7abzzEbnPRzQAmpAyFIyDRrr2Dt3kp3FTGVmo6hJuk1E07yA98dMNpJp4eSVg60DVhdeu0K1GqBinGYfNPmmcFgAABB0acttFolYNcfr0tC6zZbHpp8LXo4OOnng5N+TmiMrZNqtcbMn9EEn5XeAriXmLOCH9Xmk/S2K49/BhCur1bSCoBUG0DLGlsdzSEdtjMQuJuhpxac12K9X/fdAXexuVp7ne9v25H1mrTwutfeST87nPRzwBJeLa2N3ZVk9gCYTCbBksYSd9aboEuvTTm2y04TgbTOmiyzCkC9V60eaDad17VjuDRRx/CB1lhDDiYReUDo6C6SXl19VfzpQak6BSf8/HDSzwnVt6smHLiL2y3pY5Y3LRFliR+z+kp0zR2ooEcz3bYEyOsoYgo/Eo4kV5c7JgFm26+dr8fwJTbcI7bJRw8Y62nY5iLH4+GknwNq6TWRpmoyflXLG+untx/k2OgnGw7oc2tzisbpWhoj+WPTaHh48TDhv02rQqR5EfxdeiOU12obLO+dLj6JT8LTysdyHXr4+cSc+eGknxFaSqOl1xo5gIRbenNzE9xa6x7rVyLt3/WgoPutoNtMK6oJLz6sVSYRY2u2+ZxWg5CmFNTXzP746+trLC0tYTweJ3IONoHIe01z6/WQtd6Dk352OOnngH4QNYO+srKSsH5aXye0HdX+m8bJ+rO27GdBkjBWtmOySDL9fZJycXExDPrQkqHek80DqAejCz40LOBBp7kEde95ryS7dgzyvbNkj23ZcR3+7HDSzwnr4vOhH1gm76bp8C1sos/mBoBkPGsz8bSgJL2N6QEkCEnSx8gzTSUXG+JhDy0+L98TfU3qgdhEo3XjOQBEh37orD3HbHDSz4FpSbY0YitRYsk760anafBjv2dlvSpuiUlWVUef1qaalnew101LRMZ+nl9tj4GN31UQxDVWnAVQq9VQqVSiG30cj4OT/hMhzQ1WIlp3N+159PcfOixsyU1jYptItASzijdeX+9DMc1bUe/G/qwNYWKVDZtg1Fl73O5Tq9VCB1+pVHJLPyec9E+ErXfHeuH1Ma1Mx+dTkiuBZ4HN+Fv3PJYnmFZG1Gw/gOhgECC574+hj/Uk7PdWgswYXrsFOZij0WigUqmkruJ2PAwn/RNg3XvbGMPvCca39u/1zxoXMxYmiWJWlL9ze3sbknOWZJPJJLECW7veYp6EWnutVmh5D0jKhLUCoGOtdFAHcDcqi9fRQ4SvjS3CpVIJlUoF9Xod7XYb7XYbzWYT1Wo1sb/P3fvZ4KSfEySitpdyxTNLVirP1XIXcH9fniUpv+rDElDB52c2nKUwVbkxq5/L5cJ0XVW3pcXo9roab1uC2+GVtlRH9d7q6mrQ+TP04aGSz+eDha/ValhfX0er1cL29jZarVaw9L7Saj446eeAEr5YLKJSqYSaNFtSdfdbmiIu5hoTNkMfI57G0YzpSWyKXvTBZpZYdp8PXo/3ByBRltQlGdrRxxKazqW3iTYtK2pjDcMeegx2Ck+j0Qgz95rNJsrlcqJn3zEbnPQzgkTTcc9sHy2Xy4kpOLZcppbUhgIxN1Utrq0W6FebOCTpSXzdX0fJK/9OlXB2jj0tuq2RczmG9u0r+VV3r8M0tFSnh45eUyfx6Gw/Pliy0959x2xw0s8BWvpCoYDJ5LummVKplFjxZNVrMdc5RvyHrmutvFrRmCpPLb62snInHf9ede9qdTlzj0k1jsLiumhdTDltTLU9mLRGz/eG4RAPGF1JrYs6dHefW/rZ4aSfE5p4YqbZEtyWpGIZ+1jCL/ZBtsnBWDIvViqMxfi6tlpJz0OLpGd8rZtzlYi6/46WN7a0InZ/+lXDCe1nUMktQwxtNnLCz4fcA8IK711MgS1/xWrO08QtFtPq37E/x34+dk9q/TXeZ787DwK1vHx+kk9n4Fmia3uv9UT0PtOSk7EQxnYUWu9m2vvlSCD6JjnpPwFmIfesmPfDHavLa8jx0KBJK5LRGfXqusdCDXvf0zQJaa/1Ic/H8Sg46bOKaQKcmIcC3C/TxR72Zx0/OjjpHY6MIUp6VzY4HBmDZ+8dAWlen7vvPy046R0BTu5swN17hyNjcNI7HBmDk97hyBic9A5HxuCkdzgyBie9w5ExOOkdjozBSe9wZAxOeocjY3DSOxwZg5Pe4cgYnPQOR8bgpHc4MgYnvcORMTjpHY6MwUnvcGQMTnqHI2Nw0jscGYOT3uHIGJz0DkfG4KR3ODIGJ73DkTE46R2OjMFJ73BkDE56hyNjcNI7HBmDk97hyBic9A5HxuCkdzgyBie9w5ExOOkdjozBSe9wZAxOeocjY3DSOxwZg5Pe4cgYnPQOR8bgpHc4MgYnvcORMTjpHY6MwUnvcGQMTnqHI2Nw0jscGYOT3uHIGJz0DkfG4KR3ODIGJ73DkTE46R2OjMFJ73BkDE56hyNjcNI7HBmDk97hyBic9A5HxuCkdzgyhqUH/j33vdyFw+H43uCW3uHIGJz0DkfG4KR3ODIGJ73DkTE46R2OjMFJ73BkDP8P9gDgXwLW+BQAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 34; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 35; current eta: 0.5, current beta: 16\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 36; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 37; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 38; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 39; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 40; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 41; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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hol8SdVDELNdXtaqXBe263S7K5TIODg7w7bff4vXr1zg8PORcNfXfXzbXXnWR3W43NE3jCkBN00wLnrraqtUqpwQ9Hg/C4TDHLW4a+jfQlp3V1VWcn5+j0Whw0DSVSiEej0PXdSnKMYGIfgnUUlJV2NeZRDOZTNDpdHiO3Pfff4/Xr1/j9PQUjUbj2hNwaPoupQZVS2xM6c27JhoHVqlUuPhnPB4jGAxysPAme9mND02v14tIJIJ0Oo3V1VW0221omoZAIICVlRUkk0kEg0E5z5tAPrElUYVvnHdPfz8P6pQrFovI5XI4PDzE6ekp6vW6qWshz2PRSr9ZUMsuDbU8OjpCrVaD0+nEZDJBJBK5leEV6mdGD5Z4PI5sNoter4dwOAy/34+1tTXE43EEAoEpL0Zc+8UQ0d8Adrt9ymVfpFOOGA6HPCZaDdJdF/Vhc1kt/6KdaKqVPzk5QaFQQKvV4uYh+jffVtmumr+nfv/hcIhoNAqfzzdVXyBDMa+PiH5JVLdU/bNFUEdd0TSYdrv9UT58ERdfncBL6UIzgqBJNlQIRA09NLFXzUzchmVVR5DRKOxoNMqVeVTBFwwGP5ryIyyGiP4GMHvjUXtstVqdSsOpLCJ4tZadUlmBQMCUKMjKV6tVThs2m02OEVA5MdUe3DT0kKOHmNfrZe9CDSRSfl5Ef31E9HcI5dOJbrfL/fBnZ2fXmnqjQnPu6PybSqUQCoWuvQiCUnSNRoNn7tFyCWoYorVSt1kFR8Inz4Uac0ajEWcn3G63uPYmEdHfIcazfKPRwNnZGQqFAkfIrzPzjYpkKL21tbWF7e1trK6uIhaLfXTmvUqkNGeeioEqlQq63S7cbjfC4TCi0ShCoRD8fv+tW1l1UAktAqESZ4/HI1Z+CUT0t8RVdertdhvFYpHLaqlTbhHR22w27tkPh8M8/WZ3d5f74yORCDwez8LCICtPu/mKxSLq9TomkwlCoRDi8TgSiQRCoRC8Xu+tz5pXsyJ0TFH7GqTJxjwi+ltCDcBRoI8GQlJePpfL4d27dzg6OkK5XEan07nyDE+CJ3d+fX0dm5ubePLkCba3t7G+vo5MJsP59EWgQhxy62kST6/X41p+atKhgpjbtvLqFiAKSpLLTy24gjnmit6qM8XnbZy5zs1OX0v1671ej/e6vX//Hj/88AP29/dxcnKysJUnwWcyGWxvb2N3d5fXS2cyGUSjUei6ztZ40RQdpQ1PTk54KIfdbmdPIplMIhKJcAT/tlFnAhiLn8TKL8dc0V93p5oRY7pJfYIv+96X/YxZfw8sHgW/CjU9d9l7qn9P01tbrRYuLi64W25/fx8//PAD3r17x1b1Kuh8m0gksL29jZcvX+LFixfY3t5GOp1m15si64tW3pHgj4+PcXx8jHK5zME76sOnCri7DqDNKmUW0S/HXNFLieN8jD3m6kz3wWDAYq/VatweWygUcHR0hMPDQxwdHeH09BTNZnMhr8rtdiMSiWBtbQ27u7t48eIF9vb2kM1mEQqFps66iwieauvPz8/5mo6Pj9FqteByuRCLxZDJZKb2wd/l3jjV2hu9L8E8omoTDAYDDAaDqR5zYHpSa71e571rxWKRi1yoPfb8/BwXFxdot9v8Xpd5LWpZKkXpnz59iu3tbRa8x+O51AIai4fUoN35+TlyuRzev3+PQqGAi4sL2Gw2hEIhHr5BJa/30cI6Kwgqol+OuaL/7rvv7uo67h31RhqNRrDZbPD7/Ry4Gg6HPMGFXupsdrLw3W53qpqNBF4sFlGtVnFxcYFGo4FWq4Ver8cPDpoNZ4QCV8FgEKlUioN2m5ub7NKrUfpZ7aVqbKHf73PxzcnJCfL5PA4PD1EoFFCr1QAAsVgM8Xic3Xpd1/mhct+I4Jdnruj/+te/3tV1PAjIjaR107u7u/jd737HDR80pYaGShhFSuOtaOuMcSZ9q9Wa8hLUrjJj3TwwXWkXj8enLHwmk5ly6enrL4OGatKs/Hw+j1wux1N4yMLH4/GpUVv34dYbEaHfLHNF//e///2uruNB8q9//QsnJyfY3NzEYDBAo9Hg0cvqGZNQLf3FxQWq1SpqtRrPZ6eHBKWiKB1F5a1UN09BOLW3nKL1z549w/r6+lTxzbxgptoXXywWkc/n8f79e7x79w65XI4j9ePxGNFolHfOpVIpxGIxBAIBie18Ysj/zTlUKhX84x//wE8//cQNH6q4jOkkNTWnTnM1egQ0hkptKKGUmFozr+54p4ESxjz8vBZeqgm4uLjgqTxv377lyTwkeADswkejUR5SEQwGr1XgIzwORPQzUM/HhUIBhULh0q9TRa8OkFwEEpk6kjocDvP5mcpQA4EAQqHQR4Mt56XlqE+/VCohl8vhzZs3+PHHH/H27VsuBiLPhUZQhUIhJJNJfgAtOjxTeFxIym4GdLa/ak0U5bnN4HA4oGka0uk0dnd38erVK2xubk5F4unraOSzuuV2EcGfn5/j4OAAr1+/xrfffouff/6Zd8V1u130ej0OWJLHQVZe07R7PccLt8dcVZu9oYWrcTgcCAaDWF1dxdOnT/HZZ59hY2ODxWZ022cF+WYxHo/R6XRQKpV45t4333yD169f4/j4mDv5qKZAPULE43HE43GEw+EHM6DCGKsQz2N5rGnKr8FlN9myFYU+nw/RaBSZTAZra2vIZrNIJBKmx1vR9/X7fVSrVRweHuL777/HN998g++//x65XI7bZAnK/weDQcRiMSQSCY4rkJW/zwmzxsKnWQNLhOsjor+CmyoXVqFFkslkEul0mifBmL2Z6ftom+3JyQnevn2L7777Dq9fv0Yul+Pzu4rT6eRtOHSWN9OHfxuo9Q+XFSwJ5hDR3wHGlBoF8LLZLM97U4WmpvbU95jHeDzmdt2DgwP8+OOP+Pnnn1EoFKYsvHotDoeDK+/W1taQTqcRDAbv/SxPQldfxCI1CcJ8RPR3DJW4rqysYH19HdlsFpFIZCpoetW5fRbk1ufzefz88894+/Yt8vn8R4JX8fv9SKVS2NjYwPr6OlKpFE+pMV7LXXPZOi2KQ4ibbx4R/R2gWioS/M7ODlfWGRdSXPdmHo/HaLVaODs7w7t377C/v4+joyNUq9Wp8Vvqdfh8Pi7r3dnZmRq8cZ+o1p32+41GI+5NIC9EGm/MI6K/Q4LBINbX1/Hs2TNuiaXKOrNMJhN0u1228lRpVyqV0O12Z56HqepuZ2cHz549w9OnT5FOpz+aI3/ZzzNy0+Izip7KnqksGcDC3YTCx4jobxEaFU0psWw2i2fPnuHLL7/E8+fPkc1moWma6fenpRS1Wg2np6dcS18sFtFoNLiCkMTicDjYpd/Z2cEXX3zBqcJwODzVRTfLfTaes1XR3ZT41PenvfRU2QhgajOw9NWbQ0R/S7jdboRCIUQiEU7NbW5u4tmzZ3j+/Dm2trZ4J5xZaIQ29cOrtfTUKehwOODxeOD3+3lxxNbWFvb29vDy5Uvs7u4ilUrB7/dfepYnqzsajaZcbXWbzk2Iz5iio7LmbreLdrvNW2upJ0HGZplDRG8SqopTq+Poxlfr5VdWVrhmfn19HWtrazzWyuz5mcRAVXe0Duvk5ITXYQUCAW7ioTw8RemfPn2KnZ0dbGxsIJVKza2+I2tL/QSDwWBqKi0V8dxkYE219tS52Gw2MRwOeRKu1+vlFeFi7a+HiP4KZo38olr4SCSCUCj00eIFGhmdyWRY6CsrK4jFYjw33uy2VRqAQSOuaOINpeYmkwl0XUckEuF5ALFYDMlkEmtra9jY2MDq6iq3zRpn3qkioiYjmv5Tq9XQ6/XgcDj4M7itQZXqmd7Y1uz1ehEMBm9ln54VENFfgSp4KmZJpVJYX19n8agTYun8TGWt6XSax03R/HYz16DO26MhHcfHxzg8PEQ+n0etVoPNZuMdb6FQCNFolAVPhUDJZJJbaGdNwqGHHLUJq7MByuUy+v0+vF4v4vE4AHA/wLKoxTjqn41GI/T7fe5apIferJXdwmKI6BfEZrMhHA5zrfyzZ8+ws7PD02vUsdB0jg4EAvwyO2qKGn96vR7a7TbPpT85OcHx8fFHAzA0TUM0GkUikUAqleLSWl3Xed3VrCk4xjHdNF3n+PgYuVwO+Xwe5XIZ4/EY4XAY4/GYq/lovsBtuNnqzMHBYACn08k/T0RvDhH9ArhcLkQiEWxsbODFixd49eoVnj9/js3NTSQSiamNL+rLrMurBs5I7I1GA5VKZWqpZKVSQavVgt1uRywW4778VCqFZDLJzTPUoz8v4EaCpzN0uVxGPp/HwcEB5/1rtRpcLhfS6TR3CNIZ/yYwvs9lVXnqZyxcH2mtVVDPssCHGnkaH7Wzs4NXr17h888/x+bmJg+MXJRZ03aMGM+xjUYD1WqVt86USiVUq1UObFGWIJFIsPueSCQQDoehadrcUVdG60xDN8rlMnK5HN6+fYs3b97g/fv3ODs7Q6/XY8+FgnoUzZ/VHLPo57EIdrudU6CUfhTRm0NaaxUoCk03o67rePHiBQ+h3NrawvPnz7Gzs2Oqem3eTUrWnQZX0tjsYrHIYq9UKqjX6+j3+7Db7VNReZppF41GecjGVfvejMM/1CKft2/f4vXr1zg4OOCsgM32y3Yd1b1WC2lmeTazrPesz2PWKHH6GjVY6PP5OIIveXpzWMuUX4ExGmy32/GrX/0Kv//976HrOsLhMNbW1hCPx6e8IONNarR0V92YdG5VN+CcnZ3xtplyuYx6vc4LMWhdczKZRDab5am1ZN2Na6cuE5r676Yin+PjYxwcHODNmzc8Vovq9yk9p+bmSfBqwY76mcxyz+k6jIU96tep70cpuslkAr/fP7XQQ7g+Ivo5pFIp/Pa3v8Uf//hHOJ1OjMdjBIPBj449xvPlda2PepauVqs4PT1FPp9HoVBAsVicylHTDrtUKsWLKCgVSGK4bAT2vJ/darVQLBaRy+Wwv7+P9+/fs4UfDAaw2+3weDzw+XwcI6Cx3RRopIIgtVFGdf2NBT2zLPWs8zuJHgCLXrbWmmeu6P/yl7/c1XU8CBwOB8bjMWq1Gux2O/70pz/h1atXyGazAIBer3fjcQ41Ot3pdNBoNDgnrlahBYNBhMNhPrvTWCt1mKYZy6daeRqNTUMzycLb7XYEAgHouo5QKIRgMMjpx36/zzl0er/hcDizUcbtdsPj8cDj8UxV1KnHAqN3QNV31F3n9/un3Hvh+sy9g//2t7/d1XXcO+oNRM0dNCiSuI0ONLXOXJ2JTxNt1Kq6aDTKI63UAZlmBWDMx5fLZZTL5akiHMrDU90BLcikBSDNZpNr/NVJwNQkA4DXageDQU4dAh88JHowqJ8HHbVoOCjwYZffXe/T+9SYK/qXL1/e1XU8aG671FMdakECo7oAh8MBn8+HUCjEL3oYLHOuNT5saGvPZDLhhwzl8+nn00gtTdNgs9l4nr+6pLPVaqHdbvPobypoonQiWW632z213Ue9LvVIQKJXdwSIlV8OOdMvwG2fHenc6vP5OCsQjUYBfLBuPp9vKoh1E2daVWAk7kgkgm63i0AgwKL1+XzQNA26rkPTNPj9foxGI1SrVbTbbTSbTT6SNJtNtFotPt/Tlt3hcAifzwdd12em6owBPGMgjz6LRTfyCpcjKbs5GCfQ3ubPcLlcXOSjadrUDe92u1noN3k96s+g1VlUadfpdPgcrZ7DyR2n1duUSqxUKqjVaryjbzKZcFGTy+VCPB7nRh1jII+Cf7MET/l5GuIpqbrlmSt6q36wixTR3CR0E6tBK/r5FOi6DXeW3t/r9SIcDsNut0PTNN6kqwrPZrNhOBxyeS5tzTk+Pp4qGKLFnjTXX9d1fqhomsYFQ2SxLxOwWtVIX0MPDHHtl2Ou6CUPevuo6b67/rzVdJgqfqq0U4Nq/X6fB3N0u11u+CkUCrwth7bwklvvcrmg6zoSiQRXCoZCIQ5OzhK8sYyZ/p4svrGNWbg+cqa3KKqYyOp6PJ6P8utq7z7l4tU+AIr2U8SeqvY8Hg+XL6+urmJlZYVHfXs8no/Eq1b1zSp0ovy+uPbLI6K3MKroyGU2BtTIylNkn1ZxVyoVdulJ8AA4lx6LxbC6uoonT55ge3sb2WwW0WgUfr//UhfdOBxU/b16zBHRL4eIXgAwe+w2RfUpEj8YDHjHfb1ev1TwiUQCGxsb2N3dxdOnT7G+vo54PM5pRuPPUVHn2qsWX3X3RfTLIaK/Aa6qbX/MkMjUKTr0olw8VdyRhd/Y2OBZgNvb27xEY1aJsPpzCKPXoV7Hp/b53gci+iWY1RxCvz72m3PWBBsaUNlut9HtdtkDoJp8svCfffYZXr58iZ2dHWQyGei6PlfwhOptXNa09Ng/14eAiH4JSAzqBhYKOAGP+wY1lsXS/HmqwKMcPrW6xmIxbG5u8pTdvb09rK6uIhQKLSR448+97t8JiyOiN4k6v42i2mpTyacygESd4jMcDtmdp1JdKsBZWVnB3t4eL89YWVlBOBz+qM13UUTgt8encWfeA9Rg0mg0eLAFFaHQxNtPof1TbZOlqrhAIMCNN7quI5PJYHt7G7u7u9ja2kImk/lobqDwcBDRm8DYg352doZmswmHw8G5aYpWP2bhq2k7KuTRNI2HiDidTsTjcaytrWFra4s33y56hhfuBxG9CSaTX9ZJ0Wip/f19VKtV2Gw2pFIprj2nGXqPXfgAeNZ9NBrFcDhELBZDIBBAIpHA6uoqstksT+MVwT9sRPQmoCKVUqmEw8NDvHnzBqenpwB+mbbT6XR4iEQymWQhPFboDK9pGhKJBHw+HyaTCVt9GrNNeXgR/MNGRL8gagqJusxovNTPP/+M4+NjjMdjnJ+f82AJtWlF07RHG9xT+/ztdjv3+dN+PLW/XwT/8Hmcd+E9MxqN0Gq1UC6XcXp6ikKhgJOTE4xGI9RqNXQ6HY7oE5lMBoFA4FH1gqtpSIrW0/ALatShlJ00Zz0eRPQmoHHR9Xqd20xpjxzVqdPYK5pJYLPZeAf8Y7P4lIokNx/40KhDs/lusyb+U654vA8e1933QFDn09N4qF6vxwMm6DxPhTtqsU46neamk4d8A6uVheogC3V23W2K/bKhGncx2ORTR0RvAkrZUWEONZxQwY7ai0516ero52QyycKn73uoN7FRaMapNrdRcqx+jurnSUsvqK9eMIeI3gRq/pqq1Yz0+33eCkMvp9MJl8sFu93OUfCHOAVm1mIKmnFPv78tsVP/Pm2qpQ21ALjGn8ZvP8TP7jEgojfBIjc7ne/r9Tqff71eL09zdTgciMfjU0Gwux7TNeua5/3eWI9PqBNtzV47WXd1h1+9XuduPqfTiWAwiEgkwg9QEb05RPQmMM6uM97oqiD6/T5qtRry+TxbenJRbTYbIpEIj6tSRXUXwp81ldb455c9CIxu/jLCnzV/v1gs8lSewWAAr9eLRCKByWTCFv+xBUQfCvKpmYDO6epkV2KWSHq9Hur1OgqFwlS0ezgcYm1tDbFYjG9io/jVn3lTqMK97LrVP7/spV6XOubqutdr3LJzdHTEK71qtRpGoxF0XcdgMIDP50M4HJ55pBIWQ0RvAopme71e3gAzDxL+xcUFcrkcC4TOrZ1OZ6qE9arhj8u40PSrKtyr3Ho1hjFL9OrXXzeyrvYxlMtl5PN5HBwcIJfLoVgsotVqwWb7ZfGHz+fjMmfjslFhcUT0JqDBEX6/n6vRvF4v19zPgna/VyoVvtG73S6azSbq9Tqy2SySySTviTPmv9WXGfd/lqVWRWz82nnfp36P6tKr0X36s6uuic7xjUYDpVIJhUIBuVwOuVwOFxcXGAwGfCzqdDpTk3oFc4joTUAbXIPBIEKhEHRdR7Va5cWNsyCh01JKOsPW63VUKhVsbGxgdXUVyWSSd8zTGGl10aPRki4aVDSKnWoIZm2XXeRMr/5sOtdfV4hG0dNZvlQqoV6vo91uAwAfo9Tj1ENNcT4GRPQmoBnxtLOexjq3Wq2530fC73Q6XODTbDZ5U8z5+TlWVla4NTccDiMQCPARQs1RLyp+o4DVrbK0ZJJ+VacA0Xsag5bGl1nB0/XQIJJms8kbe2ncNgDOegQCAQQCgSkvSDCHiN4ENpuNRR+JRPi8aRzhfBlk3eiG73Q6qNfrLPxiscjufjweRygU4hve4/FMbYdRi2bmCd9o5SmeQDEFyokbR3+Ra01LJ43ZB+NrUeh6aIEmPXzoZ9PP8fv9iEQivDFXXawpmENEvyBGq+rxeBAKhaZaS+v1+lS9/TzrR9a21+uh0+mwpWs2m7wNliLUsyyu2fO9KvzBYIBOp4NGo4FWq8XjrCeTCdcWeDweeL3eqepCuib1wWOMPVz3s6VFmbQSm4QdCAQQi8X4IUgPWLH05hHRm4BEHw6HkclksLGxwUEnijhT481VTCYTdLtdrtEnDyIajbL4qXnHbPBKPXsbhalaW7WkWC11pfM0WXsqMCKPY9aRYxHh04OD2nYTiQQGgwH8fj86nQ4P7ohEIkgmk0in0wiHw2Lpl0REbwJyP2k+3NOnTzEYDOB2u1EoFHBxccHWU+2rN0bKyXrTOmoKCgaDQWiahkAgAL/fP9etv0pkakptlstOhS7kndCvZOX9fj+/KLZArvcylp6+jibqhsNhTCYT+P1+pFIpDAYDXp8dDAYRDAY5fiKz95ZDRG8SGoKZTqcxHo95DPTJyQlKpRLvaqcOPLL8FCyjG97tdnOgKhKJIJVKIZvNYn19HZlMhpc+UkBvnvAvY96DQa05UI8U9FAgoZPYqXdgltDNuvVer5fjJOFwmK/DeMRQHzgievPYrnAZJRk6B+qrbzQaqFarvKtdXftE7bcULCNXnrwFr9cLTdO4rjwej3OcgCwbiV2t2DPb8GIM6FEAjYJp6tmdAnb0MqYMb0p4xuYlYzBx1kNGWIiZH5SIfkkocEcBOfVFIifR058Nh0PYbL8MpPD5fLy7ndx6TdPYnTbucb/JG17dTKsW6lwWOBQeHSL628SYFqOHAQXI6AFAqTrVtVXLeUnkagHKfTXfiNAfPSL6+4TcZ3KhAUwF1SQFJdwCIvqHwGU96oJwC8y8uSR6f4Nc9gBdtFb+tlppBUFFLL0gfLrMtBxS1iQIFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFkNELwgWQ0QvCBZDRC8IFsN5xd/b7uQqBEG4M8TSC4LFENELgsUQ0QuCxRDRC4LFENELgsUQ0QuCxfj/XmmKuZ7w4XYAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 42; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 43; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 44; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 45; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 46; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 47; current eta: 0.5, current beta: 32\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 48; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 49; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 50; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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yIiZ/eno60ntuGux2O8LhMHZ2drCzs4NwOHyrtZSaYOR/k7vcNpvNW9eWhU+7L7Xuoqq6ZrOJarUqimS63e7YfvjKXX5SmE75GGZ2WPTfCEo2odZT2WwWiURCZL6dnJzg7Oxs6mIarVYrBL+9vY3t7W2srKzA4/FM1VZKrpcvFArIZrPI5/NotVqqPe/kx1GDS+rg0+/30Wq1hAOv1Wrdat6pPJ/Lwlf21Bt3s2DuB4v+G0B936gZBdXFX15e4urqCtfX10gmk8hms1O1ttbr9XC5XAiHw2Ic1cbGBrxe751mvXwjGA4/TcyJx+MiTHdXiJDaWFutVlFKS911qtWq6IevNO1lU50y+ajCbpzY5cex8O8Pi/4rQ+f2fD6P6+trXFxc4OzsDBcXF7i5uUEymRxpQ6UmOPrQk+DIcbe7u4tnz55hf38fy8vLYxtjTFobtbamUF2xWBxpWCGL0Gg0wmq1wu12w+PxwOFwwGAwoNVqibp3SspROvDknZ4Er+bEmyR6Fv79YNE/Emo97Ah5J5VLVI+OjnB0dISPHz8ikUigWCyOTIpRotFoRIqrzWaDzWaD2+1GMBjE2tqaOMevra3B5/PdOYOOREXr6/V6qFQqSKVSiMfjSKVSY9N9TSYTHA4HfD4flpaWxPW63a4Quxymk/viyTct+YvWIK9NzsOXLQN25t0fFv0jofwAqtWMU3ns6ekp3rx5g99//x3v379HPB4XO/uk5zeZTHC5XPD7/VhcXBQ94hcXFxEOh7G6uorl5WV4PJ6JPe/GrbFeryOXyyGZTIq+e2o7La1jeXlZXNPhcKDb7SKXy4kzvbzLy3X3VHFHu7zJZBL19Gottei6coIOi/7+sOhn5L4OpUajgXg8jpOTE/z+++/4/fff8eHDB8RiMZRKpTsfT0ILh8OIRqPY3NwU7a68Xi88Hg/cbjfsdvutzrZqr2E4HI4Ip9VqIZ1O4/r6GldXV8jn86r18jTldnV1FRsbG9jY2EAwGIROp0Mul0O1WhWil3d5et8ok45CfGazWYyoohsDtcpSQjcJygmYNFyTGQ+LfkZm3V2GwyFqtZroePPq1SsheArHTXNNs9kMv9+PaDSKFy9eYH9/H2tra/D7/bBarTCZTOJsPOvr6PV6yGQyODs7w/HxMa6vr1EqlUZucHq9Xsyw39jYwO7uLnZ2dkQ+f6vVwmAwQCwWExV11PJazVrQ6XTiqLKwsACtVit+Xy01V97lSfgs+vsxUfTz2o5I2QFnktOIKsfoMbQDUS18pVJBOp3Gx48fRT38ycnJxIo1JTqdDjabDUtLS9ja2sL+/j52d3exuLgIm8029YdfrZx1OBwil8vh+PgYr169wrt373BzcyN6zwOfBO9wOBAKhbC1tYXDw0Ps7+9jfX0dgUAABoNBhPn0ej2Gw+HIjD2leEnwFosFDodDtOxqNpvodDqqgztI9PJOz+b9/Zgo+oc2KVBmb9H3alldj3UNtZ8D070W+bnUHHPK7+v1OsrlMmq1Gvr9/kjb5263i1KphHg8jsvLS5ycnIhhj+l0eqa0Wp1OB7vdjqWlJaytrWF9fR2Li4uwWq0P2u1I8Kenp3j58iV+++03HB8fI5PJCNPeYDDAbrdjZWUFe3t7okR3c3MTgUAAFosFw+GnltVms/mW6JXvL5XfkuDdbjcWFhaEaU+975U3W7pZyLPsmfsxUfTkUWU+Qbt3o9FApVJBPp9HNptFqVRCr9eD0WiExWKBXq8XZ+SrqytRtJJIJMRI51luemazGS6XC4uLi1heXobf7xc7/LjSWDXkn1F3W1nwR0dHSCaTokmGVquFy+VCKBTCwcEBfvrpJzx79gzRaFQMyNBoNOj3++JmR2tSCzWS191sNsPhcMDj8cDn88FisaDT6aDX66FWq00cqCl77+X/F971p4dVPSUUw6aZ7KlUasTLPRgMxA6m1+tvFazkcjlhvs4ieIPBAKfTKbz1Xq9XdYdX+9CP605Tr9eRTqeFSf/rr7/i/fv3SCQSaDabAD6PvgqFQjg8PMRPP/2EFy9eiFx+i8VyaxdW9sFTro/MervdDo/Hg0AgAL/fD6PRKNJ+5ZbW3BLryzBR9G/fvv1a6/jmKEtbgU+tppxOJ7RaLSqViih1PT8/x83NDVKpFIrFIprNpjDvqeik3W6jVCqhUCjcGY4bB+20FBoLh8PweDy3suzuSq0FPnnE2+22mFBzdnaGN2/e4NWrV3j//r1oskmPo+PE9vY2Xrx4gR9++AFbW1vw+/2q0QG1NFrla6GjgtfrFVaL2+0W77ncS09us0X/puyZd9frZ9SZKPo///nPX2sd3wVkmlJ/9t3dXfz8888IBoMoFAq4uLgQX/F4HMVicSQ3Xf7g03TXh0xYtdvtCIVC2Nvbw+7uLlZWVuByue48dinNXWp7lUqlcHNzIwZWHB8f4+zsDOl0eqTvndFohMfjQTQaxbNnz/D8+XNsbW3B5/ONCF7NrFYz7eUBGF6vF0tLS4hEIiJrsNVqiWiB3EdP9vzTz6g55rw6mR+DiZ+ef/u3f/ta6/guef36NdLpNMLhMMrlMuLxOBKJBDKZzMxNLWaBst3ItH727Bl2dnawvLwMu90+tvuN2r+1Wi3E43Gcnp7i5ORETKehWXTKvvmUx7+6uiq655LTbtxkW+BzabDaDU6v12NhYQFutxuhUAhra2tYWVmB3++HRqMRIqebZLfbHelrT89P46zlHACecDM7fKafQC6Xw7//+7/DbrePzFJTKzd9LJxOJwKBgKiWOzg4wN7eHlZXV+HxeCYm3ig76TYaDSSTSfzxxx94+fIl3r17h/Pzc2QyGZHuq7xx2Ww2LC8vY29vb6zglTu8sie9MuRpNpvh8XgQDoexvr6OjY0NRCIRWK1WNBoNUYBEXXbkpB6CKhKpBJnm2VE3IGZ6WPQqyM6oWCz2Va5pMBjETri1tSWSX9bX10XW3V0fcFnwVC13dHSEX3/9Fb/++itOT0+RTqdH8vrlMCWNsN7e3hax+EgkMpLSq+ZYU7a3pufVarVih6dMQurXFwgEAECk6tZqNXFTVabu0jWazSZKpRKKxSLK5bLI9+cz/WxwyE4FOts/9jhktZwCs9ksnFsrKysjgl9ZWRHe+ml3eOBzyu/r16/xl7/8Ba9fv8bp6Skymcwt85vWo9Vq4Xa7sbq6OuJDcDqdd74umjZLJjkA4aWn59zZ2cHe3h42NzfFDD3a5eWmHVR7rzyzUzgvl8shk8mgUChgaWkJDodjqn4BzGcmqvpLmbDzilLwNpsNfr8fkUgE6+vr2NzcxPb2tjB/PR7PrVlzd9FqtZBMJnF0dIS//OUv+I//+A+cnp6iWCxOfJyc8UftssmzTmtXy0ykZiByMg6V+7rdbiwtLWF3dxd7e3vY3t5GKBQSo7Hp/N5oNFCr1VCv14Xole8VdRfK5/PIZDLI5/PipsHMxnxu5TMwrlz2odDI6K2tLezs7AizNxQKwefzweFwzJxHT067d+/e4a9//StevXqlKni5xTQ93ul0ivVsbGzA5/NN5SQjrzrt8jqdDg6HAxqNBjabDevr69jd3cXW1hYikQjcbjeMRqNIXaY22fV6XfhKxr3PZN7ncjmUSiU0m82xvfSY8bDo7+CxdxKNRgOfz4f19XUcHByIUVMrKysIBAKw2WwzNbEkqKxVzrD7+PGj6g6vzIc3Go0jlXPLy8u3puCME5IcZgMAq9WKYDCIYDAoputEo1GRY2AymcTRiZxz8jCMSdZlr9cTffeowy7v9LPDov9K0GBHr9eLjY0NHB4eimo5ir8rHXWTdi7lz8isPz4+xrt373BycoJMJjPyGGVtAUGONrI03G731Odk2bTX6XRwuVwwmUyw2WxYXFzE6uoqlpaW4Ha7YbFYRDXduOk3asjrpi67rVZLWAvMbLDovwJmsxlutxuLi4uIRqMi/r27u4tIJHIr9k7MYqZWKhXEYjF8/PgRFxcXE5128vNT5t36+jpWV1dFiu00kHBphzaZTPB6vdDr9fB6vQgGgwgEAnA6naL0l1Du8uSxvwvZaahWtsvcDYv+C6HVakWSDTnrNjY2REuraDQqKuWIaVJL1Xb/Wq2GZDKJ8/NzIXg58kBdaZQYDAYEAgFsbW1he3tbOO+mudmQid5sNtFoNNDr9WA2m2E0GkUbLY/HI5p6yE5AOsurTb+5C7U59cxssOgfGWonRUk2oVAIKysr2NjYGOl2o5ZoM63Y5N9rt9sil56GXFIOvfK5lbuiw+FAJBIZ6ZEv34Qm0e12Ua/XRcfbfr8v5tK7XC643W7hn1BaMZR9Jz9+2vCoMjOv0+mINXOzzOlg0d8TuUml2WyGwWAQnV2sViv8fj/W1tYQjUaxvr4ucs19Pt+txhfTxJflxpDyv2WzWRwfH+Pt27c4Pj5GKpW6Vdyj7DdvMBhGPOsUk3e73SNFL+NCdJRMUygURDPP4XA4Inir1TpW8BSmq1QqIrtu2vM5lTdTZl6lUhGxehb8dLDo74nNZoPP5xP17RaLRRSWWCwWLC4uYn19XWTUUbNKmts+LUrHm2wiy976ly9f4uzsTOTSj2suYrfbEQgExAjrFy9eiB75ylRb+XqE3K8/m82K7j82mw0WiwV2ux0LCwtC8PJ6ydPfaDRQKpWQz+dRKpVQr9eFJ15t3cqwXLPZRKFQEPF6l8slwoTM3bDo7wGFuCjLbGNjYySuTqmni4uLCAaDIhmFUO7s0zS+UJbJ5nI5nJyc4LfffsPLly/x4cMHpNNpkUuv9A/IHWw3NzdF1t/GxgbC4fCtEJ1yvWTOF4tFpNNpUVbc7/fhcDjgdDphsVjEuV4WPP3Z7XZFVl0qlRIFP3Q8oN+9a8dvNBpiIlA6nUYgEBCDNtTeX2YUFv2MUCLL6uoqnj17hr/7u7/Dzs4OXC4XgE8fOGqbtbCwIMJUyudQorajq/0edeQ5Pj7Gy5cv8de//hVHR0cjLa4IrVYregL4fD5R8EIJQZTma7PZxqb5DoefevWT2GOxGOLxOPL5PDqdjtjhDQbDLcFTx13gcwFQNpsV5b2xWEw0FyHRy1NuxtFoNMSNg6wNv98/Eh1g4Y+HRT8jCwsLoiMshd6i0eidbafvQm1HBz7Xp9NOm06ncXp6itevX+O3334TffOp4w1BNfEUK19bW8Pq6qr4CoVCqg05ZPr9PqrVKjKZjJjGc3FxgVQqhVarBbPZjEgkIjriyB155eIfWjt13P3w4QNOT09xc3MjJvLO0nOAmoGQT6HRaNy7Z8E8wqKfgNwQg5CHRFLYbZo+8/Jzjvs3JZ1OB5VKBaVSSZyjr6+v8eHDB7x//1600ZYbYBgMBlitVgQCAayurop8/vX1dYRCIfj9frhcrhFzWA25sSc13Dg/P0c8Hke5XIbBYMDy8jKATzcYpeCpdx41yEilUri4uBANQs/Pz5FMJlGr1UZSb+XuO5PWRjF+TtKZHRb9BJRmptlsxtLSEjY3N7G1tTV1iOuuRhfKa/Z6PTHvLh6Pi4YXyWQSNzc3onOPbNLLocJIJILNzU1RLbexsTGSFXeX2dvtdpHP53F1dYUPHz7gzZs3YmculUro9/vweDwjfQHlTrj9fh/9fh/1eh2FQgHxeBxnZ2eiicfNzY3oLUhJNrOibJ/FTA+X1qpA51Blg4nFxUVsb29jb28P6+vron2U2hlUaa5PSqntdDojOeVUaprNZnF5eSlEnk6nUSgUUCqVUK1WheCpiaXH48Hq6ir29/dxcHAgquV8Ph+sVuutAhpat+xzIMF//PgR7969w5s3b3B0dCQET5l3wCerYmFhYeQsT955+Vhwfn6O09NTXFxcIBaLoVAojA3TTSNgjebzHDyDwcDdc2aES2tVUApTq9UiGAxiZ2cHh4eH2NvbEzXh8u9P235aptlsIpfLCQcZ7YDVahXFYhHxeFw04SyVSiK0ReLQ6/Ww2+1YXl4Wfobnz5+P9NQbJwrlmvr9PkqlEq6vr0Wl3h9//CEm3tDOTmFJcuLReGpKpy0Wi8Kcp6Sh6+trpNNplMvlkew7+b2bdscmJ6nD4RBOSO6DPz3zuZXfgfzhI0dYJBLB/v6+aAN9V7rqpJ2dQleVSgW5XE7shrSjl0ol0T6qVCqJM70SudvO7u4uDg8PcXh4iK2tLYRCITgcjrGvi9YmWyLVahWxWAxHR0d48+YN/vjjD1xeXoqbjXIUFcXje70eyuWyWCsN+KDXlEgkkMvlRLqt/N6o3ZDuEr/ZbIbX60UgEFBNdmImw6KfgNvtxt///d/jxYsXWFpaEg68YDB4ZwrtJOdcLpfDzc0Nbm5ucH19jcvLSzE4MpVKoVqtCiuL8syV6PV6OJ1OhMNhHBwc4Mcff8Tz588RjUbh9/tVp9ZO8iNQn34av/X+/Xuxw1MDSgpFWq1WscPSPADqfJNMJnF5eSlek3x2V74OOpOrDe2YhNVqFYlRwWBwpHvOpNfJfGKi6P/pn/7pa63ju4AKU5rNJnQ6HaLRKH7++Wdsbm7C4XDA4XCoZq7d9SHr9XpiMk4ul8PV1RWOj49FZ1qafFMsFsfOg5ehARiRSEQMovjhhx9EE0s5DDdNY4lut4tisYjr62uxLhI87fBKwdN0m0qlIopuMpnMyHGkUCigWq1ODKfJwgeg+ruy6a/RaESa8+LiIgKBAOx2O5/rZ2Ci6P/1X//1a63ju0BOJNFoNGLe2sLCgpihpnRu3iX4TqeDUqmETCYjRkGfnp7iw4cPOD8/F4Msx+3oamu0Wq1i1NSPP/6IFy9eYHNzEz6fb+pBGDIU/7+4uBCDPOgMLwveZrOJm5/BYECz2RRTcQqFgsjUkwd8fAnPOmUXer1euN1umM1mAJyQMy0TRX94ePi11vGkkaexUOlnv98XgqczLonq6uoKNzc3I1NlZMa16KIWVBRFeP78uZgtR+OhlI+7y+/QarVQKBQQi8VGfAq9Xm8ky85qtd4aOEnTfUqlErLZLPL5PMrlMur1+szJMspJNuMgz73FYoHValXt1MvCnwyf6R8IiZu+2u22cMKVy2Xhxf748SPOzs5ECit1flVj3IfebDaLFlTktItGo/B6vaqCv4tOpyPGXMXjcaRSKVQqFTGQUq/Xw2w2C5OexkrTrD7Kxc/n86hUKqjVal9sdyfkcdUcrrsfHLKbgDymSv43gso8K5UKyuWyCLXVajUx7DIej+Pq6gqXl5dIJpO3JsrQc94lFJPJJOLwe3t72N/fRzQaRSAQEOat2hrVoAKaarWKXC6HdDqNfD6PVqsFg8EAr9cL4NNNxmaziR3VYDCIrrRUJUemfLPZfHDL8GluFvLUWq6fvx8TRc9v6G3kcyMJIJ1O4+bmRoi6Wq2iUCiISjIyfamEdFYo044q5La3t7G2tnZreuw0Z1o5j79YLCKbzYoRXRaLBcvLyxgMBiIWLvfcp7M7taKmYhdqWz0Lk2oNJqHT6WAwGMQuz5/R2ZkoejadbiN/KAeDAWq1GhKJhBgGmc1mxU5IHnnKnht3zlX7oNMuJufSU398mgNHHnT5MWrPS/4GOopQTTytbTAYwG63IxKJAPh0k7FYLKJ6bjAYoFKpiDz/YrEobm73FbyyIchdHn6C1kbHDxb97PCZ/gFQS+ZUKiWqx7LZrDjbK8c0TQMlrNC51WKxiGq5UCiEQCAAh8MBvV4vWkepdachZ6LcT04uVKFe8xqNRkQotFotjEbjSC49Jd7QDL9isShCi/cVPEVC6Fgz7XtDpcIUMjQajSz6e8CifwBkJpMpH4/HkcvlxM9JELRbqn3A6WxK51S9Xg+dTgej0Sgy37xe70jmWaPRQD6fR7vdhtFoHDFz5T703W4XnU5HfK82Bprq7clTTyWyRqMR/X5fJN5QjkE2mxWDJmYRPDneTCaT6B5E3XQptfiuwhvqzONyueB0Om/NseMz/nSw6GdE/lBRg8Z6vS4GMDabTSFcAKLyDIBIQlETOe3sJHgSIeUKmEwmdLtdZLNZ9Pt9ZDIZYeLKopd3d8qCoxsNxdvJdLdareLvFJqj3HqNRiPCcVQ8Qw6/Wq02k9OO5vU5nU7RHbff74v3jI4Yk0RPeRMejwc+n0/MCWCRzw6L/p7I45xoN6UYM4maTFn6XqfTCROaxGU0Gkf+pJp0+Uag1+vRbreRzWZRqVRwfX090ohT3uVpByfxk1+AmlZSZSC19JI739C16bVR8UwikRAhPSqYmQatVjtyPKF+gpTYk06nkUwmxbUmjbTS6XRizT6fb+oyYeY2LPp7IjvHaJa6LHgyYUm8ZNoajUYsLCwIM5rMXVm88vPK1oTSNFerJ5edd3KN/eLiotjNabiky+UaaWJJNypqbUVVfpRMRJ77aUJrer1+ZEDn5uYm1tfXEQgEoNPpUCwWcXl5CQCiGYZslQCjoUy6gbhcLng8nrHmPXM3LPoHQMJTE59GoxG7N/WDt1qtI7nrsuBpd+52uyK5hwZC0BSYVquFdrstSlNpPJQyi41uPEajUZTWUjafbB7bbDZhMcg5CdQmi4pnLi4ukEwmUalUpnK6keCp4Qi12V5bW4PH4xGtu/V6vQgdlsvlkZZfagVMlApst9tFwQ8LfXZY9A9ErXML7fA05WZhYUE4zCirzWKxCO+z7NCinu503qURziR8Ok7Is+DVoMYa9GcgEMDy8jIWFxfh8XhGBlHIOyp1vKFc/LOzM9zc3KBYLE5l1tPNhUZUP3/+HAcHB9jY2EAwGITVahWNOBqNhsgGzGQyIqNPfi45tVZ2birHZDHTw+/aA1Gr41bWi5NzjnZ+2l0pUUYe8UQTX+jPRqMhJrl0Oh1h8k8ysakohwZTrq+vIxqNivi+UvD0GOppVygUcHNzg7OzM1xeXiKTyaBarU5l1i8sLMDv9yMajeLZs2f48ccfsb29LSby6nQ6cXOjqATdhAqFgnhf1HZ6OjaRn4N3+fvBon9EZDObzvkUJqOkGDLldTrdSKdY+YtMeqXYZQ/3OFFQvrzH48Ha2hp2dnawu7uLaDSKpaUlEQmQb1b0XFRiG4vFRMebRCKBcrk8lVlPgzojkQj29vZwcHCA7e1thEIh0a6LXjul+DocjpEBGXq9Ht1u91aprXyDUq6bmQ0W/SMhn+3lKjs5dkz94wwGg/ie6uypJl0u3qEBjeS4Gyc82aKgc3soFMLOzg729/dFJx0aI61WxUchNEo0Oj09xdXVFXK53FThOWrbRed4GqRBmYMkeAAi6iCHJcm/QT9Tg26isvOUO+bMDov+kZBLaymUJ98IaEcnr/5wOBwRuCx0OfwnP4cSORRI5a9erxehUAjRaFQIPhwOw+PxiH52StHLfempTfXFxQWy2ayYSHsXsujX1tawtraGxcVF2Gw2kasgRxbkLwAj0QM1ZCcnzbPv9XoT+/Yz6rDoH4BarruceUfJMbSj0wcb+Gz+UwtoukmoeeOB274D2Zw3mUyw2+3weDwIh8NiQu7m5qbY4eVhkrL4qF9fNpvF+fm56JwTi8WmjslrNBqYzWbRWcjv98Pj8YgyXGVIkawgOSpxl2OSav9pcGWtVkOn05l5NiDDor83ctGI7AGXTXyCkmTkD6fccENt51NeS74mNZKgcCC1v6ax2PJIbKfTCaPRKCwLihbI1kc+nxetsk5OTka89Xc57+RcBMo70Ol0IiLRbrdHrAvKASiXy8jn8yNDLOUbn5LBYCAGZ8i5/3LREXfOmQ4W/T2Rz9EUnlOrGqOdTe0cPY3Y5S/5epRO63Q64fF4ROPOlZUVhMNheL1eWCwWABBHB/noQbtmuVwW8fiPHz/i/PwcmUxmZKjkpPdAzjqUaxHS6TSMRiPa7fbIIAxq3JFIJHB9fY1YLIZ0Oo1qtSoclmrvhZwwlMvlUCgU0Gw2v2jDjr9VWPQPQM62k9NmyQKQd6y7WkCpfa8UOyXcUKzabreLVtDLy8tYXl4WNfbD4VDE92WTms7E1IK7UCggmUyONLSsVCriHH9Xgw9lFh/5BXQ6HRqNBrxeL+x2u7A2Wq0W8vk8bm5ucHJygsvLS5Fe3Gq1xoqeGpbSDDv6fRb97LDoZ0Q2IckDTb3gqeJNLh0d9yEGRsWubCohHx2UqbxWq1VUm/l8Pni9XlGAIoudfAWUxUdnaMoDqFarwlzO5XIolUqimIZeJ5nqk44dlKtfrVaRSCTEGtLpNHw+H5xOp4gayOO6KL03l8uNpBmPg8qVKYdB7fjBJv7dsOgfAMXEKePOarWi0WiIDx4lmah54NWaOMqFOrKvgCwIurlQZRx9GY1G0cWHdj9K+JG/KCxIf1JCEDnTKP9dadYrrRYldH6nfv1Ud59IJOB2u2G320VxTLvdFt2Bs9kscrmcyMS7K0og9wSQp+TI7ykL/m5Y9A9ATjJxuVxwu90jiTTdbld4peWYslraLv2pPMPLJbjKqjpyblUqFXS7XZHYQud12hFJ2ORYI+GQiGi9avUDtN5xfell3wWJv9VqoVqtIpvNiv56VBzT7XbRbDZRqVRGmoxMExakY8q4CAczHSz6B0C59RSqCgQC4txKXXEpvVU2XdWERX+Xd3v578Dn5BTaxYFPTrparXYrw49y92lnl8OHtIa7RDPp58qfyaW8VBlYqVRGyojpqECvgW480zbjoBJkek5u53Y/WPQPQKvVwmw2w+l0IhAIoFgsQqPRCLHV63VotVqRVipn18nmPYlb/p7+Tr8nJ/v0+31xU5EHcFAeP5n5chLLOGEprz8O5Q1r3A2Bdny6XrvdFj4OGTmrblooF8DlcsHhcHDnnHvCon8AVPnlcDjg8/lQLpcxHA5hNptRLpcBQDi1xmXXqTnzlCE9OhpQPzwSEt1UyH9Ax4lZinPUQobKdSh/fxbUTPH7mOdUROTz+RAMBuHz+URfP2Y2WPQzohSpsisNJcDQbkx97NQSdIBR59MkYchOK6UPQLYAyIcgp/JOgzJr7qEooxD3fQ5aC9UVBINB0SDUbrer3jSZybDoH4jcQZYaZDSbzZEGGYTsiJKZlIU2LnlH+QGXi3K+taNLGX6UmaUrsFL0DocDwWAQS0tLCAQCsFqtLPR7wKJ/IPLuLZ9nyVFFZ28qk6WQ2DRONKXg1TrpEt9a6DLKc/+sc+3osfJrodZbS0tLt8ZTc6huNlj0D4R2b7lmXhkTl9tcyeK8a/jFXQKeVUxqwpj2JjFOVF/jJmM2mxEMBhGJRBAKheDxeG6N8mKmh0X/CMglrpREQyY/JZFQ1xc6a6s589TO+/LflWd5+THyjURuoClbFQ8R6DSPlTv+ynkG8nPQ61euTYZyEmi6TzAYxM7ODra3txGJROB0Ojlc9wBY9A+EEnQoD77f74vqN6fTKSrIKHxGhS/yh13ZmFKtjFb+mZyLL6OWX0+DKh46XHIScmbiwsKC6KNPFXfk2KT4Pa1NDikOBoORKbkOhwNOpxN+vx/hcBhbW1vY399HOByGzWZjB94DYNE/APLeUyhJp9PBbrcjGAyiWq2iUqmMtMEi0cuJOnKqrZyCq9zNlcMxqMuMHLIjUdVqNTFVlgZUUuxe2dxjGl+AXEknf8m1AFTeS621KfWWbk7Uf4/WRnP+6H0h0TudThGWCwaDopAoHA6Lvvls2j8MzR1m27f3Cn3n0O5KDjs6u9P3cqMI2Zkni56EIafdKk15tQEY8m5PpjOlwBYKBZHfTjXrlFtPqbfUqousAVofrYt69FPLbsrzl2fEk5XjcrlGGl3SfDyqOlSujcZc0xDMfr8Pg8EAl8slxE5OO7fbLboIm81mNu2nR9UEYtE/EsrdUx4tRckylDwjJ8zIZ1+13ZR+RzbrZfNeDm3JQqay2UKhIKrnKO+eOvmQAGlcVblcFgU7er0eVqsVHo8HgUBA9Mqnbjh05qYW35QpR3PmaMQ1ZQsOBgPR3rtSqaBUKqFcLovx3SR6Oib5/X74/X5RocdCvxcs+m+FnHI6Lg1XTez0c/n3lM06lFBmnmxlULGN3FSy1WqhXC4jk8kgmUwilUqhUCig0WhgMBjAaDTCbrfD7/cjFAqJLDjqeScfN8iHYTabxReVGSstESr2kS0fem/k5iBkXbDYHwSLft5QxvnlzD0qhc3n8+IYQEcAeaf3er0IBoMIBAJiDJbc/mpSRGHSutT+lJ+HnXOPAov+a6BWJ/+9Xo9q8MnLT9V4AIT5rhwj9bUZV5HITAWLnrnNJC++fJzgwpYnCYt+nrirgOY+JvSk0N4scfNJa5v2iMBMBYt+3nmsoweb3E8GFj2jjtrOyxlvfxOw6JnJjDsGME8W1f88TsNlBCzw+YBdsgwzZ7DoGWbOYNEzzJzBomeYOYNFzzBzBoueYeYMFj3DzBkseoaZM1j0DDNnsOgZZs5g0TPMnMGiZ5g5g0XPMHMGi55h5gwWPcPMGSx6hpkzWPQMM2ew6BlmzmDRM8ycwaJnmDmDRc8wcwaLnmHmDBY9w8wZLHqGmTNY9AwzZ7DoGWbOYNEzzJzBomeYOYNFzzBzBoueYeYMFj3DzBkseoaZM1j0DDNnsOgZZs5g0TPMnMGiZ5g5g0XPMHMGi55h5gwWPcPMGSx6hpkzWPQMM2ew6BlmzmDRM8ycwaJnmDmDRc8wcwaLnmHmDBY9w8wZLHqGmTNY9AwzZ7DoGWbOYNEzzJzBomeYOUN/x881X2UVDMN8NXinZ5g5g0XPMHMGi55h5gwWPcPMGSx6hpkzWPQMM2f8f96VSlGk8uIfAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 51; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 52; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 53; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 54; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 55; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 56; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 57; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 58; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 59; current eta: 0.5, current beta: 64\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 60; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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T01OcnJxwY4xJi2kUCgUcDgcvl/X7/SNr5IfBRtp6vY5YLIbLy0vE43EUCoWRvec0Gg0cDgd8Ph9PuWXGmkzw7P0kEom+vHtmhslEL5VKB4wzxA7DarW6zylXo9GM3H0gRkOi/0KIv4ytVgvpdBpnZ2c4PDzE0dERzs7OEI1GkUqlkM/nJ/agUygUcLlcCIfD2NjYQDgchtPpHPCGH7VeFm8TXlxc4PDwECcnJ8hkMkOXFkqlEi6Xq+8mYzAY+DHi8ThOT09xeHjIA4G1Wg23t7d9/vds7x74tFRotVp9NwDGwsIC9Ho9nE7nQOyAmA4S/Wdk2Br69vYWtVoN6XQaR0dH3Eb65OQEqVQKlUoFjUbjzv14uVzOG0OYzWaEQiHs7OxgY2MDXq8XBoOBi2nUWl7oegt8Gn1Z1J7NNm5ubgZSZqVSKRwOB9bX1/H8+XNsbm5yy61Wq4VMJsO3+1idQKlU6ntPUqkUcrmcd7IBwH8vrKITzpA0Gg2sVivsdjusVuuAVRaN8pNBov+MiL+EbO2eTCZxfHyM33//Hb/99hsODw8nXrsDnyLYJpMJHo8HwWAQwWAQS0tL/M9ms3niIJfwGlutFvL5PPehS6fTA9l/crkcdrsda2trePHiBZ4/f45wOMzLdFnN/fHxMY6Pj3F5eYmbm5u+tTxrT7W4uMi71XS7XdTrdfR6vYGCHvYapVIJnU4Hs9kMvV4/VTox8VdI9DMg9I0fhXjaWa1WkclkcHl5iaOjI3z8+LFvS27SGnO5XM5bUW1vb2NzcxNLS0twu90wmUzQ6/UTmVwOc+/NZrPcJINF2oUoFAo4nU48efIEr169wnfffYe1tTU4nU4olUqUy2U+yjPBDwveSSQSyOVy6PV6WK1WaLVanq7LxD9M+KztlUaj6TP/IMec6SDRz4C4BFXIsBtBuVzmo/ve3h63uYrFYsjlchMLnvWeW1pawosXL/D69WtsbW3B4/FAr9fzfeu71rnDsvFYq6rDw0NEo9EBcw6lUgmHw4HNzU28efMGr169wurqKlwuF1QqFbrdLkqlEpLJJM7Pz3FxcYF0Oj3UUmthYQFarRZWqxVerxcmkwnNZhNyuRyNRgOlUmlgXc/yA+RyOW9sKbx+Ev3kjBX9qK2Tv3WE60j2EI+Mo75k4u2tm5sbntn28eNHHBwc4OTkhEfGxa8dNXuQy+UwmUxYWVnhgmdFNOKRfdz7GuaBd319jb29Pfzyyy/4j//4D5ydnSGfz/edm7WyZiYcrNGlMFhYrVaRTqd5Ce2w/AKJRAKVSgWTyQSXy4Xl5WVYLBZUKhV0u13k83nkcrmhn6+41TUxG2NFf98cafGXWCieh8q/vutY0/iuP+R1CTPrdnd3sbu7i+PjY1xdXSGbzQ6k1d51jczBdmdnhws+GAxOLPhRx89mszg8PMRPP/2EH374AR8/fuyLL7Ap/ebmJr7//nu8evUK6+vrsNvtfTkAwCfzzGKxiGw2O3R5AHyaMej1ep41GA6HYTabeQOLeDwOuVzOt/LE3x/27zSyz85Y0YubGc4zwptHvV5Hs9lEq9UaiDLLZDL0ej3k83mcn59jd3e3L1hXKpUmtrgSYjAYEAwG8eTJE2xvbw+M8Ow6xk3txd1xMpkMDg8P8csvv+Cnn37Chw8fkEgk+HJDoVDA7XZjY2MDr169wqtXr3jvenGgkHW+aTQaqNVqQ9+jRCLB4uIiN+cMh8NYXl6GTqeDXC5HIpGAWq2+83tHor8fpOopKZVKSCQSvC97q9XqG5GY6HO5HN+2Ojw8RDwen7gZhXiEUygUvIhmeXl54o4043LtM5kM9vb28NNPP+Gnn37C7u4ukslkn+BZlP7169d49eoV1tbW+gQvbmPNtgCFHWqEKBQKGI1GLvhwOAy/3w+ZTIZarQaNRsPdc4XXTzwsY0W/u7v7pa7jqyNei7NRyWAwcIEVi0VEo1EcHx9z7/dGo9Hn7c5MIkqlElKpFOLx+Fi76GEIv+wsCWZpaQmhUAhut7sv026YEYb4PQn7zzUaDdzc3ODs7Ay//vorfvzxR3z48AGpVKpv/W0wGODz+bC5uYnnz5/jyZMncDqdfYIXZ+qNM6yUSCTQ6XTweDy8nZbf74fNZkO73YZSqezLKxh3HOJ+jBX9n//85y91Hd8EzAG2Wq1CJpNhZWUFb968QSAQQLfbxfX1NSKRCN+DjsViaDabfetP9qVstVpoNBp8KTDLtRiNRp5au729jaWlJZhMpjunv+IRuNls4vr6GtlsFul0GrFYDGdnZ/j48SM+fvzYN6UH/ho0XFpawsbGBlZXV+F0Ogfy+MUdaNgILxasTCaDWq2G0+nE8vIy1tbWEAwGeSota1rR6XR4zX273R7ZJkv4k5iesd+ef/mXf/lS1/FN8uuvv+L6+hrLy8s8Tz6dTiMej09lTjkNUqkUarUaFosFXq+Xp9ZubGzwnnPC0W7YGl74b51OhxtrHh8f4+LiApFIBMlkEslkcmiOgEajgcfj4Wtuh8MxtDxXfB5hN1txOSzrK7+2toZwOAyXywWtVgupVIp2uz1REwt2U2HnoZjTbNCnNoZcLocff/wRh4eHfGpcr9dRrVYntp+eFIVCAa1WC7PZzGvTQ6EQ1tbWsLKyAq/XC7PZPLTIhCEe4TudDpLJJD5+/MgDdRcXF7i5ueGmFuKAG9slWFlZQTgchsfjgVar5b8ftSfO/OybzSZub28HavzZKB8OhxEIBGA2m7GwsIB2u41arYZisdhXcy+m1+vxmwObPbGKPpryTweJfgjCtXA8Hkc8Hv+s52M55V6vF8FgEH6/H4FAAH6/Hz6fD06nEwaD4c7UWqHg2+02kskk9vb28PPPP+OHH37A/v4+0ul032vEQUi73Y7V1VVsbm4iHA7DZrP1jajDct273S5qtRoqlQofqYW7CVarFaFQCKurqwiFQnx/n70un8/z5UexWBy6HGK7JoVCAblcDoVCgUf6SfTTQVt2Q2Br+0lMKMd94cRT1GF5AMxqamVlhTeICAQCcDqdPMdcrVaP3Yob1ncumUziw4cP+OGHH/Dzzz/j4OBgQPDC18pkMlitVqyuruLZs2fY2tpCIBCAwWC4s2adZdGxRha1Wo1H761WK4LBINbX17GysgKHw8FnDr1eD5VKBZlMBvF4HMlkEoVCYWiGIsv4S6VSiEaj8Pl80Gq1MJlMM5mEzDNjVX2fnmPzwjQBJfFzWYbd1tYWnj17xk0sbTbbRCP7MJjg9/b28MMPP+CHH37AwcEBrq+vx75Oq9UiEAhgc3MTOzs7vIhGuDMxTFy9Xo8XEt3c3KBQKKDRaEAmk0Gn02FpaQmbm5t48uQJgsEgjEbjgGHH9fU1EokEUqkUyuXy0O8d6+UXi8VwcXHR9zlRdt50zOdQPgXDDCRnOcawEX5tbQ2vXr3Cy5cv8eTJE/h8Pl49Nuq8o7bmer0ems1m35SejfDM1JLBZnBCcRmNRgQCAayvryMcDsNut09Uq85EXywWueh7vR5MJhN8Ph+eP3+OFy9eYH19faDGn03Z2fQ+l8sNzVRksNz+aDTKE52EpcGTegDOOyT6O/gcW0MGgwErKyt4+fIl/u7v/g7Pnj2D1+vtqxwTc9cX+fb2FtfX130ptaNGeHHijEKh4I0qQqEQnE7nyLZXYpjoS6USCoUCWq0WFhcX4Xa7sb29jVevXnHffZ1ON7AMaTabKJfLKJfLYwUPgOfmZ7NZFAoFXopLTAeJ/oEZlk0m/LPVauWCZ73lgsFg3x642BZ7mODEabdM9KxOf1zQTrz2tdls8Hq9PJYgTv4RZ90JX89MQSqVClqtFhQKBfx+P2+Yub29jUAgMLDVCHwKNrK0XXE5rXB2JPyzcNeBPPRng0T/hZBKpbzrzKtXr/Du3Tvs7OzA7/cPFK7MklveaDSQTqdxeXmJy8vLkUE74Z+ZZ30gEEA4HIbP5xtI/rnLg65er6NcLqNWq0EqlcJut8PhcGBnZwfb29tDcwvY+Wu1Gh/hm83mxE0tKUHnfpDoHxjhF1Eul2NxcZEbOvp8Pj7lffbs2UCrqXEpteLji9fbQgOMbDbb9zuZTDY0U04ikfCZx/r6OpaWlmA0GkeeUzzzaDabyOVyvGpwcXGRN83c2tpCKBSC0WgcatvVaDT4VD2fz6NarY6cHYkRljzT+n16SPSfEYvFAp/Px33oQ6EQVlZWsLa2Bq/XO9BbbpYvca/X4wabwnqAYYgDiiqVCi6XCysrKzwBaFghj3BkZdfXbrf5jSaZTKJWq3ELbjZrELrjCl/b7XZRLpd5diPzBpx0t4hG+PtBon8AZDIZ78rKRmuTydTnThsMBuFyuWCz2WC1WvsCZWzdPMkIL35OOp3G3t4e/vKXv2Bvb29o/3ihmFgyi0ql4g0ng8Hg0EIeYSMKIc1mE5lMBmdnZ9wQpN1uw+128+Qi4TKBTduFcQDmmMvcf8vl8kTTe5aj32q1eBageHlEjIdEPyXi0VKpVMJkMsHpdPLsNdZPLhwO48mTJ9xWanFxEQsLC0PX8ONgJavimcD19TUODg7w448/8ow71uhylCGI0WiE1WrlrjXb29vw+/3Q6XQD5xyW7MJ88E5OTvDx40dcXV2h3W7DZrPBZrPxpCL2HofdrGq1GjKZDCKRCKLRKK6vryf29wc+pfyWy2UUCgWUSiVotdqJbMKIT5Dop0Rc586mx2tra7w77MLCAnQ6HY+Ie73evkSbYWvrcTB/OOHrxQYYzPGGjepiwSqVSp4dt7KyguXlZSwtLcHr9cLtdg945ItbTDNXnEQigfPzc+zt7WF/fx/5fJ4XB5lMJphMpr4MQuGNilUwJhIJXFxc4OLiAvF4HMVicaK2XcJjsISedDoNrVY7NFhIDIdEPyHDjC3YXvR3332Hra0tPmIyA0etVgutVjuQWTfKWFNcRz5qjc887ZgBxt7e3lC7apYVZzAYYLfbB9J9PR4PdDodVCrVyCaX3W4XhUIBiUQCkUiExw6Oj48RjUYhlUq55wD7yTwFhMcBwO2wDg4OsLe3h7OzM1xfX0/tJFSpVBCPx3F1dQW/3w+LxdLX247SccdDop+QYYLf2dnB27dvuTOs2Chy2OvH7b0PE7lwmt1qtZDNZnF0dISff/4Z//qv/zowwjOYP73P5+N++IFAgD+8Xu/ItlfCa7i5ucH5+TkODw+xv7/PW2en02lUKhVYrVbue8duIMJjsBqGYrGIeDyOo6Mj/P777/jw4QMuLy9RKBQmqnEQUqlUkE6nEY1GkUgkEAgE4HA4BmZTJPzhkOjvQCqV9gWYZDIZ3G43nj9/ju+//x4vX77E+vo6bDbb2Bxw8ZdQOKqPi9rXajUUCgWUy2XkcjlEo1EcHh7it99+4w0uh9XDM/ONJ0+eYGNjA6FQCA6HA2azuc8NaNQ19no93Nzc4OjoiHfQPTw8RCKRQLFYRKvVglKphFqthl6v5+nDYuGxGEAkEsHJyQkODg5wcHDALbJrtdoE/wv9CHP98/k8arXaxHv8BIn+ToRfJrlczqf07969w9u3b7G+vg6r1cqfM2qEEQeZRgld+PpqtYpoNIqLiwvEYjHEYjFEo1FcXV3h4uJiqOONVquF1+vF2toanj59iqdPn2JlZQVOpxM6nW5k5aR4Sy2bzeL4+Bj//u//zl1y4/E49xGQy+UwGAwwmUywWCx8GcPMLbrdLnK5HGKxGE5OTrC/v4+joyN+3Tc3N1MF78QwQw32oG28yaHS2iEwgYrTQj0eD549e4bvv/8eb968GRA88NcEG/YaIaMSa4BPU9Z8Po9isciFXC6X+5pJsuYYwjx34K+GkyaTCW63m9fDb21tYWVlBTabbWgLKOZws7Cw0DfCZ7NZ7O/v8yDh7u4uYrHYwNpbpVLxUV6tVqPb7fLrLxQKiEajOD09xf7+Pg4ODnB1dYWbm5uRbrmTIpPJePBOp9MNtKwmxkOltUNg9fTCvzPP+T/84Q94+/Yt1tbWYLFYBl47boo/qu67UqnwIFksFkO5XOYVaOzf2XRY7CqjVCpht9vh9/u54eTq6ipWVlYQCARgsVhGCkL8PoFPa3g2wv/bv/0bt8UWn5f1o2MNKFqtFnK5HDKZDO+Hx1pvn52d4erqamjzi1nQaDSw2Wxwu91wOBzQ6/VUXjsF8zmU34FwSu90OhEIBBAKhfD06VO+hheO8GzEHDZlHzWFL5fLaDQaKJfLSCQSOD09xcHBAU5PT5HL5XgSSqFQ4GWn4puwUqmE2+3G+vo6tra2eFTe7XbDarUO1K4Pm4UIp/RsDf/LL79wl9xYLDb05i+RSLiFWDabxeXlJVKpFN/WY3vwqVQKmUzmQf0EmatuMBjkW4Xj6gWIfkj0YzAajXj37h3Pkw8Gg1hdXR2Y0g8bMcdRKBQQiUQQi8W4E8zV1RU3rmQtpSQSCbrd7lBnWGEzydevX/PusQ6HAxqNBnK5fODLL66WE2/LnZ+fc1vs3d3dgZiBGNbl9vz8HMViEcAnX8FEIoF4PI5cLodarTbU824axBV3Wq2W70x4PB4YjUaa3k/BWNH/4z/+45e6jm8CFqlnXvahUAhv3rxBOByGyWSC0Wjsm9JPEn0HwMtGb29vUSqVEIlEcHh4iKOjIz5CsuITcbHMMFjO/NbWFm9EwUwqxPn8w/bLxTeQZrOJRCLBdwU+fvyIWCw2stSVHaNeryOdTqPRaEClUvH3l8/nZ9qKmxSVSsX/L4SjPG3TTcZY0f/zP//zl7qObwI2WgibXbAglUwmw8LCwtTTyGq1ilQqhXQ6zde8zDv/5OQEkUiEO8BMsu2kVCrhdDr5DoKw88ywHPRxTjsMNvNgEfZ4PD4QaBMnDbEsPRZ8FOfEf64tNJZuq1QqoVKp+m5y5JwzGWNFv7W19aWu41Ehnhqz5gztdpv7vne7XZ5Mc35+jvPzc16RlslkkEqluOWTEDZrYK2ah6X9bm1t8d5yGxsbsNvtfaPdqPjCMGMO1kabFc8kEok77b3ZTYMJ/EubWbDOtewnMR20pp8BoZiEiSKs8yprxlCv13lG28nJCaLRKLLZLF/njtq2EppRMmQyWV9/+Ddv3vARXry1OmoqLx4JmfEGu75YLMbX5qPet/BmJ04bJh4HtGU3hlFGDcK/V6tVnl56eXmJbDbLGz60Wi0Ui0Ukk0leHDKsXzvQ7wYj/txVKhWcTifW19fx8uXLvim9sHz1rmsV/rnVavVVy52cnPAtQZZVJyz3ZQJns5ivnQHHroduOtMzVvS0NhpEnKpaLpcRiUTw22+/4cOHD0gmk2i323zaz/zcqtXq0MCWWPRitFotbxf93Xff4fnz53wHQdxMstfrjayBF8K22Vg/u4ODA0SjUdRqNSiVShiNRqhUKu64I+zLN6rl1JeEXHPux1jR03ppEOEXvtPp8H12JqBUKjXV8YQjJiuhFVbpOZ1OrK2t4fnz51zwYrfaUdtVwtGQzSDq9TpPjz0+PkYkEkE+n8fCwgKsVisWFxdhMpm4nzy7QWSzWT57+ZqiVygUUCgUkMvlfZmExOTQmn5KxFPkarWKfD7PPd8nfb1YOKxSzWg08pJcs9kMn8/HC2fYPrx4W24YbOtR6B7LjCiz2SxfcrTbbVitVphMJiiVSp5PzzrbXF9f4+LiAq1WC4VCYWjf+c+N8LPS6XTQ6/XQarUTfQ7EICT6KRGKvl6vo1KpoFqtotlsDmxrDRsRhxloaLVabkTh8/lgt9v5PrTH44HP54Pb7YbFYoFCoRhYU7NjsiKUVqvFm0IWCgUUi0UUi0VUq1XUajVUq1XU63V0Oh04HA4EAgFoNBqey67VaqFWq9FqtXB5eYlyuYyrq6sHW8vftaQZxeLiIiwWCxwOB6+hF7fcopH/bkj0M8Ki80z04mnvwsLC2Ai3QqHA4uIiDAYDnE4n7xQbCoXgdrthNBr5yG8wGKBWqyGRSFAqlVAqlbhtNNsuY0G229tbNJtNVCoV3nWGzUJarRYWFhagVqthMpm4eFi5rVarxeLiYl+VXDKZ7GsPfR80Gg00Gg0UCgVfaoj97kchkUhgMBjgcrn4TVCv11Mm3gyQ6Gfkrp7q48o9mVW0w+HghTJLS0sIhUK8R5tOp4NSqeRZgmwnIJ1OI5FIIJPJoFgsotFocDEy0bORnvnIseexXQCTycRz110uF7e4YvXwLOc/nU7zLrGjuslOAnPwcTgccDgcUKvVPFbACnTuEr5MJoNer+fOwi6Xa8Aii0b5ySDRz4iwnpuN6EKGCV4ikUCtVsNiscDv9yMcDvNurj6fDw6HAyaTiRs9suMwu+hIJMK95WKxGO8zz0Qv3FJrNpt8Pd/r9aDRaOD3++F0Onnveb/fD7PZzA07GQsLC+h0Osjn80gkEkgkEhMJcxgqlQo2mw2BQABLS0vcR7BcLvNae4lEglwuN3B8cc49a+ntdDphtVqh0WhI6DNAor8no7504jW9RCLh22Eejwfr6+t4+vQptra2EAwG+8THjsnKVVmZ6tHREU5OTrih5Lh+bkz8MpmMG12Ew2E8ffqUN8s0Go1QKpUDnn2sZxyzqGbnmnY9z2oE1tbWsL29jY2NDXg8HqhUKpRKJVxcXEAul/PZST6fH5lKyz4/5vkn7ItHOffTQaKfEVZPzh53bW/KZDJeKMLW76urq1haWoLT6eR58yxWwLbWmBHFwcEB32Jj0+5JRl69Xo9AIIDt7W08f/6c95YzGAxDTVKYTVY0GsX5+TkikQhyudzUxTMqlQo+nw9bW1t48eIFnj59ilAoBLPZDJlMhnq9DoPBgE6ng0qlwvvaC7MUxSO9TCbjW3bi3n8k+skh0c8IK76Ry+XcTGLcF48ViWg0Gu5yo9VqsbCwgFarxYNlLAh3c3ODeDzOraZYcU4ul0O9Xr8zW1Imk/GWVc+ePePdYwOBwNBWU0xgpVKJT7tPT0+RSqWm9rFjI/zm5ibevXuHly9f8n73LKHo9vYWCwsLPE6RTCbvdMZln+9dnzUxHhL9jEw70jPYertUKiGTyfBqPuCvwcFCoYBUKoXLy0ucnZ3xYp18Pj82mMZuQmztGwqFsLW1hWfPnmF7exs+n68v4i3uPNNut3lq7v7+Pk5PT5HJZKZay8tkMthsNqytreHFixd4+fIlnjx5Arvd3ifUhYUFvk0ZCARweXmJeDyOWq02sP0phKL194dEPyMse25Swfd6Pd6LPZlMQqlUolwu86QYZhVdq9WQz+eRSqUQj8e5iWS5XB4reBboslgscLvdCIVCWF9fx8bGBu+dd9c6uFgs4urqCvv7+9jf30ckEpna8Uan08Hv92N7extPnz5FOBweEDxDoVDAYDDA4XDA5XLBbrfzOvxhwVGACn0eAhL9jDBvOFYGe9d0k03dmSsOi14vLi5CoVBw0bMbQz6f5yaYjUZj5JpaKpVCoVBAr9fD5XIhGAxieXm5zyfP4XD0ta0SC541lGTTetbIIpfLTfWZMOvtpaUl3gXXarXyc7FtTBZLkEg+9dQTZgGq1Wre614MK19uNptot9t8qUBT/ekg0d8DNsWfJAec2V6VSiU0m03ewJG9VpgfL2zOOMqQgomdbQF6PB4uNtYwk5lGip1wxdfKrLYPDw+5J/3Nzc3UVZYajQZ2u523yjKZTEPbeQlvOqzOQJhPP+rzYzfEYrGIcrmMxcVFysKbARL9lIi/sEIzh0lGezbil8vlvrX1sCmrMHDFnsvOxUZIJrJwOMwffr8fNpttaEMLBgscst5yBwcH+P3333lDi1maUMjlcqjVaqjVaiiVyr72VsCgpTqb3bAkp0ajMdKQo9froVarIZvNIp1O4/r6GhqNBlqtdurrnHdI9PdAGMSbJsAkTJu963lM5Cx+oFAooNVqeadc1pCSTeVZxt0wn3t2zHq9zr3sMpkMLi4usLe3d6/+csCnmxrLBGRbcLe3tyP7J7TbbR6/SKVSyGazqFarQ2cYt7e3KBaLiMViiEQiPLFImH9PW3eTQaK/ByxaPsx5dlaEdlZshGdTeebZZ7Va4fV6EQqFuOD9fj/PUmN5/+ymwrIH2+02rwpMp9OIx+N9vvpXV1fIZDKo1+szXXu9Xsf19TXf42fNPK1WKxQKRd+NkZl4sPNfXV3xltWjRnpmSBKJRJBMJnlxEkX0p4NEPyXi6T0b6e9b2y32rhMLXqVSQafTwWq18nz9cDiMlZUVeL1e2Gw2Puqxkl9WDCQsr61UKshms1zwsVgMiUQC2WyWV+LNSr1eRzabxenpKeRyOY9heL1eGI1GnnHIGlpGIhF8+PABh4eHiEQid7asrtfryOfzvA+AOBuRjDEng0R/D4TT7vsajohTdtlPtnSQy+Vc+AaDAWazua8XPFsPi0dzJpByuczLatnv0uk0MpkMDy7e1x6t3W6jUCjg6uqKCz6VSiEYDMLpdMJoNEKhUHDrbNYN9/j4eOJmltVqFeVymUf4aetuekj090DodMMeD3lsMSzw1Wg0UCqVkM1msbCwgEqlAoVCgU6ng2aziWKxiEwmg3g8jng8jpubG5RKJdTrdV6IU6lU+I3gIb0Q2Tq91Wpx0V9dXcHtdsNsNkOpVKJeryOVSiESiSASiSCTyaBcLo+McQjTcdvtNnfgpcaVs0GivwdM9AqFAkqlEgqF4l7TY6B/W4t5ybNUXTZKC6fIsVgMarWaV8ax1tbMZps10mA1/2yn4HMKhl1brVbDzc0NF7her4dSqUSj0eA9ANjNaFxQU3idcrmcf9ZklzUbJPp7IJFIeDTdYDDAYDD0RZ/vIyqhCFiiChMrK8ZJJBI8uQcAnwWwAhbmmDNrYO4+CH3xWWoxM+hg23SjIvWjUCgUsFqtfc45JPrpIdFPifhLxnzlbDYb7HY7X3Pe3t7ObC0lNLPsdrt9Rhpsu40V+rCdA2FGH0vwYemsXxs2HWezoFlmGlKpFEajET6fD6FQaGgPO0rUmQwS/T1ZXFyE2WyGw+GA0+nk/dlrtdq9PeWYKJhZh7jKTFgdx7L5xglpVm+6WRA33Jj2s2DvTfgavV4Pr9eL5eVlBINBmEymvjgKCX4ySPT3gBk7sJHearUinU7zfPmHQLgNJbwJjHreJMf63AwzwJj23CxPgYleKpVCp9PB5XLB7/fD7XbzbDxKypkOEv09YSmxi4uL3IWG5dk/5NT6MUWpH+JaxTMDtVoNq9UKt9sNh8MBg8HQdz4S/eSQ6O/JsGksW8OOe+44PpfAR53/rvPddd0Pcb3jZgMSiQQOh4MX8uj1emrEcg9I9FMybOrKtu3UajUMBgMsFgt6vR5PmLlPNF/YoZWtc0e1rhL637NgmXCdP6s4J30dqzoUlhwLERYWsWsbdg5hNqJarYbD4cDGxgY2Njbg9XoHimxolJ8OEv09kUqlUKlUMJvNCAQCuL29hcFgwPX1Nfecz+fzM63xVSoV9Ho99Ho9NBoN355jo5xYWExULFJeKBRQKBRQKpU+ezNS1izDaDRCp9NBpVL17aOzWZCwPLZQKKBSqfQdR6FQ8Np6g8HA6wxWV1exs7MDv9/f5w3APgdickj090Qmk/EmkxKJBDabDTc3Nzy//erqiheyNBqNO9f5LOWWeekxr3iLxcLz14V93MTLi06nw9tnMzfbZDLJbzziBpxMjKPcaNhMRlwbwI7Deu6x9bbH44HNZoNer4dCoejbYWg2m6hWq8hkMtxa++bmhqfUKhQKmM1m+P1++Hw+eDweuFwuuFwuuN1u+P1+2O32kRWExGSQ6O8Ja+Qgk8lgNBp5mitzlD07O4PL5eIGk0KP+mEjFDOV0Ol0sFgscDqdPHhlMpmg0Wi46MV71OImlcwnPxaLIZvN8gIVodU1M+JknXPYjITdeJjdtNiemx2H9eCz2+3w+XzcW99gMEClUg2IvlQqcQ9/YQouE73NZuPlwoFAAHa7nc90tFotCf4BkNyxXns8IeNvCNagIpVKIRqNDoiOPWeY6FmWH4sP2Gw22Gw2WCwW6PV6ntUmTkFlwmL598xcMx6PI51Oc1NNVqPPMubK5TIfeVnKbrfb5S44Ho+H1+izjjvC62c3B3aDcrlcsFqt0Gq1AyN9u93mTr/JZJLPQFiKsFwuh9lshtvths/ng9frhcFgoOn77Az94Ej0n5F6vc5TYVlhi3AaLfZ1Zz+FVXWs/5tare7zer8L1kab9b0T2mYz0bObQyKRwNXVFb85dTod6PV6uN1uBINB+Hw+WCwWvk4H/hp4EwYxtVotdDrdnemxtVqNF/ywlmCsxoB5BrDeesS9INF/DYQptZNGwYU3APFaetZzs78Lj8Uq4ZihBht5O50OtFpt3zaZ0WiESqXqG+mHXes025KjPhPytX8wSPSfG+GX+HO5uUxy85g2B51F05nFVa/Xg0KhgE6n411zp90XH+X5N62Yhd78dCOYGhI9MRwWwWdFMGzdL3wQjxIS/ddgmmm9GHGgbpZzC38OY1JB3/U+pl2GjLs2GtUfDBL912SWgpPPff5pzzEuTfY+DLMKIx4EEj0xmlHBPhLho4ZET4xnWOCNeNQM/Q+kjDyCQyKfDygsSxBzBomeIOYMEj1BzBkkeoKYM0j0BDFnkOgJYs4g0RPEnEGiJ4g5g0RPEHMGiZ4g5gwSPUHMGSR6gpgzSPQEMWeQ6AliziDRE8ScQaIniDmDRE8QcwaJniDmDBI9QcwZJHqCmDNI9AQxZ5DoCWLOINETxJxBoieIOYNETxBzBomeIOYMEj1BzBkkeoKYM0j0BDFnkOgJYs4g0RPEnEGiJ4g5g0RPEHMGiZ4g5gwSPUHMGSR6gpgzSPQEMWeQ6AliziDRE8ScQaIniDmDRE8QcwaJniDmDBI9QcwZJHqCmDNI9AQxZ5DoCWLOINETxJxBoieIOYNETxBzBomeIOYMEj1BzBkkeoKYM2R3/F7yRa6CIIgvBo30BDFnkOgJYs4g0RPEnEGiJ4g5g0RPEHMGiZ4g5oz/D2fnx4+qcno2AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 61; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 62; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 63; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 64; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 65; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 66; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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gC1Gr1ZidncXMzEyfWWWr1UImk8HV1RVOT095Zx5rkhFOyRG/H8P68pmHnsVigdFohEajGXkQB/EnJPrPiFh0rEc8m80ikUjwaPnh4SGOjo4eFLwYg8EAr9eL+fl5eDyePsE/BFtla7UaIpEIrq6uEA6HUSgUhkbtlUolLBYLbDYbLBYLtFotz92zmMTBwQEODg5weXnZV7Mv9Lpn53/2AGQlwOIjjFarhdVq7XPKFQudhP8wJPrPiHhFS6fTuLi4wNnZGS4vLxEKhRCJRBAOh5FMJscW/Pz8PDY3N7G6ugq32z20g06IOIBXKBRwdXXFo/apVGpgNx3LzQcCAQSDQbhcLmi1WnQ6HRSLRVxdXeHw8BC7u7s4Pj5GLBZDtVpFu93mZ3nmda9UKiGRSPi/CRtqhAFDrVYLm80Gl8sFm80GnU5HAb0JINF/Qoad3Zl3/PHxMf744w/s7e3h4uICiUSib1KMEHFkWyKR8NJWljJbX1/Hzs4O1tbWeNusuJZefH9CX3thrf35+Tmurq7u5OcZzIhjc3OTG3FoNBqUy2Xc3Nzg8PCQByLD4TCKxSJ/eLBVXqlUQqvV8pLgZrPZF+gTIpVKuV23zWaD2WzGzMzMFzUkfaqQ6D8hgwJ1tVqNu96wYN3BwcGDzjdi4Qmtq9mWfnFxEUtLSwgGgzAYDH3fO8pZvt1uI5fLcR+6ZDJ5p4hGIpHAYDDA7/fz+fUrKyuw2WwAgEwmg/fv32N/fx9HR0e4ublBoVDoEzJz1tFqtTCZTNBqteh2u/y832q10Ol0+uIHLEug0+lgMBi4VRYxPiT6CRCOsBqGOEDV6XRQKpV488q7d+/wxx9/cH+7USvUJBIJ5ubmEAgEsLGxgdXVVSwsLMDr9cJut8NsNnOXWOH3DPoZxPeYyWT48SKXy93Z1isUCphMJgSDQWxvb+P58+e8Z352dhbZbBaxWAzn5+c4PT0dKHgAfYMtXC4XDAYDut0u0uk02u02KpXKnayERCKBXC7nY62EZb7kmDMeJPoJELegDvsaRqvVQi6XQzgcxvHxMd6+fYvd3V2cnZ0NXE3vQ9gX//LlS2xubsLn88FkMkGtVo+Uux6UB2cr9MnJCW5ubvqm1gAfgnYmkwmLi4t49uwZP0b4/X6YTCZ0Oh1Uq1UkEgnc3NwgHA4jk8kMbMOVy+XQarWw2+3cervRaEAmk6FarSKfz6Ner9/JFMhkMj4fT5gaJNGPx72in9amCOHqIlxxhCvjKL9kbGBDKpXC9fU13r9/j8PDQxwcHODi4uJOe+ygqDVDuMI/e/YM3333HZ4/f475+XkYDIY7Aa1BQhjmgZdOp3F4eIhffvkFb9++xeXlZV9zjUKhgM1m4+YYbIV3uVwwmUx8uk69Xkc+n0c6neZGGWKkUik0Gg3MZjM8Hg8WFhZgt9v5WT6dTg+dUyeeektMxr2if2z746Dgk9Cs4WPw0GuJy0Yf81rjcHt7i3Q6jVAoxFfQs7MzXlaby+XutMfe1y47OzsLh8OBzc1NvHr1Cs+fP8fS0tKdrbzwZxl0T2IymQxOTk7w888/48cff8TBwQFisRjf2iuVSj7G+uXLl3jx4kWfT76wQIYNphgUiGSoVCoejwgEAlhYWIDFYkE+n0cul4NOp+MjqqVSad97wqL+k47wIj5wr+iFnVLTjvDh0Wg0+MAH9kvJCk6Y/RRLWx0dHWF/fx9nZ2eIRCLI5/MolUoP9sOLH0BarRZerxcrKyvY3NzE/Px8n+CFVW7DEFtFp1IpnJyc4M2bN/j555+xt7eHWCzGjxvCFf7Fixd4+fIlNjY2uODFr9fr9fi4q/si8DabDT6fD/Pz87w3QCqVQq/XQ61Wj7SSk+gnh1Q9JuVyGfF4HIlEoi/YxQTKHpT5fB5XV1c4OTnB+/fvEY1GUa1W+Sjn+xAHsYAPZ3mv14vFxUX4fL6+qa3se8QMSxkCQCqVwuHhIX7++Wf89NNPPIMgFDyr4d/Z2cG3336LjY0NeL1enioTHyGEAc5BP6NCoegrIAoEAnA6nVAoFCiVSlCpVFAoFPfuuEjsj+de0e/v73+u+/jiiEtNJRIJNBoN5ubmeO06i76fnZ3h/PwcsVjszjaWrVCVSgWJRAKRSGTkQRQMsWgMBgPcbjeCwSA8Hg+MRmOfu86w/nLh7oSlwBqNBrLZLC4uLvD777/jp59+wt7eHhKJRN/qrNVq4XK5sLa2hp2dHayvr8Pj8fTV8Qtz/KM44uh0OjidTgSDQQSDQbjdbhiNRrTb7b6qvF6vd683Pgn/cdwr+r/+9a+f6z6+ClggrVqtQi6XY3FxEa9fv4bf7+fmlKxw5fT0tE/04nhFu91GvV7nwxcfYtjqxjzu1tfXsby8DIfD8aDdVa/X69seN5tNpNNpZDIZJJNJRCIRXFxc8BJZ4ZaevQ9sJPTq6irW1tbg9Xr7Zt2xexZec1jdPLPGtlqt/Bzv9Xp5xoHNrWs2m/yDWV2LIautx3Ov6P/t3/7tc93HV8nvv/+OdDqNhYUFXiefSqUQjUYRjUaRTqc/2rWEW3GFQgGVSgWj0Qifz4fNzU3s7OxgcXERZrO5z1Ry0NlX+HfdbheJRIIfM66urhCJRBCNRvkxRZwy1Ol0cLvdWFlZwfLyMtxuN48fDDsytNttPtRS/HpKpZL/LIuLi5ifn4fdbudlwq1WC9VqFZVKBZVKBY1GY2BMQLgLoFHVk0Nn+nvI5XL46aefcHJywrfG9XodtVrtjqnFx0AqlcJiscBsNsNqtcLtdmN+fp4PjggEAtDr9UO3t+IVvtvtIh6P4+DggAfqQqEQMpnM0F2IWq2G0+nE8vIyVldX4ff7R8oQNBoN1Gq1gfl1lUoFi8XCqwZZTEImk/FiHNZ4VCgUhu6MhLuBVqvFMwe03R8PEv0AhGdhtqp/6uupVCrYbDYEg0Ee5PJ6vfB6vXC73XA6nQ+W1goF3263EY/HcXh4iDdv3uDHH3/kvfniawuDkA6HA2tra9ja2sLq6iocDseD5a7NZpP737GVWrg1NxqN8Pv9WFxcRCAQgN1ux+zsLHq9HqrVKrLZLN915PP5gaJnPnvFYpFnQDQaDWQyGYl+TChlNwB2th/VdXYY4rOn+NzO0lQmk4mnsZaWlrC8vMy97fR6PWZmZu7tmBM/ANiWfm9vDz/++CN+/fVXHB8f3xG88HsVCgWsVitWV1exs7PDp+AMKvwRwvoJ8vk8MpkM8vk8KpUK3+Kzbf3q6iqWlpb6/Ppub29RLpd5wDMajaJQKAysUGRz/FgLssfjwczMzB0rMOJh7lX1YxxLp4Vxgkrir2V17OycGwgE4PP54PP54Ha7odPpxr4f4Zb+xx9/xI8//oiTkxPucTcMvV4Pn8+Hra0tPHv2DMvLy31joIUVieKfo16vI5fLIZ1OI5vNolqt8px8IBDA+vo61tfXEQwG+baevWa1WuVxkng8PlT03W6Xj6q+vLyE1+uFxWKBTqfrez16ADzMdC7lYzColPVjwKLy3377LZ49e4aFhQW+suv1+juRcnbtYam529tbNJvNvi39mzdvcHx8fEfwbAcnFJder0cgEMDq6iqWl5dhs9mGrvDi+2g0GigUCrz8ttVqQavVwu/395XtCq27gA+rNyvdZbuEYed5tiuIxWIIh8OIxWKYn5+/kzZk7wcxHBL9A3yK9JBQ8P/wD/+Ab775hufAJy1MYeOvhCW1x8fHAzMM4kAbK7UNBAJ8vPQwj3zxvYnP2rVajccnNjc38fz5c2xtbfGAoPBBwsp2y+Uyt+e6j06n07ejqNVqlL6bABL9Z4R1qi0sLOD58+f4/vvvsbOzg0Ag0FfDzoTPfqEHrbjislsmejbk8r6gnVgoLJ7g9/vhcDj6PPLFGQH2Ogwm+EKhwLf1DocDq6ureP78Oba3t3l/v/h16vU6T9PVarU7PffClZv9mWUJms0m2u02iX4CSPSfAYlEAp1OB5vNxsdMffvtt9ja2oLX6x3o6jqsym4YjUYDqVQKV1dXCIVCQ4N2QuRyeV9cwe/395252X2I74vBVt5UKsUn6BoMBrhcLj6qWnyOZ/chtNguFouo1+t9Ef9hYhbPxCPGh0T/CZFKpdDpdHywIzszf/PNN7w1VVhdx8p/74uWD1v9WbXgzc0NMplM37/J5fKBttIymQw2mw0rKys80DYoJz8oltDtdpHP5/kEnlQqBalUCo/Hg/X1dWxubvIOukEPtVqthkwmg0QigVQq1WeN/RDDnHSJ0SDRf0KY//zCwgKP0AeDwaHn5klaRm9vb5FMJnFxccH7AUat89doNHC73VhaWsLKygpPg40Cm4DD+hCy2SxUKhWCwSDW1tawsLAAq9XK/e+ED45er9eXqksmkyiXyyP7N9AK/zhI9B8R9kvNAmNLS0vY2triqx4znTAYDH2r3yDrKjHDItPJZBKHh4f47bffcHh4OLC5RxilZ5bTGo2Gb+uDwSAcDsedyPqgh1C73UaxWMTNzQ0ODg6wv7+Pm5sbNJtNPjAzGAzCZrP1deMJYU5CQuffSqUykpjZMIxWq8U/aOjFeJDoPyI6nQ56vR4Wi4X7yG1vb2N1dZW70zLr53FgHW3iBwNz1P3pp594xV0ikUCz2RyaBTCZTLDb7XC5XFhcXMTm5iZcLtedFCE7M4uDdplMBqFQCEdHR9zFt1KpwGQyweFwwOfz8WCgMMcv/HOxWEQ8HsfNzQ1isRhyudxYXYitVgvlchmFQgHFYpFnPchNZzRI9I9ALpdDLpdDrVZzsbOzO6tdX1pagtvtvuPpBow+sVUikdwJhIkNMMT98GLBsnFX8/PzWF5e5q65brcbDofjzmopvh7zEWCTbPf393FycoJCoQCdToe5uTm4XC5eLiz8eYUZhkKhgFAoxC22k8nkg6k68bCNarWKdDqNWCyGZDIJnU4HnU5H5/sRIdGPiHjl1Gg0sFgs3IfdZDLBarXeMYgwm819Ahj0WgxxRHrYlj+VSuHg4ABv3rzBTz/9hMPDw4EGmwqFghf7CMt8V1dXsbKywrf0KpWKxxfED6Tb21vkcjmEQiEcHx/j6OgIJycnuLi4QCwW4959bKU3m83QaDR37rvT6fCpN3t7ezg4OOC++oOGadxHpVLhOwW/38+vyd5nqsy7HxL9iAjFqFQq4XQ6eeupz+fj1tNs62y1WvuCYqOs7oNELtxmsyEZ79+/x5s3b/C3v/3tzgrPYFZXfr+fl/ey4ptAIHCnOk58H+zamUyG5/7fvn2Lk5MTRCIRZDIZlMtlGAwGzMzMwGQy8fdAHK9gxh1szNW7d+9weHiIaDSKcrk8ctSewUTPKvP8fj/sdjs55I4Iif4BxOaMrPhka2sLr169wtbWFnw+H3Q6HRQKBWZmZqDT6e5slwdZS4lddwdRrVZRKBRQLpeRzWYRjUa5jfbx8fFAwWs0Gtjt9r5uOdbdxsZJi6P0g1Z4Zpr5yy+/4LfffsPR0RFisRjvpAM+HBtmZ2eh1+v5NFkG8/qPxWLcL/Do6Ainp6eIRCLIZrNjneUZjUYDpVIJ2WwW+XyeD8kgRoNE/wDCXyaFQgGHw4GNjQ28evUK33//PdbW1u6tU2cMqmob5mvH/r5cLvcNk4xGo4hEIgiFQri6uhroeKPT6eByuXhF3LNnz7C0tMSDa/cZT7Drdrtd7oP/5s0b/P3vf+emmUKRqlQqPnxCrVbzufUKhYLvSsLhME5PT3FwcICTkxNeOFQqlQZ64o9Kp9PhH1SoMx7UWjsAJlDhWVMqlcLtdmN7exuvX7/Gq1evsLq6CrvdPnSVFv8i3ldWy4wkisUiF3KpVOLDJM/Ozrh1Notcs/tTKBTQarXcS295eRnb29vY2trizTOD0lqsuk3Yk85swY6OjvDLL7/g559/xu7uLqLRaN/7IZF8GE3Fps1IpVLUajUkEglIpVI+3INN4RVOvBnmjDMqCoWCBw/ZeCuK3I8OtdYOgPXTCz/3eDzY2trCDz/8gNevX2NlZQVWq/Xec+OwElbxVr9SqeDm5gaXl5cIh8MolUq4vf1zbPTZ2RlfIcViYa61Ho8HgUAAS0tLWFtbw8rKCgKBACwWy1BBiH/O29tbZLNZvH//Hr/88gv+9re/YX9/H/F4fGCwTSaTQSaT8Yh6NBrljTDxeJzvSK6urnBzc4NisfhRVmTmt+d0OmG32zE3N0fWWWMwnUv5Awi39A6HA36/n+fdX758ifX19T4xMe82cZDuvi18uVzmHWbxeBxnZ2d8PDSbFNtut1EoFJDJZJDNZu+cW5VKJRwOBxYWFril1tLSEnw+Hz+/i80rhZ8L74+t8OwM//PPP2N/fx/RaHTgw599X6vV4sU6bG5dLpdDLBZDPB7n914ulyf6fzEI5uEXDAbh9XphNBr7dqUUwLsfEv09GAwGfP/993j27Bm8Xi/8fv+dFZ6t2uOsNIVCgRemxGIxfk5nxpX5fJ4XtLABEuIVkkXnFxcX8eLFC+zs7GBlZYWbWA4aDSUskBFye3uLfD6P8/Nz/Prrr/j73/+O3d3dgUFCId1uF/V6HfF4HM1mE1KpFNVqFZlMBplMBsVicaBR5riIO+5mZ2dht9vh9XrhcrnutOwS93Ov6P/lX/7lc93HVwETWaPRgFQqRTAYxOvXr7G0tASj0QiDwQCz2Tx0mz6Mer2OZrOJbreLcrmM6+trnJ6e4vT0FFdXV7xLjYnlIdgKv7KyghcvXuDFixd88oxer+/72kHlu+J7LpVKCIVC2N3dxW+//Ya9vb2BZ3jxw6LdbvNpPazLjrXaDhtr9TFgxVCspJmt8pSmG417Rf+v//qvn+s+vgrYaiEcdsE86ljN+rjbSDb0IpVKcQMINszy7OyMT4hlR4SHEM6W+/bbb/Hq1SvuSjOoWeahe6zX60gkEjg6OsLbt295Wk58hh+0O+h2u9wBl/0di6h/Kli5rUqlglqt7mtaIuec0bhX9Jubm5/rPp4U4o4xoec7a2Ht9XpoNpvIZDK4urrC5eUlotEoEokEkskkkskkYrHYnZHQwqOC+EEglUr7xkW/ePHijg3VIDNO4d8LBdFqtZDJZHBxccEr7SKRyMi5c2HK7HMinFxLAbzxoTP9BAiFU6vV+Aqey+VQq9X4MAY2Webq6grn5+d8ZnutVkOr1XowTz3ISJPNlmPTY1kjz32vMWwFLJVKuL6+xvHxMS+YeagOfti9fSm+lvt4SlDK7h6GGTUIV0+WbmPn81wux+fGseh7IpFALBZDKpW6k3ITr8RsiyxEoVDAaDQiGAxyt1rWuSf21RtW3iuk2+1ywR8dHeHg4AAXFxdIp9N8lR9WLvy1FMJ8DffwVLlX9HQ2uos4WFQul3Fzc4O3b99id3cXyWSSm0Gw1Z5NxRlUkCKRSO49ywu74zY2NvD8+XOsra3B6XT29asPShkOgllchcNh7O/v4927dzg5OeHlteyaSqWy75jBovBfw0JAjjmP417R03npLsIVptPp8Dz7xcUFDg8Pkc/nx3o9Jnh2lmfGFVKpFBqNhrvUbmxs4NmzZ1hbW+NjoViDybCUoXCYpNBumpXGHh4e4urqCoVCAVKpFAaDAWq1mpfWymQydDodbl4pHGLxpVAqlVAqlVAoFDTdZkLoTD8mwl+yZrOJarXa5wY7Cays1GAwQK/XQ61WQ6FQ8Hw0m2fHLK2MRuODPnrNZpNH1tkMvlKphGQyiVAohIuLC9zc3KBSqUCv18NgMECj0UCn0/FaeuBDTUE8Hkc8Hke9Xv8i22rhNZlRiVarfXDcFjEYEv2YCEXPLJyr1SpardadXPhDAmEGHGazGU6nk7vO6PV6XtfOjDlcLhe3oBIaZQj/y7bh9Xod5XIZ+XweuVyOO8ywz5lvfKvVgtlshsFggMlkgslk4g8dVmhzfX3NW2O/NBqNBmazGXa7HWazmTsRMWjbPxok+glpt9t8em29Xr+TupLJZANz73K5nOf/dTpdn+CDwSBfydk2VqvV9q1qmUym75pCS2gWPKzVaigUClzg6XSaD6LodDq8qs3pdMLpdMLlcnEhMZurWq2GaDSKfD7Pt/mjGlcOQ61WQ6vVQqFQ8CKoer0+kokGM+twOp3wer18GhBV4o0PiX5CWPUZ+2i1Wn2iH2Q5zUQs/MV1u91wuVxwu93cumpubg5yuZyvXKzyLZVKIRaL9U13FbaWspWe9Zsz4TPBy2QyGI1GeL1ePh3X5/Nxhx9WiNTpdJBIJJDJZNBqtfoeMpPABnVarVY+sbbZbCKbzSKVSiGfzz8ofLlcDr1eD7fbDZ/PB5fLdWdsN63yo0GinxAmsm63+2AlnUTyYRQ1GwSxvLyMtbU1zM/Pw+VycccZZm0lrPrrdrvI5XJ9VXzX19d8xny73ebXZ+Jvt9s83lCtVtFut6FWq+FwOLgv/draGoLBIOx2Oz/Ps8Age+CwGgRWRz+J6Fn2wePxIBgMwufzQavVolqtIhKJ4Pz8HBKJZKBtljgVOTMzA4vFArvdDovFwtOVxHiQ6CeERcyFEfdhX6dUKjE3NwePx4PV1dW+6S8Wi6Vvzrpwuyos7jk8PMTe3h5OT095X73wWCGOIfR6PXQ6HchkMuh0Ou6ks7Ozg83NTW5TrdVqucgZzKKaBfDYA2Zc2INmeXkZGxsbfa7ALF6gVqvR7Xa5h96wegOJRAK1Ws376LVa7dg9EMQHSPQTwurwFQoFTx8JYb+8MpkMarUaJpMJgUAAm5ub2N7exvr6OpxO58AzaafT4RNgWCpwb28Px8fHCIfDY9lMsemx29vb2NnZwTfffIP5+XlYLBae5xdfO51O88k18XgcxWJx7PdHrVbD4/FgY2Oj77os1dhoNGA0GnkTUqlU6rPhAu6u9HK5HEqlktcRCN9rEv3okOgnRCqVQqFQPJgzZs0hc3NzcDgccLvd3CZ6kODb7TYqlQqfS7e/v4/d3V0u+GKxOLJ7LCvb3drawsuXL/kKbzabB7beMk965nhzdXWFdDo9tsuNSqXitmLff/89Xrx4wefdsyNEr9eDQqFAuVxGOp1GMpm88zAb1kdAUfrHQaKfEOFKL94eC2FbduFDod1uczNHVqcvjLxns9k+bzlWF18oFO49Vwt9+E0mE/x+P19p2bho4XQd8eTbRqOBeDyOk5MTHB4e4vLykrfMjopUKoXFYsHy8jLvEdjY2IDdbr/zkDObzXC73fD7/bi+vkY8Huer/X2rN4n+cZDoJ0R8ph9kfMkCa51Oh5tL3NzcQKPRoF6vY2Zmho9oqtfrvNAnlUpx+yxmgPlQNRwLdBmNRtjtdvj9fu6ms7q6Cp/Px22lBrnwdjodfpxggyzC4fDYjjd6vR5erxcbGxvY3t7G0tLSUONQhUIBg8EAh8PBbcPz+fzQ7j1hPQK5304Oif4RMLHfF8xjLbb5fB6hUAhSqRSVSgV2ux0zMzM8X83OtcViEel0GolEAul0GplMhkfgxTDRKpVK6PV62O12eDweLCwsYGlpCUtLS3zoxtzc3NDa/Ha7zX3phR13457l1Wo1rFYrgsEgVlZWMD8/D6vVyuMdLI3JshMsOMfMSYxGI2ZmZgZmCtjDk+X2G40G37HQqj8eJPoxEW47mejvi96zX9R8Ps/r31OpFIxGI1QqFTfArFQqKJVKqFarKJfLqNVqqNVqaDabA1d4uVwOlUoFrVYLo9EIp9PJjTEXFxf5ZFyWjrvPsTefz+Py8hJ7e3s4PDxEKBRCoVAYu+RWq9XCZrPB4/HA7XbzIqP73kOZTNYXGxlWbHN7e4t6vY5cLodsNotCocDNTUj040GifwQPrfIMZq7BCl2y2SxmZmagUCh4nXy1WuW2WuLqN3aUYPEBNlSDTdTxer0IBAJYWFjA/Pw8vF4vT8eJRcSEzDoAi8UiQqEQ9vb2sLu7i/fv3yOVSk2UomPVhqx2X2yhzo4WwveKFROxPoFhphy9Xg/FYhHJZBLRaJQPGDEYDJS6GxMS/SNgwTy5XP5gOSjbxnc6HdTr9b7vYXlqYaEN0N95J5PJoFQqeVMMK9/1+/18PLTH44HNZuOjpgbBGnHYhJh4PI7z83Ps7+/j6OiIW1VPUojDjjKsI4+JeJjFGJuAk0wmEY/HuWvuoGt3u10Ui0XEYjGEQiH4fD5YrVbo9XrqBh0TEv0jYL5547R4invShznWClts2VZep9PBaDTC4XDw1X1+fh5+v59vp1mhDzs/s5p89rApFovIZDJIJBKIRCK4ubnB1dUVQqEQotFo3xCNcWGjrNlUnrm5OSgUClgsFiiVSh6DYOdzFti8uLjgvv7DRlQxwxJ238lkks/BY6Inj7zRINGPifg8Oonwxa/HGBQrYCs8O7szv3e2lWfDG1mMAAA/RpTLZe6vzzoC8/k8F04kEuGOPvl8/k5xzLjU63WeAVCpVLzwhjURsQcSW+Fvbm74DuP6+vrBGnzmRMQmAbHUHjEeJPpHwETJtuCPZdAvMBM9C9qxSLfFYoHJZOLz6ZrNJlqtFlqtFsrlMl/NmaCF2YFsNsvNOVkzziBv/XFhory+vu5rEmJBRVaN12w2kUqluCHn6ekpEokEd+4RI6zMazQaqFarfc1GxHiQ6B8BO3MPssee9PXugxXwsH75XC4HANz5htUDsAkzbCVnY6VZsKxWq6FcLqNarT5qiOQg2u028vl8X2Xhzc0NXC4XTCYTr1FIJBIIhUK4ublBIpFAsVgcKffOjkcsOEqMD4n+EbB6cObBrlar+Wo1iomGGHZ0EPrdsXZZFiBLp9OQSqXcXpsZbgC4s8rHYjG+mlcqFd4R+KmLW9iKz/oH4vE4bDYbT+Gxe2e7kHFGTbPA6aB+B2I0SPSPgBXG6HQ6mEwmGI3Gvsq5SYQldMNhnXLAh6EZvV6PCzuZTEKn00GtVvNUGNv6MvuuQqGAUqn0qHP6pLBUJDMGLRQKfb57rO13lKChcNqv0WjkDxA2LZcYDxL9mIi34Gq1mv8i2u12HkBrNpsTrfbAn9Njer0eH7XV6XR4gC6fz/NiFoVCAYlEwvPuQlMPcQrwS9FsNvl2H+j/+UaFOeewrAWb2Ud2WeNDon8EbPQVaxyJRqMolUo8RTbIPWdUWLqNrfgAuNVVrVbjKT22yrNrDsuvi/31PzVC8bGfYZxUIBOw8MHAPAlYxaHJZLojeuJhSPSPhLXNWq1W2Gw2JBIJ7ozLVunHILbgAvpTe6wm/6Fz+ueMcg8ywJjk+sL3TyaTwWAw8JXe4/Fgbm7uo9zvtEGiHxPxLzRrZdVoNPx8zaLsH8sj/r5us6+Rj/GAEU/60Wg0MJlMcLlcPP3HYMcgYjRI9I9E+BAQltMO2sqOuv38VKvyfU03k3zfqN//EOKjwCAsFgs8Hg83IHlsenSaoXfuI8BKZWdmZqDX62E0Gnk+XXjOnnSLywqAgD9NOdifxbDzs9AdV+yPPy6jfh97H4QlxEJGvS9hRSKb8iMc9qHT6cgF9xGQ6B8Js8MyGAxwu92o1+vQarXc/om1gk6SNmP++Hq9nneuCWfMiYXFRNXtdnmdfT6fR6FQGNvyalxmZ2eh1+u5qy876ghh9f+lUgn5fB6lUulOcZBEIoHBYOAfVquVOwhvbm5yN13x9xCjQ6J/JDKZDLOzs3A4HLi9vYXJZEIul0MqlUI0GsX19TWur6+RTCbRaDS4MIf9okqlUiiVSu6C43A4+CAK5kvPKgDFKSq2ijabTT6OKhwO97nnCq8tzA4Mm0Yr9gsQt7GyvgCz2cz9+61WK3Q6XZ8PH6sjYFN8o9EoYrEYCoUCGo0G2u02pFIpzGYzPB4PfD4f78tnAzlYy7BGo/mo/w+nDckDWzcqbH4Alopi5a2sdTWXyyESieDi4gIXFxdIJBJ8wgwwvPeb1dqz9lnhSCvWMsuq0cTbfLbKM6eecDiMq6sr3NzcIJ1Oo1qt9l1X2ApbKpVQLpd5Hz0bqzU3N9dXBCQWvUqlgl6vh81mg9fr5aO55ubmoFKp+D2y1uJcLscfhszZl/n3y+VymM1m+P1+LC4uIhgMctef2dlZaLVaPnKLGImBKwuJ/hNwe3vLq+bC4TAikQgymQxqtVrfijpI9Gz1nJmZgcFggM1mg9Vq7ZszJ1zpha8jzIeXSiUkEgmEw2Ekk0nkcjnelSaVSnF7e8ur+4STcwqFArrdLnfBYVN3DAZDn4iZ6NmgTbPZDIfDwafl6HQ63k7Lvp6Zdgivx7rlmOjZQBC20huNRgraTQ6J/nPDztXMBks4ggq46+vO/su2+Gq1GrOzs5idnYVGoxloPTWMXq93ZwVn53omeiZCZkzBHk69Xg96vR4ulwuBQABerxcmk6nvnC709WcPKa1WC51ON9BPn9HtdlGtVrnPPavWY2k3NtDCYDBQHv7xkOi/BMLKulGj4GJ/98cEqoQz6sUPGLYjYPGHeDyOfD7PV3pmtOl0Onm/vnClf8y9it8T4bFjUOSfmAgS/adGvIp/iqjyKA+Pca9dqVR4c069Xkev1+NOPQaDYSJLqkH3Ocl7wgqQqK5+Ikj0xGBYLEBosSXMldOq+2Qh0X8JxtnWi3lsAcooRTmjCvqhn2OYp/4k9zXOaxH3QqL/kowr/I/9Cz/o+uNe4yGRTsogn0Dio0CiJ4YzLNhHInzSkOiJ+xkUeCOeNAP/B1LVA8EhkU8HFJYliCmDRE8QUwaJniCmDBI9QUwZJHqCmDJI9AQxZZDoCWLKINETxJRBoieIKYNETxBTBomeIKYMEj1BTBkkeoKYMkj0BDFlkOgJYsog0RPElEGiJ4gpg0RPEFMGiZ4gpgwSPUFMGSR6gpgySPQEMWWQ6AliyiDRE8SUQaIniCmDRE8QUwaJniCmDBI9QUwZJHqCmDJI9AQxZZDoCWLKINETxJRBoieIKYNETxBTBomeIKYMEj1BTBkkeoKYMkj0BDFlkOgJYsog0RPElEGiJ4gpg0RPEFMGiZ4gpgwSPUFMGSR6gpgySPQEMWWQ6AliyiDRE8SUQaIniCmDRE8QUwaJniCmDBI9QUwZ8gf+XfJZ7oIgiM8GrfQEMWWQ6AliyiDRE8SUQaIniCmDRE8QUwaJniCmjP8PkfMAPlWctrMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 67; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 68; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 69; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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58V9eXuL09JT3/NdqtYH7Fa/uEolkbJBPpVLxHYXJZIJarZ56EAfxd0j0XxCx6FiPeDabRTKZRCQSwfn5OQ4ODvDhw4c7BS/GYrEgEAggFArB4/EMpM2Au00q+/0+arUaotEoLi8vefvquKi9UqmEzWaD0+mE3W6HXq/H0tISer0ef4AdHBzg4OAA19fXyOfzvAefCZ6ZYgIY2AGIryWRSKDT6bhzr81mG2ntRcK/GxL9F0S8wqfTaZyfn+Ps7AwXFxe4urrigyhvb29nFnw4HMb29jY2NjbgdrvHdtAJEQfwCoUCLi8vcXh4iPPzc6RSqZFpQrlcDrvdjtXVVYRCIbjdbhgMBnS7XRSLRVxfX2N/fx/v37/H0dER/37a7TYXutAXnxX6tFotSKVS7p0vDBjqdDo4nU5uj63T6aiybw5I9J+RcWf3ZrPJS2t/++03fPjwAefn50gkEqhUKrzvXIi4TZatfFqtFiaTCV6vF1tbW3j69Ck2Nja4s+1d9yf0tRfW2rMH0bj6fovFglAohN3dXTx69IgbcVQqFdzc3ODg4AAfP37krj6lUmng4SGMBbA4APt3NmBD/P1qtVosLy/D4XBgeXkZGo1mbGERMR4S/WdE/EvY6/VQrVaRSqVwcnKCX3/9FW/fvuXBuknmEuLAFiuI8fl88Hq9fMVlaTrh1n7as3y73UYulxsIvInP18x5JxgMYnd3F3t7e1hfX+dTcbLZLM7Pz/Hx40ccHh7i5uYGhUJhSPDMaMNgMECj0aDf76Ner3P3WzFswo3BYIDJZILBYKApN3NCop+DcYMdhIgDVO12G8VikTevvHv3Du/evcPh4SGi0ejUFWoSiQQmkwmBQIDbXbEzvN1u5+OgxF8z6nsQ32Mmk0E0GkUkEkEulxsSn1wux/LyMsLhMPb29vD8+XNut6VWq5HP53F7e4uTkxOcnJyMFDy7H4VCAaPRCKvVCqPRiG63i0KhgE6nw11wxUFDhULBx1oJjy7kmDMbJPo5ELegjnsNo9lsIpvN4ubmBoeHh3j37h3++OMP3i03S0mqyWRCMBjE3t4eXr58ie3tbfj9fp4fn2YC7KhqPDaq6ujoCDc3NyiVSgOvUSgUWF5exvr6Op49e8ZbdL1eL0wmEzqdDsrlMm5vb3Fzc4NIJIJMJjNy9yKVSvlW3efzwWKxoNlsQiaToV6vo1gsjgzQsSm4LEcvvH8S/fRMFP2osstFQHgWF+bBhSvjNL9kLKiVSCRwfX2N4+Nj7O/vY39/HxcXF0in0wPnZWZcMWoHIZH8OY0mEAjg2bNnePXqFZ4+fYrV1VWYTKaJFXbCvxvlgZdOp7G/v4+3b9/i999/x8XFxUBzjVwuh8PhwMbGBp4+fYoXL15ga2sLKysrA7uKer2OXC6HdDqNVCo1VEXIvg82vsrlcmF1dRU2m4373eXzeW6gKT7XC2fj0aCL+Zko+vu6k4wKxgjNGj4Fd73XqPTPvO81C/1+H+l0GpeXlzg+Psbh4SHPV8diMWSz2aEA2aTOM51OB7fbjd3dXXz33Xd49uwZQqHQVNbVwnsSk8lkcHR0hJ9++gmvX7/mjjxsa8989TY2NvDdd9/hu+++w9bWFlwuF++NZ7Aae1ZuOwqFQgG9Xg+73Q6/34+1tTVYrVbk83mUSiXEYrGxrrksxUc1+/djouiF/0MXHeHDo9FooNFoDJw72cNMJpMNpL5Yzv3k5ASxWAz5fB7lcnkqAwyhSLVaLY/Q7+zsIBgMDgievd+krb3w3/r9PlKpFI6OjvDmzRv89NNPeP/+PeLxOD9usLTc2toanj9/zo8TKysr0Gq1Q+YbbOQVG3s16ntiFYM+nw/hcJh77svlcl5wc9dcPhL9/SBVzwg7t97e3vJgl1CgYtEfHR3h6OgI8XgctVptYJTzOMRbWxa88/l8CIVC8Pl8A1Nb2WvEjEsZAkAqlcL+/j5++ukn/PjjjzyDIBQ8C9q9ePECL168wKNHjwby/+IcPxO+uHSXwYTt8XgQCoV4AFKhUKBSqfCdwyy7M2J2Jor+w4cPX+o+vjriUlO2KhmNRmi1WgBAqVRCNBrF2dkZTk9Pufe78JeUGU1WKhVeZTftIAqG+FzPSmtZ66pQ8CynPak3vt/vo9PpoNvtotFo8LTar7/+ih9//BHv379HIpEYiLIL/e729vbw6NGjoTp+8ez6u0Sq1WrhdDp5etHn88FqtaLb7fIiHeHDYxS0wt+fiaL/13/91y91H98E7JeuWq1CJpMhHA7j1atX8Pv93JwyEong9PSUb9eZ6MXxCna+Zf7s897P8vIyQqEQHj16hHA4DLvdPmSGIabX6w1s5VkxUCaTQTKZRDQa5bl0doYXZhBYj3wgEMDm5iY2Njbg8Xj4w48xqodg1Cq/tLQEtVoNu92OQCCAcDiMQCDAq+rYlFqhuw7ruRczTbqUmMxE0f/7v//7l7qPb5Jff/0V6XQaoVCI18mzCSvRaBSZTOaTX5PlsNVqNSwWC5859/TpU4RCoSGv+lFneOHfdbtdJBIJHB0d4eTkBJeXl4hGo4jFYri9vUUikRhKGer1erjdbqyvr2NtbW2gjn/ckYEJdVR+nW3r3W43wuEwwuEwXC4XL6Ntt9uo1Wq8GnHcEAv2YGHe98R80Jl+ArlcDj/++COOjo741phNWCmXy5/8euwcbbVaYbPZ4PV6EQqFsLGxgY2NDfj9fhgMhrFbXPEK3+12cXt7i48fP/JA3dXVFTKZDLetEh87VCoVVlZWsL6+jq2tLfj9/qkyBOxn02g0hkTPGnNWV1extrYGv9+P5eVlyOVyNJtNlMtl5HI5ZLNZbpcthh1Rms0mv29Wvktb/tkg0Y9AeBaOxWKIxWKf9XpLS0vQaDR85BQrpfX7/fB6vXC5XHA6nXeW1goF3263cXt7i/39fbx58wavX7/GwcEBksnkwNeIg5BOpxNbW1vY3d3lNfzCHvlRtFotVCoVFItFVCoVNBqNoZhEIBDA+vo6VldXecsvK0vOZrNIJBJIJpN8EIYYVqZbKBSQzWZRLBah0WjISGMOKGU3Ana2n8aEctIvnHiLOqrYxGAw8HZRv9/Pt79MHKw2Xdw8I06XiQORt7e3eP/+PV6/fo2ff/6ZW2ePukeWarTb7djc3MTe3h6fgmMymSamAVk7bj6fRy6XQ6FQQKVS4T87lnXY2NjA+vo6XC7XwFGBBTxjsRji8fhY0fd6PRSLRcTjcUQiEXi9Xmi1WpjN5oGHND0A7maiqu/jWLoozBJQEr9W6GW3traGYDDIG2jEZpbTwgS/v7+P169f4/Xr1zg6OuIed+PQ6/Xwer08frC2tgabzTY0+1384GIrcD6fRzqdRjabRa1W462wwWAQ29vb2NnZQSgUwvLyMl9M2AMjnU7j9vYWqVQKpVJp5Hm90+mgUCjwXn+fzwebzTbgx0dMx2Iu5TMwqpT1U2AymRAOh/Hdd9/hyZMnWFtb42WtbBKMmHErGRNis9kc2NK/efMGh4eHQ4JnohM+1I1GI4LBIDY3N7G2tgaHwzF1r3qz2UShUEAmk+FGGTqdDj6fj4/SYjl+YQag1+vxWvtsNotCoTAx01EqlXB7e4tIJIJ4PI5wODyQNpxUl0D8HRL9HXyO1BAT/IsXL/BP//RPePLkCdxuN1/ZJzndjoONvxKW1B4eHiKdTg+9dlT3GiuLDQaDcDqdEz3yhQ8fJlxmolmtVqFUKmG327G7u4sXL17g8ePHPCAofJB0Oh00Gg1UKpWJpbsMVpvPHhBC6y1iekj0n4FxNfwKhYI73Dx9+hQ//PAD9vb2EAgEBoJl7OsnecOLy26Z6JmL7qSgnXjHYLfb4fV6B3LnwuuIy3fFHYQsiFer1SCXy7GysoKNjQ08f/58Ymyg0WigWq2iWq3ysuZRP0Phn8UDM0j0s0Oi/wLIZDLodDpuL8V60Xd3d+H1ekdGx2etL280Gkgmk7i6usLV1dXYoJ3wzzKZDGazGcFgkKfSLBbLQAB3kgcdC65lMhnupWexWLCysoLnz5/zUVpms3no3M0stguFAorFIur1+sBZfpKYqUDnfpDoPwNCt1cWnXe5XLzCbXd3d6BTjcHKf++KlrP3FsIMMG5uboaKhmQy2cipNktLS7Db7QiHw3j06BFfkcddc5Tg2Rk7m81CKpXC6/VifX0du7u7PHAnfqixAB4zBM1kMqhUKlOLWNjyTOf32SHRf0aEefdwOIxQKIRAIAC/3w+HwzF0bp5ncku/30cymeQGm/F4fOo6f7VaDZfLNVB5JzbTHEe1WkUsFsPZ2RkuLy+Rz+eh1WoRCASwvb2NYDAIq9XKv0dxHIA1LsViMSQSCZTL5amzRbTC3w8S/SeEFYrIZDLYbDaEw2Hs7u5iZ2eHl54yOyvhoIZJZ3fha4Dh7XYymcT+/j5++eUX7O/vj/TaE4qJzYZXq9UIBoMIhUIj592xs7z4ep1Ohw+3PDw8xMHBASKRCJrNJj++BINB2Gw2qNVqSCQS3hTEaLVayOfzPOfODDemETOrzGu1Wmi1Wmg2m3cWDxGDkOg/EWw+u9FohNlsht/v55VtbCuv0WgGfN6nhXWdiR8MzFH3xx9/5BV3iUQCzWZzbDDRYrHAbrdjZWUF4XAYOzs7cLlcQ800owJ+rVaLG2ceHh7i/fv3ODs7Q61Wg8VigdPphNfrhcPhGOq3F1Iul5FIJHjqjY27mpZ2u82zBaVSifvfkx32dJDo74FMJuNBOqvVipWVFXg8Hng8noFV1O12D6xG4lX7ri0984cTfr3YAEPcDy8WLKt/DwaDWF9fRzgcRjAYhMvlgsvlGjpqiK9XqVSQSqVwfX2Nk5MT7mefy+VgMBgQCATgdDp5FaEwGCjMMJRKJdzc3OD8/ByXl5e4vb29s49BHIBkjsLxeByJRAI6nW5iTwIxCIl+SsQrp1qths1mg91uh9PpxMrKCp/c6vf74XK5sLy8DKPROLT9HLcKi/vox235U6kUPn78iDdv3uDHH3/E/v7+SLtquVwOg8EAg8EAm83G3WoePXrEi4G0Wi2USiW3kxY/kFhunE2wPTo6wvHxMZ+mC/xZ2GMymWCz2WA2m0daUzO326urK3z8+JH7BAqn3kwLK9K5urqCz+fjHvjsyETluJMh0U+JUIwKhQJut5vXkweDQT6AweFw8Mo6sWMrMHl1HyVy4Ta71WrxXPybN2/wH//xH0MrPINZXbH8O/vw+/0IBAIj++OF98Gunc1mcXZ2hg8fPuDdu3c4Pj5GLBZDMplEtVqFyWSCSqWC2WyG2WyGXq8f2iU0m03kcjk+9ebdu3f4+PEjotEoisXizG2y1WoViUQC0WgU8Xic7zJGxUmIYUj0d8Bmswk/X1lZwc7ODl69eoWdnR0EAgG+oms0mpEz1sS/hMJVfVIQjzWzlMtlZLNZfp7+/fffcXh4OFLwarUaDoeD++ltbm5yYbC4gzhKL34o9ft9bpr5888/45dffsHh4SHi8TjK5TIvl1UqldDpdNDr9dBqtQOrfLfbRalUQiKR4NZh+/v7ODk5wfX1NTKZzFwGI81mk9cH5PP5gUm4xN2Q6O9A+Mskl8vhdDrx6NEjvHz5Ej/88AO2trZgs9nuXFXEQaZxQhc+HCqVCiKRCB8mycw7rq6ucHl5OdLxRq/Xw+VyYXNzE8+ePcPe3h7W1tZ4O+ukIKJwS892FG/fvsV//ud/4sOHD9wpiKHVavl5mo2Y6nQ6fBYdcxpi1t9HR0e4urpCIpFAoVCYyUJMDPPiY8YalMabHmqtHYFw3LLw79xuNx4/fozvv/8eL1++xObmJux2+8j3mGT1NCrKXKlUkM/nUSwWuZBLpRIfJnl6eopYLIZcLodyuTzQgiqXy6HX62E0GuHxeLC2tobHjx9jd3cXa2trYy22mLWV0H2WWXcfHBzg7du3fGR2PB4fWJUlEgnkcjmfpyeTydBqtZBKpSCRSJDL5XjA7uDgYGDEVb1en/kcL0Qmk0Gr1cJgMAyNyCbuhlprR8D66YWfezwe7O7u4i9/+Qu+//57bGxswGq1jn2Pu4wqhf/Ohj5eXFwgEomgVCrxqjVmxMnmu4t7/JnbjsfjQSAQwNraGra2trCxsYFAIACr1TpWEOLvk23p2QrPYgZCH3zh98KyF8CfNfGJRIK738TjcVxfX/Mo/c3NDfL5/CdZkbVaLWw2GzcXofba2VjMpfwOhFt6Zm4RDAbx+PFjPuxBKCa2zRRv2Sdt4cvlMhqNBq9MOz09xeHhIc7OzpDL5biJB2tZHTUcQ6FQwOl0cuNM1hbr9/tht9sHDCbYfY67P2b8eXR0hLdv3+Knn37iW/pRD3/2vbMg3c3NDXK5HDfhZKYYyWQSuVxuaEzWfWAefiwgKa7tpwDeZEj0EzCZTLwTzuv1wu/3Y2NjY+AMz1btWVaaQqGAm5sbxONxXpXG8t+spLXf7/Mg4ihnWBadZ7Pl2Ihql8sFo9EIpVI51k5L/F79fh/5fB7n5+f4+eef8fr1a/zxxx8jg4RCOp0OKpUKL/2VSCQ8n59Op1EsFtFsNqdyIJqEuONOp9PB4XBwKzFa6Wdjouj/5V/+5UvdxzcBE1mj0cDS0hKCwSBevXqFtbU1mM1mmEwmLC8vz2zPxM6wbMgjm2t3fHyMy8tLpFIpZLNZZDKZqRx2lUolHA4HNjc38fz584FBFHq9fuC10xhLsOPFhw8f8Ouvv+LDhw9DW/pRtQWsMq7b7SKVSnHPu2Kx+FmMQxkqlYr/v7BYLJSfn5GJov+3f/u3L3Uf3wRsJRQOu2CRaZlMBqlUOrHtdBQsp8y2uel0mq/qp6enfEIsOyLcBZstt7m5iZcvXw7MlhvVLDMptgCAB99OTk746OxYLDaUShMLnu1ASqUSqtUqAPCBGvdd2SfBym2VSiXUavVAgJKcc6Zjouh3dna+1H08KMQdY6wBhHm+s6h4q9VCJpPBxcUFLi4uEI1G+QMgmUwiHo8PnXWFRwVxKkomk2F5eRlra2t49uwZXrx4wWfLCSfPiN9P+PdCQXQ6HeRyOVxdXeHw8BDHx8d8as80PwM2mOJLp8tY/8I8fQwEnennQigcZsucTqe5XRQTPxshdXl5ibOzM9zc3CCdTqNWq6HVas2Up5ZIJLBYLFhfX+ez5ba2tuB0OgdW+FHGleOoVCqIRqM4OTnhE3um2ZYLHXiIhwel7CYwzqhB+DlrMz05OcHFxQUfatntdtFqtfh8+lgshlQqNTL1BQy6wYh/7kKbLWY0yTzpxSv8NMYS7OwdjUZxeHiIDx8+4OzsDMlkknfosW208CHCdjDfgtjZfXwL9/LQmCh6OhsNI9zas9Tbzc0Nfv/9dx7xZttyZvzYaDT46i5mXPMNQ6VSweFwIBQK4cmTJ3j27Bk2NzexsrLC+9WFrbfsPcfBHG9isRgODg7w/v17HB4eIhKJ8LO5RqOBQqHg8Yt2u33njLkvCbnm3I+Joqfz0jDCX/hut8vz7GdnZ9jf37/TX16MMHjHDC5Y4YtGo4HVakUoFMLu7i729vawubkJj8czkKYalzIUDpPs9Xq8Zj0ej+P8/Bz7+/s4Pz9HPp+HVCqF2WyGUqmEwWCAVqvF0tISzzgUCgVeNPQ1USgUUCgUkMvld86xJ0ZDZ/oZEUe+K5UKCoUCL6Gd5utHBdtYe6rBYOBRaa1Wy80uWJUdy0vfRbPZRK1WG5hZVy6XeeEMq5Kr1Wrc+EOlUkGv10On03EXnXw+j1gshm63+9Usp4XXFDb3kGPOfJDoZ0QcxKvVanzSqvh1k+rv2WuY447b7YbH48HKygpMJhPUajV0Oh0fZOlyuWCz2aBSqXhKTBgHYFt8dqQol8vI5/PI5/PccbZYLPJZcIVCAZ1OB1arFQaDASaTCRaLhQ/akMlkqNfruL6+Rq1Wm3kH8zlQqVS8fXl5eZnvRhi05Z8OEv2csJWvUqmgVqsNnXWlUunYwBdbUfV6PbeMZqW+zBSCGVuw17GWVVbbXq/X0W63B67BcuTVapW34rKCn1wuh0ajgV6vxxtlmPnHysoKN8DQaDSQSqVoNBq8lp5t8+8b2FWpVDxewIqgGo3GVM03bDfkcrn42C+9Xk+NNnNAop+TdrvNA3T1en1o8MKodk+JRMKn0zJbLY/Hw1d5j8cDl8sFk8kEhULBz/ftdhuVSgWZTAbxeBy3t7fcV449bNhKz+6LHTuy2Szy+Tzq9TpvzvH5fAgGg1hdXYXX64XT6YTJZOIPl06ng1Qqxfvdy+UyqtXq3KIXWoHbbDbodDo0Gg1erDRuaKUQqVTKRe/z+fggTHEvAXE3JPo5YX3cbKUVj4oSI5fLodFoYLfbEQqFsLW1hfX1dfh8PtjtdphMJn6mF55VWbDw5uYGp6enODs74zPm2XRY9nBhDxpWI8BGRbVaLWg0GqysrMDtdmNrawubm5tYXV2F3W6HwWCAUqnkwUB2/UqlgnQ6jWQyiWKxOFelnVKphNVqhdfrRTAY5I49LGV4fn6O6+tr5HK5ofcXHpHYOG+r1QqHwwGr1TrRfJMYD4l+ToR5bPb5OJjltM1mQygUwt7eHp4+fcpHN+t0Osjl8qEofLPZRCaT4T31Hz9+xMnJCSKRCDKZzMCxQtyU0u120el0IJPJoNfreenu8+fPuduPzWbjJcZC2u02d72NRqNIp9Nz1dKrVCo4nU6sr69je3sbGxsbcLvdUKlUqFQquLq64juLVqvFp+SM+nlKJBJ+3GG7EvFEXWI6SPRzwkpAWU/5qJQZEyJLv7EZb8wL3+/3j/Spa7fbfITz5eUlz6cfHx/zaTIsp34XGo0GPp8PT5484aOmVldXsby8PHIybrfbRSaTwc3NDXfnmSYrIUalUsHj8WB7extPnz7F48ePsbq6CrPZzIOEJpOJD74ol8uo1WpDRh3Ch4BMJoNCoYBSqRxyFybRTw+Jfk6kUinkcjnkcvlAfl0MW701Gg2Wl5d54Gx5eXnINZaZSJbLZT6Xbn9/Hx8+fMDh4SE3kpzWV85isWB1dRWPHz/Gy5cvsbu7i0AgMHLUFLt+qVRCJBIZMO6Y1cdOqVRyW7EffvgBz58/5/Pu2XU7nQ4UCgVvxU0kEkin0xOvJSzKIZHPD4l+TthKLxT9KNgxQNjB12g0BlZP1mHXarVQrVa5KcXp6SkODg5wenrKHXWEwTRxWnBpaQkKhYK3ngaDQTx69AjPnj3j46JNJhNvRRVPvmXz7Y+Pj3FwcMDLimc5yy8tLcFqtfI+/+fPn2N7ext2u31gN8Sah9xuN3w+H66urnizT7PZnFgPQKK/HyT6OWEr+F2dXiyw1mw2uX+8VqtFo9GAXq/nZpKtVovPeU+n07i5uRmYQFupVIai52JhaDQamEwm2O12BAIBbG1tYXt7G1tbW/D5fANVfOJRWt1uF9lsFhcXF9zEkrX9zoLBYIDX68X29vaAR9+on5FSqYTJZOKpQ5vNhkKhMJCKFH+/LEtB7rfzQ6KfE7aCs1LQcbZYwJ8raqVSQSKRwNLSEmq1GmKxGC8uabfbqNfrqFarqFQqyOVy3H2Gde6NS5dJJBIoFApotVrY7Xb4fD6srq5ibW0N6+vrWF1d5VV849JbnU4H+Xwe19fXODo64oIvFAoz/UxUKhWsVisCgQC/ts1mG2oVZoFDFpwzGo2wWCwwmUzQaDSoVCpDKTxmH8YKohqNBj8q0Ko/GyT6e8C27VKpdOJqz35Zk8kkz08bDAZ+pme59Xq9zj+q1SrPw48SPKvmU6vVMJvNfBR2OBzG2toaH1nFCm4mCaNYLOLy8hIfP37EwcEBrq6u5jKxZCk1NirLbDYPDaBg/2X3I5VKh+rpR9Hv93kbM6soVKvVPOtBTA+JfkaEv7BCM4dJ58x+v49Go8FLZAuFwkBenLXhNptN7j4jPquz/7IPlUrFC15Ysc3a2hpCoRAfhS2eNsPuRXhNNluOTbA5OjpCIpGYaaAkQ6FQQK1W86EX4lSg+PNRq3er1Rr5sOn1enxwBpuww9J3s9qXLTok+nsgTNdNUw7KSllbrRZqtdrAL6u4oo8h3EmwlJVOp4PZbIbD4YDP50MoFEI4HIbP58PKygosFsvYkVXs2qVSCblcDolEgnfcsbHT8xbiCIuChFV8wtVeSLvdRrFYRDKZRCKRQCaT4dWNo352rEPw+voafr8fNpsNBoOBukFnhER/D5gQZ91ijgtEiQt9hCs7G5llNBq553sgEEAoFEIoFILX64XNZuODJ4TVgsJGnFKphEwmw1dMYcAwGo0ik8nMPXmm0Wggn88jGo3i4uICBoMBcrkcNpsNSqVy4MHI5vIxb/zr62uk02lUq9WxxwoWF2G2Y+VyGb1ebyA4Kfz5EaMh0c+IcAspXOk/VW+3sNKP/Zltm9n0Wa/Xi3A4zMdNezwe3nUmkUjQbDZ50w2LDTQaDVSrVeTzeaRSKUSjUT4Akpl2CmfUzUOj0UAmk8H5+TmUSiW63S4qlQr3pler1ZBKpXyFZ8cKNv3mrh0G+zo2l77RaHz1/v6HCIn+HtxVkTcrwmg/e4iwBwoTPuvMY/bP7NzOLLZbrRbK5TIymQySySRvaKlUKnzbzVb6dDqNXC6HarU69iw9C0yUkUiE9wykUin4/X7e1KNQKNBoNJBMJnFxcYGjoyOcnp4imUyiVquNfF9hPQJ7eLEYCYl+dkj090Aoyrsi+Pe5BoNt01k+P5PJYGlpCaVSCXK5HN1ulxf3JBIJRCIRJBIJ5PN53gLMdgHsIXCfIZKjYFN5mD02qyx0uVywWCwDor+5ucHNzQ1SqRTfqt8Fi4sw81Fidkj094CJnvW+K5XKqWvi70IYZWcrOduey2Qynltnjjcs38/y/IlEAre3t0ilUnxoJDvnf26DSyb8Wq2GXC6H29tbXF9f88k7bBRWKpVCqVRCvV6futiG7awmpfeIyZDo7wErjNHpdLw1tlarodPpjKwomwbhFp+t8qxQhVX3VatVZLNZnhpTKBQ8/cUi88wtZ1Shy5ei1WrxbEGxWIRKpYJcLh8oRpqmR1/YXsucc1iMgEw0ZodEPyPiYJ1KpYLZbIbdbofdbketVkO5XEar1Zq7VJT9knc6Hb6bYKW8TEDZbHbAHJIZaLDZcWz4xrdw5hVPwmEPr1nujTnnuN1u+P1+bg5KdlmzQ6K/J2q1GsvLy7wKrVwuo9vt8o/7Ch/4u/jZn4G/R/kZ7Lw/Tkhif/3PjVB889TLMwELv8ZoNMLn8yEcDsPv98NisQyJnrgbEv09ENaO22w2OBwOfk6t1Wp8IOZ9EItUGLwaNShj2vf5nIwywJjn+sKfn1QqhclkGhhRPY0rMDEMiX5GxL/QUqkUKpUKarUaKpUKUqmUp84+5YSgu5x1vyU+xX2JJ/2o1WpYLBa4XC6srKzAbDbzf+v1enS2nwES/T0RniPZFpullEa9dho+l5gn9QbM83XTfv1diI8CoxBagRuNxqE6fmJ66Cf3CVhaWuLGlwaDAWazmXfOCa2j593iipt62KomFqN45hzzyZtm+z+Jab9uaWlpwEVIvPpOe1/C8mPmLbi5uYn19XV4PB7uQ8Cgs/xskOjvCZuVbjab4fF4uDlGKpVCNptFLpdDNpudqwiGld6yiS5qtRoKhWJgnJU4mMe2xdVqlTfViB13PgdqtZpP6WFW2uLVmN0XG5NVLBaH0olLS0s8/WkymXifwfr6Oh4/fgyfzwe9Xj/wNST62SDR3xOZTAatVgun04l+v4/l5WVeeBKLxXB9fY3r62veS8+EOe4XVdhcw1xl7HY7lpeX+eQbuVw+YMElDOixTrdCoYB4PI6bmxskEgk+7EI8gPOuyDpLGYqbgdj7sMEZFosFbrcbbrebd78x7372etaQk0wmEY1GcXt7i0KhgEajgXa7za22WFqOvR+z7vZ4PHA4HEPegsRskOjviUwmg06n48MYWC6d+dxdXl5iZWUFiUQC1WqVR9/HCZ/V2bMae5fLBafTyQtStFotz8+L01Vsla/X6wPXj8ViSKfTqNfrA9dlU2bYroA1sQB/+vRrtVoYjUY+6koofvY+SqWSW2z7fD74fD44nU4YjUaoVKqB1FutVuOz8a6vrxGNRpHNZrlZCPPN8/v93AiEvZdOp+PFSBS0ux+SO85r32Z4+BuHjbBOJpOIRCK8ZZUNgJzUAsqq/NhKb7PZYLPZYLFY+HBLdm4eJXpWxFMsFnkbajKZRD6f54aTS0tL6Pf73EQjlUohHo8jkUjwGXc6nQ52u52vtCaTaUBwwpVeq9XCbDZzp1+r1QqdTgelUjnwkGCGoOx6bIgGi32wtJzL5eIFOGazmcpt52fkdpJE/xmp1+t8cCQrzxWLXvwAELbTstlvWq2Wz4CbFtblViqV+KQbdq5nom82m/wYwPrps9ksOp0O9Ho9z4l7vV5YrVaekgT+HnwTdwCyFXnc8YW125bLZd7ww8qWhY5AbNoPcS9I9F8DJvJZGlyED4BpJuhMc232ufC92EovPmN3u13odDo4HA643W7udyde6Ufd6yxpSfG9iR98xL0h0X9uxKv454gqT/PwmPXa5XKZj7FmwUZ2Vmcr7qx58VH3Oc/PhAUYqa5+Lkj0xGhYBJ81wbBVlwULadV9sJDovwb36Vu/bwHKNEU50wr6ru9jlmPIXfd1nyMNMQCJ/msyq/A/9S/8qOvPeo27RDovwvcloX9SSPTEeMYF+0iEDxoSPTGZUYE34kEz8n8gVeQRHBL5YkBhWYJYMEj0BLFgkOgJYsEg0RPEgkGiJ4gFg0RPEAsGiZ4gFgwSPUEsGCR6glgwSPQEsWCQ6AliwSDRE8SCQaIniAWDRE8QCwaJniAWDBI9QSwYJHqCWDBI9ASxYJDoCWLBINETxIJBoieIBYNETxALBomeIBYMEj1BLBgkeoJYMEj0BLFgkOgJYsEg0RPEgkGiJ4gFg0RPEAsGiZ4gFgwSPUEsGCR6glgwSPQEsWCQ6AliwSDRE8SCQaIniAWDRE8QCwaJniAWDBI9QSwYJHqCWDBI9ASxYJDoCWLBINETxIJBoieIBYNETxALBomeIBYMEj1BLBgkeoJYMEj0BLFgkOgJYsEg0RPEgiG7498lX+QuCIL4YtBKTxALBomeIBYMEj1BLBgkeoJYMEj0BLFgkOgJYsH433f8wFvf6pguAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 70; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current iteration: 71; current eta: 0.5, current beta: 128\n", + "Starting forward run...\n", + "Starting adjoint run...\n", + "Calculating gradient...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "algorithm = nlopt.LD_MMA\n", + "n = Nx * Ny # number of parameters\n", + "\n", + "# Initial guess\n", + "x = np.ones((n,)) * 0.5\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "\n", + "# lower and upper bounds\n", + "lb = np.zeros((Nx*Ny,))\n", + "lb[Si_mask.flatten()] = 1\n", + "ub = np.ones((Nx*Ny,))\n", + "ub[SiO2_mask.flatten()] = 0\n", + "\n", + "# insert dummy parameter bounds and variable\n", + "x = np.insert(x,0,0.5) # our initial guess for the worst error\n", + "lb = np.insert(lb,0,0) # we can't get less than 0 error!\n", + "ub = np.insert(ub,0,1) # we can't get more than 1 error!\n", + "\n", + "cur_beta = 4\n", + "beta_scale = 2\n", + "num_betas = 6\n", + "update_factor = 12\n", + "for iters in range(num_betas):\n", + " solver = nlopt.opt(algorithm, n+1)\n", + " solver.set_lower_bounds(lb)\n", + " solver.set_upper_bounds(ub)\n", + " solver.set_min_objective(f)\n", + " solver.set_maxeval(update_factor)\n", + " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-6]*nf))\n", + " x[:] = solver.optimize(x)\n", + " cur_beta = cur_beta*beta_scale" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are optimizing over the entire frequency band, we can visualize the upper and lower bounds of the device's performance as a function of iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lb = 1-np.min(evaluation_history,axis=1)\n", + "ub = 1-np.max(evaluation_history,axis=1)\n", + "mean = 1-np.mean(evaluation_history,axis=1)\n", + "\n", + "num_iters = lb.size\n", + "\n", + "plt.figure()\n", + "plt.fill_between(np.arange(1,num_iters+1),10*np.log10(lb),10*np.log10(ub),alpha=0.3)\n", + "plt.plot(10*np.log10(mean),'o-')\n", + "plt.grid(True)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot our results and see the resulting geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "plt.figure()\n", + "ax = plt.gca()\n", + "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", + "circ = Circle((2,2),minimum_length/2)\n", + "ax.add_patch(circ)\n", + "ax.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we'll check the final frequency response. We see that the response is much \"flatter\" across the band, and the worst performing frequency is much better than with the MSE approach." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting forward run...\n" + ] + } + ], + "source": [ + "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "frequencies = opt.frequencies\n", + "source_coef, top_coef = opt.get_objective_arguments()\n", + "\n", + "top_profile = np.abs(top_coef/source_coef) ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Transmission (%)')\n", + "#plt.ylim(70,100)\n", + "plt.show()\n", + "\n", + "plt.figure()\n", + "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.grid(True)\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Insertion Loss (dB)')\n", + "#plt.ylim(-0.1,0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In summary, it is very easy to implement design constraints and density parameter operations using the native adjoint solver interface. One could use this same design flow to implement robus optimization over many frequency points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/examples/adjoint_optimization/Near2Far-Epigraph.ipynb b/python/examples/adjoint_optimization/Near2Far-Epigraph.ipynb deleted file mode 100644 index ce5b485f8..000000000 --- a/python/examples/adjoint_optimization/Near2Far-Epigraph.ipynb +++ /dev/null @@ -1,1817 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Near2Far Optimization with Epigraph Formulation\n", - "\n", - "The adjoint solver in meep now supports the adjoint simulation for near-to-far fields transformation. We present a simple optimization of metalens using the epigraph formulation." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import meep as mp\n", - "import meep.adjoint as mpa\n", - "import numpy as np\n", - "from autograd import numpy as npa\n", - "from autograd import tensor_jacobian_product, grad\n", - "import nlopt\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib.patches import Circle\n", - "from scipy import special, signal\n", - "mp.quiet(quietval=True)\n", - "Si = mp.Medium(index=3.4)\n", - "SiO2 = mp.Medium(index=1.44)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Basic setup" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "design_region_width = 32\n", - "design_region_height = 2\n", - "\n", - "pml_size = 1.0\n", - "\n", - "resolution = 30\n", - "\n", - "Sx = design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", - "\n", - "nf = 2\n", - "frequencies = np.array([1/1.52, 1/1.58])\n", - "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", - "design_region_resolution = int(1*resolution)\n", - "\n", - "pml_layers = [mp.PML(pml_size, direction=mp.Y)]\n", - "\n", - "fcen = 1/1.55\n", - "width = 0.2\n", - "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(Sx,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " size = source_size,\n", - " center=source_center)]\n", - "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", - "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", - "\n", - "def mapping(x,eta,beta):\n", - " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", - " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " # interpolate to actual materials\n", - " return projected_field.flatten()\n", - "\n", - "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))] # design region\n", - "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will have two objective functions, one for each far point. However, we only need one `mpa.Near2FarFields` objective. We also need to provide a near-field monitor, from which the field at far point will be calculated. Only single monitor is supported right now, so the monitor needs to extend to the entire cell to capture all outgoing fields.\n", - "\n", - "When evaluated, mpa.Near2FarFields will return a numpy array with shape npoints by nfreq by 6 numpy array, where the third axis corresponds to the field components $E_x, E_y, E_z, H_x, H_y, H_z$, in that order. We will specify a objective as a function of the field components at frequencies of interest at points of interest. In this case, we would like to optimize $|E_z|^2$, and focus the fields of different frequency at different points." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "far_x = [mp.Vector3(0,40,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width+1.5,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", - "ob_list = [FarFields]\n", - "def J1(alpha):\n", - " return -npa.abs(alpha[0,:,2])**2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", - " frequencies=frequencies,\n", - " decay_by = 1e-4,\n", - " decay_fields=[mp.Ez],\n", - " maximum_run_time = 2000\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our objective function that we pass to nlopt is rather simple. We'll introduce a dummy parameter `t`. The goal of the optimization problem will be to simply minimize the value of `t`. The gradient of this functional is rather straightforward." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation_history = []\n", - "cur_iter = [0]\n", - "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", - " if grad.size > 0:\n", - " grad[0] = 1\n", - " grad[1:] = 0\n", - " return t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The key to the epigraph formulation (and most nonlinear optimization problems) lies in the constraints. We'll define one constraint for every frequency point of interest ($f_i$). We'll define our constraint as \n", - "\n", - "$$f_i < t$$\n", - "\n", - "where $t$ is the previosuly defined dummy parameter. Each constraint function is then defined by\n", - "\n", - "$$ c_i = f_i-t $$\n", - "\n", - "within some tolerance.\n", - "\n", - "More detail about this formulation can be found in the nlopt [documentation](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#equivalent-formulations-of-optimization-problems)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", - "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", - "\n", - " # Backprop the gradients through our mapping function\n", - " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", - "\n", - " # Assign gradients\n", - " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", - " result[:] = np.real(f0) - t\n", - " \n", - " # store results\n", - " evaluation_history.append(np.real(f0))\n", - " \n", - " # visualize\n", - " plt.figure()\n", - " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", - " ax.add_patch(circ)\n", - " ax.axis('off')\n", - " plt.show()\n", - " \n", - " cur_iter[0] = cur_iter[0] + 1\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll now run our optimizer in loop. The loop will increase beta and reset the optimizer, which is important since the cost function changes." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.8/site-packages/meep/adjoint/filter_source.py:91: RuntimeWarning: invalid value encountered in true_divide\n", - " l2_err = np.sum(np.abs(H-H_hat.T)**2/np.abs(H)**2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAEe0lEQVR4nO3du3LjNhgGUFHjMuPKk8J13jWPlEdxk3qLlJ7UYpp4R9aK4A38eDun8pqyBQPgB/BfSmratr0AkHFduwEAZyJ0AYKELkCQ0AUIEroAQUIXIOil57j7yQDGa7oO2OkCBAldgCChCxAkdAGChC5AkNAFCBK6AEFCFyBI6AIECV2AIKELECR0AYKELkCQ0AUIEroAQUIXIEjoAgQJXYAgoQsQJHQBgoQuQJDQBQgSugBBQhcgSOjC//7862PtJnACTdu2pePFg3Akf//z7+WP339buxkcQ9N5QOgCVNcZusoLAEFCFyBI6AIECV2AIKELEPRSOthzZwMATzRN580L5dAt/SAA4xVD98ePHz+/FsAA3e4rA+/v752PK7444vX1tW2a5lvgPgvfMYGcKllMXSSmtu/x52otUqnFTilpeXsby1pzOn0ujjW0fV3tuf9+27aXtm0vn5+f016R1jSNMxFgpLZtO0O3t6b7uApcr9dvxx8f39GAUd8fa4kdxNZW2amPr/132BH/qvb8W3qMtzq3h1g6M4Zm2P2/b7db8bG/PMeQnW6pvHAfws+Od/3+x4bO8diGGsa0b+5l2Jz2D+3vuWqO11EtMQ8vl7pjPHYc58zttc/LPl3t6+vvUsh+fV3a6bpP90DsQo/JuB7L4qG7x7se5uxyu763hKHlHfZtrXGeO7f3eHWU6NvFQ3ePq/TcyyLhxxHMncdLlVv2zk53pjX/vq/bU+7/zfE8uyVpLUc/nxNGh27Pf7zNasxW7Km80HcPNfv3OMbKC1ml/p5yro8OXSd2P33EEZjH/ab0kaLLE2NqUVspL6x92clytjTOa98ytoba/V21vHAUc8sLKfeXmsnLTrK2NM7KC99Fygucz1FOnqXpJ4Yovgz4mTPspq7X6+ATqGma1Xa7qecd0x9nttTl9JauLs9YXiiZcv4fv1cmSJYXatwTvPQlp8AdZsl+qjXOc+fbGcsLJZHyQo1VtzQYW1rVt84r0s7BOE+3xTxZpbxQulzd24T6au/X4Cbbr7ywLUcsL9xfPiffyKmWpc/H3ZQX+na6W1yduuytvVMI3GGO3k9j5/ra/bHVc7P41o7X67W9XylKb+M4ZEUpvSflWKXnm9KWqY+ZYszqW3ulrvk3bXFCp9QclzXHeM05Pvc8rZkfY9tServHtm0vt9vNJ0cApEz+5Ii3t7efX5dWhjmfMTRltep7viU+s22NT7lI1IcTu9U97YgTfb61cU3P7ZrnZ638mJJhU6/ci6H78fEx6JcAMEyxvHC5XPazRQHYjmnlhT1dFgJsRalc0ftpwADUs/7dywAnInQBgoQuQJDQBQgSugBBQhcgSOgCBAldgCChCxAkdAGChC5AkNAFCBK6AEFCFyBI6AIECV2AIKELECR0AYKELkCQ0AUIEroAQUIXIEjoAgQJXYAgoQsQJHQBgoQuQJDQBQgSugBBQhcgSOgCBAldgCChCxD00nO8ibQC4CTsdAGChC5AkNAFCBK6AEFCFyBI6AIE/QdB3euVkSLCkwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABsCAYAAADJ2WELAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMaElEQVR4nO2dy44buxGGqduMF1nZcADv8655pPMoZ5O9gQCGNwHGM1Krs0np1NRU8dbd1GT8fYCgvpLF288i1WLv5nlOAAAwhv29DQAA+J1AdAEABoLoAgAMBNEFABgIogsAMBBEFwBgIMfCeZ4nAwBoZxedwNMFABgIogsAMBBEFwBgIIguAMBAEF0AgIEgugAAA0F0AQAGgugCAAwE0QUAGAiiCwAwEEQXAGAgiC4AwEAQXQCAgSC6AAADQXQBAAaC6AIADATRBQAYCKILADAQRBcAYCCILgDAQBBdAICBILoAAANBdAEABoLoAgAMBNEF+B///OPPe5sAvwG7eZ5z57MnAT4S//r3f9I//v63e5sBH4NdeALRBQBYnVB0mV4AABgIogsAMBBEFwBgIIguAMBAEF0AgIEccycLTzYAAIDDbhc+vJAX3dyNAADQTlZ0v3//fhPe0rfdtlivOfKiveP6mI2jtF97rsa+nOfvnSt1Wt75nnt6aBnF9Ix4PsIoqSevW+4ZVZatdTk639q2WtpmS9vS+/ZcS5vqtU+2c9/fvn0Lw8r+OeLz58/zp0+f0uPjY9rtdul0OqXD4ZAeHh7S6XRKx+MxPTw8pMPhkI7HYzocDmm326X9/q+p4uv1evtcLpc0z3Oapul2bJ7nNM/zbXuaplf7cp2w3+9ffSRe+ez3+7Tb7W52yLbuJHTm6risfWKLXKP3rY1e4dv47cfaq/clD+W4l44WdIWw29p+e85eb6+NKp6UmT0e2TSKXP7Zc7oM7LfeljLT57yysnXCC6sFrxx1+ej6WarL9lOq07ZuR3VZ6u7xeHyTV9p2va3j1/bK53K5vNKVSB+0LdZmOabt0/cKWpcul0uapim9vLyky+WSzudzenl5SdM0pfP5nOZ5Ts/Pz+np6Sn9/PkzLMysp/v09HQTIy1wz8/P6XQ63YRXzlnB8DJOZ5QnYvoeXbF0wUtmeRmmM1JfL9texbWCb79L9untKN6cCHuVotRAdX7Y9HhEoqrF0fbmURqtoHrhR8d0A8l5LFsQjcp0I8uN4LxjnuDabbvvlasNP7I7Jd/zizrSyKmJRLa3Xnsi7H1b0bVpsvFqJ8w6bjotXn5ZW7xOQj6ew5bS645LxP58Pt9EV77FvvP5nH79+uWWo5AV3cfHx5tXK4Ydj8fbRww9nU6vejNduSSD5PjlcnmVIVrI5HpPIDxvQQuvzTivEMQmyUx9TOKapikdDod0vV6z9lnRsngCa+33rvG8Wp0G63152EooadH5KNfYPJe0yvU2TMkHe8zmaUn8c9fUoNPSQyTA0TW1Xq+9Vzdgry7Y63UHkLMtpXhUIdvymabpVs6Hw+GVyHojl6V129ZvK2g2rVI/bRpSSre2KA7bfr+/fR+Pxzf2l8rHesLWYdTtT2wUx/N4PN6+dV7s9/ubfbbdeBSfXpDMl8CiHtGLzOtRox7ZXm8LIBKA6/V6q0j6Xl1ZPU/LszXyskuV0stkqRganYda6DUi+t592n4bthe/l/4oPXJPyWP1tpeI7VLh7UHnp932wpfON6W3XrENR4tHrryjTjByBjxKIw7tKXqfkuB6cUteRHXbpl3s1EKW6yw0Od3wzuXC0mWh8z6nOTq+aN+bIi3Vy3fznG5NA7LeX288kQi1EgnfUhuXEnkomiViJ0TeWU8Yo1gSX80oo5aa/K8pxy2J0tlb3iVB66FWE3Ji3BPnErK5F3k9uYyqNSjqpaIepxRfNGyz17YUkvZmPUo/KvXYXTO8jIar9nwLJW818qb0vT1ebk+j8+pO670l2+w9KeV/HCzlWY+AenXBG57ba2wYLW1EE9XtmjbR4kSVpndqpoJqbNf7tW01is9LZ229zLbOKNEjvJOezK25tnY4UmtDb4X24q8VkZLIbeEh5dJU8gBbj9fYsqQe9tqZ68y2aBNROXpTETl66lhK/XW7Zh5atls7z1ZnbCu8Ol9bLxdPL4waHrb2zCPiWzKsrqWUljWGaCn5v+BH9HQuUdijOvEoriVpqM2nlNapKzVlvUZdKFGTljWnYFpGjiPpjbM4vVCaSI6MyfWO3nAtNzmuw7Xh547ljntp9dKey4voh6ccdgqhNHTyzv+/Ti/0DsfWondK671PL/TUIbm/Z0Rp60qNRpRGlLVtt6U9R5ri1VcbbhS+HNtsekEbYyMtsWZD6onfY8vGfY+edpRY1dLi+dVcvxWtdtzLzoh7lPvaU0SaJenpndaLbOhxolqpmtNtjcj2IEtoEflcL9NTsCWvocVTiGjxlry5PC/NNY9w5aj1lmrmOqN79f6IKYYonpYOPXpWN7q3J00579gbadl7autQL3akFl3TSq7drv07TG6EoI/l4on+D7B4Tnfp9II9ZsPOxeOFWRNn1BB6GnZUgbX4Le0R7bApd1+tyNVOL7R0UDXTC6V4omHniCmGKJ6a6QVhyfRCzXFLbTnXiP8a3mBKr6cXonJrLcuadrukg/OmF2r0psa53GR6YTT2v9RbUjOk+GhTElvy3qY71uKjpWstr9wj14Zah+5LGaklLRSnF6LJ7aVEYdgFJzx7PGp6mZKo9lTGJfnRO+wrVaSlFa3lx5iU3v6wF90bjYDuPb2Q8wL1+dwz1FsI2ZrlvMYUQ005lfKh1bFZMgouaclSIm1cNL0grDknZMPR39HKQR6lX3b1dbX/JOoVvt5505b5RDuXnGPNR9lqhtI9Tz7o/XtPL0S22WPeHKvdXjMdLeXcUn96O7lcXtTel7MtEsmoXet7LVZLtvK2e+pA1T/SeqkVuS16Xjvhv/Svi7rRLhXcJaz9bHCL/d61tfO53v33Gra3Cq/GS+/SPFzCiGfFNVZ4vXnNHmxbjUYfa4wsazuB2vBKxyxd/0irpTZxrUN4b7hiPRkrjjkvpdZG+VgPw362QP8IsGT6wKa71UOyx0p/W855WF7erZ1/pThKI47Iw9LnekcsKS0TYV0XturAcnlnl6iU62vIjZZKi2DJdqmsvLTUXNeiDZtNL2xFNHSNEl0zX6j3S56u/UWzZKvn6XrpKdEjLC3TC2vSMwfXE95W0wylsHvsXfP6JbRML3gs8ehSaltnofTEQ9R5e+261vuNvPB7jbCE4jvSRLy8bW8/mn/Ra+p6GWl7r9yand6ixLbX9Z6jk7U45/n1WqzzPL85l+vBW/4GqcXf2m5/gCqtU2rtyGHXDY68A0mvXbRZyqQmPp0f3tSLhOGFVxvHmuQ6b28/593acvKu857vjcRDx1eTL7KsaUrJrdsSni6X3e6vpSoPh8Nt+Ud9nw43R04Eozzwznlxa83Qy1zqezzbbZq9uL125q35a++TcL19z/P3yIquvDFCAtKenvYepmm6LSisI7VDYll5XVZ9l+PeCvCS4TrROqMlI0XE5JgImy4wwXqrtufTCzvbRZ49G3OV0haatVOO2wWfozR7YebI9fBL182tmRP1ji2dD10LL+9yz7xawfLC8p7gkG1dl/U9kSBP0xTaqYny3VsX2lv3VdKm23Fv/dZ1XHfketFxL+2eyFltsW1S8ie3lq2nJWKjCLisxX29Xm/6Ze3U4Yt+XS6X2xsj7CuE5G0SObKi+/Xr1zdvjtjv97d9eWeafj+aLMBtRdqKrd7W12giIZJt/dYIu/J79G8xb7jpVURPbO19NU9YeD2q56lHvX/JkyphK2H0nft3UyTG9rqWpxhqjm9BlG/ecX0st6qUPp4rq+gfbbl7c+Q6TM+xsB8tsvZc7dNDXh2O3vXnLTPpeYa2Q4iEN+ckeFpidUK/x02/vkc7mSmlV6/vkvekySt65Fu0TN6bliP7YsofP37MuYZe2vcysnSshZrKaXvQVrYShNqGtTSMUYJ2D491NGuU2VrxrJHf76lut7bTLbSjRb9Ko70vX76EgWVFN6X08VsSAMD6hKJbfEcaAAC0kfOai08vAADAery7BW8AAD4yiC4AwEAQXQCAgSC6AAADQXQBAAaC6AIADATRBQAYCKILADAQRBcAYCCILgDAQBBdAICBILoAAANBdAEABoLoAgAMBNEFABgIogsAMBBEFwBgIIguAMBAEF0AgIEgugAAA0F0AQAGgugCAAwE0QUAGAiiCwAwEEQXAGAgiC4AwEAQXQCAgSC6AAADQXQBAAaC6AIADATRBQAYCKILADAQRBcAYCDHwvndECsAAH4T8HQBAAaC6AIADATRBQAYCKILADAQRBcAYCCILgDAQP4LUvTmxkBU5egAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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20W630e/3EQqFkMvl4PP5MJlMUKlUxEEpExCBQACxWAyJRAKGYQix0NmDwaCsF7MtBg6C92QYBkzTRCQSQSgUwnA4hMvlkjKV2dRyuYTP50MoFEI4HBYy7fV6EiwXiwW63S5s28Z0OhWbCQQCyGQyUrIVCgUkEgnU63Wk02nM53MYhoFQKAS/34/ZbCZyRTqdRjqdRiaTgcfjQTqdRqvVEu2/3W5LYtDv90XjdbvdmEwmWCwW8tkk0H6/L84cDAZhWZbIQsvlEuFwWALAYrEQPZqYzWZoNBpis2qQoobNBMGyrJXKgOQfiUSkQmBikc1mEQgEMJ/P0ev10O/34XK5kEql5D2qnsxgTrui7al6J+3T5/MhGo2KHXi9XkSjUSnBs9ksvF4vxuMxlsslAoGArFc4HF7p11Aj532kUikhsFgsJp/fbrcxm82QSqUQCoUQiUQQCARW7oOfS/ulrfHaTdNEIpFAKpUSe55MJpjNZvB4PEL4ql2zOiA/qQkhK3a+b7lcIplMIhKJIBqNStUBQH7G6/Vic3NTqsl34b2k++TJE8xmM9HzqDcx+lmWhWAwiPl8jqurK7x48QKLxQLxeFwcXW1OUEMzDAORSEQMlE0KGqHf70csFsPGxoY8vEqlIovFnyG5UcRmw4YE7ziOZGeURTY2NhCPxyVrYHlrGAYACBm0Wi0pQ+/du4doNIparYZ2u/1m4bxemKYJAFICD4dDuVYGg2g0KtkLgJWIrz40VbdiJGejIR6PY3d3VxyYTkujJTEEAgEkEgkMBgOMx2N4PB4xRL/fj263uxJYqOFRWmEGbRiGOJNhGELMHo9HmpdqpjAcDgFA3suyjVkDjZ+ZCwAJFMPhELVaTZwmmUxKALu5uRHJZ3t7G7lcTr6HcgcbGn6/H51OB2dnZ1JtJRIJuN1ujMdjXFxcSPLAJhGzbTY0GUyDwaBcP8mJAYxaMJ2UREDC4ppMp1PM53MMh0PRS1liU5tVA2UkEpFgxyzVNE0hsslkIj+rNuJIAqlUColEQoIJ10YlExWqVqnaZjQaFa26Xq8DgCRXqVRKXidZud1uxONxZLNZSQYYOGjHlmVhY2MDuVwOkUgEtm1je3tbKobr62tMJhORL+njXEs24tioou2wGc31yGQy2NnZkXsmnzC489lbliU2zc9Rgxq5pdVqod1uS1CNRqMSIFmh0Hb8fj+CwSDy+fwKIb8N7yVdv98vhsfSio4ym83kxkhS1Wp15ec9Ho8QF7OIUCgkJeZgMJBymQtpmiYMw8DGxga2t7exWCzQbrdXoh4XUc02SETMJmnwvV5vhZxTqRQ2Nzcles3ncwQCAUSjUSnTR6MRTk5OJLvL5XJCNI7jIBQKIZvNStluWRYmk4lk6cw+/X4/tra2MJlMpExlpsfSnqURjYyG6PF4JDiw0eP3+1EqlVCr1USn2tzcRCQSQaPRwHQ6lSxT1UyZDbAZw7VnRaGWZ8za+TlcV66p4ziIRqOIx+PiQGyuMqhGIhGEw2EhPz5b1bkZUKh1kugtyxIiPzo6EsLb2tqCZVkYDody3VzPUCiETqeDYrGI09NTkR5IkM1mE41GA/P5HNFoVLJzPjNeEwMRP5OOxGwpmUwilUrB6/VKv4J6NO+J1QodmQRAkmTSwu+MRCLy/Jlts4RXp0QoZcViMbEf/nuhUEA6nZYkZzQaSWbP++VnsTfQbDbFt2l7sVgM29vbcLlcKBaL6HQ6mEwmkiTRzzweD7LZrDQ70+k07ty5g3A4jHa7jZubG5FLaA+RSEQmC6h1MzFgv2Y6nYr0wKDPRIrBKRwOy9qSVJllWpaFQqEgCQynIGazGZrNJgKBAILBoFRhTDhI9pR+vF4vHMdBq9VCvV6Hy+VCoVDAaDSS62E/YTabifTE/sB/i3SHw6GUJcxSptMpXC4XBoMBer0eBoMBDMNAPp+XjMYwDNGjAEiE5N8UxWkAt7Nn4M10Qjweh9frRaPRgGmaK+UEF5QShJrlMhgws2F0ikQikvl5vV4pTZnBkfAnk4loR+FwGLlcDoZhiK6TTCalzJpMJjI5MZvNJICwxN3c3JQMZjweSybwySefoFAoSIOE0drv92M8HotTbWxsSMYZDr8565WNqWw2C8uyMB6PcXZ2JtMSyWQS4XAYtm1LgAQghjudTsVgWHqyGcp1DYVCCAaDEmwnk4lk05QsgsGg6HnMHNSsnRUHMy9mxHxmhmFIQ44ORMmFkwr9fl+kBtM0MZvN5NmQtEzTFL2+Xq+LHESCYnDl9YdCIVkDXhOTAV73crkUR6Ldqc6qvpcBlu9lQOD9MBNSJSQ2NilPUPphVkn7bDab8Hg82N7exv7+PgKBgGT/nU5HCI1NnOl0imq1ikqlgmQyiWQyKQGV1djx8TE++eQT1Ot1sV3TNLG7u4uNjQ2xMSYK5ABWqVwXr9eLZrOJaDQKn8+H8XiMarUq2jzlJP5hUsQ+QzgclqalKj1NJhMZ3VR7HIFAQEZUKS+QdIPBoMgf1H/j8bgQNvmFUoNlWRLge70e6vU6hsMh2u02XC4XotEo0um0SAm5XA7L5XKF98iHlKdI1rSpb0S6Z2dn8pAZ+Whgk8lEGjuhUAi7u7tIJBLSVby+vsZ4PJbFVxs76gwrHyLLMepidMR0Oo3Dw0O8ePECx8fHcm28SRqoqiWRdLkodCISca/Xk0YfnZFZ6mQykVJmNpthMpng+vpaGjL8HurJ/X5fGiT8PhrCfD4X7ZHgQ7m+vsbp6Sn29vZWmiMsPcvlshhANpuVf6ezqzOjXFd21qnDsQPbaDTQ6/WkJGRGSSMi8ZJ4mHUyYrNZxeZkMpmE2+2W0RtmXOxgV6tVmYCgA7FkpANyLIpBl7KLaZrSdKrX69LxDwQC6Ha7mM/naLVa0pXu9Xrw+Xy4uLjA2dkZ+v2+SFuUv1iW8hmzRKcuyjJxPp+j3W5jMpmIHJDL5RAIBMQhh8OhdLcZeMvlsjSkgsGgkCDJnRWCSjh8D22dGT1JlOOW1WpVSI3BmfbHQKr2CtRRrUQisTLzzor09PQUxWJRpgMIatCz2Uw0UQDSQOr1epIxq5oxNVn+u0rC9Cn6NoMdqxWODfLzbk8tqDOzDFb0BcLtdmN7exv3799HPB5HuVwW22KwoJxC/VmdO+frJGcGv0wmI8kaZZVGo4F2uy28wmZasVhEJBKR7/3GpEunL5fLuLm5kZEQRqxer4derycCuGmaEiFZMvj9fjSbTdi2jW63K9kiO87MEKbTqYyCkJyYKe7v7+P73/8+vvzyS1xeXso1qAaliu1cVHZPa7WadNwXiwVOT08xHA5Xyq/hcIjLy0t0Oh1kMhlsbm7C6/WiWCyiWCyKM9JR+D3tdntFw2Lpw4458Ea/pGOpzUNmJzRe/uxoNBKiHAwGuHv3rjgQNdV6vY6joyNsbGzA4/GgXq+j0+mIDjoej7GxsSEBpdVqYTQarTw/lnokcAYxtRtPh6EmbpqmjOOcn5+jWq3CMAzkcjkpyU5PT1Eul0W6YQbI98ViMdRqNZTLZXFWZqCUFtrttoxRcXSwVCrJaBYbfNfX11JVdTodyYDb7Tbi8bhIWiRYNkhyuRzS6TTa7TYqlQoWizcbgPicfT4f8vk8AIhOXq1WMZ1Okc1mcefOHViWJeU5pR2OoXE6h2NmzNzo4CQ4Jh/BYBCZTEb+vVQqiT69ubkpWSnHllqtFhKJBAKBgEyUNJtNnJyc4OrqCtFoVNZO3eTBxiKlCAY9JkLUcUm2fr9fNH/6Jccbq9UqyuWy2EYsFsPm5ia2trYwn89xenqKer0uJMQGLBMDjmExcLPComyn6tBqM1JtNANALpfD8+fPsb+/j8FgIKN/9L9oNCqSIu+H1RKzYlYPXLNmsynBkWRNuZKSEnlmPB6jVCohEAggn8+vBIRfm3Tv3r2LarWK8/NzGalSG0DMHJn9DgYDITnbtiWjKZVKuLy8lFKdaT+zVZbTNGoaUqvVwnA4RCwWw+HhIT744IMVp1C717d37rA7TF2KujSNularAcDXypzpdCp6XjKZXJl84Bwl5Qf+DDMwZtdq9GbWzdfZFaeexlKJZTofPjWjq6srjEYjbG9vi367sbGBwWCAk5MTKSPZ2QcgAXIymSCbzYrx8llxLdiRZzlMR/D5fCvOFQ6HpVqhBt9utyXDYeOF44ONRgOj0WhlosHtdkv2mEql0O12V2QlykedTgftdluCMu2MQYXBXR0Z43oTXLtOpyPkEggE5H3MclOpFJbLpdgdR4o4D86xQU7b8Dl2Oh2USiWMRiPRYRm8/X6/kC/nonkv6hgXgysDeSgUgsvlQq/XQ7VaxdnZmcgHPp8P/X4fJycnksxks1kUCgWZnb2+vsb19TUajYZUJd1uF+12W66Rz5hlfL/fx2QykYBLbVOdPeZ/s+znH9M0xdaZjKVSKaTTaaRSKbGBdruN0WgE27ZhmqaMkbpcLmQyGRm9pL0ycDKjV+VEBk0GVkpAT58+xeHhoUgyXCN+5ng8lqDGPhMrtXa7jXK5LJIIkwTbtld6J5z+UfsAvD76/WQykQz5G5Pu9va2RHCm+qpWdztidjodaegwCvd6vZWMiFGGGynYkW02m1LqezwekQxYjgCQDigXg5qg2tnlA2LTLB6PYzweS+nGBVMFdBo/dUsSK7vO3LFGgZ+Nm5ubG7RaLTFmfi6dioFA1fjY6Oj3+yvbW1n6Up4wTVMyP+5oyufz0hSh8XLigGVSJBKRTSicjaU+S+mEpRbJlw0LZmEMViy7o9GoOMbNzY0MkhuGgUQigclkgmKxiOvrawwGAyFodTcRQXmHu69oV5wPZuZA7Y2TLexUU/owTRPj8XiFOPi9JGUGaK6/+n3UhjlvzGfDptdsNpOKLRwOY2trS4Kebdu4urpCuVyWSQ0GLwZQyhscg6R+Tnumrar9hOXyzW4z6oskbsp53AIbCoWwubkpO/k4E8tGD2d66ZesqNRZdgZPldS4PiQ4ADIdQ1mMUlqhUJDxRe5+vD1fy6mOyWSCer0uSQZtMp1OI5/PS0N1NptJUGZgUAMvs3J1+z+DD21L7V+wEqascnx8LIGeGx5YDc9mM6lwo9Go+FC9XpdKntWhus2YdscgxWbkNybdSqWCwWAgC8WJBYraqVQK+XwesVhMMrV+v4/pdIp4PC5zquxiU5fqdrtSgrK5QzIj6XKki1olywV2yC3Lwu7urmQrbIjRuILBILa3t2UigtmL3+/H5uYm4vE4arWa6I9slLB8rtVqUmr4/X7s7e1JIGCDg1GX5Klmt+pcK51bbSxxdIUzw/w5zo3W63WRA2hADEYc00kmk7IZgvoz8KaMo3FRrgEgUyPM6m7PXnMMi9kBgx010fF4LJs0mCVHIhE0m82Vz2W3mzOvDIhut1tkgOl0Klk15QvqiXy+Pp9PnI7rwI053BHECoLv42iTYRjodrvodrtSljOjZuZIHZR6HuenmcHxuXHywjAMxONxmetmlscGD+2Qa8lmoTpxw+kYVo4keQBCapRlGJBZrTGQJRIJxGIxaU7RLvg38KaCSyaTMgHDhlkoFJJmtfr8WdpHo1H0+3159kyySLzcREC9e2NjQwIiJQdVWkylUqLRcsNINptFOp2WWX0AYkt37tzB9va2TJhQsmPFlEqlsLu7K7spGay5AYP9DEoJXEMeRUCZr1AoYG9vTypaypmpVAqmaUrixcTP4/EgFovBcRxJZtQeDMclh8Ph16a4fi3SZanCJof65ewoMzKwnEskEjK1wM0JDx8+xIcffigEfHJygpOTEyn3GGUSiYQ83Gg0Kjd5c3ODTz/9FJ999pnsE+c0BMsvNaskcSeTSXFg7koLBAK4c+cOTNNEqVTCixcvUCwWAUAMmaRaLBbhcrmQSCTE0Rl9Oc3ADIckRZ06kUhgb28PsVgMX375pWw/BX6hOZNImBGqHfBGo4F6vY7ZbCZleSwWkwwmmUxKlkGCZuOOEwDc1MDozO42Z5DZxWXZTCKgDKGWxNRN2ZnlKBuzzmw2K405t9stz4/EygyWo0FsKKmD6GyicgMNnyvtiZrZ4eEhUqmU6I+cIgHe6K9bW1uiq5M0KSlwmzK1VkoDlGYoWXDXGrN8ZnF8PpQr1AkD2i6bS6PRSEieTqzu6PN6vUKgHE1jk5OZFZs1JD7LspDP55FMJoWYmG2zpDcMA6lUSqQ8JguUEZjdqpqp2/3mXIUHDx7IvDNtlo2leDwuc6mscrkho9vt4ubmBu12W8p+zueyCna73djY2JCt7+PxGJeXl5hMJtje3sa9e/ewubkpEwpMRPiHCVoymRR5hM1t/gzPEOEYJ5OhcrksOy1pj5lMRkbdaPe0Adp4IpFAJpORrdamaaJWqyGTySAWi0n2HQgEJEFUdfFfm3RJnOrcZjgcxu7uLgqFggzX828OPHN/MhtHOzs72N3dhdfrRalUQr/flyyW2QYHvU3TxHw+RzKZRDQaxXQ6xcuXL/Hxxx+jWCyKczG7UzuQapkIQJpWHo9HdNhIJIKdnR3RcjmXxxlOjk01m03MZrOV06RY+nPTgTpOxUM1vF4vMpkMnj17hkePHmEymeD8/HxlTdWGn5qd0/gty0Imk8Hp6SlarZbs0lFPI2PDiWcl8DlQLmDWQ52WJMnsl3ok9UcaG/e/syrgOBMNnBrX7V2F3PrMMS/TNBGPx1dIl2TD6oDPLhQKScbqOI7Mnm5tbWFnZwcnJyciRe3v7+PZs2fI5XJSFgJvZB3LsrC/v49kMonr62uZVeVa8F6ZPfF8hHA4LJMeJD6OUUUiEWQyGdH5OW+rnnkxHA7RarVk/ItSFKcZuE6UsthYU506EAhgPB6LLMG1YHZM2wqHw8hkMojH4xIoaBvz+Vy2qfLwFtUvOA6o2p9qk6FQCA8ePJAM9bPPPkOtVhP/TKfTEnQo4ahyHb+DVYEqzdEuDw4O5LwIzukPh0Ps7Oxge3tbJDWVbPm57IlwppYkeX19jU8++QThcBhPnz6VbeiUHdl7UTdfcfrk/v37KBQKsG0b5+fnuLq6EhuMRqPY2NiQGXHySCgUQqFQwM7ODmzblrFZVXp9H95LutVqVTroPp8P8XgcuVxOSIv6HBeKc5ORSEQOxPB4PMjlcrKAbL4BvziQhYbJoXJGGtu2pSNbrVZXSk1C1VQYzdmN5Wu2ba+MqVGv5UPxer2yj5xZE+UBZgbVahXtdlvu1bIsRKNRzGYzOYGJhvbo0SN88MEHiEajePnyJVqt1spojsfjwe7uLg4ODhAMBlcagMx4WQKWSiVMp1M8ePBg5T1cS3Wekl16lpNsNtF5GaTo4NTJOEOqrif/n9MPnM9mw4gEQ2flLkQGQFZH1Iups7NiYuZL+YHNC+qhsVgMd+/exf7+PkzTxPHxMRaLBXK5HLa3t0XPvLi4QKPRgNvtxr179/Do0SN4PB7ZZcjslGuh7tSiPsxSVA1+1NkpoVHn5Zgeh/RJoGxQMqtUeyAMDOpMJ78bgDwXZvlsQKs/xxEyHs6k2jvwJgm5urpCq9VCoVCAZVkiKRAcCzw4OMDOzg5+9rOfyWv0tU6nI01rNlKpe1PeUT+X1Q/vIZlMSlDr9XqiZS+XS1lHJkBMYnivavOWfvC29bwN27ZRrVZxcnKCQqGAZDIpn8uAy8SFFQ+AlQYnyZz7Bzi5wOSQz5EJSyQSwe7uLmazmUy/9Pt91Ot1ua934b2k+9VXX8lN0blisdjXCJJ/WG5xpo964tXVlei03F2WSCREM2Mm1G63ZWGr1Srm8zlKpZJMT3DhWX4Gg0Epf5iR8zq4JdftdqNWq+HLL79EsVgUx+HMIBtonCul7qyOrpBwfT6f7ApihkqtitsQ0+m0bF8ulUo4Ojr6WsDw+/24e/euzN/SeWhQbAg5joNyuYx6vY6DgwM8ePBAth4zenOHmrqxgRkkMzd1Cy4P8mBDSN2ppO5CUzu0zMhJUMzugdVmKnVdACv3pT4fvl/dF88AQeciQTIT5y4x2iGblSQfOienB9RNHyRC2pUafNRrV6UUXgMJejqdyjrRbribkLO4JGxq76qWre5CU6sQDuYzc+Q18Tq4NZaz73y+9XoduVxOrok7KF++fCkZ4e3KT13bXC6HO3fuSIVDHy+Xyzg6OpIZ70KhICNYDPL0qdvN8/l8LtuB2aij9j2fzyUwczKFshdPTAMgMg6fARuClBroa+FwGIlEQuQPl+vNIURnZ2eyK43z3ZTyOD/MwE8ps9VqYblcyqQQpSxKMSRZSmHUlmlvsVhMEkzO6N7c3Hxz0i2VSmI8NBZmBWwqsMxhNKZmR9nAcRyZc6UuVCgUJPMrlUqy9Y+yg3p26uXlpZxXCvxif/jGxoZ0sEnINB46EJ2kUqnI4Dy3zDL7ZLOD+/TV5oZaiqoEok45cIwsn89je3tb5ml5NujJycnXdqhEIhHE43EpR253+AHI+ArwRls/Pj5Gs9mUDJiZAZ2aBKbqxR6PR7Rel8u1slZ0crV8I/GSFHi/XA+SijoqREJ0uVwy7kVypabM0prfx9f4ebQTj8cjOxEdx8HFxQW8Xq8EasMwZPcd55hZ7jmOg3a7jfPzcynNs9msnPpF+1Ezb5Xo+fkke5IKR8zUsyTYYGVWqmZglHRuZ7qccLm9nZ3PQ908wflVOjMbW2qgV7fBNhoNvH79Gs1mE/F4XAKNCnVKYblcIpFIIBKJyPwtANmtls/ncefOHSSTSXz3u99Fq9US7ZXjjCRCErGaONB31GAdi8VgGIaQOCW60WgkUh7PL+Z0gLpuAGQEjGeo0AbYc6hWq3jx4oVIUY7jSDXO5C4Wi6FQKOD+/fvI5XIYDAa4vr4W+Yx+xOCqym2swKifs1pRA7R6Hsi78Eu3Ac/nc2kGqE0aOj3ZX51P5UNmJglAMslAICBkbVkWbm5uxCmWy6U0xubzN2dbNhoNEavp/IlEArlcTpyNGx1Y+rG8ZbbAE7Q4GzmbzUTvo4RAzUadtFBLKp69wG4rS3tmIBsbG3jy5AmCwSCurq5QLBblQGw1yzUMQwya10yHUJ3RcRw57m46naJYLOL169cr4zTRaBSJREKMhp1xdRcQD+gxTVMCEteH4LQCGxV83pykIMGq907jGo1G8l4GINXhqCvSqIE3GwF4ZCQrI9oNMxXHceQcVwaNYDAomRU1Tjo/dfVqtSpOzs46iYLyF2duec4xqzgeOsNMmHbIQ4KoV6p2T9LhNIiaqbI5o+44YyAFsDKvy3l1l8u1cvasehwkJZxIJCJkzjOeb25uYJomtra2kEgk5Fmr5Tnty3EcpFIp7O/vy2w6n2uz2cTx8TFCoRD29vawv78vUxXHx8cYj8eIxWLS/6D+y4pD9RvyBfsnjuPIxh0GXU7LcMJAXUPKYpz55hgaD5a5urqSpi85qF6vy85JBldW4H6/H/F4HLFYDPF4XI76rFQqItEBv9iowzWmT6lVoSrV8XvoR+oO1F+bdFWwrAewcmoXywZmuSQQ9RwCjn9x2oALzvKOF009Um068IHQOUzTlAOrOXvKHSLqbCgXm5sX2F3k0DQfPEeG2Nkm+VG7pO6rzgny51hecRaUZU+j0cDNzY1IJMwmDcPA4eEhvve978GyrJWdbXRczg8PBgMkEgl85zvfkWmGzz//XA7sYWmbyWRkDI9aFNd2PB6vTKBwZMqyLHi9XrknSkFsKKon59Ph+GyZ3XFnD0mK169ut+R/c90ZfNjEIDh7ySzP7/fLwSlsSHHGe7lcotlsSsMokUjINAIDPIfZuSONTSZ1zTjeREKkbalb1ll2Mqvkc1R3atEXmO0wS1MDPqdFIpEILMsSm3ccR+aMGciox3PMsVarYTgciizApp66Rf3zzz/HZDLBwcEBvvOd7yAej0tzh41MtYG2WCwQi8Xwu7/7u+h0Onj16pUQLzPGm5sbmQ9uNps4OzuTWVtOfJCEWCXQvuhrtO1oNArgF0kcm6bkBgZgVre0A0o2tDGuNQ96ohxAHVitROjXrMTox7RL8hl5iQGBmnsoFBL75G5EPsurq6uvNeZUKedturOK95KuOsup6kxceJ4i3+l0RDog87ORwa723bt35UB0Oke9Xpdf12Pbb36NCbvCHo9HRmLo1MxIPvjgAxweHkpTZjqdypiNOvPYbDbR7XaRyWRkS2y1WsUXX3whYjjnQUkynGhgNGezhBkZS2EaAjcuMGLu7OzIqWQ8lo/XXigU8Md//Md49uyZNBRIBlxvNv0AIJ/PI5VKIZlM4qOPPkK73cbr169ldCqbzcp5vr1eDxcXFzLnylOnLMtCpVKRLCIajcrxc9ykwYyJHX42ShgUmL2ws8+1Ua+Z2bXagCSJMSOkfMDKwXEcOXiEWjKlKp5zwPUhqbGUZMNtNptJs5TSAHeShUIh5PN56WQz06aGTMIGfiFLsffAxh+/g1mquvOR50NQ51SnQDhCxmDHrIhdcW78YDZFP8nn80in09I4I0ltbm7iD/7gD/Ds2TOEQiH5FVc8YP/u3bt4/vw57t27J9fBnaKUJdgcZBB++vSpBIbLy0ux03A4LFUFf8sEjwTgObhMMqhhM5ulzXe7XSFiZuYcSaS/GIaBhw8fIpvNih1xEodzw7x2js5Np1NEIhE8fPhQfnMFT0wjP5DIOT5GsvV63xzGzw0YPNx8d3cXl5eX0rCnxp5Op5HL5SQAlstl2YRCjVvdmPOtkC5/lQ5LL55sxe4xy3u/3y+zbIvFQmYsuStta2sL9+7dk995xHElNtoymYzsLmF2ORwOhTiurq4knX/w4AF+8IMf4P79+yIVJJNJ+S5V8/L5fPIrbfgbClqtFizLwldffSU7YvhA5vO5zDoyG+N5wdScmd26XK6VQXZuwojH43j06JFkvYZhyG/d+O53v4vf//3fx87Ozoq+p+qK1AK548bn88kGlBcvXkjZxoZmMplEJpOBYRj40Y9+hMlkIro5j+lrt9syQmVZFnK5HNxutwSSRCIh98QNG5Q5bNtGMpmUQXRuTqD2xs4upyhIapRiSFqLxUJG3pjF2LaNRCIhWyd5HOTFxQU6nQ4KhYLIB3ye3HXH4MLPZwbDQMCm7+7urnxurVaTM3S9Xq80fphIsApjQGDAByCH3JCguWOtXC6j819HcaoH8vNZcmaUn8EjMRks4/E4ut0uEomE/G5Ax3Eki0smk9jc3MSf/dmf4Qc/+IHMaRuGgWazCZ/Phzt37uDx48d4+vTpyvgd10slXVYrPIXN7/fLxoL5/M3vq3v+/LmQGkt2ygecv1bn0Ll2THoYEEjeqVRKqsXpdIqbmxs0Gg08ePAA3/ve90TDb7fbck98tmofiYFVnUrhyXoccdzZ2cHjx48lIeFhRdVqFY7jIB6PY3NzEzs7O9jb2xP/zuVyItVNJhOk02ncv38f+XweLpdLZBhuSuI50DzYi/7PXa7vw3t/BXun05EXSQZqhxZYnYlVMzZmHfw59fXbehiJ53a04PuoodFZKF6rWbg6dsVrUK9NBcmE38lshyWyej1vuyb1/tS5POqYdLjbIzC354lvzyK+D2oHnE59+35ZGvG71DXn9XK9+DPqfavXpD5b9b/VDSh8n9ohfpc9qRKR+m9qV52fw2d+++fV9/+yfwcgdspMSb3G259x+9pUvG1kSbUFde3Un1Hfx7W7/czVZ0I75Rqo0wJMKFSoO/HUbfDvg2rH/C51rEvd3ab2F9Q1uu3Tb7un2370Nh+7bceqHao/Q/Bz+V2U+NRNR3zmb7um2wnObclFbaLe5ix1lJU/o05u3bb9WCz2Tqd+L+kCeH+erKGhoaHxNryTdN8rL9wm5F8lI/u28dtwDe/DL7s+9fXftmvX0CDeZ6f/r/vgb9s16ExXQ0ND49vHO1nX/a4XNDQ0NDS+fWjS1dDQ0FgjNOlqaGhorBGadDU0NDTWCE26GhoaGmuEJl0NDQ2NNUKTroaGhsYaoUlXQ0NDY43QpKuhoaGxRmjS1dDQ0FgjNOlqaGhorBGadDU0NDTWCE26GhoaGmuEJl0NDQ2NNUKTroaGhsYaoUlXQ0NDY43QpKuhoaGxRmjS1dDQ0FgjNOlqaGhorBGadDU0NDTWCE26GhoaGmuEJl0NDQ2NNUKTroaGhsYaoUlXQ0NDY43QpKuhoaGxRmjS1dDQ0FgjNOlqaGhorBGadDU0NDTWCE26GhoaGmuEJl0NDQ2NNUKTroaGhsYaoUlXQ0NDY43QpKuhoaGxRnh/yeuutVyFhoaGxv8S6ExXQ0NDY43QpKuhoaGxRmjS1dDQ0FgjNOlqaGhorBGadDU0NDTWCE26GhoaGmvE/wXDdigEhgGAAQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 256\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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mQ6VSQTKZ5L2tVCqhVqshl8tRKpUQCoUQDodhNpvR09ODnp4e9PX1obu7GwaDAa1WC9vb22g2mzAajajVamg0Gizw79+/j9XVVeRyuf++0KW4mEKh2BU3o1josWPHMDw8DKlUilwuB7/fj6WlJdy/fx/BYBClUokPrtvthtFohNFoZOsunU5DqVRCq9VCJpOhWq1yHEcikcBgMMBut/OhIqHbbrfRaDQQj8cRDofZFesUbhRn8Xg8yOfzmJqaAvBePMrn80Emk2FiYgKTk5NoNBpYW1vDysoKSqUSbDYbxwONRiO6u7uRSCT44dIa+Hw+NBoNJBIJXnyFQsExK4fDsUtZ0AYplUr891arBYlEwgdorztD7yOhSr+3p6cHuVwOgUAAuVwO9Xqd71smk0Gn08FisWBoaAjj4+OwWCxIp9PY2tpCOp2GwWDAwMAApFIpKpUKNjc30Wg0oFKp2NpqtVp8rXq9HiaTCXK5HIlEAvF4nD0KnU6HXC7HAoeUs9vthl6vR6lUwsbGBkqlEjQaDccJlUolC3CVSoVarYbh4WEAwOHDh+Hz+dhC9/l8KJfLMJlM6OrqgtFoRC6Xw9bWFnK5HHw+H7q6umC1WqFUKjE+Po5sNgvgvRggCdr19XXEYjGO+bdaLVQqFWi1WrhcLjidTmi1Wr4PpVIJnU4Hp9MJo9GIQqGAVCoFqVQKl8sFu92ORqOBbDbL1g+tX7VaZatKLpezkm61WtBoNEin02i32+jp6UF/fz98Ph/sdjvW1tY49kpuKu1xh8OB/v5+GAwG3vPlcpnDAp3QvqI9JpVKWYABYMXZKXDlcjkcDgckEglKpdKuzzWbzRgdHYXP58Ps7CyazSY0Gg3v756eHrb4NRoNALBQp/CVVCrF9PQ0Zmdn+XfOzMxAIpHAYrGgp6eH99l+cfVwOMyCfr/rtlqtqFaryOfzHGe32+3sOZtMJng8HlgsFnR1dfE6UBg0GAzyfujv78fExAQOHTqEkZERGAwGjI6O4ujRo9ja2uI8B1nEKpUKGo2Gz8zDeKTQJc3o8/mwvb2NWCzGWs3n8+HSpUvo6+uDRCKB3+/HP//zPyMYDHIMkZJmlUqFD5bRaIRWq+UgNiUiZDIZb6pGowGLxYKBgQFoNBpMTU2xa0C0Wi1OVikUigcC4J2Jlna7jUqlApVKBa/Xi9HRUXi9XoyPj8PlciGdTkOj0WBnZweFQoGFZSaTgUajwWOPPQatVotgMIhUKsXXXavVOE4K/GdC8ObNm6hUKtja2mLrcNeiy+UsZDstW1qTdru9KyYkk8l4zejnSSnQ8yCLjJSEQqGAzWZDb28vPB4PZDIZW5jpdBpyuRwWiwVqtRqxWAyFQgHZbBZarZZd686kj9lsZoGm1+s59mi32yGXy5FKpaDVapHP56HT6dgiImuckh+5XA4ajQbtdhulUgm3b9/GzZs3YTKZ0N/fD6fTyUqQXLtAIIDR0VEMDQ1Bo9HA4XDAbDYjk8kgkUigXC5DLpfD7/ejXq+jWCwiEAhAqVRCLpdzPJVigJTwymaz7EmRQaFUKuFyuTguVyqVYDAY4HK5YLVa0Wg0sLOzg2azCYvFAqPRiGq1CqVSyVYcCbhGo4FUKoVSqQSz2QyNRgOn04lyuYxisch72uPxwOl0wmazQa1Ww+FwIJlM8jkgq89oNMLj8cBut0OlUvE+ISt8bxyRkkTkAXbSarXYZd575iORCF5//XVMT09z/kQmk7ESpXh/s9mEQqGA3W5HX18fTp48CbVajUwmA6lUCrvdjnw+D6vViomJCZw9exYWiwXnzp3D/Pw8QqEQ/H4/W9wU6trr7dHZUCgUbHzsDS3IZDL09/fj5MmTKJfLCAQCSKfTsNvtUCgU/PkAYDKZ4HK5YDKZUCgU2NOgsGgul0Mmk0E2m0UwGMTs7Cyee+45nDp1Cn19fbh48SJu3bqFeDzO59Fms8HlcqFYLCKXyz1KrD5a6J48eRIjIyPo7u7mi6Y4RrPZhE6ng0QiQb1eRzQaxeLiIhKJBORyOccY5XI5yuUyZDIZbDYbnE4npFIpb/hEIoFKpQKFQgGNRoOxsTEcOXIEAwMD8Hg8yOVy2NjY2DejSBuBsul7N1Vn1p2EssfjwdNPP42+vj62uqrVKnQ6HQscqVSKO3fu4P79+1AoFOjq6oLZbMbs7CxWV1chk8lgtVpRr9eh0+lw+PBhmEwmRKNRlEol3Lx5E5ubm1CpVPB4PAgGg2wJA8D29jbm5uZgt9v5GuhPvV5HKpWCQqGAyWTi+5bL5dBqtUgmk9jY2IBOp0NXVxdGR0fhdrsRDocRi8XYGiGLm5JDGo0G5XIZtVoN6XSaE1E2mw3tdhsajQaFQoFjpeTNmM1m2Gw2dqE7FaLb7YbBYOCwks1mQ61Wg8ViQW9vL2QyGTY3N9myJOWjUqk4+51MJpFKpRCLxfggNRoNrK6u4p133uFDf/bsWYyOjnISTK/XI5/P8wFcXl7GtWvX8PLLL3MW3Wg0stBdXV1FvV6H1WrlxF5n9Qm5ihqNBr29vejt7YXT6WRX1Gazoaenh3/v9vY2ryPtQ4vFwvdDxgQpLlI0sVgMqVQK1WqVvUA6B1RJ0Nvbi5GREdTrdf58l8uF3t5eGI1G1Ot1bG1toVQqwev17oo50t4n72ev1UjPd25uDrFYbLcw+A8XP5lM4gc/+AHHRM1mM7q7u9HT0wONRoN6vQ6bzYa+vj60220MDw9jYmICKpUKN2/e5Osmb1Or1UKv13M4pr+/Hy6Xi3NES0tLXD1AZ7azigZ4T/Cq1Wq43e59E+cA4HK58Nhjj8FgMGBzcxPr6+toNBpoNpsIhUKo1+us1KRSKQwGA3Q6HdrtNjKZDMrlMsslCjlQ5RRZ15QfIflGiX2TyYTBwcF9Kyj28r6JNL1ej1wuh3v37qFcLvPBSCQS8Pv9mJychE6nw8TEBD7zmc8gFAohm80iEAggFAqh0WjAbDZjZGSETXRKPqlUKigUCpRKJT6sbrebrR6Hw4FSqYSRkREub9kLVU7sDbqTNSqRSNDV1QWHwwGVSsXuglar5Thps9lk14AswqmpKaRSKRgMBpw8eRK9vb3o7++H3W7nOF6tVuNraLVaiEajyGazXFJy6NAhnDt3jg8+hQCWl5fxZ3/2ZygWi/jQhz4Eg8HAG0sqlSIcDmN1dRWPP/44uru7dx2KZrOJtbU1DAwMwGq17rL8yKvo6uqCTqdDKpXCysoKstksJBIJ7HY7jh07BqPRiLW1NfZIKJlltVpRLpdRr9d3Jd8sFgsqlQpyuRx0Oh18Ph98Ph9sNhtXKmg0GvT09KBWq0EqlUKr1aJer3N2uVqtcqIOAFuhqVSKlQ0phWq1ilgshkQigUKhALvdDrfbjd7eXqhUKo6NkiFAYYOZmRkkk0no9Xq4XC62aEulEt+TRqOB0WjkvUKxbzImyBKmkiWysChpSQme7u5uhEIhBINBFItFaLVarjSo1+uo1+v8u+hnVSoV4vE4C7KhoSH4fD44HA5UKhVks1kYDAYMDw/DYrGgWCxia2sLWq0Whw4d4vUul8tYXl5GKBSCx+N5wGKNRqO4ceMGhoeHuQqIKBQKuHLlCv76r/8aKysru6zIkZERnDhxAjs7O1hbW+PwDCW2vV4vf47VasXAwACKxSIMBgOkUinW1tYwPT3N1i2Vz5Hr3Ww2eV2cTid0Oh0CgQDm5uZQq9XgdDohkUhQqVR4fxB0vh8m0NRqNXtDWq0WOp2O8wfpdJpLzChUSnkHtVoNq9UKmUyG1dVV9gw8Hg8GBgZgMpng9Xpx5MgRFs5+vx/JZJJDNSRDuru7MTo6yoUFD+ORQlcmk6FYLMLv92N+fn5XrKJYLOLmzZs4deoUjh07xtorHo/D7/fjtddeY5fTZrNx8oMsMLICqFavXC4jmUwiFotBKpWiUCjgzJkzsNvt+OAHP4g333wTb7755q7rI9d1P5rNJgqFAvL5PNfhkUubTqeRTqcRi8VQqVS4dIQ2fmeMplAoYGZmBn6/n128/v5+6HQ61Ot1rrqIx+MolUpsDSaTSaysrLDG70yYVatVvPPOO9BoNBgaGsLk5CRvKqlUis3NTXznO99BOBzGr/3ar8FkMnFogZIykUgE9XqdS7HI2s/n82yBkhKh+JNCoUBPTw8MBgNUKhUCgQAUCgV0Oh2/n9aMkjxkIScSCUilUgwMDKC/vx96vR6JRALb29sol8swGo0sPOjwFYtF1Ov1XXWeZO3k83nk83m2fsmCNRgM7KJls1mUSiUUi0X88Ic/xNraGgs9QiKRIJ1OY2FhgeP+zWYTarWaXXa9Xs/Pv1wuY3t7G0ajcZfFTOGP5eVlrK+vQ6fTwev1YmhoiBOFfr+fY79kGJTLZSwtLSEej3M82GQysYI1GAwwGAzQ6/VotVrQ6/VQq9UYGBjA6OgoTCYT72GyJl0uF+RyOe8trVbLtcESiYSTnaVSieOttH+y2Sy++93v4vr16/j1X/91HD16dNfrKysr+Na3voXbt2+zoqGzLpFIEA6HEQqFuOyQLL54PA61Wg29Xg+DwQC5XI5CocAxdYlEgmKxiEKhwCVuuVwOjUYDLpcLlUoFsVgMkUgEarUaLpeLnwflMtrtNnK5HK/XftD52QuVMlqtViQSCczNzWFxcZFDR6Q4qVqFPkOhUECr1cJiscBut8NkMkGhUKC/vx+XL1/G8PAwP1dK5O2tpioWi1hYWMDg4CBGR0fft5TvkUJ3fX0dmUwGd+7cQTgc3rXZ6/U6/H4/5ubmMDY2xtlA4D0rmJJLrVYL6+vruH79OnZ2duB2u1EsFhGPx9FqtaBWq1EqlRAOhzke3G63sbCwAJVKhWeeeQbHjh3D888/j5mZmV3xkmq1+sigdSqVwvz8PJaWljgW+2//9m+Ynp7muI3VaoXP50OlUsH6+jq2trZgMBhw5MgRjIyMYGtrC6urq0gmk6jVanxIT5w4gVarhUgkwpuUNB7FdkOhECKRyAMbhTSuWq3mzUb/ns/nMTc3h/n5eV6vZ599FgaDgbPoHo8HU1NTkMlkOH/+PMc3Nzc3MT09jbW1NY4/kQBYWVlBLBaDVqvlGCXF/NRqNRqNBrRaLSvJdrsNnU4HjUaDfD7Pgkyn07G7NjU1xYJobGwMPT09SKfTuHHjBubm5lCtVvkAketO4ZCpqSlWHCScenp6YDabsbGxsSvGBgDBYBCxWIyVdKPRYEuoWCzu8sIqlQpbw+Qab25uolAosHWv1+tx+PBhWCwWzM/Po16vY3t7m5WOWq3GxMQEpFIpzGYzwuEwlpeXUSqVMDY2hhMnTsBkMnHzTalUQrPZZCubwid0ABUKBSc5ZTIZnE4nms0mgsEgyuUyXC4XPB4PV3tsbGzgzp07iMfjOHHiBLxeL1f83Lx5E8vLyzh9+jSvLQmsV155BS+++CILnkuXLrEnRcJNrVZzoo+oVCpYXV3lmlYS0jKZjGOkmUyGFVGxWMT9+/e5CkapVMLhcGBgYIATzwsLCygUCpDJZDAajVhcXMT6+jrS6TQsFgsMBgMikQjnPhYXF+FyuVix7UexWHxA6JrNZjz//POYmJhALpfDzMwMXnzxRYRCIf7dPp8Po6OjUKvVaLVaiMfjbBFHo1EsLS1hfX2dlXY8HkcikcDw8DBb7Pl8Hvfv34ff798VLmy329jY2MBbb72FSqUCs9n8UJkEvI/QPX36NAsocqUB7Io/JpNJ1sZUanPjxg3E43FOGC0sLGB+fh49PT2YmJiAzWbblend2trC4uIiH0Jyo6kG2Gg04tKlS3jiiSfw8ssv83VUq1WuE33gxv5jgycSCRSLRS7xiEQimJ+fZ2uO4j8ymYxrUanw/PDhw6w5qU6z3W4jnU4jmUwimUxidnYWOzs7bOGSwCVBujfLSg9JKpXC6/WygKOKjLW1Nfj9fk4UXr9+HQaDAceOHYPdboder8f58+dRr9cxNTWFfD6P06dPc7lQPB7HvXv3EAwG8eyzz2J8fBxGo5FdrWq1CqvVir6+PnbnqBifBCttMNLmJpMJTqeTlcL09DS2trYQCAQ4+Xb37l289dZbHJ8ld446yCjh09XVhd7eXiwsLLC3QzFehUKBQqGAYrG4y4OhKg8KE3S6nY1Gg0uHOkNMVHtLAkGv17OHRTHs3t5etNtt+P1+/p2ZTIarRMrlMjY2NjjhSDHX2dlZFItFdHd3Q6PRwOv1cqWDVCqFxWJhi5DCXJQopkSpWq1mJVCtVuHz+WAwGNBoNDA3N4fXX38dKysr6O7uZo8zmUzizp07CAaDOHnyJM6ePcslfTs7O5iZmcGNGzc4zu33+xEIBHDo0CGO61IstlPZE3vLx+ick/LLZDJs9NhsNv47yQaDwYCjR4/C6XTi7bffZgVHz5Bi0ZT0NRgMMJvNMBgMUCqVKBaL2NnZ4bXaj0QisUsWAcCTTz6JixcvQiKRIJfLYX5+HoFAgGvS4/E4KpUKl9tJpVLObezs7HCZKp0FmUyGeDyOGzduwGAwwGQysfdNHsBearUa8vk87HY7Jicn97124pFCd3BwEOFweN8FUCgUcLvd6Orq4lrR1dVVXL9+HcFgkAu7qbKBSpWoPjKVSrHApNACWS/k8tJmowTP+Pg4rl27xrEmrVa7bxKNrs/j8WBsbAzZbBYOhwPpdBqRSITrUykhGI1GuT2RNB25PTabDT6fj4PqdJgWFxfZAqY4EPCgkN27sYlWqwWbzcaBfNqUzWYTVqsVLpcLKpUK1WoVfr+f44lUAnPp0iW0221sb28jkUjA4XBAq9VyvHZubg4Wi4XLmiwWC6xWKwCwpUtVHzKZjIUBCe9sNsvdWVqtFgqFAolEAjMzM1haWuJ/p4zt0tIStra2OI5Lf0hA0qFPJBIc5qF4L619IBBga66zJZkqPQgK/VA3UafApfshhUvlSBT/pfeQQE0kElzlQtlzWo9kMol4PM5hmcHBQRbe9+7dw8rKCsbGxjA5OQm73c7ty9SFR7F+SrjQ57ZaLU4+azQadm/psE9NTWF+fh6NRoOTPdVqFYlEAtVqFSdOnMClS5e4qaZcLiMcDsPv96NarcJisbDHQkqJLG69Xg+bzfZA9n+//UqGQGcSjjyB4eFhGAwG9Pb2slFDnaR0TeTpUg0yNfHQXpDL5TAYDPB4PLBarbBYLOwx7VdZQcqKkqDAe1bu+Pg4hxOLxSLkcjl0Oh2HtUgp3b17lxs9HA4HLBYLtre3uc7e4/FgcHAQ7XYb8XgcwWAQ169fh9vtxsjICJrNJrq6uuB2u9kI2ytzKD/yKB4pdAOBAOLxOGcWO01qtVqNo0ePsptVrVaxsbGBQCCAWq2GwcFBHD58GMB/9pZrNBpYrVak02lMT08jHo+zhaBWqzlJJZVK0dfXh76+PtZUKysrWFxc3BWfPXXqFI4ePbprA3WWmVgsFjz22GNQq9W4c+cOotEobxCqyKDunM76Y7VazVYFxbpOnDgBvV4PrVaLRCKBW7ducVMFWQSdbdO0RuT671c25vF4ON5I7x8dHcUv/uIvIp/Ps7YOBAIYGRnZdX+0WSORCObm5qDRaJBMJpHL5dgiXV5extDQEA4dOsRZ9J2dHWxubkKr1XJyp3PeQaVS2VWIT8KsVqthZ2eHu7PUajW6u7tht9sRjUZZmJAVTwqAhD0lTIPBICefzGYzSqUSqtUqstksx4BJ6er1+l1WBYVX+vv74Xa7sb29zaVgnbFJg8HAlv3Ozg4UCgXvXQqh5PN53L17l9fS5XJx5Qh1S1FXHylijUaDrq4u7OzscHKGLGaj0ciJOqqhpaYL+lkKMVAnXSQSQSaTgd1uR6vVQjKZ5OadfD4PjUbD3mMkEkG5XIZUKoXH4+FwE/CeMEokElhbW0MymYRUKsWRI0fwC7/wCxgdHYVGo+E9RmGc/eKOGo0GJpOJjSP6bNo7dJapVO/MmTOYnJxEsVhkt//+/ftoNpvY2dnhyhxKklIFTnd3N9RqNa9xuVxGT08Pzpw5g+PHj8NisTxQ/knP99ixYzh58iReeeUVPnPz8/O4f/8+hoeH0Wq10NfXB6/Xywq7WCwim81idXWV78vlcuHkyZOw2+38nIaHhzkGPj8/j8XFRQQCAWxubmJwcBBmsxknT57keDEpfDrPUqkUiUQCgUAAExMTDwpUOvsPfQXgbDJZLLRxqVPm+PHjGBwc5CDzyMgIjh07hnQ6jUOHDqGrqwuVSgVnzpzBE088AbvdDolEglu3bmF6eprdBKqPHBoawuDgIPL5PJxOJ9cr+v1+XLlyBW+//Tby+TyA9xIvIyMj7CJ3CiSCSnkuXLjArrpKpcJHPvIRnD59GuFwGK+99hp3jZF1QgdvamoKSqUSHo8HExMTGBwchEwmQ6PRgNVqZcuwXC4jFovxtQHvFYo/99xzGBoawje/+U0+4AQNAeocEiKXy2E0GnHmzBmEw2EudSqVSrh8+fKuQStKpZJnA8zPz6NWq7GX0NvbywmNzhhmd3c3d45RBx8lcqhdM5/Pw2QywWazsfWXTqeRSCSwvr6OUqkEnU4Hh8OB3t5eqNVq1Go1jI6OQq/Xs8dCRehkxbRaLRa45XKZD7hMJuNyLfpDNcRutxuRSAT5fJ5dTkpwjIyMYHl5Ga+//jqCwSB7CaSMKK5LA3lofSnGTBU0Go0G/f39fEhTqRQ2NjbY3aT5Bz09PZwQplBDIpFAqVTC2toams0md+BVKhVsbGxgZ2cHuVwORqMRPT09nCXPZrNYX19HKBTiygDqsKMSta6uLuj1eiiVSqysrGBpaQkqlYpDXvT86Dw2Gg2srKwgGo3C6XTi4sWLOH36NMfACbI29/PAJiYm8Pzzz2N5eRnf+973EI1GAbyXlCavhoY1UYPToUOHOMF0//59TtrK5XL4fD5YrVZkMhlez/HxcTz77LPo6enB7du3ceXKFVQqFYyPj+Pxxx/nSohOOs+1y+XC6OgoXn31VbRaLeTzebzxxhuoVqv48Ic/jJGRETgcDgwPD3PoYnV1Fbdv3+a4O33m2NgYzp07h3a7jZ2dHZRKJU50kmditVq5CoQaRSYnJ/Hmm28il8uxECeh+355JuB9hC71yZOWUqlU0Gq18Hq9ux4+afDDhw9zyROVEsViMYyPj+Ps2bM83GZlZWXXwlIM1efz4fDhw9ja2oLZbIZOp0MwGMS//Mu/4M6dO7usRdJg5DI+6h7MZjOOHz/Og0++8IUvoLe3F7FYjEta6vU6xsfHodfrce/ePR7G4nQ6uWB/e3ub3ZszZ87A4XDAYDBgenoaV65c4evp6+vDV77yFXzuc59Dq9XCj3/84weELk1x2q/mkOK2r7zyCubm5tia7gzzUAeOXq9HNptFvV5Hf38/WzZ0LSTYqF6y2WwiEAhwaIfqkrPZLKLRKHs2breblWQikeBQg9ls5hZmj8eDVqsFrVaL0dFRFAoF3L9/H6FQiOshzWYzx+uoQqTzOVJzC1lSVNN6+PBhXLx4EdlsFisrK1xwPzk5iY985CNwu904evQoNBoN7t27h1arBYvFwm7vm2++iUQiwQX8ZIFTjJXis52hB/JMKN9gMBjg8/lw9OhR6HQ61Go1nkpHJXWNRgOFQgGbm5ts6ZMlSLXHLpeLzxJZijs7OzCZTBgYGODZE+VyGTabDWfPnuWuQqqOCAaDu0qdOt1vmogXDoeRTCYxOTmJxx577AGBS+tLMzD24vV68bGPfQwSiQQ9PT34+te/jkgkwhb8hQsXMDk5iVwux5YszYFoNptwu93smUokEvT19eHo0aPI5/NYWFiAQqHAU089hQ9+8INwOp0YHR3lUN6xY8ceObGQzniz2UQul9vl3ebzefzkJz9BqVTCL//yL8Pr9aKrqwtarRZdXV1oNBrc6NG5Dg6Hg3NM+Xwet27d4mSe1+tFX18fent7MTY2xlY7KWEa7BMKhVAqlbjKg5rCHsUjhe4PfvADLC0tcRG11WrF8ePHMTY2xj3oR44cgc/n4xgN1dH5/X7Mzs5y2RjVriUSCczPz3NWkzpjSINubGxgY2ODLd2lpSUsLy8/4J4D4MOxH51zGkgjarVa+Hw+joUmk0nodDpYrVZsbm5idXUVSqUSqVSKNRzNLJidnUW9XofX6+VicK/Xi2q1uqv7yeFw4POf/zw+/vGPc4Jp79Ab4L2JSE8++eRD197j8cDr9aJcLiMajSIcDu97j/F4HJFIBAC42aK3txcKhQLFYhGJRAKhUIibIQDwYJbt7W1IJJJd3UwkmAuFAtc0UsJLr9ezoqUSQLpn6u5Jp9O88VqtFnddFYtFpFIpDld0PjsaF0ntwRRmuXz5MhQKBceoi8UiTpw4gb6+PiiVSvT29uL06dMsoMgboTrf27dv83VTVyL96QxTpdNprK6uckljq9XieCw1KlBIorNBhLwV+jyq+6axnaRMqIyuM1fQWSVCzRSxWAwSiQTDw8NckkiVHCTIOptIOgmHwxzm8Xq96Onpeejeevrpp3HmzBm8+uqru/59bm4Oy8vLOHHiBD7xiU8gmUzi29/+NtfUm81mHDt2DEqlEsFgEGtra1hYWGCFcOjQIYyOjiIYDHItK3lhtVqNBWEikWAF5fV6uUmCjAuywvcTXjS1by+VSgVLS0tYWlqCVqtFOBxGPB7nRg0K71CDQy6Xw9zcHC5cuMAx7vX1dbz11luw2+04cuQIK3BScDR7we/3w2w244knnsDS0hJmZmb4Gb311ltIJpP4zGc+89D1f6TQvXLlClcYSKVSmEwmLqJfWVnB5uYmMpkMW5uFQgGBQADT09OcQaxUKrh+/Trsdju8Xi9CoRAqlQprcQqsVyoV3L17FzKZjKc80cQemuLVCfXgk6u0tzGCzH6ZTIaNjQ1873vfY4uIxh+qVCpYLBaoVCokEgm2wNVqNYaGhjAwMACJRIKFhQW8++67vCEoW02H1e/3Q6lUcjPExYsXeSTk3/zN32BpaemB67906RLXKu5nqdMULLKk3n33XVQqFajVav6ZWCzGQ2KA/+zaoTZtsp4o1t4Z26QEB7WwGo1Gdp9JIFJDQaeVTUKKrESbzQaZTMbTviheq1Qqeb4BVTAA4M1PIQWqn6S4I8X9qJyOEludNdl0YOm/nckiGoRC4QuaA0DeFDV/qFQqVpSVSoUH7ajVau4eq9VqCAaDcDgcrKyr1SqSySTC4TB3MlIyVyKR8LVQXFqlUqFUKnG8vVarcXllJpNBJpPha6bxg0qlEtVqFcFgEDMzM1hYWEA0GuXGmu3tbdjtdt4H5XIZ09PTyGazvG8elmAGgK6uLly6dOkBobu4uIi//du/xVe+8hXYbDZcunQJ2WyW61LJkKJzUavVeMIbQU0F1GARCoV2eSHFYhE//vGPUavVYLVasbGxgZmZGW6r9ng8/JxpQBZBsfXu7m6evdFJOp3G1atXEQqFsLm5iWw2i4WFBZYHnYnjTi+Ccg3Xr1/HysoKJ9sLhQI3tpC3ROG5UqmE7u5uDA0NIRgMsrdJdd6P4pFClzqZKO5I7ZBmsxlutxv9/f1c0kKHOBqNIpVK7cocLi8v41vf+hYGBwcxODiIkydPAnhvHCC1AWcyGa4qIHd3Y2MD2Wz2AWtWLpdjZGQEg4ODvMlJG3UG/un/5+fncfXqVS7pSKfTiEaj3A1E90RDTkwmE0wmE4/1o+QguWuxWIybLCiONj4+jk996lMYGRlBo9GA3+/Hq6++itdee+2BdVWpVLs6zfZDIpGwa16pVDA7O4toNIr+/n5OLFC5FVmOyWQSi4uLHPJRq9WIx+PY2dnhSg0A3L1Gz7azcJyUJ81IoGQWlWyRa0zlXW63m7PJi4uLnKWm9lMSbuTed258EmL0+QqFAn19fRgaGoLD4WBlsrKywl4JrbvBYEAul+PyLgpbUVWKw+HAk08+ibW1NW7hVKlUcDgc7I5T8o+aBCghCvxncw2VENKYylKphO3tbS5vpMoQymvkcjluL6ZuNLpuMjLo8/P5PBsU9Dw0Gg0nGClhk0qlWFiT8uv05La2tniyFlmkdBb2KnT6t+7ublaMnbzyyitwuVy4fPkyzGYzvvjFL+LcuXN48cUX4ff7USgUMDIyArPZzDmMzlremZkZ9PX18XArquYxm82w2+1sBG1ubqKrqwuFQoEn/VEHJin6znsEwPJkcHAQIyMjmJ6e3mX112o1LC0tIRqNsjKmfgFS+tQ0c/r0aUxOTiIcDuPq1atYX1/nKqZGo8EleltbW2wEymQy9PT04PDhwwgGgzyLg+Zg0JiE/TpnO3nfb46gD6MkhNVq5YlT1G4KvBeboy4i6junGB65YOVymctChoaGsLm5iXQ6zZOQ6GCSFqXP2Yter8fIyAjX+1JMpdMyokNNWmxnZ4fLdAwGAwuQlZUVpNNpttpo1KRcLsfm5iZCoRDkcjm8Xi/MZjNMJhNisRiXSNHEqb6+Pjz++ONQqVS4c+cOXn/9dVy5cmXf4ReUuHmYlUsCtaenB729vZxFvXXrFvr7+/k95D1QKIFmAlACplqtciac+vOpfZky9WT1pVIpvtZYLMbJN4PBgHq9zu3NmUwGtVqNkzeULadQBiWzqAnD6XRCrVZzxr/dbsPlcmFkZAQKhQKrq6v8DORyOcbHx3Hx4kUAwPLyMuLxOOLxOM9gKBaLmJ+f5/IrmoRVKpUwPz+PRCIBp9OJ7u5udHV1QaFQIBqNol6vw2KxYGRkBENDQ1yKR0XyNpsNVqsVlUoF8Xicmyuy2SzH7SiG2zkPOJPJ8L6gbqZ8Ps+dVVRNQoOGyOKiMA0lY2gWNe1Vmte8vb3NYQ25XM5lWZ175+bNmwgEApBKpejt7UVPTw+azea+7jl5a/39/RgYGHjAC8tms3j55ZfRbDZx+fJlnDp1Cna7He+88w5u376NVCqFaDSK7u5uHsYzMjKCdDqNer2OUCjEMeCuri4OT0kkEmxsbCCXy7GyjMViXMFAXgU16dB1Ut07ldnVajXY7XYMDw9zi3snVAlD4Sr6OVoPuVwOl8uFwcFBboxZWFjgMBgJZ6rkoSFNlOynmSeZTIZDk9Q997AE5V5+qq/rIfdSIpEgEomgWq3yMAy32w2v14tarcaxqWw2y9OVAHBSZXh4mC1jajOltk9q16M6USpi3wslefr6+riWkhQCuaj0wMrlMmZmZjA9Pc0zgMmlUygUiMViXHVAc2cp3EBhCJPJBKPRyJO36KBls1l2z5vNJq5evcqZ88XFRdy6dQvb29sPXH9/fz9+9Vd/lUfN7fcVN3QA+/v78dxzz+Hv//7vsb29jStXruCJJ56A0+lEq9WC0WjE0aNH8dJLLyEcDrO7TG20NPowlUpxWR4plE7votV679sRzGbzLsuWYpA09o4mMul0OiiVStTrdZ60ROtgMpm49M/pdGJsbAxyuRwLCws8HtNut2N8fJw/g2qdjUYjXC4XjEYjEokEu2pUMUFhApqJSsN4crkct8VGIhEWTDabjROC+XweNpuNp8uRZ0BZdYvFwpl4auslC50SR2TZURa7XC5zDDubzcJqtUKr1XJYhV6nJgg6+NQIQsqIzk2tVoPNZoPNZuPOtEKhwLNJent7MTExAaPRyCG/ra0t/OhHP0KhUEBvby+ee+45+Hw+bljab0Rio9FAT08Pnn/+efzN3/wNgsHgrte3t7dx8+ZNHrC+ubmJN998k/MwNJGOvumCxilmMhn2nqipwO12s0t+79497o7L5XJIpVIAwK2509PTOHHiBCYnJ1loUtydZn/QXvH5fHC73dzktF9tPM0cJo+GutNofrNMJoPH48Hw8DAnjEkhpFIpHn5DPQUUy7579y5WVlYQj8dZXtGwnp+Z0CU3VqlUYnt7mwXO6uoqXn/9dU6mdXV14fz58/yg0+k0CzOqHnA4HJxooYn71NlE5TzU/UKzSDvRaDR45plncP78eS51ogQSxQFpY62treHmzZsYGBjAk08+CYPBgI2NDfz7v/87f3sA1QePjo5iZGSEN1Wz2cT4+Dg/dJqzkM/nuSuGWoNpsPfKygoOHTrEXTkrKyu7rn1oaAh/9Ed/hHPnznF8dT+hSy5TX18ffvM3fxMOhwPf+MY3EAwGMT8/D7PZzCGBy5cvo1ar4Rvf+AZWVla4y4dCPydPnoTf78fKygoP8KGB8tQKHY1GecoVhY4onkblP9TiSnE3mnZFFhgN7rbb7XA4HADAljiVndHAnNHRUZ7ZOjQ0BKPRyGMiu7q6OMTRbDa5xZaSMTQjYWdnhydF0ZAcANz1RSM6Jycnuba22WzC5XJxEowSwqVSCV1dXfB4PBz6cLvdXEq0s7Oza6A7leZlMhmutaWJWkajka+BOiapvtbtdnM8kkrLqKC/u7sbw8PDXPJEA9FpotXk5CS+/OUv4+mnn94Vc56bm8Pm5ibGxsbw5S9/GR//+Mf59Yc1QDQaDeh0Os70/97v/d6uOCQV+WcyGfzd3/0dFhcXeX1p6Dc1DVEFD9Umd1qX1IVI4TZqn+2sN37yySfR19eHfD6PUCiEmzdvwmg0chcdVRcB4IH1MpkM586dQyaTwcbGxi53nhooaGYuzYjI5/Mwm80YHBxEd3c3RkZGcPz4ceh0Ojz++OOYnZ3Fu+++i1AohEKhAIvFgrNnz+L06dNwu908a3dubg6rq6vsJdLzpq+b+ml4pNClkiFyr0+cOMEJBbImc7kc0uk0jh8/ju7ubgwODmJsbAznz5/nQns6qNROR3EkvV7P8SDKltNnZjIZzMzMYGpqimOt9Xodk5OT+JVf+RX4fD6OwdBAik6NTqVAg4ODOH/+PMdQ6/U6xsbG8MMf/hA2mw3Dw8M8wUqv13NNoUql4q+eISFAgoO++4vKkWix5XI5Dh06hE996lPshi8sLKDVakGpVOKrX/0qPvWpT7H2JYHeed1SqZS/5FEieW+q0m/8xm/AYrHg6tWrKJVK7BUA7xW7f/azn8WRI0fwwgsvIBKJcKnTE088wRbT3NwcfvKTn6DRaGB8fJzrcFutFlZWVjA9Pc0F4mNjY6z8yKqpVCqw2WwYGhriEiiaKNbZHUaT42jQDw16MRqNXJLndDrZJaPaVfJUSEBT0rBUKvF4SIvFAq/XywPNqYxnY2MDfX19PP6QJn5R19HRo0f5mxtIkNPa9ff3o1gswmKxsCKgr9ihGuRqtcrf7UeVHGRFxWIx7kykJBZZsrlcDsvLy1hZWYFWq8WpU6cwODgIqVTK4R2yRp966ikcOXIEWq0W2WwWV69eRTQa5bPz2c9+FuPj47vOJ1VLHD58GE899RQ++clPckx7b4y6UyjJZDLugPv0pz+NRCKBP/7jP+aE+MTEBD73uc+xYqU2dzqzNDv31KlT7JnQMHsqmdvY2OCKBSrZpAHkfr8fqVQKH/3oR/HMM8/wXo5EIjyLuvPaqczN6XSywdbV1QWXy4WFhQXcu3ePZYdOp8Pp06dx9OhRVoIkO2jucLPZxPDwMI9ApYqk06dPcxu4VqvF4OAgy8BwOMwjVz/wgQ/wWkokEv4qIpqq+H7W7iO/gj2dTrdJgFEShKxYErr0MPZ+6+reB7334ZPJT59NUGy3MzDdqbEpK9wZaH/Y76UuM8qAdioLKkEjbUrXRwKRlM3ezhiKP3fOWuDFlEjYI2i1WhzPplg1KZn/DnTNJJT3iwU3Gg1eLwrVdL6PKgDofukPWSb0c53r0em6Uaz8/a6z87/7sd+177fOnfdFz5Ji9/Qc6VmQIuqsX91vsP3DrvdR1/moOnAAPOSI3tu5dhST7EwidiZCye3t/NYHSjDSz+0dc9j5Pnre5I7/dyDPgM4jVXGQN7rXbaYqEXoWe9eZzhAJ+M4Je7QeAPiaO+UDvbbf/dJnA+B1pC5Ggtarc4/vJ1/Io9m7ng9bQ0q40rXRdXdWz3S2q1ssloc+jEcKXQDvH6AQCAQCwV4eKnQfGV7YK5D/u5r0f8L/hmt4GPsprP3cuYe9JhD8b+FR+/Sn2ec/L/63XNt/RU4JS1cgEAh+9jxU6j66SVggEAgEP1OE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOEDk7/O65ECuQiAQCP6PICxdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIIDRAhdgUAgOECE0BUIBIID5P8BifVtoiDaGFsAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Y4uIipqameI/Qz0ilUiQSCbzyyis4d+4cX5PT6eTPS6USr1dKPVE0GI/HsbCwgLW1NdhsNnR3d8PlcsFms8Fut8NsNqPRaGBjY4PtIKXnSqUSXn/9dVy8eBFjY2PshP2XjK5UKoVGo4FUKuWHTqeLRqPB0NDQttB8cXERgUAAfr8fd+7cwebmJp/KPp8PdrudPQy1Ws0nm1qthl6vR6VSgVqthkKhgFwuh8FggN1uRyQSwcbGBhtdyq+Fw2GEQiFUq1UolUq+blpgJpMJPp8PV69exQcffIB6vc7pi0ajgcOHD+P48ePQ6XRIJpOYmJhAPp+HyWSC0WiEVquFx+NBT08PotEo6vU6pFIpKpUKTCYTDh8+jFwuh9XVVcTjcfY4kskkb+LmHBIRDofZS6U8KuWWduaTm++3WCyi0WhAr9ejpaUFlUoFgUCAwyKLxcJGlTauTqfDvn370NbWhnw+j2AwiHK5DIfDAafTCa1We4+3JZFIeFFSqEXvSK1WI5PJIBqNolwus9GhP0dGKZfLsRdWLpextbWFfD4Po9EIhUKBWCzGhikcDnP4OTAwgEajgd7eXgwODsJqtSIcDqO7u5s9751GFwC6urrQ3d2NtrY26HQ6xGIxPogrlQreffdd9n7C4TBUKhV7qel0GjqdDg6Hg1Nf+Xye83Z0+FC6SalUQqlUwm63w2AwcB6SUhVqtZodgEgkgmQyyV6r1WpFPp9Ho9FgY9bS0oKenh643W7Mz88jl8tBJpPBYDDAbDbzfikUCjAajfB4PGhpaeH0Bz37nfUNWleU7iMjTQZsa2vrnrVJ6cRisYhkMgmJRAKlUolGowGz2YzR0VGMjo7i5s2bqFarbLSkUil6e3vh8Xig1WphNBohk8mQTqdRq9XQ0dGBoaEhPPTQQ/jggw8wNjYGmUyGRCKBtbU1yOVyfPKTn0RbWxuMRuOuNYJqtYpQKIStra17cvsymQx2ux12u53fqVQqhVqtht1uh8vlYofP6XTC5XLB5/NBLpcjnU6jWCwiGAxiamoKjUYDc3Nz6O/vR0dHB3w+H/r6+mA2m9HT04NDhw5hbW2N16NcLufrpf3xIB5odOVyOWw2G1paWvikpUVusVjwyCOPoKWlBVKpFH6/H6+99hqCwSAymQwikQjK5TIvynw+zwl0OpmLxSLS6TQUCgUMBgPa29uRy+VgsVhgt9vh8/nQaDTw/vvvs6FqfgGFQoGLEmS8mg0V3QPlD6VSKfr6+vDEE0/A5/Ohv78fDocDpVIJi4uLWF5ehsvlQltbGywWC8rlMoxGI86ePQudToc7d+5wfpMMvc/nQ0dHBy+Cer2OmzdvQqFQYHZ29r6FECqeEM1FFSoENS8otVqNRqOBTCaDXC6HdDqNer2O1tZWmM1mDt0zmQza2tp40ysUCjidTuj1eqysrCCXyyGXy8FsNsNut0On0yEajaJUKiGXy0EikUCr1fJh2ewRkOdaLBZht9tRrVahUCh4katUKhQKBc4NUqGJcqfZbBbZbJZzftlsFqFQCFNTU7BYLOjv70d7ezunhCj9EIvF2PPIZrNQKpXQarXI5/Mol8vQ6/UwGAyYmZlBOBxGLBaD3++HTCaDRqPB/Pw85ubmkEgkUKvVIJfLodfrUSqVeE1pNBpUKhWUy2U2nAaDAZVKBRqNhp0Ful+5XA6r1Qq1Wo1sNotUKoVisYhisciRXK1WQ6FQQLlc5rVkt9uRzWYRi8U4rNdqtejo6OD0DK0BMnR6vZ4PAb1ej1qtxoVlnU4Hg8EAtVp9T5qhub6y04DtXH/N63B6eho2mw03b95Eo9Hgoll7ezt8Ph+USiV7kkqlEk6nE/39/Xj00UfZA7Rarejr60MgEIDRaERbWxvXSYaHhzE7O4tAIMCRrVQqRaPR2HZwNEd9jUaD93mhULgnzSSTydDR0YGHHnqI6xLRaJRz7FqtFtlsFsVikQtulHetVqu8liqVCu+xVCqFmZkZtLS04DOf+QysViva2trwyCOP4N1330UoFAIAGAwGGI1G2Gw2hEIhJBKJXZ8t8UCje/z4cfT29vJiL5VKvCjJk6OLzmQyWF9fRyAQAADOeUilUuTzedTrdWg0Gmi1Wn5h6XSaT1ubzQan04njx49DLpfD5/PB4XAgFAphcXFx94uXy+F0OndNWlNIXigUeIHV63U4HA4cO3aMX4RcLt9WqadwYm1tDUtLS2g0GmhpacEnP/lJeL1ezM7Owmq1orW1FRKJBB6PBydPnoTRaMTa2hrnuSiv6HA4UCgUtlVaw+EwpqamYLVaodFoeFOQV5bJZKBQKFglQPeqVqvh9/tx+fJllEolHDx4EMeOHQMApNNpJBIJaDQadHR0QK1WIxqNYmVlBcFgEGazmavsVH1Np9Ow2+2o1+tsXMno0qK0Wq2w2+0cutIzHxgYgNlshkwmQygUgt/vRzgcRj6fZwNN755SAFQAI09VoVAgn8+jWCwil8tBLpez8d/a2sLly5chl8vhcrnw0EMPYf/+/chkMlCr1bBYLEgkEigWizAYDFhfX8c777yDcDiMarUKqVQKp9MJnU6H1dVVrK+vs2eu0WjYmysWi2xIi8UilEolLBYLRzulUglarRYulwudnZ1wuVyo1WpIJpPI5XIolUrsJZfLZcRiMcTjcV7/5FmSl0y1CyoiajQalEolhMNh9iy7urpgs9nYWVEqlejo6IDRaAQABAIBXLp0CWq1GidPnoTD4dhmrEg9U6lUeI03G7BisYjJyUmEw+Fte0YqlcJut2NlZQUbGxvw+/0oFAqw2+1ob2/H0NAQ3G43JBIJfD4fDh8+jEQigcHBQRw9ehQqlQo3b97kw95ut3ONh4ryKpUKbrcbBoMBXq8X0WiUHSQ6pCqVyrYoka6bvNT7OTJOp5OfRzQaxdraGu/9ra0txGIx/lk6MGhf1et1dkgKhQIajQYSiQQb0Ewms80hovdLNQ+1Wg2fz8f3/SAeaHS//vWvo1qtYm1tDePj42w8ASASiWBiYgKHDx+GzWbDvn378Nxzz2FpaQmbm5uYnJyE3+/nF+/z+eDz+djDIJefFmoqlYLJZILdbofNZkN7ezuf8l1dXewdNUPpCFpozac5ecKU12lra0OtVsO+ffvQ2dkJvV7PniUVichbSaVSiMfjWFpaglwux4kTJ9DT04Ph4WH09vbC7XazsQLAhcRoNIpAIMB/vru7G0NDQwCA9fV1llfNzc3hG9/4BsLhMD75yU9yIY1CwI2NDWxsbPCzJRQKBTKZDC5fvgyv14tPf/rTaGtrQ71e5/ROsVjkijf9XdFolMOvoaEhrK+vY2VlBZubm8jlcnA6nZzzpJRHvV6HVquF2+3maKZUKkGlUsFms8HlcsFkMqFarbKnZbfbkc/n+T7z+Ty/J8p/0aIlA1+pVFCpVNgrTiaTyGQynCYpl8uIx+NcSyADQgaPPOaZmRnMzs4imUyyrK05B0vSJa1WC51OxykR2oB06NJG1Gq1rLrQ6/Xs5VCONZVKIRwOc5pFpVJxdZ8KxiqVCnq9np89FYsymQxsNhs6OzvR0tICs9mMRCKBjY0NOBwOOBwOeL1eFAoFhEIhaDQadHd3w2QycaT47//+7wiFQjh8+PA9csNYLIbx8XG0trait7d3276gvOff//3fY2Fhgb1IuVyO1tZWDA0NoVAoYGpqig+BtrY2DA0NYXh4GAaDAVqtFgcPHoTH40EoFIJOp4NWq8X8/Dzef/991Go1dHd3swojlUohnU5zTlWhUMBisUAul6O/vx+zs7OQy+Wc76doaedBQuH7bsVUpVKJ7u5udHV1wWg0snQzFoshEokglUqhVqvBbrezSsRsNnPUnU6nodVqUalU0Gg04Ha7ceDAAU4v9vX1sRJoYmICW1tbHI3kcjkUi0X4fD6cOHHivjJW4oGftrW1YWNjg8MA2kQAkMvlcPXqVRw7dgyPPPII3G43Hn/8cRw8eBDT09NIJBIIBoPsLdENUhhBBRfS0CWTSSwvL2NpaQlarRaHDx/G0aNHYTab8fjjj+PcuXMYHx/fdn0ULuxGrVbjMKG52kxFHwpT6N+bm5vY2tpCPB5HtVqFyWQCcPeEvHLlCmZnZyGRSOByuWC1Wtkji8ViSKfTSKVSHEbSyV6tVuHxeCCXy7fldguFAj744ANUKhV0dnbiscceA/CzcGp6eho/+MEP8OSTT+L555+HwWDgRadUKlEulxGNRrmYRZ5FqVRCKBRiw2K1WtHd3c2GRC6Xw+v1QqfTIZ/PIxKJIJPJsIGmYg+lT9RqNdRqNW8EqVTKoTwVUTc3N5HNZtkbLJVKWF5eht/vRzqdBgDOcVKaQqvVslGt1+usw9Xr9Zxfp+dIhurSpUtcpCE9KOlDU6kUNjc3uTgK3I0MSI9qMBg4tUAeDQCYzWZOc5GBSCaTCAaDMJlM6OjoYOliJpPBrVu3YDAY4PF4YLFY+DlEo1HkcjkolUqOGAqFAt8XrflMJoNMJoN6vQ6Xy4V9+/ZxOK5QKOD1ejm1JZVK+b5aWlo4ty6RSFAoFBCJRLbVMmh9pNNp/PCHP8SFCxfw3HPPoaenhz8H7haf/+7v/g5jY2NsYABwITAajWJjYwOxWIwPIVLobGxswGQyca2gWCxifX2dDVAsFkM0GoXNZuMDNBQK8Vrx+/2cdqJ/U1GLCrbZbJYPx90i2GKxuGvOtLu7G6dOnYLZbOaCODmK9NyoMEkRGtkhiswsFgv/na2trTh16hQGBgZgt9tZODAzM4P333+fi6nAXRnZ3NwchoeH0dfXB6/Xu6tNIh5odDc2NjA7O4srV64gGAxuS1w3Gg2srq6yZIwutlQqsStPG3h5eRlXrlxBPB6Hy+VCtVrl3CgVOyg1kclkIJFIsLS0BI1Gg+PHj2NkZATPPvssZmdnt8lE8vn8fSuFjUYDkUgECwsLmJubQyQSQSKRwGuvvYa33noL6XSa5SQulwvJZBKrq6tIJBIol8sYHR3F4OAgFhYWsLy8jFu3bnGuKhaL4ciRIygUCpybisVivKHIGAQCAWxsbHDhovnaqFC4c2FtbW3hwoULuHr1KnsRZ8+eZS/H5XLh8OHDePvtt/Gd73wHn/vc5+D1epFMJnlBWK1WHDlyBB0dHXA6nVAoFPD7/bh9+zY0Gg17K1SRlcvlfH16vZ7fidlshtls5hCKNjV5YDMzMwgEAlAqlZzvK5fLmJ+fx+LiIofmVKDS6XTo6emBUqnEzZs3kUgkWAdpNpvR0dEBk8nEcjjygCnUI0UFeWf0b9JBU/6S/iwZMjLyFMnk83nodDr09vaiUChgaWmJD+loNMopEiqayeVyrK+vc/Gkra0NAwMDcLvdrCgBwJuXikl0bVQApWdABepkMolAIIBCoQCv14vu7m6YzWZEIhFcv34dN27cQCKRwCOPPAKv1wuTyYRgMIiXXnoJ6+vrOHPmDJxOJ997MpnEm2++ie9973tYXV2Fy+XCww8/DI/Hw+tLoVBAr9dDoVBsW5Plchlra2sIBAIcRlPEk06nMTY2hrW1NbS1taG/vx8ajQYffPAB3nvvPcTjcSiVSvbee3t7kc/nsba2hlAoxAf78vIyQqEQotEo5HI5p2+SySQsFgurcCiluRv0DptRq9X43Oc+h8OHD6NarWJqagovv/wyZmfvfv2SwWCAz+djlVOtVuMalVwuRygUwuzsLPx+PzscsVgM4XAYXV1d2+zN1NQUAoHAPbZwfX0d7733HhvcgYGBXa8f+BCjSx0rFEIRzYn5VCqFTCYDrVaLeDyOGzdu4MqVK5wvqlarmJ2dxdLSElpaWrB//354PB4OJ7PZLJaWljA/P8/5OKVSydKQ/v5+OJ1OPPnkk3jzzTfx7rvvblsolBPeWSig8CQcDnN4WqlU2Duj09JoNMLpdEIulyMSiXAxqVgsoq+vD8DdVAr9mWq1itXVVZjNZqyvr2N8fBzxeJwXPoWp9FKoi6kZMnT79u2D1+vdJom5desWZmZmWIL3zjvvQKlU4vjx46w3/MxnPoNarYZr166hWq3iySefhF6vR6FQQCAQwK1btzA3N4fTp0/j+PHjcLvdiMfjmJubQzab5Sos5cfUajWKxSKq1SqsViuMRiOq1Sqq1SrnvE0mE3uBJEmj5yWTyZDP5zE7O8vyp1wux4aRctQUypE+tlwusyHS6/VsqGjDN0PPlgphxP261Jr/PHm7VJegtJDBYNiWWqBUBMkj5+bmsLGxwd4k5aRXVlaQzWbZc2rWIVM0QZEFyaMod1gsFlGv12E0Glmi12g00N/fz3rVa9eu4Z133kEgEIDT6UShUEA0GsXy8jLOnz+PmZkZPPHEEyw7rFQq2NrawrVr13Dx4kUusk1PT2N8fJw9esrF7tu3D9evX79nXTZ3mzbvc9rjq6urWFtbQzabhdfrRSAQYFWKwWCAwWBAX18fWltbMTY2hnA4zGH97OwsK45o71Fe1+l0wmg0Ih6PIxwOY2BgYJsaqXl/U3NMM8eOHcMTTzwBmUyGaDSKqakpLC4uIh6Ps/0qFArcYUedo36/HxsbG5iZmWFVD6UuQqEQrly5ApVKBY1GA4vFwtry3ewNpUlJt/4gPrQjrVkx0Ax5ET6fD9VqFYlEAtPT07hw4QLm5uY4L6JUKtloUeGDjEAoFOIQksI/2pwU6lLhRKFQYGhoCLdv30Ymk+HKrU6n27WVU6FQ8KmsUChQKBSwtraGYrHIciIAfKoB4FwchbSpVAoGgwE9PT3cmaTVaqFWqzExMYHZ2VlEIhGWzFAH3s5T8H54vV4YjcZtRlcmk6G1tRWFQoFldJOTk3A6nbBardBqtejp6cGv/dqvQSaTYWNjA8vLy/D5fLw4qLA1NjaG9vZ2rvz7fD7UajV4vV54PB6WB5EBlEqlrIQol8tIJpPcAEGFg0gkgqWlpW1FKTIeyWSSDSYdegqFgu+tWCxicXGRlQf0zKrVKrLZLBYXF1kJoVAooFAo+NDaqTltlj81a5zJe1EoFEilUpicnIRMJuMDk36uUCjgzp07yGazyGQybKTpumu1GuLxOHtEZrOZJXaNRgPRaBTpdBoymQxdXV3weDwssTMajWzo8vk8t6yS9IpaU0mnrlAo4HK52Ou6desW/H4/pFIpKyTi8TgbvBMnTuDZZ59Fd3c3586Xl5cxOTmJSqUCh8MBjUaD1tZWljhS3tdoNPJBvxs71y41fgB3Dz7Kzw4MDHDRNp/Pw2KxsBaf9Pq0n3K5HB+MFN0Cd/OwzYaactdtbW335KnpHVPqjFQxRqMRQ0NDfMBRtEnpCVpf1PQzPT0NpVLJKaLNzU0sLS2hWCzCZrPB6/WiXC4jn8/jzp07/O4PHDiAarXKOmfq+tx5jZRafBAPNLpTU1PY3NzkRdxsgNVqNQ4cOIAjR47AarUil8shGAzC7/cjm83C6XRyN43JZGKdp9PpRDKZxM2bN1mYvVOSJJfL4fF44Ha7Ua1WsbW1hfn5eayurm4rmB07dgwjIyP3eDkAuLNkdHQUVqsVk5OTmJqaQi6Xg91uR2trK6cnqOBGBl+hUCAQCKBcLnNB5aGHHuLmjo2NDbz33nt8UDT3yzdfi1qthsPh4NB424P/D49Ir9fzn9fpdDh+/DgkEgneeOMNBAIBpNNpzM/Po6+vDyMjI2xYvF4vDh06hHg8jqmpKSwtLbHsRS6XI5fLYXl5Gbdv3+Zio9FoRDqd5k3QLIFqlrxR08NOLzCfzyOXy3EB0+fzQaVSYX19HZFIhBceec+U/1Or1SzBCQQCnI81mUzsXcTjcaRSKS5GkcqFun5IEkh5U/odOwX0FLprNBpkMhlOYdDPajQazosvLCyw50y6bOoyIw+Nag4SiYRVK5TLpLw+PXfyigwGA+csm+cq0NqiGQjN9YZKpYJgMIjbt29jZWWFVRHFYhHT09PcUqtWq7mI1bzuAoEAFhYWUCwWYTabcejQIfzyL/8yRkdHWQII3PXyqClkJ6QLJoNJz5Varkn6l0gksLW1hYcffhijo6OsuEkkErh16xa3oisUim1aYsrbm81maDQarrGkUikcOnQIBw8eRHd3N6xW6z3yT3rPR44cwejoKN544w32yldXV3Hnzh309PRwHcXr9XJdhaSK9M6Au6nTo0ePcjE/n8/D5/PB4/EgnU7D7/cjEomw+mdgYIDtCXXnNkcFVGcgEcEv/dIv3deuPtDoGo1G6HQ6aDQargQDdztxvF4vDh8+jN7eXh58c+jQIb5IalGkPOTZs2fhcrkglUpx9epVFlfTolEoFOjo6EB7eztKpRLa2tpgt9sRi8UwMzODt956C1evXuXQRC6Xc14N2J7yIGQyGXcDkS7SYDDgqaeewsGDB+H3+/H2229jaWmJGzjUajVWVlYQCoVYRN/b24vOzk4cOnSID6DNzU2YTCZuFd3Y2NiWgvF4PHjhhRdw8OBBfPvb38ZPf/rTbddG6orm6ybt52OPPYZ6vY7vfve7WFpaYkkLVfyBu7rSzs5OjI2NYXx8nAs5VAUmjzyZTCIej8PtdsPhcHC7I1WR9Xo9qtUqL/5gMMjFMlIJJJNJVpiQJMZut3MnIhXayLjS2mltbUVLSwuHfaVSCfF4HLVaDXq9nvvgKQwkFAoFrFYrrFYrV4dJYmYymdhzD4fDWFhYQCaT4YNDp9Ohvb0dAFiuRiiVSp73kclkuGhjMBjgcrlYe7yzfqHT6eByudDS0oKuri40Gg0uOlHle2triyMe4GetyaQPJ88XAOuTI5EIy6CUSiU2NzeRTCa5c89gMPDck3K5DJ1Oh5GREXR2dm6TE1Kx0+/3o1wu48CBA/jYxz6GRx99lENqgjzjZgkjceTIEXzxi1/E+Pg4/vmf/5l1qNRN6PV6ucXbZrOho6MDg4ODHI1NTk5yLl+lUqGlpQWdnZ0oFosIBAKoVCro7e3FE088gfb2dkxMTOD1119HKpWC1+vFgQMHOOe985qBu8bX4/Ggv78fb775JuebL168iFKphNOnT3Phq7e3l4uafr8fExMT2+SjMpkMBw4cwLFjx1Cr1RAOh7G1tcWpNIPBgGg0Cp/Ph6GhIbhcLj5UR0ZGuJWZDifSjpOT9iAeaHQtFgsbBwoTzWYztyK2trby6S2Xy3Hw4EEolUr4/X5kMhmWJY2OjuLMmTOwWCyIx+NYXl7elrMhL3Pfvn2s/SPFw/z8PF555RXcvHlzWwKdUhrUgHE/pFIpDAYDBgYGcODAARiNRjz33HNwuVxc6f/ggw+g0Whw6NAh1Go1nDt3jnPSZOgoHeJwONDS0oJPf/rTsFgsqNfreO211/DjH/+YX6rP58Pv/M7v4Pnnn4dUKsW5c+d2vTZSc+yE1Bvnz5/HxYsXYTAYuCOKUCgUaGlpgcfjwdjYGGq1GrcvqlQqNjgksaGDEgCHxpubm1zJLRQKyGQyiMfjbEDJEyYPV6PRoKWlBTabDR6Ph72+er0Ot9uNbDaL1dVVbG1tQaPRwOl0coNBc/MH/dM8mav5QNFqtRgYGMDw8DBWV1e5tVKj0WB4eBjPPPMMOjs7sby8jB//+McYHx9HqVSC2WzGyMgIfD4fxsbGEIvF+GBv9pYp5KQN2Kx4oOsuFoscmXV0dHAqi9IIlArQaDT8vEmfS4oKkkjZbDYW9pPGt7kLzuPxoFKpcE792LFjnGKjwTHr6+sAwN1ozXltlUoFnU7H3vPjjz+O4eHhewxu87Pejfb2djz11FM4e/YsWlpa8Bd/8RcIBoMsUzt16hSeeuopSCQSJBIJrn1Eo1HEYjGOREgN0tHRgTNnzkAikWBychKFQgHHjx/HqVOn4HK5MDAwwOmHgYGBB+5jck6o8NZ8aKTTaVy6dIm7IHt6ethjpuLZzMzMtoYQauo4cOAA26U333wTN2/ehNvtxtDQEAwGAzo7O7Fv3z4ekKRSqdDa2ooTJ06wfUomk1zXqNVqMJvN970P4EOM7ssvv8xJZuBuDvKJJ57AoUOHUCgUOI/ndDohkUhY6iGRSBAMBlnP5na7OfROJpM8+YdaLqmSTyFlKpXigSkrKyusP91JMplENpvdpmUlmuc0UBXZZDKhpaUFpVIJm5ubiMVivPCpSEQhNUmCrFYryuUybt68ieXlZYyMjGB4eBg9PT2w2+2IRqMc7pIe8zd/8zfx7LPPQqfTYWxsDBMTE/dc37Fjx3hB7gYVtLLZLBdSdrtHqrJSKKvX69Ha2gqFQoFEIsEFSdKG0uYmr5YkXCSxolCwWfhPRUKj0Qi9Xs8NCZRrb29vh1KpZI+U8qr1ep0H75DnR+khMkwAWJqmVCpRKBSgUqnQ1dWFT3ziEwCAd999F++//z4kEgk+9rGP4cSJE9BoNPB4PNxSK5FI8PDDD+ORRx7hdXbjxg1OdVBOmf5pPggymQyCwSBKpRJ7l2azmSdTHTx4kPPc1FhB4zhpAhiFqDT0iTrvVCoVz1ugiII8QZ1Ox2oQmkKm1WrR19fHHZE0o4LWAamCdkKFzUajAYvFct8hShKJBGfPnsWRI0fwzjvvbPtsbGwMd+7cwfDwMD7zmc8gmUzixRdf5MlsarUanZ2dvO4XFhYwPj6OsbExJBIJmM1m7N+/H7FYjIuW6+vrPJaTmlWopb5SqcDn82Fzc5PTSMDPDsHdHBKKvHZC9QLqKiXdPTkser0eyWSSG2fS6TSWlpaQTCZhNptRr9extraG27dvc2pkcHCQxyDQfksmk4hGo+js7MTg4CDGx8dx4cIFxGIxrK+v48KFCwiHw3j66ad3ff7ARxh4Q9VeKpwdOXIELpeLi0iktaQH4vf7MT09zXKiXC6H27dv4/Lly+jr60Mmk4HFYuEuN6qQF4tF3LhxA/Pz8zyIhX7Pbg9Zo9Fwxb45rAN+JnYHwB1Tr7zyCiYmJlCr1VijqtVqYbfbIZPJsL6+juXlZfboDxw4wNd47do1+P1+rgB7PB72hm7cuIHx8XEoFAr09/fjsccew+nTpyGTybCysoIXX3yRB3Q0c/r0abS0tNz32VMhgboAP/jgA3z+859n0bdEIsHGxgbGx8cRDAbZeFOxhooRuVwOCwsL8Pv93MNP+UZSFZBygKIW8ghJDkUFVcr5xuNxxONxnntKxaVwOIxgMMjay3K5zI0OzflmMsz03ig9RXk+un+a8NTZ2cnNJRS60SFuMplYC02j+8jgkXfS/Hup24iug5pjyEul0JIO6mAwyN4uzRjJZrM8kcpqtbLGl8b6kUGnAiUZ2nw+j2w2y1PMqGOP2uFLpRL6+vq4zTYQCGB6ehqLi4sceU1MTGBzcxM2m43XQT6fx/Xr13l0KoAHCvR9Ph9Onz59j9GdnJzEt7/9bfze7/0eLBYLzpw5g2QyiZ/+9KeIx+MYGxvDlStXcPToUZYNjo2N4fr16yiXy+jq6sLo6Ch6enpYKkZ6YJpVkcvlcOHCBRQKBTgcDkQiEUxOTgL4mZKIPFIaA0uQ4olmgOx0xBKJBC5cuIBQKISNjQ0u8KXTaZb/UYMN6XUjkQgKhQLm5uYwNTXFqS5quyfJIz1X6pasVqtobW2FXC7H7OwsqyqmpqawsLBw32cPfIjRpY4UGhJtt9vR1tYGvV4Pu93OKQCqJFPljsIRtVqNUqmEtbU1vPzyyzh69ChOnDiBJ598Eo1GAz/4wQ84FxqNRlmpQAt2cnISyWTynmogtQk7nU5e0M3pimYjXK/XMTMzg3PnzvHMUmrc6OrqQnt7O3dXkddKFcparYb19XU2+iR6p267eDzOJ+Po6Cg++9nPoq+vDwqFAqFQCK+//jpeffXVe56rWq1GW1sbgPvPRSVjQfcxNjaG9fV1zqM2Gg0eNUit2SQrIuNJigsq9iSTSe6wIu+D/g6qVJNqoVKpQK/Xs7qiWQsaCoVQr9e3TWAqlUqYnZ3F6uoq5HI5T72iIh7pQukzCn2bh8BLJBK0trbC4/HAZrOxZrZQKMBqtbJ8LRaLQa/Xc2um1+vlNBh51Ha7HceOHcPm5ibi8TgkkrtD5vV6PYfXdBCQTFGr1fLzIO+Sinu5XA4qlQrJZJInuDVPo6M9QlpmMvyUy6WW9Ewmw/+meRRkrEmPTKH64uIiVlZWEIvFuBGG8sTNkVwgEOBoijz7+0VQtN7a2truMVyNRgOvvvoq+vr6cPbsWZjNZnzhC1/A8ePH8aMf/QgTExP4x3/8R4yNjcFisfC9qFQqHiyeTCbR3d0Nj8eD9fV1bG5uolqtwmw2w+VyQafTYW5ujhU3pVIJS0tLiEQiGBkZ4Zwx7d1mKAJzu93w+XxYWVnZ1p1WKpUwOTmJYDC4TdlCyhvgriFvaWnBpz71KZw5cwbZbBZvvvkmbty4gWAwCLlczi3wdJiSA0e5eZ/Ph0Qiwe3xNBOYnL2dqoadfOg3R1AeTiqVcosiCb2bO62o0kx5MTrlyVOqVquwWCwYGRmBx+Nh4X4oFOIcHz0k6hZpfljNkPdjs9nYmFCFlk5H+h2FQoHnApA3RkUiWuRkjKh443a7odVqEQwGefYqtcrSIJtYLMaqBIPBgKGhIZw8eZKN/I0bN/CDH/wA8Xj8nuunIe33M7gAeNSd2+3G2toaj6Ds7e0FAJ6YRlpTCl3D4TB3EeXzeZ5aRUU2MgDUQkstr1R4K5VKXPTS6XScj6T5GiT7o3QCFYNIiieRSLiVm4bGSKVShMNhHlpkt9sxMDAArVaLlZUV+P1+Dv3279+Pxx9/HFqtltvI6eBVKpWIxWKsMZVIJBwu0qS05H8MTm9vb4fNZsOFCxewurrKG7+jowOdnZ2sK45GoyzZcrlcHAGQMYpEIlhbW2PvvVQqsb68UqkgHA5zKoqaTKhDjVqj6bAuFArcJt3cuUaGnyIDSmOsr68jHA7zwUpRCbWw09q5du0aVldXeYwn5Yh3al2Bn6Xb6DlQAwERjUbx0ksvoVar4ejRo9i/fz+cTifm5+dZ/x0MBnkmh16vx4EDB/hgoLCeHJdGo8E6ZZpjQAoZym1T6mR1dRW1Wm1bA0y1WuUiKQ39oSaMra2te5qjaB3S7yC7RGNBaSZHe3s7jEYjotEolpaWsLq6ykVgkk+SgID04TQBkKIkeoc0onWnXvx+fKSv66EcXaVS4VPpxo0bHL4NDg7yrFYSx9OgYKooU+GFqr2pVIqHX5Mom7qT6O/a7QZIajUwMMB5mmg0ymP2mosllNqYnp5mb4ReMuUPl5aWWKfb2toKp9PJqgSFQsFhCHn0wWAQ8XgcsViME+fpdBpvvPEGCoUCdDod1tbWWOazk66uLrzwwgtwOBw8pasZWmiZTAYDAwN47rnn8P3vf58HwHz84x/nAp7T6cShQ4dw+fJlHqFHrboUUpO3RkUOi8UCo9HIXisVwiSSu33t1BZNnh6N3qT2SyrOUTqCDj1StrS0tLCihQan03tMJpNcYCEJHnXF0ft3OBw8/nBhYQFbW1s8UY1UGSQvI0laLpdDNBqFVCqF0WiEy+XC/v37YbPZWAUA3C0UjYyMYHBwEOl0Gvl8HplMBkqlEi6XC/39/QDAI0edTicWFhYQDAbZOJNkrdG42/ZK+UvKcdOsgVKpxG2t9Xqdu9LIEyX5YTwe57Zncjri8TjUajUrRqgo6nQ6MTw8DLvdzjIumj1dLBbR1taG5557Dv39/chkMg8ckehwOPDrv/7r+Na3vgW/37/t86WlJbzxxhtYXV3F9evXkc1mcenSJW66aPa0DQYDt0snk0nOzVOHGkU74XAYS0tLsFqt7EyRppZSJZOTk5iYmOAcOkXZlC+PRqNIJpPQ6XQYGBjA2toa/46de4hsBTlxwM++4kur1XKjA00KDIVCWF9f5zURDofR3t7OkR8Z8OnpaZw/f54H45DipfmrmT6Mj2R06/U6DAYDSqUS3n//fayuriIUCvFYu5WVFRw7dgwulwt9fX0cBlitVpZtDQ8PY3h4mDts2traWCc3OzuLcDjMlV76vYuLi/cUkPR6PT71qU/h5MmTXISg0LE5HK9UKrh9+zbeeustdHR04GMf+xh0Oh22tra4ktre3s6jD2mEZfP3MnV2duLo0aOs511fX0cmk+Gee+rWSSaTuHr1Km7cuIHW1lYcPnyYw+FmDh06hD/5kz/B0NAQZDLZNtF684Ih49nb24uvfe1r6OjowIsvvsjfPEBfX0NFO4fDge9973vc204dZz09PSgUClhcXOSwS6PRbBOxx+NxBAIBnqGg0Wh446hUKj5wSKer0Wjg8/nQ0tKCRqPBuUhSuNDsXjIilHe3WCzweDwwm804cuQIOjs7uUPN7XazZ0jSHJot0BzqNxs9moxGRcFUKsWaZypmWSwWnDx5En19faxGIOkcjVikd0hDw2mCmURyd55FV1cX5ubmOBWjUqm4oy4YDHIbL33NC3nsFAXStTQ3E9EUMxLzZ7NZdHR04NChQ+ju7oZareaB7hS+d3Z24oUXXsDTTz/NRTL6JoxwOIyDBw/iy1/+Mj7xiU9w3r9ZX9sMDaF6/vnnMTAwgD/6oz/C9PQ0f06Fwlgsxt/SQCk7g8HAeXwad+lyudDa2spaZ/IQiUKhwM6KRqNhT1Kn0+HgwYNwOBzIZrOYm5vDuXPnUK/XMTo6ylErFStp3ojD4UB3dzc0Gg3+9m//dpu322g0uKGJ1h4pctxuN/r7+3n4lsfjQWdnJzweD/bt24eJiQkeT+B2uzEwMIDe3l7o9Xr+8oGxsTGsrq7yLGha6waDYdfa02480OiS26zRaLB//37s27eP83u0achzpClMwF1vZ2hoiB8ujXOjPn6S61gsFjQaDf4WBNJn6nQ6lEol/PSnP8X58+dZ61gul/Hoo4/i85//PJxOJ39lR0tLC/R6/T2LK5lMwuPx4Omnn2a5VKPR4G8XoO6W5gEbFI5QIYa87lgshqWlJZb4JBIJVldQhVkul2N0dBQvvPACF1bGx8dRr9dhNpvxx3/8x/jVX/1VbjagFEjzddN8YCpomc1mfOlLX4Ldbse1a9e2FYIAwOVy4bd+67dw6tQpvPrqq1heXka9XsfQ0BDL9AqFAq5fv46XX36ZR0J2dHRwA8Tk5CSPRdTr9XA4HCxZohwr5TxdLhfrSkkJQUU3uk+VSsWeAG06aqZp3qDUcNDX18d5NMrZUjpAJpMhGAxienoaMpkMPT09LJmitu5yuQyz2YzBwUG0tLSgVqtBq9XC6/Xy4UdpLvKeaHO2t7ez9MvlcvEG12g00Ol0OHLkCHvoVGQliSR16IVCIU4TbGxsIBgMsncaiUSQzWbhdrtx+vRp7N+/nyV6y8vLKBQKGBoawmc/+1kcPXqUu8/OnTuHcrmM/fv3o6enB7/yK7+C7u7ubeubOgUHBwfxpS99CU8//TR7d82Dy5shz4+8UPo6pK9//etIpVKQyWQYHh7Gb/zGb7DRo/1KMsXOzk5W9lDHY3d3N2w2G78XSqNQWmtwcJAjw1wuhyNHjqC9vZ1HpALAyMgIfvKTn7DxatavkyqH5hX7fD50dnZiYmIC77//PpRKJafcSKNM6zAWi6FWq6G1tZW/2svn83EhjLo1R0dHWThAXzNF4zTL5TIXjh9++GHe36QUIcNPEc2DeOBXsCcSiQbdPInT6cXRy6RKI4Wmu7Fb7rJ5oj1VspsfNAD+toTm8IE2P/2Z5vBhJzSkpfm6m1sad/599DNE8/3QQqb8M/1/zf+bdHw05o+kSWQkdzsYPiqU46b3sNvvaZ5PsFP7CoAXSvP0K8pF0VwB2qj02W7//VGulf69s313tyYWenbN97FzbZDnTDMNqHhLMyIofUJFQVqjO9/hbtdK/9A1NF/jR3lfdL20Rnb77+avGWpeS/Q+miOsRqPB74PqFLs9d3KAmvOg/xUoZUDPkVIClCtvlnM1t443pwRpjTQ/E+J+e6x58h79DO2X3fLRALat0UajwcVQgtI3O8dC0mf3Wxs7r2MntJ/pGdD/13zdzTldi8Vy35fxQKML4MMTFAKBQCDYyX2N7gPTC80G+b96iv532Xko/KKu43582PX93/AMPyp0rf+3X+f/JP6nPNMHrdP/6XvwF3EdD7oG4ekKBALBz5/7Wt0PT9AJBAKB4OeGMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuI/EM+l+zJVQgEAsH/EoSnKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwhwijKxAIBHuIMLoCgUCwh/wfh3eMQQR7DEUAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 512\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Zs2ehVCpRr9chkUjg8XhQrVbhdrs50s5ms5vuVbVaRS6XQygUQjweh9PpZKfPYDDAarXCaDSi1WphbW0NhUIBRqMR1WoVrVYLpVIJL7/8MtRqNa5cuYJ0Ov0ws/pwo0seLeU4lUolSqUSb7bh4WH09vZCJpMhnU5zKBoOh/nvjUYDg4ODMBqN0Gq1MBqNMBgM0Ov1nF8ql8vQaDTsRVBuiEK8eDyO9fV1NrqtVgv1eh3RaBTr6+ucT2o3WHK5HGazGV6vF5cvX8aVK1fQaDSg0+kQCARQr9exe/duPProozAYDEgmkxgdHUWpVIJSqeTUh9frRV9fH1KpFIcbjUYDZrMZIyMjyOfzCIfDyGQy7HFsbGygVCptytG1e7uRSGRTHpCMDr3/1jwefV7yhPR6PXw+HxqNBtbX1+F0OqHT6eB0OtljdTqdsFgs0Gg02LFjBwKBACqVCtbW1lCpVGCz2WC1WqHT6VCv17G6uopYLMYHhEaj4c3cnstVKBTI5/NIpVKo1WpsdCgVRQs4m81yrrpQKCAUCqFYLGJtbQ1arRbpdBrVahXRaBRLS0swGAyoVqvo7u5mYzw0NASn04lEIoGenh6Uy2UOcRUKBR9EUqkUPT096O3thd/vh1qtxvr6+t0FLpcjl8vh1VdfRS6Xw+rqKhKJBJRKJcLhMAqFAlKpFPR6PbxeL9+PTCaDXC4HANBoNNBqtfw5FQoFFAoF379arYZSqcT5Q3IuJBIJYrEYMpkMb+S+vj4UCgVOIWg0Gvh8PvT09MDlcmF2dhalUgkKhQJ6vZ73jVwuR7FYhNVqRUdHB19rs9nkA2NrfaO9dkHODtVXKpUKIpHIPWtTJpNBJpOhVCohmUwCAJRKJVqtFgwGA/bs2YNDhw5hfHycPysdgv39/fB4PNDpdDAYDBztSqVSDA4OwmKxIJvN4sqVK5icnIRSqUQymcTKygrkcjmefPJJeL1e9ki3Kizq9TpCoRCi0Sgajcam65bL5XA4HLDb7ZzaajQaUCqVMBqNsNlsnOOnveF2uyGVSvlZLy0tYWZmBjKZDB6PB729vfB4PPB4POjv74fZbEZfXx+Gh4exsrKCarXK9QzaJ2q1+l3z8A81unK5HE6nEz6fD8lkkhcKAJhMJhw9ehSdnZ2QSCRYWlrCiy++iGg0yqd8tVqFVqvlTUg3k4x4sVhEqVSCXC6HXq9HR0cHMpkMzGYzrFYrAoEAZDIZ3nzzTUSjUdRqNb62er3OHgd5GluLI3RCFotFNop9fX04evQoXC4XRkZG4PP5UK1WMT09jYWFBTgcDnR2dsJisaBarcJsNuP06dPQ6XSYnp5GMpnkB67VauH3+9HR0cGLudFo4O2334ZKpcLk5OR9Qz9aQPT39gIGvXf798lkMqhUKjSbTeTzeZRKJfb+u7u72aPUaDTI5XKb0i20GHU6HZaXl5HL5bhQQSd5KpViY0k5WFpI7QZHp9NBrVbzhiQDWK/XuTBIBkCr1aLZbKJQKHBBJZvNsgdNEczi4iKuXbsGk8mE3t5euN1uFAoF3Lp1C6VSCW63G6lUCn19fbDb7ZyLo9wc1QYsFgvu3LnDebzp6WkAd52DO3fuYGFhAdlsFgCgVquh0+mQyWT4INNqtfw5qEhsMpnYmzUYDGz8yNBbrVZoNBqUy2W+FlrT7Sm1XC4Hi8XCIW4ul2NvTyaTQaPRwO/3w263w+/3c2Gt2WzyMyqVSiiXyzAajZBKpSgUCvz59Ho9Fxvbaa+vbN0bVJDcCkUDBoMBN27cAADodDpIpVJ0dnbC7/dDq9XyWlUoFLDZbBgYGMCxY8dgMplQrVZhtVrR09OD9fV1GI1GBAIBDAwMQKlUYvfu3RgfH0csFsPS0hImJia4KHk/+R39oTwppSnbkUql6Ovrw5EjR1Cv1xEMBpFMJuF2u9HZ2Qm9Xs+1JY1GA6fTCbVazQVjqjlRqimZTCIcDnNa7hOf+AT27duHzs5OHD16FG+88Qai0SjfV7PZDLPZjJWVFWxsbNxzX9t5qNE9fPgwhoaG0NXVhWKxyFVlcvXlcjkbEEonRCIRKBQKTkfUajXkcjk0m00YjUYYjUbU63X+HjpNrVYrHA4Hjhw5AqlUio6ODrhcLiSTSSwtLd33+qRSKdxuN4eF7ZAHkM1mUSwWOT9kt9vx2GOPwev1Qq/XQy6Xo1Kp8Kax2+1wuVxYW1vD2toaAKCzsxNPP/00PB4P5ubmYLfb0d3dDbVajZ6eHpw8eRLj4+NYWVlBpVLBm2++idXVVTQaDdhsNv75RCwWw/j4OOx2O1eI6Q8VexQKBXQ6HT9UuVwOtVqN5eVlXL16Fc1mk/PtzWYTmUyGc1I+nw8ajQbJZJLzu2azmSvttLiy2SxsNhsAcCRDHpZSqYRWq4XT6YTD4YBCoWBFAW0yi8UCqVSKWCyGUCiEjY0NlMtl6PV6OJ1OyGQy5HI5lMtlLp7SoUKRSblcRqlUQi6X21QpT6VSePvtt6FWq+HxeHDo0CEMDAwgn89Dp9PBarVyTl2v12N9fR3nz5/flBu1WCz82traGhqNBh8iUqkUpVIJxWKRDSldu9Vq5Wgsn89Do9HAZrPB5/PB4XCg0WggnU7zgWKxWKBUKlGr1RCLxRCPx/leU0pGoVBwGoaum4w9pdYymQykUikCgQCsVivvIbVajUAgwAY3FArhypUrkMlkOHLkCGw22yYvt9Vq8bXpdLpNXiPdYzJ6W/eT3W7H0tISlpeXeT07HA74/X7s3r2b1313dzcOHDiAjY0N9Pf349ChQ9BoNGyoAXCutlqtct1DpVLB7/fDZrNhfX2dbUOj0UCpVEI2m+Uosf0QobrE1vpN++sejwcnTpyAxWJBNBrF2toaHzrxeHyTrWk0Ghwd0AGWzWa5DkIHX61WQ6VSQTqdZkMvk8lQqVQ4KiRHIxAIsKrqYTzU6P75n/85gLvh8K1bt7iyCwCJRAJjY2M4ePAgbDYbhoaG8Lu/+7tYXl7GxsYGpqamMD8/j0ajAaPRiN7eXnR3d7MiwOl0wuPxoNVqIZlMIpvNwmQywWazwWKxoLOzEzabDUajEd3d3VAqlZzMJ2gTAZuruaRwoBDD6/Wio6MDUqkUw8PD2LFjB/R6Pd/QQqHAngfloCYmJhAMBqFUKnH06FH09vZi//79GBoa2rT5VCoVe0AbGxv8PqlUCj09Pdi9ezckEgnW1tY4zzQ3N4e//uu/RiKRwEc/+lGu4FIIGAqFEAqFcODAAdjt9nce1r+HypcvX0YgEMBTTz0Fj8fDRpc8Nbfbzd7J7Ows4vE4b6i9e/didXUVCwsLiMViKJfLfM+pAErhJFXPfT4fgLvVb/LwXC4XTCYT6vU6LBYLzGYzb5hms8khKhnbds+qPW1FmxK4Kw2kA4E8+lqtho2NDTYelFooFov8vdFoFJOTk5iYmEAmk4FCoYBGo2GngKIzuVwOrVYLg8HA+VfgnZw5XSdtHJJFaTQaGI1GuN1uuFwuyGQyZDIZRKNR9vipGEpFwnq9DpVKxd6Z2WzmXHEul4PVakVvby/nDjOZDMLhMB9yHo8HhUIBkUiEU2JkdJVKJX75y19idXUVIyMjm+SNtDdv3rwJn8+HwcHBTWF6KpXC2bNn8a1vfQtzc3McYcnlcnR0dGD37t0oFAocGUgkEnR2dmLfvn3Yt28fTCYT1Go19u7dC5/Ph/X1dWg0Guj1eszPz+PKlSuo1WoIBAJ8b7PZLFKpFB9gKpUKer0eer0ey8vLmJmZQavVgtvtZgWJWq3e9Llof2s0mk0RL6FUKtHd3Y1AIACdTgeVSgWJRIJUKoVEIoFMJoNWqwWr1QqPxwOn0wmr1cqRTKFQwOTkJBtbn8+HnTt3cvQxNDSEVquFeDyO8fFxxOPxTZF0tVpFV1cXHnnkkYeZ1Lvr8GEv+v1+RKNRrKysYHx8nE8rACgUCrhx4waOHTuGI0eOwOv1wmazIZlMYn5+Hvl8Huvr61w8cblcLB2iDWC1WlmSkUgksLCwgLm5OahUKs4dmc1mnDhxAi+99BJGR0c3XV8+n7+ngklQXq5SqcBkMsHpdHLRJRKJoNVqsaEqFouIxWJs/FutFku44vE4rly5gpmZGUgkEt4U1WqV1RnJZBKZTAbVapX/kAbW4/FwSoUWT6lUwvXr11Gv19Hb24vjx48DeKcwMDY2hhdeeAFnzpzBs88+C4PBsEkjWa/XN+VfKYddKpUQiUSg1+v5AOvv7+cFrFAo0NHRAb1ez9eey+U4B9nuWZMki0510qhSiFyv1zE7O4twOIxSqQSDwQCn04larca5MdJSkqdFOTWNRsMRSLvuVa/XczqKDCopJ6gwZjAYoFQqubBarVaRyWSwvr7OG4vSM3Rw6HQ6PkhIb67VamE2m6FSqVh7Tofl6uoqzGYzuru70dPTA7lcjlQqhbGxMWg0Gng8HthsNr4PZFDoc1Aunw4IMhiUgpFIJPB6vRgaGuJwXKVSobOzE11dXey55nI5RCIR+Hw+voeU4ovH43zot2uNs9ksfvSjH+H8+fP41Kc+hcHBQV53ADA2Nobnn38eo6Oj/EwBcMovFouxN05SK1IXUS2CagXVahWhUAixWAytVgsbGxuIx+McVdHBZDKZOJUQjUa53gLcPWgNBgPUajVMJhNHpw/yFiuVCteV2unv78eJEyc4XTY6OoqxsTFUKhWuk9BhZrFYODKgdJrb7YbX62WnMBAI4PHHH0dfXx8sFguvk5mZGbz99tubHMBcLoeJiQns3bsXgUAALpfrvtdOPNToRqNRTE9P4+LFi1haWtqUb2m1WlhZWcH09DT27t3LJ0u9XmdXnk6NSCTCVX+bzYZqtYpYLMbFl2azifX1dSwtLSGTyQAAZmZmoFKpcOzYMezbtw/PPPMM7ty5s+nDlstlLnbcj3Q6jbm5OaysrHDIeu7cObzyyitIpVIol8uwWCxwOp3IZrNYX1/nAo/L5cLOnTuxuLiIYDCI0dFRVCoVWK1WVi9Uq1XcuXMHi4uLiEajm0KlWq3GqgCqsrffO4VCwY0S7UQiEbzyyiusUzUYDDhz5gyrRlwuFw4cOIDXXnsNP/7xj/HUU0/B5XIhlUrhzp07uHLlCpxOJx599FH4/X643W4olUosLi5icnISGo2GQ2ez2YxGowGZTMaifY1Gw+kak8nEBRHyCsnDjEQimJqawsrKClQqFXsZ9Xodd+7cwfT0NEqlErRaLex2Oxc0enp6oFQqcf36dWxsbLCRMhgMnHsjg0K54FarxYaNDggyZK1Wi3XQFEq2C/XJ68/n88jlcpwPV6vV6O/vR6VSweLiIprNJnK5HOLxOEqlEjQaDafQZDIZgsEggsEgezRDQ0PsmdE6VyqVMJlMcLvdHIFR+gS4u8H1ej3/N5/PIxQKoVqtwuv1ore3F1arlYu6V69eRSKRwNGjR+Hz+WA2mxEOh/Gzn/0MkUgEjz/+OJxOJ+r1Okc7P//5z/G9730PwWAQDocDx44dQ0dHB68vamxQKBSb1mS1WsXKygqnxaioS57qzZs3EQqF0NPTg6GhISgUCly9ehWvv/46kskky9m6u7vR3d2NfD7Pxdl8Pg+tVsv/n0qloFarYbFYUKvVkM/nYbFYsLKywg0KXV1d993T1KDVjlqtxjPPPIO9e/eiWq3i9u3b+NGPfsSevMlkQk9PD2vIq9UqK3uUSiUSiQQWFxcRDodZu72xsYFEIgG/38+OSKlUwvT0NFZXV++xhQsLC7h48SIcDgeAu4fAg3io0dXr9Wwg2z3K9sR8NptFLpeDRqNBOp3GrVu3cPXqVYTDYUilUtTrdYyPj2NqagputxuDg4NwOBxcZCgUClhaWsLk5CQLmeVyOUtDdu3aBbfbjQ984AM4d+4cLl68uGmh0M3bKhRXKpVcHc3n8zCZTGwIQ6EQF1UMBgPsdjvkcjkSicSmHF9PTw83aITDYTaqy8vLsFgsfJiQ/IlC1HYP4n4FCwrnKFXRLol5++23MTU1hVarhXQ6jQsXLkCj0eDQoUNwOByw2Wz4+Mc/DrlcznKeY8eOsZZzdXUVY2NjCAaDOHXqFPbv3w+Hw4F4PM6hu9lsRn9/P+dzycC0Wi3Y7XZWElSrVc6tVSoV9ijJu4nFYigUCqxxnZqa4pxxLpdjw0jFLzLoWq2Wn59UKuXORp1Ot2mNtUNeLR0OBHV5bb3H7Xk/0uRSXYJSZJQSII+aopdSqYRKpYKJiQmurNPzaTQaCAaDKBQKcLlcLOuioluj0eCDTqlUIpfLIZFIcGddqVSCRCKBxWJBqVTCxsYGWq0WBgcHORq7efMmXnnlFSwsLHBNIB6PY3l5GZcvX8b8/DxOnTqFp556ClarlSOfa9eu4eLFi+zxj4+P48aNG7DZbKwwoJTDtWvX7rlnFDm1e86khS4UClhZWcHKygoymQzcbjdWVlYQi8X4UDGZTBgaGoLX68XNmzexsbGBbDbLaTVyyChiJi0yHShUYGxPy2xdD/F4/J7o9siRIzh16hSnfSYmJrCwsIBkMsk6dirYVyoVXg8kCZyZmUEkEuH8u1Qqxfr6Oq5cuQKFQoFDhw7BZDKxIOB+67NcLnOx/9109w81ulsXaTvUfeP1elGr1ZBOp3Hnzh28+uqrmJ2dhUwmQ1dXFzck0ClXLBZhs9kQj8cRCoVYe5rJZLga2t5mSB6KQqHA3r17MTo6yikA2qj3a+VUKBQIBAKIxWJcLV9eXkYmk4FSqeSwrNFosDicPis1ApTLZdjtduzcuZMfOFWKb968iTt37iAej3ORrr1VlLhfhZigcIaMLklQPB4PisUi5wHHxsZgt9u5Wt7Z2YmPfOQjnCsOBoPcuWSxWJBIJFi/3NHRga6uLrhcLnR2dqJWq8HlcsHlcrGXSRuiXRheKpWQSCSQTqe5bbRYLCIajWJhYYGLByT1i8ViSKfTfKBQazHl5ajwMD09zcUj2uCUi15YWOCcHsmz2tt+6X5S7pu84HaNM0l4SNpGEiCqeNPXFYtFTE9Po1AoIJ1Ob6qQk3FPJpOIxWKQSCQwm81wOp1cuadDTC6XIxAIsA5YJpPBbDbD4XCw0oMahMh4k6qAoiNqdJDJZIhGoxgdHcXi4iLnINVqNZLJJIv6jxw5gieffJJz7cViEUtLSxgfH+ccvVwuh8fjgUQiYZkbcNfQkfb3fmxdu9QFCtw9+NLpNFKpFIaGhriQnM/n4XA4OAdK+4cKr+VymZ0j6tai56TT6dDT04Ouri4olUr09PSwBvl+10W5YDq8TCYT9u7dy+kYqjsYDAZOP5Kxv379OsbGxjh/bbfbOa1JDVk+n48LZ5Tj1ev1GBwcRL1e50M2HA5zwZZsD0Vd90t/tPNQo3vz5k12+UkXSajVaoyMjGDfvn18aq+trWFxcRG5XA6dnZ0YGBiAwWCAwWDg6jspEkZHR1lyQV6QzWZjMX4gEIDP52M97sLCAoLB4KaC2aOPPor9+/dvWij0GnVCPfLII7Db7Zz8Jv1oR0cHbwjakJSol8vlWFlZQat1t0VVr9fjyJEjnBtdX1/Hm2++yTrj9n759mtRq9VwOBwcGm+68f++KajgRQvq2LFjaDab+Ld/+zdEIhGkUilMTU2ht7cXe/fu5c9ns9kwODiIWCyGqakpzM7OcocYteFSBEFeg8FgwMbGBlKpFCwWC3tApJUGwKEyFQhp4VKujeZNkL5UrVazcqF9c1D4SPk/Mm4rKyuQyWSo1+uczyQlSzab5RywVqu9Z5YCbVSSCLZ7uGR4qfBFhwG1/dLXajQaNqxzc3N80JtMJu4ySyaTrE6g9SaXy1nBUC6XuSOK7gmlLOi+UQ6Z1odSqeQ5ENSlRYe90WhEs9lEJBLB5OQklpaW+FCrVCq4c+cOVlZWUK/XWXdN7cK07oLBICYnJ1EsFqHT6dDX14ePfOQjeOyxx6DT6biWotPpuN17K3q9HhaLBfF4nNcDfQY6nOkwikajOHLkCA4ePIharcapvlu3biGTyWB1dZXTVaQMIWlo+5qgSGBkZAS7du1Cd3c313qI9kP3wIEDeOSRR/Dyyy+zoQsGg5iamkJPTw93Mvr9fj7Q8/k8MpnMpi62eDyOw4cPw+12s265p6cHfr+fG2qo4BwKhbhhY//+/dyJ257aJDXO8vIy5HI5jhw5cq9Bpb3/wFf+/SFotVpeqHTqqdVqdHR0YP/+/ejr64NWq0WlUsHIyAjr1Hw+H6xWK5rNJnbt2oXjx49zJf6NN97AtWvXNlWPlUolent70d/fzzkuCounpqZw4cIFXL16ld17uVyOgYEBOJ1OAJtTHoRMJmNZFi1yjUaDJ598Env37uX5A3fu3P3V2x6PB1qtFuFwGBsbG4jFYtBoNKztHR4e5k0fiURgMBi4uk5FBsLn8+Ezn/kMhoeH8fzzz+P111/fdG3k3bVft1wuh91ux+nTp9FoNPDd734Xc3Nz7KlT3hy4a9R8Ph/0ej1mZ2eRzWZ5Y3s8Hs5LZjIZZDIZOBwOOBwOpNNphMNhDuEor1ssFlkWQ2kAag9Op9PIZDJIJpOQyWQs7+vr62OPtl6vcxGs2WzCZDKhq6sLHR0dkMvlnKIiHavBYIDJZEI+n2eviDYRaT8tFgvrN6kTTa/Xw+/389og7TF1Rel0OnR1dbE0qv19KedK10meKnl/DoeDG0XaC0wGgwFutxt+vx+BQIBTHIlEgjuhVCoVG0+JRMKREoW1ZrOZvU3qfiIdKOW8yXumwg7pokmIbzKZsH///k3dncDdA5Jy05VKBTt27MCTTz6J06dP3xPqSiQS5PP5e3SuwF2D9vu///sYHx/H9773Pe4MJXmVy+WCzWaDQqGAw+FAd3c3hoaGUKvVMDExgbGxMSwsLPBnphkqhUKB32vnzp08d+LmzZt4+eWXWU87PDzMcr6t1wzcNb5OpxM7duzAuXPnOI99/vx5FItFvP/978fOnTvhdDoxPDzMnaqzs7Nc/CKnQiqVYmhoiI0jRXUA+AB2uVyw2+0YHh6Gy+Vi53D//v144403WJdOa4sO+/9SesHj8WB2dhblcplDAYfDwbKrjo4OqFQqFtIPDw9DrVZzD/Xy8jJCoRCGh4fxvve9DyaTieVkW3uqlUolhoaG8OijjyKdTsNoNMJisWBychJnz57lfm2CQoZKpQKj0fjAz0BV8YGBAQwODkKj0eCZZ56Bz+dDNBrlogYtCJlMhtdff52rt+QlJRIJbmnu7OyEx+PhVsCXXnoJP/3pT/mhdnV14ctf/jI+/elPQyKR4Je//OV9r408tnao8+fw4cM4f/483nrrLWg0Gt6A/ODkcrjdbjidTk5vUPecUqlkGYtGo0Gz2WRDSvMEqBpNRSfSSFKelrwS6oQqFArc4mu32+Hz+dDV1cV5WZfLhWw2i2AwyMN1aDYADfuhZ03FRpKWUdWfjKNOp8PAwAB2797NRUzS5+7btw9PP/00/H4/gsEgXnzxRYyNjaFQKHD7sN/vx+3btzmnR+/brv1svw7gnS5GjUYDh8PBVXWXy8VyLb1eD4/Hw+mL9fV1VqZks1n2zKlVlIYEORwOlsjVajUkEglWUPh8Pg73m80mrFYrDh06xB2A1WoV8/PzCAaDaDabcLlcPC+AoGaPdDqNUqkEr9eLw4cPQ6/Xb4oMgXcabe6H3+/HE088gTNnzsDr9eJrX/sa1tbWWOJ3/PhxPPHEE1AoFCzNq9VqiMfj2NjYYMkhpVI6Ojpw/Phx1Ot1TE1NAQDe97734QMf+AAbT1KnDA4OPtRYkXNCsrv2lGc2m8WlS5dYdjo0NMQDnMxmM6RSKW7fvs3Pmu6DxWLhIU2ZTIbnSHR0dGDPnj04cOAA3G43ent7WU9PKpOTJ0/C4/FwipHUQ9TN9jAeanRffPFF3Lp1C6FQCADQ1dWFJ598Ert370Y6nUY8Hkc6nYbT6eR8FnlIy8vLuHnzJtbW1mA2m7kdMZFIYHp6mqUzwF0DYjQauaBDhQCz2YxgMMiDTraSTqc5n7SV9hZbkmnRgJ1SqYTV1VVsbGxAqVRyxZjyTrVajU87Km6Mjo5iZWUF+/fvx65du9DT08O56UuXLvHB43K58IUvfAG/9Vu/Ba1Wi1u3buH27dv3XN/+/ftx+vTpe7xz+n/qkS8UClxwud9nJIkPhWAmk4m1pKlUCouLi5iamuKZBrlcjkPEjY0NGAwG1n9SPpoqytRRSPItMv4AeJ4ARUFyuZxHVZIhajabCIfDyGazrF8mI0BNIBTlUBdcuVyGSqVCb28vnn76aTQaDVZzaDQanDlzBsePH4dGo0FXVxfn7MrlMg4fPoxTp05x8e7WrVubBtGQl00SOFob2WwWa2tr7IGTN0+yseHhYej1eo4CJBIJ53ABcFdT+7jHYrHIdQNqvCElADWCqNVq5HI5BINBZLNZrK6u8pQvi8XCqTVSVuTzecRisfvWWBKJBBflSNbYvp7a19eZM2fw/e9/f1NRGgBu376N2dlZ7N69G5/4xCeQzWbxzW9+E6lUig/hHTt2cC50cXER4+PjGB0dZS310NAQT7mr1Wp8/bRmFAoFwuEwp6y8Xi8X49pTRcD9x1rm8/n7zjagdFowGERnZ+emAr9Op4PRaOTculQqRS6Xw/T0NA/paTQaWF1d5cI4Nam0t/VSTjsej6O7uxv79u3D6Ogozp07h0gkglAohFdffRWpVApnzpy55xqJhxrdH//4x9xNpFAo4Ha7sWvXLtjtdkQiEfaK6Kbm83kEg0GMj49jbm4Os7OzvPgpz0J97jabjXNmcrkc5XIZV69exfT0NDKZDHsZt2/fZhlZOyRYJzH+Vs+ZQn2pVIpIJIIXX3wRExMTaDQaPNKQZC6kE45Go5zk37FjB9xuN4rFIkZHR3lDAGAVQblc5qlCKpUKQ0NDOHXqFE6fPs0yo+eff54HdLRz8uRJdHZ2PvDek+SJVAQ3btxAsVjkQo5EIkE4HMbo6CiWl5c35Tx37tzJg2UymQzrn0nGRDneer0OnU4Hi8XC3Tn0PKjoRM0NVBSh4iY1QlgsFvYCSOVB0q5yuczpDWqUII0t/XyJRAKtVsvvQ8+TUiWU3lldXeXcK/DOoUoabNI8U7WfWoVNJhPnD4F3Wkip/ZZUEdRFRmkPqVTK4wE9Hg/MZjNkMhlLD8PhMFeq2+cwkz6Y0i7Uul2v13kqGUUSdMDRdVUqFfT19fEMi1AohKmpKUxNTXF31c2bN/GhD30IVqt1k/73xo0bXJSjGsyD6OrqwuOPP36P0SUN75e//GVYrVacPn0amUwGr776KpLJJMbHx/Hmm2/iwIEDLBu8efMmq2i6urqwf/9+dHR0IBqNolAoYHp6mlUwLpcL6XQa586dQ7VahcPhQCKRwJ07dzj143K5NjXptEeC1LhEDRpbHbFUKoWLFy9y0ZFUS+l0GuVyme8NDRTSarVYX19HqVRCMBjE7du3EY1GuRCWyWQgkUg4TQHc7U8gtZLD4cCePXtw69YtrK2toVwu4/bt25iZmcHf/u3fPvD+P9ToUs6KhN40JEKv18Nut3OhhE54KiyQCoBE4gsLC/jhD3+IkZERHD58GB/60IcgkUhYb0iytGw2y8UGWryU79t00XI5J70pPKGQqb2gQg9qfHwcr7zyCkqlEjo7O7naSzlHj8fD1Xfybp1OJxqNBsbGxvjmUyfUxMQElpeXkUwmMTY2hmg0ioMHD+J3fud3MDg4yFXoc+fO4aWXXrrnvqpUKtYhbg3/CCrq0eegB7tjxw7+jDQekaYdxeNxVo4A4HF7VJ2n50ReJX0d5QdrtRpPGqPJUBSi0rBrOulJ4N/R0QGHw4FisYjx8XEsLCxsmnjVbujJ6FKTBLV+EjKZDH6/nz21jY0NNowdHR08HziVSrH2ttVq8TogfWWxWITT6cThw4e56YWMO9UoqNhIUjFKLbTP2SWviu6bSqVCIpHA6uoqwuEwe5UmkwkKhYKVFzTkmlIVNIeCik2UiiDvm1JYtOcoXJ2ZmcHc3Bx7gqS6aG8rl0gkLBMEwPf8fmuqfb2RWmCrfvxnP/sZBgYGcObMGRiNRnz2s5/FI488ghdeeAG3b9/Gd7/7Xdy+fRsWi4U/J2l+aX2MjIzA5XKxrBAA2w6tVovp6WmEQiF0d3ej2WxibW0NqVQKBw4cgMPh2PSLAtr3Bylg/H4/uru7MTc3tyk3XalUMD4+zpPTSENNz5nWodvtxtNPP43Tp08jn8/jZz/7GcbHxzfZu3Z7RvZHKpXCYrHA7/cjk8nw4HKz2cwOSqVSuadovpV3/c0R7R/c4XBwyyqd5O0dTHRa09QlWiCt1t2ZpSqVCnv37mVlw7Vr1zhcolOMjDXpIu+XVjAajdi1axf8fj93npGnRqEuvQfpCyl1kEgkeNOR6D6bzfKhYrFY4HK5oFarsba2hmw2C6PRiI6ODthsNshkMg5LUqkUCoUCdDodRkZGOCk/PT2NGzdu4IUXXrhnDjAAdHd3c3X1QZujVqvB6/WyHnJ5eRnXrl3Djh07AIBzn0ajkTcwtZJS5JDJZDAzM8OeJ1XRa7Uat0DT7FbqCqKpaWR0SeFA6QDKG6rVar73pIElQ0RtwTQoRiaTcd4PAOfzJBIJFhYWuBjWarW46EoFJJIjUjhOMxnI66ZJV+R50YQuv98Pl8uF1157DcFgkHO0fr8f/f39qNfrmJubQzQa5dZm8phpXgQdZKQmII+ZZviSNMpisWxqOCC9NxXEaNhKLpfjLkmqhVBjB+WDqbNRr9djbW0N6+vrvPGpoWZrrvbatWusMqKpWLVa7b6DtCmlEggEEAgEMDs7u+n1eDyOH/7wh2i1Wti/fz8GBwdx5MgRTExM4MaNG5iZmcHa2hosFgscDgdMJhP27dvH7c0U2ZAzI5VKkUqleBIhHRY0fY669EqlEis0KHJqj7QohVSv1xEIBLBr1y5W97RDBxIVnem+koqE6g+kU4/H45ibm0MwGOTIi/YPRXiUn2+PIijPTUXfdoXMu/Gefl0PeXkks4nFYhgdHYVer0ej0cDQ0BAkkrtDJahAEwqFWONpNBrhdDoRCARYo1kul3kyT/tJQu4/ddncc8FyOfr6+nDw4EHY7XbOd1qtVu72oQeWz+cxNjbG81RpuEU6nWbBP3mscrmc25X1ej3Pf3W73XC73XA4HCwlSyQSvLEajQZ3AuVyORiNRqyurvJvXdhKIBDAZz7zGZ7lujUMpIWWzWYxODiIZ555Bt///vcRi8Vw6dIlnDlzhgd2UHhz6dIlzoOn02mWLJFkigapqFQqHgJDX5tKpXhIEXlIuVyOJ2bV63XWm6bTaR5HSK211FdPC9vn8yEQCLAW2ufzcUccyb8CgQAOHjzIemxKlSgUCq6Q53I5zM/Ps5YVAHuNiUQCyWQSVqsVdruddcISiYTTUrt27YLNZuMxftSSvXfvXoyMjPCmogJhV1cXenp6OKynQS809Hp9fZ1HXbYPMU8mkygWi5zGoPtE4wUpV0lzJIB3Cl9UkKL7Sg4Ora2NjQ2k02nORbvdbuzZswd2u51lXLFYDBcvXuQo7plnnsHAwACvxa0jEoG7DpLL5cJnPvMZfPvb30YwGNz0+vz8PF566SUsLCzA5/Mhm83i9ddfZ3081QUo9KZuRMqFUiSj1+v57+FwGEtLS/wbPGq1GsLhMHcNUifj7du3sXv3bm5JJ/17sVjkLlKHw4FDhw5hcXERt27dukeJQXaDUjh0OJOGmmwOyesCgQDny2m/0AhSj8eDRCIBhUKBZrOJyclJnD9/nmVodrsdtVqN7/Ovzeg2m02uQl+9epUn3avVamSzWezbtw+HDh3igSrA3RB6x44d0Gq1cDgc2LFjB3bu3MlzCzKZDM9smJqaQjQaZa+LBqTPz8/fk1owGo34+Mc/juPHj7NkgxQI7bMdqLvr/PnzCAQC+PCHPwytVotYLIZbt24hl8vB6/WyCsBut6Orq4sLRc1mEwMDA+y5l8tlHhBDs2QlEgkX865du4br16+jq6sL+/bt4+JUO7t378ZXvvIV7N69mwdi38/o0qnc39+PP/7jP4bf78f/+T//B6FQCHNzc9i/fz/rRj/72c/CarXie9/7HpaXl2E0GuFwODA4OAi/349cLoe5uTlMTk4iHA5Dq9ViYGAAGo0GqVSKJWSU5qEQu107bTQaUalUOPdL81yp6YAiG4lEwnNhKcKhgg6lnPR6PYaHh9HZ2clFN6/Xi0qlwoVOlUrFw8NlMhmnPUg1QMZapVKxJ5zJZPjQpajJZrPh6NGj6OvrY2+fUke1Wg179+6FQqFAoVCAz+fjAUyU1tFqtejv7+cWeJonTBriUCjE84npEKJ5HvTZaFaARCLh/n7a+BQJ5vN5dHZ2YmRkBH19fTAYDFheXuZ1R7r1Z599Fk899RQP+K5Wqzz/Ynh4GF/84hfx9NNP8+D4dn1tO/RbRp599lmMjIzgL/7iLzbVHagtOx6P82Ad8qxpzjStM5fLxSkm0rhTipDWP80xpnQRebNGoxF79uyB0+nkjsaXX34Z1WoVR48e5UOOOhgpdWk0GnH8+HFWdrQX1igC7Ovr498BR9GF2+3Gzp07WfrX0dHBacqpqSnMzc1xbl+tVmNoaAh79uyBzWZDOBzG9evXuaBOdoBa32nmw3vhoUaXNoxGo8HIyAiGhoYA3PVId+zYAYVCwYUBm82G3t5e9Pb2YmBggPNSVNUnD4tag6kC3Gg0EI/Hsba2xg+VhO2vvfYaLly4wLNjq9Uqjh8/jk9+8pNwOBzcFeLxeNhjaiccDsNiseCTn/wk95+3Wi088sgjWFpa4pZUiUTClXhq5yVvhqZRUehOQ0lWV1e5aaO9ffnw4cP49Kc/zSHJ2NgYms0mLBYLvvKVr+BjH/sYn/SU4mi/bkoBtMuoPv/5z8Nms+HNN9/cVHwE7obqn//853Hy5En84he/wOrqKoC7mstjx45xsfH69es4e/Ys6vU69u3bB5/Px22rk5OTuHTpEqLRKCwWC+x2O1QqFQ9xp9GO7aMeKSdLYT59JkonkLdHxUe73Y49e/bwQGm671arFYODg9xYQMqKRqPBDQDr6+uYmprapM2m5ohoNMopDfqdZK1Wi3WlNEi9PVVF951m+JLGmboDvV4vGwUS15PBp89LOeF4PM6dliSfooHtNJgmnU7D7Xbj8ccfx9DQEF8PTZ5TKBT42Mc+hoMHD7K2+vLlywCAXbt2oaurC2fOnEF3d/em9U2e+uDgIL7whS/gox/9KHt37YPL26G1Q8+0p6cHrVYLf/Znf8YR4O7du/F7v/d7fPg2m01e3zTc2+l0cnTg9Xrh8XhgMpnYmaBGHSqGDQ8P89otFArcjNDR0cHrf3h4GD/4wQ9YjdOubjIYDPxbIEiLbrVaceXKFVy+fJmvU6/X4+TJkzhx4gTPOiZtf2dnJ+x2O2QyGUv1yOnq7u5mnTrN8bDb7ZsG79Cvl3rsscd4gD4dPMlkkjvS3s3bfeivYE+lUi3gHQ1juwtNujkyoKRDfK9sTZLf7zoo79ieZiB5kUTyzm9beNCgcBLGtxfZ2rt42j9H+/e1t5pu/XfKP9Of9jQI/SwaOE4Ve1ro9PvB/jPQAyVd6/3ehwpe5OGQEoOgYhMVKsmoU96rfaPSfaGUT/vf38u1tncREfdrYGl/rf3724dvU86a7mP7OqTZzJQCI4Pa3sXW/r4Pu977Xet7eV60FtrXR/t6oWuhQeP071ToIall+/U87Dlu/TrKa/9X1hYpLOg+Ug2AilftMi6SBLb/2Xqv/iP7a2uxrNVqbWoEaofSGu1ph/ZiYHuudSvttuZ+B9LDoMIarcH2z0EqlPacrsVieeDDeKjRBfDuCQqBQCAQbOWBRveh6YV2g/yfPUX/q9zPA/nvxLtd33+He/heoWv9736d/5P4n3JPH7ZO/6fvwd/EdTzsGoSnKxAIBL9+Hmh133tSQyAQCAT/ZYTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRuTv8rpkW65CIBAI/pcgPF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm1EGF2BQCDYRoTRFQgEgm3k/wcuyZ1I5ythkgAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", - "\n", - "# Initial guess\n", - "x = np.ones((n,)) * 0.5\n", - "\n", - "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", - "\n", - "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,0) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,-np.inf)\n", - "ub = np.insert(ub,0,0)\n", - "\n", - "cur_beta = 64\n", - "beta_scale = 2\n", - "num_betas = 4\n", - "update_factor = 15\n", - "ftol = 1e-5\n", - "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", - " solver.set_lower_bounds(lb)\n", - " solver.set_upper_bounds(ub)\n", - " solver.set_min_objective(f)\n", - " solver.set_maxeval(update_factor)\n", - " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", - " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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UOBv1nLKomuEXQnwZ2AesE0KMAH+CYfC/IoT4NeA88B+rtf96RNclxy8F0VI6P3hzkv/3B2fQzCyR0UCUB596jaePXOTw2dm0dO1oIMoffOVVhBDpwqF82/7Q3JbM2PbQU0f5+mujvHpxnpmwRq/fxf03DXLr9nUA7NzQzmCdCkw9ePcuPvlvr6XnA9m6KD1+N0ldEtFS+CtkHDNJpvS0JMQ3Xx/D67Rz1+71jMxFiVRxgVJL6owFDCMdiGgVMfwLsQQR09M+NxOhv8NNX9vaLexaeJ2N03C9WZQ4c1E1wy+l/IU8T91VrX3WMweGR/nLb51kciFOj99FPJFKG32LREry/Nszy96rS2DJInyubakc27SUzrNvTKUfz4Q1vvij8+g63LK9l2RqSRC9jrh3zyBPHDrH8IUAwLI7mF6rejesVcXwT4c0dN040V85P8f7rx1gU7cPv9vB5EKs4vuzGJmLpC/ogUiCTRXIuMzMSDo/E2Znf3s6r34taSSPf7U7zkZGVe6uAQeGR3l4/1FiCcPIrlUKYj60lM6Xf3yB/cOjzIW1ug4JOWw2Bru9PP/QHcvCOZbGzFQoxuYeX8X3PbVgGMtnXh/H6bDx3t3r2dDpoc3t4Nx0dbzWlC65OLdobColQmetU8xHE8xFEmzt9eFxrr1iSyN5/A/evYsHn3otq3FMIypx5kIZ/jXgrw++kTb6q2EXpudeZcJairB5AloVqFB/7eMmgkZmS64YvqXXM5Unv74cdF3y9aOjfOUnIwSiCTwOGyfHgnzw+o1VbRo+Nh8lkdQ5fGaG/cOjzIY1NnZ6eOieK0v+beajibQi5nkzvr+jv70mejM+l535aGP0ULh3zyBfe3WU505NIVh+x9nIKMO/Bljx2tXwOu3cf9MgTx8ZzTIsTpsAQZbnUei2QqnXRaupUJwbLuvK+Vy3qdA5VYXq3X956Txf/NF5NDMUFkvq/NOh81y1sRO/21EVw2+pkB4+M8MThxb3fWk+VtaF2fL2wcjoEcDugbXP6AHD8GeOp97xexx0eZ28+ifvq/VQKopS56wy81ENtyP319zldTLY5c3S9f6Le6/lc/ddm7X9sZ+7nsd+9vr0IuzSbbleZ237ub2bcOXZ/1LqbdEqENGIaCk2duaOqVpCbdXQ6/kfz72dNrwW8aTOYwdP4XNVJ1wxtRAnoqXYPzy6bN/lSENkGtpzM2E2dHrSd0trjc9VnYtmtZiYj7NuDaub1wrl8VeBzHRMt9NGLKljE+aCrInXaeczH7o6pwd3757BvNuXFm7ke10mt23v5dFvv8FE0FhY1pIpQjlyqett0erstBGW2Nybx/B7DY9/rsKGX0rJ5EJ+eQafy05Kl2hJveCLaiGcnzXy6/OtAZVyYZ4La8QzwoznZiJcNdCR1s1Za3wue0M1YpkKxRmoYBFdvaAMfwVIpHQmF+K4HTa+d2Kcz3z9RDqmH0sYRv/2nes4fmmBqRrI+X7kxk185MZNxBIpjl8K8syxsaxQAtTnotU50/Bv7fHnfN5ht+F325mtcDOWQCRBj9+V0wBv7PJmNQ2vlOEPRDTmI0bsu9PrzBkHL+XCPGFmHx0+M8NTr4wwH03w2kiAH5ya5OO3bStv0CXgdzsKXu+qNYmUTiCicX0N6h2qjTL8FeBSIMpbEyEA/vrgm8sObF3CqfEQP/6j99ZieGk8Tjs3bek22sYJ+NefXCQYS9Lrd/HpD1xVd/F9ywPe1pfb8AN0eJwEKuzxT4XifOSGjfzDj85lbbcujlaoIpJI0omzIvscycjkWdfmWmb4PQ5b0RdmKSUTwfiyNYOIluIvn3mDDq9rzX9zn8tOLJGqatFdpZgLa8xHE/R3NJ/Hr2L8ZaLrkgumgYL8t+lj8/WzoLWl18/v3LWDR95v6KV89iP12TN0dC6K0y7YsMKJl887LoephTibzPRQv9sIiQx2edLyDFaYpJLSA9YcJheMlojXbFwMx3T7nPzOe4vXiJkNaySSes41g1hCr4mctNdlR8Iyeet6ZGQuii6pqF5SvaA8/jKZXIinY6hRLYXDJrIqTS3qLX7e7nHyjq1GZVA0UZ8FNZfmo6xrc6/oGXZ6nXnj8aWwEDNSH4+PBgH40w9ezfWbu7Kab/gyQj2VIJZIpT/r26+PYxOCX75tK+PBGH/znTf5pVu3smdz4VVcmWtM3XlCVlCbxXyfc1GT3+qtUAjJlI7DXrqfWooIseXQqRi/Yhn/cvgc/9+PLzIb1rDbDAkF63+LeoyfA7S5jTBFdKkEZp0wEYyxfpXb7C6fk9NToYrt07qIHB+bZ7DLS5fPRf+SE3+xoUhlLpiWtz8b1vjR6Rnes2MdXT5Xev3g3EyYawY7SaR0nKsYP0tYzApHrVQsWAtnZLELV7KozKJTpmDdrv72oi8AWlJHL8HyW1W7fe3NZ/hVqKcMvnT4PI//8Gz65ErpEodNcPdV/cvSNOsxlOJ1VtaAFctq5+JkcPWMim6fi4VYkkSFpCemFuLEkynemghx1cYOfG47HZ7sOL7VUKRSej2W4f/28XGQcM/VGwDDSK5vd6c9z0KqeHMJi+WiVs6I9d0Vc7ek65KphThjAaPZubUIXijxZKpoj9/qVAbQ36HSORUZfP57by2LnSZ1yfDFAIceqX9JolJOwkqy9LvLJKolCcaSDHat7vHHkzqhWDIt4VAqES1JKJbkrYkQSV1y9UAH63PkcPstr7UCMf4Dw6P8xTdPMG0Woe1c30ZvhnjaZT2+dFprIJJg/Sre50rhGytTaX27m0+9f3dNnBGvs/j2izNhjaRZlBjRUrx8fpbTUyH+6cVzXArEVs2SiyV0dIqz/AuxZPqCvJZdytYKZfhLJBxPpnW6lzJeRwu5K+Fy2LDbRE0KauLJ1IpeejqHfxUNnl6/cVJOBGNlG35Lm+f4pSAOm2Bnf3u6SCyTSoV6jLDMUaIZWWBnZ8IcPjPDLdt78bnsbOn18fL5OUKxJIECPN31He6cLSJ7/C7++v7rAEOHv1ZrTtZ3V8wxt7TS99Dp7Cyl1SRHSvH4F8zvu93jwO2oTc1DNVGhnhK5MBvJG6Ost4XclfA4bITjax/qCcWSSJm//+o5s1nIljw5/BY9bcZvMB0uf4HXqgA+PjbPzv523E5bukgsE28JxisXRlhmuULr/uFRnA4bOze0s7XXmP/52TALscSKaqpzYY0PXLdh2XaX3cZ9GQaxVsVbUPxdpq7LZQ5WsZXNsYROsS1mg7EE89FEzSqcq40y/CWgJXXG52N8+IaBZc/V60JuPrxVkh9YjQWzc1Y+L9aKa29dt5rHXxmhNl2XzEcSzIY1LgViXL2xgza3I+dCohXqKTedM19YZjassbXXR4fHmb7juTAbQUrypq5GtRRHR+fRdSMDqtPrRGB8Px+/dQu3bO9Nv7aYbJpKs7i4W9h3Nx2Kk1qiPVVsllIskSJHot2KWKGe3iY1/CrUUwKWXrqVFdPudrAQT5atolgLPM7a6KOHzLuMQFTLmSc9MhfBJmBz98qGf50ZDy+3ejcYS5DSJSfGjDTOqzd25A0deZw2BMY6RDnk03vv9bvY1O3DbhP0+F2sa3Ol2yXORRJZawApXRJLpDg2Ok9MS/GNY2Nc1uPj6594F51eFxdmIlktHO02UWPDX1yYbGnYKhxPFp0yHU8W5/GndElESxKIaOxYX7lm8/WEMvxFousyXWV56PQMbW4Hj/3cdezsb2d7X+MdJF6nPS3PvJYEY4bnms/jvxSI0eN3rZq612uGemZD5RVxzZnjOHEpSKcpntfly12VK4QwL5jlfW+ffN9OHnr6aJaaqstu47/ccTl2s7Gv3+1gS48/bfjH5qPMRw39nXhKz/KGXzo7w9RCnN//6R10eo3vZXOPl/FgjKB5p1BLo5+5/0LCZCldMh2KZ0lUW6nSS43/Snfa8USqqKXdUCxJSpcEo8mci/vNgAr1FMl8NIGW1AnHk7x6McA7t/Xgcdq5rAqNQNYCbw1Es1K6TO8zHM+dijk2H101hx+MdE4ov2HJbFhDl4bHf9VAB0IIurz5b/M9ThvhMj3+917Vz9UZDc97/C5+7d3b+JUMDZ02t4MtvT6mQnHC8STxhM5cOEFES2UZfV2XfPPoGJu7vXzwuo3p7UIIdg+0YzPP9FrG9zP3X8gxNx2K86O3p3ni0PllKdPv3tGL12wks1rKtOHxk1VbsxLBWMIw/lI2ZUYPKI+/aGJJ44B9+fwcSV1y6+W9rGtzl1VVWEu8zuo1FcnHv/7kAn/znTeZDWv0+I/y+z+9g4/dsjXrNZPBODds7lr1szxOO26HjUAZhl/XJcFoggszEULxJFdv7MDvdqwowOZzOcqO8c+FNUbnYuweaOcPf9rwVq8caMdmW6xUtjJ7wIjz59PR//G5WSYW4vzm7ZczsETGut3jZEuvn7NT4fTiaq0oJp1zfD6WcyE3qUuOjy7w0Vu28E8vnueF/7q8O5uFltTTBl9L6gXNPzOVc2nxXrOgDH+RWJ7Ki6en2djpYUuPr6G9Ap/LvqatIA8Mj/KnXz+R1mqZDWv8xTdO0u52pj22REpnNqIVnB3V5naUpdczHzXi+8fN+P5VAx10+1cWX/O67GV7/D85N8dUKM4HrhtIf+bS3gNtbkf6bjKX4c8KgwiB3U5O47at189EMJY2vLXCZhO4HavfLSVTOjPheN5jczoUp7/Dg5bSCcaSdObIvgIjlTPz70IMfzCWIGAZ/ias2gUV6imaWMKo6Ds9FebWy3ux20VDr/x7q9hGMBePHTy1TKDLanBicdHMYNnUXZjh7/A6WYgnS24c/29HLvLQ00f59+FR7DbBibFgOoSUD5/LXnYB18Hj4zhsgj1mh7Ft6/xZ3j4YMf52j5Me/+ICr4WlupkOg0jJ//7hWQ4Mjy7bl80maqrDn4nHufp3NxWKo+tG4kQuBro8aYdragWtpkylXK0AYTjdXNi1qoObUacHlOEvmlgyxeEzMwjgndt66fatvgBZz6x1R6R8KXeZ263irULXTTq8DiP+XYLi44HhUf7WDDuBEQd+4tB5nn9zasX3ldtQJBRLcOjMDNdv7sLncuBz2XMaGZfDhsthY0uPj/Oz4azncoVBll5EM+nyueoiL91bwPrIRDBupGHqkqVBHLfDxkN3X5k2/JML+Qsmsz3+1Y+PkJZE141sM1CGX2ESiSc5dGaG3QMd9PhdDR3mAfC77FkdmqrNxjwSDJkpnYs5/CsXb1l0epyEYsmCPLql/PXBN5YZBC2l89+/99aK7/O7HGWFep59Y5KFWJJbtvUAcEV/W944td9c4J0IxrMuNqWobtaDBr7XufJFM5HSmQ3HefqVESKJFB+8biB9werxu/gzs3OdJV8xsUKlfKbHX4jht+pL5qMJ/C47/jx3HI1Oc86qShwYHuWPv/o6wViSaMLw/H9q57paD6ssfGsc6vmdO3fw6a8ez/JUXXbBr717MZNlZNYwXNt6C/P4O31OQiV6/GOB3EZjNcnicr+3r792CZ/LzjWDnWzu8a2owWNk9hgXwYtzEXaaEtGV7NS1lnhdjhUXd6cW4pwaX2Do1BR3XrmeD90wyIduMNZ/On1O3rHVuFhaTtdKvS4yK8Mzvf98LGSkGXf6nA19N78SNZmVEOL3hRDHhRCvCyG+LISo+/upA8OjPLz/KEHTIwjHUzxx+DzPHBuv8cjKw+dypPvHrgW371rPL9y8OWvb+67qT5/MYGivdHmdeFyF+SVdXicRLZXTi9RXmVu+O7bVjKfP7Sg51BPVUrzw9jQ3XdbNunb3qkVCfvdiuvC5GSPco+sSl325994IleNGtfjyu6UDw6O869HneNejz6Wb2n9kSYpm5u/S4XHgsttWNPyZzkAhjoF1QZqPJlZd52lk1tzwCyEGgd8B9koprwHswM+v9TiK5bGDp5a1VNRWiKc2CpXSnSmUUDzBoLlo+9ErHdiFIKmTzqIAI4e/mBBat8+NBGaX6PXouuTo6DynxhdyvxH4+Zs3LzOgHufqbQ7b3I6SZZkPHh8nltB51451XDvYuWxBdyl+l4NOr5NunzMdBht6c4qpkMbtO9bR43fVvQR4JrnWR6w+AqOBKBKjXWk8qfPqxUD6NXa7oD/juBBC0NvmSvcVzsWzJyd46OmjfGIozgNPvJxz4TsTa1xW3+VmpVahHgfgFUIkAB9wqUbjKJhCFiUbkUzRrHwpcZUkGEtyyQyvbOsQ7Oxv4+hIgJ+9aRPheBK/28HEQryoUnkr9TJTzEvXJcdG55k2Mz4Gw95lJ3JKl9ywuYsPXLeR/aZBKLT/sM9lJ6YV1zvW6ow1GohiE+Bz2QqqpLXizJf1+Dg/E2E2rPH0KyNcPdDBR2/ZghCC6zd3Ncx6U67akVx9BJK6IVhn6Qz1t3uWhV7Wtbnz6jQdGB7l758/mw4rToe0FVU8dVP+QpeS+VgiXRXejKy54ZdSjgoh/ga4AESB70gpv7P0dUKIB4AHAPr7+xkaGippf6FQqOT3ZtLjEczEllf+9XhERT6/UCo1H4vzl4xb7qEXXmSDvzI3gFYXslyE4kkuXkziENBjj7Pbr7N/PMXFU0eRYw7sNpgOxriuK1nwPOcnjTm8dfJ1PNPGHVg0kUpruAMcusiyhTpDkyWFO2wYnE/f7GSw3YZn/i2GhlZe3B0b0ZDAd58bSt8xrPTbvHgpwRdf17CanekS/uLrJxg5+za3bVz9ghuLJ5ExjbF5yUNPHwVgly9M7OLrCAHHJyt/Klf6WLMIB+IEw6msz86lWQTGAnb0gmGsx8btTL6ZfVw5EzHGwnrOcf75UARticBbNJHiz7/6Gl3zy39fXRrJGwuaJKVLnKHJNT23i6Hc32bNDb8Qohv4MLANCAD/JoT4qJTyXzJfJ6V8HHgcYO/evXLfvn0l7W9oaIhS35vJpztH+eS/vbZMH+TTH76WfWt4a12p+VjEXh+Do69w7Z6buHpjZ9mftxBLcGp8gb0ZMXuLeDLF829OM/Hmmwx0JXC4dfZet4P9p1/nlN7Pzbu30O11kjz4fa7duZ19+3YUtE/HW1Pwyo+hdyu3334Fx0bnmQzGWWpOt65vy8oUensyxLnpMBdHzuF3zXHZruu4arCTwQIWR885z/LUmyfYe8u70ncSK/02f/TocyztcKnp8M0Ldj71i7nfk8l//+6bnJh9O2vbN85JegY2ct+Nm7hqY+6K3nKo9LFm8Z25Y7w+N5b12YOHn8tp/Hv8LryXXYvPbee2y5cnUjwzc5Q3j17KOc7Zb38z5/5nYzLn66dDcV69EGB6LgKc4PLLr2DfvssLndaaUu5vU4vF3fcCZ6WUU1LKBLAfuK0G4yiKe/cMsnfLYsPrgU5PQ8RTV8Nb4cbh0USKQCTBXI5UQytV7lIglpYVWN/uYUOnh9dGAsxHEpw1i5RWa8CSiaXJH4gkeH00yGSeW/+zM+GsLA9L3+fMdIjtfW2mPk9h4S6f25JmLiyls9xQ4Zd/fGGZIqWW0tk/PNpwrQH9OWpHHrx717KqYrfDxm/dcTk3benOWvzPZEOHh1A8lbOvQ38erad8C/fWOWAVb/V1NG+opxaG/wJwixDCJ4zg6F3AyRqMo2gcdhs9PhdP/OrNHHrkroY3+lBaR6SViJlu7dmZ8LLnQrEksUSK2bCWlc9//aZO3pwIMRPSeNtsnL6twBx+gD6/8VnBaGJZt6ZMUinJWxPG56d0yUIsQURLMhaIsb3Pj8thKzhvu9jvLZ+xKTT1Ml91qqF31FgGyu+2Z2nogOFYfe6+a/FkCK/91f3X8Wvv3k6335W3ybxV/5Erzv/Ae7YtX7h35F+4t35LK9Ggr63ukw1LZs0Nv5TyJeAp4BXgmDmGx9d6HKUwPh9lfYc7rQHfDJTSA3UlrJNnNqQtyzFfiCW5NG94uINdXqw10es3dZlaOfO8bRrmrQXm8AN0+ozsoFAB3vdEMMZMKE4goqHrRpWwBLav8xeVvreoK1/Y9/bg3bvSRs2imNTLXD0LANa3u+uiKKsYrGNuqZd+755BrtnYyXWbOvnRw3cW5FhtML36kRx3Tu/ZuZ77b9qUftzhcfDQPfl780bSGT1m1W4B6rCNSk3y+KWUfyKlvFJKeY2U8mNSyvL75lWZZEpnYiFOX5ub9Q12a70SVlZPvhaIxZLpAZ+bzvb6F+KJdEbPxi4vHrOX6eV9bfhcdo6OzDMyF8HnstNZhBF22m343faCwy6nJhbSbRbPTIcRGHcY+fT3c+F1Wp2kCtvnvXsG+e07r0g/Ljb18pPv24lridfrstv4nbuuyPOO+mWli+ZUKF6UBr6VyZRrfSCWSHFZRiOfj92yhdt3rc/7WdZvOR9N4HPZ8Xuat761eWdWYWbCGguxJP2dHnqaqLCj4h5/xudMLcQJxZO0uR1pDf5LgSgOm2B9uxtHTOD3OZmPJLh2sJNjo/Ns6fUV3fxCCEG7x1mQxw8Qiae4qBlrCWemwgx0efC5HLQXcaL73fb0ZxWKFTr43h/czhVFdna6/6bNvDUZ4isvj6TDOz970yD/+Z1bivqcemCldaXpUJx3X1F4Nbx1rIzPLzf88aTOdGhxrSkQSawoTxLLCPV0eZ04chTINQvK8BfI25OLIYjVCm4aiWJDFqux9M7h3HSYawY7083VLwWiDHR68LkdJDAUOOcjCa7b1MlLZ2d5Y2whaxG9UNo8joINP4CUIKXkzFSIPZd1I4RRlFUo6e+tiDul1y7O4XbYilq/yOSnr9rA3i2Li5wDXZ6GC/NA5neX/XvFEinC8VTesFYuetvc2ASM51jbiSdSTIXiCEBgGPSlonaZ+9bNp+YjCTq9Tpy25pRrACXSVjBnp03DX+JJW69UMtQTT6aWdTmaCMaIaMl0q8VL8zE2dnnTJ39/uwenw8Y1ZippUpccPjvLux59btUqy0w6ijT8AJMLccJaiu19frxOe1G6LItea+H7PH4pyI71bXlrHFZj6YUpX9ZKvePN42xY2VgbipiX3Sbo9DpzLn4bHn+cLp+TDrcRu4/nOc4z7z4C0QRdPldTe/zK8BeIpYV+eQP21V0Jl92GTVC00cxFbGmiOoZnfW7a6Gy1mNHjxWcaTptNsLHTw7HR+Sz53dFAlEf2HyvY+Hd4nEV3xDozZaxBXL6ujY4iq5b9pvEqdJ9SSt6eCnFlng5aBe3TvZju6LCLhg05+qzF3SWG35JeKPaC1uN3MR3KNvxW1tDUQpx1bW46XYJAJL/Hb925SWl0Y+v0OvNmEjUDzTuzCnNhNkKb21GUN9IILDYOL9/w50ttHA9GmQ1r6Zz1jZ2erIYgm7p97B8eXdYQO5pIFayF1GUqdEpZeFvtM9MhPE4bA52eosI8ULzG0YXZCOF4iusGSy+SyxxjX7u7YUOOq3n8xSZP9La5mQ1nZ5BZSpzTIdPwu4UR6knqOY8R684tHE+R1CVdPieOBv1+C0EZ/gIZDURZ3+7G57av/uIGo5COSIWQzwjqOubC7mJGT6bh97rsOQu+oPACp06vk5Qui5JmPj0VZluv0fWqmIVdMO6U7EIUfMH8ydlZAK4uw/Bn1hisJONc7+RbH7k0b9xVFzu3vjY3c2Ety6DHEjqJlE4gkqCv3U2nywj1SElOrz+dymk2YOlqYklmUIa/YMbnY6zvcON2NKPht1VkcXe16t/R+ShOu6Cvzb2sUKrYKsulWKmYVnXwasQTKUbmIunQXbunuFCPcadkKzjU85oZyrpyQ3tR+8nEaTe6cTkavt1n7vWR8fk4Dpugu4i0WjDufoKxRNYxHE+mmAkbekrr2lx0ugVhLYWW1HPKdEcz5JgBev3Nk7KdC2X4CyCmGQdRoT1gGw2fc+XGGIVy8Pg4Dz19lF9/4mUeevooh8/MZD0/FoiyocNYzF2qSvlf79m1LE+9mAKnLjPeXWgu//nZCLqE7X1+PE47Lkfxp4LXZS94beSNsSADXd6yOzr53Q7WtTVumAcWY/xLj7mx+aghMV1kplJ/hxtdklW1HUvoaWVWK8YPhmHPdVdo3X0ETLmGdU2szAnK8BfEmekwUsKWnubK6LHwOG1EE+XF+A8Mj/K/hk6n2wHOhjWeOHQ+y/hfChgZPV7X8rumj9y4iQfv3sW6ttK05a2FzkIN8ekMaYhiwzwW+RqKLCWWSHFuOlKU1HQ+2tyOhs3msci3PmItxBaLlf45MrfYjD5mpnKCcUfQ5TYM/1xEW2b448kUKVPF0/L4G0XiulRUHn8BvD1pNPLY3mSpnBZeV+ndpCweO/jGstipJSJ2y/ZeolqK2YjGYJcXf57OWh+/bQu3Xt7LNSXEwS29mkI9/jPTYda3u2n3OEs2/L4C10ZG5iJMheLsHig9zGPR6XU2dJgHDPE1wfLit6kFjS1FSHVYWIY/sxOXlcrpMNM9O007HogkloV6MrPRApEEXmfz9tq1WHF2QogbV3peSvlKZYdTn1jt7i7vb65UTgufy858NPfiaqFcytO71roDsDR6Bjo9OT1+ALfDXpQqZyZW04yFVQz/4TMz7B8eZTas4XLYOHxmhus3d5W0z9V6x1q8cmEOgGs3lbafTPo7Gk+bZylWJtnSZvUz4Tg3byu+eG+jqfSaZfgTKaYXNCMsJkQ61BOIast672YWks1HE3T5mjuVE1b3+F8GXgemzceZR5wE7qzGoOqNczMR3A4bW0o0SvWON0crvGLZ0OnJ2fvU8sTHMjJ6/CtkRpXaBewlM6T05E8u8p0TE9y3ZzDducni8JkZnjh0Pn1noiV1njh0nl0b2vm5vZuXfeZq+F32rJaR+Xh9NAjADZvK73fQ6EbfwuvK1laKJ1MsxJJs6Ch+Hc0KfWUWccWTOlOheDpW73UYmVi5ZBsiWcVbGp1NLtcAq8f4/wAIYnTK+gLwQSnlHea/ljD6ACOzRg9Yb4HNvxsNn8u+rJ9wsTzwnu04l5wsDpvgPjNGn5nR46vw93hgeJS/fOaN9ONc6wsA+4dHc4ajPv+9lbtt5cPnXt5CcCkRLcnZqTBtbkfBGUqtwNJMMstoD3QVv37hcdrxuezpxVyreGs6FE/H6oUQdPqcOYu4Mp0ey+N3NLFcA6xi+KWUn5dSvhv4BLAZeFYI8RUhxA1rMbh64dK8oS/TrPhcqxuw1fipHX38zDUb0o/tNoHTLrjBDKMYGj1ebDaRlcNfCR47eGrZhctaX8hktsxagaX4nKuvjcyGNS7ORdi2zt803nolWPrdTS4UL9eQSZfPmVZcjSdTRLQkES2VtVjc5XUaoZ4lx0o0o2o3YOr0LM0wazYKmp2U8gzwVeA7wM3AzmoOqp7QdclEMMbm7uYM8wD4XI6ytXpiiVT6lvvPPnQ1D929i2hC5+uvXQIWxdlcDlvF46f5DPdsWMtKL116R2JRqifud69u+KcW4ozMRSuS0dNMLPX4x8zfsFTJ8x6/i/loglgiZaZyGheBTMPf7XOZHv+SGL+W4vCZGR56+ihJXfL8W9M8+8ZESeNoFFY8A4UQ24UQnxJCvAT8KfAasFtK+ZU1GV0dMB6MkUjJkrINGgW/y05SlyTy6JgUQjSRYnLBUELsa3dzeV8bP3XFOr53cpLTUyHmIgkGl1TsVoqVDHdmemkiJVlq+lfqyLQa/gJCPafGF0jqkt1laPQ0Iz6XIysV1koOKLUiudfvJhA14vfxZEYqZ5sV6sEI9UQTpFKLx3oypfP8m1M8ceg8c2YOf0RL8VfPvFGUSGCjsZrr9TbwH4FvA4eAy4DfFEL8gRDiD6o9uHogncrZ15ypnEA65l5OuCeqpZgMxrMyIu67cRC7Df7q20b8/bsnJnjJlC6oJLn6tebD57KnF5x7/C7+7MNXl9xC0+dyEE/q6HpufSBdl5w25bxLSVFtZrxLwovj81FsgpJTVde1GR5/REsaHr9p+Ne1u3A7bdjMfspaUieaSKVz+SOJVM61n1hSL1gnqhFZbZXtzyCtndWS96qnTQXHHevLz8GuV9LSzFqKjiKlC8DoX6sldSYXYlke2/FLQVISLLu4EE/yfz/7FoNd3or2K7Y+68+/cYIZs0lJvnh+WEvxdz+/BzAULvet0JFpNTL77ubK+9ZSOhfnjMYzlcjhbyZ8SzLJxoNxun2ukiuS+9rdaEmduYiGLo0Qm89lx+dy4HM5CAvSrTWNzJ4UbWaortJrP43AioZfSvmZNRpH3XJ+JoxdCC5vao+/vGYsluc2uRDnhoxc9f3Do8v0+eOmJ1XpRvX37hlkfbs7PYeHnj6a84TObExeauGWhc809mEtmdPwx5M6F2cjbOzylnRBbWaMdaVFL7vUql0Ly+EYDUTp8bvTqpxghOSmhUjrOWVm9kS0VF5HoZmzsFZdZRNC/IwQ4odCiGnz3w+EEO9fi8HVA+dnIvS1u3E2oTibRbntF6NailjCyMPOXJxba08qU03xvj2DOXvU3pdxwSlWmG0pluZMvgVeLalzwczoaWRtnWrgc9myQj3ToTjrypBJsI678fmYUbwV0tKpnH6Xw4jxmzUicxmZPVEtlfNY8ThLX/tpBFZb3P0/gD8HPgNsN//9KfAZIcQDVR9dHXBxLlpSbnEjUay2/FJi5sIuZC/O9eSJ11bLk8osurlley8fv3VLVjz/47duySrqKtvjX+VOaWw+ykIs2dR3i6Xid2dnks2ENda3ly5FMWAWfk0sxIkmUqbH70rvy5bh8c9nePzRRJJbtvfysVsuS39Wr9/Fo/ddV/G70npitSP/94F3SykzV+SeE0L8DPAC8HjVRlYHSCkZC0S5and/rYdSVbyreK6rYWT0GFkZmeJW9+0ZzKqUtfZVLU9qaeOMW7b3LqvezaRcjz9fQxEwiso+87XjgBHyum5TV1MbkmLxuxxZmWTz0URZ4nMDXcZxN70QZzaskdRlOqPH57IjMCRBfC57VvWu9dttN+W5f+nWLdy1u5/37OwreSyNwGqGXywx+gBIKWdaoRglEEkQ1lJN12d3KeVm9VgZPQDrMwy/ZXQtbZy+Njd/9B92V80AFlNtabeJdPvEUrHi+ksVOg8Mj/LI/mPp7zMQSfDI/mMAyvibZF40re9voLP0O8F1bW4cNqO94lSGHLPdbugC2YQghVHENRfV0FIpdF2mLwAXTWXPzd2+ppdrgNUNf1AIcb2U8rXMjUKI64GF6g2rPjg/axwMVzT5rfriSViaNLOVw9/hcSzT2c/0ui/r9bGzv3rZLcWcsH63o+xK2nxrI48dPLXsImq1kVSG38ByNmKJFOOmxtPGMqrjXQ47HV4n89FEOpWzr92dVoIVwvjX5XMxb3r8mR3ARmajCGGEIZtdoA1WN/x/CHxNCPEF4Ii5bS/wS8BHS92pEKIL+HvgGox00V+VUh4q9fOqxRlTs72axqoeSMf4ywz1rFZ8U43irUyK6ZFabnwfMtI5l3xv+Ravmzk9sFi8LsO4RrRU+nvpL1MWpdtnGP4ps5Cwx+/KOuZcDhtdPidvjMWIJ/UsR+fiXIQNHUZleTP32rVYTavnBQyJBhvwy+Y/G3CL+Vyp/B3wbSnllcD1wMkyPqtqnDFz+Js+1OMsfXFXS+qkUpKphfiq5faVFmdbSjGeWiUMvz8jnTOTfIvXzZweWCxe52KYLG34y2ww0+1zmR6/RrfPhdNuy0qzdTvsdJl3BYmUTiiWafijaVmWVvD4V8vquUxKOSGl/GMp5f3mv09LKcdL3aEQohN4D/APAFJKTUoZKPXzqsm5mTA9ftey8EWzYXn8hTYxySSaSJmFMwnWt7ux2Rb73y6l6h5/EaEeq5inHPLdKeWqJK7monYjknm3NB40PPRyG8xYej1GaqiV0bP4O3icNrp8LlJSshBLpiUawvEks2GNzT3GhVnF+OEAcCOAEOJpKeX9FdjnNmAK+IK5VnAE+F0pZTjzRWa66AMA/f39DA0NlbSzUChU0ntfvJTg28c0khJu+sy3uH+nk9s21r4Ip9T5rISUhobNqdNnGbIVp0+S1CUjc8YJ1BmfJHp+FuG2E4snkUuUDA5PZh9ulZ5LUpcFhatsAn4yWb7HbxWnHT/1NkOpC+n5dAE/v9PGF44bY+n1CO7faadr/i2GhkqTgK4F1TjWLN6aM76bwy8P8+ZYgnYXvPD8D8v6TFc0TiieZHwuxO4eG9ELx3h9woFNGHOJv/EKvrBxrE6cOYFryvB7TweMBd71iUmiF6Y5f8nG+Bv17fWX+9usmtWT8ff2kveyfJ83Ap+QUr4khPg74GHg05kvklI+jpkuunfvXrlv376SdjY0NESx7z0wPMo/P3uMpGm4ZmKSfz6Z4qrdV9V8ca6U+RSC57lv09O/kX37rinqfeemwwy/fBE4zaZtO9i0rYfrN3cxH01w5PwsupnJ2e5x8M4lqZWVnstcWOPI+blVX1fJRWbns9+ib+Nm9u3bnTWf/rEgXzj+PJ98305++84dFdnXWlOtYw2g79I8vPQCO3ZfzfMzZ+nrjJe9r+HEKTj7NgsJGNiwAf/Wjdyxaz1CCIaGhth27Ts47xyH428Q7dyC16wynwxPABe5fNduvD4XO/vbuazORRnL/W1Wu6zJPH+XwwgwIqV8yXz8FOZdRb2wUlZGs+Jx2grqH7uUaFbxljt9a93pdWYpUlY7vg+F36KXIw2wFK/TnjNEZskMb2jiPg7lkE4h1lJZ8grlkFn5u67Njcdpz8rccjvsdGXo9ViMzEVp9zjSlb2tEOpZzfBfL4QICiEWgOvMv4NCiAUhRLCUHZrrAxeFEFbA8y7gRCmfVS1aMSvD47RnpbcVimX4/S6jQXWmgR/o9KY9J98K7RYrRSF5/Ha7odJYKbyu5b1jYbH/6ya1oJuTzFTY2bCWVfhXKhsyssrWtbvSqZwWboeNDq8DAQQii3IiF2YjbOr2pi8SrWD4VxNpq9bZ+gngS0IIF3AG+JUq7ackNnZ5Gc1h5Js5K8PrshMtweOPaWYqp5mRsXQBd8f6NhZiyWUnYTUo5IRd53dXVDfH67TnvFOy7oI2NWmf5nKxFsZD8QSBaKJkHf5MMtNB+9rcy4TzPE47DpuNdo8j7fGndMmlQJQ7r1xUaXU2edtFKLADV6WRUr4qpdwrpbxOSnmvlHL1wOwa8uDdu5Z1a2r2rAyv004kUVxWj5SSWDJlpHK2W+Xx2SebEIJrBztLbqJeDE67bdXMoXVl6MHkwuu055RsmFyIYRciywtVLGL9Thdno0hJRVqbvnJh0Yz85TNv8MLbU1nPux2GuevyuQhEDcM/HoyR1GVWh71W8Pib/9JWAvfuGeR9Vxn6PAIY7PLyufuurfnCbjXxOu1FF3DFkzpaQmfGvFV3Omy4HMsPKZfDlvbwqs1Ki3JCGJ2aKonP7chZ8Ty1EKfD68CR4/tQGBdph01wdtpI5ivX8B8YHuXRZ95IP54Nazy6pIuWzSZwmkVcVqhnxKzO39SzeDffCnn81b//blC6fC68Tjsn//yeWg9lTfC47ASDidVfmEEwlmA6rCGlubC7RsZ9JTZ2ejkzFUZLLm8j2eF15rwwlYPPZU9rw2QyE9bSC4mK3Hicds7NGIa/3DDqYwdPZen7A8QSy3s/uB02urzO9AXHapQzkFE81gqGv/lnWCLjwVha1rUV8Dnty06c1QhGk0wGF3ulrpVXvxI2m2Bznrh6JbN5LHyu3H13Z8Na2QVJzY7HaUtnP5Ur11BoQobHaafb52IhliSZ0rk4F2Gg05Pu5WCzGQJ+zY4y/HmYDMazlCabnXwGbCXmo4nsVM41WMAthE3dXuw54rTVuJD7XY6cIbJAJEFvCzkOpeB12kmZSeJ9ZV6UC5XJcDtsdFq6/NEEI3PRLEehGIXXRqY1ZlkCU6F4OlOlFfC57FmNMVZDSkkwZhh+j9PIlFiLlM1CcNptDC454T1Oe9n6+7nwuR3LLpiJZIpgNFG2MWt2rDvEDo+j7BBcoTIZHqc9nc57YTbCfDTBpu7FY6UVFnZBGf6cSCmZCcUrkmnQKBg9UAs3/GEtRSolmVyI0dfmRgixJkVahXJZj49M1eVKZ/NY+HNcMC/Nx5BQkRTFZsYy1Pk6tRXDvXsG+dx916bv6vo73DkTMtwOW1qn6fVLRilSZkZPK8T3QS3u5sRQ75MtZvjtJFKSlC4LinEGzXS4qYU4m7oNI+urIzE7j9NOf4cnrfVejfg+GAqdidRiJylYLN5SVbsr4zUdhd4K/Tb37hlkS6+PmZDGjVu6c15Q3GZWD8Cx0Xkg2/C3giQzKI8/JxNmN6mBJi7YWopV7FJonH8+miClS6ZDGuvb3Xid9rprKL7FTO202wQ9VcqwydV3dyxgGP7+VWSqWx3L469kSMzy2PPVc3icRoW53SaYDWt0+5y0ZUh0t4rH3xqzLJKJoHXito7H5svTRjAfwWiC2bBGSpeG4a+DjJ6ltHuc9La56PG7qnZRytScsRg3+w9v7G4dx6EULOO8Wh+HYnA5bDjMdou5cDtsRuN1M86/qTs7A0zF+FuY0YBR1NHfQjHaYhqup3RJKJ5MN1hf3+5ZVh5fL2zp9WeJd1WaRY9/8YI5FSy/lWArYAn6VdLwL22+shSH3caPz80wb4Yq35pc4PCZmcXnVVZP63LJvFWv5AFZ76QbYxQQ6lmIJZByUY9mfYe76k1WSqXH72JDFe/cvDlCPYuidbXv31CvHBge5ZvHxgD4h+fPZlXYloPTLlY8Fg8Mj/LFF8+TNHspxBI6Txw6nzb+LhXqaV3G52O0uZc3Dm9mchmwfASjSQ6fmeHpIyMAfO6ZNxg6NbXKu2pHNQtyrNqFzO9tJqSqdlfiwPAoj+w/RtgUt5uLJHhk/7GKGH+Xw7ZiPcljB08tq+rWUjr7zX23VaAlZyOgDH8OJlqsahcWQz2xAgz/V18d5YlD54mZJ9BsWOMvvnmiYl5bI+HNEeqZCcfp8StvPx/V7HfhsttWrCfJV+E7G9aw2yor2V3PKMOfg8mFeEX0wRuJXNkp+finQ+fQUrl1UVqNzN6xFnPhRMXF4JqJava7cNpttK0Q489X4dvjd9Hlc9ZdZlq1UIY/B9MhraWqdiGjMcYqMf54MsV0SMv5XDM3qsmHFVYIm4ZfSkkgqlV1QbnRKVReoRRcDtuyCt5MHrx7V1qeOf0eu4379gy21MVaGf4l6LpkLqJVdUGwHrFCFquFeoLRZN5Ky2ZuVJMPb9rjN0I9c2GNREoquYYVKFReoRScdltWu8Wl3LtnkP/rA7vTx3CP38XHb93CLdt7W0pbqTVWMopgNmLkprdS1S4s5qPn6h+byXw0wX17BvnHH51Fz+jC3OyNavJhhXosj3903lKbVIY/H5aMwmMHT3EpEGVjl5cH7961Zv0u7r1hkIGO5VpO9ZqSXA1aZ6YFMjFv5WC3lvdqeWC5+sdmEowleOe2Hr780gUSukRL6Wzo8PDwz1zZ1I1q8pH+3uJJcC1W7bbaHWOx3LtnkHv3DDI+H1tzaYtc2XqV0AtqJJThX8KIGafe0NVaJ67HaUT9VlvcDUYTjM3HCCdSfOyWLdy+s4+bt/fQUQXly0bAZhN4HLa04R83i7daSe6jHFaKx1cLp92G3SZIZdyytlKYB1SMfxmX5sxb9Rbz2ITIMGB5CMeTJFOSE2OGquFVAx0AdaPDXys8Lns61GOJwm1Shr8gPK7amKDMBV4hSCt2tgrK8C/hkhmjbcXFOY/TvmJWTzBmlLkfvxSkv91NX7sbt9PWEh2LVsLrtBMxL5hTC3GcdtFyoYNScTtqUyTpdi6avnZP5Vty1jutNdsCGJ+P01mF3qyNgMe1csN1Q65a59TEAldv7ASoKw3+WuF12tMhsqlQnC6va8XMEkXtybzgtOJFuvWs2ypMLsRaLt5n4XHYVlzcDUaTvD0ZQkvqXLXRDPPUSdetWuJz2dPf22xYo1tV7dY9ngyPvxV7IyvDv4TpULwlwzxg5KTHtNwN13VdEoonOH4piF0IrtzQDoDPqTx+b8ad0mxYa0kPstGwPH67XdDZIjINmdTM8Ash7EKIYSHEN2o1hlzMhLWWarKeiddpJ5rI7fEvxJLoOpwYC3L5en86Ja5e+uzWEp9rcW1kLqxVrduXonJYMf5uX/V6NdQztfT4fxc4WcP9LyOZ0pmPJFouo8fC67QTzePxB2MJ5qMJLsxG0vF9UBk9YKxzROMpErokrKVa1nFoJCyPvxXDPFAjwy+E2AT8B+Dva7H/fEwG40hat1eqz+XIq8e/EEty0kzjvNpM47TZsmOlrYrfbSeaSBGIGXnhfS3qODQS1nHbqmG5WrlrnwceAtrzvUAI8QDwAEB/fz9DQ0Ml7SgUChX83tMBw+gFL51haOhCSfurNsXMp1jC8zEWwnrOzw9rKY6+peF3QF/4NNGIwG4T/GC89FBPNeeylsxPxQnHkowFdMBGdPwsQ0MXaz2ssmmW3wdyzyUeT/KTyca8Yy33t1nzWQshPgBMSimPCCH25XudlPJx4HGAvXv3yn378r50RYaGhij0vXPDI3D4Ne667Sau39xd0v6qTTHzKZZnZo7y+uzYss/Xdcn3T01w6tBRrt7UhX/LdgB2b+xgsIxCpWrOZS15KfoGz148TUx4AI3b3rGHW7b31npYZdMsvw/knsuZqRDb+9pqM6AyKfe3qcV9+ruADwkhzgFPAncKIf6lBuNYhtVycUOL6fRY+F12YonlMf6wluTCbJT5aCKdxmm3CfpVLBuANo8dXcJM1Aj1bGwxuY9GpRXVZC3W3PBLKR+RUm6SUm4Ffh54Tkr50bUeRy4mgjGEaN0FH6/LjpbS0TNlN4Gnjoykm6wcGB7l8JkZ1ne4cbRIf9LVsIrYJiI6AhhQMf6GoJVaqy5FnbkZTARjdPtcLWvQLFnazAXeA8OjPPrMG+nK1EA0wROHzvPaxUAthliXWNLMk1FJh9eJs0YyBApFodTUwkkph6SUH6jlGDKZWoi3rLcP4LO6cGXINjx28BTxHM2p/+f3T6/p2OoZr+nxT0UkXb7WKwZSNB6t6drmYTrU2sU3lscfy/D4q9kftVnwmx7/TEy2tOOgaByU4c9gLqK1XJP1TKxYdabHn6+moZUXxpZitV/UJfS0UN9WReOiDL9JVEuyEEu2dOckr6mNnhnj/43bt7O0or1V2yzmI1OhtK9defyK+kcZfpPRdCpnCxt+p+XxL+r1vOuKPgY6PGnN/Q0dHj5337Ut2WYxH1aoB2B9e+seP4rGoTHL1qrAyFwEgIEWzsG2slMyNfkXYglCWop3buvhN26/nJ/asU5pzS/Bm2H4VZN1RSOgPH4Ta7Gy1ZqsZ2IZsMxQz+icUbg12OVloNOjjH4OMkM9Ax2te/woGgdl+E3SvVK7W/fEtRpfW20EY4kUZ6fDgPG9qAXd3PgyPP7BFj5+FI2DMvwm48EYdptouabLmVgefyieMv9PMmreCe0e6EineyqycTts6QXwjS28RqRoHJThN5lciNPtc7ZkUwYLy3O1FncXYklG5qK0exxs6fXXcmh1zVdfvYQ0VS7u/vzzHBgere2AFIpVUIbfZHoh3rLa3BYeU2ogbC7uLsQSjMxF2NTlVb1183BgeJRH9h/DUjcaDUR5ZP8xZfwVdY0y/ICUktlIa1ftAthsArfDRtT0+OcjCS4FYgx2e2lTYZ6cPHbw1LLmNdFEKi1qp1DUI8rwA7GETiCSaOmqXQuP005YS5FI6VyYjaCldDZ1+1R8Pw9K0kLRiLS84T8wPMq+v/k+ES3FsycnW/4W3e2wEYmnCJnxfYCt63w4W1SxdDXyZTqpDChFPdPSZ7MVn50IxgEji6XV47Nep9E/1ljYjSAE7Fyft0Nmy/Pg3bvSabAWStJCUe+0tOFX8dnleF12olqKYCzBSCDK+nY3vS2+9rES9+4Z5HP3XcuAmcY52OVVkhaKuqelA7cqPrscj+nxh+JGqOeyHp/K6FmFe/cMcseV63n50AvcdecdtR6OQrEqLe3xq/jsciyPfyYUZ3ohziaV0VMQdptQchaKhqGlDb+Kzy7H57QT1pKMzkWRYObwK8O/GnYhlslXKxT1Skuf0ffuGUTXdT751FF0acRnH7x7V0vHZ70uO7FEihEz3LVtnV9l9BSAy6G+I0Xj0NKGH+D2XeuRwP03DfK3P3dDrYdTc3wuO7GEzshcFLfDxpZ1SqqhEOzK3Vc0EC3vprw5uYCUsKVHGTgwQl1aSmd0Lspgl5cOj2oerlA0Gy1v+N8aDwFGSEMBPrcDLalzcS7Cpm6l0aNQNCMtb/hPTxuGf3ufMvyw2EYwoqXY1O1TGT0KRRPS8ob/3HSEHr+LPlWkBGR3kxpUGT0KRVOy5oZfCLFZCPF9IcQJIcRxIcTvrvUYMrk4G2Ggw0ObRxk4ICu0s61PZfQoFM1ILc7qJPCHUsqrgFuA3xJCXFWDcaDrOqOBKANdnixPt5Wxvodun5P+DtVNSqFoRtbc8Espx6SUr5h/LwAngZokzl+YjRJP6mphN4PXRgIAzEUS/Oa/HGlpwTqFolkR0uoZV4udC7EV+CFwjZQyuOS5B4AHAPr7+2968sknS9pHKBSira0t53PHppL87ZE4f3Cji+vWN0ba4krzKZcXLyX4x9c1kvriNpcNfvkaF7dtrPz3U8251AI1n/qlmeYChc/njjvuOCKl3Lt0e83iG0KINuBp4PeWGn0AKeXjwOMAe/fulfv27StpP0NDQ+R779Fn3wLeZN9t7+C6TV0lff5as9J8yuWPHn0uy+gDaDp884KdT/1i5fdZzbnUAjWf+qWZ5gLlz6cmK3dCCCeG0f+SlHJ/LcYAcHoqhNdpZ2uvr1ZDqCuUWqlC0RrUIqtHAP8AnJRS/re13n8m56bDbOj00K6qUwGlVqpQtAq18PjfBXwMuFMI8ar57/01GAcX56Js7vYqOV0TpVaqULQGax7jl1K+ANTc0obiSWbDGtv7mmfBp1wsVdLPfvMk06E4G5VaqULRlLRs8vpbEwsA7OhXhj+Te/cMcnlfG91+J5u61dqHQtGMtGxZ5slxw/BfPdBZ45HUHzYb+FVBm0LRtLSs4X9zfAG7EFw50F7rodQddptQGj0KRRPTsob/zFSY9R1uPE4lO7wUr9OuOkopFE1My57d52bCbOlRMexcqOYrCkVz05KGP5nSuRSIKg3+PLQrpVKFoqlpScN/cS5KUpfs3NBR66HUJQ4lxaxQNDUteYafnjS6bl29URl+hULRerSk4T9l5vDv7FcZPQqFovVoScP/1sQC3T4nnV61iKlQKFqPljT8p6fCbFGKnAqFokVpOcMvpeTcTFhp9CgUipalpQz/geFRbn30ORZiSb53YkK1FVQoFC1J0yZsHxge5bGDpxgNRBk8/Bz7dq7jqVdGiZstpoKxJI/sPwag1CcVCkVL0ZQe/4HhUR7Zf4xRs3PUaCDKl358MW30LaKJFI8dPFWLISoUCkXNaErD/9jBU0QTqYJeq9oKKhSKVqMpDX8xxly1FVQoFK1GUxr+fMZ8adsv1VZQoVC0Ik1p+PP1jv3Pt1zGYJcXAQx2efncfdeqhV2FQtFyNGVWj2XM01k9qnesQqFQpGlKww+G8b93zyBDQ0Ps27ev1sNRKBSKuqEpQz0KhUKhyI8y/AqFQtFiKMOvUCgULUZNDL8Q4h4hxCkhxNtCiIdrMQaFQqFoVdbc8Ash7MD/BH4GuAr4BSHEVWs9DoVCoWhVauHx3wy8LaU8I6XUgCeBD9dgHAqFQtGSCCnl2u5QiJ8F7pFS/rr5+GPAO6WUv73kdQ8ADwD09/ff9OSTT5a0v1AoRFtb82jvN9N8mmkuoOZTzzTTXKDw+dxxxx1HpJR7l26v2zx+KeXjwOMAQoipO+6443yJH7UOmK7YwGpPM82nmeYCaj71TDPNBQqfz5ZcG2th+EeBzRmPN5nb8iKl7Ct1Z0KIl3Nd8RqVZppPM80F1HzqmWaaC5Q/n1rE+H8C7BBCbBNCuICfB75Wg3EoFApFS7LmHr+UMimE+G3gIGAH/lFKeXytx6FQKBStSk1i/FLKbwHfWqPdPb5G+1krmmk+zTQXUPOpZ5ppLlDmfNY8q0ehUCgUtUVJNigUCkWLoQy/QqFQtBhNbfgbXRNICPGPQohJIcTrGdt6hBDfFUK8Zf7fXcsxFooQYrMQ4vtCiBNCiONCiN81tzfcfIQQHiHEj4UQr5lz+VNz+zYhxEvm8favZtZawyCEsAshhoUQ3zAfN+x8hBDnhBDHhBCvCiFeNrc13LEGIIToEkI8JYR4QwhxUghxa7lzaVrD3ySaQF8E7lmy7WHgWSnlDuBZ83EjkAT+UEp5FXAL8Fvm79GI84kDd0oprwduAO4RQtwC/BXw36WUVwBzwK/Vbogl8bvAyYzHjT6fO6SUN2TkuzfisQbwd8C3pZRXAtdj/EblzUVK2ZT/gFuBgxmPHwEeqfW4SpjHVuD1jMengAHz7wHgVK3HWOK8vgr8dKPPB/ABrwDvxKikdJjbs46/ev+HUUj5LHAn8A1ANPh8zgHrlmxruGMN6ATOYibiVGouTevxA4PAxYzHI+a2RqdfSjlm/j0O9NdyMKUghNgK7AFeokHnY4ZFXgUmge8Cp4GAlDJpvqTRjrfPAw8Buvm4l8aejwS+I4Q4Yup+QWMea9uAKeALZhju74UQfsqcSzMb/qZHGpf7hsrHFUK0AU8DvyelDGY+10jzkVKmpJQ3YHjKNwNX1nZEpSOE+AAwKaU8UuuxVJB3SylvxAj1/pYQ4j2ZTzbQseYAbgT+l5RyDxBmSVinlLk0s+EvWhOoQZgQQgwAmP9P1ng8BSOEcGIY/S9JKfebmxt2PgBSygDwfYxQSJcQwiqKbKTj7V3Ah4QQ5zBk0u/EiCs36nyQUo6a/08C/45xcW7EY20EGJFSvmQ+fgrjQlDWXJrZ8DerJtDXgF8y//4ljFh53SOEEMA/ACellP8t46mGm48Qok8I0WX+7cVYqziJcQH4WfNlDTEXACnlI1LKTVLKrRjnyXNSyv9Mg85HCOEXQrRbfwPvA16nAY81KeU4cFEIscvcdBdwgnLnUuvFiyovjLwfeBMj/vpHtR5PCeP/MjAGJDCu/L+GEXt9FngL+B7QU+txFjiXd2Pcjh4FXjX/vb8R5wNcBwybc3kd+GNz+3bgx8DbwL8B7lqPtYS57QO+0cjzMcf9mvnvuHXuN+KxZo77BuBl83g7AHSXOxcl2aBQKBQtRjOHehQKhUKRA2X4FQqFosVQhl+hUChaDGX4FQqFosVQhl+hUChaDGX4FS2FECJk/r9VCPGLFf7sTy15/GIlP1+hqBTK8Ctala1AUYY/o4o1H1mGX0p5W5FjUijWBGX4Fa3Ko8BPmXrtv2+Krj0mhPiJEOKoEOL/BBBC7BNCPC+E+BpGxSRCiAOm+NdxSwBMCPEo4DU/70vmNuvuQpif/bqpEf+fMj57KENr/UtmhbNCUVVq0mxdoagDHgY+KaX8AIBpwOellO8QQriBHwkhvmO+9kbgGinlWfPxr0opZ025hp8IIZ6WUj4shPhtaQi3LeU+jOrL64F15nt+aD63B7gauAT8CEM354VKT1ahyER5/AqFwfuAj5tSyy9hlMTvMJ/7cYbRB/gdIcRrwGEMIcAdrMy7gS9LQ9FzAvgB8I6Mzx6RUuoYMhZbKzAXhWJFlMevUBgI4BNSyoNZG4XYhyGFm/n4vcCtUsqIEGII8JSx33jG3ynUOalYA5THr2hVFoD2jMcHgd80paMRQuw0lR2X0gnMmUb/Sow2khYJ6/1LeB74T+Y6Qh/wHgzxM4WiJijvQtGqHAVSZsjmixj681uBV8wF1ing3hzv+zbwG0KIkxjt7w5nPPc4cFQI8Yo0ZI0t/h1Dr/81DIXSh6SU4+aFQ6FYc5Q6p0KhULQYKtSjUCgULYYy/AqFQtFiKMOvUCgULYYy/AqFQtFiKMOvUCgULYYy/AqFQtFiKMOvUCgULcb/D2XTGtUVOYoRAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "lb = -np.min(evaluation_history,axis=1)\n", - "ub = -np.max(evaluation_history,axis=1)\n", - "mean = -np.mean(evaluation_history,axis=1)\n", - "\n", - "num_iters = lb.size\n", - "\n", - "plt.figure()\n", - "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", - "plt.plot(mean,'o-')\n", - "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot our results and see the resulting geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", - "plt.figure()\n", - "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", - "ax.add_patch(circ)\n", - "ax.axis('off')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To check the performance of our final structure, we use a CW source at the desired frequency and plot the fields after the struture." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.52,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " size = source_size,\n", - " center=source_center)]\n", - "opt.sim.change_sources(source)\n", - "\n", - "opt.sim.run(until=400)\n", - "plt.figure(figsize=(10,20))\n", - "opt.sim.plot2D(fields=mp.Ez)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.58,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " size = source_size,\n", - " center=source_center)]\n", - "opt.sim.change_sources(source)\n", - "\n", - "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", - "opt.sim.plot2D(fields=mp.Ez)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 5f7c6fd09a1a4f0c53087b118794e5edca0f4744 Mon Sep 17 00:00:00 2001 From: mochen4 Date: Thu, 14 Jul 2022 15:34:45 -0400 Subject: [PATCH 13/26] fix phase (#2132) Co-authored-by: Mo Chen --- python/adjoint/objective.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index e56207cbc..7bfa010d4 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -85,7 +85,7 @@ def _adj_src_scale(self, include_resolution=True): src_center_dtft = np.matmul( np.exp(1j * 2 * np.pi * np.array([src.frequency])[:, np.newaxis] * np.arange(y.size) * dt), y) * dt / np.sqrt(2 * np.pi) - adj_src_phase = np.exp(1j * np.angle(src_center_dtft)) + adj_src_phase = np.exp(1j * np.angle(src_center_dtft)) * self.fwidth_scale if self._frequencies.size == 1: # Single frequency simulations. We need to drive it with a time profile. @@ -99,7 +99,7 @@ def _adj_src_scale(self, include_resolution=True): scale *= 2 return scale - def _create_time_profile(self, fwidth_frac=0.1): + def _create_time_profile(self, fwidth_frac=0.1, adj_cutoff=5): """Creates a time domain waveform for normalizing the adjoint source(s). For single frequency objective functions, we should generate a guassian pulse with a reasonable @@ -109,9 +109,10 @@ def _create_time_profile(self, fwidth_frac=0.1): The user may specify a scalar valued objective function across multiple frequencies (e.g. MSE) in which case we should check that all the frequencies fit in the specified bandwidth. """ + self.fwidth_scale = np.exp(-2j*np.pi*adj_cutoff/fwidth_frac) return mp.GaussianSource( np.mean(self._frequencies), - fwidth=fwidth_frac * np.mean(self._frequencies), + fwidth=fwidth_frac * np.mean(self._frequencies),cutoff=adj_cutoff ) From 2a8e54689f207103a97884bc2f2d6eefc4e47685 Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 14 Jul 2022 12:37:17 -0700 Subject: [PATCH 14/26] mention no interference effects between uncorrelated dipoles in LED tutorial (#2133) * mention no interference effects between uncorrelated dipoles in LED tutorial * Update Custom_Source.md * Update Custom_Source.md Co-authored-by: Steven G. Johnson --- doc/docs/Python_Tutorials/Custom_Source.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/docs/Python_Tutorials/Custom_Source.md b/doc/docs/Python_Tutorials/Custom_Source.md index 4dba5c16e..699027627 100644 --- a/doc/docs/Python_Tutorials/Custom_Source.md +++ b/doc/docs/Python_Tutorials/Custom_Source.md @@ -17,7 +17,9 @@ This tutorial example involves computing the radiated [flux](../Introduction.md# ![](../images/LED_layout.png)
-One can take two different approaches to computing the radiated flux based on the type of emitter: (1) random or (2) deterministic. In Method 1 (brute-force Monte Carlo), each emitter is a white-noise dipole: every timestep for every dipole is an independent random number. A single run involves all $N$ dipoles which are modeled using a `CustomSource`. The stochastic results for the radiated flux are averaged over multiple trials/iterations via [Monte Carlo sampling](https://en.wikipedia.org/wiki/Monte_Carlo_method). Method 2 exploits the property of [linear time-invariance](https://en.wikipedia.org/wiki/Linear_time-invariant_system) of the materials/geometry and involves a sequence of $N$ separate runs each with a single deterministic dipole (i.e., pulse time profile, `GaussianSource`) at different positions in the emitting layer. Because dipoles at different positions are uncorrelated, the radiated flux from the ensemble is simply the average of all the individual iterations. The two approaches converge towards identical results, but Method 1 is more computationally expensive than Method 2 due to the much larger number of trials/iterations ($\gg N$) required to attain low noise variance. (Even more sophisticated deterministic methods exist to reduce the number of separate simulations, especially at high resolutions; for example, replacing the point-dipole sources with a [rapidly converging set of smooth basis functions](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.81.012119) as demonstrated below, or fancier methods that exploit [trace-estimation methods](http://doi.org/10.1103/PhysRevB.92.134202) and/or transform volumetric sources to [surface sources](http://doi.org/10.1103/PhysRevB.88.054305).) +One can take two different approaches to computing the radiated flux based on the type of emitter: (1) random or (2) deterministic. In Method 1 (brute-force Monte Carlo), each emitter is a white-noise dipole: every timestep for every dipole is an independent random number. A single run involves all $N$ dipoles which are modeled using a `CustomSource`. The stochastic results for the radiated flux are averaged over multiple trials/iterations via [Monte Carlo sampling](https://en.wikipedia.org/wiki/Monte_Carlo_method). Method 2 exploits the property of [linear time-invariance](https://en.wikipedia.org/wiki/Linear_time-invariant_system) of the materials/geometry and involves a sequence of $N$ separate runs each with a single deterministic dipole (i.e., pulse time profile, `GaussianSource`) at different positions in the emitting layer. Because dipoles at different positions are uncorrelated, the radiated flux from the ensemble is simply the average of all the individual iterations. (The interference terms between different dipoles integrate to zero when averaging over all possible phases.) The two approaches converge towards identical results, but Method 1 is more computationally expensive than Method 2 due to the much larger number of trials/iterations ($\gg N$) required to attain low noise variance. (Even more sophisticated deterministic methods exist to reduce the number of separate simulations, especially at high resolutions; for example, replacing the point-dipole sources with a [rapidly converging set of smooth basis functions](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.81.012119) as demonstrated below, or fancier methods that exploit [trace-estimation methods](https://arxiv.org/abs/2111.13046) and/or transform volumetric sources to [surface sources](http://doi.org/10.1103/PhysRevB.88.054305).) + +In principle, to compute the total power emitted upwards in *all* directions, we must also average over all possible Bloch wavevectors $k_x \in [-\pi/s_x,+\pi/s_x]$ (e.g. see [this paper](https://arxiv.org/abs/2111.13046); this is also called an ["array-scanning" method](https://doi.org/10.1109/TAP.2007.897348)). For simplicity, however, in this tutorial we only compute the average for $k_x=0$ (i.e., the portion of the power in all diffraction orders for $k_x=0$, including normal emission). Conversely, if one is only interested in the emitted power in a *single* direction, e.g. normal emission, then it turns out to be possible to compute the net effect using only a *single* "reciprocal" simulation as reviewed [in Yao (2022) in a general setting](https://arxiv.org/abs/2111.13046) or [in Jansen (2010) for the LED case in particular](https://doi.org/10.1364/OE.18.024522). *Note regarding normalization:* To directly compare the results for the radiated flux from the two methods, one might scale the spectrum from Method 2 in post processing to correct for the difference in spectrum between a Gaussian pulse and white noise. However, it is usually more convenient to *nondimensionalize* the results (for both methods) by dividing the flux spectrum for the textured surface with a reference spectrum computed by the *same method*, for example emission from a flat surface or in a homogeneous medium. This way, the details of the source spectra cancel automatically, and the nondimensionalized results can be compared as-is without any tricky scaling calculations. This kind of nondimensionalized comparison is useful to determine the *emission enhancement* (or suppression) of one structure relative to another as well as the *light-extraction efficiency* (the ratio of the radiated flux to the total flux emitted by the dipoles). In order to compute the *absolute* (not relative) light emission by a particular structure, using either Method 1 or Method 2, one would need to rescale the output ([thanks to linearity](http://doi.org/10.1103/PhysRevLett.107.114302)) to convert the input spectrum (from white noise or Gaussian) to the actual emission spectrum (e.g. determined from the gain spectrum of a light-emitting diode). From 21066c2c6642ca8a5d550f9a7948dc99347a80bd Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Wed, 20 Jul 2022 10:03:38 -0700 Subject: [PATCH 15/26] fix bug in plot_boundaries for cylindrical coordinates (#2136) --- python/visualization.py | 46 ++++++++++++++++++++++++++++++----------- 1 file changed, 34 insertions(+), 12 deletions(-) diff --git a/python/visualization.py b/python/visualization.py index e3c6b6f09..4d6d1eb10 100644 --- a/python/visualization.py +++ b/python/visualization.py @@ -219,9 +219,16 @@ def get_2D_dimensions(sim, output_plane): return sim_center, sim_size -def box_vertices(box_center, box_size): - xmin = box_center.x - 0.5*box_size.x - xmax = box_center.x + 0.5*box_size.x +def box_vertices(box_center, box_size, is_cylindrical=False): + # in cylindrical coordinates, radial (R) axis + # is in the range (0,R) rather than (-R/2,+R/2) + # as in Cartesian coordinates. + if is_cylindrical: + xmin = 0 + xmax = box_size.x + else: + xmin = box_center.x - 0.5*box_size.x + xmax = box_center.x + 0.5*box_size.x ymin = box_center.y - 0.5*box_size.y ymax = box_center.y + 0.5*box_size.y zmin = box_center.z - 0.5*box_size.z @@ -251,7 +258,9 @@ def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, la size = volume.size center = volume.center - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(center, size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(center, + size, + sim.is_cylindrical) # Add labels if requested if label is not None and mp.am_master(): @@ -376,7 +385,9 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): # Get domain measurements sim_center, sim_size = get_2D_dimensions(sim, output_plane) - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, sim_size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, + sim_size, + sim.is_cylindrical) if eps_parameters['resolution']: grid_resolution = eps_parameters['resolution'] @@ -436,12 +447,14 @@ def plot_boundaries(sim, ax, output_plane=None, boundary_parameters=None): else: boundary_parameters = dict(default_boundary_parameters, **boundary_parameters) - def get_boundary_volumes(thickness, direction, side, cylindrical=False): + def get_boundary_volumes(thickness, direction, side): from meep.simulation import Volume thickness = boundary.thickness - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, sim.cell_size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, + sim.cell_size, + sim.is_cylindrical) if direction == mp.X and side == mp.Low: return Volume(center=Vector3(xmin+0.5*thickness, @@ -450,7 +463,7 @@ def get_boundary_volumes(thickness, direction, side, cylindrical=False): size=Vector3(thickness, sim.cell_size.y, sim.cell_size.z)) - elif direction == mp.X and side == mp.High: + elif (direction == mp.X and side == mp.High) or direction == mp.R: return Volume(center=Vector3(xmax-0.5*thickness, sim.geometry_center.y, sim.geometry_center.z), @@ -472,14 +485,20 @@ def get_boundary_volumes(thickness, direction, side, cylindrical=False): thickness, sim.cell_size.z)) elif direction == mp.Z and side == mp.Low: - return Volume(center=Vector3(sim.geometry_center.x, + xcen = sim.geometry_center.x + if sim.is_cylindrical: + xcen += 0.5*sim.cell_size.x + return Volume(center=Vector3(xcen, sim.geometry_center.y, zmin+0.5*thickness), size=Vector3(sim.cell_size.x, sim.cell_size.y, thickness)) elif direction == mp.Z and side == mp.High: - return Volume(center=Vector3(sim.geometry_center.x, + xcen = sim.geometry_center.x + if sim.is_cylindrical: + xcen += 0.5*sim.cell_size.x + return Volume(center=Vector3(xcen, sim.geometry_center.y, zmax-0.5*thickness), size=Vector3(sim.cell_size.x, @@ -572,7 +591,9 @@ def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=N # Get domain measurements sim_center, sim_size = get_2D_dimensions(sim, output_plane) - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, sim_size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, + sim_size, + sim.is_cylindrical) if sim_size.x == 0: # Plot y on x axis, z on y axis (YZ plane) @@ -662,7 +683,8 @@ def plot3D(sim): if sim.dimensions < 3: raise ValueError("Simulation must have 3 dimensions to visualize 3D") - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, sim.cell_size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, + sim.cell_size) Nx = int(sim.cell_size.x * sim.resolution) + 1 Ny = int(sim.cell_size.y * sim.resolution) + 1 From 290e873ca9772a9da932b8200fd5b5f82bbd8a85 Mon Sep 17 00:00:00 2001 From: Ardavan Oskooi Date: Thu, 21 Jul 2022 11:02:43 -0700 Subject: [PATCH 16/26] prepare for 1.24 release (#2143) --- NEWS.md | 10 ++++++++++ configure.ac | 4 ++-- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index 3ee3d0c63..e3023cdec 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,15 @@ # Meep Release Notes +## Meep 1.24.0 + +7/21/2022 + +* Support for adjoint gradients of local density of states (LDOS) ([#2077]). + +* Improvements to memory usage of adjoint solver ([#1855]). + +* Various bugfixes ([#1959], [#2044], [#2066], [#2073], [#2079], [#2091], [#2095], [#2114]) and additional unit tests ([#2032], [#2049], [#2053], [#2076], [#2082]). + ## Meep 1.23.0 4/6/2022 diff --git a/configure.ac b/configure.ac index e5971eab0..7a206f0e0 100644 --- a/configure.ac +++ b/configure.ac @@ -1,13 +1,13 @@ # Process this file with autoconf to produce a configure script. -AC_INIT([meep],[m4_esyscmd(./version.sh 1.24.0-beta)]) +AC_INIT([meep],[m4_esyscmd(./version.sh 1.24.0)]) AC_CONFIG_SRCDIR(src/step.cpp) # Shared-library version number; indicates api compatibility, and is # not the same as the "public" version number. (Don't worry about this # except for public releases.) Note that any change to a C++ class # definition (in the .hpp file) generally breaks binary compatibility. -SHARED_VERSION_INFO="28:0:0" # CURRENT:REVISION:AGE +SHARED_VERSION_INFO="29:0:0" # CURRENT:REVISION:AGE AM_INIT_AUTOMAKE([foreign color-tests parallel-tests silent-rules 1.11]) AM_SILENT_RULES(yes) From 6aea4415f27af0ad51c9f34a6c452e327615be20 Mon Sep 17 00:00:00 2001 From: "Steven G. Johnson" Date: Thu, 21 Jul 2022 14:08:38 -0400 Subject: [PATCH 17/26] 1.25-beta version bump --- configure.ac | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configure.ac b/configure.ac index 7a206f0e0..3c2bc2201 100644 --- a/configure.ac +++ b/configure.ac @@ -1,6 +1,6 @@ # Process this file with autoconf to produce a configure script. -AC_INIT([meep],[m4_esyscmd(./version.sh 1.24.0)]) +AC_INIT([meep],[m4_esyscmd(./version.sh 1.25.0-beta)]) AC_CONFIG_SRCDIR(src/step.cpp) # Shared-library version number; indicates api compatibility, and is From 319f054918f678c349c99e8e0b339f167d6a9464 Mon Sep 17 00:00:00 2001 From: JE Touma <70023515+jamesetouma@users.noreply.github.com> Date: Wed, 27 Jul 2022 15:14:17 -0500 Subject: [PATCH 18/26] Fix images links (#2151) * Update Basics.md Fix images references. Add HTML code. * Update Custom_Source.md Fix image links. * Update Cylindrical_Coordinates.md Fix images link. * Update Cylindrical_Coordinates.md Fix images links * Update Eigenmode_Source.md Fix images links * Update Cylindrical_Coordinates.md Fix broken link * Update Cylindrical_Coordinates.md Fix equations display * Update Custom_Source.md Fix images links * Update Third_Harmonic_Generation.md Fix images links * Update Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md Fix Images Link * Update Optical_Forces.md Fix Images Link * Update Near_to_Far_Field_Spectra.md Fix Images Links * Fix Images Links * Fix Images Links * Fix Images Links * Fix Images Links * Update Local_Density_of_States.md * Fix Images Links * Fix Images Link * Fix Images Link * Update Basics.md * Update Third_Harmonic_Generation.md * Update Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md * Update Optical_Forces.md * Update Near_to_Far_Field_Spectra.md * Update Multilevel_Atomic_Susceptibility.md * Update Mode_Decomposition.md * Update Material_Dispersion.md * Update Local_Density_of_States.md * Update Gyrotropic_Media.md * Update GDSII_Import.md * Update Frequency_Domain_Solver.md * Update Eigenmode_Source.md * Update Cylindrical_Coordinates.md * Update Custom_Source.md * Update Basics.md * Update Optical_Forces.md * Update Basics.md * Update Third_Harmonic_Generation.md * Update Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md * Update Optical_Forces.md * Update Near_to_Far_Field_Spectra.md * Update Multilevel_Atomic_Susceptibility.md * Update Mode_Decomposition.md * Update Material_Dispersion.md * Update Local_Density_of_States.md * Update GDSII_Import.md * Update Frequency_Domain_Solver.md * Update Eigenmode_Source.md * Update Cylindrical_Coordinates.md * Update Custom_Source.md --- doc/docs/Python_Tutorials/Basics.md | 80 ++++++++++-------- doc/docs/Python_Tutorials/Custom_Source.md | 34 ++++---- .../Cylindrical_Coordinates.md | 38 +++++---- doc/docs/Python_Tutorials/Eigenmode_Source.md | 59 +++++++------- .../Frequency_Domain_Solver.md | 7 +- doc/docs/Python_Tutorials/GDSII_Import.md | 40 ++++----- doc/docs/Python_Tutorials/Gyrotropic_Media.md | 7 +- .../Local_Density_of_States.md | 16 ++-- .../Python_Tutorials/Material_Dispersion.md | 32 ++++---- .../Python_Tutorials/Mode_Decomposition.md | 68 +++++++++------- .../Multilevel_Atomic_Susceptibility.md | 21 ++--- .../Near_to_Far_Field_Spectra.md | 81 +++++++++++-------- doc/docs/Python_Tutorials/Optical_Forces.md | 14 ++-- ..._and_Transmission_in_a_Waveguide_Cavity.md | 66 ++++++++------- .../Third_Harmonic_Generation.md | 14 ++-- 15 files changed, 319 insertions(+), 258 deletions(-) diff --git a/doc/docs/Python_Tutorials/Basics.md b/doc/docs/Python_Tutorials/Basics.md index 8d2fab091..d2a9c28d7 100644 --- a/doc/docs/Python_Tutorials/Basics.md +++ b/doc/docs/Python_Tutorials/Basics.md @@ -51,7 +51,9 @@ geometry = [mp.Block(mp.Vector3(mp.inf,1,mp.inf), The waveguide is specified by a `Block` (parallelepiped) of size $\infty \times 1 \times \infty$, with $ε=12$, centered at (0,0) which is the center of the cell. By default, any place where there are no objects there is air ($ε=1$), although this can be changed by setting the `default_material` variable. The resulting structure is shown below. -
![](../images/Python-Tutorial-wvg-straight-eps-000000.00.png)
+

+ +

We have the structure and need to specify the current sources using the `sources` object. The simplest thing is to add a single point source $J_z$: @@ -120,8 +122,9 @@ plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9 plt.axis('off') plt.show() ``` - -
![](../images/Python-Tutorial-wvg-straight-ez-000200.00.png)
+

+ +

We see that the the source has excited the waveguide mode but has also excited radiating fields propagating away from the waveguide. At the boundaries, the field quickly goes to zero due to the PML. @@ -149,7 +152,9 @@ resolution = 10 Note that we have *two* blocks, both off-center to produce the bent waveguide structure pictured below. As illustrated in the figure, the origin (0,0) of the coordinate system is at the center of the cell, with positive $y$ being downwards, and thus the block of size 12$\times$1 is centered at (-2,-3.5). Also shown in green is the source plane at $x=-7$ which is shifted to $y=-3.5$ so that it is still inside the waveguide. -
![](../images/Tutorial-wvg-bent-eps-000000.00.png)
+

+ +

There are a couple of items to note. First, a point source does not couple very efficiently to the waveguide mode, so we'll expand this into a line source, centered at (-7,-3.5), with the same width as the waveguide by adding a `size` property to the source. This is shown in green in the figure above. An [eigenmode source](../Python_User_Interface.md#eigenmodesource) can also be used which is described in [Tutorial/Optical Forces](Optical_Forces.md). Second, instead of turning the source on suddenly at $t=0$ which excites many other frequencies because of the discontinuity, we will ramp it on slowly. Meep uses a hyperbolic tangent (tanh) turn-on function over a time proportional to the `width` of 20 time units which is a little over three periods. Finally, just for variety, we'll specify the vacuum wavelength instead of the frequency; again, we'll use a wavelength such that the waveguide is half a wavelength wide. @@ -197,11 +202,15 @@ unix% convert ez.t*.png ez.gif We are using an animated GIF format for the output. This results in the following animation: -
![](../images/Tutorial-wvg-ez.gif)
+

+ +

It is clear that the transmission around the bend is rather low for this frequency and structure — both large reflection and large radiation loss are clearly visible. Moreover, since we are operating just barely below the cutoff for single-mode behavior, we are able to excite a second *leaky* mode after the waveguide bend, whose second-order mode pattern (superimposed with the fundamental mode) is apparent in the animation. Below, we show a field snapshot from a simulation with a larger cell along the $y$ direction, in which you can see that the second-order leaky mode decays away, leaving us with the fundamental mode propagating downward. -
![](../images/Tutorial-wvg-bent2-ez-000300.00.png)
+

+ +

Instead of doing an animation, another interesting possibility is to make an image from a $x \times t$ slice. To get the $y=-3.5$ slice, which gives us an image of the fields in the first waveguide branch as a function of time, we can use `get_array` in a step function to collect a slice for each time step: @@ -222,8 +231,9 @@ plt.imshow(vals, interpolation='spline36', cmap='RdBu') plt.axis('off') plt.show() ``` - -
![](../images/Python-Tutorial-wvg-bent-ez-tslice.png)
+

+ +

#### Output Tips and Tricks @@ -412,8 +422,9 @@ if mp.am_master(): plt.legend(loc="upper right") plt.show() ``` - -
![](../images/Tut-bend-flux.png)
+

+ +

We should also check whether our data is converged. We can do this by increasing the resolution and cell size and seeing by how much the numbers change. In this case, we'll try doubling the cell size: @@ -615,8 +626,9 @@ cbar.set_ticks([t for t in np.arange(0,0.4,0.1)]) cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,0.4,0.1)]) plt.show() ``` - -
![](../images/reflectance_angular_spectrum.png)
+

+ +

Mie Scattering of a Lossless Dielectric Sphere ---------------------------------------------- @@ -627,7 +639,9 @@ The scattering cross section ($\sigma_{scat}$) is the scattered power in all dir A schematic of the 2d cross section at $z=0$ of the 3d cell is shown below. -
![](../images/mie_scattering_schematic.png)
+

+ +

The simulation script is in [examples/mie_scattering.py](https://github.com/NanoComp/meep/blob/master/python/examples/mie_scattering.py). The notebook is [examples/mie_scattering.ipynb](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/mie_scattering.ipynb). As an estimate of runtime, the [parallel simulation](../Parallel_Meep.md) on a machine with three Intel Xeon 4.20 GHz cores takes less than five minutes. @@ -755,7 +769,9 @@ The incident intensity (`intensity`) is the flux in one of the six monitor plane Results are shown below. Overall, the Meep results agree well with the analytic theory. -
![](../images/mie_scattering.png)
+

+ +

Finally, for the case of a *lossy* dielectric material (i.e. complex refractive index) with non-zero absorption, the procedure to obtain the scattering efficiency is the same. The absorption efficiency is the ratio of the absorption cross section ($\sigma_{abs}$) to the cross sectional area of the sphere. The absorption cross section is the total absorbed power divided by the incident intensity. The absorbed power is simply flux into the same box as for the scattered power, but *without* subtracting the incident field (and with the opposite sign, since absorption is flux *into* the box and scattering is flux *out of* the box): omit the `load_minus_flux_data` calls. The extinction cross section ($\sigma_{ext}$) is simply the sum of the scattering and absorption cross sections: $\sigma_{scat}+\sigma_{abs}$. @@ -765,11 +781,8 @@ As an extension of the [Mie scattering example](#mie-scattering-of-a-lossless-di The scattering cross section can be obtained by integrating the differential cross section over all [spherical angles](https://en.wikipedia.org/wiki/Spherical_coordinate_system): -
- $$ \sigma_{scatt} = \int_0^{2\pi} d\phi \int_0^{\pi} \sigma_{diff}(\phi,\theta)\sin(\theta)d\theta $$ -
(In fact, this relationship is essentially the reason for the DCS definition: while the scattering cross section is *total* scattered power divided by incident intensity, the DCS is power *per [solid angle](https://en.wikipedia.org/wiki/Solid_angle)*, such that integrating it over spherical angles gives the total cross section. That's why we compute DCS using the flux density in a given direction multiplied by $R^2$: in the limit $R \to \infty$, this gives the outward flux through an infinitesimal patch of an infinite sphere, divided by the solid angle of the patch. The RCS is similar, but the scattering cross section is the *average* of the RCS over all angles rather than the integral, which gives an additional factor of $4\pi$.) @@ -1004,15 +1017,17 @@ There is one important item to note: in order to eliminate discretization artifa A schematic of the simulation layout generated using [`plot2D`](../Python_User_Interface.md#data-visualization) shows the line source (red), PMLs (green hatch region), `dft_flux` box (solid blue contour line), and `dft_fields` surface (blue hatch region). -
-![](../images/power_density_cell.png) -
+

+ +

+ The spatial map of the absorbed power density shows that most of the absorption occurs in a small region near the back surface of the cylinder (i.e., on the opposite side of the incident planewave). -
-![](../images/power_density_map.png) -
+

+ +

+ Finally, the two values for the total absorbed power which are displayed at the end of the run are nearly equivalent. The relative error between the two methods is ~1.0%. @@ -1084,7 +1099,7 @@ There are six, comma-delimited columns in addition to the label. These results a $$f(t) = \sum_n a_n e^{-i \omega_n t}$$ -for complex amplitudes $a_n$ and complex frequencies ω$_n$. The six columns relate to these quantities. The first column is the *real* part of ω$_n$, expressed in our usual 2πc units, and the second column is the *imaginary* part — a negative imaginary part corresponds to an exponential decay. This decay rate, for a cavity, is more often expressed as a dimensionless "lifetime" $Q$, defined by: +for complex amplitudes $a_n$ and complex frequencies $\omega_n$. The six columns relate to these quantities. The first column is the *real* part of $\omega_n$, expressed in our usual 2πc units, and the second column is the *imaginary* part — a negative imaginary part corresponds to an exponential decay. This decay rate, for a cavity, is more often expressed as a dimensionless "lifetime" $Q$, defined by: $$Q = \frac{\mathrm{Re}\,\omega}{-2 \mathrm{Im}\,\omega}.$$ @@ -1116,11 +1131,12 @@ unix% convert ring-ez-*.png ring-ez-0.118.gif The resulting animations for (from left to right) 0.118, 0.147, and 0.175, are below, in which you can clearly see the radiating fields that produce the losses: -
-![](../images/Tut-ring-ez-0.118.gif) -![](../images/Tut-ring-ez-0.147.gif) -![](../images/Tut-ring-ez-0.175.gif) -
+

+ + + +

+ Each of these modes is, of course, doubly-degenerate according to the representations of the $C_{\infty\mathrm{v}}$ symmetry group. The other mode is simply a slight rotation of this mode to make it *odd* through the $x$ axis, whereas we excited only the *even* modes due to our source symmetry. Equivalently, one can form clockwise and counter-clockwise propagating modes by taking linear combinations of the even/odd modes, corresponding to an angular $\phi$ dependence $e^{\pm i m\phi}$ for m=3, 4, and 5 in this case. @@ -1185,9 +1201,9 @@ from mayavi import mlab s = mlab.contour3d(eps_data, colormap="YlGnBu") mlab.show() ``` +

+ +

-
-![](../images/prism_epsilon.png) -
Alternatively, the permittivity can be visualized from outside of Python. This involves writing the permittivity data to an HDF5 file using [`output_epsilon`](../Python_User_Interface.md#output-functions). The HDF5 data is then converted to [VTK](https://en.wikipedia.org/wiki/VTK) via [h5tovtk](https://github.com/NanoComp/h5utils/blob/master/doc/h5tovtk-man.md) of the [h5utils](https://github.com/NanoComp/h5utils) package. VTK data can be visualized using Mayavi or Paraview. diff --git a/doc/docs/Python_Tutorials/Custom_Source.md b/doc/docs/Python_Tutorials/Custom_Source.md index 699027627..01f026cc0 100644 --- a/doc/docs/Python_Tutorials/Custom_Source.md +++ b/doc/docs/Python_Tutorials/Custom_Source.md @@ -13,9 +13,9 @@ In addition to the two source time profiles of a [continuous wave](../Python_Use This tutorial example involves computing the radiated [flux](../Introduction.md#transmittancereflectance-spectra) from $N=10$ dipole emitters of a 2d LED-like periodic structure with a thin (1d) light-emitting layer. A schematic of the unit cell geometry and simulation layout is shown below. A silver (Ag) back reflector is used to direct nearly all the flux upwards into the $+y$ direction. PML is used to terminate the air region at the top of the cell. (Note: PML is not necessary at the bottom of the cell due to the Ag layer which is effectively a lossless mirror with [skin depth](https://en.wikipedia.org/wiki/Skin_effect) of a few nanometers at a wavelength of 1 μm.) The emitting layer is placed within a lossless dielectric substrate with wavelength-independent refractive index of 3.45. -
-![](../images/LED_layout.png) -
+

+ +

One can take two different approaches to computing the radiated flux based on the type of emitter: (1) random or (2) deterministic. In Method 1 (brute-force Monte Carlo), each emitter is a white-noise dipole: every timestep for every dipole is an independent random number. A single run involves all $N$ dipoles which are modeled using a `CustomSource`. The stochastic results for the radiated flux are averaged over multiple trials/iterations via [Monte Carlo sampling](https://en.wikipedia.org/wiki/Monte_Carlo_method). Method 2 exploits the property of [linear time-invariance](https://en.wikipedia.org/wiki/Linear_time-invariant_system) of the materials/geometry and involves a sequence of $N$ separate runs each with a single deterministic dipole (i.e., pulse time profile, `GaussianSource`) at different positions in the emitting layer. Because dipoles at different positions are uncorrelated, the radiated flux from the ensemble is simply the average of all the individual iterations. (The interference terms between different dipoles integrate to zero when averaging over all possible phases.) The two approaches converge towards identical results, but Method 1 is more computationally expensive than Method 2 due to the much larger number of trials/iterations ($\gg N$) required to attain low noise variance. (Even more sophisticated deterministic methods exist to reduce the number of separate simulations, especially at high resolutions; for example, replacing the point-dipole sources with a [rapidly converging set of smooth basis functions](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.81.012119) as demonstrated below, or fancier methods that exploit [trace-estimation methods](https://arxiv.org/abs/2111.13046) and/or transform volumetric sources to [surface sources](http://doi.org/10.1103/PhysRevB.88.054305).) @@ -172,15 +172,17 @@ plt.show() Results for Method 1 for three different numbers of trials/iterations (10, 50, and 500) are shown in the following three figures. Each trial/iteration involves two runs: one each for the flat and textured surface. As the number of trials/iterations is increased, the "noisiness" in the plot is gradually reduced. However, the total runtime increases significantly. -
-![](../images/stochastic_emitter_trials.png) -
+

+ +

+ The next figure shows a comparison of the normalized radiated flux for Method 1 (500 trials) and 2 (20 runs; 10 runs each for the flat and textured surface). The results show good agreement over the entire bandwidth spectrum. The Method 1 results (labeled "Monte Carlo") required almost *four days* of compute time using an Intel Xeon processor with two single-threaded cores at 3.8 GHz whereas the Method 2 results (labeled "Deterministic") were obtained in 24 minutes. In general, deterministic approaches tend to be more efficient than brute-force Monte Carlo. -
-![](../images/stochastic_emitter_normalized_flux_comparison.png) -
+

+ +

+ One situation in which you may need to perform brute-force Monte Carlo simulations is that of nonlinear or time-varying media, because the equivalence between random and deterministic simulations above relied on linearity and time-invariance. However, in such media one also cannot directly employ white-noise sources, but you must instead input the noise with the correct spectrum for your desired emission process. For example, to [model thermal emission in a nonlinear medium](http://doi.org/10.1103/PhysRevB.91.115406) one must have a noise spectrum consistent with the [fluctuation-dissipation theorem](https://en.wikipedia.org/wiki/Fluctuation-dissipation_theorem), which can be achieved using the `NoisyLorentzianSusceptibility` feature in Meep. @@ -308,9 +310,10 @@ There are three items to note in this script. (1) The line source spans the enti Method 3 requires a convergence check in which $M$ (`nsrc` in the script) is repeatedly doubled until the change in the results are within a desired tolerance of e.g., < 1%. For this example, $M=15$ was found to be sufficient. Note that because a line source with a cosine amplitude function in *homogeneous* media is analogous to generating a planewave at a discrete angle, at each frequency $\omega$ there exists a cutoff $M$ beyond which there are *no* propagating planewaves. The cutoff $M$ can be computed analytically using the grating equation: $\sqrt{\omega^2n^2 - \left(k_x+\frac{M\pi}{L}\right)^2} = 0$, where $n$ is the refractive index of the source medium and $k_x$ is the Bloch-periodic wavevector in the $x$ direction. In this example, $\omega=2\pi$ (pulse center frequency), $L=1.5$, $k_x=0$, and $n=3.45$ for which the largest propagating $M$ is 10. For $M > 10$ the cosine source produces evanescent *waves in the material*, but these waves scatter into other Fourier components (including propagating waves) once they hit the grating. Thus, the source still produces propagating waves, but the amplitude of the propagating waves (and hence the contribution to the power) decreases exponentially for $M > 10$ as the coupling between the source and the grating decreases. This effect is demonstrated in the figure below which is a semilog plot of the $L_2$ norm of the error in the normalized flux of the cosine source relative to the "correct" result computed using 75 point dipoles (Method 2) as a function of the number of terms in the cosine source. The error decreases exponentially for $M > 10$ until $M=12$, at which point it becomes limited by discretization error (both the point-dipole and cosine-source methods have discretization errors at finite resolution, but are discretized slightly differently); as resolution is increased, this minimum difference will go to zero. Note that this analysis is only applicable to periodic structures where the line source extends the entire width of the cell with periodic boundary conditions. A finite length source in a non-periodic cell has no such cutoff and thus will typically require a large number of cosine terms for convergence. -
-![](../images/line_source_DCT_ampfunc_convergence.png) -
+

+ +

+ Results for Methods 2 and 3 for the two cases of a flat and textured surface are generated using the following shell script: ```sh @@ -358,9 +361,10 @@ plt.show() Results for Method 2 and 3 are shown in the following figure. The agreement is good but there is a significant difference in the runtime. Method 2 (labeled "Point Source") involves $N=75$ simulations each for the flat and textured surface for a total of 150 runs which required 10.6 hours. Method 3 (labeled "Line Source") involves $M=15$ simulations for the flat/textured surface for a total of 30 runs which required just 2.0 hours. -
-![](../images/stochastic_emitter_line_normalized_flux_comparison.png) -
+

+ +

+ *Note regarding convergence properties of Method 2:* In this demonstration, the number of point dipoles used in Method 2 is one per pixel. However, because this example is a unit-cell calculation involving *periodic* boundaries, the number of point dipoles (equivalent to the number of simulations) that are actually necessary to obtain results with sufficient accuracy can be significantly reduced. For smooth periodic functions, it is well known that a [trapezoidal rule](https://en.wikipedia.org/wiki/Trapezoidal_rule) converges quite fast — generally even faster than the cosine-series expansion (used in Method 3) and comparable to a cosine+sine Fourier series (not shown). See these [tutorial notes](http://math.mit.edu/~stevenj/trap-iap-2011.pdf) for the mathematical details. In this example, placing one dipole at every fifth pixel for a total of 15 rather than 75 simulations produces nearly equivalent results for the flux spectrum. More generally, an alternative approach for Method 2 would be to sample a set of dipole points and repeatedly double the sampling density until it converges — and in periodic cases, this could have similar or even better efficiency than the cosine-sine series approach if the right density of points is used. Sampling every grid point is usually not necessary. However, this approach to Method 2 has two major limitations: (1) it probably won't converge as quickly for *non-periodic* cases (where a trapezoidal rule becomes much less accurate, unlike Method 3), and (2) repeatedly doubling the number of sampling points might overshoot the minimum number of points by more than if the number of cosine-series terms in Method 3 is increased by one at a time, especially if one is not shooting for extremely high accuracies. diff --git a/doc/docs/Python_Tutorials/Cylindrical_Coordinates.md b/doc/docs/Python_Tutorials/Cylindrical_Coordinates.md index ff081b8c3..2b8847578 100644 --- a/doc/docs/Python_Tutorials/Cylindrical_Coordinates.md +++ b/doc/docs/Python_Tutorials/Cylindrical_Coordinates.md @@ -143,16 +143,20 @@ unix% h5topng -S 2 -Zc dkbluered -C ring-cyl-eps-001200.00.h5 ring-cyl-e Note that, because of the `to_appended` command, the `ring-cyl-ez.h5` file is a 160$\times$18 dataset corresponding to an $r \times t$ slice. Repeating this for all three modes results in the images: -
$E_z$ for $\omega$=0.118 $m$=3 mode: -![](../images/Ring-cyl-ez-0.118.png) +

+ +

$E_z$ for $\omega$=0.148 $m$=4 mode: -![](../images/Ring-cyl-ez-0.148.png) +

+ +

$E_z$ for $\omega$=0.176 $m$=5 mode: -![](../images/Ring-cyl-ez-0.176.png) -
+

+ +

Because only the $\phi$=0 slice is used, the visual distinction between $m$ values is much less than with the 2d simulation. What is apparent is that, as the frequency increases, the mode becomes more localized in the waveguide and the radiating field (seen in the $r \times t$ slice as curved waves extending outward) becomes less, as expected. @@ -163,11 +167,9 @@ For a given mode of the ring resonator, it is often useful to know how sensitive [Pertubation theory for Maxwell equations involving high index-contrast dielectric interfaces](http://math.mit.edu/~stevenj/papers/KottkeFa08.pdf) is reviewed in Chapter 2 of [Photonics Crystals: Molding the Flow of Light, 2nd Edition (2008)](http://ab-initio.mit.edu/book/). The formula (equation 30 on p.19) for the frequency shift $\Delta \omega$ resulting from the displacement of a block of $\varepsilon_1$-material towards $\varepsilon_2$-material by a distance $\Delta h$ (perpendicular to the boundary) is: -
$$ \Delta\omega = -\frac{\omega}{2} \frac{ \iint d^2 \vec{r} \big[ (\varepsilon_1 - \varepsilon_2) |\vec{E}_{\parallel}(\vec{r})|^2 - \big(\frac{1}{\varepsilon_1} - \frac{1}{\varepsilon_2}\big)|\varepsilon\vec{E}_{\perp}|^2\big] \Delta h}{\int d^3\vec{r} \varepsilon(\vec{r})|\vec{E}(\vec{r})|^2} + O(\Delta h^2) $$ -
In this expression, $\vec{E}_{\parallel}(\vec{r})$ is the component of $\vec{E}$ that is parallel to the surface, and $\varepsilon\vec{E}_{\perp}$ is the component of $\varepsilon\vec{E}$ that is perpendicular to the surface. These two components are guaranteed to be continuous across an interface between two isotropic dielectric materials. In this demonstration, $\partial\omega/\partial r$ is computed using this formula and the results are validated by comparing with the finite-difference approximation: $[\omega(r+\Delta r)-\omega(r)]/\Delta r$. @@ -335,9 +337,7 @@ As an alternative to the "ring" sources of the previous examples, it is also pos The calculation of the scattering cross section is described in [Tutorial/Basics/Mie Scattering of a Lossless Dielectric Sphere](Basics.md#mie-scattering-of-a-lossless-dielectric-sphere) which is modified for this example. A linearly-polarized ($x$) planewave is normally incident on a $z$-oriented cylinder which is enclosed by a DFT flux box. Expressed in cylindrical coordinates, an $x$-polarized planewave propagating in the $z$ direction is the sum of two circularly-polarized planewaves of opposite chirality: -
$$ \hat{E}_x = \frac{1}{2} \left[e^{i\phi}(\hat{E}_\rho + i\hat{E}_\phi) + e^{-i\phi}(\hat{E}_\rho - i\hat{E}_\phi)\right] $$ -
(Note: a $y$-polarized planewave involves subtracting rather than adding the two terms above.) @@ -451,10 +451,10 @@ if mp.am_master(): Note that the "closed" DFT flux box is comprised of just three flux objects: two along $z$ and one in the radial $r$ direction. The function `get_fluxes` which computes the integral of the Poynting vector does so over the annular volume in cylindrical coordinates. There is no need for additional post-processing of the flux values. As shown below, the results for the scattering cross section computed using cylindrical coordinates agree well with the 3d Cartesian simulation. However, there is a large discrepancy in performance: for a single Intel Xeon 4.2GHz processor, the runtime of the cylindrical simulation is nearly 90 times shorter than the 3d simulation. +

+ +

-
-![](../images/cylinder_cross_section.png) -
Focusing Properties of a Binary-Phase Zone Plate ------------------------------------------------ @@ -463,15 +463,13 @@ It is also possible to compute a [near-to-far field transformation](../Python_Us Using [scalar theory](http://zoneplate.lbl.gov/theory), the radius of the $n$th zone can be computed as: -
$$ r_n^2 = n\lambda (f+\frac{n\lambda}{4})$$ -
where $n$ is the zone index (1,2,3,...,$N$), $f$ is the focal length, and $\lambda$ is the operating wavelength. The main design variable is the number of zones $N$. The design specifications of the zone plate are similar to the binary-phase grating in [Tutorial/Mode Decomposition/Diffraction Spectrum of a Binary Grating](Mode_Decomposition.md#diffraction-spectrum-of-a-binary-grating) with refractive index of 1.5 (glass), $\lambda$ of 0.5 μm, and height of 0.5 μm. The focusing property of the zone plate is verified by the concentration of the electric-field energy density at the focal length of 0.2 mm (which lies *outside* the cell). The planewave is incident from within a glass substrate and spans the entire length of the cell in the radial direction. The cell is surrounded on all sides by PML. A schematic of the simulation geometry for a design with 25 zones and flat-surface termination is shown below. The near-field monitor is positioned at the edge of the PML and captures the scattered fields in *all* directions. +

+ +

-
-![](../images/zone_plate_schematic.png) -
The simulation script is in [examples/zone_plate.py](https://github.com/NanoComp/meep/blob/master/python/examples/zone_plate.py). The notebook is [examples/zone_plate.ipynb](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/zone_plate.ipynb). @@ -586,6 +584,6 @@ Note that the volume specified in `get_farfields` via `center` and `size` is in Shown below is the far-field energy-density profile around the focal length for both the *r* and *z* coordinate directions for three lens designs with $N$ of 25, 50, and 100. The focus becomes sharper with increasing $N$ due to the enhanced constructive interference of the diffracted beam. As the number of zones $N$ increases, the size of the focal spot (full width at half maximum) at $z = 200$ μm decreases as $1/\sqrt{N}$ (see eq. 17 of the [reference](http://zoneplate.lbl.gov/theory)). This means that doubling the resolution (halving the spot width) requires quadrupling the number of zones. -
-![](../images/zone_plate_farfield.png) -
+

+ +

diff --git a/doc/docs/Python_Tutorials/Eigenmode_Source.md b/doc/docs/Python_Tutorials/Eigenmode_Source.md index c54140087..9643ef603 100644 --- a/doc/docs/Python_Tutorials/Eigenmode_Source.md +++ b/doc/docs/Python_Tutorials/Eigenmode_Source.md @@ -11,9 +11,10 @@ Index-Guided Modes in a Ridge Waveguide The first structure, shown in the schematic below, is a 2d ridge waveguide with ε=12, width $a$=1 μm, and out-of-plane electric field Ez. The dispersion relation ω(k) for index-guided modes with *even* mirror symmetry in the $y$-direction is computed using [MPB](https://mpb.readthedocs.io/en/latest/) and shown as blue lines. The light cone which denotes radiative modes is the section in solid green. Using this waveguide configuration, we will investigate two different frequency regimes: (1) single mode (normalized frequency of 0.15) and (2) multi mode (normalized frequency of 0.35), both shown as dotted horizontal lines in the figures. We will use the eigenmode source to excite a specific mode in each case — labeled **A** and **B** in the band diagram — and compare the results to using a constant-amplitude source for straight and rotated waveguides. Finally, we will demonstrate that a single monitor plane in the $y$-direction is sufficient for computing the total Poynting flux in a waveguide with any arbitrary orientation. -
-![](../images/eigenmode_source.png) -
+

+ +

+ The simulation script is in [examples/oblique-source.py](https://github.com/NanoComp/meep/blob/master/python/examples/oblique-source.py). The notebook is [examples/oblique-source.ipynb](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/oblique-source.ipynb). @@ -178,17 +179,18 @@ if mp.am_master(): Results are shown for the single mode waveguide with one eigenmode **A** (band 1) using a center frequency of `0.15` and multi mode waveguide with two eigenmodes **A** (higher-order mode, band 2) and **B** (fundamental mode, band 1), all with a center frequency of `0.35`. -
-![](../images/single_mode_eigsource_pulse.png) -
+

+ +

+ +

+ +

-
-![](../images/multi_mode_eigsource_pulse_A.png) -
+

+ +

-
-![](../images/multi_mode_eigsource_pulse_B.png) -
These results demonstrate that in all cases the error is nearly 0 at the center frequency and increases roughly quadratically away from the center frequency. The error tends to be smallest for single-mode waveguides because a localized source excitation couples most strongly into guided modes. Note that in this case the maximum error is ~1% for a source bandwidth that is 67% of its center frequency. For the multi-mode waveguide, a much larger scattering loss is obtained for the higher-order mode **A** at frequencies below the center frequency, but this is simply because that mode ceases to be guided around a frequency `≈ 0.3`, and the mode pattern changes dramatically as this cutoff is approached. @@ -200,29 +202,32 @@ The eigenmode source can also be used to launch modes in an oblique/rotated wave Note that an oblique waveguide leads to a breakdown in the [PML](../Perfectly_Matched_Layer.md#breakdown-of-pml-in-inhomogeneous-media). A simple workaround for mitigating the PML reflection artifacts in this case is to double the `thickness` from 1 to 2. -
-![](../images/oblique_source_singlemode.png) -
+

+ +

+ There are numerical dispersion artifacts due to the FDTD spatial and temporal discretizations which create negligible backward-propagating waves by the eigenmode current source, carrying approximately 10-5 of the power of the desired forward-propagating mode. These artifacts can be seen as residues in the field profiles. -
-![](../images/oblique_source_multimode.png) -
+

+ +

+ We can demonstrate that the total power in a waveguide with *arbitrary* orientation — computed using two equivalent methods via `get_fluxes` and [mode decomposition](../Mode_Decomposition.md) — can be computed by a single flux plane oriented along the $y$ direction: thanks to [Poynting's theorem](https://en.wikipedia.org/wiki/Poynting%27s_theorem), the flux through any plane crossing a lossless waveguide is the same, regardless of whether the plane is oriented perpendicular to the waveguide. Furthermore, the eigenmode source is normalized in such a way as to produce the same power regardless of the waveguide orientation — in consequence, the flux values for mode **A** of the single-mode case for rotation angles of 0°, 20°, and 40° are 1111.280794, 1109.565028, and 1108.759159, within 0.2% (discretization error) of one another. Note that the Poynting flux could have been normalized to unity by setting the `EigenModeSource`/`Source` object parameter `amplitude=1/src.fourier_transform(fsrc)` where `fsrc=0.15` and `src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc)`. Finally, we demonstrate that as long as the line source intersects the waveguide *and* `eig_kpoint` is not nearly parallel to the direction of the line source, the mode can be properly launched. As shown in the field profiles below for the single-mode waveguide, there does not seem to be any noticeable distortion in the launched mode as the waveguide approaches glancing incidence to the source plane up to 80°, where the total power in the forward-propagating mode is 97%. Note that the line source spans the entire length of the cell extending into the PML region (not shown). In this example where the cell length is 10 μm (or 10X the width of the waveguide), the maximum rotation angle is ~84°, where the power drops to 59% and backward-propagating fields are clearly visible. -
-![](../images/waveguide_rotation_glancing_small.png) -
+

+ +

+ Increasing the size of the cell improves results at the expense of a larger simulation. The field profiles shown below are for a cell where the length has been doubled to 20 μm. The waveguide power at 84° increases from 59% to 80%. However, as the waveguide mode approaches glancing incidence, sensitivity to discretization errors increases because the mode varies rapidly with frequency on a glancing-angle cross-section, and you will eventually need to increase the resolution as well as the cell size. For waveguide angles much beyond 45° you probably want to simply change the orientation of the line source by 90°. +

+ +

-
-![](../images/waveguide_rotation_glancing.png) -
Planewaves in Homogeneous Media ------------------------------- @@ -285,6 +290,6 @@ Note that the line source spans the *entire* length of the cell. (Planewave sour Shown below are the steady-state field profiles generated by the planewave for the three rotation angles. Residues of the backward-propagating waves due to the discretization are slightly visible. -
-![](../images/eigenmode_planewave.png) -
+

+ +

diff --git a/doc/docs/Python_Tutorials/Frequency_Domain_Solver.md b/doc/docs/Python_Tutorials/Frequency_Domain_Solver.md index aaa8899cb..94012ec0f 100644 --- a/doc/docs/Python_Tutorials/Frequency_Domain_Solver.md +++ b/doc/docs/Python_Tutorials/Frequency_Domain_Solver.md @@ -91,9 +91,10 @@ else: The results are shown in the figure below. The error in the fields decreases monotonically with decreasing tolerance of the frequency-domain solver. The error is converging to an asymptotic limit of `1e-12` which is set by the lowest tolerance. -
-![](../images/CWsolver-python.png) -
+

+ +

+ As a further validation of the frequency-domain solver, we will compare its fields with those computed using time-stepping. This involves taking the Fourier transform of $E_z$ via the `add_dft_fields` routine. After the time stepping, the frequency-domain fields are accessed using [`get_dft_array`](../Python_User_Interface.md#array-slices). diff --git a/doc/docs/Python_Tutorials/GDSII_Import.md b/doc/docs/Python_Tutorials/GDSII_Import.md index 0403c59b6..05c89d559 100644 --- a/doc/docs/Python_Tutorials/GDSII_Import.md +++ b/doc/docs/Python_Tutorials/GDSII_Import.md @@ -11,9 +11,10 @@ S-Parameters of a Directional Coupler The directional coupler as well as the source and mode monitor geometries are described by the GDSII file [`examples/coupler.gds`](https://github.com/NanoComp/meep/blob/master/python/examples/coupler.gds). A snapshot of this file viewed using [KLayout](https://www.klayout.de/) is shown below. The figure labels have been added in post processing. The design consists of two identical [strip waveguides](http://www.simpetus.com/projects.html#mpb_waveguide) which are positioned close together via an adiabatic taper such that their modes couple evanescently. There is a source (labelled "Source") and four mode monitors (labelled "Port 1", "Port 2", etc.). The input pulse from Port 1 is split in two and exits through Ports 3 and 4. The design objective is to find the separation distance which maximizes the outgoing power in Port 4 at a wavelength of 1.55 μm. More generally, though not included in this example, it is possible to have two additional degrees of freedom: (1) the length of the straight waveguide section where the two waveguides are coupled and (2) the length of the tapered section (the taper profile is described by a hyperbolic tangent (tanh) function). -
-![](../images/klayout_schematic.png) -
+

+ +

+ The GDSII file is adapted from the [SiEPIC EBeam PDK](https://github.com/lukasc-ubc/SiEPIC_EBeam_PDK) with four major modifications: @@ -143,9 +144,10 @@ if __name__ == '__main__': For a given waveguide separation distance ($d$), the simulation computes the transmittance of Ports 2, 3, and 4. The transmittance is the square of the [S-parameter](https://en.wikipedia.org/wiki/Scattering_parameters) which itself is equivalent to the [mode coefficient](Mode_Decomposition.md). There is an additional mode monitor at Port 1 to compute the input power from the adjacent eigenmode source; this is used for normalization when computing the transmittance. The eight layers of the GDSII file are each converted to a `Simulation` object: the upper and lower branches of the coupler are defined as a collection of [`Prism`](../Python_User_Interface.md#prism)s, the rectilinear regions of the source and flux monitor as a [`Volume`](../Python_User_Interface.md#volume) and [`FluxRegion`](../Python_User_Interface.md#fluxregion). The size of the cell in the $y$ direction is dependent on $d$. The default dimensionality is 2d. (Note that for a 2d cell the `Prism` objects returned by `get_GDSII_prisms` must have a finite height. The finite height of `Volume` objects returned by `GDSII_vol` are ignored in 2d.) An optional input parameter (`three_d`) converts the geometry to 3d by extruding the coupler geometry in the $z$ direction and adding an oxide layer beneath similar to a [silicon on insulator](https://en.wikipedia.org/wiki/Silicon_on_insulator) (SOI) substrate. A schematic of the coupler design in 3d generated using MayaVi is shown below. -
-![](../images/coupler3D.png) -
+

+ +

+ The coupler properties are computed for a range of separation distances from 0.02 to 0.30 μm with increments of 0.02 μm from the shell command line: @@ -159,9 +161,10 @@ grep trans: directional_coupler.out |cut -d , -f2- > directional_coupler.dat; The transmittance results converted into [insertion loss](https://en.wikipedia.org/wiki/Insertion_loss) for Ports 3 and 4 are shown in the figure below. (There is essentially no flux into Port 2 and thus $|S_{21}|^2$ is not shown in the figure.) When the two waveguide branches are sufficiently separated ($d$ > 0.2 μm), practically all of the input power remains in the top branch and is transferred to Port 3. A small amount of the input power is lost due to scattering into radiative modes within the light cone in the tapered sections where the translational symmetry of the waveguide is broken. This is why the power in Port 3 never reaches exactly 100%. For separation distances of less than approximately 0.2 μm, evanescent coupling of the modes from the top to the lower branch begins to transfer some of the input power to Port 4. For $d$ of 0.13 μm, the input signal is split evenly into Ports 3 and 4. For $d$ of 0.06 μm, the input power is transferred completely to Port 4. Finally, for $d$ of less than 0.06 μm, the evanescent coupling becomes rapidly ineffective and the signal again remains mostly in Port 3. -
-![](../images/directional_coupler_flux.png) -
+

+ +

+ These quantitative results can also be verified qualitatively using the field profiles shown below for $d$ of 0.06, 0.13, and 0.30 μm. To generate these images, the pulse source is replaced with a [continuous wave](../Python_User_Interface.md#continuoussource) (CW) and the fields are time stepped for a sufficiently long run time until they have reached steady state. The [array slicing](../Python_User_Interface.md#array-slices) routines `get_epsilon` and `get_efield_z` are then used to obtain the dielectric and field data over the entire cell. @@ -191,10 +194,10 @@ plt.plot2D(fields=mp.Ez, plt.axis('off') plt.show() ``` +

+ +

-
-![](../images/directional_coupler_field_profiles.png) -
The field profiles confirm that for $d$ of 0.06 μm (Figure 1), the input signal in Port 1 of the top branch is almost completely transferred to Port 4 of the bottom branch. For $d$ of 0.13 μm (Figure 2), the input signal is split evenly between the two branches. Finally, for $d$ of 0.30 μm (Figure 3), there is no longer any evanescent coupling and the signal remains completely in the top branch. The waveguide regions with no fields in Ports 3 and 4 are PML. @@ -202,9 +205,10 @@ The field profiles confirm that for $d$ of 0.06 μm (Figure 1), the input signal No (generally). In the single-run calculation of the reflection coefficent $|S_{11}|^2$ which is based on the back-scattered fields in Port 1 (due to the finite taper/bend length which breaks translational symmetry) given the forward-propagating fields of an eigenmode source also in Port 1, slight discretization errors in the eigenmode-coefficient extraction (as described in paragraph 3 of Section 4.2.2 of this [book chapter](https://arxiv.org/abs/1301.5366)) will result in a "noise floor" below which the reflection cannot be measured in this way. This "noise floor" only applies at a fixed resolution — as the resolution is increased, the discretization error in the mode-coefficient calculation goes away, and $|S_{11}|^2$ should approach the "true" reflection from the taper/bend. This is demonstrated in the figure below which shows a plot of the $S_{11}$ and $S_{21}$ reflectance versus resolution. (In these types of calculations, it is important that the source and mode monitor in the same port be separated by *at least several pixels* in order to avoid any overlap due to the grid discretization.) -
-![](../images/coupler_refl_S11_S12.png) -
+

+ +

+ In the limit of infinite resolution, the discretization error is removed and the reflectance for $S_{11}$ and $S_{21}$ converge to their "true" values of ~10-6 and ~10-8, respectively. (Note that the back-scattered fields in Port 2 are two orders of magnitude smaller than those in Port 1 because the input fields in the upper branch of the directional coupler must cross into the lower branch to reach Port 2.) In this example, $|S_{21}|^2$ requires a resolution of at least ~150 to minimize discretization errors. The discretization errors due to the eigenmode-coefficient extraction can be greatly reduced by using a separate normalization run to compute the incident fields for just a straight waveguide (i.e., no taper/bend) which are then subtracted from the Fourier-transformed fields in Port 1 and 2 of the directional coupler. This procedure is similar to those involving [flux calculations](Basics.md#transmittance-spectrum-of-a-waveguide-bend). (Alternatively, for single-mode waveguides, the mode-coefficient calculation can be replaced entirely with just computing the Poynting flux in the ports. This approach is more accurate at lower resolutions.) For practical applications, however, reflectance values less than 40 dB (e.g., for telecom multi-path interference tolerances) are often considered negligible. On the other hand, there may be theoretical investigations where trying to resolve such small reflections could be important. (As reflections approach 10-15, the limits of floating-point precision will eventually limit accuracy even for the normalization approach.) @@ -370,6 +374,6 @@ Note the absence of `symmetries` even though, in principle, the ring geometry an For this ring geometry, Harminv finds a mode with wavelength 1.5490604 μm and $Q$ of 124691.308. The $H_z$ field profile is shown below. As expected, due to the large $Q$ the mode is tightly confined to the ring and exhibits little radiative loss. -
-![](../images/ring_resonator_gds_Hz.png) -
+

+ +

diff --git a/doc/docs/Python_Tutorials/Gyrotropic_Media.md b/doc/docs/Python_Tutorials/Gyrotropic_Media.md index a10250f05..c3d480289 100644 --- a/doc/docs/Python_Tutorials/Gyrotropic_Media.md +++ b/doc/docs/Python_Tutorials/Gyrotropic_Media.md @@ -118,7 +118,6 @@ plt.show() ``` As shown in the figure below, the results are in excellent agreement: - -
-![](../images/Faraday-rotation-comparison.png) -
+

+ +

diff --git a/doc/docs/Python_Tutorials/Local_Density_of_States.md b/doc/docs/Python_Tutorials/Local_Density_of_States.md index 16859168c..5ceaae47e 100644 --- a/doc/docs/Python_Tutorials/Local_Density_of_States.md +++ b/doc/docs/Python_Tutorials/Local_Density_of_States.md @@ -31,9 +31,10 @@ A key feature of the LDOS in this geometry is that it experiences discontinuitie As shown in the plot below, the results from Meep for both coordinate systems agree well with the analytic theory over the entire range of values of the cavity thickness. -
-![](../images/planar_cavity_purcell_enhancement.png) -
+

+ +

+ The simulation script is [examples/planar_cavity_ldos.py](https://github.com/NanoComp/meep/blob/master/python/examples/planar_cavity_ldos.py). @@ -222,7 +223,7 @@ if __name__ == '__main__': Square Box with a Small Opening ------------------------------- -We consider the simple example of a 2D perfect-metal $a$x$a$ cavity of finite thickness 0.1$a$, with a small notch of width $w$ on one side that allows the modes to escape. The nice thing about this example is that in the absence of the notch, the lowest-frequency $E_z$-polarized mode is known analytically to be $E_z^{(1)}=\frac{4}{a^2}\sin(\pi x/a)\sin(\pi \gamma/a)$, with a frequency $\omega^{(1)}=\sqrt{2}\pi c/a$ and modal volume $V^{(1)}=a^2/4$. The notch slightly perturbs this solution, but more importantly the opening allows the confined mode to radiate out into the surrounding air, yielding a finite $Q$. For $w \ll a$, this radiative escape occurs via an evanescent (sub-cutoff) mode of the channel waveguide formed by the notch, and it follows from inspection of the evanescent decay rate $\sqrt{(\pi/\omega)^2-(\omega^{(1)})^2}/c$ that the lifetime scales asymptotically as $Q^{(1)} \sim e^{\#/\omega}$ for some coefficient \#. +We consider the simple example of a 2D perfect-metal $a$x$a$ cavity of finite thickness 0.1$a$, with a small notch of width $w$ on one side that allows the modes to escape. The nice thing about this example is that in the absence of the notch, the lowest-frequency $E_z$-polarized mode is known analytically to be $E_z^{(1)}=\frac{4}{a^2}\sin(\pi x/a)\sin(\pi \gamma/a)$, with a frequency $\omega^{(1)}=\sqrt{2}\pi c/a$ and modal volume $V^{(1)}=a^2/4$. The notch slightly perturbs this solution, but more importantly the opening allows the confined mode to radiate out into the surrounding air, yielding a finite $Q$. For $w \ll a$, this radiative escape occurs via an evanescent (sub-cutoff) mode of the channel waveguide formed by the notch, and it follows from inspection of the evanescent decay rate $\sqrt{(\pi/\omega)^2-(\omega^{(1)})^2}/c$ that the lifetime scales asymptotically as $Q^{(1)} \sim e^{\\#/\omega}$ for some coefficient \#. We will validate both this prediction and the expression for the LDOS shown above by computing the LDOS at the center of the cavity, the point of peak $|\vec{E}|$, in two ways. First, we compute the LDOS directly from the power radiated by a dipole, Fourier-transforming the result of a pulse using the `dft_ldos` command. Second, we compute the cavity mode and its lifetime $Q$ using Harminv and then compute the LDOS by the Purcell formula shown above. The latter technique is much more efficient for high Q (small $w$), since one must run the simulation for a very long time to directly accumulate the Fourier transform of a slowly-decaying mode. The two calculations, we will demonstrate, agree to within discretization error, verifying the LDOS analysis above, and $Q/V$ is asymptotically linear on a semilog scale versus $1/w$ as predicted. @@ -312,7 +313,6 @@ plt.xlabel('a/w') plt.ylabel('2Q/(πωW) or LDOS') plt.show() ``` - -
-![](../images/Metalcavity_ldos.png) -
+

+ +

diff --git a/doc/docs/Python_Tutorials/Material_Dispersion.md b/doc/docs/Python_Tutorials/Material_Dispersion.md index bd0b5130f..20fb627bc 100644 --- a/doc/docs/Python_Tutorials/Material_Dispersion.md +++ b/doc/docs/Python_Tutorials/Material_Dispersion.md @@ -97,10 +97,10 @@ plt.xticks([t for t in np.arange(0.4,0.9,0.1)]) plt.legend(loc='upper right') plt.show() ``` +

+ +

-
-![](../images/fused_quartz_reflectance_spectrum.png) -
### Permittivity Function of an Artificial Dispersive Material @@ -132,9 +132,10 @@ $$\varepsilon(\omega) = \varepsilon(2\pi f) = 2.25 + \frac{1.1^2 \cdot 0.5}{1.1^ The real and imaginary parts of this dielectric function ε(ω) are plotted below: -
-![](../images/Material-dispersion-eps.png) -
+

+ +

+ We can see that the f=1.1 resonance causes a large change in both the real and imaginary parts of ε around that frequency. In fact, there is a range of frequencies from 1.1 to 1.2161 where ε is *negative*. In this range, no propagating modes exist — it is actually a kind of electromagnetic band gap associated with polariton resonances in a material. For more information on the physics of such materials, see e.g. Chapter 14 of [Introduction to Solid State Physics](http://www.wiley.com/WileyCDA/WileyTitle/productCd-EHEP000803.html) by C. Kittel. @@ -175,23 +176,24 @@ unix% python -u material-dispersion.py | tee material-dispersion.out we can then `grep` for the frequencies and the computed dielectric function, and plot it. First, let's plot the dispersion relation ω(k) for the real part of ω: -
-![](../images/Material-dispersion-bands.png) -
+

+ +

+ The red circles are the computed points from Meep, whereas the blue line is the analytical band diagram from the specified ε(ω). As you can see, we get *two* bands at each *k*, separated by a polaritonic gap (shaded yellow). This dispersion relation can be thought of as the interaction (anti-crossing) between the light line of the ambient ε=2.25 material (dashed black line) and the horizontal line corresponding to the phonon resonance. Similarly, the computed and analytical real parts of the dielectric function are given by: -
-![](../images/Material-dispersion-epsre.png) -
+

+ +

which shows excellent agreement between the analytical (blue line) and numerical (red circles) calculations. The imaginary part, however, is more subtle: -
-![](../images/Material-dispersion-epsim.png) -
+

+ +

The blue line is the analytical calculation from above and the red circles are the numerical value from Meep — why is the agreement so poor? There is nothing wrong with Meep, and this is *not* a numerical error. The problem is simply that we are comparing apples and oranges. diff --git a/doc/docs/Python_Tutorials/Mode_Decomposition.md b/doc/docs/Python_Tutorials/Mode_Decomposition.md index 0c5be2419..d3e44ebe6 100644 --- a/doc/docs/Python_Tutorials/Mode_Decomposition.md +++ b/doc/docs/Python_Tutorials/Mode_Decomposition.md @@ -11,9 +11,10 @@ Reflectance of a Waveguide Taper This example involves computing the reflectance of the fundamental mode of a linear waveguide taper. The structure and the simulation parameters are shown in the schematic below. We will verify that computing the reflectance, the fraction of the incident power which is reflected, using two different methods produces nearly identical results: (1) mode decomposition and (2) [Poynting flux](../Introduction.md#transmittancereflectance-spectra). Also, we will demonstrate that the scaling of the reflectance with the taper length is quadratic, consistent with analytical results from [Optics Express, Vol. 16, pp. 11376-92 (2008)](http://www.opticsinfobase.org/abstract.cfm?URI=oe-16-15-11376). -
-![](../images/waveguide-taper.png) -
+

+ +

+ The structure, which can be viewed as a [two-port network](https://en.wikipedia.org/wiki/Two-port_network), consists of a single-mode waveguide of width 1 μm (`w1`) at a wavelength of 6.67 μm and coupled to a second waveguide of width 2 μm (`w2`) via a linearly-sloped taper of variable length `Lt`. The material is silicon with $\varepsilon=12$. The taper geometry is defined using a single [`Prism`](../Python_User_Interface.md#prism) object with eight vertices. PML absorbing boundaries surround the entire cell. An eigenmode current source with $E_z$ (or $\mathcal{S}$) polarization is used to launch the fundamental mode. The dispersion relation (or "band diagram") of the single-mode waveguide is shown in [Tutorial/Eigenmode Source](Eigenmode_Source.md). There is an eigenmode-expansion monitor placed at the midpoint of the first waveguide. This is a line monitor which extends beyond the waveguide in order to span the entire mode profile including its evanescent tails. The Fourier-transformed fields along this line monitor are used to compute the basis coefficients of the harmonic modes. These are computed separately via the eigenmode solver [MPB](https://mpb.readthedocs.io/en/latest/). This is described in [Mode Decomposition](../Mode_Decomposition.md) where it is also shown that the squared magnitude of the mode coefficient is equivalent to the power (Poynting flux) in the given eigenmode. The ratio of the complex mode coefficients can be used to compute the [S parameters](https://en.wikipedia.org/wiki/Scattering_parameters). In this example, we are computing $|S_{11}|^2$ which is the reflectance (shown in the line prefixed by "refl:,"). Another line monitor could have been placed in the second waveguide to compute the transmittance or $|S_{21}|^2$ into the various guided modes (since the second waveguide is multi mode). The scattered power into the radiative modes can then be computed as $1-|S_{11}|^2-|S_{21}|^2$. As usual, a normalization run is required involving a straight waveguide to compute the power in the source. @@ -133,10 +134,10 @@ if mp.am_master(): plt.ylabel('reflectance') plt.show() ``` +

+ +

-
-![](../images/refl_coeff_vs_taper_length.png) -
The reflectance values computed using the two methods are nearly identical. For reference, a line with quadratic scaling is shown in black. The reflectance of the linear waveguide taper decreases quadratically with the taper length which is consistent with the analytic theory. @@ -153,9 +154,10 @@ A pulsed planewave with $E_z$ (or $\mathcal{S}$) polarization spanning wavelengt The simulation script is in [examples/binary_grating.py](https://github.com/NanoComp/meep/blob/master/python/examples/binary_grating.py). The notebook is [examples/binary_grating.ipynb](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/binary_grating.ipynb). -
-![](../images/grating.png) -
+

+ +

+ ```py import meep as mp @@ -291,9 +293,10 @@ Each diffraction order corresponds to a single angle. In the figure below, this The diffraction orders/modes are a finite set of propagating planewaves. The wavevector $k_x$ of these modes can be computed analytically: for a frequency of $\omega$ (in $c=1$ units), these propagating modes are the **real** solutions of $\sqrt{(\omega^2 n^2 - (k_y+2\pi m/\Lambda)^2)}$ where $m$ is the diffraction order (an integer), $\Lambda$ is the periodicity of the grating, and $n$ is the refractive index of the propagating medium. In this example, $n=1$, $k_y=0$, and $\Lambda=10$ μm. Thus, at a wavelength of 0.5 μm there are a total of 20 diffraction orders of which we only computed the first 10. The wavevector $k_x$ is used to compute the angle of the diffraction order as $\cos^{-1}(k_x/(\omega n))$. (The angle can also be equivalently computed as $\sin^{-1}((k_y+2\pi m/\Lambda)/(\omega n))$.) Evanescent modes, those with an imaginary $k_x$, exist for $|m|>20$ but these modes carry no power. Note that currently Meep does not compute the number of propagating modes for you. If the mode number passed to `get_eigenmode_coefficients` is larger than the number of propagating modes at a given frequency/wavelength, MPB's Newton solver will fail to converge and will return zero for the mode coefficient. It is therefore a good idea to know beforehand the number of propagating modes. -
-![](../images/grating_diffraction_spectra.png) -
+

+ +

+ In the limit where the grating periodicity is much larger than the wavelength and the size of the diffracting element (i.e., more than 10 times), as it is in this example, the [diffraction efficiency](https://en.wikipedia.org/wiki/Diffraction_efficiency) can be computed analytically using scalar theory. This is described in the OpenCourseWare [Optics course](https://ocw.mit.edu/courses/mechanical-engineering/2-71-optics-spring-2009/) in the Lecture 16 (Gratings: Amplitude and Phase, Sinusoidal and Binary) [notes](https://ocw.mit.edu/courses/mechanical-engineering/2-71-optics-spring-2009/video-lectures/lecture-16-gratings-amplitude-and-phase-sinusoidal-and-binary/MIT2_71S09_lec16.pdf) and [video](https://www.youtube.com/watch?v=JmWguqCZRxk). For a review of scalar diffraction theory, see Chapter 3 ("Analysis of Two-Dimensional Signals and Systems") of [Introduction to Fourier Optics (fourth edition)](https://www.amazon.com/Introduction-Fourier-Optics-Joseph-Goodman-ebook/dp/B076TBP48F) by J.W. Goodman. From the scalar theory, the diffraction efficiency of the binary grating is $4/(m\pi)^2$ when the phase difference between the propagating distance in the glass relative to the same distance in air is $\pi$. The phase difference/contrast is $(2\pi/\lambda)(n-1)s$ where $\lambda$ is the wavelength, $n$ is the refractive index of the grating, and $s$ is the propagation distance in the grating (`gh` in the script). A special feature of the binary phase grating is that the diffraction efficiency is 0 for all *even* orders. This is verified by the diffraction spectrum shown above at $\lambda=0.5$ μm. Note the wavelength dependence of the transmittance and, in particular, the slightly *non-zero* diffraction efficiency for the even orders at wavelengths other than 0.5 μm. Since the diffraction efficiency of the ninth order has already fallen to a negligible value (~0.005), computing the spectra of higher-order modes is unnecessary. @@ -301,9 +304,10 @@ To convert the diffraction efficiency into transmittance in the $x$ direction (i Finally, by investigating the transmittance of the zeroth order (at a wavelength of 0.5 μm) in the limit as the grating periodicity approaches zero, we can demonstrate the breakdown of the scalar theory in the wavelength-scale regime which can only be solved using a full-wave method. When the periodicity is much less than the wavelength (i.e., subwavelength), the transmittance can again be solved analytically using effective-medium theory involving a three-layer structure: a layer of the averaged $\varepsilon$ (mean or harmonic mean depending on the polarization $E_z$ or $H_z$) sandwiched between the glass substrate and air. Results are shown in the following figure. -
-![](../images/grating_0th_order_tran.png) -
+

+ +

+ Starting around a grating periodicity of 1.0 μm, the transmittance is no longer zero and increases rapidly with decreasing periodicity. As shown in the inset, for periodicities less than 0.5 μm, the transmittance converges to its asymptotic limit determined by the effective-medium theory: 0.99744 for the $E_z$ and 0.99057 for the $H_z$ polarization. The weak polarization dependence is due to the low index contrast. The oscillations in the data are real and *not* an artifact of the discretization. For example, note the "hump" in the transmittance spectra for the $E_z$ polarization near a grating periodicity of 1.0 μm (twice the wavelength). This feature is associated with a "Wood anomaly" (see e.g. [Hessel and Oliner, 1965](https://www.osapublishing.org/ao/abstract.cfm?uri=ao-4-10-1275)), which occurs when new diffracted orders appear in the far field; for normal incidence, new diffraction orders appear as the period goes through any multiple of the wavelength. @@ -622,9 +626,10 @@ For the supercell shown in the right side of the figure, the lattice vectors are Given a "real" diffracted order specified by $(m_1,m_2)$, we need to determine the equivalent order of the supercell specified by $(n_1,n_2)$ for use in the simulation when computing the mode coefficients. Setting $\vec{k_{SC}}=\vec{k_\parallel}$ and solving for the pair of equations yields: $n_1=m_1$ and $n_2=-m_1+2m_2$. -
-![](../images/triangular_lattice.png) -
+

+ +

+ To demonstrate this in practice, we will compute the power (an absolute quantity) of the $(0,1)$ order of a triangular lattice of cylindrical rods (height: 0.5 µm, radius: 0.1 µm) on a glass ($n=1.5$) substrate with periodicity of 1.0 µm. Note that this is a 3D simulation. In this example, the plane of incidence is $yz$ and the $E_x$ source therefore corresponds to the $\mathcal{S}$ polarization. @@ -876,9 +881,10 @@ The phase of the zeroth-diffraction order is simply the angle of its complex mod The script is run from the shell terminal using: `python binary_grating_phasemap.py -gp 0.35 -gh 0.6 -oddz`. The figure below shows the transmittance spectra (left) and phase map (right). The transmittance is nearly unity over most of the parameter space mainly because of the subwavelength dimensions of the grating. The phase variation spans the full range of $-\pi$ to $+\pi$ at each wavelength but varies weakly with the duty cycle due to the relatively low index of the glass grating. Higher-index materials such as [titanium dioxide](https://en.wikipedia.org/wiki/Titanium_dioxide#Thin_films) (TiO2) generally provide more control over the phase. -
-![](../images/grating_phasemap.png) -
+

+ +

+ See [Tutorials/Near to Far Field Spectra/Focusing Properties of a Metasurface Lens](Near_to_Far_Field_Spectra.md#focusing-properties-of-a-metasurface-lens) for a related example. @@ -889,9 +895,10 @@ As a final demonstration of mode decomposition, we compute the diffraction spect A schematic of the grating geometry is shown below. The grating is a 2d slab in the $xy$-plane with two parameters: birefringence ($\Delta n$) and thickness ($d$). The twisted-nematic grating consists of two layers of thickness $d$ each with equal and opposite rotation angles of $\phi = 70^{\circ}$ for the nematic director. Both gratings contain only three diffraction orders: $m = 0, \pm 1$. The $m=0$ order is linearly polarized and the $m=\pm 1$ orders are circularly polarized with opposite chirality. For the uniaxial grating, the diffraction efficiencies for a mode with wavelength $\lambda$ can be computed analytically: $\eta_0 = \cos^2(\pi\Delta n d/\lambda)$, $\eta_{\pm 1} = 0.5\sin^2(\pi\Delta n d/\lambda)$. The derivation of these formulas is presented in [Optics Letters, Vol. 24, No. 9, pp. 584-6 (1999)](https://www.osapublishing.org/ol/abstract.cfm?uri=ol-24-9-584). We will verify these analytic results and also demonstrate that the twisted-nematic grating produces a broader bandwidth response for the ±1 orders than the homogeneous uniaxial grating. An important property of these polarization gratings for e.g. display applications is that for a circular-polarized input planewave and phase delay ($\Delta n d/\lambda$) of nearly 0.5, there is only a single diffraction order (+1 or -1) with *opposite* chiraity to that of the input. This is also demonstrated below. -
-![](../images/polarization_grating_schematic.png) -
+

+ +

+ In this example, the input is a linear-polarized planewave pulse at normal incidence with center wavelength of $\lambda = 0.54$ μm. The linear polarization is in the $yz$-plane with a rotation angle of 45° counter clockwise around the $x$ axis. Two sets of mode coefficients are computed in the air region adjacent to the grating for each orthogonal polarization: `ODD_Z+EVEN_Y` and `EVEN_Z+ODD_Y`, which correspond to $+k_y + -k_y$ (cosine) and $+k_y - -k_y$ (sine) modes. From these coefficients for linear-polarized modes, the power in the circular-polarized modes can be computed: |ODD_Z+EVEN_Y|2+|EVEN_Z+ODD_Y|2. The power is identical for the two circular-polarized modes with opposite chiralities since the input is linearly polarized and at normal incidence. The transmittance for the diffraction orders are computed from the mode coefficients. Following usual practice, this requires a separate normalization run to compute the power of the input planewave. @@ -1064,10 +1071,9 @@ plt.title("bilayer twisted-nematic grating") plt.show() ``` - -
-![](../images/polarization_grating_diffraction_spectra.png) -
+

+ +

The left figure shows good agreement between the simulation results and analytic theory for the homogeneous uniaxial grating. Approximately 6% of the power in the input planewave is lost due to reflection from the grating. This value is an average over all phase delays. The total transmittance is therefore around 94%. The twisted-nematic grating, with results shown in the right figure, produces ±1 diffraction orders with nearly-constant peak transmittance over a broader bandwidth around $\Delta nd/\lambda=0.5$ than the homogeneous uniaxial polarization grating. This is consistent with results from the reference. The average reflectance and transmittance for the twisted-nematic grating are similar to those for the homogeneous uniaxial grating. @@ -1090,8 +1096,8 @@ Note: when imparting a phase offset using a complex `amplitude`, it is *not* nec The figure below shows a snapshot of $E_z$ for four different cases: phase delays ($\Delta nd/\lambda$) of 0.5 and 1.0, and circular-polarized planewave sources of $E_z + iE_y$ and $E_z - iE_y$. The empty regions on the cell sides are PMLs. The thin solid black line denotes the boundary between the grating (on the left) and air. As expected, for $\Delta nd/\lambda=0.5$ there is just a single ±1 diffraction order which depends on the chirality of the input planewave (this is not the case for a linear-polarized planewave). The angle of this diffracted order (±4.8°) agrees with the analytic result. Snapshots of $E_y$ are similar. -
-![](../images/polarization_grating_diffraction_orders.png) -
+

+ +

For computing the mode coefficient of an [elliptically polarized](https://en.wikipedia.org/wiki/Elliptical_polarization) mode in 3d, one approach is to first compute the mode coefficients of two orthogonal linearly polarized modes ($\mathcal{S}$ and $\mathcal{P}$ polarizations) separately using the [`DiffractedPlanewave`](../Python_User_Interface.md#diffractedplanewave) object which is passed as the `bands` parameter to `get_eigenmode_coefficients`. The complex mode coefficients for these two polarizations can then be combined in post-processing via a linear combination to compute the mode coefficient for any arbitrary elliptically polarized mode. Alternatively, you can specify an elliptical polarization directly in a single `DiffractedPlanewave` object by supplying both $\mathcal{S}$ and $\mathcal{P}$ amplitudes with the desired relative phase. diff --git a/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md b/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md index 1d74b5bd0..64fca8bf6 100644 --- a/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md +++ b/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md @@ -90,9 +90,10 @@ sim.run(mp.after_time(endt-250, print_field), until=endt) The spectra of the field intensity is shown below. -
-![Multilevel meep spectra](../images/multilevel_meep_n0_37_spectra.png) -
+

+ Multilevel meep spectra +

+ There are two lasing modes above threshold in the vicinity of the center transition frequency ω$_a$=40 as we would expect. Remember, when finding the frequency axis that Meep uses a Courant factor of $\Delta t = 0.5 \Delta x$. We have also converted the electric field to SALT units using: @@ -102,12 +103,14 @@ For two-level gain media, $\gamma_\parallel = \gamma_{12} + \gamma_{21}$. We can By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold. -
-![Near threshold comparison](../images/meep_salt_comparison_thresh.png) -
+

+ Near threshold comparison +

+ Further increasing the gain continues to yield good agreement. -
-![Near threshold comparison](../images/meep_salt_comparison_full.png) -
+

+ Near threshold comparison +

+ diff --git a/doc/docs/Python_Tutorials/Near_to_Far_Field_Spectra.md b/doc/docs/Python_Tutorials/Near_to_Far_Field_Spectra.md index 7bdb01bfd..9eb6862cb 100644 --- a/doc/docs/Python_Tutorials/Near_to_Far_Field_Spectra.md +++ b/doc/docs/Python_Tutorials/Near_to_Far_Field_Spectra.md @@ -13,9 +13,10 @@ In this example, we compute the [radiation pattern](https://en.wikipedia.org/wik The simulation geometry is shown in the following schematic. -
-![](../images/Near2far_simulation_geometry.png) -
+

+ +

+ In the first part of the simulation, we define the cell and source as well as the near field and flux regions. Since we are using a pulsed source (with center wavelength of 1 μm), the fields are timestepped until they have sufficiently decayed away. @@ -165,10 +166,9 @@ ax.grid(True) ax.set_rlabel_position(22) plt.show() ``` - -
-![](../images/Source_radiation_pattern.png) -
+

+ +

### Antenna above a Perfect Electric Conductor Ground Plane @@ -180,9 +180,10 @@ We can validate the radiation pattern computed by Meep using analytic theory. Th The simulation script is in [examples/antenna_pec_ground_plane.py](https://github.com/NanoComp/meep/blob/master/python/examples/antenna_pec_ground_plane.py). -
-![](../images/antenna_pec_ground_plane.png) -
+

+ +

+ ```py resolution = 200 # pixels/um @@ -520,9 +521,10 @@ plt.show() The phasemap is shown below. The left figure shows the transmittance which is nearly unity for all values of the duty cycle; the Fresnel transmittance is 0.96 for the glass-air interface. This is expected since the periodicity is subwavelength. The right figure shows the phase. There is a subregion in the middle of the plot spanning the duty-cycle range of roughly 0.16 to 0.65 in which the phase varies continuously over the full range of -2π to 0. This structural regime is used to design the supercell lens. -
-![](../images/metasurface_lens_phasemap.png) -
+

+ +

+ In the second part of the calculation, the far-field energy-density profile of three supercell lens designs, comprised of 201, 401, and 801 unit cells, are computed using the quadratic formula for the local phase. Initially, this involves fitting the unit-cell phase data to a finer duty-cycle grid in order to enhance the local-phase interpolation of the supercell. This is important since as the number of unit cells in the lens increases, the local phase via the duty cycle varies more gradually from unit cell to unit cell. However, if the duty cycle becomes too gradual (i.e., less than a tenth of the pixel dimensions), the `resolution` may also need to be increased in order to improve the accuracy of [subpixel smoothing](../Subpixel_Smoothing.md). @@ -564,15 +566,17 @@ plt.show() Shown below is the supercell lens design involving 201 unit cells. Note that even though periodic boundaries are used in the supercell calculation (via the `k_point`), the choice of cell boundaries in the *y* (or longitudinal) direction is *irrelevant* given the finite length of the lens. For example, PMLs could also have been used (at the expense of a larger cell). Although [`add_near2far`](../Python_User_Interface.md#near-to-far-field-spectra) does support periodic boundaries (via the `nperiods` parameter), it is not necessary for this particular example. -
-![](../images/metasurface_lens_epsilon.png) -
+

+ +

+ The far-field energy-density profile is shown below for the three lens designs. As the number of unit cells increases, the focal spot becomes sharper and sharper. This is expected since the longer the focal length, the bigger the lens required to demonstrate focusing (which means more unit cells). In this example, the largest lens design contains 801 unit cells which corresponds to 0.24 mm or 1.2X the focal length. -
-![](../images/metasurface_lens_farfield.png) -
+

+ +

+ Diffraction Spectrum of a Finite Binary Grating ----------------------------------------------- @@ -752,10 +756,10 @@ plt.title("f.-f. spectra @ λ = {:.1} μm".format(wvl_slice)) plt.tight_layout(pad=0.5) plt.show() ``` +

+ +

-
-![](../images/grating_diffraction_spectra_n2f.png) -
For the case of `nperiods = 1`, three diffraction orders are present in the far-field spectra as broad peaks with finite angular width (a fourth peak/order is also visible). When `nperiods = 10`, the diffraction orders become sharp, narrow peaks. The three diffraction orders are labeled in the right inset of the bottom figure as m=1, 3, and 5 corresponding to angles 2.9°, 8.6°, and 14.5° which, along with the diffraction efficiency, can be computed analytically using scalar theory as described in [Tutorial/Mode Decomposition/Diffraction Spectrum of a Binary Grating](Mode_Decomposition.md#diffraction-spectrum-of-a-binary-grating). As an additional validation of the simulation results, the ratio of any two diffraction peaks $p_a/p_b$ ($a,b = 1,3,5,...$) is consistent with that of its diffraction efficiencies: $b^2/a^2$. @@ -767,9 +771,10 @@ Finally, we can validate the results for the diffraction spectra of a finite gra The simulation setup is shown in the schematic below. The binary grating has $\Lambda = 1$ μm at a wavelength of 0.5 μm via a normally-incident planewave pulse (which must [extend into the PML region in order to span the entire width of the cell](../Perfectly_Matched_Layer.md#planewave-sources-extending-into-pml)). The grating structure is terminated with a flat-surface padding in order to give the scattered field space to decay at the edge of the cell. -
-![](../images/finite_grating_schematic.png) -
+

+ +

+ The simulation script is in [examples/finite_grating.py](https://github.com/NanoComp/meep/blob/master/python/examples/finite_grating.py). The notebook is [examples/finite_grating.ipynb](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/finite_grating.ipynb). @@ -902,13 +907,14 @@ else: Results are shown for two finite gratings with 5 and 20 periods. -
-![](../images/finite_grating_nperiods5.png) -
+

+ +

+ +

+ +

-
-![](../images/finite_grating_nperiods20.png) -
The scattered field amplitude profile (the top figure in each of the two sets of results) shows that the fields decay to zero away from the grating (which is positioned at the left edge of the figure in the region indicated by the bright spots). The middle figure is the field amplitude along a 1d slice above the grating (marked by the dotted green line in the top figure). Note the decaying fields at the edges due to the flat-surface termination. The bottom figure is the Fourier transform of the fields from the 1d slice. As expected, there are only three diffraction orders present at $k_y = 2\pi m/\Lambda$ for $m = 0, \pm 1, \pm 2$. These peaks are becoming sharper as the number of grating periods increases. @@ -921,7 +927,9 @@ Truncation Errors from a Non-Closed Near-Field Surface For this demonstration, we will compute the far-field spectra of a resonant cavity mode in a holey waveguide (a structure introduced in [Tutorial/Resonant Modes and Transmission in a Waveguide Cavity](Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md)) and demonstrate that these fields are *exactly* equivalent to the actual DFT fields at the same location. A schematic of the simulation setup generated using [`plot2D`](../Python_User_Interface.md#data-visualization) is shown below. -![center|Schematic of the computational cell for a holey waveguide with cavity showing the location of the "near" boundary surface and the far-field region.](../images/N2ff_comp_cell.png) +

+ Schematic of the computational cell for a holey waveguide with cavity showing the location of the 'near' boundary surface and the far-field region. +

The script is in [examples/cavity-farfield.py](https://github.com/NanoComp/meep/blob/master/python/examples/cavity-farfield.py). The notebook is [examples/cavity-farfield.ipynb](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/cavity-farfield.ipynb). @@ -1055,8 +1063,13 @@ A closed near-field surface will still disagree with a brute-force far-field cal In this example, to demonstrate agreement between the far fields and DFT fields, there are two requirements: (1) the cell size in the $x$ direction via `dpad` needs to be sufficiently large in order to minimize the impact of the spurious radiation from the edge of the near-field surface and (2) the far-field region needs to be sufficiently close to the near-field surface to minimize discrepancies caused by numerical dispersion. -![center|Comparison of the far fields from the near-to-far field transformation and the DFT fields at the same location for a holey-waveguide cavity.](../images/farfields_vs_DFTfields_holeycavity.png) +

+ Comparison of the far fields from the near-to-far field transformation and the DFT fields at the same location for a holey-waveguide cavity. +

+ When these two conditions are not met as in the example below involving a small `dpad` and large `d2`, the error from the finite truncation and numerical dispersion can be large and therefore result in a significant mismatch between the far fields computed using the near-to-far field transformation versus the actual DFT fields at the same location. -![center|Comparison of the far fields from the near-to-far field transformation dominated by errors and the DFT fields at the same location for a holey-waveguide cavity.](../images/farfields_vs_DFTfields_holeycavity_mismatch.png) \ No newline at end of file +

+ Comparison of the far fields from the near-to-far field transformation dominated by errors and the DFT fields at the same location for a holey-waveguide cavity. +

diff --git a/doc/docs/Python_Tutorials/Optical_Forces.md b/doc/docs/Python_Tutorials/Optical_Forces.md index 02fa842cf..39433bd3f 100644 --- a/doc/docs/Python_Tutorials/Optical_Forces.md +++ b/doc/docs/Python_Tutorials/Optical_Forces.md @@ -4,9 +4,10 @@ This tutorial demonstrates Meep's ability to compute classical forces via the [Maxwell stress tensor](https://en.wikipedia.org/wiki/Maxwell_stress_tensor). The geometry consists of two identical, parallel, silicon waveguides with square cross section in vacuum. A schematic of the geometry is shown below. Due to the parallel orientation of the waveguides (propagation axis along $z$ and separation in $x$), the two modes can be chosen to be either symmetric or anti-symmetric with respect to an $x$ mirror-symmetry plane between them. As the two waveguides are brought closer and closer together, their modes couple and give rise to a gradient force that is *transverse* to the waveguide axis (i.e., in the $x$ direction). This is different from [radiation pressure](https://en.wikipedia.org/wiki/Radiation_pressure) which involves momentum exchange between photons and is longitudinal in nature (i.e., along the $z$ direction). An interesting phenomena that occurs for this coupled waveguide system is that the force can be tuned to be either attractive or repulsive depending on the relative phase of the two modes. This tutorial will demonstrate this effect. -
-![](../images/Waveguide_forces.png) -
+

+ +

+ The gradient force $F$ on each waveguide arising from the evanescent coupling of the two waveguide modes can be computed analytically: @@ -132,9 +133,10 @@ plt.show() The following figure shows the $E_y$ mode profiles at a waveguide separation distance of 0.1 μm. This figure was generated using the [`plot2D`](../Python_User_Interface.md#data-visualization) routine and shows the source and flux monitor (red hatches), force monitors (blue lines), and PMLs (green hatches) surrounding the cell. From the force spectra shown above, at this separation distance the anti-symmetric mode is repulsive whereas the symmetric mode is attractive. -
-![](../images/parallel_wvgs_s0.1.png) -
+

+ +

+ The MPB simulation is in [examples/parallel-wvgs-mpb.py](https://github.com/NanoComp/meep/blob/master/python/examples/parallel-wvgs-mpb.py). There are important differences related to the coordinate dimensions between the MPB and Meep scripts. In the MPB script, the 2d cell is defined using the $yz$ plane, the waveguide propagation axis is $x$, and the waveguide separation axis is $y$. As a consequence, the `num_bands` parameter is always `1` since the $y$ parity of the mode can be defined explicitly (i.e., `run_yodd_zodd` vs. `run_yeven_zodd`). This is different from the Meep script since Meep requires that a 2d cell be defined in the $xy$ plane. MPB has no such requirement. diff --git a/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md b/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md index edc3cf69f..e1d291ce6 100644 --- a/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md +++ b/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md @@ -4,9 +4,10 @@ In this example, we will consider the 2d structure shown below, which is based on a system considered in Chapter 7 of [Photonic Crystals: Molding the Flow of Light (second edition)](http://ab-initio.mit.edu/book). In particular, there are three basic ideas behind this structure, which we briefly summarize. -
-![](../images/Tut-holey-cavity.png) -
+

+ +

+ First, by taking a dielectric wavgeuide and perforating it with a periodic sequence of holes, we form a kind of **photonic crystal**: there are still index-guided modes propagating losslessly down the periodic waveguide, but there is also a partial photonic band gap: a range of frequencies in which *no guided modes* exist. @@ -163,12 +164,11 @@ which takes a few seconds as we need to wait for the cavity mode to decay away. unix% h5topng holey-wvg-cavity-eps-000000.00.h5 unix% h5topng -Zc dkbluered holey-wvg-cavity-hz-slice.h5 ``` +

+ + +

-
-![](../images/Holey-wvg-cavity-eps-000000.00.png) - -![](../images/Holey-wvg-cavity-hz-slice.png) -
The $H_z$ slice in which time = vertical is interesting, because we can see the pulse propagating to the right, bouncing off of the holes, and also exciting a resonant mode in the cavity that sits in the center for a long time as it starts slowly leaking to the right. @@ -187,9 +187,10 @@ unix% grep flux1: holey-wvg-cavity0.out > flux0.dat which we then import into our plotting program, divide the two fluxes, and get: -
-![](../images/Holey-cavity-trans.png) -
+

+ +

+ The band gap is clearly visible as the range of very low transmission, and in the middle of the band gap is a sharp peak corresponding to the resonant mode trapped in the defect. The inset enlarges this peak, and shows that we didn't use quite enough frequency points to capture the whole shape although we could fit to a Lorentzian if we wanted. At the edges of the band gaps, the transmission goes up in broad Fabry-Perot resonance peaks which we will examine in more detail below. There is also some high-frequency oscillation visible at the left of the plot, which is a numerical artifact due to our pulse not having enough amplitude in that range. @@ -254,10 +255,10 @@ Because it was a single high-$Q$ mode, this mode should be all that we have left unix% h5topng -RZc dkbluered -C holey-wvg-cavity-eps-000000.00.h5 holey-wvg-cavity-hz-*.h5 unix% convert holey-wvg-cavity-hz-*.png holey-wvg-cavity-hz.gif ``` +

+ +

-
-![](../images/Holey-wvg-cavity-hz.gif) -
The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where @@ -270,10 +271,10 @@ unix% python holey-wvg-cavity.py -r -N 4 |grep harminv unix% python holey-wvg-cavity.py -r -N 5 |grep harminv ... ``` +

+ +

-
-![](../images/Holey-wvg-cavity-Q.png) -
The results, shown above, are exactly what we expected: at first, an exponential increase of $Q$ with `N`, and then a saturation at $Q_r \approx 8750$. However, when we look at the Harminv output for larger `N`, something strange happens — it starts to find *more modes*! For example, at `N=16`, the output is: @@ -288,10 +289,9 @@ What is this extra mode at $ω=0.32823$? This is right around the **edge of the ```sh unix% python holey-wvg-cavity.py -r -sy 12 -fcen 0.328227374843021 -df 0.01 -N 16 ``` - -
-![](../images/Holey-wvg-cavity-hz-001401.23.png) -
+

+ +

From the image, the field is clearly localized around the defect in the center as opposed to being spread out evenly in the crystal like a band-edge state would be. In the defect, the pattern is higher order than the previous mode. It has an extra pair of nodes in the $y$ direction. @@ -300,9 +300,9 @@ Band Diagram Finally, we consider a smaller, more abstract calculation that we really should have done first. In particular, we compute the **band diagram** of the infinite periodic waveguide by itself with no defects. The structure is shown below. This is very similar to the types of calculations that [MPB](https://mpb.readthedocs.io) performs, but with a different method that has its own strengths and weaknesses. By analyzing what solutions can propagate in the periodic structure, one gains fundamental insight into the aperiodic structures above. -
-![](../images/Holey-wvg-bands-eps-000000.00.png) -
+

+ +

Let us briefly review the problem. In a periodic system of this sort, the eigen-solutions can be expressed in the form of *Bloch modes*: a periodic *Bloch envelope* multiplied by a planewave $\exp[i(\mathbf{k}\cdot\mathbf{x}-ω t)]$, where **k** is the *Bloch wavevector*. We wish to find the *bands* $ω(\mathbf{k})$. In this case, there is only *one* direction of periodicity, so we only have one wavevector component $k_x$. Moreover, the solutions are periodic functions of this wavevector: for a unit-period structure, $k_x$ and $k_x+2\pi$ are redundant. Also, $k_x$ and $-k_x$ are redundant by time-reversal symmetry, so we only need to look for solutions in the *irreducible Brillouin zone* from $k_x=0$ to $k_x=\pi$. @@ -389,9 +389,10 @@ unix% grep freqs-im: holey-wvg-bands.out > fim.dat Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find: -
-![](../images/Holey-wvg-bands.png) -
+

+ +

+ The gray shaded region is the **light cone**, $ω > ck_x$, which is the region corresponding to modes that are extended in the air surrounding the waveguide. Below the light cone, we see several discrete *guided bands*, which must have field patterns localized to the vicinity of the waveguide. The imaginary part of ω for bands below the light cone is very small, due to either numerical error or the finite computational cell size. Some tiny portion of the guided mode overlaps the PML. Note the band gap between the first and second guided mode, from about 0.2 to 0.3. @@ -409,9 +410,14 @@ It is usually a good idea to examine the field patterns for any modes that you a + $k_x=0.3$, $ω=0.8838-0.0018i$ leaky mode + $k_x=0.25$, $ω=0.2506$ light-cone (extended) mode -
-![](../images/Holey-wvg-kx=0.4-w=0.1896.gif)       ![](../images/Holey-wvg-kx=0.4-w=0.3175.gif)       ![](../images/Holey-wvg-kx=0.10-w=0.4811.gif)       ![](../images/Holey-wvg-kx=0.30-w=0.8838.gif)       ![](../images/Holey-wvg-kx=0.25-w=0.2506.gif) -
+

+ + + + + +

+ - From the top, the first two pictures show the first two guided bands underneath the light cone at $k_x=0.4$. Note that the second guided band is propagating to the *left*, which is due to its negative slope (note, however, that there is a corresponding right-propagating mode at $k_x=-0.4$). Note that they are strongly (exponentially) localized to the waveguide, as they should be. - The next mode is the first leaky mode at $k_x=0.1$. As $k_x$ goes to zero, in fact, this mode actually becomes lossless, a peculiarity of symmetry related to an effect demonstrated in Phys. Rev. B. 63, 125107, 2001. However, at this non-zero $k_x$, the radiation loss is clearly visible. diff --git a/doc/docs/Python_Tutorials/Third_Harmonic_Generation.md b/doc/docs/Python_Tutorials/Third_Harmonic_Generation.md index 09e3470af..454f7083b 100644 --- a/doc/docs/Python_Tutorials/Third_Harmonic_Generation.md +++ b/doc/docs/Python_Tutorials/Third_Harmonic_Generation.md @@ -80,9 +80,10 @@ sim.display_fluxes(trans) In a linear calculation, we normalize the transmission against some reference spectrum, but in this case there is no obvious normalization so we will just plot the raw data for several values of `k` (i.e. of χ$^{(3)}$): -
-![](../images/3rd-harm-1d-flux.png) -
+

+ +

+ For small values of χ$^{(3)}$, we see a peak from our source at ω=1/3 and another peak precisely at the third-harmonic frequency 3ω=1. As the χ$^{(3)}$ gets larger, frequency-mixing *within* the peaks causes them to broaden, and finally for χ$^{(3)}=1$ we start to see a noisy, broad-spectrum transmission due to the phenomenon of **modulation instability**. Notice also that at around $10^{-13}$ the data looks weird; this is probably due to our finite simulation time, imperfect absorbing boundaries, etcetera. We haven't attempted to analyze it in detail for this case. @@ -131,9 +132,10 @@ harmonics:, 1e-16, 1.0, 225.25726603587043, 5.026979706160964e-16 That is, the linear transmission is 225.25726603587043 at ω, so we'll divide by this value and plot the fractional transmission at ω and 3ω as a function of χ$^{(3)}$ on a log-log scale: -
-![](../images/3rd-harm-1d-vs-chi.png) -
+

+ +

+ As can be shown from coupled-mode theory or, equivalently, follows from [Fermi's golden rule](https://en.wikipedia.org/wiki/Fermi's_golden_rule), the third-harmonic power must go as the *square* of χ$^{(3)}$ as long as the nonlinearity is weak (i.e. in the first Born approximation limit, where the ω source is not depleted significantly). This is precisely what we see on the above graph, where the slope of the black line indicates an exact quadratic dependence, for comparison. Once the nonlinearity gets strong enough, however, this approximation is no longer valid and the dependence is complicated. From 5cb0311fc9c41dff545be554167c21cd30e48531 Mon Sep 17 00:00:00 2001 From: Yaraslau <44506630+Yaraslaut@users.noreply.github.com> Date: Wed, 27 Jul 2022 21:15:13 +0100 Subject: [PATCH 19/26] Fixed typo in documentation for Gyrotropic_Media (#2119) --- doc/docs/Python_Tutorials/Gyrotropic_Media.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/docs/Python_Tutorials/Gyrotropic_Media.md b/doc/docs/Python_Tutorials/Gyrotropic_Media.md index c3d480289..a1b6bed46 100644 --- a/doc/docs/Python_Tutorials/Gyrotropic_Media.md +++ b/doc/docs/Python_Tutorials/Gyrotropic_Media.md @@ -30,11 +30,11 @@ import meep as mp ## Parameters for a gyrotropic Lorentzian medium epsn = 1.5 # background permittivity f0 = 1.0 # natural frequency -gamm = 1e-6 # damping rate +gamma = 1e-6 # damping rate sn = 0.1 # sigma parameter b0 = 0.15 # magnitude of bias vector -susc = [mp.GyrotropicLorentzianSusceptibility(frequency=f0, gamma=gamma, sigma=sigma, +susc = [mp.GyrotropicLorentzianSusceptibility(frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0))] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) ``` From c49efbeee91d1de26f86e207d921dfd9fc3680d4 Mon Sep 17 00:00:00 2001 From: Floris Laporte Date: Fri, 29 Jul 2022 11:17:08 -0400 Subject: [PATCH 20/26] Update Adjoint_Solver.md (#2166) --- doc/docs/Python_Tutorials/Adjoint_Solver.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/docs/Python_Tutorials/Adjoint_Solver.md b/doc/docs/Python_Tutorials/Adjoint_Solver.md index 5cf710a91..1e100a2b4 100644 --- a/doc/docs/Python_Tutorials/Adjoint_Solver.md +++ b/doc/docs/Python_Tutorials/Adjoint_Solver.md @@ -18,8 +18,8 @@ There are six Jupyter notebooks that demonstrate the main features of the adjoin - [Design of a Symmetric Broadband Splitter](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/04-Splitter.ipynb) -- [Broadband Objective Function using Epigraph Formulation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/05-Near2Far.ipynb) +- [Metalens Optimization with Near2Far](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/05-Near2Far.ipynb) -- [Objective Function based on Near to Far-Field Transformation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb) +- [Near2Far Optimization with Epigraph Formulation](https://nbviewer.jupyter.org/github/NanoComp/meep/blob/master/python/examples/adjoint_optimization/06-Near2Far-Epigraph.ipynb) More documentation will be available soon. From b00f2d4ce20e683275a4895c8dc8318677a296d1 Mon Sep 17 00:00:00 2001 From: Joaquin Matres <4514346+joamatab@users.noreply.github.com> Date: Fri, 29 Jul 2022 21:53:41 +0200 Subject: [PATCH 21/26] clean code (#2164) * clean code * format code with black * run black and add pre-commit * delete some workflows * remove shellcheck * remove nbstrip * make sure sourcery min python version is 3.7 * include clang formatter in pre-commit * fix import * run ci/cd every day * revert reorder imports from the adjoint module * remove isort from pre-commit --- .gitattributes | 2 +- .github/dependabot.yml | 11 + .github/workflows/build-ci.yml | 8 +- .github/workflows/pre-commit.yml | 16 + .gitignore | 5 + .pre-commit-config.yaml | 112 + .sourcery.yaml | 2 + AUTHORS | 2 +- codemeta.json | 15 +- doc/_api_snippets/class_template.md | 1 - doc/_api_snippets/function_template.md | 2 +- doc/_api_snippets/method_template.md | 2 +- doc/docs/2d_Cell_Special_kz.md | 2 +- doc/docs/C++_Tutorial.md | 2 +- doc/docs/Developer_Codes/Makefile.manual | 2 +- doc/docs/Developer_Codes/WriteChunkInfo.cpp | 5 +- doc/docs/Download.md | 2 +- doc/docs/FAQ.md | 2 +- doc/docs/Installation.md | 2 +- doc/docs/Materials.md | 2 +- doc/docs/Python_Tutorials/Basics.md | 30 +- .../Local_Density_of_States.md | 2 +- .../Python_Tutorials/Mode_Decomposition.md | 2 +- .../Multilevel_Atomic_Susceptibility.md | 3 +- doc/docs/Python_Tutorials/Optical_Forces.md | 4 +- ..._and_Transmission_in_a_Waveguide_Cavity.md | 8 +- doc/docs/Python_User_Interface.md | 4 +- doc/docs/Scheme_Tutorials/Basics.md | 4 +- doc/docs/Scheme_Tutorials/Casimir_Forces.md | 1 - doc/docs/Scheme_Tutorials/Custom_Source.md | 2 +- .../Cylindrical_Coordinates.md | 2 +- .../Local_Density_of_States.md | 2 +- .../Multilevel_Atomic_Susceptibility.md | 6 +- doc/docs/Scheme_Tutorials/Optical_Forces.md | 2 +- ..._and_Transmission_in_a_Waveguide_Cavity.md | 4 +- doc/docs/Subpixel_Smoothing.md | 2 +- ...nizing_the_Magnetic_and_Electric_Fields.md | 2 +- doc/docs/The_Run_Function_Is_Not_A_Loop.md | 4 +- doc/docs/Yee_Lattice.md | 2 +- doc/docs/mathjaxhelper.js | 6 +- doc/docs/mdx_math.py | 45 +- doc/docs/setup.py | 19 +- doc/generate_py_api.py | 156 +- libpympb/pympb.cpp | 228 +- libpympb/pympb.hpp | 1 - m4/ax_check_compiler_flags.m4 | 4 +- m4/ax_compiler_vendor.m4 | 5 +- m4/ax_gcc_x86_cpuid.m4 | 2 +- m4/pkg.m4 | 4 +- python/adjoint/__init__.py | 2 +- python/adjoint/basis.py | 149 +- python/adjoint/connectivity.py | 219 +- python/adjoint/filter_source.py | 99 +- python/adjoint/filters.py | 262 +- python/adjoint/objective.py | 216 +- python/adjoint/optimization_problem.py | 281 +-- python/adjoint/utils.py | 160 +- python/adjoint/wrapper.py | 59 +- python/binary_partition_utils.py | 103 +- python/chunk_balancer.py | 98 +- python/examples/3rd-harm-1d.py | 69 +- python/examples/README.md | 14 +- python/examples/absorbed_power_density.py | 135 +- python/examples/absorber-1d.py | 48 +- .../01-Introduction-checkpoint.ipynb | 316 +-- .../02-Waveguide_Bend-checkpoint.ipynb | 166 +- ...3-Filtered_Waveguide_Bend-checkpoint.ipynb | 2023 ++-------------- .../04-Splitter-checkpoint.ipynb | 1772 ++------------ .../05-Near2Far-checkpoint.ipynb | 277 ++- .../06-Near2Far-Epigraph-checkpoint.ipynb | 1929 ++------------- .../Bend Minimax-checkpoint.ipynb | 1999 ++-------------- python/examples/antenna-radiation.py | 150 +- python/examples/antenna_pec_ground_plane.py | 242 +- python/examples/bend-flux.py | 121 +- python/examples/bent-waveguide.py | 56 +- python/examples/binary_grating.py | 163 +- python/examples/binary_grating_n2f.py | 259 +- python/examples/binary_grating_oblique.py | 217 +- python/examples/binary_grating_phasemap.py | 275 ++- python/examples/cavity-farfield.py | 150 +- python/examples/cavity_arrayslice.py | 30 +- python/examples/cherenkov-radiation.py | 41 +- python/examples/chirped_pulse.py | 58 +- python/examples/coupler.py | 112 +- python/examples/cyl-ellipsoid.py | 38 +- python/examples/cylinder_cross_section.py | 153 +- python/examples/differential_cross_section.py | 198 +- python/examples/diffracted_planewave.py | 304 +-- python/examples/eps_fit_lorentzian.py | 142 +- python/examples/faraday-rotation.py | 84 +- python/examples/finite_grating.py | 220 +- python/examples/gaussian-beam.py | 59 +- .../examples/grating2d_triangular_lattice.py | 133 +- python/examples/holey-wvg-bands.py | 26 +- python/examples/holey-wvg-cavity.py | 132 +- python/examples/material-dispersion.py | 13 +- python/examples/metal-cavity-ldos.py | 70 +- python/examples/metasurface_lens.py | 364 +-- python/examples/mie_scattering.py | 203 +- python/examples/mode-decomposition.py | 165 +- python/examples/mpb_bragg.py | 18 +- python/examples/mpb_bragg_sine.py | 13 +- python/examples/mpb_data_analysis.py | 29 +- python/examples/mpb_diamond.py | 28 +- python/examples/mpb_hole_slab.py | 33 +- python/examples/mpb_honey_rods.py | 40 +- python/examples/mpb_line_defect.py | 27 +- python/examples/mpb_sq_rods.py | 10 +- python/examples/mpb_strip.py | 39 +- python/examples/mpb_tri_holes.py | 22 +- python/examples/mpb_tri_rods.py | 29 +- python/examples/mpb_tutorial.py | 76 +- python/examples/multilevel-atom.py | 80 +- python/examples/oblique-planewave.py | 66 +- python/examples/oblique-source.py | 113 +- python/examples/parallel-wvgs-force.py | 121 +- python/examples/parallel-wvgs-mpb.py | 83 +- python/examples/perturbation_theory.py | 213 +- python/examples/perturbation_theory_2d.py | 257 +- python/examples/phase_in_material.py | 27 +- python/examples/planar_cavity_ldos.py | 272 ++- python/examples/polarization_grating.py | 217 +- python/examples/pw-source.py | 14 +- python/examples/refl-angular-kz2d.py | 107 +- python/examples/refl-angular.py | 119 +- python/examples/refl-quartz.py | 95 +- python/examples/ring-cyl.py | 86 +- python/examples/ring-mode-overlap.py | 31 +- python/examples/ring.py | 50 +- python/examples/ring_gds.py | 142 +- python/examples/solve-cw.py | 130 +- python/examples/stochastic_emitter.py | 147 +- python/examples/stochastic_emitter_line.py | 158 +- python/examples/straight-waveguide.py | 48 +- python/examples/wvg-src.py | 43 +- python/examples/zone_plate.py | 203 +- python/geom.py | 543 +++-- python/materials.py | 1581 +++++++----- python/meep-python.hpp | 2 +- python/meep.i | 6 +- python/mpb_data.py | 178 +- python/simulation.py | 2113 +++++++++++------ python/solver.py | 475 ++-- python/source.py | 191 +- python/tests/test_3rd_harm_1d.py | 43 +- python/tests/test_absorber_1d.py | 42 +- python/tests/test_adjoint_cyl.py | 159 +- python/tests/test_adjoint_jax.py | 185 +- python/tests/test_adjoint_solver.py | 656 ++--- python/tests/test_adjoint_utils.py | 52 +- python/tests/test_antenna_radiation.py | 375 +-- python/tests/test_array_metadata.py | 110 +- python/tests/test_bend_flux.py | 153 +- python/tests/test_binary_grating.py | 610 ++--- python/tests/test_binary_partition_utils.py | 385 +-- python/tests/test_cavity_arrayslice.py | 52 +- python/tests/test_cavity_farfield.py | 94 +- python/tests/test_chunk_balancer.py | 247 +- python/tests/test_chunk_layout.py | 74 +- python/tests/test_chunks.py | 98 +- python/tests/test_conductivity.py | 118 +- python/tests/test_cyl_ellipsoid.py | 42 +- python/tests/test_dft_energy.py | 70 +- python/tests/test_dft_fields.py | 63 +- python/tests/test_diffracted_planewave.py | 291 +-- python/tests/test_dispersive_eigenmode.py | 118 +- python/tests/test_divide_mpi_processes.py | 73 +- python/tests/test_dump_load.py | 384 ++- python/tests/test_eigfreq.py | 67 +- python/tests/test_faraday_rotation.py | 103 +- python/tests/test_field_functions.py | 48 +- python/tests/test_force.py | 33 +- python/tests/test_fragment_stats.py | 309 ++- python/tests/test_gaussianbeam.py | 88 +- python/tests/test_geom.py | 257 +- python/tests/test_get_epsilon_grid.py | 128 +- python/tests/test_get_point.py | 186 +- python/tests/test_holey_wvg_bands.py | 22 +- python/tests/test_holey_wvg_cavity.py | 69 +- python/tests/test_integrated_source.py | 35 +- python/tests/test_kdom.py | 102 +- python/tests/test_ldos.py | 272 ++- python/tests/test_material_dispersion.py | 13 +- python/tests/test_material_grid.py | 385 +-- python/tests/test_materials_library.py | 44 +- python/tests/test_medium_evaluations.py | 46 +- python/tests/test_mode_coeffs.py | 324 ++- python/tests/test_mode_decomposition.py | 746 +++--- python/tests/test_mpb.py | 1761 ++++++++++---- python/tests/test_multilevel_atom.py | 43 +- python/tests/test_n2f_periodic.py | 112 +- python/tests/test_oblique_source.py | 91 +- python/tests/test_physical.py | 30 +- python/tests/test_prism.py | 383 +-- python/tests/test_pw_source.py | 23 +- python/tests/test_refl_angular.py | 156 +- python/tests/test_ring.py | 23 +- python/tests/test_ring_cyl.py | 14 +- python/tests/test_simulation.py | 450 +++- python/tests/test_source.py | 174 +- python/tests/test_special_kz.py | 224 +- python/tests/test_timing_measurements.py | 9 +- python/tests/test_user_defined_material.py | 70 +- python/tests/test_verbosity_mgr.py | 23 +- python/tests/test_visualization.py | 308 ++- python/tests/test_wvg_src.py | 40 +- python/tests/utils.py | 6 +- python/timing_measurements.py | 126 +- python/typemap_utils.cpp | 97 +- python/verbosity_mgr.py | 52 +- python/visualization.py | 957 +++++--- scheme/casimir.scm | 34 +- scheme/examples/3rd-harm-1d.ctl | 6 +- scheme/examples/bend-flux.ctl | 14 +- scheme/examples/holey-wvg-bands.ctl | 4 +- scheme/examples/holey-wvg-cavity.ctl | 12 +- scheme/examples/material-dispersion.ctl | 6 +- scheme/examples/ring-cyl.ctl | 10 +- scheme/materials.scm | 46 +- scheme/meep_op_renames.i | 1 - src/GDSIIgeom.cpp | 3 +- src/adjust_verbosity.hpp | 30 +- src/array_slice.cpp | 67 +- src/boundaries.cpp | 14 +- src/casimir.cpp | 6 +- src/dft.cpp | 242 +- src/dft_ldos.cpp | 14 +- src/fields.cpp | 68 +- src/fields_dump.cpp | 71 +- src/fix_boundary_sources.cpp | 130 +- src/h5file.cpp | 29 +- src/integrate.cpp | 2 +- src/loop_in_chunks.cpp | 67 +- src/material_data.cpp | 24 +- src/material_data.hpp | 4 +- src/meep.hpp | 165 +- src/meep/vec.hpp | 94 +- src/meep_internals.hpp | 113 +- src/meepgeom.cpp | 732 +++--- src/meepgeom.hpp | 57 +- src/monitor.cpp | 4 +- src/mpb.cpp | 66 +- src/multilevel-atom.cpp | 3 +- src/mympi.cpp | 36 +- src/near2far.cpp | 90 +- src/output_directory.cpp | 4 +- src/random.cpp | 4 +- src/sources.cpp | 182 +- src/step_db.cpp | 22 +- src/step_generic.cpp | 42 +- src/stress.cpp | 3 +- src/structure.cpp | 57 +- src/structure_dump.cpp | 41 +- src/support/mt19937ar.c | 178 +- src/susceptibility.cpp | 9 +- src/update_eh.cpp | 7 +- src/vec.cpp | 99 +- tests/aniso_disp.cpp | 2 +- tests/array-slice-ll.cpp | 25 +- tests/bragg_transmission.cpp | 8 +- tests/convergence_cyl_waveguide.cpp | 16 +- tests/cyl-ellipsoid-ll.cpp | 8 +- tests/cyl-ellipsoid.ctl | 2 +- tests/cylindrical.cpp | 4 +- tests/dft-fields.cpp | 26 +- tests/dump_load.cpp | 82 +- tests/h5test.cpp | 15 +- tests/harmonics.cpp | 18 +- tests/integrate.cpp | 23 +- tests/known_results.cpp | 4 +- tests/near2far.cpp | 2 +- tests/one_dimensional.cpp | 4 +- tests/physical.cpp | 10 +- tests/pml.cpp | 9 +- tests/ring-ll.cpp | 18 +- tests/symmetry.cpp | 10 +- tests/three_d.cpp | 4 +- tests/two_dimensional.cpp | 16 +- tests/user-defined-material.cpp | 6 +- 279 files changed, 19760 insertions(+), 20009 deletions(-) create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/pre-commit.yml create mode 100644 .pre-commit-config.yaml create mode 100644 .sourcery.yaml diff --git a/.gitattributes b/.gitattributes index 579877f02..17cd3619d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -5,4 +5,4 @@ tests/* linguist-documentation scheme/examples/* linguist-documentation python/numpy.i linguist-vendored src/support/mt19937ar.c linguist-vendored -src/support/meep_mt.h linguist-language=C++ \ No newline at end of file +src/support/meep_mt.h linguist-language=C++ diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 000000000..0aada4dc0 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,11 @@ +version: 2 +updates: + - package-ecosystem: "pip" + directory: "/" # Location of package manifests + schedule: + interval: "daily" + + - package-ecosystem: github-actions + directory: / + schedule: + interval: monthly diff --git a/.github/workflows/build-ci.yml b/.github/workflows/build-ci.yml index 9a1c19b5b..72ff0c8a0 100644 --- a/.github/workflows/build-ci.yml +++ b/.github/workflows/build-ci.yml @@ -1,10 +1,10 @@ -name: CI +name: run tests on: - push: - branches: [ master ] pull_request: - branches: [ master ] + push: + schedule: + - cron: 0 2 * * * # run at 2 AM UTC workflow_dispatch: jobs: diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 000000000..088c7b916 --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,16 @@ +--- +name: pre-commit + +on: + pull_request: + push: + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: 3.9 + - uses: pre-commit/action@v3.0.0 diff --git a/.gitignore b/.gitignore index 4ed1ef7e1..32b98c60e 100644 --- a/.gitignore +++ b/.gitignore @@ -15,6 +15,11 @@ _build .vscode .idea +python/examples/.ipynb_checkpoints/ +h5utils/ +hdf5/ +extra/ +configure~ # autotools stuff Makefile diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..3c7f780f9 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,112 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: "08b4be84be2d74173246b7c9b6c22212a6c15a2d" + hooks: + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + + # - repo: https://github.com/hakancelik96/unimport + # rev: df8eb1a4c91acb84da197828af8157708968b596 + # hooks: + # - id: unimport + # args: [--remove, --include-star-import] + # - repo: https://github.com/pycqa/isort + # rev: "12cc5fbd67eebf92eb2213b03c07b138ae1fb448" + # hooks: + # - id: isort + # files: "python/.*" + # args: ["--profile", "black", "--filter-files"] + + - repo: https://github.com/psf/black + rev: "4f1772e2aed8356e57b923eacf45f813ec3324a0" + hooks: + - id: black + + # - repo: https://gitlab.com/pycqa/flake8 + # rev: "21d3c70d676007470908d39b73f0521d39b3b997" + # hooks: + # - id: flake8 + + # - repo: https://github.com/kynan/nbstripout + # rev: 8cafdcc393232045208137698dbeb42d6e0dd9e8 + # hooks: + # - id: nbstripout + # files: ".ipynb" + + - repo: https://github.com/asottile/pyupgrade + rev: v2.37.3 + hooks: + - id: pyupgrade + args: [--py37-plus, --keep-runtime-typing] + + # - repo: https://github.com/codespell-project/codespell + # rev: 4d782511b3999c243feb3858cd7062270eb13291 + # hooks: + # - id: codespell + # args: ["-L TE,TE/TM,te,ba,FPR,fpr_spacing,ro"] + + # - repo: https://github.com/shellcheck-py/shellcheck-py + # rev: f87a493c6596a5338d69395905a4e13ed65584f6 + # hooks: + # - id: shellcheck + + - repo: https://github.com/pre-commit/pygrep-hooks + rev: dc89c436469616a7c48293ec60c8e21e9d95be08 # Use the ref you want to point at + hooks: + - id: python-use-type-annotations + + - repo: https://github.com/PyCQA/bandit + rev: 97501817e621db9975dfa6a2a1d36370942d1812 + hooks: + - id: bandit + args: [--exit-zero] + # ignore all tests, not just tests data + exclude: ^tests/ + - repo: https://github.com/pre-commit/mirrors-clang-format + rev: 'v14.0.6' # Use the sha / tag you want to point at + hooks: + - id: clang-format + # - repo: https://github.com/pre-commit/mirrors-mypy + # rev: "214c33306afe17f1cc7d2d55e4da705b6ebe0627" + # hooks: + # - id: mypy + # exclude: ^(docs/|example-plugin/|tests/fixtures) + # additional_dependencies: + # - "pydantic" + # - repo: https://github.com/terrencepreilly/darglint + # rev: master + # hooks: + # - id: darglint + + # - repo: https://github.com/pycqa/pydocstyle + # rev: "" + # hooks: + # - id: pydocstyle + # - repo: https://github.com/asottile/reorder_python_imports + # rev: 2b2f0c74acdb3de316e23ceb7dd0d7945c354050 + # hooks: + # - id: reorder-python-imports + # - repo: https://github.com/PyCQA/pylint + # rev: v2.14.1 + # hooks: + # - id: pylint + # args: [--exit-zero] + # - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks + # rev: 6565d773ca281682d7062d4c0be74538cc474cc9 + # hooks: + # - id: pretty-format-java + # args: [--autofix] + # - id: pretty-format-kotlin + # args: [--autofix] + # - id: pretty-format-yaml + # args: [--autofix, --indent, "2"] + # - repo: https://github.com/adrienverge/yamllint.git + # rev: v1.21.0 # or higher tag + # hooks: + # - id: yamllint + # args: [--format, parsable, --strict] + # - repo: https://github.com/jumanjihouse/pre-commit-hook-yamlfmt + # rev: 0.1.0 # or specific tag + # hooks: + # - id: yamlfmt diff --git a/.sourcery.yaml b/.sourcery.yaml new file mode 100644 index 000000000..f300aba4e --- /dev/null +++ b/.sourcery.yaml @@ -0,0 +1,2 @@ +refactor: + python_version: '3.7' diff --git a/AUTHORS b/AUTHORS index 67c49fe38..f26a2989f 100644 --- a/AUTHORS +++ b/AUTHORS @@ -17,4 +17,4 @@ Ian Williamson Andreas Hoenselaar Ben Bartlett Krishna Gadepalli -Mo Chen \ No newline at end of file +Mo Chen diff --git a/codemeta.json b/codemeta.json index a6282fb4d..9cef3da74 100644 --- a/codemeta.json +++ b/codemeta.json @@ -2,7 +2,8 @@ "@context": "https://doi.org/10.5063/schema/codemeta-2.0", "@type": "SoftwareSourceCode", "name": "Meep", - "description": "Meep is a free and open-source software package for electromagnetics simulation via the finite-difference time-domain (FDTD) method spanning a broad range of applications.", + "description": + "Meep is a free and open-source software package for electromagnetics simulation via the finite-difference time-domain (FDTD) method spanning a broad range of applications.", "url": "https://github.com/NanoComp/meep", "codeRepository": "https://github.com/NanoComp/meep", "issueTracker": "https://github.com/NanoComp/issues", @@ -87,8 +88,12 @@ ], "developmentStatus": "active", "downloadUrl": "https://github.com/NanoComp/releases", - "version":"1.11", - "dateCreated":"2003-02-12", - "datePublished":"2006-04-01", - "programmingLanguage": ["C++", "Python", "Scheme"] + "version": "1.11", + "dateCreated": "2003-02-12", + "datePublished": "2006-04-01", + "programmingLanguage": [ + "C++", + "Python", + "Scheme" + ] } diff --git a/doc/_api_snippets/class_template.md b/doc/_api_snippets/class_template.md index 35de0fb34..4de4206ec 100644 --- a/doc/_api_snippets/class_template.md +++ b/doc/_api_snippets/class_template.md @@ -12,4 +12,3 @@ class {class_name}({base_classes}): {docstring} - diff --git a/doc/_api_snippets/function_template.md b/doc/_api_snippets/function_template.md index 89c85b739..adcba9c4c 100644 --- a/doc/_api_snippets/function_template.md +++ b/doc/_api_snippets/function_template.md @@ -9,4 +9,4 @@ def {function_name}{parameters}:{other_signatures} {docstring} - \ No newline at end of file + diff --git a/doc/_api_snippets/method_template.md b/doc/_api_snippets/method_template.md index 71cc0ac3d..560b31be2 100644 --- a/doc/_api_snippets/method_template.md +++ b/doc/_api_snippets/method_template.md @@ -13,4 +13,4 @@ def {method_name}{parameters}:{other_signatures} - \ No newline at end of file + diff --git a/doc/docs/2d_Cell_Special_kz.md b/doc/docs/2d_Cell_Special_kz.md index 11cd1fdcc..655881cf0 100644 --- a/doc/docs/2d_Cell_Special_kz.md +++ b/doc/docs/2d_Cell_Special_kz.md @@ -100,4 +100,4 @@ The Meep results are identical to within six decimal digits and agree well with ``` refl:, 0.3272338236967464 (real/imag), 0.3272338244372344 (complex), 0.3272330216564413 (3d), 0.3293821216165117 (analytic) -``` \ No newline at end of file +``` diff --git a/doc/docs/C++_Tutorial.md b/doc/docs/C++_Tutorial.md index 5e158b86b..2e152877a 100644 --- a/doc/docs/C++_Tutorial.md +++ b/doc/docs/C++_Tutorial.md @@ -86,7 +86,7 @@ const double half_cavity_width = d2; const int N = 5; ``` -Meep supports perfectly matching layers (PML) as absorbing boundary conditions. The PML begins at the edge of the computational volume and works inwards. Hence, we specify the size of the cell as follows: +Meep supports perfectly matching layers (PML) as absorbing boundary conditions. The PML begins at the edge of the computational volume and works inwards. Hence, we specify the size of the cell as follows: ```c++ const double pml_thickness = 1.0; diff --git a/doc/docs/Developer_Codes/Makefile.manual b/doc/docs/Developer_Codes/Makefile.manual index 5abb9d261..b0501ad8a 100644 --- a/doc/docs/Developer_Codes/Makefile.manual +++ b/doc/docs/Developer_Codes/Makefile.manual @@ -15,5 +15,5 @@ LIBS = -lmeepgeom -lmeep -lctlgeom WriteChunkInfo: WriteChunkInfo.o $(CXX) $(LDFLAGS) -o $@ $^ $(LIBS) -clean: +clean: rm *.o *.a diff --git a/doc/docs/Developer_Codes/WriteChunkInfo.cpp b/doc/docs/Developer_Codes/WriteChunkInfo.cpp index 43bc40d5b..ad96843d7 100644 --- a/doc/docs/Developer_Codes/WriteChunkInfo.cpp +++ b/doc/docs/Developer_Codes/WriteChunkInfo.cpp @@ -123,8 +123,9 @@ structure create_structure(double resolution, int symmetries) { geometry_lattice.size.z = 0.0; grid_volume gv = voltwo(sx, sy, resolution); gv.center_origin(); - symmetry S = (symmetries == 1 ? mirror(Y, gv) - : symmetries == 2 ? mirror(X, gv) + mirror(Y, gv) : symmetry()); + symmetry S = (symmetries == 1 ? mirror(Y, gv) + : symmetries == 2 ? mirror(X, gv) + mirror(Y, gv) + : symmetry()); structure the_structure(gv, dummy_eps, pml(dpml), S); vector3 e1 = v3(1.0, 0.0, 0.0); diff --git a/doc/docs/Download.md b/doc/docs/Download.md index 730c067c0..deb875b33 100644 --- a/doc/docs/Download.md +++ b/doc/docs/Download.md @@ -28,4 +28,4 @@ You will also be able to install the [parallel version of Meep](https://packages sudo apt-get install python3-meep-openmpi ``` -These upcoming Meep packages for Ubuntu 21.04 are derived from the Debian 11 ("Bullseye") packages ([serial](https://packages.debian.org/bullseye/python3-meep) and [parallel](https://packages.debian.org/bullseye/python3-meep-openmpi)). Debian 11 is currently the testing distribution. \ No newline at end of file +These upcoming Meep packages for Ubuntu 21.04 are derived from the Debian 11 ("Bullseye") packages ([serial](https://packages.debian.org/bullseye/python3-meep) and [parallel](https://packages.debian.org/bullseye/python3-meep-openmpi)). Debian 11 is currently the testing distribution. diff --git a/doc/docs/FAQ.md b/doc/docs/FAQ.md index 6754daa2d..619e9f4b2 100644 --- a/doc/docs/FAQ.md +++ b/doc/docs/FAQ.md @@ -320,7 +320,7 @@ Only the [harmonic mean](https://en.wikipedia.org/wiki/Harmonic_mean) of eigenva ### How do I model graphene or other 2d materials with single-atom thickness? -Typically, graphene and similar "2d" materials are mathematically represented as a [delta function](https://en.wikipedia.org/wiki/Dirac_delta_function) conductivity in Maxwell's equations because their thickness is negligible compared to the wavelength, where the conductivity is furthermore usually anisotropic (producing surface-parallel currents in response to the surface-parallel components of the electric field). In a discretized computer model like Meep, this is approximated by an anisotropic volume conductivity (or other polarizable dispersive material) whose thickness is proportional to (`1/resolution`) *and* whose amplitude is proportional `resolution`. For example, this could be represented by a one-pixel-thick [conductor](Materials.md#conductivity-and-complex) can be represented by e.g. a [`Block`](Python_User_Interface.md#block) with `size=meep.Vector3(x,y,1/resolution)` in a 3d cell, with the value of the conductivity explicitly multiplied by `resolution`. +Typically, graphene and similar "2d" materials are mathematically represented as a [delta function](https://en.wikipedia.org/wiki/Dirac_delta_function) conductivity in Maxwell's equations because their thickness is negligible compared to the wavelength, where the conductivity is furthermore usually anisotropic (producing surface-parallel currents in response to the surface-parallel components of the electric field). In a discretized computer model like Meep, this is approximated by an anisotropic volume conductivity (or other polarizable dispersive material) whose thickness is proportional to (`1/resolution`) *and* whose amplitude is proportional `resolution`. For example, this could be represented by a one-pixel-thick [conductor](Materials.md#conductivity-and-complex) can be represented by e.g. a [`Block`](Python_User_Interface.md#block) with `size=meep.Vector3(x,y,1/resolution)` in a 3d cell, with the value of the conductivity explicitly multiplied by `resolution`. ### How do I model a continuously varying permittivity? You can use a [material function](Python_User_Interface.md#medium) to model any arbitrary, position-dependent permittivity/permeability function $\varepsilon(\vec{r})$/$\mu(\vec{r})$ including anisotropic, [dispersive](Materials.md#material-dispersion), and [nonlinear](Materials.md#nonlinearity) media. For an example involving a non-dispersive, anisotropic material, see [Tutorial/Mode Decomposition/Diffraction Spectrum of Liquid-Crystal Polarization Gratings](Python_Tutorials/Mode_Decomposition.md#diffraction-spectrum-of-liquid-crystal-polarization-gratings). The material function construct can also be used to specify arbitrary *shapes* (e.g., curves such as parabolas, sinusoids, etc.) within: (1) the interior boundary of a [`GeometricObject`](Python_User_Interface.md#geometricobject) (e.g., `Block`, `Sphere`, `Cylinder`, etc.), (2) the entire cell via the [`material_function`](Python_User_Interface.md#material-function) parameter of the `Simulation` constructor, or (3) a combination of the two. diff --git a/doc/docs/Installation.md b/doc/docs/Installation.md index ead7b677b..c99eb1029 100644 --- a/doc/docs/Installation.md +++ b/doc/docs/Installation.md @@ -123,7 +123,7 @@ There are upcoming packages for [Meep version 1.17.1](https://github.com/NanoCom In the meantime, the following dependencies are already available as precompiled packages: BLAS and LAPACK possibly as part of a package for [Atlas BLAS](https://en.wikipedia.org/wiki/Automatically_Tuned_Linear_Algebra_Software), Guile, MPI, and HDF5. One thing to be careful of is that many distributions split packages into two parts: one main package for the libraries and programs, and a **devel** package for [header files](https://en.wikipedia.org/wiki/Header_file) and other things needed to compile software using those libraries. You will need to install **both**. So, for example, you will probably need both a `guile` package (probably installed by default) and a `guile-dev` or `guile-devel` package (probably *not* installed by default), and similarly for HDF5 etcetera. You will probably also want to install a `libpng-dev` or `libpng-devel` package in order to compile the `h5topng` utility in [h5utils](https://github.com/NanoComp/h5utils/blob/master/README.md). -Installation from source on macOS +Installation from source on macOS --------------------------------- Most macOS users will probably want to install via the Conda packages as described above. It is also possible to compile Meep from source, however. diff --git a/doc/docs/Materials.md b/doc/docs/Materials.md index 2c5c131cb..25e6ceda4 100644 --- a/doc/docs/Materials.md +++ b/doc/docs/Materials.md @@ -188,7 +188,7 @@ Similar relationships can be found for systems with more than two atomic levels ### Natural Units for Saturable Media A conversion chart for different choices of parameter nomenclature for the saturable medium. - + There is no standard convention in the literature on lasers and saturable gain media for defining the various constants in the equations above. The following are the relationships among these constants for the three different groups of work discussed in this section: $$ \omega_n \; (\textrm{Meep}) = \omega_{ba} \; (\textrm{Boyd}) = \omega_a \; (\textrm{SALT}) $$ diff --git a/doc/docs/Python_Tutorials/Basics.md b/doc/docs/Python_Tutorials/Basics.md index d2a9c28d7..4ab0fde4c 100644 --- a/doc/docs/Python_Tutorials/Basics.md +++ b/doc/docs/Python_Tutorials/Basics.md @@ -242,7 +242,7 @@ Above, we outputted the full 2d data slice at every 0.6 time units, resulting in To create the movie above, all we really need are the *images* corresponding to each time. Images can be stored much more efficiently than raw arrays of numbers — to exploit this fact, Meep allows you to output PNG images instead of HDF5 files. In particular, instead of `output_efield_z` as above, we can use `mp.output_png(mp.Ez, "-Zc dkbluered")`, where Ez is the component to output and the `"-Zc` `dkbluered"` are options for `h5topng` of [h5utils](https://github.com/NanoComp/h5utils/blob/master/README.md) which is the program that is actually used to create the image files. That is: ```py -sim.run(mp.at_every(0.6 , mp.output_png(mp.Ez, "-Zc dkbluered")), until=200) +sim.run(mp.at_every(0.6 , mp.output_png(mp.Ez, "-Zc dkbluered")), until=200) ``` will output a PNG file file every 0.6 time units, which can then be combined with `convert` as above to create a movie. The movie will be similar to the one before, but not identical because of how the color scale is determined. Before, we used the `-R` option to make h5topng use a uniform color scale for all images, based on the minimum/maximum field values over all time steps. That is not possible because we output an image before knowing the field values at future time steps. Thus, what `output_png` does is to set its color scale based on the minimum/maximum field values from all *past* times — therefore, the color scale will slowly "ramp up" as the source turns on. @@ -258,7 +258,7 @@ This will put *all* of the output files (.h5, .png, etcetera) into a newly-creat What if we want to output an $x \times t$ slice, as above? To do this, we only really wanted the values at $y=-3.5$, and therefore we can exploit another powerful output feature — Meep allows us to output only **a subset of the computational cell**. This is done using the `in_volume` function, which like `at_every` and `to_appended` is another function that modifies the behavior of other output functions. In particular, we can do: ``` -sim.run(mp.in_volume(mp.Volume(mp.Vector3(0,-3.5), size=mp.Vector3(16,0)), mp.to_appended("ez-slice", mp.output_efield_z)), until=200) +sim.run(mp.in_volume(mp.Volume(mp.Vector3(0,-3.5), size=mp.Vector3(16,0)), mp.to_appended("ez-slice", mp.output_efield_z)), until=200) ``` The first argument to `in_volume` is a volume which applies to all of the nested output functions. Note that `to_appended`, `at_every`, and `in_volume` are cumulative regardless of what order you put them in. This creates the output file `ez-slice.h5` which contains a dataset of size 162x330 corresponding to the desired $x \times t$ slice. @@ -334,7 +334,7 @@ sim = mp.Simulation(cell_size=cell, nfreq = 100 # number of frequencies at which to compute flux # reflected flux -refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0), size=mp.Vector3(0,2*w,0)) +refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0), size=mp.Vector3(0,2*w,0)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) # transmitted flux @@ -348,7 +348,7 @@ The fluxes will be computed for `nfreq=100` frequencies centered on `fcen`, from As described in [Introduction/Transmittance/Reflectance Spectra](../Introduction.md#transmittancereflectance-spectra), computing the reflection spectra requires some care because we need to separate the incident and reflected fields. We do this by first saving the Fourier-transformed fields from the normalization run. And then, before we start the second run, we load these fields, *negated*. The latter subtracts the Fourier-transformed incident fields from the Fourier transforms of the scattered fields. Logically, we might subtract these after the run, but it turns out to be more convenient to subtract the incident fields first and then accumulate the Fourier transform. All of this is accomplished with two commands which use the raw simulation data: `get_flux_data` and `load_minus_flux_data`. We run the first simulation as follows: -```py +```py pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen) sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3)) @@ -372,7 +372,7 @@ We need to run the second simulation which involves the waveguide bend. We reset ```py sim.reset_meep() - + geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf), center=mp.Vector3(-0.5*pad,wvg_ycen), material=mp.Medium(epsilon=12)), mp.Block(mp.Vector3(w,sy-pad,mp.inf), center=mp.Vector3(wvg_xcen,0.5*pad), material=mp.Medium(epsilon=12))] @@ -387,7 +387,7 @@ refl = sim.add_flux(fcen, df, nfreq, refl_fr) tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0), size=mp.Vector3(2*w,0,0)) tran = sim.add_flux(fcen, df, nfreq, tran_fr) - + # for normal run, load negated fields to subtract incident from refl. fields sim.load_minus_flux_data(refl, straight_refl_data) @@ -402,7 +402,7 @@ flux_freqs = mp.get_flux_freqs(refl) ``` With the flux data, we are ready to compute and plot the reflectance and transmittance. The reflectance is the reflected flux divided by the incident flux. We also have to multiply by -1 because all fluxes in Meep are computed in the positive-coordinate direction by default, and we want the flux in the $-x$ direction. The transmittance is the transmitted flux divided by the incident flux. Finally, the scattered loss is simply $1-transmittance-reflectance$. The results are plotted in the accompanying figure. - + ```py wl = [] Rs = [] @@ -410,7 +410,7 @@ Ts = [] for i in range(nfreq): wl = np.append(wl, 1/flux_freqs[i]) Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i]) - Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) + Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) if mp.am_master(): plt.figure() @@ -469,7 +469,7 @@ def main(args): fcen = 0.5*(fmin+fmax) # center frequency df = fmax-fmin # frequency width nfreq = 50 # number of frequency bins - + # rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis theta_r = math.radians(args.theta) @@ -481,7 +481,7 @@ def main(args): dimensions = 1 else: dimensions = 3 - + sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(0,0,-0.5*sz+dpml))] @@ -495,7 +495,7 @@ def main(args): refl_fr = mp.FluxRegion(center=mp.Vector3(0,0,-0.25*sz)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) - + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(0,0,-0.5*sz+dpml), 1e-9)) empty_flux = mp.get_fluxes(refl) @@ -522,10 +522,10 @@ def main(args): refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - + for i in range(nfreq): print("refl:, {}, {}, {}, {}".format(k.x,1/freqs[i],math.degrees(math.asin(k.x/freqs[i])),-refl_flux[i]/empty_flux[i])) - + if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-res', type=int, default=200, help='resolution (default: 200 pixels/um)') @@ -882,7 +882,7 @@ for n in range(npts): E[n,:] = [np.conj(ff[j]) for j in range(3)] H[n,:] = [ff[j+3] for j in range(3)] -# compute Poynting flux Pr in the radial direction. At large r, +# compute Poynting flux Pr in the radial direction. At large r, # all of the flux is radial so we can simply compute the magnitude of the Poynting vector. Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1])) Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2])) @@ -1078,7 +1078,7 @@ Finally, we are ready to run the simulation. The basic idea is to run until the sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy), geometry=[c1, c2], sources=[src], - resolution=10, + resolution=10, boundary_layers=[mp.PML(dpml)]) sim.run(mp.at_beginning(mp.output_epsilon), diff --git a/doc/docs/Python_Tutorials/Local_Density_of_States.md b/doc/docs/Python_Tutorials/Local_Density_of_States.md index 5ceaae47e..fb66cb29b 100644 --- a/doc/docs/Python_Tutorials/Local_Density_of_States.md +++ b/doc/docs/Python_Tutorials/Local_Density_of_States.md @@ -25,7 +25,7 @@ In 3D, each simulation uses three [mirror symmetries](../Exploiting_Symmetry.md# In cylindrical coordinates, the dipole is polarized in the $r$ direction. Setting up a linearly polarized source in cylindrical coordiantes is demonstrated in [Tutorial/Cylindrical Coordinates/Scattering Cross Section of a Finite Dielectric Cylinder](Cylindrical_Coordinates.md#scattering-cross-section-of-a-finite-dielectric-cylinder). However, all that is necessary in this example which involves a single point dipole rather than a planewave is one simulation involving an $E_r$ source at $r=0$ with $m=-1$. This is actually a circularly polarized source but this is sufficient because the $m=+1$ simulation produces an identical result to the $m=-1$ simulation. It is therefore not necessary to perform two separate simulations for $m=\pm 1$ in order to average the results from the left- and right-circularly polarized sources. -One important parameter when setting up this calculation is the grid resolution. +One important parameter when setting up this calculation is the grid resolution. A key feature of the LDOS in this geometry is that it experiences discontinuities, called [Van Hove singularities](https://en.wikipedia.org/wiki/Van_Hove_singularity), any time the cavity thickness/λ passes through the cutoff for a waveguide mode, which occurs for cavity-thickness/λ values of 0.5, 1.5, 2.5, etc. (Mathematically, Van Hove singularities depend strongly on the dimensionality — it is a discontinuity in this case because the waves are propagating along two dimensions, i.e. each cutoff is a minimum in the 2d dispersion relation $\omega(k_x,k_y)$.) This discontinuity also means that the LDOS *exactly at* the cutoff thickness/λ is ill-defined and convergence with discretization can be problematic at this point. (In consequence, the LDOS *exactly* at the Van Hove discontinuity can behave erratically with resolution, and should be viewed with caution.) diff --git a/doc/docs/Python_Tutorials/Mode_Decomposition.md b/doc/docs/Python_Tutorials/Mode_Decomposition.md index d3e44ebe6..4565f3cb5 100644 --- a/doc/docs/Python_Tutorials/Mode_Decomposition.md +++ b/doc/docs/Python_Tutorials/Mode_Decomposition.md @@ -340,7 +340,7 @@ sx = dpml+dsub+gh+dpad+dpml sy = gp cell_size = mp.Vector3(sx,sy,0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] wvl = 0.5 # center wavelength fcen = 1/wvl # center frequency diff --git a/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md b/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md index 64fca8bf6..65825035a 100644 --- a/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md +++ b/doc/docs/Python_Tutorials/Multilevel_Atomic_Susceptibility.md @@ -101,7 +101,7 @@ $$ \mathbf{E} \; (\textrm{SALT}) = \frac{2 |\theta|}{\hbar \sqrt{\gamma_\perp \g For two-level gain media, $\gamma_\parallel = \gamma_{12} + \gamma_{21}$. We can also verify that the system is not exhibiting relaxation oscillations by directly plotting the electric field as a function of time and looking for very long time-scale oscillations. In the continuum limit, these modes would appear as Dirac delta functions in the spectra. The discretized model, however, produces peaks with finite width. Thus, we need to integrate a fixed number of points around each peak to calculate the correct modal intensity. -By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold. +By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold.

Near threshold comparison @@ -113,4 +113,3 @@ Further increasing the gain continues to yield good agreement.

Near threshold comparison

- diff --git a/doc/docs/Python_Tutorials/Optical_Forces.md b/doc/docs/Python_Tutorials/Optical_Forces.md index 39433bd3f..28131a30c 100644 --- a/doc/docs/Python_Tutorials/Optical_Forces.md +++ b/doc/docs/Python_Tutorials/Optical_Forces.md @@ -29,12 +29,12 @@ import numpy as np import matplotlib.pyplot as plt resolution = 40 # pixels/μm - + Si = mp.Medium(index=3.45) dpml = 1.0 pml_layers = [mp.PML(dpml)] - + sx = 5 sy = 3 cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0) diff --git a/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md b/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md index e1d291ce6..3fac28958 100644 --- a/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md +++ b/doc/docs/Python_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md @@ -37,7 +37,7 @@ Next, we'll define some parameters of our structure as in the figure above. All ```py resolution = 20 # pixels/um - + eps = 13 # dielectric constant of waveguide w = 1.2 # width of waveguide r = 0.36 # radius of holes @@ -206,7 +206,7 @@ The structure is exactly the same as above, and only the sources and analysis ar ```py if args.resonant_modes: ...new sources and run command... -else: +else: ...sources and run from above, to get spectrum... ``` @@ -260,7 +260,7 @@ unix% convert holey-wvg-cavity-hz-*.png holey-wvg-cavity-hz.gif

-The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where +The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where $$\frac{1}{Q} = \frac{1}{Q_w} + \frac{1}{Q_r}$$ @@ -387,7 +387,7 @@ unix% grep freqs: holey-wvg-bands.out > fre.dat unix% grep freqs-im: holey-wvg-bands.out > fim.dat ``` -Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find: +Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find:

diff --git a/doc/docs/Python_User_Interface.md b/doc/docs/Python_User_Interface.md index 3df3c2f2d..a6959c85f 100644 --- a/doc/docs/Python_User_Interface.md +++ b/doc/docs/Python_User_Interface.md @@ -80,7 +80,7 @@ control various parameters of the Meep computation. - +

@@ -356,7 +356,7 @@ use. See also [SWIG Wrappers](#swig-wrappers).
- +
diff --git a/doc/docs/Scheme_Tutorials/Basics.md b/doc/docs/Scheme_Tutorials/Basics.md index e45c00837..7fd12258f 100644 --- a/doc/docs/Scheme_Tutorials/Basics.md +++ b/doc/docs/Scheme_Tutorials/Basics.md @@ -108,7 +108,7 @@ This will create `eps-000000.00.png`, where the `-S3` increases the image scale unix% h5topng -S3 -Zc dkbluered -a yarg -A eps-000000.00.h5 ez-000200.00.h5 ``` -Briefly, the `-Zc dkbluered` makes the color scale go from dark blue (negative) to white (zero) to dark red (positive), and the `-a/-A` options overlay the dielectric function as light gray contours. This results in the image: +Briefly, the `-Zc dkbluered` makes the color scale go from dark blue (negative) to white (zero) to dark red (positive), and the `-a/-A` options overlay the dielectric function as light gray contours. This results in the image:
![](../images/Tutorial-wvg-straight-ez-000200.00.png)
@@ -433,7 +433,7 @@ The simulation script is [examples/refl-angular.ctl](https://github.com/NanoComp (define df (- fmax fmin)) ; frequency width of source (define-param nfreq 50) ; number of frequency bins -; rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis +; rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis (define-param theta 0) (define theta-r (deg->rad theta)) diff --git a/doc/docs/Scheme_Tutorials/Casimir_Forces.md b/doc/docs/Scheme_Tutorials/Casimir_Forces.md index c0e6b2cea..109d0b486 100644 --- a/doc/docs/Scheme_Tutorials/Casimir_Forces.md +++ b/doc/docs/Scheme_Tutorials/Casimir_Forces.md @@ -526,4 +526,3 @@ If you examine periodic-sphere-plate.ctl in the example above, you will notice t As discussed in Part I, the temporal convergence of the force $F(t)$ can be accelerated by picking the right value of the global conductivity $\sigma$. $\sigma$ should be high enough to dampen out oscillations in $F(t)$, but on the other hand a high conductivity reduces the velocity of waves propagating in the medium (see Part I), slowing convergence. We've found that if the characteristic separation between two objects (e.g. the distance between parallel plates) in vacuum is $d$, then picking $\sigma ~\sim~ 0.5/d$. This is illustrated in the function `scale-sigma-T` in periodic-sphere-plate.ctl above. Both the value of $\sigma$ and the total runtime $T$ of the simulation are adjusted depending on the separation between the objects. If the dielectric of the medium is non-zero, then for non-dispersive media the optimal value of $\sigma$ follows from group velocity considerations. For dispersive media, the convergence should be experimented with to determine the best value. Generally, as the dielectric $\epsilon$ of the medium increases, $\sigma$ should decrease. - diff --git a/doc/docs/Scheme_Tutorials/Custom_Source.md b/doc/docs/Scheme_Tutorials/Custom_Source.md index 88c7422f9..7efb6fe62 100644 --- a/doc/docs/Scheme_Tutorials/Custom_Source.md +++ b/doc/docs/Scheme_Tutorials/Custom_Source.md @@ -172,4 +172,4 @@ One situation in which you may need to perform brute-force Monte Carlo simulatio *Note regarding convergence properties of Method 2:* In this demonstration, the number of point dipoles used in Method 2 is one per pixel. However, because this example is a unit-cell calculation involving *periodic* boundaries, the number of point dipoles (equivalent to the number of simulations) that are actually necessary to obtain results with sufficient accuracy can be significantly reduced. For smooth periodic functions, it is well known that a [trapezoidal rule](https://en.wikipedia.org/wiki/Trapezoidal_rule) converges quite fast — generally even faster than a cosine-series expansion and comparable to a cosine+sine Fourier series. See these [tutorial notes](http://math.mit.edu/~stevenj/trap-iap-2011.pdf) for the mathematical details. In this example, placing one dipole at every fifth pixel for a total of 15 rather than 75 simulations produces nearly equivalent results for the flux spectrum. More generally, an alternative approach for Method 2 would be to sample a set of dipole points and repeatedly double the sampling density until it converges. Sampling every grid point is usually not necessary. -*Note regarding polarization:* The previous demonstration involved a single-polarization source. For random polarizations, three separate simulations (for $\mathcal{J}_x$, $\mathcal{J}_y$, and $\mathcal{J}_z$) are required. Since the different polarizations are uncorrelated, the results (i.e., the flux spectrum) from each set of single-polarization simulations (which, in general, will have different convergence properties) are then summed in post processing. If the emitters involve *anisotropic* polarizability then the polarizations are correlated. However, in this case choosing the polarizations to correspond to the three principal axes will again make them uncorrelated. \ No newline at end of file +*Note regarding polarization:* The previous demonstration involved a single-polarization source. For random polarizations, three separate simulations (for $\mathcal{J}_x$, $\mathcal{J}_y$, and $\mathcal{J}_z$) are required. Since the different polarizations are uncorrelated, the results (i.e., the flux spectrum) from each set of single-polarization simulations (which, in general, will have different convergence properties) are then summed in post processing. If the emitters involve *anisotropic* polarizability then the polarizations are correlated. However, in this case choosing the polarizations to correspond to the three principal axes will again make them uncorrelated. diff --git a/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md b/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md index a15d15f1c..4b6fcc95d 100644 --- a/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md +++ b/doc/docs/Scheme_Tutorials/Cylindrical_Coordinates.md @@ -551,4 +551,4 @@ Shown below is the far-field energy-density profile around the focal length for
![](../images/zone_plate_farfield.png) -
\ No newline at end of file + diff --git a/doc/docs/Scheme_Tutorials/Local_Density_of_States.md b/doc/docs/Scheme_Tutorials/Local_Density_of_States.md index ec91d66ca..f900cae5b 100644 --- a/doc/docs/Scheme_Tutorials/Local_Density_of_States.md +++ b/doc/docs/Scheme_Tutorials/Local_Density_of_States.md @@ -85,4 +85,4 @@ We run several simulations spanning a number of different notch sizes and plot t
![](../images/Metalcavity_ldos.png) -
\ No newline at end of file + diff --git a/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md b/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md index fc6a08009..490369823 100644 --- a/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md +++ b/doc/docs/Scheme_Tutorials/Multilevel_Atomic_Susceptibility.md @@ -57,7 +57,7 @@ The term in parenthesis on the right-hand side is the definition of $D_0$ in nor           (initial-populations N0))))) (set! geometry (list (make block (center 0 0 (+ (* -0.5 sz) (* 0.5 Lcav))) -                           (size infinity infinity Lcav) (material two-level)))) +                           (size infinity infinity Lcav) (material two-level)))) ``` Definition of the two-level medium involves the `multilevel-atom` sub-class of the `E-susceptibilities` material type. Each radiative and non-radiative `transition` is specified separately. Note that internally, Meep treats `pumping-rate` and `transition-rate` identically, and you can use them interchangeably, but it is important to specify the `from-level` and `to-level` parameters correctly, otherwise the results will be undefined. The choice of these parameters requires some care. For example, choosing a pumping rate that lies far beyond the first lasing threshold will yield large inversion, and thus large gain, which is not realistic, as most physical devices will overheat before reaching such a regime. Meep will still produce accurate results in this regime though. Additionally, choosing the total simulation time is especially important when operating near the threshold of a lasing mode, as the fields contain relaxation oscillations and require sufficient time to reach steady state. @@ -90,7 +90,7 @@ $$ \mathbf{E} \; (\textrm{SALT}) = \frac{2 |\theta|}{\hbar \sqrt{\gamma_\perp \g For two-level gain media, $\gamma_\parallel = \gamma_{12} + \gamma_{21}$. We can also verify that the system is not exhibiting relaxation oscillations by directly plotting the electric field as a function of time and looking for very long time-scale oscillations. In the continuum limit, these modes would appear as Dirac delta functions in the spectra. The discretized model, however, produces peaks with finite width. Thus, we need to integrate a fixed number of points around each peak to calculate the correct modal intensity. -By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold. +By varying $N_0$ or the pumping rate $R_p$, we can change the total gain available in the cavity. This is used to find the laser's modal intensities as a function of the strength of the gain. We can compare the simulated modal intensity with SALT as well as an independent FDTD solver based on the Maxwell-Bloch equations. All three methods produce results with good agreement close to the first lasing threshold.
![Near threshold comparison](../images/meep_salt_comparison_thresh.png) @@ -100,4 +100,4 @@ Further increasing the gain continues to yield good agreement.
![Near threshold comparison](../images/meep_salt_comparison_full.png) -
\ No newline at end of file +
diff --git a/doc/docs/Scheme_Tutorials/Optical_Forces.md b/doc/docs/Scheme_Tutorials/Optical_Forces.md index 2dd1529e7..d28fc2323 100644 --- a/doc/docs/Scheme_Tutorials/Optical_Forces.md +++ b/doc/docs/Scheme_Tutorials/Optical_Forces.md @@ -188,4 +188,4 @@ function force = compute_force(data) force = -1./f_avg .* df/ds .* 1./vg_avg; return endfunction -``` \ No newline at end of file +``` diff --git a/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md b/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md index b82a4c24c..9f17e834c 100644 --- a/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md +++ b/doc/docs/Scheme_Tutorials/Resonant_Modes_and_Transmission_in_a_Waveguide_Cavity.md @@ -242,7 +242,7 @@ unix% convert holey-wvg-cavity-hz-*.png holey-wvg-cavity-hz.gif ![](../images/Holey-wvg-cavity-hz.gif) -The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where +The mode has a frequency of 0.235, just as we saw in the transmission spectrum, and a $Q$ of 373 which we could have also found by fitting the transmission spectrum. This lifetime $Q$ includes two independent decay channels: light can decay from the cavity into the waveguide with lifetime $Q_w$, or it can radiate from the cavity into the surrounding air with lifetime $Q_r$, where $$\frac{1}{Q} = \frac{1}{Q_w} + \frac{1}{Q_r}$$ @@ -363,7 +363,7 @@ unix% grep freqs: holey-wvg-bands.out > fre.dat unix% grep freqs-im: holey-wvg-bands.out > fim.dat ``` -Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find: +Plotting the real parts of ω, where the light cone ω > *ck* is shaded gray, we find:
![](../images/Holey-wvg-bands.png) diff --git a/doc/docs/Subpixel_Smoothing.md b/doc/docs/Subpixel_Smoothing.md index e9fab5122..c03f28e67 100644 --- a/doc/docs/Subpixel_Smoothing.md +++ b/doc/docs/Subpixel_Smoothing.md @@ -85,7 +85,7 @@ for rad in np.arange(1.800,2.001,0.005): radius=rad, height=mp.inf, center=mp.Vector3())] - + sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy), geometry=geometry, eps_averaging=True, diff --git a/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md b/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md index d1a4cde8b..a230a2f7d 100644 --- a/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md +++ b/doc/docs/Synchronizing_the_Magnetic_and_Electric_Fields.md @@ -41,4 +41,4 @@ it will output the same quantities, but more accurately because the fields will Alternatively, if you want to synchronize the magnetic and electric fields in some context other than that of a step function, e.g. you are doing some computation like `integrate_field_function` (Python) or `integrate-field-function` (Scheme) outside of the timestepping, you can instead call two lower-level functions. Before doing your computations, you should call `meep.Simulation.fields.synchronize_magnetic_fields()` (Python) or `(meep-fields-synchronize-magnetic-fields fields)` (Scheme) to synchronize the magnetic fields with the electric fields, and after your computation you should call `meep.Simulation.fields.restore_magnetic_fields()` (Python) or `(meep-fields-restore-magnetic-fields fields)` (Scheme) to restore the fields to their unsynchronized state for timestepping. In the C++ interface, these correspond to `fields::synchronize_magnetic_fields` and `fields::restore_magnetic_fields`. If you *don't* call `meep.Simulation.fields.restore_magnetic_fields` or `meep-fields-restore-magnetic-fields` before timestepping, then the fields will be re-synchronized after *every* timestep, which will greatly increase the cost of timestepping. -In future versions, the fields may be synchronized automatically whenever you output something like the Poynting vector or do another field computation that involves both magnetic and electric fields, but currently you must do this manually (Issue [#719](https://github.com/NanoComp/meep/issues/719)). In any case, Meep does no additional work when you nest synchronization calls, so it is harmless to insert redundant field synchronizations. The `flux_in_box` (Python) or `flux-in-box` (Scheme) and `field_energy_in_box` (Python) or `field-energy-in-box` (Scheme) routines are already automatically synchronized, however. \ No newline at end of file +In future versions, the fields may be synchronized automatically whenever you output something like the Poynting vector or do another field computation that involves both magnetic and electric fields, but currently you must do this manually (Issue [#719](https://github.com/NanoComp/meep/issues/719)). In any case, Meep does no additional work when you nest synchronization calls, so it is harmless to insert redundant field synchronizations. The `flux_in_box` (Python) or `flux-in-box` (Scheme) and `field_energy_in_box` (Python) or `field-energy-in-box` (Scheme) routines are already automatically synchronized, however. diff --git a/doc/docs/The_Run_Function_Is_Not_A_Loop.md b/doc/docs/The_Run_Function_Is_Not_A_Loop.md index 258bd6681..e7fb49c04 100644 --- a/doc/docs/The_Run_Function_Is_Not_A_Loop.md +++ b/doc/docs/The_Run_Function_Is_Not_A_Loop.md @@ -36,7 +36,7 @@ sim.run(meep.output_efield_z, print("Hello World!"), until=200) **Scheme** ```scm -(run-until 200 output-efield-z (print "Hello World!\n")) +(run-until 200 output-efield-z (print "Hello World!\n")) ``` **This is wrong.** It will output "Hello World!" *once*, then give an error. What is going on? The problem is that you are thinking of `run` (Python) or `run-until` (Scheme) in the wrong way, as if it were a loop: @@ -104,4 +104,4 @@ sim.run(meep.output_efield_z, lambda sim: print("Hello World!"), until=200) (run-until 200 output-efield-z (lambda () (print "Hello World!\n"))) ``` -In Python, `lambda sim: ...` defines a function of one argument `sim`. In Scheme, `(lambda () ...)` defines a function of no arguments `()`. Both functions, when called, execute the `...` statements. \ No newline at end of file +In Python, `lambda sim: ...` defines a function of one argument `sim`. In Scheme, `(lambda () ...)` defines a function of no arguments `()`. Both functions, when called, execute the `...` statements. diff --git a/doc/docs/Yee_Lattice.md b/doc/docs/Yee_Lattice.md index 7464961c9..34864e30d 100644 --- a/doc/docs/Yee_Lattice.md +++ b/doc/docs/Yee_Lattice.md @@ -30,4 +30,4 @@ In two dimensions, the arrangement is similar except that we set $\hat{\mathbf{e The consequence of the Yee lattice is that, whenever you need to access field components, e.g. to find the energy density $(\mathbf{E}^* \cdot \mathbf{D} + |\mathbf{H}|^2)/2$ or the flux $\textrm{Re}\, \mathbf{E}^* \times \mathbf{H}$, then the components need to be **interpolated** to some common point in order to remain second-order accurate. Meep automatically does this [interpolation](Introduction.md#the-illusion-of-continuity) for you wherever necessary — in particular, whenever you compute [energy density](Python_User_Interface.md#energy-density-spectra) or [Poynting flux](Python_User_Interface.md#flux-spectra), or whenever you [output a field to a file](Python_User_Interface.md#output-functions), it is stored at the centers of each grid voxel: $(i+0.5,j+0.5,k+0.5)$. -In a Meep simulation, the coordinates of the Yee lattice can be obtained using a [field function](Field_Functions.md#coordinates-of-the-yee-grid). \ No newline at end of file +In a Meep simulation, the coordinates of the Yee lattice can be obtained using a [field function](Field_Functions.md#coordinates-of-the-yee-grid). diff --git a/doc/docs/mathjaxhelper.js b/doc/docs/mathjaxhelper.js index bec7b177c..96f40a91d 100644 --- a/doc/docs/mathjaxhelper.js +++ b/doc/docs/mathjaxhelper.js @@ -1,5 +1,5 @@ MathJax.Hub.Config({ - config: ["MMLorHTML.js"], - jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"], - extensions: ["MathMenu.js", "MathZoom.js"] + config : [ "MMLorHTML.js" ], + jax : [ "input/TeX", "output/HTML-CSS", "output/NativeMML" ], + extensions : [ "MathMenu.js", "MathZoom.js" ] }); diff --git a/doc/docs/mdx_math.py b/doc/docs/mdx_math.py index 75ecd3238..ef2cfc0fd 100644 --- a/doc/docs/mdx_math.py +++ b/doc/docs/mdx_math.py @@ -1,57 +1,60 @@ -# -*- coding: utf-8 -*- - -''' +""" Math extension for Python-Markdown ================================== Adds support for displaying math formulas using [MathJax](http://www.mathjax.org/). Author: 2015, Dmitry Shachnev . -''' - +""" import markdown + class MathExtension(markdown.extensions.Extension): def __init__(self, *args, **kwargs): self.config = { - 'enable_dollar_delimiter': [False, 'Enable single-dollar delimiter'], + "enable_dollar_delimiter": [False, "Enable single-dollar delimiter"], } - super(MathExtension, self).__init__(*args, **kwargs) + super().__init__(*args, **kwargs) def extendMarkdown(self, md, md_globals): def handle_match_inline(m): - node = markdown.util.etree.Element('script') - node.set('type', 'math/tex') + node = markdown.util.etree.Element("script") + node.set("type", "math/tex") node.text = markdown.util.AtomicString(m.group(3)) return node def handle_match(m): - node = markdown.util.etree.Element('script') - node.set('type', 'math/tex; mode=display') - if '\\begin' in m.group(2): - node.text = markdown.util.AtomicString(m.group(2) + m.group(4) + m.group(5)) + node = markdown.util.etree.Element("script") + node.set("type", "math/tex; mode=display") + if "\\begin" in m.group(2): + node.text = markdown.util.AtomicString( + m.group(2) + m.group(4) + m.group(5) + ) else: node.text = markdown.util.AtomicString(m.group(3)) return node configs = self.getConfigs() inlinemathpatterns = ( - markdown.inlinepatterns.Pattern(r'(? 50: params = [] for idx, param in enumerate(parameters): - params.append('('+str(param) if idx==0 else ' '*indent+param) - param_str = ',\n'.join(params) - param_str += ')' + params.append( + "(" + str(param) if idx == 0 else " " * indent + param + ) + param_str = ",\n".join(params) + param_str += ")" return param_str except ValueError as ex: - print("Warning: falling back to old parameter extraction method for {}\n{}".format(self.name, ex)) + print( + "Warning: falling back to old parameter extraction method for {}\n{}".format( + self.name, ex + ) + ) except OSError: # This happens when there is no source for some function/class (like NamedTuples) pass @@ -164,12 +169,13 @@ def get_parameters(self, indent): parameters = list(self.sig.parameters.values()) params = [] for idx, param in enumerate(parameters): - params.append('('+str(param) if idx==0 else ' '*indent+str(param)) - param_str = ',\n'.join(params) - param_str += ')' + params.append( + "(" + str(param) if idx == 0 else " " * indent + str(param) + ) + param_str = ",\n".join(params) + param_str += ")" return param_str - def get_parameters_from_ast(self): # Use Python's ast module to parse the function's code and pull out parameter names and # the text for default values. @@ -185,13 +191,15 @@ def parse_name(node): func = module.body[0] if not isinstance(func, ast.FunctionDef): - raise ValueError('Unsupported node type: {}'.format(type(func))) + raise ValueError(f"Unsupported node type: {type(func)}") # Get the param name and defaults together into a list of tuples args = reversed(func.args.args) defaults = reversed(func.args.defaults) iter = itertools.zip_longest(args, defaults, fillvalue=None) - parameters = [(parse_name(name), default) for name, default in reversed(list(iter))] + parameters = [ + (parse_name(name), default) for name, default in reversed(list(iter)) + ] # Convert each of the parameters (name, ast.node) to valid Python parameter # code, like what would have been in the actual source code. @@ -201,13 +209,13 @@ def parse_name(node): transformed.append(name) else: default = self.transform_node(name, node) - transformed.append("{}={}".format(name, default)) + transformed.append(f"{name}={default}") # Check for vararg (like *foo) and kwarg (like **bar) parameters if func.args.vararg is not None: - transformed.append('*{}'.format(func.args.vararg.arg)) + transformed.append(f"*{func.args.vararg.arg}") if func.args.kwarg is not None: - transformed.append('**{}'.format(func.args.kwarg.arg)) + transformed.append(f"**{func.args.kwarg.arg}") return transformed @@ -239,42 +247,41 @@ def transform_node(self, name, node): # print('Using inspect module for {}={} in {}'.format(name, default, self.name)) return default - def check_other_signatures(self, docstring): - """ Search for alternate function signatures in the docstring """ - SIG = '##sig' - SIG_KEEP = '##sig-keep' - other_signatures = '' + """Search for alternate function signatures in the docstring""" + SIG = "##sig" + SIG_KEEP = "##sig-keep" + other_signatures = "" if SIG in docstring or SIG_KEEP in docstring: other_sigs = [] docstring_lines = [] - for line in docstring.split('\n'): + for line in docstring.split("\n"): if line.endswith(SIG): - line = line.replace(SIG, '') + line = line.replace(SIG, "") line = line.strip() - line = line.replace('`', '') + line = line.replace("`", "") other_sigs.append(line) elif line.endswith(SIG_KEEP): - line = line.replace(SIG_KEEP, '') + line = line.replace(SIG_KEEP, "") line = line.strip() docstring_lines.append(line) - line = line.replace('`', '') + line = line.replace("`", "") other_sigs.append(line) else: docstring_lines.append(line) - docstring = '\n'.join(docstring_lines) - other_signatures = ''.join(['\ndef {}:'.format(item) for item in other_sigs]) + docstring = "\n".join(docstring_lines) + other_signatures = "".join([f"\ndef {item}:" for item in other_sigs]) return docstring, other_signatures def create_markdown(self): # pull relevant attributes into local variables function_name = self.name - function_name_escaped = function_name.replace('_', '\\_') - docstring = self.docstring if self.docstring else '' + function_name_escaped = function_name.replace("_", "\\_") + docstring = self.docstring if self.docstring else "" parameters = self.get_parameters(len(function_name) + 1) docstring, other_signatures = self.check_other_signatures(docstring) @@ -287,20 +294,21 @@ class MethodItem(FunctionItem): An introspected item that is a method of a class. Mostly the same as a FunctionItem, but can do extra stuff for methods if needed. """ - template_name = 'method_template.md' + + template_name = "method_template.md" def __init__(self, name, obj, klass): - super(MethodItem, self).__init__(name, obj) + super().__init__(name, obj) self.klass = klass self.method_name = name - self.name = '{}.{}'.format(klass.name, name) + self.name = f"{klass.name}.{name}" def create_markdown(self): # pull relevant attributes into local variables class_name = self.klass.name method_name = self.method_name - method_name_escaped = method_name.replace('_', '\\_') - docstring = self.docstring if self.docstring else '' + method_name_escaped = method_name.replace("_", "\\_") + docstring = self.docstring if self.docstring else "" parameters = self.get_parameters(4 + len(method_name) + 1) docstring, other_signatures = self.check_other_signatures(docstring) @@ -312,10 +320,11 @@ class ClassItem(Item): """ An introspected item that is a Class. """ - template_name = 'class_template.md' + + template_name = "class_template.md" def __init__(self, name, obj): - super(ClassItem, self).__init__(name, obj) + super().__init__(name, obj) self.add_methods() def add_methods(self): @@ -323,40 +332,45 @@ def add_methods(self): # inherited members def _predicate(value): if inspect.isfunction(value): - class_name = value.__qualname__.split('.')[0] + class_name = value.__qualname__.split(".")[0] return class_name == self.name else: return False self.methods = [] for name, member in inspect.getmembers(self.obj, _predicate): - if inspect.isfunction(member): # unbound methods are just functions at this point + if inspect.isfunction( + member + ): # unbound methods are just functions at this point self.methods.append(MethodItem(name, member, self)) def create_markdown(self): # pull relevant attributes into local variables class_name = self.name - docstring = self.docstring if self.docstring else '' + docstring = self.docstring if self.docstring else "" base_classes = [base.__name__ for base in self.obj.__bases__] - base_classes = ', '.join(base_classes) + base_classes = ", ".join(base_classes) # Substitute values into the template docs = dict() class_doc = self.template.format(**locals()) docs[class_name] = class_doc - docs[class_name+'[all-methods]'] = class_doc + '\n' + self.create_method_markdown(False) - docs[class_name+'[methods-with-docstrings]'] = class_doc + '\n' + self.create_method_markdown(True) + docs[class_name + "[all-methods]"] = ( + class_doc + "\n" + self.create_method_markdown(False) + ) + docs[class_name + "[methods-with-docstrings]"] = ( + class_doc + "\n" + self.create_method_markdown(True) + ) return docs - def create_method_markdown(self, only_with_docstrings=True): method_docs = [] if self.methods: # reorder self.methods so __init__ comes first, if it isn't already methods = self.methods[:] for idx, meth in enumerate(self.methods): - if meth.name == '__init__': + if meth.name == "__init__": if idx != 0: methods.remove(meth) methods.insert(0, meth) @@ -365,22 +379,24 @@ def create_method_markdown(self, only_with_docstrings=True): for item in methods: if only_with_docstrings and not item.docstring: continue - if not check_excluded(item.name) and \ - not check_excluded('{}.{}'.format(self.name, item.name)): - doc = item.create_markdown() - method_docs.append(doc) + if not check_excluded(item.name) and not check_excluded( + f"{self.name}.{item.name}" + ): + doc = item.create_markdown() + method_docs.append(doc) # join the methods into a single string - method_docs = '\n'.join(method_docs) + method_docs = "\n".join(method_docs) return method_docs -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def check_excluded(name): if name in EXCLUDES: return True - if name.startswith('_') and not (name.startswith('__') and name.endswith('__')): + if name.startswith("_") and not (name.startswith("__") and name.endswith("__")): # It's probably meant to be private return True return False @@ -428,27 +444,25 @@ def update_api_document(doc_items): to DESTDOC. """ # read - with open(SRCDOC, 'r') as f: + with open(SRCDOC) as f: srcdoc = f.read() # manipulate for name, doc in doc_items.items(): - tag = '@@ {} @@'.format(name) + tag = f"@@ {name} @@" if tag in srcdoc: srcdoc = srcdoc.replace(tag, doc) # write results - with open(DESTDOC, 'w') as f: + with open(DESTDOC, "w") as f: f.write(srcdoc) - def main(args): items = load_module(meep) doc_items = generate_docs(items) update_api_document(doc_items) - -if __name__ == '__main__': - main(sys.argv[1:]) \ No newline at end of file +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/libpympb/pympb.cpp b/libpympb/pympb.cpp index 639aab4e5..c6033cb99 100644 --- a/libpympb/pympb.cpp +++ b/libpympb/pympb.cpp @@ -27,27 +27,27 @@ extern "C" int mpb_verbosity = 2; int xyz_index = ((i1 * n2 + i2) * n3 + i3); // #else /* HAVE_MPI */ // /* first two dimensions are transposed in MPI output: */ -// #define LOOP_XYZ(md) \ -// { \ -// int n1 = md->nx, n2 = md->ny, n3 = md->nz, i1, i2_, i3; \ -// int local_n2 = md->local_ny, local_y_start = md->local_y_start; \ -// for (i2_ = 0; i2_ < local_n2; ++i2_) \ -// for (i1 = 0; i1 < n1; ++i1) \ -// for (i3 = 0; i3 < n3; ++i3) { \ -// int i2 = i2_ + local_y_start; \ -// int xyz_index = ((i2_ * n1 + i1) * n3 + i3); +// #define LOOP_XYZ(md) \ +// { \ +// int n1 = md->nx, n2 = md->ny, n3 = md->nz, i1, i2_, i3; \ +// int local_n2 = md->local_ny, local_y_start = md->local_y_start; \ +// for (i2_ = 0; i2_ < local_n2; ++i2_) \ +// for (i1 = 0; i1 < n1; ++i1) \ +// for (i3 = 0; i3 < n3; ++i3) { \ +// int i2 = i2_ + local_y_start; \ int xyz_index = ((i2_ * n1 + i1) * n3 + i3); // #endif /* HAVE_MPI */ typedef mpb_real real; // needed for the CASSIGN macros below // support version < 1.12 of MPB #ifndef CASSIGN_CONJ_MULT -#define CASSIGN_CONJ_MULT(a, b, c) { \ - real bbbb_re = (b).re, bbbb_im = (b).im; \ - real cccc_re = (c).re, cccc_im = (c).im; \ - CASSIGN_SCALAR(a, bbbb_re * cccc_re + bbbb_im * cccc_im, \ - bbbb_re * cccc_im - bbbb_im * cccc_re); \ -} +#define CASSIGN_CONJ_MULT(a, b, c) \ + { \ + real bbbb_re = (b).re, bbbb_im = (b).im; \ + real cccc_re = (c).re, cccc_im = (c).im; \ + CASSIGN_SCALAR(a, bbbb_re *cccc_re + bbbb_im * cccc_im, \ + bbbb_re * cccc_im - bbbb_im * cccc_re); \ + } #endif // TODO: Support MPI @@ -73,8 +73,6 @@ typedef mpb_real real; // needed for the CASSIGN macros below namespace py_mpb { - - // TODO: Placeholder int mpb_comm; @@ -127,13 +125,12 @@ void matrix3x3_to_arr(mpb_real arr[3][3], matrix3x3 m) { cnumber cscalar2cnumber(scalar_complex cs) { return make_cnumber(CSCALAR_RE(cs), CSCALAR_IM(cs)); } -cvector3 cscalar32cvector3(const scalar_complex *cs) -{ - cvector3 v; - v.x = cscalar2cnumber(cs[0]); - v.y = cscalar2cnumber(cs[1]); - v.z = cscalar2cnumber(cs[2]); - return v; +cvector3 cscalar32cvector3(const scalar_complex *cs) { + cvector3 v; + v.x = cscalar2cnumber(cs[0]); + v.y = cscalar2cnumber(cs[1]); + v.z = cscalar2cnumber(cs[2]); + return v; } // Return a string describing the current parity, used for frequency and filename @@ -142,13 +139,9 @@ const char *parity_string(maxwell_data *d) { static char s[128]; strcpy(s, ""); if (d->parity & EVEN_Z_PARITY) { strcat(s, (d->nz == 1) ? "te" : "zeven"); } - else if (d->parity & ODD_Z_PARITY) { - strcat(s, (d->nz == 1) ? "tm" : "zodd"); - } + else if (d->parity & ODD_Z_PARITY) { strcat(s, (d->nz == 1) ? "tm" : "zodd"); } if (d->parity & EVEN_Y_PARITY) { strcat(s, "yeven"); } - else if (d->parity & ODD_Y_PARITY) { - strcat(s, "yodd"); - } + else if (d->parity & ODD_Y_PARITY) { strcat(s, "yodd"); } return s; } @@ -225,7 +218,7 @@ mode_solver::mode_solver(int num_bands, double resolution[3], lattice lat, doubl use_simple_preconditioner(use_simple_preconditioner), grid_size(grid_size), nwork_alloc(0), eigensolver_nwork(eigensolver_nwork), eigensolver_block_size(eigensolver_block_size), last_parity(-2), iterations(0), eigensolver_flops(flops), geometry_list{}, - geometry_tree(NULL), vol(0), R{}, G{}, mdata(NULL), mtdata(NULL), + geometry_tree(NULL), vol(0), R{}, G{}, mdata(NULL), mtdata(NULL), curfield_band(0), H{}, Hblock{}, muinvH{}, W{}, freqs(num_bands), verbose(verbose), deterministic(deterministic), kpoint_index(0), curfield(NULL), curfield_type('-'), eps(true) { @@ -394,9 +387,7 @@ int mode_solver::mean_epsilon(symmetric_matrix *meps, symmetric_matrix *meps_inv } if (id1 > id2) { normal = normal_to_fixed_object(vector3_minus(p, shiftby1), *o1); } - else { - normal = normal_to_fixed_object(vector3_minus(p, shiftby2), *o2); - } + else { normal = normal_to_fixed_object(vector3_minus(p, shiftby2), *o2); } n[0] = normal.x / (geometry_lattice.size.x == 0 ? 1e-20 : geometry_lattice.size.x); n[1] = normal.y / (geometry_lattice.size.y == 0 ? 1e-20 : geometry_lattice.size.y); @@ -667,9 +658,7 @@ void mode_solver::get_material_pt(meep_geom::material_type &material, vector3 p) // material read from file: interpolate to get properties at r case meep_geom::material_data::MATERIAL_FILE: if (md->epsilon_data) { meep_geom::epsilon_file_material(md, p); } - else { - material = (meep_geom::material_type)default_material; - } + else { material = (meep_geom::material_type)default_material; } return; // material specified by user-supplied function: call user @@ -701,7 +690,9 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry n[1] = grid_size.y; n[2] = grid_size.z; - if (target_freq != 0.0 && mpb_verbosity > 0) { meep::master_printf("Target frequency is %g\n", target_freq); } + if (target_freq != 0.0 && mpb_verbosity > 0) { + meep::master_printf("Target frequency is %g\n", target_freq); + } int true_rank = n[2] > 1 ? 3 : (n[1] > 1 ? 2 : 1); if (true_rank < dimensions) { dimensions = true_rank; } @@ -728,12 +719,9 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry block_size = (num_bands - block_size - 1) / (-block_size); block_size = (num_bands + block_size - 1) / block_size; } - if (mpb_verbosity > 0) - meep::master_printf("Solving for %d bands at a time.\n", block_size); - } - else { - block_size = num_bands; + if (mpb_verbosity > 0) meep::master_printf("Solving for %d bands at a time.\n", block_size); } + else { block_size = num_bands; } if (mdata) { if (n[0] == mdata->nx && n[1] == mdata->ny && n[2] == mdata->nz && @@ -757,9 +745,7 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry mdata = NULL; curfield_reset(); } - else { - srand(time(NULL)); - } + else { srand(time(NULL)); } if (deterministic) { // seed should be the same for each run, although @@ -769,8 +755,7 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry srand(314159); // * (rank + 1)); } - if (mpb_verbosity > 0) - meep::master_printf("Creating Maxwell data...\n"); + if (mpb_verbosity > 0) meep::master_printf("Creating Maxwell data...\n"); mdata = create_maxwell_data(n[0], n[1], n[2], &local_N, &N_start, &alloc_N, block_size, NUM_FFT_BANDS); @@ -781,8 +766,7 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry if (check_maxwell_dielectric(mdata, 0)) { meep::abort("invalid dielectric function for MPB"); } if (!have_old_fields) { - if (mpb_verbosity > 0) - meep::master_printf("Allocating fields...\n"); + if (mpb_verbosity > 0) meep::master_printf("Allocating fields...\n"); int N = n[0] * n[1] * n[2]; int c = 2; @@ -797,16 +781,12 @@ void mode_solver::init(int p, bool reset_fields, geometric_object_list *geometry if (block_size < num_bands) { Hblock = create_evectmatrix(N, c, block_size, local_N, N_start, alloc_N); } - else { - Hblock = H; - } + else { Hblock = H; } if (using_mu() && block_size < num_bands) { muinvH = create_evectmatrix(N, c, num_bands, local_N, N_start, alloc_N); } - else { - muinvH = H; - } + else { muinvH = H; } } set_parity(p); @@ -829,8 +809,7 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { mpb_real no_size_y = geometry_lattice.size.y == 0 ? 1 : geometry_lattice.size.y; mpb_real no_size_z = geometry_lattice.size.z == 0 ? 1 : geometry_lattice.size.z; - if (mpb_verbosity > 0) - meep::master_printf("Mesh size is %d.\n", mesh_size); + if (mpb_verbosity > 0) meep::master_printf("Mesh size is %d.\n", mesh_size); Rm.c0 = vector3_scale(no_size_x, geometry_lattice.basis.c0); Rm.c1 = vector3_scale(no_size_y, geometry_lattice.basis.c1); @@ -844,8 +823,7 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { } vol = fabs(matrix3x3_determinant(Rm)); - if (mpb_verbosity > 0) - meep::master_printf("Cell volume = %g\n", vol); + if (mpb_verbosity > 0) meep::master_printf("Cell volume = %g\n", vol); Gm = matrix3x3_inverse(matrix3x3_transpose(Rm)); if (mpb_verbosity > 0) { @@ -860,18 +838,18 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { geom_fix_object_list(geometry_list); - if (mpb_verbosity > 0) - meep::master_printf("Geometric objects:\n"); + if (mpb_verbosity > 0) meep::master_printf("Geometric objects:\n"); if (meep::am_master()) { for (int i = 0; i < geometry_list.num_items; ++i) { #ifndef WITH_HERMITIAN_EPSILON meep_geom::medium_struct *mm; - if (meep_geom::is_medium(geometry_list.items[i].material, &mm)) { mm->check_offdiag_im_zero_or_abort(); } + if (meep_geom::is_medium(geometry_list.items[i].material, &mm)) { + mm->check_offdiag_im_zero_or_abort(); + } #endif - if (mpb_verbosity > 0) - display_geometric_object_info(5, geometry_list.items[i]); + if (mpb_verbosity > 0) display_geometric_object_info(5, geometry_list.items[i]); // meep_geom::medium_struct *mm; // if (meep_geom::is_medium(geometry.items[i].material, &mm)) { @@ -903,8 +881,7 @@ void mode_solver::init_epsilon(geometric_object_list *geometry_in) { } if (verbose && meep::am_master()) { - if (mpb_verbosity > 0) - meep::master_printf("Geometry object bounding box tree:\n"); + if (mpb_verbosity > 0) meep::master_printf("Geometry object bounding box tree:\n"); display_geom_box_tree(5, geometry_tree); } @@ -938,14 +915,12 @@ void mode_solver::reset_epsilon(geometric_object_list *geometry) { // if (!mu_input_file.empty()) { // } - if (mpb_verbosity > 0) - meep::master_printf("Initializing epsilon function...\n"); + if (mpb_verbosity > 0) meep::master_printf("Initializing epsilon function...\n"); set_maxwell_dielectric(mdata, mesh, R, G, dielectric_function, mean_epsilon_func, static_cast(this)); if (has_mu(geometry)) { - if (mpb_verbosity > 0) - meep::master_printf("Initializing mu function...\n"); + if (mpb_verbosity > 0) meep::master_printf("Initializing mu function...\n"); eps = false; set_maxwell_mu(mdata, mesh, R, G, dielectric_function, mean_epsilon_func, static_cast(this)); @@ -1020,8 +995,7 @@ void mode_solver::set_kpoint_index(int i) { kpoint_index = i; } void mode_solver::randomize_fields() { if (!mdata) { return; } - if (mpb_verbosity > 0) - meep::master_printf("Initializing fields to random numbers...\n"); + if (mpb_verbosity > 0) meep::master_printf("Initializing fields to random numbers...\n"); for (int i = 0; i < H.n * H.p; ++i) { ASSIGN_SCALAR(H.data[i], rand() * 1.0 / RAND_MAX, rand() * 1.0 / RAND_MAX); @@ -1039,8 +1013,7 @@ void mode_solver::solve_kpoint(vector3 kvector) { curfield_reset(); if (num_bands == 0) { - if (mpb_verbosity > 0) - meep::master_printf(" num-bands is zero, not solving for any bands\n"); + if (mpb_verbosity > 0) meep::master_printf(" num-bands is zero, not solving for any bands\n"); return; } @@ -1087,9 +1060,7 @@ void mode_solver::solve_kpoint(vector3 kvector) { } evectmatrix_resize(&H, H.p - ib0, 1); } - else { - ib0 = 0; /* solve for all bands */ - } + else { ib0 = 0; /* solve for all bands */ } // Set up deflation data. deflation_data deflation; @@ -1228,11 +1199,9 @@ void mode_solver::solve_kpoint(vector3 kvector) { for (int i = 0; i < num_bands; ++i) { freqs[i] = negative_epsilon_ok ? eigvals[i] : sqrt(eigvals[i]); - if (mpb_verbosity > 0) - meep::master_printf(", %g", freqs[i]); + if (mpb_verbosity > 0) meep::master_printf(", %g", freqs[i]); } - if (mpb_verbosity > 0) - meep::master_printf("\n"); + if (mpb_verbosity > 0) meep::master_printf("\n"); eigensolver_flops = evectmatrix_flops; } @@ -1265,9 +1234,7 @@ void mode_solver::get_epsilon() { for (int i = 0; i < N; ++i) { if (mdata->eps_inv == NULL) { epsilon[i] = 1.0; } - else { - epsilon[i] = mean_medium_from_matrix(mdata->eps_inv + i); - } + else { epsilon[i] = mean_medium_from_matrix(mdata->eps_inv + i); } if (epsilon[i] < eps_low) { eps_low = epsilon[i]; } if (epsilon[i] > eps_high) { eps_high = epsilon[i]; } eps_mean += epsilon[i]; @@ -1286,11 +1253,11 @@ void mode_solver::get_epsilon() { eps_inv_mean = N / eps_inv_mean; if (mpb_verbosity > 0) - meep::master_printf("epsilon: %g-%g, mean %g, harm. mean %g, %g%% > 1, %g%% \"fill\"\n", eps_low, - eps_high, eps_mean, eps_inv_mean, (100.0 * fill_count) / N, + meep::master_printf("epsilon: %g-%g, mean %g, harm. mean %g, %g%% > 1, %g%% \"fill\"\n", + eps_low, eps_high, eps_mean, eps_inv_mean, (100.0 * fill_count) / N, eps_high == eps_low ? 100.0 : 100.0 * (eps_mean - eps_low) / (eps_high - eps_low)); - } +} /* get the mu function, and compute some statistics */ void mode_solver::get_mu() { @@ -1319,9 +1286,7 @@ void mode_solver::get_mu() { for (int i = 0; i < N; ++i) { if (mdata->mu_inv == NULL) { mu[i] = 1.0; } - else { - mu[i] = mean_medium_from_matrix(mdata->mu_inv + i); - } + else { mu[i] = mean_medium_from_matrix(mdata->mu_inv + i); } if (mu[i] < eps_low) { eps_low = mu[i]; } if (mu[i] > eps_high) { eps_high = mu[i]; } @@ -1651,7 +1616,7 @@ double mode_solver::compute_field_energy_internal(mpb_real comp_sum[6]) { void mode_solver::clear_geometry_list() { if (geometry_list.num_items && geometry_list.items) { - for(int i = 0; i < geometry_list.num_items; ++i) { + for (int i = 0; i < geometry_list.num_items; ++i) { material_free((meep_geom::material_data *)geometry_list.items[i].material); geometric_object_destroy(geometry_list.items[i]); } @@ -1681,15 +1646,15 @@ std::vector mode_solver::compute_field_energy() { mpb_real energy_sum = compute_field_energy_internal(comp_sum); if (mpb_verbosity > 0) - meep::master_printf("%c-energy-components:, %d, %d", curfield_type, kpoint_index, curfield_band); + meep::master_printf("%c-energy-components:, %d, %d", curfield_type, kpoint_index, + curfield_band); for (int i = 0; i < 6; ++i) { comp_sum[i] /= (energy_sum == 0 ? 1 : energy_sum); if (i % 2 == 1 && mpb_verbosity > 0) { meep::master_printf(", %g", comp_sum[i] + comp_sum[i - 1]); - } + } } - if (mpb_verbosity > 0) - meep::master_printf("\n"); + if (mpb_verbosity > 0) meep::master_printf("\n"); /* The return value is a list of 7 items: the total energy, followed by the 6 elements of the comp_sum array (the fraction @@ -2250,9 +2215,7 @@ std::vector mode_solver::compute_group_velocity_component(vector3 d) { if (freqs[i] == 0) { /* v is undefined in this case */ group_v[i] = 0.0; /* just set to zero */ } - else { - group_v[i] /= negative_epsilon_ok ? sqrt(fabs(freqs[i])) : freqs[i]; - } + else { group_v[i] /= negative_epsilon_ok ? sqrt(fabs(freqs[i])) : freqs[i]; } } return group_v; @@ -2298,9 +2261,7 @@ mpb_real mode_solver::compute_1_group_velocity_component(vector3 d, int b) { if (freqs[ib] == 0) { /* v is undefined in this case */ group_v = 0.0; /* just set to zero */ } - else { - group_v /= negative_epsilon_ok ? sqrt(fabs(freqs[ib])) : freqs[ib]; - } + else { group_v /= negative_epsilon_ok ? sqrt(fabs(freqs[ib])) : freqs[ib]; } return group_v; } @@ -2702,7 +2663,6 @@ bool with_hermitian_epsilon() { #endif } - /* --- port of mpb's mpb/transform.c --- */ /* If `curfield` is the real-space D-field (of band-index i), computes the overlap @@ -2719,11 +2679,10 @@ bool with_hermitian_epsilon() { * the Bloch included) or the `get_bfield` and `get_dfield` C functions. * Usually, it will be more convenient to use the wrapper `compute_symmetry(i, W, w)` * which defaults to the B-field (since μ = 1 usually) and takes a band-index `i`. */ -cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) -{ +cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) { int n1, n2, n3; mpb_real s1, s2, s3, c1, c2, c3; - cnumber integral = {0,0}, integral_sum; + cnumber integral = {0, 0}, integral_sum; number detW; vector3 kvector = cur_kvector; @@ -2757,7 +2716,9 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) // #endif /* prepare before looping ... */ - n1 = mdata->nx; n2 = mdata->ny; n3 = mdata->nz; + n1 = mdata->nx; + n2 = mdata->ny; + n3 = mdata->nz; s1 = geometry_lattice.size.x / n1; /* pixel spacings */ s2 = geometry_lattice.size.y / n2; @@ -2781,9 +2742,9 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) matrix3x3_inverse(geometry_lattice.basis)); /* hoist rescalings outside the loop (maybe licm takes care of it, but do it anyway) */ - kvector.x *= TWOPI/(geometry_lattice.size.x == 0 ? 1e-20 : geometry_lattice.size.x); - kvector.y *= TWOPI/(geometry_lattice.size.y == 0 ? 1e-20 : geometry_lattice.size.y); - kvector.z *= TWOPI/(geometry_lattice.size.z == 0 ? 1e-20 : geometry_lattice.size.z); + kvector.x *= TWOPI / (geometry_lattice.size.x == 0 ? 1e-20 : geometry_lattice.size.x); + kvector.y *= TWOPI / (geometry_lattice.size.y == 0 ? 1e-20 : geometry_lattice.size.y); + kvector.z *= TWOPI / (geometry_lattice.size.z == 0 ? 1e-20 : geometry_lattice.size.z); /* loop over coordinates (introduces int vars `i1`, `i2`, `i3`, `xyz_index`) */ LOOP_XYZ(mdata) { /* implies two opening braces `{{` */ @@ -2805,12 +2766,12 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) /* define `Ft` as the vector components of `Ftemp` transformed by `Wc`; we just * write out the matrix-product manually here, for both real & imag parts */ - Ft[0].re = Wc.c0.x*Ftemp[0].re + Wc.c1.x*Ftemp[1].re + Wc.c2.x*Ftemp[2].re; - Ft[0].im = Wc.c0.x*Ftemp[0].im + Wc.c1.x*Ftemp[1].im + Wc.c2.x*Ftemp[2].im; - Ft[1].re = Wc.c0.y*Ftemp[0].re + Wc.c1.y*Ftemp[1].re + Wc.c2.y*Ftemp[2].re; - Ft[1].im = Wc.c0.y*Ftemp[0].im + Wc.c1.y*Ftemp[1].im + Wc.c2.y*Ftemp[2].im; - Ft[2].re = Wc.c0.z*Ftemp[0].re + Wc.c1.z*Ftemp[1].re + Wc.c2.z*Ftemp[2].re; - Ft[2].im = Wc.c0.z*Ftemp[0].im + Wc.c1.z*Ftemp[1].im + Wc.c2.z*Ftemp[2].im; + Ft[0].re = Wc.c0.x * Ftemp[0].re + Wc.c1.x * Ftemp[1].re + Wc.c2.x * Ftemp[2].re; + Ft[0].im = Wc.c0.x * Ftemp[0].im + Wc.c1.x * Ftemp[1].im + Wc.c2.x * Ftemp[2].im; + Ft[1].re = Wc.c0.y * Ftemp[0].re + Wc.c1.y * Ftemp[1].re + Wc.c2.y * Ftemp[2].re; + Ft[1].im = Wc.c0.y * Ftemp[0].im + Wc.c1.y * Ftemp[1].im + Wc.c2.y * Ftemp[2].im; + Ft[2].re = Wc.c0.z * Ftemp[0].re + Wc.c1.z * Ftemp[1].re + Wc.c2.z * Ftemp[2].re; + Ft[2].im = Wc.c0.z * Ftemp[0].im + Wc.c1.z * Ftemp[1].im + Wc.c2.z * Ftemp[2].im; /* get the Bloch field value at current point `p` (without eⁱᵏʳ factor). * We multiply the input field `F` (either B or D-field) with μ⁻¹ or ε⁻¹ to get @@ -2818,44 +2779,45 @@ cnumber mode_solver::transformed_overlap(matrix3x3 W, vector3 w) * t-postscript denoting a field transformed by {W|w}. Here, we essentially * adapt some boiler-plate code from compute_field_energy_internal in fields.c */ if (curfield_type == 'd') { - assign_symmatrix_vector(F, mdata->eps_inv[xyz_index], curfield+3*xyz_index); + assign_symmatrix_vector(F, mdata->eps_inv[xyz_index], curfield + 3 * xyz_index); } else if (curfield_type == 'b' && mdata->mu_inv != NULL) { - assign_symmatrix_vector(F, mdata->mu_inv[xyz_index], curfield+3*xyz_index); + assign_symmatrix_vector(F, mdata->mu_inv[xyz_index], curfield + 3 * xyz_index); } else { - F[0] = curfield[3*xyz_index]; - F[1] = curfield[3*xyz_index+1]; - F[2] = curfield[3*xyz_index+2]; + F[0] = curfield[3 * xyz_index]; + F[1] = curfield[3 * xyz_index + 1]; + F[2] = curfield[3 * xyz_index + 2]; } /* inner product of F and Ft={W|w}F in Bloch form */ - CASSIGN_CONJ_MULT(integrand, F[0], Ft[0]); /* add adjoint(F)*Ft to integrand */ + CASSIGN_CONJ_MULT(integrand, F[0], Ft[0]); /* add adjoint(F)*Ft to integrand */ CACCUMULATE_SUM_CONJ_MULT(integrand, F[1], Ft[1]); CACCUMULATE_SUM_CONJ_MULT(integrand, F[2], Ft[2]); /* include Bloch phases */ - deltaphi = kvector.x*(pt.x-p.x) + kvector.y*(pt.y-p.y) + kvector.z*(pt.z-p.z); + deltaphi = kvector.x * (pt.x - p.x) + kvector.y * (pt.y - p.y) + kvector.z * (pt.z - p.z); CASSIGN_SCALAR(phase, cos(deltaphi), sin(deltaphi)); /* add integrand-contribution to integral */ integral.re += CSCALAR_MULT_RE(integrand, phase); integral.im += CSCALAR_MULT_IM(integrand, phase); - }}} - - integral.re *= vol / H.N; - integral.im *= vol / H.N; + } +} +} - mpi_allreduce(&integral, &integral_sum, 2, number, MPI_DOUBLE, MPI_SUM, mpb_comm); +integral.re *= vol / H.N; +integral.im *= vol / H.N; - if (curfield_type == 'b') { /* H & B are pseudovectors => transform includes det(W) */ - integral_sum.re *= detW; - integral_sum.im *= detW; - } +mpi_allreduce(&integral, &integral_sum, 2, number, MPI_DOUBLE, MPI_SUM, mpb_comm); - return integral_sum; +if (curfield_type == 'b') { /* H & B are pseudovectors => transform includes det(W) */ + integral_sum.re *= detW; + integral_sum.im *= detW; } +return integral_sum; +} cnumber mode_solver::compute_symmetry(int which_band, matrix3x3 W, vector3 w) { cnumber symval; diff --git a/libpympb/pympb.hpp b/libpympb/pympb.hpp index 3b5da8cfb..2222015b3 100644 --- a/libpympb/pympb.hpp +++ b/libpympb/pympb.hpp @@ -19,7 +19,6 @@ namespace py_mpb { // This is redeclared here so SWIG will see it. extern "C" int mpb_verbosity; - #define TWOPI 6.2831853071795864769252867665590057683943388 void map_data(mpb_real *d_in_re, int size_in_re, mpb_real *d_in_im, int size_in_im, int n_in[3], diff --git a/m4/ax_check_compiler_flags.m4 b/m4/ax_check_compiler_flags.m4 index 86eaf15ef..ad166dc8f 100644 --- a/m4/ax_check_compiler_flags.m4 +++ b/m4/ax_check_compiler_flags.m4 @@ -20,13 +20,13 @@ AS_LITERAL_IF([$1], [AC_CACHE_VAL(AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1), [ ax_save_FLAGS=$[]_AC_LANG_PREFIX[]FLAGS _AC_LANG_PREFIX[]FLAGS="$1" - AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], + AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=yes, AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=no) _AC_LANG_PREFIX[]FLAGS=$ax_save_FLAGS])], [ax_save_FLAGS=$[]_AC_LANG_PREFIX[]FLAGS _AC_LANG_PREFIX[]FLAGS="$1" - AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], + AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], eval AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=yes, eval AS_TR_SH(ax_cv_[]_AC_LANG_ABBREV[]_flags_$1)=no) _AC_LANG_PREFIX[]FLAGS=$ax_save_FLAGS]) diff --git a/m4/ax_compiler_vendor.m4 b/m4/ax_compiler_vendor.m4 index 1b8ed401c..6c3db2625 100644 --- a/m4/ax_compiler_vendor.m4 +++ b/m4/ax_compiler_vendor.m4 @@ -4,7 +4,7 @@ dnl @category C dnl @category C++ dnl dnl Determine the vendor of the C/C++ compiler, e.g., gnu, intel, ibm, -dnl sun, hp, borland, comeau, dec, cray, kai, lcc, metrowerks, sgi, +dnl sun, hp, borland, comeau, dec, cray, kai, lcc, metrowerks, sgi, dnl microsoft, watcom, etc. The vendor is returned in the cache variable dnl $ax_cv_c_compiler_vendor for C and $ax_cv_cxx_compiler_vendor for C++. dnl @@ -17,7 +17,7 @@ AC_DEFUN([AX_COMPILER_VENDOR], AC_CACHE_CHECK([for _AC_LANG compiler vendor], ax_cv_[]_AC_LANG_ABBREV[]_compiler_vendor, [ax_cv_[]_AC_LANG_ABBREV[]_compiler_vendor=unknown # note: don't check for gcc first since some other compilers define __GNUC__ - for ventest in intel:__ICC,__ECC,__INTEL_COMPILER ibm:__xlc__,__xlC__,__IBMC__,__IBMCPP__ gnu:__GNUC__ sun:__SUNPRO_C,__SUNPRO_CC hp:__HP_cc,__HP_aCC dec:__DECC,__DECCXX,__DECC_VER,__DECCXX_VER borland:__BORLANDC__,__TURBOC__ comeau:__COMO__ cray:_CRAYC kai:__KCC lcc:__LCC__ metrowerks:__MWERKS__ sgi:__sgi,sgi microsoft:_MSC_VER watcom:__WATCOMC__ portland:__PGI; do + for ventest in intel:__ICC,__ECC,__INTEL_COMPILER ibm:__xlc__,__xlC__,__IBMC__,__IBMCPP__ gnu:__GNUC__ sun:__SUNPRO_C,__SUNPRO_CC hp:__HP_cc,__HP_aCC dec:__DECC,__DECCXX,__DECC_VER,__DECCXX_VER borland:__BORLANDC__,__TURBOC__ comeau:__COMO__ cray:_CRAYC kai:__KCC lcc:__LCC__ metrowerks:__MWERKS__ sgi:__sgi,sgi microsoft:_MSC_VER watcom:__WATCOMC__ portland:__PGI; do vencpp="defined("`echo $ventest | cut -d: -f2 | sed 's/,/) || defined(/g'`")" AC_COMPILE_IFELSE([AC_LANG_PROGRAM(,[ #if !($vencpp) @@ -27,4 +27,3 @@ AC_CACHE_CHECK([for _AC_LANG compiler vendor], ax_cv_[]_AC_LANG_ABBREV[]_compile done ]) ]) - diff --git a/m4/ax_gcc_x86_cpuid.m4 b/m4/ax_gcc_x86_cpuid.m4 index 50fe009e9..d91509c2c 100644 --- a/m4/ax_gcc_x86_cpuid.m4 +++ b/m4/ax_gcc_x86_cpuid.m4 @@ -57,7 +57,7 @@ AC_CACHE_CHECK(for x86 cpuid $1 output, ax_cv_gcc_x86_cpuid_$1, fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; -])], +])], [ax_cv_gcc_x86_cpuid_$1=`cat conftest_cpuid`; rm -f conftest_cpuid], [ax_cv_gcc_x86_cpuid_$1=unknown; rm -f conftest_cpuid], [ax_cv_gcc_x86_cpuid_$1=unknown])]) diff --git a/m4/pkg.m4 b/m4/pkg.m4 index 1b49cd6b6..72139eadf 100644 --- a/m4/pkg.m4 +++ b/m4/pkg.m4 @@ -34,7 +34,7 @@ AC_DEFUN([PKG_CHECK_MODULES], [ else $1_CFLAGS="" $1_LIBS="" - ## If we have a custom action on failure, don't print errors, but + ## If we have a custom action on failure, don't print errors, but ## do set a variable so people can do so. $1_PKG_ERRORS=`$PKG_CONFIG --errors-to-stdout --print-errors "$2"` ifelse([$4], ,echo $$1_PKG_ERRORS,) @@ -54,5 +54,3 @@ AC_DEFUN([PKG_CHECK_MODULES], [ ifelse([$4], , AC_MSG_ERROR([Library requirements ($2) not met; consider adjusting the PKG_CONFIG_PATH environment variable if your libraries are in a nonstandard prefix so pkg-config can find them.]), [$4]) fi ]) - - diff --git a/python/adjoint/__init__.py b/python/adjoint/__init__.py index 949d3d689..a787e31e7 100644 --- a/python/adjoint/__init__.py +++ b/python/adjoint/__init__.py @@ -10,7 +10,7 @@ from .basis import BilinearInterpolationBasis -from .optimization_problem import (OptimizationProblem) +from .optimization_problem import OptimizationProblem from .filter_source import FilteredSource diff --git a/python/adjoint/basis.py b/python/adjoint/basis.py index ac28d512c..af54f6877 100644 --- a/python/adjoint/basis.py +++ b/python/adjoint/basis.py @@ -1,27 +1,28 @@ -import meep as mp +from abc import ABCMeta, abstractmethod + import numpy as np from scipy import sparse -from abc import ABCMeta, abstractmethod -ABC = ABCMeta('ABC', (object, ), {'__slots__': - ()}) # compatible with Python 2 and 3 +import meep as mp + +ABC = ABCMeta("ABC", (object,), {"__slots__": ()}) # compatible with Python 2 and 3 -#---------------------------------------------------------------------- +# ---------------------------------------------------------------------- # Basis is the abstract base class from which classes describing specific # basis sets should inherit. -#---------------------------------------------------------------------- +# ---------------------------------------------------------------------- class Basis(ABC): - """ - """ + """ """ + def __init__( - self, - rho_vector=None, - volume=None, - size=None, - center=mp.Vector3(), + self, + rho_vector=None, + volume=None, + size=None, + center=mp.Vector3(), ): - self.volume = volume if volume else mp.Volume(center=center, size=size) + self.volume = volume or mp.Volume(center=center, size=size) self.rho_vector = rho_vector def func(self): @@ -32,13 +33,11 @@ def _f(p): @abstractmethod def get_basis_vjp(self): - raise NotImplementedError( - "derived class must implement get_basis_vjp method") + raise NotImplementedError("derived class must implement get_basis_vjp method") @abstractmethod def __call__(self, p=[0.0, 0.0]): - raise NotImplementedError( - "derived class must implement __call__() method") + raise NotImplementedError("derived class must implement __call__() method") def set_rho_vector(self, rho_vector): self.rho_vector = rho_vector @@ -50,61 +49,75 @@ def set_rho_vector(self, rho_vector): class BilinearInterpolationBasis(Basis): - ''' + """ Simple bilinear interpolation basis set. - ''' + """ + def __init__(self, resolution, symmetry=None, **kwargs): self.dim = 2 - super(BilinearInterpolationBasis, self).__init__(**kwargs) + super().__init__(**kwargs) # Generate interpolation grid - if symmetry is None or len(symmetry) == 0: - self.symmetry = [] - else: - self.symmetry = symmetry - + self.symmetry = [] if symmetry is None or len(symmetry) == 0 else symmetry if mp.X in set(self.symmetry): self.Nx = int(resolution * self.volume.size.x / 2) + 1 self.rho_x = np.linspace( self.volume.center.x, - self.volume.center.x + self.volume.size.x / 2, self.Nx) + self.volume.center.x + self.volume.size.x / 2, + self.Nx, + ) self.mirror_X = True else: self.Nx = int(resolution * self.volume.size.x) + 1 self.rho_x = np.linspace( self.volume.center.x - self.volume.size.x / 2, - self.volume.center.x + self.volume.size.x / 2, self.Nx) + self.volume.center.x + self.volume.size.x / 2, + self.Nx, + ) self.mirror_X = False if mp.Y in set(self.symmetry): self.Ny = int(resolution * self.volume.size.y / 2) + 1 self.rho_y = np.linspace( self.volume.center.y, - self.volume.center.y + self.volume.size.y / 2, self.Ny) + self.volume.center.y + self.volume.size.y / 2, + self.Ny, + ) self.mirror_Y = True else: self.Ny = int(resolution * self.volume.size.y) + 1 self.rho_y = np.linspace( self.volume.center.y - self.volume.size.y / 2, - self.volume.center.y + self.volume.size.y / 2, self.Ny) + self.volume.center.y + self.volume.size.y / 2, + self.Ny, + ) self.mirror_Y = False self.num_design_params = self.Nx * self.Ny if self.rho_vector is None: - self.rho_vector = np.ones((self.num_design_params, )) + self.rho_vector = np.ones((self.num_design_params,)) def __call__(self, p): - x = 2 * self.volume.center.x - p.x if self.mirror_X and p.x < self.volume.center.x else p.x - y = 2 * self.volume.center.y - p.y if self.mirror_Y and p.y < self.volume.center.y else p.y + x = ( + 2 * self.volume.center.x - p.x + if self.mirror_X and p.x < self.volume.center.x + else p.x + ) + y = ( + 2 * self.volume.center.y - p.y + if self.mirror_Y and p.y < self.volume.center.y + else p.y + ) weights, interp_idx = self.get_bilinear_row( - x, y, self.rho_x, self.rho_y) # ignore z coordinate + x, y, self.rho_x, self.rho_y + ) # ignore z coordinate return np.dot(self.rho_vector[interp_idx], weights) def get_basis_vjp(self, dJ_deps, design_grid): - ''' get vector jacobian product of interpolator''' + """get vector jacobian product of interpolator""" dg_Nx, dg_Ny, Nz, Nf = dJ_deps.shape # get important design grid dimensions x_grid = design_grid.x @@ -113,32 +126,37 @@ def get_basis_vjp(self, dJ_deps, design_grid): # take care of symmetries if self.mirror_X: - dJ_deps = dJ_deps[int(dg_Nx / 2):, :, :, :] * 2 - x_grid = x_grid[int(dg_Nx / 2):] + dJ_deps = dJ_deps[int(dg_Nx / 2) :, :, :, :] * 2 + x_grid = x_grid[int(dg_Nx / 2) :] if self.mirror_Y: - dJ_deps = dJ_deps[:, int(dg_Ny / 2):, :, :] * 2 - y_grid = y_grid[int(dg_Ny / 2):] + dJ_deps = dJ_deps[:, int(dg_Ny / 2) :, :, :] * 2 + y_grid = y_grid[int(dg_Ny / 2) :] dg_Nx, dg_Ny, Nz, Nf = dJ_deps.shape # recalculate - Nx, Ny = self.rho_x.size, self.rho_y.size # get important interpolator dimensions + Nx, Ny = ( + self.rho_x.size, + self.rho_y.size, + ) # get important interpolator dimensions # same interpolation matrix for all frequencies and all coordinates in Z direction - A = self.gen_interpolation_matrix(self.rho_x, self.rho_y, x_grid, - y_grid, z_grid) + A = self.gen_interpolation_matrix( + self.rho_x, self.rho_y, x_grid, y_grid, z_grid + ) # TODO ditch the for loops dJ_dp = np.zeros((Nx * Ny, Nf)) for fi in range(Nf): for zi in range(Nz): dJ_dp[:, fi] += np.matmul( - dJ_deps[:, :, zi, fi].reshape(dg_Nx * dg_Ny, order='C'), A) + dJ_deps[:, :, zi, fi].reshape(dg_Nx * dg_Ny, order="C"), A + ) return dJ_dp def get_bilinear_coefficients(self, x, x1, x2, y, y1, y2): - ''' + """ Calculates the bilinear interpolation coefficients for a single point at (x,y). Assumes that the user already knows the four closest points and provides the corresponding (x1,x2) and(y1,y2) coordinates. - ''' + """ b11 = ((x - x2) * (y - y2)) / ((x1 - x2) * (y1 - y2)) b12 = -((x - x2) * (y - y1)) / ((x1 - x2) * (y1 - y2)) b21 = -((x - x1) * (y - y2)) / ((x1 - x2) * (y1 - y2)) @@ -146,17 +164,17 @@ def get_bilinear_coefficients(self, x, x1, x2, y, y1, y2): return [b11, b12, b21, b22] def get_bilinear_row(self, rx, ry, rho_x, rho_y): - ''' + """ Calculates a vector of bilinear interpolation weights that can be used in an inner product with the neighboring function values, or placed inside of an interpolation matrix. - ''' + """ Nx = rho_x.size Ny = rho_y.size # binary search in x direction to get x1 and x2 - xi2 = np.searchsorted(rho_x, rx, side='left') + xi2 = np.searchsorted(rho_x, rx, side="left") if xi2 <= 0: # extrapolation (be careful!) xi1 = 0 xi2 = 1 @@ -170,7 +188,7 @@ def get_bilinear_row(self, rx, ry, rho_x, rho_y): x2 = rho_x[xi2] # binary search in y direction to get y1 and y2 - yi2 = np.searchsorted(rho_y, ry, side='left') + yi2 = np.searchsorted(rho_y, ry, side="left") if yi2 <= 0: # extrapolation (be careful!) yi1 = 0 yi2 = 1 @@ -187,10 +205,10 @@ def get_bilinear_row(self, rx, ry, rho_x, rho_y): weights = self.get_bilinear_coefficients(rx, x1, x2, ry, y1, y2) # get location of nearest neigbor interpolation points - interp_idx = np.array([ - xi1 * Ny + yi1, xi1 * Ny + yi2, (xi2) * Ny + yi1, (xi2) * Ny + yi2 - ], - dtype=np.int64) + interp_idx = np.array( + [xi1 * Ny + yi1, xi1 * Ny + yi2, (xi2) * Ny + yi1, (xi2) * Ny + yi2], + dtype=np.int64, + ) return weights, interp_idx @@ -202,7 +220,7 @@ def gen_interpolation_matrix( rho_y_interp, rho_z_interp, ): - ''' + """ Generates a bilinear interpolation matrix. Arguments: @@ -213,7 +231,7 @@ def gen_interpolation_matrix( Returns: A .................... [N,M] sparse matrix - interpolation matrix - ''' + """ Nx = rho_x.size Ny = rho_y.size @@ -232,19 +250,18 @@ def gen_interpolation_matrix( for rx in rho_x_interp: for ry in rho_y_interp: # get weights - weights, interp_idx = self.get_bilinear_row( - rx, ry, rho_x, rho_y) + weights, interp_idx = self.get_bilinear_row(rx, ry, rho_x, rho_y) # populate sparse matrix vectors - interp_weights[4 * ri:4 * (ri + 1)] = weights - row_ind[4 * ri:4 * (ri + 1)] = np.array([ri, ri, ri, ri], - dtype=np.int64) - col_ind[4 * ri:4 * (ri + 1)] = interp_idx + interp_weights[4 * ri : 4 * (ri + 1)] = weights + row_ind[4 * ri : 4 * (ri + 1)] = np.array( + [ri, ri, ri, ri], dtype=np.int64 + ) + col_ind[4 * ri : 4 * (ri + 1)] = interp_idx ri += 1 - # From matrix vectors, populate the sparse matrix - A = sparse.coo_matrix((interp_weights, (row_ind, col_ind)), - shape=(output_dimension, input_dimension)) - - return A + return sparse.coo_matrix( + (interp_weights, (row_ind, col_ind)), + shape=(output_dimension, input_dimension), + ) diff --git a/python/adjoint/connectivity.py b/python/adjoint/connectivity.py index dc2309e94..450e6f4eb 100644 --- a/python/adjoint/connectivity.py +++ b/python/adjoint/connectivity.py @@ -4,88 +4,133 @@ BC: Dirichlet on last slice rho[-Nx*Ny:], 0 outside the first slice, and Neumann on sides. Mo Chen """ - import numpy as np +from scipy.sparse import csc_matrix, csr_matrix, diags, eye, kron, lil_matrix from scipy.sparse.linalg import cg, spsolve -from scipy.sparse import kron, diags, csr_matrix, eye, csc_matrix, lil_matrix - -class ConnectivityConstraint(object): - def __init__(self, nx, ny, nz, k0=1000, zeta=0, sp_solver=cg, alpha=None, alpha0=None, thresh=0.1, p=2): - #zeta is to prevent singularity when damping is zero; with damping, zeta should be zero - #set ny=1 for 2D - self.nx, self.ny, self.nz= nx, ny, nz - self.n = nx*ny*nz - self.m = nx*ny*(nz-1) + + +class ConnectivityConstraint: + def __init__( + self, + nx, + ny, + nz, + k0=1000, + zeta=0, + sp_solver=cg, + alpha=None, + alpha0=None, + thresh=0.1, + p=2, + ): + # zeta is to prevent singularity when damping is zero; with damping, zeta should be zero + # set ny=1 for 2D + self.nx, self.ny, self.nz = nx, ny, nz + self.n = nx * ny * nz + self.m = nx * ny * (nz - 1) self.solver = sp_solver self.k0, self.zeta = k0, zeta self.thresh = thresh self.p = p - #default alpha and alpha0 - if alpha != None: - self.alpha=alpha - else: - self.alpha = 0.1*min(1/nx, 1/ny, 1/nz) - if alpha0 != None: - self.alpha0 = alpha0 - else: - self.alpha0 = -np.log(thresh)/min(nx, nz) + # default alpha and alpha0 + self.alpha = alpha if alpha != None else 0.1 * min(1 / nx, 1 / ny, 1 / nz) + self.alpha0 = alpha0 if alpha0 != None else -np.log(thresh) / min(nx, nz) def forward(self, rho_vector): self.rho_vector = rho_vector # gradient and -div operator - gx = diags([-1,1], [0,1], shape=(self.nx-1, self.nx), format='csr') + gx = diags([-1, 1], [0, 1], shape=(self.nx - 1, self.nx), format="csr") dx = gx.copy().transpose() - gy = diags([-1,1], [0,1], shape=(self.ny-1, self.ny), format='csr') + gy = diags([-1, 1], [0, 1], shape=(self.ny - 1, self.ny), format="csr") dy = gy.copy().transpose() - gz = diags([1,-1], [0, -1], shape=(self.nz, self.nz), format='csr') - dz = diags([1,-1], [0, 1], shape=(self.nz-1, self.nz), format='csr') + gz = diags([1, -1], [0, -1], shape=(self.nz, self.nz), format="csr") + dz = diags([1, -1], [0, 1], shape=(self.nz - 1, self.nz), format="csr") # kron product for 2D - Ix, Iy, Iz = eye(self.nx), eye(self.ny), eye(self.nz-1) - self.gx, self.gy, self.gz = kron(Iz, kron(Iy, gx)), kron(Iz, kron(gy, Ix)), kron(gz, kron(Iy,Ix)) - self.dx, self.dy, self.dz = kron(Iz, kron(Iy, dx)), kron(Iz, kron(dy, Ix)), kron(dz, kron(Iy, Ix)) - - #conductivity based on rho + Ix, Iy, Iz = eye(self.nx), eye(self.ny), eye(self.nz - 1) + self.gx, self.gy, self.gz = ( + kron(Iz, kron(Iy, gx)), + kron(Iz, kron(gy, Ix)), + kron(gz, kron(Iy, Ix)), + ) + self.dx, self.dy, self.dz = ( + kron(Iz, kron(Iy, dx)), + kron(Iz, kron(dy, Ix)), + kron(dz, kron(Iy, Ix)), + ) + + # conductivity based on rho rho_pad = np.reshape(rho_vector, (self.nz, self.ny, self.nx)) - rhox = np.array([0.5*(rho_pad[k, j, i]+rho_pad[k, j, i+1]) for k in range(self.nz-1) for j in range(self.ny) for i in range(self.nx-1)]) + rhox = np.array( + [ + 0.5 * (rho_pad[k, j, i] + rho_pad[k, j, i + 1]) + for k in range(self.nz - 1) + for j in range(self.ny) + for i in range(self.nx - 1) + ] + ) self.rhox = rhox - rhoy = np.array([0.5*(rho_pad[k, j, i]+rho_pad[k, j+1, i]) for k in range(self.nz-1) for j in range(self.ny-1) for i in range(self.nx)]) + rhoy = np.array( + [ + 0.5 * (rho_pad[k, j, i] + rho_pad[k, j + 1, i]) + for k in range(self.nz - 1) + for j in range(self.ny - 1) + for i in range(self.nx) + ] + ) self.rhoy = rhoy - rhoz = np.array([0.5*(rho_pad[k, j, i]+rho_pad[k+1, j, i]) for k in range(self.nz-1) for j in range(self.ny) for i in range(self.nx)]) - rhoz = np.insert(rhoz, [0]*self.nx*self.ny, 0)#0 outside first row + rhoz = np.array( + [ + 0.5 * (rho_pad[k, j, i] + rho_pad[k + 1, j, i]) + for k in range(self.nz - 1) + for j in range(self.ny) + for i in range(self.nx) + ] + ) + rhoz = np.insert(rhoz, [0] * self.nx * self.ny, 0) # 0 outside first row self.rhoz = rhoz - kx, ky, kz = diags((self.zeta+(1-self.zeta)*rhox)*self.k0), diags((self.zeta+(1-self.zeta)*rhoy)*self.k0), diags((self.zeta+(1-self.zeta)*rhoz)*self.k0) + kx, ky, kz = ( + diags((self.zeta + (1 - self.zeta) * rhox) * self.k0), + diags((self.zeta + (1 - self.zeta) * rhoy) * self.k0), + diags((self.zeta + (1 - self.zeta) * rhoz) * self.k0), + ) self.kx, self.ky, self.kz = kx, ky, kz - #matrices in x, y, z - self.Lx, self.Ly, self.Lz = self.dx * kx * self.gx, self.dy * ky * self.gy, self.dz * kz * self.gz + # matrices in x, y, z + self.Lx, self.Ly, self.Lz = ( + self.dx * kx * self.gx, + self.dy * ky * self.gy, + self.dz * kz * self.gz, + ) # Dirichlet condition on the last row becomes term on the RHS - Bz = csc_matrix(self.Lz)[:,-self.nx*self.ny:] + Bz = csc_matrix(self.Lz)[:, -self.nx * self.ny :] rhs = -Bz.sum(axis=1) - self.rhs=rhs - - #LHS operator after moving the boundary term to the RHS - eq = self.Lz[:, :-self.nx*self.ny]+self.Lx+self.Ly - self.eq=eq - #add damping - damping = self.k0*self.alpha**2*diags(1-rho_vector[:-self.nx*self.ny]) + diags([self.alpha0**2], shape=(self.m, self.m)) + self.rhs = rhs + + # LHS operator after moving the boundary term to the RHS + eq = self.Lz[:, : -self.nx * self.ny] + self.Lx + self.Ly + self.eq = eq + # add damping + damping = self.k0 * self.alpha**2 * diags( + 1 - rho_vector[: -self.nx * self.ny] + ) + diags([self.alpha0**2], shape=(self.m, self.m)) self.A = eq + damping self.damping = damping if self.solver == spsolve: self.T = self.solver(csr_matrix(self.A), rhs) else: self.T, sinfo = self.solver(csr_matrix(self.A), rhs) - #exclude last row of rho and calculate weighted average of temperature - self.rho_vec = rho_vector[:-self.nx*self.ny] + # exclude last row of rho and calculate weighted average of temperature + self.rho_vec = rho_vector[: -self.nx * self.ny] - self.Td_p = (1 - self.T)**self.p - self.Td = (sum(self.Td_p * self.rho_vec)/sum(self.rho_vec))**(1/self.p) + self.Td_p = (1 - self.T) ** self.p + self.Td = (sum(self.Td_p * self.rho_vec) / sum(self.rho_vec)) ** (1 / self.p) return self.Td def adjoint(self): - T_p1 = -(self.T-1) ** (self.p-1) - dg_dT = self.Td**(1-self.p) * (T_p1*self.rho_vec)/sum(self.rho_vec) + T_p1 = -((self.T - 1) ** (self.p - 1)) + dg_dT = self.Td ** (1 - self.p) * (T_p1 * self.rho_vec) / sum(self.rho_vec) if self.solver == spsolve: return self.solver(csr_matrix(self.A.transpose()), dg_dT) aT, _ = self.solver(csr_matrix(self.A.transpose()), dg_dT) @@ -93,55 +138,75 @@ def adjoint(self): def calculate_grad(self): dg_dp = np.zeros(self.n) - dg_dp[:-self.nx*self.ny] = (self.Td_p*sum(self.rho_vec))/sum(self.rho_vec)**2 - dg_dp[:-self.nx*self.ny] = dg_dp[:-self.nx*self.ny] - sum(self.Td_p*self.rho_vec)/sum(self.rho_vec)**2 - dg_dp = self.Td ** (1-self.p) * dg_dp / self.p + dg_dp[: -self.nx * self.ny] = (self.Td_p * sum(self.rho_vec)) / sum( + self.rho_vec + ) ** 2 + dg_dp[: -self.nx * self.ny] = ( + dg_dp[: -self.nx * self.ny] + - sum(self.Td_p * self.rho_vec) / sum(self.rho_vec) ** 2 + ) + dg_dp = self.Td ** (1 - self.p) * dg_dp / self.p dAx = lil_matrix((self.m, self.n)) - gxT = np.reshape(self.gx * self.T, (-1,1)) - drhox = kron(eye(self.nz-1), kron(eye(self.ny), diags([0.5,0.5], [0, 1], shape=[self.nx-1,self.nx]))) - dAx[:, :-self.nx*self.ny] = (1-self.zeta)*self.k0*lil_matrix(self.dx * drhox.multiply(gxT)) #element-wise product + gxT = np.reshape(self.gx * self.T, (-1, 1)) + drhox = kron( + eye(self.nz - 1), + kron(eye(self.ny), diags([0.5, 0.5], [0, 1], shape=[self.nx - 1, self.nx])), + ) + dAx[:, : -self.nx * self.ny] = ( + (1 - self.zeta) * self.k0 * lil_matrix(self.dx * drhox.multiply(gxT)) + ) # element-wise product dAy = lil_matrix((self.m, self.n)) - gyT = np.reshape(self.gy * self.T, (-1,1)) - drhoy = kron(kron(eye(self.nz-1), diags([0.5,0.5], [0, 1], shape=[self.ny-1,self.ny])), eye(self.nx)) - dAy[:, :-self.nx*self.ny] = (1-self.zeta)*self.k0*lil_matrix(self.dy * drhoy.multiply(gyT)) #element-wise product - - Tz = np.pad(self.T, (0, self.nx*self.ny), 'constant', constant_values=1) - gzTz = np.reshape(self.gz * Tz, (-1,1)) - drhoz = diags([0.5,0.5], [0, -1], shape=[self.nz,self.nz], format="lil") - drhoz[0,0]=0 - drhoz = kron(drhoz,eye(self.nx*self.ny)) - dAz = (1-self.zeta)*self.k0*self.dz * drhoz.multiply(gzTz) - d_damping = self.k0*self.alpha**2*diags(-self.T, shape=(self.m, self.n)) - - self.grad = dg_dp + self.adjoint().reshape(1, -1) * csr_matrix(dAz + dAx + dAy + d_damping) + gyT = np.reshape(self.gy * self.T, (-1, 1)) + drhoy = kron( + kron( + eye(self.nz - 1), + diags([0.5, 0.5], [0, 1], shape=[self.ny - 1, self.ny]), + ), + eye(self.nx), + ) + dAy[:, : -self.nx * self.ny] = ( + (1 - self.zeta) * self.k0 * lil_matrix(self.dy * drhoy.multiply(gyT)) + ) # element-wise product + + Tz = np.pad(self.T, (0, self.nx * self.ny), "constant", constant_values=1) + gzTz = np.reshape(self.gz * Tz, (-1, 1)) + drhoz = diags([0.5, 0.5], [0, -1], shape=[self.nz, self.nz], format="lil") + drhoz[0, 0] = 0 + drhoz = kron(drhoz, eye(self.nx * self.ny)) + dAz = (1 - self.zeta) * self.k0 * self.dz * drhoz.multiply(gzTz) + d_damping = self.k0 * self.alpha**2 * diags(-self.T, shape=(self.m, self.n)) + + self.grad = dg_dp + self.adjoint().reshape(1, -1) * csr_matrix( + dAz + dAx + dAy + d_damping + ) return self.grad[0] def __call__(self, rho_vector): Td = self.forward(rho_vector) grad = self.calculate_grad() - return Td-self.thresh, grad + return Td - self.thresh, grad def calculate_fd_grad(self, rho_vector, num, db=1e-4): fdidx = np.random.choice(self.n, num) fdgrad = [] for k in fdidx: - rho_vector[k]+=db + rho_vector[k] += db fp = self.forward(rho_vector) - rho_vector[k]-=2*db + rho_vector[k] -= 2 * db fm = self.forward(rho_vector) - fdgrad.append((fp-fm)/(2*db)) - rho_vector[k]+=db + fdgrad.append((fp - fm) / (2 * db)) + rho_vector[k] += db return fdidx, fdgrad def calculate_all_fd_grad(self, rho_vector, db=1e-4): fdgrad = [] for k in range(self.n): - rho_vector[k]+=db + rho_vector[k] += db fp = self.forward(rho_vector) - rho_vector[k]-=2*db + rho_vector[k] -= 2 * db fm = self.forward(rho_vector) - fdgrad.append((fp-fm)/(2*db)) - rho_vector[k]+=db + fdgrad.append((fp - fm) / (2 * db)) + rho_vector[k] += db return range(self.n), np.array(fdgrad) diff --git a/python/adjoint/filter_source.py b/python/adjoint/filter_source.py index 6c82cff27..9c23b3b17 100644 --- a/python/adjoint/filter_source.py +++ b/python/adjoint/filter_source.py @@ -1,5 +1,6 @@ import numpy as np -from scipy import signal, linalg +from scipy import linalg, signal + from meep import CustomSource @@ -32,23 +33,29 @@ def __init__( self.bf = [ lambda t, i=i: 0 - if t > self.T else (self.nuttall(t, self.center_frequencies) / - (self.dt / np.sqrt(2 * np.pi)))[i] + if t > self.T + else ( + self.nuttall(t, self.center_frequencies) + / (self.dt / np.sqrt(2 * np.pi)) + )[i] for i in range(len(self.center_frequencies)) ] self.time_src_bf = [ - CustomSource(src_func=bfi, - center_frequency=center_frequency, - is_integrated=False, - end_time=self.T, - fwidth=fwidth) for bfi in self.bf + CustomSource( + src_func=bfi, + center_frequency=center_frequency, + is_integrated=False, + end_time=self.T, + fwidth=fwidth, + ) + for bfi in self.bf ] if time_src: # get the cutoff of the input signal - signal_t = np.array([ - time_src.swigobj.current(ti, dt) for ti in self.t - ]) # time domain signal + signal_t = np.array( + [time_src.swigobj.current(ti, dt) for ti in self.t] + ) # time domain signal signal_dtft = self.dtft(signal_t, self.frequencies) else: signal_dtft = 1 @@ -60,38 +67,47 @@ def __init__( self.nodes, self.err = self.estimate_impulse_response(H) # initialize super - super(FilteredSource, self).__init__(src_func=f, - center_frequency=center_frequency, - is_integrated=False, - end_time=self.T, - fwidth=fwidth) + super().__init__( + src_func=f, + center_frequency=center_frequency, + is_integrated=False, + end_time=self.T, + fwidth=fwidth, + ) def cos_window_td(self, a, t, f0): - cos_sum = np.sum([(-1)**k * a[k] * np.cos(2 * np.pi * t * k / self.T) - for k in range(len(a))], - axis=0) + cos_sum = np.sum( + [ + (-1) ** k * a[k] * np.cos(2 * np.pi * t * k / self.T) + for k in range(len(a)) + ], + axis=0, + ) return np.exp(-1j * 2 * np.pi * f0 * t) * (cos_sum) def cos_window_fd(self, a, f, f0): df = 1 / (self.N * self.dt) cos_sum = a[0] * self.sinc(f, f0) for k in range(1, len(a)): - cos_sum += (-1)**k * a[k] / 2 * self.sinc(f, f0 - k * df) + ( - -1)**k * a[k] / 2 * self.sinc(f, f0 + k * df) + cos_sum += (-1) ** k * a[k] / 2 * self.sinc(f, f0 - k * df) + (-1) ** k * a[ + k + ] / 2 * self.sinc(f, f0 + k * df) return cos_sum def sinc(self, f, f0): - num = np.where(f == f0, self.N + 1, - (1 - np.exp(1j * (self.N + 1) * (2 * np.pi) * - (f - f0) * self.dt))) - den = np.where(f == f0, 1, - (1 - np.exp(1j * (2 * np.pi) * (f - f0) * self.dt))) + num = np.where( + f == f0, + self.N + 1, + (1 - np.exp(1j * (self.N + 1) * (2 * np.pi) * (f - f0) * self.dt)), + ) + den = np.where(f == f0, 1, (1 - np.exp(1j * (2 * np.pi) * (f - f0) * self.dt))) return num / den def rect(self, t, f0): n = np.rint((t) / self.dt) - return np.where(n.any() < 0.0 or n.any() > self.N, 0, - np.exp(-1j * 2 * np.pi * f0 * t)) + return np.where( + n.any() < 0.0 or n.any() > self.N, 0, np.exp(-1j * 2 * np.pi * f0 * t) + ) def hann(self, t, f0): a = [0.5, 0.5] @@ -115,24 +131,30 @@ def nuttall_dtft(self, f, f0): ## C / f^3 where C is a constant and f is the frequency def nuttall_bandwidth(self): tol = 1e-7 - fwidth = 1/(self.N * self.dt) - frq_inf = 10000*fwidth + fwidth = 1 / (self.N * self.dt) + frq_inf = 10000 * fwidth na_dtft = self.nuttall_dtft(frq_inf, 0) coeff = frq_inf**3 * np.abs(na_dtft) na_dtft_max = self.nuttall_dtft(0, 0) - bw = 2 * np.power(coeff / (tol * na_dtft_max), 1/3) + bw = 2 * np.power(coeff / (tol * na_dtft_max), 1 / 3) return bw.real def dtft(self, y, f): - return np.matmul( - np.exp(1j * 2 * np.pi * f[:, np.newaxis] * np.arange(y.size) * - self.dt), y) * self.dt / np.sqrt(2 * np.pi) + return ( + np.matmul( + np.exp(1j * 2 * np.pi * f[:, np.newaxis] * np.arange(y.size) * self.dt), + y, + ) + * self.dt + / np.sqrt(2 * np.pi) + ) def __call__(self, t): if t > self.T: return 0 vec = self.nuttall(t, self.center_frequencies) / ( - self.dt / np.sqrt(2 * np.pi)) # compensate for meep dtft + self.dt / np.sqrt(2 * np.pi) + ) # compensate for meep dtft return np.inner(vec, self.nodes) def func(self): @@ -145,9 +167,10 @@ def estimate_impulse_response(self, H): # Use vandermonde matrix to calculate weights of each gaussian. Each window is centered at each frequency point. # TODO: come up with a more sophisticated way to choose temporal window size and basis locations # that will minimize l2 estimation error and the node weights (since matrix is ill-conditioned) - vandermonde = self.nuttall_dtft(self.frequencies[:, np.newaxis], - self.center_frequencies[np.newaxis, :]) + vandermonde = self.nuttall_dtft( + self.frequencies[:, np.newaxis], self.center_frequencies[np.newaxis, :] + ) nodes = np.matmul(linalg.pinv(vandermonde), H.T) H_hat = np.matmul(vandermonde, nodes) - l2_err = np.sum(np.abs(H - H_hat.T)**2 / np.abs(H)**2) + l2_err = np.sum(np.abs(H - H_hat.T) ** 2 / np.abs(H) ** 2) return nodes, l2_err diff --git a/python/adjoint/filters.py b/python/adjoint/filters.py index c7eddd688..262f37937 100644 --- a/python/adjoint/filters.py +++ b/python/adjoint/filters.py @@ -1,31 +1,32 @@ """ General filter functions to be used in other projection and morphological transform routines. """ - import numpy as np from autograd import numpy as npa +from scipy import signal, special + import meep as mp -from scipy import special -from scipy import signal -def _proper_pad(x,n): - ''' + +def _proper_pad(x, n): + """ Parameters ---------- x : array_like (2D) Input array. Must be 2D. n : int Total size to be padded to. - ''' + """ N = x.size - k = n - (2*N-1) - return np.concatenate((x,np.zeros((k,)),np.flipud(x[1:]))) + k = n - (2 * N - 1) + return np.concatenate((x, np.zeros((k,)), np.flipud(x[1:]))) + def _centered(arr, newshape): - '''Helper function that reformats the padded array of the fft filter operation. + """Helper function that reformats the padded array of the fft filter operation. Borrowed from scipy: https://github.com/scipy/scipy/blob/v1.4.1/scipy/signal/signaltools.py#L263-L270 - ''' + """ # Return the center newshape portion of the array. newshape = np.asarray(newshape) currshape = np.array(arr.shape) @@ -34,6 +35,7 @@ def _centered(arr, newshape): myslice = [slice(startind[k], endind[k]) for k in range(len(endind))] return arr[tuple(myslice)] + def _edge_pad(arr, pad): # fill sides @@ -46,16 +48,17 @@ def _edge_pad(arr, pad): top_left = npa.tile(arr[0, 0], (pad[0][0], pad[1][0])) # top left top_right = npa.tile(arr[-1, 0], (pad[0][1], pad[1][0])) # top right bottom_left = npa.tile(arr[0, -1], (pad[0][0], pad[1][1])) # bottom left - bottom_right = npa.tile(arr[-1, -1], - (pad[0][1], pad[1][1])) # bottom right + bottom_right = npa.tile(arr[-1, -1], (pad[0][1], pad[1][1])) # bottom right - out = npa.concatenate((npa.concatenate( - (top_left, top, top_right)), npa.concatenate((left, arr, right)), - npa.concatenate( - (bottom_left, bottom, bottom_right))), - axis=1) + return npa.concatenate( + ( + npa.concatenate((top_left, top, top_right)), + npa.concatenate((left, arr, right)), + npa.concatenate((bottom_left, bottom, bottom_right)), + ), + axis=1, + ) - return out def simple_2d_filter(x, h): """A simple 2d filter algorithm that is differentiable with autograd. @@ -77,11 +80,14 @@ def simple_2d_filter(x, h): The output of the 2d convolution. """ (kx, ky) = x.shape - x = _edge_pad(x,((kx, kx), (ky, ky))) - return _centered(npa.real(npa.fft.ifft2(npa.fft.fft2(x)*npa.fft.fft2(h))),(kx, ky)) + x = _edge_pad(x, ((kx, kx), (ky, ky))) + return _centered( + npa.real(npa.fft.ifft2(npa.fft.fft2(x) * npa.fft.fft2(h))), (kx, ky) + ) + def cylindrical_filter(x, radius, Lx, Ly, resolution): - '''A uniform cylindrical filter [1]. Typically allows for sharper transitions. + """A uniform cylindrical filter [1]. Typically allows for sharper transitions. Parameters ---------- @@ -105,19 +111,19 @@ def cylindrical_filter(x, radius, Lx, Ly, resolution): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' - Nx = int(Lx*resolution) - Ny = int(Ly*resolution) - x = x.reshape(Nx, Ny) # Ensure the input is 2D + """ + Nx = int(Lx * resolution) + Ny = int(Ly * resolution) + x = x.reshape(Nx, Ny) # Ensure the input is 2D - xv = np.arange(0,Lx/2,1/resolution) - yv = np.arange(0,Ly/2,1/resolution) + xv = np.arange(0, Lx / 2, 1 / resolution) + yv = np.arange(0, Ly / 2, 1 / resolution) cylindrical = lambda a: np.where(a <= radius, 1, 0) hx = cylindrical(xv) hy = cylindrical(yv) - h = np.outer(_proper_pad(hx,3*Nx),_proper_pad(hy,3*Ny)) + h = np.outer(_proper_pad(hx, 3 * Nx), _proper_pad(hy, 3 * Ny)) # Normalize kernel h = h / np.sum(h.flatten()) # Normalize the filter @@ -127,7 +133,7 @@ def cylindrical_filter(x, radius, Lx, Ly, resolution): def conic_filter(x, radius, Lx, Ly, resolution): - '''A linear conic filter, also known as a "Hat" filter in the literature [1]. + """A linear conic filter, also known as a "Hat" filter in the literature [1]. Parameters ---------- @@ -151,19 +157,19 @@ def conic_filter(x, radius, Lx, Ly, resolution): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' - Nx = int(Lx*resolution) - Ny = int(Ly*resolution) - x = x.reshape(Nx, Ny) # Ensure the input is 2D + """ + Nx = int(Lx * resolution) + Ny = int(Ly * resolution) + x = x.reshape(Nx, Ny) # Ensure the input is 2D - xv = np.arange(0,Lx/2,1/resolution) - yv = np.arange(0,Ly/2,1/resolution) + xv = np.arange(0, Lx / 2, 1 / resolution) + yv = np.arange(0, Ly / 2, 1 / resolution) conic = lambda a: np.where(np.abs(a**2) <= radius**2, (1 - a / radius), 0) hx = conic(xv) hy = conic(yv) - h = np.outer(_proper_pad(hx,3*Nx),_proper_pad(hy,3*Ny)) + h = np.outer(_proper_pad(hx, 3 * Nx), _proper_pad(hy, 3 * Ny)) # Normalize kernel h = h / np.sum(h.flatten()) # Normalize the filter @@ -173,7 +179,7 @@ def conic_filter(x, radius, Lx, Ly, resolution): def gaussian_filter(x, sigma, Lx, Ly, resolution): - '''A simple gaussian filter of the form exp(-x **2 / sigma ** 2) [1]. + """A simple gaussian filter of the form exp(-x **2 / sigma ** 2) [1]. Parameters ---------- @@ -197,19 +203,19 @@ def gaussian_filter(x, sigma, Lx, Ly, resolution): ---------- [1] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of topology-optimized metasurfaces. Optical Materials Express, 9(2), 469-482. - ''' - Nx = int(Lx*resolution) - Ny = int(Ly*resolution) - x = x.reshape(Nx, Ny) # Ensure the input is 2D + """ + Nx = int(Lx * resolution) + Ny = int(Ly * resolution) + x = x.reshape(Nx, Ny) # Ensure the input is 2D - xv = np.arange(0,Lx/2,1/resolution) - yv = np.arange(0,Ly/2,1/resolution) + xv = np.arange(0, Lx / 2, 1 / resolution) + yv = np.arange(0, Ly / 2, 1 / resolution) - gaussian = lambda a: np.exp(-a**2 / sigma**2) + gaussian = lambda a: np.exp(-(a**2) / sigma**2) hx = gaussian(xv) hy = gaussian(yv) - h = np.outer(_proper_pad(hx,3*Nx),_proper_pad(hy,3*Ny)) + h = np.outer(_proper_pad(hx, 3 * Nx), _proper_pad(hy, 3 * Ny)) # Normalize kernel h = h / np.sum(h.flatten()) # Normalize the filter @@ -217,14 +223,15 @@ def gaussian_filter(x, sigma, Lx, Ly, resolution): # Filter the response return simple_2d_filter(x, h) -''' + +""" # ------------------------------------------------------------------------------------ # Erosion and dilation operators -''' +""" def exponential_erosion(x, radius, beta, Lx, Ly, resolution): - ''' Performs and exponential erosion operation. + """Performs and exponential erosion operation. Parameters ---------- @@ -253,15 +260,18 @@ def exponential_erosion(x, radius, beta, Lx, Ly, resolution): [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of morphological close-open and open-close filters for topology optimization. Structural and Multidisciplinary Optimization, 54(1), 15-21. - ''' + """ x_hat = npa.exp(beta * (1 - x)) - return 1 - npa.log( - cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) / beta + return ( + 1 + - npa.log(cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) + / beta + ) def exponential_dilation(x, radius, beta, Lx, Ly, resolution): - ''' Performs a exponential dilation operation. + """Performs a exponential dilation operation. Parameters ---------- @@ -290,15 +300,16 @@ def exponential_dilation(x, radius, beta, Lx, Ly, resolution): [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of morphological close-open and open-close filters for topology optimization. Structural and Multidisciplinary Optimization, 54(1), 15-21. - ''' + """ x_hat = npa.exp(beta * x) - return npa.log( - cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) / beta + return ( + npa.log(cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten()) / beta + ) def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): - ''' Performs a heaviside erosion operation. + """Performs a heaviside erosion operation. Parameters ---------- @@ -325,14 +336,14 @@ def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology optimization using nodal design variables and projection functions. International journal for numerical methods in engineering, 61(2), 238-254. - ''' + """ x_hat = cylindrical_filter(x, radius, Lx, Ly, resolution).flatten() return npa.exp(-beta * (1 - x_hat)) + npa.exp(-beta) * (1 - x_hat) def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): - ''' Performs a heaviside dilation operation. + """Performs a heaviside dilation operation. Parameters ---------- @@ -359,14 +370,14 @@ def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology optimization using nodal design variables and projection functions. International journal for numerical methods in engineering, 61(2), 238-254. - ''' + """ x_hat = cylindrical_filter(x, radius, Lx, Ly, resolution).flatten() return 1 - npa.exp(-beta * x_hat) + npa.exp(-beta) * x_hat def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a geometric erosion operation. + """Performs a geometric erosion operation. Parameters ---------- @@ -392,14 +403,15 @@ def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = npa.log(x + alpha) - return npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, - resolution)).flatten() - alpha + return ( + npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, resolution)).flatten() - alpha + ) def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a geometric dilation operation. + """Performs a geometric dilation operation. Parameters ---------- @@ -425,15 +437,18 @@ def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = npa.log(1 - x + alpha) - return -npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, - resolution)).flatten() + alpha + 1 + return ( + -npa.exp(cylindrical_filter(x_hat, radius, Lx, Ly, resolution)).flatten() + + alpha + + 1 + ) def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a harmonic erosion operation. + """Performs a harmonic erosion operation. Parameters ---------- @@ -459,15 +474,14 @@ def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = 1 / (x + alpha) - return 1 / cylindrical_filter(x_hat, radius, Lx, Ly, - resolution).flatten() - alpha + return 1 / cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten() - alpha def harmonic_dilation(x, radius, alpha, Lx, Ly, resolution): - ''' Performs a harmonic dilation operation. + """Performs a harmonic dilation operation. Parameters ---------- @@ -493,21 +507,22 @@ def harmonic_dilation(x, radius, alpha, Lx, Ly, resolution): ---------- [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. - ''' + """ x_hat = 1 / (1 - x + alpha) - return 1 - 1 / cylindrical_filter(x_hat, radius, Lx, Ly, - resolution).flatten() + alpha + return ( + 1 - 1 / cylindrical_filter(x_hat, radius, Lx, Ly, resolution).flatten() + alpha + ) -''' +""" # ------------------------------------------------------------------------------------ # Projection filters -''' +""" def tanh_projection(x, beta, eta): - '''Projection filter that thresholds the input parameters between 0 and 1. Typically + """Projection filter that thresholds the input parameters between 0 and 1. Typically the "strongest" projection. Parameters @@ -527,16 +542,15 @@ def tanh_projection(x, beta, eta): ---------- [1] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and robust formulations in topology optimization. Structural and Multidisciplinary Optimization, 43(6), 767-784. - ''' + """ - return (npa.tanh(beta * eta) + - npa.tanh(beta * - (x - eta))) / (npa.tanh(beta * eta) + npa.tanh(beta * - (1 - eta))) + return (npa.tanh(beta * eta) + npa.tanh(beta * (x - eta))) / ( + npa.tanh(beta * eta) + npa.tanh(beta * (1 - eta)) + ) def heaviside_projection(x, beta, eta): - '''Projection filter that thresholds the input parameters between 0 and 1. + """Projection filter that thresholds the input parameters between 0 and 1. Parameters ---------- @@ -556,22 +570,25 @@ def heaviside_projection(x, beta, eta): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' + """ case1 = eta * npa.exp(-beta * (eta - x) / eta) - (eta - x) * npa.exp(-beta) - case2 = 1 - (1 - eta) * npa.exp(-beta * (x - eta) / - (1 - eta)) - (eta - x) * npa.exp(-beta) + case2 = ( + 1 + - (1 - eta) * npa.exp(-beta * (x - eta) / (1 - eta)) + - (eta - x) * npa.exp(-beta) + ) return npa.where(x < eta, case1, case2) -''' +""" # ------------------------------------------------------------------------------------ # Length scale operations -''' +""" def get_threshold_wang(delta, sigma): - '''Calculates the threshold point according to the gaussian filter radius (`sigma`) and + """Calculates the threshold point according to the gaussian filter radius (`sigma`) and the perturbation parameter (`sigma`) needed to ensure the proper length scale and morphological transformation according to Wang et. al. [2]. @@ -593,13 +610,13 @@ def get_threshold_wang(delta, sigma): photonic crystal waveguides with tailored dispersion properties. JOSA B, 28(3), 387-397. [2] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of topology-optimized metasurfaces. Optical Materials Express, 9(2), 469-482. - ''' + """ return 0.5 - special.erf(delta / sigma) def get_eta_from_conic(b, R): - ''' Extracts the eroded threshold point (`eta_e`) for a conic filter given the desired + """Extracts the eroded threshold point (`eta_e`) for a conic filter given the desired minimum length (`b`) and the filter radius (`R`). This only works for conic filters. Note that the units for `b` and `R` can be arbitrary so long as they are consistent. @@ -628,18 +645,17 @@ def get_eta_from_conic(b, R): Optimization, 43(6), 767-784. [3] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' + """ norm_length = b / R if norm_length < 0: - eta_e = 0 + return 0 elif norm_length < 1: - eta_e = 0.25 * norm_length**2 + 0.5 + return 0.25 * norm_length**2 + 0.5 elif norm_length < 2: - eta_e = -0.25 * norm_length**2 + norm_length + return -0.25 * norm_length**2 + norm_length else: - eta_e = 1 - return eta_e + return 1 def get_conic_radius_from_eta_e(b, eta_e): @@ -675,11 +691,12 @@ def get_conic_radius_from_eta_e(b, eta_e): return b / (2 - 2 * np.sqrt(1 - eta_e)) else: raise ValueError( - "The erosion threshold point (eta_e) must be between 0.5 and 1.") + "The erosion threshold point (eta_e) must be between 0.5 and 1." + ) def indicator_solid(x, c, filter_f, threshold_f, resolution): - '''Calculates the indicator function for the void phase needed for minimum length optimization [1]. + """Calculates the indicator function for the void phase needed for minimum length optimization [1]. Parameters ---------- @@ -703,23 +720,23 @@ def indicator_solid(x, c, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x) design_field = threshold_f(filtered_field) gradient_filtered_field = npa.gradient(filtered_field) - grad_mag = (gradient_filtered_field[0] * - resolution)**2 + (gradient_filtered_field[1] * resolution)**2 + grad_mag = (gradient_filtered_field[0] * resolution) ** 2 + ( + gradient_filtered_field[1] * resolution + ) ** 2 if grad_mag.ndim != 2: raise ValueError( "The gradient fields must be 2 dimensional. Check input array and filter functions." ) - I_s = design_field * npa.exp(-c * grad_mag) - return I_s + return design_field * npa.exp(-c * grad_mag) def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): - '''Calculates the constraint function of the solid phase needed for minimum length optimization [1]. + """Calculates the constraint function of the solid phase needed for minimum length optimization [1]. Parameters ---------- @@ -748,16 +765,17 @@ def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x) - I_s = indicator_solid(x.reshape(filtered_field.shape), c, filter_f, - threshold_f, resolution).flatten() - return npa.mean(I_s * npa.minimum(filtered_field.flatten() - eta_e, 0)**2) + I_s = indicator_solid( + x.reshape(filtered_field.shape), c, filter_f, threshold_f, resolution + ).flatten() + return npa.mean(I_s * npa.minimum(filtered_field.flatten() - eta_e, 0) ** 2) def indicator_void(x, c, filter_f, threshold_f, resolution): - '''Calculates the indicator function for the void phase needed for minimum length optimization [1]. + """Calculates the indicator function for the void phase needed for minimum length optimization [1]. Parameters ---------- @@ -781,13 +799,14 @@ def indicator_void(x, c, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x).reshape(x.shape) design_field = threshold_f(filtered_field) gradient_filtered_field = npa.gradient(filtered_field) - grad_mag = (gradient_filtered_field[0] * - resolution)**2 + (gradient_filtered_field[1] * resolution)**2 + grad_mag = (gradient_filtered_field[0] * resolution) ** 2 + ( + gradient_filtered_field[1] * resolution + ) ** 2 if grad_mag.ndim != 2: raise ValueError( "The gradient fields must be 2 dimensional. Check input array and filter functions." @@ -796,7 +815,7 @@ def indicator_void(x, c, filter_f, threshold_f, resolution): def constraint_void(x, c, eta_d, filter_f, threshold_f, resolution): - '''Calculates the constraint function of the void phase needed for minimum length optimization [1]. + """Calculates the constraint function of the void phase needed for minimum length optimization [1]. Parameters ---------- @@ -825,16 +844,17 @@ def constraint_void(x, c, eta_d, filter_f, threshold_f, resolution): ---------- [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. - ''' + """ filtered_field = filter_f(x) - I_v = indicator_void(x.reshape(filtered_field.shape), c, filter_f, - threshold_f, resolution).flatten() - return npa.mean(I_v * npa.minimum(eta_d - filtered_field.flatten(), 0)**2) + I_v = indicator_void( + x.reshape(filtered_field.shape), c, filter_f, threshold_f, resolution + ).flatten() + return npa.mean(I_v * npa.minimum(eta_d - filtered_field.flatten(), 0) ** 2) def gray_indicator(x): - '''Calculates a measure of "grayness" according to [1]. + """Calculates a measure of "grayness" according to [1]. Lower numbers ( < 2%) indicate a good amount of binarization [1]. @@ -852,5 +872,5 @@ def gray_indicator(x): ---------- [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. - ''' + """ return npa.mean(4 * x.flatten() * (1 - x.flatten())) * 100 diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index 7bfa010d4..07aeea618 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -1,13 +1,16 @@ """Handling of objective functions and objective quantities.""" - import abc +from collections import namedtuple + import numpy as np +from meep.simulation import py_v3_to_vec + import meep as mp + from .filter_source import FilteredSource -from meep.simulation import py_v3_to_vec -from collections import namedtuple -Grid = namedtuple('Grid', ['x', 'y', 'z', 'w']) +Grid = namedtuple("Grid", ["x", "y", "z", "w"]) + class ObjectiveQuantity(abc.ABC): """A differentiable objective quantity. @@ -17,6 +20,7 @@ class ObjectiveQuantity(abc.ABC): frequencies: the frequencies at which the objective quantity is evaluated. num_freq: the number of frequencies at which the objective quantity is evaluated. """ + def __init__(self, sim): self.sim = sim self._eval = None @@ -48,7 +52,7 @@ def get_evaluation(self): return self._eval else: raise RuntimeError( - 'You must first run a forward simulation before requesting the evaluation of an objective quantity.' + "You must first run a forward simulation before requesting the evaluation of an objective quantity." ) def _adj_src_scale(self, include_resolution=True): @@ -68,23 +72,47 @@ def _adj_src_scale(self, include_resolution=True): ) # scaled frequency factor with discrete time derivative fix # an ugly way to calcuate the scaled dtft of the forward source - y = np.array([src.swigobj.current(t, dt) - for t in np.arange(0, T, dt)]) # time domain signal - fwd_dtft = np.matmul( - np.exp(1j * 2 * np.pi * self._frequencies[:, np.newaxis] * - np.arange(y.size) * dt), y) * dt / np.sqrt( - 2 * np.pi) # dtft + y = np.array( + [src.swigobj.current(t, dt) for t in np.arange(0, T, dt)] + ) # time domain signal + fwd_dtft = ( + np.matmul( + np.exp( + 1j + * 2 + * np.pi + * self._frequencies[:, np.newaxis] + * np.arange(y.size) + * dt + ), + y, + ) + * dt + / np.sqrt(2 * np.pi) + ) # dtft # Interestingly, the real parts of the DTFT and fourier transform match, but the imaginary parts are very different... - #fwd_dtft = src.fourier_transform(src.frequency) - ''' + # fwd_dtft = src.fourier_transform(src.frequency) + """ For some reason, there seems to be an additional phase factor at the center frequency that needs to be applied to *all* frequencies... - ''' - src_center_dtft = np.matmul( - np.exp(1j * 2 * np.pi * np.array([src.frequency])[:, np.newaxis] * - np.arange(y.size) * dt), y) * dt / np.sqrt(2 * np.pi) + """ + src_center_dtft = ( + np.matmul( + np.exp( + 1j + * 2 + * np.pi + * np.array([src.frequency])[:, np.newaxis] + * np.arange(y.size) + * dt + ), + y, + ) + * dt + / np.sqrt(2 * np.pi) + ) adj_src_phase = np.exp(1j * np.angle(src_center_dtft)) * self.fwidth_scale if self._frequencies.size == 1: @@ -109,10 +137,11 @@ def _create_time_profile(self, fwidth_frac=0.1, adj_cutoff=5): The user may specify a scalar valued objective function across multiple frequencies (e.g. MSE) in which case we should check that all the frequencies fit in the specified bandwidth. """ - self.fwidth_scale = np.exp(-2j*np.pi*adj_cutoff/fwidth_frac) + self.fwidth_scale = np.exp(-2j * np.pi * adj_cutoff / fwidth_frac) return mp.GaussianSource( np.mean(self._frequencies), - fwidth=fwidth_frac * np.mean(self._frequencies),cutoff=adj_cutoff + fwidth=fwidth_frac * np.mean(self._frequencies), + cutoff=adj_cutoff, ) @@ -129,20 +158,24 @@ class EigenmodeCoefficient(ObjectiveQuantity): specifying `kpoint_func`. When specified, this overrides the effect of `forward` and should have a value of either 0 or 1. """ - def __init__(self, - sim, - volume, - mode, - forward=True, - kpoint_func=None, - kpoint_func_overlap_idx=0, - decimation_factor=0, - **kwargs): + + def __init__( + self, + sim, + volume, + mode, + forward=True, + kpoint_func=None, + kpoint_func_overlap_idx=0, + decimation_factor=0, + **kwargs + ): super().__init__(sim) if kpoint_func_overlap_idx not in [0, 1]: raise ValueError( - '`kpoint_func_overlap_idx` should be either 0 or 1, but got %d' - % (kpoint_func_overlap_idx, )) + "`kpoint_func_overlap_idx` should be either 0 or 1, but got %d" + % (kpoint_func_overlap_idx,) + ) self.volume = volume self.mode = mode self.forward = forward @@ -174,13 +207,14 @@ def place_adjoint_source(self, dJ): if self.kpoint_func: eig_kpoint = -1 * self.kpoint_func(time_src.frequency, self.mode) else: - center_frequency = 0.5 * (np.min(self.frequencies) + np.max( - self.frequencies)) + center_frequency = 0.5 * ( + np.min(self.frequencies) + np.max(self.frequencies) + ) direction = mp.Vector3( - *(np.eye(3)[self._monitor.normal_direction] * - np.abs(center_frequency))) + *(np.eye(3)[self._monitor.normal_direction] * np.abs(center_frequency)) + ) eig_kpoint = -1 * direction if self.forward else direction - + if self._frequencies.size == 1: amp = da_dE * dJ * scale src = time_src @@ -211,10 +245,12 @@ def __call__(self): kpoint_func = self.kpoint_func overlap_idx = self.kpoint_func_overlap_idx else: - center_frequency = 0.5 * (np.min(self.frequencies) + np.max( - self.frequencies)) - kpoint = mp.Vector3(*(np.eye(3)[self._monitor.normal_direction] * - np.abs(center_frequency))) + center_frequency = 0.5 * ( + np.min(self.frequencies) + np.max(self.frequencies) + ) + kpoint = mp.Vector3( + *(np.eye(3)[self._monitor.normal_direction] * np.abs(center_frequency)) + ) kpoint_func = lambda *not_used: kpoint if self.forward else -1 * kpoint overlap_idx = 0 ob = self.sim.get_eigenmode_coefficients( @@ -242,22 +278,32 @@ def __init__(self, sim, volume, component, yee_grid=False, decimation_factor=0): def register_monitors(self, frequencies): self._frequencies = np.asarray(frequencies) self._monitor = self.sim.add_dft_fields( - [self.component], self._frequencies, where=self.volume, yee_grid=self.yee_grid, decimation_factor=self.decimation_factor) + [self.component], + self._frequencies, + where=self.volume, + yee_grid=self.yee_grid, + decimation_factor=self.decimation_factor, + ) return self._monitor def place_adjoint_source(self, dJ): time_src = self._create_time_profile() sources = [] - mon_size = self.sim.fields.dft_monitor_size(self._monitor.swigobj, self.volume.swigobj, self.component) + mon_size = self.sim.fields.dft_monitor_size( + self._monitor.swigobj, self.volume.swigobj, self.component + ) dJ = dJ.astype(np.complex128) - if np.prod(mon_size)*self.num_freq != dJ.size and np.prod(mon_size)*self.num_freq**2 != dJ.size: - raise ValueError('The format of J is incorrect!') + if ( + np.prod(mon_size) * self.num_freq != dJ.size + and np.prod(mon_size) * self.num_freq**2 != dJ.size + ): + raise ValueError("The format of J is incorrect!") # The objective function J is a vector. Each component corresponds to a frequency. - if np.prod(mon_size)*self.num_freq**2 == dJ.size and self.num_freq > 1: - dJ = np.sum(dJ,axis=1) - '''The adjoint solver requires the objective function + if np.prod(mon_size) * self.num_freq**2 == dJ.size and self.num_freq > 1: + dJ = np.sum(dJ, axis=1) + """The adjoint solver requires the objective function to be scalar valued with regard to objective arguments and position, but the function may be vector valued with regard to frequency. In this case, the Jacobian @@ -265,28 +311,45 @@ def place_adjoint_source(self, dJ): frequencies. Because of linearity, we can sum across the second frequency dimension to calculate a frequency scale factor for each point (rather than a scale vector). - ''' + """ - self.all_fouriersrcdata = self._monitor.swigobj.fourier_sourcedata(self.volume.swigobj, self.component, self.sim.fields, dJ) + self.all_fouriersrcdata = self._monitor.swigobj.fourier_sourcedata( + self.volume.swigobj, self.component, self.sim.fields, dJ + ) for fourier_data in self.all_fouriersrcdata: amp_arr = np.array(fourier_data.amp_arr).reshape(-1, self.num_freq) scale = amp_arr * self._adj_src_scale(include_resolution=False) - + if self.num_freq == 1: - sources += [mp.IndexedSource(time_src, fourier_data, scale[:,0], not self.yee_grid)] + sources += [ + mp.IndexedSource( + time_src, fourier_data, scale[:, 0], not self.yee_grid + ) + ] else: - src = FilteredSource(time_src.frequency,self._frequencies,scale,self.sim.fields.dt) + src = FilteredSource( + time_src.frequency, self._frequencies, scale, self.sim.fields.dt + ) (num_basis, num_pts) = src.nodes.shape for basis_i in range(num_basis): - sources += [mp.IndexedSource(src.time_src_bf[basis_i], fourier_data, src.nodes[basis_i], not self.yee_grid)] + sources += [ + mp.IndexedSource( + src.time_src_bf[basis_i], + fourier_data, + src.nodes[basis_i], + not self.yee_grid, + ) + ] return sources def __call__(self): - self._eval = np.array([ - self.sim.get_dft_array(self._monitor, self.component, i) - for i in range(self.num_freq) - ]) + self._eval = np.array( + [ + self.sim.get_dft_array(self._monitor, self.component, i) + for i in range(self.num_freq) + ] + ) return self._eval @@ -294,7 +357,7 @@ class Near2FarFields(ObjectiveQuantity): def __init__(self, sim, Near2FarRegions, far_pts, decimation_factor=0): super().__init__(sim) self.Near2FarRegions = Near2FarRegions - self.far_pts = far_pts #list of far pts + self.far_pts = far_pts # list of far pts self._nfar_pts = len(far_pts) self.decimation_factor = decimation_factor @@ -322,7 +385,8 @@ def place_adjoint_source(self, dJ): ) all_nearsrcdata = self._monitor.swigobj.near_sourcedata( - far_pt_vec, farpt_list, self._nfar_pts, dJ) + far_pt_vec, farpt_list, self._nfar_pts, dJ + ) for near_data in all_nearsrcdata: cur_comp = near_data.near_fd_comp amp_arr = np.array(near_data.amp_arr).reshape(-1, self.num_freq) @@ -349,12 +413,12 @@ def place_adjoint_source(self, dJ): return sources def __call__(self): - self._eval = np.array([ - self.sim.get_farfield(self._monitor, far_pt) - for far_pt in self.far_pts - ]).reshape((self._nfar_pts, self.num_freq, 6)) + self._eval = np.array( + [self.sim.get_farfield(self._monitor, far_pt) for far_pt in self.far_pts] + ).reshape((self._nfar_pts, self.num_freq, 6)) return self._eval - + + class LDOS(ObjectiveQuantity): def __init__(self, sim, decimation_factor=0, **kwargs): super().__init__(sim) @@ -372,7 +436,9 @@ def place_adjoint_source(self, dJ): dJ = np.sum(dJ, axis=1) dJ = dJ.flatten() sources = [] - forward_f_scale = np.array([self.ldos_scale/self.ldos_Jdata[k] for k in range(self.num_freq)]) + forward_f_scale = np.array( + [self.ldos_scale / self.ldos_Jdata[k] for k in range(self.num_freq)] + ) if self._frequencies.size == 1: amp = (dJ * self._adj_src_scale(False) * forward_f_scale)[0] src = time_src @@ -387,13 +453,29 @@ def place_adjoint_source(self, dJ): amp = 1 for forward_src_i in self.forward_src: if isinstance(forward_src_i, mp.EigenModeSource): - src_i = mp.EigenModeSource(src, component=forward_src_i.component, eig_kpoint = forward_src_i.eig_kpoint, amplitude=amp, eig_band=forward_src_i.eig_band, size = forward_src_i.size,center=forward_src_i.center, **self.srckwarg) + src_i = mp.EigenModeSource( + src, + component=forward_src_i.component, + eig_kpoint=forward_src_i.eig_kpoint, + amplitude=amp, + eig_band=forward_src_i.eig_band, + size=forward_src_i.size, + center=forward_src_i.center, + **self.srckwarg, + ) else: - src_i = mp.Source(src, component=forward_src_i.component, amplitude=amp, size = forward_src_i.size,center=forward_src_i.center, **self.srckwarg) + src_i = mp.Source( + src, + component=forward_src_i.component, + amplitude=amp, + size=forward_src_i.size, + center=forward_src_i.center, + **self.srckwarg, + ) if mp.is_electric(src_i.component): src_i.amplitude *= -1 sources += [src_i] - + return sources def __call__(self): diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index a9b0b656a..707eb8beb 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -1,11 +1,14 @@ -import meep as mp +from collections import namedtuple + import numpy as np from autograd import grad, jacobian -from collections import namedtuple -from . import utils, DesignRegion, LDOS +import meep as mp + +from . import LDOS, DesignRegion, utils -class OptimizationProblem(object): + +class OptimizationProblem: """Top-level class in the MEEP adjoint module. Intended to be instantiated from user scripts with mandatory constructor @@ -18,6 +21,7 @@ class OptimizationProblem(object): This is done by the __call__ method. """ + def __init__( self, simulation, @@ -32,7 +36,7 @@ def __init__( decimation_factor=0, minimum_run_time=0, maximum_run_time=None, - finite_difference_step=utils.FD_DEFAULT + finite_difference_step=utils.FD_DEFAULT, ): self.sim = simulation @@ -49,30 +53,27 @@ def __init__( else: self.design_regions = [design_regions] - self.num_design_params = [ - ni.num_design_params for ni in self.design_regions - ] + self.num_design_params = [ni.num_design_params for ni in self.design_regions] self.num_design_regions = len(self.design_regions) # TODO typecheck frequency choices if frequencies is not None: self.frequencies = frequencies self.nf = np.array(frequencies).size + elif nf == 1: + self.nf = nf + self.frequencies = [fcen] else: - if nf == 1: - self.nf = nf - self.frequencies = [fcen] - else: - fmax = fcen + 0.5 * df - fmin = fcen - 0.5 * df - dfreq = (fmax - fmin) / (nf - 1) - self.frequencies = np.linspace( - fmin, - fmin + dfreq * nf, - num=nf, - endpoint=False, - ) - self.nf = nf + fmax = fcen + 0.5 * df + fmin = fcen - 0.5 * df + dfreq = (fmax - fmin) / (nf - 1) + self.frequencies = np.linspace( + fmin, + fmin + dfreq * nf, + num=nf, + endpoint=False, + ) + self.nf = nf if self.nf == 1: self.fcen_idx = 0 @@ -80,15 +81,20 @@ def __init__( self.fcen_idx = int( np.argmin( np.abs( - np.asarray(self.frequencies) - - np.mean(np.asarray(self.frequencies)))** - 2)) # index of center frequency + np.asarray(self.frequencies) + - np.mean(np.asarray(self.frequencies)) + ) + ** 2 + ) + ) # index of center frequency self.decay_by = decay_by self.decimation_factor = decimation_factor self.minimum_run_time = minimum_run_time self.maximum_run_time = maximum_run_time - self.finite_difference_step = finite_difference_step # step size used in Aᵤ computation + self.finite_difference_step = ( + finite_difference_step # step size used in Aᵤ computation + ) # store sources for finite difference estimations self.forward_sources = self.sim.sources @@ -102,8 +108,7 @@ def __init__( self.gradient = [] def __call__(self, rho_vector=None, need_value=True, need_gradient=True, beta=None): - """Evaluate value and/or gradient of objective function. - """ + """Evaluate value and/or gradient of objective function.""" if rho_vector: self.update_design(rho_vector=rho_vector, beta=beta) @@ -128,8 +133,9 @@ def __call__(self, rho_vector=None, need_value=True, need_gradient=True, beta=No print("Calculating gradient...") self.calculate_gradient() else: - raise ValueError("Incorrect solver state detected: {}".format( - self.current_state)) + raise ValueError( + f"Incorrect solver state detected: {self.current_state}" + ) return self.f0, self.gradient @@ -142,6 +148,7 @@ def get_fdf_funcs(self): f_func(beta) = objective function value for design variables beta df_func(beta) = objective function gradient for design variables beta """ + def _f(x=None): (fq, _) = self.__call__(rho_vector=x, need_gradient=False) return fq @@ -161,9 +168,8 @@ def prepare_forward_run(self): # register user specified monitors self.forward_monitors = [ - ] # save reference to monitors so we can track dft convergence - for m in self.objective_arguments: - self.forward_monitors.append(m.register_monitors(self.frequencies)) + m.register_monitors(self.frequencies) for m in self.objective_arguments + ] # register design region self.forward_design_region_monitors = utils.install_design_region_monitors( @@ -176,26 +182,26 @@ def forward_run(self): # Forward run if any(isinstance(m, LDOS) for m in self.objective_arguments): - self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + mp.dft_ldos(self.frequencies), + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ), + ) else: - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # record objective quantities from user specified monitors - self.results_list = [] - for m in self.objective_arguments: - self.results_list.append(m()) - + self.results_list = [m() for m in self.objective_arguments] # evaluate objectives self.f0 = [fi(*self.results_list) for fi in self.objective_functions] if len(self.f0) == 1: self.f0 = self.f0[0] - + # store objective function evaluation in memory self.f_bank.append(self.f0) @@ -204,16 +210,15 @@ def forward_run(self): def prepare_adjoint_run(self): # Compute adjoint sources - self.adjoint_sources = [[] - for i in range(len(self.objective_functions))] + self.adjoint_sources = [[] for _ in range(len(self.objective_functions))] for ar in range(len(self.objective_functions)): for mi, m in enumerate(self.objective_arguments): - dJ = jacobian(self.objective_functions[ar], - mi)(*self.results_list) + dJ = jacobian(self.objective_functions[ar], mi)(*self.results_list) # get gradient of objective w.r.t. monitor if np.any(dJ): self.adjoint_sources[ar] += m.place_adjoint_source( - dJ) # place the appropriate adjoint sources + dJ + ) # place the appropriate adjoint sources def adjoint_run(self): # set up adjoint sources and monitors @@ -225,7 +230,7 @@ def adjoint_run(self): # flip the k point if self.sim.k_point: - self.sim.change_k_point(-1*self.sim.k_point) + self.sim.change_k_point(-1 * self.sim.k_point) self.adjoint_design_region_monitors = [] for ar in range(len(self.objective_functions)): @@ -237,51 +242,61 @@ def adjoint_run(self): self.sim.change_sources(self.adjoint_sources[ar]) # register design dft fields - self.adjoint_design_region_monitors.append(utils.install_design_region_monitors( - self.sim, self.design_regions, self.frequencies, self.decimation_factor - )) + self.adjoint_design_region_monitors.append( + utils.install_design_region_monitors( + self.sim, + self.design_regions, + self.frequencies, + self.decimation_factor, + ) + ) self.sim._evaluate_dft_objects() # Adjoint run - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time - )) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # reset the m number if utils._check_if_cylindrical(self.sim): self.sim.m = -self.sim.m # reset the k point - if self.sim.k_point: self.sim.k_point *= -1 + if self.sim.k_point: + self.sim.k_point *= -1 # update optimizer's state self.current_state = "ADJ" def calculate_gradient(self): # Iterate through all design regions and calculate gradient - self.gradient = [[ - dr.get_gradient( - self.sim, - self.adjoint_design_region_monitors[ar][dri], - self.forward_design_region_monitors[dri], - self.frequencies, - self.finite_difference_step - ) for dri, dr in enumerate(self.design_regions) - ] for ar in range(len(self.objective_functions))] + self.gradient = [ + [ + dr.get_gradient( + self.sim, + self.adjoint_design_region_monitors[ar][dri], + self.forward_design_region_monitors[dri], + self.frequencies, + self.finite_difference_step, + ) + for dri, dr in enumerate(self.design_regions) + ] + for ar in range(len(self.objective_functions)) + ] # Cleanup list of lists if len(self.gradient) == 1: self.gradient = self.gradient[0] # only one objective function if len(self.gradient) == 1: self.gradient = self.gradient[ - 0] # only one objective function and one design region - else: - if len(self.gradient[0]) == 1: - self.gradient = [ - g[0] for g in self.gradient - ] # multiple objective functions bu one design region + 0 + ] # only one objective function and one design region + elif len(self.gradient[0]) == 1: + self.gradient = [ + g[0] for g in self.gradient + ] # multiple objective functions bu one design region # Return optimizer's state to initialization self.current_state = "INIT" @@ -292,7 +307,7 @@ def calculate_fd_gradient( design_variables_idx=0, filter=None, ): - ''' + """ Estimate central difference gradients. Parameters @@ -309,7 +324,7 @@ def calculate_fd_gradient( fd_gradient ... : lists [number of objective functions][number of gradients] - ''' + """ if filter is None: filter = lambda x: x if num_gradients < self.num_design_params[design_variables_idx]: @@ -326,7 +341,7 @@ def calculate_fd_gradient( "The requested number of gradients must be less than or equal to the total number of design parameters." ) - assert db < 0.2, "The step size of finite difference is too large." + assert db < 0.2, "The step size of finite difference is too large." # cleanup simulation object self.sim.reset_meep() @@ -337,9 +352,8 @@ def calculate_fd_gradient( for k in fd_gradient_idx: - b0 = np.ones((self.num_design_params[design_variables_idx], )) - b0[:] = (self.design_regions[design_variables_idx]. - design_parameters.weights) + b0 = np.ones((self.num_design_params[design_variables_idx],)) + b0[:] = self.design_regions[design_variables_idx].design_parameters.weights # -------------------------------------------- # # left function evaluation # -------------------------------------------- # @@ -347,30 +361,34 @@ def calculate_fd_gradient( # assign new design vector in_interior = True # b0[k] is not too close to the boundaries 0 and 1 - if b0[k] < db or b0[k]+db > 1: in_interior = False # b0[k] is too close to 0 or 1 + if b0[k] < db or b0[k] + db > 1: + in_interior = False # b0[k] is too close to 0 or 1 - if b0[k] >= db: b0[k] -= db - self.design_regions[design_variables_idx].update_design_parameters( - b0) + if b0[k] >= db: + b0[k] -= db + self.design_regions[design_variables_idx].update_design_parameters(b0) # initialize design monitors - self.forward_monitors = [] - for m in self.objective_arguments: - self.forward_monitors.append( - m.register_monitors(self.frequencies)) + self.forward_monitors = [ + m.register_monitors(self.frequencies) for m in self.objective_arguments + ] if any(isinstance(m, LDOS) for m in self.objective_arguments): - self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) + self.sim.run( + mp.dft_ldos(self.frequencies), + until_after_sources=mp.stop_when_energy_decayed( + dt=1, decay_by=1e-11 + ), + ) else: - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # record final objective function value - results_list = [] - for m in self.objective_arguments: - results_list.append(m()) + results_list = [m() for m in self.objective_arguments] fm = [fi(*results_list) for fi in self.objective_functions] # -------------------------------------------- # @@ -379,40 +397,42 @@ def calculate_fd_gradient( self.sim.reset_meep() # assign new design vector - if in_interior: b0[k] += 2 * db # central difference rule... - else: b0[k] += db # forward or backward difference... - - self.design_regions[design_variables_idx].update_design_parameters( - b0) + b0[k] += 2 * db if in_interior else db + self.design_regions[design_variables_idx].update_design_parameters(b0) # initialize design monitors - self.forward_monitors = [] - for m in self.objective_arguments: - self.forward_monitors.append( - m.register_monitors(self.frequencies)) + self.forward_monitors = [ + m.register_monitors(self.frequencies) for m in self.objective_arguments + ] # add monitor used to track dft convergence if any(isinstance(m, LDOS) for m in self.objective_arguments): - self.sim.run(mp.dft_ldos(self.frequencies), until_after_sources=mp.stop_when_energy_decayed(dt=1, decay_by=1e-11)) + self.sim.run( + mp.dft_ldos(self.frequencies), + until_after_sources=mp.stop_when_energy_decayed( + dt=1, decay_by=1e-11 + ), + ) else: - self.sim.run(until_after_sources=mp.stop_when_dft_decayed( - self.decay_by, - self.minimum_run_time, - self.maximum_run_time)) + self.sim.run( + until_after_sources=mp.stop_when_dft_decayed( + self.decay_by, self.minimum_run_time, self.maximum_run_time + ) + ) # record final objective function value - results_list = [] - for m in self.objective_arguments: - results_list.append(m()) + results_list = [m() for m in self.objective_arguments] fp = [fi(*results_list) for fi in self.objective_functions] # -------------------------------------------- # # estimate derivative # -------------------------------------------- # - fd_gradient.append([ - np.squeeze((fp[fi] - fm[fi]) / db / (2 if in_interior else 1)) - for fi in range(len(self.objective_functions)) - ]) + fd_gradient.append( + [ + np.squeeze((fp[fi] - fm[fi]) / db / (2 if in_interior else 1)) + for fi in range(len(self.objective_functions)) + ] + ) # Cleanup singleton dimensions if len(fd_gradient) == 1: @@ -438,17 +458,13 @@ def update_design(self, rho_vector, beta=None): self.current_state = "INIT" def get_objective_arguments(self): - '''Return list of evaluated objective arguments. - ''' - objective_args_evaluation = [ - m.get_evaluation() for m in self.objective_arguments - ] - return objective_args_evaluation + """Return list of evaluated objective arguments.""" + return [m.get_evaluation() for m in self.objective_arguments] def plot2D(self, init_opt=False, **kwargs): """Produce a graphical visualization of the geometry and/or fields, - as appropriately autodetermined based on the current state of - progress. + as appropriately autodetermined based on the current state of + progress. """ if init_opt: @@ -456,9 +472,11 @@ def plot2D(self, init_opt=False, **kwargs): self.sim.plot2D(**kwargs) + def atleast_3d(*arys): from numpy import array, asanyarray, newaxis - ''' + + """ Modified version of numpy's `atleast_3d` Keeps one dimensional array data in first dimension, as @@ -480,7 +498,7 @@ def atleast_3d(*arys): returned. For example, a 1-D array of shape ``(N,)`` becomes a view of shape ``(N, 1, 1)``, and a 2-D array of shape ``(M, N)`` becomes a view of shape ``(M, N, 1)``. - ''' + """ res = [] for ary in arys: ary = asanyarray(ary) @@ -493,7 +511,4 @@ def atleast_3d(*arys): else: result = ary res.append(result) - if len(res) == 1: - return res[0] - else: - return res + return res[0] if len(res) == 1 else res diff --git a/python/adjoint/utils.py b/python/adjoint/utils.py index ef4a94aee..fb37d2b0b 100644 --- a/python/adjoint/utils.py +++ b/python/adjoint/utils.py @@ -1,8 +1,9 @@ -from typing import List, Iterable, Tuple +from typing import Iterable, List, Tuple -import meep as mp import numpy as onp +import meep as mp + from . import ObjectiveQuantity # Meep field components used to compute adjoint sensitivities @@ -21,15 +22,10 @@ # default finite difference step size when calculating Aᵤ FD_DEFAULT = 1e-3 -class DesignRegion(object): - def __init__( - self, - design_parameters, - volume=None, - size=None, - center=mp.Vector3() - ): - self.volume = volume if volume else mp.Volume(center=center, size=size) + +class DesignRegion: + def __init__(self, design_parameters, volume=None, size=None, center=mp.Vector3()): + self.volume = volume or mp.Volume(center=center, size=size) self.size = self.volume.size self.center = self.volume.center self.design_parameters = design_parameters @@ -38,35 +34,54 @@ def __init__( def update_design_parameters(self, design_parameters): self.design_parameters.update_weights(design_parameters) - def update_beta(self,beta): - self.design_parameters.beta=beta + def update_beta(self, beta): + self.design_parameters.beta = beta - def get_gradient(self, sim, fields_a, fields_f, frequencies, finite_difference_step): + def get_gradient( + self, sim, fields_a, fields_f, frequencies, finite_difference_step + ): num_freqs = onp.array(frequencies).size - '''We have the option to linearly scale the gradients up front + """We have the option to linearly scale the gradients up front using the scalegrad parameter (leftover from MPB API). Not currently needed for any existing feature (but available for - future use)''' + future use)""" scalegrad = 1 grad = onp.zeros((num_freqs, self.num_design_params)) # preallocate vol = sim._fit_volume_to_simulation(self.volume) # compute the gradient - mp._get_gradient(grad,scalegrad, - fields_a[0].swigobj,fields_a[1].swigobj,fields_a[2].swigobj, - fields_f[0].swigobj,fields_f[1].swigobj,fields_f[2].swigobj, - sim.gv,vol.swigobj,onp.array(frequencies), - sim.geps,finite_difference_step) + mp._get_gradient( + grad, + scalegrad, + fields_a[0].swigobj, + fields_a[1].swigobj, + fields_a[2].swigobj, + fields_f[0].swigobj, + fields_f[1].swigobj, + fields_f[2].swigobj, + sim.gv, + vol.swigobj, + onp.array(frequencies), + sim.geps, + finite_difference_step, + ) return onp.squeeze(grad).T + def _check_if_cylindrical(sim): return sim.is_cylindrical or (sim.dimensions == mp.CYLINDRICAL) + def _compute_components(sim): - return _ADJOINT_FIELD_COMPONENTS_CYL if _check_if_cylindrical(sim) else _ADJOINT_FIELD_COMPONENTS + return ( + _ADJOINT_FIELD_COMPONENTS_CYL + if _check_if_cylindrical(sim) + else _ADJOINT_FIELD_COMPONENTS + ) + def _make_at_least_nd(x: onp.ndarray, dims: int = 3) -> onp.ndarray: """Makes an array have at least the specified number of dimensions.""" - return onp.reshape(x, x.shape + onp.maximum(dims - x.ndim, 0) * (1, )) + return onp.reshape(x, x.shape + onp.maximum(dims - x.ndim, 0) * (1,)) def calculate_vjps( @@ -77,7 +92,7 @@ def calculate_vjps( adj_fields: List[List[onp.ndarray]], design_variable_shapes: List[Tuple[int, ...]], sum_freq_partials: bool = True, - finite_difference_step: float = FD_DEFAULT + finite_difference_step: float = FD_DEFAULT, ) -> List[onp.ndarray]: """Calculates the VJP for a given set of forward and adjoint fields.""" vjps = [ @@ -87,7 +102,8 @@ def calculate_vjps( fwd_fields[i], frequencies, finite_difference_step, - ) for i, design_region in enumerate(design_regions) + ) + for i, design_region in enumerate(design_regions) ] if sum_freq_partials: vjps = [ @@ -96,7 +112,7 @@ def calculate_vjps( ] else: vjps = [ - vjp.reshape(shape + (-1, )) + vjp.reshape(shape + (-1,)) for vjp, shape in zip(vjps, design_variable_shapes) ] return vjps @@ -118,17 +134,20 @@ def install_design_region_monitors( decimation_factor: int = 0, ) -> List[mp.DftFields]: """Installs DFT field monitors at the design regions of the simulation.""" - design_region_monitors = [[ - simulation.add_dft_fields( - [comp], - frequencies, - where=design_region.volume, - yee_grid=True, - decimation_factor=decimation_factor, - persist=True - ) for comp in _compute_components(simulation) - ] for design_region in design_regions ] - return design_region_monitors + return [ + [ + simulation.add_dft_fields( + [comp], + frequencies, + where=design_region.volume, + yee_grid=True, + decimation_factor=decimation_factor, + persist=True, + ) + for comp in _compute_components(simulation) + ] + for design_region in design_regions + ] def gather_monitor_values(monitors: List[ObjectiveQuantity]) -> onp.ndarray: @@ -142,9 +161,7 @@ def gather_monitor_values(monitors: List[ObjectiveQuantity]) -> onp.ndarray: complex128. Note that these values refer to the mode as oriented (i.e. they are unidirectional). """ - monitor_values = [] - for monitor in monitors: - monitor_values.append(monitor()) + monitor_values = [monitor() for monitor in monitors] monitor_values = onp.array(monitor_values) assert monitor_values.ndim in [1, 2] monitor_values = _make_at_least_nd(monitor_values, 2) @@ -187,8 +204,8 @@ def gather_design_region_fields( def validate_and_update_design( - design_regions: List[DesignRegion], - design_variables: Iterable[onp.ndarray]) -> None: + design_regions: List[DesignRegion], design_variables: Iterable[onp.ndarray] +) -> None: """Validate the design regions and variables. In particular the design variable should be 1,2,3-D and the design region @@ -202,48 +219,55 @@ def validate_and_update_design( Raises: ValueError if the validation of dimensions fails. """ - for i, (design_region, - design_variable) in enumerate(zip(design_regions, - design_variables)): + for i, (design_region, design_variable) in enumerate( + zip(design_regions, design_variables) + ): if design_variable.ndim not in [1, 2, 3]: raise ValueError( - 'Design variables should be 1D, 2D, or 3D, but the design variable ' - 'at index {} had a shape of {}.'.format( - i, design_variable.shape)) + f"Design variables should be 1D, 2D, or 3D, but the design variable at index {i} had a shape of {design_variable.shape}." + ) + design_region_shape = tuple( - int(x) for x in design_region.design_parameters.grid_size) - design_variable_padded_shape = design_variable.shape + (1, ) * ( - 3 - design_variable.ndim) + int(x) for x in design_region.design_parameters.grid_size + ) + design_variable_padded_shape = design_variable.shape + (1,) * ( + 3 - design_variable.ndim + ) if design_variable_padded_shape != design_region_shape: raise ValueError( - 'The design variable at index {} with a shape of {} is ' - 'incompatible with the associated design region, which has a shape ' - 'of {}.'.format(i, design_variable.shape, design_region_shape)) + f"The design variable at index {i} with a shape of {design_variable.shape} is incompatible with the associated design region, which has a shape of {design_region_shape}." + ) + design_variable = onp.asarray(design_variable, dtype=onp.float64) # Update the design variable in Meep design_region.update_design_parameters(design_variable.flatten()) def create_adjoint_sources( - monitors: Iterable[ObjectiveQuantity], - monitor_values_grad: onp.ndarray) -> List[mp.Source]: - monitor_values_grad = onp.asarray(monitor_values_grad, - dtype=onp.complex64 if mp.is_single_precision() else onp.complex128) + monitors: Iterable[ObjectiveQuantity], monitor_values_grad: onp.ndarray +) -> List[mp.Source]: + monitor_values_grad = onp.asarray( + monitor_values_grad, + dtype=onp.complex64 if mp.is_single_precision() else onp.complex128, + ) if not onp.any(monitor_values_grad): raise RuntimeError( - 'The gradient of all monitor values is zero, which ' - 'means that no adjoint sources can be placed to set ' - 'up an adjoint simulation in Meep. Possible causes ' - 'could be:\n\n' - ' * the forward simulation was not run for long enough ' - 'to allow the input pulse(s) to reach the monitors' - ' * the monitor values are disconnected from the ' - 'objective function output.') + "The gradient of all monitor values is zero, which " + "means that no adjoint sources can be placed to set " + "up an adjoint simulation in Meep. Possible causes " + "could be:\n\n" + " * the forward simulation was not run for long enough " + "to allow the input pulse(s) to reach the monitors" + " * the monitor values are disconnected from the " + "objective function output." + ) adjoint_sources = [] for monitor_idx, monitor in enumerate(monitors): # `dj` for each monitor will have a shape of (num frequencies,) - dj = onp.asarray(monitor_values_grad[monitor_idx], - dtype=onp.complex64 if mp.is_single_precision() else onp.complex128) + dj = onp.asarray( + monitor_values_grad[monitor_idx], + dtype=onp.complex64 if mp.is_single_precision() else onp.complex128, + ) if onp.any(dj): adjoint_sources += monitor.place_adjoint_source(dj) assert adjoint_sources diff --git a/python/adjoint/wrapper.py b/python/adjoint/wrapper.py index 8ecd38a3f..06d9ad96e 100644 --- a/python/adjoint/wrapper.py +++ b/python/adjoint/wrapper.py @@ -46,16 +46,15 @@ def loss(x): value, grad = jax.value_and_grad(loss)(x) ``` """ - from typing import Callable, List, Tuple import jax import jax.numpy as jnp -import meep as mp import numpy as onp -from . import utils -from . import DesignRegion, EigenmodeCoefficient +import meep as mp + +from . import DesignRegion, EigenmodeCoefficient, utils _norm_fn = onp.linalg.norm _reduce_fn = onp.max @@ -90,6 +89,7 @@ class MeepJaxWrapper: https://meep.readthedocs.io/en/latest/Python_User_Interface/#Simulation for more information. The default is true. """ + _log_fn = print def __init__( @@ -103,7 +103,7 @@ def __init__( minimum_run_time: float = 0, maximum_run_time: float = onp.inf, until_after_sources: bool = True, - finite_difference_step: float = utils.FD_DEFAULT + finite_difference_step: float = utils.FD_DEFAULT, ): self.simulation = simulation self.sources = sources @@ -150,23 +150,27 @@ def _run_fwd_simulation(self, design_variables): ) self.simulation.init_sim() sim_run_args = { - 'until_after_sources' if self.until_after_sources else 'until': - mp.stop_when_dft_decayed(self.dft_threshold,self.minimum_run_time,self.maximum_run_time) + "until_after_sources" + if self.until_after_sources + else "until": mp.stop_when_dft_decayed( + self.dft_threshold, self.minimum_run_time, self.maximum_run_time + ) } self.simulation.run(**sim_run_args) monitor_values = utils.gather_monitor_values(self.monitors) - return (jnp.asarray(monitor_values), - fwd_design_region_monitors) + return (jnp.asarray(monitor_values), fwd_design_region_monitors) def _run_adjoint_simulation(self, monitor_values_grad): """Runs adjoint simulation, returning design region fields.""" if not self.design_regions: raise RuntimeError( - 'An adjoint simulation was attempted when no design ' - 'regions are present.') - adjoint_sources = utils.create_adjoint_sources(self.monitors, - monitor_values_grad) + "An adjoint simulation was attempted when no design " + "regions are present." + ) + adjoint_sources = utils.create_adjoint_sources( + self.monitors, monitor_values_grad + ) # TODO refactor with optimization_problem.py # self.simulation.restart_fields() self.simulation.clear_dft_monitors() @@ -179,8 +183,11 @@ def _run_adjoint_simulation(self, monitor_values_grad): ) self.simulation.init_sim() sim_run_args = { - 'until_after_sources' if self.until_after_sources else 'until': - mp.stop_when_dft_decayed(self.dft_threshold,self.minimum_run_time,self.maximum_run_time) + "until_after_sources" + if self.until_after_sources + else "until": mp.stop_when_dft_decayed( + self.dft_threshold, self.minimum_run_time, self.maximum_run_time + ) } self.simulation.run(**sim_run_args) @@ -202,12 +209,12 @@ def _calculate_vjps( adj_fields, design_variable_shapes, sum_freq_partials=sum_freq_partials, - finite_difference_step=self.finite_difference_step + finite_difference_step=self.finite_difference_step, ) - def _initialize_callable( - self) -> Callable[[List[jnp.ndarray]], jnp.ndarray]: + def _initialize_callable(self) -> Callable[[List[jnp.ndarray]], jnp.ndarray]: """Initializes the callable JAX function and registers its VJP.""" + @jax.custom_vjp def simulate(design_variables: List[jnp.ndarray]) -> jnp.ndarray: monitor_values, _ = self._run_fwd_simulation(design_variables) @@ -216,17 +223,23 @@ def simulate(design_variables: List[jnp.ndarray]) -> jnp.ndarray: def _simulate_fwd(design_variables): """Runs forward simulation, returning monitor values and fields.""" monitor_values, self.fwd_design_region_monitors = self._run_fwd_simulation( - design_variables) + design_variables + ) design_variable_shapes = [x.shape for x in design_variables] return monitor_values, (design_variable_shapes) def _simulate_rev(res, monitor_values_grad): """Runs adjoint simulation, returning VJP of design wrt monitor values.""" design_variable_shapes = res - self.adj_design_region_monitors = self._run_adjoint_simulation(monitor_values_grad) - vjps = self._calculate_vjps(self.fwd_design_region_monitors, self.adj_design_region_monitors, - design_variable_shapes) - return ([jnp.asarray(vjp) for vjp in vjps], ) + self.adj_design_region_monitors = self._run_adjoint_simulation( + monitor_values_grad + ) + vjps = self._calculate_vjps( + self.fwd_design_region_monitors, + self.adj_design_region_monitors, + design_variable_shapes, + ) + return ([jnp.asarray(vjp) for vjp in vjps],) simulate.defvjp(_simulate_fwd, _simulate_rev) diff --git a/python/binary_partition_utils.py b/python/binary_partition_utils.py index c37a5d07f..53f1d65b5 100644 --- a/python/binary_partition_utils.py +++ b/python/binary_partition_utils.py @@ -1,9 +1,10 @@ -from typing import Dict, Generator, List, Tuple import warnings +from typing import Dict, Generator, List, Tuple -import meep as mp import numpy as onp +import meep as mp + def is_leaf_node(partition: mp.BinaryPartition) -> bool: """Returns True if the partition has no children. @@ -18,7 +19,8 @@ def is_leaf_node(partition: mp.BinaryPartition) -> bool: def enumerate_leaf_nodes( - partition: mp.BinaryPartition) -> Generator[mp.BinaryPartition, None, None]: + partition: mp.BinaryPartition, +) -> Generator[mp.BinaryPartition, None, None]: """Enumerates all leaf nodes of a partition. Args: @@ -30,10 +32,8 @@ def enumerate_leaf_nodes( if is_leaf_node(partition): yield partition else: - for child in enumerate_leaf_nodes(partition.left): - yield child - for child in enumerate_leaf_nodes(partition.right): - yield child + yield from enumerate_leaf_nodes(partition.left) + yield from enumerate_leaf_nodes(partition.right) def partition_has_duplicate_proc_ids(partition: mp.BinaryPartition) -> bool: @@ -49,8 +49,9 @@ def partition_has_duplicate_proc_ids(partition: mp.BinaryPartition) -> bool: return len(set(proc_ids)) != len(proc_ids) -def get_total_weight(partition: mp.BinaryPartition, - weights_by_proc_id: List[float]) -> float: +def get_total_weight( + partition: mp.BinaryPartition, weights_by_proc_id: List[float] +) -> float: """Computes the total weights contained within a BinaryPartition subtree. Args: @@ -64,7 +65,7 @@ def get_total_weight(partition: mp.BinaryPartition, ValueError: if sim.chunk_layout includes nodes with duplicate proc_ids """ if partition_has_duplicate_proc_ids(partition): - raise ValueError('Duplicate proc_ids found in chunk_layout!') + raise ValueError("Duplicate proc_ids found in chunk_layout!") if partition.proc_id is not None: return weights_by_proc_id[partition.proc_id] elif partition.left is not None and partition.right is not None: @@ -72,12 +73,12 @@ def get_total_weight(partition: mp.BinaryPartition, right_weight = get_total_weight(partition.right, weights_by_proc_id) return left_weight + right_weight else: - raise ValueError('Partition missing proc_id or left, right attributes!') + raise ValueError("Partition missing proc_id or left, right attributes!") def get_left_right_total_weights( - partition: mp.BinaryPartition, - weights_by_proc_id: List[float]) -> Tuple[float, float]: + partition: mp.BinaryPartition, weights_by_proc_id: List[float] +) -> Tuple[float, float]: """Computes the total weights contained in left and right subtrees. Args: @@ -96,7 +97,7 @@ def get_left_right_total_weights( right_weight = get_total_weight(partition.right, weights_by_proc_id) return (left_weight, right_weight) else: - raise ValueError('Partition missing left, right attributes!') + raise ValueError("Partition missing left, right attributes!") def pixel_volume(grid_volume: mp.grid_volume) -> int: @@ -116,9 +117,11 @@ def pixel_volume(grid_volume: mp.grid_volume) -> int: return grid_volume.nx() * grid_volume.ny() -def get_total_volume(partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> float: +def get_total_volume( + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> float: """Computes the total pixel volume in a subtree from associated chunk volumes. NOTE: If multiple chunks are owned by the same process, this function may @@ -135,14 +138,15 @@ def get_total_volume(partition: mp.BinaryPartition, Returns: The total pixel volume occupied by all chunks owned by the partition. """ - my_grid_volumes = get_grid_volumes_in_tree(partition, chunk_volumes, - chunk_owners) + my_grid_volumes = get_grid_volumes_in_tree(partition, chunk_volumes, chunk_owners) return sum(pixel_volume(vol) for vol in my_grid_volumes) def get_left_right_total_volumes( - partition: mp.BinaryPartition, chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> Tuple[float, float]: + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> Tuple[float, float]: """Computes the total pixel volume in left and right subtrees. Args: @@ -161,16 +165,17 @@ def get_left_right_total_volumes( """ if partition.left is not None and partition.right is not None: left_volume = get_total_volume(partition.left, chunk_volumes, chunk_owners) - right_volume = get_total_volume(partition.right, chunk_volumes, - chunk_owners) + right_volume = get_total_volume(partition.right, chunk_volumes, chunk_owners) return (left_volume, right_volume) else: - raise ValueError('Partition missing left, right attributes!') + raise ValueError("Partition missing left, right attributes!") -def get_grid_volumes_in_tree(partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> List[mp.grid_volume]: +def get_grid_volumes_in_tree( + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> List[mp.grid_volume]: """Fetches a list of grid_volumes contained in a BinaryPartition subtree. NOTE: If multiple chunks are owned by the same process, this function may @@ -189,7 +194,7 @@ def get_grid_volumes_in_tree(partition: mp.BinaryPartition, the partition. The list is not necessarily ordered by proc_id values. """ if partition_has_duplicate_proc_ids(partition): - warnings.warn('Partition has duplicate proc_ids, overcounting possible!') + warnings.warn("Partition has duplicate proc_ids, overcounting possible!") my_proc_ids = [node.proc_id for node in enumerate_leaf_nodes(partition)] @@ -200,9 +205,11 @@ def get_grid_volumes_in_tree(partition: mp.BinaryPartition, ] -def get_total_volume_per_process(partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray) -> Dict[int, float]: +def get_total_volume_per_process( + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, +) -> Dict[int, float]: """Computes the total pixel volume per process contained in a BinaryPartition. Args: @@ -219,17 +226,20 @@ def get_total_volume_per_process(partition: mp.BinaryPartition, volumes_per_process = {} leaf_nodes_in_tree = enumerate_leaf_nodes(partition) for leaf in leaf_nodes_in_tree: - total_volume = 0 - for i, owner in enumerate(chunk_owners): - if owner == leaf.proc_id: - total_volume += pixel_volume(chunk_volumes[i]) + total_volume = sum( + pixel_volume(chunk_volumes[i]) + for i, owner in enumerate(chunk_owners) + if owner == leaf.proc_id + ) + volumes_per_process[leaf.proc_id] = total_volume return volumes_per_process def get_box_ranges( - partition: mp.BinaryPartition, chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: onp.ndarray + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: onp.ndarray, ) -> Tuple[float, float, float, float, float, float]: """Gets the max and min x, y, z dimensions spanned by a partition. @@ -272,12 +282,10 @@ def get_box_ranges( xmaxs.append(vol.surroundings().get_max_corner().x()) ymaxs.append(vol.surroundings().get_max_corner().y()) zmaxs.append(vol.surroundings().get_max_corner().z()) - return (min(xmins), max(xmaxs), min(ymins), max(ymaxs), min(zmins), - max(zmaxs)) + return (min(xmins), max(xmaxs), min(ymins), max(ymaxs), min(zmins), max(zmaxs)) -def partitions_are_equal(bp1: mp.BinaryPartition, - bp2: mp.BinaryPartition) -> bool: +def partitions_are_equal(bp1: mp.BinaryPartition, bp2: mp.BinaryPartition) -> bool: """Determines if two partitions have all nodes with identical attributes. Args: @@ -290,10 +298,13 @@ def partitions_are_equal(bp1: mp.BinaryPartition, if is_leaf_node(bp1) and is_leaf_node(bp2): return bp1.proc_id == bp2.proc_id elif (not is_leaf_node(bp1)) and (not is_leaf_node(bp2)): - return all([ - bp1.split_dir == bp2.split_dir, bp1.split_pos == bp2.split_pos, - partitions_are_equal(bp1.left, bp2.left), - partitions_are_equal(bp1.right, bp2.right) - ]) + return all( + [ + bp1.split_dir == bp2.split_dir, + bp1.split_pos == bp2.split_pos, + partitions_are_equal(bp1.left, bp2.left), + partitions_are_equal(bp1.right, bp2.right), + ] + ) else: return False diff --git a/python/chunk_balancer.py b/python/chunk_balancer.py index 956ba979c..8162ca7bc 100644 --- a/python/chunk_balancer.py +++ b/python/chunk_balancer.py @@ -2,11 +2,11 @@ import copy from typing import Optional, Tuple, Union -import meep as mp -from meep import binary_partition_utils as bpu +import numpy as np from meep.timing_measurements import MeepTimingMeasurements -import numpy as np +import meep as mp +from meep import binary_partition_utils as bpu class AbstractChunkBalancer(abc.ABC): @@ -25,7 +25,8 @@ class AbstractChunkBalancer(abc.ABC): """ def make_initial_chunk_layout( - self, sim: mp.Simulation) -> Union[mp.BinaryPartition, None]: + self, sim: mp.Simulation + ) -> Union[mp.BinaryPartition, None]: """Generates an initial chunk layout based on expected compute costs. Args: @@ -66,21 +67,26 @@ def adjust_chunk_layout(self, sim: mp.Simulation, **kwargs) -> None: timing_measurements = MeepTimingMeasurements.new_from_simulation(sim, -1) - new_chunk_layout = self.compute_new_chunk_layout(timing_measurements, - old_chunk_layout, - chunk_volumes, - chunk_owners, **kwargs) + new_chunk_layout = self.compute_new_chunk_layout( + timing_measurements, + old_chunk_layout, + chunk_volumes, + chunk_owners, + **kwargs, + ) sim.reset_meep() sim.chunk_layout = new_chunk_layout sim.init_sim() @abc.abstractmethod - def compute_new_chunk_layout(self, - timing_measurements: MeepTimingMeasurements, - old_partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: np.ndarray) -> mp.BinaryPartition: + def compute_new_chunk_layout( + self, + timing_measurements: MeepTimingMeasurements, + old_partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: np.ndarray, + ) -> mp.BinaryPartition: """Rebalances the partition to equalize simulation time for node's children. Args: @@ -123,16 +129,14 @@ def _validate_sim(self, sim: mp.Simulation): """ # Check that chunk_layout does not contain duplicate nodes for same process if bpu.partition_has_duplicate_proc_ids(sim.chunk_layout): - raise ValueError('Duplicate proc_ids found in chunk_layout!') + raise ValueError("Duplicate proc_ids found in chunk_layout!") # Check that proc_ids in chunk_layout are assigned properly to grid owners chunk_owners = sim.structure.get_chunk_owners() - proc_ids = [ - node.proc_id for node in bpu.enumerate_leaf_nodes(sim.chunk_layout) - ] + proc_ids = [node.proc_id for node in bpu.enumerate_leaf_nodes(sim.chunk_layout)] if set(chunk_owners) != set(proc_ids): raise ValueError( - 'Processes {} present in chunk_layout but not grid_owners!'.format( - set(proc_ids) - set(chunk_owners))) + f"Processes {set(proc_ids) - set(chunk_owners)} present in chunk_layout but not grid_owners!" + ) class ChunkBalancer(AbstractChunkBalancer): @@ -156,16 +160,16 @@ class ChunkBalancer(AbstractChunkBalancer): """ def compute_new_chunk_layout( - self, - timing_measurements: MeepTimingMeasurements, - partition: mp.BinaryPartition, - chunk_volumes: Tuple[mp.grid_volume], - chunk_owners: np.ndarray, - new_partition: Optional[mp.BinaryPartition] = None, - new_partition_root: Optional[mp.BinaryPartition] = None, - xyz_bounds: Optional[Tuple[float, float, float, float, float, - float]] = None, - sensitivity: float = 0.5) -> mp.BinaryPartition: + self, + timing_measurements: MeepTimingMeasurements, + partition: mp.BinaryPartition, + chunk_volumes: Tuple[mp.grid_volume], + chunk_owners: np.ndarray, + new_partition: Optional[mp.BinaryPartition] = None, + new_partition_root: Optional[mp.BinaryPartition] = None, + xyz_bounds: Optional[Tuple[float, float, float, float, float, float]] = None, + sensitivity: float = 0.5, + ) -> mp.BinaryPartition: """Rebalances the partition to equalize simulation time for node's children. Args: @@ -204,17 +208,17 @@ def compute_new_chunk_layout( elif partition.split_dir == mp.Z: _, _, _, _, d_min, d_max = xyz_bounds else: - raise ValueError('Split direction must be mp.X, mp.Y, or mp.Z!') + raise ValueError("Split direction must be mp.X, mp.Y, or mp.Z!") - sim_times = list( - self._compute_working_times_per_process(timing_measurements)) + sim_times = list(self._compute_working_times_per_process(timing_measurements)) n_left = partition.left.numchunks() n_right = partition.right.numchunks() t_left, t_right = bpu.get_left_right_total_weights(partition, sim_times) - v_left, v_right = bpu.get_left_right_total_volumes(partition, chunk_volumes, - chunk_owners) + v_left, v_right = bpu.get_left_right_total_volumes( + partition, chunk_volumes, chunk_owners + ) v_left_new = v_left * t_right * n_left v_right_new = v_right * t_left * n_right @@ -223,8 +227,7 @@ def compute_new_chunk_layout( old_split_pos = partition.split_pos new_split_pos = (d_max - d_min) * split_frac + d_min - new_split_pos = sensitivity * new_split_pos + (1 - - sensitivity) * old_split_pos + new_split_pos = sensitivity * new_split_pos + (1 - sensitivity) * old_split_pos # Adjust the split pos on new_partition. We can't modify the old partition # because it could affect left/right volume ratios for subsequent calls @@ -250,7 +253,8 @@ def compute_new_chunk_layout( new_partition=new_partition.left, new_partition_root=new_partition_root, xyz_bounds=left_xyz_bounds, - sensitivity=sensitivity) + sensitivity=sensitivity, + ) self.compute_new_chunk_layout( timing_measurements, partition.right, @@ -259,24 +263,26 @@ def compute_new_chunk_layout( new_partition=new_partition.right, new_partition_root=new_partition_root, xyz_bounds=right_xyz_bounds, - sensitivity=sensitivity) + sensitivity=sensitivity, + ) return new_partition def _compute_working_times_per_process( - self, timing_measurements: MeepTimingMeasurements) -> np.ndarray: + self, timing_measurements: MeepTimingMeasurements + ) -> np.ndarray: """Computes the time spent by each MPI process actively working.""" time_sinks_to_include = [ - 'time_stepping', - 'boundaries_copying', - 'field_output', - 'fourier_transform', - 'mpb', - 'near_to_farfield_transform', + "time_stepping", + "boundaries_copying", + "field_output", + "fourier_transform", + "mpb", + "near_to_farfield_transform", ] - num_processes = len(timing_measurements.measurements['time_stepping']) + num_processes = len(timing_measurements.measurements["time_stepping"]) working_times = np.zeros(num_processes) for category in time_sinks_to_include: working_times += np.array(timing_measurements.measurements[category]) diff --git a/python/examples/3rd-harm-1d.py b/python/examples/3rd-harm-1d.py index 5ac0f5dd3..022efaac5 100644 --- a/python/examples/3rd-harm-1d.py +++ b/python/examples/3rd-harm-1d.py @@ -1,19 +1,18 @@ # 1d simulation of a plane wave propagating through a Kerr medium # and generating the third-harmonic frequency component. - -from __future__ import division +import argparse import meep as mp -import argparse + def main(args): - sz = 100 # size of cell in z direction - fcen = 1 / 3.0 # center frequency of source + sz = 100 # size of cell in z direction + fcen = 1 / 3.0 # center frequency of source df = fcen / 20.0 # frequency width of source - amp = args.amp # amplitude of source - k = 10**args.logk # Kerr susceptibility - dpml = 1.0 # PML thickness + amp = args.amp # amplitude of source + k = 10**args.logk # Kerr susceptibility + dpml = 1.0 # PML thickness # We'll use an explicitly 1d simulation. Setting dimensions=1 will actually # result in faster execution than just using two no-size dimensions. However, @@ -28,38 +27,54 @@ def main(args): # and pass it to the Simulation constructor default_material = mp.Medium(index=1, chi3=k) - sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex, - center=mp.Vector3(0, 0, -0.5*sz + dpml), amplitude=amp) + sources = mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(0, 0, -0.5 * sz + dpml), + amplitude=amp, + ) # frequency range for flux calculation nfreq = 400 fmin = fcen / 2.0 fmax = fcen * 4 - sim = mp.Simulation(cell_size=cell, - geometry=[], - sources=[sources], - boundary_layers=[pml_layers], - default_material=default_material, - resolution=resolution, - dimensions=dimensions) + sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=[sources], + boundary_layers=[pml_layers], + default_material=default_material, + resolution=resolution, + dimensions=dimensions, + ) # trans = sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq, # mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5))) - trans1 = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5))) - trans3 = sim.add_flux(3 * fcen, 0, 1, - mp.FluxRegion(mp.Vector3(0, 0, 0.5*sz - dpml - 0.5))) + trans1 = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)) + ) + trans3 = sim.add_flux( + 3 * fcen, 0, 1, mp.FluxRegion(mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5)) + ) - sim.run(until_after_sources=mp.stop_when_fields_decayed( - 50, mp.Ex, mp.Vector3(0, 0, 0.5*sz - dpml - 0.5), 1e-6)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(0, 0, 0.5 * sz - dpml - 0.5), 1e-6 + ) + ) # sim.display_fluxes(trans) - print("harmonics:, {}, {}, {}, {}".format(k, amp, mp.get_fluxes(trans1)[0], mp.get_fluxes(trans3)[0])) + print( + f"harmonics:, {k}, {amp}, {mp.get_fluxes(trans1)[0]}, {mp.get_fluxes(trans3)[0]}" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-amp', type=float, default=1.0, help='amplitude of source') - parser.add_argument('-logk', type=float, default=0, help='logarithm of Kerr susceptibility') + parser.add_argument("-amp", type=float, default=1.0, help="amplitude of source") + parser.add_argument( + "-logk", type=float, default=0, help="logarithm of Kerr susceptibility" + ) args = parser.parse_args() main(args) diff --git a/python/examples/README.md b/python/examples/README.md index 13e1e2d4f..e7aac224f 100644 --- a/python/examples/README.md +++ b/python/examples/README.md @@ -4,7 +4,7 @@ Meep simulations are Python scripts which involve specifying the device geometry Python libraries such as NumPy, SciPy, and Matplotlib can be used to augment the simulation functionality and will also be demonstrated. Much of the functionality of the low-level C++ interface has been abstracted in Python which means that you don't need to be an experienced programmer to set up simulations. Reasonable defaults are available where necessary. -Several tutorials and examples are found here. These tutorials are meant to illustrate Meep's various features in an interactive and application-oriented manner. +Several tutorials and examples are found here. These tutorials are meant to illustrate Meep's various features in an interactive and application-oriented manner. ## iPython/Jupyter Notebooks @@ -16,7 +16,7 @@ The recommended method to run the tutorial notebooks is by 1.) installing `meep` ### nbviewer -`nbviewer` is a web platform that can render interactive features found inside Jupyter notebooks openly stored on web-servers. While `nbviewer` can't run python code, it can execute stored javascript code used to animate the simulations. +`nbviewer` is a web platform that can render interactive features found inside Jupyter notebooks openly stored on web-servers. While `nbviewer` can't run python code, it can execute stored javascript code used to animate the simulations. ### GitHub @@ -26,7 +26,7 @@ GitHub is able to render some of the smaller notebooks as plain text. However, t Below are summaries for each tutorial, along with the features the tutorials highlight. While there is no particular order to the tutorials, they progressively incorporate more complicated features. -#### Basics +#### Basics * __`straight-waveguide.ipynb`__ - A simple 2D straight waveguide tutorial that explores basic meep features like `geometry`, `sources`, and `PML` layers. The tutorial also explores basic visualization and animation features. @@ -47,12 +47,12 @@ Computes the resonant mode frequencies of a 2D ring resonator using `harminv`. * __`holey-wg-cavity.ipynb`__ - Calculates the transmission and resonant modes of a waveguide photonic crystal cavity. Demonstrates the `harminv` routines and how to estimate the $Q$ of cavities. -* __`holey-wg-bands.ipynb`__ - +* __`holey-wg-bands.ipynb`__ - Computes the band diagram of the infinite periodic waveguide by itself with no defects in the time domain. Explores the `k_point`, `run_k_point`, and periodic boundary conditions features. #### MPB and Band diagrams -* __`mpb_strip.ipynb`__ - +* __`mpb_strip.ipynb`__ - #### Eigenmode Source @@ -79,12 +79,12 @@ Computes the band diagram of the infinite periodic waveguide by itself with no d #### Nonlinear Optics -* __`3rd-harm-1d.ipynb`__ - +* __`3rd-harm-1d.ipynb`__ - Examines 3rd harmonic generation in a $\chi^{(3)}$ material. Explores the proper way to do 1d simulations, how to include nonlinearities in materials, and compares experimental results to theory. #### Near to Far Field Spectra -* __`antenna-radiation.ipynb`__ - +* __`antenna-radiation.ipynb`__ - Computes the radiation pattern of a simple point source "antenna". Explores `add_near2far`, `add_flux`, `get_fluxes`, and `get_farfield` features. * __`metasurface_lens.ipynb`__ - diff --git a/python/examples/absorbed_power_density.py b/python/examples/absorbed_power_density.py index 8072a56b9..7e82f8489 100644 --- a/python/examples/absorbed_power_density.py +++ b/python/examples/absorbed_power_density.py @@ -1,90 +1,113 @@ -import numpy as np import matplotlib -matplotlib.use('agg') +import numpy as np + +matplotlib.use("agg") import matplotlib.pyplot as plt +from meep.materials import SiO2 import meep as mp -from meep.materials import SiO2 resolution = 100 # pixels/um dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] -r = 1.0 # radius of cylinder +r = 1.0 # radius of cylinder dair = 2.0 # air padding thickness -s = 2*(dpml+dair+r) -cell_size = mp.Vector3(s,s) +s = 2 * (dpml + dair + r) +cell_size = mp.Vector3(s, s) wvl = 1.0 -fcen = 1/wvl +fcen = 1 / wvl # is_integrated=True necessary for any planewave source extending into PML -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.1*fcen,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s), - component=mp.Ez)] +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.1 * fcen, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s), + component=mp.Ez, + ) +] symmetries = [mp.Mirror(mp.Y)] -geometry = [mp.Cylinder(material=SiO2, - center=mp.Vector3(), - radius=r, - height=mp.inf)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - symmetries=symmetries, - geometry=geometry) - -dft_fields = sim.add_dft_fields([mp.Dz,mp.Ez], - fcen,0,1, - center=mp.Vector3(), - size=mp.Vector3(2*r,2*r), - yee_grid=True) +geometry = [mp.Cylinder(material=SiO2, center=mp.Vector3(), radius=r, height=mp.inf)] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + symmetries=symmetries, + geometry=geometry, +) + +dft_fields = sim.add_dft_fields( + [mp.Dz, mp.Ez], + fcen, + 0, + 1, + center=mp.Vector3(), + size=mp.Vector3(2 * r, 2 * r), + yee_grid=True, +) # closed box surrounding cylinder for computing total incoming flux -flux_box = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r),weight=+1), - mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r),weight=-1), - mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0),weight=-1), - mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0),weight=+1)) +flux_box = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r), weight=+1), + mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r), weight=-1), + mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0), weight=-1), + mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0), weight=+1), +) sim.run(until_after_sources=100) -Dz = sim.get_dft_array(dft_fields,mp.Dz,0) -Ez = sim.get_dft_array(dft_fields,mp.Ez,0) -absorbed_power_density = 2*np.pi*fcen * np.imag(np.conj(Ez)*Dz) +Dz = sim.get_dft_array(dft_fields, mp.Dz, 0) +Ez = sim.get_dft_array(dft_fields, mp.Ez, 0) +absorbed_power_density = 2 * np.pi * fcen * np.imag(np.conj(Ez) * Dz) -dxy = 1/resolution**2 -absorbed_power = np.sum(absorbed_power_density)*dxy +dxy = 1 / resolution**2 +absorbed_power = np.sum(absorbed_power_density) * dxy absorbed_flux = mp.get_fluxes(flux_box)[0] -err = abs(absorbed_power-absorbed_flux)/absorbed_flux -print("flux:, {} (dft_fields), {} (dft_flux), {} (error)".format(absorbed_power,absorbed_flux,err)) +err = abs(absorbed_power - absorbed_flux) / absorbed_flux +print( + f"flux:, {absorbed_power} (dft_fields), {absorbed_flux} (dft_flux), {err} (error)" +) + plt.figure() sim.plot2D() -plt.savefig('power_density_cell.png',dpi=150,bbox_inches='tight') +plt.savefig("power_density_cell.png", dpi=150, bbox_inches="tight") plt.figure() -x = np.linspace(-r,r,Dz.shape[0]) -y = np.linspace(-r,r,Dz.shape[1]) -plt.pcolormesh(x, - y, - np.transpose(absorbed_power_density), - cmap='inferno_r', - shading='gouraud', - vmin=0, - vmax=np.amax(absorbed_power_density)) +x = np.linspace(-r, r, Dz.shape[0]) +y = np.linspace(-r, r, Dz.shape[1]) +plt.pcolormesh( + x, + y, + np.transpose(absorbed_power_density), + cmap="inferno_r", + shading="gouraud", + vmin=0, + vmax=np.amax(absorbed_power_density), +) plt.xlabel("x (μm)") -plt.xticks(np.linspace(-r,r,5)) +plt.xticks(np.linspace(-r, r, 5)) plt.ylabel("y (μm)") -plt.yticks(np.linspace(-r,r,5)) -plt.gca().set_aspect('equal') -plt.title("absorbed power density" + "\n" +"SiO2 Labs(λ={} μm) = {:.2f} μm".format(wvl,wvl/np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0])))) +plt.yticks(np.linspace(-r, r, 5)) +plt.gca().set_aspect("equal") +plt.title( + "absorbed power density" + + "\n" + + "SiO2 Labs(λ={} μm) = {:.2f} μm".format( + wvl, wvl / np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0])) + ) +) plt.colorbar() -plt.savefig('power_density_map.png',dpi=150,bbox_inches='tight') +plt.savefig("power_density_map.png", dpi=150, bbox_inches="tight") diff --git a/python/examples/absorber-1d.py b/python/examples/absorber-1d.py index e6d93633c..49fb6a862 100644 --- a/python/examples/absorber-1d.py +++ b/python/examples/absorber-1d.py @@ -1,38 +1,50 @@ -from __future__ import division - import argparse -import meep as mp + from meep.materials import Al +import meep as mp + def main(args): resolution = 40 cell_size = mp.Vector3(z=10) - boundary_layers = [mp.PML(1, direction=mp.Z) if args.pml else mp.Absorber(1, direction=mp.Z)] + boundary_layers = [ + mp.PML(1, direction=mp.Z) if args.pml else mp.Absorber(1, direction=mp.Z) + ] - sources = [mp.Source(src=mp.GaussianSource(1 / 0.803, fwidth=0.1), - center=mp.Vector3(), - component=mp.Ex)] + sources = [ + mp.Source( + src=mp.GaussianSource(1 / 0.803, fwidth=0.1), + center=mp.Vector3(), + component=mp.Ex, + ) + ] def print_stuff(sim): p = sim.get_field_point(mp.Ex, mp.Vector3()) - print("ex:, {}, {}".format(sim.meep_time(), p.real)) + print(f"ex:, {sim.meep_time()}, {p.real}") - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - dimensions=1, - default_material=Al, - boundary_layers=boundary_layers, - sources=sources) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + dimensions=1, + default_material=Al, + boundary_layers=boundary_layers, + sources=sources, + ) - sim.run(mp.at_every(10, print_stuff), - until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-6)) + sim.run( + mp.at_every(10, print_stuff), + until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-6), + ) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-pml', action='store_true', default=False, help='Use PML as boundary layer') + parser.add_argument( + "-pml", action="store_true", default=False, help="Use PML as boundary layer" + ) args = parser.parse_args() main(args) diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb index 321cf70de..311f3b285 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/01-Introduction-checkpoint.ipynb @@ -17,27 +17,16 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'meep'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmeep\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01madjoint\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpa\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'meep'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", "import numpy as np\n", "from autograd import numpy as npa\n", "from matplotlib import pyplot as plt\n", + "\n", "mp.quiet(quietval=True)\n", "seed = 240\n", "np.random.seed(seed)\n", @@ -54,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +51,7 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]" ] @@ -76,23 +65,27 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-1,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-1, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -106,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -114,16 +107,29 @@ "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", + ")\n", "\n", "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " ),\n", "]" ] }, @@ -141,10 +147,10 @@ "outputs": [], "source": [ "def mapping(x):\n", - " x = x.reshape(Nx,Ny)\n", - " x = (npa.flipud(x) + x)/2 # left-right symmetry\n", - " x = (npa.fliplr(x) + x)/2 # up-down symmetry\n", - " x = (npa.rot90(x) + x) # 90-degree symmetry\n", + " x = x.reshape(Nx, Ny)\n", + " x = (npa.flipud(x) + x) / 2 # left-right symmetry\n", + " x = (npa.fliplr(x) + x) / 2 # up-down symmetry\n", + " x = npa.rot90(x) + x # 90-degree symmetry\n", " return x" ] }, @@ -157,16 +163,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -180,11 +188,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", + ")\n", "ob_list = [TE0]" ] }, @@ -197,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -224,9 +234,9 @@ " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df = 0,\n", - " nf = 1,\n", - " decay_fields=[mp.Ez]\n", + " df=0,\n", + " nf=1,\n", + " decay_fields=[mp.Ez],\n", ")" ] }, @@ -239,11 +249,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "x0 = np.random.rand(Nx*Ny)\n", + "x0 = np.random.rand(Nx * Ny)\n", "opt.update_design([x0])" ] }, @@ -256,24 +266,11 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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y5nNv8vBz4ditJKy6Npqbyv/WW18jHgDJZBK4DoDx8XE6OjqC8TDzuvrGG3eVX1ffoLOzi2QyOWkFQ6WYQSmUxIy/QUmkTE8vkUgEi/g7Ozs5cuT9bN5cErlVqwbp6IgHW0FV1lN6dI3Btmx7qrLRbUctVbrjcP3aXHxXkeI1RSKPR4j8JvybvPiuIsUbi3R80Y3nSK68wiDWEcK1LYd3JBIJxsZGAXj/+88GSr05pRQTEx9k586/5Oyzv8ipp+6kq6srGE/r6ioJXkkoIZPJkE6nyWQyFAoFOjs7SSQSJJNJYrEYuVwuiM9LpVIUi0UOH34vmzffxrp1G1m5cn8wU1sZi2dKR0cH6XSaL3zh80BpaX87o+5W6LvquwjNDAG7aMVFL2qtL5jrHDc8sQKz7GfeDADXABuhuKdIkfntezanfUKoa5MJq75mGZZhyZIlwSqFoaFz2LVrA+997wZOPvl14vGeYGbU9Mp6enpIJpPk83kymQwTExMMDw+TTqeD11IzBmdi6UyIiomTW7v2u6xcOUgsFqNQKJDNZoNxPCNwZgxQ4ujCw9Y4V+eErqOOXseknty+SOj/+0r7xRuLddW1qZQFrt76mhlQpRSLFy/m4MHS7xcvXkw2m2VwcBU7dnyOc875IgsX7iAeTwaTDfF4nGQyGSzQTyaTZLNZRkdHSafTDA0NMTIyEqyPNeNylZMS+/evZNOm9eWe3GAwHmfWzk5MTJBOp1FKBeJqvkeoH1tFDhwUunn3OgZoTk+ubB/at0c3NZauv7+fnTtP5YUXPsWHPvQ1+vpeQ+t3BM705sysamX4SC6XI51OB4HGiUQCeGfDTSNmg4OreOKJG1m79h84/fR9RKMxlFLBcjAjdBMTE8HvlVLEYrF572YsvIPNIgcOCt28GADWARuBPQ7ad5zBwVU888zNXHTR39Df/zrpdOm2qwwV6ejooFgskslkKBaLjI+PMzExwdGjR4PxNxN3B0wad9u37wyefHI9V131CMuX7yGbjQSvvZWbCZg1t2YWNxaLkUgkgjWxwvywXeSgHYRuABG5FlA5ybVp0y1cfPH9nHTSa+RyJYExr4+mR2fWsY6OjpLP54NNAI4cOcLIyAjZbBalVBAwbFY9HDhwJk8+uZ4rr/x7li3bSSZTnLSAv1gsBls/FQqFYKa2s7MzGA8U5o8LIge+C90AInIWcMklD7Jo0Wvk8yroSZnJg8o4uVwux9jYGCMjI4yNjQW9uomJiWDsz/QA4/E4Bw+u5okn1pd3IdkZ7F5SucFnZdIdI5QmHKW7u5ve3l4JL5knrogctFjolFKXAP8ViAL/S2t9T2jGBxCRs4SlS18nm9VBoK6Jqatc2hWLxYJxtKGhIQ4fPszY2FiwisKcZ4Ty4MHVPP30DVx77XdYvnw32ew7vbd0Os3ExESwJ13ld5l1spXrYE1SHqF6XBI5mEXolFKbgD/RWu+p+1umtx8F7gMuBvYBLyilntBa/6pu4wOIyFmEGVerjGMz43JTx+lMUK/piVXGu5nX3EOH3sPPfvbHXHrpN1i0aDeZTOl7zHFKqSCkxGwDZdbCFgoF4vE4mUyGTCbj3ISRDbgmcjB7j+4bwFNKqYeAr2itw97L5oPAG1rrXQBKqe8Aa4D6hG4AETlLmbrdUqXgGREzC/JTqRTxeDzofZmxuaNHz+aFF/6cD3/4a3R2vsbwcOK41RS5XC4414zTmVffyo0+Te+wcucTYXZcFDmYRei01huVUj8C/gOwTSn1d/BOTIbW+mt1fvcyYG/F533Ab009SCl1O3A7AAvmsDiAiJylmHAT8/paKXLm1dXsI5dOp4PYt8q/v/XWWWzb9idceOFXSCRe5O23CySTSfr7+4MQFSDYjNP0Dk2gsOlZmq2hjNDJGF11uCpyMPcYXRYYBzqBXgg7+GxutNb3A/dDaQkYIzMcOICInKVEIpFgxhOYtAzL7C9nBM306MyOJGZs7eDB1Tz33M1cdNF9dHa+wpEjpQQ4vb29JBIJ+vv7g96Z6dmZYGIzbmfCTfL5fCByZmdjYXZcFjmYfYzuEuBrwBPAeVrriZC/ez9wasXn5eXf1c4AInIWY2LkzNibGX8zM6O5XC4QJbPwP5lMBhMVBw6cyaZNpdnVBQv2cOhQdFJvrbKYV1UTRmLG/8yx2Wy2vNFnOvjuAwfObPEVshvXRQ5m79HdCazTWm9vyDfDC8BKpdQKSgJ3PfCJmq0MICJnIZW79nZ1daGUIpPJkMvlSKVSAMH612KxOCmbl9kYIBKJlIOBb+Dqq7/FqafuYny8g+7ubgqFAolEItgowKytjUajwQYA8Xic3t7eQOTM95teX1dXF8PD5/LUUze05Bq5gA8iB7OP0f2zhn1ryX5eKfVp4MeUwkserFlUBxCRs5TKAf6enp5g1UMmkwlyO5hdhs2rpQka1loTjUZ588138+STN3LllX/P8uW7KRRK64d7enpIJBJBL64y8Q4w6RXWCK75DtN7TCaTHDt2Hs88s55bb93Effetbcl1shlfRA5aHEentd4EbJrXyQOIyFlMZ2dn8HN3dzfZbJaxsbEgKHh8fDxYlmV6W5XBwwcOnMkPfnBDecXDr8nlSkG/ZlWDedUdGxvjyJEjQXYxrTV9fX309/fT29tLLBZDa006nWZsbIx0Ok1nZydjYxewbdvt3HHHFs47L81997XkMlmLTyIHrq6MGEBEznIqe3SVqQorA3rNWJqZ9TR70A0OruKHP1zP1Vd/i+XLd5PL6SDezfTUkslkcN7IyAiZTIaRkRGKxWLQizOvp2btrNmpJJP5EDt2fI7rr3+Uc85RJJOyDKwS30QOHBS6yOmRpm21VI/9PPm23KbJrESo3BHELKrP5/PHhZaYeDazPOvgwdVs3nwza9f+AytW7COdJth9xMyYGkzMnNaaeDxOd3c3xWKRrq6uIK7OiOKCBQvI5/OkUr/Fjh13c/HF9zMwMEY2u4zx8fG6/t++4ZvIgYNCV7ym2LStluq171rUfZgbbx44cCD4bNauZspLGEyPy6w3NTsMl7Y/v421a7/LqlWDFIuTt2oy+89lMhnGx8eJxWJks1mKxSLJZJJFixYRjUZZsGABvb29wSuu6dUdPvxeXn31dn7/97/O6acPks/3MjIyU7xS++KbyIGDQhd9PEpkfwRCXp5YPK1I4ZpCaPZz5NxZQ1lO4Fxvfc1Sq0gkQm9vL8PDpd8fOXIkGI8DJgmQ2Sl4qsjF4/FAeCv3kzM9PzOWZ7Zr6unpoa+vLwhNMfkjzHH79p3B1q1rufbab3HaacNASfyOHTsmPbop+CZy4KDQfeOub4Ruc0dqB3/91l/z6ZM/zVl3nRWKzZt23cQDDzwQiq2Gc1MpPU4Y9TWbbiaTSdaVc5e89dZbwfpSsz1TJBIJBGl4+NyKHA+DwQac5vXX7C1nEl7DO4lxTK/Q5HE1G3hWrp/dtes0Nm68nFtv3cQZZ4yTzfaTSqWChf9DQ0Mz/j+E+mm1yIGDQnfjjTeGau/ZPc/y2Y2f5Ykbnwi1EW66+6bQ69owykLXqPpms9lgBULl7iGmJ/f007dx3XWPcsYZ+4lGo2itg+2WTBCwCTY2uVuNCJoxQbP1UuU+eNFolN2738XDD1/GzTf/kHe/ey+Fgg5smH3vZOPNxmGDyIGDQhcmtjSCT0zXE5puqZVZoL9lyydZt24jZ555EKU6grg604tLp9PB2lSTKtEEFptlYmY2N5PJ0NPTQ09PD729vezdezqPPHIZ69c/wSmn/Jq3306RTqcnJdDu6CgFIE9FenP1I3ldLUBErnmYGdDKkJO33jqLZ565nWuu+TarV78dbGluJh5SqVQgdMViMdgw06ygMAHHZquldDrN+Pg4fX19FAqF8s7Da7j11h+xdOlujh49xpEjR4IVFGZ34d7eXtm9pAHYlte1LYXOpieNj0zt1U0Vuv37V/LMM5/kqqseYfXqQyQSpZSHlTkeTHCvea00Y3ZmwsQsJas8Ph6PlzOGncMLL6znxhv/N6tWHWF4OM/o6CiHDx/m7bffJhaLcdJJJ5FMJoPwEyE8mprX9QsXVXVO2wmdbU8a35julc9MDkQiEfbuPZ2nn76Nyy77JqeffpBYrCeYODCzrOZVtHL7dNOjM8Jm7JnXUPPv8PC57Nr1b7j00gcYGBinUOgKxvvMtuxmOyczTli5LleoD1uDjdtK6Jr6pHmouidNO2DWnO7adRqbN9/EZZd9k9NO24VS3ZO2bjKTFZV5Jcw+dWYp19QVF1rrIDB4fPxCtm+/i4su+joDA2+Ry50QhLWYEBQTc9ff3x/E24nQhYOtIgdtJHQ2N4LvxONx9u9fyZNP/gGXX/4wy5fvCv5m8kGY193KJV7FYjFYxmVCUhKJRNCbSyQSQZhKae3qn3HFFQ9x2mmHiMXiwW4l2WyWzs5OFi5cSG9vL93d3Zx44omceOKJ9PX1uRPvaDG2+1dbCJ3tjeA7Bw+u5vHHr+faa7/DsmVvksupSXlZDWZpWGdnZxA+YiYNurq6AnEzy8a6u7vp6Ojg2LHz+Md/vJVbbnmSM8/Mk04vJpUqzbCardPNduuxWIzu7m76+/s54YQTWLBggTtL9SzFBf/yvoVdaATfefzx67nhhu+zdOlestnIpNdPE/dm/jWvriZjlwkGNrOjZhNNs761tDb2Ju644ydceKEGTgkW95vE12YVhinGnundyazr/HHFv7wWOlcawXeuuOLvOPnk/YyOTgS7CgPBOlXT4yoUCqRSqaA3VrmvnJmoMBMLqVSKnTtPZcuW9XziE4+xatUEkUh/sMnm2NgYw8PDjI6O0tnZOSkWzwQJV+5GLNSOU/5VOUZie2EJulq27t6qT/rKSXrr7q1Vn1MLc9lnQ/V1bTlQKg0y61Jpd6q9b1vtXwZgm65CO7xMf+TUk0YQHMNF//Lu1dXFRvCVimWngie46l9eCZ2rjeAzOgS1mzqGNp3NmY7R06y9lTG5+eGyf3kjdC43gs80QlSqsVkZiCzUj+v+5cUYneuNIAg244N/OS90PjSCINiKL/7ltND50giCYCM++ZezQudTIwiCbfjmX04KnW+NIAg24aN/OSd0PjaCINiEj/7lnND52AiCYBM++pdzQudjIwiCTfjoX84JnY+NIAg+Y4N/OSd0YWNDIwiCr9jiX20tdLY0giD4iE3Z9tpW6ETkBKFx2JZtry2FzqYnjSD4RjNDwKql7YTOtieNIPiErXGuLRE6pdQ6pdR2pVRRKXVBs77XxieNIPiCrSIHrevRvQJcAzzXrC+0uREEwXVs96+WbLyptd4BzdsU0fZGEASXccG/vB+jc6ERBMFVXPGvhvXolFJbgFOm+dOdWuvv12DnduB2ABbUVgdXGkEQXMQp/6omJ2KjCvAscEHVx0teV0FoKJLX1SGcetIIgmM46V/VqGHYBbga2AdkgLeAH1d1XhU9OmueNNKjExxkrvvWFv8yUGWPrqWvrrWWuYTOpkYQoRNcZLb71ib/MlQrdN68ujrZnRYER3Ddv7wQOtcbQRBsxgf/cl7ofGgEQbAVX/zLaaHzpREEwUZ88i9nhc6nRhAE2/DNv5wUOt8aQRBswkf/ck7ofGwEQbAJH/3LOaHzsREEwSZ89C/nhM7HRhAEm/DRv5wTOh8bQRB8xgb/ck7owsaGRhAEX7HFv9pa6GxpBEHwEZuy7bWt0InICULjsC3bXlsKnU1PGkHwDRuz7bWd0Nn2pBEEn7A1zrWthM7GJ40g+IKtIgegSnvXuYFaqjSfanUtBEGwhg28qLW+YM7jqtmd05ZSS3KcSu59/l6tNih97/P3zuv8+dgPc4dhG3d2Fft+2p/PfdsK/zLQjlup13qRwmAm+2EJnU1OIPb9t1/rfdtKkdNahK6qi1Qvsz5pQhA625xA7Ptvv5b7ttUip7UIXcsboV6hs9EJxL7/9qu9b1vtX4a2FjobGqEeobPVCcS+//aruW9t8C9D2wqdLY0wX6Gz2QnEvv/257pvbfEvQ1sKnU2NMB+hs90JxL7/9me7b23yL0PbCZ1tjVCr0LngBGLff/sz3be2+ZehrYTOxkaoRehccQKx77/96e5bG/3L0DZCZ2sjVCt0Lgj9xAwAAAclSURBVDmB2Pff/tT71lb/MrSF0NncCNUInWtOIPb9t19539rsXwbvhc72RphL6Fx0ArHvv31z39ruXwavhc6FRphN6Fx1ArHvv3024IR/GbwVOlcaYSahc9kJxL7/9tmAE/5l8FboXGmE6YTOdScQ+/7bNz26RtCIToq3QudKI0wVOh+cQOz7bz/M7cUqadSbmLdC1wga8qSpuGF8cQKx77/9RghdI4ebROiqpGFPmvIN45MTiH3/7YctdI0eU7da6ICvAq8CvwS+B5xQ1XkhC11DnzQb8M4JxL7/9sMUumZMHNoudB8DOso/fxn4clXnhSh0DX/SbMA7JxD7/tsPS+iaFR1htdBNqgBcDTxS1bEhCV1TnjTlHl0j8NXJxH7r7YchdM0MAXNJ6H4ArJ/l77cD24BtLHCnERo1e+Wzk4n91tuv975tdpxry4UO2AK8Mk1ZU3HMneUxOlWVzTp7dE190jRA6FrtBGLff/v13LetCOZvudDN+cVwC/A80FX1OXUIXdOfNCELnQ1OIPb9tz/f+7ZVK5asFjrgEuBXwKKazpO8rqHZFPtifzokr2u4QvcGsBf4Rbn8j6rOk7yuoSP2xX4lktfVgiJ5XcNF7Iv9qUheVwuK5HUND7Ev9qdD8rpaUCSvaziIfbE/E5LX1YIieV3rR+yL/dmQvK4WFMnrWh9iX+zPheR1taBIXtf5I/bFfjVIXlcLiuR1nR9iX+xXi+R1taBIXtfaEftivxYkr6sFRfK61obYF/u1InldLSiS17V6xL7Ynw+S19WCInldq0Psi/35InldLSiS13VuxL7YrwfJ62pBkbyusyP2xX699iWvqwVF8rrOjNgX+2HYl7yuFhTJ6zo9Yl/sh2Vf8rpaUCSv6/GIfbEfpn3J62pBkbyukxH7Yj9s+77mdVVaa1xBKTUKvNbqelTJScDbra5EDbhUX5fqCm7V16W6Apypte6d66COZtQkRF7TWl/Q6kpUg1Jqmyt1Bbfq61Jdwa36ulRXKNW3muMija6IIAhCqxGhEwTBe1wTuvtbXYEacKmu4FZ9XaoruFVfl+oKVdbXqckIQRCE+eBaj04QBKFmROgEQfAe54ROKfUXSqlfKqV+oZR6Sim1tNV1mgml1FeVUq+W6/s9pdQJra7TbCil1imltiulikopK0MMlFKXKKVeU0q9oZT6d62uz2wopR5USh1SSr3S6rrMhVLqVKXUVqXUr8r3wGdaXaeZUEollFL/Tyn1Urmud895jmtjdEqpPq31sfLP/wp4j9b6jhZXa1qUUh8DntFa55VSXwbQWv/bFldrRpRSZwFF4G+Bz2mtq4pRahZKqSjwOnAxsA94AfhDrfWvWlqxGVBK/R4wBjystX5fq+szG0qpJcASrfU/KaV6gReBq2y8tkopBXRrrceUUjHgp8BntNY/m+kc53p0RuTKdAPWKrXW+imtdb788WfA8lbWZy601ju01javPPkg8IbWepfWOgt8B1jT4jrNiNb6OeBoq+tRDVrrA1rrfyr/PArsAJa1tlbTU14BNlb+GCuXWXXAOaEDUEp9SSm1F7gB+I+trk+V3Ab8qNWVcJxlwN6Kz/uw1BldRik1AHwA+L+trcnMKKWiSqlfAIeAp7XWs9bVSqFTSm1RSr0yTVkDoLW+U2t9KvAI8Gmb61o+5k4gT6m+LaWa+grti1KqB3gM+PMpb09WobUuaK3PpfSW9EGl1KxDA1auddVa/4sqD30E2ATc1cDqzMpcdVVK3QJcDnxUWzAgWsO1tZH9wKkVn5eXfyeEQHm86zHgEa31462uTzVorYeVUluBS4AZJ32s7NHNhlJqZcXHNcCrrarLXCilLgG+AFyptZ5odX084AVgpVJqhVIqDlwPPNHiOnlBeYD/AWCH1vprra7PbCilFpkIBqVUktLk1Kw64OKs62PAmZRmB38D3KG1tvKprpR6A+gEjpR/9TNbZ4gBlFJXA/8dWAQMA7/QWv/L1tZqMkqpS4G/AqLAg1rrL7W4SjOilPo28BFKWx+9BdyltX6gpZWaAaXU7wL/B3iZkm8B/Hut9abW1Wp6lFJnAw9RugciwHe11v9p1nNcEzpBEIRace7VVRAEoVZE6ARB8B4ROkEQvEeEThAE7xGhEwTBe0ToBCco766xWym1sPy5v/x5oLU1E1xAhE5wAq31XuDrwD3lX90D3K+13tOySgnOIHF0gjOUlyi9CDwIfBI4V2uda22tBBewcq2rIEyH1jqnlPo8sBn4mIicUC3y6iq4xseBA4DVG1kKdiFCJziDUupcSgu4fxv41+VdcQVhTkToBCco767xdUr7pL0JfBX4L62tleAKInSCK3wSeFNr/XT5898AZyml/nkL6yQ4gsy6CoLgPdKjEwTBe0ToBEHwHhE6QRC8R4ROEATvEaETBMF7ROgEQfAeETpBELzn/wNAVD00iU3IQgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "opt.plot2D(True,frequency=1/1.55)\n", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt.plot2D(True, frequency=1 / 1.55)\n", "plt.show()" ] }, @@ -288,19 +285,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "f0, dJ_du = opt()" ] @@ -314,35 +301,12 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.figure()\n", - "plt.imshow(np.rot90(dJ_du.reshape(Nx,Ny)))" + "plt.imshow(np.rot90(dJ_du.reshape(Nx, Ny)))" ] }, { @@ -356,13 +320,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "db = 1e-3\n", "choose = 10\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" ] }, { @@ -374,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -390,47 +354,38 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12,6))\n", + "fig = plt.figure(figsize=(12, 6))\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", - "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", - "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Adjoint Gradient')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", + "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", + "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Adjoint Gradient\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1,2,2)\n", - "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", - "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.subplot(1, 2, 2)\n", + "rel_err = (\n", + " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", + " / np.abs(np.squeeze(g_discrete))\n", + " * 100\n", + ")\n", + "plt.semilogy(g_discrete, rel_err, \"o\")\n", "plt.grid(True)\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Relative Error (%)')\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Relative Error (%)\")\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", "plt.show()" ] }, @@ -443,69 +398,50 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "resolution = 20\n", "opt.sim.resolution = resolution\n", "f0, dJ_du = opt()\n", - "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose,db=db)" + "g_discrete, idx = opt.calculate_fd_gradient(num_gradients=choose, db=db)" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "min_g = np.min(g_discrete)\n", "max_g = np.max(g_discrete)\n", "\n", - "fig = plt.figure(figsize=(12,6))\n", + "fig = plt.figure(figsize=(12, 6))\n", "\n", - "plt.subplot(1,2,1)\n", - "plt.plot([min_g, max_g],[min_g, max_g],label='y=x comparison')\n", - "plt.plot([min_g, max_g],[m*min_g+b, m*max_g+b],'--',label='Best fit')\n", - "plt.plot(g_discrete,dJ_du[idx],'o',label='Adjoint comparison')\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Adjoint Gradient')\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot([min_g, max_g], [min_g, max_g], label=\"y=x comparison\")\n", + "plt.plot([min_g, max_g], [m * min_g + b, m * max_g + b], \"--\", label=\"Best fit\")\n", + "plt.plot(g_discrete, dJ_du[idx], \"o\", label=\"Adjoint comparison\")\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Adjoint Gradient\")\n", "plt.legend()\n", "plt.grid(True)\n", "plt.axis(\"square\")\n", "\n", - "plt.subplot(1,2,2)\n", - "rel_err = np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx])) / np.abs(np.squeeze(g_discrete)) * 100\n", - "plt.semilogy(g_discrete,rel_err,'o')\n", + "plt.subplot(1, 2, 2)\n", + "rel_err = (\n", + " np.abs(np.squeeze(g_discrete) - np.squeeze(dJ_du[idx]))\n", + " / np.abs(np.squeeze(g_discrete))\n", + " * 100\n", + ")\n", + "plt.semilogy(g_discrete, rel_err, \"o\")\n", "plt.grid(True)\n", - "plt.xlabel('Finite Difference Gradient')\n", - "plt.ylabel('Relative Error (%)')\n", + "plt.xlabel(\"Finite Difference Gradient\")\n", + "plt.ylabel(\"Relative Error (%)\")\n", "\n", "fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "fig.suptitle('Resolution: {} Seed: {} Nx: {} Ny: {}'.format(resolution,seed,Nx,Ny))\n", + "fig.suptitle(\"Resolution: {} Seed: {} Nx: {} Ny: {}\".format(resolution, seed, Nx, Ny))\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb index 5fed22162..c2e360d25 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/02-Waveguide_Bend-checkpoint.ipynb @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -31,6 +23,7 @@ "from autograd import numpy as npa\n", "import nlopt\n", "from matplotlib import pyplot as plt\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -45,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,66 +46,83 @@ "\n", "Sx = 6\n", "Sy = 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(1.0)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-1.5,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [-1.5, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "\n", "design_region_resolution = 10\n", "Nx = design_region_resolution\n", "Ny = design_region_resolution\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables, volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(1, 1, 0))\n", + ")\n", "\n", "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", " # e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", - " # \n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; We give an alternative approach to impose symmetry in later tutorials.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", - "\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " eps_averaging=False,\n", - " resolution=resolution)\n", + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " eps_averaging=False,\n", + " resolution=resolution,\n", + ")\n", "\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(0, 1, 0), size=mp.Vector3(x=2)), mode=1\n", + ")\n", "ob_list = [TE_top]\n", "\n", + "\n", "def J(alpha):\n", " return npa.abs(alpha) ** 2\n", "\n", + "\n", "opt = mpa.OptimizationProblem(\n", " simulation=sim,\n", " objective_functions=J,\n", " objective_arguments=ob_list,\n", " design_regions=[design_region],\n", " fcen=fcen,\n", - " df = 0,\n", - " nf = 1\n", + " df=0,\n", + " nf=1,\n", ")" ] }, @@ -125,24 +135,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "x0 = 0.5*np.ones((Nx*Ny,))\n", + "x0 = 0.5 * np.ones((Nx * Ny,))\n", "opt.update_design([x0])\n", "\n", "opt.plot2D(True)\n", @@ -160,15 +157,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "sensitivity = [0]\n", + "\n", + "\n", "def f(x, grad):\n", " f0, dJ_du = opt([x])\n", - " f0 = f0[0] # f0 is an array of length 1 \n", + " f0 = f0[0] # f0 is an array of length 1\n", " if grad.size > 0:\n", " grad[:] = np.squeeze(dJ_du)\n", " evaluation_history.append(np.real(f0))\n", @@ -189,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -208,16 +207,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n", - "Starting adjoint run...\n" - ] - } - ], + "outputs": [], "source": [ "x = solver.optimize(x0);" ] @@ -236,10 +226,10 @@ "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(evaluation_history,'o-')\n", + "plt.plot(evaluation_history, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -257,7 +247,11 @@ "outputs": [], "source": [ "opt.update_design([x])\n", - "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "opt.plot2D(\n", + " True,\n", + " plot_monitors_flag=False,\n", + " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", + ")\n", "plt.axis(\"off\");" ] }, @@ -276,11 +270,15 @@ "metadata": {}, "outputs": [], "source": [ - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(-1,0,0),size=mp.Vector3(y=2)),mode=1)\n", - "ob_list = [TE0,TE_top]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim, mp.Volume(center=mp.Vector3(-1, 0, 0), size=mp.Vector3(y=2)), mode=1\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " return npa.abs(top / source) ** 2\n", "\n", - "def J(source,top):\n", - " return npa.abs(top/source) ** 2\n", "\n", "opt.objective_functions = [J]\n", "opt.objective_arguments = ob_list\n", @@ -323,10 +321,10 @@ "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(np.array(evaluation_history)*100,'o-')\n", + "plt.plot(np.array(evaluation_history) * 100, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Transmission (%)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Transmission (%)\")\n", "plt.show()" ] }, @@ -361,7 +359,11 @@ "outputs": [], "source": [ "opt.update_design([x])\n", - "opt.plot2D(True,plot_monitors_flag=False,output_plane=mp.Volume(center=(0,0,0),size=(2,2,0)))\n", + "opt.plot2D(\n", + " True,\n", + " plot_monitors_flag=False,\n", + " output_plane=mp.Volume(center=(0, 0, 0), size=(2, 2, 0)),\n", + ")\n", "plt.axis(\"off\");" ] }, @@ -378,7 +380,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx,Ny)))));" + "plt.imshow(np.rot90(np.squeeze(np.abs(sensitivity[0].reshape(Nx, Ny)))));" ] } ], diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb index 049ea8d2e..7a759ed66 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/03-Filtered_Waveguide_Bend-checkpoint.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -38,6 +30,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -52,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +59,7 @@ "\n", "resolution = 30\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,10)" + "frequencies = 1 / np.linspace(1.5, 1.6, 10)" ] }, { @@ -80,25 +73,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)" + "design_region_resolution = int(1 * resolution)" ] }, { @@ -110,29 +97,33 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -144,15 +135,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -164,22 +161,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -193,18 +192,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -220,24 +225,37 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " ),\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -249,31 +267,47 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", - "ob_list = [TE0,TE_top]\n", - "def J(source,top):\n", - " power = npa.abs(top/source)\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=Sy),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", + " size=mp.Vector3(x=Sx),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " power = npa.abs(top / source)\n", " return npa.mean(power)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -287,26 +321,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "rho_vector = np.random.rand(Nx*Ny)\n", - "opt.update_design([mapping(rho_vector,eta_i,1e3)])\n", - "opt.plot2D(True,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "rho_vector = np.random.rand(Nx * Ny)\n", + "opt.update_design([mapping(rho_vector, eta_i, 1e3)])\n", + "opt.plot2D(True, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.show()" ] }, @@ -319,39 +340,26 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "beta = 2048\n", "\n", "plt.figure()\n", - "ax1 = plt.subplot(1,3,1)\n", - "opt.update_design([mapping(rho_vector,0.498,beta)])\n", - "opt.plot2D(True,ax=ax1,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax1 = plt.subplot(1, 3, 1)\n", + "opt.update_design([mapping(rho_vector, 0.498, beta)])\n", + "opt.plot2D(True, ax=ax1, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Dilation\")\n", "\n", - "ax2 = plt.subplot(1,3,2)\n", - "opt.update_design([mapping(rho_vector,0.5,beta)])\n", - "opt.plot2D(True,ax=ax2,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax2 = plt.subplot(1, 3, 2)\n", + "opt.update_design([mapping(rho_vector, 0.5, beta)])\n", + "opt.plot2D(True, ax=ax2, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Intermediate\")\n", "\n", - "ax3 = plt.subplot(1,3,3)\n", - "opt.update_design([mapping(rho_vector,0.502,beta)])\n", - "opt.plot2D(True,ax=ax3,output_plane=mp.Volume(center=(0,0,0),size=(3,3,0)))\n", + "ax3 = plt.subplot(1, 3, 3)\n", + "opt.update_design([mapping(rho_vector, 0.502, beta)])\n", + "opt.plot2D(True, ax=ax3, output_plane=mp.Volume(center=(0, 0, 0), size=(3, 3, 0)))\n", "plt.title(\"Erosion\")\n", "\n", "plt.tight_layout()\n", @@ -367,33 +375,42 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)]) # compute objective and gradient\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)]) # compute objective and gradient\n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1)) # backprop\n", - " \n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " ) # backprop\n", + "\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -406,1607 +423,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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zGery3W4XJycnODk5CXF8lpVXQY6q8DSWtzMAtVuPX20sr403DEcUMfKT3Hr/bN5Df+7YD076ZyJG5Ly/A7JHXenAS510qxtpqKVVQtCqkoAcctntdkPyrtvtBitv953Ta9BY3jbW2N177FhtWw3g56n3opuB6KKjtX0V+dh77CW5DwMn/Z6wVkf/zUYSYLv9M5adt73wJLfW5En82NZZmjiMEd6W6PLKaNo6q+O9Gc/TynORIelZFtTGHV63fqadkW+z+ta1V+ji5Pj6cNLviVhMbltJAURjUx18oZbdEt6693StY7p6Ep472FCE0+v1cH5+Hlx7tfIKXYSYvOMmHiS93bmnWq1ujc4m8VWMw+uipafiTif5AGkprU7f5c9jDTt8n1ZNHMXgpN8DtqwV+wpsz3ZTC5+VmbcNNPr72IhtWlQSkCKc8/NzXFxcoNfr4ezsDN1uN3N3Wqvv1117+v0+hsNhUN8BT1Zey4Fs1aV7zxKdFRfpHD0NAXg9mojkvY6VPFUIFBtJ5tgNJ31BWMuuGWkrg4259XaQZExHb3fB1Vq8Lio6oabRaIRM/dnZGS4vL3F5eYmLiwucnp4GQtohGboQab98v9/H/f19autrlfdS2sv23JOTk7AZJifhaOJOFy5Kb7ko6iIZI3hMA2FDgayQwJENJ30B2IRSLBGnlhhIT7ONWT2bnVdprWa6bSONlspo4Q8PD3F6eoper4fLy0u8evUK5+fnoS5vx1rromU76bgDbr/fx3g8Duo7na3HTTBZGaBrDwDL5TJk3+123kmShHO3UloluF6r/b1aeduP7+QvBid9QWiyzopo1P22C4QuDLFynCW9Tri1dXgSngMxDg4OQqvs+fn5lpU/OjraiuVjmXpa+Pfv3+P29jZsez2dTkOrq80bsDLAJCHdeg2BNHPPRCDwNFarCGKuvRKfYU5M1eeIw0lfANbCx7TwusEjkI6X1cpbwttxV6ql17hW949nPM0sfa/Xw8XFBS4uLnB+fh7IGNubTs+LMlsl/O3tbcrKA0/lwE6nEyS9Z2dnIUnYaDSQJElw46mxt1tvAU+luLymGZssZTLP9upbV98JXwxO+h2wFltd88lkkqql0xVWoYuNbbO65XS7KbWCfMhp5Smv7Xa76PV6gfCarbdyWxXKWMIPBgPc3t7i+voa19fXuLu7C3vdq5WnhecxqeVvtVqoVCqB1LPZLNw3uvi27Tfmvut7bDlUcwF2Uw4n/P5w0u9AlmhFX5PJJDWUksTabDapibVWUqtddCpxJelVzkryaZaeMTwtPBtqtHymijsuXOyeGwwGeP/+Pa6vr/Hu3Tvc3NyEQRm08jwmCa/hQ6fTCVaeWX7VKVgC27jclttipVANF9SVdwv/fBQifRlFEXyQVqtVyGwPBgM8PDyE7DZr2SSxJvhIeGbHbXZe59plNc/wPFQEc3R0hNPT0xDDk/DMorfb7VQcn6W2I+Fvbm7w7t07XF9f4/7+HsPhMJy3ehZnZ2e4uLjA5eUler1eKAVWq0+710yn0615eDYvERunpQo+IN3ppy4+PyPLtXfyF0Mh0pf9Zk4mE7x79w7v378PRLm/v0e/30+JZ5TwmrxTxZ2q8FSXbsmhiSrV0zOmVpeepTm69NbC85iz2Qyj0QjD4TBcx/X1NW5vb3F/fx9m39Eq063XReby8nLLynMRIzG14qDyWd1Gy/bcA9vlTSs5zsr2l/353Bfu3u/AcrnE3d0d/vKXv+D6+hrD4RAPDw9BuMKJsEr4mMyWC4CW47TpBkhvTMF2Vc6qV6Udra0m05TwmiyzSjsm7ZTwdOm5KHGYZrvd3nLrKfhptVohYafjrK2GQVV2JLptw42VN5X0GjKU0ev80Mgl/WQyKeWNVnfys88+w+9//3t8/vnn6Pf7QQ+v02vUNbdqNLsAqGzX1vXV9VXCsyTHDD2tfB7heVy688PhEPf397i9vQ2Ef//+Pfr9fmqcNb2Ler0ePAvmD5i8Y1UAQIjjrX4h5rVQussQhFtfa6KROQddRJn8i5Uz7cuxG7mk7/f7+MMf/oA//vGPaDabKenkSwcFJldXV/jyyy8xHo9TmzXamjyA8DOSWy2WJvlicSoJTwuo7bEk/KtXr/Dq1auQSNMsvSW8tsdSdMP4/ebmBnd3d2GcNRenSqUSPouNO5bwnJXPMppOxNW+AhX0aKmR+gIlPu818x9WdsyvumiqJ+BDNfbDTkv/6aef4tNPPwUAtFqtkNV9yaDbmiQJLi8v8f3vfx+tVis8YOqGazxp3Xv93j6QVkbKQRQqq2XSzlp4JTyTdmotVWX38PCA29tbvHv3DldXV1tlOYYcKnRh8k479ZTw9frfHhur7LMiJV4XgFQikk06XDzUiquKT8t8/Gqn/1riO3Yjl/RJkmA8Hod/z+fzb/yEvgvQ5NHd3V2wUloXpqpME0n68Kqrq1Ci6+RYkr3T6eDo6Cio3s7OznB+fh6+6sx627/OujgJT+v+9u1bvH37Nkp4LQ8y3maJzopw1CUnyazSUNV3KpeloIik5/3kZ2jCkZ+nvQb8PFsBsR2Ibu13I5f0lUoFh4eH4d+tVitaVnqJ4APXbDYxm82CbpwkzWr2sLVmQjPNTGAxbqcrz/hdZa6cb6dbUdneeE3azedzjEYj3N/f4/r6Gl999RXevHmDq6sr3N/fYzAYBHEQyaEuOJtpeA5q4Ul43h+dsqMiJSWr3ThTQxKKetRa8xrUvefCVq/XU3oHO/dfrb1WDRxp7Mze64MbG3f00kFLliRJcEeBp24v61pr3diqz/geS3ZtYLFE59QbxsHMfNuOufV6nZLV3t7e4u3bt/jyyy9xdXWF9+/fYzAYpLaj4nXw3Or1eojlaeEp9tFSoNbRWcHgWC2SnvkB6vU5pJNdf7w3ttSnKkaeoy5Oedt62dKnIw4v2e0AH3AlsNbPaW2ZZWaXmarrtEFEh0+ofp6xM+P1brebin11tLTNeGt77GAwwN3dHa6urvD27duQuONmFbSMQHobKrr1OmaLmXomcXk/tO6vY7U4YYfXzL57Ep7XZK28zd6r9Qaw5ZG0Wq0t70IrKU76fDjpd8DGifow083mA0ypaGw2vIpsNEmnO89QN697xzObb/vhVaqqcTzdehKeHXNKeHV9GW602210u90Qy9OtZ+yttfSYJFnHaqmKkC69SoQZyzMmtzkRvTaiUqlgPp+jVquFUWKTyST0P6hAyq19Ppz0BUHykridTidYLcLW5oGnDjVaUsbtdOXpRisp1I23E2KA9IBN3ZSCbj1lte/fv8dwONzqDeD16DBNrclrdSBWGeDxqO7j1tU6HYfXS9IfHR2FZCA9I3pQqh4EtrvsAITQiQ1PnMdP0uugDid8Ppz0BUDC03rRNWe/uq2Pq5WPjaXWDShIdsa6at3tsEglgSa9LOEpEyYZ1cLr9fDcVM9vBTi2pLZYLFKEJ+lpbYGnhY6k56vT6YTrWi6XuV1y+m9d4HSxUuLHZgk64nDSF4S6wVpW44MMIJWFBp4efpu95otkp3Vn6S5m3W1VgFp+7Za7ubkJQzAGg0GwvjrAQpOJ6tKfn5+nevHZpaeS2uVyubV1NY+jWXudzEuyq6ZAcx12AAbPj+eq8wkYEvD/Qi29ag6c8Plw0u+AltlIFIpMaKU1q8+H08bxOkiSL+vKs5RnS4Hq9uoQDxKe9fibm5sgrWU/vG1S0ck7TNrpAI7j4+MQxwNPsmJt2CHh+/1+yrVnWZOk55RczU8okWP3WcdyUQhm9fzA3xaEdrvtlv4ZcNIXgD6IasWstbftn4zlVXPOF8tZuiuMbgBhLbzKVO2IK/bDa1nOKtp4DVyIWJbjTD3bI6/ZdduD3+/38fDwkGrUYXxuF8fYrjokrk246Y63tNj0UvTf9BKYzNO9AZz0u+GkLwASUON6uux29xgSXoU8LLnRomtG3sbuSnYgPpfeEp5Ju4eHB4zH41StXKfNaHhCC089PwdpsjynMlcSXq38cDgM03KZsef1cJGzyUhelzYcqWek5xnTh/A99AhIdoYXuouOIxtO+oLQeDPm6vMh1y41K7PlA5014w1Iu/JakosRnnr6m5ubMLKaSjslO7PpSnidq9fr9VJuPWNnWniW5QaDAQaDQaj5W8IzV6ALnFUNap1fS2yaXGSfPu+Dberh7+zIMa/VF4OTviC0Tk+Lr/E6y3dKbh0UkTfI0bq56s6TeCT8w8MD7u7ugoXXTL1aeDa6qCCIZTnOuWO2XqWxAELjDLv0dAMMCnGm02kqfFAvxvbKax+CbapRlaf2NGiyj4uGHbKp8wZjE4kdcTjp94Aqw9SCM27XhFyM7Fnxuq1PqzUk4XUuPafW0qUfDochrqa1paVnd5uOytbOOd0MA3iSHVN4MxwOgzvPpB2HbQBIJR91sYtJa7VBhzJaJSrfv16v0Wg0wn3Q+6WzB+2mIC7OKQYn/Q5ock6zy7GxT1wIsghPaEeYWjVr3XWrKd2I4u7uLszoe3x8DKIYDR9UGxBr4LEjspMkCRbYJuysAIdZdb1OvXYlvHbykZSxrjzNQdC9V7mx9RYYgliyO3bDSb8DdDlt8s2W17Trzn4PxPem14dYZ+rREo7H49RATr5IQt0ggyGHVhdo3S3ZbeJR++E5NFMHgLIVV7esZo+7JuA0YUdyM/GmTTo6CViHbgBPI7KtAlGHdPB7O6/AUQw7Sc//ROBvoosyttZqNxzdYbshZJ6rzn/rQ6oEt1tdMZbmdtHD4TC1i6wSkHr/2K43at1V089GISA93suGEfQmWBGghWfoYCsV+pkxwtuZgerx0KJb6H1Ur0g/l/BW2mLwIRoF8L3vfS9kuzl5VseHKXHUJbXuqd3pRptG7Gs8HgfiWwEKR0PTFWb7qqrrbBOPFcgAT+4223LpVXDSL8U33MRSu+fsEBB6Daw2aIecZuvthh5aFdHhHAC2PCINgazIh++PiZscaew1RKPdbqcknS8V1IYnSYLXr1/jhz/8IS4vL3F8fBxeurPLcrlMbR1FoYqdaGO700ajUXixLEZJKWvQSnSNfUk+6ts5Gluz8lQM6jbSdJ/5/6i73TBLz7Ic6/A6Q4/XpUlMG8eT8Ey4xRqRAGx5CTGyaz3fluI0v6JfnfD5yCV9p9PBr371K/z0pz9NJXzKgvV6HeSsbIZRoQ2AkHTTkpI2kvDnzMKPx+PgrtOaWmtOommJyrb22s0kdXsrluFioYi2r/K8dF96Jgc5CFRLZJqw01o8rbyeqybslPSqIYjV5HlPYwNFbautaiL4/2JbkB3byCX96ekpfvGLX+DnP//53+t8vjMgQd68eYMvvvgCj4+PwWXXh5QP82KxCA+xnUxLYU1Mxqpxuibm7IOuyTISXkdj63ZTSviserltkSXhNUuvvfcqsSXBNFywjTnMUdgYnteiGXq669pGq6U+9R70XqhWwklfHDstfdnxgx/8APV6HVdXV0H8ohloFZdovRp4cm8pY1ULbxNzKlRRN9Z2nVnCc9eZy8vLrTHVmlxjQszKeXlOqrSjddb2YN0AgwlD9SC4oDDhp6Os2IwDPG3TxYWEhCbptXSo3o7eD81lsFKhXXxO+nx4yW4HqtUqDg8PcX5+HrLmVKPpgEZNdOkkHVXT0aIyhrfdYbbWrDV+ZudZd9e95bjzTNYQS/U4mFOw3XI6IVfHadGi8vjaMGSnBmkFgqS3O9WQlNZd12SnLWUq4YEnBaBWVZz0xeEbWGZAe7xbrRaOj4+DxWO2m8S37rC2wNoSHGN3KtvoLWi5zzbKUEbLrj6O2GL/uyW7Wnfbncfdbkh2luW4ANGNplehOQSq+xjPazuxJbzmJZh85DmpyCnWTRgr6wFPnlS9Xg89D3YqjyYVHXH4BpY7QNLx4VutVsHCURVGEuumlLpppRI/lpFXsgNPgzfVheUDrvPwY9l5ID1OK2bhSXpV9SnhrbSWJOeiUoTwqrTjteWpGTURaMt0+v5KpZJqdNIhojqxt8zP7C64e78D2lWnCTu6qEpstW5Ket2XXvdp4+frsUg2Eo0POMtyOjxTE3UAUu6x1seV8LGynApvVHSjMwRspp7H22Xh7T20cwXsYA29BqvUAxDCDOT+RagAAA3QSURBVHo92j+g04kd2XDSF4Btl9UHiw+pzVZrok+3adKkHz8bQCphZmNoxq0xRSCQ1rjbqoEOkbS6AB1bzXMi6VXDrxUJjcf5+cxrMIbXJKB6LbwWHa6hugGeh1UyqgIwNnpMF0An/G446XdAFWNWY67xqO0VVzefvyOZqNxj+2uM8NQE6ORdK6HlgsLvVSCj+QSKgajys5Nm2G+vLjjPRa9Xr1MtvCU88JRlp8fCScD8qloHCn9UuGPzG3YmAF37mBbB3ft8OOkLQhtpYi/9GwUtGfvESXD+vcbO6kqr+MXuMMMEIktlauFsndwq/BhqMO6nu06LbNtjbVurehA2S8+EJICtWXn0UmjpbV6EXYL2/vEcGPIo6bNKdU74fDjpC0CtR1ZCqtlsbk1usdZKqyBq3dXC87PoWusxAITYm8S3oYZ2sjHc0K2fNBseawm203i1HJcVw1uX3iYgbcJNp+JSwszFRK21dtxpmU6Hi8asvCMfTvo9ECO8WmXtHddk2Hq9RrPZ3BLc0MVX0jOxZS2Xyltp5fUB13IXyanhhXaz6fnb+X0qLtLefhXNxJJ2hN0bQDfC1B5+KvF4LQxHeC9iE3VsIjC2IYhjN5z0e8Baeq1ft1qtEKuT7NpVFhPdKOHzGkZUpaeTYfRn+ndW1WcXK80XaD9B1gCM2MQblQzHOv60tKhTejhPH0Cw8upBNJtNzOfzEA7pvVL5LxcqJ/z+cNIXhIp1bEmLxGFCipZ6F+ltDM33AulZ7ypHtc0oSv7YMXQxIdF1Hj2Jn9UPr//W8qOdeMNr0E0w2fGn22RpiY4bV9Cb4H3UcVz8fM112AoKUE4B2XPhpH8GlLRMvlHAQ6IpQa3k1FYDstRp2n9vS4EqZNEH3opqeH7MmttdZ2LlP22ltYlBnkusDs8Ynn0B2tevijkeR8k/m81SljxGeivmsR6PE78YnPR7Qi0+SUvy0zJqXZkxvRJfM9IqdNEaOMmnNXDW+2mFSXh1g5MkScXmuj+8SlaZBKPlVIIr4enW68Jj9fCxHWqta2/3xuNnrFarVF4kZukBpLwhIF0m1XDGyb8bTvpnIiuTr8knAKEObttHgacHl5+lDzBdXjtTjoo+1evr52umWzeQpJJPBT6avNOSHJCuBKhXYTsAueDpVl86scc2w9BC85zVW1LyayWEx9IF0o7esufGYzi24aTfE3bgA5DWzDMxZQeOWDeU1lnfr9ZLJ83oQx07tsbUuoUWCX98fJxSr+noLHXr1WW2pLLZdPUkGDpoec56E3os9Ux0odLxW/ScWKLk+3gudr6ghlFu6fPhpC8IS2D7M32ISXgtuVliayZeQQsbe4A1iah1fw0xdLNM69LrsAk7TYfnowuNWng9B8bljOMZPqj4JraHnbW8McGTyn15/7SCUK1WQx8DZcS6u42797vhpN8DGjNmJY80VreKPf2crPfbn+siwn9rfoCLgB0qoQS0JS4SXZtctEFIwwk7qosuOo/JaoDdhVfLf3rPeLyseDx2v9XL0cRep9MJMw5sctGRDSf9nthFfKsmi1l7/Zysh9S6/iSZNqXwd9a111KcjdvpSfD9WhbUkhylulqSY6+A6gvoWWi4oGVHHo8xPH9ue+btEExtT9b4nkq+Wq0WSK/DOz2TvxtO+j2ghLcvIO2uqvpNH1ZaaivLBdLJKj0m58nF5L0qXtHNLrRtVYmtbjI/X0lvZ+oDT+EDycaf8VjqxgNPgy11SrAmMrUMaF/W21Arvl6vQzm0VquFPfX4t+7eF4OTviBsTJ+V0IsJRixhLWlj789aABQ2Jqal1SEXSnaSkJ9tY3m19iypkfD8TM2m6whsIJ1om06nqbIf/4bXQdLbcd+M0a3yT2f9U9bMBSrWV+DIhpP+ayBm4fXf/N6Cf0s3FsBWHkA/M+vz9Ge29k9i0arGtAGEuuGawON5kbBacqQXo9abIhsNGxaLRTTGV9Kz5Zcv3fRDSc1FKEkStFqt4B3omDK39LvhpN8TsfhdXXrCZqAtqWld1QJaia8Sy+YEYuEB/62LifVI7OIR8yZoMTU3wWuymn4lN4+tSkLV9fOaVPSj8wM5PJTf09Kr5JiLh04ijuVVHNlw0u8JSzwlMpAefxWrRzMbT1c75u4zcaby3BjxNRmoZUAtvWn2OxaKaJ1cvQBr0QGkVHJacuSxVEBkZbV2PwBLes7u4wKg/f9arSDxdWHKuj+OOJz0z4C18EpOzYpbV91a/Dx5rq1bx+r9mv1X8ulWUlb/r0RRGTG/8pix6yViOgI9byur1UYZfg7bajnZhxaem23YEdp07dXzsLMIvOOuGJz0BaHucOxla/EAUgSzUDc/VgEoYrXUimvvu2bGVVGnll4Jz3IcqwyqFtRsO+N2OwZM4/9arYbpdJoiu5JeE4z0CNS9Z0aesbzeG9Xha0uz9STc4ufDSb8H8lz7WLJNSaaxu0UsLrWWPObak/C05rTwKlHVOrcq60h4df1JYrX2OllXvQf22tvPpcVVGS9fqr1nHkBn+DGTrxuIxKy6dg2q0tAHYxaDk/4ZKGqJ7d9qjd6+NOGmIQITXzHSa3MOXfhYz71tSNFynT2u5iE0dufCoSGDehm8VpbndCHhVw2B6JWonJblOZ0crHkQCpBU489Zeb6PXXE46fdE1gNlCWSJpVbbfo7+PlaL51f9nu+xijZ7TEvqrEy3Wnn1VLhwaHKQoYKGJlrB0JwFrX6l8tQJaMVAmrSzQ0f4WTpC++joCKenp1u7+/hwzGJw0n8N2Oy5WtLY72MiHX1pmS2rxGarAwqboLPNK/oenosq3ZbLZerc1X3XxSXWKMRwIMu95oKioYjNO2jFgZ+lE3+63S5OTk7CRB5uye272+wHJ/0zESN8jAxqEfMaTbShJCYw0TDAPtQs8SVJEvTxMcku21TtudMtt4uS1cPHMvZcXIAnktrpvrGJvSS+1eDrtVar1dQ+fpzI0+v10Ov1cHp6Grb1ypov6NiGk35PxCy0WkBrfe3fxVzsvGMB2wnEmAvLmJm6dCrp+D2teGwghl24NFcQCzv0HEh4VgBs1l7JyMYbvS+xxYmfy9ZdWvjz83NcXFzg4uIC5+fnYSKPjeed+Plw0n9NxEitv9O/IWIlPibsrAeg7r3tN7efo1aUI7lpcXXghMbk6uJrQlDdeD13Hld3v9Hputpxx8m3KsjhrH5ek1p1Lbnx/ZzGQ8Lr1tzq2rMy4ITfDSf9M2FLdnxo7cAJIpYl3+Xy22SWHseSX0thTJTZkdW0ntPpNJyLhhVKfrX6QLoBSPe5Yxsve/e1p55dfsDTRF0tBdpjaCzPuQAkfK/XC1a+1+vh5ORka0srPUcnfzac9HvCWiZ2mgFIJaHykJfEy9KSqzw3a4ouP5sWmx1zzI5T+ELiq5fAPoAkSYJlVhJZtZ3uNKPDNpWI1CaolefxddHi5hb0bLiYMEtPK9/r9XB2dhbm72Vl7Z3w+XDS7wEbb/LhBxBmusXKYfp9nvWPlfYIJUksO0/QW9BJOCyLcfda7WJrNpuYzWZoNBopUYwq+Ljo2M0oOXePM/HsEEy18nou4/E4tQU3+/eTJAm1+E6ng5OTkzBVV8nO4+jiYu+DIxtO+oKw1p2z2WmZYqOabI09z/W0NXX7OTEXX917/Rzb6aYad5Le7mCrs+Z0IAXwlJXXgZskO4du0soznud50crbBUjVdzwegLCQ6jhtHkPn79nuPU/iFYeTfgdssokWnqUuJXzMylvlnf4shlh4YN+/q/NOE3okG4mtVl571nVYBcMDHlsz87TotLh07e2EXRX9aIlOFxgrt2UCT0MH3ZhDx39ZCx/zqhxxOOkLQuNPPqDcuipWdrJfY5Z+n4czaxGxn2HLiLGWV93CWgkYs/I2lNFtp3Wved0aKxZuqCjH7tSjyUI9lm2o0bp/LJHplr4YnPQFoBaWoJsfK6/Z9xUhu9avs35X5LNsbkAJp1tjqaXVCTRav1fNuxJRd43V7bTzPA9dhPSrCoT0eLp9trb8WqJzgXHCF4eTvgA0g81/67BIIH9HlV2u53Me1rz3xBKDVlJru+asYEevU7vb7D72MSJmnUusNKl5EDvQw75iIZKX6PZHZUeJyYeN/X9kZdr190UevA/1cBbJCRTRBtiBGDYvYYUzMXHQLuJl3btYwhLAlsuuv4sdywmfieiNcUtfEPYhK1KP/yaOXRRZ5OfXGPliXkssh5CXkIyVJGPnlXX/ipLaif58uKUvIfL+z7N0Bgon3EeD6H+Uk97heLmIkt4lTA5HyeCkdzhKBie9w1EyOOkdjpLBSe9wlAxOeoejZHDSOxwlg5Pe4SgZnPQOR8ngpHc4SgYnvcNRMjjpHY6SwUnvcJQMTnqHo2Rw0jscJYOT3uEoGZz0DkfJ4KR3OEoGJ73DUTI46R2OksFJ73CUDE56h6NkcNI7HCWDk97hKBmc9A5HyeCkdzhKBie9w1EyOOkdjpLBSe9wlAxOeoejZHDSOxwlg5Pe4SgZnPQOR8ngpHc4SgYnvcNRMjjpHY6SwUnvcJQMTnqHo2Rw0jscJYOT3uEoGZz0DkfJ4KR3OEoGJ73DUTI46R2OksFJ73CUDE56h6NkcNI7HCWDk97hKBmc9A5HyeCkdzhKBie9w1EyOOkdjpKhvuP3lb/LWTgcjr8b3NI7HCWDk97hKBmc9A5HyeCkdzhKBie9w1EyOOkdjpLh/wHd6U0bQcI2KwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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G+k/AsOyxPthPUd+F1eX5sF9dXaHdbqNer+P4+BiHh4fOte92u4HpM3y/npNu/crKCr7++musr6+jXC4jnU4HynT+52I8f35+jsPDQ5ydnaHdbrt4HLgrJ7IMyJ782dlZp99n1n5YuAMgcE94XH7l+7kQTExMBCy9YXQY6ceE75r6GEZ8n+x+HK39+hSykPD7+/uuPs4SnTbWhCXvZmdnUavV8OLFC2xsbGB+fh6ZTCbQUBMWVlBye35+7oQ/1AAQurBw6s7s7CzS6bQrA3KQh/+Z1QvyB4vovdCZeirptSTe+DDSjwG/C4zfa3nsKS89nlp4xvBhhD86OkK9Xg9k7MNcXcby+Xwey8vL2NjYwNLSEnK53D19vQ8t1VHiy00sdYHjOUj4bDaLubk5Z+XVhdf7xnvEmjyz/Vo69DX3/IwPEd7i+qfBSD8mwlo8/YeaX5XkYa60T3ha+Eaj4XTuOzs72Nvbw8nJiSP8sIefVnRmZgalUgmrq6tYXl5GsVgMDMYIIwmvhYlDtfJKeLrrOoiDM/NTqZSbgAPcuenqkjOrzxKfTv3xr4fH0K9h99bwNBjpPxFKfv/h84nuW3d+DbPwTNrt7u5ie3sb29vbOD4+dgMrfLeeIJnZL7+wsIDl5WWUy2UnxPEbavzr6fV66Ha7aDQaqNfraDaboUMsma3PZrMoFAqBEVtablMxjj+rT8U8WuLTz6LQcGmY52R4GEb6MRDW6vrU9/k/D3PpSXiVvfpCHN+t13iZ+vr5+XnUajVXk3+M8Npcc3p66lx7X17LyTu5XA6lUgnFYhHZbNZN3AHgjuV31+ksPc7qY1eeqvDCPCQlfVgS0vA4jPQjwnfnh8X0/nvC3veQS7+7u4uPHz862Sv19Q+VqzSxNjs7i0ql8qjyTq+JibNut+s8jfPzc6eTV7IlEglkMhmUSiVUKpUA6dlxp5+r3W67hiBtnGHfAuN6xvP0OBhGaFjgjxQ3Sz8ajPRjYlgsH/Z3JJRaVi1HXV5e3htOsbW1ha2trcCgCn8MVpiVpwXO5/OBiThq5R8KM66urtBsNp0eoNFoONeepCfhi8UiarUaarWaa97hQAyW/LgZCJWDXEBYt/ddfJKe7bp8cdEJm0RkhB8NRvoxEJa993+v/07Ch2npKbxhlp5Ju52dnYCFpyjGF+HwgddhHnNzc6hWq1haWkK1WnWxfJgHwq90xdvttruOw8NDtFqtgPqOW0kxdFhcXES1WkU+n3dJQsbwbA7i8EwO+OCx6OLTOyHpKbdluU+n5TIcoLU30o8OI/2Y0Lg+LJGnROdXX3TT7XYDSruDgwPs7u660hxjeCbuwmJ4PvR0uUn4sIx9mBus0tZutxvomT8+Pka73XafRZt2arUalpeX3c62c3NzbhgGe9/ZK9BqtXBxcXFvrz1+BrXevEa69mxZpsyZi4OO2Db3fjQY6UeAT3LVu/s/h6ns2FBCd56jrkj4/f19HB4eunZZJu1o4X0hkApa6HJXq1Wsra1hY2PDNdWovp7XQ+hgjnq9jp2dHXz48AHb29uo1+vOtfcXlZWVFSwvL6NWq7mcQSwWc3JZJgObzSaazaZbvGjFNUPvu+skvDYu0SvQ8p659+PBSD8GtO6s9We1OtSmcxHQqbUcZMn2WEpr2SrLAZesxT9EeE2qVatVrK+v46uvvsL6+rprqqHbDAQ3zGCyjLPv9vf38eHDB7x//94NyaBlZt2/WCxieXkZz549w8rKirPyOhaLuYF2u+0sPD8PcLfDDy2+DhJl1x4/G/9O1XtKerPyo8NIPwLUmjMm13HOSnISS5N1zM5zzBVHSOuMO81w+51zhFpHTsIh4b/55hu8fPkyoL4jeQjNjjNxt7+/j3fv3uH169fY2trC+fk5rq6uHAFZAlxYWMDq6ipWV1dRq9XcOWjlueDRm2m3265H4ObmZijhfZLrZ+VCEDaByAg/Ooz0T4QqwzQJRwum1okPr06tZULr9PTUEf7k5CRAdo67orVkbVxFMVonTyQSyGazWFhYwPr6Ol6+fImvvvoKq6urKBaLgTKdWnclfKvVwsHBATY3N/Hdd9/h3bt3ODg4QKfTCUyqoVv/7NkzrK+vY2FhIVAK1MWO94fjuPmZtALABYtJOXojwyohWi60WP7TYKR/Imi1dTBls9kMbFGtTSQ6hILKtpOTE9chR7JfXFwELKGfJyCU8HTps9ksFhcX8fz5c3zzzTd48eIFlpeXA+UzLkD+ohVG+Ddv3mBvb8+VB2ldU6kUCoUCVlZWsLa2hqWlpcA5aN0Z/+s8PBKen4HJuUQigWQyeS8Tr6GMvzsvj2Fk/zQ8ifRR7mZSl94fZHF6enov7tURTxw1xTHVJycnrg+ebq/vyvvWXaF18rm5OSwuLuLFixf41a9+ha+++gpLS0soFotuOIYq1vzcQrPZxMHBAd69e4fvvvsOP/zwg3PrtZauiwvj+HK5HNjGWkuX6gkp4dW6s++egzZ0Rx0/V6ITdsLuybDfG4bjSaSP8qo6GAwc2VutFk5OTlyWnRJVWkS1VsxeK+mpY1eyqzV7qHuMC0oymcTc3BxqtZqL4TkYo1AoOMLr9tI8Nl3u8/NzR/gffvgBr1+/xs7Ojvs8dNM5SLNSqbg4vlqtYm5uLrBPHXMOfpWCCxjr7GzBnZ2dxezsLDKZjBu2oYuHDs7gwsHzhN0vI/5oMPf+EVxfX+Po6AhbW1tOpXZwcIDT01MX96qiTBNZFN1wyykdU61lOJLFf3DD6vBalnv58iVevHjxqIWn5WXX3M7ODjY3N/Hq1Stsbm5if38fjUbDzaJn7TyRSCCfz2NxcdEl7vL5fGBOPnA/dCBZ2UVHF55ju3K5HLLZLDKZjNs7D4DzeHgM7aUHEGjiCfMEDE/Dg6TvdDqRHFbApBwAbG5u4l//9V/x/v1718vO8c8qINHJrzrxhvLTMFdey3AaswLhe8mT8BsbG86lpwAnbKdZWl2W5I6OjvDx40e8efMGb968wY8//uhKc9o6y4z67OwsyuUyVlZWsLS0hHK5fG9jDL+PQHfNBeCm6rDkx468fD4fqtXX5iPdiJPVAf9lxB8dD5K+Xq/jX/7lX/DDDz84gUcUbixJf3t7i+3tbbx//95tycykmyrEaBW5UHC+nO7mQos1rAwXRnYeP5lMIpPJoFKpYGNjAy9fvsTLly+xsrLiLLxq61VWS03/3t4e3r17h1evXuHt27fY2trC6elpoPoAwB2DVn5paQnLy8uoVCrIZDKB6bm8V7rAkKi8P7TyXLTy+bxrw+UC4k/GZWceFxCdpcd/8/9dF88oh6NPwaOW/re//S1++9vfAoAbPfylQ7PIHP/EUpQ2n7BvnS+fcJrQ8q37MMvODD3LWdxSularYXV11Y290iy9T3ha3Ha7jdPTU2xvb+P169f4/vvv8fbtW+zt7QWm2vrXQitfrVaxvLyM+fn5exN3CJ/w3JX29vbPM+rT6bSz8rlcDsViEfl8PtCcoyU9DQ98Sw/ALaLdbvee9xQFg/Q58CDpB4MB2u22+5nNEl861ApzaIUfv9LtpjuqO7MwLtUylk94n+zaMJNMJh1JyuUy5ufnsby8HJC+5vP5oTH8zc0N2u02Tk5O8OHDB3z//ff405/+hLdv3+Lg4MCV5HyisNxIff3CwgLm5+dRKpWcVQ4jvPYSUFw0GAzcwsUNM+nWMxHI+8f7oElAWnHdzmowGKDb7bpWXZY7tRnJFyIZ7uNB0sdiMaTTafdzIpEIDEaMAvhQa61ZxS5KZiWexpp+/K6E17o7p9AUi0VUKhXUajXMz8+7QRjlchn5fD50l1m1uJ1OB6enp/jw4QP++Mc/4ttvv8Xbt29xdHTkSozavOMnDJmxX1paQq1Wc269P7RC43jKi7kRRiwWc3X4TCaDQqGAQqGAbDaLdDrt8h+6MaWfFyD51XWfnJx0lRQSX3fqNff+cTyavVerp7uKRBVhHXVM6GliKyy5pFZShSoke6VSweLiIpaWlrCysuL61En0mZkZRyRVpWk8zE65jx8/4rvvvsO3336LV69e4ejoyHW5hQ3hYLiiclsdwEEr7y9sOg/g4uLCiZUmJiaQSqUwOzvrdsilW59MJl0LrfYscNHyy3W8Xi4O09PTrpFHW3Z7vd69GXuG+7CS3SdAa8S6+cOwJB2/p3XnhJtSqYSVlRVsbGxgfX3dzbTjzDkSfVhnGQlIMdDu7i5ev36NP/7xj87CP4Xw09PTLpZfXFx0yTsmcfUz+zvvkIQs+zF5l8vlnJXnpFxa+Zubm4D8VvsatCSns/VYBmw2m2g0Go743W4XmUzG4vonwEj/GRFGdt/VZE2fce78/LzrjHv+/LmrudOlDusb1xZZFd5cXFzg6OjIyWo3NzdxeHj4KOEZZqRSKRSLRSwsLGBhYcENxlC33o/j2URE4dHl5aWL5+fm5jA3N4dcLuc2wWCVg4uH3/LrC260c0/1/TMzM26hYeuuufhPg5F+TIQ1fPgWOCwzz4x8oVDA4uKiU9Wtr69jfn7eEc134cOGXwB3OQeN43/44QeXtOOuNA/t567S3lqt5gZj+Fbe785TwnMWv+/ak/AMTVTA5BP+IZktCU0Xv9VqBUhPrQHlw0b64TDSjwGto2uvN38H3DXf6CBHbgpRLBYd4dfX111fejabDZTF/BwASeETvtvtol6vu9IcVXZPIbyGGeVyGcvLy87KM/bmufxyYKPRwNnZmZMYU8KbSCQCO95QZ+83AIURUwVJfs5CLbnO3mNcTz0EFX6GcBjpR4SSmXJVPpwacycSCZeRT6VSzs0tlUrOmjI7Ts28Np6EWXclPuN4Ns/s7e1hc3MTr1+/xvb2NhqNRmDctA8tE3I4BoU4pVIpkGHXZiBWB7Rr8PT0FK1Wy5UuWXLkRpaak/AXL7+MOWzarcb7JD0n7PJ7kt7i+odhpB8Bfj3dH+pAkQ4f+Lm5OadAK5VKbj58pVJBqVQK7Ajjx+48H3C/484n/MHBAd6+fYtXr17h48ePbgDGY4TXybnU11OIk0gkAhl1LQc2m01H+OPjYydLZo2fm1mGVRt4/bqQaK2eEmDW+Fn+A+52sGW1hPLmTqfjvrd6/eMw0o8A1cH7STaKUFRfXqvVUK1WUS6XXclqbm7ONZr4WXm17mGur2rcSfjDw0Nn4d+9e4eTkxO3OcWwKoISnqHGs2fPAlZexTLaoVev13F2duaGgLAzD4CL2endKOH9ZJ0SmJZeh4NQbut3C3IhY4hBcQ6VgLT0lswbDiP9CNB+cGaiVXvPxBWFNZw7XywWA8ksuvGPbdigVloz2Wyg8QdgHBwcoNVqBbT0QDAHQc8knU47bT2lvQsLC8hkMq4VlsIYzvbTuX5hcTxbf4cRngsRRTeU2ZL4unjqGG2GGVwoeG3UCFCL78uKDeEw0j8RJAuFNCQxrf7MzAzy+TxKpZJT0ZXLZRQKBaeg82vtYWU4Iizu1cEcdOn/9Kc/uTje33MuTAzE6gFjeE7OXVpacsk7AE4/ry3CJycn7kXtvtbk+TWRSAQIrxZaR2npVldhpKel96W/TCbSwqt6z99Z13AfRvonQstt+Xzeqcs4BSabzbotniqVyj1xTZhV92vufgzvC2HoXtPCf//993j16hW2trZQr9cD+8f756Cmn9td0aVfXV0NzLubmJhwajhuYkl3XqcFsR6vn4uk15HbbJZhFYFkZfKNajrdwYaNXZ1O514jk9bqeSxV7xnhH4eR/onQue9UmbEkxRi+XC6jVCoFNOZK+DCr7rvw/KrWnfHr+fk5dnd3sbm5iTdv3mBzcxO7u7uBEVdqFXVEFd15blKxvLyMxcVFlMtlVypkA4xuU310dOSGh3DzDTbUMEwA4MiqwzW4WGkikBaemXe29uqgDB1Kou3KmvwDEOhitL76p8NI/wQwHqalzGazTnTCBaBYLAZ08lqC08x1WEZeFWiasGLMymz57u4u3r9/j83NTfz44484OjoKqOB4nXSJGXYwz8BuPW5Flcvl3Fz8WCzmBm5wE839/f3AaDAKYGjhU6kUALiwwSepuu46BtzfzFJblXk8v5GIHg8Jr4lA1SIY4R+Hkf4J4EOdTqeRy+WQy+Wci6naa6MAABRrSURBVE9dOQdDsDHGz8oTYQTX0pWO1/a3rf7xxx/dLrZqdUkEneKjIcfi4qJ7VSoVFAoFF3ZwNh0tZqvVwtHREXZ2drC9ve1Gg3G3HcbeyWTSZdx115mJiQmXe/ATgTpJiDV1AENnEqg2gJZeOxpVoguEy54N9/Eo6dVd5JSTKIHDIDKZDMrlMqrVqkvS5fN5N++N2nK/5ZVQ111LVprJ1m41xtLcxfbg4ACHh4dudDbdeY2rdehGuVzGwsICFhcXsbCwgGq16ppeuLedDvHs9Xpot9s4OzvD3t4ePn78iL29PZycnKDVajkLz8YcIGjh2d2mhNeda1Ujz3o6j6UZf3bfaU8975Pq8gHcs+6aHDUMhw3ReATJZBLPnz/H8vIyXrx4gbW1Nbf9M6fqUGYaRnh9KNVysQedhGg0GgEd+9nZmdsMg0RX1ZnOswsj+9LSEhYXF1Gr1VzJUKWwTLLRitKtPzw8xN7eHvb393F0dOSGiPR6PRc6MOE2PT3t5teziUY3t9Bda+v1uht6QQvPECGdTjtCx+Nxt3CwHKcjscJyIDxW2GJruI+Rhmgkk0n3H/YlgxnswWCAly9f4u///u8DLa+cWKMbNah7C9xZdP1eG1WazWYgK65iF52eG7ZLDK/Rn583Pz+PlZUVrKysuIk32WzWlRb1+tRVZofe6empm/bLGJ4DKvgezbBzZj2tvA621E0+OBGYAzZIbopw1AuKx+Po9/su9tfJONpe6+cAWDkYtjuv4Q4Pkn5mZgb/+I//iL/5m79xq2gUEiW0gr1ez02g9cc/+9102myjFom1aV/Cur+/j93dXezu7uLw8BCnp6cBa65Zbw2p6Mpz0GShUMDCwgJWVlawurrqOuQ4TsuvHvD6tCWXbj030tSknSbaSCyWKenh8LlgCy+bcU5PT92GnOzA8916HXrJz3dzc+MGcuggUt5PQnX69Dj8ur7hPh4kfS6Xwz/8wz/g17/+9V/qen5WuL29ddaJSjqdzKLk0Zo64W+DxU0mtre3sbW1hZ2dHWdVmc2m5VMpKV/qynPTyuXlZbcRxeLiIkql0lA9vx8PM8QgQelpMEFIy+5bd25WwYQlAGeNmXz0Cc/jMfnHEIWLCWfaT05OuoVIN7/UhVQ78TQnwM9sVv5hPGrpo45MJuMeOrXmvhac32vWWUl1dnaG3d1dbG1t4ePHj9jd3cXx8bGLmeka81j+3Ht15Wndnz17hrW1NaysrKBarTrrzsXJT2qR9GGex/HxMc7OzgIyXu0c5GKTyWRc/wCtvCbsmI/gsbiYaVjIezg5OekSmDwOS4cXFxdusfDHtKngKJFIIJ1OO6GUP7zTcB9WsnsEjJ2H1dX5swpRWO+mm8uSG637/v6+2yFH41U9B6sGOjST9Xb24j979sy589w4wh++ocdTsQ8Jr3vsKeGpQCTxU6mUIzwlyKzmMCfA4zEvQZktzw3c6RR0DNb19bXL2rN8yO45nSbM9yvhuWOOViXM0j8M28DyCfDlsv5Xqs04j/3y8hKtVgvn5+c4OjrC3t4ednZ2sLu763bJ8QmvIGnpUnOTCKrp1tbWnDvPzLxWDnxLpyVCnXjD5hlu5EEXnMIeXgu7B7VDULeiooWv1+s4Pz93jTj+gkavxdcp8LqoVaByTzez4P8Dv3IoSTabdbvlUHdgeBi2geUI4IPrE54PLWvszWbTEX5/f9+VwI6Pj92ecTramfdXu+DYzUd3nmq61dVVNylXRTbDtAG0slyU/AEYek2DwSDg1bAqwfkATAwq4Xm8MMLrJpa8Hn5evrggUZ/P0GPYJha08lyEOKtgbm4uoPk3DIe59yNACQ8E3fqwUhwFNUosrbHruC2SgKVADuHQSTvcbUYltFqm0uvSBUmvjxZeB2Bwz0KSKZVK3Zv+Q7LTiwgjPF36xzwYHUZCy68ts/7OQLpY6Hgvyp+5+Ok8P8NwGOnHhCbv1Iq2Wq1AfZqJOlp2JgOnp6fvPcwsO7Evv1gsOgXg/Px8oDdf41clOXA/fmf1gAsShT+0yoPBIDC/TuW8TAr6O/L6Lr1uwa2bgxBKcnbi+Zt1aIw/TG2n7c2lUsktgBbPPx1G+jExzJpqn7gKaugqsztNH2aSS2fpsYHH79xTnYASXYUrqvrrdruO8OqCk6QAAu2wHO6hHXN0w2l9wwivevowC8/OOZbYVCykhKcnpBN1/BJdOp1GoVBAtVp1JUr1QgwPw0g/JtTSa8eXtnhqfZttr7TwWnenFDWTybjElK/rZzztu8O6z5vvedDC0/vgqGiWwnRGAAmlKkNq8ymp1Tq879KzF8DfLkvbZDk/jzkITujRxVEz/bxPwF2bcDKZdGEPRUjcQMPi+afBSD8GhnXK8WHng87WVpbfVN3GrDjdebbsMkPOLDmlrvF43CXkdGa8tp7qNs8seXG/Nw6P5PvVmuu0G3Xzddw1a+ckOwnPKoSOsuI90AoEPycXr6mpKbegAHBSX788x68MOZjAYyyv++wZngYj/YgIq9cTfMg5HJIkJ+H9JhnKWWnlSQoSne25AFz5iudVMZDu/aYde9qwonPoeGztbtMRV8BdtxyP6yvthpXlgKA7Ty9GFzM26DD8YPutWnXmP/gzFyc2FjHc0TDBrPzTYKQfA76lB+4edLqxPslICm0O8TXslLVqJ5zGuLTk/ktFQRpmaExM990/r+48o4RnnVwJrxZ+WNKO90EHiHLegG6vfXt7i2636+b6dTodJ0ZiCQ+4K9FxkWIIxGSmWfnRYaQfE757DwS7vZTkbCwBglaLlotuvlp2utbUALA6oBs7UJeu46J4Dp3DT0s+Ozsb8Ch8wnORUe+Ak3tIeJXW+nV4jeH9bbfZjsz8wfX1NVqtFvr9PrrdrlvotIznfxYuVpQAa13erPzTYaQfA0p2Xzii5S5tRVVVGd171Yn7GWtVpmkpULvP/H3ZNZfAWjt16f5Gkv5GFLwGnTnnE77RaAS2hR6WpWfczcnAnP8/NzfnhD2dTgexWMzpB7gwqQKP94sLJDfR8OcC+GGW4WEY6UdAWPLOH+7AOrxaLa2f8zjMuvP3HFLBn0l4jpjSbZlp6bX2T3KwCUZn+nGsl47z8i08s+gkPIdjhglv1KsgdMGhS0+NAeXC3CqLiUT2zWtfPhdLbcrRYR1aWQhbeA2Pw0g/BjSR5g9n1EXAr+Xz37TuTLJQfKJZeJX1+hs1aleeuvIkP+f56cBONsr4bbc8J3Bn7dmQw8WGhPcrFcAd4Vn+Y3adm35wmEcqlXJddGxbpoVXEZDG9LrFFQmv8l3/nhseh5F+RKiVV624n0gLy6jrgEdaM53rxqw143laei27MY5necxX0VGvzww3E2gcyU3Cq3pNPREV9HCCj5bltMUVCIY0Oi2Y3kU2mw303nMh5EKhC18ikXChg+7+q9l5vzSplQmdQWAYDiP9iOCDpZNr9cUHUX9m/K1jr8ISYAT/nQk8bWDRQRQq3yXZlXCc3MvEl+8e8/P4kl16GBxVzTDCV8cBwaoFE23MIegio6TV+8hj8JqmpqbcggkgMP7K72bUvIbfmmwYDiP9COBDpbVxEpsvJbtm3XULJ3oACiUSFxUm82jVdTAlcN+65/N5J1pR6z5sI0lfsusTntdLsqtHwmvWxhy24OoIcN4vLnL0JvxNKvh5NCcyrH2Z10jv56EtuQ33YaQfEb7cNaxmrsTWjRb58qe7KqH8WXYAHAFoCYG7KgDjd5Ke8l1/koyv3uPx6VGogs+fS6/NLjoGXXfQUYEP8wTaj8DrpxCHCyHd+bA2XJXkUrwTj8ddfoOk1+5Fc+8fh5F+RGhyTq2xEl0TTPqzxv5U66mrTsvIBJvGu37HmcbRJL1OtVHRCq2knstvC2ZZUON3tvrSijMW1wSizr6nB6BzAScnJ9Hr9VxDjA7jZGJS8xR+QpTXz4UE+PNUZn0v43qz9E+DkX5EqAXy5+KFPXTqCvsiEnVf9feqW6eV5bE0608yqn5fu/CYEATgkmPqReg0HWrzaTWphKPLrkMrdailxuMAXCKw0WhgMLjbR57hBX+n7b4aSqjH5M/Foyegrr2vVTA8DiP9mPCluCQ0SUoNPmvPNzc3rm2VD7O6z9qNprP0/Sy3tr+y64zkp3Wna81zXF5ehs7Mo0XVhKMm0LiYaFcgFwWV3tJzuL6+DtT8SWy9NiA4iZcWm8RnLkQXU94jLjSaxPMz94bHYaT/RPjxuC4AGkP7rjndZLquvqusmzdwEdAFQInvD7nQstdD3gWvTcuPtPLMFWjOgdfhVyFIePUcWG5kkk8bY/wRY3TT/Y09/IoBQw6//Kn/F4bHYaQfE75l54AMTnWlVdY6vN9fTtL4iTIm6HQBGGb1eWzGyjqOCwjuZKOLj16/Lgwq9kmlUgEVoS9K4u993T9fKqoZto01k5skvDbxhHUwaijlj90y0j8NRvoRobG5uvJMtlF2yxhYyamJOS1ZqTWltadV5LGV8OqqU6yiJTgd7KG75KirzOPwXP4kXW0e6vV6gYYYXzkYNjxjYmICV1dXLrzRphoujHTz1a3nNfsLlMbsep/8KTyGx2GkHwM+6f0MOy2iWj19kci0zP6IaD/21pc/Jsu3vlod8Hd85ft5zep60xKrzDUsYcnFSsMBFe/oAkZyc9GgB6TVA1Ur+tdKqDdCXYDOINDtrMzaPw4j/ZjwM+m08Bozq+XRBcGHkoxkUCixSZqwEVm+bkB3fPUXFm275YvE13CBsTmz+5pEC4vt9f6wBMmf+Vl8Oa0uIH4lRAdpsE2ZmgROzgnbX9AwHEb6EaHxrx8P+w+dxqGaeFM3XV1dEp7H9N16jV2VOKzDK+F16o2WtPhextuqKqSrH4vFnNUl4TXLThUcrb56HrwPfg5BF7Swz6zTb3XxoHXnUA5ux12r1VAul5HL5czSjwgj/Zh4KCNO+Jbffx//JkyQAiAQQ2vMrVZT3W5tRKEVDrPE7HSjTNgfhqlWWK17WLLNr49rzoNeheYQtMfAD4f8e6u9BclkMrBpJ7fzYo++JfKeDiP9J8KPqf1Y21eZDXuFkVNJ3+v1AsShpSfxtReAC4Em9DTjTqj4x++6Y0mNi4e68sNq40p2LlJM5Gm+QK/bhxKe8TstfK1Ww8rKCp49e4bFxUUUCgWk02nbtHJEGOnHRJgUV7Pxj8XbDyXcSCTG8Kz/syLg5wXCYmC9TlpuXQyGwW8bHhZv+4k2v+yoWgPV5PNzqSSYFp3JT34OWvhcLof5+Xk8e/YMGxsbWF1dDUzi0U48w+Mw0o8JJbZvZVWqS7L5rrY//EHfp6Up/3faYadlO7+Mp/EzodfqE9j/nmVAjbvDoN6HrxLkRBy63xQkUfXH70l4ah2YzyDha7Uanj17hufPn2NtbQ3z8/NuV5uwLbkND8NIPyL8OFRLWnShlXRh+nwtm9G1VqITdJNVeecnAnlNTOaxuUWlt7T0WiXwG4N84pPk6sLz2nltaqX9SbvsA9ABlpTndrtdd+2qHWASk1l6xvCrq6vY2NjA+vo6lpaW3OgtbRc20j8dRvox4YtH4vF4YOQUCcLauBJEVXnT09MBCau67n7GX2W5fmyvCT1tlb24uHCx+8XFhTuP/z7f9X+ogUXjdp2Lx6k5/q48bAdmHwA77Fgt0EEYbNWdm5tDpVLB0tIS1tbWXBxfLBbd7HzL2I8HI/2YoLXmQ894228UUY/Ab7fVsVphghTVu2sjjm/pgaDrTlkrt7TiYE22pOpQDwCuUYZ4iOx+Wy/HaufzeZRKJRSLRTd1l1aeix8XI14Te+KZJAT+HC7Mzs6iWCy67bmXlpZQrVaRz+cxOzvrFj+z8uPBSD8i1GIzUQXADZcIK9Np4stXuvkvIDi0QmNlrdX7pTslvTaykPDZbNbNvONkHDa6+OU9DVEI1dPTjedornK5jHK5jEqlgkKh4Hr6tZ2W16Uz/9gey646Zus5WLNaraJSqaBcLgc2tzDCfxqM9CNArTsnv8ZiMbeZhR//PgT/bzVUCJP5as3bd2n9siGVdDpN1x+hTZEN6+9aWQhrnaW3wW24stksCoWCI3u5XEY+n3fz+LhQAcESoE7o0fbYwWAQ2KAyl8u5Cb607qolMMKPDyP9iNDMMpVtPuH9vw/7/qHj86vfBacvRViJ0J9512q1HPnVyut+d0p49Vi0645DL4vFohuvzd11OU+flpjQ69J5gtoiq/eV+9/5G3KodTfCjw8j/QhQS0/LO4zw/kP5FPKH/c2w92l5jl+1aqAkY/KM5Ndx2jqxRsuJhJbhmJXnlN1sNuu079o6G1Y+HFb/D+sJ0JbiMO/GCP9piD3ihtookhDog/yYG6947GF96N8fe+9D5Fd5ro6PDmucITk1YcdQJmzjS2bgfbIPW5TCtAFaxvRfZtk/CaE3zUj/CRiF8KNinIdcr8dPIoapB1UNqITn+elSk/h8+RUEn5Rh4UfYdelXf7Ewon8WGOmjijAvYJgiT/GY9eXfGH62MNIb7hBmfcMwaiLS8LOCkd5giBhCSW/9iAZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAzxR/499he5CoPB8BeDWXqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDH8f/aq/EM/063/AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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E8AU+n/hhi4MPPvTq1rMAh1aeRTEcnqGCmO/Wx+M/1MIXCgWsrq7i2bNn2N7exvz8PHK5nKuvDxMRVbWndsCUoF8hqFY+m80G6ve1jp7vHbUoEn6DjxYbAcEOO8PkMNJ/BMIeOiXCUwjvu/Q6MELFOxJe3XoV78IsPYdNLCwsYHt7G8+ePcPy8jLm5ubccAzfyvPnzc0NOp0Ozs/PcXZ2FlDrfZGQVp7TdWnlk8mkq8X3FxZfBPUXHy3BZfgC3I/sGnXNhsdhpJ8Sozq9xrn7/mf1v5XwOpWHhGcRDvPj49x6Fe9KpRLW1tYCbr0v3oWdC+vsWe3X6/UeWFcuLByckcvlAr34OrVH3XeW1/LzOgDE76XXyj4g6NqbpZ8ORvqPwKiHbpwLr5/zU3P+GC5Nz52engZGU5FMYcRX8W5hYQGbm5vY3NzEwsJCIEU36vwYz7Ou/+rqCoPBIDSW1/l6+XzeufZspPFHfamAp0U5Sn4V93zSh12zWfjJYKSfAmpl1IopfOKHeQZhhOewzbOzMxwfH+Po6Mip9WFWPszVZoxdLBaxsrKCra0tLC0tBdz6cZoCXXvW9XPop68ZMB+fyWSQz+fd3PxUKoV4PO6UeKbifNVdy5WZ12e6DrgvHx63uD62wBoewkg/IXzS6msUkbTKjb9TC0aX3h9Dtb+/j8PDw0ARjsbVfoGKuvWFQgH1eh0bGxsBtV6LiPg5vQa18qenp2g0Gg820OBPqvW5XM7N1WNJL4BA+6/2+qul19Ff2oqr3+UjTCcxPB1G+inhE36c5Q8jO93dwWDgNszQGP7w8BAHBwc4Ojpy1laHQ4ZZeP5Mp9Mol8tYXV3FxsbGg5z8KH3BF/DoYbBBht8FIBDH6+SdTCbj2mspRrbbbTeymw06WoDjD/XU2N8PQ8LEPyP+ZDDSTwG1tL56Ps4TULIzB09SXF5eotFoBNx69q2zg44u9ijC68y7paUlbG1tYXV1FeVyOdTK63ny3Hq9HprNptsQ8/LyMrBdF/BD2W0qlUI+n0elUsH8/DzK5bKr46fHwMWMpKcY6Hsa2kvPkt6wUma+10/zGekng5F+AvgE9l98GPnQs8QUQKgrr5tlnJ2d4fz83Il25+fnaDabruw1bPorH3oSgLPrWWq7ubkZsPKjxDtfsWdzzdHRkSvI0e/hdlKlUgnz8/NYXFzE/Pw8CoUCZmdn3a43vD5O9dFNOfwmHSU9Z+qFFTqp8EeLDxjxJ4GRfgr4MblOd9XBESQLFwH2k9OV5xAM5uCV7BxQQQKF5aZ9BT2fz2NhYQFbW1t4/vx5aCw/6lo4Gef09BS7u7t49+6d2xCTVp4k42y9er2OlZUVLC0tue9hHM+FrdPpOCtP116HYPCY+gIebt7Bc/AnDJt7PzmM9BMijPDMHftKMkUyWnhad+4OQ6Lr+KnLy0u0221X/Ram1KsFZCxcKBSwuLiI7e1tfPnll9jZ2Rlr5Xks7Zk/OzvD7u4u3r59i93dXTflloTjmK25uTnU63Wsra1hfX0dCwsLKBQKiMVi6HQ6rj6epOfiRa+H94X/1j4F/e8wK++n+MalHg3hMNJPAD82p1hFS0yXdTgcBvrIKdaxU04bZ1h4wxl3FLvCrDvJ56fMisWis/BfffUVvvzyywexvN9Qo9fR7XbRbDaxv7+PN2/e4O3btzg5OXELD/CD5aVesLCwgI2NDWxtbWFlZQXlchnJZNL1vAMIaBYsF+ZYLe2wC+tL0OtUb0ZfRvrpYaR/InzLSCIzFUXS65w3FcdI+LOzM5ycnDwYgjFuqCWhhKd6Tgu/ubnpCL+xsYFarebSZz7h9To4+25vbw+vX7/Gq1evsLu7i8vLy8BQSxKe37W9vY3NzU33PbFYzHkyutBxAeOx2BOv1xImyo2qNFSVX0MWI/7TYaSfAGGuOsmqMSfTUbrhI915rbCjUKexu2YD/PFT/KkjrOv1Ora3t/GLX/wCX3zxBdbX1zE/P498Pv+gEMcnPFtnSfhvv/0Wf/rTnx5Yee51V61WXUnv9vY26vU65ubmnPhGAY7uPYtytJrOr7nX8eF8T1hWRD+jyr7F9JPDSP9EqBinOXXu40aXXl1pvpddcsfHx06lv7i4CCjzYQMt/YIfPvS08EtLS9jZ2cFXX33lhLtarYZCofAo4Wnh9/f38fr1a3zzzTd49eoVDg4O0G633dALuvWlUgmrq6vY2dnBzs4OVlZWnHjHa+31ek7MHDWGW11znYmn4ZBWKYYNwVSX3gg/OZ5E+ig3NmipLHd3YfHM4eGhK1EFEFCU9f3c803Fuk6n88C6q0VUqAvMraiWlpawvb3tXPq1tTVUq1Vn4XWUtMbwjLV9wr948QJ7e3uBRSwWi7nvq9fr2Nraws7OTmDUFvPqqmOEEdavwEulUm66Tth21jo7n6GTfy80bDE8HU8ifZRX0+FwGNjK6ejoCLu7u9jb23MpLZ0Pp1ae5NINKXTclG/dR8Wx/Klx9dbWlnPpaeF1P3n9f6Y1Aqy2I+G//fZbvHz5Ent7e27LaaYcqRvUajVsbGxge3sb6+vrzpvgxpRKSrXy6ppzQUwmk27IRiaTCbT4MixguS61AA13/FdYUZRhPMy9fwT9fh9HR0f4/vvvsb+/78ZAn56eot1uO4JosQgJxko0CnWM3Vl/Ps66+6kqbv9Mlf7LL790hK9Wq47w/lRbjd+vrq5cWo6E/+6773BwcIBWqxUgvLbmLi8vY2trCxsbG4E5+bTs9Ap8K+3n4bmI5PP5QEceN71UzYS5ft4rHYgZVuloeDrGkr7T6URyLBEfVgB48+YNfvvb3+LNmzc4PDzE+fm5a0JRwquAR0vHfeao8j/FugMPc9dq4X2XvlarhYp2OoGn1+u5Mdbff/89Xr58iRcvXuD777/HycmJq7rT6bbxeBy5XA7z8/PY2NjA5uamE+7YOkui8/u4uKh4x0yDeirFYhFzc3OuK4/FOhT/tEmHiweJTw/A398uas/ox2As6ZvNJn7729/ixYsXrsAjCjeXpL+7u8P79+/xxz/+EcfHxw/m0vlWTF1cPpyM29W68yF+zLpTpVeX/quvvsIvfvELZ3WV8BrD8xzozh8cHOC7777Dixcv8OrVK7x//37koEsV71ZWVrD579NzS6WS2+/OH2+lHXV+iMBryefzmJubQ6VScRtmptNpxGIxt1DwnpH0XDx4DF0YNDtgxH86HrX0v/nNb/Cb3/wGAJBKpR6MPv4cQcIPh0PnhgJwrqvWjGssr4UnOtttlDJP+LE7c9HsYGMt/RdffOEm4KhKry49Q4bBYIB2u42zszO8f/8eL1++xB/+8Ae8evUKh4eHaDabgWEcPA+eAzfFWF9fx+rqqgshdACHtuNqRx3Jyhg+Ho8jk8mgWCyiUqmgUqm4jrx4PO6I7G99pR4DF1cW/HBRGAwGoVkCw2iMJf1wOES73Xb/zd1JP3dojEjhzY9fgfuechWy+B7d0GEU4Xkc7SbjUApW2XEIBmPqpaUlZyV17zcel24y59W/ffsWf/jDH/D73//ehShMyekuMb5+wBTd2toaFhcXnXCnopufxtTtstkpR8+gWCyiXC6jVquhXC4jn8+7e0eEWfHr6+uAZsLNO/ldOvffLP3TMJb0sVjMWTngB0uv/5OiAFWj1SLzxa2WfHdXO+J8oYlk19QTC2CKxSJqtRqWl5exsbHhhmAsLS2hWq2iUCgEZtD5RS3aOPP27Vv827/9G77++mu8fv0aJycnbsBlGElILgqGa2trzq3Xcdn8Pn9MN2sPmOPnosRtsWu1GqrVqpvgE4vFXIku71uY686/AT/oDRRHW62Wq+1Xld8s/Xg8qt7rA8sbG1X4RSYAnEvNGD9MXfbhu/IUuKrVqht8sbW1hfX1dSwuLgbIrjl4JTxDCm5D9e7dO3zzzTf4+uuv8erVKxwdHbkqu7ABl/yZSqVQLpexvLyMlZUVlwqkm87v87MCzWYzME+PukA2m0W5XMb8/Dzm5+dRqVRcfp+E7nQ6AIL5fc3T89pYC9BqtXB5eelerHnIZrOByTuGcNgd+gTgQjDKC1I3XuNmuvLlchkrKyvY3t7Gzs4ONjc3sbS05OJ2WnatN/cJz2Kgi4sLfPjwAS9fvsTvfvc7R3gV7EYJiGzPXVxcxNramlPrOfNOMwOqG7CJiOO52VhDMbBarWJhYcH13GcyGWfl6b7zGkh8/lv3rx8MBojFYs6rYA1Eq9VCqVRyHYWG8TDSTwG11MDjs9z8/9Z8NTvWnj17hmfPnrnxVhS6NG4Pa4/VuJoDMN68eYNvvvkG3333HY6Pj0dO3eE56RCOWq2G1dVVJ97puOywOJ5NRJzW2+v1AMBV8tHKayzPFB0AdLvdgF6inhIXF76X1p49D0p83cTTXPzxMNJPiLD+b8KP3zV2Z9yfSqWcdWezzBdffIHt7W2srKwECm383WTDCE83u91u4+TkBG/fvsWLFy/w+vVrHB8fPximOep62MCzvLzsrHyxWHyQqtVU4OXlpRvvxW5BWm5O8SmXy6hWq26cFhV7uvCjhnvodfJ7gR/E5Hg8HnDxLy4ucHV1FZjlZxgNI/0EYCxP4c3flFF/AvceQSKRcK58oVBw+8Nvb29je3vbKeSaB/d7xf3GGT+ObzQa2N3dxYsXL/Dy5UscHBzg6upq5MaWej20youLi1hfX3c98n7VHV3vbrfrpuXqiG4WLCWTSTdso1wuo1QquUIcdb99UVTPS/+m1Yv8/dXVlat05CwCioIsDzaEw0g/AUgSNooo6cPaQHV/t0Kh4IZIMhW2urrqRk1po8xjY6CU8Bxk+eHDB7x69QqvXr3C3t6eU9EfIzyn4dRqNayvrzvxkG64kk67805PT3FwcICDgwO3uSVVe25+4Vfe8fp4LL0enpPOI+BLMyH8XLvdfvBSr8YwGkb6CaCbO2jNuNbQ88El2enizs/Po16vu9fCwgIqlUqgFHXUVlMKden9brkXL17g3bt3aDQaLlc+jvBMp1UqFayvrwcq7yjeqZXt9/tuIw72IRweHuLs7Mw1HrGZplgsolgsIp/PB7SJMAGS56j99clk0sXzWtxEUrMQiGTn4E2+VxdkQxBG+idCY182jDDXrKk8vqdQKLh4tl6vu4mxlUrFKc10dzV2991cJW0Y4Q8ODgLNMxyAocVAejzfwrO2fnt7203c4ex65v2ZGWDDzuHhIfb29nBwcOBy/9fX126Xmmw26+5RWE2B3y+vVXf0jlTBpwfBaxoMBpiZmXE5elbnqWBpGA0j/RPB2JdWbG5uDtlsNuCKs1ZeVev5+XksLCy4zSC0s0xd2TB33o/flfDNZhN7e3t49eoVvvnmG7x+/dpV2/kurl/5xxHW1WoV6+vreP78uZuEUywWnbrOEle18EdHRwELz2YdHpuk1+vUngQuJCy11YGZtPDpdNotChT79PPA/Qw+1vrzZ9hW2oYgjPRPBElNgYrWmiJdKpVyhJ+bm0O1WkW1WnV15lTkNd8eZt19qBusQyw54ur3v/+964fnQI8wfUFFO3bPra2tuUk4HKSZSqUAwPWyU7SjhT88PHS77pDwzE6Q8NlsNtCJ5+fguQkGi2o4Glt1EBKeaj1wT3rG9kp2rd83BX88jPRPhBbTFAqFwN5t3LFV20b5UhdX3fgwsvtuqeaqmSZrNBqO8Bxx5Q/AAO7jdhUVGXboNFvW83MLa22A4Wy/4+NjR3h/X71YLObcd1ppEl7n5ZGMWqevqjuVecbyDBdUB+A90X0FdOCGhgqG0TDSPwF0vdWS5XI5R/5SqYRSqeRIXiwWkcvlAhYvzLLTBfXjdgAB4YqlrtyI4s2bN3j58iXevn2Lg4MD1zGnzTM8b4qKuVwOlUoFy8vLTqVfXV0NDMVg/pyTgmjdDw4OcHh46ApwqBkwHUlVniOwuLhxsaJYR5ecxTWsndftrujip1IpdLvdB6W/WpbL+nzrrZ8MRvonwp/+wpSUX3xCy67W3c+5+yTX2F3JrlVvx8fH2N3dxR//+Ed89913bgcaTrwh4WndSR5uP8WOPZJ9cXHRdevpLrO6l93e3h729vYC8bsSlKENrTwJz5Jkbd1laEKlnRt66PbVdPH93VrEFNkAABRWSURBVG78Pgb+5KKicwYNj8NI/wRofj6XyzkrT8IzbifhNXYfR3ZtuWXMq0MkOJfv8PAQu7u7+P777/H+/XscHR2h2Wy6tlJaXX4f9QXuNccqu9XVVdTrddfAQws9HA6dm8wJO+/fv8e7d+9cHp6jvvldOjhECU/VXwnvjwyn4q7pNeocPvz7pOTXLcVMuHs6HiW9towmEonIxUx3d3eu7ZXNI1TkWU/uE36UdfdryimW6cYZ3PSR03OPjo5cEczx8TGazWYgJw3ct+gypi6VSq49V8leq9Vc1oGEVZWeIcSHDx/w7t07fPjwAScnJ7i8vHTvARDYT47WXreZJuH9GJ6k90MRLlJU+jWl56f2lPT+nIKnCKMGG6LxKJLJJNbX113Z7LNnz9wIaHXnR7W86uAMuu1+bMvGEe5xR8Kfn5+7n7ophj9plmRnL369Xnd9+MvLy1hYWHDnSg8EgEufaSnvwcEBPnz4gL29PTciTMdW8fqYbqRwx0Iluut07znwwo/fATjvSdNs1AJ0Kq5eb5jYyWPZNldPw0RDNLgr6ecOKtjD4RDb29v4m7/5G2xubrpGlEql4spKw7ZY0mGRAFxMSzeX/efcvJI73uhcfO5aq/lnn+y0sqwLWFpackMsGbez7p1DMDTnrYU31A329/edS395efnADdfUn1Ymkqzc1orXydZXeicstFF9RF3zmZkZtwipUOcPJVGxUucS6OgwQzjGkj6bzeLv//7v8ctf/jKQgokCmBqamZlxTTL++Gf/4aIl199rbvrq6gqNRgPHx8fOoh4dHeHk5OTBFlc6j08FKloyduvNzc25RhkO3uDwC3a1cWHSRYkutG7goSo9h2H4I7VI1EwmE8hOAHAE1a242RSjJbKqkXAwBknMDILm8XUGHqHVhcwgaG2AYTTGkr5UKuHv/u7v8Ktf/eovdT4/GbDRhEUotGysSQfuy0m18ITQevVutxtoUuFmGRypTbIryXwl2m/PzefzqFarWF5exqZsKBk2Qy+sMUgXIqbmjo6OcHZ2Fsidq6JOwjN7QW+HOXOSVafa6MacGiIwxPA7FmOxmPOI/LHhei+A+wo+/r8Jm9RreIhHLX3Ukc1mXWpJx2TxAebD7ueLlVQscNnf38fu7q5zn5nzJtlHza2jC8t6eRbYaEXd2toa5ufnXXuuDrEEwre2IuEpFlK00yIfejR+qpJaRiKRcOIdCU99gjE8Mwzan8Bj897Ri+SEHH5WKwz9tmUO8fRrIszSj4el7B4BHyw+3AACuWdaczZ/MN3W7XbRarVwfn6Ok5OTByWsGuPqxBhC+8x1JPbc3JyrqGPN/MrKChYWFtwoKh1TrQ1BFMj80tr9/X0XYtAqa0Uf42VWI5JkKt6pS68W3rfUYV4S6+t5nsxm6LZWel94HO1kLBaLbkaekX48bAPLJ0DHPpPwfkpKH3Yq8lThKdbRdWZXmo6wCiM8XV/Wy3PazsbGBnZ2drC1teUyCfl83i1OYRkEWlQ/1Njf33cLEYdgaIGMEp7lxuwjoFuvZbW8ByzTfazrTfvktYtON/fUBhqt0WfzE9OmqrUYRsM2sHwC1FqqVdYSWabXmHJrNps4Pz/H+fk5ms0mLi4uXBWabiGlajx/+ikx9rxTsGP8zu2i/X3seFyf8HS/uevuhw8fsL+/j7OzM9edp9+tcTzbZRk6UK1XwvMa6S3oohY2GYcE1fPUDS+0Zl/vjeoarJ0oFosPBngawmHu/RPBh1NJpXXqjUbDpeA4Dpovf1OGMFGKULLRupZKJdTrdaysrGBjYwOrq6sBdz5sW2p15xl+cIgl6+kPDg6c96GbSuiLwp1WGqqFb7fbjvAaw/uFNLyH9Jr0O1i2SyuvZA9bNNTKsyKSNQgm4j0OI/0E8NNzGs9zH3p9+Sk41qzz4ffbX7W1lITXqTvLy8uuToDqvLauaqWaxu/sluMQS6YJSXidU0+PgefBElslFGN4zcNT01BRUu+Z1tPr3vT8nWYtKISG6RysESgUCqjVaqjVaiiVShbPTwAj/ZRQS0rhzt9jzRehuN2yeg20rv7e7ezgq1Qqbm687l/HlCL3FtRdYjRVyMXo/Pwcp6enOD09RaPRCLTHalmtT3idBaiLnLr0usuMEp4IO64uJGF9CBqe6MKhm3py1xzeE4vnnwYj/RTQB5LWSC2Vb8FJaNbxs7pN/8ZcM2Nnkl5fjFkZVlDpVteeOgMJT0GR2oLuCHN3d+dy3YlEwpFSKw25uDDO5nHDXHq/n10XNBbPsIqPoptWBgIIEJ6iHe+XDurgvnilUsl1ChrhnwYj/UfCnzungx1pfdV9Be5zzFTmtcKNCjnJn81mXdzO8tbb21t0u91ALE/y6O6xJCer4uh+09sguUnIdDodaBjSHngtvGGmgqJdWJ2Br/5T9ec1sYqPoQIn8PBaFP4CSZ2jXC4HrLyJeE+Dkf4joHXfdFmZO+bDT5deq9EYP2slGYdzaC8+42sATs2+urp6kI7ToRJaJ+DX7vt98LrYaC+Bdt9RVfcJH5aH1+IbnRxMkpZKpQcbXnQ6HVfco00zdOv99CVde/YU+EKm4XEY6T8Cfu13JpNBPp93D//MzEzACtJa6QBIpsPo9irxdCSUVv5RS+Dv9KVjo9TyhomEnFjLIZY68YbdcPQqqA1wNxlaZz+G1+YXzhPkvH/28SeTSQyHQ3Q6Hbcoqueidfi8z7pQ8dx1KpFZ+KfDSD8lfAtEMrHCjg8qBT2C7j+9At+t1hSWtuH61tvvQNNY2G9GoWbA4R+sYCsUCoFyWiU8rTdTkiR7WB5e74nG3aVSCYuLi24EeLVadSo7vRYArtZB5xDorrYaKvB++ZN2DU+HkX5KqAXVARZK+n6/j0QiEegu88U7rY9nXK6z8XRTB+b7mQbUZh8gXDhjvT4r13SbKd0Rly619uuTjH7zDBcyf5HxR3QtLi5iZWUFa2trWFhYcJtoxGIx9Ho9R/52u+0WPmohelx/Oo/OLvD/fxgeh5F+CvgPmC9aqVvNB9cf6cQ4nOk7/98U5HTqDCv6aOXDpsaQHByuwXia6S1utqF1+qqiU3xkBV+r1QoMwPBn2vn3IJVKuRw6h3AuLy+7UmEKksxmdDod5HK5QPEPF06tENS0n84E8L0cG5v1OIz0E8If1eQ/+HRNSQK6vJrao/sej8edgOVbewpyum2TFvr4G1ro8AjddKNSqbi94TneSy08CaRiow77oPKv7bFaFsvr1o43zubTnX249TZDCOAHt55aho4aY5+9xvbq+utYcNUwzNo/DUb6KUACq6UBRruYKrSpZaLrqkM31LVnHK87uYTF0n41nz+plwNAKpWKU8+1mIXfqSk6jvLSDSl0rBWhcbzu7sNqOe5Jz6489stT21DNgZaeIQ61EL1PWnykhVBG+qfDSD8B/Lp2JbFawbBFwZ/RruQJK+/VFJxfkqrv12wAsweqmOv+eepGMybW89Qcv+oH/pgubW/1O/G42Ye/eaV2Kmoqzq9xSCaT6PV6zmNSd51pRN0hx9+40tz7x2GknwJKFN+C+y+20NJl993RMCJzUfG1AZ02w9+piKiEr1QqruWUM/LUuvvtwSyv1bn0/s63moPnefD7qR/oGC2GHNpFB9xvmaULpYp2XEy04YaL0uzsrAs9uDDpsA3D4zDSTwjfgvuuu9aPa++95tF9EQ4IDs3g9yjJGAtzmActJON3rddXhZ470Oo8AH6f3x7Ml86mY2xNAY7deMB9+lFjci4swP1mFP1+310LgIBIqQuhktyvw+c90cXJFxh9jcUQDiP9hNCHUi26L+zpDHbfiuvnldz6fhKHtfr64If13HMTDubfqcyT3CQrBUQAgYYhlu2yXFdz5KzL53XpOdDKa42BjgrjYkHBErgnvS4yWnugC6oujvx3IpF4kL705+gZRsNIPwU0tieBgYd1+FqLr4q+ttX6gp7uaKsTd7lwhHXmaTkvB2rQPR4MBo6I/lQdv9+elpNCoe4gCyCQn+e56PmyAYi9+/zebrfr3H0gmB3gxFyGExQNff2D38eCIS4UOmzDFxkN4TDST4kw9ZxWkX9Xix6W3uPv9fP+IqHuvf9SK0yC6r50bHcN23EHuM8UkPjMFLCrTfsIeB5+2k7jbxKeHgWtvU6q5d8YUmi1H919X/vwQxIuClwYzK2fDEb6KaHCkxaOqKUnyXyyshJNyeP3s5OorJZTT8Envi9+kTC+h6DnDjysOdDiHDa3+KWwFOF8zQKAU/q5zXWr1Qr0FWj/PK09i39o7WnB1crrAuNnS3g91mH3dBjpp4SSnkSlhVTi+vPmEomEs2a+xdQxUhon88W/qyimIEn4c5TeoCGD3+RC0NLTyuvftdBIhUpeR7fbdXG39hfwvvBcuTj4GQMV9vw43W8i0io9Xz8xhMNIPwU0dtcik+Fw6IQrWk7+TReCXq+HVCoViEWBYN5bP6cLh1o1X+XW7ICmCn1lWxuFdANKfreeC3sH9ByA4EguXVTUs9Fjq7JPi61Th1SF1/uii6Ie358DwFoAI/3jMNJPiLB8ssbyWp3nx+Yq5LEDT3P2fv84v4/H43tUzfbJTi8iLDYe1fSjXX7jRlhp6y6/j2542KLC97Cenk02LKWlys/qurDQwb/3vHdMUXLevZYUG8bDSD8FfOKT2FovriR+7Dh8vz7kdJeB4N5z/BzfrwTUslSKeDqrz3eLSXoSl0o9r0Ebf1j7ryXBumj5brgSmz9vb28DxUGsEdDZAGGE97vtODC0Wq26Ml8uVmbpH4eRfkrogxhWQw/ALQS+Kp9IJALKPa33qKYcFet0kSCZ/Kk5JH5YBaDG8ko0HkOr6Pg77eUPW0x89ZznyLCA16PHV+HwMQuvRUhsIqrX64HJwH460jAaRvoJ4efNCc3b87/VAoallPxafn8KjV+froKafo6k0a4zf7KOegqMqUl4puzoenPBUUsclj8fR/iwnLnG6LrYhDXyEP5AzEqlgtXVVaytrbnNOjkyywj/NBjpp4Sf7vLTSErkMPeVls23dkoMAKGkV2L5jT9+kYp6D6oF8Nh057UKUK+Ji4kv2qmyrgLbuHPWxhn9Hn/BJLSZJ5fLoVarYW1tDdvb29jY2MDi4qKNv54CRvoJ4RNKC1z8nV38phtaSyW8/j3M2qv775OT5wMEK/WYOry5uXG5e8JPg2n7qp63Lij+70bBV+4pumnK0b93o8jOa2EMX6vVsLKygp2dHbdpp1p5c+2fDiP9lFCC+Gp2WFNOWBotrMlEwwMAjsAqwvnFKIybqZiPqvX38+thDS78Tn2vHkfhhx+07FpjQFWdqUDVIdQD8PsP4vF4wMKvrKzg+fPneP78OTY2NjA/P498Pm9WfgoY6aeAT5RRzTejXG7g3nW9u7sL9I0rEf0CIH7Gr80HgnlzFfRmZ2fR6XTcefP4qqzr73x3exzZ/WvRgRg6805z8yzBJW5vb52oqItYIpFAoVBApVLB2toadnZ28Pz5c2xtbWFxcRHFYtEV/IQVKhlGw0g/JZSYmoP33W0tmdWCnuvra5cuUzdfSaYue9hLq9CotutATfacJ5NJtNvtQFGPTuhVz2Qc2fWc/GpEHQHOOfrc1ILfyXp8ZgFIWE37MSTI5/OoVqtYXV3F9vY2dnZ2sLGxgXq9jnK57I4dljUxjIeR/iOgVWvsW1f33ree2nzj9+Or202o26xpP36nztbTUINdaLrLDV/8PWfOq9Udl2ng+fjVfNyVRyfmzM3NuQ02tWip3+8Hym79DTMABLatqtfr2NzcxObmJlZXV92sPS3GsVh+chjpJ4RaOOaOAbjCFn/ow6h/h8Xyftmp70X4gzf9FJ6m2XSw5cXFhduZhnvI6wx7rYZTl18FPy1E0p1mdFIP95bz+/nZgad9+yS+DsCIx+NIp9MoFouo1WpYWlrCysqKy8dzgwsj/MfBSD8B1N2mqkzBaZwarZ8Pw1PcaF/k8t1ajdNZRUfik+zsXddRU6x7ZzGPZhd4Lfw+3barWCwGRmv7k3bpenMx4kLkN9ewlRf4IZbXXXGq1Sqq1WrAuuuCZ4SfDkb6CUFrl8lkEIv9MDnmsQEOfDiVpGEPrP7O/4xfFOTHsRpKUCegm0+L32w2Q9tYtU5fN9Hwy3ZZEVcoFNzEW53Fx9l4GmuHCYy6xx51DN5L6gLcjEP32FOyG+GnR2zUg/rvsMkEHtSiqlrPvylGkdj/W9h/j/r8qPeOKhbyN81Q0iv5xjXP6HQeNrmUSiUXwyvZfSusWoYWIbG6T70J3a+Oyr9v2Y3wEyH0Rhnpp0BYLB6Gx4g8yd8ee19Yft8nm7+jLV8+6QkKhmG765Lo2pYb1pfPc1JPRBdLXVxGDQsZ5x0ZxsJI/6nxyL37KEzzgIfVuivhNFvg9937Y7n9lJwOwGThzaQW2E8Fjiv2MaJ/Ehjpowo/gxBWXKSZAyJMSPxU1neUeGn4pDDSG+6h/9/HPQOPaQmGnzSM9AZDxBBKeitaNhgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYph95O+xv8hZGAyGvxjM0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiOH/A1BwT+4LDM1KAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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IJpPOwk9PTyOXyyGRSLiKg3oTfGjy0E+I8n0xiafehXbYWdlufBjpPyDCSniDrL1PduCu2xtm5ev1OprNJtrttout/S8/XzeRSLg4nksrqtUqCoVCoEzngyEFSa8982E5A46uYrcep++QwIMSmkzW+SU+vQ+69tp/r6o/w/gw0j8Q93V5DUri3Ud4tfAcx3VycoKjo6PA0Aqq4cKSWXytVCqFSqWCtbU1PH361M2w1+TdoHvwFXi9Xi9QIlPBEefn69Qdztbjhh3/cyK5qR/Qh3o9fmstp/j41zPXfnQY6ceEftkGkd7P7CvBw1x5vZY/f4+EZ/KOpKd+fZiV1+Td6uoqZmdnB5bo9D64G6/VajnJrUpn+R44wIPJOx2mmUgkAi66Py2H98rQgDV9JvxYHdDPZdghaxgdRvoHwv8CqvUDhs++DyO7Zurp0pPwe3t72Nvbc8o7SlLDYlq18qVSCSsrK1hdXcX8/PwdIQ5/3z/EGM83m000Gg3XSedn7Cn2UcKzU4/VDK2/h83F07l+vDfei098/7NT78kwOoz03wM+YcPiYwD3kl2t4cXFBTqdDlqtllPBsUZOYcygGrVa+Uwmg4WFBVSrVSwtLbk4Xt3nMNeemoCTkxPnWXS73cDhQivPeXjclFMoFFzooAcYB4eS+MzOK/G5ZEOn+FJrP+gwHVT9MAyHkf4B8Al7n7t5H9nDYvjDw0Ps7u5ia2sLOzs7ODg4cPp6zdgTWgKklV9eXka1Wh2qvFOQqO12G0dHRy5r3+v17lh5Ju/YnsuBmjoym1UH9t2z7ZeWHkDA0jOZp/JelRCr5HfQTEDD/TDSPxA+cWntdYIOf89/jmrQdWkGLXyj0cDe3h62t7fdjrpms+nmy/tWXhOFHI6xsLCAWq2GhYUFF2eHEcRPILI2z5Di+PjYJQyB4Jx8Nu7MzMygVCohm826kVf0WM7OznB6eop2u31nIQjvmdl7HbdNIY6fBFXC678b8UeHkX5M+JZaFWWqaAuLQ32ys81UZ841Gg0cHBxgd3cXu7u7ODw8xNHRUWBgRRjhdc3T7OwsqtUqarXaHRGO/174UzP2e3t7ePfuHQ4ODtDpdAKEp2VOp9Mol8uYnZ3F7OwsisUiMpkMYrFYQEHYarUCpFdrzfvXsl2YgEldfp3vr9ae92e4H0b6B0CttWrIgdtBkv54K83Mq2UnMVRtx3p8o9EITKhhMs13tWktuWl2eXkZjx49wtLSEorF4p1YPuy90K2v1+vY2trCP/7xDxwdHQXieZbU0uk0isUi5ubmMD8/70g/NTXlDjS+t5OTE5ydnQVcex2RxfsnmXmPWsoj+fn3DAPM0j8MRvox4Ft4Xcio9Wt+QdWSaqecb9lVaXd0dIRms+lm0NElHkT4WCwWsLzVahVPnjzBo0ePMDc351zuMLceuBXAnJ+f4+joCFtbW/j666/x/v17V6rj+6LuPZ/PY35+HktLS1heXsbs7CxyuRxisRja7bYT9nBwJtd+6dZbvj4tudboGfOr1eeBQI9ALb0RfjwY6ceEL6DRmfC+nJVfYP4eE1snJydoNBrOopPwOnuO/fFa39brquY9nU678tzTp0/x5MkTVKtVFItFpFKpO8TQw4uEbzab2N7exuvXr/HNN9+gXq/j/PzckZPhw/T0NGZmZrC0tORyBuVy2UluY7FYIJ7X4ZnM2vtDRH2CK8JcfZ20a4m88WGkHwNhAhoq4/r9vktCUZgCwP2uzrQ7OjrCwcGBG4JBLT3JTnlt2JhnJTy72iqVCqrVKp4+fYpPP/3UWXnq35UYvoWnCGd7exubm5t49eoV3r9/j9PT04CVZ+MOX+vRo0eo1WrOmwCATqeDfr/vxmuphWcCctDUYN8DUPghgC65NPd+fBjpR4RPeHXTte1TNeT+1NpGo+FKYaqw0ym2/rqmYYTPZrOoVCqo1Wp48uQJPv30U2xsbGBxcRH5fD4wzprvAQhO4iHhX716hRcvXuDt27c4OjrC+fm58y4mJiZcHM9+/NXVVSwuLqJQKLhDjoeErvDWGfwKJTL/2y9j+p+BT3wr2T0MRvoRoQkvkrjZbDqLCASTTACc6+zPtGOSjsMs2R7rj4Dy5bUaw5PwKysrjvDsky8Wi04koxZUXXpa+J2dHbx69QpffPEFNjc3sb+/j06nE1gLzZbZhYUFrK2tuSEclUoF6XQasVjMDclguY5iHFUOapZdS286Fqvf7wfWWavqUA8KI/zDMRLpo6x11kw9h1Ky3bRer+P09DSwplkHO15cXLiEHZN0TNSdnp4GrLuOufKlteoGa9KuVqs5lz6M8HyeL/NVwm9ubuLzzz/Hl19+ie3tbZe8Y/WBCy5nZ2exurqK9fV11Go1zM7OYnp62pXTGM6w9KceC0Gycw6eP+fe1y7wocM3w5qVDONhJNJH+QPu9/uBBNzBwQG2t7exu7vr6ucA3JeZ1tWfdtNsNp0rz1jX7wsPaxXVOjzLcn4Mv7GxgYWFBUd4nV+vLjPn5TcaDUf4Fy9euDieo7e0RMe5etVqFevr61hbW8P8/LyT9VJ9x9diSZINOpoIVNecsltdSa1CJT+3wSSer30YVRVpuIW59/eg1+vh4OAA//jHP7C/v4+9vT3s7Ozg8PDQlad8cQmtPFdAsV7N5JY/+SXMugN3CZ/JZFAqlRzhP/nkE6yvr98hvJYMaT3pdRwdHeH9+/fY3NzEl19+idevX2NnZwenp6dO/APcymPVrV9bW3NxPL0JHijA7ahqzU3wfQBwpbZUKoV0Ou266pj/YJ6BoQEXYzIBqN16ts/u4RhKeqqxovahavntzZs3+Otf/4o3b964WJySWC1BqdWii8p4/vz83LnydFcHufLA3V14dOmZtHv27FkgaecTnpbTn6+3v7+Pb7/9Fq9evcLLly9dae709DRAUlrlVCqFmZkZ59YvLS2hVCq5FVix2O1GG01w6uHBGJzJx0wmg1wu54ZnKunVyvPz0qUYHH2tMf+gKodhMIaS/vj4GH/5y1/w1VdfuU2mUfhgSfqbmxu8e/cOb9++xeHhocuya5xJUqpQhK40ZbZ+2eo+V15VdtTSl8tlrKys4PHjx3j27NmdGN4nPEnIFtnd3V18/fXX+Oqrr/Dq1StsbW3daZvl+2aysFAouGw9G3ey2WygBZaEY4LT19hr55/23XO6TjKZdNfhoaGZf80VTE5OBj5XHqJ+r32Uw9FRcK+l/+yzz/DZZ58BgBs9/LGDhO/3+27mG+N0lqX4e/F43C1p4KGobbI6yVWtuyrreC0lCK1ioVDA7OwsarUaNjY2XLlsbm4O+Xw+EMMDt+o/XUW1tbXlSnKvX7/G9va2i9/DBmrqMM1arYaVlRXMzc0FtuHEYjH3PrRDkPkKZv5pLPwtufl8HqlUyn2uGhqwFZfeh47d4qGgXXt+/sAwHENJ3+/3XaIKgNtK8rFDrTDbWZVUwO3gSY5w0mSWxpxahvOXO4Z1j3HAJMlO5dvKygqq1apTwOnSyTCXvt1u4/DwEN9++y1evHiB58+f4/Xr19jb23OTdzS77pcEC4UClpeXXT2e6j6/7q9z/Kizp+dAz4dTfIrFomvDzWazro2W96FWXIdh0ntiabDdbrsHR4D7jUiGwRhK+lgs5tRWwHeW3hdZfOwgia6uru6ov7SVVg8K1eUPEtkAdzfDsFWVuvZareaGYMzOzqJUKrl58v4EHCV8p9PB0dER3r59i+fPn+Pvf/87Njc3cXBw4AZcDpoZT4LSu6hWqyiXy3f68fkew3bWU7fA95VOp1EoFFCpVNzSTM7QOz8/d9fTzD//TMTjcecFtNtt17LLnAkPMT2UDOG4N3uvX2a6qVGExosqJfU76lSb75eXCE3UMdYtFAquB54z7Uj2QqHg5siHac7DdPRbW1t48eIF/va3v+Hly5euTTaM8HoIJZNJFItFd+jQraeb7nsUKlTimGxq8LniKp/Po1KpuL77XC6HyclJd5iS9NrLoCGRynNJ+pOTE1cZ4QZdc/FHg5XsxoCfJAoTigzSjgMI9JCTFIydqax78uSJq4XTDaZb7U+LJXjIcMzV9vY2Xr58ic8//xyvX7/G/v6+G8AxyMKrlZ+bm3PNNH7TjpYBdYMuFYaU77LMOD097fruZ2ZmUCgUkEqlAMBl+um+azjE96SaAX527XbbtSRr6+7l5eXAYSGGWxjpHwCSJGxkM5OA/Dv9N/5Zs/ILCwt49OgRPvnkEzx79sxNrZ2eng6QPayxRNWCrMPv7+/j9evX+OKLLwKEZwIy7FBiiMExW2yZZbZeB3Ao4XW3HpuGmPdRD4bTdbgaW0diaYiilQ0/4akxu07kocWnZiKqnug4MNKPAb927veAq8bcL2+GufOLi4tYX1/Hp59+iidPnqBWq6FcLgfKYoO6yHxpbafTweHhId6+fetEN/v7+3dWUQ16T4lEwh1CTBgWCoVAeU4PmE6nE1jAwfLfxcWF82I4MLNSqaBcLrvy4sTEhKvJ+4dmGJT4Nzc3Tv9A0oe5+GbpB8NIPwZUiJNKpe6o3wZl6Ok6s1GmXC5jaWkJjx49cu48M+SaMAsbA+XLT3Xn3NbWFl6+fOlUdmdnZ4EhH4Pez+TkpEveVatVLC8vB5ZiALeE5wFDC7+3t+cGaHa7XQBwQzNzuRxKpZJL3nEEN+/fT7rplBx6H1qn5/NYjjw7O3OuPvUBFtffDyP9GCDhuXudNWuSXQmps98YuzO+nZ+fR7VaxcrKips8o+78fT3iPuGPj4+dtPbly5d49+5dYEHFIMKzoSaVSoUm7xKJBIDgXr3z8/M747mPjo4CWXv1ZgqFQmChJV17PzdCwlN5d3l56T5DVf35CT26+pxFYKW7+2GkHxH8YqZSKSfYyWQyLpOvsaQ2x+hc+FKphJmZGczNzWFmZsYltujO+1n5QW6vPwBjZ2cHr1+/xpdffolvv/0WjUbDNc4MIzyn2pLwKysrzuOgzFZLlt1u143n3t7exvb2dmC55c3NjbumLsDI5XLOM2LSTsMhrYrws1OVHUnP39V5BrTw2sRkpB8OI/2IoJXPZrNOYMIljWqZeTBwmSN/l8sgCoVCQIZKgY3fHx5m5TVzTsLv7u66bjn2BzCDHpbUUiGQEn51ddW1zGazWTdnnjvsqN+nS//+/Xtsb2/j6OjItRczN0DSq9TWVwyqbp6Wn913vr6BngZwS3w2NFGyS6Wev0rbcBdG+hExMTHhas602oy/teGG21vz+bwjvK58SqVSgbZSjWOHlf5IYnXpWZp7/vw5Xr16hb29PbTb7TsbXfUg0WaaYrGI5eVlrK+vu269fD7vXHAq3Xq9Hk5PT3F0dORc+r29PRweHrruPLrmFOPwvfo6fV6XJPUHbyaTyYCVV8Krvp7qPZb9+LBttvfDSD8iSPrp6WlnrWmpGbOzgyyfzwfiWSWAP9r5voEQ6uJSDEPCv3r1Cs+fPw8MwKBlJlQjQEvMFt3FxUWsra25wRiVSsUl2vhaVNsdHh5if38fu7u7ODg4QKPRcOuy+fkwf8H4nTVzuuS8rsbhOiWXhwYPPL5nTZZqdl5nFOq+PHPvh8NIPwI04ZXNZl1bKC0aLbv/yOVyTjLru/CamR8Uu6ulY8a62Wzi/fv3roFmc3PTDcDQZiBadb4WvZF8Po9yuYzl5WXUajVXOahUKs6tpwxW6/C6RLPVajnlHXA7o55VDSU8S3x056md9+vrzOYzeciDgvE7/07Ldyz7+VODjfTDYaQfAWqF0um0+1KnUilkMhnn8qsrr4TXtttBNXf/p8a+dK8PDw+xtbXlsvTffPMN9vb2nIXXVdI8VEgkNrzMzc257TfLy8uBbj1mypmw4xBPluWYpWeWHIAbHqKkp2SXOQG63P5obM4HZKsy9Q/Ad6ROJpM4Pz8PfCbaAkxPwEg/Hoz0I4CJL7qv3KdO0udyOZfR1/LUMML7BNcYVlVvrInv7+9ja2sLb968wZs3b/Du3Ts3vcffHa+jqeiFzM7OYnFxEdVqFdVqFfPz804HT+tKl77ZbOLg4AA7OzuBjbkUwOhMQH4+ul+ehKcV1s4/Zty1O46gF8TPWvMcmthTUVLYbgDDcBjpR4Aq6ei+MhNlUD8AABPjSURBVGHHKTDZbNbV7rX85sftYQRX3Tm7y7ghptFouO21b9++dTvm6GLTipIstJba6LK4uOg65hYWFlCpVFw/O2NutrO2Wi13wLx//x57e3tukKfKXHWBJIVHTNrxeiSjzsHX2f68FmvzemAQ/uekLj61CqbCGw/3kp5uIgBXP40aKFFVN55JOsbuwzrgtFNM43RaKiakSArdgMPkGRNobGphwo6uLrPnLJfRsi8vL7v6uy6aJMHozl9dXeHs7MzV4N+9e+dmAdIqa++9JgZJembpNYZnU45aeB4eGjZxyKbqHnQmnrrsDAUYAum+e2u2uR82ROMepNNpPH78GGtra3j8+DFqtZpby5xOp92DX3pfhw8g4KLqiivtFjs+Psbx8bHrWCPpOZOPwzx8lZ3241PxxxZdDt5QV54qQt4PyUy3fn9/3037PTw8DLj0/X4/UGLUFdM8REhSqvcol9X1VuotTE1NBUaP6ez7sFhdya9/5qFnpL8fYw3RSKVSgRjsY8XExITLKD979gy//e1vA4MhtdVUy2+afAOCFsmfIzdovRXJr2uhmE33DxK68plMBsViEfPz86jValhdXXX9+Oxs8+fo0dNg7K3SWsbw/rZcle7SnWdSU8U82mfPZhjW5dU74fZavhde25+Kq9OH+P59BaSurzbSD8dQ0mcyGfz+97/HL37xC5ehjUJmlKS4urpCPp/HwsIC5ubm3DpmHfqgJKc1vLq6unMQaFKO5KKMtV6v4/j42FnzsEGaem9q3fP5vFtPvfb/t8+odaeWwG9hpVtMpd3h4aHL0mvSTmWtKpNlEpPyWgDOMnc6Hdfnfnp66t6Txt48JFR3r6RnCKPKvbA5AH650E8AGu5iKOmLxSJ+85vf4Ne//vW/6n5+VLi5uXHtoow7SWRaI7rHfgadz2dtmkm5/f39QFa80Wi4bTc6t067ytQS6gx8WveVlRW3iIJ98EzUhS2+0GYdKu18wvu96ZozIOGZ2NSZ9IP20qtSju8D+O4g6fV6Ac+JpNfsPz8LvZ+wUqp2PhrCca+ljzqmp6cDI69p5fmTRPUnuPJQYFcay2AkF+NlJbvflgsER1lRPsuR2EtLS1hbW3PjtRYXF12+gbE7ycXr6qhpKu14T/V63dX86b2oNWWZkhoEds2pFp6bgHQpp3osWs3QRJ2GEDyQWMkYNs+PlRQqIs29vx9WsrsHjJt90Ye6yhTPUGHGJhASgMMmNCnH+rpOtPHJrt1w3Ck3PT2N2dlZ585vbGygVqu58VpscPGXV9LrYDmw1Wrh8PDQaekPDg6cK65xtrr07JFnmZJuPZVxPuE1TCE4PZifp5YqadFZ0eBh65NeqxXpdNrpI7STzzAYtsByBKjCTUdh0dJTHktLqUMbW62Wm+Wme+zUuoVZMVpYdV+pqFteXsajR4+wtraGarWK2dlZ1wug7jzvUQnfbrfRarXcTj6txbM9lq8N3B56dOn54OvQKjN+Z6iiXk+Y56JguKGDOvzVWFqt4P8PjhxjL4QmWA2DYQssRwQtptbfmaBjvH5wcOAy8Dq/zV/MQJc27DW0IkCycUkER2OzDVaz8zqAg/fnk4iEr9fr2NnZCbTHcqwWAHcduvUaw+tyDdXSM0vPQ02Tb9oPQPhaBn/rrYY8/vN5EKXTaddLkM/nXcLSvq/DYe79A+En6ZrNpltHTQUbSaDLG8KI4JOdtW+6rqVSCfPz81hYWMDy8jKWlpYC7rxu19GSlsbvXJnN5pmdnR23eZf98MCtOk5bZbWJRvf1UVDEcVUs8dGlHxSyaCOQzhLkAaXJzDAviFae7cvMY3Dwh2E4jPQPgNbd+cWnAIUuLuvsJLsmxtR91i8/yc5uvkKh4KbtzM7OYn5+HnNzc64jTtdLaXZctfs6l54KP+oCeDhRdEXCUxLLHgMd9KGTa9TCsxYfRng92Og9ME+hlQW+BxJfXXs/x6GTdjkq3DL3o8FI/0BoEoq93NrTrauoAbiylkpFtTHGnzhTKBRQLBZRLpedNSsWi4HFE8yah4mAeBhR0kuFn4YfHLihpS8lPB+8ZyYttT3Wb57xxVu+F0MRjV9KVPGN/tSQitdhUrNUKqFcLrvwRisVhsEw0j8AGi8PckEpOGGmmu4y/13jZZad2JKrY7W0c4/jo1kivLy8DBBHZ9mRlJT2NptNt3ZKtfuxWCxAdrXsWv6iLFbHT/uDMHwLr+29epjwp4qX6KXo/Dz/M+XnOjU15bwg38qbe38/jPTfE74sdWpqKiCbpYUjEUgA/i5bc3WFcz6fdyTXkVOqB1CxjXoc5+fnrrlFZbBMsPV6vYD3QQLqGC8mxAA41Z4SnpUJ38KHufRageAB54/Q4pYbYlC2n1aeB6Ruv7X6/Ogw0n8P0HqTNGy3ZSzKUc5+dxozz2zNJdnVotMi0hqyzMfknO59Y7abclf/oaTkfehMAGbldQCGCm6okNOKRFgd3ie8Hih8v2w/pjiHugBdVhkGDQ2oV9BlnmblR4eR/oFQS0aLnc1mXYxNK6oJNi3D8fdp5f3puEo85gtorfmTDyW+atU1vlbSaAmOQht/4o1ei17DIMKHZei1AsGkJLUE8XjcvQabm/xhI9pXz8NSNQv+pF0j/Ogw0o+JsHZOLa/pVJlkMnmH9CodJeHYuMJSmbbgsr6u1QG/A081+2ptSRQ+VFFHS6ljvXTiDQ8OdgXqWmhfeBOWXedByNibW27S6bQr+XEDDw8vhkJ8H1qXV6/KD3uM8OPBSP8AKLF01nsmk3HDIklwVZXplBmNm1WcomUrv+eeMTrFPswd+LE03Xf+mVN8OQSEP+kes5GI8TXvWROCujoqTHhDqAvOnfRzc3Mol8tuNx7w3YYaWnlO3T0/Pw9stNF+ByYWB2X/DaPDSP9ADItftYWUZNIOM2184bXo6gK32nPNlLdaLWfhOSYrjPCqPad1p4ilXC67unahUHDhhO/SszSne+KY8WfYMCxpx0OmXC5jbm4O8/PzKJfLbh7f1dWV+9npdFwCUT0jekx8H37PPD+rYUIeQziM9A+Ajr3ScVX8YupqJl80o2Og2FLqL8JUYY269upa60QZfxYfPQ/G0pVKBbOzs257LFdpqbfBa5L8VNr5nYB+0g4IhhKce08lIdd36cZaHlasGGjJkIs2+Bo8OMPGkPGedciG4X4Y6ceEEn5Qrd4XiegaJz/uVqEOf1dFMLqySQmvSS4NM3y9PglP0nN7rJa56DVwiCU9DM0b6L37ghktWzLM8UeC06sglORMcPLv/P55VS0Ct3JdVix4f/69GcJhpB8DPuHV0miXmMakusqJJbawJhL+WYdc+N1mPgnUEvp6fU2g0aVnSVA19Kri43QfhhHaD+8PCNF71rn3mqBkkjKsr99POGobLy27hkTaNajDRP2RWob7YaR/AFRFpqRXa6g1dVolEjhs/JNmv0lEjVfVzVWxj9b+NWGn0l0tyTH5peuf6VFoKKFLKDSh5pfS9PV9rT4ViGFddPxc/Fl3+hqapNP2YN4v8xv8XM3FHw1G+jGhhFSBDInMh5KWB4LW03UklhJerRpwK6Th8AnfhdXkGa2svzBTy3E6oYZxMffV+Uk7LTWylMZ74k9/7r3mJzQhydxHv993HoW65vxswz5rBUlPwqtmwCz9aDDSjwl18X2SD7M0fiOJdpHRuqk4RQdZ6msTavl1dp0u3mDZjolB4DvSaP7An3rD9ljfu+BseRXjaBONJtm07ZYlOCYNWanQxRfqAWn45HtDrHbQ0vszCszSjwYj/QPgfzHDknN++ygluZpl98Unajm1r91fi6WNLGErt1SrrzJX3bqjmn1aXo7LIsHDtsj6E5H9EeC8HvclkOB+pYAegG/xtWSniVJ+Tvp8fyiJEX40GOnHAK28PoBgBxgTUDrEQmWx+jwSyLfadJN1OaQS1n+QoDwggNsSHHfHkzBhgzI1aUgNvO6K5/M0Dve9Dh6EnPmnY7911Rd/lzkOddEZ62t5kK+jrj7vQwduGEaHkf6BUKtL0iUSiQCJwzLdVKLxcNCEHC28WmxdGUXLrt4CoWGHn1Dkv+t96E//+byXTCbj3hsPBHoQKgxSwlNH3+12A++BtXgePrTqutSCD9X0K6GZgPTHaOnhZ7gfRvox4Sfc6MazvkzCsNddM9wq4PGtlFp6bXHVHnS9nir76FYzu61JQxXxhIUgepjQC4jFYk4uq/kFvmfq5H0XXA+8yclJdLtdd5D50lkteYYRXmW+KkAKk0CH7RA0DIaRfgz4NXWdaZdMJtHv950l5JfdJxcPCO25J1n831EBi2/lNUzQ6T1qOZnV9l1yEkjDCF2MQWtM4tPV1sSiahXCYm++H1p+7aTT+F9DCyW86h34enpd7XfwdwkahsNI/wCoQkyJ6if4/Ow2/5tfWs06+9dVuSnHbAG3O9zUlSfhmdFW0Yqf6FKpbq/Xcz31vCaJz/8mwqoWfuytrvjExHfLLJn5v76+DnwOWsEIawn2k3IaRiWTSSf1VcGRX+0whMNIPyb8LLoqyNTdHmSV/fq8atGBIJn5+/Qe1AprolBJr732KoLR+j5jYx5U/mv7JT1Vv/G6apnDypW0zP71/S463QjkhyH+Z85RWdlsFqVSyUmLc7mcWfoxYKR/AJT0Snx+yfmFV0uvmXmt7dNt1sw+n88ym5bFtDFHS27UzKvyjweCT8owMgK3o6soxGEPAB+cy6cCIz+pRqiQSD0VJWVYeS5sH4A28+RyOczOzmJxcRGLi4tuMrDNyBsdRvoHQrPn6ooTSip9jkJJMUiaqwlAbcwBEHCP/TKWnwHXa+sBoG52t9t1B4s2/uj1dWBHmBs+6LPS96z3oE08YaU3v113dnYW1WrVreLmZl6bkTc6jPRjwi+VAcHVUZrUUvIpsX0Jr5LVb6xR4iv5gSBhw4ZakLz8Xf1JwjJE4HBKynU1Ucff8S17GOH9wzAs+6+lvmHX0mpGLpdDpVJBtVrF+vo6VldXMTc350aCm2s/Ooz0D4AfA6sIR4nv6+2V4HoQMBmnpAKCbbN08ekqh33Bh+UYfLKpZQ07xFRarJ7CKGTX+1BxEX9nkJhGm3n4HLYJz8zMYHV1FY8fP8bGxgaWl5dRKpXcmi1z7UeHkX5M+IQPa77x/476dr+9Vi2/Wm2NsX2rqQeBn1DUaTO+VWXZDLj1TPy4fpAFDov/9TV83YJWKFROzPvR+/JDJPVq6NJXKhWsrKzg8ePHePLkCWq1GmZmZlwCzwg/Hoz0D4RaSyW/PtTy8zlAsI7Nh7r0vgBFLaZaTnVp1ePQbDtJxHn3frZdlXuDDgBeX6HlMS0DapstXXOtOvAzocWnR6Kk9ZN2tVrNEX5tbQ0LCwtuYaWq/AyjwUj/AIQl20hELYuFxf++JUwmk4FaOt1q/2BQLb5q8rWmriU2lu847kqHYgDB0qFP8mFkB3DH2+C9sNNPH7rYglJdVgPofXC0GD+fZDIZsPAbGxvY2NjAysoK5ufn3VRddevN0o8OI/0DoC6pTn+li62KMnXjVYGmsb26+0BwNhyJrTr2YaTXPXY6r55/1gYXvy2V1yEGEV69D2r0Ocefizt0rDdzETr7T1tj+fqU/7IOv7y8jLW1NaytraFarWJmZsbNzlf1oBF+PBjpxwTJqDPhALhJrn4CzH/wCx42cEMtrK/48yW52nVHsQ2z7exe4yRdLqk4OTlxk3HYy665Bk0m8hAh9H7ooejQDp1tzwEeui3HJ7x6Hsw1MHFXKBQwNzeHpaUlLC8vB6bp+nJhI/z4MNKPAbW+tO6x2HcLIFVNNihJ5ktoB7nUvviHh4wv0tFYltckeXX8lU/609PTAPH8LjdaZc3u+zvpuCijVCq5KbulUilAeFX20QPR1+XKLCYs+ZkWi0U3RZfTezmf3z/sDOPDSD8mSIBUKuW+qH6ffFimm/D/blj5a1jG3o9jtXyoLaskvlp8uvm+tJZlRfVYgOAmH+6RKxaLzrqXSiXn1vuz+LTerw1B/hRbPUipqdcpur51N8I/HEb6MaCuPZNsg8QlfrJvnNfw/xym/huk7vM1AprU44YcnXYbVkr0e9V1nRTjdq7T9pdu0htRAVFYCVMPF36u7LvXJKB25oVJeQ3jI3aPjNLmD4VACTbMqhMPJf241xikIdCmHJ0vF5bQ8wmvREyn066VlYk6nXwbRkxfDKT5DG0r9qsg/uyAcT9HAwAg9AMz0n8PjKI7/5AY9qUf5GkMExHpoA3tudfqgTYKaQVhHGKGhT2+dxQm9LnvPRvuhZE+qggjm19VGERETRoqwQfpEMa9J4UR/IPDSG+4hf5/v89jMav7k4WR3mCIGEJJb6JlgyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgihvg9/x77l9yFwWD4l8EsvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGP4fXRC+5jUryogAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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odDoPrCzPnd11uVwOhULBCX14HdNMttGFDniY/OO58d9WsnsejPSfGX7NPaws95gQJ2xQB6281sinceuZrV9aWsL29jY2NzcxPz8fsPJhrj3FOAwnzs7OXKlNVX70JGjlC4WCs/TpdNqFJ2ElSibyWDXgvfBnB6jCEEDoNRumh5H+M2Ic0R8jO/CQ8DoHj8k7xvGXl5fOzR734DNbT7d+a2sL29vbWFlZQblcfpCx5znwK0nPSgGTd76VJ+k5NrtYLKJQKLh4Hng4sUcXRn/gCP+Gcb+vxqPLb4R/Poz0nwAlqo8wkk8iu5+4I+Hp1jOO5zw8nUs3zsqnUilUKhVn5enW53I5Nxwj7Hw4GqvT6TiNvQpqFMym53I5R3ha+Vgs5lx1vU69PzoEREVNGutr7/y4azZMDyP9M+Cr5fgzIuwhHEd2fq+Ep0tPnfvR0VHAyo9z64mw5N36+jrm5+eRz+ddHO+79QRLdVT9XV5eBpR3egydusNefNbolahqrXk/6I1wZx3KgHmvNJTQkdlG9k+Dkf6Z8Env47G4ndDatT+g4vj4GM1mE4eHh06E40tt/WNqLF8ul7G0tISNjQ28ePECpVLJyW3DCKOLD117qv00cca/9a18sVhEPp9HJpNBPB4PCHGoqNPZeGrlSXxt6dWwIyxxOsmDMoyHkf4T8JjF1/eF/R2Jq40ojOGPj49xcHCA/f19NJtNV5P3pba+1aMFzufzmJubw+rqKlZWVlCr1TAzM/MgaaYZe83aX15eOu+CG2goEWmh8/m8G7NVLpdd3Z9ZdlYf2Ouv1l69BX1pojCM0H7exPA0GOmfCJ/oSl7fMunDG/Z+Zuhp4dWlPzw8DOxc40tfw1xcWs50Ou1i+ZWVlUC2Pkxfr9/f3t66TTS4Nx674ngdTLxxZHa1WnWNOxyZTXUdpwVfXV25cd0U2TCm5wJCL2Q4HAZ6Afg+vcawrL9Z++lgpH8GfAJPsvSa6PPLcRTeaF88yXZ4eIjDw0M0m02cnZ05wodpzoF7MmQyGZRKJSwsLGB9fR0rKytOeeeTxD9Hng9r881mExcXFw8m1zJbXywWUavVMDc3h2q1imKxiHQ6HQgRuC+Atv0CDz0Tf8gnxTkqZ9bja7bf3PunwUj/TGj7Kl+sPeuCoD/ThJbOzWfCjFl6vk5PT904KnXrCb+zje723NwcVlZWXPKuUCgERDg+NGN/eXmJw8ND7OzsoNlsuvl3tPAU+xSLRdTrdczPz2Nubg61Wg35fB6JRMK58ldXV7i8vMTl5aWz9CrUYQlOS3Y6PkuHkOi4sDDSG6aHkf4J8N3zcc0jqivX+XKaneeGETpKmmo7Lc31er1ALBxW9mLiLpfLYXZ2Fmtra65Ex1g+bBY+r4n18G63i5OTE+zu7uLjx484OTlBv98PxNfs1KtWq5ifn8fi4iLm5+dRrVYDjTAMV9rttuv15+is0Wj0YKYgLT0XMp0mrFoHjf19nYFhOhjpnwi/pq67zfjus7r/uisOY3duGEE9/enpqds8QjfFpKUN62pTwnM/+a+//hovX77EwsKCc7l9cnDh4HX0ej20Wi3s7Ozghx9+wM7ODtrtdmDTykQigVwuh0qlgoWFBSwvL7tuvUKhgHg8jm6360p+nN3Ha+EQDD0+r0Fr9Fw81XXXph5z7z8NRvonQgmvGzjSuiuZYrHgDi3slFOFHUU36sr7rbJ+yYrWneWuQqHgLPw333yDr7/+OjSW12vQ6+CQjP39fbx79w7v37/H8fGx2wUXCI7Zmp+fx+rqKtbX17G4uIhKpeIkt1yodMsvxvKci6dhCQk9Sb7Mvno/22+W/nkw0k8Jnyg6+onZbW0FVYGJ3xpLwY2q7CitZT07bAY8ELSM6XTazbpbW1vDL37xC3zzzTdYX193vfJMjvlSW03cXVxcYG9vD2/fvsXbt2+xu7uLdrsdSLr5hN/Y2MDKygoajYbbLrrf77v6PEt1/jXRrefnatzue0n6Pv+96v4b6Z8GI/0ToPEvLRnHVKkl0jlv43rhGb+3222X2aYrr8IbTWDpw0/xzdzcnHPpv/rqK6yvr2NhYSFUiOMTfjAYBAj/u9/9Dj/88AOOj49dbR6AK8/V63UsLy9jc3PTHYfhA8Mc4H4+PnMRutEm75NacdXc+yVN1QeopdeSHWDluqfASD8lmH0niZmEu7i4CIx+8ndpZTlO4/dWq+XkrRwuyc/QPnMfmrQrlUpOYvuLX/wCX3/9NdbW1jA3N4dSqeQ2rFAyAUEL3263HeG/++47vHnzBoeHhw9GYWWzWTdxZ3NzE5ubm1hcXES1WkU2m0UsFsP19bWrpdMLYlnSrzgwNqfsVsmrG1z6PfeTmpgM02Mq0o+TmkYBWlunRLbVauHw8BBHR0fODfbdzru7OwwGA9cWe3p6ilarhdPT04B1V0voa8uBh5N4OAGn0WhgY2PDxfB06Wnhw0ZZ00uhS39wcIC3b9/iX/7lX/Dq1Svs7e25yTgsN6bTaRQKBbfAbG1tYXFxEfV6Hfl8/sH+cxr++CVGeikkPF86DJP3WmfkqzCIn+OHAlF+Rp+KqUgf5ZV0NBo5sl9cXKDZbGJnZwd7e3totVrodrsunufDG4vFAlbe3wJKrTtJPm7POSBo4fP5POr1OlZXV/H11187ws/Nzbkhl1qTV40Ay3Knp6c4ODjAu3fv8N133+H169cBwmvCjW796uoqtra2XBzPxhp20jHrzuP4M/RULciRWtlsNlBZ4KLEe0O1Igdi6oL4WO+DYTzMvX8Eg8EAR0dH+P3vf4/9/X0cHh5id3fX9ZhzLLPG8qPRKNAtR4GKlq4mWXcg2NRCsrAOzxh+e3sba2trbqptmEuv8Tvltbu7u3jz5g1evXqFd+/eYX9/H5eXlw8IzzCCbv3q6qrzJii3BYLbbdG1VyuvLj377vki8QEEhoAyPODiQU8qbHsr661/GiaSnlYsajeUsSwAfP/99/jNb36D77//HoeHh66/nOUsJTwtvXbMMYPNMpw2y6g8VxHm0udyOZe00yw9Y3i69L5GQDv3Dg8P8fvf/x5v377F69ev8eHDBxeiMI4G7nfd5SLD8tyLFy9QqVTcfne0zgAC18zFA7jfLJO5gXw+j1KphFKpFNgsUz0EVkVUtsupujpJZ1KVwzAeE0l/fn6O3/zmN3j16pVzw6JwY1VZt7Ozg/fv3+Po6Mgl3nT/NW0P5ULBrL2f0JrGumtmm40o3JyCLv0333yDjY0Nlz1Xl57nr+48a/Dv37/Hq1ev8ObNG/zrv/6r66BjEpHnEIvFXHVgeXkZGxsbLnHHuXqsnfN6VXxES88FkfdnZmYGlUoF1WoV5XLZnTtwPymHHhIXDxUmcRIuFwV1/9Xtj3I4Og0etfS//vWv8etf/xoA3E6kXzo0fuTMN8au/iAIVYaRCDrbTae7jovdfbLTujNrPj8/76S1lNeqhQ8jPKsMrVYLHz9+dBn6t2/fuvhdZ97xPLiQFQoFV5NfXl52iTvt1NN8AevyFONw4eQCwY68Wq2GWq2GcrmMbDaLeDzunimN5Wnpeb8Y17NBSbfm9olvmIyJpB+NRri6unL/HgwGf/QT+ilAM86MdcP6tinGUWGJEsGPO313XuN2ldRms1mUSiXU63UsLi5ifX3dTb958eIF6vW6S6SFxfDcUrrVauHDhw/47rvvHOEPDw/dVFvtEdCZdVxsFhcXXWvuuLq/DvBkNx2z/ww32JFXrVYd6QuFgsv8+4uHL3rS82OoRLUfk6K8z8wzGMZjIuljsRjy+bz7dyaTCZ2T9iWDJAIeTrrl7zUHoGUnv0GHUO28lrFoDWdnZ/HixQusrKxgdXUVq6urjuylUsnNuAsry9GlJ+F/+9vf4ttvv8W7d+/QbDbR6XQCM+cU2lDDcELd+nE5A20NJukBuGvK5/MPtsXOZrMA4EjOc9fsveYZuNgwOdputwPde+pNmXs/GY9m7/VhtVnjcJYduLfYvuZey0lhLmdY3M5W1aWlJayvr2P933rhFxYWUKvVHNn9KbbqXVDzfnp6io8fP+K7777Dt99+i1evXuHo6MglH33Cq6qNM/KXlpawvLwckPOq5FVFPtQicLts6hZYmqtUKpibm0Oj0XAtuEzMUdTERYSE98MiPndaEVHiDwYD5HI5c/GngJXsnoDHrMi4Sod6CMA96VmGo9pta2sLW1tbrpFldnYWxWLxwd7x+llKeKrs9vf38fr1a/z2t7/F27dvJxJez486gEajgeXlZczPz7tx2X4cr2VAdgyyqkE3mxN8Go0G5ubmMDs762J54D6GVwWjtitr4pPHjsViztJfXFw476LX66FYLBrpp4CR/pnwO8GAYAIwTBOuPelU1nGO3fb2Nl6+fOnKcJVKJZTs+pm+8KbT6aDZbLqkHV36aQjPkhr19UzeqVvPv9WuQY73arVablT2aDRySr5KpYJ6vY7Z2dkHsTyAwGf7unu18FqPTyaTjvTs1+dY8JmZGXPxH4GR/olQkoQNplA5qZ8D0P3eyuUyFhYWsLGxga+++govX750RNMx0uM05n7XHwdgfPjwAa9fvw4k7cYRnqArTiEOrTxLalqK1LJau912HYPcdefm5iYgF67VaqjX66hWqyiVSm4TDMbvmhgc5yn5IRMTeXTxNZcwHA6RSqU+13/3Fwkj/RPg1+XV5QXCRTb6/mw264jA0dSbm5tYW1tzwhcm6fxRUH6nXFgcv7Oz4+rw+/v76HQ6ARd53PWkUiknxOFgDJ6Lqu6YbOv1eg9m8nPL7Lu7u0BNni8NU3j+YW2x2n3HJB6Pr23LVDnypaQ3Sz8ZRvongCTJZDIBBZzW3wkq9JjMoqurmfmVlRXXvKIy2nEDLAk/jmd77Js3b/DmzRs39WZS/ZrXwrbZ2dlZVy2gjp+977TwmjfQib3Hx8duQwzW5rn5BUU4moTUeQOa7NQ2W35P4vs6B1p7agN0Bp/F9ZNhpH8ClMTMQGs8SuulQyqLxaKLa+fm5jA/P++GSWoJjqKXx8Y/qUtPwu/v7+Pt27d49eoVPnz48GCzSR9K+Gw2i2q1iqWlpYDHQbdeY2zOw2+1Wtjb23Pjuc/Pz52V5yJCuS33tfN3r9H4neeo1QxVMGoGn4sAx3F1u1330p1/rF4/Hkb6KUEyUzij8a62lup46Gq1itnZWTQaDVejrlarqFQqbs83Wr+wya4aOgAPCd9ut1177O9+9zt8//33ODk5cRn0cT35SniGGuvr64G97ig8UtFMp9NxbcV7e3tuz3rmDbgo+htZ+io+XgMVjrT8DDWo/OR7tNeeIQaVearO4yASLsDm4ofDSD8ldAAld3TRTjOW4JTwJPvs7KyLa7WzTMkeFt/68btv4dktR7XdwcGBI+C4Ory69LVaDcvLy3j58iU2NzexsLCAcrnssuusl6uFbzabODg4wOHhIVqtlhuRzUWRzUH5fD5AeADuvHz9PF123fSCffRU/PE+0OpTqqs6fSW9YTyM9FNCM9IkvbqtzMrn83kXu7NMpXu8UUk3iewKLcvpEEtOvOEAjN3dXZc91zhZKwi0pFTIsZlma2sLy8vLqNVqrgGGSTE/S8/hIZz8Q8Lz2rLZLHK5nGvMYZMW3XIuWuqa6+Qhqj75Ugm0kl4lu6rZ9yf1GB7CSD8lVLxSKBRcI042m3VkLxaLKJfLqFarrpPMj2n9ya8K30KppJd18bOzswcjrvwBGHRt9Vh05zl1Z3l52bn0i4uLqNVqyOVyrpx2fX3t9tVj0q7ZbOL4+NjVxemW05rrcAwSnhOEGKPruDFu1UVrTy+ExL+5uQltJuJ90i5Gf3qwYTyM9FPAr7Er6XO5nMtScxNHv1/cH9c8ieyaFNShFJ1OBycnJ9jZ2cG7d+/w+vVr/PDDDzg4OAiMuAKCO99wkCRLhfPz865ywJn1VMkxjme+QLfYajabaLVaaLfbLklIwvM4dM1JVJKcRGV9nzX2TqeDbrcbGN6RSqVc2U97C/x7Q8+HC8qkLb8MQRjpp4CSPpvNupfG90zQMWPNJJ2flVf4XXfquvqqt6OjI3z8+BHv37/H+/fv8fHjRxwdHTndOT+L1l2n7VAKy665paUltysNdfUAAnB2/jMAABRWSURBVOFDs9nE/v6+mxbUarUCxyJBeUwm4JTwJDPzAxwqQrJTKegPzvSHZfqZfl8ZqMk7s/KPw0g/BVgzpiufy+VQKBQeWPiwRN04sqs11xhW97njPvWHh4f4+PEjPnz4gN3dXbebrO4Cy/PURpdyueyaeCitXVhYeNCaOxqNXHzcbrfd8XZ3d3FwcOAIrxtWaE2dhOegFXoLJDQ7//wdb7Stl7oGH1wEfVUeENTqaw7DMBmPkl7dq1QqFcnWWg6VYL29Vqs5y14ulx/E7rR2/vhpX1uuu+QwE80NLblH/dHREQ4ODnBwcOBq4kzY0cJpA884sr948QKzs7OB1lyOu6KLfHV1hZOTE+zv77vhnycnJ7i8vHTuM3A/TouhA8VK7Jbj4kD3nu48k3c8nmb8s9msu9dKZh5XpcR6P9WlfywpavgDbIjGI8hkMtjY2MDq6io2NzexsbGBubk5lMtlV5bK5XIudve15EBQj+/Pz1P9+NnZmZuc22q13MaWZ2dnziVm0orQgZM6eMOP2yuVipt8o0o7use9Xs95Fbu7u64G3263AwlClt9omRnH8zMpjtEsPaWyFNDo/LxsNhuoOLDl1u+pV7edmgj9WVhYYAjHk4ZoZLPZgB76SwUz2KPRCF999RX++q//GhsbG4H+cpJHX35jCnAvKNEhlew/b7VaD3a84QYauoklLaM+/NqtxxLcixcv3BBLTrypVqsoFAqBjDrPkVb0+voaFxcXOD4+djX409NTNzVIS2qatGNug4vd3d1dYC87v+ddVYLMA4QtJlwwWMeftKcfqxP0OGx/u8cxkfS5XA5/93d/h7/8y798MAzxSwdry9Slcw/2XC7nrJrWjYH7wRKqEdfecw6pJLn29vZwcHDgGlY6nY6Lncc97GGuPPej50itxcVFN6raV8TxPMOktQcHB2g2mzg7O3NJO921luTisXV2PQDnhTB2Z9srLTxddF2weE1cjDj8Uhc8Habhg7kFvx/CMB4TSV+pVPC3f/u3+OUvf/mnOp+fFEajkZtXz7hTXWPdyUXHMxOateZednSfd3d3XVacVl1nvWkvedjQTJbgOENPu/Vo3cMsHz+b1lSVdizL6Zw7IFj6o+KOqjsdbjkcDt2+fRcXFwELrzE56/e8NhIX+AOJmfhTLX3YtB+eEz0OFQQZxuNRSx915PN5F9OSPGrJaY39zRqZoKN1pyXd3993CTJfnDIpdGLMysqBWnfuPMNJN36Di+YU/DFXp6enrhYfRniSUjeqoEaBAzaoh2f87hNeu/20n4DEZcWCsbpPej95zP8HCo54T0h6s/STYSW7R0DX0Xfj9XsVndAl5Sw3KtooYdXEnE+IsHiVWXKtuXM0NROLzDUUi8XAXHp/6IY/uZbNMwwxKLzRPem52GiLMElPglH3zi28qNhjqysXQl6Xv3edimsABDbNCHPtdbwXxVG8dnPvH4dtYDkFSD4+pCoa0Y437jPPeJaE5+aV/k61mrUOIzwThJTPVqtVLCwsOMIzfh/Xj89z1PidWvrj42MnvmEc3+v1nByWmXR1oUn4mZkZ12GoO+iohafw5rEGGJ4fd71VrYK/bTfvjVYseF90p14j/WTYBpZTQF1SnYNHy0mLzoQcy25nZ2dul1pNaIVZ97BOOE6gKRQKqNfrWFhYwMrKitPMc1JuoVAIKP+Ah406DDXYPHNwcBAozYXty6duPfsLGDrE4/FAXsAfSa2ED7tGfWnowfxI2O41+vec9lOpVNy0YC5EUX9eH4O591MibFyV1qJp1bkdNUdCq6ur1ks/k9/raC12wxWLRczOzmJ+ft6JbZaWlpxWgC6tluJ4fmo1ObWWSTvKa5XwwH39Xb0Mkp4yWwCBvABJr0m7cYRXFZ9fUdAFapxb7xNeOxm5GBkmw0j/TDAWpQVVl551dmb+VR8OBLPxfq1ZW3TL5TJqtRoajQbm5+exsLCA+fl5VzrU1lV/9j69EF8XcHR0hGazGVD3XV9fIxaLBZR1jOOz2awjuyruaOHp1nNh88MW4J7wGi7wFbY3fdiuQLpoJJNJ59ZzVgFLqWblH4eR/hlQ11lltLq/mjabAPeW3A8VmCjTcpi26HIbqDCLRneYWXHg4WaSTK6dnp7i+PjYeSIMN25vbx/MpqOOniU/limZcFOXXi182Ew+zU/oZ3OB4/3QoRm+1p7QiT/cCYgjx/wttwzjYaR/JrRhxu8AU1Lr/vK6AGiTCd1nJsqo59fBkkzUaQcbS4VKeLrzlPhqfoGJRurfOdNOB4EoKVWfz7Iiu/5o5XVbKWbqCQ1ZfM+B8TcJr2Imdeu5KGjoo/0F1Wo14PUYHoeR/hnwye3H45SokuSUlvK9/qRc1r45V47depxAQ4tLmStr4hrHa/zOcCNszzdtjSW5ScYwV56SWh1+4UtrtcFGofp8najjq/i0TDhOdUeviLMBOKiEi6FZ+elhpP8EqMVWLbq2u6ZSqYCUNSwjTgvPPnwlOpVqrIWrhNYfMMn3sGOPFl3jbACB2QCU07KfIJPJBKx7t9t1Lr3uKjOuDs/7wnMn2TkSmyo+HqPX6zlxjz/nX0MiXyDEQSXaUxA2oMTwEEb6T4A/wZXkoXovmUwGBj9qYwgfXqrJlOzqqmoJi8IaDpbk17CuNL/GrS62Ljb5fD4wA4BkZHOM6hCU8OPq8L6+gCPA2YrMWYGj0Qj9fj9Q69cEp+YpaOW5KIXpBYzs08NI/wnQmjotGrddZnJOSa/lOJKcOnaSjm41yc6EHF12buxAi66xtJKc+QQSXUuAfOlcep14o0k57ZbzLXzYuGkdOMJkJGf8c8da6us7nY6rBnS7XZfN57XQy+FiqR4S75cR/ukw0j8RfszJurFm34GgZt2XoDLuZ+wMwMXkJBK3jyLZtRZOkQ8tu7rWKttliEACho31YhzvD7Hk9xzq4YuL/Dq8HpvZdSoI2fWnO9b2+33XUXd1deUSiNrZRy2+ljOZBPQHZkZdNfoUGOmfgXGJPBLfL9X5012Ae5Lze60xa/3f36+Nmn114bX1Va0frbs/kpsVAVXX0eLqokNBz2OE17Ic3W/2CCwuLrpttwuFglsEu92u+6oTdHWR1DyIKgQZ52tuQ/9PzPJPhpH+GdAHTB80LdGxe4zDJVS6y3Zc6s21tZQJNLr17FzTrZvCLLy20OpOPCSf7rCjm0lqlt4nPMOJaeTDvka/Vqu5bbw4uWdmZsa59ozptWqQTqfdlla8F0p6XRhVvWejr58GI/0TofV5Xz3mu5kkuc7DC7OQvhzVn5vX7XZduU0Jz8XGnx7D7Dbjae60w8YU3U4LQOA6mCzUHEJYt5taUy1BckIwRUXc2YfWnH/H+J1fGZLQjWffPY+lbc0kO4VQOmbb8DiM9E+A6u6VyGEZcy2tabad7/Phj9rie32iA8GGFb85h4Sniq9er6Nerwe2i9bhGtpjry3COpNey33aJOO3/tK1Z6JQG3RUMkz40lp+5QKmgz95b/zkpt/TYO794zDSPxGqwvOJr1NbdZiGWm7dicXXlfvtsOpyM2bm++jq+u23tPCc2MtJvVod0EEgqtHXbZ/9cVmEqt7CSpZ8Mdmmx2Icznui3gM/iyES3+/fDw17dLouP8vwOIz0T4S69mrhlex8QP1RzlpP90mvL4WWrTSM0BwCCUfNPkdzsxxH60nXmMf0p+i02+3AnD4NIag74OhqfgatPF8qKOJiRwGObozJPIFfdvTDI30xxifh6Y3oWC1z8R+Hkf6JCIvpJzWJ8G8ABN6jLbZAUKevMax+hj8zTxt1dD893fNe1XUAnPKN56MTerU6wFCCMbaKfEg+PQdtzGGtn7X34XDoBlxoXZ5diJok9EMlXQh43nTtw9x7w+Mw0j8DfqONL0HVZho/XuWgCp0srNp9jWs1I++r0+gK08LqVlusu9OzAP5Adv08v9+eJKTsFrjfOpq1dS4gXLi0hs7roaCn3W6743a73cC23mHeBcmrORJ/shC9FS4oOlIrbOSYIRxG+s8AJSKHTGgc6teTY7GYK9dpBp7k0X5zkkqJri25WrsOG3Lht84C951rPDd1w6ko5Oep2CiVSgWGf/LnGqv3er3A8a+urh4M4NAqga8y1K2neRy/4469BP70YMN0MNJ/IvwOO+B+EdByGr+qG6+LgJJe42Nmvklw/r0mvvjZwD2haOEngURRj4UJNNbceT2JRMLNCfCFQbTw2gfAseGdTifQvcfcBN+vbrpflvRlvip00hjev9+GyTDSPxO+GIdbLKtOXF17FZik0+mAqAQIxvQkiO7aouGCkhy4H5FFMmhsrGQG7lVuAAIegD/gI5VKBd7rE4qfp2pDQu+B7nWnajrNJ5DwFB6Ns+A6n1C1Cf4OQ4bJMNI/A1oj1x56AC5DTWktH0YlPhtxVNLqK890uAX/RhN9QHADR34WY3S6v+omKzl9QYwuVMD9nH0tHWpyUVV843rpeW9o9UlQgveALcFhbr0vX+b9100u/PZaw2QY6Z8Iddn9uJqxMB9W38orqcNENwACLjsQrBb4D7SWDul6q1KN5OeCoNdAAuo0G3a2McuuJUf9XjPsLD9qPT8Wi7lcwu3trcvgq5SWi4YuTkp4P5b39f1aqeAsABukMR2M9M/AOOLTMgL3O6v6Zbewz/Gz4iQ4N44kCfyHmmSjtdQhGvw+rAbOhYXNQfryZ+JRDKMvzZqT9L682L8evQ4lvernwwiv94qhjzYR6Qhwc++ng5H+mdAYV8nLshLjTy4Gw+HQSUtJLJbvSBIVz3BBocVUFR5Jo6RXC+9ber8hhZ+tVpyfoSo69SB8/b8q6sJIr4lF9unTAwCCI7rpPfh1eX6WzhksFApoNBpYWFhwc/9p6c29nw5G+ifCV86pddEYVHX64wQ7mvnmg6/ZaiCYbNMsNd+n1lJfvizYd7+1rn5zc+Nib78JRxcTlc9qo5EvMpoEvT9aNQi7R5rnYNdgo9HA0tKS26xTu/eM8NPBSP+J8Mtd+gBPkur63XcqSlFofV7LdEAwked3+6ll9+WsPG8m5zTzrV6E7l/PxUEXqTCS8qtf3fA/n5+j5xpm4XXsVr1ex8rKCra2trC2toa5uTk3EtxIPz2M9M+EL8dVsYivz/eTVWox9aWxt+YE1L3nz4EggZRofB+J7YcEPlk5Rlt/r3/vW2RePzFOLagbWvDnACZ+Dq+N7yfhZ2dnsba2hq2tLWxubmJxcRHVajUw6stIPx2M9E+E3wDix9WajQ8jvU9wJZRPLCbvmBvw3X61+LTaYclDWlX+27ewamn9xUwtsH5Vq67nw0pGmLCI9XnmMFRQ4+dFmGgslUqo1+tYW1vDy5cvsb29jdXVVczOzlos/0wY6Z8Jn/g+ef3ylt9V56vzVM3nv88v96k6TwU1mslnFl8JMRgMXFJNy2waFujiMC4fQfh6BdXqU5Dju95c+HgMTWD6ZTkm7ZaXl7G9vY3t7W2sr69jbm7O7Wpjopynw0j/DCgZ/NjVf4B96+WLdG5ubpDNZgPJMc2ya42fYh3fehKqcmNTimrbE4mEa3NVq+1b9bD4n+cDBCfZaD+/jvXWsdp6X7ggXV1dueSheke08IVCAbOzs1hZWcHm5ia2trawsrKChYUFt3Gnzbt/Hoz0z4SSnZtOUoziW/+wNlx/6o7fmqsLhcbG+n1YUk+75jhf7+Liwk3U5SLQ6/UQj8ed1QUQOtHHJ7xqE3R4B4dhcugmB3doNYAttTqsQ9tqY7E/7LqTy+VQq9WwuLiItbU1rK+vY2lpCY1GA6VSyRHeFzIZpoOR/olQsmtTCgc6+nVrDQP8cIDW1Zec+sd5zL0HgiO8lPQcX6076XIBoO49rJHGX4D8RY5WvVgsut1jdfAmx2QxfqcHot6HDsCgtmFmZsZtTrm0tITFxUXMz8+jWq26hcQI/2kw0j8BGmOz7TQWiyGbzQZieSCY9FL4ZTQAAbIT2j2ndXptuPE1An5tndNllPScne9vmqHbTPvts5p30J11K5WKm8U3OzsbmLRLYvKe6MBNLkg8Nq9f953XnXpp3TWGN8I/H0b6J4JxLCe7ptPpB/HwuL+b9vP5NSwv4CsBCb+aQJKxZ10tvlpaWvmwPnaS0Sc8d66pVqvOupfLZRfHa+OOhh064VcFP7TyJL0O19RefLXuRvjnIzbuIf032GQCD34NPqy1NAz+QzrpoQ0jtf8z/+/HKfw0caYbZvikZ7nRH9OtOQUSkvP0Gb/7SbuwhiG/fMmv/D2Pw6w/M//apKOJRMNUCL1RRvpnwI/T+TMf0xB7Eh57jxLLPzd/ceIYbu1fDxtcQa+FJNMNNzmHj3vwkegqwAmzwnqvfAUhz11Lf770eNxCZ3gURvrPjUfu3WfDtA97WP5ALa0KiXQ0t19B4DGZv6Cl50Qfv3rg9yNMOrcwsY9ep/9ZRvRPgpE+qvAJN66aoIQM0xj4TT9836eck8II/tlhpDfcww8JJsGs7s8WRnqDIWIIJb2Jlg2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiM9AZDxGCkNxgiBiO9wRAxGOkNhojBSG8wRAxGeoMhYjDSGwwRg5HeYIgYjPQGQ8RgpDcYIgYjvcEQMRjpDYaIwUhvMEQMRnqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDEY6Q2GiMFIbzBEDEZ6gyFiMNIbDBGDkd5giBiSj/w+9ic5C4PB8CeDWXqDIWIw0hsMEYOR3mCIGIz0BkPEYKQ3GCIGI73BEDH8f0/EO26T0duqAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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NzU2srq4im81e+N7tdjv8fj/6+/sxNDSE/v5+BAIBI2Q23lgDmdYFT3b9ATCLgPRoGll6DeY1j4r+r0C7gpcFODLazYKYp0+f1qXLmg1keb1eTE9P4+7du7h16xai0WhDq2u9Frr2GxsbSKVSpsTWCtNo4XAYg4ODGBwcRDQahdvtxtHREQqFgumxv6x+QTbZ0KqzM4+it9lsxtXXPX17qOjfkMv64Bv9/jLLCqAuyFWpVJDL5bC9vY1nz56dc+ubtfKxWAwff/wxlpaWMDg4aLrarnpflUoFiUQC6+vrKBQKF96XjTvxeBzDw8OIxWImXgDAVNlx2wPU1zIwP8+FSAqfome03m63103rUfe+dVT0b8BlPfJXRekb7eNl4E7u458/f47Hjx9jfX0duVyuJbc+FAphdnYWH3zwAaampuDz+Zo64JJ19tvb29jc3DRls1Yo2kgkgqGhIQwMDCASicDr9QKA2Z/L6+VnIgeCMEfPYZwykMnPxDpYU2kPFX2bSKG2UyRirbZrJPhkMomVlRU8fvwYz58/Ny52szn5rq4ujIyM4Pbt25ifnzen1Fx1ndzP5/N57OzsIJFIXCgyRuz7+/sxOjqKWCwGr9drqub4XLTYvHYuPBS82+02xTw2m8004cjWWlmzQHQv3zoq+jdACv6qCTlXPYd06fP5PBKJBFZWVvDTTz9heXkZiUSibgBmM/h8PszOzmJpaQnxeNzk5Ju5pkqlgoODAyQSCRM0tGKzvWrc6e3txcjICIaGhhAOh832gXGBUqmEfD5vRorxM2LlnsvlgsfjMYsF/84RXPJzAlC3RWhU4ahcjor+DbAKvpXHycdbZ88lk0k8f/4cP/zwAx49eoSNjQ3k8/mWinDYRTc/P4+pqSkEAoGmBA+8cu0PDw+xtbVlFptGsQseljE0NISxsTHE43H4fD4zX4CZh0wmg0wmYxpt5ORch8MBt9sNj8djHsu9eqVSaZj65AwDHXvdHir6NrHuOYHXFugiYVrFLsdIM2gnLfzDhw/x4sULZDKZlopwgFdWfmFhAfPz8+jr62vJyh8fH+Pg4ADPnj27MDfPgpu+vj5cu3YN46If32azmXx6Op1GMpnE3t4ecrmcibzLIJ7L5YLP56vbFrC7Ts7LY5RfTia6rJpQaYyKvg2sbr1VzPy3dE2tj5XRaVpDBu0ePXqE5eVlM4qqlX088KqkdWBgALdu3WopeAe8svLFYhHb29t4/PgxksnkufvQyodCIYyPj2N6ehojIyMIBALGyh8dHZlFbHt7G6lUCoeHh+dKeDkmiwdoOhwOM2qbUX/e2HnHm4z2K82jom8TaeXllNrL9vdW686AHdtknz17ZoJ229vbODw8bCkfD7zyNqLRKG7cuIGbN29icHAQLperKWGw5DWRSJhS31wud+5+drsdHo8HQ0NDuH79ujnZ1uPxmF549glsbW2ZCTsyJiEbbTjR1+PxGLe9UqmcO/xCjtRiY06zi5nyGhV9m0gBy/l41uYb3lfen8GtbDaLRCKBly9f4unTp0ZoqVTKDKhoxaXv6upCIBDA/Pw87t69i8nJSQSDwab2vnTrDw8P8ezZMzx48ACJRMKk3IDXqbaenh7E43HMz8/jxo0bpief02wZ+U8kEtja2sLe3h4KhYKx8pxxx+ekkDnIA0DdGXW81Wo1M/abotdgXuuo6NtACpj7T1quRvl5a4VdoVDA/v4+tre3sbKyYgS/s7ODbDZrWmXbEfzs7Czu3buH999/H/F43OS9m3k/xWIRGxsb+P777/HTTz81tPLMyc/Pz+POnTuYnp42Vp5FNdVqFdls9txcfFp5zriThTlS5CzCsVp64FWA0uPxmL77ZoOTymtU9C1iddFlX7g8OZV7ekbn2VOey+WQTCaxtrZmxL6+vo69vT3k83lTedZKRoB177Ozs/j888/xySef4Nq1a1fW1xNOsEkkEvj+++/x/fffY2trq66whlHzQCCAyclJ3LlzBzdv3sTAwAD8fr8ZDsqoO2MUqVTKCJ4ekbTy8gw7uuvWcWNy1qAs4mlUy69cjYq+DaTV5jBKGWyynuHGIZaZTMZY9+fPn2N1dRXb29vIZDJtWXfgleUNh8OYm5vDvXv38Nlnn2Fubg7hcLip+npZY//jjz/i66+/xvLyMorFYt22hYdSDA8P486dO7hz5w7GxsaMW8+OuFqthnK5jEwmg/39fbOQNRpZTcHLc+/4etZTcGnVWcgjS3uV1lDRtwgtN4NwhULBnK8mI8vct/J+qVQKm5ubpjWWTSztWnfmuCORCBYWFvDZZ5/h008/xdTUlJld34xbf3R0hP39fTx8+BB/+tOf8ODBA+zt7Z2rerPb7ejv78fi4iI++ugjTE1NIRwO16UCZaoum80im82aRdG6mHGR5OdFwfPztZ5fD7weLW6t01daQ0XfItyz5vN5pNNppNNpM4udbmdPT48pJT08PMTe3h7W1tbw8uVLvHz50lS5WSvUmoUijEQiWFxcxOeff47f/OY3mJycRDgcNoK/rLmH72N/fx8//fQTvvzyS9y/fx87OzumBJbY7XYTIPzoo48wNzeHWCxmZuTzOfn5cBtTLBbPeS+y5p4BPO7bZckuy3ZlC618rDVuojRPU6LXTiaYvSjzzzs7O1hbW8P+/r450klGlU9PT1EoFJBKpcxpMJxPT++g3S4xu92OcDiMmzdv4vPPP8e9e/cwPT1tjpW+SvDccuzt7eHhw4f44osv8NVXX2FtbQ2lUsncj8/h8Xhw7do1vP/++7h16xYGBgZMek5ODKJoi8WiycnL+gIW2LDe3uv1mqAcPSN5Cg4XRVYicqKO9f0ordGU6Dt9NaVIyuUyUqkUVldXsby8jLW1NVOXTleVR0JVq1XkcjmkUilz4xn27Vh30tXVBb/fj4WFBfz2t7/FJ598gqmpqaYEzwBkqVRCIpHAjz/+iD/+8Y/4+uuv8fLlS5TL5XNuuMPhQCwWw+3bt3H79m2MjY2ZYh/p1kvvgfPtZdmwVfB+v98Mz2StPkdhFYtFU6vP3D5jBvIgTT3aqj3Uvb+CarWKTCaDg4MD7O3t4cWLF/jxxx/rut7odjKiLA+kkCe6vOkwR6blZmZm8Nvf/tZMwblK8DJgVygUsLW1he+++w5//OMf8e2332J7e7vhsMvu7m4Eg0HMzMzggw8+wMzMDAKBwLnXkiLk0Ax58g3dcBbX+P1+MziTh2XabK/OyaPg+Rw8nprRe3mmnQwQttrl2MlcKnqOLe601ZQFJACwurqKH3/8ES9fvjS95bTw7DFnvtl66CL3pm9j4IPNZkMgEMDc3FydSx8Kha4UPMWYyWSwsrKCb775Bvfv38fDhw+RTCbP7eGJ3W7HyMgI7ty5g4WFBcRisQvjBSzuKZVKODw8RLFYRK1WMyfMUvCBQADRaBTxeBz9/f0mGMgKRW4NDg8PTUGPPEBDHnElT+lVmudS0WezWXz//fdYXl42X6xOWAAo+lqthvX1dTx58gS7u7s4ODhALpersz60YNZiHBmFftPPjFF6Ft787ne/w9zcXF2U/iLBM5qeSCTw+PFj/Ou//ivu37+PJ0+eIJvNXnhtrK2/fv06bt++jaGhIbOPbyR4LiyHh4fmM6LoGXX3+/2IxWIYGRnB6Ogo4vE4/H6/WTAqlUqd4DkAk89zdHSEcrmMQqFQ503QMKmlb44rLf3vf/97/P73vwfwqjSylX7uXysMKtVqNbhcLjN/nRFlGamWJar0Dt7W2emsS4/FYlhcXMS9e/fwwQcfYGJioinBM6i2tbWFH374AV988QW++eYbbG1tNWyXlbhcLoyPj+PmzZvGo7goL87XKpVKSKfTyGazZh/O1JzH40F/fz/GxsYwOTmJ4eFhhMNhdHd3o1wum8Afj93mloiWnIsNt0y5XM4sDGzOUZrjUtHXajVzaCCACwcjvmtId5EnsFpHPhEZuX6bbqbb7UY8Hsfs7Cxu3ryJxcVFzM/PY3h4uOG+2nr97JZbW1vDN998g3/5l3/BgwcPsLu7e6E7L2F2gK25l7n1Mo3JbjoGN3niTW9vL65du4aZmRmMj4+b47RYo8CqQFpxHm7BBZSfLdOBskc/GAzWpQ+Vy7lU9Dabzcw6A2AaKjoJGS3+a2Oz2cwY6WvXrmFubg43b97E3NwcRkdHEQqF4HK5Li0/pUAKhQLW19fx9ddf4w9/+AP+/Oc/I5VKNfU+3G43hoaGcPPmTUxMTMDr9V5p5YvFIvb395FMJpHL5VCr1UxNfTQaxfj/PyF3amrKDNsAYE61oeh5zh0XA37fKH4p+oODA6TTaUQiEfh8Pq3Qa5Iro/fSeunY4b8ONpsNTqcT4XAYMzMzpsx1amoK/f39CAaDpu20mRx8qVTC1tYW/u3f/g3//M//jG+//fbS/buVvr4+zM/Pm2aaiw7FkNWJmUwGiUQCyWTSHGHl9XoRDocxNjaG+fl5zMzMYHh42EzKpYVn3b71JBsper5euVxGLpdDNps1pb6xWAyhUOjKwzuUV2jK7heG1n1ychIffvghlpaWjBvPeXPy2OeLkME0Ns784Q9/wIMHD5BOp5u+HqfTidHRUSwuLmJ4eBgej+dSt56pyWQyiY2NDezv7+P4+NgU3wwNDWF6ehozMzMYGxtDb2+vSdGx9oEZD2Y65ORbuVBxgcnlcsbaM4bAOfu6t78aFf0viNPpRDwex8LCAu7evYsPP/wQMzMzZr/bShcZ8/DpdBo//vgjvvzyS3z//fcNJ99cRFdXF8LhMKanpzE3N2em5zaq4ZfBu/39fWxsbGBrawu5XA5nZ2dmlNb4+DimpqYwOjqKvr4+s1XglonvUXYuyhFk1klEctBmPp9HNpvFwcEBYrGYmdyj1v5yVPS/AOwJHx0dxdLSEj799FPcunULQ0ND5ovbSjMJBXh4eIjnz5/j/v37+Oabb5BKpVq6Lo/Hg8nJSSwsLGB4eLjhmC2ZjqRbv7GxgZcvX2J3d9fMtQsGgxgaGsL4+DhGRkYQjUbh8/mMC356elr33JcFQq2jyWTAj5OH0uk0+vr6zIw+5WJU9H8DZHsqU3A3btzABx98gDt37mBmZgaxWOzCPPhlSIu7sbFhBL+1tdVUlJ50dXUhEong+vXrmJ2dRSQSMY1D8rWk4LPZrGkV5vl6p6encLvdiEajGB4extDQUJ3gOXOAlltOGpIluxd9BtwGUPBs42WxlBxoojRGRd8m8ot1UXRfjpcKBAKmMGVubg7vvfcerl+/jtHR0basO1+X+/idnR189913+Prrr/H8+fMLT6S5iEAggImJCXNN3Mtb36PcV29tbZlBILTyLBWOxWJ1B1lyu0KRy755RuZZm8+S25OTk7q6ft6fzUz5fN5E/wuFgpknKIeZKOfRT6cN5MAHfiF5o3BZi+/3+xGNRjE2NobZ2VksLCxgenoaQ0NDCIVCZi5cO9aJ/fB7e3t48OAB/vSnP+HRo0cXni57ES6XCyMjI3jvvfcwPT1ddxKO3FNbLfzy8jIePXpU13gUCATMcdU8004KXg4QZXSeATtW7nEMNvf48v0Cr4/cOjw8NCk+3jotpdwOKvoW4Zx2Hr1MIchZeew/j8VimJycxNzcHGZmZjAyMmLObW8mBXcZdHM5AOOrr77Ct99+21KkHngl+Hg8jhs3buD27dsYHh6uO6GGNx7Gkc1msbW1heXlZfz00094/vw59vb26iL20WgUsVjMvE+ZP7eOG2PPPIN6rIBkJF8G9QgXu1KpZBqZWKLLDkbd11+Mir4FZD6d6TS2g7It1el0IhQKYXBwELOzs+aEmcHBQVNc8yZitw7AYD/8N998g0Qi0dI+noK/ffs2PvzwQ0xOTppyWwAmj87ut4ODA9OL8PjxY7x8+dIcm82hGMFgENFoFL29vfD5fKZykNcu+/lZjMPKO7mgMop/enp67vwAZiqkdWdUn52MWp13MSr6FnA4HOawxv7+fng8HlNJxhJln89nctNzc3MYHx9Hb2/vOcv+JoKvVCpIpVJ4+PAhvvzyS3z11VdYXV1tuky6q6sLPp8Pg4ODuHnzJj7++GO899575rw7AKYijk0wiUQCL168wNOnT/H06VNsbGyYGgkdMSYAABhYSURBVPuuri54PB643W6Ew2FEIhH4/f66kV201vSM2E3HCTvci/NMe1p6eZ6d9bOQx1gz59/K0V+dioq+BWjF+/v7EY/H4Xa7jetLK9/b24vx8XHTVBKJRFrOuTeCnWjlchnJZNLk4u/fv4/19fWG/fBW6KmEQiFcu3YNt27dMtmDgYEBk6KTJ+8wB//s2TM8efIEL168MAMvaYU5Iszn8yEUCpnAHRuXGJCTFjmTySCVSmF/fx+Hh4c4Pj42omev/NHR0aVBOXkSLu/fyjHenYqKvgVcLledJaNVBGAi9IODgxgbGzMnuLpcrrqZbq1ibWjhXPr79+/ju+++w+bm5pUdc8Drqbmjo6OYm5vD9evXMTc3h7GxMXMGHQXPEdbb29t49uwZlpeX8fz5czOXX/a4U6hOpxM+n69uEg5FzmAdU23ZbBb7+/vY29vD/v4+MpmMyTZw2m2lUqnbGjSCnogcqvE2WpnfdVT0TUIXNhgMGsEzIs1e8f7+fgwPD5s0FUdntSN4WeZaLBaxt7eH1dVV/OUvf8G3336Ln376Cclk8spWZ5fLhVAohHg8junpaVy/fh3Xr183nW5erxcOhwMATHwilUrhxYsXePToER49eoSVlRUz7ku+nkyvud3uc4JnYE1uE9LptLHwmUwGhULh3PHVnHh7Vc0CtwpM1f2tGqN+7ajom4SFNR6Pp+7wRB61HIlE0NfXh2g02rLgre250r3mEM4nT57gz3/+M3744QdsbGyY4ZrWyDbThVyIBgcHMT8/j8XFRczNzZnqOLnn5haFZbwrKyt48OAB/vKXv2B1ddVYYjmVVp4R73Q6zZ6e3gLbY5leozu/t7eHVCplPAZG7TlfkO8BQMNyXAkzAPK6NGp/NVeKXrpX7IzqJDjNtaurq070tEYejweBQAC9vb11AayrrJRMh/HLy+kxjJRzTv7Tp0+xsrJiGlrozssBHsThcCAYDGJkZATz8/NYWFjA7OwsxsfHTX26dc48m1wODw+xubmJhw8f4rvvvsPq6irS6bQJqhEp+J6eHni9XtPscnp6airleM79wcEBkskkkskk0uk08vm8CbgxYu/3+03HHa+n0ex762fI+3CroVH7q9EhGk0wNDSE0dFR3Lhxw8yWp/vJL2wkEjln4QkFai10ocg5Q39vbw97e3tIJpPY3d3F1tYW1tbWsLm5ac6oJ9YFhZmFoaEhTE1NYXFxEYuLi5icnEQsFjPpM+u10ZoyI/D06VM8evQIL168MMdkN9on06PgQsigJg/elNOAk8kk9vf3kc1mzSx8ACbN5/F4zALocDhMwPKi15bwcbyWq071UVocouFyuVo6J/3XSldXl9lnzs3N4dNPP8Xi4iJmZ2fR29trItF0PXt6euB2u+uCdsD5c+mZd+bEWAp9e3sb6+vrePnypZmPf3BwgGKxWBcIsz4nT7lh1mB6etr04k9PTxvLftG5bxQ8J/6ura3h559/xsrKillk5PuUU21lTp1Vhdwe8ABLuvOccMM+eS6CfAxFC8CIntsDuu+NkO+H2yxrv4BynktF7/F48I//+I+4c+eOsWydECihqE9OToyrPDo6alpeue+W01jlAZbWXnBZkMI02NraGp48eYLl5WWsr68jmUwaS8gqs4uujT8dDgf8fj/Gx8exuLiI999/39TOX9aLb620KxaL2NnZMdsIFtw0srIcdMnqO6/Xaz6TfD5vqvb29/dNak8eYMkFhFNyKXj+3+FwmM9JjstqBLcXXPi8Xu+VEX/lCtGHQiH87ne/w2efffa3up6/C2RNPYtPWB7KwJcUPve8FBEAY7nkoIn9/X2sr6+bnPfz58+xubmJg4ODphpkrN16fX19mJ2dxdLSEpaWlsxxU4zIy8MorHAhKpfL2N/fx+rqKp4+fYrd3d1zo8/l61LwHo8HPp8PXq8XXV1dpkApl8vh4ODAeCpcPKSnwkVV9sgzp89DL0ql0pXuPa+FZ9u73W7tp2+CKy29gnPurfVmDcYB9S2v2WwWyWQSKysr+Pnnn7G8vIzNzU3kcrmWKsho3b1eL4aHh3Hz5k3cvXsXt27dwtjYmCnzvSxrQAsvZ+Hz2Oy1tTUcHh6eExpdep5O4/P5EAgETHegPLSSba7c2zcqlpGpNS6ecsowMwnWcVlWurq64Ha7TSBRtu4qF6OfUBM0chel6AGYLz5LSJmXTqVSWF9fx8rKClZXV7G1tYWDgwNzNHUzMGjG+feTk5NYWlrC+++/j4WFBQwODsLv9zd1jp21W47NM8+ePTPn8kkLL7sGXS4XvF4v/H5/XU6eI6wpeMYirqqOk9fT1dVlApVygs5lj2eDTigUMjPy1LW/Gj3AsgmuchflQQ/ZbNa4uLu7u1hfX8fGxgY2NzeRTCbN2WytvDZTg/39/ZidncWdO3ewtLSEmZkZ9PX1mU62VgTPfvjl5WUsLy9jZ2fHuPUUO2/Mo3u9XmPhfT6fEWo+n29Z8NZrOzk5MS4+YyJXpYc5Tz8Wi5l4i7r2V6MHWL4h0o1Pp9PY2dkx6TaKnbPgZb67mTp5ii0QCGBoaAgLCwtYWlrC4uJiXfnsVXX9MnNQqVSM4B8/fmz64XO5nMl307KzyId7Zh44yQk/bJxpV/DW4CK3FcwYXPYcjNbz1BwVffOoe98m1jZRBuo2NzeNdd/Z2cH+/r4JaFEMl32ZZUUdrdj4+DgWFhawuLiImZkZM4DDmiK86BoZTORoqe3tbTx58sT0wx8cHJiApRQ7Be/1euHz+eDxeOq68Cj4XC5n2lpbEXyjyTxyIMll0K2PRqOmClLTdc2hon8DpKBkqWkikcDe3p4pNb3qaGppXe12O3w+H8LhMAYHB82pMLKqTvapM5Aoi3/ktTEoxkVpY2PDROrZMXd0dGTKadlTwNoDt9tdl4tnIQ9derbGttLhxvcqhS9F34wX5PV6EY/HMTw8jN7eXj3hpgVU9G+ALAPl5BZ5NLWsjecXXJ5/J/fN7C5jc8zw8DAmJiZw7do1jI6Omkk01nScVejSlWcbay6XQzKZxObmJlZXV80JvKy4Y0mxvA5GxbkIADCHUTBoR8G3auHpTVit8mV19hJOJhoYGMDIyAhCoZCm6lpARd8mssBFFuEQfrkZCON+WabTZFEJ+/TZmjs8PIyBgQFEo1GEQiEjQNYJyEo54HV1HS277Grb3d3FxsYG1tfXsbOzg3Q6jWKxaGYAyOuQrjwPjmCdAQ+OTKfTLQteil0OwJQLp/UzvIienh7jCfX398Pv96uVbwEV/Rsic9jc//LG4pJqtVqXP6bIOHQiFothYGAAw8PDGBwcNIGpQCBgSkvpWrMWwGrVOURCps9SqRR2d3eRTCbrymE5/MLr9ZoiG6a9mIpjhRy3LYz480SZVvbwjBUwz898uixLlmOxLvusu7u74fF4EIlE6s6311Rd86jo3xDr5NtQKIRwOGxKSHt6ekwLKa07I+G9vb2Ix+MYGhrC0NAQYrEYent7Tb++LH1msQotOV1tzpnjKGimDXnAI4XONldWF9K7CIfD6OvrMw1DDA6enJyYbjjm9Nk0w7beZgXP9+zz+eDz+YxI6UEwA9JMBydjHrxmmb1QmkNF/4bI6TEUMltf7Xa7mcfOYY1MM0WjUfT392NgYADxeNy48XSr6a5Wq1UTeaeLTWFzxtzh4SEODw9NPIElrFwogNelu+wI7OvrM7P+pOBZIsuFgs04qVQKmUzGdMk1I3jGCnw+H3p7e82i5nK5TNttMpmsO8fOGpCUnzNHcwWDQYTD4aYKkpTzqOjfAgyAMerOyjKHw2HEw0WA03c4JjoWi5mjlvkFli473fWDgwPs7e1hd3fXDKLgvpqz3ylyWWDDKDxHU9Oz4DaC4mFLMIdeMOvAbjnGAZp16Sn4UCiEgYEBjI+PY3R01MzUL5VKSCQSqNVqxlNhFsGa1qTgmVXw+/3mxBx161tHRf8WkC6s3++vc6Xp3lstvdfrNUKjeyvn57PTLJ1OI5lMYnt7G1tbW9je3sbBwQFyuZyZ/iqFzu1GT0+P2SJwJPfY2JgJEvIgCnoWXGykh8AyYhYXNZuW4+cRCoUwNjaGubk5zM7OYmhoCH6/HwBweHgIh8NhavblFkS2Jsv3JBcxFXz7qOjbpJE1oiXyeDx1veDcv/LLTIEXi0VjXcvlsplVJwtfEokEtra2sLW1hUQiYfbUsje9kTi4d+fpOlNTU5icnMTIyAh6e3vNyTNM1ckJOjw2iv3wrDdoVvCsIhweHsb8/Dzee+89TExMoLe3F06nE6enp/B4PKb/PpFImOAeX8Oa6pT9B/ycrJN11MVvDhV9G1j75GXZqPyCyuIZ3o+DHLlHz2azxnIxYs50mxwzxVHRsjddLjxynj5r9fv6+nDt2jXMzc1henr63Hw8mTbjluLs7MxU7vF1uY9vtnSYbb+Tk5OYn5/H5OQkBgcHzYhtXn8wGEQwGITP5zNtsfyb9Xm5kAGv25XZzqtjr1tDRd8msilEDtSgdZe/l3tWGVhjCouBOzlznguCteqtkeBlxx8DZ7FYDNeuXcPs7CxmZ2fNEBB5eqws8JHps1wuZ0Z30a1vZrQ0LXEwGEQ8Hsf4/z+mWk7wkQE51vRzVgFn3FmnBFlnAdITYuCyWq1qRV4LqOjbQNbdMz/OFBp/8qgrfjkLhcK5PTjz18zz22w2U2Ajn4NbBWsrrhxfxbRhIBBAf38/xsfHMT09jampKVOqahW8HBYiewgYNMxkMiiXy01ZUXkNwWAQfX196OvrO3eApbWgift/3hqdAMR/yx4Cpifp/fh8PuP2K5ejom8TOfyB7nqpVEKpVDJi5f95hJMUsHRh5XYAQN3+WoqdVlAGsKTHQAs/OjqKyclJXLt2DQMDA+Z0XOsZevQYZIxBnjzDQRjNVtsxmMmCHxkglBV3zP1b04pyEWvUmGQ9m55tzJxNoPv65lDRt4G08vIgRuuxyfw/f8r8uXUclYwFWAdrUqxyDh/w2kq6XC74fD5EIhETpR8ZGUFfX19dLlsGyOT7oOBZgMN0IOf0SffaKkS5YMnuPPm8hUIBAEwVHiP2rDXgjIFGQrcGKtm/zxs7/FT0zaOibwPZwUZ3nmOfaZkZVZZnsMs9vww+SeEwny9FxNckXCRYzuvxeBAKhdDX14d4PI7BwUFTrcZiGw7usLr1dJflPl4eRMH7M7JuRQbZKLhqtWoKb5xOJ46Pj83iw9JeRu25wMjDJ2VgVAZNOU+P1Ye8SUuvXI2Kvg3knl7e5D6VFo832WwiZ+tJKC4WtsjHWvf/dOlZUssqNR64QZFy4Ga1Wq3bQshFi1Y+mUzi4OCgrqKQB1jw9Fhr66t1/y0Fb7PZUKlUkE6nTb87AGPpmZnIZrNG9DINeVFAT5Yey9Soir45VPQtIi2PNYLOfa3L5TKuvwzwyfpyKSBaSgqdYuatp6fHjHrmjb+TrbByyAUtdalUqtse8Npp5aXlzGQyODw8xMnJidmfSxdflt9ahQ+8PlCS/y6Xyzg4ODCNPCzzZeFRJpOpa+CheBuNECfyhFp6VXqOXWuo6N8AWRBDsddqtXM5euveXUbxKXo+B0Xs8Xjg8XjMpFfm8nkfqyfA12RhDffR1hZg2Z1Hay9LfsvlMgCYOn15fTLwZhVlrVYzAUp5oMfBwcG5abWs/CuVSsjn8+cs9mUilq248sixy8aFKfWo6NtEpsoo+NPTU1MCyy84RUlXOZfLmSYcikTWyTN3zY40zpanFWebrYwByIAcYwuMjvN3FLd1nDevkdcgO/HkHl5G/K0/pThlT3+1WjXpNXnoJ2fby3SnFPxFo7Ksi4BM92mnXfOo6FtEFsLwS8d9L4C6VBvFT+vM/7tcLhPJZ9TaOoSSBSu8P/8tz6OTx1IxO1AoFEzEnOlDa8bAusjwNeXxV7yWk5MTuFwuHB0dnUsr8vkatcRyAaGw5eLH4CIXCC5Ilwneimxn9ng8OjmnBVT0bSJn2jkcDmOx5Z5UWiHeuBfncU+NqvQa7b1ZogqgbuIMawRYBMR2W7bYMrMg5/Sxco8twdxG8Fgovh8ZpLT+X24RLkq1ySyBrD2Qpcly5HUzgue1y8GYfr9fD65sARV9G1gr4ZiXBlA3xlmmsax7apnqYr89H8/JtHwcPQd6FRQN3XjujZm3LhaLpmxXCt4aW+jp6anzTOidOBwO8/xy3826f7lVuGzvLd8r8HqxItJTaNbCsz2ZNQnxeByBQEBPtmkB/aTaRApf3qQIeCCjdKPpptMLYHRcjn22TsehZeMkHYqeKTdW/bFSjcVB8hw56YLTS5F9+9wiyEIeWXgkS4GtgbxmoMWX6UoKvpnn4eNcLhf6+/tN12AsFjNz+JXmUNG3iYwWWyPi0qVtZGFlrpwCokWm8JmPZ2CQi4QM4NFCcz8vKwJllN0qLCk4eRBnuVyuC+hxQZCn87ZilS+iUTahmc+7p6cH0WgU8/PzWFxcxMTEBMLhsPbWt4iKvk2s6S8KhOLgXlxG0WV0XVpzawRbLhLWIh+557dGyuXj5eIjr5fIaLz0Wqz7cLnnbjUPLt16GQCVnlAzcCvS29uLubk5fPTRR7hx4wbi8bha+TZQ0b8BtJZyEi2ttUxJMW0lI+oM4snOPJl7JlaLbx0fLb0La2aBnXzStW9U8GLlooWiFWQNA6/H+tlZr+2i5+jp6UEoFMLCwgJ+85vfYGlpCaOjoyaAp1H71lDRtwkFJ11sCpn7XlpyRtfZdUeh0+Lz/tJLAOobXJgZ4O+swURpqa3bDvk4oPGhEm+zmk0KXsY7ZNRevo/LXp8Wfn5+Hp988gk+/vhjTExMIBQKmUVQaQ0VfZvwyysbaaxutrVcVO7XWdTDCDoFQFcagKm0Y3rNWpJLi29dfGSLLxcYeQx0M9a+XeiV8PpYV2CNE/B6Grn4XDBcLpcR/L179/Dxxx9jZmZG9/FviIq+Dax7ee6LZVechJZOCt3tdtel1mSJqxyw0agWnzc5F5+ehczZ53I5UwHIbjQp/kZNP+0ii31kNSEHaNRqNbONYaZB1ilI76a7u9vMtr9x4wY++ugjLC0tYXJyEpFIxMzNV7e+PVT0bwF+4bn/tHbfWcdpUeDSCtPVt0bvZRWfTPvJslar6OXxU7JvnSk9bjEY5W83Gs/R35zwGwwGEYlEEA6HEQ6HzSANAOaQT9kLL9ti6e0wPRmNRjE9PY3bt2/jvffew9jYWJ1Lr4JvHxV9G1j72Wu1mpnyak3Z0VXnXt06W0/+tJbJSs9A/pSuvbU3vlKpmFFS6XQa6XTazNmTguOoKZYDS+FdNoNPZhR4Sk4sFkM8Hkc8Hjdz/Dl8k3X2cjGSB3Nw4aN3w0q74eFhM+5rcHDQtOaq4N8cFX0bsBSUQyo8Hk/DoQ/yJ7F2pjWqarMG6fhTBsasKTCZNmSPfC6Xw8HBAfb3982hk+xDl2W6spiHcQk5JoueDN13eh5+vx+xWAzDw8MYHR1FPB4/d0oPu+94vHUulzNjruQR18CroF0wGDRn+w0MDCASiZjyYO2kezvYrtjTaYPyBchmEWun2VVcFbHm360WVv6+0fPIdlm6+hQZe9fZM08Lzz21DETyJvfZXORo3fv6+hCNRs1PeeCmtX+Az29tCpLTclhtx1OCOBqbo8E1aNcWDVdIFf0b0EpFWbM0smStWDfpQchjq/P5fN2hlnStZazBWlcP1Levcu/OE2Oj0SiCwWDdybryKG55TfREeE18PW5/ZMeiNTuh1r1tVPSdhszTW8t15dBOWTsg04d05znQgy2/csgHLXEzrre19NY6HFSWKavQ3woq+k5GLgDW2gK6+XL0FF16uY+nNZexBhXn3zUqeqWeRrX1sh3WGkzUffWvDhW9onQYDUWvS7eidBgqekXpMFT0itJhqOgVpcNQ0StKh6GiV5QOQ0WvKB2Gil5ROgwVvaJ0GCp6RekwVPSK0mGo6BWlw1DRK0qHoaJXlA5DRa8oHYaKXlE6DBW9onQYKnpF6TBU9IrSYajoFaXDUNErSoeholeUDkNFrygdhopeUToMFb2idBgqekXpMFT0itJhqOgVpcNQ0StKh6GiV5QOQ0WvKB2Gil5ROgwVvaJ0GCp6RekwVPSK0mGo6BWlw1DRK0qHoaJXlA5DRa8oHYaKXlE6DBW9onQYKnpF6TBU9IrSYajoFaXDUNErSoeholeUDkNFrygdhopeUToMFb2idBgqekXpMFT0itJhqOgVpcNQ0StKh6GiV5QOw37F321/k6tQFOVvhlp6RekwVPSK0mGo6BWlw1DRK0qHoaJXlA5DRa8oHcb/AzzraX7WS48zAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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bmJmZwd27d7G8vFx1jr3T6cRvf/tbcUR3UPCOSKVSWFtbw9LSkkjx3UtkJpMJ3d3dGB4eRnd3NywWC9RqNYrFIrRaLbRabYmphhwHoTN9lUol8v7lIhsqrpGLeCjLj0VfPSz6I+ConVtoyUr7+NnZWdy8ebOmaL3BYMD09DSuXLmCgYGBih1t6Wx+bm4Oz58/FwPNXiJramrC2NgY+vv70djYKN6nvC5eXiXISTxU00819TTby34FdN5PS33ZiJSpHLYRPSTluedHcT1KeQ0EAnj69Clu3bqFx48fY2dnp6JryCIYHh7G+++/j+PHj6OhoaEqgaRSKaysrGBxcfHAo7H29nZMTEygo6MDRqOxpA6f6uiTyaQQs5yxR/54VFqr0+nEjF/uosOpt4eHZ/q3DBKJ3+/Hy5cv8fXXX+P27dvweDw1fdhHRkZw4cIFOByOqgSfz+eRSCRErvx+NDY2YmBgAENDQ2hqahJ+d/S3hMNheDwehMPhkn04CV6r1cJkMsFsNkOv14uVUzqd3vOeqXSXZ/nqYdG/JdAslslkEAwGMTc3hy+++ALffPMNVldXRRZcpddSKBTo7e3FiRMn0N3dXVVbKkrIkQN4wO6JSGq1GoODgzhx4gQ6OjpgMplExWE2m0U0GoXL5cLa2hoCgcCuotfr9TCbzSLCT6uBZDL5hqsubQk0Gs2edf/M/rDoD0G5AGrd28tBu52dHTx79gxff/01bt68WVMDi2KxCL1ej4sXL+LkyZPCjqpSCoUCdnZ28Pjx45Kz+d1WGjabDSdPnsSJEydKZnny03O5XHj16hVev36NcDgs9uUkZsqlt9lsaG5uhsFgEOaX5NIruw+RgQaZbXBTzeph0R+SvaysK4XO4ROJBNxuN54+fYqbN2+KGb7awB3dj9PpxPnz5zE6Olp188lsNouNjQ3cuXNn37Rbg8GA4eFhTE1NiTx+srDO5XIIhUJYWlrC8+fP4XK5kEwmxZ68UCiIaL7BYEBjYyMcDgdMJhNisZhwBNJoNCJoRxF+vV4Po9EIvV5fs/V3PcOiPwTliSHVfPhka2c6h5+ZmcHXX3+Nhw8fYnt7u6olvYzNZhP59c3NzVU1g8jlcvD5fHj16hWePXsmDC52o6urC1euXMGJEyfgcDiEay5VArrdbszNzWF+fl7s58vRaDQwmUwlM71Go0E8Hi+x0KaZnuyyyQyUW1pVD4u+RnaL2peX2O71eyR4Ws4vLCzgwYMH+Pbbb/H06VN4vd6aI9RKpRJTU1P47W9/i76+vqqbTqbTaczPz+Pu3bvwer0lf5dMc3Mzzp49iytXrqCvr0/UwdPfFg6HsbS0hJcvX2Jra2vPAYw61Vit1pKsvWAwKFx0ZC9CCvqZTKaavf7rHRb9IZAbMMp2WXsJXw7WRSIRbG1tYW5uDnfu3MG9e/ewvLyMeDxe8/1otVqMjIzgww8/xMWLF9Hc3FzV6iObzcLtduPu3bu4ffu2cL0pF7xer8fp06fxwQcfYHR0FI2NjSVHbLRaePnyJVZXVxGPx/esRKSZm2rvDQYDcrmc2LPT2T0F90j0PNPXDou+RuQMMQAlrrjlMyPN7lQ84vf7sbS0hPv37+O7777Dixcv4PF4al7OAz8ExEZHR/FP//RPuH79OlpaWkTaayUUi0UEAgE8ePAAt2/fxsrKyq6v02q1GB8fx40bN3D+/HmxrAdKHXppBeN2u/e01QJQskc3Go0iKk/fad9Px3Pk80+2WrvZizP7w6KvAVrCys0UtVqtsHIq77hKHVoikYioS5+ZmcGjR4+wvLxctb9dOXq9HidPnsTf//3f46OPPsLQ0JDo5lopwWAQjx8/xp/+9Cc8fPhwz9d1d3fjxo0beO+999DZ2flGeW6x+EM/e5/Ph42NjX3/NpVKBZ1OJ6yvKfIP/LiKkgcM+fWUwMMzffWw6GuAzqBjsRhisZg4dqIPbnlXmFgsBq/Xi5WVFTx58gT379/Hs2fP4PV6D20CYTKZMD09jd/97neigo6i6JX+LZFIBE+ePMFf/vIXfP311wgGg7u+1uFw4Ny5c7h+/ToGBgZE9FyGjtr8fn+JW2458hk9NbigICB53Mu2WgBERx85wMdUD4u+Bqhc1Ov1wufzQalUwmw2w2w2iyUqADG7b2xs4Pnz55iZmcHc3By2t7cRi8UOnU5qNptx5swZ/Mu//AuuXr2Krq4ukRxTCST4p0+f4i9/+Qs+//zzN1J95RjF9PQ0PvroIwwPD+959p/P54W/PcUEdkOh+LG/PO3PaWsQi8VKauZpC1XePYipjYpEX8+5zuVLZDqO2trawosXL7C9vY1CoQCj0YiGhgY0NDRAq9Uin88jEAhgY2MDi4uLojmEz+c7kuep1+tx5swZ/PM//zN+85vfoLe3t+rAVigUwuzsLD799FP89a9/3dWYo1gsQqlUYnR0FNevXxf7+N2OAeXzea/Xu2/zDcq1t9lsoikGrYqCwSCCwaA4r6eCm3r+HB4lFYmeAyU/Bu7C4TCWl5dx//59zM7OYmdnB9lsFhqNRsz0Op1O7GvX19extbWFUCi06zl1LWi1WkxPT+OTTz7BBx98gK6urqoET00rHj16hM8++wx//etf903CcTqd+OCDD3Dp0iV0dHTsewxINfj0XHaDsupsNhscDgcaGhpEr7tgMCjsrmOxmFji07Wpyo5KbnkgqB5e3h8A5Y/TcnNtbQ3ffvst7ty5g+Xl5ZLWyVQtBkC0Viabp6P6cGo0GoyOjuKTTz7BjRs30N7eXpEjDfBjKyyfz4d79+7h008/xd/+9rd9q/d0Oh3Gx8dx7do14Xm313vJxUJer3fXQY6W9Y2NjWhvb0d7e7uo/ovH4/D5fPB4PAgEAmKmp2aWFDwl+yy54QVPTJWzr+ipMWG9jaa0pAUg0kg3Nzfh9Xrx+vVrkXAinz/LR0dHuRSVnWoUCgXGx8fx+9//HtevX0dvby+MRmNFH3hKBlpbW8OtW7fwH//xH/juu+92DdrJR46Dg4N4//33MTIyIrrP7gUF4QKBAHw+364zPbnl9vb2YmhoCF1dXbBarUilUohGo2KWj0ajb3jbZ7NZpFIpkZNP5pjVJiDVO/uKPhQK4dGjR3j58qVYPtbDAECiLxaLeP36Nebn5+F2u+H1euH3+3fdq/5Ue04SfENDA8bGxvDxxx/j448/xsDAAAwGQ0VL+lwuB7/fj4WFBdy6dQtffPEFZmZm9swLoL9Dr9djenoaly9fRktLy4EnArlcDvF4HIFAAJFI5I2TCY1GA7vdjsHBQZw8eRJjY2NwOp0oFotIJBKIRqPw+XwIBoNC0Lv540ejUQSDQUQiETQ1NYlsQKYyDpzp//CHP+APf/gDgB+WetVaNf3aobNgWrr+EoNec3MzLl++jH/4h3/A+fPn0d7eXvGxXCaTgcfjwezsLD777DN8+eWXWFtbO/CoUKPRYHx8HGfPnsXAwAAsFsuB6cWUeBQIBJBMJgH8mKlIS/qRkRGcP38ek5OT6OjogEajEQNEOBxGKBRCNBrd1Q5LnulpRdDa2gqLxcKir4J9RU+WScRhMsZ+rVTb/rlWdstv7+3txeTkJKampjA1NYXx8XG0tbVVfEadzWaxtbWFW7du4dNPP8X9+/fhdrsrug+j0YhLly5henr6wGU9ANFocm1tDR6PR7SvJrOLxsZGTE5O4r333sPU1JTIJyDrKzrfj0QiwvRyt4EpnU4jGo2KuAFtD7i2vnL2Fb1CoShpe0SdR+qJnytCTO9hMpnQ3NyM/v5+nD59GhcvXsTExISoQKvULSaTyWBrawtfffUV/u3f/g23b9+uKK+f7qOnpwfT09Po6+t7o0nFbr+TzWYRCASwuLgIl8sFACJv3uFw4MSJE7h69SrOnj2Lnp4esXJIJpPiiJOCpfu53OZyOYTDYXFC4PF4YLPZoNfrq6omrGcOfEryaCubETJHT0NDgyhkOXv2LLq6utDU1ASLxVKSonoQuVwOLpcLt27dwh//+EfcunWrqhVLa2srzp07h8HBQVit1gOXzmTTvb29jYWFBXg8HigUCqjVajQ1NWF8fBzvv/8+zp8/j76+vhKn3EKhUNLUojwLrxxyFqJe95ubm3A6nbDZbCz6CuGn9AshR+XVajXGxsZw7do1XLp0CePj41Xt22Wy2Sz8fj8ePnyIP/7xj7h//35Fgpe3F319fbhy5Qo6Ozsr7lvvdruxsLCA9fV1JBIJqNVqmEwmDA0N4fLlyzh79iyOHTuGhoYGcU3ZSIPadh20siLPvWg0KoQfCoWQTqerNgupV1j0PzNyE0cA6OzsxOTkJK5du4YrV65gaGgIFoulJkeYQqGAZDKJZ8+e4fPPP8fdu3cP7EpDyBH7kZERjI+Pw263H7iXz+VyiEajWFpawuzsLDwej2g+2d3djTNnzuDcuXM4duwYrFariEfInvd0Br9btH63+4zH44jH40ilUojFYggEAgiFQiKdl9kfFv3PjPxhHxgYwIcffoi/+7u/Ex5zlR7D7Qadw9+8eRNfffWVaA9dKUqlEhMTEzh16pQozd0PSkne2NjA06dPResrtVoNu92O8fFxTE9PvyH48s42wI/dbCqJn1DiEwUBvV4vPB6PaOTBAb39YdH/AjQ3N+P8+fO4fv06Lly4gP7+/kPtSWnl4PV6haHm+vp6xb9Pqw+TyYTz589jenpapMbuBbWY8nq9ePz4MR49egSXy4VsNguj0Yiuri6cOnUKw8PDovnFXqsXynGoNGBKbr2UoOPxeNDU1CQ667Do94dF/zOh1+vR3d2NY8eOYWRkBBcvXsSpU6fQ0dEhykoPw87ODu7du4fPPvsMT58+rep3KY11cHAQ09PT6O/v33eWp3Re6qJ7584dzM/PI5lMQqFQwOl0lvS028/AUu5gQ1V0ux1flhONRhEIBMTKIhqNchfbCmHR/4So1Wro9Xo4HA4MDAzg4sWLOH/+PI4dO4bm5maYTKZD70ELhQJCoRBmZmbw5z//Gffu3UMqlapIODI9PT24evWqSLfda9VBM/zOzg6ePHmCr776Co8fPxbNOGiWHxsbQ2dnp6ig288+jM7kqX5BpVIdGNCjlF3qqENmJSz6g2HR1wA55ZB3G1V9yT83mUxoa2vD5OQkzpw5g5GREfT29qKtra0kgn0YqB5+dnYWf/rTn/DFF18I99pqPvwWiwWnT5/G+++/j+7u7j3LZil/3+v14smTJ/jyyy9x584dbG5uIpPJQKfTobGxEf39/RgYGIDdbt93FUMrBsqx12g0wibroJoPqrtPpVIlg0c+n+ejuwPgp1MlarUaDQ0NIlmGzqjJ012tVsNisYg97dmzZ0Umndzj7SiIRCL4/vvv8e///u/46quvRPHMfrN8+c+sVisuXLiADz/8EKOjo7DZbG+YfMrdatbW1vDw4UN88803ePLkCTY3N5HNZoV/XWdnJ/r6+tDa2rqnoYcsUkqrzWaz0Gq10Ov1SKVSYkA46PhObmtNlXdcgLM/LPoqoZbMPT09sFqtyGazSCQSiMfjyOfz0Ov16OjowMTEBE6fPo1jx47BZrMdyb6dKBaLCAaDIp/+888/x9raWsnP9/td4IfVSEtLC6ampvDJJ5+IfnflPnX5fB6xWAwejweLi4t49OgR7t69i2fPniEQCIjBgbI3e3p60N3dLaL15X+zbBKaTCYRDofh9/uRSqWgVqtFT3ua6fer9aD7AyAMONLpNIxGIzvr7AOLvgp0Oh2am5vR09ODnp4emEwmUU5KnWisViuGhoYwMTGB3t7eI80UoyW2bGL5+eefl7SeqgS1Wo3jx4/j2rVr+M1vfoPx8XG0traKMl16n2QyiVAohNXVVTx+/Bi3b9/G7OwsvF6vaD1F19PpdGhqakJnZyecTucb/nkkdprBqWhmc3MTbrcb8XhcuOmk02mRj7/fMR79nOIMtNyX22Yxb8KirwKLxYK2tja0tLSI1NhisQidTgeTyQSdToeOjg6MjIyIlUAtgt/NFIJiB263G/fv38ef//xn/Pd//7fIc9+N8qW8Xq/H8PCwKOI5efIkhoaGYLPZSpJmKOFmfX0djx8/xt27d/H06VOsrKwgGAyWeAgQBv3FLowAABcnSURBVIMBDocDTqdT+OeRyMnwkkwwIpEIPB4PVldXsby8jLW1NYTDYSgUChiNRjGIZjKZfbcqNOBms9mSs3sO5u0Pi74KzGYzHA6HWLoStJ+ls+L29naRX14Lsphoiev3+7G8vIyZmRncvHkTd+7cOTD5hj78NpsNra2toqyVMuRsNht0Ot0b7r2BQAALCwv49ttvS2b33a5Px2z0bOTBkAJr1JwzEonA6/ViY2MDq6urWFtbg8vlElsjiolotdqK7L8oA5H28jTrs+j3h0VfBdRoQbZrJoEajUY4nU60t7ejsbHx0Ht4ampJQllYWMDXX3+NW7duYWVlpSJfA6VSif7+fly4cAGXLl3CyMgIOjo6YLfbYTKZSo7SSKSRSAQvX77E3/72N3zxxRcHds0l+ytqNU2eC/F4XNhYkznGxsYGlpaW8OrVK2xubiIUCokgIFmIU61BNW5AtNVgl9zKOFD08kPUaDR1V1orH8WRSMr/m06ng91uR1tbm/jgVyt4Wr7TcpXSWx89eoSZmRm8fPkSa2trFbnp6nQ6HD9+XCzhR0dHMTg4iObm5l0z42gZHo/Hsba2hm+++Qb/9V//VVGbbPKub2hogF6vRzabRTAYRCKREBl7q6urol21y+USJhu5XE40sCA3YbVaLSrtDkrLpe1CNpuFSqViP/wKYRONCmhubsbAwAAmJycxMDAAs9ksZkbgh1m+ra0Nzc3NVR3LyfvcUCgklr1utxt+vx/b29uYm5vDy5cvKyqc0el0aG9vx4kTJ/Dee++JRCA6Pdhvu0H952ZmZnDr1i3Mz88fWJ1HJhlyDzqfz4dEIiHSY9fW1rC8vIzNzU34fD7hqANAJOPIhhnUmlpua70fFHTUaDSwWCy7NuBgSqnKREOv1x+ZjfPbjFKpFB/E3t5eXLlyBVeuXMHExAQaGxvF+XIymUQmk4FKpYLNZqu47p1cYsgIgvzx5+bm8PDhQ7x69Qp+v7+qZ22z2TA2NoYbN27g0qVLGBgYQGNjI4xG44GxBZrlV1ZWcOvWLbx48aKicly5Q41arUYsFsPKygqSySTcbjfW19fhcrkQDoeRTqffELAsatou0WqSEnb2gxpg0mqBRV8Z+34ajEYj/vVf/xVTU1Pi4dZDkEQu9WxoaEBvby8GBgZE5RlVd6XTaXGmTB884Mfjqd0CchS9piSXu3fvYnl5Wfi8k0CqYXx8HJcvX8alS5cwMTGBrq6uAwtm5PvKZDLY2NjA48eP8f333+9riS0/I+o4S8t6t9uNZDIJn88Hr9eLUCiERCJxYN4AWWtRzz969gcNepTmrNPpoNPpRFsxZn/2Fb3NZsO1a9dw9erVn+t+3grks2rgx307faDkTqu5XK6k9RJlmlFAivbLtL999eoVnj17hpcvX2Jubg7z8/O7Lt3lANte2O12TExM4IMPPsC1a9dw/PhxkeJbjctOOBzG3Nwcvv32W2xvb1f0exqNBnq9Xhh9yE62wWBQZNlVAgmfnrmcdLMfKpVKBFapwy0bZB7MgTM9szdyh1qKVGez2RJjCMo6c7vdmJ+fx7179/Ddd98d6Ei7n9g1Gg1aW1tx8eJFfPzxxzh37hw6OjqqtoKmZf3q6ioePnyI77//vqLYAc3wZrNZ7MGp/1wkEhFBumoo379XsqJUKpVi4OFe9ZXDR3aHhFJQ6cyYjB0pV317exvz8/OYnZ3F/Py82OPWuk3S6/UYHR0Vs/vY2FhFhhfl0OrD4/HgwYMHePToEXZ2dg68L5rhjUajaFNNnvXRaFQE4GqhlmdCzTOOOtX5XYYbWFbAQR8k2heTZ1swGITP5xMZZ9S8Uk6mqWT5Xo7D4cDU1BSuX78urLXomKsaKBXW6/VidnYW33zzDRYWFvYN3tEengRPy/p0Ol3ScebnNE6lpiQtLS1ob2+HwWBg0VcAN7A8AorFojh2W11dxevXr7G0tCSMIgOBwBtiqEbsOp0Ora2tohru4sWL4kNei+DlevibN29idnYWfr9/z98hK2u9Xg+z2Sy2Eel0GolEoqTn3M8JZe61tLSU1A4w+8PL+yOAhBQIBLC2tobnz5+LrLPd2jtVg9VqxcjICC5fvozLly9jfHy8xI2mUuQimp2dHczOzuLLL7/E7du34fV6S/rlERQY0+l0MBgMIlIP4A3B/9xHuXq9Ho2NjbDb7WhsbITZbGZTzAph0R8Sis6TNfP29jbW1tZE5Vitgqcmj2NjYzh9+jROnz4tjCmq7eZCy/loNIqNjQ3Mzs7i9u3bePjwIba3t0VGG0XMVSqVOLEwmUxi/65SqYQZ5i8peADiKHVwcFCUBDOVwaI/AmgWpTN46rhabSxEr9fDYrGgpaUFAwMDmJqawqlTpzA0NCTstUjwlWb8UVNJSoednZ3F/fv3MTc3B4/HIyrZaEbXarUwmUwwmUwwm80iuYeaTIZCIWFK+UsJHoCo3T9+/Diam5vZLacK+EkdASQaanYpi/IgrzoqWLHZbOju7sbo6CjGx8cxNDSE7u5uUaoqV8Pt1x+ezrwzmQwSiYRYfbx8+RKPHj3C8+fPsbW1hWg0KjLgyLzCarWiqakJTU1NolUUOc/6fD7RjfawUfqjgAqcOjs7ubtNlfCTOgLovNhqtcJms6GhoQGhUEhEw8sNG6nlk9FohN1uR09PDwYHBzE8PIyhoSH09PSgublZ7FPlXH7ZLpq2Fvl8XmSxUYWb3+/H+vq6qGpbXl7G1taWaLVNlW02mw0tLS3o6OhAW1sbnE4nrFaryDz0+/1YXV0V+QZHJXiKR1RjfU3odDrYbDY0NTWJAZEDeJXDoj8ClEolDAYDmpqa0NPTA7fbLarT6NyeynCpXtxqtaKjowNDQ0MYHx/HyMiI6F1HySay2AuFghA4pfMmk0nE43GRFBMOhxEOhxEMBkXd+uvXr7G9vY1oNCrKT41GIxobG9HW1oaenh709/ejt7cXLS0tIqMvk8mIAplkMgm/349gMCicaWqBjvxo/01VhdW2ADeZTHA6nWhqahJVg0zlsOiPAErLtdvt6OvrQzgcFoYQslCo/NPhcIj96OjoKI4dOyb6rFPArLzzC7Vwoh7uoVAIgUAAPp8PHo8HLpcLHo8HPp9P5LzLzSCVSiU0Gg2sViu6urowPDws3rutrQ02m03s36ntdDweRyKRgNfrxc7ODuLxeE17eFrVkLMOuePs7OyIk4NKU3ape057ezsH8GqERX8EUOKK1WpFe3u7KB/V6XRwu91i/0wzfHt7OwYGBjA8PIz+/n44nU5YLBaxTJXdYmV7qZWVFSwuLmJlZQUul0sE1aiUNZlMivpyQq1WQ6PRwGw2o6OjA2NjY5iYmMDw8DC6u7vhcDjENoLssqi0NZ1Ow+VyYX19HeFwuCbBazQaOJ1OjIyMYHR0VDTFDAQCmJ+fx7Nnz0QBUyWQGzEZgXCuffWw6I8ACuTREl8WnsFgEPt7GhhaW1vR3Nxc4n9PFWbUzZYcZ0js8/PzePHihahND4VCey6zKdinUqmg1Wpht9sxMDCAU6dO4fTp0xgeHhYri922ETTYeL1erK+vw+121+SloNVq0dHRgdOnT+PixYsYGxuDw+FAsVjEzs4OjEYjYrGYWJlUsm2gAUx22WGqg0V/RMjCb2hogMPhEJVmCoUCiUQCwA9bgWw2i0gkAp/PB6VSiUwmA4PBAK1WK7L7wuEwXC4XFhYW8PTpU8zNzQk/uYNmRdofq1Qq0Ujy8uXLOHPmDI4dO4bGxkaRzVe+H6a4QTgcxsrKCtbX1xGLxaoOtqnVajidTpw7dw4ffvihaIqp1WpRKBRgsViQSCSwubmJ169fIxAIVFTDr1QqxRaEynDrPU28Wlj0R4AcgaYPJaWI0lESBa3IIz8cDsPn88Fut8Nms4kzeCp13d7exurqKlZWVrC5uSm84SuBjgEdDgdOnTqFa9eu4cyZM6JR5l6WWfQ9lUqJfvMul6umZX1DQwMmJibw3nvvYXp6WrSfol70xWJR5MyTG28lTrZ0z5QclMlkRLyEqQwW/SGQj83kyDoJXA6++f1+JBIJMfNTearRaCwxhaRVgN/vh8fjKfGTqxSNRgOHw4GJiQlcvXoVFy9eRF9fn7Dk3q97LHncra+v4/Xr11W3uwZ+WNa3tbXh5MmTmJiYEHnx8lk6edw3NDSIEt1K++/lcrmSbUE2m+WAXhWw6A8BZeLJppbJZFIIPRAIwO/3Y2dnBz6fD/F4vKQVE9lHk1cc8GO0nurz6fqVQnv4kZERXL16FWfPnkVvb6+oxttL8PTeyWQSLpcLy8vL8Hg8Ne3ljUYjOjo6RJBSTiySn52c1EQmGAft6+keI5EIQqEQgsGgOLrj2b4yWPQ1IifGZDKZkgIU+TjN5/PB7/cjFAoJTz36XaI8e688+aYSqKmmzWbD8ePHceHCBUxNTaGnp0d49+03w8v1A1QSHIlEqn4u1LzTarWK1Yv8d8gZg1SSW83AQiuhSCQi8hGo+QiLvjJY9IdA7rpKiTKUJENn6ZFIBIlEAqlUSpg90sxdnqUHYM9Ek72WvpTwQ1VnfX19wvq6u7v7DTHI3n10PTlH3+Vy4fXr16IxZbXQjJ7JZMSzMJvN4mdUkRgMBuF2u+FyuRCNRg+0uyZoYIpGowgGg9jZ2UEkEkFzczNX2VUIi75GaGlPoid3XPoikVOCDNlByV+7Ifdho+WvjDw4qFQqkeXW2NiIrq4ujIyMYGJiAh0dHSVdY+X3lL+T4CkJZ3V1FRsbGwiHwzX3OCBHnsXFRZjNZqTTaRFApC6/W1tbePHiBZaWluDz+Squxy9PHAqHw0gmk3XXj+EwsOhrRG7GSF80W5EYyaX1oL20DL2OZkyqaacZncRObZ2NRqPIn6c20bSPpso/uqfyhpIUP0gmkwgGg8IAxOv1Ip/Pi5hDNWm3+XweiUQCW1tbePjwISKRCNbX19HW1iYafobDYayvr2N+fh5LS0sIhUIVz/QAhAsxpfBy/7rqYNHXCIlenkFp1rVYLLDb7WJ5K5eh0mvLZ6byoB4d+en1euEtT3bP9O8Gg0Hsn202mzCUUCgUiMViYlYs38+Xr1LIyXZ9fR2bm5tIJBJQKpViZs5msxULX3bmSaVS8Pl8ePXqlcigAyBssiltmE4nKhUu9cij77UU7dQzLPpDUJ75Rkd3shVzuVgoJ758jy5Hsuk4y2KxoKGhQVTuWSwWUeNOgwENDlQiS5l05F0vD07ydzkImUqlEI1GRQCyWCzCYDCIVQK18qo0EYZeS/vvra0taLVaseqhakDqTFvLTF3eKIOr7CqHRV8jsuDpjJkES7MwmVLIwgwEAohGoyXbAboeCZcMLOx2OxwOB5qbm0USD6Wgytl0FA2PRCKIx+MiKh6Px0ty8mXh0v3LfvEkbpVKBbPZvOvsXmm+AM2+JOpUKvXGaoMGnlpmabmIqFonoXqHRV8jtBynhBO1Wo18Pg+tVitEL38ZjUYYjUZsbGzA5/MJdx1ZRBqNRvw+CZ9meKvVKmZ6Eip1bZUTgOQjQqp9T6VSb8zUNMjQ+1GSEBX90NaC6ghodVJpAo2M3PeP3vsoluPUOJSyGZnK4Cd1CEj05HtPOeGFQkHM8iQs+qI9uc/nQyQSKenZJgfr6Jpydh9lndH7pNNpxGIx0ROPlvV0VEiCLe8YQwMWbSfi8bgYZGjLQFF92v8fdOpQDYe9Bs3qZrNZtAZn0VcOP6kakQNvAERZqpxUIy9nZYcbGhgAiPxx+b9Tjj5dm/5dri6jqDsV7vj9fmFnRUlA9H7lIpOX1vL3bDYrTC7o38uv9TYgewl2dHSwXVaV8JM6JHsFkcrP0SnK3tjYiGAwiHA4LJJ2ykVPR4CU0ms0GuHz+UQlHhWtyEt7aisl790PirjLPeTon1OpVImBB8Ue3pZqNqVSCafTiRMnTmBsbAxOpxMGg4Hdc6qARX8IZMHvlj5bfqRXvjqQi3SoSyu9lgJf8XgcGo1GbBNo+S9nA1IikJzxV6lASex0H7Q6kf+GtwmbzYbp6WlcunQJY2NjsFqtLPgqYdEfgt2q7Oify1N0KS+fouty1h7t28uX0JTOSgMFBfDkgYY65lKJaflAU8vf8rai1+sxMTGBjz76CGfPnkVrayv0ej1H7quERX8I5L2xnJVH4qW9uLzv3tnZKdl7k+jprLpcdGSoSdF62WJbzp0nylcf7wpqtRrT09P43e9+hwsXLqCzs5MFXyMs+hop95inSjtZxKlUCvF4XDS2pOaW0WhU+NrJJbTyTE3IqbAUJ5BbZAM/FrnISS7vkuANBgMmJyfxj//4j7h+/Tr6+vq4WeUhYNEfAtlPjpbqcpYZVd7F43GkUikhSmoZZTAYkMvlRFKMHFQDfpy1aVkvHwHKOf20zKfBh+6BBpNqUlyPClqdyDEM+ZlVso1QKBSwWCyYnJzE73//e1y/fh0DAwM8wx8SFn2N0Pl1+QxffiRHde4Wi0UUvlCTC+oWUx7Fp2U8CZ0GCErwMZlMIpJP5apytJ8suaj9VDgcRiQSqdhu6zAolUqRVERHjLLPPaX80t+9V4afVquF0+nEmTNncOPGDVy+fBm9vb08wx8BLPojQs7Ok3PoqQlkJpOBw+EQabHUBJIESisBirxTwYvcD57y7qn4RraYymazSCQSos7c5/MhGAwiGAwiFAqJElT6on7yh4VqAOjLarWipaUFLS0taGxsLKn2i8ViogiJRE+rIgAlKxy73Y7JyUlcu3YN58+fR2dnJwv+iGDR10h5oY1Go3njqE6O6svWWlRwIvvpybn48rVplpcLbCjfXM7ck4VF5hJer1eIPxaLIZVKIZVKCVNOn89X0oFnL8ce2maUH43p9Xo0Nzejs7MTra2taGpqgsPhQEtLCxwOB0wmExQKhfDuJ2MR8uuXE3+oUEmtVsNgMKCzsxPT09MYHx9HW1sbt646QhQH7PXenWjQESPXo8t71L3MKuSf7TU4yP8vyo/p5AaZ5ftk4Mczf9mnLxAIwOVyYXt7W7jpkkOO3IiS7l8ujpG9+ykPX04OAn7IfW9pacHIyAiGhobQ1tYGi8Uicvip0pC2HtFoFIFAAB6PB36/XxQe0TEldQnq6OhAZ2cn2tvbYbfbeQ9fO7s+NBb9IZHFLlNJ4Gy/18i+ebt93+t68mCUSCSEV18gEEAwGBRmnbFYrERwcoyCcgjIz48Cj7TNoO62drsdTU1NaG9vF1bWNCiUJy3RSoS2H7TdoAFH9vijqkKqJmTB1wyLvt6Q6+UTiQR8Ph+2t7fhdrtF51nZMqvcBQhAiYc/1fk7HA50dXWhpaVFtMSin++XHUfvI29naNCk7YNcnMSZdoeGRV+v0ExLS+x4PC4MMigAKB8tUsWg0WiE2WyG2WwWy3Uy4Sz3q6/1vsrhWf1IYdEzeCNVmPIMKKpPraWoEy+dFsiWW/IZPPNWw6Jndqe8boBOEOQMQJ6Bf5Ww6BmmzthV9Lw+Y5g6g0XPMHUGi55h6gwWPcPUGSx6hqkzWPQMU2ew6BmmzmDRM0ydwaJnmDqDRc8wdQaLnmHqDBY9w9QZLHqGqTNY9AxTZ7DoGabOYNEzTJ3BomeYOoNFzzB1BoueYeoMFj3D1BkseoapM1j0DFNnsOgZps5g0TNMncGiZ5g6g0XPMHUGi55h6gwWPcPUGSx6hqkzWPQMU2ew6BmmzmDRM0ydwaJnmDqDRc8wdQaLnmHqDBY9w9QZLHqGqTNY9AxTZ7DoGabOYNEzTJ3BomeYOoNFzzB1BoueYeoMFj3D1BkseoapM1j0DFNnsOgZps5g0TNMncGiZ5g6g0XPMHUGi55h6gwWPcPUGSx6hqkzWPQMU2eoD/i54me5C4ZhfjZ4pmeYOoNFzzB1BoueYeoMFj3D1BkseoapM1j0DFNn/P+LtSpnYxHCGwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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vnj59itu3b2Nubk4ks9SKw+HAjRs3cPbsWdHpthYymQy2trbKFpm9RGYymTAyMoLh4WG0t7eX7erU3aYyn5/+TMVJVEBEz6N6BgrvUVERFfOw6BuDRX8EHHUTBzrjZrNZBINBPH/+HDdv3sTDhw/h8/nqupbFYsGFCxfw7rvvYnBwsOYed+Q1X1hYwLNnz0RMfC+Rtbe34+TJk+jr64PRaBRFM5Utr8hKkJ7tqUyYCm4o5x74qUGntE9eoVDgKrtDwKI/JIdplLHfNXO5HEKhEF68eIFbt27hm2++wevXr2t6vjSacOzYMVy/fh1jY2Oibr1W0uk0VlZWsLS0tG8ijEKhQG9vryjaUavVZfXw2WxWFMtQUZC0OIk6Ael0OuFrIMed9DOROvx+6WjJ2wyL/jcGmbNU2PLVV1/hq6++wsbGRs3neGkq7Pj4OM6cOQObzVaXOMgB5/F4EAwG932s0+nE2NgYBgYGYLFYxC5PdfA0fScYDIqyW7o/arOl1+tFz33K1KPa+crPp7LQiakPFv1vBKlJH4lEsLS0hC+++AJffvklXr16VVbcUgsKhQLj4+OYnp5GR0dHXZ11yLT3eDxlOfbVEpFUKhWOHz+O6elpscuTECnGTpWAHo+nrHko5dtrtVqYzWbYbDZRhgsAqVTqZ6KmsmXyE7Do64dFfwikBR+V3uhGrkM15wsLC7h16xZu3ryJly9f1hWPp/vQarW4ePEipqenodfr6xoIUSqV4Pf7MTc3V3akqHaeb29vx+zsLE6cOAGz2Szy+Mms9/v9WF9fx9LSkqj5l+7WLS0t0Ov1sNlscDqd0Gq1IhwXi8WgVCpFvT7t7kqlEmq1uqyzD1M7LPoj4jCCp7Cc3+/HwsICbt68ia+++gorKytIJBJ1Xw/4UYynT5/GyMhI3cMns9kstre3cefOHWxsbOz5OIvFgqmpKczMzKC3txdarVac5akOfmNjA8+fP8fGxgai0ahwxJGHvrLzrk6nQzweRyaTQSgUEh59ctpR+2+NRiN8B0x9sOgPQeVOT/9Wq/hJAOl0Gh6PB3Nzc/iv//ovfPPNN1hdXa3bpCdsNhvOnz+PY8eOif5ztZLP5xEMBrG8vIznz5/vW6c/MDCA999/HxMTE2W7vNRqefHiBebn5+H3+8vCfiRihUIBk8kEu90Oh8MhOu7EYjER2ycznxYAadyfz/X1w6JvEKk3WUotX0B6HpmwW1tbePz4MW7fvo3vvvsO29vbdbe+kr7+qVOncP36dfT29tYckycymQyWlpbw3XffweVyiWtWvs/e3l5cvHgR586dQ1dXl+h4Q4KW9tFbXV2tOoGHzvQGgwFWqxU2m01YCyaTCTqdTjTVoGIdEr1Wq2XzvkFY9IdAOrRS2hdvr91eWlWWSqUQCoWwvr6O+/fv4/bt23j8+DH8fn/D8WelUomxsTG8//77OHfuHNra2uoSRS6Xg9frxXfffYd79+6JJKBKsRqNRly4cAEffPABBgcHYTQay7LpqK3W4uIilpeXEQqFfhbyo9x6pVIpPPfUcjuXy0Gr1YraemlnYZrso9Pp2LxvEBZ9g0j7sZPpSewleArH0bTZFy9e4JtvvsH333+PlZUVRCKRhjPMZDIZhoaG8Mknn+Dq1as1lc1W3l8wGMSDBw9w584dvHr1qurjtFotTp8+jWvXrmF2drbq8YEWj4WFBWxvb+8Z45eG6/R6PXQ6HRQKheiVJ23xTfF8aqIhbZXN1AeLvgGk7Z0zmYzYgWg6i3TIo9SUT6VS8Pv9WFlZwdzcHB4+fIinT59iZ2en7kYYUlQqFcbHx/G73/0ON27cwNjYWE1ls1LC4TCePHmCTz/9FA8fPtzzcSMjIyKl1+FwiNJZ6WeTyWTg9/uxtra2b1cfcsqR4Cv77NNPZbtxtVotFgU+z9cPi74ByFFFXVqp5TPNcKMvr1TsoVAIu7u7WFpawqNHj/D48WOsra3V1QSjGhqNBidOnMDvf/97fPzxxxgaGhLmdq3vJRqNYm5uDn/5y19w69atPZNxenp6cPHiRbz77rvo6+sTI6ukUJfeQCAAr9e7pzNSep7X6/WiN550WCXl10sHWlZOwfmlWoy/zbDoGyCfz4uWzh6PBwDEMEmpVzmXyyEej8Pr9eLVq1eYm5vD/Pw8NjY2RN/3w6DRaDA9PY1/+Zd/wQcffCAEX6vJS4J/9uwZ/vKXv+Czzz4T76cSlUqF06dP46OPPsLAwMCesX9q5xUIBPZd0GhghclkgsFgEEcRStmlRhnUOIN8AHSUYq9949Qk+mauZKr8YhUKBSQSCezs7GBhYQGbm5vI5XJimCTVjVNDR5fLhfX1dayuror+9DSP7TCoVCpMTU3hk08+wbVr1zAyMiI837USiUQwNzeHTz/9FJ9//jk2NzerPk6pVGJ6ehpXr17FzMwMrFbrnq+Tz+dF3/u9kopkMhkUCgUMBgNsNhtMJhOUSiXy+TySySTC4TCCwWBZWyxK6JHyS9Q9NAM1iZ4/0J8cd5FIBCsrK/j+++8xNzcn5rhRB1gaJpnNZhEOh/H69Wu8fv26qge7URQKBSYmJvBP//RPuHHjBgYHB+sSPHnXf/jhB/ztb3/D3/72t32TcDo7O3H9+nVcvHhRnOOrQYVCgUAAbrd7z7AjCZ6m45pMJtH2OhwOw+v1wuv1CtFTFp90Zh/V23OlXf2weX8A0llxyWQSm5ub+Oabb3D37l0sLy8L85N2LzpLS+u+qUf7USCXyzE8PIzf//73+N3vfoe+vr4921BVQgUwfr8f9+/fx5/+9Cf853/+J7xe757PMZlMmJmZweXLlzE4OLhvDj/5Onw+H1wuV1XRy2QyqNVqOBwO9Pb2orOzEyaTCQCQSCTEc/1+P+LxOLLZrKiqk5730+k0D7xokH1Fn0wmm7IHGZ0fAWB1dRXPnj3D7u4uPB4PNjY2sLCwgJ2dnZ+lyEqz8o6Kyk415LT76KOPMDIyAoPBUJPgaRGi9Nq//vWvuHfvXlWnnTQZZ3R0FFevXsXo6OiBpbnkuScnXqVlQ4JvbW3FyMgIjh8/ju7ubhiNRqTTacTjcbjdbrjdbgSDQZGDT+8/n88LwUtLddm8r499RR8Oh/H48WO8fPmyLOPqbYdEXyqVRLGI2+2Gz+cTs9j3et5RQ194s9mM8fFxXL9+HR9//LEQYS1e+nw+j1AohOXlZdy9exeff/457t+/v6dnnd6H0WjE6dOnxVCMgxYXOpMHAgGEQqGy0dnkeW9vb8eJEydw6tQpjI+Po7W1FcCPu3w4HBafM+3y0gQoOj7QuT8ajR6pFdUsHLjT//GPf8Qf//hHABDZUs0ElXBSv7Y3YU46nU5cuXIFN27cwJkzZ9DZ2VlzWI4q3ebn5/H3v/8dX3zxBVZWVg58H2q1GqdOncI777wjjhAH7fJUNOT3+5FMJsssJpVKBYfDgcnJSVy6dAmTk5Po7OyEUqlEIpEQDkCaZFOtHRbNqE8kEgiFQsIakBbwMAezr+iprpo4Cq/zPxp0Lv+lqZbfPjExgcnJSUxNTWFqagrj4+Nob2+vORMtl8vB7Xbj3r17+PTTT/Htt99iZ2enpvvQ6/W4ePEiZmZmYDKZDny9YrEo6gjcbndZpmJLS4sowX333XcxMzOD7u5u6HQ6sZiS4zMUCome9tUWpkwmg2QyiWAwiGAwiGg0Kvr+sYlfG/uKXiaTQa/Xi7+r1eoyk60ZqGzN9EtBr2E2m9HR0YGRkRGcPXsW58+fx9jYGCwWi0hgqeXLncvl4HK5cPv2bfzHf/wHbt++XdNkW7qPoaEhTE9P11y0I+32QwM1KW22ra0NMzMzuHr1Ks6cOYOenh4R56fIRyaTQSQSEWW1e33P8vk8IpGIsAo8Ho8o1Km3uKhZOdB7L11tKXTC/DLYbDa8++67+OCDDzA7O4uOjg5YrVYYDIYDh0xKyefzYof/t3/7N3z55Zd1len29vbiwoULGBwcrKkBB+UuvH79GktLS/D5fOIM39raisnJSVy9ehXnzp1Db29vmS+CxJ3JZBCLxQ7sZ08WQTQaRSAQwOvXr+F0Ouvq8tvscMjuDSH1yqvVapw8eRJXr17F5cuXceLECTgcDmg0mroLSshp9+TJE/z7v/877t27V5PgpceLgYEBXLp0CV1dXQcKSdqme3l5GZubm4jH46J67tixY7h8+TLOnDkjZttJu+vQ+8tkMmWjq/aC2onFYjFEIhH4fD7hA6i33qBZYdH/ykhbQgNAV1cXTp06hQ8++ACXL1/G0NBQzWG4SkqlElKpFF68eIHPP/8c9+7dQzgcrvm5wI9Tao4fP45jx47BYrHUNOsuHo9jdXUVc3Nz8Pl8KBQK0Ov16O/vx5kzZ34meIqMSFNpc7mcmFl/EMlkEslkEul0GslkEqFQCOFwGHq9vq7KwmaFRf8rIzVbh4eH8dFHH+HGjRuYmpqC3W4/1G6VTqexvb2Nr776Cl988cW+STfVUCgUOHnyJKanp9HW1naggGjc1uvXr/Hs2TO8ePEC0WgUCoUCNpsNk5OTmJ2dxeDgoEi1lRbISPvZU9JNLcfHbDaLVColxl1RDQQ79GqDRf8GMJlMeOedd3D9+nVcunQJw8PDonV0I5B4vF4vbt++jZs3bx44UloKWR86nQ7nzp3DzMzMgYk40saXT58+FU00s9ksTCYT+vv7MTMzg5GREVgsFlGCWy1/njLtanUSl0olxONxMdPO6/WitbUVfX19MJlMLPoDYNH/SshkMnR3d2NoaAjHjx/HhQsXcPbsWfT29tbVnnovfD4f7t+/jz//+c+Ym5ur67mU0TY6OoqZmRn09/cfmG5LvfRevHiBb7/9FgsLC0gkElAoFGhvb8fx48cxNjaG9vb2fXvZUWqw1HlXLXxZCVXytbe3Q6FQIBaLcaJOjbDof0Eo7dRisaC3txfnzp3DxYsXMT4+DqfTCaPReGjBl0olhMNhPHz4EJ9++im+++47pNPpmoQjpa+vD5cvX8bY2BhMJtOeVoe06eWzZ89w584dPH78GK9fv0axWITRaERfXx9OnDiBzs5O6PX6PfvT0y6fyWRE0hfNtDsoVBqLxeD3+xGNRqHT6cQxgUV/MCz6BqAGEOSQqgxlymQyaDQatLa24vjx4zh16hQmJycxPDyMrq4u2Gy2n3WcaQQq36UGGDdv3hQ17PV8+Q0GA2ZnZ3H16lX09vbuKXjKfQ8EApifn8etW7dw584dbG5uIpvNQqvVwm63Y2hoCENDQ7DZbHuesUnwdD7PZrNC8BTZ2O89ZDIZxONxMTGH/AKFQqHhY1KzwJ9OnVCnVoqfU2cc8jxTt1aHw4ETJ07gnXfeEX3hyZl1VH3dotEo5ufn8ac//Qk3b94U02j22+Urf2cymXD+/Hlcv34d4+PjsFgsYjGjx5I5T30Enjx5grt37+Lhw4fY3NwULcM0Gg16e3sxNDQkBldUe69SkaZSKUQiEXENlUolimiqdRuWQhV4AET7slwudyTHpbcZFn2d6PV6dHd3o6enBxaLBcViEYlEAolEAoVCARqNBjabDcPDwzh79iwmJibEuKejyg8nk35ubg5//etf8fe//72sHn4/odDv5HI52tvbMT09jT/84Q+4ePEiWltby3Zmql1PJpPwer1YXV3FkydP8P333+Pp06fweDxlZcVmsxkDAwPo6emB0WisusuT4GkRCQQC8Pv9wh+g0WjKymn360FA9wf81HEnk8nUXGrcrLDo60ClUsFut6O/v1+MZJbmjheLRajVavT09ODEiRM4fvy4cGQdhUeZTGJKvvn000/x2WefYXt7u67rKBQKHDt2DFeuXMGVK1fEtFkqqqHXoV14e3sbc3Nz+Oabb/DkyRO4XC4kk0khOBJra2sruru7qyYWScNz2WwWsVgMbrcbm5ub2NnZEb0GSfRUXbef1ULNNKTXTKfTZUk/zM9h0deBwWBAR0cHnE6n6NFOc+MKhYIw/UdHRzEyMoK2traGBF+tPpy+4G63Gw8ePMCf//xn3L59G7u7u1WvUa22X6/XY2xsDFNTU5iensb09LQIqVU284zH40LstLOvra0hGAyK3ZdCcFSj4XQ64XA4RJqt1ESn66bTaVCkrEkAABfkSURBVNFRaHl5GUtLS2LkFQAxsorO+PtBYs/lcmWxe3bm7Q+Lvg6MRiPa29thsVjKzo3U8lqr1aKzsxPd3d2w2+0N7/CVz8lmswgGg1hfX8eDBw/w1Vdf4c6dO/tm20nNeBoOefz4cZw/fx5nzpzBwMAAzGazOHbQjprL5RAKhfDq1St89913uHPnDp48eQK32131Nci0t1gscDqdYgEha4FaWpHYXS4Xtra2sLq6irW1Nbx+/RrRaFSY8y0tLaKj8EGfHSUH0VmeXotFvz8s+jrQarWiBx7NX6cvZktLC4xGIzo7O9He3g6tVnuoMzydpWOxmOime+vWLdy+fRtra2s1ddJVKpUYHR3FpUuXcP78eYyOjsLpdMJms4nBElKLgDrZLi4u4ubNm/j888+xvLwsJt1Ug9p/01gqqsRMpVKiLDkWi8Hn82FzcxOLi4tYWlrCzs6O6AgsbW1dj1lOzjtyopLnn9mfA0Uv/RCVSmXTldaSKUueZblcLmrFaWeSy+XQ6XRoa2tDa2trWVFJrVBWWjabFR1odnd38eTJE9y/f1903vX5fAemqhqNRkxMTGBmZkbU4Q8ODgpRVhMGOSS3trZw584dfP7551hcXDywWKelpQUGgwEWiwV6vR6FQkG0r6bZ9Gtra1hcXMTa2hp2dnYQDAZFzTyNttJoNGLghdSRt9+uLY3x0+LDU28Ohpto1IDT6cTo6CgmJycxMDAgzvDSLq12u12Y/rXuWOR9psqx3d1drK+vw+VyIRAIwOVy4cWLF1hYWKipFt5gMKC7uxszMzO4dOkSzp49i76+PtFXfi/Lg3b5QCCAR48e4euvv8bLly8P/P9NYTqLxQKz2QyZTIZIJCIslN3dXayurmJlZQWbm5vwer1lbbGpyYY0EUehUIgOObVuMIVCAUqlEkajse424M1IXU00NBrNkbVx/i1Du3mxWMTw8DCuXLmCy5cvY3x8HCaTSSSG0I5WKpVgNptFwUwt9efU/cXv9yMQCGB3dxcvX77Eo0ePsLi4CL/fX1drMofDgdnZWVy7dg1nz57FwMBAWeON/aBdfn19HXfv3sXCwkJNC7xKpYLRaITVaoVGo0EikcDm5iai0Si2trawvLyM7e1tBAKBqq3GqoXkKDGnFtG3tLSIdmZqtVpkOLLo92df0et0Ovzrv/4rZmdnxZy2ZnCSyGQy4S23WCwYHBzE4OAgHA6HMD9TqZQo8czlclAoFCLlFPjJkSY9M1MlGbWVevToEe7fv4/l5WUEAgHE43GEw+G6Gl7QIIorV67gwoULGB8fR0dHx4EFMwSl1e7u7uLp06ci/l7L6+p0OlitVhiNRpRKJXg8HkQiEezs7GB7exterxfxePzA40ihUIBMJkMmkxHHpr3aZUmhEJ9arRZef26kcTD7it5iseDKlSt47733fq37+U0gjVUDP37B1Wq1EDTFpU0mE7LZrDhXSp9HoSxpbNrn82FlZUWUoS4sLGBxcbGq6S59frWFViaTwel0Ynp6GteuXcPly5cxPDwswmW1Rg2oHn5xcRH37t07sIcevX+tVgubzSb8BNFoFPF4HLu7u2K4R63HQRI5vc9aPfDkSyFvP/XjY/bnwJ2eqQ6F6WicMg1fIDNWLpcL73I0GoXb7cbS0hK+//57fPvtt1hfX9/3qLSfE0utVmNgYADvvvsuPvroI8zMzKC9vb3uTjvUdGN7exs//PADnjx5gng8fuD71mg0sFqtwmlJnWypZ30kEql7Tp90ka3VmqRpwTQ/kAVfGxyyOyQkfJlMVtb9hRJcPB4PlpaW8OTJEzx//hxbW1sIh8MNH5OMRiNOnTqFa9eu4dKlSxgZGYHdbq+7Ywzlvvt8Pjx+/BiPHj0SXWz3e69k0jscDjHTLhKJiEYWjQieaOQzUSqVsFqtIj+Aa+kPhgdY1kAtX6RisYhMJiMGLwaDQWxvb2NpaQkLCwtYXl7+2Vm5nvJXqsc/c+YMrl+/jgsXLqC/vx86na7uHY7Mab/fj2fPnuHu3bt4+fLlvmIlk54ETxV0NIrK6/WK4RO/FhQydTqd6OzsPLA3P/MjPMDyiKDsMJ/Ph+3tbaytreH58+d4+fIlXC5X1QmutQreYDCgp6cH7733Hq5fv47Z2VkxSLIRwdPi9OzZM3z99dd49OgRfD5f1cdTHF0qeKvVCpVKhWQyCZ/PB5/P96sLHoBw4DkcjrLaAWZ/2Lw/AshZl0ql4Pf7sbq6Khx1brd7z5HNtUDOuvfeew/nzp3D6OioMOfr/YJTPbzf78eLFy9w69YtfP3113C5XOI8Tdek9Fo6M9MZnibMkuA9Ho/oRvtrWoR6vR6tra2w2+2wWq3Q6/Xsua8RFv0RQSE5KiahzLNGEprkcjkcDgdGRkYwNTWF06dPi8ETNM6qHsHT+Z1608/Pz+Pbb7/FgwcPsLW1hXQ6LRyAlDCj0WhgMBhgNBrFDHmtVisad/j9fni93jcieOBH30Z/fz+OHTuGtrY2FnwdsOiPCDqfU103zWerFXKSWSwWdHV1YXx8HGfOnMHJkyfR399f94Qb4Kf+c7Qrb25u4vnz53jw4AGePXsmGllS7jud200mEywWi/ihDr00nJLmx0ej0TcieODHyFJfX58QPXvua4dFfwTIZDKRm69Wq4WAKC9/v/CbtBZ9dHQUExMTmJiYwODgILq6utDa2iqSfvZqLknQ61BOejKZRDQahcvlwtLSEn744Qc8f/4cOzs7iMViKBaLUCqVMBgMMBgMsNlsaG9vFzUENN2GxE6hR4/H80YnxlKmaFtbGzo6Ovbt6cf8HP6kjgA6/+p0OtjtdjgcDtjtdkQiEZFbT/nltEAolUqYzWY4nU4MDAxgdHQUY2NjGBoaQnd3N6xWK7RaraiEq6yPlzaloBJWei3qSLO1tYWVlRWsrKxgfX0d29vb4sghk8mE0Ds7O9HT04Ouri7hqNNoNCgWi4hEItja2kIkEoHL5YLb7T60065a7/t6UKvVsNlssNvtIvWWHXi1w6I/Akj01GRjcHBQxKvdbrdopQVAjHuy2WziTDo+Po6RkRG0t7fDbDZDo9GInZ2oFHg2mxUTXqLRKMLhsPgJhULweDzY2toSBTyRSATFYlFUxdntdnR3d2NgYADDw8Po7e1Fe3u7aHNFCTfJZFIUA7lcrkMJXqVSQavVipwCeg/1WgwmkwkdHR1wOBws+AZg0R8B5PzS6XRobW3F0NCQcODp9XpRN075+bS7Hzt2DMPDw2Jnp9JSqRkvHQZBAqepraFQSExupfRXj8eDYDAo+sVRY4mWlhao1WpYrVZ0d3djfHwcExMTGBoaEosNWRalUgnJZBKJRALRaFTk0ofD4YYET+/b4XCgvb0dBoMB6XQaHo8HHo9H+EJqQalUwmazoaOjQ/T0Y+qDRX9E0JneYrGgs7NT5OIbjUb4/X5kMhkolUrhqBsaGkJ/fz86OzvLBFfZlJLETsk+r169wvLyMra2tuD3+xGLxRCLxUT9euXOqVQqoVKphONrfHwck5OTGBsbQ09PD1pbW0X+urSlNxUUbW9vY2VlBYFAoKFIhEqlQnt7OyYmJnD8+HHRYjsUCuHly5eYn5/H1tZWzaKnBpw2mw16vZ4deA3Aoj8iyMSn+vJMJoN8Pg+FQgGTyYRUKgW5XA6LxYKOjg7Y7fayEdTSODllzKVSKXE2p8w+6inn8Xj2rMYjvwHNh7fb7RgdHcXs7CxOnTqF0dFRseNSgg8tNlQsRDvx6uoqdnZ29u2esxfUJPTMmTO4cOECJiYmYLfbUSqV4Pf7odPpEIvFhGVSyxw7qu5js75xWPRHCJn5tLNS6IuaPNKXOp1OIxaLQaPRAPjxbEsFI2TqxuNx+Hw+rK6uillxq6ur4qhwUJtruheaD3/58mWcOnUKAwMDwklYrb0UlRWHw2HhAAyHwzUJUopSqYTT6cS5c+fw0UcfYXJyUpzBC4UCDAaDsCRWV1dFzf1B0GJGfo39Ztkz1WHRHxKp95lKQqUedqlHnZpDhkIhBAIBMWmVEl9aWlqQz+cRi8XgcrmwurqKpaUlrK2twe12izDbQVDcvb29HadPn8Z7772HM2fOoK+vD2azWZjy0p1S+h5SqRRcLhcWFhaws7NTVzMPwmw2Y2pqCpcuXcL09DS6urpEvJ8+J4fDgY6ODtFtqJ5OttSdh6oa2cyvHRb9IZHGxukMTjHyWCyGUCgkklnoS0ohO71eD71eD51OB41GIyr1YrGYcM5JS1VrEQTNz3M6nZidncX777+Pc+fOiWy+/eL9dJ4PBoPY2NgQlkW9qNVqdHZ2Ynp6Wgz7oEWNXpcSgcxmMwwGg6iHryWhiT4jii7k83l26NUBi/4QSAVPfd1SqZQ4p3q9Xuzu7mJ7e1uUnVLpLfBTTb60p540sYaccrXutCT49vZ2TE1N4f333xeTcaUTZ/Y6C1MX293dXSwvL8PlcjXkvNPr9ejq6sLAwIAoDNproSFfyF5DLispFosijBgKhRAOh0W7cW6TVRss+kNAHvZKwVMojXLwXS4XfD4f4vG4SNSROu4qBUFhNulxYb9dns7varUabW1t4gw/PT2N7u7ussmxe+3wVBocCoWwtraG5eVlMQyzHuRyueiOS/0CpYlEAMTnlUgkEI/H60rlzWazImwZDAbh8XjEgI16ewo0Kyz6BpEOYaQkk0QiIXYgv98Pn8+HYDCIcDiMeDyOVCqFfD4vRF2JVPxScVY7e9O/0/ldp9PB4XDg2LFjOHv2LKampoTgpWa1dHqO9BxPJvPu7i5WVlaws7NTV68+gs7WuVwO0WgU0WhUNFelBYAWF7fbjd3dXcRisZrrFChpiBZXKutta2tj0dcIi75BKs36dDotYttUcEPOOwrfVe7glVCbLRIlmatSJ5V0uAb17qPEl4GBAZw8eVLMhpcOcpS+ntTRSP37EokEXC4XlpeXsbGxcajuPul0Gm63G4uLi9Dr9Uin07BarVAqlSgWi6KX3suXL7G6uiryGGppeU0dieLxuDDzpXP1mINh0TeIdPpqLpcTP7SDS9sz73XWrOyYS38m4dNZl8JUdP6nOncqfaXCk56eHvT19YldjxxzpVJJXFO6u1NkIZlMwu/3Y2VlBa9evYLH4xEDPerp7kPviUJxLS0tiMViIutPp9OhWCwiHA5ja2sLi4uLWF5eRigUqiv0Rgsp/Ug/d+ZgWPSHgIQvFbpKpYJerxd98EOhEKLRqHDgVZ5vCTqXU6NNmvpCo7R0Oh20Wq3w9uv1ehgMBpELYLVaYbVaYTabIZfLkUqlUCgUfrboSP0QmUwGiUQC4XAYXq8XGxsb2NraQjweF4sOPbZWQVIzUBpsQT0CbTabOOPTIuN2u+H3+8Wxp9bXIKtJ6hthaodFf0hIrJSNZzQay7LbcrlcWQ93mUxWVnVH/yatvtNoNGLhIDFT80ez2SyEr9VqRcsoCnlls1kEAgFxlJBaIvl8XvydRE++iHA4LIZv0MhtshJkMllNfeiBn/rvURSCinXoPmmiDb02RSfqPUpIF1umPlj0h0DaApv+TqY3CZFMdPoJBoOIx+NCePRlp+uo1WrRTKOtrU3Ut1PJK41uomsD5Z1xEokEYrGY8HDHYjFx/iWRSR2JlWWuVDNApbVSahU+XYucdhTZqCwkIqujEd8BHVnouMMpubXDom8QEgud3aVmOU1bIfFLJ7Ds7OwgEAggFosJ4dP1Kk16OrPTDm8ymUSPd+n4J3JoUQsrr9cLv98vvNzJZLKsJz/t3tJpsXR/tJBQoQ5VBxYKhbJU4nqoHF9Vr59gLzQaDWw2W1kNA3Mw/EkdAtrpgZ/aMSsUChSLRTF1RbrLk5DUanWZ8OlcKj3TAz/t4BQZoLnvFNcmrztl/VGTC2pWmUwmyyIHUijvPpVKQaVSQaPRIJvNlg3MIEtkLz9EoxxW8LSr6/V6dHZ2wmq1sujrgD+pBpGew4GfzE3aSaXecjpb04/UtCZTn2bdU5JMPB4XcW2quCPTnnbeVCqFaDQqauq9Xi+CwSBisVjZ0I1qIiPvPBX5SHMOpLF2Ms/3yi14E9CwSqfTia6uLpjNZhZ9HfAndQhI+NUSacgKIJPdYDDAbDbDYrGI9FG1Wo10Oi12exK4NPZPSSjkvJM6w+j31DGHpuhKq88OqsaT5g7Q69JiQ97xes7yvzQymQwOhwMnT57E+Pg42traeDx1nbDojwjaLSvN4UrhSZ1ZJCjaYaXCkslkSCQSZY5BacNN2plTqZRICiJPeC2Cl943/dDuX/m73xIWiwWnTp3C5cuXMTExIUKUTO2w6A9JZe862jUpXFctS4886WQ6085cGXemc3cymRRWAxXpACir7JOGBhsRqzRL77eKRqPByZMncePGDZw9exZOp5ObaTQAi/4QVJ7XpeIj8zuZTCIcDovxT+RVpzAaCZ7O9VLR0Tmfzt65XE5YCtIx1pWLRWVu/duAQqHAzMwM/vCHP+DChQvo7u4W5chMfbDoG0S6w5PDK5PJiOwy2uWpUy2JngZcxuPxst2eLIRK4ZOzUJqXL62Jl7bGIvP8t2iWHwaNRoOpqSn88z//Mz788EP09/eLhhxM/bDoDwHt8pRdJv2RJstQ4Q3FqqWxfGnGG/2+MiefzvGU5isNBUo9/OSIozAdOQjfREupysWp8jOrtQOQwWDA5OQkPvnkE3z44YcYHh5mwR8SFn2DSCvUaLeWxsMpbEcdYoxGI4Afu8MajUbY7XZEo9GyMz5dg8x6EjYtEFqtFhqNRvzQGZ9EL118UqmUaGFNHXMbKZWtF+oIZLFYYDAYxCguACIzLx6Pi0jDXiW1KpUKbW1tOHPmDK5du4bLly9jYGCAJ9MeASz6I4JECuBnRTOUR0/mPzn3pD9S0QM/jYimtFz6IeGr1Wqxk0qTeOLxuOiLTz+RSATRaFS8NgnvMNN0CVqM1Go1tFot7HY7nE4nnE4nTCaTaIRJ4UVKDY5EIkgkElXDizKZDFarVXT/eeedd9DT08M7/BHBom8Qaedbmgknrbqj837l3yvDdPQj9b5Li29oKgzt9pQ2S559SujJ5/NC9OFwGH6/H8FgsKyJBy060WgUPp8PgUBA7LbVwo3S9ypNRCKocQdNx7HZbKLZJfXTB1Dm2yCfBqUHS3MUaOHUarXo7u7GqVOncOLECXR0dLCX/giRHXDWe3u8QUeMtIlGZbqqNPxV+efKGP5BDjw6y0t/pE48urY0uYZ28lAoBJfLBZfLhVAoJM76ZPZTyS+JnhYfcjBS8Q2l6VKhD4lfq9Wio6MDExMTGBkZEW2rqNEnZfuRUzMWi4mptzSog7L96Hp2ux2dnZ3o6upCZ2cnbDYbe+kbp+qHxqI/JLQjVn6OtSbG7Ee1TL9q7bToWtJFhByJwWAQgUBAZAEGAgEEAgEkEomyvH9axKR+ASp5pbJhquc3m81obW2FzWZDa2srurq60NHRAbPZXLX5Jlk5lF4cCoVExxtacORyOdRqtagupCGaXEF3KFj0zQSJWDrokpp1ejwepFKpMmtDeuSQRhKUSqXwLUhn0vX09MDhcECv14sjBwl+L6TWSLU+A9Jx31KLgmkYFn2zQgsAmdjUlZdMb2nGYDqdRrFYFKO3jUajKOclByVN75F22W30vqTwjn7ksOibncrGFdI8A/Lq02iplpYWEXmQhgelGYK8E//mYdEz1ZFGFsg3IM3045FR/7Cw6BmmyagqerbPGKbJYNEzTJPBomeYJoNFzzBNBoueYZoMFj3DNBkseoZpMlj0DNNksOgZpslg0TNMk8GiZ5gmg0XPME0Gi55hmgwWPcM0GSx6hmkyWPQM02Sw6BmmyWDRM0yTwaJnmCaDRc8wTQaLnmGaDBY9wzQZLHqGaTJY9AzTZLDoGabJYNEzTJPBomeYJoNFzzBNBoueYZoMFj3DNBkseoZpMlj0DNNksOgZpslg0TNMk8GiZ5gmg0XPME0Gi55hmgwWPcM0GSx6hmkyWPQM02Sw6BmmyWDRM0yTwaJnmCaDRc8wTQaLnmGaDBY9wzQZLHqGaTJY9AzTZLDoGabJYNEzTJPBomeYJoNFzzBNBoueYZoMFj3DNBmKA34v+1XugmGYXw3e6RmmyWDRM0yTwaJnmCaDRc8wTQaLnmGaDBY9wzQZ/x8+o/cbb1GMHAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 72\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -2017,10 +449,10 @@ " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -2032,28 +464,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(10*np.log10(evaluation_history),'o-')\n", + "plt.plot(10 * np.log10(evaluation_history), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Average Insertion Loss (dB)\")\n", "plt.show()" ] }, @@ -2066,30 +485,23 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -2102,70 +514,37 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Transmission (%)')\n", - "plt.ylim(98,100)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Transmission (%)\")\n", + "plt.ylim(98, 100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Insertion Loss (dB)')\n", - "plt.ylim(-0.1,0)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Insertion Loss (dB)\")\n", + "plt.ylim(-0.1, 0)\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb index f67d5f19a..ddf535c4b 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/04-Splitter-checkpoint.ipynb @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -33,6 +25,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "import nlopt\n", + "\n", "seed = 240\n", "np.random.seed(seed)\n", "mp.quiet(quietval=True)\n", @@ -49,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -63,13 +56,13 @@ "\n", "minimum_length = 0.09\n", "eta_e = 0.55\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "eta_i = 0.5\n", - "eta_d = 1-eta_e\n", - "design_region_resolution = int(5*resolution)\n", + "eta_d = 1 - eta_e\n", + "design_region_resolution = int(5 * resolution)\n", "\n", - "frequencies = 1/np.linspace(1.5,1.6,10)\n", - "#print(1/frequencies)" + "frequencies = 1 / np.linspace(1.5, 1.6, 10)\n", + "# print(1/frequencies)" ] }, { @@ -81,29 +74,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.56\n", + "fcen = 1 / 1.56\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,2,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]" + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, 2, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]" ] }, { @@ -115,15 +112,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))" + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")" ] }, { @@ -135,17 +138,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def mapping(x,eta,beta): \n", + "def mapping(x, eta, beta):\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()" ] @@ -159,26 +168,32 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define spatial arrays used to generate bit masks\n", - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", "# Define the core mask\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g + arm_separation/2) <= waveguide_width/2)\n", - "bottom_right_wg_mask = (X_g == design_region_width/2) & (np.abs(Y_g - arm_separation/2) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_right_wg_mask = (X_g == design_region_width / 2) & (\n", + " np.abs(Y_g + arm_separation / 2) <= waveguide_width / 2\n", + ")\n", + "bottom_right_wg_mask = (X_g == design_region_width / 2) & (\n", + " np.abs(Y_g - arm_separation / 2) <= waveguide_width / 2\n", + ")\n", "Si_mask = left_wg_mask | top_right_wg_mask | bottom_right_wg_mask\n", "\n", "# Define the cladding mask\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False" ] @@ -192,25 +207,46 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # left waveguide\n", - " mp.Block(center=mp.Vector3(x=Sx/4, y=arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # top right waveguide\n", - " mp.Block(center=mp.Vector3(x=Sx/4, y=-arm_separation/2), material=Si, size=mp.Vector3(Sx/2+1, waveguide_width, 0)), # bottom right waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e2=mp.Vector3(y=-1))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # left waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(x=Sx / 4, y=arm_separation / 2),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # top right waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(x=Sx / 4, y=-arm_separation / 2),\n", + " material=Si,\n", + " size=mp.Vector3(Sx / 2 + 1, waveguide_width, 0),\n", + " ), # bottom right waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e2=mp.Vector3(y=-1),\n", + " ),\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " symmetries=[mp.Mirror(direction=mp.Y)],\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " symmetries=[mp.Mirror(direction=mp.Y)],\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -222,26 +258,53 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=1.5)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", - "TE_bottom = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,-arm_separation/2,0),size=mp.Vector3(y=arm_separation)),mode)\n", - "ob_list = [TE0,TE_top,TE_bottom]\n", + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=1.5),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(\n", + " Sx / 2 - pml_size - 2 * waveguide_length / 3, arm_separation / 2, 0\n", + " ),\n", + " size=mp.Vector3(y=arm_separation),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_bottom = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(\n", + " Sx / 2 - pml_size - 2 * waveguide_length / 3, -arm_separation / 2, 0\n", + " ),\n", + " size=mp.Vector3(y=arm_separation),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top, TE_bottom]\n", + "\n", "\n", - "def J(source,top,bottom):\n", - " power = npa.abs(top/source) ** 2 + npa.abs(bottom/source) ** 2\n", + "def J(source, top, bottom):\n", + " power = npa.abs(top / source) ** 2 + npa.abs(bottom / source) ** 2\n", " return npa.mean(power)\n", "\n", + "\n", "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -255,25 +318,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", - "x0 = mapping(np.random.rand(Nx*Ny,),eta_i,128)\n", + "x0 = mapping(\n", + " np.random.rand(\n", + " Nx * Ny,\n", + " ),\n", + " eta_i,\n", + " 128,\n", + ")\n", "opt.update_design([x0])\n", "opt.plot2D(True)\n", "plt.show()" @@ -288,33 +344,43 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(v, gradient, cur_beta):\n", - " print(\"Current iteration: {}\".format(cur_iter[0]+1))\n", - " \n", - " f0, dJ_du = opt([mapping(v,eta_i,cur_beta)])\n", - " \n", + " print(\"Current iteration: {}\".format(cur_iter[0] + 1))\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta_i, cur_beta)])\n", + "\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", - " plt.savefig('media/splitter_{:03d}.png'.format(cur_iter[0]),dpi=300)\n", + " ax.axis(\"off\")\n", + " plt.savefig(\"media/splitter_{:03d}.png\".format(cur_iter[0]), dpi=300)\n", " plt.show()\n", - " \n", + "\n", " if gradient.size > 0:\n", - " gradient[:] = tensor_jacobian_product(mapping,0)(v,eta_i,cur_beta,np.sum(dJ_du,axis=1))\n", - " \n", + " gradient[:] = tensor_jacobian_product(mapping, 0)(\n", + " v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n", + " )\n", + "\n", " evaluation_history.append(np.max(np.real(f0)))\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1\n", - " \n", + "\n", " return np.real(f0)" ] }, @@ -327,1393 +393,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 4\n", - "Current iteration: 1\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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QesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMIbQA8YQesAYQg8YQ+gBYwg9YAyhB4wh9IAxhB4whtADxhB6wBhCDxhD6AFjCD1gDKEHjCH0gDGEHjCG0APGEHrAGEIPGEPoAWMIPWAMoQeMIfSAMYQeMCba8udBJ+8CQGdo6QFjCD1gDKEHjCH0gDGEHjCG0APG/B0HFjsW0aWLiAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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DxNTv2SCQSXrVZLRIhxYgycwM9ikYzTDpG6COrn6/X9ntx8fHFZmRTsv58lyPHeRbLpc1MmaERMiNf4ME5Iq6OIfJy2p6ltGGfWqyKOnVj8CONlXHNY6+i+xs5+Pe+DvfM4fzIrZj+zyoWcK3h0nfAiAIS/rZbLZFelbt4cQDAVerVUWo1WpVC73hhV2v1zV1W0nPxOLzMrucSZGRXuvwYR/AdjOr6kx4XaknqxjE0pxV8WzQU78DaybqgLRk//ow6RvALxqkKRM/4j05UAiS8+URrsPAgM/lcrnl2MsIfB/Sq32ubbMZgeM5mWi5XG45HNVeV8Jrhpza4Oy1Z0Krbc73kSUflQANB74NPDv1ieBYawJ3MOnvAaj4cObhpeUyV1yjDvY1XnC2uSPqTjoOw3HYLHNysUONQ27qF1DS4xrw8mOFWV5pNuKO9JmUV5WeQ2nqd1Dvvg5ypQ3QKAc/H4BNMAyuSnATvo5G0nf9YanqudlsKuKD2CC9LtagjrHMScZZaGhfX2KYA0hcwXcmCV70rH0NZbHqH5FPvtGEGXbSqUOvNCnm2yA8RyGyxCHcD9plU8oow5K+AeyA4+qvnIqrYa+sDd5Y6jJZoKbipVZHnob61J5lkqgDTkN8aB9ORvST71kTavi6fP0mAutx/Ez0+ShUspfmBGAgZkkfsb3evXGHRtJ3fdLC3t5eNZWT16TLYuzqKeeXlskNu15DVyAaZ8Wpba1ah84HUKiHm/ezWZBl4bHNrQkvnNSjU4g1nRdmB0jJAxN+z4jP5Nb7xHnZffJgyH0w7mBJX8B6vY7ZbBbT6TTevXsX19fXNfLDYcV2dWaPg/RsB7NtzOp6RL2e3q7EGtYy2PkXUS+EgbZwLhMW5gqr9lkKL8Dhy9vb2yp/gQc9OAe5r+yoVG99ydHG/dckIh60cCwGM82KZAelsYP0f/7znz9UPz4K8MsOGxr535zzzYQFudS+hSMv4j2RdIFLdoBpzB12KqCSFi8xT7Lh+fZQbZW0bJbwNGCQqCkElknakhRlE4WlLwYK1j5KyTU8kDLZtV4BL4vd7/fj6OgoHj9+HI8fP47z8/MYDoe1Qp7GDtL/4Q9/+E5VfJZG32f76tQC6XF+NmuNX2Ycw2vMgUws6bFIBrb5fL6lLQBso2YqME8AYv8Csvn03tAWnI7cT9QEgOnR6/VqNrGaECXVHsfd3NxEr9fbSjfOQnWaPMTXVJOBBzheJwAZksfHx/HJJ5/Ez372s/j000/j4cOH1SxHdnp2HY2k//3vf/+h+vHRAaEsLlIJkihB8YLqIpIcsuM17VnSK5FVmq5Wq+j1elv2PgYZltYYYHR1F/QBbSO8yFmDEXeJQhz64zZY6kZEpd5joIAUhnqv0hzPFedq3B7ORh1sVdIjX2I8HsfJyUmcn5/HgwcP4pNPPomf/OQn8Ytf/CI+/fTTOD8/T2c4dh2NpP/jH//4ofrx0WF/fz+Gw2FMJpM4OzuLk5OTWk49pA7bnbxaDRMxS7Dh2DxIqlEBvKSr1apWGUeLcEBa8wDDti5rDZxTgHM51IXz1bnHpFcfAE/t7ff7tVRh9UNE1B2fHN5Tac/nsR3PE5zOzs7iRz/6UTx9+rT6fPr0aZyennqFmwIaSf/8+fMP1Y+PEqPRKCaTSUyn00p9PDo6qojP6i4IASnE8+ghPXmWHMf8I6KmurKNrQNLRGzN6+diHZD03Cf4H0BMJX3EnXTNCA9g0MD1cW/QOG5ubuLg4KBKIFLzR6MMHMfn2D/6w9cFcA/435yfn8fjx48r0j958qSaF2F1Poe99w1AgcXNZlMVa5jNZtV66HjxAZCCa+MBWf47JDUfB20B0pfj91C7MbBwqS0QPZPUsK85oxADF0iPvrGGopEAvjabBfDYDwaDuLm5qZKWcD6n/OrcAk3sYWcoX5+f2/7+fmXLn52dxdnZWTx48CAePHgQk8kkhsNh9fy6jNKAZ9I3AOoyyI8XcrFYpKSPiJqk1nAVt3twcFCRGGqoFsPgeDpLy6y+Huf5q0edPfN8HvsdoGbf3NxsxcC5HWxc+w/+D15mm3MJcKyaIhF3g2E2O08n6MC8gemFoiXQwLRwiePzOZyc0wC2xxeLRUTcrZDKBTL4eCZn5gyDtAXRUdgy4s4jD9JjH8B2ORfRZNJjoEBfcV0MMGwWgKg4Tkt7oQ1Vtbl2P/p5cHBQ+S1AemgGGAhYu2B/B5s+nMPPxTc4BTgiaiaKRiJK2ZHGezj3vgHsacZ0WJZQTcTGb6r+80SXXq8Xo9GolrjC0j7rB0tOjhbAJOC0V55kw6SHNNQYNif6oA0NdbHvQh2I6DtUe3YccsFQXB+DFDQMnmOQzeZjB6ESvakSsFGH1fsd4Bcfaigkp0pFoET4iDvJDrVeHV7sBVebVj38fCxL+oj6pBl2CLIKzup5NklI75/vhbUS1jCY9DADuMSYRj+4rzp7bz6fx2AwiPl8XiM0QoUlopvwzTDpW4BffKj3LIkz8IuHl5vDaExKDWuptMU+PSarfAPJqUUy4Mjj/RolyAgfcadh8OCkcXTdj4GEzQlemw+aCdrHM2UTY39/PxaLRS3Eh2NLZDfhd8Okb4mmlFFG9tJxoQdoCZpai99US4BkZ3NCk1b4b5bqmRNQs+ciYutvve/S/XF7uC4frwMPayYc3eBkICY37H1dIkzNJx4Ujd3wkzKMjsGSviUyydkkXUrqPVTszMOsZgT2cXw/YrusNTvsWCNRzQQqOu/HvtKElKyP/F0nBnH7uGdNvuE4PZtMWpKLQ3haAUifVdNkIaMOk74FVJXMVpLhY/kc3Z+tbMuqLZNYCczTUDl1lavGYJDgDTF4DFbZOUxY7TObBqV+6nV5sNJBkqMhEXf5D5kjLyvJBVNCB6vSwGXUYdLvAJOdp6Oy91mPV+cW/8apsLoiDWeoASrl0L7Wicd+ZORldfIzGx4hO/YzqKdeBzAedHjA4qQakJ0jCCA7koI465Dj8hqiy0J22raS3yjDyTkNYAJzXDxLn8XxagZgP8CzxDgjLiJq0hvQqjs4tte7W8OOs/d4Rh9LTdYSuG+IQujgwvdTIjwIiL7rIh7oJ9JzkbjDST080GlxTR60eCUc9IfrGmQDQNff3xKcnLMDmq+OeLMSFmCPdUR9jj6AeDkSTDiHnVNPNTedSY856xiUuCQVq8dQkZmcDPzN12iS9Jw9h78hwdku5zkDmobL2o2eywTXMmHQgHAuCpPwenfZPRh1WL1vAKQnJnfw3HrNvdfYOaeDsnTkl5GlfcQdodheZScY1GkQlX0KPOFGF4IEidhph/tDcg2kaFZnDuDsPgwkOBepykx6zQ1AuI4JzwNdVgAU/WT1HSHM4XBYLWWN5ax1QO6ytC/du0nfgNFoFCcnJ3F6ehrHx8cxmUyq2msqsWAf69TaiLoKy0s6QYtg3wDIx8Qv1dFj6csTbqDeM+kxmHAkALPj0EcmrKbe8n1wX7FP6+CXptay7wF9KM2uA9Q8QrbiwcFBXFxcxNu3b+P4+LiWkqwTe4w7NJL+pz/96Yfqx0eHvb29GA6HcXJyUhXRyNJI2TmmRTS4DNWuIho4TiUyS3oMFpB0rEHAmcekx7rubBow6dE+HGqsGmfqMY5BX/GJ63HOfFZEA5/q7WdfBl9bnaicSozBB5WAUGQk4r0GdXR0ZMIX0Ej6X/3qVx+qHx8NWAqhXNbx8XEl3bNZXJBeWi5La881lcsCGdgWjoiKiExINhXwnctlYfovLzfFHm8lvZoXGfH5b15sgyfJ8BJXLL1xPn+ynZ6ZP1nmITQpLUy62WxisVjEdDqt8vRHo1FlWnQVpXtvJP1vfvOb76QzHztYCkXk68txjBovKBxzWcFJEEKngHJhTL622vWahMN59jwXAETMpC5yDBi3t7dbGknmCGOTgPvCYTVIe6j3mW1euif1d5TSjWE2sQ9gsVjEu3fv4u9//3u8efMmrq6u4uLiIh48eFBNge6iR/+Xv/xlur+R9L/+9a+/k858rGDbFdILDqJs6ieXwNaadVkJbJ5HjuvM5/OK4AzNsGNyRNx5zeFshHrP0lc92vid71cHmYyArBWgb+q8A9lhTmQhwGzA5EFAwRNvIOnRV5hLkPDQrL744ot48eJF/OlPf4qzs7NK4uP6XcLXIv3Pf/7z76Qz/whYr9cxn8/j3bt3cXFxUVvsAuWr4fRi1b5UZZansbKUgrRUpxkPBDooMIlAevgCNMzHvgAARELYjdvUwQdgSY92s8UsOeymBOdBgze0r+D7hx8DM+w4IYmxv78fX3zxRa2WYVdJ/7vf/S7db+99AbAL8eIdHBzE9fX1Vv662vNs00NC8cu+Wq1iOBzWHHqcEaczyCLqNerwN6uqcO4BrAXgd1Wx2RcAInHbEXlyDtvimlTD6bRon7WTEtk1JIdr4xN9ZmmfPQ8NN0bE1qBgODmnEZtNvboNpLIm5XAoiifUaMFL2N4cs2avcxZb1heb1WLsj4iaX4D7xETh37gdaCCac8BpullWnjoUeTDk/kZE7Xqq7quTj7/zfbB5kj0XjfkbOSzpG4CXXqu0aCgtImovs7ahGw8OID0Tj8nLkQJ+ufl4JinOibgLBeJ3bl8HmFLKLZMKAwv3Mbs/vVc2PXBORl4G3yPfJ5Nf+8b/F6MM5943IPMgc8wdOe14QVWa4wVlCcjtcqIJBgwlvaqyHG9XEvJ5UPkh5bmffG3+5EEj0zYi3qf/YqBCRR6+Dw7rMVgTyZx2AM5XP4YOcjhWk40yh6VRhyV9S7ATCzHw2WwW6/U6BoNBLY7MFWJB+iyEBfKpNNPwIGLTANRXluScwML9ZQ86XxfXRtkqzgwEmbJyXGy+qKbAAxAPkLgH/J2FI3Fd3jJ7PIsqsJlgsu+GSd8AlSqQ8LPZrMr3vr29rS12yeonq6jsrWepzrFzJq6SngcEzK7LSKzqLzvTMk2Di1tmzkA45rA+HTZe6Qar2ywWixgMBmntera30S9ENXgAaGPv8++MpkiAcQeTfgeYPLzy7HQ6jaurq0rSqwML+fCs0pamnmLAaCpyyURFtEA1BiZ+xLa3nCMOqpmojc+OMXaOaXUb/kSMnqfEMvnZq6+59jzIZE5C/C8Y8BcY94NJ3wAOeUGiz+fzmE6nMZ1O4/LyMjabTZULrrHsbBFLTouN2J6zr55/qNkqmXlSDNvobNuz+qx+hUxdLzkNlaQ6DZYTl3hxCia9Sn88Ax4I+HjVjEpmAJ5R1/1P94FJvwN4ufDiwpaHpL+9va1Wd+GXFwUjQKiMQKxul6afchYaO+Y4RVglNtvCWTgN18xy29V7rzn+TH4MZNnGJM+KYbDqj+elGY+cy8A5+hr6g/POkr8dTPoW4Bg9Z+VdX19XdimTAC+sLuqgEpeJp3Y22/Uq6VViswdez21ydKnnX7326LN6x3kA08GgaUBgwme/cUEMZD0i85FnJurgGhG1RTtt0zfDpN8BttGhtuJlBOmRC64vMC/qoGTiHADY79ivITg4BNnxxW2wlsCkB9TfwH3QQUdj9fwMdFOpyxqB+gPUPFCVniftYFDlT62Og2P7/X7MZrO0f0YOk74F+GXmueNwXEF95zRUXtlWPeuamAPyqvoN8qlzD7+DpPAd6OowQMkRxu1nST7Zc9BPJpgOBJmDTh11TH4QHyYUIiTQqrg67nw+j6urq5hOp5V0z9JwjW2Y9C2gHnz2xHOtOOxn1V7j4Ex4Xt1V1XTsZwJzSE2df5mkV/ucPyPq2XTZOU15603kyjSCklbA+0rE50lO2DB99u3bt1UExMk57WDSt4SG49hBFXE3mQbxbMyZZzubw22YlMPSiVVtDqtF5OvHqfNPHXlNpEWb3Ha2fVvPrKR+sxbCJoBW/8H0Znakvn37No6OjmrOTY31G9sw6e8BTXgB0eHdRygNqapZNhsIiim4mqxTArejKbR8rSz8ptAU2xL5s2PxHErtNT03PpfbUPOAnYCaDwDVfjqdxtnZWYzH462kIvb2G9sw6e8BVZd5EEAsXsNfGhJDFttisYjhcLhVS07JUPK2syOwpN5D0pfy6PnvjOwZ8blvWZu7/AH6tw4kbBKU8gMWi0VcX1/H8fFxjFWuKKMAAAmoSURBVEajrRVwcYyRw6Rvicz+jdiuEIv9fAyfA8k8GAxqpanUw86hMbTRlFyjGoWaCSWy8/cS6fmYXdKzaaBoi+w58ACwXC7j8PCwKk/GAx9f9+rqamvij2HSt4La2Jn6rDaqTu/kAQN2vxaf0I1JjjZgIqgjLbPHlfh8bHaP3I5+z5Cp+Ur6NuRv0g7U4Ye0ZyQ+wXdycHBQrU8wmUzi9PQ0Li8v0wG562q/Sd8SrJ6zJx4veilurej1elWcGTa9+gmY9CztuR+I28PTD9Oi6YVuInvT9wyZOcLnIvmoTT92mRE8D0Fn+eF/MhgM4vj4OM7OzuLRo0fx+vXruL6+7jzBM5j0O8BSE4TnEtcgP0JFmvLKpAVRIa1VfS1lt3Fef9Y3/c7Y5cFn25qJiu+6rxSj5z6UogCZpL3PAMH3ojMTUev+/Pw8fvzjH8d8Pq98Ld80CvFDg0nfAuyQ41p4GADwEnNFWlZHVfXnZB5Na82y2HQgKcXBb29va0k/aoooAUpSOiNrloBTIn2TT0G1iWyg4eMyzz+A/wfU/PF4HOfn57UoCZ6BJf4dTPoWYFucq96ORqOt5aY2m01trntGDiYO/40BAs6qbPJKlrPOZbe59t6uEF4b0iv5sgQbPk8djOr4VNJn2krWz8y+j6gnKUXcFTRFJVxjGyZ9C7ADbTAYVC8VSA/VPiKqVWtYtcxi1MjXB/iFVomPySZcanqxWFR90EU24Nxi8uMeAPYTZM7CjIA6MKm/QROGNKKA9jOS33ewgVagswzxP9JFPYw7+Mk0QL32SKoZj8dxeHhYkT7i/bLJ/KLywpBNCSkgPh/Hef481RRkR1rqeDyuknwy4jP51emoajrAKb8amsx8DjxjkJOF+NqaGsyk19yDTCPR2X08X4H9KnyvmQ+kayj5Mkz6FmCJAkmPNe7Yo6xxYgYm5mQvIsJKTCxV4XkuP0v4jPD4G8TX9NxSLgDutWQSqPaBvuI8dnKiH3p9leaaW8BaAfqqTk5oSgjT4bv6E9Avow6TvgUy9R5r1YPM2UvNWWF7e3u11FCV+iw1mfxcmgqkh08BaizIxd+ZfFrtVqMGLBXZHmcCstnBPga2rfF88IyakmfY5NCSXXycpuWiv71eL8bjceWwHAwGlcZR8g0Y72HS7wC/QFAfod7P5/O4vb1N1Wm8xIvForLf2WufOfciolYzrtfrVQTjqbqckJItnqHHgZBMJI0KaAoxEx/QajdcwUfnFDDpuR+sxutcAh0c8IzUoYnrTSaTuL19X4obC5J0XaVvA5O+JVi9h11/dHRUSR2Qje1MlmCLxaIiMSfUZOEvhP4Q08cnzlVzQr30XKm2VE2Hy0+xXc6ExECB8zKbns+DlAfZeV0/9iso8XkQw3XZLlfSbzabGI1G1YA6Ho8rU8uk3w2TvgWYDFCbx+Nx5amHhJvP56kdjRcZS2IpaTLpD7WZQ2IYDNiMUJNCpTWHzgC+FofAlJRcXAN90T5H1E0CJj6HEdUxyIOVOv/U/6Bhyr29vRiPx7FcLmMwGFRpt6UkJqMOk74l2P7EywyVsiTlWdVGaScuvsHkj6iTMUt8QRgQf5f6ucsbnoXA+HwN8bEvIKJefiuiTnomL/LjWcpze6y1sDmCwQDgmnsYBI+OjqrPhw8fVlV4TfjdMOl3IPM2s5caEjILVbE9jd84/Ia590wunnkXsT29FijF2bnfEdtLVClxS/echdiyfAMdZDhDbj6f1zQN7gcTH8+Jk4vUg89+CGQcYiXh+Xxec/IZzTDp7wGW9iAx59JrIQt8n8/n0e/3K2cc/gbhsT49Yv7L5bKy5QElqcb/d73snCSUtcfHRURKelxLj8fvmE+AvmcZeVkiEA+M2KBxZP1Fu1ynULUkowyTviXUycXZX1DR1Tvd5FWHus+Ovr29vVpJZ3j79SVWE0A1g/tASciaABOaJW/2XKClcP/wTEqDhmpPOouxlPegGYEm+f1g0t8DGZnhvYfaqWq+5urP5/Oq5NN8Po/BYFDTBDi8h/CYhtYUpcGhCdmxUNezfXpdHQSa4uPqnORrQ5pjwMC14MNg4mfhU5gTJc3E2IZJ3wKaRML2PCbXcN48Z9EhqUZLZ3NlV92QY68OPw2zsSedw2ptpH4TMZhk6sxTuxybmjWaWYf+ZGm/TGKdM6A+Adj+k8kkJpNJHB8fx+HhYW0JMaMZJn0LMOnhtQfhQUTNpMvIr5NmUOkVmXZa5pmr62hCDNTbUsmtb6L2Zo68Euk5X4DtcY7xR2wvrwXoYAqTSROLeHAYDocxmUziyZMn8ejRo5hMJjEej7eiBEYOk74Bmdeea9XDnleHWml+PCeYQPLzijm8QSNgiY+wFA8onKuvxLqvnd8U7lMHH39yViCkM2cARtzF+PFdPf9qDmm2IUcHQPqHDx/GkydPqqq4XCDTKMOkbwF+uYfDYSVxNLwWsR0SKxXJ4Bx2LfHM2oAu/qiJKlxwg+1/XBt92YU2CT+6sUqvYUrN9ddngT7p+ZzcxNKeVX0kR52cnMTp6Wmcnp7GaDTamk1o5DDpd4DtWgCSqeRYU8mv6n9pICit+lrKec9Ir0k32YCk94fPjORKIpXyHKbMZtPxc1AzhNvT/HvN4tPsRlTKGY/HVZ6/1ft2MOlbAi8ePjNSMTKyaWxdSaATYXhKaTanXI9VcrVV7zPil+z4khbAE2hKHvws21Db0YEju16W/aczCY0y9na8EA6ARrPkbntu9ndTu6Vt1/Ha7n2gTjrdlx2TDRh6bNYfJn12fmngyQYAHWxM/Arpg7CkbwF9kb8O2dseV1LH2x53n2tnaEuY7Lj7kE2TdErtlNrMBoj79qGrsKT/BviuM8G+zcHlu8KHINmua5joRaQPxqQ3jB8uUtI7qGkYHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHYNJbxgdg0lvGB2DSW8YHUN/x+97H6QXhmF8MFjSG0bHYNIbRsdg0htGx2DSG0bHYNIbRsdg0htGx/Dvlxi98LdOCjwAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 16\n", - "Current iteration: 15\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 32\n", - "Current iteration: 27\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 64\n", - "Current iteration: 39\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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wPLfLST4c2cW21XydWXdn3c0fBhX9HASDQeTzebx48QLJZBLBYNDUvbP01c6TB9zNZLezsv3fbufmbrdrPN6yLJfC9/l8jik8NL85/FL2t5NHA+BjfUG/33cdrCmHcXLIR6vVQrPZRKPRMCXHGjtfTFT0cxAIBJDNZrGzs4NcLge/329CcDzD2y2kAdza7Ufl7tvxeLnLywm4skWXPcKZomftf7vdNsKUO7CMAhCa7nJqbqfTcUzKdZuaa4/J1jTYxURFPwd+vx/xeBylUgmJRAIATKadWyXcKGwLgL+7LQ5S8BRju92+JXrZWIO7PCfnNhoNM0uPC4ad4Udrgu/R7XYdgufxQN5kg81FRBcfJyr6OfD5fAiHw0gmkwiFQhgOhwgGg2Nz5eVz3TLXRpn6tpecIuaNIS9b9ACM6GmC1+t11Ot1Ey6jT0Am1NBRSPOdFoUUu50P/5C9/e+DRb62T8FY0euH5USKmJVywA9nYLeyVzfc4uajCk9kZps8R3P3ZQorRc9zuBQ9019brZaZlsuzvcx4k04/ab7zJ51/dACOKjr6FN8Zt8QiZTS600/BqB2bDjCJm2de/pt8jF2mK4Vvh+h4Lm82m8Zx1ul0HOY9RUnznjnvFK8Usp3vzgVGevmlOc/jhR3G09TXz4+xotc4qTtuWW8UjSy8ccMWPMVkt5R289a3Wi1zLufoLHrvQ6GQeb1QKAQADs+9DNfJ95Xi5XXImLwtbP4cVRL7KXC7Do7JcktW8jq608+BjJfLKS/BYBA3Nzcji2bk4sDn82Z7vqVZz1HNdMa1223HeZ4xehlXl448OWrLbXGRP3md8ue43xeRpaUlLC8vI5vNIhqNIhAIqEUiGCv6169fP9Z1LAT2zu33+x0DIPnvPCtLzzbFztlyLK6Rwrd3bzvkJSfJchGgOd7pdNButx2C53maM+EoaBmyc/Oyyy+/zLcPBAIm2Ufe5OP4O7sEyWjDY/y/Iax/YNESC5ZYA8G6BxX9bcaK/ptvvnlQE/+hUy3nfX2atcy8Yw69DIUx/ZRx7+vra4TDYWQyGXOTITy3cBidavYkWBmek4k1Mh7OxBom5UQiEbODM4Igk2zs3Z3w8+HixOfzdW9ubkyEwvY32O3B7M/+Ltg1ATJxKBAIIJfLYXt722RBlstl016Mo71isZhjRLfyA2NF/7vf/e6xrmNhkPHqcDhs5salUikzMx2AmeBK4VL06XTazJVLpVKIRqNm8qs8Y8ubbd5TpDw+0Cqwx0Oz4i8SiRjxDYc/DJOkX4Gil+OoZQsvJtHwb5dWiW32U3w8PtAacttFpWhHjbqy34PXJ6/RrS4hkUhge3sbX331Fb766iv8+Mc/xvr6OpLJpGsNgeJkrOi//fbbx7qOhUGKPhgMmumvdArZue08m3MHYpMMTm2leQl8HJRhn+PdvPpyx5ctt90EL+PkPELI97QTZ6Qo3LrzuI2rDgaDJu1XOgBl2E5eG/MG5Ax6OTJ7OByaz0DmAMi/kQ5KuSiFw2EUi0V8+eWX+OUvf4mf//zn2NjYQCqVeoRvx9NgrOg/fPjwWNfxWcEYvfwC22KUDTNo3lPM0sTml9tO2+VjZecYvjcdd+Fw2NVUp3D5u1wU7Ko6ALdyDMLhMK6vr02tfzQadUyptR2OfF2KPRKJOBa/TCZjBm3Sz0GrR6b6yqOL/Dy5uAJAPB7H6uoqfvKTn+CLL77A+vo6EomE4yj30MfGzx313s/BYDAw52sbflEpJHmmtGP40lkmLQjpJLNj/tz9eN7m/VLINPGl49F+T/4ur9etBJiWxqgoAwDzXDrWOFY7n88jm82admF0tkl/g0w6YmhRWjayPVa/30c8HsfKygrW19dRLBaRSCRuHTHUYfcDoxY+Ff09w11JCpL3E95nd7zhAiGFJ5/Hx8lzdjAYxNXVlcMcl4+3sUto+b60UGT/P76XXY8vBc/FJhqNIpVKIZfLoVgs3ur9F4vFTIsxO89fVvPZ+f0UPPMSQqGQaUHGjEj5mdq/K7fR5JwpsD+HacI/43Ydef7m7k2TV4bI7IWCgqX56vf7zS5Mk7zf75vX4mtLU9m+BloZPC5wuIasFCTSnCeyjz9FXygUkMvlHCY9nYv2giYXFLuQh7eLiwuTn+Dz+YzlwHRjZTY0934KHvJzkCY8z6/jCnW4UDDH3vb4U5i2FQG47/zS6cazuHS6sVTXriuQvgVWGLLLr3RiyqpD+/35d8ukJjtPgaKnn2QwGJhjwqiOwcp4dKlcILg7uxXuMDwGwOHck159mTrL5xB5bJC7v/QnUMBs+slUVvoQ5CIiuwJHo1GTlCSfJ/0UblWHvA5piQwGA4fPghaHXOzkQqTMjop+QZCZbfK8bD/G7ScddTLm7WadUKy2Y1B256W5zkxEKXzZrZcWAh9P60C28rajAqN2ZVv8dmIOd3daMHJUtzI7KvoFwjbtbfHz3908+5OOIHJ3l/fJnVgKStbnSzOfQrZFby8Kbjv7pOuTv7tdk7yNsh6Uyah9pHx2qK/pbuhOv0DYu5Zt3kuHnL3TTdrx7Kw/QpOZRwNZqXd1dXWrs64072XoTibnSEtk2p1YWi7yJq+JN/vxutvPhop+QZDiHdWBZ5TQbRN9lNlrn/llIhCjAWyiITP6+G/yjE4HIL3qMtXXfv9xZvg4kctQnszYo5NPd/z5UNEvEFK8NvY5V56rZWbdqHJYKSiZuiuzAJkhJxeCXq/n6pCTKcecxptKpcyNVW72WV8ixW4Pr2TSjt3N9+bmBvF4fKEbcS46mpwzBfMk58zy2tKhZgvWzpnnjdltMtxmO7soVLmLUvgyQcj+W9i8gxmC8jXsPH7u9hzemcvlkM/nkcvlTHmxTM5xez2ZmeeWnNPtds1Yaz43nU7rbj8nmpwzBfN8Dm5nbfv86ZaGK3dU2zPPf5NDLWT2nLzJkBY9/nInlcKVCwJ77MmIgW1uyzRjLjxMw81ms7fScJmo45aGKyf2sJ7BTsPt9XpoNpsmSef6+hqpVGrqEVmKEzXv7xnu2rLgRhaEjEqt5U7Is/Q0BTcyZdYOrcmCGykOWQ5r5wbQpAdwS5TchWXBjZvwueNns1lks1mz47PghsJ36/JrF9zw9Wnis+Cm3+8jm806Jt7Kz1Wdez8w6jNQ0c+BHCFlx9BZdsvkFlaVAc75cBSzXekmRThNaW08HjfvJcdeS0ecTPiRryfvYzYg/9vu3mM31rT73UvvPVtZjSqtpc/A7q9vdwWSxx1+FslkEr1eD8lk0rTESiaTM0UyvM5Y0a+vrz/WdSwUFIndREN6sMmoJhq82Z1zKCTZxkoWsfC9ZfccCtCtiQYFL4UvRU9hylCXm+hHCV6a3LKJhux6Y3fX4QLG62NqLk182UTDvtmZefKo4fP90ESj2WwiEAiYqcCbm5tIp9MP/8V4IowV/ddff/1Y17EQ8MtFQbBLS7lcRiqVMl9YipJeZfafj0Qit9pl0QQfDj922ZGNI2jSSmHLYhNZbirrzG3vuZ0yK9tlSbMdgEOs0gPO+2XvezndhgsRfQL2jfT7ffh8PlMS69Yuyw7T2dcyrl1WrVYzvgfgh9Zlsl3WPBmBTxF7JgMZK/rf/OY3D3Ixi4x0erk1xgSck2OazSba7TZubm4QiUSMM4t15PauK0XPm+yTZ4+EZjfcUSawTIW1O/cymYbit/0Fcvfn/dKLbs+t4zlbxvvdinvc/nue/w923gH/pm63axafWq2GN2/eoFwum+MDjxW0srwq/p/97Geu948V/a9//esHuZhFRu5Cfr/fFJNIhxxbPbEp5sXFhbEMeByQ5Z92xptbC2zbSSZ3eloUciyVFD7P9/QhyBbYtEwYf5fnZAC3dlvppZcLkN0vf1Jxz12xfRFSuNfX16hWq+h2u9jf33d05Ukmk7daYEu/i5cYJXrfhA/CW5/SlEgTmIIdDocOT7pdVmonosjd3N5JpSedZrYcNd1qtRy97+nY43nebWqtbNfN2fayLJdnedm7zs20t73/wOKJqVQq6bALAL///e9dTRz13s8BQ3H84tNspud6VAYa8LEu3s6qk33r7RDa5eWlo06dCwsHZDCMx5AdzXt70g2fwzM28+5pEbi1xCajfl9EKpUK2u22Y6yVZu99RJNzpmBURp4dP3dzIrkl58iSUT7GbfekuS1LV2WqbSgUwtXVFQD3+fRuog+HwybbjlYHLRHgYwGOfB95puZjKaRF+I7YnzEA4yRVbqM7/RRIccv7ZOhLCtgujCEUjBQ+73dziFGUMlOP93FHZmRglOj9fv+tfvtyYIZcbPh32McRCRcSO9y3qKa+chvNvZ8Tea6n6Hh2HIctfOmhtpFHAXnupnABON7fTfT8nSFDCj4QCDji7fybrq+vTbyf3Wf5U87EG1Xu+inQhWY2dKefAyaWcJgkAESj0VsmPuB+NJC4nZVl7r1bK2pWmQEwYUQ30fNoIOO1tA7ogLSRYUXO0GOEQuYLcAG4vLw0C4yK7/NART8HjBVXKhXU63X4/X6kUimTxTduntqopBb7SGCb2LJjLOvXmaEGfBQ9z/5cKGQjSXr4E4mEcQJyYQFgzun29BmZSESPvswbkIM1ZQTCix7zzwEV/Rzc3NygXq9jb28Ph4eHCAQCKJfLuLm5MQUlwO1xUW6Cd/MX2Ds+TXwpXHrc6YiTkQPbpGfSTjweRzKZNMlAsgefrMqTxwhaBAwdcrdnwpAMJTYaDTSbTbMgyCxAZXFQ0c8Bk0O+//57vH79GuFwGPV63TjChsOhGZts5+qP2+Xt34n09lP0AEzYkAuCXVZLPwB72bMCznbmSW+9LXyZi2+nBzNpqF6v4+zsDGdnZzg9PcX5+Tnq9Tra7bbJ15epu8qnRUU/B1dXV6hWq9jb28Nf//pXBINBtNttR/rq0tIS0um0Q/huHnE71dRGOvtkiyqa9jTPZUMNW7yDwcAcCXiTmW52XT9xy413a2PVarVQr9dxfn5uhF+pVHB+fo5qtYparYZms2mOANMwyYmsi8f8qOhnhCmt9XodR0dH2N/fh9/vN6awNH+Zs0+zW0KxSXNcevbtx8odWU7CBeDYqaV4pVdehvrshcctFg+499l3S9lldxtm/tVqNVQqFRwdHeH9+/fY39/H4eEhzs/P0W63HUcL5fFR0c+IzLunWUuhsBCn0+mgVquhXC4jn88bU587sT1Kiub6JOHLuL6dF+CWSAPcFqz8b9t3MCriQOwcArkIyKYY/GxOT0+xsbGB9fV17O/v48OHD6hUKqhWq2i3244U5nHvpdwvKvoJ2AKUIa1er2e++J1OBycnJ+acW6lUUC6XUSqVkM1mTbMLijyRSJj5b8lkEoDzPO8W9pMFM3asf1xSkPxbRuG2yEz7GlL80neQz+dRLpextbWFFy9e4ODgAPv7+3j37h2Ojo7Mzk/xz7P724ubMhkV/YzI8lPZ/II7FsNc1WoVx8fHZka7nPOWTCaRyWSQz+dRLBZNuSoFO6oc1G1Hdgv1jWKaZKtxj3GzQPgc6RSUzTJzuRyWl5exvr6OjY0NbGxsYH9/HwcHBzg9PUWj0TDFQ7IXHz9n258wHA5NPUKn01Gxz4GKfkZklZzcmfglpWe82+2i2Wzi5OTEMQmWraILhYIRg/SkS1G5OfhkJMBN7A+ZRTlpQZECpM9Bds9JJpMoFArY2NhAtVo1HW75mbk5DGXJMY8PrVYLb9++xffff49ms+m4Bt35J6OinxG7RJbIVFRmrTF9lc43CoA74Pn5+a0vPV8DwK1WUW5hvccQ+7SMckDSAgiHw0in0yiXy44GHbJnIPCxrkGWH8u8gVqthlevXiGRSODVq1eo1WomHVnFPhkV/T1iLwQ+n8+0jpIOvEgkgkajgVarZVJc5ZmWX16G++zqvUUUvI28Jl4/cwzsYiU7hCmjDnaOf7/fR7vdxs7ODl68eIHvvvsO3377Lf72t7+h1+s9+t/5OaKinxE7rk1GVZkxY44lq7Jrjt0jT+bX5/N5E1938+ovsuAldlqxTPm1e7iNcx7aC8HW1ha++OILbG1tIRaLYTgcYm9vD+1229Ql2EcO5QdU9DPCL6udaQfglvfZLc4tFwF5VrUnvNgxdebP804/WrQAAAgPSURBVBoA9/HTi4xbqrH9+yykUiljTZVKJRMSPD09RbVaNdEVFb8TFf2MyIQat4KacbjFzNlUU2bzyTluxWIR6XQasVhs7I7/uXDf15vP5/H111/j5cuXaDQaODk5wZs3b7C3t4dGo3Gv7/VUUNHPCM+m84heYmfIjcpwo+mfz+eNYCaF9J4Sk3ZoFhOVSiX0+33U63Wsra1hd3cX3W73s7KEHgsV/QTsL4x0SLGsdV642zMfvVarObL6Op2OSVy5ublBLpdzDISUjTc/N1N/Wqb9e1iLkM/nEY/Hsb6+7nCqKh9R0c+IrHRj+ixwt12WwuduLzvtdjodNJtNNJtNrK2tOTL87Jz+pyb4eQgGg44sR+U2Kvo5CIVCJt00Go3eOVRkl9vyjH9xcYFGo2HKVmu1Gra2tlAulx27vt0w8zHO+6OiFZPeWxemT4+Kfg4oek5mrVarZpe+CzLOz6QdCp+lq8fHx2bHz+VySKfTZtgDh13IzrnjcvHnuT6ZMSf748lchE85Wkq99B8Z9dmr6OcgEAggnU5jeXkZ5XIZV1dX6HQ6js6w83752O3G5/s4XovDLk5PT7G3t4dSqYSlpSUsLS2hVCqhUCggn8+bkdC0QGwrQKaoyiIZt4Yetq9Aph/Ljjr9fh/D4cfGnHK0luziO8oCmNYhaSfwjEIticmo6OeAPfHW19exvb2NXq+H4+NjI1Qm2NwFioxC63a7qNVqODw8NPPfi8UiisWiEX02m0U6nUYqlUIikXAMtJTRBrlTy6Qg/m1unXRkWJFJRWyKwTwC5tfT+uA1yIk/dt2/24BKN2SsXS5Qyuyo6OfA7/cjnU5jc3MTz58/NwMVmQPOlNq75oLzuVxA5Dz3er2O4+Nj41tgUU8sFjNlvLyfZj+LYGROu2xmCcCkCjM6wKw5Frswk5DNQrhYUPTZbBaFQsFYIvl83uQZMOLBKTzydzerQH4O9ucpFyZlNlT0c+Dz+ZBIJLCysoLnz58b0z4ajRpP+8XFhWPK630xGAxMLT+vBYBjB7Wn2Epzm6Jno0suAHL35GPZlgtw9hHg82R1YDAYRDQaRSqVMuO9y+UylpaWkM1mkUwmHf0EOBhU3uQsPjl0UmYsAkA4HDaLXSQS0R1/RlT0c+Dz+RCNRlEsFrG5uWlGRmWzWZyenuLk5AS1Ws1Ml33IgY+2NQDAjNEGYHZ42SHXNu/dPPBy5+X78PGjYFfcarWKk5MT7O3tIZ1OO6b4coen6Gkd8MajiRy33ev1zEIaDAaRy+VQLpexurrqWJiU6VDRzwF3w1QqZVpfx2Ix5HI5fPjwAbFYDKFQCGdnZyarDsC97/rT4DbQYhIyYWhWeLxpt9s4Pj42lods7MkyW3YQymazyOfzKJVKyOfzZrY8W3yzKUmn00E4HMbq6iq+/PJL05tATfzZUNHPgTRnM5mMKYNNJpNIpVLGVA0Gg6hUKo6uL6NeT/6UTLNIuDWOeMjFZdT1SkuG5j8A10GS8hgSj8eRSqWMEzAejxuznf0J2FaLMwZ8Ph/W19extrb2YH/nU0VFPweyKUQikQDwQyYYz5nxeNyYtHSiNRqNW+d8tyq8eXhs6+E+jimcBcgkpHq97ujdz+MId3uZE1Cv11Eul9FqtTQuPwcq+jnhbs8dKRwOm5CV7IG3vLyMo6MjnJ2dOVpEsaDmsb60o3blT4nMFZjlOMHhmjo/bz5U9HPCkBG728hecKlUCvl8HisrK3j27Jmp8T4+Psbx8TEqlQrOzs7MFJjHaPD4FMTh8/mQSqVMKJANRpTZUNHPCdNO+ZPeeZr4mUwGpVIJa2trZgDE+fm5GfvEyS9smcUMN7srrJwoI/+bCUDS9H3IARLyHC9bcdM5xwiBXQcA3B6SIcODMnWX3n0WNfE+vk4wGDRJUT/96U9RLBbViTcHvgk7wOe/PTwgo87kdlhMVs3J6a8y0YXxbzkhR06HZVKOPUVWPld24qHfYFSokDPwAOfADClEOzOPNwqbjjiG3ujHYHiOiyIXJHuqLUVNL758DWYSysQd+k0ymQyWlpawurqKbDarwh+Nqxmkon9AJi0KXBhkqyw5HVYKXfoB2ExTip+hMikst0w2N2vAToelyGSIjYKXiwGPNLRuZG9/HnsYa2eCjfTq04PPRYOvwSQeWTgkm2vKxcDus6c4UNEvMnb3HNk7j2K2bzwKyN1d9o6fNjrgZrrbu7/bY6RJL9Nq3ebz2QMrmMknR3xJQVPobtekTI2K/nNjnKUw6rHTvJ5kXhFNqo6bNVowrhJPmRsVvaJ4DFfRqwdEUTyGhuwWjIeKp8v+7+P6wC+Sab1I1/KUUPNeUZ4uat4riqKiVxTPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPoaJXFI+holcUj6GiVxSPEZzw775HuQpFUR4N3ekVxWOo6BXFY6joFcVjqOgVxWOo6BXFY6joFcVj/H/mx3LiEoGnggAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current beta: 128\n", - "Current iteration: 51\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "cur_beta = 4\n", @@ -1721,16 +416,16 @@ "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " print(\"current beta: \",cur_beta)\n", - " \n", + " print(\"current beta: \", cur_beta)\n", + "\n", " solver = nlopt.opt(algorithm, n)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", - " solver.set_max_objective(lambda a,g: f(a,g,cur_beta))\n", + " solver.set_max_objective(lambda a, g: f(a, g, cur_beta))\n", " solver.set_maxeval(update_factor)\n", " solver.set_xtol_rel(1e-4)\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1742,28 +437,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(10*np.log10(0.5*np.array(evaluation_history)),'o-')\n", + "plt.plot(10 * np.log10(0.5 * np.array(evaluation_history)), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Mean Splitting Ratio (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Mean Splitting Ratio (dB)\")\n", "plt.show()" ] }, @@ -1776,53 +458,32 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ - "f0, dJ_du = opt([mapping(x,eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x, eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef, bottom_ceof = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2\n", - "bottom_profile = np.abs(bottom_ceof/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2\n", + "bottom_profile = np.abs(bottom_ceof / source_coef) ** 2" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o' ,label = 'Top Arm')\n", - "plt.plot(1/frequencies,bottom_profile*100,'--o',label = 'Bottom Arm')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\", label=\"Top Arm\")\n", + "plt.plot(1 / frequencies, bottom_profile * 100, \"--o\", label=\"Bottom Arm\")\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Splitting Ratio (%)')\n", - "plt.ylim(48.5,50)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Splitting Ratio (%)\")\n", + "plt.ylim(48.5, 50)\n", "plt.show()" ] }, @@ -1835,30 +496,23 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.update_design([mapping(x,eta_i,cur_beta)])\n", + "opt.update_design([mapping(x, eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb index 3d6528ff8..8b9ffc01e 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/05-Near2Far-checkpoint.ipynb @@ -24,6 +24,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -49,69 +50,88 @@ "\n", "resolution = 20\n", "\n", - "Sx = 2*pml_size + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "nf = 2\n", - "frequencies = np.array([1/1.5, 1/1.6])\n", + "frequencies = np.array([1 / 1.5, 1 / 1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(design_region_width,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.Source(src,\n", - " component=mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", + "source_size = mp.Vector3(design_region_width, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "def mapping(x,eta,beta):\n", + "def mapping(x, eta, beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.flipud(projected_field) + projected_field\n", + " ) / 2 # left-right symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " # \n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution,\n", + ")" ] }, { @@ -129,12 +149,20 @@ "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0,40,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "far_x = [mp.Vector3(0, 40, 0)]\n", + "NearRegions = [\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", + " size=mp.Vector3(design_region_width, 0),\n", + " weight=+1,\n", + " )\n", + "]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", "ob_list = [FarFields]\n", + "\n", + "\n", "def J1(alpha):\n", - " return -npa.abs(alpha[0,:,2])**2" + " return -npa.abs(alpha[0, :, 2]) ** 2" ] }, { @@ -144,10 +172,10 @@ "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=[J1],\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")\n", "opt.plot2D(True)" @@ -168,9 +196,11 @@ "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -200,39 +230,49 @@ "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", - " cur_iter[0] = cur_iter[0] + 1\n" + "\n", + " cur_iter[0] = cur_iter[0] + 1" ] }, { @@ -249,19 +289,19 @@ "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,0) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,-np.inf)\n", - "ub = np.insert(ub,0,0)\n", + "x = np.insert(x, 0, 0) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, -np.inf)\n", + "ub = np.insert(ub, 0, 0)\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", @@ -269,37 +309,37 @@ "update_factor = 12\n", "ftol = 1e-5\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ - "lb = -np.min(evaluation_history,axis=1)\n", - "ub = -np.max(evaluation_history,axis=1)\n", - "mean = -np.mean(evaluation_history,axis=1)\n", + "lb = -np.min(evaluation_history, axis=1)\n", + "ub = -np.max(evaluation_history, axis=1)\n", + "mean = -np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", - "plt.plot(mean,'o-')\n", + "plt.fill_between(np.arange(num_iters), ub, lb, alpha=0.3)\n", + "plt.plot(mean, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -316,13 +356,19 @@ "metadata": {}, "outputs": [], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -339,23 +385,22 @@ "metadata": {}, "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 90),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.5, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", - "opt.sim.plot2D(fields=mp.Ez)\n", - "\n" + "plt.figure(figsize=(10, 20))\n", + "opt.sim.plot2D(fields=mp.Ez)" ] }, { @@ -364,21 +409,21 @@ "metadata": {}, "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,90),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 90),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.6, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb index 3e552be49..407781c03 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/06-Near2Far-Epigraph-checkpoint.ipynb @@ -11,17 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -32,6 +24,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.verbosity(0)\n", "Si = mp.Medium(index=3.4)\n", "Air = mp.Medium(index=1.0)" @@ -46,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -57,68 +50,87 @@ "\n", "resolution = 30\n", "\n", - "Sx = 2*pml_size + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "nf = 3\n", - "frequencies = np.array([1/1.5, 1/1.55, 1/1.6])\n", + "frequencies = np.array([1 / 1.5, 1 / 1.55, 1 / 1.6])\n", "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "design_region_resolution = int(resolution)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [0,-(design_region_height/2 + 1.5),0]\n", - "source_size = mp.Vector3(design_region_width,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.Source(src,\n", - " component=mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", - "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "source_center = [0, -(design_region_height / 2 + 1.5), 0]\n", + "source_size = mp.Vector3(design_region_width, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", + "\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", + "\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),Air,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", "\n", - "def mapping(x,eta,beta):\n", + "def mapping(x, eta, beta):\n", "\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", - " projected_field = (npa.flipud(projected_field) + projected_field)/2 # left-right symmetry\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", + " projected_field = (\n", + " npa.flipud(projected_field) + projected_field\n", + " ) / 2 # left-right symmetry\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables),\n", - " #mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", - " # \n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ),\n", + " # mp.Block(center=design_region.center, size=design_region.size, material=design_variables, e1=mp.Vector3(x=-1))\n", + " #\n", " # The commented lines above impose symmetry by overlapping design region with the same design variable. However,\n", " # currently there is an issue of doing that; instead, we use an alternative approach to impose symmetry.\n", " # See https://github.com/NanoComp/meep/issues/1984 and https://github.com/NanoComp/meep/issues/2093\n", "]\n", "kpoint = mp.Vector3()\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " symmetries=[mp.Mirror(direction=mp.X)],\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " symmetries=[mp.Mirror(direction=mp.X)],\n", + " resolution=resolution,\n", + ")" ] }, { @@ -134,44 +146,39 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "far_x = [mp.Vector3(0,15,0)]\n", - "NearRegions = [mp.Near2FarRegion(center=mp.Vector3(0,design_region_height/2+1.5), size=mp.Vector3(design_region_width,0), weight=+1)]\n", - "FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x)\n", + "far_x = [mp.Vector3(0, 15, 0)]\n", + "NearRegions = [\n", + " mp.Near2FarRegion(\n", + " center=mp.Vector3(0, design_region_height / 2 + 1.5),\n", + " size=mp.Vector3(design_region_width, 0),\n", + " weight=+1,\n", + " )\n", + "]\n", + "FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)\n", "ob_list = [FarFields]\n", + "\n", + "\n", "def J1(FF):\n", - " return -npa.abs(FF[0,:,2])**2" + " return -npa.abs(FF[0, :, 2]) ** 2" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = [J1],\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=[J1],\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", - " maximum_run_time = 2000\n", + " maximum_run_time=2000,\n", ")\n", "opt.plot2D(True)" ] @@ -185,15 +192,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -219,44 +228,54 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k]) \n", - " #Note that we now backpropogate the gradients at individual frequencies\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", + " # Note that we now backpropogate the gradients at individual frequencies\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", - " cur_iter[0] = cur_iter[0] + 1\n" + "\n", + " cur_iter[0] = cur_iter[0] + 1" ] }, { @@ -268,1455 +287,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAC/CAYAAABdcw+KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAD1klEQVR4nO3dQU4jRwBA0WrTICQEB5hL5CZBYpFVLhZpDsGGO8xcB4TkgE1nEXlWAWY0nm8rvLctqau8+S6XSu1pWZYBQGN16AUAfCSiCxASXYCQ6AKERBcgJLoAofmdcffJAH7c9NqAnS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkurBnf2+242nzcuhlcKREF/bsj7++jD8/fz30MjhS86EXAP83v//2acyr6dDL4EhNy7K8Nf7mIAD/6dVvXccLACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AUPo+3XdeIwlwENPUvf84jW75wQCOURbdp6encX9/L7zAUVmWZVxdXY2zs7Nkvl8e3e12O05OTsbd3d24ubkZ5+fn4+XFn/YBh7darcZ6vR63t7fj+vr6W69+pWynu91uxxhjrNfrakqA77LrUyG7vbA7Vphn/4UJHIddj8pjz/zKmKMF4Fgcokfu6QKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE8uiuVjoPHIdD9CibcVmWMcYYm82mmhLgTbse7fpUmLOJ5n+nuri4EF7gKMzzPB4fH7/1qTC9U/i95f/5+Xk8PDyMaZr29UiAn7Ysy7i8vBynp6f7fOyrocuiC/CBvBrdbk892nMTgO9V/gJPo+toAfjo3N8CCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCM3vjE/JKgA+CDtdgJDoAoREFyAkugAh0QUIiS5A6B+NOmaIgLcIiwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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oMgO2oYdn8kuiS8dWxkt7VF+RrCZ8NSl62SXr5R63XxBc6mPVnp74+XaQYKDh8d4n2ed8Pk+3KPHm+6pO2oNiXdm+t4px/+rZvrKREgGeW+FCU12YrUR3zq9Vp/enup+XbWM90gCV419V1vYCr8/wLgaVob95d5Jfn+nZgivAf8tMV8hQ/Lpma/3lH69EckIyWmrScfNd4qSB0oDwQRocGL9DYi5DpXBdm614ZOVV32sGnP33Owe8be8VtupzlUV7S3D9djweX+0p6nzC4yoxr8aDWbDvhzNDYh0UR9qnZwOJu2fZbjeVoa0a1sc6elQBwe3f+1355dJKiGX41o0HT9bBLa7Kx/2c6lyfI6pTe7hKmPisiPV6PSVRz8/Pr76WX40zVyTsc0906dej+dAH3vSWJK29vbHZRdmXInqsoC6g8Qomy+bg9PZTq4zDhcR/6FQUE++fiwpFt8oivBwuvRzPbGnn3lJqLkPiObT1XLZd2UVtYP0UVE4A3iyv931PmHuhzHB54VTirC9KSBxZX8/u3EenPfSbwchXDZUAVpkiy1U/aC/aiPWzTAYHr8vLYF8Z3ObaNOfb/EzHsw0+/l6GEiJfoVBQ+awF3/Ou6nRRn9OXj2Toox25J9Pa6+0GRlPPYjyS6hyJwcvLy5tnuVZXS10sekLk+BKpcqqqjeqj18HMyyOtHLKyg473Petq4lTL7Wo5rfOrrIp29vPn7Oa28Tbot/rKQOKZNI/pwfH0Nq5WqylAcSk5NxErYfb6fAXjwrmUCTLLZHZcjZv3UW3Ueb5VVwU6H1+JnguX28Pfm1sF9ras3G6+ouFvtk+rKz6e1besqrbofJ/3nq2rP3/09s73MlR0lWHSwFpSyEDae6Uj6Vx9zkcUcgmicisx5BaE37vLwWmtLe43E5+clWP5coj18pw526kuDyKt9fcDe/WpTE7OSnjnBFdlLAUsTta5JV31YB7vj9owtxdXjauEtxpXz6DcTnN98nbJHtzW8nFxv7ymvsqPJEB675pnwrKNvkqgDdjeyieuoTrefYBbggyytBEDcmtt+lqx3w9frYY9QVH5+rq3Z/GjGLq94J1jNJMRdHMz9+88SrX2fU9I2wn+bF3ft6qEkA7lSzlOJA0Wmcv6WC6/nKB2iN7r3l5fa98vJvr2wpLjVJkdswef0HOZo4t1tUWi19XkrZ4t4VstOt+fbiZf4ErBMz6OpT737QJvn7e9sl1vuUyb8W9fEfUyXx7fwwOtbK8vBFwbKKrA7AJZBSf3b6/P/cfP8TlU+ZaPEZMNf/au2unCyQtzshcfZO938vRWZL+SoZkuHZBGoej6kprOqv05QaP6xKdRe89D6AmdT6Rr+8e6uQycyw7mstMlEVyaqMIngTt2NaGqC1Pe5t4yvLc8ZSbLO1F0Pv+m2Pb2hf087nHKr4QHJQV1LrOrAEF7M1v18niO/JtZZWUbzzzfM569JEJBsPLna8r2bJeJzDXtcnt5O1z8NT/9m2Ssk9ku28RyOL+rbJpB8L0rxJ/Nh15IExyoueWlZxb81ktvqcmnX+mJV9WDcujE3jYXJraN+9BermdcpCfGc/txbpP3Cu41QYTZue+zVcs3Fzi1f26LRsf79gX757bluQ5tJiH1fjPTosDxdq/KFswudW5v5cPP+QWU6jj+XbVrjl7W2hPens1cUGlvv3Dpq81ev3rzpBI33VEkf9AWUO/imvo1l2B58LzW70fyoaLLAfTlWGtvJ2CVgXIQqmWw3yrmWS9FtSd01YC5KKgu3rZSiZT6zd892/hkcZHgpKkmqmfFlY30mdfl5+m1Z6xcqbT29hGAPYHg+Y5nQb1M0d/TCoPC5e3XcVW2VAmJ3x2hNmlfuReQXTwlWp5N9/C2+Bygv1U2oW/1/Ix2qJIHtkP9ndsDrUSumr+09fl8fiW8DJIsV8dyrrvweps5rtSX0dsJznDR9ahHY/mgXpPVcQCqh5Bw6cFvpdH4frsPs146x5z4+tdFda6EQK99CcW+VULBMjx77GVpLjJzgss6lE3wIT8uKr0Hh1TZsDs/x5aZYBUsdYxnXeyX19fa62yXIszjKebub1Wm6QFYZXsQZfkeMFyo2G9vX9VeP1fl85+hVnW4yLPsapntc0/tqb6Y4jZxe3k5rX1fAWmcdK5v/6kc3gK45MM9f6v24XuBdgQfkulWxnFD+QUUN5Qbq1qSeJ3XLFV7E4/lVsuV3gDS2avMtRdU6PDVvqNEi45ZZedzTur1ERf4Xvbf23rwfkgAuU9bZdNVllK1p6L6RtTcZPSJ6YJX2caz/qqfvtVUBY4eDPrVePlVe4quzyW3pycUgndb9PpfBaLesr03D1arVTkHKjH3cqsvU/U0QfZndl4d9xGC29oHZbrCJ5o7Ds+pJoiXwyvZvQnk51TOUy1TqmyrWrr6Z5VjzjlBVZ6yKrZLAiPBrbLGawVXx7o4KvNluXzfbV0FtopKEFiHt8+XplXb1WZf7bB8vt8LknxPk1d+xcyVPutl0ofn2t7z0TnB9VWVBxAv38en8ivasOez1XzxsjQGveMru8+NJ89jkGbfetsdtAd91un5wa9kqOheGy3nMkl3LmV61a0hS7fTqB7fG+VEqrY6eL7/XnLOXp8qG81lHdVE7tU5l7l7PVUmwt+V4/t2RJVxVe2rxrhqVyX+FdXdDb1yl7YfPDPviYaX7595Frk0DpWdWa4Hkl5blrK6ntj3/Kf67UmJ7MStON+D7V3vYP18FrZsst1up/ksluam25vz/T334v9sPuwh5hycnlDRaBrY3kNP/MsRrb2976/XFv3mAPZuL6uy1GsjpWdFbAMnmzu975e5zXrZCvu31HfV0xNGvmaGVGV+nuHxfL93kufSRvQPTWa2k8d6nzjp2VZ9zrsz2Gf3Ne7dVuLlWR2DAtvLpfFcEKuEQqxWb59058fzYp0HEq/LM/VrqPxe9uGeq+YdV18cx6XAQ+HV8c/Pz2+2F+b0YM6PqwvvI/mwf0y5RHXhqlpKVcZ0ASNVZumOUAkHtzZ8WTcnvPysyhiWMtaeoFZXcqtj3H6sX3b2enkL2DVBwO1WXQBjuX7nQm8/UsdIcLzO3jcLXcwlDG4TiYP7mtrde16Di5zbabV6/ZQyt1mVDPTGyvvsduPYuDBX51a/de41fqxx4pjxHltmu73gvJSBu39X7fI9eS+jul7jvEePfiZDn6crQ9JpfK/Sz6HRaWSJX5UhMqpWcCJw2cpMioLrV7rnBHcpkvfumXxPxjxHJdatfZ+QS+dVNuvZkZPXBfc9ffGVRdUuHfee9nNsKYSkZxMGdom772VXbZh7j2Ld64OOm5sX78Ez757/LZ2vdnAe+tadbOz9ZL94Po/hcVWAZD2+p+2Bukq0fMtQ9fh9vyMYKrr+LaRqGer3HfaWRL0Larz9y/9lyFzbOOBV1lstqziALKfKJKo6hWzC7+s7nOwSKZXjN5R7duxCQbsxm2TbdFGKS0i2eU6kPRhV2XC14uA+Wy8A9uzDvrAuQb/Q7ypLWqqnWqWwPNq4skVley9X53HvkYGJCUcvY/T2Vne4zJ3T8+NqVeL9Y5+uFTTPcLna8uew+NzvCa77lN5jv7l3PIrhossOVhGWglplOb60cbHgZOVyluLrF1GYfbsjVo5FGJW5/7eUOVbRuMqgqiVaZTdm7VUdnrXzuF4W6DZwkagm5lLGXgmFTyIXXPatCsr+sBwP7KzDfe/atro9ZTcGhaWMdM72sm9lSwZ6fgGnqre3z131oeq/Jx+cI71MuRpT1ukPhPeMVuVKHzz7dD92O7Bv+vH9X8JzXJNG8CH/gt0jJ+k5AV971Bd8nmpr32+o5lOMNEl4jyuvklZOWU0oFxyWvZTp+qSjwy6JgLfLL/jMUWWZ3o9qGVpNPN9zq/aLvUwKpONZuQuELw+rIOCPhNTxldjMXTR0UfdzWKYHWp3rvuArFR+XubFjn3srr7lz/XWvHd5n94drxLuXfXJ8GeTpS/Qvn8v6l11VoFlq11wgqub8r2b43Qs0vuNpf2+DvCrDBVBZiI737/rrWH/guQs7l6CVuLizeD+8b565ibl9tYoqu6jqq97zCeFLuiqY8DaeaqnqF5V8bNxOnCBzKw+1t8r+5vriNnXhUL28UFatnPxcDyDKuAj3grlFwL5w9VAJ49L4XSu0bFPP73pJhfedttXn1UpM9zbzfCY7fi2Gfue+6PPNEwb3uypp6AlrJdIjGH6f7twA65gl3KBLUfta4/pgsT3MMqqyPbvyicTJdU1W6su0Xp/mln1eFs9v7fWDZ87n74/P82WfjtOzjyv7L+FZjNvMM+trAlj1mZ+r174tQgFYsp+XXwVuZWKe0a3X6+nfzZAqyM5lXb6NVWXlXtYSLrhev/tANZe42uoF22vn3txcrbLbHxmzarV4ja1+JsMfYu4XS3yQdWxvKezwKqbfpN9rh36rTT54FBp/SLK/Zj+YYXk2U/X3Gpu11kphYFn+m1lNhR/v77k4+oOmq6yjl3nTZr3+Veh4Lqu5h8fjNIY6bsnGvUywCpbeN9pIr9fr9RSQ+LhI+kElZkttq1Z6PP+ajHUJjmXlpy64tIfvkdNfKrHVfOtl+V5+7xt4VR+qDN7bzXZwbvUeGvSr+JB/18MBFW44zyx7wrVaraZHNvqyVGJxOp3afr+f/sNE79souqChfw8i9vv9VA8v9AmvV33hpGMflrJSFynPOPh+da47kAcMHk+Blnio7LmLGxxPfv/fnw0gdC+nvlXEh857270O/SNRL1sTXHdvKNv0MjgGXG2oTD32U+1X/5kJ6/3eakCf6VjaXP+htrrFSWVVgbUnorT93EW1nujwcw9S9FH3O7aN20H0j9Pp9Op/m8l/qgByubz+zy9PT0/Tf/9VHRp//2eyLMPtVa2UqC+cB34RdwQfsr2gAav2gxjhliK3Jsxut3sz8B5tPaJxOdjadyfTfxLm0sj3dfnEMn7GJTP3CecyLxdEFy85kZxbPxJHwv8m4RlblamzzZoA6/W6HY/HyQ5+dZdluHCp7bILswk++b/K0qsJQTuzHl5I0/JdQXW1WpX7ztUKZ7vdtv1+3/b7ffkfolW2248P03b/UR0a+5ubm1dlV7aXj3B/kuNW2Xmz2ZSPKnV/4RaSCxKhj/b23tVObpWwXIqtxJcXzDywPD09tcPh0B4fH9vj42M7HA7tcDi8ulVMgqsxUp+dKtNtrX+rIP1tpOC2NlB0GQU56NVyleIluMxlVPRJ9fLy0g6Hw/RvfFpr7ebmph0Oh3Y8HtvxeGwPDw9tv99P3+fWfiZFS2J+e3s7taW6aKI+CPaBE0iv/SKPZy0UGgYJTR61U5NftvEgxvbIyTwrYl+4Z3s8Htv5fG7H47GdTqdXk44BwdvtgsusUePHcdKk5LaQBzJODH+alkSK2wue6WlsGbTUrt1u9yrY0d7KzpSVu519+c3kYb/fT+3fbrft9vb2zf/98wtHvKDnQdrtLBHXbwY8tZP2VSDiBS223X2TffKthorz+dyenp5eiafmp9BK5+npadqKOJ1O7du3b+23335rv/32W3t8fGzH47G11l4FFdlVdSk5oC/yGI6Vv65WgafT6er94Z/BMNFdr9dTtHJj0CDVN5M46Vv7flVY79Gh+X/TGGUpWLwVRZNK0bu6pUkOLhGmo7uzu0j4MxyICyIz9znRVTah1/ph/9gWZs++P0abnk6naamuOiRoFFRNBv9vHBRM3wagjZnt9ITb7UGBoeiybNlBfZC9NMkZKPb7fbu5uWm3t7ft/v5+CsLctqAoyq5uZ60IlEyoL/KVh4eHqXz5KIXWs1HVq/FzO9MWzNB7osslO/vEeeVzkceyLfRDzRv1icGMZbMPGlt95nOSwrfZbKZVyN3dXbu/v283Nzdv5ohnv5XwVis1CjWD5AhWc0vf1tpPu5/i5eWlPT4+Xp3K+3HVvlP12oWc5bmxr22HL03n2nlNeX/kc+HLc3/9o2XP2bAq5z32qDKPa9o5Vx/L9dfV317uNf4wV+acjd6zfH3P2L3Xh5fKXvp86fheVnlNOXPn9Wx4zeuqnXPtuLu7617r+UG6gzNMdEMI4S9EV3SHfyPtZ21avzc6/xFGb7T/maiyj19R7s8uf2Rdv8pG/xP5nzgvf6YuXUMy3RBC+Pl0Vfxj/y1mCCH8xYjohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggDieiGEMJAIrohhDCQiG4IIQwkohtCCAOJ6IYQwkAiuiGEMJCIbgghDCSiG0IIA4nohhDCQCK6IYQwkIhuCCEMJKIbQggD2S58vhrSihBC+IuQTDeEEAYS0Q0hhIFEdEMIYSAR3RBCGEhEN4QQBhLRDSGEgfw3XMboPpgAycAAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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ipCdtedYWGbq3Vp9JqjRCdz4kU1c2vfS9y40kxHZYh+ffuj53kfgiD/vlkTpBo3MDJrm1okLPpEi2h9rSu5dZSIoaG2VIndSWPS3oOuG2SFTXPhQwcN58XikLjyipK55F8PqaH24VdDLjYiv7z50DzHi48OYOuaoGm/F2fKFXcuVntAc5CAZA1Ddmtq3dLJx/OWrt8OmF3+TmCN8mRsLyFEqCYc2SaSBTWAm/aqzwjGSZ7lAheZ7alDf3mpSTphMv25cX5eopUx4qpqC2GD2TWD1K1TmUNT+X8umdRtRKy7z+pet4akzHQMfJcTlx+NzoPM6FR3WUCR2gOwSPyPg/4ddmv9kfOhQ5WY67JRfOhcvTI2OStMiCcycip9x4fa+XS+6trVa6nm+1kl6wPx7tk3DpoBXU+E0V3D7Xsi86bV5zv9+Pyg4+97RzL38JOl+Zl8vYS3b7/X7YT9wL3SNd1W6phCSt/X4/TBpTBkZJNEB6ZLblKTTTaE5uKx2SQinK1YuLRu6FqaieUio68AU0kjkjF12PNUInQY6vBR2v+hl3Ykj+vBa3RGkuJAs3PtZuCe7n1HzRqZJARLjufBWlKnIlOXAumJHQYetYZiMkIDpzwTMt9p9OlRGvPuN3XHzVdXyuJpPJaOuhz4f0RWiRAfWdOuPZoztPRujUw81mMxpLy1moLx4gMDPhTTVcnGb7ykaqarSlUeNgWcxLa7QXz3ikT8y+fHsnHQHfv+lIV+lI1bhIz8hOguDm5aovHxaia3iq6Z7MCZWRgXtTgcqpLV+KcFvXbdXOaNytVyta8khZ35EcCSqKlzVkLHqGgO4Cmk6nI8eh64t0tV2OC1ZM2zebzWjlnm1NJpPRbaevlQTYTy996BxfqaexiayY3jvpOPFKP9gPXZu66GUeyYQLjdRZyU03aGhsJBjNl5Ou5sPLZtQvd7DMEDzSZnTOmjYJkyUDzVtLP2kXbjeevWmBkTebeNmGY2llCCRDt2O/joPRP52uz4cHRFX1hYP42uj2wJvd7mW7lIyl6uUGh1btjsbDFIR1Wiks603yoIxuBRmPjIxenP3V33oxQvDUiaSj/wVPK3WOPlNfuM+TJKVIhCSuPnlE5jVlv1uLqZvOYZovJZWMNG/MELys4RFV1Uv24OmvK7b6yAiGqSOjVzolLy/oPMrWywI6ntkTnaOuqShe/WBE6xEzCYFRMZ0OCU7X9DKFdFB3Zal2SbL1jK9VopE8tWDFBUDeIcbotVX+4Py4DrfswXcatHRD80HHRLtVX+TkWQp5fn4e7Rai7EngdLCvlaG4ZnB0dDQ8x8Lt/2uha3lBRXZXdgmXKYa+o5Iy+lTUrAnVRKk0Qe9FY9CWFaUiKmNMJpOBrEn67t25wKc+cqKrXrZvMXXU/xw373JT/amqvrjjySN3/l11+FkUrdqekwX75pGHxi3D0MNtVqvVF9uzKCM6M7bdKq0wcvSsxDMUl7Nk7YsxBPWHUVbVS+TTypwYzbJ0JB1Q263IkmD0zrYkD8pI16JzYHRO4mX7dEhKq7lLQPNEeVEPW+UE6pBA2bs9eEaiGyPkFES4euqaom/pFa8hrpAdn5ycjGTOBTvutqAz03W41ZEkLtJVdvZNRbqCaicqL9AbavWU0VtrBZy7EJg6zOfzkfExSlAbVEZuhVHb0+l09AAUTytJvuqX1/tIqj6JJCSSA6My1plbEb3GxtqwE6ZHKzQM9/ru3JycJR890Obm5mZ4MldVjbIV9oEOg5ER+0zDZ1/VN+6hnkxenjHgURcjFB7PyIjpso71HRitDMNf0kEuzDJN1bV1o4RnQpSF+u5j8ZIRHS+PYXBC0mW5gw95YWTHvh7SS3fakuWhGjmdD/tfVaPAQlstRbq6bZ63C1NOLPVpjnWbuzJnOnHqsO8pZiasV1V9uwtpm81PDx45OzsbeWcJlOkOSdc9l16eylGZCUW0TP/0qnrZZqLzZQAtr88XI1xuT6HyauL97q39fj+qtVFxPaJjJCky8gfDkOwFeXdd18Eswlfm1ScpuJ6penNzMzzTgf0iwbRSYic5ZiasZ3J8inQYxbiTYdmDaS5LCe6EmGJ6+5SVn8fvZMQeNaoffCiSMhrqus7jghnnzdvT52y3auz0fM84dcf755GebITRr94Z4W+325GutByTPldJkJGvAh4+OY91eemVQLtkJqsH8WgBjONX31lGUeTMNRnZ5X6/H55h0gtdI13duSKSoof1qMXTH15nv3+599/Js6pG3tnPZe2J0P++muqpuHtUKhtrkCI9PuGMBqPaIRev/Botz61tbHzKFBeBGIF42u1lCEVDivw95RPh3t3d1d3d3TAGzQsdH1NfOk2fT/2v8dAxcFO8XmrLyYht8pqM3mWsjLy5Zcr7xP9F3tvtdkSgIgjWI/UsBpLkZDIZlbI0H35zyaE9qb64SMIUWXB7FHWG+q53f5CNnKfO51ZO9Zf9cX1kgNDSOd6pyOvpOD9e/WiVNjRv+/3+iwfxqA3NOUtdLedP/ZQu6FGZvdB19wKN9pBH1/+aKKbwJC0Rg0iX3polh5axU8BMN1ReYMpZVSOv7XUuRt80bpEKf1FBhCLPzPGJWLhdytMreXl5ZvVnsViMIm+mjE5eilyOj4+HJ/LzWcaqb8lQ+TBzRakkbO5yaBGu5tNLL5IPH8Yt2WksAkmIcvfUltu1GImLBPTudflWRCydFblTFxggcCx6Xq/ql9Ppyy9eKCgQ4WmOmQar361oW7olkmc5xWuo0hedp77pll3V5SUTyU46pf57QOTlDzoGlr7cOauPInbK1yNsltrobPTO288591zs9BII9ZAOgv39Jmu6JAA+wq3qy2ixavyYtu12W8fHx6N9fVXjZxvQ+D31d+IlKbDOI2KT8klB1F8RukBvybRW7TA6ZYSiMc5ms8FxcBFN12btib/msFqtarfbDcQteek6apvyIyQXLmwwMqRseXunDFLnetosOXpm4Hog8qsaP1OD6biXaqQPIhXWQXWeVv89CqPRKs30iJ1Ez2xIY2st4klHnHD0fGZdnwu3GoP0QTLg8YyqpW9ql20xlecD8/W5rknS5a9haK5YL/XI27MS7lWXDDin0lPJUP1WeyRDX1eQ/ntphPXz1i+SMFv0LY/sZysL8BJRD3QjXY8SWwV9fU7irXqJVPTrBVJWKQpreIx+SLiKprwmqOvQk1OpWMTnOBg1c4HvUK1L5+qdaZDGd3T0cpsjU0k+g9cfKt1yMqqTMarw0o0rIb/zdn11t7VjQf2VIZE8eH1vR+CNBVyQ8TIACZvXZJ3Q+0aiomPgfHjtnNmCCFDjkuxVm1a/9IwOPhqxVcpq6ZBfm7VTl7lkTXIm2fAcjd+3Obqeet/4OeugjE69/EVb4K4g9aFVIuJuDY2FzoalBc0dsw6OXTs2eFMR23aekYzcDr42upGuIhGu9FfVEEWIaFhzrHrxpPSyJA4qLs8h4TJdY+ohT+xkXPXlo/+oZJ6mtBTZF9mYLjGFYl9EBNwPrBcfueeRBl9e32I/PWLR/ked69vxfGsOyzYiPjoHv+PO+1hVXxgg66OMOv3mDBqNO1jK3jfD05HyXbJWEEAi9H5y4VDfMypkxCrS5e/esfTFRSwnMfbDddL1Tk5Q88gIllsORXr6W7qn/xUs+H5klg5ajrM1t4e+92yCWY36pPH4TQ1e3lC/uXNJP2DpC3V0FFyMk77oOJXXeqEb6U6n01oul7VYLAaBywi0XUTCrHpJs6UYEiaJl4aiiZGiqj6qtnUtpoiaSE9peOMECdvxWmRLZRNRqV2RMeui7nFbysYxMP10B+Dti9Q8KhUhkABJ7PyfJLvb7YasQ/PDaIJRG2XFeednhwyXqbXIwa/pOuY64e0fykBa0Q5rzUzD5Qj8VxyqXm5tlZGLsCUff4h9q6ZJneI49JkIktG6Z2s+dq6NMLJkLZl38B2S0aEI2RfbeAME9ZU7faRPTO91LjNe6pb3wfe685dEVM7j3XLMKjTW5XL5RTnia6JbS/P5fCBdemCRgEiOvwbK9EP1OI9YmEq6gnj9zVNeRW9OZLoeSZOk7FEclUCgErFeyGjDyd7LHjxfnpkb3lm78kiAiyO+A4NwkqRjooPSHDDTkMNq1cMOZQi+QMY74Dwy53w6EZCQaJCH5M8IWLpX9eVDYzzD8TGRTH1bm67X0hWWK7x/rOeT+HgeI0Guieh/z2wE6g/LT3S0mgva0msOy6/NrM6jZJaMSNzc7uZ9cudJeclRqB3eCOLPWqh6qQP7r4BPJi+//PHNku50Oh1+b0xeXimWhKQSBA2iarzSyBSBwj1UO3Oyk9KQ8D16pgEpEmUE4MYuUJF5XU60p4z63K/B42ez2fDLxRqzInlGYozcRdKejpPwWfqgIcjBaaub7zHW+JgWqm8edfNzzzwYXdEhOvHq/1ZNnuN2GVJ+LCUpctYxlOOhvvNz1i8Zfc3n81osFnV2djb6MVPuXtC8KULjwqBvH2QWwfqyFj6lq/yFXdct1yeOibojm2BdthUxtxwdo2hlQJQvnZUvnqkPHu1KD/XOOdY8tkoLHoRoh4zWQiR/6v7Z2dm3Rbo0GN137fXKqpc7ebiPUueLKOTJvBYmxZFik1z1zsiD6WKrFsnIUp6UhqD+OvGSBF6r1wk6l3VNTylJnpIFF4Ko1FRcRS0slUg2Mmw+WYoLGqpTnp+fD1GCUmum+TRYzner5MJyA+uprN+9lr46YbT+bpUl6HD8esxmJB/vM8/hYiTbl37qjiu9RAa8A2u3241SYs4/nbvIyOdW5EzH6jpKGVPfmaVQDj6frDe3ImB3SszCuDVPfdAYlB20gpiq8fNP9N4qP9AuxAmt9RGuXSh40HV1vnbiMNP72ui6e4Gkp1outw7N5/Nar9dDNMz01lMZekiWGjw1llH46q8mheTP9IZbquhJSeitFFSfO3GS/Dxa4zYtJ3VGoCQE7qjwiM9XwlspvS9mkLx8EZApbStlVt/dEHktkbmfywU0jtFJmgSk47yEofHpe3cIOt8doc7T39pBQpJl3ZZ6QgdNebVqoy4XpdWM/ElwPqfUJ0XoJB1G7IwoSUIMLNS+5oUyb2Vxh6Jckb4yUZ3H7WfanePbPp3sPJty+6YdSYacO65ZiGwV6ZJrdB1mtr3Q7SljrSiQ27Emk8lo5VxemylyVfu2YN9czjqjp61OlK95bt1AoBSR0a5HYn5Nte2rxa26G4m3BSmGlHE6nY6Opyx5+6NkpL5QHlROfi9HdCi95Lj9bq9W+kciUX8YtZEYWNt0h8KIh2Tq5K5+UjYsI3lW03I8nC/X2VZEfWg+FTjoeEWA6pMTHedLf/tdekRr8ctLLd4vyczJnQTECLk19+5EmOaTJL2G7bZG0MlRl33HAUnSAwDpI8mWu2+4zY59ZMDQA91/mFIC1IToXQIm6TI9ZIRzKCWqqi8MigsonkIK/rfXivRi6qfJa9UXW1Eu01hG+16vYj9aRqkxS4YkUO5B9dTNFXa3++nuKNVvGSFx4cHLOGq/9beuz8iPZMD5oh44eA++5tN3VQiMyv3+e0ZNzGp0PgmRMtJnMlKvUbZIlnrIn3mSk/R5aq1bMOqkfrBUpblQ3z0alUxb5CbZeGqvfjFDon3q2tR5vSR73RHH8qATr9sZdZwOTeP2DNS3gXIuWJpw0mWf3FYZ+HGcXxNdn73Quv3SoxK/O6zqy3vvOelVYy8qYt1sNqPieEuQXiYgWStC9ZV1J1udRy9NgmCJQEQkJ0J5+J1AVGoqSitioPLI0ShtJxF6KtaKnkgcUtYWyR6Kbj3V/rlaWSt6J0nTOCgzZlC8fksvdM5rEQ2NmHojoyfxsg1GhR48HB293OzSao8y8z5SL1prGD4HTlpuZyRbOkRG5azb8643LZh6NkibkPPz/ee0qxb5Uh4E55lkSfm3SlC73W60Z9kX3vmio/ymI10ajCbPa2ckEa+3tCZc51IB5RVJFq3jlPLJ4DWRrdowx+Lj0nlKZx4eHkZRp5c5ql4iMa2wOun6tq1Wyq/xsFSgSIi1bEXvvBYjR16TxkpioKy9LkfD9h0JvJbIwOWsNki2LQfj5RnKklkMswS9KxqjEfoWJ8re9UX9Uv99GxQ/07EsA7E9bgsTYXrprVUS8bmnM9C1mEGpXclATp/rIt42iWq73Q7bOJ2c+DfHRp11mdHuXrNPzqm3Q9J1+/dg5tBaA/v1zZYXqsb1o0ODpJLT4CSoQwSmc3kNn1AaDlc15RV5Td/OxP67Y5Ayysvqdl2RLp9twDttPO1VVOUE5qk5lci9tf7XirmMW+USOSPWY10xPdLXPf28QYJGLdDwPEpXxH909LIXW/1jndmN36+tcblOueFo3L59ziMbznmr7k/iJMG5E+CWqVakx7Z0nHRBctWcUD890tV12D9+5ym+9InbAikr6qECBkXe+k5Zk5c6nBSpsyypcN1Gz6bgHFC/3X7d/t2pktxpH7+EZ5zYvza6km5VNSdLoNA9tfAUjZHJz9VgGMEwfWvdPln1suGctxV6rYuRiBRV9VA+mUtt80YFpnTqA39VQ0ZMxfFUzQmTkaTABQeSqD4j+RKs1TEV1TmSnffFz/fFHfXR29B1Wb9jtKLz6IzUJp0NyzmspZKsSepe5pJTcGeteSe5c9wcC2XmZTEnXc2FxksdY4nN95FznYJROPWKtUr1U+dS112PSbYcu3TLsyNmO3Jsm83LE9d0vvSMvxTMcxXtv4YW8QrOGeSXFs+4s+qJ7g+88c/4XjVeUGC044b1n7bHyI0LaWyn5a0Xi8Ww35KpJvtIhZ1MXp5F29r3yzZJ2q10sOrlFlRPxykLVzBPnb22p/GRsJy8STwkIzdy1hGrvlzk8LKEZx+MijRON/ZWpOLXpCP1F9vh/HMudQwjdEaNOtazmkN1zpacGDgwi+JCseuUHLgWO3kN6UMrk2tFeq5vnDs6dc/ouOLvOuZj540v+/1+eHqeX5N99DKQl4cO2TVtRFE4+9yya57bCjJ+jvB/TXQlXe735GTT41WNt8pogUWTyWijNXFcMJIx8b3q5SErjLS4yMAdC3zWrCtL1TjScO/NKItpl+ThL7XDKPGQQni0zTRS49IDbeQM2DevbXEzOyNPXzlnlNPKTDQ2RT6c31a62NITya3lXElilH0r7XzNkNwh0wFo7Jzj1ssXvRgxUrcZDVM+chgeQfodm8wqWJNtjYl6J113PeOxVTWK0J2Q3Wm3Ine+63y/aUEyoAOk81GQ4BmN5vxQ/Zb9pSy5Q0S6qL85Xi8F9UBX0uWzAiSI1qp91djbclGstfgjQbZSPRKrFHE6nY5IlXsqOQEtZdXnJHJGap5yea1P49/v96Pv1C+d13IWrWjDd0l4+YZOSn1XO76YI7JQ/7TAp0c7Ur7ugEhgrFlyhVznMhr3aJ2EK0OSbDQHrPXqOky5fQHUU3C1wUUt1lhpxFwso0OiPun6fIAQ+zOZvPzGGw3eI+/WvHm0LP3Q9yQpfed6oHb9TkTu0mg5VOo0r03ZUmd9TljWkr3T7nzRTSU4kb/GzGCAsmImzOzN+cR/X893cfg6wddG15quBK+FHVcw1udYVpByVX25eZ8GR6VwQpGx02Dkjfnc081mM/wQo8hFBqnr82duaDzb7Xb0YA3WH2ncVeOfFJrNZqOfH3Ei89SM8vLaJImaJKi7687Pz4faG2twfPoVF3H8dm2OSfKnLP2pTzz20OIQ3xnReY26FXlpnCQRfSbZsUwgebxWg5WzUdSpXQ/SJeqQiIB3O+lWV11Xcjk9Pa3z8/O6vLwcnranXSu6a4okz3kk+cmBscRwqJRDJ6M50f5zZhMsNYm4aE9+m63mTA6ZW+Xu7u7q9vZ29KvRvHtOu2jUf8mUDtZvnd7tXvaeS1e0pU1gWUZc4gGAR+ae9fVAV9IlSWoSq142NPO3xPiLBYwG6eEUkbjhcCJY52F6UjUuRez348fuaTHs8vKylstlnZ+fDz84qJqv7lRjzW+9Xtf9/X3tdrshpdExVN79fj8Ygd+I4LVdKZhIh8SlY0kikofq0hcXF/X27dt68+ZNLZfLgWx1EwqJXQt7fNgN+6PIhJHzZDIZohcZtNoW8coxSUb8FQwvk2huvBbeWiDyyNGdFJ2S5lt3Gaqvrf7RuOlcSH7UJ678+y/fnp6e1uXlZb19+7bevXtXFxcXdXJyUpPJTzsXVMLyaFNj1aKqO1yOnwEN9d+Jkw+GIamJbB8fH+vu7m4owe12u8GZSVfonKSb/D29m5ub+vz586BDsgHaW1WNtqa5Q+UDgTQvkrvGr9KjPueiuOaMz8RgROs21SplfS10/TVg/VyIvKxITJ5+s9kMv9+kGyNEHvodMF9gkGC9fkRlcIKoerkjS4Z/e3s7/Lqo0sFPnz7Vmzdv6t27d3V9fT2Qi0dyUtzValWfPn2q29vbwVguLy+H887Pz4fShhT6+fl5eNYESyckWyoeb5WW0jDq4kNAFotFXV5e1rt37+r9+/d1dXVVZ2dng4wUxTEjeHx8rJubm8GAZDTc/SCCZ5o+m81Gsjk+Pq6zs7ORs5pMJiPD/vz58+Bs+RMvjKZ9zuSQRbqth5WQsJiiqp/L5bKurq6GR42qNKW+KCtSZK6fAdLnJEVfPK366aHYy+VykMPFxUW9f/++3r9/X2/evBmyDZGe9EhjbOmB1yzVB8lfTzJjQMNSgD/gSPonWa7X67q9va2PHz+O9Ffv0hvWX+fz+fBjs58/f66PHz/Wjz/+WJ8+faqHh4fRz1Mpslcf9LnmizbqtVY5AD5KkwGKxsKtmlU16PZ8Ph+ecChHp+MUYHAB92uj+5YxKb4egSeF1g/lPTw8VNV4szvTTqYFuh6jFhme0rbWve5My7xep7YUteiHIJXSMYI8Ozur+Xw+bBOrqlEdVF5WKZYI9/T0dFSnZRrvCyhec/X+VtXIm+t1dnZWV1dX9ebNm7q+vq63b98ORMA6K1O2zeanX0blM0ld9iJX1sxZOlE/zs/P6/z8vC4uLur8/HzkaFar1RD9y/Epmqsab3ETGSni1nYnpr9MITUHjGDUdxnf5eVlXV9f1+Xl5TAXIiAu/okM1BeSoeaIuwu4i0WOe7lc1tu3b+v6+rqur6+HKJeRpqI/LqJRNwkRCnWATo/bHDlHJF1fI2Dpjr82rdKbdEyZnYiPEa6CqoeHh+ExirQ5d6BeMvOFNo1Fx67X66HfImEvwdFRcpHw9PS0lstlnZ6eDtyg8fr6QA9Mfias/tVi7u12W6vVahSVuEK1ivdcjPBjhda1dMzPpQ1e/yKc4A4pUNWXz5Lw2qw7Cy91+N/8n33zv9kH9snbbfXZr+lGwLIC+3xoTrxt709L5j7freu1+nuoT36sX++1OXR5uKxf64t/7zVDtvmaPFoycflw3ltz3pKH6wn73NJd6q/rrvf50Pkt2R+y1daxr8m7NUY/xvvvx3L83Br6K+HgILuRbhAEwR8IB0m3+23AP+fldBxxKIo99N3PXe//B/9JO98ifomc/+j4o8uql529Juf/hDta5/Scs0S6QRAEvz4Osni/3x0OgiAIQrpBEAQ9EdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjQrpBEAQdEdINgiDoiJBuEARBR4R0gyAIOiKkGwRB0BEh3SAIgo4I6QZBEHRESDcIgqAjZj/z/aRLL4IgCP4gSKQbBEHQESHdIAiCjgjpBkEQdERINwiCoCNCukEQBB0R0g2CIOiI/wcGf68Igc3WPgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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oUQXr4S1wHvY6MXsB0wkvfy/rA+m0Wq0K8YM8387zu6HkuSRVvEBklc4EPHNynt7h4MaW9XdicMLlOZE5l1NqCDgrHjF41ET6xGXM9QjZYTzMn5Mu13J9guQ8Z+vFNvLo7iB46s7btnIi9mjOyTo3Ghhl5D0ny/xznuv2lAtyLKkiwyVQjHTJ55GXgVwJSwiVnlImFxjCPF6Ee7kH5ZVav557lVRPndi9x1FSIsDxeJxa1Kh+Ml5CXEkpxIV4UWbI0UN27x9lPIwNJZOUQlk3NB4KOYH4PXje3Ct3pdtut2kemXvCR0CoDOGSPvGcHMU2hJww3o2iRxV5Wsjb3pyk8vDeFTk3jJIq6QMKjDwva+YGi/nxe0BWyCnEBfFixHkOjKITrneUrNfrZLw838i9WRP3xlmrY9dwz9mJEkCYuUOTp4q88Im8ch1kXFLFu+dey+VS9/f3qa0RWXD9QR74PcYpN8Sub3nfvkctrCXP7tFwrrtunLvdbkoTeoFRUhp7KRRtGZvP5xUPgio91hEPzIUvz8V6boneQyc0D534nC8IhCDpHS8392y4Frmg8XicPEMKN9IjyZ6cnCTj4fleSZWezrxdLbfs7qW7h8x1nTD8eb3FDsX05+UefAbFdMH1FiJfOxSS8JY8JUQFyeReqfeZep7a24/cCyFM5uUEQ+jOfPt1IEPmCRKElHmvtxS6x82cu6d+zMNmLSia5ZER88yzulflHhXj9ojF8+rkpPH+uI7LtTsX/D4vWrHmnmZDr7i3yyYtnM1mM/XQMi88H8+BTlCw5feuR/kcEQ04weZ1DE995N56HrExJ6wnxO5EzdrQdeEeLxHsr7JlDALqdrsVIaXCTXsV1pwcGsLmrR+01PT7fZ2cnKTwyxP3LogQufQYWnleKhdKroE1p30GzxpLzaYNCgmeakDBveXGx8XY8pwqY2R+IBz3EFBCJ1tPxbjAMk6v2Hu+y8Nu5sA7FCA/z/XlBUDGlRsv9zqccEEehTAelB8jjOJB7p5e8VydK2FucFzRjq21e1q5ocoNInKa56FJTbjhcpmms8MJ1w0cZJ5HCzmJ+7i89sD/vTWN52Ms6ALrCkEx/6wrfdm+Ti5f6ARFW3q1jzkUfo28mOZy4R0n7mgRfXgqbLvdarFYpPVwz75eryfjnEdwPieQNXNeCsW6FyAgig2SKp4IE+oN+kwGgoBwdDqdtAGBflavNkuPC+oLyVhIY/j4EDp+hxJTOKF74fT0NHULYDRYVO6LwKD8CEduDKTHFjdXLhSHLgm8Sd+R5eTiOWUPo2u1Wuq2QHB5dg8zpccC3lNFKg9/PdeX59Pza7gcHPs3J2bmwFNFTiZuQFgj2trc8ztG/jynPxvP8VTRJ/8dv/dn9bnJn9GjCt6HvPMcrKX0uLvNDQ1z4/lJSN7JmzRbvnvP6wTeNgZhcw0fd71eTykFnzt0kXF6nzNpJ2//A07aHmHmHUmM1SMi3yXI7zabTbqfGzd3iM7Pz9NmIhwzZMajQU8v+Fg+ForvSGMSIL96vV5RJLzC1WpVaYdxBWIBSC/wHopOrgj5i4X39pKcIDzXS+GEhmssubekeNiTexWen8p/h2DzXIwJ7xkv1gtd0mPOy1vhuAbjl5TInEZzNkWgdO55QgS+0ygvDHqv6bGQj3l3pcnJ0zsCPMXgnvJTIWOv10tbN/F2lstlGg/X8Mo33o570nlY78/knpiHqD4XkJJ7ueRyeXmfbW5ovX+Z9UB+iSRYIz7jJIRh9gIU0Ynrh6d28uIu8uefz9ckJ32XbWQPD97z/MjkMcNLAZefuaZHLrlHT83E6wy0PXodIE9bDodDnZ2dJUeDVAKOCbpMW1wpFO1eIBfo4RMChJBAShCdFwFcAdzLcgvtYXNOAD8GnoujBcyb4JfLZfLUyUu6hXTiz0kSJYPgpEeL7vlm5skLgk+ROn/zdMXJyUmlab3f71fymh5qQ07snIIg3WOCeH3uvWAmKY3LPWQUnnnhefHeWXsIk/WleAjpYjhIMez3+4qMENZCut5jDQF6OsLH4ukbl7Hc2HlDPYaHFrHz83MNBoNkFJBt7okM+xkI5P7RES+yQlwuxxCUG3yvVbDuFPNI5/Gsnt7yNFa+UeIYYfJZTzWhG+iH7xz7V3Qu1xsn79wI+njdYCBTLvNuyJFjDNxisfh1ku5+v09hCFa82Wy+U9GFfEg7oAhMPkTICysMofF7kvpOSCi0K5cTAoLuOV6/NgI2n89TX6aH2giBe0H83sfinhR5SsgBL5w5I+XinjJz5nlVnstbv2hWPz09TduqGSOEj3FyTxeSctKgC8O9dgSca0uPZyR4p4Dn1vPCk3tlzAnwHChdLpCJe3bMMW1MRBD+fmTLjVW+dm5A3Iv0XVEUmtyw4zS4UfCox+caovYtr3hmvqbuJXv/M/KK7jBOSMc3KXghDHJHZ1wGMYDeaeJpH089HCuq0s0AqTGnHnk4IXotxT+DnhLB8PLCYq1WS6TvBy15ftv5w3O47t0yftb0V0m6pBdy0vWGas//8nf3NCg0TKfT5JGh/BCtk67vrsoF01uAWq1W5TQsqVo8kKqFNe8zdm/BicBzSE6cTjzehYGySkqKgsJ5gQnL7GPwUNCN1bF8s3sQ0qOB8PCSfyFd6fGwFQznbrdL5Oe79Ag5a7VaxZh62Mi8uoeZe7q+Bni6bpzwxiAEiMlzcu6Ro3ztdjt56/m4+AxeNrKJQanX60nR8ejYku2FnDxF4cQGXBaAp0WceNEfnjFPOXixiHoH29YZu5Mufb7c0/P9nnLAILixghiP6YKvmXcpsX4eFeLMYCi9xuBpC9drdIKtvOzkw2vnWZBZDATFMrxxHCdIlx10pVC8TxfSdc8Mz4LJ83MSctLj90zw4XBI7WZuET1f5KGge2aE2uQFcwHwkN9zYx6Wem8mFpzn4DOeQ/W0gaR3PD73EHgW7+jwU71QTEmVKrQXjrDuGAF+5/OYP6enabgn/ZicyAUxSY/FQOkxMvDnzVu/eB/w0NXDVvpuO53OOxV9iMBTMDwr9/acJEbd74knLlV7hiFylxnmjXv5VuSTkxOdnZ1VdqFhtNyD9pwwZMBcscae82ZMnn5w79+9RAyX5+/JLZPq8U0LXmh2PfF0g6cdGDe6whx7tOBRgee+XU9IvUCI1E54Fl8XUgLMDXnv8XhcOWSJZ2MczCs6QqQG/+ChQ7qz2ezXS7psG/Xmcyddkuwoqrv8buVRKJSEDgbP4wKvTHu+xxffK+Z4aXyWezDGPFSCLCkEcj1Ik/5RdnD5EYAeXuVe3Xa7TeNky6KfdzCfzyUpeexO2Hgjm80mFQ44IMVzprlH7E3rEMt0Ok1n63Ioy3q9Tmvo3guK5rlPLyS6RwlJonjS4yYUDAuFUUlpjT1ERTG5HoUoJzPW1lMEnl5wD8u9T/fW3AB75IDCci6vV8qJAvh/7hjs9/tE3BAQ4+fvyANE70dIcn9/NiIn5DHfSs28II9EecwJx4RCzrRpuYHz63rh0f+PR8z8e1soRskdAGSB90vvHuiPPC0WC41GI43HY02n0yQjHkV4CgTOoNjn3i4y9qvzdD0XRFh8OLw9ghHvjaq49NiU7t4mL37v++C9P9Etax4mexgnHW8T8oXmPf6z5+F8Z5iHNl4A4WQrrDLnFBCa7/d79Xo9nZ2dpSMTnfDdq0NJOZ1tPp+rVqtV0hJ5X6cLND3SGAm8c8J1r3RDltzPz5uV9I4S+eYU92rzSMMLgXilTgqErN5y5N5hLg9S9RAVnjW/HorPmhDR5GNj3iBf36CAk4AniRxLSq2F9/f3qaOGOfUOnNyT9/MN/BySfFw4H7QfchwqERU7Oj2n6eTlJIQRHY/HyWv1QiBHl1Ks9ALbMYcDmfPcqeuQ54Of0jt+zz3yqIMod7fbpUN4fOuxO1XIA0SNTPghOc47vh/A+epjomghDWVsNpuVcNMr3tK7bSLdblcPDw8p7yc9dkPgYfrffOMF15eqB5vj0Xj4zs9e1JMeT7HCY3Fy9edw8oboRqNR+jYLLLOkpCh0GLhnxHx5VwZeFad65RaeHtZer5dSNb5ZwIsa9Xo9hcR+ej7zKqlSZPADdCDZ09NTDYdD9Xq9lLog58v2YA/x86o0KSapevqYh+Qopnco5MaTdWLuvLjlRVbkwIndC4heeEFWnfy4B61IdE4wLxDvsW+88JB7uVxWdnYhx54X9tAeGSCM9sIP83Z6epq8Xpdz94iRHULz0WiUIijOjUY2yZ07gXpO2eskePJO7nkbo0eZvunHDbPrvUdeRMDoGvLlkQzz5V6+y0rugHiPrj9TKRT/NmC8SfcqfdLdq/Aijud9IGiEycNrCmsshLcvYREZBxPPtb3jgfeQAiBkyq0vyf68Z9DDQb4+hJCOz3c6neRJsoEBD8PzWX7+Lpae0BCCYYdev99PHh3PCOngNblB8wOpfS3wrP2waLwovNzBYFDZHcTuJJT3WCGU9XSBpygKuXmR1IswnrtljiEAWsp8Tryg6evlxSjG59uF8cLzba29Xk/NZlODwSB1mXBoSj5fXgzGE2SskC+Gxo09c+wFw3q9nrbQe/cKaarBYJCOo+RaHhlyrOf9/X06w5lvLCHfiTz1+/1KJOBODl66F5G9duBElpNus9lMO+CIKAB/dwJlzjDm5JOJuJhLl8d+v6/BYFA5qB4j/yHuKYlipOshQN4V4ILt1tMVAUsHoUiP5xiwOLyXxfACzG63qzT/ex7ZQ1gvDkiPBMHfPJT1U5/IEXvolBelvAgnPXqwvrMHwsBTJv9GPty/243nzQsohJo5eeCR+XwzDl4oDyTPQdaEct6Sw/ytVqvkgdPZAHlCvBgwL5J46EzKyVM33gHgLWnIiRcEGY8bTPduPKR0gvOUFNdE0SFHz/dBhORoIT9ygxg/Dr5nzXn5nGPwGZMXnUhTIEN4zO6Jb7fbd4zxcDhMY+T5yWHmBodx5NGmyy/ElLdjegHNn8ejV8Cae7GOXDRpH2SGtAVerhsF1pAoCWN8enqqs7Oz9J2KFMoxaLk8MG6v6ZTERyddz9X4ATVMPAu1Wq3SQdxMOtVX6fGEegTDE/EQAmEFnjJpAW9/8g0AECletXvCUnVLqyszHgTC5RbT7wVBOVFxPzxMDz1JA7iF9/YWlNMV2Pty8xyiF32YJ+9/pLjGOqGghKL+lT94dLyf55SUvudrMpmkQpavce5Z+Lx5Lo6xo3iM3eGFStYABcqLKRhFDLuf0OVGgPlywwNBMS94dLTG+aYAFJv8LykjPG+fLyd/jCpjYr14fu8O8fYt9xQ9EiK94X3MEDTPiV5wOBPG71hBlZc7Hb52uX7k3jFylRdtvQ5DKol1dNLFKLOWzJNU7XHHwz0/P09fZEuKjc/48aQ4LMwvh245X31MFPN0W61W2gvtLTVUUyFlD5fxcAgt/CxRnxy8OBYEryUvBuQFAAQFhc27Gdwjl6oHh3iBzXN+PBek41tRWXCsM837noPiHt4u5t4JwiapUsyTVCFico6dTqeyNZOCCi/e66TLl/dBpDmZIsBUor13mLQG92esPp8elvocH8vt+fznxRIvfHlRBs8sv65HWe7N8R6Ukxw/pFurvf3es0ajkdYC40S+m/oAITpRCTLopOtFNM9/0jHgHSGeHvO0mXv80qPRRmbyMB5dwhNHhoko/L45yXPvPEJ1L5578C96hLyik4zBO1/QDRySvG6SdzgwLgxcv9/X2dlZeuHc8PLtz94qiVN3cXGRjGMJFCPdTqej58+f6/z8vJKg911EJycnlR5eBM/zZYQahNi8vLAgPRbBaN/xFjUEcrVapRyyK6F3QuTK6Vae+3ju0tMKuUAyprzVzD1tqXpoTp67YlujpHe+BtzTBnjTKKGnR/KUB6TlhsSfF6FlnTx3R+6VvCnXhIRcgSBSV3ZXMk8pMA4vqHnTPs/BBgUvyng6gPvyGS/sQXLMp6ciSF2QG2Ue+NfPGsBRwGj7HGJIPbXiqSZ3Cnq9XjLErKunEiQlZ4J1pgvFw3GXH39mlzsIiAjmmNy6Q5Hn57m+P6/XPI79zteQKJS6i3vj3p3iaQE3ntRx/IwFCrsU6vF2PVpkHpkLioB+XOjHRjHS7Xa7evHihU5PTyuhsvf09Xq9SvHCrfNgMEiCTjoBr4qFQcH414sYuQXF4jlZIWjuCSFoXg3PlcYJV6r2oOY5VPeqMQIeMrqFRlk7nbdfvnhxcaF6vZ62ovI8hF/MHWHqyclJIiLCRKq8nvLg/czVZrNJQuynwuVk5uG8j5159blF0ZgD72zg5QVUXwNfS58z8oCEyCin1w88ZeEFPJ9bnj+vaOfPxLq7A0BqARnhq47YLOG5WffmMKKQRLPZTGFy/h1e3ubWbDYrn/GWQy+IQojIrOtE/mKN3FNmLj1KdB3I87TufeadALzf55W14eugvH85d6R8zZA95Njz556O9MjFoxIMPp0OfONwfmj/x0SxnC7pBYSfNIFvycQaIcwe4vjedohXUmUx+Jn8Lgqan6vJ4nshDCKXqgdrsLieZ8o9WLfuCIu3oXn46B4HBoDP+OYPF85er6fz8/M0j7PZLHkoKDN5Qv+cpyu82MjOIObE0wB4dovFIn2lth+zmReqIOL3tdygYBAriukkwBzm783z7K6QeXEqN5aMiZ9RfAgFkncv8Sl4FdyNKONm+y29rhcXF8nz8h1tyCadIxBLs9ms7CIjRM9Jl8iNF6RDAcnTdowvJ0hCesgV5HLrtQrWxiMg99R5JmTYUxvIMylCir6+yYW0FY6DP3e+U4/PwQkYNvfC/XwF3xRxOBwSwfJ5r4X8qnK6CKYTDZaVxaHxfzAYVMIPLKUTVt5OJj0eaOLKBumSOwUuCAh0HirxOfdAPB2Qe8F5eOdk6wvtx1JCCJBzHv4jXDyfe8burbvxoJUIb5S55Pe+M43n9YJHu91ODfMcFN/pdJJC5cItqRIeenoo3+nmhRa/r+fE3Yh5yoA87TESRoZyMvTcoEcyedgsVftjW61WejZkBbCunsrKZQWyxXhwb56bVAtfTw9xuQHAMYCsWG+8fjxq93TRAYjKdc29PC9cYcA9P5xHc/l8MUd0WgwGg8qmGHdicCjcMcDoopMuU+4Q+a4ySZXIzM/jxigik5PJJH1LtW+m8PQha448lEIx0vUUgIcOTrr0tJJop1jDz4SEnivzRDvKJT1+K7B3LJCw935fPptbdcidflQPF/3oPFcsnpMXxM3nCKf4HARDyHrMyqIYPLd7tYydUN67Eri2GyDPTyOc9HQyXs+d5WFlni/3arWvj88fhpYwFmX2nl2q2dzTPVmpemaFr1VexPR1yMNUV2ZkhGfwTgvGnKcXaCHzOfX5yFNRLueMx8N3N8g+tz6n3m3iBVUI248vJCVRq9UqEYjrH4VQ0iDeBkb+2g8TyiM6DDJeOnPkMuApJfeW35fP9qgMsiYK9o4d5pZcNeuP08J9Hh4edH9/r5ubm9TGiPeOQ8AY4A+PmD42in9zRK/XqxQ9fMHIvYzHY9Xrb3tK+/1+cv198j3M4XoucG5NscgoVn7vPETFkuOR4vH5MYn87MLui4gR8KP1IGs/Rcx7D3NP24sX7l14/thTE97zKz16LlL19Cvmifd5fhjB9xadvAUIQJyeD2eM/qx4E8Bze+5hePucG2je74QF+UBAXvHP8+OeTsq3jhL6e5jsX7Dp5OsdEcfmg3ERolPsJbXj60Mtg15h5hHvmvU5ti2a8SLbno7xPKjLtusBHvHh8NhNwS63fBMKhgZ58m3Q7gS5M+AHR/E8zge5g4OceuEy32iBjPFe91gplnENvkYILzff0OF1IHb4YaQ8IvlYKHrgzXw+T+FPDiaB/eEeMnjfoYc7Pjk56eaLmnujbnE9FeCFAd+G7C+OYvQzar09xb0HSSlc5zBxvHC8Jz+qkft7KkV696ul+V0+f37eAMqYk7ULHS1Pntf1lin3LJ0cfL6kaheHz5tXvL2lyD1b1u+Yh+Z/yz0p5gkvioLaMXi+3r1k/5dn4FyMer1eyTG6UfRnd6+QcVJs8zywe6vkZj1i8+fjGsc2s3gqKC/gYlz8cx4tUXyjo4fty41Go0JcvtPS18o34njHymq1qmzDzSM393L99T4dzY3Hsa4ISNfz376ZyLcMO5Bh2h2Pyd3HQlHS5dAV90ydBHzi6IuUlCq+vvheaAFP5fWcpFFgiMWtMgvLuJysAPf2sAjvZrfbpRTJ3d2d7u/vU0uT9O6ZECw0lhrvQXpsadntdhWjwxh8XDlxeSscz+BfP41H5TkwKuN4ZU5SrtzMlYf4fi8/VMf3z0uPuU/3MD2vixeUp3x8jZ3E+TxziFea52z9Gchv+3g8csIg+nX8XAQnMidrf7EW7iU7aeeti678EAjkma+F1xNysiVN5edJ8zlvL8zz+r7DE9JnXdEZ5C+PwNzI4U17jYO5wCFC55A5n8u8IJ3rM+vt+s+43ePl5fOX38d5wA+8KYGiOV0/gepYDiUPlVerVSpo5B4e/39fAvzYAmFBfbOFV3Fd+bzNTHoMe50Q3IPhVLHb21vd3NxoMplot9ulMDsP1T2vza4ixoHHiDJ5K4wTCmP2ucNDoNuC3DStRoSV7pHnxSEPt70YhrC6keLfvP8YxeZzpAIg4jxNRKiLciInPKsfNZinc3g/aQF/v4fULmce1XgxhbkGnq/E4HB/b0nkPvyO53KvDsNElwJkRHjvOU1kIx+rG3z3CP2AIq/4M99uaPNn41wDNirR58p7MFasyTFjwvr6TkuPiiRV9I75dM89l4sceeHa7+F66FHFU1zj0dOx6PtjofiBNwitW28vNrCInpsl8Y3VzZP8TyEvbiC4eS7VlZWwnN5grwrnHvJqtUrhP21WnEHAcYiMs9frJYKHhHhONzL0HiP4XojwqmseVkLcnuPzLbu+tZTr89ljoXOe4sD4kBvzVIgXMD13SEjL/DhxUJBxIwAxMC73FFnD/IAePEQPMxkT4/F+TEjOz2DIvSAv7BCaOrl7yE6eMZ8nL0ZhpJmbnHQhQ1+TfKtwfnJX7lHvdrtKi5TPBcbHw3X+Re58W7MbFO/+IB3h8u9jp7Dn7Vqus5IqpMj4PGJwg/U+OPHnuuTpoNzR8cjaI7aSKHrgjacE8rBfepxID3NyT4GJ9Up2jjzcg4i8KODeRF4o6nQ6Gg6Hqd/SG6e9IENhR3o8tu+Y98xY824JX3DPLXvqAS+P52CsnkPM89gQMttFPRfu3h4CDnlJj9V03wXlnoXfk/n0ijDX8D32PG+eVqIww7y7d+7P6OkiNypc71hI6u/3vljp7bkJ3iVxzDiz1qwfhOsFWq4PYXBNZNTTWZIqOyN5BsbpkYo39E+n09TytN1uU2oLHCsU8jPGNXdUIKl8LV3PfLu4fxsGMu/3kh4jQ8aG58xh+7nO5flcT53kqRqHOwkuoz5++MO7Kzw1icwei4RLoOjZC5zdilC7l+GFmZyAPBz08whyL8WLR3neycneew29lcm7FgaDQTq1yL0Exp2/8vF6rs89IF/wvCuBLbJeLfZwyucsb0GSHvONPC9FIU8h+N9RdoopTqJUgb0/NPcsfCx+b/pP8XQ9jDzmYbhB9rXjfW543lcA87Y4v47/Lfc+PS9NWMwaUJiDnJysXA58PSBLZN49dU/FeI3BIxX3Vjn/YjqdVg4cajYfv6XDr42s8Hep+k3HxzadMFcYSY/6PH3j0aivP3LKCweF7zFstVqVVjfXO2+X85Shrydz6MTp6R7vpfZxMT8e1T1VHN/v95VOn4+NoqR7eXmZwkMqoxzI4jkoV05feElpol2RUSivnOcbMVAU3uOHzVA4yIXXlTd/FhcaF4Y8x8W9eKHYPBeVYO904Lm8r5cw3D0i9264f54WYG59vhBCbzOCLJxEKQr6oS4+L9vtNj2/dy7QUuenPfE5xuqpEcjIc7coAiSV50pz79c7AQj9cxnwtFbebUFHCmtDbplzbCEGPMecMHa7t10o4/FYh8MhfY754evg80KUz7dvCODrZPBy/YhCdnW6gfcmf4jO5244HFZ2vJHa2O/36fB7z6e7PHsqMC8kuyy5557rEHNHxEG6h884sXp04kW4PKXCKXk5XxxLGbHO/o0pfp70r2pHGg/Rbrf17NkzdbvdyvF2CBfH0nlIh/V3xSF8Z3MAnhUEx4TSP0vXhKRUmSc3CBG6xdzt3n4lyOvXr1NelbwuZDYYDFL71+npqaTHyu9isUghofR4OAeCjkFgPGkh/l8rj/cTQgw8O50WnlPzEE9SMiIeejt5e78y+US+ama73aY2G//WVG+7Id/tpEt3RP61L6enp8nr8i3X3gEgVU+sgiT4l7nxApJUbVWju4PdXXwGksZQQQJsbHEF99PeINvD4ZAKnHQw4IF7hCIpFcA2m03qR0fBOeyd3Wr+Ne3cD6/QUxJeIPM1ZhcZeVHmCnL3FBb6Qcsi5OutiE5e5LvpMW632xoOh+m+vqXZUxHM2Ww205s3b/T69etUSGadiICYG56drh1Iv9l8uyWaVB9Gx1OMOG0YSWQ5P7mN92BU2VmKLuAAesrmY6P4KWNU5BEmzmL1L5tzEgQok5/OjyK12+309TEQJOHTZrNJRxNCNvv9PhGRF3s8V7RcLnV/f6/RaKQXL17o+fPnurq60nA41MXFha6vrzUcDlPeFKNxc3Ojf/7zn6n3cbPZpK/mcQWHiH0zgVfssdLkuP1bAQhD8zNG3VrX62/bhIbDoa6urnR5eanhcJjyip7ThVxms1lah/F4rMlkUskNosCtVqvSWcCuu+vra11eXurq6kpXV1dpOzftRt6pkZM6qRk3UO5Z53l/ntMr3Xm+kvf4tX2/fr6rkHGuVm8PJEd+eNZms5kMn0dYOAN8SSWe5cXFRcpbX19f65NPPtHV1VUyRhAsHi9rMJlMKs/s8o8OtNttPTw8pK9n4lmcuPPUCcTjkRNyA+nR7rjb7ZJDQX2D6+ce6ng81t3dnV6/fq2XL1/q9evXlfM6mDt3jDBe7kAx163W23Nazs7OktHjGy8gVJwxL8TzXnfeWq1WShUik3j7GE56jEuh+Hm69KhiiekA2O126WtoaJ3Cgi+Xy0RU7nkcDo/bNi8vL/XixQudnZ2p0WhUPE4q497+BWkTpvjmBEnpTFnC61qtpouLC/X7/UQqnA1MU/l4PNa3336rb7/9NoWXV1dXajabur6+rhAvQkcYjMftykIukfmgtcjPuYUUPc/nIeXz58/1ySefpIKgp0qc0DAcr1690nQ61atXryrfxeYeCs8A6eFFQbgXFxe6vLzU2dlZ2v7suVEIajQa6c2bN8nY4C1C7nmfL6RNmoDQ0dcVI+WbO7xveTAY6NmzZ7q8vHxn+yye6mKxSETkbY6stecL8brcGyat02w29emnn+rq6kqffvqpnj17ljbHeJTDc5DmwQnJ86qkwdybZ4cnnrQTuhtv76HG0fCIiLX/5ptvNBqNtN1u0waIy8tL/eY3v0kbg3a7XfIw1+u1bm9v9ebNG3311Vf67rvv0jeNeLqQe+IVL5fLtIGBdUWf/fyK3W6n+/t7vXz5MskO+gxRUyfyb/glGun3+2q32zo7O9P19bUuLi5SJOXFwV+lp9tqtXR2dpa8JEAP7nK51Gg0qlQSvcjABHkey8nlxYsX+u1vf6uzszNJ0mg00mazSUUIBARFllRRWi8eSI9pEbweFJfwmWMP+QyLB9FTIW+1Wul7nTxPC0l42iHvpPCugrzi7rnJPJ9NIfDy8lLX19fpNH0vyPGMtdrbbxS+urrSeDzWYDBICumeN2EsIaLn9vi6FLo9OBbST9nyfNx6vU75TebX84PMBzlwCGmzeXtgD8bJuxN8Z+BqtUrGZL1eV7ys4XCo6+trXV9fJ4X0/LKfReHtTSixR2p4h3i/HnLjNPT7fT179iwZaT9HA+ANn5+f6/r6uvKNHV4Idfn0ohHPRvjMnAEvlnnXBBElZOjdEhj/6XSa5hDDi7xTbCUCgPB4JtYTufeNE9yfMWDUWPvLy0tdXl5KUkpv+C4/r294hOq6gXz43OL4+dwjV6VQ8xabI/jJGtgQ4qf+llfEK4OsVb9Q0v99X/tZ3irD37y4lrdt5fflX6+g+32OVXnzyrxXZHkWVzq/77H18CLQsVYzH6tX9rlvfr+n1sDb1bzo5GuQXyu/r3cV+HuPrV/eyeDPnn8u/+y/8j6XlR8ytlyGvF3P7+Uv4AUcX4cPrYGvgxeG/L4+bpf9nMzzOTo2T6573n3j18+7R4591gt7+RwdGzPz7+93mTmmy8e6XvL1O3ZfnuGp+ZFUiah+Ijx5sWKkGwgEAv9GeJJ0i+9Iex8+YADei/dZqaes7o+93w+xhMeu+THaUJ4a+091r/fNTYm2mv9fUGKePvZaf+he/6rcfwjH9PDH6PG/cq9fAsLTDQQCgZ8eT7J8ub1vgUAgEAjSDQQCgZII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYII0g0EAoGCCNINBAKBggjSDQQCgYJofuDvtSKjCAQCgX8ThKcbCAQCBRGkGwgEAgURpBsIBAIFEaQbCAQCBRGkGwgEAgURpBsIBAIF8X8B7R0jcb03WewAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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AvpKCR47EslwuNRgMAmkSnTDmEDbrIcondm2PRqPgVEj3ElgikdDTp09VqVS+1zX/sYjN0x0Oh3r16pXS6bT6/X5IeuCNSuuWbz6frwnsUc3XJp/QT2120obcfyzwuNBvpfXMLdUMGIXJZBLkD8T9bDa70dO1BsRu/tVqFbynZrMZCAsrjCQxn891d3cXvOXd3d1A/q1WSzc3N0GLSyaTYcG22+1gtPL5fPBCSU6x4KvVavDs2u12uO5+vx82JQkLvE9L1twPc8SGYKzsfDJv6M75fH5NVyUpGs1O47HZDYvHDzAyNoHK+yxx22u1ny8pkCTjhxS2vb0dogeum3GrVCohgWWjLYjd6t1EexiP0WikZrOpdDodPNbJZKJms6mzszNdXV1pMBgolUqFxCVrg/WCUYiG3vZ+o7Kb9cC73W5YXxgu1rxd96xt5i6XywVH4o+FNdjo4ZZM7VyyhrhP5mtTFRORJEY8kUiEtfurX/0qvObHrmaIjXTxdMvlclh4WGN+rAdjJzXqpUJU/NgSJDY45Ai+j47L65l86d5o9Hq9YBn5ruj1EM6h1eKFA7vIIXW+bzabKZlMBt3u+vpag8FAkkKoZb1RNgSbJJfLablchvALDTibzYakjPX4LJFhpCDdcrmsvb09TSYTJRKJkL0fDofBWyBBhmdpxxyyt/Nj5wCi5T2ZTCZEKVbPn06naxUJUQKxawT5A6+6WCyGa0DC2LQR+Vxey2a290KlBEaIecJwkWzd29sLUREGF9K1hDscDkNkgOaIbINeLilUDIzHYzWbTV1eXgYtM5VKqdPpBPkNXRoiPjg4CPNvKz6IvuwPY2ETcDbzb+UYSI99zb6QtOY9f9/9Buz+ZV7YL+wzDIt1xOxcYVStY8MPa6XT6axV4cSF2OQFdBnpgQAJeUulUtCdLOlCFFbzJQNL+FkqlYJ3YyshsIrS/STaDGb0+h4DE4iXcXFxETK2aE3S/SLf29tb018TiUQIUy0ZWS83Srp2gY3H4+AJocVh+e1G6fV6IQRPp9NBlx0MBprNZmt1iXbjTCaTNQ8sm80GwuHa+E7upd/vq9/vhxAPfdNmxa2naxd6NOqw0QI/JM1Wq5WGw2EgudFotFYdYQFxc31o7Xwn98z9E+4zhraqwN6/XXd8DhsZ4qVaoVQqqVwurxkP5hGPCz0erRkDYD+bKAdNtdvtBr0a+YpqAgwvXihGxUZdzCPjy5rB27URCHOHRLG1taVKpaKDgwPNZrNQAsfalBQqZC4uLnRzcxMivW/zFB/7u3VmbNQE2RKVWUPGb7uH4ANrnPP5fChpzGQyYayHw6EajUbIH/2sPF0miAwzhFMul4MORXgsPUxMNBS176vX60H3o4yGWuCoLhm1pHy29DjxkpTo9/u6vLwM2eTJZKJSqRSuZ2dnJxAf1QVoWzYZKK3rpnbTc432Om3ZmyUQ+xq8on6/HwgBvWtrayt4e7VaLSxevGq7WamDhsDsePZ6vbXyMIgDg2KJzRpIS7hWPrFRBCRlDexoNNJ4PA7kQgRE9GOzzGy0xWKhXq8XwnE8Zrw8yuVs6VD0M9C+8Ryj18r3U/bI/EKg6XRa1Wo1eKsYJRvWEhlYrxPCHQwGajabGgwGWq1WyuVyKhQK4dqtZssYWB2W+bBlkTYCsZ4tcw8xs94g8Vwup6OjIxWLRS0WC+XzeVUqFSWTyTA3/X5fV1dXevXqVZA8rBa+CdGIyO5Ru+9Y+5JCBJTNZoODIClUTuBgWccln8+H68BJ29nZCREI76cU0a6HHxuxkm6n01G1Wg0LKplMqlQqKZFIqFgshkmzi5aNZzcdNbNHR0eqVCphwd7d3YUFC1lb7Qpg/disjwFrSHlaoVAIVnJ3dzd4IbVaTZVKJXwWm54mAmQDm0QjIWTDbJsAsKUz0n0Wu1QqqVAohI1vx4nqDUgRL3dnZ0fFYjEkGfFoIYzhcBiSXcwJSR68rU6no263G8JIW2kAqVot3sonm7Lj1uPCEDAveB/D4TBECrwWrT6q89kyMsbfdrehwdokGrAblTlCxuHv3KMlC+6T9cX1VavVkOgh8rDlgdPpNEQR3DfJom63G3Ry8hTUxuZyuUD8yBl4r8PhcC3/gRFkvSFhMF5WisErh6wZt3w+r4ODA9VqtTCXvI5102w29fr167WkLVLQu2AJN7ovrbaO7AVh4uh0Oh1JColoSWv7xpI00lqhUFCxWAx7FjlmOByG+v64EGudbqfTUa/XCwPN4BQKBe3u7oaB6HQ6ajabYXPbZEw6nVapVNLBwYGePHmi7e1tLRYL3d3dBWJngW3SrcAmC2vBgoV07bVCXIlEQqVSKZQqRZN+kKtdZHgr1orjXdmKDbyzUqkUaoWpMSUpRmE4VQz8JrSEpBaLRfgsOq2sMZtOp8Hj4t4tGTGeVhqxGWPrKdlEl/XcITHrgfGbzyA6wQu3GwGiQlJi41DozoZlraG52hA1+r38JgS392/HgNdwH9aQR73KaBmW1XKZWyQfSg4xeDgPtowQrRgZjgYVwn2SQTSbWHmFucV7tY1GkkKkYZPOdryQTBgT9id74vr6WhcXF7q4uAjVSNaTfww2woy+zu5XIgycLCLKcrkcqjnQZZlzeKJcLocuTMYjujfZM51O5+fp6aJT9Xq9QKIspu3t7VA7Op1O1Wg0tFqtwoay5R+QUaVSUa1W0/b2diAyex6BzcxumthN/x0FnigER1YdooUwrRfCvRJSQiBcI4t2sViEZA9yBKBGEQ0tl8upXq+HtkUWHLovYaEtqZLuSSCXy2lnZ0cHBwfa3d1VuVyW9BCeQhDcC3qYJRnuh3mgmsBqsQBCslop14cWaD1Dxg8ShXT5DF6by+WCpERrK6Eh5E84b70dmgVs2ZetTrDlSTbhxA8kCphTQnnWxO7urp48eaLT01MdHh6G8inG2CZ90IGZ30QioW63G2pFrZ4MadpWVeaUcJ+OSEmhHNASDd9ty60gKWQYG0VYMrafwTru9/tqtVqhjpdGBcjv26LHx/bepv2KZ18qlYK8l06n1W6311p3rbaby+W0u7urZ8+eaX9/P/AKURv7hnr4wWDwzmv+oRFrGzCJGKu3sthrtZrK5bJWq/vaw+FwGM42wAuSvtm9YgcdYrOlQY/huwjmVg5goaGxoUPm8/m1zcNv6/Xa0B/dkSQMEgvhW7FYVKVS0c7OTigL4lwDNiohNddhqzhsFUKlUtHx8bGeP3+uk5MT1ev14FFZfXSTfmi/b1PlxGKxCNqZ7eaB0KxWOZlMQg0qEgQSDEaHENsaM0JVvB17FgcbaXt7O5Am8hTGBs+Q+mSiDnsdGDheS8WBDcuZV6o3bNlZPp/X3t6enj9/rg8//FCnp6eq1+uhmwqDg7e8tbUVQl26wtLptMbjsZ49exYqVrhG1gVzbI0JobKkoLOzVmjOIHEEqeCl0q1GQrpYLK6t4ej+YjyoLb68vNTFxYWur69DTgGD/23ygt3Hm8DcbJKO7N7aRNCslf39fT19+lT7+/va2toKdd7wUK/XCy37/X7/50u6EA9iOyRDaQqbezabBS8WzQkipU/9+vpapVIphE53d3dqt9uhRdK21kYRTV5t8oaB3aAQLwRVLpdVq9XWNGJLtnw+Gx1PzJYusfCpwiBkxKuAFC2p2ZCWsBvihxwpGXr27JlOT0+DFwW58Tl4VehfSCfSQ5naarVSt9vVxcWFJK1lxu3GxTvmukj+MB/D4XCtqqFQKISDbGg0oGg/k8kEvc7Wv+7t7YWDe2azWYg68vl8IBPes729vXbwzHA4DPdrtWK8XSIuqhTsRmT+mTuMz/b2tgqFgg4ODnRycqLDw8Mgf/A+5o21gKNhw/paraYnT57o8vIylIlR8sd6s1URNl8BcUPm9Xo9XAtdcRAi88l+wjjQWIIzgZdr67dtjuPy8jK0f/f7/TUN/TF8l33HvqBFmwTu3d1dcDBobqAs0u4F5rFWq4XEGWuQxiKcH+aTfRkXYikZ4zf1iTaJlMvlAlGWSqUQkpFEsod+kDVuNBohobW3tydJur291d3dnbrdbqgxtMRnNcZo3agV7qOweqYt5en1eiFZxXVyrXiBtpCc+7clOljlcrmscrkcDvngbAQy4nSCUTFAnaw9+QoPDXLO5XLa29tTvV5fayW1eiQEyLXYEi/unXtDb+x0Our3+2F8lstlCP1J+mGEqEQYDodrSSeMDWEj74XIol57sVgMEgk1sMgFhJi28wsPEc8QIuc9nNEBgdrxwJO05UaSgnG3FSLSvSaPZ2oJyxogQnabQGQ87GsoPdzb2wtkwHdES/darVYgTa4Tj/X4+FhPnjzR3t5eGB8qFTivgz2JtETEYgnXki5ri3GDEInabAT62D6yCclojsBGnnjVdF4i7WHAGo2GXr16FaICohuuEbkPzqDSgi7PTqejTqcTeIJ2YstXPyZi9XQlhbIqNh4TTqhVLBZDAT5WXHoQ8i1BLpfLcFoQSSSy6ywEuxiY+E299/azLaIShtWsbJID3VBS2PQ2003tJKENiQ6baWVjMj6Et81mM2hPdEVhYKLtqnxn9MCgwWAQiMxueAiCMbHjjbTCmRIU5vf7/bXaXDxte8iIrUvluulAjFaNIA2xORgjwmWMUC6XC96o1Wh7vV4gFuaf74FEmBd7b7YZh/FFU8Xrs80dGB++B8eBQ386nU6QcCAXK+HY+2ZN2BI0PhPJw65BvEy7hrrdbvDkMXy7u7vhjJBSqRTmFKONp8t3cXgR4xDVc1lPRJvWMWKeo8YkSlxIJNFWcAylTZjyfqQF1sTd3V2Qd0jgUeoFl7DPWfPtdjsYiKurK93c3IT1C8dEE7xxIDbSZSLs4S4QGgt7NpuFxJg9PIX322JoOnFWq1XQgje1/lnwfZTioHXRjWKTLNL6gdS2ZMiK+yxCG/rZjWX72F+9ehWOoKSTiVDdaocYn1qtFhYmB30Q1hFa2USQDVfZpLe3t+HIwMlkEvRXfjBC1gvh2qkBPjs706tXr8IBRbbUjbDU1rfaNldavm0JmH0dxsAmLO/u7kIbNeOKLoqRRl5ATphMJoGIeB+ktlqtQkssR0fa+k5It9vtriW9CL0J8fP5/JoxY+3c3Nzo1atXQcZhLKJaO5KRNUoQSavV0tXVVTjDgv0BICAIklbtw8PDUE9brVa1t7cXygRtswnzOp1OdXd3Fw4y6nQ6ofaX60RishUtGD2cDNuAYvcHP9HcCw4WUg/fyfhv0lTZ63ij7IHr6+uQSGaObWRB0uz6+lrpdDqc64wkAemy9yw/xYHYSRerRtlSKpVSr9cLoTkhFb9ZCNYDQxft9/uhthRLZxcL5GClBb4bsrET9lg2lfdwLbbrCCK3hIXFn06narVaevnypf7whz/oxYsXur6+1mQyCQeGHB8f6/j4eM0r4TMsoc3nczWbTb1580aXl5fhcB1Of+J62GSSwhmx6LyTyUR7e3uqVqshERMtGcJ4rVar4FW8efNGjUYjnOXA5tnZ2VG9Xg/SCB4bEgQeLmEc14UHTZiPRw8J21AeAuT9trbXhv82UkEK4vNYV9yTlbjsPaP5YuRKpVKIPNLpdKieIAPO6+m2PDs709HRUahGgWyiSVXbTk1tLt7b7e3tN1pqmTsMA4QFYabTae3t7YV5sUd3Ws8RA/H69Wu9fPlSvV5PuVxO19fXIdHE9/IZdj1aJwkSjUopmzxdG4nxY6NY9ldUArRSF4aStYWUZN9jrwXtWXo4yhE5hIgr6tTFhdhJFzeejYQFR/uzBfyUk2HlNm3cfr8fkgjSQ8sghGhDaWvVbIhJEsAmqIDV/CStETrF2SyAKHFxQPenn36q//qv/9LZ2Vk4LQmSPTk5CfXGfLZNkiBL4J28fftWt7e3ax1nbDTOurVnW3BSGYuzUCiEkiu0TltYb8upBoOBbm5u1Gg0giwEUddqNb333ns6OjrS9va2lsv7w3AI4SA2W29rkydUNFD7adt7CePn83nwbG3HGt6RTZTyOrxHSEFSKNODeOwZxlHvnutGbkCmqFar2t7e1tHRUbifVqsVmh1Go5Gur6/VaDR0dHSkarUa1qJdFxjGxWIRIoFGo6G3b9+q0WiEOnYrOWEgbCKISAYv/vDwUCcnJ2t1xDYypBro7OxMb9680fn5eUjYNRqNEFmSZyAyAOwT1rxNpD62V+yeY7/a8yiiRGcrJuw+lhSMjq2S4D02mUy1CtfF+2z7NQba7vWfJelaWAK2P9L95qEQnFAEzY+Btr/RfKzlRKeKtiRay8rGtXrupoGPXp9NrPA4m1qtFjRpS/T0db9+/To8sgZvcWtrKxSYNxqNUPiN/kdXGK/hMzgsm7Kr+XweSu5OTk6Uz+eDB9XtdtdOCctms9rd3Q0a2KYOLTCbzQLRN5vNQIx0XR0fH+v09FS1Wi3UmXLUIx1Vdrzs3JPUibYhk7QjObRa3VdtsB7wQiFMpJdE4v4ROrVaLSTkmAvbnIBht3W2tsaYayPK4nwJ6d77ozYcwkgmk2r/78c12fM53nvvvZDgjUZYrCm0VWvYeCoG+iyNEJBcs9kMRsxqoblcTrVaTbu7u9rZ2QmlZFYz7na7ajQaocSLjkPumaaD09NTPX36dC3BxVgPBgO1Wq0gbdn5je6T6JxbBwfv3e5LW25p5QhJa1EKNe68z1Ys7O/v6+joKCQQLbFv4horu8SJ2Ek3ugCZELwOGh94/hJH5DH5tOxFwyaSAnw2ei2LQ3o4uMRmg8EmywvsRNlKCqtX2vIu+5lseJuAsxlcEiNIJWiRtFmen5/rxYsXevXqlRqNxtoh3iTsKFl67733QiKy0WisJdFsGHpycrIWNUTHAN3v9evXevHihS4vL4OHjq5pNXEqStCbo80TNsEIaUC43C9GkOgGg8vmoyQNj8l+VjKZXDtkiLG2J0iRZLXHidqkjr0+S8qE+qwXtHPGAX2SZM2LFy90eHgYnrOGvGA3t42Ebm5uQpKHU8tKpZIODw/DI3HI0J+fn69FQrPZLMz1y5cvw75BlqD8cDqdBq/a5hAAY0q3WvTv9nrpKn3sjJRNYF2x3oisbOLaOjW2EgiP3nbWYfCQCTlc/fnz53ry5ElwYCgTY46jjtVPQbhSjKRrydaGEQwGnmwqlQrNARwgwiEzLD4IYGtrKwwqoSf6JO2v0oOOTBiCnmnLmr5tAmyG1p43EE3e2WQblQnRukw8M6QByIQFTzszeurr168D4UI4fBfNFXg6yDGSwnkUhMtbW1va2dnR0dGRdnd3Q6KITWa18vPzc3322Wf6+uuvdX19HTx0SrqQJvDEIA50RsrNbFme7c6LSj68frFYBK8Sj81WIdgkDRov3jAldxhivsee40GBvH1/NIridRggvp+qBh6ASMKPkjgaDnZ3d3V8fBwkBgg7mo+4vr7W+fm5Li8v1el0lEqlQgnd3t6eDg4OlM/n1ev1dHd3F0r/bHjO3KLV8ggprtd2+iUSD6dtFQqFtTNNkJtsUjjqlDC27AP7mm8Lz61XSiu79FC/zPhbrdhGKta7t/xBbuH09FTvv/++Tk9Pw6mFyeT9E2YsvzAO7LkoP8WB2EkXvc6GJeguhA4kLehYWq1W6nQ64ZEoeLA2dLCTgTdAMoCEGh1EHJANaUbrdh+7fjwvagFparBnMdj6Tpvdxzu090cBN5uEjWlrcZvN5toTNrgW7hVvkgoGNNf5fK5ut6vb29uQ+GGjX1xc6PDwMBgevDEIgSoJ+0QDkje21Xg6nYYGFXvgta2bhHSiJXwQfTSEtbKQrdVGdsJYMRc28cd7WRv28zCM9vv5zWdICnIH3vBqtQolSyQ3GUdOBGNsptP7h11CpO+9914YY2ukqVa4vLzU9fV18MgIk+v1uur1+loTCERkDTf3wGfyrLZ2u616vb728FSihZ2dHe3s7IT2ccjJOgfRqgSiAjsHkLPVxN+199mX7GkMQiKRCBUxNmlpcyfo2dbZYC6obT45OdHJyYkODg5CYw2dZshKtpGCvUMi9GdJuoSYtLFK6yEJtYcUW7NQUqn7k7IODw9DyQcbwia/sFosMhanrWqAACEmSq6+y8Kxi9AW9lMyZhMm0vrxdBgEro3FwqNP7GfYxIP1wJBFuFeytXwvixKSIyzjZLBoTajN4Eezz7bsx3oY0UoCSUG+IQQk5MeD5JqjCRLG1DaQQPy2/pOxtmd02JOiGDNL4va8WVtSZAvm0VVtBUo06rLRlK2P5XHpJJ8w3HhsvC/qlbMm7CluaNL5fF61Wk1HR0c6OjoKhxvZagzWMVEcJIVhYh3YdcO6pdzLtgdTKsm1YvgswfEZtvPQOho2N/AuRNe//SHJFfWirTG242rL+CqVSogKKJWjEgZjhHyHAWYcufZut/ut1/9D4kcnXciMttRKpbI2yJZ4afmDfKl3JDO/t7e3VitKqGizprbshTAJz9Q+lcDWv0YXWRQsFusRU8BO5YCtAMDLIizEG4IY7Hvt+62+KCks9Gq1GiQQsrfSw7PT0NtI+iBlSA+H71DVgJcfJRw8mmw2q0qlopOTEx0fH+vrr78OiTFIGO+AA9QpF7OJCgjTjh9jA9lCIPbgG3tQtd1glpztNdsyPtpmbRQAYVPry7qzhh2pxF67bcCxVTbMK+uGcSGPUC6Xw9jZR/ZAatbwcC400Q8hMtJEIpEIESARHmuZ8eSshZ2dndAoYcvUrJdJaG/P02BdovsSsdgktI1Y7B6yf7MR5iZEIxkqDQqFQqhe4m/WuNv5svdj230pgyT3Q5UH90OXqq1/Zm3wSCgaPuLweGPzdHO5nA4ODsIjZWwPu3S/GW3NYqVSCRoaSTKyx7bYPrpBouVdeKSWbGynC4v6scVis6l2028iAoBBoIWRU4yY7Ogp9lb3jFZyQIAsMowSHhJkwqlJeH+2/Ri9dDgcBm/HnoxmDcbW1v15xaenp/r444/18uXLcMxmNBvMpsA7tGNo5RgiFiv1YAhs04GVbOyDE5fLZQgBCRcxUFQl2EJ5O8asHTRdm0xjXPAUKVGjHM/eKwY66glaI1woFHR8fKyPPvooaIs2YWvHhPMkOKuhVCqF8kG62vguzgqgygDSTKfT4XyJer2u3d3dkANhP9gqBAxDsVgMXZRWY0eiQJcncuI+2U+QupXbohFDFMwVnaPsSSIW20xDGaaNnqyXy3jTQAThkjwdjUahUoOIhM9iX+H9YxhpYY8DsZEuorclA0swJARoAEin709e4sAONgRJABYWGx5AuoQfVstCx+12u2EBsFk3lYxFLXyhUFh7mKM9IMaGaHi6VqPmAGYWyvHxcTj+D0/DWmkSOJxpWqlUVK/X1Ww2Q12lDe3tAR7cP08yRkLo9/shWWObKKzGzoKmJvXw8FCvXr1aezgk3gbXSjiHd2RlA0iVzWtPotpkhOzpZmTemSNrUKPzZD1f1pqVW2iSwGtl89r5xbuy5BqdS+bIylKQd6VS0eHhoY6PjzdWLlgySiaTocyJ5Nf+/n5oXOHeSbrhXUsK647oj+MuGUPey9wQ2a1Wq3BM6Hg8ViKRUKvVCudIQIBWB2d8+Rzbtm7lOpyjqHbO+sLpsE82SSQSoRQQTxMesF4xXq4FtfkYNpLP/L68vNSbN290e3v7jQS0XX+2iSUuxNocgfZlrbBdiJwg9vbtW0n3HVX2wBaqG2xWnEmOWlfCFMIqWhcR0+lI2dQQIa0/boZHlfDo6e3t7fDflA1FS2wgPoR+ai7xSDjKkvNx0ddIovFvvJpqtRqSiyxiG/KjEULYWHMIf7FYrB0mBLnYUNUmtvCK7PGRNqkiPTzpwVZxSFrbnHhkVtO0oavNoNtEEVUEjAe6HPdHqI48YMnadqvZaASP1ZaEoQkzVlyzLWWiwoX3cJ+MI++1UkU0Sci92yd9UKWRSCSCwUEWQvvl3jedzre7u6vT01Pt7++Hk82YS050o1SQzywWizo9PdX29rYODg7UbDZDFxz7BG/VroVoIpr1T4KKiAeC3FSSaDVze2YCCW+egGFfEy215LqQGDAU3W5Xi8X9sY23t7e6uLjQ27dvQ228dfCscUDKsJUMPzZif1wPZytEM8lsCJ4ThVcyHA5VrVYDOUkPB2hI3+yC4TVMGmVktiaTVkw7oRZWS0QHK5fL4RjCnZ2dEMpFSdd68MlkUtVqVdK9d1Kv18Pjuakp5jrRfdmUEAv6L9Z4Nrt/xDpECNlBopQ7WS2RQ1xIzDC2dCQRgttGlOijkvAmGR/GOjqXhJt4QFwH92klFK7Danh4lEgyJLBo/7ReC6RLuExS0h5GvikJyL9tSZIlAFvSxN94T3QcoklIPHNbD4wsQgKNrLqt9UXX5rVEETb/YXMC9mBvtFzWNu/hAB8roeTz+VDXOplMQlKQLsdqtRoMUbQUkugROYPWYbxSKlusgY0SL/eGF28rfTCWUcK1Mo+NZBl7DDElks1mUzc3N6G6xFavWGPIWPV6vY3e9I+F2EmXcwDYdBa2pKbdbgevgg0DUUV1SGm9uJp/M7hRbc42K0QXF4iGnpCILbvhvF88QJud5jsymUwoAbL1sZKC7stRfVGtGjkFo2GvAysPubMAMSh4u4TalN7Z6oD5fB4WHB4tFQ62ddaOif1tN4SNDOzTCJAhLGkQmkd79yFl23UFeUEkVsaIyjl0CeJd243MusHztfdkE4RcF/JGNGFm15OVDli/1pu1koRtd6c8jHZVW65ovUH7vVY6sZGEPYSHdYfMRLTCucVEjfYYztFoFKSJ29vbYCAZ9+h5IMh1tVptrbPN1vja6gk7Nta7jJbzRV9n96+dP8ad8WDd0L15e3sbyuaix05G1zH5gZ/t43oIoUul0lppSHTR2gTNcDgMlhpvjQX4rjIv60EDFrP1Jq0FjMKWGdnaSLxfWyZmFwpEenNzo5ubG/V6vbAgOe8UiYDvHY1GQau1CRMOeIc8rAZqH0NjT/niXm0IjJeDLMD42vCd8bEkhddHyGxlATtuEEG0QsSWNkFq/J2EEJKAPfHNPnE4Suy8H9IFVjKwxf4kjGieyOfzwWOGGPC4IVFImXGPev28h81vk3d06rHmuXcMhvQgGdkD4/H+eJ2V3/BsSULb72JtIoFw7CeRAiQ5m81CWRpjw/UjMzH+rD17XCnfYec6euhTtPxv0760SUlbrcC4Rg3bJtjqFebeHguK5LGpcYl7ZnzhmZ+VvGA3afSwiU1aKosUjw2Li9WNDqKtGngMkKXNhDPpj1lC61FAaNbrtvoQkzubzcIZBK9fv9bZ2VmQR/b29tbCdN6PHnt3dxf670kylMtlLZfLkLiDQCEr7gHPjtDUXp/VKW01AJ6wDddttJDL5UJ9dK1WC2VjtgWX+2HzUfiOHMJc8lpbK41UY9t3bbkgiUI2uk3asLHxmOyBJvb1JI7sYUCLxSKUJnKQNeM1m80C0dooQnqIIKznabvl6Ao8PDwMT+mwUZdN6mHIog0xtiLDenuMMRES0gIRj/RwtOXt7W3I2LN2RqOR0um0arXamqGxHjbHJjIHVjPd3t4Oey+qddv6cGvc7WdH97aV0JBsbDnmu8q27H63uQ04AoljkzxhwbWz9jZx0Y+F2M9esBNnYcNztEoGklAEb8t6QO8aLDaEffqB1XUf83J5H+2YZJQJvfBGmGAWwng8VqvV0sXFhc7Pz3V1dRU2E5qtJUUm3pawkaWG5MmKs/Bt1pb2U6t/0/gA0UoPJ6+RTWccmA87jla7q1QqOjg40P7+fqiPtosZ0p3NZkGPJOxNJB7aa/l8vHLaZHmczHK5VKvV0mJx/1RnNpLVWiUFT80eJdnpdLRaPRzKbTcxui1JIw7n4VD2i4sL3dzchJDfZult3S7jbDcx64a1VS6XdXBwEGrR8fYtydlORqu528Sr9FByiP5LNt/KBbaKR3oIsTudjtrtdvB0Mba2IoNxsiTIezkCEW+XubTVBax9pBdeC5FHjW10n7P/mB/m17b/Pka8UYkCKY69yI+Vg/jeaBXJY47fj43YmiMkfcOS2WyktYo2o2jDLltCZEuPot9nN4/VQgnNrBa4iXSx7jzdlXNVmSCrMSIFsDlZuGhKfBc/NnMKadkGAMiQ19mNbr1vm+Wfz+dqt9vfqInl3u3CsuNvD6Phc2xhPWca2HMQpAfDaQkF4whJ2ooBq8mR+KrVaqFECqLsdrtrda3RBB3vJRHJ5qIULupZWS90d3dXu7u7wSMjiUSLs71u20CDF8p6Q/uzITQ1ulSjQIRWn7UEYxtTuE9eT8lUu90ONeucaUGtMqE8c231YIgumnS2lQ1WK+W19v2z2f0Jc61WS/V6PRAq82yPNJUU5JutrfvmHXRd22UGovKBbfYYDAZrpLvJ67V5E5wMe6qgNSqMgSXcaHRsPf64EJuni4dmH0MCbIhvPd4o8RIG2YOugc0o41UQzuIhrlarkMm1XU92o/LeWq2mZ8+e6f333w8hGtqnJR9LQPbszsXioVyNciZb0yutP0ivXq8HkqZukgSIfS9EjTeBVEKbY9QTIyy1XUTcJ4sNLxmv1Z4eRjLIVmdEN060KoDCdeYdjc3OCwTHv22nFHIFWXTbvWbDUEjMfiYbzCabbH011xDtiLPSDWczU2Vg7y2aL7BS12Lx8PBV7gGSWq1Wa95gNJdBtUK321Wz2QxnJ5+dnenq6iqclZDL5b6RlOTaqWYgUSc91IXzoFd7OLmNamhWwAiRZMK5sBKb3XNWN+c841QqFR4Sy761685WYBApMC+cs5FOp8OaiUqI1jsnorPVMdaTjxLupi5IzqiIC7GRbiaTCWVWdAWRzbVeErDEazOdWDd7RF20hMc2LuANQhx2g0Mu0Qy9TZjZ7h0ev41Hw8aSHkJCxHwK2QnT8RbxTjAAeGHz+TyQPTW6aJ8UzxOyY4Q4tpFNbUuUuAdCUYrIuUfpQeuWHh7hTWvvbDYL9Y48i4rxfUx3Q9srFAqq1+vhbFt7jdlsdu3MVztvjHepVFrT8Om8itbA2gTW9vb2mjZqa3ejbdqsARpHyuWytra21hJu6KpRb92uE+nB451O758ld3FxoVwup+FwGBKJtlohql1aJ4NE2M3NTThw6PLyMhz0BLEXi8U1PdRGN+l0Wjs7O+GkOdrIDw4OwnPTrPG30UOlUgkHs9uGG4w5XjPvoRySChmaUDBom5waq2uzBqzXbcvnLJFa4yY9yCnZbHZNqrOEy+utIwbR8kMjCnsjDsRGuvl8Xk+fPtX29naQC4bDYXiuFZMLATKI9J0DiMZ2P9nw3HbKMHHohHhdtpUyOql401dXV/rd736n2WymJ0+eBKKlweHg4CDU6XK9nU5H5+fn+vrrr9VoNEKGdbVahYVkS5asscjl7g+itmEb3gCeAMkOxiDaxoxXyBNPSd5tCmmtAapUKuGJCuiBnILFEw3wvgkHOecgKnvQ3npycqJarbZm2NhUeFZUMcxmDyddUZLH/UoPVRT2nF2Mhe08KxQKoUKAelL7PZASsgudWTb0xztttVqazWZqNpvB+yJJh1GzZ1zwlJDpdKrb29twaI09Hzh6XCXzYDX31WoVZCqSWhgt3mMfhUSFgj38xe4jIkwOC6Khx0Y9rLHl8v5Brzc3N0HqYL1Tv5tM3p9dzHxyohf3fXZ2ps8//1yXl5drJwHaNWe9S9sUxAFC3Auyga1ygKDxdJGBmIPoWSrWq6ZcjvngviuVSkhSxoHYNN1CoaAPPvggZOQJxdrtti4vL3VxcaG7u7u1bDLAi5MUSNjqh0ySXRx4x6PRSBcXF2o0GsHzxYvK5XIhiWLDERYQzwj76KOP9Od//uf64IMPtL+/Hw6Ytr3j3Menn36q3//+9+GJv6PRSAcHB0H7JbyxmjO6Y71ef7Sg3NaLkvTA42fR2fpbSaECwSYCrYeKd1mtVoORu7291atXr/Tll1/q7OxMd3d3IczjMxk7NgJGo1QqheTbs2fPwsEtVqdlPolg6LgajUahbXm5XIZzAfBCqEIgusCDWiwWgdCiTSUQOF4Y17jpMUXSg6babrd1cXGh0WgUHj9kz2OwpYQYHgzd1dWVzs7O9OTJE3344Yf68MMPwxkLnBEQDZfp9KLxBnnD1uvaaA1Stk8QoVsxGiZbqSVaXcB6TyTunxTMYeiXl5eaz+e6urrSYrHQwcGBjo6O1iQZPiORSOjt27d6+/atPvvsM3355Zehw83Khta4sIb4TkiU1yOJ0PFH/S26Nok4CBtDa49wtNo7Dg0nuNmmpkTi/qknyGFxaLuxero8C4yFQPaZcMyWJZGRJEuKHoeXjLYJsfzyl7/UX/zFX4Qn6L5+/Vqff/65ms1meO4UJJNOp4OkwaK2IEydzWbBm9za2tLp6amq1WqQSdhAJNRevHihTz75RG/evAmeIId7/Nmf/Zk+/PDD4KWx6FmINkyyQLYgRBqNRuGJuTwVgcw7GXvp3shhIDAOm4DHVqvVNJ/PdXFxoaurK/33f/93IB7pwdvEc7QlgFRZENLX63UdHx/ryZMnwWOxOiaeLxom3h0yAdcvrR9qbT11C5uw4ppYc3h2hPc7OzuhJRvPjTnnnriGRqMR7pc6cevV0/hha0N532AwUKVS0S9/+ctQRraJcKX18y4ODw91eXmpQqEQkrA4EGi/hPM4KUgjaLZR4rU15lbeIEJYLBa6vr7W73//e3355ZchMsxm7x+I+uzZM/3lX/7lN2q+qRr56quv9Pnnn+uTTz4JXnIUtqKBfWUrkzAo29vbOjk50ccff6ynT58qnU6r1Wrpd7/7nT799FNdXV19gx+oTLLlYrbULpG4f8bd8+fP9f777wfHDJLnAZ1xIVZNd29vL3RYkdiqVquaTCbBG4WsbM1oNNtoy79KpZKOj4/1q1/9Sn/7t3+rcrkcstkXFxdhMiBpkhHSgxW0SSL7PYBOp9VqFc5esIXjkCVVCyxmQuRGo6G7u7sQnloPi39vKl/Dk0PLpQTJ6nl4HHhL6JI7Ozva29sLBwa9C3iT1M1CgkQXADkCrwCSw7MkfKM11Z59G00cMUbMD6RFIo6nJdukl9VA7TzZxKJt/+Z6OQiGUB9dc1NCCzKx97C9vR0Ig7GysgIyCOCQmuVyGeqRo97+JnAWA1UdNkGFYeGa7TqwFTpR/dqO06Y6WCqI7u7udHNzEyokWJOdTkfNZjNo67ZZYj6fBy2daMBqo5v2E3oy826f0sF+LpfL+sUvfqG/+qu/UqlUCo5Zs9lUv9+X9PDEiei9WOmGfxNB8fw3HD/WCsnbuLC1qWTK4Ad7RCbhq6SNG3BTDWS4yHe4/DZ5ZjemzThHS6bsd9vf7/oOu9k2lZjgkdqzCCyxogt+l2aOKGyJj9VHNyV3rDdmPbnvCiKNaGLTfn703rnX6Hc/lnB77N6iVRH2e78r3nXN36X43l6TrTWNzmn0tZu+15Z1fR/YionHShtxRqxW/13ubRPw8G2TgJ1TPNzoWmLerPH9LnvJ/uZz7H1tqivHoD7W7LDps/geK21sMrSS1sj6B8KjExEb6TocDsf/IDxKuj/JI9g34dss5LfhMQv/f/q53+U7vsv3/dAC/bvu64f4rj923OJIRMSNn3Isfux5/i7f9X+y7v8Y/Fh7+U9lbbqn63A4HD88HmX4H1TEcDgcDse74aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4Y4aTrcDgcMcJJ1+FwOGKEk67D4XDECCddh8PhiBFOug6HwxEjnHQdDocjRjjpOhwOR4xw0nU4HI4YkfqWv2/FchUOh8PxPwTu6TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgR/z97YW1s9ZXOSAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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qyZMnKpfL3+mavy9+dNKFcAaDgV6+fBmSUHiGdkFaq4cnyaJgsUqvF24qlQoL2grymUwmDO6fmpmEdBHZbRjEd7Lxp9OpRqNRILhutxtCUEIfYBeDTSKwUNBU7+7ugiXO5XJKJBIhJJ7NZmo0Gup0Omq329rZ2QlhZaPRUL1eD5qv9Xh6vV7wNCFzex8s+HK5rN3dXQ2HwxXPtNfrBfmiXq8HsoAM7X1C4nZD2Hm2JMX85XK5sLG3trbCz61htgQW9ZbQLDHSksJagOC5zmgFBdfL79m1CEni9RFhIIGhN1YqFe3s7ITog7lnbiF2vHHmEi13Op1qMpno7u5OqVQqhPbj8ThEFtfX1+r3+8G7J4JBekGn3NraCnuFqhtraKIZfsYBD7zZbIZIc3NzM4zZuuQg3jlRxHclXWCrDFifGFDmwY5l1BljrPkda5xthNlut5VMJoO09Otf/zqMxY9dzRCbpzsYDHR2dqZisRgWna0uiJaivK3sBi8FzQ+PjQGDiO17v4uOy++zwCSp3++r2+2qUqmskCTfab0n9DS8n+jmth4UG8Yu6Pl8Hqoj0L1ZzHh88/k8aORUPFjPq9PpaDqdBp0Pz1a6NxA2urDJHvTRnZ2dUJ2A9kmysNPphBAPHTc63g8lSRgzvhNjmc1mlcvltLm5GWQDxtJ60DZM53PRowlJM5mMCoVCKPXDS35bSRrXQsjK99j5Ho1G6nQ6YZ63traCUdza2tLe3p52dnZULBaDps+cE/qy/q3Wnc/nAxFPp1M1m81QgQI5kMtoNpsaj8dKpVLhWohGqKBAlmEsrFcLiUVJKZprsNVEEK0lNUkhKTUYDELegO/5rvtt3dqxpWJ8JoZrHblHuWMdt+DYsH4xLHEhNk+XMg/rmSB4b25uajwehwGxWXBCIxvu8r6tra2g+0n3mijfy6SzKNcR+NsWBotxNBrp9vZWFxcXyuVyIUMMcbHZIAuIGauMdiZp5V4gdP6f/+N6sciLxWIlqWTHkM8dDAZBax6PxxqNRlosFsrn8yoUCiHMxzjZigm8aUidz7VZ7ul0qn6/HyQNstrMI4TOfWBA1iX6rFfNd0C41shBchCANYLAXh9yFZ+DsbL1q9Fw3xoESxQ2JLfXSWIXz3k6nQYtGeNh1y0elw3/bU0rpAhZET632221Wq1gREigtdvtMB7sK5s8hFgw9oy/1fAZQ7sW+beNHqvVqo6OjjSZTELlCh61pFBZcH5+rpubm/D/76oCeMiTtGvERoK2ssDyBONq9wJzxffw/9lsNnANjs1wOAx6seWNvxhPdzqdqtVqBWKSXmcUt7a2VC6XtVwuNRwOV7wMO7jRSSgWi6pWqyHEs3XAVh8C0VBIuieIh4iXRUjyAt10f38/hL4kTfCmMpmMSqWSNjc3AwFy3VYmsRvaXpd92d+HbFl81pghZ+AF42VnMplQbbCzs7Oi36Ipo2tRgyspfAYv5CCkhOFwGMiDkJ4NzoaxiREb0tr5gAyszIGxYUPYcBL5wRIFn428QQIJj5n7Yk0xH6yTqGfEBrdaPNfKHJJLsNo8FQTlcnll7Lhnfs9WXvDC2A0GA9XrdTWbzTB/VGbgWTPmtqrB1nk/tJas4Wdt8/usLZyW+Xyuzc1NHR4eBo0cnR1ng/rlq6srPX/+XJeXl6Fk085PFNbrtHvQIqrvWokPaUu6LzMl4rT6LuTLntnc3FS5XA5VClRoUIXzXUvd/hTETrrlclnz+TxshM3NzdBIwCbDK2AjWcE8mXxdYlatVnV4eBiSVZQeTSaTFbKOkraklc+yCzUKvJpWqxXqO0la7OzshJCdUirpnkyoLCDDanVNdMN1iRdbzI33t1gstLm5GRoPIBnIFWMzHA5D1n42m4UIgiJ2FmI0y0vyyn42lRXICN1uN3i3NmlkpR3r2UmrHV7253bjQXCE9aPRKGidEDufZZNy/L5NfiFX4Rky75Rska2OygY2qQa5Rz12fm49WO7JGgTGiUYCPDEMmdVvrTHAuF9eXr5R/pjP50PCDEOVy+VCcpXkKyVVeMesYRttsQ8gK7su8dwh3f39fdVqtRXvX1KQQer1ul69eqUXL16EKgdbjfQQLOFG96V9JRKvG1FKpVIo9UPSkbRSSmrfYxO4SBS2ocUaMKQ51lkciLVOl6SPFdwJxyuVSgiT+v2+Wq1WqK+02heWb3d3V48ePVK1WlUikQjWr9frrbTq2pfFuxYGixDSxSNDzoBs+H+8Ed6LF7SubtV6u7Zt2NYV2yx+IpFYWXiLxeuaVTpxCPnR1iAgogr0P7w/rsNqnLZbKapT23vi9y0Z8TvcE5vJhu6Mh90cjAsvfsY4RDVzogjkkslkEgyD/W7IxMoI0QiC77PaYTS8tdf30D3aVlS+xxIZY0aCjLnFMONxUerW6XRCwT+GhqQiFR35fF7ValVbW1tKJpNBjuh0OsHI4Knb7+V6iZxs2RhzZDV+25xBFMWr3W7r5uZG5+fnoYQN0n1XyZiNMKO/Z/+PPVCpVLS3t6dSqaTlchmciGazGeQ3u89oYKlUKkFzt5IWDgfaNc0xcSH25giy37PZLJBVuVwOiYf5fB5KngaDQShTssmmzc1NlUol1Wq1YIkTiYR6vd7KQnmIcO2/37Y4kDoo7SGzvrW1FTyNzc3N4PGy0O0ms2cn8HebPcdrwbuCACF46V7CoBlkPp8H/RLCIXQlOgAQVbVaVblcVqFQeENXlRS8asrhMFy2bdp65xhBm6Th3i1hoS/imVu9kXvHOGBcbSWE9SKpqKjVaspmsyGjD3kQVfDZVsejVE66Jxc+m3uP1rtawuf+mFfCfJt0PDk50cnJiQ4PD0PdJ3qzXYfkI2yTQrVaVaFQCKGv1VtHo1HohrONGuVyWalUKlQyJBKvKy1wDJhDO3+2uYSowCaB+Tvvt8YGB6jX6+nu7k7X19ehrKzZbAYd/m3R49v2nk1wsjYpA6QNnL1NN6o1lCCbzWp7e1uPHz9WrVZTKpUKCTMiKAiX/3/bNf/QiPXsBbwxW2sJcdF4kEwmQ7kTlox+emuJJa14LPwc4iFM+lMAieBR0aqJp86CgBzXZW0hYDYr5Ig3QbswYTNWulwuhxpbivMhfIwWZGI3DhstnU6rVCppb29Px8fH2t/fD91RfLfVvSzxJJPJ8F1kwtFb2bCWsGxlRFTPlRTCX0lhs9PvXiwWVSgUlMvlQkUGnieaKA0iOzs7Ojg4CF11k8kk1IzmcrmgKRKWFotFlUolFYvFsPEwjJJC8wkhLNdhJSDuh/8bDAYr3j41qh9++KE++eST0CQAQbImuSeujXFlrGgwODs7U6vVCt6bnVs+A9KmRjyZTIY1P51OVSgUgiODQcOYQTKMk9XUmUOI1nrBkC5dl1dXVzo/Pw812uzr79OEFN1zjHW04sRGHvxuNMlOE8fBwYGePn2qnZ0dLRavuxLhICpwaJrp9Xp/maRLUb8tak4kEtra2goLsVwuh01/d3eni4sL1ev1oPWygGmuQEeVpGazGUja9u+vkxHWZawfWijWc+31esHC5vP54HlFPTtJgcyk+2QPG5lrYyOxifg7IRCbFeKxIasNwTBceOaQ6P7+vh4/fqyjoyOVy+Ugi3C/hNd4rVFvzxan93o9XV5eBs/NhuaUpHGNNtsPkbJh2dCZTCbUtVYqldDQYj0sMtXISYeHh0HHh3Tp+8/n87q9vQ1toVYLhJxGo1FIxlhZxYaxlJnZXIIlXJJmaOSSwiY/OTkJZyEwFnje0n0y1ZIc81Uul3V0dKSTkxO1Wi1J95l6Gw1Y42ZlECp6aNemXZmuOKIfSrvIfeDhWr3clmFauQmJgRwH7d9Er1GN9m37zu4vCzxydGNkRjrzlsvX3Zh0tFouIQ9SLpe1t7envb09VavVlSjLdoLCRUgicSGWkjH+hHRJglD8bS2aXUB4cjYR0+/3lUqlQp86kkKn09Ht7W0Ic6JlQVZjtIkTq7s9RNBcP8TbarXCASd4H3YT2VBtY2MjhKI2vJW0QrhbW1vBC0MiIcwnfMR7R4fiGiSFWk82JZtve3s7EDr3YY0Bv095l/VQIQg8Mmoye73eyrxymhe97YyBdH++QXSMkQzw2CBRiNGSAR77o0ePggEhKcYBRLZOdTKZrCTpMCbpdDrkDAaDQZgL60mRUJS0Uo5EEmy5XIZ7wrOixpOxskeAMrb28KN1ITGyAZ1dJDOZW1tZQk22pJBopTNsa2tLh4eHOj4+1vb29orEISm00EJUfDdSjG3ZtrW5koJDROTXarVC6/e7SMtqyNF9Z40Hc8Je48iAjY2NkK+4vLzUixcvwnkfGDbyDLY0zMpbSArIlpCtPY3sT/HS3xexyguSAunivWWzWTUajdDBUywWQwYeT8Rm90nIMUGj0UjFYjEUO1POFLW8EC4bgMVlWzWjZTeSVrxXmwSDHPP5fNB4bcbYJm34fTYSITREi5eBV2lLeCSFvn7IotVq6e7uLtRssqEwUFR4EFZSEYDnKa02KLBYbWealWs6nY6ur69DKElXG0RgZRabrMGr4IWeyBhFv8c2XnS73TAneNGUCVKxQla+2+0GQo2WsxE+2+/k77YygrGFpOyZxRgRSJBKBJwEtM1OpxOIzkY/GFOkMNYE18O6oM4Vrdtq/NJ9S7MdJ0774mhNvNxarRbKo1j7yClU5XD9zBv3HT0rQ1KIaEhMMc/sCWtso3vISiN40Oy5aMsw64FIj5b3bDYbGpQuLy91dXWlTqcTSgBtow1rHq2WIx/p4MTLtUm/dyXWf0jERrqQH2GNdF+Og4ewXC5Dx5rVcu0Lr4P3s5jwRKPZZAsICV2MxB2eSjR0txl2673xGSxCW80g3SdqrCzB4SFXV1fq9XohdN/f31+5NkDozaKE/C4vL1fOEIX8CcshQrwb2nUJ39k06zwa64XQmNBqtXR2dqYXL16E8J0ohTFAl4X42CwYmHa7HUI5tES64ui4k143RDQajRUPCq3Z6q+QMNeIFAERjcfjkBBkTigzsqeKseFsayjXRplWPp8PHt5oNAr/lu7PiL69vdXLly/1+PHjUBdtJRY8bus42LJI7rter4fyJfYG69LW+eKR12o1HR0d6fDwUAcHB0H3rlQqK2drsB7Yf81mU+fn5+GoUE4Gs1UiUdLlO235mk3Gsk+sBs77GAtkEuaLMUwm3yzdjBpvGnM4pa7dbocGIL6XFxIIMk6329XV1VVwVCBde/ZyHB4uiJ10sWqEG/1+X+12O4SwHPqBuG1LfexnYfG73W4YXOm+ZRGLG5UW+G6sriXTdddsQx88Q15WF7Yva2BarZZevXqlZ8+eBeIaj8fhcA17IpkNv2x2n8x+vV7XixcvdHZ2FkJEsueUIHEvkBL6G5oy+uHm5mYgfts0YWWA4XCoer2us7Mz3d3drWTsOXGKWs7Nzc3gseENQ3Bo7Na7s5odRGJ/10YetpSOa4XE7HVHoxXC4FQqFXR0vG57EhUE2O/3Jb2OnpA9JAUjXavVVpLBSAyclXB2dhbID4953dhicGg8ob338vIylC9ZOYLrY71zmh5jR6042j5GwkZohNH1el0vX77Us2fPwr7b3d3Vhx9+qOFwqMePHwcvP0qk9sU+iza/vC0/Yt+DPmz3T1QCtNUTJKKRBVgPwPLEfP76pL27u7sQCUG49nhQW5v7F026bDosPjper9cLVpBJJDNOkb/NjNqNQsbSemDRonx7DSwMNijVFOu6aSB+m5XHi8T6TiaTlUNx+Ox+v6/z83N9/vnn+uKLL3R6ehr0UI7G29/f1/7+ftik9pp5jcdjNRoNnZ6ehtPD6CZbLBaBIGxJmdV/OVEplUqpUCiEZhQ8dGvQbIUI78XDRRKxTyl49OiRKpVKuN/b21vd3d2taH1EInbs0SYhAxuC2/AVrZGf2/InK/VYL5hw1nqoSE6E5tFTw5B/uG/mlgoCEm1cVyqVCk/zQHrgSSKHh4ehnMt6i4yvlRkgwqurq5VxRqvms5fLZaiDxTHp9/taLpcriUYb8tu/c+g/T8t4/vx58OwvLi6ChMUcI5HYfct+Y1xYU5Zwo9ULzBlaMFUq9rOjXjFG1SZj7ZxYg2kJmsgEvZtKG9rWIVsiDbvX/yJJ18JOZLR+1WZ37YJdLBbh6EQGHKmBRA1kjTW1m1168xlfthD/oVIXK/jzGZBGs9lUo9FYOTvX6rGEnq9evdLp6WkgS6w5m61er4cWY8gFT7Ddbuvq6kovXrwIn0HyAI8hn89rb29PT548CYXjNqzn0BR+D4/RGjg7RnjFPHmCEj48vkqlopOTEz1+/Fg7OztKJBJB90X6sFqbBZtXUtD0xuNxOMgeY2CTNxS5Q4zoqbbwn/MvSMjZuUKG4BwHe5wmei/rinUoKRyIQtKPWmmAt4nzQO3qkydPtLu7uzKewNbCSgolWPV6PRjkYrEYSr44lY+KHrtmZ7OZbm5uVq6NBhrWEXOJrsl6s80UVHwUi0UdHx/r0aNHK8TId+I9tlqtEJpbb/whWS/q4Nj5ZYysVIGHa5OHttvSkrRtmtnd3dX+/n5IHmPU+G5rhLiuaDVFHPhJSNfeKDoXpEb/Om20lBNJrweJWszoYiCDa70lMtrAJkiwou8TYkR1XkIYMsHolUgcdqMRStqCf0sGPBKFR+KQied0/FevXumPf/yjvv3223CkH6TCBi4Wizo5OdHTp0/DM6Tq9bokBaPEiWPUuq7LGtsx5XDt58+fh6TFYrFYqRO1mrjVrO182PNcbYE90o8lkGQyGdqd0YgJmenMg3RtpJRIJFQsFnV0dBRKhGaz+8c7USJEMbwt2WNNcN8YBc5xsN189iwEntnXbreDdn5xcaFKpaKjoyPt7e298Tw3xhgSYo4hQg7P4TO2t7eVzWbD4d3n5+ehicbKKNfX1/rmm29CrTHSBB45ewRvL5q4YhwxRlE5zureNBgwX9G1sw7Wo2W8SXJyLbwX0kXegBvYK8wRHjb16AcHB3ry5ImOjo5CvT/RjLTa5LOOh+JEbKQbLZfBK2QBofkxiNVqValUSrVaLSSBWBQUqNtyEOm+tAYryaJkYinmZ2Pb0rX3mQBb/WC9FXt/NvlGWIp+iuZKJphifMaBBc8mvr6+1unpqV68eKHr6+sQshOaJhKvmwxKpZK2t7fDOa5ID/1+P4TAPEKmVqtpf38/SAzrtPLBYKCrqys9e/ZM3377ra6urkKykVpazoEgu3x5eRlaQe188Gc0Wx2VfKT72l28W7oUIXo8eyIYPofKBowoB66j29Gjj8xgKzggJRsdkWiTFIwVskWpVAreabPZDIfJS68NXC6X0/7+vo6Pj4OEZPVZ6yhwCPzl5aWazWaYS+q/d3Z2glbfarVCk4cN50nUXl9f6+XLl6E9mLllHxAJ0lXJk0DYc0Qa9oS2qJfOXFm91eY23rV32H9IgRA1+9h6ruwxxst2eLK/2Eechvb06VMdHR2Fz6dpw/KLJXe79uMk4NhJF73OZvoJH8iMUzqGhwOB4FXiwVqZwh6kwmIgi8zEUheLRyApeEQUT79L22HhFAqF0PG0bqFai03dIA0MtjWX0NkWykMUNIHwVAYrcUS/gwoQPLL5fB5aS+/u7oIccnFxEZ6wbI89tN/NYSYXFxfhqQZEDHhKLGTK9XjGGRl5YEvQoi++0xbE42WSLcejtg0CNsFiG0M46S2RSISnOfCKhr82dLYePkRt74EDvbPZbChnY2xsrfR0Og1HgN7e3urk5GSldtnKTiQ4SZ5x0psti6MhYDKZrHSORSUhPrPRaIT1QogNKWI4aKfFe55Op2Ff2AaM6Fq2HYTsn06n82ASOrr3iT6pdokmRSFhW/WBpxvNt/B5ELhtwd7b2wvdjDRtwC14vqx5Pou9HxdiI120PTaItHqeJ1lhG7ow+JVKJVh+zhyQ7nvKreZqJxLPlEVHwo2NjIdjvdSHSNeSeXQBrnvUNp6YDaWx3iyWQqEQ3k/Jl000Um9qQ3XCShYx3qT9HozW7u5u8MZshQByBokNS4zcazTjzlhHFz/Gz2pnNrKIbhQ8feup29Im3s98MtY2SUL9LERqz7Kw4WR0Xdix4vejura9X95Hso/68EajEbx6W7Zka06ja4E/bQK42+2uPNa9Wq1qf39/5ckTXAuSCOuY+7MlXtybPX/Depi0mBeLRW1tbWkwGIR5wdvEq4yuBRJVvD/agv420rJRLl61dRZsxQpJUWuQbSmo3Yf2PA6e20ZpIIYWSaTX662cWmebgd7XePxQ+NFJlwGnLbVSqbzRwMBG4QGP1NIxqYRdOzs7ajQaK089sFl+ElCSVs4zsDWC1C8yIRxV+K5SF7s5mXjrfdmFh8fISWCQHosmetRc9Gmx1hNE4yaDTYcbY0rNI1EAdaI8SBHvEY0Lzc+W2NgEIOc82BCZBKAlDfRsFq+dy2hW2m5MPBzCWiISe0ANkkJUf08kVo/AtOEpn8X7CMs5DpADb1h3s9ksELGNDrjuaLKX6IPmHLqjbPSxsbGhSqWi4+NjHRwchLG2DT42Q7+x8fo8aebs8ePHevz4sfb29kIiiO/Fc2fdsdb4DOQYtFC7hizxWj2eMUHHxqMnB0AuxYb969arNV7rdF1bsYKmzudQ6x49PxljY+dLus9jEBWQQLSdiUSvHARPmRmeLvuZ8ZC0cq8/NmLzdHO5nA4ODsKBIhAdBLJcLlcyrDxanRIoyKdWq4VaOwbRbkBbhYB1RgqAcKlfJYNrN08UtoTFhrXSqrZlCRPJpNPphIO/rXeKnov2Zt9ni+fREPf397WxsaFisRgeWGkTGTxsjy4uK1ewiMmCc0QghiJaB51IvO5Q2t/f10cffaQvv/xSl5eXK0kJSStElE6nV5IwfA4hIPXAtnEDgkdHZBMiu9jH3aAz02Vkw/VoaRTfSZi+WCxCq66tXojWHGPM0IQtcbH57UMuLQlICvNzfHysp0+fam9vL6w3qx3acjYy7kQ9ts2ZSIykJuViiURiJTKi0qFarapWq4UqGMiHkjc8e+63WCyGdWS9QtrLx+NxqBixxG2jEns/tjZ+nfOCseZoV8iWpKn1SMldWA3elvfhjHDSIGe22JzP7e1tSFASUdiqFqILqj0g3TgQq7xA8sqWVbGRCbnq9bpOT0+VSLxulGABLpfLMNBoVdJqOyffgzeFReZoONodmYR+v7+SEY0uFrvQ7CPBrQdmPRkAeUJUbEiuvVar6fDwULu7u6EeEo8DD5mHXBYKhVD3ub29HXQ7NsZyuVzZLDbczWazqlQqQSOl/ZjyNunNig3uOZ/Pa3d3VwcHByqVSqFZxYZ/6GXSvdbLJrWeJmc/oE/axA6SD2fFckgNiSs6zWwyzB64Y6MYZB6bYGGDdrvd8HQNdEy+Hz2cbDob3SbsKKuSFLxk1iDkXSwWtbe3t9LsEvWcIHJK+HZ3dzWfz1UsFrW/v69SqbSSi8Cg0vLNPuC8C466LJVKwZhjiGm5x9FYLu9remmw4PwC5hVtfV2m38pr0WiC+bdyj71na6TJoUhaiT6pJ8YZs52D9jrY56wbDBTaOKWOtsSS/SXdn6lBYjHqTP3YiI10GfR1HgqgfS+TyYTi7+3t7ZUjCW3LazQRIq22G+ON2BCS/2cDs1AeIh/IG28ZDxULTYi/bnPhiZC4yufz4QxgezrWcnlfK2lLyGgOgDwpn4MIWNxUJxBG0T7LvRcKhVB1gBFgUbPBozpaNKnBy5IupGTLrKT7k8+QUOyTjG3VAaV3kBaNMCRbkX46nY7u7u7CKXI8LYASMzw9HgjJ+wmH8ebsaVhECnhQ0eMPLXHgPfEejJhNxlh5I5o85O94yXja6fTrIysxrrbci/2CB0gnGg5FPp/Xzs5OKE+zdeI0qlCNwbm9y+Xreubj42OVSiUdHR2p0Wio0WiEszkI06Owkhrrnz+5H7sm1zVJMO72uXeMPeuS8V7XsGTlCzRd3osj1W63dXd3p9vb29AujPMT1fu5ziix/9iIjXRpiaV4P7ogrSXkaDsW6Hg8DrV3tnia90Y1OFueZAfXholY0XWhEOEHeiMeRKlUCuGvNQZRPZfJzWQy4flpHKy8vb0dit6l+8dK25ZZakvxqCB2jEar1XpD44RECZ+tMSCMovSOpEm/3w8L2ZaPQTxck02GWeNiN4UlXBsZ8LlWp4vq8HjHksJ1oSvSIMLj3u01MTbodr1eL0Q0jG+0qYXrtglL/g2pIGlhWBgTiN6uk2i3GR7mcDgMBpWf2Y6u+XwewlpbssXc4HESYttWaPYAejBzy3swvMwhuQObTDs4OAjSxd3dXahwwWjZvRXNY1QqFW1vb4dEoq1Ft4aYz7DAi+U6KW+0ElG0tDBacYLhIVqxjUTcCyV90UjWykasL4xxXIj1GWntdnsl67zOO6T8JZq0SqfTK09S4GdRTxfpIkq+LFj7/C3bpRJdHBCWTfBQ7lKtVsNDMa0mK61mv5fLZajPJTNtmz1IXpBws+cBo8XZQ8SjNb+2DpbNj+W23hiht702/qSRgc+DaNBQWbDSm00tdtwwUhChfeaaJVsIA8+G32Vd2KcAQ2BsDNvOG/1uCK/VaoUqBwjNEiqRA2sFowO54nWjwbNObDLHkm10THAcuH48Pnvewmw2Cx4jYxYlb6sd26gDuYBxs/cJ6UbXD7kQTh6zxDoajYLs1Ww2V8aDXADr21bG1Gq10O7N6Ww2OZdKrT4e3Ua3VINgEOwawcO1Bjm6/qxTJN1HIq1Wa6V+2o6/hTXww+EwNLjEhViPdmSQ8T5sQgRYS4fnhkUkBLQel12sFlHpgcHn4BD72JKHvF1bAA7R26ynJTs+B6+Vp+cOh8NwdCPkR5jP9w6HQ93d3eny8jKEeuhvLHy+y9as5vP5ED7aoyWjWX1b20qYazP59skckB96Z/RxN1xztNDcemu2+oAxt6EkLwgB3Z41wnPPbIUA8853ROcKTY8DkChvQutEK4XsbGeflUrQo/GWM5lMIEtrgCzh8x5bGcAY8aetXyY3wLxESYfvsaWGzDn1yNb4QpQQCBU+JEV5pFU+nw+fZ+t92VtWQ6YszDZ3QP6sC6Ipm2jmtS5ct1IUY2q/m7G1hPsQbDWMjWBtFGvny64V25SFkfqLkhesBxjVfaIWzBIlv0fdoT156l2hAN/JorK1oVZXsq25FkwMi53FhScOCVjZgr/bx5lcXl6q3++rWCyGQ2Hsd9j3NJtN3dzcqF6vB9KlgsN6jiRsyuVyWGxsYruYogktu8GZC6ut2VASr7BWq4XH41BMbytGovXHtgwuWkfL79INhURTqVSUzWaDbEIVBlUftsoA4rH3auUijIdtkuD3q9Vq+P9WqxXkCnvkJHo1Ug5lbdJq/Ws0vI2eusYZt9HuMWuwqeggMoNwmRvr5ZHwIdLLZlefKwhxIhXc3t6GGtx8Pq/Z7PWjnba3t1eSncA2bHCusJW1SApG175da9YhYK9GHSp7n9GEqNXI3weQNPdjOSLKMdZARq+F+uooOf+YiPXsBQZhnXdp9RYbBlvSJVywi3/dYFlZAh0Lfc0unofKW7DmZIdZ3ExQVGvDspP0oVzl+vo6hKfb29thoZGQQgqwGiqeHpuPJBp1k5Z0qXAghI7qiTaMtqVtUQ2aTPI67e7w8FD7+/vhFKqo3oaBim48wkv0V37OEYmUR/GwQXvEI+NrtVvpddkhWjr6Nnpv9GATpAHp9Vm6PLpmuXx9WhdP0mXjsi4heOYUD9IaGuuFEUmUy2Xt7+/r4OAgGBLmhTGCPK0hZw4krejtGBM0bAwAkUeUdImwbKUD87K5ublSKmcNJsbXPhGl3++H+l/kM7xRq8eynqnEsA0o0SSY3edW1iNCouKCiC2aPwA2R8MaSaVSb5BuVDa0hsZGwA9Fuj8mYmuOkFa7dPj3OqGcjWC9XWoU6TW3PfTR72ORW++rWCxqPr9/CkE0AWdBFvfx48c6Pj4Op/izgWwSAP2KRdPpdII+S5mVJSq7cS0ZEZbZzHfUo7TlUHhLeOycro8HZfXVdYkwW1sp3T/5ONotxkMO2VT8LvfC9TIWeJqcAcw9YGz4TOvpcq+cf2rHhu9g49MIYCtR6BTj963WD9GhxXOPRBc0eHB99shL7ie6oTEEjKVtULAVCHYtQ5qMe3Qv2NDbJg+vr691e3sbWqy5H+QlSJd7jhJJdG3ZdZVIrLbZYiRns1lYw7YaZLlcrhhEjCT7K5l8fYymrRiJRqXRZJgtKeS0uYfOfgBWFmC94xDYe1knU0STn/x73ff8WIi1ThcLb0l2XZaTBQrhYF2ZdBJhNlSyk2S1TBuWYvWjExudFKoOPvzwQ33wwQdKp9MhAbdcrj7DzJIonglJHwgo+rw3q0dDJNVqNRzOQV0lXi6kZ0u+rHeLl2zHhAXHONiuOftivHmCAwkYkhN4m3ax2o3D5rXJMgwDGwuvE08I8uE1n89XjID9eVSXtvNmq1lsKywG136O1eTt/yG/2EoVm9yxpMnLjoGFlTuoHbXefvSp0VZ6gEh4+ClPCHn58qWurq7C2sAB4NrRZtGtqcumDpdOzuhBONY5YY3m8/lwmpxtF4cUrTGT7o0NxIuUsbGxEaQie0YK986c2SYJ9PqoFMac2UiYMeX5iMhT1otnTKPjbdeL5Ye/yDpdWiRJaEQTWtHyrahmiidlT89nIqyHZpsZkASk+8OjWShbW1vBE7XWOCpNoFFC1ovFQuVyeaVGV7q3vu12e6X1l0SO7YhjEbHYq9VqKNgvl8uhU4jTttg0dN3gobDBkSNsHSeLKyqv2PuEfDAYeHt4LDyRmeyuJTpbM2nJAwLgevHcaKIgNEb6sITGOrHJMsaJe1iXTc/n8+FAIK6Tz4iGu4wBffuz2Sw0FNgyN3uildW7rcHkxXy0Wi1dXFwol8tpMBiE9cJcovNG67pZ5/aQo8vLy3ACGTo/nmq0pdyuQxyGyWQS6sRrtVp4Mi7G31bncI4I997pdNTpdNRoNFStVsOTMGz0VCgUgjSUTCZDAhGDY8fGrjv0ckja5hkYV4iQKNfKgHweBgrytsbRGkRLuBgpmxugIQaDHQdi+6ZsNhuaBNgAECgPi7OHSzOIEAuLlMOMoycGWc/WltPMZjM1Go0wsJyOT1lRdFKZ0MvLS33++eeaTCY6Pj4OjREkJCj/ond7NBqp2WzqxYsX+uqrr3RzcxMW09HR0RtdbJLCRrHdY/apBsgjbFabjCQJZh+whwdEVcD29nb4HMg0WupE4welWVRP3N3d6fz8XKenp+FYSen+ABrG3cojECaF95VKJXQaMcYYApogpPskFVFEoVAImh3XiI7N5mezkmwk7Lbv4SnDy+UyjFsqlQr/X6vVwprDCKHbN5vNcEYA3rmVOrgP5rLb7erly5fBWPE0Xgwu6wevys6DNZSLxSJIH5QQEjlB2MwViSBCdB49jgZsk3xcA80qVk+2Gnar1dL19bWWy2U4PJ1rZ97IMSyXr0siMdjNZlNnZ2f66quvdHV1tdbLJeJFruKgf2QiyukoraROP5rHIeK1pWnRM13sd2Io7IE9rCO83rgQ69kLjx8/1u7u7krIyeHPFxcXajQagUCiNX5McjTjjqWrVqva29sLTRTWwzo/P9fNzU2QBfACWDBRCzkej3V3d6fBYKDr62t98skn+qu/+it99NFHOjg40MHBQUiw2Z798/Nz/e53v9MXX3wRWm57vZ6q1ap+9rOfBS+cjYrngCdXrVbfWDBgNpuFUioMEcbHtqvaUhkSPEQXUQ8Lr6VcLgdCv7291bfffquvvvpKL1++VKPRCAmpVOr+cT32e0g+Ed7u7++HA6WpZbbfzfxyFi/6PN5muVwOEos9HAjCsLXRtoffPnSSum7bcCAphOHWc2ON2WQoD26kyoFrkVafsYfMQ4ng1dWVTk9P9eTJE3366af65JNPdHR0pJ2dnZAAjJY5YlwpcbPdcdHEkE2WUoKGgbHeGmQHUdtSPdt9iNEcj8fhDOWLiwvN53Odn59rPB6HM5ht+68tDzs/P9erV6/05Zdf6tmzZyHpaiMLjBr7Dgfo6upq5V6l+8QnyTsMASWYrB+qfIj+bHkp/GErcY6OjsKaRKojz4P0EwdiP2WMlkXCRxJAWHc2M+Ecwj0HRfNzNimE++tf/1q/+tWvQpXA8+fP9cUXX4QkBB4fIYVNbq0T+/k5xd/JZFKPHz8OoRoZalvr+OzZM/3ud7/T6elp0DB59tunn36qjz/+OHiI0YRWtHaX6+Ba7AE/1AFTy8qiQx+VXm/EnZ2dcKDKQ+GT9bJns5nOzs50cXERnumGNo23idfDYmcu7EE+29vbOjo60qNHj94g/Ci5SQp6pdWDc7ncSqkUHj+6OF4hxgKpyurQjDW/R9stT3WwxsDqqu12W8vlUvV6PXiHaKS2OYHkKW261MYSGZRKJf3yl78Mxw7ynnV7JJ1Oh5PhdnZ2Quuurd9lPXQ6neABo/GTXF2X+bdevPWybSXB9fW1vvjiC3399ddqNpuaz+e6ubnRaDTSo0eP9Nd//dcrZ/qynsbjsV68eKGvv/5a//Vf/6Wbm5u1FUU4BTgGlOpRL45nXiwW9ejRI/385z/X06dPlc1mdXd3p9///vf6z//8z5VTAUlwwg382+Z6bJLzo48+0kcffaRSqSRJYb2QW7F89WMiNk83k8mEpxvQZ55MJkM4fXl5GUqs8B6k+6QI4O+Ey4VCQScnJ/rNb36jzz77TMViMTwB9vT0NJADHhmb24YdIBoKATqdEonEG+GJdP9sLZ49ZbPbo9EoHCxtw0gWviWGaGKGUIrvQc+0GihejM1qs8h2d3cf7KW3QNvj3mazWciWR/U4tDyyzWS7U6lUSPrVarXQsQfhW+8OnZaxbTQaYTPb1mF7HoM9wHxd9xZ6f1R+InxEFuFQHasn2s9irHu9XrgfSs1I8JIvIBwfDAYr98Y5D9PpNMgieOdvw8bG/RGmnA1LuReGw+YQbJ7Cdqaty8ZHk83cL8aLOnHqe1mTtNbaBK70et3O5/NAYM1mU6PRaCVMX7efbCmdrYLA46Za5pNPPtHf/M3faGtrS91uV5lMRjc3N8EgRpO7rE97CppN2NVqNZ2cnOjJkyfa2trSYnH/wEqchriQWFcyZfCDFbBRDhPNnEurJUjrssLrFms0ibbOa8ELs5Y3msCwfz4EJpfNu25R4yXZwvZolcK60PJ9YK8TwrUlMfY6bYLMhs/vC9sVtW4e1pU62Z9HqwXeRjSMk9V8190Pf7f/v+777Tg9NC5vK0WKXle0xO+h719nMG3i5rsmaex+eGhcbO21jZy+j6fG3rQncdn9FU3WRe8dj/V9al6j1xn9fZwQm+yz0ZEdj4d4Ifp9Vtqw92CrcL7v3nzbrT74g7hI1+FwOP4H4UHS/UmeBrwO77KQ78JDFv5P/dz3+Y73+b4fQyv6Mb/r+4xbHHrYT4V1HtT7enU/5Pf+kJ/9Q35XHPvsx+KIuOGersPhcPzweJDhf1ARw+FwOBxvh5Ouw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgRTroOh8MRI5x0HQ6HI0Y46TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhiRPodP0/EchUOh8PxPwTu6TocDkeMcNJ1OByOGOGk63A4HDHCSdfhcDhihJOuw+FwxAgnXYfD4YgR/z/6UejDAZSB1QAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", - "ub = np.ones((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,-1) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,-np.inf)\n", - "ub = np.insert(ub,0,0)\n", + "x = np.insert(x, 0, -1) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, -np.inf)\n", + "ub = np.insert(ub, 0, 0)\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", @@ -1724,50 +312,37 @@ "update_factor = 12\n", "ftol = 1e-5\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", " solver.set_ftol_rel(ftol)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "lb = -np.min(evaluation_history,axis=1)\n", - "ub = -np.max(evaluation_history,axis=1)\n", - "mean = -np.mean(evaluation_history,axis=1)\n", + "lb = -np.min(evaluation_history, axis=1)\n", + "ub = -np.max(evaluation_history, axis=1)\n", + "mean = -np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(num_iters),ub,lb,alpha=0.3)\n", - "plt.plot(mean,'o-')\n", + "plt.fill_between(np.arange(num_iters), ub, lb, alpha=0.3)\n", + "plt.plot(mean, \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('FOM')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"FOM\")\n", "plt.show()" ] }, @@ -1780,30 +355,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -1816,49 +384,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ - "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta//2)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta // 2)], need_gradient=False)\n", "frequencies = opt.frequencies" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "\n", - "intensities = np.abs(opt.get_objective_arguments()[0][0,:,2])**2\n", + "intensities = np.abs(opt.get_objective_arguments()[0][0, :, 2]) ** 2\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,intensities,'-o')\n", + "plt.plot(1 / frequencies, intensities, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('|Ez|^2 Intensities')\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"|Ez|^2 Intensities\")\n", "plt.show()" ] }, @@ -1871,142 +417,73 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.5,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.5, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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R/IGveCX+xa0xF6E1j+xgYGp7jHsqxCd+/Tr+46fehr98/0/hq7/8Ffhnzx8jeqVA9eJo33VXJMrVP4Ky0YtBTZka+V/8lEDNe0yJmDhDQwA5COAKDsIMFGpIsPpz+MkDeX9f8z5cuuueUqgkyg8Dc/cVkOukfpntt9889em/izf8t9+DD/6xv4H7v/Lrs5KVT9OoBfohf8/qgXM/GTAQBRx86hk8+NF34AOvegSXvuoVUv8bNUHKpYAoOUAImv0S90Fg5myCrzsouO/CtvYBaFQCFOjL52ZpK566ITEv73/tVVz0MQtebMMeOMDyM8qBpLfTPs8vUn+ltD+QtM1TBQzl/6VMiP36vtp+9c67lGdNpwgMWkO8IOOspHd9/Efwzr/z/fihf///hu9+2XdUpNgaC7LhSB0fpBw/J9VuINpaavLOKCAwk56BGgpeer/7XfmbjYdu6AFBBoc6+3j6pvicf/LVj+Geuy4uGoGlGALfjLXSL0galTd9+Aqe8IFiHga2gkD+WzfGB596Fm/4f/0paXRf9HXAdNQ8t32tWC31k/MvMoCDX/t46ekZFOixuTcXImZAEKKDgZif05SNPJAS45oqNT/56sfwDV91H26NXAHAxDwz/sxi+JkZExicynETipGfmPFLv3YNf+6pt+EvXXoE//aXvQL/7Hmp4+yUhuSgwIsFW1xJ/91vPo3/7Jm348/e8178W7/nG/HPny9D+nzDHvRLADkwsKIkREj9MaCwbb/0a9fxZ37hLfhr3/QovubfvBf/32NGDKWOkSoORAoaDhhC4Fwvo/ADQiCtm4RIEdduSqDq1dc9hksXLgKcwBqsSqwGiEg4MLuIFt7xjrrw1Kc/hjf8wp/GB//oX8P9X/l13SGPXR3Uu5ng4NMDAxEOPv0xdU/8GC695OUFClx9pAZQPSTY6BC71QIIcp3rNw6y++CeJlA43/ZKPTHjD/RB4Zqe/9EHdHQDJA8BTtWwW4aHAy2jVXdADQX3e/eQZe6kqQGDp5572o1OcoGk1NoW2b448mQhzezXkjtrQzo1YLDYy/PPdkVFeNffew/e+Xe+H//5/X8Z3/31f3IWnT4DgDTVxr8q/HmcQQ4kmgWitTfiKgDXCgExl+C0Bg7MJ1ZDQeltWlG0gWhrQOBv6fqNA3zrz4o8e+/CS790Ky0QeBnYenDXNDr66uuvik8vjQswMG0Agbli4Bvdf/9FXys+/6nzDFzvvrqXGAFo+YOq45iCg4L3iLzsVAAOQ26AOYQaBkKEmRGGsKKBgMEBM+PajUO8/aMP48de9dN42Usu4jNjEoNtcMfA6ABgnGrjLzDAGBPrOe1ZM1Ji/PKvP42//My34vvueS/+91/2jfhnz4/Z8JvRL3BQb7e0Bge/+tvP4m9+/Nvxp17+Y7j7X/8G/ItbtdELWlmCFr3FE+TtVO8LRLqNEED4+7/xNP7iNYGa3/9lr8Q/vzWqymAgAQyRstowqN0UtYBBqcBCVGUhJs6g8JQG0j322qu476WXSu85cmkrDBIQBOKZpQOR6yiVz1SrC1X9/MqXAcy3UT8hxl22VMDw1K99XKD4m96Ny1/5shKz4KCVPJwuQkIsKoLrjF+7YYGGV3HfhYulLdE2YKttqsGhtIWHz4kSITEL1imhfI3g2rT5aAaDNkWIXqethYJZplfU4MWbQWmfn3saD3707Tp6ZT46iX273wOGauTJ/Pmb/TIooC3ujy9KV0InLcHDu37pR/HOp/6cQMHXfftsHHsPAMjT/B6/YfFp/1gORMuHzx6yh4FUtnUUAg8FDxoUuCFrS1DQ+qDhjuspCE/fPNwLBUsR5EvqgAWOEVx08Wt/GvebvGnGP6l/N3+f9oOAbrPn9nc+9Yt4w1Pfiw9e/iFc/rI/hHR0q2S8K9vpczdL1ANK16AKFHw3PvjH/5ZCQQCHqMdEhYPYhQFTBSYWw2ogYN8ZEnPxJ//2w/iRb3kMX/uVF/H8yBkCRjX6IxcAGJn1sxTHmJIABLOCAKrv/+C3nsFf+9i34Xte/mP4ff/6N+BfKBSYi8EMvo8nmBaUg176H//Jx/DeX/kOvP1r3oOv+t++HP/8M32lILov9tnAIAbKxwoUUAaFX/2tZ/FXP/YOvPOVBWoMHIYQVCEQZWFQpUE+CwwMoYaFqMpCVPB49rlDfNtH34RHXv0YXv5VF3FrZAQFBhAhhqELCZQmMEWFWV93Kb9gDI0pMKXgxS/vtidcqYtpVm95Gt1XF2cQo0DB3/mP5Pwv+jpgulWrChaPYBAQYoEChdkCCeIPJ63DgYCDm4d4k/n8tSdvt0Dm4nKuh3xrewys7X/muWsyOsYpEQx3EVA+v8VBWPtSjnDfiMAo8VkHzz1d3AcX7itti5V1dS3M2vg11yNTKKOT/sSPS/tvnUIHDsIIGvTqjHoZcVKu3yoMtf36tq7SdNJ0esBgiZD2xBq865d+FH/m4D/Gf37pL+G7v/YdpWcKzCuH37biZvCV6OBTz+INFp3+Va+Y0Sa5c7OXuxbcBW06uPm0QMFrFQqcb9CDwRIUrKkE+6BgDQZs/xIMmABvMw4+8cCjUj7TcQUDdY9LG94VEOCpKD0pJRz8+i/hDde+Dx+8+J/h8pf/YTcksUmhKft8Y9K4ZqXAuwUo4uDTf1eg4Ft+FJfvuqeoAxQVDlQxCBGsTztZTz+xjCZQdSAlYNIevBn1Zz51Dd/1cw/jr3/T+/GHXnQP/pX6/McGAsZUev9j6gOAfGf9J8b9V3/7GbznE/8Bvv2PvAd3/Wsvx790SkELBskZp3a+gSW14P/zz34Rj/2j78bD/+678JL/zdfjXz1ffOahqdvRFXtQ4+bBQL4jf4+B8D/8r8/ib33iP8B3vezH8G/9nm/Av7o1ZWgQZSFltSFAIMBUhiHUsCCAUJSFIQb83ZuH+O6fexjv+ZbH8PUvvg+3poToXA9BFYkQDFYEEigyOE1afweBhDDVdTqRxLz8t9+DD/4f/2aGAo0IkXonkk8pGP85P4/6YfCELK889alflvp/+Ydw/1d8DXB8S9SFbn2Wt5e03ua4F64VL6IgxocCDp57Gg/p6BibxjrAtTdE0owZCJHP7X5IsPbHxzQl19bojyXfem4PBz6R3a8L2D68ca0ONOylVi1G0+avAM7hc9fd6KRv1HasafPLSXWjty2h2scW8KHpXZ/4cbFfl/9T6dT28rKopn8RKAbdiWgALEr2AN71iR/Dnzn8Afzwpb+E7/7ab5u/aCsVYibVdAr/4FPP4g3/9Xfgg9/8nlIpqkwHh9ZUTVxj2/qf5YWtoOCl26BgzXXQpmeeu1ZBQRs7sNdVsAADJZDrKQnEefUjuP/F36g+/wYGkr1IDgYUDjIIAAAnMVzZ1ZNw8BufwBue/nP44D1/SaCgB48h1t8pSI/LAQH7BjQMemNRo8e/Ex/4Ez+OS3dfBK/BQKpVATPO2UgzKxggxwU8+6kD/Ic//xb8lT/6KP7AV9yDf/n8iAnIbgJTAkZOMwgYpxoApB4wpiT/Epee/Fv/0Ltx9+/+BnzmSKDJG31v8NsRCPviCv7nf/6LeOJ/+B48+Pv/Bu7+3S/HcW/2InsMRDh2jycGrvaV7botBPxP//RjeN9//114+9e8BxcUagwYWqUhEGGI1LghgIFCpSwMkRCP5fsnfuMQ3/cLb8F/8U2P4o98pYyeoMSIYIQgakNIoh7Y36jXigYJYQDBQwIDaQB4wsGNa3jjz38nPvjNfwuXX/IKiVdAqf+MBAziQoQCr7wKLZV16nWacPCbn8AbnvkBfPDev4zLX/6Hs6rAU6njAGpQCBGco+RVTQhTFxKe+tQzuPKzb8MTDzyqgZhTHY9AHhIotz+mImABEoC6U/LIax7vKpXmzinqQe1aMDjwtTTDARIOPnnYDEnkUrZUVJ3qMxwU7FGKu+1/lRmNU8rfre3y24rmIV+LfXjXL/+4s1/vWO4c30Y6NWBw0gCLd/3yj+N7c6G+HUswsHc88sJ1Dz71MTdkrVEK9gWSVCpHyMf76PUMBa9bVgraIEMPBb3kN1ugoX8p22CgrjIAVEAQLAM+iDBNOLhxqJML/YS8NOm4dhO0ykALAywebzYYSPVLJY3if4IPvvIHcfn3fm39kuebiMVx3QMCcwNkl0CQ4yngqU//It7433wHPvDqn8Kll16S47fAQKMKmHowJsaoDf+YGL/4qWv4/r/zVvyl+x/Bv/Nlr8C/Opq6IFA+1yBgEAAgg4AHhP/pn34MP/Xffyfe8gffjd/3u78Bx2PK+3xaGo54Eij43/0b37h6rJ3PA0B1Xfc6pEkM7z/+J8/ip3/1u/CWP/huvPR3vyxDRwsEpjTEQAgjVaAgPfykcJAEFEYBhX/wW9fxF6+9DT94v8Qs/MtbI4agqoICxRA09oE4ux1MTQhEiGkZEg4/+RQe/Llvl/pz1ysUHLReU9T3YJLOgiqHNSCkqlwqo8CMg9/8ZbzhWa3/X6FQbMeEWFSFRqWkmGpIoACJdZLvlKZc/x/8298mky+95BXg8RZ87IwPWmzjEUA1JACtkgA8ffPaIhQsCanMEhNiqoLBgdSLchyBZHSABmpbIKmhBkNVYyK9d6/iLsNtzhz2tP/52Caw0Jqo5LZbZ5H1+jonhdivv1A6tSe0f/vS6QGDE6R3/fKPaqH+BYGCLTCw4i5oH8rBpz6mQ+J+pI5OB/o12owPUMgcQBW97vZVUPDS++H64jUUaLZ6UNBTC+wy1w0KdMiR5kSOycfWwwy76oAGCuYgQg3YzDMO/okfx/0v/gZgvIUqZkAbxJ6bgK1R7MCAPYuqUfw3v7a+OVMIDAIaGADkWVQxAnqMxQkcPPcxvPHnvh1PPPB+XHzp/eAVGBgTr6oCIyeMk8QIjJMYgY9/+jp+4PBt+E8u/iS++ktfic8cT6oCrLsFvCIAqwOYuwT+8f/6LB79h2JU/+0v/cZKIWjl/RA79XVP+n//s1/EB/7x9+DNf/Dd+D/8nv1QcDvn/+lf/S686Q9I/gHn5jDQ03Juy8ArCjEQQgJGYud+YPzD33oWP/yLMnriq7/0lfj/jQkBjCGgxCNExqAKw0BBhk22aoIpFaGGhOvPPYOHn3yrTE51173gNGpsAss74yEhJX2PohxD0kcOHpApyX1rGRz8+sfxhmd/oK7/vo2y9yVNfUiw9yFx6WETqZIg7jNROt6Dyy/+BvB0pDEIlkeBCAtaDOpaMxVBeviaBev0OEi41ml/lpJXDQh9OMjHaVUu02Q/IWsfsExqRe6MNgJM2rEAH+9V+vBpbpBJ3UMGBR33cXv8Yqczz8Q5VZDwrk+8F997/S/ihy/+hbpT635zp+kUgcEektNUSOsv4Lv/yDv2Q8GiYrCiFBgU+MlPgGKcFocX2bbyuR3j/tTNp5vRB8uuA2AdCoB6qA9QxiG/z72UJ4IBrw44l4DFDRzcfBpv/Ojbxef24peLz3+DOpAb/hUgAFBDwYu+TjN7GzCgjZvBgPlXDz71LB786Nt0xsHLGiNQYGBMtaughYFxSlXA4DilKk7gE79xHT947Vvx/ff8BL76S1+B58e0FwYAdIHAtudnTYT/8Z98DO//h9+Nt/2h9+Crv0ygdWi8KW3yQYE+9Tb/43/yMTz2q9+Nb/sjfwv/zpe9Yn7AQloKYGwVi/9Rz+/zv5amxIiByvm1nGQb6X0USPhHv/0s/sbHvx1/+hU/jq/+slfi+TEhTAIE3h0xTEniE8YCCRanMMSgoxuAEBhjKpDwsedEHs9rTwTM3Q1p0jqeCiSkCRwUHOwd6UDCU5/+eHEfvOjryjvS68gQNfJzArNC8DTO3hMC4alf/5jERHzTu2VGxmnUwMUADgoVREBKVdAiB5s3YsHVAIGEazdsxkqdB6GtJ813bwYX4UB3JtQzJuZpiNW1wND7dQGijFTgAIDNV1GuVxvxg+eerqHAynnR1d1RkvM2r1SIgvGuT/wEvvfpv4Qfvu/Pa0xcq4Rus4OlxPrpFIHB/jSDAksnhYKFlKGgrRSW7gAIAAgUfOTh7tz4WSXQDVY99rkPUK6aoeBRDwULMKAfl9UBZu31MKyBy6Mzvvk9uVHJQJCHh3bUAWAZCHLZkrgPnv0BfPCeH5TzAzUMAKBhtw0GdBsHlUfDgIPnnsaVJ9+Kx17/BO656xJuTduUgX0wYDEBf/83ruM/e+bt+N5vfC+++steiaM0dxMANRDYd0AM+JS4nhPA9fj/8T8R98G3f+178Pu/vECrN/y1P98MZ1NfqH/8P/ytZ/DeX/kO/Olv/HH8wd+7EMjVSf5e5kMg5e+UGP/ot5/F+/7Bd+I7vv5v5fz3XB1rszEC5b4KIDDSxPjH/+vH8J5P/El818t+DF/9pa/A0ZSykiDGnTCwBTUKJBx7SCCBgmFKXUj4xU8f4jv+9sP48VfLNNPPT7zqbqggwY9sWICEp37t7+IN179fAg2/8mXqbgjr700u6BoQEII2fymrCAe/+YlqdAPzhDxPsRlUF8yoYZnFMBpAZFdDPfTx+g1Ze+Wx117FvTbNMddt19b+cAsHAPD0J5dmTJS8+CmxSTYDLBM4leHSQRs8VRZUZSEkUQpsGuuXfKPPTG0Htsr+2Z0h5fo3f/m9+N6nfxA/fO8PqNKdiv1Y8q/cZjpFYLBeZWr3wQl9Mj3iax5CBQVeKejBgG1vXAayfw4EeW7zj7wJV1//RI7+7QEBUKsEQP1i9e6a4OeWfxwXdcGlpbiBrjrQuAs8EBAnHDz3jIzO+KYfEaPdDuFqgABAcRkApXHrpRAl0PCZH5DRBy/6+lVloA0ihA8YbGDAYgaeunGIhz4k47S/8SUX8bwF9+2JGWjdBKYoCDyUEQS/8uvP4IeefTv+o1f8BP7d3/sKBxp9t0AgyvvaYX7zaH/Cr/72s3jvr3wHvucbfgx/4CtemX3sQFHf980dkIvb7bf03/3WM/gbH/92/PlLj+CPvOje5WflUm+ypAoStLamBPzKbzyDv/WJP4nvfcVP4N/7vfd051SoAUP+bg2YNCj4jq/7UfyBr3il/l7OGdiCFRlptN6/PFc/0mEkQpySczcIJAyR8IlPP43v+fk34Ue+5TG87MUXcTwlTLw9JqGFBBvtIDPnTW7I41/H/S9+GVolIUOCLzRgGRZcDAMn4OA3/x7ecPBOfPD+H8a//5UvA/MEmlJxsYElalXfLwoxS98IETRx7vBQdjMUFeHgxrU8j8nFu93S7YQ8skEyUz+/9mmaaiCHFjiQeRYEOtoZE0s1LoDAqhgsAgL0nnWq7KduinvxA3/ix92Mp03P37ZtNeDuuL/5938yQ8Gf+tpvXenr+7QPo5bzcYrAYDnVgRpvl41r8o6lKjCnKUT3/eBT6lP65vfINJfNMRUM5O0ddcD2kZsWF7pKYjMkaAkIgHWVoFcVyipoV3HppS6mwJSCfUCQZ39TIDCjr8cc3FR57ZveJQsWrcQQ5EYMKGXfJj+SIAQc/MYvyeiDi38F97/k5dtgIKsDA2ZzDYQIUMzzDBx8Unoyj7zm8Tzj4JYAQvmXuiMJvHvgV37jGfzwx96O//AVP47f/+WvxDgZDMyfXwUBmBvoYrSL0f/V33oWP/L3vh1/5hXvxde86J7FiYJsG9F8pkFA1CM/26DkAfh7v3Yd/+n1b8Vf/WOP4mUvua//zJrkb81Mk5+F0eruBOATn76Ov/qL78BfvPRT+JoX3VvNwujLUrYhbwOQyxhw2xqA+NXffjYrBX/gKxr3n//dhAwIgUmBQa4dUilLGeHAGFiUgl957hl8/1NvxV/7Y4/iD7/oHjw/ssBEYglcTPPAxUG3iZtDIMHiD+TfkN+1wxvXyjwaL345WEc2SNDiQkyCFHitKCykg1//ON5w7c/K+/WirwNPk75fULgHQDaiirMMPwMECmJ4mcsESiHiqZuHTgm9VKkIiYt5S0A29FKHeMW0lWN8+3avrrLpYw+APYAAIK+ZYYCg5UcsgYxv/Nvv0FUw79EYiiRCCgs4FMkfy+1avnx9V3/zV35KoeA/wZ/62m/d8Js7jzOgfZNM/E5I9BJifNvnOxdn6SydpbN0ls7S74z0dR/6GvzSL//9LludGsXg1v/1t2fb/IJF9/uefAVDC3EFcDEHTdr9yAUcf+fN+ayFjdvAqwIAusqAbK9dB0BZ4exO1AHb0nvyfjbCnNOtCkEvhiAHGZ5sZkI/D4H8WafpdlY3APPJh0wZcMML2yDC1lWQUAcRbh1eWJSAuTrQm3AIwKyXC+zxlOyR+ntTBFuvv53tLwJ5yF2ZHlgeeLBnbr/V+BKya7f1o1eHNqZe/W3rebsQlE36xPZ7rqeBNqXGTwPdzgJpz6RVbVpXRuuqWHsu9mzsGdi+dkIlIps8SVQFP6GSDH80NwWa4Y/mYnB/gz0v1gDFUf82QYsWm2PvnJ8jBGV2UD8x2EneQQArE4CFaltWQ3MMjyqnpta1aumGIdhtHWrTidtAd7BvB8tmZyesPXTf7fPiJHkdOwMUW2O2BYDELOiMuZcueCV6wc5U26xdXE6v/NAfW9x3asAAjZHOUPCa9+Hyhfuy3xpoKkkVbQr4Qq5mImyuI8YHfQnHg4D9xn3mDhz0YADAbIQBsPwibNV+ZlMUy+U7QAD0ggrX3AaL8xBUL0RpkGZ5CwvmZdYIATxriFyD0wsitHHWjasgMapZCE86qqBdg6CaYKgDBL3UGpn8uWNsehBgBsd82+36AEOUs+b5JdSwhEC6ndw+qqazzvVkqY6gaezWVEj/TgD5LO0qktbwGxQYDORZIrUsGSSGHmr/dB9Dnk27boQ9rx7A1bAg+UrUj3voJaszgUhsM2o3wxAJPAIjAWOaSiwCBww0VQGLI8laDSEworosBApYgGGfqyEYEDRBi705EjgBg7YJzDJXgn+eG5Ks42K/sQXGGvdCiAAa1wMxgKjTRxNADCKNXSB1R5A4zbKLQetCdgdkzwV14QCYt42E0n7abyc9P9R9YXGVdbC1tZ3qaoCVp7ZP5m6wE+fAdntbzKXg7AZz1cnM0yhrzMKlC417bqHjKb915207ri4d3Lze2K46nR4wcH7ngxvXZO7rBx6Vua8BeD9PqcDeAFohA1WwCC2M5bLtvYeypgrk/YU/PRBsDSTcCgGzbNN8lAFwm4GFGRIszmBFJQDK9m7G5pXYFAEAqOIF5EZqZUBhoAQREvK0xG6NgttVB8z4W4+0BwRt/ACAqjfaprWAvn1KgBmVdirfPKZejbn1PsnOoz3O2SqC2gBWNVMDR8XANEDonj8A7J37wz/fXuyN7z2Se6ZUFB6B5/m00oyy2JSHOo5Bvg/lPfJzR/BQppbuwV1PWQDK0Me1SZ7K8EgAQSFxLM+TQ8DEjEh6bQW6MTGGJDEIIxEGJkzE+hwZk0LBFMQwRgKmrCJEhBhBvTUbZtMxTwUKnJLAZM+qhvfugk7dpOfUIXZkbwIF5CGWZvCDbw1IZvUjXagsqPFGgEzqE7JRBqSu5q4cY7YM9IZc1t+5Xt7Z7pb8wU1baZggkCAHSkxCLO2mgoOPSwA8KMxzc/Dpj+GNP6eTR93VBvL23qMWtsu+Kul+s49348L8GE2nBgzY3fQVnSb40t2yyldutBwQIAfB6DYi9yIsD+w+uPm0XK9dYKd5OC+0KtBW5H0s32PFzUMPPRT4uQgaozBbx6AHBZaa3gdreXvj7/dJLlyZrsmT1eJEjatAAeGk6oDJ1K27YG2RImAOBbPn0gAAUCDA9m9RA+oZ+KhSAvzkOh4CujK09SYn61k6KXoL8DnY22dIWtCzxrV9vrTn+Qav/kSNcAchgWbA18LCxEBKUQL6eR4wulVVAJAB09ISAOZRg2AtKtKhkDUgjMwYiDCmOewJKJxQRQCAIACQRzVQ7D7frCS451vAAbWi0Ekz2doNpSN2cKCKghTMBCIGKOm7Kj1vaZInmHogHYDUhQN7DjbLoWSjDwdb20z7rYGC3VvpROrfnpqg6saimmCgADQdppLe+NF34AOv+imZpnkhdTuhlhr1wSdvH7//g39u8fynBgwQIg5uHMqQvmqVQUilBxQHwxwUPCSspIOb1/HgR9+Wr9eFAGARBOzvFj+Zr9Zthe69m0uqEOX97oXSHT4Slyy3nViCGRSkZlRBazhWU5HU6sakU7G3wMCiq8CMxbo6wNx3F3gJegkI+sZiDgVeBSjPgFDHCpwcBIIWRQsC9cI+6q8GIMPcJuDY/M9ulj03814BvqkMIZ1GZH+19TbTVPzRnZEk3ldduYjs+drcEqYImOqTh5kOZXIdKmqQfa4moKKI0B1VQntgEGCOdezIQAUUhj2gYMbJAWH7/O17DQdS58eUEAKB2ZZVZ4yBZCgjSQT+SFN2CXEMIHVTCAQCE2FdRSBIGRkIpAlE+gyDcwU6COQF1Y+r1qh91/c0oPYbDwc6HwClUeEPBQ7cvtq1UHQtoKgHfgpkcytQJ5c5J82O7EpwKcCBgrXRDhTyqIkFNQEU9ZwOFFDUaw8Llp54zftw6cJ9+1XhjlIws0UuZfuooz/WHtepAYODG9d0msurFRRI0iJWEOiCQqXszCVQk1+uvvYxfPN/+Spw3DXH5L727O+ae2BtiuIeAHgS9sbeNncVpDamwEFBVyU4KRTMMtlh8xV/l894PbSzgYE2cKmzaBFAXXWAGyCo3AWYA8HaMsZmFABUUODT0vj/Agkndw1E9BUBDwKDVwPSCJosKM3+Oj80lwA14gQej8HTKAZ9GsHjMSxwjUdZCptNop4KHEhBzMGgpqNlMECIUke192+r+9GwgwWQ0rAD4iCf46ATVcnMegYC5MEgRFAYEE05GsyNRDKp1AoopBTraasHKpAY1l0PuU64RaCW4hKk3hCgwalrgDAyMJAEWXpAmCBuhkQyLU8EkIK836MCQ1YRSFwN6K38yBMQBvQCiD0kAHF5/Zh9yakIZZu2KwjidjBmaOAAOgMh22dVDywHFRwQVmMO1tpV+52l7FLwCl8PFBg5PkF2lnzNQCHvd8MhHSxcfunleblZasqvCwLVMbL94MZhHvJe7OMyGZwaMLii01xe1HGq9S07KSjHDPAcFIAMC/IDV6hPvrkuVP2Nt/nZuN8GCPjKunUIaSbZBbmg6o+/EFCwlC8vOQIwyZAzdPXzV68k6XoB+2AgxxOERXXAenhtw1+kZVRBaj6g0ICgbfyBuQEAUE025JUA2YfuHAFlhEDQkQPNlLr2W+pHqA+mMmRFQEFgXAYBSscZ7DIEjMfANMqU0/rd1AHWc8m01UkMhIHCNIETg1OZd4KnUj+4A4fknjfpRAkUgv4jXeVPQUD/CgxE0DAUFUEhgWz/sKthIURw2M1AgcOAECKigkICVYrC2IPHQEgckeJyXZHRKDSDR2j9S06K9BBp9UQCFuVYBCCwgIABAgcCscReGCCMGocwxCD5JzFYiRjk3Aw2JXCEVxFkAqVKRXAxRBaP0IOE0l7W7/zSCC6ghX3bqAa/1zYwo3IrLMDBUtyBhwMAe1SDHjgsw0QVf2DG34FC0mPWQMG+Vq4HOwEgnc6qPN08CP7C+XtRqHN+3W5ZG6K2j+0xbTo1YPDY6+tpLv1Nk9tGbmsNCnpEVebkSKvMOAiosbfPdwAC+2YlXEv5vthmEJPvFS9SHVU+TyeAgplv2V/I/If6NpibpT2mdwcNCFQuBCLkuAFzFWR/c60OmCrg3QUJdfwAo0jGxfDXLgODCl6BAqCJE/AwoGqAlH8NAr1RA1tVgaGNEeBi+Gkakd0Atv4ET6DpuAIBZCAY5fs0gqdjgYDxWI2+bRfjn8YRPEnPkaVLDU5JAv+MxFDDAHeWWKbowMDqQiRQCDI6IigURJGLKRLCMBRoiAMoCiBg2IGGARR3eTsNQxcUEHdFUYhDntSKoikKETxEBUuSKa6ZO2oCY6DQWQRLJP86PkFBgaipP7NiqVKGA5RzIrHG8nEGBA6SHwMEG3aaSNQiczMkAoK5Gew7a5vgVQQNNvWgQCkJVPXefQcKAGa+7OVeLnW22fGqGgAQaNgGB7OGjeACEotqoL8AkWX/5G1vG38g917edeD2QaF009R1Vt1Tm8P6nnnh88ENWRuitY/70qkBg/vuulS/dB0jGKh+LnN4qH90cOMQDylp3aekZXbB2r0lQ78PBPy1l4KWllJo34MOdRNQwYL/ba0WYDMU7E0VHNi2toK38RjLMCAugs5IA8mVGv5aFmbd3sYPrAFBG3BWZuCroQAovf82rYGAzSHgVQFryDeNWw9NnMB4PFcFPBikEVDDz9MIPr61DAJ6DDghHY8ZAtIo0+9O45SNv4CBLmiVkigG+iJYme0bAy9VwGILtMyiKAYIElhIURUDhYY4SNxBGGKGhbAbRB2IDgZ6oLA7X4BiGEQud2DAIarCIGoCxx12oagJU5rDZk9NyPUqUFd5knqFGSgsJZux3+pnTIxRXx9RvQQw5TNrPEKpV6xQbIBACdnNQOq2kO+Uh60GG4JtioHGmth8JbzUQSiNm/5N8/e+rgB760hRFPbAAdWtdqC6PbVgRMl/k42Oq+F22uLFGIQNoJBjFDSbQLEZ/r6oY9DarLY25dqNQzz8YVkb4r4Ll+ZQunKvpwYMZs2R3rSvgqmWDGQ/VYfndKhQ8NjrruJeV6h2nSm/C3cIAlsrYt8eld1OLTAosLTXhbA1C0vHVvQRlve1wTJ7YKB1FTC03LkEE2YwsO0WFAYDBNnuJ8Lx49rnjXeBAp9C8wD8bS2BQHcuATXwazAQZy4CUQRMFZDv6v9PxyWQ8PgoqwKYRvDx0UwpyCAwjTMQ4HEUuDqeCgSMYwaA4jrg4j7Q71Jo8/phatYsxQIHBRRC/i4qgfydhiHDAu2iqAvDMAcFA4AMAkU5wO5cpSZgdw4YNXAx7EAhgKO6HxQaQhiwiwYJQGJZpGpiQgo1JAxBVmvcAbN4leW6VtezNQ/ixFIfWIEkMgvMal0zQBgigacCCJMDBIONCIEXczMEiIoQtWGIFjDXuhr2QQJQDDqv3NiCazHv5jRXIJYS73Ep5EsKHLSqwfq591++tSknBYVqv52zQwYL/ZGShybf127oglGvVfvVuZe12zs1YLD0kGfBIw0w9ArMCvX9rlBbQ++HLPnrz0BhBQLWegySR1cbOlADzAGgokzaAAX71IKllF/c1qXQyhm1QtBdUZKojhtoXAWmAhgEGCCYxJvYlIIi/feAIEeaozTSAHJD3b3NBgDkluTDEgi0qgCo4yKAKAH9EQRzGBAQmFZhgI+P6jiBFRhI4yTugXHENE7AJC4DUwPS8TQDgQwBphIwIzWKQft5Vp6uhbPPIYZSj6OHA/s8ggIh7CJwJKpCGAYgEuIQVSkghCHOIIE7kMBxAO3O1ZBABRIQNE7BXBAKCQgRKRRIEKVK/zYqlbkcEJdVqhYUfNm1YNqmCUC0301cAGFkWQAwcAYEKCBEAMQShxCCxFYELrEsSdsIryJQ62pwsGDtRIYEuYHlVWqXK8X6fryw8QYGB0BfNZA8tzlYeSBc2upc9b1Rx8lAYXJ5XcpPL9k9Xbsha7s8+sBV3HPh0n4A6qRTBAb77r42WL2hKUBdqCK/9BWBJcUAmMccyO/m+ZtJW813twBo3ln7pPQz1VCwf7a6ZShYS7NgQub+S+0KdmkSG9nXwIBzFbTqQO5lGRC4+AE/Xa4BAaP02qRnNm+Q5RZqKKAgvmFTASyZMGoGbGmK4d4Mg/uGEw4k8u6qMmBwwJO4CcajogyM6i64TWVgOh6za0CAoA8DGYqnohLkv86SpRUw8K4YC0C0c1AgWezHyrmBhDRO0rPfRaRxBEIA7wYQjZuUhBkk7M6LeyG7Is7JdxIooPFYH+BcSZBgRgkGTKGBBFMUFBIiycRMu1m9pAyqg9XJKIZqAKp66cvIksEBkSgAMmRR9GligKkAwhBDgQ8WQMgxCNpWJFWtKLFCBJXhkEQgczW4EQwVJOjJvZoAPU+5kYW60WszlhLfHhwAZQhjwBwY2H7DbgO2td2zY1pYcO33PkUhNft7AY+z/Oixtoqk2K+Le+zi8r7TAwZ7D+g83GZTWYXrcdx74eKiKiCfi8+qpwq0FWULtHVgs5t6AHBHUODPndWC9qJlpMEsI71jJTO6wakCGQDqOdGrKXE76oAZf3MXWM/MsrsFCIA5FACd6azM9+0gQG6rVgVsXoEtMw2+IDAwjcDxrQIDKzEDPB4LBKQkf9Xg99SBVSAAMhS06kAPCk6SeGJQFBAzIEv6toUYQJOgMacJFJICguSPQkDYRUw6QUAYA3gYgHESFSEEpN2EMI5y7BDLkMc4gIcdaDyqYhIyJOzOl+8Uc/BiCwkcB+xOAAlWhyOFHKBsAYycaAavA1Dq6oqtNHdNBQd6PQOECVMG1woQiEtcjKoKMPWgqyIA5Mbmg1MNCR3VkX1U/VbXZQ8O1KhX85/wncOB37avJi/t722vbICCwpLrgVCKSFeolv1wIOGO6SVvv+67cHGzl7qXTg0YrFW3gP0P/PrNQ7z1yYfwvgcexysvXJrBQKsMVG1hBwYWK5Db0aM/v8mrBdUwF0eYbUzBiaDAZ2rthTWH3BrJt3EGXhVYgAE1jSWos6MOnAQIpsZt0IOCOsvzB9BbVtiDQLvwUNmPat2BLTCwHkA4Vb3/PK/AHlcBTxPSOImh55SBYBY7kBJ4nKrYgbVERAgaF5Cat83Kyso3xnm5zs7XOE29W2Hp2fjEKSEdQ2MRGGkAMI4gDpgmFgUhyf3TLiIdTwi7SdWECIwKCKYm+HiE4yPAQYSpDAgRmI4VFgZgKoGLFOImSIiN22sgwgAZC1m7vcTQD0wdN8N6a2agJYqCvDcxEabAwJTE/RXKeSMDrIDA2mbIB5n0jToqAhR6g0ICN20Lt5BgVq0y6nta5bU60Ax3lNkVt8OBXd/DQd5mPXegGHRzB6Dftvfa9UVVwasJbn+Od9N2q1q/wY5ZKA5vv+69UI+eW0prpX9qwGDN4O5j1Os3D/G2Jx/CIw88PvPJLMYMuGPWgOAk/p3Sv6ZqQwsFvUDDDAR64BoUlMzV3+fTnTpv3OJL6hUEmi8ctQADrTqQXQT+M5f4gZMCAYBFKIide9kHAjEbfWQXgflhLXiQsBUGXJzAyhwDlatgw9DCSiHQYYVrowrqew/aE5fPFEIJMMwtpRr+QTWWTrChlPtypV80+LFu8vKIBT87YrO9gouUsvcZEcAxMEUCBTFoHPT+UwKFCWGYgDhm44/jI4EDHdlgwECDwIO5H4INgZwsHsHgIILCDqzxCfsggQNVwYsJyC6HxADHTr02Prc3esVl04MDTKjcC1sAIXGJQZBprMtCUTl4MUieaAMkAKGoCfnx7Wuhm7rBbtZUVSVyp8V9X4IDkF2RGjgo29j9BgACmyHfryy01b8FBUKxGcHPcYH6uHIid0zn9Wnt1yoMbLRHpwYMfFp6ML2UC/U1j+Peuy7lXmo+l/59IYFgFp/nPvegwPafxHVgp+hBwcyFMJuTgNwNLDBq5ROk8mJ6GPDfvaugUQcY9WeLH7CelcHCGhDYbXgoqLLbCestcQP6vQcDrry3wkAbQJhhoAoi9DMPTtXwwkV1gLnAAqcZEPTmGuilEIJOApbEqCXW0WWxGX5YO1m2DEUsx85fgN4zWEq9VTb98MbZOXWoYz8vRW5OkPn5e4BA0wgeB/AwSlCaVxEUDtipCDSMsNk3iSIQjnLQIuk03R4ScuCiQUKQmQkzJIR6RE2MMlPjwMBEjEHr+mD1PJahj0vBs7kMtLMu7gZVD3g/IAQAHJCH1cnohqKSJbQqgocEndmvWnmwXlBIOiNuJtq11Dac7IFAnzHZiIg5HEQFHgu6zEoBKzyi3gbMYw5MPbCzZtPtOaVT9tUx9hs4QADlHfbq5NelOmm5H6IaCu67cGnx2idNpxIM2tQ+GEvXbxzibT+jUKCFunVUwRa3gU8ncRsAy0Bgh2xTCTR3S1DQ1Bpb2Ggxw3pMuYHa+Mv+bepA6y7YBwT6qi+qBEAfCtr4AQ8BdgutKgDrCZFTDwjVsEL53sIAUE9DrDBgroF2RIFTAvwUxF4dgK1TYDMTOiCo4gFWoIBCACOBEGETC7VDT3sTEn2hJ2pVBjdpElDDhYeDvLAAEtIIUGLQNAFRIu5pGgUAJgEG1liEHLwYQsfVMBRImCQwMrsZFBJiiBq4OHQhYfDDIEPtNoskkLBbUBOqeISFBqmFA0CuE1nEnxgIxKJMBEaOT0iJCyAkaatCKHI3oXxeUhGAAgll1AJVkFDcAust6nzBJr2xFTjwvfyZekAKWKoetNsiufYHc/UA7twn8ZQYb9i52h0tIBCQ40mA2n61UHC7QGDpiwIMgPkDe/rmfiio0tLLtuF61Xb3ed+IgzUgkN9jBgTl91wBQHkZGyhoDcSavDKbnGgDDOiHnrvAxw+cFAgAdKHAZ88nDwJAHwbMjdCDgWVlAKoE+AWJxhoGHBwsuQrgt7ObjtgDwcJ0xD74jyKB1RWAlLJCz6ZB7nnjez11AECvB78hlmBr6gYwLigUW5WL6l7MFeHyLNfMzT2AyRkWmY9f3DABHEZxK9ikSpMs7NN3NRwDDgoqSAgyqZJAQqyVBNbHbUoCIDMxdmJrSLu/A0RNsAA2KR/5uxaLkEdLpAIH48T5nUiAtC17AAGkhlMBIbB8lv3oqAhwroYaEuQvlxVvtyRTDRo4AFBiDqQyZPVA2tG5ekAKA7wHEMxoV738TkdxT5hMPrZ7WGdHFi6Y8cxz1yr71fbnKtH3NtKpAYMtD8HS08/NoaCXVlx45br6t3doL0vV3AQdIJgFEbrtS0CQj/E5aVUC2wZsrzHVCIQFGMjbloEgz0iI2wcCYA4FPvXiBvwteBAA1NAbUL0QMNCuVLjFVeDVAQs2ZF4EArlsgYJ8j2bsJjV++a2ezz7XGvPWhy+ftYz8ktg0N7KA66V39i2mxrBX6yr4fW67LeNcz5eQqr/l2P31uy0HOYfCQYSMxHCAgDjIMwoxB31SiOBJFQMNWKxdDUVFkCmZR/m9LfKk+7qQ0LobUENCcJAQo0yuZI99CRTWUl6ciTmrB/b+rgECMWCBigYISQEhQd4bs2ilA2OrZbpFhAwSDAqoWR9gy004OIAOreYyY43Cgd6QJnMvWMO1DxDMMFcBimhjBeb1b6t96CWL87D09M1DfOvPPlzZr64ifQfcfmrAYGtqfTJrNjL7lwC0ESc+UtV2z37fbm1AwDZ5GJDfLUPCZx0I2obeGf82bsBy0AOCKmYAy0Bg282vajlsVQLJ/hwKegMlbFmSPNywcREswQDM8HsYQIktyEGEeSTB2oqFAgM5QNDUATP4tlqhuQs8ECgM9KBA7plgNaLqFbu3uZpIyAy8FZbBAIViyP3nEEv5Nb+lYN+pOnaWlz2pMua2QqMpdvm7dX2nIjn71Rxtv5WVbXe/tTKTTev1P4+umCYpMxbfFwfocFLpKnNwPdRowxkpux5kFkU3XwKF2tVAsQIE+SdxCMGmZiZSOJhDwsTQ0QpFSQgOqCs1ITFktGAfqH1q4QAo7gVzEbACgnTGRWFg4i4giI1mjUnIVDAHBIjFFVcDZ6Oeza5ucw9y+SFy1YLU6gFPue2y69vRLSBUagGgAY/I95K3aztmF2qVBEsnmszOZxy3BwV3mk4NGGxpkgwK3qfRm0ApVPORteNZZ3BQXXPhibTHzUWCRRiwfT11wH7voWATENgNtskH71T576gDtp1KTk8CBNLj6QNBTyWwPy0UAMXwz7NdQCCXzwlgQOIKmoWKZjDQiRvouQoUAGbqQJqyu2AVCIBi5Kzn6nrw1VT0PYNv3/3nZnlj+alOPx1k7L8BAunx+bmHWEEEeaAAanVBk3dL9fzG2WibEW+MPXT6XfmuoyvMneL2cxIXQK9M83ZTFxYAopus7FKS+LlJ1YMWEDQ+gaexrL/gVAQZ2jjofAq7PLNn19XgghbzstEokDARZXfcKiTAQwKyN5w671Mv5bkREhc4APL7lJJygKoDLSCEYHMoqDuUSxxCFxAAqdTE8nwRCiCYiiAZm7dZvXbMqwfYBgjWRnA5WB4/CgxYPe5BAjfuBsDFCdiGDX2zJSh4poGCFgi2Y/nsMrN0asBgHzTZOM9HH5i7D+QBFzgAUHovqB/O0gV7x/hNPRDwx/RgwM67CgOa100wMBt9sKAO6L6TAIEZ+DaGoD6mTPRix1g2e1BQZ02v7YJvLFUgoB+WYIBQjD8ZBFjjRY2rIN2Gq4DTXB3If6dlIADmPvWqB96R7xvj3DP+PcNPgy656w2++s4p6rLW0GdObj0L2wY1+l5NsnqiqTd73WzuDKBu8KttYiD8Yj0Elp7b5ODKA0SaSrxGAw5W7vDl7q/fgsNaMgf6lCpAECOhRkrVg1ZFYItN2ORqGPMiT6QuhwjkpaJZIWFiHZLIKFDQgYQEmaUTKKAwNSrK0toWZjhNbg+2ETIiwgNClhgICJgDAgwE9KR10DTNASFfO5U65ts2Becq5QDE/NBkc45JAJg4ny8H5ToVIUOCO03K7VANCe0+K0vzWOVTuKJdErBaW0KYKwWtHemc/o7S6QGDFT1Fpol8CI8+oEtPugrDKIWbXJ1rAaFNa3FXPi9rIGD7WzeBHbcZBprts21b0h3EEFQQAAcIDgiWVAK7s6WcxkBV4zX4oWruQx2P0YeB4CDAxw2EDBJ3rg5UxsncBUtA4J+RjxlY8OefCADUGEGXGp4b/1AMfxjk7lXCBpXFq6QOGCAUWJiBo8/favPUAQJgcb2OarGeJBM0ZeMOlmeRf5MyNJBBgz0TD2kdYLDnQj0loRfsmJKUqbobZoAQ9Toak4BhJxMwTWL8eYOKkF0M7nvlagCQmBC5sAoz5XdvDRKAFhRyJavet9jtFaHMGVEZTTH8Ca17QQAhQEYtSNulsGDn6AJCgLUMbnL4Aghrqses7fMm1KkIVh1XIMHCJJiyB0WLiqoOzhooWN4r5XPhNWk3P/PctQwF9919aXY3vQ7otrR87KkBA1/IvkrY3NHvf61AgSR3sHtQLSAAqKQin3rGvz2Pv1JXMbDPW2BgS9zAiUDAGxzX8+uoBHaFFgpW3Qa3AwW+E+C+Dj2ERut+KZCVZyBEHUTYugrU5OGk6gAxy5TDFjug37PMvQYEQF/GXgIA4GQqgEIAxUHOY9P6KgAwxbxGRTH+tl96qn4RK3vmJTbEPWd9QLX6401PP5HChodiGUop80jYuxHJvxOcYaw8JwbzVKDBlAODN/0sMyKKK4c8LDh1gVZgQdYnXnE9WC/UVAcpiAIITdS83OSxLOhElF0QNOwg4ydPoCJQlDn/zdXA8u4pq2QXnhk3H+RLapDbd9C/b9kGVvcrGwwOyD1/gwOom04CESmb91Y9sO9R81C/6i4GAQvqQbbKHdWgfUZV/tWlAABERUmAQoJtX1MSXFmIklIrBi0oSPnUbdk+25ID5TUmrofea7aoTUtA0aZTAwaVX0b/2oJIj73uKi569wGVF6Ey/PrBqmLv3JZa2us9qJncsxcEIFfu9aaAOQw0L8La+N/FYYgboGCrSrAVCtydNnlBtXMJuLwqYNs9DGSjYi4DFHWgGJtt6oAZna67wAGBxQ90jQtQoKC6344y0Ab+tSBghl9VAYOBSgnQnqcZ/AwBNse/AYBb0toMifyV4DNmnik/a64goFF+/APuvJ/2HIECdXmNCX2OtcqjPWabdNCeoz0jB3bswW7HmCk905RVHpuOWgChPEva9yzXhk2mScp3HPuAEGJWHQw0u24GjkAKe1UE+UuVq0EdMnnGxImhQxHrkUAASvCi9Xbbx8bAkiXxu5jX4SBkiiDnkpAZIcmdrA6wtvZIOkhsowv2KAer7SEWQMFDAjeQADkuzzyJBTXBejVE+SotLNjv12yLj4m778KlVRsDzN0Kd+JXODVg0HYqD28c4k0fvoLHX38Vl+4uUFCCQVDhcEINCaWi9ytXaKxW9wF1QMD+zph8iyrgQKBb6Su/W5M/dtOIdiTgO3UdbIGCNpv2/nSDZmj+0asCFpPhYaB1FeQYgi3qgPZEu+4C82mb8c+S9AIQALUhaVMPAoD9boE9IMDOWBgYsP+ugWyj9SonmWQqpZR7ljZF777lq9dWq9yXeitUZtdPKENLl9ajmA8nDQghIgzNFNQN9CE5t8JWUHDuhxYUfLR/F/w0MdsESpCJpCiUc3lAsAmVplHcDHFA8M+YByDJjI3yO3MpFRWBQ0RoVAQz+uJq0GffQEIL73nlv/zQVp5nvs/ShCScHA7yd56rB4uuBQ8HTjXY1z7ObocnaR+zTdC2mPR93qMmWFllD3QulPoyXjFYelUIdUzcxbsv1/vbDmdzQ1smGD28cbj6TE8PGLjPhzcO8fCHruCqQoEv/yoYhDqg0BLzQk+7Ugw6D6VVAu4EBKpKvqHhrY7bqhR8lqBg6dItgC1mz70ESzCQFQEUMFhVB0xi7rkLTKpWY5EWgGA1wK2XehCg5b3VNUDmi9YYALZhb3GYg4CuCJggPcUpAWkUA27LVo+s3xPyLJS2MuU4MUYWoz+mhAkyAQ6bcpDKWhbyXW7HB1QlVwE8SLtZjMXdYypBIATI8xuiLWktayIMeZsubU0WJyJAMVCZlVICSgfEOEjxAfDTUVegYEGhg4FiyrEj0BknybkeqHnuyDA45Ge/CEdWN3QkJIUo5wVAHMApqOEPsNErec0GF7PASdw/5JQf4gikUo96KoIBQnE1qN/f2IYKPHiY98rQWlpqaowPXjA4UBjowsFiBvZvq2Z9VTdD6Uz1QGGqVFYyJaOjKJjLpo5RQAUOVt5PP3cNb3nyocb9vR8EesXf22b28W7c3dkr6dSAgXmADm4c4sqHHsLV11/FZS1UXzjtM8nDS3RjBQqddO3Goez3LuHmb/2ZZwZ/MwhUlXdFtuym+g5maoF+5ub7CwUF+brNC5jhvlNje6rLGgzMXAVYUAeyX9r1FK1Rr+IHilFIzhc9iyFogQAoz7K6nwYCtIwzCADiV/ZDBFdUgeQkZI67yjVg370iME2sPmfGqIZ8YnETJFUBRk4ZAsYpYWSBgkn/+t+OnMTd4FwMU5LPFrBmf3sR19aAWUBbDAIFMZTA0KAGfqAgi1DpMUMkAYAoEDDEkGFhoKDHAVFBwf82EmQSoRgl5MJAcTquVCPoctfMEyjuQI1ilKe6doCYIQIAog6d9HVhCRQ55Soj6oGCoQeEYVcDQgilvtCkboYCCAIFgyoQk6oJoVYRAKSkriMgx4/IZD4LkKBvtI+9WoP+6ja5jHKQduMFhAOgdisAeV81vXtOG9vQ/LPgYIH1eicABd3uFQXAwQJQxR/Yxuz+dlDgU3f4O+bbeungxoHax8fx/f/l9y8ed2rAAJwECj78MK6+7jFcvnBvpj5vDKn534kEGRSWkrkngNrsdtUA4A5AoKnAW1WCfZEn/rjPQkxB/1KEPIMYzGVT9rcA5odtLsGAdxWYOlCNLJj5nBmYxQ8YJHSAwMUQzIbAAXMo8KkzW2A1KVA7X8Cgk+Nog9/Ou59VgeBjAwb3dxBVQMWKkTnHCExQo66KgABAwvEo28eU9C8wpoSjJIZftsn+Y4WDozFVIJBSUsDgvA2QXigAjJ36MGSlQD4UKCDEAIQQKlA4N8j3XSQMQYz/EAQczoWg26DbZf9uUFCIhAgWyFD3g1xDwCLSgLgbpP7pFNacfB2Z1O3g1YQSZ0K+jsSh1Klm/oX8jq0ExnGaKvUASAoISZQjq4fDTrZZveFUAYJAp7oZ1O2Q4xmCzq5IASEoGBCV0QssdSQsQIL1du2999anvbOqbVyUEXDncNDGHNgIli1p03FTaawyH1ibuQUU0I1RyPdQTmuHAEBWutfSMgg09+XqXW0f71tss4FTBAaH/8tTuPLkm3H1tY/h8oWL9YN30adVaqQfDwiz8984xEPqnvjmD3xzVijqaNc7BIG2sm4dZZAbn4WuuD9uVga3DwWLl9FzFHWgXHM+DQ4qEACWYaByFaBRB7y7gHklfqADBOYyaIGg5zawcoYY/AwHPQgA5iDQDCFsRw9YNHoNA7uZi0BiA4BxlDiBKdXuAVMFjicx/rcSY5ySfkeGgqPJqQaJcTwlTIlxPMnn4zEhsXxOSZSFlCwOobgUslKwYR7xUCkGGidAJKqPug1CIOyigsEQsIsBuyjQsIsKCaoWnIvk4ACyPwacV5CQ83BWE8ztEIO5KwaEYZCgY3U5sClMuugVGyzE+cyW5nKw+ARfb9CpNy1Q5jpjKhZQ4uHGEQjqXtD3OwOCKkoZEEIEOMLHITCF4mZwqkIMURZmcipCFYOADiQwkHRYX+7xcv+dru5vReq2xHcIB/XFiqKwcDH3eU8ba4fmzqU9O7UZdwAK+SwNLNyvUNB7k+Zl2LFDwMyWHNy4hisfeRhXX/sY7r/rXthw36V0asDgwSffjCceeBSX73ql+A4teancyjAbUkeE7ti28LN74nU/LUoEkF/g1YmFTgICVWDhfiCoXABuqE2VTMLLxt+2u7iCjVDQzcNK/jwcLO23ZCAA9GHAvtvSrlXsQB414NwFsM+2fQMQtBPltFDQKVcAYtjzjWgcQTPjYFYFdDKhEi9AKzCwogykWhnYCgNHU1II0M+qCBxPqQsCR5Ncw6AgMXA8pgoIAFENbLrhao4QBwnVFM3WKKpaYM/dAGE3hPx9F6WHey72QcH+DiHgXAwYIhQWAj6zERJiYKckRMQhipFMIyjtKiWhBwnkhrDmIZFWl8wFJQWSDfpasGIpv1Rsn70zNi9CiJDVNMMeQNB4BYtDQMhuBlEPiorA0JELLOs1MDjPl9BTEjIolK+LaRUKFvbzCeCgijcApI2DzoexFH8wG9W1BhPFdVEyLe4Df4ebQQHAEiyIXVqcV3eed3dvvXs4uHGIK0++pdjHDfbl1IDBE695Hy7fdW+n190YfzSVkBtQaFKftCANBdA17ntBYA0CNkb4EKfu7HKSyvZ6mWTnQliDgk7yakE/X3KQr87t/OBLC0jZ1h4MeFfBTB0wJYBTCSirQGG+LfuMzeC7OIIMBECBieoG5n2jdmrgShXwLgIFBAseTCeFgTU3wR5l4CgJDIyccDT2YeB4ShpjAIz6vQcC05REPcogUGAgb1tRDfICTfbXu46CLnQ1hhkofIbUyA8Bw5FMdLWLoQsJ54aAYRRIMJfDEiRE2uNuGAYHCeMiJDBPoDCAnFuKUwLtzlUqFAw441CMzdqwRzkgw0E29swaCBBlu3UOhgE0eUDQQEV9V1jjGGxCJkoFEihEBCq99sTO1dCBBBBmoGBp9d0HFkkh39bCIYk3wIFTEepYA6WsTurOytk9zrsXHCxkZSF/0K9LoBCyvWlVBUpTgYU9aW4/6u8HN67hwZ9567J9XEinBgwu33XPMgllurPvNSwskdnBjWu44pUIN5JAWsSTKwKbKiDXFaXex/NueDv8UD9XlXABCvrXP5nLIBDK+F2X2sagDZppZ39slYJaHYAaeW7cBbwOBAYPYPEPGxAsqQRA6eHl4m2eg4OAvN+NLFiaZXAJBkAyE55BQYKusJdEop8YGgAo/vwx9WMGbqWUAwePxpRjBY4UGo6nAgbjNGUw8MrAOLG6ElIXBrJbyUFADk7zxdZ7FcXa+KIDgSpYICKkKRVIiAHHU1L1gBHHhCHK5xASzo1ONYgxg4G5G85FxhCSfB7k989HwvkQMITkYhKACMagLoZjEniIyVweBglc4GAa4Ue3LELCcC5P4UydOkf7JlDKZZqAoLEHgNQdcz2w1rkWEEIEU6oBgQI4SBwDSM8RIpBGcBhExaEoQXJkk1s5KHCQwGqombkJpFvt70odOKmLYUlaWIIDuEBEoLS32cfZcTlsaZObY1pYyKCwVVGwY61jau2WXW8trdiTgxuHePBn34YnXv1I3z6utO2nBgzWJrMAAJ61VMugAAAHN6/jwSffUpOW7+k3fsNNqsBJYwi2Vg7A5T9shAK9hPvnXQhVNtYuiwIHa8dIFgsIaE67MFACCfXqrbsgw4CbEjdDgroMHBDY/Ppgm3fAAMANOfMpBA3cqgFAitO2dUDAXAjDoDcSkSgsw4BtizvYJENjQvHlc380wVZ1wMcNPH8ssQMnAYLEjDSlCggAhYEeEOxTKNP8YwhyLvOvwlzpDJAuHxxiECk5TQIIKWCcGEMkpBRwNLG6GhhjJBxPETEkfMkuYEyc4xGOJhYVYQo4DgIBuylgiIzzk3M1JEYMjDHVoxss9mEIA0IYQAPnIZA2uqULCVHrr8IpHJy2oCDl6wvKfW4BNRt0q78JzBq3Mo4CpVa2GRBUHchKgcCAGDcHCVRGNQSivGhQ6kECMJ/1T+vGvv7pSVwMds25S8E+bIEDVQ2W3AuzTKz3yEtybYTZE6/qelAwGLBRD0So1Oo0lbanY5tKXpbtycHN63jwZ9+GD7zqkRMpBZZODRgs3niW3st+1odWp/JgD25eF/nFkdYMPNjEdXftfTCwqGi4Y9aCB93+MqIg3CYU9OMKfJZ6akEbUOjXfa+Po+o3mruu26AKNLRjzPC37gKVCVeBQMEhL7jTqgRA+Z3Ps294q8/zgEIPAzlewBrgUMBgfdbBEkQ48dxVkDrqwDhJYGAOHkzs9s2BoFUIJoWJKn6A0YWCvcsUQxadtaLa77nsFy+tmgcBkWQVJTGO82S8Agu7GGQIXmJMKSBOjF0kTCliF5MoCClgCIyBCSMBQ0yiFCTGuSnhWIMXdzGVIZBRAusmjUEYIfEIx9nVQAjxHGKEAIAbzdCDBIBBQd0QBqy21oZTsGQWYC37DbEI+T3QyZIYY+kNUyiAQBEgNVYU7UXOxomDDi1s3AxMQWN8ioqQxR+euxd6oCCf+/Vp35S+phrkfV040C7KSeEg7zO1ZQMw9NrrBXvi1YT8qwVIyKfU9iu3463inc8zf+OIGQc3n8aDH1UouHByKABOExgspbZQKp8TXOFLIRtpPfHqRySmYMlN4KNh97kJThpHUOXXt6AOCmbuAwcFzTDEnvtgBgXu7jaGOVSAUGWl5Ei2o1YLFmGgVQe8u8ACCs3wLwJBwkwl6EFBldFOXLWHAGCmCngXAROdCAYSShChrY5nroKUP9fqgP3z7oJ9QGC/XxpZMDUPOtrDUb9QsvYzkroP9LkT1bP+QYx326Hdl7oBiVT2tUGKPu7Ap4nFUoyBgCkhsTT4U5qQOCAxcDTOAWEgYGBgtOGNxBgS4fykxzQqQiD5XUhcuRqCBS3GKCMFVyFhyJCANALe5dCrrz5Qcd+L6eAAhQuk0xkjGBPABCJeBwQKkPEawSkIVKkIAC1CAjN0hENxLyTUbUWnWd6bfPwBsBBvsAYHgAQkAnM4gNrfFg567oY2U70b6bgeyKFz+ZW11XPb4Ytkrni7S7o8HNx8Gm/86NvwgVf9pHZqTw4FwKkCgyWnZpO8nwnzQs2kNZNfOsb9hQaCPbEDlUqQtwe3n2oF4SRQwPt7fPmFbLLblnLPdeBhwI6Rf2bcVR2o3AV7gAD16noeCGz53NzI9tKMZBwEAB0Q0FzneAFtWIm2wQBrp9C5CmwaYj/fADNwa5pWJx5qgUAMf5lzwD6nlKoJiPKtBirtpWthZQU8gwbKww8reGyAYCl5cFhb9c0DQn+GRPlgQxqB/lBHn6Yk1imlhON83qT7CIkJI9WAMFDAZNv9REqTqAjnY8TIrDMuMia26ZlZXA0JuibHCSDBeoesQwy5Xl66AgVgGW67DyAVOBideiAR1wUQwiAW3gAhxNyLRoigyYChrCNgKgKRrLVBM0gQ1SA3t+aGyg9db6PJ8ha2NIVCBaQOHGABDuQKGQ5gUKRtC4USQ+jV4BYO1lSFxr50YxOsHNEBBJ9XLts2cBMObl7HGz/6rQoF9zZ7e23g8rt7isCglwopzpL5djQV0nqkQ1rLboHNLoM1KLhdINDPqypB/t3CKom6wXLcy6ZyeE6h2Vduo+71BXfAkjpQuQXMZaONZQ7C6QBBta+jEgAdKLDYgRYAXNn6oYYZBnRFwrJUcXElZBhAWF6XQGFA3AaiEFikf14Nj+uZCM1dYG6EFghK/IH8NVfA5IYR5tvOSoA8lCExRp0zICQGIjIAmIrQzkewZP9b4LidtLS0b7vZA4L/bn8tBsBPnOSTKQdyvQBbwTtRUlBIDSAQBgWBYQoYIxc3A8vyxIFYVIMgx8fEeRpig4QhRlB0QYs2OqEZVmtrOMAAOQwATwhWr4FynBvVIDfR1HNLGQ5UMjf1gKC/DzLaggKgwNAFBNKFnJh1dURVKMOQAcHaGIOEHCTAc9eCQWPEsnnq1QprttfgQH5rbWKSScJYXQX6Etj9Z/WAAFTKgp2nAIWVp+xwsmkvNfZlDRC4BY/8W2e/WuBo0smhYD2dYjDYrmvOoKBKjeHvxRFsSXtjBxZcBn5fDwhsv4ODNZXAPveGGK1mr3tLXgqWv5thwBq/Cggm9/1kQABgDgW+7OxZxbrKd0EABlcGA6oObISBhBI30HMV9NYn8O4CPyNhQgECm5XQ1iYYUz0bYZsECKRxjCiGMjHjPOZGvXeOL9TUGn2DC9tuRt9mU2xT4kLDgRhAQAiMNBJSkGGbBggBhCFMGBNhMDeDDnkcYsBIUCBgUS+SKggso2omi0ewoEVshQQHCmHI9b0PCjUMAKjaFJsx0cMBLHSwdS/0AIGSBC+qUmCwnIPnGhUB5mqAgwQre0jbkW0dCiicOO2FA0MPpx44KGJ1J0j7cwJAsHLe4gPZk7pDz3twUG0vSezXEhT4PG+3V6cIDE7q4JTCraBgFqhxctKSc+/xS9kxnfwALxQQAD2VAChSH1Df4dxdRvmF7UnBM1dC11WgVz8pEHgwaIEA6EOBz5v2mnKD6OvHDAS0rMzoG1DdIQx4V8GaOmDuAmaeAYEfmeAXLOoZ8dzzTowQDQKoMhprPfytPfel4/bt25KHOayc/BxtHvzXGKibx5Qd2FL+gSkrCYEICYQxAUOYMGj8g3czZBWBWBfwmrsaSjzCbUKCvUc5al3KoAUFX9dL/a8TTxMoRlHZvHrA0xwQADGeVWwCV4Ag383NoEBBQQNUUWLsDBLMoOcczZ/JUtvjVQO5f32GmNdVObO+ywSwAk8BBPl+ckAAZjbnpG3+5rSsfK93atu05PSdp1MEBgtphehmUHCn1/GN9b5K0AMB/7u8vxNDYPtvBwhQNqxBQcnmXBXQXNml5LtTB8rmOwCCVZVAzt2FgqbcyRnFHghYsNUiDKhiYDBQtru4AZ67CraoA+ImkPgB7gBBgYBlKBBVXAzQpECwW5mjdslolhkIy/48MhOdfb4udKp6b962dtIboFbAe3EM9pv6OG7+1ufc4t4oCkNzXhswr1ZsVCUgjS0gMEZmiUPggIhpWUVgQiCFA1JXQ/JuD4GEvJiTGf+FSbvYesEWtGh+cC0rcqDg63+b+nAAaKSJFkEoIJIN/u0DAoDsatAwkA4kmPFfbrt9xzmbzSICZbXCtV4KA3sAQctB2paiNiC7UVynj9Coxicw/C+A0rDZft2GGnN6wOAEBc1EMvrgozL64NKF+0rhVb4jc8yiGH4vz1nPs/U77b3+GggAMxiw49znXgwBUIPAnQCBT0tA0FcH9EKrQGBQsBEILKMOCqr8xWVLWIEAkMuxgoEcdd2BATJ1oB9EuAQDvKAOsH1P/aWMvasAqKEgP4OmntlzsPH21b4F4y6Be/V2H+xnyx8DOsmU+fetNE2yt7iSDUpBmyxw0eIarIc4uf2TCUSujIBSLgAqgJJ9y1Dh9/s0i0dAgQMosBkgDEEAISRoACPnJaKHQKAFFYGodjX04hFijGrkfMBi6kMC20JeNTwzEjDUoLDxicj9mnoAeXcr9WAVECJAvAoIZqBbFSFx3fPf58L3+5bUA3muBRBYry4woHk1RcRiEFgggK29IQLlyZocJGgG1kFhQ4n3bIHdVE5zG2FQYPYrQ1evwG4DQk4NGPAJbt4PSbx84b7q9/Wa3AYHAPLQFk3Od708NXGTNj74VRiw4xbUAfvbiyE4CRC0xbkGBMh/NwAB/LY7gwI/3en8+c/LcAsMSECVjigwKDiBq4B5rg5MLO4CXgGCVhUA6p5wNuBVVfM9+LoHbEa/XYvADL4Z+wgx8pHIfYd+ByJ0BsLgZhugUh/y86f671rK3iD7q9uT+8KQnj8zY4IMlTTgmmDQYPsLRLRlKuXYL9etMRXmv04JztUgsQkhEHgERgImJFUTgsYoTLIeg6oIYsBYy5cxqXIwBTlfiUcAQpBloilypSJQmsDBgfUaJGghl6FuDVB3350aDnKAHIDiIvU+a6cuyFl1RsVlQACVpYjNYAdtTJYAQW+lm1YBwckG9m6UTSQwA3YqguvsKRB0IYEZ2d3QggLg2qsNadGt3LcXTz33dO7Umv2ydBI7uJZODRhs9d9kKHjN+2aFCsC9RK7ZbQEBcC1hXLeyswc1f9gVCFTnPhkMAHeuDrRZfmFcBluBoJxrcbbIfGVr1NqXysdiNC4ColKOpHMPeBnUxQ1AYWCLq6CnDnBC5S6w+IGe8Wp7vrnsraF0vfm8zykB+4y/TcZjhn8ItdEf9KEGe7762SahItR5ILdvDgj7G6Y8g6J+T7BqwFU9Tvrd9tlxiQtEjA08WJlzBjLeBA1AT1kozyFvUzgAFyPWAsKYxK0wkroVEmNIDAoS0DgEm+KXMdJGVwMAVCMbZNY8DjoRWBql3hok9CA7k5jcaDEiub/Ze1h9OGD9vKAebAEEUxDsygYIS73/3FdbSH5f8m1Yc56eiiDnj8hTFa9BgivDOSjolcipmCeyD8Cajdhnv06UVt7V0wMGvUlqmpQXlHjg/Ss+Ga8ElEqbUx5b6h/engs3D2ANBGR/6yaQi/RgYE0Z6NXHfWPLZ1DQ0Da5fxkI9GJ3rBIAy6TdBnRS87x7qsAaDLggwjZugLFfHcgKgpbzOCWZj8C5C7YaJqDI3q2/PmDJDbDc+x9iyD3/IUr8wRCDGvhi/KMadwmQUwAIlGeotM+wa7TPXfOen7vcUP/5dZ5VH3zLjJyTfpCyLst+58/6PLgDaIz5M/GQtkVl8M+lTRbjLscR0sQFEJgwMSNSiUMYEyksTPUzIXNTAAH7VAQCLcUjyIILBRKad4wrSHDGzKfq/VroaHEq+1bVgwDoGg1igO3YpN+Lm9YDAlDaGg8I++Cgbdf8MZP+DowyzwLKJa2zswoJOudB6bAsgQKjWoi6AoaNyTXCXgE4eO4ZgYIHHu1DwW0HN87TqQGDfXK+LYh09bWP4dLdF51PpjVEpggUJaCadWqjZjqTdHpuBA8Ctt2BgFy7BgFgWRWYved7JAJmXg8ubF4c23RHKgHQgQKU7WieiW8JPDDZT6oRHAswoC4Bk0Z7rgIzREvqAFffa+PTugvWgADoS9nzBadqCLBjDAQsMt6k6hkEOAXAjEoEZSNf+7bh1qdg5AmlpvlU1K18rRWphjv3LKtUvaNUP8+ZW4cwGLzZc8rPKwggLDwvsAJcDPK8hqJKLAFcT9GBnnOf6yGx9PaTkroYegEECeyTeSMmluclbgbO6zhwDDKpoKoIiQQ6CEsqAhApSuCgPq/K1WCQkKYuiHtIANz75iTx8phc+2Bu031w4Ec27FUPFBCce8GSKWagdTho27l+uyfntviVGSh0OkCGCWTlQgyGqTJOTQDmoGBlU5Xfnva4VZCBXM4Hzz2tndpHcfnuS83vbhcIvtgUg6ZROrhxKEsnv+6xTqHWroC6qOaQkH+3T6FYAIMKAtx2DwL29yQgMHs51rK2nvPFuQiqXL4QULCQ215Q56LLYA0GfNyAugq2qAPMc3dBUvXU3AXFkMzjB1ogANCdQXApiLAHAq1LYOiAgBh6pwS4nqaHgKgQAJuVb7KhcWVWvjJ/xNRMQLUwdW9v2l4/O59/X+y+OwtQBTfVdFF45rNLxmoRKnuetAh3OT4hBnnewxwUumCnvc+k/cnec/SpxCOo1WHMACEmgEPAGCRfBgjm1kkEUGJ9lsgDJZLY1L6KAAChVhEoB9GtB/rO/OLWNLl2tG98nEXNz12hgfnk6sEJ4GApLbV77Zou4iGhZu1Dd4IOKNRqghycQQHFRrSw4POwKTU2Ii/o98D7cfnui03A4gnliOq3y4edHjDwd+lk5oMbh7jy4Tfh6uufaKCA659mv5uDAArOOLWXm1Ndm2YQUB07BwH7exIQWKpqWYbr5QvLt7MEBU7cOjkU5J+m5iaa17xpoKo1IcpB9YvThQECFnubc3WgfOeZPG3uAvNnt/EDS0Agz6CGAp/q4X59EGhdAzYczgyI9fS9IhCCjtVHAYGBTAkYQeNxDQHTMSzi3cbSEydZ3GcagfFYfP+jHCerUwo0sBmhaZL66EGgddjLjbrPUdSqGBUMivGnOAC6OJVIITtZwnrYlecYBmmgQwDHHRCiRPXrapUYbOZJqkBhSk3AaJC1FFKsga+ND5lAHVCon237mJOOOkhgBBalwIYGTkgVIBgc2MRKQyBZSwMFECaFvxTUYJLY230qAjiAUhKD3MK6hwQyI8fl/XPvo7xznRalUhD0/Xdw/9mAg55L4aRtYZ4nAScDhdJJcqAgN6vntSMaWOgZ4p6yFrz9uoYHn3xLUbpX7c4JIeGLQTFoi5cAHN44xJUPXcHV11/FpbsvNRWH3P9AmSELy6Dgkiya01506aHVEGCftyoC1WI17hz76Lk9Zp/g5Pe/EFBQftb5DqDEDbTqShs/0KosyzCQXQWVNB2df9r+lh5lVg0W4gemBSBYioqXcu9DwRDLvfaGBLaqQBvV7lUBAQPKrgX7HIM+yzSCJvkHnsoSwfZdA9kMAng8Bh8fAVPZBk7gUQAB7JatVuUgjdKUckrgyRrG5ZpJZjAiwSbeCYMYNIE4XTaYggDBIJ8NDBAH0O5cDQthyM9ZoELhgCJCHORdHQYJJk2UIdBcB0UZAlKielRJoAU3kRwfYwGFLKh7WPBwAMqxDjFJPAGS+PxZjZLAQpl5kQOBHCDYeQwQtNbrd4kJCUEmUJLCtZEM+t4a1C1CQqMAcJq/j7OH6qDAKrS5GGbft8EBGjzIcJB/sZ56+9d+EzAHBQDzOLMNigIoOrtiHTiDLjuPawObZECW3d+mdHdsis/SvtQet/a7UwMG7U0e3DjEwx+6gsdefxX33X2pqhTU/I7cnkVQaFPXldB/WK0qsBUEliBgqyKVc0XlHFXVskpPbjvN3Qc5s1uhwE7VKgQ+Qysvxizj3u3i3Qo+bsBgwakDAHXdBRkMlM56ww1bhaA1Di0QAHMoAGo1wN/K0nDBrTAwBJWSiRCDuQekp0jHxzUM+M88AcdHAgLHR0geBqZjgYDjWwIA05iNfzrW5auPJ3BKmMYJmMRfzZPUzpRSpRT4BZaqeQ5CQAgGCEFgIRLiEEEhgHaygl/YxQINcRCjvzsPGgZQLJBAww7YndP95+TdVTCgOGRIoDggxB120dwONF/hMhT30UBhdehpXQ+Q64E98zZwsYUDTJynBmDmChAGBpgKIEAVhAhRHSwOIULAxVYdlP3mZpDebNDpj8vqi1P13jLMFRTnE4ktBSr2Uk/WNpdCBQ/b4QAo7VAescD6Cyo9fd95up22soo9sOvBwcJtgIL8ddbFAxatZCxEdX+/CVdff1WV7nXbsvR9KR3eOFzdf2rAwCuX124e4uEPX8Fjr7uK+y5cWvQHzse1zkEB1XefaPWhVEb9hCCwVLFPMp94b8RB7cSw4/yXWiorx+6Bgjat9BgzFKz5xtr4C68M5P3OVdAErrXuAuZ+/IAAQx8IfAyB7VuDAp9C1RjI35hvqYYBCxw8sTLQwsDYgYF0nFWBdKTG/vgW+PhIFIHxaAYC6XhEGkdMt0QZmMYJfDwhjSNSSuBxAk8JnBhpFEiwdSk4sawlbdXAQ6J/3jFkUKAg8QVhiKBAAgpDlDUOhgG0i4iDuAzi+QFhGBB2wxwUBgEE2p0DdueBOCCcO1+Gn4bdHkiwVRftudMmSGCtL5NOdGT1AgCmHJC2XNUnLupB1N/HJEtICywIIDDrMtShnDMDQqA8dDKR1DmJS9AhpWTzUgxSwUMUFcG/x2qQ2UZd9WKD9t2Mr/C95F0OdriByYpbwdteDwd2udzH0M1LisKW9jOfy7sVPCzcJihUx6DVQ+p9Bzeu4cqHHuoq3XttzL7EwLUbh3j4I1dwN9+9eNjpAQP9e+3GId78kSt4/2uv4l4HBb1+qY9GBfqgYN8tGWmlhf2p8+RuBwROEkzoUwYc5i4c5OPcPosraCvxKhRUmWP0X0U4EHD+yl6+OgE1PRiYBRJmMHDugjsEgnaImwcCAFXjH6n0FCvb14CAlbkfSljBADCPGXABZsOamyCNxU2gn7OL4PgI3MLA0S1RA3RbGidMR8fg46kGgXFEOp4UABhpHMGJweOEpHDAicFTGXGBDAkrj9hcCIHUpSD/gkIBBRIoCIQwRFUOhgoUaBcRz+0EKAwI4gA6dz5DQlJIMFWBwyBKQhhA5m4IAhghDNjFQSBB3Q0jm5JEmEJRlSIF7FjeyRYSfFzC0NSVHkRaPWEHB6NTD0gBYWJZ2ZGnPiBEAjggBz8yWR0T45UIGTADRYnt0CnJiZO4Gvxy50QNJHSUg6UOwKbgOFENqv3ajvTgAChtoMFBPVKBVg3/kjt27fjSlpZr5KGPlpcTgoLlv93s83TlQ1fw+Ouv4uIKFPTsTJt6T8fbx//4g9/f/yFOERiwktBbnryCRx8QpcDXE7/ETlVXuUBDW5ChqdOH6p4AOi+5+36nINCeeh8N+ny2UNOmVi0IzUZy/2ZQ4FNPLVh6Mck1KrPRBU4i80MP8/bGVZAj1SnLjd5dYG4CUwx6QGAxBAzkYYc+jmBJJQCc5BioggPbBtTTBvdmFNwSQBiI8jLCg8IA8QSaJkANfw4enEZQOgbSCIyjGP9jAQBxGQgYwIFBDwbGoyMx+goDHgSm46lAwDhpXJuCgcHAhNX4gvLIQ3Zbi2JACFEmXRLXgZbZLlagYJBAQ8Rw7hzG3dEcEhQMoLBAu/MSi3DuvLgbhnPAMABhAIK4IxB3AgphQIw7hBAxhIgdZP6BlFiCGMN8pkuDBK8yDRRy/RlUTfCgABRYyGWidWfS+mOqlMxpQIiss2USQIGBFBCDGKqgr1ZQc7oECAnIc1NkNwMBbO95GKQOeTXQIMEC6fwQx97wxvKQlytAoxhm1cC1DRK02A9IzFejopK0qsHipZvvJ2pbFRbuBBSCv2ZTRHatxzpQMMtn0+kseVy+l9Y+rqVTAwbXbhzgLU8+hEcfeBz3Xbi4Rzaqn0ieJctBAlArCtduqHvitVfxqv/ym7sPZs3IL8GCPyZf06el23C3YI3AWmpjC6q4AvSCDUsjUPKyEFhYTlyMvx8GBWtInBJQ3UujCuhnDwM9dcCMP9hBAIpSkNaAQI/bMu2uT1VkSVPoBgJySwUGbFihhwHCCeMGJh1NsKAOJFUBZq4Cpw6ko6PsJuBxxDROSLeOK2XA/pkqkMaE6VhiBzgB03HSQENofAFnI5cs+HCltRUDOOXVH8lUA4UFCglxF0ABGG9NiLuAMASEOIqLQf+lW8cIu4hpGBDO75BuHYOGAfH8sUDEuXPA7hw4fkZg4fhWjkWoXA3TXEUgDWakuEMMESnIEspTEjiYmHTGwuJqiERgUAUJojxB1kvQchmgdWxfJLCmibmCgwmMmAhjSFk9iOqO4mRxRHNASFqvKHEVhzCRDW+Ncg59h2VxptZlyLCJlLyaUHUs1oY49kZpeUiwAMUGEswF4eFgFoxIAFBUg00Bihva2lZVtmtL1tdBIXSOyaDgCCZ3TPX7fXddWlUE1mIoluze9ZuHHfu4/J6eGjB4y5MP4X0PPI57L7SjDzppVnjlYc0qEzfuCR3yaIalnHK7KnBSSah/D5hVWLhNvcBCQoEC/4P+CISS+Wpmu32pdR3YaVaUAh+9vAwDog6YUU8bgYC5mRUP8rueSgA7Hp0e3UIPqAcCfp0B7ybowYCfZ2AIqhagVQeSxAvY0EL7bKMJTBEw98DR8xpPIEGGtwsEXh1YAoLkwMBX5u4QTaeyTKmUW0gECiKfi4GTpYLjDphSAk8RKSaESWIckgHCGBGGCWEckRQQeBwFEMYRYTgSQDg+AmtwIp37EtDuCIgD0vEtcTXEARh2AgXTqA9JPnMIRUXQeISUbG0Eyq4GH49gkJAY4FjmSuAkgYIeFADM4LOXiqtB4AATZupBAgAFBCZ5BpIXzoAQ1PgTOAfu2Ysv+xUQGrWwhgT9TdPzB/P8PW/TmpJQ33BxKXh1wR3SizeQAuASa2DNkboaFhWFE8gMiy5oBwFdNaFzDIDZcf6eFrPU6WAu/ez6zUO89ST2EacIDB554HHcc+HSKiValfUFIw+pr+0QFSho3RO+4asMvW27TRhYmoJV8r/tpdoCBT6uYA4EdaNQNpe8zYMOHaOvvvy1/3G2YJQ2LH5kgbEto44f6AEBuMxDYG4E5vJbAwKvEnRuL7sKYnMvPrreg4DdwkAhLzjUwgARKleBxAz4SYdUHUhjJ5BwUnfBlF0C8CMKOkDA4zGmI4khMJdBOjrGdDxWLgNOaVUlEOPfVwkWn3Kg6h0JYb3u5vPZfMBISJNM+5zGCUil3vjeYI5zSCzBkbsB4RwDKSHtBBriOTX4cQBPI+hIAAFaVrDhj+qCQIiyLxxJwGIaQRTBcQCFATFEkfEZSEGWu56YdB4MiUcwIE0JiEPIkGAwOiSpIB4UZmWxUO/aTnarHmS/OwNMEn9ACghgkoDFDiCUOASA4FQEq5ecAMQKEoDgXA6Wwy2diE4QYuVy1HeqcSnkTpydhep4gy1wsCXtbYfZH+v2LUACUJTpbIe4jmOw41oRtt1mv82fu/mXdP3mId725ENd+7hWEqcGDO6969IMYNtUPUD96+EvD03Rkxx+svhkLt59aVn+z78v22YKwm3CwGLqEKsZf7mHk0MBWebWJiLK8zvoyoamEJQzL+TXNWwtDOjnnjrAqOfMzxDgP3eAoHUbeJWghYJFd8HMVaD7HQi0qw96GBBfLs1goCgFjTqQpvmcA2nUeIISPzADgs4ww+nWUTH8qhJIrMDoRhaUfzaawIySGHZ370Eb5ymAhgII0akAaVoox07yrgQA2Z0gn+167ngLtkssJ54SOG+ToDzA+/ATos6twBMj7CbE8+ek/sZBGuXpWIY9KjRgd5wBgYYRCAM4DaoiRCDtMiDEEBF0JsakZWXKQQsJ3o0VI2HgmCFh0Lo4+LoYaWYIeimrEwmYAouakAQ2rVcb1GaHwPJZ62cPEGQ/5aGPIFsjQ0Y0FBXBlig2Q8a5XajWCthyE14plAdX1MaFiZNspMLtwkHeTo2/f2MzXA1H3gAJpRzmCkFVy7NqyV1IsH3V9ypfdfJQ4GMKtjyWUwMGlpZuugUG82YBNRzIOXjBJ3NyIABc5bsDIKjUAqtUVH2dQYHfXsUU7IMC27bUnW7THnlw7+qRHRiwHr4BgV9EpwWCpFKpX4EvqwR2W6ihwKdI0nPr3YaHAKCzDLGWa6BS7h4GKH9HZybCWh2YzUTYzDuAzgREFlQocw6MeZRBCwWYWOYbSDUAlBsNoCkh7iKmYzGvMQ7giUGJVT2QQhAFoTRp7AiCJ+xN1XBuBwAWhJjLO4Q8csH2x53NmNi4qhKr+1vmVSAKmDBBVw3QdIQwTMAg6z/wOICHUYZ0xmEOCDaZEsXazZBEVWhVhAEy02KU4sIUbMIiBQcHCWL/CUnjElpQkHuSXC+5Glo1a0INB8TIRj4lg4ICCAGYAULS/RaoaI1jV0WoRis5SLDMW+fhdlKrGjSwsAYHQIk5sHarBwf2O8C5B/T37WLSS6nM62hgozuoxHfks7iOJ1f5hM2e7W5//7U3QcFrHs+d5pOkUwcGS6mnJizBQQ0Fl/L+9rd2Xvk7VxNeCJWgBwTAdpWg2k7N76pTOijwLoQ2tiDPVugM/lJqhy15N0EFC30gsPgBDwTmHjAgYN1ucQQtENgfg4KFW5kBACAQIFkXELByy1ClMODdBNbLWl2foFUHdITB0oyE2fBbUKFBQp6EqD8ZEVLKcn4eLRCkyZOeekAYAE5BlAOFHRqi9MjZlIAaJvLfRiHoxRUspda1QK2CoH+DAoDBQDv/gQx1tL8qw+j9EqL42ZPMA5CO9dpyE7Cx+pym4mZQQKDdObB9jgPCMOYZFs0tISrC4AAhYqfrNyTnapCpl9VLIlZ1tioks6gJiSk3ymU0A1Uw23slfWLmGg6ArB5QVg2kxWOSuRIMEGx4o8R6sPbASy+idCgEEkCMxdUHmctyAZtjlNqbURAwl4JkLMOBT0HzmBh5tEJu4ZhzG6jaRt5eqQc5v6Xt3QoIVVvNJS+Wt8weDSAQ1Ya91hf6aTMU7Bl9sJS+aMCgZ8N6wvcWKLDUo7AtULAvzWIJFoAAKGqA/G6bSmC/zyoBkOkfwDIU5HOF/a1ToxKsLa/bqgMGAFuAYE0lsD9T8i8iZ4OfC63KtuzzIGCqgMGA+WyXYIBaV4GpA1OzToEtVuTiB2ydguSBYPTG/3g+RbGDgjJ0UHsygZAQgAES+W8zFYZQqQc8M/59ZcGrA2vbtqbWZdDb5mGg+u5AwWZT9DMpkioOgJQHDdB4ivI8c2OdnN+cJaaCorgR2EFDsPUaUgSHHSiYmyEWFSFEBA1YZKLiatD6l4chso1Y6MfEkFhpMKShtnrMoX4ms/KzoLbERdly6oG9G5OqA0Q1IKABhMBNHAIwVxGAAgnM2ZhLhhN6C9HtbUP8cR4OAJSYA3QBIasHHgQa9QDtdt22BAhy1RM05u48Xj1oTdGSC7x3bC/5mBvvPrj3rtuDAuAUgcHWYFdLbRUk9KGgTb0mcK/sYy1Qdf19vW13bAcG5K+daz8Q2O8J/tS1SgAsQEHv/tZGGSzCgOTipEDAgM5R0AcCy2KGgnJ3AMTIezhoUwUB+sGDQHbNkI4m6LgJFmHA5htoFipaWrioCwRpqhY0mq1b0EkUSSaxmYL0mBMBQ+nVB/ud6/VvmYPgCyVRJe3U4BA8SISQ1YhuSgkMVQrGY6kz0yRBiFruPUAg3d8u6CSzLA55RAOFWLkabFRDCwlSxwlxxS0moADYOwQUWOglq9fewGSVNMMBsnuhBYQQbObFAgj5uztxT0UAMIcEITCXw5WYJKC2mB4OZA5p5Ldd1QPfzLbqAQhIJuGzDNlkQGeK1J47pNxzMaNp73lDu+3TnkP3TUK379T+yQcAhwsxBbeTTg0YbE09IADWoaCqcJ1z9qJdq8AWf6Hee9xTM2i+e7PLQHe0QFDO5VQCYL9SUAUYum3V9w4M5O3lqicGgg0qgZ0755rLvXo4qLLbfKjhqpRlsKDCBgYkrsDNSQ/nKkhOEeioA/kYniSgcBrFKDsggIICmMHTMTBN1QJGPSAIQxS3QEgAovSCB1SSvw8I9CBAS701oGtYez39ffv3KQtdNaI3XaDt25B/aqDB3A6zlJI8wPFYDLqen+IOeRXJcawAAWSLO8nS0HkhJ1MRgqkIo7gg9F8MUQ1TgYTEjKQijkGC1XmLSwhc6rPV98FZsaWSynfretCh+UFSww9VwJJJDAQE3AYgSOELJHAqi9F5JaHtZu1rY3pw4FwLQFEP7N1fAwQCdHKlUi65tBoVoZPbUqZt2tOel9tbh4J9yOBNiikF79PRB3eaTg0Y7GHPKrUFfv3mNYUCGX3gU7XiVnseZy/9OFnAVUpNS7NdAfNK47/uUwds35rLoPzdAwR+X5t6ldg1xktA4BuyFwIIUB1bzl3nq2ykdrumGqxqVcBcqmswkN0HQAUD4LRNHTBDP5X4ggwEKcn3dlnj2SMJMlFOAmjQhnFfr781oGYw/XYz6rbNvruFw+q1DxZWw+ylNn9TiVas7tGWcDZYsN/5QMfOtu412tTcaw8qZNSCevbNCMWhAgSLS8Ck0ypHt7x3R0WAfacgy0M7SJhYDHwXEkBdMLYRB+bHb3u38/tu7hHWS9bfU+02qN0LtwMIclHKC9KpewpmxH1mUr+NmT2YAgf+XLV6UM5uTYF6ZWpAsPxBlQK9VYur8CoC7BxNdtZq2pZ2XT7r8RuObRMz42mdp+DRB04WU7BW2qcGDDZUqXKsK2ibJvL9r+1BQTm2nRzD/Do9OGjz00LCWp57IOCP3aIO2N9y/jsAgl5aAoLGXWBXvh0gAPoqAfL36s4A1L42rLxsmV16MEB+ZsICA6RlHHOjx11XgYBBTx1ItbtAjT/SVAOBLYvbQkGa3I2EfGOE0B8X2DP0atglkC/PSQyyZar1GLLFqfS3ZMbNF16I5bzN9dbUhEoV8HXO7h1wlkpjJhwccHNcLiegBqgWLNrr9ZIvszRpoOEEWzGXAHHJhFgAYZAARY6DKA3ezUChryJQKJAQIkIYcjyCQUJSgzuDBFbjhvq9YLcNKLAAzI2Xfzo9OADm6kFgNwTyhICghVsAgfRZGiC0QxT3JTtmIyD4dtsAId+8/apREQAHXNkG1KBg52tOVaW1Nl4+y99+OzU/rk3XbtSd2pMstLeWTg8YnMBXY4caFDzWgYLkjvOA4OWuXBWXuqWLW5bz5M+7DwaAuTrwWYMBYBkI8r79QCBZ2aYSwO33UFBlCZjBgW3Px3RAwL5b8OAaDAT7ncGAzPozdxWYxO/VAjCghj/HD6ihXwUCoOpNZ8PcXe7b1c8tBt8beyLxp1PI+ylGHXGisEd+dUv3vH3B2jZ7Ht1oKvekmnqZA//sDApSlLezyL7TVMrM3C9WngYRGwBCNm9sRA0OJuutcg0IFECDxhvYaBHvZgDNVQSbwKtxNTCFErQIILWQgBoS4IIX7Z7qd61w4wYRIavzokgUOJggHaPAkq+QacIFJOr3yc67BAiA1pVGQbhdQPD3wdo28VTVSypXzm1TpSK403pIyEqCXsTUhHy7C+W4lnptvT/HEhC0uN3v1J4oJ4t7Tg0YeDfoqryjf21Gw8deN4cCAPCBvz3D7zfVlWpbftvf+XMuqQafFWVgqXF8IWIIdOMmlSAfW1QCyV4/f5VSs5Dtyv3SuAgqyVPdAxUYBOe2YW/4O4GE1qNv1QFzFzgDxuPx3HhZD8Ubr+qeGqPvngf5nnzP+Nuwumz0BzX8oRj9IMaLFRSYyhLWUoidJa4XlCKrB0upW1d9PbXv5pKB/z6V/Qpd5Bb9Ca7MySDN3DQdaCBf7pYXSx7KfOKkLBD1vDUgIIQ88RdzysMdKaaOihDzc6tcDSGCFOwixSxvS9yBvj8ow3kDqEA1NWqC79ygfJ49l4V2iw0O9OFl9UD3d9UDPVhcE6U9rjsxJPfbczFYWZ6krboDSMh9Piq/s5QaCMh1prrQus1p0z6XwSIMuO22dPJjr7uKi959QNvsX3vtNp0aMMBSYXaSFerjut51L/nC7UFCpVB4ack2rWexbFuTley7gwGqd5Ur3e7ogi1pwygDy0lPJfCTPm1xHZTssm5fyFYHAoC5KrAEA6IYFDAAubgB7ypg3q8OMMOCCbM6sMcw6U3KH/vu5ezBy/M1AMg2NfbDUHr/IcrywyHIUsMkDbAZeqaYjTxnaTvmpazLypX9ZayZy7oT5Tnqc1oM+vAPTZ9RrtO6gBIBQQeSUuipNUBeJjjHajDYPRN2z6SAhB43HufYDXIwx+OocxloLbN9FnS4kDhN8kyyWiGKAbHMCYEQqjgEczPk50URSAplKYHCBK8iCCTYMy3TMCeUuRAsxmCCfXaQgBoSQMhqQkf87KYE+72biQ8FDvIxVC7i1QM2YEBx88397r4tEejjPIqhAwfdh9HciAOBcp5tkAAtnx4kAHNQgJZPq+O9IO1/8yMru8Mbh3jTh2v75QPd99m/LenUgEEvMKSXrFCvrkBBe74eJNgypgByoIpPazS2jwh7qkD5ewcgsPaSLQWMnWDoIbAeS4B22wIUtNn2sQNtz6Yi4I0wsOwqAFYDCa2nmg2OGXjOQMBt/EALBEClEgCoDFAGAH+zW9wAXglQA89UYKGCAP1sPdcE6NLUkMl4AKSUysqVmE/GM0H22V34CaTamSV9Wpo0ChnS5F9/0qigk0YNwj/ZCPVHgtg/ZgZ2/eBPUjWHOu6IbtyDe14VHOhzNZQVQAhq9PWZGDCYsRoGyW+Q0SRZRXBAIIBQoCGEiKAqQoC4GgwQbJGwLiSAdRpzu50iISy1Cm2LYG6GDAeg/K63cJB/74HBHet0COQoAHMvbIWDVWhwuVcYyG1kbtNMBrA6GXJ+PCTIsXau+ZWSa5R6KuZS2qIMtLbg8MYhHv7QvFO7L9Zhdu09mTw1YOB700vJCtWgoHd8r2C7kEC1krAkK3XNbefB+81dVeCkINCTpH3j1lpY799rfcYboGCL60Cy2LgPgMVJQ3ww58zt0iHtfTBAqF0FlM+7oA643mk2Fmvugi1AAKAln0oNAAoI2DPQ3n9fESi9f7Zepi4fDFMHTAkIQ4aAKQFpkmWlEzPGVFYHTAl5cSpb9GecBArGJL8ZWRYusgl57BnaPAm94fVlLiLSZ6rGn0QlGEiWrR5UMRgilUWpFBz86pSyhDUhUkCgiDg4WMhrTLjAUF1/gtMEDPOgUE5JevkGbs7tA6C4IOLQ725nFwfmgBCSgwABhMrNQAGgCcw6WRIFURFCBFhVnNRREUiMLsO5GhLnGIU1SLAsZ5Vz0W3Xb1n9bH82gU+WJ4BZ7EHPtZCvsQUOvP9wX/s2GwqJ0nZtAAXS7T01QY5tzr2S9toDYBUG7PPhjUM8pPbrchsoPz/N3rR27CkCg/WiObhxiCsfuoKrr38cl+++iKWnSc3/7VFGZjY+Fpg3gr2HvzR0pQsBwH4QWICArXOTl8jb9ar0WYWCDVltG6XKfaB/PQzYdh83kOGgow600vSSu8D7uA0I0h4gADCHgvYmgJlrYKsqkBq/dIEB/RxkmeAMAiOrYTcAYIwOAm5NE8aJMbIsFTwyY1R4kO1JlhxOjAQBiKRgkRQikqt/fvIdP49EULVAnocYd4npoLz09EABQ1RQiAINQwwYFBjOxwgi1uWqBTaiPnMBhgExDhJiYeDnZp6k6VgAISUwT6C4k6movZqwO1cFN2IN9trkIIACZIrpBhBkMacEnsqzFQMoIyLApjiU2ARidTO4yZWCun5CUNcPUR6WxwoBE8tbK0qDj0nQdsC9ayf2OCoLeDgwFXUJDqrjNa3CwUrqtXnddlAX8mCTPQCFDtlOtAUUJKd227q7fPcdRD1gTdpfG63m/+6zX7TybZ72N8CnBgzWZKWDG4e48uGHcfV1j+Hyhfv6xhVorI7JSvJ/Cwm+6NcmVqtO3/18+yBQVf6Tvs3ZB8d9OPCuhTuAgpI93sIBPmvz7fo3zz7WgYGZq0B3VsMMOXXdBfV2RtdXvQ8IgNpwAHPj4ecDWIKBnjKwBgNxqFWBBIyjLMgzpaIIjMkMvxj6Y4WFW0lUgeMpyTHVv4Rj3TclxvEkAHA8JiTmDASmFowrD3pwqkEBBMJuCPI3yrZdDNhFwhAChkDVv10MGOKI87pvN1AGiSGDgfyNOjPlEAbE3VCpCX4lyxoSxgIJcejGJmRICGH5WXuFAM7epbQBECaFvCSAQLEoDhqcKNevYxNEPSEw2aqkRTlI7h3tQQIgKoC3m7eTGMhwIOe8MzjIhbdHJdjaBubfEM1hYSMoyJ/iesj37cpgq13I+XJ/vX0QKHgIV1/308V+rdmurWntPT352b4wE5mE1/jKD25cw5WPPIyrr30Mly9czMd1E7vfN0EqQK0mhPKT5Tz1LpA/3gEIzF6AdaIuaYVdWzcCUC92JDtnMQVy4BwK8i7dtp4rmcBlqWp7EACWYaCnDhAad4HFBWwGggWFwAewLakE+QYaEJAPG2BAeoLJgtF0hIEBwEwZSBJMP3KBgcmpAmb4byXG8ThhTIyjxDgak34WOLg1JhxPBQaO9fs4JRxNAgDHUyoqgboXem6ExCw9WisKBwaRSP6qId9FWdvgXBR1YBcDdkPIkLCLAecHAYBzCgXnhoBzBgxDxPkMDjK98BBErTBIEHfFgDgMCEOjJCgwsAWM8gQKgyyLzcsBjF5JmIGCS1k9AMTmwbUROrqhBgSGufg4mKTODhDEVQGS32XQ0BENgUowYGKALB4BxdXgIUEy6bu/y24+wL2XC8lGNFQ96g4cuMsBaLphROKwyLI/aab3BSZuaBNZ7gLwsKAKyh2AQrmH5lILqS1FckcffPIp6dSq/QJzX0Hx5mIpXszOn3//RaQY+EI7uHkdV558C5544FFcvuuVQBr3nsY/pNmkGYDrOfPs+H15k88LIOD2rYPAQkzBWsoNc2b3cooFtaCqXDoksQWCpdEHdqW92XPvf7eRcZuqoU5LMKC/WXQX5PiBxvjvAQL2QYUtEABFKcjF1XkxrTx7MOAi1mHT6xJlAPDBg2zf4w6sgYNjggMBAQOvDCzBwNHEOJoSxpRwa0w4GuXv8Zhwa5xwNDHGBhKSXocTI02pwKBuA4A1bShPWRtIRx/os9MFkcSVQBUMDEPAuUg4P0TsBoGDc/p3CAHnouw/N6QFSAAGYlEciBBDUROGMCCY8VflgNIIRB3poMAgKy/JcW2gKTV1IhuQfRH1Sc2uBShm7V0MvcQWBOdi2A8IplIgjbWbgeTUNvdBjj/oQALkEB3f37Hc84e6miqXAgowtAGJ/jJ5tILBtsn4TZlKQOVKR2lvI2SjgXwGwn5Q2BDM6PedqD+v581K92sfw/0X7nWdxn2ula0dxeV0isCgrgAHN6/jwZ95K554zftw+a57+xXEB9zl5Pz1dlgb4Wq/PUF+7hgElvK/lPx9Ma9LTd04Av+dqnaBUUMBmn37UrWORCdbS+tEkGtcZq6DUCQ4cxfsBYIcQyDKQTvKgM0lsKQSADMokMw1daqdPTAOyPMKNK4CUWkadUBdBQYEWR1IC+oAQ2BADfqtJC6DoynJvyQQcDwxnj+eZjBw61hiDbJSoBCQJhl5kEYZQyJFK26EvAqjBi8uPvuA0sNSNwIRSQA+CMdDkO+REGLIroPdoO6DXZxBwpfsInbR1AMFhZHx/EA4HwQwzqsCEWlJRYiIQ5R4BA1SLMtgFxUBqiIgTbLSoqkIWkdo0s5H616y1L6zzh1BgBp1C2Aci3G0UQxT0nqSYCsWUgMNNo+CvfccWGNuipsh6KXzZD6sqkEPEtyLuLJmU9df3qZKJQGKmwM1/KvWgK5Lwb7vUw3aNnPrKAYPC2ugYPlZBIWiNuTzbUnMWel+4oH347KHgqX72qMStOng5vXV/acIDGql4MGffRueePUjuHzXPbMKUfulpqbX7IykGWx/mexq6L8hM1pbAYEqL/l8CxWg619r6dnDwP7KUt13z4VAud8t21FDgc9mqxb0khMI9k4RvbZgVKsOVO4Cb/RPAgR6THd2vQYKquSWL0Ysr1NWDRploIYB7ypQIAghuwfa2AEZRujiBGyEgCoFE0sA4fHICgVpUR14/jjh1vEo4HA84fnjogzcOp4qGEhTQkpJ4zAVBCb9zlwUA60DW3iVyCsGhHAMUAwIxwkUNRgxBIwx4MhBwvldUiVhwpfsAs7vIj5zNOL8bpDvXkUYCbfU1fB8DAIHgwQuxsSIQVQEGwkxJI1PiOcQI8BRRzYoECCaqpCAIErSzNVg6yk0SoKUi1PsrM606lLu8Ws9c1yAcZTg08SQVQxtMSF1H+wBBItFIH2vKRQ3g6kIS5AAyLPeMixuyfx5vbJ1M1gnYx6RX+Cg61LAkmrQpJkNaNrO7o8st86t7EFBYxS6wYwdUMjhZHva5cNPHuDKk2+eQ8Gq/XCxEnuS2ce7+a7FY04NGFgBHdx8Gg9+9G34wKseWVYKAEegpVKxg4EeIADA4Y1rer2en6eFgrlh7wfLdFSB6uFvk4byvOH76LGVuhz91lCgWXH/6mGGTZYXLyfn94tM9fb7rPVgIG8nH0yoV94HBDm2wBrq5jfg2o9sZd7MTliKzZVx9bmZiMjBQHYVQGYZtBkHF4cYdtwF020AwcjFTdACgSkEq0CgysDkVAMPA5mrMhisuBJs7gICiFirHyEFAk2c1YIYAxIlhCEhTAEhMlIkjIkVEKLc58Q4F0nzTxkQxiFg5JD3nxvEFbKbAo4jZ0CYFBAmJoxEGFiWQa7cDAoEYrR3sIBFpAlMWpeSToOsalMPLMnGN6dpDgRt8hAakKdjBieBSkwAE4hYFtICi4QUotT1rCq4mRhtn45wCAoIgSAgYNlzoxMyJLTbczYXOkgrBsqrBq2bYR6MaB8cHABKS9ol6MDCUlpqS5fac/ZGnRpQaCEBqEGhF69m11ppox988s3i/r5wr/5+BQj8/g2A5O3jOz/w5xePOzVgAGYcPPc03vjRb8UHXvWTqhRsELbXAEF9ebb/4OZ1PPjkW/R3vYrUl65eEBhYu5d9lJj3FwjgmdvAQUHZuBpsuJRU7CvvSi7CeT79phYGbH+rDlRAYAGFLRAgNcGGPAcF5vw5xxJUEebuszU6fgKi9iZsXwMDfgpim1TIgg/bYMIMBCCMLFnIrgJGFwgkjqDvMrBgwued8e+5DG6NSQ2/xBF4d8GUksYUFChIU6qCow0UpNiW62qoPpC2mQJlIcpesYdJIGIMwJAUQoL8jUEi7Af5exylrM5Fc7FEHI0JX7KLOD8wRhagGGPAOXW/nE8BY+Q89NEDQiRCTIwpB0ZGDEOUtyEDwgA/V4LMIaEKFElMgE3RPIOEOBSjv/Re+xfD5j2Aqg7jcalXACihqAeJkV0MCqdMCgxTBxA0UJG0914mTtJLU5m8bb5EMfKwxKXUe797yVwKm4MRpTB0WwAjuV68tkAbQGFf5n07XB/ZhwSUW0CtRi+7rNskUHDfCTqWC9+bNLOPK+nUgEF90/d2jlh6EM410BouR3YHN67lmIU//n9/7YoSsYfqVoDgRDCwkMq8A07xALAIBfq5goI9wYaza2o+K1cBfOMx/41/TSgfN4cBoAMEFlC4DwjU8JcRAwsqwTRVz20GfSbtVq2cAwS3GmGrDIBC7SqwxtrPRJjdBc28AzwHgpTqGIKRgeNxwi031LDnNjiekioDqRtHkEwFYDgDv6dH6GerQen2xUBd1YBcV9CrQKYa9BIzIyUJGGSduMfiGo4nr+5NkKl6Jhk6uYs6lTPjPAdZoZCtTAljZJxPhB1HjNOYYxBSYgR1MUwMRFUoJtKhj/EcQuQSpGhuBpv7Ik3gIAAqrgYF0VZJcC6HnFaGtUphuJ4mJxvRKMGelXoAsfBWJ4lL77cFBEp5nzwThQ2vIujfFhKgu3P8QfcJlieUn717trPnzhuCET0cuHiDVTjQ326ZF6HkxdXhJUioOo/Wtgf3Gysn38kIWGvbZ0r33k7oHtuGLfaxTqcGDN740bfhA696r4w+ONGSFt7zJakd218FMl64r3+azxcQtLEBwDYo6LkP8m9rKGDMXQhbsrnUQ6g0iU4swaI6YBCwBARcL6qzphIYFPQzqA3QkrvAQ4B+b1ckzIsQeVeBAwTvLsjTEiebmnhhIiIWN4KNMhgnCw6EgAAnHOm8BEe6L8cOjCkPLRxHUQWmnvGmYsC1qUVMpBHXKVsCH18AGEyQA4llFSs/8+xW0DiDLBmJK4G0UghrWUzC/HxTYgQCxjFhoICJBHh8SmpjExPODbpiIQeMacQuBowMnE8soxiYkBJjCKIaTAwJWmQFBCJEdTOwdzN0hr9mVwOnrCRQB0grWFgsuP4bleV+TrV6YFaWxFhmQFAX1gwQKICJ9Xk4NwMcJFh5Ur0CIYCyCuEJk9ly3xKbS8FfdhUOAOQaexI42KIqWCbteJQ2+8SA4F3TJwwY7LdXa7ZO9gkUvL1jH5fv+9SAgdz0fhLakk4EBUuBjT2i6/qKfE/1hK/VXigon6soWqcYzGMKVqBgUfUsBmGPU6MCAWAOA3aOkwLBLI7AAUAPCnK5NavokY1Db17aav4BoAYBQKckNuWAqqBDG1lgjbB3FyQgqwAj25wA85kJ2wmJxpRwy81KeOQmJjIoOMowkPIMhSNDJhdiQtRiCEwYAIxQwxDELUCJEHQYIgOIuygdXKckpAoMrFBX6rH3tRqA5DpRQEDqAWUgCH54oxvWmOdB0H8jA4FtwqWEiQhHFSQEYARSkG2i0CScZwaHgDFxnjBpAqliUANCCISBSddviAiNm4GcauBX4PSQUAcHM4KBghSm/pk3+u1QWJ4mGc6o55Hf2swgttk6AwoIsPdeYG4JEKCAYGsHZBVBL2WpjT3Yupz1YhyCqhSrcCBXdnAg3w0O5Fg441yUhduGA7tJ+w2k/WY/OsIAgSjnp3VX57b4hICwKciySTUUbLePpwYMbh8Klh/KwXPP7FcKXFolus6+2x5versqge0nN09B3pfN8CoULOXYw4Hf1su2hwGgVgfK5j4QSK8oW6e50d8LBU2+Y5QG2b2cFMPsGAA1CChQleWLFQQqGCBsX7RI4wZQ4GDUWxmd28BPVbw0S2FPCbCph89F4AjADjJ073hKiEF87Ds3UZHJ7nluguoz8jZLawGHS6l1Lcjfss8rC5J/rE6MZJMj2b22aUqMkap+KRILZAABExImJkwIGGjCeQ7gGDAlYGBGIo07SEAKAggxocziGM/J0gZ+Ia40YLbuhiktoShZbEZLShPEEqjbguusDGOcb2zhIBsuPX+aRD0wZcEBgqy8yauAYM+pzHWAYsxhbUGTpT1tg6Wcy31wYPelcCDbqzOo3mUZCKUnX8Ywye9yICNO3jmDgwOfWjiojl+YbXbxAqHbdknyTts63S4UAKcIDPaHtqz8xhtQTSeFgirdRuXK+ej9tqlE88mH/DG3rxIANRhshYKSzT4IuFxVMGBfK3UAWAYCM/q9OIIKCPRcHgo6yQi8bVzrl5YqiKpUAStHVQfMhztbxjjEmTqQmPPftAIErdEfVUWwzwlqxDtyTgy2cE5ECIwYgOOJEahMYfwlu6BBjmXmwt4shh422uDC6XbrO2RBJJ+CgwVbX6GdLdE+h0DdKZZ3kRBCyN/9Og35HsBILKZTBIUkwY1BYy1CAHPCoDEGHANGcoAAgYIpSKhFJKibQVWEGEE+FsECFi0egRPAEoTIHZDNhtRcApp6nY9FI8M8hwNGrR54QDDbSWq4FgABQK0iyIYuJJS03RAuwUGbsnpAEBXA1AP9vqoeAGgBQY7b4NY9iVFvz3W7v81Kh8KyP2cHDgoUPLICBct5OUVgsJZWoKGFAgrFfWDRoWupR3MbpSkzOJVysFBxujBQHR+699IFgnyOvkoA3A4UzLdVpU6YjVGugMDKYB8Q7FUJyvmWFRmvuDQZb0CrCwJUPlfKQEctWFQHDAq4XtEQbPMUpNmCRuygwGBgNvsyEXZRfOmBNNI+2XoGAUM0g69/9QSTO0/q1N2eCvHZTj2D7pUAE3aCyuu2z5SEmFWFetEmn2RqAYGEMc8zwJV6MLGsOTFQAAJhJAlKDIERtZxllceOiqCxCBKMWKsG7aycVpd7oABA9mWjtz0ROzgA1eoBnLqgUAAQZCbFZUAAUKkIDLcKrRlzV2VSp31YayKX4KD9yUw9sBEZmg+wtMWssQYCVrpUdgMIdjlfbvsM+d6h4VVme3TTacsXr6nHKiBU81TMAg3fsQcK1tPpAYPbJLHKiEIKNUNBs7Sl/KDjl3I0Vy/Q4WjOS1UusljysKFyLcGA+95zGeTz3yYQAGvug/m2FgaAuTpQdjnDDpwcCIBFKJhltJJq+zcyc8t0YKAEENYw4Iciao776sBGIGCeL3E8pqIMtMY7gMrqeomQWHq6GQCaFnWroe8Z53zN2+z89NJadm43r1lpMJWByiqO8+uLcRcFgTCAMQbIqogqjY805aWgEWRoZALrUtDIU/xOtKAiECrVABYTE4q7oRo1o3WbfVwMJwCxgoXF1L6gOa6hAweMvnthERAC8kQ+Pg5Bcxr0ZbdHV0HCQvZmzwQODtzv/GO2a4p6YOGPK4CgYKUIiRwY4QIY/bk3p2777M5XuX9p/Te9dqqClOIqAep+/8FNG33wSJkH4TbSqQGDfT6bxWWGnSG1eQqeeOD9uPTSy3M6rQKGMDf2XuqZ0Vzq/2ZLWqloM3VAPy8tlwxsBwJgGQradFtAAGxTCIC+SqC/r6DApVmAkdteJQdLXVUgxxEswUCoXAXyt6gDrNu9u4C5LHc8KRBwUhiAqATM8plNYXD3F0zDDazT25YS7vXO2tQa9TxRo2tmikG1Y9w+d1z7Sq2tKjc19cvbNX9/rSvDg1CGo6ZybuEHf9/+nr2SkKDzGSSBA2b5PsQAHhkcJN5joAAOBFJAICrBiRFACgChURHgXA32HjhIIGZwcJCwoIyxGcFZ8PP8jZ13PNScVnAgd14HLgZILV0BBNe2kXuPKkCAa0e41ir2pSqHJwUEAphLpyC3MxSLKjNTEdwVFC42p5kNWnZVz13Abhv6bVR5tq0rRL4f3LxeJve7cO/J4hiadGrAYJ+R5V4ZeffBjWt48Mm34OprH8Oll3aUAgDVmgm+19/24FupB0AZYAxUCsJq6hPlEgzIvvk2NDEE/u9JFII9OXxhgED3zVQC295CQS+Rfw6tCtBktiqvHgxY/MAKDHBxFTCwqA6wHYdlIGBVCVooYC5NdtDFJsSYaRyBn1ZhZvTVwFujTcV4kxoxiw+JRHlbOZZy8UWUoEDroVeX21Ktef7RVAFW2R6QR23lBqAqn3IscuCjlRPg4aGuIy081KBQysnALU90xDI8lIl0O+aAAJPQGSOZm2GuIpBzNQTaAwnZ5bANjGfToltaax+rTkyBg/L+BMk028RCU19B+CwBgt+/VUGAORl8DAJFuQdORUUA+pCQy6WXi7W0oc1u9lVtt/3t2AdTQTIk6H2Botivn30bnnjN+3Dpwn3b1I4VG3R6wKA3Gchacg/h4MY1XHnyzbj6usdw+aX3r/yonDPHB/jrLUg91XEAMp0u5q1ToVyeP98wAOwHAvtbNArkBi0DAVBDwZpK4H6ffzdLdmXdR02dmJXfgioA29aHATP+YAcBqGMHWL9nl4JmiyFqwAReBQKgGDzLqsEBUIyYrx7e2APIBt8be2/ozcjHYI0o8jFE8oxtIiIpFaqeNaEAhc/HGqL7elYepZaXq5cy76J1kjnDlBlsK8spcYYJDxL7ytQrF72q5O+HFQ7M/09MXUCIEFVBp3TIboagMEewWAQpowDk7yBZyIm6kBAKJKzF2VSFmpr6bze5YAw6cCBHz9UDq4Wcj0vVTIrS8ckYWl3mhVAQ8rkMEAoN5DgEA5K8mmerIgDLkABU7fkMFpZSY2i3KgS81C51rwEHOQIJh5882BYTd4J4iFMDBn15GDNprQ7iI1na8iNvwtXXX8Xluy+X4+yQ6pxuCg+Th9ppMAH0/VVNtd+nGHSDUpYq0gsDA4sd8LYHWu3UbcVmVEoBtsQR5Iu7hi4f617UpaRunPK9BQJq/m6EAQoKnLqYDErcgBkb7yqwEQJL6gAz57iB1nhJERQD5lP0BYva8Mtt7Tf+gz6gYM/KjiE5BqoikBoqQvkMKs/XkDc/k2ygXPlvbTxDb20OV9YOvgwectnbPmhZNmVuMRtboAFAF8Z6aQIQ7TlNXAHCSJzXciCi7GYIEBXBlJpEQEjQFUGL66HEI8gMhCFqe5NjDoqSkAPqepAAZEM3exabVYSk75Ub2SAPDX31QAEB2iba+gAaf2CGOp+FnEHnfOYTtUkvNCQAzl648itzN2xVei2Ty224XGKhHW/ar5ktcvk/+ORT0ql97WO4dPdFuc81m1dncDHrpwYMsmIwK5TOOF8A4j44xJUPCxRcuvtS5XvPv7fD89/2IaImOPjibozVPn9VW+laELBjOjEDvb9rIwtO4jrzHpEWCl4YlUBztw8KfKZz78a+94i8fVYrMKAQUAyVCyI0g7PgKmjVATNqrTqwBgQAZlDQAwAAqxAwRGmwsyEPNQCEAF3qWM4bLIKfnNHPQ+xWVqe0Z9cExnVn8Zz1pEJdh5u4jtBMIc02HFSHfgKhO9qD7bkkAwbKAZ6bnwmtPxPbFv1+BQQKpNDCiEQzFSG0KoK2JtnVoNBQXA3QgEltptkCFYuasAoJ+kzycwCW26FKItFn1sJBBvACB/oE++pBDry240rLYHUUKCAH1HCw1ka1+yZ/C6zn2QwJAPJz13al7Vi2CsydtuUdUFhqz9F815zj0Nmvy3dfys960eY1eVlz+50eMMgGuyefzY87uHGIKx96CI+//iou3n2pMpo5kta/K6jgFlwQwT1cfTA56KSZl3sDbM4gYHaNUnnuBAT2zU7WW/GwPuA2YwmABgpqWADmxFtlpIWD7uf65VuFAfJDDve7CgQCltWBrT1VueX6GbSv80lBwCsBZlSsB+ohYCDIXbox9qTT+ebhdO13cJnrfxrRLkXNKQFJe5b71B0ACLrIVLMsNcVBfNS21oSfOdItOhUoIlaLUMlcESPT3qGhuxAy7NWgQLNnRQvPylIFEA0gEEvHc8JUKTdTR0UICmZJXeJzVwPE1UBayYIaeDfUsQ8JjXLge71bJULbzB04AGDqQYEDoFIPcjBf8K0m9E4UmDBrrtvXff+MiuXcGRTaRtKBgv+FqRoCo6WsspF1MWLZ/bAxddv0hc6dfW7v1NskBnB44xAPf+gKHned2gw5K3Zv/zZJpwYMfFNEnU8+Hdw4xEMfuoLHFAqmtvLYORcqU3vokooAoFISFtMCXfr8t1msYMCdvkfb7Qu1pdr0FjgJ7uDbDTAEsA0KTtJoNS9eFXC4AANrrgIPAV66XlQHNLve321DDoH9QFDf3joMDBQkJoCKW6AY/6IIDDQHAZqOgMlm5RvLIkBpgi0lTJxk6elpBI6PkMZjMfiTHMvTMTBNBQ5YgUD/yg1uBAMKyAtQKRQgRlDcAXGQ7yGAhh2wOyfQMOzAFMBxJw1hiEBehGoQtSFE8LCbgcLYKAoGDZHCLCB0SNKLH5g2Pb81QIC6GYZI4KnEdSRA16HqqwhRQS+p+pPyqAZ1NRBETdEJk7h10TWQAEBAIasHe55VfYOQ4LwWDgii9nk4ALJ6AIUDp0D4oY3AHA76LoVOmXeOoep7eZ88KGQ1QcvAqwmyqVETgD4s2EXWUrdzh3y1nulhn7+OTbp24xAPf+QKHntd6dQWJaScu5fa6yylUwMGvRumzrZDg4LXXcV9d13KhiAfZ++M+3H2Xy1cr6SeioC151T9tnfuLgi4DS0IVNPUbrlsc+zerFL9sQsF5lJZhYIm47Pr+AZsxRfqVQE71mBA9/ddBX11IEe6u17mPneBH13ge5xAbVDkduf3ezvKQAsD0akCQ9DAQS4L/FAay2I/0whK+nk8Bo9HwHiMdHwkxv/4CDwdg8dRlvidRvA0gsdjpHFCOtYlqo9FKUiJRTGYzHCWN4WnVE0xbTPnIRIoBF0DIYB2EUQBYadrDww7BYUBGHagYQDFHWh3TqBhd063698QwUF/Y2tRhAHn7PMgk02NiVRNIF1HooUEwsARHIuScBJIMKCemCtAAAM86mgFdTNkyIPyFQwOpIOa3Q+q/lBiqRu6TsOU68gA/+4RJ3B2NajJoAhAZnesIHzrkLwl37p3FalhLgsFNXAAwFwLPTiQ6/gvC5c8wb5q5VdrI8kMv/5gr5rg/m8V6RMoB0vqQLvNd/Za23TtxiHe8uQVvP+1V3HvhUtlyG6n89qzf1vTqQGDtoB66dqNQzz84St4zAoVfYMKrNMmUF5aoKbNklrRrJ+WCO5OQWBjPwBAEzegqbvYkat8/t5mSgEwdx+ghYINOVwJ9Nm3RsSaOmC9/Da4bSmY0BSDbPDvEAi8EmNT/LZAYAFsFjQoRr92EwxBDET0ygBPoGkCpuMCA9MoCoFu4/EYfHwEHN8SCDg+Ah89n1UCnkakoyOkcRQQuHWMaZzA4wSekmw/nsAKBGlUV4LedzWNYptiyLETFIJAQJBtAgUDKAbQEBGHiHB+hzDI9nBOVIOsHpz7EtDunEDC7rx8HgQIEHdiMOOQISHEHWKI4CGKehAEDiYmHV5YTzxFQ8COtbfPElgo8wyI8MxJjGL7nLMxcoAQiXRkA5ybIc0AYUo6MoS5BCqaOhQUHoJrn5TObegjEcBgmBuzVhEMEvSNNXl8nwtvU1IQ8HAgF0GGA3LXc3AAuYUqINFW9Lb+QW8tFrvq1mTtnCkLM1CwA1dAod61r39e0olgILft7jfMuH7zEG958iE8+sDjuPeui7m9mSkh+zLUy0CTTg8Y2Ae92dbYHdw4xJs/IqR1z4VLGjvF/iezVEtT8ilPHsN1pWzHjvvz7iO3WQVxG3tqRpvvuezWuUgn2bvrPYO95Pf1Rx84KGjdBwCq0Qc+xz15sDOaZGnEySYYsG2oo90TO1UA+4GgVQj8CAO7lXpMfX1vW2DgpOqArDCosQlpBI3HoGkE0nEGAlEG1OAfH4FbGDg+Ao9H4KNbmI6OCwjcGhUMRv08IY2jqATMmBQMpmN1JSQgmWJgcxK4GYhsVcC8omLUuRFCQNwJGMRdBBGBFBDCEBHPD/p5kM8KCvHcDnTuPGgQMOhBAnbnJFhPlQSOO1DYgeOAXdwBYcAUZAnpkeBUBMIUiooQiGSxpY6KwIwCCegHK1p9iFocMi+CKBWYBBCYi4thkhqtBtEBQoLMsWDe/KwqUAEIkvyEMAggeBVhNhpIEVlVg2qVQHtua+9h3mGwUVoSMjeGBi+uuRWA0rnKcIC+S8GnXtu4lqoARZRreFAgok3xCX5Te+mt7X01AMbZk147f/3mId765EN43wOP494LGii/J9/t05rZiU7eLJ0aMGgrhn9BTX559IGrSlo8M6r+95UbwV3Arxpm/kH5QR0cMsvbQp7bipG3N3nqAUwv3/7YpeQNlFcB+8f6L3OZaiaMcTP6AGg+r+StaZQWJyZqYECOXQYCcxecFAj2xRAsqQTAHAqAerGg24kdiEHdBGQqgcjO2VVgQDAeVepAOrolysCt5wUKFAKgQDDdOhIgUBgYj47A44Tp1nGlFqQxYTpOYPubEtKYwJNM15z0L1JZgKmXglovCqRwQKBICIMEI8ZdAA3yNwyhUg3i+R1oiBjOnVNIeB7x3A7x/DlxKezOOVg4Dzr/JcDuPMK58+BJVASEARjOgaYRHAcM6mqIGhQ4EWEMYsQnEIJzMyQWFWFgYCKT7JFVBBu2aKmtBzZaocyLIFA3qVWgwFUMAmnPmRkIQYNcFwAhsLwHBgisgEAUQSQjNPK4fdbetw9ObCCheg97SsLqUEfXsNjIBv1cwYFel5qWpA1GbIMQe5ervy8f7JVgoIYFgwQAOaSgNbhtfIKlpfizXuq1+72YCjvs8OYh3vbkQ3jkgcfxyrtKTFybbw8Jdm+3m04RGPQfRyW/XLiYG3r5zdLvtUdHhYG5+YG9pAC684Dvz3D/N1tUgSX3R3PanCqC5XlQ4ex4t9+7EOwj1ZuqXsksk0CjFlQXaois09h4EHDfKxjQ37ZDDTMQpGLcZ0DA8xgCViNgjROjHuIGLEMBMF8xcD7ssHYXmBEosIC8kqB97gLBirsgHd0SReDoVgGC5z9TqQPTrVGg4OhYAMG+ZzBIsu2YVTFISMdJVQLG1IKByuYA8uJMPtliR4hy3x4M4hCAQAIECgVhiIg7Qjx/LJCggDCdO0I8PyCeP4dwbodo372K8CW/C3R8C7Q7j3TuPHDuSwQQwiAKShiyioDpGDHuxNUAQkxUAwIRArvprAHJM0dMHTcDIPVicEpCmzgxyGBC1YOYCCM4A8IQJSgS2sEWYy/1joPU66Cg0QUE3VYAAWCeakAAnIrgIQEorc6anojF3kW1vLCDBVnUSfWAPJTRlU0+QXEpyGX67oSqXJv93aPbY9oet6ZVNUFP7l3Lm+MNVjqBdg8zKHjN47j3rktdO9WDm21ZWS7L0wMGnW2t/LLFP0/Vdilee8naZ291vXp99uEiliWwJRhYApgNl6qOy/W5gYP5GAjknms+hjpA4KTKsmlBLaiSEwlXIaUDA8Cqu2AfEOTPelA7Qc6S28CrBMC8l0I9qQhzGDB3wb74gSUgEHWg4zKYjgoQmLvg1mcqhSDd+swmIJhuTfL5mN1nVQsUBqZjWcshHU9VAK8tGNVPk9zXSPnphgCEXcRIAgXTkUCCqAYTpiEiHjPijjDdmhDPR8nPUUS8NSKeH5DOn8N0tEM8NyKeP0Y8t0OYRvDuHOjckQDCeIx0fKsoCGEAp3MyQiPuYMMvQ9whhFgDAhdAsDgEAQUBBLAsXzy4ekJan6DGf19irYsA5LqBgSlVIxgA5CGQKan6ADZikJgP0mmzDRCSBwQUQGjcDF5FkNEHjP4iQ2tpydVQqwbls6kU8u4CpX2ZuRRcH8LU8tbVsLVtXIOFtse9pVfeA4V9aUtn0EPBPap0V/nvQEJy+b2TdGrAoK221538cs9CoGGvgrRG1J+/V9heNatiDhbyZb8pn+d52QIw+diVN2DBVs0CC4FyvzYFbnA7WijIwl8v2NBnfDVt64WsLhK1AQj8KAM/Y17rNuipBC0U9NLSVCKW1aXph9sJiNaBAEUNWIkhSC6gMN36TBVgmI6ORAU4OsZoroOjYwGBo2OkoxHT8YTx1gQeE8ZbY6UQ+M8tELSrR1qaXG2NWmtGwC1TDIRECLcmDaiTGAPeMUISJSImWV0wjQHDeVV6EiPuIjgl/ceIUypzKUzyu2BDLHcjaBoRzv8u0DQiTaPGI0xAGkRticdA2mUXgwDCgAhUgGCDLwJrh0G3hahxCEBWETwkABtfC02cgCmwqAlJ4kp8YF7Q8g3KBdIpISSNSTBAiEuAoCoCOm4GyesCJNxJssaS14MRKzgAssEVo0fFQOtxJjyuKQr72slq9waD24s/88dV57d9nWsvFWmlFFy4VKkI+bfdE9X5XUrXbx5+ccQY+OSh4D4NNLTUFnBbYSpfUUd2584+Dwf5vE2e2gpwElfBSWCgPa4demOJmoou26gbV9BsQgUFrQsh30TPL1kV7nyb7fIwYMcYDLjvMyDgEkPQTqFb9tdz8FfBhXZrKFCwlJaEjhJYuL4WgU0/vAQEVVBhGhUI3JDDNo6gdRvsiSNIGkPQg4LpOMnKwM5twPoPALwksK4QrCf5nS0GpRsGKtey4EYARAHTcUKEwEWbeEoIarWjzqsQU0LkBEoyfC9xkgDN0eZvSAjnzgPMYOhkRmECeAdKkwBC0H9YBwRfl0xF6EGC3ObJCoyZMxyQXg9c1IMIRgKBidU3LrERpFAQ3WdxP3jYnwMCgAIJrFMCV5AwyyD0Ia3chFMKNGWXgsLC4kgFIDeC5OAAKKrBzCtZfrK3vfT7lyFhbnBDc4yPQSt5XdZalpRgb7/m7oO+zfIqgs9H75HY+e/ChYWcnSIwsIJ4+uYh3vYz3idj+zs98z0VpueLf/rm4W3lq3y/PYVgKwz41FMMvPH3LoTZ9gYKimJQ1Wb3eVkt4F6D0ioCeXsz4sCrA3n/fiDouQ1sYR4fS9CDgl6yGKw2fsBn2XrFLQyYUBqC9drqyYhiBQcKBHvnIJirBKxzEixCgY0sGGVEQbK/U+r6wkmDBM0fTjrBTkJAGAEgYUhUprblihkwLHg67X4B504I4j7IcQd5SCN13TSWb9L7oBDA44QUxtm6zxG1odATgNJUqwdhkLkQOAFhBHgnoBBGRB3q6AFBoIh00qQSqBiAHLjaQgIzYwBVK0j20uIAACDDgbkuZWkjUQ9CkPyYeyFoH5xU3ciAQMuAkCfxySpCCUrMkODz2s/q/N0GKtWgcik4Pb47UgGohjACCi2o4cBDgyXvltiXepBA8G2yveNN/JndB4rd2MJLvfTWplO7L7aiHTq/BCpAbR///Ae/f/GcpwYMgAYKLlxaPbZXUXq9a28sLWZBtq8/7T5Yb4eCvQDjt/XkK5rvbo1/u73nPrDfV1DQTmLUS7NRBgvltTBdaE8dsBx4IIA2FjmYEA0g8Fwl6EJBL2v6QprB73k/uksQd2DAqwOVStADgmTzECRxGygQ2OesEkxjPRfBdFygYBqRjnW+gUlHCkzcDwqMAZxkMZ8UoHMKlJvlSKBJ9yeV+adQBx0CtZKwFnwIZOPdBiFSIJn0yEFCGCKCDmkkHZ0hoxrmDySlhDAxUmDQJC4PCiMChRoOrP7aNq8e8ASEXVEQwk6gTOHAACElEmPFyMsqJwUGUxGkbtaQwJCGd0qsCv261Vpqa7J7U3+eCEDSHqNTD+AUAw8I3vLNAAFYUBEKJOSi3DQnSZ90ykyK+iaSmNo1OMj3iiIT9ODAP+9FOFhpR83geiQwpdhYxruYuTkmX2IDIHjj/z6FglYJsfvp3YuHg6V0Evt4asAg37TKLydNLRS0RvRphYJHH3gcD/xX35J/t5UG1ybn6CkFe9PCdZeAQP7qMSgqgW33ow8WYwqAPhTs6/bY/rVRB7pvyV1gOWiBYM1t4FWCHhSspaiT3/Qa5QoC9EMp01KeVof86oXtugV58SIDAht6aOsUTLaWQZmpMCkQ5FkKx+MyU+E0glmnNV4wzimkbHABKceBCFOYdOiggATvEtIUBQCORZ5nN1dBHqII5JZqbahiyYOraEA1dBEQAyqzIIayb9BZEnVoYxwiEINOfhSLohHmdYyTlAdCkBiC0Qyfe45pkoY4TjIFs477pxB1beVBLLzOrGiAEAFMLPMS2PLaeZhrEmWAaV73CihIDqzUpoXyi3tafavPFvcArXOtepDdC6QwTWqCZ4Ag+apUBATYxElipax+dd7rtXfeH2MTMa3AgaXsVkBx4eS3kHkGB75gzKACjVE9YW9eTrkMB+0xa4nqLObjL6rRXoOdk6ggwMnt46kBA7vp+xwJrTX+XZnd/jY96KfzkEeZm1qOWc9P6/PKeVr/2eZj2nQSILB9PdeB/f6OocCnnirgtvdXHNsOBD2VYAkKfJqVMyGPXwZkAqH5vcifuixrGAgEBAcDtnyxrWq4CAR+DQNbyMgUA56yEpCnKc5QMALT5H7TSL2REIeYJ86RGwggNazmUgg6iyEmDTBUFwMnLqoDAJvQCECe1AjAzB3Rm+CofC9lG0w9ML8CkCEgG/wYpD7HZrbEIeaZEkMIMnPiTmZNpMalIMMIkrpkCEwlWsGOZNbVAXfndGSB9I4pTJCFi3SFQ12TIYSIQBGJbFijGN7ELFMWs025TbOA18Co6ngCMGRYWEnNbXGzSc7t4ACMwLo2g3RpMXXcCz1AsAvO3Qy8AAnI+zalDXDg7y8bd3g40EJhmTKa0Wy3PGEFELZkFbOi33zMulrQfi8QkAMXF+BgS/JQcN8epcDSqQGD3k17IvOFDfQVpLYHTfDzIFzFpZeW869Ve3tFKhmI5vLWUmqP6VXkfWBj1/d5JczvsVUJ8nH5rFxe+n3uAzvxmnbmGoy5OiBXNxDQq28GAmAZCvI1XRnmF8xlb2m4j4cA++tVAXmJ5zBAUHUAzq0AzrEDW1Y2rBY2MigYRzG8rI01a5c1BIADKMYicSYxCjEEYIiYxpgD9UQFSCL9a4Q/u5gDi/q3z/6vfHYFujYVcpv8+gmuMpcZEkPeV322KZVDkLiEqIsx6doLcbBVGikDhMCDrc4Y5D6DDdPTdR7GsWobCMjKgZSvKAakMQdoVnwMuoCTjAYQtSBCHskA5MBEDwkttJpM7uu+T0v9c/+WeXc9r8ABc9+9sAQI8tXcDJxhwNZAqN/0Tj3YoKfP4ACAnx3Rp+xa0Lz21ANgGyDkXG9sX9dS2wbLZ3feLedo7sV+194j0Ldj/vpPP3dyKAA+z2BARD8F4DUAfpOZ/z3d9hcAvB3Ab+lhf5aZP7rvXJcWbtr7aUpQCHcL0QMBAFy/eQ1vefIhvP+1ut51lfmFjLCjPHftteTzl2feKqfLqa2sbRZOBAS6YxUIgHUoWHQhtBldgIFq334gkGysqwRADQW9rFnjyTwvQz/vQHs7SyCQ5VcHA1klCHYspLfJvAwEzMgrHZpKMMrwOnCqoEDnIHZ5JLBOpk9xkIacAmKM4GlCGIY8vC+4xY7MNQDdJx+9CuCe8crwA96iHDWJ1nqVrrfv1QZzQ5AZeSC7HvJnXdJZQCJCAxNknQX77B9wmoAQwA0ccErym8EmL0qAKQjJFAR1MZiiEAaZmCrIok0cdCSAgoGHhFJv+6AANCC70o54YG2T8UGGA2f0bWcJZpTvEtCIChDKVwusDQUQXNxBlYXsUlgxqy4QsYIDonqfXteKwcSVDFVkOKHHbQCEnAUs98D3tbNrqW2LuydsknXYEswmSH4jIHNY+JM09+Hr9dPPyeiD9y1AwVo2Pt+KwaMAfgTATzfb/zoz/9WTnGjpJucVoz56SWYXpUAWXLqoSoGvOL1KVM1+xbWPqFUtfE91CV7W7svy2bsP/7utQFD+3gEQzDJ4Z0AALKsEkrVlKFjN1kojCrgydGVHeV8NA6YG+M+mGgS7I+31Z3dBAwhzIBA5m21546kc14sb0LGRUq+CXi+qwdIx43lpZEvteW7DsOdr32l6oa7tACIv6WzH2ecQ1SURu3nnlECBBcJCLOpBZDVSk7gXqAcII4hGWQo5RARduMvWtphYLJkpCVafZSQD5R5rImRJvIo/ykbh9pJcT65lwO3hwCbrCbnjpMDAqGECHUCQnOs+F6zYxhktZs4pBFyfx6sHgJn9kjSmUyeeQhcQrDxnBjdff1+rsd7e6qmr42bH7LEfeV/QewEymKUFQFgaImlDEg0KNjBMlT6vYMDMh0T0+16Icy0Gejgpphzc/Bb1w3xaF1x67HVXcVmhYEaNnUv5h+2DZIA9khZ8kEkNCEupJ0+tVkz73ABBOc0GIPD7VzO3HQjsytnA64aum8Btg/+Ng4JudhTI1kxYW2ZLIODjT4L/HEp5krpfLHYAOpYe8HCQsuEGF3AAGBg1gDClCgq8QiCZiyXvzDqDnztm47Cy2fjy4Neht4oT3f6mJJfgwB/XA5pOHivwsXvxrqC2DG4DUvP9EVX3VV03xHx9tutQAIYBNCUgJDBPAgjmYqAADhHEEZgURshcDQIKTArCqiQkIMcklPpdvw8J0nu2tqKChbVbXmhDluEAyDCg56/dDa5dazpaLyggOPXAn6d2L5TrVgoCOaOK2nguQQLcMfvSidtdd0AvDqx7DRQ1BO39uHshaxubkxkUPGpQsGhLlnPx+VYMltJ3EtGbAPw9AH+amf9pewARvQPAOwAA/0Y9dLnbHC489TYI79rNQ7zpI1dw9fVXcenuS5UR9Sn7bxduwAfJAKjG4PZiHnp+7u552/y7+wD6lXI5qNDdwedYIbArZ6OvX5bcBpa1XjwB0IeCNsaj3Zc/619rbpZAYAYGSzBg6gBzrQ5kZcC2u+OhcrWNJjBwaA0h9MIJIPPTxyE/o7q9DvVv4Ix6zziaIbPjSGIVAOhw0+ZZmrpVBZduUBBcfaKm3pV9XGYMnKSMMjRY2QClPO0zHFykPXV4RXHIyc7JWh6cZGnnpFHzNIlCwAGgWBQEIlERiIAUczmTwgNIRllkKNY4kAoM2u92MNWwkG9xoTHq2YUuHOSTUB1r07gXrCPjx/nrlV4YQPD7NioIBgik95sNZlVmK5Bgx6O1H3XqKQTAQvvbAEF1/MJnf42MambCHAX5e2knR7LRcxfvvlz9fi3/bfpCBIMfBfCDkCL4QQB/DcD/pT2ImX8CwE8AAL2EqqCBLMEAVYWQ33UKSr9fv3mIN31YoKDEFCyZfrbTV/+3R+dhJbSiHljGNFWVFf0HuCpr0bwyeih4QWEgX3MfFFC+sgeDJbdB3l+yN4eCpaxg/lLZdkseAoBGSVmCAQcM2U2wAgP1vlQAwCDBA8E0FZXAGz6fQpTervrEbVu5Qaq3LRj6bOR14iiZQErvVo2aTSqVbB/pXWcYILcd/effeza9OmZlVR40gOS2l89FOensc2W5ChL5Gqi3teXpk84pzAniZrCyBIuCQAGgURdpEvWgUhEoAGkEqA8Jth6CvBOUZXEG54DG4N4N73rI748LQui9HmuGoBqzb0EJQBN7IGdZci+U67g6wSkTOjsjn/ftSx4i3c1JHMJU1T9rhT0PmIpgv1uCBLlFuUdfA9ba4X1tMLDmsu1dod5Wji9tJ9n9+OqrF5dVhCUmzkbPrSnla+kLDgyY+TfsMxG9F8DPbPmdKQae/vwiHB4U/F97cNduHOLhDxkUXJRKJxnqXzC5SglU9NqmnnoAtMpA/bulZ9ilvwUyrf/eBgxs7YKsDjuUHLQqQc9tIJecuw5m2zvZCqBKNehCgNtRqSgOBGybjxdYhQHzM/dUgJ4R0++VAQMKEABzI+bLFAB25/Umig+d/Gdv/EME24RRpNupbBdDGCRwUb+X7VT84Fw/q1kQqI1cmD+azpOyWyrAJdlyz8FADK7+ulgNc8+wuWd0O+cu9rRa5thS5paya0HhwEDA+8W1zE0xEMMluSdTECpISPk52L5gkEBifA0SrNxt3gFouRsoFMPXNoLL7rXQaWFaODDDmdy7MY9HWHIvaFu4Tz04SZtzQkjwQx27kODvHcXQVz77fm5yliyttcVzIGja4vaibR3MakSZMtqek0HCNXN/OyioOsu9DtLKzX3BgQERvZiZf02//p8A/INNv2v+5orQHtA5/vDGIa586Aquvu6ncfmue0pDAyxSrfRUfO9oLm+1aQkQLCNbJazeA/f3s1gBWxhYqpgnSXvmIrBcnFQl8FnqLSDSS7PGbq2cVlwEHgbkX+mtWm8/w0BjmNAzTGvqADA3TjnTru/iA+mAvgKQIaBE4psxqgAgROnZhggGZcOfGHlp4TTK+gEyac/yCpT2zPzaEtPK0+pOGw3vupF/kcrkUHlkR4iIiAjxXFZvgj2fdvinPhc4xYY5AYMLBO0pDM4t01VtbJsHBChg2PPIbgZVZHjSz+pqcGBGLM/FQwKHQaaMpoBAlBdJSlQgoVUTpOer75C1M+zeiRO+3j040Nt2bUVRD4DGJZGPcO1BTz3YrBw0N+BAoJxnDgnUtMeVkiA7Kh++941sbo9RfvNCtMeUmtVAchurx2vdMEi4pkr34+r+XsjaXPFYvLvP/3DFDwC4H8BXENFzAP48gPuJ6I9ASvF/AfBtm87VSAJGif7m/Xf7e3DjQKDgtY/h/rvuFbkPcFapqR5mAPUFn1XKEwCCVzWATmVb+l3nPk4MA7MXbc/LuSIPb4GCqrfPtfHfBwXr+epv7oGAZfF2YaAKIuzGDSTkQMIFeTvtg4HqJhpFoOMKSNrLzGPqPQhQzJ85RDBF7XkiG/8xpWplxIllwZ7EwKjGfkzl38SMkWXSIxt2lyCTH9n000C/p2pGKk8LHQgBCgAEULBFpGRJavtH+j2QTH/QrkYp+wbEMMjcQwBIR3iwHwminzm7duQzBYW4FhSooyhYyt8TEEKGBJ7G2bMqkDBm5QZJllT2MECsU1AFAwkCwpANQQUJ2l1cggRYHUZtAE+almb5I9i76eIReAkO9P8110L34vuAwbVJvNAeLygJQGmTsg+/ZLaGhX252Nsuz9vk1fZ4phgYEBTbwiQP5PDmdTz0oYdyTJxdbR0CuPk7T5/vUQlv7Gx+5PZO5ipRNsrl//qTNNoHNw5x5cMP44kH3o/Ld72ySJT+IVW6EqG4GJI+gQIDZKduAKEHB5a2zF5Fnc/dB20GCf9/8v7t+bLkuu/Evitzn/Or5vjJTxb7wvkD/DgScemuhid0oQg2gCcZ6ELjEqRoMjiiPDERlhQTE+FHOxwOXyTOSKYgAuyuBsZhR5BoEoLEmRGqqgHwIv0PRnUDVPjFT0LX75ydufyw1spcmTv3PvtX1U0pfsqIqt85+5yzd97XJ9dauXIAAr4sq1oQW3HsyBTQwsJfIBRcqrNLMOCvtQ6EmhPTADR+A04L8GGZCoaFG5sHME0AxPbPPQyYBoBipxGYkCFxh1KGngoogj9BBL1BwpwZ55QxZ8Z1ypiZMScFgcSYOWNO0L9cQOKcMlJmpFyPXPZhfTO70xNRQ/uKkBetwSGGIuinSJgoYIrQvwoKUaDhKgZM+psp1FgRU5CDqWKAnlIZESkiHnSC14iSPIgfwfre2lv6MMspjGZ2eApIGJoaQEDI1a9DTQ+U/a4GbVuiasbQ75OaeAKhQkJG8VaXnKuZhzpHO8KGKFhPe+CgXHdw4J+1BgdWP3t9mpo5amGGsHs5SLD7A2XFbaYbyxO5n9vfhWl6K0+D172skZcrQGCagjJ3cyd36msxBTKIMh68933ce+fLuP/ZN/Hqi5+0ChhIGy/P8vByn/6DMyU8baKc3PneKrzXBBwzHv7oAe6982W8/drXnfnAGmSlkzJgHFk6KMaA0HjQbmgPTLMxur7+/im0At4+d2EV7j+/dPjR0iP9w4GCvWlRT53gt+/8RWsHFqaChTBJWDi62UrTdhBMh4WZQGCgagG8ZoDjAQgRGYTEEiV5ZhHaswruOTNmBuYkIHCdGXPKOKeMUxYIOKWMUxYAMGB4cpbv5Jxx1t/mzDhpqOTEVWsAjGP+eygIQQR+CIRjlL+HGHCIct7BIQbcOYQCAFMkHEPAMQZ9rXAQA67Ka8JUNAgKC6aFCBPiNCGABQ7SGcgTvCYBOYEp1XadqLQp5jOAIG1q0njUng4SbCfDPlODCP+6q0EcGYlIzCAhFAdGu04UwNCDpeDGmAlx53/QQ4JPNxlyHyocALtMCqP5am0OK6GhypwMjLUJDhKAVVDw88vN5umVxdoGENQVUb8l192fA0CM773/fbz+h7+s8uuT2nd5U+Y1+dT5ai3dGjAoKzbAdYhWcNnnDx4/xOvvfAVv/9Lv4NXnP97ai12DLRIFyDEpqI0NT6+q/uOs10MLGiuAcHl9PljpX4KBvtN1v9tMWxoD99meExCBy1AwSv3ktZaj3jN4FQb0YolMaE/nFUfCG/oOYJ41325nAYBNpzZLg90DmCbwBSAofgLxAJCchTBnFBAwLcEIBs5zwrW+P+WM0yxAcD3nAgLX5ySv54xZrxsUFK2BnaegbVrDJw8m8xK1kErb2DkIpi0wODjEgGkKuDMJHFwdYgGFq0kA4TgJLBxiwFUkHKbYQQILVJBoEgooBDE7sDvaGsU/QQHBdhZwlteHo4RNNvkVwxL4hm080CLYuJkmgAlEDJDTIqj09Y6MevRWXWnbmDNnRqdF8DpyMy2MIKGkHXPC2qFAJjyfCg50vm13K9B2fvYsaoptQLeaqm/GQpuwZnIAGlBo834pbSza1qCglGn5+aCAePD+D/D6d34V3/zFr+Hu8x9fmrUxlntNvi6kWwQGvsP41XFbEQ8eP8Lrf/BVvP3pr+FTz39cB3ad5Bf7qr0gbO5k3wuOMJdwINfrNbnnTuvVFgj4z7dgYKVelsnb6zbIE2jzX6BAf+r+AfugYKQtWDuDfCsGuTchrGsH9MneXMCDnQU7gMCiEy683C8JC7+dUOuztUs7X4FnAIJzEj+Ac8q4VgFvmoHTLO8NBp6cE65ngYHrcyoagmv9e54zsmoJ8izmA2Qg2VkKLGYJZh62m28/InEoBMkBSVEPkYhBTk4MCguHAgVVg3B1iPJvCrijf49RAOI45aJJuDMFXEX5XSTGIRJSBmJgpAIIctgSOAGXACEnYNLJtzgrYmmI9u29aPuqRaAQgPOpafMKCDrBW3RFnTusX5jwE7ODfJfVzEAqnNkEsjaFwUDZu0913PXBfS6Nu15r8HRwgAYOyvtNn4M+X5c1u83zOF2EBGAdFHalbs5eMx0stub68nk55K8T4cF738cXvvtr+OYv/jZefeFjMtcQ1+3DvRllkb99+qFbBAad7WTQkA8eP8Trf/jL+OYvfg2vPv8JNPvNncpn7V6ErrGIIB07OEpt4aCsOqyjjmxjw/LUBtzjL7BQRwHYBoE+jWY5y0CvGUDZ517ruQ1tDKDZfbDI2ugx3WJhtELpYUCeXL/71ECwqiHIuORQeCMNQRdjoHdUY3MY7H0IFAI4TsV/YFNDkJdA8GSu2oEnc1IgyPjp9YzrORUtwfVZYCGljKRAMM9yrkJKGTkxcs6qLaiwx1lPYDTwc3Xgow2GQJ3WAKI1CAEhEmKUQ5DOU0CIAT+dAqJqCq4OVXtwNUX8zNWEO4eAJ1PAnSkWLcI5BTxxgJA4YAqMiTcAIc9AjnI0c1BtAhGQdbtn8UEgGW++/c284CImlmTvB1sey1weIZ4BjQYhw0wMBRBClOshgpIDBPWB8IDANqacFmEYOQ/Y3Drdp0suSM26wsEBajZQog0oHDCcScFgYRMULs1t3RzuTcDqKzaCBPlOp00AlqBwqQLsPsACCtrvjbQIY1n04P0f4vP/4jfwrV/47/Dqz/4VhVaFMTUxNGVa5Kurs40J+daAwcIu3msK3ntXoeCfCmkNVcJYqaxcBucQAFweik2+dEAHB8W0sKc8HwIM7HzWWodv/At6E0Lzm33nHUiWWm3BzqyU5GFAvj8+PvqpgIC59SHAjmBEAC5qCfpthytAYN7pQ6fCWLUEGSLcionAdgtkxjlXk8GTOeM6i5D3GoIPzgYBM356WgLBPCekOSPNCXkG5jkVGEjnrNVkYJCQ0wxmceIzGBgdqlS2jgU9NpkiQpxAIQoYxAAiIB4EEuZImKaIMAFxipingOs54uqccX0IuDpkXM8JP3OMuDpMuD5kPHeIOEapi+PERXNyZ2I1MQCHwJjUD2NiaToxM0wlloC0YQCCOSkGsfVTAGXVZvWA4B0UR32h90Moci/XeUKDJjWAEMTkUAAhxAIBTw0IqJqCPgLgVuTVteS1Bov4Bw4OGkuHf0dU4QCWYZ0rrJ6HD947x/l58QIkACj+Yl49v3ufwgoUlM+6PA+BAWWuAoAHP/5jfP5f/ga+9dd/C6/+7M/LnNWYoDwgEJaHzK88eyXdGjDw6pa+8A/e+z5e/45BwcdLQywiqvn7WCodNGNIYf75F6RaCe05uM/qmQQDclwCwQYMjIj7klpsTYWmKsv2sy4sK9Bsi7qJzmKUfE49DFg2vf/AEAg6H4LeqXD5Oa9A40ZUvUWmB06FwBgIbuBHYNsNey3BOWe5xsCTlHCeeagpeHIWk8FPT6mYDP7dSYHgnHCeRUOQzglpZiQFgVz+MtKcwUnOcsjpjDyfKyS5Exq5d6ACIEf2AnYyIqlDXZgOCPEAIjkBMWdGCBnxEJATI+Ygi/kpIx8YKYlGQPwdAnJmfR2RMuPOISKXXReMOQo4XWXGgQmIETkzpsAAB7BqkTN0W2Q8QqIWirageLFTArK2GScQk4DQtAMQ1pKZAnJ22oOoQlwWH5R1m6P4y1etBVmsfBUKiTVQFVdAUB+EISCoNF/sXkAL7s8yhr0e0gIoOWVCdcJ0cyNZvQCt1uCS/4H/nU/9gmwECSVPNq6xhIT+UXvm8dV8LsuxMBMbFLz/Q3z+j/4LfOuv/SOBgmbe96HdgpSNq7lp77P7dHvAwFIPBe9/H1/4zq9choJeAO+1KVnaggJHncBG59nQDsjvVoCgMRDu6Jhdfpqk5VhoCzwUdM6GQJ1n+oiGTVFWOuRWTTd6iYG5AFgHgmLj89tQR4K/0xJcgoJaaPc6BLcS7OEJNwaC4U6DFS1BMig4p+JY+ESdCU1LcEoZ/+56Ln4E/67AwTYUpKItqFCQ04yczmCNCcDzLNoCc/YyOOhMCYwMCgGcEiirVmSaBC5yRogHBAAJEzAF4JzBUfvMAcAsn47S7LqW7IiYkKP0xzvW7MxIiOCccOcQRdGDvKI9iAiTag6SQAGHbgdD76DYA0LO1UnR0qiP+PHIeQwHPjKEaQ8sErxbNVIGqs1ZT3JVb/URIPQHDpVB5cbqDWdCDLc3GoRwNSs4XSsWzoiWo15rsAUHe+bVvkQGCT0gNFrntobkixdiMGzlB9gHOVBNQYGCv1L6Q00rcID92ulRun1g4FKFgv/HzaDAXm/BwUKd3gnT/rtbac32s2oyeEogGOWn5LleK+VY+BFgAQXs/tUb7M/KMHv+tYOBksu9QDAyG6xqCXZAwSKjdTIHIFHzmo9DBYPeZGBAQGFpNggBiIcmKNHe3QZPZtEYGAzYvyfnhHPioimY51x+N2eupgFw8RfYdCIMoQr+oBN5BpiTfpZBneakbMOkWLQG/WejVPKjK+ScMoiAORBCyhIPgTKuS78gPDnX9qpxLwISEjBF4JzEtAAxC7AuyBfag2AHIYmDIkEd/YiKRoG9ubA3MWj/KBqm2PWZlbmBU1qHg7KKDq32wKnPis0ZEO0BhXVAQLeyB1qHRG+mXW2lHUnhoL8P2f8jZ0TLGefOpKCfeQF7yXmx5MNrI+y3FwBhaErGavtt5WdhGiECEBZ1W8wHBgU+/40WZA0O9PVTpFsLBi0UfHJMT1uCdSQMbUJvkl2n9nf9+7U00hK4608LBcWnYSv1UNCXY9XZcAwFvV9Bn83m0ZvZcmuJZwCCRkvQ+xLshIJl5mSwt4JsoCHoTiW0+uMggYpWgcCZD0o8gtwGJ9ragiixB3KNRZDrNsM5JYELtgiHbfkokM61zjkwyCRGTIhTQMIknugsPgLet6DxuxgeAlW1J2Q2cPU1KNfihDgFfTbafBgkdhGuUonICExZynkOhBgyAgEnW6QTQCngbHBgQie6pXMWPXsGwEwaaVF8IMT3QP0NKDr/g1QBAex8EIQ2iHXl7kDhoqNvkxibcCAVrtoC8z0AwEHju6j24CaAwLh42Nue1GgN7L5Y26lQ4WDhjOjhoORjAAcraTgfNpoaE/puta0CfAEHQAsIe+DA8kh9Oax8/jeEBz/+k9anwJ61mvfuuX15uvTg/e+v5xm3FAwW5gNLi4kf4w7VqMxp3PBKeUNh2t/npulD0BQMoWBLU7ACBTeKVdDm8kbpJieVVSDQnKxBwWgr6iUoWMufCfqUFu1qnwEodcg2yRXtgGkItE8VICA0oYvjAXZ+wZwhNnIGMl8OUuTNC1nV4qMwuBMBM4lHfs4koXajTlBTBJBEbX8AQibkEJCjbEsMgcAc0e9CMDAAUOFgVI9Fa6BR/Gi5SyFEeW+7FGxLozkh2nZGCYlMqjGQ4EZ9KvVAUm8hyI4NSjZhLuEgZ5kYWefyHJz2IERQOoMpCQAQAWGqgMAsWiFnrmLrZ5rBxlF6oIny/WlYh7wOBwUeMuAd0mQhLOUV9DGhKnAg18aAAPJj2o28G0JCk1PeBwf1+k446LV4lte1ufhp4OCmycMBMCiHrfDl3g/e/yE+/91f190HP1/nqpEGophXBuC4kkw+voQXVr9z68CghYJP4qKY6juUv95rCijgwY9/oF9wULAQnpLWOmMjgPaqv5419ZoMtO9H5RhBQdEuYgAF3TzRzxuj7Yf1s/p6Cwjq5Z1agotQsJah2i7ssz0th0w589uBYXOUsWkHvNag7EJwvgVqNjAtQWYu4YqzrvATiz2dWcMTZxHQokUQkPDnFARXhxJRUO47TcFdJ5wTYU4BKWWEQMgTY8pBtiXOIgSywUBSc4PZfzODedLXrl56bamrWrlGZfVf4hsYFNj7SfItoCBAEDWAUR8MyaInhhCacluyMx1mECgwYmbMAChkRI6YASBzCYcbSP6LmZ32QJwTof4G5fwFr0FglsneDm7qwJN93xvQTKfHaSttlNbgQEoBUF76HnTaAwMEe6IHBAC7ToRdzR637V9gYwAHctcKBwufAy0no8aJKTaUYg5YEaIfReraZXXOb94NysGhfOnz3/01fPNv/hO8+vzH6jwGrMupGyQvH//eN//3q9+7PWBAGvzhO78icQpeNPPBQFXjflOJq+u9K1Dwhe/8KgDUY2r9dzHoGEXl5FYJpm7+qDvuaifa0BLo67U4BcA2FNy0RGtA4B2UNoEAuDkUNKltF+rVfjsyXwHKgcAILLsTDkFhAQSywmUkoJx0aOGGGyjQ6IMJeurhQDUQAhBYhGW5RiLgDolxjoRTCjjMGYmD3C/XcMcGHdXnAPV1toj8zmpQ5q/1lWSBw1DzCIgwoEV8g2pGmEINmxw1RHIkwjQFHB0kTDHiEAlHDY4UiDByX+DMSCShiSkxIjKmGFDOtctctLxMkk9Ooj0IZOaFWAGBczUxmNkqtP2uAkEFg0VAta3U9zd5h0bsFEGy4q0OoARiK0IJjXkBA0AogtyN88HUdjEtNBEdHBTNRRlHkNU1PBxImWwHwUXtAfCRQ8JYQ1srqIlEaP8p0JkfiWmSvvnp38arz38CzFl1hwMZ5uXVJhzUuX4hHzfSrQGD770vWxLf/vTXVFOA2kGcp6Z0uqqWaqCgE5JVBUx48P4P8YXv/Cre/vTX8Nf/X59D2ZLWr6z7tFAjoYykRZzwHbaytlzYLbwkPZ2WAGhhoIECl13fdbeKMYQBYAEE9rfmANtagvLgART41LzXqajY/kK9jDpxL4GvAtPQ9NTBQNEOFI0BNUAARjnlsJx2mOWwo3IUsn0vczkK2TsJkq7+Jw5AzECSv+U0QyIcJ8ZpJlwxmrMPkmoliukiL89AMJ8Eu26vLY3MFmvJr+SDvrGzEwCNgqjlsetyOJJcC2QHMNWzFQIBx0lg4BAJUwiy8YHqoUwhkJuvFXACIYFBWUIol35sfgdMsNMLY7ZVN5A8IHACwgTYsdyUtF3yum8LUDUKwEJ4bW1vbvqfpX5xw7wKB2XvvgEB0JkXuntbm3WAALRahLVx77PWwIG9X9MclLKqacHmzL3ag/LwHSaAvatvLyfQtU9/j+Ij4bKjES1LWTgDFMs4fvXFu6XCmIPOY1T7z1peOpm1hAKRj3dffPmiE+mtAYPX//CX5eyDF1+GD65SadIudHBQvmSfLyv3wfs/wBe+8yty/5deqffpVtnuJm3mFp2yrhKGh4h4BxWv0fDkuIbri87ddtqhk6Tv4E+hJXAlGqbReBuZC9zbbT8CYEVLoDnxUFBu4c03bkUhxli93u/gtkHvNUkdBA40S2VQOhio2gE5+IZRNQTcA4H7m5j1czRHIdeuQiAWRz1TeYubQECIjAkBc5IteMdJYOO5Qz0d0YDATkgE0EJAAQB5YHLVmAdSYHR4Up9iWHaI9gRGvRZC85nBgr2ODhAMfExDEEB6/oJ/Lcc82xHP3pTBGWD1P5hc/nKWMwZClj6ayf+l8j5S1GaPqj1w5294SNjSYpW/BgTL/ihpZX5poCCX68UnwcGB3WVLe9D7H9S2QgEEYKxFKNlY6Q7e+GEOieXei9KuaA8Gq27zCXBbNFpIGGVqbc70Cyigncd7KBjKAc0keZ8RLpdljo8Ca5xLs7L2IbKAEyNA6LPs55/uerNo1qOZx+Wu6daAwduvfR2vvviyvquV09pwchEIFQ5qGk3spVJf+zpefelu6QgcD/qrZj27kqgVcp2GoIED1Dyuw4GlvN6py6NXOvcICMrnLRDY35sAwVqf6zUEQPUhsEsLIACWWgKggwKnkl0zdA8zuLGS8IUY1N8QBBwoeBiw3sZQZzio0IEAAisYgFG0AUm1BlY6K0lC1RCAZaUNaBfXrhVJo8+BkUMV+EXQd2aAxnl0UGf5Qg//KFJYzH4tQBQ/gtB+3zQk8pnAABEKEEQARFTqzW6ZIBERS29iiP+ebsK3iISZBBbsnhlqYtDnkWkRrP/2AbaKf8aKZmtkXtirguuTX6qbL4LbmtfAgcXZB6r2ANgEhCxVA3Dr5bAna9UoUG+8Dgg6h5r2wM6RgD2/1SAUQNBytrcazVpPOW/6QnZanHGL1YJZwC/ZVhpru4cIMBXT0xgQVmRXyY+TX27RvFcrcmvAoCUhOOEQBg4eRpmjzlAn+Afv/0CPtvwG7v7cq43qnQeNv1SFLVOrHtO83AQOysN6SPBl0K+MOrV7f0lD4P8+qy/BGhDYW2o+chPkipYAwBgK7D4LlZuDseb6ykDp6qzxI3D9RD6j6jPQ1KmDAacdkEA7KJqCYiJAteMn+y6qkLIUQUiBEXVrHQcq/WpS9bep+nt7/8YZj3rvQVUM6ihuTDD9dkKfRr4QltJA+I38FW5aBst/AQEzV8CEu2pcGrU8SkjfnGUVHQkCBIC+4KI5oCwRA4l1Z4Vd7yGhaAnMxODbqe/fXb++iZ28m9ua7XplLqmmBW7EtNceVPMC0C5xgDqGS85WAGErLQCBqu/BoGAqUGXVzLqCbgBBoWGhRSh5XFkMdH16NxSsAMEl2cDltQGq7nwKTouwBQjD/Nd8NUczv/jyok9spVsDBr2Qr02sEOAp0nmA1h+4SZ7EfPD6H3wV9z/7Ju7+3KdgQpPdXYHLdlV/eIh/ZG8/k+/0e3eBhoLttz0kaGqjFW7AgF2/AAR7NQSNpWYwmJ/WbACgagnsQQt/gi4Taza4S3bGBqgcBJTPwqKPNH4ZIcL3Ea8Z6GGAmd3n8tqu1++3UNB3M4ODqQCBfjCYTftixzIRqYC0nQHlB3ord81bTuwzf4+uCi+mtulMi9F+Vv6iFTzuTzFd2D1SV1OXNp5Yit2EwADIwYGAm7Rw4AoI5oYQQlWlE6FoJYjQaBJqHbHTJHRmMS08I6Pt17Ezj12YfEYNUsyW3IwN+6Y3LQAmwKo2wbQHfVoDhJvMF6UL2+LDTZqbGgQHCMVuvwYJ+tC1MwN2z6GdhlWLXP76u++VEaUNtJ9wZ0LlEsFVO9+oDLao1bOBTNNd8t1+eTVPtwYMmvj0PFI1UR1stnJgxmjv/vfe+z7uvfNl3P/c27j70t1m1WfJJv3NLGG7c5fp2K1mGzhoVrjOPOI7ueW9ufEGDJTr6535EhBcmo8sfahaAn29eU6Ez6Ozr8qLfjXQ11lXR2sxKgZaARNctvLfhIGBdsALRAOCUjKudcQwIa4w4PMf6/f8i164m6Aqnv9QFTxVk44XaPW37VHWwNIE1Dx/I12CUF9/5XOM63ELrPxv/QP3dN9Gfmu5mQUWZF7mKvwIoKxwQQyNE9VCArf1J74QU31Ir01Y0YyJynkMwWu7nLYDnZkZgYvArHAgn1do2NYeMPaZF5rfdI1BdNnE0C6yDBCiNk6d+9cgQerEMrBjHnWfbUHB1qJqS1ZkbufKmW1sVZhkduG3B/3DA43XdL/60ivrvnAbg/X2gIEvZXHaWQEEQCAhtJXFIeLBe+/i3re/hPufexuvvHRXYrC7xrWOPG9goK2iEnQidr/fdK5xcNCUaKgCXwmCssP+NZqYgTrZWropDPgs9lCwS0ugDxppCQDsC/hUHAr1ozUAaDJc62ovCNg/DwPmMzASYplbAYbme6UWfKkWEqwX+pbzvYI/Ur3erGjtn91LjyMfnjOx5kDn22Grs/SqBzIAC7XOe3+NcqYEARQbEBuZZ7y/xo3AYVDnds087z0gABUSgj6rqXetVw8JVsdBfRIMEhbaBL9SXAMF2xqpdV5NBTvVJN6kMIQDK3GnPeh8D/rUaA+4vwtU0I2z56/73Q69FsEDQgWF1sywCQmlPraDSV1aXPk5dDSPepkh5RsXXPoILX5fx22rRaCcAPVLYKC03YP33sXrf/DV1vzt8tsVbrXYtwYMSqcrf2kICL6D9Gr1B48f4d7vfRFvfu4+PvHiXQkyg3aSt7Rm55SJg12VUwMIvca3dmxCHbTVtODLtJWGW2YGtq8hDLgLl2Cgbo1bz9WW6aBdWbqVz4bpQK6vQMEojVZITX7bQd6YBvT3mzAw6Bf23vsMjIRSKRYPQMCSr/cBbBG2ISCEVvhIhECUSUZWYnYYkAh/ylmv1Qh+8jdVQAAD8yyTUdZ2yrl+H4AdMzyKfljCSJetviSvS4hkhYNJw0b3h0zp9zlMiC6ENCiAo48HIVKj7PKAQRtdNt94oebbw4rXdfvgwJAUEhpNQmkXFIFmPgk2VbcmB7QrxQ4SGkFQ7M6+0+Qq7Paq9kqJOzggQnVYlNcVDuxZF7QH7sKaaaF8pZtbtiCh18S2kOC0CCuQAGBXHJm9CyxbANgFDwT9UfOrT9XvpcwFFMh+SFWLEChKpFLOqEdIZzx4/Aivv/MV3P/Mm7j7c3ddfpvZdvF3lG4NGPjCblIkIPaoroEfPH6IL/7ePfzuZ+/jEy8IFPSTvvxWXuSBxoCoQgBgE3BFv0xj7UEDB8ASEJwKbPDQ7v0SBuzvJRjYQ7b+8z325Wc1HQAjKOjSReP2AAQAoBH+WIWBuvpcwkDREmzAwI1XqKVcNfebMKCCn/SvvZdte1qSnEBprhCQz+WUQLl2hu235/kMTjMwn4E0I8+zvE+z2Dnns/SPnIA0w7Y2sIECgOGq1fqmgoC9RtRQw0TAdACFCMQJFCfQNMnn00HfH4pPB4cD2lMpD6AQEBUeDjHCwkvnLOdO5MBI6tBl20VNC+Dbi9i1lQ3QQXuVz/WzoNOMTOxc2itrWxW1cZnotZ0MLjqTQ6QNSEAcaBPKJKf5uiz8FmkNDrTEtbjetCDBopy8rnAw6OO91sDPN6O5R7Ikdy5zrMHGCiSU+R9YQAKg86rdfJQGpoS1eXWkJehNYf5RW/NrYnsS135BdWssUwuPAON7j838fd854q9rhy+lWwMGZYEy6CD+A+skvqIMCr7xGYMCXggBoO3gqavhQCiaAuvExXEJehHttZHnbc0lo135dgN8w5Hkw4KBvhNdEr9Aqy24GL1QM/BMULCdi6eGARvsXlX9NDDQrBJ8XQ9yTd2bNSAQtb/u1zcoADQ6YIUBSucKBJzKe+RZNQMzcD6B0ww+nwQIzicBgNOTCgP6N58FEvKcwInB8yx1kTOQGMwZrIEO8kBjYHEJKCoYRAIFDTo0TaBICFNEmCaEw1TgoEDC8Y68PhxB0wE4HOXzwxEUJnCYQHqeAceDjPM4Iej7CYSsWoM5y+SXmEowqUwGBLLboAEEDwd9+5WJ3I1SWwSUiX0NEgiJeQwJThgsbM4FEkKBBLA7l4HMwa5fVOwEhREcmNOiwoDER1iaFoZwoG8uaQ2G9av3a+enfZBg31xCgntKyey+OdbPq2Ue0DSUG+6aL9uaNXrO0h/g+kF1eq0yxDSDj1TTff9zb8uWenevLRmwRQm3Bwz075YtClg26KMfPcQb376Hr792H5944RXMbAFluFMNtbVoe71LoE73PPnNEgTYX9PvGxx0885CNdd20vazUTs3HXYFBhpKx+Vk3+kBodGQ6Bc+eigYmAu6DO0L93wzn4E9tmv7TSmuy9ooANAi4M8ACmLxDWiBwGsHIlAAoIGBJBBA6QzKM/LpGny+rhBwPgkInE/gWa6l6xPS6Qw+J6Q5IZ/OSOcZPCfks5yomGc9WfEsAXyyQoFtSfRbE8uZCPo3RD1B8SAnK4ZJ/x4iaIqIhwnheECcIugQEY8HxKujgMF0FDg43gEdjgUWcLhCOF5JZMl4AIIABccDaD6B4wSKB8QgBzHlXNXSScdizvKa2O1w4CrQ+4Gy2p5suxhQBncV+NuQUHxB3Iqx9Um4AAmqNVhCArC6VW+UbgIH7vO9cDDyNVibh/rr3lxrC7FLkOCbrwEFS4uAUtvzbD/H9gAArICCl9YYyxbrD7bSFE2BxLEsgJCB7z8W+fXW5+7jFXWU7/O75ju2NeffGjCwtmhU+dx2jvYHoin48rfv4euvvYWPv/hKE3nOT/7+/payjBkJpQp7HhUTAZcfVTjw2ciocKDZcSaFETHTsCF7GLC82YW9MLBF8TeYSj58KNj93AEM2PUPWTuQt2CgyhIAy+10bZYHHdP1FVv9h0CNyUBCA28AgUKAAMFZXs9nFf4CBAUGFAjy6YR0fUY+nTGfzsjXZ+R5RroWDUE6ncFzQjon5LNAQTrroUrnrNqCemZD7lVqAEKscEBR4vKHgxyQFA+qLTgERIOD4wFhiohXk2gRrg6YjgcBhqsDwtEBgkJCVkCg4x1gOggUhIOYI/IBlGZwnBANECBmhsDiDS4+AlQcCXMW7UHO63DQt++sZe+3g/oxtgYJYn4UMjFhGgJJPWOkRaiQAJLIl00kPduNRRhAwv4x9mHDwd60e16y1fgaJNhvuiG3FA9U/t+ab7m72OfT5oM1KDAYGMkXm79Z1VUkb5CpBYQfPn6Ir7zzRXzjM/fx8gt3F1ps/4CRhngr3SIwWJJedr2ytdoDD3/0EF955x5+57W38LEX7pZ49KPVoP9d+zwnAQGluQoH6/ms8dqBqjUYQYEfWAtqdq/3agf2woBP3sLYp0Zb4Mr0kUNBJ1TXgGDthMgCBHk5gJN7PdpRsAYEHgZ8f+z31vd75ksdrUBBryWwEMATAWROhKoNMAjogQDna+TrCga4/gA8nzB/8ATpekY6KRR8cI10fS7agvk0Y75O4Dljvp6R54x8zkjnjDznAgZ24iISD80IlkJQEwLJSYoGBmEKiAcBhTAFTFcTaAqYriKm4+S0BgdMz10JHBwPiFcTpufugKYjcPWcgMLhCuHqugUEhQJMB4APYlIp2oMoEzCrQOGqPShLz9DCAfFyPC7amhkR1ICCaRPMNyEwmi2QzJ0WgVooiGD9/lKlvAQELoJ7ERkQWPbCC+Puw4ADe3AFo/XHXpqfGtAalsPJAcZQi2D5Wpmu23x373tlUa8tWOS3ky8+q818YdpovUREYJJ2N0D44/cf4lf+8A38zmtv4ZMvyKK2LFDteV3e+udsEdrtAYPmTdsxgHYXwaPHD/CVd76Ir732Fj72/CvVxggLPMMXCUtW/PU5l/zfbFguB6Nc9HDgc/5RwsAl+buyoNXP6oceCuoqxn9/PxTszggcDAAfCRCIOph3AcGlQDtrKQZqoCAQiunAPNhNSxBR/QgiUM0DXjOQZlA6icng+gPxIbj+QLQC1x8A52vw6QnS9QnzB2IuSB9cI53O9f31Gek6IZ0S5utZXp8z0ikVKOA5Y54zMoBzdmcvoJrgRhN3oCSgo2U7qIp8mgLIwUE8RsRDQLyKmK4meX91Qrw6YP7ghOm5o4DBc1dI17O8n0/A8Q74IOYQ0r84HBGungOHBLatXvEgnSCK2eEQJiQS9S1lRlJVPvQcCguNzDa5KxzEQJvnQ/h+EFm0CV6TsAYIgVUgoFxE0L64AIS8DggoAnsdEACsBk1bTU8JBz7Ogd+lRUTVR2v0uAvDqdm1UH7T+nzt8UcALmsLgOXc239vlx+FA4nRvGwyy+904QD88L2H+N9850v4p59+Ex97/hUkiCwiV87ls5ayYCvdGjDwq1pr7xG9PXrvIb76zhfxtV96C594/pVFfPpiR4aTWVgKdCP7vWr2G6njsd6AzRy0Ewi2YKCvIy/w1+YK+w515f8woGCPtuCZgMDa20OAlrU3I10yGVwCghJbyVWSjzp4Eygw00EMIiAEApyWwEwI8zUwn5G9luD0AfgkQJCfPBHB/+Qa6XrG/ORJAYJ8mnH+6bkBgvk6Ic9Z4OA6YZ4zZmacM+Os9TXrmMlav+XkRdeUNvFK2cRlLRIwaVkPKeNwJkxP5CjleBURryLCdUK6SgIIpwnxOuHwMwl5nhGPB0zzDL5zRxwhzwnxaka4MyPnBEpXoJwRckLOWcwNnMG4kn4Ws7QmZyBmxHhQHwgCMTA30kP+JnAJvNPCgfSBqN8Z9QG7Hm3i188uAQLbOHOAYIsREwhMpEvwsYlhCxBkK5/2y71aAyvcs8ABqgbGhPfajoT2/XLOalTxA0gYahGAZpdY+c0N+Ggt7XWytGf7/Pdyx+bhBOCHjx/i1//5G/jHn34TH39ezQdZ2qT0h5XnNGDTve/TrQEDwNmRMC70ux4KXlhCgZ1kZwKgvyewrXK6lAit4F3kn9c75bMAQdvptgd+vw3RJ3+1+YqbjNrvbkDBDRP3eXoKHwJT4RYAdK8ZuLzHHetQMArF2zs1r0FBjdmPuuuACFNwDobBYhCsQEE6yWvzIVAtAZ8+AD8Rs0F+8gTzk2vk6zPmJ6diNpifnIqW4PyBaghOFQxEWzBjzsB1yjizHM18Vs2aaAz00Cct6+ggpqDLNYECBQI1mcwBODPjQIQrAFMW80Q8BJn4MoNT9V2IKTdOjsxX2hYZE3S8qkkjA7JFEwDZXvzYqtNtEg7xUFV3mZBVWzDn+i1TBBNXR8U1OPAxh+y1BwQi0TjEQIsQzIC+ZzntxauTESoQEAGRGQmjfe9jQLC+twYIUr7LkN4kvgwH9tje38CbFNZW7MB4/lpeq2Xwgan0y813ekAw7S2wcy7un6wFuWHNNVkbyR5m4E9+/Aj/xXffwG/9TYUCMKKav6I2+qXF6t6Z91aBgaWRTfzhew/xyzeAgr4C7Z57G9wgwFbVvVD1HvyWRh1x0Qk3oKDvUP3nN03UDyrUMpXrHRRUjcEFKNhhQljAALANBOXzp9cSeChYHGRkxcK2U6HPJrCtJSDcwJ/gEhTYdkODA9MUzCfwSSAgX5/Fp+D6hHyuToV5VlPBnNSxMCPNzmyQKwCY34VpCDwU2PHRo2Qe91UKUFmQZxV+BhoAIcxZ1L9CR6CQQBFIJ3FeDFHyTyEghRMsNkKiUHY/4FQbgoKutSk0wmcEB+Q9AiHE7nctQJ0SkUVOZ4iZh6GCXm/eA0KfDMR7OABa7YFN/CDpp+aIEFTMp057YDERWxW5jZnt0wnrPv8dkOC1Bh4EVuCgqWcyk2z1NwCq1sBW3Vt+CGt1ClRNgilT7J76pcXnBci02GuA0MRmsO6Bqvmwy6X74IYFQDuHZwgU/OZ338A/+gU1HxiA0hgOmrIO7nkp3UowANpKeLeDAgaqIFiDAt+ONHy5DDSjA3eNNJt4893NnhYKnkZLsFK0kkf5q/ly3xv5FfS2uTr0a0ZWoWCQNmHAZ+wGZoPelyADF2MRGBS0mVsHw5FDoWRNrhcggFvJkWkKlrEJJhJQuAgFeRab+XwuWw3LNsR5VnCYi8DPmSVgkcYf4CzbDHlNmlue+/dE4mNT+gAV4RBoS2OARmMQyMwobf1dMr1xknyTL8c8y/ZHLWuMGodhjqAo9UIhgueT1n8A0wzNvdzXXscDJh2sxOJ3MBfD+NLvwAR45hYQJozV46vlgt63606Jl3Bgjs6AahJsS+Me7YGOhDa66kJ8DiFhPfMbcGD57GaIHg6AdtXdw8HI3LA2n7ET/kALAGz51V+twQEw1uRuwQFW2ryWVXYVGJUF1V4Oq5gFCv7Ov3gD//AX3sTPP/+K80EQOMgkfUF2nnBTnqdNtxYMLI2gIDeC4AIUrKQinwZQIKtAcnvRq7DtoWC3PWsABd1HF6GgL9bTQEEpg4MCcv/KU7wPgb1fZO5C190I87x2ZPQWFKwdaLQWuthKs5XLLdNQc2ohwfWHWo+jrYhRr0/BftNCAZqIhRKvwCIV8nwWQVmCGWVwSiVEcR+qmAIhxIAcswi7EEAxFxU+7gDzE50o5ix5zDpZBqmbA2n4YdUAJB5INquTAkEoZpIA4FBMC+JPYc6I052IqE6JpAGRJEqy5Ls/4tmXk5NFeUxSH3EC5rN6eQcJqpRFiFtf0jm+gYNMwAzC1DklAu2WRuWk0mcEEKTxtxwU15IXVEVwWd12cBBIYdeZFmz1WGIhNBCvY4eAxrxQngwtUKtFGCZvJ+ngoFyDva9woEVr4cABwF44sDqH+1tQxJlGe+0AN99b1xysJatP74bi4YAA9ckAsvaTERyUKht0kQIFf+NN/PzPvtLmDwCDy/ZFMGlgpAoHT5tuHRj4lUYxH7z2Fj75wt3amewPt5P+sOs7IWmTOlADzngo8IfUeCi4BAQN5fqyeCodpD3+AmvpplCw23wANFDQnH2wN10CArveaQksBz0UmNp7LxT01eYdiazMi6BEPvvdi0tA0DgaUhfOuNmOmBUG9IwDVkDQ8wuksPKdJj8xqhqeEKdYthQGtdNzZoSUIdPBDAoHJEoSb+CcEaeANGfEc8Y0Z9xJXHYkmOnA70qAAkKfigMilmUOkJ0JiIQ46c4Ev1NhEmfEeCCNaxBAFiVxiuV1nGIFiNgdkFPqjaW+5lnVMQHIAUSW+wDGWZovRASKmIKADzGK34GZUUjbqgEEZQfrN1NoRsh2v7H66t6zCs4tODChYKaFsAIHKjoUWtW80GgPgBsDQp/Mn6NzUOzhAFjCQamRp4CDRTZ43W9qz+f9UO/n6wII9n1tnuLFwlqzQU0XYN1JoAGL1IRUzE5a3h96KHj+lfLMjHKYqtSJgsUWzPi+tKcFbw0Y9IPoXQcFL794t0z2JXhE933f+XzltgJShD/0tZ/YTYBG97oHAr/CXkuFYDe+M5L3I23B2r0X1wdQQO6zDw0KNjPXtaAfqEOzgTz9kpbAVm8jKFjNiqN3G291JVCvj1LoPvcw0J9vYMKRCEVLEGh00JE/5EhhwA43ggq5UewAPaAIFEp0QU4MyowJR8yAhCcOElwoX58lnsCcka4iWH0MLICRbVVEZg2FzCWoUYljACzjhftkQY5I4hiUYEeBENS2YFsWLQBSPHhACIhTRLiy4EcH0BQxHY9iRjBQsAOZ7KClPtkhULATDJOYCCCCjDLAlBQOgEjRZFoxLSSiEkTNA4KHzMBFVi80e1up70dSZxs/8ILfaQvg/A5Iv+fnInmGjilC0R6UZ2tQJHkGt+N0DRJsCazfuQQHJs/WfA5sMO6Bg0tpoRlYyfYojT7y16K1MalGzQDBfBlUe5Ag201F6SQhuUHeFwn44/erT8FfcVBg9dAzQOlbXGWU/37//t33Hm7W160BA19I233wOwoFewZiIwCwBAI/uQN29vpSSxDca68d8IL0UnKaqYtag2dJW1oC+3wEN4S+PIzeVLALCkYeWR0QALh4BvoWFGwlH2jEkp98at7lTxdhfaB1af9u+aB4ICiHH4W6WmigwMIbZ9UM6FHIMul3ZVBBSPEApCRhgHNGOKKGJY4EnhLoEMHnhHg8yBkId47VF2FOQMpIc6pBjNT7ziIeFrV9Aths0QoFPOi0/vlST6GErRcTgURAhJ6fUKIiThGIQc9R0H8H0xZo8CMNnRw0YiLpoUuIk7RBtAOX2j5HrEBj9UoEyjM4AJTcWAxAoFjaz7QHjCUgACje4b15irle9+kmfar/XW8I6BPbg1e0B/JcP77Mc8HdtQeE8t21h16AA/39luYAaG32ZWyU6/KZPO6jmST7xdyl+dvm7UgoPic1z+bvwBUWrIcxkPUs7z+13QfmU2C/xxgYN/M/+L7Jxxfx4urvbg8YaCd8pGEiv26agu57Jmg9CNRAEpJMAIxWez98/6HcJ7QQQMAgnnl737Yxx1OBhwJy3/JvRvaonqZ9nQBjdZl/22sJ7PNtfwLN8aUtiXsH7UWzgeTAL/jXoMDSUFvgH+nuZSuQfvLp58ORFaHkzmlYehggVAjwrw0wg5Uo1+OPySZS08Ss+WiEICtePa2QAYn6l87gEEHzuZ5OOJ/BmRHtQCQ9+yDNScwM8yzRC82pTz+vDotmx+eyGb8EakmXMTzEUOoKAOB8BQQQ5H05OyEYLASEaVJzg2gFDAbCNMlvHBBgOujhTId6EFNQyPSAwE45WxyOskKCBkXiDA4sURJBRXsAroDARGXnSwAtIKHeXiagUU2xG+NNnbnXzamm3e+Lo5wHgXLvdTiw+abuXACG5gUDhD1jegAH8ixtg263QimrjglbdRfPfrsnRgDfzaMr5oE1wPKf9bfbnselRP4TP3+X4jfzt2iczPdAzFIMO+Pi17/7Bv7xL8rugzp/VbnkUy/4R3OTn9erfLyP/+Zb/2BwR0m3CAwMCuSUxJdfvCsf6ETfeItqI1WV1Ph+/Yrvj99/hF/7zpcA6DYyrAPBQngCWFW9OfLuHXMIrdagsXfT2Pt1NG7XjkjufQl82e2DG5kO+nLumUCeUksArEOBrUu2Hh+gMfGtYGheLAdi10+abOv97E1TZysmphqpTnHHovKx+RJkKYX5FZS6VgCjIDoPCnJYUKnCKIEBFBIozuDDUUIlM6vPwoyYxUSAnAssmOBnPS0RUC2AAwI7crw4MjrTAa/18abe3LRczk+Qa8EBAkLotAvUgsN0QDnCeev4ZquH6SCNZvBAQbztzYPeVrScxZQAQHYlhDIWwRkcIgLJIVAWfChnakwHjLo9NirMVi92ciLX6s11pIv11351U1vAFUyEF7Y1B3JfP+6c9qBsTRxoD7Yy4OCg2a0AoJoW2rkP6LQHBaTqc90dlsqMDShYXq8f+DYZz3uAW66tzunUlYn1+QYIIUh/MUAwWACA3/7Ft/AxdZTvT/sclYd8JtHOMX5ef/e9Kh9fKUczj9OtAYPvP5YDkd78TH/KFJVOVE47gxMKVLdd+dSrgH/444f41e+8ga+99hY+9//8BUydyaBM/ABGQrPct2z/8SuWtBCCla41vzYwBloDg9HRQBmlkcnA7lk+p/1aAl+uG0HBGhD4z5yDoT65nWR5CQAGBTUb9cMCWTZAtVR2qMlWvZV7OACwe9rbHq56GLB6bVDHA4EJbmbpF7pa7fsSUwCx9uhpKvvMWSduilPVOGTdlaB2dTYHRfuNPTultr7MidH7L/QT4ca5CLtTp95vVNT2mfoJVC1DFCFj/gNEAgXOt4DMv0A/8/4WNB10C562QjMepQcRkxSXGKSdjEOsAjJEFQBUdmhk1SL0kODV46ZRALDQYPUn7W1W22BZ64W8XzWbGrrMKwYM7JzmRoDgQGBVe1DqbCWtwcGKacHms5In6rUHJXcKXm6hcGH8NpoX/5raD8bbsIHR3Fe+5+d2dg7AJAeG6cwg8bM6QAgMZM3cK3p0shwFrjtNFBD6JKdw1r5Q5Vs79zzq5GNf/j7dGjD4kh49aZqC3kYFZm188TmWxiCAaohP33+tskMAfvDeQ/xtPbDi7kuvAtDQtA4G/CS/qlJ3qXxuArFTr6FraN8lRlqDZjBdqKtLQAC0ZauXPyQtwQAIgJGWQJ7sNQRbUND7Faxlo4EDu3ZhqTbaRTKCALveg8BmPzENQTEVmNnAoKAviEzYHACy0/OQgcNRnsEMViFf2iXXdipFGQn1bkfDf5Bp5Ezo4cJWa3bN3sdYx7oJPSLtd74FNTHr8qHcGJRYf0NVa6Pag0gBUbUIFWCp6aesk8wIFoC2H97Ut2hto0wjl61cRA0cFOG7BgeSI72H0x4A+wDB20i4Wftvag/Kooecjb0DBH//S3Nf96tVbYF9p62OFgrW5njq53ySZanNb2QApIAQoOZtNSVMpk1QLw8PCFbUVXOlW9Da67Jo/qxAQWnjjSnv1oDBW5+7j7tKQgwMKFM0A6JWk+5W4MCSqzCbzH/w40f4lT98A19/7T7u/lyt1Ik2JvkiqTZWU91gkxy1A2QrjKiZR6ST7KB2u09nUvAmA+AjBgJ7aHm9rSWwJy+gwKDPPXIt8qPctsKTpa34Ee1Koq0vYAwBdv0SCABo+wnny0CwBQejbWSc5ZCglTI1q2OggbPmm6Nto4vXg6l4S+0y6iNrfWjNn2JDlbuYlLtfNWVfK3d5togi4gSQrnA1+hCxrHZZJ3pQbQ+blHEBFCLGsGCPL/Z/rylaLd1ynu/V6SZYV+HAvkNVIMt93bjstQfevCAP3Z4HLgGCaU+pXQOT/QQtIID8DIqiRbiUtuZBb0Kod14Zw75Mq30vaXlS20+s76gWwWAnBioahAYQrBMtGhqNQ7M3gX//8UO8oYvmuy/dvbD8qenWgMGnXrrbCNGGMq3zq+bAXhMY3NWUJ60fvCdbHt/8zH288nN3i7kAgEwWPQzYJA9sDw4igJMeYQqYLY+AxbaetUhhHg7scVt7cf2jgeVAKGV39dAAgT7kQ3EuBMZQ0GkJLAdF4G9Awdrj1myOazXlvzYyuewzt+wFAS0J15XIAghGGdRwtsNMw8DYQ1Z9X1fHds1Wze77bvKqz2xhbfT3JivcNW/vWpLtOpRrHVABMEdBfaN/x6rfxbXR+Clt0AGCTfZWh/Z7q6udoACswIJ+am3ZYNENNQlr04KPDOjPaDA4WNyn1x4wq0nrhnDgC7GqQfDmhfqpBwS/+KuA4O+0nbbmwvIszdGuvujLtUhVqzKESdU4AXJ+SGJIqGMHCEEhcrBEaLSUBggeCl7toMDP9aN0a8AA5kTWFXcLDvpTubxK+N33HuGr73wRb372Pj71c3e1Il1HSOfBRL8xAfmHMOCVhlwY2MGB/0lTSnfR4ADrg7+pi+6Gu4DgWTUE5ZmtoLqxlsBlawQFazm5Ub3A1cMAAoANjUqvNQL2g4B9FxjDgNcd+oKtCf1OWDGR2tpDEzGSUc00ttXOVrDMKIcCyefyYR8QSkvUFuFC9/d/C3w5FWhVjZKqXEniCADidEi2QvJANmiDxjzT1v2N6718HyAyoZZrvQOXQUG/U77nVotwsJBN6rGz/7v69ouZDZ3kZrKF5xYcACumBSuHMy1ojvXmeR8cAOuAwKi2+hUNgi3+CiC4gu2tl35OtJdrgnN7IXhh/mcpJ+n8X0xSbkzKJ9omWkgDhBrlcMyzMASo/AABAABJREFUl6Cgge7m7zLdHjDwA9soXSc+ImikqQoH5AZZLwy+//ghvvrOPdz/3H186qW7kJGiAWXUBit7y91qpZ/oR1mkgKIhoAxwHVALODCR39k+CUsbedgx/kYwsPy7Awj6Tj9c1faq6g8HCpYCaFxw74Phry2yWbJHTXbWQKAZWCPz0cZKdlMYXZpAB6t/L4wMBNgc70zwGwzoOEgm/DM0YiGXiIX2186JyFwBLGXGzFn8E1lOVuTMesYIl+2+FrtgR3yjsj2xxnGQIEcTaVTRAEwU5IyJshJankDZRlCM8t3gnYF1Ave7Ojirf4Vvt7jU1qwJt96xTLUHQHUiNkij0n5OEPh2czBnamULlOPtz7piWIyBMo8N6nrPGJB7tXAALP0OTBADTjQ708JQezCqv7X5ovnezUwMjEFwobVKWTy7vuxNCPb0tbG+C/gHaahxclo7SmeYY22guuW02fniNEoj+fXGt0V+VU3BAIA36ufWgAGpwF5Spk5AhGYvae5WYCYM3n3vIb5klfriJyGH1AgEkPPkhgabuaj6LRmUfRAoWVA42OUuM44SBiyd6EZp6DjX/OyGQLBVzj5tQcENTQc+G5fsrn2t+ipaC+xkqsRlHd0QBHoIWADBnhnLZRAowqPXCngY4CDb9RoQyEDOIrxzFqGfmMtr0xTMmXFOGXNm/WevgQzGnAQOcpbvZrC+zgXeUuZyeNLobAALIy3HSNtrYApBQ0DLUdNBoWCKpNfEIWsKQf8SDlFem+agnDNByzMnAkXEGCso5KTj16BBIIE5uLYLbT9fXQU6weDajHy/d7BAru3WQGFkfzahV0DBnmr3HoyLSzOLnzo8HBC1jpHPZFrYZVZoF3V9nV4CBA8t8Hkl7DJvjRZLjTC9mC6M+y71IFkg0vrOfBINX57BYUIgAjTyZml/piH4PfLyy0PBaG7/j0lj0HSizk4PVNWTrxMTBo8eP8Qbv3cP9z/7u3j1hU8IuRV1ZGoaW0wJ1iE2OlCZ3DNMW1A9navmQLK0pTVAKR+7v1tOdP43o7+lwwBtp3lWGFhk4KODgtVH9u93agXq5T0rhY0JwUNAU3dbeW8hqmR4AQVRQSAKCIQIUERG1QrkLCv7zCrAVUOQsnx+ThnXKWNm4DwnzJlxygIApw4QrueM05zL785zxjllvbc+JxtscIlzMEomvEOQf/UUSRH2hyngEAMCAccp4GoKDRAcowDD0QBhipgIuIr2O0YMonWYiBACoz2tkhDDhBAmWHRJJo0T0azC6lgcj4M1UHDtaG3pYWEPKGxAgo1581Pw2cj6jJLdbpxcGjVDONAfGhx4rYHUk76iWldDv4O9qfm+jgOFgRq7wwECUOb4fm4E9s2PVh7/t8nPTeaA3QuBCpJ9v0Ce1Z8lCsSGKMG1FBCs/XtIfPe9h/jS7/c+Bd08ZmW6kG4RGCwps4UDFNMC4PqUvn/4+AHu/d493P/Mm/jU8x8H5usKBGZG8I2e5suTBKjAQHmorkYIGb0vwaXUd/wKQe13+t+01zvtxqjDDDQgq+ex211pMALdtRtFMXTZ3AMFa2P/w9QMXJwIPCByxqKegVX1YmMWsN90cMAUZBVhWoIQGw3BrECQVBMw6+o96ao/ORg4J8Z1zjjPIvhPOeM0CxBczxlPzkm+c044p4wnc8asMHBOqllIaoawg5hY2sYOZVpLEtGQqk9BkFMSAxGmqHCgf6cp4I6CwtUh4hAJdw4RV1PAMQYcJ8IxJEwh4DARroL81iAhEmMKoqmYiTCxaBZysOOsJdRymZRzEiUeSfwCMpgvDbVs14ttyqnp734u8AKhXTVuQ4LfsWR9u2hDdWLoIaHflbOWLsEBsOZ3sAIHWtaRcLw4pzRr4qqZqNBh9WzPWWoRem0Cuvc0eL2TJZo0hIJmUbAGkkvNkpgSSIA1qKOxAwQDRXbapIe2qFUoaPopD3yfLqRbBAal2+pf3a/NANvxU13Hscp78KPv4d7vv4G3X/s6Xv3Zv1Ji0zd+BX1D57PcaTAxDCd675DU5Dns7IkeBdoOvwYD7eu2ozR5XwGCSwN3mAb+A1ve7WtQ0AOBvN6Xnz1AcFE7cCmugIeBTmAsfAn0+rCegAqJHgiIGoGwgIJ4QNESqHo/Qf0B8hgInqhwP2XGaZb3Hgau54zrc8L1OeHJuV6fE2OeE3ISEMg5I89Ayhmcsh5YyBUMzO6dl8Ul8mAgZgOKATEEhAkS9jgGhEiYpojJwcCdgwDCVXlfIeHOFHA9BRwD4Ym+L4CQGVNgZKayRzwCQBCTRghTUekzUCx8ognX9gdcZ3VQsNLGVV0MjHwPimah+BfsgITiQMown4QCCNplSnx+A2uqOZIueXkMeTjw99j2O/DaLgcHVi8GBzfQIvj5p0KCM11YqQwQnJmhyZMvm/2mf9bwmzvSyGSwMi8AG/LCA2Saa5/gvAIIU+kzDx8/xBd/74sKBa9gMZ/Z8/sF30ZfuDVgUJ1wfAdSbQHnpfoJAJjx8EcPcO+dL+PtX/odfOpnP1Zsj+Xce2sMc2KyBrZAMIPZr/EluKQVGK20t74OrAIBhtd3AsGadmCt8zTagH4JsR8Kip7HvXlaKHhmIMgWaXADCHLq6qv7HGg/W0sWtVBtqo0Gyf8t9baEggSID4Cq82dV5Z+TvtfV/ZM549qZBAwIPlAA+On1jOtZXn9wSnhyTjjNGemckGYBgXRmpDkhGRwkNR3MKgT0TAHOclIhD4InUQgg0jMO1LkKpMcsBzltMcSAqGchxAMhhIAPpoB4iDgqDDx3jAIJU8TPXE24cwg4HSKOswDBcWLMWg93JsYhBiQQJq7nZbB68GaID0OkCI5o4YDYUYJO3Op4uGzvQVt7J0W2vslobMsi1Zr2lnq5EDchxAYQ/KhvDvB5SkDwURMb7YEbo0vNgf5/CQ7WHzq+TrSc3xn1Oail7/0Q5LP9c+yNtQVElxlitFjw863tcjE33nyuc6W1A+eiLSjgQYQH730f9975Mu5/9k3xiVub592ctWfBd2vAwBLp4Kmr9Lbj+EZ88PghXn/nK3j7019zUJDr0bYeCHoQ8IO+3NtBidrENuHghqaE8jMs++Iq8V4yGYw6y56Vuc0WTSZC8/ovCgrWghAZEOjLbZPBloZgoTFqIbFfHfQDz6IQUqwR+wSm9HdNP7AMi8aAwwRQbM0HCgXmWGgQMLP6BTBwnquWwDQFTxwU/LvrGR+cZgGDk2gJfnoSIJhPSaFANAVpzsgpI80CBel8Rk4zcjqDc0JOM5AzchItmpy3sBwfZCGLAYQo5xyEOIn3dTwgxAnxcBA4mJL+DZimiDglzIeI01G0GD9zjLg6SLmuDwGn44T/5GrCnBl31N9hjgJKd5hxxQGYApBYNgMxgEiYFA5gcBC4gQMOkEVCEWpdn19p92Wbm2CzcUkiEJotjwoJbAKfFAA6QCA5x2JLgwBUf6oS6A1u4V6E/vrYWoUDLY7Bgb/DRTiQjiCCzgn8vcDga5+d39YQEJyJwX7d/r5NN4aC8sM1LcjaPNE9vZMlImuS+A6ZdznrSaohAiSvv/fjH+D1P/xl0XS/8EksopZemuc30u0BAyuwdrYWDoAqciQ9ePwIr//BV/HNX/waXn3+YyjH2uYkM67FqbdtTt3gH1WwgD/V/HQCsaSyWvDvqyqxCNUNbcL6J2N10VqkrnUgGHX0Ns9thkY+BFjUwU2g4MPUEqwCQQEAhncwbUwGOWMBBN1qsaxoUlpdQIwAoXxmKmZXZzXmgL5Xn4IMlN0BRVOAfVDwgQLA9XnGT08JP71OBQxOqiE4n+RvOsuJi2lmzOeEPJ+Q0xl5FjDgWeEgnZFVU4B0WWOAKH9DCKB4QIhyCmKIE9Is78N0xHSIRTsRs5gEmBk5BeQsGoGcGZmlPjMDP3McnUHn0hSAzHLcs0m2zJggWyRDiCJrmIswFtu+vldBJN9Zwk8JRb3yvrY/CySwtG0bPKlel+7AWGgQEAW+iEGUVwGBrYhYag8MEC7BgeRhn1MiMIYDueTe98J0oU1YmX+6eV6ysZznBe66ud/7epT/dySy2IN2Lwmqb2YMAAo/uaMNquVsysbbAtq0Uknb1QDB5q0Q8b2f/ACv//O/jW/+4j/D3ec/DjsyvL1PKwt2QZim2wMGXWrggEJTGQ8eP8Trf/jL+OYv/lO8+sLHUE6vc1qCAgnaiDbA7S48n+EF4GKyHwj/dsI3wR/GUFB+13bm9eQF/IY6yV5jraPss/+VR/nO6KDAB9LZCwUfiunAXu+EgroF1UFCs+d9oCUYaAgaAbBmQ11oiFy79+8bE4LsPjBHQ9lyKIpHi0cgzoaqOVChKc6C6lyYRVPw5FyhwPwIrs8J5zkPoWA+y/U8n5DO1wIGp1MBgpTOBQbyPAscABiewxBM0MkxyUwRiGfEeAClAzgeijkCAGYcwTzWqp21zqLubiiPILsGBM1CJOg2RgYlRoiEWTe7BwYSadAZFZRizxVtn5yNoCt7chO9FAR1W2PXXzf6gAdEcRq0493cjiV7TjFpiIZBqiMASFV7EIRyqjCUOa/fjVX0mQPtwV7Tgn1tFClxCw7a8xXqXNRoDS4mp5HtF4IDH4RWe6C/N/ByuRwn1Ru59i6aDw8HolLS9xj6pFRoaMu5OmfMs+QgRjBYACFEafuU8eD9H+IL//I38M2/+U9UfmXpB/2izN33pv5itxYMmuQq/cF771YoeH4ABZxaLcE8S/uOBvpCDdwlE4peSPq/WF5f896/UMA2Txeg4JmBoDhSmdq71W5cgoIPw8nwmaBgxZeA3N72PVDgtQTyYqMObfW3BpD2T+MSWB1KnALdmqjeKyUgUWZ1/FOzAgMzZxeHQLUKqfobCDCwrrhl62FmyMpcghaAM4uDYVJnwjQX80D1IUjIKsAXGgLTHvRVkFHggHNWv9uAnDMCJXAIcn/S56QZHA7IiUEhI2SRbnnOyIGQY8B5zrJ9MRLOiTFpOadAmEmma6mHjDkTKGRMHMVMQDqngpEDQCzBZCKonKLIIYByrlqDoqFmdWRrV4kUo/SHNfWyU6dzSqU/CCCIgFnAgRd2WSpSTEoZ4KBbLgkgVr/GDTiQTD8THMjr1imxiGW/TrD/ezgAGqgqcHDJB6GkJSBUCPALQa0D7k5yLPMqu1+OkoOD3ixiWgpiwMFBjU2Ta/utmhosG3n4vgyhqZ6U+r1/+6/x+f/h7+Jbf/2/xas/+/NFayS9FuCbzOMb6T8OMND08PG7eP07Dgp6fwIHBcRZBrhbwaylempbFfK8AgUyoFVT0Eeq8wK2M0H0w2XYnb1Dy24oeLqONISCRv39Fw8Fu/wJPiQouFFyUFA1LL32KNZ+YBoCUhtymAAKxYTAWncMIEEjEEIggHPdoWDRMiSqITdBh+ZBESQaIZc6lqzJCprUpk2cxT9AU06QWWQ+6wXIkciDlT6RHp0c6tHHRBEhTgiTmhRCKL4I8lfz4f1IBpvTZwau9LUFWspsopuRIFqCKRNmEq1BYjlx1UKPBQUsCkDQeheBFUUGAdUpESrQAQEGztWH8BIcrCbrX9twAASQBr9ZxEExObQVx8Xu8iHAQeOUqOBxEQ4AFGfEHg5ulDpA8NqDNTgoz172z/Ec6+djFdjOrMCqXfLARnlGgYZSvtLLSl8p/cSeb6Bd5E0Wn5x5BsWIf/Xnf4rP/0//Fb71V/9vePUv/WWBBfihFisgbZih96TbAwZdRfQV8/Dxu/iCQcELH0eNTWCCs6qYd0MBhXUo0Em9rADVUUgm/7juU9AJ1BunRsreEAr8bxcdKyyvf4hQ8ExbEfdCQWnfFgqq78UGFOzK3Fh7VFeFo37ioCBEV38eEAgWzVA0Bq22gF19ppV6bKMNSsCfmQiHKeCcMkIMYAbiVIWe3lHeq+06zQFZ+3WIB3VAzOBJxkv22oLk6i06M4KOCQMA74gYpgPidAWKE+IUECfdoVCcEDXugQZBOkxBgxlJueRRVF73KTHjgCrMTNtStAZEEGWzc/4DWpNC0MAzBgeqbbB+Y5M+gKGP8t7UOC/3cECxXuvhgG3r5RgOGK3fwdPAAWDf/fDgQG+qY7MKYvdgd1PfviZwt+Gg3GOn0PS1IFEeo17NWPgcWB9AEmDjXMuY6/3GcIBVgOScQQH4Vz/+1/j8g/8dvvWf/5/xqb/0l9U/xeYndTgl0s7o/OzsPh68dpT/9oAB1inpwXtOU/DCx52gcELAAcIzQUGY5H2ok73fh17UwjuAYDQ8yX3WlHakLRh8djEN67CFggZqgH/vUFA+d//GUODBKLuB5T7blxMAXAbbyJnQEpN5ibd1VvqJXTMA8LsQHGRaHXoIGKVIhLM+MYD0PAEXWTBmZA7FYU/ShJ9ihh1QRCFLVw2EGAlpkq2KOUXEdNCgRrIbQXwLztU3YLHqcbWmZgSYxoACgtMayO4Ei2MAAYJIEudgkoBHUbcuToHwM1dTE99AoiaSRkB0ZXedJG5MikVQquwgki2BRGZSQN2l0MCBOijmGXbyJZtOZ5ouroQbLdLexIxRBNUqWLd9DkqO6OngQLKwEkb5KeFAbPa8DgdWT3vHaqcZ2NIa+Dtu3b0AAjGYRRDL/KFHu2e9g457MjUSSwWVfjKAgzWr9Pd+8mf4/KO/j2996v+ET/2lv+wWLgTbxULQkN6+H/TV4bVuF+rw1oBB6wRXG7/6FHwNr77wCTQdbagtWB5r6ZNNbj0UVI1Au/orpgPTEgxOuQNoAQNrUMDu9XaFtBPzLhPChwQFvgx/EVBw490HN0lkHuhudQOU+i3bDusPFr+3jNedB64Oadl/lpEN4cCArYRFLPRpCoTEBA7iO31kl++rCTGkKjRDktDEkXB9zrgOhPnAiOckWok5I2UGpwmpxC+QaIc5tREPs5ELrIrqcpmCAxESh0EfATFEeS8BjggxBlDUAEQa58CAQIIcSfAii2dwdZDYBncOUaIixoBjlHJNAZhIghuNkl//MUsgJHOmI9MYaJ2Xs8/8ilAnZzZTjK7iioPdYqyt9Bfqx1rAqrRYSdURT4TqFhw8q88BcDM4kNI+Ixy4+mnTQGvgv3fJHwzbcND0ASvJABCYSLXRpNmxMtb5t/SfjAUc9OnBv/03AgWv/h/xqf/Ff1bal5iq3wSrBmkr0WjeWk+3BgxGjf7gfd3n+emvyT5Pp1Yfawv6WwZV5diAdYRZ1MCXtQTFl6DsSW6FqP0DnBDtUh+nHPCddCXd2GbXPLG+pHryV+MzAeyDAr34HwQUjEwIw4eaepMLHMhjTeUZx/XbqO/6frMEgi14hIKnhytGfaxZokkn9ikQmMV2PsVQ63eSyTWQHFYkhxBl3DkEPDlH/MxRdijMx4xTmiQ2wDkvQh8LCAgsMNgdUCjfKaGQt9rV2lLV/aKuV4sbBAKIarAjHyrZYEAEvkDBFAUIDlHPVYiEo56tcNRIiFOQ7xOhnLkQ4PuTQQGKn0DRGsC1VUBdBXKqq78yj5gwo3Zlaic3lo7xlP1mLa2ZFP59wwFQdixoKXTOGsBB/XYBqSpQnQnladIF88EaEGzNxZY/GgECBYFiJnFcjQoLCg2iPUjgWPsPIxfZTlFMEd/7t/+6gYJFmTqtwRAQ+n5D7by+lm4PGNiKRAfgg/fexet/8FW8/Uu/g1dffBl809WiCgYyoQ60zmNFyA98CTaBII5h4ELWbEiMAMHnd38akHi5bvfstAR2zbQE7r2HHODDgYKtwEXAChQ8a/LqSl+nHoQs+fpeTNzUXu9hwIFlb2LyoJCtVO1ivMluUIGWAUxqy4+ZEaeI65RBgRFJgOGYAk4h4xgD5sz4mWPGWWHgnDSUckplu2POjJNFOWQ5ZCl1BybZBGonK146KwGo/gCB0BysFFV7Ye+PUf7aGQpTjIiBisngEOupi8eJyqmMUYFiIjk7YXKAYD4pHg58Kn3UhBzpVjETuk5lzLFOysNw2eoLwMU+7R+01X+A1T70zIlvBAfA5fG65wCmtmQtHMg1wP0SJVaAPADbgHAzzUpf38PF2UqRy+fkIUEAQepNj1Rm2WEj2qVQ/B7IYg7kLFomFg1DmWpiVJ+Cv1eh4Iaao8Wc3ZX9weNHm7+/NWDgKfvhjx5IRMPXvqFhIrUliVYb2z5jImczbiPVsSN3swUXDUGvCvZAoFvNPBCY0OwF5ihbwJjAAUfge4ViUdEBq4PJAwEwhoLmsxXNx18oFNhDVvwKhg/xgp/QTDxlZlzRDABLOveZHdRdCwNSouHOlK6/ZO0nvt5I/yOdvKNWdgZAUbbWTYFl215mzEEF+8S4SuJoaEcry7HK6tQIrloClnMWkp69YIc0GQDknIt/4dZxy32KHRzEAHFIRHWStHgEMVA5bdG0B+Y7MUVZ7duRzEQoMBCJKgyoyaI/ppmIaj26/DGzHGfMFcTLqhCoKmMDgiz9hRGXkADUPtab8y6pf4HN/lS/s6VNEA1ZrzXQYpe0BQfyuJvFOrgpHEDzV+CATVPntAelPBvlvRE40WLWvBSFtf21mkv0TZmTDBBU27gABFt0ku2GCwUmAOBf/VtxNPzv1dHQJ+77wzBj1L7uNN0PHj/CvXe+jJfw0uotbg0YWGU8ePxQYkd/5k3cfekVHZRmf0rytw9YYupnbTBu6rwOSA4RD/78z+S92n/rCq87CtdN8Lbq8zBgtllLfd+j7lUJKILWdrc5DNzIXnilXlRnLrUEcr01HVjeG+BBLSfQPuppoKB06wEUuDu3UNB8tOJPUSYfu33RyzTPu2hy8Pe0n3h1sAOBNbOMr1fuoKDXFljWg0k1mCOkeN0HFie7BMKB5T4pM2bO4CzfYZb9Bqwrf2Z276FOjmONQDYzAuy6f325ff2OAS8sQqhq/V6jECC+CJEgTpFQICJy76HvgYkCYqiC3z4v39E+JL4Wi+Yr9Z3199LfZOvmwunMAaaZq2pMf3afrZgT9vYvl8nluRqDmWDkfNfAgfym9M4ODgCZd54WDuz3ADZiHRCG3v6k+ffaA28S2DN/Da4P8wy0cxfqHG1lsu81j3N9hIjqHF0gQQ/o6gCBtFLLaZnMTUV//n/6r/Ct//z/glf/0n+G5ZP9vOwXFXa9m3c67e6Dxw9x79tv4P5n38Q/+NZ/vVontwgMIh48foB7v/8lOWXqxZcBOArlNN4rS1QGdz2Dvf3cVnoPfvIn+Pwf/aZcj9MYCMy5UCf3NICBsqp2j/Ey21LwH0A7ng7cy/XRqcE5L+GgSwsHTifA1kwHwD4o+DBORgSWUFDQ5EY7MWxyrHBg5fDPqmljZTcEg3ayflZTzKgkNqHYStrq3la4zMABuvoF1DEuSh/Ue5T7umtAXfX7NvOBnteqdm2r5Cit7Q7w1RldQ1i/iE6qFAGC2k9kxWa/MXNB7VMEhQ/qbMWD/Jit3K922auMHYwCKng7TYF3+uVyV+wHg7W01e9GqXe+4+pv4KcUDwfQ1fBNQijL97Q+eu2B3nePUyJKniqseyfOrSlwoensX29mfrlwY+yYq3Vg9ZCQCqxOcifbzZIFEJgiwKkCAoBv/bV/KMGL1nZMmUwCbc/jnXb3wXvvVvn40qubwHRrwODB44e4V46evKtXHYWWyFTSO0skM6da8+QPwE3msUDBt37hH+Ov/t7fAserVSDIUPUvtzAwolDLZVmccp2kvJbAnJoMDjJk9TRKzVnoi88uUMVFLQHghVgRMu7NRwEFfZwClL8OCnptwdqqwqsjybf3DtXuwAcDGExGm1DV5H4JA/pmpHEhywFB1N2o2gNmLsKUm19I2qHl35VWnPs/lPRR5tFfWutr/ju2kCuN1MEpHCQACgpAAwvFudluaNeAdpHS9NWbgsK4Tzb3tvKa1oBoEw5KLgwO4Dh6BxzIYy87JfrnFjggyEJm4JgoN85PFcCHN+BgpM31AD2ayzLXvBsQSDnYmWN0zibTTkW1nY0BAYDILw3VXmKCLOSS1ZVbaHjNQVnQyvsHjx8V+Xj3pbsXDc+3Bgzu/d49vPW5+3jFFbqxYRmNUj2hDA0sKBQU11CnfvnzP8Xn/+Vv4Ju/+Nu4++IrAACOkwOCWAR/zva3hYECB32LdBoAm+CtizVwMPippaasI70z1UG1SANhBgBrvgT27D2C7Jn8CaRgu6GgPnQdihp1JIBNbYD7Xc3guo13WV9NTsvfBqbgPsB4ZSKvl6I+9heKsGsnzYUWyv2mN0n1f+GFXZepFsKebdW7OFek+XxZh/5v7yy2Vofyfh95DFXi/YO7caujEOKPANSfLOuwBC8agay7dvNogBvw77UGNg5W4IC1TFtwILe5rD24iWnB6rDO1+K3odVb5+hBXdVbbIDSQEO3VYIhKPTj1q/qUOtNfFgqJATdDROCAUIEQmoAAQA4HpYmhhLQypXRQYCZEbiDgwoF93C/k49b5b41YPC7n72PT754t4f7IjCZrHMxitbAwlcGF0XMCw4K+N5P/gRf+O6v45uf/prsbtDdDzwd4b3Gc4baZtVW28MAi+7CUjO36BszZgA6WLyWAPJ+pPJcTaYBcWqqoUptBAR2fcWXAK5cwLNrCXw2bEUM+NVZCwYjKFiNUOg0Qbs1Jj5D7joPIerpBNdNhdaoroBtiBoBlISGVoBc2cY59LC37ZtA/Y4vyE3goKvHds++n9j6FZDBl11bWTmVZ/QCgIb91hdDXnOr1XOfldWhvzYAWqAFBlBszB965/V+zG7GeBrTw8oKuTk7oIODPhU4wLM5Jdbv4gbag9b3APBVV00M6+XvAXNf6jW8i0VdP5dbXVjO2MxV3EJCZvF1YUIk8amR4F4OEAAgTFWDEKI7u2cpn0RrbbuYbCzUUOsGBW997j5eVii4tOsCuEVg8MqLd6XBtHctHVyomBSY5NjSJnxl63Eo3ps//mN84bu/hrd/6Z+J+sU0BAAyxeI/kBjq0e00BQ4GGMtJB9CBYnkk7WxcAcELxfpbLC8OUjEneDhYS6tAILXnYWA5QFy+XNn2pL3+BHZpFxRswcF6RhbfGwOA5MTXxUj4b9VJXzuj3PY59S3Xw9NaHAd/QmRzgqSFArdr/jRRZsjWKQbPEuoYnOUcBP1uiW7IEv643AeoAY12ONOVgEdEMvmVnTwSFbHEBJkOIrSCREkUEKhOvwTCWvwQM/WRQYOa+yKRbP3Weixjldp222wv96XgvmjnJNb2GvTxHhocMIiWoQOtRR93T7ipRsEgYAUOCsSgCuo1p0R7/E1MC5KoucdIe2DPHpkX6h2Ai2aXfjxfzOPgGpaLu605XXIlC9ERJISg1xUQxA9BAQGQRafuVrADvBoQt2d6UC5h9qsJwUPBKy/d3bUt3tKtAQNfZb3DEACQNykEmcQEDmRglONdzVHj/R/iC//8VyUOws+9WkwG9pyzLqTk2FvpGMl1nhrrhYerElKNQO2uVCYGNN9nDD+4Sep9J+yaPWNlFbwqAB3pr6m7+/7XZ/+p/QmA/VBwA+3AHj+AS3Uwcix9GrNKwrJerL7GQNDBQE6yhY5Tvb44KCzJyaFpBpL85fksr+ezXOcMnmcFAzntsEDB2U5czODEYNPIJKuYwYRdooZa20t0QztUKRymCgdRX08H0CSvKU6g6QC4v4iTfHdSR2A7fModVS2TZ5Tn2/sQQeoTRKSy1kAfrbC7BL8j0VS0O8w61tUZzdrMtfOy71eTRNUwMGQ7ZIadmiPgb5m7gbbmIhx4PJD0NH4Ha3PA8AjnTnvQ/1LqwZ6KASBgc457mmTagsX17rOmf1h76xcDqhlKYmdIPJGoC3zxP6h+CADA8QiEpKdmOkDoHVd7zZkLjPbgvXdx7/e+uICCRhZtlP3WgEGj6qMlHBSTQkBxLeCiNq2Cl4kkYuJ3fgVvv/YN3P1PPwVWIEimkYB4bhdNgf61DmPe2UVDa3mEHxzSrmVycADQu93cOOlobZwQu0GybtPdJwytDPJ3HQjG2fsIoGBP2gFD9iQPRaOYE17d6K+5nA59CMb5kj+9f8nIPGrf0ymhCno7NTLP6I8Sp3wumoJ8uhYBf74GzzP4rO/nM/h8KpCQTyfkeUaeE/ickGY9CnlOSGcBhHxO4MwKCPIXALbOGCkhxUOAnJwof8NBBHc8TKBJNAhxiqBDRJgiwjQhHI8CAXECHY4CDXECHa4EHg5XQJwQjldVcxAOLSRoqGnKqbwnHd+2Mvaq8lGbN2272q7cSK1ArTbBNL8mPAwWGpOEA0APCwUUShx+LoK+Pn7fuFiHAwmC1MxZVkeo+b4EB12NaEnqWDFAaLQHXMvun9+bF+SezdJq/NyRX9BTJK/9Lbt9yhzovueaXupHYCGxbrfV74QCBnK9LDrZaRAMEEJn+itlI1StgW6p34CCvQqmWwQGvsS0gANhgQ4OII4s5bdEePDe9/H6H3wV9z/7Ju7+3P+qAQIzEwDAWU+3M+2A7yjMaITGKJn6cbWvuhHRaLpv2Lc3VWh7haK+eVog8FneE97YLtXf8TYQXJoEd2gGtmBgIRS6a0sfghYQXLZrlnyblt9ThSMFxUzVydDqagQFlGZ9LxqACgQzcD6JRuB8Ap+uBQbmk2gDztfg8wn5dEK6PiPPCel0Bp8T5tNJQECv5zkpGAgQpLOYFThDXgOqQbAy13YhW4nGCgfxENRnKuhrOQ+BJoOBiHh1AE0R0/EIOkTE46FcD8ejAIKCAU1H0OEK+XgFHI4KDUex2YaDmC/ipFLMnYEQgGDaQJ03fNj6RVvzzjZ2r8t20jKo5cLIUQ2o15Z74+3eGorXAMSr2Rsnw5Wx4RYMNpaqkHWfDcTtlt+BhwPq6qc82tXP05sXWkCwhdCzptZ/wrXpxgR3cb5nKQ8B4utGVZNgEThzQIndMSdGtiBfBggsJrvFllg3h++BglZOrhfq9oCB/q2dbQkHcrXCQX9m/IP3HuHeO1/B/c+9jVdeuitn2ufqQ2B/AQED0w4sYIDbPPlE+p8qLaowoc4/vkBBHZj+9Wrkv/rlMR4O7OkmGC07zarITVQ9EOyEz0X+t6DgRlqCDxEIehiwx206InFdRbjsLVSMw8StmcB8TAJX/xLw0tm0CgdeQkE6KxSc5fV8Bs8nAQMPBKdr8OkJ+HSNdDojXc9IpzPy6Yx0farvr89Ic0K6FiiYrzPyOQs8nPUgpVn/nuUshVL+NOgdkapJJBKCwkCYDAqCAMEhYLoKCgARcYoIVwfE4wHxakK8OiIc3fvjAXS8Ah3vgI4n0PkadLgCjldAmgUYpgSOB6nTyOAIUKrChqMIwWJP90210uYjdXLfxqXd9K8JvrKi5KUNuhwsxQxiqE+UfRcNJJBXs38IgHAT08Izn7HQvFkCwrZ5oc7l9Vv9vfyXlzfwV3y7N3BjrzvKWV0E8nheHJm6TZMQSRaqNv/PLCHNRRsAPSwxqgxjNNthXfkMCu6ro+EaFOyZt28NGJigtYEOoKioLLBG7XpKnFSr6MHjR7j3+18q3ptpAASmIQCAWcHAwtX6DrEWFjYGO4oT6MZZ43Bmqwpyn/nXYxJYGQw2yJvrYxu6vX4aICh032ehZKVCwZbp4MPWEmz5T4yA4CYw0AuGkVqxryvfEkyoviY6oYIqHPg4FaSVU+ooO/+BLSi4/kA0AvoXZ4GCdH3C/MFJwOADA4Qz5ieiOUjXCemUMF/P8vqckU4J+ZwFCuaMec6YdXzMmZHgoiQOmsTaPZAEL5qCOF9NRJimAJpEcxAOAfEYEQ8B8SpiupoQjyeBhKsDpjtHRAOF564QjzOm5xKi+UscZlCaEbR+bLj5uV1gALqHnASwwlTAnbp29G3vNYTW3pfa3L5LZAKnhYUq+AeQoJoEWXGSWRLWAaG3w3sn5LXk/A78b7e0B97voI+UaAK1k6f+js1cUb7jAKGaWVvzgk8lTx6A/Hw3mv/KExc3K9oiQusTIvmlEujLPutvuUcGNPdkFC2CWYZEY+BMDeqkyAEKr9WcZMnHKShbErmdl/wvMpbt4tOtAQOghYN+teWjbdUuIf8/fPwQ937vHt783H188oW7mLOYDURj0PoSmP9Ayi0QpFy3N6Wuyi3oTMpt5DZLRWbZatAGPNXQrfYa2KEtGN0caFbQJhytPkqHcUIS2IaCS1bMPVBA7rsVU9BQ8Y1tpzt3WaxBwdZ2Uw8EftXgct6sHoYVgqotsnnLNEYCJOs+JlZHdWdBKn4FxbSwBgXXHyBff6AwIGAwP3mCdD1jfnKNfJpx/um5AYLZAOFJQjrNmDNwnTLOzJh1XJx14j5reOWmDlyyFohEOKiq9ECiQp3mhAPZgUdAPE6Y7kTE64h0lQQQThPitZozTmdMd66Q5xl8546aNDJiSgg5F7AzqC5wYFu5smgvWCtfHBHbw4X65LVDo7ZvyrzS9iZAiesquwh+IhESDBSzEpzWgIC4og2tj+lW0ex8jfbAAbAOCFvaAy2zqcgvwYHVYbmHqzaBsuqg2JsXVrUHYCw0JBdsr/5T72MiK0kury3uoi3qilJlULAtWcDJ5kI5BySjghUrNM7MCFm13AQEVeIESBwEDhUGgSq/fByfNa0XcHneBm4ZGIyS1xp4m5VV0sPHD/HF37uHNz9zH5944e5QS5DABRCsI+QOCKwDjBxjE7gJ71o+g4cCUkoldxgHSkjXIlT9qrGWEsOH9xkpT63lbwSkXtjSEuzpVM1jbwoFe8wGO1WFy+tjKLBdJTa5swPBS0DQCIQOEBbJhALQOpmVSUZmnbWilAmRM5r4A/pPthym6kw4i18B5rM6G55aKDidChTMH5yQrhPOH8z6VwAhXcu/05wLEJyywMA5s/jaQH1ubNxoNv3kFFx5o8LAQQO+HIK8PhDjzCyAkGfwnJGvMvKcEc/iu2Dmiphcfwinpp4o2vZHKlsUKZ5FQE6TizSn9ca6q0KD54zMCaUJ2Y2JQftfavvyXSfsRPBfBoQAQqLWxBCxAgels61ILytAn8yEAGBhXhhoD0yQX4KDS2kECWvmhd73oIJfGSDbEOQcLNslIorWIBsQMItGh828Z0edO3BYKd5IJsxgfTSDEy0AARDtm2kIwASGmBXMvJBzlQXvvvcQb+ii9pWX7lYLXjeP3zTdajAw6gScussN+kePH+KN37+Hb3zmPj7x4t1NLUHOrQCdHRBYw/tY8aux4PU/EfhQG5MJfiqCv0TJ6qDAvFrLvZ4SCjwY7Nl+Z5/tTY04XoGCRTn6IDzAAhQuP9hpC3aYD3ooGMWhuOhc6gSCVyH2e5uBdXMSd+9Xi9dfMIFmMQtMyNl2wyygwGlGPssugzQn2XGgToV8Tsizmgpm0QzwnBdQcJ0ZMzOus0IBcwEC0xokHpsTGjMCEQLJ7wK5EOIy8+qkLL8+2u8CIZ1mUATSiUCRJN9TAl2fkUNAmiNoTsjnGSE6J0yrj+mg9cNtvW3V70padMcb9gFwB4grgOBNS1DtAXRuADNs0/Wq5kBV64y9WoPliK/iNlRwWDEtjODA7rFXRjWaX82T1x5s+R5UMb8fDvzui0hiIjA4CKYeCISs2w0CC7hFb1rQ79NGIYt8SDIGBBJaQABQQukHsGgMOu1BVCh89/FDfPnb9/DmZ+/j5RfvDh3FpfpuTge3Cgwu+aR6W9ijxw/xxrfv4euv3ccnX3jlopbA1Ms26P1JdZdShGt4gwK0UBCVCCQi1hIKQlEfbQBBGYXr0xt3r2+yJ//SY1r86DQd+sHYDGITtU6ifsIe1a93qrqUngEKEsyxdAUKBsJguO/ZXTNzksGBdzrNWA/Q3JTWawrqRfk/JcBvG+Qs71XVvshbzlIH5jioq/K8smQe+RTatTUosPcBBtYCBImXE3tiYBp035wZlLjueEgi4NfKZGUWOJJjh5Gz1I8daFN+4OrTaTbWxGfuX3f9YE8fAJaA0DsjB2ZkqBByTqlZ7aI3goM9qR9XJc8VEHrTwh44AFqTwqhu+zmlaAWsSno40GdYmZ2uYBvu/IO8ZqSUUOZgJslEDwfQxWNkQnJwQK68MZCMc16aEix5SGBCAQT5TOshQ9p+oD344XsP8NV3vijyS4P7WbrU2lt929KtAYPWeq5/tZeZPQiQCnmkpPX11+7j5RdfKUJ/BAWZW7PBbLY3BwVrgpJCCwXFNAAlPwcFJvhD8EfKYkWY8gXB6Qq8sfMgd52pUZO66/62a+npoMDBzZaWYDSxbcBBoy3w1+E1Ae4anMAvn1dB/zRQ4CeEkRnJUlh5PUp7V7MWSEh+FOS9RRT094tyjYg00BBAUbYR8pxlBR8JYVbVPwHZpHvdAwxAxk4IdfU/AmbRFCzNCQf9OxHK+0Aq+KIcqxx01wKVeEUyrigEUOzKFWqZmz4SLtWw/v5S9aI6nl2aZEf9wDSZKfMCEkdwQCTqa79jxQtIlSGNDX5RBq81GKXVIGGhziecW+3Bit/B08KB1E3JrtQBWjjQTzePoeeSH27ByPfJgWakB53QqC30nBWFA1aaMzhA4KLiX8BB4EW1+nImZsQsGgQApV8kuDkh18r8/o8f4G//4Rv42mtv4eUXXykL137Kq89Y+npYv1lLtwYM+kKunSnw8EcP8ZV37uHrr72FT94ACuaUh2aDkbC0M+FHUCBOhW6SdH/NTFAP27gMBMMwmfb5inDk7kJvi7opFFi6MRT0poM9QNDkvYODHhQ6bYGlBQS51/L5Moz1TdPQp8S1B5X/7LP279rv1pOueWIEp4ASZljDeBNJZMEwz4hTBF8dkHNG0NeT1UWj/6cigMNpxpwJIWVMJFuqJgIOTEih+hqY5m1t2jEgMN+CSBUIJsLYAfEYMV3FukPhasJ0NSFcSUwDsu2MFv/gMIEsuqGrB/E1iNoXLtepPwq3XnNyssopMQO431l/2oLCvcnMTN4p1UdEbbZn229K1mipNejf73FI7HYpiJBx4r0Xqjvg4FKZR3BA7vP+GHrXHOMb2uf9nNmXjdyhUo32QKAgBNF2qcegLiIkM2wLiwEcxEwLYLZy+uszZ3AiTDG0vi6Z8YOfPMSvfedL+KeffhOffOGumj1qUDRgfQ6pwdMup9sDBms2fXfZQ8HLL95toUBXiQ0UYAwFlnq6Nb8Cg4IphiIAIgwITCsgg7o3HZiWwCChEaIjGHDqMPtsGehjWTf9ylnKMzYfjASkr1dPnzeCgi3TgQ+O0w+mLVPJmhYBrdmkXHPf6T+/lPoJbuRotbAto0KBNyn57xYHVFs1Dx+uhEmh/OOgZ7uHIKd/xgnIGXQ4QkKAA/G42GRVIxDStbyeAsKUEKaEdJgRjxHpFHE4ZxznjPk6LbYp2g6FEhF5kGUri8HBRINti1cRceq2LB4n3aoogDAdJ92mKHAwPXeF6XhAfO4oMQ2mowQ4OhxL2GTESerFOSTWfzSeTTXPTOKVDla1ced4ZoKPuLax943c6hPaFfYnHbO2ldWEJnWfD7UGwOaR7MDGWCv+Ca32YMu0cAkOvNZgwGCrcGBmhXLdlbkrDfoBPZo7fd2Jo2UCiFpA0IcXKNB+QLqPMmgZzARHLGcgTIHAICDlAgeAc0zk5RqOs4AEUhY40Lz98fuP8BvffQP/5NNv4uMv3BUtHWoQNL+Tw3dprzlY1s843SIw2P780WODAjEfWHdhg4ICBwoL+rffj+rJbg0ILmoJ1HRgIFB8CewflgK0EZ5AO4p6e9kADrZk3nLFPH5MX26gM+FQ51F/EyjYAQSraWHDoVVtgZXpUlz3rVQmOZYytVtQqfSZfmsqlf9aKCgmJqrf2ezOZiIjDRjUQEIETQexqx+Ozc84RHmOeu3TQYIHpeOM+XQSAXyaMWlMAwlsNC0DG80ZaZYzEtJZfBSQGDnncuw4gBIMDECJ6maasCC2NBkrBzkzIfpYBqNARxa7wIDgeERUzYEEOzoKFFw9BzpIFERygEDTQXYjeBggqsJvYxIp7VIXh8VxsIEDfX+jPqEvdnrN2CObPjI6U2WhNUAezBvbPd/GIPs5xsEAwYSpYvYGHNgjDQ7sTjeFA1/eUZOtaQ5WdziZ4wpRE0KeiRtAIHLjnkn6QJA+kVWFQfo+MZctqRmQRWJmcBCQ7gFhLZlZ4Yc/eYTf/O4b+K2/+SY+9vxdHVuCLXYY31rv3aVw7NKtAYOtQfXA+RS88tLd1m4MwOzFctRqvQa95rUFBgF+18ECCFAFYgg2GdLQl8CbDXogIH9a3aZ6XUtvg3fDMc+vnNdMCFtpBAUi4FycBb24BQXNIO3Kth8INswIK+mS083qo5wQAGoZM6owsDS1HmAltfXlgaDGqzBtgT3Tvruc5XQGQpDY/51vCF0R7NAgCgEcRKPA0wScrjFNJ8SrE9LxhHgnYTodMZ/OGgL5hHyW3Ql5nstZCXmuwY1yYtnRkABmPUgpc41+OHBeJC1YUL8BMVOI30CYolwvYODPSpgEBg5RhX/EdDwgHA/ummoJjlegeKhAoHCAwxFMUaIfhgl2ZgLgNAdNZrXubYHMaLarhUBqPuC6Nc/mDur6GXd9ousboX27pcC4mLzw3EzenLDDQXEBCA4G6tCofgcjOPAREiE2kIW/wQ5WWTUpbGlK6o83zJVl8IjWgMjm0lTMcVHHZyYgZxStQSINRAXS66I9IAIoQx0MRXswAgSgXXSa1iAy8Cc/fhe/+S/ewD/6hTfxsedfETlFpBqOWgcjx+WtvrBVT7cGDNZK+ehHAgXf+IxBQW3/0Slppi0AWm3ByEwHLIFgZDYQUCBnRlgBAhcPuwjMHcKS4QbqU8woa9oCn/rbjqBgr/lgYTpYK2Nvq7lUjh1Oh2vJYE4mMLUbaoOami5TnbhIs2eDkVey2KwbncD3QADUXSk1hsXSjGBFIF3NEGVwCDLxBD9Bk5yTcCCE6YBsBw3ZwUnTEZxm0HwGXdWDkw6jg5POurVxnjX0sRymlO1QpSwqT2ZGVv35CApKHRQ4EKdHxHqYUtDDk8IhaqjkCWGaimbjWQ9UYnegkp2WyhbvwMwxg25i5gQwq0OaTMqJVJ3cAQKAZtvaTfuGPdPHADFo9H1kNW0JSbrghLi4VzsGK4B67cHA72ADDoBqVliDA/9on3qtwT7/m5UycR7Pq6pBEE1SQgn4pMGxxDE8yvHJEDSy/EtUQ5HYFKxcYmLTuxXzggcEAA0kmHz50588wn/5R1/GP/wbb+KvPP9K43Rai9NqDZoFGnChs4zTrQGDSMsB/VC3JL6pUDAaCuZbsEWpZUtKaK9tAQFR61TY+xEMgSDPRVCWDmsqukWqmZEBua0puJTWir8FBPL5RwAFW/rESxkEsGVG2JWohwO5aIBg1/LOAbdmbvGTPdBCQXSvhxk000GwFdShTFogtaPnBGYJ9SvHLN+RswMsdHB3zHKYz1I+u677/zmlAgKiJWDY9kezqbIdKgLRIKxWbSFqKrskgoKBHLdMFRCmCIpy8BFREF+B6SBlNBC4cAxz9scwE1VNgUGBO6p2NIOaP4Hsb1c4UCEXIZNOJlY/BFQzlfWRy91D6qB/JrDaT26SqlqdxpqBrSX6aEw6QKjmBa2/xu9gv8+B3dt5K/hHrZbrKarD3cAFCCuprRuBO9PMQU13CpKBh4BALOaFAJRzdky5x2peYAwAgbkqfvXvv/nJu/gv/+jL+L/+td/Fzz//Spt97XfQcMl96ufjPlH5b5xuDRj4Yc1QKNAwkS93+zz3dijzKgWAqfvVmmNhDwQ2ya8BAWnMewGD1ILASN1VhH/SkSOaggIHCKsF3DIj7En/XqCgL0OZLVszwtOerNZPUDaIpCYrbWY3OfjfjrK8cHlonkfNRQ8D9tvqf9DVL9yESPUgMIJqS1h8DBAzSmCfnGTSyQmYPHwmBGZw0kiJnAUI9Lsi6GeNCSD9kq2vplS1TLnGTZAM7hCF/RbCELXsBETdPeAEOYVQnAeLgJ8O8r04yU4DIik7kZoIqIUBsucE1RKE+l2DLLQg2feNSOKZbmYFUemiqgS0cTwwLrQ+F/qKPk1/u+wr9nbUN26S2Pbud+YEJto25TlI/zDhYM9Wxq0s7TYn+IVXKWfne9HMt2xqOgcKVOuB5PhuAwQBAnFQNP8DdcGRcMsbgGBpooA//fGjAgUff/HurnpYlJmWdWGvHz5++B+HKcEX0sIc+wMlio1rA5AtWYf1Xsb9c+qKbgkEjaZgAQTaMfVkvNpJUycsOweZol/zjoUBcn50Nz0M/Az2MsBoUDarGfvrJ6ZeyJVLN4CCrUbZqy0oE/w2+g1tnHqNIAO3aKAKbXb39Nml7q9LK+4Gi1Vhb5qxz0L/w/JoAulBYKbmJs713PZyfrv2fq1zhvYttjDA7NrDJk01S/jrqG3Fyfm+AG2QoRuCQYmtYLtqokBCBTBCXdGjqPztOhM1Z9LX8zHsN3bd3cdA0vWXYe8jNOFxCSYM5eOseQqlC6tKuLtfHvWV0QO7dh71nVGIcQ8K28nK2wnCNa3B2nWnPXgmOEBbvz0cANtz0VYaahSGfbM32fbzbn3P6m8gpoUEkAAp5VRMVJFC0Q7nTOUAKNK/mceA4JaD+Nd//i7+t3/0Jfzf/8ab+PnnXykL0D1l93OGn4/h/j54LGcrvISXVu9za8DA7EL+6MlX7ZQpYBH/3OyGtg2JiW1bakOzvnMFAH/840cAgGiC/iZA4FZxjXbAro80BKV8+pcCaoBUzdWFCWELCkZR2kYdcHVi6qh0Ewq68t0UChptgXs90hbYYBuVraoqWzgAmXMUFT+C8jvq6orcPbazXZ7b/XTx+4Vt0H3ZhMSyTARQlPnYf4Nz/S6bYNd+s7a7ZW1HyKJ9Rv3zhuonQAT24mJYfKc8wldqozFaXl+cqtn8pvTQeu/ub5kvFA6KacAAhZ3GyH7VlcdqqfihrFTRaAyO+tWof1D33dHYZffd5rrXGhTtY6c1uGRqGMEBgLJjYQUODLz9gq3kkGtPviQMt/wMhnDQpAoFNFi0lJtoIoUC09YyJdFsIcgiL5jJKiCGOAQEHx/HA0JGZZC/+y/ewD/8hTcb80HVIprcQXFUXvgjdVDgRlCBgvuffRP/4Fv/9WrN3CIwYDl68ttv4P5n38SrL70CoDstTQc50/hgDzviVO639PL8kx8/wt/57hsARP1D2GMyUCDIFre9age8TwEuCcnS+R2ZlyQWz/rdNTH0dGkPFLRU+hFDQfk8YHmCYlve5u5UL1RtYNUSABecCb0QX80yde/r6+Ekt3KjkV3QUi/ERjfMTX/o/m5U+dZ6/2lk/9OmrdXvprB42t+Nvt/VmwWfWzxn0Fn8iCz1VuReV5FURs1m6p8ygsr+i0vhaONEtUZ7TAo3hYNux8IaHABVe+C1uZdOZbxZ6sDQtiauleXiXJxLZRORAIGapIgTkKv5CmHaBARvYpAakuf9VgcFJftk/9YWIxUQWiiQ+z740fdw7/ffwP3PvIlXX1ze36dbAwYPf/QA9975Mt5+7Ru4++IrTUc3cW+rACKUbUi2L7nfn9zX/Z9ocIl//Ok38bf+37+IeFMgyOKe4rUGrRoXWB8GitdUaVx4cRTMyP+s/WzLM380CEdAAIyhoJna/oI0Bc133ftLk0mpRT9nqJagvF9mY/H7xZexFOhDvriQv1H+m5VoRwal95RqXR4PvLiufaGEfu6+t7xnm48GILoMb9X/ouwDcOrr3ve73lNfrlPpj/b7LZV7f0//0DXTD9AeYLZWBiu7X1Qs9C08+IF9Nrj9mpbfpz3Q00BCb1Kw93a3jxQO4HOygAP4z532oCn/RiEz0DrkXQCCoq1dmNZGT7ZL7Zxk5oSiRcgZCOkiIHgfBDNbv+y21PfJtAWkWSD3nro+7BdpDx4/xL3ffwNvv/YNvPriJ+FNhKN0a8Dg9Xe+jLdf+3opdBuRywUmVQlIJEdqenOC35/s0w9//BC//t038NuffgufUEeQQ1zbZTAAAlZfAnPessNtUDvg2vahalOFDri1Y3asfN1sCizU6murwvEieTnBrkKBU8d9GFCwUB2PzAcNFLTnQWwWkrcFfF8ZQ3vv6td7SXlDuzuWbWfgNjr0qj8ymlkjEKrgN6GfmOu8p/ditEeHz0m+kwBwZv0Nl/dAXe0YVNjOhI1diotk9Rls+yKo+OtIdfgzQ8i9B6ZI3a4gdk6c0Pu0sSFA5gukfZm1TvV7/Yl9vo1X23bQrkvw6XtZ9SL38sXu6r9d6tP12c20QZy+DzXnCKDOlQRgZFYAdNx+WHAAYM92xl57cCl5Q0ZfdcXWVm1u68kLTW9m6L9mwGGaypzFZyZr3AMOuwFB3sszJ6rhrUfKJevT3oxQtAjU9VnOePCjh7j37Q4KLqj/bg0YvP3pr+HVFz7ZdkSq3cQ6YdAxYc6FpjUI5lQAFA90QMJQSmxqCaNsE9m0EwiKuaBoCFLtbFs+BeaMZdNvgQNeLhe6fS68Y/VcFwLrZhX5657giHQIBd6WvQUFa2nLdPCMUND7mGwJfv/xEgIGbdbZ8BftuWfJN7KNu8lzseoE6mmQ/nWup0L2x4XbUeFzrv8Sy1HKBgRzzpgzI4MxJ0hEUAWGcgy5HrWcMutryVMqgLDezhYB0VZHgeR1DHJwkvyV702RdAIkTFEmwykQphAKIEwKDnJd/hERplD7aQiMSIRE0BgiKkSCLA6ao4x9k2CjfwPP3M7kPif3Gfxchdr2vcViC8RGpqgtOFiYFQwO9H3RHlh5Ntr4MhwAI9PC2Cmx1sfWPDWcFl2ZAdRw0EWDoP4CzQ9a8Nuap5c+BwG2M6g4JNIM8HQREGwXA2D+a+ROKq15Mn1LOUfHAXFAb9pVn4IGClxZNtrw1oDBqy++7BpOOntDqUp1MgABrzVAkLO2QzB6k2r94fsP8avfeQP/7Jfewssv3S0aAgA4xB1AYDCQE4DOpwCQa13jNLHJtSxrJoMGABaCtLW37z0HYNUZjjpVLbCYNHdHM1yxsa6XpQJAE8TIlXEEBX1x1+z2NHjd3LXXhHTXnmbbZX2gPqmAQCrlZWIgRPR22fJY1KOjy5HhDgjsLIOZM+Yk5xmc56RQUCHglNx3MuOcMk5zxllh4DxnnFNGZvlu1u/k7EChaBNaLYJPBtUWNTQEBwKBcIji0X2MAgOHGHCYgr4mHKeAQwwCAJEwUcAxVliYgvj+HKaIqYBDwMSETDK+6/n2QoqsgM9Ogiz6d3EObvv1Lu3XoK2b1wPzmPVxgwVDh76Pj2K3jFIjHNHDgT036+fBzxoqcVdMC0AnSKt03oIDe84aHOiTm0iJpj2Qx1woNffmBKowoH99Wdh2GXSpMTGU2uvHuWpVmCoo0CzbYBUOCiTsAARA+nApP3MDCVZHArxjbYHl1XwKGijYo73ELQKDZUzvDg5cpfVag6AXlRHAzPjB+4/K0Zavvni3+hIY1e0FAk5dBxMYsC1ffRev27WAxbIWcJOI++tee8HJ7p+lUbdYc5gbmQ4sV3uhYDWaoX+QffQUWgIr5+jvVqKVvw0MjMrlIaCUZbyq2FRBli+pEGAAzdY6rvFoFA7c04oN0kNB0gPAZs3adUqYE+OcGHPOuE4i0A0GTinL9VlA4HrOOM8Z13MSWHDXBAyAlDJyytq9JfIhQ0BEqqHW/uisBED7W7BVj5gJJBIiEGJAjAGBUMDgagqYJoGAqymWa0f7LAQcY4WEQ2JcRcKUAg6RMUXCVYwamla2ohrtRjCyColMrfNWAwXN1mLT02Ax2e5tcxneblVu7Q7AgujYarSAQtEi1n5vQh7Y7vf2WYUCDwx+THWmhSbCIWCW71VA6OCg/MbBAXTlvtcpEajaAw8IozQyJ5SykEZ9JFWjkc5k9tr/XaS6iCvbdbskJ3eyQILWA+u2RjDvAgRAThpNDDAk1LKXS+N4J0ttwdKnYGUBs5JuDRgsbFyLz5dag8Ao2oIIE8bAu+8/wi//wRfx9dfewisvvdqceFgmjnRG3XaY6usSn8BNKB4GdBtZs/cbKPu5pR+1fgTN3mz9W30Pgtuj3WoLDApucmrgFhQsVtO9kyGw3QFXoGDLl6CUf4eW4JmBYM0csiEQhtucLgy8KhRqeaWMuo5jDV5U5lMqk2dvEmH9z2+FMig4z4zrJEL9lBmnORcgOKWMJ+eEJ+eM63PC9VxfnxPL31kgIM1ZDkiagZQzOGWkJGYJgQNUXwczJ2xoDEjV/dZtLTxyjASKATEEOc4gBDlp0QDhEHGIhKtDxJ1DwNUUy+s7h6hwEHDMjMQBxyB/r1gE7FWMdauqLgh69XwFYNe+BgWVfupnO9u9Ll6XmgIby1XgeUBUQWYaJLvHQJg2fWIlrX22S3tgeet9DyxPAziw79w0hDLgTAtcf+Ufs5m49xfptAYkeSK21wARo9UqLDUJNocDyzm8cXjXoFsEAkJuAYFZQ5hHGzjyWw30FYPkXwBDAIGtjq1OrY7MBOYWbg8eP5AtiZ950zka1raQvG5X4u0BA6DtkDu0BoVIQ923/ujxQ3z1nS/iG5+ROAgNEFikQgA0n8ZAYCaDDgiYJYTsmj8Bm+PK4rNOhJmGoKwq6usa3W0csKUoxFb6RG86sHoyKGjE8dNCwU21BE8BBCaT9vkNrJRnBAQjgeAH2ponc4HSLjc2CZOekmgR+oj1/AP9GADH+nubJO3REtJbfAQyyzHhc2qh4MmccJoFCj4oQDDjpycHB2f5LJ1TgYF0ZqQ5ISX5K91ctQY5IacZzAl5Ptc+DgD9pAkUdZsc8RwQpgOIIkKcQCEixKD/gDhFxEjy90A4h4DrKSAeIp47ZFwfAq4OGXfmhDlFnBPjziEgHyIyB7HLThFFCFFARAZpxFI7+yKAihlh0UI+CJl3Ii6w4LRG3mHtUrtLJZS+Tf0YdoBYAljp2Rig0JlHUbRJvTbAp35MbAICRf1GBpswK+UhDH0PpCB6ncdw4LW5+noJB66OMPA7wDoc2CO91sCXVYR01GJYPqzGAkDZ3dzNt6NnWf928zlbmFxT9RkgZK6AYFood2YHcQRyLGAQOCFQLCc5imP8ssymKfCLt4ePH0ocn8+8iU95R8OBT9RWul1goOmS1kA5rsKBfmxHM7/52ft49efuOoHIjdkAAJDn+r53NOQ0BoK1yXJDcBTNAJnu1Udts8NgXGhXpynw2oIPDQp6e/seJ8M9ULDDbLAGBGtOWGuAUIs40HpsAYELX70AgV57sEhu5dGtFMG1nEQsQABUOGCdRP3uGldG02NYm4tfAcSpMAOnOWNOaKDgp9czrueEn14n/PSUioZg1sOTDAhmPVExmfbgfI2czsjpLAcy5SyvtW5y1tVRGvT1GAQEtB8LDBwEFKYJIR4Q4gHxcIUQM+IUEA8Z0ywnLWY9m4Ez45yC+EBkmUzlcXU6CxRxQtZzF6Q+YmZMsdZX0Prrd5wUIaj/9uwsWm/7Tu3ct30HCgYJMq4vAELxnq5bsvvUj43+/brfjR97qvY3572tbY1Wxj1woPdu4EDrYuGUaPB2EzhwWoO2XBp3IM8imDNgJhsyVZKqKqomYQX8gMG8nsFsY7sDBDMxgJt5h0M1VVA6gwMjhAgiEr8YDDS/3TwtUHAP9z/7uxKnoFuE7j61FrcNDMqgW/b2YtOycajXzYHn4XsP8aVvy9kKr750twKBTQbebAAUU0IxG+S6mlgFgtxNEmG59bDGfXd2Ro0BXyYMOxmOaicvoID/gKDgkulgp5bAg8AQBkblWp3wXHns2hoU9DtIyiqxEwgrq8Z1x1JbKTJA5lhqM54GdglSL8S6XSsn1RpIw1gwrkDl7CIAdWcAqwDN0F0FLL4FSR0HzymLpkD9BzwUnE8GAwlpZsznhDyfChTwPCOdr8HpLCAwn1VrINoDaaANjQHJCYlEETQdEEIAKRDkSaAjHq6Q81GrW3wYshFTuR3heiZRpxLhnDIOkYrD5QTRHEjzCgGkzIhuk7ts+YIK5NJSJTIpFTj0YcwHfaPTFq23PQCQtL33ZlfbtKm5Cah9gxh6ZBO4Io28L9uX686r1el/8IFvpaBzoRPlmo+BeaHTHgx9D7xGxZkeNncsuM8+MjigCDlKWeGASNT6Qcuv47BEo1DtAoNlfp4Hfbuf27V2l4Cguz/UnABuzQsA5JAzZ14gCtK6VKYO10KSHhkUfO4tvPrCJ4e+Ls3Okv9oTAkbDinNinzQ6R49lgOX7n/uPu4qFDTRCXvnQgBIM4ZagvncAsGww/R5D2JGIPUXcFRrr8tRsR4KyklxFQoyWrIcqaBG1ean3KfZjnhjf4KBVqCPX69PHgNBvxLqytDNcOM0MAX051YMg1ENHEqlXrhxTFqcMmhaAXMwNUclpyUA5FREsruaRslpDazarA5seh5OTd0SUXYQyA6DrKAwq78AZ0aeTRBn5KQ+BGlutAQGBSmdwfO5AoFqCzg7k0JTfOvnGYmTAAIn8HRA1Pyb8t/MDTmI0x2FjJAJeQZC0G2XiZEi6+4IyXPKLTzkXiWgya3Va33aNactMCAcQoFpD7B0TFtrewDVSQ0o7V8BUXOnEfUoNKgJdZeU7xCKYF1qHLu0Nl64vQYsAWFhXrjJzgU/vjq/hN4psWoOUMo0hANc9jkoZjYDCYMKXX03cACAc2rhAND2rtoFsKzeZb62ph9Agv3W/obYAAJNB4GMDGl3rz0ARBvNcpaJnH8yqU9ObBZJVjMPPRS8+HIzL6+mLXmJ2wQGABZObKtfrJ2uxI7+3P0SRnnhidzvOAC69wzM81JLUL47EJIhFtOAQQGmeiqcHQ1bzAceCsw+5XwKTDytnZ7ovcVXvXrphlBwSUvgy4t1KFjTEvhBsBbtz6dm9WPf3er/3PpIVE3BGhSMVonOjwSQyXG0WtYcUgiy4igOStA+hhYOOKu2IBWtAZhBpNuwuE6OsslRVpkxEGaGOPgFWU0PK8uepNsF24rb6COb1Zk2yq7fyVll2gVB1iUaEKzFQNhKQetB/qEekw6pN4OqABfZ0Oq6AL/v82Mo2HRKA1B8HUIoQfh8+8O2JcsdUFarOTk4EM2CaArkd3WlvVEJK2Omfx+673l/BJkPDNo734ObwIFdA4ZOiVK2CgtDOHBlWhS1m+eYBTjJaRpU6YZgh5ABsirPqZxJV6YOIgFeq+KcZJ6e5woHhWVW5nu7bt+fzzrfQ8AQKP5EANTEIYoK1nqS+Z7FN8HaAnDyS6FgLbm2sbrfSrcGDMax9AfXiioluwOX3moiQpkWYHXHAeDOPsh1t8EWFPiJtoMCmg5YHBtrUNCfH092TQ7rYBWiNdJdfezaft/+4JHegWUt3RgK/DMvQcGKlsADgS/bVuodkJaLxpr/csPy3spl5fSagnUoaIBgg9TtIwooO1DKJM9mNlGdYXluBRXWlba0FyEEiR0gETsVDnRdORFhJt3LT8AUGMcp4JyibOObJMDRIQbMmREiI2YWkwUzciQwB+Q8IWj5MoCgf8sqn6JoEygWzYE4XFcdhmk6EMTPgEJAiAcgBsR4AKl/gfc1oDipM6LsWIgxCMiok+IhBolVYPENYsRx0jgHFDBRkOBHJFETo6pjiQQYoH+DmhFMU7jo58M+8CztnwsgrMOBIQtUAJtGQWveJBK5G3ufBbTmtj1jxwSoh05gZKMfaQ824GBRCa32YLjFfGVhR3C+YW5OkNu2z2vfk3PmQ1n5yHbZCDYBDaiwlrmpaIgV3As4Tjr3zeclHIycMDs4oACBi6jOkADYTlFKc7tzQTUFRBbsTqGgkV8v12eWItvDqLYN0ADCWro1YDAEgrXEjAfvPZIDJT77ZlG/lAHvDzsabEMEsA8K+rwYEACgOLlVo9MKkGoGnPAvUGCQcEFLYANiNCxH6tPRd3ptwSYUrE0Avc/HyMnQQYEHAnTl8o/ZCnBSTkYsE2ZX6D71gOCvNX+5+81ypbi4n185hjoomwFahIMNXs10gQGCbKsiUU+z7venqiaNuoTMpCGPY8ABjISAiVmEO7N46zOrH8Lksill+6CrR1FlJ5nXpjsI8SB+BvFQzAohnZHnMyJfNY6HHgrqPePCATFMSyCIhyvEg+zrjoeA6RAxTRFxIsRDxHSMeO4QcXWQLYz/yTHiueOkWxhrTIOjgUMMOEzyN0LqK4LKbiOrz6otMNNYbvs92v7+4ba/fVZ9Bsr7xV/Xwf39RmnA7dsBglTM91opauRpqz3wcCCFbeFgpO93C6ZVOBhsZRzOad3th6Vj1mqjMm4SrN0JgaIAscIAA1WLRwFECcgk+hpOupoXswDKNvRZ2tlkQD/BDuCg7GL0zWiLTyuLaQ+o+nx8773v1wMDR1AwqiT7Tu/bNUi3CAw81m6rKcspjHrKFJXV/QUogLMDgcdQMEqhrpZ6LQGPTAfeTOBNB6HSY8YYCIpQ7bOwUhe9tuDDhoL6fj8UrPlIbAGPFqNoQ5hR4o3vSYvybK12PsJEPIhyaUIpJ1F56namQDJZhAAwGFHXmzEDVzGCc9Ite2a9lyShh1N5PRFwiFmc+QLhPAWkOSOeCdMUMR90++IhIM1T2a5Y/A7KzgQZA16VXjQcVr4QRGugGgP7K/9EExAn0r+hAYI41XgGP3OU+AXPHSeBgyuBhWMMuDMF3JkiDhPhzhRxFSTAkQUoi0HqzLQFZnduYo/Yam/QPh9pKnC4cl3/ijDdd8ubjKFa5goIvY0e5RsDOLAVatGb25cHcFDKdTM4ANa1Bn25fQpafmInZLVc4vlPoDDJZxTAlEBZQEBW7KpuylWbwBnA5KIoqqmsZmLg+aPf8XCAyWkVdKxLHTs4gIzpBz9+F/fe+Yo7MHALCPoZcF//vUVgUCtg68TBBgpeesVtM+qgoBeIcCGNoQNz7SF+t4GtFJwvQdESlH3rnZbAA4GZFkJcmA0sHG4PA30/8ar1SwvoDwUKXCo7LMoD9kFBP5n5J426ti/b1hntuxPR+vKkrIh0tZcgq8wQ6lzIubZ98/u2Loq2oE+c0ZgTiDqtgcT/h823ja1EKvDOIYJSQkTEE4iAngLhySwhhQ8x4MlZBO31OeHOYcaTc8T1LMGPTnPUAEdRYhh0AY5yOiAnO5hJnRctwhIgYFKKah7eVO3+pAcYRQWBLsBRnGIJcHScJIjR1RQaILDgRncOAccgUHCIAVeRChwYFMQgUeUmDcVctAekvbDTFizMCH0TETXtX9rXXvftb86nfk4o7U8tPC/6zTP2ZyyhYG0c2XdV2b+w0e+CA0DGOgaOycuM3QgOtu62NRcWUwkgofC1PLYjJVtfUM1WaQ+OIEqiBeu1B6RRHHNqtQeU6xxwwe8GaEFPfAwm1x/FpAckPHzvj/H6d35ZDgx86RV91nKe2TLF7Em3Bgx4sPWvF1ybUMDmbCT/mtDGA+G4UCEC2xOBAwIuZgODgoEvgTMbbAHBaDWwTLSwu7uPFpENre4Wuw+wEwq8CeGCT8EWFPiJrB9aThMJYD/4lEL392vUnvY10/WFZVv735lwQC3u6OtNACvtD8v7LQe0+BZo6WzHAon601Q+chBQ/U0kiWdAFDERS6jgzDjPCYcYcE4Zd6aM0yHilLLCwITr2aIf5iYS4lmDJllI5JwZeRaPauFlLiGQ2cFBWxABAkA0FUSEMIlwCZOck2AhkQ1cfKTDQxSIuXKQcIwBx4lwDBUIDqolKD4IJPHng/6NgcohaHJgjTRYO+Z5qB0YRcOz9q9NF7C2K8PavvxuVeD7sbM0k45/d7nnj6DAjyV/PfgPUVXwu+DA+RwUOFjTGthzPByUZy8dLO3Ra46IHgqWfgf613wcSCDBzAw2EcqJnmq2NbNAdr4HXntgjZ9TAQV4bXJcnz8kM1m1BpPLeEZRZeiY/95P/hRf+Oe/im/+4tdw9wX1iTMwuaAlr/3Gzzn/UZgSRhVTG8NOmTIoKKkT+MuVgmoLxjP9cqWI9RXBjYBAnREzlkCQsBScpcTcD1otJreLDurU7AsTQp843xwK/LVLUKCP7M0ia1oQdNdH4FPMCVu0UHR5qFBAQb2BvZ1Q7iy3crZe1RxgmrR+IjglUBwPVA8DZaVo+RiZwxq7s60eNK5BQIUDBpBVRU6ylz+QBvUhwhQjDokxT4Q7Gip5ZsZZoyHOnHGaJz1HITcHKc1JgiLZIUrznOVUxsTlACU5uKmetDg6YdHOS7BDk6JqD6IeihSJypkIgQh3DuJQ6A9QOuohSsfJDlFS/wEiXJXXFQhE+OsJjUH9CogQwyCiqS0OvHhsNAGunzShgVnbVbeh7mh/MSMCvaagaBDLWPHXB7bhC/4FfuFgaW1MLb5nq+vyITWmhYZzt+AAPXjvgIPRFkwThJ2/QdC8+ts2r32ZdH60uZOc1oAwBoSg5gWECcgzkG3+tp1pohVgW0Bko0QW6FjZsbIAxWYBZveTaw/e/wG+8N1fxzc//dt49cVPiuawi4BZfzyY7Eb9ZIMjbw0Y1PAeLmll2IES9z/7poQ5HtoOnbagdyYaCEpZJdRlgp8I2sGvQrEHAlrZgqjvDQhs8SXH33ZC04rRD2gVhOYwhE613uxIoK5/rJgQbmw+ANzktg4FfjdFbzroJ7C9x66OypzZn7aGMRBIjQDNISvB5UThwCYAAojrjFS2Xk0mqQd142vbTfTteRgrgGDb1XKqZhOqEdJCRDnjPRKQmJADl6OY5wDkHJEm4H/GEjpZAEHDKJuw179y4iIwc0bOchqjnbgogZK4HMEMQOMIWH2vg0EMEOdDmJCW0xNNgB+inJYYAlTIowj7SFSOW56i7La4igGTHsJkxypPgRBRQakAAWF5VLqZEft4FX0/sbFsL0dtD4i9eJicGnyr7T0UNL451h/h3nf3g9V/++TeHNcK0fG4sjFVgFvhoKjkdVFr42rdIbFC1I3MCqjCr2gNDMrl0U1sg/4I+cbMahNOXzcFfrzvAStAKAiR7Lwxnx7vf1DPs0gAt4Ag4zQ3PgjYOoCpF+iqNXjwkz/D5//lb+Bbv/CPcff5T8jcBox9TFb7RPtlHlzz6RaBQZ2Kqfy1OAVf1DgFd8er4ZJ6IFjvxKZC7q9JXuqAriGNnw0IcnYwwCibmqzsvtwBVITt2nkB/k2jLQCGfgXy8YVB3XfKHZqCLdPBZRMJbuRPsFAckJ62pisR1gkI/mAVNzkJQJgwcCs5QOsp7nZOW5ywKC8agTG2K6uPgcFBsWVGRBJ1fIRAwYEdHIB0ZS8VkcDIMeA5DmBG0UIld0wzs5gjEmtAIf09a/8zDYH0UYODLre87IO2UApwzn+BxPlLBc2kPghRV/pE9RjlqO+jVlM0DQSoCCo7XMZgwK7LSy4TdwFhg4KR5b3TGimSVUAE8FRt79vd3vt27+cQ2PvaT5oYIHb/XbnQ766MrXpdxTt5OJDrpj3wAuoSHMiXNrQG9oxC+R0c6FbJRmtAKIUejhgPBahzDADYuVSZdd4kro6NwcrXAUIUbYE5pRMlcKivF4DAWeqTc4HG1ciYXXrwkz/G5//oN/Gtv/FbePWFj9sog3cmXqS+X7nyNnC4fofbBQZ9qhGhFArApdO2yDxQHfrkJwBX6YvGXAxkHSQ7dhp4IDAIKKYDBwOlgQcFtkVuH5vMUhi8brrOCgy1Rwxb6hT33oSwEwpyBwXs7jyCgj4HK2JzcydCD1HyxrQCDg6gjp9sg9smKoOl4DNZVjGLiHdbaWA7bl73k749lyocAAqpFilP6zwQASFKDBWt98wCjCMfFdnOCO1nsipmhkKBFluzYXZde58yN+2UNqYcf9oDqbYA0Pagtl+ajIyk8Qdgk7eaI/R6KN+vELDYZcMJyJc0YQMY9lojE2hm2mle585+u5FG7T5o87qo0Dx4KHBwYMkvjtbSWu9cG1uru3ycaQEd/G3Bwbq/QTdyy6q4X0VrO+l84pEtDV63ZefSr0flZjnKsEBCYlZnRIHKDCCrr8pFQAi5mhg4w+ZXi4PCqy1R04M//zN8/o/+Dr71134Lrz7/cc1kbvocfJ9r+oRf6o3BYCvdGjDIqtay9O57Eub4LQ1zLN3YoeWe5FTMTEFXj/aZW9nBDWIA9WwDWvgMXAICW7nZRJ25dmabfEddyhkyAIMDpqH0tLG2cDrcMiE0aSx+FzsQmroaOxr6ctmdeyi4yQpoCwq6TG1PXgCEyrkCAoChZmBIaRcG/pZtuF8BLmytDPWxlq+zE1z+96Tl6laYB4I2fjVbGDzI69ZMNTLp9FM6uu9sJV+8Xtnpz5svn3f9tXy3PrWBWvKBhkqm5drSJJa7964sZNokamFgLT1Nm7vKWB6vXoFAPt+G7j3JEPhS8gC9BgfsNGe9drLAQR/PgtZ2KlQ4b76+ZlJwfcHMCauKCG6hYHUe5fr0rPc3jVc2EM0sYbsvAQInwEIpOxNVie9hkADUTLvx+70//zN8/n/4u/jWX/uHePWFjw8KtZXGB855E8qjHz3cnFhvDxgApWEfPX6IN/RApFcKFKDCgRcIqkZu3Mq5AwBTNbvPis+AfKl+16ITkotgSLQLCIq5IKuqVjvxcmJ22dNHX9qzX/vc8vzuMrECG6uppqYXN990NkQFAg8FjUDBs0NBefyNf0AtHFCsqj+SbYI6QxYBMST+PVLxaZNvn3LNXnRe8hu3EVBY6SlDdea2MGuyuNOkA8a6rXkVRPd8D5eF802S9eGPIl2qaw8DmhcPfCMTwsVH4ubjqcydAziwp2dgZbcClmPLxjVZHIE+Rw4OVvwN/Jxsyz1QVftLXreHYzvXuPJaFatKLADFjBAg07vl5CIg5CRaJHNSZIaZGQAqZhF5cO233/u3/6ZCwfOfaDM+MEGOtLJAnW+B1pzy4PFDfPnb9/AiXlqtn1sDBta4D96TQr/5mft4+cW7jY1zOIwaqnVKqKJe1uvacA/+/M/089gNVqrmAvUpWIYwXgcCb/vtVbjAxoB2H4jNz9ncHQzY26G2wDscrlWsPMHVk9WfnwkumxDWgjLdFArKyrFbYcJd35o3G0hUAGiOliWUWYIX9WA3eXYhtHn4VCMQvcYCy9XG4n0/4fQr5xWNx6JMy3Lf5PjWm6Zd2/BWTDCbtnv/XRpfH6/YLa30+dU83zBd0CY0QsB/1gmD4a1VcrbnCJjcVUGK9fG2BQfepNDDwc39DWzOXcJBUx7Wk3JRDxXzWgPzGyjOiK5wttBam1tLVvRnGQBRPaOEspjDEnOpg0uAID5LEeWcjZCrFsHVoSUxHygUmOa56R9LOBi1WaMl0LZ5pFDw9dfu47/51j8Y/ha4VWDAePe9h/jKO1/E11+7j0++dLf06OUWvqWay2+nqWYDBwcU8eDH4ggCAIhadd6vIEQAoWw7rLsRtoHAzAU5t53WBOZwxJqAB8bA035NvtdpC4rYWzMh7NAUWN09KxSUdtghdNagYFQXo2HTDkM/6FQbVMLOfkSrRXsyOxfSgaBv/Bood+2B0la1wyxtmXbNTv6UW6vdM7sJKusBSE4NX7ZWuUmsBC3yALHiab3r4C7vwKv17YMhWbCwctAYAIucCA0AtjiQDKjnj6D2zYXvD5nQGay+19T45TM0/eMj0yws8mSvO/jBeJroTQfkrt0EDsoz3MKjiO8VOGjg+0b+Bs3dUfwNhiYFcUTstQaB2RnbNsrj62dlng0M3bEggJAhZ5Nk1vJeAATZWiw+B5TlIPSyACljNJQ6+NZf/2/x6vMf0+f3/df1xyaf7Vw7goKHP3qIr7xzD19/7S28/OIrKwWWdGvA4NF7D/HVd76Ir7/2lpgPrMFaKdAm8yFY/NV97NYRCXjw4x/qlpH/Dn/19/7XnXaA4I9CfhogMG9vDwPWbMkZh4qzll7K1Pqn2qAVj2+UlXPRFpC31VqnxBIKmjRYuWpqoABYhQJ7XA8F5aMdi1DfjFtQsNAWrLQ/l49tondx6oe/WV7cWmVd/nX37UZzE0V4W5tkoB795iYUFeqAi8zZHwGeZnASWyfPswj9+Qwwi6CfzyLE57Pc04MDy3kgnBl2nDInORIWADhZR6x9ZOuExSbIUzk3xNoygCLBjmemoDt/1ERH3lQ3HaQPTAcBCSKFgQCaJhl/cQLiBAtBXnwFzMzHBh51nHtnPw5+fI/UtrVV+/a+SfuPr6/1LNr1rfLVsnqWCwYAW3Bw8b6w37jvdnAA6sbXwnyrdbzwN/Ao0/ocbJkUSOe7rMUuzAcSV1rWE0g7CPELr7X51gOGgQ+xOCZywD5AYA1nrtoCsgPHypxbx8yrL36yrewGauHAtPs7SkMouHuxfW8NGHz1nS/id157C5988e6Sah3FWirbXYqDFmsnDWUCZvkiHr73fXz+u7+Ob/7Nf4K7L4jNh+MBtqJoQhfr5HNTIEhAgQHrmKPVs+0T98fHWmqmKgcDXltQziMHWkG06lewAgW9XwHgOu4GvXZpSxk/6u5tPIauzGihILgP+i1znheXWVtfhfn3q3EkBvm21GBVyRtpW0VlUxP6OonKywoHHmJZarg57XM+I6dZBH2awbOCQZrBevhR+SzN8v1ZNAZ5TuCzvE5zAhIjzzNyOQeBkc/1eGUPDPb+UrLohx4A7H04RHkfAkIICNMEREKc5ERGOkQEfR2mCJrkFEbESSBhmkDxIEAQJ4GEOIHTrO8PZcyXsLOmrgWWUFAWAEvY7VdlN27/pg/0l6j/ymbqa73s73er+BEcACjBgWpMEN71TKAX3e46b2hpOz+upb/BAA4G/ga9SaGMd6c1yKSLKP3LaAzGNQ8Yz7uzgq/VjZ93mZ8OEEqgJI5lmyx7/wvTknFuzVvWN8tizPvArEeRffS4QsEnd0ABcIvA4GtKQpbMY9bLroW/gfkRLLQGOgtTxIP3vo8vfPfXJOLUC5+EDQOBAAODdSDg8r4GmtkCAolsWJuu+Nhoim4AA04wunnNawsEBuo2LrKy9yYEn4ZQ4FKv0uxNCMBqJx2ZEHrfgvEjWyCwslP3uYeFNSgoxVx51pbwX8tz85uV+1pagxmb2GR7nkKCxWd3J77JDOd9BGxSUS1BmoHzSV6fTxUITtfyWj9Lp3MBgfl0Bs8J6TyD54R8TshzQp5nAQVmpLNoDtLZIAFyVoLCAYBinRtt2yQzFZilQKEgRJI+HgLiQa7FgwRtEhCYEKYo0DBFxMMEmiKm46GAQjweRCtwOILjBDpeAfEAThPocGzPNjkcq8aFelNGNQU2u4m0jfuAXHvOHxi1fd/+TRz/7gujSKZ7Ug8HADSaoS31uTwvu0seEEbJ+xoQXTYpNHmnzhlx1d/ApxYOakbWTQolBBJLiGXTGjBxuQ3z0tSyOv9aOyeNr6GahFJnDhDABIY8N6p5uAEEEnBpMuIXZcG2PncaAm/y8n9Nm4XlIqz6FLSaAjU6rqZbAwYvv3i3dFC/GgSq1sBfVGyQ11R3J/itag/ee1diU3/6a+Voy7JKHoQuFi1ABYEREGT7jmaCIVHnrDPaGEnaSSLRMoSCpQYGIIfT6HXTFthfETgOCizi26pfQT/FtZ30WfwK1lIfuay3S/cTJ7nvXNISXJpQ12DAg8ClAEzc/34leTi1UKx1VeeDqUABgWuf1slXVr3dbgRmWdnPZzEVmJbg9ERh4AQ+XSOdzkjXoklI1zPm00mg4Fpggc8JaU5I1wIH6cwKCWJGSOcMnuW8BM4spoXMeqCSFj4NKqGYDAwGSE0HerDSpGAQ9eyEKSIeSP5eRdEaHAQC4tUB8xQxHY+IVxPS9Yx4dUY8ngUKcgYOCXS8AwQ1DapKNvDyZMLaf6mO7e6cEgs61jvMLiLs7Wx7qwuw2K4Dy3XrByWLbu7a06fLitZ93/qxrWibbzI32gO91Iy/Xf4i5Xs3d0Zc+hv0IrvCgcBA52+QAYQIgiyCTP1vJgWvNQhKTE2ssq7dPNea1iASYQaDAmNO4oQ4xdAAgpRESicaA12YZT0DwtadHSBQnmsde5NmrXS50pu1tGYX3Y7r7oOvv3YfL7/4ym54BW4RGKylHjIXybQGZReCNO2D997F69/5Fbz96a/h7osvL2yLHA9lJWHCXv4uzQaZobHkq7Bk1ChzPQyU/kHQIBttAUydZStmmc/0GlnYV9LwsFSFDLQL3RgKujrdAQXAEgrK7zeesAcG/Pe2NAR7VlcLIHCagTUYGIWk7iOqbflMkMufgYz5ffhoayB9DwIF6WsyiUBnPN93HSQwy4p+PoumIM0LKJifXCMrCBgQ5NOM+XpGOmekaw8GGemUkM/ZQUHGnCsAz1oZCdwIx17VHKgGOfJnF0wBCCEUOAiHgHiM8trA4EreT1cz0umMeDyIdmM+IFwdyjOKDiAEIJ0BksBPi0Zp1LQBFmfEQ0EBeRvLqJpAH4V0b7snrv3SIu1ZvRQgIMjuIgNPquN9Ee4clwHB7g/41b18Ies3CdKvywY6qmO0h4RRsvsWKFEAMTiI5BZjl+Dg0lM24EAcV6mGPze1iVOfZNV4EKSObeaPgQoEWPJz8qwAh1QhISGVaJx2yKaVhgkIWdtKx7LAg2hy7awTcoAAABzryYqL5OdaJ4/65HcfiM/dBWLt0q0Cg154LFJHsP67xdcAGQ/e/yFe/8Nfxtu/9Dv1wKVeVa5QsNds4KP8eZMB57o1cZFdBzUUZEItKwknSCJRiQT3bFCwJq6rtqAPYrQZ7nhRno2jql3yc9BFHwL9cAQDW1NZn48+tsLWytDiTNh97LOlmWG9bLUMMkmJ74eoIDO6SYSAiQQOwOIYSBTBIdeT3IaFZPkszYD6D+Q5iVZA/xoUzB+cBAZOSeDgOmHW91lBYZ714KXMODNjZgcGBr9a5jQovAGu9Ml6yFHQ1wfKOJwJ0xM5TClexQII0xyR5wi+msCJEVPbVykGpNO5OC/GKavGZBL721oK1VG4OA8rFMxubD9tnJH1dkcJokNAibYXgovZb9DrVuHAWE0P7ANhE9AweekAwcwD3sQAeA3DNvgAqPDagYT/2dAZEUCjsR1qDVDfc3gmOChkpGeJGKMRESIDKfBio5B/bZAQM2EOuTExEOn0StvmBRvbXjMoRZt0jqb2oU4Y+F0wfs7NLMGL3vj2PXxDt+zXelct0qBW+3RrwGDMTXtS20kfPn6E19/5Ct5+7RsFCkYRxtZWEpe0BGY26IHAT+224iGy4z8rFEy6tLTDYp4aCnK6kZZgoXbxgLDmlOVgaNuEsLx2EQicdoC67/avfRrlolf/Wn79KZZWpj4SZS1fvXdee5Bd1xFqA3S0T9pPIgiEGQIHCLE6i7qVLmh2wXC6v0BZfYjaP4svQarOhnmW1z0UzE8S0mnGnIHrlAsQnHP9m7QezlqRBgt9MggAgIOeZRCJcAgSQn4OwJkZByJcAZhyRkzLKUqKTJJvdUJM5xk0xbpLoglS1tYHm8mgxCLR8a3xRjwUpLw0CTIvQ0WXp11o92Lrd+0eoEKZ5BmBxCbuASEqHPgm7R38SnEHjzeh4F9HXclmrlqETBuAAAzH6rC4GyYFyYPOH+W86txKYLeCHqcscMAMkKrpA54aDpCBRKLFmjMKHMRMpc2Bbp5mBQSWeqTA4CTmBQ+LY+0BiRnOaQaL8ShEMBOK83FX51sRLy2435ufkS37a1W4/GWbbg0YAJ1g6GzPl38c8ODx93DvnS/j/mfexN2X1CbTrYatwWftxyNfAps4vC/BvKElWIMCzdYzQUFxS3GnyK1qCfqBCcAj16oJYScUXEKQNcesHggaG333fTRP6hP56aD9Vpdf7l7bKlFer8ebYGzvKrEyAa2Hc2S3wnCTiJ/QBA5i6STEXLQGzHLsM5kH/iR/EZ3wcykEP3lpfm2FJASk2xKlH5+zwG4PBWeW0xXPJkDRgpY3hc0MBFI/cmYcggYZN6mh5YyQeweQHlur+cldPl392mmNtZK1zFE0ADSJeYCiHXE+tdqCOIHjAUxxAQVz5gL7dnYJ8/6dRMM2V3lUhArEz0T2x8vFUJzeSBaPnbD1nX45Dpb5oO5/uwVR1SLYIN4CBGuyPtnavYyvXXCwdEZsTQpufbuYnzrtQU4AsdvFGPUZdqungwOkyikRdb5O0KDpLPN+zKTfz8187Z3cZMdRBX97fs61bhjifAxiBYSuLVdMCA8fP8Qbvy9Q8MpLdwvIieb5ZttRbw0YeEFSrm1Aga8YOYXxAe79/pfq0cxr20C0Ly7ONXCmg6JN0Ac9DRSMNAXeDr0bCnSvbN3jnmss+Z7SVyt3eXiLp9YtKBjVd5+eFggWk+ClSITmFY/tSHEm6BtNQQcFRROkPzDh4NvZklerR50MZLEkJVg4MJVZnzF3QjOTeDZzYCCqFdxiD4QsAjBNAoJxEs/8aQalWTz7J3Hi4zmBom4XnCLonFSW6vZBcwwkEVCRgRnYpZkr824pb71+8belnnQVRZIPBHVWDEHzSZLvEKQcMSBq+cKkADAdQNFtZ4wTYDEOKFazQZxK+HIDmxEU9LDPEA0ggKbNl+2Nps1N5ZyBBhDMiSAEElMVy1gGMzLRxVUe6XPkz+WxYIjgeECedwEQLKvAOuR7bmG9zxAOtvwN7MfDB3hIqNqDemxILgu7QPFGcMAdHEAseGXRtwcOIsvicQqE5OAglHaudJch37eKUcsGyAGClLmaXPRFabcHj+VsoDc/ex+vOPOBVkHjJ7JnO+rtAYMNDcGlyawezfyWnsII+GHGqNuUTFPZ2xxvAgU+tcGJ9JqDginKgNG5ujli9iZnzS+0BI3U7jvcSs0NoMB3zhEUbB24swYE9qhtIHCDxZVlFNa52uNqOX0UCC/IyupZ31uZbF4smgLU1yOfEX8PD392f2JghqymGwcmQrFPTmEAB5CKiWECVFMg96v1EY5XyKW99Vx47QsxS3CikMVOz0liEEzHSVfkMzjHUng+MJgjjlaKJA0XmUXlnYHMhEDcaM9A7cYEU+n6/npQuD0EwoHEVHIgwlUkHCMhHMTZME4BkzofTlcB8WrCdJx0m+KEeDwgHMUBMV4dZFfC4SjbFA9HeT8dEI5XqimQOAccD8XREGFCgkzmIyiYVWPi29wch3e1N8kHXuVcAEH7uglQ86C3sRRt0Ug6qTtfjcWY6Azjq+OhnA1DBRLIjeURIJRRugEI2V1j/W4TRrmDg1VnxGaaHFjF/ZzFDFvWm2NuiWQbIpgYIcj210xABoEYyEHqWvxIpCoyqhlsCgQGASk3ZgVg3W0l8SU4QLXdaIWQVbiZ4pQMoq8jK2tJdf56aFDwOfUp4GWttXKRFvfo0y0Cgx2rmUE9LI9mlsTunwkHO64W2IYC1htY39k6htbyDnw4/gQjLUFz1rwTIIvUB03QzA3Pf1coyF0dPSsUeCAALk9+9cyA9RVSXbNp3ks5dyxhgepoyNWubJqCNUfSogHoHBhmyGoXZeXBjQOTt0/OeQkHlGUtRQEI8VBNIlE9tbUewpWuPHJqJoiAdtDbansOAYimLQigMIvGYJIdAuk6IMwZ03VqHRBV9X1mqqtqV+R+V405HgYCDtqXxfGQcAiEiUggQJ0P4zFiuiMAMD03YbqKmJ47Ih4PmJ47YnruCsFe37lCuHMHdLwDunoO4eo50OEIHI4IV88pCHRQEA/geChjN29BAVctwbO2twmOpIKjAAEAhgjTtePTx2n/uKBGqAqE2iRECs0eELywl18uQyj7PnYTOJDvdnDQbx3vtQZ9DI+SAmyjogSq022+qj2QY7mjxiUAiGU8JdISs4wr0n5MusA7xADKDA5a5lzn9C2FawJj0ro0k1EewEEI0u+MlhJXrUQ7/7V9gSHy64sKBXdfutsqQbAiE2ld02Pp1oDBHjVln6xS77ujmS2tQUFynxe782glCSzOqQdkUkzMDQwAaIDAPFsDPgQtQc4oQKCDaHtVDZ0kQgsF/d8PUVOwpiUYAoFfCXXagtXtPayrCTg4KPfVMaSDqY+lUG7tH9P9tUevCYmFlshUziowmGUCMftkgQO230rmMgMzvOaAWjiwbyap4XAFZAqgEHV7VEQOEZECwkFW3OkDCRpEU0S8lmvT8Yz5KmK+njDdSbpbYW63Lc5ZgiBlccBqnG7RwoGlQNLHPdxOaqoIB4EBv00xHqeyTXG6iqIluDpgunOUv89dYToeEJ87YnruDmg6Ag4I6Oo54HhHNAXxAI5XFQriwUEBIWUBgLmUgxvQsTHtoWDOfLmtSZzMSLezRa5wYAPBz+WlT1ZZ3aTFAWnuN/bDxdjYGhcQwVk0gTZWHCCYD4Jqwxfag2eFAyl3hYN+67jAQUB/XshiHrNIgSaJQaDE4BCeWntAms8pElhBwgOCaMZqnvqt5cyMlFFMR71mKMt/ZRdS+Q00DgLWZZstau0U4bZ9x7+xdGlZdOvBwE9Q/juPFAreUiiw5Cf9VuBVhzS7bz3juz0JsVcQRGicbnWKMc2AfQZgCATma7BXS0AKA3XHwSAW9whxidrwm4PPqwmhQkFe1BGqKt7qv7vVs0JBv9USwObe57qZTCabAgdlAqocbusNy09jxnTvb8Kgo217too2gVGu6UqSMhcVM9lsDFINwggOjlXTQ6Qe7hoEiUKxr9P5WgTj4Qhcf4DDdEQ8foB4OmO6c4X5yTUm3b44udgG6Sy7FHjOCgcz8jlLQCMNdJTPuQQ5AjCEq6ICt6BGh1ACG4VICgWTBDky88EhIqi5IF4dxGxwdcB05wrxSs0IV8+J6eDqOdDhCuHqDnC4kuBG0wE5HoB4bDQFPF0BYUIGFlBgGgLzKfDmg5FJcA0KfFvvSf5b/U/8+2phLjm4+fhQKGCT+sQVENz4WJjcUOGAFaS34MCuGRxYzj0cqHWswAGT2cB7k4Krj5X5jNg8BXU+I5bFEcnZI7blFyHeSHtQApB1gODbobfIxn6m0CFbAABi8jB/EvN8srrKRKsRJD0UmPxi9701uddW1sp13CIwWCvjqFJuDgVOE1BbtYDCmlomBlmJSOAQzUizMKfWS1nLYWYDshXWQEsQg92KBQRKrPwBEKytHNZAYKEtGO9AeNrdB1rcVSioNLs+4W2f6VDLUU7MrAa+Bg62jm2163YAi+XVA4IFRaGAYoOUVaEkEwojoWHJryQnVdOairmqH2VWsdcGBxkSHCiGCXwgDegTgTBLUKRwAOJZnBLPR4TDFXC+Bl89J+GSrz9AOF9jOp9wOJ2Qrs8l1kE+nZGuTyU8so+KaFEQ5YwFNKGR3aGlMhm7fiY7BNtQyOIsKNEOfXRDC4Mcr44Ix0O5Fq8OCMejmgmuqoZA35v5gOOx8SdAmMruA1AsPgXZawoyFluNvcnI2tJrhtbSIjAZKuwX4RFoFQhsn3v5zH3u4XlzjOwcH+Ck490BgpoYvPbA1NBbcNCnslLW/I52K1RFCKHZqWBn2ACiNVir9ca8AIEaTgA5QLB82kIoiPYgqPYgbWgPmKt5gUkAQXYXQCCBeai3tzl+rV68P4mFLeg1Cx4OCOITZ5purynQ20jLdv3mpunWgIFvk/UpuJoP1qCgT43A27qxTypcGNBOQWU7Uzkd0X231xBsAcHCbHAJCHp1e3muUHXrO1DV7N6EUP0KtmPGXwKC5UpnJxT4HRUNEGxoQQBdbgOrwVCgEwYcnDHKASxsZgVtTyrX68TYB0XxcIBQgxbHQaWYtsAEBqDzm1Mx+0mCwbIzIKNoDsSZkJCD7FYIU5AAKekMhAmIMyhNCghH0FH6TD5dg87XoDs/Azs/IZyeINo5C2lGPp3kACWNdZBKzINZzlDI9UClPYcpjQ5P8gcnyZkIk4CBAoLsMpgEBOIkmoE4iR/BQbQABRDUuZBDXAJBiPo6iumARVPgHSZNU1A0gKgmBEtlVUgoAEhBtD89CPj2Lb/p/IiaRUH5W69D3wf3Fx4KxBZ1OWjZcIzY6hqACeIOEDhM8NoDe34PB5ZdRqs1sOS1BzU/FQ4SnLMdmTdGEEhBFzIZGuK696No7H3VuZKIh4CAnGDhr0PQPgggZ12ps2xfDB4QqNUgZED9gOpOp36uL015QUJXB2ioZkFfuN/18qtv1dEjaMfrPt0aMPDDt3fSsNT7FKzdxa+ELWX3ubxfpwT1KdGBJtcm71qPKgBlstBBT7pK2AsEbI6Fyy2JaARol2xgDFWcFQ56v4JngYJlFugiFBTTyGiyG2hBbKIoJhFdOZTtTGjhoG54Fn1OrzUgsulScJ7Nbqn6BujEaN7LCwclZkzWBBtOMN7fZKHEcQAiW6Nk/TRndsFSpAvIQS2EGCaEMIE5gdIZHA+iTs1nICeJb3B4DpRn6TN2+uJZD1nS93HlNEZwRj7P6I9hlngDtT388cv9ccuyFbI9Zjkc5GCyS6clIk6iGdDthyL4J2SSo5YNDMphSKohyMDmiad9nIJsfci3lWmGmAscSJnG7du3beNHBCwWBnVcVBMiGQhQfVR5mheOa+Nkzxgppwu044TyLGM/YBMOgLFvTp+89mAbDjp/A9YKWNMarM13zDAVIPnFTnCAwAzkGQgTYohipsU6INg8UfoKKkQCbq4vlS5/1vSzUi+to2lGBUqr5wePH+JLv98uavtex4NrtPjG6HWbbg8Y5NQKOrdnHbAtidtQsJas+tY6voGA/3LpBD5L9pfq6sAOPvpQgaCbDJbdJWAIBQttgQWACfCHyYygoIDTCof4OhhBQbsCGkCBd6IE0PhNdA8mhydcyruEg3Js64bWwIRy0EZOxCUyIYDqbYwlIEyoJogd28oBVMHRJ9b/PBxYsJTk8hkJSGSnacrKWzQOqW5b1L5TX2eZ8TgpLOjxzSmV45qRJbQy2/HLdo1ZAstYIV145tWDdyzoEiA2XyKU4EMKCYi6jdCAIOrxx+XwsliORmaNR2BnHVgEQw8DyCgAUByJ2eqUS182KBiNdK8ZmkDF+WxPm5bfD0yHfnHgtYX22T5twToUjMZJscVruwkoVEAgVhgwDVtGAwcFpNHCQW9SGM0HJbRz+cIIDgQc63Hj3GoNRg20Nu/Zd81pgKiaGANrXcTdgGAmBvMftR1pgUtxxv3H/rr5f5TK6ZXuNxbRsNd0rz2jfb1cRK12ck23BgxkcDgSNuFAwcUpWG5JvJTWYUCdRrQTFyFii/E+f15T4CYAuElhz06DAgTN7gMPA4NOoPWgL5ZlLE6FVTtQz6EvuSjC6SZQUOtrkBwUFH+JDgoWWy1HjpQLqRtKpYvw12seDnRZTwBYNpeD0J7MFnSiQyDkzAUOctknXe3NDSA4++MEVS1Gy/K4krzgXJibNHk4sEh5rPVnu59mAqKqS6WPWX+LYk+NnWZmYxdLA5w5a33J+7DwXTGB05aPUwLF2F4rZaXaL4vJKtTrJviJkO0kU4QCBKWPavhb64tla2FWYY/aXyvYXg5tjZrL0iYpA5PVXrhhW/o5AFVAmPnQLxAIKILJtAZlrrC2KybDXOv+JmOF4RZQuQUEylUgB/GP93AQFL8NDvpKG5kTymNVtherWQcHZmtvnBHthsSSCduKyAPtwdocCGtcW/iQzC8hgimBOFZAUDPrFiAw69bCQA1c7lkkeTlQ5pphXTHefe8RvvxtCV60BgUrv27roUBhr/9eplsDBmVw2FsVDg8ef08iGn7uLT37oHJY/S5MYbyZ1raykY56iYC1pAIfxW9r8D8VEOQE3wFGWxGtjENnw8ZsYA6H1Ey4Nu17D+3+gCFY2V2ZF49ydeExxU90rebDhE830Q0nOa7lKNfDGA4slJlNXxouzTsiAjZxWaAUPX/eZi4GMtUtWDYhWBQ0qwkGEKPXYawsJ/qKcmlhXdDfGyCAba0n+UlkYlInHOKqlSFbvVo/iyCaxF9Rn70rdsSGwNncJTLQ6tU+2B5U1kMpA0uH1yy7BGzbsGSJu++ylaK0FbAE2j7bXhNouW7VxIPp2F3qUdxAwD5bCAbqNQduvHTtMhwrVqoRFOwZK4Atd+BBejRWFjsWSl2JxBz1gHbulN+WXZsODpBlHrTxhxBFTlMuJ02WcbSqPRjMhY0ZxUECm1khV0DIqoW6AAimMQigRotQqn0oL+oYHCU/d7773kN85Z0vljDHbe2NemD3PDdf7on5YunfKxgQ0T8D8EsA/r/M/L/Ua/9zAP89gP8UwP8HwN9i5v/fxZuVBqiA8L33vo97335Dwhy/+HJp5Fp5FRDWprIeBsi9CIq2GVz6ZxOSWf/aoAfGA383EDRqXxXXW8Kyn3gXhQuNtqCcLud2IIygILuJda3z+whtfRqaEBrPai1XF4OB+klwJIAWk97AbMLu2FaKC5OC5c9SAiGyOB1m1RQw62Sgj8vaniZ8rA6snmJ9dHPvLS3LVuqFWW11LsUfv29Bo5+kmj7rLoRyg1JDbV/fMqBeKANcH/LVIf2sUtba0da+/zWK5H6O3FnXvrfErQ/t0mIhUF8XOHD13M8LHtoqsNW5wb7TaHpsrABY7kBwaWu8NO8rIPRwUDQF3vzW7+hBNQWMUj9H1Pc+8qOOqREcFE0mxATWBDBiYKg5cGX04EoMc7xkCihbf3MWM1ZO4BB3AUJiqRsLRmRahFKr2th9y4zGWe+a8O57D/HVd76Ib3xmEKcAfVd0TxiajgYwv5H+fWsMvg7gHwH4XXft7wP4H5n5/0BEf1/f/72Ld+rI98F7D3FPT0m8++IrKPor+94AENYSoSNiOBUaTOU8+BHWB33RFPRA0PsQjICA01JIjtCmgFCfN3L/QvfaHSyDMRSMtARrNdlPdNTXi/t19azudh9cggIPAzdNqpEgoDEp/P/J+7tf2ZIrTwz7rYi9M89lw/CLAXlmqjj6H2zZPU2yqj2GPEJ3F7sBA3Y3i+wPcDSSIcCGIQG29GC9+sGCHgwbskZUk0MW2dKD0U1OU5ZsaPreKrZGgKy/QayiRuN5n657Tu6I5Yf1EStix87Me0lhBkcBnJOZO3fuveNz/eK3vpJejnWRqsoMyKgx/2N4trzIErhBPrfnGYXa+Ojyfi/gZtWcLefduhA+l9q+uGYcNovOufPDvlGOgOCsvGl++Dd9vhmdDwzj08bi/lBvFzM7L4KiERjAzqfdwWiDvGdv9sfc1gBhrkQVws+LLN3OIK6PIzgAiDUhOGugMBIY3NkbsF1OJKStmTs8EprELO+LPcoEHIjwz529QfPz04Nej1lda/+FgQgmuXP0vmLZGNnGbAcQjmwQIMwBEynI2W8eDtEB0BmUEgF/rqDg2x/MQcG+Na2e9n7CEgSQ9PKzT66OnX+iwICZXxHRPzsc/k0Av6rvvwPgz3AHMCAXUBUvf/bn+Nrf/QN8/4Nvi/rAdGfjbjL6V4drHenN7Kd2vuc2Hy7QOni/AzBacJcO+SYgsHwHUUDyIW3rlC3Lrrg9XPLXzg2xM9pKTV97AApmOzz7fE08xN24nzfQotPMj91NJtLS3l8TTs4gyBZIFrjxunua1GJGODWt97BcR6z3dWAwfdSeedqDCADULcfOLkS9pZ3rraPvY3a/WbjeGISni+Dpx9uzRo+bGoDFLJphvbK43FvSpM/i7imFD576h4IFe+f2SSGaKO1iB0TjTjMErN0chV/f6H77PNvlxWeNtTgCDnv1AnXnjGyanRtBwbigX/NCmJajOXM4f8J88fuYSiE3xpWuswYAuvXC3w/swQgOYJsnM0bU34o3ctFAXgxP4dwF0rim4rI1jAUkRLdGTjA2sQMIB0aKDDH6rax2EsPmwVTMs9kyjoVPPnuFb/7o6/j2B99roCCMCUI/1rp6Rnb7ABS8+vQTfO3H38QX8c7kaaT8k2YMZuWfYeb/Rt//QwD/zOwkIvpbAP4WAOC/345bpb//G3+I99/5kgOAzk0nomNnDiaLE3q9WYJ0GiCLlfb18Fzttx0Y0Mu8NSCwYwEMcNF9VMeCoDf26hgDwo4tMBUCNeMvzdnnoWA9LwQaKOh2rbynwcZylS0YVAh6Vf+8/+7wJsPndP0z0MCCsQbBbIoCQGC7PLXFHTBhud+tjE/K4bxZIKiOIEETwhXN0FH3bOF6UmK+hq0IGNgqOwjYarO232pV4zxGBWMr7b0E92nhgO3aldmBh73W4f6tPW4DhQgE4m7e3tqx7O6X5N8nkmPCtMn7Jbf3iYAlJQcOSyIHC0sibMRYsoQXtyBSVmxaHOn/j+w0gP3ibt939Y4fIliYbSq6U8LYZ+7ZgtmcMDfcw8+0AxddmbIGqhCz73jv0TOyBrdYIVs3eoDQwIEnF9LlOUE9FSzGArWcCD1rEBu0rzub18xszWSzYZC1kVO9GyCQhlmWOAfyDA4KAkjwag4ljpUGCt7vxti+NGEfL9x7oexBwe/8+Jv4wa99C/+HH/ybh1f+pxEYeGFmJqLp6GLmfxfAvwsA9JeJwYyXP/tz/M6Pv4nv//q38P47vxJObuyAh/7dgYPjYgP9k09f4g9+9HUA/c7Xz/Pz5+54cpefDxBwKbBEOTs/8YO6tOBFyhCMLl/2OeUeFPB1UBCFwwgORrZkbDM/dTCIOdSVHpXZDmfGA3efZwBBakmArMPUAIL8LBLDQbBFTHAAjvb7FDnRGAggti+69rZ+AMQSWuIZyKIZn3AEBVut+orwXvMtcMVWFDBUxqVUfRUQcNmqg4HKjKfCzhxUBRy19uDBvru3pCD8ARX6SQS4fZeSZllUcJCIsC5JXrMcW7NkpVwSYcmEhRISVfmcCEtKWBKDk/T7opEqkZt3QWQLYoAxm7uJDpg/qwzNWYPZ59vfxd18Tw1Pd4B9q2LMJ9CBgPH9mxbmxrLZWsPHrIHNtFmcAwe++uoAIYADo+dj4yaQRPTU6xtzYEyCuK2bwg9zr4Vu/ezXH4mjkcFgUKlvBRAskmJjGe9jDqxEUHCz3AIF4f3Lz2TT/INfG+TjpPzTCAz+f0T0l5j5vyGivwTgH93zo5c/+wl+58d/Ez/4tW/hvXe/3L7odGeAsQcdOJATO/q489FFsw799gcf4YP/8G9M6c/ZYtEZDF2LQ1DL3oZAPQ4iIHAwMCBeNsOZ7oFMVaBPoe9NhfC2oOAN1v+ubaxd35gtmF1sXBg7MJC6Y83A8uD8CKgMoOjj+e+PjJvscsD0WbkDFP29s/WDnQugmMcDms7Ss3qy7eFk9bX0rLFImF8BAk9VwMLGFU8bO2C4FMbrS8GlMLYir5dS8VQY21ZxKcIqXLaKwnKNyhLyuJbaxoKpLyrD4GIdm8AImVBsmBIoREOkJnA1y6OwAQIWBBBIprtlSThlAQVrJiw5Y82EhzVjzVXBAOG0kLJe5A9DKfeLnoH2tM9LYsZ/8n0zSm0ug3Ec76lcv8XBuLinHC704+dxTlCIDtiB4R5s9DebrGn89qzBm+D7CBDeDhzUplIw32GyZ97P37ipAkv68RZMqe4BQspgquhsELhO4yBYJEVAxlN11CRtHBnCWYmgYNxcdmunP/8ACia2Bi8/+wRf+9P7QAHwTycw+CGA3wPwf9bXP7nnRwYK3n/3S3s01unRmmqBozA4mqgE/OSnr/B7P/ywsw7tOk7PAyYdaMJfBb375XMDCQIKFAS473jZA4KZkJw8OxvnbeoBCEvgNgRqXCho+DooMKHUsQRjAx8g29Gg6udmCyIteE1tEAGBfefnp/A99b/tdlaBLZhMtPY5njcRCpNqdPclo0cTJPpl8sWjkIAEc42y+O2bWlEzSSyDnAhcCBuasC7ahxEUPJXqgODxsuH1pWIrFa8vAgbkeBFmoUiSpFok3HGpLLkRmMGlCoZVVQWHPAJHTTXHbRQAgbrwZmEESBkByamgSZayCPzzKkBgzQkPa8KSKx7WhMpQgKD9vAFYKlJNKEQShbIyoKqEnJqRqYGCxdQXASAYGHDGz21h9pT+NVfNfmBMGmT23VgG4e/0t2+ATBhKG7g8CqbSrRyo5q6BljtZA7DOe1XBHsLqobkqlD1QVQJrH1wFB8wwf1sDC3IxtT9Qo0meTsY98HKQEAFCZRBlwMJEFxsDrJssDn3AMLs2S/VsUUrZ6hzAwtgL4wbzsMzWqCugQJj0L3XfHZV/0u6KPwDwqwD+B0T0MwD/JgQQ/IdE9E0APwXwv7rnWgYK3rh0rIE+l74mAl7+VCJOffc3P8JX3n3Pz+mMo4YdxAwQiFpAqSsDCAEkRLWBR50bAcG4HQsMAWnQF0G5kRnIU1Dg7+8EBYeWtXYsCP1oTwC0gX7IFgBz0APdcduuh/kKgJuAgXDcp9oICqY3jWj8QPD7+zo9/yieRP9sBlISLCOiBe6R5C7JrZy32rK/AdBMixIauQKuR9+CfsEjtMGYgtqBgtdbxeOl4PFSHRCUSxF1wVZQN6BU2U2V0sCCYFnJqgiWyIc2Vtmy9u2oAzijRZRBSTI/WuTDpABAGAN5nzOBckJOSdI+LBklkdf9vGYUZpxXu8MGYEFOVVUTCbVSi0oKiEEiBXbCwYC85tSyly7JwC23+elz1EDsCAyO+z2OTX+kuJsPY3Ia7+FK2YEDQACC7mTZBV18xivXHefT7oYKBg5YAzHS7bE2IAd8thysJf50yh4UXAcHUFbDyQFEcKCfSfsw52ajGMfobm2dAQTW9UiDPun4h7Y9J2EWSEOEk85l9oBjudkqQYCCxUKI7TGqqft1c2yv6wL+5WefiCH+r39LXfa7Djks/6S9En7n4Kv/+Ztey0DB1QXfbzxhDeQLeBYxSBjKGJs6jstbVsO+mxgSHLnawJiCA7UBIiiYLLJeKPWgYHA5PAIFllDGQUFt9HWMFOegwNFtG4ijdXYskS2wgb5rJ+uLt9llhfJWYODWYjvuAEcgEAVBMO7B7vx2rRZlLjyjgYEAClCSgL6UIZbPCygJg2CLHyDgINvimyQd7FYZG8nKHMFrZU0brLYEW6nYtuqg4LJVlK2iXIqcpymWa62opaJscgzMqOWCWi7gWv0VMYHSBBxEUOCfLUdCXv015RUgQl4S8iIAoaSEXBNqVXaEAV5a/2UibARcEmHJZhvR6u6yhETom/1BhjIBARQsRMgpMASWT6KWHthHpuCOPrf7d32u7/fMVsh6CDST09mYDcfaeQoSoPQ2QZ77Kkh4i2LAKArPsCZYaPFD1qDbNYd1xePD9OCACL3FYtI1KK9tbh2BAyVSGNoMpT32dFNiY7dVDa4xGNULNqZdvbDsUz270aQwhAo3G0iw9rC2i0sVxvf9enm0uXJQ8Bt/6OoDB5A3yj+NqoS3Km+KsG+VMTUzoxmaAQ3ldR0Vdv3NjmDPCBzZEmDb9nYER6DAwMAvCBR4/HgoZmHuWIJrSaOAgGoDW+Dvw0D3JoxsATAfrE6VHrEEe/XBIRjYgYZJiQLenmkCCHZCobOPCOeM1x2etRcIYtVsxqHgRTomFSBtIjxJQhpTpWGvJ+6JS05YKqMgYWFGYbXer336XgDYuKWCrgYCWWwFuJi6wMCBMgkKCOp2QS0bEIAB69iuAzjoq60BcXzsCnPAeQNSQsoXpEXBAa8AssyFVZqBiSQQnz6rGz+ypE0+h3sl3VEK/lDDRFVFLDkpGIAbLq4GDIwl0ARUDghM9deFIC9hrNicnfe197ep9rzf1X1S9eGWATDu/AkmYOv1cWxuycyB6r8DJABz4ejX3c8/snto3dniHUBlNolgJ62BBDzT2jC7LcKsuFUDRwWIuDAWBQdM5PYrUa0AvCU4SJivtbU6OIj2B1gWGRt6H6lsam2TMkCqftDPns46AcYa2UY0YCR3g98DgvtLBwo6m7t61+b52QCDOZo+aIAbwOHVpy3hktsU2E+7136n2KkNRpbAGYIJUFBQgHtAQWrCZAcKPL/BHhS4LcFoU1BbchljDCJLEAGBT7wrbWdqAwrv7TeHbMFsUb1TdXA3ELiHIYj37BbKARTM8lOMgAG9W+nu8XNuCz4aa8BJAAGl0gMEZiCvyGnZc43QfqpinFe4YCHCRoRTSqhJDPLWLGxBIsJCUGah3524y2JFb1xYLb1yBevYdFBQLqimRijHwMB03yUnATlcAayouCBhBSe9PkloWq5JmJAqz0PMoEoejdDdG7U+5r2w5tQMEFPS7+WcNSdkktgUM1CQAWEJygVUth4QRIDwBv0M7WeK/UxJx40dU0raBI0b+0Xwe+eYtv5Uye3eAA5i/ATrdQcKhyWupa6ycIna5kxkDexbQvOioeu3iWtMBQMMWF4GaWsASXLTAAfggAio1AwT7eIGDphME7IHB8B+3VVwAEoQI2/Iem3ggFgxHCtAUL8jqnpcmD5PZw2ADfy5Wqmfz28FCrQiLz/7BF/70e9LHB8DBa5CmG2m9uUZAYM3xVSY7kRvZWEM+L8HBEEovCkooKh3A5pQSilQWgMgAEDLKrsoNS40F0SjoecuiYsYtnFTkcWMcyMouEk6Uc8WWBtFFUKnI4ttdvPasz7tB/YUCOh7vmcS6IRp3wZqOJ4zBQXHLqW+ez6wD+EDgOc54msC0toAgu1WckXOqxjl7ZYUyfp35n5cu+0IW0yCjKq77LVU1JqwVTEizKKcB2cGcwJzEQPBvIC4gjiBqiQyoip5HkHJhypnAEXC5UZwYGwBDBRYIiT7zm0O9C8vct8ENz7MSVwTzR7BvRKWhPOacV4zHtaE85JwyvK35oSHRf7OOYmXQwJWBQQNGMiulMoFKBfJMqmMATRltRsQqw2QAXk+6GOkBN72fdz7ylsgbW2fuAvVXXhXZuP7cGwHNQZXmEfBFCg4mwDszeFul1uhkk2lII8bWAMAs0UmHqpgZw8Swb11TOxbKmRxdV16bwU0w14BDDqmrfqLMirbBYAI/h1AsLkaGRugsSYcw0YDQJvHpF4KGlhd5ktFYw86gDCDCKGND467HRYwBwXS6Ae/npdnBAx+fvVBzMI4goIOEADHoGCWnc6FSW0CRq8VBUlXFx4GpS2i11QHlHpQkBYcpaE1I8Od6iCwBNdAwejV38UtGFQIjTzlaZvNrj4tM1YgvN8tlD4mDqcUbIVgpNtgxQHBHhR0BqNKs8PTEXN731Wz7SaRMjgvIhSX1ccAK2NAzEBuLIXo5cUlqq8dC0c+PLb0bzoIQLQBAD73zwV9loACoowNJxHelEHpgpoSqC7gbZPF1lQKWdqxBkGZOiBkxocZtCydfUHSNMvLmjXrckZeEpYlIy+E5ZSR14wXa8Z5TXhxXvBLp4wXpwW/dM4BFBBOAyhYl4Q1gAIDBkuCqw6obM4YYGAMiCt4E3YEIe20VPagf23MJgXsvLT5C+wFinXYjXW8RS29MsYFiesl9XtTT3DtGYXOpuYGe9A9SGMNRBgnmCGibQyiSsGy0AI9dRDDKe1ugcYeQNOJJgMFYGTTXehlU1oEfFECUwHVPoWxMAcqjGsBo8omq5SeLByXoShjuKrNQR5UKvoQSHNwMKaztjZDAx0jn3NvefnpxwoKvqMJA9++PBtgcKSHvulDrBP3KDXz8Mvw9oApiMZp4ytmPvsHJSLTuLvSHQeTLDrOEphwifYEeXkr1YHV8oq6VA9Ewy6o5wGF94EpGFUI8TJOc014xmsqghEMBP2t12HauG2hkjVK9IHMatxn/GTc8XY2AwHgRVBQRB2E7eJgwFVDY/yJWBfdJaNs0mdlkx3zsso18truk4NFfF6xeDpiY2kYCRVpSc3YTZvHowZS6YIGCcVekRLhUXfjZatIF6BuGdsmn/NCKFtGLStqOQ8GiEOcjStqsOiR0BseJvFGUKNDAwRpAZZ1QV5EuJ/XjC+chB14cVrwhfOCL5wELBgYOC8NFDwsWeIeJGBNSQMhwV8pqg7qpQcF5dIAgQE+BQVmdHncr9XrK+NBDZt9LuuulqkJlGDI1q9TDQhY2PL7xnob4yaPARnrO5AwtU3A4ZyUHXNYFGxHHVQKBDMghMYlkPuYSuGINdjhEr1NZA8QVAtsLIJWK1EWfZHZcQDu9UNk3iXKJtQNzKnVOURHnHnXTIsCJILFyLE1ZHilqv09AVQtNiL2gP+g6Jr5Z5/9BB/+6PemoOA4ZsbxHZ4NMLDd1yh0+Gj3CTRQ8Nkn94OCaGy0Ux9w+By+90vMB5m50AAD8TGAAYB8p9DZEjgoCKqDvB6yBIwADgZAcGujIFiYHBQQ4MFfCAEgJMfNcFAQVQhH4GhAHzt2INKoAQzExdHeXwvEFI0h5c8WXTg4iC5gQtcNu0IOd7P+NnuAKDg6gTlcI2XZRRQAeWn54VWfT3mR2qUFbPrsbKxUBeWKNZ+QFgIVbb4igV4IwJIWLLmI4V0uaoQHpdklguB5zfj8acPDmvD6kvEXT0U8FZaEWiqWLWHbKrgwisU3qBW1LLpp1uBHwX1Rmqf1Lznj1dwTLYaBYNmElFRlkBNIVQR5EbAwAoLzKizBw5rk2JJxWggPS1ZQkPGwEB5yRiaJb7Akqe9ChDVrboXy1KsOjDGo8optE0Bgf9avZdNur9M+BQAJ12t7Wz1uVLVR+8qijWt0TD9tIctHUGCj783HuwAQGkBCm5sKEiw0+PFl9XwTepbivO2AzT4g6aFob4BEkrKYTOVAh+pLxyiAWPcH9oCDaoFZ1qdOtWDrRLn0qoVaAFZVLBcR2G7XxDuQ4CWsy4ftEQxBgRrAn14bOAAHfpErLa9Iy20KPpYswl/9Lt774lf27Tfu6HwzdXB5PCdgYLUk09lZiTs93jXSG4OCieScJv3ZnTcb7i3oRhtkbbCZULS4BB0gGNUG18IbH7EEdQ8IxvjmsyQyo12BgYKYMfIQFAyMyc0kUHcAAvsbQwxPu0Hr4v7DEeAYOHDjIZa/G2CJS+kF/wwURNo5lrKBzdG+Vmk8Awi6CyeuoEWFrrcld6qFnFdQzsjqsWBueKUyEmUsxDgnwutUcUqEp4Vx2kSQPpWKzy8Zry91GuPgUiqettoFPapVgxyFqIgwo0Uzxgv1beNIPAXE0I48mJF4D6QumNFpERuBswKBhyUpKBBAYAxBBATnJCzBquqENYmR4c6e4Eh1UC8gjVDK20VYoO2i/VTba1QTTfoUpPS1CkRf5onE9qBWETrLjSXYxrqxYzruhX+MTN/x43TjHejnL8S/3hgzsI197AHCwdrnHhMH9PgIDhwQAM2dMTAHdv+jNcnnfQQISUHBLfagbkC1TZWqidz2oDpA8HovjR0ZS2NLZhJ2QoH4aaI/sibe/9TUCrfBwcvPPsGHf/INfPSb323ya5Rz/W7z4H1fng0waHosAJGGsUZRegzh+JH64Fp3tJOOVQJzXTWF3YNNIvjvpy4kHRhQ8RsZArHMwj474m1bgsrXAcFRiQZvs5CxPy8ouKoymACCMlkYjzI/erNanZVi9A4nXShT1uFykKjFJq35Ph2VCSjgmS4akJ10LfooG7g2Kw67BS16Tb32qFpIeUVKi+QcqISkcQtyZeQEnHLGw5rx+pLwWBlf2BJebxIR8QtrxlOteNoWD5n8uFVctorHrXThki2EctX8Cxb5UF6tHyZCJIJMi3ioAjulFuI4hj0+L1nYgiVpyGPCaUk4pWZg+LAQ1iXjnCQssnkdGBhowGDidXBFddCBgug1pP15tS+NNYIN5TScM6gzbY4HdgAYEpwp2B/H/c0xrzKpah8QemCcbdzPAEJYq44Aws8DDjKJWZ4NnGu2Bl4dv38DCGKA2EBBvcIedLYHpl5I1q9Z+5V9jkVwNC1hrdq5pHbtNHG9Htrsar2Hq7789ONefpncObCxiq3KOBgrWp4NMLCKjo3bAm70HTI26mx/fxMcvE3pwAHQuQkFcNANsLhgvAEgMCAwqg0K+gV8NuQ7jNntqHEYR34GCmJcBzlc34gluAcQ2MIYOZt7GJDEIewq1OROF0mEWOxyMVUnxFjscqWWmU1d9ZwypqRs33EiF6sv19KDgwTwtvn9r6oWUgF4FUOqvCDnFTlljYxIKJWxVkapwMaMNWW8YGArGZdS8VgZl614foWnrSVdeiry3liDMdGSAwQFoEB7LaHtLRVyTJC0EBwQjAmSjC1YkhgRWlAiAQW6+1cwsOZgNxCCFHnQIgMEM5agltuqg2ugYGZbEMdbsA9ydad9Z1FKdRztxzx1DKCNfxv7ZRj394x5sITRlt1qD4znAKHlBTBd+SxITkx7b/Y5o9/+LXCQIRsYUylE1gDAfp1iI99tXZB5WYk1BDEhG1BQliRTRsrCFhi7R1TA6rfYRVk1hi6ChHDv1qh6iNJ0DPwiCg+v0aXeN7W7e9P0t/dsAZ8NMJhFOoslmnKMLon37ZXvLx6fuzMoSQHRBaM5rrpbHeiecfdAZmhIPSAIO4lRbeBBi1yIsgezAfZCFOjGeXya3q7gHlDg0eHuUx20mx2DgkifGvAZF8aj7I9WLHq8AQIDCN0IIauvPkfKzaXcFg6SPBPiasptx7eswHYBJb1ThQIFdV9NuiM5WkCGnSZvmzAF9rX2BxbdqXEFqmZ9SxtQF1DZwCkrQFhQE6FUSca0sUYGrMCWJQhSqUDlBZfSAMFRhsYxVbMlWLL2bmmZ941vicfE6LEdWzJhTKHcMiMivCf/WzVIkUQpFDCQVG2w6PgUF0Q0hqADASV4HUgEUmybsAQ3QMFuvHYfVfiTsXui8hOAQDI+XDXQxtEI/n2Ou60BTUH/XeNeqXpjDuK493ngfRTnu6xf7DxhAwdHhbrdte6Eq+zqkfJNcJAMWtxhbwDAUzQnawfSPleAwNqkWYHCVL2g89MBQlr6WBUjSPAHaet5aIF2bFy/f0Gg4dVEflG8tz3e5LUbG1eW4mcFDKzZdbxNy6xRx2I//YWwBqZkBDANIkJhF+E75mEwuV0B3QQEFr1Q0uNiBwj2O+r+1t2jUch5oKDAvA5GUODkWcwRsfPYwKT+imqHBzgEBRW9nQTaIglEnevBqHejDgEJLec7h0AqVt/s6VxF169gwOrB1EDDoueVTRZ/AJQYvF1AWJpeuRZQXvbtMLRH98hVaUZrK7c7WBWo2I4ng9IGC24lACEhp4yUFqy6Q92YOhWTjZWqx4vZCmh7bqoq2KqkXGYWdoX9M/tnQHe0ByX7uiksjeV4aJ9V0KuqwfIYyHjbZ0BMqeU2SCQqYTJguoVQxvXidhseijy6mprqwABA9CgZ+0cB3mHfqTumg4Jl7UAB5UVsC0z4Gwg1dsBUCDeYwG4e6LgH5mNf1kfqxv0IECzUcEoGAQZwEABBxxqM7IGdMxrXiS5D5hX8KwcHZIDcoAVbk/Ypm/0ZtC2INM2zggRmeGZGQQYCEMw5hLiNnZQWvVEACLWI1xe38dGBBLs5TcZAHAe+fv18Jdb85actIu9XAiiYAqjhdbTBurZFezbAwAYDAE+VDDQEzBBQMDbq9FoYAcFB05Ngazb9kJu0626RoJPJpMtg59Bda6KnMnbABlhqu4hrgKAJ0B4QHMlmO+brGhooIEBd3BooiAtxi1PQJk5E29NcA36X4caDoeEtUNBRqbUtiHxQR0B3LkBn1ewhVnEADkh3+kDL/c5thnm3L0DKWYQM5KEIcCFDVGEue9e8FKTqkwVF2QSp49ZUC6rKEICQgZqUDn6S505ZvALUQPVkYynbWErSrge70SPvlWtA81qJ6yYwGW+BnerdX1vio3HcUS1izMfVQxj78RvJyqq5mV4xEnUG6J4+iyyBBGOQz+qCypH166KUDkbE2mdH6sGRDXRBcGXsW4ZOCRd9kMGwTsCBhQgcWYPuZnbMCH4AXOAR/ywzIQpIVQtqpA+LkVwCOABIAL+CgzQZb/F9BxKwBwjGZVwFCMbuqTujsHItDs0uN8pRGddzO6YyQ45TODa9SPcpgoL3Bpu4o+KgUT/M2m9WnhEwaOK8koADEDznxqtPX+Ebf/whvnsDFAAIw3ICEqIQGwcGUQcOoL93gDBBmPMEQBJZbqpCAN4KEPhYPqjv7lhYpI9AgakTusV55sI5tWJobdTfOKgQDkDBUVCmmcAaS2IRQoILGJlJ9Jqhx404tTSp+9zvkLTsRhVW84cmUROsEggIXKUfq7UNN5CQtVJZp+CRrjoCBHVlbIJJou5xaZH1UnBtNeFiSVtA6qalBqwRgObZYkU2Dg2sNRsPhNc6Hgg97NXY9bMep+6jjie9mLJOLSHZZFyNC3YMJhZ1w5MkZR6ZMoK0AL68/Z3w0zk/BB2L/eRgwNpZAYGomRa8WZTS3Np7AAVvajMEtLFPpCC6NgYBGJIU7cABtZkxC3fd3dUAAuzXzh6IF4C6BSs4cLk4AwcH9gZWxkOGI0yt0AGEKte9ChAoA2aDwAzUDeDs85dtzAE+Z48Cox2t7XN6NqiXJ/UzUHCP/IplBAVxnFyDFM8HGABh5FADB5BG/cafSOrkL7/73qG/ry1Sx6BgAAMBQXc+v8Y/o4EBHsLU9oPjYFE2emsQkG8KCHwBH+s7PI7tzMxy2ZPQHIAChz5mTzBLSQvMZ7SsQu0pInK2+lud+RgURO+KW8xI3E3YnqaYy9MMDurjxTjsHThIYXyYP7QZMC0KMpZVGIQQHplm7m4HFu5d8Vgdg/CCRGFDSuASBBQAjx/ggEHrSqlXTyhIIPteLtL3iR2LDerVf3PK9DDR1LgbdUEP+GiOxmDdQs16bW65C8zzpzaPji4A09gXRikbAJixN3Ye0NrBohsC0paqRhiB2tX4IzfcjcsvYuwbOB7GfkxS5GoFA8e21rHVNwWAcG2ey2/Ms2fMF0Apg5QhkjYcwEESqV4hmQlb5MT+drO1TYT+2wAEgChL3Q2IJ2XlJpsejhErx0bvnghtndcxMlU3DJ8jKDCm4FrMiq7Y8oI2TsLhw/JsgAHQC3IbBD/59BW+8cMP8d2vfoSvvPvevEX0R9bYPhm665pI0GNuwRroNSgydv3bJMRpt5juB0jUrY+uSW8DCHzCjPWm+NThsFO3d4CCIR+E74onqLq/ic1EE0ozAUMNFIT6HIEC031bc08HflxICC0Ri3Zt1buWa+CAJu5OZqxEBHd3SgtsxysCd0i2YwZtsY12yVtugAVmD7QDoMtr61W1Nr5+pVZmunP/7lj401sAg6tU7LWIc65OuXLp7sMdtTdkbCUPS+PYLldClQNwMMAOBpS12QECwt67aK8qjOoDYwpmoOCIGYxLkAGErGO/Otsq4MBSJ1gPCHvYAKQBsqmdwdinbG0kd3WAAIhqQW+QKN8EB6IG3FMHszWuAn699JYAoRDU/kXVDMH1mgNIAKCB0EJjWzla771hk7drDyRk/Xs1gIK9YSlulggY7/zJ8wEGJuRkRyDo8tVPX+H3f/QhvvPVj/Clw+BFaC01YXi4O0xdp7pBTjQwNIAQr+XnhDIbFIGureiF4lFwoggI3GBsqFccCCOGJXsUmwQE5DtBQUsjXULkx7ZLOwxHPWsPILRtYwuMQm2L335hNFAQF8WZWPE7GooO4KAEtUIGo5ItQAIOKpnhW3b3K+FkqflxKxiIKhVmlhUIYtUs3gMy7eiOHW78LA8daG8Ki+SV8Ms7ewYDKBF4xHuELIHT+Bb3sBtvWybAhDqBHY11U3g72b0Dvd4f2FP+k990ACgK/vh5wsAAsvgfuhpTiFxogGACEir6zUANY9+Pg4d50QPiw/EfurMEcBCBcXWBb2AAjTXwdITHLri7ee/++wYQshr4Wbjm1DwFboCDQvLMJSxucfXtqqiy2kECw+3PUroOEIQ0aW6OSec+EcADSADQgMLYHtN1rgcIM1AANEP574Usv/tKzkvXHo7duBsf1y7xbIDBWD7+9CV+/0dfx7c/aI16ZQ/SJg11TNrAGgyFIiltx+wH4W5p7X7WDYRBd9uE/lsCgoMdw7XSVAgNFJB/vgIKTHUQEkftDHSGG+1jwNPkNbAFaAPaFz6WNuiosRkoGBBRDx7lsIEDIqErE8uOyQ2xdIGM/UuUQRTAgOohjWJk00+OdDdrZruRGmfZOVHumZYpy0BNJeEC+kqkRTaQUorc27IFKp1uKZShhngt1XJjNLiYq5a89xJ29XfHlI9dEgVweE/qukCU2nsz5gMg2R6DKyAFmxwV+EQEaKIxCrv0aZhie7Vd/8HuP24Kaqc/Hpm/AAgAHBoTU7Qf6hnCow2BjX3gDlBwMP5JfyO4UlqiUhv7rLtrsAFkNNaAHCX0r3abqVEiQFwAinNf2AOWL+WZroADYh0iCg4UJ9wkg45AAtf7AIJlhjQjRlkbSdeAABI08VQPinIvBwaQcJz0rbnUR1Aw2vLcM+MiKIjlk89eXf3dswIGNvA/+exVBwqOBk8EaU6rTxCAHepEhHei0WqDK+KBLjYuJB0Y0DdxEZhZ3d8DCMpEAZWj/ya1BcJcDcXtS3YPpJ/NH7y5I4pg2akORrceoJ8QVu+gPpgVzxbZquMlsgXN0HDQq+JgUbTPYXHc3du/l1UxDeDAvRX0PGFXdHEY9I/9ew7jYhD8Q3v1oGG4FgmkScoscNmkY7aLCDwE4VxbQifems++ueOJzQOjbpuENN4ELHCVYEV8sSyRaqBXK7gyuNQBOLT33o53AIQICDph3wn+BEoy+FJK8pkSaM2aZ0FAQFqyhFReNE3zssLCSos3QFbXQLWfmBkJBjfC47wkcwZALhbPAVwH79/F390ftGvmkngrp8m949/AgQFjixYIJmcN8vBzwQEaMvyaGuhoDUDqAQKx2GmbaoErODGSArtKQAWBGNiSzkcCsgUrUB91Sz9RbZHWcrQOEr8ZQCDdFEnbjenkdR2w6zlQsLaYrTaYyAS9AHpQsHOpPwAF92jLrLz67BW++aOv4x28e3jOswIGBgr+4Edfx7c/+B6+/O5Xrp5vjTmTVTPWAJBO01/pSwQAR3dqQMBeRzBgu997AAFz200fAQLWXfDuKQIoMKYggoJMUN/xK6DAVQe9jcHchUlvNKXVwq5rRrmFnVDcEc1ucbNQd8frRdvWCAMLotIFg9GBEReH1v+DsAdC9Dh4W8liaMBhwrh0YKGpbdz7wNwit4tWLgEl/HYLIX2LxP6vW0G9FNRNgveUrYC3gnIR0ODfGXCoDN4KqoICAQiaMKnWQF4YSLjdMWTRD50NgAIAyZsgIEHyJdCS5b0K/rRkpFV293ldQEtGXiR9s3xXkJYMLMpk5UXaeQmxI1IQ4Msa3AhFaDe6P+8YgG73H42GrSLh/T7zJ2BAwEbJtXXAP4dzbhWn1QcheatEzO7zLe7Ix7VNBbtavaIzcAHmawGb+gsgDTHOKmUlCFKz5CINhhQrVBUMFPTggPVHBg5m6U1sPbQ1cgYQjmwQDDQQSSRFAQuNRWjrgG4cDSgA8PjrV0vfuBZnJ3ofHE2re9bByBZ8oqDgWx98D//mH/3rh795NsBgDwreu/mbNykM4GNFcvZZl7WrvxlfZ+4j1wCBvD8wKuQeEIx0kU2GyBZEUNACxDSbAkm+cwMUmOrAoxty29kC89F6hSnoz9mrETp0fGMidAvj5LuDrw5LjYBAd1e2oNjiEK9t6CsR0DLYtXu2e1ufVTgF6XYJdQK8FmECSKP01QKsZghp6qxN1unY1mooZaCgPF3Al4Lt6SKA4PHJwYD9CVAoqJeqIAEoF2UUNMsiV0bVV1T2MMgRGES1g6kEgAAMNCCBgAAFBJkkZXRKyKuElBaBn5DXDFqyfF4yqoIEPp9AS8FyWl3lkeQG/c6WDIgoo7CePdgQT+IK9FR/vrHzb707m/cylvoDrkziNnc7QBB0wvcUE6r3zIHDEnZCspm/ok64eS2erwV+dU15TLLQmGrhGjhYFI9Ul95wsODgQO+QkwCBWVIvi17uAEFvOhopsjMSA0DAfKMANJAgT3xLSmhThfcfzWwKfgHF5OMfqny89kzPBhjMQEHcMV9L6nKD4QbQB0f6F37wN+R3B+fy8P6aL6m53zkg0EHP4Xg5AAQAuoE/q5+BAkJAtW8CCoI9wU510MUtsEod69SGh5PTdUd2y93tniRPcWE8+j7c2hmT+Q0DayAPAIDQyV1md4ktmE/+HWvjQMIeIjebDr+G2Aa4C2hdvN0tvr8BBMt5761jtgaxPc1WoGrqZBP+j08oj5sDhvJ4Qd0qyqWiPBpQqKgXOYbKKJZ++aJshqVgNubAbjnpLwuJbE9mGRWRJcphXsWmIC8JSPI5rQlpEZVBPmc5tiTk84p8Wh2IZJxQUkK2CJPQfBXGh5OqDkzF4KBgBee1AwT+mfaRRg242l/1SdkL+limUfvC+wh849rQAYqD4e8U+nDha7PpHmA8Yxy77ymhJRnj22DBGQSgqRXld6SqBFctRJVaymJ3sKNCCG5oMIIDkjZjGDiYb5oAAQhZJ7ldPnoxoEo7xERNCQ0gEFiSnAbwNAMJdvd72v5eUNCRMVfkGxEFmzuRj7dW02cDDAwUfOWL77/xb2+BgpefSnCkGHHqFiiY7Q6mNGEABDI4b7ge8nWWoNVJQIFuYA9tCmagwGPM70DBRHXQ6cVjCyg/ZwKKeUJHDgcm3ghHhWxnzjxdGOfn9+8T4uSZT9qoUmJm183698ePePhAccGl0De9q6iEXpZEfcHYU0O1NoAguveUs1qVAy2CXxXjw+jSqLYE7KqBKioDBQHlccP2WJ0t2B57cMCb2B1sVYT/pcp4NStxV3EdlAR4vJAMQqKKVVVXy5MAhW0RtqBuCemSsJyzYlGG8MkLUi6oyjY0+4eKaBDpKayXRe0HFtB6AtYz0ukMTouAAIsboICA1TYhhiNma1bM57H39hvruPalAw2Ty1H/bz8H9JujRxmBcRf6fPY8BlZ0Hrh3QrzgeK+uUYa1wT8bfKlqazCoFqyGSZIf0R3goJLu8Kmtl0fgQB6Tp+xB0t+OsR9kYSJdUyNrwLqRgrMIZgYRQUL4eLPcGkliPHr7Oh9/Otrc8c1neDbAIHofzApRH2/7GhqO5dUEFMQy9ss1QGCHd4tLUBvY8XtAwVE9ARyCgplLooCBY88DBwVdIKMjQBBbZiKNgX432+ls931yzzIbd03jZIm3dcI3gAGEu6buedvbm2rzDijw7PD0B919CUhqnS26TIQQwBlLzqDMQLo0gFAvoGKBlQjp1OhfuQ0DucgOeamgrcBD9canqSw2BIXV3lFAQblUBwXbk9gmREBwYXYg4MF3rrSZJ06CjjNd1y/MWIlQmZBqxVIrmLMvTsVYrwxwTWLjUCpouIkZJFpgIQcDeQWtZ9Dp3EBBXsFpFRuEvAJpbYAgr5CcEjI3mVv9ZkD+el+j62vv79kXty7A7bcS8McENbkq23gGUXXpsRvz4VqZCzJCs7vgBhLMFdlu1EmtyXs26Z8aewA01UIAB+6xkOTnxNSkbgAHkvOBO4NECuBA2mPeW6XyFBxAryMhmUNgKJY11NjE9htVQ7j6Ua+h9xnz+Nw7BK6VI/kmoODDTj422Xd852cDDN77q8egwNprBgbi7lEO6GfqwyiPTMEhqh8Agdz/NktwLyjYP39fJwcEWhdLTCPqg+sJaBwUhAx0nT3BqDoIfvj3qhCayqDlmu++x41dJ6FLy2odlhEmfIdH+t15/PraAh0n71FQkVl8+rfJH0BkCWRs98YK3Nj7qpAkG8r5hGQAoQi9TekC3jQsM5ELC39cZlCtSscvwhosC+q2qRugGPrVPG/10aDQ3OlmoKByn255/F02aoa1IVW6Vf0+tvuRISNlNUxU7wQzTsxaP/FUUHXB6UH+zg/y+fyAmk9AWl2NwGkBsryvEAGyizRYj2MHXO/b/nUEsTaMm2qp/303DuNx9Lv0qsjADCGjzdvMUmGcF/4MB7LC1qAds0ak6gQxQJQASFfiXMzWCRZ7A7lRaaqFBFBp8T/EY2EJE5hAlVF0x+7hkxUsmLbC3B2XpO6gN2g+Bq6CA2g7jWGlO1skwAGDqR9HgKBVuG/zE3CQAMQG/o7km4GC73z1zW0Wng0wSL7YtGIdcES53AsK3r+iPnhbg6I3AgVDMaOa+Dk8/o4liKoDGAAIoGC5ZmTIzT1xF4ce6Cf6tRINtQwQdAE+5m6K/vOIiAM4iCzm1AtDy7jw3ovaGWFS6oExWdM1MGcv11ierv+IFcyxsAau8pHdzMIBIKQMlItspSmBNrHVIBBiXABbROwIF0auFcwn+az0eyoV+dQHGDJPBABAYaRaxXWLgBWEi+mXDbkGsDYrlvzIVFcJwEo9ME0pIS/NviCvCfmcsZwX5FMWm4PTgnxakc8L8vkkgOAkdgfp/AI4v0A6v3BQQOcXwHoGL2cgLeDlBOQTWBmDStkBQUxNXcCefZIZrse23rzVry6gKS7m7IKCKAgZyLgex6i1Zh6aNQZRS6B+LQrXBNHxvAq/PwLU3T3t8tFt0Xb8RqBbrphrAAEI7IKey+TXIv2e09IzB0DnzriBmr5PJacAbIjRNgGocmlh0xpAADBdR2PxeXMFHMhaZDJIvovsgbV0TPKnh/zRxzamUF8r94ADKyMo2G3VjjEggOcGDLSm1tcRoR1pDq6BglF9MHbULVBgX78pKDgq8f4zMGD1IRhDIBNcFuO3MzI8BAWTKGe9AeEAArrvA1sQ1AhedROoQ2MQ2iSSCXsAt+OCSbtDO1XCrPit9U0HDtBAQYy6GAGgGYUWCD1vu+hrel8JvypqniUnbABSZSRiLExIlVESNYCQMtYlg9MGS45E2xMY4q1AZjCXEpLu7FJKWHNWt0BLsARXL6S89R4E6i1QUpEd+qVi2eRvY3muTYPQRKbnlvFhIq1nknG4EGFZEkiBQFoT8iljeRAgsJwXLC8WLOcF6bRgeTgjnxcsDw9YXpyQX5ywvHgIDMGL9ncStoDzCVhO4OUk9gUKEi4swR4NEGxqN2GvZsi2ldr15y0dfuxPt/kxQMBw6/cYP6S1Vft873gV91oOawFpX8QTZw/c7hk+hmurIaIKJXmJ4L7OWQN/4IRuM4Fh3fCDdl6C5SegurW8dJGpVKPEJcFjHVTFJ+axYHglJQF21uZuizOAhKE5Dtv5jcEB2jGiAWxMwEG3VsV74zo4sBJBgW1qpxuiKxV9NsBgnFRA0OkMghuYAwJAGvWaTYGVcaMwYwrs+AgU3qR09jZAtwOJ57RFpAECsyWYsQRvb08gr1cDnMTSsQQJzTdcWYLAFgDX9fkUoLEP8KMJNSymaXJOPA/o+80XECK5JfMeHARhb4AvCpCtsO8yubIKk3YB20+54byqCygR8laxJBGcS04ihKnpQUsirEyoibCmBemUge0RgKoTVK2QckZVvXtNmjmOEhaytMAaNGh5RFozts+fQMtFduqnjPJUkE8V5WkTA8THgrJVLOaZoMaInv1S6fejEpkC80owMGAeCfms7omnRdiCk3okqCfC8uIkbMGLc/v8oIDg4QXo9ALp/OCsAecVnM+qOjiDlxW8nFFBuFRp04sCgqLswKZDfisVm3036cexD2M/bgnIFdiIsWQCF+nLJkBst9d7ADTvIerG72x8+zgMC0DckABt/YvnXcPT1+yvKksfdqwBIAsTcWsJzzBbr+920EACA3tw4HYHVYFCIKaiUSIDxHvVQkJTJZiHl9tjRJAQG2VYW9+0MDAFBzwDB1pmzMFYHGZdkW8ff/oKv2e5gb743k3Qd1SeDTDQsdEfm6ArL5MGM5fEj94g33X8Mo5/2z2OZTRaGkunM7J6TJ47vrXNd9NR94CAjliCmepgtCfwrIBzlsDr68FcdAkbVAfNH5zQuSgqWxCbcmROol7Wzt0L+L6tjnZbR22pK113fzKGR1a+trAOfe5MAZogKcwiXCpjqzUIlf06efFnlS8syBSRCJKlVCyZcM4ZGwEL6062AjkxqgGI5QGUFtCWQGkBtiRULCUgL0h5Aa+6Y348gdYT8vk1yuMTttOK8nRBPj2662J5uqA+bSiXgu1xAasrY4tvwO7GWEPwo2tBjixWASXyOAYSr4BCvAIBC8s5I685qA3WPSA4n/YswXpy1UFdzmI/sKjB4SLeCFsVRuBSxSp9YxH+BggeS8FW5PNWxDVzqw30HPchkImxJTEgZbWBQNFAPKgNHMDlx25MNg+VHiTMxy91Y3IHCEz1Fcb3rA5+tclc8Z0ygmBW1oARXBf1OFtisWYRCPdS0rDo4zpCPJjummoBuGF3sI+UaOyBBAiTp4oAoc1xv9tUah6tM2PZsQbddz2wc+2b3fiKtB6/iqePAOEnPz0GBeM+inD1ts8HGADsgzKu3WPjYThujfPxAAriNWZ9OO4eD5/q4BwJsdu+9JSi2A++ow6MC8UICKKRIeE6SwDLd/CGqgOv4xEosCAxDhJa0pgudjzajnvES9YPM8rsGgiIjNDY17P2NH2t34vlYFGAMOaIL+ODsuw8C/agYAuAoKrO2sZOpNyTqn0uAJaUQMRYWGwOVs7YyuYAoTKjJMYCecYVQGHCmkUPj3IB0gKUJ3nNqwjMy5OAg/UMfnoNfnoNOr1GfnhCfXpC+cID6tMF29MF1cDBReIcxOBHDRRUlAu76yOAlkcrjJWW/tmGigjMvApSNXAQgxjl04q06ut5xXJaxY7gvCKdBNg4KOhsCU6iNsinZkuwnMB5RQHhomqDS9G+UnBQBkBw2QoK4MzPpvWzWCOx3+RV5hmzJdYCgIoFCZtSBASAKmOJq7VdB804WOZyA/U3x3EYvxkBZHMb3CYMBZBwhyd26+PsHnqDOA+JMgBNrJQg8Tcgx8TeRXb6ktfjTnBA/XlmHEAAdnYHQbUwYw9ExaH9NQIESBvcUuH6swXANLIq6WCV7kDAlXLL5snWwdmlXP39ww/x3d9shoZxvPRAgIfXfXk+wCAassDgQStHDU9oWaw+CoaG8Zw7xsxhmRmGRNShm1EdQKS6K/iA7erQXbcJQQLceCgyBA4IcMASMP/cqoOboGCMHGdpZT29LPzPFp1dG6IJ7NiN14BAnAjyaluDeT0o1MHIAwu1WtU1iq2z1B3KApwcRT0tLPRzYXlvgEBi4PMks7BcKCXZzSYiFCYkELa6YUnkAOGcE9acUCtjSYzKol6oLLrsVY0TqSxAWoF6ArYnID+J6975EXx5Ai6PqI+vwZdH0OUJ+SLHT9tFmYNtFzqZtyJ5FJQ1sIRLXNlNvq8FXEHnUdDYAtIQyGOo43wWxoCW1eMQ0HoCrWdRGehnsSNYGyBIq6gN1LhwpjbYFBBcSsVjqQ4INv2uqkrouM+k3yThDiExkIiFDdFViFgA3GzMEdk4QmcDFMF8HM8+lm+OY/lktlc94JUbR4FIob/GDVEsFdDEQrFfU7MDiOAgAcwS3fBNwYE/xxXVAiH7xuKIPaiAAwRzN3WgRAYutI0GFXAs19bevvH7cg8ouFbo4H0s12zi7HW6Bl4RbM8GGJAGe/F43GadPTs3vG+g4Ht4/4tfwb61QvzuybedkB9AgNsHhFOjeiM5ku99kgG5Tq+33D9/83+3ReYaIMAhS7CLYlgL4iB6I1BgWe4OQMEsm5yzBXbdyYD1QX4HGOgWTw4JX45yOdgFqbn8kdbJdwmJ1CVPgEAiSbISF8mcCFvMZMy6k3Z2QEDBNmEMqhCyetAYHxH4iUjc6BgoKFjU7mGrjDUnFNBOvVASYU0iXNNyBm2PQD4BdRNbhOUBdL4AdUO+PDlI4MuT5GDYnpC2Desk14LFOeAiN/TkS1WlrNd/zxhIQwkoiMmQJBRyluNL9jgE0OiEtCyg5bQDB1hPQFpaTALzOtD35n54KY0ZiGqDrTAuqv4RYCBAoHADBFsAdFZifyUisZUjxpJkQJqagCvv8uhkHaxN/acsQXhvLIEzfON4jiHIx3EMNHCgc9HGc1LAa0Dc62O2NMDh9jnufnudODk4ACuvFpkDUytYsrGKm+AAQFMtRK8FBRhdtERmUdkM7EFKavcyAARTJ0RPjqoGH1ZzV/NMmlZqbOdRd2BCBO3sFDqwMLnm+HokwQmElwPTzd338ZW7MRNTvs/KswEG4Bp2+CqSD8CBHXn56Ut8+Mdfx0e/+Xfw/rtfnqPwwECMVE5UJ8xAgCNv3WlaxKm98Uj/jAYU/D7D6OkooqB7TPH9AAiEHRgSIMVcB2+gOgBwNePcmISGZ+yBto9llLP2sMl4FIzKAcE9YCDkcWjJilorW+uGTvI6WBjcuAMxK9DNtl6ayKVW9jzvGZK6dlODMrGM5lDHIFyGXai7rCX5LjHpAiwAYUnCIGwkY2lJScABq/0BMxb1YCgV2JJQ1lntD/ICoG6g5QwqG7heBBCuG6DviQtweZJMjAYKNsvM2LI2HqVxlr6bjxlpXm3vgzTJIMlyiJQ1uVEAB3kRIKB5DRwMpAwLUGSRDAsgbECdAwKzI7hseqxWPCogqJXd4DAyPAA6xqAqUwBu6oQIGGQokY/fTOJ6SgiAQN1RzTj4MKOps3m1D2A1G8t6czZAcAB6ZyBBZ71fJuYZMENcscpvG5ys65upFVijdkr8DLm4gIbabBI9Q5OslLbr342XaHdAxhqIAYHctvbsQbA9kI3SHiDYszv7wa3POnUm7W3BdiqDAAhiy43RJK+BhZnRoa9nwCE79PLTV/jwT35XN7Xv7b7vgMXRJumgPCNgwIjDOoIDOxbFtaW2/Og3v4v33/3KMfrm/hqRZmsDtn3YgQA9xQzY2rM26944aio3XWP3KP5Ie0vlUQ85BQRRbWDC8ihg0duoDowRMBc5JLAv/n2secOqzRCzb/4ZKIh13tUT+/q4y6VZRd8JdOR+SRYYozF1B9IZWSdNy1plwWcS+5AlJxQUMTxTAR4CEu9sS2bFhU9iHyBbFeG0cAIy8HoDllSUMUiuXuCcZJerAOFSGzjIAJa0IK0L8go4g1QuoFrBZmNyDsKeC5K1KxjYNJ3zAAYcWHolJj7sKXBgplYCHCSQjZdlgY0tT2xEOp6cdcryOa9+TtG22zZV4UxcDwuLMaGpDbZSlTFoaoON1csigII7skl7/8aSYYBAq5aTj98cVQjaPwYKMkL/RIYvjOdbc9RBAXr1nrUr6cbJ1iAXmmGZivFDIjiIQXsKGkhPlIGYRhlogEB35BK10MZHUy342vqmqoXIHhhtq/TGFCCwZGcsLJCEVa0CNKBg94v96ZvAg7VZ2rytV2n4fhd6+gA4dJc/YDlffvoxPvzhN1R+fdlaZyfn/BpxszS53lieETAwKAoINgzgYCiCtL6Bj776XfzqO1+aoidRS/AADvb8w4w1GMEB25ORfd9ou9n1YumZgwYEXFDuds4BHXaAQFkCW1AObAmAG8IT6EHB3aqDPUtwxBCMJYKCPuHQnXXldp7ebHdxMqaALEiQghvbgWQ4OFhAanMgwaG2ygLydJFcKIGTtGFhwqL2WciARCVmZx9kcMwFT/UtjZ5bgS1V1E3YAyChcMXCjIUkhG/nwcBiuHhJfBD1UhiRtJ56ynq2Qx1AFnXtOVlsrtHc8b2PKUKlNpZMmHlGwwm49AiMhbtARJUNBFiQIkw9DUxtIGqZ6uDrXlAgu3pyo1GzEViyuCsumhzK3U5JxIwDAAopzqHvIyjQfBhvlM2ULBR6P55ByZMdccq6q15gaoYEanY1ta1jYzf285Ra8jDb/JOskpQWMMvunurWAEGC2B3Yc7+NagF6wxgQSQE8+2duFegAgsX00PVaWblsjzMAhdi8N90J4+bFW8i+o+n3R/EL4nrc0/8KCn70e/j+B9/B++98Se0mYtyIoczUT0eqKC3PBhiIa1nb3ZtqgQcNjyGt73/wHbz/7hwUAK2T2NC2foqYzI4kFfjgPTiw/zah3GL4xiCLp+xQ6A4M6M2Pds2jkDyIYPhWLMGgOpixBJ0tQd0Dgtnw3AEmPTiCgrtsJiZ17W/W14sUDBpAsF7nxMhp0SEm4GAzSjYBi6EdtUwHgIKkKdzg4AAloSbWHaqAA9nNHE/UarSrgoWtAkAVI7cCMIlv/UaEhYVByNTiIBDY7RbIdqu+y6O240uEJOQ2KJ2mTE3sn8Odzo3Ck/f2GpkkwLpQBLjkSarOOMVMpBZ0aAxKFN1HR9fDqDoYQYGVIbWEtofUVoBAAwgRFCyk7qapgQNxRbXcJPobBQievKwLSb7hlmFwV4LhYTeeDawqQHCdfMpiOGhzQH+aQvvP+kz6pXmCRYDg4J2y7+aJEriKnQDVepdqAdhvUvbsQYiYyAxKGaZeiGDIN3lkqqu9OsU8OAyPWx0Nv99TuvkwqECBAAqGubQDBf62p/9ffvYJvvaj38f3P/i2y69OVs2eKTIFep1obDorzwYYAAEcmDAPjQZEpKWNOqM7Af9tAxm6W6J9w8swDuAAbVDFjGfXWILZ7YF+EAHHYMAHz7jDO9o1/7wsQaAl72EJjL6rdutA1c3uOrbyHgz1oGCX+dEWUDOiPNrhevv2OytZCWSRcSio7caAxGsP4MA6X5pKV9Qs7oZARUZCrowMoaxNnbAVRsoyYkUgSa+OUQMTzUfMZrYNJAt9YRYferawyiKY8lYbGEhDJD7p/I729M38MA5nC13XjNOnnJfZsjRTJ9XhO+YAIPRCjHmkSWNwDAyMsSS2WncgwIq3Oc+PO0MQAEGCMEgCAhogWBUcjKBAWIQJKCiX3lvIwO9sPB8Bg3E8K0vgQKBWn58CetEJSxOO4wo5na/GjDJkt10bk5musQesLo3RRfoOrwUAe9uDiXphBhCYSNpTNy4zmwthEvTawdUT6MfotTLOj9laHj7u3u929BEU/MYf4v13vhQmhdY5yqzxWvbwd4AC4JkBAy82SEzAAz3SskadCcNBhUAIwnGwNwDQJhaCWiEuqLEPbiKC9naGJqdgICwQblQ40r1vAQhg9X5TlsDdEMkBARh9Zjq7/mR8ki1KVvcJKJqCgl1QpoE5APuE4NKWO8pZFyASRZGBBHDXxpy46/OUMtwXTMFB86xQF0aSsMWPpYAKQCmDSgUlEV4GECymex2E4Uhhz3au0DbeanXPiVSBTbdvuVSQDqYYYRHQhEZ2bOBJ+4wJ/3SUUUjFQEot5DR35xoQkO8aKxNjScRiRn/W9g4EtO0jU2BxCyIg8KBUBFfpEAELwbOYmvqgBwUiGB0UBNbAxnOX7vzaeAa68cwpyyDRIBLCGIhabMxgSJSVRdKfUANjR/PW7A0ABQkKEECyORKQoOyBCuIpeyAdhjcxTATiEsuI6oUpQLANC1fcMsyUdoSrguPafjeFgPAbLTPWbQcKYh13oOBXJrLLFoeD9fxI3h2U5wMMVJhLYBHqju2RlgnFvnc5MgUGDobrj8VAgX2z00MRdgvQsRXq/jUK8WMwoNP2FwkIgDkoiLrekSUY9L8zQBAZAwxtZzU/UpU1YGR1a/XzdtktouKFwaWAWY3mYl2LZBj0+i6LLFiALCac5fr2rIHtiOCAWMLoArpLYlm4ZWOWsZEYtW2UdLdq9DZ3u9gouJD2Y+eomK5ddnvtR5crv5ldo//cDnTeFHU879o1+ZDxAPZzIYKfaPg1XuOWvvda2c9H2t0vIiO7V0rtPFJBT+5JQAoMoGqExhJYuvOF1IYgeB/sQEHZ2vgtxn4FVQK4GYACx+OZEihnMNQzgFQA256JCSwSOQhj1c/jOOnS0fz1zStZPgjNbhgAgti5ZHBOc/bA3r+BYWI8dh9A0DoSwTaPR94bADoVi4MFyKFZG73JWj++764Y2ngHCjCRXUdeVsO19h4t8/J8gMHB4vPy04/xtb/7Bx3S6hrV+f0AKo4omfGW6AXbXshJGbOi2W9n76dAAHg7MGC/OwIEoe5+6C4Dw+CG6CxBUBtUe72SbnooxgiMceOtgTq2wBkTbovouLMKi6gDAgMOQ2FudSauoGUFVwZRbospdKeVAWrbUaS8ym6bZVFLkMhqqGhp4lmM/xZkcL6dSyFS3rGbgFFAh/c8vnJ3vjsPTL4XNQb7cTunVPHjtwx0pfbfddcZ2nWW2jYPUypZ4iZjMFJbgO290e7xu0bnk9P58TpNkOvr8L3fP3xM4UMECPYTAwEtGiGFzwIGLPkVETQnSZ/q3ICC1ElYBAI3I0MDBfrZQYGDA5YYEx6mHMfjmZpQxYIGduN4hmEBNZTmCmZy1sAXuO7aDRhP5zGG9MMKEJLOEU5BvRDYA7E1SGAuQXWnC8k11UK3fl8BCKiI9hadUeYVkGARHJtRZ+8CH5uAMV/rZ+V4/e/Ly08/PgYFg+ySZxgAwuEDXH/Q5wMMtHCo8MvPPulBAa4gphnjoMXDdAJtQioMiHPn1pg4RodBB4RGH/mxDgwA97EDeo9rgADYg4JbLAEGt7FgXGhW4swqYBDAgd3SBJO+cf9hRHBwpTF3bMFQb9fNce9aV9Rp0HYjERSpztE2KNguAg4gC9ZuMVVwwEZ/poyVsodNlnS6smYXbgBhYRH2mSRWPufrunEDC/K4rPQ4OWjImZxlSGj0+Oh3H495FD/uBf+lsH+utWpQoKqfxXCvVrP8Zwcldh2gByoljjEtUXWROsHfjmWSHApJ9e9JgcGak+yuNVOkRHekDji4AFZ9f2K9dksa0KkAbPyNwh9o4YjtuSMIOMyaqGO4y2pK8xgFnTuiuou+ESiodT6WvVQw1FB225QJs/WA4HR9N4+MNdh1XXcbn8+Tuex0Oxt7JXVn9eIx+wNpoyxo0dYaBQic9b3HKaewKAyqBdd13AAItsW/wiII89dAhHnEENm9JGlUbOsYuOsIMFwr137x6qcv8TWziVOmeyq/3mBzywFEXCvPChi8NSi4ed3r6Os2SJyAgPAcO1ZA398EA/E3twDBrM7aXm9kYHiFJXBXMQyAQIXXrNVjHvoIDnaPGtvR2AJU0AAGzKaAmBtTYJRsbAf7bL70thgv633gIElQFVukxGMhe8TJXAmFSAQvkwpjQjbWpGoiF/VgsHa8x5huzy4oAEmk8fxlEXQffha/fDO2u5SKp629v2wCAC6l4qmI0L+UKhEAa/vsAKNI+GNngRQQ+OuVZdHT7KT2aq5clAgpJxfea5boiDmJsLfPJ3tPhHVJ+h446fvKEvwpJcaCBNTmFigugqlTBRyxAMfGmr0NjIMC/d7BhwGBAAjkd4w+eZm9lvtAwXa5PZa1GDgww2wRvCmsJSTHucUrALgTcrGMc9qOAWj5Q9jYPZnMrGNdwkbD7Q+M7enYA9J6J6BzaxwME30zxrWBg9gmAMzp0I8YizIDCCgdizAHCQzS80zojkDB7hM4p2k73iqdS+K7X8JdMmvc3F4BB68+/eTqpZ4NMOhAwc/+/A5QECm41ngjW7ArXUMfbW2HTpwxAuPxaPcwAQP+uwgA3oYh8HoEUBBdnIYgKJ2B4eBxUBF8ySu7v7jT0QEQtOYPE5daXoIuJLDU3NW8kfKlse6x/UI7uEHW2A6jJ0pcVG2bb8xBKaqnrXNwALSFy56BUgcQBAwAbOAAustP8zbyzG8DYGDegwVLBbwViWWwVQZR0nC+QErs1bM+MVBwKYytFLy+VGwKCAwMXLbqYYJrqahF7ls3eVIx3xB2gUt1Npu5pSOeZVh0MGCULlRLpUKeiJAWAQ9pSXhNhJQFLJh1/7okPCowOGXCpSQ8rAlLzt45Bg5chZAak5DJvAOaoaDFEDhiAUYA4EAGynhRU3E05kDf2x8CIFB7gUPPAwcFtdnHjKqwe8ayj/8k82GJx5Q1sGuhrU2sM8+NqdGvlvL59rxmUiPcABAkRoCqFxASu5naztmDK4aJFv1Qn3oHDqy4oLwPIIDRswiWHGoGEvS7yCYAup7GmAI7wXxbXrz89JUEL/rqd3cu9fsd/yDHroEDrffLzz7B1378TXwR70yeRcqzAQZW+Y4pOApzfM+1TDgG9x9AOq0vB0juiBkYd/A7emhUIQzgYRSG9gxvAwisrm9oYOgeB4ElMCPDFryo6aNN2I0l8WBPoJJ2amcwtum1Ovrpb9H3w/1YIhbBd1eUfAG2xYqTMhimkqgbkBYVQkkiIALSbt4+1HbcoGCkSYeAoShYKNpWEt63glP2EL4S2hceLwFZAiKJTJa2sp2/gAMBBY+Xgou+GiAoW0XdqgCSwigRJKgLoHiGCmtQaxzH1oRFfMutOCAgZwtEjgkAIAUDOUv+hJwEJNRFwj9fSsV5zXovAQOXRAKCQmjxROotkCXglIGCc+5dCSnBDQVBCGzBMQAQoNDyknQpkeM5UDBgNjGTIFydq57axbR1QgHu26xh3TCuoNHAw7+czB+u8DSY3amNERiP1/4AAKhKjVzVBSZU0mBgRMgKFEACulOMewAT+oSdYWLKHRB39gDD2hrf3wUQyIU6OVgIqgYDD9zO9QBcg8tgcwAOLUNp33iT8uGffMMj8ratpwC8n3csvPzZn+Nrf/pNfP83/hD/xz/6Px2e94yAATVQ8MG3FRRw6/zD0gS/d/IolAwUfPYJPvzjr19/jnsAwTV2QK8xVRcMv/dBdg8oCHXqbAmAjiVoqoO5geFVlqA2YWZUeXwUBbNezDAPZl0IuW404JkuZyPjclAoJXC0gjP1CLCnX69dx9G3LO6clvZ7Q+cePEYoT6LNc0SQAUwSmlqs4hpYYBg92zLeOUhAaN/wKrkSCJWzMwECDAhEsuNPIKAANVVZnGujuWMx24TIEpStomxFci5tBbWI7YEwCJDXypJcSVU2zMXBGE9ihBhAoJRAlPVVciOkREiZxdSjJvGwyAQsGRwcFTckpFKRk4KDMFhs156UprZog6ecsCRhEs65jy9gO/rRLmBkAG4DAMABuqK5qOaaheieJS5zoGDj7trAPBrLw/pFR36uVgwIcOTp9JLo96SO/dDGqY1Rv5zOcwl6xH6MTZXPAuRElI/sASHda5jo7IGEjevYAxw8lJ7TpXa2NjCAALig9znttgjKJBhcZAbI1gf7fd0DBNu9I9zjoHz0Wx9pmOOhDiT3FN7Rvht7aChhY7uTj1fKswEGLz/7icYp+I5kSTyil7yEzpntTsNiDkqaW+Hr+Oi3PsLf+MHfgEuymf3ATGVwjSH4RQKCK3XaqQ3e0MDQWAJ5ZY1N31iCAt4tFuMmg7m1HCDg4L8Nn3nKGbzpxI/z0BbRPAz9IW7/1WILSkzoYsdMTwmAavLJTEADnwBcDzljphQ4yJ7RdvjkAMFtOaq0uYRkTp4+eEmEx5xw2UpnHCWzveJSWtS9NKIEQCjfqtevMgZ3oKBU1CJ6b3kd3EFnbI0KKAcEKYtXB1dI8gYgLnQpJbl/TUj6TONgMePEaJB4WhJOSQDBeUk4Z8K6ZJwDIDCd/5LEmyCmPB7VAAQbQm33D4YaDA7zFQeAf6YOtPOUHbi5G/w5xrKoxH7x5dY8N3ksScg01wIxUBt74DqL6Llwj2HijD1Qgd8BhEmZsgfAMUDAABBgqgahO5o00J29tk47PgCEbo3pn7NPiBSUrOP4cPl2Y72i4LJv8vFGeTbA4Gs/+j189NXv4r0vBvolIDQxsGHswIJT61FQHoOCWRarWbltUHhDZeDv+0VnBAW3Sq8yAHaAoGMJCJ2BoQKFI9XBbBcbF4qwNHoxMi1OhSPVQRdpb1Y5R+/hM8lOggmgZQW2i26EdIeQ0l5opb6NaFxQxx0Yx3SwcSGRxcgFftfuaN9h3y+trgYuRA2xzxGg6Z+ZUKoYNmZi1CSGggYQllLxGgBR9bj8yWQ226vYCSyLGDPmTVM8K82fksyLXfS7ys4IMNurggJnDPbjs4PSCWK8yQUE2RlWVSt0XUOk8k2fiULIYTU2XBexM3hYM05LwoMCgwf9/pzb+1n0wehCuMsZYdb/R3NXKuvjwku3Wx3Pn8zpuAbYUSIZfwWiCuD6VmOZllXHcJjzXcfsBUsc8TSumV7F/tjhfJdNtZoatFTl+oRue+CqBZWtYpioBr7XDBONPZgABOBg47SrjAlrXZ04CvhYKwlL5iqGGCVKBbiH5rexQqn3bLtZwkzRdSXkf4SzBvfIM7Q4CFE+9vfZl2cDDFxoOxIMDakN54YbHa0eJkoUkICCgo8dFIz5rvUkIB4dF4T/NkDBUK4OuFuAYMISNMPD26qDyBJYkJ7dAsHtEcbpNpZklO6Vc/r6hfro5JO5VBo4GA0RswiuHcUawAAAtQ2YCHivV6Qi7R7Hk6271eS+bdw1X2rWnbX1jxg2CotTk4CEUgkbKzCo8pooSQS+S8FjTqBUkCQcotO2Y4nuhlwlzHLNFcTJQQXXirwkFCxA2ZDyiooLElYwFXASgDBthZQ6toBSEsZA1Ql5SUpeJaSUQGpnkJJ4IyxLFgZgTTivGb90yjivGV84yd/DknFaCA963oOyBA9rRiZgTS3yoAcZSgYGFAiUrQEC1GYsGHf5wGRuvkkZ5vGU8bPxTMBiBmd5OpatX7rxvAO2gRUc14I7SoLMhxRsh2ZlnPMWFn4ECGrSKwoAJjFIuKZaUDUCK/1AtfbswQFACI/yBiWAAxfwaLt9Be8tLLMxgxXGHszAgVyahzan7q7xlbq1p4Ipy/XrBiDJUsP7udYb4v/EQcH7f/W9cD3gCi54PsDg/S++L298Ad+jLJ9osRywBAC11My/9RG+MoCCIOtul6g+8GM3QMGtMrOd2AmvHvTsAAEldLYEBowiSzCAgpnqwFzpDBTsAEH8PGmwqaFhX4t9PdXvWKKU6YqjfIRv4FlzZyy2E4D3Q8dYDBPV2YD2gOHcoY2HPtjtTg4FyNg44RkiKLCFPGXZPekrKKtufsGaFxQQtioswkUBwqVKkKZlqxJ+13bbVDv6PSbzSam4eoFSceYgEaEUMRasRcIv15pQNgnyZGoFZxBmjIGpEqiBAhAhL0ltDMQAMS8JOSfklZCXjLwkLEvGi1PGeU34pVPGi9Mi4OCc8WIVUPCwJAUE8ndWNmFNwGpqBAMFSWVq3UBl8+BCb5ab4EofWj9KhfuePwDyroJy15ccNjWQe4exDNwaz2Esd3N+tubtmYRba9sRkzBbKCNAkLVDni6DUK6pFrTurlqoBhCSsgdieyB9NAEIXpMbYOye3bzv0K+BA72crT13XHe2TA4PByJLvydZMZvdWsLUtJvSHhTETWL3ui/PBhgcoqwRHIzlBij43m99hC8HUBDp0DAMMOvO/UOq2JyBhLtKR8YOg24QLEC3A90vDoTocRBZAhPus9gE9pnRDAx3oCCSJrg2/FqPEPYn2kbnepMolZeyLuRCuTcqMXfCuqM/by3YtyZ1uO4U4IHd3czOd4EZFyYPzN/v+LrQtsbsUALSqrvvDM4rOC1Y8oqcV5REyJVwqYzF/kho9CUXYRFyESo+w+MD/ONHiQmw5A2v14o1Ex4vCY9bxbYllMWMETNKreBSUUp0Z1yw806w9gjtHL0RZOgpGMgEygk5JaQFHSAwluDFecHDkvCF84LzkvBL50VUCMYO6OvDQnjIWQ0OGxhYHRBotMFyAWnSIo+cWS/NnZCrewV00Qav9Jm8pPY+7titX82/nva/3R3vxlrTs+x05Afj+laMkptjnPTJeTim8z1hn8Oie2z0v6/UwAEgm4ukF5KQAA0UmIrBjDy6mAdVPQKcMShgThOAwB3AaurlDEzX7PsYFLnWsPtnhvs138XE7NmC+H5cOz2Xyx1ybQ4KaHf9o/JsgEFFE88yDWfofY8SR0DAEJfEr//xh/jub32EL737XkedRQp2yhocoeixvAkFGScvW02H43bv+J7aAJ0CgqkOex/BcLQneBNQ0D9ftPDucYsNc//uanto6FJJEye7Byg4sN0C0HZb9xh1HbVjLKOayOnBERQ0QLALsmTXqM2C/8hQT5iPBM6ahyIvInTWE8wWhNIKygs4r6C0gJYTcl6xJGEQctWMi5WRKeOcgXMivE4Vp0Q4pYpTqnhYE15fKs6LgIG/WBJebxWPl4LHS25BkQY3RlQxUGQdMGKraO0edrbapqQ2AyANJqRRf8wtMS8JKScPVmRqgxkgMDuCBwcEYmewkACCRY0RR0BA2xMsi6H0S8hmeHny/uJa9PV6P0H7yQwkHGznxceJA4Sw2+zH3iCkb4zBBgjuHNuHmwSRunxDreDzUm+cIEaEnWxkNR0d9kmdgOM9ODDDxJQIll7cQYH+OkZM9HwLVYxrPSET8x4gWJtFkBDasT1kaMe7bQGG39/zu3Ez0pqhqWCHR1ObTJdrRBkgBmukyjGtcjQ0fM8NDWl6r6PyfICBDdhONjZ0BfTCprMCJ8kG2IGC3/wIX37nvV0LRm1/zx7ohLtX4MdzDUzYMf88magT/+LdIjMDA8GorQMEBhTQWAJnB9DAwZuCAgADYOpxLRlIoBBNjuD2BUd2Bh4r3dQJEnx9vhh0P7wyce9B9+OOY9RlduyBgoIiIZmxXQCW6HXQnSe7UVsz5Ns9lhpegUhsJShJaNu8AMsKWk7yuSzCIuQFKE+gfFKd/Vld9pRB0JTMC2U8rBmvLyL8X28FT5Xxein4wprxeit4fFjw+lLxeNnw+lLxeqvYNPDR46VqECWJbSDxDPrgRkcBjmKQo5Q1mFAOcQZWZTdM4K8J51WAgDECZkdwSjQHBKSREhNhzQoItkdRF5QnBwRUL1B/TPD2JP1UNrCF0tZ+kzod95Gp4pwpUIM/1AK2fgP2hqxH+v4DFcS0HG1CZuP9yrrg+vjJHEkQsJOYm50BoK79cryG/VAEB9Ma3AkOHBSAkRm914KxB5bOmesAEJTKd5XtsC6MatvZujqWa/3zJmAibEJDk+zCxlvxMNNoXjICEIJ80zaw3ApmaNjkWwMhR/eJ5dkAAx9gaOAgUi9yyoieWwcxgD/76St8408+xHe/KkxBCQ1nP7Gw60EF9ibkU7uY6w5N0AVw4DW5do397qKjDW3S6/edymAABIw+G+IurHHFLj7B1KZg+pzyMjIFLvypGRqZm5idB7SJsKu7BhchAB7fnURreZWx6RbbyWS277tr9IxAp2+ugBs9ogZIrihe3f3YhE7cjW4XEaYenrBvSQZkR5qy7LhzBmUBAJQXAQXrGcgL0ukMLguQT0C+AHkV3bkyCGsmXJIChCQMwkKin39dEl5vjC9sCY+FcSlZgEBlPJUVjwoIJEqiBEK6aBwDC6Fs+RMqt9wJUiXuXCI9p0FqeRFOWV7XnESw54wHBQjnxeIQUPAyEPdDUxnkwBCYHYEDAmMIIiAoT6C6oT49CmNweQRvmwC5soHLBSil9c0VpoCtb5a1sTqsbM+ywiJpdqoA263b+hSTlHVCexij07E5jNHD74drjGsDsBNuhKbuN7agsXss4JjEiDCJxaCvCw4OgEMB5CT4EThQo0RSUOAsQoVma4TYqxB6gOCqIEMnuQMJAOYbiDdhFsc1t2vb+0snrIMMGx/NVnMDCVl3U8YegCv+7LOf4EP1zusNDWl6n2vl2QADa8zEDRx0O3lgt4WNKOqlgoLvfPUj/Mq77/V6UtgGnrrBHkHI7vKzQkL9+NPqTrMDB35uEHCTAbfTT8YJ74M27kauA4IIBLpUyVdAwb5++0OBKO1AgceVh7qj6fcWtz7pj9olRzpCfu1GWQiTfXiO/S4NQ1sfoH0eAUG/ExHrYMgCpphAIreFZ3VDtqZS4HKRXalS1ai1qTuOhJAKEFMr0LKA8gpan4C8oF4egfWMdNoUIKwCEAYG4ZQJFzNSzAIQ1pzxhQUovOCxFFw2xmOxsMjAU6mSMrpAX+V3RSMRlgEQzDIuzjIpJoLGHkjuLeApizVi4RicaF0IZwUDOe2NCtck5lh7huAiIEEBAV8ewZcn6Y/L03GfXFMfxD5hBpZFReYCImWEiBqI6OZhbnr+GGp8ZPYOhPZujNo4HY4dJczZgYHdGuOrJyDyWVwKmVEUJCST/hbgp3K3PvoW5w2Ijw4cKLUgXplNtcAMz9ZoQrIDCCxGuxwAPGlYZrPz8g1E11aTB71nDe76Jk3P3f02/I3CmnX9HR+jZawUu47IHrz67Cf48E9+d+JSvwcFkfU+Ks8GGMSBpZ4voeK9xUFkUgoDH//0Fb7xww/x7Q8+wpfe+UpISSvFu90prqPSGIpDv1O7IvVXn3ZSvNWhHvwA+e/AQI8cm9BHUxegDcoY2piBORAIT1CHz7EKI0tgrMAYYS6H7021MDYDoCwL0O6qNODuEUd6dLfI0vx33T118XA9pe1G2BdvagkJYHEU3PrbL1PVrqCIy5npr1UIQQ3dhIbvhRGlJDR8NuPDBOQFnBfQ0wI6PYAvT6D1KQCEs+x88wqkJ2A5gbaLeDLkFbwsWJlQKtzVcauMh5yxLXA10mMpAgRYcjJsamC4KWA8yuhoJTJrQLPVE5aosQcJYgdAyVQBSTwpsgABGx8Wg2B0OVwIYkh4uajLYW9LMAICBwVPrwNT8GZ9IQauIcNHMU+SBgZ8vDlDYCxBiCg6hB6fzdu7xqmNM2APEu7YDY9qBN1ve5ZE0qOZpVOZYYhBclMkUqPEplow9sDK0ToxFltqDRyAhAEqkPXB2APSqrfNhQKEMG+dRXAgoBELlU1wRpmHJ+J6Y7kfQVX7/bW4OEcXbUxB21pEKSGpsdHVGwT85DNRf4+ggIdXAwX3mMA9I2BgtZVBNC5Ifl54rSxMwe/98EN8+4Pv4UvvfsUXxFh6V5sGGiI7cQcoDh3KcHHqwuqWjmtGY+2BgBy/AgYCajQQsGMJJqBgFvoUCItH+BwecQ4ItI8sDG2MOZ8DKCAct2sDB+3z2D7johqB+Yy6i8WvRqQx3AOEq0V005VASN2zMGSXRlkMITll2YGG5xOKuvqfZM4TIcS1glWHZSyCRS+kTCqYEtKqtgZ5AZdNDBAvK2g9C0B4eg2sJ7VDWIHyBARDRaQFyd5TVndHuG1J0ciHX1hFABQzLNS2Y+yTOgH7dMszUxlLv0xpn7nQIw0mzW5I8MiEOUlI6RTdDTVVsYCBwaCwXERdswkQ4O2ioOBRjkdAUDbUy3ZHH0i8AKosAGFZBQyS5szwcWMISEM/K5AQd2B1Cda2H6OMAn3Uy/vHKQDKDYz7T8POOBqERm+dA0bCRV7ow6LggJVOIJNQDFRlD0aAYLcefQF2oJ9tc6fXVHCQNEdIQhOOCO9NY9PWDjoECXKfCVBwRqG16f1lskG7UWbrsR8P7QE0zw/StrF6f/KpyK/vqUt9A3B7WWfXi1k0j8rzAQZoDTIKbfveinXGS23UP/zge/hralPgBlThelGNkMKx7rO+HntDmNognnG/PnxK/Y1AwL/fW6AeAYLd5yugYHw0Z9n6R232AbgOCFytEN7795js2ocb7+sNr7vV+1bdgXkXUOjbLmteIg8wJLv+C6hSGy+seRPM+Mus1VOV17LBss/FEgUScxNMUg9N2sQEwCyxKyhtHUBA2UTgGUB4egROZwEIeRFvhi0ri7CAfMe6glLC0oXATl0cixmrNANZAJw5mJUOzlHPCk3HhP1Zz1qa4s2iEl5C3AEFBuZdUDZ5fXqUnA43AEG9FAcCsf2tDyiTJKbSkdMlh9Lnl0rm0O/Jd4zmDuygIC1gf78PO25tDhzv9Lq5NrThKCTb+QZ6bHGcCEPzptAVjfUrcwiupIwBGNWAgNkFzAAC2tSN6+p4Wyuyhu/Bga/p3NiDKCjjTrpt2CYgQdfeHVCwugO9auFt1umwRoeTMUyXbk7NPocqdSqFP//0Jf7gR1/Hd74qhvKF4Sr0ri3D+hevd608G2AAaGV1MJlKYYey9dirn77C7/9IQcE777l+HWjoCghIOVx3d9PxWFQnRIHme87wVNfQ5QEFPgMC9iidMAz1PRKIM0Dg1xoGZ3wU2wnGx3fRHMCAHI92BMeAILIErUbAjgbtUMhB/eu83rt+1g9RmLnw8mdiZzMS62siZPWpRrm0xbM1u9RDPRAsGIsIhSoChpNvBSilfVCg0RixAuLGKAIsLRlcK9JSgLztAcKygi6PoPXcezMYSHA9tyQ18udT6jtb8KuRFk3zcbmfCNfK0Lc6ztz1qsoC7tFCPYiN6u5rbceiq6HW3bwL+PLox0ZAwKWgbuI2WrfSwMAVuwIrXZTBZOodCT5lrE4zEl1V+K+qAlJQ0DE2mGYrjXMTuD5O5S17yu/dfLM5aNuXEDRn3kXV+9a2M5IZsT2PeCWoWikAhATZxVe1WEzaxTb7DCiErp8uhTyAA5ng8jmyB6RS09b8biNn4CmAJNLjc6CgbF98uDfxNhsqw4cgobVjX+cJ8EarAzPjzz99hb/5p9/AH37wPXzpna+gGOCayaOJDLhVnhUwmJahFSIo+JaCgtkEjOdPVRLc4vub2mK2LDrN3BkXXtGuzUDA7ngvCO39SDvuBlg4Nv9NO3cEyLsnjrLZqOGw+3uTNLVXGYKd7mIvjI4Awc67Qo9F0NOhaF+g2Otsz5oB1KT6VgUINQGZMtKiOuRi+xO7YJXdujWxqg4IALIIJLIEONtFwIEFQxqKh7y1XARIqCggYgUHDCoFWKqEi1Xh56oGs5g3bwbzs08SjhjUfO3dYJdi9MVQs3stseN313Zc8TtfgNuC3EcdDEGjWOoZ40R03gXm+WFtoe1iqpu6bT1DEwDBNGS2VcvsDAxkkbSheYwgi7cIrScBYMrKGCjgvAL6arkvSm2AYBZufGS34s5/x8yF9SgluOGg0e0NIKD1dWz7rj+K9yMpgwCogJoABIYwBYUBSgoQbNNhRoqAx0PgSCFcKYYPgMAe6DEDCEADCUlQiIMECs86ZRP0+AgU5FkbWNi1z1iGTcthlEv0Av9IYO/YOAb+/Gev8Ld+/A38e7/+Xfy1d94TFmfYEP+85dkCA6P6x2Mff9pAwZfeec999ONuOZaRvorhe+NgBeK4s8kmC1qnDx+9D4BuoX1jIAD4BOAw2W6CgfDAkSUA+nFPRB1rMIYvHpmBEQwYAJBFiq6zA5HOA+Y60E5p3UDBkbvlXeGbZxNJb5HBIAI2YmQmJGJNvqNjzAzh8toAgP7ch8eqr5Z4CJCdezwHQS1VLXgM+S62a/VBYHFhVGxIy6IxE5QGNoBweRL2gAisBowOFDSQkqk9XBgepPD9J1F8/Gn7VbPPMBo4uoByba6G1hbWDsGo0EBBVwLwImMBALHtoOQ2HmnJDRTkRQDAsvp7+xNQsIDzeQoKol3HxpCU17oexXHqUPFgnJqaxRi8bK8gSY5FPUAwRbRlrKY4cmfzj3X+WT4QIgcJpAAhwYSYrJekxoGm1nUWIYAEQJ5nlLNH641NfZm3CooCQAAaSIiAYAQJYBywCdoaASgAHFQuqW8XAFMmYbapm6h4Y73G+vs6PcilP//ZK/xLP/4G/vavfQ+/rPIrgVC0ngliizFO22tYZlaeLTCIxRrl409f4vd/9PVDUDC23ZQBeFM0poq1Fgc9XOvQYE7OjPLbBb0/b3ueGRCQ726DgXDIr7evQs8IxHrcAgNzKnMPBrpslJNi94ttdgQK4q6roi2014zn7NaxSzIIG4ngNypTjOL00ZMJc2mAnE/tAhR0s0ADB0o1c1KdMwA2gZwSco4Ut6aoBdriNGsbXeFFFZEgoqS0CqUEKltQAQR1QNCTX8tZMaqUEAP+zKj3KwtmVzpK/kBnf+t5YhyI4Pbp7oYKCOSxmqcB5QC8TErm2B4BGBggyIF50Vc6PQQwcJ6DguUETgt4OQNpQQGwVeBSNQ5EVe8QZwsAKFiIY9Sa1pqyM95kZbR0nHoGQybfyduIzFB7AQcHod0nc7EThiRjzDwwLJ+HsQiyJQqgAHeChNDvR+tsBAdAWMcY7ZoHIEHqyR47IYKHyr1K09pENnkGFIa5yOwdcWjI+QsC1gwBBf/yj7+Bf+fXvotffucrQQ5oe9rm1Z/+7W//rIDB2AZxcI2ggCegoN8pN2SazAf2jR8ooWMIDCAcMgYHhnOAD4KRFRgBgv0WuB8M+LGh/t0j6utcr9kmWHQ5jBOt4z0iGJgY/fQ3trYZpHZgUY5AQQGj5XeQP1toNw3K0+3I9DZWjC1YMiGDwDmBFGxwksAuNoFYVQtLWmRWFV0wqCWysWhlrHkOXG8f9OL2l5cm0KIwm7nPjYXVYp5LETVyCWmir5QYOnomSP1V6Xd7L+02f75ZaaxEE7zSVqm9D6GGPVpiENq36oKDuozPIFN0YMGGe0c30QgIzF6D1jPIjDzVC8RtCvQ955PntdgYOzfRoi6g5hW1lYoCiS65G6OAj1Mbo1kBLOkrV4A1KZGETiG4H3cAB21pG7eY1+alBhNC8d3wDiQkuPGqsQgGEooKfgIrkGggQarGnfD3R+L26ktq1yTXQQKAHZtgdgkECHC6wiaQC1u1zaDWNjyGWt7ZQb1FCbLpz3/2Cv/ynwoo+Gt/5b1ONgkBJJVtESX5EGAFfuiwPBtgYN0w22XMQMEtP/2BsW73OXh//GCpTawdK9AuzuEP6JmBGRiYqg1+DiAwq88MDADX2YGZuyEdgQEOsR3mVAUswqHtQzq1i9Vb28FfuQcFG+8X241FUAg4kCvFxTcD2Eii820MCSdcWdiDnIShRqP8WFULyCTgINqTUAJtTV+fcka1hXRZ3EjOAUKgwWkMtOMC+Vrqmlb683oBGQX+Ttirl0Q1YFCEfjca369R6gAe+mt33ekCtwl/EbzheJKUy2lZANupJ3JGZQQRUYjHa89Ku+/oGqwW+wY8qAEXC2IESgIEiHpAsDSbgnQ6CytgxoXLCUirGBkuJzBljQfRVAfFQGqVcSRBpfT4MEbH8QmEMZqEFVsygYvEgfAesO0wi2uhGAnKa4LBhFDump9tw8MGCAaQkCkhEe1UDeapYCxCBAlVhfq4hjH69Wm2dsVDBWyaAP3BsIaBm2wf2AR3hzQZoA00MgnCIowOmMO4vwMUKKk8+UIu3YGCd95r9acGDsRbZA4O+nuJaviW6Ho+wOBAShso+MMPvucRDZtA6UGBCYh4reheFY9350wJhYDLJi4rQBiz4f09rnVHrmKjrQDCb66Ve9mBIzAQ2YHekJDR8tkPiw2HWh0+IPlTsLp8EteGzhHAkLUN1MCQ+2RQmy649icLL+vCu3+MjSDJhxIhV1mAl5xQlNY95+wJg1hXV4+Pkgg5ZaT1AaBHkAW+KRcwZXC9iGA7nVGfJEIfnc4tbLLlVtDQvGLMUDpwQEBvLBd3dQcgIsZKkOOMupUeBKjLXrlsDgDqJVju16rnSGZFy4vAJWRVdJAQetLbJukLqWCXv5QVKK250fYpIa3ZAUNeF2m3NXdgQSj+ZhswZRmCsPdnivMyqFn88xiKmjRuQV781cNRU0I98jxYzmJkWAUMXILqYFP7AuZ9MCkDAzZGx/EJtDG6MLm+XsZh7cAB6SYo2S5Sf1+5qRPkvIN5OpujKq2FYRdgMIIEt2cJqgYoQLimanCQoAJ9XNsCCdu9Th4PAFwVY0aKEShENqHzcIjH/IIYkhrJRbwJD4EA7T5ZuGmpT0MH0a7r7//sY1cfmKF8OwcNHBjbzKMd3BwczJ4plmcEDPrPzGZo2ECBDZ6Z98HMLc8FZHwfvo90k729jsRCh4W/+EwzQHALDMQn3zMfY73GQbJ/OrMbAO4ABNfYgbodLzJmbT4+uO9CEmARIjW6nKvZmbuGNkBgbAGjGW9VFluCAg7AoIqOV2naFrGvb6uSCKnKIrxkgLcitgEMAAULqcGB7cg0c4zUUo0SlwdwUv96ykC6gEoCp1UAQlpgrnZQIzoKFvbgg4RLamRnfUxX9OzkrFVphtUBFJStgC/FGQHeCupF7BzqtoEvRVMuV/9dubADDTvOVZIpyePtV2oT1skAQZaMiibI80oq5C3TYhYQsCzimrkV0JKRtgReFtCakRegbkBCDmqIgf63Xb982d6b4AfEtsD0xSFUsae+jp4cQ5bLal4HKbkKgQM4MHuCpjboQYGAgYrLdjw+x7Fp+SfYkgZlm+tNReJBmHQ+EAm9TiqII0DwiWR9dW2+Tuhyc3ElY/ciSKgbOC2uaqCBRaisqgUDBIOqIbIIM4Bwbc0T5rdfd4kGoDCCBG5MAhGuxkmITUjh/357hvm3JGCoDvUgAP/Zz17hX/zxN/D/cFDQ17Fby1V2ZL2OsQaxe9/E3uDZAIOI0yqATz4T7wOJaPhe23HfuM6OLaB2vAMLQL+T3l1pvNOcJXgbQDDGGvA73qIG0A8oavNhV983URckexr3Ka+wsMHTxUXf6wMNzWTPxrCUysJ3qnCzcMN7AtQb1ncR3kaaCRDQHf9+0fXNrv44EaEWFi+ERGLLJ9t0qfEGYKliS6DggDWhTOu/YHeQBBSgJBEi9QKqiwj6WoB8cn98Yu4yMVIABp6uOWRplMcKwMEs88smv1W3PqIki/GGwCJoYCWuqLWiPl4UEBTUx4sDgvK4oVxYv6uolyoZFTdhGuql9sBgMhZt3BkwSKuqCZaElPXzIgxAXgn1vCBtBXkp4PMqbpmlAudV4xkQuPbqBPMYiG6YzTYgeGSo4LffdV4Z8XsDBpoxUWI65C5YUQttvIpRZ14lcJGqDq7ZE8xAwaYMQUxMNR+bMhQTE7bConphFpq7MHJmWNbYbqpNBYRNmmj3Y/P49pwltTsQQLAHCagFFjNDAJmsKLa5MDVDOQIIPvP2ACGCg9m4m+UdsLEotw1qB4uRQLpWGiC4ARC65vD/3Zata/i46XSmJKzLP/nsY/yLf/oN/O1f+y5+5Z33GpAJbMKsHKyKvtabnPz401dXN7HPBhjEWv5Ewxx/56uS++C2uOwBgQMA3xH3hnUI30VQ0Iv+/cO9LSiYeU7MJsKR2Vdn0TABBfeqDA4BQVQXOCAoczDgYAFtoemayowzbRmoYBbmgHhwnQw/G6/E3I51Ft2qPpDf9LH+7ZhVy+8STZZrW6JGcCCX0Osw3O7AXBpTPolQqgWoiwpzAQZUNeqe2RSsPeMCNP99C5o0BQ0hZTCpOoI3CZmMsnXjQe6X4NYVtakG6lY6ULA9KhjYCrbHBg64MsomUQMthoS1ZfQGNLo6qV47JSCtGXlJDSRcEpZz1rhFCdrAegERzly5U6GQ2h2kNTc3wvWkMRpasilXA8Td/0T4I8RzAKgJu7SI1jrYHHR5DgJAYMqowNQV8W1AwT1jM0OzD4bJXsBYbLQOEqOGcdAJCGa1K4g2QcPcPTQSFrsDotBuESSwRUMUWyGmhKRqBiJ0aoabACGoGCI4GAXn4ZrYCVfqmARnEUzoK7USdff+WdeZaH/QmnoGDoAQRFqeRcGBff7PPnuFb/7dr+Nbv/E9/LV3viIsj/5gZEDuKR0oIAEF3/jhh/givnj4m2cDDKxjXmmlv/vVj/ClLyr9cmMnPdOxHyX4+fPPXvn9DBS0n9+xY8f9oMAMJIE2AUZAcDTw5/VsoGBkCd5EZXA3ILDda7eg3AAFXBorQCwLL9syphOLK5hl52Yl3dEWM3o7lmisaXo7zw1fhTko1i5hZwaLZJhlhlcwFpW3NWlwpCr0b6KMnLNUi4u3EUeGha3d+kWZYxvG9jXQYCyDqiN4u/irRwT0YDxPANRlrVaUwrpj7ieDqAjgrEK5VAcF5VLBW8W2VVSo6x23HW4EZOrw5XEgVhDSYwEKg5YmzYqqGOSeSQwhwypl9gZETdWQTyvyWT0CQlwBCrYA0Sag2/2rsGeL+AgTZgRMBBxiNEhLeKTgoELtWhQQxJwTpsoyV0QDBSOTZbYEh6AgjM2r4zwffw/0mwX9Ua8qiGCgtjl9OH817bF83drNQIK0EfcAQef2EUC4S8XA1NrmABzMSnz6BhKadB+DKM0M+66Bg+tF1o5Y4rT7/R99Hd/WMP1ts6Fr0RtgAlu/gQYKPvnsFX73TyS3wr/xH/zrh799PsAAkvvgG38slf7yu+8BvHfzGdu1R3i9oJyBgm/88EO/X7veDWF3o1wDBSNLMAMEB2x8/5wDKIj1BFpdZ0aFFn/gusrgDkAw01PGh9ZFonlyJP2N5a8HXJ2gdtkWiXDEZKQTVqx1CRtU38qkbthsfERoq7YLsfeJ9jPRdmbyajRuReYswQ8rsOnilarqUUlwTiagONjKSDl7m3vUNd5nhJupZRDamWtpLIMCNgMEFBIIYZHsgpQSsu6SSxijm9WvVqAICJAumUMvS60sgKB5hMix2CE2R6SmlZUSrxV5L6bU7gCgTMiLsAFpyUjnFcvphHxekB/OyOcV6dTHECB7XVbPEyFAIMGSFTFMsEfBZWxV05H7MZpnKq1az2oylFs4Y3ed5RZHw90RFRRIV3LHZMWSiDyIzREgsGOk7ooG7hyMYb/5gR7rcKCBUe+yAZzenMNVu1hBfAD4zhJcAwgpI40qhhoAwQAQ0IwBfK0cwcE4amfLjn3fAQRqng1vYvU/LVfkgrtA+j/gu1/9SG3iJMV1ilLK6tmO9Jed3D6CApOP73/xvf9uqBJefdpST37li+/5rtwopkCYBr3ObLbss/6lRPjJp6/wuz+U6/8LP/gbDRDc0JXDzhu7IbAFwJ45GMsMFMxA8REouIclGGMQ9AGJgtBSipt8JzHEsR92vv1OpJumrV0Yspu1YW/ggKn9jkSws14r1tXRcdAXUjiedaFgf5XxsKSkKoUmyMbFVzap+7FSlDVYII+3qSV4gT26+ZhLtGRxgRRL8jkzQ0jI/uDXokM6cBiBmQIDLhuwCCNBZ0k5TJdH8OWkGQZPwOPnyHkBpc/VQ+BpV8dFBxnXDVwT8toEGFcGrxm4FKRKWJOwBs2Ya8YYAGvSlMuqToj2BTm85vOC5bwgnVdhBV6csJxOWF6ckF+ckE8r0vmFCP/ziwYIFCS4+6DS+6ZeYHMnvZHqWLF5Y/i0C8zT5RqgHyNtVvsd1ONAp4GxBWNJqng3ADWOSxuTSYVo1vGVifyVVBduv3KChPr9qswX1i4z8HkF2B/NY9u4q30QgIFpUYDg+TgYRBJ6mFOWMa19kyjJvroRhR1AMIHJzN163sdA2IMDkwf+5NyOOy+pbMAo89l/IOAg68cuRMT+F/ubARhDTQPk6/X7/+x7DojEdknqQhBW6UhuxSWqrf3yaqBgTM18VJ4NMPjwjz/ER7/1PbyvoECEi7wxdEoGy8JgiqXFF1c9qC7eP/msgQJvVFuIgd66Xgt719Qda9C72EVGYG5TcMQUxPKmLEEEBCacxpDFdtzq2gS/6rZH46SRctwtJHHxaS3lGcmUJWjgIPtvxb5AkYr/XgSzTXzfZSgQtPmTE4FZwsMuZrEFMdoTVzBCSj17EIvvyAw02o5Mn9u0DQUMFHUT46bbNYAgi5aELzWTsGbwyZ33S+y7yE5Zlrhk8syOm2rHGAN9RVHWIK/A+UHYg8sT+PFzsZzfnpDyAlpeyy59zaDPM5LFGEgJRI/6Hq7T314XGS9bxUaExOLWuBC5nUEJC1gm7OwLiAjLg9oZLEnenxYs54TlxYrltCC/OMuxF2fkF2cBBacV+eFBIg4+vAAtCgzOL7qog7VzHQyZIz2DZBP+nmOj2jxs882G21Ek0dEYOKYot3GAAAo80iZb2xA2FkYr17b43xqTcTxmCMiVIEcQjxgdUN0cp7ZW+Px2xrO9zoKPXZ/LWiciRHVgVwNjEJjVALSCSYACFZbAX8wKHAw8y3iPrGAUmKN6YWQPrIYprJ++3EyadgQHoKBW0PYzpqBtUOSCUw1OaMOdnGAFTgoSzH022T+WGxo70tV7ePZxTbf3APCxqg+a/Or7bFaeDTD46Kt/B++/+2WYsLBJAEV0xhrUAA4yOdYFsN85ZwI++Vls1K/A4oU3/VX+VhYAAQAASURBVO6kkPnb31YjWJkN0ltlRLRvCwjeytOgy2wXgMEdgCAG3aGclSIk/61QucB04IZJxrYPJalDYo92AE6ip80su/VFg+hsOqMoZVCpcKyhysQxfL6HjHV6tjEQtiCP/VD0HjmpkERTaxggaL/pmY84YmYGsdeSU6W0IOUFyyL3oXIBl4uAhKKRFZcNtD6C1hP4/EIAwnICP8pOOz+9RloyyiJugtvTkxj1vX5EXjPy44bymJBPFeVpQ75U5FNWu4PsnglHXgmdR4IxBGtyQOAswWnB8nDesQTLCwUEp4cGBhwQnD3ksOcmSIvnJmBIsKrKQN1E+M+yGM6Cnh0ZtM12nxzPU0BQJmoCIsj4TIQFIhRtPAL3jUkbj0tOCg7E0HXJKaxlgV2AqRbI5J5c64j5jDW6cz4DCsoVIHQeRqZi0AYgUghGLCQDSaAtcvsN2VFn9PYHKV0xUIwAwa5/BSC8TTHccG85Cvnesu8mWPhz0pUtqlMAdUtNrZ7AXH5FtsCY7u9FUOBr8/HzPhtg8Kvvfkknb1WkaoMPzRXEaKWAJGVxlVMjS5BImIJvGBPx7pfa7hiAx2ePHe1AoPq1ncgKhnJWDsFAWJzGMurMZoKkEyLWBi48JilZo6CBDrpBbdCxBMPx5js/BwWzBcSrWsoADu4oXGHZzoiy2xlIhldCUsdgJsAsgxo4UJ15YWSl/RelGAozVruFAZDwTMYQ2IJsbZ2H5zaX0AgQdjW/CwjGJc3ecOi/pvYymxhLCb0QIdMJ+XRCUpBA25OoGvIKLGcBCKeHBhD0bz29xvLiNfLnr7F8/oTt9IT84oTy+RPy0wX18YLtaUO9rChPRQwSL+ryaG6MB3EMTG1ApIBgTcinjLQmLKdjtcEhIDg9SNjh5SwAQIEBL6eWvbBKyIeNY4hsjXnBLXiQrh5d/7wFXu9KBAQjWMpEKBDwiiTbGa7iSVC4bT+XO8YiUQvdHUGB5PaQ4ymhqRfCnMeB0LpWjua0fe4BwsgEJlk/1RVZAEFVjwoCcWpgYaZeCABhZqBYa69eAI4BAnC8nnqd0FiDGRg4BAlB7bdTOY9ygwuIgmyZqFOqRo2sxh6EywHYbfZ+oob4H01AAUVD0kl5NsAAXDtBTJSNiQmuIA0cxI4cd9EpiUvH1//4Q3z0m38H77/zJQlQE3RrHlCme4YYvhfo1QhvADGp6cus66Kl7V27SrvOZGd5NbFRVBscsQRvoDq4BgreqNg1PSZ5hW2vCNCoauQGQkiMpEg7pZBMZUlYWEIcW3Iai5Vvut4RsEW5H0FAJITy0LdRCGxhyzcmwzm676y1RjBCCU0QAGrxr/pm3TlmAwnphOV8AtUNtJxB5QnYzsD2pLH+jwHC8viE7fMnlF+6oD5dUB6fJK7Bk8Q84K2gXIqAArXEO7S30hDHAgqyBCxazLNgQT6fkE4CDpYXJ/E2OAIE6xmcT8ByUiAw5CMo+/gBMW8Go1H7XJs9wK32tz4AooCO512f5xky+cSdUBf4qotxIgWo+4X/6L5dIiWb1yNoTA042jF/yrCu+cO8DYUZSg/4MQcHrPob9T6iujXbA2MPyEIPp069wGjgZjRQdDYBkwiKA0AAMLguWjuHeY6o8ru7BcLbUSUz3E9ZaI9jUC4AUQgKlX0tr1X7T0HtaG5g6/snn4nL/vd+6yO8Z4aGM6ByUJ4RMBBcR4CGzG0uIVGlMEss4fSLTiYHBV/9O/jVd74kNGxnGQ7vzL6E8L1mdcpiS2zjysEKY2cIc1TsvFtRC68FKJolN7orfHFgCeYGhkeg4L7Sdhb6JG4ZbrUaK1ubAaI+N0EWOtcrKDhYkkykQrL2FGYNwQrkTKggrAxwWPFnlO+s5Ikx4vj7loNBBf9E+Mxi4ccwzXKdoQnQ65aNSl5017hQwpLFinpJDSSsiZDTgnVdkNYH0PYI2i5AeQQWEb7pxS+hPn4OPH4OvjyhPn4Oevwcyy89gbcnAQXKGpTHTaIlbpuGTbaQy33oZQCIoYrtNXlUwwU5Ghma66EaFSZTFeh7SUp01gRFZ7AyBhWESwXKJmGHj4IJXQs7fE+bAxOVUm3newwuNJDQ7fb1Gt3im9vYWXG9xLFnQz6ySA4cCc4UOJvkr2He27WMcfWKdHfRxhCBziTnU873g/4jcIDku2T3eNT7duwB1QYQUobHA5gZKGKfrKmLoEjjpmv/uPcSmM44t/2Y1jdkjdX678LBx+tYWudaNN4Du71FUpuYWV3HZ/n40wYKmveBwqHRduSgPCNgYGYjupNU1sBKsgadbdypGdu9+vQlPvzjD/H9D76D9//KLwPl0qy+gdagkTGwVSCG70V4HpsAE3WCX2ICEjrTnQkyjOehm+zHDMEUEIy2AuMAHqOfTUDBUbEB3mXGG773Go0+49YwXumwLWOzzFcDJaubt450NCV19arwEKasaLu2S/nrcsP/u3v28N6utaQ2DUtt4CCCgm0AA7MojJXZ+36WsDAl6XePt5FESAkwEKBwToQ1C0hYSAwvFz8HWPID1uUBVM4yzssFtD0C6wvQiwvo8gh6eg1cHtWT4Qnp6TWWi4AElA31YuCgSMyDbfPwvSMwkOcmDTpE6oK4IK0aX2ARO4ddGmNnB1ZUVRdwXtV24IQLA9smuS9E6Pfhhi+l4rGy5MvQc8YAQuaZMmtnYN/WYvwnSYssl4ZlOCxg5EphCOtu3hvDrtkK4c3Gnk2LqAaNhmfJVAdoa4GBAsT5HwF966w2pw0gUNIZpeuaggMAh3MbwHX1IIe10VqjFoxRTyN74ACBxb2RyLKWogVIAjRRFGB5GIxBkGfSf9wMRzscZM0Aa9t9HW6Chqiema2pM5WCbQ24aECjjC6kudWXaOqiDcxBgfVzv35fBwfPBxgALnxbop0+kETbVbZC4e/lT/8ePvyTb+D7H3wb7/+VX/YQtd1u2X5nwIBIJ0mCj+IIDrrnaSLL3kQVgXlPuMcEt0FpA7gbpAfswM6GQM+dAYKdHcE4eEcmILImR8Unu1Tyuu0Atd90oKABhd6rY9ACCn/o4Ej8jbXuNSRnUeDFMIvethKMlt+3OINYfQMY5rpk2LNCbQuqGJhtMPYADgo23c1WiLCqemwUVh7kJtzYXdiIHCS44VkiLCnh8wQFBgISHtaMTIxcBcCs1ViEE1azRVAWgesFdLoADwIYUhWXR2gWSNbYCEkDJzGzZ4T0JEpxvlgfHmUqjBELh0yF9oe0HrIDjSGQIEGvL8XBgGQshEcWtL83bV8DBou6Wy6ZwJvsQ5ZkxqUtFfeM4o+7eiAYAno7XR9749f3Zj6NHkfZ1gTM5nKCCyiPZmgbmz04ALCjsw+f+pYxtj9LYBZM1aDsQQcQgv3BNYDQWIN9Nkd/+NAMR2ttbFs/TH37x6aIBod35Z6w35VLcOekBhC0vgL0sqpP26O/GgwNu7V+eJ5b5dkAAzde88ZvgzoGkXD6qHtlvPyv/gwf/vAb+P5v/CF+9S//shrUBct7oG9Qpb4MTVMYTgYOmg7AfjSjK/ZHCM02wsrMt97rcAUQ7GwI7mEIOn3YAcI8Glxe53R8bvxOJ1bHEhgoMCHigGFoKe4XLYLsiKLlcgXcx9gENzASefoMNxBBdFcDzIpdfmt+6wYUSFUYSyJsFSBzS9BiUe4MFAjFLYFvqgqxyho5j1U/PlFzZLUjSCqAxCK9qRMWSjjlhNcL4fVWsWbCOSesOeGJRNWwpoScFCykB+SHh85gkbmI3vNsrpAXiY/ALYgSZvkchjLLQ7APPiSuhdXcDCnvDAklhHCV91WEfmFJWfxYKi6F8Vgl1PBTqV3bbqWpGN6kXQUQJKQEVE5YxJ0ASwYKywqzdHWVebjoRDVDwLizz0GajDT0rcA5s6/D7PHfx41BNDD2jQEwmZ+aDdQAgC6lDg50zpEL1SvrwggGjuplc9mvN6gdHLD0AIFExwoLnjQChIS9DcLoxQCgcwGcBpEK661XZV4TdCgj0pFHm614HtBYE5QQEMpct9nVC1Gd8urTPnhRe745S7CzcxjKswEGY3Hd/gQcSGnC79V/9Wf48Ee/h+//+rcEFIT49Ye7ZDtGhOkgpjYI/fxh8gds3nlLWL7smRp7BAP22w4QDIuAD5B7UyCPaPYqwmw7eg6HgIBQBxVK58Y5ggGpnE/2nWoB6BiI2M+2KDDgaNp+5SxB96D3lxoqZgDAWo1Z3c4UKBTIbtQMHpdEQE1gEp32ZvVgKJ3NU8F1KVVfue1qJwI3pdQJsNMigl9AQsWyJZw2wueqWjhnwrrkoGqoolpwQ0Xzgz8hnU5YSBZQKpcwL0obTxObEwL6cTP2s/Yxp4xKBhhCzoHBvbAEMHCkKrhsBY/6eauMp2hToO35tPWA66g9AWEL1iztuuaEyhULk4fATpk91LWVFmRI82NQMwRMSY47e3cA6P053miEtub134f1ZtwI9dbpUlzQan917EACPNiYXqmLRmpHD+iDnet2HA9HJdgk7FlZTBkEAy6jDUKCGZ3uAQKAHiRMHmW2CbMyrUmQF96m9zKwZtdF1OqUVJVQgjunqlNeffYJPvzjr0+CF837+Z7ybIGBlyj0huPEFS8/fYWv/ej38YNf+5bYFHAIDjMsdv3vLWq+ix44ODAKzuyYAxqm4UqkYAA8d6UcyyEY0B8cuh1eAwQjQ+DthjmiBQK6N5UBAgAwFD4ubUONRjuCqDaIgMBXywlrgAEEAk5bx1bs1Lf9DN5d77j4sgoLjuOJgyrUzQzqISmLbGFBB0sKQZZyAm8F26SDbRd70Z1vqYytFJTavrPzrCSqbpC2ZsLjJsBgBAmnnLCkisecsGwVJwcGwR4hmcujggMIlZ4TIWMFpROy5R1CpKSHBWi2EBmA7FRESu92bckoG1Bq3ecZYLiQj6qCpyrMy5ODgroDA5ciGSQvamVo4KC1o46VxAq0ACArqJT5ncL89GopI2Bugou2ZSIgJwEKC/VGgIRG7+9ZPRtl95br7EK/g23zeSosiABNWiYUdmAH4poQ2QLKfl0e1oC7nnWmYrBNV7A/6AACzRmEzgZB9fEU3BzN8Bvc2ESz7p+5AM6a5wjI7WrXta+uTTNQMMoXt2czEN3Wb06LZkyVaJGvPn2FD3/0+/joq98Vl/rRKP4WDXpQni8wYPNQSLvGsUZ++enH+Nrf/QMBBe/8cs8SzHzzh+vvDGcsXoHaOrTz9sY5PkDH4zBwMP9uilwDIJgu1NcAAYcBC+wBwazE2XDFoPL2b2+oFCgAL6LGFIxqCkpaT+vvfnJM+w7ox0W362nHefK8MTFMTrLo1CQJVyw3guVMkJgl6n6Vk6gcNlZjNUbRgWCeM2MRMCCvItjkpK07l7HoY14KIVHFmkU9cCkZOYkKYc0Jaxb7g1NuqoYlE07BNqGzVRh84kWQWaAcCmNP9bzDeGhwrdVPhqMEGKqq3zHX3FrZgZbnFSi9bYDZChgYMFWBgAER/DNgdSlVWYJZG4qBprS5tBWquLrO3A9tN55ScwEUcNCDgiU1o0Q3AEwIkUV1XkZ20hrJht2N8SjvJ3MDM3A+zIeB1WHfqZJto2XuWdrGe1QIbymMDotfewIQGDi0QYheDGSRBa/YIZiqkKZbyfAEk+NH2GzW1jNQEPu7bvocSb9rGyZSQIAEvPrpT/C1H38T3/+NP8T77/w1UeEd9fd/VxmDbsI4xX8gFJjx8rNPFBT8e3j/r/xPGyiwvwgIJgNddqlo4OBNBSQgiyqji8YI7AffCAb8HIoLDPyZHRBY+OJ71AWxfabPemtXgjnyH343VyPE83oWYQoGZrYG9tVsgXLwE2m7/SI8aw/qnsWeTXa8pLH2KWVQWpBTRs4kIZar7i6qXKOI9MM5Z4jIS7DgOkvSBDmVXHBcBuAf9eCbqiBisSwHiUXAVdZdb2GsmXApCWtmZxGeBlWDGdUZOCCygDmiW29hd1v8BNNjzyzurxZuL1avFlNCvTYAsIKA6LkRDQgr41BVENmBpjbgadt5uyZygFUqIw1eAmZzYBb/Ueifc8K6EM45IxM6N9EWS8IM/xhd+GpmRCNnYD4WAXibd2NRvsAMRBP18+mwDPOvxR6AAAHSZ6EMxvV1Y7/mTtr7DQVV/xsTkmbnZSyCAQQ9R9uBzH5FIqDBEjUlwj7UslXTQMLwCEcbs7cqRxtPtwMo6CJGFlUnAPizn/05fuc/+pfwg1//Ft7/K39N7J8sSNKRkecRwJyUZwMMAGBHTR8M0JefvsLX/vSbCgp+uVnlGyjgwT3vnhLAyPSZ7nl8f27uPwPdIJzaDxgguOFhAGAPdu6t4xXhfFvgAx3kCcdv7oJm3/mP7wECB/XfIXeg29YO99alFp1hpOrEiTbJDpdXpLQgZyBXwgXG5CiFUAEoOChoIKEyUFNFhWTTW3NCqcWBQtVtjFuxJInwNhNyT4gAgVAqISfRp6eUcCmMNVc3sLMdrlna96552LnqQesTu2f0tBvTN0uT9s86xg1o7pkzz4z22Tw4DACYAaHZYtwCBGObpUSdnYDZa2Sl/hPB22jJCp4odfYaBgoWBQW9QadeCwDqJkad0U5jxlJKg42Nam8UsJpKhvy9zUNyFVwTkN01Duecd5Zvdtg6COE7O52HTvSnP2AQ/Lf5OoC4tiZ130WQEMLQjywCV2+T6MmQFSC4oeLIIvij7dfku8qEwdzVdTzWrWlA575ZgZf/9d/H7/wn/wr+6F/4d/DeX/llXfdDcL2QoKl/FmOB0vX2xXMCBteQcWjwV59+gq/9+Jv4wa/9u3eCggPMyBzsCXA80WafhxLVB4IvJkZIPycg6MDANdrvSBh3gny2W/En3e/2D65zD+15rZhhzkjBTuu/SwU9RHEMfT1Ga+yDMEEWTF2EiQQYSOa+RSZoXpDyKgZC1ELQAkkmeWEHBwjgwMsCJKW1c6p4DSAlxmWTfksm7FSgzQQdIMyChXv2dboI2NhKNFqsSn2T2yqYcJT34drD+JilpX7TUocxGD9b1aI9gAGB0YjQVC7xfAMFQANTHQjQ94uCHjE4TFgXaZuHVQHAYioXUcOclwYKHgIoWD2OBIWAUyK4qFxAZRNwUDfJXbFbc3TM3RqDwYgzZjCkABTMor0JSIIHX7ONTLT7iOXGXDSh7j11BaDH+kTGjpH7cymwDZSn1+mvgV4ImhAFmiCdqBnMk2F0dWRgr2aw56cWTKhjba820gAKokAm8nvsN6HDmqzqakLFn/2D/xy//f/53+GP/hf/d7GJq1sXStpVKTiQhxEcXJm7zwgYHFRyAAW/46DgV/agAHw4QQGblBOk588Qh0oaBOPB8+klbe3eDTrqrYt1GjdAcKQyGAHBATV5tYTBM9LoV3X/4XPPJAzndY1wT+kXCkba6Uun4ZuDGx0QQZTQuFw0TbFd11zv+rvCXOxAEsUvRTe7uoA0eQ94lZ1gXrHmFWlpqgV3Ea8MUlCQAzhIJOqHJ3DYuRKetursgQnAxYRl3oMDf2QdPFW25HpQqpfUIj+neP794Gxm0Q/I8x2VfHD5t72v3SsCgvg5JRJw5A+wBwXNo0M9MnLGmmlnuHlaBBgYU/CgTMFDzlhSU8UYOFg1+iSViwSQUrYAERxEt09jDGoFDyHXGWjjD5AxmBcBDEQKVjcRdiBhsbgipjg2B8kYmRVA7wp8j7cA4io4E2Lo1hlPER6Pj6weZQDVjR3lCcKAvcogTIDCuNM+AghBxWAAoQIt5g23Kkbbr7g+T0sQ/HKvhC4+xDRybvh5J3ekXn/2D/6/+O3/9H+PP/rn/694/y/9czqJNZR0WrCPoRMA4PhsN8ozAgbDwjIIjBEURIM8GVhDYiD0nWOfmxCkibAMk2x2njvEzEtXAwMIM5Yguh3GXAYI7AEw1A/XQcGMKaDBVXCkJocFZb/A+BN3r+P7W6XtkZTM1+BVsutQir+LTFl7UDDGpOACbAIEqvng6+5NQIEtYhMqV0P7csrgrBH78gJahDHguso98urUcFb2IMHSedsQkQUxo4pOPzFeE5AqYSGxrDdd9pqT0uZpoMnrTjjeW9wFUptOhGrdXSsa6410vOV5iMDkAKMAGNgH/WBx/11QB13/SPFbOQpJbcc7g8GJ+U/zQIjMSe+eaDEhTm6sKUaaD0tu8SCWhDWoDOwvJyAZS7BdgHpxUED1AtQN2DZwkb/d+DtSI+j4g44/1lgQoAQsSzNOqyZsVT4AKqwO5qzO13Gu3h5R1L+ze4bNDNuVAjhwK3v93Iwa2Xe1Hi9hCKjk5Wi8mzD0dMYCEAA4cOpUDJbJUXM2JLR03IkCnr5jein8kPeUROdvVASUZTTwogDBY0Y4g7CXO3/vH/4X+O3/9F/FH/31fxu/+j/8H4mprsuq1F7FylnGwQAA30St/WyAQdyZdhQzIIaGP1abArXeHK31O4tR9Ml/dvH8/UYq/KKQpARnCzqhemenXAMEM5Zglstg1J97vWb3O9jR0+guOAEEBwtLANld3nqgt/a9B6eMzEkHEkh5FucAhwsH+4Hd7iwAAt4usksrzU113LG15hKDQ6TsgIDzIhH8lhNoKWBepV9ykeerBWk5C6NgqgX9S2CkRVwGH0vBkkiC9KQqgqgkbEvF08aoazoM0BMpd2n3vnHv3WXP9PExfHCpvDtWuWVTbCGg951rdgcefCf1tguZRDVi8RjiMaABhhRW66j6kM/tfiML0c6x3xwHiEognBbqvDaEKeiNDNck1zBA4KqDukkUybpJVsuyNXCwXSSk9HbR8bfdPfaEApexh3SR4FASTAHEFbSsMtdTBpvy3JTlUWkeNi6zuVvjTll/cu++YtxNS3O30OUyVYf1jDUCbAQICghE2AYBaHS4ldnaxgg7ZvZ6xkRNBhAENHETGTP1QmiHu3Y1xgrovRjVwYgIb31vHgcmswDEeBJcCl7+o/8Sv/33/jX80V//t/Crf+l/HOqs/YuQP8ZUMAcq4V5ldMwcPBtg0OnAA8X88rNP1NDwW3j/nV8Zdpb2eryj3uuXYwNHIdl0VjtjIEOLGCae36S/5z22BGT0OGoAOgMg6CbMWLeDQRGfO2XsAIFTk9cXlNo1cUtYcoUQ7NsgLEqWKlZXiOCaaeBk75PdA7/i/exqg8mi7OqEUmSRmizQEl2NxEYgr+CijEHZZIEuG9KpglMB80meYzkLgl9OrlqgIq5wl8L+SpQ9p8GWEx5LxbYwtpLwsKhr3mKREfuQvmaMB/SC3t6XmvTYXi+P1Hs91CD8t9LiKlT1BuDKqKXqMJPPXNX90Lph1tFt0wuCJB+iRG5Tk7J4PyxqOyEulyJsPatlUAVE+n8M8mTfx/PiMTO2HENKp9RcOGPuibMyCKsyB2ZPkGmuOrAU17RJCGkqTyALK102yT+xNbaAy+XquPOIkVl2t0Yjo1bQUoC8NC+phcK4Ty5sRzuCGH5ctkP7+Xv33A3LS1RGEEnKcaLRVkqBwgQksKtGO77hmDmYPQQQNntwAR0ZhF0uBgBEGmZ5CIwHoLmYT5bOyj0bRrphZChrYEAAmjWxbm0N5dpYA3t0BQcv/9F/if/13/vX8B/8z/4voj4AAsDjBgJm4G9snRt2BbE8I2Bg9DJgFPOrT1/ha3/3D/D9X/8W3n+nBX+YuqVd0WGNRmftvVDLvbDU6G0dUMjHoGAobwQKjtIgA5gbTh613QTMKCjgGJb4qD51v5DEXcYYShgHbRAnlgECQKBH0sUqJYHvKdl+Ij677g7itQMTRMxNdcA8X5j9+6BS6B5SWZKSwGkTFYIBhCo7tspV2AOuYAgoENsD6aO8nJByRq5iZbCxpIvemLERcMoZhYEHDfG7LZoJcGFsNaEwDpMAuVU1z4FDzzQkt+S/bBIoCElAmYMDBQVbYZRSHRBUdQcULQ031sBVC9Om8/FmbAFlFcwptTUuJ+SsbJDaXeRM7j0wGgga/T+GMB4Ff8t/gMMkVOJqKGDFMlaeNQiUZSXcJaNKksmTuIC2J1DZQOWx2RJsj8B2Qd2eAM0xwZcnH3e8bUHldXvcUV6EAs+rD3VSkMBlQ8rmUmiBhwZWsGMC5wGmbA7PwL2Mr8kj6vy10G9+O5Xvplzch2wPIEF32VS3xiDoLl7sE60++zV8X0ZWITAIKaPPxZDFcDjq7K+Bg0nZgQMS2yEDAnJtaSGJzmg/HJ6WMsBF1AcKCn71L/1zXfsTB8N3BQPtmPZ9bIluMwfA1s2D8myAgRJD/vnlpx/jwx/9vgZ/+JUbAwhN9+OfY6MNQhPYg4IY0vUeUDDZwO/VpneCgqMojTOwM+qcRlBgz22gwPRv9qoMQQk7i3EhcXAQ6mquaNd6oQSwKwJf/O6FcVNPjap2HsKeKsq3ICZa32HAuwW1pYd1D5Sqet3Sg4KyibHUaGtASW0EE0j7n2sRlcGy+uJu+xxifZ9tN8QATtJvZpiYxDAxE7AweebFWhklifV7ZWgIYFnIop9/zNJoqYNZ23sGHIxpGEMvX3LC42XTqICyiBZmZN7HVHBQsElfC2CQ5zAGAWiAULpEd47OEBA4EzISaiVgkTbOS7+rzQoGliXhFII0ndfFgzaNKgDb+c8zIu5TVnvqZLQ4DQuleUhjasGKsgKCnYHhqDq4PMrYujyBnx7B9tlUWjruDsccALCOOSxgbDKuSCL7CUpWvXVeZJwvCTF5Wzcvgpowgvuic9cCS3Xz+I45bO6n4xwGmq5e2L8A9NEndxKAoMaBtQBKkZPL9aQ2VAEcjCzC0boXo9VO0zwfgwPMwMFkDb8bHPTag56OIVJDw38Vf/TX/y28P4ACAG/mwTVRH7z89NXVnzwrYGAsz6vPPpGESB98R8JEOjWX4GhtZhVquvQ4sILhnVv8Ag0URABgcd59x32stzsqPVsAp9d6Q8L6dqDg8KYTRkD16PHY0c6iHCwijH7HAVynI9uGksQQiG2iERIk9wASIbH2tE6wHFgDswAWak76OL7vPA+0fcFVKdyq72t73+Wab65jYhiVGghA37VUC3g9a1+yX5e4NsPEWpDzipwWlCSpoTcCCkipfPhOnxkonOVxrR31fakGDiRA0Cy986bXM/XAtsTEQgmPmmDp9aViLQXpUr3Oa5VFuDIBRYSqdWS1sad9zNXAQd+3DHaTENFOCf1ZmV1N4DYIKuSNtj+vGQ9LwsOasOS8cyGMCaNMBRB3/ibMBRT0AZo8OBPFHW3LZWDMQ8t1AAceGYDHJYhAIBoYarpqVx1MQEFkqfrxJu1POcuUTwpyK8AprAs6noU0jXR0Gt5H26fGFhgoEAAZgL4ec3zPbzaPLXgQmcAMYN9AQq68YwIJhERZ3PDiHGNCFLIwn/17ikthWyf1KWvZG2fq9x5qnSIsuLKE6xcVE3Bgqgo1uOyAjv9e3v3ZP1Tvg7/+b+NX//L/ZC+PDnb6rb8HhsheFQi+/PQVvv7HH+JdfPGoJs8LGACSZerrmlDivXe+JJOTwgASpVbbKQMwAxBZ4CtGA47oJ/zyH/zn8hsXmrnR7Snvdta9sJyXQ+wXDSKVCod5HnS2BHNQ4DvlHboMi4MxHFEVEkFByh1LYEKqMveAoPZgwLIMNmDQ99NYCLZYyMlGNRKR6iB1diokJ2aJVKbdlJw1MJVCan9GT+YMLkNbjJNsQuNGQzpKpAap2hW2e8MEHITjtEjDMZQ5SAXg1XcoAhCyRkEEaqW7dnDS3tS1t4MHfd1KlXwDxRIPVWzMnqXwqVaccsJWGQ9rxT9+JKRUkVPBosL0URM7PAHApciYXyr4QijKFXtfAdH21z+3Jm+LVSJCWtRGIBHymnFaEh7WjLOCgfOa5W9J+KXzojkfyN0HlyQGgUug/yVqo4Z0RlDR6bS3sUVke0h5086zDIjGKpj6QYFeLS1QkbkhhvdUt6YyeENQMI633fg88vnUhnbgag0fVQeUfD2rOkZk3DRQYGzgmDH03rls3W4xBay9a5zCEBsbWWYFIDAJOE66M3L2ADqXaoG5+LpOPmXd+NmA65+qWwOPwAFrTBrNESGGfNXlgvxuHtl2uqaHXWpTDUuEVNZMnCwPBapVAF9o2N/+f/9vm0ui3Xsmj3xDFGRZXM+iPZyeb6Dgu7/1Ef6NP/rXD3rwGQGDWoGPP3uF3/2TDzXL1FfCQKiykwS0w8IgMctR/ezDihBWMxGUL//B38dv/yf/CgC0lLCTnbXt6EZAMNNmHKt5dFKFXUEHAhwMjD/rQUF/MxtMqRtQh6oQBQVxV2GCyncVARBYnPuwiWy721aleTEiRs9xg11IrPpCjMy67ze+Tl9ls6EhepPmLo8ThjLE2IhgroaUEri2xZOIRNhx0uRtrf0o0dTK3kutcv2wi+OyebUYsqNLJ+kfzhWoWd0aNTBS2cT1jBKSAsw1SX85UxOSr7d23tt0uCGiLe5LVvVEyz/wWIpmJmwpil9vBU9FXPM+vxS8XhI+zxI2OT0VpdQJj0SgVFA22cERZZRUwaWCKDeVArU2iyqElADKCTklSOiHjLwkLIsI/7OCgS+chB14cVrwsGa8WLPEEwgxBM5J8z9k8RQwYZ6SsgNkjBJ8929zbqbztk5rBq46cLmCSlNBkbrA7gIVlQuI69zIcGSnDuJAxDbrPrv6UlUIQdibCyMlYwVs3Md5HjYCCvYrKxjAHBQY+2Rg08aeNcvxw8v3CTKXBfQ3wM9Jqh8BQkqSpZQVrZk6iFJu4IC5p+JRwyIa1/W+bV3/3vH3Vo9wQShwUEarqWPsCfZltq57qOUAEJLaUnAC3I4CUMNQcmb7j/7G/w3v/2XJ3TPLYNmt4/45vlIYA41hMFDwd37zI3z5nfemdbHybIDBy09f4fd++CG++5sf4Ssh9aSoDQIIUDPUKeIE0HV+EKAv/8F/jt/+j/8V/ODX/jb++f/n/1KBwJwliPr3SL+NxaJpVVKBOI67qKPt3kcafGALQr37m01AwTX7iAkoiCyBfTaGYFxAjOoOL9O891bMWrxCHjOFHxaIThIJHThISduvWrVannIwq6DWnboZEVESYWz2AMuqTRkAGDYQ1MBLFxOxB+qj0O1KLUCxiIikNghVaGYA9QniwQCW9uasVKZYKJMbsWpERZ3gOUz4bhdggiPoQGXo0c4GJC78hRmnnL1fX5eCy8Z4LAmvt4rHreJhSXi9ZjysGX/xVPCwbviLp4S/eCxYt4rXF8JTrhJNcGHkjVBqmhoi2rNHg8OcCGmZswRfOAsoOK8LvnASQPCwtIiDD0tzG3xQt8GYuyDS/pbB0HXYiNMsgGtj4QCYcaocvyNoFodU1NsmBq7GCLhBYbNlmXod6LjiUgRE2RgjAwIKCrLMTWRzl13bcf3MCK7GgdlsRtLZQb3P1wEUjCA/zuk3ns/a/okb4K9VVEhMmrpa9Q1MYoiLoCrswMGRnj6qiwM4ONbFD6wBBfAAwNIy29jYr80YWJRh/bXjYUNaaWJHQWqnxGTWFnjvi+/BbUZ8l7VnAqaMr9cnrBNQmzsFBV959z3sJUZfng0w+L0ffojvfFVAgQM1U2ruWIPUI87U0ob6aKC26L78r/8+fvv/9b/BD379b+P9dxV0BKrddnUzQLBD2Qg7E+ZGq0LQ+xhzvke+9eD4jfILBAVVd54VpvtuBm9Hi8c4aYp+G4PQbIVd5+ssiy4mTkMynDlgMmMnnTSGzEOecgn2Yl4bGUgVWGznEYwEYzttF3k60t9Ri9pGcZFRE3tfsAEdC9Y/LKCAJG6CrzFA8DcvsmDXtpDT0E/k/YewMxqAX6d3lPfZF40EztLHVQFDqcDGmnmwMs4547IAT5qV8PVW8XoreK0A4cWa8fkl47wWnJcNj1vBXzyKXcLrRWMrbAXZDBCD+6I/Inr3xJQJy5KxZOoAwXnJ+MK5BwTtTz6vyhpYDIF5oiIEgS2C+SiZmD9jZ8DWgEE7N4BwDt5AFkHTohcaKLD4GBFo2Dix2AMVGtI2KVC1sTSMLwMIFjPDQEFeQOvJj8U5jWRGw23zgpR9rerAvg5OYwpsDTNVw5vO6U0tEY/mtAAEdlsOqLow+Q7pFwcO7iqduqDCrfonDMOIh66t88zcjKfRqpVJ7ShyauwBtXWE9Vgbc6mNoSCbOsa3cy1v68jLzz7Bh3/8dXz3tz7Cl999DyNmn5VnAwy+/YFUupcT0jhMLJ2SoHoqNHAQ840PhRUU/M5//C/jB7/+7+O9L35ZJhjgqgSneWucTNLqEVHG0vn1Ghqcs1T6ID8PIADeDBQsvnBEmtEp6npMM9qEYfSLR0HfCDbX4vEM0ZOXKou9CNAGCOw6GYSq2w6C2BkQS7IgUylYBjIR/g30OQgwcEBq+DMkRMLgU0626xuJAgqLeIxvALTJa5M6hlhmZRIsvLKFtbW+6vou3q8Nkh1+7OhEzSgXdxRpkeROKcvYzQsKCFsFLuadUBmXQnixZrwuCa9VvfD5U3GA8N97WPCPHzd84bTh9UVAxOOlYCuLuzyOQY/ksZqHgLkajoaFL06L2xAYGHhxygoIWujhNVt0QQ0qlIIh4Bb0/jHk+SDUgQEIjGU2cf18FXoscTHA4qUBM1o11UL8jfVdzqBSwObFQlXXo4PxNYwt0r7zaJt5AdazAwbZsKyNWUjZvYs42AsZqJc1Sr1XJvP6GiiYzesyhAfM3IN+Uysklf3iJyAzk23CKziw5xHvEhWmaXlzcND13Y0SAcIV2wKg3/zN1np7EiKIwfQAEMS2QOtk7AAgxpXM7ZiB0PH+0XYkggKP5JgcFHzvt2TTPGLUo/JsgMGX3/0KgDaQUwcONNAEoGDAGltHJ4V841aI8PJn/xl+5z/6W/j+r//7eO+vvi+NbcBAQcGR7+/Mdx+ICzrJHNBjQbt1FSMcF6tLGo7hNijwY28HCopW1AABgN3isbNSr+3R7LwRHPi5+k/miU0YdPYGxKqyTUBWPZ6nxg5tynpTAsTfOyXw5QmUMqgu4lNeFt9hWrAVmGEYt52Ql0D7WnQ60/fuBLy5SqbsY25nEDkrsQGPdNMxjn4AHazPxUnzOSg4SGnBspxwXlYUJFwKcMlipHjOwMMCXLaE10tWBkGAwhfWjNfbIozBpbqb4+OliHHjVj1UciyZ5m6HD6upCHJgBqgxBEvCOUnGwkVBQQsm9AR61GBC0QAwRrpU4e20vrbhdH2M0RIPaWjtktgvQ36NeA1KYV6bWosrgMXHlXw1jC0NZtSNKQ+/nZ0tIM3ZYZsVAwWc18YAJmONmgphtCvg8AzRpsDKESg42rcY+I/zGpC5bYGC8gD6SwAHCYRC8DkuF0UDB6OF/xE4sIfpO2bfV3eUkRm4a81ns7HQ9OoGjlR5wAlIGsdBGmgVlrJSU4ViItFHQBBsTgQUfIwP/+R38dFvfYQvf/G9xv7AnvUYITwbYAAobUONVvbJaKoEAwfaLayGK32+cTnjz372E3ztx38T3//g23j/i+91dgSAxo03VM3cDY64e+7aXjs+2b2UKRif+65iNhM2OLjufxwBgdFOM/fKsGj8PKAg9gOw31HMqmDFKMhraU2tyZiBSoykKgXW3Q6UeiSdaJwdLvkNiZLQvJRgHitpWT1MMuWL08KoVSOx2eJfjrsnWVz6sHM3OjgKGxtjnrhrVkn4d2Puhvgs3fleRxtkDZj4zlLpZqwnUFrASenn5QzKK/JyxnldsGXCpRLOlfGUCQ9LwsYLXl8KXm8GEjKeqoRqfiorHpUtGPM4ePMM+QjM3TAmJ4rqgoe1ZSs85RByOMlukZ4eNcLg49490Gj8sqFqTowuMdaVNvP+CmwQjaqiK2O0swtKZsys0e/sOrEPwwrcXXV8lpREdUDUqRJYNytuR5CXMK8bQIigwO2DbN4GtmDWPH1TiZDPIJ/ftxj7CA5sfotgB8rPAw64NKWDs+3JAZgYabdd974yQQV3AwR2xdspgKh71n0EtQJB1G3UPDGsepwlrDVRgts+uVcaOjnVReQ9AAWmXh+f+1p5NsDAZKoJDxtHjTVIKlQI4OTxuYF9Q7382U/wtT/9Jj766nfx3l99Tyaf6dxtENcGCKLfrzX+dD/CghKNSjOmIIfnnhYDAf4+0JM+SPaMh7++BShgzEEBD+qDWEZjpHHxmJVoZ2CLxlGCHCC0l31muEqhdb40h4MDsrgGWzMMrEmsjVNxgEDryZMrWdAZ100DvTpg2LV3wh/o7Q2OBMkRCIiCbGAtPGTusPvdlSBQkDJoWRoNvZ6krusZ6XTWneUKLCfwckLKJ6zrQ2MRqiR0OuWMFzXjsmU8qi3CpVQ8VXOFlNgIMaBSfBwLOBRjDlgOgmZQKEmJ1iQZDTt24PI56OlJAghtTyCNG1AtaJDFDHjDiILagXuVkDEtIwN0DSz4bn/Ry4Y5of3s7NW1MRSvm3KzL6AELBJPpQMEXVwVTfA1zu/aPIuiCgHY74RnJSdCqdyBA0DWhmtyNQ8LW6k8ZQTlRebwCA7MYLEDB8YMIoCDMc6B1SimcfYGH/oM6BeqK5XyqRvqMAaC6isIlzcxlkNKojlhIs/eWCD2B8i61rN6R3QG0mMdWt6al5994qDgvS++18ktHvr7qDwbYBCL0fJAbIBgyBUAwlgsYqK4PL7vVFENDAEgxlsjIBiD+sRi/SdUkoCDplu7QhREUGB1IocX8nQBQXY3jMYplGDuS0egAEHgj4aGBgqcGcF+EYkLB4Bu8ZhWLTxzXCgovEnYt43tPKwt46JBzCgKDoyiQw66uKrtkMztrICreimAxUOAzUKdOz0y0BZzf6Y3sfmIgsBAxpBRzwCB5W1w4WbR8ex4KWrkJ7/hsh90lMU9k1JCWlsmSOQFdDqDlpMEYTo/iJ76/ADkEyifwJfPkfIJeX1QFiGLDUJlXBbCVhMuFQ4IJCaCxEWIURi9fwlo0QU170BIUjTLUGjsQHp8DRQFBOUJVJ7Aj6/Bl0dUfeVNogp6bICyoV62u9vHPQEoKRWffPdNyxKOZwmgVRUgAE2IRzB4RxrpNs77cyPrJKogkrMjwIcBhmBYaDFVhhgkkSkY3Y2jIBs3SLbdiHY+bZ6SbwQWZQOu1vVgrgNhvXYsIOtiHWwOMrW1tegDzsEBa3AkVRcfMQbyYP06ufv+CjhAYwtGGQDs5UCFuGsSA7boswd4grtqb84gkKsYYj6Ja/V4+ekrfGhxfIwp8PvfX54NMNiJH4bvzPvNuAGE3Ea7lpefvsKHP/xdfPRb38dXvvieTKAACIwhAOAZ7aLLHtAmV+yEBJPdpBtaRcGz5441MvVCd6WBfqaAiuOxDhDIFN8HYupBwc77oAM7fZCTydOCMU76K5Tr7NtwIA2H3EQCgLl5OtjSjYSBA19YdIJJtDvVy0Yr9VrAiRFTMjOvNttFAIM9XK+FOG6dPU61YWE1qtH0yDs9d4FbqANu0c617IPglA1cCupWUC8FzBV8EVBRFSBgIvygwi8vsqtMS0ZaM/L5BFpOolY4nUGnF8IknF+ATg+g9QzOJ+TL541FWE6oS8alwj0axMMhoZxanITR3gRAF2EwxhkwTwLzLFgTJF3x9tSzA0UDBT29Rn38XNiBp88FDFyewNsTyuMT6kXaCLWibOVmu6Sk4GDNIEpIa0ZasoAEBVJci7xqnzIWkAUZCvp/Y2miKmKXhK2NjuGjgQGjiFN4bcLL6eIABjqwb/Mb86BkO4bTRq6uT4nZd67MDRwYEdcAsfRZ+3h9ro/l6GwO4IAV/MOTKbVXCXOEKTiQuSxt2RmXzwDCVPDP2Nfj+jW2pQ/wJsf6q1o7V20xWbLIAQKgthzKIJiKIeaTmO/3qQMFX1GmAOiZ7Hb29fJsgMG0cOuYCBCsjWJuhVfaqN9TQ404oWL0OfM42Go/CJpRx/4xbKKloGMbUQFzH8lvt0smNUQxIQ+gZeOaLyptMWl0aFtUjkGBWynzMQNid61o9diN2YPRN5uKu2WT9u/T8L2wFo2BMXAgi3DT50HntSTU0SGfhaL3LIwxtLSxMNF6ndkXHSA0SmycaLVuhlFcgbTALOKTMhBcNhEk20V00ZFNYA2CM+x+y+MFdSvgbUPZClAYddtEl1+rMAhlWPgAkGYtTIsItHRakZfXoDUjn1YBCSdhDdK5AQScHjoWAXlBymfkZRX1w5Ld1qbUPsbFwdrl7mmWayCRJAMkCw70dGkJiCI78PQarICgPn7uIKE8PqE8XcCXgrIV1KcLuFTUbbvZHkhJbB+WBXgi5CWjbgvSkpHPa2cMLO6vpsjS6ySdX8uqu/tlt8PnuBM1Ie/vw2DuXtt83s3lA+vzGILdqWNuLIGBhKPoma6JQ2AJqIEDYNhHjXsWLYe70ivrwtGevKmF5Q1B2cAb4ACWX0FDKNt6CbJjaJWNa2awOdgDOXi7mRrmqH5H8sC9FPTkpCCBwQ4QAF2LwR7sqQcIQC+5pFjwIpNfHfA7WL+vlWcDDEajtUhP2eexGOB9+dNX+MafSHCkL73zHjaTCwMg6GwIAiBweaKlAY/4fPrdAAjs2Y6dYtCkIBGgYTt9sO9uFEGCUY99jAKDFEchjnsr5X6gj7fcTeoJqIn1H99buUa8jn3r62c8yI0hgqoTSN/bpDMvEAMJlBbDDAC40+HFtM0RENAOEAR6bwASLuCZYayEuKgl8YiwmAkAxNAxgQ1FuqqhKlOwdaCAt4Ly+CQ7DWMOtmCrAIAro+UfUJWCMga0ZOR1cZCQTp9jeXECq4qBHl70LILaJCCvDhI4r+IBkpcwtpK78O760fuZlbHZJPiTgoIGBjTxkIIAZwdef+4qg+3zJ9Sni4OBctnAyqZUZQqO2gGAsAIpgdeMum3I5xMKZB5WQNUM6vaZrT/ERoc0+mA06HRDwMlufkpXj/M0DOwxxXs0KoufTcTYXxP2PUNgw3UEBbFfbHmx2WDrEY8nhjJdUzsBGb4I+6GxzNaLiqZSsAeU/unBAYWL7PIrMLtqwVBOi344PkQPvOTY9X31jC/kof6jTp/CORbZNQIEQDadiXQzOAKEocESWRoAlV8ap6C76fCs1wy8rTwbYGAl1jnaGsxGZGXg409f4RsaHOlX3nlvGp++BS4KOe/5SqhQLeO9K+0BQLTUffOKzigvvbPtKoxavEExjqDgwJmrvw2u7/RdHRAO7kwhbtf0/sKBIVJkQ5BJVbiFY7X7WihcOZ90IY/IfP/qC+UMRACy+PAkWp56IbjqolyAVSOfoS3OHsYxZRGUQBcPoFYWUHCRHXF5vAg42DZRLTCjltr9zoRiygm0ZGcPiqkVTqt8fv2IdH4tLMLTa9D6j3sWYT0B5xeigliW5tkw6LrTuMBqu0QmxtpDAIJ6FGybCH4DA5EduDyhvH6N8nRBfbygPIrHQXm6OBhwlmAr19uACFwr0rKAa0U+ryiXTeR/Sk0o1kCQhwBBXVAhbQumZv1vqoUd3R8Agj5UL+z1WIOq2L3WsNjMhL5+1ak2/fiVHaSP82sTeigNPPSlF47szzT7/t5izOAIDszaSp4V6PIrmLtydE1HxVVDxDcsM9uKuFm8JhuY0YV+N1AlicnExiJPAILhHAD4+Kciv777VYloOJVzQzXbIx/X+dkAg6N+3aG60HAff/oKv/+jD/HtD76HL73zlUO3wzH0L6C2YuM9BoTcAZPD56ar58SQzgzTcdtiMmqwsF+Awg4melZE74NIMUZ6MbaVqQ3Gdu7pfbJHsCZwB9ER5d5bxihjN2kxn1z2UPCKJIp0npw8PjMws2+IwEYFDLKfkJTCHNmHGHUPMba+eUTUi+gOYf3McH94rqLqqBWULvOqqvBzgciMclGjxIleveYKukjI3ZQ30CL69PJ4ESbhacXyeEI9X7B9/oR8XrC8eBDhvJ5kZ3x6aCDBdszLqSXume2MJ0yKxRfg7cldCw0M8NPrlqZ4e8L2+WuUx03YkscN29MTqqpV7M/AgEddPDA25MrIawYUTOXTKjkelj1nJwaJFmpYgwpZxMEQVIjzCiQNTRw9AyZpy6e7fBkysM3tTLDOBEwU/vEcYJg3V+ZMmgmHcY6/6XxtKBpQfbrNKSMBokCdMg/hvasU0IyNjRUEWtZVe1aPksgWTEiNI6OtAY13nqyfwJWQyirc6bbh5Uw+uHoZPUAAdMNGWi+0zLIGEBLk/E8+e4Xf/9HX8e0PPsKvvPteS3t9o7/uwUDPBhiM3XeEUK0TrVH/8IPv4Uvvvnc1g92oNgAce7YPY4kTZCizwwYQ7pqEDvuuUJDGEpiNgT7vGJDpKEPirLwJCOh23cO5t8ASEHb+4+IXJo8XAwOTJ+90fOF9a+b+N/OFsp0TvKW6eqVwTNL1ZqScYaKAa0i449S55VUQ1UIN15QHFkqclhXJhF0Vo8OUkoRT3YraKOxjItSqQMPKBtWrk4OEvGZUBQhpK6iPF6SnFXldUJ5WlMet6dxPJ9Drv3CQsAvN64KQ0FnoW0uH/ALVDC3NyNJeNQ5BfXpym4rydHF1wQwQRDC0qzNanXfdanEmstgaEImRZlqy2GMsMcrgCRQ8ONLpDE6LgoLFVSsYVCsdGK/DGoMbO3vgYAc4n6FH83a2pFwD7L+IeRvrlc32YRCMjt2HOX5UpM0YqJKIy8BBJjRXRl0Wu4RF5p5egRa7hgEMzMEdEtMiNs4AgbN+Rw8/+RwBgh0r3NQLejNZUwNA+MnPXuGbLr++MjzLnKEd++9abZ8NMOhqyW2HO0PVn3z2Cn/wo6/jWx98z5FW/N4NdzBx03MhBO/Isc+PGjwNJx2dtzvungcalGmsbgQEUR8Z7Ama4L8OCvya3Xzpn4jCsx+lqz2m7K9UNLRtjO3g0Q7R+rRp5Pq+iG/Hvvdz4y13M3k+ta+pSKw9LFOc1TmT2DJYKNecFnBWUEAZSBKtjymD6wWWeGlXuMrOooovc64ybWutSE6Z64Knq7vvmFPqBWWtqEhAZeQ1oVwKqFSgqFuf7bi3ArpsqE8XpNOK+nQBrY8uNDv3R0oCEojc/x+AqBi8CiFEsMVj2C7CjAQDS7elCMaEo/2AqU3E60Cet1yq168roT1jrgZRp6gHgtYpnxfQKuqVdDo5MyI2FgIK6PwCWE+o+SQsQW7ggAM4KPooo1HvCMDHMfo2lPstFo/DOd2ptAe6s7kb75HCb7sShX54/pFOt/U0gdrmiwz002Gdmdsz3AQHStUSxO4AxPCoiMoY3LuODs01XR1o+DJh5zs2ayb5bQAIkVUxz4wKCRWdg4H1Tz59ib/5p9/At35D5NeOmTYVJzXvrcN+OyjPBhjYAHcqa8ISjKDgS++8J4MJPSAY3U5mOqOboKDbRdqfTTxqE5LaOX2nHcwQpWavGijZ+2BPAMZN+4mju442BdfAAAFdRrtu4RnbaHcjOMVqzyJ9Qg0QsGQ9qzromUOwEKbgkz1heYZFKlZ4rPdR5jhzxyT7VdfPYuyUtA2yAoVEhFzNCj9jWTKoAwgX0KbsgaoW3P0NcGGbIbvcLTzPBrjrXE0JyEmEeiLQpv77Kfs4jyXS7WUrYuUdYiPQpTSAsC5qnyC76ujet4sDEBMA2b1iMKYhDkN0vzQXQ7ejMPdMNa6sWwFK7UBBpzYIQMBYAsqayVFtLAwQ5PMKWjKW0wn5vCCdVywPZwEFpwcxwFwUGAQDTF7OAgQWAQdsXhqkXhq1eWnEAGHXMhbGMXhr7Fmh4Y3VPKr8jHoGRnua4/nbAYRwnt1qOn+pr0ObxwEAAO41JPN1DhJi7UegYMu7gQNQszkgAixDo/tWEmROqWqhC26na0nnwnioOrA7///J+79m25LjPhD7ZVatvc/tJj1vdoSIbn4EvzhmCAJ9W34YDUUQIPw2RKNJKiBpOKGxnq1w2A/+ChqFpZEQAqfRIB2eiAEBEqJmXnhvAyRnwh/AT45hNyBHOPwmNfqcvVdV+iEzq7JqrbXPuSDHpu9UxL17n/1n7bVqVWX+8pf//Jiq1J28VSbB+j2AQOGmMtDaMM/goh1VMKSJAhhqOhRzLfzpJx/h7/3gffyLL32AX/7cO6hWN2ZwTdgxN/Fr/plHBfHrBAygE8K045fGBAp+TUHBHkvwGCiYLc54o+eN2v70TecbLGxIDo/+1UdB3W6qE29cB38ZULCznnYFSpqe+/tDCFWo3qUv7XgVg0+a/ASIrTUrQgqWFYequqmLUgq79/1ICAu2dd/9lDyG5CnWGpHm4vvpJxBysj4MVcAkSLWDhMSCzITKhMwJKVv/glXZHVovEBNinBdUyySoD13hppRBTCg5gfIKWpJa80zBoi6gtSBlzYqopaorYmdjuBWt1yygUiFMVgzSRHtRRU05gZlRWxBjrwOglflsLloOf19ErrwdIDS3SKjDIGvRVMPwXAMrQ5ZBqe2+UVCUaWpL6tflwYZI3ABBrOXAp8VSNrMGYRoj4CwBn++A8zPw+dmmQqRwbiChQAvTxPoODgZaRVFbe2upmzX31PUGbNecvkdDrQiWvm/3gp739vAM6Pf2MICb+9jvfTRQhj1swXPa0VEsMFgBjJhyjTJpTxhGcBADElWBAslZBFgmg00Kkb4ylsYHnuYkGWWzxxd4vQWPWzBcoP0QaJIjO+BgT9YL+gciOPjTn7zE7/zgffzzX/02Pv+55wMA8CM58yAHx273/eB9H68NMIgX6RZk1HQzU7AHCv4qftstSH9dS8EaCkdnC0alig0y15MLyhT+wfnHR5YAGHOZj2oxRCDk83B0bbcAgQuTdp0ANBJfxhoBQGh7CxwF/qhssGshTbHU39No9xZczE47SgMHuiHJXED9inw+RDogcMHsQrlX63MFJhs6cI5wB1TgkrkLiAlprSACcrKqfkzgKkisdKA2iBKUVtAng08JWB+g7EECVgMMxFY7IEMejKp++AycMuh0Aed7cE6op6XR/XtWNpuLwLvYbHyjcV2lUUhWZx+qgQavAXDlBhL0GNx6hm/KQw9z2LRhAAl1U4uhV3WcVmZiUKlasOnWtdh5eoqmVn+c0jRPS4+fuLuzOII918EZNbIE6aT3Ip9RQTsFn3Qdadl0TT/TctHbtTavs701FtcZgL7WKrCS2N8CKdQAQmzQ5vOkBggNhom7u8j2d5NJEdCH9r+76bptRPbSfrllZihASJQgdm61RpB/DBD2ZNMeOGD03goODpoi9/lw10JkD46rL1hmA7eJJHT9QlAgkMxA0UwoK15koAHmyWtsTgAHrzL+7Mcv8Tv/6n38s1/9AL/8ueeNgXGZHNmBjWvBTvypoAB4jYAB1NYaXnFfTwQFX3zr+RBAGMde5ap53PLVjL45BKaAWrQsc9+Ig2KleMNk36qOYweZH4GCmG3xlwEFft5dGUZAYILD09CkarvimKL2WElPu57WMhgMcEFLE2Olr506Ux2jO803ZpUu9BAtsQAK1iaUEZ7LUMq30Xk2O3tUPOD3j9ol5GRlfwsjG0OQWZmERIKVBDkAhOrv5zt1L6x2nRZ7QJwahU2Xe8hy1hLAD5+B8gl8p3n9dT0NhX7k6rUP1kNlO9fq70tr9Mv70O9V7d2VBAWlvz9Q+MfAIDZW6hUf7XymFEP/fa1DwGiFoHYzCLh9vrs2emEnzho/4G4QBwVeGnoABKdzL+7EGZLOLftAkrEFlLBWXT/XAAjWqmuwiDIDa5XwT8tFO0CAzubNtQXoHrwCIEtfWxsoUECqnURVl2ZW/JdNoLiP2Q0TAINhwj/rPgZu72XPUPGeBVNr80QWBAugooOCQh3wR/fgU8CBrZ4GDli26Yx6jS4vY2M93Ja5FqTgrj4PQgST0fkmK50qII0PcHDgNpEzMo9I92H82Y8/wn/6r97HP/3VD/D5zz2fz/qQHfAxs9FNx9ywhl8fYBBuHNBdCj2lQ7MP5htysCfhjUL63xjm8SjCM4LlFAGAg4NJubq1vQEFt6j3/x+BgoElMOuCAWganuekl84YuKXh0ejtWuZfdThutK/HSFhdelCCeIobZ6XafZcHcICwMY8itwu2oGAVaYCgiPmCbb6ACA62x2u9dKANgq7FXAckxhooKMilIifCOSUNRmNBNoGYBViEkDhhWZSypvVBmx6tWUsCs/Y3qJcH0PUBcn5m6X2a48/XByQL4vPSwLPf3q30aKHrLRkV81NG/6xNQMiKKIehV48dS0cvRnRc9svjKiIwYWYFA8Ze7MVDpPOpB00uZwswPONmY6nklR6VNbgKUKy5VFtD7j4Q4KEU6yFh4EC0ydS8th5dVxVNAbj8KEyWz97EAGBAoEADX/OBmoiGySEo8OwZS5Ud6nC0dNNouOzs5egO9CwVsQZuNTVwoAAhWVAgUIUsRsCO85cEB2qdPxEceHRilLWuU5w1wKRj7CUhUoYiggOjNrVirTWaoj59sRHcPNywBBQU/IMACubh8Zbjfe7GUSNu9gzPY1zwOgED0UVskfgE4KNPXuK3vvcevvXlD/HFt97ZnYfom9YFJA1p62s9KCSOCAQQnjd0FlgC0FgKdmAKAiigvyQoEHS3yBwjMYOCOBd+rT1gaf+8bwkSr2DXwYEpHhcsIYd9d8T8dysKQ9ZLQLiCPLXIj2FCJYKDCuk+PrsuCfcyjgI0pkCqYK3deltr7w64BxCG0YS3dPDH2j3wWqxhUKnaIEgS1rJqF8HEqFWQWVCFUIU08pgJmTPSKQPlokAon4BV0/iQz9Zu+ApZL5brf+1dBS/3oGehx0KtkPXa/fRFWi8B9+/rMqstn9PBA7Bv4evnx7l4FVDRbrkr91nJA7sMRFP6QFP8+v7Y78CbI2nGgdYXaPUIrBZD6zKZF40pyKfGClTPNrA4AhhT4HEEyhA4GNC1UwS4loqHUrEKcF2LsQTAWqu6FKQ+bU0B4NoNHF1TqvgyYBYoIUObBRWoxNsT5i6T2ISMK524lxOwD+5rCcyB/Gx72ZqWkTF/CvaTAoSUISxg62hZqbMHrvK1DPIWHESZ5a8dgQOKPnhEOfezgQMKRagcHFQaQU3rhCOwrAszNKnHHvhq38hfs40cFPyf/7aCgmZkBuD3tPT2yChIAPH/E2AMSGord0mU8OLjl/jNP9CKUF9429wHIkHR690xNqjFJERw0G5+u3MhXCVQBhEMDDePOlLrLoPuz2vvwX78KUF6O0PCP/c9zYGGe6AgKsw9UHCTcoyCxHPza2xzG/7GtkvhePNc8HsJ2QTQqkJZpAOEtOhxRAs9CWMAB2I3U6oiAmF1AVCXC5sRbdtmzU2goP9twnxCGl7jXHsxqFBfqSInZSXUncBY64olMVYBzlWwJEYRQqrKICSmEHtAyOmElE6QuoLSSee5XrVqYu71EFgq6sX7C6y9YFCoEcAiY3pgyA6IoEFBgQzAYY4L2ICGWjXqO4CFox4F/ZZ3S18fJuUf4hXmAMeN8g/ZEJv0ybnWgj0iZWUGiFtGQU89XHrqYQgsPAIEaxUFBVWwFm1F7YDAQebcjvrxtWRTI0ATPAYiUaW5GaxQ83ZEEGDuLrbnCdgHBeXaWAIq1wEQ9DiD0vcycLif216GsgYiWvTJ446EVSYo/aFAnyegj+gixAgOovUN/xsjOHDl7DEHlagb832aHh8H4ICMiWj3KcQ9DUBBqOkdAdTt42wRRuMSdm9mpmADCqiDgsgOzGxBA4V+rXa/Xv7Fi5uX/NoAA7iyAOPFJy/w9e9+HR989UN80cpEzuQmHYED9AXoi623Ee7RvS0yOE46toDgKHK/B/kAG1Cw58MLq2dmC+wIocEHhliJiKjbMW7QSNF94AtyFxS4IKlX+3sECK26XVk10KwpkuluWGqelNCZLmdrGrUqpWtXquzBAsG1gQMXKAzfjKRCtMIiorVgSGKCFDIWQVqRkhX7YxbkXZhvPwcATCqU3MKrq7YO9iJZag1VZBEIM9YqrfVwASGLtF71DhAyERJlDZhbRGMPAhhDNWZgeaMJeJKi5YWtRTMaUPBWzldd9+UKlAJ2oBDuk6zXbTphAAxlLQpoD8DCHDynE3QbBCQLEowAYEiHdOvfgjI7E5DU7UL2GWJtlWxFmFRRZSB7RUJrIsZutdrr1lZZ0qL3qqrLYJUREBSBuQnGltMPtTaXgb9XLQ5hXkdHLszBivOWw9IBwzxUyVN41HXDQMuaIZt6ts8xU9vLG1DgezmWq3a2wNZP9Q6hXqVzuMcWFOqdJlMGJfucA4RaQUlsL6MBfYS97EC/+e4NHBwZuf5ZoNn/ZiiZtW5AwoP/eqC3y9HAGsSD9pvhnx7cCq1L7nB7tIAS80FApeU5CjqzGcef/+Qj/Kd//D7+6Zc00HDWLcA+KJiHxzP0B0VSL//iBb72/d/C23hrfzLxWgEDxYsvPvkR3vve+611sivMCASaa85e08Vpdalh4NxndNingj/98UsA6G4CO6y/doTenpTKdys4L678+DI6KJhdCJjea58/+AlHpP060AQJ2nnLVpCUqwmJa6ch1xU1KiaLNdgLeIsV88RAAkltjWl0M+rF2b4ewAESt/MGqwUuJkwF0vq4a3Aga9CcqPIiIWU+bSPX4j5JdQ04y7I3NvnmTKgiA0AADGhSRRUNNBRhCBdkJqyVsSQNSDynhJXQGASNVdC1k5mMtTlpXZ0FXagH+lc8tiMwN+4jJrf2nEkI1Qe13bP9Xa4KIgzs7bkk0o5LQmoFh5oCEoRsrGkwtzxGs/yPXQDgZGWJQ5XFyADYZ3XNUAAAFqMylCsO74cW5K0okVn36xR3slY0Fs7jCK5FGjMQWQIPQvTjzIBgXjtzjYK2pECdRWj0tQF27sWa3E7IiQcw726DDu4pyKCdvXwE8OOaCRUsH93PUhXwpwxkYw7ZlKzUw71c7VxR1XdPpsxxK97AmEG1jDFE7Vfghkvh6GA7f0sBkYICMQXv7AFRBx6QCAoO6jbYeSoh1H/vd/6VpiT+sscUhBPsgKYbrIMLGyNQGGILpOLFxx/hve//Fr7za/8S/7vf/z/uXTmA1woYVLz45E/x3h/+HXz46x/g3bdDTIEB7+hCADqi0kVCze9D4SZF2unPPvkIv/OD3wTQe5HHm9RuBvYBAflv3gIEt/x22xf9aAAwgAEHCP7CrVLHs/vDr4f9kZzhkGBJ7IMCqiOFDVNETl3rJQaFwQwpdn3MKuhrvwOUF2XyoOwAaugtUAGhYgJlaSyOkFrqVnTc2ASVFIXU/49kPHBiDeNuSc/AWjS6e626a/V5nDEV8C7M3cqPo7fn1mMBZMcDqtTmSy2oKELIwljL2tIci1CvhUBOAY/BYwQGcwJnDMJ+CBazeyXw590aBBRE0Cz8Y8zCVLKYJ6DQGjhFNwTQ2aE4GlPQ3QGc88gGhFiAjRvAGxeFboZN2UNBpf/zTo97/Qo2PUOKQLBfkCjWIIhph3uBhWuRAAS2oCCOxLRZN87S6XNqYJADMMxJweIQ2Eo9PbaD+b5mIrCMe9ndBc190OIJDkDBz7KfDZF78DDlxXYQACwgXDs4II1DIE7DXvZaAe6jb4XMuigfhhp3Cs71M501cJDg7P8oxecDHbly+RAgJAMILa3aAYL0wmxABwkwYzTGDfyLL2mgfJvT8MtHlShnPeS6rYECCF58/BLvfe99fOfX/iXe/dwv36SNXxtg8OKTH+Jrf/QNfOfL38Lzt94B0CfL3QTs6CyAAK3ljebKA/rN8yEC/OiTl/h7Vobyq//XX2kb+rEb8ySGQBxB79+ovd7g2w/5ue6wBdO1xDEfOroQevSyXY8HGjbhUW6DgkhfR9oxnERrM2wZCAK0oLJdcOBbmSxNy0EEqZJsKMeUqHF3gFH6qAASIYkhgR1wwImwmrBTCrhbGbXqvERhz2l7fyL1WyFtgWnMgYIPJEBWQFgzIzIpQEhQNoEsiDGx1hFgiMV97Ge8EMwiBIMt2ts7BLc1OLEMMgeZ1dXu8drdEdeH3uRo7m1QVqRam9vCKxrqvR0Vhj72Colklv4eEGhNmlqzogyw0v76LwCAHevfmT8vAy4GAPS12l1vMjZIEwcC/pqBATGXgKYb1pbJ4kGre6AgWoG+Zob4yrBu9B6aTDG3WG6UP7U4FWogYAQF55QaKMgRFLCzBLqXNZMoAvwRCAxZCTMoiKzSU/dzrerWGfYzwt8LiIoBfV3cAoA5q3K1vdxT9EY3jMcatN+XUaZ51L4DAhLL9++o4ObYlclSWpdDkGwAApuLl2lkECJrAOpyfYYf77z97nB9cUTzMOoc/yyHNyNb8OLjl3jvD97Hd778Lbz7uS/cAD06Xhtg8LU/+oYiobe+aL4lCxhB97dZ3E4HCBiZAh/q9+kL4oc/folv/KGmPH7RkFwOd+zWDXkMEIxFf4C+TLgdnMRre/NmMcdvemwBpvM/osLDKTe2gOx6kj26C0GDjpxGDILEX5dyDArc0thp9ANA09Jqsb26QpBBLKp8/EPLCc3XaTygMgekTSaDQIkBTKuDA2MOMjQYsQK4SwlXVKQqSGCkKsikSpoKG1NQjdasWpCFXOjr8Q9KAbThgj0OBwprAdjynTUFTX97LJokJuypkUZqlcRKdxJYqr4emUNRLXbgwEhgcDpp758G+taWXSKtydPaXkddQdbgyDMhIqNA69XcGO7C2JmYhlTScWCgN2haTgMQ2G9SlFHRW4jXKuoGcMVfO1N2qxyxYCx8BWAAAnMxoqPU1nmfDXntGMHivEb883Nmi7IFIyBI5EBB/y2JWzBzY5cc1Lc4FcW/+wC/gwJPNQZkyxTMoODWnhaBVGWCZF3bDuj7Wd14Ckx1PYtx8UQMkQKm5FWPkQTNpRBZg1uSLeb7ewEgB4F+Qrv4IMpm/9uO2O6Uv0YW07QDEJxBYGrip1Vv9aOJdN3hMjsfAJa5Z42+5mfkL2z1z4uPX+C9776HD7/yAd793OdvMgU+Xhtg8J0vfRPvvvVFADjMOwXQ0nZquCG7w2ZcWzN/Xftdv93pHXcR+DEbemsvPwIGAHhcRH8ef99f5xE2ekpNGMeBTOhMwvElthP3LITmQkAPknSfIrlQaBHL9vfqAiMIjxkUHCgLqaWBg9b0pxa1GP1jBo5ILA3KJXz7TmlAYY5urqwChcV6C5hQpwogM7L0YjRa1wBYSUvvrpWt1gBtlEA1rObzP9PFwKgMWqT5fO9sMRYBuAKrTrjOvZmYHvQaq9/p9M3ve2U8/bsxDgjuLNbPODuUWSvSJc5qmAMaj1CuChSqNnvCsjb3kWdCUMyEcOZgx5LsEiw1puAwQ+CgOZGQdqD00sOlRKreFXVnylzhr43yHssPt6Qtu4Gx6uX8fglbNha92oSZhHUHqLKvfLwuYnLGCAzG+BIKDEEDBml0Hfg97fEonfVroGAA9RL2sYIBl1Ukk9J1wOfPIyjY2dcqphJkvRrjV0FYldWhClpX1YC+l6u5EcDdpWBAR3F9dym0VnKumyfWwN+i4cl2VGlxr+OJD3/vyegSfhhWa8XWjgECD/oQq81CgUVw93QDCu089Q13Uz9phI8OAYnYgoK/+dbjTIGP1wYYOCjYH70qoq2ztiDEX9wZLz9+id/83nv49lc/xPO3nw8fS8F476/vgAFghx0YX98fzhhUQIxEu7Fe4lF8/e4Bhhl0NrYAweL0zzXAE68n5jEbW9CuO9CLXtv+LzNE9DhUNSIsU5+zNqdqdVD1vcVDpoJuXKXii7EMRbohx6ZMUmYs0v3Iwmmgjt1a9Kp1e8ohAoNd6zH+HV0Nrsmm7xULvnrVESsxOrhwFiInbgxEtDYT62tOPy/MyHyncX3ujy4eU+KZEM86m+DsUAxQ25yYBZa2lMLuHqjOCngrYysq5NkBaxFca209CLygUBVYeqDs3q+jSpavOvaqE8aXPFAV6OCv/1YHCcPngQYC/H1nBvbul7uWMtMhyOvxKO5COCpEVgPbV8d9BTGXkAH9J1iYt4bUqta0AXgwmyxRuSJSACEz6ByYKB9AkckUk08+zxjTF4+GA4DIFHicwdEYZHUz5GbQ0D7cDuwgYWARgF2QAHRlHmV74n1D7vBcd553UPB1jbl7650ngwLgNQIGsxU9sAb2vqu/OJFHgPLFxy/x/nffw4df/RDvBqbAbxm7MgTawnlVMEA3bpTsXNNj41Y/cwdE82tzzYLIFjQmxEujNh5WEAVJsy5MiBwCgrl4iNcv8NgAr3TnkpV2TOx2sRUtB0mqUndCyj5I3S2c4quB7VSdCSjcO98lIpyQWrCZiKBk6nXusyubLbWs96Cf4qyEjkrftvent1eLeJwr5Dlrsf8b4xTOlfP2gtjOzFgSNV91JvVTJwcPTEh8wnI6jSChrpY6eQUWVTQu1DegNxS9EcsUALOxAikUElIwcK1AWRUMxLoBq4wZAXOK4Ozn35u/eUnNc3Q0f8N3qAv1/lpQ/NObERT4x2bQ1nog2GcUIBhbMIEBdxUl278bQBA+N1Q0tP0KREBww0Bhbq4QsMXiGLuntSvK4b6eh9SqLsJagWQs4AD0pcsTD5zlbNkI+6zBkUvhls296Tr4lOHr+uA98Wv27ooCAL7m90EC0BnAmLXzKlJ/X39ZoOF332ugYHPuzmocjNcHGACbC+3gABhs6nATlG4aJ80DNdQn88vQet/TT5WrPpmAgP5uMFFQsQEDUXvsbShy9Azo4qrQSLWqAvUVRyvaFM8/vh9ZAn/csAURDIRzPopMrlUfgSZE4uW2creRYvZANI8ktyhyYi2YIvMWiKxB7VMlHn8AF7hJ5RsQipxQixVIFdpRsAKVxcpJi6UWWqnphI0vOlLTDhCAXlERoN0GOTOIaPdmR6HNhZZc+VXRc/HPFlOedphhuI5KbMWTEhsVbY2eEnBircZ4TiuWnHBmwt2SkEiwGEhYDChEkAADBrFa3ibLJqxrzyTwgEEHBhEMrLW2YkJXA3D314KHKriuBQ9FCwpdasVa0DMFqr7+KnOhr1nuv1ndORG4Oq0vodBQBw/Ryo/L2JnIudEW2lqk9r4DAHf9zLEjcV96BVINBI4xI/uF02KwKURCQKG0+3ScIm0BohZ0Cdaqo7qnssUBQY+Rsn0/NdfCsLd9XwOIPTjUtWDrw2doYC9klzVAYA0iobZn+Mws3ZNHk2+RmZzcCZPcJnH3wh5IcLe2gQSU7m6w7w+Vdnb0jX5uez00gzCpo/7aBQW0c8/H8foAgwP045MiEYdJ2f0MYKDg+79t0Zuf15u0N4kODIDjmIE9MDAvsGH4edxGc08dno2BsHmG97v278g1fHcATeZGaGNabMQMkcm6MJ3QrtQFCdDYgVbQCNDo5cgeGEhouel6otsLFU1AE9EN1rIbxKWIFldJpB3evAJZtYIj2rjFysuKPQcNwWsipvAjWLC5jYABwAAa4N9t07ZlGwDSdMjmdKSWNlcFoVBObdT5Ze3Pq1HsVaSBhZmd4EkJKkOQsCTCKTPOmXHKjBMzTqnglBh3ecVdTlgy4S4ZSGgR8WTMQgYvucUlNCW0t76JWh0BjxeoFVhXaWl/qygTUAS4LwX3q4KB+1VwKVX/Vb3+h1Ufr0WwlqLfC2Bpbw46CPCIfW7ulFO2UtUiyMxgFmRNZ2lpog4KNFPA1/7W6gdCAC9G5Q//bAse7YqcKIAFZwgCELjV6pzg1qYpswjSZGyGdNjQzFwiQpZCul4hriZIg2HBrMWOUjbwUNs19cme9vfEAm46cDqy1zvXAYNoJVsHAf4LrZcNddZgV2LuMDs/84jMymDcTTEH7utoIMFYlYFJMNlgQEE/b3Iw6JZdeRfAgNB4D198/JHW8XkspuAR/fIaAYMdAiZMSquDPb2uf+vkvvj4pWY3fOmb+Jt/45c0KntzTP0sNQt5H0kOCHN3MU2b0lEcUdskhIA+/b0bY278FEdkDeaymi6QohvB5M1wTcN1tRe4W4VuDQLdqgB3Ci1Nyy1aEhEQBD80pdQK1jSLs/2bGYROG2imQt98VAs8IIjtu8keMwABhUh2zVQRNoBg07/Xb0KMXajJAIMBhQjIjpiGVTpQINbXV3iqJGn1RA/ZsN92UBCV4bVUXIqCAQUKnVGYR7OMjTk4JfVj3y2M85L10UDCXWacuOAuM+5yxZIIdznhlLS7X+YeIa+BbkkVVcpDQK7Pgc9dAzESGllZzMClCO5Xva77teB+VSBwv3YwcH+teLiuuL9WrHbtr3rdzIRTIixJUGtFTsnvWgMHvaBQdMVotgpxzxDQ58eWv22I3cyRozLqjQFo3wklbh0kwOfYmbwnBDrvuQ3IIYX561k/LygKDorGBbT4EaKeSVSrkpgaqLOZ92GPJwf+un9be/V4KlIhkUEwKNXO0NwJNbIGUHAAYCvjdsaT3AhBjm5cY8Oc7n23jCDBZPrAJAh0Hh0o2NkDUFm1d9h23qXNW5Ot0JR9NWp/17IP9tjopzkqXhtg0Hw2g4U7f2ifxicRvPjkR/jaD76B3/vVf4F3f+GXNqyCfn4HJe4pzQgS9sDArnINn4sL92dwH2yYIuPZhs0ygAIahFZABB0IbYAMtzkQCtRjWFKU0E/EF3vsmBeAgLsMBjAABwIBEID2QQFcqMA23c7mi6xDdNs0v7dbYO6ZHZtTVVf8MgIGLzt9CBqAxjQUn1ZokFyBDAVziDVVsogAhVGpmtAzNsKUn4OCh2tpinEtguuqtfpbjv6OkqQpzuC8qKV8t6w4L0n/Zcab52zMgQMDfbzL3BpBJZIhb96VaPMQofNMDVzVXkBobkB0v+q/a3h+KRWfPqx4WCsergUP14L7q37m4VqH+IJb18ukSn/N2vmyVjZWQQtYJM4oVbB4qAsMDBDfLCyUYBkC0OXpTMFjyn9W+MPf8TOYAQC2IADYBwLtObZGyzBJZPPgWT8e2CuOFMB5af1OPOuoZSlF+TDv9bYYAguY0kj9bwCCxQ2IoAX3+XxIBwkQGZjRWcZtLvFVx54ReeQKbqgqfr4bJNQAg/f1aRc7fqeuB/INaCBiYByAFz/+U3ztD/+Opex/Yf867NwaQLkBnl4bYOATuemSNSu0GAcAACJ48eOP8Bs/+Hv4vb/9zxUUHObmBhAQ/UB7C2UPVd4IXnnVQMPHBsVNY/sqjlkw+Xc4vNfPr1+DligO1JfFP2ysi6Ncdv8eMAABPfYjYEBPMij47ZzNwLBvgn5PB79cc6F04OCWDAUA0fpTsIMGBNBABgiC8sMEGoJbwivqaY8EQJKCggdCS5W8eu+BZgmpeyEx4Vo6SFhNqa5FcH/VoLxiUfm19O6ROiWC3tKYcCECJ8JnV405UFBQcF4S7jLjs8uKZ6eMN88Z96sChGvh9vycGEu24jpVkCAtSv7A49NSCmNp4esqDRTsAYLPLsoO3DdgoI9rFdRSH71OsuskopbWeAdlCJgTclUXCdCZhZxCrYBEOHlshgGCsz2nAAQ8ANABQKT/m7Ufng8AAbPyl85K3mIA2nP0z07jVpCz7mH7HhFaQK+1Ovfz0eyBABLcTRaBwsHv++9sgD95QbJIjUeXb7DagSEIUQMI7Z0IDqYR5dxf+TgyPvWXp9c7izDGJNDmOFTXcR6GNxuasD9ZQcEPlOnerWi4J8ceGa8RMOh0zKYZBrALCICKFz/+EX7jj/4efu9v/zP8zb/x7x/HFMxjj5I7chUMn5uPTf0zt8DBIzfTw1f2PuU+tir9uX9utFamA8hBJC6Zj0zUZ6wgKWmccNaCTH4Y78Lmyh/oAMCPJa8CAl6ZBjyon7B3Tf60AQRur5PTrUS7ucp9801MQ7Xyu1VZhiI9wNHdA2sVJFJF4wAhEYFqBYNwcQaANQjyslYtVBTy40v1YjvmslgrpCpN7q5lmxx9MLcvXQmcGYUJ17Xi4co4LxUPC+N+TeaqENwtjPWUcKkdIFwzYymM+yQ4c8ith8Cpcx8xHsNrRsRuhLPL4KcXZQU+u6z49FIGQHBdqwKCKqirwq3xGu3H7BqZGSKElLkzFlVZA8DAAHNjFDIzMjFOSf8tuWduOCDwiqCeLdDiD+ix1uo+EUH5z4o/0v7AuA+fYmgcrGtbyAfPA5M3M5vS42dgYMFLHIM1zsCvKzKLR3tfAuvn5axngNBOSypEqJ/fxBrEFsZ7cm5zPHt81AxzJX40HpPp/v7MBgMdJMSvToCI9hhrAF7fxmXTi5+YUfur37SKhl4MD5v5HOUU9pWFjb+2wICI/gcA/xaq71YR+V898o3jt45AwSc/wm/84O/h93/ln5r7YFL2ewCh3dAdNmL+zmbj3lhphwjxFV9H3zCOpv2XIyiICNr/jgTT4Ww6S+BuANGgveYPbHS+KyALNrOvR2WLICBmer/747bWPeLxgXB/R2G6YW3a/bhxjyJACOCl/b6DgnCePcLYhV23hFoUPlOo0qfFkgr0MRO13HwHCA+lIK+Ee49gDqMsySr9VZyMEl+LtKYz0XJ2hVnMovP3iJX3ScwotahyzKoA9NjS/pUqWAujVMHdkjRg0FpKn7LgVCpWV5hrbTn3Kawij62ItQY0s0BwWTVO4FIqPrsW3F9LYwl+GkHBWrGuBWWtqKtmHxxdGwAksBbJhDIIUgVI5vYIMRZLInOlpAYG7iwg82xxFc6MeLGgWPPhsG5Ay9royp8a7R7BwM66/RnXLDZrVsLaNSX1V7S/xM/TmQWgAwZAO325SyAcf/jtxhocgwO/qqjU3Y2gh3WZLBs55+83luZoBNfo9vUD4+gQmAWKdg8gbHy9wIBq9xjrCCRII8ZefPIj/MYf/w5+71f/ea9oSFYp15mI3fmk6XE7/toCAxv/axH5fz/lgw14kXk1j3TwESiYq7UdgYJb7z/KNFA80/3j++aImxc7m/iJwwMSaXrNfy76QIEdN4INIU+7sbrmjC4UQj0B2YuHiPTV3vVNitV/7+Z1ReFp90W34wQIJjp0fB3w+7FXE308hyBwg4BrxUoCwHGgIJwb0yDMSJysZHPS9Egoo6BFlQiJtKuiAoSECyldmll6z4ppWko1JZ65sQaSWAV0zOeCKc5GAasQKdAYhgLMnaJ2RnefKdvBmlLJrM2iSu01/Tmu9f77cydCzTBQUKCBhU8HBRLjCqKlWjs40Fvm7gRuRZ3OWUHBOSecl4w3TgnPzIXS/6Ux4JLHgMvsjAE76+ZAwFIDYxaAM5FR8T9xbQKPrc/9tanKiDb7rwfw2p5rrz/Cxs1ujHCuMUBPNvtvZ8wGwSTv9s7FWYEqfjk9EFF14lbh/kwuhAYSgrzrFVDGuWhnBtyU7XGI9PsRX3vs7/adihc//jP8x//6H+D3f+Wf4rkbtVYM75BVdWPl9tkB+OsPDJ481DLoVp63691DgS9+/COLKfhnePdv/AePWJfoCvkpLoY42vfi5qD9z9y6mQhCIPrjdj7aDHVg3DDbQ/ZcZ3SfZ/+kjF8Q/6IpOpgSprSds8kKOQIAEoTCk3xgbiFIVf9ns7gISiwFFN/uVRC83lFQZOMbbS2Cj4ozcbgGQAOo7O8YLBmFHAWLiKzBj78mKYPttZwTVgFKpZ6ux2JUdcZ9KarcuKhVuosP15YeGUeqmgmRwCiox0ZOhWZhVI1NKFRxJbOCueJ+tTQ/Aw/qixfFCtlcHeYqWaueb4SIc48BDbhUUOBVC++vBQ+WceHxBp51UTyWoGonxI1DOTJgTEimrRMrIEiZsWS2OArNwnh2ynjj3EHBGycFA2cDBXeWppkZWCz40NM1E1tFXymgUjogCG2ue7nhvcJP4zoEsPXTh7U43NmdtTivQ2L7TpRdDUf4nkubPSg35JEzgUMczwx2nlq3ZfqdAaxEI2L49ZDeKd112hlSe3MaM1uw0cnxWwYKxCzvQ5dCBFVtU+0Ze69gzO0ZncOG1WO9+Mmf4z/+b/4Bfv8/+id49xd+6XFFH+/rFB91NP46AwMB8N+QJoX+MxH5L+KbRPT3Afx9AMC/1/1Kqr8CLeQ30CZ4iClwpNV+8QDhPjU48AgFPiV2ICr+iOp3P3OMSB01C+YN08fgMnC2IFiju0cnrTWmJT/1HLRuwK3r2gcAW598X6RHi9V+0jiXLoyorsqIVKCh+706FQYKsK7Q9sA3esvfKuXcSuL5Y+qgbSfDAtl8qC1NSws3kRX3IQMJS1qwpIwCVTyrCBJ19uCcgDMTPqWiJXCtA+OSqJU01vr66ha454ILG4VqvngNUgDmlFYyyUkgs673L714zIIwrqVa+h9jLWRtrFlTz8XpzPBd0xtepEmLEtnfNWRS1Lqbbgi//0zahY8FVM39FQre+7Woa0TTEtOSNP3Ssi3eOCcFA6eMn7/LBgK4MQZvLgl3i1aAXBK1ec5EWJKV46naOMrBgLcwHhqLhTUXI/l93bmidbbyVdZfTPEVX3cSSk3XYoTBuEclgoL2/by7H3fvQjAe3EyAxRw00HALMByNoGj30hj99wQ9RZH8VQll7J4g726eg+w9BkNoyLOZf+HG+FmYi504uRf/5s/xH/+3/xC//7f+Cd79hc/roaUnaY7U8GRk2XUIegbV0fjrDAy+KCI/IaL/OYD/loj+7yLy0t80oPBfAAD9DXIeueNKok0QoroP/q76ZH7hl0br8qlswHyDH11oj20Gfx4pvZHqixvlKakm/q6Dg7339KdG3xthOuoefek91nuz6uFaBmYjAgG2VCj7VjFZUcPPuMLaI+nGPG6CFz3xGgUC9NoFHgMxIHDpbYSl7vaW32slO3ePaxUcwzXvFnOJtRnyomAhZbPq7D3Wqn/kTYI4I6cFKS0NIFyrdny8Vi30kplwTlWj5i+lUfenzMipYEkarJcTKQVvQYWVCWzxB83qbnNsgMAod2JCslx/rw54NGoFIjVQq8Y6iAy13NqtmIsOPdZOQwMD9XxEtDMmZwa16wj3JlwHG1MQWYLzkvCmAYI3zwlvnnJzGzw7JbxpAGHJjDNbv4ikFR8VEIzloHsHygKqCgxIrPX0tM4a8IxrDdi6MHG85ob1FtYa5QUI7ccJUHAg0/5sAH3bptqjBGKb6sf2ox9WUzBT6x/jYKFXwZxZhB3LeJYfcQS2I77bDZ++149U9ONquTMXntXWDSEFBCM4ABpAeBU5vzeiS/TGUFDwv8Xv/4f/xJhuecqFBRa2x3o9RjP8tQUGIvITe/x/EdF/DeDfB/Dy1nd2WQOLOXjx8Q97nYLPff4wJXEzjoJzDlDt9vtP+Y1Ap/lvBot783s3ftd9b3NcwfiZuKn1HEf5PzEnHrfRQqrChtiwGjxaJQhAQNxylPaaPwcGd3gbfl5M/XxZNPK7NUtK3ICQWqoVSjQdsD97veWL1v2HVE25RLCsw1pppxjqMbQshpTafMT+D724C0NSbu2GOS/wjoJEBhDyGcQZlDJSPmNJhCsTUlG3wNUq9Xmk/KfXglOqOCXGOa/46cI4L+qb12h+xoPl/MdUxjnfP4IC98MvWRXjeUkWpMetWqJG4XvdAoRiQH1tAWrMF2dkRd+vRaxGgKY3suixlkQoNWGpsOqFWl/gDglXUoZiTQYIblxDSqbQEw+A4LwoU/DGKeGNJbcAwzeXhGcnxl1KWBIUCDBpRoIDgtU6SdYVtD4oIPAOlFJbi2LZW1t73QjnNTats74Bkskzn1BbY14TwKt8OoOVl+0xdHJaj4oGCtICr8fhvUNiqm2rILm7L/VFt8ITWedS8loghMS5AwWviuluQCAA9x237SxfG2tr68xYg97VmsZj7k0BHbNh7beDzuiswQgOQDDwENyoPwMh0H8X0xzYZgmu6A4K/rEatfsXuP076IyBLcBtbPDXEhgQ0ZsAWET+rT3/WwD+T7e+44skOIvgrMHLj3tFw3d/4Zf109TdC21C/WbMroM95RwUw2HO6Xxd4eZvvjMo2MgeOMoPCjeMeHMjydWUlfvlw/cGnxv1gMMNY4CAoNu5AUNq04FroAKWkteFjAMBBwl7hYDiYPTNbPxPU0SV0HqdZ1fAgAbdtb4SNE5QTL+aQUG5AqXofHmL2cfiDvyw0elvVpwAek5EDTCQC/OUlXmxtsO0nLTsc8laapszkE+gsjaAkJixFMG1Ci4syJxwTmrhfnotOK+aoXCXE944Ffz0UvDsZHn/ay8ItHrZYKs4GEciaiyBK9ZYGTGnhDsrhnQ20HBibvn+sVVwDP5zIaMxBgSkOKUj6GznwgBfKzJpNcScCEvhVzp3reKogOBuYbxxSodug4XVbXAyUJATYaEACMoFtF4UGHgDqfUKuV46IPC20xPIbGvK11JUAo9RJhyYQmZVrkSQVA0Me++CDOIdRQugsQWcdK0aUyAgrLZHi8V/tFLcP8PeBAGJpIGD4iwfKYDvS6K7GciDlw/GLCcHxoBGuV+nT+zJPj/3cCajzKN2NQBZq3fAwAGa0pamJ8JXb8n3g9FlawQZ4aAiePFv/nt1H/yH/9j0F/o9bQfqv7dhmGmS7LJrMg3jryUwAPC/APBf2w3NAL4jIn986wviyNGsE72xjBd/8Se998EvWEpHjK6P/ugZqc2AINyI3RiAR4Yg3Pzd7+0BAjpUvoNopP5CZLZmQNB+JVriiKDgAEfu+Sr9oMQYc/c7G1CAVtCnyljgRwQNROwH+ChG9/2YSLdsgt7CCgcFQLY2yx7sJFTs2mkLEOKoRYV4FOAefwAFGoPwvhVl7cP9wMQdNNhzdRskUFpaPXoxcIC8gLI+olyAdFKAsF5xyguWfIcshKUoOFA3gyq2Ty8r7jJrEaA14Y2lDul/XiVRwUFVhbBjCrJH2yceeio4IFCfu6b05TRWBXRAkG2hpbD2igiQNPYgVVUazAIuyhqshMYaLEnrJORUhx4ITz1vZQoso2DRwMKYgujugzdPGZmAUzaGoAECgNZ70HoF6lUBQbmo62C9QtZLAwVwUCDSwaWvnwAsd4Nbn7KWxDNfqsktaBBrMflF1ONdNsfp6793tDRQQB7wCqziaam9IuXT9qeEGiiaOVOYFCiQZtl4KqeDBa3poK5AIoEIteu6WYipT4iHIALo4ACI7AFuBl3rH/GI/pLLV6uFwwC8+qN4jQn341fMtR9uBW4eXo3HaUWQYWngkKqBhv/tf4bf/w//884UzMZhWy+3GWZnC6L79mj8tQQGIvL/APC//Nm+rABUALwMDSXefesLFvBTx2jTZqFXX016nECNt43lfwPqH/bxCgvhFuW0yTzYBQW3h9tfe6c0swR+OhtQMEUSb9mNeJ40+CcdAETrIwqbuDDn/gGAKpDega7Xnc+szY80PUlxfG4XrOBgoQRhQevmaEpdM1QIjwI5Zwr8uh0UeNR4CS6FowC56JPxwi5tzXBnFKxuPOWsIOF60f4QOQPLWYHCcgLKCeAFVBZIAwhnZNHaBScWXKpg4Yy19sZD92uxJkOp1Qi4eO0Aa8AUGw758NbBS+q5/t5+2bsxnlpVQK3852WC57bBgIKDIoIFGneQ4cyBtADLkzBWVsbjUhhrrlgL424RlJpbx0Sv+Lh3zl6x0Bshndq/3iRqL9NgyxDcW1Dhg7qc6hVULsD1okDg+gDxWBVjmmRdB0A5gMmfae2UXhTI8ECrZeHH3etLMO3LZry464A80HAEBaVKa23d9qiEtuNP2J+JScFdFavlIChMLSbVWT53AzpAYErQYmmW6nyksTyAUbX1BhzYVMFJk70gROBJYYIjOOhHVmKjxUlMP/AKKmAcbsH1Xh1634CXn8Tsg89vjcporDpDvMswB0PyEUDg468lMPjLjirAR5+8xNe/+x4+/OqHePetL6rP3fxEuhD1Uakkf306EG0DAX1I6Aron705nhLcuEMNbUHB4+kmh4t/Ygn8pQ0oeOwcJ9aiCZO6ZQj2hI23yfUANVcU84gNalqLYCZjHsPnWTdqISB5u2ZnDZgBSQCpsJVi1gnzNpOVB457GEeCXabPD9O3FsQucsQ0gAVKGXK1DZyXBhJouXQWYTmDzndAyUC6bgDCCYxTAa5Jo/3PCXgjA5eSWjOih1pxtVoBLRtAVBnMShZAjx2A91TQ5w4GYhMhLxG810BoHruNpLJVQhSyOgdotQ40g8H93t5tcv98tQIhNRZjrljotQgWPggqnAABlQegrpCHe8j1obMD10sHA+u1g08PNrS1cmuNzMPXSCzlLCWAg80Fb1+nwEy1AFfSIMXIFoCT7lHZggLvWxH3aGz0NY8ZCMY9GkG8u/yEmjek9aFQD4QyCI2fuCUrZR8cAFv24KkjfrQ1bJ/BgYMCYwmOmtUN41V0Qku7BsCLlun/49/RlPpWp2B7/M5cBz3RXnc2t7MFPj76+OXNU3utgIGYw+ijj1/iN//gPXz7qx/i3befoy04v9nkS0qVfQcHdpyDIMAhjSYt42eB/YWws4BuUWa7xwtBN7cAwe4IpzSzBP1t6RrtsejagziCARQYS7Ba5b09YaPKQFpu+/zT6joICkgYmQrOidUBHfhNglcxUD+zV1wjCayBMQiUckutQspAVQZJUlV61tpFg9HqhUTWUKymQK+yxzvgoO4+J2ZgLe058VWFPzGwXoCUFRBcLEBxOYPSPXC5B5YT+PwMsmYgPYDyCZLuQWlBymecl4w1Ea5VMxmuVfBsIb0X0nsSrKHIkNcUAEZ6vrVoBjWB73RwBAKZO5ujbh80tw8wbgcRDeqbW1avVSCZe5dFWxvuhnIA6QlZe+fpbowOXGi3UuES/mWGZhZcHjRmoFyB9QIqF1BdUR8+U5bg8qAxBFcFDJ0p6O4CBwJH9z2Oud3wBjgCHRAYu0QeX9D+VoBJDoK9a6EFtQqxZRsEtiAGG9ZeQnsGBd7Mam+P7nlB9/boDOIrtNEWvBkYE2LdYm5rp1P5u3KoveZ+ed4FB+ET9r3dW7E9vF+bH7PpC2MyKLg8DnT+bmzBI3pB9YH3pgD+5Cd/iq/94O/j9770Tbz7uc83V0b/bPitBgBcPwUmYdIbPl7+xUv89vffw1t463AuXitgAGjr5N/6noKC528/N6VvyMmEvZY1KO6t1kmVHYW8U+L2Tz75EQBAkrsS+k2f1x+Nb7dPjG1F5y/NC2u8sfPjwEoe0Wc0vn3IEniwznRem1xoBH/VDVCg7XWtyU/ttfFXkSbw11rbNbg16HQ2k7a5FbYodHZHCQZw4AqM7cQWt46quhaIi7IGXIFskdHVgopqbQFIXhTL3REgK0pD3UdMUTjAgUJCL0NcgwU4zmO9eu14au8RFwMJBKRV779nMCwXZREu96DlpMqquRnOQFpA6QTJDwAvYGMRKhhXE/5+H55lwrrIhrlxyz3cfrtOm+bABNzqIOjNgwC0QLTNCApebL0soV31XufJp56fKyINdDOQQKFaIStbwM4OXHbiB4wZkPYY2IHrw8AMRDAwAAJDuZTGCRgBgLNWExDQF4cYlQgIPJgVeRkCWVsQq7uiLAURzM114K+tAgOE6krYAwXXsq1Q+dgeXUmQRRpAOLP1OEhs4kW6228HHNAko9p07Mmi5gquBg6232wAQTZvDWPIZMMOe+BMxgBydw54ILfbd+Z34/EmOfy1P/oGvvPlb+H5576AVgvDXQUbPRWMVgODDRTOsWii+vG3v/8evvXlD/F/+P1/tDsnwGsGDD6yi/7gKx/ii28937nJDCFL49uLOG0f3gn+46T9rr/3mwCAOinsW6l2+vvz/2hm6IEM3f07vn7gqjz8/cdYgqcF/4TrNivChXyRkSmoMlog17UMwsbpYv9svFomlYUak0B2bEN1Dg4yWzMVAYt2JiQxGtMsJRLprgVotTnKS1fMJwCrCd71aoCiqHXYHJalN7tJGAITyVkHoCkMfSqNTdgwCkXC8wL1KROAq4EEBi8ZuHYWQRmEk7EIGXQ6dxbhqnUQKC2QtIDTCclrI+SdIDOBBYJq2+E5GGkv1IaBEIWO1jSIwuvd2eXfDaDZlXvw2nrEeweZIRI+cbP6/Nziefm5RVDiDYw4ggKCVSe8gq5rBwHGEjg7IBM7INdLT2s1MFCv63A/4308Gn4/9XkAAjHuBB1wDlUNvRaGz6UDAqIOBuzRnwslBQPcwYAYY+AM3yrqQirwPTuCgodq2SvSi1Hd2qNrFWUHpIN45NQA3ZIYVIHVwYEoKPB6F7CX9hT8Zj4dkG9qCWzZA8EEEA5GBAcHvzqeWQg6PJLT8/Nb+qHx13bcD7/6IZ6/9UWNiSOCByJq/YfpQIOu4uBamDvAqn78re8pKHjn7eeHVwu8RsDAQcHvfsUuWowRHpSjoqrupqKBpukfdKagVwR78fFHeO+7X8cHX/0Qf/v3/iNYV9yN8IojBsA0nOeCdgMajsd86McAQTz+liWwI4YAw7lV8TBI0xVjFsaw+C2VSRefmLC3zldmsUZQcKmjsHFwAKi+bQUFhcBCqFRRxehqMBIqiBNSFVARcCJTbhoNXWER76TUqV7T0norCOs18gmQ1TIFyCRcyj0jIWV1MdzIPacYrOgBZ8kAx0AzszEJyiK4QpEw39LaEGjTpLquI0i46PnJcjJgcGd/f9oYBD6dIWwggbOBikX7M6QFkhIkp5a3ro/SqX3sr+MBHGACAmE9j2tsfC7j6gs+T2p7dQAKr3A+sYshw4BALcB1LkTUUw3r5QFyfUD1oMKyQi73CgIsBXEGA1JkuGfjOZmCTxSAgDJBDQjsMQHAFgAAvV4BgL2aGK02hrkPKGuBLGlsgbkOWOtkgJICwuBC0G6c6q7xfboaKHCWb62hE+i8TwXgqm6ZCmkgHoC6zLLtUxLLSEFzCzGp85YC2mvGPTFa9UhgVy75ahoAwg448OM6QXE0/L0IEGayYe/ru8rf7Yn4uRv6YdYN77z9rh4icZPTXuH1SFc1UDAVkmug4C9e4v3vdf34/5dZCT/LGECBjyNwQCFXdU47mbIABIQXH7/Ee99VJuKXP6fHX2sozHNwTm5wkn1GyxXroatsF8Tw3Scg3aPxVFCw2Xjzagl0XfuczVWVUXhXYw4kgINVVLiIuRrWqoJpBgUxqMz1sFojggxr1CMa3b5WgEpFAiOzv26sAbQZkKctOWsAEVWWfr5+PYs2PGrBf1bTgEJ55MYUAOG10uZqLoqkk2GAoaxAqluQwGZ1Fm12NFietSpTYUCBEoEerqBE4JzB60VdHpf7TiU3kHDqAYumNGDFk9wPLWlB8hr5nDblcG2FbMZGrTeBdbCGJgHWBL6vq/B322+Nbn/ieQShqVUHBUN54liu2AsQHYEBCyx0MFDXtQGB+f7E9EBKuoYcEOyBAQruoeYOAJryHyobxiqaPkdzGWQ/lgWwImdlCgwAOCCMAYfOFpR+B1tQsFggqrfDLuJK3oBDYwz6PNRi7gTWPZlBWLmCK2OFuXeqaNEjrkiSunwgUvlnmtc9Dc37EtfOYexTHdaDOJNILVqhrSHC08ABcNuy3/3uBAI6uyXx7X0dYTrAez74uihVX0gU0jqNOWglp+M4yECYQcEHX/kQXzD9+Fhc5GsDDD4IoMCnjYF9cGApMjGHNrzZAIEA+JO/eIn3/0Dpl89/7p1Wx32V7c0fDoMABuywEMv79cXj78XztfFkQDDd4FcCBY8Ic/dfDeDgkeHsQRzFApiaVRFAwVp6ZHxwOdrBCJUFtT0qG5AtMK1ANP3NLSEovV2rWHEVgvaLt4Y1yeaiWk94Kbrxct40VmqgKZSupThXBh7IP+Pf8yp3ltOuCMmi18sKqlYlrzIqVusvYMrHKeq1dzGUtSuJwqsBhATie33MC+j+p61QUrMglzPIUh+7/1mpaO362K3QW9k3+3fYT25cN3QoxMNw6zAE+FJ4Xccjv9+o1dqBmIMCAwlDoKCnGF7VVYBYg8DqENRV72ddy3gvJjcQMTdwEEEB56yAIC/2nvctWHqwYFpGy39P6ce58L9jxsHcMKn1POggwEGB2L0Wc8XVaoGctT8X6D4qUHnWWAXpgL09Tvu0ioBFM0EcHFRSQCEVKKTll9pyAXZKWd0YcT3tySZfD3Dl/zOCg58BLNwCAo+BhL3jEBG8YPFatUiUC+8BINzQWY8xBV94+/nYTOrGNb82wOCdX3y+mfm2CA+ZA96ZnZ6X/+J/0En91pe/jc+/9c4ABlY3kYFGsflwLgIOBmxVEhQMtH0v4h8BkVq7e0ju5kaa+a7hSp4ICppg79fR+nkbONBGHQyQpgk9hr5LNeGALnCAyeoIe70HN433ydmCunOhLnxSsrKuBjzcIqkEJM7t7pDVkyePmpMEIbWhNF5gYlLCfR3mxtgBD2J0N0JjEmpR5S69VG6rmb9eNXK8rC27oa4rNNSO26R49L0qpp7JAJhP3foxcL50NmHJHQREMOBxCTk3+jlam0BoFR1cRrdFxzg/G4Zgh/6NQawDg/AqvxUBgdhODT0wWklie203m6CsAytQV2V4pNZp3u3MwrzbkwbQIiDweW9gIND8INb5D26BTcMtYAhEvNX2uwZfcjymxhfkloUAzo0tUGVhAN1m1PfpPLprYNzke/s0kii6vxWIOIDPYZ8ejZ5CLeMePJJP0fwzWfUYONgdj4GCva88AgZ2y7w/qiukzecqquJdL8DItKHuw2Y87j6YPn5zvDbAoOWwGj3jN6otnx1wMC+cOKkv/kIDNb755W/jlz733IrBdMVWjDIH+gIB9EZWiDEENv8exUpKeZPd8Ohjkgkk9ONtF+cRUJjZgj52QEGw9oZe7+4/b990eLVlDeI8zyOxlltNFVhJq6AVMfrRNgfz6DrYvSYDIXxjJVf0lk5VlCJl0efKUKcODgBNP+JtK9yhj3wcMm3nZVJS/h0pnXkwBUSxfn4ECVbQyAECMaNeC+qq58ioKJbaCKe0DSAoE8Xqt2WegEICL6nHJsxAwS3X4KfuvRzS1jq154+OJwSubjH404/bAJg/N1fBYWnrHSAgtdocj0BALKd2jh/wOZZakXLazHFjbHx+I2OzFxzYmmippT+wJy1+Z5qleY5iVKh/J3butCwqBQXdhSASXAd2qFt3rPWyaKDcXt8zXMIperZCIpVjyS7hVjOu3VUQ9uWm6RJRe63LJ72iPXDg4ykuhVtAQJ+P1v8eIKjSP+ClpeNxor6oQWa78bRW6Q3iDCC4HNQgTRqMvog9boGC6LrmeICd8doAA8IIDuZRMYIDtz/jpPoN/RPL8/zmr30bn/+Fd1ohGEG/uWuVjf7QA3VQwNDN4ZkQusl0HAEEBQemnIniXhiv5Qnz0RD40fBNt3chASD45tMGKEZr2ZtOgbXsIzs/gqa4Xe33iQlc0RrnZJDSjwnqKpjOoTfoiY+EG0bHMKoISGwzgZRCB8y6EEC0S6TO9Q49/pQRQZY9ykRpxwY7dO2ldOmk/m0HCJQuIHMV1LUAV4B4yo0vFXWitplZmabEA1hIOTUlNjAKzhQEMEDZXp+C3JqFv5Nq9z/mGOj7EMOwaUgkdVt1MAQNzoxAMXdBA1rF2ZntnFIar5mYQQYGfC4pn3qvC4vz8L4X8CBRa5Tl1rw4IAhZTyNL84Qxu12mLCrPSnB3aDRoHhte1pgFWrJatHT1XinBWAhLe2WggfhYATQFReYyjn0KzCJu8moC67vyKRpiMPl0OH+9ENJjMzCDgj1A4Mc5BAT2gaNeE5vb0GR9l+ke4+Gpt0KEVC3OwCYqFqmL1/YUpmD+7t54rYABsEWDUbFGcLAZ9vrLv3iB3/7+1xUUfO6doaRvDcebsxKAfmPJLFUNKtFUugYQDP019sKot3Z+4Xq0WiNtrsN/95V8dTNbsNl0B/aDcN987t+K7gQKgMt8jOSd10gFRgFD1mL9DTTPeeWqPkmx+IFhHvUiYyU7r8dPVsQmWiLDPNhGhIGqahOlwir39J82LwfXHQTNUUOU1gc9zmmtgBR1TZSrKqLF0uXOvfEOrg+Q5WRpcvfANYPTBVxWlIeLUsXMkHVFQWnBiHqrKrxuQgnNjZswZkYx5UZm6TKTVVZUt0FkFbxBzxAkB6AFyk2lnVuw3DAZT0RscewoqyELBNhvQlRWxHLVkQ3wYEFZ18YGyFo6qGqxA9vfJs+/85FImYKcG0uQzgYG8qkBAopBn94Qy7JDxOsJpKVZ9a1qarT6b6yxMDn780gBxHncAdCyTwZLNVw2Y2T2hBkFFUlgjS01/gWFUS1ScGAP4F02ddoy6TrLrF/PiVsRLAQg4LkDbKc7uxF8X3b5dMRtsMo1ROZgjzV4fMRf2OKQp7MEe4AgEh570ja6uwFjDKCiVstFdjNWxFxa/tI0Iij44lvPjYEOvxVA2K0d+9oBA7E/ZgDgE+TgIA6/eR99rKDgX3zpA/wHjSnoboMYiR8pOf/9Gk6EpZ+Tlwdt0HgaEsDB3ntzM5Bb40mfnN0HqNvd0A5YOzgQS5sBQNZS1REtMyAQcFWrQ0TfFMsm0AglFe4MZQ9WeFbCdpm6JaLgQEGB1+T3bn7ZAtdcyMQJqABIBDUgLgIs6G5ki9q07DzOPsJxbvrv9eptMMZAmzNpSuMKFHMj5CtouQLrHXC5BxlA0Ep7C3B9QCIGscYOFHjzYb9VI4NwVHe/MikwMKntVrCzCpEa7wGN26h6ccAQ3AriN6jdrJ0SvTtrdreMbGw1PGc41DoAgLmoUAwUFGMEIhvgQECq3JwrsrlqfxugclCQzllZgtMIAPj8rP99ugPyYimCS88M8BRC7kGCTWnfWlvArnU4Pw7zGx7FprNitFh94UdmLxO3NF7vftkXHoOTNJBxtE9juWytPJm0lLnt1QSVCw4imDuoaNuoGtMmMoKCW7LJdj6JGHNwUEZ6vPwnj6PSx674N6+H92p7PrIMLVzG/vSsBJ/aYjZYraK4oLqcNVBg7MEg96Ap+xEUxMHh+I+BAuA1AgZOGd264AgO5tcjKPilzz23KFyjdWpXEn4z1x0hkwzJEeB9ffoXwolVSHMrzLSOL/UDQPiksfneXmyBX3jceLsWCXdwUIt2dpMKkaK1AmyViRhAMFKBoUFNiwvcon4DKhXEAirc858NgLVfbIYv7Za7zazNfVqlO4a5NKbLtkusjtqOJtR+u2/knksfBUCk6+J5KuDouf1MCYkSeFHZ6ilz3q0P5WoA4awV9S73SMsJ9eEecj0BD58hJU1NJL6gXNQ9sMJk9XVV260Z1/tWcCuow7wBCnUCCtH9cDMFTw/YgAFFiyyAhUeFbwQ4MQPE4wgmEDCkDh64BfaAwFPmpv3NjLRkUE7IpwW0JKTTgnQ+qdtgOYHOz0DLGXy+64BgOSsIsAJT4AViIAFkvQkqrBKobNbZ0ZrS+e3ral5nwwfnIWH9Tm/5cZh1j3qNEJc+qQoSKtbam2BV8xPO+1RrSPTS2bF/xtk6XW7BwAQU9IY3F0JPBw5uunkQm0BX2dSsKvteYw3C/LwqKJimE8CO7sDIFgzvzaAg3ItyAFKBPsckGNgCgOA+hmTGpHt4vHjRHiiIo4MCh4/74/UBBpZj71Zsm1y3Xv1jMq0hPA4Kin3Wm8AAmtsbRwI190JiLeZRDXwDaAFyj43oSvjLjE7NHdFw05hTzoDeczw2DhA1QwiAJFba36yG1PKY/EJEQX3SIkJXqz+QqiCTN9TRyPyYKu71170+fwosAXFwLcBc5U5nugUSrFWJG2jGRNiCAL/3EHUh7aH/zVxTOO9wLg5oEifknJDyHVBX0PrQqu8hnRpA4OUMefgMkjJkvQD3BhDyA8rlqi6CyxVeAAmAlVke+zV0ZeivhLLL2AcKacmQa9kNaDwCCnrRASwUjEzC0ZiYgVgt8ggI7AUKlut6Ewjs9SuYSxMTkwUSKivQwUBGOi2g01ndBnfP9PH8TP81QKCtscVAgeQzwLnV7ShWPKiEtRWzdI7WUwMDBgSS0VxMWhxoBgv+vThmq1Zp/EnRsO9RBQfEFbkS1kqtpoH3rXiVferA3TspZu7ve3VKZRsAlRPF4kfcUBlBwa5cemrwqhU++llBAcKMOXx6bNT5uYECBwTeTCwOi9zCWkV1iBs0j4CDWNxvBgV7bgQ9gXoTJb1GwEDgvm+nZY7iCeJmdFAQYwpugQK/mVHfqkwUJNmnUHdHoAh984e3+vMpQ+GvbNzafB4QY7Onfyk4aI2FhODtjVunNO6L1sFBtSBLkCpIr4E/d1ecx9y1Ldbq96AcFzD6WlfIUWjq5YyBojMbIHDadd91NN//5v6W4xbRbOlHc83+hTPSKYOlgNYLKF+sgdKiEe7LSWMPHu4haUF9+MyC2C4gvtc+C7lgvRj9zav50Mly8Xsp5uF212oyktXFwQSqYkyAoFjOGTODc0ZJBGIBcwWqlrolIRAJKNUGEKRaVoWj3vKIwI4CPnQhfCogqOt6yAzEY+2NyA5oUKa5DPZYgru7XlHSgACd70DnZwoI8hnIZ0g+QdIJkk+olFAqWrMqL0Pdqwe+eitjIjEWSoOaFQRrMS9u7jEDErJvUDQ+gACOCgbSs6gE4ETIkiAJf/l9SrBmW535c1DgLbJ1j0oABBajE+TSpjYGUa/EOhstf0UCMhqTGgDu8rDrUnIlI3Iz6H1vHOmR1V8TXTuJtWNsBAcuekWAQsCfmf761pePmQJ3IwxswZF7xsbrAwyAxhoAQUcBjW6Z56KBgi9/G7/8C++0Wv+uSCIo8IYzPrwOeCI6BK/N+g/Un+Hy9jyCgqjMHgMEj2FlXUb7G4V2FsVucZoAEDbgAAHDckImp7VU4cAimys0tYgMaKXMajWlLdjy4ci5CUd0EOVWxi1AAOwLyBkQlMAM9B4CXYDPrWfXWgcB6daTZ0q4kNy4PBIN/tYlERZOWJZn4HzSjASjopF7kyS6nHvzpIfPkFNGOl+wfnYPWhLqwxWU9bHWCkqa8igecYmtktw2eeoUMqGqgFtXMDIEFRUMjtSsBaM55G7HutUmeO9eDG2s//KgIB5rHhEQaMBlAuWkIOi8IOUEPi/gnJCf3Q1uAzaGAAYMJN+pyyCf7Z8CgmsFrkWsAVEoMTyVF36VNbTXcrwY4EwGDpRJEI3r2QHFbQ4QFARRS+etorKLgRZgfbRPAezu1aN9mkhBsWcVZQMJmUdQ0DJ49kDBLKuM8m3gIF6jSMsMn6/+MZ09MwGRVe4B4NJBVvtMRxFskrIxC/bWEcsQgWEcaxUQC1C5gQMSQCAKFET5jx/9+CN84/tfx7e+/O1N9kHUKUB3UQ0/fGO8NsBgLsCjL2JAcREJKv2ioOAL5j5wIFXR/UZxUwDbm7h7Lvafo3mnBNkRO3XaeVZmR7Sgjw0g2PmcxJeJ0ZzR/j7RLjg4jjVgkGi3QTEp0sCBMw6cwJQsbsBcKYZqWYBaCYXH7I5MVoEQfeOMc7GlVCMYUAFEh4CqX5aE5x0URJeRd4Jc6yjIvaGMyLbx0zzG1C3CyYFATjgbSDglwlKAhRUwLJxwcoBwXdTFwBlIJ/DpjLqcNP5gOUMunwGXB+SUIZcHlJxADyskJ/W5rwUlry0KX5VPumlBz4F3c6qef+7R2IO91MaY2SHhDtcKym7xlXDXa68E6fA5BCdSYlNgtf3toEYBD20yJgZQMDEEMbiwuQ1Od/b4TOMIDBzUrG4DWe7UZbDcoVLCpQDXqpb1tVZcK3AxMPBQZegR0noUPGH9ZJZmhZ+Llv+OFL26KS04Ta8QyXd+ZB+jgeGPotSCEPXMBQCVNG1YuO+TCkC4t8u2u+R3sh037lVmBSwOCBKFRwcF3s/C2IIGCmoZAcEsi1pswc4+3xOacQ1u3x10RFiFYf7mD0NZAv+sCfsEsXLsqsUrCOxAxW/LExmFdr4mlzJTAyJ+jB/++CX+3h+9j29++ds33Qd+2jNbQF4c7GC8NsCgme1SQRb52xhtu36fXO9HrRUNn7fgtxrYAqBP29z+dR4z/YemzPxfBwWd/rvtJ3yC9+zpg6w3xOD/oCdTXwhCnWDMCZlvkqQDEQYSJd2LAq0jEABCEi1p7IE6/vPztDoA8F+c3S6Jxjm0n7Yv+0H1wVTIEJkt2IKCIkqfxrazDxtrz2lh6bEI01D3QWcMMhNOueJkQv0uswZlZeBcgZUZ10o4pYTT6U1QXbXpUbmgsnZMRD5Z/MEJkj+DnM6Q+89ApwvSSeMPeC2Qa2mPs3UNoOXtA2jKP6Y4xswFWtImzuAoc6GlOt4oiNRklVR4h0qRqr/VMg68jwTBK0GmDP2bBbgCSAAjt2vrLa/T8fUFFwktBgisJsGjcQSnO9R0hixnIJ1QlzsIZ1wqcFk7IHiowHXV9XO/VlxLxaUKLuu8furhuvHAPF03VR8LYU2Cc9Lg2yKCc2Jkbzse/M9MahJVnabjQEXpYDxBFaq71Ir051X6XvV9s7dX/dCz0eOAgCi4/nQhNlDQAg1lW3DslcakDXvvgFcfR7K3l3LQeRYDCBEcCNkL1Bu7AWj9WxJrLFoSoPB+1ckigmSGVA5shxhr8MOfvMR/8oPfxDe/9MEhKIj3fAhobczw7fl9fYABjDWw/tnOGkT/D2PsR/3Ft95BtKXjDWhK5AZDEHvVd5+goeawQWZQEK3cm8rtZxgDWzAPMgurXRIDKPuf3b3uTjn3d0nZA9vUwmICLimVBmULvFtxhtP5hl/33Bo77pW5o98mejtcP4BOKdZO6fnveXaJSG/5HEHBQ624roJLqfrPWANnEy6rCvdSe/14AK3q25J0LSyJsSTCKTNOzDglxn1mnBPhLidcM+OcgaUCa2VcE+HMGfn8c+ByAXgBika5k0XAy/mZBijmE2TVVsF8vUDWC+p1BT9cDwv6AAfgwBX5FHhIS8KmF0AEA94PYK574CO6FqLrQIxChi0Kqa2PhKclcs5DIyO6FghTcy3w1KHy8Lp2Cj6l89ILFC2nDgz2AguXM8SAQU0nrBV4WMXcBsoQPKwKBu7XgocieFj7urmstX22VP0egM26cb/7KXNjBXIinNgofWEsObp+9DqpQrMLSMuAE2QwMtSCn/aKG77DntHvL6KM3xB/47amHO/X+HtRxjWZpzdJrdWp6dXGdbDnQognvhk8fuZGjMsha/CEwX4AciOTLLRNAUElXZ/saEGDq7oJdAAOUqVDo7MFT0MP92c/+Qj/6R+/j3/+q9/GF54ICga2ANgtVz6P1woYAEBLVwn5rA4O5pSOW0p/b3jqjj9/DBTMfeL3rF1XdH6et8aNDJfN8I+2nhAREBBDUEPLU49VPtiQ7aDSjkMkaK4FEp1vE/hau71od0NSP5lTku0aGvgaL3oGR+PCjnRYWNyBWiS7PjjS9ksnp/Y6SnAyLfqE90DBZa0q7E3Ir6XgWmQDDPR8XcgTFhPySyLcLQmnxDityho8FMFDYdwVxl1OONeKtRJKIixMWNIJy90JtC6gdAbW+5bB4AAB1wdLcXwArhek9Qq+u7QSwGnuENgi9oMSbUr9EVdByp0V8IqJRBg6BDbJlIbjj5UMezOqoTOlrx1vNCUVXNaetnjaj0N46vUMvSSylS5eTgeph5ZpkO807TDf4SoaVHitYq4CwUMR3K/FQEFtAMHXzv21dACxVlS5vWaWRLiWhCVVnDLjLDxJaAZyaDtO1nY87BvNPOiPjbrf2Tex5TEAJK+c6NsKvXKif92V1DxmOdbAgP0m+b1ugOAGS/CYXKa56Rd65cfwmSgHXpHFP5bFswUCBQQOEJi1TwSxwP0IQtQn8QY48JEOANCf/+Qj/Gd//D7+6Zc+wC8/Emjo1+Cys7EFT9R5rxcwEAuSmlgDAPjhJy/xm3/wHj749Q/xzlvPm597HkdYyoNg5+hhj5QHoaXP7fnZiBDSerCxeo/WYTzFuFj3QIInBByxBkIe9ugBAgRNMThgDYAbC2kGCOrKcYAgRCoMbIOybeIUK7TduuKW2z61h77le/TjWiU4z5ho4ODG/S6ibWc9yHCVERTcXysermsT9Jcirbd9RPu+PnJm5ItWf1sS436puFsYd0vCxQDBtf0TXLO+t1ZRlqESViac8h1SPitAWB8sg8EAQl2Bh8+AqzIH3j1QDCQkrxAYFKxO3Th3ox9+xz0QgYB1aFRUm1p5ZQqswXxvh78cAPh5VC1j3LpSWmdE/xx5qeOdQkdPuhYHNK2h1Kl3nzydAStUpJUKHRCcWnBhAeHigMAs/msF7q+lsQT3jTHogEDXS1FXVKkaiLjWttbiemGPSUmEJQnWRKgSRLN3bK5Aroy1VGRiLOjugHm+B3++B/nNtUwO9hAFJSvEXVG1+JEn7F2Zaqd4UbWnAIIjf8XuCC6DsPdvnN2hgRXla/zFvV93OesxHtX+KMbaFOpxVj3GV+NGqKprNbPFWJWKMlXdmy/hv/vJR/iHf/w+/snf/gC//LktKDhiU3fZgv9JMgZAt2wtf/Xlxy/x/nffw7e/qrWjPbCQzIqM09SJOh1EpKgO0sAAoLn0M0tANObUt1QdP04ABONNA/YxLcXYmOGTnnUxjwgOhqMYa9BiDW6xBrc0ady8RDgGCN1maMEWwKjA52MCbdGOjVNmwXEDFCBU6wtBkdUZnCqbuRuDS921oBv6WsSYgjqAgtVec5fCPNJarWJjRc6swWjXDhDWU8Klsv0TPFTGQxXcZcJdTViTYGGy+APG0gDCgxZKKg/anCmfW/EkWS8dJHgTIW9B7Aq5HgiGPZfALRAQmy5R7xB41B1w050yKHqqFUNTJAMLsS8ChWsg4PHrmK/BmxgZGHDWQNKCyos9PxtDoIDgWoBLqbhWBQbXAtyXgvtVmYKHAAwua8VPLyMguF8r1rUa6JT9dWIuBC0E5tKHwVybHMms6zGTrs9lWLedKXMZxDHO6SjI70l7CaCgeGXew+FzdkLDMTcl1/dcBv69J7AEzhDcYgtabMHEFjwVFIzM5G35HHswEKEHGgqFLAKLMxBlmL1oERljWqHGA4Ug9+bZMzb6v/vJR/iH//p9/JNf+QBf+Nw743kD3X0XdIuf+8AUvQJD/noCA6AplBef/BDvfffr+PCrH+L528+bUnVKxwECpPc1I0KrgeCUT+wrDli1sEdYghiN24Jy8MSbFTYhTctzDyzE8Rg4aI+WxaE5wHX/oIF6H4SIRwc7QBBlBwhl2rSTHzteG3AACqLAeKI14dYCJ5AwQEl90wCQzOoRi3uDxXxoHsvoFx/mUexRI+GLR5YHUHA1wQ+gtexlVqWyeLqR6PdOSenktRCuRfDGKWnsQoGCjyy4ZsI1C+4yY8mMM2tw5LWai8EBQjmD1gtQV2B9APIKqldVnKHNsHd1bNa2K+G9Yc2TGmNgCtaBQevESNzaNc/Cuu2iyeSRcH+x7CkIAda1tbKmAAxQSwMMr3wNU3dDZO062MoWW0EisSJFDghipoEHFnaGQIHBpXSA8NNLaYzSw7W0zAQHBNfWsKmvEf1bXU7+fiZGIUGtFVV4WIfzaO4CW88Ek0dwWWOFg8q19fAYaP2n7Cm/l2RGSgMO/yPs51m+zMfenGMAAfPjK4CCdsjwOEjcR1wc6hbW3+QdgFBNzDp7QKTrYJc9gK4BH/+3/+cP8Q//9fv4z3/lA3zeQAGF3/WpaMbp0fm/otv8tQQGnuP64i9e4r3vvY8Pv/oh3n37nelDUFAAtKjZGkCCf4YErWVoXKKpMQToAYaBJcgeQ0AhGjdSesD+DQubUTzV0BZ7RKjAxBpMh6nhM/2SXXnqggSqKtJa4M2RnsQa+Lm35+hAAUDMy3m04NMgGMImDBtybCTjvxGWfrMg7Fo4QbiCsEBwVWsqLRAmAwRK53pnyMReetnBHNr9nOcwjmgBxrLOtSgrcUXVNM1K5nZgrALcZUapqyqMhbFWURahCtaqjx5/sOaEU6oaoMiMSwEWJuR0Qj6f1CJcz9re2Uovo6yIHR7Z6PemVN1an4crVGAXAAgnUwzUAcOBFReiRcKYFMQcbJZLu+9NeU2Aoa2HG+cf2YtNZ0PvYUBJK0zmE4SSUf0KBBSI9dRDZwWcBbhf1c10f926DZwlcIbJXU1z2W9UaeCgVI0V2FwO+SO1NZkIfa1aDJM3KXKXhBoq0qprKiNzDcyBYKDzfRztLV0QaDbyHPH/qnu8PX+c0m7Hn9eZgQIJ67C1n55AwZGMjL74+NKe4TZQ8HsyO8wTcQJTr7bq2VmrYMMesLEHgu7QVZCAxhT841/5AL/0CwYKgkyiwA5gAwqCMRev4Ynj9QEGk4Xy8i9e4L3v/xY+/MoHePdzX4CnM8ZsBb1HnS3weHsyQMCw4FI7polEfc7HbgM2ejCm57Ro3Ohv8zEFAYmdAJFT/t3Sn9mD8UkYrqTwBHBAftTUvjqAg3aOPG3meA3BgtuvMrLznRsWBaT3XC/71mGv3W/bwRSA+zf1OlITJolzK1NdCeZe0HnQjxAoCleGBYQRckpYqlpvzAQWdTEBWvYWGIFadYlkU3jVVwEA94hshFqUpSasWVothVMWrKI+7TsLYjylisQGDKoFrHFCXp4hLXoPqFzV2pbexEmaMjBFOyuEeHei5d8ssEnwEqlinay0IUVsz88b7rP225j9z+E8o1W7/GznLuRrIhmQMTAQehis5i4qFoB6rcrQuMV/v1Y81B58GlmC+6s+XteKB2MQngIK4n5ktmDV8E+LYqUWkOhrkW196j8zaDgABjt2AsxtFIoHFWeN+mtP2WNse0x8j/m9nRjB8X4/cf8PIwqpyQW5AZ/J/t5Zm3ug4OgnJVxS+BdBwW6cRPv+Vm4TMVBXCOchO6sYQGhStXa3JYkZKnZoJs0+cFDw+V94pxmwQGeHdIpowxbsgpphqhkvPv7oYFJ0vD7AIIwXH7/E177/2/jOl38X776loGBWrop8dSJb6eTwt9VhGQJ7oqs8H7kNeCdfd47G3bPEBe3g5Na7cUSKYfjmfjvCgl5HIPqfeAYHDFUiAF4dHPj5H6PRXavkCAAEy7AFnLWLNOFlUe8S8uaVJlbqVNICSGouF7KexUKERAlgsmIkeq8qBOeUILVgJcJKWmnuxEoDLsmjybkpdC3zWo0CZlRSJbBnJ7fTNwHAVVAJjT7u72vE+irGHhTBNWn65JktuyEnXBOQSQveXKmXW2ZKSCmp+x8A6mrWdcgXt7UnR/drtvpnq8ws8CiAvYKkX+MoO2N30DQESYF33GvGqDloGM5bD/iEcw/n7SwHZ1Q7V29mNJct9kqF92s5TFuNGSoOCq6lDoGoM2XtqXtxeNDhkgwIZCt+ldiyWbyYkaa6ZgsCzEQ4M+GckjUissZE3EsODwCxTixSucJZGPHgVKDvs3mP6QU0Zb2JJZkBQ3vNbtdBIODw+b3XngIK2DuAdlBg8HEABIey8fjMRlAwy257Xx/DqUf3xpSd1QACaXr3kXsBAP57DzQMTEELJA3THf8+BDXtemqb3xcff4Svff+38DbeOrz81wYY+AJ8+RcvDBR8q4ECICjXAA5arAEceSnN08JMp5XDAP783yjSarQzewnT3hSEnMaLAT9zJO7eaBtA1RlBlVUDB0JtA+zOwc5hPQvGwY+nbhIU3Q7gwAXxY+Bg88N9AT4GAvQz0gPQoP77Rm83RYDuWz4Y0Qfu0feULBqEazMIFHMRUK7gzBAmZCHr+QBI6DJ3TgwtcqU334V8YkLiokL4WsBUsdr9X9JoGc5BZu6KipbiKgCLDOAgsg019bLMuTCuSbBkjU3QaoqmHEJPhv7oQDXr49L0MJrgOLqXg8U/WV9VV4EU7HYJHIXx9tjcFEiwcNzlBlj2TlYWLh1YcU8876Yg3DJbe7VL713Q26oDD6W0HgcREKxStZBR1b+vRV0InoKosSYKKtp1kv6XmMA7a4FdblDPRvDslfOSWvbKKfHw75x0fToAyIzehpxGF4K6Da4NFHj8CTz+ROqQ/fGkfQYEFia4bWze1XVDtgbMyHEzeA8sGIu7ey9vgYId94GEterrsEmdG8tlOwIrNYCCnXoLu5Ol56vMQdVAaAMJnBbVM0ljS6hugxMB4B9Y9sHnQ6Ahw3VNbxTne7oHHO4wHdNwUPDhVz7AP/r9//3RJLw+wABEGlPw/d8yUPDFfvOI0FIZTckRpaZe3dds+th8PzSUsCQC/uzHL/E7P/hNAGib0ze6sgQy0neRuou+1KOVSto0BVSVEkdRcCDiyGCLCWwD9Fie8djKdoXugoJQtbaDA22rvPaYA9gmrDvgAGFjo7MIm34L/j4CGIAyAbIHBqYgMz3EZMkATUg1S4ZZN2/KnR3MEkABQ2jV+S1XpHRCZbsVDFRzIS05CqnSAiZVkBtNy55v3usZuMW4CprluDfcp6wxKON7tVYUItxfq7kWNG1xrYxTQgMID0mrKOaVcJ+l1dI/J7XGE2TXzTXW0ND52xWOEpX9vvKv9txf75/pBtSemmFfC+iyX62e/XLX2+qg6VCg96Wn513s3ObS17X2HigPpbReGLF0sb5WNSi0VKy1Z6S0+hWTIs0ErExYwEDCzTXg99/dBksyV5UVwzpnHopinUNRLA9KTexg1ZoVsaVDu0FSLQW0gYK1sQSHQanA8T4D+l6LgDxkpkRF30FCQYSloNDnoIECwk2mgC2kMoAC4dzBQQQFdQQERwWZRMyV2ARGXExNmA6g4KlyvKV70gpIRguElgpwRuIEsiABFl2Pzh4AwD/70gf4/OeeD4f3/dHYAdvT9pK+twcKgg588ckP8bXv/7a613/x+fbcw3htgMGLT36ogYZf+QDvfu7z45vSxVUDB95TwQWSKBp21sBjDXz8+Y8/wu9YGcr/zX/1t1tTHLYN2dwGIQK4+fKaogtWTxzEylS0iJRkSjr370jfPK7wZtkT+7vPr1MECEoZtIqMqigKhHMDB8p0wJgE7n/PyZ1B+QuFsssDiyD7wW+PRZ2L5bXPo6wgTiZzCJQXjWTP6lft4MDmrKy2qdz6WJE5AzxHLouaYfCNV9UyS6qIMzFOWSPNH9aKKqkVPaoilrnQ4waAHksQhwc1OpPgo4qiPH25ogqhJotrKFo/PxfCxWjjvIpajUz4zEvo+j/rcJfgXfkmBRydq8NisXPBsfIv9jnBtuukZ2jcstK2xcG60PPz3QUNr3jOJZyviFa3nMsT6yPC87H0tVe7dLA2FyhKZsUVEq3q+cj9dncjM2sRLAMDTIRz5tYw6dQqZs6ltBkLY7zXDjSbQbIO7gNlCgwUXB8MFChrIM4aANu9Vla7gJCyKgypDLA142oaNqmcMBZBSgAHevAmv0b3QgcBbZHM8SyWgtzAQgMFWvq+YmQJIpDdHYN7S7/roLl/pg4AoIGCnXidzXBWlxjE5lKQrPKNC5CW5l4oLoONPQCA52+/q+Jxaq7gXWx7ESs0/eWIflOrwsYGFBBv91EYrw0weO+7X8eHv/4B3n3rnX5Tgf5Itog9XU8qQGlyJzhbAFXEpmH+9JOX+Ps/eB/f/LVv47l1sdIc9cASNPpunQDBIzXASbl83SwCNTd8MVVD2GTKvUKmciYbi+5gfhgdIPRgQ3RgRAktK0EUdmuoA8Eb3bS/h+sIlkKba+qvh3EIClxIzYDAv78TGCWpwiOAZb0264YAFVKABk7VonNbyfyr3KySZL7yMBUABJy1Fn1OBdeV8VDUSl+rWZC54i4nrFJRTam4C8GVCIDBrXCUcjbcIxMMLXBR/0KxQMO1ah8GZk1pYupggLmqDzpZm2fqSsOVcCzGZfA4XLfNa/tV3FT+IkrF63NpEdVSpXUM3BuJAO/P0Ot72HnSbdAQGYfd8w3n7G2DpfbKlkU0oHMt3fXTQQB272cPDu2xA0xKIXHUbzfup7OK/nxJ3F6L9zMnGChgZNbaBgoG3G2EHnzKlr5qrAGJA4I1uDHVWCGpqANTsG7qROiN3QHhyXpQkLJyUkeAQBm691psgovWaXKG2BUMyvPYdWDK39/jNMS4NHeRWdszILglC1UXkLWink61Wd4mUV12P7WvQ7wOUZfCABBEjT9Jy8AeUO3rpldORNNDnUlzoBzck1HfTS7cFz/+U3ztD/8OvvPl38XzITvvGBm8NsCgpSRKxS5UjDSXaE+FRsE4CpNeMpftzQYKvvxtvPvW80YHZ0KPJdhLCaqrWdljWlCM/qWkyl+jWZMCBAYAs9iFDIWP1yGGlIfLw7FLgUiD7dwKa5vCvsgc4g44QcSAgwMS/yBRZw+EAu3YwUEDFjtDLYnwXvBt7oKC5nrYoQNLAVAgqWqjoVoAK79LZdXn6xVYCFQtR5gIoCuo6IQqQ5nsyrtVupIqC6KEcwKWUrAWtuC0uqtcKrQegfrevQfD2GwpWpsOGPaK3vhwhZSYtKENgCuJKZXalErv6AjklZvCcaZAlS6Z0vWCW/obe+VXo9Vf4Mq+AwBX/kpw9NgKv3YAh9mEgFs+3deurFUHDREw/GXPdw3n6gq/A4B+r2YwEOe/nT8hpBX283Dmp8eS0BBX4s2R/F4xEXKK940GUJcTb1xErW23gSUHB4kBHoIN3ZXgLsxirEDtBaQk7LVyfcI+g4skkIEAgSp/qV4rpKsSuoGYugthAgWcRkBwIx1xz3UwG0dHchBAAwMzwNyVXbMVPriGy25WRw/MZDtvs7IMbKhbYdFjpqXFHjhCyYRWOVGkt5ImRLaAmlHXVuROsOGLT36Ir/3RN9S9/vY7oRjUDboArxEwcEv+5hDjxgl6Y0OLZmcLEqlnnwD86JOX+Lt/9D7+5Ze/jedvv9vKjAKG0A2ZN+rOfHo9G0EXjqzXjU8SgJaCtSIsKnJt94E6WgT6KgcauNHX+4aIm8PeCl+XdsmtZ7uxByEa0XrCK0hpcQdiot7BgAMEx1nN50YNcIjHHFQYkNHNIqt9idFZkRtBT37uADa+zxjtLrUooKkVRLUHWREr2KtFf7cyiAqE1rYtVNao8CEirCxIVYO6ClSxZEooBvTdEu2tmPeVUFSYroii8pyBQ2QWojKKFHZiPadrUSXUQUJUPtUUTp0UUVfAwNZyb78X5/4RAHB0TX4d8+h1IUYl2WI4HgEMPvbONzIVfr6ru3ia8j+e+715b78Xf9vcAf35vuL3a1JmZ3s9Eaw5W5ITdWaHevE0z4BSF0QHB5m6HFJQYHEFzlRa4FytVQMN7T0FB6Ob7in77HDMMT9AcCOMSv0mSxBjCWaWILgOBD0TpkLjOSIg2JOBbWHo2VmPIzPF0R/0HMv0eQQ5FwCCyfY5cFPMBeOy3fwq0CwrqyAqFcDSwUhakC32J1t8W4W0Pgw+epxQPG2JSGgLCn7tX2rMXRu0nZtpvDbAoN1yYgjJodWqH+7sAYBNTYNEwMtPXuIbf/h1fOvL38Y7ARS4UIr54s2fV0uvPmf+u03EffvRZBYugPVq4MBYgha1+9jtG689bohNylSjnEYKrVYZXAtOVzXXwsQeNIDAWlJZr5X2AUJwPQiKbpL1avhMLQ9iZxmMKJ78nBooJF34vMoQK+yTLeBJiroUTPBQIzwEC2ekBO10Rtrmtwhh4aAMBZDEqizzyNLMdPtau3W91hqej0prprF7Gt3oy56tV38erdXYpW+jrNZRAeu97hT3PPyaAdwEAX8Z5foUxfr/jXM9Ym0S04YF8KyPqPT9NQdfmTvzka0RlWcujW6SObhyWzAtBpD6YzJs7W5LmgpaqdJSoj1m/zxpTPtsAwrMum/tuJnbax5fQHkxxe6phWzPXwEQTCyBM5wu22bXwcwa+LoYLo3cJlSwE2DB7dH2YWcPBlBwIN9FjFFpAKHCs6UgvdJuO36M9awAQGidGxHXSa9dMJ5nv88NFHzpmw0U9DofnSE5Gq8fMEBYeD6dT9wYZBbzDz9+gW98X0HB819811JFepAhgIDQzXfXYgs0+rfupASNJyxoQTvISudlwoD85mv04B3Y5sDIFsT35rtewy5ghFgDAtLkWnB+ipw9SNzAziFAkDlqV1dzZA8EVTdIKcPpEVZo5/G1b1aR/t2D4itzZ79dCtM3sZ9XLfpZAMLSfgtSNSCIGRmkmQoCVJlb0AYLpVktovSmjPej36ORadhzSairQiPhZ1+3pzTG2IV2X81Mvlq1RaArtFn5AqO17n/7YJqOvVG2+8rV/e9PiaeYre2jc/3Lnqe/vneu8xyO5zfO4ZK6a2aOBdA4o30XQLT8XYg3EEBTQCUFQM7bluzs/2C2nrsovT7FENdUmlXrtPU8tOOluQ0rNAHpiXusgQIvj+09M7wXReougYElmCtlegDhESAIsQR7LIHvtRKeDwbRzv2tQAAHaKxBJYIRofoeMWhuLDfIZbFGXrUZhBsZX9Z2zeLB0V5rRY+g9wJBDvmUA/AkMG/l3AxS6qzf0RhAwdvPh3mP83lrvDbAYB5DhLyPnZxZ04F2h7QL429//+v41pc/xPNffN5AgfrmvUAIOkLfAQUxJWjIEa61+SII6Og8BkvujWEl2HNHzH69slVW82BzT/gGccu+UHctCKl1U9A3kcYeZF3MTejUFgvgLgZJnTlonRVtw0iydEjhppjBrHPFqcUFYLX4gFrh5amPiqSQz1+wWLyBzmZIYGO8tC4MB0kBahB8loecyMxWzBtq2452tlg26XI5HabLXYuyCg+FUbLgspIFxekjkyr+xKaEy2jptqJLtk70s13xApOVbkvNFfDReEzBzj55z8qI342jgwLA+wBEQLABCXUENLfOM/a12GMEInDZm7M4T61tdiJk5iH6/5R1XZztPU01jOmieEK6aGcKGlinwGb3lwHfc3Wv4E5Io3MXQrBqtzcgAWxO+cbYJcCrg+6MDSAgBuUOBsBWXIwYyFZ22hX/BAjmTINjl0GDQD2WwJ6UsMc2sQVB4x3Jvyj7nDUIHoZh5mGAYXcMAe6yL+dryNwYUqm77PFf1NbMLtuLMrbNtu3uBAqLZFwnOvaYAgD79/cGOnhtgEEVveECQDvrlZE18NEW3nb86OOX+K3vvYff/Yp2YYygoDUkqc4YRPdBAAXrpbEELU8Y6IvIInzbImnKB402aucZh1Nw2N5PBwgzKJg/56/PGwTBtQCRFmPhJzkAhOBigIGvbWlbUSHgLIMreNsOVEnZEQi8C6K0jZS6kJvr+s+si2cizE1ziLRZTgt0ipNlARV2SO8uKWR1C6r5B0Pedfvq5p5MnyFPqyKoP5SMYcBQXKeIBsydUkKpvcDO3SotA+JStaujAwSmaiBBRWKxBb+n7Oxmo/iaADZBjJ42B3SmYR5z0OSeop3TNP39vVFg51AArlreuUW+BhqgQFCFwjnCrvvxc/TnMYvgMSYjAhPPGMhMlh3QAYGnDu5lCmTuvTaGIlNNfqAr8rimmyU6KvJN6V29gv73HBXve2U3Qp51P7jLcr2CrJdz6z8xr+29veWgewcQOAjYAIIhk+AGO/AIINiAgPnvR2QfQd+Lsk+aEMb4nSBrEV9z5RzcFc7SzPVXmpyv6j4g+2xr7NbAAbXzaz9VC5AYsWV8DR/Y117Ai09+pNkHMaYg6rvgRjhizHy8NsDAh95rvbHSiEjbQDtWh8/PRx+/xPvfew8fGCgAdFM3W7FRd0FJmT9vyBP2wJ694iHAIxG7+2CgbyQ+cCO4cHFaOxwzIE3fB/MGgchuYGKLPQgAAegAQY85sQgRJFBSv77VR3ArRSgBbIBqScq+OEBI2YqyBHouIvE4HFCFgitDK+BGbc5g0IUp4FWsBsHYYjxuCOu9seM7TdRr9UteINDsBm3U09PoFk5YE3BXKh6sRn8ECExkVfgU6CbW11vwKEbFV0XARAEQoEkD/Vva9w6RAbZKfs/6/qsYDnRg5xtfb+d/4/xeFRTMLg2vJeB1BW7WEmjphD140NMGe7bSBSgFdO0yYlDkT1lLw987azLS2+Ie+P55gbQIeSmrgoPAxP1Me2vusBkZAgqfi+6CW301dsCA3i88CgiA2wbRLPuALvtAJj+JBneCunxM1pIXKxrvxUZOT6PLe2MMKhSM2fyL11XxOisVowK3YGniBAKFmIOd34LK9Bcff7Qt7jfP9ROCDn28PsBAuuHRWANdBh0cANNE6Xj58Uu8/wfv4YNf/xDvvPW8wTcjvFTJWfrhgNqNRWgK8VZFMT/NWu3+RxaD2wbxG9iUC7ifs99Y6SzBwBbE39n5+yY4aF/q7MFc96DKHv3pLEK4ES746gqI5zklnTsXkJUVIEhVYMBZ34OoO8HnTuoIxuYxRUSPnfU8TTEo6z4j2E9tHS24TWtgYKjiOA//3d6sKun8ePMeXsD2/Lzk5i64sjbwuSbGUhnnZA18itUn4IpLhQW3qeuBiXAttRnbCbRR2HvlmPVvuvk+YMc0VoIb961vcHKwYXOPar8Pe28rgqIrAUAr8jPn+c+R/089v/ibbU7i+e/MibME0W1wyoQT8wAGvOLgwmj1AzxlMDM0CHDtKYNjl8vS1s9T1o79tV23bf3uKaZ+7eojr9ByURXIBE5pt8iYW7Lbm7W/rwZ2IAYVcnAHGDuwX5QoyDFMzIC94BIzAgJgCxLg37sBCuLzqM8bwyzo8dLQ54RQ1hhsz7tc7sXotmzkobx3cNDml1sfE0ncDCs/DtVVC+5R8jjEPmHT7X/x8cte3M/bAOwAsGGu/6fGGACRNZjBATZI9eXHL/H17xooCCmP3efnWlisVoFtopYnrJHvrWCIvx8XSSv+EXrF29+NsrOgnZ7L2zdVYw7s2h5jC46MkRkc9O91QNXiDoDmB3WAoPOnHyQKQAFojMLAJniRpgYSBERFz5ktPqEGYeWvtQ6J5raJwvQgbqRZRo6MA82/3wHOLj4ccwYBQ+TxVBBmr3RzrBvfqNeUgbyA8klp3LSAeAHyAklncF6w5DOyMJai4OBSBZlUMS2lICfG/VqQV0amamV6FSB4eWan+vcU6Bxn0J/r+0d+fFe02RiGKoQlja4FVcAEJNa/ObAWQUhyYMrmPP85tsBfHwMOt+emvz2CFE7UYjESsNvS+FYcgfclWLKXICbcpdQYgpMDgkRYSEDrPehyBZUHSwk0ULBem1tRvH7AU9bLjnXOHuV/Eyxg+FtgH/esIPL3BOzW9hP2VAMqQ4aBAQKTZT12wJmCmTE4dhEAIxDQx1HJ1+m9GRT42AMFcbjr4BZrwHDDssvdKI8VJCgT4/FRKt+qAShAop7wNGo3Bi32Ssrajkl1RauzAoCqFbwTgafUN09bWMoCZbrf++7XtY7PW1+Ep6YDW133VLYAeN2AgSm51JRfAAeUuma0iXrx8Uu899338MFXP8QX33o+HCqKkh7ME1u+GmBwyzOODS0X/HVAFwJEzVfXW4haGiMnwGho3WDmRnDdhf74s7K6A58iMzjQWXCA0IZYxbxpU2+KKJlgb/XtOQOioEABVdZOiCxolcQcJDjNaXMrAChNPtk4KFggA5KftKS5K8LFYPbVUgB6EhkgfxQtCiPrXO4aiMGl7tclItDpToX8ctJ/52fAcgbyGZQWyHKHUz43gJCL4MGKLDEl7aRIwJUF96tGvcf4g8w99RF4vIlTjPoHsBv57yNG/PslVt4P9ou/rY+j0joqBPRXf17d9XA0Fy3FcAosPLECAu9keUrW5Ig14HCJgGB9AF3vtcDZ+gBcHyAPn6FeL5DrRdfM5V7XsxcZ8pOdQAE4gXLWYl2kZb4dVErKm6h/7ali697ZRh/hbyEPxA0g2M8jLYf7CQDqAD6og5YngYG0CwRmJR/OzO7nOA7BQvjSXqDhU8Yea2D6HdpDxg0UgbDLCStER6RyWzQAwNOuUWp/vvnBcN+BZoB0PRKgkMsiy0fY44hefvwS73/3PSvu9xxuNopXdwU2oOApbAHwugEDG/G6OzhAmN0DUDAhMgLaRmptN6eN1AJRXGPH92JwzxzE45Zk2+wGAJxF4BQC2ZQSfwpbcGu0tWJ/8+5y69PQNuN04D0fmy+91hQEChZGoGAlqK0HhDS3glGt1Vw1rqQj+xL9qbcubn6+uTjqx5HawYJvRO8+t167cL9eFAxcrM68AYZ6VUuwXr1yXBdrRAxelCHiJYPyCVhOoNMZdHoGPt8B52eguzdUseQLJF9xWs7I+YTEhEsRMGmQXk4Z11VLILt7oYjGGexVXZzHXtVBf/2WAgYOlLCMrz9W3KidR2An9uoUPAUUzOdUNeyindNQfXFKUNmrPuiZBkduA2URCCcGuFxA1wfQeg+s+ij3P1VA8HAPuXym6+R6gayXYY3M6wPAsEbElD9S1nWSFmA5NVCp8TvZap4AjcYPTIIdHDMoayNY3I/upRAXMFclbG6CqUyxy6ijGAFgyxCMp3e8do7eUcdJbJp2fGl+nMdZgx5fIMwgdPZTjE2kJIDkdl6edg1jCAbZP16kLs6EMeA8yLiWauqxWtMFfPSJur+//VVlugdDeNgy2/iNp4zXDxhIp8b7oGGuDkGBP97QK/13AvJuPxOkbQ9p6MBkDxTkBcK5MQQtstc2vT+v+NnZgkFn2uNhQxq/vFuXLrL7u0y9Eh2hM3H6e84gOFAgJNYWybrBamATpG1C3SwTW7CXirVxE8wnveMuqIDXpegfqw0U6L8HBQj2WC8X1HVFedCa82UtQBHUVbMZnD5nZlBiUE5IWa3BdM7Iz+5ApzvI6Q50fgZ+9iZw96ayCPkC1GfgfMZ5uUNeEjIxLlWQiiDBejhc1b1wXUvr4bBy3SjG4d5sFDC114m86qVPZf9Dwo32KoiAK+S+Dh4rh9zOYwIoek5olQEBvPL59N+VDWDZmwefA3UhwNpYJ5yZcLckZDK3gbkOTpmQpIAuxhJcPlNA8PAZ5P5T1M8+1eeXe8jlHutn98P6kLVASh3WBgBwzkCiYX1wzuDTSQHoctYYpuWs8wBY/MA0nHV4zM0wj1v7iLqkmDMJ9jobzjUGvC03sAUGwL7cuhEDezwMDeyBA//tW+OINQDDAoetZ0J8bgwCAO0VAUzgAECF9otw4wMILNF0oa5LaEKxe0NCoPyvq/7aGML9o+0xsjZPGa8fMLDhN9yH6/sXHlPgoKAJugOM/djK8sHmP+LUFqe9AQCj/3APFPCydSGkBeDUELg+7rMFcewZWbcAwWMbctjEQTHc/BzCfJLHH0hjERKsHji5skomazK8iIs3jtLfveFKuDUaILANHYsx6YVAqCo1O+QlW1zBem2goNzfo1yuqA9XlIcV6+UCWQvqtaCuGncQrQSyqnecE3hJSOcT8t090t0Z+dkJ/OxNyMNnoGf34Dd+DnT3JrxWhtQKWs7gfAJXApMgk+ChViRKGo/AXu+g1zyIZYHb9Nv9nXsR6Gu9H0H87GYa7XhewREA9hoo6evj9+IxXeHvNVLy11/1XHqvhLGXw615UPdBjy1YjBVIDJxZixdtWILrT5UlePipAoKf/jvIZ/8OcrlH/exTrJ9dUO4fsN5fUB4uYV3I7trwdbHmhHw6QdYMPi9Iteq8+El7iuBwMR6PlJorYrdokH32lcb0vTGAjQZFU6Ur/t1iQzfkxWbI02WTvx797r1Oi60HHK8h/9zMGmgcFAG2bpm7tT64FKycMbADDljQ6xr0uW86ABhdCreGyDAfDgp+9ysWKD8Zwj4ds8h+VXfz6wMM4mwEZQ/0SXthPpn/8tc7KIiY+ZXBAWlKUAtO8Rx8TmjFd9pn+wbXCljZ3AcBFFjUuqTuUqggy4E3iwgYK+89MiU+ZkDA2z04XrLPyWZOO/Kf/X/zKBg3WSM4CSgBKGwLvxBAKaSLzuf6xFUeFb2jctFMkqHDZPAbxu9K7bEG9XJBuVyxfnZBvVyxfvaA8nBFuVwh16KWYRX9VwRkGpAzgxODTxnpdMF6XpCfXVDuT8iXK/Ibb2gke1nB9kh3b8Lr3XMuOC93moFAAFfGtYq5FxJEvJ0wtzLMevp9jmZlu+1gOHbbPJ7PPvNeAhrApuWyT/k8RoDQz0Hf64GH9ITz8Iejzo96Xtt5ANDKE8f21N7KuGUceCzBdWIJ7j9toKB+9u8g9z/F+tOfYv30Huv9ZVgX9bKiloq6WsChrQtiA2M5gZaEdFqUVZAz2M6XmJVZ8Fopwbqf45G8U58bFJuIdD3gjQkd7lKc4tHadJItKP+h4FBjDcYg6FvuAQeoRCYv0BU8aDTw9paEx5MBQd5PACFez95ombL2hIYfJbAFUUelS7giggMspHEJZPeMrEPi9oLRirER76RShzHN2wAK3n4ernfUdfFbs/zu53FjQvA6AQNgHypBJ8d9Mh8Y0qpt0Y6H2GvDuW+CM6zOb++SaEVDdnOCKdYRjwg/pLJRaqBA0rIBBQ2RY9yE8fKHn90BA03J3oDSremSfafRxWHD+2Yfg4l2j9amUJW+/22UNoB1h1Hw8KkoOPSa9q40jMHaSP23ocf3qGCtGMIAXS1OxFJPeWezmrUn16Ig4OGC8nA1y/CKulaljitQruO9JyakRVmDdL4gnRdVHM9OKKtalIsHN9YCtlRNunMgo/+W5Q4pJ3BR9iCRWNqiZjAUm2pXlnHMCrfPLfpcUy/TezQiIFQKn4a/42f2vAlzSOjsaorlXh87D1+DNRHEy1b7704AJo4GgmhqUkS9HsEpQTsWzq6D+08hn/5b1PtPIT/9t6iffYrrT+9x/fQe66efYf3s0tZEedB7W64ygBMASItGr9dzBhugBHStEDEkF8ipjnLElDxZJU6PR2rGRGQNOA2WPbDP+h2NOQbgKUWF6o5MuEXu9fsslvlke508fM6A9bQe9uRWNxyMKXBAga4ljxiLvaJHhTAhEtIOiAgWOXGrgivErWgbpwRZeUgJHUbUBcnYiCNwEK7VU+p/9yvmPgiXU+3EGCODcnifHwEFwGsEDEYFNo6PPu4VDb9gSCsqNx9t0UlntTaggPoy1IcEgRekAACGUD+DTV59Q/WpMQi6mfPkPtgHBa2N741N5/XXfV7mTTUq2XFU+5yY9D36mbj5B2Vw9AVbsF05iCkGmRSTTPXjO5CI535rbBWQshIe18Ccgbpq0B8AWMaDpAoWLUCC6wUUaESngkVqp4jXgrpWrJ+tKNeKeq2NOvZBTCg5gZeKdClID/49+1xRhmGx9LoqtZ//nV27u1XyCeekgYnaU4GQSFDZBLUxSpjS82bFP9fnn8FYnG9g3CdN+WJUEsDWhxmF8eAZp1HgR0AwxdfvnsdjSmoGDvNgaEwDAT0zgWA9D8x1sF40wPD6AFo/0wDDT/8t6mf/FvLZpyif/jsDBPe4/vQzXD+9R7m/4PrTK8ql3FwP9ZrAC0Pqivwsg5Ouh3otkHMd3A76HWMG8gLKC2paAM5WgVAZRrHXKrq/3615nzNgKxv3xmzp35xfvKIcAPT+2/tkstbdiwcf36yReUS51YwAbEHCoUtDfG5U/hyBg5bKWa0JExFQQr2UWoCFtKtrrBvh1xKyUVqjKb/KXQOUuvvb9NesuxxUteqIN3WDPeokHY7XBhjsDREFBb/9/ffwrS93pHW7LWfXpAP4IhrRqgUGarAcATm3ynhxvmNe/ZDikzy2II3o3wINvVSuxxTMjXuA7SKPDEEU+gBuCv5++aqcBIZC1fk2gC7/nE/XbKW5hbbb29392m61BbDQgYKyCvG8qd2NScHcGoPyEe15D6CyB555ABX5xxUIcTFXT3TpeLxAF0lSK2qpqgCKoF4rymVFLaLtpeOp5Iq0Muo1tT4Hu6fMGhFdiQM4ADwKGiJgqVjyGSkTcgWK1RgoAgjvA7koWFN4rgrSBWlct0fIkzbvCuhmTvrw7Vmwh3XaznM4h/i4fw4C0owIdGU1+7r3rsLB4tixUJQhWK/brANnCj7TYMM9UPDw7y4oDyWwBXVnLbABhayg8VrBuYIn5eHdC5v70eKSVFYsxjKetZpmWiCUsE6GhMsM4NUCz2YKPgKFGQhEEPAq+7/ajWD7orr3xxbPTwWuAFq9FScJvF2xgwQ/1z03Q2MoAjjwlvRSxRS4/s1sqeXl2uUHsWZQldXqD5hLeaobEUevS+HBnGNWibs1X3z8Eu/9wW9qTNznng+um/GA0lmXnfEUgyqO1wYY2H3rSmoABd/GF996Z7Rudo7hC28Ta0AMays0FM6BNeUh48K0l9CW/tutEBZYAgcFXlt/rX1TN3BwAxDoud+2AmeA0CYN/bggahvf02iKgQNF+YQy/fYMCqK/Fxj9zh5gttpL0ccNqPUQgYKfb3x8qnRritUVYgUKq6WSSAFC5oSUQ3CQCEQW0KKbnC4PoOUEXB/A2QACWbbBABI0tchBQa0jfUz2d9oJCvEARSQCJcIpeTZKKO2MCFIrUAtSWsBpQVJIZcrRlKZbY36vw7pgn0NPB1173EX/8pFd2df+4G6JPu3298EhHDw7zdq0TkgJ9us8OgdbDIM/3ecLsT1vF/ZxTvxKHBQxxCoVXq2C4WUEBZ99qhkH9nj96T3W+wes9/cWcGhMgYGCcjEGaa0btoDXigpWZqqOgWmUrNqeBSZSqH0B+ydpAgX5jALNTCk1NO2qFvsRDKGnsAU7t2pwE+25anzP223U13f2PbG06piJCSRqgMxMgcuyCOBmmdYnzk8Wg/xSlSwTSJDOIAhtwAFgr9tma6DA5rFCq1wyJSCrK0FrShQIrSpjq7qStW5OaWzfRi8AXTc4W9DcCfr44pMf4b3v/xY+/Op38MvR/Y1j/RULHLWf8+s3LMI3tqeP1wYYzCOCgi+ElI4ji6Z5EQy1NhbJQYEVShqEISdoVDuHG2/LPBbZiQ1FLK5AmIG0NLeCIvwOBGJ3vkiBxdP2m3sECmbr8HBR+LWHH2jtPg0cMBScNJAx0csuILz2v78dkwFXYyT8JqwkSETHQMEP7prx1tiZGFeCTPZbov55JkIWQIRQmbDku+GrIhV8FlQrcSvLBVRWpPMV5ZJAOWnMQE4oXJpvmFNFqdToFYnsQNWuiLQWUALqSihXBq8rykMC5yvktKA8XJBSRn34DJwy6HJvU2DUpVQtTGPV1RKrayrxTMLbpLSAS/08vMT0VFSqm4GuVveGozQrEevCzBW1B+EC+ybKAALmwND4+/MNnc7B/e0BFJCBb2FGivn1TeDuz01rXzyUMtaCRfJwr/UIrhfUh88gq2YbyLW0zJS6rijmWmr/zAfogagAWjAqmMBJ1wsxKZNl64k8GPGsdQywnEHLGbScwOdnqOkE5DMk3xkouMNVxr4brROm7cGNe3VPgNwaMjzsAoG434FxzwMdmLjbIDM6OIinQ/1vphEUeDvqx2RY87NLAAQi6hqIDChGcNCuD9LljhAqibk6CKlq8zhtjkVI6QSwVbstpMaf94CpBZAE8UJtPlnDOdu6bAC3X9WLH/8pvvaHfwcffvVDPH/7eZORswshjhrmIE7LzMQAGrNwa7w2wCBe9o8mUADsB9TMR3D06h6FppPI+nM75AIQMxB09e1kIQTBOQCCwBi4T3CPAlQrcJsFsJG50SK0DRRrBmzp4mOhywRjPvRYpaEFUnO70i5jAaALC8EmOr6llIWTJyYFC1WvacUWKPTpHC96vn8zk+Hfj1H3hVQoJNLyviKELDp5HRzU1vuCzs+UBqwFtRbkZ6v6jK89TiCtpTEEIoxa1G0RRUDMw2/nX9GzGKrmuJe1gK8FqRVYetCiN0RKJ5N15RRRCkQKYmna+Qe6Ba6goBWT2gEGXvHRhddc8z36Rnup3A4U+lqfIzyGkwrn5mCgA4FW+vrJ52C/F1x0nhKmTJ7W7N8wHHGObC6GFupW0hjXB8jlAXK1csdlRbUMlOoxJ+0ebq823ncHAcQEXhi8MNI5IZ2TpiieF+TTCfnZqdW64Ls3wM/eBN29qUxBPkPyqYOCAAiuVQJjoP8iYD/aH8P53thjMxDQ97f7HOjvO1NQPBvExEiBIB+gEoYVvppcPSB05kB/cff7UYYxTIaSsROQZvBEdnm85kY+KEgwgMCs4IfZGQnTF5TAxvKhrsoYRIAQy8If+dfcaLT1+eLHf4qv/eAb2vsgZB/4149i5HxmIkAQ9J3oq/8ji1l4G28fzOFrCAx++ImmdGhMwTsD7XIUXxBRVZvI9sSFnwqXl5/8mX4/uBBu0kSxocgUNbznE4wMgeB4AXRKtFvXERTEzRTpY8hObYCQr6wMg9YUqOSTp0jBAVO7bMcK4cyGlLUpxxxQ1kCnh4BiQABoKXWrxL4M8ZonILCDnGdLxUMHidCiznNirATkZlnp+VcQTvkOHJSHCyG2tEWUFfluhRR1HUjZNk0BgHolUDF3gqdAuaWYyNwHW8DgwY1STIlbFUYq2s6bvR+EXTSxAdM6AYNZ8W6AQO8G6j0+hnr+dozh3EKRlqNulgBasO3hsGAs8d9oTMbUg+Ip5+AxICEF2FP2nEFo7pjm0tuZpwCWyNwJdb30KpdWzliKVi/cAhay+8mgVMGV1F2wc/95YaRTRn6WsTzLCgTuTsjPzshv3iHfnUGh+BXdvQGc7iDLM8iijzWdcDFQcC3Sa1mY7tH01WPmDlAD6AoZjIw0AYM4Ihjw421A/was23FrX+tFOiggmNvQDRjbcAQMoECrVbpcDq2r7R7aTWhrRGWYfoNcO/ovit6PaufuhZH82uZqr7pu1DXD0Gqjnrm2iVlqLeMDQIigPDBmbUR3s6W4/8YPvoHvfPlbeP72O37VG+ZmN06uzb/qh41LHKYfrYzyP/q//CMcjdcGGAC9dvQHv/4hvvDW83bzgWNQ4H8TOlMQXwfQrKEXP/4IX/vB39XXOEHEXQgzW+AWlFlWEyDQYEK0vPMICKJfMAKa6BsdqCFnC2gEBX0zSViYXVnMLYTFNxORVvgiQqJksFt/iJ2nE0XLhK7EfajFPx17sDyclZH2N6DCwwECBA0kHI1oqRwJpqvfDqJWyCeXinNiSGJUsVx8YUjSazylU99MYkF/VYPDhjs9BYtxuijDsnCLSFclb+DE6GNeGPnMSOeMtCiFzDlppUT2jm5hmLKkquWasZBGRDOUj3WfYveFBbAqjdacyz1r74fQD8JdCqX0eQyNYHweYdYRBWAQy30LgmUfa3nYsXpjsdpAQgQGr3wOERTMFUVjWjD6+h7mqv2GV90srTvq0AynrSUrTuQFinJCWpIpyRVABpFnt2zvfVoY6ZSwvLEg3SkoWH7uGZY37rC8eYf05s+B3vh58Bs/D9y9CZyeqetgAgWXIliL4ForruaCXK1l9yp9XxzVEFhtDnxf6HQc7zfgBgu4s7/99ZmFmMEHwW+JVr50mj7KsQR0QBAqo96SYR7zwpzQIvbdyBGNHWDIGDxpxxvkrgVUMGAyD4BsAQKMEeINQCho7efbvjzQFyZ5vvNr/xLvvv28sasbfTWBhPY6upEbjUfYHH/0yUv85h/E3grH47UBBi8+foH3vvt1fPurBgoO1ri/HN8/qq7lnycAf/LJj/DeH/4dfOdL38Tf+q++CqUoK0YXQmALYlORCRDEOIIYKOTpZnusk4ODvToLewjbN5P7UBttigm5uqXnC9PjBzhBWKkyD68Q0ckSCNgxhs1dYoIULQhq8TqbUcLjLDyKKHsgVTRa295P03f1tLtgmivv7Qf9S6v6l81V4QChJkaRigWWD59YwQExGN2X7cquAkjmH/ZgRK9eRzkNOexqjJplksx6YVUO+ZzBp9yq33HOWvAmWQMdYlWAPgwgsClQglnQ80KPVjCkMwPXhw4Grg+9H0RZ4VUepbELu7y43lK75lYr3xpFubLWjyaAdjaVK387zwYAvMGQMSZHjAH8mv0cktYNoQgIlrMWD0tZffQpgzatgvfcLm7ZiZYujoDAgUheQOmKlBMkZ/CygtcEOeUuSLkC57p777WWRVK3wZ25De7ukN+8w+nnn4Hf+HnQszfBb/w8+Of+PeD8hjEFb6CenqFyxqUAl1JxaWwBcA2AYC0Vq8iNveAVMP2WCEhoYO7a5+zxaO/59zF9Lu7vOHxJJFgckbPoQOuT4dlDPVMEgxwjA5F7cqwDZF2f5LFfnDZGDiAbGQtgI38jfU8kZomruxCislBMbnGIP9gABEnh3P2IcfRYnXfffqexY7eh2laPTbZtG56yPzZcOj76awMMvPXkc/fJiCLIx1DwU8aLT36I9773Pr7z5d/Fu5/7ZQDQhRe47qH8aAAEMEBQRA28W4CgVtyMOG33MbgR2BZiAwcRFJSrbaZAJW98u37Mft4kBaieUimaosNQCtAidZkAYWU7ksA6K1q1r2Ilj5k0GwAqQGZhEa2JWBK3vbYzD3ugwIVg38QysEWwc0EVrERIpWJJjFWAcxXcLQkiAkmAoEKEcMoZOD3Tev6mTNiUQzWFdM6LWv6fZi1edFANsU9xr3bHOSOdtASu+5b5tCCdlqbomjKKpVRhC0EMasb72Cyp0ArcAcH1oozBetHOkN4WuKyQEko6m5tkHpqN4W4QBUaUEnBxSn9kDcbztdFaltcOBEpoXV576eCnnIdG7mtRMDLfO62X1uKayjoChFTbvdwAF5kEpTMhedFztOOl0wIpEoCAnkfJK/i0Pnrf08niCZ6dFRy8eafVL+/eUEDw5v8M9OznIOc3gOWZsgTLHQpnXFZtyX0pBgoEuL8WPFTBWiqupaqccct32gPej2IVgMQCi5mQbB9FYB5HQlf6ZCl83sTL9/DR/t6U3Ga0IEMNju4MARO1qpTOeKKumilS1l3WYPzBTsuTKJUvvh8SBnAgor8rxoDG+CiRAzks3XWqbIOmOapLQRVzJShAYJ1P9swzL6zWzpsHxmOoutrc0Tdccq8wfvjJR/jt72vDpceYAh+vDTD48Nf/S0Va4TUibKjuVx0vP7Z+11/5AO++9YW+GIlHH3igr9yN4IBgE0dwBAgOKCJCZwpoesMJKDYE3hB2AwVrQNqzRRiQhkd6W0ClsEfnaqxD4gywgpFUPTWT9JE1+Coz6Q5JFWsxwGDgACZI4ngMDETGt1kvvpkPxlrrbmMftogcb6CzVmUN1qSlhO8yo4CVImStJHhOGcv5TaU5TeBw6nnl9XTGspyQTp8ifXZBvjtteih4gJpfo3bSU3YhLbmBgdZgKZ+06+JyUuvXuu01yrytCIy0krsORLrLwMGANYNqzaEMEMR6/k6dV1PO81D63JiTppRHBe3AoLlDYj14O2ZkJWZA8rOeh/ahWIE1A/kELAqKqFadRxHIckLvTGhU10bwqiVJyTrmWeyBNzES6P5aUsL6mTFGS0JdMvi6tnue/Vrtvsd7nk8nBZOhXwY9exN896a6EJ69ibo8A5Y71OUZ5PQGrkJ4WAXXVXtlXCtwXSvuV2UKrqVqI63aG2kB6Gsfun5rEe25caNOf1TiQLDyw1LzYMKMvhc3MQxhbzsA0AJSbC2vzYghUgaBEKxt2+8WCEpmdfdofwkKdrKWyFvas/Y1sEgjAiAJYEoqS02OBAKhjQYKZCuLm+Ss+wBBdFlCSo8/aADBgIG7df8qjNbHxo8+eYnf/v7X8cFX1GhuVyj1Jh3x2gADR0Jk//xmE2kVqliwxxjxTaOl2Yjo/a6/jXff+iKGHNHIEAADIPCFNbsN5nSiata0L8BYf77/DLWc34gKKLzv4CARQBMo8OfYYw3GH2q+WPetC9BQbQQHIrp53aUAaIAOMIIDqbDGI+oa2ItETv3n9e89CtpeKjKCjQIgiWDdfmNoQayyy4QkCZj1XmS2zoRmMZ2rQBa1MgoIIoyaCKfTm2ZBZlTOIO755TWfNIL8We+st6yazlarQK6TyDRlGi1Izknb7GY9Jp3uQv76eeyvEUCc3hi/j7dBwVM6RHpAZZ0UsneKFOJ2/t5bnnPSGAgu3YIH9BwLMJQI92yDA0BQ7LXHzqV6vr91JqxrhtSKdFo68J33Eboc1PiD+Kb/YWxHpvZ5MuZArMGZEAHXjJwy0ukBdT1NDJFt+nj4JanCO+u99qyDIcjwjZ8HTs8UDCwaU1DzXYsnUAAguNbOEtyvReMMLNhwXO92RQbcIdhtYe0pzdGy92DdzQT60+ZG0B+4tbf9WMQYQYErzOBC0CqUEygwpqDLsRgPsiPDiFU7U+pxVX4v7aQSJUDcPWDybj75p8hkWNoldOUI2bYQQNgNKDXYhNEaxWm8AW8DKP2n9xit8TLbpc96jOz8iIA//eQl/s73v669FX7xecjoeHy8NsAAALqvyfrNO8V98PF5MvvzOVDjnX3aChEQqD3/FJZgTilaSx2KAsWfSOaCyDH1idz6HVE2A8EXZ6CgOMouLSK9beyiSsvTzxpb0KhqGOoGPCuDKSF5JI4ZHqvVO/Dh4MBZB6m60PaKnoSpHBrqzENEo5kLBLlaoCIIa6nIFhS5Gj1YyygkBwtKYN0KHRwIKhhrEay5swdVGLUWrNXAQb7TokJeijapRc93bwL3n6I+3EMun2F59qBKuayqmHcs32Zhu8JfzqCcOwW+Awi0QFZoqetz13ytQfW5j9wCC7FeWzyBrNcRFFgufrVsAM297+dLiVFQWkxFWjJARj1bZWlWMtWe+/fsScUACAAcg4KrRv2X69rORb/Xz0dSj+/QJkMyUN0aCMY9CM2zJhygNHPYN39sU2yrUCqwaCtkrOqOoOUMuj5AltPgkuHrg6aXBpfMcH/c7WIxEFhOCgJPz8Dnu9ZyW/IdsGiNgnp6hkIJl1Vwb4r/WoGHwBLcrxVFBJfVQIG7JwNL0C6qAmCxVEB93TtMEmmmjlP4xE/fh0Df28BRUaOeNjyCAlg0vyrZTDTFFKy9toSvZQk1AhwIBxnm91MzU6pSBEHpC6AxQUnjp5z99NIjc32WvYJtfqRV9LpyYnNnAkIqBzUwOLAH0t2+et0JIK2JsAtKpuE2LaEz4H6ubuTO44efvMQ3vv91fOvL6j4wQhmNLXhkvD7AwKPzMLIG/srMGsQxVAkkwo8+fonf/F70yURf0AQIAKWt0PuSR1BQao8lWGtnCURGQDAXCSECUiUUVit5Xj4Kf4IrATD+SmMKOihYN5HpdQowk9LpN2Rp7UV9LrVhiJv0hrhtGj3eoFcW0ZGtuQ1V9QO4QJnnHcDTuvuZiZeh9QiIK3IFElSZmwbCai2K9yqbtYB4C7xR5kDbFZ+SuhFW6elfd0tCkYoqhFUYp5RwOv8cOJ9A6QFYteAMnZ8hXR8gD5+Zhe6++2sPsAvR9R6sR2kBiDoY8CC6fBrBgDM5O0FzXlyLggIeP2K/L1Xz8L1PgzEFdVVg4IAgAhli1kBHu8HEqqS1rG+F1B6U6Z8HopAOvlKpCkCLNaoKYCn2BnAQEM/HPwMA5PR8ElRbo0jm53U2Jq0aH2PzvrukKJQon+ZW4HMpIF6tHkQB1jtgvYA8q8NYGYi0e8237nWoZEjnZwYE/N8dZDn3rINVNMgwsAT3a8VDrXhY1VV3mZiCYbAEcBDikaBW+dx22i36x/ciAQIsNk+FnD14ZH9TDzLUwMIxrsBdCc54NqbAQYEDBEu1hdRBjolGTsIb1WnQYa8CY6YUhArABUypxSLa6t4pBb3ftRNAK9ZUUBpAqHAQIPA0BoHsmvUET6n0Kon7vL7DfTd0ZYcBn+f8Rz/uoOD5Lz7voCJ+8BE3xusDDHxIVRoJ/T5U6I10Q3de8Q0UQJHWJnpzN0rbb3Jq8Z2vCgpirvHGRyf6vTSlIRDZTxtTQERWwMi6A9YC1E67KShYgXVFdQtyiI4FWhYFK73lG6tRr3Z9GqGvbYTBhGqIG0bJg3Wei4g2niT0im9jUYJ2C2LcRLs29PfizLtrkRIhS8JqAIEKNBJ+tUj88M1mTVWzqOJ1V6VXKwwcCCk1mwRrrY09WIVRjD24JsKZT1juTqCiViTKA7BcgbufA5eLWpnrxaLtVVFEZasXl1rQ3lF63VArAB2UNgrS72MtaqV4lkkIVmyNoKIiLhacGSjvGRQAMBfBxFAkbteheftam4Fz7gGJFgsRUy89kFBvbwHnjIpVA3grg1itP0qMWcvN5+T3t7U2L9pIioZKkz7fqc+3+54bKEiPzrGEOUY+SP+0rI45JXOo9xAAn6QThBfrx3GGLGdIOhkroIGF11LxEGIJ7teCewMLq1RcVgUEa5nWNMxlUL1QUGgxzdxAwZJTaBylAAHU6f+n7EMASMmLeVG0nxBudtvjHlOgrAFZV0uVY61XhbsLBsZTK1Ju5vxAjkmtxrR5l1i7ILoqWKkFSBarYm7RYpY7BeJN7yX2ZbTAMjpctFVlD0gnaw3gADvgIJkeIiudPPgH4o+Tm4AYWIMjXdZBwbfx/BefN4aG4jGfMF4/YABAlwJ1cGCQq1ZpZTV9ehr1CeBHP/4IvxWYgkcRVkgp2XMfPAYKvI7BnAKUiCxaGAhrov+sXZfHFShb4BvFwUEF1WsDBbuKyvzEeimsVpYLtbzo54iB6uVnqfmPmbNS+OgLXvvFWElpiDOYbY6AgKei0KCx5e9eo6RY8ClbzIaWNlaAcC0VKSc8lKqZEStrp0JW9oBZelC8nUypgsQKGBwgZAJWqVirMhEPhXFXGGtOONWKUyVcmXBOhIU1D53qM22+Yx0bB/eN+btp3pAhC0TQ01tbSt1ctS8Ol8xeXY3I/PnVGB+reeABpUSaXug/nZRSjoKOkrMDExBg0hr+FixJxENsBC9ezndpIKdZ3/E3XbmnDHhgoAXmVQMzBQCuQFpyODcHAtP0pfE1Zuog1H57Ltvc3DIOCrzFOUdXjR6jrdemBX1/6aOIAMvk7x7AT7jHpD5v8W6qadH7ns8QzppyuAoerFjRpeg/BQMaXHi/Vlxqta6atQGCKr6OzSamdvlqkbMrYcaSlbI/JwUCS5p9/uNePNqHPj9iytFXtotIf4x7nf0eYQQFia3/AGDuL+tXUUcXaAMF10tzkw01Mbzolcs3MRdoRmc9qUC8hDEndYsGfR1jM+KVOyiINVNiemePQTgGB3QADsjXySwfJga83dsbuuxPP4mg4N3G0HS24DZLEMfrBQx8Mg1psfn82SEvU1vMY+QtNaZgBAW22Q9pl9Dz3OSHbxitqKeWWVtYB6AgFiLxnP69YB7drKMboaHIxhb4P2MNIiiwPHbE6m0FRu2qpaZR2/Z7tjqcLVD0ykBdW7wBO0qxBe/WOdlm34kPCteiJx8RbWyvuplru3dFNMXKgzhV0CRcSwURcC0CZFMoBUCqqCvZaRoI80BQzzUnwloJmQVZCCsp23DK5uMtlrmwJGQS+6z+WzhjOeUeNNUyQaoFGpnCcCs0UuzuEw0KTFrnx61C9GjmVqugJAUIxOoSAkBZesVES7VDUXqdeIUX6aHF/OlVdwovZjWX2pkBK77EOSsgaOmWmnLJp5Mq3FhTIBYgaqdt81CcGq7GlKwgvjTmQbIG8RExKmvsg5jffjgvizOgZNdhcQWtIqKnfLLFCljLYmFtQOQKGpRaF80xXiisfZv3OVXOXQyxSc7+Pd6WQi8ArgW4XrvrKqYgOktwqRpHcCnSAMFapa3fNioCONC0xJzUPXBKCgrOzFiSAoMlsYKByYqP4GBviHRFKEBoVLUt3e4jAg0HKt2d0N2gCghqkGFrq0TZQIHFyQwFsoAe6EpsdSvQAw6zsgUwtoDIjp9Sl59+fhBMKrqNWLthFSsCV3sqtN8IBwdkhiIxAKHGLjQARX6Ok7XUJ1tvrLEGzrrs6bIfWqChuw/YpwMOqwPz/YgbAXidgIFTMRM48En3Ca228COq/dHHHRT8zQgKzMTcWHsHo7clVYRQRe9hNfRQqvqsiuyDAj/GHPRDtrOI+nMNOLTSxxZbMBQCseyDulfpbqLgpKqlSsJtM7UCNo5ma1XETayKhBhIrAWDAIA1nqCQomNG71IWx170bArPm0ieBZNb+SaYCsiCOjUOY62idQaIkKiCKiFZR0wNm66wE8Vaay/G0tKtBNeiwmqpDKaKVRiXIjglwqUKHirjfq24yypYT0kp0IUJuXgQVUbiDEvgGAHmcFM7V+XgUsJa8cdgOJiFkHqgaRKAtekPqEfSAwCfgWoVE1UIqrLk0wleerlnhPCQBeBgwLv93QQEy2ksMOTFmWbmAejCfKrAmFIGl1WzJfIKWosBhAS+FsgyZigcntuS2nm1fxbIyednaqk7KMgnVdJpGTqberhKtIJ1/tUnjZQHK2ywxHbvsd7fapdfBFhXVey9xwGsNkEd4ggiILismop4LbWtWR+RLfC4gVOmBgrusroOzsYS+LrNbKmCjFZYyBsX9ZsWhv1sjSAdXew21bijd3zO/HfYQEEmNDfozHjC0/q8JoeDgla+e1+OxVNvKcZSlRGrkTUoSJQ0rRBodRmIupE2W9mzrDa7A4UJ5O4Iy3iqMJktbiSJ9W0Im3o8uP5ujP8Cq3znBLa1pAezuSbCy7/QlMTf/cqHeOftERRs5M9M5xyM1wcYxBGoHa+ZDZiscgls8/LRJ9pb4cPIFMR63IcC3X4KHQD4T6sgkPZ39Z8VaQTEDAoKtgV9ZqPRmVFH2e3K3GIJ6TyopW0iiGxAQatAB6hlV4tF7dpx1mvfVMGlAE5qCatjH8SpZxgYcvYmQjwJiBjLEYFAXMTHilTnfoFS/kWAymQCViOEV2uSlDmBSkGyeHUiBlagctWcYytA4mxBsTgEf35ZK06ZcS2CJXWAcFkrHjLjflUBe5e5WV/J0iBd2Lpl1K95a/37OukUrBwK16aMKDSXYUJOJ4uvuABgED2oP18EdLLGUFX/9nTbBGWJCl0BZohRk1Jrd615vYCcLYZA3Qcxa2KTSRF7F+zkyov54lvv+lB0CeuKtDyA7bVyuSpAOOnarVXAIdYgnp9WFFwUFPi5ne40k+D8DHS6U6Yg+vUdHFDCKhiKj/l92LsHbSk6zRvW9OzyiPc3FjRrrkXpVQvdZTAzBJdV2aoq+npcG0wdFLRgPluDmRjnzDgnwl1OWDLhLqUeeEgBHHCImPcz3zOGuAMd/eftvjuT4CBojzXosQb997xkey/ENrpEB+PGAMOmjHZbE12Otb/LClnNwOHcZaSVbqUgS5m6jPWDEMMaKY3X4/I6ggNhUvnO0oAmm67xWgfS/tZjRhdqXzhhnZuBK4YyyIIm3XvoDQM/MFAQGVjyu3WT9d4frxcwmAM4jIohYw7EP2J34uXHmpL4nSnQcGhFuxlDGE6ngeInoqAPCieyBfPo1hsNqXxePjQCgaZAfQEYBdf82XXVDWUbSFrJWdtM5Tr8tq49q85lEePE0tB6Yw1Ekb0Qg5R6aS4Fn4YIClpE8CREffF2YFPaeQ80bbuHaL8F4lYwBJwNHJBZX+4rFTAlXKyEKZFZmgRjDWqLK4hd50rt96ZKBRNwLdQAwqUwTsYgZK64X1XweiDXOTFyYhDJEMTlcRTz2KvNHh99xONwAx7aRroQYUkJabkD6AE2vQ37qqWuV6WMpoNkTaOra0G9JvCkCLzCIOcMXqzAUqizECPsvbLgJq0yotpYP8Mjy8uqKYAtk+MZ5HIPuV5AJ6WN69WyJnYqIXo56lYHIuVeH+B0BzrrIyy4Tx/P1p3wjALCtVgdixATdKs0+V6MDG7c33ic6E70EsbXteAh1CK4tGyD2oIQHbhGpsABAYBWtCsnwom5MVp32f8lnBJhYWPE7F9uQX8w2n52k4zWpe8/Cu6uxKFuS3MrxLXcZUDc/zE2SuVXYAvc0HH5Y+tGBuAQemv4fJeq2R8ux1YNNtRS4tVcoO4+07/BuctR++eyNpZ5Byz2y65pryprAUbWIHVmBUSbujk2FXDUMbQiHxZSURlGrCUaTJ95Sv23v9qLF+0aWEeL+MZ4vYCBjQ0VM/lpBLF4kdcpCCxBWHDbspUmCkS6MqR9+uxoJGC3KE9ToOwpPNQ3FOn7ahVMbgT3RUlfCHFDNZQdEXYJ8CQZOLDgw6H9g6dtidWxb/+kAwVGAwfeslknuptYG/o1ggG3FizA66hsc2vyZEyFcEbiBE6L9rSvhGz0LBWzjjkj0QTFtNcN0ALbamMPWs2DEHugwICwWLbCpXiaV8VDYuS1IpEGJHpeeKZe2MXzuOdx1NZ2uObw/cwErlq9LplPuTKhign7fIcWaAdYu3Cb7xaVT+pbvTyoIi2r5uEDY064uQM2dRaWM+h0VhdCPpkFvmjK1eCr51EAiSr2tr9yL1xDzhpcL5DLnRZksjoB6foA9ih0VxR+jkCv9ZByLxJ1ftaYAq8NAGtVLPnUMgButSxeD3PYO9651Rbc729Md/NeBiIaaFhEhrRD/dcBwbXsxBLYmgRMySeNGzhl3oCCN5eEu0VB65LIwAEZOABYiqZexgqpB3uvAQILjCVcW9wEOGsFQ0rNb94YlyAD9Ny78h2BgATZYutkh7loaa0xZsVHShZAa5kJ1K9FSlEQ0BRlB6nRnWCd5cEkrQBSInNfMgHFuvDuGI17YOHWGEDCYNDWrd4RAGRZFiR48clHeO8PfrO1AfDRD9l1waAP9+KWdsbrAwz2IjthIEF0Ml1Qvfzkh9Zb4dta5rhtCFdM0VRw5eGWWG3HFc3HA+Dq5XgkJqwFrfsgca/e1z5DvfpYboieGnV0043QNnPYYE65/axjKAizedN+h6AwVpoPrMnIIBMagvW59mDJVnshBE46czOjXQcFFtBFSeuQEyvFzfmMwgQuBIJa/FQEZEF1iQj3fm4BHFShkS0IlLVaQoxSFSAwAScTvpkJl1qRV0ZOwGfXnhZGTpUyDWyQj73mM3sjVqZrwY6JNc1MdlpHp5Na0kDTYEQETgnVIrflasq8XNVy9ywW/1ET+OpGWEZmYDkBywm4FcTX6gP43XfzsfYYGK99b1kcVB7UanX2IDAJUq4aNNsA5CPneX42xBTIcgbSCXVRl8JRy+JYRbCnEu8zfPH++Ni7vz2WyNiC2msPxAwDDT7ssQQtOHan8iPQ3QgzKHCG4C4zni3qOnBAcEqMJQEJolk06xVzAaFb+67FbHEa/hGtlu4cg06tn8qOhdyt2SCrbknPTZGGVxhTvMJgRbviDDI1uhNYjDXwi6gCJNb7GX4idqhMAbTdGoN6bnMejNHoDhcPZFVw/fInf4b3vv/bvUx/9HME1uFmbBxxRBGb8foAg3nM1iYAiHVJ/N77+PArH+Bv/sLnOztg1u+AJtsoulIkJPBIhdsMcX5deVdx+kzafUvo3QeHPgL+3RYA5AZXZwu8pjibNdjor0b7TeDgqWOKIB98wx6AOA2SCkHSQki2AKXCLAlM7pUJuc6AoHiE8Wi1OOPR6EM9OfRCTBlYE5AW8x2eQGUF5RNSOiERq3UNwdW6YG4upYEDHxXXoiIqFNtDMcGkQVo99sAttsTV0r68ulvtpV7tnh2NaA1uq9YFmnjKQT8XDySzaGlrHS1COKUMLHddUTVARWrVXy9K4ce4EwAt0o5T70pojAGfzhbRv7SAPaST+uqNMVjN2pYamBfj7PRakvpIvUdUKGYj5axrIV9BJ41Er5cHqxOxEzjbuPxwrgG41HTS88unVkTIuxNeq1rqD7UOgX8PVXBdiwGDbe+BW/fo1v2dq3B6aXTPLnB3gccSVDuveSSLW3EwcM68CwqenfTxzMDCnpGgLAGVC2i9AP7o9QJC4aC9PaeupZ5KK5xBpCyUpAUkCagGDqS29RZjvGyR2cTEVN6JSo/DA1mL/+lptclF8Mga3ByjnGxyE2jy1AsuCakx6awBsdYseExuAzOT1AGjg472XpyZCMY2Oqi0b7z85M/wtR/8XW3N/DnTXygDE3BUank3y+lgvEbAwLQpateLAz1T8OLHP8J73/9tfOfL3wqTCrNkDLW2RTNRWC0KJfVjixi9o6sq1t32SlqOPOMCS1V920kw8EkU6GcvOjL4qtkfoxshKN1HUSKNGwrooMAUQle+hGj13aSgLEBmN2Bpcs9olPwVQyGTUIRJPMXOlECrt+CH83zloUrgot9P1/a4LHcalAaAK4OKgJGUlu/BBrvgwKRNC0hr7xSAa7XaB9zcDN16456C1YQBzQH6eqxpqo7oYra1wFQt2pyRiuBqQWWraFCZSMUJbt0wlpSRFkeYTgO7Yl60qVBZ1WqcrMMWQGhzLGlB5aVH87vCpaQKtPSKkZ5bH/PbgR5n4XPjAXKZ7zQ0QYp2RiwXYL1YmqWBhdVZhf3iXDEjQsHLyc7VqwqeUCjhWrRl8dV8+WsF7kvBddWaAU7vxzLDq9ReMfPgHrW/D+5zA0nSYwXm2IEICPZ+JzGQU7oJCp6dMt7098x1cLIMhSQFdLk3182D1gtYH3qXzSHaf2ZlDAyEZl6UNcZHJKuCNaaIZFGXkQOIwNRuxg1ZJSGjrLnHPK3a4qFuyrKDoUyvtEeVn92dQFWDiKmiGXBq6THm/i/DcYPsjv1reixKN5dalpleadclTaGXXR30Jz/5c/zGv/pP8Htf+ud492/8krpTWgbFNJe3gmMaWDuep9cIGBwMm9wXn/wQX/ujb+A7X/om3jWmoAe4lREQzD42CgUzfYLts+5OYDLflMCCdAwgiKLPiv0eAnFs/MkGBpwtIMAadkQ3wh7DEQYngGtLRfQN1XaTxzW0Nr89B1zbzuamXPzfHkhQFoE3r41szMQShIpm9fLQWwSvHRzMAUZx81NelNZezqDTVfsLlBW0nNXHuJjP2VwLNiH2mEFcNF5jE/DRwQEqjeBAZAAIa1EWYQYJwJhXPhx9uldz+pkPjzR3RiInaumTRawDZGVIBu6WBLRwT28d7UGJZIzJg1WGW5V6N7fNxp9pxXg8bkDSAvCyCdpzH32k40X2e3/42vbgzEyxIh9h4YRleYa03A00N8q1xyO0olHT+Rq9XS0gVc95DDK8rBpLcDHLfK/M8K16AbfuURxH97qlxe6AAX19uy78eEegwBmCsz1GUHBO6jo4JSAVZYio6B6jYq6ay3132Xhp56P9FgCBBBYJtUBSBWQxGQAQtICUuhXrrszYM2CUZRBTXNKYQZIKyYt+r02MBRemvAtsKQCa3THEGXR3QoLv/v3OsU+V3UOAtS1Rou4WbssmuBGam2xHD734yZ/hN/71P8Dv/8o/xfO/8Uu2H5QJ1bm74Rd4BabAx2sGDAyDEQ8L7+XHP8TXfvAN/N6vfhPvfu4Lo0IFtkE3kdIhgpesjEYxWTAVRBczgXrtbb/x1paYjB0oJgzXai1Lp3y+2LWLgwuh0dIcSyCPinc7FbYxrJ69lkxaN06GRs3Plngr02s+YwTKgtQjd2sxHoIC9/kZKMB6RW2BZw8aeGbpSRKCzmbWIFq0tJzB54fmW65SQLkAqEhScZfvlAolAVEFweodYPQRO+3vyvjKhKvlz2/YAxFYTVIAtcUiOEBIrI9HwxXFnjJo50M9leyUGUtlbfok3Itn5fB9Awfw/1eNVPegRGEGJaWOJfhee5Ik9XXDqVXoU9fBCVfRHPxrVav7apkg19b+t5eSXn1rodfsjzX6W6GdzFhYa0EsxaLl0x2WfAcqGl/gDcH8nGVwlz3hnIsBgh1Q4NUFL7W2/gRH/v5b9wl43K8c73kEBHvrwAEBM7cAwyW54mecsjJGb7j7YEkKBrLO6ykRzgzQeq9A63oPrA/g9QH14TPg4TNt/OXtuBtLN7EFbd1kiMuF5QwSQZWqwLvdjQVEpeXa/3/I+9dnS9LsvA/7rfeSmftyzqmq7uqe6e4ZkLBN0bIoyiFecZlRyKRNAwKFj8AMAIJBSHIEbZoOhz/wb3A4bFlmOGSJEhnAANQ3kCCHsmErUD0YEBTFkBwi6aAdBInunp7pW1Wd2947M9+LP6z3zcy9z6nuHoY/lTOi6tz3zst7edaznvUsSZS1I01i2PuOLKJ5dMn6R8ZATMpApBK0HD/xIi40c+pjcdR1bGI+X5ASPWWHjWGySH5R59iMsgr3rd31dxAWTG9NAZevmQM7WegsZLEfzQyrvseT9/4uP/Xr/3P++v/4L/PVN/6I/kxKn5RJfmDuZUqWa/R8/z99nMJLBAwUqVIo7Tnn9OTdCgr+Y7761g8dA4LlwDhVxx69cLmhKc7jK2f9Wgw5F8/rQvPnpO02k9QYes4nZNHFmgzLxkj1s0m1a44dyZbNRmYK6li9CjMFpw1rZsSsoMApwKwTaWmHXL37ly1+Kyh4gX3spDx/AYugJ7RA5dN9XtQon4KCoJ+zSCnkGMmLiK168udyrtlra2EzDqRxQLo1NBWA6HNtfad0XmF/RLRiYUn1GeICGCg4qCWLxtxdzE+PtLCYTi8ABZ+2IdxXjmZE8AtPhcaZkrc2pPtu+QIcUD8zC7+DOGp+uOg67oT1lTGwrtT+OxXmnUTclYY/VBq+RNxLBqFen1ksmvVfjXxVKFdr7LX6o0lCKOfsbDNZ5E6CxXsYvSN3wap5SCwAzItBwQwMZuag5vvHkO5E/cvnU5+Rfq98fV9O4Z4xsDzue42lnkD1JPWfTPdt5S2bxuGNigxbZ4qeICPjARn3yhaEAwx70mFH7vek0vCLCsQ/ZZ5NTpIxQNPOPy/j69TrRFX7JerPc1S7PGrFDFXcXdK02Sy2rQhVd4fXuTtVRSxSjS9c06qvRtVH3JMSnQK8UnpupQyvsvaCmhVN/V/KfSnb+tG9QOZNf7l+U9dwdH+Y0gj37T1w9PWT7/wWP/Xr/wv++p/8D/jqF/+wXmdJz0yBqlQUBkeyxvsCt3L9L4a5erw0wODOIYYn7zzha3+7MgU/xL2AYMkSVEU8LKiBevfN/LeAqkVlwRpQ8v46qBK1FXGdPgoOiizhjlq3/mZ9ndN87Gl75TuD6eTaM1k3TZiiKVmgUP29uivOpjR3mvkUgdHy68kVsZamLV/r6B4tjiVASLrA59oOOIzHoGAcyGGYathTiOSTjVZbF5c6+2ZQwVnQ0jcJI7IKSDfn6lJOeN+p2BKDobAHjStloVHT1WYWDlZBYeNyiR4NMWXMqXPTZxzLjWAJCO5jDdL0vRkYpKwb5kx0lsOhUc1JKkROwAFozOaMugTOTpnpznhQy16rJlJpBgTz5qqAYD8cb6yHkDR/H4831ul53bPJKRUey8ekorlkCTYrg5Bqvb3DOodxBYxXv4vFeWtkqyA2AiGVXP4CFIQXMQX3gIIQ451ywfpsamvvel2R0qtgwSBN130PSFh+7/THpyyREd3s7/Mo2HhlCrxF7Y5d6eGB6gnMuNfUQdiTDzvy/lY7gPb7yS8iDcPkE3GnZXQxtqpeFuKa41FV+lFoakGQJHOnwBcAguU6kSn6l5KCUDqdY3CQzOyb4rS6Jgej71nNsuDedMJkkWzdbDl+etQ1NEWMcUftmDFz/xfQ4CDmeUW/c2lUluA4qLNSWWQFB1Nwd8oW5HR0ThMo+BP/voICcgHGMussCssylwifpkzryX1/6YSXBhjMNJM+hSe/+y2+9rf+rGoKavoA7gcFkwr3mFrS308LlGmOHx5zJJxZiBBZilYql1HrX7kjzGJ+9akSYemeV53uKpsA+c456sXLPDiyaH5uuoakCHoJeGBKJVTF8Z3Oc7X8rHwvL0SK93nL65Hunzl5HvxS6dScJ3Ygh2p7OhD7gTTOLYKrM189ls53NgRso+r2+s8UEZVZ5eJ2qBUUvlnpYgcQDQZdwMSAs4ZDUN2BC8IgalISYsZbM4nG4NgtcXncm4uuLELRJdzHOKSTv5u+rvkpfXUqONDxkBnIGKeRvCS1gpYY6ZjBQcqlTW4W1cEYi7FL7XR5POWtUuLIrndMc6e/27Kp7gfdWHdjZD9GDmOiHwOHMRFKu+DlNRmjYjhnDZ03tN7ReS2pW3tL5xJ9snQus3GaYmhNJhqDNXky5DHGYczdTiLLc49ZTYvGIiZcMhx90mqEobAaQ8ilTDG/EBS86NmYe1IHp6zBfRUL96UcltqUKmKtuhK1Nr6HZamVB1aNttoiMjTDHhn3mjoY96T9LXl/SzrczqBg6InDSOzDp86vHLP+S2lyzMyhROGlD4caCOVpcz9WBC6O07VCcwKabDJALuXOeTYz074GSuEjRgGtN5icyFHXs+m8X7C24Wrb8gXLWX+/gJflGq5llmX7N3P/l9r/4POs38Ji7V5WLZyu4RNLvVjTl6Dgf/R/4N/44h+e7usEAFIxnjP6znPT6HtSCkvhYWE6nrzz9qeyBi8NMFgeT979Tb7+a39Gqw/e/OMcC00+Byg4iXiFue/9FOrnpDe6fi9FxLjJZ7z2zT4CBxmSaJrhPvym+ScdOZU1mOxv5dgtbLnJ6kSo9TwLOi5nNSMvG+PpMQm3RPUCtQvcVKNcI8gFTTtHZpU0O8YAUs4nF/GlUoQv4N4LjbZ0NMsTpZlIIRKHkTxqt7RTv/xkDeIKcAgRGyK2AIIEmJxmC6OcpvwevqMtoMnIXLHgqihOVM2t9rRCMDM93lYqP9+vE1gCg/nnep+iVCFjmkpPVatQT+wuQKivb0sEb0wmZZnK3YyUjzFjJRMkEcQwVlOvlPW1Cy2rAtljFzrKKeTMZAt8X2Of2xJt78fILkRu+8huiOz6wH5QUNCHxBgSsWyoJfAqlTSaFmmdofOBVeNYt46+SWxaq+/pMyEaNsmSvSWkhLdqge0y2KTz4fOcf8yzKHKMRVA4fTwWTtZnqkzOfN/vOyogmKN7uZMCOO12uAQD96chjtOGp2mXxsw23NXJ8E7lQQqYkj5g3KueYH9L3l1r6mB/Sx4OxMOBOIykfiSGSA7x3l4UuY4flD3IpQGW2HlDnlxiX3QsBctlzThaL6oxWl1/Rft2aEdCM5fZVrtjKXqlnKiGRXIioK3Hcn1brmVH5wbzGp7TZNQ25QYKWyBVRU6ZQy+6XI7XcGNkAgxGFmzBxNglJragagrugIK8eP0qgi/70bR2nIKDY3C2dCJ9+523+Zlf/Tpf4ssvfGwvFTDI6EV//Vd/ZjZ/qMI3FpsnnNCo9wCGeiwHj1ievPd39dv5nlr+BeKsA0sKOKhIc2oudHLUtULpKJkGkJkWjMU2fG/6QBZIUcFBRe4zVZfn34WjyToJuOrGX9r+vggQFL7kDijIlIlRSomk1v0sKxtedEyuZhqlVMfGnBNxDOVrfcdI1FzfaMjeYdIc2bgy4VJW7/+UImZzrsJQwOR8p2KhCkadcTgbGYOhj2kCCLXNbS1jW9anwz3CRDjqv1BTB/r5nJJIKc0AAYhyDDZeVCMfU6b4Ns0bWs5TpGwlF8BTIx+0yMIwCaGWT0Kj7er+V+rrE1P7XwUDiV1hCm6HwPUhsOsDN4dAHxK7PuiGPMaJGctJzbwmJsxbnBHWreMwpuLw5/R+NnVDt+RyPZ1TdXvOCogUJOdpwV2efwUGy54EMbEwENL7E/MMBJb38/SY7r05/toeAYNjMFAj/xkwHAOE5fNclqTOwtc5heXKpq+MgDnqd+ArIDitPAiHGRTcXpEPO2UKCigI+wOxDwoMhvHOvAJIJkEyWO90F0ypzK0C9j/NcOiogkkH2txGfK4NOl03BNVpZWtmgFDOo4gImD1Qll4L+ThIum+Nm87LzuvkffOq5HmlsLVkZS+kaMaWazjwwnW8ruHK/C4DvLtswZGHQ7mGmj6YQMER8DLH4GC65tPUzfK655ny5J23+fqvqo3yX/rP/tLdCyjHSwUMKhL6xk9+YwYFIuWh5hLhn/xReSDHoGBOTEz6AoEn7/82P/Xrf2HxtzWdMCPO2uSiUsBJlDHIUj6HO82FYB6ns3hFjnzFJxLoHmMQoNgEM1/Dor/nqcf8EZ1W0Pico6pMwYIxKJN6oms1D2wLAAEAAElEQVQXeOruhVDosuKZUO6T1v6mqdfCBBZKaVGuwOTo0RSgENO0eKVxBncqjsrEpGzCsga7DuyJMQCk2xSqUlG6BdrqFJgyFjVDsmIJFnxUgFCtaqvavlYE1A1Z32cJEurH45+lxYY017Kbe2vZT4/lBrSknev7TAtuysTCIrjCLkg67g2fSrOp5SI9gZ3KFmRNHdR8fAUFuzFy0wd2Q+TmMHJ9COx7BQ5xjMSgkafKABaRjgjGQQyJ6PSerrydNmko96A9XpIUYBiSM7iccRPrsVxo52sgHzcqSmUxn5wM0+KZLM7PGnW/rGLPCtg+7TksgcCph0X1r6ibyOxroa9z+rPKqhjkqHpDDa2E1tpS4jmbFnkjNAZMHIrIUCsPpC9g4D5QsB8IhS1IIRQQfgwMjLe6+UTttHmq+p9y98ugYWIcZUpF5qnNdBH+oWmeF64b072wRQuk610ZTFrxYPLEGCyFe1Pgc99apwNwEaAcixBPjdqkdDKc1mRTOiVWUXm9Dd/HOl61YQLHjG9NJSyOWWi4ZGPK75T9aGYHhDtajgkULQCQCE/e+dYECpY2yvcdLw0wOAIFpSFSloLMxMDCgXA6TiPvO5SY0kgAT77z2/zU/+Mv8tf/5F/mT/za1+4/iZzRbn52UqZqCD0DAm2ocfxnL2oyVImHiSlYWjcfsRpKYOnZFuT4Ahr0DltQ/vYOGBAdcHUe1wldI7P7vMKrsQd5XvikOqHlhFCYiKQLh1g3m6ZUgxQxZcPXMssly5NjZRTqR52s1Y5UrCmdlQ1wwJVrTGJm9z1rJ+ZAn0mide1c9ZEMY8q4lHFiibbQ0MlMfvfLzpjV8pZyj+ptqeABFpsuaoM7sw12ctc7BQpwN4o9FaUto1BzOqjQDdJRm7uUNEWZCkv1ay1YrIxBtQfup85/yhoMpd6/pg92fZxAQRgqMMiEoIu1SkjyVGJLEJwrjFCGfb0umd0hq92zCWX8SJrKvrAKsq2gaZHM0XUvryPnYkfM3IdieZjK7JGnzbxxCiFTaU7lFuvtMvI/BQLOmKOo39ljBkCH5PyM6qWAgts5qlTzrdpno9pf2wIIjhogWcFLtTY+KUesoKDfkw9afVCZgjCM5BBV1DtqFcKptiDHRHVCrd/T+VicDI2lehtMVUxyqksy8+eFKUjps9ePiaaXahhUvCly1LGUAmSnAUbOmltapojh+1z7ylGCKSmfirGFxdCpophajtZA5FPWwWluHq/lc2+bFx9ffeOP6nXOJ3f8C6fCzhr0nl5rrUAQo+n1X/0ZvvGTpQvjZxwvDTD4mYKEfvTLX6lZocXNSce1skux3qc/IwB+4/3/kp/6L/5XiuTe/GPzD4rOQErL0cnkp/QNSMwDY1rj76OwFoNoBgILQDAxBMe5qPkoOSWxpUzonmN634qYZ/R8LCS8HwyktGgJzN1TENFvTgZPJVSvaYXKcuRiYZsrnRbDZFQkcSQH9U+QEBFvVTMQNZIWq5zFse5S3yinTByD2hINI2KFeDhgjYHelGdhSMZiVoVBqGg9Z7xr7jRisoUVcEY363rdMR032IkTM6PndAwWZgARYiY7CkCoqQmzcNizR1a5cDfPPbVbrnTzIko9PXLS1ETt8pYESEUEKwu2qSzUtYlQzgUM1Y8pESIMIXEY48Qk9OVfHI9BQSrP65ieFowxhElAqVUgvQg+JFxIeKuv74vgLhi1qHbWMMaENXZWq3zKdSzHZ0x3myDV+2hES9HAzFoNkXvvOXB032dHymPqv0aazh5v+DCDAJin42mjLWsW7cin1y5W2OakAdI4zKAgDrOmoD8oKOj3KuQ9HGa9TojEsTAFJ6CgnqMoLYJxDuss4u1UsUQxORLfTF9Xd0xK75IKEPIJKKhpqikGPl1D0PUjomtfbWykIMGWGGYBEo6i7mX0/fkAwh2lQPnbCg6QWt4sU3w3gYT60veVl5VzXqaHyxa9WMcr43sS5E0va5BF2f2nHsu5Lwaqx0xZ25cNlyoo+Kxt76UBBr+4oEfmPbi0qdRdoIRK1Te+UFLAJOSYShPrIfzGd/8rBQV/4t/nq28sQEH9u+WxfA0x1KZCei7cYQrmd1n+aAFcloO/fD29z/TH5S+nRhscDZTJ4OKkguA+IFDp2Lo5nPZXr1/D3fxaBTW1VDPnOSpzYmejxfIakjPZJkyjWgBJpdObV/rM5ppGyEjK2Owm7kDlE8cnoFGOTCmFPEZlCoa+jINaXVFAwoo5xVLf3zUY2xQvdFl03Jvz1jlXZkCmR7IETNMjLJ8uQUSI+veTMC4vv3fcXOdUw3B0r0+iUycaZU9NuKaN6PjvapRTPxe5u5mmcs7a+S9NQr5ZnFfAQkhT+kPvgaZ0KihI8RjAzWkvQzRJC0bzrGcIITG6NL3HBJCyOik6UV2GWM31ajqhhAByvAfc2XBKLsmK+iPYTBmPBmMSJioYcPbz3W+l++dmWVo5MDe6moHBvNlTb30NAuq5ccT2ltLZu1VJCkpKzvqk34EEtTpO+9ujUsTqD5LGqALeknKjfgSWvVEqKBBnsd4pIHAO23hs20xNqmr7bXyDaVpS6VcyOWTe0z8jpTy1nZ7WlXvWkDjdk2JPXL6OMveNMQUk3InCF/N5+bW8aHCcgumaNq5tjsvgmQSQ6J4Cxe+AF+OCxaM+DvCm91n+4t19Zz6/+rN8/PvLj4vzPE6ZGH7jvW/fAQWf53hpgMFXJqZgedRoWGvW7zhs1RuY44zQFg/pCBS8+cfLS56OhOPVd7YGVnBQl4R74rny4XjT/ywg8EL17RIA1PO8AwJ0UJ8CAabN7n4gUH+nZrXuQ5sTrVvy11m0FjiX8OcIHAQmCjDbhGlLBH+yKp8WPIkYkglHudHpZ3bxHIoIMWetbBCnPQGyyOTXkIzBVK+WKeLQ+n5rPdY4kpECDKQwJnJ0n+p1L2/K8h7VACZRylRdSYzkmscvCvmsQOdUx1BZhXTPHT8Wqx1vUFNnTuRIi3AUWLxoQSvXd3okZvHk9L371Ff/AsfydSaB5j3XrN72ci/AXoKD5efWCDnKhJtjlsnt2ljBJC3c+az7XNmAZf6/0v61pPCu6x13Stjqa+qJlq8XX94nPJ6qkYoDpMRhcg2V2Cso6PezvfFwmNwMUygVPTVkr/fLVuXN/LWY4gviFBTYxuNWjYKCptN/paX1soMlpY/GZJu9AAXVMXQC2Iu15PRuV48AvUdMUXcFT9P9XNwn7gMKJyxrhhks1M/rdU/rbv1ZZGpzrD9AahM9mDfg+lzL944e6NGxeM8a6B0di3Tp6UYPd/akozW9fr0EAwu2QBsGzq2ZT5aqTz1eGmAgzJvd8feXCOoe1qC6DUmlFgDMnD6YQIFQS/hgsdm+6FhSWne+X87tU0DA50G5R2xA/Zmc+gvcBQKfxQicTt7p1Mon9w1t0ChOyt9PtJuZf2CNPbIur88MmMFBiejrddkicjLOkoaRMOjilUKYjYOK42Ats5oSuKAAoXiqZ2N08TS6NKfyvmR1P1u2BM7GYK3HFJYhG1ncwxp2c+d+nN6ren9rKmYW+QkxS2Ei9O9CPHYODCndadu7PGquWjeo49bMWgNfjLdYiJ9ONqkaxpqcp+in0t9W5nJZJwZv02TN64rXQB+KBgDBGkNWAw+99gXNLyWVYKz+Xs3fVrGfc0vrXyk0fS3TLWxIOfEXbbjLa6nYPIqmIuanZLAp40QZG5fMZ95bC5MQ8EX39tildLGxM5/fUkvEdDbLcz/VFukGJ1HbU08NyKZW1YN2oSwWx9qmeuFkGMbjVIHVwMjgNDVn6gRFn41zYAXXeEzjsa3HNAUU+AZpV5jV5k5ba00ltBMoSGKJafaTWFa7zID5s9cSMtPzvQ8ohBOgMDMKWuEw3+djoHAkzObuensUoCxTVSJoCmyx3sIRWLjzgKc//pTtWMp+VO9GKc2kMt6VyTh5TxZ72ylb8OTdb/P1X/szfOMnf3kCBcsz+JSdC3iJgMHpcXQTFrX9GRaphcIa1BrZUhf75Lt/7y4oOKHiKzqb8vXL98vV9+AEBMAxEFiCgOl3lwn0RQg0fe+eAXJncHy6TmAJBOafn0a6d3OBLxrbqZ5OzhMdqnXJ+jEXkJDQZjnGmeP7WSdG+WiMJTuvC54xWOsQsyd5zXfmMRKDVROjU+vhIlo8ah8NZeNPGklZbQwlZijgYFV+pzQmMSNYr4tx8XCQo0qOE9S+PBYR+jEwK1UdaWZnQgUJJU0RrLkXKFTRI3AnhaJvUzev486c8wY1U9S6yB6P1yQZlYZqWgCjXRqnOnprCDnhojbv8c7QBCE4w+gMKdsyNlQ/EE3CFEfq6bYYEGuwxmCdYL3Fem0AVBv/+NIcyBmDs9DYk83YLGzB60Z8z7XU0rKlDbmU5k1iEi6JVm18yv0EjkSAp0CgMjKV7ndSARVzLxPqAjzP9TLQ7r7nqUlPWR+W1tVHzceKA6QyBYM2HitOonM/ieU1GcRkcCALMS4wzZmqJ7CNVxOwdqXanwIEpF1B0yFtd9TWempvbT0JYUy61pyOb03D5U9dT+pZy9Q1sIwf8gkQzCeeLwvAyAzKkDr2bWGS1NBjDsoMd4Kz5Rpcx0cdU+Vs6veOwMJnrA93O/ZWsbYGrdPLT0y2ss65XMf8QuUqa6A6raOaLn3y3m/xtU8BBZ/neGmBAZyCg5OUwtIIKMUJHDz57t/np/7v/0sVGr7xxxYbmD6ImcKx88A4BQ2cDILTyX4KBPLxwnFvy2dgZjVO0GkFBSdlQZUZqIKf06j1RSBgmrjzGd5/U2EesCVIUwJGAUIWMGlmDxyQcxFQ2ebIvESCdvOT2pfBOkypWshhwFiHhAHbBmI/YKrpUSlfrJFR7aq2tHMVuzD6yFk9342FoO+fQPOmrhi3ZKeubrWL4wK8zNe92JJeVCJVn88SQNYSLluFWWq7ulxIq1dCPAEKS73C8jgSrsERIDDoIqnflyPaenqcWRfRKOAqirSGSCZiiUSaZEgOUrZK9deeD2Ww7KlDMWESpGCOUhK1XNEYg3UGW+x8V61l3VrWjf5beUtX2gl7J3hn9eNCnW/KpryMIOu1VJ+QVDaSVGJCSZo6cNmS7ee8l/cwApPQUPQ8XGViyEwtxENczPEFhf1ZY+V0DZhSXJlq175sUZ5Ku+Rc0gY5huN7bq3aSFMfqaF6g+hbF0rcazWQ8RbbNkVg2CDdSj8WUCBNB77VttaugAGj7a0xjjHPqYOQjtMHFRCoHuVzrCnle7UkMJVfrEsxzGAhit7/I7am/pNS6TABCIuIClKlvvLR+5/qE+5bi2fqX+r8Ji32g1yARDp+vkfPPJUrSOU8FlRqpVUnZmNxQyZqyc57UjWhE+HJe9/ma7/280fpg+8XFMBLBgzq7VsGAXMAN9fVL5mDJTh48t2/z0/9+l/gr//Jv6zVB0dobFHGB3O/gHtQ4h1QcAcQLL93f+5rvqiqfahCRjONjSNk+gIFcI1Ol1ReSnnBGtwDAE6AwfK400hm8UupPASTZ4BgRWnBVH7V1LFuBGscprEQeqZSp6Afq/I5+4bcH3QRKr0UnBuKhfKo3gZBS3tyVHGa3hpT/kmZwCcTNMUCEMx0rZIT4nSBRywkOQJl99JvnxfQUUu9zNFkNsbgbMnNIoQsn7K4KrBaPrPlIcxDdilim6OqRRnV8lagz0QDbNHIOmU6LJZ7xmQ5qu1vdTTcGSH4YnDkuQMMRI4NjlpnWLeWbedZN5Zt64o9smHV2KmVsLdmzu3LHKXXCHF5HUApC9b7ZY32Msiim1P9ma0J6k+5f5WWtgtAYI0yCULW9sXDOEX1umnPXSuXTMELx8Y0Pu45ln+bM7XldAUCBE0pUBoKHV1LFc0V3YAYs/AFmeeJ/kxbmIt1yhA0xfzLt0irugJ8o4BgyRJYBQVTX417wG3OcxpsAgT3jN8XNaiqV3X0pPKcmZcFWDDkGQTnmUmwQtmwK1gue0Fi3ryrEP0z1mU50gNUnxwzz/Ea/U+B4jE4mPaH6We2aGfSlMnO1fJ7AobCEbhcBqoLZ9on3/m7C1Dw1aP0cT2WQPrTjpcKGNx31Hy3Hi8GB2+/93f5qf/bn+ev/6n/M19944/OD7M++OIEOK+slqONeXHcAQdwDArua/N8GlVM75+nqHOyZlb8X35eQMsJKDjN7d07SRcAYDkx7xOf3XeEQuPX3CnopF5eec4gBSDkctbJFNI5lcXedYiNSLBgPBIdOXiwVf3cIlVY1WgelXHQKoacMMv2zNMNL/fPmFJnbbRF80naJ6eElC5EuX696B8xRaMix4ZSsS4kcwQ20benHHo5H7Fu6kkhZVJLNYApym5zpOiWyeNg+RyXbM/yOEoVLKLpZa77FBzUkaTMkrIG1TzISsaUqMiV6LnW7LuiNdBWwGqJ3BbNwViNi9KnWSKbyRJ53Vg2rQKCtXNT18Bq/evNcalg1T286Do0fVXaoKfZbdRluaObuXPvTgDB5FUgIDkW8V+YhIDVgKeCAslJLb1jmMdjWsgaF+NBP5j58yWzdd94y8W2OKcJFNwpNxQhlzbFE3t2mqao7386N3wBA34x79pOAYHxs5bAOgXqpYNlKBUHyiRp2qCuPTnPgtv64bPWmnCaHjy5vnpUhowyxhK6zi6DkSOtE1ID9VJWKsegYNIbvGBtPmIA0rRBHzUyMkAufqMnO3A+3S8mkKC/vGSjs5UynuT+958C1c8PCr6f4+UFBstItiCB2u3wFBy8/c63+en/6/+MX/mx/4ivvPnHFrmeCgQqY7AQHy7YgzuIv1Qm3NEVLEHBnTbP+XjiTjOpDF4DUz6smltMaNUe5a8nUHAySV9Ui390ivccLwpoQCPS5eQOMU+TV6O1cuUZTM4EydgsuASxAISQwBmL8yvERSQ4xAYk9uSo0YnEcQIHjL3mVIvQSnLSBkw1ajrdmI3Vc7Kl+ctk+Vx+1xi9KWHUTTrq3+WTroXTIlyfXe3zUBZr/fzF3d4mwOFKG1vflhbXXjUNxhfTpxOQ4DRuD+ludcRipByJ3F6Ub12K26broi4iWsqXkm6MQdS2WSl0p2AgZNwAjdESwrrJH0Z31EQp5MJMpWJfPCn47zZR6pxl7U1hCtyiDbNa/055/gIwzOe4jklXU7/HyRp/zz1bVgNMKYL7wEAMTCLASuOP2io8hXA8Fk7HARyNhVz1MGbRoKyM2ztr+un4W37PFHt2V6Pg4q1i3f1AtbByE2D1rX5dgIGmDHxhCdz9gCAeA4JqN50Xa01lByoYuA8I3GdANT3TO+vSYq0pn56m0hSMzkZYukNnwgIc6NJup/Ty0RvWNXnJ/Ew/W5a4VzZZg7cqC9DJZo/W6hc1nMuTkHneL7JVAycdA8vUxnEwCOaFoOBeh8nPQxWU46UBBnWTXx7LMVWNM6pgqYKDt9/9Tb72zV/gV37sPymtmZcveo8R0DRx9dbde+9rVL/8YVmVjkBBbfN8Sl3BjEDraDsp05orEmZNwVxzP4OCMKF4JkHbi4x5Fpc9HVbktFLm6LgvR1sn/ylIqAAtkYlGAYIppjIugysAwfoVxmckeF2Q4wBxBNcizaiiq6EvPyuiqxo9VYvUBbU6ubgtN+h6QUuRYtUfiNzd4E9BQBjn9wmjXvOSRn6BjewUpRXqVnyLNK1SuK5RkFBythUkZOMw1mOtJ5uli9zcrXN+djJfKkyAeN5EF5EQzELZ8qwxlmT0ecQkhFyaM6WMFUtroTXCISQ6FzkEw8Hb0nbZ/Qu0XZ4bA3XO0Hm1/vW2Ov+ViguzKNvLcQqFludfI0ArQsEGRJlBgp6O3Llf9V7NtrW5AAAdazMYCEjoS15/KKWB/VwJUCn+zzEGJrDqvI5PUTp/GiPVObA+zOUYPX3NGkiapgyMCFb7GtyZB8s5YN1sUmQdpmlVM2AVqOrHe1IGpcR26W+hDNHMDnwaGPg0IDBd5uLWxcXfH71UCdoDWT0qsq4/zpo5PVbzDqXMIdVJkWvAX2aFLMWF3AnYXrg+kwsloRo11ZyX7y+f2wQKjptIUVxy9ZTKTypIvJOC5uj1VGj47SNQkGBRVTYfp0v3Kag+PV4aYPBZR6LmUNHICeHt977N13/t5/nln/hrfOVLPzzleoB5F5z6CBRgMEtWXpznLfnkyZJ5edSHvBQTLQdhfZXMTFEZdxc11nOsqtWyQVSAsGwik/JcN58X1B9wpMquamxbviWik7K6s9XjbsPeebLfmfQTSJiRfTaCRBglF3FXJqS6AagbnS0pButA4kAuizRB9QW4dlJm15yrlM162sTh/mgJZnCwrJ9cnnNaiBqXjEAMMxiY8rvpSPOQa/Ono7rxuVpiyun6IvSqOd12NZWF1ZyuLFgEMSrGFOOwC6FpPnkesvgoCyAwg9I009S5dsGcQbA1FmMs3loi6rg3Ju29ELNaRXfechgNfVK75DFkhpgY4mzUdIe4Ya5yaGpTIGdpjUyAoHEl5WCL9a8pIz5HLdtbnn8FN4vFnZr2KykQI8XHvz7aozs0L5BTH5IUXsAOFMOgWhZYXAXzoExBHofv+/lr7b/RZ2qdjuFqNbwEki8aq9WaeDFWJ6fcKjCsN7+O+QoOKjB1CjyzsaTiR1BFhRTGoLJ6MS0aUxV24D4wcJ+o877j09aRGg/d5yqq13q8boWc1RrFQCSqHbqpQU0BB1k3fymbeRaKsNgsXpkFcD5dn8trUV5ruT5XAfs9jEie1um55Pn0kMX/2fqjOTuBu+XeJMKTdz8fKPgXOV4qYHAfAlo+p7j4nbfffZuf/dWf0ZKOAgpksfFmWU7G+YHWuR7T3Zu/jNBY5rBq6+Gjspg8i5U4bRtaxCtH5TRKHdWujsurLq9OpOahS353AQrGmO54/eupLKL6cnrRzNatS1BQJ/Jpnh5mMHFfaqK+Vyg/WzrzOTHYMskseS4FW9DHzjS4plFa1820LrFszj7eyfFK3cjvy/vfdyzFW/XeFFCQi7jrKCosKnBiII1KIacwO8xN/e2X+dKTsjDjtDRMnNda8X6v4KA/HIu+UqPXbB2kAhBKxDn3tVgyH/M4Wy5smmueqcnTKGjaYI2jlmk6Y7HW450lFoDgSxvqxlrGBCFaDiGWxktxEk2esuhVxOedgoDOWU1PGAoImHsBWIPa/oaxbNhK404e8icvnqvAVERzVEtxsHHzmD29T4t7JFUv8CJA0B+UHajgYDiQw6idCsuzjyG+8LkbU8CBtxg3qpmQd1PETn3GE2iwiNaJLFiuxdyrQMHqMn5nVi71LQvdzCRWNo5cqoDy5F640LgkCGFmB2ob69NKmRpwwHF0r5e+AGHf5zoiBnUgzTXomAHB0fpVXUhFU5cuQy7VW85oE7FUXk91NDJVLZzetaP07zKtcN8aXQCGwBy81b8VOwVuVQM27R/3bNzLiF4FwKW8kjzduGWQ+RsnPgUVFKSTeaf3efH5nSu+/3ipgEE9jByLLo6pLOHJO2/zZ/7mSW+FJZVTfq+Cgczdmz6eqDqEwkrUqFuYc1inaLR+LACBELi3A2Ipn5MsOoGPFsPZVCNlpsYxFQwoa7Bw2KtoP8/e/fWwi4k3bdoyg4J7rV0XR43CYsrYPE/qivgnARI6sccpKhAg3q3Bt2DJU+OYiUEQi7UW75gW8LqYK1Ao5VplA5ETJqZO7KVwUD8kXVwr/TuVry5zj/patdwxx6B2s9WDvmwKaRi1t/097ozAseWss9qfofW4blAzmXHQMrGx189rmZjz5NyW3KNVTYJYRMKc3jqlnKfIOh6DhEnfMlOly3GlpmCL3HdhKxQgNESEMTK1eI4us/JSTG3cnc1iGs6ijYdU1DcDwJou8MUcc1b7LwV+6YXnC3NJ6RQR57kRWDWLuY+Sl+lexGMxYU0ZDIcCCgpDcFDL4TQMhENP6kcFA7UHQYj3P/NaHWCNmgc5S/IRmxLGRWgoG4y9S1vDfF3FcOtIvDjdByYB4+ytMqdCJyAgomCgNCjKC/FrLTkMOS8YAnXjrP0+PjfziK4Fp8HFp68l5fuVeTBZwUGlJGBaw6CyFccgIYs6XUpt21xstCVX/UzRvxRdyQQqT8dGXoy5T12j8wwGTljdpQZs0nnVFMvipZZB61jOUXU02khKEyH6F0/eebs4Gv4yP1pBQVrqaO6CfeDIYuWzjpcGGOiN+/QjA7/5zhN+/td+hr/2p7/Bj7z1FWJmUcIh0+tUMDCJmBbmQKCT4uj9S64yp9kGeMph1X/TiaRp0GnLz5PoVkwhGmY3uuOrm6OHXH6U0QGhfQryBGIm292K+HPm1NRlyRBUs5zZ932u6V688537ndCFPqMe52ExiWb3vmNQkkM+WkREBGczLixsZi240uym5prHUurovNPzXoKEYms8tSytlHMuevScELtYeHPSnHIuZWCSdOENI8sSr/lCyyaV5jbQeYxHrWzTGElB/91tUqMbbnQW4y3BWVzfkMeIaUbcasTEQG5X6uBXgYpvFaoaRzZex0+tbFC15DzGpoUhTSzKka4lL75Xrz3OUFGjyhJbmFIpYubIUmyjegfrCFaONpJZlT6n3OqYqf4DpgI+WZT/pYCMC7vfwtJIGj/3eWYxkEq5awFQWWLRmJxE3LmyeAvmLobyfoHcH1Tk2h8UCBR3wdTvCftBXTj3A2EYtFvhOD/v+565empEjLfaz6P1OCAVACYpoaFt5HRZ1mhf6f8K2MT5+doXSvU0UeMzIDjSRy1bIhcwMKUKwnGqoJYfVjAwaQtiOp7H6WQeZwV80cjRlVRgWHPqyyh5uZ4kKI2/dC3J1agqZ6KRiY1ZMggAgQUhlEBK6ZPNtmyaWcumcwFOGvAvNs/lGeXp42eu0ZTgLS9G+2Ldr6CgliHft4Hrn+h5xFJOflc0LHzrnbf5+q/+DL/0k9/gR778lXu73mY+mxX4rJ+/NMDgznGyc2XgN999mz/7az/Df/oTv8QPv/Wjszf36Z0sf7cEBKce+bHQU0BxX1MaXCdrhiQa2IktI5TFqK3UQ55tS5dipZo9iHFGpC84lmClAppYBkrtLBcyR6DghOVcfD476C3d3gxlYJ6Ag3qPYNZvVIBA0u54dfWooGBJMycyKegrVKbFjOmF/eit5KNIc041OKx3s598UuZgAgon1Hk+VRzXTdIbTBEW1ltUb8+U56sRWyh55LJgLFvZxqH0cxi1C1xa9LsXI5jCGtRUQi5+9qZ8dCljciKliKSIqQASME2r11DGkOTMXJ1yTJNPkecElOJ8T6p6/kS0WcfRMjqt9e2IMhViNA+N8VPlBNYRkSNnx+WoFZiU/6bqBpLqQqTS9mGYqfwclcKvYr7aZnd6QZmaYtWSO1XZW3IuHf5yZmr7PQl5X3R/KrhUYSuFKZi6FA4H0uFAOPQLYNCX511SCSHe/6xFCmBYRHLWkJ093nBOBIdSOhnW9MKkCShsYZ5KpgsIWKRPjloLFyAwlbgZV9aH+1MF1c64j6odqfbcyz4eMEe+xmjPCRHdaJ3Rae9quFWZwBKBfdZ6MpXPAlIAwrTSlMArLMbC0XqWavVBASgJoqhvQ8xFarB4r0/dRBcM4wvX6DDq3Kjsb73g+lHsdK+PekbUy1nuIUX9GJKmd+0CIJDhW++8zc/+za/zi//2N/jhL33lDkuwnG93ruv7YAvgJQMGp9e+lJRUUPBXfuKX+JEvlYYSheaerClZBJL5GBBUZFZz6OMiKoiUnvOJWsaKM1pepvO1qI5PzzcXB7l7DEr0hdIJa8Ci1nURKcCUY5rmy3Tepb3qAhTU3zkWzR5bz3prJjpLF3OpKoej01zWhU9GSszgIItGF6E8i1hByqJB0GlQPrexTVgR2iDsjfr0t6V23kpeKNZzKYcTrGgTGF0u81HEnD+FWs+mfD8FEINxXqMvUzKidROJcVp0T22XgTlqrKmFoCK05SKabELGiPWWnBI2ad25XVDQDrCy6AopC5fGVpAk0xjG1L4fd1MfEwCqZXcV+CwMcnIIk47ieAyqej3XTde3s3p9dCqOnPLTfk49GFdW/rJpVfBV73mIL/b9H4rCv5SkTqLP+86tbJy5bpw5qagvJ7JlXjU/8/4U7UKOCgrGvlQbDFOXwiUoiPueOIzEvugLhqCmTinf+6yNM7rZGUMKyhosj8nLoDxjsUV3cnrPp/GgAGiZOpm1JvZIhHmv+Vk8LmmeUkJZO2f2UatKQkr0MR/N11Om1BgwaW40VVlXBzPzKLNFd11PlvbGL1pTpjJruNP0KZ6Y/tRPlSTIZKNsQhRwuVIDZbrnGYCUBzAthtqzYPFsaiCxTDPClG7MuKM+I9P5FOCaUDZmbqdeAqITxqDuIaAgQk2SmADC2+++zc//2s/wV3+igIJKhC7BPMcM3fSM6rUs/n3W8VIBg+VRdQYiirSWoGCKcusv54V/d/nWUbfBQo/mvBDSpfn3NRqqfQKglsYIReiyRO/Te6bJtOROvXNVGUu6/yEuXmfelOeTz7kwB4tzrpvyfeSDhYkdqKCg2s9aM3fxq33pj1O1sgBOgkEngGp/FO3XyoaQqw6iUpOa6khl4szPrvS7DwpOemtwNtEY4WCNggMntPkui1BBjFY7CEYc1pR1lEWzpKL2PXatM5qDNVFL1VrRqBWYSh6BKlQ1OWEKM1BzlJp+GCcgNm0USfOyyQABjLtbDSFGMKMju0hyARMGfT3nIC5smkMAV0VPMq8GeqIcCS4rKKjCzDDqZlc0ErOQcpy9IKaoqOat1cJZbaM9qZrflNa7IrMOgQWQuVfzUNmLU2agNgCq6v4wKAiLYb6Wek61Bt96clRFv+Q8UfjifAF75b0/4/5MqYRQqkyqhiSOBaAMMwM0KjN0CgrSqJ08QV9eDGhzacimAvOEmGI+JHNzMOPV5ZMqQq2VKdZN9ziXe6wOoQsAVkSitRKj3vsja/TE1Pa4brYvYgcUFCSGpGBgiNoK+1PnqRiczZhkFhormdYTZ2amqPaYqCLtpeFWHSagbbCTCDbPGgcKOIg546whh8iJ1cikZxJ0U3dZ9QmOxdqX5zXzaDm8Rwypz3MBCk7XaWN0HV+ydRWYiSn3/241x/L9ZfqzsrfkorTIgjGZ33znbX7hb/+sMt1f+tGjbqRLQMDi8wkMVFbm/58ZgzuHwLd+tyKtX1KktfhxuudPlrRMqrTPMhquyPRkUEmmuGxltKtcoeErI2HsUoHHFEWlmc6cXPfuK6GDkxC/RmOn53/Pzg9HNGaxXyovU3QEMEXjM5V/bAdb0wrLQ4MymWgyk/WskpSbVDz3c1YwNdb7mk70D+lu3bs1CWeEISVc0EY7ziQO1tBFw2jzlGYYU560CNUbwVZKroAaLV9TAaMi51zAQdkQij5B4qgAIWkTJeO8+iaU+15jF0kJ22huWYLFBEcao+aAl88qzUK8adNIajucYtI8atErJBeIwSIhanVFUvMmsaF8dPP40VesD75wuWn+uvxcCjiYHPkqKCibXg4lQl/U4Ndc/qxiL5R9Ka2bNrAFSKg0d3V1LAN1OsejypEKUMY5Ms/D4bji455zmShz68jVlMc1+vOGI6tyWbrJfcr9mT5WZimEiUnRqhR9xnGRLsr1mZXUwSQoWz5n0jSXRWq5okxtjWvDInF6D00RmkrTQtPNvgJF56GljX7WCUwCQnsHCOQ8U9dLS/RapRTKxrQEBIegoCCkzLDUFbxwfgouC9iESYYiK5nTkXYOMrw97jWxdK/8tDUlpPp7Ol/qb+eQCFXUWHIJy3Utp3ycJ61PuY6Do+OYfX0RQLh3nX6RKLayNJmp/fSY0ixAPN0/Fp+rZ4iSHL/9ztv8e9/8Of7jH/9F/tibJf39GZt83RnmSpxP//37jpcOGCzvwbdK9cFf/Ylv8CNf+tE74sT79tAlS1Apn9N6XVDK7bRWt1JmSgEpX2UFklHVtCymgORMWhr0lGhnQpBFOXznyqTUZS8qEu6qUF8ADvKc06pHZQtqo5rqNjeXjS0bxSxuwOK8EkrtHbnmLRBUa61uwGWC1/NOSSfBYYzEpHTm0bkV+nFpitNYQ1PyoWNM+AIQKtPhTE19FDW05BKh5IlN0BxnYRiMwxhHtgtVuvGF5jYlNzwiiw0Joy2BKzJ306aQMCkplZkStrAGpGJnfR8S/ZTjvs5/LzxkHh8vfKMFHTo13amgYBxIQ4mMYxFbVctrq+PXlBJLcR4ZDsceDAvnPKyb6fHFe08+EEvnynu8AFIR8913Dtocy2kFB2U01pRPcouF+kX3qdwfkfsXgHtv2/fxHBZvU5t52QUIMK3+c6sG1ykYoF1huvXcrKiArVRSNDMg8BM7UNmDmr+eNn5mVqCmPvNyDctz+XJIxwzBEBJDnP0oqllVyhzZGENxpCz+E7VRETAJrp2puqDZrOrUvXIyrLpnTclAQrQiIeWFJkHBgTb4ussaJDLmxAzu6JWXbGvW9ue1MgGRRXXCyQsUpmsqYU4JTCqsnaYeF7CJLGbSeSkQK5UdEzA7HqO1WgOYQNvfe/9t/vzf+Vn+wx//Rf74m3P6+76LexFY+BdJI8BLCAzq8fY7b/Nzf2MWaijVnqdo7/RY5mpeBAryYoL0MR3ntYAgEWd1QhA1tRAS+KSbUH16tdZ+oqeO6u2ZKNzpWFBTS23BneWqrJSi++BnHpXKsxTFcJnETQEFjZNZJDZFewsaq5xLNcTBaK27KYBooq9iVk/9siDV76dCE8aU6YP6wC/BQW1aM0aLt4nRGYIzhGx0cllDU+g5J0Kb8olwMhcQMLeDNkmjCrX6VWtmtcAtm16taojmGCCImskY50l14ysOZZXfmQxtFsp0B4iJpFGOKECtaS/itPK5WKPlbBVI1pKuOnYWlDHMdthHNfv66kyj/DT5SaVGSxqrRO5pGIh9FdEFYogv9GAwjcf4g3biGwc16PGNllXaQ3F2LEvL0oCnahliUHagpjTCQOwHZUyG8YVeANZpCqHeZ4NS8jg3R3J3rlbqbrWYP6X0t9wmTdnJBDAwdZOQkn0owMgIqTwjWTy/nFTjkjATFjJGMF67SJrGYRuPbR1u1eI6/WdXa6RbI6stpu2g2yhLYD25mg0tAYH1qjGQRe46Hbc3Xq5by3x6znMb7woIQs5HxlRDUiDQh9m9MsQ4Cefq4a2Qstphx5RJpQVi3ewn5rGuJ/Y4yHihYVUdn4VhVSMvRzSChHlTyznrekUkiKgz52esdxpBL75Yzof6reX6epoKq8fpWl3W7xwjYtMs3BWZAp9YNByhMDQhziXcde0IkotGQ5/Vb73/Lf7Cf/6z/OX/6S/yx978igZ0SVM3deO5zwdiyRZIQQL3pRGevPM2Lwog4aUCBjOSevudt/mZX519ClIq1HZRn0xU8EnQkKkTqQrq8pyHyrOZB3BkFJRzZqRsslEINtM5NaiwaPTslznlKnw6FX4ZWwKa2cFsYgcmYDF/Xs9reUy/Vt/7BCFMm5Nhul+V9nNGjWY0MgdT6rmnevJ63kAVSlVxkxTls7UeaxyjHIstI+BiaYRj5AgcjDFPoGCM6VjtLIKNmWCFMZaWvxmSnVM9ISlbkHOeKhlcKgYpBYk7I5PTooi6pFkjBKn2v5WhsDhrEeshjpCcAgQXIfRaKmgWbaGNIVmLNfNGPj0LY7SESoRoImapSjeyMDpyC7Mj3fyMtwu72hKl16+dm+jkKd98UpUwGxvlUtKYjyN4UBVaEVZVgybNm4/EsdDoC4U9xpC8Q4YR6yxxGLHNgG2186WMg3bEXNj8TsAgL+yjx6FoCLSFdiylnks/gPveO3uHaWYGwfiSVonxeCWrtePGzZt9FehNczBNxjhkCyapM04sNsEFsFTbYOMjKTjERa0kWVYffMbzdV2roGCzwnUtzdkKs9ogqw2m2yg46DaFJSi9CVw7pQsqQ6DdN3Vvqo21qi1x/bpuOJSfVRfC6jtQKwz6mI/SBhUcHMZY+l1EFSSm2TCtzkkweBJjFBo3gwIzsXY1jaCgoKlfC9xXWnzUtIgC9kQmO3DrWlrnkChkEslkIoKLQl+1gJyeY3kOZR1c4sOjIzOXLJ6uryfgoFYmTMC6polK5cvRZiJWe0kkGGOmj8UhNM57BlCAjYLMCoT/7rtv8xd//ef4P/0pTR/krJUYqZysOSFEJh0Bx6DAlOdyyhY8eedtvv6rX+fLfIkXHS8RMADIPHnnW3z9V79+1I8a0dz35wEHy5RtWn5EgcJvv/stoKDvfKr0z1M1AoA1DisJjxpcTB3TcppFVtVWF3Qxws09kkx51HWiGLfIoZ4omwtar1afiUy2puSrRHP7JZqAmWKqZX8W8MbQGKG1YMJBQcF4mPLvslSGS3WbK7XR5Z/EQLYObz22TOYqGMre0qeMjXlqnVsPnUBKZU7gJdZqA4jJ4O9liM2UHsnZ4F0qTZl0o7bkaeJMJY7WEETVy8ZkTYMUTYIzQiz5U2sbdTl1RXdg/WTLjNWcOr6dS/bE0MDU0hbTI0aIQ0CK4dF0+4xM4jPb+olmdiWytG0z5fDFNZM7nqrT7UlEWer064XmPHdnA4iQTVKqv2x8eRzmIR+jpg+KwC6OgdTXltbx6HxTP2otftuoDmJU2t+2HtOkCfBWQDM/4IW+YclQDCNhGBUQFNZgthUuC72zC/bEkI1oqV+M8wJp7BFwyhU42Zqjv+celSYDNSUhOWOaVlN81pXcv4InO20IiUBhgqwh9kIqboanz9c4h21cSR20+E2HX3eY9Rlmc66AYH2GtCuy68A1sx3xabogzyVvteT3PifCygosrYRrvX9IiQilBFEZghC5Awr6MSpoWDTDmm5zBkgFQJfvSfGmEClNsizeQFOqiOYgY1GaOolQF74aS9agBBtiHTkFxLW0rlPtgTGElCbB8Vj+pp5mKkGiLZuus2aqkLDI1I756Chl5dkYyKWJUWHmZNHQqqbDdEwXL9dgtNurL6XEJWiLqK5gTHAIkf0Yp5JPHcso2zFpNXRV/ou//nP8H/8nv8gfefNHdf8pDPCUJV6c+6eBAj4FFHzjJ3+Jv/Sf/SVedLxUwOBJMX/4xk/+El/98lem70/C7ReAg888Clvw2++/zf/mv/h5/VYBBdp2NE3RK2hOvG6htnXFsKIoDHLWMrFqrVrU18BUq6wnvbTUnI1Jquo7M/sViEgpmSkUeSq1xKk6CuqwSAsBUYhLl0NlC5wVWicKCoYdMuyRcJibxiyV4WJmyriwBQoShtnb/2Qyp5S0AY8VzChHGZMlVblciFLK08VYIxrJGBUmmsquJ1PslhMEU/zSE7aIHes1QgUH6agsM6XSB8BorbMVzVNOlQ1omsHAbMscRggN2FLL7zyV7m+sRURp9+AHTD+QRjdteJNffvEzcE2DbXUDUcq5KSK0bs47F2GaNrlpdBOpNrbTxlfHqy6yIpEsYV5Hcpo2PqnnPA6TW15KWTUG4dig6agu39XryNjGT5tkPeoClctYFylNi4qB0H2gIFWF/2QMFY7eM6ek4MAakgmar4dZjFjnzaIREEfAqQKFOQ2nFxzLPVLnuzrqpOm479C0kIKA5JTpEWcnt8PT52u8xbYNrmuwXYvfrjRtUIHBaktu12SvwCA7P9kRq1aganHuVhSkNOesx0WK4BgUzPNoBgdMngSTADipnqCmDlLOEygI4XiFrNH4kRDRoH0wrNAWIN/eF2QsjKtOPTWmNtWLNWZqKhYDNBEDtK4jZG24NpX3ZaZySsp9o6x7atKmS0hdL0RKG/KiE6rpV6rrZEqzsLCuwcuqpMJ8AQp+kwqOCR34cg7onjEmuB0CN2OcUjTAVOmVjcExl1cC/O/+5F/lD33xR+6AgBcdLwIFS7YA4Mk7T+7dH+87XhpgoEzB8qL1rhZySJmBacWCJNqZTrLWulawsBBRz4fAf/mdb/G//vWf53/7b/5V/p1v/sRU/rdE3iEpt7APQucMKWal0izEqPQ2SUVX8bAnH/a6WOao6BiUOsy5CK0sqeQ8c/UxLznGWFIZymToaVtRkQ5ZF9SYM8loCWEuC8xYtBHOqvlHzmaaMN6ACT1yuEGGWzjckA67uawtV3W4nxbhyXhFFj7sJVLDjUiT6Jo1icyYC9VYIneDTOrmyh5UUFAXNyslN2+rCEkmXUIVMCaZo4SEMgDEfCRKyll/w4hOysYaXNE9dN4qiMo6QZMosxwNSNLNTVKl5qotc6fdH4vRjzhNLWh3Qou3NR3gMI0vZkdpssudrJGLJfKUSqhivm41N1aqtsi+nUGB0yZL2ToQt7DALQvBpJUobpQ13MhJxW0xaBvrU5EgTJucigDnuygyd3Oc1N01HaM3FnFWgW4RcuUyr3KKyhqkwkiEQBi1z0QcQzGFKmBkDEebmvGq6Ti1Gq4VClJFkL4pjn6azqisSs3NV2va+c8zOQckzj4jdS2+sx4XEGKtw7henSvbiO3dlAI5erZiJsGhWzXYtaYLzPoMs71A1luy3ygwcC3ZdZMd8ak7XuZY97SsKjiMkT7pRq6lhfo7daM4PSZzIhbzpwQ++m/WUX3aXFzO29ocS22tDc4KjYPOSQkwdkg4KFuQ534mFIMtwqj+EbVEVG/i/Fy7ddE4JUwneNPQl41eTdMU3ISi6zKYIiHJxWGzljMrQCgzYtaVZdT9VUpKyWY9TxPJxmLKupaX6bBQ1u1QgKEYbNNBGyBlYknPHELk6hC4GQOHUCzVKeZtk5eVslZ1M/7Db/zoYiKfHMIRCLCLz+8DBC8GBfeO8ul4aYBBpUeOkVCJOhY34D72wC7AgSl5nCy5lBrC33vvW/zFX/85/vd/8q/xB1//YaCIDZnV9Us6LtbNZqXswdrbYqKUkTiQDjs43EK/m+u0rSNbiylRFsVYBlOiiPrPuCI2gslXvFBjtVTHGe3lUOv2vTGIRAKZMWt9siubcsyltE+0HNAcDpjhhry7It9ckg63E4DRG2i0TMw3Oklq0xeRRUtqU5TqI5IjSYTGrRhimujFvTEYSVMZ0/R8jC4+VubmKcvDGtUC1IoFUyOBcu2nZik532V0FFyAz1k3RsCXvr65pjmYm7NM46hMuBodNbaj7TrEOIzRtIoplsepbIzG7YmD0w2v1pKZqq63UyMdpa2bGRg4PzVRquVs2daPrW58rp3dBhf0pAridUFTEddItYPLlHRWDKXlsxr4GG8ncd10TFHwHL1blyftwqRuKx91HIpG8q4pToQnVQCxiKLSyd8v/p26B7IABFWcafxi4/Dathrfkt2qNCFS0HonAmdmrK0IxqgmpuaXJ8Fh+Ue1H+73CsZKFYUpOonULRpo3fN8NUW0Qrq1sgTbC2R1Rm7WpHZLbjb0CYaxmAgxg/3jcZyna4hohDyWEsM+pUlEuBzjRlRzs8QHpT6mgPIy1tM8p1Tse2faTeziMn9vpJYh6kanaQOZdEpm3GP6a2TYTX4QpAJNqu6kmmyVxlSM/cQaZOuQboUZDuoASgbnaboWG3T9qMK+Q1Rg4OycPvTG4I3Q1aorM9eFLcfBBMIAZ7w++aK9kZTIMuozLWM45azpxJJKUOdNyE2LWW113RUhJLjqI5d94Nl+5DDGqZKjsWDkuMRztpVe9JKQObDVZ/b9goL8Qib9046XBhh84ye/wVe//KPzNxYiubIczhHBCXsQF+BAyqqgOR3ht997m7/wn/8s/8Gf+kX+9S/8yCQ+1Pr4XIyUMkMIfPj8lu988AnPrm+xknll2/LWgxU/+HDF6xvHZrjEPHuP+NF3yFdPSfsBay2u9dimgW6F3Zxjtxf4i4zfCn7TYGyLYEhZ6A8Dt/s9lze3DINONFMQsZP5ugOGPiZuh8z1EPj4duTj/cBNHziMuiGfd45X1g1XZx2POstzO2KvP4bLD4mXn5Cun5J2O8IwTgk8cZbubMP549c5f/wF/GpLjoEkhjgM7G9v2O9uGUOAdo3ZXpA3j3APvkBqt2CbQueDs2CCTgLvTBE5RXBK70+bfREsWiMYY6ao38jsqjb/KxRhSsSUGWJkGAO3h4Fd3zOGhHeG882K8/UaGlcmZAIUZeRULSdmWpCycISYNAedIp13nK8aNu0GJwYjs3+BEUNyHuMb3URqWSozBV6thsU6xLfksqHmakFsPTQN2Xiy0dRBwpIwxGTo9/1RzrLEQQr0asFXUuvfFAYYD9g00uRIYxy27Yqla1/KEB3ICEZIOTHEwNUnz7m+vCakiDiwXjUQWplQGY8G1zY02xV2vcauz3Qz9M0EDPI4kPo9cXdN3O0YbvaEfiD1g/oD1IqEPhDHkRzAGcvZxZbzVx/SZq+LpYiyMM4WMNCR245gHEMfGYY90QTwGeN0UVfwZxRE5TzdJ2Dy7GitwWI0bWS9zn+HIk+K4+A4QFQgIjGSxx6JAVutm5eeCzW9UcyKTLdBNmfI+pzcbEjtluBX3PaRq/3AYVTzKmOdVtSg69CJXPSoW2pfqgt2Q+B6t+fydjeN73XbsukaGu9orJ3mTo1Y59czBMmziZkxWKNlfKXY4GgeaqWBwTszMQbOFjM0EUiRfrdnfHZDeP495PYp6eYS+h3eOVbrDavNFts0mKx0xbi/4eqjD7j66Hscrm/JodqPC67xmPUac/YIe/EKXLxGPHuV6+h5eoi8f33gk93A1SEwxkznDdvW8eqq4dWN56xxbBp9vo55T6g20CnrM26ahovths1qRet1JOQEKQTG3YHx5orx8hPizSXx9goOe+IwEPqRGCNm1SDnj7CP3yQ9vOS2ueCD28DvPNvz3vM9n9z0xCw8PNvw5uuv8NqDDY31R06QNVlxvPGXr8t4kPK9CgqK2eS9LMEMCr7ON/7tX/zcoABeImAwpw+YQcHp52LuBwhJUwukzNyaUfit957w5/+Oloz80TfUXEKt63QxSVmmbnEpJfaHA9/9+Bnf+eBjyPDaRYv0W7ZxS9s3DIeP4MP3Cd97l/HyOYyRpm1oNivseoUkwWSPsx2N29PaNV2TaRqDZMNwGNj3A8+vrnn6/Dm73W7qTtdaQcaDdqbLWSntZk0f4dAndjcDVzc9zw8jt70i137lsduWdWjxK0cI15jrD0gff5d4+THp9oZw6OeJCpjG0w4H3OaMbUbjmww5DIR+x+H6OVcff8TtzY1ujGcP8a+8QROge/wWtBZnbAEy6mjorcHbzBgSzhpMzhOSBvVZsEZovcUXcVPrqq+B0petrR0Y9Q9DYX9CzIQQ6YeBm92B/TDQeI3w26ahcYYQy7mUTaNKHDIlT7io+R5CZLfbkcLI+brT3K20rP2KpuYuy1izzpGs00gphFm8WZz7xPrJ2U7pbkOqTnaVSjVFXGgcSRzROEI27Mpm0gc9txpgKjBAez6kpI6GKWHJWBI2R9ZO2Z3Od8oYDHoOxvUlynWEYWRMgV08cBN3JMlY3+I6wXUW441q+UzG5EG98geHcQnrEtbpBp5yoXbjSBwScUjagTINpDyQbLGTtVrdEQqzFUOPiYIJnlUKdNaokM9ZjDMFFDTQtCTf0SfLLgm7EIkCcR+J7DTfb31hcYxWm1VtTZnHrTN03rFurPbdyCpK1ZDSkL2m8nANkpK2g05Jc+bVnKk4R955xq7BrDbQbVRT0GxI3RmD7dgNief7gQ8vb7naHTDOs16vaZzVjaxoYKbGQ2VMVqtzFRom+hC43h+4vN0xjIFVo+C7dXYSApoCnEEtxF1SpTwkUp7hx0LegxF9j+nrmjooKYOaNpjBuebfw3Dg8PH3GD56l/GT90nXzzBhZLPdIq8+xjsF99laKCmN3e6WTz75hP5mRxrG6T3FaVWH2Vxhr28wh4G0G9m7M57vA8+vej666Xm+H4kps2ktqfOsx5ZDamhag7UwDDtlKUQ01eS7Upmh93K9XitTlSG2ui7kbBjGzGE/0t/uGfY94WZP2h/It1fE3Z7hds/QD+At/uIWFwSiZ9clPrwe+M7HN/zTD2/48LIHgTdff5VHFxtSWpUuo7WMsz6f6g7JJOisgdAdV9fyO/cBAnJSUPA3fnYGBdVd7XMcLw0w0GNBA8A9fFyk8mpSQEIGJrtUM4ODb//uE/69b/4c/+GPax1prOUJ5b52ZXCTtZSuUuTr1rFddYQY6Apab50idiloxDYN9myjKtluhV1tVZ28OUdWG93Q3YaEIwYYh0ge9wwpcehH9sNISEwueMaIWvSGgwoG46BCxLBn255juoZMq1SZNax8xBrYeMdZ51h5Q+ME04+64IlgfIdsBbve6gUboZbHddst7aPXsdtz3dgAGQ5Ya2m9p2s80RnGUprkcizxmjb/0W6NMqUAfCltar0tVGZeGJkXgxRr8VYmUNAVYLD8vClCI2WfhTFox7PUOEJqiTnjvdfFzVrIxwvhNEzKx6rwPoTE7RjpQ+T57sDzqxvCOPKwD2BccVJ0mmNs5qZHGIMxVg18ykZdjX+qGZA4T3KegGUsznMpG9UyiMWI0wi2AIMknsMY2A0jt4eR236gHwMhxiKozWoTTcml5jRHGUbovAdn9VmkTONaxLUkY8nFpXLMmcFk4srTfvERr7z1CNlqtCubM8S101zJcURyLiV9LbQrUrMmuW6qmEjFTdKEA27YaZfCsS/dLLU3wPR6oSffXpdU1jU2ZGLjGUzWNBtqh5uMBdeSXcuQLAdp2CHcxshhHLSBWGF5ctE6qC5ISCjz4Kyl9Y5N25Cy0upr70GKGAwh5ZEkEDGkHDFoRO6dwRExYVQm6KS3w/ScS46cZkX2em+i69gPies+8OF1zzvPbnh2c8B5z4MAD9YdrbNsSk+FnFVZf9+Rsg5YYy3rtqNrMq23rLuWdePonGHl7BTh18zNkDLWJA4yT+96HEbdiKrQd3ks56K3ckRvW036Kzgl4nIswDTgnaFrPK0r88XaSeRpjaV95XW6fU9e3VD7V8wlVIK4tmh9EiaONJ2w8oazzrEPCVe6fG4ayysrz+Nty4POsM4DZncFw15ts21DbhLJNRhj0MIUS0iwH0bc/kBIkcYYJEeGAAOO0W3Iq6Rzca/aENNdY1Y3+MNe94emKekGnWutszTW0nlH10acdaxbR1vLOK3QWTlay6AatH0GKJCaQp1TBktXzyfvfIuv/82f5Rt/+hf56pd+lOND+KzjJQMGi6OAAjl1Qcu6UFSQIGIwLPy6jfCbv/uEX/jbP8t/8m/9En/8rR8tbn6zKA3QCFWMahVSordw1givnTVIWGFIPFo7Hm88XaHacopI02HOHiKbc4xvkPUW2ZyT/JrkO5JtyK7FuIZDMNjbHtNHxOrg7cfAbt/Th0CKxTozZ0i6ODHckHc35BiQtsOsdqw3ryDdBkvDprHshkgis20cZ41l7QQzHrQWXUTPrVsXKrQtGoJSs0um3WxoH7+KPT/XBSUGMAabM77r6dZbwtCriKzQiykoRZzMqIpu5ijGGWUMQFsqOzsrjE1hRI7dDzVv2Bb9Rld+5m3BeEVEOIrgRBsqpZyAjsYFBQjGUNstK50nRxRuRQchZfqU2I+R5/uB73x8zfc+fsY4jnzh4RbnPV3rcSXCWzfrUu+cy4ZcottqvlPFciX/HcUTMOzHxC6NDDGSipjLIzRicKnQwBjGMbA7DNzsep7f7rm8PXDbD8Sooq6VM/gqJC0Li4rRtEe9twP7oWFYd6wxtEGwIZGHQNjv6Ps9/bAn2kx8sMWefRn78AuE1UNid86h1GYbA40xeJMnj4tQrJC1ZjvRDypSFHG01tN1W9qNqC/GFL15xqS21ymphqSzgj1c4ffP4Nn3iNdPOYSRcdjR2sy4F1y7RkIiBqHHsEO42um9GGNEkrJZ9fpTVoX4mGAftNuitZZN2zBsOnJaKaiCMjYMKQkhGYagIDMkpZgbsazxdF7wPmOzNoOq5cdyqqz3rVYe+DW5WXMImV1IPNuPvH91y+9+dMX3nt3gvecLQya9aniwajQaT0VZvxiXrpQhm0opo+M5t16ZAu/YtuVf42icziGlqFWJ7yPqL1BYrgo7qqhQXQ8FF4/nojUVyJvJM6RGryklQhgxo7YfJ0W1M/ae1ju69Ra/WmOL06Mp/SBshta0tG5LuL2dtFNasTBOdt2ayxdyGDDjgbVreNQ5vBFuBotBWDeW88Zy0RlW4RZz+wnsr7SNtnWw3mrU7EeyaUoZaCZJQHYHUs4ceqfrdSzrVjDgNqTOI26DaS8wm0eYcYe5vSLvbpQ5MhaajpwizkBn4fHGM8SOdetIGB6fNZw1hrWFtTOsnQY6tfQSZvtoNWJjso5eggI7pQ4KIFjsd0/e+RZf/7U/wy//xF/jq1/64alC6G6N5ouPlxcYsAAFJ+BAKqWSIYu2ZpWSwPnWO8suVj9KqXwptqLzBGqMTJGZxcAgxM6Qzhse+Q02Z85ay6POcd5aFeAISLdB2g6xntysiO2W0G7pE/Slhj8PIMOANQZjA9basjHUCRu0tIyM7qcFqecIw4G0uyYNB+TGYDY7TAqs1o9oNw/ZNIZD1GjZGUWrLUGZhpzUWMWvwLdEvyG3m6nDGkBrLZsH5/BgS+48OQ6Y0KvzIZkmZ2KMWjN9OBCMB6clm4dhIOPJ2aEE80yhtSWisSZNquiaw6xUW1OZBassQecsa6+iJ1UeUyow9J8r98e2FosumreitcVSxoWVjK/vL3PklIFsDVKEcH2IfHy14/2PL3n3w0tiiAwxc75d83Cz4rzz9IVebZyHpK1YpeTEpZSTgm6GyXqSbQg4DhGe7fdc7vb0w0jO0HpL1wpbgUYylghGOAyB/WHgdrfjugCDXT9gyHTWqD2rlHptIGWNYPqYGVMmS2Q3RHbDyEXXsAoRNyTibU+43RMOe+1Zv3mIPH6TcP4Gz3PL+1cD739wyfUhgAhrb3mw8jxaezbO6kKaYYwj+5C46kd2o4rhjAhrbzhvvQIXaxBxhAS3YeTpbuT5fmQ3Kutx1jne2Ha8cf5lHpy9jly9z+Gj7yC3zzkc9lrV4nvsOhEOkb3LXB6uue0DfVCA5Mt4AXDUJmKlv0FIHGIiIYQQlAWQjDf6u7lRJ8UYM0OA2z6z70f6MSICbeO5MB0PuxVYyARMHLSMNY5QAGcWAeOKWFQrJcYs9FHdBZ/tej662vPOx9d88PQG61Q/0jaelbdsk5qkudJvoAwfUobWaS3/ARXJOQHrLd4YNq3nvHVsW0tjoLFajltFygnBoQDImsomgQmU/iSGoaQxqsHRck5OJkZmri4CGMcAw4AMAznlYtTV4OwZXdfRnV/QnF1gV2sV6fmO5FRUK+4CzEMOz6/oi07DSkmR9reY8VYBQlbNjIx7Vq3FdI7OCQ87CwKdNWy8weyeYXZPyZcfk26vyTlhmk5LTJtN2Uh13UyFOhzCiOwzYdCgIaVEjJEUIzHVuqcWYzva5oLWgGxuoL/BDHtlR8SqYdKw47xdkXNL5yyvbSJRhM2q4bXO8NALF42hVQJP2WNbdS8y6Q4mMfFCT3BkJV0Nosp69uTd3+Rrv/bz/PJP/FW++qUfnva7/DlTCPV4qYEBcISmpq+XjEGebSy/9e63+bm/oY6JP/IlbW1pah5ddDLF0rq1NTpZnMkYk8g+Qyd05y3jymAl0RhYOaMDO+z1OTZrpUC7M3oct2NidzWyD5ExKI0/UeM20bhMU6jJTHFcjFoWCaqRSBmtahCtn88pwdCT4kiOIyZGbIpIGNi2WzZNq/cgRUxQrwIJaniTXUdqt4x+w2UfuHze83QfOARF/w/XLT/YwdqsWa+2SDzAuNO6/UKfNinTYhlvrkkYklGqPBwGYjBYLMm2SGl61DgBtENbzbMBk3GKQRd5K1I2cb2vm0bzp62FxoIlY1IkxoGUEl6K6IpM9hoFSrbsBrVflhjIwWCTxWDLe+gESmQkZppC4YYQudkd+Phyx+VtT86Z1W7g+W3Prg8cxsDaG2KS0mnQgy3dNIwj13EnttSrNyTX0gd4envDR1c7Lm8O9CFgyLTOsRkjZNjkBmsCyEgfEn3fczgc6PuBGLSVsoI8Ye2ElVPNBUlTHbugG3QfEvvC4mwOlsMw8sqqwScHriXaltSArLbIoy8wPvwy791G/uH3LvkH7zzjOx/dMhwCzhm25x2/97UtP/h4w6OVZ+M1PdGHxNVh5HoIkyCsCl2vmsB552mdgtzbMfJ0P/I7H93yzz684ebqQAiJpnO8+XjDv/7lh/wrX9jy1qPfixNLetoQ9jcE22JdC8kx5oZPrm653AduR7XsXnmD9bqZrp2Oc4yjNxkjqTABpalNGOl7w8EJvTc0JiNphJyICW4PA89v9wV0BBJC6yJ90v4Fr1xsMQ4k9BAHjXBzPHre2l+jMETFaOgwBnZ94Pltz9Vu4PowIhJYXe54/OBAuFgD0NjCANl5A45Z3UFtiuRiGuTQlOK6sZy3hrU3dEZ9QxoyEqMKC11DMpZghDEpk2TFUvsxGixOMo1VR8TaFn3pW+DKuTSu+gToJnqIPXG3x4YBh8UZj1lvaUi02zOa83Pc5gy7PivrzJrk10TbcT3e8sHhht951vNs15Oyusc+Wjku2paL9QY/3mL6GyQFzLhHcmTlOlrXqg6nAAZzdYM5XBKff0y6vST3ew3EnIphc3kmKS9F6JkcE0GCpheAnIpVdEhz1VkpOfQlRbP2KzbrDe06IIdrCFpVYcYdXVZ29aw1DAliNnjv2XbCuc+sTKIxpnSIzVPJdmtnTckEDk5TB8vW8aDpg3d/k6/9rT/LL/9b/ylffeuHy773+XUFy+PlBwb1qNUEOVPr8avXv6TAk3e/zdd/7c+U6oavTEwBlKirHLWle2sySXTiOJOwNmF9omsiMe3JYw+HXsWAlTZ1HbnZEJotV0Pish/46Hbg+WFkKCKflbect45HK48rhhuaa0pA0SkYyLGGEIUqtR7xaohjVhtiGJBDUvOQw450c6mDKg5a9mYEYpx6IAAqgGtWhGbDJ7vAu1c971zuefeTW64Pgc5b3nz1DLM+55XHsBoT5+16KuNBBLMGh1EK3HhsTIymJRnHYRgYguCtx3VOF6Fyk40ri9BiDE/GKUWgU6PAzkLnLI3JtGUT9CTi7pJwe8m4v1Wlu3VIt8V354QETYZWIkESQypuhEMkugw2412j6QijFSAGNYJqRFX9YRzUhU2KetuqOEUp1DTVgmNVQJirYUoGUAGhlqUqKBgi3PQ9V4fA0/3IRzcHDkOgMcLFSrUY+2EokWzUkrVsSONIjhFL1L4W3tI6YeMN543F9VfIza1GPX7Fev1QUxQpc90H9iFxaQ1DbCBlHjXnmHaPnEckBFhvCWdf4INd5B99cM2T//fHvPe7z7j6+CkpDHTbh4Qx80HreeWs46xxJFt7g2iN/X5M7IZIP8ZJNLry+nOfNd0zxszlIfLBZc/zT/ZcfnTF4eYZxjVcP33EzSEgkvFfOOP1sy9gc0LaNThHaM9JzTlPb0c+2o18fDswxFRAkccbBQVm9wwz7skIq3aDb891Hgv0oXTfRDtPpnEgmcwwKEAPWPbDwGGMXPcjl/uRIWW6xhGtp10H2j5gbUvbrDGhGE3FoKAMdCOa+i/YqVomlN4gkLFWx5LWyQfCOJDCQCMtjdGNoi30ckqJMRQdxXBAxh4JIyZlGutoJdJkg0+DmjbeXnE43GBiwDqLX21wmwtW6wtcrfAJCWlsaaAGQ2GXGlucFQs4uG9eOqPdRmOKDH3PcOhp0sDaOKJr8ZtznDU0Zxvc9hyz3pS0iq6F0a247gNXQ+afX/b8N9+75jsfX3MYI2ed40uvbPjyxYovncOr6w0+x7IBD0gYELMvFuXqkS5xQMY96eaSfNip6FcsNK2KQFt972Q8KSgHUGl7Z8CTtboLXd8lJ1JWP4Kn+5GrPrAv9ZyNNTzoPI83DRet5bx7gBtukP4W6W+xhxvYPWVrawfSFtsYVj6xtonWJLzJGJunfi6gIHC61+VTDZ30vKjtyxdW0m+/85t87Zt/jl/5sb/CV976oe9LaHjf8dIAg+MinPsPqbXS9WuAHPmN977N1/72n+OXf+Kv8ZW3fhhyxMgs65ifUyYnVcyaqnIdB6TfIbfXyNVz7M1z0s01uS/GQKCb9fqM3Kj46GqIfHQbeP+658Pbnue7kZQyrTc83rZsvcUZzaWvXIlickCMxTuHVVaZIcrkgNbHzKo7I+WEAZx16kEQQrFWRsGKiEZEUBBnaVhjbVHGOw4hcT1EPrg58P/53jXf+XhHfxhx3nKIhjdfH/lkN3C27mhcYuM6jb4Li2HW2mnRJEPaj2RpCOLox8TAQLID4jpsY4u+Q7siwmzAUnUfWpdcSqSEotTWPLQ3en9cDqTdc9LlxwzPPmT//CnDfqd6ju0FnD3GnL1KHjOMGRsiNgZsFvIQiCaSTSCbFhmyWtn6BsQyEmiJNDmy9vBg7YmxwxjDo23H+aqZulEWOx/1d8haFKeRjAJQJvOdjhHDPgQOCfYRBYpD5tArMGicZdPqxI/9TkV5cSRisbbDScSXfLyyTKKgYPeJUqjXz5U+3VxATqy6R1wbVbHfDCMgxJyKda1j052r2j6OpHZLbxqeHwbeeXbg6bMdt8+u6a8+VjHl+gzfOl45a3n9rOULZx1bpz72Q6hpBX2OvbO03vBw5Xm0cryyamicIBjWbWbM8MFZy0etWt/Gwy1juuLWep6et7zz7MDvebDmQdfQtWdah289qTvnNsLzw8gHNwcuDwHIeONxZVyY208w1x+Rby9BDObsAS6OnK9fKfNaBYregJOITYFwc0MmItaTXAvZMqbEfkxc9oEhZXos7ZB4EOGQoAkZYx2N6+Z1RWSxOMtsjlOqZXzJ1Z+vGh5tO1JUoPBg7Vl7aHLUcUegFfApFhv1QM5C7nvioSf2I7akIK1kkERmxGTh8PRjuP5IN8nhQLNas3rwCBN6LIl2/UBty6G0KNdnF6w+F+0gy9T0Zzk36yWq/iETh8jhMDAOA5mklT+uUyHtymM3W8x6S/Zd0S11ZNdxiIndmPhkN/DB7cg//eCW7354TRgjbec5jInWCg86x7YxuNrvIo0a2NTGYqFGcVpGCqgw2jpNaXQb2F6QVg+VrY26bmY0PVJ7OjRWMDmQk5obiTPErH4QKWV2Q+Sjm55+VIbmwdpzO0TeOGsBx4NmjR13SOhJu2tyv9dzaVqkXWO3Z8jwAIkPkHiGadda0mwKswR4uW83m7UEUjwWalO7J+9+m5/+5p/jV37sP+arb/0Q1Zb50457NNdHx0sDDOajLMKk+eqrSrxSK/XIhifvfZuvffMX+OUf/yt6U1ModNPxrZPyIOhv9U/7G22yM/SYwy1y/Zz89APi04+Il8+Jw0HFhr7BnD1Cug1ZrJaajYHnh5HLw8jTm4HdEPDWsO1URXzWau3teWNx4y320GtezTpMs8K1WwiqXh+yKtljzmQLq/VDsm0wfqN0WywpAimb1NLiY9EQ5MgPPDPVx/djYhwCYVCUvB8S14dx9v2OmWi1JKwagpCBJiNNgtiTsy/R9JzXzTmVch2DM3eHaW0sUl3LnKj5iiv/WlOZggiHG/L+inj1Mfv3f5fbj77HsL8huxZ38Sru9R6GQF5dwJDVXjklJBczkZgIVzfsLnuiZJxX/wGaNU17jk8DFz7zqLWMD1dsVx4Ry+OLNY+3HRedV7W2aIlXTgFJRXhXS97Eoja9niilAiFDyMI+wiEbDkkYSt8GtWYWHJm0v4b+hhxGoliSW9H6rSrGndKOK2uwwy1m95T4wTvEZx8ppXn+EG8MbXeuG7IoiOxHZT6uDoGz1rBuV+ThVs/V+KlnvPaMMPjVmi4/xrcbHr1+wQ++dcEf+oFH/EuvrHnYCS4cyDkzdA3rRjhvHWeNY0hKl7668Vx0lovG0eQBkUxwHQ86T2OttvcNCbGesb9VkZqdlfQpo34OtlUH0GbFvk9cHQJXfeDqMNJ6zck3TmgJ2NtPGL/7z0hXz1Qc+/Ax9nWwrmNl15MbZmvUyjfdPiOGfbG99qR2i2svpucREYYMJOGQDfuoz0830Yx1Fme9gqKMsntZK3FIOi6MuKnr4EXnebztCI/PWDeWnCMXneNRa7nwGZ8Gmgjx2UfEYUcaB8I40mchSIsVj40BiUVrFCJkhaeHq0u4+h7hg/cIlx8joWdcbWH3BdXBNI2CmdU54i19zIhRoBh0imgn05QJjqlh3H3zNKao1sohKiNCIiZTGkFpZQbNBlynwYdtdR4gU+XPfoy6rgyJMEbCEDFG6MdUbJzzUUZYCtCqaWAVRxulMzo7BwVQyhNXaijVbthXjUnW7pg1CFs7A/0NMuzVd0QMjWs5bzbEnDlrHVe9rtU3h8A4aEqntZq62TSWrbcYseSUSLfXpOun5HFAjMU2HebiAfnRDpGIkVgATjtZdwPan6Y2RjvdgxYsgZT0wU9/8xf4lR/7v/DVt/54Wddr/480icYRmUDq5zleQmDw+Y8n732Ln/7mv8Ov/Nhf4atv/bDmBVVfuxB05AWw0LJBABn2EEdk7NXBcHcFt9fk60vS7aXWaoto3j0XZGu95jXz3Ca00qzbzvHK2vPGWcvjtedh63CHZ7jDldKFZJ1gSd222mZbPM6r7kBzgoeQ6dyK1flm9ievtFM9subXJQUwvaLsMgglJ7wTto3lQef4woOOfoxc70astzzceDaNmQZppiza1iLJqsgKUdlzu8ZEh2DJYypGHXNf9taqLqGC2zoPakdEaxZq3CK6qR0gfQEFMhxg3JP6W+LlJwxPP2D3wQf0t3vEW5pxJHqPMS3ZduQkSNKF1Au0xsLuknC4gv6Gcdxjc9Yy0rMHxPacZv2Ijcl8Ye3w0vHKOmOc5Xy14vFZy0XnWDm1vnY5TtG95Fj0KZUE1AVs6tEelc4espCM1ZK4AtyqwEvGPfn2GeH6E9I4klwD64cYLN6qqlqV6oL0pb762UccPviEOIy0uz3SrLCbR6z8I1ZO+wykBGNQO2/VetWIS8sKu9U5D1aOLz9c8eEXz7WbYjhjtWn5V770gD/0pQt+3ysrtrcfkn/3HeLVc3Ce7pUv0py9znq7ZuMtY9Lo+Lw1bOIO8/QD0iffhTDSnj/gi698mbM3XuO8NfxXZy3/8EHH/rbHOsObjzd8+eGKBytHVzz3SWOJrIQYYUiJMZSKBmDlLCtnsIenhI/eZ/je+/TPrrGNpwPM9gGyfoRrNlgN4DV/P+5hf03cPVPVu/fI2SuIaWisV2dM0VLHiJZLDlnoQ2YVNcqOKautLtUrZY7ycgEIroyTlRMuOkc8a7E5cd4IKURWTnhl5diYSDPu2H/3PWx/Rbx+TjzsiSIkvyK0W+jOad2GGBWMq0ufkIfMePOM9PQDwsffZfjkE/IYCZtbvBXi+TlydqFVAb6haVa6cUVhNHMPhpSP283XTo1lCZmWxyhqDues+sEIomWovsNIA616TlQBpjJntri3lvUoJzaN4eHGszt0xDFytvZ84UFX2AItkZTi5pWN1QlsLNh2sow/os8nlk5dQvchcThEhpTJWQOPltngyvQ3mqYYbgrbK+qq2Q087B6WR5mn+XlzCJMTZMgqbh1Toim+GWTtD5IOBy3pHXuyEWg78moFbaP22STEhqn7p6afF709ltH/HVDw5/iVH/uPVFPw/8PjpQIGpwRMFrOoQFj4fObMk/d+S0HBj5/c1AkccMw0FJqVQlMRStfBmuMDrLXq6NV2YFScZNZbzPpc82q2KTYJik4frjzb1iECZ43j9a3mqi4awV5/gL19qvXcY4+xVn3WjSGNFud0weqDLkB9aV3cG2EfhJ1NeONw1mPd3DREygZLHLUvQtA8JUlrsCUcaF3Lw87ygw/XtM7w+nnH5X5ERHh0tuL1jWfr1ETHFnOHXJkasWSTwXW4BnyyhCTYPNKgbnZN61k3ZQG36tBRG5tAsUyoalxTa6Vn33NvBEsq+dUeklqp5uFAOhzUqWx30K52QZv2SBxJccDQYNCuja0z+DwihxvS9Sf0zz4k315pzb3zmLMHmAeP4eKabvuYC2fxK8OIwTnPZtWw6RxnXlg7aEjk/oDE2luiqJiM0+QtSvHFRFl8M32qfR/0osWonezKGzoL7J4Tnn1A/Pi7pBhhtcU4j3ilIFNRT6W0ACDDwLjbE/bKFvmHz3D9Dd3qVdbe0XlLH9PsJWGA2mL7cANmjzWG17avER9v6bzjoy+eI8DjTcN/99GaN7qEvPMP2P+T/ydXv/s9xl1Pe7Fm+wNv0Pzg/4DNq78Hv7pgTGrJ3fSXmI//OcPv/CNufvd9+ssdft1y/gNfYPMv/UH+tS/9AV5bv8rve7zmo9uBDDxeN/yeBx2vbRz25kNk97zodQykMJVuiQjeqcfF2ivrZm5uCNfP6J9dM1zvcKuGdigdJcWUqh6gaEkkDDDcEJ59BPsbjLXYEBDb0qwfq9eHtexSKv4G2rOjL5F03UxzodZzzhBUa5CzrkXYQI6JxnesHSQv5M7R5IYHTkv9PIm1S3Rxz/jRR4yXH5Kefk+jz1A6fG7OkYevIRj8pqF1rjCIauOe4oCJI2lQrUI4DNoVU4R0OJCHA4w9Jg2llbih8SusM9iiEaljsrYzTlbIeTb/Uhth3dljzNBox02bAiZFtNLZ403GNY2mDowKpJEp6aY+njmxdfD6xvPff+OM188cOWcuVp7Xtw1vnXc87KxWT4WDzivjVNTrtc9EcoWFqMt1zqUrpbKa4xjoozIPMWU1ODNa8tlYwaVByw/DjnR7pZVdMYJvcesdshl49ewx3rZsW8cHNwPXQyipKF3PjRSfiMJQmPU5jAMguqb4Rj1sbGnIVkox9VZEvTdlLGarrCSSJy3cvD9R0ge/oKDgzR+aU+RLEFHXg9NeKHd2zrvHSwUM7j1OQQHMoOAEaU0lRqdCxZynzmCptKvVmmVVjeM8drXGhYAXQ+q2xBi1L323hs0DUncOvoMx0xjhlXXDeecmxf3KG7be0Iw32GdP4eYZ6eophEHLU3yDjB4Ze0yzLqYhjUaVWVXD++JQqE2CpJQjyVzzOqlbBW8srdvgfYexu8kYSYY9NkbOmhWb83Medpbf+2A1lbt1jedi69mYiM8ByU4rO8p9yig4MBZ8J3SuQUIGF2lSBmNxvqVtnE7I4vtZa3WFuf5+/jxjxJQ2qlqWKWHQpixxLM8oYawr3Qk9joxbNVoeuj5TWtN4ctL0hbWWxhlsGDBpIPU74u0lw9OnxP2AWMFdPsfd3mDGEYaRbvsK1rbaUdAlVjaydZmtAz/uoR8hlU6UOU4mR2KzpmqMnyZwQkVoKRbRYjEo0jr6rOVzeSTdPiV8+B3C04/IOWMfJOT8IWwg5Nm5zUjCt1v8+gyz2mCcA0pOumwErRXWjWHlLSElOmeLiFMw4w7ZXxGffqBpq8MtTYx86eKLnLcb9mGNFbhoDd3194j/5L/hk7//X/PRP3yf6+/eICJsv7jhcYg83F7gNg/wrsX5DhkPmP1zwofvcflP3+Wj//Y73Hz3lpwzZ1/8Do//+Xd55Q9/wBs/+K/x6I0vcNknYi5RtRfM5XfJz9RBL4vBPgKzuqAxGzpn2TRqjLXylnVTKjIKUMxBRYDGOb0v6zPGdss+Jg5BfUCCK1FwvyfdXhGff4KI4FLCrbb41QO86HOpvgExxqPnl7TPKbXr6uxrUPoDVLrb6WbgjWfrPNllxEasS4SccCR87ImXnxCvPiR9+B7h2ceE3YEcM3bV0JBx6y12MyAkGueUvo+FoTIaIcv6DOk2NGc7wrK9dzFG0yZbIxI9SI91DcZaXBaiyaUhkyGZPDGDainN1OaCnAkINA6z6lg5gaSi2HWjoljv1OpZW0ibKQ0gpSrM58DGRL609Zx/4YzDo06rj6wKareNwRyukP0iNeq6SbM1Zk2FjGOcmkgVzDL5FIyptHQv82/ldd10RdRsQlCNzait5s3YwzgQDzvy/hYZe1w48GD9iPVmy1lj2Y9pqtyoLeuVgupI3TnmYcY2LeawQ8KozdfWa9z2DLtaqz9MphhjZbLM7ZwFyBZK3Qh5kQJ/+53fXGgK/viRbu7e/e9f4HhJgYHcAQRZtHnMk/e+zU//nQIKvnRMvxx1qJuYAi0JySloQ6DxAEDs+2JPZcFaxDY4v8FtXyEPoyJTDKnQWPgVQyEvOm/oPBgpdcYkpL/GXN0g+0vS7RXsrlRJDLimwXUdpuk09ymQc5xq8J0VXNTNNaQ6oTNDKQecqytmYODKxFt7o8IzMcghwrhH9le6KfuGB67jolmRWvU2sN7RNJkVo6q9+0yMRt3JpptnESc0xiIeGgzNGDXlIUbZD+uwpcMhFE1BjWBFS6BMNV+pYC1HJJbFLKUiolQwYpzHdmvs2UNWrwXsMEK3wTx6HTl7lby6YMhqBWyNo/GW1lucCeAbgikisRhVQFVo0+SvsM0nWOtJMWG6LV3TYcXjx4DZHUijJVsVHUoOkz1uBu2L4GNhbDzZBVK2SFLTpVorrQZFESeZzhgaAfY3xOcfE66ecnh+DcbQrbdYMWTnOQS4GiKHkBiTxXaWs+1j7KtfZH17DTB1JtQNYKCxhm1jyVkrOraNo3UGubklXT8jPvtQmyrdXOGGAy72PFw/5IHzEAbMxx8x/s4/5pP/+h/y3X/wDt9974bnY2RjDbY1xH1pgmMc2TVarugajfBSIu579k/3fPD8wG1MPLgeGHcDOSZe6XtWP/gv024fq/1wHDHXz0gff4f4yQek/Y32HvANsnlEuz1j2zg2TcAZTX81VnTziCNiDHbVIc6xfvwA++oXidvH3CbLZR+56uNkjrVxXpmEoae/3kEBTvL8Y9i8RiNbOiN4ScQcp2eWkqrWJZnyeURKt8A07GE8kGNUYO4cBE8W7ZKaIpgx4sdICgMujqThQDzUcrtPiDdXyv7sep0fjUITYwzON9A0GN8STWQYIykFhhxpVhdIGHExYJzHH25pvMeePcR2a0y5XiGXPLdRxtB6xKj4GTEKBig9JrIsNl5tAQ2la6F4Oiuk2Gj5rGia1JLwgrbPrhFsSuQ0EFNPDgkzHtgwYtrMA3HEMSqjEfbI7R4uDyXyBpoO/Ircar+J2zGxG2MRE+bp/JZr3vL7wOzBUETD2mY9qkbIeXLTkVIgpAjDQBz2pGcfQL/HHm7oVhc07Za83jJgGdJs0ihGdULt+oE2m1tdYOOIJak+qmlwTYM0HpzqunLS6rFpt8qx9B2Tkl4o/IqIliSW6oOvvvVDaDniXBV2315WTozPqy+AlxAYHKUTRCtzRZTufvLeb/LT3/x3T5gCzeFMN3LKB5c0AhQlKEgKjL0Cg3EcEa/NbZL1BO+JnVXtR1QhzhgiMSZCzuRYxGhorbmXhPQ7BQCDahTS7fXk9GVEcE1L03U0my1utcV0a2g6BvEkI6Qc1XLTCskK0tjSYW2+H5PzWyq912NRFhdzo0q9bdstEg6Y/oZ4ozoJKvXqnLaM3ZxhVlvy5pxxtaE/e4DZnsNqg28aVTiLKcyE1Q5z6OBuWnXMqw4MRkrP+oVXgSngwgp6wkUYWTud1fxaTe3kUgqYjYVujTl/RDNGmm6rG+/qjLjSJk77ZIlisb6haRo672m80HaemA6Y/iFpd6PtX50hxYi0Hdm3GguGQB72+p7DrdqoOkNsW3LnwWszpjQO5DAq7VsMbkwXi9DJQ+zUa6KkqUwRgdpismMFOgfeZvLulry7YbjZMe57XFesiJuW6Dpu94HvXvfcDJG1N6SLFe3ZQ7rXfgA/HNhYQ05ZG/hYj6SEM5a11/yu+mWIGrWMpcHR7S3h9oDdqY2zSxlzsSf7Boae8PH77L/7Pjffe87hec8+ahOvrRM2r65Zv/4K7vW3iGePuRphHyIrZ7g4e4x7/S3Wr7/D5tVP2H6yZ5/QyP15z833nrP+7vvIaoN7dYSmRcaBdPkJ4en3GJ4+I/YDbtNhdte4cU9rlAbeNg5nEmuv5a+SdIMz3Ybu4UFFrK+9hXntBzh0D/n4euR3L/fsxsS2UdbkQddhG72/OSbCocfc7LC7G6S/xm82+lyKwNSW52bKOqHP02gTsNCT+gO5vyUd9pqmyVlTQK70x0iZPAbiYST0PSkkUlIzHRl77dgIZN9iN2uMU0DtN2e4swc05w9x23Ps2QN6cSSXwY1aEjwORLNi9fANaDr82UPM/lorUM7OMeePoFvrvNGdScFBtipErnltMVgxWKP0P9YSs9rI106VKYETBdzJCYLXtKopYD8FqsNoXZ9zTsQQGIeBfn9Lf3PFeP2cvL9VJ8H9Den2mni41XQMKADaXGAfeJJrSO2WmyFxMyZuCjiubaJr/4baebX2iKjrYVN6q3RW03Y2a+8Y17Q03kDbkA5rwuqG4faG4XBgHAbS1VPi/hbcx5jSEMs0a7r2nNyuGbP6FWhaSRC3wfmtindLi3WsIRoIRXiY4oikA3kcCaWDraSg7NKCCcgiPHnvt7R67sf/Cl/50o+UdbAI7ScgMD873deONQqfJ40ALyEwmI/jXMuTd56cIK36M5lzOEcoy6is+OROHnYqPjz0B4wYsjMkt6E3DTdFrX8YMyEKKVty0lyaouhMI5nGJNg9x9w+JV1+zHhzCQfddMRYTUustzRnF6wuHrI6O8c0rQrXsi5cKWZiijSiLnRGMj5oj4Bl2/qiTStVBsKOyFBalIZiQwtKfmz8GrFXmjK5uWR4+pywH8gp4boGf7bGXTzCP3yMefiYvpRGamtfjTCsK+yBaH1zHZjWOGpv+Vyej6YKypPKqq7W+tyogCDp5i+pNKPNtda3tMYV1C8AT3YrZGswNNj1KwQs2a05JOEQhGgs3nb4tsN1Ld4Xq1jJOCLEiM+QfEve3egi4zyy2iDtimRbjXpjVMUykWyFHBtSaslto6ZIKZJDIIVRIytXF0oHoUPCiDSO2uzIFkOrzllGZ/Epq8e6MUhQA6Ockt7PVgFabjbsxsxHu4Hv3fR8dNWr5WpWRf4bD97ApUHZrH6PdFstETMGk4TOWULKrL3aSKu+JEJpHxxDRGyEMJBDTxo1Ws3FFMw0nu7hhrM3D3y5tWCEsy9uefyv/gDbP/AHSV/8/XzYW75zfeBmiGwby5tnLa9/8fez/QO3vD6MGG84/+4NpMzq1TXdww2mKe27a9Q99uTQQxim8zJBz1PHSMAX1gvU28JIEU34FrN9gHcO2hX29S8THrzBR7eBf/Z8z+8+27PrA4/PNWf8sLP4ZqMAuHVIr/c9lai7MYbWWVYuMxpb0jBm8rMXMiKZPGoKIY0H4mFP6veqQRJU8BvLdSGkfiAfevIwaOBQExXGaY764WuaBtvfQhjVQny9xT98jHvwOu3FY/15FqLLBKdVD2My9OlAHzLd6hXWm0dI2GGJmFWHbLdkV5XwRcEeA9pIvqj/q4jQGAUKRj0arFiyKfdZlClL2RTaW19L05ZS9CB2MpHLWV1EY0yEEBj6A/3NFf3VU2XGnn3E+OwjwuVTxusd4TAgxuBWDc2jB5huQ7aO5NfsgoKCq0NkF8LEEjTWTACxWkkvV/Xatr0tc64RbYrlnMMXkakVDTzS0LO/voLLZ+TrS8bdDXF/S05RK126FXZ7gbl4lZQe0awfEjEMWQhZyEmjdMkGEwUXMp0XdbXsVrRpwMQrJERSf2Ac+hdsZQUU/K0/WxwNf6SkbauDqzn63dMgt3xx/2u/4HgpgcHEGpSyxd9459t8vThCfeWtH9Kf5xNRx/JzADQvKFKUcGU7G8rD60OEUe1TxyA873ue9yOXh8B+1LI8a8yMTk3GScKaiIx75HBDfPoB6dlHpMOt0njNCnP2QMuqzl/BXryCO3+AWW00Gs8ZiYHU98S+J/Q9MfQ0UeuoV6WfQs7HaCZlGIvnf2MNu1FtcUPO7ELAjRqlt6s1pt3qZmg9aYyMuz3pMDJ6SxwCHSBN6RjX90h/QIZBKyacAqliMK2bSK3byoHZoqMyMUnroioISFE3hRSUfi3fS1XzsVigNGdfxI7WkltHtiswa1gl9rcHrvrAbszsE9jGsm49xjTFgtWSrZAk47YPcBkGLOI3mJJKwDhNBZmiNM8J4oANvdZQZ228Y6PBxiKejFE3gJy0ZHFUgKH5XE0X1L4MPsG69WzHhnHT4XLEpYHWMTeacl6ZAqA522DPHkB7xj5kLvvI9y4PfHDZ05amXmedY+1XvLJ9TbtF7q4xxasf65AokxW2N7qxCSroM91K38MXrcbZQ8z5I+TsIdF1+mzbDV3T8fj8jO0bHzFc32Lbhs0br9L8d/5V0pu/n+/sLf/4oxt+5+Mdl/uRi5Xnk1fX/MuPt7z53/sjPFxt2Lzxj7h9/2NiP9CcbVh98TH28VvYV75IbjZkAdsedKyMA22MuKHBdi2mWxUaPGHF4Y2hc6WmXkSrFvwK9+BV0niGrM9I29d4PsD7NwPvPt/z7ic7+qAVQo+3HfuQuWjPsGcPaM42ANrVz/mpQqZ1hgfrhmAatpuObdewbn3pMqj3kqxmSfq8g/qc5IQY0XEBSLG+tWnEhEFFaFnPW0V0TaHdFWiaOKrYUgRpGtie4x6+ht0+IIhXhT2ZnAyjyexNZBdH4hBYGWHvDeftQ9pNB96Qu4bsyqaPbtikuXQ5F0C+nHPZaOOjbFQrMM9DOzmeQoX7KHDTFy+6Le1cqhVZ2g69Hwb6vmfse8JhR7i9JFw+5fDJFcP1LXmMmM5rS3CrID23W4Jfc7sPuo4VUOBENQ1rb2mdljP7RRq1HiKlNTsZFw8YRMtMmw7Xtri2xVqn5xwCbnOL2zwnPP+EdPUJ+dlHxMunpOEWDrca0MWItQ25WWP9BpKWpR+S0JdqsZgihszKBy46x4Mx8aA1+OSQMUOIDIfD8S5W9p6lz85Xv1wbIhXRt+g6Oluuc5ctmFjwz3+8lMBgPjQn8/W/+bNTQwmgDNb6K3rjTikXsopkEEuWYp8MhBJi98MALpOz5fntgU/2ke/djjw9BPYh45xnu2o4sx5rDeLAm4TLA2bMSOyJ/Y407MkhYLoO8+AV7Gtvkc9fZzx7zOg7gl/T2pbWuWK+kTDmBhsTpj+oknwcMCmTxdIsL3/hvtbZhm27ondCa7VU8HoIDMVgxBnD9RB5sH6Eu+ix+1u6QdmR0e0n5G66ThuFtCuCWHK2EEECiAOxKuqpQsJqoqRH6VWfUkkD5LIYFfV2GBUMxIEUgzIXQZW/YrX5kNKxLUijVjHWkaWUKXlhjDuu97dcjvDhLnA9ZJJxdEZbV7ssHGLW5jTWYMjEbBndirB+hUHWHIaBMcTZHyJRKH5D61fk4QYJKkZru4a2bScKW4kmjbYiVVSpaRIrajurJXKGJgmdh4eblTZg8gbGAZ8GnE3asne9xV08xK72mO0D7PkjQrOmv03sQ2Q3aIOnEDOX+5Hn+8DtNnFxdoZPI8a1+jCaDoxDJBY/iFomKuqv4FfI2UNszricMKst9uFrpO0j4uoRhyzEmLEb8A/fwr35+7j4/Vea+3WetH7IfvWI71wH/vFHN/y337nkg6cH4hh531ue7kb6CP3jDW9+6X9I9+oP8Gj3TFNnviF156TVBXtUWW6t0EnGdlv8aovZnJP2N1ppdPZQ+3kYP01ht/D9xzi93nyuLbzbDaE74/Za78/lXluPjynp/QuRPmXyao07f0R68CqtcwrU11vENTirwNKvtPRus15ztuqmDqra+Eb5MJE8ldaIEUzWczNG02ZAafvdEhIMYhkjZLcmN1v6bDgEFWCKgPWlzbGzdE2DXa0Y3Ernnmjf0j4lDjHTZ2GfDVfBcOjBpMBZoyyEDUKzXsNqDUbTHzkHNSfLSZmw0GsaLGoFhWbD1LHTWKegxVjE+akyQNlCKSDiRAFf2IcqXoxZGCP0QY29+myIYsntCpoO03W41UHnfZvw6xXd44fYh4+Ri9cI60dcD5F9yOxGXYs7ZzhrHGsvbL2ltVnF1MMwCcmrR8C0M+SoYM03GLfCOl/aY28ZxTDETJ8DvfccugZeOSc3W2zTkUVIzz4kHQ6kYY/pd5jYazmqdXjX0CdDCLCLietx4GYfCGFk5YRHnWPcBFKwPPAWiRmGgbBob1+D1ifv/TZf++af4xt/+hf5yg98Zd7c81wyPgGEOhnKmv/9ehcsj5cWGGTg7Xfe5uu/+jN84ye/wVe+9CPFcCYd3af8ohsoix73Wc0zsnFTMxGNzIVxjNwcIh/eDLx/NfDx7UjA0bZCMp6mNZzZBtMI3iZczJhhNhMSY5DVBnP+APval4gP3uKKlqvnPQeJrNaZ83Xm4aZj4w2dzTjTkEqemgoMYpiZgqybrRQvdXJCStRounPa1QXORETUsGOIias+IDgMhvPzL+DE4NsV5uxD8kFNnaTbYM4eYh69TlxdQHfBaDr6Q2BgzybBFsF0LWZ5LuVz9YxKU14zp1AqC4otcxjIcVBXt7EnDj0pqhWwOIdrdVEz1mswYtStEduQsAz9wIClx/JJn/nkkLkeItkIW5twY8b7TOMyPmijnWxBEvTJcBuFyz5xs4/c9CP7IRBKG+q1t5y36u2wXZ3ho2Pd6vcbJ5ioC02Omk+VKriyDvHaahZTG32n4kGgznIUBmHtHePYw6h+87I6Qx68So6JPOy07LVZE0VLH60YVo1l03rNkVqLds8TxiQ07YaaXs1+RRSDNbpxWZPm5mAYTLvBXryqSnYRZHVG2jziYNc8OwQFHMNITpm1t2yaB7TbR/hiQHQImQ8+PvDO9cA/+d4tn1wGYg8xCiT45DLw/7K3JBGu+obXNw/ozh9ipLBZIXF7mdiNe8QIm8bzYOV4uHmdVXeGW52R99eajvItsd0Q0TmgjbY0uq3Rq2lWukFnyM2GMel9EdH7tGodTcqsGlv6Y4hqUJq13gffIM0a++BVfQ6+YbNqwK/wvqVtG5rSGa92/jR5ADLZaFvv6B05tUgMU2WNGKORtxikUd+P2EbGPjPaFftgeH4IXPWR3ahj3xnDqnFsW9jmRGxAktGpJGqu1IdUulpmbsfM1ZC4OSjrNiSL9ZnN1qpYLgvetRgiRK2koICDFEbifkfod1NFh3VO9Re+RdoWsSow1K5LChrEuHK/I0cblSYMi5230I8DN7sDt/s9h0Mgmw7pLpSNes0UMHxGu1hz7MPXkEdvEM5e42oUrntdr0JhQNVMy3DRWsz+EnN9pf4fY6/Mkiusn5l9DkSkdGvM5NySxBBNQz8qwLodE89uD1ztdux3O7ocOPdnnD9osNQ0yfMjgbsKQj3etphoIGf6PHDVZ57eBvp+wBE4bDwmN7TS0GLxWZDEtLeo4NDw5P3f5qe/+e9q6+Tf82/c3Z+yzABhufedMAV1P/x+jpcWGCgo+PrU+4CyINdaUWACAzXvPW3WKCUmpTEHSRvfyBCm30lZ63tDDAwhsetHbg4jV/uAPi/PIWheP4ouBs6DkYhxXn38i1UyzQr3+A3C+Rf5JDj+2eWO964Gnh8S2+2GN1654K2HW75w1vKwNXQStUQxaj6bOECcTYyk2IKmw4502JNLgyTTdJjzh8j2VS7OX0cnrOPZfizinZGMJ2bDxcWbuHaLe/gF9WwA3YDbLbHdMkjDECHsRoyNrJNqZ401OGcx3mKzSg2nOzyJB1NpvVuoyxwhDkgaiYOi8LjfE/oDYRw1JdF2iHHYrit0rNFFyTWIbbRsDEsyngHLiOWQ4GZIZImIC6yGSNckuqhpFV8qOXLKDClziHA5JD6+HXi267k6jIxRAcRZ5xhTi7eW7WpNt1mzbjOtF1wYJtGoGEdCMEVomstGYEydrMqYWK9RcZPnhcoaIXhD7A0MgmweEMYDNqMlh74luVapUwPnnefNRxvWBRi8ctbycN3SNU7FTlkXIEDHm8wCLFmM4zGBazckUSEWTQerc25GeLqPvHs18P7lno+vDwwh0lrD+dpx1njW3mCMYUiZqyHzwXVkxNN1gjOaUxYjuMYw4vjedSQTeNZrSVtKylhdDyNXu0AfE42zvHrW8cbFipAtj1Zbzl/Zwv5Ku4eKJTcrxsRUflb1KvVeinXTteM8Xixdk3m4bnnzldLvIGUebhrOO48zRaTrWkxR7kvTwfohdvOA3Px/ufvzWMu2/L4P+6xxD2e4U1W99/q9brKFKLLiJHASRxIpdVOxQFiObMn/BKBFNqGYlmCEifKHozgykQSxgkAWYEWB4QGmZItoSqERINAQ02KcCOzXoi2KkhAYkSCClMTufnNV3ekMe1hT/vitfe6t16+bbEZBhLeBQlXduvfUOXuv9Vu/4Ts0kjQ0DdY6rBEGhDcaYyQx0SGL+vWCczAy5hJhr1I7RyJcI5Wqg96TQmGaFMcZDsPAyyHy0WFiN0YRiDKabeu46It4NCRFkws6S5I31fU8V7vrYU4cpsh+qk6TVvbDXPdHwogQkfF1SirgQxXkzynM4rI5jdLVcw5iQHXChNKNkuaf8tU7piYEy8Grqhx43fkUGa2GGDmOE/thZHccOY4TOWWssvj2Ct9sUN05dtpLTAOwQv0L7Rl3U2I3Z26rZ4XXio2vSYFXmLv3UfsX5Psb8iwxS1mPbjtU20txpBclQEkqixagcSoQ58hYCjdT5oPdxDs3e957ecd+f+C81by19Xz+rOFq+wY2J6IyMA+opjt1M4t1WOtRQFKJmBVjLIwhc5xkTNoaODaKuTPEJM9RPx4FoPjZD36B3/9X/lWR6f/u3/k4itbvUCglSp8nIa3H16NC95OSgre//vYnfPXh+lQmBo+Tgi9+7ov1qwKWeXw9Tghy/cNJ3qgCw3SV+iWn6mBY5zWlVHW7Uo2OOLWKF+lQpaogSM3eVK0ktJcAr7uVZNttT15dctAt79yO/J0Pdvzdd++ZxkTbd7z5bGT8XIC8wawdWNH7jzGSs7h9yf8hj7MoI3BhpaUSGPaUaSRrjR4P2Cg0yLOzN4i5MCXDPEb2QVTBxmCZUqG3a7rt9mTqEVINQiEzJnHOU0qqpVxUbY86cpchK2mrL0Gj1DZrjlUqccEWxBPgMKdYD9ggic08kua5qptVVTAt80ZpY0oVkBawj9ZoY6X16z3KTBQiMRfmmBlDYk7yq0uKYjQ5IyCznIkpnz7jfk7cHANjEAnUDKy9IyqNdp5u2+FtwhYRtyolAZ4S6t8rkqIYKwj5JTGo/2LqrDNqMEXXJEqjsJSUmGZNKhrTX4qIkTlKFaYtRhUao3hj43HW8MZZj9FCQ3x93XDWGlZeY6YRVc15qAeWWSigeungiB5C0g7dOYpWFOMZsuI+ZN49zHzjbuIffHjgxf3IHALWaFaN4XLdcL6SObs2lqwcq5VDNYpxCzE9SgyMUHR7W4gKbudIToHjFLg9zFzvJw5TIqaMd46bY5JOhjFY26ONpuvOwc+QhSoXK8pW1c+jeZX2uuA0isroRqSotbY03vHapidl0eF/0hkaI6Oeoi3JdSjbiXJnf8FQNAZNY614lZjFIlwLIFCrE75AL7PhqnRabHzAFiBfV86BkerYugbvPV2j0XcDUU0cQ+ZujFwfZkLKtM6idfWWSIWYssiL5yyAx6qnsKztMSTmKFLDtiZJ1ns5JI2laE0sClsU+tE+WqiKADlG0iyqqLpI1406UsDW9Zwjrxw7JQqrQ1tYBLuUke/IVYBtDhzGif0wc5giIclY0RlFawyNu6BpL1+JOUPMHI+R+zFxX8ef3uiKKVCcNUaSgtsPiNcfkPd3kLPY2/dbiYNGHE0fxsUSj8QALaJiIk0z91HxwT7wy893/J1vXPPuh7eMx4GmNfw33tziFLSXLZvVJSZMlPEowHDboH0jiaAWYSKUMMSWhDznLFbwlX2kSkYVJfTmR4nBVz74BX7/z/wof/73/gRf/Pw/I2qbn3A+LQn+kiR80vWtkoIf/As/yOf43Cf+DHwKE4PlQ//kv/jn+MLnvviJN+aTkoFFLatQk2EgL5veNnUunvgb939PXiML19akic46LhvLvk2ErBiTEnmDVyplSRgKSmbkvkX1G7T1lZO7YT9lnh9mvv7iwO3NkekY8bsZBVz0hied4YnrCEUAWaWOD7KWOXuxXjZ5EQyDcl4OqRQkMRhHyFkEepxHu5a1P2dKcJwTc4LjgjuIleNeg2BB6IZjyBxjZK6azs4qNt5hrT5ppVPKiaKkKq1LnWiGIlC0UA6XaqNULIIqRYBQKZFiFOEfJapiytj6S3QjqBay+aSjXildztE2Das+cJwLU0ySsKUoyVTSkKVTIGBsoRcaMisnAkCdM3ReXlufDgGNsw7XNPi2wyKHlEJGNyWXOmMtD4FxqdSXxKAUWUs5oZXBKEg1CU01kQxJzFrmKWJyoW23svFTEExSnNj4Bmct204LAMzoqtkuojBquK8KjIGEBpXQOtcxAqKtr05viZhBWxGgidUcaB8yh5C5PQb2Y2SaIzll+X6ryaVU1UHLum9puw5lpCKdq0lNLqXqZgi10CA6FMMwsD+OqDmSF3W6mMkpM5XIfozcHgNPNoI+b50WQRosRWViSifq9jLWlnFCQZ+SThGZVhRMtFyttrQ+s+kappRJSZ6DV5lWZ1ScZB3ZVmR0mxWHWEg54nVCNwVbqEJb0m1afhdDmxpI6ps6ASEXTZQlg1HiLSIJgsVqh7ceN2acHcW5s/5SKDpvTmty5TSaXJkPuuJ0anJb13dKQhGUUYdh1be0TYNzDm0WvxRRNSzaoK2XQsLMpz1WlAAFSQkdIzYlOWwXzNCyj8XqlcW/uBQj/64qVRwQ0rIsNDmIE1NKHOfIbg6EGku8VfTW0rqKF6GqS6bMfo4cgyTv1oiYUO+qN8HxBnW4lqTg/oY8z2jvpSO7PkOdXVGatXg0LM8qzqgSpVJPkRRmApZpVtzuB957ccd7H95y/eEd8xxoesvXG8Ob24bXV55Vs8F0B7Q21QuiRdmmAiyXcanEf0PBGzhrNa1xXLaGy0Zk1E2aRIxrUfQEfuBnflSA8p//XeInkZEYV8+vTC061SclCd981j2+vvqoaP6j/8kf/Zbn6KcqMXicFDx0Cl4dE8CrCcFyw1NNDJZLKVWNcBQYSQ7e/vpX+SO/+CfqiyRUHCnDHdvVE3JnUKqh8ZF9EsvW1iuhJpZETIqUFDFnCcpLBut1ddxriKNUt7nqkueSyWRiElCezRFVIgZFno4C0EFRbEN2PcX3TLFaQfstvj1D2w7rPBFgfycdhGkg727Q7ZqmWbNymtg5YSkUqUj2pTDHIt7rSu7Z4k0eFr6wfshUl6GM0UqAPbVhQUknkMxj0ShKPjXHilq05QV0k1nolYUodaCgfp04ExbXVI97U5O6+tNKxJB6b9n0bfVOh/ujtCUNWZKAAEFn6TSohM4JmwKdjiJTuxLlxM5qdm0EBStn2Xae1lu8tQ+Om48SyuVAOFXoWlflw3pI1JafaGQkjDJV6pkTm2SOifvjyN1+YB5nPInkFb3r5P6lGTUd8C7RdSuR4LYWZx3eKDH5Gu/I85ESBnLKgBFxGuXQaIwSGddC9Z9YAgyir5brHlkkb1un2bQGhSfnzKoxPNk0PNu0XHSebd+wXvWsVj3Ot2QjM9JUhbaWAxQyOkXCPHI4aPaN4r7RnLeWTWt5sZOugdaadWtoXaW8VnGa5b2put5sTarKI/0LDeQk6pPECUVCZ1EI1CrRNGsuVyuxFo6hVsYDaThSwii4nSppe4yFwxyZKfg0kI3HOIe10sI1etE0UPWQXDxWanJgNGRdLYGFsrcA9aTFLvcXJT4P3lpaL+tsCAlvNRTYNJaLznO1ciK/rSM2TeRZ7MBTMcRQyEE6boYsduTWs+09F6ueTd/Se1tphnXNFumEqdqRK64RuqXzZOORvmISb5FcsHV/Up8BSj3s4aXjlTOLjK9ShVLthFVG1nzF2SzT75hEsTXmAjPc64i3wuZa4k5MhTnnU5LeWs1551g5RaMierwXVcxpgJIFyL0+wz75DGX7jLS6ZFaeWCRVaaxCzUd0OFIqwFlNR4xrUaVInM2JmAIZEbAqy+i4SkYL3dOjfBaPBuPqe82kRS+mJLzOrLzC9Abbe9am8HpnuWoNWwsc7kTmOScp6oD/8+/5j/hCTQrEC6ecCteH80nWe0ZArcs6eszC+HiS8PbX3+aHvqmT/snXpyYxeDw++EL90N+UDNQvLh2CXMopIXho1Tx8o1ZCZytF8V++83P88E//CH/iv/W/5Q/97X9NvmMeJTtOgYv1E5qNZ91YbqbMIWuKKTiV5DDShaBAk7ClSNtf15Y/QAo0VnHROt687MS4qA00jeVzVx1vbh3PesvKgB735GkvOudKi42v7bkbI8e5MIRcs2/Dxfp1GtdiXUN68S7lsJcVk6JIf0471t2lJCSdwxtBRD++b6IeJknBYv6klCClWyuVTGM0rTM0umCLhBRdP9vjbgFL4Px4Lqt1VVirYLQCkzJEowUbYRuCbnC2wdgWbENRRkYBdcMsYiads5y1klwZVUQjIAQJYnEmjwGFBFNnCioO6HmiyRlnHF3v6K3isjPsZ7Eqbp1h03pWXvjrJQaKShXZXTtBqlaDi4e0rpWhsacWZqmVEznLhESralADKWUOw8TL3Z7re3FTXDuNVhrnDQ6FjgGdAzocsfGI7zqatsVmK6qQcSRPg3CukwgrZePIGDDplBgsSZSplbzVSKWtpQp1ClZOcdFo0saz9ooYk9g7N5bLznHeWjpv6bxj1bd0q46m7U8jN/EUkH0kHaQMKRImzcFnDk1mWBuG2XN70Z787sdYsNawaRxnXrNyi95EwQq4RkbZ8qqY2plYEgNyEh2JMJFTAFWwZhA9/DRBOOCMIcbINI7MwyDYhZzJ2pFNQyiaISQOIbMPATUnojZ472mcpVS9e7NQ4vICsn2g6ipjZckr9bDPgZNXQKnMgJQoJdBacei76j1eF8aQsFokgVfesvGGhgk97FFTdS20HTEpVFbkURzVWl1oOotzjk3nuVh3nLWeztlXlFDlsBPcg7KNVPQ2EHRDsA3Rt1KcyCAbk8V8LCNjw1evpTu6jMQAHva/QezRG10kThjpzA1BjLZS3UexIFkywjSRbaRo66HZWhmZrZwoXerhTtwIk9jSq26DWq0xT94kb19jas65GRPHGJhjoXOa3ivOfI8vmZIljjIptPWs7IpnveXNreP6qqPkxDQ5Nr3E5YvW0ViJ18uzRBtQglkJJRHSTA5AjDQqoV3hXGtW2nHRaLYe+jLD/QvUfBQ2lhEzKIAv/IbfRcAQY6n35gFLw3J3VTnhWUx+KK6WBGGJh8v11e8gKYBPUWLwGFPw8fbJJ40MEtItWG76UiEt3wOCGUha8dWvv80f+ukv8eXf9+d57W4r/6gNjEfKcQf6FjPtWfUXrDZPWXvLMWkOCQIJk6GkQgqKpDJZ5RosldCFYkFPe87aC946E0/3J+uG4xzpveW1dcvnr3qetJo+3KOPNzDspX1oe4r1HELiZki8ey/e9EbDWet4a9vytD9jc+EwxpBvnkMYpUpIQTj5cWTtG7SSbFwkRJd7V07VuyQG5QR+651l3RjWTrPxmk5nbBwxCRGiWRYr8EngmEKdc6tCLomsDVOGYyoMRRNsKwh80xJ1i9YNznaSGChzqiJhmZ+LV1HBnDJqr6GzmmGEMM/YEvG5UKaCdUoqjeOtWDeniHYNTbvBdWecb9Yco9i+KqUEGe4NOo6SVKqEppollap7YSpgtYJXldZ15qhrtSXdAvGWSFhliKqglLSCD8PA7jBwGGdyShhE2TKVgkOCrI6jyAWHAzZ32NCIqlqMxHkiHA/EKKjt7FpyY2qFWFDVOEZUKWt7vwSYZgF5WkfT9GitKVmh156LRjNHj9dCdV05Ta8jerjFHCfMpOnylnXzlJW1uFUnKO8iIyjFIjubCYcjx/0NZv8cd7hnkzLJNnzm4ozjWcMhZKaEgMusoXWW3ip6W3BkynQkxwClYKynMZZZejDSSq0z97K0recJHUZJeIYDrl9hfSPz8phI80QcBlSMaOMoTg6gVGSeP4TEfkroWHCHgW0/cL5qUMqglcZWbIF0xqRr8EDdM6AzlDpnp4DS9aAV3YBcZhKZXAw6yWFXOktnSmUkIAY90x59d4cad6QwSbLZrin9Oba7oISCz4oO8SZw3tG1Ldu+ZdM2rFtHY4VWueAwkHdLLKCVAdsSbGTUDUfdEk0Ar0klkbOCVFAZtDY14dMIA+FBx+C016U9UgcWoEvBpIiNI53ORK8pWaOUo7FiRR9zTe6Vwlt9EinSj0Czj6XcXRyrpkhAaYNebcC16Iun5O0b7HTP833gnfuRuzGQMpy1lje3LVYpnPWoCXQc0XnGkOn7xJN2xT/xpKdRhc9etBznyMpbXl83vHXWcuYterwRAzcSCsEu5Fzlsoso3Zoc6Uk4Dytj6U1mYzLsnqOPN6TDvYiXOQ9uI51QICjLFDMhyzpczimJc4hl3TJKUDLF0XU8uCQIuj5braRo/tIndNK/3fWpSQw+ngmd5jG/SlLw+MYv1fHysxH4+W+8zR/+K1/i3//nvsx/743fwS/f/V15Xdeh9IF8vKcMR9Lz9zCbc+yTW862r9P3F7QRpgJzyegcEXmwfJo1KgolRlSa0GR8yXxmfcXKdbx11pJywRnFxhvOPPj9e6jjbbV1nmUhbRwowxQLL44z792PvH87ElLmYuVFyOiyR2161ts3MMZRhr2wAoyj5AjhSOsVrmmqq5p0TpYxWVrAXo8WqEbsajunWTmDS0fsBMZ0aA9KN9I2hVeDhnzhdHhmpchFEzMcp8h+DBxCIaiGaA3FtuBaou3I0aBCJoeMtY94+FQZZb14LSB0S+3prWJlCjuVGAgQM45EC7C/p+w/Ir58n3z3UsRoqp68PX+C2VyxbbfgZXZuTMaGo8hLGwhGeqSLkZWqXQOlzQlXoBZhmOWTF+SzV0aGNmIApEshhUgME/M8C1aDcjKtWSoBVRIlzOh5QBuNLhHCKC54KRKnmRiFTZGMA6/IypC1JT+i22oUTgvPXc1HcdksRSJMGHC+58x1dFqRvMUoI0H4eIu+fU58/h7x/iVhGLDOYZ+9Dp/9PCZ+FyY8wTUd2EaefaXV5mkg372gvPs1wjf+IdNHHxBDQHUdenvF9ulnWG+eQn9Osg2pyCHmTUHNR5iPMI8ieawUpBbte5y2hEzFwRSMXuhzwgDIKQmrw2hxEm0EhAcKHQJ6HFEpk32Hsi3LXKuwVGqSZMzzTAwTMQR08XWUAColeU/Lgbg86yVJVKCyPiVnBVUhNomYI6FEYirkBDbB2kKnNCUFQb0f7mH3knj7grS7oQTRftBnV5irNyAV2mYr2ChtUM7SdY7NumHdtTTeVcOyh4N2GYWVGgNjgRiFIXIfDbNdi8R7GFFxlEo4FNI4k21Db5yQm2uBIxiKj+3zZYQIqJzRYcLMR+w00CYwbSsW1EYRkjkl+aYmAEYtANJlfPQAKpXZ/FHAy8ZJUlCTpbR6wt70vL8L/IPrI79yfeTmMOOM5o3zlsZqtt5IFZED5XAv6p7+BrW6p+vPebO7ZP2s57vPG0KSEemqAh314SV6vENNo/jkuPIAdKlrQOdEqzLeQ6PkmbrhDvXyA7HC3t3KGul60QtxHbnZAHAMmSmW03Ph0ZlUw5yMsBRV9LDOXLTC1K5VVqALfOXrb/PDf/EH+fJ3kBTAP8aJgVLqdwP/J2T89qdLKX/8233/r/ahv1VSIDN9TgdfLFkE+Urhb7731/hf/tU/wL/9/X+Wf+r138F+ztxPsnxzd4EJIgCUD3eE3RH98iPc3Uv8Gzv8s+/mfPs6hwRjWZCpRfaOXtrOSkR8hj0MO3Q3oKc9F82ac1/V3dKMPh4w13ek2+fk406c24yj9Bv06gzqIT7HzG6M3BxmppCZQsZV9G7nNKZbIUSI5uRShjLiHDntsXqsFdDSEqyXcSIk1Hgy+tStMiWh4xEzj+gcMN6iTJSdIO5PD2A8+c/qb0uFIWptKQeOc+Z+mDnMmUm1JO/IthHlsLlQwoQPimAbroynV0IT01Q8CJx4+U4rklH4lPHKYLJBzwobRAbaqYKNIxxvSLfPidcfEa5fkocZbQ3N2R4dJnyOGJWgBJEUzkaAmLEQjRL3X6PJ1uCMkTn6Qk9bZrHGfLNtak6gqxmUUlhlBaiaAzpJuzUYRSmiTqhq618pJcZAFeVvtUJnaR+nlIgpEuZATIlkxAwG15FcdaHL6tQNMrWjouZR5q1xkLGY1kJPnUeU9WKElcVevOxfEj96h+O777B/5yOGl/eUkGk3HeGNW9p5oo0j5ukOfSaUv8W8LI1H8t0N8/P3GL72K9z+vX/I7fvPGXcDymm6qy3rt57RvPkW9tlb2PUVuIairbyvOFerczHQ0tpUVL7GNGsCy8irJmi+l5FDVSBMYxK2SxaMhzVWGEJZEruMnPJpAQ0q2Z6iqiiHeaMLOkVUjhgq4j+HB4fPxdejrvFCESGgpGU/1MSAJC6AIUZCFCZGrF1Fg4BibRJwM/OedPeC+eWHxJcfMd3tyTGhO49LqWoIOKxxdLbFloy1hZVXbBpD3xi8e1BmVHBamxIXBZOUUuE4zbzcDdzuBuZpQhXwpqPpelKciDmQ50IeZrANK1vBtY+8CB7ixuPZbZbnFwbUtMOMR/QcMdqxMi2db0nKnuKKJgs4MEcI4SEOLTGzlMpmqp4CvkX5lmI8qdkw2BU3Q+SD/cS7dyMf3I7sx0jjNOvWMsdlYFzQOVKOO/JxJyDtGlfN+VPO2jM2zYrivSQ4YUAf9tJdHPbSCenWdb9rTkZyqdAaRdHQKlgZhb7/kPLRrzC9/zXCzUtySLhNLy6L7YrcXXBABLtkfCm4iuUsgsq40VL0WK2kU0B5QCB+LDn46jfe5kt/6TvrFCzXt0wMlFI/DfxPSim/8h294j+CSwmi7d8Fvh94B/gFpdRfKqX83e/kdR53C4BTBbxUArkmBbHAlEQ9brGx/Vvvf5Uf+9kf4Y/9zj/Db7z8Hl4cZ5zWvBxkoR6UZ91fofuXJN5n3h1JU8DvBlSKeOsx1tOvriAJ4lxrLWwFDc43JNdQtCKnKGYrwx7lGkHTOuFgl2kkH3ek/a1o+FdwlusblHWYkkU2VVm8lTl/5ysgUMvnnGJmCJmjFWtetBHWwsmgqFoZz9KWI80wjw8GJrYqyVnZfCyVUE6YNGNKEg342ZBUJjlLrtx9VTc81Ye9PuCTTGrWhbkUDiFxiDAqT/KNKJsNsdo9Qy6JJirwI03bYZ3DK1s3CgLgUkBRD7FDK+YC2hSiTmSVSaYIJXI+SrUQJbDnEIlzQMdE0ydsmvE54kgomym6BpMKwA4ZkhKOuRRLVhTirH3IUBaQ2SswIMFaqPQQ8LSuBko54lRi6zW2WAoFb4QOarVGYzDW47sVtvE4CppyAmiFrIjakWwrDnF+RW435HbDkLR4ZOQH0JwEE0lQSphFEKZktGtRPkAUT3iVZ6mqXrzP/O477L/+Ifv3bjk+HyBD2hYadc9x85K+7XFKKvzSresizORhT7y/YfrwOcd3XzJ8cM/h3SPD/RE0pGOEVI2lattZrbagvRy6KVDmWdxNlZZETVl5/9RuQUHYELqQjKXrzoReiszR01xBXlmAgVY7sQf3LaDQ2jFXpovVGmcKvRdTHoVYVjuV6nMqAmrM4n+gUmBBoT+KZALWt/a0HsiSZMQQmUNiDtLdydWcyNSkTeks625OhBzJaWYOiThKN8laLfckBklO5iNOG5z1GJXpdGJtCisL3lWDMqUWGMQJUxWKqJLPKbEfZ24PIzeHiWkWfwenqa17T+cbSpogggkJV0rd45JFiVHS48pZgMcli119Go6k4568F38AYxyoPRhf9R10lRSfT8npN8Ug40+FCkpRrKMo0ScopiHajuOUGEJmillGHhoaJ2JgrRO7da3E4EmXLNop+3vCcSLnjG5eoG6eo9bnwhxrZLxbwkyeqnBSLui2E8aSa0QAyggo2RbIZExR9KZgDteU6/cI7/x9ju9+wLwfMI3DbXp0vyX3VxyU53qQtfzyGAg5PzJalrxyoRs7I6Dwxhhslu6u5CW1E6Xgr339bf7AX/5Bvvx7/xy/47PfWVIA375j8B8D/3el1E8Af6KUEr7N9/6jvn4L8MullH8AoJT6KeD3Ad9RYvD4esAPSDIg8pyc+NAhCi0mpMzffP/n+N999Uf4N773x/mvX3wPuzEu2FvevRfhjI8OEbfe0p0/Q7/8AG1vmO/2TClh+xvMxQvc9gq/viQaJQHKGJn/WYX3hhgOqHlNGY+UaZQkYBrhuDshVJkn8nigjAPxKPQ46yymOIxSlCxjitY6LlvPm2cJZxTHOeFMRe96UxG+8hmV6zGuk4MxZ0kG4oQqARUn8v5ORiST0BtZ5GG7FartwXlQGl2ybJYs3gDFWXLjSWEix5biHEpX4N1JjevhzwVFTplQNFNWzNoRjOIQqtRvFJrSlEQgpUsF101s58AqirOkXjjrCglEMch4JAnos4wDaX+A3R49z0K3OzlHOXS/oWwu8Tlj2yNGGbrzDauzc7qzM9xqDV1PUpZYlDhm5sXJrRAR4LkqBqNri/qEAuIBsKLEa6EofTrMFAhNCU0ZAzYHWlWIVmGVkba4UjRGqJJaWUzbYYrF6YKtFNocAtE4ohWcRlaGbFuSX5G9UO7GmAh1DuTKw9z29B5Tqu9F1ZY3lW6aHihqZCxC2fSuIXdALnjnMRhKiIT9kbDbSxALQn8rKZJG+XrYHykhYjB45ymdDJa9a3BW2tOGLJ2QUiiVylpqVi+/V/vtR4CsVITmGXI5YRRi0azaM7S2GOvRfkSVRMwJZQ3aOWEZKCNskqwwypG1RStNY6SK8KZ6JVSZZpsDZdhBcZyYCJUeCRVsq5ZJLyfg6ZIopKyZiwhqTQlSktNaa3Vy4rOqYIrw4UOaMWGQvVYKqSR036M3lyKQVqWhCZNgT+IAeSCVgZLW0Hbi6mhEx6FYh1aGRAWolUKIiWEO7MeJ62FimAOaQmOEthwzcj9di9aFKStC0Xhl0abaNy+dxiVBqG31UsGgqWqTlGmghCjqrdpinCi5UrLQsMcjeThQ5kESA61RTYvut+j1mZhMKSMAZOOqb4ojoRhjqvgHWHnD03WDM5qQMr03PFs3XLae1irpFmR5VrlkUgjEEGFW2JREUXY4gJfZvzzjOhrx4sbp+jW2X2HXW8ASYyGTJGzmjFeFMu6Idy+Ybm6k4xMibtWiV2eo82eMfstHFQsB8NFxog5oTvvUVqdaqyEVTVNknygjNF5VGzNKF95+56v8yF/+IX7i9z4A8b/T61smBqWU/4tS6j8D/tfA31RKfZmH4ptSyp/8df2Pv7brTeAbj/7+DvBbH3+DUuoPAX8IgLNf+wsX6vy8guhi5ckuScHfeP+v8W9+9Uf413/bj/Obrr6HfYjkDKGi8n/l+gjA378+4s2aNzevYa5ep7l5Thon4bLDSfefmlUbYzDGyaHuNE432LMnzEkMg0pKJK1OMqSnS2sBpyB0JkrBeI/qN9CsKhUq0jeaq97ibc/TvlJzlDjOrVwVcNEiCzunUtuJHq0L1jYobQUQF6UbUMJMGQ8CjjFO6FZa/AoETv/KsxBkszYnNHpBugKvOFdW1TcBK4kxESWJGpt2JJWZYjg9j4W7PKVc+fII172Ku9RprbTw0yRBJE0wT5Qwkqql67TbE6ZALlqQv8ZRXEc2DuUaTLcWRcjxgFOw3mxZXz2hO7vErDZC3SpSZsU5kKNwsXPJ6KJJ2UBWGGUxxlWA0AJKe0BmlyX4LclBTigUOQTm3Z75fo+LhY1vaJKszcLDvFUZiyotSjdywNTZdzCR2QbBgGDIRmidU4HjlBkrkGnhgEPBZdGtt/W5FFed9rSmWPl5rBOAWXWfs1rhtaJtGtbrNfPtQIkZYw3dWYfvxFJaFDmDqM8pA6X+PYmlr+9ats82uMaKk6PV+POO9ukZ7tklXL5O3l4RTSdJThGaqaoKoiVnijGn9ZSKjAdDLqfPGbImpERImt71NK5HxZGUZgyLYI/DVfGgQsWJZAXKYrIkBMsMffnsdt4z3xyY05our7HO8UrbvD5nVfIrKnsoIziQAnOEIc8csyEWyAuQ0YpqqPWOxiisKuiuIzW1Im1WlO09oUBpV6j1OaXdUFxfRYWkZZ+niTBkptHhxzV2vZWk3rViZ509xTqMaYiI4Nayp0IF205RWAIpI2qtCkwSYKC1sl+zfiQ0hiRxqiztOuHlFyWg3CUmUDE42jwAnOXWZUnmwyyJw3iQYimFE+2Xek+LdWK05Vrh+KOgspMKEufWzuA2Leetq8mCvPdtK+yO3mkxbDAGmhVqfYbTGj3Pp06EcssodInF1TjKWky7wp1d4TdX+LMnqHZFymBVJhFJSUYi5MVxVepqbQ3GW5rzFebqddLmNZ4Pib9/feTrNzKafr6fRWZbi3W0VRqdJA54u8AKlzqrqreWQlbwX37jq/zB//RL/Nl/4Sd/3UkB/OoYgxk4AA2wgVe6G/9/vUop/yHwHwKoz6jy8X9X3/QTrxQYJ070QpGJpZySgj/yW3+c3/TktzFWfm1IDzKj717Lw/ul53tW3rB9uuL86k3s7oY2ice6X/fothenNCWbv6Cx1qGcBWtQBpqzK9QCMDMOddyRQ0DQK3VBZgGqlerYV4r4umvfiGKi7QBwZeai9ay95rWVtO8NqrbY5W4URK88I6IZQpPLVUmvoW3PRPO/W6NGydbV0jFoV1KdrLYyUlDCgdY+CQUTsL5KFrsGrKNoJ5m9sSilq155FVipCULRBlxG2YlUphrkpa26gKSE0yzUyN47rK6I5frZSgriUBcnmI6U6cC8u+N4/Zzd7Q2HYSRZD90WTCN6D6mQlUfbFc36GXqzx8QBbzXddk23XuOaTtTitOgA2JjROpCnmaBlrg9gtYHiMFljcZLFk+SQyYkFA/KwEB+ShZwy4bBnursm7faQheLoXYezTU2i6mgVQ7KGYDS56u6nlAi2CgSlTEiyZudZJHIXd7f5kRe3UfoEti21glSlBYuIZblWDiHbEBBaqsozbnVOc/kM+9oLuLsm3B+Jx1GSA2fxq04qIWelAlzU2JQhK412Frdu6dUFtm9Zvx5RVmP7Frft4eySuHnC1GwIdkXUnpAE1aLiBNMBFUaZLYMcENqeun8Lj3xJLL3Rdf+qKl/cYH2LM0awIUaRtMLUZCOVKnqUQKmC0+Bqp0fFERUGSIGkM1MZCDrhVmu00SdF1NMjPuFoauKlNRlDiJkhJ47FMWqIRtaQMQZtFLrx2MZhrMaq6q7YrGB1Rto+Zbrfk2Im2o7UrGUdV0poYxRquIMwkoZ7DnGGroXzC/rLpzSbM1ReUZrThB2jLLaC/azW9N4JvddIQnYCKSL7cumyKusFA+J8xZFkKJpCrgkvD66K2omFuRNnTIsih1hBzoZcpeBUyZKMwqmrp6KVhNV3qG5NcT25PWNUnmlOpCLzfaMlybUKvNOsnbzxVCwUSaKXCtxphS2Cscq2Q1+9hlptUPMktug1NkkRpF+NxSi0c9h+g1uf01y+RnN2xUw1k0OSo1wFuHLtbOi2x697MaZyFvvaW6irN9kVz9fvDvzS872cLRZe7CZWjaXzpo4RZXSQS00KrEbpqqdQhJGTgZ9/523+1Z/+Yf7MP/+T3zQ++KTz8Ntd3w5j8LuBPwn8JeC/W0o5foev/f/N9S7w2Ud/f6t+7dd0VVDxSSDj8bWwDpZxYMxCb/rb7/01/thX/xX+V9/74/wTl99T57ESbMeQGebIYU7cDQE0vHs98Gzb8uamZb1+in3tc6gsvtp6tUVfPCO3G6aUmQEMqCI2RUUZslZENHb7BK8s0fbQ7iunmVoNVb5z/VDS2lUnxsDjBavmEVeOuDRXY6IKiKLSpnwvCot+xZAgp8KYRMRIWoaJ7B396rLysw2lWwnoxnro1xS/ltdwnWTOtSIuJFAZ4221xRXQG8aDc/I5ql1r0Q8Bs1QdB70oNqoHidnGaJRXNFaChjGaTdey6Ty9t6JRr9UJqEScUfNAng/Mdy85fPQBu4/e57C7E8T66gzdrinGcYyZ3SSjilyKAMxcx1m/pdmuYN2iulYSIi1gS41GpUIuA9OcOYSRaRblSWMSXSpgPbYVLIDSBq1ECnepnk4KkMgCLDmRwsy0u2W4fkEYZX6uXSOUPN+Ba0U8JSMUQDQzcg9zLMQEU0iEmEk5VQZJeYVJMqdKVVQPcsEnjnOdzUp1Zyi2IbmeY8iMs2j1ay0J2cXqNezF67TPPoufd6jdSxgO5GkQ3IQWLriyTkB3zte5dpH7YT1N1+MuZnrRikU3HXQryuaK2W+YdMtuKtwcJo5zIOeE04rWWPruAmOP4ihakhw4tSpfPtMCrCtlSYYUuSjmVNHsSmF0xmiZNXtnsaZS74oAAEW9sz6rFCQZmQdp0YeJUDJDGOmtpvEWja+U1Pp/fzwpMJaspB0/FNjNhdthZpgDqXbDGg/Yltb3qL5DG0WthaVr10bwI9mP7O8P3I2B8V5m0VopOqvZNJqVcaiSiYcd6XBH0ZD3d5R5QuWIP6+z6Nqs1k72kUgMWzadJ65bGlPkvSFgXr8wGnTtAC6KiVoklkteVEwzaqEw5giuAoBcQXdFumpugjlWgSVDqmuPnNBhkFFnv4bjXoSqlEb1G0p3Rl5dcsSxnxJTkuLOW+i0OFm2BvR8QE2HiiNaKKTmAZtg/EMXpzsjcwarBZBY42vVbpCvlBOmSinAGHS7xp9fYjfnRO0ElJ0LiUIoilCoiUKmaTfoi2foccA39+imxbz2OeL6KTeHxHu7iXevB27uJ7iEw5xeWcvLSEqb8iDKV3/FXHBFOgU/+jNf4sf/hz/Jb/9YUqAfjpBf8/XtOgY/BvyPSil/5zt4vX9U1y8Av1Ep9XkkIfgB4Pf//+w/e++r/G/eFkzBb37yPaLC9eiSoKOqaVIGDcOcuDsG7qbIs77FrC8xzwI6zJjVlrS6Ipie+zExqwQkepPFftkqYllm7kVmwasiwiIx1sovk3JNXGoyg6Lq3Et2LwFTY0tAjffo4Z58/4J0f0M+7sS10FjMaou5fIY5fx3anqg9U8mMaeZ+kjlbZ6SKLI2jXz9BW4/utgAU68muJ7meIUqiRJHg2jrPpmtovBabdm/RjRff+OpqtuAMlo7BaZSgFJAoZYbaArRa0VqFM4au4kHQCmcdXScmQZvW0TmD1wqVZ05OkimSpolpd8/x5Uccr58zHfaUpdpQhuJ79ofIh/uZF0NgCBGrxaTmtTUkl3CrBu/W6MaxCP6UlEl5JiCS14dQmKr4kVZJDl91xDiHcytp/WNAl1eSg1NlhHRr8jwTxoE4HsnTKJWKqoqSych7rtz3TCJH0cFIRZGQ0cqc0snrQbpipa5bCf6tWcBn0k51y2hC1UNMW0ncjCXohv2c2E1ZXP7mSC6K1hmejIk3zte8fv6Ey/PX8JdvYOajdGnCJCIzNfl4oGwuCqK1pVrBhRgrnaWmJ/meYFfcjokPbg+8f3vgxW5gDAmtCltvOW8tsRTWrsdpQ0m1a6D0SbNiccs02lQNqQfK7by0Faj3wRR01PhQcFZj4KS1oJUo8+mKWVFproC4mTwd5eAlE8ZBaMPWohbtz2/qFMgzzEozA7th5GZ/5H6YGeZYNUE0URWMV/SIy2OxHkx97jkzTYHdsfDRcOD9+8CH+4H7MRBzpnOWJ51D4elXvayZnMjjQJ4HVJixxuC6HtOtUbaVZ5EjKge89uAMqXWkvoE402pRhiQLeG/p3lkt+5RKuURbMYvS9gFsqGq3TBnB+ziNUhqtDFo3KBshQZwz+2FiDILbQSlaI/4sJhzR/VGSfhDzsHbLsRjup8z9mBhSRmnNVolbpnMazwzTjnz7Aen6I9Lh/hQHdb9Bby/Q2yfkbit23MoxpkwowljKWQ5fCpXBsXxmcE5X+2uL6XqSXxGUBQyxKGLJjAnxdpgFQDKVxNb2uNUV9vWCOtyjnKesL5mK5W4auTsGhjmduiQ5lxN26uOXFAicWHUAP//uV/nDP/Ml/oPf82W+99cBNPyk69thDL7wj+R/+HVcpZSolPqfAj+DTLP/o193grK0D77F9QvvfZUf+9l/mX/zi3+G3/zke5hzRhdVpYDVKSlIubBpC9vOQYTOG7zR5IpRaN0Ks74SKc1mRekvuZsidzMMFBosuIxJBZ8yRuvKQdUk7ZiV45AMhykyhcxhjgwhncYZSydYKZl9rpyVeVljOGssjTboHIj318QP3mG82YtmeOPpL7dSFWzPQRWydwwlkQgMMRNCZKqrUAM0hn51VVXjFBHLMSSOx8h+TqKNUAqdM2xbT9IJ4xr6foX2RkYlzp5mg6VWGKffa1DJFTAmFDqxAfZa4es8kaWqMRrnLH3v2fSe3husypU/Hmv1XW1jQ2AejszTSAwzizmJGJx4plQ4xsyLIfDO7cDdccZoxXnfMGcwTcNqHfFtBG2wVu5LLtLhKdqSlSFkxaFiDcRLImFMoBsmNn2H1zI3VhVnoeqs/tW1qavvg6myybVqV5+c26daIYSSJRBlYdNMQdbIgrfwRuGMtFa1UqfALuh8Eadyj7jsJ+yHbcRsKhbupsjz48zL48xuTGituDzMXI+JfYDPXKy4XF+w6a8wYUSlUTpV1VhHhFYeNt5J8REliYixZNOSXMtuzlzvR967OfC1l3e8e7vnejeSc2HTGqZeXACdcXiTBRMDp9mgUVSApuh+5IrUziegsbRdQx2ppFIoQWiKjcs0yWA1WCWKj1ZL8v3xS/AyRg4M8/DsvlmnA06CVrUtnYpijon9MHKYAsdZkPMpFyGyONGbKNoS0YSi6p4vxJjZzZHbMfLRfuYb9wPv3hy5PU6kXDjrBYO0bcX8zBiP8k0VUirEMDNPI/NwpK2UTXKE4uSZ1fn7yhtU77HJc1SREGTUdRqE1YRHOrFLR7VIUxIlBQDl1FGg0l9VzijtQHm0EbGG8f7A7XTkdkjcjzNDSFil6L1h7Q29bei7Hovsb2qnb1+TgttRHBads6xQGGdpG4OfJ8gj4+1z0ofvcLy+J0/indBerEU9c31G0YYZy90U2U2J+zFyCLF21+TjGk0tVKpPhbc0TrNSBqMEZyEgS+pezKf4sp8S03GiI1I8XPSXMvKxLWhDdisBVWdhHnXeEJIcx5vW0jhhUDgjReBpD1ftDFPHqL/w3lf5X/w//wD/3u/+Mt/71ickBd/pDKFe/9jqGJRSfhr46V/r9/8q5798T203LiI9P/+u3NQ//rv+LP/k0+8hRAHEaFPQWZPrfDvbQmM1q8by1mUHH8Hnn615fduwbS3WKChGWuxKk5sV+zmxnwvXx0jQmc54XJNoXGKyWixppUFNVAIYC9pxiBO3Q2Q/zhxDYgiJqSLKRbxFgv7KW85nx+vrRlp9tpFgHANxDIT9kTQGbBNJbYvNEadAWUOyFq3yibK5tFzHmJiSxlSPAa0MKcGUIocgAkq3Y2QIUq11zjImMNbRtYU+FTrboLwDI26Hi2nMMhbJ8KAyWQohJaYwCxo4Z3SeYBoFRJiqtLK1uK7D24gLGhsUpgh/WihS+QH0aAzZWLL10ilQVkY77YrkO8JiTRsixylKZo+MKnZT5H6c2Y8T67nFOUujpNrOUAOew3qPdo48zMw5oUvGaqEDhpiEfqYEKV6UFt/7ZZXWroGowiW0b2jWW5opEO2BkGUmW1xTqaH24WeKoBVEdIpTYE61o2QQ5crGiB68Nwqn1IluJ0Hi0YGdHtzxitL1uSz7T17/OCduDzOxFO6GyH7KHCLch8RngxxK66ah8514NeSIToKTeRyTTm3a6ow5V9e8/W7m7jjzjZsd71zveeflPc/vjkwhYpXCaF/lrR/el9C1tVBsEaCbMxa3ANzkRlGo2Isi45RJFbQSyfDIA5PBKNFNNLpULyB1ekYYSylecDCA6o0kFKsVzXorWJ/aGWD5mfrJFTL+AcGEhBQIMck6SVJQ5KLQaJlbey8MAyUrZtHnn2Kqtu4T9+PMborspsBulH3ojOzJMcrrFt9h2hWsttIxcY5sPdmIlPOSqIr5V4YSRFY3Rlw44Od7wnBHGQbxkgBZh6aBpoVsiCEyhRmXGpR5EDqSw6smCCfbevmllCMTGIcDx1jYTZmPqsX545hy3lqe9J6QSzVxgxwyYz14x5iYq820MTI600rMvHw2FAUxR4bjTLw/EqeAaSO2a3AxCBjSNhxjPukd3A6Bwxy/Kc42NSnonWFIcK4sXjsBJCsDRUZVoeJa5iiJ+n4KDOPMkCMGi1WFdbutYNwsRUcFQ76+bZjSmtvDzN+a4NlWRJh8va/ASfVRDMnEqfdvf/hz/NG/+gf4d/7ZL/Pb3vrC6fu+RV3x6ln4q/z7P7aJwa/nepwcPP7gmgcgzPJvf/3dr/I/+5kv8X/8/p/gv/3stzOlDBaUEvMX/8rPq6rlDb/xtQ18BP/k61teX3suWsPKatQUJYAbR8QyxVSr/sisCtoHQownqlsqBVsKidp2tQ7jGpSbKTqQlSaXVOdM8t5TxTzMUT6d1Yp7J9rh675FuxV6tcWvW+KhEyaEdzSbHt+vcG0nfGAQO1yEudBYjVOCeF1gCymLGFQohSEW7qfIborsp8hhjrVK1rQ1eZmTbNSQC1kbsK724nRlgnBSlpR2ncwwxzlwHCbmMFMqKpl5j56P1RQliW1s2uIagw0enQzoBkX1f0dAjEU7khaQod5cYIsSWlu3ovRn4LuTxLk3mk3nTgvlvPO0VmMpJ2fHXJ/VA/9bOhdt29JNkljEXDUgtAAkFzUi9XhBKo1IHi6shHqIVPpYs9F0yTCoW4iSVJRlHqrrFi21UgDRvqhrsxgtdt9FVetaRecUvdWo6SCqmkXGGGKW4ySJUvoRGHfBjOfqf6HYttISHWJmjoW7ITCGxPV+EsVPpZhj4XLdctU3tYMm2BCjHiqc5Vqq9zQnphQkMZgTL48T1/uRD3ZH3r85cL2X1rJRik3nuFw1nLeObSugOGeU0LQqgVjmbGIuph7hAkrV6DDK4E1D36w4mowNEvRDkiRARnLSJRD2R3lo4SotZjla3DyVDZgS0FbTnZ3TbM7EcvfjHYMTNXehK9bHX1GkugJHjZb2etM0dG1H20oySn02AvgTzEOKwl6ylGoi5Fk8QjateJxALXp8B/0ZJgVoWgE2bs6h25K0x+o64quz62WsoNOEDUfcvMcergm7+zoeMqhGRLLQmmIa5nnmOExYLxWwMfo05ly6rFqZqj4p49dcAiGHExh2qLHjGKT1nmpn9AFwbEFlXHlYR1oj2JCoUfVZacqJDWathbYj9SuaTc+8P6KtQTcev27Rqy3FrYim5ThF7qdwimmHKfGq6ZeMxXKhUisNynmJ01YsqlM1cDsp6OZMiJEpBI5zwJfEYYbOWFpjccaJ7kWOrJqWy86irlasG8fdMPN//Yfwxqap4zH9yLunhoya6P9XH/wcP/aVf5k/9f0/wW996wsndcjTElTL061h+PG/8atfn6rE4JOuxwDEhejx19/5Kj/6n32Jf/d3f5l/+jNfYEoC0jNZTFoWoJZRqjoIPqB2xyciW/mbn65YWYVLI+qwE9EcKwF9AX8tsramZFSKJwqjKpnqayaoeqVonGO1aolZZk1OiyZ9TEmoWClXjnZ17qrXVGf+x5Cx3Rb75A1UDNimIU8B3zb480v85TN0uyYZR0pZQF0qs3aaYqXS7J2hq5WmrtibnB6AXaK4p04uba2VNpu3sjkWvvkS1JbDJ/GQECzKk6L8ljlOM4dxYp6j6H7lLMqO40Eki1OAxovG/2aFyWvZWNWoRyktqoxKk70mt5myCugErt+iYxQ6pF+hXYvJiU3jeLrKNNYSq7xu7wznneO8syJqlGMFG9m6jmob3hrapmG7lpmkNZoUg3DcvaP1DmskaC1ysK+oSH6TnoPGuhWt6WhMxzTMpJirTz3oVG2fFRijMEXhCtWRUShtqRg0BacVvVX4PKHv79DznjINkgBah257iu2km6I9WS0VNlVRMNFah2u9JBlOs24c553n5TFwmJN0J3Jhd5wwwBQju2Fk0zh6q6sWf1UFBGpmREGYP6nIoSwAUAnK98PM7ihU33VrOetsNRNyPO09V71l7TS905g8y15baJ/11XUuqDyj44yKA3kUTwWNkkNtWrPqznC+wcZCMILmFsGYagt9OtDqLTklsQImda7DWE3TetqLM2y/yCdnTvz9x9SnklEYSpE1Zo0+rZFYpBNmrGPVd2zXK9qmwVpTD1fZU2oxjapiW5edRZeWjTc82zTVT0GzbcR0yhjpPqhuKwnBNmCsRbcryupcBK98L/untvlLFUlTKWDyjJ6PqMMdHO4o0yzJZMlVQl2OqjlEyjhh2xllnRh8VQyU4SFBEGEeTamF1SILDQVf40drFaVYchH0vXmUXCwHoQJMUVRiKVYrxlhpzEriWcqZYhymXeMvn9FPI7bxzOOEbhz6/Bn6yRvEbivg2iqCBNT3YapwkOC3nKlgaGNovGPddZyte1arlsY5we0gs35d9SwWaqJKEVOxRDnnEyjYwUmQSsWR82bDatNy0TmOcwv/ED571hFzPuHLYv3Z5Vz6f9Wk4E9+/5/lez/7fSxG2tXcmsdp6ifhFH4t16cuMfjEkULNplSB/+Kdr/IHf1qAGv/9N75ALNAYgyG/AjpUWr6ulKh/uYrcPWykl/CEA9zfwnwU7XJrKWWFsm1F9UPnjAiDoGh0plMJnwOuWHSWdpDSMk5QzqB1S6MVK6sYjpowz5X6I4siZlF2O4TEMYhSozWKKUli4LuefvO66CVcPMGECes87uwCs70it1sZWYwj5EirFb61tWsgdKdm0d+vcz2jaqsei1WSPMwpY5SYCp11nk1jxdREV9nOuhiFjvSQFMTCK6ZMIUSOU2CcRf0NBW4J9ilSSkLniE1aKFVFhG9UbU1ipUVbjCcbRdaZXCwliQU27YiuIDVlG7Ky9ErkiBtnKlhPbI+dlraht5reFDxZ5pFUjIlACSna4B2sugaF4CziPKMpIj3diC69yhnFg+jNY6GjRdOh1La6SNp2lAmOh2sOcySlirkwikYpqYrjLFK82uJdS8gQDKSs0KrQaIUJR8xwizrcku5eita9Uuh2JUFjZUF5klZgHEvmpnIUS+f5gNWaxnWcbzZcdI7XNy03Y+B6lOou1AAVU+I4ToQQOA6jJJVayfvQPOAAlCQGKcOUC3OWLtRUKZYxZXqn6LYepxp6p7lsDRetY+1lP6hxB8dB9Cu0eQSYrCDWFNAFTAmU8Qj3NyIMVgq4FnN2hSoJ351jfM+UhbprKt7CaSCMApZDiZYDlrmyOnKR/2qlHZv+DLu5RC1a+1X98JVnXT/3yQmySMLdNY5116CNJaOw3tN3HX3XCENCmwoMlfWWAYusx7Up6FazsQ3PeivOe5mHpMMZwd9Yg7aXqLiSQ8JIxV/WW3K3IXtp/0NBlVnwLzmL1HRO2JIFa5EiOUeRiU7xNF4JGaaSyXPATQHrE1lVnAbSnbVVnhcF+tHSN/Ve904zN5a89ngDwxylU2s0a2dZNVrWRB2NkQJoRWctrZX1Mi2jXy0jrBAi2beodot58iatbzHnN7gwi8psf0bqrxhtz3FIJ0rrReuk4HGi+bK4jaolcdYG5z1dv2K1WuGbBlvF1Qql3reMKxLfO5UYdUY7jUXRueqHggCPVZhQ8wEOEeVusL6n68/J65WcLb2VcVPV5ShFsEQlCybu3/jZ/zF/6vt/gu/97Pc9hJN6rxd5+KXR/Enn46/l+tQlBo+vj48T3v7GV/mR/9sP8ad/z5f5rW9+QToJFYWqjMaZhx80VJCW1jgrwc6XmXZ4Lt/y3t8j7e/IYaZoi16foc58RetLRttZjTcCPvLW0OpIU2ZMVIJj0KJAZmpw89rQ6YaVzsw2EYdIOuxEASxnCVZ9z6ga9iExhHKySB5TZjcpsl+xvlxjt0e0ymjrwLdk35Fty2GWOacqhd7JfLMxGksUms84itBIrcRb1+F8jzfSnj5PMsMzSlzcOu/oG8eqcay8xetyku9c5uJLUrBkvjHL7HdK5QTYiRkBjzUtOrSYrkeXhC2NtO77Fc77Gmsqqh8oxhIxzCkxzIlhEpxEMD20tSV3QlKDS5lViKQUhBo6DzK+yGKtq6PG5hZtM7pzGNVQpQRObUPQaG8xqqGxijQ7oSVZTd94MSaiSAu1Ut9euWpSUKpmQEIzTpmxaA5JcTcJy6AxMgtuDKg4o0NFaStNcS3etTjXker7U2FEz3vUtCftrimHO/E88MIS0Uiyh20oridluYvaGlSK6DCiwhHGAaUVtu05b7ec9Wc8W6+5m2XN3U0CjJ1SIecEqaoqRkU2inICPj585FykYzDV5x6ydBJsga3TNEaYJmeNpXOKM29wYY863sF4TxqPlCwytMX15GYrbBkqi8M7CAiDgELOQdgS8wx+Er0Ca6sypaZzLYXKzggDjKPY95ZcXxewcvgOUdao1gWcYiyaMSu8ETVBwvgQb9IjgbKcT0FIlYLOir7x5AJNIxgj4x3e+0qb1Fi90CrrM81F3P/Ge9xwy2oe6XKu+iAOWo/yLeqRgJpdxgq5nCy9ozaEpJmnREeiaxTeGKyxwuqpoyStwHlP26/EbXQcBQPV9STXkpq2YjYUuQKppypuUIc7J0Mzm8srB5YBvC6svKVEJ0ZDGrZei6JoKahCne0L48qEI2ocKkgyY4zB2pbO90RnT6qoqqo3HubAqtmgjQO/Rm9fQ8dAKppke/axcJgEr6AVbBtLazWdE1GktsxiKhbmk36CWZ1huxbfddimkWdeS9BUFUN1daptysxaR2xrmK1QR5fOs9FKYkEKoi67vxNZZucx6zPs5gKArQ7MxjPlQoiFkDNWGf6Lion7d/5ZOb+WTq7VD14xSn37McKv9fpUJgYf7xpoJS5Tf+Av/yD/8b/wk3zPW1+s3YGC1WJpWz1OTlmtrS0sbxSNBjXt0eMOXr4DwPy1XyQMk8iBrreotq9zZX2q9KxesADSYm1cwaogeugJaY9rjy4aY6vkpTWgDPv7e4brr5Pe+xrp7iUlTCjboM+vWD15i3bzjGPXspsUYxI/hJxhzjB6Q++2dM2i7GbkQB4CUxCsg1GC4LYk9HiHnvaoMMhcP2fhoPtWpJLjxMo29N5LdVHvqVUJqzM+R5qYcRFMUujiUKXKltZnIVKu5QEwlyWpmaIiIoyFhJZZoMo0rcOfneFzxDuL8w2ubdF1bIAS45uiBM+xnyKHaeY4BqY5V+UxScg6K1WUMxqdI+FwyzzcEnY3hPtrSb5qIChdjz5/gjIFugb6Dm0dpY5WxABLhFSclhFMaSy6FKzK2BzRqYpS1fknJ4lc9YAvQIs+AUbGQTFzDIXrIXBzFDnas8bS2GqTHCf0tBf9eED5jpJllq6tdLGW8QdJJKEXt0RjHdo16G5F9j2lWYvkdJBuVGvF776UTNnfkW+fw3AgUjD9Bru9oL16Hd9fkteXnHnFPmimKDiBVEcmQmdb9k/5JoyBLlWwSqsHwKQSRHZjJTBvvEYfr9Evr8kvPyDe35COO6ncuhX6/Cmct5Rq0T1Gka7pnGHTrKEkdLdCH+4x1omSaCmS7KZ4asuTg+zXOKPCETUdYR7qvW0pyqBdTyrlxNLIKJIJnNeEvImSVJmqDHq6aguZUkQJMsvi0UXj0Ww7TyxCRVTGYozFVNXJJdjriktIaYZxjzreoHfPKbcvKMPxZNdrVhvc9hK3ucBvLnH9hqytdGIqEE7orGBKpimBuShigXUj4wxVXRJRGm0svm1R2zNc29KGyKwtwbaMds3ke9KYKRoihilKS9/YRyJeWroEj9k1qmR0iZg04eJAF4/oPNHoxMYXohMQHyBjizih5gkdBgiDrPu6R3XToV2HbtbYdkPEEhBTqv2oCAmsdRjvyXZNCJFhChznyDBn0f3I0i1ojWLTaPo8Ym4/Ir94h3z7khInlGuwZ1e4z3wX3bPPsb44Ay/UxAykKGqPOc2YNNOUCasCrStMKII1xKxO8VJYnhI/Swxi3LS/J6eM6xr0+ly+d/8S325wzZpJK+akePtrX+EP/5Uv8e/9c1/mt735hVNX1qjHmI7FVE5CzcfHCN9JgvCpTAzgEe6L6kf9F8VQ4ns/+0WxUtUVVJJfvYELpctpVX8V9HSAaYc63hBfvA/A4f2XlFxwZz3eWPRqQ2lE7TBmof8YI/agajpi4igcX+vJbY9enYvud+376OIEVKNgnva44zX7d36Z4y//Ert3PiIOM6axdE8vWH3XS+x3/Sb6i89Smo48ZeYExxCFQxsyBw9dEhlho0WaOZdMjoJx8AocCT3cooY7yv6WdKjaB1pVbwTJ0nVOsjkBKOicamKhcc7hmhZv1jRJYYuvFtPigBeXcUJeZGuF2jOEJG3lAgGDNh7jFLZxbLZrujzSllEO2pwkcFmHck5qmwpknGNkP0buh8DLw8jdMDFU4aGVt5xpTaM92ls6r9FjwJYZNdwxf/g14kfvMt3ckXPCNh67PcPmDH0PSZTQdCtSM8vmFv62phSFshqNpcQJQkARxQCITPViPjECSgUSLolN0YZYFiBW5uY4cTsGdpO49zVGcYZGk9FhECOj8SDrtA8SZLR/EGzRFqyYcJl+DWR0yZi2w51fUbozUrdlN8PdlGS+WkQZ0LQtXltUiuT9HdNH16RhQluD3/S0T97DXL6OfvoG6+6Svt0yWMVoNTFXESMeqFRLoFquXEpVKFQnkTGlHnQrOgN6vEHfXVOev898/QHji5fMuyM5JkzX0Dy7RG8uKPWg2o2JIUo2P2dAac66LTpH3PkswNVFh79foxYraG05AUFThHmkDPfk417ubbtCG4/uzgCYU2Y/RQEKu8DNceIiZLqUcabKgVdaYj3aKo0WCvK7KgqDPCOrFXgr4y10ZQlINF9umdzLIkJXaYL5QLr+iPjRO8T7O+I0o7WhuTjDPnuTxmra9RpnCrltSHMm58iQJ+6myGEOKBXofOBsTlytJGHVrcXXUl9Gmg6TWxGAKoVOG6K2jLrFqZY0ZUwJ5CDYi7nIeEiFROfEJ0T0DxVZ11GIojKOIrZEmjSQ4x6mPXoaCSGgcyZlJcDl5QjLScyUHnsn5CKaBKsNej0L8Le7ILO03yNjKicgcMqSMAxTZJwDc0yUIiBbbxTrRtOnAXPzDeLXfpHD177O8PyGNEVs59m89QyXA3azQU8X+K5nLoo5ZlKOlDij40Q63JEPt5RRunqN0ljbUnxPMk2lGiNU26ZHrzbku5eEORDujsx6j9sM4EEdb05gwqZZ8XNf/yp/6Ke/xJ/+53+S73nzi68AEpfzatGZ0N+iW/Cddg0+tYkByM34ytff5of+glhP/o7PflFmUo/SBqXF6nS5tJaWlzXq5BKnwoCaj+T9HbH6aMdpFinX9Qpz+YyyfkJaPRFVwWrl6tOEPlyj9iI6RAzotsOdPyHFWdpr63MxvjFy0JQSUWEk7+9IN8/Zv/uCu6+9ZNrNGG9Y7waU1qy6FaZZ0V98liHK/3cMhZATQyrsY6ENGWeTgHeK8LStKniVMVah5gE1HSj7W/L9jXQLQAKo3AwedP0FUCOSwwMlRYo1gnrebHG64DqPTbMgxHOSVifLGFs2Rqx0o7GCJkNRYlqjNa51+N7TrTt6ZlwaMHFClXgyU1mkmHPKBJ04hondceb5buL9+yM3x4ljSFhjOO882jpWnar67AanFYrINNzD7XPGD15wvL6jlIJbday9gzhL5n2ScK63Y1lXtWugK1VOxQgkii7CgkCwEaqkE5cdXVfdQleqKzQXMdGZY2SYA+McmWLGL8QFpcSCNo6kQexeMVZoY8goJWlHLAVjO5x1FK2xxqC6FZqMaVpUf0ZstwzZcDfOfLSf2U0BoxQXncUZhWvWaC+vm+bAeLeHlJlu98z3R7rdPeZ4j7l6A7W6oG03NK6jWEs5meYU4chXkO1yLRoWmAdTLUVB5YCaBsEQHG6YX75Pev4Bw/M7prs9aQ5gNG1tjyvfkps1h5C5GSM3g8ymN40DFN56dLvFlSha89Moh2/bE5stpdkQsKSSZZ5vaghMUdZ/iuRSMP1axje4qoMgWINxjgzzzBwjqWIPTqf54lSao2AVsmBYVCloJbgIgxIraYRNUao1eUadwHmvXNWDRANEMRgaXtwRDoMkI1Og9R51eYXnNaxWBGNROhHI7Baa8TATU6J3hn0/i29FabB4lM64qnKolRY3Qecx1lKUxdgGbTrAc9wPOGYogVz1FsaQQWWUSrQYDEo8WGoHQIHEkBKxaabkiTjuSPcvCTsxaysxUYyV2NP0wsipCp8PCq/lFKNyinWPWpRtMX5DTpm5QAyi81FUdR2NkTFEUhUCcxo6p3AGeqsxu2vSR+9w/MY73P2D99h/eCDNiWYjHdLV1Tlld4u6EgxKwZJzElfcGEiHe9LdR5T758TbF+KUax16e0FZP4HVJUF58fBQhm71BJMS5vKIG/bE40Q8jlKwPhGlSl0tx99+5+f44f/0X+HLv+/P8T1vfqGCtz92zj1KCh53C369SQF8yhODr3z9bX7wL/wgf+5fFJepV7ec8LlfaXdBVRUEg+izqxTES3yexFiommHYvqW72mJe+yw8/W6m9WvcVJtgqxVrp2lJqHlPfPE+6cUH5BBwqxWEGe9b0iTqW6m20rVSWKHGVuMRZOaZCyUtMpiZNIcaxGZUGGmNZzKCrC0xn2Qzh5gE/Zy0oNZVobMC/tGqnCyWyzxRSuabXMz8imLlcFeVUZGnEXW4p4SxIix7GYm0DTpsMBXRXxYgFgsSWdgJIUnb/JQcJKFsam3BVA8I57C+RU/SXlVxkrn6ya0tE/PMnDRDUtzvR27uRz66PfLB/chhirSNY972tN5z0cs8WVehEKPBJPF6z1OgxCya/Y0X98LVBt2u0X6Fco1UuKqKyJ42IXL4pemhNZ0DlAi5Ui9zqvdV6KhCeTRQzGktllJI5BPVKVYlK42AXa1Soro3jZR5FGnbpvLrXQN+xWEWkySlpGrbbJ6i2h67GgUtrQ2pKlfuDjM3Q+DFUfjwWpWTWNVmvcZsrjDbS/x2RxomwmGUJGya0PuBxl9Lmz1FeS6LAVcFsIGA2F5B6j9iYBQt+Gm0rq6E8jrleE+6uybfXTPtB8I0kQVZh+sa/LbHbC9hc0Xya3b7wPNj4GaYyUUxJelgtc7QrnpR4Lde1iyaaFtwK44VH1EKOF3Y+hVlPogufs6UeZLkZRpRacZpL9S5WkzElCvjSJ7bcpAXOH2eUkcWJVbapNIonbFUzEHWtVNY97Vq0NpKe5rqbokkHco1aL9Ct2vMaoNpO2zjTx4VeQoQAiZFAbhpqimU8Orvx8BHu5EX90fGKbBqLGEbaMm0KtKqhDMCQtQLCfZkliYdKO06bLMWDI7PMIldesiFnAoqZtDpgcllzKMk56H6F+yNyIDrcS/Mh/0tDEdKylBxH7pdUUzzEHtsi3GNzOW1kkSiyLNSaRbgqVoQ/JkhFkKBjBZjqJQe6Ix1/OdrsqnCKDFwGiSu5lfj7ZLEiyHcIsqWmENknoO4RE4DZdwRnr9HfPkB4XAQ5855FDpnd0agsA8CcG+M4mLzBo3SmBjoUmSAk/JhCXLefOWjt/n9//kf5s//3p/gi9/1BRKqmka9OiqXpnMVLPtHkBTApzgxeJwUfN+jpEAv/ZUClAc+uEa+blRFj1ZjHuGchpME6+J02L92hXn6JnzmN3LsnvH+bua93URImbPW8uamYeMNzCP5/prh+S15nsnnEdVvyFlmo3EOlGwE4IMCpzG2Q63OMGdX9K9dEWOiO4xoa2ivtvSvXaLXZyeOuwAdFSBiHHMSrjuLnSwy331Mp1Ko06JX1kml2K3ldftzUndG9j0JLUCo6VDR7QMMO3Su9sVmFrvXOFdkdnyFSQEVkV4eNu5cuwWLzkOOGZwhokQ+tvpJYL0ctKXahCShVpWciaEwlchUHNMwcBgG9seB292R+zEJLsFojutOZqxlOdiFP26bBt93tGdrsAbdONqnl7jXPod5+lnYiGzqVDS6SpQaI0mFrij45TOrNMufs/CTc5grl74eHFqLPbZv5P4kC6aClpSpc0KRge69peREb5XoSxiFCrOMKlKURMl5aHqyX7MLmfs5s58TzhgZyzjN+fqJjExSkLGLtlUsK59YLfdjFXVQio23bBrL1eYpzWdGQcJ3LfP9kZQS2hls34hxjhbJXTWP0hlScsgrkISo5IfEcEGnQ5VJrg6J8gVZg/UAFUOtBrduUVqRNwljDH7b466eYT7zecLmKbtQuBkiN2PgdpB9abTYdU8xM8SE9T3GetnPxhGV5zBM3AfYj3JYrL2wjjZ+jWl2cl+rtHOJcug4J8+h9wZiofcWVxHpy3y35CT3IaXT+s/zdFoDIhGtUdkJkj2JHPYDXE9RrLzPhX2U0jJ+0eRuC5snmKcjbp7pSxGjqinQrjp832GaRnAKSkhruRTmKox0dxh5fntgDpHQGta2cGhg8oXJwaQCRkWcUxTNSWehGDE/wvq6LwtFyT6dMhyD4AZyTfqMEsBsKYJpOlFhi+hkSOcx1tgqcYN5pgSp/ovWqLan+BWpPaM0K1JBbLjnI9qvMG1P3t/JXqiJw4kWTJ1RFcGuCM1cpNOxCZU13kiM72zFgGWggsf71y5J04zxjhwTdtVKnD+7Qq3OSLZjjoVjSNLZm+RzWJT4ngwHpps7ptsd2nt67+HJG6ANw5z5YD9xN0ac0Xxm0/DG6hn9Z8CWQm/fZd4fTufXz7738/zAz/0YP/W7/wO++MZvpaQgipYakhLMysfPrmUkrh79+nbn47e7PjWJwaPc9GNJgShCvZIPIIujqI9xPpfXyI9MiPKD77syFtX0AJinb6Le+A0c+2f8w9uJX3xx4N2bI1opPnvZcd46Sldb6fNMHEZyTNKyM5aiHdm27CdpSQ1lpi+KmBSd7oj9Je6t/xorpWleeyJa9Mait5fY86ewfUJutxRjRUrUarxZnLZkfq2NJWGIOYsTYEoY0oMUblXyU22PalpoV+T2jLy64m5KDMdMzEk6IL7jbOMFVJak0jRhOmnjywfNVYnw0XMpi9LhY+e7xHGOHKbAMMmhoCiM0QljoQIVrTGUqB8WeBF1thITaQ6EAEV3qJxxJWNKqlankRQK8zQTwiwtv1xOc3CMw/Zr/PkTVgXcHGB1JhoQl5+hXHyGg+2Jc8GpjCsGZ6TytVoqQ0kYZ+naLJ2lMAlLJQWhsOZcu1IaZRNFUQ8JW3XqkwDPlKL1lvO+5b5vMCXRGUFlO61OBwwArkF1K4pfE2zP/THwwT6wDwlvLUFbut4SlAgx6RLqe4JSAoUHBTVVZ7Axw5BhKprUntE91TSrNenZS8LulhyjyOu6pho7VUXG03ORkyynUJ+/emWMcBISKQCRnJRYeZu6Bl0j9tftGraXMA/4MKFLEdXLzTnm7KpSzTZM+4mhYlVyofLeJdCXuuaKtuAs2nmScoQpMpG4mSeuj4E5RtazQeFo+x7t13Jfw1wTG0GPL34kGy9mS6u+4bxvab08N6OqiFaOlfIZZK/Oo/DZq7R30RpcImfBPSi3RCJpqJRUvUQQKeSQCyGJF0aYC5Ndoy4/h1aWZrXFbN+Dwx3eO/z5E1y/RhknMa6K7eQUCWFmnmZSEDqlRmFKwpUsnb3xQMgDyUEpDqwR47Pl0SoNpoKXK3j4pBw6R1QU9UaltIC1k6JNGacfKWk+OqFUeajCl9hhXO1+ba7I2yvS+im7ZNgfREBMCp+Os/M12rVivDUeTvFrAfSZWth1zoARtpfVGoPEWJUTGjGNs8t6UZbcnaNf1zT9BvPkdfL9tSQersFePMG9+RuI/SWj7hjGwDEWjuNMnCM6Jja2RWmHMpacMnEK6JSFEQMU4ziEyLv3I9+4Hsil8PyiZ3yy4jecP6N/QxRevb+GHbx9/f/mX/qb/3t+6nf9Kb7vjd9CKUnOI6XR2goL9mNn1+MOwasJQfmmryzn4+f4HN/q+tQkBsv1SUnB48ZLhfic/l4efZ1lZpzFbviE8i65AvIa/pb6CAD97C3i9g0+3EV+8cWBv/21G27uJ5wXgZLPbjvixmON2PBqZ9HO4jcdentB7i64GyM3c2IkYX1iGzLbxtKojDVruPwulN/i3pS5WjGW4nqia0nKiiofAjZpdKIrchBp34p/u3UkDHMuTHNgmiZIBa2WlmGtypUCbcnthtlvuT5EPqgmQyFlOmd4Y9Oitg1X29dwVgsae9hhVJaKgsVzXTB3nGavD6JHizpYqIFlN0zsh0nudSlMna/OYctzOaVqkhTkTA4TcZyIw1GqKjXS+zO2XnHZGsaVq0mfYu3B5IitinhCIdRgPW59hn/2JrHfopQldeekzRP22THuI2qa6aJjlTWbTtMp4SKXomDBUKR46hiUaTwdBiXMp3VzAhouoxptwPiTr4BxrVg9e8v5uuN47PEl41WmdQpTonDIcxY+umtQqy2pWbMPiesh8vwws5sSXQeuLYxFPOsxRtTTqmiUVdAaxXljGToBioVUaJzFWEs2nmwb1GaFPX+CPt/BdCSEIMq2qCrxS2U/LF2TGaUjytpHOAypIhd3xdNhS610qi9AMb4aBj1ySixitaw1Fdzao/sNWVnyEMmmYOxM513ttCguWst5Y2mNMEYUBW1ETTQVEfEaS2QX4eUYGYaZoTE0RokGR7PGrLaoaZCDPecqUhNpnWFbHG3RbNc95+uOzlu8FWdCFeKDo2l9/ouplGAMRIypxCBJVRL8gWpkPYpQl3SKihJMw1xHgbshcjjODEeprFt3yfr1DXZ1iRlu8SXi1xvc+qzu5YrdKBlbMiZH1h7G3qCK5rJ3XLaGrVf0BvLhhlRGooHY9eKOasyjBF9RuTVV3fhBovn+OFO0WFsbI93K7MqpOygvUUdJNSbIb1UfwHpM31OKRnUb8sUz5v4p98nx/n7i/d3IEBLOaJ50jtfXnsvuCm8c2tTu2+N1o0QxESNqko13eK0wJIiBNA/keRLwstJkLBFL8Vt0e4Y5ewP9+igUyYV26jvC+oJg1sTDzITmforcHyfiPNGSSL5w1l2gtxf4TUc4VKyWEeZRLJr7MfLu7cgvfbgnzIkP7ycAVs7wue0bgr1QCnbwL/3NP8ZPfeGP8zvf+KdrNypK9zeLloTSguN49ez6+In28a/Jdz0+H//of/JHP+F75fpUJQYfHx+cbtLHeeSPLrW0M+EEHJKg/tApkO8z/OyL/4p//cOfACC3Z+wivL+f+JUXe15cHxmPAd9a7o8tYxSzFoxB+Q6/6VHWYC+eoi5eZ3Rrro+Jb+wDM4amyRxCZmgtvS6YHKRatucUsz1VEPNUmI9CE2ytpreKbWPQww497TElYp2n2VzQnD/FrNaMqbAfNAeVmcZ0EhwpJxc3S7EtsdlyN0pS8Cu3A+/cHBlDZtOJUlvfejbnW/rG4htPuXuBjpPIgzqhkJUlZ11Gi49msLkKG80pMs4zh2FifxjRqtA58TDPWdD8UmkstK86QggTJUzkYU86HMhzkPlnypy3W3JusEZz1orF7aqxnDdaqn6VBQxYZOauuw0Og+3OmVXDpHs+OgTePwzczBnrJ863idfOC68V0KsWr6UtLBlJOIECyzTCeKgHgnQMSI8wFlqjrJegqI1oSxhHSTMqTTTWE5xh3TZcbXpsCaR5whsZaUFBWStMkaYDJxba4yFyOwZuB2EyZG1IRQ5kbSzGGlGFGyJmPuJz4axxKAxGt2waESzCGDZ9i/cNyrcEbUh4YmsZ6DnmkTEHUpTnIaZFiqZRuCaKVW6aUTmTkCofI5Kxpe6vkhMlBXKsAkK6ClO5joCY/0yxVB8ThbGa1jj6pqVrG6zxwlf3Fu8Lm14OXZKIa501hm1j2DYanyfMPGPsCmctqWh0EpxDKorDlLgfZlK2rL3mSe8oqxW4Ht2uyEqjrOj9qxTwRrQV1r7hbNOzbhtaZ2isRqVJuka1Y5CDzKsJMyXKPTlFEWMkKXCCfXgI14sFuUNZS8qZKRZuh8CH9wMf3u64vd8T55ELr3lj5Xi6fYPV+hxLwDUW3fVyv4us82XNnzeaubP0usVqOGsdz3rHeWuwww16uiOHgeQdmUwxiJ+CtZxol2RKMaBExS+nxBwCh3GSbgHQesvcGGK2J1fLh/0v83mW4kEblGtRbY+2Dmsb3NkT5v6KPT23t0e+cT/y95/v2Q2B3hv25wKINhou2i0WUHFksQsHSQycVjStY9U1rLuG1ijS4ZZp94Jpd0MMM0lZaNbQX4gpXJQuSMwFbxq8aXGNvJZSmjJrVA4kDcesxHV0mJimiYZEWTuafk178Tr24jnNFCgxoXx36raMMXN/DBz3E/MYSTHxKyvHG5uGi67nrD1Db44A/NRv/z/wO9/4LQ+HVCm1e/0Am0ctsuv6255vj+W6H5+PX/zcF7/1z/ApSgze/qak4BOujyN+gcXxTj0CS70CnqpL/Gc/+tv8wM/9GH/i8z/KH/zlf4viOuFxh8xhSqTqyqW1wlnRQAAEeNa0+LM1qu0xz94ibV7j5Rj5+u3I+4fIEGHVi0NeyZZBF1wK5BSZguh4347Vq6Aap5z3ljc2LZ/dSuasx3vKy/dIww7TNNinb9B1BtMYfLvF6JaSIzkESsq1BWvEqKcUim2YkgB3bsfA8/3Eh/cThzFyHjLbdc8bUZOMx282GCuCMWU8yJy17cA1J4bBx731oBYOpUi7LUbGaWYOM96KoYuqKoQaaXcTl+5NEjGiGGCeYRpQ4wE9jBR1wK4CbU48W13QWMWhc1WpTrPtLCsrwirMg1QZtX1N1hSrGYPig7uBX35x4JdfHnm+m7HO88aTkSmBM5pN48iuVj45QnzUKRgPYpkcBKB6kuyFB9nmUkdZdhL0tbGoaMEE8bOoc+yp88TJE0g48skqGGPRTYfqVmTXENKDHkTO+VSYaaXwztJYQ+MsNg7ENGHmPWYccUpz7lZ0q5a1t6IAqCzWN7SNQ2lLVIYQE/dj4eX9zPVu4O4wEqMwXNZOc9E5tt6wbgxts8WUUF30dJVcthW0KR0krZDWdZwFe6E1qVre7qfE/Zy4GYIAtFLBWsPZquVyo7nCs9UaZw1KK9rGcZkatgZ0iaIW6qBJI+ZwjSkZ07aYxmKINK6nSeCdfUBuF05Stcu9NFXrQdQCLcvw0WlN5zWu8aw7cfdsrK4A5QWLNAmIN0ySFIQZwkxe/BtADjEr61nlLHN1pOgo0YKJUJoqF564Pox8/eWOX3r3Be+/uCeGmacbz+6qJ7PiM2dbOlfAZGn/Ky1rcx7wWrOy8KSz+NKQkkMrWDnFWSNJgR1uKIc7sZfOrYhQeCfdppRQugKJU8Jaf1J0VUXEkHJOgheaNDE24sRYyiMTnweMkarJYNZWcCptJ/u8gGpXpP4cv35G2k3cx5HnQ+bD+4nb/cSqtTirOW8tl72jTxljmwdgqzYVyqIwxtA3jrNVy8opzHhPmq5J1+8wPn+fNE2UboO++gy53TAleG8n3Ynbo8TWdSt4m/PWVkdFizaFYBJjVtwMkef3A4fjQGchx4DXLa9vXsM8u8GHSWiLTXvqZlgj54LWqspqFA6TOGxOsVBcd2KDfd9nfssDE6MWRhQB9RalawehutZ+3LF1udSrfYRvhbn7VtenJjH4lknB427A6c/fIsMq4hlP1b2mtkB/9v2/wQ/87B/hp77wx3l6LXQuhZixeKM47x3P1x7rDGdrzxvnHeetFSnPUgRZvLkQDvbZ6xxwfLSfeX838sH9TFYWYwwxOnJUYDKpOnXdDoEXx4lvXA+8fzdyPwSc1rx+3mK15vV1I+/5cEd6/i7x5hbaFnRBr3p815NcQ3Ydk3OMRhGSSHkWbVBZs8BYNOokr7lw0EXBUPjX2rdo35Gtpd0+EQWwthPMgZeWcFFV2EjpE2Jb1fslePSaeKWEIdMY6J3McTtvTvoRth4kp+5NDbikGZ0CLifmOOGSmJa4JK5+l+2Ws7YjFmEhdI1j4zU2HBE/l2rTrBT4lpIdu8OO93cTf//lgV98757dbsJYy36K9I3j6cozrhqyV9LEqJ0CZhFeydNImQdBtIf5BFQ9LStjTjgKpYRRYHyD0jMlWpQ2WN3gjDARGmcgasyCidAWfCuAReMFn4LsfWfE9dOYwrZKU6+dObnClXHEpZE43qPublA5Y5se1axp+0tC0xGUEXChMxijmKLw9j86BN6/n3jn+YHr3YEQEq3TPNtKEsm6obIIcdqijbSV41yYcxQqJkIH1kqSM6tEfjYX8YMYQuZ+Tnywn3h+mPnofmIMGecMl5vEW8WSTAPGsUb2W+8NPhuyMbgCLg6wv8ZMe5iOYlDEBa5vsXHCdRvGWFg7Q+8120aU91oreviqYtCKdpIoN9JFQVsUgmFwSp6LN/pkaa3iJAJgaRaaY5jIo4wiyjRWzEF6FUGe4sngKQNaC0BY1oVDmYacEuMcuD8MvPvijl9695oXL+5JMXK9ESnuVWPZdJ7z9QZ0oKha1MRZGBAJNl5TOkens1CoVcGEAbN/KbLZ+ztcHDFG47ITV8wYZJ+FSYCHtZW9CHo5rei8oTWalRMatEH2syQ7pQ4Ay2lkW8oyMjESH6xH9xuMbYT94Hr06oKgPdprtJ9BCwV2wS+JV0mNT0s7srJdpFMpkcYaRedERdOnARMOTLfP4eV7zO+8wziO2ItzbLtCbV8nZMPzw8wvfrDng9uRkDPbzvHGWctnLzue9A3nSiFpUSQnfSpqbg4zdyVC9my9YXPhWZ+9jhkO5N0Nykny0hjFeWt547zjo7sJpTWuMZz3Ip9u9Ktl1DedX/XsKlQvBm0ezqiPX4uAErpWYpmvfOOv8YN/4Ye+fdH8setTkxj8mjsFjxKFb/6+JRl4+PWV936eH/ir/xo/9cV/i+979k/xd1/+XQBUmul9z+ubhs8/WeOMYQyRJ5uG7z7vuWjtg1KdERCUvnhKXF1yN2ZeHGeuDzOHKdE1ovy2cYqLRrEympwKszGEaLgdNVMU+9vpGBgVNE4zRkE2qyqxOb24Zn5xR+k65m5FPr+G8yu0X2FNgzYarQ1FBXIu1UGMysWfaNqGtdM86xsOZ2IffDdE1n3L+bqjbTzGCsjGtl5AMZMRkRFjwAn/mWpDu1QKSpVTteGU2Nw2OrN20CrDurFcdpaLVjZ0aw0qjBXtX+e2KVaQV8Ygsqmt1QIoyjNp2GGVopDIaQDfYZSTeX2I2GIwWd5LKQVVRXkWx8ohJO6HicMwMx5njE3c74/c3e84Hlek0KGLIY8jOhwhjEJTGvaUoY4RYqV+Vlqp0lWP3lqwhTJXEZzZk90oHOwkn01jTuCpRb0sl4pUt04qOh1kplpBb15LwMkVyb7pHFedZeWl+2DTBGkiz0f04Q5un5OHgWwsarXFXBww6yv67VPwmmKW1m9hDOIOen2YebGfeHE3kXJm0xo2rSV1VJGpWgEViCUzRxhS5jBnjrU9a7WAY1de0xmNt9Ucuf58LuKjMMyJuyGwGxNGa/G26Ge2q8gYEuvGoIui1aC83Nt8/xL2L0k3z4mHe/HG6Dq01dj1GlfvQe89K6+5ai1j72hVxmo4b63MoRe9CeOADLpWzuYhCdM8ANx0jieMCSmQK5U0hwkWRkIMoryIOq0HpSsgsSbHmVIBqUaQ/0WjiyOFieNR1t/9/sh4nEgxoa3ifpgYguzPlLNQaXMBgoD5UsSmgo+JdQl4FUglwDSI7PW4Rw07TJ5xqpzYL6bGQJWS7LcYUDqAqbRo6+mceFjsO0cKVjwVLDQ6Y1SW/a0f1vDpyFs0QZyH2KGKEl8ZZaBZofotKhSMVbSN53zd8WTb0lo46yyfOWt51kt8aqxCjZMUDaaqoOZC0SI2pY0ADnWcYNiT76+Zn98wfHTLOAz4lNEXwmwoSBy9OczsdqOsyVy4XDms1qy8jKi81WijaZJijoprK0XfYUqyRzrHk97SrS6xF/fy2jnDPKKnPRdtx3ef96RceLGbaJ3lrYuO1zfiDaLm+QHXsKyP0yj80Zm0dBBegR7WqyYCp/FBKXzlG1/lB//iD39HSQF8ihKDX21m8nhU8InXJyDqv/LuX+cH/h//c37qn/m3+b7L/yaEGRUENOKmPSu/4tnKo1A8WYmAxVlredo7LhqFvr0RQRo4IcknLPfTzH5OhCj0nsuV5bW14zMry5NOszKFPM+kxtJVoZvdFLk7Rj4Kkti4RVe9UudKGIn7gbAf0FNiPN+RjjvSNKCyuOLlKoUasywrp2WOqGJVEEszZ+0Zam3xdsXKG+7nhDZy4PTukeuiUhjfgVaiTGgU2jqUESCZUrriDSrVT4sZjFOFlkSnEsUqtDNsWqnKt61l0xgcERXHEwVQZvaRUkTJrxiNMwbaFltlR5NWpBIoZUIpocXpKujUFGEWmCTdi4WeXLJsblfpS9vWsWotMSS0Vqy9ZuXAlwBxREVLCjtMOpCGPQwHwRbMIyXMQkeNkXJyv0woowTTIXKJKD0Lla3at5YYyHoip0JOqjIocvXxAGM8uK4G7IrhKCK/vPKagqN3so27xvOkd2wbQ6cTdj6iykyOEz7NzHEiHHdCKZwHSk44RNbXNY5iRQdfwqXMqilygDbOoJXmvLec947z1nJWOxTi6aDE1Aehot5NgftJ/O2dUYTGSqJmBC4rXSEJdLkIlfIQHPspCjisCAOEOjPX1dTKK3BWoVIgHK/J+xeElx9S7l5QjgeUMTin8WnGxQlXZko80tUA/7R3qNAyNBI8O6tYeY2tFbcwSNoK9O3AeFKWOZDM16OwNEoQA6JKac7zJM81zJIMhBliIqf0zeshF9EDWMZNAsARjZKQUW4DccSXwMrB2muGRmOcYtVatq0TPRJFZaxMIq6lHzjsvoAqCaMijQpkNVNU3R8lyChQe6wuWOuEgmm07K9S3WArVZs4o5Tc101jmFrL05WD6Gi1mFF1KtGScKpgKQ+0Xol+Eg+MQ5mE9gmtNSYtFu0tSUkXUyvpIF51lvJkRU5Sjb+1bQU0aTPmcIOKs3QgqqFWTGLrHU/JplAI8zSQjjvGuz3z7Z4QZnTrKbXwMKoRTxyjMVaC7dW64c2Lnje3LW9tPRetFcaTdxySwmAZZ8cwWl7EQIhCF76fEhetxywMl8OOMuzRxxsuzns+f9Gybgx3FxFrNE97z9OVY0XATntiFW8iBoqrZkjl48bLMsopPEoATmfYCdmBAn72Gz/HD/6lL33HSQF8ihKD7/j6loAN2bBfeefn+IH//Ef5qd/1p/gfvPbfETWrUk5ufXrc4azjcv2U9rzjqhd/dG+gUxFz/wHcvyAviYHzZNdWXXwB2bXOcNYbPnve8BvOGt5caZr9h+Sb92B/j+nWXJ09o7t4KhQzrfgH3nCcE8/OGraNyCjrWDN8pShVKGeaJsZxRE0zKiRGWzjO4WQIYwGvQZciJjLTQZDM7T3n3QWr7QWbRnMMhWMsNL7g4kyYjgTTErQTK18lYlDGGDHjsQKgWmZkMvcuGFXkoIkTNk2sSHSNmFT1jeLcK84bTZtnzBzRcayt2nCa26sq9qS1FgDX/4e9fwu2NMnOw7Bvrcz///c+p6qrr3PvIShfw3aEZUIAAcx0D4MwKBDDAfDgB3IaDQyDpEkFRAYZlB2G+WL7wZYlWZJJ0aQCQmjAngZBPxEY48JAkEJXzwAESFoybYsPkkWie2YAAjPTl6o6Z+/9Z+byw1orM/9/732qenixo4CMOHUudc7e+edlrW9968aMOIxGA0e18OIAjMHqQRA4EhhanZAss0FytngegZQRl3GL918O+KantmACvnpvj8CE99/Z4vnbI54eCFtkzG/9Oibskedr4HAN2u+00NNhh7w/oMwZJeVWmowZZNXfvAMccqzWZJlVuGUh7JGwz4zr6x32hwNmex1hYBov1CfOVkxJCmje4XK8QLTsCyHtdvnUNuJCDgiHA2LaAemgld7GCGy3oP2M2VLyKO0wzNcYcQDna1C4xIG0FfWtCFwG7fSGvMHT26DR/9uID1yOePZixJMbxiRWAIwDwrDFXJTOLUVwSAWHLBAhlEE7gQ5WAjnM10DJmOKI6WJA4I3FWUS8da2AYjOpFXYZgFsRGEmwHRijFEjWeR/ma3DSgjcYJwzDiMutZg0MDIR0ANIOGYwLAZ7aRmCOuI5akTCSdhalw5XKhBBQOILCBIwX2Bdt/gXJIDrg+nqHbWTEKJiQwNlSVNNsBcO8loEG6ZVkwKA7E3ruCoIJfe3QnZHnGWXYYcZ9bOkCTw+E52+PmD94C09dBOQiePb2hI8+qef1MhLkwdsQPkCCBvhJCOpKIe29MkRNX87EAAIgA8AbUArawAdaEImDxgahmMvV7h0ZyGcAQQQbaEDvYSSUibAFYy4CRlaGKo0gGRAogElaKjNbQKeMFguQzc1CKGHAnLKWB97vMaQDnh0Ft++MuIgTLgbNpBmu3wLff6umKtJ0qU3IBDhkIEEgqeDqMOt5nzNkf8But8N+v1N2peicOGtl0jEynpgGvO/OBAC4GAP+lfffwv/guVv43XcmXOx+C/TV3wSu74NuPYEnnvoQplvvA8qEnDQFdD+rPN8lraOxHTbgYQQko1zfR3j3q4gc8L7b78PtaYtDVhftxRhwgQPC/a+DdvcQHBhYjrcCALR4NykWOxBO67BFoOHreOnzP4RXv++V9wwKgN/OwMDHiajO1770S/gjf+uH8ZPf9Zfw+z7wzXrhAaBkuDcr7B9owZr9gHG6hScuNkpn7a8g97+O/PZvIr/zdW1+NG1Aw6RR2AAgwBQZz14OeGIz4Hc9MeEDW8bwtX+Mw3/9/8CDf/JPsHv7PuLFBpff9BFc/nf+Vfy3nvzdGAPjyc2Adw8Zt8aID9y5wK1pxCABZXuJ6fYF8tVereLAuMq1mjHePdzH168PePd6RkkFUxDNk7fKevmdr6Fc3QOYEZ54GtOTz+G5i2eQbt3B1VyQRBDnaxweJOyQMOACcTshDKPGJDArUAiK4jXbA61PuBTkwx7p6j7ocI0tZYQomAJjpAPGXQLuHcDbSSsxF3MhHHbWglnNfLLoZg7BOraJ+uBD0DKqw6iFT2IEhQAvYSVmkclhjzwfVAgdZoAihuE2nrt4AvTsBZ7aDrj/dAIz4anNgOduTXhmQ6B3fh3AHinvEUUDspD2QDog7w9aNW3OmlJpefvMBUCEkADRkzmNEjRgkMHYS8aDRLhfAq6vd9gdZqScodXvCBwYw7QFHQBJMIClPuxtGNRVxYQBBdNBEMOICAYXVVYDEXizwXDnDjbjiPkwgyID0wX48gJhCKAI5LJHHCLKQJgPwHPbAE4D7sQtcplwGdVivDMyLrAH339b3SpSIHED2t7BxXAb16lgCtq1k6DNaiarTX8RGXF/D3z9To0q5+EC08WTuD1u8PTFiHv7jAepIDDjchrwzDbgMmjg3AUXxHmPEAEZAvLlBYo8BVxMkFQwjAPG7VaDD4nUb55nRLlCPBRsDgfcwgFTtH3KCdhpYS6SouWK4wQZt5iFcciibaaLViIc+Ar3qQCcdU8pIdSA05aMJ2LV87LW3ujPRClqlQMq4itY3e+Q6B4QJhAmPLN5AuWpDZ4YCG89qfLl1qTW+rMXEcP114D5HmZJmMcBPA5aI2GcNADYwLnEoGeQBMICzATKsYEYWAEmeGCm+bVz0vsHtVw5J3ABcL3HuLvGLSoYB2CfNQOLDtdIV4Q8DaApgtGlrxJrF9pBU6OFrfhT0UC8/W6P3dUVDg+uEecZT0ZBHJSRirt3wF//GuTt30J69+tAKRqj8Iy61VLxLq2EXZkhdA3JGU+QbtMuqzzkzYBxM2C6fYG4vQSHgFvTiA/cUQD4gScTnhgDvunJLZ6/PWD79j/G/F/9F3jwT76EdLXD5slbuPymb8Lw3/4f4wN3PorDExNK0kJhU1R5VwBIGDW+AIDsd8jlawjQ1tnT5dOg7YXKzHSlcTGHByj7B5C8r3rmpJ56VP315hfw6c9/Bq9+7zcGCoDfzsCgX+hucV/70i/jj/zcn8RPfvdfwSc+8C2QMi8iPINdtihZA3nyBBINLsulIKd72F+/o0r2sNMLFgZVWiEiJ7WcLseAyzHg/ZcTntsGDPd+HYf/6r/A1//BP8RX/9GvY/fOHuNlxJO/8TU8Nx9w8T9k/K6nfzdubUc8yIztZsKzlxs8cxExPZgxP/d+0IO3NGe+FMSnb2G+uIOECft9wVev7uOrVwfcP2iL1aeniIsg2FBQZXZ1D/ntryHt9ohf/y2EJ34T4bkPYbzzPsTtHZTpFjIKkA7YXyv9Pg0B07ABD4N2mQzBcte5pmIxk1pGaUa6fgDMO2woQ3AAHXbgdA3kGXlgSL4DyBPaKMkCt7QKnfnrQJr+CQBFYxu81C6CpgRSVEWpwWMt+ImkmGV3QNldY77/Lg5X1zgkAV3cxrh9gPdfPoM74waHXMBE2AyMTd4hvvWbwOE+5rzHZmCI9cSVWQFBSRkyZ+SUWyEiaEnWUIr2wCiiQVNkXlcRSMmY5wOudte4NwPXGLE7JMxmaROhFUWKEcO4AYtoc6ecgHwAZgVegRnjEDDyBsN4iZCiVaLMSi8PI8J2i2EcFNSECIwTaHMJ2oxAZCTK2OVrbArjdhSUQTBeBsybCSQFm0iY8h589XXg/tdQ3vk68u6B1oW/dQdBMsZbEbeGCfttRBZtEDUGxpPbiFsDa0DY1dchb/8m8v13VKFuLhHuPI3bt57B5cXT2G8n7JLS+kNgbAfB7SjYFA023VBGjAxsRki+hEQAh436/EPQjI9hVB930cDVEAqGnDAcHmDc7YBZ72sjajX/ncIIGTdIpCmUh6yFfcQ+dvuCQez8DkDYBHDJFqlvUfIwn7f5iovFnAAaS0HWmjxDQUEYvXw2gKx1PsAT4vgOnp2ewK2nNvjgrPUNxsDYUEJ499dB12/jcHUPIRKGiy2GW0/U88LjCHVduewK0NQVZZckaTQ/xNISSTM+YC2FpRQgJY19OOy0ZgVHYJ4h776L/PY7wFwQwoBN3EKGDYhGYN4hXT+AbEeEcbAMJctPYG15TRw01ikX0Dwj7XfYXV9hf30FTjO2QRCCgPf3wW+/A3nnN5F+6yvI776l8mmjSpefeh+EAuYsuJ4FX9/PEMx4sJ8x70fki4AJE+TiDuLTt7CBxnlcfuBZbJ97P4Ynn8R4+RR4m3D79g7Xuz0uQ8EzE2Hz9X+M/X/5q/itf/CP8Pb/56s4PEjY3Jnw7FffwdMiGP57A57bPofDbXVFAJp5kEWAGC3zaICUK219fnUPcZowTRFhEAQwSA6QfI08XyNLxlFFAiKrg8KdzlrprZUue+3NL+DT/7c/ip/41I/jxVrH572P337AQHPmTvyc8Ytv3sWnf/aP469/8kfxiQ9+q1oTpDG2FCMkBYRRSyIPrMVNgmREKuAgKCQ4WCH9EhglKL3NF5dA3AJxBOWCKTLuTAPGoGlftziB3voKrt98E2//49/CW//4Hbx9SLgVA0CE6c4beOap9+HWrafwxPu+CTRdYJg22I4RlwODLxh72WsP8ekSKSXQ7adQnvog8vZpfO3BHv/0/ozfenDALmn9gwHA7XEEDUr9Ewh5f0C6f410/xrD/oAxzQjzAeHpAzjvwcOFptVkwnzYY543wMVFZQqIWmytfyYAEEE+qH8b1/dA805z3w9XwP4B8mGPvWTs9k/jFmWU7VaVoZdd7veOGNDO1ABc0FgxnaBMgTZEaM2PCLAOdgCVgpIOSFf3Mb/zNtKckO+/A7p1H8PhAcJ4gYuoipCvdgj7+5D7byPNO+2Id3mhZ4GAkvIxKHB/clhe8n4uWuRIuac8z9jtDtgnIA2aA16sEJQiK0IQraE/DRuleQ+tBC9JBqEgCiPygJgZnKK6T7wWhxWwwjBqFLhki4CPtvcAJCOQpoXO+wS+3mObBLFk9WfmA+jqAej6HeSv/1OUd76GfP+elo4dBw2yIgbHCbdufwBz0dRADz68NQbcGhh87x3IO19F+fo/RX7760iHWYtlvf1b4DvPIDz9fmy3d7CZLvW+FMYwB/CDB4hbFaxTMNYE0AZOw6iVBfMMLwNOHPUMiWgt/VLASZv4xLxHyVqZkr38dmAF7+Ok9HRSliwJNMZAoN0CiyCVgv28wy4DF2G089gabp1NBbOzISjISVW1n24uRZtxpgQ8uEI6JMiwQbz1JLbTLUzDRuMf9nvw4Qpy7y3M999GSHvMQ0TCkyibDahMes69IqkbNcGBAWlAIBEQ7X71FqcV5PIh1iNFsmYZ5etr7P7pb2D/1teRKYDGCWG6hIwqGzBsMAdCvnWhrquVPHDDgSlAKAMpY55nzAcFuxEZNF+D5yvQ7l2Ur/8m8ld/HYe3vo753pWJAUI0mUxxwGHWGKx7u4RdKrgfZ+ScQTLg2e3TCE9dgXPG5vItxBhx8ez78cSHfhem5z6Acvk0LueC5w4J834H2V+h/NY/weEr/w3e/a/fwNv/zdfwtV+7h/sp48mv7xA3Edv3vYnb7/swbn34KTxlqdEHk+tEpIWm4lblvqWzxsCYmLANhDEImDXNOEmujGutOMnB6mhQ24/uQ1wOrkYDBZ/FJz76wkNTEm8ajz8wIE3HE09yqX6a5XjtzS/g0z/zx/ATn/wxfOLD3wER62cvokEzUjS9zSgijOpHijEiRi0mI8JAHjFvNtoQ5IKUUrp8EmXYaMcvEYzMoFHdCRcDgN195PvvYr7ShjXE2qpURJAPBelqh/TuW7hMV3jm9gbDE0+jWM3/kQGOzyhdup+B8RIogjJcIN96Fu9aB7rfurfDW7uEORfMA2PDwCFHpGkExy3o4hbixQZ5d1CFNyeU/RVwdQ80bgBS8CAC5GHCrhRsNhukomWYCZq6JN3Ser44SsbuwX1cvfsWMO+0ccl8Bbm+j3J1D2V3hZRmTJIwX24xBU3pE0B9s6JI31mIxeDQgEGMqB38uBNwVnCFYgHCjFwEh8OM3fU19tfXur/zAby/Bk1brZmfEtL+Gofr++DdlWY73LoNjAPArMrb/McL8HLqCDp4sY/atZEDDnuNPt9jQOGMYhUCi0VH51KAQmBEpSN5AFFEwd7y4bXqXiazUIcAyhMQPanLLENriYs0A4i1UQ6x1VArGUgHxALQ7j7KvXtI13uUbOmi+yvkB/dQ7r2Fcu8tHN65j/nBToX0dsIYAsJ0iXDrSWC4h4t4CRm0vG9grbKH3T3I/gHS/XeR772Dw7sPkK73EBEMD64x7q7AV/c1tffytnbZo4AUAsp2AtFtxHhLFWjJerh8v+tdh7bnjv5sfmI02h6WOZIPWpxHQoQw9AyQlio/pIz9XLA/JByK1Sex+B3thgnsDzPCYda2vcNU91TqHjtzdibPHDAgySj+O0Ta2OvqAeb793BAQLn3Dnh7S2tYxAiZD0j7a5Sre5DrK6Q8A9stxu2lApgQK2tGcWwF9MXWKmuXUiRu0e9r6trvlJa7bPcwJ8z33sHV138T12+9pUzR5gJs/TGUHcm4KhkXlxd44vZtPWsWsFyNBVGHS4G5AfZ77K6vtUz4vAela+3iev9d5Kt7KPsrlDkBROAYEC82oItbkLhF4hGHrOWZ7+32uJ4LrgNr/AoLgkQ8cet9aqHfeRZgQnz6OUzPfAibO8+ghAk8ABebApYLzO8WfO3Xr3D17ltIVzvkg9ZmiKaLSy6Yr3bI998FdvdxMd7BfmLskxqLIoIkBBo24MsnQVkLs2GYEDcbjOOIcVDQmiUquzCMyDlD2PZh3ABx0F4V5OdKu01WULCuU7AABS/WiqtnXRAPGY8NMDhtny1H65e+HK+98To+/fnP6KJ+5GMalVsACe11JSpSfeJ9HwQAPPHBDyPEAWGcEDcXprgI4+VtjJe3sbnzDOacUTiijBco2zvYYQTPBZdJu6dtAuOJkTHtCHT4MG5fvY1LGvDs+9/C4d09whhx+f5beOKbPoDbz38Ut595FpebETwOmEWDujIEFDcYn/ogbvEIfvIDWkM+Trh/KBgOGZEPuE1b0EGbCV0Mmh5454kJt7cR24sAvhiQ7zyh7ZetmyFvLsG37oCfeArYPgGZLlGc+RgmbLYXCFELq3g5/F57e4Q0h4CLiw2eefopyEFTpjCPwBRRNgNkf6H+8stbiEO04JtSFambVeTt4/ywux+VlVmBzaUxCGbBZW3GU8KAQIyN5ZbHaYNkCgKD+WeDgYsSgQjIQMAmYoBgM06IMbYyv6eGMQXEGuVMQ9BI9CFaAyFtO0whIAwjthRxh0ZseYM8XmKTrDuctfQl0tz9aQjYRMY2MuhwDczXtTMcl4RACianccAwamoqivrBW5MZqMUhFhTWsSraoIkQRN+jTAO2JMg5aR8riShlhMglChekaQukAgqssSa3boOfej/46aeRt7dQtk/iUEhjU4gwsoCvCWFMKHJAGQn5iScUGOQCREbcjOCLS9B2BI8RNGkwawgRwzhgGxnBCr2grxVh7iXh9lxksRcwsMgcMEwBF0QI01aZAIEW3AkjZNgAwxYybnGdCjapYD9nC57UaPkhaLOyi8gIhwlj2WG7HRCiFp0RIttfLYFeUgINQd0GSZmCmwYRqUV7cQGOATMIGLcKzMeNnv0xQDYD5GIDzNpVMI4Ttk88ic3tOwgXCiLYnru/A5CirIgUUNCYCCrGdDm47dPd7I4BMICufv/bd55EjFHjMaYNeHMJXNwChguUYQMat7i82CqLaCJhIR/qAzNCHHB56zZCCKpA0wGcrkH7CdiqfCi3LlGee6DzjhP4iacQnv0Qyp0P4np8Avsp4ZmwB10kXM0JTMpQxYsBw+WIcQy49dz7wWmPyMDFrdsY7zwDiZPGRhBhiIyBAsbNCDzzLMbnP4rN1Q63p0s8eP4+8iFhfGLC7Q89haf+u89j/OCHIU89icvN09geCnZZzaMpWmfdJy/B17fBzz4HLglDCNheXmLcbM29ogZFmibkwx45qcGCfwTQtNF6Gqxn2AGCuOxbKfymv35cmYJHAAMP05ePDTA4PfxYoroQFotGpBWhLHrzxedfsMAbyzcvZBQ0aZU8CnjqQx8FADzz/L8CL66hmxUqirsA8KRV5JIwoYxbzBSxSxokk+yWTMy4GBmb9H6EDz2L8uEPIv33v4T9195Guj6AhoDpzqV2lnv/88Cd9yNfPok9SFNz7HUSCThO2D7zIWyf8fbGgm0q2Owzpt2MJw/a5hgANlZF7LnLCXcmxhMjITz4COj6HcjVPWDea2DQMII2l8aUbCHDBSQOkDhppLNY6h+6i2+r7mWTmIAhxvAW/AABAABJREFURjzzzLN49s5t7S0w76xA0A5ipYSroCduGQ3cAMBiOBCw4jAwhUshNJ+cuTWEGBRUqPFYMI4ThotbuHz62ZZr7PP2bm2ARZlbS1UvcZs1I6DMlpkSYdZe1FiC0q6bMwQcg5UIViofcdB+B8OEMG1w69YWt57boIyXyFZNc5e1dHQ28BFIC+oMkTCxts0my9qgZGtXlPKFZCBZ69+c6pkUwJSmVAuRuJ1ZQC25SILbFxe4faHNwryJmBz21vbZPqzeP5hB4wZ860nwE0+BLp7Q5l7jBSRu4BnXlHagw1NaofMDz6G8+xGU+2+r/9pfJ0TQuOk+prbPPkrW+M3uDlMYKuin7mzQ0GJOKERMm4jpzpMmZEMTvHGExEnTNQuwL4I5tWZedQ+YsAkq/MN8DT5ozAzP1yilW/dhBKTovkOD61QfH58PCmoFk4Hc6fIC061bVvhnBE1WBtuZgP589q8VtKIghVgBMkh7ApAUrc9gsTYkRZ/ZzjM5ayByzB74+9n7bG/dwfbytv7QYzlcRsQNZNhAgsoIcESGyQYPzOvlBBHGzQab7cbqd1ir+zSD5ivNlupkBDHrM17chmzvIF8+g3cPglv7gssHe7xtrgRAZdytMeDJzYDbk4LqWqsBwAygZC/Bre4uYcLm9pO4fP53431PXCA//zzmr/0m9u88gMwZcTtieuZJxPd9BPy+55FvPYddvMTVoWBvciQyYQqsxbMkgQ/XWja7a8anC6qZLnjiKbu3DZBJ3BrrGTpQECogkI4xqPrre1/Bi737YMEoPIrZvByPOTCw4cGF/WIR66Kuij8Igl2WrLRNIVX6koGQqz+ujLfMJDaLFZ1Css0T62m+t4CifSmYjQEFWUGYDIzjJbCdwc8Bw/YWwnP3VFmy9ibnJ55GmW6jTLeRKeKQNKgrl6UNQvCiMwoarg4ZVylhnwuKSC06EggQiHU7BK5TwGZzB0wBHLeqZMw3LTyY4Bz1wocRRai+dw8I6jzEfPr2vTIHZBURszEBUX1xG2i3yTzri9nvLvaqVwwOBDpWQGwPSgVonV/OlrsWCIkFkFyrKnoK42KkGRQHFUbu5wtRFW1O4JCs0p0CBCFlb5AbgHGBzzGCx9GAS1RQEFWgCkctBMWDMkuiTq8a3W7CtIggBwFlMmqcEePGHDgB4BmUWEOws1qpFKOCGW7V41CKprV2nRGpZ2CKBiVKyZpVAj0ntVJkHPSQhVirrlEYFBhsL4FpC+FRz75AmTdidXGoZoHwCExb8GVWwXfY6d77PbU+C+KxQIRaX0Sc+XDlRaz7VMrymcyVRA7EHJRRsHiC2PqEWCEjiRNSAQ5FSzJnaF68wMK9SKpyK6IdMsEDiGe1nK1lMw2TumtCtABAW9qUj86HMkikZ2SIFcDUz8NkbMGkr0+se4Am6sldZkY3y8POvxR1kwaVcSSla5VdmnuhTtxAZNDXEEdlbB0yh8nWMWr/C/LeBVQNBu+yKJ5+58O+EXM1BCZwGNVVhK3WI+FRWZMaKzMgj1uU6RZ2STMiUtZCb158SrP91Ai7d1BL/GIM1lUUtRaLD7Y91XsVgek2+HZBHDfgO89g2D3Q9MFBKzbi1lPI2ydRxksc5oIkpdYemjWCFBBGDgHT5jZQtqCcKijzfQCKBZ0W9O3JZdya8RP0HpjxWY1QG0f664b0xfc6fhsAA2MNFotkTIGViXxxVTuaKEA8WMfR3GJDAdncWtA6vmmelJYtDi0ZLZysvW3qnW0FOGTBNQm22yeBMIA3t8DzXoEIqQIt4xZlvMSMqLW1vU6+AQARFWQAkKxnwz4X7JKWnT0UjXIHNIJdoL7fXS6YMmPHAh4GTBcaC6EVuGpYlPphw6BzQWt3m9FAcC1/bMIomGIDEwppXwY19TJg/Rn89ykErRJ3hs6szEEFAqT72oOB6uPtBKOXBUU/UfeH5m5PfV+Ndh/U8qZo1e+GATJPWt3wsFsABDaA4OmQ/ZxrXYdhrELev8YwWsGXofZOUP1J9cgKeYkomNsI4ALMhixjnFSJJC3yIkUr1aklmC39zLr3mX+ZSlxSxz07YwCMOiBGFA3zMiCjCjL7W7dUadqagthYfYlVjAdZOpwFQAICumSEcVI2pmNuFq6hHhDavURRQNG4QAZRe5b6972C7f219Sx3bAEFpKKtjv3eitBi/WFgVSzOpirwMiiQGrLucSlA3sBbfDMAYn3+9fkg5pOAwM9JPTfD2IBNVQ5uPXpQ2oq9tHVvZ7/JL3UfeCaEggR4yW6RChT0KnbKxoBenX8crEOmVohEGLsYn1ANB2+qlM/IigLLmRBom+RhY1lcAxAnHMmiYYNDIZNvKsey3R2NXdBsEpAq7ENOKuus/LX3sQlQNjgyIRTR+B4hbOMFhtsjMF2ANk8gWO0QUNB+MIPK4+tkWStruZ5hsQb680hR26uzXm1C24e1XgGgQZw9O2DtuNG9xd1Of9WURArdb9SThm9k/DYABkC/OILWcOlz3/8qXvjoiwuruwNuap1ZvfTlKwAyXtaAmmxf5GLWnSHWVKT2u88CzJ0bQQMM2RB1QSqEMVxiHC+1yWnJdjhCR2+qL2suwG7O2Be1+hUYAGLvVSBIWVmFZB0NfURWpiIVxpwF+yxVsJZAiHFrhqF0T6zVADzovn/Go9gC88eKwIr6WHAiQ+vCG6UpoZjQNcHGUSPsV757qcxBE4QaiENLMOAI2392Lvimqz3u4ICc1hPtrqlCswBhBA0HjYewqpcyjCcBggdJtiPHTdgPY6PH7WuwskkuVMVdM5aVoOlxHZ4hqYkOJuaRhTAELSwEPqhFygHEVinSAAGVtHAv0JrW9DV214wf0H70Fjk6psGVbm2f3LFLaA10tNRwWxvYWXALm2SVgXJqiMWdFGX/3IpdPIcpJqrxBeoL11oXut7CxkqE0dxuULdBaXc2d2tPxrCpkgOyR8UHTaulnPT8jhsrHGINkuz8afZHOjof7j55GCCo/mYDPcKxyoYajPZezry7nEpegAQ1hPRZHCgTcPpO2ntL7we3da2goFgALRo4WMoLg3akrxmKFcMigDmCOYKGpRxKZmztzPBxI0mVc6lyF4D27GBB5IJ9YsSgRZ8iK1gLUHDiGQObIdj+A0MMmMY7GDZ3qmzQ5yIcMnA4lJrOqimtpW5vZO3BEUg74gYmYytIXSZECGbMWObzQn3LsK1L1C8XbP2+8OZdvPw3X8IrK/1F9u83BgWW47cJMGgL+9obd/ED3aLmbvV9gU/R437VPIxrlxs1VpVkgVGQBgagFvzcKejklhGR1hYPhE0MOATCwKUeIrIe6JrSYodfFBDsrHpiKkAqxTID9P2L2AUpWFyS/kk4Ew5UsEt6iANHIBUUIQylHeLe4HDwUS+4AZBT1KB3OqsoqxhqsFrmCNFSCC1yu4QmrE5UpGy5vA0MwAGC0fG1PwOFbk9OvVxQ/UEAIrqLKfCGMf6Z8gxJwxFAwP5aXQHeYjkli/ZHe0NTmhRjpdtpnNS6jgMkKAXrFldaAK42/9Ktq4iueRH1iBRWQRqZEMKoa1tT9mz+2WtLmHshqYI6maaGTuEDLXCtB2f2u9U67RWugwLqFIPdHXaamIMxHAGUo4KV6L7ufv8NQLsbwdtYS9GGQSEezb+BldiBxKXicvarUEC2OB1nCjymoNjddbDrbcpJ2l1PQhjCoOsdlQGjmHV/e0UaAiRp34HF2QCW52MYK/tyBAgMfOl5D+2zs5MmwwpOXh8QBa1Q6p42sXTX+pFac7ESG0jweITV68mapVuvrTGSqQMFvm5n5QWAwnoPmQmhoKVc+tk3eTYXVcZzAa7njF3KagRlWFv3tgiHrDJulwrYjDEmw1jQuxOZcc3AxlK550GPpDfLYrYUT6j8zkXf+5AFu5SNvZXKhERmRC4VIMSgDGpkvQcBpO9vQIHgz6pztqr3J0NVX3/jLn7op1/CK9/7Kj7+EdVffAYJ/LMAhMcaGMjq67sGCv7a972Kjz//YhVcPR3e/816Y/r7vrfWxW419wIlGYKckx7kvfstPT/dBlvzlilkRagGCmJoiN+b/ChjkK1vfbGGNfr6Ogebh32f7X38c2Dt4qXvaxcmEHYpgwi4HCNEBCUopcpENaugIn1pYMDBwnqNNI6BzBdpnxfgIFSGWMEBVctFqc0TqMzp0moteVpYqOCgwNw3JuCl2xufd5sjOU5RutesB+YIDlH1imTt1RCSttTtAAINI6QHB2mugVxtzkbfO2NQXQiTBmaFaP7taSFIiysnabEi9fyxdncsEGQWRGMZBgDR2YMYIAYK0AfOunuBNIaGnSrO7kM+BgFugS6AmVul3BRv72rKMFdTWSbqhaLrHK10soRBA1G7wElxJqNz61SqlRjVebACC23u3IGWRmlX0NLN0VkCF+qptPsr0u4NYC3AoXecxXqNECEEAkdzH4QMkcliIIquO7MyTSFBLCBxcT4soLAHBceAYNL1dZeHA8Yk1QhZ389T59yBfjRrNQS9h2KgQKwVdB9/g07ZHd3HNVN3BhTk0gymm2RGIYCK3ccCzAQtqYwmf/w+u5H04JAUFBgY0JitYs2lZCH7/LP2baG6JmyuhDESrmatRTAdUpXHQ+D69wCQzMjr5fGxXC8arBoIE7MChEiI1EACswa0tnlIVeS73Leubmv0hTfv4o9+/gfw2U99Dt/xvIICAhpbi39+IOGxAwYnjvERKHjh+RcbwpYGCPrv/e/WaTZ+8fapNHrd/k6D+QqSAHPKil6LVKCQsh1u1KoKiEy4ZkLkjMhcgwO9YpnHDNSDnxrzMBsbkUsDBQCsQ95yMBGGoBchZQFDa9kHInMlJGxi0Bh7gvY2WJ1Ov9QLinv1PgUAOe3tN97AAfkicwBHUzZ5VnaElTVYtxLtswwAboDAfMauiJz+9Wh+t1JE0BSUT9YpTOhFCtSQfGBnUQKGYQuOGZQHUJgrQKAabHZQwZ+TRomvrfA+p9yAgYICD+g0ReXWqj+HuWxcCK0j45NR2aUIIgsgjGLNbAZjgLj6noNahMQKvKijjEXU4u/38RwQOAfMwlAVVjZl62vfH46ZgFAEM6sVFjgiDlGt7DzfrJj6+A8pTVn1cz/lYjrDEszVZQDMpSzYPXEXYLfmIgbWC6ytMCGxIBQCe48ONOuagEr7Uxy0SFCfXuluhMXZsAC+MHWM0lDnPRfNbvB5OyhYMEuLM67fKF3enfHqU3eKOypjKFnZj2JsQGi+75N30gKuhdncMg0UZLtzPSjo7+MpmQFIO2Y4LXu0g6fu0y7l6ko4WBfPlBUMHFKxdN/TchBABQfBlP+QlDmIs4KESBnjKiZBumdwprZ31bpcZyLEAMTE2IeCkQkxEYYYEBMwBUYMrHISALNUVg12Dtseqox6/c27+OM/8zJ+7A99Dt/+/Iso4kDCXC9VP2Fh0Pkq9ivxKCDhsQIGp0ABcAwKbgIED7OO/aztSwMD/QFxdsDdB4cKCpr/ElDlvUav7gPj7lI4fVZptFwWB7+U0mIbVm6DyhCQ5ukWIczZ3BWhvZ5G8xKAjEkYEhjJL+iJ9V3Q293/k/3DdnkYAiG1AHQivn7mVgiWVuX0PYVjC4Vo6Uc1C6UHBC4sk61RkuO9cXAgRSroClAQQAxMIYBIC5lEY26GQBg4YIgBHAZQiqBwAFLUjnMOEFKq8QaLdC9jDHicNFq7goLJaPfJnsP829LiUfY5G6DUOQMadEhMOkchTCFYDZpS2YOCc+xBQEtpLBo5vqLte1dB9de7wnW/clW4oVmHNs92VmHU6mIb1UIqgswELvYcrPNkaIloLeNsispiPmoanVgRp3Nz5o4xOMFknGIJ8g3rDWgdoCxqnU8hqFVLAi5AginbOFW03AtkjgPKYa9xBCfOxTIOYgBq9o9+dkAw5z6AuZ3vfc6Qzjo/d76dhaSi3UZjBWe6F8GZnGjZVzUOQeNvzt7Jzq1xiinoz4JneLjsOKeo2EDFMsapyWg3vtwocjlb0OSZswtzKhUcnJKN3vWVmavhNEa2zrWNTVjLY4/hWrO0PejQNS+ISXuERCYMWcvhz1mqq6HujQFnANhbjIq7F77w5l38yZ/9Qfzo97yCb//IC8oS2f+xSMsCM9nbg4NTowcM58ZjAwzOgYLXHhEUrGnnsgID1fK0N5pTUzrKEshJQOBKfLbD7IeoHz1A6GkroB3oHgj0r+MUVlotQCRoAx6F4Aog7PAD5v8lp6thebgMoCBDEEkptFPAwOe0FhiO8qOfygKDstQo8c6tIGbdigUfLqhjfUF9n04xZZu7C0i37pxe9D3wwMw5F7VgVnQf0CyoQMAQ8iIIKZBgyNqO1QHCOGzVH8zqF0baW2GguVbiq1HdQE2l9JREjYKfjNpWC2vOzYJNZpHsc65uqFTKESWfiBCFkXLCFFSQlSIYPPEj6BLGoBZhNcUKV6agKtwTcQaVNehTpsJwBMrKag9uouMDU7WQZpbGfIjGS3BnvYr7wPNs5yI3NqlSVat5dwCm+uA7NiN156OBAw0IdpavD+Tt11uY7W2z9mIoUgUvZdKUwzDUOSk4IAViE7eUwHomCM5sHJ2LOEE44tABgtkCjrMsg44f/WxzPdvu844kGAxMRtE7FS0GKIQAsNc1MKajv5N9Lv3qTp4DBS4700Nkh8u/tdxR4OxGlxlgxV0MWGRYKlNRqrHkcvKUjPS1chk8BFoAhX5Oi9fvAMFi3bvXUdlRkIQRiTEWQcyEFBQgRNKaB7G0+wBojIEyPYK/++XX8cM/9zL+6ve8gm/7yAvwljHOXBUV77Y/jT1wcLBey35NbxqPDTA4NZwpeOX7NaZgvRo9SHBQ4L4wt3h6QOCpgQCwq8CgVD+TZwJ4LvTe0gXr5U7Lw+qD6fQB9NH/vh/wUo6FQX09JiQmxCIAa1rOueEIWAqQSbRNcBYIqbA89beeGnmcNms/z4qCM9nvnAEHrCe8BkeJ+5WXLwpYVkSWlvnRgsbkSGjOKWOXdG90L9rfLNepD0BSa2FkwjBnbCJjEwOmohkjORAKE2KIGMYIpJ0qrXwAcjQ3guYkU9BnKNUN0tLkeuE/r1wIyRSVs0vOPok0FxZDFWwUrSgodmanELQ8M2sWiLDZXOYW4aiuBGRGTb1Cp2jrepsPiOgoqLPg4SzNKSsWOGZn5odYrxyCxmiY9Urk8Sfn5+yV4daMhgOv9XlxANYD+/VaJ4I2DwODMjCjAMQIpbkUqBBGjgtpSsSQzADfcCaYW0ZHiFpgSTTq/mDGhWcOOXWu6ccdfV5a4HE/qsUbgJEZkRlzVDp7ygp+iwgGc0H5eWEhFIa1TYaegRvupGAZU7IGBbksrf1HkR3rkV0+ifWuENHy1DjtLuhHDwrWMrNz7li/AmWxnNJ3JX9qnHLbNgPPKikGwWAMwRQ1YHVktrVRICAiFqxINZYlWUThr3z5dfyZn38Zf/m7X8G3fvgFNVZJEETX3nN+CqEWbvrnNR5bYHCUklhwxBYA3dcnQEGR9jc9YgVwkiXQmILGEuxmjVhNOVdwcO6Q8pkD6MN/N3cAoR/+96GjvRwJMykKHkLnuuAWlORD7MIJa6nlUAiZ2/v4Zc7dZVgY91nfP7Nq8BhYD3AHDopxiUIKXBLI0pUAzbtfHnFBF6HfCZ8m7Jc+x752wy41gFZEhWm/bAsfY3BgwBqZnDWdc46M7RDU8mFW6yoQhrgBh2wR9gMgRoP3PnC3rqqC9XSuWJvz9C4EMfDpFtFs4MapUZ2zrlcvWDK0u+cQXGBLXTsAKAZ+2CxqKhnCpdLbNfLfXTb9vN8DS+PsWfPTt7UO5qIhIsxBFhHbj2S9rnK+F3MG0BeBWYOYJTCAKdxyMoDs1FqLsNIa0BS0KPra7lJgKIszcARCqTEP4NBcN+sz4RXtLLDQ3QaHrDLFi6Fdd1lIfr57GdPHGPXneoxq9U6REYNG26fCGLIgRUYWwSYqeBrcZQrNXJAMFGcw6fSdFPTWenMleWZU7r4WNFDgCv6U7ND9tJ91GZf+N7r1S9eGB+4xq8vSrfUirI2zjDHtD+NJGZpVXu7RZOijymT/3R5YNDdv6EAEVY1bxIEAI6MgCyFb7luSgl9984v4s7/wg/hLBgrcAAggZAMHQrqItXndijVwm677so67b9y98dkeS2BwBArOgEo/X6WzEjx//BwocDrKQYFTqPWjp/9WoOCQpQKCnv5bX+r1ODf/9d8EonpARws0HAJjiIwpMkb7HFmprdhffhPatJrAGtmvL3Uf2BdsXUig3xg4yEB1KxABfnITLHeZnJddPk9lcQzMrWnrU6Bgb593KevXs34+xdb0CH8aAoZAuJwidknBwcUo2BdGKoLtEFBCwQgNRlP2QAMUJZgfn5tvvI4j33w0cIPOfdBcCHtjoZQ1WNahKIJOCKpgEWFkZAiz7U1ArJ5uW0cAIupPDaTWOCRDa0cs09H6girF5qlBX8s8/1NWt4KDFpil770MtI3MmHPBEBhTEQwWK9Fbr4W1mFOUVhyLu4JjJ+ds69wzS/2c59V5WYOvdcqvnkEx4V1AmRFQsC8EyupSYAN0ZGeJAxCCVSjMrCwHP/w8OEswF2MKkrIE13PGAzvHV1bS/JALHuyTzn/ON55pv/ebISBlwSYCm5Us2cCL57QzJsY29vdycSfR7uWipklR5dW7D3pQ4IzMQm4ACB1AyCIa8LmSO3X5mECizFMQoBjrVIRRGCc12pwLxgAc/AcdOOllazHwkPrMjofIY/9/FkIoenbVlnLXbAYQEFi/ZxJLRLe/86AKk5cA8HfffB3/y7/zGfyH3/XX8K0feqHGZWhejgIBrZBKtdLjKVLYz/EpUPDS33wJH8VHj//IxmMHDPyhXzVQ8BCduhgiXXQvoIffEKobK3//y6/rfxUDEJ3Q7qmtli7oPq+GUnvrZB0Uk/Hw0YIK9fsabRxZA7osxWYIqrw2Ay+s4khdQEzg6u+Kgbso5vNo+dTl7r8PZkUuwAFBS/wWO+GFQCzIXbhWfcduH2QtfGztPOhtzuUkKLieMx7sM/Zzxj5pM5yUysmU0SEwxkPCNATsU8HFGJBGLXZyMQSNRoZajgJWH77NGhYnUWsILHLxsfR/d+l8qT5TBxKyLDIResBZVaEoKGBqsTBAb9GaAFqBg5YTBggDTKEW7lmO5rbprcE1SzCnFgTmKWPuSivSamm0dfYc8oLR/KpzLtiUgBQEU2CUyIjSAoCF1YIsaACWOZ6cswCLIlxrUNCflx4UeLZPnzFU17kTqcwGAnJBItb+JObD9/cjc/gGjo01kHB8HgwU9K4Oz80/GMP1IBVcHxKu5oIry9W/OujHfs61wZOzkP15dlkwJsIcg8YnTafJ5mBuGNbcII0FFiho7+5lvbb2d/29lA4UFJOLfaZWDwpOyTfXiyI1rOjkPDNEQURgvTQBWM6Ka30uB0g7CzCcs4DJ7heT5ZyianlfwvcqjzNMHncLxKwZQ9nOSAz6ukNoZ6wYNVKK1m1QplZf83/xdz6D/9N3fhbf8qGPIxdpMVv2pHTDOj1s9PrxR/7Gj5z9vccKGPQPvS5z/F7GObfV3//1L+BH/rM/+kivEVh9S4GVNirmu79pnHMP9K8JmAvAvh4CVwprDQiGoL7EIailNhp16z7HyM2KU5p3WS7Ux9ovSLRcoz6Ku2ccxECzH+5if0uG9CEqfIq9oB54ahHWriTQhE+RprSSWJChOCOjHz0ouD4k7OZlrMcpQTpExjQXbIaMOUUVphtXvi5U/broDXZWaUArMuRBW57itbZmSwcUa80CWDVKZ0ZMia19yM0qdEuDgVDABUuLlguCaAR9XoMDEFAUI+hWN0DmYKevcOkugQZSUMHYUdntVSqtrlGL1h5CQWRlYEYhFGZkATbC7TwFs4tMSDr76tarsLmcujkDaNVHT7AbDSyUY0bGQEFfFEzX2FgtO/OcxeJRDChJQUSotQ3cQmZ4lc9Y+z2cOgsOEOfSMg7WoOD+IeMqZezmgnu7hKt9qufZWbBzQHfIBcmCUvvh7pEYtPPlvgiIC6IEzYu3ZyF+9HuZzShyw6oyfWUpN3yckxe9snNXQi+H/CvKghA0ziOgIBAjFSBywaEoAD1wqQGAu5kQnb01NiQRqkt3zpoFUBpVdAQQHiab14MtXsZL0pyLVTg1/p3f/1l8y4deOPr5TcDpUcZaP940Hhtg8F4e2sdawXm6h3Q/D6x5/3/vK0rv/Nvf+Vn8qZ/9lFFaivZYjOIVVbJFrIpgIJjZjIZsGVwEhVQpONj09zo11szA2p81BO4CXrheCPUv0o0MQTQh4XXD+6hgXxryC86WjmUKJ5gFsHY/ZBg9SPp7sbvcImqlqtBsa72MrrXfRRcXAhc+Ur/XJAZPHUJVSodUkLIyBS5Ed7Omo+Xc+XuB6j7R4FBGllD9pTpil0KaAMSjHGt7JVO2XSBlfY5WoS6b8GwWeXWbV2bCmSdnoTw1ykcRQQQjcQEya4TyyqINKKDIKtRd2EOUbsUy26Q/gz1F7KCgshdyDAr2Rm+v/d4ODHKReqb0XGpAVoFSvyfdZGa2kgEXFK0a56Cmdzn53xdpVmwpSxCpNQrKaUamAwWpoziK2H0ODuwVoGUYzV7IGEM9u1k6V7adfSJNgZUaGrZkY9ag4JBlwRTsUqmg4P5uxgNjC673ucZ0rH3uRBr1PgfCxqpQM2cEBg6JMEUNpk1ZEEiDVaVQzWQREbuL9Mj3En4fZZmiCnRGRccWrOVFz1KSBUufylJwZRWp9YdJmWpGSRLCkBkzF4yBcMiMFLVokQaC81EguLoZaBH35XLZz1U/j/VwI23txnU57AaaF5iLbOwMGUgzg8xduQDwLR/6eFubFXBiHIODRwEL71U/PjbAYP3QR/LGlH5xYWMnmMiKDwvVQ+xWLezz3/vKF/Bv/W2ld/4nH9BNi0RN5xulxZ7cDK4bH7hgZgJzMbRezkbK5tWt6oNgHKH2zMApMLBmBxQUKDvgaUsOBjzOAH7gTqyrAKBgShwEKoLERQMTndrDyme4fg0ReL8F6oUN9OIRWkETOTGPmjVyYjRfoVN0jZ4/WAzIbEGIOReUFWMA6BkogZqSKVrPwK0r7r6OnLEjpYuTKS8PQBO7uTWQslvDCgTcwpNmefXWiVtTx4Vom5BShagLWdgYh5VFG6FBcpVtMWGfTZKz7UfHpi5SdyuIkRZlnnJrG+455D0o2Jsiy0Wts9xZisr+Kq29GWz3Y3ser6Wxz1o1kINmsZADGddA8P6S5+fdg0gHDBlyxMhUANYxBf0aL9a+smZSD28ughA0ABSi9HFhaGYPGiPjMxe0s+mxAbWegkBjH7p4mV3KeLBXpkABQcLegG5KGSUf1xholixjpoIYCIcsmArq3Vi4PM/Qo+t76aP/7fW9rPvgz7uWZzgtJ3pQsEiTPiOXYqAqTxDMIIgKdlJETeVMxQrDxaJl53NBXxjuVLZYstRV4L3JZjfS+riuU3LZqyxqFUSqFRLdUPPX9kBdXwfCEgA4uPD90f+nCh76RfPsvPdiND82wOBRH9pdrQCBTWD2IIBIfy6kVu8vfeV1/Nlf+EH8h9/119TnY1cjdlSWuqtIFUR0OlWFtKerzJkwB14IzXXg0KkshWWlLqvMF8IqV/b40K3BQF9u2TGNpuu1A9UPvwfSCVsBrPlIQEJp7IE0gOBjQQuegLRyCgGc/lEFb0X8tdqbLbIwur/u1zR7bIKBArGP+voWHZxMbDETdnMBc8aQGGMqiGaF7LNWG9xnLcgUiqYHJqhV60pBWQOfCwwItGdaK/614DmKEehGceVuz14KqpTNQLVoM6ni6gEAmeuGbANKt+DVYoQJdekt8ebu8BzymonTgYJTablM6krLJWOIXbg5AI5UXRT7UkAclKaXoEDe9LCCm0ebd6ngwNakyIKRqevYAZci58Fn2482sgi6tlAokEp7+1ZmLAFGVcwOtgqqgbC3IOV9ZRB0TfUj29rKAhSUXI7OcbHZENMqwLmTLd2zrGOJ/K72Smfx/zh9Kk/hC+ooWf8yLP5/yRLEwBbI2RRh6JXfCRklYgxUCcgRNa7ByxYfgYQz1WO9NsxaNvdrd04+P6psXn4sXbleuAxo2TtutDmjUmW1bQStf94ZXD7uvqENlz73HkAB8BgBg5seulKOdqo9VtQtD3HWwH6hWODNFy2P9D+ylBFIo9U3kZCyVm7zQ5YKN+o1eyaD0lkps1FX6rP21LmHlTEGjst3tsparf62xw1oTW4rnNG5DhwILOtzN6TZj2UFSF0bseci86UOVklt4V7onqG/8OjWek2HrVEvsPyeRGrgIpP2XmAGqHhevK1DpiML31H8WvhJkWUAW4FRqqj04pwJpTDmpNawUrBsFl6BFLY1QSs2AixKk54aLUyqMVTB5uiVDW/UUGjP9yijwwyn6Rj/L2m/8jDXjdTz3uWsF8tWsJiDvviWB1v5nJmopti12IWCWNhe/zy1LXZmb/S3rqzYRxlsgPMhS9+qCnZuN6ApWwdh9f0XJvbSotYI/uZKSqV0Qafmmkml+sF9Xd19cOoc9zS909un2C+XH8RunaJ2ymZTPm5A9PLB20kDa8De3LBeYAfQ+5+tfHQf77J2G/RK0OWUvr/+/FFkVa1EGBilAFscp9ImkZqJ4jVOlpVp43sqMw80+ewpol56vpfPPUPgqbvr2C6XlYP1Z3a2pDfgFrKbaMGo1HNnP/AujO8VFACPETB42Kg+I/teaS2FsUS9haafv2gVp/7KH3wFv/cjL1ThyBactwkBEvqoW6p56KkU5AikzMhRFBRUJmEZTNZbKutKcT7vVosAJw+aN+tQn5amfwW7TJ6S2Lf91MNGR5QToEtQXAiLWljBgrfcFcNGvyJo6l4PEPqxiFvo/vNhqBdoERkAIYg0dGf5uwUCsZS3RHbJQkHMmpY5WC2CQMWq6hEkMESKKV+NA7ipSem6UloRHFGL0lnULPqaxS7sqdHHUOs6WGtbNjbG2SfSstUlqZ875eZCqAqWUc9FP2ihsPr/OPuoi3ibFSnz0OGgYBEHUWSxfrFIvTuVRi8rE/IhY8lC3fSLqPPvn9/33QczwEJgFpTcwIGvb23TG2ydYa16WZUa4VhZndt3oLl1/Lz42QGWufVrcLI+h8tHpXqO3U/NXtKXXT7ofRg9VTlYB0AiZRItGymQ9QtxQI2lJVrsexGcZVvbIVIZlosyr970y0cvG3oGszdczs1jeY7JZJaunWDlAguEVKjGm3jp61o90VPOqyG3DER9r/K5L5jm4MBltBb5OgYDzpT4cylLciwXCT3Aaz8jWyv/e6bGFLzyDYAC4LcBMFjLjx4gODgIZpWyXbNf/tLr+FM/+4P40U9+Dt/2kReqjHRlCQCDl50ly9O3FDYFCqzpObEDCmgNkfqiNU4pryuXAagHrR4GPvZJuYtA+3/r30T/PdYDUxkHv1gQtFr0XcQ0gFqABYQCLUiUiaw5jvpHyRQz2YUkphp/sA6Ecn8ho/mSHYj1qNcPOneHu/qRRX3MGjin6MQtfM+HVyo56MWOjENklMIKzqSlh+r82P7eaE7353XW4HsZ/us9qPFn8OExcyS6jwJlX9gyryIxImk9gshZHTWZUVhp0Bj0jCxAga2hKq8mYFV5YbH2BCzW+xT1WAW9/36l7Bt4+ZcxXOkCPt8G33og2c8bcBdgs2JdmQXW4LJQrNaGCeWSqAUNn1lfrcxoAbvsClX3C9RZ2Qxrz2vngFb7381nfV4edfi51MBngKMyLD0oCJERrEz2NDA29jF5LZPgRbyoltNWC9Y6L/LSiFg8R8eGsRlVlW0V0qp83e/62js4qPvbywVqcqFvZKZMB1WZpmfYqBV3T6yzPaL+lgZ4UmOypKXbjiHULJV55XLoa9P0xa5OVZb0MwIsZbQ3wntUIOBugv4sDKEZSH4P/f0cCPRsznqfXv8G3Qf9eOyBwXr4+rvxEABNyykaV3D3S6/jT/zMy/ixT76Cjz2/THlUPaKvMDEvKsxVC9vqqktE9XWeyuV1OhZo3y/m6UoKvvkrH5V3/qIlKxD8QrEDAQFKsva21sEOXSi8v9+qgyFxAFnt+gAgFEJCowwTYP3pm3shstu/x2vtArNnC6pV0AmADvjWvxUHCaUTvNDuZKirhNr7vT/WfQAcM2FPQAqn/bO9pTUEzeQI3OI72sXX/ajPhAZk2B62PrOdD+4WQ4oK1AB9NlE/iQZViVGJi9zsgpRVEqyr8lXrxMBBFUzEzeqw+ROwWO/ewnDGw78RUku6By/BFCWxR2CblcdLP6un58aVT9bTt9wvG7rXYG60dqWPO6VL5O6wxnTVeS9OnN4xJtLURqO+IzESaQfTjAJJrZ0yYHPmtrYAFszcaGeiuvCYrAupzpcd4HKj7Nf7X+z/pbRsJDIwptS5Ra1T6dYIGjUf2JrvEJJBDE0HbmDBz+80BEwD43IM2I4R2zHi9ibiYoi4GBjbMWATA7ZDwBg0ij5Wg8OrTp65i/4sBd3TEQq1tEYyo8Gf8aRcoGOWoFrZVZbZ3lpr6HPyS1+vyS9iRvBKo1bdMQsdFb0qUd3AW1nGJvSFx/reGadkdE1ktjgBV/RrENDLQI+bcNmht7ytj8vzxXphCebpxF0gtEDDfxZQADzGwKAd2fOjXlY77Xd/7S7+mPW7/tjzNy/qFMnC75aBeu7vgqD1IA8aeyAR9f8FrXgSsMo3t7Gg23ACXVPzV2ncgalIAwIKBtqlQm0LvAQFumB23Fhr1AsHkHWo4zCAOepBdNYAp9mDNbBe+8mcBqvMwUOsAsAoSmJTupoeSoQa4twgVD/a0XYLJFDW+hJJM0X6e+6MzGYIiIHMygqYhthSPG2NY+2+5uCrKz0NVHeNnQ7joTqrwJWvRcIKKSidgnJYuao6XU0umpLYNfU9slKG2NxJHl/i/QmcHu4twbXgJwNgLvm9XLWDF3fdxKI9GhKZJU2wIFs2qrWl52ZqC7yur1GVLGnBrUCmnPiY2u7jRdZK15WVkC+X7lF1OzEgWbXUVAEXa0GopN9rcKQszq6zBcwdKAi2zuaqC4QFCO+ZubP7LxbTZAI+kFLy/twxlwo+1OrXttweRBuYsJszyonz21c5vZgCLgwYrEHBpYGCKdiesHcStfNEp+8hiEDGJjafmFbhaxkYlrLIzsA0tqyt7WmW4LQcs5bV3hnUP26QYeSdNg0cMEeEEK0keUQG1XLZfUpuMTntQaK9jD4nn3VZljL6FAA4FyuxZhkBYAr9anWPV7dh6Vbx13z9TPbBw/TgqfHYAgNguRinFsY3UAh4/dfu4jOffwmvfO+r+Fi3qOd8tFNs37QLSpVCcwZBsPR/OWgo5hns3RNLwWSv2B0sF+ZVwHesAOUDcJhBdpm0YU6C1/D3Dm/ifeFX1djI+sPXkrg8AGEGyqC19UNEiJNaJnUhpDPnpQUgLWVJPcRsf+sCwanCahl4V72uJr7Pz4XSEAYTrKq0Zr8YpCmETrE1JWLVHkPGEDTtazeX6lfshwvjMbjVpVUjt0PAyO6jbcWkmFCtukg4epY1uCFStqGmzNoaFheopYGD2U4Asc4zsgXx2WHztXTrZGJeKBSnh9nmtD4zqri6+dm/TErN13LVDl7MdRNNwaqvnM0H298Szd9l1lrx9Tx3Hes2Q2i0diCMgTH1gIaW1HY9LxU82tnr5i5Qv79eMgMHRgHH5lOoYN3BAWW2gMDza+vBvD0oiNztfafQbgK3RKEqVCFAGGbNygIUjFGLQJXB4zFCZYoOWTvznTq7gQzQDgoILicFABeDgoQL6/txOUYMxkQMrB/OFhCk9vw4dQe9cmOgoOtt4CCtwJhTOSIA3SAPeuNmqCyBgNJeAUE+6HxyAhWVayQFkmZINXTQ5giAQqydTWE9KSi0jpvEETEMkDiggI+aswlQKzmK0CPJZ4JZ/Z2idoueut9zZV5fp/va9chg6eH15yfed81K9nUKPrFK2X+voAB4zIGBj7UgaT/Tf19/4y5+8KeO6RfqPtfXsIMYrB0pdQdz6adHRbCKBageuDVwAI4tbQDHh8pRNtAUvrMCDgYcZZeEctC2wJITSpqBUhowKFkBAABihoQIitoCloYRNIxAsa6BJQMyAiLgOMJr6RPs0heTG+LBd6tnWIEbDyhaKNJzdGHdDL16yDOIA4YwIMSg5YGLljsNuYCmqELuUKr7ZTRf6mZgXB0ythY53+cs98WihkCYhoiLURXYxj4m67i4iaHSnf2HCzV9hnQCIS2F6qLkLtTkpQJQCFqgiDTwLJH6P3vqWPdtWahq7TMOLvSZqjVbrUFvYwzombZWuuomWc9PwUuBYIiqXDMCMrIWKoKZiIBV/CyWddMAAxMWve7HwBgjWaCodv3Tz1zdN650m8JAi43xFt31zpn7y6Svp422oYh9Y2u7z5Ya+Q2u7cB8dn4LcNvtv7Bo6WxusKwyiKJNjdzV2NIMQ3UrtNTnsgjudKDqjMHFGLAxQLBxMDAEbEfrGMp9O3HCwABLBqX5ofdP2NpIs4A5NqBjTJNYDBAeIg8ctFeg7WsoGZQOoHwApYMCgrwHSgLmA2Q+QHKCJAUL4uCzl2chAmboIChLwONUm5hR15a7sgkGIBSo+Z4sjbv1qOAAS8Xfs3FNb3iXKKUiTukNjyEZrOaxnw/rErIY7bV7UPA5fOKjL9TfdqbqGxmPPTBYgIIT1JMu6g8s6Jd2jhvyX/ev58M11pdnIYoab6yb7IrBPqtlThU8dLM8nrsLwz5OwKk1p9nyDKQZkvTylDRD5oMBgxmS1u4ENKEaB1CMoDAoIBhGYJhA4wYUM0SmJRXYgQO1DKiWHvfUwn4obUo1ir5a9D0oyE4VJkDKsXC1dSPSvgOUEyhEcJxQW/cSwEUQEGrxpikQrlLBGAm7QYXmPrUqfafapnpvic2ggVoOEC6jCtqxp2B7xeBKIc+aby+9SLG9t06LHIaqIKgeF42boCL1WZNYbX457eNcF6s6pbgW9KzP0daXzoCX9fzaECAsKnvp3iKDbb4eDLuO4u5TbMdA2BgYULBF2ISAgVHXtIEaZ8Xmk/MWY2MgBeCAgQLygpVpwMuZg/e8tp3/fe2PX89Pz+6JvS8ZCIOBg1Dn528tAKQHUwZUYmjn0gtIrc+t90HxZmneNvzCPrvrYIzqGlGwDITeOndwLnLy7gkHkLBrTIWDHLX3hskCcdqwaNxBAS2cfD2t/lBQkPagtFe5dtgB816BwXxoMi3NtnidTDM3gss0hAgZRgUKwwiKIxAHZRN4AIVoHVCHZXxCJ59Py2bbNdcRDlaztDPanwNZv4qvCbUzAoDmncmKWN2oax3h7//aG6+p/vq+v4ZPPP/xlWyXbxgcPNbAYCHQ5HhzXnvjF/HST71s9Ishre73SMryootZggBovloq2bPL3xQaOnCgkbTtMPQHYznPRun1Pjan1XowIAYGxC6Q/x+KU2/SELbPjhk8RGUMQgSGCTxtQYOCCowbo2pxEhwUo5zB6qd1gdCPBSigRgs/1I8o+RhZExslqzQh5QSKI0IYEZgRrdRr5IIYIjaJMaSCKTE2Vk0uZdQqaOvUsFPKa2PKaxGsZaBjsGehU+BmJVyFGCQBKNpYhzmq9UKkraltu4kAKlbJUAISaSqVlMWJrpXiPN6BO8UVTMGeV1wZPdDVKboCO57fcghAankHADtosSevVZ8Ii4ybegaIas2NwdwHGwNbQ+RjUOD0tiksMtfYjfM2pRvCUN1eTaQ24MX03ta2giyiBUtwcn7Oxqz3nkypGvAijqtbZStl6x1s3XYpa/CkFGyH1m2z/gVpnEl1zXTs1qWtrbMEHlcwEDqr3Oj6Umx9y4l7p7FH4KApvzLUVQ3GHLj7SXluYw4eQRbcBApkfw0cdpD9tRo8+2sFCDkBOaHM6aRMIyYFAswVCCgTOunnFUjQuKqhPaO18a5Mpe1jW5SVwu/ve/3s5/QG3eCHjbkaapT2ut6UWst2d6f67wN4zZmC73sFn3j+hSWQ69g0l9/vZTzWwEDHUtn7eO2N1/HST7+MV7/3FXziIx9D601fOpYgt4u+CHwBaL6uQTAkAsmtq56UAuqTy4nN52UEkFu+1A6D/3w59QY6GpLPQEqq8CsQUDBQv08HyGGPkjLKnFFShqRU6cl+fiEG0BDAMSCMqmhLmkGbbfPnsld5oIZurRVuq2HfCQShBbTtA3Jq6iVQgZau7Vx9ie4mAQRILSaC+nWkYIj/AOQRFEaMcUSMIwYm7ANhSII9A0Nk7BJhlwQXibHvip70qUjLPOQW0Lex1rWR1P9XfbMmYFGSWl29S2chGNDAoQd2SgY4g0LBEAZwsEBTAmYL7gykaVZMhGj5sAuLwalLWgaktjgIU6y926k/zyfo7mYZruYXCZQJZG1jGQUcuVKokUXXvNbuOLZqT9Xd2MRg66ksR6xra9ZsnpWBqW6mh827A17BBH0f6OUBsxZ09ihr22f9DAsG5sz8Sj6990QAJz23UoAIDB3wck8GQZWtnsOCIdLJ9tDrM1sVfwdmB9YMKj3LFgfRA4J0aK5IyUtZ5oolKoCRMABF/fUC1GfSmB/rEUJUg5GZcVIWOChYxGWUbAHTM5AOqhznfQUFZfcAsruusi0fZpVrc0ZO+UiuMRMoRnAMYJNvNE6gaG7SODSQ4N/HEYjRlLJ1RO2Vdz96hb8GBSd0wnp+RzrBlT+MMahyIjTAW9lmWeqv51cNl0RW4GANPh8+HltgcLQUXe7ra2+8jpc+/0P4iU/9OD7x/McWm4fKEpTGEKwC+QAA+ytFrWnW4JSSFbl6XYD+ve0wkFFcjVI2C7hHi6ERb5LdL1X0tQ0lV/+aswWHvfrf0gFlTsj7GeUwI9vFKSlpIFhpKXrEBDAjM4NjBA0BYUyI24yw0WcoMAXugXMcIDkAHIE8QzMF4rFAoI5ENVDQCpX4z6QDW2kVYGT+xJwsCii1NQHUb9hZAsgHIIxAVuaAhg1CjNpJLRMOuWDggE0EUg7KGghq7vI6bbGvEeEZCWtrdoy0pGFXVtdRjAGg6L8Y4xGiJnebMAmmxNzCZSIDBbDiLMcNahZBXFjmf6tixWlGJidTrh0YxmlGxmNMQhxBIVQggGzWN4DIEbucMWTCzK3A13pdPa/bC3FtLJBvHRnffN6HNu8zFm2L6wlqkRstXGncMGDgaMBG1ywUrV/CFnDoMT/rte2L7ZykvdcxPpXRMHZv9YLCGrND5lIgEUiIGOKk80t2V0hdYkRBXVq5YB/loec1EjT+pTuvziIMDHBJ4P2uAQI/sz2z2N01+F1LrAF9EMBaSlM9L+bGCRpcWshqFrD2jjglC44MhJJUnkhuMiDNyhL0oOCwQ97tkK4PyIf5obKNe9kWA3g8IEwDeIigOKp7wcFCHBZxVgjx0eWyf3ZAWHILvF2xGQKo7GI2VoIUlIQIjlZkOx8qAIPFc5AIxHyvd2/SX74vDg78nADviTV4bIGBjhXlaKDg076oH+2QVof4FqCgu/hUTCkDkKt7kJQqpSVuyeS8KPIDoFKDCAEeJOXggNjjDeywAPAgGmcnKuDwaFwHJB5DkA7I+3ZZ0mGGpIy8P6DMGVJE0fXqkHIM5kpQtqD/f+0LxWolhFjjEKoQpKAXmkMVCNwJBA+sJXv+RdCRr3fJlYZVgamgQPbqT9RnszXuBS3bpYl6gTFMoGkD5BEIo1pEcQIPGwxDwMABc1ErduaCKcTWSljKUWBzzT+mVifivOJqwrUCx7XlZS/MHintdGzJOn+ZAJGaFuquBWUM1GUZhSyTpZsrltSsB1Cesmar4nI3lLvGjgLMzCoJg96DkAFRC5fjhJGt2yTc+tMugUwBOXguOB8BGVe0NR3R6Hl3d7SYAqPm094U74pF6iwyX4PKIBFZZcug2h/G/gUFXggEKq39t5buPb+uMCamz+ioDJGBAsr75bp61Pxq7ynYfaHQ1laKra0gxBFTH0xrLjEtxhSwkVYPZX1eT8WXOPMyBjurh52uaQUEB8h+p0zjQ+4ZRXU1ohTQtKmggEpCdYtyWLAGXmwKIhUQqEhxWdAbCAlUioE/lbfVPTqr4dODgrTbo+xnZQ1mlWslLYEYMZt8I/AQUKYRdJgRD4MZQQeEaVQDxAKuXUFTHIyB4mrhy00yuQMFvR4AcFoX9HrAXbgxQoZJ1yjtIRz1VIqVnGeACvCLb34Bn/6ZP4af+NRnl/qrfw8pDTB/g6zBYwkMzrEFN4IC/1vzHbXc/1WU/2EHACh+qVxRm3Ur0g5MfXnfJFY/ozgSZUXiflAA6GEBGtpbHTYRUSVkgGDNEJTDXAFBSe0Dq3bDAFDs4oQyQHJBBJCZQYE0CCdESNjppUmz0vrBn60DUhRaWjP08HtN9T6dpwkDLClhcyU4U7AIMpr3BoiW2RTUIW0aJt2XcVMBAiUNUKIwYDNsMIaAORDmQq3AiWjhkxojhLpNi3SqnppfuA7yrJRnz3SYgD0lZMX2m+KgDFNQpaBnbqxKbAgDQiDkQjXHvtbUX51zF7RH6at9QFkFBXNLXbV0LzkRkEshqiuBAkSGulcLC5cJnK1yIAky65rWSqAn5gpqRW3W/vqjQLgTNLevLWTVgqpLtz1a156VsXTbbMCrCLT1czmeq7t1Gu3dxWk42LKo+fW6Fme6uv2XTgmomyY2cNixRhzGGqCbmGqhnVI0z77H9u6d6AsEBV6B18NO5zbvloDgsFOF6/76jvms0f0xKgBwyt0vx6BrjsLKHlaQG2q6K1WDlRpb6y4doLkpq1vIgVJROeMy7rAD5gPKQQ2ffJhR9jPStbpK88Fip9bAgAg5KDjgFFDmDB4CJGXwOCjbMGeEKYGHqPsYIiQOx8q7k8U65cYYwFPAXeb3OsB/pzunYjoAngnm7zlMIJNx5bAHj/bnttEE4Bd//Zfw6Z/9n+Mn/tB/2gIN1y6OfvwzsAaPJTA4Ne7+2munQYGPo6ARMStWrRUHBXLY669f39cD0aXPuFUuWRYClyodRRYYY8ExIejh6IJPpNbZXCLUik4dgNj79SxB2c8oKSHvU/0/5KIuhXJMQVIuCDHUyl75MIMCQ2JAiQncuS4ot2wBWfnP/eh5Z8qMBg760Y5wY2YaW2CC3wHBvvkTURVtB7YcZHl65bgBDdfAYdsBBGUOJM/KIMQRYwzaj75ITQsT4KhYjCvdRa0FS+miNDdLcU3H9mlUvRR3RihEiGVUeL67ODUep7quXF0LQClU60OcVWAezNlT3Ec54Apcis+zd4GhubrEQVccqxKrAM7SVkMcwSEgECERkIS0WA+30tM95FgwRitqXrtS3pCm1sXU3DRnsUDU9bpKKM1iDYNatkHXNYtaYzetKwPLOA0DhmvXV8npkfa/rm3MCrws68lpaU/FjQXI7OXIbz6nXsyqruV8MIZgr4F8PSCwQD457BrrKMfzdUrd2T1bQHUrUlCfubMg7D53WhgJdU09jqL7uqqsTu5Wg8FdpTXdOqkyd3DQyb6z8o0JyMomKCNqij0XyDQgAhApCKWAc1ZgmQ76zK687ZkBHMniCgJy0hoxFtztOgD2+m0NmtHFMTS3hRte01Z/8bBTVmtShkso4LWv/Ar+yM//G/jrn/wxvPiR7zhS+uuxYA2+gfHYAYO2VE1xLWIKToECr9Th56oGIJoPvFL2s1qwwCMH+bW3YCv76kwBVSrfMwNqdCoamKhP0zERZdaL4sE37jooKXVMQaqgoCQ9qOu2oWQVtgKAYodV7G84d24Yac9D/n3/fyfO5xoUHO9P/cW63tUt42BrFTvhl26xpnbB6LBTl8L+GjxtgcMWtLkABrPw0h4SJ0gYMIYBQxxqqVTPWa6vW333vVLISws2HUBmKXq9CMz7JSW7elFxajanSs3yqGCg+m1DWfjHA4fKCpQ+kMsW010ztTZBH09wKm6jO8tNgRntaa4tEOsc06xZKj5HGWqQFZUMiaMGJjJpIUFS370qsePKfGslxp3Lo65t2ut+9SxdR3cvqNt+ziEqo1EKSEZgaBZS/expdv+81jUfTs7xkfbfYmd4VOUiZbD55ZppwxwQOWAAoZgL5Oic0okiZ5XJmmsQn+yu1F9voKCycp1B098rl0kUR7NaGZgP6ovPSZXaOgj01P2u11yaS3X5H/a5LOTM+v8kiwKAXCCm7B0UPJJ8M2vcS81TYCSoAsz2nlxEM7RKqezucqpd3IDJYZdJJWUFA/79KgPsdFBkVvklBYhje580K5M8H4CB8Npv/BL+8C/8Gfz1P/gf4xMf+VhlEW5kCxYTb+6ER2UNHjtgsB6vvXG3i978GAAcIak+NaeOLsJ0EfhnwTl9dGzeq0DIKUPs8PZV3wCAmVGCHTan6qMGeXEMKClpoIwzCWcyFOphzKIfHuxoTQEqKi6tNeti2KEvRRDOlN48+v1TowZ9tTzZtZXoP3OBUIBFTnOzKwQO5KrCmg8VFNT4iSzNpQBUkMXxAB526jOMI/J8AM8H0GGvPtFpC4xbFZRhgMQJxFEt8jCYhO39cLb3Rbpo856STzW/uiSb56GlUEmeFz7Gnpr0PHwYyCrAgjbslZn+sMCLIqn/ttsC/72uNsEpUFDBiysvUwgLN5W/XQ0yjVWJlZwsetutcDFLUpVmCIP21GCtR18szgSrZ3FFvChqtfbVO8sx71FcgZ2K5enPIYdqqdMw6u846PoXta4rUNADLnd3uOtvEWMUBl3blEDjhCIFFLOyBx6QG6IaJXZGiaMWv6ETZ1REGSx3e3bACodrC+BTpqDsrwFj4np3I9aAO0ZQIISi6ajq47ZS6WlW12Itv9Pf4+PvjtyXMIBGXiaa2j6eGufkz9GvSfv9LiPM5SA6uQiovKQYTLZrpL+wKnt1LZywuC12RGO2koIRW7/s4MrY4ht1QMwIMaCkiDDFJhN93n7WQ8Rrv/6r+MN/58/jJ//A/0WZgtU4p8v+WdgC4DEHBq+9cVfrFHzvK/jER19YIqUOvYrXePMyfuthVJqkuRXUyKn676XS9xoh6/4uV9YAIIGPomVTUSq/AGAEFCRF6gDIT8t645kUAAbS6m6FAWRQUJ+fU2jE6oB0hCxZwEwo4Fo4pbbnDS3IBujQdnVx+O9zc2z63IhrZTCB6Yp1NL40wXuEVo0tcGtPhVSusRsOCspeYyj6i0cd0AoxIB9mDSrKCWXeq3th3iqtOh1A4wYYpupPJKt+BnQuHCuUU632VWbKkfXdWV7VV9u7PZzSy6bAalxFc8NUcODCEp0yczqcBV5Webl2PXgpJ5kCB1s9KDjyK3cCVaw4TFVibqGXApoMwFULt5XM1n4aAQKCMK2NcPuQDmh1MRDGwPTBp9IzHH2RLqDNt4vs7t8LHCCJq3/2n/e6rkHBTcXEqpDOrMGcFgtRXXNF51Kj/q0MOVmhHfLASgcH7sIrGuvTl0CnbGfSXQb+YW6DvNs9/D4lVVw+AjEozRrFb3eUxNylK6NKus9r15eISlrtvWHgsE/ZJpdZ3KxiZ7CC9q1wt6eyRDpXb+oF4Ei+9Z1Tq1xkVnlpr0OhsQt9y/LFeQAgOS/cxVX22zquMyR6+U+BkZFBMYCzAogAoNR4rlSzrxx8vfYbfw9/+At/AT/5P/0/4xMf/ra2li53F3VwdMH/WQGBj8cKGPSqvoKC71vleZ5EqEXBAZVG0axoL/X/taZHihyL+r2KXhIHBWKd+3o/qJRsbICgREV2lBmFBaEUiBBoZU/XOTID1pXEy62L9iBGgW5igqentcNY7PKIXR4psuj9EAYN4uMYunzfWKnEGhzpgVNxQHELjVr1tiKoaV+1f7mfYlJlUEj95EXQWT+8FAy60IsPX+OcssZQdMCrLlMMkCGCPD1zzohbt0T1w+MllL5NkKxlUCnN5v9dsjSLvGRXDKbQFq6DU5XYev9jPXZKd0vJQMgKUmyNj5QYMQizKbGh0eA1j5mX5YwX6bWukMwN1lvbqUtxXc0VAKrUtfO6UGKuDIDO/dFbuAOIUo2ZOSW0vEaI11NYMDCrAN+jwLguwLeObHP19CxfE2c9nA6Odt4KgyhbRH3HIJCC67qu3d4vQEGfOdOBrQq6DrtFiu3R/gtXOdJT55Xe7V1LkqGV+HKTR+vzWdPjTqyfuw0cFBz2SNcHlMPcspbmdPIucZdeLTEc3cn2y7y8x2huOXV7HMsCwIBYUZkQPHWbWiEfttRBz4rw7AAyg4qH1OTrKqia69S02yS5bOtkXBgiKAbEcag1XHq2tnfpopQFGGvHWe9N0Vzik/Jfr6eDOKmgw+V/KQKaMyQGSPZS1HpXfvGf/t/xR371f4ef/M5/H5/40O9d6iXPBDlyJ6xcMEdDTzxBjeabxmMFDHxoRagfUFCwbj1ZF5JWPysmdK2Iz1phnRg1wKQo4oZ9XlcYPCp45MNQsPuzqs88mCLuDyhgzrICBAvcYwLHiEwzIjMkJRAxSkw1CNEvN1lmQnstZxcYYRz0wkwjwhTB44AwdVXBLGDKK6CJCV3hYGAANZCvGGNQZYHYWhdBYYDEUhupK/oCWKEPLNZcmz5ZQE/S3OCyP67gSLO6cCha1HHKGlSUMoIh+OLBjhY1rtR4rs9T8+AXG2zK1oWwR5z3lLyDAgcLpcsF7xSVt0oCIoRF87btnC2U2BBUqQupNWlKDCQ6T8kKYIGl8qq1E5yBMTeYK9YukAs5V1CwDurU12WQ08u9woUxHFIsyM9kvniedajZDEdCq7IwXYGlHiDM+6VbZt4vAcypADmfa4j6O0QQSuZaaEXEKEZlB3xtSqrpX+KHlGhRrXBZ7bR9DZHTYCs7+1IWLsdqbRIDGS03vWSguZX1bJSsVHvI1YcvhW09+84a3dn0ImzzAcWDducD5HC9yP3Ph7kCAwcEnupX58AMygWYBgXZMZjLMrf3tj1t97UpKpcFxYIl8xlZQFYp1Ysjstd04aBBjcRV7ogFaoZJ46fUiBoXLlIp6hZYy7eajh2DyrWoH2GK1c8fJq2QWasknpG5IqVmU2iWpli/iIIcSC2z1VjrAPtrNdKyu29C1SM+XvvaP8Sn//P/I37yxf8Dft8Hv2VRJXddWv9YjwEnWe/+9d/QiokfxUfP/s5jBwwqKPj+z2lKRz+6hVwCWe18pv3bTfh4TjdceCtouPvW/1v/JgSlnxzNOpUfnFjnBWOgf8NGezUg4Ae3VudyRWxFfGpqXj/EA6KSWRkJ5XDQOIUpV5owTPmsEPB5ERN4GhAGBQQ8DXpZrEIYDVMtAuLMgZedBQXkAq3dXkS/hrWaXqQoGVIVRc7ChL7sKJF5Mns6vxu9/7CvydAAl37PuSmOhAbcNFq/qEIs2yq84VHsVtmtRh/362wKtiqDlZ++UcelgQLzRbaRNW7ErWVmIJOl0jWavgl7619PpEoskCnrbAK5o5MXqV5mrnn+c6V7TWEZfS32XH2aVT9fj3HpvNlLa7XuKCzqX5UYhaguN4tcXzMGTp3rXnSgwDM6elDgwYYOCk6sq9YGAASa5SHOStT1lApIpFuXuq+k7jjxwOO1G8nTlisrkwwclMV6QsTOVm4A5twZyAmECFCXgruK0q9MRtRa97Uy0+JS5HYu12u3ch14QaBTqcx6NYrJAgHHUO9QKbLkMBcMJimoJm5le+1vtI1xYw97WaC5+XqCMmB1OFopa4QIlKDyZpz0vtgZCJOuLRfRoMGYKhN6utR7AMXGEIQYwNNQGQIex9pwyeXaWta6q42cOaC0YF1LKSAWgERjCNhlfzamuOkAZSScie1ZWWMqTP59+j//t/GT3/a/xSc+8C1+y2z/uZObYaXDnAto5/jUqGWUv/9V/Mjf+JGTvwM8RsDA6ZHWZerFhcXmv+WLufB92d8zBbXmOJu/3nNZ9fC/9vX/F176f/779TU5RpSUwUwQ8x2VuqJWaMXf2Wkto8PWqLUeUi+2EYI2ADGqrWccFgU1stYXCOMMtjSvsrHaBikj3hAUyX5AjVZTpmAAbzbAtLUUwBEYN8oYBI3qF1YaPgNVCDgocMHQ9sWAgpMywRouMYGqQOBKGyLGSut7tTGNjcCCnhP345mLRuX3cbBlH5l8FK9hr0Uy6j6H0iwh/x2R1uK1txIdBLhCkOY6cIWwqMQGGNvD9hnwILmarrkeNSpbFobBAhSsacNFlseJ1wSan95eywOq2sigAFNivhDUUrc6a5xDAPIMCVDayOn4M8BgAYAs97+cYjU8nmcFCk7NsxLIJeOUSJOcm7A8sV7qvqFjYdqv/4k1bOewi3voP2N5BiRnUOQa36BAomc5UouPgIIuioMp0/bWffGsGtvQgwJjspwp6EGBpzL3d8gmbZ9D/Vn11Xc5/RqYGBf3FhZXkooegUXthX75RAufqeFs3QhFvw3BXFZWbZNi1rTjNAN5o/EJorVWlBUNoMMMGeKNci3EsJSzg8lYZ0O90dIZOUu9nM3KzGgtkgTig7+hGju+kqWouzef1wH6mSw7LS4Y6p/45r+AT3zgX0OtvMgOwFirZ1KoMV11rqgQwsAk+0PU32lG86vHTPpqPDbAoEdCNSVxoQiWoGDh+9L/NqO1Q6+WYsRxwC9+9R/i0//gf4+f+D3/K/yBX/lfg4YRLJoD2w9KGcJ8MiK1L89JVm0wTGM9qJ6PXwuheGU/P1zOPHTAQIMiraiOFVyi+YCwbQWQamqjpdOsJtZ8b+4+mLbaSGlzAdpegqYNJE6W1zxC4oRCAalo1TutIOjCoLmqAVh1OSBCQKwAIgBIRBg41Mhr4dh8i+a6QIqt9gNxVbA9Egdc+JpSLmpB5Dmpa2HOKMTgOFd/JXJHN/sx8HztDpBIzpZKZqCgL2xySpH7GQjmM1wHMp1yJ9X/C+f/772Mzg1GIUBOUJyPMtZKbGGNO+thsREUB3WNcLGyv3DeePWiTs1bUJ+UY1/9mtVYga1vaEk8y+dE7NA3PG7aL+ZKa/dnoC+rezRK7vzaBizhLpIbzmSXCdFcRVZXZdaYGylFmcM+z34BCmx+NbaAGqMZqN1HN1zigOKti1lLUCcxtsA6P5YiSEbE9BJHBGCIkgZut5EaCRwnBbvFO7o2N5bZFQjE4OGAvA/gcahZAev193RwjUkwINBXOBymJmPXZeoBeNZQZYFMxtLYii6xpXRqgLTKqZwCeM4onE7qgB6w8DSYG7fNCwB+3/u/uVvnfm76UaDG2KkU2/atSTbTge8FFACPETBooOBFLFcMOAIFBUski2aQaaObqKyBde/7z37j7+MPf/Ev4Ce/7X+D7/z9f85e/Qf+RT/S/9+P7b+A1/znE1P7O8PH76zn74x/EWN9rqb/n8zi8RsCIP3PFIDVmC5naTlCQOamOaG/ZGl7ODhYuNcfARQAjxEwOI+EjpkCBwWyDoxhawLCEWC1Kl/7jb+neaS/79/FJ57+H/1LeprfGb8zfmf8zvid8dtyGJtR+9RUtiC2AM/S6S77txhJ0DMHd994/T2DAuAxAgbnQIGPCgoMaRVZ+r9Iw62RQObWGfDar72GT/+tH8ZPftdfwu97378K2V8j/ZevY/i/voDdd/+U9grvivD0BUMWo2YbkLY27tp/0rjRj2lTq8wpQhya/84PxhFO96Ao83N3jVz6Er3niq/U4T5E6ztAMVZ3ggSLLRgmIIwowwaFI2ZzI8zZXAlFkAx99f3iAytu9QYvXstdu74BIR9A8zXocA2aryG7B5CreyhX9yBX95Af3Mf8YIe02yNd75E9kOpE/wctJMS1hbQ3h+JpQNxMCFNE3G6UVvTOakH7tMOi1yutWA+OVH9yrRTY56v3EfN5WRq1zqsGHnVNU0JcvD8NowqEQecGjsdn4BQV3gf1Lc7Aca2Fsr9etOZGTovqbX109FH5bg+KHcaapbKgY/1nVk72FG1e/eJdQNnaDbYuM36qzOx6fr3f2ClZnrb/UtZ0nZXSilw9wjmwFNaFr9u7+vnnc+fRq0D272/7mq53yPt01GzoUe9N2I6I2wlxM2G43CBc3gJd3AZf3AZd3AZtLiHDFjJuIcMWOYw4ZOCQC2ZzJ3jzL4EGJ/uIVgLzbHOyksDe12Heg7L2JGnFmVJLY71JjpnrY9lSebB9/8Zla18srPWc2C0q4Xoxtpt0wcJ1O4wgk7fjT/0BzO668RROi+kq0KZqxcq597qLrUaEvxsT8Pop9/ojjscGGByPZfaBxxT0oGCBukQDkGIRZBBe/9IX8dLP/DH89U/+KD7x/m+GpD14tEApoAp3r2/OsR1QkaWgIeKTPcF7UEDTFhgmlDAAQf1hEvRw1KjfXoh1eeHS5bHDc8NjPiorSx7p7T7KVZOfKuQdsfb5ssBRYF57PtRAogJYBbwTfpr6+4/u56WutOl6iAhKl6LEQdOthAnFajeUUsBWMVFi0MpmHlCaWsMqioOuDR+nlrbCS8VAQj4OOiwF63rpy6HZExqtX1Cj0okXAYhSinamq+lxFilfLIDTo+d1Yjr9PjreUupIxIqt5MV83Hff5tuUbp93L5aSRaFocJQV9uozEgRokfml1EBNxAGSTzgxpNR+B4vW4X2tghUo8ApzR82esnudLR7Cggu9eZf8S1pTcSVdSv141HPQsqA8CNGCOgEQlWWlxtVZhMVnVIBqoKTMtl4padGd0pUQLqWCgqN7c7xbBr7ew12tGUj+g+W3wV6L0WTGqSFWz6PP16eg3Qfrn3DQdYvt+1rOu+u6yuNUFSus3XmTqw68+LRsdVBggbKSLSh6ndK4GlqC2eJEbtAFNWajMxYAgEfXA84a6HyzgYJ0hu2G1FJ9eO2Nu/jBn3ppxaSfiPs5Mx5TYLAEBX3craxAQX9vmZUx+MKv3cUf/fxLePX7XsGLH/xWSNprXjdnKwcKyzOWFpQVNRLVA+OWaSrHVg1N2/ZhFfnEAYHV9EcYtGaApaZUg6kLQQ3kX4od3tlamFrrzpJAHIFpU3s+IM0tyKdPqeTusMYBQp1F5YLzpsYd9g9JEwLr3z4tgrhKiprC02Um1BoPzCjslmIBSIuY1Pc3y0ef5/h9atCWD49iF6uxzzqXvu5EL4jtRc4+/2ItVgGSbRItJ3oxjy69zqPoSchS3zRSi8QEZV9XoJ9X/Xr1vn1q3ZmgyRbkZ1H0wd47szXI4VoLgHJqSoutmQ7sZ73A7AP0/H171sWfuStgVFP9XLF2oGA9t6OCYP4+fcriau0tHUTXlNCazazXVNp+VACeO7CwBta+hmfSxE7WMTk1aiaEBkL1a9p6CHQ1Jvq1td85AlHo7oOB5/W9oXqvLJ3aA377jAQvfFa1+vEzMQi5U0CElSygY5mwnGiXclcDaTWNu8mqACrxWHZZBUxntkBBFSybERcmBQNhUCbU3Mwn5aq+rfZJKVkrUPJs2TjeSMqzMo5ZpxASiJf9XU4ycHFQ4zAOVbcIR4CHLrYg1Ayw5IbtQm+pPgMTSASvvfk6fuinX8Lnvv9VvPDRFzuA+egg7zEFBsvRswWC9lmDELte7IXwxS+/hj/xMy/jx7/3Vbzwu15UxVoKEKwT2qCXsAKEkjshybXPuuS8qKK1oI/HbQMH4wYYN5AwtZTAqPX8C7T2fMqNOlpTgESwVrYE5hExjNb0Z6gd9pxFII5avc+tjeJpUwW19CizgQKu+bL10p0o+MQE5EfTlbrElr4Ij/2ohY64Q+NdmduDRhiHGCBd1TN9Ldtct368ToRXbwwKJrDIbDihuByQkIOKTmiylWctsIw3F7ChKohq/QXP2VuCi1bHwus2rNaxj4Z+yHikrmnrPObOMgKHmlVAIWhdCX8GKWfZmXNDSj5h5aZm0dVf7IDomn15xNHPjbq96nPAF8+5XtOHrRs6oPWwwd1adnNSMAsD5Kv02N6V4MV0fK7hxFlkPj6Llk+/OIvdPBbnPKiSl8CgIpZenbTK64k701c/Deb6BLHR2rSyrrla9FWviqC8B6sUWPrDuwdt71eKAlPRqpCSrEOlWGGvE3KruguqxT1qV9Ogxk5yXFceLlcjB4RgPVXSXkEK76vcWkyfAxCugYNmPnFY1gk50gfezrpzJQLoYguGRQZYEqmgwMGXZk5oQzIR4PUv3cUf/fwP4JXvfRUff/7RYwrW47EFBjexBV6qs2BZgOOXvnwXf+rnXsZ/8snP4Ts+8gJyAZi1+hZgtGSwcmXTtlXCAvQge8oVsDi0fhC8LoC6DUbQ5sJYghEybPTgDhsUCpgLzFcnixoBvdhSSq61sg3kPdkDhhjAUUAp1rKzDhCkzAoSJFuBnG7dyIrrVFBggqBa9cfC1ddBOnfCw9SL7oGWRBXPIaYARK/lMGnHvWFEGNU3SikgiAEyq3u+Lj3tlk+NL3AhN5jQGzofv/t1vV7FmRgDMiUnybrKAZq6FGOj83IrB0wnaggsSq06K2JpWlX4eyXEECpoai9gVmOX7wwDCYTS7Y0ApPUYvP4G1ffU9XXXk1BTCgUJtKrC1urHW6roDYr1kS3i7vfPgoKamqq1Jc7Ny8vYVrDle9pbt/a1uJXbpy06Vd2vL9DqMEiua061QFGzDntLVZjNRcFAgXbpy9rvoM27Uwyr+AL0vvH+LAKL80jcxRiw1ToB6p7yUCprEEo5Qu3n7szC7z20dGoatAAYDZauHDWPXplMXcOyUqxH29l97W4EXv2833tnI8QBjgSAi2LdYQSfkFmgoKl9PSDgqIDAjKy5ANliotYFmNZyVbuAEgKLyVTCEDfaDn1mZWFn6vbKgAmzyvHDrpXw7kEM0OKM+liYaVubfoEjJI6qdzjWInKuD9ZFo8wPjl968zX88Z95GZ/91OfwMXMf9Cv1XiD/YwsMfJxlC1aH4pe/9Dp++Odexl/9nlfwez/8AlJRXyUzteIbcayKlKdtVYAMKIr1oKp+eKEiC4RpwGDS2gBxskMwQeIGswBzklofYC6GELtAnv5QBLtHAQJmBQYDE2b7HOMGIYp2XeMIhATKVrO/ZIiX/BWpAsjTZKr1tXInPNQmoNO/VHyt/L8FNQior6ZIw6gxEXGEDNrrIDpLYDnWXva5b1bilSXBvKiHXgs3jYbMnbmJYwtS8jxtXiqKanmXUgHCwvJ2y8nZIqPDydoq62Ex69aVgVsM/p5ktQC8DGxnnR0psd4XSspa9C5yFECogHr61Xsd1DOZG0XNrP0kXGFwR6nbvPuW4BVUhahnm7tz0injavHVzc+qyHzvi+49iWiraTQmV6AArLfe1/M6GcwZYgsmdZ9t5xZr59qsyvV66tPaPFZgi7Xwj1ZY7Jp+uXL3+RO/5zNA3J6jAoJHOYuJFzS272kwhZrtczpoxb0crWT3iTvTlwv2YF0aJ7Vqo4ODscmF7r66y8M/znIuD9FMVWQs3Ai2V/ZzEuMkTF4tZBWHI0CQrfCSB0W6XPV6KyKnZCqBsiCQytTBgiQzE4YQMIyXQNoBRKBkLs8N2V2mdu7ystNqHXbPq/ugC/QGoPqgKyJXg7sNFOQOGLFosfVf+tJr+FM/+4P4sT/0OXzs+Reblwnqbn6v47EEBo/CFqD72S+9+Tr+zZ9/Gf/Rd7+Cb/3wC5WdJgK4AIviG+7vDAN4MuvYL3ZOGr3vlpBba654Bo9CtXiCQcGAxBEljDgU4JAFKQv2pSDb93Mu9WBkWR1iohrdqxG+jJkFQyDM1EX8xg04WAlajkBJtdCMB1DW0VF54hZRFZ6nTxkRNZ/nQ4b1HdEgHSgrQ5QsmNPcNdNWLSARlJLBsN7pLvRSAM/xyJ9KxIsiUh5pvQ76rECtL9wSYisk41ZhX2XOwJ+3I/agr0qbp1lru/f+bbc+gaqQFhHoBlKaFeEWWVwqMY7VkloqMhOQ0kdvDPXs89gYnOZrhFYrTF66mOH9N47Ogc17YeF2vtwjC3ddKMbdGuoIVWFqVm9dE9LqfwS0csqmXHmtFP01T1jdPShwarYGn7k1+Y2sZ4EFCgoQlVGQUkAjgIMvdLA1NTfNTfvv67cGBKGBghpIF8LiLAKwHiJ6Dr1TqLMHctgBxBYnQMiBQIMW+mJrMnbTnemrn9K4UYt22gLGclaa285o0eWpufWPOs4HILtxoj1rxMCk/pCXc68MnO2nA4IwVNbVM6eyeAYVFvL0YTJ1CIwcgMCCwqzPGQhj3OjZpGCuNGpxB3GwYGZt5LYI9D6hE/rsLwBHReSS6SMHBYvQGQZ+5c27+Dd+/mX86Pco013srBSiaogtV/fh47EEBotx6rBK+/EvGyj4i9/9Cr7lw+o+EJLqI1Dai0BG77jy802sSHE+oPYv8HEiIKa6DpwlGDbIFHBIgkMRAwKCfRbsUtavS8GcBKmU2qQEUPcBExCZERkYAmMKjCESNiUgsiCLIt3MWmkwxgBia3WbA8BeA16OhG9L5+p8i91goNY7LxA4q3VuG8SYG48zKAXIpLXS1W9oLXylgCePANegzsKhVj0r84DQV3Pshlc8I+ajqpI1xW7ovq8WZbNM+ucUABSUUSGvAmjBcjRYIJ4LgBDNksxtLd1a6IK4YFkQC8DYBUwtIqi7dCrp4iCWi2sWrLsiqFHggIKDGoE97/U1ZrfIaAlm1j72Tvj2gGCRxdJbuE7/nplnBaGnGBhXdN/gnGqqre9xbOW7j9aTQl2vk+tZivao8PXMymhThu4XdE0JWFjuFIa2//3e27xP7n/HINSzaHtT1mdRChCGRawQrCmYzAcDSntQUhlDowYc5/0BoZQb78yiSqBXPx03NZWuhLEF7tkZ0GKqYgaX1Dt+DiMQGg5z6LXegRprpNF/CspsX+pvV6bOQIrHEHA05a/sQEupBnY5Y06CfS4GDnCjTB0iYWLGEAibGFBCwSCEIoTChDGOCHYGtV11qMC/nuUu0LsOvz+WRqtndKqVD50xyGVZWdYZA78NDNVfP/zzL+OvfvIVfMdHTsQULKyBRx+PHTDoD2RjBo7/XwT44pd0Uf/id7+C3/uhF4Bu0bMISJQxmAGACEMYm5CKQ6O3QFrO12vq26gCMhqadf9XnCoomIXssCozsC/ALmXskgKDfSo4pKJMghQNmumpJEO3YyREYkyRMSXCHAWbyCiirZ0LM0oECgiRIwIHgGeNOyCz3dc+X7uc1T97Ypz6KQO12MZibwzxEmn1LgaswxppcI/VSvfmP7y91P1gBhNBYgQOe4SxtVE+aqqzprzXtQL6NCZTSMWt894PXSft5XnFvs76euuSvp11UANQV1Hxi6CyFVNxlGMdYhN69e+61tAdQ1M7BxZrBkZJFWZheDMm98NLiJofHmMr85rTeTDjZ7y3ZLvPcMbA2Ba1bqn+Xb+Oel6ksjCei06hWztj3d7TnHqL21JtES2jh1uRmGpVrgPpTq0n6XuT+7qJNKUQpDUNBuqCDTvL3QJ6b9z7bu3WwLR0QPzUeaxNsUSOzmI57EHzXnsLrGosxDHd2DTrqL5GbJlTvL3sgvga2Cqg2lm1ujpFThOHdAwA0E7Kif8gYw2MwWMAfRZKD5YNrPRuA3XHFswF2KeCXSoqT7NgnwqSFBySK9ulPGUGIjHGRNhHlalJgCSEDYKCoKAuJ3UtXMDdHjVrgaMaXH16eHcWaoB3GJTJio0xkDAiSXN/ZLFy8ytW5pe/8jr+zM+/jL/8B1/Bt3/4OKZADBQUae6ER8UJjx0wAJZuhMXP+kX90l388M+9jL/03a/gWz/0QvXfB9bDrhpJPHtFYwkiIUTfvEkPQtKDIJL1MJyg5F0g1ZSZOLR4gizYJ2MFMnA9ZzyYM67nYgCh4JAL9klRbi6yOBxMwGiHNzJjkxlzZKPJAlIRbIeAgoKSFOlK0LS/IWhhH3jt+hZK6a/eBNORL7YbZOsjgJzyKIgCBcaSNXA/nzIyATB3TQVvxKALan7iw17RdE6gpG1uF82H3IJcx3X0bhwDaeVckZP1M4odBk9d80h6j8/ogjgrSDDqcB3YqWtFC2XaB3M51S01ILKrYeHsjWVz+LovFznDesKqcmHLly9BXUccVFiFWAFBZTw6y3zdFXQBZlbz7/sQtLoXvJ5cm6On9HGscQR1T63Oga/lI89pAbAchNud48bAOGsi7IF9tJQTdo7FYgq0m6V1u3RwlWd7xlmp4xhr500axlqn4ejpOxdLAwNd0FzvJ+cOGNx0JrviO1IUJGDaqDJKB5B3ADXWwP3dZ+9Nz7yMkzYxmjbGFCgD4TS3rDqrrtkCE58njz/b57Maqsoc0Q+sAoIdEHRgbza63YssKVOg8tRBwa4ChIJUSjW61vI0mAthiowxMTaRMUdBGtgCIFWeGloBAmEYtvXeEe8h2e9cqmxjez5e7LnEEZqBoIxBhmaiLdwd5kpwPfX3f/0L+DN/S93f3/bhZfEiMTm8cpDpW59Z8vV4LIFBP04FwvzSl+7iT/3sy/jLFlOQuwCUlNWijUygotkADE1foYwKvSQOIC9KkrXaoLDT8R0uWwfFRHUnzAXYG7J1UHD/kHA9Z1ylgquDHuTrOWM3K3Mwp4IirVEJW6DMEAgxBGwGRhoDDoUNFLRAFTGk66syCAFB2QOQWz+50tI+zhU1ApwWpCNBePIvbEmcNYClYjr4AulcyrCBpy96Bz8yGhjjxiyilgu/nNAJi6wDAwuA1lvjvc95TS1LcyPA3BxeTIqKW2F5Yb0BosLZ/76fH9CUaQ2AC8dW7UJJtG5qHky7Xmy1dOz5pCkMDVC0Ai0lVmG1YDw6el8BYjc8XqB3EXRtqgvb107v9xY4DNytakB4QSiIJgtTSMt16/7/Uea0TFM7AQjq2hrYAmpp2Xo2u/Vks1Q5KLiqle8o6/OUpGyMt47u04DjeHbfF+vWB83dxA7ddCZLwqK4WbGYl+jxQwnkbZm9oNS6LoePngVy1mXo6qlYbRWJGrORBEdKa8EWnAIFJ74+SvlbjVrsCF3sT93rUNMPU+c6OGTBXIDdysi6ShkHl61zQcrqql3LUybCYMbWZgjVwComQ7MIbo1RT1FiYzU1jqvFZ6XuzjkIMyHYx3CtgiUBYM4triBboGSx9RYR/OpXXsef+4Ufwl/8149BwXp0zpf3NB4rYPAosS9ffPMu/sTPavbBt3zohUUgoueGBtHSkwhkoFVqvAEVBwbKGCAb3Ve66H4fN/jAHgYKruas7ME+Yz9n7FNWd0Iqi6jUITCYCZtYkDJjzoKLMdQaDf0W0xBAXRAAGXyPFNQwASzC+Jj16IfZMRrkQoqyi/glV+Fwyp3gh7QUAKwxCRBdY/UtGFCJ7X2986I2tJqrRXQjXWsC2POWF8LXL6ErD40/rwp3HUDFCCrLzQ1OsFbPLpRDBxJ6n3jIRwrRo6jFLep18NQJJdYHdnm+9fqcO0DzvWBTasSaCbAACN08K5ip8SVyBhR28Qg91W3rfcrC7QFltVbcpFwzMDIcrVs/p/q3i3m1OVVX0A1rKbaW5T2uZTgCCKHF5tjaSikAW0WUWJZyoANIi3Xr57liMh75TMaIyiHZmSRPTa4goVXuAwR0gto+ujvUFJan/knnRlDfvFbgy6LGkwfFVbZgtaiunNQ7YrEFnYfyJMdk5+lIHq1Agfvi/SOtQMFVSrg6qJF1dcjYzwm7WZmDUtSF6yMQIUZ1I8wxYM5WO0DCenYLcKBilrSOjBuL3Z070gs9kO1cIQBqPIF2rEXNTBAR/N2v3MWf/4XP4D/4rh/H730IKDg3HsWd8FgBg6PR7wWAL37pLv7Ez7yMH/0eZwpk8TsuxxMEkQERQoIKJY838JFhaYzEQNYAJZH1tegoLy+uUVqkrPvAGt0lC1Bwb5dwtU/YpYL9nBUVd8AgEGHPBUMgpMRImdGdcZU/yCDS31V9FMDGgiQWkFjjKApdkM8pf0C/qMpTEZGly6g7QEjOuhMEAK1cClqtS5esVQ4lBI61/bVwBAVrx5vNuo2mMFZWT1W4KxeBrCzIDAt8zBqvkVdKwgWxlXWviiIAWkiKIoIJZbHsjoXlVhXeChgAzQLshMOxy6CbozhoPa/MdNVE5wa3eLU4C4cACgIJHZgxQVVBQh9D0e9xffHO3+2W7Dog0p7rrJULVP94X1mwMjBem17K0Zx8Rot5redkjE/d65WSTaarT5VDX65je9kADY4NBDBroRv0YKs0dqaCAY9J6ffb5tsH866p8AoAC44K71SmyM5km6fNjQiBLN0wogEB3+vO5YBozMy5u2PAxd1ZveKqBddMAWfxKHk5ciGcUj69G0Ejs/z7nvc+RTV0LFRdxwb0kki1rueiQYa7Lq7AmderQ8bVPuH6oMBgzkUVv/nxAZWTQ2TsA+EwKGgoK2GmDHJBoGzytACZDfw0FhYW51NjuLpX0LMa6jp7fASARVxB6kDsL3/pLv6tv/0Z/Hvf+Vl864caKOjttnp+F5t7YjMeMh47YOBM6xplf+HNu/hjn/8B/CeffAXf9pEXj0CBj2qNF/0nBlW2DFGQYL+XCgBWJSbGGOAcY8ChXqqGavUQz6lgX6Qe4oP5vpwp2KWC633C3g6yR9BKEZDRXnMgDEEWTAKgvjKG/k4MhJgIkQQBBGZBKEapwhpHEUNo5Q8DOiuPHRMoS0Cq7L3Ihn+tAWbLPfHPDg5gB55h4ED0hxlAFKX0YhjVb2pCDsGyQiQfCbbeYj1lgSWzvHLuipuUZeGocyEBoQIDXTcvJhVOgQTpfL9HQNFHZ1WfshQL6hz7QiwaHCuLeBnqFAUX/SKQBXgWVxoOtpZuBpGhKWV/wRvWtZ/zQtHZvOu98w+sqczGvlD9uYMDSwn12IwVs3A0p/rwHThZMy2rdRT73K8jVmtZ50VKBvo5ZwPQ3IOtXvH2cz633w7+iI/AQC7H59LdbufOJQO1EA8zunOpc4xxySbAwetN62pBuHWuVjrYLfNkQMCLrnlNgDUosC+by8DX110D3dfOzjRsIMey1P/LYlj8nhSLcdC5mEvBmNgadDiXBSh4cMhVniZjBBbyNBcMgdVwQUTgbNkKKkd3SY2tGBgxFXBkEElXxVGLzHFk1LifE8ZBbwQkOwMAbE11jUWAlAt++Ut38ef/9mfw7/7+z+JbPnTMFNwYswG9lz2ofNh4bIDBOfuWAbz25l185vM/gP/0U5/Dt3/kxVa8CErTnFqobH5zsgOTzJXgFrluot5OpqDR0lYpDSKd1UTV+qupJzV/Fpo6kzLmrJkHh1Q0pqD7mC1jYU5qjXhJYGSgMKFYEExgwm4uYM4YIuvBD4wkBXNiJNbMhoigB4/MwgQq2tVP3J6lGxr9rv5XBwdiQCAQAFmyBiwNPPTComZ/EJSJg0BI14hF8UcRt9YIIYwqr9AExhq89JbqQsHmY2FWjG50ReY0nS6pWQ62FqeKSAUiRGogIVLHJJgxLbXxTlkKYFNqbb6dz/sUIOiURFVsOJYz+ln0TLN2EUgGEpgJofgcvcSrbkRVykCbp5/f9Xy7gL1Ky7uOccsW7fuj8+MUPZrFq5U7VZH5z8WVWZ3PDfNagZO11V1KAwOnQOCpdXRg5axB4SVACL7fXUyH9Gdynfbb7fV6jn3t+ySNkj9VfOfU2YwMMKmsityAQrCvY8cmVLfDI94hwTG48ij+Pqc+61E66ZZxN8KaLQjUgEJl5mztzpalrixUO4OeEeH7nURrFMxJ2teWnrhPHlugPzukgpLLkTytrg4m8Jz1fpPVhAma/RVZ5fbeAEIqsgQ3IIgbjhwa0Knnt917BwXelVZlksnAYu4DAwX/2oc+vlxfLN0wZDId3ZqfGuf0pY/HBhicHAS8/mt38UM//RI++6lX8fHnX9AoeFNi2QPfVsP3TwogLO2SiNR9zdU8IvNFoipMUDOaBCoEHAjUgBJDgqleNj3Us9UxKCK12JEX5NCuaMtiHCiCgoJEhJkKYiD1m1nE7RQLSmEU8wO629bR/clxSsAZ5+8d6ZiCGvlkisAUpdawOWYNgIaIAQUxJGqVSYEyFbaONRCoU2pViXRWp4/eF1uyQNAUa18tzIOk1H+nAiIJIGVZ5KQeH2rFTtRCMKEQFCS4IE5ECMXmiqaA3TLuwXwPkPwIlU6wVbq7AzHZjNF65NZbJyshXERBCwwkKNMJFl3f5HOEKqtgKYAn70I3TzjDZDR874LxmJa1Fb5cT6nvw8a+OEDwZmBtXnGxfqfmBfg9PJ7XKUbonGVb52c/KKSqhwgopG63HiAkQge09C709G2/14t9PgFYnIpPpYtGt7Mp4nLj/NkkVmBaz6YBhWAAVnPyqbIJusYKwsDorNzlPapAtJtrDwjEz223luv7XeeKJVugJR90TYOxBQwA1rCoKtE1oJaCUy6qXp5JsTWFga2icnC2eIIsKhtTlgoKeiBLovg3FQHngsCEQxZMIlU2p1hQhGucRcoFgRi5dIHqRRDtQhLMmKLlufDYoVzPgp9pqb/zK1/WmIJ/9/d/Ft/8wY8fPXoDB1RjNvxOHf3SexiPNTB4/Y27ePmnX8Ir3/sqPvbRF1GKdL0NXNk0JUssBn8NrWPJGpAJPwDmdyKQczT67cmL5gEkRcQCdTpL1Q5FX6PAMw/q69jX0v1M52hz8Nd2ISPd+6MpHo+oXb7GmcVbZSdAstHIgJWB08JGZj0Fu2BMBMGKNVi/tClpQAWIkZv2Nw0gZBPAMzWryC259fx7mrhXqO6jS7lg70AsFw3wMeFbXHl0j8vUlFQFBYkQg2DKrSoaF0FgUQCxUsD9fHtx1lvVrqhOWbZ+Tvz33XrMkJMscAChlXQ1etZAQhAC254x+8/VuvDcjnU1OheWPscKWuyz+LzkRKqa/2GdoK0rfF0EVNxNYwweEbhIjZFYW0I3za3vluo9UBYBcZ3yeuQ1tL9jkQVAgLMHi+d5+D73gMXZAQerDv73Rb7x80mk9DYBU2DEwPp/JJXlCCDAvvc1Xd+nRVyDHLs1zgECETnapyO2wMCBzwd1zfwAFZxK9RUUgI4DANfyy0FUv17+OQkqOPC4gVPslhQBQpO7gBtz0u1fQRaGmKU/mJxnk32ZLKi64Kxe6O996ebkWQi//OZd/Llf+CH8OytQQAas0LEu1cXrv3MDW/Ao47ECBv32fuHNu3j5p17CK9/3Kl54/kWLoFfzVi1PqQiLRRWUZIJQuwhAYw16+grQzSNWOh5Fq84FVBKhbrwLU79Q9XXtddY+RPdl9YMt7J88/B9UL2Gj49SCWPwdrV4DOPqdymzZrKjSjD5Zf2KtUyaWOiYAiEN9PWUM7FIJWR6tsQb+B+gEun329FAAtYYEE2olxWT0eAMEp4HNWmiJAPucKyuQVtXONP1IkCwAcd0VzmMzrPUCYmLEQBhz0T4UMSDmopUmAyOTIPcKuFKkgjWY8Xn3AtgV7SlAkHJRH2puZ+XI0VOUxVBQpTEjkVgVnFnOet51jm7V+FnjE2sL9EKszW9tfZ8DLT0I9XNypHx9f4HKcjB7YBp1QvXM3HAapKwBVZJSrUkF/ccjFGC2NYxBlWgMXN0mFSD4OZXmGyfCI+3xmr2aO8A6p4xUtAKqMweuJG46ozEAPHsZX2UNdgYMnE2YrJNmL/fqnpywKH3uDdwcAyz9vfV97MDBYm0sFsIBDXVMq7uOPA7inOwRsV4JAk9iXM/dn4k74ccnfofJCyidkKd8/Br6OkvZ7OcNaKfTzx6LG5E205UcXAYVNwDrv/orX34df/YXfhD/3nd+Fr/ngx+vLHYgdxVRB7bsHJLHbHTzdkDegz+o0XzTeKyAgY+7b9zFy39T+1F760n3dwOEAE+pU3qtoLNGjTU4uuT+2YURVAFaYytABNnQe1OC/eWShWzzl/fANrfkAlMtsDGGooGFRQNhElgLaxQ7xOYPI9agGK5/pz7G0F1Ahv8uGkpfWT1efIZqMFUvPjUag4QXZ5w5Vr5XRAFCMWEvsmQNAhOS8WV9z3ZfVMnNv+euht6iWMuvKjo6YdW7CNzy8nLSfXyH+0o1D9uQus2Duzk4DRtZEDMhBQMIRTAErYg22ddRCNmCkNwNUlH8CeG7VrjuMuj9yz2tnIGzbg8A6tIgQmJVcoly5wrRz6AeJKiFAxxb5LotJxTDam69snUf6bn59fR3QBN0FcQYYOHi91E6a/P0/NDNqbdk3fXWqPhHXz9igiRBIgU6DrD8fUjU4vZYGDYr/FH2t0hjC7XSqVTQ6oDg0NXyf29nlBC5dJ+1rG9MhF2QhctBGRGp9+vU3fK1PcUMLO6vjTXb0rMFbDKCOxdC8Lt9LjiyjqyvXoy1FG0SVmOjOnnm1rRT616bIDAwBr2/cxZ1uQqBwQreDIMQEzhwZQRjIIxBv3bZrNletARXq8UT0V4FynzQInW7B4wKLuyc2P//3S+/jj/9t17Gf/BdP47f84GPL1zY/ny2nApy0FiYBXN1AhQAqh9/4G++hI/io0d76OOxAwYOCl75/lfxorWeBBpV7cFwzEp5U4FZukvWwEfv0ioAfvVLrwPogUJrVmH44CQC1xfrDhCp78+VesyEkTVAcDMw5qwf09D+nLPGEhyVRLaDOw2MzcD2dewOtloRakFw9YN7qpP6cq0aWgcKqCwLCIlVI6OCI3AgdllCEQNKpIKzLFkDImqswYrKTeZZIwZSXgZZrUcfkOXKae0myCKdzxYmcEu11LzqWe+28RHMf+wgbQhqjRUBoqA2VIkiEGZkkWqZrRVIoWPBCyyBY09590otFakKLZVSvwaaT7LOmQTJYgp6kJCKto5NgmYF+57AFcPK8sPSJeBz6pkL9YmXZvl04GZt3er7uDBFBw50PgEe6U1VwTiQ0b+9ARjaN4LGrojH9dR5ydE8l2unL+LrJ9aESJJA2INu1TRz0Ktpvw/f3zXgcybL+6DUYLiyPKcedOwxSTed0zHqvdeYAtY7HwpiVhkQUlm4w2IHyh71ngHHd9bHkeu/Ywvc5VHBQFWuQN/MbZHuuw6MZED5fa3mCavfosGgYrE0YjFAgpgLxsA4lFLl4ZwFm2HpTvWsBK+2vJan0xAQA2OIKlsjq5yOJreJl+eUXGOfGT1jXA1G+96//jd//mX8xX/9FXzzBz6GDEEo7QUrW0Aeq6Gf3QjpQcEaEAANFLz6/a/iR/7Gj5yd52MFDPyhP7cCBUBDrJoz3/LvAxQI1CZApJG+PSL2C/MrX9ba1HWYcJX6+djH5rn+Yu6LIno5RAAJjKkIEulBdKVQRJBHPamBc73EcybMJtHc/6Usgf79xg7ydoz6dWQ9xH7Q3brwwCQHBwBqdbdSgDK3tDugoh2C5nALFeNdTN+TBdsRmksBSo8FapkKa9YAWNLN7Yf6KXV7EIgWv3vKAlRFCnMRLC2vXtB6tbOUs1G6J5QYqZURQzDBzChBf3cUBmIx1xBbXQW2S58RqUV16dI1xF9jXNCxT2csXVdsvfL1tTulfEXUQkloICGKUqYZziLo664p/aMtWLkGeqvb55M6ILBuRnMOGPjaugIjEoRcGqshfOQS+Ubn6WcjiSzAwNm1IyjaCgTteUBA4OqziKzR4q4INGhWX/Dc/vpcfG81Q0gsnkC/PuRWu9/PrAPXOT/aOZ1zQLCaJkPQ8x4zV6ZrDIwshBmE2K33msHxsb5vN43ePblmCwK1gMPehdCCDR0UuBvhWO4ALmcKCINWaCUGc2yxE6KuqCiEiY3ZK4IxM1L0uK24mLPL016WAjiSp9MQcDEGladBP4bAiKTvpcC2xRU5rf8wpqsu72qZvUx/Nt+zgy6/B0SNLQhY1oNgW2tfs37cpB/X47EBBq/d8ND9AnEXfegKjDvWILKmJgbbLBdIv/rl1/Fn/paWUf4jf/N79DV7lIjeD45qSejXblvrpVDrWusIDIGx6S5gTxUurNVQahaD5+7q8yjVFYMzBQGXU8B2CNjEgI2V9nRfeDCmIhA0lYmhtf5r1bR5xRysI4NFqxGiMQZECv+D5hRWl4L7hr2BRwFa3QNZC+b29Sn/b6osg1vLSwuw75TmDEF1KxTBbs5aAMUEbV8KtQ8y8jVlUqoxl6ygoP4Kg0k0Yi8Wbcud/VBx/TmKUo+ZlKlyptnZqLXlcIr+PgUKeqW2nreasnreFAgIEhtgNYBAbP5U8bm0YNW6F12MwDkl2wMw93/3/nDdb+mmpqdFFYOmfSaLcYiBTOHq68cjl4iYID+2TG+a601Mxk1rlwwcBFGaH4Fr9dMeHDg93DOFzgr0eyvSXC89KNCzuWzo46CgFuBZlUE/d1ZD1lbruQQ754TNABQheLOjKOqy0fkraFgzTICyOWl1P3304X9rndfXKPD4Aafz1y4E8gyEhRvh0eWOB3UEDoii7svImpE1uMElx0GbgLMCKk8PK2CgKZ7KriowUCNrOwSMgas83diHy9Ql8Gls7Bowki2QGJh0o7L3RH37h7Uib2ACClcg4SyaK39eva/Pw7fF5TNwDAqk+79T47EBBqdAQX9uF2fYEZcrMJgVT24Z0IK4/FXz+fzl734F3/786TKUCxrHfqCoGBbBbNvEpIoC+p/awLVdN1fcu2Q1u4NaAZNX4SplYT30lu1m0NrefoA3kbEdG0AYLQ93YP0IBlAoa3c7dJcVXrt+QemZNhEPAurAAQAObimTpgH5JTCXQhDLNDCXTWalydwy6QFBn4GxZAqk/t4pBbWMI1iyBL2g1bXsMjh6qyELIgkABTylFGQiHFIBEyFRATOjFFUOWRTkZdKgKMqiGq1wVSD1XMji01HqXKVsj5T18ge9gih6rNrPGGBp4CCzKrnKGgAVIADH7vFq5eK01d1S7I4DOL3S7rF164GOCgq4GGsQBCWpwCsgu5NLtsMVVTgzV12386CxBwWn1s3Xsw82k+4/pQiki8+I5njpK3naI/af6t72sSLZ51e6jnl+hou6DQ7J3AelgQI3Cvq9789qEbUbHcgOgQFkYyIJhQ0kWAxCkYICAhc1VIjRrXHHAHTan5jquQjd/zurE2yNnH3oFVY1SFzmWCXTCgpy17PjnNyBMwcMysY+RQXgg+ieSHBanhfyxAHKEBi7mWuvhFOylJmrQbaWpw4G9OtQ47mGLtajBwULX7/vHx2fY6CxCw6uyMEB2s+5gq5WvK6yPabX1nrvvTAFPh4bYHAOFBwTOagWrGYSiCkuZwvc2gEg6j7409bF6ts/8kIVAi1vlBY0jqPl+r52OIrohWPRtBmy10i1OElEDBm7xIiczU+onRU3A1fL132O9VnYAQRV3+IYqHZcvOxYg4H1Gf0QD4QGCoo2/UAFCOk0cier5lgAwuw8yILek85vqXy7xW2w0r0FisiRSwMHMOCw2qsa9duBh7WwX4MCTVdraXW5yJGg7cdCWXQKooi05zgzPG21n2M/1grkJpT+XgabdQSc9iXqfHAaHJACt2x/6OzYGpidYmXWLMEaFNSUrhMP6tkaVilIWZKsPu9UTHAGpfUjlmxHINL52uu6YuoBDHAeFJxcw27d1hHo/aAb/q8f/dusAd/i904whDeN/nfOnVXAYhA6IKsxMgoavBS0l92NFscTA5AK1XgYPyeAyrQscpTJtA4c9bgVlWmorspgcQUKCPq4gt5t2YOCh8gdjepbGCMg1i6xTDVGR0T0AdHOCZvMO2SVkRdjwZzjkTxwltZjCEZu7oNN1GykTSRsQkAgWMGjZUEpoqbA+5UTQs1YcHe2u5i9OBxsbft4OP0hahBnZN/bFnDoDM0aGLz2xl28ZDEFLzwiKAAeI2BwHhQsL55jr1BX0PpvQ6lNBw0k1u/amYKPvLDI/e1LkPpnnNkcAOZ/19mEQlZkBurGIL3UTAFTAHZMGKJoZa2sgW2HVGqxnqPa3aQpSxp4QxiZsIlBEa+h2jHoxZiCAwOA8gFabrgDBe5KkFO5xHqACWplLMGB/c6ZeAM2940f/iKPDg58nPq5C8qeti7lvMBloqOI6rVSaJUM7YNbRLK/xsJfjpYhAjShCNQj1vKLu/epsyB0Co+Q3MduAiRDZWOwmAN2i24d8OXPU4PJloF+58Z6XR0ULL/Xr/v1rkDgDChYC1z9tjObDBwU+5pBVdkp6dKsdmdlXEmfOyP1/YAakOb0PvcbsRqn1sx98L3/fQEUT38J45oWgt1jmZJ4HJJUi2/9ta9VEQYXZanAQCzKpNw01qnOPtQNQdX/XLBc735k4Ox5uREU9NYs3RBXUI2R+RgUGFO5rKFSlDkSDQT19RYAyCqPYhi7XWBLQ+Tq3p2YseNsmR9c3Y2nZKkr3zEyApnMjAETK4MQCBi4ZYQEu18ev+Xyf70TguZuaiXFTHpyY9t6HeRXndDOZgUHK0ZG37M9j4KCH8CrndH8qIbJYwMMfJwEBasIV7KcPY836H3iYub9F7/8Ov70zzf3QWUFVhtF8EhbZwHsfVe1vsn+Xq1qMotPC2EEVtQazLKNFLAVYI68yHHu/cxAu9x+YDyPeQhUYwoiY+E+qKCgJKXy8gxK+8YUZA08lKxNgLwLm3dfoxC0rrgIxHqDtHa0pEAjPDzeALgZHIBbwBxgl6lzI/jwGg8spOtBWlGPzUryWA1VSsFeidVSIAG7P7wTkOv2q04rtngPTw2zyGTSV/a8cU8p8iA17s7LYoj+nUCViMe3RDtLKavf1KPZvMmVK+oAOrKGqwLzc8ot+l9/TlW5nhL+2f+mNLaGjLI+NTQW4/T/BSY91zcos3OKrL6vv9bD5u3/VxpoCNKi1WGP8LA169drXfVynS3h+9q/BuxnCkSW+0osiAIgMLzYmKfNAbBYAJciFrNi41HPq7sVa2odtd9xYwZojOcppqR/ft8HX/OHg4KHxBXkzvCo3UiNNTBj5LzsUbmq+LJlLqjCZW1eVAE7quUeOVr/Aw021NRlXrCOPTDUvYbWKiGs4rOWgCB2vn09D8ey31+YyBILyRS+/RoZgPS9IWOyC7WXcbfEIo4BS91T3xsGCn7qZbz6/a/iEx99oa0THg0cPHbAAFghtZPNOAqIuFoQizQ7EH75K3fxwz/3Mv7KH1SmoI8ErReLu/K3q0NxtqwnoAecA4hYA2dACEIoJkS9v3mGB9FYtsIQjvLG60vaZVVF5dXODCiQKTI78Oo+OIDSQZkCAwVUrBubFEiatdc8AIexYv0YIFE1XwSokAEpFZhiNDERL+IN3JmbTNaRoParyNAATCoCsRK5/owRgPQAoVoNvYLUPfSDXCBA1qg1jwcAGIGVdQkmJEopFqx1fH7cz+guGk8F6+nFUxbFsjStFziCx0kdGasenCrSrAOPb6EiQND9VsFBDZDZ895kNT8MDJzTx9HmU0spQxWsmKUTmTXiHYzE6hfivjEGLN7B/bYdEjxSUmiWZaSWaudAt1dQpyLnT80bTPDiN75eCBbs9ZD1wur9+pS+lvuPo30FTu/tel9RLJoyi3L4yNhnTTWslyOqy0UVe6lKVi3+cva8As03zqSv2bsYfY1j0HV3izOYYlsDIgBHa77OEnGA5L/jZ77WVKATcQUVFKTl15KBlBQQ3CB7KFpTNW4xTgD0XsRR3QpEVVYHCBKL1XTQzKFkmR/n5Kjvd0/ZR/bCQp1rxORsLdDkGRXnGpFZ4yS2XhSue1qQkN0ZgjEkxtKiGaU9Q3BK90AEr73xOl766Zfx6ve+gk88v+yt8NuSMVhe/LZQRzmxhsgdwfW09xd/7TX8yZ/9QfzHn3wF3/GRF1tgnSE9l3OtJn63MUfVu7q0m8VESf3yrCBh8A6MTMhCNSDG08GUntX/6ytk9TaFC6qavkPNv7cINEx7pfHyoboPFBQkICWUbH3ca3dAfzOCdgBMoBCBlIBobgVQPfin4g0ceFVwALWEs8J1FZyBNGWsqBkdpaWgRWjQoghAWCpIggfUKVOgZa8FXNjKNXskueYzu+WQi67caV948zW6ddD3SVBgYMVjSMHYxEtgpucDlWly4epn1OlmsctfFYn9nwrloAGNUiy4UTtPOiCIpybv56EDAmvB7mMdP9Hnquv6UltvsjUvOnMHYIUtwyATCktXGfRYDTuNvajYV78mU1gwpXy+GNJNc9d1MbYj0De8Xo8CCB5pb823TwXQTIeAgKL3IQYQad0NBiEVzXJJpBk1XmQn19giPnteASyKoznIWoKuBgj6tT4HwE5Vq/T3qc9v+xpoCQqO6xUs4wra9yZrUlI2ICfzBXbyJ+uEpCiLQCECcRlrIKHo+kvBECeEqCXKZ1L54PKmFEFmtnunYy1H18/jctQZGU3xNnmfbyrMhHb5KJiyL9pKnkMFxhUUdAwQBCgk1qQO9ed1XakzPDpdd/fXXsNLn/8h/MSnfhyfeP5jFs9SUCvbocmYm8ZjBQyANTjoRgcOyJpxeDMgdwi9/sbr+OM/8zJ+7JOv4Duef3FxCX0TKn3naLJHjJ7y5531+kCaNTQlNitbwQEoaInhrtVpAdUGTAIsGpnoMy0f3A+yMgaGLOvFTObbO3TxBKlSeJgP7VI6MAD0IrpEF7UKBQCFaN0WWf++8NKlYAyCxxv0h98jsYnMVw0VnEIABbLnIgME+pAOFLRkAB3l1UfWypNKv/epix5zoH7FMXD1hQPHwMCF7FJZ9R/unmklZ/u69A4IvKJlDyrXrG19b6KjksMs+v8UCFECJBzn7Ps69GMZRd4UaC/U7bgcDRcGmWSx3pmUsQhF6WyyIFIG1WBEDl6oSV/5BC5oa1vZgpsBwboIT1/LYP3yA9p1yCT6va1Nv17Ace2Ms+Wa7Y086Kunc+u++u+c2dsadErQ2ikFoMiaOpgL9qRnNFLBnIFYlIlJhbQbas384Ec6s87GOBg4xcY4Vd6KSz0CEPA3WwECX4/j1s/H9QpOuRBqhpODgjSbnDkjfwCIuxEGA7AekyAFkAEQAYdB6xwwIReyvileLttk6Ep++lr2Dac8bsCNP7IW8P4slSm4Qc5rwLbFlvgzAIuS8j1joBlydmi6PXb9U2V7XV9lqV974y4+/fnP4Cc+9Vl84vmP4dR4FFAAPIbAQMeKLajdAvslWXYKfO1Ld/GZz7+Ez37qc/j251806639ft+GFUDzpwFHoMAPTzvgsvCJAah9xdXnxAAzyKgmBwocIoQjhqjtbgug1LlUt/NiMFBZDUeS9SD3c+uCDKmk5jroLqVoKzidayk6PwDgAJQCoQLkrOyHEMSbn6xcCrBCIMkXrRjGty5LVgLBsiCPS/CGQPBwo0GgcQ1YKUkOtfKhsFOEZIwLV0tB6xGs0+pWa9gpr1MCNZLGb7QYA65+VUfzioN6V8Iyn1lPn8UXmFXJoJq7vAYJvh66Fst18LUAmtK3Y7D4glc/X1Py/dVYv08rf6xrGrEEY4GWzX5OretybanGxpxSUloRdGmt9wqpPtqZZwhG65kNiUF0sfspnV2z7n16ZuBU/4ZH21vUaPnaUtxjREgBZcoFibUoj8YUUddd0QBuLI90Zpc9PpbM4Tk3yUnQdeLcnGJK+rO+BgVOs592IZg8lKwxBS5vHiZ/gGacAOpagIIFlfe66JQTJEQEDuAwIILw/2Xv/552O67zQOxZ3fsDnatcZUYiATB/QmLZEkUS59COZVmEQDJJpUISAEWF+uWS7ZopO6nIU6lK1VxNbNdMjaOybIoSKRAgVMlUkYQISVZGxncAkJTs3OU+wwOQmqpc5Fr49u6Vi/WjV/fu3u/+DkBJ9U266pzvfffe7969+8daz3rW6tWPgXSjMrooP+W9Wd2tweBzUGD1LTiS7zJYJJ0zkgB8MiIZgKeUDx2ZEh3qnxjTYEDLQcHP/jbuP66ggIsCAWHII2twqdwZYHDxdU1q2NadRrFwwvVb13j+68/hhU++iI88IbswMgGFyINDgNZvu0PDPSiw757Jiz2wxupLut0tEwGUddZpAI0CBE5ZlDFlnbyy/7zvnd6/I0Prws2eB16vso3jCVYBDHFChgTiAgYgKBdlg2WEIta887yJ1LOJWYq7FHpkTEU2nzKruLeu/FXMQuq7kNX1oGAJKnwjWBjtL2Br8QFMl7FV2r21YBvWYMAONJZGsDhGiqN/J1ceu+8VJERaOrYDIG0xeof+uaPd9GKp990DkgVZ2tNAQso15bCDsbpM0NrYyigYMlLXOzAwUdC3eYe+nc+22U7xIUSbU1+3S/0r8Q0MAfXMMvYz1w2VFkq+cuKvbe2eHhsnHbepydzYF6K2jUeumBhAOQNcR2PHz6EFSMZSRvdqZFJrumMO/4wtYFXqJicxlkHFAEJlEJxwW/Q6ZR5MCYOvRG6u74gcJRJFHF/QihmRtoTS6hgTL5kcdya4rtwyuQ6oXCeCyXHmDbYzJAEtOFBWVRgCOZahLF3QP5WlIe8zEXzcgYIP10EymyQww3Fe7gwwAMKLjtgCOx7/IrlP5sVPvYSnnrwnk88QG7OzOSYEcpwwMSEQh8HTR/ivN+IfCwMPCBZMyqJhKIFSAi1XFXEqKLBzFsBCDTCItgpgtFYNhuGA2LWufTxBBAV98IyBKUCuzcv+PLJPTiorOJM8i0iRsQCbQqLwLLCGiXYKETCwQI2ZZ0sS7dBOkSWJU2AAZcEQMIyCN/ukLUar9oFWlc7bgwHbwjguXa0+wHHx91C2AFwBCwMoqVUsxdmith36UkcFNQdS+3Van/icCEo2FhfVouY3o92b4GjnQitHy91A8fxeOUfK/tQ7hAO3arPYdwMg0PfvpfpY/8rWvJZTgJQ50NiMABKMYVgPxm1s46NxOwIAR0BrN3bCwdj2s3ZpguFKcCGgqEKNDO7Abi/hzUayW2OslIPcxx0wgzkDJbv8gRpT5r61N/QbAlVm9mwAV4YgynK7hks32lMGd7IclIThs0uNJlVW1V3ayfpRgKOJwKa9HXRJWz343rWAgqe/FEBBkNc71iCfcifcKWAgZfDKQQvE9bGvvfUAn/3WF/DSM1/BPfPJ6Lgx+joi6RTOk6K1iIb75TfEBeUdXQqoCpg1yAZA6z8DVOnbYCJAB5UM+sok2CCHuSGsgvFde4orIFwf3GdAwclCXMCUW6vAhAPgbIOs+dcDSuklBQkGIqz0NeG4uxWqVeiKFRgqtKKBjd40/b3d9JA/0YKKAYQGBBpAECZsovhbbgFq/y5kylD6UPaUUAp6AhSsTawdZjsY1s8jFwJ11zfTI7SpADhm88mKa8G2JK9AIdX2DECsr9/OX90pqggE8sAaba3a9h37Zojv0LfZpXbr22wEBCrYq30MYNrPIELWv4u2a2GgpNq+MX7I/op7hqrxjDBuuzEL7C3/GfsRFf3RuIglsiHxN1EuNmvpY2BhwxTM2+pU0QxpERwA+p2LyE6z0AvtXbbx5Way0ty/6wreZKUE1hsB6OvNTk42cpwSaFnUbbCAS0F67H3AdqPLu2MsVmBVqW4lnamy1d7mARSYUSqg4Ofw0tNfwsce/4i/D8f327EG0nKXyh0EBpPSDcTrt97EZ1/9gtAvFr2pnZNUk5Vu0tngt/tVRqJ0y2+2CgpuBBiwDjJRwnVJi1NQ6lYw6h1EjjahAw15EV9bXuA5BYyy0t/u3ze4MFiW6Tki7lcezIrFQCRlMNIs/Qm3k40s/kDoNwMHBBEoHIRwb+GdKSUM8MgeuIsBFRyYFRafMdroBzi2GiMQaAQhF6C0AadUK7V7hsdiALAAJQouoqz0Yg8UpN5227kw95FAeyt7JhZUZNe24qpUezqcVWENXR2pPqmXTWeo+t4NY3PvrIVu74DwHrHdrF6x7NwBIyAQguX65cgUlF57X4UQCgBl6/NFXGtJlq0xSIFBAGIYjF2+MGZDw8xiIOxYfOfmvc+U8Az72oAlAwVsiYoiSxA6ZPcSYliQXVFw2VAxNhQAc6pKWuUjbikf+wBsM+RY46+i7EaQ3az3p5TB2yLB2YswyAUQcFAEEPjy7kyuR5jFpWANGlb/StMgjn/G9fceyOoDcx/s0HFp3/eW5c4Ag4sYiJJ36PVbb+Izr34BX/v4l3BPG9U6h5RqSW7V1gc0kcdR+Df/hDFg9dk7KLj5c/flM7MG+o0HPZsizosABmMNUgblxVkFNposJR8EDQOhpUR/XXBl2KSyRCLeTrGkcN9kcRD6PEou8LibdAIIpBGpbOL+UCqRTUCGYBgDXExHfXmEGmzKUHOlCNUKOnqf8/RuA4W0UxAOrmwcbGiVRRSG3c0Bb0P5nqqZNwQK1XV0CTsFuKTjsdah1mkw9rQPWRkM6XtyOtwUlCkw1uDDqsBoB75GFeuVVg6f06i9I/A6WX/5rol5UsPjHJZ92+2BQAUB8diJvta/kuhMl6+puzBZ/6bsbe1j1wHahbpHcNO1dXy35h1PtEQsY3tz3FYU2ILerctqJQtTaG5TwJZFOzhAqvEG/VOLmgYW92QBfrr5A2/rUB5yJw9Z62lsABso2JTd3bYKGGZAhZIyvCR/S40lQ8rgNQFXBNnMgUO7WIxFavrMmFS7SZwT1997Dc9+83kHBX2GWq/PQbmkL+8MMJAyGeiBb6yg4DeFKRjcg4LFEVuQ0DVoAwaKCwhiSUZiSpe3tYKCm3daZMplH7gCwN0KlAADA3kB5wzKFRSQsQiquN0KHVn1jf+uvnkfzBOP+b06EEJ5EcYCFEyP+NkfKoOeC2D5IxzNbi2gAJBGfsVY3wsWBEnl9UsrlKPCkM6cTJ4g5KmcUAoxoZXVNVgkgxpWBeH1SeFvggejdqyCXH4w6XsFNWQuBkos0vOoz5JNjGrAa3bl1YIFcwsZSJiVGQvQgK4SFEnZuvoPxkRT/+Ci0frH8dCMj1GJ7dezARyj6flWfe7PDX1uq5DqWJDz3I0HDzQ+OWYB6Lr6/fFTrsJ+/oRjBOzm7A6sNW3TPc+owvDZg7C3VZWqso1J2QCTTwEg7BS+UVMBIMg9wzUjgygAATCDt5sWCJgrIYCCfaChGnJm9EBHgMpL0mclbRcPtvRx1KJpb/VOlBKA64fXePYbz0uegsc/3LZ5L3t3stgba3/zrtwxYID9wG+YgjfwWWUKdus8O9RGQFxaulunPH9+mIDMgbIvjkANHJSb1YNnAIA3lnX8kIEv/6gOPgUBbOzBsshAzIsuc0x1kHo2maMs+e15itdGgBH9Z4GtkOxdWQWXxT7QUHiZRV3pc5sENllLd21QYo2SmyiGUXHlUIXaUOjNSm/9R4Hn5wYW4yXhG63/Ll6EOtagZRWAhvTtfaUA+mCqpu5Aq8ym9at9aLk2xIqrisss3Z5dALBjbeBnojjqWICGeVGFEteIP0Ld28+P3oY/zL6n0PcGBC2w2MdtU/eDMlD+UyZjV6/QMz4m7Xj9HBPu7OZ5BFLDZ4hh4EAdQTwvhJSzxD9RqTInMJyULrxDfO5W4w92qx1YZW5gBXit8VYOFtRo48IHcnoDpYR0JfEEdPWYMBXZWIzSjotL/aCl1z0E2SXx2a8/JxkNH//JKbBvyqiPTrgY7hAwOG7w67fekEDDp79U13m+F+XShDVhsm3NgCs3K8q6gjehktjWIoXxTFmEBGVCWhZQutGUoJJ9kH1ZTgnAIYM4gYsCBLuZKf3GsTgADQEEyJ8ACjSmgVWQmbXjgs1dDNRZFQq6EBTDLivkgTD240EgW9sCuBjIFOvin88gvcFzGuFbN3sxH6X/ckB/jtqSrC6x3WZAAZiPt14pRHAVlJi/R/xNU8k9YKGgtKxOHtRlzAbQ+NGnJTIv/n1iiR8p4KN6axt73e38jtY/0ZazenRjMfqp5acH/a91SHG5Mto+rsGpjz5epV7Hc6NtL3lO0992zuo0Alh9O/ZzWp9DlmMw1VM+jsoGRgGuHpNdB60tc2hLe5eRa2HEht4SEBhLIMHZPJbPVlaTzwzSDV3S1SKggJLI+3wGKJ6zOG3r5Bc/9VXRX2UdXscn73dJX94hYLAvTELvXL/1Jj77ez/vPhnGAaqdFMtYWdFyP3E6y2434UK9NnMx6KC72YaCBCWBsYGusqPUVLii01LEzQC17PS9GIuDc1hsgNXZPoe4BKlmBwgGQosHSiNaOjvlNhqkEyAAoBW4o+QhQRifEcSx7KjH+O55D5AiXagPaJ+jdWRg6qJpn0cNvcir1KHZnIokBfEeJAA+8i5RyT0AmCmw5jexnnL/odKKgGDQ9w4YZvW8DfvS9TWA0/WuwrGrt193i7bU70fjz5ciA/sYnnhbr2tgVAbzkVIPCvRnl8Zp1y5s43NS4nOaQOYZSN0B/61tz1kIo7IOQmAHcCBZzQAQOCdvZ2mXxds6Tfq/mfMpVcBgsQ0pWu0KCiz4O8Z6GSgYGWw32/5ZVkqSlJtbAidhF6hfyt3Ph4YF7EFrHRcxidWDhw/wvG6dfP/Jp1p5E0ofzOzMzJQhmI+OuwsMSAbda2+9qbmjvywbSpifZ+ePaRWZd461nVI7VeYkV5bVqguCM6XQUSRBhDf6NWfZDB1okKhljfPI2rJJQosbgBMjIaMopZCuFnhq0PUGvFypdRCER1SG6obwCa7vQL0g1eN7EKDneyCg77enH4GxoDhD0wYLMiaHCmuIDfmbv9DaTRty91TugEEECnzJ3RImYiMg+uf0CitOyFEQpz07JfB2ABKAen2vLHoremLFertZvWdgJtbL6h2UQjpUIGjrPCod43NJ4fpfYKxwOwaM49jeRaej1vsR2vRwdY9dH4N54/g4GhtAO1f7+B67BQZlNjb753f3ppQqMCHycWjt5kumo7uQEyz2xXaKBZVgCNjzUvcXco40MFDbY+Qm4hEraG3nYEFag9COcWcXDCSYOyElNLtPcUEMADeffwQFZVWGt5S9XAY8NT5vLAmW0AG3OOaIVB/EeBHTFxUouMlkHxh4/eEDPP/NZ/FV3zqZq+xV1rXRZb0eu8SOTcrdBQYArt+Ku0x9RAZP9JlZaajvEHhojBRqhGgIOxG/vg3opFG4JGmtaLkSWsnQKCVAlbcNMMoavT+Yv7scB4AAAWRlGzal9UrdXq15JxWMuS51bBVPQJO9daC/jwN7BwL6a0Z+27by7edeKcSEIgEQ9GuJwe1a4gYgAGgSjlwQxsOYirPlDONk90xZjStz8ajlnJIqE2qiqc01RDlXEMuAqN3eWqggAMDeiu2XpXbpZuVHk3aKSmSiQFqwIHVs7tFUtbXEpyAgAL6dkr0Avhx4AR6gy119WzYMCF7zw3blUE8c1fMSmJmVyCScKeGeu0Q7B+1EyZbXSfs0sUkWP6Qyq25YZAChwOOJEgDfMUM+N/OomVM217TOlMEn42G4uwaBWZJl5qn+1saTgRQiEFaZd6NEyB6AWPuQBwJ5xkhSJvkXgrIt/4zFgWG5CknrbB4t8tmy2w70zvXDB/i5bz6LFz4hoEC4lQpmSXeObSsUjbtHAwXAHQYGvh/1J1/A/Sfvod12dTJpFLUZW+DDkH2DLwcIBZBUlkmsXGIG5yul8q9k4OYFnBfQ1WM1AFETIqUrHYQbCx11AyR9YkGLSr2akWrMKqCTTegFlK8EAORFBqQOUst7gEUGIw9owqE7wNpm4lNsqKtZsQlvDTk674zBIMtY2MOBY9rmmA9i20RAGG3YPbttxMAUSCPXcwPm4NBfPirRanCBJSjfYkLIllMB7XIrAEhW7eIAATBrpKOmA7XKwLHSCorVlS8wV1gB1DDQKo+4AsaUzIUls17nvh7AuK63Ua4RgEUXWQQKAdQ4OyOVlT8Tmp77uh6176BtXWHHsdi7qS6U4SqPCAQuMVdAO+61TUiZTN/ILS9iZeeljlmdi7RcqV3FFi4EWV2gQGH3PLqdfDAAdtbV2LMLxiykReTIumpfC2tgs5jj57JBVkyJPCUNMiQSF66VlGgnkykl0JVkoE3LgnS16PLyRWWxyn2Vx0wJnK9kvmRJ1cwpO0AoqCnEC4AH35O9e77yiRfxkSfvuSuboXqAzY0z1mXDlPmhXD98MO8T3CVgYEKUkoKCz6lP5p4zBYwUlsqFn3ojtsldhNkOqJyrMtx0hCUTjID4z/IigzZfyd1sra1NXkWmkbco6wqmJPQVgNz5zvogxHS1+IBzIBBBwWPvk3rYNQYIPGtiWH64iwlI+0k9mOBzv5Ve6nQsKeq3bVETnHeJAWioS9SGezj0CaL6fBC9n3ckHJsKjqlb6o43Inn0zj2wWFUQEbmRJENO27xsHo1dM7hhBw7q7/TzGr/Ud7u1whopq5GiCstm/d0CEJiugKFg+USg1bsuZoFhdu2j1HNbm3rWINwyZ2cg53gt9T6jNo5j6zagINS9UfBHir17fn9NY9FeAsKxaH8BMs6NSWnW3+smaShFjAtUOErLlcYxqcxQ8CB/7SFBfrjBEZTVpMh9B+8SmQIPqK0AwcFC0fblDeAMoiwAwSxn25zunTDn8uafGUEeY0VCBjMBWwJKqaSsuRKW3MhjWh4LRtljoKv3CTC4eh9w9ZiDAWNwOS8OFAwUmAvhwfeu8flXnsOXn/kqPvL4U5XsgzanLgvnNnF1bbqJzLZy/fABnv36s3iSn5z2x90BBlrkpZ+T6M0n78lBql3uaXtjIRLFCZ0EbKloNetYuPTbD6/lTlx/m9KyX5q1Qgbu+/7aftVozuB3/lyXRMqSRC51CUyMO4jLF2Uw5hYUPPa+CgquHhPf4NVjcv7qsQAINGNh2gOE+bppa7c29mJW4lXseycoGGOxmI28Vejb3lABQrPbWg8KtpvDpUUAYMuLYhm5ZuJ3MnamUTR6cgQc7Ni2B5quELetZRAeobTbzk4AAXA7UBBZFmAczGRKFtDA1puabCtlsKVla6z0tW2rGMcxis04UqbWl0d11N9YHZFzBWG3BGBeX96Pm3ddIqCxZ49cIyMqe6D44zif1XM23ptxbtQ3F1kCzQwsS12Hj9pmvpvqQmP5aX9NnvSbvXWyZCRHmmf2R9VtsMs62cclaR4Dpk2BgdL5N+9ILhgAWJWte+yvSarid1pwQIlQ1k3G+DKWx7JKTLLTmqylx95XQcH7/hpgf9MCpCtguQLnK3BWoJCWBhRYLMPnX3kOX7JdfgGAdRdTqJ0FCPDBBj5wJYyK68dPvoBfe/k/G14D3DFg0IKC+91ZRgQIXpRONwBgLgTfKS6wBm++9QC/8K3nAejGPALfwAnIA3DAen96H6rAdGozCXq8eUeoJi5I27qPhgfaJBoNE3CllNX7ZPBfCVOQHntfpa4MEOgWzgIUzNrLjqatDRwcAcMsdtZyu2FnekIRLUEHL8kWyOYO47IplR4sgVHhqDCKKrtNl31eXm8cJ3MVtLoe2sfB1gAvACB9aROglHOj/I1GlfcZMQh9AFJqYxlG+Sb6BFJSgUNKXuIVLiits6Bgdh8N5GKr97YB2MC5tgcxiwA1EMG1zjzT5UfMQAQEvRKdlZRFaWyb7+W1AwcXkv7O2tqTfxX9z9fXq1sIOj20SdjOAQqgoNvlGRjJY5DTM17+0D0QcF+4/R2OdQDYGgaMssY2JWHtxKheITs4aKDeZssSqXvX4/aTRk4tKAjLmaNsmWXHHMoWVXSJMiDi1v8BrPLExriBYU3JvK0VIICQlisUBUPCEq3AjYKWkKkW24q8lMbYaKpkxpnKcLp6TGSxsgR09Rjoff8jZQoem4KCDWr7MDwNNgD85tMv4MMfeEp3+dXsoMwoZkiRAhiVr40cbQBBC7GuH15X/fjERw+7884Ag+o+qKCgVdQxKKZtvDhgDbkZKLANTb791gP84qvP44sffwH/6//mZ7AWhrGTCbJr4JIWR81CqSXQamuSdX2wBaLkBXzz50Lf2aAsFonfuhK8zsuVTFgDBMuV+7Fw9T4HC+K7umoBQVp8C2djECIYggOiypQAOw/WrkQ7wHZPTHpANhWSNNOcVJkSyz+YANLvIwYBxxabRROPQAHzXmh6yeGe2x4sitLlal11pXE3RAYh+G3tHAWlf2tAcME3K/XEHGBREmU1s7bPFI/y3nylgilgUBLBnLKM3xBHMb9f60bqQcGOIXiXFru3+fyCU+1t4EKi3gGPfKeaoU/80wqmFJBFcECAvKO1TzE0Ie9IlIaBb0ALCnpAEH/Tj3cO4FfGelS/Cg64gCbASXYu7BsjMgR9pH0KoOCHI2PIZYywtQli/yDucFsWIK0BIGSg3IDeJyCA8wK6+XNJEnfzjjAmIZ7JXJSNGxio7JgZadE4e0zcBrQIUODlfSJ3FwEHbOCAMtZO15RS2+NDj9/zeIOkDoONtBHNYLEu2K0CGrO7nhzJmPQLbqc7AwyOQMH+OzV0GUNjBoyVQgAFBXjj7Wv88qvP4zc+/gI+9PhTAMSIZI2rTZDtaEFAThlp6UDBdgOGpJb1oEB1A/A7socChaQb8gAVjFHJhP0SmuBCHYjoAYEFuTg4kBTGBRWp9szIEaLvmhAA2rz3YNn8Q7+DxKUruxBmTyzJgDMI0PgPBwqQTI+8JVemElE8q4cIuOnpTEMqEKgKvs82KZ9rqlYXBBEEmL820O0NQ3AGDIR7RQXWR83v3okZQJaVKaawLFvcerOzZM1KbZSUKeEmDmAgLGL8gL2nlUG8jtxnAg6m7oAAyohqvcxSPVuvQfsP2952LY1g7FSbo85Li3y3+uWl7g1gLMwgGNRyq2AjCVy2dqGiVDiArABBQZ/UbVOwagxG8bEdAcXReB+/2AnQlLK3EcMCkw0IWDvnneFhNPnINfvo8oYbgJD1s2z5nJFyBmVlEhQUIMtut7zVeUBlExm5CmNLwWV5a1msMV0SU/CYGmFXwFLZAdbPBYRNZe/K7ICgFPjaji0GyjMBScCBp32ASLzata0+68sDjSnwmDtjzw+Gxp0BBjGm4Mw4i9fELW6bLVANFHxLQcH7n/ItZVdmWSkIAoPBqgQLCEsi5PyYgwKn8zdBpZR0EN28IwhzsAyvKVG4heWH5jJwQOCBLVcVCCg46GkrBwWO5Ntd8oAxknf0bku9FBgYS+Db50K25/WVFSNwsDE4aQR0ytIRlMAQAcisQinZdqosNDazsCt5AWOVc+tNqJ0u61QBSZNRfggIukCt9wwMTIBA3CWzCdJqFLFaiAQZIws1CmuUa57XG6GK1YolDRarCkoFZA8S+rayenRBie4miSsD4u+iy8Mt5Q0WtOZgBfBgTTLq3MHTuF5NnY76wILrYvvrXh/SnjWb56zdfSNLv4Zrhj4A7rZRIHARJGSu9HcPEow5pOpeIO0veV4K7WrKK7gMBuN9mGp9uaqWr7kUc1YD5qqO9RTyslCq11ISOYM0BQU9AzuTOdLM5+SOyBxRljcEZGIBBwW6HbowCTktYAMFaQHlVT4Xk8PynrYTLikoONyGvh9TIcbAAYEHFl6FIENxHaxF3NBRx9hW5lZK4Tq2iUWxJPUhqC9BoGI1zI5AwXMKCu5ZzN2JcmeAgYCCPY1yBA6AnuLi+pcrKPhXP/MCfvwDTyGydGthCTLXAZoBlKSToQBLioNzBa028TYHCMiPqQDZgHX1HAcuQIAdxey7KlJG8RiCsBQmLUBWd4JO0LW0lFXc+50dJNS28qkQGy/ISfMausEAGbsEiNYiFjSrQZUc0EMDDtRC4mQrFlg+F8jEg1pGVhVb4eHfNZhR1yxXwZnluWlMl/VBh0NmINLLvZvgXQCB0VbZvnzUntc3uBeToGL2+zpuBQkp5zYvwLY6EECRMdYABAAw68rvOykXgECf7rf5acyzEfz0Bhi8Lg589Fln6xPq4u1sW5VbP+zyeAQw8Ejtb+9Sk+4QxgmaKDAGtqGOKX93OYxiQHRpM7gIaCoFyKUeA9yNZpJ8ut4+BB5SDDwMxkZc8gx1VdZIe6HAYyCzuwtSwg4UBCNEGMoKCEwRVnlb5U9T+4H8sXVCwhqInFmJkIgVFDBSUpCQCEvKErStbgZOizAJJodLAfMGsmWOIwMtyuII6pvVXoGlVTlsrgQzytbC6kKogKCU2gZWNjbgIyaOuFrF2CokxkAhcnZ2to+PZUz86qdexFMNKDiCElLuDDCwomPPP1sps3ZwUFBdCAzgjbcEFPy6goJS2oEbAYV0VAcQIK7sJRFyWpAey8B2o/ToFZBvVEioT2xhUFl1E5kBSlXh5UkyfDLq4IxsAQgra5yeKv61sIOBjVldJfXdjQkR18K4sTLIhXhOZKECule4AAXfNpmNSako17OhGjjg4viXYYrgqkYnm7BDENPJ/JlJhO+q9KBGV5MGcVEUkLPgnB+Ge+AICEQQ4HSsKSRq//Z1bZZyiSStvmUdM8XWNps1u1RKVBmWXjk1IBSY0/3Bat/55J1tOaClu+K+a6XhXdFZPW9Tl0t9YTuBBpdVs8+HvYvHB6EeqxUOn+tcqee0DwJYeM+BAqBR94GZWADu+/DSeO/GOi1LbScDA/ZXVzqxxygJk1AZgmNQMLKMe/kDlbmPIoNWErCQyICCpo0nwsJSl0SEnDIW3Zm2kcMaqMj5ykEClvft92qJ7UmaoAg2zrK3TZTD5jZY1SjbSgsIZgZZYccCGgEnMjSRbnFuWF+JhBEUfP3hA3zuG8/ihQAKXK4OW7YtdwgYtK/rjT0aX5G2ATr0ynjj4QP88quVKegHMFCTUdhTCwMpAIRShN1rAEJ+DClDGIRyBXaLQAdnXILTvFqwLk2gNUhdggvjpDT/1dYNRAuwZADrVmSyljpZgbHHPgO4UZYgkwAPm6RLkriFzAA7W4AhBdaAg9yc0b83AK6qBXel29PqxlGegGVbhSbPVzsBSpcC1w7809LM7wYEpBbIab85CGiCtDrXQQ8SYmmUEIappFkisKqCUsv5jHKSWx9Y6OgU/wUXwjCKPdyfgKr4Q8ChM0Jn63ISlAmDlg774pH7wY47g2MKRcHbItcZ6HdmS/vDA44VEHggY1huaq6JUbBm05YnxvwI8PaAIOZAYcphhVPHTgZ35SVQEJXhBgBqsBgQOCeH2LsmE4GSyKEly1LEjQQkZGJsTMgg5MRITNjUzdDK4bWRwzI3tgq6R7I4goMDOXwECEY6xUoxg4tFjJqBVZRJMNYAGvTebDpJmkb5G8/ihU++iI8+UUFBO5ppdySWOwQMJg08OoGKsmIQDDPwhq4++I3AFESr2pD7n28bsjYsESEn6biVgFwYKTEyExYFCCsDi9NbC1JaxA+vy21MubVbDodCId+AI/Tk7IAYHS1VtTJj84kpt123IgNWJ6B9lraQvzYho6hfdVIBwJqAxRBtYvAmE3OLg7TwI4ADXV9MG1CE6qVyE7ZklS2nDSDQ1WMiNN0naJS4vs/A4tylQY5U+COyAA2bc6R0AhhASB97KkOcvJBcxnUcok/2YgqEGZJ6mYGyTq1Yua2Chfis0cqY2oi1zbpj/vUWG1N5HeI7nqzHeN+PwM6YZRf7JOWuPyoQuG1fBAc5XKrE/hgABoYIgwgW0PfLJF6hz0/hoCDIjMNx37tdzMWi7hb77il8LX6pt4xNGd4SFESWsjdMVgZs9cXWjPG9THLWQA0T2op/XnKayGFlElQOJ1qwLCqHOeRAuI0ctr8a1G1u2yM5HBmSKnPZ9claWPSJehHsL1DZAokzquAAkFH8+vdkb4UXPvEinlJQEBYzQB9/kTW4U8AAQONK8ANaeizN3bk3Hl7jF771PP71x38HH/rAPUGzeo8tIFsAWDfGqp8zEW6UxV0ogZOs2y9gbDowE7Eo18K4SYSFxNpOtCCrd0AUpAkKrWAjqOrSSotlmMUOiOsA/nnlgnVrwYCvSDD/X2gToroDtMQtMdYkrhJi0shplgGbAWwFS04ySB8FHFAC0yZR6aS+cVOoqfoBKfgBo7XVBHsBVWh2hTq2oFH+2s63AgApKhjMs0hGUOD3QNe/ByWMCYYxBhEUqJDJ8Vhxv2pvxZJdZ/eSm7dtxYxxlP6Ebg/XDkQqkK/C+4QrGlCy/+W4Hl27EaHMGIEREOj75RH7hMO7eJ/osVbBdP3CJluCor8NYAAa1gdAM/ZH5RLL0sQvmd88uBxmy57fDSgwmbRBjkWZBFRm1pr5xrtH4wsSYS1qnAWQsGTCQgkLEzZirMRYOjmcEmGFgYSMnPMjy2HmwNIGOdwbZpvKYrv1FuaBBbgyC9O7pLASgUSfxFgDA0lWPdtb4SufCDEFAQXYk/4H5UroxUnvQpiBAmML3tDkRV/UJYnm71Kby0HB6muHK4pdmaWTCrDS5gN0JfKB6fSWBsZsSSjAlEh99LY+l+CLnp1XlX82qWzyxKUu/QTcusm3cp14a6D0bIlM6RtM6ybvqkuDitafGetWgJyQWUASMkATpLtNImtRAFZwIOgoWtoZSJXaEwXHACui130pdsIT2Fu/sZi1O9pWGGise+4VzND6P1AyA2Fi3Rn/WrzKUUlkbg+9JWx4sFukF7PCBTcVx/PaZm2thip62JZNxW5TOnDQnbxcj6jIadyH57J7VkEfnzeNS/KSxRKj2jLt3xAHEvrHXIYz0MBd/zEX4Ip3fRjHPgAf/6eCNgegt3RtFpnJPWW+X/o8iykwuRWZ10ug4Ix82hIBhTVXigCFJal7kxMW2rBkcldD4ZEcFrmVzOi5hRyOgZUzOWyGWZW7Y1dJhugROc7iloVvUi1joptjBkqJqNlb4akn7/nvrIxYg6NyZ4ABcPll/bruwjfeeoAvvPIcfvPpF/Chx++hjCgk1LkKSGcDFfFV65qwprpqIQ5Mo7eI2KNmSQNl4vpcoB0DPYgpOihlMLZ+O0Pja2H3ca1bwcrsE25TZAtU4de/cyJC2WQiIUF8dGjBAevnxdrGFL8AW62TUGQlRNZuHTiQXAeaWjpJMCHKCrCsDTdA0FhbjeJTa+sKAwUTSlBk3IGBoeL3jrig/CfKxRV+ECbWjwjXnSmucFRwAahJXihLNfy6MVjYWbCIlTI3xUH7Aft897vvJwBCP7+6Z56uwy4W4Hb9FPvIYRHX/rlt30hV6vyNyiWlFjQ0VukZ0GDtNIotsXNXR0ArVjLE9h8CXwVVyf7qSg9VWDFHQVwCXVShM9eYAQ4NyszVYoYAgRkoKMp8AgMZpV+T8uxbImwsin9lxqIAIYOxFN65Gf4i5HAPgDi4bq1kEubCVo1wr9UvlNcfCij48jP96oNxUcl7WO4MMOjR/vS6ASj4ec1N/eGwJNGXeU1K9IFF5McKjc0nvzKwkChpczV4NG2p0bQJJvR9yu7fzSaef26DeIyiclDA7IBgLe1Es0lWBi2WQIqu58NntuvgphPQ6ivNqFSw1jcHcMCszARDlzESyATQGeW2s3j3iqXNC3CgTOz8CYUCBKXC2AsNe3YHBprfNpWMjdueikhflolG5QMXZPZ9ChYAB1Q1aK4FCByP9WVErffj4BL9Prp/N89O16Gj/Ed5+WcgwOaS9VXff1Zu30/cgLhYzdhXdj4F4GC37AHeCDgAuAjy5Jqzc+EyqOLQnhZ7ZdZynxeFEcZ8aLNt16ChrQ7k7Uhe2bGkHHxioCgbkNRQMYCwFo0/CHJ4UTmciy29fjQ53K/uMjkc4yZm+sKYAiLCcqCuZ/L2jbce6IZLAgoGxEJTDBRc0pN3BhicLXH8CdKSXax+8glp1KTmboEMltlyECvRHwZU5oCSuhgCSJCBuHk0rYEEAkQ4BKFidQXCfEercGLcQx8/MGIIRoDgKLZr2H6p+rbss70PIEuIRoV10jrzoOCASNqvACB1WSQy14oIzZ0lrDcc7saGyaCPygPolEpVnb0SiYp/pPSb1K4BNVj7RmFS2+LStAzVDrPcFD2g+6qZwqEKGCJYsN+79Upocs4D+7+hln1NLl4xO7Z7p5PHLtWhNwZ2gA21j+z8DhSEH8c+e2/6q1UuvheeuhbtXM8C1WMKHPRECnOhCvcKFKzSEfQd1jwyaBPXV2xbX9E0AFW7xEWTB+dEWDdRzrlIlsstEbCJO9Y+XypNGIVRB1q/xMIiFJCvlFoTYeMKENbgZlhJgsdrH0Rwp7eO40Hf2wBQDwaiHDYmBGj1hJUtyNP+vMK1UC9SQ1IOvNmBgljfM+Wole8UMDg7fYla+uWjTzwlnU3wtfmEFhQQSSY5mjzEaCR/hgkjtSJikMySkwKGrVlyY88BVMEOkHYfANlTVBEQ9HSc+MQC4taXa1ahaT2EVrMYCApukfAOqAZaXDLk8q8bpDKhKjhIYF2vq5OSu5TKQCMw5Uve1Xk2F2JXNXOO63cxyPaKw9p6pDx6ywEYMQT7OsTrLpXO6QEgyG0dU9W60eMXAEO878yqreWyhBkJ/zNKdGT9jAXacR2mbEw4f7YPz/Tfub7b7XcX2loVd98XTR8aiJNrY8px++0e+OV6v4qiO6ZpVttaYhsZsAIG8yEcb9q3a1t/thlj+lpLTsBWJM6KZZkzcsK6FWRmIAt4SKwvW+THhXUpOLju71RQt/NA7DSCBT8zkwawVoBQl1xXly+wz5Mwk8EAdkssLwVQNvqBYkpjPWYJqFLobwRw4GNB9u75+QAKbgsILl1+Z4DBJXGUUAf06130JquJkCBKylgDpkov5UTgjTxyVAbW+KlFoWSCDD4AOjCl03ndsNrgI1LLm71zbZVDX+qywhYMHPnnADRMQdMmFqQcwID8bQFBDOrxyF8KS4UMcQMAoQIGGrvKDBxYfyRtLwp9FAf6aNDPyI2R8D6k87nWaabwo7LolU8ji9BSpbexNPsSlaexMA5KG+uyKpseMPhxvaZarPp7qjW/hVxB/dW4HAXtxWf25d3UoVFqGFv/PQAYMXF/of3pJ61/9GuQBXEODQGETpzUdu0B4Hu0uSOfQ1vYbzrANXqWbawmgcgVHFBh5KxuVmbknMJns7oJKZHKM9P1VOVZGvdJMSRSWIEE7QBCZHMFJACr9sVtZbC1Ty+Ha9t2c7ArmSpzILkXjB2QvsyBLYig4N4HxzEFl8jfSyP5zgCDadG5A0hjGSh4ISzpKKbRWJPzgDRRUUV1ZsWSDkRKBGKN0J88Ok6UxDL4SK/PJLv39TEJgOQJGJVINY1oqj6SF6gTN1FF3LEcgQFzFfQMwSVAAATLZiKgDBzEOnZvO26ErvTCqP9VDwpGluFI2feKITI10YPRLDcaVHmSu6/JEWGlbyMfD+G+I2bJdW1jiVoQVbx3q4DifR61vBtl+cOoQ2mO179HgG5kCfplFiTXved72dc5fHGXXG+5QgHexT5ugUT/LLlPW2bK6jaln4dkQgHQxG9yfIG2OXQ5HsSfvzDXgG1lFGkrWFzZAhtRMHoGICG+E1XZYqApspUWRA2Vo8QhzgiXZXAfMzBa8n3BkQMALl8puDLc/ady2Fa+JCJ85+0WFFz0/j5i195ZYKArWaRo47z+UJM/fLKN3rTGLUQoRcAB68BmpbKYIb2km5UsBKe/Fr3GolGN7orFEaMqww0ANnYGglIQcGUuLPsAlkhXjYRVpBP7YEJbjjgDAz07MIqLiOyAtWUvkGZjM7ZRrPrMSpkJ/f4ejeUzsQB7JRAVgN2zCSrtVqH0dYulVxAjxSDrsduWaRVEZaRsl2giYAXrdXLOQMPQFQUg6JJdR5hyeZRyilaPtz4UUHvActuyq0/X78Dlvgdai1CuCbfsgHnzuHcxFoCOUbD5EyZvTewTz9u5cd/7AtkoA7A7tHNxxOv6ujVM3uxdwrWsvzcXLStIkCV48o9U4C65+uuXojlhWJkDBQ8jkADQcDnjrJi1n21OB4Dg7zuIc+gTLs1cBTNAkLyfWhZ2yQkLVXeGuWyzGWsku0caKPjKJ3SXxFso/dl+CrNyZ4EBEJAit2ki74/oF5W1SZe8mLIXqAYs5nQLnSdrYyo4IMDzHCTegwNAUStoJ3hy4WbJil0QBUkvZHbBKlSBCVAV/+5V9bABAXvOohutTJkBYwHQugrqjmdSzlggo+C8U/T9QNEfKXm7h5zX33ZKfpRhrVUCtd6PYiDXpCxSqm+RG+Ef60GKGG0pE1D7yvNmAAoi0STbAuAJt2LJj2o+TMosn/2jlB923c6wPJdAwGic7K8NxwfNczOtcej74Vhp52q9to6T0TXRPSnn9XsAE3vXRgsgInAw5p7ChD81700WqzHBqHM+goScCQuyKO+yX/9vCnktRftANmrb9P3OzM/e5pLtrKn2aZxfmMvdUezAESiw5/axWgYKFu0ckcUiv7OCgu9+/3X8/CvPCdP9wXtTRd/jo9sCAit3GhhYeeMt2VDiq7r1pCPa7joypqDI2SWJPwzFJhS7afK+nJFRkAsjo/rGkJVWgky+2yoSG6RWZvQkIELABmmevVR3PYCGEeiBQM8K1OAn1C2V9dg+0O3gvfSv+fUjq2ICIi7D1GZ3Wj+uwOit+9ad0luF9Zz8Bv5MoHO7hFnVT7BZbotYdoxMtMAaABAYla32XwSGmY0RCFuwblzdC3ov6gKkuJVSTbl5jxT50Zh89LIHwj+scglgz1ih2ViS6+TvX8Z4ipYoUGXBHiRUENHKghY4mBvDmUHUGAE2g0B9+JUprCihj3eIxQAJw9qEEIMWTRZsDCwkroarIAMk+l8CCHs//6UVALNCA+05G+OX7jsCR70htgQ21v/lVIFDgsYYyDljCl74xIu4/z+9F+NLa73s+e8Rvr6TwCC2Tdx6cgwKAlJX8o2SHCaWxBdI5BO76eScQMSgVECbXJ93tNdeiMQyi0z185de9sJIGFkUPRDoVxTU8xUIZAUBMUraGAP92a40lj6UNoS4T2LgH8J5V/QqDEYbPR0FXtp9+oxp1n9RII9WaPj3k3KlbX5uV3jY+moKyaIAb7QUAIIkZmmtl5ry1Cw2apRWJtoJqlm8y6j0dPionI0heBSwMHOx9OVMDEI+cc1RueQaOAswZ+Pt0cZae6Kf6qPVRHIdNdf3LkNg7DYkD8jjZqO0JSezlWQFgX5uykA2yDPHl8e3E4NA4q0EMAgYiNszG1BYkAeygXayAWnM9DxKiWN7ZIxNY0sCQOvbObIEiwYaWn9lBQuJCN81UPDJF/GxD0ZQEN+JPJSOsBOrTTnbEn/lgAER/Z8B/CKA/48e+qfM/Oqj3OvBwwd47uvP4kUFBUAHCGIqw/p8ECUQSWrfAvGPrSy/3XRS5wSQuhYyC3uwgLFuNXhmUZDAzFhwWXj2gnImEI+E4JFfcmgNBCDg1wUg0IKDwaSfVcUFptCPkuSoxnEkAixfhLMD9rNu4o/2eOjTOvcZ0vo8DZeAQSyXNqjz76AmuNNfGHX51KVkUZesPLlm/Nszih0YC8bRWDyyhk4sLb91uQRiosulLz2Abtwr8bpHBAz2O2u7DGkzUwzmSTT33ajMcoacGYunxmDMQXIIDOo142Djmk7YgqDZ2NKwD0rdCli9qqjMoYECS/E+Mh6OZIWmbKpAACJHBRS0QMFAwEJpuCnRpZ0aj8f5/tyCbg5FtwPGSjSyND0zG7PhmlxNCZ4Y7irJSozvvPU6Pv/Kc/jqp17Ex568J2brQGeJ8E6gQQM/6rT9KwcMtPyXzPzPH+WH1jSvd6BgBAh22d/8JglIGZmSZ+UjXV5ncuYq2ZaXEiFLS8LCmuVQg2c2qiABEKBgZZQWc/cuJ9iD1h9prIBdvwcBMUZgxAjYsdxNdD/XtPNs2NVlnQxVKtZ+xsAUta4LgUnyF8TJ1udTX4Nf0Y4VPW9AIAreuVDe13lG6/bKvGzdMXW4RuUfo6DjfaL11gchAdWq8BYMzzlrDR9ZvWd84vKb8b3PRFi/18VcLn2JLhigtmETowP1ZceAslBOt2n8jTVOqpHtdo2xP0iowBf7iPkZkxCBwOEY7URVHY+8O+aAoAQwYEsjkxxPRFiyLOXbmLFkazPNDBj2QVlC29ra+gTydM+238CtZYa2lZ1hUEiWRCG7YgUKmDAKtr/ACCwAKupTy7zF5rbsg5fcTQuOZfjIVdO4aQMgyCofIjB48+3X8flXnsVLn3oR9598CuBNN4ob6CoGmHgHEIxB8HdAZRQePHyw64b+/e5cef3hAzyroOC+rz7QTuxSjMrIKiEhEQHQTtBdxK4ooxAkU5/e5kqDFCUNZwUIORMWlh0Dd37xMCCvBvEHI1nVC7AYUDaPRsYOBBhyPwICvpGT/UOYzL5neZtlzdu0yyhImk6VUhaFaSPSlosoOACJZXKUEnWDTMIICtZSHADUnSSrsO2BQGOdndBxUs29sPXvgxwQ0UIz66wHBDMwYJaFHbdyyZXUKK4uwI6IdmzBkX8cOGJR/uKBwWyZQgqAwdwwQOuKiaV3pwHn23gBDUEVMcuKJaiw9Xat6+5RDCwqQGjSqO4T9uxdDGNQcXRNHLfVlaXnEof0wbIvwJKAdYN+TgoEZL0/gaYKgqgaGztQoGPcV73o1vI72RHlBgJ4o4SkxhlA4KSbNbG4GVjn+jZhFGK80g4saGdfWqIa33tj3vl8mdGkMJ4ZaL1xVpmVCghs1YEBg++8/To+/00FBU98BLTdiFUSU2FDdZUCAhGvmq3HNsrqoFgEBc9//Vk8gSf3Havlryow+AdE9DkA/wHAP2bm/29/ARH9EoBfAgD8j/UYWvdBAwr6Xc7KVgEBM5zM8vGR4Ht0p4yUMihlvKFI6zHdrWstIpgWJmxJKW3AQUKx4JnJgJyVnYUTBBl1H9oI4j0IINRAwSEj0AABBUq+zet2cW+CWrGkMySFishubCnlvXvGVn6wsAY2zvtim6yY8C1oMzk2nwMguC0YsNIEeYV+SERTQNDTtS1Vu18BEinGeqw+dwYIdwZDtHwCxWkgwb8rsOqp8EvlLwUQdM+era6JZeSKOYrYv01bm5CMCqJnAs1laHcar7snlKQJdwCUQm2sjYX8l9b95IqeLo/j9nwFxYlI7mtxMAxAQUn8nCHjg5OCnU4htjKGfEndYsZFCnKkbKCyjmXITH4AquhEhjCRGhdJt3tOqujJgcKMUegDGmNwc9b3sn1Ybf8pBhwpmqy2zcLPyuyZgWZtIyCqymOLJ7Cm/tw3n8WLn/wdfOwDHwLWd0Qel4KeLiL19xhAEPIg1es0tbWPSVRQ8MKnXsQ//d1fm77LXwowIKL/B4AfGZz6zwD8KwD/OeQ9/nMA/wLA/66/kJn/DYB/AwD0flnctQcFLUsAQAaqsQTW2M4a1IZnStrw2QHCg7e/jedf+TwA4CrLxLbEQZU9kO+c4P4wqW87IGEodlJ6Y8l/Re3fPitaBAExGKh3DdQJDKGpIhCwdnGkz4FdGQCDwS6FnDKI1AxKyr7kK2TKcrl1SSKwCs5MAhAAaE51u70kk6KEnTab+WJ37XkgVEfUf/N36qMdAYR5kqje1wi0sR9ACwiH+05kNNn4lmDxmKWzMYNdOJGvkjFf6WYKMlDhgPRH9FVbxri/CqWP9q7tp98ngKBlZfbWHHDQ5oFK2ArvFIT7sqkygB4Ie7Du3lOUJwO6cOAAQMGDlFLgMkaf3rTD2TE9K5ZOOBZJtkMtq6LCxGlx6PhPdc19Vpki8uQmsIxh+/RbyhEi3e0xZRASOKUOKOTKKJSx64GhbTsAC9b+QKiSMVAdKDrYzL3N+xBeAahxW4QgJwJLGwEVALz0zJdx//0fArabtv06/WTJIYiT5tsBiIrsgmlvFjYUM/341RBzNyt/KcCAmf/OmeuI6IsAfu/MtadAQXQdzJSfPRu6EJxk+9/r738Xn331F/DSz/42/u7/7RPIvCGnLLE5LJt1lIIOuYpl0afb1TY481qN0HJQoN9HywVjwE+Kn80PKE8foPmttkXZpKJlg7Et+33gYyXrhJZBKSjfGAPgyoUB5yuktMCdkEUQr4RyaHIpFbALJTAV2O6MK9t5Ul8pITk9S+qqqFZXK1DnwrJhBQaMgP323QKBaQCoVr8XLrNSrZ363gvgoGEJ7itSK9csXLZltQA4oQIGdesAgZo/rMVfTpmt6x+BgRH42q3b7wH25LlLpqCSRRFdsTyc0bsN91HydQUNjfcyoegKC2xCtuO2vJcuxiP0ZQZwfUzbZ1Sr1VL0ZpLIedJ2zqrw5Deka+6pgoKygrYbBQFboMGrPHHjDDiWJbr9M0HliLIHDhQoO6NAaUG2LaGT9JUBBXM9CGPQgQXAAYN+lL+hWW8rq6OcvsTUusvFYggA3P/Ah0DbqseqIds+iwEq2hYAFahrK4FYjxtDQwnXD6/x7Nefw4ufOrc18185VwIR/Sgz/5l+/V8C+H+d+V2/+gBAq+g9psDiCcpeEfYdoNT4g7fewGf+8FfxtY9/Efff/+Nyan3HXQyJskTcJ5nMmesAlGyIMlLanfiCCOrH3c6CbQ+PNlSpghEtWwBU90DZqgtFkagPPDsXJy4LaGBjEwDwwESnpBM4Z6CsyrJkgDLEdSOAwBUSgJQWsCraXDQDpO5PkZIotZxqUBQlUW5rEmW3pIS1FCxITs+KMFUaVuXLjFA4G8Vt5yMIsLa/BAQs+vgoADT2Y9+3femFVbR0WBlhzyIHYCOxck1hzQJijSI3AXhmBU1fHiX/wLt5xqU1+REIAJi2f2TeZhn+rPSgvoRjlwLfRhuejYCC3LffDbUDBakyBWdW2fRjPYJc+76kpEa6uAUsRa8o/foby9ufTaklqtaugQJTagYKDCTY3yBTpvIEAKlMEao8u+HhQEHdk5wWAQkqh3LK4JSRU9wqurpttk4eG2CIfVvlNNAI5ANZHeV0nMdxNdcufsvapVRgIMBqvaCXWIGSnkqQtM4oAGvMgYKs6++9hme/8blGP16COn/lgAGA/wsR/c8gdf/vAPzymR+1qw8aCdoEGrqvi3lqHdffbrj+s3+PT//RP8TLP/XruP8jPwZsstKatneAEqzitGiWKgEJ/WCMaLWr4cUyAwR2sgUB8Pdw6i6yAsx10FkwYYh4JS7g9UYmrE3cpo26krK6KAm8XClAWEDLUgECX8l981Xj78qRObCjGkHNJIpvyUkCgzTLJLYCSyIFpJ3/FmjBQCl15o6WGwIjYDAGAMA+PuBSToijANBoUcTnz6zWnbAK1Chgw7oqGYlxQQMUjE24ApwGX2Kabi1y/niUjhLDWBkBhX70XIXPt33WaCmuHD+/EkdPP0If1GWqR77sBiwos3AuLwd5m4wAA7AHBgYcrMy2U5+B3SVVUNBk49MldYmMPRB2wADDkrQvIigoNwoKinxWQIB1BW8rmAuw3oQXa0cGA84QWFwBKIFSQlqunEngJMYH0Q2gLgZOiyjMwCbYtVkNPduvIcpnacM5QDhTopx2GT2Sz50cjowtAND651U29zrJasWqd8om4ICTvpHVXRwfrz18E89+83ll0p/yd6q6Ylz+ygEDZn7+UX7XLklEAAIVFNQGjsds5rErRhuwr/3gT/HpP/5P8fL/4r/C/R/9MWBbfcbR+o5K+kUmBa2CbNW3bvub22Cs4486AX9cojyk8LcBQP27xmCf3r+38/kpgl9vZNKWou+5KSgIAAFoJ3HKju6RFxEKKYPzAmwL0mPvk98CAAsxHcWXMQeJIO6EBDAYqWiEbpE00X2WSUsiRcl8vDaZ51ZT06ZB6QPHqwQuWaNnFFBUPiOXTw/8Yt2s9NZqtHTarW9lp86oqFqgUDPJmethunnQgeLv0y3L7Y5ETVv26ZRpGI82e+Y0pe972A+zPpDPsR/q+vvWGBhl80vedyNmoQcMQAsapO0qkHqUjXtGiY6OsvEthJqEJxkNTm75DkFBYA2IC8o7fy7HomzZVn2/spMrAJyJFKUvyp3zIt/zoiCBxABRAEBUgQT0mIAHOpDPMnACRgCHsXwbOT2U0VP5HABCNLys7SY5C8QuSAhCQe5LBLWaACRcv/UAz77yebz4iRdw/4mP1luE95yVv3LA4FFL7cb5K+/YgiawLihPMF77s3+PT//xP8bLf/tf4GM/8mPSaebTASRaNGX5TRNstwJIdSDqAPTBCLjEycBYwvqrlDBSFVbE3AvBT9dE/ca4CaejToKBda1AYFvBNljtuZt+zzJ5bUki1hvAGINF0G8BhD0AZFVGSSDa5DeF3IxeKIuEsd1VIHkLDBzIfWqWyaWQnEcVmtJEAjt6y/RsbvkqLG9HRe9YAKKp4plZE/q11tO6PQ4HPeMgQA/GgKsIFix/RA8Uin+uYAGokdnWIPHrVOV3Jw5Gs5eCli3wB3U0w+HzzcLHhb5QIBABgv0mrrcHbtsXFZpXIBABwhgwFB70hbrMRoABwA40QH+/z8YY54GU2VwYxWQsqa61j9n47LOzBMYaEEDqjnS54kykuhLWG5RtBW4EGLh8UcaA49r8gWwBIExAXqrLclnOgwRjdE0Wp4ydfJYGEfkdnvuu5XS/HN7ksx2LwZjGWAOgclP1Uq/PWBJSsQ/cpPenhjV47e038Nnf+3m89MxXcE+ZAr0BgFEqpLbcGWAgJVjQu1OBLRj8rq6zZVz/4E8FFHzsn+Fj/5P/uZ9jIm9SKjdg1gBFhGA7YwgoyUBzsyP5ZIzbe14ccHZZRIf2Hg0wiCAnDLQwAIkLeNsEDCgQQAAGDRjgAmybCBkDB5Gf3MwRr++9aIBhliEVhamhZ6s5heOigZMo6qh59XGUBSRQAVLIERHToQJtshH7GJv50iYyfunOejxWOGcAgAV+7qwIACiDRFvhXWgwVpisvQIb1QRcCTjoA65qoqm9+wFoZ8bAUNmVJlhydMGFMts580wdYkzGyC3QA4FRIO6uP0Z9ESpCsRK7vrBjFPzabQCcAwQgfMeUYSjZfhdAiH1ABQ+XNhGLbTmbE9EFZmPasvFlCJvnoMDaFwwqmzIFa2UKjDVQwyOCAlbWgMsm50fy5QYh0EflS85uhPCmIEHBAucFfPOOfM8Lkl7r7oYyltHQa2rDWGzDeyWnw3y+JKOjoVfWGo+1VWgn8RaoQGBYp4Lrt76Lz776BVnd8ORTFVpw0fdw6TstdwgY9MiKx58vlOsf/Cn+t3/8j/Hy/f8CH/uP/7qiW+1gC7ADpPMQhHMACbbMRn5D3eBDO8hODjgAQWgFxeIsSPVHtassGFCUXswV0rMDht63mxYMmJ9rtCZwAywrCyeltZZlBwCgPkKL52BKQKGK2LcbEUL5SiRS0V8mtY11mWIhtLT4knZCsi/UfUnheOweU/r2PSoY4Jzyb0CCP7tTODMLwvpx1O/NWPGaKfjUsWaR2kRNwFVdzz0GCtZuMTr7VIkKmoaHL5agaluq9pZ1aKz8CRBwoAAbU4Ha3eUzARrQDRz2hzw7tf2hfxsA1wMGB2kRJExAg7ZLBA5ABA862rg2X5wbs2YdgeHofmmW1UVQkIAEViBwAw9iDqwBcQFzcfdBAwq2G5E9RzLG9GFSpnVLDUhAvqos5bpKTNO2CkhQFsHYBZPJkmU1eX+44RblsoOCboHsaWDQzeeG2T2W0w4qVo3D6B+zoQZjTsr122/iM6/+El56+ku4//hHwo+5Q/c2EsflDgEDLX0nXXJchvLan/0pPv3v/okwBf/xXx9EzIb7rauAhMgk9EttgAoW/HNneZx6p5GQmrAFgDMD7hKIwT4jQNCj9zBZfYAOoplIXFnV3NuMKSGZ0KWAttWjhUEJVFYhu0yIIoFJQFVKGbpuEaSR86Ty2qzdBG4F46TJokVpzU1+rlr71h+jTaFGud6nyh9oAcAkqcsO3O2YoNDHO1AAb7fG4iFJCMMepCVAISmNaiKAQc4ooKO9EbrxqD2tbWq1avvOrrfS3z/2ox87uH50/9hPdnzHCPBW+yauyun75V32CQWlUr9X0OCBcJdYBh3r1gYROOyPaVsF1sHasW/fo7nSRtFTBVMOBuLyOpMTmqfAVh7YEujATDoTaYZB2cRlcFbObEUUOHdGyLZVlpIIZg3TIjlT2EDDtrarpqJcpggUbDRB5VLXx2fKTE4HudwAhgAGeNu8DXjb7yLisVwH5frtb+Mzv/+L+NrHfwv3LKaAxVHo+shZg+Nyd4DBLQDAqLym7oPf/Vv/HPf/o79e79fdl7fQeZ2wIPeNucNhLEiACQ00LjGxRTuwALe9uC4BKhGFhxUFjcugbPUdjCVYb+YTNb6rHuMUwAES2KwwZgEFFO5hrgz1g4GFhvR4A32TmCExgXQDJnkEsy4F1TdvlnyGMls2lCbHosL334W2dQsztkW0/L1fDhRN7zPs6cOj8TsBnHF9dxtsVaOyd8FWyRRRBQvSlvPHj9p19BexzQ5LmAf6nZvvt6/PDqjxPNhr6t+dCvDZa3T94sA/grcKGkaAAVB5EcEe4P0VlVa08Zzp4QoUgAoWRseO2nUEkusKHOwzGsZcBSZjbF4bW1Dq/BdwIG3MsW1Ld21sc5tuCXuAsFyB1htwDoouaeCdxSMoaPCMiVtqQEIjp7v+9Kq8Wznt71PlNAB3ETgYGL2/3/gMKPgOPvMHv4KvPf1F3H/iI8ez0HXb/JK7AwxOlwTnqSgBxLj+/p/g0//tf4KX//Z/KasP1sm+b13nGUhARGNWOrDgP6NmKN6qNOlvzfekz3QwYPXsAUG3/HAHCkZLEZuHd4MzItgTA5eLhrh1lLr82wRgkG17nSXwT4VRKeTAQCzd+YgeKX3gwNKfKHwAc3p5pNTjtVO6UJ7rIE6vHa3l3r2XCTSgXdtdosI5GWxF1IAFABKdPSuBEZF26cDiRIk247W3vGx+DHz2Up9OWB/Viy+4ao6Ccf0323vXL1HRREDQsYqVNcMeXASftzMO4Z47AOGAr2Me5KIdUJi+Tzd/ZonRfAniaD4Dp9pO3kGvi5tFnFHGZZOA722TQNltdTcmYQWXBKRSAUJR9kw3uHCQEJ4XZfV7JaflWWFM2bHe4GrerbTyNd4ruKmgzOv1D/4En/6Dvy95dh5/avi725a7Dwx88CUwVGDoJCQUXP/gu/j0H/0jvPxT/9JXH1DOsJABkfGTzrNiuGALx2ziKi10hgq6VEo/2Xqr3lcQVEU/AgU4EAwAJCagKJqd1JsooS4LEgUkuQtU8aQ0/a3XnclZAxc6ZlVo2lPAljHqz+Tp+/qEo1PL8ci/P7Pgdwq/1mLoGtDzQyUThWbsK+AYmIVcEZIC1lwG8vdMsJWvmgkKx8GCN2LXX2HcT4OqwnU7/2ooPTXbutrqceos6dN16wHcASvQB+Na/7jlGufI2X4B6pp7re8Z0OBt4fT1wHqNoECPtewD0Mc5ICqQJs4B/ncmBYbMmbVbl9bY2YIjZiVJenkuCoSWBey216IunuKbUg3rRBfkCeD9zNZUxYBCPG+Wgsj42F+Rwn838nqn/P3EBT0yKtSxHMENC0qiv/7wV/G1n/nXuP+ExBT0Y+BRyp0GBrLfQU/LEGyz8dfe/jY+84e/ipf/7q/j/o/+TTBvDgYoowUHwNhxCkw6tz3GR6P+TJkNoCi4OmHmoGBQKGV5rwzQJuhbiiL3PGi79gYVEFhAEGnQjy0XStWaOZpoVNbqliChPUl9YRYENI1652DFzpT/bel9u2/z/mMqcGchzZT/qE9OWleNZWNtmaqrIAZbRZCAElJUd9R2Bceo927aNSreQZtcAEwHLyN/JgrQj1Gbve1UHSd92+yPEsCAB+NO2DV5xC36BmjGOhsws8+9hRoy/AFoXJF6YfvuAUiQt0XrtqDQt8M4hx40WNuPymBuUWBcqEyYVX03ZhmDlJJa8JrLZLNgQQInnRtULssbbV/528mdlMe/K5r6KSV5jl3nKyHsWCevI2h41HJ2I5e+mMEFdK4Pm/cC8q5/8Cf49L81UPBRRPdULLdxh1i5O8DAhNRokPesAYDrt7+Lz/z+L0ujvv8nNBgGLiy5AFhIFIFbFeGeo31SrYysjEtU/aOW3jrrn2P1pCQgJ1wnkykHALXIu2a0dKPdM0w+n5RAAwjiMiLkBViuNBPiFXiEYB2FFQdtwiIQgC0IwP69zUq9AAC6DGKjSOBhyufY2aGfeQTEunavyaAGCqZjCtr79SDWCdxq/UOUTFzT7cFW3XpvLIvn2Wip6pbCnpaR4h+CpwloGpSh8htYz6YU6ZLy2tVz3M++MiesynEwEHN3hP65Vd+EOhrI7gFdcyxcZ7/10RSPD1x28b4NExEBX+hnzxwYxwC1OxjG++/bdzTXBvNE78Fg0HLl7QNob6Ykba8Z+5jWRu6ckjlAK3di2/eJkfr2s/umPJ27u2vf6zIDMF0/97EQDDMCBOhf/9m/b0GBpYw+wxZcYoxxl4BBX4hkcpvSC+Dgtbce4LOv/gK+9vSXcP/xnwSXrQpJo7RIFRTKDiDI/ZOs2+np/KNyokPedQmI2AWIrhAQX3PxY4AoLMpLnQS5kvU8mBi9EERQUm4tGWugSooGyUeGg5YZEv8hO4bpi3QXTej/EQgICasQhX+gjF0YmbXojzlBA/qp7twAPMwSRZ3ZoIWIwLip1hFr/gYLtrK2X67c9+pLRINwoZESnj2zdwv0QEqP7Vwk/ft3xZXhgHIHoIxHUHTSALer7wD0cccO8HozdLHFHB5yuxNzdqCw9GXa8/H97adHVmn/vjZn7XediymmDjZgaO3oSX86sBBp6VrPKTU6l2GUALAzf4ytggO1dGULezVUShG5YG2/BGB5JHfsfYEWCMT2GoCweP2u/DDk8hGQtTJjkPLSAoLQf6CM6z8LTMGTT2HmRgJwmRWalLsLDIAGHACi7K7feoDPfusLeOlnfxv3Hv+wDIqkTIFRY0wiYHNyGrIBCJDOEwGkSnc0l26DOHeW/mQQx3c7KrntWsoYomQBAGOFOH1CP/HiToqOdJc92nUBZL7vOoCJYzLXhF3OtpGlesF37AGWR2mejyz4Ry09iAgWdAQiXo5AZZIUukhZlsgSeSR2BAjiwy3CIKgvlTWIaRSNDaDS+fsXkPMDEDDzw+/cViMgFRVddIkAPn76+AkA4wjyS3Xmg9U5PUPQAYJdH53pH8Dbw5ctGpibtYHX+naFutiGHpybiw+aGbCdj6lVNF0syp656UofT+IGF2B5FcVTS9KmV48hMYO3RebjclWXOJprAhFUHm1urCUo/Eb5j8DDbcolgHBbGT26dsQM2GcbK8vigEBWVSjAS7nGFHxcVh/AVh9FYBfdRMAjtcXdAgZDN0I99trDN/HsK5/HS898RZAW69IZJlAp4KwUq9PatnY4+Cdt8CxLDcjqqWhjEqK7oWxt/fpBeFsg0F8/sjwOJnZzGXA7f9gI6XZKB5BlQj19PfIlA5012AufaLUegQHd/Gma1TGuxgDGSqBvi1G7Dvqqj4EYWppHYGCmSLvrmkjsopZUCLjida3R2F5fSGAsBYbryO+o1wwVq77HowCsEeW+Y5tScDX0y8xO1JuBhsXwuvsYOGYJppk+T/YPgArkgGaFE6l7zK+7NEb8WDsee5eDgEX9nK/qMtXg1pPEP5XFazYj4goSfHtjUvZuwtZwlJOWzIRIjCpmDVxW48mYq0WzokYmp3Rz/VHkUF9Oyr3ds/q2f69k9IQZiGDADSgASEsrN7VPrn/wpwIKnpbVBzU/zC0ZAtVtR+VuAYNYusFx/fABnv3m5/DiJ1/AvSfvyeRiHdyWXpILSOMQHCCo4PXBbtMyLQoqhEkA0Lkb0hwkALdjE876HLv3ni2VPCr9Upv57/bBY4XiBOioYOq+4wQ1PAEEu22hLR+7JWxab/YpnicJnABglGWsqUtn6WJb67sEele+z4KgLvR3VNr2/VLRuBcDBztB6e+rftXmES142K2jjoB2EJj3KPQ7IwAoSzYzYJscJFgUeYwgj+P7Uv37lR+x9HW/VG7bPzHQzao1AqFAbeuBcjwzNgWIGBPw5/DtiJcrNPsMhFgf3laP/dGUemKdWhy/x7QY21qG4EBPgjx7ZjdnWXMoJJWdCt4oy3mK7z3gT87Lo+PfNXEv9rwcxo3JaOC9ldMTMGDv0DADptzTVVX42g+vff+7+Mwf/gq+9vSXcO/Jp5rVJnHZcXzmrpwZ51ruDjCYNgwJKPj6c7r15L1wisFmerEm5kgiUIiyKv5NqdrWauJ0hcgkiMvCni3+TQLawDYKHbMLiuk6rT8/GGAAumCuVlnHCXQY3R1KjTA4KCOaqg9g6pX/JWprF+iG+t0Uue04VjZ3FxTbsW1bKyCwHO1caupV9TFzvB9wMlAugABLtGKvY9W9FMF8inY8uEewDnfBVyHwqimmnKy9O5dXlcdREN4CEGgKbQAt6No9AHVMAtJWzE3sBOWrzi2S0KxD9zFTBbizILN3kIqNhbuCKV+dw0Xq0azQ2f1ocjzc8zZlAAhmicVm41SUbIhtSBJ4yApgWZcRs7oTsK0CEFiSkFmMEWWugdeUZFzbOO/lxcAqZdueKSrkA+aPm+/738T327/xQbH2is8FhL1o7t/J6PhbCm0f5/VJGS0fx6wAQI1Vv2NUAY/RsnPX3/8uPvMHv4KXnv4t3Pvg/RYM+DOPZDbX+jljeCzj7w4wsNI1lICCZ/Hip17EvSfvdaFstj44C7tia9+NAisbhFVgMG/1OAAsjzmgcOagdzlwAZYBULASB+6RTDkAAREt9hHdrd+pU9zxmP32wvP3x+Pgmiv+GcJv1saLPwOdWRuEBregwJS/g4E2FztCSlaYe8FiD2zXugPaUpZYraBEEp/h1Ru0RaDAG5dC71+OZbul368LbOujsXeUPNBa41bPWHoQdhIUDJNjRUU2bNdS61DkWaztRptu/2OKyV6zoK5Dd1Yko0kAM7KCjJ1rfiPBuKxVoaTiMq7OKRuwhGW6j9pHoxLjTCKAGLVVB1wvjte1BhpTohqkajEmZQPlRUAHM2rwn7BJclzZBGsbB1yMRue4TOkNgPkcb1R5Y8mP5nlXDqzc40yDqAyNy2egkdF2zuXbHCwAOCWjgb2ctrE9BAKRTbW2XN6nxzJee/tNfPbVX5BdEj94P6zs2mcL9efvPlO9sme+JuVuAYMJKPjqp17EUwEUjNKCSkpQSSUr/cPwZS0OEmoKXF4eE6snrJFuqLMeKAC7QdhUPQzs/SS7AAJGlH0zCHE8mS9Z80cMwxnKu7+dT9zSWBFCR26AZGMH8WByglVYmsJSy6ffoEXTO1s0usQfsAtX/7vVdqes00hXcexcNGqN7RI79fQ4guL233ftmh8h7OwoGvsMIDjqqzM0Y4wzmN3jwD0j25KXsRXq9+CxW2RUjt6nP6V+VQEZ2r8U2Id+dQ6zbNRzm9L3cWyrfAWLuyBzu+SlruPXdqECWRVlLIbd6sK4tfHKBaDCsO3Ewbb8ugAs72PgS8aMAAUmrdMS3oELDBXHZY5NwJu9d+/jtlucabajsTc6ZzI4nL/t7rNNWua/bDmdlkY+cxZXwvXb38Znv/UFcX9/8GOIWS2BuR5zML2rmfb8CXBwd4BBNyB7UMBA2GikFnf7mcwFAILuzpbrXM/s4AAAOD8WJk4ddLsUuD2jAITBHCb+5LWmmzHNQIANMDpYvjKgoOLz+7o8ggoLd24/u1BCnaQe51E2yM7wG1xKWl15oJTMf26WbNnqBi3uZzZrt1KnQCtYh3VPSdkC84UrKLBI71GClaCMTwXLjcpswsb7zFaF2OeZf7O/T6N40C2/VRcatL+2aoHL1reTeqq17nvbc/0c6z5+xT2g8YDEPg7h4nvZeA9MBeBjxVfp9IGGlwIO+2edKb17oGxAlvgGAQldop9tdXAgelnB8oQxqGM5nrcdTSHzKOlz40Zn5hYialcx2TwbWbRhD449pd3KkiOZcraMFVz/ORhjJk/C92i0TWV1I6e1xo8sp7V2sf3s3EBWN0GEAHh5H67flkD5Fz/1Ep568h427PWXd1NsFEUF/a6nPVi4fuuNfeOGcneAAQDMmIIAswpsjPQUVz0PAPstdQkp1eYyxkAGlQ5IXxd/gFaBinhnvrWRBX9hcAnyrMuORqlQ7V/RKvYDLG7FG8slW3JnnMXPWu+2LQEK7AxDhCJpHUSW6wZLxLUGROOZORHgdbeySuM6G5D3s4JUEV0EBJr6mUKU99Rqt8+XynsZ5NS5noCRdaM/s86PQbNmSWvSJBEsCwhrpd/zlQibUdbMOHz70WFMi76HgSvKV678myRZ1o7LlbZ5fd99sqT9uzmJOtoDIShsv8Oj9sOsBAaiWZpX1ELVAFkDs7KqRN0A2+rAypfgFgMI4zHfBGRuG2hJco9RfMQU+EStEmVJdhnDSXOVjGQLVzkrrx6s7L55xjXwMpMt7a6e+naU9ztrWsCjGyATWf3DltN+7pKsll0Sn/36c/idT76IDz9+DzeOa8byGazvS7r5HGs/BIBQe7TG3D2JJ8aNjjsFDI5BQQ8IZgMXUFAABhjNjnxEwJsPHwAASmQTJoPPXA8jsABg7iMb+f47316/VKWZvAgTtczffbRtKwDfujW83aS1a2l2L28GJDcgK7ZlgupaECgtslbdlt4VAMRq7SRgKxocRbqXhWZQsxUC9tnrYEFYpiA37MWMXWpKtQMEmmYYy5UIIo3yjpkdR8rL7zlQ0JfKUTDkrt5DxVgFk/fbLNDIAma7WBjP9GmU43ojlHNKsvzOqXgSJm27gWevM8t3ViLTAuwBAdVsjjtAQG2mP9b3nL2j08JAEx8UcgyeytbYl0fqzwBISNvUFVRRlqAUUA6raHQHQXOJ9QBBbtuq1t1YNmDbsxyexwCV5dJc/L5BTwoKy0BBMnCQXZY8iowB3qWcIZ5um95+J92xNYAF3oayurJFLUC4tZz2c2Pm9shoA4DPfv1ZfPmZF/GTH3gKN4UbA26kq+xxiRnEIg/IAAJa9uDBwxpz92u/+2uDFpdyh4BBG2ho7gMr/aAtZT5gQfIlUWQQgDfffoAvvPIcAOCdTfYmH7odAk3ujIJXIsQpHPlrvS6Kew8GV7OjWpikhbn97CBJ3t3apAVJoc0uVa2pJnt1nUzTAUpgmdBke7zLX7FPwz7vlEWR67OJNZMas9LY8pcL12CqvKj1JYraKDPGCmARgZsXF6rjFzEB2bIDlJfGmqXlapfuWZTVgllqWum7kd0zaF0uzYw89L0CdXlofAe//+D94nPqFz/mQbMkIEES02wVIJQCXxqpVq8oLIL75C8t/zOmBaiAwNrbQNYwMY+CgZEl1oDSo3cN76vn7Bvl47aOfdgAkvblsOvXpSoXcmVTszLytjpQcHBgY5YZvGiacsvcuK1ALu2OoKMyYLsopihPytyEc9FF4K6CtAxBQQGwqWVqsnRTebJN5AzQG2TDXhm/TrgyGucJJmeEHTI5k4hVLgOpoH4GOqAwkNXvlZzWfrjI3nJtOwD40jNfxYceF1CAeF3fUAQY/CcGkJQ5YZWCnRiIoKBZnTcodwYYjEDBzieDFhT0itKLftkYPti+/fY1fvHV5/GbT7+A/9X//WdwUxjrxAoGERJ1KDUMQFF6FyY24MKHu4EG6Daq2A+sDXsgsIF9csp1tS2UGMFmg1AH53bCK5gtpEXrlpOt8rB2YXexJTBSItHtJDEdiQi5sE4mgJOAAxg44KLBWLZlM4OpCAjLDPAignS5anxoDg7UyqUub0FT+gC+GRhYrtyCbfd+yDWRE0ywhr5ygTFmK4C5RSIW7yWyNd6op+3nTEH9XEFrv7pGYmjEgrd8EawZP43qprIgrlwQAiLUuVvPP9sQqgEDKaTRxslEWbd55/je9nX8q/39u2ecTdPM4bMkpGJgsQRdNR8HdKWNfSddfitA4qYFCX3sQiyjANkwdn1MKzAT8GXt3DEDeg/ph+wswaZyZgvyppjxVTiwBj9secNBDrMaHwIW8gAo5CLnxbCrslqlrberswrAeTktFTwEAUfsCgD8xPufwlp4rJviYxkA2/tJmxs4IL2vsQZvvPUAz4fVeZda+84Ag+g+6EtvGVsn2SDuEW0sCcAbbz/A3//95/EbT7+An/jAUwCAVVFDMuXvCnAEFEQJIoCFqMjGYomaM7PBNWIF+slZSv2tTUoBEQwuwMYCHIxMteVR2wQpZ6OvlKMysS9MAIGSTGIiwqINlKBUl7MD8o8JtqwdCYSlBwe4AeEqgKmrauXZ0uQUlrmZteOJqcp+gxavr/ZADCDsksEgL92eD3L/opZUTSyTAxDoKERrnFEJVGsUPk2CljMWy6zE5+7u0zNZ0XJSoJAKJPCzAGkBmRILmevIXDiW5tY+96VjCgA0MRrODGiAmyupCASGMTbAzk10+N63KA0ImDATk76Nke/Dzb5sqWApYJa4DQrLcY0hcHAQGYUTSaV2wbF5qezMcgVcPVZBbtLMiOlKXEX6mVOSFRUBFKxFZMsG/Rtkj8mSwhUIrBN5A7w7mTOSNzkRVggQIAApsbIE4h5ORNhU9phhl2hg1On9H1VOA+eAQJTpVqx9jnSS9S8TK8EtzCuzGF0UWIPXHz7A577RBuJPpJGXOwMMvqpICNgrUDsGKEDQcxEURHQby7e//zr+wR88j1//ey/gx99f6Z1VBaBRPBUAjIEC0IEFhBPaqVaaZSj6eTa4zoKBdSvYwFi3CgK4sA5C9u+AWALAWJ5GGSgZpONEJZ2ohAzGkggry6RdcnIEy4rqOQFgzbaWCCiM9RI4wA0iOMCVbCXbJHBZVbGbX1cDG/2F7CXiCoLoIohCdKlUKqsl5daUWVSBgjXgYP0aBcao0PBzXPJqh+odLrkY/CcW4zC6vguwmkZuW1CcWrqAWL+UrV33GyvJn/0zR8m53P2SsrhGeublNqBrYL0ftsFBmzX3be7dKoL+c/O+4a/DP1uJwEVXJbAAr1L/ST6FzdkErDWJF41SfQMe2Dga401wbAS7Or7ZXQUKCvLVKVCwFRbsAg4AocomkzlcBByMZA78WGjPA7mTvatpIHPkuiWTyxzaVCYX1ngmbkCCGShToy525C3ltB2eAQFzZPXGqQCpeVuw1Te6DLTdLabLlP/1wwf4uW8+ixc+2RrNHRbZlTsDDCIo6It3Egc0F+j1qEjlnJQ/+f7r+Id/8Dz+5d97AX/jA08hrtBadVTYACraOcYIRKCgHxv2c+AddQt2FMU7G1zmJuiROrNM3JWLg4G1m5R23AZoKeyDvUyQe9I6JgJuIEg8gbASkImxJkIuwEqEtQhjsJJYCwslLIm8fgncNB4TYTkDDmhT5bCCCgELya58ulELXT1Wg7uAc9brgBXgyAqE7xy/Jw3YAqpvteyFw1GJwNH+yvjIjXbplyDdpvTXN1ZQT53uArImQEGPOS0ewIYAsTBhOiXOBqJ618AACEwDbf2+e2U9+n6pxOt3Qj/MK2urM8/YGwULkBRUQ6Fj2UBFWABxe9XvKEVBwuqgomETuj0sdiWO811MTBzHSwC8x6BgNYNEQcHqAAHOEBzJHQMCJnuKNvZtZE/SyZBAAQwoUNhE7tBWsGTCQkncDmXPXBIp4zkx6mIfWj/G/o9jwI3QIKf9vbiNubB7tGNK/m5lzxJ4G+h1ElNQwUEhiePy+xPw+sNrfP6V5/DCJyooMMBwqdwZYDCb1D5nJheX0TUMfPf7r+Mf/eHz+K9/WpgCs7KtUVf9jflwAG4G1IYajW+/iWAh3gtADeDyercDLwafFIgC70HNjB1YC2MtxSflWuqENLZBvu/bb19qNHBKQCrkyHtLhFSEoluy1G9jQe7YJD6A9ftG0uArJDJ+0XU2Kyo4SAQslIFsqVo3pc804I1IrfgNKKtQ3eYPt8yU2Fuvowh3U0hTViAci37WEiyoI6vA+hFdv9co67ZtzXKRsWHX7n+PcN2lEi9LzcHsVhP5/dkBQE0H3rEKZv1CXr5NWHVg9gWXAIDAsnRsgMcWGDsw9tlGa83KWe9BT8GPhLxd1wj6cPGpPtYo+miRZhiwXpCWRSxW3nSlQmAMpmxCASJjE5diWj265Z2Nq2YEfN2lkMH5CgxSILAHBSuLwjdQYCyBAYFVv48MkfdE9iDIHiZ1RYpxshZgSUnqRFvDInicE1gTcYqsTlT7CqhAwdvygoz2+od5b4q6D770cYVW7wDALMUKAZ5vx9NwGLCx3+v5B29JoPyXn6mgwN77zNS4M8BgVxhT1DW53D8YKPivfup38GM/8hGsgSowIbxujJxop+BTABBE3Aj1nlWQ4/Zp3F2jARYHVwQEM4ZgZd4BgrXUCRmRehRys9TgSTdMSUwS1JNYPjP0uyAfJlkFt25Fkud4p5QGHCBpO3XgAOovXIgk4r2xLAUQiCWl0dM91Q1U67V2oByOFmtIoDNjBQqoUqVb618dMTdx4te+rGPBq6NX9GySjDMejxW7zl+sPuUIIkQAMQIbhwG0WZ7DgTUwMNBmnRu7P1o/fAQFkTFotwMegYCeOUN4YnzcUST56MxM2bsFhjr37FmP2sfZgYEooawWsACFjJQyclZG7YhNUPdOZGyobwh7MDB21VxgwmagYMN5ULDqPJHr94DAvsd2n25LwAoIZJ2tyhsxUISNFOOEE7ChYGNxNQhAqMylBS6yWucmq1Mw6Kqb+HYyejZWmvHajZ0tIKK1Sx7WBFuq8g8YYFfefOsBvvB7z+HLz3zVM/6mox8Myp0BBsdIsyuqoEflTwIo+Jvv/yiALmLW3RJikQNtx42AAlAHn1xfBYcxC0dlNsiiy6BOQJmUHFiCChTKdFK2qH0yATah8wxpS7OQK/f6XcCBtUU2YJJU2QGgIjEIukurRtRiCg4ySYIpWaObdLMrDU4zf3egwDn0lZdIP1MMYLOkLYMlWUWTKXIIspr4VY1GjZHWl1ZIxkjrrJqeAMRIa6u6jZ9G6QyU/fB5AOKgH4GMmHPCnkMwqxYVLOTcMAtNxD1w6M/fbQt7AQT4CiL7fQAFR5Z7+7aDekxAxEzxW/9GQd5H1B/1deznxXSzWsAVGJCAbCIN0CVkWpCVTeAdm8CwwNBmf5b+BeXBtTI29pWlMUBQgdl+OWIPCor6wVnbZSvsYMHkjzEF7jY4AAWX5I95Sar80SXQQf6sBW6cALLHDbL1ryQui8ylzTnx10NzAOho5ArQ+3EVSz92IkY+AwTi720cRX2TQcLanWEFSUDBL37reXzpma/io0/c210yiosYlTsDDC4Vofd7aqi18P/k+6/jH6r74G/86EdhkbRW4sT3GANz+gDgrU5+oAMKCkYS0LAZYhjyVKj3zGwvpIwlsGjfHhRwCfTdLSZlPznNv1eY/XO9TiZnzFBbIEuFWJX7xpI7z9qRE4Mh8QbE0EhaYLMvCg765YxEGSlnEYhxs6vU09i9lK4KaWylijBkiLWydqyABXmuJtywt5KY90Gds+JjhGgaaS3HggDzH8c34nioe8bRMW6im+2cWbd1PbiAuKMEMhYL4az19K2DsAxIoGcBdt/9OvZ7RMsMuOxGGB4Ln3s61+ZWD/JGK3gskv5Sf/dBcpnEyl1ykuA4BQpLsqA4atiEJcnY19AbZQ9izEdc/dC3CjBkahp3TTsHdssRS2XGrI/seuYWBHubWPxAp4V6psCPR0U7kEEmf8rOGGm/FwiLyYWxyVo+LEhunKAAsk+EsAdFb2MAQdoIt5bNTat3QBIAejApbdTdt1TDYVM52pfKItY5/e23HuCXXn0eX/rZr+LeABTcpvwPBhgA0oDMSsEyuW+JwPju26/jH2mgoa8jhQjmI8uvAQvgKVAwa9AvN+Gu44PaOdA+Q//2yNME124JUESh+rGUgwcMShrMBg/+Se2xRN0xDUgEUJXedHbBWQObBMWguxNm8m4JYrkaQPBbZq7CEWOLtQ9Y65NCbeUEK8BtYNUIhMWgKmDf7hY0BcADp44jratgGgEGKzkNhMesy8OlBi5aZkKOmcVkgCGCBftNzzB0t9+VXnhGAAC0IIDD+SMKv+ntyTuPDkf6NhoBNp/seZdW8MT+BuZ9HvubXNknUCpYCB4oJ4G3YvkeswkKFKBgrWNvpvMAcFC8Y2tKy9Q0OQrQucsO2jsTycottdKTUCQoKpBSAkwbGwBI7m/dGyC3LcVYS65KzowTlzVF9qgQT6fQ9A4QpAmh2GBYRkAgHh+xSsAeCAzvXVqmSavldXOPKonB8N23X8cvaZ6dj15IXnSm3GlgUMeZaJ4ERcra44llHeifvv06ftWWJH7gKRSotV9EYGQQtjRGxH3JpXbkplo/s1igkUmADjobiLHOsYyEXgQFlxKDZJJAyaTLAWEOqhRmAcODcEyhRx9fo/T1fAQEMQjIQAFRGylsghCoaLh5NTbgJgyBMxEGCpKgf2EY5jkiRmWndEyYM7sQjLECEQxsXFkB+2dxG0cuGn9eKFF3x3azc7XtuFEeBhRWFaYZ4i+tfUy42eZt69eNLI84JhHAhLWvAwCAqGUZDEAQ9q6Jw2JgFRUQGACQ4xUAzEAxMKZjrRzNi9k8HjEBR5H0fWxOk9dp0vf9fFk0vmbJhGUlZCrCIqTKJuQivvRcKkiIsQkJ0LX4hHSSvdkDsT1rExPB1ZwvFTD1hZSWzZBAZGJCZgYyYd3Egl+ysQWEkqzdSL4zv6dyCKige1Y2sGT1NnCgjVPiXO1+M5PJwJwZOKU7oltw534in5MWKGyxKt99+3X84qvP40tPv4CPPnl/vuzyFuVOAwMrFMCBfc6QCf8nb72OX/n95/EbHxdQwDZRSCg9BmErOnhS7VDrxL6TN2Y/Z4MhAgQAvuxxJ4zRDsimdAPQ66GgJRdhNsBStw3i298SYYGsDhCkJAo3oZ2YgEzWcOOmnFdqMhmXRDv6dEltMhLrlt2rKiYozLDMXg4KlOGJLiCgKrj9vdjbLQq/UdDgKJBqBAY2/WsKYd2qcgAw9ZU2jEvpBRmmKzziMlBAloLGhC+rPicDe/bY2we2BdKujIBFZCQikO0Bg90baANtZyVa/s3feJ7HgvY2Qjbee1Tiwr6YXKcK9DkYmLnjgJN9j9r3Sy7yeSUB0kTKHOjnVBqQQIWRIQo0E+n3ChR6lw9wbm4Axy4cnrWnPFrmOkN8+kV/nBO4yKqJnC0YmiQDqrcfATmCdHjNi95vVEZAy46nJG03AtfSHm1ipKZN7On+YTCleP/xDCAAxuO1Z1P7hE1x3vWg4E86UCDsXsvkPUq5u8BAjE0YY+WAgGV5XCHCm9+7xi+9+jy++HHJTV0YYBVwVEQnFVSAQIEaoiSdPqXIb1HCGLz4PvrHGQ0ADlqy5QNIBFL/vu83AInR24gC9Uki7MLDR4E/cdIBx9QoUCfhkmg3CUeD3SzSpk0MHEAsDZtfSbVGnG9xGdGlJUQm4EZ5H0ZR1REMlAAUTCHIZw6xGS1FHYsBQcvdLkLMVnTMV3gwk/SjGeWpvicFMDfbZfCoRGABwMHHzYELw9scHWgAbsUYnBGqkdK30q/W50l7H5WWKWjBgJw/DwhKgY8Bq8rRGPB5RISka+7ls8iaZSNXbgYSlpywbKWNS1AGJxEHZdHvGQBEEAe0QGE6X/TADBRQlK/UycgkLtWlABsBCxPWwiBiLN7WdOh6i2xMX6KLIbIDI3k0i+2I8sj6BbjdOL79qJvri8j0xVUTUUYKCJJ3SlTdB196+gU89cE9UwAEkH5L0XB3gQHgjeErAwJz8J23HuAXviWBGh9+/ClsYZATi8DeGB6pGgECoAIy7SlLQ4g9pTuicbtq7jpvR2FRuIytPnLEJ6TWgZTCW1jYg6UwtsHEhPrhmnfgvaIG9r5wYD/x5NgBGNAX2Efcj9tGLP1gQvLoQhUmaIVctUrbJZ4xEUu/miMusVpLdRNIXEFVBs4YKBAwAXakEMqmftNkQVQKAhQgLEi7ICpZfCxuL1OI2QKqIKyQlVtsGFwjnbvOt/0powCLLoweMMxcGKPxPpsrwB4A9Kly4zWH73Oy8KDdYga+S6Bg5TIFBEdj4WYLAFHnxapAccmSFEw+i4K42SQOYdmKs3A9SDCq+YbYlZ+Bhjaw1N8+LMHbl1Fehr54U7OuqQ8yEiAsgMubDYyl0C4VchukS8Psq7GM5NIZmdQD3CiTgLH8PXQfdEbaqFhsWpwXl3SD9VV0G5jRZGxQIgFA3367Bhp+9Ml7zbs37oNHtFvvNjCwEgECAddvvY7Pa/KHjz7xlFDSrMKCxWe3UT3WAwQAuMqyrxX1fs4JjzoKFOsHZLMMbXCPyKpF/TiakFeo9OtSAFsZcCR4gSp89/SWASJ7H/h1vT9sBALsnex3I9p5SnnG+pk1uQMzBg5amprR0tOXVnH07EB0FUR2IAKCrfAUEOyKKdlBsOCjBFxtqH1yahc4BABxQdHWiknf9KDhBuzjwoCDlZsL9tSIygfGlvzoN5NKz5jnU/U4W2bLeeN4sO+z6wAFi0wKEGr/S/Q9YaWCRSn4JQUWQd9TAhcJSwDie2uTd/NOQgH2QOrM0CMiTa6jmjHIRJOvNu9ylrwCVwxxMWC+0gM4BoXA7WRTdbXtmYGRbNKv05LRyiE30vSgzeeYgiD33d/phmGdgCYDY3QP2YZQ3zVQ8MxXcf+Je+8JQ9CXOwUMBrK2LSRbT3ru6Cfu+XI5hgS9GRhIkCUxxiCY1WnsQzKEnEmVzsBCKrxTADMwEK/qledRYa5BeDaxZELKwTgprU7yuzo5j0oTFTui3cJ7RAYg/u2XxZ19NytRDh+tGwbGQKDfwMXAwKXkT5cYgl4BzALOrL1yIrcSE8GVwM5P2sQe1NgNoNKkQKuIL/mRrYwU4YiKX8NnStR0whoVsFlBATicLY9at1iom18RKPk1B21j1256r6ysAZGwNDEux6j1RPJZAniD6WxCIdW5fzQ2fPyAG4CQSL4vmVBWqln6NIlPzfK3KTCoFvJC25it01foE7BJ+8Cj8Pv2msmieI3N79E87WNKFhI0weHcLKbEylkZdcktMJJRo3cbFcNBLmMRSD27L0cWtz54pAf87KBuuXMZJIiMIADffvt1/MK3ntfkRfcrO2D3OSlXL9kxdwYY9A0yap/XH7ZbTwJh3qsfO3H1OycyX6IzunVgJGV8q8wA0FJOyyAkdoRQ3wtFejQp4/nFb0QXptsxWOnr6Odh5+PMu/Agr6T8ievVm9N8OZvYDAwwH+8V0WeDbDNDjt0GfenHYA8I6ucKCty/TPvI6iWlHSCIlKEcP27cxjLvFKEVSnSogG8LHG5THiU+oHluKDOgNG0jPS7L2HTeJMuIxwoyxFJfi4j/Jdl4IY+sX6OGU3CQMrXj5MR7Fg6CR4GpuZnKWp/dZ/lbiSVwsdTAVNujRJYNTuJ7DAgwfGMeALqXwNigOVx9QoMvDfHUzuvo6pPzADLpcWpl2oW2u2RYvWuZau5KqucMKESQ4O8RSq8HemPQXKl2fNE+qps8iWHw7bcko+FXPvEi7j95T/qQunf3+g7eJ9aBcCiX7wwwiIW6v4AwBc8qKLj/5FNACPlLJBPDQEJWkFCKsAgWIMOo83tJNFxiNULPXq8BSh0p2DPKtVdCrdyZT8pY+iOjcTISBKk71IMD+d28jACLUZMMSGdEkNDpnQgKZoDgaBfJWc72GFTYg4KmTUiEflT8I6vA2AFrsxFDIABgzBBMo6oHjMGsGEnJhceKENVa9usGZeTzNXn3bpT7mfv3JQKCI6B0tp0WUENft/5xOUaU2jFDNmbqePAxkwxIEq6yWYwdOzIZL7H4uLOEXwUepAqgDVBd93uU2BLXHMADbxUg2AKABPh862UUoZUBURHdft7XC1tQUI2UZih1hgaAOuMAAQAASURBVAL2p2pdB8+7ZJwcy9DuXJDXbgyqzOpZkWhANnXs2rZf+munrxQYyJyX42++/Tp+/pXn8MInX8T9D94zL05s0eZFCW0b8eDYUbkzwKDv9wgOrh9e49mvP4cXP/k7uP/ER9DvQlaXziXJq62dXZQyNGaAUQXYVaLANPAOEMz8vRctbBuAFyag/OZyqUr4Ajye3POUELgNaPHJrl8VXDHVXRcJunKEoSsSeDege1AQt3idMQSXlp8N20NfWPy/Zgnug+uucvvudSXHHgjMmIFL0dRA6zu9VLjIBN+YgyXWgQXA9zowi3kU9X9xPXhovqNrZ9edAQHAHAgcxcDI+XmdAEhMTojFuQJNgGUbTX8mzXhRUBBXrlxN0Ep8XQOSo1IDcm2iyFxZN5k7tnKFWDJ9LimeY/CmKxtYcQfGMrTJgImqxM5GwF9UvhosNTUWgJ2bdrIid1rOyskmhqurQwLt6l669+6NnL6MZH/fvsAeGLz+1gN8/pvP4qVPKVMAxpnU1wYRIig4pwXuEDCwQt1nAQXP4sVPvID7TzwVdiUb/Vg21pEOEZBgk0YScFT67SpVRdawCd4DNuBrx40Q7Mj6jhNtpph3g/1sj3flyAUz/hxN99COM96PlP4OGdc4XCbtVtvWYj02BQgb4ImodtSBFvdLBlAgOdofDRQ0PmSoUJgmmNDfhPXpdg8DAXbcIs9njMCS02FaZGDgPx0Ufxu1VutKFPm7lBYsmMU8BAryMhf9/5eAw6XregCg1a/nB0BgFggrvyX/XbTMD9tNb7jPnUC7sVV3Dcy7sRVzXAA4vVFQX/qEPjtGYQIONtSVK1nHP0HyDFjGP4uDWEa+b1f+oq0sAM63irbzETA07WsasugkD+OkfVD44d7JymOps4Pwj0xY9YreDjeMQI2HsB0NI1gYPp/olNwfAS0guhvF/f25bzyLFz/1VTVq17o1+qjY5nC8afsmUAAHg9celjsDDEZKLIKCj2mjSs9qaN6M7wl59JNuLmIgwX5xldTFoIoM6IPh5MNsmeJI0ffgICLwHYAYvPeuy6d5nFsl3qROHSn7cKzdSe9EIarr67VdKQAwUEJSlmZTBsZBAYnAk2RNhI3Y472OUkhHJXepOAgAgCTMTylm8Qcr4ULCJ6AV3mfcAmfzPOhHa87YtF56Vwsgr2WR4QBwZXokt24XoK45B+BLXIHajs48+DN4hypvE+E/MphncQH9XhJ2frSfBABvO+DR2s/icEwxDd1VC83ZqWUORAHsQIOV2ZCdjTU5dw6R2Z4BM4EvSkrlENUI+LhXg4EDBwtQe1Q3L3OF1cvYW8gKAC4vpB9Tcw7xGACmtAcbw3sPuFC9NBoq8W9pXJpmALZgAajGIjcAaAy4rIrtSq3argBwpcGH1w+v8dzXn8VLz3wF99//E8D6Dnw/jK5UGcsgKgIQbAmUggN7t7HuaMudAQZWpqDABq/tSgYAKLtBK52XXKFRqcgr+ZawNXLUBs8OUVLtiFH96rMwBAGx8/Yo3Cy7Uo8BnXYYKPP+GmuDyW/pwn2nEz4CAch71p0MpX2JdDdDIiAtoJTFV0vyqsV9CRUcWIa1xEKJWlbnrDRpTMl6FCFvedsFBOimLByQ/YFTuhXSYU8I2q+pPto45yjRUxXQLXiMYydaIbNdBaN1E9ktoF1KNgILRq0bWDBmAZgvJ4s56WdlnuClKn8/dgsQMMqL0ftwAeyE4awdYxvKX82FkSThlCuGDjAcbag1SqBkz437K5y1gN1tZXEsF2jDflh7/AvaMWdt2m4HLVZs9nZl2JbQrVytwICCrJpS31YiEIxK3MGCAYT2XH2D8b2apF+D+xrlzka9m2EIuFuZwxNGYCGyC9oybXWaqnWulyDv7RmJNzz4717Ds6/8HF762d/G/Q98CLStbft270ts7cMiV9UciOAACg8YEnN3VO4MMIiNfxkUTAYv0AxECoqMU6ogAdJ5MSYhok1PLXw0wW1wBIEVQKP8OCJx4AIaH0xCXFDuvfXfMAztseF9pu8WJrMNSGdhCLajG7Fs+4q0wfaBTymDFCAUp0ip8dMUJcfMu2CAAcyaDpp16Rm61NDVkjNEnzqrd/AaAFqlD6BR/HZ8llltltbUBbELBmrGRVV0LUAY1FTau7Nc+rE42pgoDoFm3TkAziNaXdgFq2QPAiKAmNa2O93niAcurz03EGX3e2/aMsy+XVsG9mjUllkAg61o2jEMXfxLCxIqcEBzvNbsaNr143Q0RqljpyIAqIB0tqsj1ax7ERCUDVQKgALf9ty3gzY5FeTspRfpXiiCgQAD/Vjz8p0xYsdM6Y9/Fz7H3wdwgMBsAtIue7CgdTsp7ykccijBRfQLgAf/7z/GZ3/v5/G1j/8m7r//x4HtZt6GbnQpMGHRU0fgwALxn8ST0+reGWAghXH98IEGGkpMQeM+MFBgg7g53hYfDNrwJFv64frt78r59Z06gJrBo0gT2Jsn/TNCvaOyPqbk+uPt90bSu6K/oOS769zHvG3deYAvOEYp1QmVstgokS3gtADbCkpZwUAGIbfCJGUk3Vt2gazBXm3JiGNecspgI7V+SsKayjQ1NABwCpZtqPds7futt0UOiiumMO0V1lEwF1Dl1CzAc1eCFQO0K2bMumWWvd0dMAwUHPzaChbkWAsYAFF6V/b4s3RxKJeSfrV0awUA9tuzbRqvB27bpjUddwQLtmW4tZu0KQZtKm1XSu2HEWgAsAMOcmy+rfNs/PZjtx+3lgQJBGUD5FwPChb/rG1p2zwX2+45fLZ/YGcRojLjrcZ2nZUhMp/0s8uSqFbr+TmzUAPLhyDC5HtzTWCM4/HgAq11U7AQqjR9L6khtBGmsv6zv/d5fO1nvoj77/9x0HZzqKdAkgYbVOTdE0TISSQtKiCQ4oH4n3oRv/a7vzat6x0CBhEU/I4EGgZLm4JSdWXq6LZToGgHjin96+9/F5/5g1+RU9s7sAFUAUKLNOXvYLSEDr7MBgzYjYaia5G533Og9EcK3yepXVeUULWVG853VjE0mtg2mRkA1EXAKYtUoQRKCbRcCVCjDOYMlNwCBL4C23tmINvG80X65BI4WBQc9KmhOVGzJM3KVfh8lNFxZ7nSOcvfBG2v+F3B0QXGqOm/g0Kp0ajVsKVOYXXgoP/eLROdAQbo3yWHZWYXtey+zPz/hwBAf3ApSr6naVt1Ek3xg/b1H7U1jVt2A607sQEK+t1TJk9Ag1yzBw0AdmwDUPvgKKbjKD35cktQkAGAN1FSxhKUmxYQcAULxAxeb8ClgLlo9OVWZa43/ViOeO+k7C/B8XtqZSyl5J9TAyD24KEFDiKvKQ68CBZOAAV5VnDiHMj7kazfyXkAL//0b+D+j/5NZQqCwSQ/tgfV9yAGUgahgDkBVGQ1CgpAGcSybu764Wt49hufk0BGX7I/LncGGFxbngJjCqw0CjQo1QYUtOjWfxqo8Osf/Ck+/Uf/AC//9K/j73zjM6Dtxs/HuAT5HbV+MqAOmAGl3wb0dUDA6166QTIAC3KxTMxe+bMpfAMAIdaibHWSdkCB4/V9mzZtZeaYTEyfrAoImAi8rdJeeQEtSwUIfBUiba/8/nwLcGB7qS/K9fW52oEWCFgZZU2Lyr+3VpOeP6X8O8XkSsn7DCJkB0DuOC4EjQBqgjvtXSw6GUrVJyeCB4ChMgxnAUOsVm/JHkGZblbsGJL3AgA07ewrLd67dqYI+kM7Y9fO8PYrNnVPgIZSzDWh/YA5aACOMwOe2aXvLCgQP3cFCIgAgTdgXcHbKmBgFaXG641cF42Q3ugIpYkJiLJEPujxAAhSVmud6udwfQQNlDWmqTP8opzvjcHIRPRAwerrYMHKSNYDFQj4uYHRBygoWM/pJmMGirhjK1PAUg9mgIDrh6/j2W8+r/rxo7t278udAQYOCp68h4aiQTv5m6Ue2vBOffkxvZYYwIbX/uzf49P/7X+Cl3/qX+L+j/xNObmtO3dDI5gB7MVgX/aU/tA1cAgGAhDQycfB+j8EAFH5h8nKpYIKedeA8ieUsb9z1gBNDyzMMsjzIoIlL84K0HJVAYvy06QfyaowAAclgAOx1kWYkmAEF8ikIGEECPxhGAOAPj95DwCaz6lTSixt3ij93jrwvh2wPLHtp3WvY4uC8IrWEQVLp18RApIluWaNGWCYsQtAPSZVD8fjxh1zHRXqXj82jEkACa0b4SQIGFCzI4ts6q+90OZ9UBz7fA809aDNHTwkEjCMABoKtSAhftaLbGvwNgAS7s640iY4au8ZyI2rDyzR1ilQEL4TF2EItlUAwbaKTLG/KoPYloqrPLkoSwAgm+I3MJC9rTnlFjR0gAFQoKHMJa9owALlrIp9JMdj/5HwjT9MWe/jFOr+ngRv6rsSbwAVcc2yMAPymzAuWYK0rx8+wLOvfF6W7GvGX3nmvLZ3BhhEUNAUb9DSNi7rrGtAAtC0Fiso+ON/jJf/9n+Jj/3Ij4FZNZYpzx5hAkFoTLaei+CjZwX883kwUEwBdQregUBgAQ5BQD9hzSoY1LsvbjVtARREkFCKTNwiwtoACi1XwHYjuMCaC4oTCvnnTFmaUcHBogxaYQZYqNLkTRf2tSDsipMbqEpnxgKcWqa1Bp+qBWP1lkA/+a1vZ+PhQhn6UhtLNoABu96/B+WV8hQsABi6I4AWIKC+CQCcZgysa4ZxFR0AqNcPQEDZ5q43qeSp9r9t27eWYjv/dzFKwdrkVPuC0wJjGqyt47JdT6o0YRfieLdXHZUe8BLQpNzN4a+tuGqAwCVQcPOOAIEdMKhggI2p7d2XffvGdr2BBjlUtwIAly2MFiwguC/le6pAIQAKpATebgsSeAcS6ng4Kevj+Ju4CahZVj/oULtewYAwB1zbgIt+zrh++ACf/dYX8NIzX8G9J59qrzsodwYY3H/iKW/0HW14qfh13LALr/3Zv8en/90/we/+rX+O+z/6YyKAbOORoq4EiGJyrdUjTABTb2BvtfSD5AAQcHAR9D68U2AgsAFDIBBZB6CyEFZM0YdClABOIhAoAZu4DThrdKwpesD/AgoOygZOgNABgEUi+7UZHpAIBogJVCTXge9vYa+hCn3U+w4A9IsvFUrUMgH2T39Tfail+ln7QNaZe6qb5EfBnRfL0Jdqb4ahVeOgoQMMFjg7Yhcad4QDBrlXnS0CHKzss9p1DR9KXAwyVP7AHgDMWIAZAOuU/8jtBty+P5pXiTQ1uv7o2zn6tC0Ox4CzAYaUkSmBU8ZVys4KyPgOAAEkKdsxd/WM6jwDv7vliE1MwVpjCUrxuTAFBeZG2G5Evmxrw1AisppWZvIEcJliQAApVVlPBM6mKJMaHRUkECdwSQDWChKYRUikDPHJJ1ewlLO0IHdBfSbnrc8tkcqjyPoenHLnKrAgzon+Yp2H5iaYleu33sRnX/2CLHnU1XlMte2Oyp0BBqeK+2SOC28bXvvv/wM+/dr/Hi9/7J/h/n/0152WAVcF63SODRwggATAgcLhw8YWzK0BAdCCgrOAwM53E1d+0n6v9dSy1QkJyPzFVmRCK05irFXB5ytwKSDSepHGHQDAFYEKpC0LQGQ559QqoAROCg5cysmKBUYFCLJR0H6DqGiduvUUvzdAQCfr1kVgM1ch6W23j8I+dOsALYVaJoIkFrOW5AW0rfeBWP2KEA/A6q0bVVKXwAIs3wRFgVh9qo1vHWgzXMZG70ukMXswb8fcir8lCBgxBR3DJlUo7Wdg7Ps+2T/+i95y1TZpLdPQxmmRhtJlurZsV4CZBOUmDc41VqGoy6eYS4H3sSHAHhj3cyCHzztQ4KsPSp0DARSAt+o+CKBB2nUCCvr5YGPBSi9PoGDBZEr4bkCCtb9IX4g3VfApqzwU3zulpPJI28FcnLcFCJRUFlBrqfPBONm5qwZyvmmLLk4s9mE+sUMKJVy//SY+8+ov4aWnv3QqpqAvdw4YzOnAQPcA2pkUPtdTDgru/xf42I/8DQACFppO8eckwfTcDRybhm4Wzymm3UABpkItCrSmjECB3/6AJQBaxX8WFPTvQqlF/aUIJwkRXMIYbCr8kgCExCIsqEjSIhRQWcGJlJ0hMG8KGDa5h4IDIuhGNpDJqzNefK9tGYECt5rsPG8OBNwlwNFaKioQyjD6GiogDWzFvpAm6pRP037V+tiVPgBLvrT+VL0uBoAaXR3BQhN8dQtmgYBWmclDFCAY4IgUQDfWm3etY2ifRbMd+xeDb/V+R242uaSLjrd7GlAbxN20x99ln5HmQUkpJPXKEncTgnMNCEgfZLVwDShInxKlABSyAoJxbAhqK9U2B9x1Y+6xITNmoIBjfoKiIFhYm+JjnIOxosoYPQDey5iL8sSuTQnMCg56GZOCjNk2cTFw0VsE90EpTuo24KB7rjPzGxTEWT0P5PwpGR96Yyjn6/kdKIjtEUvDTtVxeP32t/GZ3/8lfO3jX8K9DhSQ1f9CuXPA4LC4v0/xtVNENhoKXvuzP8Wnr/8PePlj/8xBgZUWHJgmkkEDAG2GbLSdORsk4dwIFExLFHInCqWst9RnURUe5vt3Rz0gypi7SXhpQDXJ3S8Pvlh424DFBjdXwW3KqWw+oZkYlLJGXAu+S6ggoX1xrY66DCptzdUNcLQuO1hIWEUoWpAVcxd1bRTqUdyGFbumF06oghuACDqg+rU7fyoDiJHXTpfaeVNCQAsWFChIew7AgjzMjzsI6NiDWuk0/tyXBhi04PUS3eq/6UAAgD1L06++iSCgd7PZdfrsWwffTvpyF5CL2n+0XFXAsFxJWy5XoLzItbZqJ2UQZbV6KyAgO6dAIptrQlqrBpICQ3QQgz7NPt/NB4vLclay3nBkzR4WkzHx8y3kCQUwHMd6Pd+ORRrMq2nx/pvVx8abyiIOcv6WMh7AIUsAYN+2nbvK56qdC2zfa9//Lj7zB7+Mr338S7j/xEdOxQKPyp0CBju2wIJ9bJW1SFHRelA/EUsAEJUVr/3gP+DT/+6f4OW//S/wt37kb+w6aE/jBHCgtJKlQ+FAS9VrB+XQtWEkfH+4KvAIDnwNcHgsh2scHCxSNwKATQGCUm9Acmvf27Ox/vf1baK1+yhho0htsl6asC70ReMbIIB2m7gVdGISIZGT/yLsxrgANrk9ToD5NBjgbRVGwPynpfgxY2J4DcwCAATlNGqzo/Zjaz+gBnNCwRxuqqLRRFFiuCgA2BCCr9YdUJCAtwFQ0GdWVkFbL7gLRgGP/ju95nQZCMb4fS88O2tKr2sCb4HbAQETxGcDcAcs2lHfmgsMgPcJ2+eUdcmuzou8SDyOAYW8+LERSIj/DCSAdNVJ2N8F2GNlKw6QfT6s43nhjE3ok1lJWf38Ol5tvpvIDfJl1n5TeRK/hzlh4LkHX16fTiZRDwD6gMVJSw3LLWT8pcRy/rSch7qnd+mxuaFUvlqena89/UXcf+Kj7cqYeK8TrMGdAgaSoVAbO8YTkIIDTf5gQMCVVNnw2n///8Sn//g/ldUHP/o3xL+0LLsAkIYyjQFfwO0Eo98i1HNynIl0sASFX4mK+myl7I2qRymgXJE2lyKCJgpFE+qCGEQw5k4wmi4vF9LYzCZtFH4p1b/qVthN1FFRi8XSfVLRYCQLBh21vQWu2XuwBa518QKd24C4oLzz5y0Y6IKqHAioD9WtVOszUzCTxPfNboJRsKlibwKvDGBYdPW6+vtyroLJLY5SWQLipMAhAUkFlN1HAQVbn/VLurRODohHgY7Nca3ygdDZgfeJkOyZALlkHLOxi7Gx8yM3zqOAgQjwOpAQBfilvqbYzyHPB+fFgQJlcSs4OAggIT32Pg9WNPeCR+zb6gbKrihqJP9eOcR2i8GdFQhctjUpJfHNF1KjZAFh1b8AL3rvjcCpkye2Kml284Z9DPIEGAMBuWDoxrH79QC5SZg0GPOn3GMnaPn9b9Tn6TJeDMD4vGiEss3RyOqF2BSkjOvvfxefdlBwr4KCQT3//64EdypJJ3ACiAnA1oCD6+9/F5/+o38keQp+9G9WZMwldJY7ktq/nXAc5uWuB7rv3FxXn2BCsqIAJjhQEZdG0uMdPaeKnzJ2ApNsQubF7ysutCA4F1SAgU6Ani2RytMJ2Cc8MurUqDAJ9gm+bW8iy+Kl97Jc4ESA7SQ2KkHheABbjBE4YAd4W4GbPwcHtmAIBjZzLWzgIn5Wa0veJkoi7DvsqV8Lq/JQV1URPCDnENih8HrGxGwVuFUcnFEt5k0FoAJjALsobWsvYx70/gAasBDrvKc3K2g+A48j4O5XA3Cx3BadhT4AAnb92cRcMzfBUSBuA0a0v+UWddltjOm51Pek7U6JlKHR1Ts3A5CgrAEtC4rlA4kJwlIGKIPSCs8gajEKOk+ok1G74jEdBggK3A8e572xrVwNFXF7XOlpdkbP5VHZgFxjjG4rR+TPPvh2CgCANv6mX+bYgQG/LoznnSIOvx9u5mTXNiWMATJZbQC+TMCB/rIH4HaPGBNEFdwcgoJenp4sdw4YNI0PVHAAoLoVZDEbJ+D6rTfwmX/7q3j5p38d93/0J2oHqkKSmw4mekeJjwdMz2sPOqiZeFovd0PYIMrV0kERV4D6pAhjwdrcO1r6g8hrKtHqaYVxZROwv2ZQDoPkIlJPqfGnsk7CXTs2IIFVMBnLsXUMjv6ksXhK4zt1mnnmLpgBAmMNuOjxDWUVwMUbu/KYKYVat5gVs4ilVUT7NyzCvmHr5zj2OuupbSu9n4IDzx0RgIb3ZWQT7PdAAxZgbovQx7y1zz3D/rjirwfq5xMrBHZZOsM9mrHZgYKuovKbnGsMQQBUUybPH9eCAut35jIfA7oAxwECJVAWazstWUBCXsQdsC7gZRUAkK/A2wJa5TtfvQ/YOoAQU4zb6gYH6FVJTOeLt1HnLqAEpyp58xgtBoCFXD6Iy7LANjNrWBcFVNXYOCk/7PlW+oBboJ0LvfIPv7kEbC3OpnHjTcHALeV6d83OACRD9tx+7+ugbtPIOL32gz9R98GXxu6DWzIFVu4cMLDSW+6kihYWscsFDx5+WwM1voh7H/iQ081GpXHwbe6Uo3dULBPq6cjFEAdBMyE1upbCinwuALKDnho8uIT6VWt/aJWN3iVMVBoJaaCNZejrunun8L4jus7aZjEf2QQNT9utmtT7tb7Bj2dWTw8ILMCK5+uweX2nAgIOMQUDQMAbV7q5E3qtEFOLnQuiH7W3HhtXQmBaLvpVR+0f+0CLRGh3iq/UczFQrKn/CCx09z9atTUsM+U9AqvAEAwMr7N6uaI3az3U1S7POqa3zYP7RLHBY1sAHXLFpqzE4zRMwWTu7OqluTo4sQJF1j7ZkJbNI+t9fBpAWB6DuTZoUTaBH/MMokgFLOH0AOsSPjN0VKmQBewMy0Rhe19bbIoAXDBrwDAH11ddOUUT1mYqDY/GbW9w+Mc5kBixWXrGr61BhBdAQC/rL7mNe2UP1AEXDcB4zuzQ1M7tKCONAWBlCj7zB7+Mrz39W7j35Ec954gz0I/i5tByd4DBhY5is7oVJFw/1OQPT/8W7j/+EUfDTjWbE9+sT2qFK9Ig0W7jkzqBKqeVDa6EZmAZaxCv8SPdb+WoK/rc1TcyBoPf9vfdxVqcjEreT84w0Wi/XK6fAE3Qm9WbCEMh1rgPSgADnRtBWYJyliUwsFAYZV1PAYJdSWloKTooWK4qIDgCAyO/KjAM6jwVu+FtV2o/lHYMeTFA0bxXljbcPXzu096VbnXNXpGO+np/bPi+Ib5mX8WWEWzibXq/eNExRaUCu/UGjZ9n02V1mcAWZDcoztzoo3qAkBZderdoTBBCP7COuVJAiwTmeuzQwhqICKBwBQgpo1mHj20uL6MxQbpEr+Yjh2Y/qHPS5tpV9j6hEGEPPJqskOcPlLl/HfR1H+sS3mP+2xPG3JlYg1Exl4F/j597BiuCoCCru5VAFlh6/X1ZkvjS07+Fe0/eq4DhktvoZLk7wGBUdgNC/rz2vddr7ugnPqJBReQUNAdLY5qv+ojyApqOmSK3HjF6qYNkGqzVHDugPE8EEZ25f5jm8ueqnju8XfzST8h+EHe+saF75kjhREAQgQFCoCH4dq4DiyOwGIIICg5KDXRSEODUJSEty5ghyEuNWO8DN2dR1rFcAgKXwMvsN9F1ENmllHZKva1PEHYXltZOgdXs+CUB3bVFj+ubWBupQI230VgbspgEDVC0OAQq4k5CSr4fhsWZGHCkBWAm+XzpHQEZT1nBRVLXkjJV/nt7l+6znSNA6r0VjTdAVUbUZ/KbKJDo+7bnUGAItK3qZw6GSpASUS5c0jLxWc1bYdzPt1V6h4ztCYBxa/ltJbfyu5etM9kZWdUoB9UtdP32tyV50TNfxv0n71WmYPY+R8+alLsDDIYNEpSz/n3w8IFuPfkS7j/5lC/nIxK07xS0om3GGBhwb4HP6tEMsgNhtpNcbb2lPIJgfy/KwYA+HPiXygw8nXXDxGeOQIGvZFj98275oS9308QsIclRjRsIbpRMsoQwKzgY+T31uggGjB1IV8scDMTla6OgzW7Vx2kB2ffLJQv9vSq3yLMxLRfAzpDBaC64XRtZvI2v6jFqvGwNUOB1rdkAtxXIQp2nqwXlRsYVF9JVM1ljU+bvYuMlBqfytoEWZSHMJbZZLhby1UekjE0FBwts1Y1sshPibTRRj/vSYyzKqM1MDgaGwAyXH/rc7+vyF1puScPvkScAgJEP5OdE5uerzmgy5pRw/fa3hel+5iu498H71e1g8RGxSs2XEQCav87dAQZNUX+RfrO/1w8f4PmvP4sXPvUiPvrkPRS9ktIiyxNVoZApBVMywD5L28iV4I/vEedJKuoEvXfr306eeVv/UysEol917Ma4uJ3txQdeoqIvKDMHCO48F8HOFQj0aVz3VdBg1ZxhPmV/9pZAg9lzKfLcrYHlSoT7coU+6c1u45dunfXUrzqwCnaBqMAuwK9ZweLXPIJSf5SkMlpOuT2OlH6MZak3nd970mZO86sv3wCBr+wxhiAms9LPFo+Sl0dfsbIbN0ATj1IrqnEJIb04NG6EzKCxTXZQlctxmVHp9ZlRAZ6a1Q0AOEPbB3bq3cgov8mBnHg3cvXot+aGhsrBeIuRW7ov+TG5NLIARLL3we/9PF785Au498GPwcCA/WuqjQAUAdCOdzoudwgY7P1LERhcf+8Bnv/Gs3jhEy/io4/fqwwlQbOAEYgyEqEBCQAqUIj3zwdNdyG+YO/zugUqPpwse4bk6DOA/cY3F24dM6b1T41Pb+IgYjzDBGA0ZYawiQJAM0Q9UOqXvseSkm600imZUmRuc/KAM8pQQZ+GMycKc1fweWk/5wzKVw0QoEWWqCEAh37XN2/dIx/iAKT5wivzo59YwQLAl73+0MrR/BmVR440B3ZtB0zbz2MM5ECbUMnZJbXSyyZupwgUdI8AW9Jq7oA+x8VRzoNdvgNnjdoA04tgaqiU+gcqaAhtcmjIzGTVTKHvKO6x0RY/n5ZJWvrFPLPIhHEEwqDchnbvZAsFRkVOn3AF97e0uRFiBq7fUvf3p17CU0/e8+T+JSo6r4T8GbfLQdxGKHcIGEiJA86ZAgUFX/nEi/jwE/ewdaPRN+MCsBF01zHJxy/I6xauBOACMLg8PGdD56JS1+9xKFaig5t7jK6p37vB3r0PhfYCAjCwyG8iPSfh3i63++v7ik+BRHagZrnLya9L8JSIsYLNCg/qzquQtUAu/U7MkpRlvQHrjo+kAam2np3yMpxOu5StxghcAgKDfPkxKNOWcTYNfxEUxPbUfjc6Oi3NOQLa3OxxjB+sAPgLKWHe7JbBAl0SmtpOPAtqBTrr9ChepbYbrtS96Cxi3SfDXAlHQIEsPkEZBQLa/AijVx8EoTYMU8rNOf/e3uTE9+jL7trJ2/P2ir5R8vqvNM07lkePKouAI3m0v6Zvi0aJUp6qzN1x6rBX+F8+3gIYGDCLrgQQrtX9/cKnVH+VeXtZHRPUcTR4kTNm6J0BBj3y7EHBl595ER95/CnYTmT9jwnSkEQkG24BaNgEoBkwnMYK4pKib8jwiWL38/3pRvbXL7PJFSdfnze9dEP4NhOyZxnrNsbcMArNpkWTY1AgJvfV9qUqbthBQaoJVyjDlpxKwGgCdG01+S4pNj2U/aEEBotyXm/AnADL0LbegKDW+npTl6vFoDOgyffg5VKWRwMCuYIBT3MLgq03ZwqCnsynWD/rw2KHDKRCAFamyMJ3triLGChmbdoBBu+vnmkYlXcDGg4YsL3lr7W6BAC6VS1y7UFbAoP23LeljTlX6kvIj6HxKxa7QpoIy4GCAogYzOjPu8246l1OlhnRXBAOMANIbYLTYptV33V7Td1uO7btyOiKFqsp/ihjh8ea37TyeCbjvC1iiGLHXsbt1AEgUVi63YGEHkTEa+J1VnajtD/fyMR4n979NVbMzcjT3zAkJu65rwvT/eHHBRTMNsiyajHL3PB2CXYRTX7XlzsDDIA9Wo2g4KNPSKDhCLU2g4YZiVuQALSKDABWHqOxIQ0Wjs0s9ZGiP3N9M9n0QFT68V0jcPDj3TPmoyZa4PInioxozCYYAOAKHBRkybXjrY/9nn4t6fksip+pAgQNpPJ9MFAnEyVUYRv2wgAyGJsIU8CzRlpSFl5vGoZAGnYb5EqwF6cqwCMIsNiAyaY4InSzW3uW5tYFtf2eJsuPRv5xDumfjV1x4KS9HAFDCNL0sWegwONqLAMhV+DQP/e9LpGSBlrF7ud7y7YqthYADBRftIJPtCmAJubIUwZr4qwaryJAAdwuifWVBRaHEFmFQarmfXvsx1iTPTSA0B3r1OTSryAUNv4GbdMCgtaHHXdwtOZpt3wO3/1adjlkMiiSMr1Rc6y19GTHAFTlz40ciXLdYE0Kmrl3i87IpIS6L+8MSJTu+nrR/i0u6Q3bffr64QP83DdFf/3k409hKzw1/uK7Jr9AjhlzEJv2EmtwZ4DBDBR85RPKFOAIvaqly2iYA4KABKAOXOuArbSDwesxGNjvVul7PcPBkcVvv6vv2U08/b7pzZgZm91HK7M5hbp/D6BOGNughRJgPAoRISdjVzgofkamCgBklbdM0BzlFsasTTaQMAIITOCyVXBAZAQBLOMfmHXpVlGvQwGuHgOtq0d2g9t9JAhwcLFbomdlEBxIeVGKW10BmrLW6F5bhwzdRtcoYDvOQRg3fc574ZnMl0nyLyX/WBmXEFDrcTNRufVgwQaDzZEGNISH9+VRwMKULaD2fESd4dgQBPSg6kjJhdepglnatDEEHKjaP3YAwAoMbIviJrB1iRk2Ld5A4w9YAxm7TZ+kKQfjrd8PoE8YFrOHhjG2AwURgNq9DtpqC2NvBgTqNS0I2IIM6uWPyaoN/K5kj/QYuVyu8kfMBu9ClzFVke4ZBzQHTIFv4ZqGVQhzIYU6x5kw2sPlkt5YC+ONtx7g8688h99+5qv4ySeewhrke28MJpJz2ipVdjKjELmha+CAIKDjCIPdOWAAtKDgo0/cQwlI1Qey/sgUbOGWiko6iiMgMEZBrmcctewZN8BQ8YcTM7o/AgA7fgQCDABwaSffBoALh2M2WUNVQsViyl5fJEOETARKkmiaCPq9TticCKu2KUENZWUOdgBAAVlmVDqMw/kOINheAu5a8O+WrIZV0ZFYa7rHREzKYglZ+g17/L1De4w3WqkC2d0CKQADBQfcgIPsArMU6X8u2I3V2P99icKqsZQApEQKvrIDBrWVg/W7hvbhChZiuwH7XeGkgcaVmkn1puITe2Xgy26YgugG6Kxd9k2EpAXMIVL0laSdq1LzV7xlGyfUGKREWXS0XacggEu7jbcvfw3goSZR24+9We6Go7HXughM4Q/aB8l31XQA5fEJx4Bgi58DGCh6zMZyBA4j+dPLnkeVOwB2sqcea+UPqQzpWc44bzZYtkg5vnEEDaYbAhgIw7g3HGH1CfPBR/dksNk9rt96gC+88hx+8+kX8KEPPOWGXO+C8cLkFdWN2F2uMgsQieDAVuc9gSfHFcEdAgZW4uqDDz9xr078gHZ7ut2KWc/Ec6Dw5lsPAAiqO1MuKX/g2N8/AwF2fbQiZ0BgNBHtOwBH9g6SQkX717TJkPRDAikYQJigFSwsASgslGSSFhOwXH8L6AAXdoFHAAHRcrMtZgFnDwrCRlkMztQqQHUZGM3uLW7WdL7yxh2qrqisYmBgBAEdG8BJXQuqrDYdW2WrgrawCNfCrcXV93tXjSrknIkJbpvCqsCiMpPzKWlgbVrc+uWeVdB2oZ456CHvGSBwqeyAQssUjILiooXbsCylKqUIsqyNR5ZtfKvT7UyERLVdM4CUMlLKyLmCMGET1jGrEOMVQoAj9RUZtBcDOBNfEQFBA6bcrTBoQx2jR4Bgg8oVlS8lgIitMFYuLn9WHsuemdyZyRxgLHeAsezJMPkyAAp2Q65MpTStuiO47XerowNFdDrCDmq/GQMdf3OWU/vCK8/hi0+/gA9pTIH8dg5giwr/BNLgbEVBZONbBy0LKPjcN2TJ/j/93V+b1uHOAIPCwOsPKyj4yJP3fG5FEjQOul44ADIIXEHblr468L/79uv4xVefBwBcSHxXHxDrOOjcngVo6svd33DOgABwezAQJ2QpVSlZHd31OXnHCg70L6gKyiQT04DCohN1SYSVNiyZkEFYchIEy9LORIILhAIjpMQCzhMhcf0c2QP07gVKktTF2IMOIDQWcPjutHnslF5Z9TT2yAILdC0rEChFcyapUI0goATBav3V92s/NuTxai1N3DYuDC3GgwiJKlDIJVrAASjAhFgFBu7DjPELsZ3eqxLbO8YB2LkuCG4EBKISG4Gt2NY+l7gCanmleVtDGS8QXOlYWxtIsL/Z/y5IywK3x22PDnMvlAJz6fjYBVqAZmU2Lq3NjDUI38+wK9aWkSWI7Ip/LpUhGAGCdSvYwFg3Obeq7FmZXe5s3MqcCgqk2mdkjinjKHNSAVYCMjHWRMgFWFX2rAn6neU7tyDBXQUGCBjBpSwv5yCxAY4CGLxLOBgU3PfT+L1G5d98/AX8xAee8rELtEOh11em9wEFK1TrbwxLAfDmwwd4/pvP4oVPCpN+xHjfGWDQgwIAlSFAr3jnFgOYHSnKPJSJ853vP8CvvPo8/vXHX8D/5r/5uCvSS2UEAmJ9yuB8BAJt3eqfkZ/uEiBYbWJ3E9OobGA8WUclKcwWUCDWf0pAKgISlmyTlbFkwsYySZkBTsCGDQslLEkUaGYAif08io1u2ac8kwqoKXugAEFZgWmSKh8TA3q8LwfUtVutAzaAeyBQpI9K4coYYGxdxf4DquvHSvWvzl03SyLcEIfzQpEmktiOm3BsDxTkR4kANEt2R7LtPQQG3d05/PV5EBTXkTUb6W0DwQa61gsgGrhdmwsLBvdfJwWtKQkATqkFCkQLUoKzChzBQhivTdAocH6c6vc+Oc6ZmIvIEhhQ6FmCUqrLILapjeG1cAMIDCQUBQy9zLmVvIHJF/3cyZxEwKYggRWrCxMm7UGJ9LvobUoM3rRvA5NQAB+OBgTMgxAyTHuf+DUDo9JvdYup8hMaE3dkNALmKtCZSdLGBpqYudaHGW+89Tp+7puiH59S/XhU7gwwMFDwlGY0nM6jwfF+PDaNz4zvvP06/v7vP4/f+PgL+NAH7s1uUx8xOHnECMTP8aczdsDudyluICL1niFYuewmZ0Xt3ACfIbVXoMsMxRKtExRYMqOsoqBSImCTicJJLJgNwMIQjVQEHGw2IxOMo0NmQfO6lkAEsk4Es/ZAE4DABGg60rob5QlrNwpU4FiYFlt1JgrKFH8Uog4EBgJ0g1hZIzZH+n9fR5v4RqNGCnXJEo64pNZ1Y/EdiYRRyCTtTdp39nm0WiQ2SYoVcRNrXE6v2IklnN+zZvPo9xErMANff1HtviRVXB1QQPNdFRItEhKA+q/PxCqNcAEkzFiXZkzPVxocga1LoGDdqstgLUXBQWUI1sIKDnqDREGB/+3a3U3hyhAACgiY5B8xUmIsSC47Vt37hgPZJFtRELK+j8glOZ+5rgbIykwSsFuVxno8kjebNf3AqNRmGnZRX6JhOAMDsZi4bG+C3bS0QMavGCgYXNOXOwMMziIhN38GpZ9vRMCbb7+OX/395/GvfkYCQWLMwqz0dGTfsT0zgK5KI4YAmEfz2r1GAT12bug24PkktQm6DaT5Bpk8hTlMXFK/ALBuMlkBRdMZEEgron1BAidZPbCmCg6Y1GpIgtA34gYcAKLEEgsyjkE1QAUIUIBguot3LTwrdSLbP2+ngkYhRWvK2qt+b4FApFbXUlQx2bFyilY95b7ZirtvRFltO5BAJGNHQIICBrWULAi0LuPiHTgABsKoab1zZdYbYwZtvwbeWD9TVmfBwKPS2kftv6QEIqibTL4v2tZLpiFQoM7tQPYXxt6QBDcaCRD+HbdgLO3VHD6X8GUUkzGKJzBQwPq7CHR5AgqMJViHLCWfljW7dw7yBiC1J1TjKThIISjPAE8EBzamuQBbkngmqYc8k1GZgggQvBaDLmhWHASgUMt8llTdouOwvVV7l9ltBqDg5195Dl9+ptOPF4bPnQEG9tIzdEVE3uAJ5IGGR+U7Cgp+/WdewE88/lRz7syuhTNAsPs8+g3vzz1qoQkYStQKv9uUrbCCA9Sbh8ma1P+WQChFlA9I0TWzWPfMWEAq+Ml9YsxiMZkPL3EbVFP0s1CCVamVgIT3Fut4JkUBCQws0v77zM+qMmrditOpPRDoraeiSisqpJap0fGqUsD/ph4YiOI3JbUSY2FulNQuvkPbKkGAloGEdu23NEyfOMbKiBV41LIbh94fFdZFoNCDAYT27/3cMzB2iTWLf1OQxMaQAdIPSyrSJzfGkhWJq8mEfFP7wP/lpGwNQgCu9iU0vgate6f/bvSx1OeWbXxyrBsAszbn+DtUGbip0rW/sL4ppuBqW94GFJwts/c/ss7PFhcphjkQ2APUKWEMgrWRPduNFr/u3HseeFWmJQZCEhHe0NUNX37mq41+nAH7WO4MMDhTCKoMAV++AkhwG9CivQgKfrIDBWfKESg4XVneExwGcDIIWxLWIJMAncyKhpXqy8z6PWgB+8wAkijmtchnUeJeYxSuaL2fuHZ8NCnTwYy8xTY7DV1nQTURHCS9iBUgAHWiHsmZqGAAuFC0cz0IMKU0C7iqPtXWKl1Lq4Qijer+1iAYTQnFts6hgZP6QZPTqtWFU1Q5WTsxJ6wEbChNfMdKAkzEoqUK7lwBjZdxAZUa7cu7EcCzeTFzvTGH/tLPxqRZPxgg6P3cPSCIAC0qqtgXwLg/rC/kM2pcDSzGBlhWA2uMJaFhc2y1jgMFDSBNxKDSBY9qrEJ09wAVMEgf7JmdS23ej/9m7E+MmHdTEtEuNsuMk5xoKmPsujjmAQNlNADKbQD0frUCHIwB2OViiYV2H86VKLuA8wp5Vvp+Ha2UiW7VN996gC/8nuRB+OgTJ5j0rtxpYGAZ7SyWTRSiJTPSJSh2HHCA8J3gPngUUACgWQIjdZG/EVH6Z72GUTs8BsAYnbUk3xJHLHb9wQZG1mA9YvHlEwhcdI100lttMmmqMCSUJP66UkRBlGR+RGqsppT3MyNasiMr1gMTlVa19ciZqAEIRHTbeedt5JPNabjxtXZ4BADkHeEWkrkFzlDUvQIa+VSrZarXq+LZ9LM/H8cALCfCzSZ/ExGusvlY5drEBOQif1F28R0bixISmpgVLAyis7XBBBTs6ev3whKblb7/+n4zMACMAUGMH7jk514LOyC42Wqf2L3P9Il9z4mQVvm7JELahDlYKGHJJSgxQl6LuhhsxU4ZuHwMKEA+F3M/1ORg1eWDCuY4KI1Lbd237S2KMRjWYRkCzlmNEWJlCYswfubzW5B2MkYuezcyJqxOQHXh3BYQWL82tQhf0viw/n78efTbM6W/vgcWPbNnspYAfPvt1x0UPPXEvSmgPyp3DhiYQrYxOwUHCQCL5VyIXeF+5+0HHmj4k4/Pkdalxmbe+5esXtN7ogpGU5xsJ+z3elBAgg6OwhpnJ0e4jAFCzhaHAGxqQXrsAVUfYynk3+W5c5Nhv3Tosv+VUpy8OiHNYiX7R4dtZYi8j9K1dorXyd9gHQUQYGxABAKmYEb0dAywmrkIesUzAgMjy7R0YdkpJZRNAwQRWBN92aLLMf2dWfoOieWcunA2yI+XpEpTLdVNHcyUpM59BrkWFMRGnffLD6NEIAAAfcbOniWIoMAo7eJjPVDagSUY9Q0Q5kDom3WTvrHiTA4FkEAGEhhpU1CgTEICYdnILdt2bjCWMDfE7QCA4hJJXZ4arMUYH2I1PYoPuQ2jSVoBTyvMIktzIonqzzIPKJEGABYJpFBDBChuhEgfkH8HLEfAeLJfki8RDJxhCOLqHQB7MGAgO77/sD2Oj42AwFkFPbquZ1ojIIhMwQwU0OC9jsqdAwbALcABGRITgPDG96/xK7//PH7j6Rfw4QNQAFy2mkgVbyziPw8Hwz3qZK7HmNtBmeBMupy33xjSZsICCdq7QhWgXOhgKSOdym3g9QwSJVJ6AKaT1WjrWdS8+U1rQNx44PdlRNfF5j0KWuvBQPRV25K26CLowcARNR3dBDdbmYIBUzZbL6UB5CTnU0pqVZ0TKhp35b8pYLeKzL0EBRqZgZUlViOTuKbAcv0aEnWcAcGPWm577xEgkOMIY7qW6N+Wv+frdtRPmx7LScZN0W3ARyDhKies6v5ZmBoKfKGEm80A9FZBwiYAetlKyAMifz02hNjnS1jdW+eMmTtc29kj6Js23r+7y8bArBogEBeefF5yUsOkSK4AAJQyaCvIWeYEURoaIREUjOQKcFm2PAoQMBUg95cSm+SM4o+/3V+7v/g2Rvsl41GeTfUd3mNQANwlYGAtpoPckXGAggT4EpaiittQ8BtvP8Avv/o5fPHjX70ICoBKQcXSW9aNkvdj+98xT3zvDUjg3TGbSxawp3gHWYFCAUlCtdwnQToGCvBjVWicYQ0uTVYDBNESskHtv22soBYNj9pvRz0HZgCYA4ImjztXX/VsWdttaOkZIJD68CEgAETZANUqnVmkjd81WlH2WYVoXzbmGp3dHJNrV3C7jcFE8b83eymp8johteLzYq6BmIOgL0m1aNJxkCy4KLIvUODlQoNdMxZmAWelIKd9n9Xv+iEJUjCGx8aAAQQDbGsxZV+wqAJdUs33sZatgoGc/PzKFVCHaiLuR8KkCcG4zp+aSx84cjJE90APDlKS1MIcAAf7fM9YUQTwFyBDlyUfGCHSareTKwBOA4F234T65iZCRkzKri0ulDNK/OjdRmW+6XO9aS8T33xbYgok0PB+U7fdu50AKXcHGFjpXtozVfkEUkCg5jgT4cFbD/CL33oev/WzX8WHNabgkmGRutZ2pD4rRzc8gybJlH29UeNusM9sf7nxpy8krofeV3uUNRGooKHef/8iMW85gMPJGi0eglGD5wHBaCLuwAGOGYJRPvdHBQR1dUFpGIEICqz0gVd5II16MCCfRxR1CD5EG4RlvuwRa2P9RAEUjMqR0u8TAI364Exp+lIHXD4hjPuS0QbdLoAmyZK+TCwUvq84UDpbPov7JWlQZiJGTtJ34vtWS5vUH572Lp++RIan+GCUp0n/GUBJSElyfogSZiQFDFsibCyJqJgZKxE2JOQCcCoNQCC15pmBlGTjtxlA6Ft33Nx6kC09OVq3KylA0PlEOp9oSbhiTWqW5kYIEHOvzOUJUMeq9fMlAABU2SJul3BsIENGr38ox29TLtxmxIICe93S367fSfFNX33wIu59UIzaNPrhLcrdAwahJII3ilGsXkiU5IPv1SUdH3miLumIZUy1Uff9AqVqc+1CnS/14RGajPc2gWbK0c5HBQlA8gkwDSO9gdafC8wVRtzpDMAQscfJegkMnKXBosto2i4jUMD7+IHop44rC6J7oF+PHd0GfcwAgCbq2o3KvFfJfQS2AQH5XhmDJaVBHMfe52pR8NG6kn5qN5wBzivjHhDErxtuX3L4vVXBnnGpTnbexqaAgRpTA5IAvoUtiRGwQpYCGp29JO1Pkv5c8hjkAWiBniK6oyV2s1U7RTh1IBHWUhy0gMf5P1jjSCgB0PgQQKx15ALeQnpxgucBSUohHlq0FyzKohcwV/mWQI5zfKMeat1yORohnQES++ySLAHmboAj5W8yRY7VL7Ev3o3qf7cyHDhmI6Ju6S/rXR9vvPU6Pv/KczWPT7g+vu9tXGjAHQIGI/QVD8WAV9OZr3/vAT7/Sp9GuTb+iL4f3a8+sD34KEzr1G91y/s0IAE13sFyBgB7q1rO6yTP9pvwW7/fcb0rsq0gQL6fAwL94N+7D+rbRZdRDw7ie3lgoX2GCvtbgAJbsdCvebdiyr8wN4o+67Ni1HVzPgAA+964CQ6isW2J3CU3jjVhv2Wt1K9t3z7HhwnwTDTcGrcHBTNaX55bn7UhsF68r2Nfz1Fd4zcu0Bgb+b5ocqylMDa3YvcxNftUvan73oI/AA1g8Pfp3nvWx7kTVqLMdXBC/e4sQY7iZiAsYKwhcnBJGgeSAVJXhUWuM2twIGy8884KHs0zOR4AKnSukVVNn+GyJLJztGPntKrOWMIdt/4au7Kf+219++2Sxys09rKkf88zZTaK30u5HovVbxnpsgFYePOh6q9Pvoj7yhTM3nHIThw0yJ0BBsCAJpt8ZgAPdJepFz/1Iu49ea/1SdtFB4q+n9zAXjHddonKDB3Kye7cyVHeyCoXPNgBhUvL+ICWeTgqDVrXA/3EbVC+/Y5aC2fKEgRLxv2h2IODPpEVx76Fvqe+eAxeM8UBoN1pspMIqUaz1psmcuV1lauy+GtXbYfF/jMAYPc8syRrxAicXaM9GruxUGCLtCG9HS0Owa0+VuATf59oCA6oe27dQle/d6AlgoBZ3RsBpu1tgbdWT7Y68ySuZsGpvURumzrc+r6vs33t1+X7fUqlky052MZALqz7jAAECTTmArBG98P+MLp5NAYFo3m3Zw/UiKhNXBU/BaYRwm5U2UF/YbJjCASa+XXhoVp2QzZ8b2Q/Hs11FssRkyNzfPQjPU+qv74p+ut+yGjY67lZuVT9OwMMRiCgHgvNwAUPHj7As9/4HF781Fdx/8mnmqtsEPWbYzHaSbOELz6gbgtJQ5nRXPt3sdpgz8UNRmtDyRKCCabKNaD4iP4BDAGElTOoeWb574RQGPDdIS/cf1EhaEGkturENlEyCj+WSy4Hvy4RDBmkRCj+GYBGlxcOPmet8dD1rNqvj7Su67PruTPLsGy558jfKu/YbSs7E/iDYtVfspuIzqxYhkoDCT1AiG6Bzdpw3hxaVz12AcT073L0PtlZGQG7V0CdLthT2qONlGz1ydFqnTZbpbZfOG7nZmW2oieek88BHJ3UcCOlE5m5qFB7ZTqbg8Y+WImuSqAFB/K3BQLlhHDsrfuZwm9ij5p6TuTiiMHqG4mS79jqdwuvOIrnui09PyqjLjXd0p+y7w8ePsDzXzdQ8BR2760yngZ3Oauq7jQwEAeXNpYO1OuHr+PZbz6Pl575Cu49/hGgqK1DUQDtMPbueXGgnEWkZ+o+HNx8sJFKuI5Owti485rMhfp9vtWtAYmmlk2xiXLJrXMIdoalnfpEAbhxVcxg2wLVzCZ4hsvEIVkP1ceJ77YmaJH8BWzLsJvAtbr22iwgAwNz7Xu0pNOOn3ED2JK1keKP/laEv42gHWiLPrtgtISCsSfLYRmIeTM4mV2o1xvzwnxRqMyYgV2SmaAQ3ov3A6AbeVHjVhoBhz5p0qXtywFMl/leiFU8vezX24hq4Ch12tRaIWk7UTgeXXkOCgIYoPAvtOKwzjneGCO5QI8sI9q/Qc5pp1EpUzl4axkI1EYMctD/dnoh1szx87ssfVO0EUjtE66/9xqe/cbzePGTL+D+4x92/eUbxVECuOo0q/cohdyR2rozwACYIMegVK8fPsCzr3weL/3sb0ujbjf6w36QVGKUeyenn9i6Z44q1P12Fm2jg5kaMFDGg94H/uh8N1lm1fIPOvXCRKhAoU4W3+p4NoH0Hj6gJ4+n6fsPjnf3tueSgzYyo9bLBpI00Cq0Ehjm+5Tsimr5K5hYdP0Zk1iPSwgQtGyRogDIFXlUAnKzfdWj0tdqN4pfjlXlb+lxj5ZamWLsfa1VEQTpjstgtVjjDajeGKxp7WVAQdpNkBkD1e2gQGHB8T4kM/eAAzbcMrDs8J3bA42bcPbOWYNxO+AQQUNkGizHRdy47GjHxnJgZl4aNzZmbIMmAQhh6W9oM8sm6hnxtO3qfaR5UhhfOyWMgUzaNXG0TsOlE7kZZcTw3j8kmVfrO1D+fvyCzBvIpGkQ4VnZ31xjg2Vtb+X663U8+8rP4aVnvoz7H/hJua57b6LAGrDVs4R+anp6Wu4MMDgEBVxw/fB1fPb3fh4vPf0l3H/8w6Cyj6MWFBmOk3ip2wfppDVQsatIHBCTWO2ZC6BHveyiSSVXaSdCAx4COzKj1CaFOiTcToYwQQZAwq6r7X/byIpdZfyjR2B3k9O2kiVKblFpFmCJPygCDpJRBYVg1MEGWcJlroYly8QREGCtULNFIpNHtttZtgXkdqQLNBwtC+xp/6j8K807VvyRZm6sPdRjCM89KnuK14ZQ9Q/PgsmawDP9F5Nr2fFLQoW6DyPGY9cGap0+SrDqvB1o1w59fE3c0lnYAfLP1lYz0NCzDfbcKBX6WIwzY+lyPpC4IZOMHwMF5r9OCnzJWqGTl4eW+AklfNwDLRvaPqeTb/65l2m3k3G9tU/NdtS0k2/NecRzA5k0fM7BOp0LdR7pluuHD/DZb31B9NcHfhK0rbtrxI2qdeDN5bKm9tO6ybi/NEPuDDDYDZQBKPjax7+E+49/JHB7bQfJSqGo2ML5bgDEjmloqZMLt8YTbY+aqe5pWs/59wlYsPAfE0bb5TqRLqGr77IHCRUlp+H5erPZZMH8GnRWBqUg4JOjeBALKqYk67SJQCSbMtv4N63PZoZ24ACp3SnuSrO3cWJP0LIRcKVKYIP6qgdlFEB3ZPmnTvmbAIdZh0Hp70AA7WlfhL+zws1nahSfuWIaZdd/PwAKdTfM/bNmpSrw2m5ngMBt2+W4bVrLif0zDdplv/vg5tfY3hoVNBS+zDQAdZofzc5+RcksJ4iNqxEosO2cIyjwGczbHhBwCZb6QFEDY/ZvBBiO4qBGyj4ChAkYeNdyrTdyTJ65wRFkm8m9HUOwl3s7w/JEGbk+3Gg19/fbb+Kzr/4Cvvbx38T9D3x4xyhYfSQ3DwG8aV2C0ePCEUEez2frHQIGOAEKPgz3vwx89FK2sSKzNrSBwMGGbNr3ksV8iS3oJsQACDTX6XFilonCBaw+OAYqCIoMST8YqfMHpi5ijhIomZshzSeb3ssf0zMLdq1/jEDAFGg7Kf0+DhR0sqZcAUKCswcwUJxsmaUi5MSArQkn2QqaDDOg0uML4NHt0UftQXYXqPAR7Z+MGQC5pWZCOn52cOCtxK3gLkFIA+espege0vY2xiWb4IMpxqoUm8yQOAYKTZXcv74vbrMFax/WBieAQKS/q7Vr7dS6DUdW77B5Qju1lVJmKlm71XbaLNkPxDVgbeSfC7xtSrnsnojtBszH2ij+IgJNH2cGBvSvMQYWT+Bvw0WUkLVd2SoYmDGU9jn+3bXx/njrNoj9MlD25i+PsQRBjjWyaiDLvCQzGFo5ZjJM3AUVELTyqgMKOwNJ9QTXZ1Y9cEsdsDtdFf/129/GZ179RXzt41/E/Q98CCgDptrrQzALSSRdwQ4cEEmbzlzkWu4OMDgDCsKABwao1xHXCYE7cEVI6V0PaXq/3WSZUmdhkkaQsK4OBNgUh09uqSOXqFDq83gQEUUxHNoGTgp/UwAEE/AQf5tOsBByvpuQ9lm1KxEDVFShqaLfGJzkOwHCHqSs1lTzVgDYXQyJJGCQ1GqK1m9Uflm1kLXSFbpgN7TKv7d2naq9ZLGBgbJK36pQbKy1kYCWDtz1X63coH0daAnAYoqCLwWwkMFkUeikSq4DBt13oLIP9sxRqtsmpLe38g8YgZb2Bk5ZuT+EdjPWKpPMBaYkiQZgy/ZqexlI2HR89YDBx10218R8jf+jjDsfawFQZbJ7MVA2BZs65krZt5vJmsY40d/PfPyXrPuJ0hcjZiC79Hzfd7eRXzSQXWwAQf9iWQJIUFkDNSbcT6/yyEABhfrMXMgH8n9WSIHB9dvfwWf+4Ffw8t/7Ddx//49XwGB6qjaGBh2i1otPgIM5YXCXgIENROmE67feqDEFH2hBwaGwOGgseYBSSlNggF2n9XU8PNe5A/pJSlx2zIADgm2tQMDOAz7huGEN9oO1qZ29Z+qUe8p1MgF14gHglOu72+TTe/WgIbIO7vFqkLtpHZ2YKe0AAhUAujMmkQoXIiTKIBLvgcxnAul68AIR46MMbm03tH11ycpNUZEFC60yAGL9U9mAm04Ym5BmrsJxxhDN+q9jX9qA0goCjDkguyZlOFjQ/iNK+llS7rpFHCzlBruiKrJaRWracJSgJcDAoWugAU9H1m1svzMs28k2bAPUartB2ym2H6eEZIAhZWBJ7rbZmKq7IYBQS5pkLgmr2aOOwQgIpkDUQJUGrnk7soHS0I6uvMeySA8eKn+OFn+kyPVzb7hwfx1Q5dYFBRuVM5ksUtbAZZbJq7zIOVaAoFa0MAmdrPH3D/NIffi1bybMxZErZVbKhuvvfwef/sNfxcs//esKCjp9Y7cxeaozUA5XcNBZSaI/kHD98PXDKtwdYBDK9cMH+Owrn8dLT38JH3v8I+POGFFjZ4q5EGJHdY1/ytE69bXNJmHnLtjWhiHg9aYFBBEMqDXqgsbqfmKhNSswcMGuEweok8+vCZPPv/csg913a8EC5SyikZUhUGrMJ6aCAGMQ2GUAyRxwV0TrWrCfGyhIDJSEYQY3AGFtcu3TU5btjKaNFhlvLpBh51CFdM8EyTDQPj3ZXyO3j4A60rbJruhceOpxKgYe6nEYQAiuh6ASGn+8tWUt3bywowEYRKXmx3rXgLWhK7A9ULC2bI53cya2J27ZnvYZy+JtKWBqqW3p82EBqFql2cBCErAgIEuAljEHdrx+l8YZLWI4yvIXAWrrmurapmzH7TkCC9KAu3blUbseWf9HRosxZ8AjyStnMomAnLX/MriI/CFO4JKAUkDLlVoFAhAoL6LvM1pZ5MZI0jbRuREt9w4onNIB8pK7Q9dvfwef/re/ipf/7q/j/vs/dEE/WZvsQQAA6afAGoCSL9l/Ek9O73pngEFc0vHZuCSxsxD2VgO3fy+U6+9/d3+9fR50zLBMmYOBdTOaiIayZ6BgW/eAoGwtJRcmmaP6UOpKBahAg0yGLUzAABaqEqHWzRBR+rY21zAHgRsRu8YFiD0USGmbkMbiUVHBvMmEl4arrgWSwEQi+WmCCOTMrRA2P/uoHFm0SjBLv5VNAlLdChPFX8GBMQZrVWLrCt7W6gba1qH7Z0SbNnWMiivZvwDaAm2alit1I2SgWGBVlv7W6yR+QwVNCSBB/6bwORM1iWDi38P2bP52QGDnHhgoL1NEHNkCUXa83lRAFZVUbNsIFC60rVuikZbOS23XvFTAkDKgwMDYBU5ZQZh8TgYekrghirkiHGRVwHDUnpfGpv1r41T2roMGmEaW4MA44chYAi0Y6NmAGRDoQUAEAAPwdiinNukfu56TKG4iAmeVLcxgLO4Z4PVGwYHWgYrrfgHU0Hp0cqhxDwQ52VP8R+VA33z63/4qXv6p/yvuv/8njvUSUQdOuNaTxluk1SWPX8H/8eX/0/TWdwYYAAYKfk5AwRMfDZ2H04pfrh0Li+sffBef/qN/uL/GBudtnrF7Tv3tdL0/OmQONMxFS8MNQIFOfB5Muv6dHQgAPumIUjMBGfo5TkD93RCl6+9If2NoHRUXHE9Km5AGgJFAZVULrexcCxJA1q5cSB1IcPHad51Ztb2QNSVW2JW8K68DIEBcRGFtK7DeNICOFRBcZHj6EtkcA2eAKKG8tPSpAgK+ecfP0XLVMATMwfqNTELnZ++BggCxuIrE2jAKzW5Mm6Kycw0QYDSuvwEzUJVZCwaatpwBZnvmI7ZxpKkpL+DYlssVKC/a3qkBCiBp2wYoECmjsEjip+Sck7si+vwLtX1DNSmO0QEgGLlgHhEUeHvab3rgBRyzl8BFg4XD5+H4AWR8msWcErCVCnDtlimDtk2XD2VY8CLZ1qZlE5Cnz+ASzvWP4xLcFWq8WL3eEx0g5eWf+q/FfTB553P35WZ8AKhL9p/5SpsxcVDuDDBwUPDMl2VJYl9UsEl3llt3oIGCl3/qX+LvvPJce7LvwDOdd8FfNiuUEjhuCp8yDim2WekCesZLfzaPBUCBTMLZBAQcICCbog8TUVE6UgIXTZCq5+U9bgEO9FmGEKisKoQBcAKVbbq00RQZQJ5NzvBBO48CdRqFa0fBOgMQXQUDIMCmqLZV/jKDtxsRkOYWisBtEHDlJViw/j2AAFHmBF6uqhLbVnn3ZZE65AW8rWLtWvBV2ZTJseCqLMcCEGjXeAf60v+eH/u7fB08UEgjWjswLuCCYu1njIsF5Vo7l8374CJIniogODjmCLjyAuQMygIMaLkCKzC4CBSSAui06NjM7nJrA0Ktn+sINel1asyOQNcoLsPu8cMEBREQhGuP+mK2LNGNYlKmYLZf8VFJrWVNj3IPoAqvM9ddKPff/6FHq4O5Xik1YwUIMXfPfLnZBmBW7gwwcFDwxEfPK33nlLjSMnY8dGAEBac67cwgcQVnzyEA7EJIgmFU+LJk56OcJcTBlCAgEzzZb1KN7qUkO69tm8cAQAEFqR8+Agq590FApb1Tu8OLu64uTUoupQEH76qwUwZ2QwEOyhgABOIk7EEECIC3exOoZH+5FUg7gWrKqaGzx2CgggBxF2BbwduNKC0NFHW2gCWglAuL1WtW0zYex5TJBRjp3gnk7hz1lZK6blRx1TFeFCAoCND+tOArLOzBipxsHFdgQAEkeDsa2BrSqNbpfR9WJTZM2hWUUtPmARDwJUAwAF7t59rmUpVy2Oa1vWubs7oSOAS00bIIUMiLuxk4Lw4SUgAJAgpyAHa5gjJqV4/E8TuSLxfH7+lVB++i9K6DE9c2hs0RQAuF+q3LjdEM36EA2dk0A13qbhNGzVhLU6j7uJx+rGsNQmXOWvHvsn3jcyIgv/D867fekORIkUm/8Js7AwxeeuYruP/EgCkAVCAmCNccWAMAQ8HF7A13/f3v4tN/9I8ug4KLQGAgNMNz5HtVXqzgxDdTDeDAruWwXIZSoNBN8TKL4FpvKjigooIWGsBX/XITt9TOQqX43f3asx/bLYKVFXzgcVI+Mlq3Z7D5AbWv2ZJ+qK+WCLaUyN8hTNaLFmwfMHgbMGDnuOjxDWXdXBkxq1IKSmrajoACAE0RnQmUBCCmq6Uq+6yf+cp+rL+1GBBlbgo5G0MsQVkMBm2lYQoakCCNWN0HI6E1Kl3Mjx8LQOHQag3xAzOXjIMta/cOEJSb1QHYe9P2CWnJMjeVgXEgcFuQ4HEKMbbjgKGJbT8dy8BuPL+LQil12FxAppo2sCR7h+AgVSYSaqS4wWLH9Z12IEAqofcJMqmTR2SMja22Wa6qDDJGp3O7jUDBEBD08n4m30d19vMX+mGkU0YMXQ9cwli5futNfPbVAApOljsDDO4/+VRQrBaUIT6omhGqBQcAquIF4KNRfy+g4B9IIMgHOlAwAwJHQnHng61BI/I9t99BDhCALEpI38vXvSs1j22ViaCCkigJVWyDf0Dl+WYkGa1vb1ftDonb+4dI4KkPdrS0cQYKqE+eNJucXTEpRdSCgybpB4RF8FK6v5hbUwO/tgcOGgAIbgJ3Hzg4WBswUG62RhlxKbLuvRTf1XFYsgAbSgkpFaAkydao0dPpCig3K9LVIq9Fxuhs4BWgRd0HgILjpQZgrTeinLDIZxOSXXS29I32RYzROBJi1rbDfgMQKPBRFs9dwNsgjmAICiKz04GCwz4ALvaD9AFV61OBQrrKFSSsC5AFCFBexLVjbgYFB5wXBw20qIuhdKsdaD8P2niO0bzoxvW0fQeFovHUGyXCLLpYOgIHOVXXocmmILvcWHEgX0BmqKif/1Ae7YJuD+SQBoseA4LqvhkmO5KH95UZt+FRIGJkpiftP7z/CAz2de1AwWciKDgbGIk7BAwAoI5W7MABoCLMdscJA9UnDGXvtOsf6JKRn/512bACaIXbqJFnDX+RTei+++QNwIFLqCvB1t8CjMQMXpNbUpTFj0xlCYK0aKAN70EC4D4/mqH8aM0fBb31gGAEBux+HSBgstUBMwpvj5KHzengAKgBDkCTeKSxrkprsU782pZDwhT9WUAQlVFZtyEYECU1tyAoJ2AVocgoKEhIIWkJLVB3TRLBvSRwFLQpC5CM+4WbZFehbseMabRYD4AdIEi/Mxx0Wf+ETKDVgh28SCfojxLk9NnwmsDb6KfulYe5xDqwa66yxlUz6Qs5P+mPVfqDKe1AQlk3pCUjXWVQUpCmyt9cOx7bod8tLgGbAoScvU9ivEfjypGOqFn4YrtP2vuQKQhuS7uXgwOVi5qBQeJRmD0hFxtLWYq8Txd3YCzmDiD8/8j7l2fPkuNMEPs84tzC7LSSmaZRWVxJZpLW6gbqkcXFTE+zwAKgkcmsgawiQQNB9hhntBatR2bazFYLtdqm2SCaQGclgBmTbAAUWeSoZ8F7swrktM1CCz02MpOYWZi/YVD3nHAt3D3C43XO+d3MAlt3wizz/s47jkeE++efe8Sx+m1b/giX6Cjk9huO8mZq4q5TYsa/nUniEnKzAzZixu6SQwMUnT6SuQ9bD6+lAQDpwULlLFn76e+//LRhCkagYqfcL2AAOKWUnDAUferqVaJwROEptEWeOsdJlqH8i/9AVpx69csO4bnnHFDnI8M1/VJjV7TDj9A961K9jnLNcWPvXannRAoIyC1+RKZkLZEOwKlFRFz98wAELmIG5NqWtgP4TDxvNFCbeuVdptwdWJwvy2oyHse1DRCkFhCYpzoKGeg5FUNwu00BQdqhXYMmnA6zpdVbzXIFMutCJsMQsvIk329buU0SQcs9NWdDEl0KE9Mu7nLJevFVf1NY6AGBntMlvPlCAeYhU4jgmDS3JsjwNuYwRnBaC3iKstTVKHnXQMG0XVJCCCHnsmxRlt0OQdqSWanxlBCWDVhkLMp7eDZK3yncZoBgDAPpNDvQON8D5FbhAzBbiW8KCGxsVGMplvpRWbPEHBL5rSBhke3QJibGJbeVBwn5XbVOOTlxkTY/WoCtqjdQ+vLMGdGcDz+t1IcKOMQxEJjR9BeULHMfn60cy52LqzFaThx9vE4vcPWkOqfAJxpe8A73DxhYqRAWckcXtIo8IHJnV6V0/Yuf4xt/8fv40TvfxcNXX9ds+wnqc2XYcXbjUMeNxAOaG3Aerv5lZiCoQUsrEJSubOKy5BQrNZnEmSlw75qPtbH/hsqrQAAwzhugJn4HZJRefg+AgJfbDLUfsAeVDP07zmLbTaIbW4zajL6yMFOWIKWLQMGshGzo9a/zUOMSMygIiyhFH+eWc5eca+Az5ytqdQfAZbmQGdJYjLYXOV8ABHaK/6BMu4qenBCm1DVhyYm3FK/yShiMFfIxaGEOgrs3J0LCBmBBTAnbKmOOtpC91vy4CUDgLWmSb83k4BbAFfLS9lmiBjTTBsQN0Hn0kteBPN2VOQHsZoxovofQ78re5PFhoVIUo3m0Hn/jNOlFBSh4tlIPMpkzVd5DTmH33PmiUuRBHcvUST+TITMIXr4jHbSnfxrd41kBUBBWMpRFqjIQGIUQLtTXPnzD3JzPqQMDLWCzZ/MMvLdt5sCAnZtBwbvfx9uvPTxR53G5N8DgENFZx21oenaG4fr5x/jmR7+HH73zLySRMTMFA+8/Xu0elzJqVNeQrl67JQMCdnWu2QTKIOGqZPtCjA/FK81Mln3VwPX0bB6kGlaY1ceHA/QdWgBQ3rMoMB4l8syMv5fNHdiXISAAiqdm+z0gyIBr65gXuMRCiWE3oMC8IQUFVbb7TrzajH4Y7JNXD9OY9ksHBCrzdvnqsXx3qNCXWbJ3KJs5rm3UNekHstKW9+XcmsiAzUwIQQwTBSAmxLhqrFy8+rRuiHa9A3BEEpKJFvN2QG42390Kb5xDPJwCYPkclABKYJR8D8DscvmiRE4IBXK+R17YS8OIcMtsSTGqp9dNY+fF/TTnqXqJNsRT9nELqu03JyCUtQEMLIhMasBAE90zqJ6Ume5pHQ817KllBPLsg9HUvtrztvsfliybUMYFFTnJ9kCHIQ7bqWajex3Y1U23r59/gm/+6e/gyVcf4+Frb+2sUpArNT1yb4ABgF3P0Qrnjl8ahTjh+tnPM9J6+OobDVMQekVIcU+uY+M/2NcN1tE72KCCBzapHrQ7U5K4ipvrquyLHC+AwZ5wTukPP4yk+9IZ49/saz+53JWjRJ5qXypJTs3+6rohKEhjUMCcPZ+KDvbPnNCfRlmLChevUpICyzvR0s+nDvY1vQEYsBkIRpGimS4Hi7f6BKtl2Q/vyA+t80Go7KCMpr5ecs/+XE16i6EYFhrT1pSWDOBybF9ZIF5XOW9bQSmBFl0Lwc1USOsGcvkHAApQWMbvYPJrwdtQNtnbdmhH+5ew+0nAznorCYr6iXcBB3Jdt+iXn7FgcSD/zJaGripv4bwJKKj2OWDkmTf/Hu0+cNanFMs19BL1Tk4c3GMCso6yfY0eGunkHX3cy8HnNrWAamBD3GvU+7xjZftaHVrX9fq5rGj45GuPzzEFB7K+P8Bg6oXvWG+Njf7ls0+yUB8+eMsZYJeQZdeYkZ7lGEySRnjSsL5+bVPlbULtReSrSudjB3JKiGF/DjP7ge06siN0x+84msM7ovtboz+KjeX7tKGZvt3GisPvK4PQHzm1oJUHCkAZ0CNPZrRKHtniN2bUNoRlAYcETgQmBjMBWyjqt6Wnc+hAAQGV6XB57vxyhTxne8IMTKdlNQmf8qyBEsJsieh+76hNZuthWDii2z8EfYN9i3nJUsJOLgJdlZAZr7d5Bk67xoEl5salLE3tGR+b0hgMjBj7M2k7AN1UxmqtCWu7kYE21sN+x6Vk6Gd9ZP007Ko2qUibXY9TYw84N/7qjzwNQAIa8GC6xTN27lx2/w9exv30xrwHCFMAQBZO6MHAqM/77UoaVG9zqzO8LrbV3vz9DnQRR2eWXT+Z2ZDrZzd49NPfwpOv/3ACChrG1N9jUu4RMBiDAW7+Nhfh6bMbPPrJe41QuaDn3LjNoIhXlUe66/k3IMDXyX7Plj0d+aBOBekto+h89xR7krEFFap1nnF5r/HA3i0Xxr/q85rrT5TKI2/CAzmhlFlfXpWnsQdu+07FVpgkpeDdIlKlLkueEUHKWIjRilVoQarcx07LokXNgkWeAl2uSiJhAwQqRsD2jWhWeYgoTS9f1x7DT5I7eZdrym4zHGeYgRaIOCG43y1YrOuXa2RhPU653w/DZettya0ZLIZEOkuHdO2DahVKLgsiiQiO25KM8WkWn8qUdojd4jslfv4CjI03km57ODZn8h6MzV1zNhqbNhZaINDqmvb6WZmBm4HxB1BymJRJ8PqWgfKpcFeNfX1r9dD9NpRgH3CL+leewBkUtNPQ9bqJ/WALwwzDGbUNuVH79cHXn+Ct1x5W9S/2YDa+ulfN5f4AAwAjw+v/tl8qe/rsBu//9BEef+0J3nztYc6lNkH2je3uFmJPvTX1mNXFg4BMpnFB4CMfeFZGcKh8VIUgmVHIwMGDB87eBxqUi/MGdIRopRLd8bYn7imagQ9T16tiSKh4Ihnw2AsTjN4sa1Y4ZiGn3aviNNaAgoZqiweXV5jURDeyZyqda3PnyTw9TjmpcxZmyO9rxgKoDEcLAoZzsh0IaJOtOprV7l8e3IjZ9cGG6i1eIFXnFmp/8F7M+4zALKTUMmpNnau+MzAw43BZScbN7bP33QoHFqp1P4ACGnbKcCEwoA/3jPI/HMtTzfhpp9GZbI6oc5MbNde3HvRI9ibavXft9jjesQ05oAGeF+qacX2p0rGt4U94+TrWIoHBHShfCm3AQj7H87ET+xEWd874LwBc/02xX288eFgtvREILgF3BBL2WYt7BAwOvHFjAHXf02c3+O2fPcIPvipCXVODDBtUWHaR3oe6wTCjovaAgMfQBVhzf+3oAU1dS325AwkAqk8F27FMbNs5odo8XaqBdjDqZgNxJH/A3om07jYVT57KrODHWBGmhhEJ6Fe8dOBAs7v9MQbn5adleWEXV20S3bJHatnwzUd6ioAab8Er9tlaEG2ioA8dZBagGAAOC6ppn40R2M3jaGPHbf6KvQOnkp3ugcKUUWh6UmvgKyBQsxl2jgcPF70DkPuChcuYE3BVfzwoMzs8WGbZEkrbD13ZM1wb813aeG+9j9GKfK3hB3VtfZpOPzCswFz/HI9hrziLjun16fkyY1cTo9Odew7X3XRqozvB+bPtCUXPJu6BQn07arb7aqR2e4AKrhv75d8p2DUDeReQ4N3EvtwbYDD1zBtjDABPn13jWx++h++/+wHeePUtJJWqNbDdoGpoK3p4b2E0X5FZBx0BgRrAFHDgz7PSDsyW7irGn3O1c74zcddh/XfdR/fz5zav2JWR0tgLq3n92aJzIsKm5wR3QJC5flYZACyGz2EAEGxIoAIAQBIFzLK3HNMkr4XKQi6hLCDVzs8mIBsQwSsDmhmoqOZCGZuxaIzCAQAA9JPK3iCEpRgDFewuXTww5sTs+pkZ1Mna+hRhoRq7xzCzvSm7M1POer1ehoP3ym82Cpkxy7ReAzrajsAJwDD7aJCTofz0YHLc7uQYhFkeSFn4q5WLC0EMZRb3AUFDr/f6Z+yo2DEn6mkZEUUjvQKc0y2tHh8a/JP6099vVDevDzf2+pOz/vH2woACYQIURi85KX4Gc/vOwNx+Wb1NX+aGbQDCmXJvgYEHBd4oP31+g9/58D18790P8OUHD/ulWJjdnOO6of0D2rBEcwv3e58VKPWUbX+8Ph/VfY5LeY9i/BUkNMaftBdlfdvcKZzt0bl+R6hpcm+HskudVWeyLJtjYCEZUNBOHxQkzAACJYn1g3TVSAMM2djJNZxXO1Qv20IF4LKQC4Cc6Ka/CegT0jDQBSenWZXlWcsX97LiDxGVJ6ix6Vrpl6cfO0XWycRIjr7Kl6fItl/lQyhAAc1c972neqPmtofvNzVs596xMh8+54btfaz+sr5ATs51n8/uwJHvDy7pEUABiHulnQXSJIS2K/IB6Iz+IUvggOMIECSM9WSrryqHZuC93nm8nyjtvWfGvgcN9fkngxVOZzp9qLrGQgcEM/gEc7KIkQGr6CvO9iMDWR6DpbZszbt5Cdw8v8G3J/Yr6DWk75/1pLZvCw72Wu3eAANgHxQwnFB/8wO88erDCmlV93Gtl41oizZ3kMEIXXeDbgAIPBgwIJDcDRnAZslPrj7bThO7CFcGN9GSovKBcv0sunimQ19Syv24ASiqv9AjdAMFBEbUgSgeugzaPYDAgDP4fAAQ3IwNOO8RQD0/u7zDzpu6n7WSt7g/eyPpV2PzXp83/iFi5u0ls1voPaQi+yJnmJwJCBSz7EWuW5GLrQJJsQcJ8rACJrJY2D+0/t0avBEYyLIgAUij920edfzOUfpbcH0qtxI74NOABpZFh/Jnnw0sWU6KHfOyKIIY1klr5n4GlUcDBEZg6kzYoJLfMSDY3G8vY2DsuADn2YNzsmjObE4dGvkGALws/Wh6Bc6wEnF2UuTLlyyqk3X86HMMJIxsR7dI6Oj9JmDnk+c3+Pafvoc//spjvP7FHaYAqscVrLfgQF9rt9wbYNCi2T1Q8PqrbxXhDxoqoG/INlZ6tNbbiO6q63cOEBgYkIHLWd9szNXAmdennOQJXnud6EGQ49JGA2ZW4gU8FQ02WpReOjcrECgIPQQuA0/puxYgREIOMxhAkOOD8IIDCPahqkqxW/sNaPf5SzYU98grPqXMY6fINwZ4lW8XtH18NrOlrx83rAypzJAp0KBAITdtOAAJPuTg5FY/t/V8L3t/iyXb+248GF/Td9cD1rfa94VtL/KtqiAYMJtlBUnk398xDQYmh/3nRfpO9df1H7c9ZVfyLIcCIrcGECQe9KM01k/+dToj7V9x/w2rsu1Rr5gbeC/GLY9Rt+/EsyOAWzPmVHThyqL/iAgx6BqSBgjYcABPAQKguquxHWedqxFj8MnzG3znz97HP3/nMb706iDRkC2vTP7PjOoAHNg1e+XeAIMzoOCPv/IYr7/61qESzcecEsnrBNg5yTpU41s3CnEGCODquJ0EBAYGNiBPm9oyzSvl7ICw4utfg4T6vabzLw46WGxOMJ1mwGOK0lGj9KgDMKVC4UWSQZcGDEKZhSEAgXUuuOQGjGYx2CBsvV4XKx4o9WEyXH5n7xU3Snvg+XulnTYx/mb8rB8lHgNK+L+DdiggAMUQqlG05ZdEZowQSBYXdCAhKpsgwo2ynG0TbgAbXEEtQ7iHO7nMDBmrTDYHBsyApbRvvLwdPpKD/a1lIe/tgYOAJllwKhCABQVI2ZPsI1utTLIsGrncpV/lijf5Fk0i4QgQFJk5veN/575XZGr65ix7aYa7fZ1t1Bf8u04Ot7qMHYjw97yr/ruFta8CgiDHsvEPDN72AUI0hUXy/sSATY4G1AA3wOXIZiS3nRj4+fMbfOej9/Hddx7j9Vcfdh179KwMBvKDCzgAHYHoewQMAAxf9OOGKciGWs/di48FjTNbhufoUR4wtHfqsmcHoCAPVPSgYOU0BAQGBmwQyL5y36oOTa0CCLfNm/h+2i7Wdh4g9AghEmVEnu+VClK/DKXXAEHQOBVkHgiBC5sgqMLTxTVAyN4fYlHmIsxS3wHIA1CzRy0YoNECKhOPbas9tsTcGf9OObv+AZiCHyvktopeviQvqABAAFhkMY4xCbAKCiIigGRgIRAC6fx750Vz4ykPFz5qPN869u3YgYQMAsx4JRZQuIEruSSeg+lLZAJwlksgVlBQPMkWNGTQBPu9iGIOA9Dg+lcOUwDOYMz7XCc7X/ncv3AqqbACmXAg66jPTeQ7Yi+9TvJlBA5mYZ89QCD38vforx/pPF9W3YwqrpVZAYEALQqal+QAAlKQ/sHeG9ffaniJTF5cbIfVwVUh5wEM3762Gz//VEDBH73zL/GlV98a2iv/LM8ETsvAnrXl/gCDbHyL0c2Jhi0ogJ3L/tKu+EYIzSTtanoI1fcYTYep1hNsQEH7Gjb4gH1QYICA3TPyDIsp3hkfsI5roMGW47Ub2eCy4z0O4A5UrG40RKkgiAgrMEXpAGNlYKHQAYQAMQxG4Ym20pGQOMf1IjM2NAxEAxBAQFnHoV/Ayi+gM128ysV/R4rY4v0t9c2oPd6UOBtEU8TMpR+sud3rfgB4b21cDMiZZ0SBRN4ELJEQQVhiAKnHEwhIJAxCVNmbIpQsAAMIkHi9l6MzfsM5/hPQlExuDSBIqXiym/ZtYxFMRuuWsIGxbuNxclY2ADr5RCIswYEpUopY39+AEplcjKXqQjOxJMpmJsvr5gKouimP1dTPO/ZDFJ0zYwlSo5NGgMscFWDOXvp38HLfM+75nGZfawTbvK6DKIS/M4Ci44QRI6wsQCwSsAVSvSHAWLal3y0QoLAlx2y6OrfgwOoeQFn/WF2tDlb14Tvovo8/vcbvf/Q+/uidx/jyqw+nDFh+lt6vMAcT1sBXYFLuDzCAi3dBmAKbffDGqw/rATf+6aie+pgJH3Do06Gu8x30xcsIFFjdUuJcl5LAcrJyHuj4ngzxiPwJbXzKQIQN9uAEY7IUMFCcnbKEc4PSCYiJsIaUabwl6GIiDGUHZCAmDSkElsQbo/U2HaglFwEVQAAKSEC1LkIvjgHGK0o3lT43Urx2nnljBgC84jUA4NmhlYtxW5kP21pE3bZXaStThGLshLlZiBA3NXxbwhIJC4Us68AsACEQJPRiq/TXAGHLHnV046YYuqoMDFgVPnGAwIBCBwgYWFVeBgbWHVldKicAY1lpH13sfYOBV2TAYAxLBRiasIQxMPX6ItIP7XpkKe07d23frIxNAwg84NwDBR6cQmW7F84cATDPYAK1wb/U2Pv2Gl1/phTGwCls1UNZdyTO4OB0MsAdytRWNDL6+ac3+P2P3sc/U1CQTxvYKPL3OFH1BOCTZze759wbYOAV9NNnN/jWh+/hT979AK8/eNjTTc7OjSidPeFXHfNkQ4wKEanybZ4JQaWrjqxIJLQol+siy8dkffEDTOhpt+109KxjemNfMSWhUaaghi0hJKcFJFqgIEqpFBuYGwBsBaEDmKL0mAhb0NwB1BSeR+mWY2ADPCh6bpGyTXWs0N7g3TsZGdBCaaMjRetBQFa02oSth7s65bo6o7amlEGFGLgCMnwbzZbsN5ln6juoMSfCEgm3ICwBWAJhCUGeSVsFEFgNHhNASWh3hsg86vuRceyOlYGxMk3pgBXXLMpm79uEDPQr1UNAsKakf0Um68afi8wCAUsIGTBkJiGzCg0Ds1mCJw/ZBcuR6ZMfVYKUbddL7asWrvJ9dS+keQYU7IU0PZAd1hnHxn/yxetTuiwEdz8uYDAlLqyoFgNsVnyoE+jZAgs1hea6i0vzHh4UvK6goLNhVofpxuRRLE7zb//sER7gtel59wYYWBFQ8EgWf3jwUJUXINyxUqUqZPeNMgAOCOyAxr/+9GnZONMXzCvIcSC2qlTgwMesiIElBmBLYhyTUlmBxIBCnK+oHlYQDZ4HX9Bg1yXouh1kOZLgBmU1yGAgwT0TjspCGXxGdZlcN5a6+/yFjbkCB162W+JqUOZBQtnuZ3AgqQUaIzWly2Vub84Qnrx7RSzxeE63GbY2EdCM+gZU3lbxaAUYeBAghk+U9cqpMmZm6KSOcs5WKdi60UKjxCwWHoiykVsC5VBNAkkdkLAm4GqJ1jhAkiQDmcHJOVM/JQ2dqVGLKutqbYlWwFmgY1luTn6eXZmxBCsDt+uGDcC6qWw3zvJbkwcG/EKyA8TgC0BIGTAsFDLLEDV0YGBhiSEzC3mfKh4xPiLPlUiSa+GZBa7AgonSDI+fktaJ14EBE7eXtbw3GlBQrq36PvoZAy0oqI7dARRUSXYnwMCLMLMG8PL2gEVrw23GDGWwBwgARsm/8aAg56FY5z+LFRrv1IcP8inZZozfLf+m0l9G55p9/MFXn+A//vEfTqt0r4BBAQVP8KZ+JdGy263IICyo2IODbG8agdrmX3/6FN/56P16J3oUPyo5CcUAApnRqcGBxdqTggNKjDUkLJBV+DhXHvlzADIoKRthGYhUXigUT6liBgYDbfYufvE2oO6Ms+KpWaDI1QaiL21890zR7ICuGK5gRQ1+TfMSwuivsXsCtTI1IAAunmxHvXJhBDwQYOfVeiBgxmvNCYjO21UjlpixJc4KujVubbFpo4Hktxg4/ZvE401gLByAmJBWMVjM8lVIrBtY2QMOEjhYXDulxOpBiVyDyiK4xE9jEYa4oJUpXNjAwBA3iYUKCm43YwZqlsB+mywLOEhDOZr4juRor93JUQHDEpNjYAroCoEQ15SBwqIfU/JAYYkBK5CTHAW0FUBguR0+kcy8UyAP507G/o1m/XhUJg55VyR/B8VBcYadgrBIGxcjZro1BNpd9wXonTRbcbw6xxyOE3przgL1gMCYAs/+1OEiynaD0DMFM0Bwyi44J8de/7vvfIAvvfoWgLrNvF60n/6Zvr9kObjfHhS8dfBp5nsDDOzbB99/1720eh8BUONAeb+zr7U3PygBlLNDv/vOY/yv/i+/IfsvAIbR7DRZiIuUIdCkOQYYAhCY5Z4bRLlERGyk2d9JOyYTOOlHYaYAQZ6d7P1CeT/x4s+VFgQEarfdb7cxAgRAGYxyrE+K80i9yqLv6jUuxlp4cAAgA4S2DD2tgWfVTS2dgAHPCniKu/VoPRC43VJlvMygAUBKqVomVd6Ru3aIAQiqEc2QXUVCYvOWkvR1oWawREZi6SubrvVAzCAFoTJnv4ADS/7MWmyQ+AmgWvUty9jq7WQ+inWPQMHKCZtS1uVvucaDApPp7VYAwe3GL02eHnRdRWUNVsqsgoADMSiyj3PIJrMJW6pAAm3WXyXHJhH3BsetPZFDOLiQPZjYZmnXuhDkPbdkYEDDe+AaHEDCgYDt0992b0vaDuWPZzarsKRjIIGeofT1Hb+H95wLSJZ77QOCcf6Izd5B1k1TQNCAgZO+TQ129PfrjinYs0n+QZ4pAOpQFAH4+PnT7DQfgQLgHgED+6DEW6+VnAIy6AoPDgDjoM1YtwChLR4UvKlC9Rn4bVxqVnK9jKEgGwyy38fQ83QZqH8WCZEFICzMWFQZLhpbXxLnY0L3AZtWhtk6mEfuaix3gPycPWiAAcbv74EA0GfI2zHPEkxBgQ1I1N7TXvHMARF1cTqfkFopTgcIziQMmidrCXBGbxszsDahAvNmWzBwuyUkRXOb0uB79DdQFGAMBCTClhKiTRtwDFGIlAFTSgISUyL9K23KSUCm/NUwQmAwyDFsCmDNWLncDmJp2xnd7Y1VC76S9vlKznauTnhgDc3IX20bdiGDDIShMvRMAVeAwMt2JtfbDQCSYwz0WAi4igGframAhBRyHoIAAyBsxhpQDjkszDmZcQmEJXFniKSdOCt3DhgvoONkOopxHzEFvuTLuehK4jk4WABwkA8O24wDc1KAYhjLjAVntD0LdRhCuFxPtUmkwBwMtDN06hkoRd/4fBAPBlogcATYgDpkYxvZcfQ3w9wmtYDAnp3BubuNBwUPf+0YFAD3CBh4esSEY7FP27BV3JM2Mqsr6L15oO6Ef/XpDX7vo/fxx195jDcf+JhP3QE8ip32ifwc7exax0AyiGcgYWNd4Q9AjIQEwsLAQj5jmA4yhnUAa0/emy6016nbKYlAHRYYLZ7kQYA/x9gBOebnlGMXEAR3/47GwxjQjOZMZy+Wy8YIEJhMLW/gEkDQsgPmyX621lS3Ga2RwZrRsCFQ5em2Xu9sRUpZo+CsTyN1XFQDMWOY+JlQ+rE3WG1pZT5KgLMTj1bFq96JZFXM1Lq+EDn4BNl6AZmxjH0Cbdo4AwUJywiACyFkJuGzNeGVJeCWGFeRsBJjiYQ1UQYHSyBsTFhJGIaNy3EOKed+LEE/5cUaVkxcAYSsJ5xhmgq8klHRbZm610EWlKbMjKU9GwJuGARKDESo8yGAbYHMTGEGFlBmChYDBBqzW1z96mmMfSX3PlC3p6MqZ+0urORA18RWx9C+dw7sswWVnYGzQ+7lWpBQsSr+Hd37jQABEV0UPvDl3gCDt5uXLkmHVij/T+qxyECQ/d6bsQ5my1D+8Vce483X3pbrDSF6A2V/TzIH9jzzvgCokhWjacbIvCoDBcYkmAKNJCDhigGO7eIjVC0V2i48crQa2V7pVzOst88uvTwEAkAFBuwensLL+zCP6x0OTowZAi/jdl0BP6OgzR+weLcxA5Y7YDHxliEwgOABAdCGEOZtlL0iVWrlN7DEWNHeV1G80UU9X4mNo2yrUrQErK49JwCD1Th5cODbYFRmMh8VPzsH0PqxKHRiXaGRgSUISAts75WlBECyOa9iUAZtA5IAqqjxxMTzOHgqllR3yO+0AUHZmcQhh2pikNDNVQxIzFg0FGEhj4WDhA85CBsTpBdvABYGEGWa7hKDTrt1bGdS5styEwYyH4k+Ywc9b5jvpAZKnCeuAII5NOQ8KAEExejbglKLq9DGjCvUTN0G4MrVbbQQ0hXuVkYOyFkmMjQ6JusX6qebAr0BhnvWrDC7tnCOKqgND5ELgw4WnZs4pR6sADL7wIOCNgSzV917Awza0sWhJijNVhozxsaAwsfPb/C7f/Y+vvfuB3jzQS9UP7WlYw2cxGdKsoyHAg+zx0QOGABgXeNfPFkaGjFmYCGqaF97RLVgkgGRBqnvAYUWCOSaN0KJTVerllt2gqDmR92x7dpyzA9Iu/ZMXM8/f7bKmhkoOaeWpwcFW+JqmmELCowOb0GBTyZsE+DaYh6yxF776VR2DEAHBrz32gKCmuIOVbKcTcGTaYqaYQ+NiQcnw6ZdZrI0D2fU7z0omBWiMgbA2vZBFpmxXAYRAIBNQiSr8s8hAGEjrJwQiLBSQgxRgVhSuSwVS2OyBoqhG4UWvOxtlszw/XIbihCkHyh42QiICQskCXS1xTkg+9iAUARIn2GnGDjwC+nkFfYmbdL2f9vay3cCxEnxIc0CJFDpJmEv5UZX5lg5r8AzPu34K989kFrNVqi8RC+NlluvPhjnHI7W2fCOhl+oqmYRir6/k6635kbvkPoVLw0IpMlgmzmkXmdazt3jC5kCK/cHGEwapUruQA8YRg1kyyh/X0FBfoRrKFtP3j+7izftVK+lixgoCBKQhEEuhikbqtAAhXxeDRYAVIABoHJvX5G7EgcDeR8hUt/P/W8fGvDHKhCgO2ZU2ui+VnxIabYcabftjJgBK4t1t0lwQB3vBgrYKB/r2lFwqvUlD0C9hEZ6dYgA55LiiNxUu0H2vB4XINBnz+dpdk45AjVzMystODiKCtjUXVtvwgyhTPMT71rOA4glyS0iyflbkDBCYkn2Q5D8m0A5jHOlORZ1XkfYTUo8Agix8z7qayNKqMem7Jb8DtEhm3qRlvQZGTm3Y0uoQjhe5IadKhk6WRa59vu859rmO0ndywMS1XrJM6tArUu8uJgFbJb61rUd4oCXoIt6o1kb8lmewAwI5GvpMv3uXyc4XZ9TBpp39SAgEGVGYfi6Bw7p02c3eP9nj/DB15/g4WsPD8feqNwfYIAesfnNlo6ZNdD186f41ofvDWMyXg+ESUfxQ0H+uCHQKJqqY+URHLS+lAGD/KMmFk4ZBHC3TZV31k7HrAfwXUdjU+9q3+TcZrua7TBA3iP5tPc+M7Oino1A+Z2NVpUEOu/d7N+vpT+r+hjNDDUGJDAkeO2UWEEA5QTRJc6T4KycmUZnaxaUefb7c+7bKVr9EsBzDwtwCtY2cr3b9+g9pzrOLQdiQ2MziYFhljn/C7NMiyNgCRFr4pxrU00JDS7hMyD/fkUp/vG00EL7n0363GsXaxM4GbXTfn3hFk21xxs00CbAAXXbyDmDUg+szkjb5CVWcAMMwm+O6QT6JMLZCBmNrZepg46YxUMAoBfnc+HF1ej0vXqTD43m+Vn5LlbPkR2KNGcL5J1KPe29bPPm2Q1+66eP8ERBgd2vrekRWLg3wGBoTNDvs9KhOQKun93gWz97hMdfG4CC5iaR2vu7TqMdpnyC9kRH8sWBBALyB1IioJqFKsAA+GSuAhBKrahO7IIfjDQdxLtVPPUa87NGh44ZhzNPrUsVy4OBBLE8/r3d7jJo9QQf6yaCxDA3vS/JB6As3g1WGhmysqDkKhAC9cbIyijJzlPVPnzQAgC/Mt/eSn17S/seJWABPeUq+44BgS8Ve2f3dVOFidBN3ZVzPHUtibejL4+Ol5LWVR1TQmIFBahXRdxbDOnSdjpsI/ttcXByybihNupV+M21hW+DfBy9gcv66WDcjMKu3FAUnWHp7tLP+vFl3/jX4OOSUmOcWl4jOcwYgAIAjLIBwPxSdDiwVd8IIQcUuDkdsHE8Nt57rMXNsxu89xMBBZZzN6ot2312usW9AQatoIZd2n/FzAXICYTrZzd4/ycFabUCbWUY/L1HX09jdwxoPsEKTMi06u5aUQUHrqMZGnVfp2tBgwcPwCi+23sKVkZ9/w42+ZQ3Py13eF4u3pMhytSpzPyQe9t+gDNrIMapLDhliVc2DW/RXDZmBmLAuiVNdJN1ABChi0iV2HJi0vUj3NoSE3bASr1Cm/M2JwDA5mgvAYfzs89MA21pV2uOIyBwtslGWdftN0j8Koqz8FibT7MPFgL8d0ZyXojmfOwto1wvKX7cbqM2y4meNGZulhjyanu2z9rH2iaDM2sLQgYhrSGcZcyfH1eDE1/AsU93GNCX6KHRO7aGvxwaGH9v+Kc6Gzilt7WSorM3dfA28yrAtrqaggSpF7V36d6Vhr/lXtfPbvDoJ4/w5GuP8faDN/J+AyCEy5rv3gKDkdGuCpdJM9fPn+LRT99XpPUWxA9ve2BzD/3kLDAGA/Z9eikjoDAPMcgJ7k3Ike7VZ35tmU6qgEMblmAKupqaczXazME7W+JLutuLWPvjJw5ZzgFIMICQ1IMjyPcADA0k6PQtAwKqwC2JDFClEYMsMkWy0FRMsnYEs2agO6MDuLyDgW7xFPNstTYAmQHwoYDRNKx2gZYjFkBP2zX+o9a76xrxtZLj7kAV77aX0NOmny9XNyjn4wAVaDj6jLn/VoU9J03aENhvxzMr7o0YHJ/bYXS2eZDkWYedBLkRLd7L/LhdLjvYll+FXmh1cqNTGWOP3xv+HR0t51ymp2WfGnvV0UAqOpp4ChLyLbNR37FB7n2unz3Fo5+9jydffYy3H7yl77iV+w7Ah2zPy70DBlIMETrDDdQdR8v184/x6MNv4Yfv/gAPX30DeSJ0q/Da/IC0HXS2Fig0Ha/N1Dnzbr5OjfGXaUx1x7RzyOPpqiO/HEN9WDoQ4o9N6jC4hnMHH1w7iOPZKW0cL7MILIAgEel3HaAnl9UoEcq35qexbpbpW/WHZcrCL+0a8jMRtCtD+nUgbJ/NFrhk2pVJZRRvhXtOK1IrL8T8nCk7/XDkn7Fb9MVWtmTUrFgPHoCEKH+5Bw2eaVgTV6DBnlN/Yrjce++VRm167vPOpS2rufTkAQOdSpTbdZqKIOv6j6YJzKYO7MYQ9jzsl1doqk+bEIB3zFogcIFOLg/2Y8eM8KZAIKCsvU4gXYiCSUEuMYj02V7fnbBB1j7Xz27Ufn0fb7/6ZSCt1f0YQUBCrtt4Fdm2/K0AAyL6XwP43wP4nwL4u8z837hjfwjg2xBA/79h5v/y3F0bNDWiiOyYluvnH+Obf/Zt/PA3/wRvv/p6ESowNma+k1vjGTuQn5EyUKAWOHiEyqnUecQi7L2pr1v+XcIL5RjVf5vjQwP7kkv3DfmmDv2z5yCm1LcGPsM4nu4nUA8SBgABwUIOmkWekJemTUlXoVODIvO5Jct/tHaENfHe9+jbRaGKITlH+e+txDbyHG1fK/KzRn9soEf7jhXq2YTVc3WrQ2JlKO5/AMuDB5vlUy13DUyBw2h9EOC4je09L5lT77/I2LIE/vPNHhD0YKA4Lof5T5W3fGBsR8cGOuysXrtTGXbCAeAZ1vGAIdh7bOuEwQxvgtFyRAyo4S8gwQACAbwpi+CMNwBKq+7bf6/r5x/jm3/6O/jhV76Ht7/4ZQcoyv2IE0DyzVmp27lR9bfFGPzfAfz7AP7I7ySi/xmAfwjgfw7g7wD4r4jof8LMW3+LQXGdfErva7l+/gm++dG38aN3ilCrOP7RI3cAAeAQqQcEBgb2UKvrDTRRsoSRtz82/q3GHRrq9pqXUrz/vlOnhvnw+/zgo+q6USwPEq9z92NJDuhAwgggWMKbhQIolJUoSancirbWJrWEuCsGEEvrHa3aN5pfbdtG949WXZt9qvfYOADoNM2s9Fe0f30CYRuuOSqzmPcssarpwYf391f5evuZPR1YcNvtJ7SZC+Az4ADU4AG4sM31xyi/42iBnSNAMAID5+PoOx52dR7QgYrZ8bJjVz4Xl4kR74z7tE4tSzKvX61vScMBDTvAuo/V2Kv+Ed2jQShlCtyoLgAhd1ae2x+t/82zj4v9evWN5h3NltlIkwyPDA4Arfv8ff9WgAEz/7+AoefwNQA/ZuZfAvj/ENH/G8DfBfBXxzdtQgfcGurirV9/+hTf+Og7+NE738Xbr37JXccZyQEj41s6D+UAYz3YOkDgjtlgrc5BeTZvhYUAkNfNP1toNBeqMfgEgOJshuyLlUvByhB123Y26A1waEMmRvmj0HY5ludougwSTgIEW4kyeC+SAXC9wBRQwEKWAwNXTXp+66UPV3BsPP7ofhNZopk3AKWfHSp+2z+pFA/k7GfFFJpU2slej2VzmNMxK/sAwBkkbrLCO2N1h3ergGNQgcqXBzx4SNbuXH8auvQDDPrBfF7/oFqn+8FZEFgBgkmf6JwZd0xkukOxu/OqdrLr2pe+oxF+kZJ1aLWz16M80K17Ncq61cKZMZZgFRs7oOwkBX1msEqhMAjGYAaAkj40FNszqW/t1H6Mb3z0u/jRO3+s9su/cwm3uqWpUIMDQBzZ+fv+m5Zj8EUAf+22P9V9XSGi3wPwewCA/wFOgoKE608/wTf+TEHBF1/X/ZtrlAHV7ktLqVWMRGoGDw5BgQEC5lQ6q12fV8sZdfZeEe4OtVDAwOczJIFukrYNosGgkh/OJDThAaHeTB2iUurTWJ5ae+IJSOgAgjw7KEAI8MZfQYEDCX65aqDIMbmYdyXciTfcZowTSrzYlH15cwbSql86TEDa+jyWUd9r+4dXPLl+RbZdjop5HObK2j41qNYGJkNubunFMYOLUyDA/r36dwVw0fsWD73uS7mPuXdlIhkrBhxizO+XGsYhfzLaPb4KYfgqnegXUsV+2d1TYOBIhllGE53lrs/nehDQhkbtfOfYAOicm06n2R0vdHruVEZ6s6rEHEzmonqTudZhUIqeYlTnQoBAZgY8OKgMvv/AtN1Tz+NalqO6Xn/6Cb7x0bfVfn1p8A4mbwP15EBKykB/iFpd+dyAARH9VwD+R4ND/5iZf/qi92fmfw7gnwMA/R3iWYLhHBR8qTnXN5I9ZI9eHwywuoKnQQEMFJjyB6RT2/1dBx8OqNkg21tN5Wy58N4Va2GDKms63XZAwYMGGWSmzIsxqmi6C2J5BYnzhEUIDiTIczuQoO5wVpG6r42lZ8PYyqMBAXMvb5OE1qTK3dqft9J38r69fuVr4xR2Q4WaDLyB9DIX40gKAgJAtk0lZNPIMLfbRB7Oh9c/B2DAvzuze/+X8+7kwYG+O2VOPzrgIO8eVCagAA5Rp6j4NUXQrVgK4E59BSj9BRj1GWQZ7LIDPkTgQaQ7r0a7RbYiwx120xl/njkzZ3WYv7Ytv2I9VumwXH8dE0hACIYLRC2dAQdABTyI/TlFf1XPrF9AQcF38KPfMKe2ead8L5J6enCQ7536cTgonxswYOZ/5w6X/QLAA7f9qu47+dD5wOhAwezaqhE/L1R7EhToYOKU6o7tOsQu8h4B5iMUfddiRl83W5bABiCFUNgLIlGweow3BQoGEjSAWwxWUq9WDT/r/X0sj7dynu1n1BnBQAEJPnFRwxekhsKvKd87f0M+6Rw9vq1NHy0GL892sT7BWwEFbb/ZVr1tqoFkV1erjANoJucghl+UWyhGL4lBpAwSVnBYYFOwOIRKhtW0q8wkDIrzSjtAkBLMgFFaGzCwVfIhTuPx4wzSrk9k72X9Mi4NOCVQBgexgFiVTwFHsSSQEYHDkmVgPYAbsLRXrxGQ6oCAvl8XZhkwpHbulB2YMQM7YCCZvIEicwcAsk5q2IJOV33OuuhU2RogQKHUM7h+HKLUN9hiJRiDg5dSep2+79RqyRUycADpJ4CGFbTP2nk75d+0UMLPAPyQiP4PkOTD/zGAf33qyhmliCanwIEC7+VnGnUk3JdeSPJTYgSv+4OlAgXtAPPn7lBDfHYAjjrb2eI6GoUoYS9viHQAcgoAVhl0QeVs54YolB2VUVehcUZmB7I3Z4hcmQGjhQmivIrXlxxAIJQEHV2ApJo2ZDkmRTHsztd3sh8p6yH1bwAgA0BnDFnCBbzeije2rXLOtopSVsXMrSGctDM5IIYQs4GT9ZKvxKAtVyrvBcFAQohi6FQhWhyUwyJGPBtFB7IYw7nZFRgYymfrAYGXS9oyGOBtFQ91vZX3Xm97mRhbsCcT1z+9TCgEIC7Sb+Mi4Gm50ncVsMRhAaDydCxCSYAtoQnPsFi/KhW5sF+Z7Oy4BwP5mgEg8OdPGIIR+9KFOj0Aa8GA01Odjhq1x0vSN13ZVnfaDkiwsKUZfH9bCkBK6nu4Z7lz820cmPw8ysyp3bVfUll0c3vMrh3I/m9ruuL/EsA/AfA/BPBnRPR/Y+Z/j5n/H0T0nwP4fwJYAfzB6RkJvriXzvTLOxP65UUKkQo6aLJpKkrBJ35oY4mfa4asTC1pGacOSYeQB90eKOiU4OxdRwk6+Va7ftawkBlYu0f0RrYoLU7OMG1JQEJIhUWw+jdoHHCDb0rX6YUA/ICos4HN6Ctlm+OGnnIu70F7ysfJ9lAptzR4wwJgXZEZgJSAbQWnDbyuBRQwg7dbYNukjSzs4OsyWXEnKw2VMy3qHYcIVsOHuIjxi4vsiwuwcAEDUGMS3DLdwYBWCdUY6wKVOVPojdoIMCUnk6QyckAJ64q0raL0t1XAwLbKeFB5gRN4XXvZjBLNRoyWGfkYQfGqAKm4iMwUKCCEzDBgKQChhB1KOKYKV+QwWfld0bwn+xvQ9jlgdzbBHViCDAj0uiEo8IDAOy0jYGbPa3TPy9A3VWkSq9m+itXIlkIsRhLIBp9TKkBgFMJo2Lbqvm3uig/N+TwdwJ2DXXBY2a9XX9B+nWAKrPxtzUr4LwD8F5Nj/wmA/+SON642r59/otmb35PFH9SI5048EtJQcLN9qQMHgDP+u+AggpFAEWWWSwsOgnzChOVTbLBpqLIdOwBBIe6zAwNAMByYF1J8DFQomuw5uovz2JQ6ZxTOKSfHkt1jAA5mnbmK05k5ahWlsQdWT/thIIFKnIAqUVj7jSi7C5SvgQDPBKjXC8cEZANnXrCBAA8WXIiBtw02b34WTjIFR4Ek4dNYAjVqvFyJwYtXqvATaBFPndVYiKesYMB16aq/Wiyz6/faKhUwuAAUGEuw3oocDAwYYNpW8HYrYGC9rcHVHWXExhpQqEGAAwsGoDpmIS4FwOacjADyYMGHGarfe0bCA95BafojgJ4lyL99J28YheG97wYKZoCg0jd3CSWE2Omsis3LumfOFAxZhMwYODbNto1dGoACH4I7Awpmi9DVNkZ+d06tXXfafk1AwAk2/N+0UMLdSwcK/DzP19Ea8SHyGg7OPdrPzofeX1Ao6z5icgZCf8OS6VKZrbKQKEqLmZIBAakzBc4GVR4j1+c3dnSddXpOW3kfe9cYO3BA1CfSjUDHyy4VMv9cHlDH2QDAL4XD3Y+2OOagDVO1irb1vvKqmFyFBLJnOwEC2dtV48abMQkb0rppWIGF1i3fe57LIARQ1OTKSKAQEJYIxFUMGSepT9wkpODyG8iJhpYrCfFwAkDSl9OmEbcA6cihj2V6WWV59aGVOrEyZXDVggK+/SzLLO/LAMH+TmR1ICcAQ1mZ4qe4CMugQICWpQIKBiK4CUEEC0GghBs8kCVvPIBOB7ELbR2V3eV77+CZX1J+JbMMBgZ9GOLzoKAJcboLq3tW3r+Fkw4BgeaPtO16BAiGYMDVichNSVSn1sql9msCAK6fPR3ut3J/gIEr15/+XFY0/Iou/gAgzzDI2Z8jtDUABG6fp/2MiixTS4yaQgYgGQDMAEI0gLCJQj0DEICM1nNc3JL3XJiB4uKUgWMSbLF/712MmIRLEnjsPl5+eUqiIW83yFAj87ODEKg7eUXHzYqnz0weRHMvySvQDhAcxWW5ZgTM21JmwIcG8vHW222MW1q3bNg4JZkil1JZkxcA+7a0d9W1eCkEWT1vWUCRFJBtCMsGLJLwxy0QoJDjtExB+iMRiA3UetClgHgmRwPje+1TXWMZ9pt6/CkzBUgToKBgoZUZr2uRF5BlNpSXyizLK4QMrMISK6CAuABxEVkY66LgwYceWEM1uT/n/I0CFIZepskNbkzdIS+hk++o+DBo2VlyoI7YTGDIZGb20i5Wp8Teh5kvSxLEZSBANg+AgL/G6yE7/2UBgqpN9+0KAPzlp5/gmwYKHvjFi9IcVFT3bMFl3YdsxcTXqjz/utwfYKAKKC8TqcscS6f3iqwFCL70g28kVL9dZaJmapnEhll8NntbsTYkSTywDBA4yYChAIAReDJzIS5lOhtQgEJEPl7Re4AorVmyYrzqQhB3XsbUAwFgjsxtINqxM6BgGKNz7eMQd1ufqowM1cjbaoGA/h6CAUsUtDZyYADq+Y6YAXYGbwQGNt2H7P1KPXMSYltUpiEEYZaIsUX9+mYK4s8vAKeg/a30O/9euT8NPP4MBAwI299LSxcnR2YLqm2g9H+rX/6rYQMFAGndwLcis23dKrnlBcMmcgsquxQtlCAAIS5xDBKWK2D9rAAFy9+IV+BbZRIo5PwENtZB+7clNHZTcymg0I6mrzzQ0jqfiRebYR6xnSXGV0KcynqKY3IOHMj0PXVUHJOZDbnlGzgG8y7dZdfwywPd67nQ5skZUvn3jg7izuBT1mdzdsDrrQHQa4ot0//Q7Jcd8NPn9+zXCHQ0oOCH734f/9sf/++mdbg/wAASk8mg4MGbeT+3MU8AJQDex1uGq8ChCBXAGO0qSmC7r44kT50yysCj4AxMjssVT9SDBAL6KY5ABgsSzWimrKWt5CgAtUJsDb8/NgsjzDy/VunMBuUsbgccIvOXAghmZcQMtOGBCTMAVoPjkwQ1S/6u7ID3dHndilHLCaiqaLe6DSkG9dZKlgsiQFtACoyABGwytZCDhCgoqgpIG06pg5HnOvLkRnRm23/MyHXG7gR17ufFby50sHFmVjpQkNKO7BgbRHZYkYECxZDDXiEQeOPCvKxbHZqJiwDEZRXGYFtln/5FuFWwINN0czjN9fcOJLRTcyvZpXP93IcUq3YpukhmmKiDUoVFD5hMdThyuNM7KA2T2TknwHmdYmWkW9r3BGpd01w30jv53AoIAFMw4Na0qI55r53qr96e1U8/fPf7xX4xl1lZRDVDrcen9qt5jgcF3j6Oyr0BBtfPP8Y37StT7UvbAIDz8DNabjpgG6fR7b98/gkeffgtPPnqY/x7/9k7E6o9Vh3dvG72XpCLsXK76EiIBSQ4I1QMVzJ3D/nTnGag9PhopbG8fPOMMVAAIZcNkOgl8UM3WLtB6dmD3QHplhZtB6TbX91zFF/bK6N8Aft7wBBglUTA7MUqG3CGIfC5A+l2RVpX8MZI65bDBUNPd8eo5VfakoCDtriwAkXKMXTy4R7fRuTDO7amBNUy916RV4JV27i6ZKMUJOkWEA+IzDDZb70/azLgJvXIU119H8oGJ4ISg6KG7ZIAJEQ18gM5zfdpn0SqJ3s1zMu2McIixj0sstCsGMVFx+GWxzMpI0fLlZyzLKW/KKuQZbAY29fMvDEZ5rno3IODo77fMT0pyzrvB4GFKtK+Tw4gTJhMYyiT5tfke6F3ToA765P8Gn7fKFzg9zdrFOTr7XcDAkBBHJKqfx+ECRwrwNXYKL9PT1EF8PDXft3NckMGbhVIGN2rBRvu2CWgALhHwOCb+unkt197a3xCBwj25rfWjXn9XL93/fUnePu1h/vXu3Zi9MYnZ9I7Y0MGBA5WJ+OB0ZL43VUeiOSO+6U126VK5WfPIOTqDwHCCU/OD9RmUA7XG3e5Az0y1xq1QKBRhF12brvdsCN5WuleIo9eNwIFWSE6ULDLEkxAgU+Q8zkE+fETAEAxVMc8GKBAlbdLSn1nOtxo7EDZy0UIOZmOlqsyc0Ez8k3ZyeI95in5xXwaUDBQhmVYiHHL4AC2LsKq10bFCwwGy/PZPFOZJZFBvubSMADaNnkvYnBgxEXTJFTDBX0y1eZ+KseqG2wJpLk5KQnzIhEUEgYmSHuGK2UHsMAQCRswyTS2GSx9l/UWWK6KfV/XfXDQdYY7jIXcGHGcWOtAMg9AcmEyBcKPvvOSx4dV4wwj6ctw5sD83dpFigBU34Rpjb/81FUrZ/qmAbq7QKDt9x04viB4ooCSzaGl0IOEvdK0+fWzp8f2sSn3BhjYS0+/HmiljVNZRx5SpEG+d/3T38KTr/8QD197mBlPRzTPH5U9XI3FlWCR/vGhhB4I5Mxtt7+dGsduEOd76jPYbVN0A9NARHV+/zbtmt3DD5SM3ns4IPMTC+IGxsgcKAPTnY/ufAzQd9v+biCZQcm1SQUc+CWuh0UVYN5Ual89v7zPPSfPALEQgPW1kRzdWhVWzFBlo2avlhJoAMAyGKBQJ9Jp4mFYIsKyIFwtBRTEBfTKFwQUXL2S1zPAcqVrGSxqrKPIUZmErPz8yn95CezQtYspuTwOEhQcRNjKk+X9AA4yawcLMpg2+ec7vwLwZ7r9ChDjCgorKBLSuiGGIGEZjYGHQb4BNR+78sYn52mMysCT5W0DLUHyNqDtGXQm0BKaMRpynN5W0stshXmGe6UByPvjwuu2CD/tkT3LyQkdWKh+e53SMJnmnGQHwzOnAwfl6PXa9Qi6cT7xlN15qZUJGmM9cTZ2wwKN0S/joDzD3tZrk307URcGgUxn2RormdVxYGhku5py/ewpHn3423jy1cd4+Npbg0TTcbk3wODhr71dhH8mlpOFOstqJQEFP3kPH3z9Cd567WE1i9g+zdqWmpCsbpdLIPtyYGz0p2MHgHrJWKtzBQT6ATycsmQshd/X/e4BArfHBr1ltPpWJZZGptW+UTigyyVAPUCr7ZHCaOvnj6vMumml5kW61uvW1aL2zc6XEIAtgUg95SjGEAgC2LYARLGCMSVwIqTAIGgcmkLVnwxgjDLq5XEaKriSlfjClSbMtYAgLhkA0FKAgWTav1JAQbyqQYFjDth/P6BTko3cSBUcs5zCMlVWviETwLy5YaLrJ2wArl5R2UES2oLrBxTKrIVtRYgrwrZmRibdRgROOSHRGJkwmdnRLWo1kOtoGmheK8Lf44Wm4w769YzO9sdOjZHWOap1gmz1TMIMMHBznv5wt3S6ZbFn9nrDl6LL22PHgGCo/yt5tDIDugTBJhzAzbbVw5sBBuYfzsKObWheK7lNQrEVAJcDxihU92iZghs8+tlv4cnXHhemm9nJe6477w0wGIOBHaMxoWOsLa+f3eC9nzzCYwUFLRCwz7ICwGiRoBkmI6K8AjDgY5jyX3BgwZHs+qCUB2H30SjzhjNAmAAADICDv49Vp0WVI2/aA4627MS7anbGD9yWFm3pOEzaeVAM+Pm6W8zUjmg4QbzQBhzoN9XzMZb65GWsjWUgKl4f2SqWus5+TKBtc/koQjMzi7EnTqBAsIx6H1oIwOm1CgCALI/AGas8vY5CBgN5wR4HCBAXAQVuH1NwgCAqINDf+VsJLXsgCrPvE6XNZLEfVoAQqtAaJYCj3IdoA5Imn/IGAslUP5/Md/sZaFtlSqBbAAkpIS4y4yN+YbwOBF6CfCtAQCGHakooxq2kqACqni8fa1AM1DHvNr/mRIxbum5j2LLwx+PFM4uyo9cL1AKA5iNy+RGz+zTndX1kVLeOdWp1ykQvTNaemSWVH4UApsbfuo9t7tgDX3xvyx/O8scVGQRCxZAZA12AwryYU1vC3y3XvV/uETDoPdMRpXOmXD+7wfs/eYR/+bUnePPVh7Lgl90r6xLuRH3QH6qTWmCQG986SnWctKPE6hg1105R+iAhUvbPjX+nLIAutDC6rr6gB2bTJWAHIIBHyq29blgfoUR93YR9cYs5ZZAgAIE5SJzbwEEoFCvbI9MmeEFjw2zDRxPLLPkwJ5npOxAnYFPvOm0Sb7VQREwl/sopU63scw0GhsvHVCvjBBQDZXPo/TLIHgxohnwHCDwr0LAER4BgQqSVukK9IA8QVEZMktBG8J92JiCpx5ZWgHTRIAU3WG/zdNFq8SNOEq9XeQe/cqST9aVyBjCWtZuG6wGBgMWYFzwClemLGVS232PQrPf2644VKLD8jp2kt+H4OTV2/HZv/LuQmdtnIZ/+vud1BDBgOXYcjfb4NNHvhI3IOt115Jnhv5P+b6oVsn4qx5Ld3PY1IOHoMTfq1H7w9Sc5/F3C2l4283vcH2DQmMdhQ58oT5/d4P2fPsLjrz7BGw8eTr+1vmGno1zQQXyI07rqGBz4c3oAIef5mxVE2eXs4LJte7MutDAq7eiYUf07FGErvkuBncXnKP+nBkjjqRVIcACBA8p2zh2IMKMn9DeDoUBg24QCt2V4SZgCJFnbP+cNpA2Idj95hn3zgAD4tf3Jjvvipn7VL+ooazNGQDFI5rWqBztfwleuT+7DQJkhUMPUAYJQ5GL/Wk9q3DhaZSoAgQiSEVMBBDWIQcFC2sBJprtJUuIVyH1YyUIJ1K4sqfLnbVWAVpbmJSf3YZ8eyd33WzcP3gMBOa0Y/ikgMPaG3Iwct5Qymu8u1CyBbRewUIVzcl0vdJIaT9Ru5WyUK2NGoHvGWZ0A7OqF0b2PtitdnIY/p3rcn7d7zh30vQEBHujxlLhmlm2A7YjNSrZfXyvhb7uFgfJS5je8N8CAm98j5GfFSOO2PH12g/d/9gg/+OoTvPHaQ823KR3Bd6aKMeiopb7zzIoLc1amsTby5WfuWD65pxlo7bgbveuoS8zvQ819fF7G/MZtXtde/G0vX+NMqd6xMj6yg3ICaE1jdwCBN9iCVNlI6/lsn/61a0jnFUv8oGRi52lrrAZN9+n98rzug69jsq4g15WMGJtFowB0K0naIjt2jvvoD1OQOKXzeD0Y8DkE/nPC7ADBnmc1KrlLNN4QgUBhkQPG1qQVYFnMiwMD7tPLzPJ9B+YELP1HqUjlTJzq6aR+YSjXFu1XAC9qB22L3dX0HBjw7ZHX66Cx4c8yDw4kNJ96rgBBw96YqEeg7c7ja/T+ALxeqMY+HRjPgV5o69ZFJUbtNLj1JfcZOnaVvPpzz5bS72u6176wI98fk50bhEkgrS8RITmqYMb5XD+7wW//TJzatx48rEISl5Z7CwyOFFabWvb02Q2+9eEjfP/dJ3jzwcOcpMTN9RVQqJ5RgEKLLi8ZgFZC0/X2jb92ruF99tB5u4OHnaiz/QdARJ57eblUTv4Z5ZuJpX6J9Jw9L9U+4JMBAuknhf03LTYHEMoULkoGEAIAzrRguwgVeQrbrRlxmntsDJD8LUbIG6TRSpGcaWVnTLwHqnkD2QNVI1TNPEDDDPB8XOwWluZIkHYivZ+1U6DSPrJmhzIrloibv6mgI9NYA2N4mIFF2mc03dTPwQdQT6s7Wu9jr8zaKDMIBQgAyG2Up+gaW5NlPgAEPrfDA4ID9qZlPXV3VV5k7J0to2ccGfyhsR/t3GmmUSbU8BYHdbmbHpeSl4KmAgQCSN5FjXduGxbdla9nzgABGC//5e3X6w9KonxgVM+o3m+n3vcSGOwprlH5+PkNvvXhe/j+ux/gzQdvIblzZ6GErEMUEPhzevppXM+9Mvh6wS6TNGfn+ifuMXnAhGFwF1H9GcKuXlMwcgfk2hbfuT0YkHoZP8A6oJwBcnyaMQnmpbIZkbQJQDBAwOKllm1nZJh1Cl4xQgwFAUGGVcnnKPKa5mnYmV3jeFoYufF8ZjW7c7iJRedzmyS1sx5nCwSGY8HqfoEbReoO5c9/ABV9WoCCgATKklBg5sBCYXwcYMjt5OLh+Vwu+1F+Tz+W1YL0i9qwbr87LZ4zYgjcGv17bdW20yhBrgMJl7rDQyGMd7dGuj2tzpnub3JknC/pg3vnvogIfOsXgkDuGNTAMwQkEIDABSBIvcpGdm50/0iFtvaLHZDIAEP7xFn24N4AA2AfJc8a+uPnN/idD9/Dn7z7Ad548LALGQxZAnueAwWeHcj6CeiUp6/Iyxh/L+tm0/7Scb/jnAfAAw5utvvrqkccIBV/eOP6mRmEmKFRAFADAs4DwwZLMTxyD6KYv1lhRkeS1Wi4UiWAxhuFnoOBYSll2EzDJMwdQFDtK7+75VezMaq3vTGxOiXXUWce5ggA7BmYvWLY0trEbmwArkTHHeDTthKaXR3y/EYFOMgfBx7cts+yLy+SXNulOUDIHp0XwEk/sk0EbNpqtr7+mMEpgCBhDgZaxyi/rW947FPkR4Z25nAdU/ju9851w0rt7/5bKTTY0JQkFxJQQ59fXhBvstCxtxnunORu3urQkf3KpQUYXOR7BBDuDTBoQwedQR9c8/HzG3z7w/fwvXc/qOgXYNCRcRkoqABBAxI2nwntHrQddPWzOmhWtoNRHncMdJsTFN1Q8IY9uh43GizACCD0SLitCk2ut0FjhkFoaaqAAkE8NdJBSazb8mgZNIQ+zGDev3me3jOFtV1jRID9hhpmTLfG3xnxkYHP9ymmcGjk/XjI1ZSzR8r4rMGfepQXIQN/nQOdun+rTjOg6fucnj+4pZwn7RgaEZe8Ew8m9BkOAHSAQn9ntoCrEf7i7d+2/R2YHA8Iqm3XH3z+04g92NWBA53YljQ5wZ97Vv/tifFFdNmZcjR5Y1f/KRhIgABYO2jjkQw0qOH24ACqw1w/tVclquW7Z7+AEobQqzM4AGq7NCr3Bhi0oMDvHim+T57f4Nt/+h6+95sf4I1XH9a3mihI/5y2tM+1fR4U2ICwe9pA8AOg7fCz/j9bP4x3eMCjwTQq7QArK8XJvSzdyJ/mr7EBdjGQ4HqHdeg84OyZ6mXm40QSx9PBCf3SZYADBBOA4ONwQ5DQeZADozArDQPQL5giT/WKHxjQ+JUBsP5Ug+B8nZel7d/RBntv8WJq9vzDfL37U8qevfocJ+7qX2OUYAxFaNgJOeCvmzETAKZri+yWw35x3Cd2AQH3+U+tI9NWtYE89c7B5pGhn+k3+9nqslaHvQy95Uu32qVd4zdcndtbRSKsBlaVFjD9Zitsx9A4Hu56HuybFeYyS81YB8CBArVfrYgMRHjmrXrugUjvDTDo4/q1VzQFBQ8edsd3y45GIhrfp65HDwouHSw9eOgfejTYyr2GuwHIN2hum31+UEWU45WxN+UapHfKeTwED6t7vg2uEXColJQpcy40nY/ldXE8+ZEBQhVqwDzMABQmoV+lEjhrStn97ZR7xhzybYAjb4/dcZPLnnI/W/yr+fecMTWj8M8lTtpwnJzwXM+94/jI6B0zAEABCgTHOsHlpBiohN+OJSwVynM8K3Gmxg3cmDJAR2AA6AGB14mj/mL3aatoP0eG33v3Zvj39Jjpn/ocDyLccwe66khPteUWPQDIhr8y+uWcFa1To7+5vt6/e0wECrIvgmpDjrG5eBEyg3lsv0bnka++1svAgXutYbk3wACoB4CVll4xof7xVx5PheqLCFPuF6j2aMj9F5i7Z9lJ9tG4rr7NBbPBBJQBdXYw+YHUMxnjLpHae4yQdSqU1y1K54suIXHV6+zzDKue5AHDyqxfqnMdOD+OsxPNWw0WZlSdxfIAS0rkvDjUCCAAYxbBgwZggOxpulGKimKU53Lk1dkxOy/xmHli5s4jO+tZlXbQbfQyzm/m3l/kYrt1TNhpqnR88QpwDwj0oL7xWpt399e+TBkQkXt3zu8eqACIEXjYAw4mB+uLp6gX9wqzXKmzANKuawHBTL7AZczmEQAo96iPyzmlPuV9z+mm4HST13UVW5RqZ6T9/gSF4snltUmdYfefl6EgibERtdGVd8Iw9FDsQ1/avlQdq4B5fXQGCkzO7fnGGtg5Nk6PwMm9AQZnGP89pDUTlAixdCBrtBwPypy1goPM26AssKvgIAbCpt5hBGELDE6ipPYU2h4ouGSg+cE1IhBSWwdbV74TTj2tMQTCxkUmOQZvRsSMfKZcVfUS6eCUAbdyGchRBblpjCCaAnJUndwLzdQeV32lE7xS3hQMyHfr7DrZ1yUs6v3a2Q+jUila1F59CwBSY/xZn6FkBtYtYQNnNsmOc2Ld5oEi3q9fzG2hSk/f1YM12Sf9kcLAWFLN0lgyFVASqrxisnO6sTkABJ33yj0IGslD3v3ly2QmjyWGLIuoLJgHDZbMKoCh5M60YQu9xW7x1d/tV7oxChnsylg3zgKCPTBwBARa3WR66WKdpCUMdgtBqTrLefuml1ZnECOVunmjb+9AqpP3QhIeXHpw7ccL4IHiDsB05/s+A5Q6f2z2690P8OaDOnxwlMDNqPvbEX6+N8DAyixL9iwoqBLbqmPW0azjaZIIUwcOoAqTubAFyYEDgLAlRmRn+BLlgRjNGOk6+gYcPHvRlpES3Bt87YDbiz37c+sEGf29cR6UQQ19AJWBuDEiFQYiOuVhqLwc020UuYyoui1xyU/QXu9jalblHGtjNzRMgRhA0EN5LjE7RgLzQdcm6O3R/gmQjzGiGL5Vjd6aDT7y75VZFa4yB2CkJKAiMbu2tTbq65dBrCXegRDMeAXS2RiiJM0QLiFgDdIuKzGWSOCtAARjUDL+dcp2lBzof5c2Kf3Yd0OTDXMNCNatBgNrSg4o/erksyhoWAJlwCC/5S+IYVkBIbQGgIfhiSKjc33MSruGih1vWQKT8QwUWNkDBWfYgREgOAIDVTK3e889XeTP76dFy3ag8owQqErsy+/L4xAE4JgzHxIFMlC0YyO2KdeCxoDA7j9indrn+y7xc80p+BNNNLTjJwmyXHyoY6/cO2DQlgDgpkFao3N8MbHp96zyTk8xE7QxCeIaM5CIZZ4yYwwQqBiFJcgHYkgVIHTqFScowyDKEIpmFwhIsIGXjWum7Qsytw5jX3MPgXoqjmiKxvdKRuQo18u9yoD0g9EbDk7cofM9VH7XzGKPjrNnC004NMqbmvexucQAOn0zktNEKbdMQGvsVk7gpAAAwg4UcCDySEm2xdghG7s1K1TOsvfNWoElOMNHavBCa/yAhQICKSAgYENCTADrB4JEgxpgs35eK70692AsQr/TxBlQxocHTHJOAQUr14DAgMKaEhIDK6dKTkVud5MTIOPTyylAjH8IAg4ioYCCGBRUibFYKCCyhRXYGQceUshitPiYQnDyAeq+5+U6WkulufSissdo+v0jFvMIFFwKCGalZzULa5CZzGzU5a83+HK8AIL+2AmGALgzICD3/HZMfaJTEr//bu/UpoM+cybBcVTuLTDQoZazNz3SAhpl5n83HawYGK6AQgglpkrQ76crQMiAAMYYcAUQVL8ioQAEAAISlEHIIAE1SMgRUPUsF61bARA6MFWnB+Pzc6DYDz7SXT2iHg3S0cJFNiCL9zUejECLvM8PQjlWD8RS+/LjDEXbxtnsLav33dFPMyXcAoE2LFCMWWEERgZu3QoIMMN2u8nfLXH27BLXSWFtMRkFkt9RDd1VpGwAl0BIIWX2a4mkNA4BKWFBAAeSfhjkeUsre5P50NjVJcuY5D/vDfuzs/eqCf8tKNjYAIMAgjXty8vEdCQv68N78gpEWGINqGhNWKJnFCTEsEQq4Qcd98YoeKPhp9yOZDsr/m18f5wV04k+xGl4xPpLFeoE22rfAg4g/dU+Gk6BEJVRICJE5orpBOZOiuVreQfF95m76h9grIM+DzBgIvRgwK47Chn4vBS7xvSXr5usaPgevv+ufPsAqNu4ZbhH+3M9Lyj3Dhh4mtIjrTfd4g8jpSZ/6/15ALlzrE2i3ogBJGMJSBqNgQ4kWIKcR/NBjUcGCZEAlrSuBcBGwiYsOkg56X5mIBIWpgql2zF22dF2bFOA4JN2WroVKLhhdyllq//BYAQKDWsymw1ET9EB/UAEcBoQjGjsEXKu2Iyd96y8LvNAMQcDR8zAmkT2Ztw8GDCQcLulbNjM0KWUco6FKdPW2BVAYDIDQgi4ikGVccgGDwhIYCyssYMtIERV6oPE03xvFOUXB16QPH8kyCJsY9eIC21ts0NiIKyD2BgnBVJObgUcpCynz9YiwxeRmcnJgILJcNkClsAKEhIWCtiYcAvCEhyTwAERjCVxYRKcd1lmw/RTbq2cIc1GWMB0lxnmfB/H1gAOlJhec6HOxcCbhfISqcNhDokwlp7NNEdF9qNiOWdOijGOdf3nL76nd4C57pFjdBgikGP7QMBeg1DGQt53IRgoeqvW0bbM8WP9dg/cebnNXeN7x3UmvTNhBOAeAgNgjrRGIukMxwAcjIo1no4bsCX+ECltXYxIjmNDBqI1oAcKlu2a2xvQb8ALwFggJ22Jsbg44GIxeq23BwuLhRfIX6P318D6xkCMNeixMqP22kHr+5pH5keo3A/GF2UGMoXtlUZT7zODYhg1aMCAnfcyAEFr2AwQeDBwu5nny5mKXcdNAwBYSGPkREhMCEkM5BIjgKRyFdOxICAFztJMEBDpFYOssUM5fBYaUBAdLdoquUr+Rn0TMnDeqHin3l4QEcgv1QZhwKpkWuap7NZtw6bhBC+7I7kBJjuR0xrJgYSkAIFxFUnCLyzXLSyGP4GwJmAJmj9ChA1BwzNJzxeAkGfVMHKo0eRU+u5AjjvduDgwXOm04uAoUACy95/bBOU8Y4ZmbCYgTEJONG7YzLJfnRnA5SSoDoj9TJYX1TnA5wsC7NkzIJDrOhgPVVgBvUNqxwIJKPjtn5WvJFbFHE40IAGj3jIuT5/d7B6/V8DABGxI6wdfHQjVlRYM+HjP/CHyZ9GLcxIQUcMECEw342HeQDYopos1edEDwM4QUbkuRoJ+pw9XDLCOAkvWAnzikMQGr1A6Tx6gAXqspqbq9Q/KQM97Rp6kv8KBADu/XQRplLgDYDog3eYpNuDMR57OlFn2vEljL1FuFjIw2tvT32sSo9Z6ui0gWFlitCkrWOctuJdMgRAYWIiRWDxdJEIIst1+J9DHZwMkfk4K4pYgdHgMRdnZcQEJNJ2u18lTx4gYphJ+069M5FFgrAFvBA5KWzMh2pcxjWGg8ju/OxsYgIYV0lB2I/mtUFDgZLclQgwCBBIL87KlDYkDUhQgnighsYRlEmsiIoJ41hHAlsrndRlATMOETjPYAHZne7RldGjIcEL7dB5Mdd8GSj2MDbX26NjMJKhi4TJtdkk1mwkUfWMMKIDspNjxpuafu76x6470jXc22jwBO2e23oW/rgUH/jk2pnz55PkNfuunj/Dk60/wsLFfrNeoCq9Awtli9vEBXpuec2+AgQcF+dOTI1DQNEILBtq/bblWpGVef6CCtmuGTEZWxQYwZwUxnVfMyDecfbGxHcycvX5RvFe2fwoaaDoFSc6py2jBkeniIThG5ACmc+VbADAy/ntU6x0xQKHgaMYa1NujRDmLiW9ANbOgBQVJPd8zoGDd0hQQVAmlpvguQEE5IZFEwS1RFNeiCXYLSdstMch+BQVBwYJ9q0Ay+Htw0JbMpIEzSKAklHxKynoZIwJ57oYNCwuo4EBqbAlpJQTS7H8HFs6UPfkhqawdQLCDV0i4BXAVAz5by2TXJci7JaacIAokLCFIfkQO7REii6e9BR4mdFqp6H9XKmB84l27exhgcK9tQECeW4CDd3T8NcVBAcAkzgXE8HtHIzOaoXdQ5Dhw5aqW9YwTxKV6Rs6ZGH/9MTP+9ujWk88AwK4nf30NAvz+Snc58ODrM7I37/1EQMHbA1BgPT0acPMgwZ84KAnFPv7gq0/wH//4D8cn4h4Bg4AGFPxaLdQWlc0bxkaBw2Dam6+fPcWjn72v+zZnxGqzZs9iquM+yfIO0AMDe3Jt9KnkLKAGHHKPc8BhoZLg2E5Vsuvb6UpWNjitMSixOXZJPsDM+I8GppXZcrd3Ku5VKwqTTDGXfJURYGDU8eoMsjwQyyDAPYtL1rwvdq9tAv/b2SWhVZokWfMhUBVOCES4WsTbvYqU/wYiLBSwGDNAAg6WIMeXSPhCjAIGyDL1BVAUgKAKWHDpLutWgLP04Y1RGAEwwOLZEQNI0o+/EC3VTe6wIQFboboTByRKOWcCedIqcLsmYRWChNVs4S2oZ9vKbyZXK1sCgiZhBjfXTdqTkDTvQPbVYRlWkGhGclEJ2QyJif3W32U8fC5jweMqojwWvO7y+gWoHR47biFJz2gintUx85d42TrmyPADc+Of79HUpQLDg3Fwys5oefLVf4m3X30dSGvVEczOuCW4qnplqVo3b/TVJ39TO817XebeAIOnz27wvovJzBQUDX+7RjJDUAGDhOvnH+PRh9/CD9/9Pv7+f/4uaNPFgCnA+9hMtgCKLXJRuqmycf6J+tcl3/AYHPhrynHtIBV4qB8wAw/1fYCrQOV5qMsRTTWf4VH/HRl+O78dmMDcyJxNoGlLNyfcDSD/dbOyLoWcNFvVkjBPlKsWxYJ4t9khIqH1ZYqkGJMYtA8kSX4rLmTIC2dZfHyWmNUCAomHU04+9KDglSVgoYBXooCCqxjwhUi4WiIWAr4QS8JiDDpnPzMMCgwyQFAFmkXKQ4Vna/8byxYApCQJiEkBQWJNhtVQFxLwb8WIW/HJQSHiFhsKCFAZBaCAggIOYoCwL8RFjoEQdmYojOQof5G3bV9pzyYkQ3U7zfrsCBTke6D2PPcMmNu1+7yjYjMS8rb+LeJqxomes6db7O9iuUzu/gk1a9CWWXb9JU7FpUa/fs7g+OA+o7+d8R/ZFn9cy6+/+mUBBYCzH5BvwFhlKKd5Z1xnz7WmMuzKAG4a+3hEsN0bYPD+zx7hAxeTmTcWcNhgzQdzrp9/jG/+2bfxw698D29/8cuyPxkYaKLyw6+noTSwob7cc1WBkdayjDv3V5F4c2AEEPx1Sd+8HbTcABE5tzGa+d44XWY0597AlOvqwSfXjO/lz5+V1iMpOo0qA2+n+aQ4DxIOV7UkMWYyVUuulyleMo2LNd66buKd2mBbk4CCpQr2i0ETQ7zkkIJN59pI4rTTleCcF2VGbIkxgwMDBEsQhuCVhRQYCBUuwCBkkCBT7SSXxu4prEINCLL3xBvA+lXC0UeEfH8n/VgRxXyPlIS6T2whBaGwAyRpE4tcc7slxCWCKCESsCYgkIRbZK0ByQeQZETC7RawxDoZ0ZJyL5Gln9lhMxSiMi1LJAV/yOxLCBqWUTla4i2R/LbcGhv2bYb7MLvdjZ/Pb+w02+53P3YGTsvAMRnd6/PSK7sGHcfHLzb6wL4dyecMzp+V5G3K5mxGkt/cgAR4u1OApj3x5tkN3v9JbR+PnL17Aww+cDGZQ+QGHIIBOca4fv4JvvnRt/Gjd/4Yb3/x9YzkyBBdVwbR1ZoXdL/DcF9u2OZ4tGtMm9RvlwFEh/LzIDUAoburQU0dyvfnnC2zxL+RsjpSaiNXasTy9s4fVePWD4Lg3j1nYlORS06BI6BduKpdk8Jiw0uwTHSt8sZgTUAj5jzxWyjqpB5mEmMIVsNVDFpi+b2lkNcryAv1pH5IB126r3i3ZS7+lRp7mWcf8hz8JRJeUSM2YwlK4qF50S0gYCBtMobShvxZ4txnKslr01D2eBAiQAExxJynYACBSJiDAGFX1sRq+CNut6RGN+B23bCEgM8SYSGb7RGwLvo32WyFkNc0eBF5+rUNDBAIEGjWNlBQ0K6WaGsblNUSRTLhABC0Xu7I4L2c8dRPHQQwHU8zp6Sc1888eBGd8vkZeqBmjnNl5byGQS6/e9tCtQIdvNG+WSbfJ4kA3iQ3jQGbV1qDhCDAfGBHrp89zTkLD18rU/YDYdgfrNwbYPC2i5lUJnOPxmk9m+Z769efPsU3PvoOfvQb38XbX/ySekV6TerzaaVM9uf4kN9X4180xwpAGIAJdz01+zmzEDYq6mE0AxJADybsmkuyXjto5BUVdbumv5uKTApVS5sOGM86KZT0JCoZ8pKAJUDB1qSQW9QLV7UAAUCeZrZEArOsZBmjGDIiyVqPidVoMZYUBERAmIQlyEp9K4kH+8pSr9pnhkzqP5eFN2J+pcNirNS7DYUhWGLYDRtMAQFvIAUCAgw4MwYyhhrF6fovUxDvhghIUa1hBFEAhYgQ5GuFMRE2IqyBEZPEmVeSmR6BpL63W8IvSUIsV1vCbUhYU8AaEtYk1yb2KyPWMj2SJ4ChTG31yCX63wYScLh8cgztx5bKTI8RIGiNvk/09Pus30v9q82Lxli/VDBVF1Wevzunc0j6W2ewcKbM9MgofDLU/YDT1ycNfLN/38hPrh/d/5LC3oZoG3OoQQIKGCAuIAEoQOH6+VM8+un7w0TGo3JvgEENgGpAsEvtOHbAezrXzz9RUPBHePuLXyoMgQEMAwZ3afzRp7iABijo7WkQUW6vbwAGNftbYEH+muY78NGO0aie5wa01rzZTGV3M4iOYm67pQFE/t1rwFRWtrMc0ACbBVJYg5w1jwISdgGC5h/kxWo0kWSJZdXDJUm2el7whQUkvBLKWv9rSN2SvtD6Ze92AJLsjf0KcKMlfEtooCQWWuKhGf09hiACgAGCZMDAAEEqQKHNqK3aikrfowBWQICUCkDQf3ECELYkHx9bEyMqQFgTY10I6xZkDYNE1SqJKYVuiWmR7Tm5Wr9pl5Q2ZqD9lkL57sT440tnGQJyxzwYqLxiatgElN+dcXRtMjWQR6XRRzzQRdGd23hC558z1R+cD3V6fc97r/rjjkEfyKLTT6Prdq4/LI0MzdawGfucx6Y9hBlQHZSPO8aAWOzXow9/G0+++hhvP3hTjwf4jJa91rhXwKCmghxCnHScvtPIfg8Kfv2LXyrsgG90zxi8KFqcAQUMGm8WI2z3u3t6Kq6EJ2rmQdVgdz8ePW+nvlUZDrKOV9y5wYCK7m5Y6llAQRgCn+JhZSK8ToQzg9GCBKoXrWoXrAr6Gu3yyLSEvNbE6CuB9g0A+QiNPLP9OJBIYZ81aZd+LV8GrNfx9x/88eyAecRtDkFUhqAHBP53Ki/MCR6Ue4Va9zsBBpS2GiRwFBYhyL9o/1AAQgqS6R+JkZQR2BjYUkSK9Yep2kWmXqZsofI1ILBQmS8/+iqlLazzMsHAEAjsOUTMODaavhyPv8rJ0PK56YyRcR94+h1jNbv31LgfsZT+3BfU88oC5N+A2BYKIDX2pTYBIHtvAwiORVCQcP38Y3zzT38HP3z3+3j7wRsudJNyHeYpr1LuDTCo6JdREtTugCjnXX/6c3zjo+/gx//gn2n4oB5s/hl1pzhGnuX5d6SYdkqH3IGhx0/tsYpNqJkFuQKDgX9ykLdPn7332YE4q0fLgrh4dl5YxsCCou8MFByKHoEECznMQEJUgGHrT/ivKi4OKNiU0QQU/IoeMADzr9mNymwxlzaOXT4hLGwAwScR7uQPpFX/JgUIq1SsZQzKhw2sdeqmc/8bcGNNRARFAQnBQgsLQFErExHCghAiFsjiXinJUrwry9oOGwtgYAj5sLEwPfYp63UAyl5EvvYKHQDQV/QhgjNAAO54CwZ8iMCDAQf1Mzjr8qUmbGj18iNj6o8flQEIILxEHTEz7COK/0J9/CvTw+VoW4Oyi4J7D4afcWPZX6zbcn4BCHonAMBfPr8pifKvvlHGJgVwNZNnVJ9S7hEwaGiyJplwHCuqO1oFCl79cg8Kuk46oq4uo6n0wPS1Og97dt7orkN2oTf+8nvAKrTn6PU9i3FCCbyMQThiORpmIBsc3VfYEjNGBgact6qGShSyqGYDCQwfcqBmwarScp4pAKBgoQx8DxgAZHYBmpsA1NfaPY9Kn4Q1MUaOGSAFCpbwNwQDzGN2INnY2howIMvg8rYTYjOwGaMyPArUaJU2SJKMSEEzsZU5IFo17ECgsCAqSLgCyfoBgAAElaHlESQ2mVPH5rwMOfu/Ju/cBnBBvR0QYPc8EyKYsgIjXeeNSwUS6nPzcf/XH7tjkbF5V70w15vn2IBeZ57Vo6My1qP5zs2Wef+DcK/WcahfR++Vr1H9xMpvGYggQmnjgOtPP8E3P/pd/OgdAQVs1wKw1HO/2PYeWXZ/gAHQI2b9PWUI3D4DBZZTMESkdm/5UW1TN8jGHbXtoFmRdvfXmg6ypu88aAcDlULfSQmivPPjuoHxAlThXcohaCF4YFBNBc1AYXNAwYEEbBWbwDnrlzJIAAqbYIu+9ItNWdPP1p1op41SfY7vFpfoMDMm3ojseZ/KGoihEYNBm0skTElqXM02UIYAXBiDFgxwKn11r39SAG+r9DsKDUhQ6pQXAAQKJTnRgALhVj8JHRE0BCGhCGFuNgfgLLxjJtJvVzK/g7xbmefDDhwA+9nzng2w+5HbNWIFgMbx6ViBdn/D5FTI6A7OzcssJ0CB15eVrvRhqhM68vRbHOnIwTleVwKqLzODTaUCWb86gGCJgyg6odyIS0eqfqcCDgCAqCTKv/M9WRwJIjuGPzflJxwxOvcGGMxyCS4FBb/+xS+54wcD5BAU2EAedO5Zx67yGPzvFkAM6jOdKdGU4Iy+/fDGP8Sy/2hQtMcn511ShgPNjnk62rx+oNDSZrApAM7Iy3bS88TVLyCBe5Cgz7ABVOacy3bMz9U6+vo2A7yb/sWNAsDLmfUx9TA93ZzM2DeeZXIMQA4TqPE/AgPWZ3PG5E4/1L7HSoHyFnqQoDFWJAKHBdjW0i7KHHAGCjUTFEPMDJCfjcNZEiKVGSh4oXaQR9Xn0PwwdfvGnvwsLNqBAb22AgNe/zVA4Ehv5We40jkyl5QJYOwMu9eNwFwPviQdmEvrAFlf3VDrQZ+/FQLYlsd2jFiuYr4l1QbdGIAOHLh3a4EAMLhGc+L+/Dv40TvfFabbXT8CB1qb+nlNuTfA4GJQoKViCl59XXAbu4bOv+kcep6Agl1AsKdY7ZluXzWQdgbV6dIa8VB3/PrY3GiPjg/LDExoXXhb3eaA0YgR4FQSbkAgYhhQYBtkLUigwXkkSF0wRAETFVAAKrBQ3j0MlLs7V0snkREzeaJv7c7e6GLGXPf7PcPhjUZ7HFz34REYyMm5E0Xt+4S1bf5KUJyCBDlvRWnDkpdAbh+M3dHjdZv5EFPThoOcmizOM8B2l2K2G7Xb43Y+mkYt53h9VgOFLkTQtqvu70MJ+3qKsW+4uzLy3rtzDpyc5vjno+/cOjQzfecpfatTkC+U2jXFVBRbkXMJYxTDTELiV+BgXDH3c9w3236Z7ddXvis5BYDUDx6ANODAhTVm5R4BA4+2zlFh179Qob7z3Uy/QBEZmTIlTdjICK6hr+38DkxYZ5hU9wgUNIAgnz+ga/lSZqEtIwVnaNlOmSBmOfcki2AGogX1O+CiQuttPC4bEJtgKECBiB07EOQmXAOE7jwgMwkymABYsk4VyvDsxbwMY5rdvp1+OqN223OnMdeJFzhiudw5LQgAUIcI9sBru3+2L8S6fzula+3Nm4JA1/YUoya2OaLdMTxlOfKGwHesTy4zUND0xd18m+rcvWN2rxNAAk3faZ2aDiykug2PAMGkrdmM2kwvWTmrXyaeewcy9tiCo2fvPd+XHf1C8vEL25BnhhJSrc43gBCi1NvAgF431Xv+biMj70KWVX2N6dTf7YyyMShoyl4IYqfcH2BgpUXXk3L96Se9UA0IeHAAAF02JwoA0EYSs+TOV3CQv7EeoyhbvY6CdkLPSrTlABTwDEwA4G4An0DWXvGpZ0eeTpsNsK0GA3LuYJCEvg5DRsK8Snte9jqTGg2RGwUIjecBgkvYkek+HiAk5FifJe4YQADKuRkNmWJFrg/5bnUmvOTP6zzBk8Z7eC7qa3KVD6jfNvbahLHy1XtK+UUoXH+uBwiAtHvjnVWTjXboXMCp1jMx4PqMZrMo4y4mOwEdXTbiHisxGhttnY6A4qhPNYZ/xhCMwB8fhYR2wN8wxn8m7n/Soel02eBew9KFPU1flY8TVY6P6XM1+tnYt6zX4BnUOC8WFtOd5Vgb+rTjI3Danu/uf/2Ln0v4YA8U+Gqy040n2LD7AwxOUUqGtD7BNz76vZy9KcV5hiQKy2I5hJT3ZYDg2QNDjaPzbeARgGXR+KzeIqCAA99WNr87RIn9GogYlQYUVIPIy+RUbNCdEw0QOCbEFWoMOFdUG5BjWV6Zb32nbE1MpR4PkLqIXX9vcNQdKnScvxdXMQ6OVvNTg/yc4qM+NfLcPYW/F9NtaNxOSQPZiztU1O7YCf/pfDkTFrrLuXulBQpWdoDH0TtPj7d1bvJs5G+j8BsGo1L+B4qf7BmDsNS5sMQd+tuLgoKjUKa/pq3b9PxxPz50Zi7Nb6i+0Rzy/ckAqdcrg5Lb2pJg7feoTzSAQEd2Oe8IELT9YnS+7stObWW/LiitgzIo9wcYzIrP6oQJ9Xfl2wcP3nQnxuo8aVMx8MyGJgFi10Fs4HkQQLGgO07lfD3ORMBCOkCjdHY1VEJjQTqexW7josbNKbik9w7B7ZOEMQpxjLAnxa9bXlGtWwMS/CAdDTK5QT3oQ8xKIQ8yH3IBiqH/PEqLjv32iFbz643vWZs9j38CALzxz4pY25iZ5a9nhuw84BwjdFDIK7+BARyGihzDUx3fM6iXlp348jC2PKKazxqagzKUkZcPhcxaULAZEVSMBZUciZBBwwwwHDAOe2U3ZNT8Hm2/zDJLnMYdGYGTYGCqtw4KeWej7fsUShhB19CYAYIWIAJQQHACDDTHq5ABIP2qPadzav8F3n7gQMHIyL/AuLy3wICJalqVSFc0lHmeD197c6z7VZjZcDND53zJfS1RKsTO6GcvND+XpiAhf8h3cfX0KJ5SAQhaLwqsCFdBREqgqB6z1T9tIAUTgAMJXpe7wXY4qGJrBE4a8TaON7t+lPizo6C7fIPm9/S7ElWRwE8FDhoAOb5sDAQAzOf0cwJvq7ZXmfrHKQHNfra1AratKL609UbxTIJXI3O2faYMiUrbUq8Ec+zVKUs7pjeot9tnHpWR0WDOLFl1nvds3blZXsCxzNpnDkqWEeDA0UBmJNMl8yyJEIC4VGCB1ZBQXCp2wSdNlo/fBJSY4yAEMa3wJBTVFheurPSiD2Pab9M51g6WdG3t3DKYlY6qwX99XuzBwUFdAYi8B+Bgqre8vsoe/bjPek8fIaBjCLzOeVEwcAkzkMdU3Rf+Utcp+OFXvoeHDxr71dq7ppzNb7Fyb4EBUAvj+vnH+OZHsiLUw/aDEqNEoY62syUodV+IYAMOek7vKTqgQLFQw41BYTt/CTCjQkDtYVqnVOVIoShBTgkUUXnf2VNvDB6nTZ/j321SBoZ16nmqTMp5EzAwywA+AwbcvVr6DgC6pDS912F8GKjBgd9XbU+o2gEYSGr4s9FfbwsTsN4Ww7beZsYgG8GUpK/Y+Zvlmlgb7hu4HHMPVOLrauBJ/zIFYBOjR0TgmIrBS5umYrQgrvGQ2/ZoZTornARze+MCFJZsYER2AdSO7AAZR3eRHaB9y8CR+8vByW65EqaOCLRcFVkvV9LWIYIVOIxBQpMoCxemzBXb6ZvZmCqvqNtdErULa+Yxs6E4FyYae7Q3+k2okHwbtaHOmlwsVfb1d+1bHBk9r22iJQx2ujLLJRgBATu/ZQbs/BEz0AAB6PTXCuDlegwA3skQQQECLkRlOQXPP5YVDd/9fs10e7lZCPUllPsDDHYUUhbqb/4J3n7trek1PgO5TvxyiSpmYMNSgIIZ/tlKY80+zyrwwNiwR/RghCYu2MYE8wdtgB4oxKXyuCgu57N7RyizoZFfCgBo9w+MzCi5BzDEDmCG2qv7DABBW49RaT2yGShYb0vbmPFXQMAKEnhbFRwoaNjWYsy2NRsxTtLOvDGY5S+AY7bAFF6UKXwUCaTeEIXi8WZvNiVZMAhQVqnxuMyTisuQMt8FZQey7Pq1Z8mIQFh1Hnv+r9xjU1lm8LRWQOrzlCHFKAyBAYRNV2ZcFlm8yWSlQJ2WK2l7Aw2cBDRkRR6aPBinK2aybBmuxlu3bbmn6iEX8sxT6DTvKYc13T04JflghpNJJTs3brKjAkzDQUTuWp+s3LTHkA24UD91QPVFgUDraFzKBjT7WjbAO7EcnVmmgOtnT/HNP/0dPPnqYzx87a1mSr1dZNtxLKtR2dF79wcYtMUSNfIHJX6QQUE9BWlMPQsbYB22yT8AgHhVGICG0vPzjflompHty0ChGJtyTI8HMfKEfvlZDxbyIjb+uWkTDNPOcMgvPFGWI8W0Nz3xEsPf3H+6ZoFV0SV6sb/WD6ozYKAdELMwgtGont7cYQqGoMDCBdsK3m6LQTNAoEAh3cp56XYrRkwNW2q93W0w8PVbuSbDEEiVYMgGLlxFUGKEq0XsLCXVTz0zkK9drkQOatQoLi7pzpTkQFlOy6BfOwDMJp+syEWOxBoO8QmJdp9UGIJfmRzDKnKkIOMtSN4NxQXMCRSvVIFLvWm5kn6xXEm32TaVIybgAMP+nctUqXsXXcEASiJ1lScFSD+1OihjCSCzlgDqtQ1MFZrTYfut+P1pk8frc8o5NXAg72v46/17jMokLNmFJPfyBJye8TkhrNcNQcAoN2DETHoQ0AKA9hxve8JS7NezG/lKYvPpZG7aML+HtrHsb3X8ScCA+wQMOmWvSOvDb1VIq28IZ1S6e8b6DCpncbyqGQAUFCfMQg8IasAAYMYwNICBK1DBRanGq3wtuQHdAQZXHzqT4T4rO6ED2R4Y9p38gvmyywND754/XRa53VeBvr5/dKWl4iy80NC2ovAnGdRJ20/p7ZJLsA1BgbAMjLSuSOtWGbJt1Wdkj7fUzdYWCCbfFaAYwLoo0xYlJyUuACMAMYGTGDneNtDilFiIQIygKCAgx8yXK1HScXGAQMIQthRxpSwbRdmVUb/2fVpXPAw6tVfCHWtOvCWs8g5qdEXOCTaOSsglVaBgWy2ngzOYn8rSJtrEkOUIbIhLBFIAB7lHWMwzVSCBBVCGg4C8pgJjEU/Zz6qJBjTbaZSh75ejpMSq//sbpPq8zniIs8PFWpfrtC5Zh7WhinKj/NjpcsX5nvYzdfvMMck1vMv6K8DY+TgxawD2dAO28PIfsAAzALDHAOwZ/51jCGKWr5/d4NFPfwtPvv5DPHztYWWfyKMpKu3InjGgUDPfF6zreX+AAVAJ9y/996h/7WFz3IUGBn93H2FLqoJK4xDcrAF3lwmjMNtPHgSMVquzayzcMAAMDIBiuZYc+9CtbubrkjfHnWe4eIf39k8Y+T50MwEA3bkzpTgx/CdCBdOwUUXFeVmEIud8zwC0AGFWrM38VEROhfLeuAcFDSDICw25NkouryRADFr92H7hFYrqOZknTEVB0tUXQIuAASxXAgiWqwwG2L58aDSs96pMJlk+rQyavu3Zl7SBA4s801oAwip0PRGV+P7tL0XpEQmVz/KPYgSnmlEY9Wfe0ilZMhKIFSBgQ1xsnMs3NTiop88CRAhBQzP2pcOTUzgrAwN0YMvOGci17scnKPgW+Da/uT1npM9G22Hu5fsPB5/59kHZdVIXDfRQZ/wBgAip8uonAGCaB+DBwb7nv2v4BwsS+98bgJtnN3j/J4/w+OtP8OZrDzuTTtVfW65d2CHv4PiPJvnecf3sBnvl/gAD1yDXz57i0c/ex5OvPcbbv/brdoIcRw8ERuumtw3RmsUtoZY0oOuiW1MBmXEYsn4tWOASOxoBB8c2DEGD22Z/rovpmrdEcaIc7PmzMvSynVzvYtzdPebb++fvP3dyT3+9TyIFHFCzUILus22YByv7mOSZFADmgLzcL0SJcaLakG07Mp6UkSE7KkaDUySEJaLkGQgDkJmAV/4t9/sLmSUIr3yhBgRGcTYAgb1CpdArRaAGqj4UYwDXPumcgtw7rQUgLFei0ENETiwLUf599t8VLx0AbZI1HxYgYQNSQEA6L3IFBymlwsZcUlqgVTFmVHI2KDjquv4E9aGX6u/nCrdAZOR1N+P78HPLO8e52e7P78eU7SFjPHfqOR2xO/qgM/z2e4/+b43/gdc/N/o9+zwz/v33U8rP6//vDd7/2SP84KtP8PqrD2GfYqjeWh/VfsiLToCEm7+5xjc//BZewwPMyr0BBtZY188VFHz9Cd5+7W05Zue438ltZNNZAei65eycj58L0loTd0kyw8UB/YZnvXKnijA8USjTXAv9U4z96IMqAiiMQZiEJ9wLdrkR7e+zZc+g7wze4Tr0Fxnzywy/PLM+3k3tIRQZmGLQ9StIY8ClF0SYH0QBQoMuC7CKIcNyVW65rmLQmCXzX1kDUgUTrhak2xUUEwKi2EJAYutIoC0AEYhRV3xUQ9MaLqO+oYlyIRBoWTIoCMsiMXEDBcoMULwCXb0i+69ekf3KFggguMpGmM0YU/ltHzMyefgxBsg4C2r/TCZlU2YY5K84pgXlM88RnDRRNt2CvhBkBse2yri7/axQxtsteNVx9AoQ44p0K8xBAoAtIAJIgYGkCYXGxOzIU7pCkanlGoQl5oTEnGdgBsbCMllWoWJgYOct8gXJLEOKDlw5irtiY4A9Ruawj3cXMPozJgjqBMiQZ86djiq80B17Qf0z8eCntH/r9c8M/wUs8+wLqa2UhqJ0O9//2SN8/90P8Marb+W8GHkX53C0zyEZZ2Y/hkwCUslZ+Opj/OGP/3FfES33BhiACNfPP84xmbc1JuMb0DMDHgy0n8kFxsPj4+c3+PaH7wHQNQKHLVybLU80t0PZA4sqVNbsDBQzcKgpJH2gggMwOxBQWARu8hu6tzw56OXBZ4zykYc/MOKj+07vf1D27gUUeVX7PIOjU1PByLNNPPWdNmUPAljnoVPagAVKf9/68Sq3bX6b58jbKiGAQEiaU8BMiJqdb0lznBJoiTlhLr+hS5ZrE+WMJQhLBGlYAFevZMNPCgLolS+U/VevAGEpDEGIkk9DMuUuAwSKMBiaEoafNR42DaTfE0m6XQiLhkAA5k3kaLM1eANtt+AUgbSCQhRWISj4uv1M2npbAPql9JNtBW4/QyCdQaBT6VIkUJOI6JMQqx6zI1NLPhRwECv2BTozgeJVYWCGYRkq4KplCTJQ6EMzux+BygIubzJshz1GcHj+TmuO7nVo8A8Yitl9gQPd0zMpncfvaf5Klr3Rn7HJFR+y40jOpHZG+t979wN8+cHD7pMywT0jpxXoWMqVdkyCdzQJso6P2McnzeJ+fbk3wOD6+cd49JP38OTrT/DQxWSOAEHuAG6//K2b9uPnN/jOn72P737lMf79//NvYOu4oFLaBt0jsVvWYQQQMhjQg3ZOsKMUs1c2Aw8CHFLVmysQ0VVtRyGcNOw9xX8hIPi8yoQezXFhB7QEDOgQIxIWwZJ6DCCkDRwDKOnS0FevZEYg099rEAOxLaAoMxR4jeqRyqeFY1wRli0nI9o0uwDk/INhcUbLT62rGAL7awbKcgmuvgC6ekUMVlgEBHhAEBZhDqIwB7LAtFRlYyAx6zZ3Y60rWWlJxnsIhJCEPYsEhBARYgRFBkcBBdCwggAE/U0RIa0CVOICuv0lOC6Se7CtWZ6k/7CtebaC5XMEl5y4J1MAWa5hWVBNV9TFi7BcdYAgz+ioZL+AjSUIhTGYAoI2TAPUxk0q18j4b3c85WY/E6J8iXrniN6fGn2L0DWPb53FGSDw547KHXgQfPmLb2FL7cfbi0Pl9T8z63fhSCKYXHObFmq4fnaD937yyM1u4D3DdH+AgQcF1vgtIPCN7ht82hn0x8+f3+A7H72P777zGF/+4kM5NujTAz/0sEyHcRV2sF98ABKKR1YfK2ELo5yaRwz7yKzfzN5ytH+KnPcMyAWl/d79qHSnWPjGNsmq4lkDZAaGXUzcQjb2GVPiBFuQhykCQWPli1D5WNcyt31bQWmR8IIBhKtXZBqbzlQQintFaObkS7XmyVjZYJF6twY4BkbL8gg8IEgjQBALc5AgqREGBjIoSNLvGci0px9TVT2p9NMQSKZOghACCzjIIIEQw4IQFnBalSVwAMH2hwVhuRIZ3n4GeuUL4M9+KfK26aHrCqy3iMuKdtEo9qzBQKbyt5GrgQH7rYsbVQyBbntAAA8IRsmbIWIIBgxgHhi89vfevrbs+Dgni+Q2hIlCuUS3XKJzRu+evGLH2OAfOYMVp3okYNxV788VV+6SjQ1g5hzxJKIcum4Bgl8iKwF4+uwGv/XTR/jA2cfahezLvQEGH3z9Cd5yTIHlEOwxBMPO0hisjz+9xu9/9D7+6J3H+NKrb2HTA5vrDDPAuIckrWwHnWrGKPhM32L4dZvYb+6CiNlz/Pmjcuk7v7DuwY4yOVn3eb6H7TI5laRR1lDNECRQ1NkhMhVNFpoKChCSUOGkND7Lioe8raCrJIZrvQXptEVyix+RW9GPmsWshsWvyqer8Q0NVgYKr4ixCguShQwcCOC45PDBpo9dE2MDsCUBAxsYKXFmD7LzxaWtPasW87Q++S8kYw1YWINAiAoSYhCQsAQJNcSw9AxCWgtAsDyJdRW2xtaPUFBm00XJrTKZ5bonU6CXqy2JbKGDlh1QwGAzOUAhh17yvSh0YYQpGFCqu8XRnb7yO4EhZX0U2672j6VyUblkvMr++b2O6n7W2Pehgf4elzACZ0v7zvbc0TvnOumfAJJ6uzCBgQRgABB0fyLg6d/c4Ld/9giPv1bsoz1y763uDTB4S5HQEUswyy0YdZiff3qD3//offyzdx7jy68+rAahTwo5l1wy2HfqzXpKCag7lH02OFTHPRgo96iQamUcG+rvVN323+HFPZExI9DuGuUglUHl6lNdUy6ySQQlt6M8m6DTUh1rU2XVKxggZnDYCmhI9SqVzBsoLCA2VsDi6EkMmRosXtdyXftdgEnp1u03j3ZkrDQ0AIo5Ls7xSkGB/qaIBGBNYtw3FmCQmJEY2NjAgYhjTTLNdgODkxyfMQaRCBSACAIRYWGJf8XEAgqYEJIwCAsDMQmoWILF9S3vYAPFNf+WEMImIZK05sWlyNaR8EtSt8srH8kVqGSbWQEFYAYUbJ0HUEDaBQME2LK3yhbsAYGxfspdI5eRofTnlvPG17TX5etf+jh+ObpmDopqMDAKD4/ld/zMWTnS0Tlc2ZzoF4w0nZS4Ps/eJ+h01CpMkCtNtR5jxtNnT/GtD2V2w1sPiv06w7LeG2AwGjid8ccYSY5AwSef3uAfffQ+/tlvSPjA38vuD8w71nRgtmj05Pt1RS+s2tgbekcY5cMGEqrz6nLJl8p2q/cCKHsv7wKo38mWkWkPWOefgQFTvTKI6nPzAKUaLHigkNmEUJaZzcyCzcmHGCCbTspmjHgDFpfIiLKkshmvaiEqk2VrxGYru7WGKtjCRA4MKKXNDhwkEDaWlXETs7AEGQhY+ED+rYmxsqyvb2wCJ1ZQIPX1tbWJdKShAgqECGEFKAALBSxM2IgRCIjE2FjDComxBZIwQ4iIS5T+vd2C4yuSR7BICIeNeVnKbAcDYpmRmawMOpXt4GM6OYnQzSxgxwBMwwQGKCasQAUEGl0m1a2Nnu+3RyDhyJC2923v2d730vJ56ZZR/H+ki2d6+EVxTx+qLDcOzW6rl4mimslmbcuOHnD3S5DQG/Swzfih5ppEEv7+nQ/fw/ff/QBvPhCmILj+ZPWblXsDDEZI+kwuwYwpyKDg1Ye7gOBsB7Rjnl719x2FFM4kELe5RrHppn4wVnRudVJd4ZeZvnT0CqNnUSOLESNg120oCZz+NTI6rgaYggGHJVowQDp6DTD44xUiJ4mHUztjhDeN9xWgwNnQ+1yFlIGCnYOrciyDDQB+BOdFqiola7GQMsUtAwFLbBtNNdTtNlyQEks+AQQcGDvADKxbElCg565bUrbAgIGztYNwFxEjEjIwICIsMSBiwxJI/sWA1bEISyBsCYiBEbgAihBfkS+CLzKbgZPOatApj/Yvh3tepmzhqf8CDnzyIAe3XQGBmAGA1WAW+vQ1vCgmPjD8FS5o7jG6f/u7vUd73awczJO4qHTPmtRvpmeBXtfeRc8Cta6d6dlkP7Tk2wxE4btdnp5OpDkFsv8IHBARPn52g2//6Xv4k3c/wBsPzH45/XWiGe4NMDhKMtkbTL5U4QNNNGyfYSXfc3TOBBAUb0q39YLNI/ymcrtr6zWVijtGFRAqNx87ABX1ffbR/l09ifa+seG5lOToUPjmdlkopcuhIFl93ocXCDqYWkTOvA8YNHjun+3BRAEL0eWBWB055yYYYJD5+86KOtBQL2hVEiFNFI0A9W9JUCtrrddeag4X+JkFCTmZcBgu4MIOrJuECAQYJAUGsi9Bcw5MfoP+YGt3BJKcggACEWNhVmMfhEHYEpZImUVIO2EGSVaU2QwhajslZQ+UvRHZqUxtZUQPDs7IVrd3pxJqqGDGBNgzPAiY6Ss0x9r63RUAzBybM06NlXZG1ssa+23Zu++ecd+4rV977fmSl4zS+1fG23cP5qw/bTGtDBDs+SS3CSgyNRaByAPqscPk1eDs2MfPrvG7f/Y+vvebH+D1Bw/zMTSMQt4/KfcGGMxotBNgMBcDBX+kOQV2X0FtXE0DAerGrJ41AAVHgMDuY52W3eDrO/q8SW8xHnAGCG5dd6DGCLegwpdLWUBfxTPX5vptNWCJ2dOUvzFQBQoskmAevqF08/zJH3f3YZdTYT9ngAGYhSbK39HU0rJfvMQQPFgooMEDgZzgCDSdetKTB9O0WsOUoAYp2T/ODIDNLLBkwlG4YN0YKxd2oA0xJLuP9p8cBXHVD4rwgmrFkCQ0EAg5ZLAmYQ0k9yBgoa0CCF2Ywc1ooCRKXADHIlMLYfIvMp6uLLon25F8bd9eToDJAXMGYOS55+OVhS8/Z9T/nUHAQF/5+1SJ1jsGeFQu1QOza9vSGndO/cmj+h2BmFZ3VosK6QpCBhZMLlEt7aaueDRdrvkx2STrj9bwez0C9LrKOxp75ZPnNxkUvKGgwD/LGIUzOO7eAANgPLhmxZzDoHTMzz+1KYkf4EuvvpXvYcbfgwNU++VERpke4sPew3oOQEELCMoxBxCqe+y9YX9sHWScxEYfzkDFi5a4c08DJ6u+ZwSAVAbDCtbrGRQA3nqgoJfkkaMAvkLjOZZnhr5SWKV+HjAY0AAaI+fpC08TVgOYq3vPwAM0ZyErilDfy58/K50hUcMutrAk3ea1BpJ6+GqsMhgAqnDBxucBQUrYZQtyaIeNNRAWIDBBFiSUPIKNxfAzM1YHECLVYQZhDhgxiRwDIU99JBhAKMxOlnEj37OybeVr2wVjvHyjP6oD3H3Ldg0E/Dl7YGAPCOwxmWeMspUz4OHSMjLuIxZgX0f2RQy/00MQvZR1jclEO0803cKiozj1LGz9APlTTy3X301HnIGCfgaa/P25goI//srjDAp8MXDiQxR75d4Ag72pK76YgQeK/f6rT2/we7pOwesPHpaBbz8q91RKgFAyHhzk/Xoq6eCLGiNlpZu2wKfiWm0SV80ijM6f32u0PKp9biZWfaQ+r2UV7lLWZrBVJbWGmarnRtjgBKDrCXnqbt1k4EZF88YYwBni7BFxGWjJv5YDfCMZzqYa+XuXc9ur61kl05wH27Xz3L0ynJbFyGsMmMdavNcyq4Ahxn8D59CAzx8wQGAhg3XjChDY8wwQ7M7rzofEdUqoAYJ8toJU2TqAEIMsQ65hhgjdp3KUfAXOX5I2xoZM5tSDgbvIt3oF9GChff+RJC6xlXse7jS0OeieyW14QDBiMo9YzBosjOt3CV1/VPaBx851e/pw0PTR61enf8zTPio220buT90UXQAZrPo6eKfBrj0LCIAaFLypnwHwxYcvskvDqiAm5d4AAyuzV/W0v2/kT55d4zsOaTGAnNRhSR5AjlUzyj2CuqXJ35+1M0GMj1FIBg4AQZpb4EJBgWUfxCDaQPAgZvq+g8NDxex2tYtrrHvGbW/kDUrcGT/+u3cecMQBKzJC53KcK+ouQmTk43om83YaoiMMupJ26o1KEe6cd+IcovnBme7Zc0KOjAOj9xLNILTTC9uZBS0gGIUNgMb7vaBY4pS8h1Ru3YxNoCFAiERYWcJetKVu+mNufzeFN3+SfOKdXSJfK3ttfKlBuss5o+dMAYHbYe0v5xdQ4BmCI0BwNxazlAtVSlWOxt/RYkOm+y4JcVSzmRxgICpsqO+DgNNDE0AwChvkffnYGAz46o7CB3vv5MHBXrlXwKDtEqOcAF8+HgjV6BYzcPlLpvbDmAFy3m0edFwSO7gwBgYelpBVFCgxOHAxdAT97LIYw2wYUQynGUlOUr+Na8BztswGz2jQzMqs4+0Neg8aMvjRnAGgRueFnuN8To7tMecBuYF3kybPlksUfeUxTg74c2bLZ59J2rprgtYoLjwLXbVTDEezC3xiYVssHJc/DLbzWsG9z4yMkvg8A0FCDysjA4RVx4aFvWzqI8BZWQO1wrYy6ycvI7F2dg+fTGuAtWxImU1pk/sePvqFiu8n28Dgz0DBESC4i/G/RI/dZbXBUSn9Rbed4Zfj5PoS7QKCbhEvuwd6MDAKFZj+9Um6+X6uPlYyKNApibPSAt8z4ODeAINZN5l5A/ZBpD9phCrefzGQGeUZ1ZbRW1GCiewY7YOE7B0ZSCgAQZ4hSlwWy7FORll5L9CBaYbSBiSVgcjcMwLyzPlA2luec1TuoqxGTELFGuR70/AcH4bwOQte2VczGgZsQT50B4/sUg8MGBtkOe7uezJp6kVKP11r/Hzfh4DSZ1JTx+CosxA0HOCOpx36pf2Sse97oWmYpOAAKACBmLHC+hMLQBj0IysvO2dmlC8z7sf1+QZQRsm0uU/tMFxnmQYuKmGa9yTsJef65CQ6BeT2Hi2DafWWkGjpR+05cq/LwcElTo71m7votRYMAMXoy3FtG6Jmf71Al53r2YGzYMAebWEuoACBDFI8U9EAyY+flymJe6BgVo7EfG+AwSXD/+O8+IMsE1ndp4LrPiRKlXe4BKX5CWXJSldscRxWeObju8Tl3hQJYM1cTQxEYHE0r4GEBZS9Oasnay/cUBpylofwMrxqX/ZCBqMyylXwStwPRn/+SNF6JdtRd0A3QNHcY7Y9U0oVKGgAQUvHtlSsgTq5Pw88sfrZM0XXGecLcj8uAX6j59uzgqtHbcTL78SYJJO4+x1UZ/RufpnYAKrDX042t9115+clnZVxWZNBtkfeZmdctBorse7rc2Ty7BjTHZjrtVnfzf4Ke+NT7mvPGIU2Acmwz0DB+rHqn9xnYdc5XTNMbr4MGRgDekk5q9daEFCu130DZkD296t1AujAALnzLWegBQKj0EC5zgFkTZzN9W3e5ePnT4f262Us3Wzl3gCDURl5Cx8/v8G3PnxPlol87WHPKJAh0bowm6E3dI0KLISs4FVxwq1vzahW0fOJYIFFATCAGPV+TFgAbDrHGxDDsyRAk/Jl+pbeb/F0Xyx5CQtebhLQrBzYgVy6NQsGsTt/vxkYsHudTe5pf89YJO/oMs89GA8KjuL0R9R8m7A3mup3tnjd7D3zmVden1+3zQxMxEsR4R3KNA8ErazKiV5eflZElQuRMDz/bMmxX2MxHPXr12aQMIeE+yz0YfkRpA6FMYgU1DCnBty6MsqN6PWWvW85t2IPWEN1pKwoBqFNVXL2GOvXi15vTOViIUfPJDSNZszmUfH66fMyRmcdkFbfXMIK+IRXO9ayAS0I6PIM9O/SHLcSIB9E+taHj/D4qwUUlDbXdhnprAtBw70CBkefMH76rP6gRD426L8ZZZs882gpiBGwuDgyKwCinPBlx2w2Alf3o2HGeMj3MqAg1MKiD/MG6Qo6eB1YAJrB9oJU9d5Uw72yN5uhp3v75+1Rr3vMQEvflWccv4flOADtrAXPHGGXh2tBwWgBoHaKX7sGQGXYTrRVbezVWDnjlQ0aASEU4Ips2KCLCNEpbziLwod07tBPZtntM1alrBXP5UuOBrY6GY732z4rd5FvLVvSZElUazNY8iQpWI/aH2LCvsGk+ueoP+/1Zd8+7EGRXpIZS/ShTcAxlwA4sDKVup3U2TCZxTIn3sDCniMyCpudMUAvqq8ucTpmuQLkrmmBQAYBdn4DAqpwgtvv62HHASC24TY95+bZDX7rZ+UriUBOxSnF0Uy1c1vL5Qgo3BtgsCdokICC951QvZj2VJofw+xOvgqOYvZWI3uFHjCU6+v5zShggalaucyDBX++gYUr2xePFiQpL2AD7OolhxWA84bh7BLO3fLNw8FV/p5dFOQo/8InlDJLnoh5X6SMAMFRsSOD5pgCAwXtFL81eXBQVhwEpD29wZolL3o5hUZuIXlPljODFViBglJVBUjYSoToliyeUazAZUl+IiPfN3XfIAeDVeNtzBpaK8xLYGnwAg56oAVgV7523Vn5moxjIGBTkEWUgUJIhCWgmnq5RKkrJ1mjIarj4EN/MBk7wGveaxufHvXp3f5sHiT6sKac55hQaxNAwptARg5W100RxcK1vHybmrMyLA0gOmvw76KvZiu8nnE0jkAAUNgAAwHteiUjAODtkZVRgmEkdG988+wG7//kEZ44UGD3tWL2Ka8ZkneO2NDiGIzKPQIG5XfbAB8/l+9RP/n6E7z92kN/CJ37t7PAALkeFhw0i+Tuor9tyiIAJDejwa9GZsAAsEEqNGNhFkivKcyCXFsDhoUKJZgggzPfONZvuKcEX6S0Sxnvle7MUdvBD6767wgEVAO2vedB1aockTyQOCeTGkAAhPZlHXwxEHiTdSmwSZuvVi/m7OlmAzYBBWviCgxsug3UlPfoM8a3G5ekpUBILMaqXt+ZAAUH/p0NOCwhVIBgcbS3z8IeTwus26QRfVd879ufRkkKCtSQJsZG+rGpJCsfhkBYkxLj3IQNBqDAZPyi8gUMJACLLWgSGGtSz9mAmLIwG4uizcbI5KTyNFkWI3PslQLzfu3beTTc/TRer1OsTWoWNDdRDiMl38Ks+qZsno4t/ip0USWiVn9gDABsO8vcydtAQOWMuH35/k0bEeq/Xd1c3w3gyg5dP7vBo5++jydfe4y3H7xRnUvuf9tr9TBHFED1AaUzPty9AQaVI0qlQZ46pPV2RlpO8A610uGqQ4Uoo03SnGx51II6PQKVY4Ze/YzHbMh1owCGAg6AHjAACjScImxBg13XvB6AEr8aFX/q5Rh9XvY64iiOVo5Rd06L2IEx6h5du1d8jgijBguWH5IS50WtAKVgY9AAsYQNLNGMmcEka0SEQEiDFG0zWlZMUW6pGDI7r7t240JxmwC8q6CUdZVHkIGAgIIlCkuwBJSliGPovnp4ZLhMzh04G5RZn00AWOl3A8YGFuQrjoQ1Sbx73RIWBVRAUEZGjPSqiT11uKAHBTP2wMvXZLZBjY0zZMGMpF43emfL24gkttLAFhGqRZpMlsHJNZjMnWcK9N7pUclGHkU3WFjTZCMHhbH0Dg0G1wLj/KtZ2Tu2XJivclY/tXI57VAM5HwGAFTXdPUzATqpNUJp7Y7ZFgC4fvYUjz78bfzw3R/g7VffgHwFdCvLcrsXJF2em8pTi9Oq3bda8RXzcm+AwchIPH12g/cyKHgLGRBow1TrpgNuFJzIZNbGa0FAqY+3ZqH6m2OEFHISo3dYufFgDRxIFdkhQcohB7sWGHi/nr49frOXXs4M/06xVgatv89RAo8/p2yX363CMtjFRIViJZWtAjEKBSAQAaT5HVcx6IySBGwy137J1pmxJo1DM4k3yWK4AtUGzE8ju7S0ACAqKDCqe6GQQYH8C9lALQoIloHBKoyByDPQmEZtFeis2DqQZQVGLv1dwW7S/RsLkF0QwexWZ6QgUxe3BAoCCkI09gAKIALWkACVvSRSFNq7sfMXlZYdK/PONayAIicDXUsIysagfP9BBSjfeBgDApM74MCYyfls/7Z+7EIL1gY1cHDX2zl5H3XK48xaAr9qfdP2vaNFgkY5AKOPo9m1vfFvbIa3LX6/O1a2J3ZGbcv184/xzT/7Nn74le/h7S9+SfZnG+Kute938KZoxzKIynca2N79ZL+/N8CgbfCnz27w6CeP8OTrHwj94j+iAlQAwRqIRiMEQI+RAUprt8/VpjmZct3YgYLqmG7bB3CinTMADQCyIpXaUYfs/eDPXXjQIV50isvLmic+us2Mnj7KJ6nucVC9VvG1SaNMlMGAsQgZIECyy4U5IESOiEiIiXWVxwIO7HdiUgAhhmtBKOAguRdJ7JiAcd1LfoH8jsoERDX+gdzfKOBgiRImGAECM1ZBjX4kMVTysSIRbXC5B15xjpVmXep+7HNrirw3peAZ1H3cKVKQjzuRftxJAcJKSZbN3oLE+Dc5ji0gBWUVcj8PiEFDCS9Jxga8TMYWnvGgoACyniUwORMKq2Oy9sZqRFVb3fZKDks2DeFfmfWc6dLPg/uas/Ki5fPQQSPd0AKqNu4/9/zNoURvQ+QFqu1qhsYJW9LVPa24/vQTfPOj7+BH73wPb3/x9ZI569bMkW0BBERJf0O+99KwCL4EkpyFPQR/b4CBb8ybChS8WRrTN6RvxNx4fXhhhuo83bNXuGESfJ5C6b0FJHTgwUITzVfdItm8ooJbjTHo0T7Qo/1ciVPv8XmVbtqVLwdGnk78ljJTPFRWuNQLR0YrMMBBAkmBLcFNm8d5twkAxQAiBoUE2gAKMXu2tAVszAgpIED+rtDZKERYKWmOABfjNXhvK3lmjDNWZpS8sRLPlSpjdbXEGhBEyp7r4sCAfNbY/TWAYFJLGyilfaAN6dPWj+UglT4dIgBJqmUAKUlcPgXJJUihfAFyTbIOwILYAYRbbFiTeuxJYrUJQNgIK6fPRcZFtgoUwmXAK7Mw7m9hai4BX8d9PJ/l+3x3tXM8ztweZ0zdUXk5OqjTJQPw5GVXDrt+O2KTW3th+wCcsRnHIeq6XD97im/8+e/jR7/xR3j77/xdYQKalyJbxEZzoYQaEEBA+jUnsRepYxAsvP4Ar03rcO+AwbWBgq/9S7z96hvyPXagNDqnpnFnqG/Q4ACuP/1EfiTXWDuIt+vyXQAs9Pt96MGBB9LzRqyDV7oGIGIFMHZr9W9wGdEczUA7iNkdPoGKbMnJjj1QSJSTtZKCgCSRgzxlyDzbQISFe8Nlnu26yYeDliSGbtXchSVQnsEAWA6C1LENMXhPtsqOR80QeO/1KobKUH0hRgkneHZAAYB5wwYGIiCeybaJ/A0QwI+rVCnRqk3yD2XBKIjCIoEZHAJiiKLQQsRViMoeIIODNUm7bEFYhIUIK0dwBH65bVgo5q9B3m7AmoA1JSwE3V9kvDdbYSZjADkvw+dr3AUQeIbAtgMKMJCwQtFrBf6jN2CXFOeYWJ8fHa/D//T/RypjQtenCcU/ArMjo9/ahcG13f4zZcB2fOPPfx8//gf/Kd7+O39P7IyGDVgZAWkjDRuwggQigDexDQoICKgZBAq4efZUneYn+MP/7A+n1bpHwIAdKHiMtx+8VVDfkCHYafS2wXX7+hd/jX/4f/0D2ZeaDpbLpbRYDwiqATsBAezBgz/PAYhcoxHo+NsoLxp2GMZCBkbogoE582Sh+0i3Q6AKJKhJVLBAOmWOxEs9ARDsI0VrQj+dMZRpd6MFetrFi8q0xOLBzgzVFxQcRDVSZrByyEGN1KLhEjIwkDYB2ex+p62MH97q8TMyWB7oUgAoFlYsRHCIQIggkt9LKH8TCDFJmGHNQIGxMJRFiNiigIJfkiSFrlvCyrK64OKuma0hcYmM95I3z4ZmPDuQgUEFBjjrr1Mh0DNlNAZHOuHzGKu/quL63pjWP+kMdtszVuAFdD7Qyf/Hf/+f4u1/++8qKDA2IIAsh4BTDRJIwxyq+xmpZhAAMAKun/2lzG7wOXeTcm+AwfXf6Et/dQwKOpbANf6ZDnH9i7/GP/xXf4Af/7v/BP/Oh+8VeqfqhDuJJmdKMxg74y8bemoDCFoA4fZVQKJ75gsAhVb5X3qvl5SfsJ8WPTFQ9rOqR5EpO/YA6sm2IGEjwsKFNUhB0wdI5qwnLh98WiCe7bolbIsYq5UBTow1JWzLeDEkYDwrAfBJb2ZYxEjZlMOZkYpBPO9FWYYYbIqiHAsWIthuFQCsklOTNrGcvJVtMLCuoqwspAAUC1tV2AGuIIwBlkVaIUQgLAIWQlCgsIDCAg4RFK8QQ0QKJCuAMuVQgDAHrLIOuNJPNK8m5wUKEljl/WIyJkIOvbRJhW3iZlBZG+vQsgNRUYDNXe/AgGM8D8Oeel5X2v5eHfsVjMHh+S+oO/buZbec6eOZwc/n9+d2zMwL6vdaH9f3evvf/l8M7m/PN+cxuSttXCWASxo3Z9clCVPw4W+rfXwTOWdiUu4NMDBQ8OsP3gAsMdCDAj+gZqBg0mGuf/Fz/MN/9R/hx//uPxF6Z3StlVHnwoWoHs6TLXdwP/vwwx6IyL/+NtiCPcWze+yOdb2EWh2AqDz9NHu3oSB3826V7g4UwCQGYmNJTGQS9mAzFiEJ9c0KEhYKMuk1iqcrU/HEkG0LupUT5ZXGfaddmz8SqedawEC07b1wQZDjBJbcme1WwgRJgcG2OjCwFiCwrcB6K/0/bQIMHDhgBw7IgQJxlzW3gAhYrkBxEZkvCxAWAQUUgbgIi7CtEm6IVwjxCgsIkQkbCRMQFSzkMEMoYYbNgQRWhuFFZGzrO/gpne0Mg5jZgD5XgxyD4MGAMDJpoLda1vM4rr1bPo+xtQvO/xbYgz0m93RY4MV1eLNDb6TeP1AzAP65XAOBUtx+zSdgvSeYMzgAkNmD6+c/xzf/9Hfww3e/j7cfvHEq2fPeAIMfvvuDavYBgMtAgZUjUOA71QRpWgfibXO3LR2OR97UTqEQ2h3lZ5TVRIQV8B1xn676lZWJ8e8YjGH9PufAZlcHKgyCA19MoQYKRPIvRAEJFBBCBJMyCUyyEJLS1xkkWCIdA8ysH+KKYpwYed5++w0GoNetJtbhwkMWox7Q1zFQpq4Xnzuw3oIMAGy3AgbSBkq38pcT0me/lP3bCl5vgaTgQEMKbIZt24bKh4iAqOyLgYIQQXEBhwBargQIxAXhlS9IH9kiEK6A8Jmcl1bQ+hk4LriKV7iKkotgYYYNMuWzmtEQCIkj0iJyfCkyBjIQIDPyjhmoGIIZGFC55pyNARDo9MyI1rZjn2s5DuNN8x3+tsIKO+CgSozdfL5YfY3X1WfeotLVnZ62REFbPxVoGYC6rgIaitGn8hfIv4fgAPL75vkn+OZH38YPf/NPZB0E9stT/fcglHApKDhTJHzQg4KqOFDQAYLWc/LXj8CBT2j0jwiTpcRCAPtZk+0MiBZQDM65c7mQCjQAk7dxIZA5oDynoZLdi5y8LWbn61LNCimMQQYKaQWg3q8aOwMJIJ2Gpwl0CcVwMZfEwsTN9zKiLvKjQKGSMtfVA1At4WqAYGagLKM+0iRcsN1quGCt2AFePxMgsK3gdRUwsK3g7VZAwLYWpkD7NY/GSg7N2GyEIMY+SqiA4yLMwbIg3f5SmITlFdCyCouQFgEvIQLpSlmEiOjCDDKbQcDB5oCYyftlypjUyI9mFLQ5A8FubmBAQdRuAqcHATs0913LYeLiIfgYG9p8dBvrs+n9Pi/dhN4Z6/rnzFmb6GSMdHIIYPtkpSUAqg7OBIHqQTZkaYa/rf+d9JkHEQnXn/4VvvHR7+JH73wPD199Xc/pevyw3BtgsJ+QcwnNJqjs+r/9r/EP/9V/qKDgSwBYhX7yXiNQUH3ezcBDM6BGHXEbrJkQ4viLJTTAgzNg8aLF6nri/jxB0rK5h7L1+g4YtIGWk9+RnA44R+3lZ/rwjKMBzUIYi5A0Xk4RpPkJCBFR2YRo0/GCTcdDXntCkt8KMGDYVEl5nC2HPXwV/TtacGjkqQYgswN5VkEbLthuRYvdfpZDBXz7WWYJ8j4DA9sqSlaBAW9bRctzSlX7UiBpVyqACnEBh18KSLCwgrEHV7cl1HD1CkARHAUUdGGGEBHiFeBBAhc5bwMZ+5UYZzIefSDHTyUczSTogEDLCvA2BgItCNijvWflzoBhTp3PvGrTbzw4NjW2vlygQ+5URvp0Jr8ZCGhLiLVOHjktAQCF0v8vMfZ7yaAnc0KuP/0E37B1EB68ATBrzoFVbr/cI2Dg4pkzuq29hILO+TSKRhrw+r+V2Qc//vv/tMopyOAgP0hVgIEAonEcqkOnDShwHXIYZhgOMO2YI1bAFQphDCwuKC8c+gD6ge8NsB1rAAJv6ynQUNX1aODwkRcDmMklbKimBmEAFkZAgUL+TToVDxRkCmRYZBqpXs9KItq6CQAqoKCk87SYkQLQGaeKss6hgYQqedCDg7OAQEEBb5JrkG5XcJLkQ96Mlu/7jCVzUiRQkATEcLUA2yqgwO6tvykueRtXr+Tz6GoTgJAiKFwB4bYkK663BSQEAQ6schZQVhZUknrOgVfpCTUQsAPB2APrOJyAxCJbYwCOGIEMBAYg4ETc+y5lFitvjT/nnzuM59607YGhfSm65MJy+My944PnVxomRGR6P221npuAgW6WWKtb7ClHCebD64DrT3+Ob/z5d/Cjd76r4QMLOVgoYxBCb8r9AQaYd/hyggMAAHzSh8V4rn/xr/EP/8s/KPNI8z1rtqC+zoMDMVosn4MDhaDjPkkn2+mEuQO35+x5ANt+p5dw1snB9ZLilPV6HPbsHsjkQZ8BkudvlXJjb4hTvo5XT9nVZQYazpears0LiADIKaFNDkLZRyUfgQpwaOft+5kPQc9jDyz0aU1t+nf1dVZjQ1sqv/OUQgcGNFnQGALiVBn/HDK4/eWYIZgAAmYFBpM+ztgkfMAEIgbFlD2qcCVKlPR+HUBISZiBWIBEWK7AdAuOV8LWhBVl6qMmLxIJ8GxlHHKA4Lx8tT+IXIuMZ4mCp0HAJFdJjr84EPCFt61/31n+Ezf6aAQCdF913SX667C+kwO/Kp3WXh4cCzAr7ewb+x2jgoI6VNknjjsnA6h1wojRdNd2oKApOdx94EDdG2DQDaBquxiVmoopgmUKuH7+Mb7xF/9IVpz64pfKfTKrQOW+pMkkgLSjHeMkjt+yCELbtjoCYY/Mc1RZY6ZuBJwFEKfLHQfHWWptVkKsni3sRTHonApYygPIEHf7bDcGON+iCe2QxfNesN6De7alAh+5T9UD2A948oPdAIM/L4MGICc4Av3sFAdq866KIZsYpmzIynoExAxeb5FaQOByCHhVMGGAICmISIy0rkNAsNc/SavOgSuAIK9G+d0oLjK2nGHlTXMQFCAYaAgG3nT9Awnp3Jbcjwkw6+Q7kO1Qvq5ew+TAUwCA63tj3G+5rc/nFIc/xQrMwMDg2q4PvBRdUt3wxe43KacMft7WOtm4DbGEx+xeDhBoi08AgdMdBmTt2IgZGORgXX/6SQMKvNLkyvYdAc57Awy6Qs6IAxjGVQiwDnb96Sf4xl/8Pn70le/i7S++XgakGX3ADXgUCgmu0Tg1AIGARcMLumIcJ+hkcTX+LsZGChCywQTknCa3ISe0XAwQmnJ2sN7Va2nub3epDHpqjtnJNjiMnvMysfsdIfdLyjRhaeL9uvDMLNwRsocAtF4CgGKs7PiAOuyM1l7du2S1FiAUMMB+RoECAnjmgJPsW2/ldwMKXrjvda+gzNt6C1quwFh1eLrlh/KiSglY1wIQbj8D4iLMw3KVQUIFukZeGAA6I9dWtvlY+T1e3KkY/1mMPp2UY2HXXj5I2A1felBwCSBodctddYi115GuesEchd1E7ZblvBgQDBiCJm8pOwx2v2Ho0j2zKkFzCn5PcgoGTMHZ3AQr9xcYACeFEXH93IT6L3KihlwvHi8jugFv8XD9670EOBToFDIjZYBAUM+AE5gaAJA2VXAOmSt1BfQswkwvnFbaM+/8cy7HVNw44xdAPfisnMg9GNbDK+sGfA0pVStthvNIfJMZI1UiXo65W6byAESUk6fvIRf3dHQ1S4aV8ufUrz2wre7vVkCBGeET/YkigfPUq50SQj7/sKSkZN8GXgFaNA+BWfIOtG6WR8NEctwoXAoIlWzHMdpp8cZsYOwB14dGfcfCi/59jspkanI3ZtoE3kv7vddT3tGwcTBiLVtms73vSwaLp8qFgGCqd1plGprxOgMDeq0HAwAKIOjAAIABe+jvzaGsCKoPc/Xs+6zYr98tiYYvodxrYLAbR7GYzPOP8zzPhw/eHGTXBlEChIpK5HiVgcIwlkhRE7CK18Z2ziL1CuZJGEhwyk7qqF6NBwq5egoWgB4wxAHvvienvYMW8niR4gYvNQZ+dwD6pMTWsDZGFYCj6/LTmoo0b6rhHmCsLHs6dCfWms/ZAVkh1jUwRaDHynk7lCF65dbV0wySr6+jgf1CRGyAlOt1COTcvu+IvDeJ46cADgmczLuxt7O+31xbATjr20H/kbt/U1LSTPANiJnIk+cak0TCwtl0RgnxUZ/Y2hrUI1nKzrou0DZ7gX5QV2IwvsKkngMQ3Pd7YLfvu34PxAxuukiKr4L1oRDdynp2rK4aW3vZdcCL6ZAd43+KMdxjVkY5T/6ZHgjY+RNmAMAEDDTGn5pzrI75WVeOMXByG7xHZb9efX0+u+ZCxubeAIMpCNjxuq6ff6IrQv1A1472pTACOdap2wCAsOR9PFqZrAEKGSToPTJQoADtToVy9IrdJwBFba60aYrDsTI6k5FbAYn64umsh6P77i3KdCc07u7RDcTRM/I+FwbqX0Lvww1IKLervKjq2oN4q97/4jLxXMrhcCovfZoMZmAAqABDBwrs2Rr6ktk7wjjkZEDrp34hrxMfe8+5BHBAQA07oADCKeBKBh4c6LQxTgoiN9TXNdeOjMiotlNAWE44esW+VHVKdX3a/t9cQ94YdWBgwC7l6+d9n32ITB2VnBMVrb8HcVg8g6CsJphz6LNlNbM+afTHxTpjIIthOTMzq7tmAs5GAH2aRAjAWOIzrMAQCBTAkHWa6fl2FkJTrp89LfarZQoatutwtlZT7g0wqMoIDLh9TAHXtnb01x7j4WsPy8pRvvjQARuFaIzBUvZnQKAgYLTC4hAoAB2jEJQ1aMFCrk8zwEbJQnlbAURb9tDjbJGljpWY36It3cC8AI3Ls2o6uGqrM4k5bckyi1VsOCtLDfvwhqwk2Qgaz+Zko+pYntbw6r3Loy8L21CjwMwT7soRpboT8811opC7O22bri/gAFCETr/LF8q5A4V/xBiUeg88+cZg5qRMDyK03mS5PpvMSKjk26z10fX6k3PcX6jNmj5OBra0PsXguDY1T7QZA6N4NfBy+r7UJwGLOiomB6d7LmU15bLSDy7RGXLPybvs9fWZAWyvmYUid5wQM/aZjdrLFRiBBZf4Wn0ht7FRHK/6d2na9fpv/rK2X419ylm+8rLlwpOg9v4Agx0wwI0RuX7+MR797Lfw5Os/xNuvPZRzhvf0A1xlrZu8fKEImbksS+lAQPZAZ9ni7nx2AKMCC4CABTsX2TSeXtZTdk06xKyjtEAg779DPsJsUO6EBzpEDvSI29fsjvF41tkltt3mhQDIyaO8rfWsku6+vTeejYrzxrvlgvfitsBQEZJ/x5Z+7+jyfY1cHzcQpm3RVWjSX5qM+lP+ySwuPjFuR+/RGXXLmajOaepfLVs+0AIXJr0RERi3eelnAM6zjporgPE7uiQ2issJihro+v8FeROVcwI4BycVWSijCbjQpzu3m83Q6o2XoS/y/glQmPaXEYviQMBwAbXWdtSJwlMQANRtM5j9Uq5xdmnECoRllxW8eXaDRz+t7RfIbI1jiciYPoxBwk5fuUfAoEdXI8FfP3uKRz95D0++/gRvvfYQjuAHAOwxoY4Bla9dU8xrpktpjD0w/PRzSVbcCUFAFdUkC7owk8WnAADv0ElEQVT6CxTwkKvSGvbxi51a7GRyz4vLJD6qe/I5FSLP140Q9rFSzItYdQfqtrJ9bV6IBwkhRsnKt6TRZEBNWyZHHQbUs30/wBTl3vSwWfG0rFck276HzVuqjZSc1Ny7Vsbh6pX9uhx5pncpB/3rcAqcD4u092tBWdsO7fln20KTHW2fATnSkEcP0tRwaG4FQtQVHAtLQDqzYjde7cfAi/b/artnMwEMGE05bqwmcOycXFQG/evyRc3GoZnRddO1BIACAOz4LCQwYgIo1I6pu392Mpu/G+Z26OmzG/zWTx/h8def4E21X3I3dWLM0VEwwCSMWnnT/x4zBj0gUKbg2Q0e/aQIdWtaJHf3SaNsKLe9TahWQQOQP9FKFJsx2hghAwv2MGUXiscyCEX4inng0Fa4G5CTl7F7uF2VAjE6q73/4B6nymRQ7jIAIwDQIXh3HAAG01L72vfy3M0LYfnyHSMBV6+AbGofFTqVsGZwcDj4LHfEEtkm3lZXLIYOe6z+ZpWFJuJZwp0YJ5/4WSd2jhJBd+PeoxUqy8336z4qo/6zk+BJPp/Dcm6856osjSRGuuv9R51stUd7ftsGvg67dVegRQGAAUUcxrsrUKBLOttUS1AAliUDgpydvhev7sbABf1fXjzLYu9Lsx2jqddUfw+ck90y6z/eePtbj86f9sEdgHDgZAxDAfq7YgH02tEiZd7MZA0/sT0AcDshWZ4+u8G3PnyEH3z1Cd589aF002x7ypuSwYTMho4Awn65N8CgDRfYgDL5Xz+7wXs/eYR/+bUnePPBQ12v3gNli7OVMurWFkrbknyy1QvaL00LaBdSSiGQojmyxnNPUzROfqBNPNqqZi3qB3rv4AKjzrPzXwYwsDKj/GfGv9k3Sta5KIwAdHIroGwQ7lHjIQNL171fhCOgdRVDrJnwpMsHE8QD5FWX6t1ZnbIDBUcydd5/XhQyqVgMHAA1A0DN1wxHXqsem2VdAwbm5izNJR+y2v/k7XiqpZ1DDgTYLAtSFscAgoAD1PIcgIKL5W/vbiCM0zh3Ip8qsqXlqsjcvgMRJGRggACQNhDjEvP5HRCYeajAZf3fvW/nmNh5ns1s9sHrzF3n5EQ5AQx2911wj2FOxkC/yLktc9Cw0Q0A8PbeG//W6fRhq1a7rgN9+/PnN/idD9/Dn7z7Ad549S2sicXesFaVUYGEMlKpsAgOIFidZ+XeAIMRQ2ANZaDg8Vef4K0HD/Ura9I4zVA4bDgDc2vD9ZBLRquAgdZmDBoEMEAZhppUd6BBK1YBB19Zt0/CFxcY95l3ewEYOPxKm7+8HbxnBqadd5A7UiuGUacfKC/P4HTsjRmZ5IzQBoRFlhc2gEAGDnRlPV0XQNg8/UtB1/G3aVzyqpQab7Wt8bbtzlHvjJIaevvEMcWrGhCYd2pgQA2W91jRUNg88lqbNjmdAFf104mx8WzNIiCN8jkMGGOjzAu5dRnyss0p5THJAGjbnOwdkKbw4vJ3QCvLfrkSUGDydn+zzONSA4JQflv7yFcoIyog0NLUL9r/q+0RKODG+M8dEzn/BfQBcBlIADBiSWbnT6f/jXRLJ9cd739i/Gf2xY6PyuZsCwH45PkNfvfP3sf3fvMDfPnVh9kGhZxwiLyCZ7D6UAEIch+CZxCOyj0CBj0gYADXf3OD938qoOANDR/kL6zppQYS7NrK5k/sY+8E6omeeSW/i9F+7Mafbh9pAQpokHtEuM0GPDQVHIQHRgZ+d+DOjg3BxuB53bP41HoS8nvH8DehhoaJO5knYjIFfH5IxeAYe2MfwglGNytIsAWAKAJBvz9wFUHLIp8oVkVOOo0LyipgvdUs+VtgI8n2TxtAqZoKWI1ZTs3Sy4XyF7GEOpHTPFBNfJsaJref3EqBFXXtWISppzqiUdu29MX1IYlZ10Zo9r0B9t93AANRQjtkKznGBbZAU5a7BwhAibVvBMaagRmg4MyqnpWCGsVmJoTJfwoIHBgzloCWpZa7AYL8LQf9rfdjAwbuA1zlPMpt1HqowH6OFJS51Ber+r9zOMvdco4NTjklzdXymKPxn08cAYQLmQHMgMaIdehB1EynzDz/lmXeY6DP2BQrPpL1809v8J2P3sd333mMv/fFt5CYLc9QwIDZDN1v+4Krq2cQKoCw0yz3BhjwoHENFPzgq0/w+oOHSIm7RqxQnRtp7edYW7s4onvsAmssy2FofOG841LgYMesBH8GAZZR7pMkq2n+4xofHh/vP8co7OupdhCfH6DAPsszenYlllbWBP18ruaHhEXukFf/2wpIyB8kCqKs7dsD5ACChRfsC4EU5D5EQOQ5QIgYx72Bmh2YGKVdQKCfL87MAEUk/baAv1cxQJoVr4DBAwcPvutxo7If4MsuxBZapaWtpjkdUFAggEHzB/RDUMYWMG+gsIB4K0zCtgqLYADBAwULjfhkUJN9SrJc+Z7sGyB2V0Bg33SoAFiwY9a20TEGY5mPjBGw3/flfQYMZj6xZjL99UOHBJg6FMPxfzYXYHb9zv6j45UOcSdVeHxm3PeOdbqpPt7ee6+YbfmrX9zgH330Pv7oncf40qtvSXs7NoAVDBARkuox9gAByF8Jyp8hgUm5jLhRuUfAoPxNLIkaBgrefPCwYgmGDAHXjTkabL7zbMyHjdwCVVuaxk81qwceV99+Bzx4cN7WACTI/Zvn71evq8v4eL+vAiTdw3raNTSntoMol8FA3Ruk/rg/Z3TrrpoDIEYA/Od1QyBQWOR9YwMS0iLGyliEtIGpBgj5uwTbCoq35RPC/q9bWKhNkBu2jEsO9LMPqpCBxa+bWLawA6EyQjUYUKNk28E+Wawgl4UpY5b8Gg+sZ4qxqvqgb3uZR/0d1WDGDBpY5SzTD9tPR7NtB9kOy1X5yJICAqy3tcwdOJMVzxl+psJU9n7mx0kg1rMyMYcLMjtA5bcHAxo8qXKiWl0GXNr363E9Cn3mdxwc724AYOiUYMJgzMY/xiT3kZ6dUfKjR86esyfHI6M/0kGz+8+KF+fGjP/606f4D/78ffynv/EYX/qihr9J3V8GwJQBAtQWsdNd8kAFDFyzB4BMedyr0r0CBtaAT//mBu//7BG+/+4cFLTKLIGrhvYNXDe8/N372nFmKRvJl2RFb+TrvwIObN8cQABO0eaT5nVqB2wp4+4xZQ8GSGEOHi4ve8gdmA9WufZYOQI1OAjGpcIMFatxAigh/wYRogMJnNxnjCeGCkmYgrBcgZdXxFCpYSKjue07BeoZD6fadS+g0KxNKByFC5yXmlqjpJ8rrsHAggQBApL8X0BAYlYDxZqnw25MoY9e+kagsqOAMAfKNN8mgMT+OrAQAiHQgriI/DmtPUjYbsFJ2JuWRSifbi5yz+CsClO8XLnLOgTCtJwFYgYGMhBz+qoKgza6ayTyrvrudxgcqBlMuducqby8nDX6M0N/wtkuzzo6uTk+kuGRwS/6affW7hk7suMi9796/hR/8Bfv45/+g8f4e6++5eRRdFUCIzDVBt+DAZRtszvGHtiUxwd4bVab+wMMLC7z9JkHBRKTAS4HBXUsKZ+Kv/7F03y/vLP/6Rdc60plR1l36MXBXVuzBM09nKK1686UI4Zgfp39cnVqzrkEqByWA4P/Iii9VnKs+ygngAYAq6LxQPI7AthakBAKazBiESpDlePiKX/dkDjJ1wTVMHHSjHs/Ba+te5tT0M4qqHIHJuxABgT6W5mB1YGB1QGBDSwAQceCAYUtsbIHLKSHY9FG/d+4JCIgEoECEEECvjRdIYL1uACCCEIIjECEhQpIWJYFBAVo262EfhQgtCxCJ3cDZy9L7i6Z0MAAQkSy2QV77IBuj8BABcj0mOmq1nm5pM+3/d4GVRs2kPP8Tcb3PlV2DPHgsOy7BAlU15278EhfVDpmdt1JUGCl6HZXGrn+wV+8j//TP5CcAmYgGXBTQ29NZuAAJPZsBA6YLcgu+z5Rp/nxV5/gH//4D6f1vD/AAAIKftuBAnbH2nwCAD3a5rpTtaDgr37xFP/RX7yfd1RAIPnrLu/RrcGOjYtP7S332IHdJ91ttM0ZBP+7vveoHmeBSSvDmfLz7TM6d3zzUj/P4hQPlh1QEFZhC/o7UAUSQogIMYKMRcihhq0BCRpuAIPilc7J5+ozyDnODch17csCjtd1q+S5aYX7RumqeKgKDjbII1dlBlJibA4MrKkAgXVLch4LcFi3VAyYAw7SBn0/yx+YVsNPKksiwhIDFgUMS5BtSowIxhIEEKwgxMD6G1gCIYQFMSwCvrZbcFh0OuPtAJxdFZDgpkFmuStr0M3q8TIHKrnnMIEmbKZmZsFeqICVHUipBgOWC2XsjAcCnqFxXfmiPg/0/b59VTt3G4QwDx810H97RnimLu+IC3bLrpwOjPzW0BAvquejC/x7yf4fFRTIM+w6/8ySdOjBQa5zCw5039NnT/M6CG/YZwAm5d4Ag5u/kcUf9kCBlRPfeenKXyso+Cf/4DHe+8k7+f7WWez+WyPu8zN36uvWQR3jAWS/hA0406n37uc7NTAADlPqkcfn75S2piPEfjSIZ8XeIxMwRm23QIGV6k6oQUION4iRCkABCRZqYK5BAl8hf93QDBYzbDrecCU5V7p1BYyu9jMKZkYpXlXswKYGfU2MxFy2uQYDa2L8UoHAqoAhgbFucl2ChhkMGAz6ly0AFkjJC5AwAZEQbhOWIMaeiPCFGAQgOJCwECMmabNAhIXhtiOWJQrLtt0Ke7MDzrAUBueuMgdRAWBulkIld59YeAEY2A3VNH3+kr7u3240BoO7VbtOy5GpHh29xLvee48X1VeX3q/V48AlurwuVa4lc9bjFo6OgTJxDCCDgktLZg1QAISBg6e6DkIJr+/f694AAwEFHwxBwV4JoOzdSL+iqtMQAX/1aQEFo0ZrQYHvQFtTgdMg093D+vv6uWBoKWcpSF9ic9APAA9iDtmQM/VrtmcMzaXAzAMwq7NQ2yQsTQMUIu2DhBBI4+QSaiDInPycoGgL9LB4rNV0PAUHbG98NX6BMiWrGKic2W6Jazt09ZbEcK8KAjau2YF1Y6yccLsaCEj45cbuvPKvAALOlPeeghcjLiAhkOYT3FIBAYEQAuG/WxO+EAlLCFhCwtVCWChgiYSYGEtgbCyATLZJWJxAiPEVhAgBXTnM40CCsjpcyVzrvAw6DGXiPW93MleAUM0osNkcDgzI330wYMyLBwKMEroB6n5+1Metu6yKCmZj8wxwaMslXvVenVs9Wd9jvP+OUdGLyl3CGW29YnLyDiKHqHbm0uRvP60dGIRvm8IM3Hx6g2/r4khvPvA5C/OXuzfA4PvvfoA3HjysQMGsBI3JWGzfwAGjgIPAjKSg4D90MR+7r14qv0kaOYKqzu87u+9gF39W5AXwwNFncPcG5Kh4MHBr6tIMfSpx5NuGovTXjYzxpWUPAFwExvQ6IgFeUk+hykdAIZFnExiRNUnOQEIaMQkRFKMy0XXIgXfm7w9fQOU4Wqt9NM3NG6VRuGBNnL3XNTFulSH45ZawsjACt441yIDAgIX+/myV+npg4FkDYwsMGADAK0tAUNkuxgI4gHC7Ea4iY4mEZSN8ITKWjXAVg4CCxAhBQUGChBlYwhMCMMZhngzOnLwB9EuPO3lnmc/Wc9iZTTBjBjZIToblZmQwMAECVj3r32eGLhEqhRMpL94tgMHrqO3Fx+JsHM7030g/vYhOGhUKd3uvu5SInv7fmKs6er1ihfJ/fXJu3udAgfRAt4Fxgvknz2/w7T99D9979wNhClBdMi33Bhi88eBh/r0HCsygO1sGAJrhyeUkEP71pzf4gz9/H//0Nx7jS5oIEt2NiIv3a9O4IghbkEEciQ47+cs23N39J9efASe+bja42vUbIlAJfPUDwDKaA+Xr7HzPgowG9onF86pyCQhrZU6BMvhamXMdV82ZiEQZKLRswgbWBMUaJNwau0AKElCzCQRloCGeLZjLB7fg2m24IICBguLB+jUGWqOU1Au1ZMKVzfvvAcGaGL9MaQoI1pRwuxUwkBi43RJSSpkaNUDgvUcbJwUgAL9cA65iQKACEq4ySyDgYE0FDKzM+EIIsi/IvoUJSQFCZMKiAG1laYfgwjwEFHDm1kqwTsOXyDuzBlFyAJzMjRVgOFCgJ7RgIPGYFfAJnVo9kWlTtWFfrk6oJxB7I+49WUDG4ssYdyPDX59X13mmi0b6Z1Qq3TIoxEdmcL+8KPAoerB3NoACCvx00RLGLADCkqMBdLOp4K610oKChPKMI6tyb4ABcAAIzNPi+kMSHiAEFAPx8S+u8Y/+/H38s688xpe/KFMeE5XOHwCAyqCWRiZsiWWKtAbrbPDlMIMO1JydGujOqHkvTnZmsJVnHT5qetIKSSSz4hVL+Ua4GgOSpDE5T/ehDOxq/G3HA1KqNWcHTnsm7t1GdZQ57mr8iXC7lUFO6uUaSBAAwDqzgRCoTImMur45pTJnXwa/IIQQVCnY8w/evTJISjhY+CxpfNpmE2SavwEEKxsIEKO/JsZniXM4Yd2AlVMFAj5bCzhIChSMOYA+E6iZocWUmAMIMcj1IQTcboyrSEgckCKwJgELiUOm2TeWBaEENARsrGwCKUAgxqYAIRArKBAGJyhIEHmLQQ8xZnmfkbWXeZ4lYJ4+12s7GBBo8wVSqkMEawMEgH52R9t3R7rBxsrImG4oRpO59mT9tXL9+P3PjDP/7AJouDqnP2/8vEEFhru97mnHbVvZ+cLWtffeP7qXU77OPcvPuvHnm070jgUwBgSA5d+cZwjaWhFRAQW/KaDASnaEp28r5d4Agxko8G0pg6IWY1HC2pFJkNbvf/Rb+O47H+D1Vx9KmIHES7RYZAx6P5ZrbKAvQT6XSkYDWkypAQjRD6zgB2bfCS9lDY68Crlnc84LEBPV8zbOg9XeKw6UQ0zNOa4OMwpuVFpWhhx42wNgM5lW+zfrE6UOa0DFKAzZBM3aCsomEBWgQCSs0i0BQRcs8YsqAcj7vCzakgkFU8CoPdWkYIDhwYGcU4x+mV2wFzYwluB2S9iSgohty7kKBgpsmiNQwIGVz6CgYGMBCTapGkBISRccjADkGVdRTkgBSExIIelfDSUgYWPCwgELbVgi4QsxglJhcSIxtiBjMShIK8yNG/sXyNreyic1t/K2/QWkHbMCMyBwduy3Y2VksEbveCn43gMEI1agP6e+94voHaL6fqZ3ZkyDB0hny1kwYPXx14wYgnylsQGoQwZ2vbEDHgy0QGAGZlqmYFSMVZyVewMMjop5+KOSUIT88fMbfOfP3sf3vvK45CywMgmEPKc0EoFRWASbFmIKelEGgQFQ4ry6mlXBewcL3ABpEoCAYkR92bhPXLHBaJ3VG0V55mWjcDTlzIpPeukNugxSTgzS94lav0uSbfaSG/OzU39eRZc6hevlYNfsUZxWCqMhmmh1dVuDhg5ycmI7P5+cZ2BAgCuakKj0i0IR8tQjkKNScpPyeGpb9lCdd9oafZtlUH7Xx83oCQAooYKzoMBK0vyLlYElcQEHKi9hNQhILGwCE9bkCdB8JzAHcJD34RA0VWDLsxqWIBnZkvPRTEFN7cJWfLGc7VgLAphrRuASIHCmL1qpkgYPAMHemJJrBu/tdI4H3x54z8oZUDC6x1l9c6a0rEELCEZ66AgEtPc5YgYA7IIBu0fLDiyOJgilm3aJh0A/Mj5WUPAn736A1yeg4Ey5N8BgjxqZdSlrPFsI4uPnkr1piYwl/6B4BtZBF/KLSOhgUOYgcAEJAECRVEkQFmjsVYHC4hIWbTBSpSBQBZFssC0YGPvGe15QKxjPTMSWnmxkI2Dn3GDsvRDd79gAOU+3Xe6BHW8HmRx395zVpWn4Dexkg9z4eTqaytPeNzqA5sNNo+L3Z7DA1AEFScIsQMHeZQQWAG6URUnazAzCDpgardCZjVNjmEZrEHhQkLhQ315Jz2YZBNNqarsXCyHsxFx9SMESD8Pk/bbECNqZpF6U/xbAIGZ4Y8KGkD1n5prBEQXLTiGPVxjdk6/J1v6y2z8DAUABAjMP+5L58CNA0I+z+ricU+5xKuE3zPMRPOtpgNvAto0jybdqjxdwYFXzr36kb2Z6prxjD4rkusa4dw7HEYjor/UgwD/jbN6AXVMxA3lsFNbQ64JWOv69zH79jtovYwr2etaetO8NMGiLf+nR0sJAbVOePn+Kb334Xv4KI1Dm9DLKF8JMEUSdh511JonS8kChAhZogAIAsDAKI7AAjLN9FzT0YqRqcAnQgLvO0WoecLiX31A6Qj7fyWkvDnhmcI4AwUiJzQYbgG6K46xkWWoFcp6DHm/BFytIWFBCDZX8PLAaPbBV7jobwcDCLYQ18WBh9L4zD0MfAmAMfisyiUsfm3mpBg4KUCggoJpFAO3PJKsSImkbKM2/kaxImFJCIPH2EeW5S3OvfM9Gedq9QwgOJCCDBQ8avNFILPVKkCmLSLII0rolMBE4FGq9DfOA4UCZvWtf146k4/LHr13iQb1PFDwDAvYSgGeG7QyofhnjCFCm094j2NhgHSv2uwbbGWjb84w90OcuaJ2Zy/QLcA4AnAmryDXt847B1AwEAGMgYPs7IKAnGitg971y9939XoUr12q/7NtAuXidP7l2VO4NMJihqaFAbZ/+vXl2g9/62SN88PUnePiaS9TI9GEBBQYWrgJlj8GAgDW2JCaVKZA+RplQ4un59gBitHWvKR/wjbO5VQWXRum2AMKu25jzyGKGAxxw5/Pk/Jp5KM8qZRSvO+vRzOJwQDPg3I+jhJn83EjOkysKCShyXJxSX8xw5uf2IMF7QL7Yeavbl+eNW2hHK2MzNirAAAEMwFzByznHCr1lnkaUtYGCDahWKgSQDXxQTRcCA4lk5UECYog5dLAlCyOEbnpiGiwpnJ+hSwu30xdjoAYQNGsdUJnm6BkGM8IEMUIbIOAs0DRp9FJ5tjK155pcgTkAOJPvM/NYjwD1HrgE7j5+AA2HxqKL8sK6BopCYTs92PZjyL/vzCkBFMy77ensggsN/yyEsjeuZiBqJkvbPwIBeT8KI+DDgz63yNciag5M9byd7nqjK/4+/toTvNUkGgp7ozsuYKbuDTAAxsyAzy2oGkXPu3l2g/d/8ghPvv4EbztQAGicXC9g1ANrCVQbeUctdvscwNj7gmOmg8n2I58bM6XqXshASvmZvba8v/JQ3P5MZ5Tww9JM6xl5NMsFig0YG7u9KTvAeND5v/6ctmRGB0WWrRwl/ksVWDCgYMBq0+sySFCadJRI6cvG6FygMnND9t8CjkWQbZMHMFZ6cYD3vTzaMb9nsAwkRJLzAqt2SowFASnIwkUhBSSyGQ66kFEoIMC6QgsMjhY4AkbsQdmXwQAoA4J+W6Z+ioFU0BOogIOtsHw2XdZPrW3X2RjpzDPZ9rPztApjGUyUvAcELRgYgekzGe53GTuAZ0vrv0O2k7gKi+YxpI7GzCkBisy8IRpN/5t5+pd4+MDE2DcbYbC7lWMbhjIAIMfKhmcD2rBAu8+uWS6YnXSt9uuHX3+Ct9wyx5bCAy55cf5OR+GrewMM+hWh0DeOnQsAYFw/u8GjnzzCk689xtuvvg4k7/eV++ZrqGC7JdSDJmmCoo9FelbAJyeVa+xkM/oFNNg9kH8X8OBLfY78XWL1+aV8zZU/1420dllnQEDElX+OAxJ7ZTQ4j6i3Geq2a327+qePYpKzj7MYqyPn1O0jOSAkgMnocAUKS+pBQkkY5UyRtkahHXfdEtfGNLhXiNQAB63sCDzI+fttAYyz2iPcbJDEmhxKeSqhgADRLFWyoYIDV/3qWPV6A9IgNBbJ2s8vlWzn+WNlP+X9xTD0jFT77pmx6avUnQvM6f7ZNLuzjljbXHU4oIDCFgwM81OADAQIZeyMxk3bS2bjJk9pdPvbcTNiOwUsiP5b9IW2xJVjMnRKgF190iZF7tH53Xs2eqQ9XumUpgqj3JNWlrNPiNt1VVsAQxBQXePuHcG1kCYd7Pr5Uzz66fvq1Oq3FVx9zVHtAUL7xL7cI2DgOsEAEDishP8fe/8atNlxnAeCT9Y5Hzj/9hazJoEGtJfYjd1Ye2yLN9y6m5JFiQQEkrsTu4Nb82KQFDgUKXktifLaa3sdnpA1Go9FQpRIgRBJNRrA/HAMRZAgadoSvq/RuJCSZY8ndmJ+zEawuwF5Ymf9YyI2bOJ7T+X+yMyqrDpV55y3uznyflJFdH/nPdc6WVWZTz6ZVQcccfi9Izz01XN46r4v4+wtdwgo6I3wohfJUyjupOPo74HI7HvKJ4huRwYMecllazjZLxX3xwAHHlyIASgVcT2Q8za78+fnzIEEpXMTKLBbDPNntcoSCm+h754iW5qmY+cv1aD9qWYDb+2vaVoGvwEF84ZqkECcWQQyar6RYGVlKdvaH7LrAig1PFG9VkQOQSQveFUm83NSTklwYZZ8VtMIcqf/9WYhbCnBKfZQKH75W66R4UCRu8fae7fA0T7Gv/feW0pthH1irgECzw7Yh6T6M1t0fCzNdd97zMiZ7TGDwnFZAtqenStCo+g7Je2a9HesGfoeM9LLM+s6Gv4+lSxbYeoaFKTnN2xR3m0dLBadjHbfzw/vgYKrl/DQsx9M9ovjJPpInVfSu7cAAlCDhHk5McAgVA3ZAwRgxtH3DvHQsx/AUz/5RZy95XakT7AC28ABtPHS8qjQbQUNChgGHc01YABc0iL3wQFQ2I3KuLs3653vQwMN5bYGKJbYibXSGqBbaTc7d8YeVPepn1PWtZRPUnTI4EzAG5DyQYK8o1+idhgIsQIJQ+QZiyDxVZ0rrV64PLus1xYjau0TQgaD9j5EQGutCCAnem2JxbbKweLRsuw79XVLuZFL1/bWAlhacKcGAtcKfkL1HjW74QFBzQ7YdEsDA5mKzue04taEvqda16MuWTwNx8SdM2c7M9C2/a3w6Pw562UfL15rouctG/dqd/d+fQAyBwVef9nPNQDgVzkV4WZJ0bTL+xvl8OqLePC5R/DUvU9k+0URsiIna2cJoA5AsHovjbYTBwx8g6RG0QYhjji8fIQHn/0gnrr3CbzjljuU83QNBFRclyuO26IUdtB9pI3gWITcQADrX9tnoMETAflvbrJYH7T9bpur3IB1YEGzAVyeSwvP3UNBNgZpnXwDoKvMahao5QW1ftfVNnlGRWYzcOB+Tyy5Fq2P2iSQMEgGvLEIO8wBAkEMTorhQ2QXAiUjs2RrAmVjFELOTbF7TcixdW9Qa0oaKJM+7RiATTFZYHsm+1azvg+k2PejPCkHIGQ52YwTP5UuX5dBwRogWGsvIIOCpU9Me0AwhswOjEOYgYGB5JpAGQjYPhsfM1DQGStr40R+l2NF5FE6LSXIbux3oCHJ7lr1hqv4mnHfx7Dn3+1r1p5b/+0a/2of7NsogDP8lf2Ju64NOnz1ZTzw3Efw9L2PKyiIAGk+DXEXIFgZfLMsDNgTAwyaKM0BAnDE4eWLePBrH8LT9zyBs7fcCbB91U5Jr4rSmTWOBwa711Ov8h+0ScYfQBM02H3ctp3NHlBoGdK+/Gb6dkUpf9PseKHU3IBP0mowBj1QUZ/fK03WIB1bN/5d1F1VgDqDqJbn4OQuek9nlsQMDmwp4bTNktAm52WQQGPAAUvG+xh06h8JqNi1AAJDv6jmhLHR6NSljGxl4++NTt5XUtOw8xw9DfRDPXbMrmvVIe3b/gpNM9FkrYayvxkI9t4PGEVuCICUBEfK/kyBMkBAnsHgV8Qk2s+rLVhKBwiWGAJhBEp2wJbUzu0l9xOQkGdo2Fgptl2iWqEdqnGyOkZMAEB2XCjrkYwTaBE0IB9adF5apccSyN3tnHYfrMPIwDx9oa7B8u+OHWjJs+H5rxr/hQ930XTctD2Hr76EB775MTzzrs/hzM3CFNhHW4jDIkAgBQis77nWzU8MMMidqERuTVBw6k4AUQQLBwgKZNcSnWusaZd6ZgkGAFQGiVOIYW74u+DB3yf9doOidRzVQHfX5I8/BTcC5kCD3f4CGDSAxV6lQt9t5N1oO/sNtAee7e89MrVBCcrIfQhnCGIWIwQs5A/hSI0mAwkkICGyTndk6Hx6wshimHYcMUZKXzIcI7s8BQMJAhAKJgBzuroVfzfDIa+yTEvfiDi1V8hbQjtO5M1SN9UydZ2PZbannx8icW1xiZr5ISzT6Qp2R/NDbMGvFsMDSNu0gAAwb5s1dmCkIO2RDH3FDLi/A1lbUQEEgkmm0nPLxmleikVyGo6L6az80ag2aJC/Kqv6wD6lY9ipccqqIfcdxV/fAkn1vllHda5Rz+j7fT3D30sq9NvTPE328NUXcf+3fxrP/Phncfbmtyko0DYwZ4OVnU4ZBVF/y3Z2QqkCkvNyYoBBDxCAZfbBg19/BE/f8wWcPXW7MgVxDgiKxltgDgAgHjcMc9uop8G3AB4A5HBDdf3s2qKU5+Xrq2FTX195CvVza0+iDSyKJy6UPvpeNf5+8PUGnV3XKl7Zpfqr+SP32VwiDPqVwiHI4sUcSDx9zgxCAgmRMKD8MNEwEEYewC7UsJuEReiBBIBy0mGDsvfNVmfgLwGCZHzQpqaJlEFQAOC9UEI+p86gXvNMU70bY4brvlO0TYaG9s/P9DEQYJ+RZkZidMCQL5rqObPQDwQkEMfMItQAIeSFvjzDY6+2lDcgr9AHaeNAzVBBCNJeFiaw9RoG3x7WZoD4e/6rkDpWWp/tLtpgbWzA6Z5CL/mQ6AbQAJNJm+Xsl44R5rLuM12B8nitp6l+7y5A6oSOF8FBHwyU+3r1698b8ThvU8Dha6/g/m9/As+88zGcvfntKXwgDo78lVVet7EHWff3kdvJAQatxI4ZKLjDDaYGKDBAsMHwUJwwm5U7M8beuHaiXh5ANO6x6H4tfSO1uta25oxCg0oEQGgBl3a9V+vZGWBN5F3E4uZgYTbglgabvkn6vwjhyAARcBAg3+PV32HQvyNCGAASYzQxYWAZl1PIYCFG+dR2jNl79aGGtMogzUECgAQUlko9x32JHWhR0z5ObQaIyAMMEVJthMAMisquFUbItddWoFYZIhRtkvtbAmthyEbHwBqQP2FseSHQvJAKsHmQQArado1ZJkWbKEioF93ptcnSNMNWqMBYmhBIp4iWbSFLRWs4QSQlyWU6a0pAgYKAOPXZzo3jwtqg0D0NEOCTq+v2IqBkG4rH7K8XgMqoL3nm1XVybYsRWKEvenllzXouGPWOEzSr0xKLo0w2iPD8qy/j/n/ys3jmxz6Ns296K8C2nFeKRwJpnVLIfopYZA9Y2IYlRufEAIMkTGdArhUULNI/iZloqI1C0ITZ55BWBs3MaPeu21TaA7LwClr16Bn/pAB6YKdxvS9dcNCRdY/B2ULRtUorvGNTe5LxGTKiDgOIdqLwwoDgQYIyCdGBhCECE4lx4pDzEQYiTCABCcUyxVSsoS/zuxeUxULeQAoVUKam0zmEVQOUgECcJCPaGyBj1wovtTFetraDtoUZIt8OOcwj2wLQAkAid1b5hzACYZCcAczbwgO2AJFxUFkXoZ8FkAC0F/Pa2h5reQP2exUM2IwpnkogkNpnq95qtwO0HYAKrMlG2qaaNaiuW9UrvrSMYsuI9ox/j4Kvz5s/ZOFY75L2/VYNffO6jc/Xa59/7Tu4/3f/Kp750X+Ad7zprQCr8aeg51wDOOCo1y/X58QAg9qj3AsUNO/nUbens7j4y9OyX0GDm7RbdZaa3l/6dUPLPgDFKwn7v8MyNH+3Smtg17L2AKABEJry36gIaRhcPbNB4jAiAYUQEkBAGEA4BodQgARWkBAdKIiQWQcTzZMW5YuckrxlyXJ+ARhemFxsIvdL35rxqXMGzBuFM0hNA8QTaFKDEydYMq4ZJEmyqIABGNhJ1jTHmP8mYK5tFRvjIuhYMNmHIcmZTN7jmNskDNo2ch6FARxGNVKDAIUwCGALQx+wsRn8ktWxnARjdmxBnry09HJ7tJI567BNCwyE4re1XwUGbLZUapuo7SFtYAxCPSbSeNgKCuB0VD0mdF8TMLjfRY7JvuO//t10wvbwvLcA02suC0BxgQHYy0boc57/o+8qKPj7eMeb3iLv2ZJtwaZsAQdy7uGVFxbrdIKAQe5Eh1deKEHB6rXOI50fBDBv+K0DkHfX0FHXGIIb1fkbz6EhT1wrgUuDKmxRwwv3zid3qL8OSGgpvLQev53rl9prGSUgGSYOWdGZQaJhkGlCyN6qhBqGOUigXQo3UBgxqPcqoCCDhER1O5DAnNdJGA0oQNcQ4H4vJLeR8wCQWYJGFvtAWwzQLhsf+8cOKOx24GkH5gjsjgFm8O44GakECpjBNWvXfBE1JsbOhAEUNBE0BNB4IPvHA22XUcCCAgFpj/rfuArYJgbk0w8lqxO1TQS0IQEFAN2FeMg1SN0WxToDKnva0haJpZkaYGAOztKY8AANyONgZQzItvITk7VJCQBoEFk6l6DPMlTt2yxLlH8THMwdMqlvi639wenELc+5Bi4iX1u9zuFr38H9v/dzCgre2rtK6rLKJtfgADi88iIe/NqHcBtu7V51goCBvPTR5Rfw4HMGCu5CHQedFYuXdcEBFft/79VXZs+UzVZc6weJXlEaw+bxlShpmGs+rtet9Z5FpTiAlreRjiw/Oz8xX9Ea/Bz7ICDFW5F/o9MW9gx7BwMJapy48lzDMCSaexNI0H9DGGTZWAMJXDIJDEmqiw4o+C9xLoVCDQTIdmmAfAJhlx1Qwy/hggwKkH6XYADH3wemnchz2gkgYAZPx8A0yTnmxcYo4MG3T6sE8zI9K6MAYBhAgwADGg+AYVSAQMDBG9ogYRgdkzCA9bdviyI3BMihBpq3A+Cn4PXbAUAxo8O3A5n8UYIB1TRlW3gwwLHL1BDHAgiwgQY/Bvbt/6YebAxMuX1AATztMpMDHevaxuk7FO7/9VIKdYn1K96Bq1UQWu93DbquPH4t4do9SssJs2dWBv4/+L2fw3/2I/+J5BR0ZZuZzmaxUIMdZ5kzcnjlUloc6Ref/lvd6p4YYEDMOLxyCQ/UoKB5sg1RR1Nb3IaCzvVUwXIEIKsXHr72Hdx/+Auz280GYgsQrBnx2fkLHb2ntTrXLCkKK1QPjHogqUdVexy8Qxs8AM3B0C1u8PMSEAD6irC+rvWYpAxNQTpvSb3NAigMYwMkDNmwmcdKlrAoBsn+jkBiEmJw4ACUMuuBem54u317n2odqu1FeponmSedDNNxMkrxdQECCQRMO/Dx6yUQUDDAk4YTpglxN6U24IkzQGgUopA+xEMhIIyDeqYiaw5BjL0DCnRwE3D8uoCEYQSGEeGmN0ibxAEcDkBhJ23BB7DQQ5dFsDQJ5ARGA2z7tgGQAVkO6eSphbZvDga4BGcGsHiagYHE2HggEKe9+r0VjtbvY2oDOWAv7sI8ANg+heiBQmrM/cZ33tS+4ve3GL9az1X6bS+9Vk8BrPWbB0m9sgYuZuc7+dh7OplxjHO9CwgouPltKFiaVj6OFVoBCWrLDq+8gAee+7BO2b9rseonBhjkl+6AAjX8TEETMBw4SKUBDmgAmPH8ay8neufHnvvLosxkzllq3NRRU3KIll4H3tf4N86fDY4tIKX1uPrWvuO5zlt05EbMON3nWhRIT2YNQNBTity6rlfUa6IQNNlXvVcz+ESilM2jpQCyGHgYRMnSkI0SUQIJOXZu3ms5BTJPwytXYITua5XCU0X2Vs0IeY/U5wrIrILjIlxggAC7Y3lHZQjYQgfH30/HYMenXQEE4m4CT+KKc4wCekz+rU8LekAQSOUsQCGMQwkUFBzwMIJeF+aAlDWgcUScdglA0DgpmDuQd0whhgMg2O8RA9GM0RluYBu0ZnUkmXPMwMzyA3zoxtgCl8MRjZGpwUDKNfCgeKvHrPpqUj0Xo3QeM1J23ux+ch5P3sBt0C17jOl8Seeaa2Bg07j2Rd/XSss4N0HAkoyb58euHqwBls8zSKDAGftWkq5c2zivehYAHF69iAee+6jaxzuXKTGcIGBQgoKqEIkgVFgmEm/4M6LT31CExhGHr73sskPfIvcg0sYcEhVmbcEx+h+5c9RGOwztzrYPUl4AAnPQsDYBq6qblSl35GKg1V7HtHOKxSnWetD0aLul+raS2nqgoOdxACkOTmFIXoS80zwhzoME3h0rkBhBPhZOAxA1cZEktJAMkybOzaZAkrr1yB+aEY8VBdtaQdZcPGMA8ytW4tVrDMHr38/G//V/MwME8XiHuNslMMC7nQCB4wnMETy5kE/0fbBcstnaPoQAGoIwCAeDAIVxTCAhjCPCwZgYAkw7yXG46d8BTyNotwPd9AawHgs3vUHe0QBCGBwoyCEfP6thCOTWTaBrkn8+pjokMmZTC31YoGYLbFtzNyxs0wMDzUTPVt+ui10ThuypGoXtDZgBK0t2c9cV47OH9fccw0usxw3TX6g881plum3ycqjv00sAbMl7DRD48AxQTQXPydHXBQj02OHVS7KM8gamwMofCzAgov8zgL8N4H8P4G3M/Pu6/38B4L8C8F/rqS8z86Nb7jkHBRWiLRpUh3IKHwDElDsuR0B/H772iswjfedjOo80AwhNXQLGUeNlAhJo8IxByB282A8dkK5TufmrUg+tTw9A1KU3qOprV9AigEy9WcczhSE3bHsdyMoy1bt+fq1grqcY8td2pBDK96bcplzJoP4NZLDAkXIMtgYJ0w48jGIwlf6WxMUJKdQQNR9BwxKZSaA8u0EH9UBuBUZSt381ZqtGiOcr3plXWhiiOqHw+HXxRnfH4OPXU+iAd6+Dd2qAFRTE11+fAYJpNwETy341WhwZPMnfLOO2V0aBwEMQwBACwi6AxxHYTRjGATwx4m5C2O0EINx0EyhabJ2FtRlvkvceRtDBTYgchdU5uElXNByAOChA0JwEZXgQpiR/msl/g+zl5fry1xBlwQzUAKEXKlBQMAMDQL5Pp/9muVcAoZ79VOcKXUt8fWkMXyMY6OqsLfrKir1rZdgL2n5T0h5K3Vz3iyVnZyW06sGAplcX11hOE/wxy25dnC0yr+e1gALgj48x+C8B/J8AfL5x7L9h5r+w7w3bL91p/CQ7ZQyAHEvjmEDC4asv4f5vfxLP/Pivy4pTHMF2fhjkPLk4ZXxiVLrUJdbQ4DqkgZIYBSik/ZhRXDODWlWfC6SfDSSA0khuBRatknIwSu/B7p36pI1xq2MIc+/D3mkLg9ADTbovySAdt+f6fVMCB5QUxEaFqkteGyAiDhI6CDHT1Y1QwywfIQrap7DLAzuxCUL+pxUY7QXWjJMDsMWqdz6BLRmkRlJhJ4/AQAHvXp+zBMdirKbdBN5NwhDsdgUgSLHjaMbLAwNVgUFWaSNlEmhgRGOeOMh3DAAghtJjPxAjSb7NNaGKIePNjKmFG0BDCRAMqEWjzNsKd1X2Nsa87BMwmDJQsNBAJ4lwLVTQYgeW+m+WdcOg2bhxyYRFfo07p74mld44LX73QcEPFBC0iteZLcPdCpH2wEBjNkdxr94zMGcF0kfymqECKGOwAAaafXXeNgIKPro3KAD+mIABM/9XAMr5rze6dO7tKRvycURCMmCMQRIZ9YMVfh2E7MUfaJKVV9LW4Vk+tcwRGEM6p5jy2Jl/z+TpukYIIk5CrTpjJ8bWgEAGFx5IzoxlOrBh8HVk2fM8NimatRyEwVe+EY6xrOgwVz5kMvTgqEGz9pTszOOK0xwgYFeGGhSk8eSmQPo5+RZugFtQydOBHv3P5F2hL6AyUNkwFUlsnMEC++RBDwp2xwUoyMmFYqR4YvmnOQSWOxBrg5BkNwcFqV1CmIcW7M1ixDAEYSICIyACkyzjykHqQxRy+McuJPdpWXuWbtOgMgkRzJMoXPMcdRwUi/M0FW3NO3fkbiBBdcJsnQHLG7D2abEDIqhrAgT7gIHV8blPfpA/7MG8A+XmmCRnZY3p8+9xLfrJv3f9Hj0d5e/T0lN7AADA2xnKixK5ZxQGvg4TqJMxOzfdc64jSrtmifiaaHirgoI9gNa/jTkG/0si+kMA/z2Av8HMFzdd1TBe3SV8E3KzE53AWNdB+MZH8fS9T+DMqbuEDTCPwIz4MJZAQZOXUIEFAOma9LnXBBiKWoDMA6pBw+AUrT0X2GQk7bzEWrhOvTmGV3kOrWmLeykcqgZnfa9WSXI3tqWSwzCK8pFssvwupnT1fA8UaNDuvzZgXLxVzWIKpTRZBMtTcNO9inUSwlDRgA4seLktycSB0MIAWR+tY9fmnc5AwU5A6qrhUWM6kGIi9egTiBL2IxvSed0NEFAIqPMOgvUFl6C4WOIETAQmAQozcMAR4BE0HggYoQGgqOFDkTlI2Jq95W1/ncwBFAmErXUG9k4kdP2ymTcANL3aRSBww8dkxYDWCYJLTJ/LxUopWfUz9tVJdb23OitLAGDB+4d7qy4AKO7RYKfqfIHhwP32bEGjn9Z2jxnPX30hTUk8c+vdiVFbD5Pl8gMDBkT0jwG8sXHorzPz73Qu+yMAtzHz/4eI3gzgK0T0f2Dm/75x/48C+CgA4H+U9zfBQCXQ7tLDAI6+d4gHv/4InrrvSyLUBAgUudvf4UAfuL6mfwYMQA0agAZwCOPsfALm835rhVIBBqoVTAUYaMjnAiiNrS8teW0ZcD2ls5R4sxhf3ygLJ4e8tjyQpoXpsa5CLh5ZDSiTodU2oAQICghqkFAwCUN098xKobvC3Io8SjCgHqsaJkkMzKCgkIEvVe4HDYPQ/UNEwIC4yzmAGCFJgxzBIRR5BpJn02YT5L7WFygBAktCxCAzFYZRZCYzFGTfbHW4XhZ4asMRDMuTCaCBdbGeSt7XIGsAM3nLvgwGYgcMyC1WAEESFs37X/X+xZjyY9KPx2os3qhxaO8MIDOg9k6OtZN3i3O2s9BHoXRg0ns2dE+tj5ZCAluB0BZZUCiYKjmvBUgqA94C/M0QgjEGN+U6FA7DRvv1tQ/hqfu+hLO33p10Hzvma2mVRis/MGDAzD92Ddd8H8D3dfsPiOi/AfC/BfD7jXN/E8BvAgDdTDxDUy1hzhBX2fkPLx/hoWc/iAvvu4Azt51Rr8MnG+XfPBxUYGEODha/A5BfpAIOQA88sF1bAYd9DGV661Zm85Y8hCXqrTXomgOuRtO5HZY6fVFSXsZY/Cbvrel+LwNqKOqaVZDLtrEpRYZ3AgghU9bMksRYgQQvn+aStHJCflDDM5jJQt8dQOmtFgazLBQG8BBB06SLW40aVYsyJS8Q4m4CDSRTE/Xd/PTEMNk4UNltAQb2bmr4/fRFDwhs+iKGUcHDKF4wkaxz0POkzehQFMZ6sj7IYDDA+i2Ta5AxgKac0zj2fUzrUsTXfejQ97M6F2iNqWuxAS0Q4MagH39ZNzaM3ELxKxYmZyZdy3Pmk6MAhTWWz8r16iFgTyBUGf4tRt/vK5zRth2afzQM8CGBBAIAmWlDodSPRdu0QBur/foALrz3PM7celpkTdC/QxMk9Mq/VaEEIvp3AfwrZp6I6H8F4H8D4P+17eI5IJgLNlM+HjMxgKPLRzj3lYdw/n0XcPq2M4iA0o9wCU/Z6PP4BmfoSwBRswsFaLDzrdpNsDDfx7Nra7CxbCiBDmion+lLobT6yH154Hlk3ImvuWtle53ySiCpqHs2UDRkj85avUfvJqDUAgt2/4UQzCzjObrz/JROBxL8SnNLS9ImOS6A/NmKkS1Q6EsIQITQ7Ltj0HCgCwwNkmsQggAoCsAQMYxyH8k7EDCQ8g9Y/iaZNeRT1DUZMntHCaMkZkDDDPI9i6AgIAMChEETC2WNCJCukug85qKwfmo5QJYnT0DMRrQfd33ZLi7J3Zg1MAMDrj55cy4nGg9qgSV5NUGAyZI8I9UAAESIS+NuK83MTmINXVQ4MA4shK1spy8b9Y9sruugLhDaIpMFGr8JILzxL86tQgkNx5XHN6S6mKzrv+lx+vfo8kU8/JWHceF9T+HsbWcAMNhm2im7NwvzLbT5H9d0xf8jgMcA/LsAvk5E/4yZfwLAGQB/h4iOIW/xKDP/q403LYVc7KMCDDBQzFm+ePkI5776EL78ngu489QZHEcXITUwSvKD0qwEL7oyhCAxnarDV0YsKRx/bOWzoksMxKqh5AgMByWAcef4OqXHNtYkryndJdRdx9HTdh1bs230UHqnLMiFi9i7vKcHDFRR7jOwsKDkqZ5N0qkXKGRjadcmAGH3zt5Od0naFXkUrdYDAvoesoQwp3ciCtm4xUlAgmXWTw4sRe1D0S3H655nfWXpC5H5NeSdM1PijJ8ZePur6z9AgUL9jYUEHow+b4WyKvm0PozaOg8Q2V73Spz+2kIOnbZdCwn0QMAMAFRjbmm8bRxrQEcPeWfI65/aeVpgO9Oj9tI7KN9T343t3fah993v9amAc+O6ibWe7a+1MDDpvjSUrHuhLFYzs19PeqfWbBVJGzBCBgjGIiyUP65ZCf85gP+8sf8fAviH13RPb6gbgKAGAxHSTy9ePsQHn30YX7zvSdx56jQmbY0JGY0RUXL0rOxiPkGWRB3So6ua6Z/S6BfgoXF8cf8KE9E1lI1zff3srjnBsvJeUHbg1fhaBwBsotVm9+0UrTcV7+fen8v4ewkYFCwEnUq2BhTsnksGoFc8QNDf5br1Op0xTeeMaK00t5qU54pke+uPwXnUMQJERaIm2fvEnDPDFUAywEBAiilbvyJP/y7Jw9ff4uQptpqBAlUGsY6ldxPq1rLI5UX69UMDCPh3WgMD/lxfev24latTv88CEIhpznsGBj6ZbSmjvZnM1hZIfo3CCSnHmRzPIGERMLj7znThgt7ZO6a/ZvCXPPvZc/z55f5WWLSuX8v7r40/ILYlqzOuD6cSAbxw5QgffPZhfPk9F3D3qTOY1C7lBdAaACHV7d8yxuAHUjoozIOCaDZBhX3xyhE+9OzDeOInn8Qdp85g1/B2SBOAKMcTAADHkdOSqB40FENMWYZAACh/tbAMT9TNU9WhEc9rAgoPAgA3xxrYB0CkGmxIUEllCXUX+zw4WKDUWvdcKg7UZDYASTGxhXdMmXE5nSyDhaliFaZ+djnQZxWstAyETddCNiap/VsLQqVj+ZVELJVcOnKaJXNx1IUC7GeWFVV1Xktevdblt5v13Zo97s6dhbJ69+6Vqr5dZgDYBgjqa3yp6thkA+y8FhDQBbMSG2ALaHkQoItomfEvktcalLVWZFFEhTxabGgVQi3Cp8WxPtsJtA3frLTqvYfBX/Poy/ssycixBG5va7tl+BM8csbfX3tsU34rcFDUgAiXrhzhka+JU3vXqdPYRQYRpdr5iT+SdJsBwlo5QcCgDwjAmSGQ760DL1wWoX7h3vO4Q4Waim8Hyj+8oO38mpHyXaggu/SEmlGYDUt3ggGKxqHMZhQXV95/bSR1nz+n9LSBbjhjqXQotuaiPRQwTwQt2829CWJv5BXPFxkFc6Rst7uzJZLalDLmgwwWbEW6dKwBFOK0yCgUHjiAcm2JjaVeEKq1GNTs3Z1n3MyoNkl0Li9+tQW8heqVA9cBDGz3ImVcnLnxQWXdi5wBq8NavTugoFkajM7qzJ0eIxCGNhAIg2MChhIEpGNz1tRLIzaGe1lp90oVG0rF4Wxo2MaX1zXF9o3XMQC2UfgzEDA37kuqZkkP1SJsGXR/SQsY+HOmyP3nabVfvHyIjz53Tu3XGexUZQzMAHSpddmslk9XgABeHEInBhhwZVxagMDILwMFj997HrefOiPhVZSN426cyuRkmROvuRpEebsEBuVtVwEC5u3mF4Ty1zvToBcNC0ZS6wwUAALAHESk01cGbwNdtzNqqRiEvp2A3FbyyLI9tpicesjnD9zIYAhB5TBITRJYWFm6llP4gIG4S3HSNaAg4vCuwkpweyMI8LR6ubzqAnPj9xdSmt+/LntwR9ddNj+racx5drz2VP0KpRkkyPlpoR3Lu+gVN3ugG9rpZc2vhAaY8kJYKaxA+dPfCQik6bBjPseBgKjKzus/L6FrHU/2OumtiRCUDSVKtig/yVg7dPTLProFaOsXt3/VyfAHUMqhiODWYLhRtR4gaN2jAAYdo2826HgpT4eBl64e4aeeO4fH73lS7BfbyCdMBARmIQaMPWgAhNoi1OUEAYN5B6hBwcTAC5cP8eGvn8Nv3nMed9xypvgeO5Abs270evgfx5gNNef+aQnaJb4lzLpWp00ai8K5SyrvZwUotAZ0rg+QPO1QgZhuFbsu++yM7qBEW1nVoGDm2aRr23UIvrb2npTDPabM7PdAGSyEYFP0pgZQKPdxHAvwAFZDAsm8LkACkIECMFdyXpPMkOJ8CmgNCGZrrdf5G7bf7t9gcPoUrEi1V7++J3/tZTa/uvjdMCYt4195qJzOs2NDioPX3zrhaSpJhHrQe2Cn8/Fz5RfazwO6Cgxww/vHzOgPDSAwpHFjIGCqHKDCIWq4xVs+LQ1knUTgGTiYg/AsjxRCLW+3iaXqnVXb1Jr5WPPGrwcIWOna7cZ+L+P6cO/5MfZt0EtXL+Jj3ziHz91zHm8/dVo+285i8QMyIBjIHkCIBAS1h8GBt6V3PDHAYHI9x3cOCx1wBQruPHUGERkUMHNhnFBt1400cXlCS0/aIJmqJiCD1itlTffWQKFtHAFkX7IBEorTmyGPgpHwm513WIqhtQBACRqWgVrfweD2exGBFCAEpVAMMIRA8sliU2oFUNDchDjl0EOMmLEJNiXRgEKHTQCA5vTBpSlZlUEB4ABBBgMA2l9h8/HnIgmtEYPW5y3Rrj3lXLzOhn5dg99lUFqCgBQO8nHvRg5JnXSaqGy21Q/j/FsnyiL4D6EVK/r59llptxaIs7bh1B55SmbNCqQPbYUh7WeliCPL45mBqHorxnIs1boN2DKGtMoaPvVjSP6KzvHAIXmhtotKndLTN+5p+U/TsOayZuy7xzeAorVuuzXi0coH8HXccs9d42Qi4NLVi/j4N8/hs+8+j9tvOW12HxGMwJQMP5gxgcrQgsnXUAGw+NInBhi0AIFHzj1QECNX16XbpdJq1JkCrH5T57rmyRvKKkhInJGUgjQuBmcGCW3PYA4itjzflxYSrr0WH7rxwAzI7TBD+Y22aRUqNtgxBZwUVwgEiqLkQpgDhYEIIcjiOmEA7BPG0DwEMQyae+BzE9yHczxQADpzuXvRAwcEAGQP0455Y29gIIT5sToGXdHPdk/7V7Bt0cZRNiw95Qs0xsRCaYXcWlR1ZnwG2UeZ4aJ0bSMsVIABl0Nix2zaJQxEkAMIwgC1FupJpW63TnuVQEDbyk25ZDX+QBAwYCAgDAANxmnIwpUtIBAx02V+/Fzf2LF24GT8yY8pBdTWBga8AbjPUvMN1yNAn0lMu6t+Kuf077/2/BtR1m5n9fMBRxsHL129iE988xx+7V0CCqJWMLDorRY4QBVaIMoAoRMAS+XEAIOp6kRbQEHNDiwZo7rsowRvSNn6vGrg2cD2hzK673sGcr4HGg1NvlLPmkbzg3URBLjfU8yZzsa8mH6eOiN3SPF9/Q2S8EHQmBsBIRoYYIQo7x4iIxAhkPwdYskmDIMaJxYQQNEtMWwJigYcWOvtaWwzTMkTX1fTCQgAbTBg2rqXkFYYmioO7WLQZnDSdgIIc1bNt5t/iy1xaytzpqr0Kr3RyUyPaw/4bQVxBJCyPQnAcQRiyBnzBhJCbidjHTJI0FADQa53H0JbaisAiXFZbacKDPgQQYSCAMcKTK5d7K+1R4wOBOjYsXFzPWMG2g6DrTuh3maATefmqt3KUINnfW6E/gDmBn/OFsxuM++X/0Pr7n1LIQPgldcyKHi7MQXIcl9iDowtCP5ESPstieHEAIM60cOU3KUrR/jw18/hcc3erKVRhw9sX7oR2gKs903XiRR6FBQwD0WslaEaeR6xDxZkQss76AEIwC5qIU27f+sdlgaul7NXZoC8M8esyJgzkvaL6KwrOoHIg77HQAQKDiywGRcWL4dY2ALKbELQmN3kQYKGHGYgIRmfufdaAAV5EaljJ9FtcYEo9fgz/SxGpQcGEgXNpbFh/QtWmbPIlPXcnrGxtjHxt9qmV2yBI3O6m+1ieS9F2+h5pH2cGAOp8SpAnCTEhWFYAAlTDvv4TyUnkGAvvk8baYVboRyfG2AAYSsYUFYgNtqGud8udZssjZVjG/upbTiNf9820l4OZKuYAmw8c6U3DEiUz96iM4C+wV/S0V4X1/e/Xl3qy/V+HXjoJJT5Gn7n1Yv4xLfO4bF3ncfbbjldnsdApD7pWJ+XwUHrSWU5OcDAbVunsXmeCRQ0SoAIbSttlBZAaijAJeOeru80RkvvtAbylnoezwZh3h7cj5Z3AKD0ENLJ8qcGBkYXtkoTtbuBbDKsPZuWYssAgTcZoWNfx0DOCJEqOkpgYRwoGaQxECbKbMLAfZAwEJKn2gQJoUxULD+8g2yIum+BEp118gXYhxDCqN4oJWMDzjR0z9gkr5NlKi4zY6dy33Fug5SzEw1Y6H0sYWpD/zR9KKEbNfQE1ya5jUYFA2Ow9jGamhECuiDO/oIIg4IEoUAmIO4AHhQURAFQ2i6zvARtp9U2Su20EMppgAFGGSbYp30mMHZTHiu+jYC8b60cwwECZc92QGqDHTOGCOwUNFNg7KYM4gCUQMHa2YHGuQnkxpaUFltrm15nyL2z3rDi9eZWve6L15c7vb/Xm+m80L75EpjIz6BVZ/KVVy/ik986h8d+QpiCVj17IYHImTWwNopYDyFYOTHAoEaZBgq+cO953Hnrmc3Uv6havZ84m0VeTAvltQDBVgBQG3//s57c1hrkPS+gLtaxC89AQ9xpH5Xn9oADMJdDayjUNVsa1LX32QID2fPR++vfXnZ1KppLYMzHzAhNHSPEAhISk8AuzEAshi1mI9QFCew+0c0MnyAHVN5ozRMC7YWiKE9ZW2IHYhTZxVjS0WZsLDF3N8VkaHb628DBLmbjv4us28i0tv4WUee28NvBvY9th6BhAJIMdvs9BkqgYQzI7TEEjA7IjUOQdkEF4lz7DABioBSGCAaclCngYLkJGv6BBwRxW9sABVDr53Qs5wzU7ZOYgT3BmlV1dVwghwjT1E0dIxRI+q8maLKCBMmjkddhysaRp7lz4fXXPvpB6t82/J5BTNfZNZV+3Fc3+kKVfvM6Pp0+ta+fNgCGtFhdh3UoQMEpAQWON9yryLMUHOgNXrhy9CcjlGClBgV33XoGDEFP1m8CJFnDuMrAnAQmN2mDAz00K0S0iS3ooVqrN1CCgR5lPqfH1svOXTMACUntvLHX7Z0BgnROiZopADsbFHt0VQ+W/ECvAUHePwcEHgz0vNS2QeLkqU5q0I9hoQMxPjv1VMUQ1QZI6h8IGIgxBaGvob+D/k4JjB4kmKfq8wwskx51e1oncdiesnLONHVwsWqaGRyTXRcQqLHZcSzAwI4FHMh5Bgzk2h1HxAjsYpR7RRYvN3JS6tYWLW/IwKS1wxCEkg5kf4ExBIQAjBT0dwYKwy5iHAjjTkHCpL8pYGRCJGMRkKdoqVeVmQhNPK1ZBAULP+i2SSDAtY2FcXJb5TCBB2ysbWZjY+dYG6lde0z0AJr8lmMGxNJCOVE+Wi0hBBmbQ4TuIwwsOoVY9MIOrLN2S6CwpfT0AjDXi62wiG+jLbrQlwGlbkyGeuLC4E8OKAwVPrS62PlpBvNKqUGBjY/vLIACTyA2c3G09Ka9MwOXrh7hkWcfxinc2q3biQEG1rYGCp74Sfn2AZA9fnWS1fCX4MA+yGpDnyBUOusP2/+dqxcBiMLyKsN/aZaZC4NpHd+vmzIQLSLaFijoDYA5Uu7eVs4vOg0ndMxZwxR0otRdKcV0vHWv/oDoMSVLoKB+pyUFaMqv9pK8UrR2R0oyFIUoRhIYA2MXCDt9/5EDBjDGyI7KlmQeiixeKmlyTwSmIMZoIIAKkECaFOdCL7pwUndRqYZQeyva1eGCngdqDIEAgTkgON4xdjHOwMBuUiMUGcdTTCuzHU8RMcbU9w0kzOSuxYySgQHZBkIIOBgECAyBcDAEDCEKKFDDn0DCRBhDwAEzRm2jkaYMEHRcNwFCUIqVLRxkdLmpQS7AwfW2Sw0GrK32CeXsFEQYg2PjxFgc6L2NvQGWmYLIOREwECXKOUbxhgIohSYGN7bt2QORMBPKxjBn/TY47yoBhUbZoguAOQho6b99dd9Q6avC4LtnkOrnbOx5xiKU983H/Huv5nvZfneO5RTccUu2Xy1AYL8LUEAZFLRq6+3j33rmr3Xf58QAA6B86btulZyC1NB6jq0IGM1IMBBJV+YiUjAgypXIsYcMvKRIzm6orBuAGiiU8aPBdVajmTjmzjQxp2cZOqdAs07fYiZKAFHKo4c7zCvwFw4k3gD02VAQsGONUSmC3qXjcMf9vdrUnC89QOTfuX7XgTLtDegUnYX4UG8lYjMakSWDN4MERoQwCTtlAZgZOyLsYskijBQkpqrKmYw1AEnsWz1TciDBvFUZuOKxkohxMU/DSSedURsd3ggIzAM1g/P9iiEwwPD6zoxSTADg9V3E8SQMwfEuJhAQjVlgZLBWgVrfH4KxBhq6CYEQKCawcDAGBIo4GAg3jQEHUX6PIWAMhJtGUo9aGJ5RAcIbOIBDBDOBh5DapgYIgwcIlDO2ZQqkfXAGTtJ7tokDaX5hNUvytAXVrJ3MCDMLy2JAbIkdMGDcAgNbv181P+aMFM0NaDHlsGMg67bekje1FQy0E4/Ley+Rto4IAJDfr2XwW/R+K2FW7jMHBB4MbAECdf7Wryko8GDAjhtDYJfVU0Rn93Z1aNnHXjkxwKD30nUjE9Sjp8wGJLfXQAKJp8cKEgDg0qsyZeSz7zqPB75yT+og3mP2STOjeeHpqTLwR6jB014wQfcpLT0xY9RnQ+N0cpwVQJAyEnMAMdA6Yk5VdOcRlQPGBsuEjKI9gp4fz/cB5gqiLmuAyEIYE2SBDkP1g3pPgd1IUQUp3g8nJiiEBXCQGGGtuHaEOLHEqAOB2d6XwYEwsYQZmIGdeqgD1AjBPFFOSnUiBU76m0i3UQ7uII8GQIurXiZ9yBkM+Gm5fl77jJbmdtjgeIrYaWjAwMIuMl5X8GDswPEk2wYIhCnQ++xiUuJmrID1UEIIhO9rXxjHkEBCZNa2DClcMQRCHIDIogpjiIgsYI05gIMZj4CJJSlvJMlS5Aiwhos4AGBKwD8owxMBUJqdAqdo+w1Stwdcm0x+24AC2jM+9gkXeEAgdeBNgADIoMCzBb4tUq6B9VdnCNNKodhmIHullxS4Lygop6avPnZWPOiZv4/+1pyK8hjSMblWfzeAwFYQ4I/b/e+69XRxzj5goJezsA8oAE4QMLCXvvu29Zf2wmMW4TLLvOUAyivxqadx6eoRPv5NWYbSZjeMoVQIABKYgIEOLXbemDpL9jRIO7spNzvDEvI8SLC8XwMJcowTgADEeFppxdtqI75WanDQKz0FsRRr84AIQI7rVoAo71cclxQEpcU9AKSEOGlDBQd1aEEbo1jEDuwajOwmCKxyjUKLiqKX+DbvGDsSI2R5CImWRX62LSpiBmciN5ipTCjqfWjL2qy1hHQCBxU1nYxPBxTsmJugYBfLvAEPCo4nAQavT2qgYs5FMCNeswVWBsoMT9AYdRAtB+yiJAiytEZpkKWhgiaBzr+AKrnWFFjieUMQyzFEIAY35hQ8kYR9AjJAkC/ScQIJ1hbTnm0hXaVcAyJqG0VtI5buBMYcELRCBltYApFpu2+LBNtgwI55hsAn5Vq7lSABab+VtXi6D58CpUPQvaYCBb3i22dJt90IMLA6i6uomPxZAgB+259nU3Ltml6YoKcv6vKCgYL7nsTdt57ZBKZODDCwlwZKIQPtWIs/KA4j5dQizh3zpSsX8ehz78fj9zyJO92Ux0Hjx0DZwVsLb6Tzqr8RQk2bx8qQBpmiuNAjl9P4vOEcQUVc3htPAGpY9Xlema50Ik+rtQaOP6elKHrxtR6oYJfBK+8kv0cFRhh0doCdExmT0vwjLDdBbIEpuzJLXvexGSWkc4AyYcdKNO0dADDArEvlkr7fFHOW9o7BAZgw5URFqI0CkFdbZPWIM0gIhDSVq/d570JWzihZFc0ACTiYG6ClBEMzXOXfbHwMHMQYszGqDL8HBX5/XeoErSmhKE6xbbt/GEhDFBETkeYeMCJTDpkwaX3l784Q7AI4IAYQxAizGlLxvjjlHyVla43QaIu6HVKfabQFcw4XGP40QMARKYfAwP6OeQYITC41JkoyszFTUU4eCAB9ZqA1lReYj++Wh2zXtUoKBYYywdByEVJuApfs50DO0XEAIU+p7AyQRjW26rM1INAFAZVh32L8WytB+qqPRAUj4B2H2b3mr1yUi1ck0fCLah8tRL5WTgwwOFPRI2teWKtkZlnCCS9cOcJHvn4Ov3Xfk7jrVJmzYIMwsj7MPAbrWHnXzMsARAklGt6hZwZAKQBGONB9E7FsO7AwRvFAARTGk6MaTbvpsG3WhJUiLraBQlxD0sBSBy6PWIf07wsgKVIEKtY5MKCQZaNAQeuQlWuZkxDrrElXepS+2BrGTtkD4vxpafl4SQkQrD0yXZ1BwjybmGf0YF08fQ1kI2TMQcsrZeZiSv6+ZQhC6wexokLhR8br0L6h4Rxf6YB2/ocBtzqkYH3MwgmWbxBC6C4Cs1aYpR9wYDB8kh1SnYkITJJZn2KClqV/HW1gYICBYoEoDwisD/uwQT3lcCY/w1GVMsuMgNtXMQJACQRqL9kvAAZgtqAR0PGMFwq7MxMzqp6SD6HKdmZGPVsIDWEmJ4e53zhVWdNjW0BACwDUxr/l9c9Xkc2l/5EqKeNQXutBwD727Oh7R/jQsw/jS8YU1HVcuPbEAAP/kt0Pd2xWvISLl0WoX37PhcREMHO66UGg0rjrcRtMhSdB+b6AKJMl4JCoSQcYhsEYDcIBt8HCAVAYzwPQLLnHSm/hkzoRZym+1lIca0g6HW60gTdew2CyUnkzwFqZcrlXmi2KNF8DQZVMoL3XQGgxCoDLf5CbuHnfJUCwmQwgAQh2nNRaBGQwSfqv5xAlOTl59YwRox3nn70fScgkEOtfoe0PBmm513cxbUe1qhPJjAyfdGhhhXFQUFBnr9nzTDGTrlVQJCGSzkqgYqbCTWPeDkS69gHS37WS8hSg8o65DQIjAQQg4YLFNjAgAGT5A21A4Bcj6vVTv2jXTF4pZ8YMffscAAUAALaBgNay4QAs7aV4ZqljG3KpZJZ0IObMqA+hGlNoIQfPjMoV5cNuhO6S4w0QsAIAaq+/Nvy10Qfmhr91vW/Xka7fhl383hE++OxD+PJ7LuD0bRI+IJTO6VI5OcCgome8IIu4jL+muoedd3TlCO//6kO48D4RKmACp9Tbh0CFxw84ZeHuWechtM7zgESu4XR8SQGNA6kSErAAzI0ngAQaAORMYX3Zmvbt5Qgseg8bB5O/3p/jSyssA5Rx3JHk64KmfDFkQ7ikhO19y5Xh8vOWEjc7di7dc4gy9UloUcYQCVNgme8dkRZ8SQrEBqqMVpETz2XUKzWILJqx8R4WopAESjXcE4BQKgHx1GVKY6CQkzoJiBwwDuXMhFHlarMTAJn10iuj9ZXgpyz6GQl5TYOb9PfBIMdnUxcVWAx6bIBT/i0RNsaqzFaQfkJUf6+0cYsavKPNDgDr/bG/RLFeb78b4LSOmdfhAL/fg4DZd0MgMunNk4e7b95uycVvl+O2yPHFnBVlaAhVUdno9BYgzKgv9RIJS3lNzW8+pBPnOssb4tZ3H9a8/Zac2nkG/fOMMZjdu3EfKybbQMDhZbFfT75PnVrt93H1LrmcIGAgf2tA4MGA/LVeqr3NdWICcHjlIh76nXO48N7zOHvbaZhUi1kMAA70QQUIcLMb0j53PD8qU/tcncc8P+5BSeurhHa9V1SDzo2LgICCCjgAorgO0C5LWbWteNq1DqjlQl3qFph7zDVgaIEFoA8YfOgC6Cc+La1NLkxCBgcyn5vBk/PIGGnxrAQGGJnNrsxT7SW2+5TrdiT3GwJhigAF1rguqxcm3wvAAF20RpYTHjX5cETAjsTYj0GAwBvGkKYuTpEx6W+ojHMOQq6dX9tjcC8RgiUUUhFWsAWPCnZAGRcBBtKP6pURZdXKcnEqM4T1x7NMTtb9mDNjA+ZFWXt5GxhI5ywAAqDsc61ifcrPqx8bx4GSAQDmAED2NUBABQDM+Dez3u0HNjP3TQfHmNHawTGnyg/vxIrq9RZGhXN2bH9dmvqqsgk94FN+JdLyTnTb7e958UvG3p9X1Ilmu3IowT1zZq+AOTVD1r4Bh5eP8PBXHsaF913AmdskfKCzskXX0LxPt8qJAQblNKMOILBPs9aLl6igD6+8gIe+9iE8dd+XcPaWO4Dd90v46bYDT7N9gxtdPQfOw5Da6AEeILTBA9sSpWwJaFnbbWEa4LbHYdb9XO2q31T+XQQAnUG1hKCXSgmeqABEFj+29/eAKULoSGZjVtAFDK2PNY0VZbnErrTmdufpnUjsgRnLIciaGQkg6LPJ/rrbxY6QZvpBZWXhCQMHiDKljyBdnkhyUkZI3HsMOdySlj8OOQnRFjuKzIgHQWd/KBDg+RTF1uJGVjxLIL/zPmMr/DLJBgosH8GDAaPJxyGse8YNQOvlaPLuAQG/XQMC+1N/+2Ppi4Z+Wq7F0YGskOdesMoNua5r79oDAMn40/oY3Wd8JpmgZLGycwOAaVFPlaHZ8jlDRdv1dJTf3kdP+fNaRr+lv5YM/dJ2IbVqwQeKu2op7mVQIH8DDi8/j4ee/YBzavNziRxA0Mcvte3JAQbaWh4MNAFBtbKZxbMOr1zCg889gqfv+QLO3nw7MO3SvVOCGZCAAO1eL3uiO1Zvy3Xzc4fc66ym+ZlASt7xAKIHHGqjmZRXAzjMntXX4756RUdaAwC9hJmlWFmroxZKpwBS1JjP79+/DRjMc1tiFwBLdpQaJaW9kLDoSy8SMEHAgX1AxX+syox/kg/ne/n2aSm/AkjouQNUEcDiu5ToWpvtcgCAw7AY9+5NmQPQnUe/FJ8vE+TsnQmtefVi6EsQsDVObgbSZFoo6IYMfanHQxMQ6A7PEsi5OTm4LkQoFjvzwhir3u+NP4BZTs8SCLDpbi0A4Mdn/Rt6v6pqe45Lz15SqYuwDTD4+2/VTXU9e3P9awPfNfyta/1zZzXFzMDXle8a++pc2n3fXdPpjO7FmQiHVw7x4NcfEaf21F3gOAFEICp7f8F8L6izEwMM/HuuAQJKvVMa5/DKJTzw3Efw9D2P4+wttwNxN7t3rUFoKs+ZHUcGFOWBiqws6Kd8LH+cReOH6SMtbtCAqsHV86rL/UA+5u+3VErF2vYsWoOrHlizAdUI6fSKyME0XO7wJockCy5Bw1QDhjhnF6YKLBwwZrkLQI57Lq2UW5feNK+isLyOJcjpzxl70Lx/x9Ax5/Yx6foQExbescWirC1AA3fOWlmaS27He96xnd+Kk+vmTBZrMuyVJVDQKwMkmc6WQB961qsqrXyepVwA++S0BwGygmPe7xfU8mOyOR6Lr37265krbG0Wit/s/qEA6Hb7OWDw+12N1qtQVMfkJr+3Gv657dCncyxojc1efEM5zAx86zrT0mZ/lnSiHSLC0ZVLePC5j+Lpe57AmVvuAOIEogj5ZgcngECqJwnrsj0xwMCZ1G2AQPcdXbmEB775KJ5512/g7JveCkzH/Yd4Iz4JquPa0KcTevns5X1ab5BiRnDgwg0+0t/27IFIgQMA+GVa83bMI3UGEoASKPSrPR94eqtlANAK4SSNWzI3W10ELxdy237denn/DJ4iK0AIJViYWJI3W8wCc5WvYTpzKL1EX9L87EYPWEss7Bk3f1lrPnSzNA7XiWFpf5B3XDOEtYdc39NKy2tek8e+c8RbMqnvubX4XJ2aiUkhHn3OEibMK5OWle/1iV4ybx0X7zEBHhwUIGAGANw4jLECAdc/BtN+1Xt+HA5a6R5gAEpwIH+36yNXpdWk8wxdIO8dndGvnZRaLv5YUWt0jtfH2j2Hqv00HW9ylECEw6sv4YFvPipO7anbhbrT1XtB3AQIAK0RBicHGMBi/lw1tIGCGOEBAXHE4dUXcf83P4ZnfuKzOPumtwGxvEcqLUWjjAH1jm8pLVDRC09UqDylCPlBamEOt20DdACUuy2GhlPf6xnZ1NieDbSO/GVfBcz0GqoHpN2rW5H63QOMUSHIX7ZBYCAhDOn9o4GFmMMPQou7bRZNZkvZtqYF1slSVns/qMyAtubj72MA16ZJyfXuVx+V9g403Yh6OqfvL8A2/bVWZl2+Ot6E2Ncy5Fxda9paDLrFuBUkGChogQOTv96mbF/Z9m1fK1rf9i0Q4HMCBgUBlo9hx4vtYIBB34gjaJqw5hjNxt6Gj0bJNjkA4HVTyC9h1yTHxY3bCjBYOyBtLzdwXxfl9zeU33NI5kCoBwyWQwT+3L1L616ejV6wRYevvYz7v/VxcWpvfpuGD4QTIA662J1AWem7QeykR76dcoKAQQcQdAZDAgU//lmcfdPbCwM205At+cVefvENKmbk0++Gm+TCCwWA8OChARw86yDHygG/WHwsbGnA+cHmlZAfdEXAMaIAGF4U7ndZV5NRrYQyKLDP3ybAEAZJCAyjKqYh5ScIq4BEaU6QvAIPFJYWtEF+g+RLjEvzHNFOxOwBAg8GWpnj5M719+k9py5bPe19Fsvat1xvHXr+XB1CS0ufu4v8x9SYgaBooOiNeq73TCPaMq3bvgX8ahCQ2rYCAi02IMF8noDIQkEzg+KUdV6c8rjzY67WdW6Mdccb4MABVYa+1C9wYN3enBSw9xiGma7rlZ4O8sdmhr/FkMz3lfeo7cA1Gv/NxTribuEUkc3ha9/B/d/+abFfN4v9ylkaAaAIikCTPWiuhlGWEwMMKFYoqwcK4oTDq5cEab3z11SobaM0Q4rF8xZCDq1yrcq0GiTsAUA+CVSgdswGahvdAzXzIM9YQetNaq3BArRAgMnaD86GokqLnEwlACs8hWGo6ktJGXHyUPI+hEGUUxiVVRl0aeMBIbEKbaBgS/GmbQYsYcoDBZUEQktEPXnSfHstm3pLwmdBrzoA4eU497aAOTBeUIg/CMqgOFYrsVzj2tNMPdHtyBS1tI/1FWsjVuPPgChdBX9E0q8Si4Dyi6vk7rs0fbVu2xro9YBAwQg0gEBiA+Kkem5SUCDgAAYO3Hgrx9ryOPPSbo4zoBxr9tsAQwMsMIVKT1WOSvGMjjx7usf/7jEhtcH3ej/9buj9G6S7N1/WAAYeoB3+0Xdx/7c/gWfe+ZjYLw0fiEPEmSmgCLCtq2rZS042CxzxiQEGzbhQiyl49aUMCm65vW2o/P16ZZUxaF/fTELZo4iBbxBpdQKQBwEwA+MZhv51RXf2innBky+Me4sJmCkmAJAPJLEpOQCs8U8Gcqp7R9YMAEHVcsjKiYIoIBoGlZUoKA5DBgthkIEUBnAYdVANYAUPZIxCGHQlQUtaRF522G87oNCau22lZWJb8fEeGABKwx8avz1wsH9JYr18j26ceTnMswSet5YWCyTbWQL+d5ol5Glquw+RJsNRMvj2L7M+CgogQMFYH8L8Q2pRYwVMlMIG7MZ2ayrpPu1JsFkY5XYRGmgyAg4IGAiIE9JYizamyjHGPr8g2jhb0GUKlOvxlbaHQdulARYcUEiyqEMMWqwPZNZgg96pwx++7zYYkX7YxDMQ1w90k36e3WoFKNg7N+pAGgJ4/rXv4P5/8rN45sc+nZ1aCkgxL4h8PHNQgAMG5mB7Xk4QMGgptoXwgWcKau+1umf1oOovyuf6M2sUzmXElnvfSu0p20aD+kGa9g2DAxBzADBnHfI5m+m8VNfOIGuxBg0lxU5JlQrOyaeWkw0GX1QOFEICDKyGXbRsAA1jBgskCk3YA1VuyhiIxzOAg4QhBgUVQ5CExhiy4ZgoZ1jbtL7Cc2WRpRuq3VJxONcMBnycOYEA2/Y081qIx7fpEnCe9dcl5Vr1q0afm3mgtu3DRHp+M79EQV+Ka+v2QPlLl372ylaQIGEDUcD2hq323NKOA5XbiTGAPLsw7nHShMEp70+MwJRZAp7S+OJpp9dGiT3fgHElv8txxTtksODAODlAUAIFJ6E6tLlWmn2udghrnSPnNJkR74yUd9tLB8vuXshFfw5+EXx9RK1jU55c49kU8Pyrr+D+3/0reOYv/WoFChw4UGagDisYOJB3XHd+TwwwaHo6TrkdXn0pJxrefPuiUOQ+y8puFQj4OJ0fgP6+9cDsIXf/rDq0YBvOc2Zjoip0D6yDhnTfnhdXPLzhOTZoOg8GonksBgTsmjiJnJxHw/6cWg6tYgpbZWHKCkHkwGEAD2MBFMIwpDwEVkBgAMHCDiAHLsIAMrBAktQ5qScqiWsZKJiRYeZkJNZm87WWWN3ODDgwkIxFmXjr92dZtxidtqeZmJw6Ubfquy3QO1Oe1mdTzGPAzDNtMD/NUJG1NRGgDBBQ7ucwynoJJEst8x4gwdiHIjdhQxuutV8CCB4MxCm3mzf86ZjmDWj+gAcCHKMkr1XjiWv2bZ/4VtUuNp6MdQOQknt5arQbYwYUgOvQMb7+S8bf6RFu6d1r0LephDKAxFy/iwIVlRvvStBFwzCzIRko2P787Odfexn3/+5fxTM/+g/wjpvftgi9y5sKUDBwUN61X04MMFiiPo+uXML9NiVREzWKYqgLQA4ihqJTLj66wQzInwYgKFaCaQzSDQq2VQql6xSuN5Qe4RfXONAAQBE/0PLuqspVtchAAEA2KvYeHhB4MACUCix5tVBvR89xcq6Tz4qktfQeYsjNiyyAwnggMeQwAMPYBAmgARR2iyCBNT8BZkhQUtbJ+QZmK7v1is8NaAGE0qCo3GM2FgUYKPZnunnNy8S0A7eMi+uzHJ0CXmibxXZKfXGABwgUdOqpN0IVqGuxPwBJm5lXG/VaBCBM2v5Daj9AVlWUb1bI4yNJbgFZ/gHlT7IzZ8O/pe22sDrXAwZ4yqEE3h03x9H1jCFQ0P6BJFPiAI4BCM74MUtcJQxiJAudqiEHjgkoCFRwuu5adIs7jy0sUuvZBQB7TboVkP5jMwdawCERIyE9Q2SUwYHV33TuUnn+j76TQcEtb/cVW643xwy+mAUcAAkgLJWTAww6s4sPr76o6xR8LucU+NileUwkFDTZcQMHgJ5TojmJOWpnHYZu8k6+vq5uu8MWnbVF9fUeIR8s11/aaRs0oOxwKN+dl9C+lQowrAElkwG7wZquW2EIuoAg3TNToTO5ACWCPtZ30vgoEQFq9BNQOH4dpIaGQgAPI3g8yEZnFCODlIMwgGiXww1hAKaQngMKCPp3UG80InujQAYKoG3G0xuZZt6AxZlrQOAp58QMlJQzduphTrvkZZphKYzNdAxMU5a/A3hFGMgzZAtjoVCE5ukDEh5wwM3AGIYBNBxko2TgYDxI7A8No7TbqMAtKkiIUfpznAQYsoA95ijPU8VJml9CChBqFgFA8Un2Le2WRlERIpA7NMMEBuJaYQLLHdgJEGCOwO5YwVuct5kbO+z1y4ohFCciGxJ5nwk8xAykph04ZoCQ9CagQEzr7wECXP+odUp6eLtuMwAA52QAqwBgMWyyVJzR5RizXOxdAXlfi+3rO8+esWC8vQyaeWMAnv+j35+BgllYxoXZisd3n51Zll45QcDAFe0ch1dfxAPf+Ck8/e7PCygAkMBAndikjcgpZgNHXTkBp32UPnoDOG97mhJapqCIcQ/2IRVvVNOj1+5RHidFhhwdYPBgYVJDl9bjHQRgVEwCT7s5arbjjffiuu5LoKBRVhVbi32Z3SQkVM8U1IirV2qe6qSe5XgA7HaymqWFG3YGEA7k2ZpngDhk7zYMAI9A3CWv1UACKGCwqZHqJFmUsf5S51yu8qe9aNRKuMCMSw0IKuMC/VeAAWUJsDtWQxMTeMjbE+IuK12eGMzyd7VNrFgfHHRFtoFS/wrjoAltAaxGPzEHCt5I28iDBAwjMOm+cRQZhAHMCupsfIcICQCFDBCI5FsSKX8hswjW6+sPBC21GbldM0Ynef2cgV2dQMi5DYkj2LeHhQkM2O2O22DAgzgrS21j49udYo4DTZOskBmV2amLeusJHJihBAr1Cjhfa+cetKZH/A1aQGAJBLTY2w2lsKkU5J4tHbjhJkvMrDt5dt3zr30n5xTccrsSLRUgAEpQYCChVVyC4lo5mcAACgpsmeNTdybvhhBFiStNaEo27WM9DnY0jF6bjqFiE6yRWZQSixngaQIN1ukVJNR9yxSCou0EJoJ2xh6o2LCOQrY7MXfOSRWkewY5L6FEv3kw8DRHv3P2b/vgS0mCK9fYSnKz4hOGWvLRrxVR8lgECDRBgr43Tzs1NscSalCFXHukzENSFhQyaCBTIInG3iWQkBedKr/UuYINsmEB1gGBMQdmEJKBmdqAwP4qM8C7XfZEWVkEBQIco/ydWAxBjIiRlW3goh146rcpDY4hAAAFBSEoOAgCFMIo4YQEFLQNYOBgHAFjEqadhIOMRUgAQfMkQgS0zcw4y+wbBQhBvzaZwkW2hKy0AEMMvrXZcltpe1lbGRjwiZ8dRifvy4xOnLRNXKhgE6MDpDHSHB9WZ2uHjuHbtK5EqJjGhVJQ63nnXHcs5QQsgYGeI7Vl3ZkW6DH5LLGvnZlR/noPBpRnnj8jGXfC83/0XQEFNiXRX+ONP9Hs+vaU9rIcXr3UPQacUGBw+OqLeOAbH8HT9z6Os6fugg8LzBgBAwRAMuD5d5kEwpQzSxOa9h6E0eAGu/Xj8xbDNZDAMcp3aLnquHZPux1QgIMEGoBM2y2VhNqHdqxLlQE7yjV18vreVYxMxNUY5L6YrP3x6j3lHfVUPU5hENGM+t7TlMMeJo+ATd5pAoSe/JlEGbPFRqcpUdZs9VPZkCnk8SB5nYmypl32SO062slMB6FeHItARR/MtPOCQrX+URkY8SQtecEZlAYFXXicCQxkkFAAAs7MQTzeIe524ImFIYgRkwIEJJZAk0ljBEcuAEHrmwn+C5Q0BPkdAkIIiIPOcVegMIySyyHAgBDGEeFgLL3ocSdtwaysVASNIgfW82g80D4YNbyg8rRpeMTFQjAGEBJ7aPk2CShsaCfbx/NZIPN1Bvohnh8kIJgVC+PYtgvlzPJA6nwdC6cBXUPZF12jjtcaJmixA1sAAVCAgtlMLw98eu/p2QEHBowVYG/U7Xp73cqQP/8v/wD3/+OfEVBwy53l+dS+TxcMNIDd4VX5NtBtONWWBU4gMLCXFqbgtOxMsjE+S9GbJWEkRmBu5GuwcPjqy7I/HKjnUU1D4QhgKKbNMKIYOc2cJSDP3R9QMgQALMeB1PhLR1cP1QMKDxTSK9YGvZPc0gMHe5T6/Nb1STdZve094yReXsihBSJXF8vzgOYGcAQmoXzlHYNYe44yTW1FGSba2GTNMXUHDoOAD3kJAQOAJlnFZHwE0I3J4GRDslOjI+Ag0djGFjiQUCzVrM9bLM7oFICgXtXOgAH42kCBJrLx7rhkCI5le9pNwMSIu90MDLAyBwYGlpgj6yOkDAEFAlcgIYwjdjFiGAfEGEEHQ7pvGCdgjMIIII88qraLY+OB+QKSd8WUz6MIpuhi5tVKcamNNuQQtdoo7YtluKCVALo7RppVUIcMLA9kLWSwD0tQzTSQfSUgoMoQNgFBZ1ZJet6CXklZ+3tS/UtMQVFaDlSlE1t5VQVDsAAGZms6AGkGTUH920wav09+zPbd/+1P4pkf/3WcPXVHeWwBTMwBQC1zkY+3j596+v+BXjlBwCDoS8tXps6euqshrMpIEpDCCHCefgEKjBmIMuXxH31cbzWWCgHuXjVgcKGIRItTGyiY8UkUI5FGNjJa9oPCqFkPFlZLrRjQGABAHxHX9wH62bWtecNUveMwZs/KFJ3FtA0kAPLNWq8YE0hAogSoUpCz0pKPJaf5WCoAjqQhhR0QZL1xxAk8Hki9hhE0sAIX1hyElF2YwwfkptcRwYOEZHBaACH1G89gdYwNMwpjA51pkGSpwEav4ThJ26g82Sh2NfI1KODdlBgC2/aAwPpk/ttmDAyACi5lAQhR/w6MOELyPThggnQTHItaCyPAMeg4cu8/UWZhEsjVkAdJX8FIyWPnMOa+RpSjdW6debiFjYBpsX1SG7Xax8KTDhAkhsfYj2mSkAHHGUsAXskjaPXvGNtMVMMTlr8NMFDNEGmCgSX9sEE3+CRVX90kVmMFzbibsxB0yrUPf8R88XwWwQKjWZ3T1YMNVmAGBBZBQIcxaDgIz7zrczh76o72dM46N67Yt1C4YR8XJi6eGGAgL/1heelb7wKAROOsemVAZgbgFDGQDPLzVy/igW89iqfvfRzv/If/Png4cNc5g8QlGPCgIaP4rDjYzjGgoM9vrQY4M6JAoRz2jfGnsjKfvDjfDQgAc6qsLqOXvdQ5GJPiMo59wiIZo6BUbLG+QWIbFFD1Zi9YFbckXPVYFUCNDETf7yT3ALtjpBXhjD0Ag6aYmYKoINEtVSreqRodRgYJUtH5AC9yK2LRt7LRsX8xGyD1Ws3Y39CyIM8lUGD7PTjo3n/fJK+lojIQFsjYvwi4SYPgap15TOl40gVUvdOmtvFtlIFAYgxqlsDCBPW0w14xgxkCirzjoZJfNdYz0N6TFegYSADoLpsMoN0bUIRabcDyNCV1LX5Zg2m09/BsQGJPt/Wdrv5bYQT8+6UvSALlSo96/rXS/2d+6Ey5HFrBFDRyE5aKjv+jK84+nrpz9bITAwzspc/cdveMwoH/DaA1jaP4cAhQKNTDyxfx4HMfxVM/+SWcMaEGJzq2SUyx2NcEDW6bCxBRgYUwpv2FIa2MqDxWn+M9iFbCXvHCroNtTJ7ZK162VBK7Mabf6c4OFBkgIntHy+Z2YIHsPAMScGABWJ1f35y7vVRSDoICEYwCFExxRDgQEBUchBIgYHIsgq9TB1zVRsf2se8HnfY2VLOlUMgUP7Fk6kM8exADAxAwIu52QPoAcf5rOTDigc+fSd4rA4pwguUchHGUbe2DKSEx/aZtCrF4/1ZR+SnDB10tTti+gAQQrH/U4i2ch5XVI3kqwEJah8CFCMoww0oZlN0CJBlTvejitVv9+lpAQLoue8mtxdG4NnYLpV6oSNaQ0H0eLNRTFckzBDG/h903XJ/Okz9tNgAUync0EOB1YGNmQGH8C+DfN/o83NSucyXbNTtm/Vvs1yN46t4ncObWu9dtA04QMHjq3idw5rYz8sM1ENfGayZMM0kNYXHE4eUjPPS1D+HCe87jzG2n01k83pTOyefzbB95AND7wpfzLrg4Ln/ZzlOwkPJazYi6Z3ZXWaxLLwbYAAKoaDLADw45Xt9zid5K71jUUcEPR9AgsvAf+6gBAzn2oAsWgCJxKYGGhUI1e7JWOCotLk4hDUhoP3uhDLCFFQQgCGMgAAJQeaageItXbQDMRF93XwawmSWR1HDL4k5aqxSbt3i71FwvHwhxN8k0y0AyC+FYkmh7iYdLeQYeEMj9y9yCBAgOhjRLgQ6GcobCeJCnKdqURd1Htg5FUCW8IfkNxdiU6/Ja8yacje2Rtsvx2wIFHvhuKkTaNwfpw46Jq3vqrA9bsu8GJmARABAhLo37tTGTcq4wG/c16GWy+2ZdVwCFwTavQ991dJ2BgNbHoWazAerzaqo/hQyreiwZfGOjV9kBO551AFssFAA44PDyRTz0tQ/hqfu+hLMKCgog1iknBhic/aGzspEQrG9Eadhk1Jt3mHfqoyuX8PDvvB8X3vcUztx2prguo7rSc8jbdUihBQoq0OBjk/7cDrsgA+SgeJbBHLv/ltL6apqwLh0Q0EPJ9TUbSutDTBkcZQU7BwwbwAJQAIb6eVuS5NJ77UVvW70dOEiB7CDvRQS4j5oQSgXn5ddcCrasbO5fGp5gIM2s5Wknc/2BFJtNvT0MwHQM3g3A7jh56MMosW0KATwO4IlBuwkYZYZLjIxQzVAAIGDBit928gsGEKqZCH7Kos1G8IDAL2YEnbJoix+RrmVAChjgF6saciIoKgMwbzpO4AAwwDbvO+ncGhBo+7VAQU/zNIuGB2gISXZpFtAwV9u9BLpm8twSCJgBAGcQGx5xktGGshhuTU6Rk5PThcKKuLyuKiy5VppfhlSZsL1DjwVohQx8GNAb/5ndwdw59c/3dbK6OjZ629tRuUWSg3F45RAPffUcLrz3PM7celpkZ6DA1l3plBMDDGbsgDNkDKR/APJHUapixCgAXLx8hHNffQjn33MBd546g11E0X7Hen3wVBYNblv+cnFZOSDYe8UVc6AnYAkwsD+vgcAT07ChLCbL1ChZt9eQstx32YtITnLxzkDrvbnylJk1Q50tE3wFMACZsgWK0EsPJKzGIuv3mSZVtFgAB5Wc2Pc8FIZpsaROxoUSks+2KrgbSZZ63h2n5Z7J4tLDmNcAGHbgcRSvTBfToYObZNEn9Wzjsay011vYyMswNvIMgp+uGLLBqxc6ohBkaqJ5t0O1bashOiCQAEEY0u+07LUzbrZschPctkTcAgNpu8USAD1QUDB8M+FYvoCLnc8AQVW3feLkHQDApjfNUGw0hHJsG6uWljHewpzKC6dt7ug2u2aL4dxLt/Xe279zAko9JzQ/o3AmO9v1UNl17FM6H51MCkJaFO3i5SM8/JWHceF9T+HsbTI7j5mSrljTLycGGKR57h1AkD6jauez76i6D5J29MKVI3zo2YfxpfuexB2nTmPXUHK7SbLO62Fej5XUgALlpOEU0YEyaEjdyHnMxdx1QLzm5JmUA6lJa6J6xx66LpDsAgNQA4DrRspWL0/dGtuRaVZOAKF6d6NmPWCIk4SFlgCDPdMnPAKlh7s0xamXmAlUtOT8PZNX4t41gQOTTWIUGqUGYJa0yJz2cdAAgcknTsDBTQjM4GlMDAJpfNsSPEmX2KWYv5tALgN+uElAQr0UcloKe8/piiYvM/ryeiGDljpb3vZ5MKBAoPUNBQblZNCeNyiVqWTbKUugIB8oxl9xOVGxfLrNzpgl2AHyTtoHEyBY64MzarwBAFQmMyq8MoKFAdTn7DOevcwKB2WROS2PrSV0sz9Wl5ZOs/q2mNDWu9t+Cg3jP2egC8fT7ah7Q6vKXk/vpvkJ9Z76nkSy3kZk4JI6tU++7wJOK9NN6d0hY5YG29ssJwYYeDjdAwQGBircWjTUpStHeORrD+ML957H2285jWNt5XoFMEF18wZs6ZYWadReD58ADLKsag80dFgGGXxeSXUG1VLxg7wCAAD6ymKfAdMcx07hOdRLKGXAKYSSQVMJGiY0AYMZMAcYWjkMAGY5G1tl1szKrpXmRsq17kTNJCO/JgdzGvB6ge6T7wIkCla3JccgAyWedolV8dNG89z5WORqLH7HAtg2EwToZ8inQdEAATUd7j+mVMeH9wQDq8lcCYCtlIIZ8r85gYPWyqgih5X7ou5rJTAvAICBrBYAoAG198tuux7HM6MHd3BWhjyG3VjOfx14SqC+BgUu36r1meB99FmDzZw5NLX335FB0mE8tx9m3As2oFGttS60q+5XvFKlG9JQYQZY7NcHn30YX37PBdx96gwmhvvGii3WtZ5HdWKAge+8S4DA/wbKDv/ilSN85LlzePye87hdhVpmIeWSUN02Nq34IpsNDt/IRC7pCyVwKEBDyAbTqbRsJIHC2957MAHzAdUM02SqrBg0utPkLFWoZL3y+BpIZVkQAgYBD5UcGAy/Hn0CDDGiYBh0VbxWDgOAlMdQymvdGHCqLWbyKw1Ox5Np3bMAaqF/LNU1r7kh+9x78DxPxfI2alYFEIBkSZw+dyPdd8OStN1ida9mKTQXy6mocHn30huexcOBDAKW2mINqHHO9VikXgvAoGyNPst9hsndU88Zt6jfeZ+KBTXujbsa+PS9DvGC2YGDNJaDeIxNJ8r5F96RAraP3ZYDFNIBQlDWtBwCGfiLo9PQY/vqMHu47tusxyr7IVUoncqSjG2Agg0YcnYRGoyBl5F7qBGDpPV58epFPPK1h/HF+57EXadOY9JjRhnU3/FYKicGGFh32QIIaiMGAC9cPcRPPXcOn7/nPN5+6jSmFVi3S71lW/1qG5C7o0Sh3U4FEZyMomcVtoIGd7tcydag6tHWjVhZD3jZbWpZy343mCqZL0UIiwVoiTd8454QMIptQf7H1RcIi3CMn1tuzEsrXuxkd+05G1r7GeiqPJfWtUX7dIxZxZjNps623oHd2hr63mznHeRr2mEq7btunCx+YXRW3flaGIURdLIweXAtvxslu1mp2Bjkvl8YenZhHKOiAfiwUHMJ9pWyGhNvhgEGFDR4CqEoOwBn+IxEW9CPwJzh2zReCx0m0KPWVy29JoZLvnLpQ6wqkSzXmbC26y/7u8X4t/QasKzDaulsIZdaZarv5H6WOCrL/MUrR/joc+fwhXvP445TZzBBxiaxjK8aIFRNOysnBxj48DA2gAKWRmUAL149wqNfP4fP3XMet99yZpY41TIFBhyutfEB35+59JLdoDEyK+ho2Rs0uAeFSilR+i+XJuJNg6A9gGoGppYvqnPmaBuNwrMwYRrq+t6U5MKZLiP5SFHezgyDgYZFsLC0ch3yO8yVlH+JGgW2vJd9jFrt9XrBbDV2uc7JyOXGdCfNPTRGBYiqY7P9W6oyQ8oL1H5PHm5f936bKpPfLS22Y+9SfGSjAggJONR9wT3bgYHZ8um9fuL7yJY8gAYIiMjjcIp5vE4LenD/MVrqsNYYlbfkQn9ROn8O8O2eWXfJf5abNZMa2dPLslV32bu1AJGbbFycl57hnndj7IAUn9NW92Iqxoec99KVI/zUc+/H4/ecx523ntE6EqLJ0f/muWxa5eQAA/07a2RIhnTPYL145QiPPncOv/Hu83jbLae7Br/+3fqAXNeXbDUAzfdnjJsPZMdIwIMfPGugwc6r6T2g7GDNKntPUP82B1djEFkbZNBQ3iM5ALpv2sC5DZasBsAWBbIooMkoQDLfPWAYqAQLIeRwxGCOFmyVwPLLhanyvZkhAGZUZ6+0jHkLEKzFQ/29vFfaeo6/plFSjXuemL9N85wF9qQlj6U+16jnLFyy6BkulLqerm5kIRgDTTN2xRgBAA4MNAEC6XVWT2YsMgSddl1LBrQvQIICWLWAhT0jZ31nICAdU70X43xsJgm5sbr3uNQNx++4sZmBQNHDHVjweOgHqbOAvt6y80p74p7h7juvQGNfp8pFrzUMavc2worKL3RIBCrrPmbg5Vcv4tFvqFN76rRca/JmAIH0XMcedMCULycHGMSyU6yxBMzAS1cFFHz23RI+aDlQMyTq/y6c1xpUzc8Ha6k7fRpwWQMB0AZP12wHDUCZ1OdrXD+7NVsDqHMI5mxAU9notsmDmTGBk65uAbGakPaq1ao6kKxxP0DCCIMCAkROgCGEOVgIgZWylPsKaBBPS2bQjUq7TUiJnjVz4MECgGsOMwCVUWgwA61kTztngS71f4uu2OyCw/raiJWCC41911tW69AC0ivKuJgFZIeccUq92LFC0uYq0wQIGYlJMCvGXIQK5NptQmmzIXMQUCYFVmxAdMYfUrXILKGCCgTE2B6Tfjxez1i0V7HxCIgO089PNEF81HawWLjvU6WuSlK7Zl1lx9aMfw2S/D1rnb6PPgeyTgfyotv5gvk7JNLK/bbbRgJeunoRH1dQcMctZ7TujMAS8AqkINExM2B27duv/4kBBjWSq5mDunhQcEcDFPjrCoXVAA++w1hn8XGilr1o5TAMrjMdV9/WMdU2G3RAE6UDGan766gz4OqPytbxxBoEyDmdQbWgeJizsuHIThmZ3NaLKScz6hRIjfwcMIyBgMh6nDFEuS5QBgrGJGSggAwUkqwY6QNOXIICC0NkqThhtUqhNDpgwPZ1Eqa88U/A16rpHr00C6flYQGYJ00tuRZYjj2vlYWPGUupAQm1DzVn+cBseD5WGx+fqW0gW+Z7yx0MJHDd3nBhAU/vsgN9xXu027zb3i45sAcEEhOqQCDaMR2Tk7wOdgvj0MbgtY4/AGkMIgKDGv+BCDtuAHconS3SKoDC5FiFlDhXSI3gO+OSjpK/pZ6yY8V4cLrLOy9A1uHXor/txt4H4KnU4YADC+59/eOCey8jDIiAl65cxMe/Kfbr9ltOzyvoa+FCCXbPtXJigMGS0pvlFLjwwdtPZaEugoKG7ut1JAAzj9jfX86dl12rs+l9jpFj7gMRjt0H35ooHXAsQzYlHjRYsUHZK61YWo+CXAIDLSUk+8p7+wHvmSC/QI4piSwTOIAggGEMlJSUbCtQIAEKITBCVEYhCpMwEDdCDyKwQdmEBBQKJqHFIKwbzKUwQTl1CrBWLMAAe4C2Le/D9tUgz7dBPRZmupEXf24qNXPVZCXc7rrvlvHszKRtjWenGKw7l2AJcDa2BSQk5oiGdrih8w5Wmu3cmSLIcKEBtEMDkeeMQA0Edmm8wW3zdY+749QGEqrbBUp6aqdjbxdEd+2I+yBB7bwBBaPKa91kpaWjerpJRdH0/mv2Epjr6zVd3Sre5qR3mDJgmEKuoDl6FpIeggsgO/0dkWUg5l2Ygp/+ZnZq8/PtmpI1yHWTHwYQlsbsiQEGBf1fK0RXXrx6lGMyt5wuOpayg63wf2dnpy4rtJz/+tzq7Af969HoznpKFPS+UyXmAYNnGIo4oPUPzh1uRmu13slt9+KRWwCBV0rsDFWMnNoqJmNVyaayTuZtBmUAADPkYgTGoQ0UZD9hHIIYiyjJiwMxdkQIxOmeQRH6AGBKbELOoM7NkoFCXqlxhT3wGrDOF2gYi61gwM4z42H7l0I9dRsC7X68tQ9vmYMNlP3aFj3KHmk+pwbBdc5JNvjCDFlojQzgqfGPth/sDFILJJDeT0DCjEUAkMINXkDlC7tt174rbdsMD7i/GRzkdt9NERMYu6kNBGx/BCejb/cEVlifWI41mDwDyTz5COzIwDk0BCdsCAVCjm8DFBg8lW0YrSFNJ2lVCLMhX5SeTvLHrE8D6IYwveHfRzdbsT68g+vDhd5X/RypYBAWS0MhEySn4Ke/eQ6ffVcJCoCKlNISOYNeZgHIW17rBAGD8m1b7/6ihg8sJgPk6VbRQAGVN3A2eAYOhkCbEnSAuUKtKfT6vLrsmHN8L2Ylu4PrmEGVaizBgi2Y0QpH2Gul0kMIPN9cyhvYCgoMEERmBxJKQBe7PZlVUbEqKiQlFhQkhSCgwBiFkQjDpCBhigVI2JGEXgwkkAMJkxqMHeXcBPNAZZpoBgoJlm5NTAQKpsAaYisgqJNrJ3A6x2TtwcO1gjigBHIAZmDOF29sWiEDb2SA7JmG1E/bTNBS6GgInhngdI9IBvAINosl6jOXpnV1WQQgAYXFUrRrBhKpXWPZpjcCDOyYU7tNvD7G+uMrt1EeY3JuYEJQo4MgvycAQ2RMgTDouwwR+lv65RAJO3ABEACUICHJrlOpFV0EYBXc1nrYX2vn9EqdTmrnDkSwT4tPjfO692skcAJzxuwVDwpuPV0wZtWls6JdurQ3C2rpxACDXtEQM168KlM6Pn/Pedx5yn8QSZWRUgU9yjQBBPJXGTjQbe38FAQdTkqLMavHuVDPHmr1xdgDCpR6+YAcgiCmBBQMucqqdMqGJB2lA2Up5rVU18YgvBGgIK2V0/Bk+ovpSZKhWT4BBRCDHoAQKXv/gZTqnIOEMUq7jRQwBlIAYAmMnHIQ4IyMMQnELrnHe5w2vcqzCt1CxRlbAIE3IB4QWBt4I2JgIBuP3D7ekFjb7CZuGpL5vvxOtYHx3bjuVv4bI4EoLYhooC4BPbdvHDIbVLSh9veRyrCRUdbiNXEaA2L8ZYN9ctYGgCDtaSvH3Zj2nPy2a8temGDH8kXPXeTNYKDVftaGvRKCnJ9BnVOQQdEmVMHuAw6IxGAHTp61p9RT6Yh3Kaer1kNyTM9t5DT543bOUjGjDywb/i2sF4Ai96IHCIiAl6/mnIK7lCno5Y21Es73LScWGHjn/qWreUXDO289kw5E9SgYSBSLNWhKJTPnQK8xXZBoSQBjig9JrJqZMQXGCB0ARiWxDBBAB0uidngVOPjiO29JX8n+nVJ5A1E+zkYlIcW9dgpifMzLvgGxWodqQP4gSg0KWkYoEBWr8dqUzQwK5iBhHEjpT8Y4EHYRGIMBggnjQBgpgAOBohl/RlQWwWhn/QCe3Jv7tLRuVv+Xhau/ZkBEDm0jwsCmxDNvSIRWjsmQ7Ox6NTICCmqDwgmATFG2TTlPLgRUs2e+vULVp8wABMrbQ5D2kb8WIiIFZ4SwI4yDbI+BEALhGIQxZJAwDgG7yNKWnIFehIw/1imsbG3EBIRyKpeMo2re96wtM1BotWOrLRfZnoV2NGC341iwA7sYZ2ECD+hqMNAaR/53DdZitD4+T06+UWWC6B6judcY2NKgz8GAnNNmaFtgYA0I9Eo3ARNIbFbeb9e0wmByLPNJrp/p31cUFHzuHlm8yB8rwIDfgRIU7Nt6JxYYACKMS1eO8JGvy4pQd916Jh0QkGtWX/5E5wFYspDtqoGC5rDpuRlIC0gQgAAArFnvrCiCzKMOpMCBMwI1Y2ve/sZO6+krQPOl2d3XBo6idqCMe8kx/Y2cgb0EEDKgocSScJSBMIETCBo0q4iYwUEBW2RMpplN6zKAIMor2IjhduxzyUvNB+SbhiGw0p4aFgiMuMsAgXcSHpgQE0BgxhwgIHueRk0HZSgmzusldGPX+kpWaiRfNHUDEHD123uWPZbAU807/W2sgQECORYRFTgUxoWB4yliipwAgAGDGCMmxxiYQl+ipfO7ZyBgv+XrwqEABkOQ7YMhJKAwMjlgIIY/QoAeB2VFiDAhYGQAQwRPAhgmNfTSFNLvmBg2b27QfVPNHlQA4Ua3YQ0IaqbnRgOCXhtF5hmAA0oWB0AGao6J8+G6YhowMAv9yD65Vx3e7JWtgECOz0MFrVkX+4CCef5LHxAsTt8EmmDAGEi5Z37GS1eP8LFvnsPn7j2PuxQUyLXLQKA6tHc5kcDAGuLSlSN8+Ovn8MRPPplBAaSTDE5qnBqK5oOb1DiRqBJT8qMm1RTMmrNxNKiXbiBhUI8d0qHTmvQhTZhKVNeodUSYf72x1Zmp7hELZWvsq7euQnEvffnRwESQQWtMiYABoxdRAATTsiGQU2aEGJTq1KSOAL9vXoc1JRcjEu0ZGQkgjAHYTcYmEDBZP4iYWAwJ7xgc4szzZAbIAwRAqWnzRktqOie8udLDM3aYszJbMijJ6LCFAfqgwLMEuxgLhsCMT2TGsf59fRcTGNhNUwICGRxwyl9JOQeuf/r4rU8yTLkE+ndU4xIoOtYAGIcBQyC8vou4aRTQEFmORxBGltT3uNPZJjq5iwKkcw5BMvkG+T0OQTBAAfYJkTjT4GmftavKVtsU7MKKVRt6Vn5r++3L9ORw0DxksHPbUp8+S+BLzRRI22TjL/vbgMCHdlIIpwMIlsBAW7/kOpsjsla2L8ot9duiT/dlB3pgoAcEAkpZ2Lorkij/fjx+z5PCdNvxBSZgH4C1VE4MMKgFcumqfCXxifuexN1OqK1zragtdr9FiIbOfB8a1Ru02cxeEThbBAYwDLrCOhNGABMxRs7Z36MlgCmL4BGvn/LGdQU3yMIPUqAEBS303hqsvSeOri42WcwzJaOyCWMEJlKDqiwJJRkIUJo0lJNyD8h7PGIQMNg+KcIuWNuUHb72fGo6NEIbyMVIbdsMS8Eaec8TSCuPhaCGI+h5ChBSqKkCCdY+SwrMGxSpa+l1JlCg56bjeizlgETdr+GtInSQPM05KDCWwADC8S4WgOD1iQsgYAZK2qF8lykyhiBhq9w2+lc9zRgImASg3TRQAgBTnHCggACI2h8DDgYBdRq8xjiwgEowdpEwQsAnRxlTHKXBJg0xsBr4qO3CRGmKlxyrwIE0igLC7W03Yws8oFPZWQ6BHWdcX1Khlfq3yJTTtv8rbaF/9wQDWxNDARTGElj3aHOIVtouXc+Ys5T6bhaSNaNvx/K1VQft6NOeDvW5A70wgQcC9p4GBMopto1QgD5P1tl5P55Qprv3VcV9ivVfQJzmpQY4QcAgb79w5QiPPCtfmTq9GRTwTFB+/fUIo+jl96AdPg14AqDKKc075ay4PVAgnfN2oPuaQEEfPTKt0l9CZc/fa2n6lxzX3xXCBZwoHKrdUsioGKVhDAgdwMIbDNbETPOQyiRFWklSBKIyBwYerKK9KVf1VKtesfoMUQzLBABRFNSu9jwVo8Wor6qKi8kAgFKvlccZkJmjbj30b80epL7m9vviPdYJXHjsxXnJ46/DMp5uRgoZtEBBCxB4Dy82to0hmKD9TBmLwNnLfB3ATYO+TACiBroji1KWEEemtaWOyirF/AxfJpX3aAAfJUAWhZllYO1m4AAoAUKvtICc7Td5eieimDGyAgoM2PkZIr0SiHJlQj7ZG59ieyVMcC1AYEty3VqxHC5wDRIyS5lCoBoOyowrEuNqIVupQ37vNb0JbMsXMCBg71YDgfK7EZglB9rTTGavvHYRH/n6OXzRObUtfbFoy/xv/Wt9+YUr4jSfwq3N64ETBAyssxko+NJ9T+L0bWcBlJRNt3SEbMrWQMHFK0cAgAPXUU0RAM5zg3kI5Ly70sODetHCKJRAwRvSAyDH01w17Zljx8z4V1oDA73Bmzvtopjyc6p3A+T9AOR3HOopc9cGFETGPkt+HUZ7z2ipTFDvQj3EgZEyqUm94GxEUNDQMMCime/e0EzY1h89KNhaAnx/Fe+3XjSLTNkzUv5F9iTFSBhAFcq+3DeSLGjjjb1tb52+62njUG2Prlmanq0zYul3sL/U7J8DUfJaTU5bi4EGxSm9CNCsvXoswRIomGKeQmpjwe61RpEHEBDEKfFMWs8t7DME1w4Eet4ysF1/eBn6HC4gy9iYyjqXy4cxPUiQ4/MHe725t640o1+922z9DDgdkM6n4nzbb+WRZx/Gl99zAadvE1CwT3+1G/v+KOpHEtwvOqf5bz7z17q3ODHAgCCg4EPPPowv3XcBZ37ICdWhM/dzdr0VP/gHt+/o8hE+9OxDAGSRHJ9gBNigz+dHty+DBWoqCn/uONhSqJS/fqvG1ACDlbVZAUPR+XPnTu9cIV05L/+t58rWA7slS18jryiBLJORJEkz6gWmGK9nfn2Pzm4VT2fL+7eVoS+iaNRTiSJHMxiemk7gANUKZI7dAMqUCT/4vee/VMgakPO0WJAZaIA0254CYTTYEGWxnqBx9VGyRIEhYjcJE1ZDDGjwJDLjGBFjZGAMCMocIOgVkREGAUy9aa+13Ac16AYKQko2LPMMDgbZbzkIlhhqsxQCyV+LdY8hgBQoyJpCmg1eGSjvza2V1Nbut5W6ny+BAh/6iSj7PoAZ00OkU/8CyVRAAEGTdw0IAKpbgMUkIi//614vApgZyDWdsaYvWrI0eVn4DpRlN7pcrlHlaPlcZvivVUemvTTXj/uAAJpdn/tcKzH5yfddwJnbzuxlp2b73EELaR59z+zjPLxelxMDDF64chEfVKR19rY8+6CgauAFW4nVf3q1vjkFHF4+wrmvPIwL73sSP/H0u3BAGcnanVivNoUAWKem7EGgASAWwALc+R4w2MUHVVVL01O/h/ypY19wf1vUl+zPglxKfqlLAYh0R/dLjIFEVkAXMBjFWmcee8Ag+1wdOnqh9BJ034pybL6gvdoGcGDo3abHJgp7RY7Qx1juQlAmCoQ0M8QDFKHqg8bhWT0mM/gMooAQWEMkEqsPAyMQY0cRQxgwBM01UCYgJXQyY3QJiACwY6CVgFgXn3ho7EBICYfy1/IKDBD4mQk2rVTAgSaFDjkr3kCBX+VypFAsfBTIGbuGUl/q0612uhZQkC+e3681s8cYLAMH4hGrERv6q9lt6ePlvrwy6BYA0IyVV3piq46AyY2Q+rHcU8+r9GJaYdjdow5lAllHbtWNNQBI+1f6S2vZbQ+UatDQ+gsAP3Lb6VzbhQ+z0WwZRUrvSV4MDFz83hE++OxDiYmQ/tJvmRMDDD747EM4/54LOP1DZ0rEBi8uYPYVNSsLDXB45QU89OwH8dR9X8LZm98u106vS/8lxd3JkgZdNpeKDssOMBhYAFABBgcgHFiw2vu4ZY9qrn/PPPwaHFQgwE+D6dFfxfXFvecdzbMbCSRYi7C9Py0rUg8YRiTAYPHY3uJKtq+uR6vUyUatKUg9j2mf4nDEppKrRSk3oQcObEptXn6WMGDAAJmBIN7zgDGyzkqQ9h5B2JFkwY+RsNOpnmOgYpbCFBnTGDZNW1wqW6cpBhJg4Bc38tMU876SJfALHY1DSDIzo5amlm5Q8vuW9bevCkl7GcsjYEBWCPTgwOLkxJws/LixT69NG0zLg2t91gBALa+WB3xteqHM45qFZeVmSUekc5xO9KFMuHNKubR/LyYGNt7XAwC7jzHUbYe0MvYtOwSAdt/HpuKAgV9KnShxQWBI+OD9X30IT77vgjAF2W/plhMDDGr6pWiMag37TR+80d+HVy/hwec+jKfv+QLO3nw7MMn6g7R7XR/kxFt9CIeqL+MN9j11ewTagKEGC1IdBxjsYpTAYUvxnaGH7uuB3uz87mbLDELeMxvkilrL7O36d+l92fSuCOBA1xxYYhgAdL9d0ZWRVrkXayxoxhtYerHECFPSlGKGBFZvQAEk6Qp+Tj6k2e40COW+myJGyEyEMQxFtvtNgbGLAX5O/C5o0udoU+GWFzoCyjCOBwlFXgGV+3oLGxkzIExBDRBQrHzY/A4GsoJOX8pcMXBAOaSX2uRGFsIcHFi8fIT230HWcVi9l+u/a1nzLQBgYZk1Y7ikC5Zqye6MUhe087W8wWfOs4vqpO+hvNW6nPRvvSZAqz8039vLp7hntjkA8ge47KWAwhGtbQ9Nu7aiqhCN/zAXYUKKm2FK3+Q4uvICHv7Kw7jwvpyzwGVlm+XEAIOzFSiYAQL/JTwgfSpXStVoAMARh1dfxAPf+Ck8/e7P4+zNbwOm1/Nx3S7onKLhgu6i8stqiugApMbzgMHAAoAuYHA1TvtTtVfkVCP5FtKf7Sd0WBj3xD2+CyBLC9uIyuQdI8sgJ27OQUP9bYB6+pdnGIASNABuOt+Gui7N1tgSd5zdr7i3/A2d41YKhUc5Zsgs7WLKMiVkaj1Yj1kuxqDevuWr1PPkWZmEabTtMtlzaXndpdUpAXTnysv7l8sgt7LiDQgMQGIGZJvSUtZm9PZhB7YwYXVhd56BNntVwnwMkvsvMOfcElaDZ23KeYG0Xry8V3r5Q3XY0PqozZWv5WFfE10zgsX492N/i1Wu2VWtQ9Z/UjJr6m7vWYRqv6vR8uOLqizrwGX9t+B0LtkZq3TarmCmtzNFxUtbI8mZGh5Mdka+okcUcXjlEh569gO48N7zOHvbaatxs4/W5cQAAzW18qMDCHIj+Y5sDVgatsOrl/DAtz6OZ37iszj7prcC03HxPKp+5wMNoEABJZtQgQYDDHpuDzAAVBhEpDfJMcatC1j0FOIiDeZkNEPBNfOyh2ue1gtwX56bf4XOgwZRGPYVujXAUMd3D4LJi2ZKZqksZVpvpSH9Oe179g1AOsdZIdYKSGhB3rOYRsuEoQZOLMmfN2FIvzPLEmZ5HNfyUaVFOdq7W0gBuS+uJcHN4t8OoG0xfF7+W2nvWTH2CTlrg9TA9cI9NTgAIX+4DQIKUnvCJdbNaujkWB2t++MSIKoBQP5q6ILxix0Hy8lksVTj3fRe3g5Z/hTkmwpOJn7sm4zyo7ctgARch+7r6L2Z8XesQG1XbP9i/dZsS5KjsAOmIxNQIMbzVy/hwa8/IuHvU3eBozAK5pTmZfXa5cQAA7BO6FkDBDUYcL8zKHgJ93/7p/HMO38NZ9/41hQ+KMqs8RpipgwAdKPYnwxfBRq2AQZ5ph808jfXo6ekW9T/khfQlKceu+6BUOVocK0gEDIaDkMhh4MQZoAhmldRhSX8x4aAEjRIddWw9WtalMLLd4qmBgTAMiioAcEWGttO8nUNZlgqj8rrLA4LuRyDsiwM8NgPzax9pKYu+6yx0VpKdp/kt60gYC9ZO1m62usLc7p2ERyoxU/fZqH8XZbEAVL5jKXVST0Itffw7w99dqAaOOnvGQBoGP84dR2p2RhfGd95uwQBeV/Wc17v9cCCZxfg/gK0t87LfztOZfHelR3R85Md8bKoAVNTRp3K1vYmyTAW6I8MFFB0IWvG4dUX8eBzH8XT9zyBM7fcIW1JUXUn53ssAKkTAwzIvqSz1pi+IYsGld+Hr72M+7/9CTzzzsdw9k1vToBjliDSQ3XphFIZ5oYrcX7yku2awlOGQ9UOMLj9RVjCPzfRdAulSL7s52B0ZajXZdDgB8F8QNQyLOqtdSaTTQGeKMmJKej2kAEDBSAMmWUIQwkYoiQ8ZsDQ+N3J3XC7ZsWLtxWn9OdsAQSFcaru0yq1MYvsLiy8qvyudigW+0omap7wKveo47/uMaXhXHLcGsY4yyL/vRYGppZryyNMz14bG67ojMz8XpTlCgBgTiugypc3lZFiXSLbTvV9jFTOBojRBidrfaww+Jj/DsEDAIg+MwAQp3J8s/2u9OMNGNep4t4R8PpOryGn74pz0jVZLxbsju5f1Hm9pL8u/b/gSFZ2Q38Uz5nJZY3RMF0Qj5M8ZGPK750/kSvnszEB0peet/D3vY/j7Kk7AWZwmmirbZva408AMNhszLoNyzh87RUBBT/2abzjjW+e3bN8Xl5yZJa4WFeNPG1TdebKU7Zz9gEMckoFGtIzFkZK5dXPacHtsmsjZxuAamim+TItde1oGFQelI/WoMCAQ8iKhCmAwoDEMAQFDmFUL0OuizMjWAMGe7VMTa4M5+77LC1ksmS46ilzve26DNWJvt7aEi5+awfkojJeW75361hxvCWg1r5G5VvJamuhliW5FecuP7pbarn5GXD2rsbQRFRAwK4nIFJOFvXfZ/HfZlkrS8CylwOQwqocgciguFMwMGWdGKdyTBtTUI9lZ/SWxnFdQjGOtYLOwGd9l3VbBgy1E5DfaqYTk6C26znAG39gPwDgbcw8lKo7crU22IbZZerkUl5DHeBYOpesH3dx4OHwtZcl/P2uz+HMzberjSKQrteenCgFE0vl5AADE2aL5t6Agg9ffQn3/+OfwTN/6Vfxjje+pYkEi0be7dL1ddNz4yPnFEK9Q/4M9m33tQEkx3IcvkKOabs3WFr+SMkY2PsAmCuG5FX0lIcojqQ0OIocTD4pO22aP9PVN+0JKpegdGIwTyOAhiHLpcEiIAwKlDJYYL3P4MFCEMVkHrTPkBbveA4OrinUkF4vA4V0TsOozQ1bBU7ZK55+jVpD35RLoqobXpe/ugUu6v3AttyCutRe+zoYYsxkAbi+q+evxHDrwtWYsX4mz87vK8bYGX7HJHimIAEBZBAAor37Tg0K6v6SGZI5CABHYQVqEGDgoMEOEEcZvyo/TmEF+708dvMLyBqgKbtUx2+h8wz4uzE8AwqVjvOGkepnbtVv8iJto17rOH/uFoen6ncc57ZhZgfs/UwukP7LxgYYwqcAUoPO/joWNuDw1Zcl/P0Tn8XZW253dklCDeCQGCO2TvwnIpRQAwLb7nm7QBsUvOmt7hjQRcpm+Ip7aWkAA26B7BDAO+SO4QYS4AcQ4AcMgHIg6TXdXAa/r1VqmfRosiUwwFHWtTdFkrwTAHESWZlcGgMoVdMPHKt/CDo2LGQgxj+BhmFUsKDKRsMKRJKTYMwCa44CEQE0JARNYQCl+GVIg0/+zResqkUmv52xrmRd6My0s+3hFkbQKayC9vQVKChd347tQb+P59zqM7zUj6ilnDtlCdC06r5Gw24pru7+PdLMomLc5Clf+RyNg+u/MkyAhB6MRairHbCtn9R9xPrFDARwHQ4wox+VHXBMAHuGYErjlqddOo9tzNp4BZpjtjtek+wgY07Ha5K3jlmeMlCwcUs9kGD3rBmFWmit0mAKVnVbcSwDgNr4J6PPlZxWCnMrxBJBIYB3eu9pcswpIOBAQwAsmVNsoIACDl99Efd/+5N45sc/K1PqFSzkr8cYOAC28VQnCBgUgMB++8aenSvl8LVXBBT82KcFFBQKKG+3OkazU6Rchw2KzMCCizN6T9lAgwcMeSABCSzoPXp5DIsDaWbhysECYH8w4IGAl5cDC6X34ZSOr4urNxkgUBklDyTIPx7GpHhoGEEVs8Bq/GesQhhV6WRWQbyVDCTqBasSe1B0lSWDmTeXVz5zxt7HQVthMqAJgtM1qV4LHnZdNvTZvYDFD7L0DEKLASkAgYXeHNOGvC1gUWWfErYc/areKinTZJhAPrdtD9lY706/KEAAUBr6mg1gxxLUbEDMrICNV552GQRMu2KcpjEKAMzdMeoLV2M0vaMBdzlQAgUL9W0FCSnG3tBntUy17vN6l+NgiQWoWROu9fwSc7JUiORakxNikpHY+PxeHhwkxNkoKfz9zs+mxfeKRxbMAckzFSAslZMDDOq1CDYowjLR8K3aOuTOt2EP6bRLsbUWINB9rdBCq1AIkpEahgI0sAcLk2MXHLPQAwsA0E4EqkrhebZkmQEBe7Zk2nUBgQcDSclwBKYpe05+kLXkZAMHSMYcAHgYkrdv3giFAAyjgoVBwUIGChhHkU9KVBxAdJyYB5v1AGUUZrkdiW0QJeU+l1Hgghl92PldUuRIir+VFLUYIrP9K2wP0Gvbec1bXvuW2PKNKlkpuucXBqAPAEqAvOB11uwb5Xhseh5xAghg5Kxuo7Mdi+BBQq5z9V6N7RlLxL2ZVRsYgTjJfXa7BAQ4xjRObXs2NnVcApCxuXFcyrYDBDoukx6zsckBHAMQxDsWfaFjESOkT8cMEETYLs4ec1sZ/Zri61XdNugyOTYPfSYWwIOA69XpJqNa54V5H0/XFf3f9/W8ffhH383265bb5zdhzucri5DvkeXQKicHGDRQYjfWSKRTEj8pQlWkleGACpEjAJlLS8wKDvx9Qu6EYagGU9WBNnSk3Nl2aeDNwIIONJ6QDBpPu3kMD6ZIdVqL1dd/p61IfOkzB0UohXPuwBJL0FM8SenUFKXFMTttlmZfVF4IG1hQdqAACgYSxgN51/EAFnoIFZuAFHogp9BGpMQny+twQIFd+CYvdFVaAm7udUd64YItgMCDgcX8j9yOrRwQL//UbxvArVCGvf68ECJIpQdOnbGhnuHxvzsx7ODDSva8Thxb2CFGmvJlCwqgiskqIJB9DiDY82WjaGe70+a2nwGB3OZbWYEUGtgdy9jbHQtD4AB8dzzuMxZN//jz1eCn8RghLEKcLEyeHFaydkxGMmS2fDIdxgIQ6jh7qsiC81cB3lY+QPQ6qAUErkGHFyUEcFQgVDAFKLer8bCWgA3UoOBOPafNKkjOQg49bGE5Tg4wAPpKyVgA7ZmHr+o6BT/u6BcTGpDihsSUBqo0EucEkWFU5QqdCRJzR2eeA4Ut9fRlUhrTOmNQsBNcw8asKFPsyoOVGigUCNG5u/MK5rNaoGClbAIFbjCyH5hyg36NXAwTgFL+OazggQIGAQR8/Lq01/HroPFA5pE7kEA1kxCNjZjygAyjggAboD4RVADXbMooPGBovJsPF9ixOmSwFRBwlWVehXwSJdoDc/rMLp1sdXSsQbGgTK+vLxWnHIt4e0rINXDscm18GAlIFDU779T6w3oc2xgiXgUIacqXTQ8DFCC4MEN6Ga13EtSGNl8CAyvMAE87adslMDAdd8fgjCpf0k8UkD5jxDZLKMp4TLsH0DTJqo01OBiWKWwxpBkcELML0Uxeqg391dFbyCBnBoZrpreVXyEXLtY7P7gHeqswC1AkVvvrlgABKJThg1tudwC1CrW0gEJlG3rl5ACDljGxAWohAgCHr76C+21Fw1vu0BNZlEQVQjCURWn+MadBYewBDUGBQyzroAhxFnaMhtrK+jYTeor7ufsYCgUyAEmINApI0AGbklpmbEJXkrkeNcreWEiTBbcYi8IrscHrjU+V7k4ulV0oRyRQQAYOKABTAIedGJzxQFgXIv0bZD3yYRTlOY2gEEDGLNgaCZwBA6l8ywWYMkgQrzHHoVP9aaa9pO6N9y7zBBZAQfIaHUNgAMwAwe64zP/wVPI+rI5Toj0DUveP1kJHrfazNhQ5zQFfCiPZtuWFuDCShdp8zklKUlXGyOLYNB4UFLVgafmdAEIYGklbHhzo/nIlgwwSRCDLbb0F/DnwBniQMKX29SECTDuwhQ8MIMQJ2B33wYDPAdo85tJOOZfjHPzWxYf69igWZxdwMNOk88c4fZVCAnC6tWYG5AX6zEDLpmxMvCyOO9CajleAICdPI+kfALNZaYevfUcTDX+9BAX++Y2cmsaLLInyBAGDhZLol6sv4oFvfgzPvOtzOHvqDndCTe2R209g0tnJDiRI0lqAKWKYZ2YAIQQFAcI6eNQMAD5PEhzXB40xGjH2KSrbjjGFk+SyTCP5wSavuo4QUn4FBcU0WhersgNBrO+mIVnZN+2AAaBpyvkS6oBRdOCgAkwtA+P3cdyp0ppyoiE7wzKMid5MoQTb5gjsdsIWTDvwMCaZGqtAem6mMznnGBiK1/gp/Hrlbv5xYRTINUrxUpWhSPuWmII5KEh0Mucks15sOVHOBgR2x02jwdMEjpwUIscI1m9aJy/MvnG9D9VqynMoQzE0UBoLpN9EIItb+4RTCuDxoAwjuQRUDGMCcxjGglEzQKk23/W7/LcHDgADCKnz5+Z1rONqGycnRNpj1r4LgCBau1rbbgEExipYO2nbSpWWqWWOXAI6Z2yobhfP2Lk2aQG3IkmRcpvXTsxa8fkwm5LEt4QLemGUHhgASkCwkIhZM1liY5wzYblOFWtQTEk8dVe+vytdILBnOTnAYEUg6YNI9zyOM6fubLBQETa7e2mOa1KGYcggIYUpGMHTt+QAAiCGIrQ6ZNhPqS6VOpa1UNqgwKNNtzcN0iGBICCU72jvp4aI7BggsqMgsp0IrOeBbDUuM0jOK8G64jKlJYAHOQQToF8pk7olgDCOua8E5TrNiMQJPB5I3YcRNFidWQarfc5NlRqRhZlCGsjFYiIaF82VXQ4tbQYFcpI7JiChzjzvGg6jle08fw1HxGMBEAYCBGiwyFH7aXRgARVQyK/rjGZFIScv04BBCOnbCaQzTWjQT8gqWKAQEA7GbGBM9pMYIOYIGg7AURMCYwAlDTeCsZPzJgAjJaCfWCcv+w44gALGkj0oXnq5fdfa1kIEDcAXff6ASyqswR7vdpsZgjVQYICAfHjHsXNdQGBjREN2S4CgnnWVxNZLNq20dwLxW8rWkNcWA9tiBoBFdmAGBlrMQDDmMgODw1dfxv3/6ON4+t2fz/brekHAgk94coABMENPUoJ8EOkbH8XT9z6Rkdbs2jyFJH3As6B2Y/Ebw00CCLznxrEPEqIYoERnWdJaBRQAh157r7mWmOUT9PJF+pqNFcmqc1qFPcUxuoFoIGFw6DyaUhMPh8wjM+p6UAW4O54DhAFJkSUvskNZrxXmKPlJmfkF7wAaJYQgCn4EYZf+mgyNijT6mSKQ4s9RZdWlnqEAgZCS13oy7nmWiy/mrkkxa/32gfeKjCmw/tcCBeZJTjsxPrspAYK4mxIYSEAgAQUDCKWntWZoRATWV4P+kb9xUIOjQCAESiAhjANokLrItnr9FJCMPhSLDQrulN0iQJW1Mm1DyPQ0cx4CXKDSjuy51DOeJWi0z6b1VXxoqLXeQCts4EHBtIOFgrqgYK0t6v1VnkcBBmy/BwTjQQIDM4bAQnQrYIBJPpbmKtetdzluOIEDz26mc2w7JUtqyM/lgll91nrvJt1bsR8tMNBMhLUyjPkcAIevvowHvvWoLnN8F2ZgdLU02n9Fz5wcYGAx32rf4ZVLeOC5D+Ope5/AmVvv3oa0Gh6cbDmPbRhnXlwNFKQzChigRjJYepaBBgUHErnYYCAWsrTlT3vwFRSVvw+wQEWVbEpKPEvnN4AQi+dtio8cpW3vTJ34NjnFJs+ze3mj6LZblF7vXTQxCnBJURQ1YJA/YMIYgd2xgoMJ8mkfv5pZSEqmTFxzHmaWthqiDkW51eOxd01TtoSZWA0JeeDpaG3zJH3IoAYF024qAEF0CYscOYGEMswzf0+vVHMIKCAGAxcBIYTEukwDiY4EEHdAgObQxCDXGjNk7BU7sxKnPB4aJcdzNxqi+l183LvRpnstL34toMDYm2oacLOuFKSdXYIgjWH7+NnADPjpwovMQGUoAadzqPrdlb0DcL4vVwnixScGPMHjWdw4ZV0OgJbYVt9XKkZArm28HwUBPD0gUIcmAfB4E8yWCdP9UTx172+J/dpou7KsGPrt7updlp3PkwMM3CIlgAz8wysv4MHnHsFTP/nF9D1qPwCWhJwXhkBh/BMVN7wBBVBoDPgtQAHAfBlS9xwA7TBDJ9bVR+JA6pRA0RFLhbisKEvvo5TJKhDimD7eQgoAeLcTWbv9TaBgcqjjhb1SsyYNpVa+mFELKJVNBKB5GewpZh+Xlgo5ZaX7ja5QL7NMouoU82RsG7rqo/sQiuW+JE9IgQINEhrhKSLNm075IEP6ahul/rBe4koYh6eYAIEHA23mwLeZkxtC13ONkbGSyJ4K1Yrbh4xUkVPIOQYo+n9HUdfe3JacnAVQkEoNCsCF/khrhvg+75V7DUYotPN4NDmaLFy5JMyaDQDWgQAFydPZCgS8oUyyB9b0jn/fQm8nJy6PG7Y5+uM4ZzahM72GikUA1vXsFodrCQi0+haQxnkCRsNNAIDDq5fEft33ZZy97fSqvZIyFP2FSWVUh1vY9flGOTHAgKsGPEzfo3ZCradzLAiG661qcPJ4kH/bQF6JHzaBApCMKZRI6y3BPCtVR6nDBOyZgU6W64w12MeDqiht74kCrF6+vDsl5VeCBeIoXrkBhRgTW8A+JBEzeCoWSAKW44ZmEOy9FhTceqkU8wxcuOVKc+WwhfqzNrOER7iExhk40EPgKNMo4w7AIKwGabwdOyBKjJd3x5AFZkhodq0rNJdCYu8DAKNULfYeEEJEhExRo8lNVQPUmFtlkN5TaPwORZ3isha7noOCOpyQ8g1SroEmJAZNbjPgMwwgnaKa8hQSjZ3PlQRRm9GgM0/MGHZBgR9D6+Agl9qDc85EKhVo2ALbSBOBAaXCZd0AAweSeFuFKVvOcD1l1IUQuqEBzwjYtN8OCAARYm0k5WHXpWvSX1Iw7KiB1iwyjHl8ETAPS1oY0xdXp2YI1voSsB0IOBBQfGLaPY/Hm3B4+QgPPfshXHjfBZy57exqj+DCTg1O5wLwuXC67/DqpcX7nRhg4AV8eOUSHnz2g7jwnvM480NnnKLOnlL9t/8db2s0ybpP7OH47yAbeM1bN2Nf04QdsMCVxyA3rgCD7dtQupRcDwj4Dtm7ZosSrNgUn7zJlWfEYGAUOZCTSWJNfJ6Cy6gnx6QkwOCe3Vy+tQZOjZhgV+Glaza6q9dSGp5PAggAkDLp1dNBBQ6YRD4OHGB0wJICMLnYO+liWOn5lH9TAGgnt42qoEjU6hAjOBJiYISBEKqwghn2xBxM/T5jSYg+zyCEIAChAgRlfgEhjKOAgvEgL141jLJtoMB+GygYxkRv22yTGSiwNu8pcZNVPRYa7XejC4WQkzjNSMMAAUpwYJT4cAA2lq2unzd2S+MBKMeEyXsGAkowxQvGEbjx+iWxc6ZjaFBWs33NPAS6UsxYLzlW+4KAJCfk65xtev7KJTz8O+/H+fddwN23ncFx1zalWgC2uJbaKRBLrhxHgDXpmwCwMulffwS30a3d1z45wEA7+fNXLuGhZz+AC+89j7M/9A6YwD0QWPsoTu9DJ0TAxctHAKCNRQg0SNvmq/WPGP59wULKZ6gGw8xb9WXWyStmZAkE1HRW2nbo2N+/NZArilNAkqdT5+/qAQNzBA5kahY5oGTsggCHXUpeJLuvW4yEWiEYmPdayccpPvnpAMGGmGi+xxbPMT+7Kc+6TnYuPP2n02WJwByyV2TsQozgQJC4NAlIOrgJtJMVNDnougvMwC4kgypT3o6BaUyrZ5Ku7RAORsTjHcLBAI4RcTeB3MwES0YMs6mLWf7RUbPBtUECB/bODTAgQGBAPRthERDYfrfSZWIUxlEYNLcc9jJL0GAIloyJ81jLLHnPqHQvVgsf028muLVSxuXZP5qwa8/XO+rPPfs/kMaAya7FAnBQeW40jF05btAnQKlPZMsxL6ZX7Npiu+yfqH3vRV3a0KP2d18QkACA3NdMULJHrmoPfeUhfOm+C7jjljM4nvr2KK1v56qbP9lO6sgOAhJUXxxeuYSHnv0gnrrvy/jFZ/4GeuXEAAMOg9AvX30/LrzvAs4q/VIIn5WBTX0srwXYAgj1jktXj/DIsw8DAI4npf3NwyNtICJBcg4w6LDYDhaAeWfXfb26zQZYiz3ogAD2ndZtt8Mv6QGYFxeAcXX3q7xxAghzgGRfiisAwzgV7IIHDAXDAJQJRfbsRi1nMmtNLdJ3bipF7/UsKsX8nJlSbLWXb1/NV0j0H5FSpA2AYNdGSzJTBuFgAHhCGA9knYZJputh2kki6E4Wd7KkNrBktZP+HsaYp8D55MTGNEZpXg8KeMbMBr9QTopht6cl+vULPBhAGCSmnfYPefVK59mSAoT9wwZh3l4t41W3lzRW2p++XbKWLyJCgMxeCfDLsRfgIMh5BUCwabb+OXU100ajr+t7LIUBhAUYZn09zbVfMIjAkg5Z1h/ys2Xsax3J28CCv1+6/3Xqz1UQ0Pnw2oIdAoAn7nsSt586jeMWjc2cm9I5twL8BSyIHYL7MieBaMDhlUOxj+89jzO3nUG7DaScGGBweOUSHvqd92tM5kz6RETdENYI/jdQNZzfoeXFq0f4yHPn8Pg95/Hv/8N34zhyMvg1OOgDho1gAagAA1BMddpSUkdvsAAVCGjRWiaCWhw9+egN3KYooKR/3FExbxkU2bszc8opWAUMBw22pU7oBAplUGfKtz7xXCcTsR3zBmSTp6SybwEv9zx5VpjtA5DpP6UEuakQHRsTdDtOosw5wtbUBwXQwU3Fx3XowIVrOovk2P38LJHWAki1fNdnJZi3Wi5g1J0f76bD2dz4FhgwdgCQFRHLpY8pecaLnp61U6uNyhcqF7Cqcy0QXUgoroCDQepMDvBZ+44B1rcJN65v1/22xQA0AYALvbQMobx7Q2esEicyNhJFToPfne8805MNsICs22+E7uzqzQ4IWAIA9T59q1TefvNp7CLPdawBAireCkTyrc8AgBgOlCIBhIuXj/DwVx5Wp/lM9Z7zcmKAwUNfeQhPvu8CTiso2AIIygYsG8j3oZeuHuHR587hc/ecx9tukdkNhubMhKZ10RJS0+NEM8CwDBa0Fh4wACV6TqctjLSOIVqKbdWdGugj2/rJ9ZjzfS4TaHaMQEQy/YzUDmAOmGyBl8QyxIg6LJE8ag8aNI8BQJHsRRtkll6j5SmsTTXyClSPdxOMWsxBZXy4+GRjyO+DIYdi9J3q5Fe/zn7+Wh4Dg8iVuMrt0FCNsQXF0skeLHCU5DYgA7neanHeWC3R2Wb8gfUFcqrPa2dPN5SGrJU7UH0Ua5EpW2mbnBMylP1LXtxeugIHQDNfJM0htHs5Vsp5vsnQpWRlV72WbPUduHivLaC2/AR5LdemAeSsVwE09cSSjgBKPTFzrPRAIPF+V8FC8bfDGCzIzO+rAYC9Xw8ELDmfpW6dgwIAAgpaVdS/djhADEcAZ0BAQGAuAMLF7x3h3FdL+9iHBFJODDA4/74LuOvWM7II256AgFE2oA8rvHz1Ij7+zXP47LvO462G5CChBCD3ITMpvkM7E1EAhh5YANqAQS+3Wukf18nXRpwzbE0QgLbMTBazff4myB18qXjgBHCmuZqyUNAQBgR3rGAZ4m4xLDHLZfBymikIq39juMwAVmnoN7MENTPj7109N4OT/K0N2+C6/RU8su0zoBTGuUwKoMVgnrqJoGkKqQGytQ8vWdmyspzPhO8lu5l3vzXhLX0me8HTrWjf3H4b28MfSQshsXbOmEFCYhIUBOhxkXEQcEBDGQ7StpQ2dL9TMl1dOn12ob+uxcMTcHIAoHCynPHv6lRXtf31gk+oK/WknVI6VnZNBywA87G+pCur9u85TN4+rNkXoLQxct3czvjSAgYkCjDLgghMAgAk5tQGCJcuH+GDzz6ML7/nAu4+dcYWKUWgOSDx5cQAg7tPnbkuQJDPk/sxgJdfvYif/uY5PPau83jLLaexc5JM2wm9SclMgf4mpXgKwMAZ7W1gF9L9Sf5LOQxID10sLYO+1rntupa85Dpuduw+W8eFrrJ3mgMpLxeJS3t5DKQswzgm4DULS0T5XsBsTfoUcsA8/rhUnPJoZ1VvoBzdb/2hUkHxt96eVSUpUss+9kfz++2T+OpZl9ZqnnD7Nq+9sVaM3l6ZE14Yf/OkV7zdFgiYA4BrlX+9nbPBC5CAkAEC1wCB1OhD6mpsgrJB5UeXtvfPWe5Lq1+m8EoGANraaaxPsdQJMfZ1p1RxrguWql3YYv2uSFsXoNAF6VilI4skPAcW5Pwq06WjK3+QOnLml9iz2lUp7Ex6L+0u0WQARuBlgPDC1Yt45GsP44v3PYm7Tp3GxFmuHri0yokBBpNrDABdUBC5Dwh8g71y9SI+8a1z+MxPnMfbbj6N3VRKsf4NAINPrrLDCfFasqLudoBhC7sA5MEA5EEElAOtVWqk6t9zSyfvAafobmb3nCq0a/emqpImK7Ien96Py8Gv7z+AAOImYJDtHJYIA+YMQ2UkGWgvRLNa5pRjM1FtARDUxqillPYqXhk6wED5EIDMnqwmgwIOTJQULXuZJQNWVXgrZQsbFVB5VV6ubq+yMi0GoEH7+ppev6zlUnXkKpAQ+gCBBge+oMmjgLEDUqU9+mGvD3Zi4DUDkIw+5gBgAnf1ZKqh05v7j/0s01xDb/y50o9ZB9jzva6Y3D09yFgqpVNT2g47vsYCtABAoVEqHQnMZeWL2RaTU5K1viczlBnoAAQQXrp6hI98/Rye+MknceetZ+TZrO1JeVz0yokBBkv09xZj5w3dK68KKPjVd/423vKmu7HTKVju24jYcSyTyAOwq1hU8+mtkw6BZoChHAwlWIAeNy8a8OiZN3d+YD4ACgXpZGPnLsmHIR2bmZNMOAKTDSz3rB6xbDje6j7YuwSRGxGJvBQ4BCgocIBBwgyiLAbyIMK2BTD0chhssCzmcSShxZlRK16gF5/eYqCSspk/nlt1WSgtwKi+uRgeyjkdujcZN5OLVaIIUVjFkkErKulqcG3gCugDLDnW9/i9TOtEtxsu03STvGMOEhyLkD60NaDOE/F9cFb26W+VXEwmDIn4+BDABLftxn6sxnzk/ni37lCP97Wx7qs/EKXXK8e7lMDmHXMa81F1X/Z8M1AwUJDaijnR70ul5TSVrMGyXkzX2AayvPx9p0ZFavthZccRAwi7iWe2IwLJdiS9QRI2MG/ghSuH+Knn3o/H7zmPO06dFsDn2Oi8dHhfOCcGGFin9A1dNPLG4kHBW2++G7tEl+pz9AG7qRwUtT4kAnYqeDN6RhF5wJAGg7ar95rtPoac7bfzm/Sk/NzWgnOFDBzitZ8t6msNDBgQYBbZW2b6ZHQ0SkXRWiLXr3iXgQKp0iA18FmJFArEQBRYWAIsgIXowYIdHxLQ8EYyj+49Qg3ATGFXd2sbL54brl7yVl2NvnHL+2tPzf+so+mk9L1Rr4mWbfQtf9e6u23AqDN11Oie7T47a5q2rORY9YwN7diTVzDPV+9LRGKIzPAYKkCWUd6VZWpeQUoq5Ybhb1dM/7ZZp9SnYukUbQEBMeZxzrw+xm/U+D4GpzG+I8ZAhB1vBwlE8tUSQu4XwaDZD0AfwmSJNgioAUBtL4B5n0xyIhTC5Ci2g5QeGECYIgqZ9MqLmij/+D3CFERWGShwSP02v0KznBhgsBX5LXnDPnzwljfdne/VaeQaIdeDwwbGTq8b9D47yoDheCq9ZMAxC9rP22BBOqHNX7VjFuGoO1AxFipZ9cIpHhAYQ9JTFAmYRVFC9gwbcEtr7gNIc9wtGUkAARxAmAOGcaAktzGo9wAgOAUSdDAFItmPFrPgQRYpYCinSm0xeDM5V3+X4pd23ipQqG+KpSSvFZkXb8Wzl/RKdQYAOjTVvuxVub884H/Nuk/1e0uiW6sUMrB4NxU/E5PhgYIHCdGNU2u/XqhB7mNAYbluXVCZxlYbBNh5FjaNsc0E7BwI2E39cX0jxvQxbEwzdrp/iMBOx/Uu2O8MEkYKGAJlLzmBGwcSTN8ZUHAsgQcNXo7pd0cP2jE/Hr0utGv2YU96tsHu7VmViUUGLdKIIf0yrVdgbULAK1eO8LFvnMPn7j2P20+dTh2vBAcmmeVygoBBqUDzgWWlYR3pO8oUPPYTMiWxFwOqG943+FRrvCoPwS+pSoGwY10Exg0IAAk5S/0arIIzLJa80/IGW6XlVS0Ngpb3sFtQHBGcFIYpJ3muKZN+3fyMtgBKCUSyXD4hgArAMO4yWBgds2AKhQgI0UIQ7ICChGeOVVFtS/pcNpS+NA2aU+ZJ5h1AsARu/f1big3VMSzuWzemWwz9lnN6ZQshs5W08aVVp9YYscQ10wNmTIrwHRpAwO6hY2IGEDIaKDLAnd2av6fb3icJzrMBPSBgbIAH+DvOIGDH3BzHVo/IXOjRLeM4RBu/lMbOFAghirzT2lzMsmKnAvwJjCESdiGCp8wgGEAARAdanF1222C4Nv1n8l9jAzwQ2MsWVGVwFaFA+u3W9UL6X9ZVsv+lqxfx8W/IlPo7T53Rd2UEzv041ci/cKecHGBQ/V4Cs0SEwIxIIqOXX72IT3zzHH7tXbpOAYvXPkVgYGAKuUP0lGDJJrQfbswBESXQkLwRBxSIhUoSQ8fYTSXFlpJsnCLyDl9cqKfUz227awwQANcHCgwQyG+9fwMkzIobLObJmVJJykYBQyBgDKEECup9jIFTCGIMnlEAhmjJjSLfiZCAQspFIM/KZLAwW4LUSkvW7hXbymieE+P399guO6c0FvmRHtxV1SjAbt1HWzHQtbLPJIS10gqnr5WhErxnMYpE4GrDe/bmeSZmTu9hiVy2FiFQUrKA89ow3y/7HEgoKuJKJfYWCLD9BTCIZb+w8ZmYTM6MQB6zefzWQGAXYzFuYc/YMm5dCSAgZoYOAEIQAxXI3FcgsBrDyJgCYTDWMaIAB1Ng0cFKpSfwRi4RD3kMEPVzHbp6T3e0dB+AWf5UDQS26H4r1r+MFVg8d5ZvhQIUWHn51QwK7lBQUJfEGmCLO3CCgIEvNSjQD+VmYwrZCMx4SackfvZd5/H2W07LoCMxzjU4GEFJGRq6mwKBoxqZRqdoddLBnbdjTkBhIMIOmWYSJC3nCbouQQKAjKTdu9mA8cXpqlJWmB9YijO2yhIoqBVLBgrO+2jILbMF+jfkGGIGCxEBYvRDIByDMIYcfhh1aV1jE8ZA2DmQMMEnNAo4IGSWoFhrAVwsQQrkQb7mMBcKaStL4ACBP2dL2Mees5QYulW5tdq8/UnlG1NaX2Usk9ec8Xeg2p9np7QS3ACUSlZlTUDO8oYe1D7iAUJNydbsge23Ev3rNMRWsz5dIFD1ibXwQAYA0l67GBOQ38U8Xndx+1hdGqe2HSE5P2AzRgIUEKzDluCgNXgmqM7tgQN91xZQN6DQEXcTOG8ZM73xMgfY89JiAnzfrfvtOFh/7oMCc15e8UzBrQIK7P1DJVwPDtbKiQQGRUnegKJ56xEEvPTqRXzsG+fwG+/W7E1ox2JREhGCXhkEMmOp1mEMAgQENGhDGs2kj2bO68X7DuO3B+TOZfvTfRQhywdxpXcOJAMFnJNSgApN23tr0XE6K7XDV58zQAdlVARv71qFSCTWR4seRa1osrLpnJ8GnBhv80IA9UaCUJWBRBkacDCQINtTxSRQkZswDiFRvsLUcGYg4CnlPEWqCDMYA1SDMKcse3HzLaAA1Tk3KjG0zgexZ9SxY982BZCr2nmJVl4r9YKIXpmVRsfOp3ReBgAlOFhKcKOAJj3dy/K2Zxgta+BA8EE+J6JmD/LmtfSHFhjoMQMMYDfFIlegZgbWwED+XYKANQwYmQtjE4ikPwSeGSZffPv50ks6rmXm9xSyR6nHiq7J5fEbBQi2zcbQ/tkAs/5dZd86IAiQ8IHlFNx56kwbEGwEAnU5kcCgRkW+cwSIAZXszffj8ypUhgICta4e5DIAGghgSgIbtdE4Ii/6YrGiCiAAJUvQK6zsQU4+4Rx/YqXJWAw1BSRjTUSYImMIlAeNGz2JCl16OGUDZ0wJG/iowAGGIAMlRqEBoxibMQTsYpSbMVwyRP5tRiQQFcZmaV7vpHUyJRTZqElHU7JQlxGsgIEQIfFMDpLwtItaRwZGEgVpoQbrNIEhNLIOvpzkQzpA2XmYVS6Cb8e6bd12nWBYH/f7toACMwpryaFrYR8g08fAfL/tS+9RbDebbrHUBqXersNHdk0rrGRgcEc5wU3AnsSuKVBi4Ix940nAYS+Zy08Ba7Vn8kx1bJqBMlHk4xn4+dINGzhAEKv2v1Z2YDdxk8nzgKAGA0tjEijDNUX7hbptlkOAnt3rgYJ6psKawZth1R8QKAD6uQE9dsve0fbXyecHQ+iCAbvvS68e4WPfPIfP33sed5866x5a1mErQ1CXEwkMrJhMBicchnwl8aPPncMX7j2Pu3RKh5VoNgylUvYgAZDGGwFMxBidp0bMwEBKacmDOYrmWEtIsbIWe5J75risAYql0gIF9b6UiMWZKZmiggGNv9s7ciAAQbftGIMoYGJTNoQYTPmQ++29TJWJ9uCeMkr7U0/nRYAQAiPuhCWIIAxqECZEjGosOMhgHwdKRiJClIqdb1BrtroYIRmaYvoa+gzNDS2cmQIAM1Cwm+IMEEi79MM9PSNhz4jMaTvvK6u1ZkyA0qAAuUlt/xAyEJBtO48yWwRtd2LHHsk+iVljltyGIRQUNQISoE6zgG5gaUmiBoVLa4pcD0Pg23uNIbhWQNBvlzYgCEGYnNZsI2PztgKCtZQUn0fSK97Ab8mXsXDxmq61c4E5M2DHevkDcrwEA+W6NrJM/6PPvR+P3/Mk7rYPIrnSqt2++ujEAINaGL3Ge9FWhLrvSdyliRopbMWZ/kpeE81BAiCNxyiZBA8SAPmOz9QACgAW47Tt+b/2XOtw8wWUCjlUA2gun/I3c36WZfpGaLyL1TxGBgfOAIGAUZOIxsiYSJbMHSIwESXPdDfxAkAweauhH6hrbGqDYiWyuXyqNR3ls4vayQPATBjB2AVTCuKlYGJ955gSFSe9nU0L7S4/SnCuf6aVvey3FMK1gwmfOFiEDtAGBTuOMyORPEoFAgYCPACw/QAQFdlZKKsVf/YGptV22fjrb6UCgipLDxgMLAya1R6IMA40A4MjQophA5SS20aTRcis3wTJG7qWsu9VdQLqPquN3ugcgjpsUBdhDNtjrgYDwDJDMA6N2US0ca0SJ+iWHmvpsKKuXvY6wPw4I6KCFeXojb84B0AOFQPlbIK61Hq7BgKyfzlMAMjHNOcrPorsL10Rp/a3fvJJ3HVrBgVeFq1mFTW1XcOcHGBQ9ZIWxXvpyhEeefZhfOm+J3H3rXOkZTMEIiz276hbNQZGrR4MlAavLRIyDOKdHpg3p0ABUCVkIQYFC0C7Eet3AEpAAHSSqYBiINm+hDargVQYLpojbEu2M6aEBgLs/YY6rk1CzaOksHc62Dx9vYsZFBhLMZ/FkL3UMFQVR5t67pVoDRiAibOxMBAjISDRvmOAhhyq6WdUUsutmDOQaWVgHvv0cpd8hSx8Cz9YLkPK2VDtYbNo7BlF6EcVGybpt7vqedbHiuQy3d5FTqDMjP/xFBUEZDDggYBnDjJQKDtyvd47AIx1/0uep/VjVuMvq4iGEDTklD17IBgak3NIv0oM0hkAnLZNTr4koO2BNfJ5LU+tVs55XJVx8h4QrEFB2sb1gQLmPNY8CKynGvaKhfPy+MkX+DHXSwSuwzt1jo+BAQvXeTAwhnUg4I3lou5CPZ6QkhDN4WG0GNEMbC3BHICwSciAe4RrtwbA7enpJutBJcjxi7PZfQ50epT1NTv2whVxar9435M43bBfuUKlkxH9geq8XjlBwCBv1+iSiHDRfWXqbIN+8cUGJcgBA2SgAEjHMqDAASlLODmtyEABrIJWYzo67bB1mlhreWX/jnWH8wPKx6bS6Y1OUQ82T3cCdUy0AkIOKHjFlb2YiAk0i3ObpwpQ4dkAZYzbSu9LvvX0xi2F1YpbDgcoe5EMJHrZEQKr9/MyXmIMvDdDXiMyr4IDi4VHKmfOsNbfWBADCWQAh5ZzATwj4H/HGBMgKPfLbwMBNTjwv0MgvO7fX+s4GshKLw3EyYxQlEaO2VAJO4QmWEz3roxJBgOZrvYGycZO2AgK6qXI96W1t3huXtdY+ADI+iKFFjbeDzC5iD4SsTqG1Cf3NhJCWyBAfrdZgV7eQAIJC4l18kwpW5d9r2diWYDGpqQToDlkSE6QBwiAJJgDAAfbs6345MjUp4DZ+9l7WB9Li6+5/gUIS0uFXMR+PfLswzj/Hvl0cq+0QlUGjFrOX6+cGGDghQ+UCEw+PfkQzr/3As7+kAg1NVR1H0ZGmIDEydWZLAbrgYYHEnAgO295hcUEFvRhB+5ZVno0HjV+tDpd2l8NqtQJGzecJWwW3laGoEtT6EaSvIQD/V0yCkEAAypGISwvjjRbGCm4+jXq3U9Yo+LdLb6Z3nCDFjAmqVd6oGDp1vNBK8qCoOEN63+k652zhjYgim0iUW47ZTuSezkE7Kao+7QxA9L0vKCoJwRBGJHVI4/Z+AzJWAdMK1MPPAhorYhn+4wliJHTdq8YY1AnueUYt8a1jcI2OtvFstM6FkMopq0Wa1sgj51hIyDohejqkhxNZKVsFHavbFHeBvbsfgNzYoqCWSNt94Cc6xOQw3dW+6hKb20spTDCtTICDRDQiqHXn2JO9WnpKCtOX0VwXh8mgfuScUv7keU9hiwXRmkcV3VyDWg679jrV75PHThZgYQp+NCzD+HC+y7gzG1nZn3O4Uff7Ko3KL2znyVXqPdGOTnAoIXiCbh4+Qjv/6oI9WwhVINU5TAsjW8QaogynW755m8I+rlSppRAZAaOUYYeWoY0/86P80xDr3gQMHtflB1Q9ucfyZPCulFsGUBWC1bGRxcAUSCRj8qDscwqrC3DWhifjl7149aDgdqbqRUZgPnUqFoxtR9ZlJbRWEtWIjhkb8qNLC9lDhCISBgqAhDl0DiIrIn0o1ATYxiCfttdoegknvZOjXwEA1NADKJSMlsQMATG67sIoy8OxpCYg2Tl9EXHyMBAiTkIgZrgwAMBCyv4cELOIyhBQSDgpjH/HvW89HfINLZlvMuUVPFa/TTVYlVMlb3RuS0PrqW4t7YrgNSBk9Kmcsy3iuS1uOJAQB02kllB0FlBempkTEwZhKUQnf7uVPtax06PDcjy22Ygge0Mgcii0p+qhwPKPCVh3XJ4yc88A1CETP3f4lmVTva/Wg7ZDOhU/WmgRt/Sa27Sr8MSgMPLRzj3lYdw4b2/jbO33gXwfHIkVd9nCWavXL8BSpBAs7eo7rlPQsK/rYVuJsZP/XHX4k/Ln5Y/LX9a/rT8afn/j/IXf+eH8U//6R800cGJYQz+v5/612mbAPzadx/Dp373U/jlH/17+ORbPg4w52+iS9ZO/nRsAkez9Du9oSItIoAC3vCf/k/xj/4vX8ODz34AF957Hmd/6B0w+sn+5aSi7EV72srvA5wHrH9fvHqEjzz3MD7v1r6eLRjSYAKkunMPp6ZBL145wrnfeQhPVvRUgYQxLzV7Z399/SOAo+9J+OaL9+n3wPV963XdmcspWUlGFcNQz9P//T96Ab/wux/E3/vRL+HNb7q7O8ujN8Oj/5lnefHv6DLZn33Xedx16+nNVGBL1i1hvnBFPIHffm8VM0wMif6s+k7xW2U5gQv5MgMvav1/9Z2/jR+++S75YpuGcmwq4y6257nvJmBttsL/8797Cb/5zz6Oj/z5z+J//T9+e7mmwULYIbgAdhkWKGcf/Nf/6mU89vuP4mff+jn8+Tfe5bLc4bZtIasyZOCp7JHCLGQwkCwj++g38lfoltqyZuV6FLfvghcvy/g6/54LuOu2M0XMd6kta+Zt6oyLl64c4We//X78/Xd+CW9542kAeWwAJeNwLWPj9//lRfzVb38Qv/rO38Ydt55J46IOBbRCL/4T6LUcCya3oX8a4oW+9my7pTtNrhcvZ/1z961nCnn3rvXF5za19O6LOjvg8XvP4+5bz8z6SeudUx9L78jJHpltesPf/5/g+3/lv8Njf/Ab+Pmjv4lfOfN38Mm/+FPYapugf9lt17YJEPv4zFefmQtay4kBBp7mfcxAwY/8Ej755v8QFKc5IGAdaroPgH4nfV5YqZpM2QAPPvsBPH3PEzjzprcBx/8GIAK5Bhn0b24QSoMfsA5MJTjQvy9ctikpF9KUlN5aBbNQAvqGyTqmDcqn3ncBZ287jdRB5UFzWbjtVAMnCyZRxvLAgMPLR/jQsw/jqfeJ0cuKj9IAjUX+RXv9d2ZGVIrcK8dXrl7Ep373g3is+gom0E/mrBOEAHSThF4xUPDu87jrVAYFwwaKef61xjlwO1J68EkNbxU1Jkc7s16kcpvNlIHG6lPfIgQwXrxyhE9+8xx+XZf5nq2dH2g+1W1EmhMfRwcIxpwQap8g/xf/7Yt4/J9/HD/7ts/hf/c/u0PkXs1SWCqhkn8gyxOQnIH/8v/9In7t9x/FL97xOP78G++cAYE6VFAve72WP/DK1Yt49BvvxxfuFaMhtHkHCHTAt2uuHAKk3L5m9O6+9QwsoGs5JGBO9+6FjCYNgg/I33QJ+qyXXr2In/32+9MH30z+ktvTGgtlrdeS5b7z6kX83Lc/iF9/13nccevpZgggf1ck9/ewxRACOPze83j/75zDhfecxztuuR2YNC21pX9N98LrYSqODapnbewcWk6ZJupZUriKvtC3dTjCCjO6+vYFBQU2O6A35tdAQNMeAXjs9z+Ln7/4t/Erd/8t/My/90Fg+v68fqZ/1c4QB0DzkEAMoqgAgZNtIgUIj333Mfzi734KfwF/cf7iWk4MMDDhf+a7j+FTv/uL+OUf+SX8zJs/BsTdvAF40t8x9wq/DeRt8lMBCZ/+578FAHjmJ34DZ25+K7D7fkZqCJC57hm99cCC1NYBBt1xePkIj3xNZk+UUypbODoDA6BvjHLnBA4vH+KhrzzkBuVx+e5NJsVKCziFQj6HVy7hoecewVM/+UWcfdNbgd2/6SBYSnkZHjD0PiFr3tSlq0f45LfO4XPvPo+3nzqt1aRc/YUa1zkCVu+kZ9RofPybee1x7wn1ktBa3gFQxU7179HlIzz8lZzzAvhVEss2tjZkclOunLwCAxwySGAGXr58hI994/14/N4ncfsp/faHzpgZg6whUSeH7qIsSrUbkXM9RmMX2CWGBvzhv7yEX/3uo/jrdz2OP/s/v7P4SJb83Q4McjJbZgH++X97Cf/glUfxN888gR/+M3cXIMDHtmsg4PMG/OwCnzcQQvb0fuu+J3FG27fn2RV9ZPYW+T39+iSHrn2NCWJCXloBGQywAoRI+S4FQNBjKTbO8m2XTyjou/3U6dTvcwxcr4MkNgPL/b6Ojb/06hE+8a1zePzeNpMy+1Q5IcfLTS5mAGOc6Zajyy/goecewdP3fAFn3/gW4Pj76OmVstK60mzSIfYCpYE8uvICzn1FnJIzCrqT3NnJFW7dlmaXLVvc5JRmByjTF4p33xME1E6pVuTnL/5t/MqdfwM/8+c+AEy7whbl+ti7T2JTKAAUQDQsAoTHfv+zYh9/9Jf/ZDAG4Amf+e6v4VO/99fwH7/jP8Inf/inZN5TpzHINVZ2wxKB6+6bNz/9X3wRP//SLwGAdOrd686qhNRA5IDCrEM7sJDXzxbzcnjlCB/8as4+rR7fLdTY9gjd3vHoe4d46NkP4Kn7voSzCgqybJDlY9cAmTmoR0/63GQewIevvoQHvvkxPPOuz+HMG98MHP/r/M5AkgerDAwscRhmgEGMIOmSsKIUL165iI8993781n0XcKcZPQeqep8lXpWfCu2lq7LM6OP3Pinhm9pIVPRova8GBLVREVD2cJlI1KukA6Q+uUjXm3K0YGaiDi8f4cNfP4cv3fck7nDhGw6UZnl4NmbyyaEowUJrieXff/UifunFj+A/escT+OE33l19hyFXfUnuHsgOaeiI4f+n//IF/NKLH8Hf+5Ev4i23nF5e/KYAZSV9TTAGojRqL10R+aQpywsAuhg7ay+mL3V4+QgP/c77ceF9T6Yp0Ub6EGUAVwMEEspAjjsGQVis7Di8+OrFtAyufUVv3z5u71cnJROAF6/K+PrSfRdKT5jQ9YAX2ViroMrw6MolPPDNR/HMu34DZ9/4ZnEavIzrT2xqpb13TGl/BgemTw6vXsJDz34QT933ZZw5dSfAUzF2glae4UDBFtnp36PLop+fcqDeM63LoeqO7UkyYhy++jIA4Ffu+Gv4mT/3fnFqfTFaxGSlxl57SQlVGwzCZ/7gN/Cp5/+6MOlv+Q/xDD/dfecTAwwe+85n8Knn/7qAgjd/bBGhEUcFDQ1A0Akr/Oq/+DJ+/uVfxq/c/ov4uZd/SZU6UDYU5Y7rgMKsQxtUd8j38MolPPTsB3DhPeez0Zh9i3but5QgphMO4IjDyxfx4Nc+hKfufQLvuPl2QaIVEOgyKBVYmD0XwOFrr+D+b38Cz7zzMZz9Mz8MvP6vK2Sf5UROLiKzOWBILEMYABAOL1/EX3724YKerUMRQGYepNrcqGm7XLoi38544l5ZUaw1V70GA7Z/i4E5/N7zeEjp07O3nhaFmk7px+STVwAUVCqlY2I4DVQW9DVkPkIO3ZCTF3Xi2nDnZWP2ytWL+L89/yE89q4yfOPXmbeytPS3X+7bL9b1ndcu4v9++Age0/AHmVxNntqF1jLbe9S2rWPypJudNAPPwHzsbGkfBg4vX8zj91Qev9qLxDlABghpB6pscQMJ5D73zHnFO+ufG/FA0WeBLEM7ZvK6pExlUz5xKr3/Lc6WvZDK8/DVF0v9sJvT44V+c7qj0Km6nfQHyzscXn0JDz73YTx17xPJ6fHXsNPBfuzMnptKpd++9zwersMfLabVyyTtr5zQJJtsd0x/AsDP/tlzc1CAEiDJvVAABBUHxMZEgO17tBGf+YPP4xcO/0a2j3GavaMvJwYY/IKBgh9+dFvnnZUKFLhzfvVffBk//8p/jF95+y/gZ//sOfzcy78E0oZLjWVZQbDOGKWxOh3aA4XDqy/iweceyZ26ZiJ8LWdgoaPEXMc8vPICHvy60ne33InkqZo87HzPnqR7lYqzxSA8/9p3cP/v/hU886P/AO/4Mz+cY4bOCKSBLAcykncgqQcYnr96CQ99/ZHsCWAS/jmxC1nFl3kcLsTg6lu3/tKKYt3FbCowYH8LQKCyOvzeER766jk8dd+XBfTZoO+xMEVRAEEBlLZLBZqM0nsVVKqisLubjDIQKOXCoBnI8kzMC1cP8cl/pJ7qLWfSdQVM5PZ2XYqq6/ZLVy/i//rtD+Dz+j157922PNvVZM/Kwz3SKctPve/JgqnxwLn9ImuADQBPMr6+9iFh4m69C2BdMYCnEhA7gMBAYn9SPom+hAcEAHDxyiEe+VpesXUJFHgTt5ZrZOcn+bz3t3H21J0gM3px2s62JnlVBhGqH/7Jzzr9cNypvGMIzDFq6FTRqyzGD8Dzr76MB77xU3j63sdFv8WI5C3DAFrZFjXYLqpRtfvh5SNhIn7yiyXTCiwDAZPJik49fPVlkc9f+lX82Nc/0B1AhKiyiTCGtSjMjjnI4OCxf/o5/MLR38R/fPbvqn1c6NdaTgwwSC8NZFCwbzGhAwrtuQQFf+4D+VxWlKbnc3GPCTDOyhCd3pOI4eNCh1cvuk6tnnwaINqx7b5ANg694hMFmXF45RIetJjeqTtg+RVyvIPwKwZlBgbc8ef/6Lu4/3f/Kp750b+Pd7zpzSjm2bomSMS4BwTwgCGDg3ScAg5ffRkPfvNRPH3P4zj7prcoE5HzOAx41Xkc9vhs32RfvSjKRU2U7K4o5saen+dNnb8eEIBZjLYHBbXSsOvX+mtj/jIT4fDKIR78uuZ03Hx7VroKPr2nJLKR2mbZUAUEsmFiBi5ePsSjSi9bdrcdk79lvbeMOq/OLl05wk9/U8JD9kGYUJ23lBA4B2WlQi7kf/PbNafmGuRfF55U/i9k+Z+6K48tOy0HDvRBJUCwvwYS7B0t8dSYjrUV71r9dBGwKjBKRu/eJ/CON75VxtcSCACcjrhO/VC/gOpLMkDF0TEDCS5lebIwlQ9882N4+t2fl/6frg3KvlBqq9Q53XfSF/Vp4VQ9gbO33JGZ1kIOaIOB6pwaEBDHEjS96a39ulTOwNbymT/8fJrd8Ikf/qlcp5VyYoDBJ9/8MdnooSH1zmW8WaTPBuuQG9CMDEd8+l98SUDB7Z8qQUG6X6MkNKcjm+1ZdnxKdTm6+kLVqRU0GAHJzoiyIdzOc61artEPr7yAB577MJ6+5zcVFLgBsQE1rhUb9P/Zj/wneMcb37LS4Vj/t8Fh3kwJlGSn5ly89h3c/+2fxjM/8VlJZFSj18rj8OGbdI8CaGTa3WpzdCUvfnVmZZlsYIbPZ+GC2vOcgQLdX59bGKWuDOv2Cji68gIefO7DAvpuvl3pQXermjYtvFc4eesiTw44AEizS1o5LxlgUSGCfcpFjfn7RC53x2K7KWtGktfM+2fG4ZUX8FAKn71dlTrS8XmZy7hZVI5Hl1/ITN+pO/N91aiJkTMvz+0HUhugAgn+ETa7Yal/tmQ164/s4t9WR46p/k/f85sKml7vM4gOCAANQ+iea/35+T/6Lu7/vZ9b1A9r+mypHL76Eu7/1sfxzLs+h7O33J67IGc2AWwOlXvmytqSVv/SqVLQUb3z9vDrfNwnUPCXfhXvuPltrgKlTEq9qG9T6Lr82zOuAgr+tkx5/OFHpQbJPi2XEwMMUlGjLsvH2mA0wYqRNsNPTA4QKGTXBv30f/FF/PzLf08SQf69D2WhaplRUJvyAXI5vHpp3qmt0azeKDv3+l3d/a9cUlDwBfFkjIZjFvAD7dRmXNM2kBRiBZQcyanhA/EEzr7prQ6ML1sIYqP46oGSPQWG5iz845+RmOSb3p6ZFEcjslGCCQA4tsDnKwCpfazdjq5clJi/n7K5WbrI9S/eAan/NEHBNZW2Eju8ehEPPPcRYVJO3QHzoHxJwDIBYGc6iPLb+r6r5xxeeSElcp295XZwFfOcfRqcsEnhpPp/T6asPfXe8zh7210AporOqcDrBrq/BsUJNFn4bO9Se6mu/lcu4QEDBbfe3b68BQ6AGUAAkiuQLp3PXpndvHwOsAiS6mS4w6sX8cDXP5KcEnKzt5rU9wZAIMczKPgPDBRUnnAJBtaYRD2nCj0WoODUHeX5tR6ryjo7xzi8eql0qnS/f+/NeSjpPTnV8fC1VwQU/NincfbmtxdDt3x3uxYzGaR9HhDo8c/84eMlKOiEpnvl5AEDIBuBPHNYCptHFAEawGwU1VB0+E//s8fx8y/9kkwZ+fN/2Q1ot1Cw366eW+6j2fbha68UnfpGF1NaT9/zhNLXjJzFm5VdYfCNNSFk5WD75MJkVA9ffUlyCgzpFvH73gDpGN6WUfKg4Oa3l4AJgOV2eyChSEefYTQiuwGhSpNiSvSUnIW7xNOeGboNA6ijDApQcNvp8lwHzmx/ArFOBvldHbNl9796KYOCW+7seL8AUL2XO03EUQEHfX72lJ4QpTUdtyHTHkCgqL958hb+2L3elWVTia94+4V8LHy2VBa91hZoqkBBow/L9h59KLGMGtP+ysM6u+F02j+TUQ0E7JyZAeNi7Ej9BRS84xbHNC3Fw31p9OH0eCI8/0ffwf2/93PqNLxtfoeGvLpesTsnhxczKDhz610F8yWl0x4bSwYFX9imn80pATAD8oUO1fu/+nJOxLz5dneu3iMcuH1U/c3vWq9loBeX4YM3f0zTNPYbqycHGDQ7gHmJKDxismAe1PghgiEN+Jk//E38/KW/K4tL/IVH9LTG4Bg2iq5qkMNXX8b9NqXv1rvmjVa8R3v/Ev0mnpIorTO33t0wxw4AGECCC6XAyadQEGKYD199Gff/45/FM+/8LM7ebIO+58FtC1d49uXwj77rQIEOmtnAX7qZ0YhS39zSAi7S7AyXKCbH64FzbaEWAwUX3nMeZ247PfMMC2bGKdcaUBVv6hgjUVofVaPXYSI2hSbgQKKr/xVvVIU+LUGEL/vL6PDqpTL8Mc2zr6/p/sbUXH1xGTRtBQGN81NOgYEC78kvAIJWgtu8/vKuMuXxnCaS3t1nStBgBvw5XE+XA2pQcPbUnW1A5g1/1X9z8purg3OSEpNY0eOFDNa84eJvuf/5V1+W8Os9j+NMwcS1dcS+oYqjyxZ+deEhAIlxdc9iJx+qw9JF7MKB1ldf0vDor0vOQqt429IDTjUI0t+ftkTDM38Hn3jLx2f32VpOEDBoDWq4jq2dlxkZF3gam/GZP/ycLC5x5u/gk3/xI2XMqio8HMz2rZXDV1/GA5pId+bUnX0QACwDgY6iKRKhHL3JdSedASQggyRnpCrP4+jKJdz/jz4u85CtU9eJR267kNoaSNBEw/u//UkBHR6pt5Tu6oCZl8PLR035AMh0r3+HPQfT4WWZfXDhvefFaDAX96RZfM976qVnwUXfzTHtpLQSE9QoDYO/qf6Fp12Djmu7Z//+d9yQe6ZCJPf/xkckkbcHmlbyBqy0xlwxvowJcsfL6/vjuVcKUFCHD3qgoHVOj8ky+dzzeAIFkpzqQiaVE1Uoj5o55Qh2oODwNc2uV1DfBEQNBnUTg2Dt+81H8fS9T0j79nTkdTBZM6cqyd2Nz/SoDIqYq2cWbSDnHF59EQ986+N4+t2fx5lTd3bTcgrb0rIRLlyazwv4zB/8hoACm5Loju1bTgww6KJyi+ElJVtclDY/bUI9+3eFfllLDgv7AQMZlB/NnRrYpIzK3x3lUnjCX54rLWAOkKCGuxmvNSre9mvM7VuP4ul7H8eZU3eV2ehLLMHGbO+EpD3o6CXhAB0gMN9nMj28+uJcqS8NmDVFXjNBLaVe138G/kIpLw8GgALMJqX1k1/MSsXdv6TcXZhrq/y3MBHXUZZBx76lEfNP9X9ifv9OWza9yc6YK6YkVkZ71QC2ypb+U9+vCwRdjlCDhSIYE+Tl0zBqqe8Ne49jGb+f1JypyhPeMo5n55Vj2eqfjHb3Ov/cPZiaYsrp3dm58U7kbLz5cHLNTJWh5hmoWSohm+Uu4Kne+TN/8Bt5nQJlCtaZqn4fPTHAYG0gemSbkZ8I7jO//9m8OFKiXwZHM2M2MNjong10uSVCeSTa7bRblFV6J435u8VVztx2uo1EG/cgRwlKqd4ZcEbpw3jq3t9qIOl8XnFdyxvsKJfDqy9metAj6YXwEDrntAbSbNA7WWz27BYGWY4J9xLFMFMs+flVDLIGsgAOr1ws61/coFJg/nkL71Mm6l0qmYgtZSPgAFaMdlGp/ePBQK5/Mb6a97+2MXd4+SIefPaD7fG1VOeNnlq3/9S6xYWg+kyUv0b2PX+19ITRM2pJR2po1ZeF9m6O3xs0duX+kvPSGr/52mtjCcypSom2yWmwG88dqoJ1LirRYWpa+r+qQ3GboM/aGJ4y+/XLP/JL+MRbf/paJgjNygkCBnt0DCfvz3znM/jU7/21uVB9co1d5hveGg+ZVmuVRD+2lPpKnRc7uyHpmr7eo3CdQNl456PvHc6ZCI5VmKCVINYHTDOj9I2P4ul7f6trlBbjhCuGPYOCXP8bAQbS/ZtKvZWwtXSvuaysbQ6/d9QEfU0GLF1cxn3LYyWQKJiIltLqteNGI76qFLeUJVDm6p/G1zUa69Z4KxaPanry+xqkqn9ePnSJhtX9i9BWmSCa91cgoXrU4eWjGais156Yt7EHHTV9XpZ6/O41Vut3aZzTBWXXCCLr4vXnmVsbTlUDqO8z5q5J/4dhMwtlnwH45R/9e/jkWz/Rvl+vLIjw5ACDxdJsSfngUk+oszyE0pByaHt6vhgSXfTkm9Xd0OkpuLXZFzzVfUr1zrL4iShF+yBJCzwAjRBCQWeXx0wOSanf96UfiNF4/sqltvyvGQjUSr3HFGxVWiVzVRdRWu9vGqUmA5YevwBoXdWa4aeZl9oBGBvKolK8nuKYssXwGTZ4kis5KQ99dev42t9QLTNNy23aY4R8uMrrnwJ0z9ilft2XxuSm8bsnEJNrnNNTg7JrZQYaZaY/F5wZdmG9dp2vU//78Geoxlznnef2q23nrqWcIGCw38D8zHd/LX1lahFpLeUudDJPgevw5PemH83TuDEI2urQBR2dd56xD+lAG0hcM2haK1uYlE0yXpanyH9pnvmmyq7cv+NJtm6zRan5+3eYiOtRvEX4Qz29ntG+ZurX3X9W/329yLXwUPFBpBs4vrCl/6wo+W7dHah3/X+R4buGtrhWo9c/p5Vz0QJlN5ApqMfXFjmsjLXi/mv6fyUvrnxoWdJXhNfs16K8/iTkGOxRRKif2iBUoC+85F7rT4/Ub7Anv9lTvTHl8PLFbfffOpDq+3/v+WsOfyw/yzzJfeV/DbMPrhsU3Ij7bzEUnfubJ3wDPfkU/mgxTTewzJTuDfEicx/4wY+va+0/GzzCLUziRuPWK9cPulvl31b57xsWvBH6Z1kf7We/rq38iQIGDOAxJ9RPvPUTTaW1n5n4H9poX59R6ilpe4v97r8veucfAGgqyw+USfn/tXe/sXJUZRzHv79QAdHwL2g0tLE1AlqqVFIalChiEUEJJL4wNcH4JymRYKUNUShEE1+QNJSoFYSkoTUxNBIoVYkRpAajr1r+FBBKRRtq+kcM9QVqYqRUHl/M3OVS9+7O7pzdM7P390kIuzt3nz7nzJyZZ2d2zzBz/wxz8OuW2Vj6v+qZiCGMZf1Wij/kFxmHHF9V1//vkxeVVfY/gx/cZtKk/u+3L6sbv3+0meKPbv//wwrHL6i/15vowuDoTju6KKj6PqjW0c35JDn4gSp4806r2ye9uhtb5TMRQ8cfX1GW4pPw0e9Pf9B4s8no/7xFd511PvrxNVP+qU+/N3t8zfS+0Y+vtPGHPX51e++gW8DEFAb9NqJBOnWY+E0oClLutAaJ37aiqW78KjdcGlS/gwbU271PUv+PI37qyx+Djq9B13Vb+3+qzVX6p45Rx0/RP3HU/6cb9fHraBNTGPRSt1O7md7RuSrRVDuvuoOmVx6inTutmdZvzp3WsIVZG/t/XPGrnCmra5jtZ5DCsOn9368/c4yvlGdrhumfQbaxURy/+kn3248BSFon6Y+S/iDpZ5JOnrZsjaQ9kl6Q9Om6/9aoO7XbTqXKf/1M/V2302tVYwyTf2q98k/Rjjo7rSrrpylFQS+92tH0g8ao4lcdh21Yv1OauH7r7uua0v/DtqPK/q3OPi9HUQCZCgNgG7AoIj4E/AlYAyBpIbAcOBu4FLhTGv6H1OMuCgZRZcNpyqAZdfyqg6nXTnGQQVl1cDalf1LFH7afR1l0jGr9DtM/qU3S+h1FUd+2/u/XP6nlKgogU2EQEY9ExNRt1bYDc8vHVwL3RsSrEbEX2AMs7RajnyYXBY7v+JMWf9iDT1Pyd3zHb1L8UR+/bn/89p7Lc50xmO6rwEPl49OB/dOWHShfG8g4Kq02b3SO7/iO7/iO38z44ygKbnj0hp5/o/+bNzsRSb8B3tVl0c0R8Yvyb24GlgCfi4iQdAewPSLuKZdvBB6KiC1d4l8NXF0+PQt4YYg0TwP+PsT7mm5S2wWT2za3q30mtW1uV/sM07b3RMQ7ui0Y2a8SIuLiXsslfRm4HFgWb1QnB4F50/5sbvlat/gbgA11cpT0REQsqROjiSa1XTC5bXO72mdS2+Z2tU/qtuX6VcKlwLeAKyLi39MWPQgsl3ScpAXAGcBjOXI0MzObjXLNY3AHcBywTcUNNrZHxNciYpek+4DngSPAtRHx30w5mpmZzTpZCoOIeF+PZbcAt4wplVqXIhpsUtsFk9s2t6t9JrVtblf7JG3byL58aGZmZu3ThJ8rmpmZWUPM+sJA0mJJ2yU9LekJSUNNqNREklaWU0/vknRr7nxSknS9pJB0Wu5cUuk1VXgbSbq0nNp8j6Qbc+eTgqR5kn4r6flyXF2XO6eUJB0j6SlJv8ydS0qSTpa0pRxfuyV9JHdOKUhaXW6Hz0n6qaTjU8Sd9YUBcCvw3YhYDHynfN56ki6imEnynIg4G7gtc0rJSJoHXALsy51LYl2nCm+jcirzHwGXAQuBL5RTnrfdEeD6iFgInA9cOyHtmnIdsDt3EiOwHng4It4PnMMEtFHS6cA3gCURsQg4huKWArW5MChmZj2xfHwS8NeMuaR0DbA2Il4FiIiXM+eT0vcpfu46UV+Q6TFVeBstBfZExIsRcRi4l6JQbbWIeCkidpaP/0VxgBl4dtYmkjQX+Cxwd+5cUpJ0EvBxYCNARByOiFeyJpXOHOCtkuYAJ5Do+OXCAFYB6yTtp/hU3dpPaUc5E/iYpB2SfifpvNwJpSDpSuBgRDyTO5cRmz5VeBslmd68ySTNBz4M7MicSio/oCi4X8+cR2oLgEPAj8vLJHdLelvupOqKiIMUx6x9wEvAPyLikRSxc81jMFa9pmcGlgGrI+IBSZ+nqCp7ztrYFH3aNQc4leJ053nAfZLeGy34GUqfdt1EcRmhlQaYKvwIsHmcuVl1kt4OPACsioh/5s6nLkmXAy9HxJOSPpE5ndTmAOcCKyNih6T1wI3At/OmVY+kUyjOwi0AXgHul3TV1C0F6pgVhUGv6Zkl/YTiuhrA/bToNFqfdl0DbC0LgcckvU4xn/ahceU3rJnaJemDFIPgmXJirLnATklLI+JvY0xxaENOFd5Glac3bxtJb6EoCjZHxNbc+SRyAXCFpM8AxwMnSronIq7KnFcKB4ADETF1ZmcLRWHQdhcDeyPiEICkrcBHgdqFgS8lFNdkLiwffxL4c8ZcUvo5cBGApDOBY2n5DUQi4tmIeGdEzI+I+RQD/ty2FAX99JgqvI0eB86QtEDSsRRfinowc061qahINwK7I+J7ufNJJSLWRMTcclwtBx6dkKKAcv+wX9JZ5UvLKGbXbbt9wPmSTii3y2Uk+lLlrDhj0McKYH355Y3/8MYdG9tuE7BJ0nPAYeBLLf8EOht0nSo8b0rDiYgjkr4O/Jri29KbImJX5rRSuAD4IvCspKfL126KiF/lS8kqWAlsLovUF4GvZM6ntvKyyBZgJ8Wlx6dINAOiZz40MzOzDl9KMDMzsw4XBmZmZtbhwsDMzMw6XBiYmZlZhwsDMzMz63BhYGZJlXcg3Cvp1PL5KeXz+ZlTM7MKXBiYWVIRsR+4C1hbvrQW2BARf8mWlJlV5nkMzCy5ctrgJykm2loBLI6I1/JmZWZVeOZDM0suIl6T9E3gYeASFwVm7eFLCWY2KpdR3A52Ue5EzKw6FwZmlpykxcCnKG77vVrSu/NmZGZVuTAws6TKO73dBayKiH3AOuC2vFmZWVUuDMwstRXAvojYVj6/E/iApAt7vMfMGsK/SjAzM7MOnzEwMzOzDhcGZmZm1uHCwMzMzDpcGJiZmVmHCwMzMzPrcGFgZmZmHS4MzMzMrMOFgZmZmXX8D1fcW/zDLBwiAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.55,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.55, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Dgm8ujda+BFtmRtXsVmfaL38a7zBSfFDV42u+A50XBKgFIdxnv2RqK0m3duslICBO2eY/U39h6eXwyep8XlZ79vMvv1g0KVcf0ZONrY0qcZn/tUDgYcC+v6Dq/e970zN43WuvYXTmAw8RPrerRULNc3/pzi38hz/8JL77TWI+kOdzGdDzbwoEkH6POiGeAUGapD+nCce3T/D4D70HH3nT9+L6l341aLzbgMA0gwCMYwGAaRSVrv6GkwAB6/Hjf/K/4PEf/w7c/Io/iEde8+VIP/vT+VqpFCcwnLaA2/et55YfAk5+5m/hif/tv8ezv+E/wqO7Bwt0BIO+UD6HKAIvRqdJiFnIUxzACg6sx4//8f8imojr7xdNx/gKTMsg1wQQlXvI36FoE8LkNAkBFCIiSAQwiU+KOZI+cvVRaV8CAJqZDhLqvrWlX33m5Vv4lh95Et/5e57B7/jSRzGq2sHeANMyWW9KrLDQAYUWEgjFp8bb5GtTjYN8Cs04uSYEJR2/fEuFnk56mGuA0HwatPhjRB1IsNrS4yfN6pLzzIP9tS0UmPbgOGtSbpbx54Dmc14fy0B1/PJLeMcPfzM+8rXfg+tf+rq+HMmg4HDQJo55EtXUDwBQwAc/+8G5+fse0oUFg25aUQMRMz7w49+NP3Dyx/Cnr307vvUrviEPsnK+ruDKRtuZ6bN/qSgAzDj+/EvlpbnqtrzsqMfZ1KIHgKCrKXjrTTxytTgqtWrEqhzN3zwz1bJ5FVq2+aumg2Yzxg1mBABAmTFoDqV+Xn6paFLspbTbVfVBB4Fgcp/tmpfunOAbP/UEvudNMhMbmcHMHfuxe+5KKfIMRLvAZz5/C9/yw0/iu75WoMCukYHbmQUUCqL7HFog4KkI9zSBmHF85wSP/9A3iiPgr/2twNkrct5dS5zA0ySCnpN4+DODx30FAGCWmf24z4L/5Kf/Bm78zT+Dm//O78WjRw8i/ew/E6CYdNaSuJgTUgI79/sWDLjjmv/C3f8f3n3nQ3j66tfjdfylOPvnP1PqMlLWFlAIIO20ZFAQZJbPZmJQLQIRAcNONBE//dfx+I9/Bz7y2/8orv2yXw9+5V9Kwww71SoMqlkIDhQiiPb5fkj218ODHHvhzot458d1SatzVCVmTAoHYM7COTBVcADUvgptP/vM52/h9/3Ik/jg73kGv+O1j2Ky4cP9jrIQle8BAgtmavKg0ELCp2+L+ePDuqTSVjfYbymbaRRlO0JlTQgev/xSmQm/dj4ThoMBYqf9M2HHEEjws2HWdx/JrS4RTYcJ87V3tLfMtLcMGOgt+S1lzPUxG9fbOmqgwJ2vHHlf+zpkTWu74kaX6hYNqUeYkMeHUmdy3Qd+7DvF/P3GP9lZ8ni+9EsDDGamg9Jo1tEzFDz67fjWr/imPMhW1/tUkV4zA9aO7c0ZQorfmDUFgFJzox7PAhDozoyBulNn7/rbJ3jy4wIFjzaagsR9OKihwHVkpyGw72YzzPnPM8LlF2E1KSxJWVOlSbmWNQVNnTRQMG0EAjDwwsvH+OZPPYnv/tpn8NU6E6tM7nVp6mQjkEuhOWwzve/62mfwhqvXJLuooSA4LUFwQBDz4FxrB0TYixA/uX1LBpXf+Wdx/dd+pfgU2HnTCEyjAICBwDRmLUELAaI5SCLQU8LxT/8NPPF3vxfPfPk34PXxtdj/zL/Iwp4nlmu1zZMDBMAawuqy3/4v3v17+Iaffg7f/6sex+umB7H/Wd1zPpaKpRAQDAiCwoCeJwoZHgwcaNiJxoACjn/2f8ONv/VB3Pwtv1/MEz//L0S7EAe5VxyAYSfv3LATSKAADANAsdYmGCSEAUgjQBHHn/80bvzg12ebMwNgkqInJhCL+EjqQApwBQdQcAgORH33+sznb+H3OihIC/0OXFaskOYBzEX7RAQmFlnC2vfAeOm2aMqeeuxZPPzgo7Jk1j3DmmEGB9LiDcWsQMHXfk8Rem3K8i0AmBwodGbEZnJQTcfzt18Un6a3PpOX5JqpZwkOlmJPtMfNvNLGiWg1KUxR6trXCQUwktOKBPRGkZM7L+LtP/JefPT3fJfWjx9v23qy8dfqyGSJ1psHBEhP+8CPfw/+4PF/NtcUrPqpLadfGmDg00Eo+IYCBVuF3aopINRQoEIvd34v/IBKAAJ9IGg97H1wlUevXqs7GpXfeDhYg4LWvljU16YedJqCJaellZS1KZUm5ZsECh56ZF4vDRQkFIE/eQgA8sqCokVgvPjyCd77QyK0f8drH9XfdGBgberhob35+Jc+P4eC2AEC0xBEUhM5bJBvtAPJtACq3r/zEt7+F74FH/0//hm88dd+hSzpG0eZ/ZsGYBrBBgjjPmsMeBxrELDj0wROjDSOOPmZv4133XkKT3/pu/H68BDGn7uLpJ1MwIIdJBgcdMwJCyPxS/u/j2/6F/8jvveX/wf4an4t9j//StYIAMimgxCCDHtRNQcUAKdJCIEqYCA6A0XCC6/8Xbzz730fnv3178WjVx4C3/15IARwHESrMAxFY6DaAwyDwILBQxwWIeH4J/8yHv+R9+Ijb34K1770dwiUqcYhUERS+ZYSSd8CYyISnwRmEdAMTCTiJjBnkwCz9B/TFHz1ax+t+2FvTuI6nzkeEhsHiDOiB4SX7tzCN35KVse84bWPYgIQmLOJIULETdFYLcABVAhaxvWvh4I3LkGBTybo8jtlUADMZsQIOL5zkpf0Xbv6qBtvl+HgPI6fx58rcSK8T5bdV+oj6hGpE3MQl7K6egEADgAVcDi+80KtKXD1sFQ/Jem7kL1V9BglfQ5l8/efuv5f4Fu/6r3rhd2YLgwYtM6GlZDqqnwkVVDwlU5T0IOCLR5o5imtzz3+fA0FW4CghYHe2mXLlS25+/BbbuLhh67NrfjNrMNPQFahQIW+rZ74yJu+v3aUaU0sG+umTa15Jd+lAwXJ1YepcVstQdEiCCC8dEccub5TocDkmIGEr4L1vMuf4hMg3z+jUPDdb34GDz94LWsIIjkIWNMOqPAXhyOnKdB/x59/CW////zH+OjX/Dd446/+LeC7P4dsIrgPGGAV9rd+9m/j3T/5ND70a5/E63dfjvFMgwvp+SUQ8BBg1/hk518a/z7e+8r/jO9+zf8Zrw8P5Wuz/1kMWfuWgj2jgAJGuYYoYIoEYMqaBQoBL7zyd/Hun3waH776Hrzh6MuRzs5AYZ9NEBQH8F40BrwACTQMAhHO5GCQ8Pw/+CtS/7/rv8f1f+u3gsdTdYQs5owQBv2rQpYFClj7qcGrTO9F9R+0z75w5wS/V30Kvvq13mfhQL/Uc35lC+ydcIDwmc/fwnt/WBxhX/9aXTKbWHwOCIjMmMrcGKg0XQ0cdByu8/v7td9TrT44NB6QczBk+880CW5G/PydE+dzJKuf2GblDRxo9utq64yZPvnx89GrLmKl1Sv5SRSpfwqXfGRAUB8TNnAS4X388ovZp+Ca1c+hsdKbF9wEiuC00MwAJXzgx78Pf+Dkj+FPXfvjoimoKnnumNhcsJiFCwMG9bJAriqhsg0R5YapfQoa8wEwh4lWsDVOcR4IQKTep9+Ej7zpz+HaQ49Ugq43EwZQ2831gM+F71M+ot4j6h1t2ZwBgs92e2ABCk5uv1Ai3j34+kpD0AWCHnydQ5OSoSnEuq4cLKVUTAdJ1bL58woUvO61jxbWQzNItKOJn8y6Q9nCowO7QcH3vVm80z0MEGSGG6DyAw4GWlNBAwNgueb5z38ab/+Lvx8fvfYduP4rf5NCwQEzwbiHmRXMUdCAgNUc0IOCNzzw60qhnWmAYgBPCSEEgQPzA3A1SLpiwMMCRdEUvPeV/xnf+8v/A7xh9+VN83cGpRDcx5CfX+VLdd4pMV76+b+Nr/+Hz+DpL303HvnXfx14YiSMYmpILM+wd38aBRLGvQCAghSFCJ6KuYGcueH4H/9VvP3k2wTKfu1XgPevqBah/kc0CnRovw1BVkiIBktgISUZbE1bNbGsXvmWHxFH1dep+YC5QAObts/X0UI/lXmrdmW95kX1eTFH2ARGYAlGHVTcTyTag2AtytIMCSYIHRyoPLbZ8fGdTzvv+jf033/pGPNjZkpE6UvtjPj4zqfzkr5r6sgIfXYNB6U6eiJ36/hZxRlp1Qbw/hiiQSC1xTATwHNAOLmtmtA3f58G11J/E7Lx0/XtaiLbvBtOI02smgMK+MBPfA/+wMm3409f++P4fQYFjbP6DAY6k7NeukBg4Cug7og9x5EPVKsPvjH3mLxUx9+z1T7YXyNedWaS46r+elk6xXNvfkpWBxywl2cYsJkAnNLC9Wb75CPqPXL10cYjmg62f2b1FSh4x6fU+91BwWYgmD2wrqsuFKgKt9RR7PoTZHOB/3wACto0w5WW+YAZCJijIEF8Ct77w+/EU29+Fo88dH0dBjhlwT+DAbXxe9MBccKPfv4v4e3P/wFd5//vAqevVNoBMIOnPTBNFRB4v4ElKACAWz/3d/Dun3xahOprfp02Y5I2GKReRaQh+xNUdXYg7u+Ld/8uvvlf/r/w5/7NG3jYQ8daivNOSxUslPMv3v17Dgp+fT4u5fNYPIkWOAGMUQRdVmXrzCxGIO6yFgYUcPJTn8Xb/9K346MP/5e4/qt+C3B2F8EcF0MEh6H4I1BwkBAzJFCIiEHANgVCSsiQ8Jk7J/imTz2Jp978LF734LWq75r2awYKwNokr0p5dYOHYkKGAxPFxFBvPPdZqECbQ2fJmAocMOH5zx2Xdfg2PlAQOdpMwLqpcuCbA8Lxy5/R8eH78uoGzvgzhwOvNVh8pGlugCo4lUUktUTuRrMln5LRChJMi+AB4fh2WZJ+7eojWkbO9WLm1DIRXZ5AVWUwR8Of+F78gVsCBd/6Ve+ty+6hYHUyu1xXFwcMKn252YOQtQS2/IWg3pumfvmq98LUMuIdnzQEqxOAldDXw3ldtVV+ueb5l7VTPPa0eM+eEwZmgVKakr5w5wTv8cFVmlrorZE/mLpQMNcU5Drxv+ulRpsCOCj4/Et4xw+vaFJChK+nJSiwwXIJCr7ra2X1gWXZxqpetXgIAOYg4J0IX9JB/enHnsW1h653VhUswABzFv4VDDDDOxH+6E/+Jbz91h/GR1//x3HtV/xG8NlpvZrAA0G+l4OCWVNoCUIAh4Rb/+Jv490vP40f+PL34NFf9m/X3UAFfuy36qZ061/+Hbznpz+CDz/0Hjz6y3794R+cM73wyt/Bu3/qB6r8e4AoqxvmLc2c8quOcS+rHJiBaRJAYK5XN/yK3wic3QUTgdVp0cwP0GWPFSSY0yLRIiQ8//ItfL2tDrh6LWu+UvL9mbqgAPgJQ79+zOflO7/2Gbw+938lgwYOlAmyeSOpM1JgUepnp0QTfkmWDJp6v8zk7a3swEFvgpUbxM369SXNS/re9H0l+BsgsTccHJzHsc6Pq7fcpOoNV6/NxlefQnYEhFvhgQ4kSHsTAc/fPi5huM38kSdWjjCIwGyabBRICLFuXD9+AvjAX/3+GgrWzNOAq6f2ffglYEqYvyPagM5pBMz4Mz/2XWWd51e9VwYKANlhRAU8l9s0t9VKNjsjIA2sjXKs6/BvvuUZXPsyhYIFGPAvujnQAZgNAPJZvryo6/Cf+rpn8fCD1/JLDcw95X2aR9BrtAWaju90zAdW9EWVYEejovUCoNKorDoa2tIwHHYy9C+6TxZcyNSzVp+t4M/14ouyAALkzn1a6/+Zt9zEo1927fCKAnfMYCBDA8syxOwfkBKO/8Fn8fYX/yg+8tX/D1z7N34DeH9WgMA0DC0U5MIEC5cBWQ2m9nIgB005+Zn/De/8+9+Pm//2f4Rr/8ZvqH7bS9QbfBeuBQWc/PO/iXd//kN47jf/J7j2K/7d/nXAMlB24h942Dn5mb+Fd33uQ7j5G78F1375v7N8/04+c1n8cfOZCBE0TXj+p/8GbvzVP43nvurbBMr2Z6JFiANoGrNDI0bnp6ArF7orGxR0DRJOXn4JT37yXXjOrcOfsnMiNT4z66BgL7qvsU+/XENBFgkrswSDBg8HS06Jxy83YY5VWLdjaJ4R9yZYS+MFNM7Lj7zX+SzopI3ns19q/A2cp4QoPjrg5COevsGZX0s9uPtT2QM0Txi0UTwkJNXqgGTS9uTHnsDNtz2nSyrrcZbdBCxDgqq0mP1Mv9NQFOaagnM4sbdpTcNyYcDAulq3PtWR5gOfdRtK5CUdodOxIY3ljfY5hXy+bZTn77wopPjWZ3D9y95Y28ZXYKA9ZqltuBfvnOAbf+hJfN+bn8EbHvTmA8p+BdsZukncWX2wxNKtmrADAwBmJhZZkrhuXmnrzDsZtlDQagtechHjXv/aazkfS7PfDAtYBgF7tUIgvHhbBpWbb7uJNz50TQU/OxMAnw8GmrgCz//UZ/GOv/Sf4yO//T/DtV/x7xaTQQ5WBJnZWgq+ZFbPKZepFYwn/+x/xRN/57tw83//n+Lar/xNRVhEp/3KTeoiDbpn+Zl51e4h4Pgf/1Xc+FsfxEde95/j+v/uKxZq3SUPAU5YLEVWfP4f/1U88f/9Ltz8yvfh+q/6TXLOYiz4/tgLGtMAVJW0bMc//Tdw46//t7j5FX9AoGAapa0Sye9DEK1OCOKLMO4FHHRJZDD/hAVIOP78Z/D4j3yzaMr+rd8GjHfBYUBQM5pMYSj70WQNAtZBwUwNn375pPIpaJMfxlqtARo4AM+dEm/dPsETGvzn2tVHwNkE6Rzv8ox4ARCkwuftA+D485/RJX3fLZqIpWT2+fNoRFHMr0899mz2yVobb/05My9kINA6C8pnTIQX1JHxmbfoLpvwmoRiapD7dSDByuPfayeDPvAT33MYCiotwXzvmF45e+nigIErbc+v6YO2ocTXfAe+9bd9i3Rq+SUqOIAHAv1xnvk6oacqRTkecHznFm584p24+bbncE1XB7RAsCTggNJYM8rV71Xs9AeL+cByZKryzanxLSirD1yY5vYnlf9F/2GVA2a+znwudEObh67NHAy9VqUXn2ARCjR95uVb+GYNQ/yGB6/NOn+NdoUIlkCgjUT4wu0TvPMTN3DzrT8gS7LGu+iuJnA+A4swYKsKdNbPKeH4H/443vHZP4Hnfusflpm2bm1cQYEvTxbq7hWObkAxZ6Us9P46bvzND+C53/af4Y2/5it1JmvX2OzWQYDfq8CFHZY2zi9GftbzP/mX8Y7P/gl89Hd+AG/80t8xy2+VuqYoU9nq++DDNQM4/gefxY2/9l/jo4/8F7j+a76qqjsAxafCQ4Xdq7fh2VTDQ4aC3/J/x/Vf9Zvz9aLpjurkKQKcOKg/hoZknkYxNah/Qg8Sjn/qs3jHX/hW3UXyq/LqBoKaM0JEVK1ZDOJbkwI1GrNlUHjp5RO894ffie95s/T/UqMldd8B/f0SHEBFukVMrLY2buzq5pgIokVAQL5jnY5f/nSBAh/8zScds3P/49bXYNkAJo6GdRjrfFtXD/XzOhUHNZYoEBgovHjnGO/RXTbf4Lduz2NMMTUATb0hFkgAxDSVMyh/vaPhKhQ0WoIWDLYu47wwYOALaXHpAWmUD372g/i2v/g+vP9r3q/7UUuHna3RJSCvXPBppp5BjqkOChJG8+PvlIhoD8mGPFthYL7r37y1Xnr5BN/8qSfxfW96Nr/0Pi6B33xnLRF8lykpb3jy5qey9+z8x8X/YvkBrS+GdNbsc/F1H5LgJAsOhucBqbZ+vrGpn27hgXr5kYMBH3zIHK/MRFA2LHoa17/kd4D2p9gMAxqFsAcDJsye/0c/gcd/7E/iua/6Q7j+K38TspagrVcAGDqzeqtzP6tXwU8h4Pif/DU8/hPvx0cf+S9x/Ut+WxZeJuzz5kQhOi1PGXg4Dz4GyB1N0I/+fnzkzX8O164+smqzlRv4NiwD4sy5VQfL45c/jbe/8Efw0d/1Z3H9S3570caAEbje84GsLQwMmrrugcPxP/1rqin4g7j+q3/LPLu2gZoHBKAGqBBA06h16CAhDjj5Rz8uG1J9zX8nezeMp13HRbglkEQajlnr3ZZB+ncj6edP3zZHxmeKUAK2TQ1nTTOHA9kbwm1NDns3+o6JZl6YAYJqYauosNDgb2o+yJqCc/gPuNxnHYcvell98GzOfymrpNVxeAYMTtUI4NN3jvGNuovq6x68hlE1CQYNyY0rLSQUU4PXvsWSN07ZUf5P6eqD1lxwnhVuWxOdZxvZX6yJvpQY3/zFzsVlukyX6TJdpsv0r0b6io9/FX7ix3+sO6O8MBqDf/6fvpI/24T1xdslotX1L5OZJLl/MFoD+rNkfzNHY695/2vwc+975eDsVm47t4Xrk1cf282Cfvf28Fwm6sTf15O142GZic12UGMX2jNn7OD8D94JE8BMzSXH+maDQ1oCn5Xetsjd+sr1Rjlb3kSQtQRhXi/t/gR9nwHRDizuT+A3KzowWwWA3m6EPRt/qwkomwC50L8UcnAe85i3z7LMTh3iKGYVdnH8jJXTp/l3+HgRPogUgMohzrfRavu4dhFDU+nIFgtipsmxf9a+C9Ei/WoQaPv1Vn74DaUWNQuNj8NqO+WO1tfaWBvlXSEX9m3IJp5QlkZmf6aqrZbjeyyNQyjNttw2nTYC+u/PfGzRJ7ROzZUt/VUeX6SyV/2U2rGlDZsOfOHGZ6szu+5QPf5r738N7r7vFcBpKi0M9/KD52OqLxMwH0sB4I0fXzDZ4AKBQbuO39RHOaKVqt7rq0SVDTMYAaiWz+hVAOaVvvIitjAAlAEUOP8gav4DjJJNn7uqkzVp/uJ2Utd0QHr8gEqvfVnt2IKH7CEggJ1zWVtzEEJTrq0voRcwYM7Cg1qB4gSHBR4yNbYJmWTBhCw08T0IGKAvZBZhoLcxkK6vh3N4q477v7AAPBrvf2IkMFJijf+vfVi/M+vWVyxtMCXWQVaWsE1WNtdAvpTe+pvdI4hAQcJHExFisJVEBrjQ8NFyPBDl7wEBgSJiAMJg7cmoo0la+zmoG+Q4Kex5oCNn6kGnDX37WBu2bZl9JLrmhj0skBJPo+zz4Jc/mjknCbj5nSRl34ZUvhMBYZD+TjEHJSqOiZDoizru2B4OhPX3ycaYRcdmlj1GbLfG2o6OYl7I/gcs5fNOilJBWlfN0ryl1DOVLjhW+SO91QkByDtiwsGBrwO7PYCD47RkQ0MzkxhW8l4VAGxDq7zLJaEKSe3rEZC6vqXmaVnd4KBgq3xyY2uvLC/cOcG89Uu6OGCgfwnArdvHePcnn8DTj0nsa3tJrJKybR5Wrd77Nlb3q4SYO7G0fK4WeIe3XV1K1vTef2BRsLuyt0LwvImJCtWv+C14R8w1erec3QsQyN+68/ZytGV2c1A7cA9OhH7GabsXHnSAW0hZCABzIBh2MxiAEySphYG4Q/aM1+8GAmMC0igCPSXGlFA+c9EUjIkxcsI4iSAYE2Nilh0p9dqJOe+pkBQmgL6fjCXTDgSi/B6GQIgq9CkQBpLvQ5D2GyJhoIAhUNYcGCBEkMCBhiMOYcAwDBkUaNqD3fJR+y4xHyZQHHWXUC77TzhI4HEPInE09JBHIXbb1B9rnRZl7V+YrWyQc6LxyW1NEcyqQUgJpNtBc4ggNn+ESSFB/kYKCERg7fMpOSBgBlOxe7MfmFZSjn8ANOGXtwOCXE4o0QGTlo/hfbroQF6AZtzZmkjHfwcDBy5343a5dnXc1uvy3hUke1UAmG1otQoIAJ7/nGyI5zd0snx5B0vu/O1FzG3HUluyeRVXF4tzYcDAkhX66ceelUpVqDQ4gBJku3JhiRpbIXbr9jEA6Ja9DQzArp2rV32nWuuX1u+N0FunwrVldeUaNC+p48pO7II2cedFnYGAz+yKuUCfet9A0EutdgCY18m5geCACjprB5ZMBYe84Zs6rEwGDghoKBv8IJQ184uaAQcDHHcCDJBNfaZJhPbYCPQxiawapwIBIwP7cRIwSMCYkoACJ4zuPhKCWsJQT/p90pdgaqdoLkV98WIg0QBkYS/fBz0+RGCggEDAEAKGAAyBsBsiBg8LUYBgMDhQwBjy/Y8QowzISAIGPUgAT6DgNAk6q6c4FABUgd5qEJbamacyu6s0CUsrG/wzrQ9QBGgEs3ymNGWTECEK4AQX2VUdFxmijekCgr4oKb8x6++b1x4A5wcE6d7z6IAigW2WxsX52562NP500sGYBkAO2mR596DQbpWd70t0rvG77HPhNrMC0O546Z9p22IbVDz58RuzrbE1p91UySo9cAgKPvTYs/hjH/1Di+W4UGBQhwm+lmfbS3AAzAEBmFewCTGrVAB1KN58bROABH3bzpLm3s61gr6yxS5Agf9u5epCgU/t98ZbuAsDOUMOBqpjC5tA3SMQ1EOSPeoLoCE4ZJPeCASrMODqsQsE5idgQBDLLJJ1tshhBzhNACjmoDumGRhbGEgCshOrhoCBcUo4nRL2E2NMCWNinCXOkHA2ssKBXJOYcTaWz0mPGxSY5mDUBksdOLCQxoPTFBgc7CIhBJnx7iLhaCifBQwIRwNhoAlDJBwFgYghBOwi4UoMGGJAJEYMYqYYSLQJBRIGDLuhaBLikawkyJAwqBlhD5AuORz3Ur+6r0I2M2STkQcE67Su7fVzCwk1IAQgpCL0DRhM2A+6fI1GAUIzed0HIDCzE47lDeuZGYqsu3dAkK8rZoYMBWWcmW9QbY9vzJutep0X4IBtzFAYyGVYgAOnXFDkODiO+5QMAGDjetnx0sXWrbQIACRMc2drbCvH7Dk+Hxuh4JGr11bzfmHAoIWCpZThAKgAIScu13khliNmfd2zeNv/+Hu6JoM1INgwAZYseVlMTSQ+HIYC0xbUUODLt64t6C4T6gDCUjANDwW/eH0Izh+RcNV/4AsBBG6Hv2SOaXpe/AQGpx0YMEGyJSYAp/pXMEgM7B0MnCbREOynhLMk2oCzKWUQuLufBBqmKQPAOCWcTQIB+ykVbYE+y5sVllJrPgihaA12UXZNPIokQl6BYYgRu0h4YBczKBzFgCECRyFgF0VzcMVBwi4GBIWEIRTTxGDahCiaBI4jaNqrDX8EpRE8CQAwT6AwyE6AbptrWZYYzgcI7nsOBbIGCHGoAME2dyJmcFBByMWBWMK463LAEGUGrT4IpE6lOTQq5hqEZHKaih+ACfRZc9p7rNdvBQSTb7NdCnUZI+WHWgVt9D+A1afteFhGjGr8o7lJoQsHWXCbZHZ1QutZ6p3L9aN10wOEEkcCJcx9U5cBqJbi1w/WZy3ka6t8tHRhwOBQoW0mDhQ1fdvje6TFAE7uyN4E3//mEntcnA/nPgT3AgRZvvoZcAsFGzQFwV8D339UW7CUmV5vX4IBO7fmP6CP9ECQYQF1/eqlm9I9rzKw1QPt/gT5nAtlrA5pyVYY+NgD3qP91TAZxLKrX15Z4IAge6o7jYBpCOzzBPEbmFKtHciAoML/NDH244TTSYT42ShC3mDgdEw4G+XvfkzYTwl3x4RRP+9bIJgk/jsnZ1JLVh/LLWr7F1Cg0sf1c4yhAoRdJBH6Q8ADgwj73RBwZQg40r8GCUeRcDoERCJciWJyuBL09xEYiBUMCDHI5xgIQxgQwyAQMOkmSlE/J3NMnECkWzZ7QEjTvQMCBfF96AZPsm2ORYoxAE5JHU31XQ4RTAlgFaohSt8MrPAQRMsQokYTDgiQ+maCKMs6ArKFg16qzjhhN7l3U6IizgEB6Pgh5Nl5o0UAUIm6tTGqyZOZFOD0BtHKnmfVC3AQFA64hoOg4zvlejofJHhAsNlb5aho8imVbbFDvlGpX3CtUc73X3i23zBqCxQAFwgMrNALXWWWemTVAkFC2bDo+5vgIWt+BFuhYAkI7NghKGiD8nTNB0AFBdUSol5mqmO/cECwFLnSPq5CgSt7yJmY5loC0xxsCFWc7cprqwzgoMDVV/na9x/IduV7BAKmmB0JzwMEph04GwUITg0A9gmn+6kLA/tJNAvTlJCmpIyUBAjAkCo2TUGBgzV7Galq39o1DCokAiHEgFMCQgyIqgnYxYBXOpBwZRfxwE4/DwFHY8DRQNhPAbuJsTdAmBpAYMKUgBgYUwaEiDBEkEEBRekT4QAghFgDgusnlCVH31HR+k/uJ2kS+RkgEBoScghqjYYI0x4kFk2ATSOZpV8BGtk9wZafZu1BiCCboar2oCcgPRyY1sBS+57mr87MsAYIppK332dAsPdHNQZ+0zutqGUQyOcFKPLOmRkOPB7U44tpTsB80KwA0cnIWN9k5RAgeJiQ+zpSIpEngd2orec9TJCrkfb5S8kiPj7toGDLROzCgMGjHRLaEg0QqBu1goLbJ3jPDz4hexP4MJf6Zw0K1lJtLij0d09aAmAZClyMgsp8sGZKAM4FBEDfZLAEBEuDDJrjFRwsQIEPWdw1HbSxCJrVBt6XYHG1QS8OAUqhqiWGVR0KDMg1DgiWVhh8ATUEPSAwU8HpfsLpOOFMP48TZ43BmBhpSrKUcUpIKWnVcYEAZjnGXIBvi8aAREsQAoCRMixQTOJPGQKmGDBGwlkUHwPTGJxGwpVdxOk44XQfcWUXcVdNDVemgKPIOBrSPQNCHCL4vIAw7JZXMlgf3iI9oNoBlaACCpxhlDEA417gAJNsjUFJAUHrWM0JCJD9h1Qos/bLYKYF945NGzQHbbP2YJ6dANsCCDMTg3SSdUBYrMMCB1JXvlKWHRIlMiGVlRtmZoE5REomE9n4RXnXxVJXVv7l7Nl5Dwd5hYjCgdVroFJnVfRE/xuss1KJ+HgTj1wtSx63SMULAwaHIGBV64R6Fuuh4PsbKMgBMDpQsPWZXkNgeTcgkONzKIju86umJZhlzDn+LPgQ2BOylqABAju/BARr9mdLPS/cLwYUdDfj8RnqZv5wgJuywoAKEIQdZisMnD9BDwgmsPgBHAAC8x84mwQI7u4Txkn+eg3B6X4jEEypggFO9tfae63aDKh0wMsmBZKZLksI4BQTQkoIU0CIjBQJo5oydjHIqooYcKblfGAXRMOxC3hgFzGmgDECQzoMCDusAEIQv4MZIKQEpH0GBGLur2QwsHSQsNqvekkdDZfgAIjiG5GlrQIB9LNqLESbMAAQsLExJAG6k+IcDvQSMLC4YyHQ0SQsAEJrN/c+CK6XFEBAEfLZsRJYHsvYgYADC9MeGICw5snvgtwu7cz10WgP5HdybRCC2LQEfVZnmMOBFrb4HcDG1XYPC+Tf9YYiMR/cyEv2+2lZZl4YMLC0UUkAYD6A2dcX7qxDwf3ma9FkAHS1BIejGbrc3wsQAH0tQT6+3WywCQh61bgIUQsrL+4DCnIdaZAVnnSgP+CUmbnJzwR7uw1uCEbEa0sOGyBYcypsVxicpoS9agbOsqagOBSaD8G5tARqLpimNNMQLEFBz0ZdBevK0zVkQk6JEJAwESEiICUChqx4BpAwullg4hJYKTHjyoC8bHLaRYwp4YgDUjBte8CECRwCJmZc4QCOAYlZ/A4gyzsHljofAiGYD0ILCNMeSNoPeIJsvawrGQ4FTYpD1ddW+1IvsWzuhGmS/mSVaPe8RzgwQd7a3Rf9DRbeY7/iaw0QsrDjuXlBfkHIK6WIivbAAOGQ9gBwsjZIO6n2cxMggHUr7Ln2QK7jDAg9E8MW7XEvtTtftkOjZwh7jpcvt27LLo8ffksfCrbk68KAgVfJ99KaaPQd3zsavqFjnujVqWPSLph4zQDcNRkIOjBw3yaDynSw0BPWVBr3GJxoduwQEDTJawsOOVluggILqGKCn5MLBV024FlKFELJtruMJKpOdV0VotjiEeTlhqIdqFYYWIhiC3vrgCABYiZIMicsmgKJRdDTEtiSQ3MslGWHfcfCs4kxjkmDHOmqAoYI+lSEfl0XpFXF1bFqqY9N2Jy6U777+5RrKDR9sHOME4ODwAcTIxFhYkZIMpsbR5GEgep2TLYxzYAcgCkhgANjTLKs80pi0R6oAEzESCyajAlAVECIYUDoAIKBpy11lJUMU61F0L5H3mk1lZ0hKQLVttBA7lsVLLSJE3iCrP9k2bpXpwQFDpjyKsBDcFCabw4HUMhf0xpInqwda0DwPgh+5uu1B968gHy1H4sSFseyRWmnJcowIX0o+7uoiWEJEIgBhgCAdfM84WlMDKTXS3bWNQlrsmpWtFw+V3f6jFZT7qHg+pKmYIOG48KAQW99J4DcyL4hrFJas585Gj71dc/iDQ8+OhdsmNObt8O1je0bbQYD+uW+gcBrB5Z0uU691k29JYiawUNQYECA5hiASrOwJZ0HCup6cMsRDQoMkiRTGQSoqrN5OFTJSCzqW6CsX3eOYKWKqHxfgAGmzp4FOSCRagsaIBDhr3EC0uE4BGY6GBNXULCfXPyBlEpgIgcDbaIgA1wYAtKYEAZGSoSYdJlVqP0LEBwMbpiOFECmXG0GBEHNClDuIhDCEPoQgaI1CD7oUkrYTwQgqZNjkpFuEtGT1F8BPe0ByYwtsSgrpiBBcQYW89UMEHyAJFvJEAxMR0CXO1bmKgVVAgoo5AI5M8NSP2vrk0VwgSW+Zd69kN0xlvISJOKgmB4GgGRHQkJ5/+4bDoAZIOSmcwIOWNYezODAbqiag4Nag3zcjXmqMbDfSj3psQOA4E0MSz4Icm2BBNMkSBH7FXbIBN76W/XvIX8NCp5pNQULk9W12154MJh1Gy5kaomIcOv2scQpeOxZPPzgtYMDXN2etHjOPi7BgF1/SABmIQhkIOjCwAb1Wg8Qqu2Ul8wHC1CwajpoqtFvdl2eV39cg6Tu6gNnGqhAwP/t1E0O4KRmgia+Sp1i86qYDwGQ/QnyFsYeBmAahHpToxyUSLUFLRD4cMWjwsHEyHb2MTFOpySOhw4KfChjmyX3Uggi6G3kSvo5UMCYGESiNQihBMTJ2gRwmeCmetBbel717AqYyxTIQIACdZczkgY1yhEOA0nkQ6IcPKlNAguEcWIgJoTk9XsBExImljIOpj1gQiLGpOaFlIAUpC5aQDCtAdKUtQgCqUPWIkg/bSDBbRUNoGi12n5WN5pWWtk2G3D92BIn5BgA1qlz55ZVOjnWQQIQYrOevoaBHhxAvx58v0Xm13vVKAhA77sZDhwIcLWksSn76nfofSYQlfHQtAhElnuJrZHDSzOQuA8IZjLxWgQpG5dHahu1r8d5TN9t8lBRQcGX1ZqCRbBYefaFAYOlMlZLU6w/OjggAk4+V9Z5vuHqtcrpJtMxIS8naZ/XNx9QdWFlO7sP7UBvR8ScjwODch5AvBOPT9mE8IWBgqpe8jPr+lmFgtBAgZkFfJwCOHOB1dFSZgAR5tOU4QBQu3Gc1w+5gTn/1hweKKiZQHPuzAoZBirtwHwjowwEXMIWm9mAVUswJsapBhsyKDCfA/uXUl9AhxDEoY8IRxpy+GxiRGIMUYTnlBg70yakolGwVQjtygP/mPNs4d7VpvkVC25JYyDMAiJ5IDiKlKMoBpIIim0STQJhdP0gWIzcCGBKmHRUYCZwDGKjV/PCBEJkGew9IIQQEWIERZaVDGkUdb13ck2TtLlCArPufcAJBC6gIBWrf/qA3+2D3Svziyndk9VDighgFbA26WbpvxkOdHDcEgiohYPZ+21Z6cCBzarPDQdLJoWtYyFPqmHJFzeQELpahFzm1U2qpL4zJLimMMfFQ8nMy0AtN3qJqA8FvSXf50kXBgyy3WXtGq2hrGRi4Dgv6dAwypg73VQvQX5ep7r9eccF59l28zwwMN/CdCURFZVjdXzd2nXIfCCPvwfzgev49nULFPi6yU6F3NnOtYGm+tmKF5zk3bWQszp97r1Ipd4IfqYmg4gCgal7s2aAUG1m5LUDrLMQ9SHwuxpO+TNmGxqNBggeCnRnRJ8CERJzDjksbWOhgaHREBlDLKYFH87YTA32XX4v5/1eCL3Qx0BxCvQpLoyKfrZvebVDds4gwIdV9iGVrVxDjDmSYqzgon52AqsWARUcAGJamMAYKADBBJX6HwTSJW6sWhefj44WIel3Z+4q5oaE7AwbdCg2kxe2gb5cQaX/dS/MTgYOFFicFpkEgALgl/RthQOpS3vOWmaxCAd2jyU4iKuScW5O2DIuVhvFGSjIiRkkkEJCT4swgwSiPFZmTYLl300sV+uqGRslm/XYaClDwVvFfNBd0dW//S8NUwLKK4L1IhdBf+tOcdSwvRUskXsB7DfVPXoTbv+Z3L4FuEcYAPL3rvDbHBUs6IDQr5fe5kf2by0ZKFjashRR8qa5ugcoIFdHpa50St1qC9qHkjgSkno5e7tBd9Fps0JD6inke0md6X1NW0AGBgoDFLragcQFBFLq7HCIWktg/2yHQ9v1sBXMMmOWadggcg0xxGqjoykaKBQhn3dG1Jnq5Kqj1T6sbZJ0vyk2L5oX6KbEMY2AnTMQsM9LGzSF0AGEJAK+QELKnuYcRHswxCBtp5qcSLqsUX0QAovwmqijRahCbw/ab31gLYkg6f1grA+b8ATQh9zK/Nfpo+3ljdZAOMHeGyEBcr/dAgeMZuK0oWt4u7ktxVubSftbeq1B9jXo/mjjGGl8YI6qpv4nKpBA6o9yXkiAaBLs0eRKwivlbYW7lyVwx164M4eCntCvf8oLn+t0ccCg7SA54lX5H8jgihfuyNaW5qiR7DouYTEyHACzTuUbLR9zIGDX9LZB3qQd8DDQdvK1wcLK7onYrQnrag2aZFCwxYRg1x9M7pGbtSkzeGrryWsFFtSLbSay01Fq3pjGsYt8K2IGApVWAEHMBAYBCgkZBha0A+B1IJhQAKDd9lhKXMobUKZYAwIQFTpgAl9ryQCgqasvoKz/gqUZrDfagewritYsUWv8EhgRBE6M0bsgAFl7EFEDgmkQdIcCjATEjhYhBN0COqoJwYXoRjZ9OW2C9muu+vaB991S22eXEjMkMlLM2gS/O+F54MDgYqY9cNlezIbL6VaTwoGSNVqW1pyIug4r4rAvOh5ZUAgihYSQIaH2R+g4LBJqSDCtAZWNk9ZMcK1s6U0wMxS8pQ8FtcTrlL0tf5MuDBiQBf/IATCmUqMZEuT7ye0TPPkx2e/6kavXkH0Oms6PfKwGAKCoOL3ioCfoTDOQz+M8MLDkS7A+QOT2NkA4ZNhqtAVb06pNufPIJRL285sQaEGj0quvFpwWyiZ31kybmrDj4d3OwHK9YAYCecAwCDgAA+y0A+b1PrHO+vUfowABp7JUkRUK2voWGJCHiemAMhBYTW+NvdEzjbWz67nWrN+vumY2TUv5aTUfLaj0fCbupWweEADrb82ciqXexQZP4JHBAZgwFUAwQcaMkRiRVJA5LQJJczj/h6JJgPONkRUvg9N6OVAA3NiwMsj75DVdq1NxC3msJgVgGQ700YkIKbGs2ycboSjnLQOCl/oHUm/Z3eK1AGpfg4M3179mfl3SMMjx4oQ95TFRmKG88+Q0rF0tAvqaBFY5U5kXULQKltr3qp1oVlDwZdeqcRL5bwOTXhveAmcnXRgwyA3vD1URBgBQwMntW7jxsRu4+TYhrSx+O3AAaOCKzuNMrPSo7r5hwHXmuRNdXV4rV/lMLl54qs8BmX59x27TmrZgLVWDAuaCpAUCYKuZZaHecr2saU4SqsGxCwQ1QCL7Dpjd1oGAaQVyHcbch0ToI2/JnU0F7i+rCYFZ94bvAIEBQwsFPl5eJDONL3vkt8Wz30lR5UPbj80PwJ+335dz7n7ujds6uAM1VE5exepm6nIdcrlNU1LO6ffZef+c9XyUspVjE4Co9S3mAgKShfSdAwII2bEsEoOYEEhgbaJiaiAUEMnmBgDgSYSjh4UGFPJY0GoLt0Dxoh+RmhCI8rMW4UAhILB8YUalPchxKxpAyE9aGBN6iXO55lqD9uecVxGI5oORMPPNaKFgCbAogNjvfeJgQQIaaB/fBglek1AgoTYvSL5a4HdZ8J8PQEG5lmf941wh8XGhwMBemKIpsIoyQDi+/TxufPyduPk2Cf5gzWGdHkCOnV3Fo+48bqYxICyoc+4RBnrmgsVBwDd0qOFgcyq+BWvJP2lpo5Ulj9g1ILBjtT8BcLD+LLV102oKeo3o7bLAMgi0fgSqFWDUMFBpBvQv6zkDB4MuhtjqWVXV41QLQgMCOVYgdUIR2v7l9bjTE/IFAOQ4BRHqRMU50Zi4tA0KrKFuK6kl98Vqe8PAX2kDbCaK+U6lcty+o4J4u9bXoYcqoNQjMIcJO99Lfu35JD8WIRC0vzMwopgYJJyzzCwTyZsk5goxLRBYNQZOk2DmBohPQq7nVpvg+jr7sWJN2LWpB2xOk5gdEQ0IUMPBTF2e6jHSb93cAoI2V7df+ImCJcvFUrK7UveFXk5VPS2NqRUUUH6YhPGe3ITh3iHBKflKmWeauXnZiPqOhotQ0ALBln7i0gUCg0Y4AnCKLjx/50Xc+MSTuPnWZ3D9oUcBcNW5vI0slN63mMz+bZd5IHhVYGBN6C0l61BbzAcAaqfD+0tLT1uyk9m5ZS0BsK0eF8oFOCfD+ricdOpWhHxdZR7wL7q+3Nk3gIvguFcYaLUDwFyQWT2ZurWCgYXZfU/we6EfnMAnE1ig3AZ+104Pb207oWorba/8sTMrqfpZbuECBDjs12L1LNewri1nd76Gh7X63i3UN4CZentiRkzA6Oqbx2JGGLmp70nrKfUhwTQJBgll/w9CpAiKsdKUmeksR/aUTKoZYD6JaFXmq5MEdiaFDhz41QqAMy1wrWE17Wo7sdo8fp03bXFC9Kk3pi7lLc8KqQAD0asGCea4yJiDQm8VhoeC67okcTZWAnMoaIFg00TzAoGBvSyyPjXP/wEiHN8+wY1PvhvPPfZhXLv6KIqKvYYDQIU99Z2xPPXGZnCcCTFgHQg8DMwar9OIiwVv/Ag65oPZ9R0YYPevHZyrOpASZO3jmvq4Mq+gCBk7dy4gABagoKkjV67ZYDjTInS0Ao2J4L5gQK+7Fxho05BD5G6AACfURShBhZII/UhFEBHMBq5QYG3go0danIi1PiwFKe20lFr1Np3XbDP34bA2MjNN1R6RkBKttIeEVx6Y6vYgmrVH/j4pDGh7jAYNS5CQtF7XIIHUgRFldUOGBOu2bL4IC5AssZW1CbbCvsJADw50LDnobAe4dfwoEUXJxgpa7RK+W7S5ZkbXCVFv777YyBRQzfzb1BOMa+Os5TuPswUYyPwQPCSAxHFR91FYWtkAV5e+/IC8s17+3Lp9gic/IT5x1x661tdKWzla+WPlrKDxMKhdGDAw4dgCwvGdl/D4D349nnvsadEUcPG+XYIDYHntbC1aaqF+UDvQ+gy0TkUbnELmOWkP94ShG2zbcwtLm7akNaWEhwG5dh0IyrUdKKg0Kwt1Y6TSzVyojp0HBs5tJsCrBwNrpoAhzEHANAHRaQFsiV4k9zcYADBy5MjJNpvqbTyVILtQKiTABeXhTvS+Q6mJGkkU6mBRGQbUdKPX2xLQWMWGUHgAIYEWl4NWDp9BHDXbtrKVH5xNK7TYTr9QkJCcM+4MEvQd4WrMyRfo34UZ4sLLW5sV5PdL2gNllxoQcs0JqGUfLVr0BLqPZONXqobMsnNjQNlJyn4SVkBgZdw12LD6zWBr0MSofRLCTIvQXdmg9/IyiFDkz8ntE7zz4+ITd+2hoikof1egoJErq6bXJl0cMABKYRUQnn/5RTz+Q+/Bc1/3IVx/8OE8m/ZLc+T6umF6qZy1yvbx+JdmtAvaAa8ZaGFgq5ZAuH3hvJuFtb/zwtClSluw9Fi95tBcpAcDwBwI7NotWgIAHfNBqy24FxhofAZ0JpohAJgtL2Qugt6rrscVGFizdZfsqzagAQKvGRgozGDANABmBhDhTxg6IEDTHtgXCLD4/mgi9REnie0/jYDuEJjGPZCSHNMY/2wAMU2lTGvbCtv+E0RAFMFPLkok2dbUw06ODTu3TbVCQBhAnV0pg8FC3BXNQqImqiTNHEEjCVQMCgkjS6hgUkgwJ8Sl9hv1swllA/AxYBEShiBbPQdAt961dlQfB0JeviftrI549v5QVA/4+r2pIYEhSxJtXFwaW4rWQB+NEtdjWXvgx5/YAYTsg4AGEg6ktZHYx0Dw17VOiLW/gMYvWV3SfM7xlyfJAet3r0lQSDioRbC82aZOOibb+Hp8+wRPfKyGgrrcnYmplWEJCjZoC4CLBgaWmHH88kt4/FPvwXNvfgrXH3xDPp6p1+DATA82g5l1S09ZpcKp4xxUPWMJCKoGaxtvvVMu2gl79N9TzerxXkCjpSLPbts57Z/ehQG9aJOGAKjqFVjq1L266sAAsAAEy9oBO7bVb+DQigLJ+voL6aGgBQLTDgxagbIEzmkGOjAQFQaQRtA4gqaxBgH3PUPA/gw8jeBxD+hfAwOeRvmNgQEnpP0ITqIx4Ow8qbOmaV5eitY3Qv5OukV12A3yXioYIA7y2YEB/N/dUYEFBQOy/ScMFHS3yl0ckECYOpAQuWgSUpI8DRwxEmNQLUILCBNbON+6jPbdg8KobboECbayQRygRYuQVIsg8RIKDESddZvT4mTAqJoE5glgtber7wXUz2YGCbP3x2kJmPM7yjYb15m5zMTRNy9on82AoAd7kJDrrO0js17jK7i+QEVuzlvepUEjGVZaA3Q0Ba2W0Z9aNS84x26DBA0rnc0NBwABAFpTA+etGScc33kBNz72BG6+7dnOLokNxNwDFBy//OJy+XChwKB07OOXX8Q7PvUN+MibnsK1q4/ML51pDuz3mqzRG3tU1Vlmdle9x/0CQdtRF/X1zXE3Iy4A0dhrs6CsocD++dS+Fn4gbHPUBnnyMAD0fAiAGRAsaQjs2GLOtJz2cJwfCNZ8B5aWGHrtgAUhajUD7XK6NvmlgoeAwOrQgGCgYiaw8MCDaQZ4Ao17NQmMAgM6y6e0B9IIGATsz5DOTvXzqUDA/kzAQOEgjRPSfkIaJ/A4CjztJzAn8JQkWmJK4MRgDZnYbtkszaFgEIN8DgEhBPlOAbSLolYfBoQhIuwiwhBBCgcGBBh2oN0VAYajK8DuSM7vjsQGH3ZZ+8BxBw4DYogIcYchROxAGBNpGGgq+ygQS+CiBIQo5gbqAIKZGKS/rLSxzpJHliWMPU2CX9lgWoQI6fvRtAb6LykEksYREFs1BBZVi9ADBGTzgHv/uDPueS1B864XQFCht6BBMCYwQEisb6eDhFq6LwjmZtzzxg27Xb62NSnohIgzyDj7vvOhqJ/dm/Is5NHK73/htQg4DAhAkPGto0U4+fvP48Yn3yWO8lcfkfsuyCQAmPkUHEgiH9+Dh/Dg4jUXCAzmUHD96sPyUnitAKvaxsMBlRchn3OdwVf88e1bcszUpecyGRQgWIWBTqoI1V6sLOwaKMj22QXnOvnxHAZ6hOBSz9HQH2q1A/px3UlmExCsdfh7NBmkdQ3BvQCB1w60goITw28bvAQFQ5ibDExDMATSuAUa+levD0HvpxBA0wikfQaCDAPjCB7PBArOTsH7U/Aon5Eh4QxpHDGdjuBxxDRO4HHCtB/BHhBSQhonCSHMjDSlDAMeCnjirCkAHBwEQohqh41BACCEDAQ0RMTdIH+HCBoGxCsDwjAgHB0JICgY0HAkoGCQMBwBw5AhwQCBwg4cB1DcIYYBUyAMCRiTBiVKanoICgIJQCSwAkLejTC5kMja3raUtG3vfExBkYjq5Y+6smGIBJ4EEOzlkaFqDgji0U6IatYKYlMom4xRFIHD1AUEymMgIwvJKvmxyTSp+rXVIDSAAFXpV06KegPTgHh/gy2OiVXiuRNi1hwA/bgGAOyNk2sdHAAo2gSb9YtMOBxu2e6F/JsZIAAoWzs3gEDaPnqNgePjn3wXnnvsw7j+4MNFu+ID9i3Ipupcz68AtXx830e+vV8+XCgw8IX+foECuNljLzk4kGsdKVeCWwTZ8Z0X8PgPfr0e6wCBHfdaAu9D0GoIDr0RVDq7fN8ABSGiqymYCcmcq1XfgrYvttW5pB2wQxkIWrVXo/I6Hww0T1+DghBhEKRj+kEfgi0mgzYqoWSdZ86FuXoaKOhpCYZIWb3sgcBMBqI5EA3BViCgaY90dgrsT8VUsD8TINif5WPp7AzTqWgGprM90tke035E0mP2TwBhkjLvRUsw7W075lT8szrmhLkZAWpGIMSdaA/izr4LGAT/78oOcTcgHO0Qj3YIQ0S8siuQsLsi5oX8V46FoyvgtBONSdwDaQeaRnAcEBUQQhCnxRHi9BdY+m3ePElNDJEjRiTpYlOjPWDOQZGsvds+MAURxhkiGkDgAFBg8CR9YNIeLsJfIIADAN0WmlVTxEk370kGDeJ4SgYICXNheRAOLNl7K+9b+d1U3jfbwtgDgra1AQKj+CFUWgStZ5+WxpoqV9zxNWhNCj04INL6OAccrJgcfGbzXhSWF/YQVfIjvg4dDYJeJT5xb5Bx0T/K6Uy2BizibEZq5OODDy/+BrhAYDDTFKwlLp238i/oVbZV6u0TPP5D78FH3vQUfuf/9Dbci5agesYaFLg3YgYFq1oCpdQGCspOf7VPwQwK7EVdz1LtfNjRDiD/PS8QbNCitKPFEhQcCEZ0SEtgToXMtUPaUlRCYL7iIGfRLTGULJMzFfTNBgUE1FwQzHdATAZbgcAggE9fKdqB/Rl4PMN0eiYgcLrHdDpiPDsT4W9AcLrHNE5IY8K0T2D9m/bi5Jb2CWnSvRuSAFXWGqz5GASN1hhINQeEsBNzQtgpIAzyNwwBUcHAYICGiOHoSDQIVwQU4pUjYDgC746KFuHKa5D2p+cChJBkA6lAhDFxCXimsEgxiCo/1uYFpBIx0fpCCwdei+CvW4qu6LVGNulH4hxJDyhQAMhvc0xe0x6AxO9Cd1FchgOXFt+9BhKkofX5LSAUodv1Q5DsFy2CpoRlIMjmBNM6ODjQDBYYMm1JGlHBAQfV9Acdf8iN3e5zDw7W6kbrsht1lp0hxGb+ataQu6kfhGpn3vja1ytcB3hNQbdaNmqej+84+Wg+dyvpwoBBCwXn2l74gMqoQMH3146M9woFS2kNCOzzASjIm/ksqNINBoA+FKylnnbAvnaBwGtfeg6FrXbgXDpFp/U4JxScZ++C80IBUGsHgL7ZYKB6pYEHAnMkjCQwYJqCGGS7YTKnwWnMcIBpBE2n2X8gGQx4DYHCQQ8I0uke09mINI5IZyOm/YTxdKpgII1TBgSDgAwHzDkWcVpZsmg7IyIKCGUoUFgoQBArSBjORsRdxHS2RxgGpKM9wtkOw+kR0pU9prM94tGZAML+DHzlNaBpVA3CGdI0Fk1CGqRDxAngHShNGRBCiBhJACGSmhlUfppmyfal8OYFMRPX2gNABX4n2U7PpmUyOIiqtYqJMIYEJIEDX6NFxCpFqH6eFBwslKs4MqoIp1D88Fo4yO/TgfHJv595GWBjK2egdqgrws2vZNDcz+LGGCQc1BcuwgFqh8QwzJwxKWn+4VYr9LQHJOsorNSbAEGvq+HAxr3gQKxoDyo40HtL/RigGMBt0aJaJkK+/vmXX8Tjn3rPtkmzpgsDBs+9WRwNZ83VA4I1HZWlLhQ8jCLEOgGK7HdbGrCTh3lkvu1AsK4lADwUZHHcgYI257Pac1DgYeCghmAJCPwLtlZvC+14v1DQXX64EQrWUm8Pgh4QbPUjkFUGutxw2jdOhfpZfQbgoeDsbv6bTQZne4wGBmd7TPqX9xPGsxFpnzCdTV0gmEZZgTDtU4aBcUzWmnmzo47CQNZmT5OEBx7l2DAETGMSaNoF0JjAYwANCXEMSHsBBB4T0lFC2E8YjiakcURU80YadwjjBN6LD0S8MiJMI3D0gCy1nB6QerryGgGo4Qi0Y7BCAeIA8E6cM+MOu7hDjLKKQd8u8T8AIaD2P4gcMSkgxCQrBTwgDMBBJ1SpLz4MByY8Ad1fgkR1rxGAAiQOwrnhAOgLutVxzN5HndFyMTN4R8UMCE7wtksdAc2u4x3/tqfmcz63BQ4Ic+1BgC4t1fwe1B7UgCD3P4eQzvl1cOC+V3CQr12Ag7XUan8oiPn7h96zLB8X0oUBg/mSjoV0T1Bg6pdmFtz5zept17QWQCXE8+dzAsGalmAJCJa6eGsyAJacCvVmrVNhFwga7YCrx+1hXNfKfT5NwQwKUKAgD+gOCtpkqzVaEAD6Sw+XgKD1I/Bmg4BtWgLzG0ind2d+BNPdu10tQfErmGsJJoOEfUIaU60hGGU1Qq5fNcEABQ7kc+kzSWfbE3MOuDSOCSGJJmECENTgTKqRiIll4NahynZhjO4ZPCUEjWwUk5g5YkrSJiwAQ2kCpYSQxHGSOIF2EkmQ+Yr0z8i5z4a4AwVZJTEm5OWBa/4HLSBYNEUPCUABhba/+MQKBwOkf00JRXPABQ4ixN/gXHBAthyxhYNlrcHs3ay+SW7MRkC6MVMXEMzEoOYFcvfK/aR5SJsr/7nVMJglRcamEkBoUXuwFRCkFnKmtgrYxdQI+so/weFPDjh1D3BgPnHPfd2HZHVD79qFdGHAYJPA35LWoGDh2pVMQTqdJ8GE7uy31Q4AXxggWNEOrBYDB7QE7JZzOig4BARr5pXNG0H5srtyez8Btr/mU6AzAFahn5vS1Y85GgIq2LnYjqPPtn/BnT+BHTanwi2xCLzZINJhLQGlcdm5sGc6GGW2zd6pcD8hTaIdQDK/gXm7lFgF/X4fCU7VrnMg91ra5jBLUUXlviQrGZopIKeENBFiSEh7AS7aT0gWKjoGpDDObh45gVIZdEWLnGBBncLRFYCXtQcUd2LmUPNCUjCYGNn/wAOCj4EwgUESQWkecrmTfJn9pHIKAghyADoLtq/qeKhmBTMzmDxLNgPPApNUaBcfgUOp9476Y6U0NplJc0CwQEn6g3oVw7r2YC1lIMhQKr+bAcLCSo3tgMCiOWi0CLPUHd8PyKauwE/IO1/6+26EgwwFjz3dh4ID6QKBwQYBspSaju/VLxIx0d7CjZzo7DuzTtFu++s1A/bXwcH9AsGadqAtju9vPX8CryHoQkELBPkhzmTAqR5oluq01/mX6sDVW1VudhoCKARYtvXRlhOHLjlFyNK1qPH0DQ6sQsxO3GbRNAOA0w64OjwUnKhabZDGmZYAaa8z4BUo2EtsgrQfkfYTeFLnwHFCShJ/wFYTSLMsC6vsLLgL+bpEjMCEMLH6FIhX/EBZYdy9n/UrczUIUhl9fwP910tlaWQCp5DjKdAosEATI+0nUBgRKID2p7NhnKBzsyO9Z+7LjfYgDPIvAikJJIxmiuoAgsVAGAAMJOYoABjMJNWUqe1H1qX9Phg+mZi1/GfHdmEDgFl27OPaP7+8KjoHZ+R3qtIaUOq/lwvv6mzJXgUIjZOiCwW8tEkT0DctrI1dfmVDYPdb+LHrPgDBHpq9QF0m/HjW29Y9n9soo7YI/5V7Ht++hcd1byDZMPD86cKAwZaZ5eLs1Any45dfFNJ681MaXML1wJkNx323z7lBD5kNqPn76sEAUGsHsvA7wDVL/fGeoKDVEpxnVUabltq2Y0LIxc7aApvlFmCw+eOhaIRADQeu8ADql8ey2NvRsJgSgF7oYh+PIJsN0qRaggS/4iBHLxz3SONZDkhUQYFGKuRxL4KTRRPQagFE8AZwZMSdH9AmhDhgCqJFoEjgKYhfQWLEIcxWIGRgaJwL2l0KARGc+fkLKxXiLkhkRHIrFQKcU2IEokRN7GkXkBKYSco/7gHdk6ELB5zE72DYad8ReOWofTlMQEwSJClEBEDs/1CB1QCCj39hWgQGMDlNgj5m1o/q9plDQS9ZHZvWINiwpHlJRAgGDTCtQWNS6L0K1QRnNQN6fbNkL3vby2cRubX2oIUDJ88LHCw8bunYBB3HDCicFsHKn4NBNePYLKS0qfIrcEA1ps1AYSn592Dpc+/74j2WoOBduPmWZ3DtoUcPKFyWn3NhwMBisAPod2bmLjxkQUVBzAeffHexyVT3qZm1XsZi9/XkSPOO0mvUJRjQc+fWDjgY8I/fGpK3Pthz6gE2Q0FPS1BlqmmnNbjrQRKwyYTgAWHpTbFaTzqpiUGi0AGqJVhkE6mRDAJ6M4OBrEEgZKdC0Rp0gMDMBraJkWoMZlELLVCRi1IIDVMMv/lRm9cQAGKZpe/qmQ3rckneMaa9aAZoSMCVpLdlDC5egUQ9NAFXQ8GS9kHyILXk4QBAhgAf3yBE1by0MQ6IQDsXEMmiJNqGTKHTWLYZVN7bYQRCyILI/hJY/A4A1RwkgAfpTCGCQgTFnSxtdPEPDBAYqlVQ/5XgTFWkC/l3+rzJQuOuvJuz/kVFW9BNLRAAh00KqqbepDXIz+m8u3q9aRDk21x7sAUOLAXTQHVhYDl/Mskp5hvy96B71SLEZUho62Rt7Lf6csfnsin0r+98t98e374lERPf8oxoCrpjurvPCn9cHDDwpezaf5qObDNJraiTzx2r+uXpdU2Bq+zZGtdZY7ZZrH8rx74wMJBnylhPvb7Rs5AVjcE5oGBJS3BoFtKFJf1pUzfwkJAvsscVbcHW5OFgCFbiupao+RLc8RYGCEU7YFsdL+5ymMqeBki2r0FC1hLwVEGBAYFoBiZgcvbivE2zCM8E5MA6IRIoBiCMCLsopobE4CPRCIRxBCfGbiGqIedlidbxzByxvZ7z+KT5DB4Q7HOooyNSIIRhyJoOCoSwiwoOEiWRgsQ9IAunXGwW5eGThMfFuJe2DKX/EAAMUKevnfTzMKn2IAIpSkEtzLIDhEl9KyZSfxTVIjAwgwQGEKM5E7r+3dZT8+U8BlOvNSDKRFDK6RwBK0fEaqxTeLJxbullchMsUz32tQfb4KBXH605oXdNXX4DFa1zHUf6kLBVi9CHBAAFFIDDg86SLFi4pvruJ0Wajm/fwo1PPClhlM0R/6Cvwy8BjYFYOX1qrFazSipd7PhzJ7hhYSh1a2b7TR0IxHVN3zj5ev2j32cU2PoTuKUr9wsDLQhsGaMD3CDRpoqqfwGgoKmrLkG3/gT5mlpb4B/nn9ZOZIlI7MJ2W/1dRDVWz7Oaf1+yFPR+phmw+stQABF+Fqcgz06T29Ew/53UOU53QLTtkBso4HHsOglK3gIw7IBR1v1bndIuyj4HQ0Q42snvJ0Zv3wMA2RcBwMwfoX32Ul66+Wtm9ZRBhmbfKbpzzf4K0I2YgsHCLqrmwUHEsAPN+pPL8zjmNq3gYAI4JICjmhRku2fiScwLIVaAEAHdmElYadDvFjxLHBA5v68Ga/nVwTrI+35nf63f+ZS4+GD6Z4CQFytkrQFQhxFmzMc6u6aypS+YGRonuWpN/z3CwarWwMq8XG3lPgYKbD4XttGSlekeIMEGDSSw36SqqZuDMgFYlwvN9exkyfHtE4GCt91sVuctAyd3jvl0YcDACro0nteYwLmCjz/3vFSq2mSKV6l2gC4coKG5FaHWu860AsD5YECz3oMB64ZbZ8dExYlJfje3BXfTVvMB7hEKiPralCUTgnM4lBO1Q6FPNsAEVnusTJtkwKDaQatVGOV75GNUBmiIBdXMBNTCALx2AEXYJyf0mTMYLG6BrEIb3F81gFhvsUshiG09JYTdAJ6KZkCaQ8wBsxDGqZy3lBqqOg8EbE0tLJgWoTqXVyEoQCgEkNOQCExoPIugOzbqZ4Qo9dQkTgkURKLzNMpyxmEnfTboGpa276cRRKPAgkJDoChhf1WLEFFMDBOrTyPE3GArY4D6ffY1u7UfWh/smf9sXJS+L1+yLNSxbtWkoAF/8iZEuY+dEw6sQIfgYNMwVCpm69iXzQoG88x9SLDz54AEWKAnq1nfWAtm7Nl1Tj4ADRS0Y58eO759ghsff6eDgj4MLH1eShcGDPy4ZS/GEigYJFSVqks6/AvSwgH8/cgPwisZ840MOGGHgzCQy9XAgKfk1nQA9BveZzFvgUr1MZ8WtQU+VVDQHm+gYCkt2dqWoGBmQsi5q/rAIZ+KHFaESn1U4qKqm/qQH4ANBPLsDTUMaAlmwl/qzcNBykAAThkYiLlAwWTgUEwGZHZy1pj4wwBgEHU5swhFAEiuTZxQ597Afj9Cv2ff/wLfv9IGBNd/MkxENUfEfI5CVJ8B99tUws8yBjE1DDvxt2BW7UGQugyy451oENTEQFS0CESIQUw43IGEFABWO3buuwqs3hC61o1bMF1LHvxNa0D26yWTArAMB5a5JTgoDy5wYO3izmU4oMZHrKc1qCCp3vb6PONgvtZMC6TaBA8JhCxAtjgtwso3A4WlnLQZcwK9MRnMoEDPH995oYGCPgD4Y6l3QSddGDDwhWyXqvTSye0T3PjYDa1UXdKhdrQZHMid9DGmEqLcQNYBFsMw92xDHRiwYizBgCfjmelgpZFNdYhSLd1kwq17Uc+EUKXesfYB69RcVhfYsT4UdFchAFWddR8Dp5aEaA5y7qn8aDbIVoPDHATyd/unxysYYFZhL0Bgx+COFSBQr/hJtzb2UOALp5FrKIggozgUaBjgBiatngooFpZVtX24uW7m2LfUpksOgL3UCJaZNsLlWy7oN/Bq+arpdtRrQg0Qdm+9j8EBKIAiZ4GVAYGitFcwKJBQy6ZFABGCRiCNISoQULWRl/kblHd9/Z2urfaeXvVYFvid+mFYHCRUWgPYe7US+MjgAJj7HCz1gaVkWoMm0t/cpNC2IdzKj5Xbu8+r46JeWWkNlByCO5bcuOiBahESEGtQAGZ9fFFmAIflBhQKPvZEhoIeBMz60RZVgaYLAwbtkNOLiAVIox7fPsETCgXXvE3G6FfhwK6v7kRl1mGpskdX0/B5g94LDHgavpfGbutgq9mg0hb4NDMh+HPnmA0uagmAdSiQnFkdttqC/mNkhmFwkAuYn1bXR77EmQxmIICiFch1tQIDXvBnL3mUa/N3ryUA5nUa3CK2EEQNvrp82s+orf9S/d2EpL+egqjk4QewzmxG06ZgVHaXngd3PmYDtg6e05TPVXVin6st0FGBxCGTR7duyo/1sdJWspyRQZPSGI0SejyJ9qDSIiQFBwcJWZOg5gbpu5SjR7agUPwPyiTgkGag1297qQ0jPAt8tAgHQBGpqLUHW1KecNl3pzWYmRG4+EFsubU3L2wYH1MpBAAtFcvX4qh4j5Cg/gZFc+oKxrzqw9QFAnfcQ8E1hYItQNC+CWt1emHAoLXFZR5tKuWFOyd48mM38OzbbuJRR1q1Wq38Yg4ImhZmXG0jlvvdu2agEny5PM1ssCqHy6ZNDahctzq0VB3ebt5oC2ZpZfBdGZ1m3rhOSyDnF6DA+RX4euwl0/kYHJT/17Mb3HUeBKx+KiOGF/hrMJBnowZWKX8nZhGAQNESdFT/OYOxvLoZCryQ13pthTxTwT2rW6+BkXomV+edqJuaj67Kc1ahC6rmBga6e2hYHRk0ZcmZ6jrMx1DXJesMtFeXrRail1LKxeWErD0AkOvSNAbiYCd1SAYCK5BAVPb1iMFvC071mABWgX0+PyLfj7tJn1HBgfc3kApABQfmXNdoDwAHCOdKjdYAKG3WaA0M6JN/aL9Ysy8Hx8o8trrZI7OMgyzj0Lk1CQBALIYhBwpy+EBdLQABSJbUt1DQFmNtD5yt/HaBwMCXWLu1fzkYuHXnBE9+/AaeeWuBAumE/pdusDN1GQog5OctORg6ENDHHoSBfM4da2EgB+WpytypCKo/+td8KVWCz/12O6vPMzPzYnbHy0MaINDPW1Zo9FJrRrC2zajXVENbXnk85Sy1L/1MK2Cg5H0GTKCtwUCrHfAmA6AWpB4KOoJf/iwI/0rwR1ePVISZngNRmdk6oeWBll122r5qxyz1zKuVNp/Ksbb/zbQwK/DFBl8GWax7XHCC+GiYZquu764Goq1v+x5kLGDTFNj1Vt+JszoeRJInWoMEynUOq2cKHW1CDQqmBeXOWDDTmLrPvttncwLmibFhTT8gAET21BoQgL7gm2mT1PfgcJprDQLsXSYc8iVaGzerPBNy4KlsVXSrDmwMMEhoHRcXIQGQOnU35gPLGmdAACgU3OpCwQwI3MEWCOr6Wq67iwMGKC+Aqcqt7yUAt26f4F2fuIFn3iJQkHcqawRpuY/XHtjJziBdXYBZY7Uz2iVTwcxM0IEB36a9F8LKLJ9np3MuWxu5P3lQW+DNCPXDke2NNuj2VMszoOoAgZ1bgIKlul0qr6V5+OICAcAKCJh60Ooh+wEUGKjMAc5M0NMMAMjCKS2ZC+y4V3V7EFiAgJQFfcjmgSzkKcq23PpdjquDnD5y0r44TZDohnAbTkGKaJtKVfZwa5D15ijt4YQTofBKFf8BPiBUQITENIgBCDu7BihLOhXSnBMnGtMN8wSKrh83sMDUaQ/fFta3FRJ4Gqv2KB1K4ctDgmuT4pMwFkig0jak3yNRAwqAOClqNu193zoV9MmAoxkrqm2LW+2BjoPS9+eAoLddTgvapbxZUHf54rLWwPqAgQLcODor7sr4KbKijOEGCi0kmLnBgMBDAtPS8kf7auONjUTcUFsqddOMnQIFN7rmgzUg8OU+5Jjp04UBA6BqjwoOXrgjUPBhhQJ/oV/X26Y2fndtyJ3Pp2cCS78sza5m2oHcqPUyJqBu1KXZweYlh74U96MtWBqM1mzN3odAv68u3cz32xb22ZfrUBZyLu8RBpZ8BqrjreABls0EbWpNAg0MWB3JFttlBtoDAQ4DECISCBNL3aVJwvOmxHpM1ttPbFtSKwjo9tPjVHaZHDX4Udk9ULLKrg/PipMHRvkeCSi7TxKGGHLEyCHS6i6UdUjpIKGKg1hYAhgWF4IbUKA0iZOitiOlUd4tCgBYghBZW1k7hSDtNGsrhQRdwsxTa7oxjWPRGJi5YeaTYP091ZDACgm2VXEgkVEBtUBIJtQ28kEW5VvggCTfxZSoq7SIOoAAoKuPWEkHtQf3pzWYP25hLGV2Y6n0yRkkOHNDgAMCBYQKGizDNt6Y1iEf8v+jyJfGYUjMBwIFjy5Bge8LdZGqMm+tqQsFBm1iZrxw5xbe/ckCBRXbagvVQT/qv4v3dud7jeSJfs2RsKcdaBvzkPWuY6nL6dDSOthvZ9qCRjB2VyI06SCU1LOFVQ0BMAMCzdXmbaN72VqCAdiT2jJnM4EBwYqZYAEG0nls3I0zYAsEbDNR0wzosjvWvwID5RirRmBMQBoFBKYkgn9k2Q54SlKnY2Lsp4Qxsf6zz8CYklzDCeOkEKHahFHLlBQi5HMHDLQhogZ6AoAhBI3xIJsTDVE2ngok54YgESjls+xOuYtBd6GE7kIpG1CFIPcWiBgwDIP0c54HjbJlopyGfIw4CTQMKvA8IISVtrPvIcpmhaEBBdMm8IQeJMzMDda2tnzSNDwOEthBAgNlQKK+YOilFg6kjfR3kjOd3croVQOCzs69BiH7INjd7zGxPK/WGmhdoWgNgkJqT2swi1myUP7VYwoKHhLyyg2DhAYQAPS1CPYbBwma20rbbZdZOmmgwJ8/LxScJ11IMLCKfuHOCd79ySfw9GM38cjVa/n8VjhYS74B1sIS3ysQbH25u/5eK/LZQ0H2XO7OmBeSV7Ga+aBY8ZuLW7MB5toB/bwEBEAZ8w562bp6WoIB4IB2wJkEKCV5ij+eirbAhEmeffZgYMlz3qeswuhDAYbhIBCYRoDjDqCICfLocWKMqg0Yk0DBmGzWLyBwmhjjlBQKgLMpZQA4mxLGlLDXa6fE2E+MxIz9mCQEcJLvKcmzAMwCIkmxpJyDzvRle2n5uxuC/I1ybBcDdlGA4CiGDAxHUWBhFwOGGHDFQCHa3hMCB7Z99UDAECJCjBr/acr7UaCJNMm2usABAsaxWMcMDkLst+c0auFLexoodCHBfDvy5+CWP8qKByICUgLrPhCmITJTQ954SCEhcRH2Pis+q36CnoGgeadqDcIaIIiqP/t1mBbBRwGs7px7g7u9z6RBwDz5LJngz3NuFeJrSxlp4dwqPFG5SA15ub6TmhUqQNBrJtSAYPs2ZIhRQdOLuWPZObktq+eevQ8oqMq5XMxZupBg0EJBrtTmhViDA38vu8SnnpDqAYE9d81kcC9AAMwBoBeNr9UWtFAQsEGNfsiLNqfmhe7BgM/4q6AhaOuorRPThgD3AATJCf6llQamHRj3fRjIuvWOhqC3ssVBAQ071arE7A+wBgRMUSAgqfo/iTZgVCgYuYaB/TjhVL+fpQICZwoJd/fydz8mnI4TznTr5v0kZoVJP6fEujZfIddCJrvGyf4ctomSmQRUsMdAGKJ8DoFwFAlXhojdELCLAQ/s5O9RLKBwFOTYlUjYDdFBgmx3PATdyjrIZ4GQiGHQsMaHACFNJR7EuK/hoE299s1tT11IsPYVtf1UTEA6UFm0VQkqFAEEAQQ7rjESqAUE1ALfPtu7YQowuONrgFBmtSuAYL4ATotQIAHIEWPPk2YrFOZBj8CNpgA63nS0BlY/aMwPS8BgdVXV0QIgBKYi7B0gZEbkHjQUQMjlkSLh5HZ/9Rxwb1CgNZR/8sKdk/5Fmi4cGNRQ8GyGgl7qqeDb9b3dBnEnesLfjs+OOS0BN79r73UotTPiLVAQ3efsV3AICnJ5VzC8k6rAHTPhj8apUDKyFQi21E9rMtCP+q8p45oz4QoQYBzn2oEODMy3Ow7lfNYSxAIFcVDTgfMPID3vtAIch0pDMDZAsE+iAZgYAgNTwt0xKQgwzsaEs4kzDNzdT7i7TzjdTxkETveTwMSYspkhTQlpEu//NEpvLsoV7csrpgTSzieOhYQwiIo8RNk0ycwFu0E0AVd2MYPClV3EA7uAB3YxQ8JRJBwNCUf6uweGgCtRoCESi4YhhAwIKRCGEBEHnblPe9A0goNqE4iApKr7ROKnMOwkmuQ0OgXZVP9t2zql0tYtJLDGn3BaIYuPwCHVgBA0YE4YBFpJl0oyqxaJMyDYW2x93lTu9rmXfFMVQVb/ZtHEQEC1giFDgUKC3cFDwpbkZnESNbEPBzAB6+AgEjAtmBQ8HAQ3EVuDA19HlO9nF8sAOikMBHUMDSTvgmkLbNWCxVKpoaGu6+PPneCdH7+BZ5ymwFI/6NX2RJDVeV//ySdwFVcXr7swYGBdpacp2JQcua0Fy7n1OSGtVkhtjUHwakJBFqnUxEuHlMV29OuZDwKwuCysyoT/7kCBmxd9S9THahmOG8TY/QPmppleveSd01btJtu0BAeDEJnJwMccsABELRAswEA35cA3IvRzTP9hkNkYETjsGi3BDhyLliBBtoceE2PfagjSHAjujqIdOBsFCF7JMDDi7l7OGwzc3U84G1MGgWk/iQ9BAqZkmy5JWyTdo5qZczfpmXaEB1V46c6JIQAUA6JuKBGDbK9soHA0CAgMcRQwGESDcGU34IFdwGt2EUdjwNFAOAoB+yngbgMIEyfZxCgBMbDCgUQkDIMK5zQBCADtQTQBSXw6CACzgCxJ44JhZoOpNi00KfeDFhIcIJj5ogqgFKKskLDQ66opEK0CIy8rBEAk0ACkSnvAKCr3NT2yf5eszWxWC56rvm2oJBPR1RJHzuXxWgTJQgPIS+OHmRNYR1TycFXDge5kLQq+Bg6SlocgTrIW6XS2TwrULHEADiwLGQ5y3ZHT1HCGA8C0GnJNvXy+CJwWEGxJ/SNXry3LonMwVl5twYwXFAqefuxZ/NGP/qHF31wcMCDCrdvLUHDQL25DuvW5Ezz5iRv5uxfsrwYUHEo9ILDjS1oCDwUWw/+gwMwj+3rOzh0COh9b9x/oQddiHpiregBwsIxLwYjOFaZ4GrsagnbGmJPb5Ee+L0NBZTowCAgDEIasJTCzgYHApGBg2oIzNRHcHRNOU8LpmHA2KhhM8v3nz6YZEJyqxmA/JkwKBeMomy1NCggpJd2KGbKkMYmJAczgJPWzlIhCdraTHRHtr4BCCAEhEuI+gSJhGALGGHA6TNgNAVd2Cae7gLtjxAN7/bwb8K8dRVyZAo6i1MHRwBhZ/CEeGFjNEMAUGIP2vwmy46GYMY50lQCBJgLTqHZ87as8SVyfAeJ3EIcuHJBGouy1vx0na/9prAFBOojGRgCIWDXw+lKY4xslPS4hmSU+wqjaBsxm1d5ZrxWA9X4D9XvGOtv1kOAnULXzbomBIMs+nZmh0SLIzVM/HHAvccqlWTQrBJTJBBFSKj4HACGCiw9Gs4kaUDQAJqS3TNAOwQHseQ4OPFSUh5TjAPKS+ntNtFAGccT38nG5/i8MGAgUuCWJG9J5rF4GBR9+y0183Z//3fWkGssmBbvAe8audbq1d+VegaAXuW8zFHS0BXWGO05E5zUXOO1Ad8BaqI88WDg4WL5uCQo6pgPTDGS/g+JYyNN4WEvQaguWoGDYofYnmJsOOO7KsbibaQlGhltFULQEd8e+puDuvpgMXjkbcWoagzFlDcG0nzCNDgjGCdNUTAgpMaZR6jBNojlhFiiy/Qp6cGCbHZFuXkQkf0PcAUSIAyOEhBAJUwyIkcBTBMWEYQpIU9CVE0GcKoeAcQrYm7+DmhgSpC5SYoxRVl5cSQxm0R4wy7LLgQG2iWmrPZgo910CKtOCrFpQDQklad9xP4cDW+bo+4SFsM59o2gbGGN+VyhCtAcJBQRsqZyCAOsLLZsVRgDJNi7MgnNNvm1a124THw8JmGsRjBv6ZgZ2IijPpd0zOmOL1xoQ5e9rZgUPQAiqLWAFAqc9EB8IkeKJbKzuA8J5krN+VHAArZseHFDnt3lJ/cY8aG3Mkh8SW/l4CHwuDBi8+5M37o+0VhrBoOCZt9zEw+7+PcL2ac0kAQC2Q1g2WTbnQ3Ot/C3ZfTWBAEDtaNiaE2aZvzcgsLv6CJD2uN5a26W5p00Q2mbrlTWXgR0AZa1Ax5/A14v6HuRoeT1fglnmauTcoiXIqw5UM9CaDgwQDAq8dsD7EtydJuxHxl11LDxVDYFpCn7udMSpagd+7mzqagnMZDCNYi4YszkB+lcAIE1jBoI07bWa0xyMrAm0bogTMAEh7kBqaw9xwMQRHIPY33nKs2iyOAFJAyup0J9SxMRuJQRHXS45IEU7ps/WtprAeABR4UDIgAOKcAikoBKAKYAwqrreTAtlAozdEUj9TBiQclMtCDdMPCu4lI6ddNFCRA764wWlaigEDgYIxE4ZDoQMZIZu+Wi1BrN2wfq7ZnVok98SJhj5ReybGcI6IKjpQT43OahMCuU38tggUN9zvrR8wQHBRkDwO61uiR0DYHVSAmDut+YgINdr7x5rcODK2OanTW1wP/v9Wq4vDBhYmONZOvRmHqCyW7cLFFgchPPcvvtIKn3d239ajYC/3mf1VQMCefCrDwT52OH9IazsvcGpJ3cLkR/W+NTltXJ6GEozYDDhX+qkCXiz9jyvLm4353FAMNMS2Lr2nunAnA3DMHMw3LvlhyMDpwtQcJaKc+FeHQpP9xPGMeVVCsWpUAxePImJYNK/Uj2spgM1p7RaggYKfH0Vh0sRolJXRRgyB7UTEygwOBGY5PmRND9BwIQIGAMhTEn8ESjh1CbTRLi7l/vWfBYgxoMIYMKVGLWzJaRKXJnvwZD7JFFrWlBNQo550GgPOoDg62O2Q2WTxClR5B4G0v5os3+dr9sSCbZnlWN5Vk3bNyFae++yKcHVpHesMy2CB4Ty1QFCXslwDkCwzw0g2O637ObLrwoggLNJwPtc5FiFrnKWxunzJC/7t5gv6ge6myykLL+W5ONCujBgcP3L+oVeH8rr5KkusVaq7a1wdXul+vvl0MsQu1Z+RNOJesRYgYA78AXVEGxdmriqJSiDkf3bCgQVl3RewhltY1aV/UBNVjZbfWDxCawODArgAAI6kKzVATP8ssNqu9/s9NADAnIOhgumA7cMMWEZCiYHBacq5PcWi0BXH+wniUUwTpNGMoQGN+LsJ9AvovgBEDOIxR8gYQCmESHuwCRr7DklME3iUQ8Tbn3NifkZeHMC6WqMEEifCXnuwmhbtAactQZDkvLtAyGGJMshBxLzxARECjidDCkniSeQtI/UOYWsmpDlgLCVCl3TwihQsxNHNh73c0AApK8tbLy2JlFEkBlkJN2sKcE87ohJnk8GD3rOrmsDAzVFbUu+9P75N8o70Xkv+yVAgOXAL3WE29o+w82KhKvGpaJjzYBgJPVqaBDgr6+1mIdNlpbD8q2NKrk5Nb9r5dPSdZYMCp5tdxE+8DvgAoHBUhnvtVFeuKNLRt7ahFHuPLd0GqcJ0OPZKxiovGLXstXus14ttzMVHl4FH4IVGMgDUnVw7lzYW2nQAwIpN2Y+GC0oLKXWc7rVolh9NFVnP66EfVXu3rHy1H5mvMBbi0/gtzLOSxBd9MJ2GaJ+96sOGDRzMhwbKLC4AuJzIMcTA+Mk/gcWkXBS08O8OCb89W8iUV7k2ZgJdAZREHV0oK7Doc2Kmef1YjHiW0AwR0RZpQCEaE6J6ogYAyjK3JNC+Rc6L/eUSlkTS/nDJIGOxgRQYMTEoAnYI0noRAcHioxglj0ZBorAEEDTXuMJjBkIBA5kyaMta8TuKIdV5mkUDZCZn7b0l25qtHjmxGcDTudY0RrUYxQIOWKgfi0avZX3sHZSdJoCmgNC0me0Pgjyz8YJ8xUwmHBwYLDgUjUe5TEMMNUKkUEGquWb5wEEQPLN7LZFzqEl9XcHdC+9SZz7Kp97Y1RzvLpn5/ghuXZyW+TXs2+7iev3YF6/MGCQ7WAHruudb+vYgks8o6SVg1Dw3LZkL4QNodK45bjBAeDdbmoqXSJDO3xfMADUM2P97l+8pfXFXTjIJ7dDwVYgWGu7tfegt/d8gYJU/aUWBFaTbGBDUYLTVOOVdYR2MM8OdiF/z0DgNQQKB7NwxnGY7WvgoSCp0J90cM9OhyyhicdJQxU35Ysa4CcGILEIyhSDRiiUvI42OAZCSBKjgCKBJ0biUC1PlHX4YqfnNHSFitdCkBvJKufZgKKVIPlnyxcDEUgjIYYhZIAZhpiDIe3070DI5bOy+mQhnMcJiEigEAUQiIFIohZnk9/6ouvi80jO74Bs1YJqD9yyRoSh2nshxFh8U6JCk4FCt7u5vuP6zXrvt8pmgQATatVsPNZwgHqcWrzloefJneaAwHD7ByCv68/PdnAAZgUuLNaLvbO9cSovgWQABgRZi6DfIRAKdPaasLgDDgj8vhMEqdf8ZKY2A1aoKtXl1b+dcdyOzwu9fJ/e4326dbsER7r20LXZ76j520sXBgysisj9fy/Jh6G0XayyCs69XRluV+BAslWabkZ5i+RYBs7gzrcwYPdc1Q50YKB6wZYGBstDCwcZAtrv26BgDQh62uw2ZOihFQgzMwLQWX6Z6uNVuYOOMWVOVcFBdPbdzm8BuAFdf9/Z4nhtf4MqpHGSNdh+fwPTAli9TlybAnw44hCAkJDDDU9JbOxAFBV7IOzGhP1EEvY4ksAFM9KQMKjDnzgclt0WLZhRG+1wE3BZ33JRENugR+Q0CPZZ9lJogGCQAEc5hHKM2EVykRXLY1NixEg5vxMBA8v6ezHVCBAMqcwCJFohgc3vwPp7mkA0gYOsYslRE1WDkB1VKcD6Yd6gSTKwUDdtH3K92fpQ+xNWEwMSlEAEBmzZYLb2980JPnUdgFfeyyVAkONOe4BWyNVwUMqeqj50aKyyKJByfnJRVlm0CHrfg1oEQhcSQFSNWX6dBwOLomZpPAf6UODH+jVtw9pzgCK/bjrzQX0NL3yu08UBg/ZFsxfM/X8otZVq/QVo4ABlQqEPq+AA7ly1FfLKs32n8dfOdj+k+4CB6sVa8iUwByD3wgGzAWlpO+QtULAFCODObTYHHaDr9d9S/Z4QIatnbc31UO6at0+uIEVbQ+umDOSE7IBojoYGBBkSOrsfprLPgd/0iPX8lGRTpHa8DIEQGBgiI42y34A3CUyRMEyM3TRhNzGmIWCckmgdxqS7K+qeCGrD74U9ZvcZ2KiEcdULFDgwrQGAKlxyVDPDLlL+PgyiIRg0lLIHgnafhQDZmCnQ3PTArBHrEovGAuJ3ARQ4YJIXP6iQixQRokEeAUmW51WAoGsgOU1AEA0BccqaBH36al/Kxkbv0NurwK317Z9YfVlPS+9m9p3qAIJF+TsPHGSfg3yyMSesjV35VCggQbwCCZ0dK+EgAaiAgNhpDWi+5fVa8s20NLb7c/naaoKzbTzz8quYDzxYLdXbPF0YMKA0FdU2gKrzuOO0UMV+a8vrDRTY59C0lA8Ykq/W3mKcDndqMe/+c9tZtmoGgPy9ONjB9d7UfO9lhMp13Rkx5Rfr1YCCatBps7VE4p0BsQKn9qcrpgNRYSY3GBSHLXPyoiZ6W/ltkwGgBoFcR+WzxbYvAYyoOBnKk2SfAxXAeSvkBAUAtx0ytG6bdz0SwCzCEFMAhiQ29kAYAuPKELKPQuIokQ0VMuRZSXdbrDdHAjDbIMn+Tk3d9DZQstQK5+jMF/6vMVi72VIMEN+DfAw4Gsr3vHmS/dXNl4ZICJCw4FU7JoADg3XGiwSEINoEk2gjCDGpoCGAA4ljosUgSCPAsWgQWJfAaihjsC2NTLD3lhy8t9rpnGb9Csh9y11Tj3tWMFYA4PxCeLlnE5v2tTANaDd13lEPCPI4BwOWj/PAAYBWa1DSEihYmqwQACMvc4U5EBIVSKAAYOpDgkFAo0mwxxb42MxXBzUI/gIPBVvhoJZfjyLnbAGuyPriQrowYJBftrzkBe6F6UOCJanUJ3Dzbc/mSl0CiKoqtbMD6xqC9kVbWubS0wrYfTbDwBIIHFptYMuBmryt+RjcCxR0tQTnmGkCBQQIdV22lL1cZhkU6rLYjKJoCWRGv+QQln9c7pFnKE4zkI/LBjig4EwGoQCB0xKIGaGAAbvvecBq6oMCYVBjFgVxPAyRMIAwElfbJNvM374D+l37ioGCHbc0pf5nnw7F7gCWNUDeL8B/7m3XLFoFgwnx2/Hmg7KNswABEdwOjEF9GpoMsAiFyTpzEFDLdml9RYIKENFCDAUA8rbOCpNw76bfqhtwoKAPXk3Lfayp2f7Pc37KBMfaidQmugQKq8nNnirN3r3CQcdhtQ29Xu6P5fc7g37+oF/1PSfvI3IAEvTn2ScBwJrWYOtYD6ybDnpgsNRPZvKrAwP1vjcHZAEuFBg0AhFOMFcvTF0px7dPcOPjT+LmW5/B9auP5Bmj612ZtH0jzR0Kq59UT2k7R/X6HuwU54SB5qXp2tGblJdALcBBmf02uyHifFBg6TxQsLQiI2fNBp9qoGlu3PUlUGENqGgOOjtwgAD0plTzz65OWl+CbJPOfgaiMTBeTwmqKRAgYBPa7i+r9oB5bgCiAERTewfZcGdIjIFkljqBcBRsZl+KY97V7ez+kGDvbY7UpjCTuK/e7+V88920DSYACFkzQEFgQDYRI/cd+r3JH0SeiPbA/Fmkl7BqDSix7HUYJMS4LLGUrZ0Rpvxu+s25ODByiO2eE7Cvl0N9zjn9lv7WqTM/UdLJTr6zCekGBrL2U9/fQz4JS3BwHs1BzjlRrTXI8Rr8884xvtlKmVxfbkw/JyRYXZg2IYOCdQ9NaaXv9sZ9dOphDgXLY1lXfs00A83vNryDFwgMeipw7Tx2iQk+Tce3b+HGJ57Ezbc8g+tXH+3CRfFVaMhP/0ZC9bKVuAXLqeuxiqYjbIKBni9Bb6Dp+1/IZ3LOSxtSY0IAzu9T4IvZf4b8aaHAUk9bkK/bVopSFoUDkNZdx2ww/40fbIpGoAIBD1IhwurLtANgE9RzIAAj+w7YVtzM8+oiIkQdpYxpBkg03x1oVoz+Nj8leMtCUeWatq473SWeo/anTuO33dSbKJaaZGuZLPtWDgolv20/Yv1PhBgjgcCqTkjEKiAKIBCLiWIiIFAs73fkokHgBPDg3mPtDYfe3TYt9r9tqXdlpTVojlVw0AjBnJpZU750AxzY+Fk7I24sy8axLjsSkweFKY9jwgv1+9uDBFAJwcwo1V6ZgjbkfWmlwSIQLAj1ZfnVAYJeHa30swsDBtmBJ6uE3ezXXjx3/fHtW7jxyXfhucc+jOtXH67pMtsCym9bQFh6FW2m4qu8vXa1A3DfgXAJBmYvxyEybMvkNQTVDKP5DYUKHtj986ndZbIp4frsY4GiW89drz0wbUFXPe3BKkOAxrnL9scCA7l8rRNU/mwP8TO2Dgg08NSDgXw8uc22FoCgV0/EwBAoO6kNABBFxb/Ty9ole9T54izXlZY6H3d1X0rfTvXm9d9tjuZ7T3NkmgyG69Jm4qiOuT7VeUD7LG/6WKyXJtPMGvUPLJ7qVgcOEBLExJC49MuohJpNDZYjB/c90C+lBBaF3Xn6I1DFM/Ap553NDOrggGxYWICDXgVrMq1BwwrF9ZXrVUW9cTIvX1yChN4YtzTuce13IB/tgxvzSfFkCRKA6v2mnMcCCvaYKqud8mHh2FYgIE6L8qtcv1xHW7a/vjBgYAJuBgjupbGKOb7zAh7/5Lvx3GNP15VKtWcsd35br3ZA/jybza1+bxr+IAwAlb3yvKrInAmaQ0810CzMfv3vzblOS9FqC6pSchnU+/mZH2ppeisUEMq/PAj7cvl2Jaf/yYDg89XMN/MszdXHCgjYP/MbaLUoLQzYrpseCEr9wUok9bNSd4DY0V2WZ0LeBPz9hNUuj5/345y9joqXe0LNOdXZr339bfFZWapHuGt3gWZ1uZa4uVCsNTUgiBsCYWLO9dNCAmndyZLM6ATHAihofVbOw21/bJPvn9VxV9/6ri/uurgRDoAGEA4kv/NgOYaZSSEL2VZrQA3MU8fEsGUMrMY9BwtQUOhCQmtumK9uKPeYa5YPVdP83DIQ+Pfp+PYtPP7Jd83l1+x2K0BwAA4uDhgAGQ4AuFCiKvy0Io7vvIDHf/Dr8dzXfQjXH3y4+k0rTHIM9B4gNMOLp0eXoeZr3YEXYSBf01E19mCgR81tajUhW9NMW1Dvf5CL5gbqtbRks9waEOQwFBwqj5SjaJZWftVAAICDIOAFWfJmgHMIMbi/bZ1UxzYK/0h1vYVKYJW6lYxN0s+cfdx2oMxQmoUZN33PDzydvlhpo1rIogymld3clnR6P41smqkdN3t1Pt1jfftkxSSqNQiACLglSLBiVXWuRV8DBZnlYwYLB9NGc6DvS4fgIP8iT7gaQGjSoaXFUi4VmdQcRxGsB7UGiw840AcdcBSN8jSDhLm5wYABC5DQjK05/5YOyIY2vx0gAKdmUvvIQQF/L1AAXDQwAJbhAMDxyy8VKLj6SP0bnxoNwQwQgG0vanP/rt3HN5zBgDtH7eCwCgNLDe4AaUtqtAXz3RLngtCnQzXTGzz8obUIYWuz2JyzpbbJAMj9uvCQQMuRHfUpTug0wh9OSHUEk2SBVwVTb7axBgLRaQBCPoas2g7BYAGQEL4ToFH5KCU9NgoQJJvBTtmbnjiVzaSmEe22090tp5dU4H4b6s6Ok7JvQgkKlTeYooA2fHSYrfTwjp3k/DjItQmhNduA1kGB839aZGsP5tweFhepgBlcm5R26wFuVFBwqqzqfa8mEb0Mtp2kl/IYUE9jluAg72ygYMm+A+MwBLQpmxP8d261BsAstkGlNdj4sIUJ3Ow8owsJ3tzgtQgAMiQsxUkAMAeFTXkuHay3iiBDgZ/ULt6r/P68UABcRDAAKjiwdHznBTz+Q+/Bc29+CtcffMOK8Fj2MaAtL6a/1Zq6a0k7kM91tAMzGFih4y0Q0JuhZe1IAwPNDHlr8pEhD10nf+X7IhAAMygo9+Z5PfuyVv4G7rj9uqMh8GVuYSjPSt3n+pq5iWDGdSt14uHAzz4rrQDRDAaCLuuL5EBg0gA801hDQP4+yaA4juDxDBj3wDQi2bbC416AgBN4dAAxjbB9EjJQACXCn2+CGEudu/0SZEdJEe40yFbUFAfQsAPiIAARB2DYgYYjYBhAGguC2h0pKcp21goKu1nQKOT2snj+ibWuGWDUphzfRm1b5WEigwLntjJQKNoE1P1W29PgLTvo5Wu8RoHBFtFwYSY5r+zm/c+Cb25SAGo4ACHvJbAICL4CVtKhLYmrLNpvTJtkccjzCoVJ/qo5QbLaMS0sPmBhjGTd/qmCBKsv1FoE0yRkLYLARJ5IGVhkTbU+YuukDJhPIKHmg6zpXpFfq+XfPnJfTDBwiZjx/MsvFijwmoJe8jPr1szgGnfLMsAeDFieSiNtMBcsAcFaHpY0BEsvqkGBaQsAtHa0QwJyKfWCpvgBw2dpKxDo121QkH9f10cFAtX5e4eBJedB5jLw5VbrZdOBUT7koCAEmgFB9H+DeuQbDEx70QikvWoJxvyZ0igCf38G3p+Cx1H+TiOgxzGN4EmuS6NoBtI4gff6ObFoCyYGc9L9FJb7ZQgBFIMCAYGC7YEQQLuIMMhui2GIoGEnQj4OoN2RgEEcQLsrAhC7K6DdkexWafEEglzDIYLCDhwCYtwhaLTJKZALIlX+JgM5IiBxVqP7rnSw3QwAuqDAjXanOC9uggRANQqs+aq1X9vGoxoO0OBBJMheAXwYEIAGEjAfA9agoFqx4LQGvjpBbvmiCl/5rHDQbQfqv/9ds0JHo5WPOS2CmjMqQDCSNKOdwgSTBwxnolisiYW0BgXnSFscDXvpYoKB65DHd17E45/qQ4GvtMpxZw0OllJ7rmPrWos50DUZbASCsh/8CpX2nAi9tqCz3K63lTJQhOCsCtpHund0S6APctfdExAAVb1vcoA7Bwww+s6D3aWFCgKtMFl7TbOqlMr3LDScUAlhDgQxaH2lEZRG0QzoZ9jnaQ9KI9LZKXh/irQ/y1BgIMDjGfjsFGmcMJ3twfsJk4LAeHYGpIS0n5DGKYMCS0QkKb9u39jbxjmHPI66/CtqoCEFgTBEhJ2YFIajI9AuIg5R/h7tBBaOroCGowIKBge7I2B3BeHoikBC3AFhEEiY9qAwgOOAGPQfgClR1hp4jQJUczy5RsulWWpHPZC3HnapREgVs0NagYQQ/PUHIAGsQFJ2KgTm/b5ajuzhQBol39PKtAYIBBnH8ihWFbYv/lo+4N6VWuYSmdLeTTUpZC2Bd0TkWmvgx9TG4XiWp3bMdPVRfW8BAbr8kcysaL4IBLABAmHm/O4goWSi1eps0BRUZViQX4uFXoCmTrpYYDAzH7yId3SgoEdRs82CluDAn++lJSDI57wmoLPCoJpibgOCbnICb5uT13zpXWtbb3nAz4h9MuWfPbJ3PmczX7ceCtoOHdIQLNXJ/QDBTDuQajOB7V9QwYATIH6p3NKsKgaaDZgeCsRJUKAgapCeEAiDAgHxJILfQUDWDkx7EfyjgsDpK/L37G4FA9PZHtPpiOlsj3S2x3R6hrSfMJ2JScEgIY0J0z6Bx4Q0yQZOaZ+ySYEnXgQDyqsmAsJOwCBEAg0BcRcQhpBhIAwD4tGAsIuIV44QjnaIRzvEKwPi0a6CBDp6ALQ7QrryGmB3pCaHHTjuQEFMEki7DAgh7kBBNpYek8QkmEjFY5LliABlOKhea/1r7bo2U46hRMkD5pDQrnAwUwaRhCOKDDdTRgXJJtDlXUUeP6yv+3ehu9oKQH5TDwGCu9o0Bey0CHKPdf+i3rSFpbrzOV+TVWwDZ1LIcKCz9Rkc2I0zHPjPdToXIABOwFs4dYMuM3FAACGXx93Pm6ire/fTVijw31tAmEWOzM9eB4SLAwauQphIfAoWNAWLt2jhwKceHPhz1fceEABdLYGdvw8omGkKWijIqvIeFDQR+vRvDue7IjhL8dmell/sJd1FLfiWtQN2LVW/a+qiZ2/tpD4UlDv6srXLC6u4Aw4IUlrQDnCBAauXXjAfS5GlHqbEsr6evINa0RQE2D4BFtaXai1BhoI9aDyrNQSnrwAOCOT7KXh/hunuXQGBU4GC8e7dAgfjhOl0j+lUtAPTnt1n0RSkUXddNECYSrnFO9MKam2tIKCQQEG2VKZACgYR8UpE3FH5fEW0BQYFwwMPIF4ZEK7clWMPPAA6uysag/2ZaA8UEMKV14DDBOad7KcyHBXNXdwhhgEUgAkmATU+YCIwMaLCAXHtYOihYNa+fiY3lXJnSCABBNmeuDgvcpA+JgBYti+OcFsZ631nIYWBIjyzkOibGPqQ4AFB7mmgatrx/IJ3tAjW5hUoLKQsNLnENEhYMinYeGRy18NBeVoXDqSmunDQwtMsyFueGGph2VRJNusu3/MqkuyflWDLoFcBAViUKRUUWERD+8k5zQNeptlvj19+afU3FwYMfOErR8MOFHTjbzf32PbQTgPdKxSs3bP36K7/gFfKwwn6BS1Bz3yQN/pZhoK1rFJ9yUxluLaByOL+EMA6DKw5ktYPn0FBAmZaAr/kzYcnXtQQOBiQLBZhsTVrMVBddgMEEnWzQcFQmQ5YNQN7NR+IZkC0BGeAQkDWEjhNQTo7w3j3FOl0j/HuGabTMwWDU6SzEePpKN9PE9I+YTobMe0T0qjfVWMwjknr0G36ZFzgeoJFGWz9IgKAYQhFY2Bag11QbUHAcOVMgODKgHA0YDodRWtw5QjDA0eYzvYYHriCcHSGNO5FgzDuQVdeI9+vvAa0m8BRoWBI0vKcgJgQ4k6lkQS4GbOjAGGiErvAikPaXu1+Eb22HsEqXxg8UaUh0jdQhb7AZiQBEtmjgfJ8mAgIzDZPzfKqXm7qZtgLk5VZP8zndUyplu6ZcCvjwCEtgtyrqZfe8/x5P+c6AAfgIOaxSnOgkxsOOrZSM646bUGjCTiPU+Ass9yBg+y8qOf0+tnyeX8/oAKEakn9qyy//KT5ITy4eN2FAQOr7AwFvUqtZtkHKrEnVPT+i2lx5rogxCr95AEg6KnCZp26AwTuexcK/HbAK+YDDwXr8/O+VkA+y9817YDT+1Qw0I35refqhy+0a6tiQ12uBOc74D+rlsD2LEh6bElDMKHseNjuOmgpEvV9Qz0MkGkH1KdANQVdKNB/mEbQdAqkEXx6Fzi7Cz47FRAwLcHZXYyv3MX0yhnGsz3GVwocpNM9xrMR4+mEyf6djZhOJznmYGBkxj4x9gZOcHCgxfFyM1CZU3koCATspoTdnjDcpQoShisT4pWItB8Q94xpnzBcEX+H6WyHYT9h2o8Yxivg/YT4mhEDq3qXGYFZG0v+0hXpPwwF92g2ahQ4MJkaNAyy6bl1L4u8pG9B7nbbfJI2F0hQLURSM4MJeNUgIAiI5D0aWE6S5kneBflc7+7aCN01e3I17jgQaIQnm2c+BBLacMBdLQIaSGift5Jsvr8GB0ACh0F9snTFk2113VutoAGK0MLCkn0/P/AAMFSA0MBC/m0u0fxZK/fPcQoefLjOz/3IL01ePn7bR//Y4s8vEBhQIS0L/mDJO6QAKwIc8wptSe6T767PLfgrVM+9r+SnKgsg4M9tBYLWdHBOKKi1YWXVwZadIwFvJ/WHnXagBwMd/436YY0dr30Be/4STlNwCApsEyK7jrkGAstWL8Z/+x5TkFm0qZht9piXGTooiDPzQUdTcAgKTl8Bj2cFCl45w3h2humVM/EtuHtWaQnGV/aY9gnTmQLCmDCeThUQjKoh2LM58HH+u5TMXyIqFO4IGAnYM2NHhB0zhjGBOYppQs0T4ui4A9TZcTDVdVZPlGcOACilali2t4iuPFA+a8PM4MCZFdDAAVyfEa2BmIOmMN8C27f9lPNLmAJXGoQYipNhEnmHaPNhkueHIP0SunFTts0rO+R+ZQK0p1Fben/scB4fmyV3cJDQmBqsfJUWwUFCqX1fL/OxwmTrZjiw1QpIMrkxTQHkRjPtQQsIbR0sCurDs/CDqdUazB5RH/uCyq+efOykCwMGx3deVNL6sG6d7FMzS9+iPmoay0ec+l3/w2P1db7hOBVVzxq1957nf1N1gl5nauzm9tcBwqsCBMAqFJQsHAaCg9oBbyZoB7PWRNMkH8iqqPOWX2pfPvMpqACBvYMhz+DhPFDgU7sZUYYCAyWCOp6hrD6AQIFsJwxI/IEaCpD2QBqB/ZlqBmTlAZ/dle+jMxec7ZHGEelU/AjS6R7TfspQYGaD6WwSs8ECFOzVzyLp54nZaQvmhQ9ESMx51r0LhD1ERV6EMKSQp5M2ZakviiNkyBrFR4H2oBBAp3ukEDCeERACKJzJ0r8QtL/brFfOY4f8nli/Ze03IQzgAAwKByw6fKRk7aL+BioIYyCMDkoi0QyMfFecmBETqV+DOBdm7QE5oUhSL0yEpJoDVRhIPAFW6esYZlWEdYThfDtjqfPekrscaMgF7rGdBzNoOS1CXtEAzJZ+Li1jXIIDoHG41GWbzARZCVC0BwcBgVDGmnbDtC12Pz/Wbk3Wx/yErb2fSzOhTW0FHpBf/p6mSb99a0U+ztOFAYPHP/ku3HzLM7j20KO1TataXZDmlexTb1qHhUr1VH4QAFSltfQ7O+bhoPrtQh7bjmYwYMfuEQiAw1qCpXSvQDDbRVI/b43aZffs2vFc8mUs2oImEJFeZMdaKOh5V3UVGE13sm1+Z5oCHfRCcCsQiFxsAirxCbJTYUIVoGjaS3CiKvbAmfydRqT9iLSfJDjRxLLKIOlfLqsIOCWwE3S91QWlPAWupI09JM8HvJB/R9VMMM4v7T5fHBxVXaxQlsYRYYj6dwCPI9I+gsKIEHWp5ngGhAiapH5MLc4UZEtECiAEYNqLHT0MQFCHT9X1S35VGAfzAZDyDzGAkmytPEEE/8S8Kju6XdQpJwoAACZ5ZYogB0T2MRJRXlJol64+FDio0SyTGgcKCgl510FGo0UogMBoFC8LgNDNYnNt/q3e08p4r4Ag5etoDQwUehmcyYX5uFvO+UY9vwCf3wMln/76Q/LL3eP5Oy/iRisfqR6J23RhwODm227i+kPX5MvC0pxcDW0l+9Q0SrdS22srob6iNXDX2H3KYiZvl/LP6OSzIc4uDORzv7BAkEtiL3Ym/RUgaBwxu46bq6loCFZXlriUmkfksuYnqqBkrq47lNoZo9/mF2iggIr5gKiGgiGYk6EtVQQsYqFFKaSk0QzTCOKENElEwhyZcNLohBpwKC8nXFFFSkwBEcgcCLzTjE8MjEkEJovA3MOEI2Ni27eA9PJ5hVldWHm8r8HOlXkg8TWIgzoj6pJGC360lGbls7Jr1EceR41rMCLEKGYXkKxWoACS0H5gorJ9skn/QBgzFQoc+L7TmhUGUNfvoNUYLZfFhgvx3DfluQnGBF0BoAcS+y2Ml9PmdytnuQTvAQwS7F1zWoQVQMgjnMHNPQJC9se4Z0CIAFIfEvLMYkU25EqsgaAaf93x+poFmOhAQOsQSS1FbpBfdo+8NfNbnxH5WMmOpQJeIDC4/tD18oX8opkidPISlZX7+EaZVarex19XN1ot5BnJwYF1iPYaPZw1CoepdbkjrmgH7LoDJoMeDPSiGm5xKqTytfgQtEDQagfaAWsTldi1YfmFyTlZTrmc6xOqWYpQu7F9V1WxPw+g0hKALNJd8SkgqMmAnNc+AQNJixkIVCGNwSL0dB+D7GDVs6NaLVBQlTvJLDsxwpRgavoRADCIGv90ErV9INA+YWBG2k8YE+EK1ysRJpQVCR55gXruFEgDNWkZraxDAMIuSj25FQrDFQt+FDBcCbJUcQigQWIdUCApz9JMyxrT1Q9PGm43DAJX9t4kVZHHgIEII2xQFzhIDIxJhbUWllgUD7YNNtlSVTUXDNb7m/E9HuiPuQg6hJg09Mv8+unAfc3mnj+vJQveA3hI6GkRWkCwZMCSUGsAWkVrP3oqWS5KHTj6WTMxADI+s2t3r0WQZ9Z2e/IOGwc0B6vjsPs+25XV/8bfp/ccJ7fqGumnSn7dUfmVJ83tL38JaAxaS3y9tteukMRrKh5tlOPbJwUKvuyN+kNHatboaACh0R6Y1TXnbQkQWoehWRv27FIdzYCdX9EOAA4I3It6+CVdTj0oqIAA6ENBDwgWhFo3wmPrT8CsM5aEpe1q27nSlpK2gxihzBIBmSnmVDWV5MsDQaslIBOUoRaUMejOiCr8RTuQ8mfTFmTgsnNVxj0ERPDEoMSIPDSXEdLZqNcljVUQEa9M1RJFJMY0Jhy1cQsmzqGQVyIi2xYJsvlRFO2Jj2sQhwCEGgzCUOIahF1A3EWEo6EEQLqyQ9wNoJ3stSCaBYGF2YzMbxIFlrDQICfQApgEGihEmeEHoCxFEICpIiSy+B0kyD8DhCmp5sA6zmz8b/pGdW65Di157UFw3/sXt+8Zuu9Z9x1jaD22Ef5MOxcAXTrIeSXA3Ekxaw+4Mq6ul7Eahxwk2FOdFkHxG9UeEz0tgq8Py10LCk29zNKB8Rg4rCWYhWTv3n+eh83y6+PvVCi4rr/z91gf9y4MGDQg6T676GA5LTR4Vr/MK9Wfrz/ri2SAMHtG0R5U5zMguGtX911vzQeuk7nPvTDGM+1ABwb8C7jWYVYHsCUoOA8QZHJv2siZY6rBKwMZI68d9r/ZNinrl9UkuT47D0ZW3WxCwM42v3cfrDVIZVBA0RJ4X4L5JkhczAWmLcgagU5LWWAqIiBGgHdy/ZBgvYsiIQUCDREpBISdLAlMRxOihToeJ0z7CZx4FuVQ9ksovggGCPYZgFND+bzpG6l/DQgsTxLoaB4NUYIfRdUQxCp8chgUDIaI4AIh5X0Whp3Ug70TofeOce6fzFMxKQCyx4I2GHGJkEjqEGjhjVPSQEXsxiKbKjvhuNZHgNJP8mGiTZCwmmaQP4fvdoM4akP4crNHQA8QbJfBFe2BhwMp3zx7S3XlN2NjLoKX/I+qMci0CADPxiFtkzzmhBoULHMH0xwQzg0EM42B6wXUCRlFWIYWCpX8uvbQtRkEbJkIXRgwqNdMz89XTN51THOk9bEnqkrt36WvkdgKCGYPl7QGBK6j+ec5DcH9wkA7g+6lFUbNPgX3BgULQLCksWgAoYKDe0xWu3nZJYkd3VYbVDAAGRd1vJ9pqqo6sevtt05DYEDg1emEFgpQtAK2+2Guw9yI7qFBvdeC7Ezo2tfMB0OM4og4RIRxAh/t8l4IzAnTfgSPE3iyyIa2H8J8TwSBBH2GgkHqAUFbNw4GAGRTRW8PhbyhUiDZfGmIoh2gUO2lENSsEHa66ZKGRKZhAEUBBdEgUFFb+PqzOuUk9ZyKrTyEoTSy+hyEIMGQLFomheI0aAGwbBZ/6P1a6iuV+QV5Spw1DffLCwffueY75TEOc0CAjW1L2oM5HJjPwVI6NB4ZKNw3JOjOlV6bYE/ZGmVwcYx239c2bavu1VH5z9q6BwxQ83dHfuU+uJEQLgwY+ELmZl14czrVDAZwcvsENz52A8++7SYenUFB/3FLJous7iHbUxwqwMoSGW6zsSTgeh2sOrYNBtZAYKn/22OauXg+92pBwWxwOqTGU/PBLJTphpTHeIha0n8Wga0Cn+1iC75qcexLNk0YWH34urHz1TbJOsAbEIgZQa6x3RGz4cdBAaWmLv2DWOtEZ8Q07IrGzIBxGIBxB4x7xGFE4KSrF9xmSBovAAoCvZ0TAYjJwBwaq1UDdZv5c9S8jBTD/FwIYmLQ870dGGGgYMeHKNs5xwF5C2cFAlCzffOwcxqVjhpXbdDMAZRSrkPTHNimRraNM0G0B377ZqA4x9k7l8cj945t6TO5+YDqHbNr0Hy+F1A413uXJz0OEBBgIYDvGQ7c+2Tp0NiUfQ7sfqbVY/NfcPtLSKbXIQFxBgry7M7D1yYiSzDQOefH7er2ndu6YpTbNS1+rPKrpylol54fShcGDHxH8g4uwDIgWDIoeGIDFCzVbwGEetczubYDCfmGfpCPsw53aGtgy8sWGMg29WqAWu4pFsHsPKrMNSgQVbgDAqBAQW9gWnL+uU8NQXvLepCWA2X5HZBsFzcic84G+x5QDe7k7jWHAUKBAP8ZXkvQQkGus0Zb4DMwDFmIiSMeqRp9UKhI4HEPMIOTmiOmCcyM4O3uqWyGVGzxEGc9FGHPjSNB+31LalcY2PdiXoh2Is/yTfORj6nAFnOB+AWASCDA/CuGnTSIHhMTw4CZKPVaAyKBhDTleqUQZVVJVgWQao9ElW77agRQHoirDYKycoK7Unxr3+nBxKuSDr17biYt+fN7BAhWk+16GGQFQA8ONsqmKhu9caq8t5QBP8OV8/s4BAkgdQMlBmcoKBO4uVmzqfSlMdtnqKMdWJIlW+snwz/6k9osE9xNU/P7pXRhwMD3m2oNLKEEMev9DlKpT37sBp552008fPVa1zza+1373R4x0yJ4SGhXHax5pFbn5yAAFBjwQt/W5QPz40v5bxNBru/BgQ1OPW2B3HwDFHg1ZgsFa2aEc1HK8rW2Cs3KA2ZnMihwoNbH8mK5AC+ze+Z2L8/wg3nxM3BLFUOpO1KgkrpKBQp4crOZqoCyzM7UFgMQYhQhHgdwSrKhUHL3AmQVQ3LCH6iEQiXkW2dGnzaqWTeltXZ1fgEVTNi7kYHC+RAQyWeFCLK/Mao93DYPa636UF8DEXaEmOEALFH2Agl4sbZhSpRnv8wFwtl/9+oHKiK1W9xz9KP8m7XXgujwC+9Td5bs+r3z66lL4rQHaVqAg7IWo9UaAFSPUQ0ULE7WnA9Q/o0DqKCN4CEhR2mkUne21NKDgty/BcgD2oL83V9Xawd8WdpyHZI/vq0ZwK2FSe2h1WZr6QKBgXYOJ8is0wGNRkErNrFU6pMfv4Fn3noTjzx4bfMLVAd2WUpO2dPahBZ/t0yU54GBRT+CjZ3Og86hRO4fwDMoKEInVT3zXFAAdOgby2aELRoFqgelFg6ysEaZJISFWvFHK3DCfFA37UBdZw4IzLySElqQmql6KWY/CzKvawlEMFNULkaNnKYyO19I/dgQr+p0dX73A6PYWr7r/JZ+w0ABgg4UFHOfqcQnENvvuKw+ChEW+S8Eyf2kWoQMCSgxNVoNXtwwc84TDadJ6MHlpmagAAtYVBXXtJj5moSuR2D+AffhwN43ZoED+Dp0cEA8U4FvST43W8YwVo2BlNHVn0IC6fFlSJD/K1CwRIdartyhzXsPDGZq/gO3z5CJIr8MCno+BC0UFPhaftDFAQP0Z7nZNm514GaKtz53gic/cQPPvOUmHr16bebs0rMDVg9EHzi8UK0F7LYXogsCemArDPR+ezAtaFcOaQv0I0zAyceeTwHyuXNDwSyvPlPmcxF00D//wOPNCBEFBJK7l89eZd+0Y/mcm63oia0+GEWzAsx8MJhVmNlA7kK6UsRa4CIAfRWnfhbBV8xW51qjne9zDvOOy+ti0J2mf/T6TKVibvrYauqqe6kzE5ZZcN7yl2TjsRLcJ2h7BwTVIii3S7AnfWctGFHOfl3SWfa39q/zpLxts9twKNfTGgzMbrQAB0CpV3bLhp0pwX5jJoU1rUEvS1vGs3xNfgf1fnw+SADa8a0Aw9bUg4Iqj+6Emz6VUwsyKNikVuXXI17T7e69BAWHWvrCgAGwDgdADQhWqR9+y008/NC12Tp+NPdpU75npxGAw34Nvbzn+zQNvCj0l47Pfn/4hc/q8gU46Myt1k0I+n3V0dB+syUtaQu8I+bab8nsonX+K60SofQbSJ1W89EGkPztg7umHwK6DwJF6DX1BNR1VUkNDwfzevHHy8zYjjl4ala4bNlIa60vWtqi9EFHo2LnzwtTpa7OUYd6rs6cq0MVdnmZXp4lEmjiXIdkv7N6U1AABd1CudQhUGDB6qxySFx5T7vblS/Wk5UlrUsA7Q8MnE9r4JPz98lbD/ccEinq+QWTgu/eyChRpXOPa4zi73FOSIDKDj+htLyiPnQoC/P8NyeWJqTluyuApuM7J3iXyq9HH5prunvv41YoAC4YGADLcACUyrp1u1TqI1evdVQs7n5L6lcnTCwtQQLQB4WZSqwhx16DLmoM3EvjX5gtnYAgv1lSk1fXUk3U9aDdGbCX0pZBpzcb7Qk8ADOB54XdSmpDtuZCodYWzO7SmVFUA/N5hBjQQIIHp4V6Ult6f2Y/rwsOQyX0E7Q/JclBSrLELn8G590lWbNhy/B0DK3Wo1suJ3NO7OTbB/RxVZghgTT7RM2yTlXXE4AQgl4bZWknip9GaOs9jQDLioolaKjqez4ia/ZSvlbqeyoAII1RQKGBLQ8KgDiaGhtYnyrvbv3+VRONWWXq8QOvLFPZhVDEYipagwyXfqti/7QN/dCfXwN0rXuRtbVJgbU8SysUeimHLD9w3QRd/OfreAESvE8CaZarZZD+Pu3E6Rzju+TfHe6CwPKxF+6c4N2ffAJPP6aagvmjZ/c+DxQAFxAMesn3WYMCq1Q5v73SrP1bbcKSZsJS1XidBx1S+bQ2yjyLcxe1L8uhF2zpPT7o7ORmKTkDrTBb0BacO0ONyne+LwSwBAWHkp+x2IEqHgbmL7y7tAsBAPrLM4GuYDoYt2GW6Q0bZQX1OwgRNtyL/Vt28pO9DeRvSjJ4muCX75KdcUqYwBgnOT/qDoojS/wC24Y6gXP8ghLfaF6eoO1VVicSAnTJJgEUCAMRIsm+CUTAEGX76SEGuQ7Im03J907ESF2KFochw0JeiaF+HL5v5tj5hNpckZu3fKdspy8BgAxSSdvH2sPaqwXV/C55WEDhAnvaDBxyPTZdYlbTnZS1TA0cuAisReZ5sxWQNQhbk/kW9JYxkrtGy09wgM5aN7zgDLzwmqy9Pgw/ORSq7WkSvE+CjXs+VoKtfjBNgh9hWvNFfnCTloBgq2P4C3dO8PWffAJPP/YsHrn6aHWftom2Dim9dOHAYKn7MgsUvPuTAgWPPnSt2xhLois018k9S8zyHiD4321RHS3FGVgDgkMw0NLnUoz1Q9qCrkMdPEl31tiv33A++ABzge4HX3++GYB7UMArkKDvdv5sqd3pj2bXuNlmYzbpAhHgjjcQcAgKWjMBOac5DwS6RI/DIFAA29CoDwIjcwUB48QYOWGcGPuJMaaEMTHGBPdZ/iUFhcRybWLGlOSf9ctpafRG0RYEks9R94bYRfk7RIGFIfh/AUNA/ryLct1AQcGhwMJAvdDShBAEEiiywIFqE8j2m9D+mGfO52gjUg2Cb7MMCw28tqBg15qfgt5EjjfdYC11L1EYYNgy6Q4c2LI8FzXUZvBFc9W8P713dSs4sNcazPPtYWBtKXXA8sqO3piXm1Oz6jUJwetKlAJMW+BNN4uQ4PN1AAaAw2P00u9euHOC93zyCXzosWfxcDOpXTJ5t8mPe2vpQoHBWtdcggJfSWsirbw2dfINI981L1R+10s9WjwPEMh13AWC8+xzQM2XZW1B3/ZbaQvWhGP34eQqrD/ILAKBfl60lbvvnZIuHOkd9+XbAAKtNoDr39d/F+qmrQvn6FaEii67C3EOBEln9xAh7WFgSrpR4pRwOqUMAqcTYz8JBJwpKJyNcn4/ub9jkuumhJGBcUwKGXMw8FEQLdqhB4MQVDswBAwk2xfvYsBu0L+R8t+jIWCggKMosLCLAVdiAYUrMWCIAZFY95gwSGBEFngYAukW1lJfSDrrTxNk85wJxeY+1e22tc1M9eyhzgFdq1XI5ocO0JL+3nvxn+/NNvA22771bYUDQhbUoj0o5/yzqu2wPMznR62NvB2tQccREZg7IhLXoJC1Bi5zWwSdHw8rAeq0CIFLzVheDBAssBXgZEB7E8wnhfN8LOcLTTmWoOCpx57FGwwKXA4Ob6xV0pY6uzBg4KukraA1TYGlJRWPv5WHA6eF6qYtsnkNCOx8PrdBS3AuIHCZXwqistmE4AXmhlQvkaLZuZLJ0FzT0RDY+VZL4H9/SBtS50D/NDBQCYkVp0oudeKv7957lpFGe2K9zeL157I6KIg7gKKYC0zoq6CeFAjGxCLEVbifJsZ+nDIMnKWEs5FxNiWcjgl39xNOR4GA03HC2cQ43U/yewODZJsopQywFiLZeq0Ph2DhBwi6YVKg0s80BLIJ/N0gmoAru4ijSLgyROyGgCtDwAO7iCtDwFEMOBoIRyFkSNgNEVf0HkMEBmIMTBgSkIKI/YHddtZhyA6EEqsgApjKtBJBTQ8b2tBMDG2fq4R80RbUGoXaFESm3ie354B+J3/vkpuFVMOBXG9wYmVwgMAMXT8BcxAEc73BW6VJaKpgg/ku56xyRLwHkwKAwEVr4OcYa6m3rF1KK8eD+iBUDthcRvslQNg6Y1/Ml/s8g4LbJ3jPDz6Bp77u2Wz+7k1U1+CgTFw1vwfyc3HAYKFCxFGjQIFPrUAGtnWuXjoPsa2ZMBahwGkJ/O/Ok9/zRFbzUEB6fub9vOBlv7j+3A0w3UGkqzVotAN2nfu8PQa5u/3s4W7At5enV65DMJCFSKM5ANYbSzUDi+vrc7nCDAomFC3ByM4XQOHgzAHB3XHCXiHg7phwNsm/nzsdcTomnO4nnO4n3N0XSBgnxjhOSJOAwKgbKCVm8JTESVH3Usiw25EdpZl0b4QQxHEwylI/iqI9CDEgRMIwRAyRMgw8sAu4sovybwj4168MAggx4IEh4Ghk3B0IDwwRVyYBhKMITEF2OYwsE2NWIcMBiBTBEQUONHJfH954Q3saIBjAhro/GihsggS/KVEQ+Gi0YL6XuGHCz2PrykeSIGs68899nKK0nfoTEKukNS1CFco9LNRP09DtYbag4gIEgi362W0gl+PMWP43wIGxnM3wt06SPBwQ5toDC28N7i+Dt9+1muPDz92WvxfuFCh4WB3lvTba56GXWmCq87ec1wsEBvNjfvWBaAru7xlrPPxqQ4Elb669Fyhol9UBpTvcFxQcUka1mctv7UItVvW3AAN23UbtwMahQf90NARbgKCaURaBMjMjrObGRiZnOW3qqfhLUDEfqKaghYLyDwIECgF3dbZvnz0Q/PzpiLsKBq+cisZgHCdMY8I0TkgjChykhDQl3fk56YZKsgkTJ9mhMOc7JfhohUQRFGQfBApRISBIkMIoeyUYFIRhjzhE3B0ChkFg4DVXBAweGAJOx5QB4WwSOHggBQGZIeABZjCLRoGZS8A69WIbIOGNA0VwMJuzDLOi1fIBgToguNau0ojF/8AAgM0fwUFCiAD5ZZEutHALCH4h34pGrM3VUsh2gQS/nwuplgCuLzeQYPc5BLpVhnh+rMlxu5dCedp2zUG5hRsHDgyUXTgwDYHTHCzFyPGCeesEcQ1erIWz+eDrik/B1uSzsFE0VenCgEErbgwKntE4BcA2olyqxPb+FXfdS80fSFuE2joN1tcBcyAA1qHAYvifZ4OkefID2VI91TP+e4UBX2e9+ltspYNQMGELENRCw7QOCnPTPOqcRe3Lq9R05lZfRK78QR0NAxCK+WANCgQCJtx1WoK744RXVCvw86cjXjkb8XNnE073Ca+cTRUQTHvGNE7iS7CXjZWmMSGNZ0jTHpxS/mubLzG7kMu+KCFkMIDtnBh3+W8YjhAH2TBp3E0YhoA4MOKOMA0Jo8LAa44STncB45TwmqMBiYEHdgGJo3w2xY3aiCXZEG5fCdktPUC2V2YJeSxxChigNH/Jcn85R/uqJzxlGLBIgZwF8SIgIPZn2aphWgLttv/Xs8oS0c8gIYf9ZWfmW4IEYA4Ki6mTP+6UB7W/AVCvvpgOwYHCdQsINt6vpS3D99qs/F6Tl0PtvV9yPgWPbISCViMMLE9mb90+WS3PhQEDX8ocEeqtEtHQUkL9nkunrETXLJ0HCHodbD5x3q7mmudF939HefH9M3O5mnycFwjs+7mgwJ7VLduKruWeYKAGgUNAYHWw+nL3oKAJSVyHLDZgmAMBMZdNhxwsmbC0GTSPOhha/H7NRzeTGRBKfH+G+BRMsJUHtpywaArk3xwKfu50ckAgWoJX9hOm/YTxbMI0cg0II2PaT0jTHtP+VGBgHB0YTEjTWLQFU+eN0h0ViSJCHFRjIGBAw4AQd0i7K/I3RaQpIU6MOAUMA5CGBD5iXV0hQndkLXuaD2Wk9vr8PpBso0ssDm4TUXZwg2kJDAaWekrT13lyTopNG/NYz+wpRoUEAil4ZEBAcYYkMu1GQA7JnL83cNDLYvO3PV6NHzBhYntM8DZIINs2/oDmoEnEXPf1DXCQ5ClINlvXfOTvakpYA4RcBwtjpL90SzyXxfJ1brwmF1rbP1CWJH5IoeCQbKplUn1udgGKfHwIDy0V4+KAgdHlye0TvFNjR1ekxTUAmID2cLCUfL2+cOdW/r38Xc9XX3DPn902fj7nJjZShgIHPrE+qz1+aEMWoA8F9eY++oSlmTUw1xb0ZlpNZd0rDPQGvrX46WYnPAwHrZ/EAhTYEjd/3AEB672qWXMlPKxYWtZp0t3+fCqaAva+BU5bIDsx64qAhPyXVXtQVhQUXwLxGyi+BOOYcKr+BNPeQKAPBQYE0/4UPI4zIOBxn8vc1RhMIZebeQci3aAoRISUwIP/7RXAnAFziqAg15wS5VUNp4SyLJHsn7R7JFmhEIkxMHLQpgQJyssBSElNCqY1IFHlCyio9mC2R3qtJcjlNR+ayc/mxZyS/RMbQMgmDiZxNEWyEACAxgMgJsy8+jupfTe2vBdAeSfylsTAPUCCz8j6u5+1HT04gEKdwi+RVn9Cd1+TEq10DgiAgwS7V1Pmqk464+XytfW5ntl2KbVywf+2xCm4tlk2VRtqtSfRl49/+M//ocX7XhgwIJRdEp91+1EDrWBFjq7lBTRQC5q2XYkor26Q73J8rdEsFRg5AAedfM3gAMjOMQdTp3MfAoJNWgKgCwWzwWEJBID7goHz7i1u9e9XVFSAwPWqiuxTUJkPtkFBBQRLDlo6KIr93dWF1YdPrn4KJFAeADU3mFRsSFwCiz2QMCapr6Qza4s7kJxmYT8lcSRkdSLUsjOzxgNi9SGYwLpFc/7M6lPgNAQZDpyNXiDA+RtMCRwBcJCqDQGUgjwn6N8UkCZCiC4/KSFw0DgKCftJHBZ9ueSflLnEYiCMUwINQefhYkeWBYqt1sAJX2ra0EsWbcvF9p7BYAEE0RJB+5FCAAXpXwYHbHtYyKycUPgke/Wb3PaPxbZ3pIKGZuIgh+4BEurbdVPWGnTgQHhpQXvgrEGV9gB9QICOmx4SDqaOMG2Fbx4rO+fsfL7HhkemprL86jl/akk+9fLVG/tb+biWLgwY2NaTNzuFrhxZOnAAYNYIbUMbFDzzlpt405//3V0qW0pVqGR7t3oAAN9J3IWuDLkcS8/ysrcpizcZ2PGtQABgvmwPWIYCTYswoN/nMQdqGKjgLs8A3P0XXvjq5VQK8I5Ei82WBzcrlzcdlDIXkDgABe3M2Tvi+a2Aq8wH1CsvqPpnA79lyX8Gi9CbUObaRVgW4bkUhIgqRbNvqqj+AUnU/nbvcY8Yd0iUgFEFXhAfA2qGRfMxAAAadgha/jDsxKQwDO45sXp+nb958uXy5YXWg/xj7Jp6C/6zCpdc1/D1bz4BTvjFmH0J662qO23ewGAFBzBBD1ROfaybE+UIhbpjYeuHoslekd6ma3q7biLf5G46XYf77kACM9qVDfYQdk/ujQ1LcGDPY1glUV970AEEAJWJQe5Vjls6pEXJecz1U8ZPYBkKtqjxu89s8nPty67l+1ZV19Tj1v1agFo+Xjefu5U8XRgwuPGxG7j5tmdx/aFH87F6iCv2Kg8HQA0I+bfu64vOkfFRbTRf+YeSfzn9hj0llygAgP55ArIdqo3O16a1jtxGMFwFAs3XGhAA/Rf/XoCgqxlwMFDGy/p5biwrx1ybJipwsJpm6tDU+VsG7dUlW72UF/P35hEErxFYNCOQhq5lqHMd11k6kAVTt8dAGAgIuu5/nBghEpgDotm8fe4CAbgiwj0EJCp+AZwSaNqD45DrKXVMCQYCcr/av4AcIIS4Q9zJXghxF9QJMSLuCGGQVQsSLVEcFQeqy7WWGK6Zcx0CYNnwiKyOF8wJ+bvvC6WS5HgIczhYzZSaCMgyZUK2+Zuv5w19efs7409VewM0kJAFoUGC1oNoLkyLkPVzuWz5nq1g83Dgf4OUIzUeNC/kPFIur/kgAMj7nfh6ODR+yu1qoetzdxAI/KRki4ygGlb86FBrE+Y36z27bivRFNzIUPAo5hQ4TxcGDG6+9Qdw/eojpZPZANRUZm/TnFaV4xvm1p3iyHjdoACHhXP7TEvLz+4DAlx+14Jo9NRXvgNXHajTeSoguA8NAeCg4JxA0GoH2oHNP3FVa5IzUkY2g4OEuuxVMjXxStnOnUIDAVkwOm1BFvpF+BcNQTnemhHqyYS2mX6P+m8kZLv7EAL2xNhFQtpFTIlxZSfX+yiFRLLMTmbPE0IISCkhDoQ0DUjTFV2muK9WIfgli0vJL1X0qxQECOZLF+MuIIaAOJDAgi5lvDIEXNG4BkOUvzmscgi5zESlLnz9sJCjfEZZegZvTiBGNid4rYGGD2bwXGvg4eDVTpbnBQO2h2l7d5aCqPVSgOtH3IEEqv117gUQenBQ8r8CCFnNMtcgZEuB0yLk6nKaBH/OzvfSkilgKxCcdwdGAHm/DEC5kt29HaC17dd7dhnPPRQ8i+tXH64nMyvD3BcVDIjozwH4OgD/iJl/sx77dgDfCOAf62V/mJk/deheb3zwYSBNLnBOAYTcifRMvUbWVX7Tii/cKY4arfql/etTW98ebGfPbhq93fYXKLHIFh/YHF6yOfV3AnQC/7w+BJ20yaGwAwSt+tMGtXZAq2Y37osnfB+drNSjwkF5TK6Lmd24/d5LvWso5GIvKRPIz5ozFLi9DlptgUU5dGaEfnZKL4sgjCTrqSMxhkg4YkJiwpXBD3UDYphkxk2E3ZiwH2VJ4DQETGPCMAkUpBGYUgJPCdNk/geDW6ShkRCtIXt9RdsoNJEPxZ9SAvzESKAoMBAGaFyDgDgExCjRD0sUxDrgkQVCGgJJ6OQoS27tOdG9JUtL2NjOkYUrjmBKtdYgDLJzIyIYU4GDKFCw2vbyYbFuVtPMrlLe4BaaWyjY8v5M+bP8DQ0keEAAvBCaA4J7G2EmhyU4yHnI480E2/rY7C4Wx0F8ENgBAmBjiWkRAtx44jQJ1TNQYGEpLY2pQN9cMPNJOHDPnI/mHKEGBa9RCP5HHmCasR1wUJAnzW7CVz15nr7YGoOnAfxZAD/QHP/vmPm/PtedTHjZV0edrROLpUrN05y+pY4a3mehvny5UqnzyR9huIb3jd6BBOCw5rBLuM3La4fuCwYOIPYSFBzSEvRmOe1gdmj3sdpHRD4ZCFR1p8/ZpPGx2V+erYRa4ufBDmXNehsZ118Ldx0RWM0HFRTIdFniFVgo5KxFkHuYT8Esu1reIQZMYFxh9w4oENhMehcJR2OScMK7iCv7SeIe7G0vhDr8sWgIFAhGiTVXVnNqFMQ1KPCZBEDBZvSSaVkFI6YCIhJzQQyzMMm7KECwy1oCBYQhYAgaKln3VjjKeyrIb4cY1I9gni3vZ1DqW2EgxHocTlMDBwkY3PI9W60wW6kz7wOwPlAucu/PmulpnrzpLZcLfZNC20Je45Sv9ZpG5goQTMFfz44t7529GZbgYAkScsY8JGgeDRIw5clGDxIqTYINvFTt/HBQObikPdgCA3U32y4vgsuy/bIChfnNZ2P8cQUFj87H+QPpiwoGzHxCRF/+Kt1MP/QAIeWXa8l5yae5o4YXjp3KzTjauTf5F6Q+vwoJcI89lOUD5HjPMDB7a1pV31qe/Kz3/EDQMyGsOQ35ss9MCPlYoaTy4hnUuB3o2HzVpxIBj/wP/f71ZbADGBgGF3O+Tpxn/A4IshBSCPAwkH0L6tgF87pGmWXIZAoDBWBwCvIg87ghJBxFwtkUcDQkvGbHsi/CUaw2SjKv/7NJtAD7KeWNkvadjZM8GKzF6aAGDKzdbOnhbgj5u/kQHCkQBD3vN1i6osDgN1gaQsBukE2UrkT7LNs2Wz2Rf0F8G0HGCItpYL4GOeCROkWCdZkhpxwZkM3As7UPSEXU/UAqycG2vWs1JJTj1dttWZu9SzOTwoZ3iRUG/E6CLSB4EwNp3fW1B26cWU02LpUxhqxT3yMkmPmjBBLbkI02LYyxzSn3uSMzfFqTGagnOZlpsJx1n5fj28e48bEnMhTMgGBDO3yxNQZL6fcS0TsB/BUAv5+Z/1l7ARF9E4BvAgD8G3CV32gKzPaV4aAOvVndE460zJHRpoCtbSzNI51VfSHrlKe68V2ntf99o9tMlsslq6nXKavO2ToRAjMYmIPAIaJcAYTWJn6PULC0V8SmXNFy1RVR6eCg0ghMZUZDOjfKW9KqYEhTKZ8TDMj37dSfG/TrHSNrGMiRDSmCzexg9ehvpx2GiBBEvwoEYHAqkcgREQlDIFm+GFiW7bVbKu9EwI+cNLxyEgCYUrWlcvb4Tylv5wyU47mOV6ROcFNM7ywon9V0QBaToGzNvNPZ/qD7Kwxk3/tbM1fbNseQfQ2GoBACKqsQMKve0m81zkBeUsi2rC5JnZv5khOAmFextMv3Zv3A9wUv5CstEVVtP9fIKSS426+9ufm9a3/USTkopAGC5q8F7dZ3Jxet0R5kOEDo18ss5813k6MGDAoGFjIaWIAEAF5j7EFBM7qZE9bHWpfJ3qQRqJZEtz+pkskW195W/0tsY8dXoaCZAK4Bwi9GMPguAH8CUvY/AeC/AfB/bS9i5u8F8L0AQF9KnJ1MKqHVgQMgv1DtWCBQ8ESxyWgDdVUwB9QxZf9zKUkXFBptgifCDUzQXNdv9N7OgHJ8AQa2UH2HcqvdDqtr+1BQlthtA4Klvdct5bXK5FTCjW9BznWRnQ4O7IvBAMMkApPt2O7gwOqSohMMlkRAzDaK8gIgD/yN9iBEAKFAQYhZeLR8RJpdYvV1SwUOAsQxnoaAHVvY5AROIW/JzBrrwD5P2iYJRRuQmPV7aRODAGuT1tcwrfSh0PQdM7vb0GfBiexcgAtYFAgB6i9AAAXbTjm4zxKsaCCBAygQyL0cFARXfy4/VsdEsrOhyrYMB+bnUfYPIOu4MkOunEyKxmC5P9Sag1l/yP2xhgTvX2DtUk1M0IHuBgoOvlNMc0AwbQF1tAdYgANuJlEUwFjYbO3g+DOVdxVBAd5Bgu5NMduhEmhAQXLoqmNzWtUKLIHAOVYwURpR/Cls0tKTGXUq5oNn+lBwDl+xX3RgwMz/0D4T0fcB+MHz/L5eAtODA6DH1ce3T3Dj40/i5lu0UlNjJ2wrs6cxAGrh6BrUg0LOx6I2IR/oPKDJRw8E2uO+QyyBwNaOS6EMiN3zjbYAZbDdCgVru0l2Hwk3L8mSfp7aDU7KpTYj0/o3rQEgEkHlPkFAINdpFTc+VnXIec13PUP0dVSbEooAsN0Tcz2G2C0UEYmDWNDVKkE0BzFIdMxEwMCCykMgMMdc32Apv8UzYGZMVtdajMlgWyu/7fG8ohk4b6JmXVdeRaCHYzZB2HnKbRmDDfgFAE3GBmg9kcKHCjlbsWDnOzkSKMuKRgcEQQSb9YPF7bYtcSr9oRS4LuBMO1AgMfcJ/d2aGUEevdIu53iv/FbEHhBa4M7f9Xczr3yibSaFrWNRnj3V0yivTSj1xRUkAKhAQf60E5pzjLs+3+iMv8352bleYs6yoZg3O5Dg0kx+3QcUAL8IwYCIvoSZf0q//p8A/I1NP3Q2m1487gwHJthcOr59Czc+8SSee+zDsqQjjfU9XUNaxS5WsNnB8nc44WCNs0GbsKWs6HTEQyaCbpkOQ8HiXusV2DRQ0GgLTOBvgYLewNVWeWX2R620XCxHAw75q1d75uVoqYYDzoQwMx8UEG3r5/9P3r8025Ycd57Y3yPW2vteoHoi06yRWfoAGmioJoDMmkhqkQSIkUxEAnwYqh/foKvM9BrqA2ggWRdEooEEqlsmM5IgoZ7IDPdmgt0fo5kXNdFEA1nz3nP2ivAeuHuER6yItde5mexqHYbZOXvv9YwVKyL8F+4eHgPVsW03GCj7HDCEZViGlqqCwOq7gYKUZYQFQnKFhFG5Ui2X/z9LfVMpo9YyCtffTitwuDaIpipuFA6YAJar2tLEZJPoKTaQAKCCQrngpH2dqRt9vWja1ggKxrfy6Wzb4u54WcXwGA7keG+Ht7z2/gb+RuNM3+2XSn9eTrAv900OwB4ULD/30hmtwARyTgnm0pdrHmeQoMnk16ff/Sk+/nBiPvD3/h+6xoCIfgHgnwH4HxPRbwH8HwH8MyL6n0GK5b8B8B896aJ+NFsKsoyTBuaDz/DJL/8YP//OnysUtJqCaWEeFC75PADVvDGAhF2eznTQBxWwze9YK3C8nvwgaaUsKvMOEnZmhAMoEBB4GhQcDTDOtOM+9SJcurRWc2NHFTiwSaM2onPOUb3auL2Ofa9iq7EZ+5HjDqqAERToZrHtKhAwo8SoOH6tnU7KFaAvyzA4fBQ1si/+M69jJ8wG3/Ngo+9iG7n7BKqZBq7pMt7AAYndgVWIEWfwgZ8O9+1Ojxln6GQd2Znq9o6o55XVbTrS6BcLG1S034GDrHBqx5S2VWwy9bmH5oQn9lFlaWzVEhdQGGkTGpNDPYZ82OvZAGiW7sDAqVldXSKzzRFhNHXTQ0KVXz+pcQoGg8Sn5uHf9qyEPxxs/vFXce1Ga9DctBbSqzef4/u//BOBgm98s6N+7gqxb3b3aLZWMMvFOMoX8D6agmHlewoMnDEdTH0GRsc6wSYbJlCAu+YDuK9H9bgvsi+zKppcsBvZEACmMkqUBumkY+k9B0slu3Q3tkO3zXf6/Wd7n7EAB2jqOe1xdTdDxVZgMkHXa5o4t/Vo1Hmf6Xh6LRNQhR+wN6/ocSMhKSr3CqL6ZMPPs5aPnkmaFQiJwRaa2JnvGGi2TVW3s/LxA4m+TPyz7gDh6ckWYvO3vgffBgeyEVM4YIVVg4PyKL25zkOCTzt/HbvnCfMCuRlEDSQEiCHMQ0IndMu9O8e/w3uOoUbuMRpQnkU3a1ehXotsES3A2sSrN6+r/OqDF3XprAnB0v/gTAlfeWq0BigvvEDB7/+ZQsEJO3xz3XsSy71E60D86b6SDMwbw+eww4cd8ahTek9yHGlcdp15p+b0+5ya05ekgcBRuucQ1WcRkHL16mO/guQ0+c6tXMepPd3CNQYI9kynacWVSwMAZd9YkPXf+zSLrEbN707wO1ujX06a8laFfLbRhh3XLRJlqyfasYBo2JiLpu0o6mEtFi2L4KZoAuIhSBLxkJa1mefPIaotWN9RmbHhzC7qtElOgHphan4KvQZmVtb9eyH/nyLUZaHqtwb+PuXaT217UlByuIOmmTPvUYUhlfxtSHiBg/JsdzVNku6Z6/pgYk3ZzbQGAOrUj0HfdnrE22pm70NCQKPd9WbgcuyddEo78MQReznGT5MODSC8evM5vv83P6rya3h+m7+npOcHBvdIj1kK9a//VAv1d7C3x58YXY+2FV8BDyF7QDA/CACdFmHySNNRx4F24F6jGlVQ75cxGtVNM+g74dpxAW2ndcbZ8GyWgCoER1Dgzx/ak6eP5EGnAsKpTmIIBcejWGAwku1/u8uONAHlDh4EVKgXCMipbs9ZRkZF+Cdg28Bpk2tsNyBtyJv85u0GMIPTDUhJjnOAwB4UtL75JYnLY8TOIdOEeJmBEUBxAWIExVXaybJKJ78sQFwA+x0XWaqaLBBUADS8cpnJESJoMMND6kFbT32xj8wYo3fh3wdQFxnaN5enCjh/v07bNKu4Nko3kuW6LHHpdjS/PRxYDn2+75rpDrQGYHXy5X72j/UTNv1xojXwzz7rs+6W3xgS6lkKCEVjoKPz5hnTWOvcZ/N9gOCoz2/22YCiahFev/kNvv+rH+Hnv/djkV+j608ze44Anw8YzNS3XSpQ4Ap1qDYFOhpsX6T/3U5RQgsIJR/Osu22n1bx9DDgt50FgrtaiXlvMPWeH4xq/GjGmxCekupITH8PRslPdTI7Ss29ynXNr+Aol/tr9J/7aWLunDODR64w068QSR4IdPRfhH1O8t3+ONV9t0dw2kQLcHsE7HvaAL9Nt+dN1kPIWwLf9Htm+c4SKhmALJ50pDUIohEAAIq6boIumERBvy+6fsISQcuqsLCA1gugv2lZd9uwXiDxH2SFRosiWf5yjSZpWoYeEO6uTNi9Lwv8Ux6vvDz9rYJaPqKrn33BHErI3VH3mlPREqDCgsUjaNeLMRFdAWGUv/visU3epMB92yWdpaAayZ3WABpAbCY8j+weferMt2NAgBO8fts8fPP9+88GZHM50m9rp0Gr+eDffIY//NV/iF/87r/Cx9/4ZjVhAYeDlyba5AnYeT5goGkHBN5700PBB9+S3Xeg4IzH/m6O8sBBb5/Re0iO+xXtnsngLBBYKqMTN0qZggLV/Y3ts8JBPxLutQXtvbEbxYzu3KgxD4BAjm2hoGgLUM/r89cDyaxLPCv86ytoO15/jE+Ne1lpx7RbIbJoCmzUXzQEWwcEm27bRCuwPQoUqPDn20MBAd4EBtLDI/ItIT/ekDb9vG3gLcl2BwlIGcyMdEtl+iIn+fSBjiywEUUqUxPjGuXdxNDAQFgjaImI64JwWRGXiHBZEdaIeL0IECyXCgXrFbReBBbWC2i5iDYhLBK6OCywMNPEUbYx6zaUkX7JLe/r6eF74/reqiVb3psHhzE02M8zMVnL7ZoUqNY/ryXo4aAswsZcRvE9IMjzzfuLHsZLnjqtgd9dlrS2E82k0Dv5TrWYKiCpPNi5QU5/HWcatcmVe0Ag9Db+uv0J9xsecx8KRvsNEF79m/8af/ir/wC/+L3/tPjEEZzMG8kdBxezVS5H6dmAwXCZX5d25gP0BeQF7wAKnkKOZ52DZnCwu9d/D1DQp94+3mkL5BhTDbZOc0cd7CiVjs3BwThP7Tl+0wgIRtv7TrlPs1w2NWUAASMAmMHB7D7WJe1ARjvdfZ+sUOtNAjmBkpgCwPp9u1UgeHwA3x7Am3yHAsH29h3Sw4b0eAPfErbHR+SHG9KjmBTy44Z0S8g3AYJ0YwGEBAUFlrUULFCFlVXmJtohdAGloIAQlijWgLKsckRYg6ykeFkQlgXxsiBcVyyXC2iNiJcV8bpgefkCtFzA6wV0uSosXIHLtQACLSs4ZiBHUFyqE50tTlW81VvB3Ji6Ju/XJw8EsHfILHVPwSH7fUCzqNeoXj5llN6vGtus2spcFmKbAYJpowDUeCCzdKcNAR0oaKEXbYQNHnpHxPI+BlqDmVPiveRBooMD0Qh0s62OtLzAyf56ks4+Q3cv4oxf/5v/Gn/4//qP8IvfVShweZs62vv7lmvnU+aRZwMGe1KqD//qi88qFPilmYFOzcNfHgomeWjHgqP9s3t9CSh4n3w684CYCw5MCLSfXmdgcM8DXDonx+seDgZp1GGONAG9hgB4v873KSDg9E3DUeYoDG1uxipthgLGIWgzUfOcZZaAcwA0KKB8Ey3B7VG0AQ9vi4aAH94Ct0fkh7dIjzekhw3buwcFgRu2t4+6/Yb0IBqC7SELJNwy0i0jb7l8Z2bRJGQpAx/5MHG7YFXQZwgBohkgQlwDwhoQllC+x8uC5SoahHiNiNcV8bJieXlBvKwI1xXpYUO8Lvr7pQDCdQOlDbTdgKuUD4FFewCnEQpByosCmHUVxeY9nvOHmb9H1uet1wQUVKEhztiBoMmjKj2b892mJvlTfBvyy7r7FVvJ6isB2W1nDwm2/6AN91Bwz1TXm8D2M4B4blIwTW5xXnTC+6mq/i8DB/4a7cMP8uFQ8KRdf5bfV7/9r/CH/+V/jF/8r/9v+Pjf/Z/vn/8QCty+UoaDfV16RmAwfsiiKbApie977ckLaEwI00KfQ8v0Xs2570HK0+tN8jEwHxxDQR9sZe9weEZbYDlgjEcgu1GYy+pXDQOWD0sje3M/ktxta86pgHDUL2RwLX4GJFqRAoIWTPTXaIa2NrXQzAe5aA52UPD4FvzuLXh7RH73Dtu7B2xvH5Efb9jePiA93LC9e0R6uOH2dkN6SPL3KN+3h4R0E0DYMnDLrOGVGRsLECRua6sXCIC8l0gCCMtjQoSsZ7AGwhKAeFkQ14DlqkBwWRQMNqwvBVyWFxfExxW8JaTHFcvLhCUzQjOzoiYCgFXraQ4gssWxCMwZTQwKruWcIRoPDwTek79/p40KXruLZhQuEriM7os2S9X9Ptxwr2Xw8f37+tv0NFQ3ZkYBELh7lgEpc7H/Z2vzDnibdVsG6d64s9cajBwRCxyMgor1cCBPWEvlDCCcGB0fp5N99zAfAzhw/mgmO3YmBSdvXv32v8L/9r/8j/Gv//3/Kz4yR8MzaZTPJ5TFMwKDgfngi890nqcGfwDqy3mqWqor1CcDwVMq6N3KHkrFosb25mxx/vwzRDnSEpT9HRSU+P0tFHSDqcNbFhuoJfes/ehjBAL+OK8xGNtv3bW637P8tmGZ90Dgf/vjTXB4IOpNCn3ygoCIxGtbh3VBy0pZoc0zs/vL5Y90BkJOG3B7qJqCx4cdFKS3D9jePmB7J6aD27sbtrcbtocN29sKBNujfjLjITFuzNiYcWMBhMzAjetiSsnKjrmsjxDLuyGsJM+2BsKagUW3XdMNyzvC8hixPEQs14T4GLFsC3jLWLYMJHWCTBnLy9qOFwDhBYDHWn8pyKyEsKyQBY+iKytXftTWhfIO9d1mbt/rU94poCN36Dt04AcbsVO7/kDjV6J1aKa6p+67ZakI9l3FsTy1kOCP9aDQcOiJdnqUfBYaOAiLar8whgPt26gZwQ8AYZaaQVBotrWLmp04/+x9mNu8NqP8O4DQQcHH3/j37vetwynR75eeDRhwaIPMvPq71/jkl3+ssaMNCpwQtbT73Qldf4+RY4e/DoAZDJyx65TTvEAvlWuQ9+akDg66+4/PafN6JiTr3nwwXzVxlIyh99q4fV6PQKDsn4DAPSAAykCqfLfkVaj3oGAkNNrz9tfvkzkZirxgBBZnvcw2thIzQh9Rc/9AWvg2xXC7gTdxRuRb9TFIDzfkh5s4FT6qH8EECkxz8LhlPKSMhyxA8KAaA4OBW67agsx1nQX/9JFszQIZja6B8JAFCNYAXAPjxoRrIFwfAJjPwiCtAFIIMrMhBuRASCSLKRFp3IO4gLcNFG/guABrQKMSmBYjF3OCf7858/n3qgLV6rq9V6BCgmhT6Hj9AQe9vfalB4L+e69hKHLJH6Ak0WsMdqBQrnHcp8xEUQbOrcIY5Ka70NPFSVHyLH2z9Vc4P9DrhefwmK4vf99+3P9oHBk90OwBgUmmJDZQMIOXkVnDHcdDGSVrK4x7RUnPBgya2QdfvJbY0X/wU3z84UeQVcHMflSdWXaesJ1wHa4PsBOelu5UotMOidU5pA2+Ydf3KjXX+L2mYNo8XWryOQACPeZ9oKBcd9Jr3svdIQjoAaci+929D5Uj/UjLNs6gIKvAGGkIvOCwc+5mpwgD+ZkJ5g8ncsx4kHYWz3qJ4l+SAbAGIlJISAoHnMGZwSxTDdNt01kGG9KmZgL1H0i3jLTJ37blAgFeS3BTILipwDQgyBMwzKpGjyTjvpRYZLXSYtBhdmAgZEbYMhBJPgMhrBkpZNCSEZYE2jbkW0RaNpm14J6PssRboGUFWMqjTO3U8hq9ksY8pO/E3u/w3d55r4AIRNL3aFoBqGkhO3VQUIv7bGnjzhWl+RxlZA/H1BzpQSGgA+IZKBw87iw1PHsGDiQH2JsWUProFhCAsrjZvTTr95p9X01f7oPYDSHBh2J2rfrVb/+2+BR89I3fcX4fY6jZB5lzUDDItyy49Ef4kD6Y5v35gIEWfVk6+Xuf4uMPPypaAj9vtjiwlFNJX1anlh+kV//mb+s5XQWaVp4n2bnqeVaxngIIAJw9bpyGZpCzQFDOmUPB7O79QGW3/wAEALTTEptr2PDthMPo4B2R84+YJQ8JPRT0I8leaNi1Uzf6jTaFj+GEgZ1jQkEEh7cxl+lfT6pXNXHiUxEKbdrhv+10Jh9nnmeaiGodRlvOGVzhcPB+D9+t0xqYQ2ABBK8ZQl2DgMp7H66+0Wbb5/YJdZ8AoMwIcHCsjcqAyKeRD0KfZu26XAO1jh/CAUVIpL/caQ94DwjNYA97e32fx2nfBxwCwfv05w5W2Gs1TPsHBzMOEursg4FPgc/rSFswggI3a8zLx3/5n//LadafDRhkAK+/eI0f/MUn+PR7n+KjDz+SHaVQ8h4OdvNnHRwM0qvf/lf4w1/9h3pdnft8VHE6uJitUNjaluAaub3kuq3krAeEphJW9dQoH00eexiw40eVrAMCYA4FR/DeawR8dmZagXaE5DpDvdGptc8p6Ajc2/1EvXw0i7w8q/cpcJqCGRQw2mWN+7QliRAXA+3goBcMzHWBmlZvbFAaSocPSLAggzuyOfwGQlH2hyAqeAoBIQaEIDME8k1mB+QtIIoaQGYhMOk0OBYDdhLn/kiiQUiQUb/JSG9OsHDEgaoD4hoICxEiEa6RsFL1NVgDYVkC4qKzFFYJhhRXyWeIARTEdBCCfK9FEoAQQSUiIun+rk678qvlbO+zfV/lHQ/e79G7BTB8v0CrGbKxMmsmiGgoaAm+PRy0g6M2AABITRmQAwVGdXZszA4lr9J2/WNb8KRZsrHNDA7k0hY9Ukp5umYJ4EwMcIMm3vexsz7wQNv7lfbppfzyGBQGkPCL3/sxPv7Gvze8bpNfy+thfy2ZYIh8/OQvPsHPvvcpvv3hR4eDoWcDBq++eI0f/sUn+Gn30DWKnVNVNTZ4v4Ie0NiC/PV/+7f4w1/9c/zid3+M/8X/83ut+mZQaWa2nVHiTg1G/ksPCegAoaFOoBmv94v7DPwfZgv87Bf3aRf28SOse1AwG9megYFGK+CmY06Xm/bbynUNlNqVybiAYPvOfcCYYSqjpipAzkBB6ppihHR2KbfCI5br6fxkdh20XZ9QBD1852DCT4WjhBsm6chDQFgXhG1DXCJ4WRCuGTGLBiFa9MLBCP0SCeEBCCQzERZmrATcWEBAfAzMpKDP6zLtHQ8FDET4B0IDBEsQH4PlGhGXgHiNWF8udXbCRWYrhMtSpi2GZZEgSEtEWBdIqGWS8tAQyaVMQF15QetCfa/lq75H0xZg8H7vvVsiQso8hAMZ9KpINDl30FUMnQ8VCsp6GMCdtuD6mWb54VQEStUotNqEGSToReYZt9MmcLB/NgUEYrDZ0ahqEAQA6rPR0LvSDZh2adw3D/tC/xvn+3W2+5a+2QaldkAFBQ8JAErwvXuaH+7rseXP9dmAlMorHTT38nGWng0Y/OAvPsFP/+BTfOuD/UMbCe/sWJ2/QT22Tb/+bY1NXTQR5uw4G4HsSHJWibjdzbkFBV+hRpVJ71m1HscKyJ25o8vvjDYtp0dAAMzrcrnlAAaAaiagZjPvR0Pud+0YmqHL/oa6YlqzfKnCoIeDxgbrBiT30tEzzwSHbYuDejGTDwYLvTmEVfAX4Uckkf+Shg5WfwNmBuWMeNmvYyCXCgjxERQJaSVsDzJa394lpMeE5RKxuumK5nQoDohUoOBI829TFQOgUGCA0E5XlFgGEcuLWGIaxOuC5bogvpBYBvHlBcvlIrENXl5KwCOsV9DlhURE1NDJsq5CW0Y72yxaCBi9l1E6ercGB8Pr3QGBJnXA7NtGv0gWcNQmWo2o9AdJ639yA4M6GidtG2WwpZAwmg65u93gmckdG7o+pF3rpAKCnEsoJgbJvG7nRrCWEfidNDT9jvrDcsx79O1uFxvAeNDp+vUmX02/Psh3ZxY5AwXf+uCjYVyVPj0bMPjpdz/Ftz/4aEqhUzho5scC3Ig50RR8/29+pEtbfqtWmKBFN7DjWDonV8j9B5r4/NoA/EI+u8oEVFA4dbtJxW+210rl69BZDcHRbY+AYKcd6Ds9AMXWWG58z7ZqV21XJzOzUQ8HcEYFGUf9w6URFNxL9sRqFBATAgUA6onPGcwS8jdcrshszlu5zAgJIYDiO1Gv6/oEcV2QHh6xLRHxRUJ+uGF73LA9LLh8PWN72JqgRvadMyNtWcwNOddFFwd5t3cvtxUbRFzUHNAFOApLwHJdQIvENFg0+mFYooDA9SLhki3o0fUCurwA1ivC9aWESNYIiOFyBZOEQkYIOoMp1M5UhV7GaRZsUgQN4eCrSsOeZQIFp1aHNRUATOaGKnyJAOI9JFBdV4LcJXwL9LMuRrctt3fb7dgZIKg+RbUYTosAoC5LnAFbChsVxk+lARwMzUzv1b9T+836aw86QPccqIPOfgDY58VrvO5AwX+mg+adVmySng0Y/M4HHw0p9Bwc2NGAVz3V4Eg/wccffntPj91LsDT7Pkq9EszyWCuS0yh4bQK5yuTlS+OvsDeLzDUbXz0MlGx4KDipIbjb4XVaA8A1LP+cRUNgK6kZILAKCAcHrjMyc4L2k/ocaufstAmiEp49u5xjENBrCUajyX5LX1OZnQNikA6ROYOsc3S+EOH6Up3apBwCEVhXKlyWR4TlLfJ2QXq8IT9csLzYsD0+greE5UEWT8pbkimOWwYnLiGRObPAAWd1aJS7minCTzW09RGC2hQokPg6kIAABSohkSmSQIKCgEQ/XEFLxHK5IF4FEuJF919fSghkFxa5QMH1JTgs4LgCumaCrJEQ6ndIefbvsJ+3MMK493m/dV+/oT2H3DG7uBxdGyGLfqk5P90+ZEeR9OScsAskUG4AwWvXLI89IGQctwv3GADcYlTsZkh0fYXcr65ieVfANjea94v3tQJtH98/0pk+vu/f5VbuOaxvB2pMh34AuLvwSMs7hoJ+0HzvvTwbMADqwzZOXLgPB3V7TRYc6dPv/hQfffjtgf0p7ipKaZZnak53w9YJp376EexUm+Af/tDJxu8fV/azMHAYzdDd8x4UnAeCXmswGQ3ZfdV8IL2TX4+dCyCgzIVWOODa6bHll+V5AnMTDz+AVN0qvV/TqSlM2KyD5PqkpXvxZWaC/itvxx0WIHZ88mYENn8EKpoCMINjB5tEoBck6vS4gJYFvF3BywW8PSJcXyLcHhB1YaW8pbJeQtKVFPMm6yUICNRFlOSPy0JKOeUGBkZgYN9DVD+AqLEH1IGwLKIUCGGRtRJojYhLLOsklFUXVzUbLEsFgmWRbesFWC8CBUEWVEJUQCCDgwiAdtMObeZJcJoAE3Jfxfv1Ar+uAupFBkqwK7/Rt5Wd+cC3ETclUx8IfSrtA4A5MIuAqSKMdB2JGSCQE0SA882hORwcj+YNrs5DgpThBBTqTff9IoB7fSMw7h8tfZl+ftfHe+FPtNcSD9LID8yysYMCVAfaM+nZgIFVOPOWvR8YpMIBgAIIgEDBJ7/84xIHYaQZ8GrHcSz8exmuX0vlr5krXsEj2pROpvFZ35NNSe2OuzAzgYG+Qff1azyakk+DgrFjIQMaqe8QCHoYODs9i6HC3t6z5ogkeEoxK5Bex0FL0MI3ICDt3c0jxbigWbTGCw/9vYTxdEjqvnih0QgG1LKT+8oMBeOEQKGYtigBHPWZk3YwYQFRFGG5vQC2R9D2Qpda3iQqoi63HNKGJW1AznVZ5swCBkliBDTLLpvtQPcBKEswD19JVBCnUDwSZYZEXXaZKKjWYBFosGWVQxC4iUtdftlgwLbZyooUHQQE1RhE3a6wQKHEX5BOsxoSvQOqgaE5wn8V77e+YwcNVEHBA0KvXXONsraVe+3kXhsBQMQDSLgDCCH6oUtTT+9pDnab3EGsQH4PElzx7kGhPON9GOnzxINt/1D9vP2s9WWsSRj6TwHHUPBh1RTs+/J5uTwfMID17dzCgatIvhhaOKjHSHCkP9I4CB8357H7ntyPmXrmnq3LOv5mTfeDBjAGhfbbKJ2p5EcgcAYyXXab5DUF9lX+6khnBwU5Y9fRedVo42NwJuUGEAocQJctRQaYdloDy7DVlcBcRosCDToHXT3OGVyXvGUUrUNR5c/Kx8qkERjUCI0+lSIgeQoKVYMFjiBKMiLWJZg5LEBOQEyyAqGWqwj+LKsxWiAk/c05y++0SbmnVOtFWZfBnAqcmno2VQ4KBOXhre2FOntAnx1RZhIIEMiSzIiL+kXEqgEJQVZQVKdCVuFfTQaxAkGIAJFqECLYNEM8qUpU4Q8gxH+g9xtAVfh3+z2oYqctcADdt5UeCO4Z/M1J10wFnADKO0AQbb3Tsqnkp8HgyWsOdrc9sc2mQNa6Xnrt9oQJKNTvx/1jf2///ayj9Zm+3vsrjZ6hgX/dWCGHDx5jDwXmaDiDgns957MBA2APB36HmRT6AvG064M/fKRTOny9aCKDde3tbIE3+eU699cIGRiMPjup60HhTMqDWn9kHmgaxonrD5R0Oy1daz4YQEFOcy2BV5cObKejt7rLmZoUinbAno6DG9Lob2TYanumNShAoA8jywnDJDOSdp5i7KkCxHIx6jca3ybyZoRWaMBtt6cloKrASepxCEuBAZTPrELbbNEbzIubmYE1oRU4NhWUa7S7lEp5c79IUfE2fA83TXOyCv49uZgEFEDRxwtRkKe6ngeDisAv+4LOQICsk2BTN5kMPqho/HyUxtJHmy+Jln8unbLAIQjv/X59EK+oUt/aRgi00xo1wm5nOkCtt5Z6KNi1l0lbUXAu1ENW+xxIUxaFgmkPTNFmoODgoJ/yO9MaPKV/8aDgBe0MFCwfT0lHfSWw7+/9bY9Ss8YE2mcwjYhdKHGtE7Xrn0dasSuPoGCUhzP5fVZg0KeZSWFUMBb84VM3z5PdeSOBultet7v3LO2EZnfSESSU/Nyr8N39jyr3DATuDcjtOcxS3ydvQqhQgFNQQHkbd3KjDm7nWNR3hq3DZQ2Hqw6HLEu+gtSrX305vNYgKCXkzAUOpHxE/Rw1rG2GdJrV/YDLNeblWIWFHVpW40NrVy2PzHIDDwesgEA2YyZWALCy5lKW/ewOexK0L37ivOYyMn+ws6lrEAyMI9Q1x7bvdDene+Cpbc24QD7X0VQ/4iOgrIIYQAqHdd37932/Dexp2wjFz6AFhJ22AO4d9O/PmxDKtrPtpd9eehqIuYoBygpcAs8EgHMawEFV45fy4zEAnO1rGo0qDMS0PDARsuhU93bwUXLHnoWB/rnO9vv2DCPQATtfDapwMOv2GTW4X4ECl7/3aaLPGgyapKXq4cAK2wr1Z9/7FN9SKDhachdACeJyzxY/Tu3rLVN2mvzu8Tf701zlL/sHd5qpu/qK/T5mEGbsIGd4LLoOrjMfHELBbtTTC/0u2fYCCC6Ddo/GBnmsNQBKkL8CBzKoYiSqauASFZHH7/Ze8sIC6GyPHTjUx1E46EYc9RokzzoAC58G1Wqa7u0/DAwFy9fx/ntFdja/DfByu3HUpsv1Se4idbbG5S/v1voSnGsAo3fbv9eov4vmiKTe9TB9qC3wx41K4qy3vmnWSgNXPMrbHg40DwUO6DiK6Cid6Tv9YKloCly/2ZsdGo2CG1xZep9+czSAekq/2edtlH9ANAaNFsHVmxEcDKHgS/L6PwowKCPaARy8/qKNmHgPCLxAfV+1kq9NPrSoBwQeHDvqMo8UuLN8HZkR+mP7VNoYjwO39J1ba/dzI5g7HtW7Dm4CBdMpWH3yFMMsKlJ3vF+bXjQKbScXdUSObO+JEFWtzEBZwtYgoTTmk52kP2o0q0Ou5R4HKB1zqT8H1z96/7u63bSBdl9/zlM0TD6NnmukOZH91HSOft90fQ2MWss8WdbLWNnu49rnV/luvYbAthfzQTiAaTu+h5lde7kD9sOpvbkCdflu11KgLuY4BWg3s0dMEfJpM3sarYG2m+E6Cyf6n33Y5QoK/fufmWkt3TN8/UP0ne6EJl+9RsTatC29XUzKut+DtZdfPRScMdPM0rMBg1nHOkuZgc++eI0f/mWNmNibDHog8AWdc93ur3kveVNGJe52BO4VeqNjz6RRZQbOw4x/Fl8RR8R6lGrnBlQNQYWAoTq07+BOQMH9jJzIde+D4Do5S6F5KVTqiWzWOuHudS+HByhzWM6uqjb3GQn7tl7PV4NMegKjhvtNYPE71M4qAWUaomzT73b/rhEkruGQy3NphTK9DRGVtRQokI6eJWwyBbHFlzUltGDsmJkznxVgM73tACL08JJit6M81aAePeUdj/IS3M7pVF5L3P2epZlRf3Qo5zkcwJlnDKjtqQjVDIdQgdrMcqfu3qY+x30/5PePhP/I7NAf+z55eUof+hStWW9OAFQ7qTfygNAHgerl171kvjNnyuHZgIGl2WirJC3cz97UQv22c9TotQRlFIVOXtn2vhKMKoXLh5866YWsn00xst17QBiloXB3z1KOe4/KbPtmKuDqMHVHWwBUEwLQmhDKPkbVJjwBhIY26V4iERrntenF5uGSSS8zWtPeXpzvRO4HZj2X+ndotxzV19nqjzlXUGAAW8pIYGxJOqAtM7ackQC3LSMzsLFENcwsZrQM1t9clqH2KwzmwbsLrkFGXSchkIRDDsHMMro9AAsFBAKWEEAELFGgYQkBSyC3jbDE4EbhrNcTe32BBZJln8mPxrAfuaN8P/dunvKOm1o30IJQs6u2hWYthEHiMiVX6+xO08h1RG/HnwXrci3zuDCQcCYFQm03zkeHMdYagLkZBfdp1B/122aDLN/uh1rY3VHd4/b39fueCgUTeTAceDlNgveNk+2uvrJCwV+p/LIw/ZPu0jOid6w9Ss8GDI4adC8CSqF+tw3+ALQvftTx+nQm5nSz3wQ+z4VsnzwcnLnVTLU79C8YXazvTybpjFZmpC0oGXufUVCXdsJ9EK2sOKSd7eVdfhs4ANCHhAVc5zQYpRylPjez99q/I81J+z61btoU2gQd3TM3MLBlxsYZW2JsLGCwsYDBLWUFA/+XcdN9KTNuibGlhKRAcEsSy2BjhQ+nSZilsspiIIRAWEi+r1GiMsYALDFijaIhWGPAGqnAgP9bY8ASZYXGJQYsCgoLBSy6VDXp7BGz16uHJqISna8WDdi6lzRrq2ff4dE5NPzu2sMJKBjfyK5mMODgAAA4H8PxvTTSrvl4IE/UGtwVWIN+aSRcPSCMzLTAfrB1lJ5S6qMZDcPUPUvvFM89CMhG2IO/evMaf+zl1+ReHryc0sf13fO384zAoP09q/INFHz4NCgYkuH5Qe0/eLoHBU8m20FqOjLCe2sLaibPNr2uY7uXu8ZDnep5VKe6TWHBqW2qehRNXv18/KeqKO+nWmrWYZRFa7pRV8my/ksQAZ3YRvcCBCb4HxQGbpsI/secsSXgMWVsOeNhy3jc5PO2ZTxsCY+JsW0Zt1RBISs8sJoc5BPle8lX5l3Uw6L2t+8q6IMDgTUGLEvAJRKuS8S6BFyXgIt+LiHgEgOWCFyC/F4XwrIRrpErOAQSJiUWk0aQGSRM9b3N6u9+BF9K+h8m7doG9jMNRonkwVScAH19tSh6s9kI44uezLT6GhTJ8x5aAz+ixYkS3isGdsLVAKFM8a1NejqL6t9KUnnv+2NpyxVcS36Z8fmbz/Anv/wEP/nup/imaQoOUu9keXZ89GzA4MyL/jJQYOk0FfbJEyvtNg09z59Sec9oCsqx7wEFTeTIO7VrH6lt70g4WjVsl0wHRn7Uc3R8O4VtCAR2nB9RTQFBzQhl9Oa0FDx2X5qqZ0/ZheXadg9bMdE612LCyG1naiF8s2oKMioU3FLGgwr0LWc8qGB/zBmPG+MxCQS8uyU8bBkPt4SHW8K7m4eDjJQycspIW5bFkjbVUiR5tylxAwWjelfquAJBjKLJiRoeWdY4CohLQIgBMQoEGBS8WAOua5S/JeCFfl4i47JkXHLAGgOYGUsISMy4RtkGLT9Z+pghAa0Yva1/7PhX6+9uue8T77NP90bquzo0uxcR4NY2MDgQq4kfwXeA8NT8n9EOmq9BE6Wv1Rr0sQ32j+PiR9w5Vu6BIRw0h3xJOOiHBKVLcnk1mGnyfIpwxs/h8+7h4G/fvMaf/vIH+PPvVJ+Cp0DOkU9Nn54NGNyTGY1NZuKosQv766Bg947PvvQBEPjN03UFDi41unWvNnqCeX5fiXvtiwOZkVd1P9pqL9MCwamMkX+akouD42n//QwQlP0HlafrQE/N5T/wHh8+v7s/kznYmQkk6AqKEgWNLetZjpVQvfWFs5oPMlBNAgw85IybgsC7LeMxyd9/+7AVIPhvH5OCgfy+bQIC6ZaQNkbKurJiFkjICfKZWcIns66fYMtcu2iIFrSISKIYkkY1DLpuggQoDAIGa0AMAXEhPK4R0eBgjbiuCdc14uuXiIct47oEfP26YMsBWwReMMAcsC4ZFCJiZhAxlkigDITgtEFExXHRoKD4G7AsSvS0JY3379PeqSsINDHwnmri6q4pebW6nIvwnwJCkWyTMLuj5+jb1GHGDAiqwt4v2Qw61hqQ5m8oaOVyLn/3szN6HODpfawHBA8HABqfnxK90Av7M31xBwf20+DgN799jR/98gf48+/8DN/64Nvz67xfddqlZwMGRzb71857856jxml5eo8ITwLBrLIevV+/z2dhJE5HtrshjQ9u2Of7DBTMtAVnHJ2KA5XXEvSd0yx1o5sGBsr2/hjanz/N3Anh3zhpdeedHKGRz6tCCwUJDywa2yCOdEGcCS0M/O6dsjgMbpnFlyBxgYHHlPH2lvDulvD2ccPfP4qG4O2jbHt4TEhbxnbbkG6MtCVsm0EBI90ScrrJ33ZDtrUVckZON33cvUaFNNJhiCtsyecQF4RlRYjyF9eI8ECIq5gS4i0jroS0LrhtGbcUsSUxl9xSRr7INdMaSzlIEQZslBERQCFj8atLoYddDwUCALJaYcYwIucT3qfkxdfdJ9TBWZ1v/ARE6yJ10BbeQZFe7BwOqcRhcM9wFkxGoH10+GSGwhmTQg8HwAAQBmkkA0bazTMj59E2due28Unacn0vQDh4rt+8eY0f/fUP8Gff+Rm+eTT7YDKge597Ph8wcN99+ds8z59pmOMzU0lOO5zcKfgzJoMZDJxx7mum69g90VKtXas/tq80M4fIEcicgYKdtuBe0g6selcDjepzd7wfZbtS3Kk/Jx3xMJreIHUmkHJKERZAqTHNqDK35/e/R88y8IMonaSuoMhhAenCNYFMw8C7orafCVJPmMVhsDoVmgkhFyj4+4cNty1juyXcDA4eZZXFtAkQpNsDcroh3R7A24acbuB0Q8667gKnoikYagxCEK3BsiKEAFIgoGVBXK9ItxVxvSLniJwyYmIsHJHzhpVF+GfXiGVGQ1ZHRcKWCUuo/g4JwIpxTRSZ6tYpAAsIaIAtgYNU3utuzY4nvE/A6mmCX/SG2L1z3/NYeOLmel09de3DZgU0kID2fOYKFJLiE3wY4J4BOKU9qGehX2jpLBwA79dfSbbJfdd7nzh2/hiu3vm8lFNpf9wIEJ6QfvPmNf753/wQP/7Oz/A7J6Yk+nu6HA1/H2Xn2YBB/8CMNqKhrX1QRlZ60ExpPYOD6ZS9vs367xMYuFeJ7yd3oG9Euim7etqrvcppXf53FakDAjvmFBT0U62GHU+ABEvh0tk0R83K4myY3Omx3XafDiLEtdqBXsXc/oaHh3LNGSy5vBIV/wJZB0CiQ3IIVWUZFhEG2n+yqsPlnbfvOEH8D8ofy9TCnDO2pE6Fm2gX0pZlueXESLe8g4J0e4d8exRtwe0BKd3KQkw53URzcMeUgBAQ0oqsCyDFuCLwVc5dTdNwhQi3pMKbkDb5ToFw2zKWSNgSIeeMlEPxs7C/BMLalq6aDeR6ETJbIVg9NijISRby4tRqD+x9PuVdyk3lnRoUkJsR4JYBL+BOhCbAUHPpXqtgJ1X7fqMhVFMCmSahmBEU3jh0x95Ju7Y0yKO2DamrpjXo4oEcwAGozr6p13OXxx3hdxIIntrn+lkOUpTyoyAY2aCsHmdfIu1ry2j6pU8FCn7/Z/jWBx8NzR9lmz3noB8n7LfdS88GDGRlsFp0r7/4rKx9YFBQOtUODnzqN41G28A5EJDvenx33Jd1NgROqLO64zwglNQ1urN5H0HBk1MZFSgclDTqEGm8fwYBAyg4Oz2r3MnbZDGHgmH8hS4o006DMHm+MmMCBKiWQFYJVH7S/DERAkUpOnWkC3pMCEDKEhgoghEBjKL0jVZHLj4KbM6EAGeupgNddTFvoiVAyg0U5HRrgKAWpXb1ISNgRcYNASsoZWTKwHYDUUROGyjcZCnmGMCZ1LmSQXk8pW30HAEq+OEWK1K3DfskomJCAO+hwH6LuqVbbOop79KkBoUCAqSfrLktkQR1xF/rc1uPp3W4205OKwB2a2TIy8AOFoAGGKbPd2AWeUoizOGgXHkkaAf5GkdgbXJY7umPf6++V8+xfFl2DBSOIMHDBDAfZBJaKPDmg+EpEyjYA0G996u/+/W8/uJZgQHDlg199cVrfPKXf4RPv/czfDyY0tHAAeBGWm3HYzJhNNq+BwJ2XX/s1IzQve0j21Azd9ceBtips3wl9PcZ+SDM0gwIyj4HBW25dNqCo+ThoH2E/XGa7kEA99v9sbPk8snQmQjOPlt3TqAgV5VzI0CaztjVLVfPZIQoD07EAGzJZMUANo1BVlEPEIlECxRlNKJaA+nauTjVmQAkYpnfT0AgUb3HAIQgUQdDsLopwjLZ+QE6i0BWPaSg0+JClGWay+uJYOS6MiImGgOvOXDJ/A/sHnI/vb++x9B8r/mOoQZMkmcM7rn1mSA1LPbaAgDEsjR1qzGQ3w0QmN+Ee5e79wjs3mUxDcm0lgoIIaJfvbCZ+lcLR68fTtdpRnR1t5ocpD3OYAHVD6E+yHG6AwWtr0GrNQD2cFAEadd3+UHQ3VlR/v6Dc76K/recq03UQ8AMEuCOsduOQLeHgl118AOzLs/7vtgyifKOX/3da3zyVz/Eh/hw+pzPBgxMAP36i9/gk7/6IT79g5/i4w++hdKAQY3jZ4EDYFf/Ca2tHqiA8Js3r+WYA+o8CwJH6rBZsvCylmWrcAUSGuG/hwRneZymEfT0QGD5t4rYVMLB9MQ+1VXqeNi57GYKHEHATjswYuR5Eib0JZMrHMzSUFMwgALdRsyyfLGeu8uXOarFKH4DSTUFFMoLJdgrDmDcJN8xIAZCBiFmRg42WmYsIFw5YGNgYUZiwsLAkgLWyLiuC1IGHhNjTRmXJWg8giiBkTIjcgTnhLiubXY3Lf8QgCQqihxCMysBXnPQzUoIcZW8xxUUxa/A/AzickVcVxH6S0SIhLgExEVmKFwWDXq0BFyXiOu6aMwCiWuwRNK/gKsGQTIIsmBHBhQE9SVIN9EU9FDASd6lAsKT3mPpcdzyxQU262fdN4+h2K4UabXhTv3W61H5B3AnJCq8z2GhXG4ywpzO6vHmEAPqIzjQB/KA4IXs3ecFhn2XfNd7DQ6cmVFHyYf3NqE/ylh22gSglt0RKFgq5gM3qA2z/NERFOzf9asvPsMnf/VD/Pw7P8G/+Nf/u+lzPhswAGe8evM5Pvnln+Dn3/kJPvrg201lhJs2s3uXhN0iHwYHljIBn6t3KNBWkKcIUn98c97Zx3THMzrbFalTjh14QKrAnlZ7Eh81plOVsBmdDJIbiU8BwGdgpglwTlz2JP0nMJ4PPZwpYl2WTpfkooC3jnwQu6CBIAcFnSBhe14Tln3ZOE9zUBDBkllG4kap8HCwgknMZyEsWALALMIu68A0g8Ex4JrFP4EDI4eAvFi5MFKOw/DFgMjy26P4O4SYEGIA3RaEuCKnG2hZwNuGWEwIYovPHg78I1JECKJepxDL7AQDghBXhOWCZY2Ii0BBXAPWi0DBskZcLxFfu0S8vC74+kViGrxYA16uES+WgEsIuATCNQRcg8CBaAdEwyCaBcJiJoSiKchAvu2hoMBBfY/H71C0Jry596iagmpV6IXlAAi66aoVCury0cD71HOr47EAAxvMTzQLltcaBvygXY82q1Nx2x+XnEhfYv2YVfDBSLw+w/m+Czg3IPsyfbG/XxMqXdNMm1AOo3ocAPz4O3ufAj/Y9GZcv/9ef/z6717hk1/+MX7+nT/Hxx988/DZng0YvPriM3z/r/8UP//9P5OHto7YqbFGcDAzKcjJ9e3+7Zs6j/Q7/8W//yStQF/5huPZp0xn06tUIVGvFNxNGkiwbf5yE9JvmvcAaobP0dld7waCGTpWPQ0EfOc4W1Z3lkrZ6IO0nYV1wg4OOMlIfmaX6x0QgTq6NGFiZTGwwQPiHMCpmjBoWcWFTjXMDRxQANnAFUAMC7JWYO9PhsR4sc5GoouGIVZhqSPpNQa8jQHbFhBCwrZkpC0g3TKWS0C6ReT8Amn7Ojht4nfACXm7tYJzpDFQM0FYVgGFuIDiIkGNgkxTLLEMFpmyuFwiliXi5SXiugZ8/RLx8rLga9dFIEGhoP7p7zViIWhIZf0jNT0AQN5A6QZKW4WAGRSog+VT3iEpS5oWCOxW9XTOgruq5Or7DApKnb9T33emR2DQN0mMjNr1MbhMbdT35tvz0Wyh3cNoWfRwoM9HTY+G6lg4GOT45zxjAu2ftdv0pfvjNvppO0BpQOFAm2CgANQ1Nz7+4KNGRsn1qpzpTQj3oIA4i3z85R+LfPzGN+++v2cDBt//6z/Fz3/vx2I+8Gorb+MCSmXcwQEAcIUD71vw2Rc14pTFQTCb7IxGpwIU2AlRAIdq6120Pddx+FFArw0p2gRrVNw1moM082699yynI8Md2VFtv1Obernfg4DdqfoGOrVdf1v3vQ0SgwII8pykDT/rPmo/R6l5dnabXVlk0yYMUk6QSD96ie0mZbBgDAcRING7gomwqDPibsyTGC/XWP0FQpJ1CoKtOUB4sQb8tw8R13WTyIeXiL9/THhYJdCRRT3ctiwzFlJGToycV3VO3IdEbqYVmg+DC4kcoswwCCGIuSAGkJoILPqhBTb62iWWyIcvLwu+ftXohwoBFipZfhNeRPG98FCwqvlgIcCcC4vwT7chFGCrgZskrsHB+6sPW947RavD7ITqHApq23afFMAOCuoiWfs6r3faXxbOYY7lsqU/6EBBwNhAQdsSW6do/jfYt/F70FzyVzwHNHP7fsz6L6COwt+n//L37fsyy43P486x8bBfdscWwd1qQwx0GBhqE4CB2YF6Y0u9RT+YGUJBZyZqoOCDb02f1adnAwa/+N0f4+NvfLOobOE0BQUOiCrBOmFDGMMBIFBgsam//WFV78xMBFNyA46F54GHc1M5VR/JZTiyhwT732sTev+E3a0mv6eN6N4zDZ6l/V2b+d5XoJbgbIRkgsiO6wHhXjYC1yVP2TXG+j61UyaunSTroqjEpS5VYAv75/fl0o8y+4wSVaET6uUl3OxAc0At4CKigQMvW7bMAEUsxFgIuIaAdyHhsgRcNsLjJvDwsC0CBLcN724yndGHR7Y1F7KBQcrgzLKyYq7v42h1RdIeLgYFgyhgEHRtgzW2YZCXaOGQRUMgYZADLgvhxRJxCfK5RvlcImENwKIOimusmoKFIM6GpinIW519MNIUzKBg9O4s5axGYasPHQQ05rLgPt33oi1oNQUW6vq96742oMAtLJDvKHpABgRktM6zagya1sx53777dA8Q9NkLnKDtw3b2/eHzjX+32584qJkUKlHXL8PBwgAUvDbBBmwjrcisGEdQ4DbhLhR843cgps772p5nAwYff+N3AKCd6jPSHHRwANR3UuAAKEtb/rFfW8G9CYupDhzAgK9wI0o7cO5pPZztgAoD1Wt+DAk1P+QaYpvfUdrvOwEDg2c57DhdHkdAMNQOcAsDdswIECwNlzZFGQQJDBiFs9SCAofBPAt0RI4AolzNCeppTg0UjL97f7OxCtqV2QAOkJLaqnMLB9RpipiBuGINC4JqEwKALevURWJsBCxxwZYYL5aAh8y4bqGssPiYM/6d64LHtGLLjHe3VBZOerilsoDSo1tMKTMkfLJBgWkMBuVvmoNIhHUJusSywMAl1oWUrmssCyq9WCOWQAUGLiGUlRVfLBHX4FdaRGMyWIKYSpbefJDTARTk6lNwBgr6VPwoAoYQ0H33bcAiGdZtUidZq07fBp5c/wdAEJhL+yqjVa1U7ahUTA6GIgUQSl90sg/oAKH0YZLrpoz6Psxl7TANQcDdewoDE8dLObbrn5u+Wa+Hth/2oDCDBLlevVTwkDZ4qN48/RRNwT5K6zg9GzAAUDrWuqCHqeAHcCA7hnAAAK/f1LUVPv6nH5VblFFl8/sEDBQbtK+k85fTHFdqT6oV8iwkAB0onExfFgRmaWoymGsIjjrDAgieJgD0/uJC5k61AyCAZRDkOsgAXe6UqwpQOkMLTsM6crLvOlJSsKhhnalp7E0KUYRMOc/KZnICZ+GMhbRxq+DItYNluDrDWYIGBcKmPgQpM2IWQZmYsQXgGiNeMnBbgqy8qOGTRSsAbHm8FLMtzpRUU1ACJrEETQLGsQVMqx5CcL4N0KmTtvQyhkssyzYoAKhjoZ4jswyqhiBSBQKZwii4aeaC6lNgUCCit7bZ6hsyTLP3Fo58BpzA90PDUv+dL0HxK4itpgBy28y8bxNPqf9aaQI6INA2UNqEXY96YSR5K4CArn+Zlduuj6jH+T5Myuy/h35sAgNn+ujdqNuZFaqPCFetwh1I8CxgPhaA839rX135bOSP5reBgu/8ufoUnIcC4LmBQZ9smOa0BLJdxYtzSPTJwihbcCRLZ1Q3UxhoXsZ9G4+c6LUG5gSko8qzkABg55swS0f2tXtmj74zmHgoA2MoeB8gaE0L7bLYo8FKgnUMNklMkkYGUCDgZl9Vx6tJgbKEJ84JBQTkgsUWJTCh739ZgG1zWuVuZNXbqyfCRfKtnuHMoLxVzQH7OAcZyAIeFCLWuIKJkANhzYTEwMZ+aWZGXiISM5Ibuhqj9wABAABJREFUkW6pQkGC/GbW31qdM7hoDMx0YAJppBixEAcmoAJR0RgENX2YUCeqMwoKHMRQNDwxiNYhkFzHpiAu5btN8XJAYH4Dfkoic4UElgpFzBjU8PpuciewbLvTEBRtwbIAxSRlQj+UY6tzIWmUyxoOu4ECK/MOCpIDgnttwNvEiYFCrjSG5CNAkD9rvzIgYvM/mKXdvq6PcCAwBYXyAO/Zl92DgVE/PbjePtUhY7kW8TEkNOYGZ8pEFfVx8JjUfLrn0PuOoOCp6fmBAfO+0rht46kzdbbCZ19IGOU5FNiLSC2BuhdT6KxUpjNqKtnWRDYzoQ8AXkVlnsLlsGIAae5HHoZc1u+mI23A2Urm792ZOMq2O1CQ8zEQ+I7Qq1Mt+VIpbVXt8kxcAEG8Bqq2gNgYXgL9RLJOkGStgrypSUG0BhaVUHpbDUtr2xQOiBkoMxRQASF0naNPM7hS4C1wwK4z5QymBOIogJBuoBBBYUEMESsk5kFigcoCB768M5BX0u9eEKG8Dx1sShl1Sy2ng4pmy+mYY280p0T9Z+NpG0wHiMAneeJiwhOYqFAAqgGLpHK4YEUq/OV7rkBgU0oNCqYagtC5gg/eSwcEZSaCQYEuhCW2+m4bQjUhhAVmQmDI+wHX+t1oDbp2UGVbLf9RG7DyN1AokHAACAgkU7rRj15bOChlYT4HB33eeFstyx0owO/bnzpNo0HNFAgGg7adyXfSV8tO/ZT2VdfCUEhQwCHKFQztOZ1GxGsQRmkGBa//7lULBbuyOFdwzw8MjkjSmxQ6MwPATRjlfcTEtuNooqANtQMzMj2mUOrVZz6vxTnJrj0BhEnjupt2Q4y+QZyrVEfLGJ+ZfpU8BOBcR2g5HWVRovjJ91AOkI4wgxFYNASm1EsERJZY+xHSZzKwMykU4Q8FASuzLNcXbpDOkZGBhSog2LGW726I7SMIDgHBwwEFKTyz+ZqfgpVziCDcRPAQIVJA1OBJa/DvYu/5DvcOrKy7LnRY7qOIbmfmmpM7rvh/6I59QC0ugG7BiVoozxUGrI0WwrFj2/bbZjC4og+n3pEBAZvALBoAhQDTFDhQYNMuDEwIvj0kOEAYtIXi+Dl4J14vRaYZ4z0oe0AwUA5QaCSLrDmHg3uBwU71IaYNBUodbPqz8hAn0kTA3wWCUX/dXY9GGowy8LQ8m8kQdSBRNL1cAEE0vHZv6WPGcxNKRnbP1EBBmZ3Xpd4MNknPCwzOVJaRvwFrcKS/+IGGUf42Gl7rzQW2bQYEs8p1RM9NakfWhVD7/TNAMNPJ6HqzdAYCTpo+igNoozUY56EXRCKEKhSUUeykI+w7wVkWTWlkkBBMK+DgwNT0QP2eSyPW0SyFCgEZTlugWFHmfxPAAVwEkAICWVnYaE0/gRpRzxdprKaFIXCxzo0HXHlXDU0RxgoJZN9hkNZpdQDU+tXeb3j/mVbjTL9t7+qOo5ccM8CRRkM3aGO+UpR7+DabhxWGiVy5x/Je6tTD2XtxSxI7k0DVEhBKNMsgK1gUKAixwEKp+1178FqzWVu41w58qqYzyXsPyoDTFuhFM+01B8NX7Z0Rn+hfNbymhwXJ2rzuNedN+rWnAMEZjakDmDK4G0ECTNOoq2kqIJgjcb1fpyE5ejbmPRQcpRNw8HzAwFWaWQc6BAdmvHrzGT75Sw2jbJqCmdNK50Mw1BDMOqruevM0EOoKCDU4SnDPJKPHkk//eDtI2KfzEDC6jlenTcq45GWvLQAGPgXovh9AQd/OZyVrMt/aRO7gwPIRHBB49avKfgA0hgMmfQUWEMlGqAoE6tSmdoQKe75+LH0nwOAjCevqQZ0dMX/X/QI8/ZWP7cOjOnIPcJ+QRh3gvbp0dLmnPEtzrEFT1cMVPwF/CrCDqnZJ4qoxqE6FzlRAsaqWGyigps637aF1NHzfttDv6NuCh4Pg2wt825E8WayUp2gN9nVpZFLYv9+7sDBJcyC5AwVfpu9WGNhBAnQkwWgBwUVWrbF33PWAWt+6/Lz64jONaPiTGtFwBry2/U65PRswOAytCwwLgjjj12/c2goffnQOCABnStBm22gP9loDTCvnII9ewHYQcBcOuvPPqv9PNdZhsuNO0Lu/nfuzq/SdHLse7h4UjHLvc6Td3pRdrDOEewUgvZeOkszfYAcHrGG0s9yJy6pGg/UU9OI13sa+8xl2qkfvsamz7pqo759TqqYOALuwvjmDfZ3W75xz9SRkH9VwP3WPe0fKg0TeybLYeepCSqX96qJKZb8/NnhBjHqcU+sDfd8w6CemWo99RWmApHyvGhrnrVphwJsJYICw9zUwKDC/AoMBoHv9XJ08d/mbQIFvD2XfASj79sC63fvfFFDwxTXM0VE66mNOwIIXdGfSVwkFp+7b9o1VswetDzKqkHbpzSe5+Iex194NzBp+7YOPP/x2PcZrBRxQnAEp4BmBwaHKZVIYRlqffven+LisreBO61RhjUrynpZgV8FOVKQGCI5H3+PUwQEw1ZLMzz97LLrrO60FuY5yd7xTdaO1ZXv79W4aFjpYOMhedp9Pw5WqNfCo5UdJdQqizOkW5z/zW/FAIMM9fgo0mhDw++qWeefljusX+cn9PHwV+pwzoEso23bOCdhuUsY2jU/PZbsGZ53fz+BcQYGTCbD7QFmmaqnLdVlRMagKX23x5GzyMs0gimlkWQUsVDVPIQBxKccUlf0gnkDjFNhmSvK/M32543rn2eazMyHYNg8DDhbaGQnVp8AER689083D9RDupaP20HczBgG2zWvRzB/Bvls+o2sT5lzXpMas4IcC6LZNUtPHWArj8071d++h6ToLBYea1jrfifV3WUvdTyYtqs3s/Mf2PdkOCvrUmwzOmF40PSMweJoQtSkdn373p8NCnUEB9RX7q4KCXQb2HQ6AuyrUXXoS2T7lHHfsUdn3naA/dXLL/vb9NKx7zXpUQnbn8z5LdfTkR0zIrVM6aUQ4Vp8CUvOBdaBNnSng88TRikKGhdOtGiM7p3a8zdoMaatCf7tVEEhbhYC06XEZnDZdE4CRtw15S+DE4G0TX4+bLiKUMvK2CRxo5EPJbv1eyjG3SzEDEAAoaycQKAoUhGWR7xRAa5TZCMsCioSwRNkfCLSsIAMBXWuhwEJcCijQstbRuW6XeBCLZWQvyItKXCvLvXa489Hoj3MwoMcfOuDmVnuG7vu9ZLKAUNvX2R6j1yr4ZcQsH71GzY5tmhURmgXInixID47ZQUKYH/seqUy5LAVpbY2efu0i4KugN0gUcwHK72KTBJrjGp841/cbFMig9mBBpHt98yQ9IzA4qP5+FEPBzfMUm0z/ug+hoJdgXyUU9KOQw2P7Dgl4+vgYuEvuoxHgU+GkvyQwFfS7uw2yFNxxo47Qp0aJ7Io3ADhaiMXnJwClJzQ4aL3kNSIcAXWlOjmndABDk5IBgQkAB50ODnZahqLqt4V9c/PwNVpfEgjYtgIAnG7gbQNuD2Vbvm3I24b0IMemLYG3hHTbwFtCviXkLQkM3BKYGemmWoPEyFsqWcuJd3DQvI9AukaC/A5LBOmaCXEVjQCtAgFhiQhrBC0RcV3kc4myGuN1kWPWBaxwwLp0M+KqEjbLbwAIQfpY50DYqPlBVdswciDU30PBLw/WXNdve0q4bw8FXltwlIgIgdmt8VLbhN1nd06XVWDSe3RSP6MGeB6ZJ55uTujvd6e/2WlC30cv6HqQHgCOEnUFfCY9CQ70NqZpnMCBh4J/1kNB/xzv2Vc/GzAYjaR3c1+BFgo+/PbeIWtgxxlWmH77lyFV3zqfPEIBdg3jtPngTkN4H+eyu8K228/Dr+3h1snpPxOgvr2NLjAq1qIgJreWAFSRR+00rFHyZoWsnWbrnW1hYzV/u4cc1JkDk5XE61cRYSBqUxGzEAoDoLKOg6Ysx7NqApA28OM7+X57BG+PyLcN6eGG/HjD9ngTGHh4RHoQbUGybbeE7SGBt4x0y8g3MS3km/xGlkBHBgr3wMBAIEh0IllRcdVVF1dZWZGWgOUaBRaWiHhZEZaIeF0Qrxds7yKWy4pwWRGvK8K6gHIG5wsQE+jyQjW0ARRYyqOBAhu5z6YRdiYAPWco9O1lu2u7H00N6D9n00JnycK2i3Ogk/4QOABVR9kiQwfXHbUN+S7twlsH+tSwwl5dcPwA7zXyzncE3AQODvtBOz674/Sd2um9VqLRJHjVyXuk5hoGAb3scXCgyZZONk03YyCzBmU11jT/o9AY7B+yt8vUKR0/wUf/9OO51y6wE4r76IW2/SRl3svv+6osR9ea5QPo8uLH3qNzTlJ0l5+5LbV9H2ftpaG67ejtqMQjyJMOrj3fH9MCgV2/F/C9NsFMA7auermvV39096+Q4UYDflU9KtzjFbZ7QFAtAZlZQN8LkS0TjDJtkqKcUxhBfQg43RooSA+PSI835Icb0sOG7fER+eGG7Z18pi0hPSSkx4TtYUPeMrZ3CgebQsGWkW8JWwYyu0iK7r0mcAlqZGVSIhWSrGEQ1iirKepSy7QELC8ibkvAcl0QLxHxekNcItLjiuWWEK4rkBhR/R9izoio5Yl0k3foYg9QsMBDNgsgqhNjRB+AqHEOPBjx+09MfgODuu5goGwqSsZ95EXSfoQxgANIlqxuBkcEXrM2SyNYlu3nxv+Zuwh9fnZCecoAhjfFat9zBhTujnpPDIxm+7xDI7UgMAUEbwpsHTTu36/f3ZkI7DoEp40CAM549eY3Q/O3ne9l0e6aTzS1PyMwsNjdPtUX6B0NPxo5apxOJ0fRDbJPXspolPFEG6Yce/+l76ap9A20z3Ofr3v5H/0+SDP1ZkAbjGXWCVrnbGK2t8HOguk0MOA2NLHg9eDxgGNPASW/3UP5wEpHqZSwQY8ueVvP5Woa4AzkIKNj0jgFREDSPs5GVyG0BVnyL86D+ZbAt1SgIL0VUEjvHrE9iFnh9nZDehBtQXpMAgMPCduWZV2FzLixKD1vGiExM+piSq48DJJKGGMAq86PXx8zlkCy/sESEK8R27uAeIlI14R4jVi3Bfm6YNFrR3dxCgGZAsKSgJjH4syFIC5QEBeFggUl0JDNEChRCQdBuPSVAOd6g/Na5/GBdZBa22dpFyCXF5ZjrQ/hdl3HM21kBsso28fjzJ3i4Cj1/UzTV55Qfc+0pT3Mn+kXmxOsb30iIAB7SDjM89PTqzef4/u//BMZ1E7k11ArMDJ1yYbDPD0bMNDxZKfVksJ49cWv2ymJu5Ofog5qC3jqrALcqSj7Cv0kh6bRdWapm6ZCvhIzo6noZ1Vkk8ZZtAVH2bmTXdMKjEZIkkuu2SzndJ1cd80RCPjj/Pm92cHnuygKuvKZPdMZrUivePYjNxthE0WEGMVcEBbIKoHV050AAYGQgUV9G3IGwg39+guc63LCzFn8CLYkmgKFhQIFbzdsDwnbowCCAcEDM26ZsbFAQWLG5rQGAGocf9QRpWkLZE0DwhoICzFWJmyZsGTGdctYrlFmOnQFSIFAdAOFgLAl8OqeI3dRH2xKY3FQXFTwCxRwXMtvjksTYyCjgkCJG8D70fwZMJi1hlnv0Ov0AK8Zo/LfIoGUmTLcNopMXR2d9EdtS64No9eg3ZNtd+GAJloD4KTm4D4Q3J223ueZ0PTXkreI6qSRYRFFd06/ul3Oddc5fIbJAPAgeSgw80G93p2XMphdc0baPTMwaCsngfDqi9f45C//SMIczyJCOWF4dyGQ5rwA8xLfwcHheb4ij2DgCQ5Op1IHM3BOb/cA4cx1h8BCaNSwbvR15qrZwQGAsuLYbpW4nc61uxaNd420AmescBOdlOxj/513xx9BQgsqXDriAAMDg4SoZaFlmhMIW6n39kmL+BfQoo54cQWHDUQ6LbCroyJcWfwHsvBHusmyyuJXkLBxhYKHLGAgcMAKBxhqDVptgRj4IjHWIGs2rEHHYionIgHhlpCIENaAdMsISxRlyS2DV8YuPLF/rhBBUWYu0LKWGQocFnBYHRSs4KI1EBOD5X+2aJF/p2feJ+DV+Xv4tMdunqX77Vum7czuelU2VSEl59A4nyfbjIdmq4v3ktQ/a++dOcHKj3yUv/fscyRj9b6zkfGZa5p5j9lBlvWRZUO93ggS7DpnBpmzgeAgvXrzOb7/138qEQ29T9wTgWBk9jrK6bMDA59ef/G6W/tgUhSzoBCNzcZWGWsriIcDAE7dNLhNI9hPagW8fdOf11+vSw3c7GjWVGT3AAHYj4nGDW+X3yd4w/YgwG6b2U0J0jHvVhubtI8znfDuUpPGNlPx5rJ/IjScitfvm/Ud3twR9IuYOHi3aFAMqkGgTUY6yZZ8RvGHCBcgq7MipwW0LOBt0el+MuouAjWIWYK6AjZHwpxFA5DAxY/AQvYmRoGCW64+BlY+AnMiZAOJCQEsDpyBqiDOzEgEJCZhGXf/UkZRVl4scQ9IYyBEchCw1M/1gnC5tlCwXIGwVC1BWOQ58mRRKX2X/aJdR++wLJfrR+BFr1lNA0THfvVENKzi7bLs+s6piOCS2IEZV4K4m3pw9mLmKaBQL2jaAesvqfY779nnAAMgONCm3u0vPRQoLLD5+XhtgocEAOynGZK73iQdywH5zkQCBX/zI4WCj5r9s2d0O+Reds/uc/+jTc8GDNRBG4A0hPHSyZPxH/kXq3NwG3PAxDww0BAMy3o40r+jFXDClYfnD67fgEylYDumVtbehuYctPprD0KTnm2Yo9gFPpmJoG1nTkvgMuVHSNPr9dlssjwGhdG5+yZtHQAXwdtDQREg+jxnFreZeYqTEyJlZcEsWvFAAkyJCTEASzD1eABtAC8yerZL03IRzYEFMlplCmO8rODEoC0hbAviRaYjhi2BzBFwC+AtIMeMZQnigJjFR2AhEeKBZYSftWAiFTRpGkOjNYCcY6shyvUUeqDmhSUgREJcQp21sASERWIcxItMV6Q1ysyEi2oI1gtouYDWC7Be5fnDAo4XYLmopmAB63cGYWOBgsaBMtd3mHN9f2fena9PArdoFigq5cE6vnZw4Efp/nqlHN0NR6KHuR7DRR3egoM9y700guidSYH2fghNmvWncAOT/gHuOBMOTaldP/TUPpO9U7Dr8yTfGj+kvPwDbYI+Aw/65N29d/mr2xsoUE333gm2ucjuNh4Gei3XZ3/3ep8vl54PGEA6KQB49cVr/PAvP8HPvvcpvv3hRweaM1+YdfQPoIy+KhCM7GPAbooJtfbcmtqKOhWopn7vto1yv0vNvd3owJ4JoWyStFeBcddljO42teONnqHsvy/Uc7kENfdmZsxK9cyKfc3FcKcjA3b6Nm/fZQctvqTuredQ+hR/3kzF60ZjgbmAQoCubqeAwAzkQFgCIcaL1NntQUY3ejnCvuSZVZ2fa2jmnDOWEqiIISGS21K/WJ5SLsL9gQi3zFjI+xhU/wI/GraZCL2PwTXI72sgXGPARR0Q15cLlpcL4jViucr0xeXlBcuLK8J1RXx5xfLyItMVry+B60uQ/7u8ANZrhQKDBNUYJEBmVKivhAFBAjfLTTcakDua4qCy2Ooywa1i6Jb63qnv3ffGxwSY1t3RWLFRsPCupctmbs89Cp40a1/3tQU070932tZxj1wf405/47Y9ue8c9dfON4OL34b2nRrATPYNQMHBWPFPuJvaPP/6t78RKPj9PxMoGPan4+fhwfd+0PLZF6/xw7/6BB/gw2mOng0YsHbgn33xGn/8V5/gp9/9FN/6YAwFY1DQPabyarxkc6nIv36jpEVthZmmI42A+z2mQT3XnvH4ToPn0f82Y0MhgQ0EDrQIlhrnnCZv+2ccNs6JNsXeQ9EaoIIdqO3IRpqCe0KfxruHv3d5a0ZZKMjdmzuAqi3w1/FQkLODAa73SZnvdsgxUI2VAGgQmxYQIqTzyqY9WAnYHgEEFUbyGWJE1miDAVIfI9D4G4RQzQpxjQjLDbdlUzV9wLZuoHcJy+OGawYeUsYlMDamMivBmxKSe76ohWqmhACo4yGwkgDBEoB4WbC8iFhe2OeK9eWC9Wsr4osLlhcXLC+viC+vWL/2AsvLC8L1JejF1yoQvPw6wvWlaATiFVjkk6NqCigWLcHGjJRbIDC48e/s7PtKsDankKAD4KMFu4p2ABUKvCnJ9vnP/rul0B1gOfZztnJfYQcmiFEatblRe2P/2/WfBQ6au33F/U1zTtt/9t/nqZpvyvRihQUBBV3evEwrPgAF2XHndq2MKFDw4bfb52/60/2zWerlhTdrvlL5+Off+RT/+3/9L6dZekZgwPjsi8/wJ7/8BD/5rmgKoI070LxC+JFVgw1eBaaV+9UXn+H7f/MjuZ/THXp735nRtBw3Ep60e6k+36c83N2z9s82hIRCq6Ha2XYBMw6od+TzMGmgR3nO3B42w62pM+Fumyuse4GovB23dF51mlqdFVHhoAZZqt+LL8EECjYVLsmOy63w9CkS4SbLsiOCsMQgwopaQMgsbJpVe7CGiLC+AOih1i0HCKAAWlbkEIEYEeKCdXkHioSkYYfDGrEtGljoesPtbcTyQuIa3N7ewNuK7SHhestIj1uJY2BQIH4I43cXQRUOiAoMxFU0ArQErC9XxGssWoN4XRUILlheCAzElxcsX/sa6PIC9OIl6PIS4eXXgcsL0PUFcrwAUbQEiBeFgisyCLcsgj7pO0ncAYHCwJYyErhUn6N3RYGxJXk+ItHi2EJDWdUmQSHAu+S1Jq49FBDqX6nTB/VZqqe/5n7EXHwO6qbi1wOc62f8deapG2wBe63le/Y3O1PBncHU+/SjPou+/5RPBquJodEmSObkgwfvCziWF8AOCvYm2eMB4+5ZB1DwrQ++PTizpmcDBp+/eY0/+eUP9KE/qipMdmzaAUKvPShwUDxOKxy8evOb4h36v/wvvtMJxEGGTlFtfcEztY+lszbBcq4jepO59lnEIIWmYe4g4USa2vJOkK0v/6NO5hQAdCGHd2Gt3b724n2eU2mIRAEEiyyo7zmr3ZhRYi4MNRB6qxEUGBAw11ADvZPdpsUXM7CRzAZYQid0mBUKZJ+ovmXkui4vdBYCASGCNvG6J52qF+ICflD1+sMF63rB8vId4tt3WN5esL14xPbuHdLDhvXrNb7B5WFBujHSg0xxTLdcgh5Z1MOcxqNr0VxI1ENS/wEyH4JFQCCuhHhdJLDRiwviZZXfHghevhAguLx4kung1mkJNgUZ+8zuPRkwHL0jClUAEAsgpMCILCYKe0/R6kM/ZtBP6wkMCkIYAIEFuXrvel3bavVfGLdRf+oZRhjW/7LdwYGZEeD6msn5ozTsawbPMBwx+w2435+aw2yfyRrvhPSdRd3AY7ODZFw/ufk5S1MomMiM7tFK/v2OPRQcp2cDBgIFP2se2sMBqBZWQ8xoBdQIDn79xW/qetcWXOLIoQXHROvf2Ujds3dSu988iah0YOb/VUDBVWq775EWQfJ/YurNTDui17Z0VIFnbWQEAyMIOFwBE47O77iRC7nLEJ1ItERMXAChjLZMM6p2UjOXjlLfH3ko2MqjcBmJNu85yzvdIAJnC3JeJAKzAkIkcGKEoECZgRwYC5NqDxaESwS2B5Qof1sE4iozE9YLeL2Ary/BD2/BD2+xXt5h+dojlnfvkB5flmiI6eER+ZaQHrcmVHK+ZVlsKcv0RgnBbEXu3oMJpQg1VQRQgK6HEJrQx/EiWot4FdNBuIpzYXzxQpwLPRCsF9D1pQCBzTY40BJsmWX2ARQMsgj+LbVAsPHBu4EK18QKAzbiYMQscLAYUMK3t3H1I73eHgo6INDv/Rob5+q25mFgsiwgrNvc0U9Ww/tnbeGg3k9SrRtP7mtOaAdmgaj8be71q+In0jxBIyiaOA8geG2CXF87B+pAAdhpEZo09TObw0/J8+CFjaDg3rt8NmDwZ9/5Gb75wUflt82EaeAAaAAB2I+o9RAYHLz6u1+XBSs+8sElZhAADGm2h4HWg73meTj//c6zB9TzqDw7lch7oWsco9F51SL46TiWsROetc0J2D3D6Pf8zAkMeG1ABwJtJ9lqD8r5u5uG+qhEKC6F6hxKFgWPGBQiCOoJrqPABAnNzEQyi4CpmAn6ZO/HRqGSJRuV8ihIYR1hMINY8tQDAscgK7eqeSGDkBhYWEBhCYRleQGKCZQWIKxAvslCQ8sVdHkJuj2AX34deHgLvj2Cb4+gh7dYbg/g2yPy42O7yNLDpqsvbhJBMbPCgVuGeaCvtRG2LbMclggKhFAWTZLFkZpFki51hkEwEFgv4mi4XFRDsEqcBotNsMh3pogtixZgK5oBrmYEliLeUh4CwfS96LuROiBaHQqk2gWFgpGKQFNQc4rBAKlDJhQSVFwDOdW6nlMXlj2fq9+WiyJYKxiU4FjmQG0wbMd3kLArglnRuDN2fWqXt/r7+BnaA9tr3wOCfoD1pL7VeQ2rwhA2hbRoFTptgvWltvKqnJ9bABrAAntQkwu4Y09oRNDtQAsF3/7wo51P1Cw9GzD4VgMF1aEHaD2ju5panIBGpPvqi88kONIgYiKHgR3sgGSbl3gAA08hW3vGRlOACgrWGM8Agn/+3czpU561447iXiV8kmZgBAM9CJTR1B04IDMEwHWUoTRCYp3yRwwi9R4IsXiUmwlBlQrS/sl+iwGC7jx8L3xMmJpHv4UU2DS7HhBYASGBsVAAVG2dmLGoeSGrM5yotWVlwrBcQekmDop5A9INWF4A+QZ68XXQdgNvjwi3R1mC+faIcHuoSzTfHnfLM3PSSIo3W2Uxlyl+PgW/3PIaxVQTab+c8nqpSymvV52CuMr25aLOhGsXrKhOQcwgMRkkKdsjs8HGWbUFcOYDOc/DTf9OSMs26rHLgUKctIqVmATktln1ozp9s0BB3mpdzxkFBji/f/1WrZj4RgEGCqyVeAcJTjj1moRe+Pvkt9HgmGF0hhP9zGikfAYIHE7ptu66kz62mTFh/SvXtt0Ga8JYkwDsnBiH0yT7GwE7KPC3AsYaAksGBT8xR/wTmmdLzwYMRoldvd75wDpA8HCgmyRiYgmONLDJTEh2SrFdhe1h4Ihq58/H7s71m02vE01JfaqzGoT5WOdOfp54PHWf5SojIOhVqAYEvqP0zj731Kw+FKt+EjFAGqyKWGaiEoNDBOWtwAHpiE+UBHutAZNMUWMt66KS1JH+pnmK1AoXoA0j3IQUztwAQmJxSOSNwSFjY2ChIHZt5uLQGTKLkx8DSxbhvMQLQryAOAkkmBDS70gbwAlBt7NBwXYryziDM3i7AcxgW8MhyZLMsiT0JAVZWhlRYy/oYkW0rPJbgxNhqZELJThRDV2MEBUM3HeNWuinH5qGYAYErNoEMSXkHRCkQdVJXOEgwkb7tQbbd4OAoFWsfu61BQHedt1CgS2cBU5tPb9bxz0k1DwVYCAq9dxAGMTOqZqdSa06DxZw1ktbX3HU9p/aL5y5zk4wToDAds3Ms/fy5oWphwHA+tIBJDhBYu+19qmd+XaUBiZZ/5izZ7eUUWfnGRQ8NT0rMDgjzIaA4OAAECj4QRMx0SePD+2W/tNrB3p6PTIfzF74LjkVVrmY22Fe0bavXxnwjInhqWmW5f7S+1u1Qr3xHRhpCHogKJ+eyF1udp7Pwd1WO8ziU6CAoHpCyrXRMwAyzUHpL2vTzzpXvdEaEBADATlgC1kc1Rhim+6kT9xvAuBGrJmRAiEyizAzuMiYAkIkAYkNhBDYrW4YEWOUtYQAgJMIoiyfzPU7nIAiK/ecALAsDZ3MscBMCXslrWkLiho1Ru1kSQS8aWzcCodZVz+ErYgY6sqIWctlFJxoN/VwAgS92YD1nFnaRd6EaA8EEiT7CwV5314z4LQFfnXJSOJXIOLWQUExHSQFhG1fz9+njlvo4QEIV0AgcNGz5iLsCiAMZhuN4OCoL3gfWPDn3PMh6LUEMyB4n762BDrTgUGvSWh8OrT9j5zAJ3oToNs6yuKRQ6WHAjEf7K9+73GfDRj4orwXIQ8YmBccFPzwLyQ40kcaHKm92lytw9j7DoyAYFh5PQoCZerbUaqLCunvASDMtAcj/wvfrXwVkODTHqcGvyeOVqeBwDrKI3NCSamMpKTDY0hkOgcIti8AhAjOaQgHOZdTVHC45q8qg+TgAFHU15EBxADOKlyD2awlvzMBxQoHUIFm5gUDhI0SlkhlmuMG0RxIaGWdXkc6dVCd3UIgRAQQRcQIYJFnIXtHJqCm7wYY+nb0dcDbuvX3fJnuWEZISb8kBjhxEfwMFBBghk49rG2RUacdmslgBARWrrPkTQheU+ChYIlUoEDKF6W8gwMBb0K4CwXmZ1DArK3nT6rjrGaEvp5PAYGLiaG0B7Rw8BQgOLt/lmZq8zNm2XtAcLe/ZZTpwQCqsOc6EKga2c6Ma/fpB3JdGm3u5T+PdmIOBc31yU2zPnzY5wQGHQycYIMWDgC8+rvX+KO/rFBgaVRl+jp65Ew4A4JZ5TzTcMgd7wFh5y/BtSwKsXI9ogWHev17TjlPSfchowrzdvMECkx1fa+jbK7Zl6oVVABxqh2jAUKI0inr5JQCBzp6k1PFshzsn6mdylSQPRwsDg44A8SMLRBiRjErQO3XC+bCqqi7B4AAiGNdBINSVgHm59hrtsBuRIui2jbB5ZfeJej0TbWJBqr16l646V3e2X/vOvLCegxGPhVR0p97NC20mgiOgaDXCpjTpFmDPRQshFK+pqnBARQEBYkQqIlTUB0NeQ4FqqEZms5KKTY5L5vIyJXzHISJVUsWIGoyW3sjY2f/Z4OD4xd+NibCqdRd64x5oKlrPk8DILiX1YS6emfgGnwMqG2pwEAHCBYy3Bllpv3ivXz0ZXoPCkr/z72cnL+7ZwMGwLlOaZY++6INowy0AtbSCFrP+hDMNAR+cRZgOuAqiWp7L4DgV1Lzi6ZYRTAI8nBQFiiiAx+ML5M0H36a6KhMS+pHpLrtSVDQqVhpWpg6GmJj+gCyboKCdMIjOMgQnwPOYE4IZR4z7sIBKTQugRARkUieNxo0MBUBRiq8DBKanA9626RlVWyiGdj03Re7txNw1lbqPv1tXZmdazMJ/M3cj9HclKemBgkHHXnS5y0zOwyg9cQ6pXAfc2A03fDAA6KUkaURDFj5eSCIgQokGjTdg4LibMgVAKR+n4SCu/Vcn93quYJAU8+tYgMAsvEAPBxA/WPMrMBuZsP4jgMg+AoBocH9CRS0x9Sb+3yNBmL3+l6r+wYJu0FZBwOmqTWhnKk14XpHxXlP1X6O0pGmoGTdvbJ7z/lswKDYebrtZ0a+Fjv6p3/QagqA45c1c3yZ+RH05xytujecN+2OuwdBXgDfO97v/yo0BbMZIKeTV1M3v7024BgK7Nxi+x4kirqCGqF0kIdwwOK4RdkBVsAUDojVvp1r35qY6/mREFkAYdFR7jKAhAWdgBvAAvTYrd0CALidL/knpyN7/Nk0stv/QycPACONANACVQ8Dpn0xs4GNnUemmkDetDCAAufX0ZhsTkDBV1LPPRwQBHoBqJpMINWGFk9uy+3Pr1ITeU+4jbQFT7neqA+2TTY481rbRhOgWtm9KdcDw1h78NQmdQ8KRjLxnvx4NmDQgL4rWV8ofaUMaKHg438qhXpUZjsifioUDLQER0Dgt898J8Igx43CaKayctc8Axtn01D74AbTs9RMSdxdDa5DxNOgYDI/2vaLE5wd13eaFU4ob+IJD/EWH8GBhE9WKAAXJ0QLd0YkXxPrzAUIIIBJAGAACUALClJWFRR6kZBqlrUE5UtZJdC2M7t9OLGPu8/uvk/QG8euItjPoJWwfGolEm9+ao893EdlH+A1JF0+cAwCtn0HA0AxGZgpxgMBTPiHdrns4tMBDKCASz2r8KudxqSeN0BwUM8pRjn+CA4oVkgo5ZRRkUnOJWA8zc7OGAxxv0ogAM71mUXRgfGI3JXA4Pr77W0YdLuJfGTwTntgIND6c+3h4Ezf6J+hKCQxh4KpJo/qvqPbPUsw2FVCLcS+sDwUzNQvh2kABc3ukaYAnV3rRAUHxnbckXq3Tnvq7UnH17S8jE65q17bXVc+ex+O5pro8j+6idvWRg27n6FDKLDtuymnvO8Y7XfzCdhSrMgeDtTSqDYbYhL3AnUwy1nXN8jVrMIstkiTATESMggrAxzr4j1HoGBmhxSogAMrmOQyTY/1N3a/tyy2/OQDAOl5ti3nXL3/dRv0mSyKo49dMItj0H9fyAlxFbryCYQQ3DaBibpfl38OQR34ZHtQ4Ruy/WYRzly9x48cCIFjEDDNQA8DIuxbZ04DAqJqYiiOhmYWa0xj/ruHAKuLFQqG9XmUtJ4bHBwm1wFQ8UVot7fHup8Hlx2p/s+mM/2Rd6obX4OKin9n5XNfG5A4uOZsQDU06To48OeP4OCp6bM3Kr8OzAf9dRv4OLjnswED/4wNJPhCd++5hwJv63lK6pujaQsOTH7915JGlXHm2GUdUfmBMRSYmtMdtrvuKD21AY/ObUm5ZmDYDrzjxFeQKEaBAwrjTnMYudHtNnWqjYzMxsoaxZKo2mMNDohV2ETpgLQTMkDgII+YtAMyYFyYigbJL/d7BhQ2AAQqgLAFndIIVHBVCNgSsHEuMHBLIuAft/r9tmX5TBmPScDilnIBjNuWy6JDBhicq2+D93/wdaipu+a3oEK5CnRdinkJ5fcaA0IgXKJ+L/sJayRc3HeDhIUDligFHFw7iAR1GGyBYAnnQSBA8lXaH9V2ZxEM7fseCIC9qcAHL8rY+dhIobZgPKywx/X8LhQ8NQ36Dx788JrT90mzOrTPThX+gPOaIBPeYzioo3wucGD3rcJ/3ieXfML1aYMOjhkFBPyuo8HT8Dn18p+/EZ+4n/7Bp/j2KE6Bu4mXh2fl27MEAweDpVA8IJyBAv/9Xp2eVfoz4Sc9pcrv/avrgQConRLQOrB4IJDj2vPOzt7o89Wne9EY5ZgODnBOZXY3HUIEiY8VcwsHoyOL7bWUzliL0PxkUUCyOGeVIEgA/BoLFGJZSZDJIEC+BHZggAoBDCDrs4288HOuoLCwmh2CxvcnARCkXOCAuapKs472PRS8uyXcEmNL8mkw8HBL2JJAwk3DBcviSAIInEWDUIPxcQEEqzSDMAZF9Q8HBKTSNqiGgALJaDvK90XhYF0Clki4rhGXTbZtkXBLEWskiLo7Y0FADoycqTE1eChYYiizCWxhqpGvwPEsDSf80YJBcwys7XF1KLTZB2Y+UOda6oFgVP8KAIjzbKnDp+v5oXSd7zuZWs2opP6RzvQd+33j7/76XpCbDAhAsfPXkQlXuaB7gmsrM0C4l69RYr3IECY6RczIEjeSQ5+5KfVHwYt6udbn4CjrzwgM5voYRi2kV39XSWvkUzC6ggeNp+droh1w2+9VrgYG3IZ7QNA8V7f/TJrBwZHa7sgX4p7KrKzXbqvwOABg9aauhWZ20aCX9ap+KiuYHY2U2iVPHRSQRYU7mI7VTlMQnwMilIhxqooV4SHXUVcCWW0P1AXAosY/pfmtQJFCnavPLILNIGFLGTEzIoJGSZSRvajRrfBRIgHeUi5Q8O6WsaWMd7eMhy3jtslnSrJqogBBlgjKOYNTRtJFh3LKbpKIQYO9tP1wT8IiUxW4AQIBRIiRQDEghoCwCChsMeC2BMQt4LoEbImxLQEvVkbKAS8AABExZMQQ1cSh9yKBDCJdMyIE/dS/GKoWgEyr0PoJHEFA0cYNQUDL/AmxOcaJ2jrOvIMD4Gw9t0YR9nXcQ4Xt696dnbtbG2Z3w4Nd79F33O0fd/udVIe1txYQSO/nASGg9jfF3Huyv5wd9hTU6gdMI0H+WoPv+Tg7QAsVvYZgDAba+UzSswGD0rBK4I09Krz6oo1TMAOCEaUdwcFMgO5MA3oRc3o5U2mOYMDucU874BvOGbWV76LuaQ5G6Us7NTYaARW+qCDQuDf2cOAXKuE8XO98f78KBACOoaB33ILMVABqUBi5hgKB3V871AJxFHR0SaWJWo2owEA1iA80uiGAnOXumWtAH1oCFgYeUgIlSKhhJDwkAFHgIhfVen2h5jfwqBqDd6ot2LaEtGWkLckyyx4S1MQgSy1vYM7I6QZZREnXSxgIurLCYoiyiFJcIeslLIhLUE2BaAviEhAjI64ZzLG0o6SdeQgSxTFlYHEyUcwSJvjFZHCNhHWJWEiCEF1jVOGPNtYA9j4C/rsBgH5ttQFWNzSfpxb6cvW0dSzclVwHwFbvUI6/W8+P6ngJKOXh2OruBBBOpsYi8h72hP6Wp/ove0fknASZCyCAzDOfCtBGcvt4Agl3Ut9X12cYl9uhaWTw3Yfp74PvzaCi/XT11P0cpWcEBvbQqVRwH3DydRfR0NKYprDbPxzsEkosfJ9MfVVUXOicXlBtW4fpiTAwMyM0Jx8lbs+z5xqZO57ayHtbmj+byoil1QgwLByrCN+iNUAHB3oVLloE3dKX8YRS2tGRK9GuM233w03rsnyUOGcCCcjdudReo7yvUPNBhGgjOgAcqPgi5KAhrYOEOBbTgCzzmzNjywAtERuJkBeJKXCwBIMJwpbbWQE5O5+BLBCQE1co2BI2hYK0CRDk7bHAQE438LYJGLCssgigQII8ctRPCZJEQdZFCHEFhYC8rQjLRYCA1WeBGWUdSyIkyggUSj69g2N1RiQsUWNFOCi4qklCIkICS1CfBtQphRammKj6BuwAwGsA5CFr3XK/qZWIVtKTY10P4n0FvGA2AL5Xx+36XT3f1/GufhsUNNoC++2OLccXN8rh2POOR8QwHZk5d0Bw0J+VY11/m91ABYBOwZT9ft89SDh+AJcH6oQ7jaHGgHN0Gb+5XbunNR+MZNP+GrXunknPBgya5StNcGhlfv3FZw1pNee5sw6uvtMY+CkjgFVikaDcbfNOL5ZmDWfkKHJkJpjCQAcVZ1KTJ24B4auAg3IfnuermBMUAAocSAmWBku21rlV9qIpoOZa91PfKkcw0Al0f7iN2MpN7Ys5Pg7utIMTHUlbJ6z3Ms0CU0DUxYZEgSwzGxITtsAaIElG0FtmCYUcIiglRESUFfMA5JBxWUS4rjFjS8ASA0IXkMDerfkliI8BipYgpxvydkPWRZUEEsSZLisg7MqKIkKQkSqFiJAzeMkIcdH9AQmLgAOpJoTlefq6FtQUEAOwqmPiZRFTwSUErDHgxRJwjQHrQngRY5kuaNBg0wkXGkwlHPgENEK/F/a14HbPLdsHGpSZ+aDRitk2pwlr6vjBYjztBdrrl3o2AAKnQRiGq56kLxPlcAYFs8HNqX7NoMD6Mu0evLYAqBDHNAAI1H2WZs8567up295vs3MbkCjfWKHgB/j0ez/TBf1UK3VntFdgFtgDrd83SM8GDJBrXHAR4tK4Xn3xWpZOHi6IBDSFM/XqlRq16861knmtga2iZ027AAPQdBpHQV3OOBA2+wYQMK5kbeqrxchR0wChPotrNO8DBwO8LaWrHaKb9IMKB9oBkoMvDwhAUfMfeIMOhDLsYbrfgw7wQPc3nk55FGNvnAcCdh00IYBVoEZdbCiGiAWEyCTLK2fRIkRiJAUEUMRCXGzn7yz7G5AjkNaoUxI3XGLGTQXslsU3IUPU8raGQ328XD89BHgoSPu2xFGcEsUpMII5gXIAh3o9X8IlOBCo+CbYqN9mKVzXBS/WiEsMuASBAfkTp8QXS8QSCasDAvl0WgZSAdBEG9wvddy85/eE4mP9cfP0us0XYG9G+BL1vPensW2mpZhBQact2N97/nizNJt51Q9ygL32dHDILjvW15pwD2inLHoY8JAA7EHBNApnn6kfzPln7c1T+2fhqin4g5/i4w++5bRJJpeOAMGZpryfi3+gSXo+YOCjd+ko89df/Aaf/NUPpVAdae3Ps+/dfqLuRdRXQP5qpHBA1iYrHAATm1eXRg1ipBXwjWXmdXpWSdA8g0s7QHBwYPkYeQLXZzmXg/34x+2bag4cIABgdqpXxPEobOAEN83TKS3DYLTXqYfbbYN9sqG/av3f2X3JOvAQCyhQiFjjCoSIFMREsBBwy+I8F4lxAxDDIr4HgUAhqX1+/myJGY+abQYjc0BcAOYNkQPAqz5OdoadGzjLdMGcBQJ2ZdZoDNTHQM0J9heXgBCBuERxRFwJcY2Ia8RlCXh5jfj6JeLlZcHXrgu+dol4uUZ87SIwcFUoeLGIL8FColFYSGIjLBoLYQkatocTaLOlpxUE3LoFzrOye3/jd1d/dpqnQtWDfXbFRl0/ejNqjjmaujjqw/pDerNCOaZ+P6spGJkRZqkfSJwyHXSDnlEfd9Si+32tNlcAoICC7shw/dsEFIB9UR89h4eC0o93A7muNuDV3/0an/ylyq8Pvt3dsDM/jdIICnxfdAAHzwcMABTnNGa8evMan/zyT/Dpd61QJ43JV9TumCLaDRCcQ1ppSuTnxHZTYzqv2ObELjUV/Qkg4Omyfj1p4SvCd5yp/ZSe+nukPXhK6s0JvdZAtjk4IHVAY7HbV9uqmhH0OMbcO/tLJ1dXSj/SgIB3NvOCpBtxTkHBpcEMCQIBalKgEMFhAW03cFwQ4ype+YEQsmgQYlZbeWYQRURkRKjTHQgBSZzvdOTs09uHhLfl1+biAYi9f7uJc2XaAjIFQAV9TjcVsJj6GIS46vELwrIiLldQXLCsEXEhxCVivUTElbCsCxYV/h4K/smLBV+/LnixBnxtiQUG5FOmN64BWANh1VkIMYjmIJh2IN1AaRM40FUNxYnj3mJFJ96XX8nQ9hvodcG1mhF7ud68PX01dXx/L2/G8poF7n5joC04a0a4N+vgLBDs+r2TfV5xgHX/2fJA8r2Agu4YgQIA3HMT6/v0mZb3EApUfk1lE4CpYbp3hHX1+NWbzw/z/mzAoJAdMl799m/x/b/+U/z8O3+Ojz/4plSdCR3tnIjKjuDsx2HYoGdwAAggNHYsPfbelJxRwzgEgY787gZCgeuIuJpfjiChcZwcaA/kucb3uscLXmvQwgFgZoXyLgilNTYmjF7F+hWmdnEa+161U4weDjwUdMJFtzUhm71vjJv8T6EKCwI0bDMJBBEJFFAUfXteQekRHAQQQlyRA+GWCSEzFvsjsb3LdL2MRUfQC2WxzS8B/793AUvcJHbAY8DfB8JjJKQlgyIhbQHplhEXQtoiYlp1psKtmZkwm5XgZyTIX9BZCAIEcZUZCeslIi4BlzXia9cFLy8RX7vI93/nxYKXq8DA19ZqOnixBLxcIxbzOygwUIGA0g1It7q8cbqpiiO5UMSpDTds/cDo/dg7crECSh0uSxmbVmuvKaizA2z76JinQfeT0ghGvDOsbXdOudYKzmgLvHZxtM+ne31f/exg4En9X9pNxaSu7+tBAa7vM1DoblvSqH+/17f3z+ih4J998E33nK7+OVnEB/3eLsw8Z7z+4nN8/1c/wof4xvS8ZwMGZlezh/757/9ZsclMm9VMHdyZEMT7XODg1d/9Wjaj1hdgProG9dP/am5GjjX9dKi6aw8CU8/oo0RUz1PnOPbOTt1sDnvGI+2BXvYw3RPZTduz+1PQjVmcrJhbMHMmhHvprImgvS6qgGMT8gw3MxrmEEk7Z7seCqqwYc4iZNRGDxtZDzQSZTQXFAJIpveFGItpAWkTE0NcwHkD0q0AwhIIt0SIgRFJfA8CRRH8kbDGjCVkXCLhEgUcrkvAi3XD36/y/e8fEx5uCXEJbgpj7KYwLmB+WYIg9c9jz1GCFxG6qYlqNlgqEFxXgwH5fHkRLcHX1lhg4KUDg4v6HkhQJDEfrNGWn65AIN8VDPKtmA44JXDapL5N3kutT9Zg5b1wEvOIQYJ4wUQ06xHsrhHc6Hyixu/Ofd96fC/trru7d9ML3QWC0eDh6Fh3i/ua0V5FDnTC7zh3hNQ9X2ogyIMCa34YTkMwuHzjYzZ6tsnzjfr4Bgpmz+ZklIeEXer6r9dffI4//NWP8Ivf/Vf4T37xfxqfg+cEBgBevfkcf/irf46f/96P8fE3fqfAQuMnMIOB0TanJSDOxWdBD8IoVkIvQIG5UDxWk01AoIcA7yl9J8nyq3YTU3eqGL4DCJajkfbA0qwbmj2/mRNaKPBaBCsg8yvonucuEIx7pFFJ0W5/W/7SAUUBBbaodTIqRBNroQMEDwXbrQLBdoOprUUITTRXJYOirqcQwCGC4yK/VR3PYQGnCAqrAEJcQWEBLRfEeEGCCMpbZizEWEPGJS64xoxrzHi3Ed5tjBdLwNfWiHfbgr9/TPL3sOHdlvH2YcODBkHatiRTGm9JwjSnNvphztjZk0OA1DeLehhlRB/XiBAJi/oIXNeAl9dF8qJ+BF+7xKIV6M0G12I2COpoaEAAUHoEbY9AAwQ3UBYNAW83icWQc4GF8j4OZw2gvA8z7/Cylj6GlhUSmDruq6F34vPOfnqdZsTejdabqjXK2nT/SV3/5GojvVnRn00u7eFgtG90q0OhOQOCganndH+os4d6WDBQsJlBI1CA607PFO+p/h4YQ0H/PD10+7rayzmFgldvZND8i9/9V/j4G988zOuzAYNXv/0N/vBX/xy/+N0f46MPvlV3eA/dUSEfUnVVab968xt88ss/xqff/Sn+V//579rJO+FpKdL9httXkLEwwq7yt4FSJH/3U6jnEQGskfrQAoJzmRzCATCAH7Y7vH/qoWCfjocd/Xn977M20Np4tSsgCYbD1jGxaZAg7yIDpjImM830Gh1ARqMdFPB2KwKojFDNzJBbwDAbPctQG6RggGUVSIiLbFtWcF7Fdh4XEYjhsQDCGgm3QFgyYU2MNQDXJeBlini3JbzbAt5tGQ9bxNfWjMcXGW9vCe9uCW8fN7y7ZbzbMratRkn0YZMt+qFYVFowkD5Wox6GNtzxdQlYbEbBGvDyIrMNXuqMAwMAP+vgorMNngQE6QYYDKQNSFt5D5y2U+8A9g44gHMAgoAAtpuWP4CUgGUp9aBRy8MJfTfjBDrjpMJAjRMA9/nUuqw33vPJucs09579nubh3gFP0RIc2Mz3fSJwr1+UWGSKL422hisoeI0CWRTTWpplEOMK80v1+4A4yo+cBaepMwcNNAyv3nyO7//Nj/CL39VB8530bMBAoOBf4eMPvimdbvOmRlqB45E2O23Bqzef4/u//BP8/Ds/wUcffrue301l7LUHs4Y3qxBDGPD57EHgVKVBqdzynWSkCxPE3j5fzS5tLAhy26sAH2lHDnuMQYF4J8T36XyaTrIzbwAj0D5AD5IVC3snIbEFEmx+PbNpWVRjlAEQVwUCsewzWGgynBso8MLI4gAgpV0+awdEgJoRSDUHFIJAwHoBFtEYYL0AaQHC2gJCXBCXK9ZIstZAJtwS4xIZ10j4JxfgXUp4tzFum3w+pix/OeNRgeDdTYCggEHK2Bgl8FAalHMkXQchkEYhDAUM1ihAcF0kHsElBJmCGANeLBKk6IWLR7A6k8FaZhkwaHsQKMobaHsA0qbmgg24PUp5bzfw7VGgQDUFR2XvKggQo2oQGZwVELCAt5vAQc5AbMUhm9OyefiHKFoGg4IQ63aSFToNCHzYbKm/5+oxUKeYHjn3Afvpck9JO0g5ULfvjunufQ8KTveNpwd+MDood/SByRqNAllfmOCdMUegcKY4Z4NBycPeL6A85yCx0/KOZF0DBSof76VnAwYCBaIpKFAwsr3M1E1+NK37mEgK1RwZjeTK8e6FYMTkfRpU2FMVvrUTyb6TNjUFnCaamdEwqxaBSCtXhYHiV+ECRY2erwGEs62iS7OAR8ORkdt21Fn2upRTjkIsc/69gyVRXR5VAuAQAkVwDGKb1nuVGEKU60hjEOSnzUDVEnC6VaGU01SVLSpMMStwiOLguqwCCTcR/LhcgdsjaL2ImSEtQLoBYQGWC2i7KSBccFkitkDYMuMWJcTwNQJfX4DEi4RJ3jIeUsbGjJuCwsa5rNa42cqMqS7HnHL9tCiL9mmrIQaSsMULBYlWSAIC6yJmj6tqCtYo4aMX1Q7EgDLTYAkAcQLdHhUIbo2moJgLDAQeH6qmICeBhDtlDi1zDkHrCIFjBsVVzgsBd8fGflYCyfEGBRwWBQULYKWuJ0DRvLheYAi7fT0GHOBa1+au4Wc+lQBAJa/77J9unwfXmF3vULV+AAVTIOj7yMP+0Wn1bABlgyHTKBBPIAFgchrpYnI4UQAHDpT1EKdxOyGrGpO5piK/fk816cW8fgwHzwYMPvbmg1G6BwT+txa4kZZ3ZGxAw/swFO3B/fvfNRP0ld1X9JmWYODoJduhgj4pAFjydnFbjeweHLR+FZ4DPCBYekoktOmxJ0Bg1Gn6fc31d/dxvSZaLQiRPK0FsJIpfgAH+U4htmUhlgaFLYbMIMgAk670uI0fsawv0AmonIfe/UAdqXAIIuRCBC2LaBJuDzJ6XS9iZlivxcyA9AiYH8K2AmFBiAvWuOLFErFlYMuEjSGrNjLhn+SALQNJtxkMbAzVFIj5IGnVzWjDFVuSNQd0kSI1K/jVDgskaACiEraYFAIILQykG+i2HZsLbqI14Ntj8ScAZ/Cmmpq0Ff+Ow7J2BnMOUd55TlKuo3NiBBctQfUd4LAAFHdQwAoFBgR+jQwPB8CsLvv2r+XdafSaoDssLdsGzNzs24PCl4lq6PPSXXb3/UtBwfv0k81P6wu86UdaOHH9beYGOWcCCcB+UNqnDgrGjpQH+XeAUOMtVEAos/N+78d7+XjHI/TZgAEA7Kb1lGl5J6HAbX/127+V2Q1WqMzjwvRwMM2YJ7+naQeGlXxoTxvkqaRqLrDJNqy/CxxoHno4qPdp4aC5HdpGbrk6q6I80gychYFdx1nObwFhCspaZInlWWSJXa7RLQOJ7ZxkQyZTjfdwkEVwKCxyWLRcWRwHOcAFuxgURgcF9zQHCaIupwDe1O8gLuDtEXR7VN+Dd6JFiAtovYhjnI1SnbMiKBZIQIjICEgKBJl1eWcGMgcd1VYYKO+Da1kn92JNW0D6T5XrBRJCkFplaxYs6qAYg+6HQBOlG+hRYw/MnAlH2gHTyBgI6OJPZ5w+vY+pVIaJ0ytRdRBtQKB1LuyhIIOQtM5aWTdwoPtsKW7geMBntcuKvy7uo6sJah1ncnPrbXDA2JvUuLuwS081Q/SH99qCHgrq5g4KSvLb95rVoSAd/Va/K4OFHSSY0GfAFksrzty6jzT7w+ngs3TUh1vWntDfN5ruflALB4D/WDQG/BRSA7pCroITOHBkBBoq5UZwohupT2DA9o2AoNEcdEDQV457trNetVngpdrH6m8b6vb5z9qBhJrHzq+iTz0k9PuGOe1HJAdQMAOCHga4O+/IpGDJ+xZkkuewFQkNCjLEsS4UbylCCEt9btbhlr2fpKCQ1FM9Z7FL++/M4JRFRR3COJxwqmYJ1sIiE7ZR4ESmLaq/AQVweBChv6xFi8BxEW1C57BYAibZCDZEhBCxUCw2cV4jQPG91N19+QoU1KWLm3UKXMAh2pJ0sDnJTJCcRENgAYmcIyGbI2Gqsw1sf3HydN99ufZlauW6SyHUkbf5ClCQMnXfGQoCEFhAOTY6KFiQYTCgoKVlZ8tjZ651+X3qMWmdLYPcEnBJYIsUBLI7p5hLVN6UoUPXuI+g4EgcjjQF+lT6Ufu7naNhOdT1mb1ZtelnO7iYJQaKjd5DAkzb6mJSNKupqhnWA4LTLDcxBg783p4ytVRSK7OGmm6vKegGyfdiYzwbMDiEgdE0xZLa7Q0UfHjHPNGn2cvejfTnqrBDIGie4Yj4OntTDy4sisvd3GVvUnAVaQ4Hci9vTpjBAgb7jp7gCApGQOA1A74TbYpvcH1L1vHJs2pZqFZAwgJLRxhlWCuCxLlSGxwAACWAbZaa6NaBkMXiFPW4nEDqtW7lxv6BsQmvaU7FFJGKAAOqMOMsJgoKzmSh8Q44qdOdTm1EjGIbV6fFHSgEFWzOS57IhBoBISIM5t83K/ARHdDhqA109d9HIOS6fgFxLlM+mxkF3omw1wyY9kVNCAB25dhkL3MpR0ukPh2IqmXxZRgXeY9E5XeJL2HlFmLRyHBchlCQctUSmNbA6nJiHtbjYR3WCtXX46AAwMRlZAumISAcwsEkzV73ue2d8O6hwO1r+013vZ2GgZu6NrxXn0o/Ddf/TQDBDaj2flqAvZ3SN04cA88kdqaCfWrfzKs3v6ma7tHaQDQBlS49IzCYPOTdl1H0gw0UlIiJ3TSjM9qIeZjck2aDnnjL9U7Qbye0d6n3kyhp0PQbFZXXkDjoGABCOX2SC7/dvu+0Bk027kPBCAh2naj1E4N71AWTNT+ATi6QRVPIjK8soMDqtcU25D2CAzhoWtrnHkEVF1eETTzeVbhJ5OceDrL7DpAb+fJ2EwFngs2cFi0mgoICdIaDgYGMepcGBkpwpRBAFGokRnRAABwG4dk5hLHoH0qkQd4HfzLfi7y54EP9TIIeBJwZptcK+DKTsgruOzVl1kMBLcseCno/jhDBQTUIzkwzgoKUBQKKCSGrj4bW5ZGpxn00qanD5JwO2RiACyCIsGcE1oXMzY6APRyM0lMXbBtvn/dn952rJ/3fdAB4tu90x3hA6MyxrN+L8yIH56dl+6j0tyWK7hltNoDiIOgHtY3TYJVbll69+Q3+8Fc/2vsU3DNnDNIzAoNBgXMeF/AuDaCgMU3soWD2gr8SKBhqCUb0+1WmkxXWwUILGb7xkvvfne6299+fnE5AgXWmdn2ze/fTvXwHGIN6YmjnKqYDnZtvdobgxvlmdwgHcGCOn+q5HKKo5BGiCOXNhLcK2u0mqujtpqpJEXZkTqE66qUQOjjQDsj6EgcR3vQg0+JQhB+7EMym+i5lYoISkLwWXXVd/8Dd5N4ra+pwybuLMljiB6jQl82qQfA+Fx4A9PgeApp7DJLlfQoEFMTUQlSmgpqGxWtawuUKplAgwEBiCgW5+hN4KEgQx03RHuzrsPfbmNXhqBF4Sh0uBW9VV+uug4P3mU00A4LjSw1a+l0IGCUTjF9h2g14TPPq67T8buGAZLvBQW+2df1kM8Dyt3bwUGSVA4GiNdjBgaSd+fs9BrQ+PRsw4BB3lUpUZvcLeAoFbr7qUwu2Zsx1zDub10RT4PbpD/14SkMYgA1QnmkINlOtS6XeRiXWO10OIaHN0wwKVEPvHKbmzb5oC+z3CShIGnwHkM53f1HZFkHFxG+AEEg7adIbj+AAVd8awlLrUK71hwAgh2pKuBKwbeIoqI5rMDV0XMRngKgIRUrbEBAoxN1ouP9s06351Qj24e+2Xgxt7/qMp9OkLntfCjmMu9/58PdRqhBQYQDAsYbAAYEFlipagmUFLRdgWcDFF0MWiOJomoMWEM5AQaMl6ICAmcf1V3YigrAlGenHIFPuqgZADvNwUKopo1mboE/+zY7WMOi/30X9/v03/WQeH1MOIPR9vazGqg1wOML2ero7qQjyDg4636w9HMhzFDiw5zo5Yt+temmyS59pDAcH5m8nu8Yhr/9RmBKCvvs6Ei9L93Yk5gtY1C+1UG1UJ8e3UPCUOOXHQnwAAuU83lX606nkbwQ21P5u9ntNyLGmZJfXxqHmnorONX43McmJ1jIL4P1UCOehYJbVpCoCA4QYqMRniKxBY+7AAQcgUhRtgY7MCdI4Kd0UzNR+DpJ1D9QfgKKs+Mc5y2fUWQ3pBiQ9xqvao5oZymhZNAhek8CJYVPxOLmCHWgaZPOxMJaCes8XdJQGC933kBK8c6Df580Beh0xeVBzLIVwrB3ofTGIqobAgMFpDhotQe9PEAIQV52NEJEgRX4GChKwq7/36q5MUpG6y8yl/vZw8B6aZb3BGArq5e5oA2Zp1t8djZwBVFV+BwM2rCj9k+3XdnqoPT6Rz74ACzj02oXumJPyo2gPejgAqs+RHtvLL9nZAkYfdbPKt3keng8YFGHoF7gBmqV7nUmhTOlQm8xH3ntzBgQjoWmpVNzupQKHlfBJ3qijCt1Utv7Fj6Fgt3iLHsP3QGCU7kFMr1Uo12fN8b5bKVMEtUPrb/G+/dqoY/UR+iJRab8JLCDgOlfWfqWHg0wM1gE/M7Co/jZQlCh4FMSTPmkZOw97pgjmBAqLODdt4l1PtshP2sDbBuKL+AwUSBDbO223BhIobY1zXWNmSCzq9ywLHZV9Cg6cMrJbt4GzzJbYayMYI/OF7buXaCLcTWj77SLE63aEgBCCbKNQYIJCkCmPIUibj9Rd1wFBXMamgh4GzNeiOB3aLA6JRZB7B0M386CaDtoYBZvOPDAIkPq01xTcg4K+3to+DwdPTaMzzIXGoMAfOwSC2WDnq0h+YFeuWwUyQ/vTGRz4/PX+BP19zmZpYhoo1y198WBwOSM0IthshnqE5fGE/LJrGxQ0/fpcx+PTswED40FAhY2jrREcNFM6zHvzFBC0L1oOzPWYUSM4INTGtlSOpwZi6ogUk0p7Bwj0+3CJ1wILAy3D9H5PSEeOQ05d56tofwZpeQTUMK/e7OA1a0SDC2iKoLka1t/ftWc/8irjAQUWg4PI5GBBMhZAOouBqmmBggh7SuDgpuAVh7sECptsA48hwTQJ9rleSvQ+2m7FBEHZPPgDMjYgBSBmx6oydM3bVmAg31IR+nmTtR1YXObFVKOQwLZoksyxK2UGAPmEJiEUYW4bVKhHWUOBygqMBCgUUAgIiyzZnAMhrLFAQlgWGUmZz0Okoi0IyyLnL2vVBvR+A97x0msGehgAVfOALXlNoQUCDwlmOmA00xFHUFDMYECZDuo1Xb6M76UZFBDmYxsv9EM5dqDBcX+aqzZzXxUElBuGtn/1avWSBQcHenxprV6LbOf7/JoGYXzzmgdgKMgPtcizfWf6V+uXR4BAEa+++KydktgDxwQK7O1o9ZumZwMGO2Hi4QCoZgXkGiby9/9MwhyXkxwQDEfPPW11wtqDSJEueQILXsXl7+8ol7m99iiNYKDbfrTm+05LMNEa3DOjnNV8tNN2Wnr3eNBrDQApD3LfxTnQ5mkTAnPRMOhgHsQi1FMWIR9BYi6AFGkcdX6zR3UqWeZ6/UTcwAETIdqMBQISiWkhxAgEHenbPPycpRwUEpg13j4n0QCUKXtcpubBoGC7CQAoPBgkQKfyIQRQzqBAyFtCvgEcqjAvSaEgb/UPSZZXzlsWCNiS+ALeBGI4QbUMAgpZP6FlME1FI0AIBgJFkAMUAuIaRKu/RBXuAXGJyFtCWCLCIgAQVjQmBNkomoSwynENEHRxG8pvnaIJt2KlmAmoCnsKYxjoQxvrOQYE4NZ0YDMNDAqYsYOCWY/txxd9vbU6a1BQfAwAFAdEoGgASAVrUCdEAwHqvnttQQsFcyB4Ul9wsL1xxrN7zdTqrBCADhC4A4TSrzqftDOzFEaDLX/cwej/vlwpT6FfTeuNkm9bdp4AgQIL0z8KvkedT4GDAg8GR+nZgEFxWmvUXfZSRNgyBbz+4rOyIFIDBZi9uKHCbDzO7bUUndBnDRgEW6jD3Ze8qWPodtdXxgH6d8K9NRnoNbyG4AAGvpJ13wcqumbFwW7OL6hdzXEEByb8IVZ73S/zs20/gsi+Hg4Aktj9+iJTmDcP38EOH82uoWrbRKxRUsmUmiUYUpkfTmJeCAQgLLDAPWIK0Ln7InEFHMxDn1nMDXFVB1YxOUDn5lMJ6lM1DJxucsztAQgBMW6gsJXSjgASEuCiNBcTgUJBehBISDcFgwT9LZqDvOWiPShwUApmULaxlmkDBUGEPwVCXAPyLYoMXhhxJXCK+k4ADgTOYXfduMhUwrBExOuKsDoQWK/zaYZmMnAmAhAhh/q9LKvsBD+X7aFoB0ywZ9UQmFPsUdAii1EwNbMTVaAFT6G1BwKpiShQQBCmKVBAFQoCtSBwCgo6INj1ASfU84dqeOz7IPJfOBfhzvpbfA+cKZkimB0gGAyMIME9S73hcR+7z3tAGWyNBlbvK1uo9pO/1lV+G/nVdVGNpruDAobj9gM6eDZgUDxuMYeDV1+8Lksnf/Tht9tyGdhhjspvqKGw8wdaiqbSETXqL2gDoQIEncD0+Wtu2hJtrRAntAMzs8nR/YBd47nbIRzsszscLfXcw0FQbUH5rnAQWWz92cEBM4pvgI3ElkBgBYTFN0w/4nHP3nSys8dSD2+QdPrQvASCqjzregtEEvIXCgkUYxn/MOdqWmDW7wYLCg+2bXHRATkXbQJpSGDTKPCyFEAIcQMFQnq8IZsNPjFyzqDIje2fdZVEzigQkG8Z6ZbLd86MpIso5JyxmTnBHD1dVYkE4AYELVtZByGoUA/It4ywBnBmxMwIq4YWzoScGaGbdkkxiClhWRCXiHBdBQouK+hybYFguVQHQrcCZRPIqQOBRktANZxxBQHpJ0ztbzE1TCtQTAIOCMDYTa+dJXJ1z2bJLKBpPbW66oVnNQlgJ/Sjg4LgQMB/P4SCHgjuaQmGs5cmcDCD8SaKoGllUSCBzaGbCMQm9OV6bGsdaD6a/bZ91uf5PPtBl23vzLLNc0xG7/I8/tna7/bbeh+COBp+8pd/hE//4Kf46MOP5vVnYD5ooOCeugDPCAyKSOUxHLx683kp1F1EKEdwPQzMytBvJxjl6a9OS1HgQMe4dcqJgwP4ymAj6I5ku4o3BgHgjGZg6gQzI/jG67fmqfGROJiCNE5OFQhr+AYH8t5Yb5nQwgExI9l36KhB1QPiuQwwqmmhOA/qXZdIzUvkTvTvugjd0JeOmRT83PAELuFns1tvwYpvazptP3oTc4PtY+2Eqx9CqhqDXP84JwWFdr0AsvUCbg8CCI8PsOBE6eGxeY4NjwjqQ5A0+l8IYh4x58KsxnLzM0ibmCC23HnZO22KJRvRBmIEkpkeIWcsphpfBAqgmoew2n3NqZCAKCAgJoKI5XJBvC4I1xXxsiJeLzJ9cL0IHCwX0HoFDdaJ4BBFPTtyHNxpA2oHaxoBZGcC6ECgWc+jAwK4Y+x4n0zLBcDW6cESvCCpNbOpo139nAGBiDAqIagrKPi6KBeIDgpKY1EomEZ1nSXfxzgnnkNnvB0wuPOBol4vpkjOpZEXoclZIIA0j9YvjkDBpyf1uYP+tocCN3oH9vLlCBIIwKsvXuMHf/EJPv3epyq/nOajT50866HAeVdM07MBA3uPNoPMw8HrL17jk7/4gRbqt7sz9y/Kvh+ZSn1E3D6NTBg9HJQ7e9tYUY21lbbpAYYN6QlagaGN64CU5cx6vAeEWWfQb59V3s7XQIRhQAlD6rQHkVBmBdjRUdWImUgdlFX6M3SmQAUEUO24g2WRjhvHLsv+O7WfotWopgPhApa86E1sYSag1h1S/wi7TgsMhACBhTpq46olKM6LsqAQZ3EYxHIDrbLCIC4vQLcH8MM78HIBb4/A4wNifIewPCAtERQegUjeqiAOh6o1CCmCExBiRjpYAMqgwICgaT91GT8APPTtsIIJ6nMgABAQVvExiJcV8SIgsLy8YrmsiC8vVUtwedECwfVFE5GQ46oRCVVLUKYVKhyY9sB1qDm3ZgEDAKDd1jyvgoB+LcfKJ5f6AtRmRFQPLhbme83SruGuZZ9FNHRAMNISNIBKJ7UEIyA4ElITQVuPmw1OBsfbgEkyU2/PBCB24KKDPdMmQEMYo81PAQV7jv62T+13J1AwAwLfVnq589kXr/FHf/kJfvq9T/HtDz8qgEilLPperMq0ERSc8Q99RmBQY3t7OPj8zWv8UEnro05TMHpJI/uLr+6lejjy6qfxiMw5hgO5RK71ryzr6e7oG0CfA0+oQEuwhyDQgtCZ1IQl8h5Q99Jps0I1JZRVCRvtQS1gX9YGBKSAIA6IUhcCqLzLrABgQMBOhehzcabBAPO+rV6Hm+uZyjeD+/5IvuvGugqejKxLh53bDjzSUtfkMVAws4I5NaYbsGygfANMQN4eZPnh5a1Mp1suoOUdKATQKh7/SacCApCplmg1C2jxAQCw3BK2TFiDqNKDwlAflsAEUtTPJQBhjYgm/C8RcQ2IlwXxGhGvEcs1YlHhb5/x5bV+f/FCgGC9gK4v5W+9yPNeXwBhERiw0MQ+6NBgOmFKLQA0QODkSS/4/fse1YWj5AV6qTMnzwFaLZaHzB4GGgDA/vc908EQCM6aEM76LD25n6oFQdZfEoMR9d4eFGQbN3m3tQ5wH14ANKU908qegIIRTKLfBuA3X7zGD//qE/z0u5/iWx98JO2LvJyRMvA57u8zggL2cDRIzwYMgAoHlkak1RfFmPbtevt72HQ5s3vbd2CvQWjgADIapvaM4jgjdjG74Tiy3HAFyRMgMKh7dxvcqGns/CiOtAb+vDvHNPHFAaA4aeqom9Rr3ZkWTHsA7AHBfgfaQ4JkqIlZ1ublIKtnOvlRN3lmbr8eadkDMFYBQ00TXsBGWkTFDoBYZzekm0BCUs1BvICSLMXM6wW8XsXE8HABrRfEx3cIS0RaIra3j6AlIryN6hC4YVs3bJHEKXCVmQ68ZNBDQiJ1Cr0ljfVPxc/Ap0AkdmwFAiJxNlyuohlYXkSEJWC5LlheLliuC8JlwfLiivjyguVyESB4ecHyUoHg8kJhQECAri+B9QKOFwGCoilYSrAh1lkDBgI7MwjvfQGO+oSjNJIxAft6V0+w8+bCaadxL9elZoOHATvvvYEAaLQGVQV/p23fo2j/QHf8u873WXU0LZ9PAQWgahXm6Wg212ya4NEAdDRA+eyL1/iTX36CP//Op/jmhx/V0CmWS6pwMMrt7AnO9GPPBgwqPUnBvnrzGn/8V5/gp38gmoJZUbSjgJ6oDu7H1RboAcHTHODhQI5gBIwcZ4p37SwNoOAMCOzABzjdwkamEiPUnZPlxAlx18D63w6IGkBQc4KUpfs9AIRgz2cLwjBgCl5ZKlnVs+7dRldOgCufQSsziCgjhMHj9PWlbkdzTikld/jsdZS7FVAQLYJ5l4dAiBB7sYFCtOWS40UgId1A8QbkG7A9ihZhvYKuD+CHtwIIj++QH95iXS6Il7cIlxXx3YKkNvzt7SPC5YawPCJeItJjQrxkpMcN8RqdQ2Lcz07wz0N1NkJYg0xDXE1bsKi2QDUFTkuwvHiBeF0QX4imIFxfAteXCNeXCgYGBFfwchUtwXIBwgo2MKCITTUZKZ1cn8C9nON31P7wY15n1h74ptFue9/kQr/FawoG5/XXDN2+Hgbs3BEQAKhagiMgGLTp8pV12q4PDmKnHTjl9WBwxpO+PIym+nxPA4Vym7N+E72/hNcgzKCgA4K2OBmfv3mNP/nlD/Dn3/kZvvXBt5GzDHptoazS59G4n7Y0MyF89ub14aM9GzCwJIX6Gf7kl6J++fYHH819BQ6A4F79K57oAHy4Ue/bYKkZaTvtAeDq8R2wnoa1PAKBAxodpdKe+bji1TxXU8nddMIfod6+BQQi7VR2GgQ5yyABHhJA5X0UKFANQi/g7Zg+m1YXPETsSJ9Y769aisHosggalu99iGZjqnRQjmaTpyD5WWIAJVZnPlHbGyjEIA5rMUQsS0RcXoDSI3i5SUjmuALLFXR5Cbq+BT+8BV1egK8vgYe3uFzfIj+8xfb2EfG6YHmxYXv3DsuLq8xoeLhhe9ywPSzgTWcq3CwoUhfTwCUfuyAsUYBgDaItuEYsF+9IuJwHgstLMRPEFbxc6/d4QQKwZYGBLUuwKh9psEwtBLCl/N7vA1oHmgW4sIcDrwXSw4YjffezXLt+r9tbxXt7rL9nr0WwTU1LeIqG4Fi1ts/oLDVCtPZh792PubK2Ebbc52mgIJvPmUzHfXMLObPnGQ0sPnvzGn/6yx/gz77zM3zzAzeoLccoIFA1203lx25cJtDxp7/8AT7AB9NnenZgYKT1k++K+gVAp06ox/YvZjeqm1y/XMa9KK89MDjo626jPVBtAXs/glmDmtjd7jWgp8BO7cAq7Bw9j43ld/k81ZhGx1RnxBkgiHZBfRColpWHBKB2AEzOkcvubGqZrh5YGg2EfL0IfX0xZ8cysm8DLfVQ4IWPzWEXz38ugqhCQ01WS+z9LDGoJ7nMsFgoiM2eGDGI4FqoQsIaLoiXCwIn0HYFpUfRIMSLAMKLB/DjO9CDgsLDW4SX78CP75AeHrG9fYH0eEN6+4D0eMPycJNgSA+3YSAkAE0URB/tsA9cVKYaGhS8vFafguulNRnY30X8JjhegOUijpVRPjNFpAzctgoDG0v5JnUm3DJj44wt1Xew6bzAM+UfSQAnZmAj1t8MTgptXAcMpO9pbBqaq/yBvSbA9pdydXkcafpo8tnDAIBDH4IxEEza8b009DegqRB9cn/Grg+h2jOcBwW5CDsNQr32vT66Povl8eh5Rs/yp7/8AX78nZ/hdz74aFfC4ifFpb/zgOBuX2+O9i2ZfPyz7/wM/4d//S8xS88GDAhw6hfxKWjSgdDsgWBGo7958xo/+usfNJckVO2BH3HP4MDpD/YNhMY/ePA584noK9oZyvYjC6t0BgmjWR7tyaI12AV0mqbZMW67OmLWaZ1J1ZF6P9MilPunRi1ZO9i+A6LiELebnTT47iGiQIFqHcxEIcTOBRASlYMaFwyGaAoMCixm/qbhhiscyDlmvugXNLJFhMItg1RTsASZwbDEgIWAawwCDgoJSyBEIqyRsFDEur5EXF+AtgcNhvQIWh+B6yPiywfkh7fA7RH8IJoDeniL5euPyA9vBQ4eNqTHG/iWsD0+grek4ZS7UMp96XehjcNapx3SGoumIF5W0Q6oQ2FQGMAqWoO8XAsEIF7UkfKKBMItCQTckoBA0RKwlPVDyti4lvv2lDIHgYgRCdgKFEi5M0tcjBQYlLmJKVBNCccxBIZwcKRF6Mu3+dUPFTuBD2A65fDJMIBu/304KGbQiVNirwKf9Wv3+jQwF20u6XkW9bGAwlCbYu/B+3vxvtDrHZv89Z99/ICjZ/rx7/8M3/zGR8NnKwM2jAEB6CDBJfFZMPPER+ODND0bMJhBwdAZrIOCGZH69Js3r/HP/+aH+PHv/wzf+3/8+6X6GxzY+SOzQgGI3VWPVW1HletI2zF6jlFd8fku9is3WjH9+wwOhlqDsvMJsxd26RgSADinpgSZuaDju5H6ctT5dNsaZy/dF0nJnyr5974MFo6Zco2lkMhFqms6Ax2JcoWCjUXtvmW5Vs4irKqQwtAUFsh8DUic+ghYgmgN3gbCEgLWSLjGgFUh4ZYkCM6aSLUILyTYX94UEm7I2wOwfk0dFR9Aj++A2wPywzvQ7QHh9ohVZzfkx0dZa2FLYAWDtKUS2Yjd+y+QZlEKdSaEhDheEC6XOptglemGwWZTOO1AVghgMxuEBVvRDmTcNA6Dxl3CTWHglmT/pvu2nEsUQin343IGpKyXQEiBELJUTeHSLGs6KBx4M6NpC6KDAoshEALdDSoEdCLHt6munxqC+XAbD/fP/Qbetx37zB3Bwny+vQnQJ/dvToYbDAA2uuay3wYc5Lsy2oMYNb/maTzAqD96W/9oAPfNDw6CF2kePCDIIISKyWr0trwj47c+6Kfs79OzAQMPBTPT1kgVdca/4DdvXuM/+Jsf4l/93k/xzQ9a6LDq7mdElO1uNO8r2uyVl3za9TsyGMHAobagv5Gr+D6Z975V/r7SjeDgsIn4ofK9dBf5fUdYctV2YnTyXsPr+217W6F0HrqgDwWBBW18mQFimSKZg8pDjcKUiEtYBR/1EGo+YPUz4E5QbZzLKnxVYCkkuGe2CIKByAFCxhIhZoWYEYlwjR0kLAELMZZA5W8NC9bLgggWZ8XtUbQIlxvwQvwS4u1BYiDcHiVoUtoQbg+ygJOtzZCzwIAuCy3LQGtRjpY59msXaLwBulxFU7BcFAbW8odoJoO1aAduN1Zhr38M3LYWBh5Me5DMfNADwbnyXYLA28IBgRhAwAKGRN1lsEXJJFuHoIK2hwI/o4SK5sA7CjLuBhIaCPx7nvTDNDxnAgJ32+qddOe4Iyjo+7kn93G6g1xbJFWNBt3uzQ79dUYam6PnaPLm+u9yjCvLJ5lKuM1YHYi2231Rf/aFOOL/RKc81jR/mmcDBvegwNL7QsF/qlAwIrk6PrDRdwsHIJSASEdNdzRz4J5mYBdlrTl3cjfdvJs8SXYzap5hVKS1Gpo6UM0JQNNpteunQ694YgQy7eQGmoHy3Oc7xmE1aSqPn/Ghz2ifISJSQFBISAoIlBmJdM6EtdjMVqSIgbClcf/YQ8GWGbmowqvgSl1PaGFzg5oKwkZYYka4idB/iAIJl0B4GwjrEnENhDUGXKL4IKyBsCRxnFvDBcv10kJC3sRpMW1AviGkWwnDzGkrCzfxJms3lGWYs7PS28qH/QqGXXhiiTWwinkgrrAZBgYDph1IOvq/qangMbFoBzLjtiVsmfGYBQYeUy6gtSUp15t+AhiWqYFBDISFqQiGBYQtZCwDdbm916jkTNg7hcp31RaQjy4oMGBRLpUeO3X+XFvw5DSqhF/2mgB6M8JuuqKfzjeYyrfLEsZQcLev2wFDK9mD0+axARpXePCQ4B2xy1PekTMzKBhpC0bpqDykm+7i9nTbLXkoME36mdf8bMDgo39aSciq5tSpbLdvPNL+2zev8R/+qkLBeycHB3ePw74h9HkeUnPXQBrG8INrV6HLXH52mo4ODuR8+W1ag1PYDLTmhJkW4UnahV570F3qTEjmI/NGF92srPJWpoXKSnqUEyxkLoVYfDEyJNLiVtRF0mKDDoESdIYD6TEmP93DZFaTgoOCW8pIWaMKTipRNEETqAi2VbUFphm4xIDLxngbgDUG1SAQrjEiEmMNKgj1GguJw+JCqFMf0wbW72V1SM4AJwQn0MZdWwUsEQ4WdlhCD5dIhAoFNsUwZWBzMJAy46amgoeUcNtYNQSyXsNjygUG5C8XEEh6/tmytN5EtAXid1L7l9oYSP04ohrYRPjvoWAxbUGwzpxhkSxtQS2yFTdLWWrdnpgA9sU8Mp21jfbuImmnAxJNOoOBae5U6rQFZXMPBU/s7xK4aAJKXBNy2lKugAAIJFhXWLbzWLYcPctXkXw1bUzTAy21H6B+/qZCgS0DINqS+/d8NmAwqnr3xqZH6ve/ffMa/8Gv9uaD2Rrld9NMoLqX/hQgaJ0P22doTYV97aTdVzfWr6GebZg7Gd3OVGujhaOK1qBAQPdmjuBgqDI9AIBhqFZu97Un21W7m4T6rnV0wyGCWCEha+hBzqpBiNL/MYBMyB0cbBCnwyUGJCTEDHAISMgIGaKaHhSBCTMTZDL3PpeFdXyygIUhhAIGUbUDHhIui8xiuCgYLJR2kLCf2RAQ6AXiKveRx0t1hFvWcXA1dkikBgaEfnXCDBRHwaxmgH5GQQ8DGzNum2oFOONxa2HAoMrAIKs2Y1Z+IYRKyJkRA5fpiD6J6l9mJixBZ4gEeb/ml7B000eDlmWkrvxcaOuiKfCaAyvLXf2d1F1gbxbz3ymAeOyTswvH7q+1u0d/3/ae3N+bwhBITM7PBk7Dfg/Sb93r86TboXIfohYSwLWPMy2CgX55Im5XSQUwngXQP9SdROcOa5LXPBsc+EGcJfMp8FAAtHLySG49GzCYvRwvgs5EfAoEfP7FHgpGl38CB5f8jNLIxsRu39Bk0AFBbxLpr235tf3eWUWOY9EcdA96ZE6we/fhn0vSUbZMyxzBgcthVU9M7qSHHcVoH3aiLRQ0UHHHZlqWrdbno6x2gBDBlEAcKyCEiBBX6YQC3KIsAgdMAJNMwFwogENGArAwgYOuCplpCgg9FHizgqnEq/pbFirakkFCbiFhE2F2iQHLhgIJSwDWmLDEgKv5H5igo1YtLrbygKggSLF1oJP81Pz3s2hKZ89qoVAzyijGwE1H/w+ZsXWaAfMZEA3BHAbulxkJxCGLVM8oUyx9MiAIQXwOFjI4ENiiUlZ9TAl1ECXREpjGpQBBozXgVvPCeVzvB3W21FtOsNgf5YWY5ou9PavVkt1bCrm/1+gakofunv35XZjgUfKDon6H17KN+r6m3xuAgkGChSm3tW1tUORBIPiTh85/Bw9xIpmMajjKXVYZtXy/lz774lWZsl+g4NTotaZnAwZlGpr+LjahRk7RXTj4jZoPRlDQL3bT75PPdv+9dBYKmufiPS3PGsXoXgYIDUnfyWdvuzo8lpyvwQwOLOP3AMGZI6ZLPM+AoO9Qe1gApg5bTGXC0l79nbPYyDmDA0t0N7tXXLFSRCI91+7nfA6kslp3IFPdEtdV9DIHIGZkZh1liurTxwXwAs6m11lnKUAg1wqUEQPhgQjrMoCEztxQ/wQU6ne3L8r7rfPv4Zzs9qOXply1rta6XWGXoTM1vDNhN5Og3TfXDNy27LQtWj7DaQcCBylzAwLeJBPIx4qgMv3TysJ8NuQYLTNqoWAJGgvBmWEoZ9UabDuzTDEdOLPMUV2tVZpAOlMHOqW3AC5nbey+d7J2V7c1cEB7od8U3mDfbCG3u8Dhn+nEIM4f0S9O5dNscOQ1ojYwKnAgFwVQ4YB1Wz8D4MxTeeFf4MTMFW4/sN8HnAMCYDI7rwP1cqmDaz4bMNhV2fLCUZbrHZu460v43DkafmtgPuiJbrZvV1GcfPBpBAV+31OhYKdo7AfvXyKdgoJea2CdkcEBAHa5bK7IXWflAYEMLEahl3soqAAw61yLtzyLKC3OcuUxfIcnyxQLKBBASVTfHBQQ1N+AlyLpOK6IYRE/RbU5E0HXjsYeDpbod4qAT0HNERkeIm5aPplpZyPf/E+FiIW4gMItVUiIAVhiVECooFCnPYpQtFkOtp1USNoyvSXQD+or8ysn9rNWaxCnNrCTxXKwgEN+lobNIjAQMC2CgUDKjC2lRivggWkbtPulq84CAqJhMWBqwImClIWW0zW2jpw9FPjfxfSiTpxiPtg6IDD/glTqa6mrLkzvtK6aP4zWVbmearr8+Lq8ENMe8K5zmC/nDtyFgdF5PVycAIQzg7h7adT/9YMjqBpetBMtHHC5SKs58I5/UzgY9Pl7zUA7CCqDTdrv6y61kz8ASkTDP//Op/jon37UTH9tzuk+R+nZgYGnrHvVytPb51+82k1JPNICHGoIZiU+AQSfZuqzPkAGMAad0b6RTG8iqUEEmFUiQh35HZkRuivWDBKhBDxq4ADwJXUfErJ7SQdwMMpNBwXWydrcerbRGSAds7+9OdNrp8spSAeskGBx3zlkgC3WumkNVtkfM0JctVVShQF9JMri1xFDBG6pwEGkgJAJj8gIFPCo/gciuDICAbcklBFIFitKJIJw0el6PpXfiRtICETALVdHxSUU7YR3YqymBOqmRlITSwGoavmj0Y2xTBnFo8YQ6KcQ2qJGBgG9r4XXCtg1j2DA0kK1DGKotv8lRqyRcFkEDkQjQLgU34yAi5lYFAperFHXqECrSWigQE0H6SZagnwrvgWwZbPN8dCmeXIWf4hJHS11lasQpxDAW3YwiwIBhBMmAhxBwR4ShjDQHTNc10V23M1LQWcTlCzhv2WwR80AaQQSxWqJIf/U5+j3cXUz8emeWXX4WN1v75tQVzDfA0K/r04nd5d2MujzN6/xIw1etAvup1exvzPp2YBBsakB6Jfp9VqDUsFQ6e3zL2rwotbRUK9hv3EABH0F6CtVj3vcqo/OpukUxC6NGkEl0voMX1KRAACtKcKBwG49CAB7xZ5dYwAJpdNznG9w4EfSI0IaQEGZSuc7W5tSN7pGifgUBRZCEM2AB4Sg3RPLnHMpiyifLHBAIQIkyycTASFbp8egDLxcI5aU8UDALTEoZwQQHlNGCMBGYkM3AR1Dxi1QGSl7QAgqHL1gNGH5CDUxpEqZJiQfttTY201YAijOjHVqZJ0m6adL+t9Hqbfz228T/PV76yw48hHon9U/L+x5NR0BgWlOvJbgEkPRmJij5nUXOEpmLLSzOfS6hDKbo1kaO92KlqBsUyBgzjIF1E/5PPIrsBkyAeCkUGAuBh0cDBu7jeCJWiiYAMFTYECOn2sPvpw+QC+n/2yAFwpw7rN1JnlH7LPpSTLAbSuL73UQYEA02tenPRR83Pj6lIFeuYKR0qTf1PSMwMCNtSk0cDBKVvifv5Ewxz/uwkSe8iHwL7m7nf0c2Yh6/wdPtvdSpxjs9o1BY6bpINTR3pG2oF+VbZ70obyWgLkNV7w73N5ZXbxkvxS17iPt4Ho4KEP85kGPex6DAit4N+fe1LVFTcu1BXPODSBgUfBQ7QExy/z8YkBn8TsIizjhZyrlaiF2KUuZiWo+Y8mEB8pYArDlgEcNXLSlgMcgPgI2kn7caOhktzh1+uYqX29nfwSK2cFS6CqzV7v7faHrdc9AgaXeDOKDC/k8Hgn9WToDA37mhmkIzFzSA4E4Zcr0TjMlnPInGJkO0tZpCRKwbS0QDOrlrk4CsNgQpe76fX2aScgpFFQ42C0x3F+v00TsNBO78w/qCU0Gcf53GezJdWydEm+7dzrHXTrSmzwFCkYKkFETKIjVg4LPD1cIsABZsgosiv/o6Fn+1kHBKMzxDgruObFqejZgQOpYw2bnpro871BrAODzL16VVawanwL7vGMu2DlzYPzbp5IfhQOLilfu57QZPtkzFJjQf9YYADQNosmPNxuQq6jO+eYeFPR5udt8diYEe/6+01CtAmdHUxnNUtScdZc2EwMOQODAj276aVbTOfUu5dTabrMzN4SA4mwISIFnC5VsHvm81x7EvWkhxhVUHNoIW2CdqgjEzGqbjtiSRCbcYsBDylgXwm0L2ELGJRK2HMTeHgOui3e+C6chIWduhKxfSTDf7gvfE/L5yekMV3ih730ZQqCy7wwMnHG+lKmcNAQCMR/stQQyFXFiOkjbWEuQJDAUtpvWrxYGvBlhZ2c2OJgm6gQ3VeGvf8U5cQQERyP+09MYgR4eBrlsvO/rZY7hAKhmhToWcPViIgBnfaKdPqqLvk/023oomMkDHTaVFN3YxfspqMWx+jGggwRN1adANAX2LGPfAi5agmqK/UehMVCihgkfES6e1jwc1AUlBgsuYQADXWWJg0ow6tf6ymBpBwfUQlzfEMoucg1hd9N9DhqwL9eeA4Hdu4eEcu/7RFCfeBhoRT87P4GyyqT6ExRQUEhgNQ0QICYG1R6w3aZ0qAGAgwaWDo5iFPvrvdTbcg0SfPZDlE48RHVQyhK5z7IcIEs4sJkt1mLaCGFBCAtiBGImbBABk9TLfmExHSwxghlYU5JQvgtKRL/WQz80HvpVk5CLPd475hkkmFnBA0LuVfmm4ndqe6DtvPOg4x0toGSJBhXIax5stwl5E/5BBXBOrL4Ndbt9v8S5mSAQdpqBo5kY6xJFIxAltgMRioYgUNUORAwcDPMGMghoAkF1ULDdRPgXLQK3kDryKzBYvZOKEyLQCX8V/GreGkFBAYIOBnZmgf3LvZuve0MK308DY81BFZg1XHKASNkWEOqxu1zc6Rdt+64PdOf3UOABYPa0pftzvxlVngD6es3aN4AEQOTXnw6WARiZEOSGDgpO+Gg9IzCo7EUwQcMY+Rv85u/2YSLLMfZlQoHUHyc3P8xab9YwS8IIDnoQaKiZ7AmrGk1+szt2n47Wez8CgvKcg8rvNo9wZJyR5pDRSKfWfC4rGzqNAlMFBNYmRaQCOUtnlyW+IFRbJAqkJJoHs796PFcbrTBI1RDsku7jnCsc2MjOOYD1poXimBiiBJUJCQgbQlwRQlQoABIRYhBhvJAswLRlxkIRaYHCAWm8fzTz+T0kVE9+Hzkx7Lz3A4njXiY1J2iZZD0u62wBg4XMXAS+rfFQHbtq/T+CgvLqHRw0pq1A9bcK90wsgl9fSVQQiCSfl0hFO7DGMPQZsJG9TTk0we5hYNUVKQ0GZBYClQiGPliRj2DYBHvqfQmGDoYHpoN7UAAUKKAQinAXH4MAc0CUqYEqFkJsokzuoCBEDIHATy8cmRKeoHKfJROK/hNApw3oYED7xhEgaMcvJxpYKCjMUmM6oHnf2G87goJ9bz9+dvs2goVI9VH6S7z6ogYvMvnVD94sH+QuUKDghN36GYGBmA+c7gXepGDp8zev8cO/+gQ//YOxpgAYw0C9AtfPowh8fW0cOEbavWZw0DqgULl+0AyVoBegff3rbt9XeMuit/odAYG/5BER9+lpGmcvMBQcyIQRARzGgBBUOBeNQi49i4cDLIv4AGy3KRw0eZ51zu6cAgfYZEGgbQMWgFJnWihOYgkIazlfNAgRCwiRCVlH8QmkAlqD/gQgccSm7gvvP98/FNND1m0hZDwmRsjSZgJBpkXmCp1+0SdxndDvmYtveCmuowGJlnmZZQeZ7kiBNOocCzhklGGUQcAa7TMUKDBzwHSa4YFWYBifgVBMBT6Yk2kHbI0DC1TUgoAzG5RARS5OAVjqxwQKDpMHAmAKBbLmxAQKDBqCgwSKYyDwZoRGvDw9HfURPRT4QRPgVOwTQAAB2WkFPCRk7gT/QR+56+M6TcE9KGj7RiPmp8mHAkYuu6ZNsGd6pYPamfwaQkEfQdPy9o/B+VAqisGBag06k8LrL17jh3/xCT793sR8MPkcQoBFKpslRmeDMye7vWNkqwmoICB8M1KhyfYmqNOg9flKLp96P7d/BAk9OfeXfyoM3Ps9SvW+lr8IEE8AgaRTJtUSlFGDCJgacEnqCC0rYLMUyGkJ1HdhAuqTh+UGDqrfQQRn1oBOGzgsDhAMEiKINtEmhIhVR3AcxK0yZ9IIgBIAyRZWygykGBQa9qBgoYK3blXBUbjgW2I83Das+jsEwrZlABk3ACuCQIQKa/agoFBQQ/ubqWFecsVhzNaRcE42FO1dk2oMUCDABP6yhAYIrusyhAEL+7xEnUIYW38BDwKmFYhEZeaHTD2U78O1DVSoN5ELR0Bg35lbf4LeyXBXUK1O8h4Q1CmK1TTAYYEBgp3jtQQcnBlhBwQ1KmH/eZT6vsEE/2z/0bZeKDbqdQ8D5osE2Q5IsQaqdfJeHwmM+8lhH9n1j9Rcx8kK92738Ve6n8367EnvV2UFQ+TXH/3lOflV8jMyIbBbnGuSng0YAA4OChBU9nr1xWf4RKHgo4mmoHm5wO4F+5dLeasnTkiwzOOHQQowAgSr8419jexIEVa9dyq5BjBsWJ6Gu+c7oxk4BoF7TX1/lH3faZpn9XOYJxLIIxVQBmcsalERugGsU1dLCOOcZUlcTiAm0S6QzniYAQIAKobOwWhu5PSVs2gvrPOnIE6IFFpAyAll6WHrqEMEaWdNISLoyG4NERmkMwxbUBCHQtUoxIBNQeGfKCgIIDizw4LdmgKPKePFGvDuJsfctoyHmPCYAtYt46H4ZWRsAAICcsrVHJDnL9GbFUa+BdB3WjQGBIQYYCGGV10m+rpUILguEav6CrxYg64SuV8DojcPXPW7gcASWo2AXwo5agNo1jSwQEMGBfBgoILepscWIKhxCdhPlR1polT7RB0QlHpWGuwMCEjMp8VvYKYl0GMMFgwIQjW/2Rvdtdk7bbVk2bVZHh/SJVdPvNYQrTaBCK16XQ/wMGACrwcF2z/M/om+cqaqr8fugWC3ZPYoI9bXNLIlubDWsv/1m8/xyV/8AJ9+T8IcH4n1nRyzPJQ6eR/xng8YiHt+s0nmlQe8+uLX+OQv/wiffu9nzYIS7mT3Nc9frC/QmeqvtAStSvpyDRIaQJhpD0jVYL36DCjeqeVW70HBdtBMKzCsWEBbwR10+bNmo4y+c+m7xr6uWh5EHVg31EZJoLCoqj5rY9IRv3pysmmPyDrvAObsAIH1ORikKl5E7DvwIyPlKHGulq0EAKlGT8wWrpbAOSjo2EiNQKGO+kg7cQoRUUeAa4xg7cATa6jkLFEDG9NDDHiRQ4kmuHFuVyFc6iqEjznj5cp42DLe3RKuKeLhlvAQE5Yl4OGWsKaA26bnBgKljJwYHBjYJDR0liUmJa/MrUcVal2EySsQwiLTNEMkBOf9vy4y2r+usQDBdZUARC/WKLCgwYcucT+tcF1aP4EeBHrTQLP8cU7qPKgaHh31m6DfrWcwCGFcNASlPmTsBERft+KgOzYw8DAASB1aRCNgI/0nA4HtQ136mNG2VZ/jXRv1Qhrohz47QOgF/Zn+xWNCAxq6Qy2JNc9ETUTZgKo1AO3u6K7un2vQZ57REni5MZIZo/ffjLG0BHMCWewWCjKo/eUf49Pv/hQff/BNANyUy/Siu1kI/pB/RBqDUXr9d6+kUP/gp/j4g2+hLi4yprgGBrw9RhOV47g5Tza6l8TJBQOpTaYFhBPaA6C0pvfVFBxpBKZakv7ZdqlvxG2Td1fslkltL91HKysdiG9PoiKQZ3O3CQSNBRDBMZwHBI5gs/laZ7/G0qkH7kLR9p36ke9ByXTWU2Sqo0WkAyDBkQB9ICqgIM8aBBScCtjAgcMCbxeOIcq2RWAhg5CzmBwSFBgysEVGYkJaxksVP2bG45bxuDBerhGPKePdLeDdLWJLCe+WgHdbxqYahNsmAZa2KOaEHHRd+GTlxcMO2LQDgGoG7mgIXiyiFVhixIs14MUaCwRcYo1EuF9CGrvVIYObQdCaBXQGQVkdMmndqaP+ai5y9cUqsQMBACXcdhNhU+vDLo20Tn04bgcCdaZBrTPNQl8joX8CCDL2MHCvfcIdU5b+tXbp2nqgUc9gV+rKpWn03cDLhLWVRf1V+kvWexVQKP0FHQJOdwt5hrJRf5/QEgyBwGbL9TcdyQ17Zs6leF799jN8/6//FD//zp8LFORUzQ4jP7auPM9EiR2lf6tgQET/dwC/D+D/w8z/U932PwLwnwP4nwD4bwD8b5j5/3v6ms6c8OrNa3z/l3+Cn3/nJ/jog2+7l5Xc8b4g9zAwBIDuGnUb4KpUbUBtKAsA3gdCfveVnQBVmcuPMnKePnj785Q24IwtbJJK3olQhseD1EOBOa6hbr4b/dHmIhNXODJIyGSNVgABCghFg8QMcADlLB2imR9syeSctROvnb7XJNj9/foKRavQFMiIylU4+DV+KYipofx0I0AAQaGhCADbRwSYzdh7n6sZAgiyZHBcsUZZxjg5UNhYgYEZlxh3kPBuS3hQP4PHnPG4Rtm2RVxvGV9LWbQIt4RHPW5LXLQImYGUcnFKLEVgQFeeV2YeRJ026LUDqwr862raAQGD6xLwYom4LKIhsLUKXixxBwOjsMQhuAWMcgIeb08yB+iT7IS/B8emd7jXhrp3PqsLzZTDnWYglLoA5ysw8yGoUBmKxqloB/hLtE3IOQbzzYqEXOFgmA5Mtftj3VdvjydysNCBghRbCwu6rb9ms92ebTqQakfkJe+n5Uffd3TZ0L7l1W//Ft//1Y/w89/7MT7+d/+9oqUmMnWklYfTTvvr9IPck2YE4N++xuDPAfxfAPxnbtu/APD/Zub/MxH9C/39n9y9UhlxyeerN59X0vrw24cd9/Bl8qBpNEQ7K2C3pKlNtbNTGkDIbvtYe2DnMlqtrFV4f22f7moDPEme1hCg6eEJWiEHJpxR8h0Pd9vq7cf395oSIgUFBwmlI/rvyPu7nluS7L4T+6+IyP2cmhvfGBiIrCrNl7CkfqlTAix5pCb7xTBgsLu6yCaaLxJkey5tYQzo1rcDg5A0VIPsqa7utoEB+oVsajy+6HNOdVOCP0WfU5TG/gKsZ++MWHOx1opYERmZez+nmpL8MIBz9rNz584dmRkZ8Yv/WrEWySyRzA/BgQAHHQQsJoINCsFJwMzgOhgMoBBS244GK7NBwr+XE/Btr3RmqPGM63svIQMdDPiZYzM9iAOjOC+qz0JIQIhY4gKmiJVlaeTKuoyxNEh4I0dccsF9EUi4rIxzjvhkLTifiqoIuToryqtAQinc4MAtdRyLrS6wuAJ+dYGZCcyZ0KsDT1ILSfzEJS46xRZxcAkH4YjXgyWEZhpw8v9G+q8Br66sHPD3rb7vFQB52QIAgA4CZADHHATU7DSDAXlv6sHWf4DRImJfeyaPkhjZ81ijhtsHLFP1Ll3xUZnJ72779Ld9HwvotckVnA5Vhf5A9bNh0+b93oRqNvjK+YxAMDEt7BXOePYXP8dXf/K7+N4X/hDv/upn1feJWyZNpxp4X7btsfpxzzLcPvv454dV+I8KBsz8nIj+i2HzlwH8Xf372wB+ilvAwJVnrz7C1/70myq/fH4HCvyIVOY38sg+tEe3NlgCqDH+HSCwzbAdDGzUAz2ONWzfWEdZ7qpEd81fYk/q8kck9wRRq2eFAzsOtWiTwHw2MnZA4wylfs8V7108qgaBGcRoaZL1AlUVIWgT59wGd7O9UW6QUIl6DxT04B4WYNejzSarM2P9igeEPtTtdM36QbtiAAhOXo6pOqNRtBDNSU0riygoIYDjAoQFISb5OyZkEC5ZIOGSGXeJccmENxi4rEHNDAIJ1dRwSuqXkHDJpXNYLGxLIEWdmIUuDjqLr0sOiaaOhKOp4EmKLT9BEmfCRVcbLLraYIkuDPF6UfPApc9LkC+wOAJgWTLItjogrzJwltLux9Ez3k5KN+kAnFfUVQPD/l0iLqDCnY8X0EGAfXcGAtX8ZIrA4ExoJifgUB3Q1v+gZxFAVQm0kpvUxHWb/l4fEI67a7s325bPbuiXdJD0dvkKCnpdZ7BQjzMc90F96sastDOxPBpLhvLs1Uf46p/9foMCc0Jkc2ImNFO1Uwtu9IV69upn+OpPvom38ebuPv+xFYNZ+c+Z+d/r3/8TgP98thMR/R6A3wMA/C9kGxM1KPj1PxIoGMuh1LM3aLYb+ezjP99s6w5fK+hn0woCJrl1cABs1QO0uvjjYdJoJ5R9MwQcyVu1uMYITFUPdmFUa1V2jma/tAEF+2Pyxa5zIvv1XjUgcO2ERhUhEKqKUH9EB/feg5w3oADgABYGUOACpGG1Q2XAHhB4VXOCzU4VGuqs1O5dHmap1T/BBpsIxNQGnZQEGNIisLCcGigE+cz+jukOiAmXKErCJepyxkh4o8gqh4uqBqM/wloY59ICKc0yIMq94yE5U5+5cS+DofkNPKmZDtHFHBCFAFgIAgPne404eNFIgmsDgctZIGCVz9jAQKMOvtY118GfWE06pYASeiDYWz2w5yOwBwH22x4E/D7OTOCVgVth4JZnEH4fewZrp3VdJRgnNPa9DgquzbaHQmMWVhgs6KokryxMYKEe58bZ9rR/vUVtPhhPZuWrf/b7+P4/+Bd491f+VvVVkStrgCDvZSI08WXbAwQKePbyOb72k2/ie1/4Fv7P3/tnu3X4TxEMamFmJqJpM2Xm/xbAfwsA9CvEh1AwobVdIJjdPN332V/8OX7jX//j7TGBejOIS3V2uwkOqsLQ1AM5Pvo4CDOfhk09tw3xJklr1lDrb28b40b12HuwXNnrkLarFbazllol/4GtR6LrkNDJnQQEZ24Qu70DhaGjmsHCBhQGxzQuuQGCBVOy6+6UAnahcDlfgKwKRg2h2ysK7O6TdWbVjqwzViKqUEBJ1AFaTrItySviAgoLkBZwXHCKJ3BccLckrOyjK0q0xScpILOEWjYl4V6hwMdJWDNq6mRXbQBNUbeUzZakyMcZmGUuNEWgJi3SV6rKwFnNBZemEKwX8HqWkMOXs8LARa73etle44Pr2/kD+GiDMYLiAi4KCDGhRsW01QXUBx1idRDccxjcVQPc570icB0E5LyuwMDw7PnHzJc6/NTkRfqe7Me8fwFXc8NhmUHBbJJm+3Ylt76HIcqA9lMbWJiqCvrV+RDTlWv9bF93bOt/MK6M5fv/4J/j3V/9OzpWNAhh8yugAFDpAQGY+LKhg4Q6Pv7at/B0Nml25T9FMPj/EtHfYOZ/T0R/A8D/75YvPfv45w0K3n5nvtPeDewG0nmDfPYXf47f+B/+Cb7/X/4B/t4Pv9oaR22Yur/afF4PDrQ+WnYfqx2S/VTU6urv99s2RgcHVk8uqhrsVPfac8dbIJh9xzYRyZI8+W20CGfaQdmKDktKRU7SvAYKbWazDwsuoo+0Gee8xmZSUUCgtMhgZI8acz/oeyjIln1Pj5NXWfJmoYh9cie7Fk7KpiA5IRCTDGymHCwnAYXlThSF5U5gYbnbQEKIJ/FJCKmFYHYhmd/QsMvmuOhDNDPLagguXBM2OrFJlgTq6gAi6kIQmwNhoJax8EEwcLkHX87gyz14XeU1rwIGphQM1/Ta9ZTX4ZqGAI6pObzHRU9Un21zBlUVR6AgOCA4iEL4wKBDt4JAt80dYAbhh8+q3kf7XmA66KQqK+x+OPZBHRTsTdJ8JesERTdXNaD2FPq2gcJofqg2+lqJG9SDvYnmrX3ueJGH83v3V/6229bqY5DAfrv2yXa23ZjiyrOPfyY+d37SfABu/ymCwY8A/BaA/5u+/vCWL7UlHcckNIWCvRun7z0UvPurn+kPOLkJtxaJ2DXcyJ1G335vp1F2n80krvm5bY9vlbPGNjRGDwcGOhVZCywHwtFcoeuk/OkMp7jn/NQuEdlEQUCBTVXoH34DBei+pL26s+Q2nwWtfFAbLWnfZ7JdUwoKbIkbB7fErWTU5ZAU5P1CoFVs2BbEhot2Vu6aQO3bJntzYZR1BWfJ1MhZ9tmsiLBzCEH9DQghRVAI8urNCjGBTk8EFhQO6PSkKQnxpOaGO4SYsKSTOC6m3nGxFOCNRJpPQZzQwLLywe5bdjfXAk4TiSkA1EccTNTiC2wcCNczKK+gfC/g5MCAz58IBFzO8u/8iQCWMx+UVVSasuYHXUMiu5YsAY7SAkICSOI2UP9FNRmQQEFMQEpgUmdQik0dsGWm5gdgPgkH/gHA3Cygl71bBtxt9yQBGdRnIHDkaGj3zSCvG++1PqYa7KkE3dZxkDVlc6bMdZUcAWGso3UMblIDdKBQzQ8War0zNQCHtvqdAf1WT//5MXf64fFzN8GUiZgV7ZPHCacrz159pKvzro+PVv5jL1f8HsTR8H9JRB8D+GcQIPh/EtE3AfwCwP/+lmN994vf3ioFM/mnltLd6NeCgl9C6eAA2AKCL17qPDIRzGBgl1Y3T5dutwekAcIIB526UOHoaJowL52EOemodh8dZl0uZVW3ddlcB3+CUxSADhayq2kFBjRg6CJEEhAmoFD9FErWzl894ImAEjQiI0n+BKDJ1aWAODQLkaoOrNBhUFDWDL7IwJbXDFRI2BnYosZBiAQKATGJA2JIEfFuQViSmBTUxFAh4e4NAQczN6S7zichxJMMaGlBQZDET4w+CmMduLiDvNr3oik7FnrYRxuUhEQMymdZSWAAYD4D630zE1zO4Pu/bDBQTQZnlMuKfC9AAL1u4k9w+7WLCla0COiGlNSXxAU50u7TYKLz7UgJCEkBIFYzhL3n7v0WBCRxVbuGXgXw24ABHNyGIxXupucLbb7KOuj7+yk64cOe9X4mMM7CByjwfXFf+f6Qvs9y74mMZLwM7+gGM2c+reLRZG/WD/9Vl0F9rqo0oNsUDob9GxRMxseD8h97VcJXdz76Xz/0WE//i7/bNfa6hOPaTTwg05ugwDegatMK/We32NpcffYKTR+OHSCYPVQTtWFbXJ3tYd15SHyMcmmo89zw9VCvUWa19KJKmX0A1CArsl/DGmg/YFK33Zl6hg4ECNwpCR4WDBQCQVc7uGVw2QFCFtsyAeJ3AOufdPA0x8MQm607y0DGmeWfDW5rBuciKkLhOsDNQg+TBg66hIC4JFCKiJ9EUEoIKSIsEfFOQWA5gU53oHQSc8PprocEMzcYJFBE1BURrDETisrcflY7lqbGGCQANbZAzqCzS1XsVxV4GDjfq7lA/sblDF7PyPdnlEsWkFrXer3yRU0zN14vAQCJGBkTgBzAJNkyOee2+sNMAFHgidLS+3GECA5LNStUILDrpiqCDfYGAiMEdO+vDPzDI6DvbwOA0ZLov/N6euhxt0e3Dq6z/mtPOegGSmqTGwog/ZttfzONAhiXlcsuV2bytxTr+HQcYrJ08q5DHMaowzKDAzuG/1mdbLYl+wIFvBmr/v/LlPB6hVTSNts4wtz788YR6ioUTIAAOIKCvf1vg4abFYIjIOiux851mNi2rHvoVQO3/w4QBMJmuROAavtvuSGoUw32yjWxw5fOVVN3rJd6+F6A81mosw2tq82KqIcFC6HbQupGCU5kCZLyBWZPppLBOQB0qWpD1VXqigT1SaAgXvCrnkGRJYDIXKGgXJqN3NvKx2J+BzkEhCVi1YEvpARaIuLp3JSE06mZG2aQYJ/pLFiCSaVqIw81Tj+h2cwnN9DLxhZmWJcLtkyEzhSwBwN5RTmfqzKQzxfwJavKIuBUVGm55RpR4QoJAeJPUAIjlgJdCAmKsfoZUBJzQXXydCBly0Pr9QmLKARxqUBQYIoLUDRSZB4hYKK+yOv1wf51QXwPEGalfy7as7PZ7+hYU7OCM/F602/d91rfJbVr25v6eQsgyKDLN/XNFhfAvid1td92EDCDAzvn1zRFzysk0NAt2TcoGM0mB6f3eMAAw0l3QpcMbGzLAq88Nc/+3b/toWB242Yw4H+/a1TbbbcCAfBAKOjMBwcP1AhNoyngqu9E6zDbMRnVB3A8B6Lrdkz9T9q27O8t8UfKw7WOsHYEk2IQQW3P9gxJgxHFQNWCCGA11YDFqS6bLB4iYowNDkoQGZxI1AMKWziwE4gqeXNBSPp3CAih9KAD1AHPBkE5kckwoTNhOgf1OVgRlghKEeV8qUpCuFsEEk6L+CQsam4wSHDb/KqH0bGOpu3fnaNUHj64EHGpqwbqksKLriowGHDb8llgoNxfOmWA19xUA/UjYFk7Ob0uAEChIKQEoMgyRFdCoM5no6oDMYHiMlVXON2JgqTqCgdTVhZINEphwT0zTEZLa+3NBv61tdLjcq2H2euC/PZQtzVgvqXnmkFC19Y35UZb+019mP9snODINoJzEK/LrZut/qFwAKBb1t0AwUGA1sWffRes2asI43ncCg9E+OnHP3NQ8HRwYJVf7V+35dGAQVWTPCDsLPHbfNdR3LOPZUmiLBlxSsHQQKYw0O13o0Iwu+G3yGyj2WQ0M+xBwa7T4TUQOCgKBNPiKMHkY0srPVMN7KP2DdQogwXDc7dXneF9OfhwdhjyH5hpAaYWACsJKISgSyOJEIklql+xMLxR7PtlBXKQI1AAZXOYDAhRZo7ye4M/TCoIRUwJpRTQEnUglYyNVFzMAwDQVQFlHATXrHkJCIgBZV1BZ/E5KCmCYsCaIuInCeG0IKazqglLc1w0SBigoEro5jxn8RSAw3bdxQ9gRqnxBdySQl2hYTDgVYG8ZpTzpcKAKCkeCHj/egByPXKpcCDVDaBojofiW2DOmyGl6sDZ+WUoFIS7N8DBgkc1xYAtoFRIFQi6PBYKBazvm+kAnT/BzEF30153Nox34WiYuwYE/vsztcCb3G76wU9jn5+qwX4QHSY4oxTC3ODA6jKDmREOjtr1FBD8OLCFlSkkHKkJexNSLc9e/UzCKI9L9qndmVuu+qMBg9rBuleRjFogiE0x5zqVeJ59/HN89V//Iwku8WZvPtjchCNlwG2/2qhmxdmdOqkKQDfMbWxtu8Libb/7SyxeNRhhALDLs4UDwDkj7gAC6vH6533sZ8aO1T7Ozr4xqhhmUohDj0YkQXlI+5sAgAqrVz2QCYgs9coAEsua7hgSQohAuAggkDi4SZAbQrgDiqoIGGBzbC1cCiKAXCTtsfyTWcnYvL0dPZcsMrmDhLLmbuUCrxl0WcXskBLW5YyYIsJpQVg+qZBQoyyqrZyS2Ny7oD8zOPBQ4IM5cZGAQxZ90JZrKgyUi0KAOmGWdUUpZaMOlDVvYIAHO9aY+tmuX7uW1Pwx9NyrmaXCwBP9dwecniCc7lQZWIAk8SA8IBRQddRcmSsQZBZlwFZ1VBBQKMhudcctbdNK1Li/tqUmNhraUtctTfY5BALdMEKB7bdJVey+/x+2dJqc21y2bdMpzd3AfUv/PWxngoMU139XU4X9DgCKteOqJqNhRdim+uOkVOv47OOf4as/+R3JrfDW59GtcrHvDv/2yuMBA+5tWQSTTvSpUwho5gQv8ygU/Nnv43v/8F/i6ZufbRdtj9gMKobtuw3pQQ6IjhS5DHDgxfVfYtkDn+m+83MHAO9zQJj7GdgZjHAAcisI9Du2wkBuIdXBv6oIHq6piRdjo/dQMC6ns2fYx/dfhyNEkhDCFKRjrqBQ5DiBgEKMrKpBCYwUCJk1hn88icxeJXcC59iyKdpSt7rkTb3hzU9A7BTI54tegFUd5oIMoABQuKY8LijdwGh/FxSEGIA1o2RRImxgDSmiBEJYVoQ1ocQAOl86D/1uGWRUE0Lw56DtIkzajw81bLEa1HTCuR/k/UoM73Rp/hWdueCS6z0tuWyAABAI8MqJORqaIyaliLioanJaEO+SmFXMlPLkDXm9e8Ot4rhDiQvglniyMyGsasUoLNEhs7aVphY0B0Nrlxk8bY++rJD7LOclBzBYECGEpkNinThR/3qTOuA2dMmFHBTMegyDh1r85IXIPahq698brUYnvYc47T2oTKDgdfpxP+AbKHhFAe3zCgrmq6W/14Wb7469rc+zj/+8QcHbT/ubTKZ3NiAYl4qP5dGAgXlCb+AAqHZdm1a1uPpygZ/9xQt89c9+F9/7tT/Eu29+bucHxoFzTHyCHRUBOBxkN+cxcUxx9NlLYrc+LG6oHBvbpm7Un4c1LPLhWx94biS2eK8aRK1RoTbgMzOi9RUTSABk8AW8bKezftqaDI4Qyjpg63ztstrEexxcVj3NWICLwopl8bO1+RIbngUQQMgMpKADQCAsXj0gksGdCKy2eaIgUn0I4MuCQgG4LPJZjKBwBkUCpwg6X8S2Hs51tms+Bx4QAGzk9JIFDpgZuGRwZFCQQVZiLIjjHsUgvggmrxsU2Ex70XpHG6RcHgf0M/QeUlocgRqjQUGgmFOlwYD7ewMEzmTgz9P/bjBfggEI5NWvzohIp6WaUeqKDQWBoK+4e0NXctyBDQjUr4BVMTCV4KIAYFktOyDgFvPBPr/WBu28IkR9IJKdIxFy4I2SUK8BbBDX41APA7sg4DZusrUOQDBuD37nozKafCko1NjM3flM1f7tAX3Z3m+O72cSit/3tZRfXzcd9Jn3QaF+z/4evjstYasUaB2bsvAwKAAeERiIFN3DgYxDW9WgzsJhCSV+D9/7wre2ULDxK3AdnTkq7TSYmf1nlza9v8B4VvpAMIpbc9sPeQI+zs9gChZ25OGhGn/1GhTYNSTCvmNOXaAHAM5U0IOABwQQUEx1qSDdiLpeIVsfaYqCZhxjhh7LLTQyOCAFiL3aMmrEPsDDgicSeVm1jpEIa5DOncii8wXEARAKKzQAYK8ehAhaz5CsiPctI6LlOrj/S0l6dP+X4JSA8z1SOiPeydI8WiLKvTgP9s53ceOYGFOcO+D5a1CqmI2yonnq5wLWARUXSe1cbL3/X6LGSwi27G+mFGx+S3/HVgtku96lwoBfYjiuwKhQoMVmujENHWh1MGwwQLpCwxSCaja4W3og8KszOpXgpEAwqAQWCEpVAgsEtWr+CAOCrOqAhJDmru1N250/xyzAt+o5Rd7vUozBPRRUHwCnDMjTvK8IjNd4ozK4z7vPyJka6n7zJ7CbqOl6/OtwUL99DARjf9ZOaNJHN0Coq2vqd3f69mt9uttX/NjqQdyuZmpQWA5hCws76tGzj3++MR9s1YItFDi8mpZHAwasg47P5iWNa1ANnA3pp6+e13zXT9/6/PxC7TUC58G821BegyzZSVCtvkA1g3g4qBGvSoMDv1SmVbb9eYMs1V4PlAL/nYktayweDoAeEAiqFlgVW2/TnQUzaqZF9g+Y6qYFrGYXBQTSS8kNDmKQ2dzegyYzNzM16E90MzdZ1rYyIxYBhUiElYFEuQKCKQhZPc6TmReYUAIhhYi4vAGEs3QE4QJJoSzpkkNaUM73oOUEvv9EBp713APC6Yyodvbgl+vVWXbaDKpyPtcgQTtn9U0gPX8KBHaDrZkLQpB1/xS3bYQogCcqlsUUKC7DZA397MwBHgL60MU2CvUwsAkRXUNFi+IR1IfCO1gexnS4ewIsd1d8CU7IANYCBwMCB6smlTKFwIBg5QYDFkp629baeUbXJuPOc1bNW0CFgjAAwQwGRtOAHKsdd2cKsQsL/pg7Fe1XjM0exZlyUG2FsX9+b+3T3EC/l5wKcFDg+sFZ8qVjc+vw3sd5cabiKSyE2MOhV5Fdefbqozp+vfv20+48R7UAGKDgr4tiAPRw4HLswFSD2hgJkmVqLwvjDSoA+w5pb/aNq4qN86+rc2fYoN7HFTOSLu17Hg4A9yABHWl3JogdWWo85/qQbJUC2U7zB2PYVts8GhxY7TpD6AQC6n5o5gJ2+3agIJVSCGiAkG2pIYuDoMEBZ5VfiwzyY+kVhPFDHajkJmBVvwIOYQ4IzMiBkVRijkF8EZZAiPGEEBdZ2rieAf2b17MsiTvdoSx3IAv7e5J8ALickU6aD+B8j7KeNgF+kLmCwjgDB/qB9shRz5sYDBZE/RVzQ3aLKUcHv1kZf8tHIhxNDv0x26A//laf2yBUcDEQqNEMnV9BSFEgILb8EVRfTx0QFAcEe2YD70dgf+8BwVrKcRvTEu1zzTGx+Zyo+r14KJB8FKgmrw4I/GzeDe4zFcCXPVjovoQBNDb7Tcyd5Hy/mpERnXJQYeCX2KfVfQYF1Pb1tn7/2XDCN/Xxvo4uaRM75cBM2xzSkGzJ7yPf7RIGWkTDsb6DWnArFACPCAwsZKeVwoNq4ODg2csXeO8gTORNCoD5GIzf3auffW2y3XVvrhHJQ2AKQqVrfd85UZJ+hlDX5vYPk/7IzP9gcq5bmga6h2eQ2KZmE/vJ4ZyrD8HexZh9xq1uFSxUFZD6UlUjJPYBSdwgkjZRiqgHKPKMFQZSDEAuyIEFFlg637GH9smAxrLapVUFI6PsAwKkHik0/4OsSkJygEAjIORVwhPnC0jX9cMH/dElfcE+yyvKZa05FgwUvGw/k+yBfoAGtgP4bLstmbRB+sYVwofHt7LnrwCgqhMeAMZw0CFQBQGJRZAQFotUeNommBpjEhgQuGWHIxDIoN/7EaxFwLJlnZwDgQQ0Ojh/bY5xeE5aIipUKEhRJhE1qmSAW1IrQBBCg4GqItTf2nEi3OG8Pf6jyd97qDiquGZ77yY9FMGsMEEAVVMiu1nHA/q1EQj0712VoAMCPY4/h9l5Dec97uPNrCDnGVK72jgFBgAgLnj28kUX0bDutjF9DGqBr6NJqTvl0YABYCfbUn/244xcrGevnuO9H72PD7/8AZ6+/fS6+WDHOmZNcbP9BhrzJVR4GaoA6gHB1sFzweZBYt4FhKoYHkQo3A/QNDw49vmexDYBhBkc7HUUs86Ghy9UQ4l9wM1EwWpKEI8KBQRV7QwLs/YnGQIHVBiIRfqWwkAMWHNBZFthwB0rzCI0BhZTggeExFR9HzgUMIsMn4vMAlMQ/4OVCGkHEDhfJKNgvsgAnFZQuQBlBWn0P1yG9ML5AjrfI9qyvyuJhGbAAKBCQ70PHgYm/grXBvlZmakL1MUW8DdeBn/bZwMA1xJIaS4DUQhaOuoatVAjO0p+g6Uux6xBioblhx4ISvUnUKWJJdvkqiqCQcPKvAGCaXtyDX6WkTJC2goFVL8WciqBLGBRICBysEDO52DrJDjzLZi9f0iZfrf6ffEAB3L2rGYDYmoAoArBX0m/tjfRcQMs0Pr68fV1+nxftvMjGoCh/cBPf/EC7/34t/bHrx2HQ6CpBdcCzQGPDAy64kYjuwzPX77Aez/4Oj78yofNJlPLVho6IsM82wm3LyTsBjj3YNpbe62AQAzwAAg8AQQ4k4RTO5i3g/a4TzdXGGHA9vVylf/+gTnluGO5Mmvc2WrKA2vnVvRNhYQgnXTg5gVOQS5jZlEJCnTwKQFcnR8EDrjI4FnhAPJQ+cDQVnwa2gYIYmLITFiLOCCmIksYR0DICghRP4+BEMJJnBRrLoGL5l64AAoJllcgcEE5W+bBdQMKyBnBPnNxAo6yNx4BAyWX18H8D+IcGHbv6wwAnM+C7XMEAC37oToWDnEW5DVuQUA/D6c7MAUZ/DXE8wYG1O9jlUvTfAgYG4Xg0wIB0KDABniDgqS+EgYFfjVMoOZLEMnet9cGDWMGUetjZr3fQ8qt6GA9GxocAH2fRpCH2nvvw5QC+2503x38Dbqfm/RtM1PoDUCwCwPdpOHGspUQulI2mwXynr98jvd++Jv48Cvf1fFrdt7bOk9/80p5vGAA1NgGBPEp+PoP3sOHX/lwSlozCCizjcNne21y17NYS4Zrt+wGt4Hee0BQVWAGCKQOK2y0bZCwcwLA8OBgDgLu/XUHnKMOYtSyvA2tfTbNbzE7GsmadNI6RKLqh8gASpHZUSmiIrS49KixFTKzuGhEAjOBSJbtUWZwkJmFpFUGUJqD2F5uBw8IYoIXQOAdQFhJ4xwURgjicX7Rz1rGwYgQokACWEwNJYM0twCKDvbLf9aBgo8iKLCwtmiCCguc105ZABeUy1o9/2fqQpisIgC2wHBY9gAAuKoCyKoCDarklYARAmJqKzyGEM4WnrjsJTiKCxgCeYWBnFuUQnMmrOmmuV926IGgLUVszfwoJ0hAk/MjoQ7+bVlsv/rFBnxLXR2omQys/XiloJoS4IYPF0L9Ic9eLYfOeLP+wHV6df/B8boqo4DZ10fPffJSO2HeER/1b1dVT9oAwQgDM5+o9n6//x+tbeNVKlsywEevnuP9H7yH73zlQ7zz9lNVRff722m9H1AeNRgAclHGi7o33j/kxhcvrd5YF2ss9dHgvrEQOa993acHhDYQTgHBaBvzhrm/3hfTB/zTgUD90e3v23Wo7/vto83Qgjv55ZHtOviHXQPYUEQIcg0LiTnBlINMDRCIpKOu6kEkRI6IKHUZIjHVpXxyyQUQImi3oy/gGsUxA0Bh8bli7gDBOvpqIyZZmXFRSLAZn0nDiQiRTgiao6cDhZIBzuIcmC9ygjUpUb4ZFkjf3woLABowAB00jMUP/nLLhmWOfwUQYKmNWVMdi6+AZjbUJaIGAgWqCuQ+SqHlMqgwgKYOZHBdduiBYOZHcKQSeCAYVYJELWaGtJO24sDaRhyAwJQCUxPq/Jdzg4Aur8r1Z86eM0LGdNLA43Y73yNAADrHa+vXqge/wuMIClbvsdzSx836twcCQbdowL05GgumsDA5jTHz64uXz/FbP3oPH3zpQ3zurafI3NRlv1/3W/r6OlAAPDIw2CTuYODFq+d4/4dbKJipAdVvoBL+/g3f87G6AVwB9N72frh7GCCoiQFozirjg9SVW2xxg2qg9ar7To/QlykuuHwOe51SzboHoMOyAag2EqGaPah640qIwhgiItmyQSAydYAgS1tJrJo2CEBmsClKxx8Ly2qGEJEKIxOLilAYICAzTTv+mXlhBRDVxLAWaa8xKyTk0kMCWDv1FkxpBAWZLZ4kZw/ZSg+FBIUFLiuoaF6CARbI5SQwE4TPWUAarjiuF+msBzNEjSngTRHAsWpQVYJmCjAI2JgDKGgq4wYHXY4GbzKoqY4bBNh78RtQCJhlN1x5iEjYQIBtH5a2fysMuGZ76EdgJgMACgGkKw0mQIA20B8BQVRJoLYJMCxZVX0O2WW5nDxr7VEbnjW1/7fZN6tCWWT27ZdLTwbn/f7D9YITD34xGRRgMB1s+7n9YED75s8GG7cCgY0N3cRy9ps3jgeANy9bPymT2m/8+Ov44y9+iM++9XRrwnDHGn0XZuYO+f3rCe0eDRjM1t++ePkc7//oPXzw5Q/x+dlFRQ8D1252T4n2en2o9MpAPa6/oZsfuBUQZN8KCeDaUDqv1r1y4+D/EOikrm52gHYVp52TVBjmkNRAYb/D6vwnKDQosCWVRDozDAghIlBs/gjlBkDgOSAklf4zcYU6GwxMRbBSwB0gALgKCRQIcS0VBpqDmZ6SAkMk+c4WFvR892Ahr2AHBy3dcZaUtObUOFMVSm4JjmooY/+3RhY9mKaYPwFFuTcdBNjfaQFZUqaJGoDl1Gb83gQwmgRmEJDnECBqDjZhilcWx9Sa26De72MYsPtvJXQDn7yOjoXeZPC6QGCrEUwdIM1HIWDAm+fsIc8YoDHBQSDWBsnZ2etVVbN+ZchJsDNObYr/fOrBT23PGv3kFjPIgVP56wLBQ80Js/1GE0MW1sJHr57jmz/+Ov7oi9/B5956B0V9owDoREgqMvNX2ytEfjza3/kRgUH7O8BBwZc+xDtvPd2FAXnlzY3ek4rqMW4Agq5s9m8V3iQ6uREQrB20vXeoe6zKwfvxs1ukKO/NzMNrOzAfz1h8R+WlzQ4chlJh0M0AiBoomL2gJvhJarOnmwDBpGVKAckNFKlQzZJnKkKaDBLBwVk3a6ynREBhXACQPvF+9tgGjNItURtDMUNNEH6JWj9wBASKMsFetJ2pkiDKggz6VDKwNKdGcAY5MOhUBZ8hcb0IJFm2R58Tod4rbd22BDFIvogOAsxUsFEDbMBvToJNAXBQEFKFgFwUAlhWDYgJoL+vWe/FLDTxHgAA8yWs11YXHMGAdyqcrTTYv68zIOB6T2H3hBliQni95wtEaj4IAGlGQssmpmfJLhCRvHdmPswHYOBh/UutlvurqYhx+Gxb9vq5TV0eAARH48StI8TWxMz46OVz/M6fvo9v/fp38Jk3n1ZYAOQqs0ICEXX+auHgR83dk0jGx6PyaMAAaANrBwVu9cE1ILjFhGCN5yG2G28PAnTgtMZAGv2P3KBff7wN+WW48XuOiv43xjKr8qGD5exkXOldh1AdPQ/BdQYFVeZkBw52M0qr2OF6ZapgQDaDoVUggaPUNmSBhJAQicTUADEHRNbBIkg+hEwSmMjCGzPrgMwRHL2kLCrCESQwo1vNYP4ps8Hk4q+v3tg9T/WjgcV7q89mmYkiQkhISQeUbvVDAZeLvjdFYcdXgVlgwVQDy564Vyz7oikElrb5wDeggcAiJgK3WqBAIg6WIvL+CAFjbgJTAAzs9mb/oxmg3PjAP/SebfxM0NSBYxMStXsLoPoOVNgrOHy2bnmuGKjPFYBqNiBugBAA8AAHOw//tVn5XiluVtzq56755Pce3P/tTBxvUQhuGS8eUj569Ry/+6fv4w9/7QN85s13Wh0GEKiAAMDOeC+bphWbNP/Wj97DW3h7tw6PBgyscXjzwTtvzaHA3+xDKpzc1J+/UtI6uuFDq7TjjAO4AYLPGmiAYK47DSKa3DTSYfEH3CuT+r6ODCZ17Ovi1QyDg813vF9B/cGdjqvkVmE/y+kqaDJltkrJZnVCtA6NQpDjEQGcgFJAtNYZp5kZCgkMJG5QkFnhoNBmtkkpYGFgJTfbJIGEtXBdzUBog824mkEGrC0k3GCiF+9zPedgA4YOHmar9mveTZaODhairn5IQVSFGCLS6dRAoTo2NkWBq6pQqsNjUDjg4qTp2Um4dMykUCAydNSBPjgFYFAEVC0wEMiqBvg4Aj4fgV82KMGGSrds0JYaNtBnBwHXr7/dA7n+1A1O5kx4i6mgqT63qwOd/8DgU2LqgCkGYkawZ4/b8wQM093RJyBX9a09i6q+aQ9FKLZiGjDTgh6LgOZA6EqFgsmsXKo073cKnDKoB7K3U3AAHtQfHikAt6gENwPBDaDwO3/6Pv7wCx/gM28+3YwdUzV52O7HkFom4+N//f1/uluHRwUGz18+x2/+UKHAlILuJs6hYBcIhpv484+f43d/8v71yuwM1IXndOujNnr1wPv12o33ENEteZzUd/s7/W/OqlzrOvn+LQ6TBgfyyawSo18BjjuwYXZDY2dmf5pZwSkH4imtkBCidJw6ABFHoJiJQWbPIUQwqZlhR0UAAzm02WgkQgYdmhpWt6Jh1ehbfgZaILNcA4VxgDoyW1U4CA0WAqEueUyREEBIQbYtKSLpLPUuRhCxOrkJKNTvUUAKT8RXARhMDwW2VBJFAidhmdy3sdg9oQCG/u2dBCdOg1mvg0QPZJfCWDMX6gT4PucaYfCyZoUCubamJLS8BVyv+UOuLwCE0q4xQh+0XExUcq334g/MTAUGEQYD6N43daCtLuBmLqi+A7lBtgGCAfYtzxJQn6fqWMiDOkAG2Uovpg4wC4ezpjDbAQIPBUcTMmCn/xnq6+L+bMDB7O+3lL1J0aw+N0HMbPONdQGAP/zCB/jsm3Pz9wwQSm0XbRLZjQsOCn5THfGfvv30EJweDRi8eClLEj+wk4aT/Yd996BgDwiABgV/+IUP8L/77//hX8EZbIunPtMMto1x/+4+ZPCftXFv7gCac4y3dW0aqFXU1Xleiuuwhk7LtjvlgJjBOcu7PUcjnfFQlAG+gwTWDq0UOR/nd+BVBNJ/kYJEUQwSXjkTVbnZICGqKcpDQkIEc29qsBUNq7Q6ZBeVyZum/IC1liKzXzM76KwYaNt8idpjmAoQgwFCQDDzQmxhmiMR7uKKFAKWSBUUIjFi0HMhQgzQpZIJMSaJAQTMZ6rqK8KjU5uWaYbOndUCuQDrqgBWNFshay4CBwKXLGGG701BqGGIgZXL5lo+9DrWv4tcSw2xCYDqszkqNqLQtKWG/ZJUbJYaEramApr5DphKs6cOHPoTcH2GoM/QdKzqniHoschZFoI+rwSgyHJeQO/pzqqn4c2077WPdwZpk9C7qnYqTd/fEY/73t5PjnW+ZlL9ZZfPvvl0u3EYy6rZVkHAw4EVP3589EonzdPgftvyaMDAgheZUuBvrDld7JU9KLBH5+cfP8fv/+R9/MsvfIC/8+Y2t8KmTNrgTCm4pYxwMP7EtUZtx6ifPUA1GPfzsErUGqP/DGjKCG0+mVZiePUzTt4AQU38c+Rv4NYybyFB/A5ELQigIMDAIYJKkNmO2sArJATNdYAREiT/gUGC926PFMSuTW2gohBBWRWTDHF70BlUIK7xFepyucK45KIDWhvYSik1i7Kf7baBDJI8yAHCKcn7JcqglQLhlEQZOMWAFIAlZtxF+WyJQYPpzMwOEGdGihJTwe4y53ofuxgV03XjsQ4IElFQ4YexMQ+sDIUAMQ/c56J/A+dcsHLBebXgQgUXVQjOa/nU101AIQBBzEooJu87hUahQK6XAIABwczfY76a5DoMyN+lDfx1xYHBdd6HAQWB68+PJswyC10FBG5eMjUQEVW1oD7DmwFYlVBsZ9L2dgQF/1l9P6oFGLqRUS3Q4lXOVpt5mfV/rwMF1td351uv0fXvHzqy6myr72N36qGvo1Jg3zn67qMBAwtz3AGBSvLVRGYzPLSGONLsWAwK/vAL35mTnJUbYWB2M45odq8cta+NQuLOf/b9zcTpQJTopSyXl2J4b4epu/toZrvFQcK4r0HBkQ1bz7yGf6Yg6/FtoO8gIcssVU0NZAqCOr4JRFyHBFEUHCRwg4TMLIMpxwYIFHDJrEGU5IE/54IQJChSCFx992xwtIHuspa6TQY6romcvGnCHOASmQwtA90SCSEELFFUgiUG3CUBgFOFhYCTmhSWFHFnkBBkeaVF2UvU7N9NEo/N5jkz+tpssaA5+5lS4lQBH1HwkgvuC1fzwLnIgC9/S4Ki+1VA4aLwUBQOXu86yT1bFKQAIAaGRa63c7ZXgypTCUx98UBgCsyuEyFeAwZGU4FtH2CgjCBw6EDRdhU3HXcTzZmA9EZeKV0mWrd9hIBZX3zkED12oNVvy5k3beICoFsWDuz4Irxm8fEA/M9YlX3/P/UD27mMLXvEzg6uY5UJ09D3cjNNv3jVoMCUgltGm0cDBu++9TkAvYfmpk3RlcAOg13q33z8Ar//k9+cQ8EvGQT22ODWhrwHA/J3k+yA/QfvqCFuUp1UZJ80SB0AalY4i4pmRfe5pdTZzgwK9rzfbbumxmYOU0iQOgkMMEfrDWWpY/Wcb5BAKn0TEUiXPjJ6c8PotLiSDHbRAUKkgrUQ7nNB1YTURlFWc2TrL1ApAgWXXHDO6jhXeONENxYvc4dAOEV5NThIMeLJEhokrB4SSgcJiSCKgovSaKAgcCBTmJmPSdf2WFYNMM+jCd7n0vkKHMHAJ5eCNWcHBVyvz63XZtVrY9dniQGlFIQYh/3VhyBiUFpCr7SENvMfIeq1lhiOysBDYODas1JPTsNZh6DPm3ts043DxNiJOTjwDod7E7TtSoXJjdNNtT/ysw/MbfC+er6fvKVvHdXmzv+Rt31573/VqjyODWO957+9s8OV71UoUEdDg4JuVNzzA9LyaMCg2jgB+DWtgfqBcCZ08WSD9yn47FsDFGB7ox+iBHxaCPDltYBgBwaOVQj51NbmF2zhIOrbPdUAtv55FxJos41iBK/NNNDgwGylB7XOq/YG+UGQACJw0c/IlARqkBAiCBdwMCVBIKEzNzBhDSz+CIWQg0jdIsdHXHILYnRfHBykAqxAYUIuMtO/uPSOdamjzqarVO4GwrF4MIiBqo9BSgGnmPGXZ4GEuyVhiYQnSxQwWBUQYsBpZaQA/OXgwDgGXyK0yzUWGQTQDQrmsDlzHDznIqaCwvX1k4tAwP1lrTBwzox1LdXH4EHXg4FCjAUSnrkU7kzl3oQyml+WRLhTleAuClyZQpDUkbD5aBzAwGx5YYUDBwgHZoJDGDh6RoDNM+IHdIrRPZ4TEXoGA1fUAv93BwUDENzSHwEY4oXotgEQxpVf3b4Hv7P3ecG8jc9GAukm+7Pp0s/bMW9RFa6MO/beliR+Z/QpmK1ImZRHAwbEmjdA3oA8Hw2jf4BJTE1B8AAxQsE1JWAEgNcd+K9JPLNbuec/cQ0K9h6+sb1UB2Wtn1zleU1N1jLVgMnOibrBnskip3HTLUlHDBv8vcpAQTe7Mw0RbRniQSO3z/YgAQDn0NbWAxqVj9CBQlUNbBQ0BSGB1Jt+hITIoiZYSt5IHhBk5n2fM2glRGQQBQSDA9daRBIPCKWI/O1AwQZBgwSznY/RByk0b3pTDOJacK8z5FMkfHIpWGLAJ0upgLBEMS+kVZwXTyEgKSSMTnYWqdFkd/9cWHvM4BpJcGXoSo1SYWAtRVUBqO+AmAUaEGTxL1AgkL+5+mOYQlCYp9cAEL8OgwFpSq2eZlIIoTe3nGLAKYmaIgqBANKTZE6bDgicE2J1AqUdGJg6D17xF5AbLDCwp6Jdg4F6UfTcQ6+O+OdBNwyvNGwLfV4Fe35g/YJVi28a8DtRc9In9cChfTiodlRmg7d+y6/88mWvT77WF+/25QN0tDrrtRgmqR4YrBl2Joi93+8utcuaCYGCb/xYVueZT0G9E50P0F8LxcCF42xGsnqDDQa2jaxtDAT87NVz/N5P3se/+rUP8Lm3nu4CQBhoYdZQrjWu8Zh75ciO9aByAAV7/Yin7A4OLMWxUw2mfoau3hKRsLQf1gGXLPmK3jvJ0V4AloyJFCNsUQH3Y2aDg6OTGE/GQwIABDm+rUnjrIDgFYVdULgACgUjJCyasEdSNztAgJgYJFGTJG26JyAFxicEhEKo3olDERNFRgmEUJdU9ufNhRsYFusIqC7pokBYM4tysBLWxLhEwhIZpygS/RLFF8LA4C4FpBxwDowUSouNECSXg3jeo67Xl+tHXZ0ACy1ssQUs30CfqticCO/XBgZ7QHBxSoHBwObcqzdYXydAFQH9l0jMAoue75MldlDwJEWcgrxWlSCpo6GpAqEBQQoqPrDkpdjAQM1fcYPjYCnVAVfOZWIiuBUGgH72EmJt+zX2h/PJMbBnFx/Evs/ubzvunn+BLzO1YOyXbumTbP9u0tLJlLeX2Vd2Vd+uPtuKjuszRlEUQH0e23HkNTiT90xZ8IcZ62e5FSy4nzt6bTubmDKT8ojAQARKEQdCHacYvROigYAHBbu4P3v5DL/7pwIFn3/73e7wFazRXjcyzgNNB7eX/gCdow32VYP/0KWgmROM2q1hkykApg4QA6YakDk22dlwFQ/AujY6aSpp73NgXluv00n6z/Oqr+huFpuy4NUE/TvE2Nbkq4mB1FmxQkKWJZAxLoghqXogqZVjARIRLqUgJqceBEJas3i8I+ssfzLTCXbHC3IhLAhVNaj1d2/q3wGS9IgIhWVwz8xYSlC/BQn/LCsjGGskXHLEeS04pVBt6NXBLhap69piJgDzNt9kZBelEFzVgTVzdTg0IPD+A2Yy6B0N5TszIPDn7oEgKAAEPY8lhuZnoTDwZIl4Y4kCBVFg4EnqVQIBAmAJoQLBEtR8AABlBWWNIOniQFgyK+8nQFx6fxoAvOcj8BAA8GWEAeAQCHgDwqG+717ht7XjjWqlNyP8p1CsL38d9bffZ3+nCjGb7dzBA7udxBRHm+92yzHdb5pa8DOFgm9/6UO88zefbn19eAhLf1AeERjYVFLhgKWh+vwB3oTg4QAQ0vqdP30f3/rid/B551MwazxA88buth3V7xY42LtXND5M7mCmduhbDzs1yY//GsNilm39K36Jxe5Gg3dysoNmQ9TtHJJ0mmYekLlnVX9IXdmZaAMIAMCkV8c6uVs602vOWEADBqCZH9SMYLBAQfMypCTn6JQDaPheAYSAGBeEuIjfgCoIoQTEwgiqHiQS1YCoIAXgk7Wo/A8skRDPCguXrKcsV3FV80IIbvZM+4NlvQw6ZbugPT9imw8aQyBgKUDhUP0ZlhhQWFSPxFSX7aXKHnOY8XEaTO73kGCDfX1VCPAqQSncQcGeYyHQ1AFTSSzgUyRZdZAi4W6JOEXCXYq4W8QR8z87RV2dEfAkyb8l2t9RYCIAizlmqkKwBPWzyJcaWhrlMk2DDTCwrk0NMAioMSFwWxv1ZTAHXAMBeXFg4IFAtxn0dkqBruCRoFRUn+f6e6MZAX+1MNBU4XEk3M6sveQ+m3V3l+yWHz0qB8pFGZ4P3z3ZXTTXCbt2ewAyQoFffVDvwixmzF8L50OgwYGQwVQ1EPsiYHAQIUs6LIvV53fMB6NiENtVl+0HDWXvo81tGXb0nZ5PjtEvwekBIHSft8+qD8UOHByp8VsAOiBkfxr6pm6jACbtdkiUABGgs3Qyag6qAVRUeuAYXMNuCoIcRiHBvocbQIHZQQh634Xd5VweEkLtUNmWOaZFO9dU1QRWKEA4g2IClxXIlwoIZmK4FEbSfxcCUkhIMeOyBqSQkULAJ2tGIotFID4Ayxpwf8nVPyBFMRFcqHQe+QYJvoySej191jZUJPNj4LZUUk8eQEFh9d5nrqrGygYFcvNny7V6OOAas8HiNXgoqKqFM5fsgQBVWRAgNdaOfhUeCPZUgjsd/L1K8CRFLInwJEZdheBhwEwGHghW/VshQMNLmyrAWYDAck107fBoOaFrf+289e/qOEj9+wMQkGsVq2/AVCHwQKCqQIUFIoBiy65IwX3P9W9exTo6LfQhw/fchzrLhfuu3xCoH/jH/nv2mf+82wnH/fuwa7dx73wD9tuywX5VDNxFGL9CaOaDCgWEqVpQX68uG39EYEDMLpSnOa8V1FSd2MIBEeH5L57jtzXftU+4NDYk3+AAcTQaPtrW6aC+R2Ywu/nmjGIyk1OD6052Hszo1IPmNdvDAdDMKh4Oap12KrXZfAsxYzAnmK0SwWKpODjQmW0AyKBgBARLLe0i6zFz7YigdlkCqiwLKChwaWYHv5zRz8qcZ3cPC8PMLTRP7eqkdTlLYJigaX9jAsVLywxYBBIoRHA6AfmCFBfEuCAFwiXLGvqovgeRItZoYYwZy0q4X4tAQCy4SwH3a8En+rrEvJHbC6MO6DMPfeuA/IoFayPehyYXRogkAYLI5wUQXwTfGAsYQSl2ll3ShyEeocD+mYe9j0wYAiFnhQ35D0WXggK6msCbC0LzdZCYBGI+kJUYNAWCk6oC5lPxROM4PFmi+h+oQqCBjJY4AEG+1PwSZkYAZ0AzVNYslRZG2ieeOmhrVmpbs2WFfh8PBKN5wP6OTvWyueTGX8AZSQcg6E0K0SkFARZq3NSC0YxwtdgMpf36zf2Rh4JxQByh4AgWZiAw/vxRt3etvx+LOUfal9lt98XUBZ3rdkUcDbdQ0M1bfbAx+7m/NqYEoIvVTVzqQNKZExwcmPeml1/awSaNzH186F5zjchob85N3e+w28roHVHq/G0HEGyonYID+e9vO++9skfl3it2dl7+PIwIKhywOtpwqUDAaHLXNE2seTZ7z1qz09o+cvANKDANgBAEHHk2U3MKA/sO25kYGABVabVlDqSkGQFjAi0nhLR2KgLFBI4LEBcFhJPkXCBVEIjF/4AinjBwFwifhIJPQsYpBZzX0gHC5RQ3a/ptPf9Fw/3tSe82owawiXdgwZEsKuBYch2QrW1RHbfG9jSDgllYYvkdUSVQgFM0FazBQIp00/mYL4HBwCx2gwcCcy58osAQCWo2UKUhGBAAlM89EOS1mg6INQvl5dxgwJkOzGTAB+YC8uA6mgmgoFBDe/tgXM1XAGggMIcAoKoCQAMB+YEBBgTIOiAwFaHzOZDWMOtN9Ai9SdcmPQS3HHobD6C7xxN7wZFKcAsQHPX327rs9JU7fX/9PoXJ9v7oyfp5cirw5Oee2eoDyyI8QAG5evpIpNegAHhkYLAp6pC48TUg4Pkrl4VRoWCvYfgL3AYoZwu0/W6QaABsZF358tD0OnigJvljgARq8i/4dkAYvw+gefTOyg5NPyRqY1MNHBzYgUZAAMtM2yABqNe+S9Lj/uYKEP6z0oFCUE9vzmsDBCJQcOmDu0pr5233Vs0WnSeyXQM1IVCI4AtpSuEAXO5FRVjuVEVYmoIQLwoIlwoIMRByIKRCWLIDhCXik0vAfWF8smY8Wbmu9T8Xc9hLdWnftWiJviTrHB0MyCumYYLHnAzeSXLCD3V7KXD+B+xIve1XmESZCC2McWFGiu0cAOyex3gOFsVwXIJ5CrriQGMSeIVgBgQxAAsdAwE0JTVf7oG8NhiwFNXWlibtiFw7suZLXg1wF7Kmq/ZAEFO3koDHgV1+xQ3y6AbzbkWBBwX4z3sgADBAAe12IbNSncG1Os1g9fC+yI53ExBcgYH2E94Osu0bNlW7OgbofR8Awd/j5uXRrqWJPPb+2S8kToGNX12fXOvPXf/4kPI4wWCmuaBtsoRLH7qES/7zrlG4maq/6ZQv/e89oBANjYdC3/7Mg192rvtYY9mDhNq/3ggIBOmYuiUxN4zz/mGzKu466jDassb6fYJIkSxOojpwy/WN4oi1gQF7b+ugvYIwAIO7ZwILzvNbfyvEuAEEAurMrt4S74CYs3y/xiwudor6efM7ICLwegbFpUHC5Sy+CMsJlE5iZsgJiHcdIJziAp4AwsqMRBFvMPBJIlzWljegiw54F6uH/1G+hVkxVcAGVui924MB29eng67Hcp2dxVcwJSGAUIpEiEyBOgWh5knQ+A1A2y6Xfb/uI8AsMdSskbPQzxa10HwIIqGaCm4BAsr3QFnFXLCegctZICCvDQbyZbftuMpLOwoRlHOFA9g2p0RBlagtEIQtDPgAXQA6FcC9t7sn23RY8d+x7R4Gum1t+mT/fNCizb1C7yjt1cxarvRFs37Ijn0LEFyFgUkwoM3AfwMszOs+9O9AvbaUL/U+km73cXksi/D18WsoDxinHicYAKhxDczirhf9+csXeE+hYJtlajsL7dZ8+gurD/gtskz3CzSQsIcAQBqD/Z6OThUUrkBCPbRt1A+KAwGygZXMD4E2p7YXNvrIe3d8SKfn7l7Jaq+AINujXuu4eRjru/FeOHBo9R5NEUkG/Oq3kG061gOCnhgBDQ6K5FHgPHQAs+AydhuDdtAXgNMqkGD+BTGBzvfA6a6aGWhZBRDCIiaGdAcKZ5zSCRwXLJFk2WBhnCLjUhhLjMipZRr0kGCBgnxcgG6JoF4mL+PXgdvdPw8GexDQtrdLM1cMyF0q/dtmidynmq4A8Cnq2tJN+399CONlEpxoz6mQ1rP4DayiBFC5CBBcztVcwOf76kfAJYt6wCxOhgC6qITjxSrbC2dqQQcFsYFBBwTVxu8cA83ENZoCANTZv/xQ3da9xzCznXw+AgHc66x0EIDWBvxkBXi9PshqM52wEDazarjXPRjokoHVXYe6DXDwWmMCgNqBlNwmj+qwLX8Tnr/6CO/94Os749fkXJhxS9yCsTwqMKgy1+YDmU8/e/kc7/3wN/HhV76Dd99+B7PG0DWEbjbK7lhoIUwfVELfaIgqFDTHSbRzUBig6kx5GyRUFYGcmQENBEZv1woTtt8V88C2W9h/YOsxeXC2Qfvbm3lqOGva7rd555UBOIir90hhQEHBR5hjZiCIk1gFhDWogiAJk3i9KCRc5PpHyBKz7sR0qZl7D25XyJYq4nIGp0V8DyhUPwNc7sXEsJwQ0gLOSVSKmIB8BoUFlMRJ8ZQi1kIy2LMkGcrMODlI8LkG1lywMneBhPwywWbz394vX5pDotuGBgp+n/r5xI27+MbofreCgLu3r1O3mu2QJEUykYVtbhkPLdfDGKlQAhRZNEeANCiRBCbyCsEZVNbmP3C5B5tz4eUeYjbQ1Qh5nbcPv6pgAgSIbXCntAgE6CuCKgRpaUAQUlMGxqWE5hPgVy2YsmDvgelgP5bZrZg9z8D8vhkT1L7CKZn2s6XuO6/D2LsfqQZ76sCDYYAnfQ4GANjAwO1jg4z7vk34MUEiooIIz179DO/9+Lfw4Zc+kNxAFhdmLLN614MHcfi+Ai+PBgy24Tj1T5ZIes9+8Rzv/eh9uahvfq7z/t3AgP5N6ugmZbjYxZkSrpV68wb5qN4bBwwjLBgoPAASSG1T5H+KsHmyvbdrV90biPcoEMieWaFbfrk78F/7Tg8RflN76AXo2NNyyUBIMN8QWU8exEHVA0JapNPXY7H7Rc6rDOzGBnno8AH5LbedKWgQQ4EBXlUCXi8CCjGBLmdgOVU/hHBawWsA4iIqwprA6QQOCae4YEkLCiRYkkFCYVUGUsQbJSgAQD8vDg64iz7YIhK6+7HTqY8l+ltxbT3XpPjYCnnym9fqEWuz76MupkAOCvq0x/Z3UrOIwYAEJWIxF5x1yeFqjoPNobCc75v/gJoNOK/Vt0ASZazoghONs7VqSPekFQRIU0I1DwxQQMtpDgT63RZXINZ4G1vHQNo4Bs6ev71n9agcQVydnHTHIve/qARbF8u+7PU7M0fCURVo2gZuh4FxYug/kzPqK3hFTdgvbpBnU6MN3hg//fhn+NqffhPf/fU/wrtvfqaOX1N/NVc604dTa3gHvKw8GjCYkpOW5794hvd+/Fv47he/raTlZaHeVr2BgdGGbT93zamjm/KqLbre+LG+PqyvI8f6/fxgSCCzV+1BgtXDfsidyjXFwJf9q+5PYFumiwCG9+PllQx8+iAfdRBECJoCuMICi0pQA8wYJJSsNj0PCJJhkbOYAbpMi2py4FVPIsisj0M7qRrPvtYaLRmNrUjIa6ci0OWsnf8Z5fyJ+CHEBCwnMTOs96AgUjLHBZaTYYkLmCIKCGtBdS60VMYZkuWxaPUyIO0FIs8LIHDzrbSO42Fq6PRezMov47gt5LKElyb1KzBItKyPAdD0xi27oTknJv2cNMYA1jzEH7BIhXqfPAiY6SBfRB1YLzAzQY1RgLEd6DmY74AFxnJti1ICxaWZCcx0sNxJW0gJHFIHBKxhtz0MdICApiK20MPze7H3bA0BQaX60xt0sI0n39PPrqkEu7/rJyPUb/oPAgMHEDB1Qrw2Xtjvmo+BwsKzj3+Or/3k9/C9L/wrvPurnxEzFXE/BjykjGrRpDwiMJif5LOXL/C1XSiYqQOjajAAQLUrX4lKVgddP9D3QNJ7pjpVoVMUbIC37306SPBV89I++4cM1yXc1yoePvxmu9zetIFrHVjbOAsPCsg1s3z3oKge9qlBQr6AKIvM6gGBJLxxiBLhkC9nsfeuoVcQAG0Hq8DBgDZ9SGIBCgraAeigYCoCh3sBhsvSpGKdJSItzVkxyD8KYmvmuAAUEWJCChEcZZDIsCyPqOpAKRqbvohszxCzEjO7bHf9rerO6K+iTeyViQLVZON2n825TERzAQC7701FkPG35i0oGbhkCT5lYNCFLXbOhB4ErqkDrAGMJg/PqKhUKFCfgQ4KVBnoVIKgy1/DsgWC2g4EBgpUgSlQIOTufh75E3kfALvWAOqy6Nr2B+ne7zPev+l7V24d1kblYoSA/u9fJgzs+BccgcDOfrulk3BY8scAePYXP8NX//U/wve+8Id491f/jrRXDSVfTedeNa4V2rmqN076HhEYbC9Eg4I/FigANjdsTyGYrp/3ZTblnV50v1/vyFOD9IiXWzsPb1aodPpAJcGOBXTOK/Lij7V5lKSmo7XjhnJNkqzbbH/uYWDcXu9KpRf7/nhQeT+LsxC4DSJZB5HgIIF9DPsKCKIWyOqC0KkHFILMEi/6UKrZAVhBSGBzetW8B1y4i5HABSCf3CeqCmGDhKkIRAIEM0hwNmYoGFCIOluU10Dtb4MFVjQ1M4O/zg0MXGIbvbS3Jt3acxibldnssLMG6msAdfeT0GAgYHhv/+z7BgE5gy65KUQlKwgMUQn9wD+DAXMk5NLAYICBMR4GafZJQNWCmOT5c/4C9Z4udxOVYEHRe2xqUVUF6muqMFC02yoO9rwT5/4zZNe7vwF+GWHn4c8aH4Tb9oL5ID8zRdyuSR4LEVImg/AtzoMPMRPcAgR7MDB/WLbb6s8qFPy7f4Pf+B/+Cb7/D/453v2Vv6W+Kk2iJOYGCMPY0v1+t1z1tvJowMDPvonLAAWf150mDcKrBCMQtKms+6EduyGAzWPh6dvfrGHwvwoIgDSGWocrkFC/fwUUrF7+WEMDoslfY+FhD98H1eAl43eGQWmzTUUBmdn6WyF/zBncAKENPEQsaYAJiMR1BpnrTDIhmlnBZo4hgoIuOdNBl4o4BPJ6BoVzm+2HCOQLeI3AeqkrKKjeS8mS2MOB3u8NJKwKG+cGCX7w8N7oNoAMoEBkIWsbLDRntIhojmi2LSjgVNszbWRnf+1vU3OOyzU7McHfv+ZINg3eYp7X6lhKNXXxkM7YIMAnLbKYA/W1RSesqwo6ALgNBup5KRSQORPavaSgKpCoBLScmi+BX8oaEjioMqBQwINSJEqQQgFzU4j0/mVwd/+O5q4iXre06aTPrsFZAKuCTdVXIMiBAWxzxwD7Ej923vdlpy0dzM5vAQHZ74EwsPn+A4HgIb4HzHj2F/8Gv/E//h/w/f/NH+Ddv/G3BGg14Zz4jOgN4qCW4GHc2FEGbJx89vIFjq7+owEDfyF++vJnvU8B0DWOfoXBARR4xUC+sPO3Ta/7Abi7QXs3y203R8ndY9Xz3IGE7vd2QAHANI5CLXkbfKPuN0P/cBUePByMj8N0puqAoBQeBid0MCEbtz8fwNWvYiVZtLoSIZDE9o/EIjcXdUQLETHFBggUgSBqAucoMjNdxLygg7akbw5ATqC4glOSZY4UqsRMek1t2GtQ0L9SCGLn1sGEcd8GFJ1dsg0opiYYKKjaUIPeKCzIACTJncgtZ/O27f249/LKFDp4sMs9TEAd/BH2+vPNPZoMGp1+ZYN+lertOZWBv763oEFjCuMxWZGLLVAs1oCZA8blhYPPgPkLeAVopg70r7RVCRwQVMjzS1fNbBAXMRvFVFUBNsXAAcFampnI4KBwg4DajeGWZ8agTGFVocySoILngOAdBr1qcFOsALmQ2JQdwHxQHIHXXUFwZRDfhYLdehyd6/b7z/7i5/iN//H/iO///f873v0b/yv3fbm6LaOEUw/q0coUDvwqlGcvxRH/bbw9rzseFRgoCf3ip231QYWCPYq8BQr6G/fs3/3byY8LMR/Xb+fzazafPUCo3x0b9T4oALgeRwEAYWKz8nWwn/Lftf3d8kmgXZka4I6b3XvELIMCm+XYzKfr2PRvW9d+NEPdOqUxSJWDrFCQg0TQS8Wc0gQQOObmiBbW6n/A5QIKCSEtKOd7XVVw35sXiGSmX/QhjUXUhdzUAwpNReDMdeCh6DqzoNv8zPMc5NgUWjpog4UYZcBRJzQyudoBQ/V41wh5RE49APUOcXQlHK7d96pEDXLmWOxGYpjZjdB+9GzW+BFu+SlniWPhEhTV4EIazZJrQqMh4JCHgImvwAwC2C2haAmbGhRMgWDPbLAIFITTHTgkFPMjiA0ORiDYdzJtz8vMyfToOQHQnhWYY6awAIqoCEyMyFQDlnUKoaoGllxuvkRwmLnPZvr1It8OC9uBexzYr4DA3rFv8Q0YfDKubu9/YLq1QsGv/G1UQO4Wnc/hoPX1xT2f/TPZlux/iH/6//inuzV7PGCA/qTffevzqMvV/F63mA9GKNBjPPt3/xa/8f/+r9q2a3abOli6TvKg89ydqe/93tjwpqAAeBPHXhyFzc9NwaD/PR+EY+P0SH20rrGanQKgsxwPBbKkTu3hehCGrM0fvej3nj+BAKre6yvJUraVIKmOg3R0gRglkCTXGgFBoUBmbysoy4oCLhfxP+CConBAqhjYcjasl11A8DI0RaqDTVknKxq8mSzSdBBik6o7T3cdjFR5IHN0M4XBOb/1MremkKYgA0Jo70GyCqADB20DZBe9Ft9e96Xdo+evDf76PPrZv+UcyKtEQ3Tb6uoAgwUDihsAwA/8PBkcqjOvg4IHA0FdmnoHpiB+BCMQmMkgpCkQrBbNssKBQsGw2uSWZwQQQI6QmA8Fcsub06GY5LKDg7readLN1ePDQYGDwMNYAX67P9bRQLv72c7gfm3QPoACptCPK53J2JuDdfstkOCO9/2/99+oUmB9/u1wQNj23bZc9dmrj9r4+PZTHE1mHw0Y/PTlc3y9RjR85zbas/IAKPj+3/tv8Pf+5DeHQbrNltum4Wl5XSCo37lhn93GNwm/CewBKzAGYqp1cB1mZ6aQxm8rI3y0yU0Cq4Pnozq5MddbYVCwamfX1uFjWIc/P7CAATm1wOCAkFg6ukDSmca63QOC+CCwLWWjCMQsgBCWBginO1EQaqwDsVeTW9ZGNVCSBwRTDoYByf4uBYwJvIUeFgAZsAwcpvbt0RN+zyRhyXn8wEahi8PvzQ3ezNApCWPZ2HonYA6us//Oru+zEu6ZAMagQpPB3wZ+G/D9Nd9NeRx6ELC/D4FgZjIYFYIZEFBsCsEECFYfMtrA+crzcfRsAMAaNCmUKQQoIH1OCrXHntX/wGU5mV8uakAwQsEmkuzNcr+VG/v1q7P1K8UP8LPDGxx6CLDf9ebgEQ46UzGhdqLu90QpgDvGPhzc6sXpJ81P336KrRbel0cDBl//wXv4jo8dTTo4XWkftyQ+8lDQ3TR/V8YOcQSCmb1n/O7rlAcB0K2/ORmIOhPENiBTk5dLA4SArXKgz4JEYNzenAoDtv9OpzcG6gEaJPiyqg3jWgCcQgIXkRiFDwAhThQEA4SQxCdhOUkaZrNpX869D8J60ciGawcIBSuQQ5ullpaStziFYa+YkmBRB2WAV1iYQUNaGjB4CHAmierUaNki1UxhAEE0T+erFXA31tebW9RNAwA/+Osgv0lAZCaAyVJBXi/TwZ/VVf9TXUd5I3/rdQwp9UAwcSo8Wno4NRkcKARHQHAUwErOdftcUCCszAoEYh5KgZDV4zQF+f0UyFwLRCXgHWWg9nftxUNB9QMBKhCM0WSlTJSlW8tD+kJfdn2qrvfLGzV0ugptVHHZgYAzyLzGOND5pe2UZ69edFDAk3qP5dGAwX+nWabqJNaapUmcO5L5tfLs3/9/BAqqzcfKZODfgYHe9jpCwQ3Id/SAXCHb6Vc8te/95MZs4X7PKxBqqrBr3AGCmr4MDszXADqbITvE5PTGbRn7UMDOtroplolPfQvWIIGAiAiJAxLlFhI3htsBwda/GyCUReXttXmaryuwnkHLSWIhDMvfoB7wBggUCGXN4EIQg4lKg97O3c122z30iYVCpyaEZopQMAhBAWGJ24HOYGEY6EChVxbM5BBiky4t019tJ+NN9cCT240eUxKbEsClCyBkQGAQUNa1AcBF1IRiZoFs6lO5+ToBMvAzSu9IGOwaiRkmpNgDgb26FSIGAmMMiqKOhEdOhdeAwEJf23Ng4a9veh7sfFmSp+VAiAYRCgJZHXeTdxXcBYI2tAX0asEGCqZAMDEl7PRNt0ziblJfu4PeaA7e/bzP69KvLNM62z7yMLtOb1QPbqzTrYUCfvrqZ+Jz95XvdlBwlOAKeERg8M5bT2s8fgBt6V3nHOfeXy0k60g7R5Dh5s3UgREGptLqQxvvhDi7z+dwcFMa6J3eg/x18nX3hMr622TKjF86M4cDOZ4+I6xez0QI4qQAJkZQSfPYpLjtBFsinu0XQyBkBkJpkMDMMvDHgLUwUuEKCCsBaQIIuUCz90WkFEFRlsBxvoCixcjXQT9mUFpqKF3ktToqSuTD+w0gxLiiXDS6IrEEYgTgA2pxLm2AK6X5K+gAON71OugFAkJACKEBg8JCTDKoh3SuoBCWBJybmsAuGE8FBXN0dH4N8lvbNs6uzhUCzCHQg4A3CaivQLmsFQTKmoFSkNfcwVK9LpNrMhaKARm5XhOUItclOVhQKBAQcNdkBgQad2BmLuC9OARx0eRHUfwGCioAjECwahs3ILDVCAYE6wOeAwAILPn9YuEKB0eDoK1QkFcJK01EbYmpWZhwAxSMQNCZE670dXuV2/u+HWY22E63jdfgFjOvf6MgAEi7tr55hAR4M0McztOZGOY/cqU+6lPw8oXkVlCfAjMfXIMC4BGBQYE4yhSgRvQTOCCZedksi3R4MmglceIYZfZuyciv/J1tI/IzoxkM1AY2t7nuxaqe2vZ5+O0upsHWRnVYDkfbne8bANQ6tlUL7D/3DjAODjResHjAo6kGvGNO+DSlFJ76MRSdQQZdpshMVRmQdMaEjB4QWAEhFkYKjMwSXjcGOYbEQoiIMSLEBazRExFPsrwxX8DxJCqCxUq4nBQQzmCvJOQLoPAQ4oqQV+T7s1xjlih+Eqgvy0DvBkDOZXCgm0vH8hpErQkkcJAiQggo+rdBQg7rdkBUB0a+BJ3xxroagj0oAC1pT38ToCdUQaBLR/wAGOA1VxCwv8e4AofXobD+rc+/wpJdo5hiVVTCEhHvTj0QnO625gIfxpqi+g+4uAOduSBqvguBzZV1VUFpKwzW0hwKZ0CwMm/AeK/9t3vA0wRXn6YYIHRqwcahdL7iBHCD+Q0OiO1HvZlqnCjdcH67Pl/XFd3jvtu3e+ub9ZlgDwnOJ6FTEVzdxvOfqM3jSiCr27NXH+FrP/4GPvzyB3h3ohT8tfExYAYKqRdtBQNqqsHG58DPdNE+BzS4xP8J3//7fyBhKLtVBb06UJdz1c9GM8JgXhiP408A6EITe+/W3jHH2a72nF6ArffsrNxqhtjsp52oXjO2bXpNxd1P4IDKqp7tGeQSH2keRVSMC+JdTSQZHkg3x0DgLLbOlaVDJ5ZZzpDrsCtlCh2EkrkDBGIGO0BYi5oWHCDk0q9kSAXIQWIitKWOCSEkib9vKkIpQLlo8KQCxJNE2rt7Q5wVfUKeuHSAECkgpAvKmlH03orkq6BwWcUz2Yk7R+vsgTaTr34GlywrMpYIXERN4JSASDI4poSyZoR1VXPDRQbBLPEaOCZZ8+mgAJYPYOZjYPEADAZ2fAXKuopZJTN4XZHXDGTdrlBULt6p8LbzBlokQtkuikFcUgdG4bQgLFHgKLkgRAoEki77tFldwOZMGNrfHEKnDhTMlxyuzJsVBh4ImungGAhm7T4cDJbmf2PXJZIBMDUVgOy5RM07Eam9dmqBT1TWQYEzH5nTqb33beSW0q0KOJ7V75pyZ0DQ+YJdkffHyd4O2JBPTkMRQBFI0H3I5FHbrY4Zg5LgFerNebT3zz7+mUDBlz7Au3/z7wK6fsSqUHBdiHk0YADoyZJQUdxTDcyGCNSZruwng9izv/gZfuP/9U/w/f/yD/Dur36mHXz0G/CBYcYc5xtHxL5RzWSt6Tpe1zDY7FNo7ecQEB4CB93v3ijd1WM2QOjUA+QeDlh8mUFcI6lVPwNVMYmtA2uggAJkAlJs4IYiYUCz/lTUWVRg1Tu1kwxEm06yvT8AhEDI3AMCBYijoq5kWIkRFQhWVREuGp8/BcmzEOOp+SK4uPzeFwF3TyRY0uUsZobLWTIpavRDupxBaQUFkfhpzSj3F5GBIXAQFqBcLEZCbyobZ81cLFRzUThgWOjmsESUILPwuIgDHBUWBSEzOMs+VBhhSVUmtdUOtmTysA0ZDEyWDpaLrDYolzxVCPJlrSpJUZ8C73A4O18AHQjIe4GDsMQOCmKKCHcLQoqikpyaAkCnO1A6NUjQVNlYTlvfgXF1gQ9IlF1AIqcOzBwKueBTAwHQoEAYiBBAukJHlSN1ypW22/xtiFpCqkiWoZIaKOgr9FjSE0jl/EqTMSAVgD4Hje+fHmg6uFamUHAABNOJ3Gv34xFg6xfbuZJPdUvUlIRapVj3nTt2zCelTCQJl/7ktxUKnsph9GvWTm65xI8GDJhtKY3arWED6EQ1gOWk1hmuWLjx/OOftdjUHRT067UByKzAN6bO52CS79x/d1p/8m/qvt16XwWFCglVSXBkSf2A7X0CaJSsXrdwa9Sw5TQskfib843chWpWsN0KxCatdkowUNcx1vWMBEbzNTATUQcHgChCuailYgsHthew32kW09e00ewBAgXaOCquBIQicBGL+R6QKguEZKaGFBESqnnBkvc0X4QTaLkA6xNxVrycwef7Bgjne8SYENYzymVFpgBaM/JZlkqWVX0SqpROboDcDphcZXSbXct15dyk9WqbT3I8glybcoGCyCrmBbUUkbMcHZYboKCqB/ZPTQW96aRXCcZzBBoQdCsxgpgGKKiJJCXQEhFPAgTxbkFYkjgMeiBY7iRCYQ1ZvDR1YGepYQG2pgJVCLrVBejVAXMo5AMgkEt5bDbwQABsoSApFKQYtlAA71Ogr6YSKABbgrIWzEhUoBp10psPrkHBL8ukOJp1gQkQXIGB4f1u4K5Jf14TR9eBvvXNct7UQMEggfS9PasKBjRztPLn5yamHgq++8Vv4+nb72j96p0B0B7PMRHdWB4NGHTFmROAQTUAYHAgC3elV3v28b+RLFb/8F/i6Zuf7S/ZxGegRp1TKOhAoDaY/qYcFpPYAbSFw0Wy/SkokNqn2sCrg279JTQVYQcQyFPoxPzwIHAwh6UZHACwUaPCAbcBhijI3TALD6kDlIMD0luUmUEqrxocUAAoM6I6DhIxUnXCompHtVmVqAf7p1IdtYLAxcroACEWCalclzvmghSpBoTpIIEEEi7akSYDhpAQl4SwsCoHl5rql/MKJAWHuxXl/i9Bl7stIKQzwnJGvj8jpIhyviCvEeGSUNa1zv5lRk2wOAntNjePewDdgPlLK7KEY779l1Skvgr1Vf04Pr8pEDizQbw7HQJBuHtjG67Y8hdEAYUCcXLNGTUI0TUYyODd5YbjCgNrz7PizQUjDADYAMG4dNfa8p5S4KGg+hUEBYcuJkXpocDD/KeFgr2BuTMBDJO4hwDBCAMbteDGPp2aodTkaVYFwYMCd8qK3TRd/jsqCd3xnVo9QsHffLedo6/rAy7z4wQDNHOCFLvhpYMDc0R89urP8dU/+z1879f+EO+++bk24NVi0o0b9ONSI0rJT9THo15/fx/2BiWvclJ9tQfZbLU8hwSvItwICBWWZoDg4cC2Xyt7cGC/6R0SS/abNnBQaKscGOWFID4HBQDFgAQg6qI+ykBi6gChdaqEGKmbaQFzj+263foApyCs2C53jFmVhFxcJyuvHhJCUKlWO+UYCCGckOJJ/BFMRchnSciUdcljvlQ/BL7/pAOElM7g9YxyWRDuxQ8hny8IF8nwmNNa7fDQayGz694psM/+15wRyTkj1iV75GMh6LMwibYot37ifFgvctc0VE1StULjVHPQXBeBay8VkNxywwIKUc/LP6txc16dyYDC7QrB3ZPmPxAXFK8OxFNVB2xVwQgDmedmAh+TwysDIwzY43XUbn3/4Z0KTRkAmslgL5ZHBQI9XlCAoB0oqErBCAU1Z0U+jjArDbKd4C1llNQniuxrAcGRyjuZcb9+3976dDJQYIIpChUSAHBIqgQP41A3LrW6/vTjn+Frf/rNHgpc3Y/quFceLRgA/c2jGRwE4KcvX+BrP/kdfO8L35LcCuYEsteIjCjNx8DBgKnS/sevKqtuHLL72C251LoLJHBVDMRe71UEmelvzAx7gOBAoAXlcAQ+gsMtxeAASsN2zBvggMz+E0gAQNWC4v6ZehDsfQpIDCQSm2wqNI361jpcqWbmvhPdS0FbP6udtADLqg/2tQ43UZD484WRBkhIgVRZkGWPMTEon6qKgPVcAQF3T0B3b4Dv/1J8EE5nCbt8OSNe7hFOZ5TzGfFOHAX5khH0le/6ZXzTUgP69EAQAmmwnp01/CMQuFUK85JkFUIQyRlkwbBEYfCxHCjKawyS5rpobglaMziEbrnm3jn5ZZmmDpA6FFYfAucvQPX1BLp7o/kPRFl+yOnUqQMZAok5tyWGHgZsiWEux0GIRhA4apPjigIfgdAUAWDbLtv7Fia8tk9lc1mC2KsEHRhQS28dSbvJh0CBlYf4PF1RCDYw0H2nDZ43qwNOGZhN8h7SvzsR0g4p76n16aIoKCRYXx2iSP2qDDfHcx7OXZWCHSjw49JDy6MGAyt1ljzAwfOXL0R++fU/wtO3Pi/EttOAeAQDitMlIIA+C24w3bsx1nwL5AGugNgq3C29tCBBoLhREapkNaoIox8C2S8CGxVhGrXL739j8WRrZo89OCAGqc+BnW9dmVAYmaiCQGHxN2CgZl0sBYiRkDiCYx8nflUlIQNY0EDBFAXUbVSrLWc7n6H57RYXAYV1djaAwkpIkbe2WwcJUbdH80fwKkI8q4qwqB/CWQYudVCEOSpezuDzJ6DTGWGVjI/lsiIOXv018A9Qg//U4vIv3BT8iEKNbeDjGgCAX7IoB4zwMRgQub3nFrfA55OISVYnhCTmEU4RgTWIUYooRa6rD2Tkz0XqsQWbLg5BUgA4PWkrDKpD4V1VBUQhuBN1IJ06dWBV88Babo83sBd7Y2xrvoxKANAusbU5ABsQaNuaMuATi5lvgM38Q93uHA0JG5VAtB0A3qfgAApqmU0wRiVg8/kv0Vww8x3YUQf8gFrcGzMByenc1sfbFSC9nnUySAMkWJcZl6oigEv7nWElxrNXH23NBxPl+nXKowGDMcc70FTpfqyVi/fs5U8l+INlYbQBtR5waDwkiXztOKuDAQOB2pD09SEOtkEHd+bWmHViOpCmve1VBK5LXm5QEUZIqE6N3pwAR/mDvfjI7uXfmuPjHhwQg0i7RiIEvcYUzLognVHRVQgGAqyfFTA4NEhghsYjICyMDSjwTFEYTA8AkLmZHkKg2mH7frt0AxLX+wM0uy6tZePklbSTvouxQkKqYGCmhohleUN8EdalMzMg3YFOoiqIH8IZfH4CvtzLaofLGXG9IOa1phn2UQIB4FpiIDMfWOyCLhLiUWKm6phrkNDagw9uRNqGuBRQ6hMeWSwDWk7gvCJ458RTC3d8y3l4oLH4A9BVBTWh0cx/QBWBai5IdyggXFQdWHVFwaomAYOB+5yx5j4S4Rh4aNaWZsW3JaCZBh6iCIwgAGqgUFUCgwFQG/QVAvzf9plXCcCzJYlbKNisivL9zOYGDn3NqAz4fT6t78CgDgANCLwyMIMBPUvdNj+VzWmo2gm0yWChHhKkjhHkVITmgN7GOIOCD7/0gToazqGgO5euPm0kmZVHAwbAPnza2GrX5/nLF5ssjNMGeWAqyKXBwEwp6GhzKJ3tyRoc0QYSAKpL+oL7HQ87RypChQT5Aa1jo89dSNCT2UTtamewPamdcgQHFkKZmEU+I1aqlusegpxzUTWl6DVgiKmBWZYjts+5UxMMFFi1EJN1Z6CwKiSwAZoqCcxS7U45YB5meu7K6I2ymZ3NvlIIoFUcFtNK+CQyEgF3MYhXOHEDAyIsUWEhPsFiKxrW+87MgHQHyrqK4SJqAZ8NEO5BFn65FETWwEG+whUChwHdmwhcSucNCPiES0TNryAMnTiX1gVVQNDZ5ZAYiVxmRHKpkeNJTCE+j8ThOYwg46MTnu5ayOLlBI6n5j+g5gJOd0BIuDh14JLVL0DBwEIT3+dSYWBl70hYVOlSGNA2s2kv9jeodyCcLC2cma2mIECog/seCNhgVE0E6GGgfg43lzYgGFceALdBgW8X0+23qAJytfy2o2Xi13wHpoPoBAiu9fXAQX/PTT0w5cBAoSrGWr2CXkXget3lPCSi4TfaksQr5o+xEF2HmUcDBtawx2KqQYMCn4XxKeod2x5xAwSF2wWVwaFXC7b2p52rb+TnlABJES0NJNv53AAI9nMzFeFwVQPFjUTVReO6WU14QOngAKjDJ8sSNfLqDAXtAAMiSXAjBsCBOtNNUxAEFJi528fSOLPer7IDCpsETaRZ/szUEACLj2CllHbP63LIellMRTBAKOKUuBJSJIRLwZIIn6wFKQbcqVlhUUi4ZGAJATEAl0BYQkI8JQTOoHSRREwWOCmtoLuLwMPlXHMx8OWMGknQ5R8AUAMNdcUFJpqFOybNjVAhwOVN2E2ktG0EmwRKFuSINDzyJneCC5tMD6m/RSoMoeUtsMiEQZJftTDFC1iXIBaKyAW4rA0CLqWIPwEDFzUT3BeuMHBZBRbXzF1K5K5tDCUQoWQdfD0gEDqHVTJFaYCBurogjAO7/l1NAQ0CyG3fA4GmDMj96mCg86IfgMD+3oWBo3bxS1AF3PujVQXjwHkrEIzbgNv6fDEduD6fuU3u7F5wS429FgM2uw/arxNLlsQf+dTJ23M6AhW7CuX4VjweMKhNanLCdnE+evUc72sWxpZwibqvjBfYA4FvEGtxMOAaRf3+DhMQyWAFNLUA3B5cm7ECVG/eLYBgcGAqgvyWW9UAoMZKUPI8UhPkc7sy3CqvJ9eBgp3bTofQZQBzSy3hzoaYWz1NNejsg0GpuoGCnAoNfh5U71WG+BjIfSMZyEOvKhgoLNx7jK+FJXCSAgKydKBrEZ73nb0pCHUyPLn5gUg7bIMDyGsgpBDwSZLO/y4GLElMDZdSqoqwVF+EiCVFxPQEPi4Cl4sEUkotkFLg0pI2uQG2ztJn98qvKjAAqIN/mqZe5urRPXbC2E5PqlMqA0mc0qgOJi7lsqkGQ73lEAd1H+ptSY0sKiF3OQuWTYrji/MduJReHbjPGZeVVSFgBQeBhKI+Lb4tHEUgDEEuQb/E0KlLZA6sDQjsX135Qj0IzMwCDQ56IBghAPV1AgLADgwAVSFw97fej4PJw26G2b8CENCzmr4+VCHovscNBPydnvX9BVz9x2QyZPUUSKiqAZzp1BQbvU8vXr7Aez/4egcFV8/LF9edu6d1Wh4NGByeJYAXL5/j/R++hw+GLIwApn/XGadrEPY39G8vX482qNkjEdx+vXTECEx1hV4g21Ebij3MtcUpNHCdyPbqAzwktIvzaUDhmtlhvI5WqELGCAdauNWn1pPtasE96NR1CuQ7BFUWaicCgEHuHqnPQOD+bwWFwpZRUfwTEvfOY7EwIuQ9ELCWgoSANZTOr64wN9mYRY3wJQbn7Z2l804UEKjglANSFAXhbiX8ZShYooJCDDgTV/PCOZsvQsLi4yKU3AVP4pKBJQsomPQ7ztjH+zXO/IfBn922ek9oFhp8p3RtSh2r6ixUM0cuXD9vM9AH1jtErXOUFUSWo0CDENn76juw9r4DF11pcFFTwSULBNybOSEzzllMBSuXCgLyb//eF5aYGn41QWsHAgUpoAKjB4LdGAMKAt4kEAYIaKsI6o3oAUDvCTbPvLtnvlcbJhGb++G2HyYw+qWaB1DPcNanz2AA2DcPTEHhYCLonRGn/b/r+w0UiNEmhnq0lYEIUZBZx4kXr57jN3/4Hj7QSe14/EMgGMuVsRJ4RGAwrOLpLtCLXzzH+z96Dx98SS8qX/mOo0dvMrCHXvZtQGAyNTCnRStmIgBEBWgzrEaegTV6Y4UD2aHCQf2RBg3yPXcOAyToT7i/Hw4KbL97g9mhFg8DwP4MYtqJWFbMUOs/60Ro7ETIfC4CQh3A4gYWMlMFhaLmghKad3mkgIWbbLwWwn0uoMCgHJCZJUdALFjzqCDIwGBBbaxoSAGJQQ9giQIFgQjnUpDWgBSB+zXgFBkpAJ/E0FQEjhtfBHFcBFI4SRC+BTDFoHr7m6lG4YA3Hb67Ff46zwZ+Ur2KCIcRP+tNp83vVG9rq8Oe45oHA5ut7Txg7OusUCB1ixKMzIFBDU+8cnUgHH0HvDogbQA456LLDlGXH1qfcNFIkSMQ1PutD7XvcwKJcpRIoDASVZXgLjYokHaCukywRtikZgbwOQs6CKh5C26c/dcL2j/Hm/t3pewqAnaP5Ap076+uIPDfvaIIjH+PMABg02ePDuQbUKgTjbbNf/faGGA4S7Wvl/6LiRsgwJSmZmJ48fIFvvFjGb8+/9ZTWW59MN51J961t6FCB4DweMAAfeMQOlelwEHBrGHUL6FvEIxeMSjFORA5INjrZ90YvSlFbzoR1XGe3BcNDqr6ww4Cuh+TjR46OiWh7dKuFY2bmwMj1Q98pC4HCl5NOFrp0Gq6O6uYPUHEeX//6cxj6Ci8quAGMHGoiwILISKBKigUXXmwGiyUltAmUKgKApHQ/AUZq1VR4aCsrdM3SMilgYF/cC/qVX9eC6KaCMJKWGJBygEpyGw0BcIp8a6KEEngYRYbIcQoKwrlQqNbVjYODLNbM87WaFyy69WZ/dnYrJOqiUndv9rVullsjQoH3F5fX9cQAfUnsSBDs5gD3ndgpg6c1z6R0VpknxrNcPA9sdJBgC4nEHOBKAYeCk4GASkOTqlAVHUgUVMGkkFBBwLcYHAMSYz+WgIe/CeV/xROg9P9HwgDt0SSnY2Fm/uw09933DOoBJvPgU4psG0jFNw6DjBDTcpyzzwgADLOQAO6ffTxC3zzT76OP/7id/DZt57WpY5lc1AclrHP79WjbXk0YDCeJMOZDwwKxrJDj3W9sf3tSNG+kq0xsWsQk3qN/aI1CvtNg4NWD+5kRpslGUCQTT7qDnM8HEHBSgcM7ivbhuNUBQOFIVLXLiT4oErMMM/0LqHTKEX2T6r7+/rspNZ7M6uQToXcjFfOh6q8HPS1BM2YyIQ1SO6DXAgrqTmBpKO+zxkREfe5AChyrzKAVIDVHIek/cRAddAwBWEsZmOOQVYixFCw6GwxEOGUeVdFsNlkChKVMZiS4KRlGzRCSHKPw9a2PCtj51tnS+xv2dYOa9t37xNZu9Lz13rY8imiKG07zDuzo/r2szqgrAxGC0ksWQvVMVBhYC3cnAmvqAO2by5qaqjQt3NfbeC2JYekg3ognJIEwDrp/Vu8MhSjBhRCt5S15d8QWJAJQgblrEBgiYoMCnrFpb9Jtz1XcuH9bN+dqx/UXcc2X03Q/j6EgQ705yCwC6G2fXIKm8HabajHG/b1CkI7dr/FKwXdd8ff31TIHVMHhraUUWaBP//4OX7vJ+/jW7/+HXzmradaZ9qoxLcW/8wfPUvAIwIDoDnoAbL64DfNp+AtgYKjR8GrBF418FBgKkHb334Zh8ffYexPVQwO7LelbY2Ntv1tHfJonbUGYiTqN3rFQuI/DJG6RkjgFjOhAkJVECZwMFbUmSrq7BE7M5tbZjQeCLxtXCP0cUgglZpjXBBCRAoRCyRxUi4CCImoSs5EERGleobfF1lNERQOQq5XFJdsMnILqFSc41wu0sHL9Q+4ZBk4llgUFNSfYVARUij4JMpM826wQ4vszNuwtoNDml4euc/DJRw7uo2cqm9mzrZHUNBuS//7MmzQvN1RW8Z7VNexMzd1r7jn2ByJL7lXAO6LDPzjdq8OeCCofiTFzAftPgJAsGBR1GT/JYrPwCmKuegURPWxe1iVoKSxLhwQ1CBYCgNkMGBpvTm3eAIVDsQvowOEelNve3YAHeRrAjrS6a8+R36WY4f2UDADgj0Vqv7+9VUD/r77ez+Wa23Rf/oATHpQuTomcLuMNuAXBn7+F8/w+z/5TfzhFz7AZ998B8ySZTZwW712tLLAPyezlSbPXj7HkczweMDArWd+/vIFvv4DcdT4/FtPj85fvuo6v2tQsOmI8LBGNXZyY2Am30ECx2TnlYNr3fHsITEFo+5jHXbd0Hb0s7cWohkNEiwQh6462AJCg4OdCm47s3E70LzWge5Y3lO9S6KjS+k2znQKBQiytI3zBRQSOERQXBBDQg6EqIAQinb0hRFTUw/SSrgnCWQUCuGsd0WqYHdI6iaZkSWcryUvbK/yRwwCCYEIl8wbFcFCLSf1SbiPpbNN+yBKEvJWWUjBmcje2/U5njuMz0W9Jfp5veo7M6VZqV1/ByatfsCnq6PVz977kMRjvAFzJLxFHRDVod07XwwOQggVBAzulkg4paAKgfx90s+8SiBA0CJiLg4IIoC6CqVk+btI1EiMcDCu8LjhOam+PPassNwgspzo5i9UVyMFjFDQHWvmi2I39iFmqQEE9gD0gZPnrhz13+SPTeYD1n6NiBDcuDBxqTn8TTt7/52fv3qOf/xn7+Nf/toH+Oyb4lMQ0dSCCDt/2r0Fe+cCMJ794qd474fv421+e3ffRwQGcmWfvfxpDV70zttyUfu7u/lKe1+3eyeU3iHlz1+96Pa/BgW9dUz/ph4KiK7Pmmy/o/JpqTc42iE9HpEYFaqisAcJe4AAi3TX4ECb+OQJGmY4kw6uuLS9ctIGCeyP0h9VVYK67C4EhLTUjo7jUiEBUSINckyIDhBCJkR1Tgz6jygiEVf1gErRIDUF5wwgiVkhBlLntFLhAGgDTOe4mIGgyxQLB+Qig8N5LTilsPFFsKWP4rhIiJNoi0cR8Spw9XehKyaZ1xU55hFQmkltuAUbZQro0zf5tmz1kn2srcurr+NRPfvAVegiXI5RCEcYsLgD3nfgvJbOBGRAMJoNmimoh4JTCh3MnVQtOBkIqErwJEWkSFjCuCyVsMQBCPIK2LJUg4F8qc+MRLlszweberBTqqTvAlNxDl1sCgmuQ6q6WZTSua/PVQfVCRDswcBo2h3v96fu6w4+a2KILHUmNJ+1Dg7cfoEZxUGBtdo2LZgXmzp4OPjHf/Y+/sUXPsBn3nxaTcsM2YGIOp+F+nuzsaHrp6UYFHz4pQ/wT7//X+/W69GAAXHRiFC/Vdd5Frgbiv2bNAtiYQ2U0Rrnn3/8Av/kX78v37mhTh4KxpmQSaRU96XtoLsDBEfSb39e2/PcKxZYqb3X39LpF0E6khkkHAMCVR8EKujhgGfDh1XWQcHQ4W3W4w9R8KazIuvMQgDSIh1WWlCj+aVFOsi8AuEMssx58SSAEBMuTJK7gRgRLIGIAKSQ8ImqB58gi2mBgFCAlcRebfblGGTwySQZIyW50SQuwgAIkjZajnPJhCVynVmGTB0kBOj2IIGUiCxWfrvX0drWxlV5dita3ZpvjUHCfL9rxf+urxeAWreH1s8SE1m9LAKhhSIe4w2spQUjyk4huAUIALufDQjMT8RUgpnp4E5hYEmEJ+pLsFS1x8AAWAgOCM4CANnySsiKE1iMCoUC6LPy0Oehxn8oAtDMTRmQGBB6LB8yfrwHAxRM09LvAMEeDIyq1EP7s/59/7mfsYfJ9rY/1R+vzsVocFDFFTQ4aBW8DQ501zoW/ME//AB/51fFfGCEUSCmQWZ0y9dnatpoRrCzePbyGd774fv47he/LWkA/jqYEp69fI6vWZjItz4PYCsDgW+/SUDvaPLnH7/AP/mz9/EH/+ADfPUHX+iIEBgcAt1P7gGBfe6BYE8huOajMJ5TZx44mEn74vfz9MquLjNIsBZ9CAhlhXQW6OCgmRey++VWNmYDjZA3htGFmxlx6c0M7f6rVBpEKiWDgpRkXbtGxaPlBFAEF1EOKF5qEp0lnZBiRCTCSsBFzQuXwggUcba8B2vBfSSkNeBMMpivRZQEWYXgJGoHCDMFIRcxM/SAAOSS24oGog4SaiAlaqAQJCVkjaoHoDq5XlOifHvaTTA1aVz+PMKsA3ObZomCHlK3Fm64X1K8F3jIYGBcWXDNZDBTCJrjKFWV4ORevUrwRJWEU1QAUJVgqX4iAsx0kSRalO+1za+qDmTARbdEXluUSIODsjW1ycVUIDeFwCtpFsWSGSJ1yZ3gnIE0DhPUHbMO+BMo4JBuAoIZDMzMBjejp+/PiKYz61m/upl0OUAgrUskKCwRzNQM0r6dAU1fp9+Sqswmc36bb+afefOdYd+mGlzD5HpMf4sg4+N7P3hPxsc3P7NtG0N5NGDwtR9/A9/94h/j3bc+Z0In7GZ6GcjDwa32oD//WGw+RnJWPHg0S5l+9kAg8DCw12Bmxf/+eKunnrez891pcYFQpTRmbPM52DZ3jA0gIGt+8QIUCBwwte1lbRdLeq1No62qQMlbKPChfrNbo2+do82WdKbE5ni4nptaEKKqBglYJI5+ON1JzHwFBKSTmBjSgiXdyYBcCKEwUmGFAyDFKJJwJlwC45NVZutrFmhYC+Osr0v0dmwBBKmy1HkcmEop4qOQ2QGCfGeEBFMoPCjIPR3W0g89lh+Up+FdxzpdAYKxXAOEWZ2u1cvXSRwMue43CzzkYcC+41eNlPEkIbN6qVuo5+FhwMDMA4EtQ3yS4sa5cInNbCBgoH4g630Nd03r2fkVrChnhYTLPXhdNaJln4SKVUnrLoyPZgnI82a5L5jBSCCs+or6PDIVSVvNvJOYiIbBPzbTQY0lodtxHQiOzAcP7rfcRzyZWd/ax0arg6kFChnSx/GDAeGhv39L2Uwg7b32xc8NCr78Af7um5ow8MrA92jA4Lu/9i28W086wJnrAezDwbXy84+f4x/95H3883/4Af62I7lqh3LvMfw9mgxmPgRdmFL0kDAe10p3T90DtAc8m4draBPFNC9Yvant3xE2b5I+HQGCnH9TD+Rzkgh9UGigIH1NUc9n1m3EzrtjKKXF0DcosPS9GHwQxgx8RDqjASQ0roGCKQaXM2g5oeS1AwSUDMQLwHcyk0snhHhCCiSDOolpYS2MCJkVXnQVwX0uuISyAQSBA3EyPCXuVAQAFRTklPvzKKKR12WRRR3kxJ+hecMDLeoe0AbmPlnP/DLvXPr59lsIeygzSPg09WlRJ/sB37bZ3z7uwNGSwyW1imyXlfarDZIHAwUCcy40laCaDZxzofkRUD43EFgFACifKxCwAcHl3MJcr5cGAi5B1qzN1wtr7Z9Z1LKCZremMr34NthvVhyMg78pEBRkObBTCQqOgWCEgQ4EdhQrQCT9/mTV1DnAgVwH/U7bVbff3t8KJGldKiQ4QND6FmJNENcAwffLIyhcU8aulT0oeGFQ8JXvyPjoHVQPntlHAwbvvvV5mB1MgqNQbZjQ/6dwQPrpIKUDwL/5+AX+0U/ex7/4tQ/wmV992q1KMGD2pTY4N/vfUweqkuC+MzUd+N/gtq2MO+hDtgc82+U+faPw7zK4/Sw7ULDz1FbtVYQpIASvHoRhcYLCgQoERAOtkfgnUJQgS8wBXfxhPWepsJshmeOVwcGoPtTLFqTztY5SQYDzClovHSDQcgItGShJjhkTLOxwTHeIMSEGwpqBC8nyxkspEj0xBiw547IGibGfGKc1uHXyAWtqUfT2Aufkg57DgKAwVE1o2wC0ZZPoB2PbZo4CN5jza3mAO8FBGTp5+/0biH38fR9x0AeYGj/33/Ofd6Ck4ZV9/AGvyCzRzDS6QkQDFZnJwOe8MD+CFFpSrEUdEqsfgWbNpPWsQHAvHfjljHI5z4FAE2MdtfV6lSkAIdRnT3TsKM9NCvMBQn0PxAHR1IEAmJOuDyK1AwWWpp6hgh8eAASTfmqsJQ2fBzQaMDjYnJb77qHJdudZKKamMDS/i/R54neg5+ZVBAcIcg6mZu3DwHRCOVRpNqmcKQVfNyh46/OymgW4akYAHhEYVLnLpvH6ahfTeMA7kIwtLaA5U/35x8/xuz95H/9SvUP1V6qjVCcFDQ3sFhjYmA7cTa3Hdb/RyVk6htaHwPb2cKDAYw+dd8L0ZdzEDOdQI6WCgkJC9c69AgjS+k09ENMCB8DSLjNzAwUoNMiXe9XAHJuCRlcMBcQB7D19nHzqO0rOedcpjgJVSdXAAJezpOI1QFhlpoblrgJC9T9IC7is4HgS/4MUkQppfoVQs/JFisgReGKR9YKFWA7IiR0cAGuSuPuWsncMrTwb4MZSPxsGxhioriiQj/dJYG82/1dZDlxRARyrEmMo4vH97HrFYeSwt7Z9NM2kEGpegzGU8Z0qB31kygkQbPwI1LHQzAf5UkGgmgwu933GTC4tMdYNbZwimhmvQNfEZmnvAECkDoj6jNlsn8KgFjRA4Lqf5qIYTQroVYIugix2gGCAgdkZWf/kP5+11K4f3VFl9wbVYRPg+sNgddXB384lQAb9ERJqf6nnVX22DBQmbXqM3THWayqoUdunh4IP8e7b72BcvXUURRR4RGDQFS6otmoLbjOUoONW4F41IAJ+9uoFfvdP39fgEk8blfqb4+6U/XXNVBAmjbHZ5Dc/sT0t28caqpHxcA7+lhNtt43HAwbTw2znClQKGjtJn7zHbMZEPaCoAz5E2YFTEcgyKBKQXUeWqsgDlAwKAVykEyNmcFRZTG3wM68xNvODP6UQgFWOR4EkemHQduMVhOVOAEFnaZQkhS+bWSHKDK46KDpASBx1aRzLwJFCDabzpHBVEVYPCaokNOe5bXKecVY8i9E/llv2qeUBcv6ngYiHmCCO6n/t3GazRw8Aso8HAlRVwDtyHuU1sIBFPkBR9/7IsXC9l4F/PYtzoZoP+HLfKQQGBLJSh2ubnrVtmjprOH+DGMURUUFAlilSS1edkhv4JSslQuwBwBJUmV/BA1SCa0Cw1zRGR0Ibp8fiJ2DjNj8h8wPqLf2w7e+ysWslGhQU8FVTgxxj+0umFAD9mOJVhL1MmYCYDzooqDFhbjGeS3mcYAAddCg+CA6YJbjEN//k6/hXv/ZBi03tZlbWeKM/3ti4JuoA2cx5CgKGj3YD3RPR2b+Cfqc9fMEOVno4MNVg83DVEbb782qpx5kBgn5kPgg3qQchgTkreBNQJA5ADZYIAEVmKqQxAXx9bR8e/sZ6qWaLqVs5ANZcBZwzKBK4FOlICzdAMAXBHLviIq/rBVguwHInaY3NQbFkoLQYCAYIKwM5UEvhy4wSgDVGZAbeKH1IXoMETmgpfdPcs94n9RpDLj8IAm4sRwAwzr5vKbnw7jEf6rNwy+/7fUwJkL+BmsNAHTX9ig7LdEiEOQwEquqAz1thSw8T9UBQlYF86R0LTSnIq5gO8gWsDoczIKhQkNu1orhzHUKQ/oOCrMBJCRSXCgGUkrT35a6HAk08ZWqCQEFLTNVMB7erBBUIIPvdCgR7hXbf6KZZX4zGSATfJw998VghM5VSE/itD9b5DwoDxCSqwURFYHLKqzv00dhC7jz8OY2Ty+poOEDBLYmvfHm0YGGhj58AAQAASURBVADA6eKqIAAbQPBw8PzVC/z2j7+OP/rid/CZN8WnwG66NeTq3e0aFXAAA7pTn/aU4Z0/urzne4XE0GHOPqSwUwEhoDayPMBBhYXhPGZe5+3Suc7Gdd7e1CA8whWcCubmhbijHgSKYJU1BRACqLSgKERZlIMIUCEgUYOD0B6fDRwAdYAmvSL2SHEpAgMVDuwbDW+wXtS2CoGVUsBJvcPTSe3Aa++gmC8CDyG1FQwxYYkLlpiwxBZmuWbxgyTwuZtAgmV0rEF5Ur8WX+BAIvUB6GABQAcMVm4ZbGcD9Z7kfu17139ru82qa3e38wPQQe+h5+EBwD4zCABaymODAosBEYlqsCjvYOhhwOenGMMX1+BEqwtONFlpsHEsXM/iTGvLEVXBMpOBQYEHAqBBgakFYkaQmT5RkIF/Dwp0NY6P6WEQYE66U9OBS1Q1Uwl8zhnYPg9QCfb6oc39dv36kfnWMlD6wXTsk+nWGbaaWKSLtVTwAgpM0Mi5DgocJJiS4M0UVVGo7bS9JaLuPI6h4DuDUvDwScKjAYOZJCMfODgApoAQSNZ5fuPH7+GPv/ghPv/WO5qEiGqjHkHcOp4jGLCbpkNS3/C66H64IXOcQgQVbZDcAYINygXSIRWSjIFTONDqGByoCjaA8f5DOPohTNUDBQSAuhjfTCSKP6GSM4UkEFFyUw8QmmkhkzpAyawnxAhexWmQwhlY1T6qQCGdVpDOmAJAqyhIOYNL0JnWdrbVZFmA9NqxBXphjSSXc+d/QClVB8WQVl3BsAIhtSBJISGFiBgXcIpYmWrQnCkk6KVbN7H7Sxe9T9bsi+nBQGGzVM+vbBjamBcVbhns91Yy+E55b/+9wbyPi+DrJttTmMGAbduvf1dXmi/ZNH8B+XsfBCR6oSgGEXMYsGBHVR1YLe316oITNSDg9dIcC0c/gsFsAC4ol3XfbLAHBHU5bugGfYEBBYO0AMupbmMNCy4qgUHBbSpB4RvMBsBUJQDm49deP2SbpX9tb/agwNJT39wvy4W+2i9L19r6HuuzJMhZM62WK5AgVVPFwJ3yuMy9vj+CAnPE37uoOBgvtTwaMABQB/36tga58dsbIBgc1IRLX/oQn3v7KZhllsuQQXXWOKNrgACmpgKZIecpDHTpUIG9p0LPQ12JiSokdIAQYg2AAWoKCMLtcABYhR8OmEfqAfOBeUEYQK9bFKcolqRETLlXDyyGAa31ISR7EE3yNwfFEIF8Aa+x+SToCoQREGTcn88MymWVTjYtYKxVPfDLfTin6qDIPgYCaajlsMjv698cAk5xEWAASapnlpmxZfurqxJCqB3uWvp4/6ygIBH+grhYeH8EvacGDEAPDddKPwOzAdV9vgMGtygHXZTHXTA43vdavX2dq1kdbTWBOMO2iJBJB1SfZyJVXwNoRkP9fiD3XmEA3MwDpaALXax/E5deIbBYBHkVs8GoEjjTwaxQpOpHMAIBgj4XFqdjVAlO0lY9MLCpBMF8CQaVwLbpioNbzQZw+/n7+PB5bA8E/n4/BApC/T5jNx15bXf7/XMbF0Ltm2mAhEAEUHQTIYMo6p0Wgbriwfuv6dG3ZpAZFHz5v1MocCrLTPWYxIoZy+MBAx94A+jNBwYIFPobTi3h0ne+8iE+pwmX/CC6FwS0cyTcoVCywDtdg3Pxy3lIFtSdD7U6o2U3I4kQ1ADB7jG1lLVEKp5fgQMzkbTBvAeE1yk1bTSjUw9mgCDKgwcEaoAQZFBHySDK4GCpZSM4RyBkUIhyjXWWRTGJhH++B3ICRelwkVN1HJTlhkWz0jVIsGL+Bl3xwZksBG3J4LCqbXapgEApoVzuVbI9VVst1FZLFjvBQ0KI4BRRIBESSxEJNpeWKljSBEdZMTmAgvgjaEKgxGCFDIOFMRqgv1djGWf/IxQcRU3cM2/PilfBeRgwLKLirM631LuHggYBkaCDf/MDaH4DWxAIoaWwjoFULRiyHJbcw4DFFKh5DDKwrmoeaJEKN0Bg3ztYfihOtwXBRSLcwIC+1gE/xqtAcNVs8GmBANhAwazcYo3aAwL77AgKOnOun7CVjM1krVNzb+ijnUnBlm82SMgCZ9aHa53tGpqTovU6szFnVKPnUPBOreM2g61NiEkl33B4wR8RGExO0l8c6tP9MkKXcOmp5lbwzog+lOb4EzHs2Kk8gZbcN7b6WQODw+k5EciggCy7mczPidXBrgBE4gQHZFjWwzjAATNqcA5riKMPxczJsl2v24s3LQC3A0JTDwwQBAA2gKBAwEVkewMEm5FVQFDbrnXIFgiJXCfsIQFAC8Hszz3ntuRLCYE0noEHBFMvTKZFOg+dcAJWBwlVSZCOOYSIGGS/AiA7k4OkDbb4/tIhlxKRk9wcAwUuCgmAAoIlFwJaTgF3bjvt29oQgJqvYC+nwfj57HhjM7er7PMcyH688/lwT/aeS1fnqHWVJE2mDEjCJgMBUDMPRLJliWoa0LYYg4ICIO1ttZwF+rpJaqQwYDDq2mCNRTAG5XIqgZ54bYs+XwSFKCAgbzYwYOoAhrZY26RTtHCzQvAwkwHwMCDYK2OP3kHrFSA49PEyKHDBfsjMi3US18PBUT9NZsKUN6J2hohOSWBTeAOC+mWMKoJdohDm4844CR2hYBcGthXePRcrjwgMJrO8vfcU8PwXzyTh0pc/EEcNKGGSwgHQNPL6vfbnxplwCgS5b2g1V7oDA/v+9oTaeVV/Am6AIKwg6kGIYrc8gIPZigWgxf+eOlkO1Xrow70BBHc9A7iqCpLBkXWZz0j5UQLORIkBP0s1W0PDcgYFDT6k3t2ocNDiyfMICT5aYnSdc3eP/IkVpx4E1Ex2IdYlZxQXcX60Dtt1zN7r2zpiGuy5FBOidsxMoiYUc0aEzKoNFgwUmGPruIGabpiLH3j7eAGjTD1PbuQhwD6jBgj6nYcITfarPnOjz9oIoGZIBB5WZ+v3LGujpJ9u4dEDtXMyEG0KgYLBqAqUrPdb26C2OdjnRzBgA3++oAtbPAvI5c/LA4AVDwL2OoMBCm2lgfoVQPOAlCs+BKNTYUs/L+3uVhgArigEu59slSv/dgQCAK8NBVTzrAwTt/rq4OBaP60VkSXiLU11XTJOJM+5fkYhwhzISSHBn9/sZ65CwZGJYBwjD8qjAQML11kvkLejDLT37NVzza3wbTx9S703nc9BtDHRZtKuIVqpJoNrQNCZE6yROVi4cj6yL/WAYKcWoLPXPIEDeUxawg85Wl1ba+oBemXEHGDs/IEBFoD584H9TqDbzuiCfoAmkKCmDwJQFBJCIASb3XBTEVASEM3MoCGSywoKqaoIVTGYQIL4CbQOewSFLrxs6f+2JZFEQQlMA8HkICoCEVilXJ+4CUOHTZYCOkQ5v0FNAEUE7cCXFFFAyOqbUCagwPq+REIpbbZnt8GgYa9sBno3Y7FtRMNMBn0nvVcqE5u/g07xmVsbrPUd6owb6u3r3ORk1DYkcNCDQFATQrS2qKqAgcBGFTClwIPmXkKjAxiQ89YzHf1cYuuapyBA1MwErm2NMODb1oPVgXIjDNwIAr7sOayOm/0g2dpe3872gKBXdNtgfwgFfvLm+moA1/trCq2vZq5KglxXvgkQALQ+G/Px58URFOyYOzwUtLFl/0F9NGBQ7T0YAGEoz159hK/9yW9LwqW33+ma8ewyxeGm1H3qTKH0jYzzFghKRgcDG8VgdjoCATJAexKANOIBDuxNg4NS4QBwSgj3ZhLzuvXxv4G+fZG7SjNgsA+OEt34dzYzJP2g+O9Sc47sIKE0JSEEUREoQlSC4kChg4YMLisoLltIKKU3N9h7n5Apryox6r2ioTO3c6tTXO1A9MGzLHMc7nc781FV8N7hcDM78mpCCLLCIURAfRNGUGCnJoDlmneDcmwrSMYyG+gJW7vuUUe9V+rcawAERj/I2GqZPZC4pc5RjbGmCsi2EQTQnmXzFdhRBWw1gV89UNWAh4LA5gS037JO20BA/zZ/AehAAtduqpkghA0MlF8yDMycCAFMcWC8U7eoAOP3ZnkMxmFtE07+Bihog+h1KNiYFWbnSgzxBaMGCSB5ds2MUAFBIrhKnhZpH1x9EFq9GcMKBcxWH7ix5MgsDQcEfz1NCfsX6NnL5/jan34T3/31P6oJl+QGmLF+HgipbXH0WAciZ5+aNrA8aVw6MO7Uk1WKAhsgFEd5+sriBsQaHpCK8AGgnvd6Tj7eQZWr7OGmLQiMoAC0ffx+cl16dcFLiX61Q8cPvXjQfsPBQmYbiNA/5KwdfgHamuSIGKNGS74GCRlIvfRboxmq34Gf8dFOR0+jmjDM9mpGDVURAOnwLXkTUxAI2AOFmdnBOviJmkAhVrNDAwW5ic05jLpOfrz+7Z7q6yDR2mezULIdJEyOORbXTCQscx1o/CBE3Tbg9nqPsrKtXd+AQHFtYaoKrNU8UP85X4ENCKyXNngctI+ueCB4qCpggDBEKWQzE1yBgdpOSnMi9HEHPAgYqAG4Nv7UcYfdffFQCWxnwXuJjDxkXgXRDRBoLQwKXB/sfQradgcFnB/WZ1dpazD5Fha4J/2dEMEkr5RZJw+awdaWpOuVI/RtfQYFsyWWcpAdPO/8Iea7AI8JDDpbT/WNrxeqQsGvfQvvvvnZ/ptuIK3e/90eQ+MCXKcyUQmqM9tAqGAdbMRiupk9GDHGCIAbINgw62WC8ZWgKVRLPY0Z8Ehn6Qb+oZ3PICDAdcoDMLAO4jVPOdqsUOIacG3gYwcPt6+/1PabGYx+1ip39UJAVGmumwVClIQQ9eGyWV7J3UyQbSaXtrLwaHIgPm0hIa8CYwYIAa3z13s6OjFyva96nzXtczU/uFmh9yIfzQ5mJ56pCaR24kihgsIShmQ2RnFHnbvrvP2AP+9oMe+Y0Hei3bppP3MxdcW8pIM8f25i+tr19rPFDcgf+QpczhvzgF9FUNvAQdKumRPrxmdg4i/QtYGHmAgOohOa+lRXFBRs1CVmdr4EO2rNzmXvBm/rE1zbmYWKt32tGRwBgO2z+b0JZPSAOrRVv/oAduJl+wreQEGdMNl9Pei7Wc2/FRACUE0MkMmeNGdufmKAqAeA1FOfDeu3n+1BgS/+PRd4OGhZMDuDIPbKowED6y9mM/5nrz5qUPDW592XdBjzdDVc0LrN0xlwHQrYqQcOCNiOB2Ab31x/LrtOZKyHOhbaiGz5BmS01+REJPt2aghQTQteNDCpqjq+AJ39v13YJj136oLBgPkpDIBgcDCWraQ8/xzgZiYjtQEDWElSHVdQKAIKsUi1RS5OiCkp2DQfBJqqCTsmB++XkDX9sk95u15UKtTENmidhznJ1fu8tsGirT9vy81YB4ZOUfADwSwgzcQ3wQYDsXHGGgvCOoZDSZEZYPTROG2WZTOq0Xvb7TO9mXBdkLd5mlpFFq5XdSKivs66/4Pq3dXZOZwNjqsbX4HLebuCwN1r8H4Co1k8DH+fZXWLPL818NDkXhvsHcHArSYCUwVKaeaBqg44EChOSepVmu2lHm9lcX3HWMZkcvK9eXZZf8zuWB4KaLq5g4IKBHoCXb/dteGd5eI7JgPO+aa+GxgAAWht1+ZxVeEFgFj9xKSO/URVoODrDQq6SpXjm+QrJ29u8gJ5NGBQ4BuZXlR2PgUjFMyKoe6GxGaSzcOhYNOo/O+QBdwJ2uFo49r0gUMDHkChHVdNCfoVpgYItuZ2oosAaA+fTdLs7woNBgzkwntyvxQSzM6vQeGAhufPlDt9X6/G2HI9qMA6DVb1Q8AhkhA6QezJoXC35IwoynLAqHAxUROQL9XkQPHSQ4LGua8+CTWoEjUFAQAbkFU4mIev9X72FqimC1JzDtVuzJOodbsmBx0ciIa17c722QVm6crQSY4msIn9FcAwu5rdQF96MGghZXWY0O3kwGCzTvxa3cd6++dysoJgNBFMUxvbSpYrYYm7M3URCeX9EJ7YFIJZNMKYJJtnBwNLMxHoktdRFRDHVHlOuaBb6mqRMU0NsFUfNz2Dw+0L6LucThBSCBih4HBJoT82dW83f8/e1wqPQGDbuvaJAxDYdj639N+jgt/13+YTZjsfwIEdzCZ1sqT+fVk9Z+PXNRCYFZsU6Bl6RW5WHg8YsKdS+f+nr36G9378DfEp8GEiH7BsYwMFtTE44uw6Tx+roG9UUyCYlYHwhg8PvigtTRpVg4P6LQMEztNZV+ugx19qfwVqwMB6TADb1Q4GCFYTbuGXd2ou9UO/hG2v+KVyBOBC7DocUS2IdDkadPmZA4VICUHVBPJqgnmiV0hQJWE5iU/Cem4qwuXcAGG91DDMxk5AHvoiN5D4GUcdOGzWFBCW2EDBOZfNIGG2HFI6pYDm9OT8Z/xM3JeNOWAyyE7adLfUrnZuk3tXp4OxnTdZVr+gdda5nwcHL39eVQ2Gulf5WAf1o9gCExjwSYvKJVfz37X7SP45MtirQYe295CWU1MHLDBWSL0yYDAQm1LQlrICOQ8gUHo1wIJemZr3aZ63mkV1ti96KAhHQDCqCe1Ot/u4uc87/afWf7N0b6Z47Z/p5L3WoZpyZ79d/AtqN2twwOoTVvcnAAXE5mBOjRK0js9fvnBL6p8e1NmqGjAb48ZVCAxsVpOM5dGAgUhijVafvfoI7/3ofXz3i9/Gu299TvF2aMrXvDNHO85gn9o4raB9dnXwv1JaJ6mHrJ6uru63AM6ggtQj7jaKwTY+/KY82K2hbYN0yIzFAEF0CdZ4CqQONr10MHZStqbdLmF2+1Yzhrt1fo29ra+PQRSEACCoLW9v3XoIEcl8ExYJbcsumh3MKS1eZCB2kRarkhA0DPPZCSoAyDpdb3N2mfEAoKxZgpqsem4hoKy5DjBhkbDOYUl6kjK4sEuIU5dDWnhblaZt4CXb5lvBaEJrb7ZAa4N/aY6YXNpsnEs5hgIrDg4MCFjVEkvOI7PiA2iox5rV3wYH1kFd62fOghPnwS4UsSoDlpvAYIBVj+/uW+Ft+vXggUCv/QgFp7sGm6oMtKyGEi4bYVEQlL85BImwqaG0C4C1ACXvxbUQNaBwMw18umeLESEBt2IgEAsceINnG/ClEzY49+HjD5MY+fvo6vXQzIC7ysBeqQpzAwCGTBgAyLMV49Rv5GF1Cm6G5sYR50Ru+z77+M/7JfWH9aeu7t32CtKms0qbePGL54eHfDRgILNSGaSqTebLH9TVB93FA1rr3xtcR6lpNLptqNTJqvYTzPPGNCG7GoZ3Z+Y0Sq12DhUYzD7bnUPR3/Ln8rDGTR4UJvZeSxjCoObUyKixwAHJrBgVDowLAo8Isu24rHPLAMYIeWPxUfiss7IodwYLKViHxNNId0lhIcUTQjyJktAlwEngsAgg5Eubqa/690UdCtX0AAoIFGQQgjAGYqm3vjjbtM8QTYEBCCx4SAjrKoCQogKKRFY0FWEWWMkGXPYDrt7DTWkVA5sPzR4AcHPGBIBued54vPH3vNc90DnbeWCQ/VQq/7T1nwUaGpIV8XpBWWX/suYOBsqOH0HRZ84AocFcrPepAsFyEkVguRMzgb1Ppx4IYtK/5d5yXKoysBZpN6tTBVbWdN6ldfzr8Cyt9vzgYc8Ss5rqCiEHRmRZlZu8sR+qBtjMf4CCqhhsgIC7vnXj1/LLKhuwsEFYAUDNzrUPtX5T9wEX8R2MUVSAMBz2QIk2vxKDcmLnRE5x+wrnE+eX1NsEbyz1WDvjm/vbpq4vfvEc7//oPbyFt3cv2aMBAyvPfiEJkT78yod4963PScMG2sXzxXtEA9cVBADPPv7ZduNGgtW3A2larHP/2zR0dFM5NaTuvXWgPRTYOQ3nYI3cvx/KZgnOgQwsdcvok4VwBQSAhioIKRSi6qBQWPeHDPzO+6FW0aDAcgD4ULk+8p038c5C+PqQuIngYuQ3UAjENU6+h4QUImKKCMmrCJIhj9dLpyBIBy+DMl/ugRjB5/t6f+0OlAuABcAFCGgDjj8ZzhmIMjujwAihACUgZwZFEl+UNSOkCCQZ7BBFTeDkwjOH2CDB2oDOYmelts0ZBKgk34XxVRXhFo/82hqcZz47r3yuIOCi+Dkg6GABOD4HH1a4QoGr/+BEeA0I6nWZNTagAhwtcQsFpgqc7kDpJFBwuhMH0hEI9G8OqaoDRWfpljzLh8W2Vw8CNWcGy3PiQ2LLJeGjU6kwAEBkbn1eMhhxsB9U3VAhgNCrccG9N1io81YFgsNMs0fmKDufvcGyNYjt9ztpROEgoCleBgMhiYPqBA78z2x+wqtGg/Jbv6CxDGZjTl1Sv+cTp+fAFGqMhL3xzZsRGDI+vv+j9/DtL32I/+v3/+n8+HhEYMAsEQ1/60fv1dwHys+oSxfH74yyiy8jgQF4/vIjfPUnv7M9EBFq65oNvJPG5N9YZ8nU0gt3agB0tjRxyOqUAqL5gzLUqQOB+vcVUq/Pkh0/1N/rICFEBHVwKySHtUBFLXOIE9uDzHRAIlGurseqnRn72P86EdQK+YH1MlTZEujopamZ9Igkja4HhSWGDhISEWKQv6OqCDGexOdgvRcpuAOEVCV+G+CqeqADWowrKKwo6ypXOweEdUWBAmOk1mMbJKDIrLTkCghiMpMBKxQWEEpFBn2TzZPOwNXXgKz9ANX7uSvODMDOCWoXBPK6ccQDAM6M3SA+AIgCdtMEq2MeX5qJwUChKlR6Hgw0X4Wj8+CyiTPggYALo6xrrTdf8lYhGB0MXf1DIDXnEEKK4reyqEKQTluV4O4NNR2cwPG0AQJOd0BIkjWzaFjrIqm5VwcFhYFLLh0IrLnUZ2XNLaCVJdLaK1lBOoCqCZACIerAldEPFFGn/QR0yts0oqTuI72YDP7z5HLSV9u92y11Zq81HgbXtsR78lWbtQOo9nyvHAQ0QGEStSkkraMOxihAIu1DoyoCQ78/9OubcmUi+r0vfAtPRyjYGZ/qczE9X9cvQ+IgvP9DgYLPv/V0XjctjwYMXrx8jm/8WFInv1MdNUyCt+Ufk7JnUhga17NXH+FrP/kmvveFf4W//9//b3frIV7WvQQF+EYSh/0VBqwOIxDYLGkPCLqZPNpxds6lp/HhQeQy+FQMD2j3AEo9yNWDNcqXLZWLFPVhQ4MCe1V/AzP/WJ4EIhIpfTLpnGUM9CmGgakvWIWDsFLNuBfXgqgKQQoBKRT9WyAhkpgbJMUuYYkGCwnLKfWAsJ7BFEEhIaQFJSbQ5R5839QDnAOQVwQ6y0AYAsolowCIpYA1qyJj26mNGR85s0R5ygEFq2TbWy+6REoGX5hkmQ1SBBLkPh7I8Eex/CcwUGfYevENEGYDUZXbzUQA1AGVQgaFdQMJCGqKCX8159FBwQAAVSkYoi1WINDzCCleVwnungDLnSQwiouYpNIJnE4dEFxYrDMrMy5ZB/1i2TUbDLR/BgdQc0J7LgAP0P4+uL9BCPpYIogf0AgC7bydMgA0h0KS4+yHmQa6JaM8hCOugPCwvsciDSolAmRgc9wXsqquNrgDAhesEWQNAkAEm7WY/N8BAtBBwlg6nx7ff18pT9/eUwrG77b+e3pcZ4J+/vI5vv4DGR8/9/bTq5aaRwMG3/jxe/jjLzYosPMmBwfT0k/h2zcdUDx/+aIteXzzc+3zKtOX7n21W9UoheVY8prBgJkMDoCgHnOPQL1DyuA42TnbsPM81/fd9zf19lkf3XIye5iYxdQRUNWDFejhANxcm4vOblQ14EyQ54+xTn5+hIIasGXoDO2WVzAge5XOMVFQ8wEjhR4SlkS4ixG5MGJgZG6penNogIB8FhCKC7Dey9/BrT+/T+CLzgjPn8iAcTmD0oqQRMI2Bze6ZACxm7FaGXO0d7e5WFsTE0/1Y3LNnt39pKGdVG90m237gXSyTG/mkFcldze4euWAKDTW0+WZIVBnIqmrMQqDcm4hgGNCTVYVBLBMLXjQudRzaueyV0Kg6j/gt9WlhzOzgQagkvTGT5ofwd0bYjZY7lDiImaCJIAgJoNTBYJLceYAVQoyA/c547L2MLAWYC1FTAlc6nMATJ4FOwcQUKAZTKGOWQAgz+asnUWIKhBBnVrgU1PHsIWCRINKMAsKN5gRjvoceQ3iFFijwbqZPw9wMFOE3fJui/ci39E+aQQE69dV1ZA2zV3d63Gm9b7Wx4d+v+G7PO4HvVdgt+3ot6kGR/rgKx/i828+dY/oPh08GjD44y9+p0JB4RHyxwvrtu9ukwv/7OVP8d6Pf0scQd78nLNDOXWgyls9HICgNqC5pNSvNNgCgS0z28DACAK7FHqLNLezVl0/2y0UAAvhGaI+qAU11KceVwbKiKSmhYI5HCRdVpIJSDEgI0sI5ECV0iMBqzuVEQrae6cguL8bGFj2PIl1kGJBACkYCCQsOeCTyLhTBSFFIBFjiYS1iIKQibDEE6JCAYKAAtazSuBLdUqky70oCXkFx09kW0wdIHCKdfZKNyyHI9fI60x7cn+FUcUcgRCxWZZ2MIh6KBhn17yuGyCQzyaqhy2bjQGUAzhn5EjNhwIJiAXlAlAsooJA0l2zZrEUJSR07f3QLOL8HnyR69SWklIkUVaimh2DQEi0n5lcd+8E6p0L6fQEiAlBFQJbcSAQIAoB4kl8UdIdMgiXQSG4ZDMNMC654L4w1lxULUAFgwLGmudtf9bu5W89nQoDAEJvbK2+OuqbY11OikEEG0D8cyZQ0GWmBKNLT10KgIIui6FfyXKtvwFQ46L7oOtHs/C9PrJ286q4dpMkbqqAqRnV/KETH7jZv0HETuFu8Hf9vNZvE+5++LwbIzYnAB1fZn18g4LvfOVDfO6tp3W3o6WpwCMCg3fefrd7P572LCLi0aUhEJ69fCbBJb70gSx5LNnN0qVxVtuUd1zRIwA7Es+4XGymDoSIDQy4RnI1Chx80zE72jYc6GEAG+DgYXUzDVUKarrRkDRokDteiIgUFQJkudMa9DgzOKCgcjkDMQC5YA0AscxqSuYuH4PvGGvyIIzZ+NrfMTS7aMhUFYQUi7wWRlwLLpGwpIi7LICwsgBCZsISCKu+LukJQsygNQJhkRUM6wUUogwOl5NkSLycVU24r0vkSP8d2e1ryxmD5ngbvUrvfg19NUXtFT+IHhQ/uzalYLPP0FaK2yd4UwiXvo76+4wASu33aDbou/097ExLCNL0zalN1RQoqwcAXCywGMCFgGTX2yIUtuu98YcYYxK41QZ094aErw4JHO/Uj+BOgeCEQhGXIgpBLoxLmQPBZc24N5NCljTaa/YKQWvve5knDXCqGFCog4H6WiFAzGbmh5MiIdExFKTqX6BXbpaPwkwJs2RFwH5fQ0G+X40YgPVprQcItcN7WH8ZW3/JBUCUdjz4P3CIPdD4/pKia/t7o8owoSPCxoF8MIH0Y8dxxMJpxF8HBe+8/bRLeidK2z7MPBowALb3/OhCXjGxaMKKr+vqhs/rsh1nHhiXvAT0DcZHJJxVcpj5X4OBraS0f1NBUg9Wz1fZs+yf9AwKvM1vSpfZ0S4DPt2oPUikf7NG8gpofgdFzqEYHHAPB4iEyDKz83AA3YQoEYaDOicEdVRwgg5y4TpzGjvNS0ZNLxxIHQwDI2RVDbIoCJdMWDJPAWENjEVNCzkQlhCRljdAcQWtQc0LCVjPoKDL1e4/Ef+Dy0lA4HKW5XPqLU/OOc46ygfH3bft1cliLrsDkxm3NvEala3IvTYQEWcry1oBWVlh0JmD3GsdibpuLbZ3EvmvmRPMVj8Owv48NkmGjs4rpmFGZCdUQPq3wQkBt19ni1RIQVehpHk8grsnAgQTPwIOCWsBLpmr2eBSGJcyB4KtTwHXlQlH7Rto7RvF/Q111wBV9UBUMzWVxS0U3MW4hQIFAVvFE0keUXmct9kqvTlh2s8cmRDMX8ZkfQ8HO2rspt+0ezjrNw0oPKCwrJjoIIFMbRtUD/cd+Xunz3f1mpmH/aRzzG3AuD6e+TN7PkDBWJ2/Nj4GXdGxpUbp237cFXum7Nl5/vI53v+BLnl0qxu8rwKHoPJRhsGBmA6uXHW9+bs+A7swsE0sszl0fZUQwFZvRlvWUlUDmlyc2cO6S8Ly/T77ow7wUPpWPwMqAEMHvcAIIdabQ3V/d4nBEkAFwF2MiCiIheU6DXCAHLCGggTCWtysCG2JlnWa9mr3+5K57h919UEgMRNYZ3kKQTrvCSDwIj4IS2CUECoopOgcFM3/IF8qIODuCehyBquJYUzSQ96L3q4lsO9BtjdYetPCjtxaj521XQaVdzVJFGMVuVxNCtJMxHGUWST46jxpOb3t2P43O7PHFgg2Er1XQGwwnsQ/2Du3NuBrF2fXFJBO3a6n94OYXV879pgN0yJMpuRMBqceCCwgkXMsXFdWlQC4lIKLimKfXPIuEJxLwZoFAi76mhUMAHQzQd+exT+G6nbxK6BqNmsq2RYK7tQRN8WgsQigg78+G4Hce3MynJgOXE6KZjp4WB8DGswGM/Ns7VcHleBB/af2RRS1e2wTvUNIGJ0nr/T/nWmBCF0+k257X29/1HHMsnMioDkaOigo18hiKI8GDGaK+sEKnVbcPoWBF69kSYeRllxsG6i5UZkNss6rFYA4rgBbWWwjZe3AgPkVDA2iDC3aH712i3roFkTEYgs4qGHsQIGTq7sH1mS2zRf0f4GBaapR/R4HvU5OlQhROloZR2Q5I+nafVs2lVkiFlIMIGJQKLggIBZGhNhbAwihAKtfmQBRE5YoyoDwRN+JVliw7zBqR1pYOr/ChJUYpxiQAqlTWMCpMNYowLAkwpMYsZYifgcMJJYlYEtMiCfnd2CAkFcgnkDLBVifiDnhco8x7TO4XzooFXaz2snAX+V3a2dHcjzQTAlRfqsOqBoAyFY4SOTHghi35o4jv4hxRcWevX62bHGWdbA7z4ecY2pteHNNgfl1HX5rk+lwFq3QBydKsiQxQ9qhKQRrMX8C4BN1KrxXH4JzYZxXBwW5TIFgZjqQttvDwQi6zZeGnLOtd7wlLElMBzb425LEpL450TkZptD8CUjDiHeprLs8MjOHQ570LXp7rKtkZzaodv3W59YyqrHWp7q+9GH9KFVIqF9S5WNqbpis8urrd8MYADdpvDIGwLa5y/DipYxfH3xZliTujYGz8dKXRwMG3Zxh76QnF8nfuhcvJQ7CB7rO0+9OUJsQu8aHUD1S2xIX+8KksxqdB6uNaUuHLfOZVp1RZzx9vdo+FpLU0DHaoEvOU51YHlAEmMmhng9j25iB/QiOAGqK6BEQAmR2H2Jb62GmBa23+R1QALL6HYSggzyTRkcU9SAQIXFUIGCshXCfCygwKAeEwAhZHNnWTOLIpoEUCrurpJ2qzais5KKhZbkpCYWDAgLXTnNVKDgVcRi7KwFrZNzFgAzxTTAHRfM/SOqguAmSlFcgrTKjunsi5gRdBUAKBl3kQczvj9yIZlJom+YqgS8+qBGxC2oUdXZnkMC6xCyvoB3nxOoTceCvcNU3QiM2VmXAVia4cMm3BjuaniecMnDDtbRslSBqIaYtYVVImrwoVSDwwYmyqliXsnUsvKxFYYBxXwouq0DAuTMdlAoE57Xs+M1I8cqAqV8WmyOF4NSBuUpwp/Dbx/ToVYKrpgOfa8SbDiyZ3CQFvXx9v2/ZDQ402OPrCKCDqk2wDAp8f+r7UqD1p+bFAMBNWlBzOkQFBWifBw84PgGS8z3YLeNYMIwLUyiYwAwABN3+4qUEL+qX7PvflGME93avPBowmJ7l8PyMF9SDqsVB+Lau8/TmiPYTTqYKyYXwbIAwr1t/82+BAWu8KlZ1dfWNustpzjKIMjVXlEA6AzfFg2TpZEfp1bnH/t45j8n2Ls1ovQZBMhdSqJXvTAsZbtWCmBZIg6GU0tZJl6LyJ3ONBR8oILEEc0mBxGObClZmRGIkneWLE5eCRmYECrhkXXpoMuwAB4DrdAPhvJaqIJQIeQXV5WEng4JCWBm4K4wlBgUPRmFb2ugBQYMkZUnEhCKzLOQVSNl1nrkCWR2wZyYFX0bzwrWivh8+ARI5GCEXAZFKrksXkZ0srEBhx+CjOtpA7iFmNBH4JYoDBHSDtE/EdMP5NtMC9us3HG+Tp0EHJNYU14hJ0xwvNXSxD05kjoXmR5ALcN7xIxBzAdS50FSFcqgQWKly/o457AgIjswG3pfg5lUH3nRgbZbzVCWoQDDrb/buJ+23cYtPUJOHyZqxGhBqr0+d9adgCQVtxmMDhRESqskhxNrPVTVh79x83We+EDtQ4CeJvmQAH2lwPz9+9b83vF4pjwYMqp/NTG5x+80G2I9ePcc3fvx1/PEXvyNKAVfH3TkcANIAoR1HtS2N0pazdel3boGBjL4R752XECC3lX+QB9gAwSpOZFBjkhXDkhZIWE0PBfvhbH3xKaIB1ExiVT1ARJdNzISJ0bRgXuUhIYYo9Q8kMeAhD2IsQCaJxJaLBEWKFLAwkHLBajP5ILOvDSCQdLiBmp3WAEGUgW2na+BQMjsfBYWEoJAQiiasoWpeuCuMlVsMhCWIecIAwYIkpSWBkmZ19E5arlNlLsDC2Dho7XU2Y9nrXLvvc7svvqN2SxcFYA5yJpiyAEj8AfSdba3OxDdgTwmoEGCRJN1ADYyzyXH2eO2cD4p7Vv0A4wHGwKAmNtIshyu34EQtYiFXP4L7idlgzfypVILXAYK7EDpzwpFCcBMQKMhuTQcjFOReJXD3ZGNyuuUeAW6SZX2bDbBUB9XC0OBh0g3Z38CsX+XanAI1FcF+wiKnHkKCe1Y3mR6Hc9iODdiFghnIAH78ahEN7dfqlWJ05wUAR5DwaMDA5Grr6G3DSFnjRX3x6jl++8dfxx9VKJA4/kdwID8oXvfV+x+AS67d6K824i0FjhSbPdHWBtEa8cbZVV8LiyoAgki6+lAXb3AkIJDO2NlUA4OEguaqOsDCQekeZi4VDsRckAGWJEbaFSh6t/joHORadysiKGwAIaucn1kc3EppiWMihRoCduWCJRMuQYPABMZaAs5UOkBIoQWGsVlZbTc75bwWnFLAJQOFS6ceJIY4H07MCyWKT0R2gBBKy8UQYhQVwad+ngSBYe+kNTYEe3/NcDjur/dN7g5LWGVI+6DJ7M6vmGi54xkbc4c7blf8oD0qAAAsSZI5G+6lYQasQx2fsde4Brt2avd7FJxaEGEpj0FRMxtucxkIGLRohaPZYC1zlcBWGpzX/vrFoQO61WQg/jHNh2CJHhrMV6D3IejTk2MLBCqdHy9DdP4EZko4gIKbbll3zx0MmEe/3R9NOJUHKPDZJw/7VpZDZ2UE6Vu1j9CfjvVvhQQy3y7vl8BgRD3X2P/IxhRC/uevQoEd6SMdv2RS+45+3iaEot+2w7vh4LA8KjCwMnby1y5qhQL3hWtwIJdf5UXbUj/f3uTZjZbAJO7vocEaNMwUD/MlCJDGyaQqgcUXRoODEDSSIKD+BkmlVQcCVUUIei7apHhIHmIzwx26t2xitZTs4EDrZ7M+dSDiwAIixcnEIYmMGaIsPmASs3epsWgqKOTCEkCJI3IUM8NaQg0MsyTCZZXVC2uh6qOwFm5OhWXr7T0WURHQSVCFSlUPThEoEKXCzAspF9zFIGGWC+Oi+RdWSC6GNiMTSLD19bXDHb2hW2MeaudbyezeTDrfQYGokdzq7yn4AlvlYgINcqjhdxT2fLmWSbSbqet+/axw8OwGdoDg2nUYPq/OX+R+05v+ZMBhNBgQMNAIhdiGL77PRX0K+ngEXiUYlx/69jdOSLw6EHXGb6sERqdCcSK8bZXBzeGMvULAg99ANXe5tlL6CcZ0SejYl+y0i04R6O4LOSUn1P4WMygo+/3r2LdaCyky53ITMFYYkERwEta9VxEqJADil6DbXU9oGzBuPYICv58fvz73ljnKo5vc+sfCnNLtl4/g4NGBwd6kb4SC56+e45s//jq+9cXv4LNvPe2kF2lUXKXPgi0cWONrF3d7k+3V3+SZpFW00Y6NtfbN0xPSRkCojZNIGm2lGYWDbD8YROD3KyAoc68aQA46y/ngk0HJfq6THTt/1lChIB3gfBhThqYOBMzfgQhMmrWxFBCtSv+EEJJEbwsRJYjnfwmSWyCDqv+BxYrPQeIMPGGRb9fMqiKQLv+aB4sxFeHa+vDCXMcXUR/qJygsHTBzwFrWFhRJASHFoAoFIxbXMSvABaB6QwedrdlD3PqrfkDfto29p8Dfn0HitE7HO0/pOTUoccqF7sP1GFfq5KvnVbROSt3CgTn52j5HkT9viUO/qyi4OgFboC829umzaW2uwqTuk1nA1IBgFrHwWkwCoFcIxngbRw6FJw8Gqg5YHIJE+74DUxgok9wG48qCm+ISTFYejL5M7t7tK0Xk2olTC7oIsepXoHA2g4JyrY91b0QVcP4GHhIUAMSE68aIYLOw7Qz9KNDebLyo+zgoKBAoGMcvG7s8HMzKNSgAHhEYADy96P0eUjwUmE+BXUN/gW1GXa+vu1mzrtdvG4lvdHoxhcADgaUatoZahoPan16YCDquGyBIZ9vDAUHTHut5Bu+IGEILMw7Ig+5sXZuEUCk5SbA/f68UsL9oNmtk7v0OoBXXG0B1YBDHRSoKDrTWAUHMDCbnWiegs36DBJ29lcJIpCpCaWvD73PBmhhrDsiJcV5Jw8uGDSQsEZtO24eYte1r7XRC7XxMSk4sioYBQpVyAyEYJGgnTXr8YDMSMhlXr7FdJwBhkril3Yz9j3qfme3rLNBLVQ68cjHCQ72tB3Cwkf0ng74b8GeBvcaOdPPc7RUb6SfFvmfPqu2+97wWBwNyr7mZBBQIVlUHLrnlM5hFLbRiK2BGEJC/tzAQQDilMF1dsMS23PC28MUAOIuPSMnuHmd4MKwQ4BSl+owDE18YvZY6AaiZZHPGCAO2H+t97iHRDf6jn4cDyaL3Uu4bd1CQwVf72U0fa/eGe0iIRGB9zgNRNeESAVx46rBovDAef9Mk/aPkxg5ggIJf/06XJXGEg7H4Ccbhw4DHBAY6+JB2HHs2448mUACgg4PNoW2cBeq1tGNPf8cDhDVMtJtsDofWUG3b2FAZbcY685yNOjAaIATIMSMIRWmBwMgklWwmBRKnLy7YOCLas2oTSSX7uhyTLSEUy5Kt/rQnxVQIBwcsURPlWEE6Hz9jo9Bo1wYFNysgDQhCQTIaxhCRHCRkFlt/L/ECpxhRGHhigKCmhlOQZWXe1FC4QUIJfZjlsdTOvRDWILaOQrJqonBBZsJKhKgrKVIOmpNBZnekUjAKq3OTgIItP5Xh064HTwf27opfsbN38uJwHAuVKkASa08ylyH9AIBhluhUCWA7mx9m+zbg+8He4Bo6+HpJ1f36rnPxrMycIjdgYbdzkJztGc36pbUwmHlQAfpshxaYyNIl70GBAUBEA4EakCj4hF99/IFFVSgzFZi/QVS/gr2wxZ3PwMREUJcbdgO9AwGgh4Hx4td773Qu3/CmfYdrE141Mkgw58+Zw2GIsKWJZkLIer8FFHooMHh4nX5Wutb2jNpS7aLbDBACa9h2uwTDiL1xmHfFmxB88VDwuWFSe1R65cLgbn//RwQGOqQ6OHBtEkCzyRgU7BUjLznsYFKovyfHH71aPRBcUwlK8T4G24YqKxQmd49l8F8z10HFGp8Qo2YXk/YqdaG2AkIGG02TbKfShIHWV48JQwwQ6vW2/a7Lx3U/O7h+vzk55q6FjzbkulRUOwTSDoLU2YiJEEIExwVLiCgg5AkkrCwxCQp7U4MsZRxNDYVp06EDDQTstVMQFDACq2NiZJS1eYqvBUghg2pHbXAAdQYTUIhBWzG1TtyzErC1oDcg2EG0oQPxbz14yDm1fbolscN3iSzK5qROOx1WN0Mr1hn3syIPAXX75llr57lh8wcCwmhn3oN0eybXLANMzYTIDFaHw4IGBLNER1ZMITAIsG0ANjDQInHOTQXmN5CsXQ0w0MUcWC/YrCYwf4BRCbCbhB70Di/wpj84GLkmjqO7mWYNCuyZ946gaKsQzIRQTQT1/RYKDO4AbPvaqoy2Z9IDAvRXAwMWFgYgBHggEN+vGheB2zPSXSX306MJwYpXuj/35v74tb3G/sWg4PgBeTRgIOFbAwAJV2lNUWbRvaPGZw+gwL5zUxmu7axDM3ukNdoRCkppHRDzFgjsGct64KgPT9aWGRmiALDav7pXIQIzJZASQueI6GMbQDzfmC2GvDUiDwgWatlVDrHvNOr1mUHNDCK4ewF8V+Knx/1MosKCBsfhECsoUIgIccESt5Dgl5IlisipxanvTQ3sZn0NEqyTB2h3OXzR3mjNhECMEBogFGi0Rpt1WAeeJZudqAa6DCrUK9zNYCZXRy7gLbOH4b11GB4cPHx4MPC/GQwrh9/crOBxZaauAW2Qr2O/e47GQbv7fDz+/k9vfnP21s8c/TNoM03xv+1hwJSEEQhEGfDtpZUx0ycAzV9AGxjYNRVEMwmgOiLGDQywDPyXy0FEwol/QPecHg0ik5s98/WYTGs7FWnw8Zg6nIYEcwLdxixwEFAV2l4tqJMw2w/b+wzs9LXMPSBY01coMEW2aD8JtCXjAmS2fas+31L2lO5Z6YG/bSM7SVbz4EEFHg0YWLAW9rMXndH7OAWfG9Z5XpNhrsmy/lgzKKgzD225RrLdZ9hCgXVGY6mNtrRBw0L51vkiY1c1sIfCVAOR4NAcVnQHDnpNfe6HUVYkW4qDHhiszKTGzumI9XMj84OAJ66Qei3L7zonNQs8E0RJMDskxbSBBLEJuxlf6E0NAgdqK95Aguv4ySkI7vxtBl47Cp1JekAIBF2+KJAAoIKCrZeWbQIMADqvgrFpxhva6qzPjh7D3DEMQKpV38ugcvXHzTfJmt0A77bPZlD2uXcC9bOomaJ2i4A1Plv+rWlYBgD2mw0K2nekHfTA2JQlpyqpd42HAMD5k4QGBnsJjWx1S2cqUMUpKUxsYEAjEfZBh3K3mqCuKBmWEU6LKXj1+asfdJ93s3+/HcP+k+90PiXepOCXjBooDGqBDXd7aoEVxtCmJv1tfZ/l2cqBEVnTtYTmP8BuMmasXKA+BexBoF/tBlyfhBIRXrx8tpnUzp6zMHyvwv2oFHK5ep8fERjoEKtr882c8MJFhLI4BQZ5QN8ZdRfW/31DZ9eqMUCBm9oYuVoj9fRq3zUo6E5rKEf1aVy6Vz95zYDK1FQfMiq6EsABgc9J3ikDHhbGg9dKu86BiyiD7GwWwCawTo3w56LxbS4E6VDlo9+RrH8Xj2aVGzVcLamJoYOEZBHqyCkIzdSwpCCOZIncqgaLjTDODq26VGfEZbhxtQ9ygAAGQiE3exRQQGGNVtlgAUCFAwBTt8NbILZ+f7IvDT3IHoSM371lMcBeGfsn3znPBuv2vX1ovul3Z9A9Of4IAQAqCAC9GuCBYFaSXii7zCMMSB6D/ZgDNvibqaAqBEHvVVl7GNDcBSir8yPQENYWxbJo7XeeM6no8JyFMEQ87Wf5PQzMgaGXpw5gggiY5BSo6gFE3jcz6bUmcK2FHPW3XFDhAJBJVwUCABbsvRBLtleydkYbOLilEAHPf7Gd1ALH45ZBgTk9thdufflfF1PCWAguoYSFibRPbBaAPVl1K59+ir6veTzbe/TvgUnkvZ0b9xBI8b/vzQmFqHqvMqA24uzCPBNsWRppA2/XbAADYK4M2HYjIEVpfUzQrXTQfbkUCQ3sMrHdnCdAM/WxRcvT7HdIFr62gYGFrqWYEOMCThErk2a8405FKEVWNVw0NkLnZOYgYTtrNMVgey/kD3tRSJCLi1DaTFJegYt+JQwDd9ggYP9jD20rcWf/ERiAOZi0393/4dmAbGUWUmsGAEAbsG8t1waMUeov7ndH2PP7elPSrKqmCAA9CIhKsIWBMSKhOQt6dWBRZSARQOozINEpJdQ2PBhwBrrsndsoltNig7MPRFVMYUw65luqYDdg7/kH3KIk1HYz7uuP1ceVsD5M7gs2ZoSHFqJ5W5Fgaq1d5yLOw0CbjMnUydWH95+po2JX5NlLCdP/x1/UhH43jlszX6JpNQ4eiscDBjoTlbXWhGevfoavaz7qz7/1tMo7ctEMAfcG3wMoIPfK7fObbJsPKJFIsgtO7qi3PQPozAhCjMfHttNu10PhwFLpsoV5juADhaBbm8zmJOb2ny1xKlmXImrAY5aASLy6Tsr2s0yDY/jdoR5V3vThdQ0KNPsdxSRpcVeFBAUDS3xzigs4nmoCpJmKsCrjiMMidcvSvONZKVxNDONs0krXafnBRff3sGDFoGEs4XVo8Uo58hO4+t2DL5fX6a3tu6//1YNjzg86ggKwvYfy/fb3eNoz3wEPA+ZbYqaCMebAkTpA+Qy4hFzwYMAZuJwFAtZLzdrJDrpvfo44gEsAJTTlwPraKANzN4tX82TnG+DNfXpF5IfaBetWLGzUBTVcdUtXh4yJN6gF5Gb3BOk3VyVMCmKePepzf9llOq5osYR+3/6SQQHUDMHTQX46ZhG61US+P3728vlh3R4PGLjy7BfP8d6PfhMfutTJBc2TtPWvEznVbQr7u/XbrbEZdDBXp0eg+ToYS9hnKpIJaQaZsUZ2joU7AwEFDN7rcrDg6k/UJ1Ky4ulWInb57dQAwYfyBDZPHbHrOifw0KUiNUgouvKgFM32uGoF3LG1wzKTQk0/nC81y1+1lQ5FzAsyuyGiCgSUJMkNpQVYTg0SQgKC7MPpDhTOVUUoKTpfBGoqQuljI6yJcFlZwzLvQAIDJQyzzSszzSFgnAZS2ev5/gpGzBvLL2Ow/jQQ8ldZbjk3X/d9/4E5DERgmuJ413dgVAfWe812eQHK2mDgcpbnJq/g9VIB4ejZqSdgg3qMoLiAi6SblvDnUb7fRSokJ+3Ham7ogw4NpgBgM+gDwO6S1m6/7bJWm/QB/ZPg+10LvU4k/Z6ZAFIMIPPtCnzY5wJ9vyv3ug0D3tHPfh/uM6tHxxiTn3vxSrMkfvlDvGM+ca5fPzJ3+DGrgwJHTs9efYSv/ek38TbenJ4r8AjB4NnL53jvx9/Ah1/+AO++3cIcB9VTCjdAUJ+wrowwMHZaz5W0ujWo1tiGYxkoMDcqsG3m0GUqO3EPB0ADBF/MUWwGBUFVu+gbn2tQs2Kdn52PAYJ8KbZDDF/n2Tvu1YLqAWuzlKoUoLmD6Nes80GIOvsxfwOBAl51tmOfeT+EcdbjkvEwBQGElEBxAS0ngYTlBCx3CKc7cE5AvjgV4a76IqSUJDlOoe2KhkBYOeLOwjAH6kwNGVSXtTVI0CVqYQsJRwPRL2MA3psdd/scQMZRSPtbjj2WI6XjKJ/O1oTysGP/Moo3DwD7MEAEt3qgNxWkGKo6YOGJu5UFZi5wvgOU7zt1gMqKcr4HLvfgy1lg4HJuz8x6QfUl6FYhaPEmA3tmYqqDiMBBAZF+Rwd8Skv15emgwL93joKdCcD/7pV7ObYqDwRHz4TNrn2/G0HI4LoioMz63TB3aN3rd2Wb/Zv3u7Om6O3/4xjzmz8Upfupmr8Lo9YZ8BNbd6x64u145P7ZPX/28jm+9ie/je/+2rfwf/neP9tWTMujAoNnrz6Sk/7it/WibqMhekAA9n0H+oAQUl68fI73f/Bet70CAhqJetXAGl/Q7UUbq+0bIJ9npbsUCAyRstPkoWme4mgqAVrjNCggtE7KPvd06+vcXQ/an3/Obajtath69vYg6AxFZxpU2qyBKAPFYjWKLZliks4rQzqZGuIg91BgM6HCsNj8PkY/mV0UQFgScA6SItfMCimB0gm03KGc7kRFSKf/mbt/67UtudIDsW9ExFz70PXgJ9vqKmb2TzCM7pbFW2a1bd2KRRb9YhSZZBUF6gKj3b48Cv3mdwMGhG7rRlSVySRLhoFmFVVUQ/KF5ySzSoL8A/xkiCdZEmD4rSEz914zYvhhXGJEzJhr75MU4OodwDlrr+uMOSPmGN/4xg0oGzhvoHRVlkHcDFu5w5aLKPrJzWD18QslSYdsVgFPXA2cng4SEm4HsH0S5bsaT6HJj8GT8/Pj7571mFgN2cfj59O0OWflbs+7u+X4u2M2yCcfK2DxScEAmeWfT9gB3AomvO/ugv0KtKv/zfuDgICHe/D1Xp7vwX2gAYbtKmCcDWzrsHuEdI4oGwgF0tcUIpTyNp20BPmyuhpYA3y9nsBcY8ACgVeWvo5zubIew+cng4yAm3I3ggOrGNvQ5S4A0GJCs9wF4GnFLlsfkbtzUOBKxwBwpnu+FnaeT9VZDgr0ar/66Ut87YffxPd+/Xfw7qc/e/IrMp4NMDB65Htf+l28+7Z1mWqwfPdZDOUTBUgnf796/Qrv/eA9vP+V9/GXv/+XD6EGBjjsR2sAB0hSqldkoaUtSe0BQ6dSjwDOYpTVBE0whadxY3pJXdBAJVkN/tMRmfzpmPGmm2Xtqh73uPGlz4FMWpV7qyCqYNr9uwxouVQGV1HipJTlXHaZlYngxmj7Dq7S9IcX0WiUCXR/BWVCKgWUhBZFLuDtAso/B11eKHsgDIL/XQugzAHXKzhvKHkTN0Oiwc2QiXszJ2UR2Js5SeGkCmAn6TWwN+kYOYMEQFwOBhKAWWHftq7+nQEHC7ILvxf3hQGA+XhvAgwaj+eSSK7R2EWQh/elGNDta/DU8djvrFwEADxewD4TwYCUH9ZKluoquMUOePMjGlMNJWZAAIDFD1C97+6C64MzBAIMPu4uhACan3R/UALlJvcHC6swSwsH2kPXywAKcvG/5+6TKoU9DkACA8d5rORItIpvyq6TcZC7IGUJ5FgWB8TcAQLwyWTvYIyBDnLXQEFWHTFb9NMh8K6CgkBsg9EDGZ+it/pzYW9f/vQV3vvhb4t+/PTnRtZoMZ4NMPjaH31LkNBbn+8vBnAA4AAQ7OIxjiI3XtiXr1/hvR98He9/5bt49+0v6Pvjr0SAYJ75phvQNikz4I59BipJBUMG+41gG3WaRJ8X9UdBqVPZ3AAIIkK15/E3gKP7wy8bMETBxs8ON/Yce7BCyARkku6BSFVoe72r4hoAQLoA7b45ZWn+TlRzQWi6VWtd6F3l+SqwLSUCpYSadgUIV6RSkPYHoFzA1wcBA2UDKTCgu0/J47YLQEgbqFyUTSjBzZAPbobYEjqyCMzsrgaLR5D0VPKYBLnGPS4BGGMTooBc0fpnym6+LMksKHsfPb++tVEJnyn/+HoEBE8BB0Ldsv9tv5eIvCbH6rxiVcB4LvPnxu8+Op3+2UkDRUbAfsuAAACPGbCaEzMYsKqkxg7E1sYHd8Equ2B/GOMHrg/g+58rILgX1sDcB/sD2nVH23e0vQoQUIbg7L5AS8AGoCZwkvuKyOgKyH1ncQHaEjtd7sCpZ/YgBPAipAVbCmFj9BRtrIEB0F90X7l+hvQ7K9k1j+6mleNFuUvQpm4qeynBaxDEhkr22hkGNzAg+6PLuBxk79zjxA2zKJvt9+Tk54sR3gvnNHzncb0lDwoK/vAb+N6Xfg/vvvU5VTLp5sV8NsDge7/+O660AQj6paQrH8rtkt3W8bLHERaJm4CCP/gG3v/ydwRpLaJ5KeTrGpKs0M1tG5MZlUTYDJsUGAplCNFwLlzjDfIYGOjzGW+qQcHEMw+AgDECgUNp2rMp2jFsTqQgKVEHCCWB6tWDjQZwwE2UMDDWas+aYjVJb67swo+tpVoYFXConUsGXyvaVpH2HansSFsB7w/iStiv4OsGUrDQAcIFaFcgbZrlcAeqOzgXbHlDyRmZSZkE9pbQNWkjp0RoTNiCq4FTHkBCSVZAR9gEu+4xd97XjHHIF3zz+AEKbougnN2tQQNI8AWfeE1r+hMV/S1wcNY1cHwOf30GAytlHd+38UnjEEamgPy37KNzAaoIBiihN8dyQIyhm6G5CxKdAIJVQOEJIOBdQMEACBQk113l1CqvMxMaEhIaUDXjoJ80ek2QDaSuN3O3MaUjKMgXBwUN1JtLNetXAC8HD+Cm7IjbayjS49H4R4AwyDN/XVses8paoh5wzbq/uX/H5S/+3cjfjEkW6144AIJVHRjTMfEkKe7oWYP5D+hDj/V6+fqDDgre/oIGdNEJOuvj2QCDCApsDE1cliWNToZFb8aL+tZIv5BFz1Ny4BEBgjTTQAgYIaDJJm20QLE+Oz5Fcr2evT5fgAHfePGmGi6KXxB/L6L4VYEmRqQCe2MZ+/zw88ONo1anzs0AQkmEnC8A7X5moATaAS56TtzA252YsK0BZROBacGEjUG5ATuE5rT18CpuYZ13gHJCrQxkAu0VueQAEArSJnneyEV9tC/keXAxCEAo/XN5A6XNAQJSQU2E3IQp2pnENRBYhEyEggxMIMFK7lr2g11bYxSQaJDxZ/f1reBBYAIYJoTNdeCfIQcfVsDJgETvDyF7IkOAQMqdXZgt/tVYgYFbZYL9M48ABKAr9NNjn4CGeNvFolJzFUrrbWFpwwYGoAphZgcSibsgzfEDDgiCy2ACBAeXwRlDcK2oe3VwLO6DUdZRNiCusUAWY5CFVaOyeQAi1OWGXGT/a3wOlztxGZQLoHvfgIIUDOuAILamfpLMADTQTpUtxj4DEoDXqwdGA8h+wIP0bG8HkE0aZ8BnIMHkNdHN+8jmB5zL4DOWYAAExpJMtH6vB6MPpmPkYp3Oy4f+7qufvlT3we8d9eMjv/NsgMGh/NoUeRsv9q2+7fa5l68/CD6Zz+KQPxaL+lDS32UHCJZ3QAEgpBQ2JPiwQWVjnwgtewyKdwUE0vSFUxlJR8v/rOkTeOwE2UGDX9Lws50SNL9rJYgVrUKRWW70nIoIdqIOCnaAc+s3kV33VoHLnd/zaROaNJWMtgMpNdQm7I2BgzZUn2lIKYG0MubeGnJrA4OQL1ViEDRwi+qdCO/rww2AcAXa5gxCzhtyygKAWNwMLXUWQTo19oIpF0gZZoYwB7EcNvNYnheYKwOuhJcs+KpYUPwd+S37Hb1E6sZAQijnS6fln51xSOyfBdbBZDYGBT4xAmdlguNr8tlO50dL3q/AyaZ/alEmy1mPZagjEMjQmAF0oW9gwACOsQNJ3QVZFR1a1diBJwICCzJ8uB9iCOrD9cgQBEDQJl9TMhBg1Rf1AqeSQSkhbcXTeOkiQMD2umXwGABwUKABuo2yBOIyBpfaqs0xMMsLecXkhQcOAtBe8ohNiSI4iD8yLHncY0BQ+ro/0WUwiA4uDmZz8Z7slbBnlq7TMKczQND10Q3GQA7SdYz9xBP119dMf5lR68WnHh/PBhg4Lb1s5nOCyOJFCp95+foDyW749d/Bu7/ymcP3AQS6JzAGevEfAwhAZw0SOkjwnw6Hict4ygaEG4Omz8a/ZybP4iEsQ4KDouAJFNzqBLkaVje8QoJ/GnVk3kjpdRBKysjFGIOQpaDU2UrEW42DfAcAD37NpH5jBSkCkSj/fjVba3q9G4iT1JJI0uq4VgZXRtoq8t1FOf0dvO0iHPcraFdWoe6S1VD2BUB4ACcBCCllFO3PYCxChT4GkGAtfQslb+nL6L0zgB6kOjd6OVyb24TBSdnfyFDE17s7I1Z2lOvdmwQB4+v9evdjreICZibgVpT/LQveXrdxCwAA58ZS/y37HQMfY4paBwryXgQDSedn7IBEv1dQrV6QyMDBAAh2YQEQsww09RD6N+qOev+Adq1ou9QjMJaA9+oMwQoUUNZ0xEzIJYNKQSoZacuy361a6OVO9ra50e4+JaAgMgX5AtaYG8nUQc/UUSDQ2YInyAqbJxsW6B0LM4vx5JLB9sFCAcffijLPtoYRs84da7yByQ6TewYWbNySx4fOo08BBLfKys/PGV3H6IEe1V8f/USyD770u2PMnX30CamizwYYmN+EIzsA3JaUc8g7NM/zj76F733x2/jVX/nMqC3j4Zr5aqo3K7IFJLoNEIzycotdmQMbs2A7YwHGzQd07mntLhHBFtpSo4OD+a5l/c9QfwPUCsBBcc39HaJgLTkpGBA/YSNGSxGli4TN+eJzZEqg/d5BAgHwqmsA8AKSgnh9ECGdxCXhQZ+JpY5wBnJOB0rVR5UCTFUZh/6pB6RSgdJArUmVOM3t5ioUMMqD+11HgKC9GdoOoiw1E1JBTkViEFjcKREksNL0B1YmEZj7Wvn8IsuzPrPjqU5m/ApwdICAoYvg3EBoZhhugYN5PBbQ5wo3AABrImUKGejWOzAq8Die4tIAJvE4KQ9CZ7/mYLOUjC3oYMDjCgBYj4JD74IzQBCDCReZBm2vDgp430/jalJAYe4+oATasgTjKijId5swBeXSmQIDBQoMHBTk0Y3A+YIK4FqPHUsdEExMGHAgcoeCQQ16DysokDRuRtaOsIfVvKWEF2tr8s+embvXxDDQ423cgMM50IxyeQYnp4BgAgO00C9kwM72s+kYm5ezBwv9ZSn7v/47mn3AAxLupahv3xvPCBiotzwq8DNEdjIs5fH7v/ZtvPsrn4VnNSxH082T+uISB5DAR4AAeByCIVnbh4vehEur3zecndfsp3qk9vkMVprOQygqWtLTzOztoc03Lml37DQhEKzRJuezk3wm+mEb9TiFxgBrfGjJhJIvMq/93s+VoXP1cq0J7V4yFqTNckKiB1AiFZwZtFewKl8pztLdCzdHa7D6SwBAjZE2vaHLRdKqSumxB/sV2K5aG6EDBE7iWpD6DZsDhJwykqZzzSCBk7gaxJ1DT47peNrOhpvc/eu9gtqwy09A36rtsAEG+GuYjnEckfofqfojAIjU/aqwzOhKOx7jqWMmV58SYGaBZAcwYOxAq4tmRvUICCIYmAoTxeBCbm1gCpbXNo9nYgwBpTQAgrRlcSGUzZW/uAzukO5eyPPLC3UfCJvARVwJXCQrQcqBTx1KtbZH4xA/s3CL2ZqjWetxAcCWxcEkp5jSuuEQkfjv3Y0D4KCEgSfIwtStfn0tG1ugYtxAwRvJ5sAIDIAggoHhJpnnOcYYuI6xeImTu95T9r/4bXF/u5ubjzcIpVuEwTMCBrDFbeMqntX2n8bLjz7E1370LXz/1/6hFn+Y6J759/z1EBTCUB92YBIMIAAdJKBvxnFt4rMIcKaNZq+doc/F6IWFAlhJWeh9PTRNh419zIF+wxsosKj6uT0tEWGH3GR7kviCnWov8AJRgI0EGXAyjEUSd7DZxiUQPcgNTISUM1ouSClr0FMBaz8Euj6Ayo5UrgNAmFO2eBKqNIezA2BunoTQrru6HwR8CXtQwa2KT7Y1iS/Yrx6kRWUXyyoVUQ65AE0AAWnxlxkkMEQYNlXYPd6DJlBw7vu8NQ5+/2D129N4nJaVreAOHJjXLNFZPMRqrPz28voEAKgr/tlih72mvzkH5dp4ImEwzs8eF7E80YfsbAEwgoFWxZKzv4e2x3tvaHS97z0MzFVg5Ytrb3oUQcEK3FJK4hoLdq0X90oCCiyWYAAEMZ5Ai33R5Q64vJCUxLyB852CAmEIuFw8nsCbjTXg2prGFxhYaF7M6zH5QKkbIyVJ8KKleDYIa2BuSD8/NckHUKCdI6MSfkweDgxCAAcue+L7w+7QYw4/OBlpUxXYkRl4qm6xw9rr5i6fFD2i/vo23v305/rsYnzCcJ6d11iNZwMMRGSSK71jpOe8CP35y599iK/+6K/j+7/29/Hupz+DgXkYgEX4u+1TEIhtqolJMBZBfmAACRFDn8ZIzGWG7Vx8/jNLsuD0YYJONjynDCJVCSmDQM4aNOieY8B6yJtysGEWwpiPb4fvxNyeSEEBqVUAyZdmAnJS9qAJUJAzwWZxB9sL3cQZSA8yb2MOzB9aCviqFQovV+DhXsvG7siTUE2xyMutWvHDpW8ieGtV9kJZjBYsAGZwlcAtC0DsAEFKw3ITQIA0/osggSkhDxXieo/5rsQDJfgIOlhZzcdboAONFj5zlonS2howOIGF0WUR2Y0huM8sf8gFXQGAs2Jdc3zNrMjf5PznEb+zookHitgU0QoM2D8O760AgYOAsXxxfz30DYkjiZrsBEEe3gPgxYvS1oMLveOoxRJoiXBnDS4vgLKh5c3jCJAvCsDvPJ7gqi6DCA4aWwfSYDRMbii/pMaEM0v31qyysgFb9isM3Z6Dm8nAQKfudS3aLoaTdWZ9RC72/dfhhciYAAhcbk9y4ReR1fb+NKcn6xY/Vn+/669vS6ChHpenmyB2vnzMuHhWwAAIGCgEbKz8ODbkon4L3/+1f9B9MkBY2Hnx+29RrI/gdRPQlf6CRQDUekcdFXck2k79TzMldWPzx+tA1uFM4VPriFmmmxVUqYDlsYOXAQW5WQ35y81+5n+W6TJ2AjJJcZ/KhIqkoIJxl7O4KNTiLGyKSWoCbOUFOGmFxJRBexaaPt2L0r1egOsDyCK4y8X9stivyGUHc0NWIWsllK040q1hKVzQc6MMYR+wu1vD2AOUTRVBA/a9AwS1zNJUV55CXXkrHkNIYMshJwO4SSxqQtgrTzWDb51fJz/tU4Y/BAzQlK46Pzd2o9e2sPfa0Ge2p5XFEX31NpsUfPZvEu19vCJPOe/HxiS425ESFj9wGwCCbObaAwy5DU2MDBBgep211Deq7FcDrqZMZR+2qebA0fPtaYda4piytCB3MK2uA2ssRnO1z1RGliAVjyu4MgamwP/pPSsdR/nQcfQgEyByLRMktVc/x7qott+Acd8MANDXP4CCVlUJ12Clj/JynMRoRbuyd0Aw+uI5gIRBVvuWWbC2K3ZgpVcWemlMtbcR5yTvv/zZnygoUKbbfotoBAfhfDn8Oxt/ZoEBEf0rAP81xKzemfk/vPX5DgzIFbI/LkdTUPA3BBT8ymfPF++WqRGtIt8EC5AQic8ZQS7mNvy2bXJ7bXAjHNFnn5AKFKKubFJW5iJ3C5gIibLWWLDoYJLPAX4No1itCMFqQQDEgjwpyfVntk0qQIJT0tOo2DSXS6oYMsAJzIzC1F0Llwzs98IeZK1GuD9IFcLtAuwv5PmiqxzZ4yx4uQWL7Hx9D6lv1hmSm3qtdrmGtiZEshbmYshFjm/dHdsOryVPSUCCAR8HcEZlpqNg8nUNY0UxP2YeA6d0qje7Sd26YGiM2wE0YHgOPBGSPKL8zYfvVqH5j60J0OFefdr5Hid0fi0P9O+scMw65apgQVkCZgkWjIDA9t/cLdRAAU+NwRD3npYkXkhr+wxlA5mBVdO+BrHLKGKX0XKR1uRJ4mKgmQYCEAQUNNDSdbA3FcyVJ6ageXbCUh4ASKzlr70+h/S0sdM7GCVqsCR9nmyfcOugQEFZXwdbr8fkI9DdBuT3nuyX5J/pjNTR9XgcnwwMLN8jCsX61C2g77/80z/u+uvTnzn/PQcHDqf+G88Y/MfM/P95ygd1jykeCO4EUh/ctBADKLjFFJxZ5MvAlsgAKOr3zyOAhBtjdfzhb3v+CLMBhAjUJErVrknK2vYY2vGwgnNyl4LUV2AvYqPwYjnchcA9Sh3QqOSqvwERCqz5mhXN2QOguxZE14prQdKegI21sUx5AcoayJWkuBCsD33R3PC7ERAcKNsmVeas/awrGes8B4xuBnstKo+hdjAcHIhDVdkDZoueGgHC9cHp3JTzAAKsl71bLBHpEw3C8pzKfIQ9msYMCpZWE/UOeXkADdndHQ5zLcQb6/0y0/M2BwcAau1F5X+ghlds2VMCS+28+pPhvZvX9OT+M/rawAKrohdlX0dA4I/12AI5AgLdM6AEKqIMSFJvwoUM6xQ6iQ7txlP2vUYahzO0IbeyxpS9gqEDgryBycp9B1DAa9fBfePTluNAiG+xP5KAA5DIj5USMpudAK+gGpsTyfWP+6UzB32d6tPlox013H9krG4EChPTezpWboKn3p8xLkCNjRkcvPzZH+Or/yTor9XPhLgJkzUjM3g+hT/rwODpw8zfOII7QZ4LTX4KCm7+/hOET1Qk+psxEfHsKEskObMCBgZW7MHyCHFjNzA1BQcqVAAFBxaFm5BSGViDBnhhEQpBk6QFeyyLIQTyAxgD3QahkIC9CXsgxQPkCEvXQpLTaSzpSpUIOWWUkgUgtAoOeeGoVxG+m4IH9etCYw6gwjpSuWB2Qe0WINCFtf392DDqN2UBHlmp5ZQ7QNh3z2jgXLrbIBex9tqOKJiWQuvw17SjnsJ0zcFIwARC0jAPYZv6nChlOGCIFpaBhvh7q8EMMEbL34KzDAB4IFlgx27dA4+dX5jT8brq06WbYQG+IzBg7mDAlPu8zyIg4CYsQQQFwxR1TlN2gYNRAwDAEQSQNgijJPtMgYHHFVi8iyp9aa+8uXsLeQOn3AFBSEO0yp2PuQ6O3UPH0zDjLY4MCUI07OOuAmcJqIOEyBZ4lofFcuwj0OJpD8X19BUPtWgOLIHtbaNLyWVg9PWfFRs6ZQeeCGJP9zEgoOBHN0DBrfvPf//223+WgQED+Kckq/H3mPnvxzeJ6G8C+JsAgP+2vNaUyT24E9BZgyFQw+gXBQxvNruR7hnfmxCffWxlR602zSAIgUEYToyBMSFGi8dBOYsXmHVTUxMhYPgFiiqZNOBFXArGGgjzxB4YllgCx7gSdmLxYzLp/cNiBSQAJ0LBwEFiaUFs4IDJIu8rCiVAI5VrktbTrQEtye9XImTKyDkj5QtY08HYGIDWU8NQjgFgYtkpo6CUr7VyNmtjKcSBx0FCqwIOagWybgMA3MTC9n2hbZ1BJErFQIJZf4AyCgOpGjfNuM7zHtB940FrjwkjA4vh+GTHV1eHB35Cz2UCDB7QFf2z64s0gN8DAGgVx/1eXQnb9z7RuaErX8rioz+6F9bXeXV8BwO2b3Qv+fwe209xjmdgwACAugoMBCBlUfhayhgGErQDogMBVfYW8CrdDzfZc3PToyb/dtYCRQ3eVtzKJVga4nUPLMITQAGAQ9EqSqSGRq8NkVN3GQxuBGULxIVgwYYTUxCZgwWAO2wN2wMWQIUgJ0ktE8snN5DgesUYphBnFscZKL/1uScodGEK/paAgrciKBj3zwzaoxvhhufUx59lYPB5Zv5TIvrvAvhnRPT/ZOZX9qYChb8PAPTLgs90KWXQMTvhxz/7EF9zUGD9qE1LKjhQuuZQKMloHH9ONpH17Fc09K3NcmAHgDNAEDc6z0pg/m1KHSAgCwAw3wAlQd1I7lJATt7foPd7kNxiJtasBULJBN7ZWQNWWvAWOADgJXcTa1dBuzwNwA7PWuCcQM18k8IalAbUJP0XcoNWm5MULKlcpkzCFCk+AAWjgGNwmNL9K2tPmIm9r4kJd+Ao4H2ZWcABqrAHJHElBhCIE6whFFkQIvRm1i52vE9752zf2Np7c68+N98TK/eIDTtGSirvVGGEuUTQQrMLJAKGpzIGPo/j/jZK3pWruXqMIl6d19k56Xn5oxXJIvKMnCET5cb1HlxN7t5An88jgEB+Y7ohhnKQ2r/AQIDOR5R/7td/ZgMUGMTgVgdvDgSyg4TYErnq6YhroPc1iL09rKgZGzMwZR2Y+2B2Jc5jVckysgUlU48zIXtd0hazuxJEtQ0uBN8XMyjoYJIjkBwWdZTRS0PK9jq3DgiIMDTmeyzu4FHwurhfpr3I7j64AQpW8Uj6XXcp6Gst/L0af2aBATP/qT7+v4novwTw5wG8uv0lAOQPHS2h4eVHfxyKP3zODoK+qG0NDqKFM4MDO8YnOsEF1fQmoGBlNU01aLm2PmdKutoGDkhBARQUKDXMbWANUhJEnZoCBYbcoU1aterBAE2m22tnDhJwEBSxwU1lCUDakwnN5EGNe5OyyQYQKliKoTRopTkCiKcytFIbIGeso5YtQEmfu+thAgpGCdOKFtbo51noy/Ipi7NgkFwGzQChApEijkDh5rCmUVFJxj0RlBcvOoIuh7sK0K3SlDyQbQkYsogQZxjM4l4p2bC3B3BrwGsFAMw/P5/Pk5mCCXQhMCPxmgNPvu7zNZc5TYBgOF+dkoJoZwFsrNiANwACHLJcOBlTEJ6HeBCvSljHRkfgXvo8ljKOgECAAATQtzZkI9lIWrBr6IlhQYcKCkpWMJCSt6i2AmgCFgxIKJgY2AK55t2FYKmhbQAF2PcOCB6Tj7C0ZNvHtnaWsG37ifz1riuo//5TYg/m8RgoUPC9BgXj54AACg5sQQAC/01lDIjolwAkZv6v9e+/BOB/+9TvCzDoPqNXsffBW5939D8sieWFUluDA6C/DvTHW9bKkybL46OdwVNAgVGZwKn1ioALsO8ODkRBAqDuRkBTejj3NDlmBQgkl0gawUCdhQmUmnY4zKDakLPVOVBhoELBopNXTaK4MWoioDXPa2bmASBY4ZudgNREzCUFH57zTuT160W4EDIVpFLcT+nCpF4fAQpSjEaUv0SXUwgic8sxd0Ww7NPRT1JYrFZFASizIcpIejfIxxJgURtz8aUJCM4gYFCYK192nF8MdrOhwUkxkI3PlJYqKQtMZFOqabo/5mugx+Yw7zMQcNMv/4RzAeAKMlriPJ2PMyVx3mfX/uz6A+egIHfA4bs/ApfFdY3Bgu4a0KJZp0BA4wTMNcA67Vo7I2B9OazjofVAaWoQGxiwQlaxuqlkIfS6JXM2j9/baXxuusoaS1m76qIgwUHBAQx0oJAjWxDjCrj58wgKHpWR4d5DSi4jnwQOdK0HcHBrzPfCU3RGiHk4goIjM3dIS5zjfkJq8lPGn0lgAOC/B+C/VFRdAHyPmf+rp3yxsVq2AIgSXr7+cW89OSGtWFrSL7GF6w+fs8+EBY3swq1x5nJwS56O790aK1osbvgVzR3AATFrvIX6hjUCursUJGAxpaJAgJCbtoeGwPbdwIEkIwOloTQgI0kJZGKUSXgYUIgjL+4nZolwto2+J+hvLkrkssK/xlr0RD63AgtHVuESqtZdB2bBIspR5DWKbMIMEkLAmV1/PgNp44nK2pvl3xezK6nQ42FY85X1PCvPIBBjiqb/3uRzpUBfA3BGwB55AguUN7fCB6U6W+HxlB+ztC2Fb3EeQyqffffsHMJcKAIEm58p4wB6hLnprILE7d44B1sHf2Nxfy+GAwEAQ5zABAYO7gHLHnB3gDIBMUbghA2YQQAHRsDc64+VwAbstaOsyjSXOyZ/Xc557HthLAElLZWe7d60qofdhZASuQHQAw4Dg9TMaFImgXlkClZuNrv+Dg762sqyPYE58gW9wZDdkv1nn9W/TaG//NN/LqDgixZoGBiA4TsTWKCQQWSAH3hSfAHwZxQYMPP/C8B//xN/H6LoX77+AO/9wW/h/d/4Dt556wvyOjfA4wpyF7CILIK+fgIQAIxMwlPGgV7urMSj8QrD76Q+/3nE70+KR05BqvihGBXQAKhLgUh99ASLOE+UURLgsKnplU3CAEhVZULhrMGIDaWRl0oujVE1krfo3G6pzArtGaG+SyICKnv51Csk4BFgv3UjYABE0OREoMoAARkGKroLIiUBDIUUKOQssVoWo2BsQrtKSmRrcm1SkWCj3doyW6e8ABLOhNEbjKU/9MwqfUyRqs/eijud/j6ucj2DYvciOUHJsgOF+3NWAeg+/Hn+8Rz+nc5fzmGevyl/sIIEFjDMKQG6xywOBMBQbvbNVw7dFTO8GIBAACjdTdNTCy1osFkqoWcPbAMrYNkDBgR2BQASE9BdAqbcHwMAwLGk9VmLb/tcrPERFUm/Nw0g9B4YAyBI1pky3J/UW1bn1DtUGkPQa0UYONA9w+IPGQJwz+7DW1b+UxlfoiMoOP3NRxgFYMhusJgAT0n84j/Au5/+whEQrI5LU3ZQYDmeA2PwC4+Xr1/h6z94D+9/5X28+/YXupUBuei9dSVUOQsYMAppBAgA0D8j30s4S1VZjtnf6MrdfiM8N9rZP6ccl303pVE4KjX9GPvArUn71Zk1YO5AQYW05EjD4w3MaUgMJBaym1iEB7URIJRkgocOdfTLI0LHhgiz/vruTMj4uSiGxSJRgJdE8RtoEEEkrMK5IMraKvkiIMHqJFjmQypybdJ1YBFiASVXcLNV/IuMpXC7oVTVZ39W7dEDVqfqj5QJgK49qlbSq0cla5Z40nugBiscGKjzw4iBeE8EA0+fO+BptVmYF2rsAEfAfEEszBGzSLp7MIDqW3EHT4hJmNkUjyEJBYhGdmDz940NmGsLNAZqvdHmmI9NjCw2ADha/k/gtwD0e22+5/x9/XvVDyMyfQYICMISZHVVFnUhZJLsJ4sroCGWIMgpBQMDW4An3G8BqC3Xqr/QrXdTsG6FE2bFfNAHnyTmQOf18mcf4qv/5G9qmePP6+8fjxmPcUgX1jnLVRrHB69f3ZzCswEGcQle/vQVfusP3sN3v/I+3nn7HXmRMgB2IMCTkpdiSHCQwLYBSfPuB5CAJ6HAYVDYhMyhWIWxGJEJWIGDDEYVAVc72aARfiM4GC7MDeF1yhoATBKYiJxQSLIUAAI1RiWh6auChEZw32UmQgVhY4DzaKEAcLBQ1LUQrZTYaCVjpjH7lAF4zMJqzC18o1/TrJYtd6CQA4VZFFCUlJFLRirsoICysQjSPInb9cAigNsIEoAOFIZr/zSwwPPaPsJAzI12omIdlOpCeLIrS1WuGp19pmQPVjggkv7WHOc4gScAAubH+1zIvZCArO/Xfj9FK565gR6LIp8Ux8qlsP7e9DkDAvZbFsRJaWQHNIVwyQ7kzasPPtbN0AoOxViAWIUQePp9AxxdAfLeyAIAOGXvVl0xO1iQ3xLGYHb3daYgxhV4IaMlW3B2Uo/IxWXWDSGm6Z6CgkD5P8oaPGmo++Bnf4Kv/khAwTtvf77HN0xgYAAiB6DQWQL7Z26ED16/wm//4Xt4G2+fzuTZAAMbH7x+hW8oKPjC2+/4RSGgFzGJIAEYgcIAEmhgEUCElx/9JPzGJxgzuAgggThQ/Pa3Ir4ODhpQFKzsVwBJihdxg1dNm0fc/H0ix3lFMNRqz+5I2d0KDQRtiuiYxErlZkiKYuHuyyxEsFr7WICFYnRmIhAkhVHSo4JFo1OLOdKNOZRbXV9ql8lQK8WUfyYUBQp36u/cckImRklQClME2pakHXTOF3DbQbVA2y6CWlEwUEEpxCJYqVtVeLRIJTxsi6X7QBgeW1vPikhZFGEGqNYe+Fe1xXSCxyd4v4cqTMAKOMRBtodSEmWc26BkZdWr9o5ACMmx/Qf//up89KDL+IengoLHOmTGFt3DeyHmYKgUeBZgKT/SPz+NJWCIWR32PIABB1XL2IHN2QGkIgq+9aZFuz/KPbGqPLhXdgZhb292j3j0v6YVVoxxQG/aGttIbNOlCQEMQCiDCAg8aNhAQWAK5B4KQOAABvpz0mDCwWhanXSQicLaiKwamYJ8DggGMLBQ0p9wfPVHfx3f++K3g1HbmYI1K+AH9r8iSxCv1AevX+Ebf/gevvPl9/Gf/f7fPp3D8wEG1EHBd37jfXz+rXcOlbZc0dkX7P8zNsFAgrII1u8aQBfGbzqCYCYtC2rUJfMJQGg4gAdGlc1cqysPbq0XSnFXSReShws2XBmotavPVOB3cACpGeCAgJzjKOj6QJApAarIGXYfj2BhxSrsjVFJrgs3sWprIqcLDBTs3MQIDwBBnq8th0QWwNQDn+Rfws8TsOWEu5ywFcJdzsiNkZNkQ1TzdRJhywU5FXBWgFB3IFuBJXEzSCyCrM0hJz+uyxTEdsCZE4jg1kAUFK4VaNI9ylZ+knoJ5s7JJKRNFWoVq5pVkC1FmAnLVXToU8cMDn4BdwppLw0vMT3v5TBfovR4MyEDBaUMgIDKpgcMtD9wVPbDscPCTWBiVfuhadaA1xmY3AUGCK61Vx6MfQoqS9XB6864r03BAbxHwR4AwZPuiWaAQOqFFCSg9b4GPTYgDe65mCk0swERBJC5DBQIdBcC9ZLH+rcDAgCYQUHsXukyNJ4bDc8dHBiwDusS10b2zQ2WwMDiDAgMDMx+/+k4n2R870u/6+6DQ8zA8PudFYhjBgSRKTD9+AUDHSfj2QCDn3y0Pmkvz63X1C6WkwX+3Gg0EwIcQEKSQMYffhPf+9Lv4S/9n369C4TFprs9sn/HrWJT+AoSWGsneF6uUcitDgCBLYBRUxkRA28GBNtvAGDaxDYMhKhrg1ktQwMH3MCJFdUn6X0AgJNWJtTfHRvrhOcTWIgR0pkIjQklCwWam5VZFkXAJL3egR5gZQBhryyvsQCMpsL0cNXNf2mgwNKkMnBJCR/nhLud8HFh3CmDcMnAnhibAoRmfRtSBAhXESihAhsr5cncQElzrGH0/FSF7ZYFH0BE7xkA8H5VCr/1ACt9zvsuSq3uKsD2zmJo7QluBGlo1W7GA5iSlS00KdoYawC4Vex/z8NesywYteac4WiKzxXI6IdByJ25AE7newoIjLYPgX4OCKbiQV5FUH6wW/tyAfqxVr5o+3OoWEkBDKj1OacZLhiCCAauCggetFnRfWNc94p7ff7QGvba7wVjFZ5yH6QmAKFkUkAAqTDq7gIc6w0kYdoI8MBBAwKPtcruDAKF7+hrgNwjPMo+BwUcitW5C3QcTLrmyIPBdCYLre4Gz0p/BQhSWYOBmxb8I+NEd7z79jun8QLAmglYPY/LP4OCZBf9ZDwbYPCNH4j74PNvKSiYrpIF1K9Gom4Z94+RggTGy49e4b0//IZkNyjoWNZcfzKF1OMbSDlh785nIAEMtopCrIrBAAKrUOfaGQSyuXQFFIfQZOgXIfqshg/KjSRFkAqACqrSEdEjtvUGso6NOfUAFwb1Dcki2o0xYEjaIqvrwCKomS2ACqCSUNQqkszJDOwVkHBJiPubA0BgXFVI1jYCBABorSGpIHeBSMClJI0zIFwy41Ia7veEu8q4ZsJWMl4waf8GKc/cUkJOUnNhS9r5MWWwNXdqZbRsWhV/PTe5GMY+5a0zAoeg1HH9DmWAuYlC8+pvUoCJyiaAIW+a9te7Ogqz0bw/wVOi/A/R/cAREMxgIFbvOxkWvyBgRt0d3ACSWAMyBqRKjoq05NV75AnzvNllUNMsnR2gUEAoAoHgc/bzBkaFP05CH/U+WPmlF1UIrVfBrQ6Gu94LH+8dENzvDTs3POyMB3UnXGvDw96evP+zgt8tC/tngECYBPHxlyT3gdUbuMtZ3Ae63Jl6KWNX8OoOmAGAXTIHC/5SAAMxZsBidWYjaRWbYwqbLc2ZgVJ6bJjtvbB+UfGeuQw4hddO+oPA1tvn8cThRt18HuhsdGAGZjAwswHH3+9/uvsggoJHxrMBBhZoKNZpf33YRjxgx/6ZiUawzcsAPnj9Ad77wdfx/le+h3cj/XJwJdwQhqtPOYMRFITeHCNIYKeM5Xl/D818xpF5mNiI1XDEOwkwn3AAB5QA0vKj4eYQ+rCDCwqUV443DzpgEKZAwEJr0iipMqmSl/4LyQBCzriiia9cqASJuiYtbKTgydwJtQlAaMy47m2ymOSaZL0jtky435MKRhGQdzXhkrUOfE140Rh7TbhmxouSsJWEyg0beufH0iybQQHCUAuh4dAG1tZlCQrCenEPEaPcJhA5Nu8BN9DWpFLjdhHGoCpwsOyEXdO8DFic1AU47pOu9A/MQPTRAz1jAQrmzsaUaunXxIPLZF5UIEDhTec4z29mB6zb4CIYcKjeGIR9G8B/lCAL+tgUi77GgS2YqxF6UGEbgwqvGmR43Rs+3pu7DD7ehSEwQHC/y+sGjFuTvwFMbEFzpmArqTMCiYXJQ3AtUHcVGCi4y0ljcDogKNTTfg0IRJfAqPwBBwAt7OUFAzA003IZ2HAABcaYUQjQjgHlAA6lUw5yD32tbd8M7gJzLYxgINYHOOwDnMj7edD8yfheHn4nPrb5xcWId4oFGn7nyx0UPAW+PBtg8K6CgjgGsatvrlJzHOgpcDB24ScfvXIm4gvT7/Pi8j5G7RyOq/+LkhU0zhOC7iBB0wy9wlcDpXDDRCYhHn2JsKcNPYAC7heEJWyQ7eeI4TGbftECjTr720xRhBsqpwyA0FIHCnt3faMqQKAmiJqIcFVwUFHBnAQMUFMfaZ96BAUiKNldEDYKAddKSNQEGJSELcvnX2wZe0vYs+yBS1M/L4AXkO6PzJKO2VjOoQIo6mIwgOAR08ociJXcFXwXgkEwxhHW7AgkZI0pW1CjAoXQ84EUFCw7SRpQAI4ZAvM4YwNisJ7thRiwF787nZffauYCYdbQnqmuAdDBzJvMbxU3YMxArBmQkgecQanjtgLKUdGvzisqm/h+tDItnoBSBwTcGxbNgYWRJfh4l3iCBwMGVf59fK0OCK4KEJoFHS72fGMBwNe9YSudlk6q7BNZSq8UHNpKdpeauN2gGTuEnOABgyV1tkDtZ1h58SFjICp+YNrzi/sg3iPD6+FvBwe5X/+nyrvADBxiByg/AgbOLfizccAoy1f7by2ZASPb4mdvHPgnHwko+L0vH2MKRtB2HM8GGJyd5Lin1lfRXiYSQU8EfPjTTr987q13hsUCJibCXhuRw/je4vMuYgamghwk+A9ZtDs3gHO3+laoGphuyMe2LCbBZ6BAfcHMko4HwGHVI5QZYS0oKQjKrH67nDJyUkFJhD2x+j6B3FgqL6Ys0VhFkhgbxN/fOAnLQN3qAbq1tLPGMoSF2aGWTSIHEFtOuNsyamPULWvMgv4zcdcSuEigYgVhs1gJwWwCVtx/W+RbWi75TEieCjwAB2EZ2KCu5GXtmSsobw4UhlLOChZWPR88ZmFVxnfwqYcaBZNPXt7vinnYGzcKHBkwMfeAuzmmLI6hiuRifktwEl0E0ZVASVIEByAQ6OJYoClalQsQPJ/rzZK0BobRqxNWTSOMqYfXJimJxg7cN2EFHvYOCu73Dgrur9UBwUPlw14HZK/viXCBHDNlGu4XKfSVPO5mK4QXCgpebBmZgC2NmToGDjJBS6TXKSbgESB8a5zJK/fH02i8HNZkwVb52oyAbw4k5MAazGs3K+vZcr8p330e4b0bItR+a3WMLtZvy/WffPQBvvnD9/C7XxpBwZHJWY9nAwzEhpcFTDQq6XgRb15O/dwHr+Wi/t6XBRSAj+6GFaVzhuTOFtE+b5Z2M9mrxzGgQMmWKYCEFEGBFSwZKbelRfqUm/PsswfL9vx3u8tksryUBeBUQEqv5pSR8oaSM7K2Vk4kTZOIhFr91KaFaILPsKHiwkmVeELjhpzEOmpTAZxYUKlWRm6E1giXEHnf+HAr99NT33hlxh0noCSNlRCmoii4KQRtDa3BWKkc/al++Y774iA8IxgY6NbAHPHYfAhZ9sgBKGh9BYqfBbrvfiiaFYGBWebBF2+foQAOJp/88jpOMRNk5+gA4WRuJ/Nbzs36DUxAwH3J4bNLS1F+OCiUcV8sC5sdWIUYdwNP65WqnrJndsuyad11cK+ugY/3iofGS1Bwv3eWYK/NQUGd9lOmUfwLqyWPEmNDuGQNwC0JdyXhRcl4UQgvsoKC3IMON/1+IUgq4W6lxMVtNsQBnLrM4jWLDNOkqm4ZHytD5pHfHQyVGzEDKzDgQIC7zH6Kgm7osh2YlHH82nQKw5Y/Od4tPfbhR6/wzR9+Hb/7pe8OoMDAiMuiG7/yfICBWrgGDpYf0cdbqvGPP3qFv6YX9fNvvSMyKi6cobcTHTkv3FNoH5uxx2VBNlSjR0BC9L2t4hLiDE5u0qMiWij7yD5EV8WK7rslBAJKJ43QJvW5WordZgCBCFeyQijAtTJeeNmjqHgqGlK3wlpGbVUtIcbDNI2mqViV5S7fE3nOf07iZsjJfLJiiQENqWTcW5yFXVNt9lSSrHeGsAdEsmcku5BC8JWUmfZL8hhsR5cZwTZ195IVemFfcwtI3N1vL+2mdV94rMPYkEuo/PW6zfn8q6j7WfjerAha+p4RUMA+J3l9LGv71LnN8QHu27cIcwUDHl0e6WIHCJ0qhs/oEWNi8Zkx+Fb2HKMDg9p62eJYk8BAwb0GHj7sovQtBdHiCSSWoGntAgygwPb3cMkpsAM5e1zNpSRcjCGYQEFJAgouARSU1AGBsFD7OqZmFRMwLpw+zgWCFkBsBc6mG+e04uDMKjwaL3AOBljXbpbrpzKd+gfckcH2Xmem5TPjd1c6I+qVN9Ffh3nZr3C7ubGfETCQS9bTDTGc+FMW9MOPXuFb//jr+J0vfVfcB/6howRfMQKrYzwF4c10FCldRrRO8YHf6Me4BPm9I3V99O01PXQeP7fy9/nJqAA/3PyjopGfOm5f0gCxmNfNKYO06hvyBsoXAQjlTpRqlep7GYx7En/iERzcGk3AQRNr34SmWVOtsVdxqQ1IyQIZW/e/toQHLR6U0UBJah0ATdBEE0qVSeRjUmCQSCpFAn0d/VpEGvqG1WGfcyZfXU0e4JV1q4eULvPX0ySsu/tBwcW2YJcWIwpvjsI7+t8PwngU2MPvm4Adgs9kLgwc5nWGnw7zinMa2IAcmihFEJCPiqC9mVV4to5RgLMyBC08WhZOLFQkAYiSdvjgrzXsCgR6GmI/vrkOMtFhf6ckjFjR4MG7LePFlvDCHhUMDKCgZGwJuLNYg0TYsvYc2e8FENQHzcJpkEJfYY/NdTvitVoAOfJ1a329WAH4CYuwdvMAEQDI50bXgQOB8NzAQMM5EFhR+I+CRe73O6vsthmyutLEI/LmuuVsO374s1f41g+7/hpGZAvmuI3FeDbAgFhrDqAvSMIYbHjrhv/wo1f463/0DXz717+Lz+pFZZjQPV7EW2DA3vf3JtPjrCypty1VaJmoswcElvLDWLEJklpJFPHQCViwSQ7BQA5l4UWWvEdD6983UBAsT6OpWywDbMecKvwxoII5UL7aUQ4pa1rdFSh3QKvI5Q4pF2Qi3BOAmpDUes9oS2gQjaWcgGsipCSWV1rk9li8QRxDVksTJS9ta8XCo9qQrUAQMZBILi+xdPZM8huR7amY4SUv/lqN/q7b6XTMC8+KEMzdP4AFZRAO/t8Qt3Cq/FZUrL8e2YLblt3w+8tAtCPrRAYUnjK3mSJeMQQYFUBtR+H/ZMFvUzj55HDvM4b6HYeeBvbPShpzr80x0MrToVIiZxmBID/0vUsmz7wpWcDAf+uScSkjKPilkvFCXQlbgn/nzliCtisouIL2e3VHiRsB2itEWmaPbqBxsnL9HbTN7h4tv80JHRy4kTeyjb7O+ntn7p9bzYQGMHgDDPQ6LOPCPi7D9TkpEOAjUxBBQhzzvbgCBfNn/vhnH+Bb//jr+PaXuv7qcxqmJH8Hlm41ng0wAKAQTGINLM7AYlWOHz2Cgn/4xe8oUwBXvAYODt8Pf88LthIK8Ttncs4+Z8cT5UIDUBBFgEfYBPljBAsRIOhxOGY99PoJIFF0ywZODjC49wcwQDB1GfQgtzgsetxSxcomAGG7gLYL0KQPAeUruO3gcoet3AGkddQpgSqDkBxtZ8h1Sqhe2nXLDR9fCWlv+lxYAKBbWQYI5P0k0dbU67fLZ8bpV4a3lCan8yV4y9exCX3Y9NpXfnwPvYl16o2C0PeH1Z4nQigvq2ChFAxiMbgieLYeTgIQY+DWUeDKbIbzWc1/+vtsT9prKzBxNq8VJbwCAVJuW/+elDbwpuuwPrf5GtjxTE4MoIAbqs+LMYXGyP5r476U4qYJ19pwyQAyHfb0lpOnJ94VYQnuSsIld1BgDMGntoy7TBpzoO6DTNhIWYL9HrQ/CCCo90DbgesD+Pqg2S/agbRq+c3FPX/oKtm0lLdV6CzFZdDYAyCAgiR3+qlLIOwHW5UIAoBPBgRW++NssN2b/gVymdxBgPwfdUx/dfo9mzfO92Y0amf3gYMQivf/47D3+QADi6KPw/WcoIPgHfYxg4LlT2O9aMAaFIypJb0ssH3Gvnd+Lh3lNcISKMxsgn3nABT0OiQifU+UhQvmqCScEiQnCyQ/WG+2ASCYr7t1UGBAoYVeATFXHV2oshabYSLQdgdc74HtTgHCDm6X3lGtVWzlDrkUUCUQmoOjnIqWZ61yzmTd20iDrCSq+1oZW60DDWtD0q+SClMJxLK874QRKFisohTeobDuvR8EsckD9n3g6xmXmafH+TPz/WsAsS8riILLyZTHDbBAREjo7AKF3zobk7HU97cquJXAehQYEEHAK2Q++gFnm99wXm2azy0Q0CZFzfyUay8PCo0CezM+9s8c19qOY7aasxMs+wmQ/VXZgAAjMTkYuBSRYDkVXKsE2p7tZ4slsEJeBggu2YIME35py3ixZWUJBBBctOJnajvoGkHBA6g+dEBwvZf7/XoPMGtRrfW9DutsGdNF/WIpQ8l8LCusN7mDgkfiQuJ+OMrhIxDA/NoJUHySzIYxAHLeDJlag6yhpcAnkg9Jw7oODmzecTyuvtf6K+oBuZaLLz4ShP58gAHMAlZLWV+b3QlxPAUU3Brjjc+HhVz1CgCOCuHst8k30REoOEhQZ9ZjQMEorMgqUGAVWFs2dtoZHRxQ0HZ2DrV2diCCgrqD61VS46a2uoBudpX+bEVn9gdJt9segO0iAOHyAti0iqBFsG93uMsXKcncJO6ASOJKKAElJ5RcUXYBBw9VYgPuihWBSV4dMQ6rBOdFYDIdysCawqUkylUep0ULN7qSV4e9slJAjJ5iOXaiXO8Nw7/HxjUME5FEmIBBB5QRPAJrqjFMb5z/BIKBp9OsciwTVgpyguw6xlO82bxmAW9VNQ0YRMUcr/snueaA7Bkyq4HXgGA53+nyiG4j6WaZoOm6HFio5O3CE0ma7ZYJtaXTvewuhJRU2dMx8yDEE2yluw5SfRBQUO+B670Cgnvww8fyqOBguM+dLehn73Ed1pabmxgDdt6AKng1IEpY8cgEhGqRK/fQDASWxprNabFPZF1GWW1r9RRZbWvobgIOnUgMHIRdzPqDno2GYMS9wXiK/hqMCABD2vON8ayAAQCXyLM7ASBfXSLgw48+OFzUKJhuCSUbKwZiRp0zjWhTPCiI1e/rh4g7UIhKv5FtxLXbwX7bXQ/+O8eMBw9kpNBZ0cABoGVr5UYlb/8cWgtbhLwJi/0KsNKLBg4ArWEefIdaw55zAdU7iXje1T1Rd9CdNiYq0l0ttYptewGQVllrCbkyMjIKsVRqSwkfJ0v3Iila1Cw9rPtvh2tNZqV1xqFkLfqSx66MWd0a3n9+Qgh2o/P0mlyA/npUSty0yySvW1HPw4+dHul0N4GFyC6YmwkY/dPjufTj234GbgPdWzLUSz5PlrbNyV6Ls3lsbqv7bLDO+fa17q29z691bzHMfq13Hq+1x5FMwKbPd7oWymRm0jLhdjEKQDUhJUaqjJQaUjWQ+vQ9bP8uJeGSFAhkzUIIrgMJMFTXwfVjUH0AXT8GdgEFfP8x8PAx+OEefL0H7w/gh3s1Ah6/v6WEd6jmSYEaalVjjVTJhziCuYS0l5VexIvccg3YHrnlHpj38ymLtxhuCMS1JiCxosbIHKCD4fh94Khr7Hm/bp2R+Mnrl4P+ikD/APz9fgon9sh4PsCAdTuQYzW/SZNacqwK9ZVmH3z714/RmytQsIo8Vn38iaZ5y3qIwzejLbDeBAQ13k/YBCAyCsDK9XAACW5hZuEi7To2+b4gftabNoHBoKRpYWYt2KazCnvOIFS0PealX+W31d+YNq1KV3fwtkv1vrojqUsifeqX4CV8N/ltcy2kqpSrVkIsuUh1t0yS+lUarjt797nedQ5eT74XfIF3YbTmMpmkJoF1Y3ysw9xh7XzhjxbqSkHJcw7Px70DdGUK6Jq50gJia1yb5wEs2J4YtteosKP8OAjHIDQj0wHALe9bw/i8wfK2X1yyBU+c241rvPPx+gJvdo0zgF2v756kUudOvAQJBIxpznEofs+JgJa8RgYRQEzS3ROSspiJUFiqgY6dRPW8wx6O+9f2qzABGkxolT4TsKnbQB4n14GyBVSvaD//t8D1Hu3+YwEF5kbQe7tdJa4oZiFQkqubSnaF7/09UMYF1PiDHjOg7oGhlHTvK2Gl1S2A8wwMOBCw63TDPXDTYHuCkB9iBXjcO08d8SuzviGMMv4nr3tMway//L4Jhp/rQv/RKa5oMZ4PMIiroRGthGA5B6T1rR9K9KbVKYhf70FaowUTDjT8ZZa7MRLxeLdG/Myje0/lZQuHX7EJK6BgX5kZBaeZFSSwmmkmWMiiiNFBkNdHSA1UFzEdQO/wqG4GAwXcGrhKXAKrJLY2uW3fJUBu04Y/+xXYr2h1B112tFZBd58Cbb33gLkWXuSLKu+E0lj+kQjAvTJe1Ib7MvarZ17T3Ql9vUvuPeJLTm6Vy+vr3vOr8VSFtbc2gAGbo7Wa9t+bwIz8jR5IqX77qMwM0Ng5yPrTQI/b8BiQIDhmmt0s65ndiO/dGpHtkOOP85L33nxu87xuXVcAT762NUknwp2ATIw9EXKDNBYTpzFyI9TEyCx+/+hmWO4NlRMlERjC7BRmFDD2CpSUpdtilSDMWuB7Fzjfv7ZHi+7buazxZgyB/4MyBPeg/WNgf5DH6z3a/c/B9x+DH/Tf9QGsQYjtuqPt+8k9zaBMaLsxQUksjAyRD3mbroVyWqEltfSTKAdQ0ABP82wDQxQAXmAEIsMl1+2c4VoB38cG2fFuyID44dmV92R9o5P9YzNqv/RdfO7T7wyfX8UWdLYA/QY+HuQwng8w0CEpTgnmTjBwAAAvX/eKUIa05gWNiwYsFtuueTBfIjiwDZhAA0p9yli1SbVhDYCWIAHB0lkBBRMmET0yDUxCSsaokDdSSal0cGCAgBvAGUhSr5/NEth3OWgODAJEIBsoaHuvfc+tgXdhDZAS2l6RDCBchJq0FLtkCLdJ+2JmKzNcgVKxlQtyychNrS3S1MIEXHLGC8aQCmbKGOjK5ExZnTEDYwDgcb1sPdzlMy3tChRE0OLtpF2JYaKOTYnpcZIdj4L1SKos6gAUgM4sxHNeDQ4HNWsbmN0d4zkDt4GXHF8eZ8bjTeYW5zfP7QxgzdcUmK8rALBb32Agtc4gMZMA5ExAayhIUiQLjBLPT/8bBf98PeD+8ZwJDSQlHHIHkXuiziqV4znLNezXKrJZRRsf5aTNvogCMAASV9D1wTMPDq6D+58LODCW4PqA9vCAtu8C9qvck17WOiUxRhNrQGkDt8kRlLXqJIkL0d0IDgpSdxc4KCho6CxBa8L4WOGoM/ctcBsI2Pvxxfjek+TxE4YYkCPAjaDgLLbGvmzM7qufSvGi3wnFi2J8wgA4IgixM2N+UnwB8AyBgQ/1iRtJGRtKfD4UL4q58AdAcGPtszEDxhSgK4CRpZDPpEBf9gPiabAUfZNai2OgK5sIEmzuN4ECcAhiZIiPMzd29oCTRP2vmAMGA62Csrb3LfJoEcNICahdsFs5XxMiXtO9VaTUgJZQK4sF0hqyFujhVtH076RV/tIdu2uB2q7H35DKnTd6ic1pGgM1J23zjMGCX41ObWMQ7kTHm3eOL3CBRLoG3GNRciLsJ6Waua1BwW4EzEKhAQEQKD0kAoH9vRVQkHmzZ1gAawU8goK+p1YWt7f5fcKGtjmMLhwK7/XgyU86vxkIRBAwz3V5PZm0gydQsmyYXdt1JCY5tirtMgmK7Ocy7ptH94w/BwoJk7ABg9J6dM/S2BY5kXUAlftiKFa0XyXAUB+pXtHufw7c/1xcBw8/l1gCZQrq/QPatTrA59aG3gwJASBwAyH3dfMCG/JovSwoFzAl7zgpQGAD5+KgoGJkCZwtaEcw8GQgML3xVECwHAs9EeN47DOdLRhBQaT9V7+XAHzw01depv8x/ZWwAgVPAwQ2nhcwML+4sQaQiPVXr3uXxM9/+h1HYNHCmQHBQMEsjxUpe/LcdST9W+/SxuzmA4U7PAYumsI1gXJrY9bG/rn4qUME/Ly5AlCY4xM4wSv2MY3sARbgAFmVfBYLnvgCblXrEFjls95Gl1IFEYNRQSmhDvEGQr1SkiCrvn0fkKoyAw4m5Ddba5rWWMF8p4V7pChSyRty3lAhvRCsolysPNcFySIWAEcBTmEvzAVMfJhSIgNPAIOlOJJeWwmEJcSNlxXE+Nq12Zc8K7Zxb/SUcWsmxR0QaF2KZK2qGw3K+IqukBdms88nvn1mccd5PZ6VoMBFFRdwZDxsjrA53rgnzuY4++JvXUf/rSrzamAJ/tN9JP42XcsAUuZ+BOKe6/JD1qIL7cF6jOgT6HvI3CNYUOF53niL36eeYmtdEVNSQFCvQL2GYkX6eL1Huz6A738u//YH8Mc/90DDtlcHBbzvYq0PcQUJrTFysS6cEj9EWZQ/tN01ac0Sq12C0gGANLw6goK9jSxBDfdxa28Q2M03nwJ4HBREpiCuxAwCV+sh+4H8s2O59L7f5yV+9foVfksb+n3hrcf116HtdUhJlw+nR4HC8wEGK383M15+9BLv/eDreD+2TlZLbljZsDjDRT0ZWRVAgyrdpAvFfAoOMqgHZ3EHIRbfZ1N7E5rKT3V6PlPXA1jQYxtIMFDDCQCP7IEhoAgOgF7n3i9j07LLTWn/rOmM3JCKxgSwFE5KiQZLAxCQ0JCQ9n24oRPu5Y+PIYGHeixLhSQw0LSLYL6KQMkbilofpWT1S1KvVY+jwF2NoQFKWJK406KVZ49sUyRlkFKn24sGnEmPadYwzHFYSto83NI9U2qs4KBNio3VAibZ+CnBrWG03p1ydSw5xw4EbgOE2/Ozc+uPPDAe/poCGRg4bTx899Y8z5iBOMfH5ufX0Y9hVtj6vixkfv3UU1upx/EkUsBjAvsGdewsJEbXzFMLLvUA1ABKYsMjBQWxWBHfaxqiBRiq++CpoMCvncotygTKhFRycBcUTUm+k1bY2x2wXcD5AqRNM5PWoKC2kSWwPhM9vqDL4n7BjtfpKTzAU2RvxHOzS9Hcj7dAQZpYgtG6H/fDy9ev8PUfvHfUXycTO+ovHjeSf/Z2RMSzAQZMafCfEDf8+KMP8d4ffgPvf+V9vGsXFYrMpvWn8Ngv6DmqMv8gkVng5u9RH6QBBVWdERyYJWmbegAI9pU3OfcnvmYvEjpQSDwCBCZ422PTHcyEloCSxu1CgPQx5oZ0uQvzV0bBUGrp5YsbIKWN9x29pfF0nVtD62EKyHkfz4dlbajuQlmWC6hUcNsko0EBAu1XcEpIeQNTwpZ7VLNbFX6S07V6KjZzK6+vZ0VnCFoDKmGweA0cMDUBY8ofea37ZqlNqqD4aRS9DVNszVGrzdPAABQ49M8vf2fywT+maCPWW1leOXXA4+4OLeCzAjLD59JtwLGa6xlweeoY3DDo7hiinqVivvsVKEhJAlVT6sqgF5oKCuBJe41O96jpsq4QWEFAE0ZAe2YYIEBTcLBfJZjQgMD14RhkGEABFmDA+h+kRMoICCDIlw1UNi95TpcXwvJd7oDLC6TLnboPNqBsCgou8pgKdhYm0bpQOjDgnskzAIK4zd9gvLEJNoG5CAhuMTeHtT9T4gCskNzL16/w3h98A+//xnfw7ttf8E8YoOTF5I+gQKHmpMs4Nq1ajGcDDIARHLx8/QHe++Fv4/0vfwfvfvpzcmNQ774YN9CIE/hIvUQKxj9WdVPkYbEAoIKQmVFVoJDZ1Qw0Uh9+E6AQAULGKL+BNdU9j5kdmG+OWUjHQMYjQOj1/o09yBaUYODATzYSw0C6BHADBU8WaJQ0sj8lETQAsvkpT+J5mRu4Edp17+lA9p66Fag1YLsC2x3SpYHpKgBhfxBLJWVwvYpVFSun2SPsJpkPvpySDDt/UhMQsQSv1K9nliWuem13yPW1ro5SvKY7lCpL3QRBFfKdkqRsLiCFZ4T56NOYFd2ZRXtaB6B1Cn9+vR+jH2tkDkZ2wD4377Vofcc4GXXRj+dglD0gbAGFz+mcHptvHBYAfHh9wchEJmOOzbC6FslAQJZgVG9HnDsoyJNvP5n1PimELpbtfE3WvIFqY4wyygAzM2KnTS9ApOCAuKE93I/Fiq4SazBnHnBrHh8UxwAINIA4lYy0ZUlTvNwJYN8uHRTcfaqDgryB852CgjthCsoFTNlBgcUJRZbgrFCVjVNZZ/M+nMjjl3necm5EBkDg1wJ6v9FcYKwzOf6e/ZZVlNW1p1YFFPzwm/jel34P7376c1KVlsh1mJ76iR7rOgxYg4KXrz+4ec7PBxgE3+3L1x/gaz/8bXzvS7+Ld9/6HBBiDiQgEaBhR0xgIOZ5Dhe1ujKheoUU/GFQypAqgiLbwKIgSC29pkBAmAUBCJQw+KKNMQBG2ZCm53H4y+G7AAa3xGqsbp4IEITF6OxBYwEKzAASIaeMVC6uIOW0pHRpLwcaFHDOwIN1VnwApR2UyVOdSKObzwZr2iNRkloHdkyguxTqjlZ3pS2vUnu9SmSzlIEW4UWx9jrg++Gx9B0Z9h29/cI5ik81I1MW9gXivkgM7LK8EkshkkCLR0VwkIb89QTCbrEVYLRGaExoqRdiaYtE+ag4V0F+82eWZ5mCIqau9KOiPXN3HH7rSdd1/NzQDCh8/Wzew+u+jTr4TUkZEn1NYj7Gea2zOuR5ZwdGpiCCAqPui7IEUmbbCmIFWh8QBW2NyKIyB8LN/gSTYJJRQ8dTKzrkx6re24TrPpQ0Zk0Rtt4HlmZ81iExd03oKcfCFEjKMZULoOyAgIM70N0LqWh69wItX4B8UffBHVgZg0ZZAEGzTpM8sAStHQHBUwMF6fDHqPDPdum8fWf3zyF2JDABB9fBChS2etA5L//V/x1f+6Nv4Xtf/Dbe/ZW/IJ9BdWOGo8yZ9RjwqGH78vUHeO8Pv4G38fbp9XpGwEAE7I9ff4j3HBR8vt80gIADrsfVngGBo+75Jk14+TNFWq0CJCV5wU0VUILlEoeJDa4FwggQmrTdcpBgkx18ijTOIky7fySAAa9foK/lRMMNNPsr98qejmcAgdvEHlQFBwAaCCVl5JKC9f0goMA2q0Uhh2hk1ijklB+QpuIoBhKGKzdb8tr0h7iJEBucsCIMqUj/BSiNSda9DV2JC4CJZKD9eaJ54oLOjEPs9ubnXAQ8WaBRI88MSQYSwJrnLkDrvlZQhYDM2kCJvfpda+wdHg1wNMYYkjyNFQgYFe65wm7MrkzlhW69R0ULjQsQVkC/l+lWnGCf1xBr0JWwvbYKSnzqvGXK/WZquql9XifXLYKBmMkRq16WnDym4C5LtdCiFmBOoWqmfrc4IFAloBY8tSYX1uVNUOpjcMF6ssO90hXC3A7dup+a8gezND3S5kdcd7mXrFKpycEwiDQmJufhNcp0KFJG5SL33qaAQJkC0lLnXO6AvGks0EVYAg0WtniCvUlr6cgSND4CglXcRYwLmrOLgKiUj7f1sBcWl3yOOSL/7AgIHmcJdM1PwOHX/uhb+P6v/X0FBbpPyBpHqc5Ro3SlywB0fTaN6F7/2//oby/OUsbzAQYgvPzoJ3jvD78h9Mtbn+s3FYmm80s476e4OPGGtfd0vPzZB/jqP/lb8pOtAeYWIO2vTaybQprUeOyBsgcMRqUOEEBwn+o8VlXT4mZlM5shir5Rf52MXlPL1aqszeg6VqnLfAQI5jc39iAidok7IGz54kwBFBwxpEBSKhtaLqCHjzWoqAhNuW/AfkUuuzdbatddL/c5bSkXpgHU1NCuChB0vVTggZLEH2htdQcoFiltrE8QdLxUOAvQYGyDASAr6+xpmhlEeuy8ISdJ2apEoNbZA4tFIRYh+CJn7CQdILOyB4UYO7PWOjDmgIF09PvHMRfo8b8nWnV2MYzKVDaYAZDuSiB1JVB4HeF74ffC3IY5TWBlrjy5en2e/3ru/bUY3BrvpVvXKx4nqfuG3Oq/7TooNLEESeMM3N9/7ZZ7C4yBlxOeAAKwVv5hUDgXtsJi3Dzo14N0WebABhIMIMReB/75sJq6GLL18+H1tBW5DzTAEGWTOANjCWI8Qd7AaQOKgoFUBCSkgiuPrgNPMfZgQywBwSC/pv1wq+ZItPrPAoxX0mDYcw5wI9C4HUuQZKG6IepN68wIlWv//b/yd/HuLwtT4Cw121yT9qDoAGE5ZtcUUXevf+V9vPv2uydnKePZAIOXr19J9sFvWExBuKnO6LlwA0a/3OEGBfDyT/8EX/2v/uf4/b/yd/E/+cH/DGjiShAQoJS4W8ZAMgUZhRJ1gMBEnYdwFjEI0jO/cGAV3KCj4HJQtsGUugcWslVZg7egjaNnSzC4jvXfB/YAK9dCQdoIqFeI5ZyBXaqXkRUy2S6g612vt24WjJZWzaV5bYLToE9Twn4x5HO8h/dZGZi6I7Z6lbe1eIqOAxhIC1PS2/2lLihJ0rAMIFhBFtJjcd5AnKVCYy5ISf9lIDeSeAOCWOIKvKyjXk4Ze22okHoGq1K+cxlfwE/7MB6rBTAOeb/n6PffHmoXGIPloKADgtMywPGSLtiAs5oGdk52HufnML4+1ziYx+p6xSJQc2lpq3hpRYMswNAab5U0pwYCaDuoSXlvBwT1uqD5lQWr1fc+x/tg5WZrU/HpIVYjuAH0dziABOuiOjQ6syF0EEjrDR0vc3DJaZ8TDzDMpQOC7eLsQVMAwEUyEG65DgQMBJZgAgS3Sm4PRcgQLXgMwYGw1xDkbATQJ1ssvrzKLIlsQdIvnLIErXZAwH0PAMCv/vL/EGPr6hoMElajNLtROkzExiTfX370E3zth9/UQMZ3Tq+hjWcDDN7TlI533/rcdFERFE06vDagNff3BEqPGS//9J/jN//Z/wK//5f+c7z77/1H8r1WAWrSGSxlECe10o/sASl7kKD3oIKCHmclf6yt1hHOZIxrzswD69AUdMQ8eub+O509EJbAyvPGsYNBaQQIjS02jKXuATOaAofCotC2omCgavGSegVTBvJVUpTM93i9Exrz+iCWvbVsnRqynEeVHVEy77u4MnLwp5mit5iAheKnxW+FN8fjuVuEei/5lJDK5l3gmIvcuFnKuYobqQK5IedNlJ6yB5kkHSuRFMBqTcpTZ5KueTt1YRjL4cbGPzYq8yGnXk5hsqROTjXeMXNlPVbEOYMSDntusNDPZfeBwYhlqIFROcvrpK/T4Xyeci7xfIbPLK7XeNyx/PUy44B6Hw1rjVxIlIDXC2gNaMoWWACguRPUehdX2o7Bum/TPXAjQ2rFsvXmZrqGrYMO1LpOf0xJYl/y4p4IANkbI2nrdKtJIGmIHRBYsaI568BZgnpkCexxBQhWl2DdBwTq2x8BwVkswBlrAKxdCnoI/XzfvyH0QsEAPKbkwBIYMz3rHKDvgzgpklLTonPEP0UsQIFJLTY66jdQElDwj/8avvel38M7b78DhSk3x7MBBt/9yvt60ixKeQ68CH+PgT7RjTDTe8DLf/3P8Zv/7D/F7//FvyOgwG44jxLlDghU8zLQ/UAJiNkQFnSYAPfZzrtx3v+zAOzTV+sYnfZnvaFiHr0BD2KJkj+Ag9Rr9/uo0HQYQfOFkrsYuIov14+TGEVdC0X961bvnLJkCHDaHCDQ3afQHu7FkrIgqLqLha2uhUF4zUDvZDAzyChVVDCug190+Z2zN8JNRgYGAFi1Nqhvla3yYy4iGFtVgFBBqSgo2FzgI2/YUpHK0eqj35mlrAFJNovRqCVpwCEA5COVame5GjO1OlfbW16LiaJ1PcJrUNLLENMSWKzGSrHPJZCjcj7rnfAm52PjKdfqloKxbAPPNAjBhVljCdD27jbQv2F/T4CgBdbMWpiPHUvVuvcTOgcHN0cd758DKEgJrtbm2yVZwSKSe8lcB3ofUClSkyAXcRlQ6oDAMg1S8biCs1iCvXW3AbO+z23Yg3GcNQeLLMGtOhIOGPT31n0KjmMJFKgD3gEQLGIJyJS+sQRDnEGUd9H6c7pM2ZqucwBSgEAA14Nx+fL1Kwlk/NLv4p1//13RSfrrt8DBswEGX5jpEdJAQ3u6DOixxdq74A6L9vJf/wv85v/lf4Xf/x//7/Grf+4/AIffE2CgyK1BkJzyR9RwiD3oUaTyGWXhl7mot1VZX9CYg2/AwIJ0LJo8AoTK7CWSm4KDuURvvDw7S3VEAQnNAQInOcfWGCUxKoR2Lnq8kgg5X5DyBtSrAAQVklx3cNvF0mg7sL/QYiu7B0SZgLSbyIVjsHiOc+bh0cdufeIX4OJGJgQAt5J4Eo6swtGCKanuR4DAGkzJmyiC3EAsPemRdqS8IaWMBHEvVCIt90payKX32rBiLjIXqdfv572Y9kq2PSbw5LK5OJPLE54YKJHP8hI89N+5PTo7cFT6x46LcAXdZ/jUczp+4LHrNfugYwqa1SOw9EPLNsgAvE5ADDA0t4EyBtT2IyDYdwcDN0HxE/fq+NptSSL7eVIBsWUyOigeAHHZhsJFKNLwqGnjIy9WFABBA526DfYTQLDaU0t3WQAFBxAX1s3AwFxgCBgV+5sOGh7thmlHliC4q13nzHEmgOylAAJ7OjU77SvAtck1pgZVPsP8f/yzDwUU/PrvDHV8wixPx7MBBoAa39CdYZb4fAVugQJDatzw8md/gt/8v/1v8Pv/o/8dfvXP/Qdyow9RoLaogLUlprbLQqF2mgedPQBqTzXRXzlE3gNPswwoqd+VLMFB/f9K+zehmGoACEQYKV8AJSegNukM18hvxlBpVy8ba7xC8xaznJOwDgYQmLAToTBQmlpU+YKcLwIG6lUAQFMrqu5AMUG6A/uuvtZdKrVNQVNyabqvVJ7rTQc4YDgVqgsXxSpHe7jMwZ3AlIBqMRRXQBkCBwR1B2934NZAuYo1xU3cCyx7BFkXQ59n+wepZeBvJ6BpECCnEKBqp/borX0ep3I2YileuTb2qMAkkxo2XQT61Yu4+8YxaPFkjhRfBYfRJLg/6bndGrO/eU4787/JAgsBjxNo1WMJjiyB7O2m4DeyZA4I9qvsW/P5m8LAE/eo3bCR8o+FKiK4BQYAQNHlpm2Q/T3taSC+FS1YZJk+qahiyu46u1XS+BYgsLgaAwTMR5eQwZzIFpScgsJfF5bKNK6dswq+1EGZPzYek9c8sdWRJTg1RBUUGKMd3T6A6B247u/WZNQ5kv8MC555+dGH+NqPNOVRiyPJTfS4GwF4ZsAgDi92RKlf5AkUOIXjCyeL9uM//RcdFPx7/xFGFCh/UtsDkktOIfvrCQAnocdDkAhR8IEDGLb/vDEfM7+IYClzVjcgE6GR0NJgiWNoLHEDYLmhnD1o4lqI4GCcTj++xffFPvR7E7o7AoQUAEJujJpIA7QKylZAparL4NqLr0wggQygGSCw2ANzN1j09BR5zbrW5k7glAMt20EBB6EbrzlPTIT1kocJ3hB0xbn4HqK8hXh+iLC3YKIN2nQKXWgk6TyHJi4XpCzsAUlferbAPwZaokFB9xV5XDG+ierk8A1jn+yYJkwiOLkFIh6d1y1lPynl+fNRLL/Z+T3900Y3D8c0ahoYA8k8wyACgtafc5UmRCuWQONrPAjQaggs9udqb/q5BfAq+kFlHnVQMAACAwMRCBhNbXE0xg5EIAByF+EIBnJnB1L2IkUtugxU5swug9j11MDAyhUVXVAzKJibRhkYiAWmbP2UAxwt+inQfL0ppv1zCyD4zTBlHMR4gggKOLzuvxORdoUx0c4S2G8kZSGdupaYgq/+6K8rKHhn3CPDfM9P99kAA6uQJha6uhEmd4J+sgv0uCnUfRBBwX/85/7DacNw9+Ewq2TivjD6SGj+EjjJ5ohBIgCOeBiLIic6Xz+8oZK4SbWokIGESckkAHszOlTLjNpPJqGmIjhgUl+sHmr27aEydshNuCexAIoCBGELSBs9SXBXbUBK0vhph/SZTzkLi6DCszdcqpLZYGChsAMFi9rmuvfGSgYUDEAoSGDaHSB4wGHDAng1F7grYcRal9mEkmUiEAoAFeK+TLVThvZ9qCIp0n+eUwM4q0BiDU7MykIkeLonJYnST2Kndn3c1z3O9kzlvTkw6I8dBPTsmRGgdBAxv/fYOOsVMCtkf++E7v0k53frdVr87RYlN6AFRRJrEbTOGsBAQnAdDKDg+tBfs8wArSHgMQaP7Et5fQ/7EkdFRXFP0TE+wNJ3jRFQIGCf8awbU/oKCrwDImVJmwvPK+QW3Ktkz1jVwp1F8UuJY5EZrCBhbzHTZn2uOeyJTHQKCgppRojGqRzqSAQwd1DafmHXMrZfV90Rj/ixDnVwloHt0X0QGIMFcyGp71HPABJ4pD1odJ4v//QDfPVHfxPf/7Vv4523Pq+T6QyQ2hs3A4SBZwQMbIgwntwJSGCE6mL+YQ4L1ST7wJiC/87/4CZKB2QB3aK037NcH39edS6pH59ogVBnxIhDyuSg2Ka8eqvoF5WMFdqJefQpBfbAmAQFB0QEUpcCs1j7qGOf+37p5Hs7kQCPRVoXkeTi59bpvZw4BG1J+VTJtFAmIVhgCMKWW1VGQQUvswtY2pRV0PgEKFPApK4Jozua/3c6lhHeSKBEUn0xQ46JAq+lsEPdBtpcZt8HcAAAVHT9kkYOGMVolhsXoZxnoIdA/57lLM97Qxbo5nkexnQMDvurv25qks6BBCL4XR2n/7lS9AP0mSwwmlmeT3qO/vwJ1/PM+juJTTpkHMxMQetKYa4hsAIFj9b1WJ5nAAUl1BpYxQkoK+CFwSi71e9AQB/tPX8tgAGrQ9AWxYl2vWy30nCBUb7EwFQLSDUXQqwX4NUmZ5YgTXUkFoWlyBqyTHL3sDdorJRq8xr5Kyzk+oKJGGR6MFCnsWQvFRx0IECCGNRIffmzP8ZX/8nfEFDw9ufRi6/p3J7oRgCeITAADA9ouWIAzhqsYg50vPzX/wK/+X/9XztTEHOK5btJFYMp/QAADhNgePFYz62PqLR/zn8LOCJK+4zPY568IWndAEoNipJpQKrKIPQ8emMPyIByUlq4QSR1S9hTgzbi87bAtk3Nguk9jhh7InUvEHKV1C6qbew416RrnoMEouAP7ExCyrp97WaOOeAGEFqFRf1TKPVKubivl/ddroUWPMJ+hXAdRQHd7hiOaxXF31gyDRZFXo5L3EBIHYjUGgo9yW9QYj2u/lbZZNswC3sQ6i6QBbNGRTwJp3mPjc9H4bISNrfGWvABLvwMrCDst/D5A1W5sqiYhy28VPR+L0wWl70fPv+Jz9En8MicJ1BO8zzdsBgjzA+gwACjMg3cag+gfSSO4Gw/RhZrSCGMf5dNXAbW3bBYa+P+Goq4BZoq+iUYyBpLkKQJWcPIDLTgKphrEOwaTGjptnsAA7fcBhVwMGBLQwkuT0RmdFlyq46EA4IA3AajUFZgnkXfIxYT5q5bAwp1MNBO49mcnb6hlgN7EItV+ds1ggPGTJcJKPhb+P4X/wHefevzfSpkJZTTk0EB8MyAgbkTdCsdWAMBCLqgYbz81/9csg80piAGu/mIPrs3GRwUwOK9g29pEjYAjp+JI25USkKvp91vcKQNMY9ebjrqF0vBQZnAAXID78IakKLSVQ47AKAxdgIySRthrxZXCSVJZsMSJDgwIKTEQynZRAW5FKQCv8Hd5aBR3gYSkHtzGGcR8rUDhGogQeolcN3hfRe4qeupBZCQXWANAtiud6ie6MFdc1qkxT6kLNYgNWCXdE0GS+c7SgIQ7HcRWAJgrVwPFx9r4fYmStOzZfzIo8CLgCDM65P1m7D5nSj5eZ9HgPALnucY6HvCwjzlmpsQt7l7sBh3JsvjYLjHHcSRs8TQJHHhEUlqcyQcQ0ShTvXGXowxAbG+QAQEVoRI626YC2AEA1t3EWiWQYMUiZJTEmt/boH8FDBgVTyhvzdvYw+DaAxKNLAFUX4IM7AGBZI2elJHYgowP9QP8ImMbKy/psXsJMPM2F+tK3BW8eANwevp97iJ+tKAZuI+Nyu+9/1f+wd499MWaHhkCyBn/qgbAXhmwMAGwwTryBpI4R8e0J0UL/pPPSXxlFIgq3Y3C85OsT59gicMQbQ+Jj8UMS+RpM0jxUp8nIGWxVqIefTckPLmgUfUlGRoxgB0cMCVVFBJbMToWujBZzHLoVrFuibBibFYTakJlPjobmiMDB6Ch+audDkpSADEt9o2BQk9LYzTBm7XziJowNcIEIpWWtSAr5TUkmge9EVRMc8jVnybfbgxuvtkzblBLEjbSwEguMvp0b1zriTNVTXQkLcE06QUTeF0/+loNcl7Exg4U7DAeD4nwHg4hxVImD6zPMfhtxYjzO14jotzeON16PendARtN+cjwFNYGY+BIb3nqQcfUjnZi2f7sJSbgADbpbMDaYNV6ewg4AgGaoWm0cJbIM/lis/cBHtrQ1GsM7nhlS5ZixAF14EwAyovAiiwvhTZjJCkxaWsg2SVbBCKMUurSpO+hr448qCy3sueWzG7CSB4TNn0/acN/W03Hls/dsXtPa3j5Z/+c/zmP/1P8P2/+vekuB8w6Cg3GHGq2ZbjWQIDwMCBWDuMpNkACb2RgCCt3/yn/wl+/y/+HU9JBBYLcyL0vKd1pPJnC/5k3AQF8+blhjalMA0jJfFzK5VISg86QGDLo1cKTYvspAxob+jxyjVo9TM5VgWcNcgkPkOg39xeddFrIkhTnSvg1e2IqvsAadfSsnvz+vMWk5ABXDVw0SKMvXvdABK4ByqmTW76WtRPW0GUbwIEysJAxDQxmgW6txgcafZlUFeohBiF9ZmS6W4phucaLO7cgyLU+XGk4Y2ODoVxhnFWICoCGQr+R3v9pBw0EKxWnCjZm+MIZoCTc7T1mM8RGM/zKecY5xrP0c5pWuc1iBjHKVDxD6h7LzVhA8ombiYAXFQOVAInZRZIg1en8x7qFMyg9MxlMAECZwgsnVDdBBZA2EAHMLDqcDhnFFgA4V6bBxVGIDDLiWF5SOSIgQMvfa1sQexPcQYKthxcB/UKqg9uMPRqkxq3pGxwq1bf5ESe1tQDNC3omLinDHoUhOgVQju6qmytYPpIdVBoygduGtBs703fDTrIs6R0vPzX/wK/+c/+l/j9v/J/wDuf/qxduEkHvVlsgY1nCQziRRBBZlZZU9Yg4eWffii9D7TMMbPWKYhZDCsBQfa3IccAClKeqKdH/MLxdW5HUGCUJDf3VZoANMXgwixlKQ6Usiq+0gGCWjQuDLmpa+ECZIAOBe4ln3hTcFCZBSjUhj2JMEwsOLmpJQEg4mZfhKS/HbvVJRBob8ocACUlUGrOJghlCG1Sw9hbYBJa8CXmC3IGOKsvMRUgayGlJMF8DhD2Hbw/DABBAJPUGPCSsXMthGEvGdf5xJQvVwoTSFQL4VSZBMXI9txL5NbjPphT2+JnHxtBcc6KcegxMX32oGDD5580TNHHeS4U/Vlp4CEo7w3Oc3WO9trQdAvQZjVr0GBj2CWz5WkfbdWrYnJrYqSk1N1NmXsQ4ifYf1S2bhB4M6OLxw9YTQHPIEglxA2Unk0Q3ASRGYjZBOYimFmBPTCJrd2QC7YcIeTUChEZWzB0sVRQkBagYNOsgwwIGNAUaNof4HUkLGU0pIgu7yGMshREYL2uYO3aCoRaQuqePgONh9etZL4aWb5nZ+Ag95brnNXWpoQf/5t/Ke7vv/Rf4N1Pf9bdGTwZqBZbYP+eOp4NMLAI55i2CMCpIHcpEOPlz36Cr/6Tv4Hf/yt/F+/+8p9XocO66lIw6GgRhYsNdFBgwiOyBKuIVQA9ADGNQkSOEoKpuPdDt40cyqbOiuNQkEQr8TlAUMYAvMk1yN3Bl/OmF+8IDq4ODiBhxwoOkEUQJC2v2AaKcLYMWNdHHxO8ac61GlDg3sluJ5TMB5CQSZiEkgi7ZjeYgEhUUEqRokL1KkLQ4hLs77SLALUYhGpuCLmuxhZYASWx2sKJTIqQorI/AwO2JrO1N48bLMChfv40T+g8h+qQAXDcSh8cygpHa8SV4WQx3wIRcdwCCCsL7ZayN4t8rkcBfKLzZAvWA3AodX0D3El40iNAwd+zF5L2Ukl+7hSVUgB1ZGuq535r/x3meQgojO4BrUSof1vxoWWtgTaVJ16Agb22ZcdPAwLyXE/jhCGQPk2sckBkQI9D6qDgLicHBdspKJB4AgEDe2cJ2lX+tiDQAAoOBdN0PaVcQAJSGw0uFNlrhfpanbEDc9Cuv6dryFr0LqEbhHbbu1GZoW3wlnrox//mX0qg/F/8z/Hupz/T94cDguhCoAEUmH70OZ2MZwMM4ojgwIgeqF/v1c9e4Ws/+pakdPzyn1f6jhUQJGUN2kgfrtwDHrW7AAQhgtt/ghmSdyoAxOmj+eZhSYvyvugRFDg4qIPQkA2tPmAvy2vR+8I+WB6vPefcKctc7kAKDmZ7aHe3Ahwc8F41S0bcEEl3XrQMDtYC6zo0edIZhA4UxCpIziZEkHCXE0pOqBq4KPUS4DUSKkkjp1JyBwh198BEtgIyeRtoxaEuQtOOZ7O1CozKfaoUJ6+lEQQsKPd5RMbgAAoiILB2ubGYU7AwOcx52XTnjC6NzyeWAMBYChoYAixnwXgzvuJkDG4BeaH/fVbFElifY3w9jnielMy/JV+f3EErlsfSULtilt+RrJN+DcZ1DgAKOHWR0AT+aDrP5f5bzTFkF1gaIYKLwDMLAiCIDYys1kBnCQQA3Nc2gIEeTNgOQCAyBMAoC4wdGFpv2z2vDGImaJDhCApm98HmwEBscNq150p98Hue2hVo+3lhqQVD5TI0WUO8ArLcKy0YJYXKGg7ZaANt38HBUQcQxAAlMNtvqjIPMl3qmxiIGPf0j//N/0NS6v/i3xFQYCAgsAVWf8I4iAgKnjqeDTDQS+4jIiOhaQgvP/oA7/3wm1I7+lc+A4/8N6QF8SHZosgP04C+IjCQm9BcCmPu+UwlsaJDUcYJvVwyj8BkLnzkgvFEMQwXIY20eNPNrD5NKtwRq18XPZ1yh+2EOcAMDkqWoDk07I1QMrBX++hROLj8G1boyCAIUGgolHCtAhJKlTiEa2WU1LAVZREyvEZCTiQVFnkNEKxXA7JVXdQUyLyO5wDg12wej/uhR0B5SjWrNcDVUp4aZlBgfSOgbqW47kPL3OCP5vD3+rg697jNooKLAEH9sA4CruNn5XN6/vOFepMxA4Szua9S+2YwcBZhDnTXiDF9dR/LXFvOPzO4CUUvQaqtN8gCHBzEPeCs4vK4ZZiXZ6E/Ej8y77+BGTy0/U4OCIZqhAoOVoBAWIJea0BYgYbrbsWHLIagg4Gd2wAEIkOwGpEd8Nf0fi9ZQEFJwkz+OwEF9aEXlro+wPuv2OMt2Wn1T4Du7lkwUT22zNho2wvBKFy4kh3kcq+JwRZXFtZfWioH4KDf+/G/+ZcdFPzKZztLYGBxMFDpoA+H0z1fMgDPCBgAcrLKbgMYwcGr16/w3h/8Ft7/8nfw7qc/A27Sz8BZAlXOLsxdzgdAEKhED+SxyOK4SYBxU7C5CbJvSvcqWGoAJfRqjeOmjTnQS2vRfoq07TDphlaqkopsdm4N6aK/CT4FBzHeXP5uDg6ICNcqPIxcHhZQoOCgJKEfoYVuHBzw+OijQUGBWhVJGLsIEkomZGJhESrhLjNKJRUmhKLBUclKMM8AIQYqGougxWnYrilXoPRc9UMxrGGjmUUwWaP9A9MXukCQ327dGiiaKcPWSS5YkfodK4ozB0ta9ccBDJhyGdiIsEcSYU7XXZbX7VFWY5qfUer+vFe0OF6Hk3Er2nrSMksQEF5bxWkcz1f/dhCX/Pzk3EQM8q73UBEGRFgBAFby3BoOKTNkgICDm3GuB3G6F+K5WRCivHgutOO+CwZJbwVuMQUBEICeBAiuGkh4H9iByuxgwcCA/Q0oMFjcJ34v2+PACHZQkPXR3Ad3Gk8Qsw+2lCTjwIHBGSi4alfLHe3hHoeeFHW/KTe1iKCDg16LZGYHwvoO9H3uMSkp7LNJDwCy1qwygJoyA1HuZ61DA/LXvc7OX/w7ePeX/8Kkbya2YIoriKCgp/PfHs8GGMyMgT1pAH7y0St84wfv4f2vvI933/6CggIFAykPylEW7+hGGG4+QPx1ww1pbAHFw2tcgyhiCzqjtg+BLGTHpQxxM5wzD/LjCwrZjsnsACFeE/uV9oAADqiDA920SdOXAApnIuCJmlGqEix4RUJujIyGewUHJjRug4PxdOR1yWSQaors2Qg7NWnKRMl7MOzMKES4a4yNI4Mgv10apCmUAwTr9ggPVPQ9MHU7Y7u+vhemMbuYzt6L342/Z24ixGPWHv9gCtmAXRwcQIMxCTEewVgPjMpxrJ43uwDIv0M5h2OEw6JNYCBYvwcgcEPpn4zTJkE32IAz4CPPz87XzrNPU8i71qedzbqfFILXBVFQECoFdrkQLEWPRQrHP9sfmK7BrX0X5EKXO8pYrNIN29plMDMEZ4Bg1wyFnp2wvof9MpF8/gwUSAEiAQV3mYQVTBSYwB5cbGmIm8cWAMnSEfdrjyOo++OgwGqYTDJT9kCW/ioZx3UP170H9oXYEwNjsfbDmS5wBlt1gbmoAkCQi6YMkBot3uU3MgVDESoFCcoQGVvgoGBWAvrnLYDwrIABcKQ0P/jpK3zjD9/Dd7/yfm/NbBcWAFUFCNTgDSm8/3WwBMJNCGCoBhYXYoXQZH4kEfLaM4EogXHt4MCUBiX5XGawUpxS/UyR6Gyh2WgqvFv1jR5dBsM12hOwEahdR7eCudQyPDDLLgOM2GhCCxbOyFAroxGIRLBcdwbQkIhUsJCyCDrNIPTqJF3sds0JSMRoTPo7ovRXAOGuJWxZYg7uchYhpjEIESBYPrRVgSSIgjZXgrt6dB0eHSvANg//HRUC6rJw5gcEKe9MAlSspwJ2oFFf9zNle0K5n9XXf7SU7q3RJnBgx7pl/f8i4xY//ciYqwXaYC2co0+6km2tu8qOP6bBfoSxfLAqArPcY6DiHHMEvOF+uXVyE1UdXAlDVcJ27FcQYwjua1WmgHHf2hIQdKYgpCye7K0cmBljADs7IPetxRAJCOgsQcw8MHZgKFykYIHOQEGTzANzGTwJFJzsZzmB3Nfd/s5SJtrXdgYF2kDK1iHq46PFbroAvdEbdWDAWneGiPDyZ38sKYl/6b/Au7/SmYIOPtIIEEJcwQAK4hbC46zBswEGq/Hy9Sv89h++h+98+X18/q13fLG8XDI1cEo9BcV8OjqGGzygcgDgcucbwDvh4Xhve0wLAJAV8cnOFDBVUAU4WVGj3OeSiwhezYMmTuCmGyJDcnLjWIED0tr7gAi2uneGZAugwB53PQmrdVAIpPs2N+lqZp0VQQmFAaoVOyVcKyOXhr0l7A1ITWi/Xd0FImjI/ZU5ScOlmY5slb3eeSJNjaSGxiODwJzAqWJvhI0z9rq7f7IxOygQgCC0ada5ZCKklEE5hxuFu6I7E9IrAb9w/0Ta0NxAElTKQFJXBhHQkqTKQnWVgwP9jdW6u68y7N1meyYjugqszHOk1H3aMWBu4Yv//+tYziE5YKDSr7lVrIxjVb1SPmuBo+Ia9HgDUwIWiEikikFfn6oGRsWAND035W2K4oz9W+0b4PG9p9boYIw0eBEiezRAEFMOryGo8LpXjyF4qO2UITBAcOY2MFBgil/cgWlgCYQZgP+9ZXEj3GVRkDNLYP1UjjUK9iMosEBDiylorbe01vLTywDdOM7WXcEgOwCT1+xvd9voc2Nq3kQfkBmYFvScJIX6x3/6x1LR8K/+PQEFtj1cL5n74uhCaGEOwMwTPj6eETAQYtxuvQ8UFPzel9/H595+xy8SKbdPwYVgtL5Hi9pQxD+WCBXBYsVAGhtDzJ4FETcDEayJofiySAR5oiw+ezt+eBQ/bpPugupjFsUhIEGCo+T3HHGeCXOjrCy7QefhZYHLAhzY96yMcibkJjG6xh4kktKlewOoZLHSc8N1Jw9cuq9JSh2rFZJakqZNSJ2WTCQhDDM4YD4AhMYiTFCTAgXGpSShTFFxlxKYhcXYckJhQqMOEJK6KDIJGs9N0TOR3qwEKexgF+F0qy2em8KRh5T6z8j1FOEk1RrNyqz+3P3UrUrDpdWaALJupfTGUDcq5glQsM9N+2MVcDi9/sbjTaz8T3qcPB6HHRyUw/GHXgrTOZ5VDrQof2syRGUDtksHBe5CDJH/p3RyXjOJw+NMXePxvReUjsmdqn87KOBQkIiFQbiGgkSRJXjYm7MJlpr4GCAAOiiwQmTGEli/gh4fdO42ECYP+jtPLG88g4KQbeCsgfdDWOzJKcNoqFxqDEHpFSRhHShjcai0HWI5jK2p/HR90EhlqQEEiNH58qc/xtd+9Dfx/S9+W2Li5h9Z6CUzVGdQsNpeL1+/xJJO0PGMgEEfP/lI3Ae/9+XuPmiQBXEvQYgqBYVsBIL7fmIr44gIAeDagNbGm3J2I9iwgI+UNCCSrfsXSYliTAqZpRoeMQ8WJJXa/wbGymlPoSEBD1iT3GztAlimWIPcABzLKKdESE1qCgidz0hJ8pgzCAUZhcxvKX0S7luTwEEmrX3QkCoNVon4JdfsAaD0ZZIZSqBjQ7KF1OqJxh5sOaEioXLtFkmTOIekgqYmAqGnTHqUuAKEs/EmHgbzMEo6FoSdICClAu8cWa9qYV7hvRtMgW1hTZTxIWB0J80V87iFgmyTWJgVsR1nlYb51DFnE5wd+9Y8fsFjUzzeY+mLcz0JAwREY38BrQ1A22WoHshz9cCU4b0FnErOncrV0AWTEzabX9RTZYAAdhwFA6zHcgWlnzW3gaUeCkBAyDrQexF8AAWrcQYIotsgBhfe3QAEsd9BJstA0PsnljfmkILcqoKCULjIa77c2nvjHltWLg2VI2m7KEMUjEJb81XFSHO7xDW/oQ+IRJY2AE2vJyANkd774TfxvS/9Ht759Gc1xZz7xhncSeayCu6LE1Dgv//6Fd77wdfxNt46vVTPBxioz/CD1x/gGz8Q98HnFBRYjAeAocaBNS4RhUxHVGY0YOpdxV7+q1cA4FG9RtlpM93DTU+kxwShsjUOghZMEbiY8gUgK7qxHeIlqKCnMKUM7CrULP2GkgKEIKgjZboaxjJYql7r1ikgAIHz5huSkgjOlMS/n3kECDlJgNMMEMpO2JMyCAYQ9L1bAOFsyPUW2N2YNMgRQCg3yszglBTXVAEOOYGq3pStg4QIBm4VDj2QBIt1lt+QkSBWkxxLAJX1f8hJOkmKkr+CKoFTFUFVJcqd2lVcPboHBRSk7i4ienLFvD5Hnd3ChfAmdQhu1h8Axn04j7muw7Q/P9E84vHf9DqYUiDqVqIVDdrugsVYwEkDc6eSwvZ3gygHlwsICkJlBBAIg5M95M9vnXv4DeYOBkwZMcaSxav0QwcEjFCP4HjdYv2BpwKCN2EIDBAkdSmQsQRagMxBgfY8MFDQo/vbEaT6RUxOAh7k42rtrR11ZIiscqTVgkjJX7M1t5gOayhlQddnOkFsfiEPExFyY095fu8H7+H9r3wP73igfM9iGH8kxpfgAESi3rPxwetX8vu/8X/E3/79/2x9zfCcgAE6EvruV97H597q7gNA2fSBxgEAcnAATgP16KkmwW/043/1Ct/84XsAgKtVBwMPN+cE7PwxQSsFMoGhm6AxGrSCVyrgAnGGAwdwkC4A70IfdUsyqUuAYHEBUWi7kA2xEf0EGWjafhjoLYEB2c6sKZYpC4pJ2tI4ZWzKoLRE2JvczDsTWmJkoyQJ2DnjLpvFIgzC3hr2xHhoHSCURm6xtAa01K2WeSQSMJFAKsSkNLMENxo4EPusMil7wKgQ0EIqjGozv1tPD5tvovmGjk9nRG7qzSwBIhYwkEjAgbowUiIUDgChJHchAPI31av7DAmElIWm5rpL4JUprtbc1eQW9C1to3tgWbXvKdR+zK8fgh3D1Wj1cakS9uJpVcgnzIficecaAGc9FYBA65xUEVx1IRyaDi0sRqPsOXYbHK14kw0Ryg6XJU5xPteTvTn8lroMWPd7ZAmsh0EEBYeS5sx+fwFzQGEHA1Z7ZHYZXEI8wVkMwZCySHJ/jAyBKf+K2DZZUE9onMahtXVrxzVWF6vIQE0Tt6ZUk1wk60y5chvNoCBf/LUKCao+6zgZ3QhRJ1iMQSOAlOQTgCCf+Z0vvY/PvvWOSLNUJJX6VD/1YMfoQoiXw+IZPnj9Cl//wXt4/yvfxbtvfR63xrMBBi9fv8J7f/ANvP+V7+ELb7/j7YHnMbsUDBwADUzRyian/1oDfvxTAQX/8Ivfwf/0//xXD13GnCqMx1X+18EIMxqxV/pisgWQnZJTCV9PARyQ7yi3ILN2CtzVirRYhHxc0kERrCLLm1icZLm8zNIOmLMAhFaBpL0IyFK2hEHIChBKE/Ygq2uhEHngU6aMmq2ammYZ7FWZhDRaL2TW1tqCSbOERDeizfLZm8CbkuSYUq1RsxWydI4ExHKMwm+mBSJzYRZoDR/yeiThkhr4yEnjFhqrD9FqLACNpJxzg1lbF1CSCE+qWuiqXuFBRW0HKCEVUUhcd/W31r4+AFaR+EPFPDnp8fkKJKxGrJVgYwYjT+lZYONsPr/InOZSwk+9Lgacp6wDDul/hz4Dsawwh7LCLhNCK2KdUg1Kez6FeQ/JpRkt9bNhvwvAAQEzDo2NvISxXZ7Z0xKOFw8XwcCcehiDCreShyqlAn7FZToHFdp7CRgYAZoAgMdHLTojrtZVKyppwH7PTpnl4gAII0ugDBEfGKK7zg4FUHC27gYAT3UCRMOkJHpAGEX5yGfe+oJkBBM8Ho3IDLYWTwJDBsSJCwEQUPBbf6Ap+9qF8dZ4NsDgvT/4Bt7/je9InQJgKHRkBvXKpaCfAIUyl3ah7cZ+9dOX+OYPv45/8MXv4C/8irgnarzxdRPEGzQOs1IBmUMCCzChgbxfgAN0C9L+NgtyT9IAKJdBQRyazsQgs1kYxsHS75uybEAJxmyCVimLdZq0AJP62qju4JTF8k0ZJWVsIOyNFEULE1AVLOwkGQN7Y1wT4b6xllulRUEVcpAAdBoW6JApllWeR2WxujLLMZikW5sBhKzXfp+Wa2ClzY/LPc6/95Mfv5j1elrLWKKxvfROkEJMxGhJfJKSXQEg0cgeVAWCrSpQkEBFAwi0XXobbmMMgG5R0GLNzWUQW4cb9JwVsN0wywvDeiy9NmddH2+MJ1eP/ITzOrTTXaV1ugtFmZlQvdABgUWgx9bEeRtYAq8JoO6wWDiIeWxHbPvoKXsIgO+j+N7qstgpVR6PIc81bXHFvok9BNFAplN7lcLVfTbHD9wqThTjBx5lBzj+3dzYkXReZQS4Dvt+GGb964l1QICDLDz0NVH30cAQLQINT0FB69kfFtsR2Zt5uOGga8faot7i3iWmynQFqWEB0ImecrbAXp8Y6w9ev8Jv/WGv4+MB6TfGswEG73/5O3hXYwpI/0VrPQ5jDRpGZAyMF3oGBZ/5lXew6wXdWw/usZuf2/FmB+SmZhZLEol8MxifZBWxmQktASX1ZSGyHg5CN0cLksDeRnSov/6Y5TZbi4BXeRMckAHe0fuRizXLLEVcxohuBQlap31gERiojbzAytYsnxooOWOrjL2QlGANbgZjEYzm1FZRp7Fk0ZIZ1jKgv8pSPtmsdlvHeXNEANCBAU8CPRwjgE1Z6w4OShVBSbXhLidwTmgkcynq8eBkloX6W/NFXQsdeIk1lRQgNPmidZMEBorRR0iRi9X4PPtB3xse4/dWBXf8MXjLywhKBlfGWVaAHs/m5SVmp3l/krkxN2DrlhVZ4Nb8vek3maYiRRbxHWMJQjfClWJY9RpgVRDWhZDNBx2mP+8fmZYBTAUJCmwBjQ0+GREU+DEmn1wy4cgAEosY8g6rCgYW99accnirMFHSOAJjCjKJQWTtkG+CAfTiX/48AtEAhofJAn4x53YG/bPZZZezBkXW/ZQhssyDVKSKpAGBdqwR0QIg2Kd1sJFJWFMzGoxRsjqqzWgGs25DOqjrNkyg4IQt+PC1BOILKHgHI31xPp4NMBAkpOQNCXKcWYPViMGI/lqTi7wCBbbQc+ex2RqwQYk0jYgdINhm0KhEkfVAdzwZOCDSyPUTC5KbIEiWJkkzmn6K9dbn2YUu7w2DNcdqcTdlDIik8A5JMaZOxWZpf0rit0upYMsFVVmE2kQ5FyKxohJQ29HNYCzC3MENaUTGQAd23s7ZLaxuecXBTSj91euyrrwEAsx9HnEOxmj0MrB9LiVVWBvZnYG7xl7MRQIkJcZEd+0IDDWoSDIOcheiGmjlFThXyD8qVY9axs3CO3PDF/+pQfG2/rcVa9Lny/oP8e9BwQcQsJwbZH5vNDfI/Ka5WeGqA7gZfihcLytQZHnquq+H8sJBMVh54b3JPhlSAm90IgSOe0f+hger7iQuKADYk6bXUm/Qdra/D6eXJLhNipNK5cHe44BkPtGTM91T3vmUxs6H0V1QkrBj/nfqLZGlBsHugYQ9hsBSCruL4AwMsJxcOCdlBQwsWoGqhSvVPq9/jL0tQgDfkiGytbdeE9PaGxCIDJEBgjOdsCcBdzuJW6lQcoMRkN8v6g4dwIGyOoOuurGdLWX/uw4K8CS2AHhGwGAcYmHeYg3Cx8aLyyMo+IfqPqjohUKAdW9yUyrDaJAofqJQfr4JvQwAqiw8uj6Ag0xCL1swGlH1ugV2Y3EywaeAIPc0Q/KTxCEo7VBj/lCgRwXrPr2uN2Ja3FxkFeBSBmoG6c1V8oacN6HQNdBwZ8KWgmANboa90CFwamVtxREtLQAHa6tPfxSmZ4BgBQZut5XlIFDhwVklJXBiz5TYsoCiuyzdPi1yuRl7wACSlo3NF3Uh6L+kVlV+5OY2xW9KJAYqReUbLPXVr5G/3i1wD0rlXi0SwFQx8hYgjUzGBBA+wfxwMj8OSqbP7WnXLda8txREiTgfXQeVO2MwVxNcZQA8ee+0bqVXA7zM1soFzL0Oh1ymcX9zY9/zFSLkPfsKtuelXPhqRNaLAhjobdC7u8AaHFkw4br+wD6CgSlmwEDAwS11vlJyrnPcwJTxsnRNkfrkDQjYfksdBM6AMKYjPgUU2L+lPgCk0qkxQJXlfm7J3c2VWbZik7TqARw0xI2PswtloOA7vxFAwRPZAuDZAoN+7exaPgYO7JoJKHjVQcGn3/GgEmMSAGDuUT5bmT6PRF0YDRO4DQ4snZEXCkIAQi/gMZb1Ncuo9ZNS6pciWnRq15B5PrINKx+tfSsicCsYo5ScgAOh5EhvLjIWIW/YitCx10qe5hjdDI0zWsYAEmbf6dk4AwEzs/hYmFwEBUPGRAAI86Vx+rWR7jtLBVPBolkTMv2Ku5x7dJL6e1nZqwyJschkqY2hYMsK9QdlGiOWEYKTDIB45HIDYj788HP+swL+iLJHNx+KN+HIGKzcG4cKgE+Yn/zk43O8Nb9Ym94rUYa5DtdwuH49I6nxMdBsBgXWlfCsxPDNfcNK21shriQVPwsgrKLVWLkxfI+bggFQbIUau7CvykJsq99QhRXjZAwMSGbC2l0wVCi8XhdphiHDYOEC5fmiDBcHLmf87yEuJbBOuo7LZlZx/9lapwAOIiBYAMIKvBEoMHflUScouGOlWwI4sMvQTCawgIKUaMiss1N6FBT8++8M7oenjucHDNydIIF6hBEcrL8jDx0UvIdvf+m7+MyvfMEzYSqr8NLP3rJoXfcSgCqBJXsyARf8YQoOGmSTRXBg6YxpoSC8gh5rwGGarKFIy+lzGHqNwVpew0BuVmkDjJE9CNHvc694u1G9u2SI7E6hdCw0upc8D7izCNHNUCxYUYFYSTSABIvojsFWZyOfWEP2vWJrmaTRdW4h7iCukO2NCRTEjAmnhDm6EzhUaLTgU9X+SBBEuAAH2rO9wdKYTCDnKYYizrVb1VHB2t71/al/jwqYD78YD5MgCsn9m2RVIuNzQtJkcZfdi+vufIIdm6c5/TueY0aYH+UFoDm/js2uIUMizaGPTSP/HwEF0aXQeOw7INdA9wyRkEGkEp91Hg29INriWkYFDnSsFff9INwT+f2yLVanBz5iSOs1MCD1V9bFiPLEDqwKER3SCyeZEuWJzDcESE8prYMrwFw/c5xKUP52gfrn7IIpg6DHihUrIyCM6YgRFNj+XdWLmBnOqBNyswwl9qwpVEbOHQBbCx9AsQGUlVN9kOyDcYlxCxR0cPyU8fyAAXAAB0AHB/L++msfvBZQ8Dtf+i4+8+l33FJk9Rc19BQ20bFr+hkQYWaKQhSPVtxjLd3Mkj9PrTd2qbrwe1NqMfXCF710ZtY9LRX0eGUFHXyqk+/VbkhWS4+SAwRzZ/vVss/tV8S0yNVljME8rNG+yAXpcufRvpQ2BwiUCqhcgptB/KoV5IE9lhMs1Q27Px7cj3+rINJxazAKaAgWzSyUbdFa/F76efGzERTMneasSJM8IewpVGiEgQUgKzgg/ZtympiDXusiN9Z8ZuoKgvy/AdRGpRpL5ILHehsxncq+uxpmz5uMTehKV7anWLdRGdv3wgzjNIe6IhEAWM6/vd7PpyvqszkCNgelwR0YEKAR30Skkd0juAFoOdEGDJVNY466gYJmSqGGAkK1uxhiRcG4Z2Y3gg81B9NCcbvihwQBRqteQMI6zTGOJUOgG9a2VAd8AQygswNy/NFdQPsDMAACbYHMzbsd9pLF+yBD5ivR0wibpBHGcu8OClIABKM7YAAAQfkDGN1q+nuRHTJQHUvdW++J3jtC0xK5s0juZgb8cakPQM58RnDAjcGJPI1VQIDeBwSAqafYqxtpNT7QQMNfFBQAzwkYEI0nrgoxpneY0D5sSe5I63e/9F185q13JNgNk1Bis/qnry82gT3ONzmHjcAN4n+GVFUkpzVEK+1KLdtN2qAWJKAWUnYhrS/BBA3P1GkEDLHVcKs4NPLJ8gdbQEVs82vVFt0/OFxGpfzGamLt4WMvHILtAtQiLEIuIlBScTdDKZobnkhTv6RwktFrLSgOW+ZNabbViC83u0osG78SoygLQVXWxK36xpBAUWCvotRbFes0WrAzJpHnthForK9QGSkL8CMSKjHr3yURqEF6SSg4aKR+ZdIiScCpsjUhZlZ2LLKySqGas2ji9YvWZ7QgJcWKg2XOQRGLcvb9foMyiBXhZqDS9LH+AvMseqEyrMJlV3By30ghnw4Ojtc1Aq0IrnZzKTK68ufuWhALshcPAnADFGBKm+7XLyVojQvyuicxG8Bo/nFt+olElmGtRoJ+DN8hjW2wyp29WmeoTMgVtFtDox20i/IfGhrVHXx98N4Ft+SGTELkBjeVGw0idzSOgNIECpYNrFTBn8XVBLeCLYOBgejCmkvdr/pPmKNW5PloKM5j1gcRHMTFHzJvyblFeYkZjejgErXFNVDw3a+8j3fe7qCA7Ifned1gVIHnBAxOBztrYCPeiI2BD0Jvhc99+gvd/xxxxic48gr5x1EDa1ASiX+ZrYiOmqxMSEkEzkpBHC01E37ZjaG+QWIjH412XzXyqVethDgpfuWmuV4F+RtgmIY19xia0mx3EoOw3Uke/nYBmhZpypIfTpovvFm50WQV5RQcgPSmJb+RgX7jzWs04sSOxs0SzVmKDBUWy10AAkApg2oDJevNAKlImY9tpO2oZ6TFDA4t9dIL0rCAE4a4NIjhGRhCJZKkMfER0I4CrYOmWF8jpk6Zn3QVExODOlcpc0YhR4XkMTLEwdLkA3Ng193WZGAB9O853z8Gb73JPEtObtXKfJUSb4wUgEzSQmPRBTFfW2BkM1oLsS6wa22ApQv/OObncXjAIT3ebyBmBMTrP7p1MIKdGG8znaA9jUDOroP393BQoL/Z9gAIbnQ4tLbH13thCrjdlBe9g2UBZZE7FNtg07rGwJs0sLK1jIyQrFtfuxkQypr3e8syDQz7R7bg1piNiacMnp+sVIm+9pOPpHhRzD4Y2IJPMJ4XMFDWwFOVzPoL8QbxMjG04dIf9N4K7UzC3zpsSAdiHgGB05tBeNmY/eBGIcncNJWNBKEmpUUTiWAKONN/M/7c6APWG5uyVh8LjXxaBWrG0MgHAOqDpASlrBp2bGNq3RrRele/YRglmAu4bKD9AZQ30PYgrMESIFyBtkn+fi5D4aSmYKApOxD90kBnDGbE3oUCDUAiMg+tAVSSA4TcGBlJED3U3F6AA/mulZDtx51Lx54NbgxolgI1rcrIwihbwFFjAYfzcCtkOo9YW+OsTv4q02JYOne/WWEbUVBmtdKuClgbWJm1XnLy/XuLMYhAgBsOgGWuJGfzjOcd5xrnma7tkGvv/7TQVGcS2C3keN7zdTZQYEoD6ErB1/FkJJCwgF4n4HyfPNaEKJ6HxVAYIxItfbv+qf85jFleHGSF/QPGQkRPAQT6txsPxhicyAiihF7Zx2q2kCt+K1vNRL00dWxgddb22Kz6g6zQVVjIixlAzMyWgdoZ2khdAvZrm1ndBE/QCb/o+MlHr/CNHxyZAj+jyBwDY9bPjfG8gME0iMVek6o95ADBxqvX/aJ+XnsrMBGSUdUBSUQLXR5lYYnHjbASiLPlRSnk28+uBj2WeBU6U8CkVuNSBgVhM1G50QecSWLepAKZBDOOpXj3Tj2lAiqMWIcf+h4zB7+hBhWZ6yUEEVEiiR7erdZ4AdU70PXhSQDBshsoZVAqAqSSWQJjfXAguApwZAv8pgfATEoN6iNJVcI9AAQrUEMpYyfpSJegzZAg+2OvxmTIcedik1HYWwUzWx8iOgoGxQAGDnTJZe3nFQ/sgFuxYd69HgTfjJDv6XOTwgrKyiLRjdrORMi1dYs8J2QwSuOeVr64EboivQ0GrMPfPoGXuUS21444Vaw8Wtuh0JTVqI/ukLPr3PdNtxbjMDkAZYcSaUZBZuxVAanGDlghobM9EssMb1orINYNMEBQUgAD4XF2j5yxBjFexV1D6MwiGoc0Q318KiCIcQV11/LFa/mAsgGs/V9SnlplC0CwvgVjNcKxNHVD71vA6G4AlxFh3R6z3v3jgU1o03s2Mgg72PVBNjviDXSC6YOhBtjwYXlwAKivRVAQmYIICACsC6AdDjKO5wMMKGHwXXFUEzN7ALx8/YE2lJDWzIYoCSKQZQHYo4LNaLNgHSlWxEBO4KZ5uAZ89Zg9YrgDghgwZILVftOQuwsj2xAKEOaxXu4IEvrxjTpNSQwYCYYVgJBLBif1FZIyK7tMhHKV2vxFavQPu1dBQdulFDPXnrZGXiCJkEqRzV82ERQGEPYraBfhgbqDykVaS7fsLgarhUBGF6pFYQV+BCwcPcWjLFg1GSENLNICVI28hXRlmX/JSXLTKYlV0JpWZZTcdAMIwhqEBUegiZMoKlGuvXJcVn+hPOoeoPFetXz1ee1X3fT2NkbCr6LjI3tggVOmaC2A0/ci9da6JaVeDpe74t0bULRGw06SXWJ7fD9RsjHldK7hH4GLRfG/8VxrQkmMUlXJVi3Vy4y7lMDcUBpjywlQypwZmh44KtJ4vW2Q/peT9NwQIMdaOdEsCZE1jQnITcHAeo/MNS8i27FqRBQDAnPqgH8ABUHZ+5wx6wEzmZtrzlht8NCzwAFBBfYreH8QYPBwLy6D/aEDAo0t4MZo+w5W/0+UDZQJlJLIvLIFYRnckJbJZH0LygVcLvK83GGuQjl3uzU2DTiTlWEtTl6fXWDkqyt7oDbtzaR+MU7CTb+pTjA5YAsVXURJgasAPQyg4P2vLOoUTCyB68UBeZydsYznAwzORgAIxh68/OgDbbgkF9Vue7uvpRQwg5QCaiSqJXNfYCtGIYvfNwMwXtS57rkFShVKQ8DQGDcwnkLcnMPj9P4SCJNSplDrqHVquLAoFxFERSa+A8haJAa6uUxxmx9vuLzsoKDt3YfIdqVSQtsrUsni5+UG6wzIpVdEo/0KbFdvdUulgln8iZR2ERIGCIhAsCZDdjPFTR94IX2dVbkLiCANHhIr32IYvIU0S5dIYRCyNl9quO4UaPm0pOXjOKPiVxS3CZzoJz5TUrdAgVXbu+6Mh9qwc8PD3gHBVcFC1TlHhWtzBnqw2aUkXImxZanK2JjQUk/BtBoNJSuQicVb5rlPcQPGZLQ2A5c+18aMh72DA+DIGthccyJsWeZq1/hSSBVEAop03OyivefnN430SnyM+iZjER2oy7dLdo1gs9HnDCLp6plaGnp+rPaGVRc8KzVsDMHcqthiACwNU3e6K/uDYpANFBdEzs+0nmYvOUCIxYjqVQBB3UeGQP92sFB3tOuOtu9iMFQ+uBA4saS3ooHb6MAh63hpWU1FelMgpDpzuZPyxO1YcOrRJkZ+oL5iCKvo4mRl5VMAB6R7IGmbdOLevj7oBLnMfFC0KyOxaFyFvS7GaQcExhYcQQH3NY4swRlT8ITxjICBcTFt5IJ8SKrey48+xHs//G3prfDW52XTDzmwuvAKECywyly9tpFMKBDk+kuU+3FWBgSAObobAyC4tSEjIJg3O2OM4AbG/WDHNiCyk0TDpmQR/xL01kDYUkHaAvXIDM4N6dLQuIm/cL/C+jeMcxQfIrc2xGmk1ICWUCsLo9AaUhEA4TUUuHlgkhyzgLghcTtEIRPtcN7N2I1DMRM7ATsXFT4GJCgha7pTThkFouBWLaRrk3bNBRlF20QbQLAiJnNWSjz8rSpycwBZmkBB3Auja0Qem67/zq2nyCko2NsICh6quEMe9q5wW2uadrcCBtLPonHDlkdlGkeBKLe9MpDVrQZo/Y5+AnPfCQsyvAUKrlUea2PstXrV0SMwAFJKSKRprpzQsqw7dgClSdnxPQEFmgkiwjNr8mi26xqu+XgvdnAQlYnLgYQhcFWyXehJe6NQD+48AwSnrYohCtxKZY+VHm2WOE7AQUF438PreWALvAbBVRT/LZag7RXtWt1IOMoDvWeZMMTYW2yBug+wXZAud2jmNih3Agy2F2iUcW3AVe/Pa+vZIuZSq03u1XCqdhgAC1ksSyys0bRO86OBA1KdYAAhIw+ZTpse+0wv2HxcDoRjmjxAAAREEijvXRLf/gI0t3zNEMQ1jyfg+m4xMR3PCBg8Mpjx8qOf4Gv/+K/he1/6XWk9aU2J0BwcRNaggTXyWcsZhw1OpD5ASNqb5cfH4SV5bzAD0ScYvxMpMNuM9hwKEObmTdYpMJb57RuQlYo0alUi/FtibClph2XCJWek7YX9kKLfGlINu+8Qmk8dR1MGwUZtcuyUGpoGKnJrnVIDwA9xnRpou4CbujDUxwjt0+CVyuzbtAgZm9FVYBUc1KQMgpS/9RgGzYQoTejJRGuAcJd7+VsO1kpct2H93W00WgdmeawAwfGc+h6I+yHW1agKUqT1tGRUdEXbQYEp2atq2pUFLuaxRV0CBgoSKWPBWm9fH7MpwSTg4IztiBkGvTJkfORTUGDdC+f5Xiuk8l6Czrfpe5A5V3ELpqT3CkkxLbt+RdMb4pTnNSBSJpEhTBp15oBUDljgaqHU9wWpgnhkX0SXwRkg6PUDgFhJkFqTczaAYEreNsa4CId1kc92Gnpob7xf0WoIIjQ3wQIU1Ad1K16rA4I5s6kheVsDQBnVlNTntoHKJjFH3rRqE/dBAAUPVUDBXhnX1mB9Kow5M1m4loPyWLTDamxmRCzbfi4aFV2yAOARS2SxYN0WtUynTV9brX/cB1E32HHNXWSMEJHs8w9fr7sk3gQEn3A8M2CgC6cFe+SJPL786Cf42h99C9/74rfx7qc/p4rGMhU6OEiqvVsDMgNI6iJQusBcp4WgQXAA5TX6GoOARhBwqgB0eKMU7taWv4cxvctuglhxC+ipXZbW5cFijb1NagWhoUkGhNKsl5yRyp1aHtJ6mTb1M24Xof21eBHyDkoVRAxGRVK/W5wAo6EhIaEBNUmmxV7lWiQNPNqnDF1jD9TqsLaogpC7or85gqSPrAKBYMVQhhgGy4TImwhlBQjiS+wAQYzjsSLjHoRQHMtaABRdS+NesBQ6AF4i3Ra9kWSqJGYrkCgs0KK+sylZz78O7g4DBU2p12FEYkDjLlaBhMvLPQXZ2mtWuz8GZZ02vZjPg/kACuKcGyR7Q0BOkjgRpuF8G/e6F/PwAj8BmHmQ6God5LJ0FpE7I2CBq2c1GPyYem2GdEoFiL2iIJ0WFPIMgRgDYNR/oJWHoLPHlEVIoieWgmatdTdirEfA+4NkHKiR4HFGgRV0UDAJoeTXO3ltAtL28ZLWfJGAxE3AAMoG5Au43DkoeKgNV2UKrg0eS2Nt3Mf7cTi871FOSQgkhmYctcFqj2vldoW9GORKBobMBSAEqrIABXltfQ9FxtiBiMaQuBsS4j745hkomKvcziO4U6czOR3PBhg4C0ip0ys6Xr3+Cb72o2/h+78moMDrumvMAVFTaxSg0JnRJIL4TAUcWESrVOLrpWujNYf+644yzwQ/MC6TE4CKOiqJYLbKdcM5636w6POzLm52LPdn1oY9J9w1xsYE5BwQrQjYu1zA5SKbr1UwN6Q7oZ9pD6xBLkhFLATaMnAVhoBb63djHK2BmcBNOjSSBTTuFjOw+zWJzC5z6hHNjwGCeIEsCBIY66tTL4NKVijFMiHqFTlvDhBqAyr1vg7WatU7ZoKkqlywHGxQ+KP7DrvA6UV3TED0/cIsr7MDAbkqTSJaPOYlQ2MjiHC19fYr2IfM/el+R4nP6/5783FbhsWcVjdX5fNhysDicGxvhDTQRk37BMixTLmbq+CxTuI2WmuoROr+CFMId1m89oTJp0v9nON65BRkr66z3KPCRjDDg4Sle6hYjcgne4K6UkhJ2QOa2hUTzhsSVatEWkPQYB3bE9sI98Gjg4XZg6UXzqCg7pKubJVQ697jjFYpiWEdKCkY2LIGJffqqCib1DrZ7pDuPoWWL0DewPnOAw4jKHioIus+1nLU9+oqe1T+NYATUNFQmJBzEl268EKu7lFfP7tc+ijHEmsxpjkCcD3h35l0xew6tHgkIrmPPvzZB/jt2Do5NqCaAMGhWd6BPfVIlJvj2QCDwyALNIyg4LOKslglb9ULp4yBbhorDgQASGJhVxKQYD7YQuS0U7zI/rUJAJiQGViDacqDalZrp4MRSafSqroA0MtvBlAwd3Kzm8MZO5D2ZBDldtcSuAAvtn4mUqSGcJcvuvnu1Hqvgui9haoKgtaQDKFXRtasw+VIRwHFrLEajoBF0BC1rhGaWPnMNwSc596ttQgDmPuxDz0dqgY55gJu+wAQCgiZpSPdniSI04IWGfA8dzmffsy43uZftb0R2YJuOYRdoaAAam3LngiyV6nxYl0pNYDN3CD2D2BX7I0TamvqLsBBIJorYcuElJJ/b8tjwOQld1Bwl3vaormrVnqoqL/V/PA5A/fureiBfE2Zq271J2x2dW/MWa5xn7O95/9071MyMJP8voiK2SL8YwS5rYetL6NbhpnFMi16r9p+QPhM3A92tr4fiJw+LgqECgE0AYKbPQhiufJbKCrpWS3uQx9zL4NmzY6MQVTwsbJOU/INStMxcpF7L5UsmUplk0ykyx1ou9NHAQHGEnDZwPmCB40pMFBwbcDH14qP94b71npczS3Z13rPkgJWgCDK1NzA5svPE0i8VSkTGGU3a6aTg8cAGOJn4+9EfdEzT0ZQ8KsrUGAxJbIg4YfXgOCp41kBg84ayEK8/NmH+NofBVAwfFiVICAAIalvkhOAegAHXnBGX8pKD68WOVp9UeD7xoqsgv49UMYQwVJJamTvGliQ1YocEGugzHoRm7FGux8rAIOSGA1JGzDpcHDQ3SpbufOgJrQK2gwIVLRWgU3ei9uu7RVZBQRHIZE0TcloxESioG9sWm5Kwmm76aXQ8wCM2n2a9jgLL9J5aPEmJhJrxWhMymAtshQBAuUNW8ooOSNr0KY019FqjGmsxjgecw0O5xQzoO8Hv5ZamCmh7wkJOJMPsvr8t5xQuYJTQkkVVtFRajVIu+seRAhc9wboHo7DLPSSM7ZMuBRph33JPcq/kD0fc+2XJXptiQAPzCqQNts7AxRqRACQYFWNZQCAnBoSSSdOuS+toNQ4EhE2neuWe/S+xHNol8CkKaMk8yYy+r4rZBPI3puCTtZET8oUgbkrXCk8cT8Mx6NQcrjWBSDQv7n2ssNaatj3vln7w/H0/vL6AakDhFmB+ILxeD+tTkPLDVOS1EMihvSCLsgn976BgnS5eP0SKhekuxfA5QWw3YHzpccVlDsPNHyo7QAKPt4rHppkrtyUe01LZcsKgkjiQqzImC6LMEi6PraHDCTGPQEsZDcw7Im5ORjQK57e0htF750VKIhBomSycBk3wn1tp8fH2ALgmQEDGXLyP/7oQ7z3w2/2mAK2JZqGmJHapTCD2q4R8FJK2ZgwAwWmR8ssLXCkgqNgsU03LA03/zNr8KMV6DNQIOVxxQpkpRVnK1GzjDyAq3d1m+qzV4R8eg0eS8kLywAQmo8YaWfpnJwIeXshqLRUMCTlsNVdc537NkuAC4l2rYfIY8tfjnUNKFKJWhJVfP8LsBA3+xkYiFaT3YQTmCADBJYapcfH9QG0XZCK7IEDQNBiS1KyOaMmSXc0kBCthnhN4t6I+yLTyZ7oExVXxbQnqu4vP5AKtzuWPH0BCQ2XlMRMH2EbRPF2BdtaQ9LrnZSGN5bAwIGAAfnbWIKtZAcEd7m74AijLLKlKup2aQywBXBqvYjrLtlBhQkJogDEejew0lAzeUbFat7GdGw54a4IeLkUwiWlHnSbSIsF9YA++xd7AhzWJqxL9niV3jK6slmFo1K4tR+89HBSdkJ7EFhRoRkQUNvH7ACj+MN+X+11XwirItgSJE3jBCSsAuVSAjeS+xNduXSLVz7T9h1MvLz305aRSu4BhpcX8u/uU+JCuNyhlTugXIQx2F6ggoQp2C37QEDBv1VgcK0ND21MyT24EDhUt0wC7JzOX7BHFvgn7aU7aDvbE1l+6LAnYun2GRyc4EX5vSSBhoP7AKxgoHfVPdVpviJ9zPEFr16/OvmejGcDDJwtgAQavveH38D3vvR7whTMVua88dWHMIADKOWpFI8Fe9nYZmAQUOQBBIRIXz/+CtWTWNOcMhJlNBJlQE3v2QogCb3c2GhjxhX9no6V7PbWA7ZqY6dXJWgOaEiamy7f8fQyykhoSC2BduCuZFC5iODiBm476PJiPKCdQ0rIede4gzEq2VgCyuJbpCy9FAZQkIsr60ctm8lKMgFp9dlRa4+diC2j9TeJCJyz1Ge3+VzvwV6NsQKpgFvpACFbx7eMnCRYkSHFbTp1iIPkPOwLqy43Bw8BGHzCiz2RID0z3HdPEMooL8AU4ODAlL5F/QOW1SDfi24DA5DiMgAuSazxu9yL71g1PrOwloGUtlTB72oBe5kyatZqjSTFkvbWNFq/4aFJHMNDkpTJxvAaDPO8ATiQiTUMbN4vSsKdPm7ZWIOeJniI+j/z4871MtRqTqp4D0rh0b3ADgSWgKDJI64PaLEPgQUFWsGhxT63+UiJYc3m0RLCxOJOc7mZuhLv/o4swELvM0Jg8OJpGSNQdw82vXnfl8sBGNDlhdYr0CyEcpFgw92CDBuuDfi5MwUN93sTFqE1r3UR97XFxOSkRYig1UlzP0UL7jRQa8XHttxZpEwne+JMhuvfnLKAyAEsjIzTanz4Uc8++FUDBa2uQcFBl816KcRk6eOr16/w3g/ew1t4ez0BPCNgYOPl61d47wdfx/u/8R3PPpA0o8kPE6tBMZ+CA5D0cmfIxnn5kSCtIeUGkd5n9/0NlE9cyLPIUYt3UIWYUpF/GeKczQCqMAfMLKVda1OWorf8NV+WgYJ+02ivhQgSjGUk4GMSBF32hlQSqGrHvEZIHm+gyNXO2dqimvB5SFolbRGMZLXRUxBQtwCBCtvBHxrjCFIefz+AgqFpyyooCvBKilx2AQkaAHWsxlgUIGygVoVxyBuIdtkr3gKWgjUZD6TMUED87htc3eATMLBASWjGRErZA6mI3PiTvTe5ZTLp+sGoVnEj3ZU0sEndn0pe5fCSNbdeLey7AAg2AwTJcrjJC+3MsTSyJ2OPCxpaGBeS37vWhvsq87vPhK0mXFPDJRMeasLODZecDkxYnPsQB1ESLjp/BwWlg4LNgFASmtmtdov814C+W8AAsBRYgqfDahEutyRnTBsjypspm37coezwvo9VBqs0J3IwsF9P97fF0nASGWfVS8ne88+Fe21+rzVl8BgeP2D3ad7BpYB3Ac1Pve/pcif31d2nHCDg8gJcXmhcgbgTugtB3AfXXTIPPt6rMAVVAMLeBCRUN4TksLUxLtrUxBhSoLMHtk8N3M6gYMu6J1apoU+Q47IX8gAgkbIbYKF9yzB6nYLuPrgJCg66zH4/bDwSif1SQcF3v/I+/vY/+tuLo8t4VsDg5etXXub43be/IMFypEKZCBY/MIyYoWDRhxqm6m4FSEvilx/9BF//wdf109OizEI/bKD4uuexnKBNaxwi/yqQG3LeNIpb6ykwUBODmtRQ31lq/Vcm96dhAAc9370Cw3MU4KFBCsEAoFR7DW+o0NWgyyHegBm08WhtJLW+tYEKtelaa/VCs17IwEEuXgJVAIMItJU7gWkEApR0JWp43Zo8WdS0CaqzeAMt0wxjGvImj6EaY7rcAcxgrkASgGC9HKTwUlcO8ts03LgHEKB54sO+0H3k37dHq92QlK3IG3LeQEkC6q4IFqhQSS70MhFIrXCrjhiDsxr3gFqjUmO2QXQZREAw59nHSnzRfebrpvsOLJUSrdLkljqzlQNA2CphL8B1r7ivjEth7AoOYmCZzd9pYKzBzIuSUTJhS8L2bTr3zVmCGOjXgHYNRYNurI3dt2aZ0VSV87H9EFMO7bhqHbaHe68y6PUCrBfBU/Y2W/yOivlKZjrL0Fgb32OhxbGPbFNuXa5VubclZVGrlAaWzoMW45iNgO3iTIHEFdyNcQXcgw2vVcCBxRQYY/DQOiiYq2Pahq5NWp0DsjfMVVCyuA/uLNYkiaurpAgKFtkgXg3yhgyP+8K6PZrBZwxCKi5jZzD//le+K3V2JlDQ5cQNYKJnGvco696MoOAdL6O8Hs8GGLwKoOAdo198gbRXoaGpVbAG4MwBMYHRwQEoSczCH34D73/5O/jL/+jXZMMAA60ULYxVfvGwwPOwDUMJZG1F8waLEM7lIgpVfVSb+aq0OUwhwk49lWw13DdbTRmEeRSxPMue8DGqWl0Z18ZIVSo9pBLiDbg5DZlyRtM6A6xtWT0oajhHcj8nKI1gwJgCStp3feRh9GJLSlgVloKrpTbu6jfNYzoEd+uF96u+NM6JkgIVjyXQUs1NizppPYXGrQcocgOzvpcEILAFUc5U3qAMWt8Pvg8CSIifd9SfBHgYUDSWpFVlD6xEtIC5BEZC01r6Bfe1ouySSXGfqVccxFgPwFJZxYIS4XinPnlTsu6Td3qWBp+80a2y/ydlZRYTETbtlim17UV4CziQTJmSxLLfK+OaCC+alnpWcPCU+VtHwq1I/ENRnbilcC6ZsBG6D9+6B5rwN0v+5tokANTXXi1E3w/h88OesDV3VssAgroN6j72IlBg4IWGjBkLDYqGPY2qzYeg858ZgjTE15hb7+zekxLxeu+p287KmcNAgr0ewZT/gPZKsYJl2wXp7lNa4vhFr1dgcQVVC101cTtdq4CC687iOlD3gYGCGHsCwMFuHJaZYsGmd6ln0hhA3JKCAq7SEXbqKOn74TE5norsi7T3fcG9JbQVbHMdZfsJ8Ji4wc3o2QcL98F0neW3RrDqTLr2BnpsPBtg8HVFQv2kSYURT6zByQ9EmlCpcgMHLz96JYGMX/pdvPvpz8hnDuVHIy3YxpvdLAPmnlpkxwT8uJQLUhYlw7yJcM0VwAUAHBwwd392Yx5Yg1Il7kCa3EhVOBttovfkvaYWF2Mn4IEaSkr4WFvqJkqaIw+kKn5CKhe7wmqt34NeEDxPcb/KdQmBgW6FBFagxxjITWQKllXYTgukN0kTFK6lrO1mIrueuQgYYO4gQa9z00pAs/8T2svBfp8U9MQbn1qV2AMAVNhdGQ4QoqUw76uBJZqUQhS406BszlC9NlZzIVeAhbXgXLCVO6TSXQuoUugnNQZRr9RoVnjsWeDHouBvT93qToQOApb+eHWd7deuELxM6wgMxDrqhaWSBnG2JFkTUtI5lJtOwCVnNAZeTM2gzuZPicaASAK2NNLE3XXAoP1ehf8+Cn8D9jfWJ0Ulau6kNgGFR/fESelhiyWw0sO79igIHQvP9rPcIrqfEe87ZeeSgvJcvDcJijBfHCzM+R5kMFBk75IVU9qNKZBOqwYU4rwG16CBEE1L5Hwn/RDKC48ruHrFzl6M62PNOnio2mRLgcNc4juujY2sDIHFEBhbYAGnvt8PoOABpP0fDByQgbFJtrgMt0JsbVcAoCDBjAiNPZCgUDMGzQ2l16tF42Z2O2I87mpMbMHLj37yRqAAeEbAINIjdvkkcHBmDdRlgLS+uDHeAMCPP3olFRN//Xd6xURg9PXEWuUD+h/zjNluGt1Uhxs6SfU95IJ0aeAkjYQcoULAwaVk8A7vHtZKwgswuCXsmYMvWasbtipxiyn5zdNaE0tfwUVj7WzHwEMjlKb+Xs1+SASQFtFJ+eLXiqDotO0gyiCuLixioJK3Wi3bUZiSUeUd4QIdQUeEzm5xVxBIzsEuogYiUt4k8FCtNvmdLqys2xsAcK2gTHIfN+2LgQB6wrDXuDWky51gSWZwMlBpN6QBTJuZuZcsAJWHa+R08ES/cqy5YO4Wygoaqwh23gBmqbeQilhEAK5NUhEzyX6YKzVaVT6brqUZxip89rcxA4Mvvu2gq1nX3Qfbae3JorLrY+4b63+hmR4pb9iKdcsjr4NvrZdLSrpX3/wcjOHYHPTAg/yoPqzTAfdgla9qA6TQ7TMyYGaQeOnup+0J4iauA2tSdAMUtKtkIsx7WW+CDsKdxVLXkgIxLyqkLjKmJIF/lLsCDwwmgIE1GdwXuQ5AwYwfmcoEwM0IoOytk6U5krkRLgIEWu9/UBnCFrWGh2Yyqh3iTAZQ4Jkq8EyU6GK6Swkviqa3KmjcNFjVQcF+L91m69UzQtpDX4M5ldOuOeciwM4BWA9WZla3oxogXpqdVT+ZQp9iB05jkeIIsQ2RLZCGgb/1RqAAeEbAYOUzEZIgsAZW09hiDegEHOh4+dGHXhzpnU9/dvSZz/6fSD3qI/YdbQ/0n/kFp3Q6AD19ThVAs/LDm1QdjBWt8vZCorQBgCU17S5n7Jlx1wiVk1CtIM1jT9IEqQGI4IC1qqL68YoYz9iJsSd57b5WEGXpNkiMq3DW2AwcEAH0AKoEpiznndWqiJtYhYyzAakLIQ5+OA5Cza/Nyidbdzleu4LuaAiYYkCoSffLdTRvN/NQnKUmmFuFqzbVojZWY7RMBn1sD3BLi2rz95mSW8wR0DljVIM/dpWDPg9z0QRrq2dLbA5QqVVwuWDLFw1MFOWamlXiE+pdKnYe62+YcRsLu6zYgccK7gwW9swYTBY20uZ73jrnFY2f2AYWASH9FlqCWIMZF+cRWxMnjYWIoMYtwaatxmOwn9YHsLVhq/C3Whe1hG1tzB1FWe8D6hk1semYK9lgdTdTOHX3rINDlcFmroMOCuI+9lbnzs4p2C5WWrj02gF3n+pWe+qAwZqVzTESkUGb46c4yD9hFe5O73+YIZAUjHjNgovHFVjMyc7CdF2r9f6Q/h+twftoABhdCCl56qrU4BCG4FLk+V0mDaDN6kJImuJKyiApKLh+DFRhDZYZIWfyOwRQc9Zgz3IBCncjQhrTiCzh1t0OJqfmgPnHhu+t4JogUvf3b7l7/Ym/BuAZAYPZwjOrD8oacCqyiS3gY8jAjT8kr7/82Yf46o/+uhRHeutznfrzA5yDAs83Nj/hXFJUg3TmxhpUili5uQyR8eluFAAAsG2fAhKBMzzewFwKlyb1CZoymy2HoBwFB8DRD9dReKiiWOUYewNS0lbE0HiDCA40rcksoEPTEJosKeoWlzMHKfu6dNZHnpl7wEABskRx864WEcaCOubqYYskLpu4OJKCgJoO1xQQmp00NoH0PJwNSgnWYwMpOcXHBPcZUizHrZazKco2AQJTOi5odF/N142vD7InQpCkZEtUMLSnRS6+F3O5SOYCSaXGpuvGibxX/Xh9Q2BWDCRMoeDOftUc++5vdTDgpXJ7Ct2qMM7QTtfSQ8vmIMEAAiVpppPyhlIydgZqk7oL1RguPY95r9h5EIzxmM/jQX3j950l2O9722C/V9cKwPcyhNEhc4tp/BJzA1e9h7NagSxteaeN1veGuchcjrS+f50pUyuzrcW71Qjx1MCyCVBQQ4MOoOBOrXYBDcYYsIMDAwV02CscMq+sOyopU2osiLvz5utmMsAYoyyVDSvEPeBuJP26vKaljoN8iiPZ/QhgrmdRUsLF/0kg7V1IV5V4FIk1oevHDgpo/xhUr2j3Pz/EeoA162k4NRI3rwVVl83jkxgCBqloDYhuhwiY8No5nZUZZNkEsITVTuNr+siK8H/8sz+RmLivfG8JCo5RJON4NsAgjsNFIM3ziy4FZLjbYBovf/YnIygYRogPcIvULFkBBzMo8KhijdY/SzHia3KhyRrYQ9rMJH3ql+IJAUQo5QU2VgswAxXmUsgqQDFW5ULVIo40pCzOQ9gGqeq3s+SZU0le6Y8IoAogQ8ABJaAmEFWtpBiEAnXVMwCBlAESNwDDDSLFX6PvWILqshpABaSgQCyPAtq1AhvWm53D6wliGBo4mMfcLfLwW625ISgMUFVLGBP7JOcwxJUEV9KseLhexTpEP38vxZuzCNBaOkBoysqAhT1ga3olGRWUN2x5k1gT1nLITcrAzg1djPCOhVyG2IFZkQZqFdf78Xwst/4kMItSqGFRJkq7asBtKqCqFHMu2PKGLY8Bi1JQqlciPTuXQ2nhGoBNve8swapGwCNrQnmTc9WAUHElAFY/AKoMZG9UjLuzA8YIom5VGpRLSCMR4/FJAgq8gJD68Q+pgQYKyp3WDLiT/WOsjd6Tlvlxfj9KGjVlaEOpHrHPBhTQFZ1+Ge5XT92dUAGJK2BJLhI5Yz1gNJNKZdJqWHlvD4RNhBdblgJX+k9qWeTuQsgkQMFcS/vHwiRFUPDzfwu+/7lkg1iKqLsSJtntrpKt6wSLsUDQSRt58lvMgBviQQ6LrmDAyoYfAivHWIWXH32I93742wMomMHdY+NZAYMVa+DvkTZJAkRvmXicau8fmIKzYwWfm9+p3MQ32ZpaINduRVmakVGEVpQnBDVZARBQkhbHHqCmhv6nfkkU4FWWN1HCJV/AmgJ2xyyotfRKhs03lAwrEGP6z4razCBBuqNZDwbGphRugvRr2BkgbTNnkfG918Ek3AJTYMK8NfSueSp8atzB/UIP5WOluUxGKVkAwv4AkOYJa9DXU8ABtyS03jDNpAF/3Tdr87fHmMp1TKccoxMGwW9ruVKilpM++Oj1VxR0cdo1YEwBgrmjNN5BfL+Xfv31favUyLQowmTXg7pCFcVvvTBqdxeYL966663y6sO+hsVO2LXS2AK2GIPQOIfyx2iXO1Fk5eIWLOVQVIrEOk8al8BJ99HiXAwgOiBo9RBdTvXhkBJ4APDTegDw+Utwa5Vz4CMDAw0C7MDR9sUxeoVSAmu6rVcmnPeduuEoQ1JSw9v2vaGa6BkouHwKnC9A6RQ+lwuYsrQvbpIZYpU82+GeZIB6MaueppqRchawoAwfrQBiyE4xEGJ9W5rGjZgc2IMMmruAWqMtAJ6SGAt0dVBAuCtSw+JFSXiRM+5iXEEGUp3dBwEU3P+8t5i+Psie1uBPPyUrHpVSZ1Eu8HoRDLlWTEmY4EIALNhZNixZzMHZGMDB8T0HBT/7Y3zth789uA9WbMFj41kBg9UIWLfHGyjwYkVf5st5+VEHBe+8/fkRWEz0FU+08djitCNGsRYt5zdaI8eoYkoVlHakrXThvnUqU8CBPIqlkAQclKIWSkJDAyM7MGAGUmBFhDHoxY+MejO/8tnYtZaBNAuSev3qSVcrMwcg28WkbczKxgrI91oLtDBDo8zxuN/YfN9N6/lvn1IXhBYUAUAXU0ShmpvOI1ZpmyPNHRTMZZqTWGFWeMrrCsx+82HW0+2odEgECQMoCCzS7OJwpdqqZCRYQFNMpSwXdX2IokKSzAVLjVoW3nFfd0+vNUq4xw9cB0AAs6AMIESwq9c0Vr7zjpgIyitnrzTJ+eeiwK73nsaGu08pQLi6awG5AG0DqQuKUgalEqp16vkwEN18A8BpdQA4LRYOMpp4sgqXa8FJ3isbaL9qFhrpGjVQ0s1+qERJwyOT7DkhE3RvMQNJaWdmwO5t21VV5MNy7xoTMxcRGuoFXHrdAH1+ZWDfeyxHVWBQb9yPczxKbPwjbE1G1jLZs3NwkAkmixq7fLAA07NhWVSSNcPIaZRjVrHzRemPL0rCiy1jyxJXcKdxB6ntsvf2B2C/R9rvOyj4+P8LfvhY9/vDIfDT9rRlNVHOHfrtKiP2q+6ZYEDCgECXTytQMHdGXBo8Yf+PoODdJSh46ni2wGCqcwEA4oVOWaLJE+TOVB/xy9ev8LUfSfbBO299/vB7ZwzDk0bMWNAAorZX7ScQBY8EvrV910YjwVrRDdBS6uiTCLQn5I2UagXaLpb7L10KrGqR7Z1EEvG9N0ZL3Vc3VLzTCN4UGITKLG2FIaCkggEmEKOX5tU7Qr52rBFuzIAFkVUHBnBBsHNbGRjIkOwIaoxC3NOLALQkgZCJEtJV/aLXhVvBGIvrQ2cD8loBGyVoBVlou/T868E/XsAWaW8XOkQUs9+zCcwJ1vjoMEKK6yENCio0OElVOfO3xHmj26IOSZpGP6ceCEe0H4XNHNQZC+1EQBAVaLSgNHYm7mc2FIgRGMQmWrFuPq4PYLNw96tYVTODoH0rYkYDxSC5x84pAoTVOc11AmLkfdiMIgeKsrrViwJNG6kHJwIdOC72CaMBhQYoGdfT94BPISnoWOxdZ2EuwsTMRYTKC/B2B+QL2vYCLRWvLugBfhr0J/79NmR+hMsgRbCSsk2azmwliAtbPbbYG6TXV4mywQBIZZErgzQcggrJGyE1MAqSGjZyxebKlxftl2Gg4JcupRe3KhpsyFXTVe+B/R5UH9CMJbj/uYACBQbt4UH6QNQ53qu6OyfZOlhX2BuB7ctxiBUYdY3JkyEVRz//8qMPHRS8+/a73UW70INPGc8GGCxsNB+muxoEHFCSWgBgoX5evtaUxC/9Lt41UDCnF1EWqtyCxGwDWEANJdBcVXE1NIDI0tSiEAUATgzivoIHmyPZscipc1AKwYhCh8zgADBgMHZf7MfRNK8ADszfblZZA7yJFIz+J62NTgIO7ArEzmJN85GtBG5r8KIlcT57a9JGOuxmUveBFSKx6ntFK+e1JP8upQCXT7mbBRArzm20JDEOnHIv42pK0C+CWG0UMwBKBwbxNYuwtr4Lw03MUiRLLD5IamKIGHeq2MacKTG5mACjo7ubZEUPdnDAQrdD9tahCNMwT2O3QgCtKc+6Aw8fnwICE5Ztr+CruEyasQXVGKvWj2l+8ERoDwm0ZaRyFRBsMTnbBXR5ECtuuxOAcHkBytchSI6SZop4FPbivFbFg+YSw48UDxqucZZ1ljbhpqDjB4KryeZUYpT/cZ5Aln1YJPWWd9mnxkDIb9k5krucaLVvA5ClTZgBLzdsDEG5kyJCoQ/BgwMDaFnq5rFFO/PhfpT+AmMb7i0n5MZIel9KgS32RlHCNEzuSraOlJ2WYPbIBKnWCQYlqdFhZY0vSNgdRKhbFccaHBEUFII0AcspBBuKCwFXSU3k+4+FEYvAIICCtteDvJa0lwZuBG5pZI5vjJ6hQX19VdpbGeW4r/p+ARCYBpAW3wtMwQoUzOMxjPBsgMFyhAvTEBETqbXB+PFPP8B7P/wm3v/yd/DO2184ggsT6NwAtKAIco9ZYJbFSg0oLNaJ3dgTnX0YgZZqjaXtbEuolT1HOYV5tJSRNL+dKAFGWWkw4kVjsxkN2AUcZKrIRLjPtOxbDmBqySzWgJUIpaTWAIKxQ87aiiEc0L2xBBY3YNTkHgDBfZXCJPetYa8aiWyFShaAxeb0sd/w2bv0WSr3pWTk7UVYOy3rbJUZzQVQtc67Va+sVatKynWmwA5ElsBzvi2iGl1gD8MjzavEthZV3E7diyUvvumrXPxQ0tkU0hiBXkFZFa1aztivcqVSBvY0WpoFY3pULMIU5tkDaKeMGgvGu1o8QadU+eEeba+oD1fwtaLumkK3V/eVe155az1iXB9bTqCSQXtFLhltq8itCYMwu9Asyn2TFLik6V3/P/L+7ce2Lb/vwz6/3xhjzrkuVbX3PpfuZrNJBQmQPyCJLqa6aT8IViRRymOkZlMU5BiMZAV+yYP+gcDJS5w4QmLYBkU1KcRvFhnTVPwQntMUKdlAXoQECBDYYt/YfS77UlVrrXkZlzz8xphrVe063U1FD8HJbJyuvWvXZa05x+U7fr/vpRQHufFBfvz7Wr0JGiiowGeVJjb+R2P/P3IUFBWTsj6yFLZ/axbm+gBArpr9xtL7rLEizp5cdVlVH8i1tbCSNS8UKesGcTFuV6vh2kZYnQXd2TzoMSgYa5VgXi2Hz4mFqyrpiQOEiuBdDbtabafzCtpNFmtEZVfOfAShvJXzedmqqMIOtNh6E4uBgVSkkprtuyOlxjbI+hgu14ngpXoVWPvACyZXdC3/AyMYxmk1MmKZYB7J03hBODRQkCariLW12tZp+90C57XjqWv1IpEzUFzHQ61gaxsnD4HgW+2FJ9oNH3znW1WS+JBo+BY35I9RLYDPGTD4rNNU+2Suq2aLUv7w22fzh5//MeYPlxUGwFB9plqOtrKgMY2luu9JqSDBBYrGtUz9FPO9kWtythOB83WdaD7fbdD4qTJgK9FuRZzygIxYitjmkIpFKStIFByJmLWmKz5tK+u1OYSZj3jNmDyfvh+swXZTW5W8AYKCre+x2KafKigYl8SUC0tMjBWkzLm6mRUDCcADD3+zsG2xv5YMuKTCpjPz376ScgoQnCNcggNY2y5r+6USAKV0rJ4Sq0+BfkbbIJwBgZyNh9aTYLs3l2qVXKs7Wc7ggDpeciUptV5yzpVAmWvf+TOqT613nXOtLuVzqbyBhVZZaAooqVrpAo9nyaUz56XvxgoImld//dhOT2mye5higlTsRNVaZY+PKrXKlkg29rOiKaPeW5pi1eS73lsuR22htQqd5LySLZsuXC5P4tqWsR/93h6DgAeSwEsg8xmXtE24nfbU2fMTOYNJH2r7o5Ekf/LxUsQZNyRHpNe1atMCilaAsL7WxFsW483NsB/Irjv7BFRQsOBYYmGqCZtThjEmTrNZDs/Z3ATnVH70fIw1dTMpixYGbxt5nwtDsHato0p5RVAtZiFT2wtwXivWe1EnSFtrHEKUgndav87muWpdt9YO1XndCjXxc/DC4Fz93FmB0OkF2TBOSBxtbLcqQQPD8WGlgJxtrK/T8AwO1tevdfNvFRwXWE2OVtvpZuzl3gYFzQvlgVz0s68Pvv3hun81UPAkgfvyNfKTYYTPFTBol3Am2T2e5i3P/ve+8yHfuAiUuLyXTwEtqzBAaYTDKkmSeiBrlYNSiUPGTs1I6C4+NnKXlXdFiiHhJxaj5hqYowUFWRXCeuRltRU2JrD1W60UJaJ0zlOboLWEZxUNh8XbflaZEGoE6UXpvtmHasUfrpXn3z741I9Pg4JW5+SLAAEAAElEQVS5LkQtCGXOdkKZY7U5zfbvLQxlfV5CNSqx0uWQHJ2TWn2o/c/gyNW+GoBLcCAXJ+leTAoaZytNX7aG6jNtKP6xzr659K1Sy8eWpnBmpz/Iy6hqCTj3knNG/EUPslxYMJuesFYLnwYHzWPBNjgBV8v3jfjWevGlVIqoW21Wn3KTXN05Wzn/Qm3wGBS001MDBa1K8BgUlPT2mBanlGy6lAwQI1K0vkJ7t5dVsrfag0/owo0EXB5suA8VQ+nsFZAMKKzksaYsWAHCo779xcJ/yTxvpkGrP0D98yUnwsZJWOfpebFvRlgPdke4sFQv2U4ForXNcAkQclrtxt8atysoCWfzoGBxxisoSAYKrGJnMcaHJXFa6ryslYOfdD52rtD5TMbR5ULx9v6CU6te1t6jz9amXMuNbw0OkGLrSxGrLHinxmdKBbxDUq7R3DxomD/Zbqyuhi0wy6oFBhZXsmGVruZWDbtQpjQjustKwZNXrdw2RdNK/my8pPbx4nDRHCZX74hmlNUAbv24VlIe3iagRSf/4hoDkHnI3XjwEuvt+uMUDT5HwOCJAXexsrSNKwt86w8/5K//5tf55l/5DX7uK18jljOQ+KxrlUG1ie1CVTkkJMuD397+vG5I7VTa9esG4HqA2V4ToDG+JckBWyjzUtUKosgy2aJS3bWy8xdSPTECXhhWcCBS1mAd9YamY8pErZ70PCxZn3uIslYNTJLE+rGl2bW+4eUaBw2QmW1tazfHYpGpU84rKBhrAMpxNnezaYlnCWN9Tc2sxDvHEMyeeQhay5uXw9ehLfgK0EdthXPFoJ6s2gmyPo+1ZVSd65rh0lOucG9N5HoDSr0J60k12QlQRClJQZbzuNALWeVacapjSHI9GesZPNjDObeQ2on1soz5WA/fpHIXoGlV0hTrRK4+C42R30rsj0CBEQyt1F7aWG2nyafMjCojv6S8/vnxlXPGObWKgxY0RooTcozrpvwWOIhn9Qlg4KCRYZ8wEXrKK+ABEHfuYcXG8PSDNu7l2EDVTuStn984KKuSIpyNgy6klsX5c3XjouS2+iM8sJSuLZ3mKlkBwpM+/Zev7dJuuHoUNFCQxBEvKgWPQcExRgPtS2ZcEuOSidV18HIuNsOoPniCE7adIzdA7l2t7giiGVcckrETvgBF1mrt+RZYb7KRCKGsXwPQOzvQuFxwGIj0P8F6ZaBAa6JmlSXmiC5jJRta1eCBXHUeV+vpvEQj075lOW2XqiDeo96hweH6ykNqJNoGCioJ9IGhVBsX6i/Mntw66H4cR+Bb3/6QX/pHX+cf1P2rVQnaqG7r8I8QmdXf8Nkb3ucIGPxk14d/+CG//Ftf59f+st3UfLGZXV6XxG+poMF6/fXfxVlJ9wGBxDZnkjwAB2/5rGfb1h6DA/cjkOlZ5laZ64u1FFbntdaTquVy9dA5f964k4EE881XfLlkHV+i77c9552+LU1qP/dBz1AuKgecB3jKzc40WzpajU1ti9BxTkxLy1hv0qVzqa5z1tOMSetC5eraHnkwhMUhUpjF/ukx58DukYO8IFIZ8Y/xuCi5TdJ22nvsCqeOciG7amOnVVVEfSUA1rK1ONAFiRfgpH7vyntYLsBFc2j8EdJFGkGtjr8mw/zM62ITaWDjwaaZ89m45dJ9r467RrxqC+XlOF0zOFTP1c9VznWxwz56fXrx91J5FyUVihRyTDjnnn49yYiHRS7Aj8iT7/HJSxUpauO+tZlUrVVYizaXI/tJtYqvzP9L9cS6+HuK70CDAYI6hp4cM5Wn1MZMyWfL79XdM/vKf0hWafyMcVuazXQzK7oABXM0kmHMhSm/DQqOc+a0zsVYgYHNR3uclUioQucs/bAPdSNr74Va0pdMFMVLwVXr9ssn/2DdEMhi9tbrqqlWPUiViSjO1i7jLTy9Xl0eYM4ExBqMpAYKzP7aWmUrr2Dl0UwP/WWeWIdXXkGda49BgfgnvCMuQYHrzs9GLypJdT1pvzHltzf5dt++9W071D7evx5/7YPrj1MqqNfnDhg87DK2grpd3/r2B/zyb/0if/8Xfp2f++mvPnlCv/zex5/IsDJrrcog1XDF2QRuzNLLnjZtYakLZPs4KyyzebdrxIJ8krnxXTIC20uoMkeklhTjYovOMltLwQdI0wOYqB66Gqyj2InfixDLGSDk+v7WE1j9v1Z+euw7vwKDi3sBdvJRrESqlEcD1jwK7Pfm1fd8SQYOpsWAwbgY8alpm8FOKZNAH5wBmXz+N7siIn71N1CMFyGA1DRI9X19cxfATa3MXG9u3VAe9ggfTOALV7hm19pY1ev9rvdP28nKdagrD0FBnNbWQguAWu+iOhB7hs3b4C2Wc6saOG/uexe+CtJITnoubV7qnB/8jMc/N1/ci7XE/jYwOf+IyodIinit7nfnr32gfljHwWf8e1UrPHWtLnopgSvn1/2UVPCJ91jExoC1ChSynJUhjQDq8rkyIfnpe+7DWa1SEwkfkPzUm0eA82c3Qd9RXLhwbORsHHTx422uNTDuH7h7Nit3qSBzPSBQHgKhRma72HiK79e0whZMNKfC0oB5fAgK7sbItCROc2ROhWlJb81FFYhOGUJt+Yj5B3i1aO+YM7HlWFy0DYQGnN9WKShQKDUfoN2nBiCMwKwiZIRQrHXbfublWvU4Enx1Nayg4Ew2nMzcqrUP4rLmUZTVi+PiCV1W96rkVr0zNU3wFRRWUNBtHspEfTi7TNaKwWOw2NoAbVmLuby9BwG/9+0P+Rs/wf7Fo3u7fg5+YozwOQMGbw/Edu++9e0P+eXf+kV+9Rd+nZ/7ykNOweUkveyVX17tRrciZkxnKY6qJQ5arKbUQCE9l/grFyDXBbuokqXFbyoqM6JtwXoYjvJWMAqwEqaaQ10yBzfpxQxcLokrHkItX6rY5NV8vjfNWKi997aPtKpAqxK0iFK9rBRcjrJSEW9leDYgkigPAMTqWVBJTpegYIqZJeZVxdCu4Gqynlea/LK5nPloJxQjRNnJYUmlbtAFyUKn3kZ6ugBulRz48CE/8nG/AASx7pXNFe5cvisP2kgqUmVbbZESvOusHLy6NM4GItOE9pAbiXSZaeErpGil4UeZGs2TvaknLoO3PktSubY62vu2V2pk2bZpqlt5LLTfkdoYpgKNKsPMilPs5PaUjP9HVC4+qyK2miA5We197f1WoNYiqNu4rnNnBT9PvU+TcRiAaT717fe111PvaZFo9+LH3O9VrVJPgtrVcr3rKyh45CZYHroJNlOv9XdTm1/VC2BZx0119ywJLgDCg2j3y2vNGwlne+PqUxBLWb0KUoExZqZs861xfFrV7jTHB3NxueCKNIOxh5yDcy5BS8J8cO+o1caqZlj9Di4fgj20VQ6di1FnqLhHKshI9YZdrlON99SAgbsAB0GgZWGcrbAtPGtNsqzKlJZJcTkeSzLjolKygXA551Gs1tMX7QKtVQL6zVkRosFcJlsl6Snrac6AEezQCQ/3oH/ynQ/5t/7zb/Cf/KVf58985Wsr++ipmfZ483+bX/DZgKJdnzNg8MQlxin45d/6uiGtr3ztRwKBdsveAmOlIWT7q1kC2ylZS3P+cjjvKKtVbz3NxaYhlzVkQ6WmbzW+wDIjvkrF8gVBirooXy62F+XVEpczuWWZEc6VCqjlYt8RXMA5wWUhUhF4KeQs6+b2ABhgo6lVDLy093hGnpeLa1nLg4Y4itqCqHUheOpqOeqxmIRxWaVSZ1KkqJBLfmR2kmziuwtDk6hMkgkXaZBLsucTsZPYOiREjESql20IW6LLZbXgESBoMcDNnOlSakW7JxdmL0kFVyzIKLja1qi/R2Sq9+1Cs94258Y+j4udki9aUXJBkFzbSJ9lvNQ2i1YNqb/P/pCtbN4yRCqxrUkpVw7DhZLGFkT7GablP7cJ2qL55Hht4+RiTL9tFMODhfcxw/vB62nOlNIiZluPtv1OOUsAq6eEkVS7c8utSQGjnt9zTp95vx/LV21D6Iz5vwYR9ZR6QkxI9f//ScZNqRvc2+PmMUBYn9HjCN6L+9BAQazVtcYTaGmFU2vppcycrJ03LZEpngH6uKRVPnw5F32dyyrgvZkMLZ/Rh28VBpHWjqRWF55YQ+Tc9c5Z0LoWrZYYRaru6LxONVDVKplPJYE+TNGs4KBZXzfb+ss21frcFQ1UErhbx/RbttPO17aBKUGeUoSsYM13IK46v7KCxFzfU9uDHrs+/v53PuR/9tvf4D/+i9/kz1xUCkTMOeeSS/DWrPuXaCPA5xQYXN6L1pP5+7/wG/zZr5x9Ch6z6OGS1fkQJFxea7knFStj1f+imBmZquDV4cKG4iIS6yIUHUWckRfbSc97ig+W4NU29ip1fOzHDaxyqfVq5dWGfFVrS2FZTyPF5ZXFrC6g6quLWB2cdV0sF++tIcxLi1OvrVReaMlql/e7kWfk4iiQCmQpxDZhOQOr9S1UPkGrXqz+6A2opZZTriySUYE5KSE9cmurJcwlFvrqbeCKMZhVbNUJWoFYWqwe2chpbWZdVAoywtIW1vo7llwqObJmTvDQFU60OcJZ1SCpBVVlNfmod0Lww/l0K4qkKmkUawfleXogTzMm/qNfAlYhWNUp+rBK8JTPwlsM0URxj6SUTWJ7CTwvzHQUOGdMnOWdzeaYSoRd+/GPr8aRuRjfl/bJD35eW3zdRTrgpSSwke2aJLACIfshF++1KRLaifuxkVC915L9mpj31v1+BL6062vrIJxLxI/ig2Mqa/negAGr8+djh8/WJw9O0WwWv6EChFBNvII61GvdwM5pnOv4rc+58WFiOf++cx4J5mNy2dKrwGGpXgYNoMdUSOlRSmq2ep1qWdsi6dEmpsJKBlzBTqs4tn6/TewHiiCBdeyupkj5zE/IuaDIKjn/rDXKAWvQ14N8jBqgVfKDiOu1bXZx2TqbKj/GnT/XxncDhj6cqwTNTKrZTrcsCj23lhLWhmy28Gs78tG+c3kI+v3vfsiv/PY3+A//wjf5U1/+qmGX+v5LaSmM8mDYr8/i4t5egrCf5PocAoNzYffDyt785l/+DX7uZ772Vk9mldY9Kgs/1U54TKCJFfYbci014tZKYSk3ZOxxnbON6KJ6sIKCyRY7dd4Y4H5a3ddssaqL6AXnYF14H7/ruCAiZFG0u7gTpRhiofYmNeJcQNURxZB5hlUy1q422VQeJdStpczHpxVbmNUFgjqgWhYX2yTPJkVlzW74rEpCeSSjbCfRVB4uSO2/zBlQJIxU6URpxPlYzE0yCThx1qMs53hlLk6RBXlQ/m3gY0kX+fDxoVNju6yqkmvee5V0lbK+jh6gtRZapaC1nMRBmh7q10Nn44GLQXkBYlZDm6qi+El8FoCzVXJONEvnFlu9zp7GNXDp/DnVtxz3Hljxer+W3Fd9fXumlz4EVdkgj+2HH//cdiJr7P8ngpbWeVVP9k++11omvvQJQAxEryz/Jgd86l77cH5fzc+itQ4ayc/36yl9ycb+P4PWh6Zeqdjm3C7bSBWv2cx5nDOw7GxtCkApsq4pa8T54/FbqwaJ2rIrj2zH09lZtBkXPZhHFwD9/Nwu+wYgT2wvbR5by1FXV0THWVW0goKL3IrVuro9K5HaXjUPAFX5461Rlz+7goMnE0Gb+uZxW0sVoXrQrC2rizX3Qna4Wk43UNAPlWNSqwQrUAyWUlsPGZf5MLk8vd9YHg38s+9+i//5f/EN/o//42/yp79sRMMi5gfRwpfUEP2Ddu3l0H17if3xbQT4XAIDgMKH3/4Wv1h9Chp7Uxqp6+J6ChSUi/IOFx8vh9GSyoM+l9ZeukpZ+8ypoWTXobVq8Lh6IF1vp8RloiydeflfJLxJK3VdXq2sWisIaxRwWwDjYvGeiQsJXYImlykFXCCoJwukygm4vDWtbfAAgdfTyirHW1+PnF9TTqjvra9PtSx2kIq5o3kF77hQPDzMZXh8PTaD+hFfaqeiXFbPdesbSlWj1fAnBa2ul6UI0ErQdjpJFRQsjSCZM0s2H4bTYmXYsfIgWqnVXtc5wCV4Y20Hlxm8o3Nt0hvZMyME9WhoJz1nrQURLvXrUrItMs1UK9fcAbnsrbuziuKSOLnK4ho4qKeK0qox+QzycnpbLXGppJnHs+x2tWXVp/Mk6knqTIasJMD2vZcx5Mts7pOXUeTr+K5VkG64WIDfZv//WF+J9l7Lha9EdSk9s/z9qtBor/XS2vihfLX6WbhzLHTxnZH88jl3YEnncTPGtLp8NufRtjG0sdOcBIekLK6wCa6uRwWKVsKd9eucGOm5NAJiu2eck0tzrtyhi/XMKlxldQp96no8vyzm+elvUL0MLpI6r1k5B2dVgBkLrSTAxpVooG39ZVKfaW1r1TUqic0ZefylUNsGPF0laNLPtEBJ57bBY/r+hWTVwI+eEcijcd7G+AOOSSOePkqsbGNibSVdgILP2mPAqgr/9Hvf4t/5nW/w9/78N/mTX/4qqZRzq1Zsv9LKy3CPn9m6MT2uFpx/yQff/vDJZ9quzyUw+ODbZv7wG9X8wTZHyMKDwXU5PC5BQSu7XX7NY8pUuvzHVoLnTHpzIiz5oiSoDh82ZmfcTjnJU9JiUqUHAKGCg2WmXBLQ2mKt7gHB8DH/oEiu8Z4g0U6HpdSoXmepe5ITxXeoC4jKSnhp1wNQUCez9evOnvrrVUvh9k3BKh2hNxdGp6RixiS9U0andKp4l6tRiqxGSi5WdjOWglkqEpYGIkRWydRTUdGPL9uEa2qjyKrAsAlTbbHbs+cMCppqw0hbMMXMoRozTc2gqVYNHizuKnjN5gbnzA3OxpRW4mReF/lSLnkHVU2i8SxPy8k2Lnc+8a7vtvbWH5TQLzfItecu59PkxVA9RzNfBia17A05qyUuS/NxflhybSTHJtnrhocldrlQREB9D7ZIaz25yWUOQ3MlbJdz1eEwWFzwpcXvAy14OLvG/aj3Wgpkb+P2ASBK9pU52ob06D5Tq3CXlYkHRLILPsFjMDnFs6HXeNHTX7NKHo2dzgupODvpA7ui4E2qV4NgbRHTHz2Gc/t7qbbnP3KWnIm869wSIUshq6yl0jYPff13X+dtcELnzS+gU6uS9U5X99TO6dltcJne2rQf9lQuMjAqIC4enHoeyMuw939ZyVy5BK1KcBmgRTHfkibLXR+vrsmWIkJpqaVwXmOdexv4XgCC/EQ7KbuuVonOLchWiVnbtj/iefz+977F3/mdb/B/qKCg7UVWMVkXsIfj/HxbVtCkZ3zw4Gr748/wlc98DZ87YHAGBb/Oz186GtYyfxFB68RrN2zlFFwSQdZ/Y/1LAf7Z974FsFqFPqVecMV8wZ1A0kJQJTkIxaoHzgUkmuQQt0CcKTk+AAgs00MS2qVHOtQTmVtJWKtUrV0lm7Occ+Y2V+1TS+3tFd+dB4wL1XGMNXBq7QO23twleadazK4v5eJEZb3xBTBf+84P5GLZ6bEofTyDA7MvdXiXCa4Q/fmu50qObCfx0NzMqt95uPiv9fW9s8XLfQYHdwV7j2ZKW1TbKS7XRbuh/La4H6a0/vm0JOZ4fh4tB773uvaRM+5Bq0EbOLjs/jnB+YFSQYEx6dsp5+Kkvb7SR7yBi/bBA+Ol2s65/M76G88niLpJkj1oQHSy3vslUbaOtfXZ2iB/AAikHyh+INeSets020bdNmez861EsLAg/QjT+AAgrA/p0ve//o63iH7uomIgbn2Ol++V9W5feAS8Fa6UqmV0O4FfLLUXnIkHfhYVHCT4kaDg8AhMzqky/lN+0J/vvLIJzqzB69riMPc/izyvGE5Birx1sl/f9xOHmnWMUjd3J/hk8ya4vM6jzgk56088D32dv+FiPjfXwc6JRRs70JZLsIy1RdpaCY+Cqi4MmqRk8P0KglUc6Hl9sjF8AQrSfAYHF4DgshK0XnIhWXXeqmS+gsi2Jf4oFUoDBJd+FY9Ip5fjofFLClY9+CzFWzvo/J3f+Qb/wb/5Tf7kT311XS1KsfecBbQemDLGu7gc500OelktuBwNl/vj3/1P/y6fdX2ugMH6pv/KP6jZB6sZ7UW6olUNRN6uKMHDzzVQ0D71B9/5kH/3v/wlwJz8wMZbk/xdbgIO1n5hzNlIRM42yKAWeCTqkWSLMtkIMmZ9Gh4ChFZBWElhZd0UVmObdlqs1YNSmcvNJe2Bn3pdBAusp0pR/9ZCQrE8e0vbq1nl06mWfWsFQ+vJcD1BdrbRVMtZB/R+IBUl5cTilTEKk6tZ6UGJSdcNuV3W55S1StDKk0NQeu+q85rW00rt0VZmcpsc9t9TXdH69i4+5rqgtnZC68s2Fndb6O/nWPXe8YErXFswe6/sen92g+OiKiltqp7BQSnVdrqV/vWCk1JPVE+VXFcgJudKQVF3lkGV83u6JNhKfR0i1lJxzq1eC0VtHEisEsX+YouMF9yWVk7tN9BtKGFD6TbWZ/cDS628tLKp4qy91pmErAXYMPtzpakpMxr4aItyAx6uA9+vbn7tlLZ6BOQf916lEm+9MfzzBShqVYTPus+1YlBqRazdZ7MWL+vHy7bT+KjC1Ay9DlN8SwrY+vBj8FwNBfD1RC6ElHDiVp6Kwirp+zEFszNrn7JuGl5aOFqt2hWtZkV+zfHKFYioSpVYVqAiso7xIZz/6+pc7p0weKVXauyx4OKILCMyn8xxMMcHcd2X6wh1HdF+Q259/ubk+LhcDvZ9VYIoy3T++TGuoPqtNa2N4VJAsxldYUZwb62rT8hS8+Nq1YU0tPFLVr+IR7ykx/uD1CqoVNvokuxN/vt/7h9YpQDbq1qRqL3spxa0ptJoS8wZPMEKCv7wd2u2wq//2Gygzw0weAsUrOYfZSVWaW2gtqpBM+OhiHlyX1yXlQI4g4L/7Z/7NX75t/7SA0AQ6+RphLT2fd4Jg88W6FEcOQvJ2X8+C8F5XDAzE9KCuGgf44xoQPSsTafFwuaW+lbs/fnKznbhfLpZ30ReHdNKXUnEBQi9MZpaTy+bdC+39wSok3M5u6L9dLyljCMlTkgu66lVak8ZbydFCR10RvYpOeEQeu1YVOk9DN4xxsLglTm4usGyuqt5J2/5GPh2CvGObe/Zdo5NcHRqSYvBm4dB76ys2RY25IzIRVqWQfv7ozKcnJ95qx6tRkwxcVwMFLw5ztXG+Zy01kBL7hoMaMZS1jsWyZWZ7Wpad0WSTjBL4KpmcW7laqze+a1PDqyAQtui6UBMbRELtIjrS3JTAwjtPaq0ltfZTtbMmAJEa2vIetq3kzblfNKRYBt2CUMFBXvysLdy+WQ5GDHVnnYdJuZOB50KnesZhgGttZ0zCe3idzSSV2dgw0BBDQPyvQGCzGrrvRoHfdZ7lbOLp4rDO4dooZSIpISREts9vrjXlRB3WZm4ZJefwcHb6aFTqoFEsayg4DRHjvOFmdfF+Nl29ntVWO9VTGrY5WJHuGghrx/PVYKyjmNrN1a/lVIsCbEosUAohQFzEM2Bt645KeEJH4NWsWvgfAiOwVeQ7x29N/Jhr+DiBNMBmY8QxzWhs4WYrXyo+vyLCuJ7SpzR7bURal1Y16eUaltEau5CA3dxhjhRlmZpvKzI6awCa5wRq6Q92EljXHdU0QoMfHjAacmuKQy6Cg79ChJTOas9VoVHLmsLaYxprQIhnMmZIjWEzwBCqgDhT375q6Rczq3SCg4eadKqN8T5ILQeiD4TFHyD3/gr3/yxoAA+R8BgbR985c+eP3lBzNFWVm03re1rbb5dLCalIvNcP//Pvvct/t3/8pf49//cP+B/9FPnn9+qBE3605LJmg5VlepNrkZ0C85YpZjzXoFq+lGtNaEi3qn+Z30u8X71P+CiF9tQ7YNEt/ZmYjzzEubpXJZ2HunMsx/nK4AyPWw7KTekGrQu3DlSxiPl/pZ8vDPE377/ko1+gfoldMj2ymx01NFtOrpi7FyLTRbmZJtpym49efuUienMefByXpD64OppxUDB4JVtVz/nHYO3r1v1zDT75nVJXSvwl4sqnEHjOrEuUEIqEJOVh+/HhTenhfsxrpPdO2EIbgUzqkrnM3PtyTYW+pQLUi18JddflqBoKxdaSqWrhli2WdUy6OOT7IWVavNaaLK01r8usDKcH3O8nAiSjQvTVCNOTU6p6s1BsmQrzYa+svbr0lRbXiVsoN+T+z2HWDgtmVMsHObIaUlrK8bV57EJjl3n2fhMQtkNeyNOler/FhcDyXXySDDHOGp0cO425Grac0noWit2pbz1PhUbP0055IvgpFSPAMFrOIPjJ0i1l+FHGdaU0NWw6IKTklqFqfJT2mFhTrbBTjUX5DilB14BbQwtyeNUahWNByTB1WwMHiz+l++1jvBadj57I1CkZkAUcMpA4e1t5jwPQlSzKG+thYu56Kvapg+ObefWisHgrVoQtEUcg55OyHxAxnvK8W51GSwr2DR1yqrEAkro0Hhlhk613Vn0LL9st0SxyoeUXIORRsrpfg0AW4mvXW/rkbq31scVDIicPWMuPUH6obYLhgegtLWQmiHn2nbMMGfjJZ2WzP2cONaKUcOcxiWxuV7UzO/cZ1QBzg/3ovkoDz/fsM1ltUA4t88ouaYw/uSgAD5HwGAtjzxl4VqrB8I5xIPKtr208G3lpMs2wz/73rf4X/zjb/C//ze/yf/wS3/2QX9IBNPZA3PMvDme+OHLV7y6PTDNEe+ETVCuOs++91z3nk1QOrXTbfCe4GuZVRUVW6B77wgkehKdKL6oSRqzRX+eTiPH+3vmZSFXYkxx3UrAsnLGbGzy0z35dIDpZJNQFek36P4GuX5B2dyQN88ZizLGbFbPQO+FrVf0+Ao9fEL+5I9IL3/AcnuHAtc3Nzx/7x38dl95DkKMkWmaOJ1OLClT+i16/Ry9fg//7H3c9bsU11XmvqOLhS5ZTzP3DqdST1F5LWs6pfYxjeR0BgFnMND+HmrWuhM7CQuW9hdjYk5pDQEKTum7jr7rzK+/9eUUtDS3x0fZ8cVOfbenhY9eHzjej1CErguEzldQ4PFeWWo4VO+1ysQKnVZ/hiyUKkNbZWFZMH++GnhVFwKnghNvKK1kVoZ5gRyztWbyOcHSTtGFlHM1tknMMZGS+d5TCqLK0Hf0XSB4Z1UKbfLaRpT1+LAhzTOnuyPHVx+z3L4ijUcb9/0A22vKcEXePueYPbdz5s2UeDNF7qvF7nhxIh6c9dD3nXLTe256x3WnbDWix1fIeAfHW8o02nMftoTr52yfvcfmxRUubIh4lnj2lUhtY86JJSameWGcZlvkpfrnO6XzzoieavPMnPfKmgESVna9ricvW2mN5Wc23OncokEq6DoniDZAbXQKe9amWrGxM9f2wZxKBZiRZbb/5nkBKRz3A16F/RDIw3mdaUDAqh7V0Ie6l5RCzpl5WRiniSVZCV69R6tltmAguYiAFqxWY20+03IIXtOaL9DVsbukQkzpwVzUyiWwlp5xCzqndSyZcdh8PPHx7SfE1x+Rbz8m375CpiPBKZvNhr7v8b5yOlIkHu95/fGnvHnzhgyE6yvciy+i736JvHvXxljMTNFArpd6uJCMnl4hpzeU25fk+zeU6QQ522Gl36CbHbLZQzeA687rY05IMktkTQtdCGz3ezYbxXmTyRooUuYYmSgsRKZ4W4G3kHI2g6eYWFIkRguIOy2Z2ylyP0Xu5shpMV+IvvM8v97xhRfPuNlucV3lfz2WFdS5//izK/iTcwV8rRKsBO2LSsG/JCiAzxEweOtNv0UgaNpUedBSyJguVKlxn+WsXvivvvct/s4//gb/wZ//Jn/qp75qP7bedq/mpuZqDGgpmXEcefnqDd/76FPmmOmcsh888+CJgyf3ntE70/MqeOdx3uPU1a6znWo2neNq07PvPVst9CL4Yq99LsJhmnl9OHEaRxvwvlAC5yTAnMxJcZngeCTdvjbEPp8AkG6DXh/RJZO3mbxT7ufMYcks2YDR4IR959jEA3r3mvTxD4k//D7TqzuIGflKYbff47oeKSYFTNPI6e6WNy8/5TROJPXozbt07490Ca77PTJ4nCi9CqMXhqy16icE9zYpq5H6GrlwqIvRcAkQgq6nUrNEbajZFuicE2lZmOtpIjlLYgyukpqKnRqcOGjKJRVcVVK0ebukzGFcuL898ublPaqO/dXOxpRTFpdX4uI66sq56mAfbUMTJ0gGtJbbS6EFuAj1lO2sLOuUlSmRiy3W1qO2jT/WknZKzVCnBuAskXGeWZZEzIlcCsF7hmFgv92y3fQEX/DO4aqSpvnLd9i4fn2cuH/1hvnNS0ufAzNymQp5qyyT5/UUeXVKfHSYeT0uvBkT92NkXBIpZ5xalWc/eG4Gx7Mh8P6u4/nG8az3hPGAHt/A8c3F7xjpkjJ1V5Rn4GNhJq193AaG7F5EjqeJ++ORcRxZYqxMf0cIjqHr6II3oKBqQKCpYVRW4BC8kemaIVYpzZzI7ud6Yq0tu1Js/Wi2tumiH//4+bc/52xZBSllljlxOs7c3x3IOZFz5rDtz9wDsQjh5v1vKoB61i/NS6SQU2KZRsbTSEwJcZ6u9LWVopXCYRtIXIGvAe6mpFlVBF7sgLD6d7gn52PjFQze5Lm91upczsRp5PbTj5g//g7zR98nv/kElyOboYcX7+CvrtF+sPmZIvNp4vbT1/zgO98Dr/TPr/BJcG5Djj2ng52+x2RzJaiwC8q+U/TwCj2+JL/8Ifn21YM1TrZXuCSQA2VWStDz+pgXWx/jCPPIZhisWjHsLC66KDELUykcc+Z+mrk7TZzmVN0K7X6mnEgxElMkZ1gyjDFxO0XejJHXY+R+jMzJVFglvcPNdsP1ZkBwtbUla9x921vsTdiHBgJbBcw+yvlzb7UQKij4ww/5+m9+g9/4y9/k57/yVf441+cGGKwlooYIH1+1Tyu1rYDaOuwqQNCGCGrJ7Q++b5KRv/fnv8mf/vLZMbFN9IboBCErBC10Tth2gef7DfOy0DnhqvM8GwLPN8GIOd56SxZ0Y8S9Vv4tpaBSaknU5GMEIUkh2PGEmCG6gG72+LAluZ4UNpRuaxbD1E1lc41O94gz4FGcIx/M91tCh3rjMIjvSAXmUpgzjEu9d53Dx8LQ72Dcov0W7Xtcd6LbBLrtgB+61aNfCrjQ0fc9282GUgpLjcJ1JZvMJpuzntRFqFclusJQWLXQ8VFEr2p1E3SszGcDBZUB3ZQJdeE03/RS20VWQnVibZrQIn2lGHhIsY6JNgMdzgU6LxAhqpwZ2BWRz0vkeBg53p9wGuhCIARP6M6l2RYs0yZ8Q/Jgr8/Go1EdSjEia0mRZYmktFgbxwl95+m8Jzi3Es1yrifPuDDOC+NsRMh5SVayjqmeUC0IZknp3E4AckpWfkeYUiaEQHBq1rvOFl1Xy/pMI3N2lG6D212RqxOjDhtyGJDQk4oYsbRxesQhkgnVLtd40xB8M3Syqtb5+4Qu9EgY0E00aaAqOhihcSyel3ejhY75QEIr49sW5SVllnpaHk8jcZ5scwQWYJqEyRmZrKvVueCdbWzB+uSp85QuUIk3aCvkFCr4ikyzkU2LgHP2zKX655f6PNvzXepf2pi+HA/tyikTl8Q8zpzuJ1JeCL1jXq7W71sVACrGzfCC42w0ZstaQVLGiZ1IuxJqtcDjfG1FiqyWw64ZfanFrgt6TiJ0RrDtNDPnbAqJkh94T8E5vbBrbYXaMxcByeZk6bB5H6RQvCOIsq3VAhc6a0vVtp4fOvrtwOZqx5wWtO/RfgthS+l3jFNhTHCcK+E7OEJtmznfIerNrKrrafwY3V0hV89h98yqov2eWGQlcAYnyHzELSdcP6FeiS4wZUiLbayLFqZSuFsyd8eZQx0DjV7lVVBVXJ1DZPM3MdDkcKJrlsScilUlumCqKi01GtrAuOo5vv4RLjgLkGi8IFnBXiMxnqtIT4CCn7kABaucVy5+w9vX5wgYPH1d2puWKoYVcWcyolqPFUy7KwX+6fe/xd+ujlN/6qfPN7XUky1YOSs7AwbibLJttXDTKbINkB2bYGXTbWdleTt91laFCEvJzFmqHamhdEoheCHFREqRvOnZSMKXBU0LaUnM2RH9huQdKew4pcLxFJmqq5oTITjY+z391WCTp+txmz0lzjY4tldkbxKw5p1+mhPHGNcytxNP55TdcIU+fx+dJzrn2Xpl9947uN01OmxWgxDxgQ4YMkRRyhIpdfHMKbEsC9pFwNnJzQl9trO9d/LAMAjOhLFmGuSlVgycLUq2oFmvMVxswFof1vl/dqLPRgWEUiVqkQtuSZ19acE742wUlFwy+95xN1nlYJlnxtPI6TjhfWIYO7b7DSKyyrTOcq7mbaDrYiq1bF/rwNYCiAuncWQ8nZiXBaXQec+mD+yGntR5O8liwGCeF8Z55nCaOIwT4zybSqKSnEqV0pRa3xIRfF2Fc0zEnDmmyDSOZsFdNdsGphKhZFxZCHmhA5wbwG+ht7Gfapxv8X3tkZs0t/fCdacMLpCyf+tZurqZGDi29x9LMTJhGEzKuDGAZWNzYIowv3zNogeSBJbqElmom2J1U0xxIS8LJUfC5fNPhWmxeTqJfTRr6kqe6zrS0NspoSsIwWKksWrEPEcO48RpWphjJCN0ITBsNgzDgPOh9nbt/cVy3jib06d3502+jY9mtjaNM+M4EmOk3waWecbVUvm+dwxO2LQDRZqNnFy5EGfbcCAnvALiUKf2nxgwb0MbaSZellK4CGuloMkVB99aiuVJM6b2LL2zA07vzs6G5Gwn52Uhp2QbtHNI39u9vrqh213hN1vUV+5GSjiU7XsT2zhBzOj1O8jz98nDFcdYOMXM3RQ5LqlaAUOnjsllvOvAD8juBq2HQvEdbPeUzQ1p85ypeO7HxJJY18feCdswsNluYDkQSUxZKXPClYUchShwKo7b02TE0WjqAur7L1WJEdTWbAt7guSVKcEmBJ4NnjeTtRNQre2zwlCrsla9au0iqff3IZegtbdWbpQ0btq5hbBu8xeg4B/+wq/x81/5ubeVGT/B9bkGBisoaEmFNHCQqvsda0sB7DTw+9/5Fr/y27/Ef/gXv8mf+em3ezLNBaxtQCoZJNNrYe/h/a3jmRvQkuk97ILDL0d0OlrpquTKtt7hhiuKKEuxcuBcSSopq/WebGsiB4dPC5qT0QckkIIju577OXE/1Z7WHJmj9XS3QRmHwj4oV7v38WGLdHeWQw7mztXvbHGfUjVGKWtPfJTEcTEb09Dv6XYJ90XB7a8Zgqd//z3C1ZVlM7T7HRe6rif7jugDeZyJoad0PRFlWhYYR8LgwAVbMIudMGOxPvz6s7ShYamGKRfWqmL9zqDVZU2s/N1c9HKVdlqQkAEP2sQSMCO8RCoRzax8hLPRSY/ziU3YkFFidlz3jn0QvNj3FWKt6pjrZAjKpnNsOs/V4Fdy5CUPoslX15IgkHNimmcOhyOH45EUDRgswVNSV21vO7L3Zrud7OvHcWQcR+ZxZl6sv5lLtsoNTXfe1BlAfbYZSCTSkjlOE0uBOZnELlbJXq+wc8Iu2H/7sDEnRucgVzfGMJDFIWI+HfveNpWybWDkojrCmRhoELxybGr5NIviwmCAJs5WpvemeLifFg7LzGE5ckiFKUMRxaurANEkkL1CwHIGbF7aWMqrQiOvJeCcjDAsOaE54Ug4acyBgnOu9o4jp3HmeBo5TnafM8LoAykXVJVBbfNrZOW++uzbqLKPa7uhlJVncAhqilTMgbEQSSXiJbEPUsebHS42XtDltEYHN7+FUp00jT5SGfhV+WlOf5GC3QDB+CVaLdjFBbwISx0vi1B9DQyULMl8Raaal9CslC/np688Dm8MR1KKLOMIy0LEiH9ud4XvOoahY7i6ptvtcN2wpl0ChO1CH7YMumFZIuyfkbfvMIc9xwoIGrPfO2vfpLpOlb6n9Dt7Td3mvLYNV8Ruz92cuF+srH9crNXXeWHfeZbsSb3jargmpYmZREoGjrMuRKccl5njlCx9sqo07HTuUIUgwkBGTneU+QBxwYkSQs82bIlXW24GZYqQRel6x95Dr4UgmSDWzhSwqjW2pr21l2HtA+rhx120EM6HoXQBCv4+P/+Vn1v3wcdW4T/u+pwBg7rTf9ZV8lvgwNi69r2//70P+bfXFKsKCi4YoypYyRQIYhMmi7UoMpGtZrQ3eZMrBRnv0MMtHF+TD3fkZUbUodsdcvWCUjL99jmTVNIZQiYbqSWbc9a0FIIYu1wyCEpWR3Yd45w4xcJxybw6GdFljAknwqEi/7TtiKWw9zv6661piottmiX0NI+edkoxMxX75JwKp1hQSey7azrfE3bP6XYD3YvnuKGzTa4UW4SypUqWfkccdqTjSEFZ3EASzzLOUBy9doTe5Hx9bRU0hcdZ81/1/dKkPeCkrL7olrduJx5JM2WaKHEiLbOdHovxQdSbVan4ztwCsZMmy0JOEwUrd3pHPWEpspwofkDizH64ogTh1AlXrvBio+x3nmXqUPV0G0folZuN53rwPN917DrPvnMM3hb2vvVina6W2daxMvOVtMwsy0RcZlKKq/Z8EZjVWPSSE1oJnss8M0/2XkuOuJJBC77YUPVOV76AUlnbFJNbqTOCVMyIwDInpskkdEu2+bHY8Z8gJgedRQlhoFkmo878C7Aku84rQTPaOYJYW02WsZa8zVGw1CrDkm0zygWkEgGLCFwu6kDxnX1tMiLnYU7cT1YVK0BQpXSOrvcMvaMX2HgruUs1MioIxTtybT80G+sCa4ur5Eha7D57AS2Z4q3aMS+ReZpZ5pm4LAa+qqRyWRxp6SghIPV+ZwxcD85ZOwYQyZh0tZb+d9Tv9xxHpds4+l1HyMJ+53mxUa5c4boT9kHYu4KebpHlhMSxSohz5ZrAUoQFteyGYM8nF+v1lzhDnMlxrqoMcD7ggnlC+NDjXMBjVbcoNoZybenlAptq1hXTWfHR5mkDfw4jco/zwnQaYZoR8Tg/4K9f0JEZtgP9dkfYbFBfY8i9J4udtLurma6/QQ8ji9swqx16TtE8AcDUDr7yi9reGTP4blurwMUkjmFgSsL9FDnMhU+OM3dTXK3MB+9YklUinVOCFly3IaaZXFKlbxRSsdTJuBKic9091MirUuhV4PgGOb6i3L0kHw+2DoYOt7vCbZ8RhmvK5ookDgnCoJmeSMATRG2ONkkB1l5YMdij/WeNvV8rBp8FCi7UeW9dn91CaNfnDBg8ca1Vg5UlcAYHUla+wbf+xYf8zd/6RX71F36df+0rX1vNUppX/3rKa7I6aWXqTC3QkSUi8Uga7yinW7h/RXrzibFlxyMlZaTrcPtn9jPVI76ndwOzk0rC01VuIsXKjTHVAVmAGpazZCzFLdfTXjG9fUyFWMGREPEiQCDnxMYrwW2spE1jUVd9M6z9xpgLc9UozZUItaTCEAI3uyt45xn5ekcOipZEyql6lCc0L/hhR9fvGP09ZUok7ch+4DjNSFaKm1Df4UOwHtzF42rcjVYe81r7amJa+MYmD2KAQOaFkmbKfCJNI8t4YFkWm8wuIF3jR2TEBUrO5LiQpxmZD8xxpisLgYwnm+FP9UKXYQfLiaHbs5PC+wP87E3H/FPXfG8TiEXZ77a8d9PzhZue5/uO696zDY5t589ciLqQGdHI/iwFcm4ciCbpA8l22nW1V1hStLJm28RSYplniBlXMh5bHIwMVXvZJZrhSxzR6lhpfTAFP+D6HX2/5xgtXEqKQk6M0caEV8GEXWX1tvAuGPNNbNzmCuZUhMEXezbzATm8Rk5vyK8+MmlrjOA9ur3CPX8ft7mh3z4jdzuSnWON+yAO7baUHG3uiZpuPdvr8PV15WIVicHDdadcdcLew9aLcWqmgxHKqjIBF8i+N3fJ0JOdr8FbZ+mfqxkOy2iAIjln77EFLFWOjK+pgu7RMwtiPeIiVqZvToHeOUK1+Q7OxnCopFavBfKCz1u2A3jJfPn5hp+96Xh/gJ0khnyCu3tkPlDGA3mZyctMSomIsqDMEki+o3RiG1ZR82dIC3k6kacjZT6iabEqTQgw7HD9gKQN6jqCD3jXsaic1S2wGkfF7Cj+LGV+KlExLpFxmjmdzNJ96wfodvgc8L2ju9rjtxurMKrp/3N1rsziyH0Grlh4zZvTyDiaH8acMoqw9b4CgzPfwjtbH5N6pDODtpgLy1I4RQMVr08L9xUUTPUUtNQKpVXJbB1dssmzS23n5rrullrmUuzZFc7Ps3cg8xEZ78h3L0kvPyLdv6bMM+IUGbbo/gZ38y7sn+M31zi5YpDeFGd1zVnbBHW/DiqPchTK+jWt5fAAFFAegoKf/jlWA5HVz+ePd33+gcHjqxQoaeUoijp+79vf4pd+01IYDRRYW4HWd714KA1sdVLIJZGybUpyuof7V+jhFeV4S3zzKen2Jfn2FfF4Is/mPeA31dlwPOL2EywjnR/onTXPtD18tROyEzUkXExa2TISWuSmU+i9sg3GHg7uzGjuvJIxmZ2KmlQvN3lWG2BCJxZyklzLBzBgsBQz6xhjZgSm7EiS6WZhqxv8MBAo1p5IkZJriImL5poSHVkWMo7jkq0MXGZ0mRlyQvArOauVn9sppKHjVi1Y+QM5mdNZdWO0fPUJpiPp/o7xcMc4jiSUHAYYFiSCJiGVCbCNV6YZxgmZbjkdbwlxosvRuAGhQztjKJftFbHf0/k9zzvPf/fFlo0Xvvx8x4ISup6r3YaroWfbB3a9Z+NN3z1UiVzr466l/TqOShakOHLniX1A00JUW5icKp0zAJqXiWWp1YW4EJeFnKzq4wuUlZmYkMXsq3U5wXyA4z15PFgglyoybHD7Z8hwxW7zHN/3Vf4lTJWNfl74rLTsxBZErb4JiZoBgfWwg4g52x1eUl5+n+WP/ltOf/Qxp0/f2O/1ns07N2y+9B7+S/8dtGSrnHVblvpzUmN6i0ekbsACwRVyUXIHnTPQ6p3Qe2XjjZnepwm9fYWOd7YwjycLQvIeHXbodg/djpw2iO/NyKnmQEiJRjmJE9EpEmeyD4iz6G0v1q6gqIWUiuAr/2PTeZMW+2qMU1s1uQhZjdXvxNE7pffJnAFDonfWqrnphS9deZZ5SyDzzsbzU9cbnnfQnV4R7+5huice78jjgTzPxGW2Kop6Ft+Tt9eU/hpKR8GTx1TnfyGPR8rpDYz36DLiyGYstRtx+yurJIUeUkB8T1f9/rPq6mly6YvRuEdF2/OvVa9Sakts4jTPiKELtt2WTA+DgRH6DaWZoK2ugR0LwjGNvJ6PvJwy91NhivY+vAq73tqFna+qGXmYk7JGS+dziNWcbd3LFFMDYEZpYK6M2+qJYoof4+OsaaS0zbj6fEiuFUV7vkExu2cBlhHSRB6P5NOBfDoST9au1dOEn8zUyc0j7mZG8wyaEck4zTjdmLmYtBrTQ2DQ1v229cgjUCAUPvgXv2ug4C/9qoGCfwXX5xMYiPUHSjNpaddlElltK/zuH36Lr/+jX+LXa+BSzpAveqNtEW99nJRtgy/TwZjBiwED6mlF5iP5eEs5HWCeoWTUWUnTBWcynb712JqmNtKpg6rDF+wEYuxV63kJrUErFRDUfmCx06d3wk3vjSxU36ZqY0bbhIgFSjK9fI9aVolScxAiXUlG7PIdMQSOS+IoxYw65sTdnDhlJZwWhlOkuMSmc3RuQ/AFyQs5RbKPgKdEhTJZHzwl5mz9rhgjORtzPLizDrexbRsiXpPbhGr8ZLbRKyCoGQ55OpEPdyy3b5jevGY8nYi+g+01mQC6MM/3xCZLVNg4QVLBTQscj0y3n+LmEz5nnPM4p/jNYFa/2yvK/gXd9jk/tXuHfbfjlGDGFBld1xG8YwieIXjzUmiyuApsvGvPTVZTraJKJw5fOjT1+JJYZrFSZMnWJ15i7Y/n6mNhxK6SjU/QevZQOTXVupr5QDncku9fU05HSzSsipRyvMPdvItLkWFzQ+ivKjDQeiKXc0qemmlNU/y03IvUyI1i4EXTgsSR+OYTxh9+wt13P+L48R3LMRK2nniaEBU22yvC9QtIS+0Vyxnk1nln1aGMc9Y395orKLDfqSL0Xtg4wU136OkN3L8kvvmEfDAjHUTtvS6TARGa4Cg/6rna7y6C6dZzfGCQU8T8GTdOKN4j6ghdx27Tsxs6+s7Z6VBkVZqkbLTPmCCpcQC8OjZOuMqOMShjryxbz7wPNveIbBxcB8Hf/YB4fMXh/iVyvEPmE/E0klImpUhUJXUbyvU74DYkb/4Yp/tj5RsUvBS6EmGc0OM9HG/xcUY2G0Ja8CR8SWiJZtqTo7kM+oi6gK9GP7FQNfsX6YDZXC1bYukSDRjEaFyXNja9c/TDQBl62OzIw1BjtD1ZA0sR5pw5zYlXp8inp4WPDwuHccaBGSh5ZeuFbXD4Ul1h6zpFC7FKdo+nqqRo0uAhKN559h0P1kQjMyuDM3v1UGWsa7JIqYTNkghqZCZNpbprGljotK5JxWpP4gPaD5RlwgNpSeYeWzLMM+V0IIeA9x6ZOugCJYiVCPNCUYfW2O+grNbslHPeSduPVM9kww/+8HcfVQou9rg2wtdslZ+8cvA5BAaPeAZPyRfrw/zgOx/y9d/65dX8oQCs5ZnzT2s9HEkLVJ0s8xHJGVlmXJzQvKBVWyzqkX5jfb1hg8YFr2qVgn4w043dtTnHqUNKJjiP+bPZwtw2FCkRzdaukNqPI0e8esRbD35wsE+2YJfapS+FtWSaSiGl813JGZIUesQ21+neyrDRTtTFeVy/x29foGK8g/s5cpgzU1nw9yPDbsT5QCpWSjYr1A7VQNFIyYr0hTQmMtH8Ii7eg5RydiisQOAs7WpArKwAgBTNtCla37pVCsoyUcYjjEfS4ZZ495plXkhhQPqduZHFzHGZmLKNh84JSxB2biCLN++Keaa8eUM+zVZXzBkXAtp5/H5Ar67R51/AvfNFnu3f453rd4lS/fyrlbHWnnkzDArtvVUpkltLe1BKro2oSJFM0mytqLKQ4mzAqZRaaq++7yVXUNBssR+ZoJRWmKcSKU0+SM5GzKxl8VyXFc0ZrbyQXbdl2/W24GInEcmLnf4S9rNCTxJZ0wGb9EsUBrVNU1xtDOVCXsxgKi+VAQj27zXoaY52smsS26K24GolIZaS6Wh8gkC5AEASR3Q8ItM95f4V+e4V+XBvtrtxoYUwmXxS10VRqGFK8nBNKJhEOLfApxZMVvvJRczO2oWOQZRBM0Ey3mh2CHJOOfW6nqzbhmpujVaq3zhH7oSUFLJS4oIvjnT7CfLmY+KnP2B+9UPy3S3xfiTPkbQstiN0Dh0CcuOq54onu4HDOHNcaj++Eki3QegyuJQqOBwJcSJ5tY1JbTxqiBZlnc2SHR8gWZpg1zIpVGr6aKuGQBKr9BSx+Wxpj/aMMkpCSOKRfkvptmsrJ2NjaKqWwYcp8ulh5KP7kZenhbgk9p214fZB2fuMO36ETPdVYowpWfo9RRTFr8mF1LHkxdj+TvxqFtbOe9aqNH6BZUaYRFfaRl8SZOMSoIqqx4tUwHcGzpKyjeWwQZ+9a+3H/Q1lGvEtQbJWn6TfmMV9KWixlquL0wpAUK1pqdjYdsGqCPJwP6pbVAUF/ze+/o+eAAWX11tg4MfzC+BzCQx+suuDb3/IX/vP/yb/8Bd+ja995avUwuhboMDqgy1lMJ4NNJbRkHE93amKmRX1g2mxN3u0yYlEbIFuUa11sq22tuqQnGqv3bT+ROMcm1Slcg9qmcukCctaWvfJssZLXixFsfaVLBe8J7qesbKh46P+oKQFnY9w95J499Kc53zA3byDf7awv/kSUzZWvTnaRV4dJvrbIz50ltdeXCWENfMoIVHfl7cF2oVqwRsCQ2cyyFC5Ak1t4JWLqOdztjrt3jdAEBezV02LWaDOI2U6WvUm2omvdGK9Sxc4zYm7JXNcrBy66Rw5e9zg2AxXEE9wvCMf71juj3Y6G2ea37obesL2Ff3dLf72Ff37t8h8x/b6XYK/wfc71OmqajB+RPVsKBlJbcOum2M9IbcYWF0mXDzh0oTGEykukBL5PAIrEd1yE1a6TMk2ri5WvNLK/gA7q1xk75HxSJnGSh5UW7SW6mVfMhpnqARNe40V5AJrDK5zqHaUatKzZHtPSQUNPWG4xj1/j+ELL4njDCrkOaKdZ/v+c4YvvIt7/h5puGZxPcfF+shQQZRX03PnCGlaY74LNglW0FIMOEgcDagvk3lkqCKd+R9IP1iPd3sFmz2EDdn350yRdtXWImLl8Zxz5RbZnGzLqFDs/UtCI7jocIug9KjqCgra/VPOMdYOqx6VSiorReuykomniWV6w3L7CeXVR4wffY/4+hOmT16xHCfSOF2Mww6vAyFscZs97Mx9cizK/ZSqy15CMVAgRY0bIiZHteAiqVXOkeJt3pITEhc0dOADJQdEZ0uvdMFyW5xHXSA4T0JwWaq7pqFDC0SzllhaqozO2fwv6kg4FiNhmDqjlvpPMfHyOPHD2yOvDhOnxdZBI+4K+wD+zQ8pr39AfPOp2Wb3A+7KDi0lbMx5tV5aDxhNeumTKTmkVm8bR0s0IAQUZ8TFwgqqVaQ6jEYkQmYGtCbQ1qNixrIzwobiOqTfwea5racp1mqCgXsbvtXzoO9x3ts4L9mSJhcbu62KJTU/oriA1pTUy1VbgA++/UEFBb/Gz//0v/Y0KPj/4vr8AgNRm/BPXB985/cMFPylXzVJR8mWZdBY1+0qtvE37fCDisFSrTftl6EuIP0WkZ7Sm2VmUUfWsPq55+qUZt9xLpl7AUrdCFNcWweiilbEqt7sTW0pFnJKdnpeRnQ6wXIiH+/Jp3s7Gaqiww72N8hwg9s+N2lYfJQJXowElY93pFcfE+8PAIT71/gUcerZ797n1JVVj3s/zry+P7LpOiMybnu7Xb5VOWq/2Af6YUsRR4i2wHvv2QwDQ+dX7XRTGLRKgDQOQaqhUnlZMyLKUvkM0aSJJS0WMJVqrgCG4ptNdPIDp1PidozczomMsivmOtllod9cG2HyHTuVe4Qsr8kC8TSZjPRghMZ5WRjGE+l0oD/cIu/c4p+9B9sr24RcWFs9UIlL7Xmv4mQ5g/am5lgWynSyysc0Vj95k/HZYmHRs7YxWqXBfmjri2pN/LsoH6Zo93G4QvfP0ThRToeVTNdiZesX2yab5grsylrZEMSyCkqmiEP6QC6ZmBJTtFeyqBEprzcvCC8KwXmu9s/YffkT8jwbX+PZu/j3vky++SLL5gW3U+JUw7MEEC/VF0RhsdCuFvXdfBnWHrCxZtf5LaFH9w52V+v7ks2O7Pvqt2DpjOViA5E2t6vVNMjKKyrVPt2IxtZ60JIpyWJ9kULxQnFCKUaubODMbp/xREou63y1caGrx0ZOC4xHyvGO+Ppjxk8/YvrkB8xvXjK+uiMeJ+I822HFKX7TobsB/+wZ+vx9/DtfIm2fkzfXnKbMKcHrKXGYIkomJkcQz3YzoM7ItEw2fkyvuayZJ5ISxXuTirbkzJqWWtSyKmQ90FhsvFPPQo2nK8LQedJ2wCnEGBGg846+71FvKZgxFauOFZhquNCb48TLuyOv74/cjzMxJ7bBXFf3weHuPqJ8+l3iD/6Q5fYOAL/fIS6g+xu7l+ssM8zbe2HrQI+fouMbuH9DHg/V0c6jm73xTsLGTvJhsJ/nHA1Wm8Iqk7NJn7XJn1sLw3mQwKKOJJC1UOrwsnCjslZCtR7YVKrjaedsrVj5WfbCmwm7pMmeU8m0oCa5mNsWGPj11afgbZffJ67//5Yrtqu2E8ScANpCDXquFPzF/+RR4NK6Va5/l+apnXPtbS/kCgzS8WAP0zmKeAv9GHrolBILx3HhOJ/ZsEuycCWpiNac+4TBm4GJLwWSmRiRl+rb7vHB40LAhQGcxa/m6nqWK+mP5Uh68ynp1cek+zfk2cgvbrtFn72Pe//LoJ6uv2GWZidr1YPiOxvsQJlnlrsjOSbyvCC+x+9uGDY3bIJn33mmFIlx4f44ctsfLV3Nmfe6iFaJnCFkHwI7VbquMze6ipq7EOiCqxwKwVNJc/W9r/nqa3VgWgNYqHkHVGMbW+TsGTnncd2AJqDfkrstUyqMqazSJxRCcYw49hKYEbqbL1Ccw/uA7K7g6lP8/R1pOpJPJ1JMlJgoCvNpJJdPSXGxE/h0Ql+8h4tXdqpWrRtXugizkVqSrG6X7XRZbAPP80waD6TpQJpncipk9WS1TWoN8mlDs1onA1ZxEiXh1tAeKwp1qO8I3bX1LOOEXFk0rdR2zHnct6tq3tfUu7y2ugo1UCgM1vIqZn4zRpPOjVX7vu+fs/3CNe7mC/jpcJYr9jvicMOxOO6OkcNiUcSmalBKsTM2OVp6Z5yRONnryMk2fB8e9ktFoQ8w7Gtlw1N8ILt+DbxZMizVpId0PlE69ThN6xw3bKDN9KFW5oodCEpNXsyRlCNJEslBdlBKR0n+DPZyplR741Z9sMoBtbpDpfrPpMMd08uPOX76MadXH7Pc3xLHyUK1th399QbnHbrZ4Potur/C37yDu36HsnuO7t9ljtYWHCnMxTHlWMvgiV3nmFIhNGXOPJqZjjPdPNEcvnKKSPKWfChqXIoWhBYshKi4gLgOvCmQigsEF2qQm1CCQ7cb+uDX9+2dWVI7ZxWLVM7VgiVmxilyexy5PRy5P46kuNCreQxsgjDkCTm+In7yA6ZPXrIcRtQ7XFfbVY0PVXMzSqH2/xUd36DHl6SPvkd+/RHpaDkf2nW4/Q08fw938059nwV1rp7kq8toSiSBVF1AczKuj2iww10NdjrFzBiLrTM1QK+Us3OlZbsEtt2G/RDAC6KZgiXX5lzTPXMmt1ZUC8lDKZIqB8b2szX74C9/cz3Uvr39nefIOl/WffEnuz6nwODp62Gl4Amd58UieQYFaS1nkxeWk52op8Ot6XH7geQd2QeK27DMids4cRfh02PizbSYa9ZiVrVOYOOVm8Hz7rYz/wDHuvAQZwQjLDoVfNfTDVu021RgIMSYrBSYE8WZt3yJC3kaWe4PLHcGXvxxpBdnnIZuj9/cAFRXN0AyvQtov0e3V+gwoP6eOE7E00w+3ZFvX6L7F+yH97juioXj5EKJzWTHM/aO3leTIYzxKCoEMce+LpzL3+ZPoIRmo5otT73dX8tVNy5BmU4GCubaNmhAAFY2UamLEKqI97hhixfHMuyg3zJHYU6QxIEXQjD71W7o0S5AECYSfv+eMdf7Z3DzJXQ+ocuJPI348XAOoppHMoU4z0x3b/De2akgLfhuQJyrLO1MqVUMEaueuBBwXY86d/ZUiIk8T6RlIsWFlLL1Z0UpaqmCRd2qaza/pgtgIObZb772MLYc+FqiV83mDOkCne/peqrW306Mkpdzq6xKIkmLAZhkmxxJQeNKAHXSrSTRmG1BPC6Z+ymxXxzb4Bj8c/r+BSom35tSYRwLx2XhfmotBPPOaMFADlYOCTlSKp/C3qY7x/Cqv/izs1Nt6CgaqsTNZLbLnGoKIsBZbTE4c8xU9SbtqwqFtpBe3mvJHom1CoAYATAupGUiz5aWWkqsa4YZlaV5Ii0LuSYJog6pHBQr6SfiPDLfvmZ685Lp7g1xnsnqkO2O8Gw4BwANO7QfIGyg25C7LWz30G2J4iE4c8rMjr4oUxGWZSZJvQ9ZoN8iyw6fFxxpNSUrJVNisY0qLvXUSn29ZoIknYED6TfgbR0soV+fkbqOTh1asyi64B7MdXPUlMqrwTgnySpO4zwxjifGcaTEmU4KgxOuO8feO3R8Sb59ST7d2XoUI37obJ3aXpnNsQTGFJkq6INKqJ6PlLvX5DefMn3yclUKhKuNVZjq5otziO/QbsB3A947M8dKiTwrs1CrlOXM7RExACgm4z5F80p4M0ZO0SoiKjVNtPfc9IF31FMiZvbUOXI6kWqyZJlGcpzPUfN5sSrCRftYFH732//koc3xJSj4l5Ak/qjrcwwMGgvT1Akffvtb/LX/y984EzXWvuxn3NBKQlorB8X68afb1wCMd7doP+DFkemIwTNlMQvPpLyaC68WeD0Xbk+Z07SQciaocD2YjO2qS+yCr258qfaWDCGKCL7v6YcN3fYKHbZm1BITMEN1Oiu5UJYF3Z+Qwy3ucEfyM2lebGFf7NRVpzylmO/BnDIpK0ri+fY5en3EvbijjxFxat+bsqWPzSf6rRGCYgncTgm04PJsRkFxIUdXeyJq0kLVeovPKPXMl6jEwjzX9slyjppOM3k8UeaTya2W2UrtaTFOR/2ZolbuLXXy4D1aBOd6vOtJYY90G3JM4Bxd5wiuY7PZsNv0bIaADw51kOLCNGdiDMyygzBAMHJkUMyKdrrHjbeU4y0c7wwgqJJibWtMo73VaItGbvcl2Wt2XQ9dZ6S60K1th7Vs38aiYich11HChjkLcclWbRQrU5rVrq4025but2RzMTwuFu87VxmXikkPN505MfZKZWNvCGGLl2In9GT/lRTX8ic5rn1zo38vdH1Pnyw0qlNlxKJlD7nweopm6BRqAJQ0yVtmXPLqpOfUeCuWzme5GZ0TmIxfAdjJ1VVuTm3LUSVuxp/piEXqCRSmGJmyhdicZvtd7f13Kmw7xxa3ekrYPTWSZsE8QWJV7eRiQMWr0oVNWxQqH0HPa0ROduNLIaXFfAZmawOkeTKvB6eoD2gIVpEopbaMIilGsipl2KGhh901sr0mDdfkfk9yHUumRh9bKTtE8+b3nWWeeAcb15Fch3Mdp9PJwLUzEqB0G3TY4x3GYzGZTLVlxqpwWMYB1FCvRpYOVjnQZbJgomFj79n3dW0s4DxerR8enAGDt6idBcg2jinVSyQusCy4PLPRAkG47r1JUDUh84m8zJSUcZ3Hbzq6Z9e4F1+Ea0tdvJsSx9kcGrtqUWx3qhgwW6YKcDOuC7guIBvzFyj9FfRXyObaqn0+oFVhpGTy6I2sGmdyXCoZ2Nbp4hLibf5NMXE/W7Lo7WiJp06VTS9MUsgKOhd8UPpozosqHmImzwvxeE+eRmKLfk4LaKgtblsfPvj2PzGi/JPZB2c1xeW+9lCN8JNXC+BzDQzO1wff+RZf/62/bj2ZhrSeknOsn3hUXl0X78I82ml8PNyjOeN9T/EbclGO0RLAjrGw4CkeXFBC74hFYFnW3lMLIbHgH2xRLrW0WRnVrtvgN1f43bX1kFMhq7BIYiqRJYKELXplZCZfTyfafUKeRhDjGVgJ1k6dsZJ+7pdYnRyN/Xpz/UV8yXhVdPiUPJ+slKhqJ4NlZBt2xGwWuAnoHHQs1pvMHsneCJPiqhHHmYzXLlPwRNt0qvywNFAQJ/J4Ip/uKdPJZIjjyXgELXlQXV1gTSqIiuUxIIgDj8fhUb8ji6IeuuK46gPa9WyHDX3nTYPuTM6aC0xl4c2UuT0uHMeJOSZEjFB53Xuuumuubp4z7I+40xvceIdPE37o8f1gCgawRTOlSqBboHIrmgrBark23sR5VM03IWQsXlsTQkD8wCliORY51zKpee9bUp459YGdkPIFeSpmWyjv52TksEbImiz6eFt9+INWr3sVguvp/ICS0LSc++m1lL/GjacZWUY2YTDpWm+WvzFXcmuy6oFMD7XmaQ0Js3tqJWPHvndsvcWTy2I8B4CiAZHOTvO+EnVdR3aBjLk3LkthyZmppkreT4ljtMjnubbuKEZu3XeOLjdlAmvMdMuIaDwgi3Q2h1MRCMUowNt+h0SHsuCDI/Sd+V1olUTnWDkx0QixdeOvox5RQVINQBYL3vF9j99d4V1HcT1puCJtbhjdlrs5cXefuZ2OLLU83XnHdui53npuOgv8UhfovVJ8tlTFEOg7R54nurKgHpsH/Q7nCp4eIRompRi/IFvKYcmx5k2kta0iLlRlVUT6xRRSQ22RVC6FlIJ4QdThnV/lgkAlWxaT2wrVifNcHe0ksnfQ9YpD6R1sg6KLtaFEFd3tLSK926DP3kHf+wrx+ou8mQtvpsTr0ZxO98Gz8ZXgKc4khMOOcDUT9iYT12fv4t/9EuXqfcrVu2Q/MMdqQywOX0GoOME7c8B084yMo/G/UiUVphknO7yw5ra4RjAUsXCyvseFQPGeBc8xFoZUOC4J75WUMzKNxNORfDqyLEvbgNZDKWQ++M7v89f+879poOBnv3beppocf93Lzn9+a0/7Y16fO2DQxmTDjR985/fWPOqvfeWr515iu56sHNQbLAV4eIOX2SR90zTj1JOGSImFHDPTUpgjRAT1nq0GXJfZDJklLiyTyRq3mnmx8WyCGW3IdFgZ2PaSHIR+de2LWLl4QVjILChjsupEnDMBx7D/AqHb4ocrys075MOdDeTqvpV8t0qnxpg4LolUjYxyCZSN59mzL+Nch796TjndU1JC+429/zjRh4HrzjHlTC5mDtKRcNm4EZo9DsXVU5nUk9n5eRTIyVi7qRIH42TAIs6k8UQZD6TTwYiU45E0HklLrGVJxXd26lLn1/aB1Px0Lw4vDpcczm2gOLx3DKq1jD4QukAXDBQENeb2Ugozyt1S+KP7iU/vTry8nxgreLrZBN7f93z5euD93cA7z/a4+ZaOme0Q2HTBSpDVa6Aw10XNkdTQfLMylWJZFnYi8wa8vGUD5C5S5kIpyjgnphwZk1nfQsFprqfbypwp5vUv6uicIwY7BW+yZ4yZ4KqdbS6VY1fbL8W873fefAkWFXwWZhWCc3jvCd0OR0KWyax102wKipLIyxFfEjf9Du8yXj29V16ezI/ewIzp31tYELCaJm2D8mLj1+jcnVeYD2bMVFIFfKaqEd9RQm+s9mwGTEvKdQMvVfZWOMTMWHkLU8rEqpBcUwqdsgn2OrtgkcGOZBuj2qgt2PyYc4v+FpIzYNWpZ9jfsJHMphOG4GsEsm10pSqTStXAayuNgckcVazXXsG2iiLdQN5cEceFhY7YXfPpKfLR3cT3bkc+up94c7JNbwieF/ued65AwsAGZec83nucyEpUDvXUPHtFo+I1g4LrNzhf8M78C6RUt1IxWagVP1Ktdiz2uQIueNwyo4uBAleBlA6yzmqb34I4I1u6Cya9inkMSC1vZTKaI5oWXF7oigGDJA6VTK9KL8nWBRFTlbiaZLnZU3YviFfv8XoRXp4inxwWTjHVuHUhFTN68964BCUuhM3OSIe7K+TqHfL+XZbuijHCEjO+K+acieKbNq1Yj1/7LdKdIByRZaEQa3V3RqYD27BjyvBiY3LaYVCyGigIPtBVS3TVQgTmWJgkMiFoLMgSSdNs/61A8rw3ffDdP+Cv/fa/Zeq5n/3aW/tUEXc+yH7mvrauvhdr8Y++PnfAoF0F+PDbH/L1/+wX+Y3/yW/w8z/ztbeJGuuNlLc+X7TyDCpLGVVEzL4TbAEuonaQLcK8RKalELNN0MF5enW15A0lW1+xzCMbIltXuApCiMfqITAak9kHc17zA8n3HJeqCFYrmU4xc5ozb8bI3XFhHCdcKWwC7PyGm3f+BLp9jpvuIRmIyX5jut9LJF9/VsxlVUqk4nh2/SXCcGU5D/X0hlpJTZeR3vf0XVitYb0rdGmCCbIksm6BgtQTtJSKEFpbJicrx9bFybgVqRILjSeRpxNxPJHGI/PBgEGLN1XvcEjtf/ZVYtWhXY/6gZgtmCnKBp0TgcJOPCX0+BDoQ6DzSud1dZWUkum8GdWkAq+OCz94M/LyfiKmzBAcX7juuX8/MaUtqo4vPfsCfShseqFzBVcrICXF1aFSZELVXCqN3GQ8A20LnfdVshroUNgokoQ0JQ7pYAkc2Ta6dXq7pmYpxsfI0SpDLnDVDVWvXQ1cpsRh7bOX6qVgDoKhLhypYJ4DxcrVcy74bI6DXj1d5wn9DqaDubzNJ1w2QFfiyD5s2Ox2bJfCtgvcLZlTtMjtSNsoBU8hSGHjhauga0CTmw/o8WTAuFa86IwpTr9bQ55izsadqAmiubnd1TEdRMje1Bveu+oAbZvFrrMQrMEpu5p0qrFmOZRsxOGwqel2peaUWBMSsYqBuI5+t2PXOzau0JFx5NpyiZSSKMVDMMMiqWQy45c4XOjPVtvO47yV3nMSpqlwXIRXr49853bmX7w68t98dM8PbyfGJeGd8mLfI6o8229oEdKddwydpyB0JROiMIulSHZakKXgS0S94LuBUBxeEkELGkeYJ5OUtpYWs8V6z4txJHJGvafH7Mi9c/W1d5S4mC4/53U+UwTJsspLRaxtophLrJG3zSWQabJ1wxX6zvwRVJwRsJepykN7uHqBXkFxHXm4Yun2vJ4sFOmjw8xhtgPO4M+bYi5Un4Mr9J26xTnzPUibG95MicNx4bRAksgwCAsB8Rl1Gcjm7pgTeckk31vap5+MgxMXpIxmkyzCs36D08Bu6DjhkcpXWFVkLbCrRJvPS+aE0BUxfyOpaZ7r/mME5Q++90/5q7/zK/zDX/j7fO1P/OtvtwTEURHMj93bfgLdwoPrcwsMPqySjhUUwCO0db5hj2+aGXVUNngpSAmgxsSVMNj39RsLIZJAEc8cIzELpVSCU6iOeMHXWGAg9sTRwzzSE+HwChnvzNu9ZMxD3Fd5Vc/tacJnxWcQZyXvOWbup5m7ceLT48ThOJFSolO46j33G89V2LO/ucEtx3XjKL6DbBvDtnM1WpW19PvJYbH89QT7bsvuZl+DcM6VjFU1QeMLGKkoOcfc9bh5i8sLfrfHyQYkrAxZAfs55XzCOmv7rTe2nmjBJHHLQozG8EbNG51gEdLS9bh+U/9suvWII+DpiiMWT8gzoRSc80gwYlToLCvd1zZOKQXtlDQEDpuO10PH9abj07uRUiAuicOS+Uhg6Bz7PvDe9ZbkejbvPCOI9Ug1zyb/qhKwdSSpMb+tBVJlYGrELpxDnAXKOD8QxCRQoxxxpwhuIZVop1fBhFRizGuJExpHNNWycHKUHLnqtgw+MKbCris1T8NO2Tln2llO5ZyC2F5rri5rKQtRBF8serZTYRiu7OvjBPFomwrgu4HS79jsXvB8t2csyoyylGqrS823EAse68gMknHzPeXwEpkOlNkItHQ9+L1JDIcrxmxAZUkQi5i5TimVX9McEq01MQTIxTZJU3/V6Gu1NM7BV9IhEZ0NiEs25Uh2niwOpz3V2XgFHakAzjb2btiw2W8JZcGVWH+GkSgkB1N0+IwGmy+aEyCWaBhCBQa9SQdDB9oRtGOzD3zy8WuO+cQnx4Xvvz7x0d3E4WhSTacGZK83HddDx7Ntx/UQ2HaKdzYPY3HVTAs8jhml4NEEwQuh7+jEESShRHzfUfxodu5qihZJ0QipbiIvhVQJtHFZkD5fjGuTcF7O39LmtWQkY6TQ6oVBVbmk6cRyuGe+v2M+HUnzBCmhhVXv364ijtJt7ee4jhIGDkvm/hh5M1r74LhYZcc7485sO7eGKxVvCYul7CjqSWHL/Zy4u18scG6KTLngnWeXBdThg6+gshjpMEXiNBKnCXU9xQ8GCjDCtOSIy5FhiAy750x46Ab8sAPfuCGFZYnM80xajMwbc7YUXPGIBCT0lD4hvnJrXOCDP/qv+Kv/+G/zD//Sr/K1n/3XbQ1fm4WP9io4g4RH+xs8DQo++PaHT3z2fH0ugcElKPhadTQ8U1Lsuvz4OMnKDGoEVX++qaVHEFIFBgx7kh/AD2S0xt4a6t04pe89u01g23XGwpcCScldYnz5hvn1x5TTG/LpaBtm6GBTNddhyzgnIgVdoFsKPpg0aEmJtCwscWGeF+6nhXGJlFJq9LKpHaZU2HUD205XaZp3FsdcSpVtjbK2FMaYeD3aKWnJjjFlNr5j6AdzdawcAEnRwEaVcYmoqSWWniUveK/0Xaj2sYr4ehoRaoRw/TO1Pym1oiB2v7Nz1UHPg/eIV9SJLaybHX53ballmx3a96bWCB3ZdYgGvAb64liSEJK1OmIl7AVXA1iUczJj3UikU+LWw7MtkiNBzc3x49uRabGqgajaidZ1+GGD68znnKgQ2wStI632GyXZGFI1i15dZWDGokeNFY16I5/l2mLwNZegGho5J2Y9LODJsJzM/z7P1qZQrafWSNdt2A4bsvbVO94W0JxSlZIZX0bqxpdLIRZIiG3ARYhJiA5SMhc/QdgMO2Q5IKdEunsN82iAbf+MTjO+E/zzL5D9QKzOdxYHbKFQnozGkfj6h8TTJ8wvf0C8f21M727APXvX9PTDjjELp1RYsrKUTEpigU9yNsLyIqsXSGmkVKneHzWnItSWkebZ8hzmGl+cJgvUKgVyR0bxfagW3IWoQoxWnSgI4uuYrLwQWbDnFmcoHRLsuTeSr6iSq8+JVYeCtby63jg/vgff4/wGlx1+mMAdbTyoMgRH3kAflPeuB3723R1feb7lZ59teW/ruQ7C1oPzshInl2IVOnGCJCWq4NUTOkfoB3pX8JKQbGZoqgrek33j6zg8QkzZElN1WYm91vZq6or69zpvzdK6nVzN4bRQXVrB2jVxscyP8cByeGMqn2VCUrIWXDOAqyZw+J7se7IGxpQ5jZHTYrLj27GlyELXmQrm2eDZB8c21BjogmWlqOcYM4dT5M2UVvXAcTG10BAKwTuWaFWSedYKsjMxLsyjKQY8mSFszXgtzrWCNoMecPGEpInts/cYrp+ZisSZ5XNMmeMsHCQz5cVCqrKFOCWnqB/AL8ig6wHsgx/+3/mrv/O3aqXg3wD1q1dDblis7vu2V9X7vO5yD/e4x1fzQfgZfuYz99DPHTC4BAVfvQAFjysDlzc5c8E3LKw2lEXBVXAgxSI9/+v7/xcAud9RnJWxl9SSuBTnE50Wtq6w88LGC5tgG1I+zZxOL4n3H7G8/gHp/pa8zNANaG8OWrnbcorZ+ANZEJcZcmZI/YpmPYlBLIO+d8JpgXFJ1Sshrzp26nvfeI/ECV8iO+fpN56h+o+3HISxRsrez6n2bp1VEHxhGwLeU+1zJ2tRNOdBxE6+ZLL3Rryqp9jLTbKl3a2ZD1wMaFULrQkB7QY0Rlgqca/UwKh+wF0/Q6+fGTDoBzt51cXE+pAdTju8OvpY6GMhFSMumsVvQrNZpXoKAdA0E8cDw3TihSb6K89Gd7y3CXxh3/PR3cjr0QxbbnY9z7Y9u81A3/fGmPcKxUxQcDagVJUySU1zTLSKgYGAYOXm+ndzwLRAGdRX+aUtHiVnO/dpS/JrwS1maCVxROJsUc7qUB+hLBRmSpooztGJN4lrgVwSOadV4pnFCFd0A3MRpgRzEZaijBnGxfTwg8eqTZ3Qu2pmNY/k20/RnCine5TEpvf0yxa/6ZBhQ9GwphhKXijjgXi6Yxpfcfjke5SP/8iCZ9Sh1+/YYHA9czHAekgwRlbL5EGVIBZg1jv7yGJRxFpBpvXvHZodvoDmYmmTKSFpRJYWRbzYvSiC+GyjUQOdDhZ1ne2+a2Xr5/pcCthzcvZccZWZT4YQahWmgrtaMbBnr+alX7kTzTgIH5Ao9L2Nq2fbni+/2LHvbd15Nnjevxr48vWGd3Yd7+w8O50Z5jcEnfGbHdnZXM8Um6PZDNK8Uzo1oN73Hd4LLickK6Sq6FFvlYzO1qHsO5yYG2CZRpvD/WZdo7QbrE1YSbSX6yttyks1h6ptNCq/plRiZl4m8nIin45m1EaxexE646ToQPZWjT1W/5FTNDJtW6cGX3MUOse+c+c8BaJt3CIU33OKmcOceTMlPjrMvDpdtB86T+8s+n0Q8BiAiSkSY5VTTjMlJUvW9MKmq9H16sjTycDxscPvj3hf6IfAZgho3zNnOC21veIKRQsziZIyUTILQvCdtc6cyYMB/qf/+G9ZpaCCgkTtuD7apxpvox1mL/e2x+Cg/f1yf/y7/+nf5bOuzxUw+OARKGjX4yrBY0BgJiRniY3Wf1OELODVQ1B+9w8/4H/5//hf2c/o9laikkCsi4aI9R0DiY5ET2LrMl1e4HgH96/g9Q/JH3+P/PJj0ng0V7FuMB12vzP26mKTIWXwBbwuZGdadKfVaG1QXOlMTidwp3ZqSBnTlc+Jbaj695JtMC/WsnA+EPyG/XbHPiiHznFYEsclVzZ35lgJsoKiktkGK4EXceciVa4Lqqt+/NVPQJ0xr1urwW6q0lzmBKl/V6RYyVNcrklvxhorKZOKkv1gX7vdoVfPcNfXRohcLaWr0Y0Ltpi5gNNAkMzQ24mP6iFBjuRYKoEIlvGefP+KeHhp5jLTTPCBF37Dzf457252vLkZzKUvJpxzvHu949m2p/eu+pTVlpNrLnoFMNtWqgOc3Uip0js9t4ycMaBXkIBYrHJcWOaZnCJOCqGGa3XOKh1MBgiottGtly7VlKgsI1kPlZmsBjBa8FJurZwGVjyl2zIMV/huh0sCWRhzYY6ZJSWOM6RO0KL4boPzwQiMpxPzYST1E71X2G4Iux1hCKgm8IO9t5wgjuT5gIyvmV59RH71Q8ZPXrJME243IFfGr0ndhsOUuJ0zb2Yr5wfn2ASr+GxU2Thw8z2Md8h8tOpV9Yug2hNrBZuqaiFKpeZCQC1fVxC9Zk440JGwGeicDUOb/2YAtMyznSpTovj6zEqmFHdhySsQzECopPSwPF6tga1K1J69h5rF0HvHs23PTz/bMWghXfdsvHEjbgbHtUu40w8IdydiWpj6jry/xu9eoPvnMOzN4j8WyAmH8Vr64Bj6jhA6nFPzCknUMbdUs7Bs4UmdbfzqrJpRjgcL7gqBMmwsIbEbbJ7W1y+qlNUS+swvWNu2QuXb6BlMGPK9WENKNbGoLQS1kKVxMfOgU8wcFyvBq8C+82yDsguOXTAyq5sP6OlkKhpRa0e5sMp42+EnZZPqeoWrPvDOruedbcfVoOw8dJosXyVZiukSEzEmI1EXwXce7a1yBlCmE+l4i05H41EEZ2AxTfTDVU3pTMz1v0QmSa4GYQUk4MIWcV2Vi8Jv/OVv8rWf/XmKaAX01ACniz0KLESttWmfAAiXVwG+9VR7/TOuzw0w+OCJ9gE8rBJctgzSBSBof16/DhvMLlt4Shb4J9/+PX7pN3+Jf+9/8L/mV/7prxD9ppbwjAQlogQs7tSXSMgLuhTScscSR2Q5EF9/yvzxH5HemGlHiQlxnVULwo7c77kfE8elcFhSzXS3gI9BMgMzbpnpspG4tjtfE9mUT092+rfoZfOch1pqijM6H+B0SxltQPthhxv2+OGG7faKYbYwpGOVelHf29KcynJBfUeJHsn9Cl0FIASKMx96DR0azAZ1ZZdDLTFCTVCycoyolRAFsgaKQvaJxSemsGXeeJauniaGDWH3jLC9NqKh6tkmVKqHeW1riCgO6EKoz2cmLpEUEzHOjHMmxpF8+wn5zQ+Zf/hdllcfEQ92b2S7R1+8z7N3vsTV/n3ifs8xFrI6hiGw6yznweKB7RipovZ+65hCakukvneaXr6BAHX2vppZD7ISv6bxRJwnHJlercrSVUY/y3i2zBbBdd0a1iQi5BRt80qJFCMx2ZjINZmx5LKaBiFqBM5hZ/4EAtpdkaItDDknTnOs3ys4PIM6ejeYasapMdhjZrq9Z//sFu5fo32Pc7IqM0qNyuZk/14Ot0y398yHEykn/PXWAprcwJQdd3Pk5TFyWEr1lweP0ilsveDnOzi8hMNri5Sep5V8JerMMdIHsipZjTTnvLesA+dQ781p0ZkPiNkyFyPDLiO9HyjF5JcFm39xnpjGE8vQk4M3VUg9cbd1pkWiiyRWE6pmZCXVsrpWt5qRUm6ySIVdp7y7D1z5LZJTfa/3uNuPyJ/+EcvLj5iP9zZ/dzvC8/cJ7/0U7vmX0Ot3iX4gVnmlU8V7T991dCHgXN28xdQXZq/o13lcilVFktuypMCcHLNuTU7sBOk6nDc3zaw1P0Fcnb/Kqt5qYPSBXC6jPtu6EDoD9a6HkJEGJnwA1xtg8t265izlnO/SOV0jk3dBuOocfrpD37xBRosXB5Bhh2yuKb5HxaopWt1mnXhclcq+s+m4HjzXg2erEZcWtAjJdSQpTNWWeErZDhTFlBNh2CPTAe03pLvXlGUml0R645i9w1f77HK8J/kBxRFytH1BzPjYUjizGZ3V+9k29a/+iX+DljtWypnv8uQeVVuCRYRqeL2u++0SrFLwiz8hKIDPETC4BAVPXU9VCRLnG7/2bmDtlTVC/R9890P+xm/9In//F36DF3cvANOXX14OOwx2Ne40HV4zHxNCJOfZPNHv3rDcvSZNRyMquYDurmB/Qx6uuJ8zhyXzZjL26lBd9QaFbjkg97fockSWCRA2fiD0V+yvrtl3Wr/fTsfb4KyPLmKny+VEfvMJ5f6NGXbU3+2evYfsnnOze5fO2fdMSR+ELdnALKYn9wOlbkr4wXq7aqQbGa6Qfl8rIP3FScqwbkERyZW1X/v1UMvaSiwmw7xf4FQCs3Yk7yAMTKGn+D3aXSHDUI1U0hmgtCjdukFqZeb32ZGTs7jiOBPTQswJTm/g9iXph99n/t5/y+H7n3D65A0lJrQPbN65YffT7+O/8DMM736ZfvcC3dzQDd5cHsnmjrbeJ0XQtfdqplj+PKjqYrneL3VmYlJ5CxnzmB+nkXkcIUeCYps1NQGuVH5HKcZJ8CbH9MHX8KlUWzkncozEeSamSK7Ew5KMi5Kbg2QBxgNlOpmEjEK4dmzcltkX7qbCkhLTEsnJSq4bhbC5wV09J735BB2OlCVXG21T3ZTxaGYxg/XlpBTzoxiP9u/zRI7JdO1DsMCxq+ekzc3aQ74bF8ZU6INHcWx8YeNMxcP9p+S7T8l3r62cm7PN2RZdK1Z5sbK9ATb7Z4+E3symKmjLi5WNycaFkRxxJTJ4jxdT7BQBcmQeR8ZpZNMH1GuV5Z0Lt+tmqJ7VIK0uKCLN3tphPP0KDGK0kyiZnRf84JnLSD7cIa9ekj75HvMPv83hux9x+vQNeVoQ79i8e8Pupw50BZz2UBxsbohi6hffW1WrD47gdfUUAXOMlJIt7A3qhu7MjZCRe1+474QkW1hGXE7MpRAX2AdhKIqX+jNdrZ6IVnAubxvrOKO8ShdtfRhGcxIUR/GxoSojDHrrz6dqtQ3WQvPq8NV3Y98pG0m4u4/Qwyvy649JhztKWozHsb8xLk+a8aEnKOw7szpWEXbBse+U697Rz7f413e4ONomG3py2NL11wwaOImtf2NM1dLeE1S4Gq5w+xv0dE9eJsiJNB1Z7l4z+oCPC7K9ImrHjCcVa+shhVnPrYFcaiLmxTUnA4wgKyBYv+Rij2qNHBFwpZAqy6BZHjVwcAkKPmt/fHx9boBBQ0JPES5+ElCQ20kPaDwaivD73/2Af/u3v8F/9Bf+AX/6p7/KP//n/xzATtBUK9dKgAoKGkfKHMllIeYFIZLmESYL38m5Rrr25n8uN++QNs+Y3Yb7MfF6jNzPEUXYepsIG83I3cekVz8gvfkYFgv3yd0GuX5B//yLvNi9y3a74X4RlrpOdtVWueU8lGkkHW5JxxMi4I+3lNMBfXfC58x29w7aW1+uBeRUTpcx9DP40JNFED/QDIdwntINsL0ihw1JO2JRqgS8lhIdIuefWYCamkPWwhIjpzlyN0YOizDLhhOZ05SJ40zRxG4R3pOOZ3g2nZiyQC8TMc9PX8Xy4DuXWZwx41OMTJNp5d1iCY55WYjTzPj6nuMnd8RjRERY3hxJp4n9aaKfR8L7I64sVTpaSA4WCsWbw18BXDs9yTmfo7RKCdi/afuamplQT125wLJMzNNIWhY8RjikKIoZwjTbYnHe+rze4foeDbZBWdzwiVxq5HapHAJvHBnbCTO6WCiVucJF8niovV2LTR5uOkZVNt6IeGMuHFK2E3tQttuBYf8O7vkb+rTAOOKGbgWCktPKcG+tBCOtNta1ww0d/c0OhgF9/j55/w6TG7ibErfjwmGuFTMpbDy1D2xKnnJ4TWnBOGBeFhWISiXpSt1sALL3pGCVOTZ7Sr8x8xsEtyR0mpCYkJoTIXHGuYJzzvJNa7k2LQvzNDLPPd5vrRonxYpfhPPzbeFMbUS2DZlaOagLfq7tjBgzaRqR+Yiebgn3r0i3H7N89D3m73+H++9+xOGPXnJ6NRrHaGvk1P6dazSm6vAXSTqRxdEjaN/hnVQFjjufIEVtPuPX0nTMhRgzpzny+nbk41cHDocjkhNehE3o2IiyLJE0RrJGNj6YQ6A4G2MV4K6JsU2zUHkZqShJzc2T7Y1xh/xYjZXSGTj7jtg6clLjk73d/77mygx5wt1/CrcfkT75I/L9a+LJVERuu8GFfjVPc70FteViMc5BYR8cQz7hXn+CvPoB6fYlZT6RnPnHcPMe8vyLbK6+yNGZmdgcC2MVFHoV+mFDv3mGuxltbE9HjEqVWU4HS0adF1I3ELHWSJGAqqdzHYsNjDUuOl9wr5Zsclzkcu8qD/Y2qd+PHbdIFRxQ29+tHf573/mQb/xnX+fX/xigAP5/GBiIyJ8H/nfYYfw/LqX8ez/q658qjzwFElr74ClQ0EBD/f38k+99wK/89jf4j/7Cr/NnvvK1NYMeWlui4LAyrpds0Z45QjZiXiYSSz73G31ngKCy0PP2irh9Qdm+4HaMvB4jr8eFORW2Qem8DWoZbymvf8jyg3/B/NFL4mEkxYj2Hd07H+MOd7gvTmyuvoAfrjktmVTMG16FtcROPU2VbCe8kgpeFEJnkipRhu1zXBjwYsxZaJu7OcOJelx3VmuYPEzQ4sipoDETYgKX8BU4WdqYfWzaXit/2U+IuXCaI7enifspMhXP/Ry5m5JVQWZTFmxPiUkC2XXcYMztTlsoDqwmwZUl7QWSGmjLObEsC9NsttMbPyDdDnf9Ar26obt+w/zmnjil2quFfJopxxl3vEUPV/jNDl16yuSI9bXnbKVl7xzN00bFVzmmof7S3MnaBtF4EerWEnOaZ8ZpJs4zWhKeFrBSQ4SacY46nO9xXYcbNtB1Fr5SfTKWJMROyARKZ0mKUmoAUj1RtFjjcjqQD7cwT+R5grtXuMq83+/f47SY+dDkhFM0f4L7ObELSn/1niki1OFOt4Tgcdsrqxb5YH1nWJnWRdWIdt2A214RnkfyPpI21/DOl0hX73E/n8OuMoXe2e/feWUfgPtP4fCKfPeKPE8m+ex6dHeNbHame9dzlUpaEqIo2XmWbqD4DaHb47vGf8i4fsaNJ8o8W7BQSZTFopi1EkTVebQky8iYF/o+4TqLqZZiYTcUNf6GVtOZ1ZWuWlKt7QOqVDgRl8iyTMTxRJkOJjFejpTDLe54SznO5NNMKxThhLDxdNc79OoGvX5B6nYUP3CaF5Bs/gg5Vetnc8hcSb8V6Zss1UDBnAvjknhzmPn+q3u+/8ktx9OIp7Dr7HR91Tv2nSdPkSKTPUvfEZyBW5GHoCDXNTLXjS+mZMZqMXNMhal4cBvEPRThpQpqZQUF9nkv1goIaUSPr+DwkvzmU9LhlnhvFTLxNYLcWdsI0TU0a/B2gNsEJUy36N0PyT/4Nunj7zN/ekeeZpz3+N1A9/7BlD5hS+ev6bzpLI5LJualmmbBu9sXlGR/1+MdLsdKSO5MzZKShaJJNF6Ti6AdkhLed4DWPciE2qmuty3nhHwGBI/3Jmol4xIclFrplgqIv/XtD/mlf/R1vvnHBAXwI4CBiPw28LdKKf/ij/UT/xVcIuKAvwf8OeC7wH8tIr9ZSvl//tjv5QlewWW1gPbnh6Ag57L2ZwD+4Dsf8rf/i2/wf/oL3+RP/fRXq2TrgqcAq9uWV4ztXG1kS45reVxV7HTXdeYctr8yaxQfGLVH+hsOEY51UZxidcpDVqc4OZ5Id6+ZX77m9Mlr4t3JKg+dJ8bIVrDSrevw4th0u7XVETOEsIFuh3v2DqRoi13TjotYzO80IsMJnQOhFzSY1K2V86xQfr5PtrhZvHJBUV+IczFToTkhLlPIK3BqNa4zOLCfm3JiiYXjaeRwmpizcEqRw5y4mxIfHSZup8SSYRgj2XWEfiCEYJWIZmwizVnQSrhSpWhSLYpzjDU6ORFLYl5gt3kB4ggU9mpyteHTW5gj/WbD9p1rts+uGXY7tPM1eM9K8WVZSOpJRQmIASbvERVrtVC13Rd9wZUH0SKS2yKaLH1znmdijNVDfjTPiLSsC6f4Dg0Bt7lC+00dQ1YtSCSKU3LvSdlTtDfGtxifIYdhTdV0IiZB3d7jNq/Idy8phzfkaaS8/th68q7jKlwxb4JZHedCLBYYM2bhhOfq5ouErsfPBwYyfrdFtlfWVvJd5Xs0OZtAl5Btxr+Aod8DSux2LJvnnAiMOXKK9nuCKvvO82ITuAqCO71BDi+Jrz82trwKsrtBr16Qt89J/b5GnFsOgxNBl3E11Eo1LCf2e2bXIQSr8DiBvkfdgPMnyukOlsksoYGSpMpzA1mEGE2TvqSMz8U2AmqVqOQKEJ5+7jYqhJwLaYlMS2KZjeRmeSDWzlEF7Txht0OeTWjKhNAxPTtB5xneuWb4ypfxP/3fIz3/Mstww2GMRCyuxLvFWhSpShNTrk6MxgUo4mp+RVlN045z4uO7I999ecf3Xt0zTgtB4bp3lF1f/TNAnHA4TdUdc4N2glNHce4hKMhnm+mUjMh6qgqow2zl8xztq5VSpafUtcZW8uolh4rZdrs0VQ+KU7WcXkAEv+mADukG5Oq5rXPdjhI2xOq62NZSPx/Q4yvyJ39E+uT7HH/4ivnVHWU2EzV/taF4pdu/Rm5O9P0zQq0alGIumwaOHQcPN1fvol0wmWKsFUVxld/iKclk7KWUGnmOjQ/Ah4FShKy2rraN67J63QDBg8Z1KWgx3kQDB4KpjLR+zwff+ZC//ptf55t/5SEo+Axu4lvXj6oY/CrwfxWRXwP+N6WU5Ud87b/q608C/+9Syn8DICL/Z+CvAD8WGPyoqyGuVXZUWP9+KQX5g+9+i3/nd77B3/vz3+RPfvmrK3O5pWJCOxkaOJA4XWj7S01+c2StAySYzaxT010XcSziCMVzSsI4zRxjXkl/LZY5VNBBOsd+5mxyndIY8DmRY8LVmNrm4e0QYinMOVvZf/sCdd4Maa6eGQkxRXvPXW8a7VKM6a4jrhjZEDmnzTUAFYsRtpZsnvJZQIqSXcbHRLdEfEh104Wi1qf11hgzcCAexNQcS4rMsxHlMmZp2rwVjkvmboqmskiFzXHkxWniZloI3uHVKiPNYY+Szw6EKRGnieU0WoJhisypkokyHGNh6/dcf/G/j989Z//ul9i/+ohumei7js12S7i6QnY31ibZXLG4QKps4bIkxjgRUu3XOs/gPUUFc3asz6ldjZR1UWpN2UymxnnmNE3Mi5VWJUWIY/VmLyt3wVdjrbkocSks2ZjTaZkpOaI54SXgQ6D4eiJMhfEYGaN5GojA4JR9d83+2RZXHezy3SvjwojixLF93jN1gWeDNzvXXEjiGItj0QCbDd1uT5dO9JIIwaFdB6Fq0Wt1CFr/XVHXEbot/VWC4pjdhoXAcoqMxX5+CIVOxbTpnbJlRu4+Jr78iHy8s5+ze47cvE+8/gL32XN/KoxpphQITs3QyHd0vmUi1JCkw0zWhOiCCxaYY9HfioYenyMpzSZlTLECG5MdlpyYl8hpmujnGV9joJ1KJSGWM0B467nbvC+5MMeF47z8f9j7s2DLtuw8D/tmv9ZuTpOZN29XtwiSFmlEUA4GbRkgwapi2GYYJASSL44A0dC0SVNkQOS7QuFXh8MPckcFG1GUGAUQcPhFEikQNF9YVSBAWmaEbcmwzEYE6t5bdZtsTrP3Xs3s/DDm2nufvPcWqgCFzShoRWSczJOZ++y91pxj/mOMf/w/h2EkzrF5pIgXhrYOt95KrOk6Ntst8bVHDIcD0zwzuwDXT+Hx50jbp9xFxeGQiFkARTCKkBNlnoiHOxIdNgTxEzFWMn1ljpXTZX3sp8jtMPHiMHJzmDmMMysvff0xZTZFS3tKS/Y/z4mYE35p2Sj7ABQs1dVcxO1zTpkhiv7AmEU+vpaCriKzXHWViSul0Fq1IYUGFmqR2JYmaREs+2G1xVgvhThjhXS4uaT0l5RwQbIdc5YYY1u7V+W5vc4sBmvNB6TWQmkE4FKLTATliNWibthZzZBUu1+FQyqsM6z7nlXXYVXCNTVXmYCqAvJj0w5pzoyqVvGKURrShDEBXUWfJKvzOCu/Oz+Xaj3xOSXmchxZrOrUfvna17/Cn/xbP86X/8jD6bzv5PpMYFBr/T8rpf4O8L8E/m9KqS9zIj1Sa/13fl0/8du73gbePfvze8D3nf8DpdSfAf4MAJff/guXV/4sZZoTul9AwV/8wS/z/Z/7Ap+42oMxSuG0FknUpvmvEFCgjKMYB9ZTvaeG0Fi9prkOCnEnz4V6v6dqCTxGqebfLeYyrjG7UQrlOtx2Q4kZu+5QiOiK3XTY7QXK90cyH0BGsoHcyC1Oe7r1U1x/hd7sRXY0T7Jga5U+oWl90pJQWUkZWIuiWm0jgWiFzhBrU05svS5Vl3FJOXzmXDC2YKpp/W1pmC3gwLSeNg2Jl9o2D4jnQTOZ8ka+zs1zQAJrFhBRBY0vaniAOMWlWcRUhj3jYWA4DOQkAjmlFvZT4W4Wgqc3micrz1X/Ote/7U266YYLk1npSnAObSWYZuuZqiUWKdPHqhnHWVjrMQvxR1tRWVQao10jR56hyUbykrKhZAYpV+aY2B9G9sMoxMAqvW6dkyhPsmjhS9Y7FxhKYUiJuyEyzuKI6Np8f9CiBqiQw3xMlRdj4vl+ZhcTpQhX4I2N582N59HmNUwa4XBH2e2pMTVRno6Lq7eZiyVWxS5VGVU0nuICxXl0t8VbsCXKqlNVWhv6bFy1It9XhaoDeIVFRvdSgjImSpqpBqyr9EqxseJCeuE16uYZ5eYj8s1zao7ozQVqe0XevMaLaPjmbuKD3Szy4VrMdB6vPY860evwWkBYLJWpwJQjsSqUmei856J39FbTawjWCx9mMUZq0KaYciRu7ocRF0asC3gl+voitNRE0T7rube9MUyR+8PAfj+Sk1SEOiuyyxiN1R1hs8GkxyIjnBJ9jByK4i4bxnDFy7lwsys8O0zMWczPLrzF9UL2zOOOId3jYo9Z9ahe3BulcuOkirqUsavYIM+p2Qoj1ujn+8+26qjsUdl7ixV11VIZegAKOLVqc5V4EJuHRSrLJFgj11ExFRxSYTVC6GjVP0l+1NLfU1rGYDdGWkiqVSBNEE8NvyZqz5jEdjtXqZBJ9WEpSRiU77HbC0IumBCoKcv76ANuuxGFWyXx1xnFJpi2l7JUQNvnrsZjt2uC1zKNUAt5adXOM3WaKPNMSWItX3MS4JgmKaIqjdNOYtm3SOc/DRzUWk/Op+366rtf5U/9rR/nb/wGQAH82hyDGdgDAdjyyXP1/29XrfWvAn8VQL2l6qt//61KJksb4dVrAQX/xx/8Mt/39heOD2MRPFoYnyCB16rSBFMmWbhLNuh6iglMaMZi8cVRVMfKBZR30l4oFc2MCxk/ZdYhtdnWcpzRDVYy4Go85vo1lLWsrg/i4qYWv4AOulUj/q2pNsihU8QrXMZe5LE5U/DG0oVHdGsRi6lRlL1UOTH8201C1XQCJkpTSxaddK0xVWGqENyqUihhYkkmXNt+LpWs26FdqpRYGjgQArMG0/Tkq+jO65JwtmPtNLkYUhHRk95btLVsgqN3omqnWuVn4eHUluXVeSDu7xnvbxju75jHmawDvrvCKEi18OIwczNGYqr0wfB0HfgtVyveevQWV08u8a7iWpmztvthMuhZRpcOh4mpMZW1TuRa0VpMbXRzTxQy4llJeakSVKkU5CogZzdM3O4PHEZ5Dr5pzNfmQie9epn/jgWGnLiL8Pwggi3jNKNrYe0U18FinD5aXC+6Fjdj5MPdxDdvBvZTYuUNt49XlArhOrDtLwVc5ueUcU8CnBeC7MZfMRcrz9JYcYzTHlwgGUOyGmfXQKa0UcplcuXYWKm1ZZSNEIkhpUyqBZyhTmBcwSex9N46xcaBGV7C3cek5x9R9jeiGul7an/JXnnev5/4p8/3fP35gcOcWQfLm1c9xogngtOGrEQ9MmVxFn05yShkUZouRA6z5/HKNw2CRtQsCUpshEHbgrBmjpmSZ9T+gPYdWy3kO6NOQWIh2p5Ho6V0P86R3WHgbnfgMIyUUrBaUYrBrjq0VRivRYSrkR6Xdt0YFXfPbvnGix2/enPgo/3EMGWcVVx1Tub5tcHrKlNRZWIYPGa+QOWEW2/lvS1kv7Z3VJXKZ+9E8vt6HSjJ0FvF495xGUQvwKmCThNaV9GGUEoMU865EwsoWCoGVYh0S0yQvSqqoKXxX4xq7R8to6Eqzm36pjyMSYsyYnuNqg3aepTriBXxkYmZOUsFBZY2RG0GS6BsoPgN+smb6NUG+7hNymSpRGAshBV6ey3CUVUUUNdOkq5DFLLxOjiRvA8d2gdU8FQtbaI4Rw554lCkYluVwjqH1hPUA+SCqrmBA4X1MmKb26G0nF0nsvypPd5oBLyCBygVfvHdr/Cn/9Of4D/84Z96AAoU3/o8/LTrW3EMfhD4d4D/BPg9tdbDd/jav5HrfeCdsz9/rn3vO770wuz8Na7zSsH3vf0plQJOJDqQcr+Ut0RsRlFl3EZbsvYMsTCWQtUQsGRbqV7YsWKvC9op+r6yiTJ37jXUWrBKtN2D0agcqb4nW4faPpFu0jLDv4wIVSRLa1n/YjCzbNRDFPvdQrNu9dA56Uc6HEZbEc8xSpB6mhtCb/3SFtyVlj9b14nJCKC0oiiNsZbgHd6K3rheUDWykDMcwYFqWYJr71lrLep9OaHniZomerfC9gFnJFjtk2zai03PZRfonfT8F+1/hRJuR4rE8cB4/4L9Rx9wuH0p5fPVBRpNZzdYpUm18ux+4tn9TK6VR2vPizExGUe/VbjNFa731Jqa3WpB5UJhYpp2TCkzxkTK5Ugq08binDgtGu2a+IgsmCUI53oiZUm1ILMbRu73A+MccVSMlewMgHyq6JScKCkxRrgdMs93EzfDzByTyDwriwpLK0od+8JzEQJazIWX+5mX+xmlxFlu5QxPVo51uMD0azKQhglTXqC7HrO5YrW65pAqIStmJZ9hLDBkcEWjs0YpCM3pEqRErI5stwYKlMzYxyxTL2PWUv3ImbFlkUo1bwYjmgXq9pb88iPy/QvyFHHOo/s1OVxwNxXevxv55x/ueP9moFa4XnuebANWLSx2hTccDy7F4jeSmAv4mCk5Yyn43hAcrVKTxDZZqbbIRCp6ToVIpO4HfDcSFk0NU8UwDJpevzoG46UqOafEfhy52x/YHUam5mJomwXplCv9ao1aiUYERstcv7IMw8zHu1v+xe3EL39wx7/46J4X+xmjFE+2nk2wWKXpjEaPd6j9C+LhjoPRMFxLkd8YnAngEkp7FPU4Zt07y2UXmLc9ncqQE2ur2DjN1itcnlDjQVxUfWhqmy3mKN30QtQDUHA60KSiYq3BW0vvHVMRsR+ti6hZHhOtZm5V0jHuPCDqWtFBiLkyV8hJEZvvy5gyhykxpywGR0bUEI1SjQRZwXVUbch1i948PZ60S/tHFaH75aY5oWohGEf1sq/WXhKaLgQ2fUfXd2gXKNq0CmphKFJdu58y0ygjxp2G3nqMnmW6KCdaVARtsLYTKed2zuRPpc5/9vWL736VP/NzP8G//8M/xe9/5wQKPkvw6Ne6vlXF4N8G/ie11v/Xr++lf0PXfwb8K0qp34oAgh8BfvTb/c+vEhA/7ToHDL/03tf4yb8jnILve/sLn/p/lxtsWsA2lNavmqXcq3TL7h1TLuxi4X6uTDUTkmZSYgSiDHReYVGS1XQBUSpLTEYyFaPANOY4tVJcT9HyumOS8vAyui0MWfG3t8cPXpvffOsLVxhSkRGZBPdJEWzF6YpX0BuOo2jBOKxTomMuaveNzNeOtobinQtoa7FVgIpxBu9l0ztj0E1k6ZgztqBBqSQtCy9XRASoEY90ieh4gClTzQ7tV7j+ik1wTFV6wKvNmu060Dfr3HOt8FoqOUfiODLcvOTw4mPGl8+E7TsOaGVYX2+46MwpA5gTh0HGJFNVdKsVF5czYZ0purB2TsSadKHUSFGRjG6s8kV8JUOEYRg4BE8fPM4IU3tZN/X0aCRwFvm/Y0rsp5n9lIgxUzRYraXv3yZIyFGEonJs0xWFYUqMc2RsAkR9szNeOcVF0NgoVt7BdZiuI+bAyyGx6SwvdxMpCkh4cZi5GzNPVx029DJ62tTedH+DOdyiNjucXuNUZcqZmETbYD8bcahUnGWgFasNRtujoQ2c2OapLKCgMmWZONnPiSkmYhKA7HSVVsi0g8MteXdD2jeyrLGo0JNtx93dxIuDAJ00Z4zVbDrLo5XnjU3gUWdYlRHGkWo9NqyPFbm7SbgdpSZGoxgmRbSW0sA4OYrdtHEtO7XSisiFuTRXvmlmlRLW2aMpldbq2NVarlLlZ81zZDgMDMPAFCMx52XlkixktIAn7SmuCTQV2M+ZmzHz3v3Mf/Vy4J9+uOP9FwfSnFn1FvCsnZF1bUHfvyS9+FBaQ0qhcsL6gOm26C5iShW+ZItr3mhwmkfrgGfNlauUOBFUwdeIHm5Q8wGV5ya93fZdK+MvYHdx8cxLqGiAXWuFNloULL2TGFcMWYk8s1NVNDpm4UipKoRhmlOoVG2MmNZhmaIYv80NnM4VYlFMqRCTJC9OyQSPUsvUkjyUWDXoQKowVeE/pCJVQbkXMsEQTGsTl4zNExtj6Z2Vz6YtoQtsNsIv0MZSUCQqY4L7ufL8ELnZzUzjRFCVrRdtld441Lxv7ZFWgDcOY/3xbHn1fHr1erVa8IvvCSj4az/05Qeg4BP/7zP/5pPXt+IYfHra/P+Dq9aalFL/JvB3Ebz+139DAOUMKYjq1Mkj/pfe+yp/7u/8BP/uH/oy39/aB58JLNTpoagi/V/VJhFQLS1BHT3ib4aZQwafNMUGfJ/wzmMtWCsHOtpiVx0dmUOdibuR3GReK4pqOwmCc+bFkPlgN/FyiIyp4IzmIohp0pOVYxsMXZPPNaaJCbHY6Srp77WyZMygU8HWwtjYx1oJMU0bi2la8DWV04LKGdUU3VTNaNfhfU81Bu0s3huCNwRnRX1RnbLljCh0UQUo0J6Bro2X3A7AOuyp415yzdDj5j1u/YiL7SNM39GtOkLvcK2dYLSMg5XGfM+lyvTBNBGHA/mwEz+KaRTLWN9z2T3h9U3g+VXPzSEypULJhbtD5OO7kWe7kUeHWTLgxohWiMkQ2japXQM6nZprtYqc8TyT0kytHuEyn+4BcDJDgaY3AKUq5ua4VrVY5gbrW1hFeBNqbqYzSYItIpNjNHitWXvDdee4Cga//wh1+yF1PKB8z+bqdcz2dcbUs4+Jw5R5eZhBwRgLh5SI2eNtED0Aragpix/GeIA44roV3kBtGvKcGRQ5s1TRpUbgTJWqWDs86rF6JdbJcy7EVJlSZoiRYYoM88w4T9iS8U7Kv4xNLCnOIudsjTjR2UDMlUNKjE20wwXL9crzPa+t+a3XPW9tHf3uQ7j5kDoP6G6Fvnydq/41pgz3s0y6ZDjeS8XiMDhRYxtdNK7tQ9/ec2UuUK3wW5qC94PnurQb1fGrkNtSmsVlL7/SttMyFiliQZaMkp/RpgYOc+bFYebZbuTju5G7Q6TkgnGai97xxlXP65vAZdCY/TPqzUeUlx/BNIDzxG4l+yFGbNNfWaaNZP8UtDMYHIGOtUrkIYpR1v4FZX9LnQZZx90afJCRZzSpKko+bxt8cuZeK+EpKGclbhaDLZaiMioX6jyI50WOrVL5sP2GERXVoixzLoxtdPZuygwFktIUJcJRRRssolew8lq4I040DDSVXBVjlmz+2SHy7DBzNyViLnRWc907AZW94cI77FIVpgrY9Svc6oLVdoVfdRgn6q6xtFZVqk25M/JiNzFPEysDXnuSPbVTadMyCiA3sNRUGj/rAHr1YFdK8UvvfZV/4+f+hICCz3/pTNPlU/7Dd3D9S6tjUGv9OeDnfiOvcX5/zwGBaozOX3zvq/wbP/cT/OUf+jLf/9YXzmP8Z9/TE/PjRIpJkWoK1AC1UIoWpnku7KaCLhO+l6xwHRLrKrfdaI0zoLRlGhKkPeN8S9zfiXytC5QOqt9wiJX3bgf+Px/t+frzPbspsfKWN686fsfrW3qv2TSpXj3tJasPK7z1WFtxDm5jYcgCFGIqkvHmjLaKVBS5GNnYGrRxjf1bqKXJvGb5pWj+6yXJzP1qi3c9zhn5ZUWSVWl1lPWEJXDW5kHeRD1KJlYlVZBaqXEi726pcZYe5uYO92jCq8Kqc6z7a4y3rbd58hNbZKmLsqIvfmTEV8o8NztZi+lWrN2KNzY9Y+oBWD8/sJsynZcsd4yRwzQz5w7XWgUahIjpHCF0hBhJeZk8aFmJhCZUlYOGpl6GUsdD8/yS5EOU6qq2zGVEK0UsVebxrYj1CCGxoMIIccCbNWsnQS8XjdOaR53lImjCfAcf/Srzu/+U+eYO7R3hzbdZfc/v4nsuP3fkn7z34kAu4hoJku1VIzobNjiyUseKhc6xgSNpEZBlzG5s5LTQCGOKZp5TFEU3kNnKuLWeGOqxiNzwNC9Vj5k4R/EwaEbNTkvmXtpImnain6Ga2diisdE5I+0DrfncoxXf+9qW77kMrG7fI/3Kf8H0zfcpc8RfXeDeGQlvBi7Clkedo1RFLIXeatZOPgvxAEkUHClFDLGsSFfHkhvIrjhtMVZEpj4tiTjCwdYk1tS2Loo4TWpFrVJtcdYSOk8IHaYZbIlltYCoORcO08wYI0bBxcrhrRDi3nm84rdd97yxcazzAXX3MfnlR5TdLVTQzsm+a6XuoqxUJRpxUCtQRtxHg7L0OPb7kcP+GfPth8QXH8l+rFX4HcaKWl+pxKrEp11LbXFxAn31Phit0GiU1ahiMNngsmaOiTTtKPMgydVSQWm6K+K4aJqviGueAYv+QmkV1ErRCm0VzmoZ1zZw6TQbK0qNgYia7mUdhTVDquxT5hv3I//kw3u+eTNymBObYPn84zXpaaGza7YeVBqlNRMn8Zgh0iXDWm0IwVKNIVZNbH45Y8rsp8RuitwMEyUmdJDzoBQFShIr8azI4ITQSa1He4lPPXbOjp+FzvJL73/1qLPz+9/54ilpVeoBQPj14IN/aYHBb/RaQMFSLiutf7lwgn7xva81RcOf4vs+14iGr67qs6vUZp63HEVq6SLWZbZEqgc5YY1rm+HUWxznxNjKh7UWtNIYJSIudRqBCdRMGW5It8+o00z1Hfqxxa6lJ30/J755M/Dx8wNxzgxOPOc//1j0AoLV6MMLzHjXdN9X+PUj+s0lfdZ0Ee5jZj9nBiZiEXU9UQQTwtNxJFNJf1OYuU3JTUVUaf2xFFEqoWpCG4VxhtAHIWVq1bgHJ7LMq0EjKwmUeZ6Zx0isYkh1NGQadrJh0iwHbvDYeYud9/gugDkzGFl69qUK+dKvqd0WffEIfdhh5okcE+Vwj3r+AcZ5ri/fRl93bLzlce+5nRK5wqZ3hCZ3nHOmVCPvu83Ge+dY9Z28t1KZtNwfqxWdMwSrsapKH1ELqBAXO9My/WNnEZBM0YVA1684DBNTjngjxFG9DHLXQp0jatyjwp5+s6GzovcOlc5oLjvL1mvMxx8xvftPeflf/gr7D27QRrH93Auucmb7Oz2/9ep1Uqn0zjBECYbBtHKtC5h+TV1fYPyIckEMsaCJeIngVjqSpxI1GVKCrAsxK9CarAUQnPc3l2ckY65iA51Saq8hFt6agjeqjZY1edfmveE3BVyH6dcUF6hIufdq5bBmTe8sn7/q+a1XHdvhQ+I//39w85//MvfvPaPkyvqNK66BsL1i+9oll520BsYs97Gzit4o1LAXmeh5ahoFUsaOWQ7DKcuoXNevcCFI5egs9C4kZcWSOIiBk8oZq4TI1juDouJMBW0IIbBdrVj1nfgaaC0ViAaWcwPkgcLjlSO8tsYouAyWty8CT9eWSybM7TfJzz+Qkc4Kxll0t0ZfPKJ2W7Jfk22g5CrSeCw8FNmz1MQ877HzTmb9X3xIvnlGGZsdtLkCY8jKMVfLbox4M2O8cJ3OS9+qve5ykBmtMFWJhboqlHhATzvUdI9OicWFUcylxHnyONq7qIVWiU/GgMuazlZSLWQFzkLvHWtv2DrD1sHGFOx4S92/gPkgQCPPhO4RBsWYCs/uZ54/30t7zhuc0bx1FShVBNL0fKC+/AjmERU8XhfWlxsCEy6NKC/3Uyvhh8UscX6cI3NKMvZN07wxqvGVZgFBzUBKIYHyGB/rr82L+4fvf40/+3caKPj8F4+iRopT7nreZv1OwcF3LTD4xNXOcKUUv/j1r/Cn/vaP89d+6Mt8/+e+KOhZNRnJs4LAg69K2LbLXVfGHdW1as1iE2wdKo0Ev6YzkomMtjBXxOY1RmoW61tVNVbZ1rub8GWmzgdCHJgPd6T9juI6VOhQq0d43WG1lnJdLpRUqE7Ktb3VrL2mS3vM7YeU59+ENGO3V4TXM513dJtHhKgIc8KqCVcVqSqCtvROGNydlZLfsTesLVnZ5rVSmqOfhiLuYYqCIaEmjXIaJo/re5wR0Q7pM4ryXz07xMXEBdEVGGYO+xGwaL9C9VtUuEGNjjpGsTUd9+hpwNYZXaR9o5wXxnY5iaikXEhzIbsNdfMEEyO+VqK1qPsbmYE+3FM/fh9XCtcXT9lcP+LRyh2Nq5Q2bIKm00JIaryzIw/CKsNKeQwVoyqz0+QYj73aoAo2z6hsUFqC3aL2qLUV+dxy2sBaK7z3dH2P9XumIZGKZKXeeBmJypk6D9Ky6Q+YNNIbz2WwaCUTMp1VmDRR7m+Ynt+we/8Fd+/dy3jnfcQGz9XmkqvfvuW3Xq1wRvNimIXo6gS0KOPQmyvMdKCOAyb06LCCNmnhDHR2YZZXgpbeMKmSam5gsFXk2jOu54EP1daBtNvIBVMzQVeKUOPpLM0ETIvUcVhht1fkKaC6Hr25Eq1/BIg96h0XneNR7/ncNnBVD5T3/wm7f/LPeP7//ia7D3ciOpULq9eucPc3mOuJzsoh4otm6y29UZg0oucDZTxIedv3R7OruVQZs0MRfKDre/wyYaROzG9R+kRAQUkNHEiLwuaZoArZW6wRwqpxDt91rFcrQufF5EmpxjwXcqCqhU5XroPGXQbq1rJ2hpVTXAWDG14cjZY43GOosFphtle4x29irt+gbp4Q3Yb7uWAp4r1hBBwYLYdIzTOmzLLPpgHGvey/UkQ8KHTQb8l+RcQy7EdWOuCRMV1asqRbRiuTKfrY8tIo1JyJ0x413aOmHSaN5Cw+KtUGGTu0QUiGC6m3xSOrhfin4CgEFYxmKmCdYhU0VyvP1lu2rmL3L6iHj5k+eo90fwPWYx6/SWcDax/oG1eiAiUVilFNMVbjF47L7gX19hk6jtj1hrBZ4+cDvsyoPMF8wNqV6BcU4ciUKJUur8AvMs6LWN0kPijE6UQiNyJFvhhOyuDkaZLjeIQ1kPWP3vsaf+7nf4K/8oe/zO9754unWAJHgPAbaSPAbwJgcDzjGjD4yrtiiPQf/PBP8XsXUFBF8lIEcj4J1WrrIapyJolsPNoGUFYEQ0qCeUTbEZtG1t7zuJcy3pAqnixIMU3UaMArdKnUOKLjgJoHTJ7xOWKmSL4bSPqAcQG9ecxm+zaPV57XLwI3uwljE93a8/ajnje3QrQyLz4gvvdPmT/+CGJh9fiWqhW+63H9ChXWSBNb0VVDMUJA7NviVWmC2EaZrPgdxFKO1RZvOqw2wjhfrG5LRqcJNY+oeIA4YFYrcXesalHEbfPMMOUsGkSlEGNkv5+YDjMqJTbhAtdPmOvpKC1rtBKlQ61wSmGWn10LtWpyKcxFlNXmmBinRBwzyqzoHr+DdQEbOvLNR+T7WzH62d9S44Q53GEuHvN0/ZiyvWLMMq9vvKbXBa+KuGW2jEohLYOqLRaxQZ6dYR4PlHnCEsXUpsyNqZ/F0a5YMPUIDnSbzNBK+r3WaIIPuNAxjENzlqNJWLesNWfKsEN1K3TY0q+fMKZM30ieixAMtEwzF9KQSGPCWMPhmy+4+Pg9whvv8PjxbydWT2+lxBmslLSr8dSwwlw8htWMtg69uRCAquR9dlVhjcxfW10JZHSRZ5o1rW0gv8pZ2iMH6GImpKhFAlkA0BXvDaWqptzX/q3rRLOgiK8FVt5fNQIKg9U87j3WaC46y+Pe4J5/xPDxexy++YLh+cB0O2E7S8nlOKNPLUcgZ1XFa+idRu931OGeMuxExhygWXnHLIJeRcl6DD6IB8FSjleNZAccfS1ygpqkJZETpsz4mihErNFoH/DdCt91OBcwTjQwajsMVVVkVfGq0OvCo05zqR1OWToDerhBP38Od8+pdy9Q84jVoC+uMdtLzNVTuHqDevGUvfLUMeNUosPgncLTpgG0RjU/Dl0LrnECXOgo3UokitcX6Oun1P6KGC7YjaIXMuiJtXI4J6RZpSG0SotRp1aCVaBqJUaJE2oe0WmSJAmkMmA9xa9IyjVhotKAr5aJhTRja8FqQ98FuqQYbCUWhbaGziu2TnMRJElifMn08fvkb/4qw/NbcBo/7LDO8+jRb+PNbeDtRz0vD7MA9M7y+oXYMG+CRt+/JL/8mPzyBRSFtl7ic57R8wC+o2iL1g5KbrwUIVAGMsopemt53Isjq00jOo4nsGUcKAs2UIyntGApU2WfXsBeiPJ/5Ye+zO//3JdOaw+p0i0AYakW/HrxwXc1MHiVw/G1JhP5N/7IT/MDn/tCIx6dSgVV1WPV4LwvDqc/N8ViYqkE11FtQNkB4iR97HmPto71+gk5iNDNIVaqVnQkmCfyrKleEVNCpQM6TtR5RM0TpiR0rjAn4jyh/Mfoqyest095c+v57a9tALgfE9drz+98uuHNrcMfnpHe/+fsf/XrDB/dCDrPlbB5BtdPUP0Wvwl0upB0xVnQVuNVkZLefi8ZjjYU17eRHtXU8uTDe1vpraVzvfTIYkSVJM58NWJKlt+XJDa6LRAvRLspZ3m9JNbO0xS5O8wMhxlXMsUrrrevYalCVtoJE75fb1hdXuFcsyattVUhhFA5xcL9HLk/zOzHiXmKeAoba7l69A5hvcVeXZOffyDWtbtb4v6WvL+D5x+iLh7hHj3FdJewusT6gDOSEVslmcox41l4DcpiSdgk5LtsZbbb1YjNGp0UIOqPymSOLEUlTnG5LK9X0UqjjcYYQ8Uw5ZlUFASxslbGCI8lRSEUxgFHEanWJO2qmKH2PebiEd2TS1ZPNky3M/P9jOvkvpVhpu5u0OtbLvyFkNuy8AMqyLht2EigKQltHDWsUDaA0hiVCVbhqxyGzlScbp4YFEj5qFeQ2hTLkvHVIszwxS7aaoNDk3TFWogKStWSJStAaZQN1LBBX2hR/9SW6tfyPpMo0m1a+ffCG/R4S93dUIZZ3l/nCBcBv/Wsnmzonoi3QHI9cZD3Kp9D4SgQmwPkJBMQyjTRLxtIMTPlQtUes0zdtIkMjdxDqxEVwxwbMVkqhNSMTgmbI65GMIWilJTErRJejl8sykWvYjHVyQqCrqxNxXtFiiMcbtHjLfnFR+S7FzAPGCouBPzmStby4zdI6ydM4YrbKbNLhZmIT5p1VGxXsK0O7Qy+VioybaQVOOfoLy6pKWJCR6yKurkkr5+Qt69xNxVu50rUM72eiSYSioCJzhog0xlz5GxJK0FDTOjS4kQVzRJVsqhGWkd1PRNWtAhEOU3WuK1YlVFxQMdBDmFtWYc1fdgwV2m9WF3pdMGTxF9md0O5fcbw0Q27D55REOXUte/w3QVvbq/4nU835FJ5uZ/Zdpbf/tqGN7eeNRHun1FuPibeHWSqI8u0mJpbvI4TBSN7r1oxy5snOhKXXqGsY+Vk5HNtQe/vUPNeAASAtVQbwHVt4kU+8zLlcX6OgbQPfvLvCCfu93/uS1KdUmcVqwYKXkUD/w3H4FOu5aZ8dTGU+KM/zRfe+aIs2lLJC++gfV2qBufFA1GnFF+ARbZ4SEVGTHwvjFo7SUAZ9igtrmMXq2usMXSutmyoYMpMmRXzPmKIqDqj8ihOdFWY+7qRIvIwMb24Q20/wFw85cn2Lf6VxytpUaTCRWf5nquex66iPv4G8wfvsf/mC+bbAectaT9QxpEyHtBxJg97jPZ4XfEWKdEPtzDcUQ/30i7oVuj1I6rrycowZdFBSFmyqlrBdz22RgEDWYhVFrCqCLJvSF+1B7DMM8cGCvaxMDeSzt2c2U8FVwpKGbwzPHn8NmGzoX864vKEb5mV7TqU1tTmWliLkCeHmHi+n/j47sDtYWKcZ4KqPOodyjpeu3yTsLnArNfM3jF+BHcffEi8vSeXl5ib55jbZ9jHb2AevYGqEeue4tQGr0/9daXaIVoKtURcTWhdsFbY2OSCoaALEAuqWnGfrP6Bdr5xfZuoqJjSQEfr0c6loFJhLpo5VzorjGy0Fnb+NKCbpKvGAbCfE7kYvIHLizdw3/O9PIkR13dML3e44Fi9do3frATYpJkuwGQb0KnL+tYot5J+LjJ6q1wAv6KWKllVq3S4JhxTpz2k+egPUhdbX2NR2knAqkCZKXPLpCktw7Z46wlhTVQQq+wT2XuV6ldC5tWumZJpiu1EJKmKMJBRIgPcGVCjEFb9ZsXqtWtqhThFwvWGy9/+Ju57vpd08Qa3s5hzjSmz8VaIoimi0iQ2zjmBE7fIasUrQlTvSptGkOdlNM2oqPXQ4Ti+rNJ86ieXDEkORY+MI2MM2laMLpgqf6e0kE1zVY0CoPBa+Di2TKTdM8z9c/KLD5iff0C+fUk+TBhtsJcbVheXdE/fxr/xefLmdaJZc7uf+MYh8mKITFXR+czlKvNalUqY01CNoermW6A1rutQl4+woaObJ6IJDKpjCpfcjoW7WHg5FaIuzD5Tp8RaGeLxNDGE5iPFUjVQIrS2xAgLJMTBshqH8j3F9RzGxG4uDLFgDawwBCueJ3o+wP6FTKoojV5t0f0Fur+Ebgu6YmomDwM6zpTxQBlH0n4g7kbiLBbkfvse4fp1Hr91yX/r0QqrFXdjorOaz112POkt5v4b5OcfML24owwTWH+s9FFrm3QaZFJFRWYcZc6YMtOrggsyldUZxcqAPrxEjfcydZUTKnRiYe/FjXZI5QiG0sJ3OyMR/qP3v8ZP/rx49/zA5754vK+LzMaxWqUeVgv+m4rBt7gWP+rFenLh3lSl0FVkJYVF/ulVA+DIqp6SlHuGKKpdG79GTwe0C9Qo0rRq2AmSo7IOW4IPR/EWR0ZFETfRKjWltRlTivSjQ8AGkU9GQZ4jeXeLuX9G31/y+npFZ8VPIBjF45XF3r5PevY+w4t78jCJaYrVKG9RIYh8bZVRvlwzuhbqfIDxDu4+Jr/8mHK4A0BvH2F8B+WKokQ178Ug8sGrJOY6fauWmDpjs8Kp2vQUtCzSNvol44ptsefS5vblHi4mSfdTYcgKWw2+GB67Hr3uuHh8TZcO+DoLmU/mKE+jpjlRgJgS98PEs7sDX3++48ObPfsxEqzm7esV1Tj6lWF98QQXnBylcSLe3zO8vCfuBuJhwucsh6SxGB9Q8waVNxi1lvJs6++JhXBs7YwsUxlaUW1TgMwZFScZNy1O+uQ1y++XuWVtcMaTtWrrBKilGUqJZuzUwEEwAe27I0+BRvwkR1COmAt3U2ojV4667rl++3vpuxX+8a9Q755DgbBdo69eQ3UbcTpsQlpFOj7yjEpFu47SSKeq9T5jLuRyCvBeg5p21HmPmgdI4kePNmA6quugBqpbODhFWgFpagI2Y8sUDdhAnXZYv8aEdZtOEfJlqeD8WlocWTToq7akBs5Nm8W3rY1StUZ1G+yTN3hkDeu3nsi8/sVjzFvfQ3r8W7gpHR/tIy9HsX4JRgP6aE1OFmtdpQ3KdxQThHSYCnMqgDynxQhHtzaT0xzHl1UcIc3NwKgJJSVhoTtE6EcZIeiqpq8vjpewEFIrUoGoCuHzzANq2lFun5FffEh88Yz55R5KwWzW9OsV3fUT1m98XnwUwiW73cx7+8Kv3ky8//LAlArrzvH61RoFrIyiN1BcW1dV9pm2DrO9wG+2pKqYlcfZFc/HQs0ju1LYFWRUcSrgstiisxAZhe/jGpN5iYUa4Uw4o4la4ZyTmGsCyXVMpTLkysshckgZq+Vw3TgtU1DxQL55Trl/Ia+7uhBF2BYbVLchZ5FBVlWBC6gQUN6iraYOhTxMDC/uMc/ex24f89rl22h6pizaL5dB06c93D8j727Jc5TD1xps8KgQQImnQp4nZlWZ8sBULUo5HIW1hdq4WjZP6MM9aryDYSdGSlqjXKDYQPFr9lHGLw8xt1gpVbOFLvAP3xfxvb/0h4RT8KB9xekriiM/7DcCCuC7GhjIgfSVr3+VH/uPfpyf/mM/zZc+/0UqcvMyHI06jvMLCyLQNJW+48CBlFprZWoP7zDn41hVF9bUPKOcVA1qnIVBDlJedqujzaZJkRwzsal+GaswVqOsR4UVap5w2xXhaoObJ1IRFbJyuEcfXrLZOnwv41rBaOx4C4eb45yxXXfYNfSXW9ZvPkY/eh22j4huxawcYxSjJ10LJsloYL59Rry7R2mNVwauHgMywnaIIkAzpsLsKitv2QaF6gK6eKzKOIUYGpk2ptjc5RYijCgfVqZSjsZId2PkfkjcjZlxqnTWsLWBZDtMt8FvOzq2mHkngbGIrkKpBdJMxlB0T5xndsPIi/sD7358y69+vCO2vvrtkMBY1n1gFQL99gk6T4Rxz3R/S7i7J7a58pqKbNosI5iqFsnazptRJbUxzYVjEUX6VknDvKQsXJOaqalSoliwauvAn2bXFeJ7YLVu5WeRP845k0qmtFntOVc5ZH0nGew8SbWiWTErIy2t3ZybCE5iHzPzZcfrb/+rhMdv46Y7EY4xmuo6ogkU21FRTdpWHUvIy4GstJXxyqIoJVOOin4SsOoooFKNdzLKNg1S+g8dan2JMoZKOCOPyQGhShRnvObkKCOYPXpzKQdorRi/EvngqogFZlVk1A0B7TXXB4Q0kdcVYmOxHXl9het61NO38blQfSCGC6buER/uZ96/HfloP8sh6Q1XnQgTUTI1R9HTaFbkeAE5c6rteRS0zuSURVehVqyWA1zXBgjjKG3FPFHnWUjHaaaWLEBZGRHy066xUCu1RCiLwVZqI1SL74kkKgsIJacGNgSpGe8IFyv89SPCa2+hr1+nbJ9wv098Yxf51ZuRX/7GHc9eHsgp4zrLHBNbr3nUGa69IjvIZcSmWdZ9s2ivWpQStd9QcBhG0gGiTRzKxJgqs8pUk2TyRim0EonmHn1MrFTjF1ALRmus0TgrDrO1QjYe5QJxrBzmws2UGGKmsyLalbp2L+aRurslvnxJLQUXhYRs1pfkWpqGSaY6R3Ur3PYR+tHrrN+8J8UE4f50OkwDHG5wYcOT1SVTLjJ1U2eZyDjcSyywFuuNxOPtSsZlw6pxsBRznJhSJRZNRsYlparQpJ3jQUiM46mFoJynup4a1kxVM6TCYS7s59jiZW2qqVIpWEDBD3z+ixjUsYVg1OnrAgo+CQjOm+nfPlT4LgUGr4KCn+JLn/8Cx9EQvn1wUNu3ltsbW/NniBmrYdTgw0ZEMEoU0lxOIo4SZxSHxryLiNa3boI/maRhVhZnDabzGOOkDYHGz4mezDxN0K3lh+cZNe3wfo3TBj0f0LO4JCoX6B9f4Dc9xju6R4/xb7yDev17iBevs8ua3SCz947K2obm9lYkgE1RDo80HdGQGDAV9k1SWSthy1eQrCIZLEZKg62UqpuyO1VG9ZZLNBPE0GmIifsxcjvM3I+ZGKWcErHkZj5VtEV7h1IZPVWoo7gO1sLiKFlMJc2ROBy42+34+OU9ty92jIeINYacCn3nebLtebxOrIPlYvMEd30gvHZLPwwUBXGeMX2PWV9g1hcov8L4FcoHyWrbChF2eXsPzT1R1YSiUOKMXngAJR/vYTEz1XmRe+6k7y7eEhrv18ztYHGtLK3hqKo4t/FLbCc2y+0AFmZSlknSKtnsx7uJUkW4Z8gQMTxZP+Xi6i08hZJmSs5kIFd95I24pjWhkBJ+QcGiM9Gyd5GykY2glJLMf9pT7l5SdrfUeTpOLqiW1Vfrj1m/VuCspyZx8KxxlsCbUmNpF7QywtdxvQT4IoTgJTvSbR/CQrBa+Bmq7UvArYQ9DzJmaT0zmrsx8Ww/8asvRr5xN/ByLxwEe9E1/Qsa4KuN22DRoQcrHCJx/pTPYpFKhWvPzWoh8Kl5hDRIRSRNlHGgLEJJufld6GVstbH1jaHWRK2G2sBozflUCmGpXIS2HleY9QXlcI+PM8ZbnPf0rz0lvPYW7vopav2IfYKXY+KjfeRffLzjw4933L3ckXKmWzk+tpXdlSduDKlXVJspeQ9V2jwoBBwaT2mTKWWuYD3ZOCKWMU/sY2UmU/UMWuGtqJEue305XZaKmGLRcRASbzYiEZ9M0/JgbtWZzJgKuVYus3xdRpBqmiizWEnXWYSvals7h1kSrkOqrEPH9uItNAavLZf9mvDiOXmOmNBaZDmh570Qqm2QKta8h2a5rbo14briQ8A/FpChLh9Bv6E4T5ozcymklMklUzFUEmrxOEkzKg7SJk6tUmAsdGuq7ylhwziXM4+HkxWRAv6v73+NP//rAgWfwlo8fv/bAwffpcDgFVDwzu8/MrZR+tsDB1WhtPRHc11IIafZ7MV+c8qKKVc6v0HnjFohxkolH/vhMra0CNyIH7p4Kmiiccyuw3iHCqC6DcavMNrjui11HClKo9dbyTZqFoBQpPxZUahuhXn8Bnpzia8Vs97irp6gLp4yrR5xO2Vuh8TtIJnL1mlUUKz9Ct2vUd6jjBYGvWpiBtpQopAta63tHsh8udEa3QyGVMmopklwmtsWnwVlHfrcRKjKNMIQC4dRnAHvDjMpJcAxFYgoYssYizJovYxBVYiRkiUo5KKY2WH0GlsjJkdynBmHkd3dhEYEkD5cB775eMPTi57rVaDvOszmCf7pQBcT2a8wMYILmKsnsLmG9TVqfUnRnlSFHS4lu2UGU2yRBQgmapwkmKS5aTyIdbIsFJHWLSkKCa3nuA6rMnQmMBkxsOmdJVhLirFZWwuQMi6guhUMUjquOaJSxAUZgUI1AuaUGWIlYck2cqiWJ0X6vRZHFeN3as2td6nQqsrzVBJWpFgOVBndyk17wDRwUHRFl0ItSSY85lHaZ81RsTayXsQwNLlZqxVYQ7BB/l7r4+dgrpQ5yH0s5ah1kHIhs/TxW8tFy0y4vPd6moBA3mtMFdXWr8KQsvgPPDtkPriPvH8X+ehmYpgT2yCtutDUG1WK8n5AStDdSuytc5OvblWVYEUat3dG5HONhjii2mSRShNl2FHGoa2LfIw9ShuUbfbMWlHj4kCpIcu0i/Q+WvJQVRPr8mL9PY2QM86IPoGLE845usev4Z9+DjZPiKZjPxaeD5Fv3hz48MWeu5s9dy/3MiasMjn6xvCPmDQx37zAMYs9utFo41DBSItJW4oypJqJVfbnVGA/V+6GhI1Qlcgdr4Jj7curoo7SomskTBqwV7WiWtVFG+E3GK2PzoW5VmyV+FMKiLymbhWXtnq9+GYUv+KQFbdT5j4WlDZcJsXUWy6vPof1K/zlE9TNM/L+XoB56KFbUVGQo6ypJXYpg95cCo+hvobrOsyjJ5jHT1HrC4qVuDDXSEwjWUu7rJZyVlGU2ABFzgHnJclwnurWFL9hykI4nFubdSEcKqX4z775Nf78z/8J4RS888WjLsi3BQpesfz+rPPxW13flcDgE6Dg/Fo26QIQlh5rAcmbKlkJ+WdR2lp6PTKiptr/XwRbJGNzvoOQhaBixASkAosjGIvpkbay2awhW0f2HdF3mNBRrUXlSDQriurBX6CmUcYalRbmrnYtcMjc+fJ9woUsDB+ofs3YbRiLYRgKH+4iL/Yju1H4B9edQePp+0vs9gq9ucTNDSWHTmRntTkrBYoscLAGv3gU5IiuCdIsN8+aNp4lYiy15KPYy3LlJkI0zZndGLnbz+x2MrJkFQyTuKLlQpvkXZ6ZkMPKPJAPO+I4MU0TSTtquGRrLnm9N7x56fnGczlAUkpMw8gwjNzsRnZjZIyZwSk2q0u4GLGp4leXqBjJylO6DXp9zWQ8JCBXVAZ0wRh7Kl+37EfVIn3pOENTV6xppsZ2H2qb5TZWyEpZDj/db2T9KbHh7p1h5R2P1j13K9+seIuY9pRKcD02rKQEGSfJQNNI0JXeadbO0nvLIVbmormfKy/GinGFIc/ihUGmMzLu6TQEI9UCp5WAOU1TYNTEIuqLJRViEcfE0tT7ktb4RdRHauIoLCp06G5NDWuy33CYCrskmaM1UKvChg06je3f7RsvQbfgJX3i1EBBLLLJFBpvRE1T3q8cKDnN1FLb+22WyhliyWRVGXNixjBk2MXCi7FyP8v9MVrTeyv3zWmCrqg0tv6vEo2MsCK7vnmNSCrROcvFyvNo3bPyzeWzxiMoYB7Iw4467CnTIEZMTcJa4oyBshhNLffPCCsfmbSRDSeVm1gyc4YxVw4JJrdFXwWqW2P6S0ydxbRrewUXr8HqkiFXxij762Yn638aRlJJYs4VFG9eel7vDVtdqDffIE23TCViQkB1AbXaYIwRe2NaG5UmTpUrw5Q4jJHDYUY3y/hgDdvek/p6TKTaRxFNg5KP4GARSCNOgEbbgM4RbxzeaYI1jKk8EJmtWuSwVeiwK3lfenMp9tv9JXdD4Rv3My/HjDKazVR4FDte3yh6c0X3eItevw7zHjVP8rpGrL/RRuKp0lQToNNgA3r9SFxjQ0e5uCZuLwVMGEdMiVhHcrZkRkqKYt1clIyotlivaM/dBjkLrKO4Ndl2THMmNxErGQeXQ/wff/AL/IWf/xP85R/6Ml94ZfrAtHhsVNs6nIDyA0Bwft69Ag7kfPwxPs/nP/nv2/VdBwwetg++yCesO4+X3DBNy+RbMkjLDkuRkm9uBQQZSar8s/v/OyCGQ7adFIWmVBfWFG1QtjGRl+voDtYOceObHrpmTnKmTKoKw185IismC9PaU7skBMlaHn4WdSaxq+3RxGTOEJMm7gv7UrmPlRdD5OObmbv9gFWFOTnWTrMNnm1/ibl8IqWuUtCrrZSvjaOS0Aq65pZ42Tu2wbPyFpMHdJqp8yhe4hpKCHJIetE8P2YLR3RQyaUwpcRhihwOI8NhoJSCMYopJnI5Ka0vvV9VInUaKPt78v0N8e6G6f6OMUPeXNM9epu3t4+5e7LiMEZ+uVaGXUR7JTbANZNyZkyZmA2z1ujVNTpVlFlRYyYpxyFVpiGjbKLTmewK1Qjit0aET+R9SXWE0vq9cabGSUDBPB37wCwiJXLqyveaj73MGktVow9bNp3lat3xaNMzTzMxxmY6hAjtuB7drckpiSrfPKDGezZ+y9O145A6MIZUDcFLm+jQRm7GmDEl0evK1mu0k3DijELHAUPBKotVBtv3ZOUYUoFpJpVZsvci7zmWgtOOajv0enu09Nb9mrq6pHYX7FNlnyq3o5SEO6tRnQjqXHYXEEd0nITkCOj1Vhja2hFLIbbPbYzCGkMInnUQ3QVTI+mwI9VIyomMxrmeWCq5Conrfo4MRZF1JbX7UJXcl8s1WJV4Y+N4unZsvEaN9zAP8uyUEeDienA9aRbSsdGazjkebXqu1h2bztIbhHg8H6S1cmis82F3JCJLYJZVo4wVMbTjvpV4IBVFizqufHErjLlyiIXdXLibMuOYqCkT7JbV1QW6RpQz6PUavbpmbi2iMeUm1y2gTntFh6XfOL73rQt+55MVn9s6uv0H5BfvM+5eogy47QXm4gpdq3g3+E7233H3tv0bE/sxMhxGcUbVikPnmJLs36WUfS4MJlVEAdFlHinTgTKNVCXAwLjEyvdsg+eyT80dth4FiDCOagN6tT2W5c3lE2p/yaFoXo4zH+4mPryPFKW5WCsGDENRbJ1irSuuepyx+I1YWmuF8IaWz/eAFCHiQ0UporFk2zPT47KjpErOMM0wJ8hF4qN2lZpnFpCLziewB3IGGDHTi6kexznFDK4e7/Nf+Pmf4K/+kFgnL5UBxScnD06JV/3sc+4UfIETKPjpP/bT/Fv/p3/rk/++Xd9VwOD0oc9AQbvUK2iq0piuSpCiab3MutALlKxIg2Q7WRe+9vVf4H/zy/82AOsma7r0OFOtkMCYDm27k+DMq2UdJZs30RZGyQw1YorGGGHzznFmGEbSLO5eRomYjgiyiJAKnCYlYhLd8CFV9rFwyIpkClk79gl2Y2UXK3djoeYZTeVRb3ltZVl3l5iLR5haqCkKEcx2RyEnb8Sgx1vL003P9Tqw9gq/nyWYTgdKKRStKPNEmSdMJ4fmMraolfQUlyZOaU5z0zQxjoLe52hF0ASOUwCGVpJLMmpZDveUuxdMz16y//gFh3GA7j30G7dcvv07+N4nr2EUbDrDuy8G5gyPLzyXnSZoIc+JmU9FK0vtLkjZsEsD9+PMbi7s5kzWifWsmDFoIyp13hZRuqQ9V6qUiRuTvWY5sGkHgnANKotazeJ5oGo9lpdPZXHNxq+ZVoFHmxXjOHG/rxSEgDhXTXA9pl+jxr2AkP0t2nVsr1a8vvEUNF3wDFUy0uA93opwTUpSond1yU7EwEvPB0w6NNvbQDAOZ4C+w8Y2npvkIKioY2uj8x34lVSNlspbt6J0F4xKRq9up8ztJD3YKZ9+ZgierrtA54ixQY7CIOXg4jrmubSKkYxSOmfpQ2C76lg5BUMkmsxUZhHkElYwzvaoLFW8VCuxisKiMobOC9kzOENNll4lnnaG1zeWrc7ow0sRvcpRstJ+LdWCKuPJhUrnLNt1x6PNistVkDHHaY+e9qjxnrK/pezvKdMgxLYoRFHKwilRYBdbX4kFx8zZerBnGZ9S5CK6CYc58Ww38PHNgf1hwJTMxhs2XrPtOkzXU7s1SdkjJ8SoStBw2WnevPB4erzpeedRz3/7tQ2/43HPxfAh5f1/wvjB12FMlK7HvSb6H8UIMdD2a1SJ0kZSbV8CtRbmGJkbWc44wzwHSm6TGsikhuz9pc0oXghLjCjTJAqTWoP1+LCGXnG9DkduypzSMYvOFYzt0BfXUqWyDnXxiNxdMsTCzRh5vp95eYgo49G+4sdKVpVDqpgSsTmyMrX5jKjWRhIRtdbBoJTa2sSVOWexj9YVOyn6qPBObLJzTuQ5UqK0DjQygmm1A2ceAoIW9xfl15zq0evDtZHbao4pKn/9h3+aL3z+C0fn0s8eQ3wICD5xxi2VglY1eJWI/62u7xpgcI6EhGj4GdfSSjj+GapaEO4iXtNkgdXx9vP3f/UX+NN/+8f5X/2e/zU/+Y/+LNugj8pui3RlrPVITlRHVGeOmbM8R5FWFqdDGX3KOZInMSWZcmY/TIzTSIkRp2HrjTgnVulptk5kEw0SI5FDKrwcE/dzYcZQrUG7ZpysLEpbrLXMOR4Ja1OG3AnrWG8zNUUIQoxJRRBsZ6V/uF0FHm9XPL7oWcU7rMrkKBlypVJmQ55GSprFMrpkyBGjfFM+kw1gGmAS++BEzhmlwapKZzXBKLxuhjZpFhGRYU8Z99TdLfPtnvHFHcOHL9jf3hNVobvZ0U0HLn/L7+J3vfYWj1eed69H9rHivefpNnDViSeAFrIAGbFrHTPsU+X5kPh4P3E/ZarSrKZMQkmG0RzaslHYWo9TCTVHyQxTPJLMapopMVFSoual0iM/U7uIyYmSo1QOcj6Odxpg23kue8+u98zzTIyynsZUcWEjQlQuUKeRsr8Xvw3jeHT1Fkp71h0clKOYIPbESlFyIs2VGgtWZbzReK1lbHbeoeOApuBMwReHU0lIaF3PnDKHcUZrCWqJSiqKIRVWYUOpRUSQgOJ7atgwRpmo2MXMLibmXPFGJHg3STMaIevWHIWZDxTXUcKGIRXZF00zRGSYxap33TlcGkAlVJkpeSCNezSaUgrO+rZuGiFQGZSzWO/RxrYRV4fOllWNXHq4dhVz8xH19mPKXhjrygWqa8SwJPe/VkVwhotens+2s5h5j57uUOOOfPeCOuxEMXEayeMoB0YjzSmlUUaUTrVrPec4ixdJnMH5I8eCKll5yoVxTrzcDXzzZscHL+85DBOqFrbB8No6oK0jZAj1VGLWWqaVrjrLW9uAfXPL/Diwdop3LjreXFv6+29QfvW/YPwXv8L48Q2uatTlls5YcX91nuIDZdij/BrSLGV+Lfuza34gpTYV05hEta+W4x63SgStjFLCv2kVtpJmiRPzSE0zCoX2M15lrEpwcUFGUxXcHyZMy6RTqTjfo7qtgFHrKH5DNoFpTOKQ2vgmzkq8q8qS0exTpcSCSpl7Mluvue4s2UJnFQGRRi7t54wtpt5P+ejCqd1MFxLrPhCMOVpFm5oxtWCpnHJNgfyLq2hp01SvJvRWKZyV00jrpl0B/MHf9qWFhnLWiq0Pzq9PBQNnPLpXr+8EFMB3ETA4gYIv8oCV+UCM4OymnpX0FPkTJZfz66vv/gI/8bf+JH/zX/8PeHJ3DcBGJYrVR4OVWJu3d63HEUetOB2KWqoLS/lHCQVcOAo1cSiZMcNuStwdBoZRgsDaamqpeKOkt7tY3J5pBBRgzjJSNeVKVBC0wVqLNwZrjDCpVWaymZUXzQFoBHcr4zPYICVd4499r84anHNcbXpeu9xw6Qo2JmoaIU+UkhpbWERoSLFlBwIOrBM5VGdEIMkbWDlFMBVjKsaJK9qjjePx2nPVOTqnRVd8HFBpJI9ixRwPA3E3kO4Hyj4RbyLTPJH3I2VOrHNm/T2R77l+h0erLUMG4zyrfsXl2rF2YkttlCIrJcqJVe7d/ZS4HSI3Q2TOlc2cMEqz8ZZtZ1g7TbWKkqVXWtsYKccpFJlZX0BBnpa/K5RFo2JS5Jiw8ySCNymSa0GXjC4V69asvZDbOmeZooyJWg3eWHTYSCn1cEcdDuQcMbVgauHx9Vv0XsYRs+3Iysq6SCI8U+aKRxGMeGvo8Q497aiHe3JJlDwJ8SwE0ZQ3nnB0ymyVniq9fFsUyTmsX1O0lX3lOlLVpJKZU5GWUF0Igoh0dSokp0jV4FwvExBKNPKzdsQobYRFgttoWRvBGUzN6DzDfIBxR7l7Qd7vqdqiV5mqPX13xVTgQmk6LNoHUQO0VoxsqihV2gyrOqJefoP64huiHpgSql+hVltK2DBhOTR2fEHROXkua28x8wGd9jDeke9fUne3lGFPHvakKVKmU7VIa40ywsVYLm2S9LNzY7BnIavWnCk5U43Iex/GkRf3Oz6+2fHx7Z7dMOONohRHZw2Xq0qqUtGUkrNCGajO8Nra4cuaN3pFjsIzuTAZ+/JXyb/yyxz+2b9g+MYz0q6ADxSbSPcDaT0QVx16JWDcrIVYGbpA5zRXnezTRxvHfjDEVDCmEkxl5RS+7XNnaKqhSmTWc5K40Eiei7aD1hqdJ2wacSScV9TLjYyuAjHGI59LyvB9k9UW2faFsOeMZuUt1xhC33Gx7Vn3QcYicyZlw9SI1K6NAnfL6be0PNpZUQpMqXA3JhkBToWqIn2XuYiFTRBJ6pWuhDbCq/TJPzW3dZ8byM1nxEKtGyAwIvHujDqOopZGfrVxaG/qWxx4r55tx8NMPeQV1MpX3v0aP/Yf/4lTJf3buL5rgMG3QkIPUdUryAseqNI9vNmFr7z7D/jRn/tf8DM/+Jf5A09/N7/80X8OgNk/F1TqepTtAC3tgSKjU4t5jG0zTrox+nUjkEh5SMxSUgZKYZozwyz+9Icp4XSlM4ImnZaesG8ELGrGGgtVE2umd5o+ajLQWUvoPatVwFpLrpWpt1x1MB80oSYe9ZZg2zz4QopUMuteracIDwtvNKtguVxLb7WbbjEqE+Mi9yqIXjcRolrK0TlsaYO49v5XVrOxmo1XXHWGees4eNh0lt/yZM2b247Hay/20Uk8JOqwF5na8UA6TOSD+DmorLFYUork+0L8+JY5vM/KaPqa6K7eRF0/QbmADx19Lwedt4LkY1UidbyIMbWvtRZyFh+HaRrZ7/cc1o7Ja6LOkCb0MbClExiq5UguLEmcB0uplCjfW9zj1BQpwWGmiEszqplrqYsIPuJUR2fVkb8y5oqeC14rQn8pVQPfU/c7sdadZ3SW+3x5/Ta161GhTQZUmFMmBUOaNDWNdCrj6iyuceOeur9F1UImk52jdL1YbmOweKw6KfvlspTqYc4F67o2x1OpxlHbeKEzqqkR6jbKKpmmM6pxemozQpKWAa4T0HDW6hBlyHa4UGEeYNpTxgPpXuSsy+FAaQRIbTpct2YbPK4alO2wocN6j7cGpxCfkmlG7SfyzTfIH79LefkxZR7Qvkf5Xtoa/SWHSWbLxyyg3GoxqnJlQh1G6nxHumstiGFH3B3IUyRN8Zj5K6UoWqOdRWsx6VFayyFp7PGQZFlLKpLjRKyGaYocDgdZf+MgnJOcRcTpbL2KhXPbYwZA4xQE7VnrwtwB0VDun6FefJP03j9jfvd94se35LuCKRrrDCpriJl8mEiHCd/2XB32aNfjbHMiXXve3Hbcv7ZGlcRuTKyC46ozbLxiY2WfO62PZXK1jO/mpv+g2jQMclCbnDEx4lTG1sjV+pIpJmKMHEoWN8ZjAtOd4rfSbVIEHvVio/1IWfxqxXq1JgSPUYqUEged8URUqvT2BF6CkbUpAU/StmgkXmkt0xFzysSmb+GsVAu6YDBG7rlTCq8rpp3kyyRbqpU5NfDWzgMHwudQSuToZwFeuiQhZ4IAdqVAL/OeJ4rh6c/tNpwntEqzKCYu597ff/cf8GP/yU9825WC5fquAQa/5od+peRyBAO1AuUBYFCtb/OV93+JP/7zf46f/YN/kS89/d3iBjbeAaAPLzHWk3NEhYrvtsylQobKohRXMW2KwSAHkfRFK9ZanPPY3IxlqpDvRirBgOkMQcNFsFx1lpVXbJzB5RE1DfJ5jCO4HuWszHRrzSoB1uGDo+8tVmpVlKCZQmXukF6bhY3TmCwqfYvNcjVOSnntfi0z215V6nAHcScz6HFEldT6cxpjfSMi6cYmryL2kyNWy8hcZ2DrFJcWnq40oXpKdWw7x9uPVrxz1XPdO9YG9LhDzXvK4Y4y7EmDzC+XXCBLiT+4gOoUhYytBjMkzO0dYf1NrLfYoLHuCqcNtipMTqisjoe/q5lOQzCVi2A5zIaULZ0twoKnoEsiTxNpssx1QjNh8tScJhu/oLUXagt8NYuBUI2ZmiQLLGfgIE8zJnjyFDH9gB72MOxJ62uy3xL0CtfiwDAn5nYwe2PY9pfozR1lfwvDvQgMVZGyNcbg+0AIF+CljCocFAEGZdaSde/uUbVpLmThgggpbCId7mRdV021K6yqDRwo8ZMHSlEULe0YY91RLXApja6Kgs4RjD6OK26cYWUVzkoFTRCFkDIzwgdYKm0KdTTesapS00hOB9Q8kA93lMblUFkyb5UiqkZ0nnCbLWvj0b7DhoCzrskVF5gT0zAw331AevY++cWHlP29ZPTeozeXlP6SfYL7uXAzRXIB33uZ5NCVfP+MNN/D/iXsbymHA3mYSVMkT7NUhxoo0LppFaAozqBybesjC9egiutiTRHSTEGTC8xzIc2VPE3okuhUZe01Xlu80VwEw0WwBCMGUK5G9FwwWTe+VG1rOxIZSPMN6fAx6eU3qbd3mCHhq0N7LXP+Xkh5ZCXOrXMkDROq26MPd+gGFtfdFde9452rnjSvCFX0SLRSXK40l1b2d2dk6sVqRL+lJIkHyz3REi+qneUelARxlLiiHbUovBI1SbNMCFfIaDEeKkn4GSVh8sTGeepKsfKOZBy+XxFCh7bCCUoRBuWYTYAkvgW9VWyswpPQ49CURDXe9SjXkWvlKlvmLNMRkwxe4al0ujYABH2b8DkmC0qhbSAXgb0VqQpXwOgKSAXOW40Z9+jpHhMPQlxOTXVz2gko0M32vmmp0Colx2qAUqiqz1re55UCSWp/7G/9T/npP/rl7wgUwHcRMPh1XUtL4QwMLGSOr7z3S/zI3/tJfvZ/9L/nD7z+uyGNwgYfdwCo3Q34Drc15OJRtcdbx1wSqWoK5WhusZSO1HxAxwPkKAInvsd2F1ht2xytwdZMpyylGHqruOwsl8FwEQxuukOP95AnEZIxDp0nTNjiuxVDhouqwFis13gP3kmgrU4TrSVaD1nhKeh5L6NWaT6NUxpHKqeRI0Wl5EQcduz3BygD+u4l6rCTEpjWGO+xIWB9h/ahcTVqG02KeGfJGpwq+BrZ6sTrAS61xSrNtvc8uXC8trJc2Iwd7tHzPWV3QznsYB7J40yJmZrblIC3dOvQzGfArAyh6+i0o68ZFw/YtMcWL9MHyBzx0fUvRuqcMHPhwmlyZzB0Rx8KrRUbb1kbMEUC95wmnC5UJTK3ajnJyol9XHM9ihsdv1eFdVRzRn5byXNk3iu0cyh3j+pfMK+fE6/fpHRPMG2Dz6WSYgVmrA506wvC+hq9ekk93FNmaQeUW09ZrbEXV3SXV/iwISstFsfZQO+oMVCGO1L21BzIc4C6QeeIDUFIUCmRxwO5COFW40XZUkPRiytkI2638V5FFU8AKmvr0RicqaycPmVKrYrQWyXgpAXiRjNsYkWiWVDboeCUKIfmw45cRtR8oCbxGnCdOD4W46ALmD6geo9dB3R/gXIOlLTRtKqYWpiniTzcMt4/p9w+F3+QnNBda9Gsr4nhgtt94vkwcz8JNwIacfVwxzw+4/7lN/H7O+oQqVFsduX5tpaRQtQfVT2uA4Vp4PHse0fr0UJNicpMjoW5zIDHlMjawKPe4o0Q4zqredR7LjrDhdO4eUfdHcBblFtsoBWmZFyaCWUgpT0pHog1o7VDdz1uq8gmQxHyoPVWuA+5UGImjzNuHmX/+SBKnRUu3Iq0sqQLR1867gdNqoXOwVYnfI04FfC6CT9FmVBSCCjWXuJEDROUSJ5nas2kw46IoRTFXo9EvRKy7nK4tlFnY5wkVyBxix2d6/H9mi0ajMMFi3Na7LapzBpmpUnGQVZYVekNmHhATTKRonKU7Hs+oLotV+HiSKJcO8OQxCtkEzSXnWHrYG0hkGG4EyJ2S9ZKyTi3IhbFzCKgJVNI1ii8taIhUSZMmSiHO3GbXOLGuEf5Nt6oDRTT4rM66V4cQUIFzCenD979BX70b/1JfvqPfOegAH6TAIPaPLyPxMOFLbiAgkacoYn0fOUb/5Af+Xt/np/9H/7v+AOv/x5UE3NhntCzOK/ZNMtGrBI8slbgPAlFrqdRNdPmxXWJ6HgQzexpECa39zAd6FaX2LDBAp2yXAcJqE4p1l5MOMzhmRgeHe6FlKc1tluhtETqQia4HlxHUQqlMqZOmBxl9EhVqi3kFEnznjwtXuuCUqvxgkyNE2/yxqC1MkbANEbqcEMcbnDDHT5NonTmHbZb4foVtusxzrUsiXZvEzUO2KoxTTGvKyPXroATkuc6VLaMhOEFplh0nWT0a39PHfdC5krSg1UKjNEYK9lx8RltNbaz+D7gvcdXaZfocYc2WtQJjQNjJKNLWVTTpgnGhFWOK7/BKU3vPDEBSpzd1l7Rm0rZvSSbTPXC8xB5ZMkAQQhHFVDLgDG5fa80QqtCFaTVkDN1ruSUKbUQUyRq4KKDNybyGxbtrmU8MFf2MZGKzPP3VvOov8JeP5VSbz7NhatpwKYBmw74skX3G5GhqlYOq5worlBMonaG0nnKsG8kMMBYihbGdZpGoqlUXcV6WkPR0s9eTJ+UUuhc5JCv0mYx2rEyjt77JoHVRq2oqDShxyhja3J6ykiYVUdTosbTlDaPqtQ0EcuIyZI9G62xqzUhBPm3VkRu9OYS1V+g1xsZNzS2VVXF+bGMO0o6YJeKW4ryd12P3lxhrp+S+ivuJiHxvhwSYy6ssW16pJL3N+w++BX44OtwN2IzeOeEBGqNvJ4RAR5lFxm1hYxGU75TD763SCsLL0d4B3nOlGzojWfrFRrLVTBQkYqM1axtwe6fQY3UzlJDAO9R1kjVLovEsx726HGHrQlVNcV7VN9jkiaZREkigWysxphmv51FWTSPI8rtqXsvImurhBkPhClxSUR3lbUSJU0ohDKiJoNJDluNVHVKOsZdbQw4B13f1owQ8uZ5Zp72zFMijpG5v2LugSJxe3EbLLXKuLc2EluaNwVxxKSBLqzFc8N6lJqpVSS9fS1knalBoas5ClLpOEA6UKc9eTwIoLcek0ZUnrlePSJYy9oZYhUA642md7D2FTvt4HAL805mzqkQepncMA6nLcaAbmJ5zmg6b1l1jhATxoiGSKmZmkSaHRDpZyrad3LvdDmrFLQKghIwXY8dBtM0E14FBd+CiP8tru9+YKDUp5M0WEBCGyks8vUICv4H/1v+wBu/R2RKl5JximIfClhrMc5ivYO+o3Qe58WRsKgJNckEgtMVaxS6zZ5zuJdDLyWRXu3X6DRgugu23QV954lFgKAzCj3txK7zcEPd36HjhHeW0K1xnUd7S/WKuU7M40Qal4AkzN5MI3PVciQFqjSh55myVAp8B8ZTlZFxmtZLro1RC4qaMynO5MOe+bAn1YRZr1Guw/ZrTL9G9yu0C6fSV62tlFiIw8j48iX59hYXI65UahEZYX2fKBbKZkVdr+RnTgc5tOaRPEtmdnykRmGDbXbGEpBtMPI8jJEMNCV0msX/vBZRHEPIg2meiPs9825HShllAzpsWIctfViTg6G0OXpHQR1uqURUsGgXRAlRLeoiRlC91mI6pDPKKIw1pFKk1Fe0zIMb3TwkNCVF8hQ53O243+0Y04R5tGVlA+byDYy7QilRipxSJeZMGBOXnWOzXmHW19hHB6kczSPGB7EDpmKpGDKmJhHTUQZocsbBg9qgvKb2nYybzhM5yez4nIu0CnIkp0LRFaODtDaMOoJFa0TXQ0yARP61ZhlrNErJQbKsA4Ai5l3CSRHRp2o9tVoBHu21l2ke1/gfZRrJZaSo3J5z1+a+FcYKwVAFkUKufiVg2yqqaUG0Sjaea8bSPEKMEUExpVC+wzx6Sl1fM7sV+714TtzPmVKht7UJy1TysGN+9iGHr39AfnFPZwPbzYbVxabNsTuplplzIKChrQfVOAbKnNRFlTZt3l/LlEetYsM+JRSOtQn4zpBzRWuEhDndo3f3qGlHShOzNfjNBrtei4y3abLLsbk8tjK1QnQhsrO4kKXe02Lj8v6Wq8Ys8sGzlPjFeG2SPbQ/UHYHdIKVshTrUFoqkTntGZlYcY3ru5Z8Lcw7I2Zz/QpTxPY6pSz7YByZVCJXQ7Urqs+AprQES8h8yxpp3fySJLmpBT151HQHh0A2UoavSmJgZWH8C0gXW3fwXqGwlOqJZWY67Jn3A2UWnQ2VZjbdBavVhphFHt9pjS0z6l68QsrupoHrFs9zQmmHth3WO1xR1GaY1QXHugtseo+bE1p1EB3JWXK0LJG7pigVpxRR1lFLU/RUDSAUJG5rULVVDiiAEVDwt/9n/M0f/ht88fNf+Jak+m91fXcDA6UfcgtevY7tg/pJUPDmf++oXS42u63n3ja97+RAtP0K3ffUfk32K2HOKkl9chICoVU0053WT5yno/FMSRFyRhcZF7QuYJdAfhjFqe1wS93fYfLMOgS219esNltM6CGItvxhntnPA3UYSa3vLZr9S3uk/WpyvbVUsBasR4WORce7VI4mNtCKKKg2a+3Ad6I+iCJrg1lvMZtLzGoj78f5JnsrS0tRKWlm3t0yvPiIctij0ywmM9MI04E0jQwUuqtL1o8fY5w79l3zNEuvfsk6rMVYAQnKSK9aO43xTtzPjGkkSKn+1Hls2uUNWOQs4Ob2lnh3R5xnivXQbzDbUfwnXBAL3FjRyLPRVglPgYrRpklitvKetfJ+jUVbWS8FMKUcJber1pR0JqaS5TWmYeb+5T1TnggWuvEgVZ7G6DZaU5GRqUOu3M+FbWfpt6/J+9Iahj3GGNx6i+1WAhBUUzSshXMQrFpfXzmxsMYYqu8oJUvGEiNpTpRhbgz5hFYO1waqCzRCqUaTxBuj5JMQ1eJOWOsDYtS5qZZa3BtLbmoeBactxRRpVSDkLF1l4qNk8cXQvcd7i3dOPqM2KOvABdl3phFom5eF3GjZ51opjDFS3VpvyTmjc4Z+DRdPydvXOMyK+7lwyFVG1BpbfZnJJ0fKeGC8u2e6uSeaGW88q01Ba4M2Gm0N2hm0tQIErZFD1xn5OysGRYsnwyKmg0IkgXM6TmDoNGH0gFoosiXKgRX31Psb0rCjpBnrPXOe8WRcXZ/0NpocN22/C2gRl0AKKBMpsUgSaqXqoa1t+74IOJhmjBnJpaCmkRQjw/PnDDe3JMQ4S4eVxBATKNYz5InJKTr36Bgv0VbaSk68EoTwbDClkg+DxBNakmKbX0qSClVF/EPsUsU00vZQihM/Y39P1YqijcS1JcNWuumISFwwxhL6jnXoWfmAWXcwBfIqcNh57l++ZD8dyFl4DCZH9LzHuA4Bmlmewbyj3N9IkjdKG0GFHnx3jPUyjSUxwljLpu+5WK/YBIuxBaUiJa4wcSSVTJzms6OpHAmbYkpWEAp7Awe1SHlgaX8r04jyf4q/+cP/IV/6/BdOs3mfIYv8ra7vUmBwGuX7lv/qTE3wVVDw6v9XTaWstNlr1a1R3Qq7WgujOXRk7wlo/BwxaiCXjG6BatF0P2aYphkYpSiod38v2uHRH0tllCTz0Yd76jTineHi8pLLR0+wQex4MXYpVnO4u2Xe3ZKacx0LoFmC5HFmRuSUxVbXUJWlGE8xrul2LyIzFYoSy2RjULZH+xVsr5jGA7HfwuYKs73CdCtRJzPS210MYypQ48zh/pbD3Qux6J1H6niQue/hQJ0HYkr4+cCFBbe5kA3QTFJAMiqlFdoaanBgNMa2v2uB2ASHcbb1WRsbegF+pVBraaOEE/Nhx7i/Yx4nYcjnhC4FtYoo1zXlR8i1tHEpA50cSMoYWTvWnsClO1U0rDHkaW7EOJlQyEkqBtrKe1BKkXIAB1OayaqgvJTyS0Uy61pbli1aCLkJ3uwnTacMfXeJqVUklgG1XoOTwCqBs5FA0Q8nc7RpMrAtw7UO0wi5ZZqoaU/Mo5gHGShVi3RsbU5/CqqqpDSjU2tlxPmobHeswJ1dFU7yx64es56aMoWZahWqyOuj5EBIMYoRUZ6wXqTFbb+Wtb8cqNo0mXFzqlSxABMJpCh13L+4DrW5RKlGyrV9E8mB/ZQ4zImcy6n8vRCVl5l0LXbmmcKUZnBg+oANDtO5TwICa49gwQQvoMx6kV02ToR6loPMyEFJLZQ4kmcR2FkCu8qR2kh65XBH3e+Er9QFnNXkrqN4L22bhaFeFz8TUc4zzlJDy0KNGCgBsIAaa2T07qzVUeepAfzIvLtj/9E3GW5uxFLc96h+JWuwW1F9xyFNDKuei80GTHc0IgNHdRqlrchvGwsF4m5gmor4gfgVxfakqkg5H30qVFUkVZkLWCPqsVpZiaOxNoGx+CDGyU0zRx8PlKaEjt5pVv6S1WolzS5nKd7hfUCVwvjxx8zTKAXBWqHfSIKmdNNnmUVbZRYpZJCecT3uK6nE1FrEvbRkjBJAG4LHB4tRHTX3lNWaPI8yAt3eurLuwXjrcQdVAQfHccQGClRV/P33/iF//Of+DH/zX/8PPmkDcNoVn/H9T17fpcDglUupTwbHs+sr759xCt781x5UGVRD35UK2rB97SkA26dv4tZbVFij+w01rCiup5hM1QbrHDlGjJKRQzV1qHGFutrCYSdOee0BKx9QzeZVgpuUhlRO1HVHHVeoFOmCZ3NxgVtv0K4FGa1RaMIKLq+u5VCKswioLGSW9lXrpYSpRVTFdyJoFFYUvyG5FT4WwiLsQsWoNnpmFStzhRov0fMeXRLrVUe4uEQFGXGU3pdkAieUWtHWslqvePL4MSWOMB4og5MAFTw19VAKqxCwCyg6b/9oKccu2QwgAKFUCWJaS+D1Du0dOrgWDI4DRC0wWkyt+K5jc/kI6wMpF3m/vpMA163BhcYI1m10Dpy1rPqA63r0Qvw5vr6sMYyBWcbRjNJoG8UFMhrJfqNUE3LKGO9RwXKlK+aipxjF6vVr3Nu/Df30DaaLR6SQSN2WyzkRi2hKbL3lau3ZBM3Ga3x6TfwHasY5R1hvsf1GDh9jT+pny3tcWM4taxewalkmdYxy9Diy7egKFGUpzfwotW2x6HPoLOqDKs8CbhdSYa2UnE9VGpAysmkkqRbY8aKZUW2gGP9g3ttqOQh1mtA14TX0fYfpgriALsAAIdsegcHyGZfVp6QSpmrF9lu6a2kfdDFSlKHYjtn2mLmQp0IMM2FOjCnjNFx4y9ON59HaEuxISb+DK285PH6MzpXtdkt/ucWFgHINDGiNcsKf0K1SoKyTPW6s7HfrT99zIkiljcUZx6oCLhBTEoC+ZIUlQewovaOuOri4QJWMNZquX+G7Tiopxkq1sxWnq5b3pINDl4ppLn4lGqovD/aRCVb22ZEr065SQRWsNlys11hrmwKhKEWqTgzZ6FZo17Far+R12kF5XGMlS/JQMsp4gu24dD3uySjaG35N7bYcMtgkJkO59fedFvJq7zQ2rtDzFjVdwyQtRwGnAkpLW3tHwyWtxaLZedbbC8JqhXFeEhBjBPBbxwYovmOcZqp1qG4lolfmKCsn63peS4ydp2Mc16GH1YbqN9RuK4JfWbw2jHOsVz2rLhCcaaFSRliD98T9hj7O8F8ilt9KJLSX8+f8UrU82Ndfee+X+ON/98/yM3/4r/PFBgqq0g/2wXd6qVrrr/2v/iW/1Fuqjv/V8Mp3zz7X0tuspZU8FzOPyFfe/QV+5O/+JD/7B/8P/IE3/runA+kIDk6vs4w4mr/+3yH96V8Wwl4TByquI2tLzCJqsYyTGsBbhSkJPQ+oeBDb2hzPXrtVEpTm3IddNNTbjHOaT1lYQ7/qaEBz+py1nLKdV7O2pV2grBNrXCNBqXjRhh8zDKkQsyjQLcSxhVEerEgLqzSimlvg6ea0WdvGKn9wz9q9JkVUls9ehj21ycfWaZAS9PLeW3ZPTjICFrOMUM3NqGQRDSpFZti1lkzNGWzvMd5hQgM+LkhmY9zpUDxfO3oprXspBTov90WdgZtaWBD7soagHuWPacqHR/XD5jh49ExolaFa6rEttbDTAbQz2PUGffkY+/o75O1TJn/J80aCu58ScxGNgN5pLoNl5bUINlmFU1Xu75ny2TEwnH2GU8m/Od0df38GxNr/kexHsrpqG//kfCmB8AsWR8kS5T0sFapjG6P9+GNpV8CptJu+w9f/lu/VgDrL2vQrz2/Z/+f3yDhiVYypso+Zw1y4nRJDFBlkrxXbYLnuLY87S5hvMfcfkT58l3L7nLTficLhspYWDoGW6taS+SljZY0ZK2tyWW9O2nPKWDkMHsSBBnoefIbcyHaz7JkzY64HV6uUHYWE4iRcnWkmz5E0SHuupPSJfbRUO+wCss8rHeqUeR9jSeiPv3S/Fj0LE8TwrVUPPxkPKg9GxOXmUa14cEyFpswqonFLHLKNwNdbTWcQAuG8hyjgdGnVHmPJ+bWAnF8rbp7FWGW9VAUXI7wlDaj5YRyQFz46i1a3ovierC1zEqXb5TyQCTXkPGgkSHFlnaFm7L//r5L/5//Ph/oEy889G1MU3RnDV775j/iRv/uT/MwP/Xt86Z0vntb/q/v//HXa9ft+4Af4x//4H38qevjNUTGAVv98+K2vvPdLDRT8Rb701r/WxoqWBXV6CMtVls0KFLcSRGk8xQWqtkfrzFoWlAtVK0jgjMX4XnpdNhzncI/XEZ0vXws1N+c1nYUL8OoH+ETvqJ4CHzwMoIudqzbNv9zLr2bqMaaT/WcuMh60LGbhD4n4TDIKpwImdE3euG3yV9/P+fhnjvJaomgiv+9Wp+xukYZtB0tt/b0KmABKJwm6zkjfM2XOj3etpc+/ZDvat1Jt61cqFyT4tqBdF2C1BLoWmI9Oa8f5Yc0xS4ATX0O3IL3YYMe5/RxPjbN8PdMIkNHGfAo6Z4cTxontdb9BXzwihy3FdsQijO1FvOYYQ2sl5kLMioFMKcKUttqfYt/ZVj8x4w218SWrUlDEubBqc3pOx2eo2jrRDwLieRwvgFamCRW1wcVW3pQxvPIpzbylmqRA2aabIQGu1Fe2ZyONVW1P/ASVH67xY1bUnumDipU5vl5V5hTT6+lRpiR6+MsBFCU9F1nflqHaVrGNpeBsB2GLfvKWjCpvdtR5PjLsH+yBpWLV2gQLIF9AwQNAsACaBkZr64sv90yyVIVQ0OXeYZ3Yuzf1xMVkTS3tw1IgW1DTcUGYdihqa8hTOopwnV8P+RAW7U6g5vhVt3XbwM0CDIp2oIVjJHvJSkvu7JA6/rR6FmeVIteT70vM4lUQz0jQ0hpUKCVk3Fo1neulKmLERlqlGWyrVC3cmgcxcFlbpzh+/NYr1eTa9gAtXgpv4yw2w0PwoZf1LHE1K0tMixJu0zFQiqJFDl9rS3FBJhi0QSXbbJqh2PDponyLXkFbY1Lp/jf52R/8y3zxcz/w4P3/RqoF8F0NDM54BuckxHbDvvLeP+CP//yf5Wd/8C/xpTf/+7KYzheu4uygU6cgtGScfiX9fespyhCLsPlFAwBopJnS2kLkStUW4y2aNq5zXMCvvPMFVdtCPcvq6hFtf6e34lRCXpBmbYg+K8OcKnMR45mYG/lwKWEiDPRSZDukCrNaxsnabVLmNKt7/Jkiw6yhsa9j+/kalSSQq2aGUqdB7KKb7wCLuUzLurVJ6CY3XH3BvJINLMHuvDQrPVwroMCHVm5uAVrpxq04gSWUOStJ2xOYenC159GyhYW/obSVZ5SilIXzIsAi/IJjRvLKe17aSPgOFXpqK6NmG0iz/HujNEZXHEJkXIJoOpLj5LnYev48WuukLd1Fc10OcumTCkDQxxbC+VWXda5t44kIXK5w3FKNm4pu/f3ayteLpsOrr3l83YUX0L7SXrucvTZqAR5tvE8p4dso9envdZnrXrgGZ+9X+BryerJ9lrl42aupiFJpKrR+vqIpjwsZsMWAVCD7gOq2KETYqU4Xwpdp5eTPfMatpM754Wrdg/24AInTffnk2mMZaywZsmSnS2zQjShNq7RRWivSWLHqtg7ijHETOs4YL22uT3vPqqk1fgIQNDDzoC1yXmVrhyJG/m6pGJR2v4/4Wlb22fOox+ex6BXE9nFTq6yptqDrol4v5h1414nIlvVNgTS3SusZv+jVdfiJ77xyLfHyvBr1KdXQs5vW9oCjIAqhKUusTIXjZklI0giKGXDagdeo1KpzRSSRMf7UPjpuuPLg533lG/+IH/l7f4Gf/cG/xBffOQMFnwBAxz/8Wp/6wfVdDAzgAThAAqGqhb//3j/gR3/uz/Azf+iv8KW3vu/YI1rYnfJfz9KvluUcNzFQXWgqgaoFl9psclvVoJ4WdKmVpBSmqcCJrrlHuxbIOX9si/FO/WSg/bQS3K95C85K/AsCbgs4FlqmdHIUyxWmnEm5noBqFq2GpPRxvlza/s1BDfXgAFr8whOSzVstvdeqE4tAh2pIXCmDto4yT3KIx0nEXpaqwSIZWzLGp1Ob4fjxTln/MXgtwMA64VKcZWbLc6zt96dxQ9n8DzLPs411TlQ9BZ3czG9aNcCegtIy0bKoAqpXNvYRFLWWTm0tneJ6pgbUUguWgJQjSyVpMEWsdRE9P1xtfUxU827nCAy0UpT2+6rlWYnBjDqusU84wcGxFL+AgkXX4myZ0ripMgHRvOdle9SHr/ngtdXyXz9xcD94bZA1hWprRQhxAk4evt9TRipVgkJrNcNx1O3B74uM8S4HUFqsihvRrZZK1gpLAw9VhKZsqmi/BqXRxoLtUN2MXkrY5228s3u59IqXQ/8TYGD587JHl0PpU9afJAoFXCtlt1/1bD0u7bujQVMMR6OvGj2qVbN0/tb7aal4iAZIa4VYe+JE+HBqSdpWKWj2yGh7NItbMuZ8BsxOz722Saj2XBbAVsvDGKQkQQnGUFOlGnmNDDhtcNagbRCC5jGZ+g3Gy1eB7Dmv5WwNLwlgXtw9W3JY6qecBa1wXdoCt9rIxIOOkNvec13TfziLNcoc15YQ5f8CP/s//nf50tu/t20XfQQtD3hF7ed8p9d3OTCAIzhQAje/8u4v8KP/6Z/iZ/7wX+NLb39/K+e31Lae3cBzxNh6o7WV0ACK8eIEtwTwXI/BppQHcESyNS1EvljqyWP7LLM7ZXoKjZQ+TWvJSsU+n8ACfLLS8KBfd+pJPSzjiWlQKa1sV5bgKAFysW8WoNAyVCR7UklhjeibOyuHkDWLLWh9eBjps0Oq/TwZvbNob2QyQbXDOBlqSdIyqRmib1oLi3bE0qfPZ6Nwrx6y5lsHrqUicN5/XsrNDyopp8Nl+bWsoOXrg2fRQIFaKgcLSFhGRV3l1V470Oarl+kUSzWB6gLZeKZUm25BlayjyLOoSyXqmHppas10VYMRp0+tK6aKDv2yxpZnIO0DOchl7ZnWXaufXEtqUeI/BT05bGtb0XLls3W7LDu93KkjV+bsOssYj1SedlAcf/TyG90iqEYaFcrIhqilrcjTez0HG7n95hiY2/s+D9Tnh1DM4gaZSmUq5ZihmlJJFFJRpGZMNrW9HFwvh6aZZOw2J5lKWXrPn3jW+jiR9InK3QNw8HD9nd/r8wTiE+tvAQLllV/Gy0RCSAK8c+MbLHsqL///08GMfMa2p84qBgvILm0sWYjHrnEEwilrbrHxHIyVcg7STmsh5UKmEltPPmWpkJ7HIJs1yYh7ZEE3G3ERl8taZLS19ljTVsSncUs+LU6+8tlfBZrn66p5Y50ADqc/f9oae7i2JaFa9lQ1p9hojT9Obxwnu2qWZ3Dk1qjWPmig4HM/cAZWXrnOq92/jus3ATCABRx85d2v8WPNJfFLb/9eqPk00oQ+OwHONKnPN+/ywID5lcM1t7LkQjTJpT5AiqbK6NjRo1xJxq2WMi8KFqeuM8Cgj78XhKD1qxWGTz7686CyoPPSkHuGY2UjN0CQK4wxM5VKXDKn1jqQA0d+gkliq2qTABhrtOjoK5n1Ps58lyoytEVAQlIKW8EWQf3WdpJhZgvaofIkgbGcyvLnwlKLA93Ccj+OMB6ze/WgzFkbsayctQeqXkhEZ+ScszL5khHX8snDarnHx+eiTBvusCzGS6rkh5nKGQHvQaL96royjmpE1GpKEhjHXIgFxpSZGu9jeR4ANUHV+eiQmUrFGY0VjrX0xRvQrYiewCIUszzLk7e7BMBlDb2azS+B8DzrhlMW1LbLp/7+PFydH5fnsfnh65wARi3tfbeWibxfAQjn7/X8PS/vd1nnr1YIytkeTY1TkEptYLg8vMdVYsbUtPKNtiyALCMmRa7zqBxkZLCBw/oZQPBIvDyv3H1aVaY8PHjOr9MaFCt33VwjFfVUpVrW4KKbsgDqlk2ruj4R9FI8ZqUP9hScWh/nhOVjxcMd/76acPRYqdoek6Sl2pVbj/2kXijfr8fMekmsytn3OEqyL/FH5ImzALUKoVSqM+QiInJJq5Z8qGOlctmn5/Hys2Llq+uIz1j7CwBYqh21fX/hZB33DJ9yBmhRPy2lYrVoohbdWs6tegBSMZBWsxbOSKtmf/Xdf8CP/F9+Utrfb//es3Pq1Po48W4+7dN++9dvEmDQ/Kj/45/gb/7w3+BL7/xAQ8pSKaj1nIOwtBBOm1gY1OaIhEGsepeFHfOCEmWBp3bwnl9HIKCF2CSHKHLYcwILGllEpxL9J8HCq1nap12votrzrOkIYlrGtFQJplKEMFPkMwgwOL2mbqVoq8Xx8QgU9EOgYI3CKi0bddkE9WS8k5WYIBltqSahsj0yzym+GRNlmfNeSqRVvAbgbJMvo6RnXAEJWg+BXDX22H8+BuFW+luykmMAOE/XHjzA9ozavTdLCV1JABJ9g1OmcuSFfOJ19DHAHjOrNh46l0aCK7CfxXI55iJthfMpBrUILUswdda030uZqRZR6qsKdDtYjUJ+U+opG28H7nJPl59wXt7/VmXgs9X26YvwO72aX4KqrcJBFSn47/D9ZvjUKoEoenICBGdAeC4tmz1+lNM9liux9gIOcgvochh5TNMOOB7On9FGOQcCyz1dgEBpB85n3+OH92hJGAzLerSnic3lvSxmQ20qqC4AwJ4mPBYQs+yt448xrZrW4mBZ2h1mAdfuCAiWtsEykXWeeCxx5rw98FlAoCyt2G8Rd6zWZMRlMtdWPTBahJJ0lapBa9sKoHgI6j/terV69VkgYGkLvFrx4AzsZKrQPOqpDbJctoGX2vZoPnvWtq0FgKIM2goXSECn4ivv/gJ//Of/LD/zh/89vvj298vSOCffLuDgv6brNwUw+MrXv8qP/Uc/zk//0S/zxXe+wOImKBFogZTLTX2lH99Kzku/LLfIkXIllvLggF2CTS0PS/HLddrEIpFpaIdqAwvWNE+HZkmqGihISnSxT5WF0yEFJ5CwXAtKPWVRr5RQOWVMS3CczjKo9BkbFF4FB2CVbip9GpWEi2CTIhjhUzijpf/dNq7TmtKAgtEKqx8CBEo+MYuXEtrSB3XLp4KlfLyMTj0YsXsFGBSEGLr0AM8PjPM+4MOM+PSZlz23gDalmsVqez5LADoHCrqBlvMncx7wH/Qji7DeYxHAOabMmMoRGLz6LLQG20TSBS9ksOaYZTmjW3LbTlOktVArZCVvoqomwFrP3mT7zA8CI58ETeeZ0qvjzp9yJH7i+kQHdKmcVaC29d3emPq093t+Y+unv1+5158OCqZcSJXjus+1tW1qeXCPS1JgNbmewEFnTRM6qmQtxNATb0gMvZZqzPlzh4cVgVfbHFSpaiyf4/SZzu/T6euDBOLsqzl+tSIZbTkCA5a2VzkRn4+8iG9rb51GQs8BwRIXl7bkq4DgvDKT2r2uVeS+S+V437+dmGN1laqBqYSz6oEzmswCCKRtK5VL1Z7HKXM/v16NlZ+2vnN7Drn9w8qp9VHb+144QAII6qfGftvGWEM5xcZaJXGi6qPvQTxrvVal+Mqv/H1+9Of+ND/zQ3+dL33u93HaZWc8uPM2yH8N13c9MBBQ8GP89B/7KVGEqrXdxCZsc2RQLwh5AQanDDPVNnHQ0DCcAvlC1pNMW7Lu+krpd+nNLiUx1TK4BRws2bZJ5Uiy+f+y93+/tyXZfRj2WVV17p08BciLJbJ7/oMAiRFJ5Mx0t2FLETnDIemnkNPTJJURSSmUDAPOQxi/GDEQBEEcI5AISSYJDdHT5BAIAnJGHNKWnfDenh5NZD8GyKt5e4Z6yEMexXvPrlp5WD9qVe3a+5zv7abN3J4C7j3ne84+e9evtdZnfdaqKgMKGxCAgoCFbqCkjfMyRvtrjH3JdDIFGOt7taWK3OT88MZutIAOhgDd2MaVkQhqIkLJDYUSrlUEYGNGoVEIKgiVGwqLp5wTe2ywpCLUPBsVGpMua2+QFW98zxOwtemRFZDcD1XAU5zTYoAr6i8WUyZF3dnI8GSSHfsiUBhWBYQyeyLdS9Jwjs6jPwtA7UUTpWkgBoDsppabJCtlzXzeBBzkxiCSPqcmuS1GySPJXgjCkovhbdH1Dl28S9jTV9a+nA2XG79d7+3LHP2kwJax4nUHNCBUgpwhAGGcaFFn8/Zi+ED+Zu8/B70KCpzKrubNjn2ciNG2ikdKr7N6gRsTPpXlLIySNKeG9nlDZ2MfgcBAQ2tnmuwdzcecemjSYtcxvydbvQCUpMsPIddSvQrQjvkJcfCBvXx5GCH7nhMREJh+jDlXMyAwNtLAwBbAscwxBXKTvkEV2duagINHhRTMd/Zga4wLK1MJQoWd1skP0pVxXq+AgIGaOQ/CcoCYewgWCKE7DYVQInASNrWCJZmyyQyxHWyvjcEsSxu/8+EHeFsPRHrztR+FrVaw1TnRZi1zDV6yvNLAoIOC9/ToSYYMm3rmlsTVNdSe6p1AgQnsDApinDJOfgBLWsxRvoEF9bYtyYYSodTm8fvNlQHcoxqIjqm41wS4opmpvEhTvxjYgqbhEWnzTlABp+wuWQECkxzgE1D9JQtAeKy0X2HJKm4s7EHR/hU2wbwu8bbTlHRpZdj1y8ZritHWQCPbKpHIljCvBR2ALicaSwdw+3yK6wm7AyDMNAWJM1jjdTjHgNpWsfNkwcBWOziQzH2gNMYGgFJDRs/QJwYaKShgRiNhOAwcjPNmpFNXzIopwAiqgGNgtetPncvQpydY+EBuSG75OzhoCmb6r47qzN7frIZW7bD0pzJ65unOoKB7rByYmTYYosetJ8Fdcuo5NdS9+NXYn3mhc9zdvjuajwB8TkZHQkDquGqoMxrwBL1krIOGvkzG5l31Vqs97JC1GAbo+5/0VU02nyNIiGzkpjom6hqbR1ayOg61ia4BErABLaluBYGTMQYJhSoK91Dm9hK6MjoJMQ8iMsGmNwwEuIwswiCAMKtbE6ep5wclABWPcwaCI1kbAwl4/799H7/wTbNfb/REUaZRzqLdcoBAPueAWWJul1cWGOxBwVj8KGYaPzNBaMASFFxt8qoA/Nk2xilXimYobQYG/W/ztolqBwlKP1mSX8xRyInUuxrbYY+0Cb5xc8rLDH+s7woUXGvzLWqjoF4rq6DKa2Opu6H5BkLjFGJyIgiVEwoncBOhKEyehFNTZxBSIo2Lj0mXXiKFrDrtKPt8c4HdA6Mo5BHtz2XF7nRw0PMpLOnJGIWuiMzz62NixnSbaG0bkxfbpERDxVqFKnvIKYhJ5hg3oJKArTDVejRBn5sfoCE4hA8Ye/aJuSvQGFuN7Z37EpB5vPE0j0nrCsuPIFiYgNAPYLqnRFtq4CCWygIOZI4ao7Du60ZN82OazrMENjqbgVIbHufk7N4Y4uv3OjI+cVleBwV7L3TVlz4vr6Yn2pTjY4mbFu4I4YYh/JV9z4tVdl4zTzjKWWDf5rDB88bueGwNO5Brq21qG52PWdfYWJqugW82ZsBlfM/M4JSkr5PIlcnmLmxmf2MEAnEeH4EBc/wiEIh/jw0Q/b5RQ8kmO+T1la3aOjgApB8/+PApfvEP3sFXv/gePvv6m9LSVNRR2m/v/9AQwpNnT0+/fyWBQQQFb86ggGxyQdayT995YpBN9gAKauto0MMHdW9kjS0whdMmyU6q5BLRLma/NRHiymKAtladMSg5ITdgIx6UKzDGzgxNHk1yy3L3uKoi35kpMKGc69+qhA+i0DYWwW3McsqwKlMAyrw0PFbBrcy+Jrk29Lggx7hgT/ST9lnbehvNyM7erDAEYyJoBESRxj8UaBurid1xyjgRChFyFYYkem5WZ/OKga7wVtnYkWa1/SRminWeO3OpDBQ1KJflFT1x76hELyQO+eB9h9cV62IebuV9f2atu+zFIPO4ZAJXmduNjN0YWQ6vj8+Bc6BgOZarUhHo/IMyz3c7I0Lu2dBCElyhDhAimwQcj3/ssz4X2IHAy83N5nPTkvQs2U2WwyXNPxiZjR6Df5iszQxcZAm21obcjdnxaAx3PM70jHR6H9CcGLIDdUNKCY1JWFllzTr0kpUjhQGm9iA9afP4nuTI+N7qvtL1Mj4M1IRGEcwAVBuukTkA8O1nT/F3/+gd/MYXvoYfff0NSXhXXCTLjCXUsCx3AASzj5/Gpw+veeWAwSkosMmz7DzyaWVrnJsJaOvG5jvfex8A+hpz3htZQ8WrmJkUy55ljWVqgKPJSgXZyS6hUTSkAG/VAQKrhjaQgIXymCnJaBhnb2lO+FmVpheJUPIgtHoFLjmpUfNfoTHhUUl4XoFq4RsGWEEP5yTLeMCeR5FdSXEcOek9HuOB0YM9AgMW4hmFeU3/SRv1dWJ3YrKlGbataZ4IJzkgN3jGR2NhxmCbkt9i6CDGXYFzw27KftgqGpaX0mltonWmNrP8fRYKMK8XOqdXBi4mXkUanBJhYw27sGweU5LsEYAMkAJiO0n2jPvcr8iRH8kKTWmD5FKwMxFxPfxGhEy3wIG3GoBtY6vKXdmwBluKK/k0ManYwk5WIpMyx6ePkvFmYHCUlDezjxbOKymChDaABMtVmldD9Vj8KG/WBubehpW8Weig8pr5ugUKoo45K61B2LLAmlU2XdRQkLAlmQsepgrjGcfF23fnuMyA4FjPy9YbpueRtV4BzFBiyQuqtl0Z8Hf/8B3848+/ix/94TfF/hid0/oYk+wvf9A7x8IT7eOv/u6vHl73SgGDFSjYd9H+Ew7/fDMXNqQoiLIy4zvfex9//z9/BwB0na4CgIWRjZPFJn2nxLrXHSdO0UQym0RJgq6SwFJlcpekk9eUEAursXKgZkMUP2/mmScFJ/oqO+ZqPRur4Zf23BJWa6MZMOlH8niismYAGirL+etVPagMBQhqFCo6IJjHakXHrkIlEQxYWOHI6K4SnmKSZUpAasKCpNS7hjlhI0lyYmZs5i0S4bpUQhyA2qh0zkDBqsguh5qjomwGkVL0CPH7cO1y22Tt6GZvdD6dYATxIjU0vfJ65Zp+fU+WCri8ieHiBnCMeSwKHdUbwjBYvbO9TyKHxg8SpF+cbQshq5R4H2+wexsS0gdtqWnSZ1MPkDVcxtgSKRNCh+NvfWcA/SGGB1jMU+znqYDXAAQOQAKliWoPYbBYOuyHy11kiyLzFYFuB1Cd+VoZz1iijkkaZkq0z23yua+vvt8K9b6WIRIdWRdGNAKCuKLgiCWI4eHZ8YtMQdTz7jwhgJgAZjYFBJT0yG0A/+DH3sVf/eE3pM5s4WJlyZRwEPmmm7kDBvAYwNMb4fVYXhlgMOcUnE+/EWvtQEFjBwfGGvyLD9/H3/ujd/Cf/o3fwi988ycGb8gyUKMQzGVOrLGJU83C6MSRberg75ui4JTId5rLalx84qNP8pkGnAsl2W8+qeG37GtjLkoCgCSUY2I39O2Aupq92Dx/YP1qijp4iVCAwyzMx9Z4yKFYldVa4VWYYOWBmwKeY5tzWSZYckgqWwA32xAHkCNsZ887etFHBmE1b2J39pUgGnoCSTw5yalt4g2KR+gepClSiy1Hwzp1ceJuZBNLnD9hD05WU4sDKGCVg/lHtqogN9bzgGi+ZNl+ZzkO6u44g+ywKQEZiaDbQMt8KTmFlQCStNZAaJvuKYIeNjj2xgz8C0goSNhkyYfstaHAx9iRWGKoJW4gNhueGEI6S8yzYgl63ZA2lJQcJNiKoZKFKRnDDWF1zUvKXmS/zOk4YiFzIrRhTw75Ly9MnAGC2DZLpEzoe71YmI+oJ2bu677Wjatlhj6HWwA2rYOC2fHze03vZ13oORqqS1tjZE364caylBjAX/2hN/z5pE4Ikax+Mkdt5aOdgYSHgALgFQIGc6PPOonDq6Nh7qCA0ScFA/jge+/jV/7oHfyDv/ku/s2/1A+smNeqJkIPDUxKfmUw7XND/P0+t3DgusSfOZ3NCh40HpiZhXeuAFKfACkBqUrMLpEg2q1JtvUl4xDtd6+FhraY8MZiQpaYfN9/+QJuTImBDTzQsMCeij1KCprZgVXm81GiU6ynD1eSjVsu2au6BG5IwNbUY2+2OqC3YfakYwKTPXMu85wYqWLd6Y06PVySJkKSUJjx+76+HQD1JESDM4DSrSoHpIqUWH7HFubhUbaqjo0DH+4Gb98gBVU6/YgZBPI5aCyHhz+I+t+wz+Ax8aH+sSH2R+oeljWsUAKnhorkuQZlVuCQ8V3GvO0aVjaCBYTI0kWJbaOK8a3T+OtX+2z22fAsQMHZnLWE4GhIr8S4ZGFIJEsfckirz511kvNRKEzqvpfByBR5u6YqmvHWnpCVHOp4AMdgx+b/AAgc7MTcLJ3neUwQjmGdWG6xYUdF8qrY39scm529s9+PfSGlYm+IWUG6zDEJtaUkzIggXn9xubH3c3koKMCiPv9/W+5t9CEo4B5Drdxjqd/58Cl+5Q/fwT/4sXfxV37oDWy1w+AM9O03GQMtXzQRz7ztefIf0YAdAe/RsCvzg4k/C7JcLAqYkx6N2xjEjKxhi6pUamssyw2RnM4s+TyxJhZrA7CPfbpQL6rnMenKHh4BxOMarptQ/ZyodQsQvNhGtgDo8Uxvg8HwIeHp5UCa1Xnf3oU3DQMiKundddd67fvT9oZ4nAmXkvFY/y6ZlqDADQd1RWISQPo/IyjRRI6QWPtVYvjytS0pNBlYldj8o240r5rU+nu8W39ja/HTHfUn6uCGuNc/goPiAZYGFItt9YRkqbdRvWNcfx6HXXsV9LpneoM2v7ecxeJ7nWXuRoDeOKkxI8+HaZw0wXmf5DyvvNkWXqm1K4ZF5mJzuSTbj8FypxRQQxnEG/rEXucE7RkQxN1Xz/I8hj7VuTM7ThaOqsDAqkLDTf293Zed8Vjp+J1+H14V+Mz9i31+B5PYpUymQ3pf27UrcHBrdd5ReWWAwUNBgZUWRmGe5N/+3hP8nW+9g1/78XfxV3/ojUHRCSVPXbdkjLFHVyyCMmevBMChId3tKKgKMU5+Wy5nFCCAgY6LiWS24UdpQCXIBkOQde/3ZERbIpT011qYuyDjoE2dQZhl1bLMLS5oZcyL2C/hOtssxVaRzMsuheI8zrQ8imvad96mCbjNG1etSm7SBlEu0E6NYAAAtJ8mbREBVsmERwYOAih4tAAFxX6XzOAyhsNlrH8pKVUvr42ADaIYmTot3xojsfTJxsIgZJ1rM0A4w1QZ4ayQpEoUCgwcSHZQUF6i/gkj/WrgIC3AQaaEF42RfEWIUr6Ls6BG4P7R54AZm8TdE2zMTruv8n3m4nM6JQe2L7bmy/yyYV7NPTCmIiY5x1CIsHe9DetEyrEeRMqYJnK9YV62g4L0MF2y6ufdyqAFGLCwSK/bGN5zs96642TWloNsivGWBGoJHdEEHMnbcqTjYxtKRgjriCyT1Z8M4k5j63MW3u9E5OyePdZAgb2+LCgAXiFg8NDC6KDAMtoBe8/49rOn+OVvSXboX/3hN8RzIPOUgEIQvra2nlSXgdSSxGWhoEAHbR0/vk8AzgBBXAsOhEmlqN1AzwUA5/WyuZkanJOjpF86PXzmCHVgoG1D96Ciwhw8xVDmZW5nO4sdxWbPQMGyzmnvbWU1tpHG9Nj+AXAz5bRqF7ModvK2jArUlI31tddtMSdknTrJBjtFln1a+OCS+mEyhRQokBpU3T9fl4QM9aOw46dt61ySgIPcWD1xXTVAMr8LJVkOlsRDl13G2Y1eDCdEw0lpVIaFkocJMunOfdqnAygIpwrObZDpntQ6FeSUNRlNdoAkAlLrTMMjEHLKoFqRkeWwpMS6WoFRJnAcx8PmyhEgeOgcMMOD3NzQQBlHaL6PIoIAcmTOrkDufsWQoyL14i2JmZa5MhEgGIOwWn7qyzJDuxDaljMNDNlD9Ud0JG6xAvMy0aHnvWI2+mL4amNwoK8mkwABAABJREFUYh0PuVdhklwnYhTVJUSSJJ2aLKmNQOeh+n0OfRTqZ8zEAxFXsNI2J7OcMktJs6GOv/kooAD4BAODVbGh/eDDp/ilb72DX//81/DXXntD8g50EAwVlkzK7cgBHhlyuMpW2ZccxkNZbk+ctYLpsT/s1sqPOyGGuGucIdMMK+S7hvv26AwsN/iYAQMQqP87SnS2opI8ovdiWSkiK0O/ql7sOwuuf5eDRxAzd2Ic0wCBgQIz/DGuOQOCVVwztj/2lYVxkMn3HGAN50QAFss8J0yRPDbGQNenX3IAAgYMVLHbSXu+Vz7aPshKBFCG7KCUgQxkyrBMvwaAwUhN6tKUNWAWo2HLBJFFBqLnGBW89ZcAgl7XBAUCpkTnNsT9/pmBaUdMfwASkLQNqeCS5HAhan2Zpmw4I2EmyhkZ4kU/b4yN2sCgrUI/s8GKFDyAm/OgNJbNqHQOUJXlu6mpsk9tyPfZqCGn7EBX7L1Q10l3El3N51WR8GYfl6aJbE09pXTwO2vXspizBJXbLLqle+j0IJ0x9+OKCVjpP327XKFlXzo1D4AyASw5Ln08ZOlgZFUll4mWzsnL6PfV5nW2KRWAzpz56wQwsQYOVl4mp2AunzhgcAJWAQDf/vAp/vYfvINf/4KuI9VlIgQelkg9NoWiHmtJetAS2TpYRach6WxVZi/67JAl24Y3AgGiOIm6wgIOUKe9chfcBoBVyZt/YYABGEGDtEk/XwhD/OgO+7/8na+4UMosTzR1SmNmMyDC08CeFFQRQgHahpSFQs1TAtBR5vPMEBj9N58oOccz4zYZttkQK6yPgMsMhClRUZ5jp827Lc4b1syhg0sKLMF8eA4fMAbGFlCVM+ENBCZGTgWNBFAaDcwJsuFyAxAy/ZUmAHJXokbnzmA3rqCwdqQ4BuqRR2BjJwXCt4ZdtwNcpB6pAmlDzheklLGR3P9KUnciaIJtkmPBa8OWyPNVek7LfiLfMmAPmQemM7aWdKMqAS4bQ/N9yDc+W+0YaHPGjJTN7X0oLBirRZtmUHDURu/uhXxfDkzWLb0w99mSCT3Qe9B63qv35JWdKWYGcpZdWy8sK6S6kyS7uu5XL7y8fo86Y9jOWn+jRJfandhvtxXqk2dP8eWPCAqATyAwOCsffPgUX/nml/GbP/E1fOa1N2Qy2TZs6JnZgA54UYUSJ1LKu00zrMwbvliZEwhXqNioohkIEHUvCDRSSmfLjvSKHlIBNKzCO/DQsi3HGxH3QGLy8u1Q5uScGPMzpgLALlmyQqhPAtwbLVnPDZoSnGy5maykIFw0fGIrK6zMTEFcCmixwHjUq60Dj8dLH3owqz7PvQ+i0gH2m6xYmT0lW3NunnVJRywBd0Cgx1mTn1ZpxjSOBQGUQCkBrYLtuOssc+GSisbDSeOv2IUUUJpmIWqsOo0KegY4l7IIIRj40TZdCAIK6nU4PthO3DxsB23wM4jTRcIPKeOSL0jZ6GiZK7a3wdYYWQFC3BsjGvDYFoSxAbCbB1qbfckLhk51xnAccRKgEDflunXo0BlzsJrjNocsb2X2aM+S+VYx/KGZB8wDHfyxYjxnPWc/MV1n7brHeO5PUSTXda7n9H0mAkMPasprNhV67b363dqz1Bk0tn/YmRLimAxtXLZQmIKPAxQAPwAGXt5/9hS/8M0v459+8Wv4zOtviqJm1kNryPSiI9RL6rkJxSYS4HoqettWbHIeCdGMiuXaPlGOQEBc303h/vaYfXKxehher1i/zhrEz60VMVkzHg6zv8/+/pdEA7BA6LMCACqERulZsiQxBRq+xzCR4QlOshcAhWSnvo7aYvdW7k36jJvCzJTfLNAG2I6KtXueK/M8GRKkJro0a53j4Ti7XAI1ngIINgcIAgrOPW3mAlCWg3Ry9/0YQFFwwKyhWmt8E+oYVcAB1T5eRi13YzL2obwGgEPSBmvLAAraVdtyD/ORgJaF/UgVYDkiGNx27MGWWDc6kqZUjeMWteDRiMdxAY7lNhq2w/kwMXRsgKTQdJ6HMRjAlo53RvS5zg+f62cJfUcAeG63/z21e9ZBR6ym+8vUGY9Zl4168ZaO0/6w/nbHJh64Rf1vTfxcHblciBD14kpu4c/Z6/hV/tcZ45s17HCk41fl6bOneOf33sbXljv+Prx8YoDBCdujoOBtfPWL7+FzrwtT0NQYMytXoOjN5sElpZ13La+jUUTuxnCF9VaCs0LHZyDAJ0y8bvfEsx7oVwfyIIABCmDgADAcfT6xEX7vCaWzCtsq7pcb+2qKrPQ7se5spmNk8VlLDILGTxHqEZ2Y/RLAMWdgtde89bNRfZ5Vj1Gx7UoAUnPbgQ4u5dJRiUTFYUfqpqThDxg4AJKxBPWqtPsVqJZXoOAADGwalJkNainCKqQMZmMM4jWEQjl0IguDYAcFZUJmCa/ZeDHDD3Wy/op9WRT0OkNgfT0BHKrPj9sytwMAlSI9mDI4XaRduQhAaBWUL7jkC3Im5CaHX9nBYK0Rqi7fjcA/ysY8NsABg/eA+VCVJWuc0Yp8JtsNp7CjJ+m+HZKTsDrMx8pqvgPrOT8Dgpjg7Al/Uzx/ZdSs7fac6NXDfzMa+rPPdyBh6v+hM5eFhitEp9GUdE6nYOESwALQZRd6/ajf97WLIOkMHMXQTglAItqDWedbef/ZU/zc74+ggHc1eVj5xACDo/L+h0/x8994G7/1k+/hc68r0mJZn82AS4DJnNFCRXeKmZfscBiOs+xbK1F4V2h4xQKsQICLQFzKZUpz8qyI9xVj6pPRA30+c5NeY1nfXaQjgGBrM9Ng9Lrg0fj3CUr3uJ+GDYzeLRZvBRA3WCkan7VnHh08A2CZ0Lc6MbGfSDfRe/pKsKSicfx2fQs4WOntjwpqZF58nEJ9jU5MARCkmFw4e9aBeidu4O0Kbk0+B9S4Qun2BNQNlAtQBHSxRsWHNpVHyCn7uAOyc6BN9A3ARffhN9bHypwjY6Agpx4KMZBDXEHbiwkUKNjhCmwbuCooaG1sCwDUAkoJVC4eGhFQUYF88d+kfEFKRTeOkfrbimPWz+4ZmxVwl+/O5wSUOZJN1agbeZODlHyPlXg6KBfbcTDdTJSMZbVCaF7ut9oqOYYxX4a9HJyfcK3142C+B/2FUYch/I1zPSYXiL4ynWb6KxuzhGOwII8c9ZZ9FkEi8PJ63v+eqCUP/8RrFtcmiP165/c/PqbAyicaGHxbO/Xdn3wPb3z6TTdm1vlx3nW0J6+PcpzaY1nM19OyQpURbS+FSM4b7gBAjT+Z+w3As89VmJxGHirb/N7yJkxH6wfzxAwYWE1JDsPxpK8ZQOi54Eadd+HrBlIOYwkovcnua6YcRXEKSCicPeYnMWA6XT2x72fa0doxfm9AQAxwp+zXR9XCQcPRTODpddX+WfFY3Vfg0J4vDAG64Y+GMwKCtoG3K1rdgO0KMIO3q1amOcCjlMS7zgWoBenR46He8CPKCUnP8m2ICkqWuBGr1xpYH7+F/hfDIXN+RGQ+DOREUEBtQ3uhQEEZBHYOPbSnXGSVhIKdxA2cChgsSZhZwgvUKjgX5HxBTllZA6Al2WGO094oRMV8i7mLc+JsbjTWeLZ7/wIW4hHilYHWsuRVBqAwn54KwBPk5nIr238FBnz/C9rHvK3NGev2d2AUHBYbr6XO2jsyNAMD++3QsK6zbJ76McSus1L4TnQT6StTEr2QTF914ObOjg9ad3rmat1TaJ4Ii1IWF80fJQhT8M43Pn5QAHyCgYHFZN79KQEFAJDmQQ42chb0y50JIeflYFbFia+zcsUADALlnwUwEEGCJWrNAmf3m8uMvP01sAhELlwujCZwBhxSVqWiiD14nC0g9argoCYBCqIQ5dU21onJQasY8LxqYmiONmGV9BNDAzMQiLvudWU3KbqV8op9eKh8guLRn0aYNlOq/kvbj0BfPY+gbkq1b8C2oW0vBBDUDay0O0cPG+iMASVQkVyEBiA90iYBoEr+CkpI+aJ0p80RBnQ76AY9zQ7j7I4ep4VE5qTJGRRIuyZQcH1+uz11k/bUDZyLAITySPZBSAVscpI2WHgBKSt7kAXIJrp7XKwnDkH7HXPDmLgOopVB0OTbFVBgzrpRETxH4aXkwNqCLgsWuhKQ0OUiAgHbXnsHAnwVjMmILS29oae8wi+npyIYIHRHxWMe0WGJgGFycAaGAWGZJoUO3Ffk4PPjwgfvffv1owugTME3xH593KAA+AQCA4Is6Xjn997GuwFphVU/J3GtPmFT29aCP28c81BIOZfo/U/PORauhfCB+wl3tXo9OQYmz5B4CkKXc6DsgsClMv2dVegEENAEFigVNZZZQzdG32qOGQJgIEIMO5zFgENvLROjIh2alQpdAYFsHhArDc1ihIZ19CsFBuw8FpoYliNvZV10PD2JsAVgUNV7lpUHs1fNL56LATXGoNVxnA2sEcGoedL+6+BAN+WhBFR5n/Oj7h56HW2TIww5FDYOMRRjSz7jXgWWIxHZjxkU8PWFAoM727NtEhpprbMhzGCuQNJcCt/3oMrYJElczG48jsfknLVbMHaLOeJMHIlcpZRlbqQMFDmMtx4ABTB8uSg7wPYaAtjLQhw1s5kDEAgyYQCAEBgzC2sBIgetbzo1gIC4rHRaETMmj5rzwzvd5DJ9h24i1U3St6q9J6cFoNDfC7BgnQLAg4T3uPknheff+7hP4FBL1gwBs0MMhGOjgSd/sg8fjHbqo5dPHDCwJR3WqTbxVtRfBANuGFTq0vXPjoX/jPaayyECDWWFmhcIewUEPLY8e1cqrAMVe1aFQDuzxXLVqLvHmUsHDZRdIImyKuzUFXfKIFzlvqqEOWVk85rUY8sMDzUMYYd0O0YP9PFcxUKH0wZXQMAMb1w77wpuVGo7QLhgVwagRAkg+5t2Smns/GhwQr28jsoYWNjAvOrri25A6xWotVPMtpGRPbP0nALDyA0APSZQu8o41issNgtKyLaMUdtJDDVeQFPPMuBtp6ENGMybMA2Jk61Ju7guQQFreOSwPVn3Y1CAQJdHzoZQrpJ/wE3YA1/aqPdoeTQaq/HQMXcQwHWUwTYCx+hUjHNE+y8ycClLf6cscyRl5JSXQMHCD1XBWGSgXlYuduGBgRWY9GHcNKtFAG1gKa6EqW78dzkiWveH6KSeUdnBIAOjTrJckxOg4GPhhjqM+UP186qvp/DsMsxhegAAbc8DwBX2w8irp4tEw48LDMTyiQIGtiPUWaceAoLoKQKg7c+64Ec6ORppK7eoemBAjON1aa9QlkmFPCBubk2EzxSUCdwMEszrAu4GMWQeWcqC1s3oG2jIBUgKFDTjXbRPli13YYAi++QHRWHOIEqS6Ea2694YdpDm9xj93NXWvZExWCs6HAOBYHxHL6cu2ZddPzmzQt0DNJozBeNjfRArLmsBh0b58+PyQ6vv9UU3lkcGtNVxrqri5ZQcEFhfubK9vpANBUhAAdWr1K1e9QyFLKA6EagJsyObgdFAhcZkzSE8Y/1fr6DWwvJKfdW2OAMS21S3bjy4eQiBU1L3uTrgsfYS0EMJl0ciMyxgwAGCs1/n47FzGGy1RPw8yqXVc5org5dL8fkmYwIKlkDBQh8YcxUswRW91jvZsFaZbAAYwgPOClh4wIFx8w2zBiYrysm0csTGimNuyMeki3ihizxvhkjkIRf/nnLuQMHupe3f6eGF/vWv5s/vCXWY/Nu9rc5hzlHd5EA5bq43iBKePnt/cGr/PMsnBhjEbSIjU2BlpGK4x3GdHgvGAtBkrnqsBFZU/aJEGgyAKwkAewrKfhONkjxEgMCEwF0A67YXPm7dgzwwGLuidWUHAuLxImc36C6ACg7kVT/PBSlnDy3MIIFI9up3T1qVtLMJKkAFZsSm5KC5ujSOq4EAMOuGOYux3Rndts/qb6HfjkpQWIcgyXMyDCQEhTGXyfh4KMHqdgYKzIDa/PDSYIfusByZ1D2ubROlq8l7aBVMVRVYBTUS2j8DhfJw6JJshYwhZ8foagMH8fwDAQV9HGwsiJswIJpT4GD3VpskKWUJeHh6NeYASYNUyjxQq8fjAJj17TrAjObKGAZ5XBXvc5svua+qcM9WQcEKKHDKSC475GCBw/0fJCMLltRDBHEPiQUYIG6dETDHxPTP8PrRdJAZVQ46qLMsGq5U/UO66sbkkQObgFnv3tC5AHYg7yE6/shZEJ2nK4bqC9gOpJxkX5E//t6/wNvf+Dm3XzpK43NOa/Gw8okABk8mUDCXQ1DgSTT9b5uwwhjsjcasBM4mDaXUlQLgExrAKABT2cXdphABb1fMaNypOxPaoFQdUNhEr1b3cerZbl6Uu4fLavTcU0tZ1pFbaKFc5H0pkghmf+ei12XIjnqlC0f02iLVpp+T9ssuOcj7xTzs4E2u4p8OAngY452xDX3qLEx4jnk5NIVYBjbF6MzQfgdJUUHIDcaxj6xQiM3O9YQZIvuMmyrdaazjmDa4M8yJu4HTXRDFKCfJtuUKtCSrHYggCYcJyAmyS6SwNwYKOKiumLUeT3rsY2D93z1Rn8eBtXvZNrEu0yRKvmTTAUO5gJsqfyKt9/EY2N8DcIz0eFwBsvCKD+cMIPMmF5GrMGdMljyBV+UFRCNQCHk8zkgheMKzrBwkN88JgwMYmAFCWELK1v6YIBrn4xb0aABP1qd36R4bl5RApndMznJxRwXZtsYOTEK5wE4+Ygs5AOB6EjJgDcp4iKOP4z16voc8grOggCUF8Ge633b3lLnL+OPvfwdv/8FX8N5Pvou3Pv2GPA8YQO/HXV55YHAEClYhBEfKZkiiAMQNVgBPiIrIeJcpPSkBf1aKCTIYjQjQqTD97mbyy5wvMDMEMb68XUeBNKXWJEHL7sG6KUBUup3VUCHVelIiEVj1innrbAFvL0D5Aq7CGOD6QhScLo2L681dOFpA0KsEIQMLvWJjXwAYaN/o0R153cHLYUtumz2cmZKfh0HHzsfUAJJ63nP7I6jyTXkWtOY+dBTyRwIoYFvfb6VWV1wPKpas18T4cWvCGrCBSBkrQlIWQdqYoJbfdGYYlwRp3ry6wsG3AwQZK+ImJwfq8yVP5iXUYKuyfWYxA6AAwZqqr1QuAgj0dMF7xsCYgWayZPPGAGTwjN0gAsPcGVoUWbhyGRg4NpCdi7NvFNi3GHpwsBDkhfooHMgLgJAvtU5sHhnSHZO2ar/pnthHGvZ5Ob1zHfSOgwCVJZknF3EkuInuaQrUOfXxtnBTu0+3AujhD/mj69jQjytdz9bnQc9TKWDVC8iWFNv1N1gCQX/8p9/Bl/7wl/DbP/FP8dbrn5V+CXkHHydLEMsrDQxuMQXL4sIQmIJh1zVRLO35vx6pW5v00QgvFAEmlmBFg8XkGWAbgUL4bVT6pzG7A1AgQt0FkyuDWV4H5WUsyVCHBMqk9BiBUgWlDelSfLMcrpsqsioCnC8BxV+7gbTNdaKBjIpNDWbP4u2e0H78ggL38Tyne3k2rMawLCjrmV3ZlejJXBUcGUiw9mu/iKcqHo3FEyPVuD+Tzry7kFS68lRexoDeU1iTPFi9cKiR0KQvAwdE0GOa+0/HZX2dlZPQXDBEev8/v/pPpTUwiZcrHjl0vij4HXfDH/N4ZkAw6QEHAyZvd84dp8mDwTPWwBgFKAPnbMIiTEWWuzMn1Z3JziA3CoAOZOcUEEVAEPMLuKFdtwfpHD/P1XUOg3ITcNBYHJNykXBYLiK35QKqtevVVtWQah4KJqN6plO1b07DsfG3sR3RUSAC59wdJQt1XB6hcQMVWQZE7QoG8OT738XP/he/gt/5wq/jzdc+o+OT/vzQQCivLDCYz6M+UzXezzu2IP6rsPXhACTZKzAGrgi2fsiLo94JGACKgk34a3Kk3xFwSPDjBG6p/3aq/ymN5XWYJm2Ij5mAtq17wtyan5/uJQhNSg1oSemuLqzcGlLJcv9cQO7tXXoymMZx0ZqsnU/ZDaSBCsoNcyYxgAAWjtt75t1F78ZYnnuU2Zl3A+xZlHRRoKPt4lYFIKnXR9rHVKqAB/mx1HcACL3scksWhVKCHc7kmfmAECgJsgTL5sAUrx0270ljH3OtvX5RLiJQYAWK5s2E6g/xa0tk28lYB+VzGykly/vVsFK6q01C23a6fm7X3MaeCDj1q2XT2xw6o8zt9cQgyvOO54+zcEaJG2uQixsXYw5Qeh6P5ydEoPAxyM9yNcEsQxFQmyMyMQRtq0tAsNQ3gOscW0optpEh5xMaeFB9Y/MgaxgsF/087+8LlT+bD3NI4MjJimAg5kXcoe8t5MFl646CMXMcAm9tw5N/9V/jZ/7Lfx9f/7F/hDd/+DPy/CBnoPyRQglPnj09/f6VBAYzKLivhC6ONFpIRCNm2TQGcCXA1+eDIlhR9HPZUWAGErJskk6lqH3I8HgvN1WEaZzQMGPQ7A/R34BnaQPdEIvxPQESGEHBEehoEOGLAMGEtW090YxzkUQ/QFD8JmjYBcEBRHXlJgqjJwfFrO1gupbjN2eAx4TBGPcdqN7IDNgY3unZeDEFkAmpZB+jdGkdCFifWAuUMSA7VNpi3RrOj4moh8VyAVKGHBgBAZOAMDGlAJXAKSiyPDI/MYnUDI4nZ1ldcx6VkLFquhwQTCB1aIzq3Ds2kaY2zzm+74Vy7rR/UmA89+OdbbKcDl/9YYAhjNtc5v1H4mqfARSYrC9Agb3GedS22gHBwTwambgRaJrx5/RcQEIMOVjCr+UmxGRgBQNp3oNkHp+p/dbuFgzgsOvkUbjtBigwJ+QeXUMpoTVGwqj3liWGYOXH/vcQrsUC/E6gQN6PoOCMBbpb33MbHQUA2Hpo9Mmf/kv8zP/jf42v/41/gLf+8l+VZzhIM53w8oEEs4+fxqcPr3nlgMHLgYJVmeNsFbxd8eRf/TfytXkEpghsudiJZzB4BUaBZY0/N0D+g8REMzAgXZtU88T3r3MXVjMuKOJdAWKQy0UoU1LvA5qjtVl7baEZkCACO4COG0WUgAKTxqCMrrQVZFmWrdgUcnqPWwLZbNSYIFcIENLEoFNvz/raBDXkExglOHg1JuSL8MqRAgOwU2JWp5RMkSRZK0+MdAHadfOwuxs19eS5NRApWLOEugR4LNjaFj15ZjWa3YEXFkYXbRqrY8avbkKnyoXC2sxltarEfm8KVRq77Hti24LXkEFbXzvFrQ83/9KG+Z4ZzEBqGiNWcGBtclmZ2qP3ock4UvSup+z0cXOc3rZdG1oLTEzIfzgBBe2qbMO13phLFSl1PYGUkEpG2zYBCCVLroEl2l1fCDgoRfokF/D1hYME78MpI78/b11aZBbdYAaZmvKp3HB6guLoPZvBNIB9b+nyRd4flMlBUyp5yG8CCTDyPA0Lv8QExAgM47NsiAEB2LGeyhQ4EDVQcCNXouv86gCPmyz3dNNuoWNt6//i//4f4Ov/zv8Zb/3lv4IYtpv1wsuUaB9/9Xd/9fC6VwoYPBQUDMp6eYF5oTLYf/yn/zV+5oP/UL7arhI+YO7IWEFB27alAPCmIYRhL7Lqmd1OhwED0pXXMKHNG5gLkRhjE85tGxQ8bIOapNvFQuqTAPHIADCxUFU1IdtET3mg+UxxSTWDsKqXI5+foNlaJRnMlAwAyskNuCcGEXVvGHAqeVdiAtCUKBT3cth5NIt8Cxu72aPpiZ0LTzLLHvuUkgxjZSA3cDugri32mRfiNxlUX9o0/M1yGiKz9CVrnDwlAX7GNBlDZEAJkB2jds+kvfIMiW6gpGvsQ27HbPi5wY9sjmv+h2s40NTT9yYXlABqfdMn7SOXU5radNQeoLfJwY7Kjq0MCYCAyZb3jfOWaQIHtG7bnHQ2ftc62wQczidjuqrLECMl9dbVCHJrSOpUoLTugW5XaZe91g2+bC+ABOsXywtZ6pFYJtmKTsKcZL0EnVookU4R0S3IbXBCrF4rPbPSMakUWG6B52EYaxJDLTMgDHNjX0mCJf0J49ocXDvzKg3XvtmHGGe9z5vdWkKtAAQccBMdHPSyydTX3/g/4N/6y39F5Nr6flE4vN7DHTzEPr4ywOBlQMFYzrv2yZ/+S/zM+/8bfP1H/3f463/87/UvIoKsXXBscsxxRPOqsaLFLLcg5x09SJEejJ5ObNOE8skS3cxA2N/1Ct5y/z5JLFyEl93DAbIrtJX6cBAw0Z4xPkrBMxvo3d2AGA0eYn4mvNWlay8ktzKGD1ZlHMV+neqdQcFJtvSqHH4Xx3f8QVfeGh9mREM8zU+Wo8Dle0ayEIolfTZNDCwXX1kw55fIfc1YpLXxtBg2aTIoaF+XVQmZ/f73raJZi75bZumAwHMgYmz3Ie1JfT+JCAaAuBPdvo8BiLdWSPd2AJgT5mx2WXmizEFcGeJNC0l0y+4K80oZMkZDQxIKHcLsEXdjSY27B6rxdKYAqi3RzUNNxhaEhOa67Y3kiZzJ+zb2uZWcJeHPw5YAwZKL4Q4IJwHN5oQQsuvJWTMYGACAdMldv0yAwFYBDYBg2FMltn/PlniOU2viYCV2IOoOpI1va5jB4QwKVnqfkPU6dQbJAPtoht/6N/6nY87BskwAdvfJWB5qH18ZYPCyoOC0Q1VR/LHFfN743+Pf+dz/Sn7/H33ho1X4B+UH5QflB+UH5QclFAawvT19eALE77FlLxNef2WAwUfJKfBkMEpgYkWogiiffP+7+Jn/6t/H1//t/wRv/Y/+xx9PZX9QflB+UH5QflB+UFbFQndDSfq5vmLFeu/Ly+bcvTLA4GVAgQMC/ySs9yXCH3//u/jZf/738PW//n/Bv/Vv/JvA9Tm2//e3cfndz+H5F/8I/OJfSxLO9fku2ciopViWCTO5AJfHToPR5ZGvbUW5yNpWo3LD9plWR7lxoMWGpUaMw01J4sY4llUcT6yLm/kA6wxqKyF2udsi2fpz3gkQgc6LVOYBnSt1uLGkSK8/W1Z0trFT3FxlzhyfEzB73fdZ5Lvx9WVlF9Dlcd/10cY+0PUI20L7GOtys6PzNHbLy+LSv7hTn32nf+9v1Cn1eQ8Jp+Ptu7jL3rwJFbCs65Bf4BvpaF1vnMb38vXH2IZY14N6el0XG/ysdvuLR0EfrlS6sVxPmnQ8x4CRVj8K3cVwyeG25auN1f68ZDJu/DPLpF1/VGa9YiswiDwHxkMF8+6QlD4GfaljrMeX+5411xd9efr1+eH4DmM5rzCZdf+j/wHo8giP/m//NrYkcsdR7mP9Y87R7pNePkoi/isDDO4tPL3G9wS44vjj732AL/3hL+Prf/PX8NZf+itAfQHLeAVUgLKszWdbr08y6O26aTZwiBuuYu9uLB7JpL48Bj167MCAygWcik7w7EmEfcLbuoK5RMXburFsFQwWANL61r+Wh0BhXfKhcE9luaVrzGJfJD35eyurBL2oMDzTXpLq5pUSvrX0Hbv8yXpi6SLSbhWA0MeLGwHF4oSa+KgKJOYOmLBbHXxJ2ZwMFZQW4quNsRraARCkCBLCOC9oxZ6gNAKE2ZCyG2GsDWvv0P6sGZjEefcAUGCf20ZOmlAimyQBACfA4vfcej5BMMz8MvXWv8e6w+u/rqjFd+XZfTtgAsuEkbMiCsnyv5RkJUDKsuws6dLjAL7tXy49SZkDIIiG5GiuSRP7fHN9Ym1P6TTvZShRTr37Xl5OhxUxzH6mjCcUc28rhevksxt6ZdoxMK4qmbeKbnEHyAhk79WXpidJl6lTljG2PAVLRLzK/LIEbrn/hpzz4HDMfev7m2hiLRGJPo560u+XfR4PcgbsGIM5kT6eDfQyTvMnAhgcUS6WAJuodzRRwh9/+BRv/8FX8Dtf+E289Zf/ZyLcROLVWWKR7eBXLn2zmpTAdUOitFdgUXDNWKxYgstjpEePwZTQ8gVIF0f+Zjh8yZFPmknAfZ24eCXDcb22hrY1MFd5fvCALDEPdQv7pWPvGXizwrMnYZbPDhJ+zhQYNyCHZ1lSkN07Zg7bxwkODihl2JJfT4ayTH1SJUU9SVQ25YGP2by5ji21iistlgrZEwfH5VIzIIgb0wjwU0CQgqcTPfKoqCfjO57iOQIDBkaPfAIFA9MQy+xND8l5ARDYd0egYJF8eAQOQKSJdZI4x5z+/Ooar8OiL8OzqFUBA8Oxwkk+S7rpGYkn7zt42s6WdRsBQkh+zWW/E2Kcd6s5B0zzDhjBQPT+Vf6WbMG8pv9oxdNKdm/Jrb8N4xaWO9KkT+K13saZtbA6xT0ZAsPWovyksE005R1wPdOVrvvCuSnCJsoS71QuaC+eS5/ayq6UwaWAtw1kIDDb6ZMHNsATYItveOb/AG2b1tucwoMwQrRhVj4qKAA+IcBgLjNQsM4lAp4+ex9vf+Md/PYXv4q3fuhHwG0DpQpmMaDp0WO5VteO2/0IEMVgoCBOdpvoq+xZ9RpxeaRhg8sACFjfcwQHKQPoZ3TP7YmEE4F9gwxqsglJPD+971HPQBnDDohbwALy3VGnOsU5rwVf0HfW2cBe0bswSaY9gHFzmSlzeN67wTeAUgDB0ejrmvdIZVIcJzNCwJrinIHNIoRCWVaPxMOjllRnZAgm4BfBgDMGRutjzXZ5teKnyhKsKPyh789WDATjzrNRvQcQzJ/pmHhdCQDbWGkdkboB8ZUB5/U7BSqRPQAd3mtM3uLef3bKKlXZVMkNhwGEAkrbnoXbrmI8ws6IcZVMXF4bvWr7e1eOgPU8D4G+oQ6wBwT2+4OVGwIi9HWQ5/tlOQJtG73VqbB+7VyCPjk6jdDqHIH0oCcNDIQxX8lLbxmrfgx6sl1BNYHTxQGCyTP0OGdcX4Dy1gGCbYAE7O1ABAdDaPHSNzuiDDb97+2Qf9aW1SaRDOD9Z0/x5YceA7AonyhgEPuyLWbKHz97ip/7/bfx3k//Nt587TPgepXJrJusMC59sl8eyWSaqcMYR9Oy2oFtAAS5AJdHQCoyIXLpgECv53yBn7sudlAZ1vWyFtLnEhESss7JIkuGwpHCM1BwRsHoW27A5X76mf3pGBXICW073jsaLjVklGBAYbcDnVGTYZ21r3UHBm/F1ryL/QmswFm7Fm2caU75bKI6A2MQPZw2hYViTsFeqeUB/LWAAo9q7CaXANi4Iyq/3scWgliyDjf6ALgBBlaeJbcRaIQ9O3odcq/bvXUYAMtItQ79dlffyS0SEYiyEg8F4NpBgh7yJcdPbwAXcCsy/7iCUgFFBs5AwmpzIGAdrnvgnJS3U15AnJt27Z1gYFjKGfNbQh+P7MyiRMDpY8rApX92q12+ZNeMY2QEAhDooCGLboTqSUsduKkngQRCSgUpFTG/9Qq0At2pTAACyVJjB/x1E7C/XfWAtOkk2xM7MDCJ5hgCqvtNPwjwcR0BXTnNYyMaA9/+8Cne+b238bWPCAqATxgw2BXuSuL9Z0/x89+InWoeQwNwUQTMMmgAqDzS9dbUBcsOxWnVrx9i7zHpzIzGCSCQ9wUNstdGZaAx++toKPpM8c01ieU0O5LtajNkcmaNm1nEjbl2RsHigwf0s9w2UIY77+EOMOA/7vdeJdBxSD6zut0FEuw+Og4DVRljoveWFJVvoDljHHZSrvOBNkYHHrIDnjsiwt/QDVoNis3h0x1OflDhQfnZBVkONqJwjd/hiMVZPeyEWr51rT2Qm9L1VqKBm+8/siYr4x9Bs13zMv0msgIFCtJfBhI621YwJCYag8UNyAK0ZU7yGKrj1k+VtIqtwnVnyXlH5SXm6xIMWLw+GuYY56YQjrhX1qWBw1eH+mQVFtoxaSMQqE3GveojH6onc9NXIuT8SHzCvIGqAoR8BaoCglRkzA0gXB7tDmNb2oHoRAwJyJ0xMFvgwCd1JyHaLQBIDLz/oTi1HwcoAD6BwCAqEevcp3/yFL/wzbfxWz/5Hj732psa+VTqDV0JDTugXR73xKO6gS6tx+XRhXuHzkN8+RYgqAC2BtTWwcDW1EA0Dgpw30oH2BAFlxwRS3w+JRqAAgjIpQy0mhnoOfZ6TOuOSvw0M93uZ8r7LEPdDIeCBFGudQ8S9J7DRk/WnnuynxftWOVF7OjVoMCGbH4b94kC7efH90RSZ4O4ezmNeccMHSm5oTmu8PRvAgjsNLPPC3TAIL+z35CfhqgN3gGpBwGrW0VBwdwant5Hli8a+giaegSCd78B7uszPwmSgE37KhmwMpCQMyhrDo8lrPFFY8s9JOf73LcKFO4geADcPNDsIw0/AYUZpN07r2OuwIKm5zhHbU6HXKbDlShABwxWxVtyP7dvVfdlKKiHT81rjkCguW6UcW46N+7Vk3ZQU0nkICEnQkkFOZXbAEEBAcX8rLBb5ADS5uTJVNzp7KCgAPkijMGCLbauf6JO7bs/9R7e+BhAAfAJBAZzMVDw1S9+DZ97/U2ZcIIMkHS7LjIarMCVK+dHst0oJUGKEz3YE6yCMA7LDvNdgGBrLGwB5H1rujEnq4A026d+LESErBMdxKrcWJgDIlBj5AkojMZCQhCGFGJyizt6B326in8B/R7RQ40gxOPhZvgtKTJmDBubkOw7884u/brT2u16Sl/2TAdrok+MT9vnft0U276VuT+HCY7AgCs5/YwxGr1zD5hH79cBgY61hhoiYIhgAcDwWeymeQzn9w8ts/EHVsZ8zQCsAFPDQ/vKhpCnvpK+SUTeTyYjHSRQAAnAEG7gmPgb2Lc52U0r5jI8xeVPky/na2+Waa4CYb6GJX0DkNW5ao6NJ8HtjXWsyU4HTPPnoGaH4d45JDDIiOpHZqAyqxwpeHyInmyyw+s1MUoiZBByYtREdwEEZAvR1sEeONBb2QOQ5JRZeBEAl0eeS8Ypg5UVmUPIAPD+s/fxC998G+/+5McHCoBPMDBoGEHBZ19/E41FcVbQCA4yQGYkTFWWR4IGbflSKqN3bWUwGNOSwzsAwaYsQWNByVttAhKqCgKAoxO9hBFQIJCAQkmE4AAodKPBwWvqivKsnFG08+9nb1WeKfFwN06TkqVWBzAgAKI4oBgU7amijAjnwMDrZ+tMd22Bg4dR2dr7VezbKU8jRdCV2xAuaN3AzcyQK96pmfZ2GCYHBxwMPnz8rf/96CwCMunnqlF3zML0IFvRs5oe8bPViNwDBMwIIHxm3mIEASvPMP72nr4KUe3gSbJ7kqmpE61gKquMGGCwOWy5PEDYNz+AXvbGmNF3FNNrPAOFHcswNzbqnTvm/yI8MIOEFW0fwSza8VjFJhzW5E6dcgb+ZiDArPqT5aA2+/4ePVmygIGSE2pjJGIUlnNQ7gEIZIdJtdrDSINe8oce2wRgZAoCKIg6AgA++PApfuGbX8ZXv/gePvsxggLgEwAMZiRrf7z/bAIF4eukkykppVooyyFHlCQhBYLq+vKllXHCaFBi9qwllNwBCDYGtioTXV4FHGzMYA0xAKMQmsBl6hO/pARKrO/XQEEUIg8hiOh9HvbxHQ6L20hEj5Xd6MxKNiVJ/Epz4peCAd+vfFCwC68qPhwYjb159XrNih7d7aM/swd23+l1oPwnbweYgEBQeCv6067x36r3U8OsXukeALAggHtGasXN15sNYOMeUkiGjxMhMYNU+QpwwHBU0r2M0qFHiN5Hdt3MnESjsAJMRx6i9dNRH1k/WR8BAHFvPzF7HxFbWE7DDAQB1TqHrf0D62b9mfr3q1fvnWkek+U6BRDQmQjgUA7CPXqjZ9ary0FcBn0Uwz8Es9P4rB69Kg8BB6vxnoHA1thBwNaav79HT5ZNQEGpDSUTCiWUDJR7AYKxBWoXPIkb2NuFmZ0xFhkAl8eaU0DYDPS0EXh9+8On+Mo3v4yvfvFrnSkICN3eHoH2W+WVBwax2AS2RMMICuKEqdBJ00Q7bBCvOqXiHiLnR/DlSwvB3AkedWBgoYR7AMG1NmyN8VzBgDEFW2tOMwM9dmqxUqNAAQUFVLsAkCTXlMQDoxCVo5u9e2fVHU7KEfggsrCGKNmUJL5nynYACWD4sa6Bkh0OHYkBOPto9uxPKNHZuLvBCUoKQQECx55SNHTjPfvnK493FS6qYPeC4nOW2zpphbI+53CcVXNkdANoIE2mvxysZYAgkz2Y5Bwh3gMEa+sRY3ArV2A2ODGUYh7gPX102j/aR5bmuJFcfLOPKIAEZ1ykIjFEg4Gl6T0xM2f++XBtHkJBwlz26wj9mUMekCftTkyEdfBUgeUaf10KHcGAjcccxzf6/shoWxUPywN1y9k4b41RmQeHyfQkgLt0ZUkJtCko2AiPM6NUwiXfCRBstZcvAY+7dx7YhZhU6ftHaMJ5AAWWTAkIU/CVf/Zl/KbaL+aeP6Qm6yPnAH2igAEgoOCdb7yNr37xPXzu9Te8w+flLMQiOLkxmEQJJoIk6gESBzKBnJYY+X0WQucoMAICVXieT8DA81px3UZAUPV14+ZLcc4mu7wCJfVYaUqEzTz0kIBYcgLAQ/ghFpvPs9dV+UzyezkCH8YapMRK1QooqKoMN4JkCifqKywCVet9flSPic6Pxt+NtCqRVbIfwnWxn1dPO+uKWL9oDO3VlekNQ2dGjlURzve2783gXdENk4wBIaMDwpJkPNjGg6Hnhcj3DIacPSwgo0LDP8ogkMmGGk9TTEd9dMQSWPvtb08uwzp2bJ4im9xMlPE9fQMA29Q3GzNyE6CQSWTV5iwfMC6ATTMejb3L0L4nVp6yfTQnQtp3Me8jRUaCLMQxggY+YtDmSixCXbNBaswOBoytaa1ffwZgb5WH6JooA1ttDlg46NGmoVdL1rY5dwsYpNRQKKGy6MiNGYUIjxvjwoTHOcuqsMQoIMjp6h0gJMoolpTqydPdeRl6wxrpq5bIGYPrpJMiUP7gw6f4xT94B7/xhXfxWU2Uz3Cs/rGVTxQwePrsKd75fUnU+IzmFACjdxJLUqWUkinMDsUadVSPzPtZDAzGKMaJGh8Dgq02PK8N18p43trAGtgk78CAvQ1DvSlM+ARsTbJsSwaSvjeEvBGQSZA20Jc0xpKJdkK+UrhHxRQxKVPRwxs9pFGUohXGgJGZkEgATTNliJD4JTdEsoz5oEBjTX1cp3hoBIRzzHq14mPwgoCVru/lJTwhYE9/r2Kk8rddA4+bTluzIx78m9Ub3hR0bToOW5L7ZyIwi6EpSQxEZgCJxeAnIDOhwhgIGZOMqdyrnBZ9dwQKohEyhmCbAMHGY9+c9Uvsm2yefSLpU2WpWOcoK/hhAigxuErj5pCDvLmzoXfNmx7ak+ehAwIDcHNOCHXZlWvF0NiS1PiIpXwEwBUTYOPS6KrgwH6zHVD48xw9K3EO3at3RnDcmYHmdWdstb83XQngUF+ariwEpCq6ydiCjRmPW8KWGY9z8hyErEmKBhBKIlSVraTOy8DszGW10gKy0mKVhPydD5/iF7/1Dn798+/iM4Ep+JgxAYBPEDB4+kw2f3j3p95T+kU+t9eZ4pUvRfjYWAN09HltMRbb0V4sds+ZiovIdjNA0AQAPG+M61axNcaLxiG/AJ5n4ILa9pNdQAErgmUki4kmRtsUEGT5vHFDIqAmcfUSCJsqy7PifabXtQlR2Z/m7SR9k9TbzAT3VEtOyKgiiEn+pib3TkmuNY80qQflYQh0ZTJ7YGZkrDWr5D6L55tymanpzdiIBX2/KrEO5gXNceyjMsfBV0lTs+Gb46ZHS/GYzYixGjmo0RMjyAoQSiagSd5JJcAScImBSgLWZHfCrpBiWCEtn74uK2bG33NnCiIoMJbA5IAZgS0Y++VWnyRl7ogAVOsXQtb75CYAYgYIgICErd43nsA4pv7ZHfPIWDYg5ANVdvbGwz7m8aKvMop5O0AHDLF0HTgyNqZfZkBgY7AFR6Wie+5b6PtZN8w6wcch6Abgdj6T1Tve/xYguFtfKlNUMqFxch1RmcCJsTWZM48bS3iBCa0xioYVKgsIn0Oh5sCsygzKAAknx3EAA9/+3hP88rfewT/5/Lv4zGtvorGMfWQKVFw/lvKJAAZPn8k2ke/+9Hv43OtvukGwYsooJs4AItzEAJjQNFPfcN+18c44xbIUtkDFVYZ4OtzDBltbswT9X/MJ3ph9osdSdZLLLAn+gVlXBraqoCEpcHChDQKjAhuN/hEl5xu4LZRdBweWzNlDGpkIhS0hMgkwUOTNC4BgSV9GsarMgSY3aAYFc5Z/BAIrYxOV3S3vEzDPU4wIMLIuxpDI53KDW8rPpua9oOAIoFmRPiQBPUwDQNALUBJhE9cHaDIWulOx9pzIAJh2CumszB7qrTY3k0MDDdzl8wgUzHHlW/0BwDVoYjGYAy1s4EDzK+QzUdgCJOS326JlcewA7MIa8XPgvjkVw30xgz4mD1v+g4XdbKWJhRjE6K7BwVES7BkgMH0kssI7Ct8Ms43prvuDXpDh6Cxn/F6uOddFHQScg4KVzjQA11j3Lkj6uySftdT0VdiDigpOCVtjXIqEF2oDCjMa6VyZQqEPsROA1bWPxQffe4q/86138I8//y5+9LU3RZ4VpFO4z+r+L1teeWBgB0p87adlnWecFzIY40SZkbzggg4QbCRq6+uj98IWjJJO1miUmv7zxMLGvtJAAIJ8/iLkExgouNbmk3sFDIzibOZuHYADCQgj/K2fWd/UEV2vQIAJYfw8/iaFjpmBgW0iUlkAQkVDZUJFQmWJUxYS6eImY2DsAenzbWnlrgSQZ4rP+x1rJWcGxoyOeB982H5rE7CKVdLAjphyNwof6J8dlVsU7FlZ2kN32cQYilfMwhYAIJBHxDgxbEMVYuzCBjMm6KGdMSbu34ffpTDtGo3yNlfbQbV+x8wesq3oYGkGBbOXGsvssd5bJIxy7OnfAnGxbiY70sb75lXJhHRtHRRshEytM23Ktm0EpMaaDyWskEVAbYx25UxeuIcM5lVRMe8pxvNnYAAc6wQ00ws9aTNJulNv/6LPY//dCwp8rA4AYwWjsYYPmvjfApI6gmZOQOm6ipuwnchJ1asAhAQFZxQ3C9sb79lWAKMj850PAyj4YWG64y2YGY3OdcnLlFcaGMTzqN/49JuHnosNiH2/AgukoQQbk02pdwDdOA2g42Ct7YGgCUiAg4JNJ3MEBXFCn4ECAIp+e8KexdBGr33uh/09o+KaFdqc57CvkikDiALQepQky+JKlvBGSXKaHpsGyMlcOPdezUiQAoR5POJYyWtXyCZ0D1VwBsqO+sb6edWvCdKuqhQlp0DhEw1U9UqoM9RgJ/FaBTxBvNkkSbFVlURiGihzjQzdVSpECXDju63mCAr2vyGs7Q+wN/5E5PL2EJvtRvgBv9nR2Oge9JyMGwEcsMin0HIGCDYLWZk8nxixdX3VMCagbKkD6k2AQlbAUGixzC6RgugecrBVJGchhTmsdo+uihS+660H6QQ4ewnAw5+OCG7MZQMYEvdFR532Y0WjVqecaM8cNHb9WVUWttZQkNCSjKWUBmxBV02OjPV74hjqMWAWnLSIloFBfi3f6zsfPsXf/cN38I9+/F38yA+/MeQUWN/mhwjNA8orCwwiKHjzBBRY8Wxz7kzAUFhCBzPlGWnsKGByT3RAgO6p2gZFG8PzCWZQ4IrkQGnkScvZ3w8BBPbZsj+OXCPsQUEELWfJkDlZuCIhJQZqQsnS9pQYVWluXQ+0AweVuq4w8YjeRMz2v6XcZnZm6He2hNDe/0dAbO7vkgmpEUqSfiqcvM3EIsmRqo5lHoqibUEyD18uIhZPn6AeKVSZpgAQFvVNdxr+e4opO2AR3sG5kY+sQbXrJ/bgo5SUJPZ7Hs/GjtGZAUFPmt0/Y65rBAXzkuLO+pkRvU1xA7NMN2fZLDGuvyZcWDLoCycUqgNAsPraUkvxxnuJcmO66kxuquq+CApmZvOWPqjePpuvItzpZOasdFUCyTOS6OfWSKl/S96T7wUsSwi1MS/BwapYEuA2GAWL5uv+NSW7ruIqIM3J2LA0uwbGILZkx1Iz44PvvY9f+cN38Gs//i7+2mtv+BglBQcN5311qzx59vT0+1cSGMyg4GXKSkn5/gYIiUS7Qe2vjFHILEEnJu0IJdrjenOyVKI+sU2jpgVMjMbJfpc04z96sf6d3mJlLESp0hDnuqccGtEQy0ZSQW4qvKoQtspIWbxjTtBXcmp70zi4ez3q+USPMQ5J0/5fxUWfB4bgxfC9rAZpzHixjfkcc+lgh3DJ5CChsShqACggbEm9DvCQrBZXZ4iBMoO071ducFDBLLQ/Nx4Aglwn4Io5xGUjk3BgEK0uROPzE0KiJ3XKV5bJhYS2JSjY9xmF9hs4MDWZWBJ8ExhgAhOrYu0AwvqHWOve1Ms1BgVwrxNDO/pzY9sBDIAgjkcfo/V4gHRFB8PjyluYJybPERTMc6w2xrXyTSNqsv2oJCSS+eY5OYnwKAMNssTuMbOc38IApwZm3cnPxtNkZMEarPRVX23Xwzbi4Ey0vTsKOAXUOZk+u8+oRYcGONJXACD6qqXulLUm+kLGQJ5b9PlCChw7Xf68qZ5Nww0N6sgAIHVkeBN5BNqwwicp2BJQNjqTq/LBh+/jV/7oHfzajxlTIJ9/XCkEZh8/jU8fXvPKAYMICt46YQqI9sqoLq6eZfW733t/+Xn8OwoYY7/eOia3maBZSVA0SJ0WK0gODoaknAACgH0cf5XwN9Onq1KJQkwUAYGLId9CkHjO8D1jGqyYYjAhCxE8VKbdpKyQdcOMiYGbxwBjv5+Bgheb5nFosqetCnmxdcXdWvOlQ3PpiUoJtYmyflSS9JV6GbFtsVAwQrZk09bKAyMbVBsDWU6qtTXipQGVIJutACg6rziJQI/bv5InuB0lSUaAYFvCGiNACgRIbtU/01ebk4dLs5gHjUZk0dc+mJbLQATfJ8D2SCCwf1dyAmrTpEgBBMTk4RUPiUAyyWObrd1HS2cNnM1jcTQezKyMDYDW67LyKAzwx2z/h86zlBKulXGxjPmscqmd2Fjm3/NqPTpyAiUnAfo6VivZmd+v9KE5MXPb5P19DsRsbCOrWdKK4RxzdhY33CXjtsZoJHUqSMsQDnIIjU56bK6XfxZApgOmJLoGWVnBBpQkLENWcAAFuIoNABwb+l/5o3fwD3/sXfw1BQWr61Yg/Z4S7eOv/u6vHl73SgGDGRQcFaF11sUAw5wYBQDf/f77+Ht/9A6APRCw0sIHK1BgkwmLOlhsOMbMEkhRcPcA4+S8BwQc0aVANxIxTtqNEO2T8QyRs9Wrg5amljnlPUDYhThSn9zxdSX4vq1vHKfpmlmx1SZG1ICY7Rdh3o6DhQUo2GpFbZLoeRb/bUy4oAFJAIHvrx5zPULbhngwjXuzm+GV34S2Zxt3ueDCAOc+r2S/AxqWN5bGqOoNMfNOyOcVE4XGzY4IC7aAbBdKmUd9w6kFKODpzBD3UnViKzgYkuF88ttEks2VImvQtO5FO0hYlOTsCQBYnlhs836DpzUQ6IeO7cfB72UTVAHshiYsRmUNfzUBDpt4pMJg9DlUPUQFBwXGGpzNtYvuyi5wzoll/V9+l5R12xpL2AASdiJADk1LnbOJINuazOG9PIl2Ky9kRUpcCq1tTKGtIaY/5z0B2IU6LUTiORQWklP9JXIyjiOw113A/Ymfc76HlZV+lVf0EOykfGKeDid1MBUcVAUHtp24AzMoZsa+/ENlCmLpsngMAG4tVbzXPgKvEDB4SKOtuPcTvBRMgMBinwYK/uGPvYsv/d7nd1ja1eACFKyQN6AJZkmEV4RLBqQxjWh2qvW9WfByPsIxXR2p7biOPhqZjWyNct9wpLgHLnWIVJ0JTZu4yhjmMKF374AkPk5ad/Nijdr2DWUGI9SRtAnYkb9iNKj05bjSwChdo3VnULBa8hbpzNq6wgBCXBi6oRQZ7QsHBY9T2LNB2yToX+/v7VPFF+aBzc1ChHoHUJCxHUuMnUfjaADFDH4HAqN30k8ZtPGYAEHc1z8UIgEFrBQAUfKNeoQdI98zxFg8CeX2e5VEmnIqvnIO7MlD2joDgRhCsDE5GgOgx+OpJBQGMhpyE6CM2jQ5LaFx8yRbQAzlNVSyTglNyyWWSeZjd2kychIQKjZYFFdfJUTORh7RgkcylCDhfonBA5TYQzfEpKEcPWmSgaS7AG5NQu1b7fNnpQPkNW4o1JMqRR+I47Lf62QcQxnbtf4C9vuAzOcmrMCCjO/k0ARda22I+vZsdVGFhpoMHAASmg1yHp9mrYmgYMYBxtbN7N2t8lD7+MoAg3sabUqvcTf4CRgUUdMEEWMNAAkfGCiwQTszRLeKxUctyxwaW69MyBpHBo4naQwFHIEA80ZnBegKL9zXnrIMfyTy3eViFr9QZULHG1VnyUiAxf3GslqyGJXByqP2BKqF8vb+NDCn49kggshVPB5SD+as3JOItGuP0bzanktWJZfV8FPCoyyg4JITHueES7HDWczzhhtbUcxd0ncgS5UZQ9pbNGTBfAwUgDUlDBwbyFgnO8PCGQMHByMoiEdi704NhMiY2CzuACGhgwMdODnTQhG6ZZcrOCACSOXmkpOETYhRAvi+LNoI4BAIRE/MlOy9YxB3ZaSSQJVBqeE6JtUAFbIeXo33JSfUVn3+oK3DmGelNsblxhq1HjIKW5AjGJSFDBGgG1qJfKIlRV8jI2LrN1NLzio0JqTcQ45ziUnPMWQgeiC53oo6IDJZH0WPVaTdibRnhyw9RPfGsNTLlvmXs57zOYp9GGG4bgEUXsZpfmWAwUMabTqHSXc1o84WJJY/GgQc/IvvaSLIjy/oHX1VP8GpIptThrolQUnj5JpIJnvTCyCICs5KDU+IcVFgT4lGEDAb0ZXiWxV7MrOi7WRUOXw1xZaEptySZfOnaRfHEL9Lg6MXkoeiYhi9g5hQdeRRH9FpjDH8I+BPwIYkBSVUNDmPQY2PgZScLCnKcgZa2CRq31dC7SbkBJScccmET12y5xk8SgIIHikQeJwSLpkEGCh4yEnG0ZiUuLVtZEXm8WkgX2vOoMMjmguJZ90AXHi8R59ZGFgYZ5ymes3hA9tRj8CA7QmvwGB36p89xjh+Yt1aXGP0xKCUxQNTF4qYQI1RSbMR3FMVD5VU6TMg1L7N0zva5/MpADE/ZyD0/3CPMAbSv6I3KqmXTkBimU+FMzKae7fP2zSJivVLsK6aGnit7XC+OROVZFMj+ydAjfw66UeRpQzRDW5Q6IYcEXRdPJz6LpmQtU25MTKSvsqSxUokrIHLPzSh+M9H/jP1HKkzXWYy0jLpeAFVz2DbTsDCfMZGZKDm7ZvnFSwrRiNunx0rvZpjEQys2ENh6GhgC/y7gw55+hKgAHiFgMGtRgd73T+zjmZRVnJITAcHH3z/fV9H+qNhyYj91ove2P0EM8Bs1LIoOlaKgiBOlQAC+U3ho2neJxyAHe0bQcC8PSrCZ/coPAC6Nzf1WJwa/kJp2gEtDUl983aoR1uhRhrOvOZbCiG2wZR5LA1KTTMQ9640j4eTtM62NhXPjfS33YuLSKC1tkymjMlglywG/5ITHhdhASIo+FQhXErG42QntEk8X9gEMUh58DpkcFao302tWsQGWZLHKb4fgYIrR3RbvUp8iobC5o6xBETYgQJJXpOT46htCg4MEIQwwnDUrLWABCSQHPJDSU9fIAJR7g03ai9pfJbFMzejZe0K6Te7th2xAQYELCQS31v/Awdyov1fDRxAcktaI2wk8gBKOp4N1AjZ1wRoKYBlxOfUcE2Ea23ukN875wZ2KinjZiEr+0wZNwMFUUcAC1nSPrO5UxUgWMikJ/RS2P2QnEW8V/ZvgQFjriJ4Nrnoy07PAPTYBmN6GpvzM4IFRl9KPocjjkpnD0bdLN+F8zT8v70OnuXQXiMgmJksH0N0Gd3VDS8PCoBXCBgclRkQGHIOifUghm4AIsqICfjOs/fxd771c/gnX5BtKIGg26DX2z0pxLlZJjXrw+yaogCBAWcIAAwswVBvYweMPtP7RSYgu+LeKzs/bGgyNPE1Ptk8sNZIUb8AhHjo0wwS7BwHDiAhJv8AGBKDVtRbpAtnpTB4dkExxP5hlmWAprQbyzhVZlDTgUICpei5hbRhTezoitoSKdMyIcy8N1ui+LgkFAUDJQOPUsKnSvLQgb3PBFlm5krejr/e0/JoU/Ie4Me1ZtIfQLZplTEDWupAwYyW9AkN9PdyrqErnah8bR6NdQQQj5flBmoNDgi49boPyTqtt0PpOeKk0QWGHEcOJD2crJEAHtK62wqFBFtGSw9uUzw/wNtnciO18/4HsBwD6/8CUkAmXvOWZDviQnIq3xVASRmlNvwZAKKGklQem26VSzKPnm8Nl0y41vM5l6gvWYxAtCRjqug0ZLUyssC5LCXsQyYRIFSW8wM4rQ+xArBcGmpAwA9RW9TTEhGPxizaw6jP4qusElKHhTnoNgxHR1dmXHSZZw03YfQw49EJrvNqoqivARyCgWjQI1E0syJHskkIIGLS8x8FFACfAGAQi02eGRwgaVyfxUP59rOn+KVvydGWP/ramz5J2nSWQAQKpqJiaM1zGKJXo9/lTJ5p7vec6hqpzwE5zlRvAAEDzXsj9ss++yz4l/uZ7EnqVxmyHC/xHiQkoLU8MAnzqYC7MTBAMzEfK48mttP7we9jyqz3q+/axgH0NcnUztwpW2myGJkXtSEl4AVkCeXWGi45DVtPx2L0bVxL/qj0fIJLIXxKWYJPXbIAAmUGitK/F1XyhWScqF7VIE2e9zQfhG4ipeITso5XThkGFCpL6GAAB+j090q1xT7tTM3BnGpVWAJmnVus7IECgjDPfICIfOITMUDNAQI1CHsAMcSc1qGFxBLuNpmNRmzVnjksExkZZz1OQyH7pBQK/U8pI1EC5wsKCFn30782PT6coKtU5DS+cq14nhMoVZQt4QU1lAy82GQezfsbrOadswQH864QOSh4nDPsfJHOGPSQ1XzAkg2V9orPGQmV9JBJUQ88U5J8FnwEuQ9OwAoMeJhvHq+q4wWc6zSVE04ZSAms86VOYCGeIGnb1w9sm9qHuM/qPDtm3R3bHV/jtTGUM3xPgcmZgO1OPqdrAOD9jwgKgE8IMIhoMg7azBwkED549gRf+Wdfxm9+8Wv47GtytKWz/IHuz4rifMdEctXdBcPAwEJSLMo4TCijpAwA3OHFkd0jeHE3FbbWhyYhYkogSgBlcBKDk1LBJWc0ZQ8MJJi30NLIJLhK5RFtA9hRa3PscAYDs6fgCDwOonuQupCrmZejAEEVNDWAckbR5YTPGyOjgvTI50Ky2VHjhI0bHuWEuLXrauOokjEo5iGXoEjy1EW3rjW24JI1Vtk2UJV/4DoZKNPQ0eMmV3SkY4WUQUjgJO8zJaSUgZRldUvoG0muOgcHtFA2O5ZgADAhnGDza1X3yPFzBYg6QNCHkSYeyrOatC2yBwwxvjCljdN2ABhCM0M7amiHsR3W/7Edc//rKwUATdsLcC64pAKkgpII10rISVa5JH2lRxllaygEXBPjeSNct4RCTXcXTNhKP04dwDD35iz+RxqaykR4nKeQVbK4fQeyu1yRMMY7WQKUZYIfs1wNJOi+ESbzrCAhyjywl/so8yLT+xwbBwUOBgwIbH2sPoJOAxE4ZQHUKifNGJLW2dHImPbQXQcM8rjePgMJi/SQXR5H/NPezuGcuKHTEchdgQLCR2cKrHwigAEwAgJz4KWT1d6zIK1f+OaX8dUvvofPvf6GhgO6MkrUJ0TMsL2Z0E7TXvbTRIgG78zb2QGBGoFAeH/Tk5uQS0DXoKxKmcD5AkoZoAzKRUFCEZDQxjBD1aVlRs351tJhlYUJxYoBiZ7MHAbpTIFJZYxdJzUA5Oje6F2pmxlzqacoHqF4nxNQLJEyMR7p4Slbo55EGUpcVRH3qp+TC2PYoGRhCHICLgQFBFdVdtc+XjqGS6/bxkg7kVMBgYTZMUOVioyTKj/Sf1nH1sJbMbv+dA7aR2b8o1c9K2ifb3F8FkLRz3yG7VpEQGcPSM+fjImJRMiURfZ0LjQFObfaEMMDK9Bs+RHiOlYAAeTM88zGwI1NVmYtAzWD0gWcC3K+IOeCK8v4XxsjQw7oykjORl0qOUDYakItjK2mcQOexdzrcfkeo7e5V3TuZf0uJvYtnYmFPOU4z6AhKpYEamMQPL9C/549bEDkUG8j9UeXeXOq5rplDVctQXO99nFaysmZTuvAgBQUuF7TMeSUcVkAhc66xaTfYzk61fOhen7NAiiU0HcxDHEGCICPDxQAnyBgEMs8XATg/Q+f4ue+8Tbe/Sk5cMkQs4cDJmZg2LhjpQMnliAmpfhzg9BYPWgyijsgED2dlWFxwWHwdgW3BuamgWij32ZLLVQb5SJCVIoDBKSLwvuLG6CcMlK+4JKyLJEb0DaNaBvYyewMgObkuyXVO3ijIzCwtqRUxAimLLneepSqAYQMQlWAkJXi3WrD80phOaYtHR0dRusuW6YUl1RFQFAScEmyYuGSyBMOqW2g7aoK7zngXtDV28g6vtzaITCglJDKRcMJ6vm4obIwkI2fKkWlvUGkip92W+KG4YEZ0x0gCCzBPkxlSnoBaoZisFzHrVUQsW9MRK1Ju7iJp5eyAwTSumfDBEt8fVB3BTMOBiK4WcjMbp7pGFASD5RyDLtdgPQClAs4PxYGIV9QckFOhI2Aq+YfXFsTGj4AhK3o3Cv8UnNvBwhA/n5Hx88sz9zOIE+k3nZWACqhRRpCVXUwmLdlfsV6Zq0XbdceqjIwEPUcV2DbREbqdpdO8zELMgMFBVCAIEDh4iyphR8GoMDQ8MPIujks8fGine7vY7gXulWIodB4fQQDsS/tp4SXW5J4Vj5BwOBgsCCd+uXf+/Jw4NLgnTBcMKwUimp0ce/FJJCP90h6Rn+u4Hbx3OD1KJruWeFNlFrdAH3l1gB7nQUo1jNlUEriCZUC5AKUiwCFyyMRonxx5oDzBbRdwbmgTGi7MmD5CaYwVn2wpqzV4EQAFGO+K08acO+AcHVaPaWClDJKJmxMfmjKDBCKKumtMbYiy0ntLAsAnkQVE6jmDOq40sABQZIMceIKur6YAMGmHlAFri9krDbxiHi7yoMXLiMlUVhMBJSLKL1cHMixe0Fb92ZTlmQQAwnRi4pzIIzTAMTmuPtMuTuI6UZmTtqLhW28uHYGIUFzDTT3IGFcuRDjxQaQ7ql3DBMcAGhsm8iMyo95pSIz+zFgoBsZlRFSGeF2kXGOAKE8QikZRbP4c0uoTc5VKElCTszj9t235p4n7KVuXGdAYMmKlscyGNwpWXRXrL9hYSuaQlUh+dKpdrpb3iNIcdbT9Fm9dt02yQg2+Y4NHJheW+k0oOu1XEApDXpNxnAE0pSLhxhI5SkrQ2fgSJrbwYI5kEDAWbcY5KlvdtW2UI/21xBW6B/DZE5AwZfx3k9/DW99+g3/fO8C318+IcAgjNSUGPXk2ft4+xvv4L2fehdvvf5ZyLD3bjcKExhjSHkIKC1opcWYxAG215fxciIYaC/E2Lhx0deu7MJv5/Z70qHGS0sB5YsY2ctjMTi5AJfHaoAuovy2F0AuoJoFJJgQpeyKQ9XRkLATu2Zod2OcJrQdxd39htQNXvCgOSWQMhucMloi5CYAoTIhE4ccCXgi5So/QsZ8XA/ucVuypWGmlIGkHpB4QVfpMwsjtE3G7frcFR3XTmuzeeJhnGxZH4KSM6Vnyi4FZcepjCxC01eksb9WJSZ0TaBsFzqYWAIHDQdlCKpR0vEHPLSgH8WVC773geaEHNV5CWAO5CaCaMwgum56y4NxMDBtoExlJD16DE7FAQLKI1DdwOWCS74gB4CwMfkqntYYNaXulZ7MPdB6KZ+BhKyANEFBaa1AvY7hn5np2Q0S9VeVJ6PjPVRFBEplWCVzr7zPIVBhBuoICGx8ooxcn4t81OuD9ZqBaCoS8qFykXHLRcaNElBHto2CHLGzD8oUGfvmYAHWQpnlKxG40z6UNAOAePkI5p78ydNgvz4n37mMMJYPuqN8AoDBAhSoMDz9kyd4+5s/j9/+4m/hrdc+K56CLTUgE0Tf82q4a1n19yk9G+oSQcDOMxvXhw9xtXodlBpfX+zAAF9fuJA53aY0NS9mKyUCZRX+XEQAcgHlfw169Cl5f3khHurlEag8EmXYighNHb1TouxZ83RkfMI4OBCYlbkbnilPIt4jxkNVeXns3f61TeqUiyiyVFAtDyHJ0rIWEikZcM+nhe1kPdxDY8JUCkxBIVXGGjJAfaEe0HUABHx9LuMUQdx2FW/LDVmzAZLmmpIjAhcN6+Qiyq5u4FzA6q1Such9UgZzAVoTMNFqCD3Q1LK5BFmZ81QWoGC3RPHIE2XbdgmBPWi9HhpaiCsXBCBYXVdsxFRXT5JcA4I2eZ9cNwUFdT8OsS2U9uNg/X19gXZ9PgAEtE2YtnYB5UcC3vIFpWRsLIm8ff7BM+NZMvqGlsYYvYDTaf5FWr5tErqKMfrImID3MjUYFIwypfH5ASikDMIVltAX9eWurHTcUKdNx2gTBmd7ISzBdu1y8uLPfJzQWnd87tBrrGFSzkXqfXnUxy0XtBd/JrotgGs2hjRtGPN4aNBxFnIxsGDtj+fRhCjDabGv+0ZKkyzpWJmsPXn2vtuvN19/Q74n+ljAwScAGGiJxogbnjx7H1/65s/jt7/4Vbz12mecaieakad8ztOEJ644LBMK70pzFMRDDyeCgYnyNBRtCo2vzzu6rl3Jtat4P9wa2qZtsH0/W4NlT1LWWGImjV9nFxi+vgBdHoGvF9DlsXi/5YULkVOoyhq44nAvY2V8TgyOGcQhuUhjv3VcnsRm7HScUlZhVWDgCWKqDNAEGHDKEv7QZWYX0C4jOSYZ+RhaS27ESMlCBZt4OtQEFOwAwfVF93zMSA3UfHy4hALIDFPdxGsrBcgXcA0A4fJY+iYXoNjRzA1gBQQKGFzxo56Ct8Hgr0DBat4fhRJMYZkC61+M4EA3miIoqHUAuHDDZqbCQaWBgi4/zQxLkB+RmeCBmuG5MRYOpMulM2112wOEps/MV6A8dhB3yRdcsuTorDLiMfUuoQODef6Zv26eNw3GNnjipl/AXaYO5AlAlykK84Wy5n4sQlP3yHtMjo65AxYuCIDAdZvJjALptkmoh6uEmo70GgDRZxqGSxdld67PHVSjqG578RwoBXx5rI7RdR2im8DRkoEL79kdS/TvjorJT9v6nJ7muV335MNv40v/7G+J/Xr9s30crT4z0HtgecWBwV5xCSh4ii998xfw2z/xT/HWD//o1OnxB11p0nQ8C9Xr4nHz8wIl698vDOMqM9qQvSHoycMZAEGIU7etol2rCMxVBKg1Vop0qp8KD6Uk+zKUglQy0qV2gFA3yTPYrkD9lAjUdpXPzEO91K48pox5fcCuT5Zep1O33JOM5uTJ0McdiZOvVTbKMFlyWFM6MJUp7n7VzH2JH8qxzjF2uPfYYi7IMkaqIMAAAZoZiheijK4vOiAIISA0ZYFsnKZiCjtdlBJVBcitArkKm2PAFhBmQOcWlYv0aWqu2M0wO6ND+2f2Tub9mA3fn/z2njIrML9/U2cnOcOwfNYKsAQjuAwbKC0d83FmT7RdLZywHg9KJH2rYTXOBVQfO0CgKkCELo8EQLeiAKFoIu9lmaMj848QY9fAUS4ST7kDG9aeeE9udU/7hjxJOMroc30tRZ6syXnQZNCeA3JD3ueVH1P+gMnHwBAsdFvbKnjbul4DlrrN9VpKoEsGPb8iXfLg/JhuYwMI2wYqpYfoyqPebmMN6gZK3RHqSyIDMIrhmLlP4ueLsrMt07x/8uG38aU/+Ap++wu/qfar2xVCAAfDyD6MNXjFgYGWGJN59n4HBa99ZjLaq2I04tixpHHI8TnxHmtqdU3JjrG/Q2UWPZxoWOqG+vyFCw1aQzVUvVVwbWirU40ApJRAWamwrSKXjLZ1gJAfP5K65gJmFiFRL4Quj0SAZgqbEtA6FXk0HrEvhqz8mHE8J4MdUdTSGE8M45CcRwH5k66ymOOHPQNed7cjrBXcLh9ijJHaKgNs106JGluwvXCGgDfzgurOA9o1Txkdbg2pZB8PUQLwV7/eqmx/l4vYX8/+Z3jiH/Gx8pq8lOXnY0UVdKT9dXOy46pEkBBpUT6p2wnAfAgo6K/3jwdtAqCR1eC3Bi6aR7JdQZsCjrqpgalAy84g7HJ0LNHt1vxzZ+IgD2nK4mdjo2KC69HKCxsr9bIRciqw9ZVLDrwtzu7e8lred7I+j4sxBSYjNkaTbuNtQ90q4GzBuW5rOYFKBrluq0vdBgVMOwZuuw4Ji6bfWBk4tKYAm5Z9IeGVBZNyBgyOEsUBPPneB/jSt/42fufzvyHhb2awbeU+3/MjsAafDGAAAMx4+idPJHwQQUH04vW69e+nv9sKGPT7LGOuR7HZSWCaJUCtEgoPqLVZcAZA0JrH4Tic80o5oaUGbEAqBcSy1THZxvQAgBdIpQJFjrnlMGm5NfGI9L3FuMUgG2OwEgCLlXU609tsCXimvBbejdfB2hHYCb7KsjFcX3iikSk0iR9ed8l5PaFIww/AWnBDIp57PTNta0b/Tg/IQj0DHRpLSmBU2f1Pw9YSb05gbDImKfs8YgWsS3AAIQc4AbvYvjx8MVYIc3ghG3ofpiR1sH6LAMGvDXHXU0NioCCAltmAxTo9BBTUvhkTM4+sTQAFZ2PCiYENDtYoVZGR1sSYtEdArT4ewrBdgwdaO5OlOTpkbJuuBjief+iOhAGEs+RKS6pUefIkV73fUpZ0rFiz+h0olIvcx/JbnKEL7OCBvA+yPtWr67Pg8ChQcN324rp3djZjdXin1wCIbmsJqTZxfloTcOBXBt2mYVdcAgOnTKXrNw23OAtHCaAtOEAxt2oKLQBjOPrMYK9sC4An3/sOfvZbv4jf+cKv463XflTmge6W4OAA7YA1eFj5xACDnqjxVbz1wz+6z7gGdsrnbNkVxcE78qYi9RqUl997ivc1Q63zspyIpO27B4ICrh0c9NLAVQHCtiHZdCgAN/LcBADIOYuSA8CbKA/3VFsTZZeyCFdK4NopyF3RPmjGAARQYMzAMktf8wx2S6JMACyJsmosUKlby94fgMsQP5wSrIDjei9Znu6ZeRb17AEFlsDyPzzkI0si1rQ14HFTrqx22M5S6Al7qLbxDokHNFe9VsmO5AZi2if+xaV/RwolMgLcrHbeV8Oo3NJJ3r+0/+yeOKy934Fs+dtzUubSqsynWruBBBSAGngOz1iNixoIRkKDJktCAHW6aB3KozXDtl19lc/pHDyRm7M56IZ3Xm1hbMFDZSlQ5hbDJ8tzyWWUdeCw3ktZX+V5GFDjNuo2HYcICiIgiLqNW4Us8ZQwVIS/FbpDaqhenIns/RuoedNvrYLLBZbDQ5qDYaFTpiTjEECC9ylRD0eH3LXVXKcFMPjj738XP/uHv4zf+fF/grd+6EeCrArSd3Bgjs1d5Yglf6WBQTfIT57Jkg5JNOyJGuPl0/rrlXcUv1/RPUuAMIGBoMwMEPBMn8+goG5dkbHG3FWRxRh8mw1/AAUPLa0xcmtgJnAjcNWlYiTAxT1RhOmVmsa4VVB4QshD/x2HClZKzDPF7behMCDGU70vEIliYxYFWS7AJnFfTpqQWAuGzYJMkI9o0d0Yhg1xHhDyeQgomAt5TsjDPAFRZDcoxV1CYHwwLa5Z0NyulI4Vzg4xHIUYbnk7h6G/WK2HzXtKeix6JvDaaRvu7Z5ZbmhXIF2Adt10QzSZKzuGLawiGZeZTkl+UqH4QPlopUNsUybPlaiD/hDm4npTjgBIXoHWV3Y/Fc+YVB4lLJVE1mMiqy9T9s48lPeBvYigwFjCZqsNTnTbHUX0nuaohLrtdFsTtsvycjrQVvatXMDb1mduyqqDOzvKTZe5q44cQIKtd/fxbOdz3cKxWp58/1/gZ//o7+LrP/aP8dYP/4iM63C/2Gh1dmM4j3xG3t13rzAwkPLk2VO8/fvvyJLE1z8bPPjAFkRQsGIPVkooCtUANA5yCvyzCRBEJG2gYDKQTnueKbqUkFJDbUIzCywWoaCkEzcDK8qNEvVsXstI1s+IgjdwVFqV3zeAt2tPWlSAoBf1a2OfGiiwdk8sgWeLW597rHWmlpN/zinJ/WvtACFm6qZrz0WoG4YEqyx7VPIkrH5IS9id0FmNyTNbxq+jsjtIaLPPvb9DhnXMrqacNXO6dK8u9+1dV/emnI+VCRCYkgPDzQtl5l5LeOYdBntp9JefrXIVQojhMLRRpY80Bj2UlEX+ssqlGmtTm6QGKBVAjgbSudHMM53GKBT7zkC0h3peYEwSNYpaAQJfX/T9KTR+D9w5B1e5A2eA4EyGUoIokMAAKeshopM9lCAnYxZltHKXd/s7+uQxDBjqODA2iyK7TDI48aDbWA09DVzAgV7TdlFOO912WJoe5mXhOdVpAgY7YmRsHQAZQND8HfJl72FVTXSn/G0KchQAuK56e/Kn38XP/Oe/gq//zV9TUCBznwwc2O5gPIYO5Ptb4YTj715pYGCg4L2ffHdY0kFHymsGBWex1cgITJ+tQcY+rr4DBW4sF4aDSJeqmXemmdEpuRIDiuIBBpqcvDYn6FAZDUeKQhMzeUuRBCv9jLJtlpPOhQqAraH3GPyu7/rnww5zB6ED7xNuQ9/ExDBPrE+QPpoBQrl0oVIhR0oe47UEK45tC4bJR3lmOuIubOr9wLK/nQUKyngqlOUwYXlcBwIAEJeRDpnwtn21gYOcJRve9jUIu1dC45/OigC7mOgACo7yAmaKknmtV07Cb7t+3X1+pKjm61tgL7piFi+9AtAtn62d5SK3tyqG91BwaMxXoqQeeHKwJkmI+os61oXyQZ25ia2iBmDrSaLmKOj8g2bBo24vPweP2LajvTEMwET5oQ5+fLsIq0etQIaDg+4ItLEvja2bZT44AaclJTFwrAC9MShL6CvqtpQJaUo+nPWa3O5ct3WwPc37FPf5CMXGLOq1Pv00LJv17J2qPZNkTqpjsmOD/Dp0U6L99eT738XP/PO/h6//z3+tr54zILEKQXA48O8jllcWGPg2kT/1Lt56/Y37FBZwDgpuMQP+1cw68A7tnxYDACaExgDoPQhwS5hzRrtuoJSEns4ECvS0LenxaR/jp/NyRTX6UWhSyUsP1XYT8/ij1nvwpM4SqF62BO9tvG3ryi2lrpgBSaLark6LumCFJY5D3RdeN4A9mDFA0PoyMAcFC+ULBOOfADYPayGFAxjQeC9pTNeM3byjG+yVqG9pTUnWY8c46AwKbiVKrcqhAnpIjPPOYtjE+zKplQ1/GzhIEEPSKlDUow1GlTS7XvpR6GLKGbzp+nxWBsGW9ekGOs70HGjMPq5jvzE3EMyIdgCCVvseG85kfbQ5eBh+uwEKhnIL9N8qK6MV2B3JzcBOv1EpEr4JDE4C3Pnh1tDOdNtCr9nzZt1GlJAuqt8uZZQtGxPLsZj023nbm4MDAAoQoseCYentYQIuNzz5038poOBv/MOBKeihgY9Wnjx7evr9KwkMnjx70veOfv1zH90YAQ9jCA6uP0yIArqQJKWyUUDB0/Ckr+0q8bxWneJMutwmlVGJcWXFE0Zx7vvBBMXfx81ATFm5gbkIki526qJ6sFGpxf0LVsrNkXbeexDerizdlwGqulsf0NlCk6c7sZ50H8ueA5Pnw3pj4qTgPQFGF5oiWISNTpVx7F81VmgAldSXZQJYGVFTKt6nlEYwEPt/2h45sgQ9OUoZg7DufAcIZjBwMwnwHsW0GpyX/R2kn522VZmzPRm4iRZmkRZfYdIk4ZIMNJjhJ3IjQBoGoryBbU07s6wkYM1W5zYY1zM59l1EgT5+QzPCPKybxu6p0/MPnYPAXfNwV8/ZuMzJgyrLZPk6lBDX7u/KPXKfC8Dc9Ru1/lzTbySHEImXL3otF+n7uGlb1G1Hei22c8e8mbNj8mW7Ik76zc5bcBk76wMtXGuXY9Pg0duXiokuCKEIK0/+9Lv4mX/+9/H1v6Hhg4+tSF3swKVP49OHV75ywGA8UOLNpXfez32fvojU5NH9//T/ebsSh7RxFqViCioluTTKqAoQJe7ecaTRc/FEIFdi6hWQXuPewB3KbDZE0g0LYTG6+kxYgO6F+wM6FSrfB4+fSNqpdKgpCnlPbgyImyQDJaPw9B55uveRsgN21CArALEVDH0qhB3gQnx6yAs4We7lfSC3ArIt4wu0bQkMx6q+1v+zYrbxmMFAAAkUkin5FkNAhGGviTmxcO6zBy1/elmvRufgzrCNcVhPbGWlmlcAIassWwy4qOzkInNMc0QcHFTZOZImBgiT503AjrU6HM/J0M7F56GtlgjxeYqhQ79+mofGRM7LDimc8qf5PwDOZSawFUeAYFi+uDKUR7IfAQ6RRKasX20fgajfShn7XgHCx6bbZhlbgYEzhyfI6rwio4OCqdzp7Qso+AdrUBBDf8PndJd8xlMYf/V3f/XwulcKGPRGf+3OoyeTKmyLT7YODmIcU9Hsk+9/Bz/zz//+8e12y7kAKD62QeuTJiuy7NcyNUXVAQQE4acQTyRursTkx91joHkpUqvn+agq1G48H6oQggK8tWzJ2wl0kHAHQJA950PuQcZNr2hok7XrqAQm4zCbfVjetnh2zt73cu5EuCb+NuVhPB7U7wYE9PO4bAq6h/vdYOAACLiCiWN4lJT4cZYAqC3xbf6OPIjagQKjMwerDcSYG5BKBwmpyJkWlgRcNwfVdnLpPVS9VDGE94DdOFu5OQ9N1zx0Hq5KzkCtsKWrzHzq4Q71i3NQvvh45T8HcBP7vFwm/fZI5H/q949dt+k1y/aF9st1x+2MYGA00OH9TSZOioCCz4zXrtg9//s+WexO8+2jmV8ZYDCeR/3G+iJSgw/JbBXW4AY4sPt//7v4mX/+7+Hrf+PX8Ne/+SW40kUnHoYNXlbLuRAnDWvs137bzyVbnQtgf0dvJYIGT0IK3w/PPVnRsBPmOdZ5pAAohXia1f5s0mse7SK7elASgFCOU2KVJ10VdErylpIc2jkpx1lAbyjP3W+Zwz2z1KUEluLs+bNSkgsOFW/cOGpeXtlSGf6ecwaWQOAIBCxAAR8BhENwcHsO7D/W2BEwAIKeV6CrDOL3SreRJUKy7QKHPVDQ61nBgtyLQXnTPU0aEFYMSchn63Nykegnt2wDg9Sbcyvj/sZcPCsxDGcOR5iHwDhmt+48gAC9/10es3nLD9QDxs+tVlmITm67fpeP20633aXX5A+te9BfR8Y/XH8vAJjg4f659hz/7Qyg+ndv/fBnRkBg1x2BAmMLXP73cvkQUAC8QsCgg4I3sVI+TklboQNwYDS/bRbBjCff/wA/81/8imaH/oj/fqbjePgsKLWzTSdMOOKcSuOwjBstRaU55i+4UNlnQWjm72I/jH/uBWEn7MGINFoI36qEfAxX7vaMaWc0DoqXgrdGCwVBd4KfW+VoSeZwz1te17Sx0JFiWiqlhaKNAMByBPi/CwCwNP6zEty/X/29L7QHTIDLCA2PYmUDIjgwo9KBhNPLU9IvMQcgsQYMjMd7duGi1xg7MAOGGcjO4DwaOGvJxz03Uzo3iotNrg7veY9DoNct5yfMWIX38b6rwiEhVJeOjn3WwYM5SrQAAw/Wa+G7B3n5Q63mZ90Gdce7Hh6wc2eAwJ85gYKD5+3C63eUVwYYnCIho6MBOGughsnBgaSywBkFNfJPv/eBrCP9sX80Hrik1O1uBULY5OVmZrf84MbXB5Qcpkl62X0y1uueQmm6Q4i9L43GA7wcA0CLJZyR4gVMAfCgFCJgiKxJ9NYo/h0ffY9SPrjm5sZAVmI+w6xc7f0N49+Nez/z/TgEEAHBR/D6p+tc0YdPeHoF0M+bn6bcPbNt16PWRTR/RAegQQEDMDINwOC1D3N/Ag8cwEP/TQ9DkLMLCiD8fTz5c5qnxmpFdkufOYBaKx8BLAAAXW6r7zWo2DME8roHAAD28xSYQlYYvtt5uzfKPmwUGB5grds+kl6T+q6N/LG+PS73JrcfyN0Rc3kDEMh3U79Pci5L9n/uQaAAeIWAwT2NNuXoCoYNBOh7Io1hCkB48v3v4mf/6O/gd378n+DN1z6j15gQZf+NgIzJm5xj0AcJI/39fpLc3Fv7HsH7KCsyTu9/ZDAXQrTz5EbvTd7y8Dcg3hvlfi2F7wYaMtx7ULz2PGBUwkfrrIdm3FpzvQ4L3PSuVt5/6gafydiEY4owemdL7yM27bwVy2vOAICPwKDHA3N1x/PiDJEcEv3cutG/1L8XgKH/fqbkQ732H2H+VozSfo5xMP7z8eiIwMCSfU9YhiWoXYQfHjTnegccX7NiBRDAwuQ5RwYgeqrNDdP03ZFXG5+31CEP0B1W/tz02Mn9AyA4X45+9PsH2IDJoA/9ODGAS0cg6gYtvo/PneGDWF4ZYHCzWGcyrwGCev8GEp4++w5+9lu/hN/5wm/KgRUA4ha/cnBN1EJ3TNyzGNPkBco1KxS+nwDrCXcgfA9B2sBd7dqdKXEkMAMgCF6afxaAQ/TsXBCjJ6Hvywg2aAIX/hkwZi8v8jeGcubNTQo3KtoVCGBTqoGe3QEA+47GpYWsrfDemj11fuDCwDMdeeD99+7n4bIhwDVdc1YsEYyo/zDpjUnva4CBSNp8xDLc0w4rYz9Rf64xDjpsBkQo/AO64bc52cMIYuh9C2TLRwDrAT11z4LF+Rfn3r3zDuiGfZHwdxjzXxjxrksOvP45XDXd55S9iveaPx+efVIewkwuntEfduK0IDosi9DUrIMWZ+zc3FJ/V8+1E8ip7MCAXHO/PXBQ8FPvPhgUAJ8EYGAdFkMAWtjPplcB02uePPsAX/rWV/DbP/FP8eZrn9GkxKiegd1ZAPEZq+cPz54EaUJ+nUbuArs0EBzeYz8X10o6KI+FwO0ckOHL8PnOixvfh17Wly5s0p/HQuj9vQIP4Tc7AGHXTOEK8woj+2DfLxOhYpnyUvzt7GXpmLl3RR0EiOFXYbdDcwZwINueMNSZdAabPbh1r+EdjK6WoPZ31x2VFQuwAgpLdiH+aKhcv0Gifg2hGzAnXzyXoD97qPPSQTuu81zvsxL70PxkIgJRRqIsQwrrVx5AgR9oxP0ETgcLXDsLZmzEiv0CDucdcGD0J4PRvfzF9wuP89jTvz9ktctRoXHmzZh2fn80f+ZjEtbO+T1jGleJyGtkpxxfhX9WEQeDFvaMOsTyU6S2+0rekQMxgqd83N93OIYfhSmw8uoDAytHKNKVVQNTlk795i/stlHmySjJ4GGcAKskwwGI3AIBIwAw42+GYTYU/vckcZbWcw/5FqdUcq8ifBa9p1l5T/cQ5Tl+Zx8kixNPjxin9AgOxkQz7EDEECf2z0cGYsk+zKyCg5EmyyJXVCEwzKHTZYEpnwIBViDQmi3VZlStcmMO4y7je8ugjePCarR6Rycax0g+Dp76opwZ1JeecwEICAsQJoOCBQMCM1AAOlg4quthPUMd+eDaucQ+9f7Uvkw6z7Nel6lAh1j69QwotDqFzTRkFnJspGIHc49URwyGfGIDVl7+gpKO/fyyxt76efV6FoY6Yp9WoHMFGMIG0Q/Sc7OOM9kw3TUCwTju2eeCA4YYUlqBBa/gjRpOY8GWOBr6/DwxuJeeU/DyoAD4JAGDtQnq3xooCJ3Kltg0GChF67qL1+3HLgTtBAg07oJjxoKZZcfPyVhU5tFBRlDoPLzc7hE1KlHsI5uYcKAs9WIRMO2nBUDYGaaFRwuYBZM+Hhj7RZ1NQAVEtHGcPGms7YHDDBrsWrvmVrhliPFTN/rKFLAv77KwgJwBXxUINB3PxiwMAQQMxPH0seQ7xpC6kbdxI2Ifs9G4dW+dsPDEQ5k98KjoZyCwMrSxF6MKI1eENl52k45gNAPD70vkh9Yu6zgYkUlOmDmA6b2crPtX66b/WX3M7mYiAQkg2SeHCJnstSArUGCunncgQKHBj0tmy1n4CHMv5qb4dwv6OToi8bfayoXtXXvzPH5xC0DGa2ZAsAJt8R4jU7a/X6zHXXou6Liuj/p4znKSqcuPgUAawGEOeqzrkb7yasEeHFYyKsT8IEBm5emzeUniPZBpXT4BwOAY2cby5NlTfPn33sbXfvo9vPnpN+UayoPi5ZBTwCnuQTAK85LmCUAAkMkdGYHWOhvQWgcCFTyAgMbdYGyNFUAwuPVrADntmycOruqXOUxCO4HMuA4XkgTYNjxEhJxIaj+gah5AQ2cDeAca9C1c4U7GaGWbZjw8gAKi4TUh755l/zgKrdK7cYlaBAtAGM+VQLvCDYqY4iFMZc8KNAEBWwuAQMe0NTgArI2xcRvG8igd7Wi8iAhF3aAEIIUxaqr43FvX/tpt0oNjoxABQVT0s8eH8DfR2A5nK4LRJ9J7a93sfjaezHxevxMw8BCZWZU89XOhhKxDn8GySagBBQUIJZG+z0g5w6RfwMHmgMDYBAcKq04ExnkH7EBABACdrdo7H0N/RbyMPRCMZQVX9mO9//0ZY3MI2PR9DWMF4HC8op5b6TjpLrohM9jJjIAFRgbhSkAmRkrk+s/eQ8c9GYtsPbAYz3tsRc8xkpuvQNtcnqr9ek/tl/32/FfH5RUFBnsrswK8Np/ef/YU7/z+23j3p97DZ19/ExvPxqwvm/I7p+J3YixCCIu4WkTJXWnxCAzaCAbMcJhSM8OxNWEUtto6u6CGh4MCv3WOuUzu7sVn6kIkNCmFv0VYSqIAFrD0qByZTwzBCu+mxXgdliB4abrnOR2Y9WDI0pmGmEXOkyCfofxDRazMgCrdGoDA1gQM2GsL47lVxsbHY7l4/HKcZFyAkgkZhJKTLL5lqDPOSCzXM7Hg3eClA33eH9G3Znilm/ZeXbi8/zx8YCyAgQUiCxGIAjSAkBSED6zDQd1uAQLWvq1gbFVkS0Aav3R/l5xQtK8LJZRkniSjJMK1cQcHDcj2PhWkVCD5BkdA9aDQFHc2FmBKVnWno52HIeNY3cwPOShtcXE0/vLKA3A8AwF9XBDG5XicGvguHQeIHjgaywwBcwb8SiJcSTR7SoxMhEpAbrqICAoiIKCgqd7p7KkwCkB0esIy210JmlF/F20HsM+1sPL+s6f4ud9/G+/+9Ht4Q0EBD3d8gH7V8ooCg7FEMODGWf94/9lTvPONt/FbPymgYLk+W/s1Jty1+MXJc4E9GDAhrfrejIcAA3bDYsbDBKb/awoKWMGDGh1unrzW3JPrDbGDB2NJIEW7+neSz0oiBw3d2IyCtAQKYUZGjiSyn8c9NX16IAirMARRPwOdsKYDTYgpIHzKWZmFDgyYeZ/XADiKn8NBBgYQAJuNYzVQ0OSarTGutQ1jKa/A1pqPnSi8fdt9i4QwRjl4qSUlMXwknpYpOQAyNqkbYkxGGLRQPneAghkQnJHhPv3Cc5qCA9nfH71eoEHD3QIrVqfWupxH8GWAwPrcxqfpWHQwve73KCslCRiwPpdXwiXL55mAaxJwkImQm3ibZoBAGVmBqnmXd887/YwDGxDDkG0Ym30YEuG7ebzukblbZQUU59DNERCI4NjGzIy/jZFvYRJkJZZZz5njYWOX1PjLe0Lemus3GUv28dyIUJhQNQTXSOYlQQB3pg4cLSw3gwR9i1v2wuuPfX6GfT6XD9R+vftT7+Fzr7+506QPhwRSXnlgMOuSaKTff/YUP/+Nt/HVL76Hz772xoA8405YtLjZ4kCv5UPPEPstQDB7kxuzg4GtsnuiUWDMuFRtiwGEGtqWA8JJatCNBk1EEzgQIbpWdCNUm4OBkhO2Fqi3QM/Zc8j6hKYBudF39zgu3pJdnH2kAwnWPt4ljw1AASLoI+Le18mAHTgAPEg/G+uzNdYwAhwMPG+M61YHMBBBn42jhBvGHpjHJxGhZPVwMomhQ0NlGRdUFhenJfVe1XDQnYcjB+M7fDwxBXbpKvY7l7hXgROdPIIDv3YBDs6KyxbulyH5fgRj9/R9Sc2NhxgSMfiXkvE4gITaGNkAQgNqEgOYAVQFdNCQw73zbgYCq+RV0yvGPO489XDTB8nZ7o+DMt07hgXmUJmBgRmsRWdnJRum5+JYrfRc0jlleq6khJSa67iSCGkjAQfEygKtWCHyeZoJ4KRhOYIQb0TOyu1Awj19Z/LW1qG5OVH22x++j1/4Zndqza7FZOM7RWdXXnlgAIzCZJ339E+e4he++Ta++sWv4bOvv7GgP/snqyVTta1R9DyQ0YuaEfwqZGC058wQPF8osy40DVcFCrUxrpV3wKBNblBSSB2BQU7klOclm+EhJHQEbZ9tDSiJsTGjKJW9JbFDGwn1tvGYp3A4PlGwtcfMYaqTMAy0dLilgRJgjLVfNQkvkYCFVdyQVEETRnZBHjJXds38NB4BgTEEK0DwojFebPsxNgBhYzcruU5HdwXXmNBSQ2PNLUBSq9OQs5zeyYlhOQ/ECnwmI7wsCuQk5r82HzsP8wDYDeA6PJb52BtdrZI5Kk6N2/joZ8bQR1Bw3ckXD4Da5GhlZLIa/M4adJDwqCQ8aoyrAQQNJ1xyQmuMlOQw9aYAQTxQaV6N824x54BzxnHWJ2xOhv4+5lYAcOMcx8HKPB4Wr4/y1a+9LdcrMGAhgo0ZrCC5Ye3w2GdRx9W2BwZnOq6PHSGn5jpOQIKCa5bXysIUPGYGKwPHqYG5h+eYJXyaiYSH4c5Qso2lIrFoN3Aw32P/b9FBPejbb3/4FH/rm1/GV7/4NWEKHFx3cAD8gDFYlqNOHUHBm4eJU8Cetnb6jblnUx88c4y/9hwCo/SMJXBhCaDgeeO7lNlVvzNA0Jhx3drAFMzeDwAkPeGwGxsRJFN6jZPEzli+byA0TiiZ0bZuhIjZTzoklhgoJT1TXeUFACjt6xDZ0qOkIlNeI3jopScUqfDTcU5ESUIJEqa4IYCNLEaMARisdPSOBVJAYOMp3qkAuOvGeF6bjh/woja80LG01zh+Nl4rJWegQJQboWWgsYIBiAKszMhNvJkKgFiM0Vx2Cj3QnlaaGfEIDkguTszu/R/Rz/cUCv29W0Gh9ZK2TXUDBmHzUzinUt2bZp9Pzia89Bg0NzASOiA8UuZsy0nfb9hywuOcekgHIhdGR2cALRkgDf0wtWF2LuKS1tm5MAcohiBXBhmIhruXyCTNMoUm31/R2Tir3Wo1eAT38/PncIExZwIE4ExPBAQvtg6eW2ue1Ds7QWH0XWYAkY9LSQ4KrpVwyYxLJrSEJcg2Bq66RmgoOaGSDAgrGECC5+ykZLrPnCIMS21v2Q3TMf73dP0HHz7FV/7Zl/Gbar+6bqQlEngZ1uCVBgbAni148mwPCkzJL3+vH8/Lpc7iqBEMWB06W8FKAR6Dgm0CBRanjv9eqLGJwmKAoCs2oVBXyTmWkFNIExCJcMmCukvOaCzKD0h+hLtxoSUzGhO21lBSwlYZyISswi6XETJ3BmCV8n0EBExp9M/G61fe61ECZUkJlAglgISSE6jC44abMwmqGA0gHI1vGE8DBA04DBsY2/Nia3hRzRgJqPuzax2U3bV2QGclJwJqdcUGCEVtMdNN3ztrQeSC7d4ezPj2V0KPu9o1sWQalVTiDg5W4mIrH2x4lv03U6vxqwgKAiBY3SeRPoCl3aCeZGlek7V/MwOosmCGdQvswEqOgHkc5L0YFQHRL7aGT10yGrMyOGKgHpWEyhYvz3isHidy0lAOe6gkaz6M2t5lmefcjhkQ59R1iHjjUG+8HSbu7fo1yBEgRm8LoGCjnpC8cU+lWxk7r/sdoKAxdqBga8aANgdsW62oTeUl6DjgWM9FHdfYZCVpqKeicULL0vrxtxziPg15AQ6yXoKmDByx00Bxye1qX45VPwEI+m7fng8+fIq//Qfv4De+8C4++5rYrwgmLdn4o7IGrywwWM3TnlOwBgXxNxY78vvpNT4Ai0m4er4JMzCCAnu2rTioTWi+2cNh9T7tt558GOi0qMzEwHRAYPWMtGEmUmqTsCVCaQwkuY9MM5EGCS8I7S5CSGipvwqTwAO1uOsHB1b7z44Awby6wtpgStf6MpZ9gpElTtYpsUjaG7OPRe/JsrMGEXBPxly0bbWXRGNNLrwTFDzfBNRdqyi70VMd22asDZIAtysg9DSzMgahHxJ5wmUGNHmd3JnI2jdZPzDDG5ebxnES+9vzYwwcJKGVnDWAGiYHB/qZF+/P/mf0klOiARTEegF7hWpym5TB6A9X5kn7gBJreEvix1Xr3fu2e+AGzmZwbWXTPJvaCDmxg2hAjEvXpglAw6OS8LwCQAWKmVAxKuJtiozawhARw/V8s7qeJVjaaqU5QTnK0UNkCBCAnzRsyiTzydjBTCT9CTGQ8zjdwyT1toX8gRbqyD10cAQKbum5lgiJgUIiL5eccN0EglkeSCLGpifsml7bKiNlEj0cGDgCQI2VgZS2W+5BM5DH5Iyb5PaM+3Kc9sniosbAv/jeU/ziH7yDX//Cu4EpCHkMPvH399Rv7i6vLDAARr30/p9I9uZXv/gePqc5BTMomG39yvabwNwaX/9tAAUOFuzZ4ZIYD43rcodVBTdmVV1VGPs4fWV2T7I1xpYIj260xxOv9NU/hyZQaXHaUcutMPYZKDjK0rdmjv1hHrR8FjOPqykxNM2NEEAQ44YEoT4bxFNg1uQhpU39KQHNm4L25aRzGKh1UOCJcMoKrECBsET7PjLghrbof4KOi34HDaU4AyIsSdY+MVDgS64SHYdN9INGpCBBzxJpHRwQAwwBCKzj5n5VuOERIJjDB1avOddjWTfSvInW64Uknpq5TFyp57yokfMk1ASkyUWvDTvGbbyAUWx5h3wAcSmbxrKb3jcBm4CDrQHkoBsAGkhj1ZkBpL6M9J75ZrqKsc5HOluxBIwA28dHwwJJmQJPcGXBNIlJ2UBWNrCzg1HeY/GwuoEGTcA0BicTwNzzV1KS56RkhvVc381s6JmeGz5vYvCPSjMDC9O/8l4AAg15O9Jr8Bh/P6O3J84643anZV6p8e98+BS/+K138Ouffxc/+tqbXisDB1FAjDWwzx4KCoBXFBjw9P7Jn/QliZ97/U3Ycp54zWzIj4r97gZhMNxrXlJ1VEjjVBbPA1Q4RfuqUbDlZf0+liPQWH4XwUQ64Cd9ba9RbRqLs1yDmOwWjU4ihGxeWZolVP1+zwNgv9EI0KnQDPFI5iUe5uEAIyiIIAHAYllZVGiERqakpb7MCXK8RY8bCkMTlvWRAARb9w8Ac3pEZ5p0FNiWKSrIaeyhIgkzqKcW28DmDa3zQIzRicXHJ46JjYuCg5IgGdWJ8DgLXZoVPBQFBVlZhMOES/QpwyxeElMHCM7Zs4Rj5pwD84zmlQ82FWZQENmLFOoyMxlz/axuKcmKDErmugGbaWSNgzHLmHOSRDebF96HNMpRq+fj0WSSukw2NWSNJbmwUffSKxGKzotqfa4ep7AGfb4ljF5lHIdmRoaP98CYV7usZGe9HNPmurJTZl103kKN9mxhVhsHzaWCUbQNNRGK5iZtCSid6pFxUW9bxoNdL0n95TrTc4XEqfGxOdFz/nfUc7T/LoXPTdfFthKt8ymsREM9r6q5y2YAOxv0ne91UPCZ194E0Bltq90cUlhd8xCA8EoCg1ie2uZFP/kePvvpNx+UKOVINnbnSw4uYIpRlGhFj/ta1mo2fcuSDVvRXPEVTh7jb9Tj/10ahNL0SR0U9baAqhZ3A6AJhyIsJWdPbrvk5Jm7fWnWCArs/ZzwF3dOjEWUBHmWsrRZvRFmbNZPszGeFFukGq0kIrRqXrQqGO4sgolPZRZmtzYxHGFZX07kcXLL4G9TMyIoWGXASzvn+h9PnJy6ISoknpABNkDe2xhJgqhkwF+yrBApifBIE0cdEBTdb0LBgoG8kvrWveTGWJ4Tm+m+LQPg0TMnosFLb8SSdGrsgY5hbHI07kcswfGucmf1E6OR1HCq0w5Kmt/SAM4JFzVAzIyLgoWWmibVUjA6CalJUuEVTdgadBkqoRJ5IVdWotcJyHww4zivFBEaWH93a64xfIfM1TJMY9o8q/9BckMOBgDCbP8c7MOA5V7m5fuxEQXkKxOIxesmyN4Om0tQ1GVeM3ST15Ao4VoJMakw1QZkcuZzLlGGJI8quZ6zRF7Tc4l0WaIuAU4OBETHZUDbTLo0cTTGcW7vyj1WeaEivv29J/jlb72DX//81xwU/HdR/sIBAyL6jwD8IoD/j370v2Xmb73MvZ4+e4p3fk82f3gjrD64pwwU/hlAOBrsYNwsozuOu08enTAlyRJASjGLPCGjSZxS3GsxYEiKbFnRr2ZUJ0JtjEvpMbTG7MrNSszSNYbAMnVfBhDExD7PZNaYcSwMmXC1MTix0/fSXYStNtmatLFTjD1haBXqCWNkrrt/MCq5BvGkAEmYtLjhTA9avLrH7cYxO7LvFs+2SZYBbD6KFrPl4OnDkwmvmxiiSsIcWFjHQF5UZpdM+NQlOzh4lCQL/pITHmfCp0rGpRA+lfMACooyQH2DHXTGwEdnaJHWQWl7BSsGEJA0vBDYgwgQ5I79nkeHEs370UdAMNZvXUerX2UgswAWyoTawm8a9ERCFyQAqc+nMvZ3IsJWKxJlzbvBkFXel4z2LPc4rnZN1BdDtn/q/TEX5nGeRVAA/2wMOw75SJEpWICCDgw64LKEvFhiOwB4yDDuXxJ3C1zuXRJKgbBLF63/ZuxJyijNNp1Ki709ZJ8ISfhk0XX6vrF8bqsTHmMfTrVxAuBjFVf2GJj2vQrCviCZCI8nPWhjZzJE6KGxWHyOxw+OykKnNDC+872n+OVvvYN/8vl38SOvvXFyg4+//IUDBlr+U2b+P73MD62P7eyDd39awgeDggc8xc7/ngzPvHZ7uW3vLQQYjL/djwijd6UeDpPuLshZUbROuJRBJB5BJkZhiZluLEvsNpY11Lt1vkUU37yUJyqAuH+BGJ+wV8ENQBAFRfaOX8eE5zgjAyBZ7IsCYEMTYasM5KT5Fd2L2FpDQcKWmlCZDHUPaWpX73YHDAoOEmi56+OqVKV4d0NJ/dWUt4yrMdYSz+5JSpLsximhcfOVHPaLxgk5sa9CaK25kuvj00GbKbNPXbIvj3tUOlPwqSJL4z5VjDnAOH6kLEQKXo6d+meHTnljlX6npEZbXiNAIFaqnERw5LsOEOT3vR871CVnASIzYGcMjIDA3OuTHQG1bomyrEhQgJ31nhsxiBjUGJ9CRnbZUoNPkLwDEBI15ET4s2sdQLYtjYvjAsCX93YjE2QjQ+UodfBjAA19O/EeMlnPs7kc5RHNxfMJFqDA2t3f7zfOijk6R85A3HqbdJCjBz3Lfta4fgOhBOaDG2FrFHalPAYIj0saVivUNuo5YK/rbu3TsgIE8yZHw4qm0M5k8hRYMCDYi1s2wq6ZhvW733vfQcFnXntzZ39iiECedxt7UHh98uzpaZX+ogKDj1TsQImv6d7RR7Jk1D4H4xKR+eke/tNXJ6yix5c83oTRu0pJ6VeLNWaZqFttyE3i3wIERoDQkgjMljTBqLRBCVi7V8rkbLfDuJHRSkjiTmAgOJ3oAhO8QytzEpXFTKnIxiIZ0lbzIqg2UGJQFerf2JLWRPFHcGAlej6xTcB9oMD6BRgVdgQ8Pp7esA4qjTUojG49jAqtAEpDqj2OuTX2NfGm4GKJyix6No/U+DwqCY9LwuPUAcGjLCzQJbAExcJEpMbWDvJRXtvOk+/t0/0o4tbPKTtA6KEW8dA9xo8AELRvYp9Zm6Rfe45D8rlo/a71ms6xWNXT60cEpIKcsoADIlDDEC4hYiRtg21V/DzLrncJArBftKbsXd/b4CFjE43MIwVol5xcdszjpKEvFnMMGFZ3RAC6nW67qvVLkPAAaCcrM0NwtNvpQ4xkegn5L0QoyFPOBIXwCN3Y6TUPuu5Iz9l4RV3XQc+4tfhpW8OYrQDBrb035tIW8gHEnIJF+IDG+x6GLk7Kk2dP8fbvvY1P49OH1/xFBQZ/j4h+DsB/A+A/YOb/73wBEf0SgF8CAPwP++dPtdHDKYn73wLMYWmV/M0I4OCgfOfDjrSGAbrRIE+CJfOFqWfXq0BZfoFl5VNJuHDYrEQVVi09rjjuGpZ2yXrAPr49JNcEL0F2OaQhoXAFBqJw7I8r3aNZACHBU9gNBny5X2sdIBgYykj6qklJkIzyTRMMt6pLJ/X+rYV2mUdn3g9FT6jHDc2L67aFDoUfGBXdnI1POsiZs4McW/dMiUGUUJjwAg2pJSQwSmZne2YQZwptxeBICIE0fJCG0MHFFZt6dUnDRWBQvSooiCf7qQdt50QoW+CgAAmckvwmyVnCtn2vyEoEBfI+6Ty2ue5T3zy4CRzEQ2lgxxQrKKDWIJQaL+sq4psAyiDawCmDUsYlX5AzITfCBqP2WRgEACVlYbs26a9HifCiMcomQNs2KzJjtBobMzZdbvr4RCPzOIeQnBrTFZieDalkuMsck/k9AlBLVCZWilvzkRJDAav2vrJszpwdyEnJ0TlYM4RzG3Y6IOiWtQ4gje6MOiAT4REyWlaQUAQUbKbrynqHxI9b11l4JG6KFp2fGPqadR6wb/NZGVnO/t4TDV/f5xTE56wOjbtVzD6+99Pv4Vd/91cPr/vvBRgQ0X8J4C8tvvoPAfwjAP8xZB79xwD+EwD/y/lCZv7PAPxnAEA/JNzlk9BoO2WqP7QDZ5s7powiOADWA5ZINpf4pW+943/HYYiDcg+C46BAM6gbTiZk9J0RG6DGgzQ+pxRcof0pi2Vc8mfPAcacibPTFGfhmL2CeR38fEjR0QSNy0IrkwOgwoSa+q6BRB0gyNI/GtiDpNcm2PItBViLzf+jMjjzCmbQE70fW18f+006LwA6BnynR4wghwgT20OjB5TGteVj/W3rY1Fmswf6OEloIRMGluBinuwMCOqmhvf4RD8fOyLYufC62BGsRwNzYiQ9Wlo6QBQ+G0AAJMQwtweTQrN/mJiMVjsgaFWAgIGZMKnJ6jmxBqAMtCrsQb4IO9Z07qqBvTZGooyagXKteJ4TLrXhUSLfoTKeinnP+Bx52I91vCIgWBqYUIZwjNCN3vySCGiab6QhOEmkhadRjKEEgu93McnJPTLyeNIF5UQPGOgb5lIo/cwYcrmpTEqqiWyLvstoRdocl2Qe6bpbeg4413WrkOhKD6ycn6O9NlZlmEYHoZ3f0H0Kou2xEp8Zda5/r5+tEEoEBW/N9nEq/70AA2b+6/dcR0S/DuCf3XNtBAUzU2BIG5iAgFTGP/PT3cKAmaf/wYeyucRvfOFd/Lv/1x8fqDh7G6nAI9AQL2APVvcEI1avmjV7N26hbOiaE4E5+/p5Bg4PKQH2GfJn55LPgmEKgACPA8e4cBec3u6VfNhJcI2BCwdwANVvJKs18gQQSm14PhnX6DnMm7bEMm92ZB7Q+tjcDgqGNp+wIKZ4DNAlSJ+nyILkJIpN2R7xRpuyPuPmM7v6T/U2z+1SCI9zBwQRDBSSHSwzALQNVK8dEChAiGzB8nx4PTUSrYGUk+aUBSAYkOC2Zw8a+p4Hi/Ggab4YMI8sgbAZ8X2TG7PmnZzVWVkDpAzwxQFCyhc8TkU266myOVFpjKsam0wZn2Lgea24bslj2ts0z47Gh6ifPnqvdx2XaALrudXU8ydlrmIdjsBBzpD4PMhBdKP75WNmCiOoGQBB0AV9lQIGWTnTA4DOFwUI4v1TOEwubBtvO0qiO0Yfm67Tzo8MzsyCxr017F57g7xGBXF5uqp2aTuCvdGP7drPKVOw2mphXr67qpeVCCgsvH4PKAD+AoYSiOgvM/O/0j//XQD/r3t+F0HB8c3lZQy5EcDcN6agccCAvg3lb/6E7Jgo9dR7jbfeocc5Nr2sENBzD+xPNnAwomsOCmMEDPIb6N8c7nVUVnF02/VvXkYWhX9Q6pYg1g4SxLypCUxCsSJlFEAVgYCCpp7DRowcAEKmhXFttpELHXoMMyuyyqY+UtxGEfeQwnoTIFdw2udZx2FmQTJNIaEmeRNbMY9HvcPJc7d9ISzWabHsFACBfD7lEURA0K5iYHdsgajQnQEnVemUwCkDnAFKnqDIGZILo33BCZL4ZxPKxuGAMTDD4Yp1AQocwLTWAYyBBPBBnSFAgAicirQ9F6BlUN3AuSDnC1IuyExysqEmf9bUAYJR2dfahm2FbROuOD4AdmMUKegZaB6xbGfzStgxkXnb56QygxqWoasKgGpD0f0aatBlR7IRtw+fQU0ObVgteTV9EMfTdcGBHgDQdYEfXa6OQ6PhtNLWxjNmmsp614kP13W97cdh0BULutPpaywwDiLRsBeFvHZ7A+p177ZjfeMZENi1Q70WP42g4NQ+hvIXDhgA+D8S0f8E0m//LYBfvudHhoRmXEx6oyE8APNU5IK+s5soyojsvv1hP7DizRDzmQ8bWU2a2RuYX3cV1cL+GtC1eqVmhBwk2LX+WYhXLeg1qeNYsVkYZg9gzhKn2pPCPF5tNVl4JtpJICRV3FnjpJIsVlQxbM3W8VtipSYVMQ/G1ZKUWP8eDStNj90b2HsAgSm9WUnsBo+7YLcGBTzQg4xoOGDpkkkUn/4uMj1AP1cinkhpDM5cz2ViYdtAWwAE9aoa9do98W0TA69e+DBalIQhoCTL+7gBqYFT8UYKGGCXKwMHRMm952n/rT7vfB6FuRRBQds8lGCgwEML3MA671pbGJ1Y9yLhBG4ZlC5AugLt4gDhki8oChAKyZ7/tcX5BjzO2c8wuWuMtHN8h0l0MHC0TwNwPqcSS98yMBzJ7vs4LfJztpCsXHaApj8oApuSOusR2bM8zTdZ549pdQsjJrMO+uBUF2jrVR8YSOCUcUnJdV+1jbQCWPg4dZ2NwUrOo/4+1d3j0Mkr9b9TrCc6aLZ25Om3OYCFwbEM9QQmhiC0L5b3QyL+vaAA+AsIDJj5nZf53Rk9Mg+mdZ53vgEEjGzB+8+eDEdbDvc8AQNxEukUkA/CDjgDHTopOVEyfUowJT3cxx7oIul3FyHpszHecSWfM8oclbbeJHpqrWpWuFHRSumuYtWLpW9DLDipF0pZEttSVpBQ0BKhNN3G2LzuRsGr00zmPJ4gB+y3RAX29GFMJpoBwRwmWVGjO8VAQRkk83qAloQW5QTfca+qlrCdH5kZLZMzPavxidSzrWaIytlDBjMgCOED4gberuC6aeV0DFsgXpOoJyYCygVcEygXoLBuStMAXKzJHRTYK7GMo85L6lNxLQ8hd2AJCiYw06oAGmzXQA+N9Wdlo7BJ3VO5gOkKzhdlEPYA4ZILKmS5nO+9rwa3H1YktT+So5g8eBRz93mVxnm0MjS2Ktdl2sAAG4sgdVoBhMhMRap9LrZD4SrJbt4lMxkAVbCQbM7FfJCZ2XmAPpClsMnfg/pY5gAWTOfJv8ic6mNO9BwwgrA53BF1nt+Mz/X00B4gJMPqih69s8uD6QprBI0gIdZlv2toBwPelqk94U8A4+q8h4AC4C8gMPi4yuy0rATQYjhMnt/jAyanMH4Zv/WTi0TG8NsVslxNMqfWuH/ulKjP6DUh1usehAro68Q9gxyIVJ1fu9Q+AaAwhrr1ZWHGCoRktVW8N7Rp7yqOdWQFBAIQBBRwKqBUxHOwhDGQU74tsZ53wLISQVmEmJRpHvhh39E+8WsGBL7BDu3pbh9DYA/kYp8TIasSawYUGJ6cZ7HU6PUAo0cBjJ7NvKzPlh0KALiKYg6AgJpS8QoGuG4CFFoFK2MgzdB56AkiOibMQC7dqBfoUtoUeM+kHr2KTNLbKjjY9b+/X4AC7uCzMwUdFHj96wbermEOrttAReYS5wLkAspXoFyAluU+ESCkhJIvyPkCTqTAQEEdOq09eKdhjKxdQ/6EvQ+gcgi7ncwlqHHxuWT7MwC+y6MxnjNAKGyhK0IF4cKArgbcFdL/LJfIqHWTiSFckDQcgmlly47h0bF7KZ2g0mZz0L5LRY2iXUtd3826bnaXrax0nX3+QF3cy14XU2iThU6NHbH2GUhwXyCABL/zQTtWYGC+0v6eQcFBzxyWVxYYAGt7GFFiJNjcwyHp1J//hu6YeIC0Vsl2AyBYgIHZ4A6JVIMgYQ1/h4aEiRg+kzaEHOf4/Vym5V877/8QAKgXqdQuR2o3bsQeDQ5EeYsXJ16BC34qsKVwSBegXiUenLIcAY0eZqiwBKVx2WNcHz11k1QFGBIKzfvuND31eDDQwcByvf9KaXRvwZRX1vZJXoXRo+MKlJnZmT2CXT5Hq8IOxHh8Nc9tBAS4Phdj2lp/VbaAZ7ZAPTTiJHpKvx/BgWzgBCZQq6qQbc7ogTJ6BJ/6N7F1+tKCXPS5FpdQSp9PoOD6/LQdAk50TtVNgEIuoJQEIFweg3IBldoBQnoBygXcNtD2QjzTfNF5SL7NVrPNtBbj5GMVQID8MyB9NIfiHcd5NLBrRIDOoUwZjSxRtwMEtnMa2n5Fk9U3lpnlIKwBsocLuEr4MIJQW1Jq4MDGjlnYKdMDN/QCKQigrCwBert3gIF0Z9XgCA1LVo9K0KuDrpu/v0Pn9vdTHcLyXtMBNIAEhjEJxqrZLf2wMv3wCN8chabn93Mi/suUVxoYWBn7mRefkzMM7z+TbZRXqxvibyj88/seAYJpLbYbWVeGbbjH6eQdKjMJg9FNCCh6dd3inl4n//zA+KtS9rhlq10JrE9n0SoIJWhUtcSxS48HpyxMQr4Ki9A2CTXkMoQZKneavgbPu6421fHG7WOLczLlEC8dvFnGkMG/okdjPytgW1Gjo6LYszy7sWGWU/nmUI7VLa42MEO6vRC6vW7gYFjZKN6qhtS8KCIwrkDOoHwBN5MFTeDTMSSLVQ/hoybtZdskrAF65gAMNCza1OXEftOW3zG3zhQYKNBQAtfrri09/qLLLJU1oFw8fIByAZVHoLLJuLQLaLtKomLKoPrCxytryIvDuC3HSSqhlPrsCEyhN732VJ7d+CXE/RmQMhIlWSpKpHIAT76tSUCCr2gyb3T3HO0mGuVhB47V6O9WtXgi68TqaMjKdNs9ekGET5NXLRRkuSIh32UADXrtXQ7QChTYGBxc13+wdrp81Y5eQ1bvOSQC2QMExCBqO4AQ7Q6js9AzMFgBgMHuhCKg4MuHOXf3llcOGFhH2/teThS6lqcffnvXqUcUjHtwds9DhiAohhh/axUDEAiIG8BokG/Ue98JafrzGE3b/Tk+w4w/MAp5qIt7bXYNMHqh/uwQu05578mVS4gHJ6BlcLqErPLiAEF2tRMFaSBBEpNopOXD83ukryvBGOvdsQPzGvo5ZmosSuyvXb/PtGIHBbPikMunWRY9ymhUjpLyjCFYAQI1oGxzblLSrIqZqoy3gIMmiqxVqXNTQEEJwk2Lr0rcwBxYA8ryGSI4sAcFGQl/L2l1lkRDn2NabwGnfNgmV4I1dRYkZyBfwDUAhO0a5t0mY1IzfA8EC3F9lLFasm+hlidzpwNKBZEpgzhru7RtxiKkrBnutrQ5gINeMy+zPMQEY5MFqgo+40qWiZVazrsFoyNNvaEXgK4bPCzQ2xqvmVlIeXtbv3nPv4QejQCFrM40AgHoBmAUxgykYNFyJhYAwdi1GH5btWYHBBbtePLsKd7+/XfUfr0x/G5tE4/LKwcMgFXjg2DGeJN9SwlPnr2Pt7/xDt77qXfx1uufhYjaSeFuFE9DBvMGLQoIRnoxZFwfJIjNhvm+jhjgi5RZiGY0P8Vuo/F3rxPYe2sL4QcgAgJRQjtPTkECyiXEg4t4c6mAFRTYsjMoq+C5CKooSqDogX0sGJhpXu2VXTb1CBB2AC62HzaPxof18wH23g2URhw8w/g63ChQzpGxsPqo8XcA4KBg9Kg94fCI0q0yr7hcBBy4B5QEIGQM8424gSkHg3hSHqKEF3I5zEVtO7faQcF2XbeLk/QFJd1Ku3aAcCVQuRzMuwKhl3KghzUNzAz1Qb1ntm3NMN0xZ4BgWNSrDKEecBFP3lgEIiQNxeW0SM6byj4fSutqYGDeRyKCg7YdzzsHBZ2ZOtMNDABRNwDgbH1twCwAAgQAMDk+fEunAQ/WmQIOVX8aG6D6hnJxoNCZHdvaSx0gbj6PkABiYQ4OAUJ4NuFAthZMssnLk2fv4+1v/jze+8l38dbrn1Og3pmNewGBlVcSGIxlBAWrpJ+nz57i7W/+An77i7+Ft177rFKmOplXymB1rxkUzMp8Ts4JS7DmjOuYYLWMC0/G+a5iQndvOTL8J7FDnoSPgpAJVSheAKcsCWIaToCyA2SKupYxzMAXAQjBWxKKtS99jJRvtmd5xRosGWEZ5pkzqg/Wz2NO3ON5cVRs/ORtACMlGut45IUGEDLEbtV7O6LYeZsSwlrbjQ0AWQtvSrVVMZZDE/aKmI/qfNIHY1uOro0xY5O9EIaap3o0NnP79MQjogRw0j5K4LTJnDJQYfPO2KuU/W/PhXnZsapdVmfWbzlnKGFOoDRwybqXhGw4VTuL0BS46GfGTD1UDnb5HZGZimGqOQlUX3sYYZp3OjbSTQe6AcGwz/riCDjcW15GP/qcyyPLqbqLysUZJ1u5Q+bwAD1B1++jXUEMW+TKPX1UZhfr73jS71YWQCBe8+TDb+NLZr9eV/tFtAMHDymvODBYgIIJdT358Nv40j/7W/jtn/ineOu1z3RjbpP0QPMPBgY4BwVOK3ZqereELGRcs2WQG2CYadMbQrer6xx/u/dEofCM3XMGD239fAcDtT+TDBwo9WaJcg4QVCkOCWNcYXkIoAxKW/egSMECbNnTIm6v9Rsyj1djtGNzpmQqAw2L5X677HhgjJ0Czp5YDNXKigodjElr6omNYZ3lPIkMQTSYsxc1PzPl8b0raVXOqvxi3/bYOy0+m+5PoghZQxBiyMTDcjBgytHiylV3MdR54e+tXnObdmESALU5QPB+VAbB5h2nLPNPvUQORiGO1ek4xbECdmG2u+ZKfKYmUFK5aJ9kgDYwC2AYWIRmXqt85nR3GCuvH3Cb0VzlD1giq+2DEcIIrpt07u3m3EI/uFo1/QAMc3IJHO4p8xy4VzdOK3Pc4TCjb8CxNc+5QS5yf1anpgFEskRZHk7SvymakQkccPUx8zodsK8rxmCwX69/VnQaIOG8jwAOXmFgECz67CkCADfp1D/4Cn77C7/poKCvvarnXlHwOoFJ2CKNGA0O9+/c2ERQcOb51U0mYVyqVSs48IU8K0rslRmFzBaaEfgdCTz+0YTGecFbyrPq8CxWJU0qYJyL96MDBG0nMcv3w7p0izsmZQ1SV97RIM1t8XEK4xbjv4HJQWAHVqEdH7fIHthjdn2w94aGzxEAw1zmMNKKQYrA0aj1CBjPEr+sXknDBxp3pxDfNeqUHQAEg0QJQArfzaAgyg87OHCQQOQUq9DkrPfX70zxpgykJtfaMsrtKgajAR5FT2lsr/VDUnZEkyMF5F0ESJrirAI2efYQPUkur32EhcGfDeJKLrVHxvlhzwvUtcsEJaAUP8yKDSy0NuQjDEl598jAEZO5clqOGIIICCb91B99rh+AUR8tgcPUjuGjk2fd0ouUqD/b5UF1k+WrcNP8myogADa7C5gasF3756yhQ3N+WLaicyeBUwAHgLEHZ+3bM1NY2K+eVTKAA/kh8ABw8IoCg9CJM1Ognz999m186Vtfwe98/jfx1mufHVDYOGBHjzgCBbz7PtZlBCyq5BfJVbxtruijF9iuQh970mDlm6jYiqFj8k0YrpOAHIOIoemr4OXN6wwgwPSzKuvNPTk3BMyukDzE0DpwoHIZjBSbx4SQ+S8NDpU5iQUvwgVLyn5hnOVWx5Sl9YAlWzl7AAxe+tz3gzI7yfPYMUlnLNLE2rgSzEVWJdiSUetji73nDCbZbwIDMIsGaYyLL8yAGysfe+KRNVAlyqmAmEGZAS7gZooOATDaZxuAokatdW9t4TmS0vFIEE83JdEPRODcbCB64iJG0HbXGOk4yfd3UNlxXkSGwkBClvYjl860FNZclTr0uTMbt+Z/nPsOEjo71laAwP5e5a5MoMCM9C09sTfg20LnrO81gPH4Xk+Pe4hOFH0oupBSQroUdWCayIaxQMAyOVfkymmC3seWn8LKDhAU/LYODsyrj7Zmlb8zsZ5PPvxgZ784JvxG5mBYJXQfOHhFgcFB0QF7+uzb+NlvfQW/8/nfmJCWlI6xwuQ6RN83jOTi+55xrUBgkVwFNQAmgMIwyLXtWsHcXAjcGBx4JlL9BEaVSbx1cEB6ihtlyT4fld8eGFGS7YXvBQf2m7ED2ggOdO4ytt5fFvO2fkpXj++xhRzUaCEuZbJEP3nw/rnAYtnYFMPn1nfYa31ToKW3DuzzMZadEPogMjVGwd/qxNWzYt7HpAiJknrbAQwAg+HxLGpjCgwM2OvlkfS5bj5lGfu2dM7DOynvQIG1Z1ZFlnzJpifR54B4WzY3dKzK4j7JxnmTeyTtC2oOEJDTYBx24TR9jve9AgSgjiDBQhc4GaPY99FzPZkPPW7en0XM4GbJawnc1PO3eWjLfLmpd6rLWQMYvmvuz/NeDT2HeX8U1jzMX1nNwZfUE6vfrIDADAIeqg8BnTuqD4kY6QK0qwAU6WNlbFoFqu2tEJJzkzgxTE3Gvkwm1eL9R/VgDuBg1+jhOrvfk+99gJ/91t8e7ReR3MtCFPfkAJ2UVxcYxLBBKE8+NKbgNwRp7TYZSUNHAzgeOH/WYvITqTajSaOQ70O/S6g6awvgTIGDgsAc2MlrszC4oW9VdtRS489m9LN4Ulx5AAcz1ebvs8Wcb1S5jiGEqVLj3+bFmaLeIHkGdYOdXUFKAXvymNLAXPfrnYc83AMFaUrR6sqRmYlhgzNQsMq8PqGNncK32HnskjsEeTA08/MsyymOWwR2ERhEQGAMgSV7hiWkuDxSpkA3nkoJbBsA7UBBDhnxuLEyhHR1yQE4wBXApYvNRePmqStl9+xThm/qFAACEADgqlh/aEgLrXZ2YNscJJyVneGfEiIPS0yus2fpKlDS9rBWkbdNwJHm40gYoRt/1yVkPau5GrHuYc4DGPYnabeAcFhpsAIFuz7lNsg85Ru6YFn2YQFSQ7wCBREQzPpQ7rHQiSt9mJsAM+g4zJ78SaGk+mf2yG+tPFp+vgAEer2Agq/gdz7/63jrtR8FPFfhBIAYm30UKl6UVxAYTN6/hRGY8eTZU3zpD4x++Qz2oABYdbSjuqNCdAAOEiyZCjAKKXyfEtBIJ70qbGYwrvv7nCipI1Bgn52t8/VHZPLrBBho221JTqzzHYXy7am1S/yJpdauLAH3pOR6bZMBBKVgI0jwuh8YB662T8N+86Yhl8DKAhQM3wG72PK+TcGI27XmucTrjnIO5ueF3+sf+2un5aIAQFlDMTHpzpbwKUhAuajRL4Auh+tAoAyvQNglMNAFsReSfUcdICTK4Jy6x9v6hi9EtbMCjQQcUPIcE0oZ2IRq95CThuGkr8+Xyx32q62vP/L2V/eaExDPiocz0vC8Yb4b2LG5oaEUB0GAsGsGBuN8P6hDXCXRzuZ7YAkO57sVzekgZYD0w32T79AFw/UxMTeEJsZ7B0BwUs50Ymt8uP2wPHzFmBpbZrqG9vorhNeO7uPXnZRzUPDZcGUHMANrYLkGw/PvCye8gsBgXSxRo4OCs3IHCtPCxixoUom5PtL9rQsz6/a/UFkuciCND31r6hWrJ4c+hEJnZZChZ2JBt7oNb1IV3E7QbUrd8EtcmTy+ZgzBDgyktDfe94CEM28JGJTZzXtx23tSbEAqAAQFDsxBIGcFaYrUb33DkA/1k+fLroyQreIeshxqapPXJ/ZVXDpozzyqSyhLtmFaE64XYt5kynb864Cg70RpLIGDAD30agAHCgrigTad9Qxem4FswCe2nAEgGfZ9rgvT4ZQtkeybQFeZr6V4prxRvBRi4DDaN+sJkIuQzzAUt0KBVmZAMM/xO2PahyXnPlaWb3CrqGwA6vVuE3s0XBrm+kky7WG4LBaLqWvIBrBxvcMrvUdn2DPts9xUP8b8hQRO4uEzSZ6V6UNAdGJrfOgUGSCwRFvXh6YLc9izIIJlIv9NZNcoJkYnW2pKnq/iGyGRaOwe+rmD8j8FBeN1HzWEYOXVBAbTZPYlHV/YJxq+dAnGZ1R6yo8SqbfbupAzd/p0AgcRCKw+YwCJksT+gCAQJJmzrZmMuhIYl0NZkg2WgICyGocpMU1+mweP825KalYq80qGObdjFYdfPEsQPzQvAaM3ZV5YSuA6Kiqa+ueuopnvpDT3DA6oBgraSrz9kcE4A0Yx4W0W9EV+gryNn3fDssuwj4ZHwcFOqYU9I44AgbEEdQIEhzvusdHY6OEexXOZZJMe9jHcgLBGn/RvYXYqKG2gmD3fmp4sqTuHBmNH3OnlmDAqQ1O773SQJwDoPLX+ddA2gbqFMV6NU5QpGSpaggKaZfBGGWQ+tjm2zYy+NGoNClZ15ybJqbX6XhdDn+DGPJ374Lwh8hoSa8nqzrLvCLOMt+RqzQBB61ET8iIvYaUToz5MJYteXoACmvJvDkHBsHKH/DtnPOKy6jv6pucUnICCe/v2znF4NYEB4ArgybP3ZfMH36fAvPszcNAHEAiobtWp4TPJClUGgZOSDiJYjqgbwEmpplYHilQMnCkEpazqFbzpsjyNIedsQsE97yDQapaMM1ByKyAQWYHgPZ4qq9jmB6D/1U6KDoJOqN45m39VBqqVaFTeQRgOAcEcn9fER9KYtQCCAA6gipBZxs2VGXWF2Sb6c8UMxBINhZXFpitDX0TjH65bggDLw7D5FXb5a2RGuF+/BAS6EsTDBjKdIScRdiBwyhiQXOCHACXJwRHArACBSMIXRwAhCSvAXEH5ouu+eVhR4kmIC7AgFVKadTEvocvS4nUOFnI5YRkOGKDVGM+Mzj1gzu6xYu+sn4+YjCNQcFSIfL453ilBn9jrDEhjO1+GZQz6gmbmQsGC7choICGuhvjIOtGAACU9V6NI6G3Ow9E9VlBKz8MJp8aCwiqdwBKc2hKvWHKHUxLl//ZJTlz83Qlb8EA24RUDBqPAPv2TJ/jSN38ev/3FrypT0Dr1DwAx483LOGC7gZyTSaKnQCE+qYkrdl6CL38hneRNwUAjoBBSFiUsFOlV93TfdBfADVyKJCJpTHUlFAAQM3p3SYMzELDJr4pqiD1HunmmN08Uk1ZCXoPSFWDEPUYalDTN4MFKjMF6m/bPvieH4vZ16pHm4KU1BXo51N1XSdiWwaVTrwbuuAEoo+d5xgwvWIAVCJjXvHubVoYjgIBhx8WUA605eTbB+HP87IAhiIBgZgsADAmIyZcQw5dpSU6vRUQlVbISkCkjEUYGgeXcjGHbajcaImPIAggMKMyHgEWwIHURIzPsP3BkSANY6EmNZR+eOBrnFcj7CGDg1py/hxWzhD67rzNq83WL0MYhGLX6TiDhoTrDd2O08dFwkYMFO0SL2Zd1H+lEuf1eL546R0eA4IwlWAECOyfB2j4b5yOmiZLkxH1rDn+f26xVaEK2MY/9fjvP4BUDBr3Y3tG2TaQgpmjEjTU4mKgrdGdJJfrZ0dIgMp6Zm4IEYxHYlZ0AhCTx06TKj3Qr1lx2AIH40bC5CGr1jUX8mStlMBuTwAgcJqFpxvqhcZlB0lzcZTTfEncrafnZBCxWJU0K8kSRLsHEKkM6KnluOoTBkwmKCUA3KmEMBg+0hL64Vc68rZXSXbEA2q75kBeZp9QV04Li5MBU9c/JDX9j+RcBQOXOCsyJh1Ys8jusUiABCqQAg4iQFSAQkaSLEJDIzgEoAAQE2CY8zJfxsCtNMO5AoYHBQFGwoOBhtV3xMBcDfT1vZBWBrvxeAMNLje2K3bFrw9ju5oLdcjF/oyEMkc49G2bPaFXAQR8svfekE1U3DHW5G4wCL6Mzor7wMEJYSkmaJLkECgrgaR63WOYx0L08pO62V0o6BAR8BAgsZGD2wwGCPIsnW2GDNaygIRKmWzcvevP1z5ngWeUX97g/NAHIgUtn5ZUEBk+ePZUDkX7yXd8m0oEAJd+fYJiiE9WyRHkBDAxx5Wkg2E+gy2LwWJGsKixZqhU9HzkwyOnRpNsA2978uvVoTLJaUm1H5SB+SaUnnrlwTDSzeZdMQRFEgGR/Wx96J7Q+mZdKeq8AbHUAzd4aEKh5LScU5qCkrP2+86KGh1aIucwCJVednXg51/UQ3Dwg/nxmKA6B2gwCbLzDd5ElcKXlY6zeDdCXHLY9G7D7O4AB2wZmhkI1fGK5BbBYsIIEET85qd7CDONpmCRyASiDo7SyA4WDLcmZu+y1OsxDk82b83AGgnFc7xnbO42qX7OYr5O2Wu+pUNQTZgaQwbXqbZUZiNPb9glh7uzUkYzdy0at5qHPrQfoDCjgPDg8TJIQhTW1Tchu7jWyKiu9OB3uNuhEymgWMjAQMDNtExi463h1YDiY7MmHH0hO3Mx0D787sVexf7EHI3Y086fx6cOueeWAgR89+ZPvytGTFmOewIF1NoC+qgBRELFEe08+fL9/BzMyi0IWT0RXlwEcwCZ7mujRFnazy1WTrPquZLYj2Y5qA04Zg4GeNDQcDgDpFNidcbIzIbd4nqPc5u3zMIvRfWBQ3lxJx5Mm5VYLj+2gjbNylWEQYXUgEAR1EJgTofXXVLRdayYk1pdifaPCjYowzrUF5XqmfNscDoggwBSVjVcACfEsAzuR0g38vUAA8qMZCNxDjvi1trGWMhEJJH8Afb8DbVYCQG0ECqAsUxTaBegAwJYoezx6moMDWLBQHzdQvgMsfFzjau8PPGue5+jZ/AS6x8m2XA1AIW8DZdN5NpeNQQgyFZcVntY3+VxrnoHfdcZyDh4ZrgkY7HaRNT1pOqM07M6cMfY0sAovpRd3Cblq7CMY0Ot2YED15HhWyIl9sHHzDWHYsd+XjOk2+4UH2KvYt4s5Y6DgvZ9+D7/6u796WK9XCBhwb/RPvYu3Xn9DPjWaJoIDAENc5miHw6hMATz58H28/fs/p7/pg34UV7UiIS3Z1KWPIwflpTv+NTs3QCd9q06NMlegPB6273Whj17sVJZKiDrSZfMuB/Rr7/PkUcpkj23exSPh87v3kGWQs9G+2kb7PIKcABREEe9PqztoqLwMnlbcBTF4zQuQYNesiiebTcAHaowod6U8gwYA47kSc/0nenhnJCIIuKGAOXw+gLowZp4jMIUFvEXhM+CcERh0+gEqULW2K8YQDEBBdaMDBf2gHxXMDg6g9xUAQSAiJGSAJE3E5iGrnJncGDD1MMQMFhyI8hIsABjnI3BzTKV9+3EFMI5t/G1k51Zl7HxwmJMDW0J2T5EjAkZZWuVETKB6mIups4luJMmM68JIhpCU1m6pM+zVdUYAedwq5hMgzXniVoHL4uAz1YvSzBPduADe7DovAgFzkIJuHNiCUc4G+ZlK3Dqht7kzAu/91Lt48/U3QibAKEWn9mr6PDo/4jT/HN776a8J6DiBLK8MMOhI6GtyHjXgwjOAAy9xshx3rhnFJx9+Wzv1PfzN3/mbo6fF4x1nHVnDbf1JREgKFIQiDR6PA4WQWGVAIXg6zA24sCum1U5v0RgO+9sfCPRRslltZjD4sJ2x+6LSzlSABGT19tjAQttU6FVJqwJgB0jN227eesxbmJ4sX+u4jUBAv59pzeiVHQnY3NDY10579vdeb6hx5QaU9dgMj6BwNPUMXGJOQFROUQkHEGA91Bi26/ZHZgBWhv+8RXdcF+6ZdjKjWxTr5/8/9v7l6bLsug/Efmvv8xWGHjnCAlDFkR1he9pNggAqSwOxRYLEw47oCACJAsGgSLODktpTWhNPeuqwm82QKAkmqEIBUE+EBwmSLTmMzKwCwe4eeODHxBE2MgvSv2DgO2cvD9Zjr/06996sQkjx2Tsi87v33PPYr7XWb/3W2vu4ndQDwjDotcQBIEwAA2WknHXPD2khR7DgYDXImhnWkLOAcO3tYyq1b8BoZN4ogtjr5+KwvXfUDdZ55lnbOR4yW8hRYC2qgaxzMcbSLy1jlbD/fNVK89RBb2RkHbCc67iZ3ugdjMoscG1nBEldiWPDE91wDRAYGLcLdsDaKcC3dneqUxkA8MYv/G0ft7o3w3X2qm+jFXFq38Tbn3sbb7z2aKxYVx4MMHBQ8NqjKsQWOkDbSUA7EL6xANB0bgMKlH55XTs1ZmcDrbIFJsowTJKgJoIyE08+JSDZRkjB2x4UWKDaHBlbRaztsd0+8UMcLCBh36imVGE+gJp5XsRwcGhr305vl7aJoAlkJH+JxEFJiZBoQ942AQpGDdq/wCZ4aKUTdh867jJuLwCB041Gem9tGEMOH0uzf7n3RDcmDXjo7lHrPHqLfT15xhJoTkDBCAIOtHOybkC0HsNVKCAwl1eVs3NXz5gtWOXCy3vZOz7aOcdq/EkVbgsYsn+uYMEMEferHTpWIYIFrVwrb7MO6OZTO6Za+9n8i/cYOiUaew51mNTzBCj4Y6by08lO70QYk6jfDQhIrmade4VZk1ZfQm+APFSUEvnYZdqQVG9QYIMsn8DBQgRzveyFvp3pgzPWw2RtFnbr29WXBBkCAnxVjiOLwIyxsQeUAERwYGW0Vd6WSXFQ8Nm3rgIFwAMCBg4K+rKih0Oyh5zXdape14MCv4LP47F6CoBzOqmnR6vyMgW3CVgAGmrU6c0rFVQfDujZgMIsws3sIKAwcDCrkOt0ZGnXYdswh+faGnyjckFAhij3TCbghJQYiQiZgJxESeeckTLAZXePAEHIK1XYeudNq3v6dQYGrkoQCooj3r7p3MAIaH3i9tu+broBD8BadURQsKjfAggU5nb5YABxRbP9HU9qm7wWC0PdFFNgaEGt/3wDaOhL4wDr36aHrqElSK6uI8fVpkETF2HGht3YJFTAmjpQ3iQ3hjGl2JFXjCWAMZE5hCcb1uDKeef5LF39HCwEw9i+NAmN7DTP6VmMHoA2jOLWgIGjtLrjAAtA0K46rNq4Xm8ksHjSpBtgUQULpjvkr4AUYxU+KP14BgIMxA6g4FTPo02oZVmV46twuM4mu429cAworb2aAYCFEHrO3Q2gAHhAwKBpNKUqCAsUJeVE61BqEjUMFNjgH2FSTOnZ4J1Z6edmHcvq6dxPPJ2BGtVYqqcPxHuiVSrNpOUaEmhQvQozKxCQ7yrUhbFzwX6wCzkXxuGesBpFE279S4mwqVBvibDlBCqMDJblwkTYSBR1Vo8gJcKWNlXOujzNQEJq8y8ArEMnOn6DF2AsSVTQMzqQL9sjx/CUHe1TGAszUD1TsKKfR0PRxitXQMDBXRg/s10rhXygnYhDlA1AtsinK21y5QaaAwSrfttPbV8GEq8+X/+W8GVmRK6td19nFFbAzaAic0/mIgHEDlpnQMHGtBnPW8YSaMbT2hZ1iXXQ5Tln98xuNMnuHAFA8J65YxNW9W7lpg1V9dtf74EZOIroi5150B37UbCrrthZ9AaAU91Bzg4AWyZslAQwqJNhukO+V6AQGaFML68fZ7k370e3H4yWxQLX3T8DQGAiT/doQWEEjuf2qi+eU2Cg4NQWtuXBAIOhDJ0wQVSy7ml6+RIUOOCuE8c9tU5JR9pscBxjNSJAMCCgPFOk1kAcMrbJr70EfWYT+xogsKtwM7MKuBom9QgANCjZ0LEoWcKWDCAIMNhU2D+UM4gYGzFyMdaAsClwse9ZQUITR+StU3qTsvK6Z5Qgt2PY99lZ6ceAwvHK+GTtE/3t5H4zcHLGCBTzYLgyOjaGBgC4VLDgns7EtbFNX0w57cSqqMWA2rhY/QvaeWdzuD1W7xn7Ul9g18hCVHkGCmZtAK5vh3mblCpgyElfrwsB4JmAnQip8Dmj4I2sgGFVGmOjjZuxia1euDzXoqzHOoI0NGKVyhzkpWUTBpatPmCUmT5UwJUd2AsLy2jfVTf89Dga3bEzgwsLmLhSbxjDuCUZs6g7LgGFXkdeqx9tDGbGP/4ez0f3OXSl/3WTrnUCtwABNobMKHph4T5BUeHM0rCPs7HJubuBKbDycIGBF8P6q1/POnUEBZHu6UFBjKVFz83Ojd5QU8IBrw1xowSIuFUMIfxwSUmZQoqUsnmTuwIBLvq5MPZS9K8Ix36IEhBlYIYqKPpSDV8C1byCBGyUkAjYUsLdRth2wv8nMz6UCHc5YUuyuc2WGAcTMtl3wmGUYbLksc4zWiV/XaIGuTI+MRZf80aq8j7T1xbnduCmX3rGR/qlHau49XGzdTDmYM7mloUGSqlAYlcjat6Zja+BOqAyPfPuIqCIMd2J3HNj92YkXy2Cg8piBWAUgNLYVyqHXIGCAQRwZRYiKOjbw/6dT8GBtclAjhmXCBbc6CTy8UqJkYqGHgrrPJ6zd3274jhGgGltXu37wOGcVRHnQE4wzzOLDew8Zej8042hrDYvITNNzpHOP3MOxJkQ/WAg4f4o+GlhBwP3u+iRwsDORcBspzui3rC2me7YzFnIhHRfsCU5tqWkfwXwbZQkl5Jq2DLOR3OsViX2f+/Ime72sVzp79iNdgIh7Mch45dBOEgBAaSdRVFAQpgDet0IDs7bYOXp86d4/O3HIdHwHHTOygMGBiMguKZ7Yqe+3iGtiBZ98uCC4tYHR4VnlOhZUrOBw0jpZtvKEwD65KvJPfpJPVO6PSuwFzgwEFAQBVuvc9Q/9mjSelrC4ZaLCG8u2I6ELRNeOQruE+Fuyw4QDlSAUBLhYDFGEn8jp3uHJZ99R4Z4rengfhvfs+So6JkOiiG2M44VKnCz34ZkuDBIybVHW/oVAhHU9fHaHgwYVbuXEowonOGBtr1pg86nVER5seIoAQQE5ISsSu4AYwvqaQYKGnYrdk54bCEzhoTUgYPQRVVGOpBzc9sg45CP4mBh073xY6iLiLCpN5eL5cVwaBd3KyHsSe1z4xgC4zi2YKG9ejXHrK8BmWeZCIfVST8nAnYKyb16oXjTIjN8pbyIHquAoGcI9mJjUgHB/X5gL4yfFVbGoLjuMMfCGIeV3gAEeBooSAnY9oQtQxlIwpbYQYIAPh7G0ZmtzrnqyyDbYTxMTwK4WV9bHTzsxqquk2zelRJpH4ieZFZGIQCTqF8u2a34+9PnT/EltV+P1KmtUns9QHigwKAFBf3fVYmdaqCgR/mx1PXt1bvsAYMZY/PKe0p05fF4SzqK1xSEUaRW8mTq9xM6GhD53NJ8vUAXBQn3R1UK98f1wCCr0b8zUJAY20HYc8IrOWEvO/ac8KHCuGMJMVgfbUnuv+m9DCCQKkHfGwJwut7HBS3Ds6Lj+7i80fErIDcr05i2UYU+ZiPbsyq98Yh1ugQGLA/EKNsZu9OMVSH3ilMisG3dnQlZ5wZ8/lVgKkAgUPVkNG6bbOWF6rjE7ZEP9Xqtf83DyonAB2H3+VtZgqvbqJ3YMlhGVR/Y8hok7P63TYIDuGFGVuNnjzfjb23v55adf838inPrQEzMq6xBStWoWBik2LxToL6SF8AcnCovkSkwZqqwAIafHgfud8ZPj4L7o2AvwM+Ogp8dxjay6477oygguA4Y3GXycKLpDmMfq6Oh4UoFfJsyXQIOYq5T7ce+xH43vQOg0c/y22UdbVuZWAhuZ2jYg9z7rwAAFTCTAnGidhZwZQ3Cqc14ofv+9PlTvPntx3grMN3ttTS5el4eIDBoJ0APCvoxNkX2TEHB1ztQcKnE+3EIH0RaNFL1t1KiwAwc1DbGZL++zJJ97Pkx5meCbLTfflRmwITawEEppa5imNS55gcAKSXcZcZdJv+7peTKfFNmQOqVweWQ8EIm8CHggLXdJclYZTJ6bh0/7Kl4f4aCBcuadqoUmBpe99AX4yN9zg1om8a07VKv73RDZv1l9GD2o3is3eZQz/LEsZTP2hc8AjhTwgIIGIkJiYEtS70jIPC2qlExUOYMjt7vmriujQtBN95hQoEoxejxF7Y53tbbxiCGt6ytdvnYVmknAF3dQzrvLA/mqGAgJ+waxtpZ+mLLMl+dKUCl9WdjZ+2MtPP7y5fgZQgEJH0hgEAYjQzCwVwBQ5CXcoO89CsMVizBT/X7Xhg/O1iBQQUE9vcWvXF/JAUFhPtDdEc2toAFxKUDHlJIiXCPmtNkoRXgNv0IVGB3k14uYwhuA2FPBSgVHJDPCfb6FcHhpyUa+P643ePZ86d48zuP8dZnI1PQAgqt9eROY3mAwECK2gD/3Ixv6K3CwDsvKtJ69BKJGrNnu2LgqgyqUR7pXgBLzy5W3naEi07LpYl1eF1GT6sHBIW5QfkGCO734kItSkNuundV3swzI0JOBiSyXpdQbFt2JryyWcUTgEMsEwpYaWwU8diYhO5OBKG7qd1Tf9b/UdnNMvfPVl5UYHBdLBuIcd6qiFYJcADc+56O18KQxHqdAYLei+43fkvJVkar1UjtSmlS40PqfW1J2YIOFKyywvu8Cuh4ZB8T6fukHmoxN9cNv7IGLEaOEoFYGbLwKt1Ze2dtBStzwMKOFDBSEaNSQNgLsCXtVyLsxRgD6dce6J2NG4Dp2EV28P3MLfOILRGPE8lKC1RZEXJDXNOXlRen/k1WGA4AelDws1Lws50VHAiD8LNdQMF+HM4ymt6Y6Yz9aPVGIkJhAQhHYWcfC8u4bFl+NyCXSMaxskLykGt1I3CFHg4lgQANOcUQ3IaEIwnzlguBk9ECqGPzPkusXQMKPiNOrU2nyDYAtz37gQEDaXqPAVzu+h8APHshnfr1EJO5pSQaWQgrERkzV6ZgFic1Yw3MKfr6PAqf5e89akx1VmImcO9lVXCwZgkMFAjyr4Lt9y3mkRF+BlH4GzEKE8zoF+59lYR0EJKrpQgOIMdykjCICZdKV0p1iQ+ABiQ5dcsXMvh5nngZQyyzVRjDePSxbKrUonkRezKvwpQVDcoxlp7W7GPsFcCsk7ssJ2RabOE0t/MplkxKtWdyY78pKHDvzgFCjWvHjP145wakFzm3FMahRgtm+A1wAhpDJvDOYCLsi/actdleZyChBGNHKkDYOCmjkCSElW2eCFtgCZg7+KXGrWcHr8mPsGJGbifJd9iV6chEok9SccoaiSS3UOcgGzvwPuTlYFYvfw0K9qJ5SB0o+OkuLEFlDERvLHVGIuBg1xt3OeF+L7jbEu599yvRE/byx0JFwB0b+xXDY3LOcYVeBFodfo3+tfBbYsIByUupgABgs8oQO7At6nFSvabMajQDBREFGPEXQxDXhhQeGDCoRZ3E9oAWQ83vPn+KN78r9MsnX22ZgqHrVJfaHE0QQWPhRn2zCtnbX87PibAf7QA4VTUBBWdeXt+QlGoFDb0Co6KPk3xGMYsSqHG/+lfP0wt6+q8XcPucktDCeyJshXEQa50K7g/zBhgliVFORbwC84jpKMiQd8NTEc+AAjhgYtjaH3thLIWq2ceiE2CaRxBAwWpZpmVeRyp+5kmkYopClNGRCKmgUeYzqhGlVdDNCOtjesMSl4waKPANqRagII5/PzdcyaFuIJMJ2LSeWyKP50ZQ4Bnjzg5prBtKs092efTwFyXJZwBwENXMbQ7K2T5kAo4ETsKwbSzjz2rYk4YixLvs5mfX7sLcACJjS4rOq70UbCkJ6CJJfN0Pfaudgp+duZlr14zZmRNwOq9UpuO8yiyGnxUgHEjYGII6S2qMD+H9y4u9XVPCV8pSKRgwWTI58qWL5lBo6ED2OZiDAvtsOiM6N0dhpEwopSDlLJS7jmsGhb5DO56o4TFr2CWdWOsyH1sfE817qmwbAwkODuQt6zwItrFtBMvNqfkigHyPq11S+M1Kb496UPCJ1x61SYsVBfz/GYNZaZBgOP7s+VP85ncD0gql93giK+D6TT+TojKo0kqsaN0UHslacGI4JYpBiY1U6Io9sEkePaESwMIZ2p2CgoUwJFK6NyUcpcjk1pm3qeHvhdmvTdVo9OUojLts9aQOEJEr060AnMQIWJcdqEt+MlMT+22KKmAGrgYFMRGzMbpdDHv2sNYbrd6EJfP12f7uuXW5I95HdufOwACdpxmA5KrMwIDF2qXedozqkrCc6nJSBQVbsg2pFByQ7Vxpnml9453SY2NldDkc6Rr5RBmFgAOSh2HeeIImQRaGb5ivk0+ANaNA+1oFLbnQidFYMSF9KWBnTgrUoOjcz9rvuVTPM3fd3YcDrgUELzOvCrFsjQ3WsIqwajh4AAfCv3VL5FZctoOabpMs1PACe+itghkLNwBV78zyB5I6CdfqDEtgluNi6laedTGnQcFBonDMzpnoxKj3+iqvdK4bkPi87r4xBCfTPSzvpfg35uXU/Jz2ue1wRXDQgwKvu10b2nYtKxHLAwIG0noFvm3pDhgo+NMJKFjd2e2iAwRZbgWd7GesAR9m3NhZBqaa9LYKR0wn9OAJ2ec62VM/W1GPl2IThaRfVJlaR91lo1aLPk+BSCnueR3E7VKz6bOqgFsiYk5B4J2Sq9ccXCfkwZLAA1SPkrQPyZS4taHvowAKZgmhpujEfrF+DzH7oLz3o/b70rMDyeaMk2S+nmp0Y0OS5EeJlu8JmJWYk5eIHAS1fQ/MBqcCgnGt+CvKBMQlpD0oyKnmG2xJAU14z0V8A542IlRcJ2V4Yx1Slj0qkr6zQEMMO3ROWmghgoMtwwcFAI6kXr/M1ZJqUums/cMx7Y+mfyflmrE6AwVnyZKX5tWWqyEqYMmBAIOTzl8LIySG7T8QE93MXXXgMym+d0cIhcQujAl6kQRNOh+TTsycDOzLeBVuHYpV0D/qizZ5OTUrnOJS6Bbk1vDBSv8Ba0Bw6lAtgKY9z5I8M2oITv7SKVtA1m60uTmWyzPrKYLss2OJhq+/+miaNwKgYQ3sK/wQYf4EKQ8IGLSFMWcLLoGCs+6y2B1QUR84WnbT2pUyOAjYsnk8UMpJvxfxiJFkIHZFHo3xxnzSznIN+s1CZqVmMAOlqPKhzovRbYotYUiyg1NIIBKacFU3q58Jt2UY1+WLKsghec1o7D6beFaUbKjgIJReydq4s/93XpzubNibVnnHjVmixzm9V5ovlepLZA0OCJvARdesQ73XJHs9KC/sc25LVl8S48g8N4IUWALYunDZIEaAwIIp8LCCAQOhpem4F0Bw7LDX4TpbYGjMimhFZw1gr/fOst1uzncgjambEvXBm4ADOgqAIjF4HaimD/rXNS76IC5lzMqCxMz2Pqu9H6dL5Sw/5dp5ZR5wwtrjbgpXcsCSPmN9ZjJjQHpVsuZ4SJ9YOEffe1KkD+/CGCVKuD/k97giYVbqUsWqL/pli/3+BiswsNJ73nbr39I6ZLNwVKxbfI7JjuWAZJeTVENwyrwR1aWk/s6HkJ9jy0qJ6jMMO81a8sSWJH62yykY2jqyBrcQBw8WGMxKDwqi7pzRNui+c3e+/UI3goNd7gDzYY8ADqrXUyF7ZAbG549CMfPGx9ZUp7Ku8Zd6FG43Neo3J7FYotVptfzI6hDRvgiMxa7hMWwyo6OIe0bDARVZE+ZKIKlXVb8HBd4P4g0l3vcaACbnUfN7NDb91r2x+J51yWKXyi6weIkEaVRKFDzQagzdnnYM0swQylgQPpR1Z0odj7tUx2NLhDsFBBsBxAfouBdAEF96VQqa1xZ3Jb42mtIhrAHbezAKUr4D6ettKZINBJDLVgKlgnuVXkoMOpIaIA4gd90HAJp+sG14bW+Da8bIxok5hBlKDUHsNgcUxJVjYRRfYl5dLFE3TJ63rIM5I6z7bqgKsnBoLppXlUgTNQk4ErAVSSROwpqZU5FT8dVIwHV6IgKBnhmwXVStryLbcwkrCUYPxt+AYzNN25v0OnYFJPsdGa8FBeOOlbUWK1AQl9Q7qOmb8QGUBwUMznT+uytQ0I1AHZTxbvKbebTKHrAoZWKgJHgSVdHksqJhhQKAcpLNblIBHQAl8Xwsk/Ugqkk/LLNqlqAUhXtEtNScYwptxuD1S3XavcwTZtsgR7Ag5+m9FkI/o/0aAVKPtReuGQ1nAKEBBX27uFV0lgtk1Krca8z7oAQQqzdWOMSsw0MmEyz2f1QetgHLyuDEpYxAZdqbphQR0IOFLqYAEHK2RDeZN03uQdQYsa7J1u5jeIfFh3KkbYE7GwcFBRY6oONnwHEPKrszBnDGwF5wpSDmqD41ZQ0hgBD34ad8AGkH+E7Yh3yHu/yKvPXWPKlSZTKBsHFGRgGR5CXstN6Tf9YH0PljFHDPENwyPhJCZAcIBg42BXD7IXPJIyCM9zGvoLt/YlhSGoG03bmC6dtkxqKLQzjU+0BM0V60XkUYpFQSEhhbFj3xoS01TkVfIiiwNs8YAWcKgo6z8fN7neg3wwKWx1QUZBZGo2PrOdbKCiLrWLTMUv8eGNmi2fTefGmvvRnSWAJzSuvYjX0126ZfT9bxa8HBSTSljzBMy4MCBqvyzFYfLMIHcWAcFEwyqwG4tEkKEHxwBQjIHuv9EixiUe5UoLvKiWLLhZGRJIxwFGysG4moogcmyt7qfAIA+vXPcrydCkZVx2IxUgBXJ08BaDKs236dG00R9rR8yVIvXKagGwUXEHbbMPUOLGPdmByVnCMqOqp5H95hh/VtadgboR7HB55541F5mMGxHfau2tcga7ImENbG08n7A3T2TpRwnBNxt7/6UitM8wmMKUhg0P7TyhIc9wIIyr0CggPYd/CxA1zAXZCfrbMogfIG2iSUwHwA6U53XGQPReTtQx5auEcdd9tbACShj58eB3ZK3S6Q1/XDCghcu+fEoWhz07HZiu3mWAEcso0RiREK7E6V7fn9ZzJjQNPm1Cyenf1a1E2obpQZ28L3olOTGFtJF5N2V/phxgD0TE4Ebwjj1o/rTJ8BQE3elXMOvXauY+t9Zzq2f7mTyNNtLEG/tLexOwA8s1zLkxfP5NXJ+u4Da6WpNq/v0MMvXx4MMODur5UlKHCUugAFzJUOja9wtgeUXT2aBECUidgeCjnU8M1BDIUnZQRok6VGtvxnU+W2DZno853opDpzABAVXT2mn0+w4mwL5bkBiglXo4D1pRes2Ram9c1p1AhXr9ziWwpnW9My13Xc8Hj7CA4stEMJouiYgKMgZ102ScnZG4CasIu3q/MkZm+G67dpHfdzrwLdt4YBZHWFCgh3DHDuN0CiYayA1ijO3jjYK7ItVUDj+QSaZ0B8gPafKSi4d1BA5R4oewUE9q8cFRiEwDmlBKQMzhtwbBUgcAFr+IFzfQtg2l6poQWtO5SJy8oO0JbBOb7il27qBznebmDUj8vZ2BTIPNsgQGFjlkRTkuWV8pkH0N/sXZLX8woYaev+jYO2Q+NSbqhmwANzubHdKCWPAShUd6QkQuPUbKSreig1G2/tCoheRifY+FzL3Fyrx1b7Ssix23VsD/DfDyCQpoz2hsLLrp68eBePv/ebePszb+GNVz8JgJ21BhQc2Jd5VzfFoM+lUx8MMJiVuE3kTUyBDVJ497r8HKjRolFEfStZIgL0He6SFnU9QLjj+Xa8QLtjWiyzdyhMt+P1C6QecepP9EPd8ES/F+sWtAYJqIK3UsSr+sa6OhhAHY8zxTbbcreZ6CTKmlmUnSVvGXOTei+oMHJmZ28OAKTszYFR0Q3tcvB1zg5E0GPjQISmLas9+Pv99zcizz5HGK/44pd5Xanp657qnK46KLswBZElOH4GKpJsWH72UwcEvMvvXMrg9QAApyzgIG+g7c4BQnrlQwAzmA/1skWCJJbyIdylDWkTyjoTdHteS2yT8ckkcnRrP8iB9z8mzAYUSF8fLsZzU4Yngn4Je2DwXMd6jqGO3kutb4is40lmmBRI3yw3lugoxIzqMXY9lrOEcwyk9u+BeVldIMcuA+czvQVt00o2XmZb6l7H9ttS3wIIXAfz0dqZ2AiVnScv3sEX/+y38I1Pfw1vvPoJnXOizAZw0JId77s8WGDw9PlTfHkBCgwYDv04AwX2HvOu0LErBX0Ia0AkrmfKyItNXCJA2JhwNIqNcJhXiFa5rV6ysn6Bz6jkpkl7oQN6Wkr6oyJ+BmRPAU3sMmXYA4jwR+peuKFiz5RxHy6YKTX7DqwVhIR5JEQgYWAOnhCa0E7KksVOJG92HNgbqlT+rERaMzIDMzCQuvFwj/CM5gV8yeg4HmjG4y7J3gmzmlo/A53xCEqs3ZtAVh3Q/rMxdHDcA/s9yv4zYL8H3/+sAgNmsOUahBwD5KxWJgMKIJA30N0rKFxA2ysCFnQQLbRAzODtlWbVQiLCnmpeTuZ2Z8tr+2EmG3E8gAtjEsajdGORibAhi6F041nDQHu3xHFWehZweBtkogZMu2EKsuPL4XC73KQg4wUSZuCEZq+DjQivIL+0/McxuWY8rAz6KjAUM9nowbTpWGm7OTtjx/RvtzUdW0FNBdZovmvfG1iD6CFwme73EW0MlQNPXryLL37/t/GNX/8q3vjoJ+Q6wOAZ4ouwDOgDEx2O+Rx++vzpeDCUBwkM4lumVu8+oPBXcXsLCsrhlKaUHtXtwZsQYMBEMuhEQNpU+comLpzEMB1EnjUdFZtN5ihw9qqdWcbpNUK18rRnSqKPT9kznUHQOlZQoN+te7gVSnTXN30fnu1AhXBa18bbOTWi9uCq3IqyOKZMfKkSSX/LpkStt9d7F7O2VEPbMjXRk+hBT3wLoSUgWf8vmmTdK+0Def/LcU2i6kDDrPR9HROhRKEFlsBXHUxCB/c/q2DgZz/1MAKXQ4AC88gYHDs0oxDY7qQzSwGKAG8uEkow5TmEFsqBlO+Qkiz2zYVclg4NGRUWYG3zcNUHwDjnZDzbeXZpPFSlS1P0+eBxo6BNQQK4MoMvO7dmQLMHBIniOaFNF8EOYOuxe/BfUnUMRO5pKvMDyMnVebF2xXlo9fngdBRNdVR8tboDhqSydKOebRyYAKwTWlkiu97YgbCct9qWMZftyYun+ML3fwff/NRX8cZHPi6hOe0YBwdUQwoGaRjrsY3l6fOnePztx3gNry3PeXDAwF6dHN+SaIUmnysoKA1T0AxcQHZPX7wr11koAfBlWIImdTqkA7ZmOyVdq61I8jAF4iBBYnlnAjcrZ4Y1d5+b37t2nz7E6ZUUrwKLCg9GqRNG65uTW3t3Ya4MTJk1yrv7u6qu7RVfGLJLIiS5iEneXy+KW5mbiSI39kb1OYAxs3pGR0dWoA+FnCnsJYulxYEB25dRAcZjszIFW6ReDgDoMkRomKCyBMdlUHDcg/e4bHHMMUApsnMfM7BtrszwM4BeqQoOd1XRDaGFtCPnO+SUXZYkZzEYLdDFPrC+PgOd146F9HkF/FsAKmaQ7G2esrf/PAwUi3nZcX6dAU1jgUzuzVD1cn/WrqQn6bTv9JHNK+pkfN3XXvWJjM/kO9ZRdJNtlHVBCSa5s+mkqpfq21sZ7ecIpl9Wz/ZgwBkDq39p9/agYvPYBn6+rPcLf/47+Oav/THe+MjH9Hx9dR7b7Jb51ocUYumbY2eZfXz7c2/jD/7lHyzb/KCAQQQF1zAFUjrjyPF7aZDek5/8NT7/l/+ZnGe7vBHB90MjYw4SiJODBKJdlmeljEQJST0nET6luFMAB9CBR/QM+3qfeNb2z853xKqx3x6xhok67zTL+FPQo+yIb1ajeRYiMSaktc4uqCelV8aDkrD6Rcot1Je1jgbOSL9nktUHBUAhUXJGkSbI3xxAgu18uOlzoqG9myUnWT9HLwIjlTtbntQmHwHOWE0Kh/ZI58R+roAtMjjTMihiVWBcdBliaQCChxH4uAwK9ntpSxGP34vuhkWUAE7ye63OGhwwQwI58NACbA7nO5clptoPJU68SduBEWg2IBm1E0/HQuXXjFIJzy8cgYLKtTKGK6p7NrekzwKQCcxABAPkoKF1Alooj4vyQ+E56OZbK88Uu2lZ1v0c4uulC9l+EDopZR+ju5SdRZCxqYxbBJGR6eh1be+sNHIdmYEoS3EnUMQ2to6mXMYVPQH41q/+Y7zx4V8Cyg5OG0ACDvSNcio0et8JOHAZ6koEBZfeIvxggIHRIz0o6Dto6MIhr8ByCjpQ8N67+Pxf/T6+9Sv/Ff7O974onlRfzDgZrI9AoSSAMohI13Ark0CEnJKg3TR64TNE3lBrM0XPBXRoHMtDIkedtEb1+gStE3W5MY23z4REQYG2pW5ecwE8xAZ0QyF/J0q5T86Z1NHvGJ+hzxSFmRwkmKe5aR+XoptLAcHzbI3MsO2wPTH2fwfULiqPPo/FB3rWvrbvrG/jy4lY2ygV7InX0JcRhBhQNEDQbVbUgIKQZDhlCnpQEJcspiThAdVtKAfY8netm3f1jJL27t0roEN/s/qmDPBWmQhKoGAIqrFetB0AyiTpK+YSnYyDj3w3v7P1v8qDA4UI+lNriD7IOeZAAR3QaXTbjfITvhMwyG//jo/Yd+v5PTGSjS4Kdbf79I/o9ZGOAQVdRLpPBuvxZHtnmK5Fr2vNEev65QpdG+1Esy14DwSijM8Aj7brjb/1iw4CKIADYgKjAEwqM5ZvIP0wh5Yyvc6Y9Fl5MMDg8QUkNGIqnAgON4MZQcEbH/5FPUcXunQDPJu0PVCAAgKivVHwbECB1JJgnUDl7eDSKroZEOh3pSt10rICCFtetlLq1iaKn3N2QEAODnIVVDPOSVSIg4nOyJ2WRlIX3sNQIjhowYnVNemxAog3F8BYpKXrU8e6xryAnq1ZeRFVSVZlOHgRM16zV9TW33aMSMBDBA7hfC8x2WkCgKHvPagbFgVQsIcNjcohOQVW+pcTzL5Hg13UyAPgcshcOXY5Z78PzIGCg1QAzvAwH+0CEIiAw+bfFW32/m37nBr5v24c6py3+Z5dnrOClZwyAEIJoN8YhcgGns0x+2VFwbfM0yJ5+hTszEroQ/OYz06P94/92iTaWR2PZs5HPQRAdFH87FWKuhWiizo9xCmrnhEQ4KAxZR8fAQ0BSKeBW2nKjHUZ8wXGPIKmzSdgx+UYcX5qXyIwBVBmIYYUGnAgtaX2bnh2A1Ng5cEAg1WjGyPqHxeCH9kCyAA++clfB1DwH1dAYEqxG+QmrAA4gm2Bwo7qbQfFgvvWEwGqR9iXBRJ3j8+R+NECAfP2StG/uuZchbGuP++Sx8y4qzCK0CU0CWUKHGi70zbkxiBHdI/QxggQuFPqKzp3Ce3jffq8jwlQMICQjd2BzZQKylaU6ZQq5WMN1HrF4QpwojQW7YtgwFXApE8b4GDnAM18HbyZIlvz+RwCyx4FPj8Y/cZFvvIgpSZkwO0w+vHaealev9XjXGTTHJQCpgLad2CDvBshKRFfZPWPJPoGVu6attrxRmkD72sMVB56gO9AQVcqVUOU1yGgrqxDbNcAgbbNgyxdkCE/7Uwml33ag16RA97vwcYqhVUsL6ODWHUQJ3vnhm2eddcBhex6ilKu4zNhNqc90PThgvlwpmDRfuujSSHfltXm3QHnY0j6W8YgsAZhVYLXRzqn3hekOybeBgqABwQM+kafAgLAB2u6X4Ge++Q9ySlwpsCQrt+L3QuJ2796HbINropzUGKgJJnevVJvDCnmwtoLe0PHRVbgaDafYfX22I6Vw3eqw3HAs8lLbKM1RuuTDH2TL0EjFUxKCax/7eU4dtzBwqwfgoGryv0Cm3BBqUWK3X6XsELs1+Bputen/lc4b0qZAm0selDMgREINLvPmwgQ0G0hvAJD2j82r/r+9HpHanE1jwZl1dbVvDifH8YkBGBAKcuqgeNwVkjiobqLx8rb6+YQpdDDlkOgnhwfu7eZC6SfUkazcLsBgFe0MRr+lRxfMQbDfE6b9DvdO1Co4L9lFJxN0/FZvjSMazsa3XMNCLjEgFwJDPwoz3TpjPXiBgQg6Br7Xv/Wpa0vrX9U11DegG2T/TFM9+gxUNYQbhKg4I7ZS+rbi0Doevvgb8A0loZL/c4KAuxvU6dSQzxsbEIFCE+eP/FtlOOOideUBwMMYhlAwUQwLnmiT977Ib7wl7+Hb/3dP8Ibf+uXMGbIBmUeUW68leYh9PT7Eij08Xv5sqxjMwHd0ARkbvRvBAN9XDiyB2YMbH1vYwSMvtM6BqHkAARkq9uK3EFpFFZVmmmiXAHMPd1LZZj1od8i+xIBQ/Q0Y58jxO5hBri7+4KWPvXCJ9QpRy+p9HNsUpTVgMZLPbyjbRz6VK+Rdox9uTKSkdodDPxR5Pn6bgMGJKclb+DEtR0ZYwjBlDogoCDfVSVt97dSimyqwAV8qH4EC3uwmi+zdgFLAPYy/Q/A49Wx/1OgtA0oEFoDNIQfZuGfph3RGAfPcBYGacZy5qVeMA2rn3vmpXn+RA92TggiU8nlVPfI4871j4UOSFkCDrqH8p0DAjK9s92B8obUMZkVyN2qb7WzVoAoyE6Zvf879quHQrAeK+aqzgwAhGJjwiH88+T5D8I2yq/Lebg4A7w8SGAgZQ4KRjptHLgn772LL/zF/ypkh5bh2kEYzCMEBgqMg9LjHY0yqUABWBrISzRwh9BjkliTLNYkjdnfA2UXKq/sB9jePFJGsOMAJyVQFoGiTEjbJgK73Tmlx47W7xpET9vm1B93itW8MJoY9ZvKLKzQ3G/iaU4YBr8k3ONmGtWAQU+jRk8qbh9sQj5RKNR73XrMklmR8tCnAEK/WmtmxeRjMq+tpKzKZQORGHwuRb03oYTJ+sPKcYhn1/Q/QKHO0/CUV6uY/lRwYEr0gK259/Ddqi1+n9D3Idz2gfR/N59puwPKLv2dcsvkTEI/kcaezjVgNPZTAMAY9MTsHreUzmD1c2SYzz0jGfSOAQGRBUbZd/DBYC6ie8q8/2Ve2HwmpE3mTdqy6NC8yRio3mHVO66TbKfNwGD6+EQHrT6wafsMEMnxSV8AS1sQAfB0XuuzZmB3OTbdfPnBi3fx+Ltv4u3P2jbK0PpeYGFDeXDAoGn6S4GCH8rmEraOdLK1a7y+AQW+PAXtdba5y4HB4+iBAtAr8r5Vo9LjY2+E8xpAUO53lH13MMD7LpnTujWjCOpEOWal4zOJYCb5S3cZabuvwmoCaUK6bUC+A99XAGEhB++LowNMAM63qln8xu15jeGYGP4BgPUCFEMcM1R/wXNwsNYbIzsO+G9NQl9HpbJ/zM08mhlWi21bv8p17Rybdl18I6IqL19aoa8MpVTbQMYKsNKavUKMGibGiK2/u/coINTb+z6wCHwcoe7t+A8gwPpwBgQWTMFN/a+5AgJ875u+5/ufXWTHKlgA4pxrcjEGHdUCgSVQnV1zWtby1NDhXMQLjv0aQwOWNxCXsKre4f2+dULu9fPL6p1tc5CQtg3pbnOdYztr8rHL+NhOmwoQ+NjhYQhgdNAmfbcEmkDtizgOM1Bw7PU5FilIwGKNhz6Y5p+9ihUcPHn+LLxb4fXh92tZgwcHDKS0NMw1gADQvam//9u649THFFFCBoNtf4IJUOgUjzyitL+njhYzsKD356AMG0W+KINwmtKbhQwmwlnulSnYdxz7ARyC3osZrMJLAZW6SQw4323CHOzZhTVtGenucIBAxw7eN/C260tzuFLMpThAQMpgVmUZYmXy3F5wzqd3H9Pz68PYG3U79aQvIfbooWHhOcwMUnyXgBkiu5fFWYE5IA3zBUC7zXA0qL33bUaMgyHq+nco7tkDgDADsleynl90gVcEw0BNyj0rkRGYhRGom/9dHYd4be/lXwsGYv8byL62/z1/KLmyb94FoTLOCyZnaoiunnN+wD/Ntm1fysD85svrOfbvChDY3yt1TikswIALjvvddY48dwEMVOeklMDbBuwH8pbFsTkEXKTtADZZfsslgHXTOaWAtsPlgnUljDslq96JQBNYOoKj3q/3dA2jYTgAIlP9s4zVbABBQrNSZKyg2K/vfQXf+PSf4o1XZRtlDqGqW1iDBwUMBrZgklC4Kk9evIMv/rnuTf2RjwMomqwmcfv6kMsd60ooPjNMdi5GySvqj0DB0WSWx84ma/TKZkbnRlDA++HCWe4lx8CXL3bp0p5jkERQeT9AW0baRVhJ9xxmVcCpMNJd8CjtPgC4UKWmoTF4bHWFDoIdu8LeNP3dH95raORMAVwduogAY+Y53GCQTpOuYlEvR3IMUp3fIRHL8zg4yTxLCryYIW/vyoHGbFyWheFQL9/aGo1NV88Z/b4qwxiEfj83YF09VkZr1f+zhDfg9v4/kudLcJY+lK2dqRqECUCbMoWTPjhv/Fg/z3w6meO8XwCDi+cMgCuGwBYsAe8/G5nJ+0P0zf3hTgjvB/gol3XOfrjOwV1GKQX5boPl78ce6TmYRudYOwy8Bbm4St8CcwdwNXeOKl+MC7pnZrQp+R4dAJoVa7FUUCAvXGqK5abcwBo8KGAgpZtUfEHYKeHJ86f1LVYf/YQqlgSQbEriw1WCAPoAmec7sVyd8QiPlKKThnvvGQhIdAFE+skZPFFJ/NEsclajxVX4zJsthYW+OwJLYOc0gtqxHQAoMSgRqOhKWgU5CRiElZLGVZU2d0HNd5AYdVGhCUg64XoFJpUMH8v0uB86AnCzciqwXWm8gpE+7IHhVYCgj02iempWKM43a4PGuCX5T+08BHRJTsBW/YSufx0cACHLHmgUVD8Gaa0ylgm905NPvLPhQBl+pZxDsto5KIhhnGv7f973QN//tsXzFCCkXAEa9jGHIiaSWnnZedhd23jdEXQZGOyOXyyxf1GdH/PKe0dE+v1Yg4J9BxdWR+Rc39R5WiDslexlyEcBUUJJjFwKmAlcCFySxOk55PkctiqJVB52dUisfZrUN9XjC1ZgAgZmujKC76YERqwx+La8MtWVFPDl1pEBQLVfDgo+2TxzzFe4jjV4MMDglC2YKawQk3H65bXXRZHbGmlfS1rqoAT86XFw8/Z1fbVPGKXEe09qChJsjvXtSQvvqRPQevOXSC46KbO696iXjwJKguJFEEnZANvAZr3V71Vl4U023msMP9TKyt+ZIW/OYwz5a9eUcK9lAttZyOAaoxToSDtuy51tbxMAIziwecNF9WkYs0XSJ5ZLH628z6TQS2WWv6HH++QvJgMHYegT2vGNfXsJFCwAgYMv63tKdUysb9H1/zEBCAomjMUBguGIYcVb52E0Eg76OqAxM0zd96lczWTq2sI1NCBfa4jNMvVnIYNr9M37KlE/llK9eNPds3IBEKyYMq977OsY9tMcB8qybXO/YqoCgcA2dYxBY78GpiCM+4Q1OCsPBhjMytIYNaBAEjUevfZ6wFItOOhZAwCC5oqMGbiArSuLUF4MyASipPJ1QcBsEE0R2aQ6m7DTtoX4s8bVSOkoskRBYnBipFRwZAJxQsKGsu8aKqjo/KpHapwsJUkQojSJ180UExEs21gEqFLdlhTk8VjA98Svz+2UmSoy0v4bymw52iImfjUtPmEnmgS8HqhF+trKmQL+IJRi6hQT0IGC5OPRb8Y1rNi4dknp2bKv5lgAyZ3xr59ZDbOt9iA5Bkl+dHBgyygtodM8+/dTbgg7MbOCg269eakvTqeElim0Z1wx35YGss/ZWOiMPmejkZ84XtsWEg7t9ARGTZQ1HSeMyITe7oFHSqDEADFSSuJE5ATu5G/WRgsh1L+6EionIBOS5h5U3SP1IZvLKYeckOsz862twDlLsCoNKOh1nK2OMPmzjZf0XP8+AQUme5Zo6E7tol43rXLQ8sCAwfXesoOCz76FN8LmSEwZYKVgAFFEBS04AHSNstrz7U63ca3dGTO3ZaOqC+g7TCD5at87oQdE2ZiC12uMkndFZoKtv5sBznkHpR2UCUUzfI+UwPfmbSTPNZDmT3a7QxBWTQpaCapnC1uCoa5QIKXK3HPV32aeLM88VS5BmbErM+6dHS5rxTtLGAKuUtJeVnH2VRJeFs+SwxjK+XAcNmwO1Jeo4CM1HTecMtBFYR6F3eFASfaXaJbT1c14zt6BAaCjPq9Mnms7Sv+GTPq4XvxkT30JjR2yC1wBsBGwS/a5GC8D6RGY6653BUIE7sFLj31/NvR9PkAno77pzqyosXbA37BClzrLuqigByqnzkOYJ7NVTxqUadskD6qytZEn1lLewEHHEHZlQLIAiW2rGpiLyD0giYELRyTdZfBBqmdGo+2GHkC6y558mLYNdJeRt6wrotqVCWSrE+L+BkR1b5XovKx0rP1mQCiEfCOYnwK2HhBEpqDZ9K0DAfpM+25Jq/HFUABkR0Ndkvjo1ddvZHYuhxMeEDCYKJi+9EzBZ9/CG7/wt+3Heh8C/LWWZZdu7MEBVSaBOQF3BNr3mhhWCvqlXVgp/Ji52k/WkIw4b5MlA9bnEOmOcaa8uYD37Ag6BfagmILbMo49I2lCUNqKr1CQbg0Z0LZPqwop5QTSdcVLQd1eqaBge6UuWQx7H0SDBVCXdNMLXx42sWGUoMiCp1NKJT/OsuavYXUulKpAzOgcejwDyOIhbanW/aAKEmLdzizUyiBFUGAga6sbS7VbxhLibnC2tzwotceI6tJHColQPh6Vxen/Tvtn+lfHT2W3306aWROAjcXjApQE1kGmcsi2yT4PBBzYqgned2nzfu/ggLZN2BsDqNb31/T7rO+BanhPZHdqRE5CnUOJLMOynnUsx3ebKNDuQF0rX9o2Y8CY9XyWLarNaUq6a+WuOoTqaFqCm8+S3hExxuB4eT2DlHQFVK4roAwQ2BJp6nZG7A219de0r9vQTJ/MeEmfT5fgWn22TZxQ22jJ5A9oQYGDhDpmT54/w+PvfNl3NJTxUJ0SwN1QmnDCuRP9gIDBSRliMhEURNUE/y7bZupmlZTAdIBKeFFL3iSWDhLvRT2XpElRtgymAQjA2jCtlElH/w3FKHeNk9rbvahsInyalEh5lyU+hywzIv2XNXOYS0HS1QqWVBTXF4/PrasTztYUm6D6Zkd3H/KNjlyAO2M1UNqz9nMRwWr2EqgUc+PpGLjvwcHKG1iVKz1iC604W4HKJFDe2pBGvgvfDSwcp8uaAcyNkW0YFBVgtz3sSF2a0srVSCK5UopAYXzfPfy1wf3rwWe9WKGE/iV7KRBJNSiL/oPCDUscUyBApeh2yAeIDqAIbc45iWyWwwGR7w+h+4EMgJm57noptMPL97s+Rxq5AAE3UrmnZcYqxucvckgcEKz2UBhCIzaKgbm5k9hNUqDFSXcJDZ4t6Kc6FxNw7Eh5R7IVCtsO6vZOSSZzCz0T90uJYIBSmusZ21Ez7JWyMtLX9HPNGQtOGKBMzeQecU50gKB/4dOQaAgAaXNZNFk1AG5vEXZQ4OMWwMFkLOdlDQ4eFjC44NHNQIEpur6LRDkRKG2yyY55Skp3cb6Db7Fb1JMyRUYVIMCSDy3BKRoGK7Os5A7tA2hi7U1d4wYk2g9siYD7ve+6R5qhTbqckRTA5FdkOWMuxXck82Shg11BcBBcMmBAk90Pzfvvdj+kuw8tAIEIyUBnX6KoT+PQRT0dAQiwsaAAAMohdWGeeAPXl0vJUb4RkJUuL8EMl/x2tFI5Y7/i+Ftim1RkBAQdS8DOEnQKSY9ZLJOT7iuB+C57ZfSZG0AQgQKA5vXBPWnpW0URkEj6Pb41MNlnIiTKSDmLlJYDsFfb0gFOsuqGyqExfDV6Uf725InBpOvufexjv5s8blf0t/a5dPcEtM+o/pNyyxLP4Z7XAoIZ2H4JGWPfE0DBGiUBCL6R0L3olryB738q/W2rFAwgBB0Tdz0EzvWL5S0tdUwEAwaMN91uuwtRyv2v16WS0BvCODZmZ7r8ZCwGlq4LJXDeXP6ijXry/Cne/PZjvPW5t/HJ1x6hoNqpKThYldUqiVAeFjA4KT948a7sCLUABWWBDpJ6NJQ22Y3QY0ebUqybvo1OFI4ABKUmlbJJYfMbYK4MZiCgWT5GmtAYJrLRf5IEVMT71IlNaiR94x3dj5xsX4NShjXIMKbDfgvZ2qd0Z7JdyUJIYLuDv1zJGIK7Dw0vNikTKnuMbS9KH4P2v10cWqlQ0oz0ASBYvzJXD/ASs9P0w9xw+M/xS39fG6/ut9O8h1XoqfdQPI55WSE1tGXaUCAOXAQDh4EC/cuo4KACBa3/pOuMmzN7lGCgQACCgAFGIkKmFiTktCGlTcCBAnRC8iVoLUBX+buj6tmWInMyrBRBOVpQdtLPTV9bf/sP5+PfjmOfbDfpqWvmno211XMwQjXsMwXc71POxFDKBkWUNtn4TTdXo0M2MuPwncsB0h1YSfVLej/6Jbxno99J1YGAe+hiPmP4hNGGT+r7BrS9adM6yfi4Dg91ZXP0mioHxy4825yfufyFsB7E6QTJrpoGyp/9+Cne/O5jvPWZt/HJjz5CYfibN4ERHMQ2vUx5OMBg0glN9qbtHd2BgqOjRNsbCEGQ/F92KpG3V9xj8TempRIAgtDcVPYQo5Oburln7hBrRe92fEmpx+sskahJ3pI+YbDsBGZsBsK+/UFILRYr+Qj1dagwY4pKF9fHWlglxLd7+m7CDjQI2QTEKTNTWGMctI5LDB+oJxOT1azerDH9SEtPwNrUG5gpyhmzE8cEnfG4hs4L87ZZxjpbNjfUpwOTnXdyWSH1gCCjgBwQlMI4oH8DKDiYFSxoyIBVlgr7HDkm0CB7mI6Qk739kZUpAIgYuQEHjJRk0WtJhERQgJArg1CsXccaoN8lEMZtqq3/6VL/+qB88OPcJiGeeKLANLx4ccwtVKTyVOcC4VZZq8jP8j9KzZ/iA5R2kB7nu1ckZNk7Iv73qEAdV+qWa3NnQkJt3UWwa1+XZNjqzzGEIjkVW/PbJT3uwMxCdsroNfqOKmPnwIByw9I9/fFTfOV7j/G1T7+Nj7/6CAfrE5SOy07CVXBgobJl4TJH71oeDjBYlGtBQSl1V34b+gSAWOimCBAARXWuoCTmyXxUBsGWKdkrM804oUOnWtr9+gMI6BC+n7sSZL9hNJ6l0oC6lhh3izcyhhBEs1EMcF2ijQqqIXZjBkCE0oOBiJZPE9ygfRJ6rGEJAihgFpqZtX02RiYokzADAZXRMdowz0XjcnhnQs2eeWNhvPysBhSM0jsqova53NPGgU3ol0DZPPaQQQH2DhAczAEcVBC9HwUHGByukeorOAjK3l4rbAllGcCWCJQEMGw5gUgARSIgE+NIhGzgXK9BInAiYQ9sbI97zf/pAAIzmAIw5OLK3diLy/0a+lb7t36+blzrZ5V/Ww8wo61x/fxrlvH24YLu1cKDrAUZu1beuEsMNd1hssa6LwSzvHXTXz++75WxjI7ILXqlCwkMOUlpq+GkGfN4Nl6drvRjqldcd8oPt+lx69vZOERAQMmhxh6YumfPn+Ar3/sSvvrpr+OXX30dxRlhAImQWDeUW4CDly0PGhicgQJBiC0oiHFT6HciAoGR9Nykiu0AKYOQGoAg8ZuMBlVHmhsjOgbQItkZCJgh/EvxQcAF2oxmXArGwbvmUoA7DT+EVwQ33nW4Z//cnrJrcwaoCsBJcls/PmXoJmlvIgBNopoNpiWqhXFostlTBUZg8ShtXJRJAIQ2nHblxPiPzE77u/ztvJTpzXnxeQQJ89p1YNKe5+OQMCx/Uo9FQACcFdgDQ3BEQMACAHYu2A9hDeRcxs4cAMFcJ1H0bhJhI0ImEoBwFGyZsFHClhSIM6MQC1ugAIEBJNZQQ5IQAxMF+asMgit6A4bWt1HJD73b9af1aWzE7HNfJuxd8xQzsMDUE529+wBo5yAPxq81jJEpGpa9dfFrd4qax9b2pYm8tfrjEKOveq+OBwObjoVv8tW9/jr2z4lOiQCAp+1c6MhLnrONU8+KxBBKfw6u1+ORDR3lLzWgHIDIFoCnL57it7/3JfzzX38LH//I62KnqObjFI0nJAY4TssIDqzeN5aHCQyImnWe/eoDMzoRFBwKCAaDxOLBOEjQH/cCfekcgTRJCslilyFJKk4yYECbQI8ycTrBW3RPjVDPhj+asjp32EGLLxHzzXhKpfksnhiV3CIWyFbvGwXCmZvSArPYliK90bTJqMZszCGpFwm041DSyCIwV2MxYXNWYjR9C2NviGPb7dzGgFyinSPN3BuVdt5crGM/jzqWpgBN2GBXMFAKsDMrUyDnRECwK1tgYGAvEtkspeYfrIrkEYjc3IOwpQoSNk7Y6GgAApPcW8aZwMooMAEJAiDyTP5s3JmBXJq5e3UfWj8C728M43OjJ9r83o31SR1v8kon425OkOVVHS7e18kdad+DMnLOvorEgULR3IMAFBrW0sN/i3aaDAFLfWKMV6MXFewAeAm9WHWdsyIrsBDqfbUen4yNjbpudSOGHsIYvPP8Cf7en7+Jf/qpt/Cxjz7yMUpgZ7EzVXAArmw22f/KkM4A7JPnTye9UsuDBAZPnj/F4++cgwLoWEdQUEML7VQqbILA3vn3hZEBkHovhyoomQu5TrLG+66egCuFMGhDrG/hTc8Mqd8TjVryEn1ZEe4NtslhUgQaBQQTAXEv5gQpT4FM8E5MIZVS636Edgyei/0NczuR3I2IsGvda6KaeDcpjMPVbE5UjFFhzTz/l2B1WoMz8TZDvw7v+GjmTQQK9ZqGBu+fHeZRDwj6PILIEhwGAoowCT/tAMFeijAJXETBsY3fOEcSEaCGPSVgo4S9AFtK4CSsw0aED3ECJ937Piex68RgYmRQAAZo8w903DmOe+/taR+e9ls/Rnrs5vH7ILzR2Ty057+MV8qVpl6uLvH22LNs/ORgBAjZP9dVJClDAxGjA/K+9EjnEEVwUwCNaowh4Vg6Tm8EOmkECz6Pwphc1OM2Xg0Y6JwhDrYndMfTHz/B73z/Tfzxp/4FPvbR11tZYqFtMhgHxPbYzwWOEUZwEIrZx9fw2qSHpDw4YOCgwNd5zkGBDYgIRgUFBziwgPKBiFBUIN55IUjrKKwsglCcBFVQqCyC2IkN0diuPAGOxmcGBHge9rCEMPjkaFF/X8R0VWiZqIKF7J+zG1kDDpFxmKjE2o5JX0cQEIFYcw7aLHcr1lsu0Cp0pOAggYRRhCSqGUgwqvlqNicqaaBKWyPwIVww8cavYXSmijcOjrU3tYfav8GorTzLwGr0YzJdaVBk7hc1+KaofnocA0uwHxo+OLgBBPYZOAMGCgo4oVCRhSEMFEg+ATKAo+BAqjkKOYXkYHmxGVjYAyZCVoq15gGp/OXc9lf07qZ9VfsrnjOA1eEGaATE5KU9zGg2cIrGZuKNXj0Xb/BKfawR9EhpZU8eJx962ZMqUCN794Rm2WnVIQoW9G2pUYdc0h/2t9UP1l0MBk91iNT5Vv3H1akAsAOuR0Akzl6cR4HtWTFPKx1Q+3vBVOvtfuf7X8I/+bW38LGPPMJRqg2SezMyk76ojlFI8qQOkL+4jmrz4OAA0oHRaf6Db/2jZT89KGBwCRT0SNINkX4pCgri8is5UZZY/c2LZ/jP/uJNAEKtGotQTBkFFsEmmtHcZmwrmmvLzICYAo+IMgKBI9Q3xnUNOPiXWBT1G0MQneDsQm8IugUOdm6Puvt2WB8PnsgEANTzxvGZFUvRMYdJqDWlp9kMg4K1okJOI5vTKua4umFRLnoyeaoABiYEYV6dlNjXvXcDkM8t25EnTQYjsi7DeKBlCWy1QQmgYD/G0EEPCvYDnpcg9+H22aEkkoOJCYUKtkRITEL1H0lBARwcICd/18GWEw6SiiewMAYFlT1I8DyguC8CrL8IaDeT6FgpDnohgNMoS9eO28jOQQFsiNNnAHxMqWuy+XJhPg5zcRKiM1aofq/hnrKQwUvyV9vG3t5m2WlgV9s+qP3Sl0E3X9AdtjLGz8UlndeyoqLvar1z0HeJ4WzI0TCRosdB8zbER0+BzYkuL2Cfj//4V9/CL37kdexxF0j4IkQUYiSWMBszjETw0E9svoMDMJ68eKcy6eE1ALPyYIBBEz5YgIImhBDYghIGZmZkAeBv3nuGv/+Xb+KPfvUtfOHbnxLGwAaELd+gIrZIs5UwMc9mVBSKSPP1a8dLMdRf28UQFmPn4o6ReVwnjJ2AgSSZ4bNlZK0Rbj32VXMCntK/C+UT+vjmpW6KsAyTp2SCbvRyC9Yim1OXnXKjkM/iumdsQAztREbHPwfht/ZegUG8bytIC0ukrlC2sVidGoVb6ryfgoLC9fOSKWhBQWG4lwPI78YUHAByItjASxgBKIWwpzk4yDmpgiwNOEDSCQDRjLrfJfpk4Ut9gq5fvK8mYwZcHrfVmKVESAX1M4AVdd2AheXDTuj1MBcbI1SqM2HjHWXxpeTQ6xP1BXf6gj8wfRHraCtirtVz2m3YKCEnEnbAx4cr2wuZs4c5RtSGK4HbdPksXNozNdZmAPiPP/y6j409xgBABqMo8AJLXY01sApZSAHh+ifPn+Hxt790FSgAHhAwaHMKpMxAwYotGJQE6rl/85Nn+Ad/+Sb+UJEcIEotCkAxDyag5QgSEkTfXUKaDbLUOk+FmaGKm0NmOMD62SdjyBTvS8wQNxAjQMG+z4CDXuP/1WJGqlFnnfEH0Ai11K2i/8XWLsjmqUzqtCXCoYIm4EzYnIwK1iwebTkhPZPTjkJf5iBzFqudbQBk7ZuyIjw8punLuWdmZqBldrpbDKU3eLF+rojUsB+QebQaD9i9Tn9n/5toXTMD1rNyAL6klArXhMQADpik/r3srYqPwQKwXcVkTcZtNmbOwhWuiZccdMLEI227aj0nZ/otxqxjuMDCRMW/y/n7xMieyyL7rtHWPgCNPAIIoIGn83oYn9DPM8PPDNdn71+/MTat+5ZUhyjrSGV0MkD1+8vq8l72VmwNMO/3pDcuWie3WzPWgFtwULdR/rq/W+FSeTDAoEVCtBSn6BmMv1WhMIT6Nz95hn/wV2/iv/y7b+EXP/y6K8MDKuA+KLrpS8MiVEUVJxJ1SnLlqYhQt8LMXJmBSPNWgFDXnccMcVN+s3ewWMwtZownUKXbFsBB2iJ/80Lxx7XsUeH0a93jmETBsOfch+fvSdjnXTfE2Vm8AChA0BSsABCqR2kx7aRMTkT+K+PVxjAXrEDnfbMChzNPp++f2I8WFrzM5HAHGLQtC7W1DOVoe1jHohIpF0Is1qdh0HIiHGUEA7mLd1zYLVjrweAkb1HkxLAkuqT1PpQdIBKAQAw0a7e6tsPbeJtnCuCqMevH6361gRPG3JiGTQAcvE7bYlVgtKAU1wMCYxdXhtdKL4/3Vj0DAd7+US/E3/vSe/orw28M7mzlS0wU73XbTK9lEsfAAUFO2Ai+EiYnAvVORqIKXo1F6PrAypAIPgHiFxnUML3s7g4IGGDw8Nz67FA5CCj4UvNuhXNZtvJggMEbrz1Cm0Q0oumIk67xKAwU/O9/5V/gP/pbn2woUu7uV+ObLYtgIAGo3t3AebnRGSdPzxCcLRnbi9C/IkBoPgNVmOxzVDqy3K/WswpT/70FDUCrEKx4skzX1n4THKBF+33X7A48pKP2RAoKKkjYGGCSLHZS94uLMQUhHo1IOYfwiNb3QKvQIrVpw9QnC/UhKAMDzPMNgEzxzfqm77sz9mbLaQintB5NVR7WJm76edzGOOoMSgJ6KZG/nAqwuSAxzi0z9oMABrYsIQELKaRMDaNgmEDmETXzasv2uQU0pO13ttwrL+12EE/QvSeMTuehzTGhbgUE4njNjOWtY3ZpAyfPjSlQkCB9bgl88b6reemAbjEvPwiWMTb5vqmLLCN1oBA2r5r1zUwX9Htf9Mbf9Ff8DmvjBX0GVP21JWo+bylhCyth+qWyvQ7p9XlNYJzMB74NhIY/0hdBcGK4Rqf9tBTUfjfW4Mnzp/jydx7j6597G49i+GCyUqEvDwYY1LJ+MZIV14MnnlAEBb/4kdfHSa2Dl3V9SJFHDyyCbYVuHs2qUlHYqwfQGhlbMnZAvt8fZVgyth81O9yOHxoDjhOun3zxb1KPx+jNXpHPAAMA3/wpWez3pAxLQhe8tG8opcIou9Pp3GZRrshJDTNjywQURf7KrTGJAluHe6hyTDM5j0qXa5jgkifm48VRGQeDtugjUzozxmbLCRnsGwLFcAoQAWoIN0weM1ULOmz29sFM8p6J+kZR2XxlSwlAQdHEQQEEhJIsTkqnOwz382ijhESybJHce1PvDsEjtXpMtKMowxa4x3bOFHKk0W3DJpOtmccq97tuzMwjNbp6K+zx7S3JMtusXmlSZouAmjiL2+dlT1NbvWdM46W2tp74WjYjqE/a2ffDmeFebtj17wkAMOMfc1cMDJj+mukx+xz12JaSzzXro7hUVi8C7+xLZQ3McWn1ua9SO1FzNjbS5jUYjW1wB0T/Egnz1uRyTJ8lTkAEDs+ey7sVvv65t/H6a4/8WXSFbgYeEjDo2AIrkS2IgwWosZ6Agx9pTsF/+Xffwn/0tz7Z0Wrt4GnStCfEzQCCHOKpgm7qim4CcfVkzMDMQIEZIPtnCWH3R5F4cRkFq3SaO6nWNqRtApUTNUK2pREoyHUVLPg9T+LK0/aHfrZry1H3jrCUW+YKELakyk0V8H4wkAv4IE9Wc3AGjfWrN+yBOa7PiOgc6Cnby4Ag7ghoY2PK1oBa7/m042BNnbE0jI1ZvdGkIRQz4IHCDvNPvNTW45wV0u4tUAVbEvZUsCk7REyaDChtI0pgFmNQqNLYDgpS6DsExmDSPgMEM4rXWBKneVFZnr70WKSC7aqQzUDOvOa9FJUvXnqv146ZtIm9TeKhEvbOK42hrwSZmzEMGfvulrk5cywiAFoB1pnh9v7t5LMMgf21gmu8+wAC7LcIBHbXWTzor3ivMx3W66+SgU1XwwiILQqmLTDlilwS+YrUdNOlskDn8KFlUob2Yj7/Is7rAUGf7GkG/1KJ5zAznj1/hq98T1+49OqjxUVpTT/gIQEDAKctvXAZKRL80XtPPdHwlz78ug4gmlgjUAcx66EBIJgi5FqrMwVtgABoQUFMBIvhg8MU1/C3goKf7WKI7o+CUook6HWIu5bSMQZa/5SQiHCXaQAJA6NQAntA0pI+jryKfffF6pkoUNKFnUGwcgDICpgIshaeC3AklhUJSVeFqKEUHcBtTBrALC69ogP71SCRzTFAYAbG8j1iWKc3NN1DnXGZedXMCTsBBwo2liVrnDCwJQ1A1VvP5l5zjOEsypYIGRkHSYZ8LrohEQObG1TGVtJVRmXGKkVGxBgC+TwHBDVsUut9SZ6iQjZ5Wm3pXI+NmzbNYtr9mIk8yJzfiyRKHupVHyjVQ2U0Xql463PaWsRo3siL8xMVCFmIxJiCGDoY7lt4CQjOjvX1qveza+q1Z4yAgYH7Q3/TG5zrLyCnqjNyEt11l1M4N8kqGEDAAdj/HmyvZy+yTBY14TVbjoHqETaAcDL37K/PP6CZg/L7HBAc4DG3CDGvqMpDX9558RRf+d6X8KefEaZgXi6zBg8IGEgvcfhnbEFzltKkfWZpAuHdnzzB7/+FLEn8xY+83iA8QGKFq0E0dOfUD8vNDY9eYm8aUNAcP7/QliHFvwNLEECBCd+MHtyPoLjN+JeCnIDCqQEEr2ypCqDGnMWICS0qgkRNYyyZpy/XgIV0+RSJVybCwYxcCJxY5bwmGpYJLWix3+F+LuBtfPAalmC2K2ALDlrvp22rgS3t17Duv7CEEGxJn8wP3T64QJb9lYQV/Tjl1fTECGZNdCgTNs7gLABoA4MLnWaKN+Nhj4g0L3BxBcyW0xQMmDI+y1uchUkiKDADOQNx+xwHW2EAAQAASURBVFHB9YrWHvqUApBTgLzpXJeNnBjMSTZmqhpBGUD28ALUgV3S1rN28Tg/e1BgyaSeJ7EIDbRtaoHdqqxCK82brDtgtQIE5shUHTXqLbn34pmsDKYhKxQcRMrMsOgpgoS+EvvfM4fSE/pQR05JxpNr9C9uBwVAXRLqxWSg+Uv2E6D1eldBwdc+HUABa72pud1pm4EHBQwuF8UEzXeAkMB458VT/N7338Q/+ZSCgtJSq84I6PXD4M1KmHP2kfkyrXtLMQNymk2ppQcF+2xyH7KUB8lofIkfp1JwZ3A7EX62G8OQKpQGoJlgPnEdacc6L/puFnqY5y9cVw4wtjAAvgqhqSrrmLT3vRQf3BdGZgYKbBOggmp4TLf1no94JzyArQ1JvVA4VW+vVZPlfPJ3m/SPGVRbvWDHhr7WtkpiH3tCk7U/b2nwvq9dTx7rApztn7EGAmd1j8+Vly+1x2KxkFwNzVVmJ4KCuFfD+Zjpcz0pU9+lojkYMnY2aDaLRjC3JfLQ14y2vnWOenuviClbSTYAQEAGEzbNGL3ut9l28vG3GSiwkGcEBfd7mQKCqc4CsJECBlXaKV/WEyndHu68VCIoqAfX58/sSL/ks5WHICf2nYQp+C0FBY9+YcUUDE9a/vKggMGEIJiWyBoUAv76xTP87vffxD/71Nd1b2oASddGU6VWRRme3BPjkqxri4EWmwiFangjs7GJXHeDywDvQRmpMBcm9fAFJSMRCiccRQ25xX+LAICVoJUJbV9Kke2FL5Q2z6DNPWgzhyfXdgfbLHX5a/QzMHqffm66Dry5up7RpWgNSzSK0QuLRsZCPEBlceTaqBTlWFR67UMwgC3zbIz+vLDSb6Af+yWNcVvY4UKWi2ZUdeyXjQi25VIfP71Ur2h/IgjwY6GuOKtveGAh22GSHBxYqOgaqWzCcjw3YnLeOGZGNwPiiRo4LgUoSQG2UdZAA+aM3WLUMKRJGQdAu5qjl0oG4bD380JkJ5cQhkuEDfDkQ2FAqyzOEoV7g2pzWtjCutQPpfvtCg3d5w7YM1dlC86DsZryPU1zpTbLlbLwD8gTXmNui4HWHrACL+/c2VLeWX7bsAdEeKbsrVDra2FcIuCHARSswwdS1lCvLQ8GGPDisxXz/uuAiiX+4Yun+O0/+xK++htfxy9/VJiCug5M9zXgaqgt+3trYlctIDhTfsB6UjmjYcCXDJAQAH2H/BHAwQZgD4kz2lKjzBIR7o9KXZZSlAFgHCQK7xW0QhdDCdauGLOLgnaX63fL+r1t5cK8P3qw39PQcg0N66d7OnrliV5T2nBOO6f6Zau3FOv/lylnmNOy9zNUMRK8TxKgO7ppv5u3gdYLjeMgVazKywxuDM/Ffelj2CWWOvtqme2e2b+/w47Pzo/Ps2eKTSaVVV1JUSRBNTPkJWeJwIdtJcvYpz15PY0+v/Y2ixGYahyalwBoF3Nt86wfZ6V3LIzttPdQcIG+bZRAIHBhbNCcgyQqZQwH1TYdDOQgoC1wNs9ev/tKFaXtGTXGX0hYlOQ1dnqxsH2nhgG4pK96XXWXE+4y+V9LAG3/JdxtLSiw1T6XwlmnRXWGRzW4Ym6zFdHJHOyHyS1VveagIM1BQWQKZnMljuSl6f1ggMGy2Gig7axCwDsvnmmnfh2feFVfbekvt5ZudB9ED5dw222mqc0Ahq89IIheUSyslEEf6z0A3OUkiTCZkQsjI8nfreCnR5J49MFIqSAdNnlYkXLBXSbcH2kavxsWHgMuYACWCYh32dahkwOAuiZ93PcAGL39pusmEtdvjtJfO6Om7V4rUHBNrLqHW7EWOcnqB1vrD0ZY2gewJjRZmKccRtHqZ7tZIvQuX00ARZvYaX+Dl5PV+NuyKtkhN9CPBH+ZlO0FX/eHj8puNLxWOCS+DZu3aK+0oICac2KJt++fmbpzVoCl1qU+k0k3PTJWQwGCJZraZmRF70tJkk63nMD74WNm7BsstyNxFUZwHbjJmEVV0DBkga6OY5ZD+30zq5lzYfccu8CPR4dnxjo24ECT0Q0gIAs7sIEcEGxN1mi7odOd9387wofWmgtLXF/r4is7yMJpwH4IYEgpYfdVRwk5seYCJNwfKjtFdFdMPOwVRwQDBhBMV91lBQP2Ku+sS0azOBYfmgAGX/5LFVxfpbsRpgu0n9XJg4o6AS4cg/2wZ1C1GyavVR9oe0lyCn4rJBqe5Q/dWh4UMFiioEnnvPv8KX7zu7Kk4xOvPVJFIxQPKUpOEE+DVOEcXCfGmfcWDZD97SnSadWIXJ/FWK9tfyCoWV9Wkgq2QtgLgUhWKmRibEzYSVcwkMRJN41bW5avJCemKT0aPZ7VksXIEFi2fPu57jAW1+ADaCj/KN8z5b/aMS2uTO2BgPdpEOQzQLBaDsQh1ARUAV+FePYkGwFRAjYdxb1IfNnW+u+l3QhIhHx89tkmQHXpW10GJ+CAPIlNxix4GTYW9hcG2DD30Gf9EX6JcuY2hNsrZxT3oLiCIuwOXahHACfGEkA8SSZyeYWOB7Eat6LKuMiYyJp1M62OJHScGDiS783Qb97UtMsMfxgz21An7tGwJeiGTe3qixllDdT5emmupmCUIuuYUZ2ZLRPknRLKCujSV6DmINxBAMNsUva7PQItWxA3LoubQR0GFhiex7GR5W+Qf95Su+/KK9tcT8mYt7pqpqei0zIDBBY6+JACh7jHxMww23joELX9EMbG5x61TqT5Xzy7QTgP2v0RwEdQb3L77otn+Mr3voS3bPXBDQCg5YDm5cEAg15hzIrN96fdjlA25wwUpKSxPlUuBZ3CgSjeFSM8Mz5EYeDRzrAoh02+D8f4YgUnmQgbsiS9HWL498LYkybAJcau69D3QtiTJlNtYzLVKm7aC50ZK9/HgNB9rpvTxLi/IPDgIXVevT3jUlmeEX4Yvc76t1eul+hAiQGaMIvHw1yZo1WIJxfGroaGSF4dnErCDo27FnLvCRjjt9KOYGhCP5tSs7Xxd1tutnO1vrd45BbYhgYQUPVKpF8YvhMaG0N2Hr2214Rn68y+P2f7iszuGeI01zyT7Hn6jExhxQkJXU0BIBSu1DYSgwpEwx4J2AroAChlWZKjY5aSJImmLImkRokD8xj5uPVuBAftmG0pOSjoKetokJzRuWK+RqMEyHw1w2SA1h0Nu8ZeGALgLjYpoPVLdDMwz7Lv34ES90ywZM9XOIV9PUgTPlNI/nx5HdUCsjmYbsIJyrjVcGSrs33HWn9u/dwCYwljmYMnDl3LJFyyGQBcZxJGloAAvPue7lPw2bfxxiLRcBUKM1DAOB/fBwMMZmXG1Dx9/hRv6t7Rtk0kUY0e2G5eBKHEkikXH3j5m3XkVgkkPbqcvexmJvQx6cj+MvT5TMioG5kcLALQvhEvhfX05FnykhGfQna8tocX6+lRvVXrywgEVvuOm3DFXfl6Sj/2S8OeXDDWsatX50ZzNOvfAZydFEtmIwOFESBgHuI5SAwNHQUbS98fVJevjRvmtLsErjY46o3LTLFtwauQXAP7XAGBKRdSMECT108PnT3x/2no7f6ESf+e5lasQEF9hm+tS4T4uuGkrxtOJPkDRwAIB+nugUWUbYHuXZEJmTMyiobkclhZIvJ9MGN7yfGK+zTMNm06o6xjoqj19bXz1fsHARCg1VO22qQv18hXf+5dopqPEu5vMfa4csV3XNxsx0Vavt/FVoYA1+uoGMa08OYtYOBSHs6sT1LoX9ZrLQ8ngoSeBY6lv/9G9fmWS2DO1g9fCNP9dX33QaxSvOsVvtZpeVDAYKV2rI+eTV8oUdd7J6qJIuBqDAwgCL2sk1BHscwmy4QRsAkW2YRqHOcTLyaAMcwD0O+Jhteo9iDhQGpftLSFXc62aqiAEWHaxDp7oZIBgUjF+RIbb/Oo7JbGue/LxYBeymw+u+9MYFbC5RvLBCE3AT8mIZ4trPXfdWfAdtvZSquabjhTeLHPbQMg8yZi6ECUWg0dGIDL5mkkVXzemANUdoBZgYHOLgcHJViUzmBf4/JcW669l7EPRMIaGCiA/k1Z/26glGEvyDpIX4hjpzGwQzw5Yll1QDl5aI4OYEu52fRotU1wLJe3sG7B8oohyDP5CDrEuyN24aQeU2/R9BV4Cgr6e4zXd9+7ZyzfDZBkV04GUHIFC/tGzTtEzrZmvlVHzV6QFN9XEVk1+U71XRV0Xf6Ni4e1mwiBmHJb0TAKCkz7m/X2Iqdaj8q+As9ePMWXv/sY33D7hebmZrpOhilW5bQ8GGAwlYXw+Vnz6snXAVaXmetAESWZJAuAEAc/B09hVvpJNZtoqxjv2LYg8CzkNTM0/knKHsgN7W1qh+6wdmxqyEr7NjVbpjh7QUyMZV67Cc2MhrPY2AoYnbW5tr2WaqJI63vh4kW51oZFb6BXuomqEjyYVRkAtCXcMXxDoAMMTvmlXsoz6/ve22mWMRkYcCBhtLb6cwYIygEqBZL5fQgQMICg4EBb7p+nNP/LDsC8wePtHRBUGO2gQAEBUwKxbCqMdMixtCGlDCJC0pyOxLI0l6Dv3WD4PhYemsvt9uP9eL3vl14BAzsQwcC1rOJZYb7kLS70VXe4Yd4uPVPP8lnD1YM2nWUODLMsj2TOvlEY47oXWMm9Wx0Vc5amO2bSyArMYvbmmV+vk6XEHBfXF8FWzBiFvtgRa9OmzpUBApDsU/Dl75hTO7dfIIeoXke79zWAIZYHAwxioe5z8z7qVz9ZlSFQlSAlAId6JIRMWZR/AAhxoC2j9IwAjQPeTzod71DXoJB7BeRCoLAW9qIofYmJhj/cK2XUddjQOB0DzNkFzgQSGOOE9bH63C4UQGhBQKKq1FpgMG/7bIxi4cXnIZ4Xz1vM9BnYubZEb8CeVwUdrvg2314V2MxT4hrmmdGqAJpYrJWYcDlbdun5ATQHBBY22M4AAVfGoAKCo2UL2BLyxLvmIyxiO8kF4Nnbk7pCZ+9bDrkJKef6AicQLHzAlORzKTKuxhjwJgkFChCQNuSU62uNS31lcykjQGAWGbubjFds8my81q/JnocJoszMKOuzVRrnnVs/Xpr78bD3ugH38NutsmphT5ODqJcsTwpcdZOAhVRlSm9mYxDbUutedZPXawK6rJ/jEr/ohbvO8ra5x2SdOO80Baua0+kx+9j2xHNGoS/9WOdUE4Mz6QuRvv0Yb3/2X+CNj34cdNy3ddTJx/ZyFLJXctVxWQGEVXmQwMDKFBQwq/Lr6dJDOpQKQEn20ScCKVBQh9yNky8rW/R0FO45GGCvQ1OfoIGom5QcJiXpBEiqLO9SAitgOHRyVrCAChaCUJrHW2ntVuxnyZO9Mpu+S34mbNZWMxxR01oFegXmE14T3ciMA+Kdp38BG6vbAUGtV73W4rIxBG9KzB1tRA9i8v51pVWtno2isIp3ytpA2Myo9MrO8gg2ugAIDAyUQ4753DsqCOAiRp6VJC7KKnh9u4kff7vUrd7AjtRuLFXWFQOQ+Z4EDFBWEEAZoF3mRslAyhIWSfkUIOxEvnRRwkK62qBQY6gGGvzKsbJmxPFayc4l2rqBTy81jetFM0Pf/22MYjFQOJFXYC6zUV5NVkGDXvIVJIif8YHqpssgoNNJnf5d6l5rp7bPHEnStpoz6bZC5T06FX3px3uzsAaAp8+fiP36zFv42x/9BGCAPoICPgCyUEKSpyk4MFBwLSCw8uCAQZzsU1AQOnZMtCrqkVCgLJMonTDwAMJOg/N6mPBbXaKBnCZ8dXFeAAN966gvUKyiTJLXGykjUwKnjLskdTbfrzCp4kOTt9B0Qd+fYdL2ysyFMLbPjZAJ3dH27yp2PTy4xpV9nz9rY/ytV0bh2n65463C0Xhf3d8S8w+AavC9Xyn08+WNgYZHW1P0+xkjczFkYGxAOcSAR0BQJBsf+w6wvGwLRwceAAEJE1A3MATXAIQOEDQMgo1tSvV4yj63OW8OErBt4hmlAuZDwAKzbuBTQJwGgHCn9yqWo6PycCR4/s4RwB2Am8aq36jpEgAwOQJGNu19QNrJtaMn3Bj9IJcULfJLyKzrJPkCVkYnqyGVsaiAK9LxH4huio6LtT3q3XJcrXP7tjkosPwWd1bmtoJDHc9sBVDHLGudn/z4B3j8nTfxjU9/DW985GPAcd+NDYCYcEoMogJOG1bg4FqQ8OCAAXACCs6Mcbha+jr5pCY2FiG5UJtqW8XzGn82MgPlaJ8/o3CHWG+4l9RQP1ThcyOpVCul7G0go1pdMCOaH43e2I4R3PRIu09iiwCsgf4xfr0soX0mfPqdIpXcC2i8JvSPAalmqG4MLbTXJt2dUbRP7L9VPghQFZ6BNTl24bm3KL8ICGxulaMFBODKGgRAwMeu/Lr85kDAmAOgggUA3LAHnTI9TsBBv6U2peqAG2AwEABUEGBA4djBeZPvKpfYuAIE64eUlXUoIHnblNQ/MAhZQy8G3AQ0x2RfA3vXjxHCOHlT9GCTOxDO1U6c9+UtZVLRweOfOkVRL3bM5aCLrpRbYNRLaWsdGU0Udb1EinBfWi8d53qpHHM9G/uneVodnSa3JepZSgJIzaHUOdYyCXKvc1sRnl12PP3xEzz+3m/iG7/xJ/jbH/6YyCXCmDRX2/tYhQcjLheZg6fPn84ro+VBAQPr4CUoaIzy0Uz4iBRlkkpX0sF10KPRKTuMvpkWi+91YQJP+uqNp01mLAR0KZCtQfTJijqBDRjMMrq5M6Ly2bzh8MwFszEImhmck3g1gDZmbY9Vo3EaVwZNhRQ9WNB2cByj2Mb3WwKwoHDvJuyRqlLg5m8dzcaL6Id44knOAdrezq+eIbAwgZ93jICgHGJ0e0Cg17N5W4Abfo/7RpBwKcfgHqFf4OwBEYFxX4GDglsuAhKIE7gkIGm78ib16wECq7yCq2FIWQA+BQZB50pKG8yblevg1HczPoux6cdn/BsMz7X0/AdR+pBkzwCcOiM/H/l1vaT6iPrfer10hU5q2zdpWzkutAvetrGMuhVBtzpopb0BPcylYZzdMTlzRgKD8/T//X/GF//st/DNT30Vb3z4Y42tGuwUo9oqZAEHKg4zcADUnIVX8dqyOg8KGABx9YFmbzpK7CZRP4EAOYcSiA+ASgsQWD7/4L135T7HfZ2svcGZofOZN90nfrny5WmsF8CoeDsaNsZiRUAnxjOChhuAQW3TWtCIy7reRdPuFgrQFUrK0q7QnjRpj1BmKqQTNC9NCWCuX3cfBXWplK/14AIrofdtckLi8wKIcP+ZqDE2bRUmBuUl55aNDx8CDLDf+/lsYGEGCI5jBAKzfJErkg/1/cPN+Z5PYICaCJxNHnkACGTnFlV42seUWVcqSDhBNGQFCKAd4M2NENHe7IvQhKYQwlGzsXG9MTH2nSd67f4Q87IAtKfzt9VBUoc1EGiclJX+KaUyRRN54Vgnk2FgopdSI6e9I+MO1wekk071KbDWqV29EfQPpyzzWNkCB6RpU+evApxGB53ZCi0CCv453viogYIzO2UGP0k7AU2YLzXnIEwTW7L/1ufexv/mX/7BMIZWHgwwIAhT8KUICiaKtCrRCWIG4MtASLYejgDhyXs/xBe//zvyczkCMp6g5wlVN2UI+JgqbbZzQqy3ifPOSkpOv1pcLxpZ6G8DcJAfvN1DmdBtp4qjr69e11DSTb0rvdxTx9YOjmAhbyKoZUel8QLoSbka4iCU3CgaRdtNO9u+HeKNvTLsf++EvgUJaTivehAngGV47mh45go/KBQLITCD9/s6XreCgggIJkCV+/5YlaMEilW8fhyl6T9OGXQc8Bf/pOzAkhLA+w7aIHNnvwe2Owc7tN2BwaCjKK1bAQKM7h0MU+z3sKjsdCzm4+GXTpR+vc/53Omfy8Pv5/O3eXbUb1NGryadWh/ySvdwCC35/UIxBigYV0mASU2uSA/8P2h9xPa7OSSxHcBFPUpdfbnXPWqcBXxqqCqlBiAgOJQAtH3zMFtkdb75qX8qOQWz0If3wVHBrWXDEAlYYQUmTh3I36fPn+Hxt+uOv2flwQCDFhQ8AoxKDOCAopFeomcEJcIOEJ785F184S9/D9/8tT/Gr/yr/1SFCVii+Q6xN2vFY6w3ULvMSu2qwub9fkrrSnPWcVzPKQDESAZjOwUOQIOSh9Kj6+j5z5RGNCx2faAeV0uPkHP1/oJQUkqAJZ3lTYBTEiEVClmT0JwOFpZH2lKCF3KE9p3EwWcKfaL4B80cf5uBhAjdZ4qPJr+v6tbUqaNFJx6Tz68A4tAry2vK4lzu58iq6DxzunV2/7PljPGZRbMAZLMCwHTifu/zAuUIAN/AR2By3JN7yb6Pn8/mRv/78IzzuTEye1fO36ZuIzPgSaezkJKHlq4IJ3X1ZEDCQu6QVH0T9VEDGICWaZj2U2hfcDSmhj/qI+2Pi3pTGiHANDop250c5wJhnFTvFMjcSpuAWwuTAA3jDEBAwrS0+uaND3/sco5WBNYGDjgBVEBMElKg7PkGT148xeNvf8l3/F3xo1YeDDAYQUHHFgBj504oNTl+AJZVRglP/u1f4/P/ze/jW7/6jwXJATLZKKG+5HwVl+8AyCIBjI+9ZoMfe/XgDCSceW9dcVo2xG/72O0AHPQ3L1E44zOi998DlRu8y6a+9jx7vgIXIgK2u8YrxL6Dtk2UyRBj3iAbUnHLInCIV9IFkZh4I/Z98AD7c4BhmVNz68b7OTcEUw+xudmC3ehAworZuWV5oVRNWRtArjdPRF4AUSl4O3Z6r8k8C2MPG/tbirWnOkgyV7JuIs4BGM5YnFgHK2cM0gUwcPM86OZAZcD6c8rci24e0IKBFVCEOSIx6XSmd/b7KtMxfDTTPfahYwxExrOGiHIF/9bm96uHtN09eFmGv2IxsGrzzgCNGfntTkDottVn5k1GZYPOLQg4sJBVA0htLM9thZV21VoH+pvzAzggYzGUvSCCsdVPXryLx9990+3jJVBgzXoQ5e2eHolsAYKgzv42oKBFZE9+8kN8/l//Q3zrP/kjvPHhX/IJZnkLrly63p5SeT1TEAQUxigoKGATVGUQGsE02v6CwZ0qXUrVEJugAg1wWJaoBHuh6wBAU7dwXZ+0RP1zk1K7SZN2bJJnnfB5k/vlrSah6fWk93NDEAXTWARetLFX8Pp9lqjUbPgTqVUOm+Ze4TXHdvdL9qwtQG9IgAZUxDrHMwywHUfXtnhSDbswEJKWAM/sh8aEuSi1v4GTzWUBBfJGMe0vU1YnCrhvOwKQjeyRg9hY136VQgS5Q5cUYVyzvgqLSEC/dtU8dfikH/We9fZlOOY5E7P6rMbdPgc6PYKYCmDCHA736Nvs504AQR8ywH4/MgTHDj7uq4zv963O6dq9lGmro7abKDV7U7CNH1ABQ9c/0zJjLRag5Vod2eicI8yrTV80bfWM13ER/b3J9rJU9hYcBG/+alth9Z86rNE22W+pggCC/yWWV8A/efEOHn/vK3j7M2812yiHoM20PBhg8MhfKBGVuJaVYuxL4xEUPPl3/x0+/6//Ab71K38ooGBCHy73s5oNcnwGS2iDYai3KOKtIMHjfb1w2nETDNsdLEz+aGhsy9DGCM9AQ/wej1mZIPYVCODjmNZrKPvhdaVENTmJa+yXUwr9DXCxzTw2EHa5j8WYYQrW6hhjcCaYygjVxjTtGr3v1vMuk6TKGMNtY7AXQBtkrMyjkgMa14zn9QDiTHG2LWoLpTb6pUqM9FlcCoiq1y/fVfHkO7DFPbGJYs5o6WTzzE435A85JREIaP2mYMDr3u5vMITDJv0yy6CfluiF6ncBSTbW7Tg358bz+tKNdRxnz50BwEcXc28ALlXjcss8vgUUGEtw3AtzYMY25jyp3jmV7b32d5RtTPSPG2Q7PumzoW87Y7/SP3Iqh8tG/Wi60YFo3gDd/4K3O8372lv5SCy/U5F3bTA7dd947VIp1MTkK2yFfV+BAvt8Qf4B4Mnzp/jin/82vvHpP8Ujy7mjdoXCqjwYYDBFPzGMAABXZwEDT/7t3+Dz/+Y/D6AgoDO7d/RoX7ZcAi2HLTkbQQEXARI24fmIbe1RPAG4DwKRQLrJyMxrv1gWXoPVST5HpmA+FSmTClsCkMBlR7rbJBUkAXTs8Gl61D0azIAJAcAeY45eYvS0iTtwsMgPuAQKmuV9kxwLb/dJ9natVIjHTgygfJwYEmBMKG3uO/nOxc/jgurlJ/P6NR6fquHjUkAZ83BQOYAcwVqYb2dzOtSNUvQQq4G/ph9WuTHTzZJi6erWeP0LANDHrf26ps2Xx7nP1B9Yj5TBLMapzmGrs+gen8fevslzI7i1U8Mc9raaI2I6xKh40zkTUFDud2//XO+EZ6psS1WTOwHRUWlk8gPWO6t6ybNrvWTOHGLPjx2ETXP3DuCgupvdrIgwXa738vqZ4T8BBVeVgicvfoQvfv+38Y3f+BO88eonzlbbTsuDAQZSKm0/38FKJkBTqKPmuFRQ8Hf+DwIKLj72BnBgsVkgrNW9beB7UMAHO2peCqlv73uoJ36AMjUCK9ULxrTfjKarg3/uvIeZYM4y1okS+OBBgfAhQuqurSlG//w+BPGaciUoWCWHXpOkBYzesi+xnK0oMQV27BrDrL+zZvSv3kEg4xj6rH/vQbaPrdKlCcChRWZ3L28zT/IUwKwAADAa1P5eEWx0c3bFFsyW0tZwnxn+xdjKCfL3hvGNcethb4btrj4b+yk4+LmW3jMNn6sRHkHBdEXK0VBTzW0piRMg+kbDs+9D38zqcQpYdmUr6jsRARygMxAwvVkat0u+tvRz5gxcds+sn8f58OS9v8YXvv/38I1f/6rs4wNzjLS9MQF3UR4QMLi2Uy3OLEs9RPrMWLOEDwwUfOSX7aLL95w+hzvDL8+TJVIaA9SlL8wJKKQCk1RxsKzlZvWI9T6UM7jsYw+YIMySggqqcs2m1MwIV6/dhFau2RugMN5zTtN5F2TyOk0z0GFMRkXuFk5oJ380Fq1ynBmIni0A0CVzAXVMuV57gsyH3f9CXsXVCaK98bT6mAL1nI/7RQKUAgQ9tybcjX0b8xOqX69lUH71jJg018fVHbzN2IPmdryWmF5WLnn+YUz99qskTvRagCVzvM8TiOWE/ZmNqzTv8tgi5vIomJvtzSCAAE3SmlPW+cTIXJjLjBasMWmYbjfDEIC4tcEdFatL1TlCs0N+s702S3EZX8k3UGU8lqpvdO4lOgFxo55ZAZMzHQhAgTRP6zTmRmQgZ9gumg6+iRyoC+tBChDCnNWVUD5OZzZiWTT7J+qmmJDaPw+SE/eF7/8uvvmpr+KRgoJ5YZzZzAcEDHA95WKC02T4Jk00DDkFcrJfM13StMpwtvvrvSs4yAAIurJE6qEJLk5cWHO6z/YbH0K3G60HFDEuWZGz0cOL4l46l8FrN0Vx+gY874I42Vfnr+sRwxp2PwcFKSxHTMlXKJiQkm6LK8K7VUHd7pqNkDgCij4DHAhzhtt54Wvf5sIzKKgeFOzhDWh9LDT2ASXVtTJmcaMf2jYFahm837sR8XmRJ+Jr8enQB/ogue2VuQnEBch3jeKi2FdYgIgbytzYT8ZIwc2lug/1NECgbwuhnCUsP9MTF0CBx9yBGlK4NK4ARNBTjVnHvRl090bKJnMrj5lc/6xXK2gJBoTt0iLHmVTX7PfiiJj6TwWETcEEy2q8/V7n4qbe5g4Loxs4qLpi7hh47fscC/98G/txNShYFdMzWV+Op4wppSThS8sxyJvomm0D5TtJdM6brEqw3/KmuiZVPUN5BAVn9oELpg6kO60FDTioV3bPkXnx5Cc/whf+4ndkx8RJ+CB0ZHe/sTwcYNApg6ZQAtvO52aRoUaAGEDBk5/8SFYf/Mof4Y0P/2Jzrfyl6d/2nfFdlQzpEYTK9xUKJAo+J8lkLS8BDrhISP04wEU22uBCYNKwgnmMQVgiSjbFFT329m+XsNj3R9NQjUXb18ZIBOVfeFAGs4RIouTGHimLQJrQKiiwJYtQQW5AwWzTox7Y1Qpo5dgFJoIDVoMilDLkmcfI1ngbozdpeSG9hx3P1w1+qAA1scKYBJoaEQBDwl1sP08NycJ7WVDTDTW+WFbFUfVsc8NAXJYGnXtl5x8XVOly9UFbV/Z6KiunsscowKbLWff7GqEq0UOeg71+RZA8qiwBuC1ZJ3sGJQEflui5KsEzdYC3GstL81mNiuyxY31wVF1DyhoAIYwBX6kkDIO+e4OS5xtQ3ur7CGDyfi7ntXrX6ZO2mD7pz63MJ4DKhAJKm9szW70XAYE7I3mreucWUJBy3YHVwkRxvFZtRNTn0YH0itbvsT+iDFNlKlpQ8MlxS2l/qIa9uZwRBg8IGACuIJp4Z6BrfN/8CA4S8PTFj2Sfgr/7R7I3td2rV0qhowfaCMDUazYAoTtSOUAgUg+RXGngTpCsTThKGdjVYMYJt92BbBOXLPvjy452CdjQxABXI3wRCFj7orG+spA+s/ekelVB3TMcHHTriSMgiJuNmJCCErBtYhBT3HI1LGVbCYoVA2yqUJkEtAG5KlPztJQC5v1e48SytI+PUpd/9mWWVW31KUWmZFc/XlDybVZ+BAXRe+kMyJnCOotbD3HQ2o6LO0PiRPfMQj21gafnTp8Zk0fjEmG2/BsFCeUQJ8HGM4C9hlZvHjFpVw8KJmM6DfGQAfGQaBnndM98GcgN238PNPW0X3Q+ax+x74RJAnjuXgHtu1DrKQH7fdU3pdSwhjk3xyF6Z7I0mbZW1qdz9gb94U2wMTUnZkg4lOOcDMjFcNmkDmFFCyUN46TUAoKgV2hTYKB/DSRg28CUW5bAcoEigDuzC76KgUeAkHIHBsLeBNIhft4SFPi1ARQZSL8iJ+7hAIOLWcE6iTrm4Ml7P8IX/vL38K1f/Sd18yKjePz6qkBHQNAq13HNuW2Tieq1qOKiVDS+mcF0AHyASD1k3cuA8iYeat5A9ma5Q9bO8r6LoOp5MXsY6D33rktO1hpLtYPnGhXcku6cPAMY48+T+0zXrselapRahiCGGbaK1tnBwBXGMJYI2AJA4EQyJgXVmECN3X4PywcxkCBADXUuqrdISgS4IenrMAvbGDiy/piFT6Kisj7ovZcGKOl8pfH11TcVp+gx8e70ljNjunrOiiG4oS4yhqxGPMgYm4xpDkFg6cw4NjXtafUi85MDo1DPTadjStFg+LjV+SlgIMStb6Sox7wZ65MCIHcgiSpIIt0UbBNGwQxj1DdUVL/o5j6yPe8i3wLXy/pVxfaKAdocDvtsKwoiQMGV+k7HwsfGnI7ASp4BAkRAcEHGgPmcF9kwYw+Y8+rz0O+BADgNFLS26JQpuAXwd+XhAINZsQ5qDH3yLN+nz3+IL/zF7+Kbv/7P8OgjHxeajD2Q1pRBCMOrNptnTQbDh8Cz3VXp8AGkTTxTRehMuglSEqNPFu/UjY4obIBEW1iTfBy6e1kVJDqJ+w2brUQvFEC/W6L86ZaXfVAlek8BjFA0ggYQdK/yqXccqDw/HlH7rM7MIuzLN7MlZw+akE9gdXi/r16SCdxBHpc1cICcRhalB2IdY0La/sazvKSsUvAy4zzVPvFX3Pp8ViV247CdzPQr7tUxBi/97AoM2FYjcTBkzEA6WhlTz5vKAWyoGzzpfZ0Jguxux1k2d2JbOUAGOMrVYyqx6yz0tIGC3vhE5sdAHlXjM3qjJwbAnBD9bGyJgIQsBj7JZmGk6+/Tdtfsc0B3RUFCzb8g62tLRL1xB83TEnNWJqs/HITMdlUFLuu7qFt0PHpG8uqQQSNjwuw0tuBEP3IAswB0xVj14tnG5YItevITtV+f+j/i0WufDM7PxKm9sTxsYGCl6RwZjCcvfijrPH/9q3jjo59weoVX+1lHQQT8pRoRyU0Hpi+qvFgFFFxUQMWrsa2SmTKQCpgPUNrkTVoGCI7KFMQdy+zd47PlctMyM/yBppqtJ5evL8cgLEvsr7BEb/WmyHIVGLjBM46ArRQHCb6xTwlxewv5BE+TlAIGJRkfIjDJdrzNCoWgWENnartbQHCzwqLs97DvvrNc6Bt7pTAD8tLGQG5YV9Ruac31apvievh2sLhyXM6e7VPUnklAoizTAJAWOlNQgKJMEh/wt3AWA3wEbISUM3gXWt3HU8fYx5OLAD7b+dFi9O9nPA34zSjqmQFCu3vg6ZwGnEkRBixPQAK7M8JcQMlyENqluk1+RTlGIPx+SgAXDjiAlv00/abPXC0bnZbZdvCm38I7WPqxAGXVNSFc4PIVwEAA2nw2Jlp3QOeMhnrYHRKzJfmiLZIlib+Lb/z6V+uOv72T+j7KgwEGMcnpNHuZEp48f4bHf/Zb+Manvyb0SzMwi4ke2QFg9L703he9rypR+icoMGagTEBCKXIsm0AfdStlZQkMFMw23JHHqMDN1pJ3+QZnO8pd3EDmxnIxM50IxdmaIISDBxU845WgLmlXVMCWFBQYOGNJTJPY6rEM+TQ5ITFGm7mOb9xGOvZBH0oxA2KK61KMU8/vAQEnPa5g4FAgcLAoIyXdqw1hHuZtaQ7Il7Nk8mugwZkpKZMf5XkhiRa1z4gEINixrNRspgzKWVpeDqDsFSAYg1DSCPhokuNzREavAoRh10erXwQD0RDFHJkrWJ8KdA0c5Jea1zAWxfUcC7jpX+lbDj0uORmSaFhcNuLovd8VKav6Cpk7hg+ifkM8p9Nz8bdBV0UW0nRIH5akrt9nYGDmjGjtz+Y2+X8MZoKFEwwk2FzilE5t0ZP3fij7FPzGn8hbhLV9167aAQzMrmv7YIAB0IKDerBVVU+eP5UXSnz2LTx69fW63jcMzDRu2iHBOlFELQXnq3pjwLzvdYYkghiYnB1O+Jaz5QDzncbRDkf5rAYKmwAH8QaOOboHqmDNOmy2dlw9dADVS+/av47Xz55y7lGECGW4TVR61dCfxsp7hTkZl742MgzZH+NndN4mlaIx5uMiQCDbQTCwOb5WfqsK1RIWa2WCF2NKrEuybFmCu2pA8t0EEGwoaMFAYSGXS+GGMShhfQHzaOx6abhW/UQvf2ZAZ+XSs4hIx0oSM5MzBjLaKcnbGo4IEtKGlDYFBx1AUFDAdIDK/Zw90ERFSbSrMXeE5NBmPHvW7YJXOgUEKcPZgY71eV/zugEKWzg2yc2wnISKHKsHH598d2lUr9ALIXmUumcZewGg1XHhumazrf5RQc8NLGSf3DkBZc4OWy5ByohjcJ2+tzkqB4js1fAKEjhV8JWyO6t9efKeMt2f/pq8EOkl8gnmm/+15UEBg6ZM4ixPnj/D4+98GW9/7hsVaXFRzzAMDE86uUfpq8nBoxcGTAQXo9cDIqS0iQeU5SouR33pkqFmlmMcjjEX4K4Vamvfuo/aSeVx+x4MTGJnp1nk15QZ/drUzXopPvM6ENALauyBKCMtJQ01MCK0SV+8UwHCAaIDnI5TgEAsOyLysfvOgXTUbWSXQG3mzXRxZ45hg7y5AXFmQP8WkAIBAQEHAhgoAgSKAgCbq8x1jnp/vQxL3DRsFSd4+fumACVlasrCNCIB2qkowaUgIQMoSX4TgJA7BoF8PPlIQNLfKEnM3ZeJbhUMRFZu1uyQ/e5gIKUA7qLnOQEEVD9HQ2Q+3qUQUENseuWoC7foQPS5GXFFh984sA99uSTHl0rMEWDd7jkaxWZlCVodF+t0pZ5rdFzPPsacnHgs6PqCl9fzhep4JB8H0TcOECBOZww1WHny4h188c9+S9598AtvjPltzSwM/WLn3RD2eVDAYP0aVRKmQF/NHN9HTZbcEQYGfWxnGiqg6SSJFC3CsUWN9fY0UKKkRiqlDaRggdVQERfPIyDbnSwgfe4Fa1VWBr9hR4Jn3p0bFcE1NNY1SDWWAQ13oZoekAHtONh5M2BAgamLYlX7XkGC9j0UEDiboEzOEO4pO6A5IQ4SlMEhoLIHsXQhm365GkellZIwBMGA2HcGYZ8AAmcLtF8OBQKHdhADOGybWWYcMFbjpuH6uRabChnkijYncno2g0EEZGIQK0jgqowrQCBspuzLARypAr6UQce95vdcGMvVro+WI6OVvjSW5zkhuQEDhdHoGOBkblt10Bood0L0xBEsAEAwxDEmfkO5WR9MQIHlRqBPolwBiOWDWt3VM48rRuCs73s9s350fWVRUjtBRANIEObL9EBuQg2AOrXf+4ow3a89GnTh5MkqG8n717ua0sXxfFDAwIt7kUAEBV//3Nv45GuPGmFyoTHkBgDUd3VrjABgPwEEzXc/r96Tm7vKEaNEASCR0qRKi5rBykQAbcgWdkQLFtCBguFV07F/7Mm9wY95GsEb79vfeOWxUWel0xXXbnzmzynNnykAmMXLZ3HrWT2qFypVTQ1IEBbBY9YOEDQnxLzOssFYHYnR3qmCEzUz2553CN1QqgakpzcXgKAA2EsHCApwgFGKhRAqGNgLOwjgAhzMFSwAiNvQHld6GvmMzuzKy9xT9tpg2TuU5DdKAhZ2ImyJsCtISEnYhSMRskSEGoCwJQHdSJKhT8e9AoTcjmWRxLwKEm4bxyZHJqwwOGMHotNhBsnCQAMwuHFuA3V+94ABMBGtRmq2knZWhnrcqA8qzV5Biku2Mhi+j0FkNhD0nPzYPiOyjUCj52ZhgUMVSZ+H04TftFrnOt2MPXvb2PQJM4gR9tVRAGt+aWeLJPz9Zbz9ubfxxmtv+POiLm6a3HzWnqQEdyCvKA8GGMxjLRUUvPW5t/GJVx8tk5sYLUgY7g8ME2OWxGWeWYzdRgO2pv4UVaIKKpUKGFZgQSjUTelTq38nWMtyYvC5/l0xIfL38lPqk6y9Qdl3XT3TQz22nT27j/GVrvUreSCScQz8iKP4GLOuIEFj1gCQQ6gnhHkcqBVNNgzAQDyd0kgeAw2QXVGcs6RCM/j7hCE4SssOMAP7URwM2DVcWIGBdNIcHMz7DwBsY7n79SnLcum+dk9KBBQx7PeoQJkSIYMFFLCABNmrQDcfZ0Yh2b0kMgilQMFBBxBsLCMjxBwA30uMo4EBM0SWzR7G0jxUCwFFMDDLCfHn4ca5rVUDojHmJqwZyy3yKJ9HY7kqEZjEZ/f6DcjK2l0AEBeeNNNzxVMsFiBgosevDU0WrWvhFiTMAEJG7dcS2vb0+TM8/vaXnOmuWRd67qLZ0emi0EsgkvlMERqO5cEAg6ZooyMo+OSrj4LR6EoH92aebG94DBSYwvWJVepEMk/t6hiuTyz2r0RcET4RqNSkq5S4pb/DbaqwzQd/alzDbyvGg7trz5SEPL9+Ts3xURENaHtRZki9b89KeFfF+u9QJWn0auIRJEwT27DIB5ntj+AV01rOcj00+WwW74yA4GBgZx5CBgezMwQREOyHMAN7AAMVHNT+Kt3kLCeu6T0EsN5azu4JAEe8p4LkXQ9lYuzKBuxE2IsxBsKCOEAI7cmkwEABwgFgYxnLCBA8B4E3ZYR0HA3wWScB4xgCmNHVq7i1gQFzNGZgoPDcwbh2XltxvdLJXjWbt8td0xWhQmfyNwMfsU4VyJhThEbP+TV+PzGjK8JqyiJirbcjALhJd7PpkFF3y7bUAFgmoDMGBGRmHMEdNbF458VTvGn2S0HB6ACNfWnHB3BACbgyJPSwgEGYGTNQMPPWAR3IMLCF0VqpMAh2bU/tuXCDm2M2qYrehyHx3ChER5hlcSd1IkK2rYoBQZwIk650DINeZ+jUSq+zZyGAFbvBfRtCO4A2Nr0qEZzU9hiCbQFNvYbD5+WtO29laJa341JJPBfoQlVZ9YltM5BAaQOhZnPHpC62NeV9ZUMjpwmWJx6lGZO9jIAACgB2LlNAsAdGwZS+GWubI+VK6tEuSFeEE172nknnREqSXJkKJHRAskvlwcIiMMMBwkbiZh6q+FOCA4QNoqcFLHSMUAR7H8QY2meMS0cHMHCrkTopKVQxRJlbQN4MWdumS/JVwpdeJ8g1l/VC1hCR6DitN0TOYmjPrnF5tJNv0HW9gzPr26irrT236GtrR2IoAJCKyH4Y7FRChhiaI7AHVud3nz/Fm999jLc+K/brUsKpt9f6j2fgwCuKJz9+Ol4cysMBBiGG9OT5M88p+EQABTNkG24AoHbsjJmaG80RFFi8tnCdZLOYrt3Tir3GJCxi82bFeKocj5OQO7bgslHtddvM8B8nde7p5lXMeIwRw2PE8fdZ26w9TVnZHT792iiraT3j8xQYjspJvNYeJNj6+R0W6iGAsuSCeJWt706SpWIctI97cqU5+1UGlkfgKxF0/PajCDDQa/ajOENgQCECAvNOrfSswTXlaqN/4z0TyJW8sTiakQtmwsGMLZs8EracwDvLy8X0uzAjAJOwBwzLOahgz5MUgQr2EMYwJr71pRk/YBjD0lLVTSjyAtN4yQDPSk6EI3wf7P9K1y1kKRpI0wnAuV6Ivw/1c9mf6wXTCb2uA6psIlxzSdfZ5x7URF0X27bW0fWVUfcBZGWq4SwiYbAMIEDrm0E4iJFZxsbAgScjAnj646f4yvce408/Mw9/X2vHDBzUd0gJOH3y4x/g8XffxGt4bd5heEjAAFBQ8BSPv/0lfP1zb+P112qnRqM+6057YQ1z6+H25zx78bS5H+sNS2AKTKB7UFC9NTSxXbu3lT5W68haY6rAXIjkOGHmoccyQ/R9EpocQxN/tu8A3NO0cmZEkht8qy80NlzbN2tbBA3Wtr5PZmUmMMdJ/QBgP07QfwQKpSa2kXqZGeK5JrLfxbDsqB5OTS7NYtQW1XfPgFvwGQ1JBKJ9YiGzjO+1LIEBAnm2gd4ADi64pdcmp52Va55hdRKAoMr0YAcIiUnaS8CWFATpHDm4sgc5kfervHNVQEeGgIuiZFvW4TpimM7i3S85frP4dWQYbRxnQODMa10VPsZKXis3/cqUWXJqn5fSh6OA6/SC1KsaMNMNbR5JPG90kqxco+9i21agZqaXY7E8F6DqZWZ2hmBLkgy8JfI5BUChH6EQIzHp9vwVHADAV773GF/7tDAF/eMv5nT5BeRzNJ779PlTWbL/2bfwB9/6R6u7PCBgEEDB2woKoqdlxYWuK+p8KDgwJKp+gn5/9uIpfut7X2rv1aF4VwZ6kPX6CApm8V3gLBFLfoiCA2AwrLPfzspKEKKQRwMC1Jhn9TTr/SKy7eVTjCnpb7qczLxrsCuGmVKwpLOdrB/I++RsVdQsnHaWDb+DQz9yAxYiUDhKyybsk7X0NTbaJnbNwiZNnfXvYFR8LrdepRsSrizBWS5BD+gulVsN/1mM2kpvLD4IcAFU5U6wuC0AVc77wUAu4KNlDwoCQEikiWIB6IEDuJNy7dhJncbxKwEIrCjs3oMFMHjoq2JzOL4eyuVkcm2UkzNDOdMN0l4eGCc51j5npR98w1WQ6wY5LvphphusnS+r76ytq4Tbs6RYeTYAcAAEsqR7SyTjVQQcMKiGE1g8ed0hxfskJlQCwJ98+uv45Kuvy307GwS086wvMfeioGUNnrp9/DresG2UF+XBAIMeFMRxbbz7cMwKkSmICg7knHrSOwoKvvrpr+Nz//WvTpHkLPnmKAGhqrCthKyP8/bFmARL9Kqe+Bw4WIlCE6nFXgh6L7IX8hUoOKOPPT48EX4DC/b9SNTEjS25jIiwo0Xm0u62Qf1yuZUCPdOru91bz6lggZ0mlGcrUNAJ04YdKlAgUyCIMVHrm/H5s5ioHbJ53OeuGEtg3pCBAld2HQDtSyJTUBMX40K5JqdguKYzrbeEH+LzVvmOXHSZotB/QtkyFBwA0Ndj5yTjYwCBuCYoEmpGv61UOXvumBQ2jtstQGAGAq7pJqL5vM/lXDZmLKEcXzsKUR/8PPWDnRedicg+Aq3OW+k7YK7zgMu61+ubzFArYAlzTUApIatOOMCgIitmSqhXNPgIx2ySffLVR93xcB6svm39gcrIsv9QEceT55LIKEseH8lZJ6L7YIBBXNLhfcUjqpqhLNUfU3AACCj4yve+hD/59NfxcR00IhoGzV+6o/eZPssmJtbCBsxpXbkvuRbqlaRnh4fJnUBNaKKlifUZ4ZkR6Y+Cz7Vepkj8HqNAxfpl3X2uJpIRUgI2Skik9HypSkH2CxdqWGhDFfKDJZtX15hGivGatfH9Kctd3sN5DkT4eqAAaLgBMQYackGuMKizJFCr2syo9KAgKnhAPS7NNwCEfk+JlE6v8+GSsV+tQLiGLbDSzut63SXF3NdhFaaycjAjF6jyruAgszA/FlqwWLB0V5v0ZitVLpXZioEGBMitrwIC0VBfKtEQmi7ry0w2+mdcAgR7KVMwsHNx/QCYnmgp/DP9kBMBRwVcogOocxx0boZjcq5eE8Z8AJ6dDu31Xl+/qc7VevXhqwMVHGzaXzPkOFuRhe7YpSl2BgoQfkvd93d//BRf/q7k3PkLl0LmzKw8GGAQQQGj87zeRzFQ8LVPf11iPuE3QvW2lCkSYGH4gGTS80FO61Gigae6loqz47VUYbHDqfMMeiU/E4AeCMTnGxiIgm5r5IFe+ONz6+ecCIWrcAlIkBhboSLryVmPJcaGhL0UFXZqUPk1CvqMOQjYTc7F2quwsgMORABhFbJ0UhPeuD8wxD6zXkdAAAyA3exSa3plYAd6QCD1bq8lZVzEY9bxigpMH37o2OR8Td+Ox3qG6rqinlcvqLri4NoS67MKsfX14wIcigL4qMlt3n0qzHGlyhmNEn/pAQDw8iBgxvCcsYHACAquZdGa53agQFa6cAcCEFbDtDrCdMhR+Gr9YH+Tgu6s7GEPElCgDkbQfWj13pnOA3rn6GJ3+GqWeplRbG1eEtAzFyr/7hiM4ag+jBB/+yDKs+dP8Zu6uiEy6Zee8WCAwaMLMZOzYvOoH7R3XjxTUKCdyvWlKWSannXSaIZJgWyBSVxZAyIC6drVzJBd7ViMHQ7dxCgB5ahGOZZLIMd2ZZVwfD05pYlQTHICZmDkDBDU73LuLDvas3eToOyjiLB7pySDUtX7BUiXi7J/Nu93NlH7hCQrfd7BBmqNJrVK2e5z5p3NwEJ9Hl0EC1LfChiANlHqTFDjk/vloTNQQKQeMSo4sEnuS+1yZbzOlMAMiPXG5uWAAbTebb8ezMM7eS5tOwu09by0EkaeK+NygJ1J6MelgoJF3WO9L4zLCgSswjuXymrlUiyz/JsYUrDExxk4ljoHqj06DZE1mIAC0xG36IdEcB2RNa/oAJAT3Imo7IB8B1D/xiWLaXzeWb7DWYlT2xJV5RlU2SrNfchEPs9khSp5KMqYQ89DAvmmdMNW1RjlzpPjEe3KeXjp2XNZ3fDWZ8R+Nfe70O4HAwy4+7sqsVNnpQUFsmTk9YZ+CfdZgAPjJYui2y0RUBKQJVOcEmFDAkoBC4vmHrPcbwQHVs4ouWGyzxgH7r+fg4J43gz990JfmF8q7twXE0hLPJLPbZayAYJLqxaYuZG6A1zBQgAJ2dpkccTQtlNPSwGd5UFY2a1e2q9t8qRuq42x7rHMQlaxHX6e7l1i95Y22s3JFch2gz8Sh/HM6Kzqfk2JbegZj1vj6305856pmw8ZtZ/3QFlc2/+3AICX3w66BcPxnJkMWP29jkEOogwcLODIto8+tAGZ7HNlRt9v6fWDgYHIwB/qjbd6TRkCItFTyZawyv2aul2h92J9+rLSX03+Q0iKzAC2lECas7JlwkZJmFBlBGT/jHNQUJ3ONqwg4SFqwAH83L6OUozp/tMOFFw7hA8GGMRiGcB96RGXlTlTIKCgyd6kel0i8hh4IVW8ieHpp5CdCU3HRHCAQzZksZoQRBltKQUKn1CSJvIEqv/9Gt0zAU+pImuj9OzcpLPXPX/1CNy7msSn7bdE4zIiyzdocg5UaGyr2kyyP8CWyEHBlqgBBNOX6gxFDb1+26y+GQ0LZEpygzIVASBkootLmICavAhA36neAoaeXQBEuOOKCDnPKtU+Kyr+3ggcwVO6BgDMDPpq6VdzZvjSO6XXzM5T/Mrz8y5tmnPN8r1YzsBMc68IThbZ+/U6O4+bc+S86+oXM+6BOeOxWp58mYGqckDGcOjuqVyETeJUlRwLfSfXJZGbvcAdmNbIc8M69DriTD/I8VlIof7WfD9ZxXIrebUEAVSf1SZE1uTHLZODAtNLPShICh6cKQh/IyCIYKAP11WSp4ID64KZHXvXw98BFHAgauGHluXBAINLYtcjroa2CecI0lL65RfG8IRdlwMg8CQ5ki2LDdUeBKBoIpOuGcmckSEbzwjllEFHQc62tKwmhEWAAIw5AWfldAlYuN7QtqPvBF8GJ7+xg4kMYTUKcyPsR2GkSfA5Cro9KyYhbplc6LZcQcGW0iB4lAibAgIHCgYETIhx2TDZPCHb4QbkQMGUJBAUZQAIWYFD70WuExiVIUBc7aDKOSp+7s4huWnvDc5KNOQGBmZruYcjC+NeZaH9G887e9fFywADbuywzfX297tUWQ8OT7Hz7iY3numEGa19ZrRXTACwBgJXzQ0tbUiggoLZnh49GGiAMI0OzrJN0PkPAKzznxib5gwQyTyVdulbQQ9L8JMOKWDgSOq8yD4gd1n01UxHrAz2SkfYNY3z0K1SmN13pfckufZCx4Trz1ZDRGdly6nRS1sW3ZW9vtCNsypLML7vpgPYfZsAMexkdgw+EfvmTkFBV66BqA8GGFiZecMWi3GF1ikCO/7OizZRY5YYYsWTUQgAS+KSbW5zMEDKHsjuY+L5k6I2yglbljXnucjGKzsDdBRsnvgDHDoJ6j4CSplla+t8iC+yCnmVkEMORuwckSXyRMQtEWKsEZgrW3/URMgjCo+AwFiCGSCQYyp4UqUudjc3XLFEo5OBZumYAIURJPQAQejX6h2xev+rxDFK7e5zlgS4K8DAoWxEMAQyV5XijR7WIkchGgfgOkPf95X/Dvu9PRAVcATSfuxGTw2YA4LG6Nvv+nf18qAeTNTjzeVSTzeI8Q81OQIZ1LAGZ+GBa8BAnAuxxCRJ4BwQjDsAjvMfuE0GbO4381/10gbZx4NSxlYYGUVyLo6ElEQfyf4PwH4QUg4rFVINRd5lkae7xUYDUT/I32CQuyWMdl5vvK28jN6bXVvZgvU+ClNAYPXT8w0QwK5XBjSCgyhfVoPexxJ41gIEPzu0550OFMy6Y7FoYigPBhgMtokgsTH7PSo28/TDsXdD9uYbv/BIBC7cqi8J0sGMGj0QT18iChomE4OuACGh3bqWtoSNgyBS0mVB7AjeQIJla/fzepWLcOtGM75MLMVEI2pYipiseG1m7zX7F0SGYAshgy2JUrQd68z4pwAE5nunLyqj3vrZfumFO5Dg48AgXQqIRKAAEGbggJL83hsF2QZVq8NhA5PCo+HoQMEQMgnGIAKAGUiyPrKxmCmks42YZm/Aa07AbTRuI6/K3IyGvQUCBTT9zVa8xNwcntzHZFOntitcQDxAhixhBCx5UxkfoiEvIOYMrEBBPOcMFMwAgQHhGRhYzX/gNhlYzn/VSxvZDprAljL2wnqMNfQpSxg3CsmIYcXCNQxn9PAvAYC4+ZFcS821l4o5P0MdumMGBIAWDMRQZuOohHGZAYLhDa2Qapjs9XYmAnurMYX5mvSgJbYDto1ym1OwYiF6wD0rDwYYTIsabGCkXGJHPXshL6z4um7+0A4UY8iKAkAsKoB0X/sktgQWlsushlzpJGYFDBCAwGzLgIC8JVkKROv16ACanboAU0LnAnG+IxhNFdnZ5h+zlQyXyijwpCxBFbwlO9Apw0y2DTG1wrUyXLA2xDZJKECMCpTxGfepjyCBCiPn+t4Ii8c6QPDcBO1DjMagL43HHePDhHPq2IwCWiCwAkmxj2L/9P228mCi8qqudpCJzjWnibz4qdoon5fRqlELR+yFQ3brqNB6kFCNPLmxi+MLzI2hAQVWRRsBgiTkKTgo5Fn8UlWqoI/mCYrXMgQrdsD32kcLBlKiOsYfiAyI1enf9ZKJsCGDc7uj5s4AJ8ZeCg5kcGHspTKbKwdiVWbhgbN9Kvq+vbTrYdVxa3nsQzoxx2MG1maJhaaXUqogoIIKDGBgaV841Di8HtkBAlXHE3y++uBCs5flYQMD4GKnvPPiKb78nce+D8IwYMytotPPdNyrIjvAlKpgUpI3vDlIIFc+hZv8QzmeqjHKRGBIuIBzu079LNZ5dVdM+qK/z6W46dnWof294vP69yMYEOiFrqfkTGEmwpDNa7Rcb/Dk4X1D5U9VjtQYFN9amCRUEkFCKUDKsheDsQjEZQAI1i89SOjLLMFstRd8Dwh8OdQMDBANIGAay5wqKO0km/Pm5sVXRdvc77n6pmcx+a02rh2W1PwGwHMvQBYyIgcM/pbCVJUlowIISzoWYNeObwwbFa6KdQAJ0OWxSWSRTAYTOziIq1dsxYkvv7txzON4OzMGNEanAQMUDdIYq2569QYZOFiAkMx7zXFqQEJyvVRBAjXOy65zpn9vAnBZN1ifAHMANft9dq/V864pFXzY80YZPBuXKTtg/zDaFSCA6GBjqFRgwMQiNwEgSB1lrJ6+qG9hXOUUvEz59wIMiOg/BfC/BfA/BfCLzPzfh9/+AMBvQ+bXP2Tmv3qZZ5w5a/bT07BN5CNnCjpAMFGMACCbBwSf3d+9bp8TcsoOEgo0kZBF+ckrc3U/A+WJjtS+snkzoGDP5Mr+XHqzWiyzZLRVWb1gKTb/1t3ZZvsNmNDFnIGqKEdFeD0CB6YcmT4keqAGwmxMfGMXJvV2VEFSHRNWFmHjjD2EGTZNTOxBwqrMlp9dCwjMO7lkKJZ9xIckxcb5HY0+qzkN874qsE4W2kSBZXvHDqDxcwQDMCCQ/ByCyFgDHhQ4sAJypOxxcxtfcF2TPwDBDiQcrC/CwQQghLwTW+IHzSGx3JOplUI73mfsQJSBfs17LwPR8MT5XyFTX4n219hHd4yqj1KQAW6BsugledcEstzDgAIXGav+RWxn5drll5delLQqL6MnQzc1zJyNiTEDCMcyXQAD8fXdQCNzY6XvvQOIis7vChBI9dizzn7pk5blluWm/74Yg/8rgP8lgD+OB4nofwbg8wD+5wA+DODfENH/hJkvza+mzBKl+s9Pnz/Fl/q9o2eAYPH+dSpxixvAcboqLgcLKcNYhEwZytrhYGqFEcAWhPGO59SnVeMuNDLq45leWuiqeVH4bnRjvL89JgKVePysUPiwosF7VuAW9D2AOKAKXmNc4IYERMiUBBRAlWSpRuRIwYCUmiNiGc49ixBBAqCrUoCpvYyKcBUyqN7/ZaXU9Jf1X+gjKsc4p/kI31uAUF1sn3UBPMxAwUzJxYbPJuaMMUj+m/MLBrrNaJABhOS/EWUBDBEspIxs44w6vgXKIiCMNddqmLwZQGCIYe8BgjXb9ANPDNbLeqF9FnsExM6aQaFuLwNxbHoZ0EpRJwMgmcclguUIkIuAXns9dAk66S6lBnS9rF4AKtvhIGeCH28pszE505eRdRlCc66D5k6KM5iAyxZxESdyAN7AwLJZHUqBvaxF5jQPAOHpi3ea1wD4tdbG2F79m+h6cPDvBRgw8/8DaFGgls8C+BYz/xTA/4uI/p8AfhHAX9/6DFr8BeyFS4/1LVOvw2M6nYERZerTvfWsjv7lyFWpUTBEOERRccqNQCb97gorCGOlQGO8NGzi0Q1ufCXtsiO0vL8EsXVmuHw+8ZBdqdcqmbBFo3cKBKKnq8K28nDHCoQxgY5JMCScNhmPlBsjchi7Q5ojot97FiF3IAGAA4VVmb25MQInMxSXPMdoLBwMmIdi/VTUHLrnUg1KndczkCD1J2bwYXJS9M8YYrupBGNFqZUdyrkyAyAHAJVFIAcIiONoYBxJO0W+J/3rr11WI2dhviYPqMD3KjPjRwoQZ6tXNtDFce4TR2egz4yO/c3xbxxfMzpl74DebXLQMDCUZOl0x7w4UEi6Qipxo5fqG2yrXqh+1GV9oFWxUQYQ9FjQVS+tt6xS/TkrnWnAwMdnEZKjAOjsIZEV6O3HEI4bx8ZBMdc9N4kTQCz2Q5eO/uD5u3j83Tfx9mffEvvlDQx9Omn2DYTBf3A5Bh8B8KPw/T09NhQi+l0AvwsA+B/U4z1bUL9Ktzgo+OxbeOPVT8L2E56yBHFQo8IEVOG2XS2KPXg5+p0pgYqFGlqgkDvPxjxXRoyZVkGsLRmefHr00tIyU9FTv68RdGrqULpzzkpE41a/PvY9oO4eCJiBm3m6E2ZnqEAcH2VzBLDtoiBTRupAwsHyAp5ShBrtWYSoJB0kAAAT7jAXyJmnZIbCqWZTPJ2xMC8yNf3VggFXSmz9pl5LMa+yNSwAV+PPpRp9vYaBds7HOOj0pR6TY5NF5gYIGFCDLp3A9tmMu55LlAJoCIAhbXCwEACB/J6rzAWQYJ74oXlApcgLug6qIQYPM5BQ5JaH4CABBDCuGuczWrpnB+L4DmCgHCPjE7xSB3uz4vO/9p2FPg0ssPbfDCgwKICB1nEB3r8+qL9Rc04s1+gq+Vy/3KozL+fkdP0+AwK9zZgxbbXBsFRwKntgwwrACVQOMBGevPcMj//st/CNT/8pHr36ushhQ6sEsD1Yv+tZg58bMCCifwPgfzT56R8x83fe7/2Z+Z8C+KcAQB+mpqntAAI2iE+eP8Xj77yJtz/zFt549fWa5NHFfRwUmFKdGR9XsHr/prQDRcGrWQKFgNpbik/up35bEyNfFZp8bsUgSs8FT6/ZXWecaBNSAcA4+WbhnV7IRLjWiLsxZMxqnKrQNUI4bYs+TYWOQGo8qBqRlAeQkJIYopJGFiGz0tDcgoSCyzjFquQeSUddzkIqkbY0g1GN/qH9F8DACiCAgX2vIMD+2vkqG3IsgAQ7BjTg4Kb8gtB4v0qBgAEFn3cpVfCgYyUG3oyX/t0EGFgIQeSuji2nrDKn16rR45SRKKMQPNfHlhsXRBCIujy5G+NTWQx2OI7xKkZt7ICACAV7PRho2KAgB7NckFW/h/yNRg46Bs1ZhZQ9RJMioAC5IwPcrgPi5w9eR7VOTP95uM30bzDqfIGtnNmKJhw3q0HnSAIyvsQAFR0nBpDx9MW7+OL3fxvf+I0/wRuvfgIou1zHaNmGxjltE36vldKfGzBg5r/zEpf9BMCr4ftH9dhVpU/C0ZoIKPjxUzz+7pv4xqf/tHZqrawP3hIUlKMd5Pi73aMv0RABdaA6CnSG2p3i0+uIxgnE4fNATa1ijdbGoc4r4eu8PG/TZEIvkOtQJnU7FbKm73sPib1NPdU9FPU2yeuvRsO9yuRGA2kDyi4gIWXxnlKesggb5lndQDUkq3IptNKHC2JYxQy/9EXrUaIHCNqffBzgY4ezB9q3vN83AMBBgo4T9yAaAI4ADIBT6nhsd+ex5ez9VRmCXMFDyg4CpLMSaLtzAIddAULekHJ2w8cpA0VBgo0tb6pUs451RlKA4KuJSl1ufEBBAddchKJjc+v4zlbVzNiBy2Mr4+uGShkfufw4lQHpzjFUY4BKdNR9AMwn+onahNGr5B5dp0Xd2/8G4Crd1OslAE1Id1K3pf7sHEWv29UgIF53rX0Iv1uOQcowTfLkvR/iC3/xu/jmp76KRx/9uDIFyfuGuNT2OVAoPp7w/69jDf5DCyV8F8A3iOh/B0k+/B8D+G+vubCPQUVPtIKCrykoiJ7ObFKWc8MEdDQs6vFuAngiYkSGnec6BQtAjZnadXKwa+OkrOJZK0rrWoXeCN0aGHBzbKIoGpDSCeFEwPqY90B5R+p75sWGep96mymD1auktFdPkzNQghHRHJHIImQE7zIBMVcEWAuizVszFhEMxFhm40E24YJgQAI74EYkMAPl2DUeIr85G9CxBRxp6eNoDb717yxUcEOeAffz4h411JAqSCCiChqUUXODdajHZKxCyuC8gZMAhGFseQNKAdF6bG01kbE0cbkxc81FiEzBLWNrajomil49tnosgoHI+nAEe97RowxYWKCXgzQL0UR2bZLTMYzlLfLe/97GAead2pdIyWAEAx+Y/ozHVjrKrr3CJugHrVJgB/y5ScYxZTx971184S9/D9/89X+GNz76ca8LQxXOhC0gAJom7eMXa5FGD7op/76WK/4vAPwhgP8hgD8nov8LM/9dZv6/EdF/DeD/Dnn3zO/fsiKBwr8pKPjoJ0bjiIgGgTZRJBqrFgmuUPrF6axeq3aE17YRRPtuNNBkUjefeyEa2tgh2NnvsYoToYxIvJlRE0Fs6tyf3941fJwJ1wIEAI2360bOjq+KJbiZEQneZmNIKANF6NTG00z6W/ieSXIRCiAJbQxNIg3djHOfx/02upDYpGD0GkDQsAMKBBpmgIt8XgGBHgBEoz/pY74BFFih3ogckL49lM2jJGN9BMCwAgrbnYKEe2ES9nsFDxsob6BcXNFy2mo/zgBC2WVsdblxIR3DDiToyLy/se1zB5Tp6Zmf6ogcFQwY0PuA5IBDGOcMLFjf11BE/X8sM2ZgzWbG8y/rofDcoIeWK1v09/byhR69RoeG387YmqlNCHaAxcUXgNA8K+HJe+/i83/1+/jmr/0x3vjIxyH2qfY7E9XnUWrs2AwcnNYplH9fqxL+FYB/tfjtvwDwX9x6z2FazkCBHge6SdeDglWIIB4/S9YC1oKZEtiiGFTjpyPNra1aUfWzsjD4Pc3eJ5ZJfUMyWd/u2fNjcpjWX/503w0ENe0KtwzPmWW9D15QVIAhz2NgDPr627PVW3Ja2ijpwZDkakjM00yHGo5UcxG6hDbbt4IBxHX0wGhAfGStaj0YiCGTPsbcJBOODEGJgODYvb84ggS7xwwMTObx1Phf82aaRfEFuDEpUfckpkCTmiJEKWK8jl37n8C5VIVoQK+UyiIUAUecZVyxRUCgAEH71wEC1bEH2QqiNIAEGybgfGyniWsrdsDYgG5crwJ6N8hA0+8d6xLDNxxZBTsXEorwcXwJuY7fZzoIgW0b6m/lJEEVwLiyJdT/al3q9Wy9/wgAXE9ZOdH9tQ4qY2TJtPHZBaCMJz/5ET7/r/8+vvWr/xhvfORj+owEeSOfzPnmMpuFDhB6cFDH7ELr/4MLJbx0aYefJdFwAgoGFHp1XHSk3XtPdpjgs3tH/iMIIIAGscufjga7WMWJ4PUZ5YuksiazPArorJwkinE8nnKrNFKdmPH8QZA6ELCs76UY+KyolykrggIlnTeptxoR1u+03bUA4djFS42e5pGqcvLEN3JjAmoROqMVzAbHqzfSLEWLSw07QFDBQ+dJGhtwxhBEQLBQ1h80GJiWeD9LNIyMnn1MEOBAqYIE5gYgkLaPtjsJC5XiY8gG7CkJQGAGJ/XAKLcAQQEGJ8kIhybkpTiuQKPQrxpXM/yzcbV/4BYQ7PcoFgKKYSAHfIfrmob9WRWq8tmAhT6HYybHIcwTx6ufE9wZ+UaO4/krYL8qQYcMOSemT2KSqtXxA9Kp3MnHqa6PhaiCTQhwtanIB4Bta8btyb/9ET7/r/8hvvWf/BHe+Mgvn9SPfQwdtFhugYXFva3srMHT509Pq/tggAEQQMGPf4DH3wmJhs4CnMWwPiBlNzPAl+rdvEkkLNnqj116LlDb1dOKK2MaKGQ5zO39ZsWAgSkLD42o0dX627kzAHG5PRcUxwwM9G2YFCIC417q7IaE3MtE3lxJc8rgUkYq2jw+j0sTwJLF3YCEmIcRE536OnFoa5yrxhA0zIGC0LAawYyHGYuGJeiMxy2gYFmuGb+fR7ExMo1qyjpl0HHITnwFMgeVASJuwSqXImPMChj0XgIQbE5EBkFAgyt2j7OPBmY6rl14LC63dfC3AgTHMWd+TsZUHnU+/4EDyBlsIAuocgAoWMbABr603Ha66YPSO9zrnV7nKBMip6SaW9EzJ6ftmeRrRHb1Fv2eAhN1Up7827/B5//Nf45v/cof4Y2PfGxVMTgv1eWREHMFB5B5GEMKT58/w+NvP8ZreG1ZhwcDDEwomyWJH/1lF8imTAUnUJca2yRL5uDqC/zg3/138wpcudRrVsR7NaHTOEMnhDQRytbLPxfIi7TxzEO8QIkxUDP5pZKV9bDlTVABdsQevBQ977RMBLMBBDNleKJczKOg4wBwdJ6mAoS8DZ4mH2kECHwARY1JCkbDQELYDfMSddlkPWsbmlim0/s8xJs5xptPPMphDgyVUKMb5hqtpu3PAxxcomCtTLw9Zh7AAcqhXvwO2YZor3OIg9HbNmknFQkvHDYnVLnSXvMZpEKwrPyzUZ3lLjVJy5PVBQ0g4DIwP6tQkPXBJY/bveh9b0GCqz5L6qQqZ3bN6tZ9CGAln4tQ1U00vFSyHlP943KtOShN/om2+8xBuahfZ3W7kplxYGqgwPohz2VIQMEfCiiY5HRo5ccLA3tQ61cBAWD7+MiOiX/wL/9gWfUHAwxg4YNvP8bbn3kLf/vVTzQDN0tkGYvGbxoEliDrSJPQO/+n/7XdUWJDB2DUEABPnJrtCneOLtvfiNuBX6VgDiGAHpX3ghmAAAdAAFVI8pHHe3fFd6qz/cWD8YfG+Sw+OSSPAe6xW3/RFej9qnDBTDGW0ioW9QYBrI0JJ2ELNolDI2/SX7xVKhoEJM0mDp4lBc9yuhtm06hJDosZDmcNBBBE5sAMiIcODADE/IuT4m8EHPo9tddG5bUCA9d4XqsSxyve5lqQ0LNXy+eEl+6assUueQf7DsosxoRtlzmuYQvSnQstpKBK2p+4GlOdp/MxreMYc0OmIC/uN3EG8vq53897O2c2Xq6jtEnxHEsGHS6JgOACCPgA9Q1jrm+QdJvnI811TWBjnRXRe1+T4n4NO9DU14ClXtswB11uGYd8MgEFH6/M1JB0fhmUE3O7dJNL3THxc293OyaO5cEAg3ZHw9fPjceZEmEFB1DWAAVIGU9evCsxn1/5Q/yd731JWQWWCcpFH1d80hFOJtKlOBo6oTs9MeQ8dBTdEgwANbxwHODC4FLzDPgwr+pEUD3BR4FBOlqw4MJYhdbzHUJsGMBAa8o9WuXV9Id5tnotjmM0dPH8S97tChwkgPcdZFJSyNfZGxXNxQBCBpO1oQq0G6JVEmmf5ORGpQMEblSOMem1izOvCiX1DE0/ets7NbCy8yHhbOa1XwPurEznd5xvx5Xzf1Kvi/WIrB4Vj/NShsi0sQeUAM0Yl/HUBLQPajwtfKCMgOcqKUtwDSg4LQsQ5W2Y9F2t+nEug7NQQB+WimAg6Br5+YPRN5TUQTNwbucaSOjzULSdTXJkuQAqbymLuRcTO70OKeziGQCAgwLLlTC2wB3V2+v75PkzPP7eb9Ydfy+UBwMMIiiYvVCHia5a/iLf1GMgArPEZD7/3/y+JIJ8+Jf0RAKQwZBEJ+z3YOtOReWSEQohA1gQI5fS0klNZUL9ZoM/q/8kzt54EDPEfgIIxGOJtPxcWFnfU49dhJWIhWaHCAEVHoS2AgS7h1J/h9CZEWH3ZVT2ubZ9E6DgPdYZuxlt3niYeSLMERwUvXfKorRTbqhoyrkChMgUGEjQ88LD22ctMqAHBmGxfPOseEKW9hXlrX1evpsagWld+8Sv+qW95gyI6Xzyu4b6LxNgF4BnWu+uvjOKuLuJTzsBByLTvnysAQhac8sq75/X19Xb1oO7diwbliCEI4cdKfsSQDGAGiZouoPGa/zHNRA/LWeg4OegayRUoPOX7RXExb3wqGsknOm+XaNrPHyS0eZXzNp+Sf/Ofp8lZXerPZCyJzr7Bl1kyZJbvXcKxzpAMNisC3btyfNn+GLcRhlQ+zg2wcqDAQa+zfFZLH82mJ1SY3H1xdtjwpP3nsrmEr/2xwIKjHJLkkUqL1fRePRxVI+46D2wawaqCIo9zsek8YRvQ4KnrMK1jIPdqxdUE+CFsMaaMhKQSw0ZeFgkATh0lcyJgu6SiOTz5QQhytvcgKixa+q6mhcr5Rg/Rxo6gDqGbUlaAiXIjVEBoMDowDj4XWlCX9HbHEHBskRAqmEByunUWDezLspDTNwK95eDk/G5Je9gAgIoAlot03X5RtFfekYXS3YFvSpcKjgwPYCkuUZxA5rrx/Jsbw6O4G4GCmKbw7xz5gdwUDztj063NfP7JfJ87HpWR8eAybJMQEFkB/jgpo0XdU1Kco3qGmMPrionSX+nwLipyOJ5Z4AAECclrPqw5dDN7pykoUi9Xz0mwGG606zV/yRx/smLdxQUfE1fGGiJiOflwQCDN147AQWReo7HgHawXclngBk/eO8dfPH7v4tv/vpX8cZHf1kSnGwr5SRGiXOSl16wDGLKkhHNVOAb4ZQDlFqBp6zPitnSq2KKskeKLqhHrXuWxDpesRInhUgMPB/s4MZZjpcoEcU3RfvFd7XrEoWmKxnsur4sEoKGUM61fbFaijk81xKzKtFhRgVgBwgA4BuXLDP5utLFqM2YrOsc6lgO9UgWcefp9Z2xt2Mny2d9c5ZmTnaKM/bdIJu1Pau14fJbAAYXMsSXZQVmzsAScwAHQAUItGhPV07GcLrMedUOSlNngvrwz6y8H4anASVBR9mzLaSRUfUN4PtQeN0zABwXu2tValy+hhKGjbGuKVHfaN3kz2Tud9ddrFu435QhCHtBGEvADUtQGQIA4HznLIHnacU6DvbMJkjbyU/e+6GAgt/4kyZ8QBxA7qI8GGAw3evaVxRgFIYQJ5xt6fnkub3F6mt49NFPgFmWGrGh1KwUTynyzm8+QKyZvHevIFnc0ACCMQhBYUfDRb3R4FEw60+twav0cK7CCgAHCeVfxOORLGuh+Ujj5ILoZbmWZLIn0CaIXmL/LBN9oigA1JgfCYInFYwGFFieQd5aULDdVUDQLTFqEnVsvK4pwWDYFdPVG9eUPmv5RElwqbFqIAAEIvhrVC02cbENsY7r1QPSRwrcOqAJAL7CYtEmv5e1T7+3Rp8axcSNzFwRJpm2r6Xb2dq5VTBAE7YEmIOHwerMxvhSu/t21ApCxi6yYBdKDCGgAz76+ynYblgffaY5E8A4psP13bwN95WDt8uS/A3hgVJEhlmTNvf7Cg6ogI5dQv7qZyVg0DPIVcd4XSd6JuoY+36qZ+JSxQkgOGMlT0NPJ4Cq0Q9ngMDzkZQhCPWsOzbWjaXMTg2G3PqCi3lBiHPTmYLf+BNNNET1YOy6/19IPvQyMAPr8IG9xTAeAzSR8btfxtuffQuPXnvkTAEHpCaoLoGSUYBqlNOmDAIDdyQAYeYh2Hp5oFWUE8+hVyKUUQUob42idHrdBNb6JCQjUlOP4t8j7ScgowAbVHAXSTWeDHRBUJVO6wEB+Va2a6Rt9+/Hr+ukeX9Z7oFT0hdi1/0Sraaxl8MbJnx8HLKTpWcH9x7kbcU887qTZHZDQ/2yJ3vGNvbT6OmH+a+G3xIsEXImGjmhdtvZVsau8ebi+LDX18InnoQWVmWwbRK01fPokgGe9ckqh+XCTn7DfU7Lgg05u75Pmk15pLevmZvx9wnr9VJypLrC33Zq+QQxWdIYyv1enJGUQMd+wRGxmHmUw7ZtUb/Y35vAQGQGTK/EfrjASE6BwiwkE/ucwntYNLlwyRAYIEgZJjusTmfNL8ByrFjZbQIcIDThgz7R8CSPK5aHAwwuCWzojBEQ1M2Enjx/ii/pOs9Hrz3Sc8zzq8qI0wZ50a50NHFx4ZDBErAgXpzmI8ySx/q6TzwJMWwXkhXVQ46xSYqJS1kF+hJIsDpYO7slRW2XdsZqIaiUtxoyMICg7AEF0LASqsEzXSlu7Y82p2DttQGYj4H1waxc43l2pV065NkKV5b23OY+20x8T+h8knneronujP/S8NvxcL7+zrNxuWB0fKw4zDW2JD0FBGGL8nquAod+KScA39kw3w2sRFeRsWqzvnupMoKCWfEQHaUWS/Vecyx3r/Q38Xs138/An197LkMOqBcJr2R6RXcN9eWVWXYHFT2zgY996YgAL6dfpPrnYGDKRGr/Dmzkqj9WDsAEXE11FiUFBeb9B3BixxUQxJCBhxI622R/fVRJk+RRAcLT58/wxe99pW7uNytXgNuHAwxmpVNOK0Bg/54+f4ovffsx3vrs2/j4q4+wM3SfcwJRlsiEDU++093SGERqaFlXI3CBvmLPlZ1tYiLJiRUoAFd6OsPheeiBomfM7KwEl8AMBPTfLH2aLDmK3nazTe0Maa+E9AwM9Bm6wVAVN2AtnR0qEdrPi89iONjaY0uWzAjJF7ndbByu9fC7uXa1B7q4fn3e+l7zt9y1nr7fIwKAM+Nvhr9TVPYPQH0fBAcuYGoTpU+8lhSmTvjn7IAB8QAILr3+1legBEDhlYnz92xcLyz7rOedz424Ekr2PDmaca4fu7Hv7VG85gLjAyDIjf6+AgSX5Mdkx+TGHaN2iSXpfgukG2pRCRsxOUg4lHk9GseDgh6b6pfes78WBPQAILAFMyZFvi5kcCKb03FQICDHDQgE0J3yABLY6kYpbOG8NTLWY6bmrZ1qmwDGk+c/kCWJn3lrBAU3MpUPExj07ABwCggKA89+/BRvfvcx/vQzb+MTrz6SvU1IfouKzPdIz6/I1ZbtqmCADAykY660Oi8HwWMaPJ1YuoHt/Eg5dhLLJEPsRV/GozkOA1gAWsAAeObx0iT18btOSGnbqnBq6KDPzC2WhRtptptp69Ej9TGwdkVv0/vV6GrtJ1s21BuW0MeXyvItcP61Zx7G3l3FFaf3HOZ5C6ZuBQAcZMQMf31rJKufErqR25fezF5FHF+NTgBsKR0R/E2E8hZCQtJEmZQiaKjjdxNgAPxcrawDxdDbXed38jZVrN14cEE7zt0mM8Oy2EWWfDMXWm+fe3noQXPH6DTs0C2ys2JpdO8F5gJKurW0hQiOXc7f76uOCfswDM4I0KxqmOqXmW4B5iDgLAx5iU2ZlRVYp+DFX8u+RTDgrEEKrEG9531A3a6BOAxfoA7sBV3Pnj/D4+98WTYvevWTOr8DK2d1vBIgPExgoKUBBWHwelDw5MdP8ZvffYyvffrr+MRHX8dhWq0bHCJ5uxogg5eJQGZEDCS4wpIcAr6krIDWy2kacC3K4/p/jMNCDVkwcM2St0DxNfkOMTFysla+2cXLSqT5TEgt2ZDaBBwTkLKKt/VCNDA9E6MZq+JAoDRG4BrjsQRvet8ocPG30zKra1DS5wDghAbu4v5+rwZAnYCAoJAYwNEBgMLsxt/BgDMD8t1+A1oltuwK0+v+XV5HLHCFAOL6NkL9LQFIHWDIATAA+uKkABbqX4MwaMc8fG8qbX8pN3LDM8VKPWCYjHP4fckcTca2Nf72sM7o6PmnoM/ueavcACPo6nZrtHCkbPS2w97+2bAJpl/CVt3OQr6sXgng4KrwY+hT7vv72vDRCsh3emk5HpEZOJE9QBZ2mEw1osRS2wKVDT38zounePPbj3VHw0fhKtV/lM4Zskl5eMAgCsAFlgAsOQW/+d3H+JNPfx2//OojHGiVHYDq2YCRwuAVVVqC3AQk6NPdyFylrICpgbl1MOP9eKbwWNX5HVqjGOlBYwd4kgNh7EP/zJjRHmm6TQ2/hQYuJN7ETFxPvOnGDuHvimKLdWyMx0vS0zZm1cuMY5a9vy6VqwBAPH5J+dgxU0D+24oJyIMMRBYgzn0HCWA9J4IEOGPgXQFUQI1WfrxZwTDmRD4ucMaAK+uq5yeqgCFTBQsZATgQIaWtCUeAj6n8OaMHYJXDINUq0qcDaO8ZgQAY4hDafKA5KwB082FG/S89z+tYn5XcAOf0tP0d5Ebf82ArtGBvhuSiLEAECnfBKWF/8ycBnqcgFQnhg6ZCLTDqvf4+7Oh7AsQ+PJGfW8sUUA33u25MTINUkN3anJ25YeL8cTrfASAxI4Hcfr312bfxydceCWiA7biaYHaGEd79cYWuenjAAC8HCj7+6qPRM/IbypdE1Su6LzyhQA0oELLmJOgNfDAaZRXuLZ87BP0SbZ+/QXL0kMi944XhmynLSR3lpFZAml3/bCzOEm4agNAJTsHgtYYWTUtQsYGubulpG7eZ4hsA3AoshD5ut3M9G7kJAAh9dx0I0FbeYBiO0Jel1FCAf2ZhCFiPWZ8XrgDAjL+ACJbQsSoxI4RnSWRDD6gVytpcYd6ADGEFHDQQK1tQZS3pb4lIjusxG0/7DMoDq7AC6+Tzfy4nl8ECgBVgGFsfO0L/hrG/EgTMgHPBGPaRar6c3JCDsqyOeXV8nKEpxxwoWNjB+nKrMkSx7870ifZJv1KmZYK7PrPzopzEPgauy/m5VKaym05tTwXbrT7rgcFReB6KA4MYzly/8/wJfut7X8KffuZtvP7qIwl7N84RVSBAVMHBFeVhAYOpRzYHBT/48VN85XvKFHz0UaMgzVMCqoontGBhL0J56gr11tMBcHRAIVFWZReVVXwCLiO5ExpwuFu8l064SBVyo/w6I6jnNNT55H61XlfSaQ4Q6m5fFss2w3UEgYmfPcEN49g0VdG//oqbU2q6Kr6sio96A1KMPjVPswMK1/TPqiw8mZdjA86VkIUFzoBAKWgUlYGAvXADANi/s3+HPfNC863J2ZtO7v0LQKAGMGwpgAUCUjEwwBUoFFagAPms4KCXwSlYd6A3AgU5K7IKuR1zIAD8K41NNEodzb0EAh5io6nM9GM8ODgfgNzk5jMBtInMZHjuh73giy2PaSIvPPTbQpes+sfPCX0Uz+1DJv39ri036WKHng07M2MEeiDggC1cvJd2nOqUlUFIYPzw+VP8vT9/E39i4W8WoF1wAg5i3S+07+EAgz6EcAMo8AEr89hp8xidaIfSrTJFCdC1uFWYWqBg1zoVVG/o904T2rFlLrq/pyUuxdH7p+arAxSfhpFqB9yTli8BRCzKUkhDYmHj2QQgYDR2YfZxOsA+DpHGvtQFJqbNGJC+zwEyXilxo/R2mLdJSClXg5K1Xzx/pBqPBig0fXQFiPI+aynTlwUC1djXuTsLDVifmqF3b59FIfVAYJ+AgN1YYLCACh03O3apmPExY279DsjWCz1Y2BQURKCwqZLMxcaZBViQ7JsDAjJxE3pwoEAGDN8HUPDxzqMXdjLmy/GejXWQG2fROtanAc8T/fVByU1WjyYRO6iOYwTKSDm7vrOVJTV3aeKMLMqw/W9v8MP8tzb1qrH0By41vu0ERP0ZDa2VmV62WRBtxwwIxLrZeNl1QBuS82oRgUkYgx++eIbf/f6b+Oe//pbYL734AJ2DA5Q2pHBSHg4wwO2g4OOvBqYgCJV5UMAo43bAUF2CKFtTQDam4qV0S0vscw8OUM/xx0za15DV3QkzEOP3DYqpfzb576IoU/jRQE6sW2fWprWfIucPiMa2th4nUq7czJSWlvYycpHfE7GCAbi3ab9lHbRMWTYrAiAJWFcYD++MRT0XVOcsaexWVmAFsMzYl3DNURg7y4uE9hKNf2mAQA8C5Hud7zNgsNgsswEG8tfCOzSAhQgUNt3cRj5L+GGj5GzCAfFydwUJhxovEHCoLI6MERREdEuSA0juAXLdlbEb74tj3Y73NbkgDugC2Ot1luWAHAYKMQ//ALgoN6Yr3n84p4KF3hHRqvn/9Vc0Rwcdol9KxRfTMEkchmv1onyvn2dm80w/98+esQGRsYngob/26G5OJD8mBn70b5/h977/Jv7Zpyz8zSik4bNbwcFJeTDAYBbT60HBk+ctKHAhCsgtGqYzlH0UmSgFdeCi0S3qilMwrpUlqPeZoVErU7YAc0rwnL2uPzbMGkxJ8wTEkJ8fQQQQBWpe+YtomVuFdkZjz2LZMY59dA235aSrOLYZkh0ISo2RuXqbRxLzm9QIHQ4ScB0dDVz0jJo45wwIhO8fJBiYMQM7XwYDOxcHAhEEyDH9Hto6OD1HO9crKBBQllRgKjCQsdpLBAnHABI2OlomQdFsDxLMw7V53oOEmEi8AgkyniNQMAm5ONY3Ar4VGIhj63ITQIABvVX+x0pmpIp0nvtBYziHiJAKKxiov8fQ3ag7gJn+6LPxbW7bb72xlXPapbLnujA8Pey4WHV0pyDt+I16ugcBVv8RQDSXDfdT0wIi4N2fPMPv/8Wb+Ce//hY+9tHXPaeAGXX+GlDgFhz01W+2NJ+UBwMMAAxCBrSgwJYk/nJINLTf2wHskDcwoAMfPKq/+WsQ9IKRIVDlQuOAEM0n9ApVTidUc+F4L68vrD5az1CHCG4iOGgFh8/mVPP8M2pzBQT24+Xj2Vb/VQx7SzylpQ8KhoTVOClIkOMYQIIYsc6AaKU8HAPU401FK5A9AwItmGpB1oxGnoIt7ev9KE3/xjCBfIYeL1NmoP3OFYigsjllNom7IqCAkZOCgWJ9KSGexITEhELFQUMiwl4EEOwE7OXAlgg7teGGLRMyCFtOrjgLWN/2zagrHeBvAJf5YJ9pXG2kYyy1HYGCj+M149yAhDFpcBpaC2PbAL0JyOvH9YOQmQgWrH+JyMF0ggGBkJBNbegO2p9rxVR/usao9vrv5XRfe5i0fgOr+nPQ12f1bWyL1uNH7z3D3//LN/GPf+0t/PJHLHygcxYMQOZtRgAK+nsm6BmmX8q88qE8HGCwUKpACwo+8eqjOmiT25TZZx7P3YsqtvCDJSJKfeoDWsBQnzyZby0NNqlHPD6jCKNu6j2DWFovQY8NVCJ7WyprMNa9b0dL5XXt4OrZREYg0ti9UjODFT1YYG2EWopavE2y5W6Dx9kaEyIzIBKjTkrNgVTYkgiueEw0GhDtj6racBEY2Ky5Bgxcww64lxgMx88TEMRxmMVHxwHS8TuEIUAiFGYJ6xgoCwAhEfv3wkW8pJRwMCMTgxPhYDFizALuDrDP5y0RjmLsETsQoKJhJ2IZW5I5bwp1BAmiXGNuQuOrno7z+Rj3IbaYE1LCGBlgiCBvxvj8PGSGiJAPQh/OGVeTBCcInWd+QVdYHwFVX9ifWSjx/eo7WLtUlzf6G6PzNGtD346hDd3BWS0PsyfhdxJrjr/+yTP8w798E3/0q2/hlz76+vjsWl0FDBUopO55DTg4KQ8IGLShBBO8Z7550dv45KuvN96rFUeo4RiHD9EQ+2EWoWyq0M0YG+gGMPSVXJT+mb0wmBD0VGE93t6832w5pjk6GFjQ8PbbTKD8fhOuLdZd6sAuyLMs9515qdTMOAFoYtvzwk6hzWLXmajxODdO2EiM5JZIXqUNqQMxo5CEFQoLyPP19IVdYfYGRD5aPy12uOv+RkMBBACADhzocZvL1wKCA2JQDHTZ8bOQgQECqd+cJejHuz/Wz43+mH2Xy82CkXr6I0DYkLCXgpQIzISDGVtWGWEBeLwzdmJsmcCHAl6b1wxJ5CJYorezC7YkmQJIYKJ6zmqMAcz2LDgbYxvTPkk05tvMAEEMAdl4GsCT8MKYC+LPXhb2EA7KPN8jAoWNCHsxkFBZOAAOFoCgW070A9DqCOBcxwGY6rkzHdfURVkQgE91XF93b8EV2Lc/rZeNWe6D2ROTBwbw3773DP/gr97Ef/WrEj6wa4nIsgXC/WgSM6ghheYnSqes78MBBh0iB4Bnz2Wb4wgKZjgpgTyBcEWx9EYOmCTyhN8y6CJw6Mvs3pdAgCmWeqxWpU0EW3gKQWCTC7O2gXqQ0AtWDzBmbbL21DoCuIoVmCm2HiDM2wSAtT3FFD47Lb0lwpFIlviQes4kBuXg6m1GgJAg8VROrRHhhZcp/WkdM6+ndIi2LfTVallTn3U+AwSMMc5sHuWMJZitLLhUEkkf5EQ4inj9hUcAYGV1PHWK9yyOG0sxJaiWOrHIGpOsatiPIuOSCNDjlBgokl9iczEChEwEBustddWKgoKi40uQ7+AQ0wWuHl+gG08E8NcBAgsFrRiCFSDYy8j2yPOvlJligEA0aQzlJAKOREjFcjfGsAOAxugClz3slWOz0muxPcD1us2Yi5ley0TYFRRlSPvkXMKut89hoG/R5bWda/nK3SQ6CuO//3fv4B/+1Zv4w199Cx/7SGUK7NlpMvE846VjDYDARNTgybI+DwYYRGTOkG2O3/yOvPvgk5q9eVZIr5NVBkoVQxQABSVopR/knkXcweOKkJM6zCixaFRnQjMzqMBtCiEWf+cITFFT8LypAQ4xHgms0fmKuZiBmEtZ7733aqU3aIlbQ9N7nQWMjRMKsVPSWwagBoVZAABQ3NPkYkLHbkTAJGzCxMsk1GM2VZx+HvBkm1ndswPgFhDMEjZ7luAsPDObM7OSrO4eCyMHAQIOGEknQlms479bb/y3TEYE2rmYTpRwCeDA5DZrO3MB9kRgZmwg7Kk4e7AlWbWQWCnbpONGsiysqNeM2fhqf/fjC8zH+Jpk3FVCYcwhsH/GrtXQULmK7Yl/+34vhyZhRpaGSI9H5sa+C1tDzNghAHs3XaDPvPc+uawLgDovgZ+vPrtH1Wc71brbaiRpjwBL2ahJxnVX2dda/3/J+7uYzZLjTBB7Is/7UXvnGwuS+k9jAzZg+GJHI4k//VPFnZFGFJtN0bsw0N3VTbVAUaJA7YwGGnhm1h7bMODFrtdjz+wMR+JSvSS3upo9gAdLscUmtRxp+qvqZpOiZPvCwMIXa0Nd1U1hAe/FXo1Y78nwRURkRuaJPOe8XxUlzTcJVH3v+c+TmRHxxBORee55WQR/vTkjVjwo+MCDT7gcL712/6Mb1sDKzbdvrl5yaYABUEHBzbcFFFz/6A089siVhSKWvhV2wIQ+sSgEwVNcb+jKpLFKQBDeGgIE4sEQFR8bizzsETNwikG1siZQ/ZSyfjpZvL9Fr1WHr4CgDvX3MdC9oGDk4S72WyYOA1AW4ZhyoaQPKQm7M5nykpcVwEBATiXbHVm8zIlQaGi/8Ih5maUtmKtnGVR3maS0DQiMJbDzGPEsjrXkTSIFUKl6XB4IJMhYESCgxsMAAkj7rmqaPYq6L/2y+N4D8mPMn9sDhUpza/tlxpyoAQdzEgM2ZcKcGBMDxyzybAozZ+vGFiCI47yzf4FFH2/1r1+rw4cNDOgdOTe5IZZHsBcU7JWVCvhI+lLlRfbDlGSRIckP4SL/zMLgiAHtS/vsPfIv++9Rj5WQiO431hAxwBFmqfYTMVXmiZVJ4JqciLnNXbiXEoKCn7mO9ykoILJ/fmG2GiLwa+SsFbOPz335Gh7BI8PzLg0w8KDguS9XUOBLYQEQgwNL9vBGX+jG6pX94XffKPfz5+1YTKopfaJMBAjsvFMo9zge3yqHoZPoppT108laIavxyJSX7ALQ0nij4j0CX79R6T9Vb8psb8moyq4+z6hiKkaF1Es4KFNwTLnS0FQd6PLtjISFAQFga16tLsG6mJ7lAIHtvwgoiJplghjJSceRgYOUqIwnDwQwdcxMc7cOFJzgPfky+tJtBBLkt11HCzDqqe2+sBphDw5mAwes97VxSEDSsSE4Uhv6Pvcvo4YNmFV2ra4uHGSgwIO9ogN4yZ7tWWTKl56RGfWJf0YFDPqsnq+Orsu+ni0LMAIBJ+kvaD/mFlgmFdgomTUCCBNhAS4B2QeITQBk3Hk9fgpI8GxBn4/RMwUGCmzarZmuCBREkxAN02XUDy699LEb+Hv/7O8N63epgEEBBT+na0cH5xk4AKCx4AoO5Dcw60pfDC7oPQF4691b+Bu/+zwASDw6C0VZEmdSO2LXgMJEtJpFC6xn2UZlzVswYdqaVlaVRDSdTPan1AoZUOl6u4d4FO6+K0DBYtbFawHVkQzA+kVOluMGENbAwam09MwiEMxiNWegeJqcGLOCBVujoqxXkTn0MO3dtsJYRdE5T8oDAtnXgYLVO0qpQKACzonFCpZeLgCUME21bUZVPtXoXKREsVNgGasehbP6XJiVaEYpNTar2woOZChQma0iMyNw4f4dMUAGCsAV6HnmB6jP6RfA8cVYH5uiWRJe1s6336uhxHa/vfvqe3dAQP62YMCOnZLouibvMwzsVf1jYRLPeJyCY2X2y/rLbh3vww5RcqbdoQcF7cfDYqZgT4jhljLpL33sBp7onOa+XBpgYDkFBgr64uNc1niWYZw1rkwQOglZYsdMBGIZGG/dlikj/+RD13Htyx+WOb2pTN5bgAR7wCjcwLkdTJ6hAJxC1/s13h4QInMDN6P4ew8KwixyuCQysz4KEMxT8NS8GXSDpQmOkuR15dEmB8F5rKKEjLo22lqUhirmxpul0GD1Xmev5HqWY1qp6wztX1RmqVCiAw8zoTUiYXHV7pdHXQUFA+NACeIVu/E0J8LBknMtlGCV15c+w7aB0ydsJl7dS9lXhzj5q59VA4xn1iwfDLdIjOwq071c387QufkWXtnZv1sMkB20n6uJaoSSEOcdGgPTxvpk5mLsR0nX5fdK6CY+vn8MjEBBzBrU6a+Z9+kswIMBama9FJ3l3nNU9WKEOzDZA0w/nprrB2OrTywMZzu4Ux598ImQJSgrtGIdEESyYYn4139Ocu6MuRqVSwMM1kGB/DX0X/ZDBHSChgsMIMgcJVUWjLfeuYVPf/15fOZnr+MDOmWkXKP3OliszXWRgQVfiuLTHjUFcIDQ1iBFqAz17KjEiSfmAg4AoYCPWS/yioHN467CASwTKPvtvnhBQ2oFKjsg0Jd9OQjLMk3UeKo+BglU5yc7g1kLhXHH0VK8VjeLT/cZ1kCbUNkLt3mY5LedAfH1NyMSFQ9oPCjwzwltJdULLPelgkpuxtMBjn1SIHAI6jTqm8gbipRg30ZrJTJ8EcM2Ys2i3UtGwcn6aO56OSF+RpPVzShrVPh+29O/PeDzr1rAnq87CEcDd5C4vSwqxAr0ZGpmDQNBwEAHpgEUQO3LKL9jBAC8nG/hwtI3hZrlhhHsi2cKUC/ZvWhWP+vFb9eFs8wZWDoGh5RCHRABAj8lszxvYPhHdaLuh+mREg5IS0AAq58DBHaf0QJMVm7pOj7XPxrbx6hcGmAQvXS/jIOPw0CPVSYBJQElK1PAyhR86rWP47P2wQqz64aoDfGTiyPq38NEASqjZhc5w8zJ+IeqJIllARcDCLJt5zCIUslkbhSD/pbsdSpIPE3iTRRAMIrxdqg20ZJ2bOc699vrsxiA5UwGK3U+svaNy1oGesPp2JG1DPuGnagxuTVlUBC7ayND914wzWCg2zbV3BiRAbvRgwLvORPV/aIodIwRSpxb+olaMDrV+/QA1ZfRehS9AgPa4dJ6KcPbD4vvR98ijY3g5fFo/vtaGa0vMlLQ9TpfVw0XdX0r9V3v3wjw+e3Sp64/gZb9OazoAAsDWd6Blbwi49E0Zf/OkbwCe2S2DU8xo8lh6Z0Yv+3Dg0K5FYQtfzieEut1lc+RagGBgYDlmiZ7dIC0xfYaLqEYBPJTjfrSLh00rDBiCPx2f98eYDagwMIHO0i5SwMMRjGTRiZo5RgAELnkW8atO7fwya8+jxefvC4fXHINWhgCbilf6PV2zD+z748MBQ/loPw+QPIcAODAklR2yMBM4unNAA6ZMRPjwLKu/UxVMaRZ43NQUEDL3INDMvROiOxpkx9AbibCTkAw9MI7unct/tsvWBKt9V4XRtlfRrFoO+YBQbyqW1so2DcqWzF6AwFihLhLltXCylhpfZQYABCBUQp/ArGS8n/LcaeEqFy7vO8J7PIirwIYe9dyrO47S5VZMoZuQTaMmvke2sDvH5W9ORgGBLrIIzJZiFIW0GL9Cijn1kkAqg7wYaD5BNamlwMgDsf4c6MyQ0NRJp+N7qrgJWI3ZJs6R6ayC1tTYj0QsL9rOuowtfqpfNZ74BCEH5ayMgDM3SHZPkG2Jq03wfTpEhyED/H14AEo2FkuDTAYlsbALXY15zFUOBm49ce38AuvPlcWR5ph7IAM+rNUDWq/oE3jCXVugldgkz/XKQmG0OoSSyYRuqnPPqeyeM3BTUk7au6CgYS66ImABKQ22afUa4WGX1Lw+8HAHvQNxHRY1IaegvarKNZzh7dZFP/ItVXPDBBIW7S0nxfUHsX3b+SNaW9A1Ecq9+zBAVDHWTle7tu9d6eE+veNjF1j8J2hp+68iMLs77tVGhq9YUjqtwP8fsYygU9/LhiWfnpg/xxfRu3Tv0sECqLROupf69t6vQN9TicxO+YALUDAVJ0EMcLiKJwZq3YP4x7AkBo/RS5ZX8avauodmLnUu9VP1XFpHRk49mVPmDDKIzokWuinHgwc1hwBoHEGegDZ/5a6tCX6um0vbx6Hlc+MowMDtGHDUHXIucsp2Bs+8OXSAINwxlTnyRBcxzYnVteeIVM6Pv4Vyd58/OErwmqhTRSaEhWgMKH7aFOv5VEVhUfevSyvKTlmAwuEM14Chf7DQzLfmVanOPp6nS54VdjkWwMxEGg+utIl28SJM8t6MC97y0oOdo5o51HZiv/ZZoT6e6PhazrKro+OZbTrsVfjXr1jGzvsUcGi4rEislP8B23M8Ns1EVWZonPhpQUVofnwR5Aw0HwBtTRibWGud6xggMkl6dmKgdSwCXIulYt9EmdXrc3Qw9qneNeAXl+i/vU3sn4V5ocLK2TggBmwYU+FVVQnASiD4cLj222MwkPASB7r2UXvQSpm2/s+ikaLZbmXjovqz5Mcl9hR8Z/tjnST10sm73uZoy2g6E/qjXvPBJylWPaWtovDrfO3b+Ljv+3slx10JS93NeXSAINFoTVAwKEyA4Cbt2/h2m8/jxsfu4Grj8hXrHQdmKKogEpnshOGjI4FgD2iXdWq7A+qLXLeKjk715ShKcoDiUeRIQ9uV75Lmx8m8q+/RoH2uQK7v77mhM2Ojzxs6p41KlvJej2oQpfQuGYXtjxsq7M/tgoE1l+lKcn1uS/2QafVikZ1iECxa+8RAJgaJaRywgzKufwG5zqrgc3HR23cwTxdGQupe4dUfjORHNe/y88SiyGqn56uAMHVAsxU9gFd2ILiHI+1Eo7JE/oWwCJB0fcrE1XWh1rg1+gUdwtr6hKK9FWj+Hdv3PzxvYDW6g608tfPoDkQYd7jxKTxR9P8s6Kyto5Fr5v6Dz7ZgmU9CDCwv2DRXCNFIbNRi42AZutwtEzjlFowsGWz7GKihPO3b+G5L1/DDZ2S2AO3veVyAoMAFCwUXWnk2tjnd97EtVdfwMtPfQFXH/wAOB+l0wCAklD0et+DTQNqPJVyq6bsofn6U7ySk79GqVK5oF83/5BkkQ5TkszjxW/az7FyeUZfotyAze+1owqbz6QltNRZZKyALZ3bJm/2bR4uKON3YKlsou4Z1WH0vXbgtBh7WNz1NW9sBQQMcMgeABAqnjw3AIDyXA1/AQT1d0XGpRf0TwwMpHJdLxfjTyD9LYAg6djTfWmSbUpIlIA0wcaCAYa6eFALDlrmrWX4+vZeK/fUv/21fbzcGX6/9MAawxjddi33w48P+WvXjO/XF9az+jZl0GLxJjs2d07MMiz6Z6+XynmdXrL2Iqy305628vWs2929aNk/ZwT0QEDkzsmVxBr1Qrnb67dv4tpXWqdW7yRrxdjGjnJpgEGUkLEKCjrv5/z2m3j2tU/gSx9+EVcfeD94viuKCmg8mDqVTSTZlsbVJzR/fU32KJ6mNIZM7uG9I9lPrWDmpVDmScCCX0DFrvfCWR7b6fQ+9hiFBHo2IG1k1aaOOvOGqtx3Z/MsAQC5Nmu9Gd9udqy55ymB2q7c77n93nSN7t17gyNgYO1bz/FMwLHKQc4AsgAEkw8DB5wBnltgAC7MAc9zGTy8sl4tGf9LCTSJ2ZOVAwmeLQBNdVtBASsgICRwquCB0wEgwmTAQt6igPY+JKct0OQ6pEHXt7ND7l8fJ+rGWwMGtp+zxxO18yJjZHVozt1Rby93dS58DfdICGCsl8pKj+rE+I9/AfdPJyV9v4gF2FoPIGyzvQ3kyhqzsGxzBwTysbVRABqWzhVh2apTe+Oj13H14cchcLm5e30gt/oiKpcGGPgyomHEA+roUGac37mFZ177JL70s5/D1Qffpwqw3E29mMocAAAd/7SCBUA8HrskmOS9tnhOWKj8Z2+gf8sbOYRuH/7hisodiAgBg1aKmRpluVodtABAtjWu55gAb/ib3ynul0JVA60w2HZTkSBOrfu5A3JWc99mDZDQjfoE1xaREI4a5/tRRmCgOaV6NBHIitt5BQgYCMgzAH/uLBnx8wyej3KvLOdzOV8BQQ8KODd9xgCQEijpwsJpKsYfZuynA2iaVOnJcU4HUXR6PqXJAYW7BSjIvQ46Da0CBfvXM3x17HNbx41+uC9l5d7RkSi+PfI8W8PjmByNfYYeaPm9IXO6vQz1oLI3ueolmW3gfjtmITvg7gGDq/Vq+5g+srbYAwAWgGDgmLR9cJ+kP2jzvi/o+Ke635Bs0E82Fjjh/M4bePa1T+Dlj3weVx/6gMhlgsiSe4tE+5gx4LIBgxWl6EGBp0jPb7+poOCz+OAD7wVy/QSIGJpZGxjaGQoM5uMCLKCcs7Ps9VAD0MEa2pAXbWOwFTT0SH7pQbcGcr0+UeJaL3A2vQbUxat5BrJja/LcDvwGGQ8UVtcm1g7WLn0fFUBnCkzPK23XJb5Zm8hfOb8RpAWLE5d7IB7CMjIEwM6QmY59UiPfAITCCMxLIHC8K8Z+PoLzrH/V+HPW33I958oYYF6ZPKosASiBHCggM+xJ/vF0EBAwHUApIR3OlkCB3PWOOSA61nyFJMyDhSEsHMiuLZd5PbV8P/uyLyPvdOTZL4x+7ijnzgGS0soaABdWXZc1ACXMI7tV1kzOtK2rbpoKWPB5ITkvk0jvhy6yOoXM5BpgBmrbuXZZbZOVNtosAQjzfVTksDneahzTe+fvvIlnvvZJYbof/IDq9gzKGIMDYBPnXB5gQNFmVZAhKHj7Fp752i/jlQ/9Bq4+8L7i+dR7zCoE89IQ5aMzRFaWTEFceq/qBO1jSLEzfvJzO3HLe02Nx8QMbyDDR5cqdJ4qxVQ1Gb3sQQCbkRrFqtcFwpfK1jgisAEBy7i1gKzUtJs3HD3rIEyP9T3s7eoMAVefAiL8kLioYenG86ZxGIXJjBXYAQZwFDaA5yP4eFcM/vGuAwSOMZgdW2B5CcoW8EqflfBc0rZOqRh2TGrEFRRQSsB0AAwUHM7kGCXQ4dCABNAEoqNjIAQkYE6FiahhitrfQJ1pY/3bAIbvQ18CLc3sDy+91c7bBxZ9LPs6kB3JGHBhOZOTSrqfa08q7VlzQ6YFWEjGCIHAiTTUgwIWeqaOo0br22igh4YgOa+1G7BIot1TFoDgAjagBwHz3XB/+1jG+Ttv4Zmv/wq+9LOfFaZA7RunSe7PSf4qOGiZsPXaXRpgENFoEX26CgrCAaGdUgah/rXOC5Cin5YVCtyItlsGvdvt/lk9ILBn94lb3mv2oAGowtr6H8MyNEh91npkkMx4WHza77/HeDWASj87xVXaY0OZGdNAHWBowhIFWNRnegnzyr60Y7Rvo22jfSND0Xg5HfAdgYHKoDlmYD5K3xgoMDBgbMHxrijt493Sj+z6k+e5JI3t6TNKJDkG5Ay3/uXDmfTD4UxCBsoaGEjAdACOGnI4nAnLR5MoxJyWIIEPpX9L2CLq165PC2C4z325PN4ZgKBfTwIA3vj3x1XO2Fid+5QXUkI9rh8bFqfLC0mh8wL3d7ttF15/pIdG7aXnDzP9d+pea5fheTjBHhTG7RjXwZXzd9/C0//Vp8V+Pfh+kdEEAQOcASRQPkr7I4tM6LVpqVQW5dIAAyBCiq2AkQ8fNKDAx3gGndFnEVvnBQNh2N4hAlxB7lvINaTTe6PWec3wQMIBB7tm8E6L99hSVMUQdSyBxa95GbNmY2zMkGFdYTGc0koKCpL68qaEzOgcDhgCBm8onFc5bKMuLGFtVlqteFbAJjRffTv7uW4wwj7w+TQ+Z0B/43gUZoCzGPv5CDbGwIACZ9mnYKCAB87IdwU4cM5gnfvKXH8DaPMN3KIYNFUjTBOBNOcgnQkbgOP3xPjf/Z6Ag8NB2KHpIHKnjAFPB6mT5SVYH6dJFGKehXnIwvyxhRV4xgg0AygGy/r7+9KX2p8AQvpf9u8AAdafAQAoDFDP6DS5JOsyBqD2ncmY/vY5IcmDcsv/cOwByOWFDJi7JuQ3bMpeXy7bqLZpBJZQ2quc5+87Kn04BQidg0iHbo6a/tmFuY7rdP7ut/H0N/59vPLTnxH7xZJZxpwAyiAmsIEBzmAkgAbJiINyaYBBSB8BZWCUREPNKSigYAe11izMUnaaEbOnL07oNuv9QsRqeNl7zcG1UalK1qN5rZczhI1H1AMHuZFe7xGw+732DpGnUuLO3Mas1fiYcgrj1nZvoAKG/r3TpM1flZWPVVMyY0C7FRlg9GfQdgYU9JkctJ2FnsLFfPYWbzB82zuluGk0iuc0N4DMAAE8IHBMwRogkP5jcM7Id+cKBIrByc33KrzBIQcMUiIH6FIBCpyzggQCDlmO2Vg5nJW60HQo70jzEZgOAnDs2EGZkzSBeYbNcCDOS2DYgT8BArMDCmo894LmQV82/dkbIx7kAewBAitA2yeTfr9lzEI9BsZLTojqmgLKBsxd08byYNeGA71jx3rdU9o0L/Yv9OuGbvX1oJIf07ncPWMbvYM/vymBiebaF70dOn/323j6X/xNvPLT/xhXH3hv1QGoTAGU/7V8A5Ae847jRrl0wACAAwMtirTZB6986Ddx9cH3L8MHPc1k93YdVeh7l6S4WRZMAYcDlLOm3Dh03wvw6mM8mgcqRQsUYW7odjlQFaVu6xtuPa3Uewl0lt6KGR+vpFi3wS55zWLX/t0HHg3bO+o7ExHYJ7f5WLUpMlVY7BLbSszalBhPBSTUeHTANnh6urS/JavWvJTSzlulV1K94Sj7OpDQzLYxI+KnFrb5A03IwPphPoLnu9L+ul2OdQxBPs4FEHgwIH3sgIF/H/d1oHkikM4PTCkDOcmMGs5IB+kvyox0dhCwoECA8wwyEMAKSKaDAATmAhAKADiweqxZ+sz6lajmlKRJ6saI+7OwbF3fnNqfkafb92cEBFbAtpexRpa0Xz0IKIABaPNDgKF8ARD6Hyg6pcgYJWUABFiTMjwFKEyHhrWjgLWTNnY66gSdI23Y6ncPkMq72/meLfH9sVY6fcqBPgVah6xhbHsdsLPQAKSdv/stPP17v4ZXfuofiVPr34OkPUSPZ8gX1AQoFNagCyms1exSAQMpS3pO1il4o8w+KKDAXxOBgghN2nhaG1jNdR5g1Ngee4oPaBE+UA2oHXP3PSXuDueZDanAVJP4lqzDuIzATe+xrCqqrSS2LZpT340oVaCgIIAdWKDpTIz63e8pYKggAWkCZgEJdDhTxL5kEspUOWJADUVDjUoFihFs2pB2KKLGM/KGA1iAgRUD0kxJ5Nx6klFS4T2CgjIeXUhh8Womi5SA2RYBBjISkq4swUjImJFUK+W7RwEH8xGEA6AfIpZz3V9OZRtZY9QKcGliZXykzzgdADbjxAGLwHpTApXkLQ/87kd/tkA6ZN/WmAE3W6SXsQYgWJ/eJ/kCnHF08oVpKqEeHgBxpAk4duE9AOmi+sbrQqdv2NrxPutPUv0pG+pY6DGe0wIolOmvPaN8EQcBwOvf/UM8/ft/C6/8tX+ID/7IT0rf9awKTe6vsgYkx+S3fXptG4JdGmDQoMmOBZDwwS/qOgXvjw1+OX8FFDTnjY63QABAAwbk0s542nMjyg9oAENFvCteNOCQrRM8Q/1uENv0MATnLT7aHj0rYjZG1OWGsuqT2eQ1Y2q6vAcASmIMfDIbmfKiBMwJnI6ayZ5EoVq82lgDzeY1Ort4nB4gWJp6E4bh6pECajCUc2FPK+4xJP6cJUjdFb4ZGZEeFOTK4shUROsTZzTgQN9KoZSaviFKITigTinSxviy59NB7kdIUu/EoHkus1IYBxBJyIEOZ/Keh7MCbOhwJoqaSRVk0vaqACEGfNYf97k/I493oy+NHcgeEDQJoq4vWUNBJ8jXSLbk97Z8ieG/KzNLFIQL4D7WawLnpGH91oqxqZHz5Az/RXSme1HNW3Ihg559tfdgRv163qTxfQ2tHKvRFkeL6zub03FCef27f4Cnf//X8cpf/b/igz/yk9sXMNdHGFCw/ah187aqL5cHGDil6afznN9+Q1c0/K0VUEB1UJhnZnGZ8QOHR/wKVaE3DeyLARrFbu8X0ex2r74YmgUaGhBAQwUCG6AB2FTgDTL34GcUJjCFZclv/jrNbrd7lr9z1N5z+cAMpQQc5xqj7pXYdJB6pEkozTwXGrrQ0Ycz6QuLlULRf+4o6aFRIem/BiRA9y86CMVYNI3Zglq5TcsWDI2IZw0s5uxBaA/GVoowMFkVmyTrcc7alkJNUkqYjDlAKukXnDNo8KHeMNfAxplPStR+NAarBxXLdsvSnNbHOdew6zxrCE2Ot2Bu2ZeSjzDXc8RaoHzc+yL96fvS9m/2ZbCwVA/uLDznc0MG8nVPsgWAMpc+MVamkS8zmPPs5K/L8XHX+XuPu3Xp9Q+Nv+oYad7T9KT8doDA6j8fJWSSNWTCCZwTkHJlEewZCeA5q26V92Qde8VJJOO5orIEDA0oeOC9jtlK4flhUUBgrFwFqeNLLhEwkLf0yRrnt9/As1/VZY4fehRocgVWGohoBzgokGxcpVGCyymgIDKodk830Edzx71CLTTgXRQqUM6hsvAMO8BQKNRUlXwvyI2n4ViDENBseDC90uIVaro8/6hGbBZFVhLYvBKbDpKkph6fXKNeEHNLR/sMbK2/eJwzwAIAbOEQ6VezhjVhzVaSLyOjGBZfAk+8oZ6XjEH9vQ8ULEBoVMyTmSbxwlOC5TIRNGZPCTjeRTocwCkDmEr/MBMIE8iMS5bjq8UBAalCKrMTAAcKDmeNd1oNEbmFkgbK0d53Sjq+UMGB9lv5nNKoL2EAoQN899qfa305StjdYglsOqmeU+RrPjay1YOBU2TLXqnKFmDT5Yt85VwAgoTxcmXpDCQAlaHEfp0ix9y47h2li+hG67aUKlvm6o9pkndWAFC6PqOGrxpw0NP7FRyMSzyGI1AQvETRO3tKzTdYL5cHGMALn6xT8Ozv/AJefvJFXH3oMTX0IuyFGSgJGal2ov6unwnZakTfWRUVErMooh1U7LCsGFUAbYxwT2hhRlXKjhVgBxbMILbK1xT21GCruMotIJB6xvUf32NFWTnvpigrNV48c/1ErS85Q2fyoFDFSafyZAcSSD/0mrS+KS09TrbpQFgaFHlYMRzkPMgt4nkxv7kPaZlnWfY5ULBVTFnZFFs1/sUZ1vvbb7b3J0vky2KQvdcWrFuwOe3Nv2/nMTbrGmgde0DgF0IiR1c3qyaWd56wh6IWRYlxX8rN5NxmURoHFFbuXc/nfX05ZHzMWcg1DJTnYigbGfOgOygRKIjkanGdhdlGRVma4m8lAZvAXAwsoEbWxiIQ6pQF/b8DBJT32dCJ5Rl+rGVIxQ0Y+/pPwCbYXSlx3taKt08Jr7/rQMGD76v1LWPOhzRTyyT0AKKMa2UzOOP122+u1vnSAIMGFNx+o4KChx+vQgjE4CABsk4qLwCCZXsuHxgIiBc0S06dqjFd6B6bZw01HLn8554RW+IeFHgAwsGC2OS/ye6pQHZAgVOJCRZaEHAhiL5uUcXatlp8c4C0vfuYpqOsaxGvtCQ+cQ6Vlp8Tf0/FOqcJM1SPU7KqId5E721SbS/zMMu7EO0z4EEYofEsy/6uTdcSYa2kBEAXm0mVqSI1+vKbgEkNTAfsqFPGkug3UMh7SgBQZYfbNiAA1FURB8soF1q3p63XSnEWyo5lX8qLWaXrrlP7dBco6EKQfbvujZVvFJqoyFVJCu1kxzONIZsTecA+DGnvYZ63q/ew+v5AFBLoQrHNuLN7n6QHIfU1x2Gk0/r9a2OOgkTEfnpjdE+gjMXXv/udkmh49cH3Vz1j93A5TpugoC8sOXfXXv15PIKHh6ddGmBgA+P89ht49tUX9IMSj9bjRMUICjyMmAPqAIIljtROPH/nLXmcp9cLFJ4Wyl3m95J6AACZoVGFjOPdBhwQDiXJRlZ9y0rx6vO0umUt7E4ZR8Kw3D+X6kJjYpzkNxXDrff2IQcTummqAhoJjR2bpnKNZPEq5WbvoC9iIIcOqWTRS52TUtf2DuNB3ysueb9lshSAxuCQ68dqlC7uHUhFuYKDHjCMzm+28/r+vljoSzYkZK6AtIw38+aMMqU6ZdB7moB60c4Ylfho4MXJEwMAuKM0XwgM2KnyboP8lz5TvI9jxwraK+dRf+R2XPf9aftW77EC8i5aLHfChUGFxRNQxyW3Ijn9oHIO1SfZ1otQBmBHLkgoU8BSrhyzA6AFdfVmy4ftAQR23gYgGOm/+ihuwMHiPSJ2yoNRm9HkQYFbv6Efb2VpZ//ebsxErNPTv/druk7B++yCeg8DBG67X721f0Z9mHyF8dlXX8DLT30Bf/eVvz9sp8sDDIAaPvjI5/XTk6jGGU4lcJINIskmVXpYjlkM0G3b/d/5Fp7+F39TNqgO9qZzm7hivQcjAwethyXoGB05HxtPDkD14vT+stCL3ngBDnx19gnIoqhCad7D3ssSuvrivYrm+FSNyCFVDxMAZhKq2u5LeswzIJMCBdtvVdoIy5A3Lt4jjQRel9sNBd4ZmWb9h9Wn+4qsGIsFzXdCPxXQpTCSFNxybpVCB0iBqX7YiLNOcW69L2EAajy31NKtjlf3OUW+FqN2561S0F5+OoMk+7xhaWPUCyDg7ucp3IWCDubRI1LiW+WUPm0A3HopwC5BdBSgzkJaOhDTmeiI411JnO3CQMK02XoP3pCOATBFxtwMqP7u2R15RRoCAep0SNER3pnwxefF9MdPYafK8x24seeOQIHOYKJovRPPEvhjjh3gaL2GoE0aoKrllZ/+jCTKl1Oo/u3vOQAE7dRaOVaYdG8fB+XSAIMQFABNJ9jcaZBTmLKyCRhTAQUUgYI7b+Lpb/wqXvmZf4qf+u1nYkPJXC00ZzdvFA1daCChAAQFAeAMHO/CEl1wPFYPz6b9sMs87oTfvG5wFvJixZBS56E1Qm/7ew+7/zKetmVTTEFMNhm9q8Ok82tHuQjAgrYuVdqjDNYo6RU6ep/AB9Sd7bO2oIFxGYGFNWPhjy0SYh046MNebnpSSbY7WH20jYF49kzZcYIiHoCDXYGdFSO8ABPdud54RfHbhoKNvLRIQQfnr9Kzo4QwwPVdBWzNMReGathLzh3rcxCG0W4fORA2boOEZeplKWfQluZPnS6wfREbELE8zb06XZmNsayhueJM2C2MJdVcH/nim7IkxnAae8K55HMt2AB7hZG+uwgg6BiChh1Y6AcgBJwr4+bqw48Oz1+GDDxQGIcqFqBgA6BeGmCwioSKsu4UJgCLBYuSFMPOfjEZSKM+87ufrosjARKD9mVBHaZ2P7NL9jFWIgNJVnVLRqHb+u8517XhbUqdZibTdLZYD6B8PMTFfc1LCIv3zCI6EFgYVDnsjCec8h54fU3xHmSJNzrK2rXXiLYuTdwNbOoFbQRidsSmyxf8VgW+o/fs/r4ttuJ9tfL2Uu31C0PilEFpCBfGQkBNcsbIMyxsAyBzz9equP4Gfy7FRsBWEuASfND4+AgQ7AV6o3MaIOCvtX1TTXzWcGboQJg+IKoOhJvVVGageAB+H2RI/nSG/6I6oACCVgeQgl/OM6BhRczk6pkgNGluw5CWHDoaB1E+SxQy8AmtnkG0T4AvdENSYL5TL/RtNBhD7GVxcX7VLdzrm+iezCcxBVYuDTBYMgVrQu1+K0Iog68BCgoKvv4pfElnNxjrwL0iXYkHj6YqcdmeG5BAZ+9plrAlFXZyU5QkOU7nOe/xEkZljQ7cMKpyeb2uafOtOL15Db7teto6oKyLElm/+1JZJUc170wYGgKCNYZgI364WufFPHgv8HAgwIOFe8uHYH+/pjJB3VeM4anvCmA9eS/yaKLz7zVuXyqzAhaC4xfuW8tDsv4kQslr0jNXAYIDjcYymn6AJYlC5aQLxy0WMRrVdcXQL8I7F5B5qUyQQ9CDG2YBrF6/jcKQwDgycgp76L/k2YEBv6RzBQPTEgTsCiFgLE/pLDxvAQRG7KQr57dvCih46gsyO29nuTTAYJFoCJQGW/+gTU0YJOddMevshtd+ES9/5PO48vDjZV47AP2cZVw8RWvTwOocmGVoAazTjZgLSCD9OhydvaeZz0zGGnBd5KQki/VAARh6CaPEr13etTvuY42jGO9W6dd7EJ0ZAJsmzr1jGqhXVINY9CJr2At9eReKhd9+Y0MB7CwLE2ehgbLt3seMS1QChTOsX++9un1DanIFdO8xmIUtKhd5ABgk7cHJVDm+55oTgcTIgwtB0ml9XNdAyNWp7EOWXW6TeMNc38k5D9CsiX7NigIWgBZYGyO3ViJ5AZby7fddUMa9fHtgAwDUre2ylwUJdRuw1G+Rbju41Rqnujx6+KE198XIMHTQM3uNs7DNNHHPRgcAYPWjXubUvn0L1159AS8/9UVcffix5ph3UKNyaYDBEo05ULBo2N47kD/s8gHO334T1159ATc+eh1XHnliobT58ANDj6c5N1J6LB99Ya/kLL/Bz0tWoMAKFMgUxdF9Qc0BhTIFzWfrdlOdwmG5lfU98K7Db7MDRVBKO2wpUQPII5Zl0ZYdBToY4EshXCJuBrCIB44YgR4ErALOPYajGz/N0qX2DhcAGXuMffOOI1nRa6wu3d/c77D9K1VLKLfV5+h+ajYXf7kxkMHnp+036njgLRBxkTJU7Dv72yct+yWW1SkJv7yquqA6Jl65Z/GqG+cGuG8yAqyC383WtPoc0EzJNF3Wf/9gxIL4pY/7WTNdzWuJQh2RXnO5BJ4VyMnYgBYIcPIsQUftRwZ7hwz7tl2w0eG9RuNQHIbzP34d177yPG589LoDBd7JSBiP5csEDKz0VG7pLFootuBiAMCt22/i2m9/HDc+9jKuPnIlPjUd9mepY2ncOFJu5a9jDlgEpGEUJtkvtGG30l2HsotCWPOwo9hhAAL6BLw88qoHlNcpC8JwCAjgjIAznv05wweMvN6N+o48gf4+Vp8VgWsVdMcAlJOGl4/LDrqx8TICmWCosWf5W5tajFIxOVz7ZwESyvtEdWw3F2CAqA5B3V+mlRIhYQJIh6a/zh6o8lNyJ3pw2QGH7xdIGPd/avu/oJ4aEuKAWQQCoOz0SH3wQGYw0HkjansHU7Rbll192bYL6Kl5VzRlB/iWX0kkzxwAp+u1LZ02YgSC/QUArILq0hKr7RTXuzXLPPgdFQLh5ts31X7dwNWHH29tjeZxbJXLBQx6JdgpwEb5WfG/Cbj19k08/9vXcP3nbuDRh68Mlhdqlx3anhlIRQmWz15SrNhKJ5qSM/TcsQr107OzeFMHBQtO6PzXHL2iKHH6IOs7ZAEMBEQAoKfSIhSt97Z9o1Kp5Q06uTmnAwSnKvvIu+72LxT93lhhUC+OTrVY86klUtq9onIexkIGWJO9uTX+zXY5T7btmAcJzd891e6r7MGAggMZRQQQI1EFCUQtaLBtECHRVK5tasPe6KAmAa8Y0t1lzUBGoHGxo9vT1aN41Itz74EFGbFbozG/yowFpWE39B0idjQMpVbAEIEFAO3iT/bISKe5cEc0q4hNnxl7ELABZRl5O77hbDY9tKNbokkUvc0J79PZLStv3L6J5798TUDBI1f0RP22CVB1zcaYuTzAYAMUZFQlB2Ch2ADg1h/fxAuvXsMXnrqBRx+6gszjzj0OQjR7FnqpXpBs6/AFaGq8IQMM7EIMu8CCCRtYaPrO6yB78U7IC/3vAYBV1IOAdKgAIEDQI6Mkb9O1V982K9sLhY/6br4zlssL71T6AyV/ITo/un0Y827p5f03cwAA6IDBfhCQ8xIAZGf8y3DSc6yV5yyhMP3QreSGdWO/cY67jp2ISnNPIBARpkSAxs4FDHB13CByk8gAA5BIz020AhYAUsCgtVqMmZJbdB/Bwf0aM8M6XXBMh/lWgYe75qXuldmiv4AWnHk9VkCDHnefDC9gQa8v4aTDUpao02ee7WOrTefQbIIAOx7Jk0Uy7HkFIO/X/0AFAV4+jpGaWLmtXfrm2zfx/FfEqX38kSs6Bdb0cAAOVsrlAQbALlDglSNQB/kbt2/iF159Dp9/6iU89vATyKYNB+VuHitB2V4bIM6IOZDgo0dVwSlgmCZUX5wLCKihhqWQFVSuFfQoPixdjkDZ14MAFZhNVibX9rY2Gfg8wxKRci2wmuo5TiP1NHV/yr2UffdY9j/3dAHzEIyMHx55crXt7cn9eM+NoWfMenwGN/uYVVnp8D/OWc5Rw2/HObNuc9kGgPBDfV2x1Xcpybp7RKRAwbYrcJhAOEyq0Ij1OGOiKiNTBkDQfVWWEhFyAQkGNkSeqDQlgwsoWOZ47C5brBK0EhtlywjvKaN7LJhSHR9gLAzbvcppGwLCqtPjAQNb+LPTYwCaHIW1jz+FIcFIj1FyoYKqxzJiHeb1V6/X9pS6NDT76um+2obziYvTEYv9euHV5/DFj97AEw9fKV+IlzcagIOVcnmAwUIQqTVS3HpIQO3cN2/fxCd+5zm8+JGX8IGHroTKre98fx8AYSJW7kQ0RtqyN5WkH/lj3lBMn0q8tQiahF4rovaC1giW97S72hQhcl7EgIXhvm3z2AA1SolrmyyUV1ydrq2sndi1U3s8ZmNalewRe/+coZke6PSeCqTuV/vcjhmgYKCtliVn0vSD7miYALh+6cCAGfrsAIQBgeMsx44OAMhvvR8YObOyavJw69u1tZDKbDd9l2QsQCIkqKEn4JCoAIZDyiACDhMVoHAkIEHCDEdiTESYSU27goDk2QZUZsGPHRK1WcBCbeaL901/9ZaxH9qBwf6+eVuxrhtez/nzhvkhbOffBxnVEJDt93os9U5PAxiWYMEezr0eW6nQQo9tOTK55tWshtWCdoraqlcXRW+5g153EGo/HbMBh30uyJt3buIT5tQ+9IQsk6UyXZqhr9GGQ3JpgEGfWOU7faQsmRlv3r6JX/zq8/jck9fxgYeeKErOykho50IltULUC58X4lU6yBmJYsxIPvMrOJfg6VMiko/eYQkYQAjQOVDM9B60jUFiWh63pXmhUTzay3KvsMpDwvrUn8aWVBBQ//o2s30tmIATSG5u3YOJ+qxFFZrzASAPrmnqTeHupbBulB579qExr8Rmr9ByGyLwzACzeChHzg0YOOaMGXD7cgEBRwcIBCBYfbj5G5VE1P5N0j8GEA7J/04OEACHOQlgmDMOE+FACVMSI5M17JCJXcgBZdE8IsKUufwmp6cjhumifdP/HuUzjfRC74A09wquOcXIR7qpt7H3Jpumi8yw+bwRr8daxqcFbkuwUB+l77JTf5X3Rucg7gEAgR7r22Vdn7dtM6N+TMoOWV+lbqwVx1QfMFjMEQDwTbVfL37kJTz68BWVdwYSITGanCZyrIFvo6hcGmAwirF6b6gfBG8YKPjwdXzgoStDT6f3/IHqdQHLQdOExso90O5YfRf546JjIljOC26StIhChqFea9ne6jGVdUe75waGxt4vFKC8FB4zOnY+9LfFpEvbaYP4uHQkaF7AJhMsF5uGvt9k66Arm7CmqOyaiqZVABcAom2mKtgOxHE9J7tzwK7u/r0GgGFP6VkBYKzgPEgbAQLPDhx125iBY65g4MgZOQtgMCBgx+bMBQjMmRc0qOqopkyp9lfSvjMgYEDhkAgpZRwo4e4sxw5pLkzCgRMONBcWgTXcwIwSasgEpFyNEZsxAjuPSiBA5nYq5YX7Rvun7Hf9VA73xtxfegGDb9eNDH7zHLfdyySAe5bLXiZF7bBzYKRvjh1YSAmgzCFDas+rBnSKdZf+jWTDt4sHAJnHSbWhE7NXf3PVNXZt0eHc6iEDtVY8sE6g2DklAQWffO15/NaT1/HoQ08Ik6dtZ7/BXo8RLKSwVS4RMFh6uob8es+1BwWSaLhs/X6PPyXn04VQzmkFsdw7L9kdL2QAnKDJzb2wtegcC5ahXObuJ7/j9/OKaS0z3QTIx6V9Ypp5nJZk42PT0g51/6j49c+NjC9torFpO+bj09aGjZLShkgd80DkgNiIddB29IAhd+eDHR63cdEct/t1xiQyRF2T9ArPTlkDbFm95JxrHx0HDEEECI6zZwk0nKC/v3fM5R1sX3boenb6Z3JjO6VUAIG153sOCXeJMSUBBcesfynjMElbHbMBBBk/nJLUn2SsHSiVzmEGUtL8iaR9wYSUGMTSJ0RAYm76qXRDPxxP6B97vvyNQcAeALDH2/cOSHY36uUQwCJPRPZxAQG9TNrxUfFyB6DJFbH9PgRkcngEUPNFRH+lHDELlSG1+5dn79BbpT03dFdpO73Q2q5tk4vpawBFZyetCwHFkyfV4wJoqdTZ3s+e2w+/b96+iV9+7ePFqeXSEJJXQ/ob+m4TyXtVcBDc1JXLAwwC6sjQo0eNISjo0DfKuf123XEvgunv3UxNWQA5OakKnvz1iVlL0LAEDEAFDUD1oIEx8LY6RorIv6u955FzmKBmlDRrfNruazFqYBmuaRCz0wDeiyu0IwwMoFNI6wltsHMSiWFoWIaaDW9tlx1IEO+yooMCKCIgADE+/jgRLbORx/p31cj0HhB4fw6BsQRH5iZkIACBC0NwnCsguDtnBQEtGJgzGubASmZGIsJd+2J3ImCeFRhUkJA5I5EcP5skRJCZcJgI+UjIKeOQDIolzMyYkTGz5BxgZmDK4Fm2Z0JZSDBnyUFg4tKRCR1A0PYd9eGe/hn10Wofct0w42Xnb3qwTv56Nm4tURSo8sju2bnpN7jfsTzKtv51OSJAlUcLAUVyWEJBQAPWC1jQZ1R2LzaSvc6ydo7arm83O9e3m7/P6vqq+gDTz0cdPPZ+x7l1TIp6V5BAVjfTNQ1A697SbX7zzk186rXn8Vm1X2bnkt6DiApQyBSEFLoATVQuDTBg97coSfRCK4mGn9wABSNA0HsEW4LaU+ZeUOu++rC1QXh/POU90SX3jsG7oXs/zwh4BbSWqOaT1Kyf1uLSVmp8WrddItsyiY1PBgt9e5nwCivQsjJG/5kn6hvVFJx5ATNakFGWbkU71rxHNIo1hyEEHfTeKzoFFHA+DRTcnZeAwIcUvIGROnuDY30ohj/ljCkBmZMzOhk+Y+Mw9VO4Mg4pCfBMEGqigAPZPkypUqkQgzmBMBPXPgsAgu+ji/RP30f+XK+XejDQG7SR0xHpl1NAwJosAheXx3VZnBcJpSKDc8gomFddwTpfSF/Zn/vBnPTFpxEf9Z0A0TcGMCciHMGgxODZ6ReulH9TcVf/4kxQZcAA4K13BBT8prNffY6CxxX2OwrprZVLCQyqonT7gC7R8MqqF1DuOxAQv/tUobX7RtO8RvJ4V/96b39k+AAsjJ/sW9JdQKW8rHiPb5QXcBEwYEDgIklrQJy4BvTKKFZORwImYhwTYcpVmI8Jui1e5ZEDkGAI39B9R/+x0Z2sMcFS32pgTFJ7kODL2rSnyNiMQAEzF8NiY3POdUwyQ8FAHXuWi2B/AQNubd94m+9ZglJPd8LRHTpQPZ7ceMssWtL6MqttzyyGXACIHK/1E2A2M+FgbZEZnEhkLjEoMw7JaFVtMyVTQXKfxNR4WoV6vQ/9Y+cXY+UUVB8i2MvK7QUDW0Cgl0PpF9cfWPZrX8ZJpEL/pwSkXOVwToRjRgHsEiYiJ3P8Z6Kr6r4lcKr3WX11EAFH+Gm3KudEwMwCMg0gEDBlwpwYExuGXYID0SXLZ5X7Anjrzk186msVFKwVg9aFY2PHKti9V66/dMAgdx1sg+UNnZL4W09ex6M2zxOmyNnyRQBIB9ngIKJQIfhzTq7rQIABDOn1viQSsGBK1rxkoKXx7B0qw8CL0ISVye2Iko9GwuXfw2evj8CATJ2r79lTz+N3bhVEynV/InLKaKycUobEnE2YmVpBNuOfGMipCjGwBAhkyF6oO3IGB0ADENrB0hqgPWXofQagwANi/xcIFiEKBlqiOv7Es5dwwAyZgmYKddIYi/VZoqrpcuYCBsp9XcJhfRa1+QeE5pxRKKmpf2rH7cGFFcn9rZ7YGjgA7kf/2PNPZQg8ILA/a6G6Xg6PeT8ovzc5VF2jiaPI++WQk4SCIqB+Fyy6K0N+uz6YXEhqtJjWWi5TFM4ETtO5QB3HzLaOBmNOhMkZ8VPL6LIRKLDTe7agL+zGO7CfObg0wKDpVK5KGVBQ8KqsU/DoQ/JBpERVAD040F1dR6mS6gajyUeJQbMIyuwzEruyhurtPfp54fGN5E/KHXJXA4jMLuYX0+pWDBDcHTwvotg80zHyTCxxbU0RRXHp0QIf5i1MiZBnLu88JVVAbCyB/mZSxWTbRkvLJ2wFAMh7HZBUuJV2zoRjymMK0BS9ggMBkCgGB4AABGqFkVC9AD9EYm+h3+5AwUoZ6XXx0ET5EmvoaWb19hTUaHsdkiX82dU1ozkzYSZJ5ss5l74oBmVq+9F7ek2/uUTEPsfApi6WKYzeKzVvUunpMrYHGnbkkVmxPrpf/WP3tB2jRMJ7YQn6/B2Tw6OFejYAgQcDvRyuyaBR8l4ObdXKzLEcHiZ9KRCOOSMlEsOqcggoSIA0wpGW/Rnpp9aBiVmAHgT04ZJVPavFZgck8qhZZHxGCw5GA23BQsJywex4uW2T0PxNBwoec0xBk+e0YeyNNQC143JULg0wAOBQeS22ouGLT72Exx++IkLJ3IIDoHh5cn3XYrT40fZ9AA54VvooqyCxXSeD5xhUvwcFfdwvKolqL6ekdVF0DvXwUiLMDM38NRpMrqMkhgIYf848ylKOmI4oTBCV3juJlFH/zokCZeXnAyXH+WR17VURFalgpUw7gUYi8WAyGnBw2EDjgLaBAwdA9VD9hhkeO1byDLoY4vg5rdHZKs6pA6Edk8TAxKiLwEzWr2L4MxMwZeRcmY9EMkNgSlMBdDY9MetXAj3QWyv9jASbvrg+dVGS1ezYIcnYPainWTLclYL2uSJe+W4V3z9tTsHKNR1LAMTK19+iX99jqwxDkmHYYD8oWAMEazIoDhCXfpx1gI8MVHYyWAVCGsV73keoA9M5I4v28G0ZhGNHQCBahGtdv9oPVnAgIyR6z97hstDIYkx2oKAPLfqp1G91iYYNQ2DXOafDX9uXAg7ctaNyeYBBBwqY5YNIL7z6HL7w1Et47OErmkQGAHXgJac9fHjB31PuK7SW7Zo0xJDJhRW4goPDlECZcUwZk2ZBGW02QxVYFoQrXhq0AjRUrtH+gmIB5NnAjnjJ0HvbYBDv2QyRIlIDBQoYrJgH1lPNUS6EF749JZH8FykZ27c3UabPkt4qpo8uWyH3X9JxCQgAmN2YlCAnSgyWQMCcS3iFZpkCmLKM5UPSRESSuPApXmdU/PoFti2Uc88KbC94RCQzECZoYlvql1CuibkW5fDTeS9jOUUOgRoqio/F13jmzu/bK7OA1jN7feRCVO4lClDAti7aCwb8bcbvHr/MaKVOY64sZHtwoGA5+0LZqY4hKNv691t3bumURFm8qK1H/V1MmL8H2jFuREZJ690YJ5cGGPQOqoCCa/iCMgWAczDJcgda723qxkKztL1tFCMrHpclobECB0vyyBBlNWHCEVk6aZYEqUYZQ1aX68GBUBqihC3mOxJiH+Mtg75SIW4QGPVXG8OYkiKU9pobGsbAUEKtdwEpWvcSmGeUFehyiUkz0iSZ6XO2350HEpTIsFh/jIxLv2319+EVAGHC5lbxBqfsa05Aqd+9FMt1EYhZx4TkPSgwBA/BAatSOJsSDoCMyQQcsiiuIwOHzDiQJZKm3Qls5dnAKlNkZbwsct9v42WSe+XbA4LeIwvX+NBzgdbrsva+l+L7xzM3xWGj2kd7WIOJhHmcWEOFahUlHs9ljYpUHB8sZFC27b24vnRGI39bsifv1wI72+flr+5r+9bap59yPCoXBQR27BRA4N+tvNfKuIxAat2/HJMGCHow4MdkIsI3b9/EL+niRY89fAWj5tmzBHxfMsQ+rg27SwMMfLnpvpL4xCMCCmp2uPMYVVmuKQEbP8JucWnxQ6Ka/EWaidoBBIYY3DNlD6ZJ4oU0M6Yp4ZjlU6LTZGvSE1LjlRFyUsRbFG6t69r8Yl+iKS17CgXWzKN5AxPJ1RtAU3d5fr+PWoFNRmmqgWPgbOODg70yAiIBXjc0nore/oBPK3S9wYHts/q5hJ/e6ADxmIu6sI1zU7PT6XXZNoDQjceSZa9jkgHQIeHAwEyMAzMOYHCmXdnuVoXRWhR7E0ir4h3Pgd89xTQAA8D9AQR7+8XrmAi8lTwmMg+uBXAWkjS9dEgkV8y5CUtODGCS6ZrErDNr5EEj/SGA3LZRw0Rcv2lgU0nXZM+rhEj26v7RPt3uAIF3yiK9s+WoRGUtd6B3suJxuR+oHpIHCro6Z6JN1qou5FTr9q07Agpe/Igw3VsY1TOgazrGWINbb9/Ez3/lGh7GI8N7XhpgYH1sTMEXP+pBwdijoy24broYAIjwxu2bABQpsygDv2ylX5KyZLkDxTs+AJiQFSAAB41105xxUAV8nBEKONB6ZmtgIPLK5G8snP6Ld1ZC/eCOH9BmAQOSyzBNtIzzFeVTFRUSQoRvJfI+/efW43eLldGpgGAtUeh+AYI9Tml8DpX/mbmsambhsGQeIrXjUQwS+5QLHCYxPqOkNwBN4huwnJ1ipY8H+zhwP5ai5Nd2Js3GuhyuT+z8Ub+cAgb2EgX9eV7HNE6IjXvUfmnDkvLOBuCYjYlbhiU5KWOURW8Z+zhl1vUZWMFbDBCsHjlTI4+y/97kzs6JWAG7pgcB/cqJa2X2esnGHiprKeFTWjKXAEostXuvxWJNQybrdIZAMUHVOQMwQGiZAwD4xa8+j88/9RKe6MIHo7Lm2PpDzK3T/Pdf+XvD6y4NMABiUABUhRGV7JHi6Bx3/1949TkAwIEIDC4JXIvsYnb5NSwKNOlxOiScscydt/n/MZ0LzBQYWQYwrXtmQCS4sZBGS5vKMacwB1z4GdRIaOO16zSoouwSFqW9lt6mf5+9mcL9e/bveCHvE1iAAWCbAjzV8Fw418EZmz550TNbrHVrKG2iMmXOrusX8OJEJQeF3X67yK6N5pD74peRdd8urK/hwUGqYMf/iIy/7bd7/Ln2hZZ2amOnjV09PECQ63R1hY7hMXBQGJ6JAHUsjOWZwThkySE52JoODctDwynRF83ML78D3bLmaPjEvD16BWhZAnJ6BUmcqfq+Mr5YBryAqswltGpJg2mlk7d0yCiMVb7dEbADkwcDCjI8EGgZyNoO3n5t2aS95wD2aeaWSR+VSwMMak7BoFG78VcG9MZ9Sc+5+fZNvPCVa3jpYzfws1/6GZ17TTCWf0IHDCAJfpagJcyAeF1Jva9EhAwSSjf7RYLqGvYLIe8MwZZAjyi7aJ2D5X67JlDq2nDcr5oBDw50G3bueHWxPrPYXx8VX6UtJbS22NMWMwAs6T/ZVzdGxmfL8IxVon9P6+9lYzSeVrU15cHZnceDdjWvNcqi31oVtK0SLRTRWunbY2Swm/budp7CyCyet6OOa23fl0jXRMDNvuRS8pdc7lImBsjlLfEgDLQCEpDicJC9x/dL3uDetV+V1V+/qUt80XcBRH8C9Z0OUBYrVeYyYi0BG8fjHo90ZO9MWKjAgwEfwrLcAQMDk2MF6memlwyBmRFf/p2/5JxaINTwkT5ZzCJx2+caPtgDCoBLBAwMFFz50Q4U0ECBBPcYDZ3zt8/x/Jefw42P3cBVbdQzU8RUBRbQOB0vGYSZNQkMSuVBAAOzIN4DEWaQMAmOyjUa98gtfVsNqyqeIAY3+vjQGnUr+6uH138QZNFOAQ/oazL6GEn/FTevlPqlSEfxxfj9dDsAAPYeHgTY+xgQ6D3RERDYCwL6MbWX9lse22PK2mJtwgu9WDcio8fBeSOjv8NmbpbRq+0FUxdt070lun//3tQck4Ol/d0JnqFU57cwPP2X9DIpqMOS2THdMk2EAspM1ziZi75k2gN2YJ+M+XcCYlmTc3bqjr5MS91xhlZ3ePDjwcJBWUvZ3zodUKdq9G4j3dgnt0bMgIUJkvZf+Vw0kRxDxxB09qjokbYZUFsibrGot/wwtTGXGbj1xwIKjInYI7OXBhgsQEGnxJcd4KFyp/Zcy93843Nce/Xn8fJTX8TVB98PzN+TA/m4pCcp6cdaHN2qG+K1SWb5nDybIOsamPDPrEu5civsts59v9QnEAv4SKC9MEfx29JGDtFSd+0pypZTbfmlp7qkqbufq1PfrISKxyNyVOMPxADAzokWDTHhhrvH3oSftbbaTV/vaO8VRnbjVvfBcv4FK3uxyq58ttE5XbN5DRIlfVkRNqDQByV3ye7h2R1zKkpd3eyTQvAEgOEskV5b7+vf5b7Il9uIMuEb1ucUfeHeXfJfYHO6cMYAT7XeBnwknClg4czpmZGDMXIoemdiNKMgChEYK2AsgwcChWEpbcTV5gRWmszGWL39ZxytjmW7HqN698I0vHH7Jp7/yjVc/7kbeMJN2d8CB5cGGCyYArSgoDYZUBaM10Klk5x4M+P89ht49nd+AS8/+aKCgrv1muOf6o+g0yhJZxIh6V9jFRikn8V14EAHswGF3AEFZplSJkKC0DMAzBtfSuHm50Ct+qies7Vlbzhlf7fDlZrhvDx2Gk2tG9M+inpETW/Vfc377xXeyfHpE23ulnGPDvf7OpPkfo7BLzXjPmjtSItE591r6b9jC8RWxZ0XKc34fkuXwNp7dagO+mRB28an1Xv48109m1ixcxKFRXCfkLd6qSvYroLZvkgvW9XYtpXOwHKOdleaFrxAuKe51w7dENU9+lLigilxgEHOaxnKvlJrn7Tvw4lrQCCh+22MQXkaF3tDnGNA0MuSszOghPIt1qbRpWfYfTtHKlkCI7j59k08/2UJfz/+8BWANf8CsVj5cmmAAbAMH5zUQbptSS7nt9/Es699Al/68G8JKMjtWoWUo7ULtQYkg4y1o0DUgIUpydDLUKDA0LBCDBQsk9lTihnQJDF5YT/8R4kp1cDVvxF1HtHm3mjGHsI+K1iV07jOIUDwx4P7hkZzw5CPAIE/p795ZLxPtP+7yzYQCJSLttcC7A7GeW3s0Xk7FNm9xBPWjLkeK8afUneNeXR2HsXnFSWadHe7v/PBQnjdv+GGPV2WwfnjWPEStbQyou/sAEI9x47585dvdb/kqL/udF3g608NkOmBT68H5Zr6rgcbC65Ge/Sh6bXR7IEQCDg7k6yCvY1h2TeUt6C0tqUf8zbO5woa3HhmzVM5v/0Grmn4+4lHrjROpYGDtXJpgMEYFAw6Cxh22PmdN/HMVz+JL/3sZ3H1gfcCeW69KgB0bOmeeqAqM1FaSfclEFEDFiYFCxMRkKbCKMxKL2QQcuZCp5kwGJgwQQGW3sGgWtZE2mZ14Nt+T4OFgKADDP5+/e9BTZr6WuHB76IUooN7yornEh3e2t6uzPcDJgyM8hoI8ACAi1pwiqo7t+Gvi382Bgj+HC2bU3/9G1EwajqDXg15as/xyLbZTu02lK1zTF4LFmYHOrzxW5qP+9ur6+Nm6ppmTVYWAHvojZ/wBt2pI3k5TVa66ugV7Pb5BFhjVAEHEHRf/WrlWA/KdfHzQxbR241OB8qMC9koeQMAwDOQByDAZI5XZGhQSBkD1jEqO+vYJbYxq+9scsAziDLOb7+Ja6/+PG783HVcfeSJhiHz4GCtvy4NMFh4uLZrDcF59FaYgjfwzNd+Ga986Ddx9YH3AXluFaeV2aG6sr6sj/dgqcAABQdVgRESOMk26XkpTcJJgcBJmIUqAB4g1PjbiC5smqhD/v10GW/8+wSZYs5LG2KVeSnPiTx+ouWgHFHCI9o4OndP8YHb/lb3SI+H9QPGdRydv0Hn7wYCDQB2SsrOYeGsFsfBdXrYLOOfs+vrnEUuunpx7qO6KyW5NLYSu7FxD5EHk4tJ07FIR6KF50yOdB+Z69cdr7LogYJ6XNTKLquCberlyyl9BgyNwCljLRo9o7E2Act6b4lIX0fu6heA0cV+e9RA3ptzAkaHKdW6m+6TqtRciYEOBFqmQaq5rQebkCliHVgNqMrJ7OwBZ1DO8ACAuJW7BnA3jbK0GaWobSFnN6DjVMa03UsdTmZAFzU7v/1NCX8/9UVcefgJqRNVWSvgYKNcHmCACBTMFRSogR95UABw8/abeObrn8IrP/OZwhS0Bs91bhZUtyXgCxoUVD0XqgqtAQs0FRQoGa8J7MECRFiKUJgEoQKFYRvZjwBI1d0blFgPqE6hnd2z6o5OOBrazIGttXjyPc9Ej+45Lr2ya2KB/b0C4W8M0FqVIgUdhQU6D2U/EJhlYZwOALAZf2Ygz7rdHXN1KaBgj8EzkGzgwAEDAwOyWk4SJk0ZNRhQ0L9pMjmZTgMKBSTUdUhM2TZAwZrZ999Kn4W6YGRUg7Y6hXE55dxxWQkJubrRCe8QlUhuG72oDpId97rPQIMxrD7vwnRgP9XWh1Oi0rPLdnbvAC1AgMrLLhlzbbPbRljJRxQAjFxtBQAiBiir/oCMWxJa6ebtt/Dsa78oOXEPPSqyCmEdFuBgY/hcGmAQ5xR4wxZTPNZp53e+iae//isKCt5flCDKvXLTwWRMgh23/b3gjChTU1S6TTYQHHvQMwsgAqepiZHWUEQU+R8VN3AtU3cHHdZ6ldgnDKcosM7YlnCMtVn/fiuA4mQvPTh/IbAjRshH7RaxQDiEX5/VKIuoTqM4/gIMANuAYO6OVTDA8xHMavTnYwMCWLfBLIafMzDP4o3lGWWJPM8kAHK/QSlGIiVluOs2bGxPkwKAST3ICTQdgCSggNMETIfKtE0HYRUikEBTUZ6NUiUGQA0ty6pgjZat1O2Kdzfqp+54oxeacwZysgAN6yzSap2i81eft2T/Fs+/gFy3+g4oxs8ZvlXdFwEG6GwCSiaUOyrU6r+e+aTiEA7kqtiGzinqw3D2+l1b9WE0Uvaq7MlHfTcZo2VMEmmdDCCQGH9KOH/nW3jma5/Elz78Iq4+9JjIIGuum4Jgz9Mm7w0G5dIAgxDxOWO3ZujO33lLQMFf/wyu/sj7WmMX0UNASRDhufWU8spXZMgvvaUeD2ADJaJAO8FZEZpyn5GxXFNYAYNyr9SzPGJAR64VLyBde5WfTbuVvYvzRszD4rwB2GiSqGh8/8b74bq/jwWWOhcmUI9vMQeNgj4FEGQFAR0gON6VcWqGfz5KXzV/lUE4HisIUKVYgARnAReWCZ6Dfu+K9Wn9S2rUKyi2v2xg4XAQkJgm8HSQa91fno9lPx3OJFRFk4KArOAgjQECEcBGyRJKDrwp5NIPK/3U91HZ7/rKb+9h2UaGPDLii/2dMQ+vXxqvos+6+90PWSbdtyc01LOq9W/Vd9TlkJyi+9rcsh1ydILe69urh1DkwmWA6jPO9T2YQTy7MZr03kmebQBBuY+bd4Tp/tKHP4erD31Argc0nJL1N1WbsqNcGmBQh4Tv1O7vFih44H16i/0Dgb3XVL4KFCuQZoBosmEUUy0U6ZbQAI3gEDojNSqRUmrAz7YwrFLP2gaNMumNRa9c+vqaR4kOIFi7+bh0147or1MPxRQS4EFFp1QiMOHZiv48cojfxwEBBwgqs0BFsAHwvAxJBGV1BkGkzPKM0ne5sgU8z8jzUc473gUUJCwAwfGusgVm/OvvfFfO5ZzBMxd2gGdu+jgCB20/JlnBDwCR/CZlBNLZoQAEmg5SRwUBOJxJn+cs+3IGHc5kvB3OwDkrg8ACJkhWx5P20JBEWZjYUtZq33mAIFdtG8ELA4EeADTnDq4L7mnn+nr0Dkto2J2+Yn9fp7/uVYab38kBgrXQ0L9Oeq9rx82SJsGXBoKtXg4wyQkS7ipj1MABpWaMnb/7bbFfH/pNXHnw/QocMsAOTEC3kQs42CAMLg8waATJQgi6TV3Hl/CBBwXWqP3AGD7OJWQFRlFO6a43ZeZKUZbO4HEvNDDlORAcuRFgeHDgNZc2cO9Q2i6kxHaAgPsdf24bR/8MEtV8+5mn6fZxF8NmDyIoLa8HGm+GPGjw7IAxAw1QqMakp/nqlKNqfMrr7G2LniUATgYF7EGBBwMGBIwhON5tAAEf74IzIx+PBQwYEOCcZeaMjXW3xq4PKTRxZgMEKSElAQQGFIgS8vGIdDiAEsl3oQ0gZH3Hw0HeczooVer68XAm9QZAupKe6UlQkutG4KD0UfXMdsXy10IDXo/0YGAABMLze6ekXFf1TGFygGVyaK+TXKiUR9fg/skuO7n1eSQlNBTpvTWwUJ5RwWV99v3TeaV9Bs5POe7Lmp4v11ZwVOz9AFcQB+AAwPmdt/D0f/VpvPKh31CmwM5T3cgswJYmvUcFB6UNBuXyAAMnTMtpicoWuLLIKRjeNxgwqIMFkZEEYsEDFstxNUrNx1t1P7s4Kw/YBQBVePx9gKWQlPaw3wPFo4LAo3cMhGURhwbaWHTzvB1F36NhVgwQmKE2GhqdEvLAIWjXhfeix3lOTX+0NJ/S0Dp3uMYBAbCjPDUOKOg8jwGCGaDyvisKDcCumQb+mLZeUXKeKXCgwO6xAAXzUViGo/R5Ps4FEMy6z4CAgAV55lo4LaUEHAGaEkC6dr+yBdNhki8IsgMOELDGgOQZHO/K78MBxngIODiAEkv9D2f6DgAONU+gKM3Gq++9Mm7BwSl94/dvAYIIDHRAYSGP6PQOMGbpHJPJwbmNwTed1jMG9yi3AEo4CEDJH7GwUMmZ6oCCTzjlDijQNClD8P3Rdw0LMNLr7j7DsFmZZag6LKfKcFmbWDu39l7sDNkzUvhO5+98S0DBz3wGVx/8AGqIAXUMs17rQwo+H2oF814iYGB0mgMFni1w5fyOMgUf+g0XPtDzHCpjQBS79dxaS/qy4j0DTiitDDziAhp6b9gEB2hQdjlu72G3dBQ6gDCOyFkF4l6AgI9FazswxwIV1iWo68KoG1gIwBR3Cqj0oYErbVd0YKEKatCmVl9jbCwhCOmkOGD1TIEQIACBsfHlRI8NaNq6pYRbsMZ5FuPgDUKu+QMlbBCAggUgaO5b38eWoc3qTQmkyqApgeYERsZ8lGgBQxdqQRaFillp5AxM6vXPAsxoOrg6C4tg70uTp2r3fNzXl9p3co9R3ywBAYCloY8YghVAwPNcwzS9E2LP87Kp1/c6pwcAIWDvZPT7IrM+2VRl9V6Agtzq4rquvOeoTe39e2Bl55Vbdm3R6XJWhorMHq994rG9U8uQGDtCkmj49Dd+tYKCtbsYa6CAoIKD9XJpgEEzt3tFwZ7f+aZMSfzQb0r4wK5p0FYADlhQqg08SqkoxKb0g0nvGVJyhtRxVwxZ80LOQ+6Nmh3f8oABEcJokUZfX2C/wnGAoElMs3frktMAhAlq/e9a7raxaFQFQInUc7QQQVooH2mDBMypZLmX9iWJH3Ou7UmcFM3nlu5Tqo/ZPBZIovokn7VpAMJGHLAossIcLDoB2CGsJxfHTy7Ga0pANH4vWHqWoP9oDWeGX6M+5yzswf0qI+pWNu7fc5oSt99iiukWKBgxBANWUm4TgHVgAdhl1w7Q7mRV7r+U12hbSiuzfWKpBwRESTxZc0RUTnE4tPLpZRPHFsBHoUGrX6TrelA8Ylg8cBiBK1886DDmdnby7uua8yo48MxmTabcAQqC3KhhccyB7hieemmAQUPV6XbPFpzf+Sae+dovy4qGD/rwgXVUdtRLBw6S3JublaiSu9RooVRswUKIrI49dQcAx27J5ZL8VI0a5mM1+h0FHtLlQAsSRmUQf+wpyFWGoAMEPRgoKH0Qg27L3MYMMddEtZSA4wxJVNN96nVY7L5RPs7YExF4ymrohcorfTvnQveRtVtA9VVwEJRyUlUO7mUd+AyObxbVKmV8QqdzVm9AdqKOWU1iokkTnkgVK2vWclbwxAyezJs4StuxZPjjaEo/g5jASJgOMhQZGTQnYAKmSYCH9agowQ6IlJ+pePNEqQkllFyDkoxIUg/NQ7B8A0yT9GGyqY36bmkqsilGqcsNkYfCJ6/F31u4AJhoAEGgdB0oGN5iAxR4QCC3XAL2Rj6BpYy6fR4I9LNKvKzKK43rTW7mDU3KmqQEch8u8nJagLwN2yNa+XQAAUAD4O3eRR9u5fxF+k12LHSc7F6GWeT9x4aU7DwNPfby3TO/RX/bfgtbSgPGoODdb1dQ8NBj9T5WPz/G/bNLroE5LNsOwSUCBlEcqZbzO2/qPM/P4epDj7aJUWUO+lQbjghgU8JKW+Yj7HtcNYM0izIt9zpqjMpshBu1npr2Jchmlbi6PuN4dDF1q6sIBqVp4QFLtWyAHLfpqyCW2Cfa3E9Q0GSyDwo7MABAPAuprDvL9s3CFCta54QSJvanc5pEgIvsenBwb17lmA2ITu7PiwxTVwys2thUtLIABwZoXDsIywIdq1XkSX8a99H/tVBHgownSgn57gxm/d5HbpMPiTREsGpA9F3V8EfJh+lsakCBrWGA6VBBwXRWtulwkPeeDmUaYzFCZHk6qmDNi+tBQckPcf2x2hd2nnvXkXxH7XBKzH5QdoX15MRdTN5ITv2+YTEwQAk8M2iicj9KSZ+jcpoRymgon/NR+gkVwINSNehpox3XdJu85JIRiBy30YyDyPFaAQU2xdZAAU2HCmIdcOV0AFw44fzdb7lEw0frKzWgdmvcenCwXi4PMACGQnl+500889ovKSgwpFWTkErjclaNOHWJXQIQOBGMmuV0gM9aZsrOW9dpYBkyrzpnMeysn02eAMyzIGTm1jv1xU8b6kFCxSX6zEkzzYOs/bmctNl2i3gaHCjYWQQ0VaUjimF5PU20rXBWCud8zwb9fpUe3S+Ec8szDYSZfVZ8YRvs/FzGcJMLY+DAxmWCePo6FjHPNZlTE/eKN04EpqMoqaOMSbIZATprgdJyuiJZH+Y2hJZdOCH5T/fa84ACBqLpih4Q2HoGtp8OZ81aBjic1WOmZFMFB+aFLUDBDkDQ9EPTFx1Y65mcxTWVTRJG597Bwf0oa+tO7Ck0+KJUG16oYdidlargACgAXu7lfozqHuXXbM2W8jkY/vxR6RKhw4W5PCg4nMl+G6uprrvBNj4BcDqr45JImILf/TS+9LOfxZWHHq2hGV8W+mOgF3mHLcBlAgadkPmvJD7z2i/qilCPttf0bWlwFpDYst63LqlMRTFzmlThzsDZe0DHY/HgMKvx1ilhpKCBj3fVwwdwECVBNhidAISlTLPSUqgrKIKeZUAaZa3nN8ZzNCg8e2JGvHieVOvMWby1ea5ARe0RZQBTKl6J5HrNei2Xx3LOIEwFENBh7GH6cEITSmj+OoXj45h9eGVtVb0g2amn+nxW9Naa/e4Ftj3ThgqMwUHXKDq2ra8FILDOWyZjuQzQ5rnWT5mENE3iLarxpDxXj9zG7OTWNGAG6WyF6RCzQxEN3ZuBqA/7BY6avjucSZ8dzqS/gsWNjCnwgID3sASDfhitK7FY778JDe1gcnYkNzOhhH0K+VCIMk1iA1pGUo8VGZ3QyaeeaOyReeicC4BfGhEzlG7/dDEZ9eG+po+B2s+9fGo7LZKG3b2l3ZeUfW8HdoGCed0pWxSnh71OkfpJnUmN/4IlsLFLCTgcJNRVAOxU76/65/V3voVnvv4r4tQ+/JhVYFy3WrF2k7kdwxu5eJcHGHRFPijxBp597RN4+ckXceXhx3fOKbAFTqCKd5L4TDpIKMEaM52JgrT9ZxPAMxIz+FiNK+XZAYRUaL1CAVKqnsMsWdV7P4S0SFjcUyKkuKC+5G+pRZ7FezPa7aB1ngmclLacIICAM0BiPGg61H2AxjMVKBy8p7L9Hh4IAFhPQLwXQCA3ujgoMK/U6tacg2o89HcjrCGj4ErxSKFKkcrUPkAM1QIgmCerY00UgihUOhwkt4VleiLlLKBWY9k0H8FnPyDbtgbC7JLgrG9H2exR8X1l25FxKIzB1IIBCxk4GhYHoV35JIYgAAM7219a24FpoGVy6kd4W3Awyl86FRxYCMw7HZ6VpEA+Fbh7+bR+aOUTJQzJDU2/LqMny6fLgRoZVzvu77/l6dZGO6FMUwEHDYsb3d6zA/q8VZ3SAQJjCEoegQ8Z2DtOB3A64PwdSZT/0pMv4urDjwMYA9f7XS4NMGhW/TJQ8FUBBVcffnw5oBaeWD1eFFyXyMiOUZC50poQRAIKwOplnyUkzuBZKdjDmXpcLvYXJBeVz8zuHdhekPw7rQnTWvY2zFjrfdXYyxwyp/QNTU/6Pj0Vl2dRll0WdIlDBnG/ve8aGhY77ox7CAaAkxkCaQcBBLzFEoyS2rwx0vMWxmgEEJo2cL/dJ2lrO6ayZDKHy7ymCu7SQUMDslgQnb2nLJfMzuBT9/0EWMJWkH9CQOx99aVXqvb+o76xkEJHv6LxtlLZ3gIDw7bfNDqo49WHImEgTdtYBj8Am6KHCtQKoEjlpgbY7BxGBg56P7eYUZOQSARKPAYIQCuf3VRFsnuZjANL+TxVNoElCLDjERDQY6F8+vvu0lmuZH+unlOO2SydSa/V/Q4HrJreHrys6ZMgvNWwWeT0jtMXPP2AJMq/9kt4+SOfF6c2GqN9//QOZdB/nklfK5cGGFhpQMFHPl+QVi/8cbzXihslLAsmsc85gHQeWBE8ZyCr0qUZAAP5KGzC4aBZ34ci2KsgAVjQXsPi6z0CAx31Hq6Z4G/pgXIUb+sye4nbzF7qjAWAopQouo+cvPaWMbAZxPfkdCdwwMnMgLRDZQfYG5lSn3tkCDyV7UHpltdaTqznkXHIbGtRGCCwJDI1SAugkF32uuYLTGdq4DLg5tSTM0zUAYNh1ndURuPxBJYmnwgEpF0pbPP97V1DAfWjSlDP33I9AoDgvPhFqEcqAJ/gbPEAsr6hVPphBBLI9QV1IA6dbK4tbETfD9lcAYGy28nKibqqHJoQ6qri5GRd04IreLLzyjoYe3Xtmk7xYKBjB9izA04/LRguAK+/+y1hup/6Iq786NUVlmBq6r34HHuzDofaLSLcfPsNPPPaJ/AIHhq+7qUDBgtQYAoBTgm4fYYPh8Oi9IkI8/kf35StwxnABxFEZkHbeS75CKyeWAMS7CM2HiQ4JVuEGmgzitdKhNYjpeu3PeLe8pLK6+faHN0UH2/wTSmRu4ZNeQHNQG5mbKy9Z1fHVYbEKZkhCNB7euMDoDFAZQGpLWZAt09mB6IxOXhf3dltelp7KgZFdnBd6c+zX8Z8ec+1AwsVACtwAMuyxEA3716e7OfeNzWM+rPvx9Gqnd57ikBYCAKAhhEYgYDVdu4VMC8PcVbmUNvOgAKhGPm6ah001FMTSLmsYe8SnK29rN0BZS1r30jCqDF3jOTavQEKQKtTgIbxMblsjKO9CzqZtHqNimu/0bLlkaMyBIV6PLr/avF1NIYzzy1gcPqpYUK33rGru2ymxriHbNYWGEgTynj14BbAs7/zC7jxsRu48sjV1fA3lf9szFAwJnPZB84lvP6lD7+Iv/Ol/93w3pcKGLx+580KCh55QnZSahWCKhpGFXsG2u9T+982xolw6/abeO4rH5dTDj9QPK6Se5Bypcw3QYIKvsV4DSgALV3fDeym9HGwEQAgN6D9dviVwvWy9aEWDwjsPbziaWc79BTgtqdZ6rEGfqL3jgCAnsMRG2C/y7jZAgLAMG498lJDCnsMVNt95P4HPNVDVFSFu1gNPzrPogAAlDBEDxqg17RhC5T7Nd7WHvrZ2sEq2/zd18aL9l0F/8D+dvVn03KfWJu6w4CCV8hSEfnTtCGh5C4hK6Cz4wOg0N3DwELpRwsJ6bHRR95oh0wW+bVyEXkE9suk3w4/cLZdtr4f0egiIHRQ5PiKfh28h08iNPC6BwyU1Rvt3DJW5TkvfexlPP7IFdz1Bqq8sKse1XEq5NQEEDdjUo7Lu5/f/mYNr9vsvEG5NMCgAQUlfOBAQQcIChgw2YCTyW7QEBFef/smXnj1Gq5/9AY+/M9+BneZMNGkzuEBJrAlpmf0HhsbIOCB+czF3hmUZL8BBf89hgbZ6/aiNKh9ibYrFQs0Bg9VAMO5sIPSeKE6eqgovs5waOOOwER1wnYYE1dG7wkgflfdl0fGH8AiJKD7Fl9t8wbKbTefvL5HIDD6DXQAdqWk6k7ooyfRXe6e9aj3UlEEwQOIBgD0/e2PnVKcAdiK+4/bcNx+99J2kS7m8tv9b0BBT2JLBrXFtqIwD4AS6qG6j9G2vwdl/T4U5sftQ/3aZVlkqAcVwNCYCs65f7IIDOQRcHLjQKI/vlY40Imp9f7JvWv/nvLzRL260KeOueoTCBdGvy7pDGpnzJgJmp0t+sBDV3C34JelLZK/9XxQBQkE/eQ4DHzKgm3nb9/Es7/zC2IfbcrjSrk0wCAKH1RQMIWAwMAAq+dTxKdrtTffPscnfuc5vPjUS3jvQ8JEHGfGTCSAEABIgYJ+5AOscXVjDfhMk3ryKlCQOG8VfuqEer0Ehg0Ye8N6Tvu3E8wIvXcN1IAFf7yhRbWcxd7lKXO6FwsJkX2imZp98nfskTb32mP4h/eu9VkPDfSmeAwCRgxW1EqRTY5SAKOuTL5exjqYg+R1PuLfa/u2ypb3PmwPoGmIvW0SldA5XfHQPKiqv3ugAFiopwAFFw82sNDIzcLIOwNeXmYFOHT7WxDSnl/YpLP2ec29dhQucodWBvbIYXdN3betb4aMVceqlH3T2bLNTtWn3rnw4a0ICHhWoOQRtHbIgEBvgwDgbubh+LV6J72WiGo0IQQIM15/+01ce/UFvPzUFypTsNHPlwYY7AYFOQYEzbbT2t+8cxOffO15fO7D1/G+B57AXZ1/fzdzM9YnoAMKE6aknTShAgUHCDxQGMZ5gQXiXy8rhv8CnrCVEb23+EjVYkTn5f4AEDRXrWn1oB5DxqM5d/lem9PUIoO/cZ7uqNf5evb11r97QEDrnHN0+uKa6No9ZQ+Tu8OvO7nsMUn3412aujNa0ENUQJX3yhJcPwVgwQMFOaWCLYK/jtGs8BAB6vDY4DwfBlocH19TqtQZCO7P7cuF5G/d2Thdt+xkVtaAw2YZMIoGAqgCgnF4oAMCne2ZfdU8MLAFwwKAaoafzdYwS/TAAQQP6G/efhPXvvI8bnz0uqyD4HMOVgT90gCDMKdggNByXgKCerzgaXzz9k186rXn8Zsfvo73PvgEjg4M3FXaISk/O5OgOXLg4AggJWqBQjosGQXrLJ/p7QADEHgFo7LqCXtgsETDQ9rW36crYW16NOrqvFBEK+fuKtHg7uq6mnm+YtTL9SuPP/XYwvPdAAIRCNh93goqyUHt9rb8RaIGe8ve8HJ0WlokaLpjVHdZ+zW0rJ1nXj3qexpYKOdxNXXZUIGvBy3bkoJfstkDh1qCW7sjfrPrzRV562VzEwhEZUOGTpO3ceEADC0y732oxDtT3SfK22svoEN7/ekSBof2Jo/tTXFAO5sDAMcg5kX6L7OOc2JMtAQISKRUAvDm7Zt47svXcONjL+Pqw4+hzAz7N+pbCd6oWYdhDArmroOy/raOe/POLXz6a8/jMx+6jp984IlFZ93V7aRgANDHOrCQCKDMBRwcIUpmUhohpdrhhMoUFLAAlIHfeAVDnmnFG3aMQZt3AX26XoeBYdmgbtsyadsExe1Mgda7CCUdlZHorxnm3ZbxHsuWV78GBnogsOirQNH010Qhs3LOWj2D9rkfTRb2eW9sg0MNblV3qYxoEuts51j7mGzKbzM29RoPAMozC6VfwUQPFHw99+YyXLxQ1z5tEnIa2N7vp2wNQaj+ztGxoDRVdwm1JecPrv/9DS0RvE+std9APOD70ocOO8eptS81RyCr5W/sChwocCDA7IxUgxfy2NsaY64SZPwxKQgAif1hqe9E8jwkwpt/fBPPf+Wazm64YrU067RZLg0w6LORfaetgoJcOytn6aS3bt/Cp7/+PP7xh5QpCMbQ3Swdpfm8mhXaKihjDygzEpHSQC2rkMpvCsBCebvqFexB9wtPv4qQBwBR8uXC4KCXp6XR2lOiwdis4thV25f7QVvvjZzuedaaYPXvtFWiVS57MCDn1fO3wIDvx9xca+qhXme3mO1jOsywVelNn86+z/Vnn8PQf2Z5T6EOGZoZ8E04GehOdg6VNp5slb3ynypPAPa1vxLaQ69UAdgsAaiXXzwq9as8k2A/grbwfb5jeaewz8uxHdffz7F80WctWf7xON5T/Ejw7dn0nzte95sTNlX7jaXuLPU5RX8GutODAJOtrMa9ZwN6Z9MDAOa2fXw7e3uT9OEEG66MxMJEZ2aASUATM2YIOLj5x+d44dXncP2jN/DEI1cM/kIWhcsePg+b4NIAg74zSyeGdM42KPhPP3Qd733gCeRAeQKSfGilKCjbRaagfB4CYzKg0LAKBirqYO+35SaTeNhu8I9KYzg2jH9rOJY5Fr3BKc9wG/vTlWJlFRnTNft6Uc9nEysHh73t6g/3CgxAaaSRUvb3GKmotVBB1D/szuVGSdXnZP3BkHFsAMAbf2YxbGbkZZ/+bva3dZM6nw4MfH9Y+01FjMkBBXIAQfYTtaDBAMOUSnRfZIktSasqVZOtWb0t+6oBoP1dXqwygdndsy8VUOj24H17ORnJU3SPVaYrfNbp/bF128imRoDgovqgyhNrf9k5LYAz50quqTrT7uHBRKM7sa5XGkPtdOeunDQni8xVnmDnosqgf9YcAOrjzMWmlLYkSThMUMCqTHQGA7mCg5t3buETrz6HLzz1Eh575Apy9mwLqX38Ny6UUBuzoDa0HVN+dx3Zg4L3PfBEo0QBYHZD5+5cG9cjvKkok+rVmEczN8xCxyqgC0EEgx9Ao4Aig7pFQy88zg3jUvZ19+oHuJVooPdlWsQQODbZkaEe3HPdi6/PWe5b3te3uxnBRkmVExUEEAqqL0+KQENXSpw6ONYbjabP9FVGtKT3SEwZHTsw0AMBAwF12+SCy1cSzQuSOuvf3NUL9ZzwnV1j2DAoS1JoCxq7JscICaRAwAMDAQuUCFMGjgq8j1xBwkEztczrAkSpSh5BRdgZjKTsgTKxpRNk7Xw9lZZ9ZX04Ygo2Q0Tc7cAyFOTvswbIY8M9qBiWsju67+jk6PqLyH/ZMnly+42BLQBA9aXpDK8z5Rp259y73gydpk721uTObIh/hrcjPo3CWLEjZ/Bc7ciUSACu6hkDB6TDOIMBJrx15yY++dXn8eJTL+Gxh6+I/No1OnaNmV4orK5cHmDgWALfgY2HbJ1oJ7l9TfigAwW9ZwV0cSDXuXcdSzAR4e5cPRrAdbRUWWJEiMFCP/iB3oONhdDvbcMFVeGsAQA/qNG1A4BFW3jls4dK7Uv0uZJedqduR5TDNHUj3SsAr4yas9xGn3FR2R5nxL3nYbJlU9JcH3kvkjlmF/bQtCHtzEuPsFdOs57nx/GxAAA5fpxzCAaOORcQIMBgCQoqIOAGCOyJKPgxLCBAAUFaggPbZ9uHlHAkYCLGsYACwmFK0uaiI3FIwDFLv2cAIPl0AFDBge8TDw76drXzpB/b43tZgBEQ6EG5v66XzeY+zUPDn42R7sfRHOiOPk9x5n6MLS65Z3n3uhLAImTUh4u8vpTrewcLAFwknXqdKcf7stCZunOLkYtAgAcAa7qyL8Y4cwaOYLEbDMw6jg0I1LEgO4gkUf6Trz2PF5+8jsceuqLP4vLGGU1eIqgLuPTl0gCDCBRkd8z/Rvf7rTsCCv7Jh67jJx98woGGpYdlxVOro3J0LuHUAYae/ixGZmXwy+/qza62R6Cg9lDMo/c2BWDepb+vP+bLvN48pUSfcx/Fn4El4vegwV+3VwH5ezaKSEtyY4mIw2QgAJWWrvLYeJ/2HN83fT+uKY6+NMoMrbKy4kGd9WUFCAYWloDgmDkAB6z/5L4GCGzceEMUiYbvUmtn+5tUDhJVsJASkLLtYxwSIXMW4JWSfthMQcCcwcoSGDNnSvWQnDlw4MDGh+uu7TYPwEHfZ2uJocDY2NTtevpWPgiAkwG7l9Ol8XfH2tcK9d1FZdzk1IeL5DzSY9yEjLy+BNA5Vy2YJ3KJpTuYu/J+G8Cs15fA0nn0bT9q86iQm8pis9+mTJgTK6CtBr53pN68cxOfeu3j+NyHr+PRh6+U84xDz6g6LEr4jsqlAQYmQNHiReh++/LWnZv4FZ198L4Hn2iFMShrBnJUKSLCEdUoHTXMY2BhFCv1YAFoBUDuO65nAwx8dZzSiZiANYq5vne9f6WU9a977hql3JcUvEzjWbqNKDYNtPHpsq+cVxWPP6cHbBYW8v0ww4E2FgAgMWo0cWsfLkrcInUzPt4DBZaGhqjtO/sMrGIR3Sn39wamJsLW0oBgMxrludKnPSg4ckbOopw8IDhmLmBg1t8ACkiQ3zYWxhKUNG6QnIK37pqSgQP5e2BjDbi0Z0qMAxKOOeOQUumbSRtyhig1Ns2I2PD3sxw8Kwd3/laC7C5QsAEILsLYRQbI54b4ukQGyht0/w6eicobQO+i8u3DRIDJs+nCFjQ0ISOnLyM5JaB09nK+1Wn6cgTI1nJzgHEfjErRSzsTM3qi9K07N/Gprz2Pz334pQIKClNA9XemFhy4YRmWywMMHCpvkrcwNvTWqL/xs9fxvoeeqGhdtTARYWKhdfriO34bGTIoEY56jQ2Cow76u3rcYqVARcnASib2DrmMvIw1IGDvNoo3A6Iw7ke8OSo9QGjBgfvtKOf+XK9w7sIUgsz7PaYqiB6wWf/IfkgGcGLw3La90dKJq/EYxq0N5fMYqUfKyu9jbsFBojFdX67jUo2wlLGba/8VdsDCBhugwLMGBgTm0v/jPk967pTs3FSAgJtTWIT2YAHVxMhMQAZyUmUHxgSS90iEmRmH4K3J/TWPclg/hw/2sHM9kPOljTbuzwcx0G7n7WXvevAO97w9AL4FBfF7+PP3lmQUG7q8krwMHc06DhIoDBk1+hJtbgmwoiuBTX25R1fK/vUk3XK/gaA2jos6CjPzIlzav09RLzqG37pzC7/y9efx2Q9fx6MaPvDFswZl29vHlfa4NMCgonLd5DFaSwS8cfsmfvm1j+OzH76O9z94RQSVjA5WqjER5iwGYspCUVqcctLObKoQoPFS3M4Skk4QY6QVt4EPCKtQAUQFC1Gi41rpE11GnsYpYKCNMa8BhLj9/f6IKej3JxeSqWwBF4WzSGLLrbKRY4SZgeQMvwE2iU1L2x8Bt68ChAnUxPqyeSU27mgZt4Yd3wEKoqTK3JyjP5jr7BQW6J+JkUFIOoYBVQDdGMYsXhiTgCBKVMblCHD4vhqBgh4QrCag9ckayNJxJQiq9ZkMVHE4Rpq4sYE9cjMZEirzRhb2MZpZjiWKktdiQDDqHztvaCuD/Xvj1KNEUWAJ3mXfUmalDbdBfJMnMgQI2/IMjNg/lcmFjFrbswsdERJxI7MNSABALIu09YD+CC5O1VEV3L3qSTm+DgRWdb8rE8m5i1BpCaG04ZPChti41ePfvnMLv/K15/HZJ6/j8YeuNmO4L8YaEFVwsFUuDTBoWIIAFMi4FAl+4/ZN/JIuc/zoQ1cEzZMqpaSDwSlW5IRjygUcACge/gxBfVESzkhZHFnro6MoAgrEbfjBjFUNT1QhWCujOehr7ICdaxnpUSIaUPcBKN6l7Ze/XV0GbIqVNlu5Gnxkr2zYKRk1IkYz21/LbvYUNKvgJHkks2a6ZxYvRduBSPs1CVs0g5tYn49Z7yqdwEYzTfS0RXHf8VMWQn4QZHyXTGMWRGtj2IMWG8M8EzDJVChKhAMSkLOOPblA5kfLGEtMQGk/GR+lf7JsJ6Li+s3aR5lZwciyr30+gWxLaKH0J9Vwgm2XXAO4ZEQipCRAbpLXwiElkCrQw0SY0CrU5EBBUtq5nQV0Wt8A7TmZWnbH6u/Dd3vKFihYC+2tMXojILAG5teSStdnH3Any04m2fVtUuDHAgbyDAUEus/JbGJdqnpmBesIAf0E0aHuq+RFv47KKCcjCgusAYE1IiVS08tQie5XUHCgFI7hbxtTEIACG8fLkFcLDoB/Q0IJa53iZ25+884tfPKrz+O3npREjcwo8dqk3qBFAomBmdQQ5ARONdN8ghoPDw6S0JqTOmJrnkQ/CAExUtD7QgHC0bzQmcsUOJ+oshXD6p8xGux7QYGVJXPAISBoE9LW65qL90qlzmU+L/MCEYsioQqBvRUFCjyWhEARDD//15TNFCD4tcIYG4tR6QXWU3zAMpbdvQlYx4MpuUySxEgQRWnsARLCMXw2JVBmoaFmBheUZEiPbFArVS9Z/YdJPK+zCULlOwiezdVNSZR8zoVZSVFGKWoIwecaWJ6BgYKzKRVQcJhkuuIhyT/bZ6BA9iccHCg4UCpTFWsCW23/KQAEHgxYzTcXq/LMFxwJog7ICXhAbnfq+SeAgjVAEIGBi8pwPbH+nBK5+7nrPb3FVe5zrvtMZg0cqCosgN47ZVNGAQjgml903KhznINR9aM/Z20tj1GxYdSv0dGDAjtPxjKFY/hb79TwgQcF0ZiN19zYri9wiYCBlWH4AMDNOzfLVxJtSocpVssmN/aAWAaHLZBymEi8TB0Yh0SSFZ0IpHFO2UYxNoBP9lqrM8qABzqAAE2sAhoBsDIHBs0EYpRVvDcxxpekgfU+r0wMgv5Wj3EtDr6nePp4zm6xDza2uaWX/SIee8u+hUFriRbQseLpaVIhjbzR3vicQln7KXMASkZ+CBAgYGAxhhNhwoQJuWQ+U5pAc8aBGUcN1RxzVq88I2dZdjWDcZwZiVITSvDhBUwJc2YcdACOQkaeEegTDiNAkNR7SgQFAU55TgkHEi/rkGSbUCnqQstuAIJT+wMwdkA3uM4cEntHJexjrE4Gw7JYLezjRZBQQz+j3KZRSZpzcUrpZfm+gYJBKWG9KEzoz0unyacxfRanN6re67+RTgTaXIC9gEDOWa/XKYDAxi8AGcPUsgQTEd56pyYaPv6I2a/949beac/qrJcGGIyMnDXBG7dv4hOvPofPP/USHn+4Jmpkp3BJaWobJ+bxz8z1M5fay2dTUmXKqwBBMqRFIeyZ2jNwtMo79okqUbxqNLeYCqMh9zDas7IfVRiESkb1JgH1SLUhQNUTN6+8UTT6sr5uOzNvgTas4G9RaWf9u7o4znK/LZgDOE9yQ2CbWJ+8ejEm8oz9oMBvS721NO/YtkXpBlds3CZlB5ioJhcxF4CwGMMM0JSUCciYMuNAScJbc8YhMY45CVsAwnFmHJICA+pDSu30RWA9z2AZSgimJ8JYAf/bQMK9A4J0Ql9EJJI6r1KMhEIFCawAYS840IhOs2jNKLcpYiinXL/Sl2xgqsxm5gLoq+x1+1Eq64rru/sov152vdz6UJIfA3Vfu8gVgIZ6H8048mW4ANUKKAjfy+nxNfvaz5DaAgR+zYYpGMPfvnMLn3rt4/itJ8V+heNWy5bZr+83fs9LAwzWyhu3b+GFV5/DF56StaN9SdCGIjTKlbkaO/OAmSvdfpBPWxUFO4NxyBp6UIr6YDFB7TnrsF1zWrVE8/dHAhAtFNQXYxgOCmJAls2NBZiZmeqymzDakQrVLAaClGJGky2enbIxYyHz0Pd5HcvZCWMgIH9Rjm+toAfsBwQLpoAqtRcyBUT7AQHVevu3jQS7RI+oRAwqZmM3bhUkzBtjmBmgQ8JBjfqRMzhNOOa6ENKRGe9Jcv7MjJwqTW3rHADLXBPbN+rTkVGwUIEZAwsXUCIctI8spNDHYPuQQSK9f6ptPwX90oMAa/uRcvXDssSuGSWfwwDCGqOTeQkO5JrK9Fhuk4UvOWPogMhx6acJVWZrXyxl1vYDcMf0/HK8Dd/tjba1M4Xc7xTL7QjAA2gAQW9kgVgX7tKD+teuixwmu9e8OG9832h9hjXdYoDW9AoAnKV2DL/1juTE/ecfEabAhyG3vP9TWGFfLi0wsAa79fZNvPDqNXxRPygRMs56boYmfKnilAQr87gAMJWFM85UmLNXsMQ4MIcggbMY35kZmGhXhzUr920IQD8+oqREAwBWWO9l5LivIycZHFJ3vV6VXBPP1MfYh0Rkv2jdKOMZwC7Ks6cSewAAbLEFS2YAWIKBdt86IPBZwT5mTVYHqiyBfVFzDRB4I9S+7Rr75ZVuBQsFKHiQwHJ87sbwpNMTDwoSDkQ4YJJzDCRo4tuRa9LbkXnXcslA3Mepqfv6sscHp1APrl/2gAG4fSMwcJH2l3OpOYuhmeaETYCQsxlags0msbAlQ2Upy70s9MOQ8OUaQDjA6x5zPKjIq/VHNiaB+lwDY/tUDzqZ9uV+yO2YKVgCAWDJDCzXIbFjbT1G+s/KAV2e18BhqucGDGhXIrCyqld0PHp2AADOFM1OVHPivvDUS3jikatDlsCXlthx4/UEkHApgYF10Bu3BRTYV6a8hwYsvQLLAjcFK2yC0XT6V9v2LMnSGfbxpZyBaSJkEM5Yphh6kICknrnFgN1zIwZhfeU/3ecGf79EcDRFx8/x9tNzDsXw67ar4xmqYTiDJStWINEnLELbBGjp7y2j0ZeRgpHf+tcpE8C3i/y95w/xdGAgZAg6QNCzBLWOaM9DI7L6RztijwCTW9RUvxOSdIcZrKwJiQYSDkAZqzJ2BSQ045gI78FUptP1WfLH7I1PO6sFiMfyouoBver7xRiBSIlKm2Ixu8CYgR6QTbRsd5S/XNv8hHbXyur9yp0kbBMAhJw1/yDFCaMeIKTEjv3R+xo7iaXz4fWKsZRI6gS4ODmXc9tXPWWBslPldktme/YOWLIBIwDQr2Aq54yNNrDU9813CkoYoVa2zyba0tO+jpFe8d/uCMexXnOYpG3eui05cV/86A1cdfYrekvuN6x/9WRmA6v7wMGlBAaAgIKf/4qCgh+9suIhLBtqIipJ3qZgiwemp79nSuIlk1CDnKpyFVKAMCtI4EkUbDWs+tsGZlquieDrYmUkBP2CHmX/Svuc+bfXET1ealWBAtdloU9dIvmUpJ1S/4UX4I4FlJ2cQ4vjXpmMFo3yQEB/XhgMRN5puScCo8Tdd+T3NBQ1jVH/EoF028axAF5NrmUJmbGNWYjh4qTHU/dxmImQs4Jgq7Y+dvQNDQDheI7G8nhN/FZ5jsI1ETOTQgDmwNd9ae9ZPvVu442SePCoAMFYHKH2EeaDlHCPU+TVIVmCBO98RHoFoBNXRqRNed1qmlEz7ZXXyPjvXbocWNdzpf5ojd2cGZiqzjqA0K9lYOeXsdzFENbGs9XRxnMffvRgQMZrBUpnidSpfQ4vfUxAwWIcN6UeYVQGKztHtk90feP2zdX2unTAgIgqKPg5DR9ESmKkIJySlWVuUwMUZvWmzxT9S+IPFZAAFoPKLFStAYUDmXIVoQZEsIEq3FslFAY3Vj1p0AtLwKxJHdxgOUxlxXEAjpLi5k8IIPp12v2996y5Pip7mBNgnyLp26yN1dX79YZI9sVgwN9nRFcvgGg09qyxnKGindq4TGMtY9eQSBqOYwMKYCCnmjjHIGUVuJ6TUFbuA1rQAFS6mrtxEhXf3v6vb3M7b094ZtzWXTv3bIxun9rGepHWPZcBx7pOtgEEa4eGRYDLOXAhBmAMEozF9EDBhztNZjNIdAjLCYw1oC/3j2RVzuve/T7I60hWN79V4sZHt6tu79BtQDsuqRh5Kgca4OAWQljTzVF911jGKBG2Mmdyi2+9cxMvfOUaXv7YDVx95AmA51Vbxc4hMHAKoCS9AigLngGEW2+f44VXn8PDeHj4XpcGGPjwgYGCqz96Zaks8lwVBfNYQTTKlYB0ABHh1u03AADvmVS55o6eBTDZb26BAlCF2yvTapB3vGdUxQAcbK3xvlZ8U/gPipR9lojDLYYdAQkgzlJfo7VGSTVbX0mMFEhkhPwzFgBAL248/QEQsOvXY9fVSAFoxx/QjkFetOamu0b+5XwLeLTTsQmsQEGCvlTAgoxbAOrl+nFbWANUQ6VvJzWOqsnAQpOjbVup6mntu2jbHACtsF1R3+KE0IHYCQNeuqYIlwMFJNgHtXpFTVrhKBdE3qIyCd7wN21bvr9R2933j3szMFfnBWj1Sy+n3c/h4kWnyuuaI5O6/Xt12UX1mGzXNpr8OdTpPAgzs1WiuhsIsO1+iqwHt/a7JMjq/Z778rO48dHr+OAD7wXu/quhjWocAgX/TEmmIHv2Cihj7VyZiC88dQN//5W/N3y3SwMMgDZ8sAAFhrrysSiNqjACJd14XwlER7x+501c++onAAApHwEiTGmShk/VA5uNqoUmERmjoEIKXnpg8ndbUUVCsjBqbmefK3OCXLXsAS8OyH6npIAYVABQ77K7/8aSZCMlsJieM1Ace9qlBVpVoO05FBwrz+/BQ/OIaphCr3UNDCwAQzBPzDd00FCs43fBIsD2U/WCHatQrnXbYlgqCKzGSq/vhu3arLZF0lTQH4t2BKr3n/cY//vfjhY+ME1LHIMEourNFRYB1g+irBOhhBqA3ugvjVUPyIA2VmxyWmR0RT77V+51TgYWlLl/7qhEstqA9BP1FtDqrtP1Vi21Xepd9uqtUel1hVqJZjxHU2N9zkvRLx0j8PKTL+KDP/KTwPEuItsEqjIMpDoGdfyxVoyo8quJgPM/Fvv4xY/eaKbsR+XSAIMGFPwlDwqqt2aggPKsSmVWgNAqmaaoknz9nW/h2a/9Mr705Ofw0//830O6+6/AaaqeQZow6bmS5OWoWogR9GDBtoFA0VqxAzs8LiA2bKNz6nbYnPL4QD4ihQR0jMKKkO3w0TZLX+WtdxrRkCNA0RivAZDo//YGDMDpYKA3YE1DnpYkVz00r11bNoHQGz7b34IJJouVBqEKV5gWZn9cv/KOdvEG1e/ba2Tw76G9asXa9iJWYl0Nfm0vHoME/b0ACUABYCqpANAkjfplsCPg5QJNQzlr2YFWGEZedHTtRcuafI7kbE1H9df1Ze2dOilbhL2ac8o+Wh7sdUqvDwIQ0DNd1dFQxzTPzjbNpVIffOB9wPy9XXaJS7hwkvGXJlA+gpPMKTNAevPtm/j4V2oi/hp4By4RMBiGDzwoyLMq5dmFFBgoQKExb3YHnL/7B3jmG7+KV/76Z3D1h35cDs/fA2VLPkrKB7lYjwMK5mU1cV1Uj7kHCEDsdS08Zfs7AAQRa9AI4OB+UeHgrF6JNPXvhWygcbYGqJVVkzOoeDSzKDp1377A6Djtsoj/rRq4FY+29w5CTdaP01MLtb86wND/ptFxtGCggpGV3gqU3MKTj961tYIr97u39mjbQu9VGAEFCirTtm/BJBABPLcgwc5xzyisjj6j74uS3LYiQnvkEhiEeaQSgwP3QTYHxjQ4tFsurazpHnZX9ixKBQhj3evP8yUOU1bduxXu8kBAtjMK0LXjAOjuv1rqgT4mZ7YmTQBlMGUgTaBcgbyAg4ybt9/Ec1++hpc+pqCA0XwdNip/LsCAiP6XAP73AP5nAN7LzH+o+/8SgP8awP9bT/0WM39qzz1jUODCBxEoyLMmB2SUzusU0evv/gGe/v2/hVd+6h/h6g//FWC+K+9w909RYriUgDSVzpLQAzWMgtGzHiwAFTAAY7q+bbz6MxK09u/AyCAwZOW58YPDQRTB+L1e5CnBwrXiA7GLY90jI+S9ef/BNQs3xflyoRHboLe3AEGjIPrnbXjHe977xH5qjNp9KsP+GXn8F30v907N+KQk+sGR3G3JZb8BBHCWe3iQYHyAYxLq8wiW3kd93UYycYG27uVuddGf+ymLzbbefqFjNkI6W4WobYsOaNk5AETf6naHEXaFStvn1p+bIUQfPmwYgQ4IFKDgHNP5e3I/7yi4d5L3ZAF1zEBKMnQzJLKACZxlUvnrd97Cta88jxsfexlXHrlSQll/URmD/xeAfxfAZ4Nj/w0z/+VTbxgmGvrwwRooYLdPryVmvP7d7+Dpf/m38cpf/Qf44A/9WAEFAEDznxYaByCXAEIVIBhgMKDgjpl34Kc8TcHAjsvYowo91zVattl2191rGSmz+6WE7rnsp71PKxtK7z4AguG17jwPTnju8swHfcz94vknFjrhgxXRs07x9YfP6gwiTdUkFg/eecnUsALZyTRcO40BAgCQbtf62/mVSSjPY3/8Psnaol73F7CdXO4FyO0poZ60FQTNeDp2pmO+2IEFO6emVdBK48V6NwoZNvlrnp1eAAHnkJoOn+8ilGMS60aYgTTV93CpBx4cvP72LVz7nV/AjY9el9kNrqRqKMPy5wIMmPm/BioNcz9KG0/nYWcUAFD2zxW5gYHjEQDwL7/7B3j6/O/glSf+I3zwf/hvA3e/pw9QZTDf1YFVjb0HCjWhqwMKbqA2gMHdW35vtE1obNbp6tONUXnAel2GZeMdTohJbz9qx1ja8MjWzjslfj58/Miz3wMKIkDRgQZiLiCA9Rj7ZD0A7Scy52Vd/P5SvR0KPZ/6CR9gz5evFgAgiaEvzyrxM+cT2zWUwPOx3sNkd5ocSHBGngjE7j1Mljmr1mU19PbX7UcFCAVgNBX1ZbRyf/fuezzrU1mWU8/ZWzaN/n3SIQujToP9abnP+hi4vzq317d93toaENB9XnZx90/LY3LOi/GLw0HsG9Xl9mXu66zDMeH8zht49rVfxMsf+TyuPvwYOAuYMK5lAxf8hcwx+B8R0f8DwH8P4H/DzLf2XNRQ582URB/LcZ2UZ0iH5NJRPAtAeP3d7+DpW38Xrzz2f8TVH/y3wQYKgKJ08vek80hzC2ia3MCKwcKCVQC63/1A3igjTzQy/M2+07xPue99VCL+CRcChxcAHCP6uDmfgn2oDE93n63nhGWvYt99D66rts1zuR8bG1YWzK/JTWb4ObvjPZDw4MB7LXkf+3BS6b38FHj5QDH+C0WZUt2XJmXzqYIIVhk1Wz1XJkHIAwMJfDqjZeDArgcKwBhfsxHu2ZVPEYP4ew8rnS7nfza6IZA7T617GSbHHgTAgcvxE0BBqVQQCvT7doAAuY6LvLKBapNBb28g49Vy2Cgl4HgUcAuAWG2MGn4w4/z2G3jm67+ML334RVx56FGpg90+TQ4ajMv3DRgQ0b8A8MPBof81M//24LLvAniEmf9/RPTjAL5MRP9zZv7vg/v/EoBfAgD8D4DiVfWxHRfXaSgfoHakE7IeFGA+tg82vaiojtMEpASWD4EXoADAgYUOKAAtsyAvZC/mXnLFo4oUwJan2XmZjUHR6xYeJtB6g71h6OuwVk5Vumk1Mhp7nAsjkxbHUkQvY6lA5HhnhJrzctdfjjbewzCcYlQLTzi4lQsXNKCgAwQFDPi+dudwruAC81ySs5p+j8aI37+n9H3njHwZSQYEiADrsxKeQzX+TnEiqYeVJpFdl5ErNlzp5HluwgyLcgpD5MEBMJaFLeMfhI0ArID3lbCRe9aC8YnG0VbfjeS+1rbbeQ/6IGJ/gKpXOx1bGYA2fLDQtfr7+6NvR7q2YwMMCHgGr2/b3uYsxjBQ5q/Yc3Rq4vmdN/HM734aX/rw53D1oQ8AzCrDDhwUZ3TcR983YMDMP3WBa/4UwJ/q7z8iov8GwP8UwB8G5/5nAP4zAKAHiK1xF4sXRZ6wggGPrnme8fqf/GELCgqzEAiXdd58bBQV2+8TX/SdAACkQUlEQVSUwLPzZFZYBSAyQHrOeou5nyPGYEkzrw7O3ohYGRiDe41Lj8pqvLoX4O5cfy17IwJIbM5fM1A2ZfEQ6yeNF1e2wdrYKaPG4+zaJWQXuli2p6mlsibJ5Xni7bg+tltN0zKXwJcIFCggKGDAgICNhY51aMaLq3f/3LWV8vqV8cgbfKD0C3mgQAmYHSNgQIGSGEVNLDOsxtpia8AyBAVN/8Lu0sipnedeaPiMhQHekT8CnCbDcruuXwJZrlXaAHNr4PPPSta7sVDOMQbIy7LXr6FTBhRdCwSAAfWc1TLQtUADAmQ/N+xd3sHeNU863q3vrO9ZzkvyH8+zhBS0HkwJ53fewtPf+FW88qHfxJUH39/VTYRDNEmCJOaP3/YvVCiBiH4QwH/HzDMR/Y8B/E8A/H92XctOWfkEEGApkEF5/U/+UHMK/kNc/cEfE4MfUa5a+OhQXdKBklIFCkpnihGaClAo53VGaEHv7PVYAnoxBAF2bg8CPAAoMzRQDYedVx639EgAAGtG6QKFgeol9mWFem7aHE64esPjFM0Q0PVAgcklq60kqi0M/EZp3qcHFFPnFbAaPhV2zmDCJp27CgqOdysgiMCAXWf5C5mrUXJywd0C++zGSJ+BX5ekvVv6iFKqwEENPzjJtcYK5CzGgPXzuAft/zwvwcEG41S8zChHCGgBwV4wMHyY00EBIIjAQC/HWwamkeNyH9dHa/Jcd8T1v8/yDaDId6lNAemtPAMC8BtZ7uX4XwddCywdzQhsmWxZ/S1MUKqRQVNb5/N3v42nv/E38MrPfAZXH3y/1oFleDODTV80K1v8OTAGa4WI/hcA/jGAHwTwVSL6fzLzzwC4AuD/QES65BM+xcz/3a6bduhtkRSyrIV4XzzL7IPz/xVeufof4+oP/mXpmJFS51boAACzCaB11rEduMw17gmU8AOABatQardGc/ZV6ujD7BVDr0R6sLOTUgawpJX3ehtrnsaeTPYNhoA95WylxJBTVTQ+Uc0pjBKjThPkKzaTxKXt2crfSVzP/FHx3FulNohVnxLLNCAAvV1hEzxAUC+aM0inLDGynDLXKoseUCbNKPp5o890zPSAoAcDBgKYcwsIBn3NPuEuJeBor1XBFk010YoygxKpMhdDQPZOfpYAZywm4/mcA3ue8yjLPReAQBu9BwSnxqJL/6W2fbvckC3qmU02e2bPA3on6xGYj+RYDg881hMYhOE1vqzNHrkbnKeMXpHltRCSXWfjwztmeo6Pz48SUXe9I9AyYz0bIBvrzKtvp1PCiPbO9jOlkmMAQOzXv/ibeOWnP4OrD36guzALuAYacLBV/rxmJfyXAP7LYP8/B/DPL3hT/Zs3vacSC6aE17/7LTz9+78uUxJ/8MfA87EqVE/hjB7rOruxX7Mq8JxbRsHq6hKkyuB1N2INVWxNAeNosAWsQDk3YgV6JeKBQA8COtrSe4WhgjhZAAbv24cLKC2OsTP0fYjHKxhihn0xEmkCcRJQZ/Fp5gIQfFzPkta4ZvIIOFjzOEY5C6P35VwdVmMJmuQmcgaGKkBQLxkHkvF/vCtVPJzJ0qqadMRZ5zwDGoNP2JslL1UQINAAgoA9yEE4wT65C5dpzUhgzMIgzOrRnDDtsYK7qRp+50XicFb20eFMviypxp9t7ZGOal4kB6Prt6jPfJV8hvpGbsgwHu1BwQgQBGBgM0dkZZYK9/UcGfz7IdM+TOAZIR2TJdynckxEYNxtQ0iAyLHJ8KzPMjk2HeBkvQH8QAsWBiXUscBqaGDE3kT33BzvPQiiVNiu17/7HTz9e7+GV376H0tOwUppmYP18hcqlHBPpaDvlbABiacHiKY/f+eb0qh/7R/igz/84yUcwfNxqVDtHisysejsInzuuNKhDUgAmsFbLkgJ3Ht4URlNQRtknw+VSQ8IAuZhFGcGOkQN4JSvskWlj0cDlUlhOUEf1CkZ27Y6O+oZmMGTMgAjChpYxPXERu80WIFxiZYejq91lCFaUEBliVzLnzGQQODJAMIswn/2HtDxKP1tbWFTtXIG4QjGQbYPAB3viqHMWaIh2TyMfWULFNh+AwecTwMAZPSxeo6kRp/SBEwHuZf7a6wQTQe55nAYA4J+WjHQsgh7WB6gAmzbbbkgNrjcOVvlJFCwV4aBC8mxHL6/styuL9HKMfltSjV8lCbQPCOUYZuOZ3KsurY81eS50PL3Sce67S1mYHeORhAmKQB3OihbQHj9T/6ogoIHH12M4aYoi1nBAVbH4qUBBvFqc/6E1MRmz7/7lsRkfvof4+oD7wPyUT0sjUH1CjVxMSJ2P/dHHpvbQVCNf67XuPgRgHbgAm0mdRdbGpZoatkaKHDnNh4GOpbA6r4z+axXHvearCSeem88at2KDVV2BlDdMmc1JHrcqmshh3mWT9NmXDg+3ZQVI9MAgm4+dfOu3XYZzwQV6kkEugEKztvMGT1AEPYgVwaqKBwNiOgHV8iS98qYnwEcQTiAWdvyeBdNg85JPiMK8fqt9pxzZQaGzVWZHcs1IJLfpJQwJQIdzuQcAwVq8OlwKEoSadK/wgiEStS8rB4QFA+MAiCwBAXDPqpnSNsoy8Paf+SSv/TFbfCd5H3vTh4FKihwMrsXDNxvOZYSy3Gh8k03plTHnILU0i95llwD6wfOVYYN0OWsXayAAjrevC7dYIEXZTB1dy8gaM7REs2WCgHBAOAuQMFoLDeVqOBgq1waYLCKxEsymICDm3fexNO/+2lJ1Hjg/SJcOpiGCtUDBAB0OKhwusecwICWahuA8APXx6j9oOyN1SJx6IKovldQfQjF3v8+lZGiiTzI3rNkjT0D2J5yFj1jy/vr49NSsW5fb/hXQEEBBN4b3Y5bl55sAG+bUFuAAmdQcp4lcwkxmNKlwwE4HmvowWTiaAp4qqGFwh7MoPko9EFKoJzBx7vgnMApA5gktECM8qXMeVsIIjAgzbwBCHqWYAUQSMa2JJXWfphqH6Xpnvqn9NGif6geJcv/qA5JDw7KLJMyA4VLW4hItixkmCdyj+UioOAUGb5w6e/V67+LKFwg1q9butWu647vBU3DmRfAUuc4xisCuK//yR8qKPiMhA8inQPE43anjbg8wGBYrJGkA8/f+Rae+fqn8KWf/WxZ/EEUKrUKNUsWV7JpYH0i0HQoLAJgA8SQ7cpg6WPlewUp8mB7BFyQNBfjXpQJYBEUOUWvo+mgXgeASRmMea5Z+qjInWC0Xr0fUD13SX7zArVt6PsyOuapyAYM9Gi72y6eoW8/i1M6VB7Gpw2tN0lrvRBOLSgYGJzG2Ljf0frubYNVQS4evWMKLMmWkYF0qGwBp+pNpoMsDX4m3jYxg6eDxFenAzAfQfkgM23moyim+aheqJ6nY5+mQ5iYaH3r/671b/1LOrUstWDAtqdD7S/HENBBlaeBg+lQcggMAMhHZKj2p2cNor65h37Ri6rHXkCCGCHmVPcVo6PPQgUODNapp4rXS1hsjvNE8gxKU5VfYw2KbtDxYsmbwFB+pbQgYQ9Y70t07CT5DWYfFGDoc4UcU1BkuLuumfInD2grNmIGd+SX2f3LeLdnZadoF/et+5u6+YTKsx/odI4kG3OacP7d7+Dpf/FreOWv/1MBBaZv7L2b97s4SLvcwKBppAnnt9/EM6/9Er705Iu4+tBjADLKqmimbHMHEIzmAiP5qSlnP1Ap3DzrTLJBpu9aCabmLPaXfSuDrVfGeRalyVzocrhBTOoFAiro5kECOlsjA7N6UA4AgXIJtWDCIszgkwJHq7+tzbcfMgC9oPVAwO0LFQqwCgiamQkmpBazdgJaQIEZnYiSThNGRqdZp73U3dHVi/euR0sEuxgirmPUAQVOc91WmplJKVv91Dgogc7eIyyCggCa7sr1x7vCDtiU3QISxOiQo6lpg6Ie9mNvCByAKwZgZ7hgyQ5MTb80YICmuE/KLIS03h92lOoZbHEpi5Fr/geXPnEAoagTCwHpXSgJeMPUzjCZDvLlPKCCg4PJbC4ybfJbZVfkv5ddcC4AH5oz4/uPDn8G8rsG4v32CAzIxaeDgV6nrjllxqI1+2L2dgEb1+67MkPK6p3e8wMludAzXufvfkfC3x/6DVx96NGlA1IqdHGmwMrlBQaeFgRwfvsNPPvaJ/Dyky/iysOPq8Cn1uNSpC3fU2g9Ls8iANp5nkkAKlCYtFn3AoS1AbuXLusT4/w9rE4ewDimg8zAewWfZ2DqgE6f3OSO0Vbmrit0cMqoP7b2vqN26RRBDwTk9BaVrwICF9MzloDNC91DS68ZHzU8Jqb931GOV3W4CETTwjh5cGBjVUACFzBQQELOQBKQQOmgn3vlAhLo7D1iJBQYsCUx5ly+Etcku21NY4360PfVGoPTMQM9GMAmGLB+M+ZgvS9882/1BVl/AABNqnI0rMIpBgjGAjTJpGrUE8p+1s83E7O0cdKwZp/z1AMElXNM0kcFxFteTdBXdRity20p04mye69yKwf3AYEeBFxElwKtPrV2WXPAtorX8aZ7rO6ejQTA6dDmw6QJ5+9+C09/41eF6X74cT3XOR8rxRYBOwUcXE5g0MUKz2+/gWe/+gm8/NQXcPWRK82pxUB5j4srGCgfWMrqdZnfNp3Vc4LFSOTPOBml1jV1m2n1OLCCyhGgefPsfX28hwDU2LT+JjvX2AUPfgwI+CmO/p5APF+6f4c10DSg9xb5AQElGa1Z0CuTkMLzqH0vINjrjaYJZoC8Ecpuhx8dUZPZ7NbSREABBokULHRAQbz7I8CHMobhv/aW/UqHDEpH2FdIy1z6+ajnVlAQZscDDkBu5PtY22MboBkQqGGcQZjAAIKyNexZnHRYAAH7l12HbPWBVd+uKZJJAhYKUNC+YHBt6wIQqLRxYStNxxhAMCBn1xhAUBbTz1jwfdLILWe3PYumZ9Z96zIr7z9oAAMbUdkjtztlVvZXA7qQWzu/BwDe4HbPKZun6E+gbaep0+vTYZduH3/Iy/RETVTm6T0NyH393W/LMsdPfg5XH77S6pjg/RpGERAG6kRwcPmAgUdQlHB+5008+zu/gJef+iKu/OjVFl1ZuACoDVZyDsa0LADwpKGE4q3VGK/eCIRqQK1EAy8aqMMPiADtVL2+pEN9j+4qu0v0jQT5U1kDioBAkIXbexuL7NutJJ61MkLCnsaLFMAapegVSQcEACCi8Ex4T2IIHOJfGCI1QjWPsAQJALQGqinOIM2oo4KIpFqAGikHFNIBZYwWA8QuUdHGelYWQA0/GDj4Md/OsfcrJBZwCbR9O0qcjby6AJQVpVniqAEQ8KxAlEuw0f67295eQ08kPVcofIlA1vaX4wQCpYO0ZdEfAUCgVPqgyXcq+kVDFRKDQPLy2wMFa3c9h0Yy6/RDu/T5XN6vKfcisx1giBZ0O8n7d7S7N7TAut60c4dvwhlwoRR0Z5ve9Hfdo8+j77EUINDrFwA4vKeM3/N3volnvv4pvPyR/xxXHrniZAEtMPD63tgq0ndCKlMUpe+3+/LyAIMudFBAwasv4MZHr+PKj15pFAVgBrbSsmT7JnZKVBQp5VloWVMdh7MCGESILfbrAEbz17T6ssmXceUOCPQg4BRKrNzEKQJABs501gAicrVZAw+NYumUSgUKweA7dZqQL5E3MkokCsDC4psVQGt89H5DMNB7n30OQTFUA8+0hpOLQXKjqTFSwIrywhIUgLkCAz3WggVCoglpmkofswe6HijYB8fysRonC7dxBs6MWXOhC1fjPVOhwg9XubbnvW2/AgQyYhDgAcAImI2KgQFrdyK5SQJAjLLcbmZp95QcQKCpTCUFswNiBhBqAqm1Pfq2N8fD9neMJVDBAuAMPjsA37M71gt/1vLaU+tAKKtAZPQ7T5lU1u6zngTQfk8HWLbTmj4vNqkHQoPxbcDl8G8BRHj9nbfw7Gu/VJ1aZR79M7g8arI7AzZLiM0ZFHAAzmWMbpXLAwwwAAU/dx1Xf/SDGHkOTfHUbDqoUDta1hA9tPOcIl0oSxNUz0ScWvzA7o3gRlxptTgF2Qx0p+TL3kMNJ8jf6H2WqBqI0fTy2h1lIOBjZF7OaK/X43lNUO28zliFYMCHC+y3G2cjw9QbpXIuUJoyb5opKZ7OBpy36l5FYU4FCwoU7Mt0AojZ0d7ZxcU5HOcVCDcv48ZV0MfRePZezynt3YVnrA0zfz/b2uoqN7W2rh/cYiSu+2wafmURjMlRxU1J29K3LWvIcqxXioHy8mryOdU+aZmB9h33MJlort9ZdsjqSZ9Tdn+b80e68R71Yr+9Op63ysKz36FXAPDZD+D122/i2u/8Am587AaeeOQqzOXsx259VsdU0eQAgiW2VnBw8+03Vqt+aYCBnw7Wg4JGWefYW/Al8rbKtibk5bN/q0H2ZKyBX1WKW6N6SvKHVqR7x3Hc7GSBWAhBHfjRN9052Lf87VUsA2fRcy8gYKVi7Tt3qm79/N4Q+eMOrTcAYWWq4So7sAEIIgNlLcfom3d9zMzgQmcTATNXnyIpDVaT5bgZ2y2zQAAdnJfrAIPzcgtoACpwQFfpVUJ+2ScXat+83b6A74uWFViT/75YjDxDmBkqNzFkoCDBWAMCEnPDIghN1wOEZfsWkODad1WvdEC98WpD0A/XDu54L6v3UU7rkwLZA8aGvTes+HPSgdG5WyViC0Z6pWHDgNff+RaufeXjuP6xl/How1dw7Mb68lEi/xbOapmqBCCDjYlCKon4j+ChYfUvDTCwBj6/HYOCWRXEPFAkza1o4GkBuHX7FgBIR7r4LTvFuRBs4ATFaWU86Bd5EoPzdpfee1jQaQMg0L/HiqBFZTQdalRWv0nQ3Dg4r2mXgXHy17rt5RTDSuftAQPo9wXGyhsqf40vY93EoW61FSwK7Q31ah1goO6cfswDhKQLXBSGt7SAV/VjI9SUwPvzTtDCoDsbN5Lbvk298bdzojZd0/XNcOHavokrUGDiBUhI4AYggIVFsHyEZS6CBwn6Nr3Bj/Kgmu2BTEYvuFPm7rtsbuqpGCgsWMDvg75rAYGDTIv2O1Fvnwh87e7PfvkavvjRG/jAg1dwnLdsFalMV2Aqgw2YVH49OLj5x+eSc/eRz+PvvvK/Hb7FJQIGBgp+PgQFRjFmVbw5t0q6LcXtKsqTiPDWnZv4hVefAwB8Ly8ZhaowK6ovgKHcekNwo4HuhGG48EpEty3fannr8Mz2inLdBooeKpPB/hMx+MXLQGltt+XScNnfPpt9QVfjYsar1/XFUG69I7e/E+pSNdX422tyzUcoAECoZwMNALtM+xZctK1jhrK2n8U7w2ouhlA7xqKci2V7bxv/vv388V2F2/dklraT/A5pP+baLrMCgh4gAAhZBO/htbqGgGaGiasQ0DSgNxKnyt6O178/ZY/sAUu9t7hurKlO02sDAOvG2lBfd+e2D4x1cbRGxppDAQCff+olvO/BJ/C9Oa+HDwCARE4zgJRIE1NrHoEHB+dvv45rr/48Xn7qi7j68GPBzWq5NMDg9Ttv4doOUGCMQWEQzPg5BRNRs9+6cxOffO15/NaT1/Hv/t9+Ft+ba8JXzyoUsAA5uOpdrZZ2eEfjo/GwBre/OClYny9DfRodbrY3lspfvcWfV1n4BoP27D3PPqHNjm3R2WuAYAEGvC5z1Zo3Pmwz9R+usdMbA89lVwEIPQAQ19exDrXYB10XB7ZK06a82N1T/ct2Hxj9ABAAp7WV/cpuowABux9QjL0BBJC+iwcIZM4BWhZBK2lMAoD6HTVfMao/EgF+mcJ7Caffz3IKiBh2Q7+f70Vv1bKAJa7RUpOwh/KbEQ3l03T20JlwIGDkRPzkA0/gOPMCAMvfaqPIKmshrczKsDBsoW1AwMHNt2/h2m9/HDd+7jquPPyEk/q4XBpgcO0rz+PGx27g6iNX0IcPPCjIzJqkxAoYOPDUWg/rrTu38OmvPY/f/PB1vPfBJwAAR0UMPtmrAAO0YAHok8T84Fy+y0J4AsNU67r0ukbn+XPvV9n89kA57/49c2+u8f3UmwvgYPsHfTDKH7Br+pBByBB0+8y49X04D5TWcZBPNnUt4/swMpBwDBrQtj+tKEzf52vDbjRu9wCivW0xKtYWx9k5BajtYGDKvHunHsp+AMVTs3bqAQKgoR1tSg8IrA1tKmRleu5f2dsq98MYl2fufOj3UyfRcNy2547O88AMOE1fDx2Fbl/vOBwzD/VDqStXx5SL3SHYpwINHADA62/fxMd/+5raxyf0pus9fWmAgbz01YamAdecAumMJSiYuXofkYf27Tu38De+/jz+yYeu4yd+5AncVQVyV5chtRDCkhJ0DFLvgZ34bgsPEjsMktsZeWT9fS5eLnaDewEKo0tTdCTYFQn3RfvEylq7j1C/3+fPa/pZ//agoDBd7uS568xR394N+mzUH9PgwFZIuQcfwLbRHumq/r3K+ScMvf417L2O4PIuE2SNASLCnBlTosZ7zKjgwPZZiEF+uyMdQEggGSONAarIz8DCKSV6/dAjD/aNZmLcizq4z/Z9d1lEIdxbpK4/AJ+MCwgTNgYH96qrFyHGzmFgLB0FszGhTVKWoOYUlEW0YSOTwMgk02Bff/sWXnj1Gl762A088ciVOnYprb7cpQEGVzpQUGcfMGZU41+AvAMFs9vvvZE/eOcWfu0bH8d/+jPX8RMPPIHZDfy7GZp7YExBO8Bs0NWYbWuQ9g64xpu6gPGJKNjo3v7cXZW513JBYBDZo8azRRfuG3kMA6+ir1YINlbKCISN+uUipQcFZjjtGZ4o6L+WNzKyl6n0YIYSlc4wIt7acKL6/YIZHAKaPaWABgUWBhiqoaggwMbURnTD3bsDfP63l2e3MWYM23uH4GJftZblz1A/rGUgtA5Zm0/THO/vFYCHHVUpJdTV3Oporwcqc9AxYm47CoFNiUqYa1L7NYFL7oouYYDzOzfxiVefwxc/egOPP3Sl6x5afbNLAwwMEABopiQ2iMxQGtfjPSgQIMH4zru38OvfeAH/4Ke/gL/8w4/iqJ86tcFVp4WhIjhwMyglAWyJSKPuKMoikK49BqY3/BHabAZfQEtHHt1W7tL309Ds9Va9Mo9o8Y4NXMTW5br2rz3C958/fkqJmkjyhNV4mNep49COgdQr0HexPptAQ+/bQIEBggoaeHhO2R/c8i8CjojafOq9RO3rCcDR5wqhLu1PiVSJxsWPo2bsBIzfaLz40rAJbv8mc7Jh7CNZ78/pZX4Ufunrciprcz/KSM6tROxUJPMlDMaxzPskXP/Xz96x/SYLF9XVkZ7u82N8X/k73VVbE42TCYQ5y7sSy3fuJpaQFjGQlUb45u1z/OJXn8fnn3oJjz98RZ6VZWYRoxAPw3JpgEFRas7oN3Ea/U+AAjcozUDBUaXiD+7cwq//3gv4T/7qF/BjP/S4TBmxwaSBQA8UJlU4fhAlrivUEdUuGCmSkbKIwwfunRArgy3q+V69zFPigqeunzbOaW8NP1CViv+s61TO7c+pCsUrE3UsGpou+3twO11tVJeRfjPj769hrp5Mdtfp45pjNnwEHOhvBuYkn8ueiEofTajt3YOCGR4wQI/pc3R8ZNf3fhjkoL/3Lgy0p0TMTGoSxdz+RJi5XkMEYGZMBBwhY0G+FaZhAeYyBvzY6sfFYkwAAO0HBHvHAxB47jsBwF5ZP0XOlyGocb/eT1nuS5SvFLI/AABelXN59j5Zr7qbF/0LnK6re4Ym0tMesPU6+jjol4kIc2K5aU7lfbg8R1iDb96+iV967Xm8+JGX8OjDV0qIK5NWojbhsFwaYFCp9QoK2P3eyoa1zjFQ8H/6q1/Aj//I46WTixdi23pDSozjvDIIXbFMZKvnCa+1vOYCimHLm4yVRlefoB23DETey5l2JQXJAKnxFOp+8yC95yjnUAMeJuc1TEQ4mtFVw9CAPKc86tQzZxRcYxh93Ncrqqvuqe9ULmxZBGZ5D6YqzweNe89Z19oyCjwLi1BulQhTrn3s+9ZCaID0nfWPdZNM6XWGKqM5buf4chGQ0IIBDoAA19WtFQgkIuTZpmixTtGi8o42DjwzMBE1wNH6vgeKRWYbL7NeswUG9rBJe9iAERDojYkH+zNzY/y9jPfyHcm2r1fUl/dThpvj3RgAlu3YyLYBYwB34fQtEe6CyzkTgKMKbckn4Q097dzo1LTHvhLq6g09DSydteOgrU3OKaEY+wJytHzzzk186rWP43Mfvo5HH3pCxpN5GwoO0o6uvDTAIMoA7Ts0VF5uMHzn3Vv428oU/PiPPA4gQMg9MHAGA6iMAs/LARhmG6/JDYc/LwQELuI9Avs9yDXDsPYF160SfdrcKxMzJqZ/TBEl1Pn5E1VDacpkIsIxCc1MJEloBhQ8SDCQXTxH3wa09CbW8hVG5/lFXMp4AhcmgbkdZxkGECS8cACBk305D7paHjAbOAAwMa96e5aUCxhYiPcDbX/6cRCxCqOSqApeBQQVCJixT0yVLUjOOoJWp8VOqP1sDEIPCBbGgdbBQJSktgML6JvVe/bNNKSZO6NiYIDzUs5Nxnv59rLds0IeCNa6RKBg50sGJZLfcqxrvRFDNNtGrpn2x8ISqDHVPkaWfjqiAsIjC7tAXOUckKTTSE8D7fdI5ISVlzxBTwNj1rZc191+lXVx9Xrrzi38ytefx2c/fB2PPnyl7Lf8Fw8O+rr25dIAgz6mE5XkYrNEVdkSgO+8+wZ+/Rsv4P/8176Av+KYglExj6Si9ArXJ6Ka7cyS0NRPh7LSzzVvntEhx1PCAiOPwYOBEY3cK4zIe/Tn1e1Aqdwr3azPa8EA3G81LmZQsgEFMRyFds6iTAQkKM3MdX1ySpqRrgibEoNnBxDYAQFeZwWKHfNSu2VBiuXQNiMqc+dBaJKVLDOeAdBE6k0SKPMSICSSPOXMQM4KFmpC7loZgwXu/rbXra0ZYOPdrk0kv4tRqPP/NJNfGmBrbQwqfSsG4pDSKiCIwECfH7RMJr5gn6JNQIvAAbAEBSNA4MEAUGXcg4EeCKwBvBHgvy+horwWJmK3jbKdUj2UQAIIigNg58g2K4CUb0zINdLPwvISqxOAOjaOrCGDeduhs3IRPQ2MdbWc5+4xvPuy2BguABbAt97RKfVPXsfjD10dXtvMnFkplwgYVMEDlmyBKCBTALJCFCut8ta7MvvgH/70f4G/8sBjIoBKy/qYbV9svwcIkrgiG5MqOT8lyoCClRFgiADOqYDA7rMHEIzAwCnKI/Is1rzI0bEUWd2Gal5SzMgGBkxxsHibpmBIaGfWr9mxBwiknnUizfIVL3xvaZccxsVmojgUH2Wte3bCWLGM6kRXkDAGCEDS30qUZcbMhJQIxyzBCt8nSS1nCPg6UBCBgfZetDhvSjKFbwEOXDGAF+07pLQABMYSHBKFgOCgc4k9GPBAYNGPunFqljq7E7ONUTXWBg4AOi1XpwMFa4Cgl+ktFggYM0G+nCazdizY5/jsMpVT72MfdazhIh0fsPFSw0lwDNNRAfuUuQIBx5RN2eWgFJ3Z6upZ6zV1r3mc+SQdDVQ9DVwcEPg8ChvLBmxtHH/7nVv49NdlnZ3HHhKmoG/zwho0FRiPvcsDDPacRDKAmAQ1ERh/oOsU/OMPXcd7H3hCELpTqjPzYpBYiSgedt6kT3qyKVGAG4xOxawn/CwHW5+YEg2wPUqnDRWsg4KLKJOxglmp08I41f2mgPLslJEpGUZRHGY1TaFAPQ8kGQMzRIHMqjx6CoAbSWpLZEwiQFAQvTM4GN9W3sv9Lq+lYJLh+kKfH4GENBEyEw4AZmIcmHHM4iUd9N1pzg1AAFIxHpkJOWmegcVRWNsz13aX/pA6FQo2+76LjYX3vkoIiKh6hanmEwjr028TEgiH1AKCw5QahkDAQWUHosXIov7r+w7AhfrP+kZJHVXOFSAYZZG4DRtFJV6Zdx0U+FwR6dc1GV/qgbq98sLumlNWPG0ybn3Q2wMEA42aTZ9RWaQyBjNv5jEY+Peln51iutpPXQUupqOBfaAArg5NTow7PmS7dPz+gQMFjz58pdFBo7LHVl4aYGAlCofZN+std2ViQiaWFQ2//jw+86HreN+DT2BGjd1izkBRnHIf38EHalmCqGxNw7nIvGlKWGSiA5XZoESFHZCEOP3d7K91T4kKOBBhtOU0Fannyrb0x+X6qlzs+FY5JY/JlEPd5s4wUVEci2sH+/sSZUM3n1HQ/7yXuWVUmiWyUffVZ8Z1MaXgQwigumRLTa5FBQpMzWIpM8uHe5iByUDCVGfeHDIwIcl4z4wjchlXM0kS5THnBiDkTGXb6iH7DBzoeJhotX/bMBCVv2b8y3YACCKGwIcMDkkU6IHSEAxMJ/YZXL9tiHMJ88hvpbaJSp8Rau5T0j7rwQHpPfxsFEDHY4f+yQvyShnlCNwvUODP3QMOmlwCJ2e+/+Vve05/XI7FMtUmm9Jif+/U9TMaTilb00+9ru7HkDX3wded4plUhykVXTShYwocKPDj2RcbBnte8dIAg1F+TG0bUr9RBPabb9/CpzQm84EHr2BmiS9nvdfZpItLKoMgd6hl8oHHlbI1B7eta7vfjLo/f3ZhiUJ/Gbo1eiyRJKRpLPmg95L9KPtlpTeIFw1F34rUy1g3NF8aWIXTU8xNCu8eoHOCtrFqdG3jvUurj+33CiXyNHvjAmARj/bo3IOClCooSLRuXCJjsyfE4FsnWxDUdUMDDPptNlaAi8dq3iRNhANP4Emm286QGTWHNGGG7DuoF3rIST/3qh4omQeKwipg8l6peXA7+rIzBt4QXIQdOEzSX73iLP2sfTB93/vK7fSAQEFCVtBlOkhWogEyycp1SZFFzhUc2BTVieX0GaxTVUmmaAJN3kiy9XGzIUs4pkfeoLBoRba177pwzsksAGKWaCG7K2BAfrfnxbJd9ZBdajIN1ORT2U8LQFCvWQKCSD+vhRF6/dzfzxy5sAS7bTzbc/t8mESaU/B1sV+PPXSltI0HBKNHlkT9+LDUYeXYv7YlREQEnQpGeONtmef5W0++hA88dAWZ62plxdtCG7dlSGMZVXo4VWIQe6bAWmKLG3D2XN3HqkR8PNyYDctKhyaeyTGunxRN9c6sCmTmKmym6LMey4xiBOpx8RitnkUpuvPKscUQpAtlOkcKBYg9jKXBiUFBn6RmQtnHoxMFHmcnjFNgcBbUNHyvbgOkolCojkm5sgI4MUBUqWVWQwLpowmsgKGChOmQkBktSKDUxLCPOWM2gMkxVW39m507spsxCFmC0/rJwIBnBgwMGHAr36cvAOGUPtrXTwCVkIFhuWKfmVyoR+aazz1A0P2khpytb0l0zTFDwBEk3j0xgCmFSaU9OEhoQ0OFpjeQoHLlRdKHjPaWrRlEQAwA/LVj1oCa49bPwGlgYA0InJJwaCOkz61p9HN5uZXb+Dvq839gaj/u1OfDWPjgs09ex+MPX10A3bVH7lW7lw4YNHOMu2MTgDdu38Qnfuc5vKgrQjFb7E2vJ/kdJXcBChQgjAKwT2X4sipqo4O8BCJs9BLc4Jyk3gdQyWI+QEMgE+Hg5nsfLBlR+UrzFAEXOiEb5LK9WATHGYE2IbEtOWARdhELXVkkoQ08D69MIo9iFJNeo6F7YwO3bwEInKGpRkbbh3Np0PJ5163lJcu33M0jJoCSxEQdYMhMODigkJmRk/vNkk8hgKF6/9MhyTUu3MCZMLOFFXyiWw1JRdnvViJw0BqCquh7Jb+WRFj6xylMY3FS6TftJ2f8/W+M+uce+sY+IyxjoAYjEgHeTmf2rEEFCIb8suY/2Sd3bJaMhTgpMzDldtZJmhY5IzMTpokcmJP6GPMDIGB6agfdD/kElmBhDdDL+UsAAGyDADt2yuJmQGv8w1de0clA7CDy6oUrj+jfFRUMGEv5rXdu4lNf+zg+9+GX8PgjV8q4Bqo8rFRXxuGOOl0qYLAVG7z19k28YGtH6zxPQ/CiJGvmax+39UABqJ03AgZ74zm7hk930sL/9uvDcgUMUEV/hopgZ+h2lrjXGerCPNsLIFFzXM7Ra7pKjdZCKPtORVSuRIoEGHsTkSexN0ktoqLbfRUcLA1ONTbEuRoZ5vLbWJzNWDHpKmeFQUiyj1L55vukfw0ozEZVQxQ/A8jKBHES1ian9oNiByK8B5Psw/Z0uX72iy9+nFCnQLf6JJpauIcVEDDY/i59hiUIKP1ywT6B9gm5PrH+YUoFKDAqaOwBwqxMAbOggARgJpR9tvw6w8bz1CaVMmOaas7ITFXmpZ+k8aIZSYDKp7EG90kuy75APrvma5a3jhYok3NocBzl+NqKh8DSAEud2rLbnHuVu3Hqli2o9kr+Ti5cWdhJUFm86MUnr+OJH73ahMD89b6UIWxyin3g4NIAgwgUFA8BwK3bN/HzX7mG6z8nX5kqxdN8aRm3LauDUQUKAHA2tVON+ob2Uxj18l3131v885pnUfvcSaUua8ucmc5TyYoX4aCTl05uM2+rJ9JfE1170RIvg7zuReyZzz7KI9gCBBWxO8OTj8XotAaneqfVII3FtXik8lJuQInxYWURPFhIRECStR5ZDQMYyMnAAZQVEA/zzLxLB4Q5adKhvRawWMZ1a4526S8nAyNPLoqneo9pjbEZAgGegbwEAWRCjiVg29sfDYuD2i9szAEROB0ckyDvkKmGGeR7GEuAANQkROsPUoBnIU5LKq3gjVZXQZTXFPlcWwb9IqUHf8Ayya/x1t3vvUuay/F9hr+3Cf6e/W9//t6yZVxLClbwrP55vl3OlOmUAzKWv3nnFn7pNfn2wRMPX1mEwuIKyDHVLjI7AxUcrJmkSwMMPCCQv7XR3rjtv0d9pblOc6gKFVtjtvLvgGWCF6AUkgkv6rrdFxGv3SjVlV7g+ud6xTz64BIgHqJcT805hfl299zz4aW1zwD3z+7LaYt8tGVkdOTc9VXuerquoHQa5xAYIFh4onluDBDluQUCHgR4Q9R22OJ9KdJonYdat1uwQGkaAgUBAwoU4H/7HIaOOZtIcxrq+OjHzWoJFPewDxQEWD/sYgQ8EMjzEgQUYBAAgo1+sPYm99vavQELHigww8I/rP0xuTCDJSYYQLBcA/meBpVQZ5LTKmjTV5omQoYAOwsHSfV5CPSj17sX+QuaqD2/AQH9vdyxztgD2wY/Eo1yjn9uV6+L6F1fzG6cWvxzo89CnymDafLwpoa/v/hRtV9OHvr7wdWJFXwmttCQnrnFhOESAQOgKhTAYo4SPnj+yx4UdF4BUEZqFLPtgULLGMjv8jeoUw/Et3IW73Ww9nWQ59PiYP+5ZsC9R8CEMMuAld9tLf09QhQdNMza0FxbOc/KKDGoVyJAi47NAJX9K0ZolKxWktnsvizGf5chMiNUBlSXI7wV0wacZq2GqShAxxx4sEA0VaCgf1OaNAhMyMmtk1Bo5woWuGy3s1Y8i9brGz+O+sSuUl20yrFve0+X9uGakuXDGTTPARswj9veKnyf2l/a2No8a3hHjDLNrMBBWQPOLswwCXbQd83ZMZgMTSasumctzGmv5MF+8zYregq4N7lzrbG5cy0PbM3Iyz5anlfuG+zEUufebx3blz06v+qj5ft4YPCmhr9fUvvVOCHAcrxSArkzbGwh+7yR7YW1Lg0wiLKMb719E899+RpufOwlXH34MSAfx0lFPdofJHdZOUtLYAAEhnlQ361409p7RmXU0eP6UFtv70m4WjSDvLvZ2nfi633317XUr/+m7s6yiBcG3kJkiGz/6vQ1OiFcsGaQijEK2AMgMEx9W3nlZ2NWa2RAoFCtBhAqIOB0QMQqTMoqTERASpJ7AnJjH7CwmxkjqR2FTFSpPS8Xl/H90YK2nQmCG2yA748QgPVswaLdV9q8a3eWBfdruy9AApV+kX0TmLMLM3AbZkiaE6LtneBeAbGTYg4MMAZqvcyFemmn3K1R7mt93exb3HNsvb1h9WeN2IC9eQOn6tJyfLDf2rRnU9bqs2C4ITNuzH59/CvX8PLHbuDqI0+0DggwsGFzyTkipwvKMtMNezDu70sDDMqYDkHB40rpdh5FVAKPi3T/5Br6THuR9aHNHGZXTgUGIcLeJa/rJ4XOQLOv9S7WvL96afvM0buOmnpNALd9l+2ypURaKrub6rNmmJg3wwVbgGARSoCbTjp3pO4IxNqmTm+q2rEzSuUaAtHdJVigBKQJBLumHjP6u6zboffh2iLN3+U4W45LD8aWf12bbOUFlLDNTuBl+4F7a2vO0kZMICgoMJYAjLLeNrEqaIZnEVbDDKpn1thLALDpqaXd3f7yKgOwf9GydoeRkR3sDvVff+qIZQqvv7DejMrAMbIyUE67db3pmYEcnBHj/I9fx3O//TxufPQ6Pvjg+4Hjn27ariq3WcZTEueAVLcnQmGjbt2+udqflwYYeO+vAQUPPSaKu/EuVBn03kKfSAQ0XlUBCwBSPjbb/Xzzvvh9QwQb7o3csB10Z1cWMUFKwQPXakDBvtG5cVk1GisXnv62cWkEtPNINo0UbxipCwGCFgywPottUYCd/cyyALzUO3kgAKRJQwgd7e3ZhYhZANB6ulFiHVC1tTOgTJHa19P8OwVee5igGeUDnAC2ivF37cv++XuKvZPXFQrIWMFAAwB2AAREYYYOJJA+y4CCPK+83ZDRs4Szey2rCXkrt18zyqfov/FtdujFi3gX3didinfdVmqP/tt+H2d/nLG/+f/9PVx79QW8/OSL+OCP/CRw90/1SMAsOjmUIWUhQ000JAbSJLkqkH65efsmnv/KNTyCRwZvcYmAgSn3BShQyrF4dTy3SgRoB1RPEwJVYJFwfuebsu/4p8257bVLcdojooss6MV278bfL5PZV6TzkhbHdyqcFQMBrBuQmH9cJTH31Ume3G22BmpB1TljsyuJ7URAAM5LMFDWq+092kAlubZiAEgKA1MqoMEDBpomVRoKGPoxb4BBz6/jmzrwjI6R0J9dnYZ19x69P76WBxDK7rI95VAuYOui7QlA2zNLe85Z31VBYgEIfDJAKO3Xga/R9EfS8337Tl4mIiO2WlYsZ6RbFv3nHr2li/bqqg0a/89K5y3GwOJ4rIu2Wj/ULbptY/jZV1/Alz78n+HqA+8D5u/V6/bYK2IQTQDpx7qSnq7g4Pxtybl76WM38B/8s783rOflAQYAbr59E9c2QEEYfwTCxibzonTf+TvfwjNf/5Rsznfd4OkBwsWQ+oIiarbzYD+GwrIprCeWVUO+p1wIcATP7M7jUXvvrW/XTk0/lN+5E+IADPjtKJbtjJj3YjnnMSCw+3YGjYOlI4vxn4/yN1WeqLSRzkqQDMoly5DMC+5ZBiAEA7JnQ4m2Fe93NPvHsf+x8ZfT87jt/L5SjZX2s2Lt14MJRx5YigEA0DTpLIMMmI/GA4AAjEGCHlvolf63VHrxHmEZ6og1nWNlB0jY+by95c9Md60CgjTYH2zvLMs1MpbOxys/8xu4+sM/KeGDcmxkpzKM7SuANAHABMpzAw7O77yJa19+Djc+dgNXNJFxVC4NMCig4Of+iwYUVADgQEGe0StwoA7GpRdEOH/3D/D0N34Vr/z1z+CnvvJMZQx6ZQlUmrXuiCsdZmvVjm9pVw7PQWjE4vsNKrFxvJZ7JyZ33mkTvVNzLg2OhcpgjyJdAIWgvRuD33qv9nsECvxzGgPlDVsACMJzm2r3+wwguLHZLBfpQQAqaPDXBOChhFumAHicUHxbNLH+QUilff8xcNpqp1FpaOCU6j09QEgT/Ne5OGclWFJ5Jw8OhOLV0IWhiPrN78IiABDFjnl1DC90S/giAegZ6Yz+/C2gcB/1yf0r2/pk1Qnp9Em0v+mHrfv5stJ+4XRZAFd/+CcE3Hf6xF/TJL16wJkmUD4WcADOQAZu3rmFa6/+PG783HVJZNwolwYYXPvys/LSDz/RZiznDAsftKCgCyn4DrMZvToYXv/ud/D07/0aXvmpf4SrP/ITcmw+Np5UGRajQbZW1kIGEVLEEiTsFvzoHkEZJmfuKIuELn/faW0GtF6/ELLTBVeu2r5udV+5eQS4Bv0Seb4X8X4sMc3uXECD7+c94ACowzkV77Y+xwOFAYAAwI59KGC4XLfdp1FhYJ3aX/PyL9IOWhbMgOys90xJDf7GmNkqETiwZ5XxAadHluNslNQn166My/L8srHYt8vx6PTEHr1wr/I/vO9mH+wIP0Y6ozkn0Bkj/R7df1S2nDkPAvLdjVkziJNeMVXwanooA6+/8wae/eon8PJTX8SVh59onZpBuTTA4MZHr+ODD8mUxDZ7MzsAUNmDsi/PsA5Z0moZ//JP/hBP//6v45W/+g/wwR/6K8B8V87N8jdiF5qyNXBG1Ko7tuqxuvO3PDD52YOQQIHmfiKiP7ZjGZSVATc8Egl9b3DSUpBNeZsnRVPnBQ/ocDlnCSaaazZLDMDWV8yj2k+U1EYoJ+2rkNVzTCLspMaq+c518+huX9BPvKfvgJY5cCU0qPoeFy6Dthoa9r3vEBVjSPTe4fvYeCpf9ZnqmCnrPqBlUprEy/HYIQsrWAyiXDQDaxltTYkN+B4dsZiJETExfUjLirZ7U8ULOA+7mKU1oDkCdc1mLNc9w1VrskNHrOmHi+r4QfiM1MYM7ZLW39gnSr5fJrVtMtX4/J1v4tnXfgkvP/kirj70qNhHSpth4UsDDD740Aec4XcZ48yOFeg8OmbAxXwZaDrx9T/5Qzx9/nfwygf/E3zwB3+sggtAGhhuulJHAQKodM9GOQW5rwl3X/8w4aofZCMKtr9HWCe/e6dX7OuyogAWSjvwbCmlNtmOqG6vKPBwep9/xlY4wp8beauUap/6WHGhAQEcDjruAJrMgzTjjwoA8gxMBxnHChJKDxa9nytwiErX55sAwXIU+tusX/UXolA/pkbT6HqQ6QFB6sZHN576a0lnfqiEtvcdjaVFrHjMLF3U6IehmEEIZg8jtSrjO+Xal91AMzgvColZacBHn4jr7h/1Y3s9Ldhj+d316ak6Hthw8HLj2HF/bTPu5F4EBzpleUzcvPMmnvn6p/ClJ1/E1Yce0/tIXgJ5hiIolwYYjDPHnXD0Wc8mMEEy0/mf/BGevvV38coT/xGu/uBfFkE7OqR/PLbTleQH0ODQYFA1dR6zBaH3r3TkZgb7SOj9uSMWYSNre2FUImW2QiXW28YL35TjAOBpx4YlWMbGI0XvlTyb9w2AFST0mfpgm58OWD+SSxQrdfNU8Kj+Jeac3bWtYWAi4KDPUXBaQQLAlKtSdCCBkobEChgQEFGxSi5Mw6JeDUh07zDos63FXv4synCcBOOD87wEB0Dp+2acnMAOjMAAgLpGwJ4wV1TuxfPXY9kSWAd5KgIKtoHCqeOjVnVdnkvxXrv9WHj8G+DOMVohOxCFxAKwxzOaPub5OAQLRV5LxS3t9lQdX24gl/WADgAflY0eALEi5zOAw6E4FcJETQAzzu+8iad/99P40s9+Flcf+oCCB4CR0YSvBuUSAYO1j9SMlRuxWymsgILv4Olb/wFeeeI/xNUf+jFF33ZFFa4GKABl2lKpUhlIsVCFFJEOEI4M9ylAYA0ABFnbRSEMlMHCQHgFEg3gkeF05y7efosmtIQvU/LWvtNUDbFT7myAoTcCeg475c9zWrAKAhKsvi7eaJRwVMeujLxEKpPSVTEgAweLFerY8ICQsjIH2rfTQUMMHUjIuYCLyizU/qY0LcFdGXPj+P5JfXyRErWh9U3ZrgaDiKTeXcyaIm8fLWNUQKMdd6BxMVsjAAIA1WRC96y9Y6IpESgYzMjwumExk8WAgIEA22/nujHQyHrf93ZsI4cjDGfZjzUm4G63L0qGtdM9IAhAIJlxl5PrPeYKCCRKkwAc234v79uOE+bOwVgwO0s9L3VeB9C9ri992T8Dgb5vbtTmv0iyawuczt/5liTKf+g3cOWhR+VdKQNckybZ28egXBpgUFkCwIOCYbxXPcNFo/7JHzlQ8OP1wKDzGr3fAYXmccYueGPbn3SvYMDO62OHI68gAAGhcRgpCW/k+3a+F0OyQiWSU+5FOc+dki+hgtQofqHbE5BUsJhFMeg8debKJAjFr1QdFCBEfXtKbgnVpCCm7rNbXbyRmYFk3gAjdV4iGzgwoJBnkH1mU40D6TS4ChpmrZqCA2M+pgmYZxAFa6hHSioyChcACW2Gfa1rv6s3NB4Qyo4lKIQHhXoPDwrtnAgIUD9lk2ysdQDg1LyiiCEcsU8RKPCAoGcGPCvgwYABAbvfPEsfe9nudAyv9fVFZRgI+jHVLzelVENYxv74a9y4LAtwTYHRLjrOG3yV9zmr/BvD5vTGrAvWWfLeYt0KhxVW9DywQ9dvhWhOkKX6GXb5e/7ut/D0N/4GXvmZz+Dqgx9QpyI14ABYjNhFuTTAoLAEzb5u22hinwDEWQzADJz/t39Ywwc/9GP7HtsnMgUIE2gHybB+7n6hIj6BIeBecSAABHvAQFCPoeLwwCZ431M/7dp/xpWmyYEBiLJ3v0vS5CwGgIhEeVAS5aoggDgACMVYlP/kmTb9zIlSXQRI9wfx5HEWe6dQugzl1oNwcWcLfylYoMhg2Hk517U7PEhQICHbNutBQcKEFhwMQhFt1XeAwVFJqbl+cxpewBIJiHMMwRYYWAECEQjol5Ouv7Gzr7viPz7mWQFnCFtHhpesogOCuwGBBwPdOJGqBPqjk997lV0A7YwESmAPBmfVo5RgRhmA7CeR7Z4xxPHoxkNT/Tou1NAXL5tzJX2hY6Bk8nMLDuyZRafavQMwdy+6fuWc6BnNuHX65/y738HT3/j38cpf/6cCCvx9S/3s5f9NCSUMFgCSlcIyLOnCwACng6xrgAQG4/y//b9LouHV/xgf/OGfiDttLz3orwHi6/YMlD/rMgIFETOwAxD0yuTU92sEEQDnoyqcWZTMrGg4JV0jRgW5jP1Jvro3WffV5D3TPxHtyUbHo/Ua6+piqe4DYAuMaGUbQ3HKolBDKplZFHsPGDivA4URSJjQGBIDCZY7QYAYkzRVgFAenQsIM2NexsRgxsSiRAyQ3+9jxR4QOIaAOiZoFC7yYGAIBPp+tTr0/SoVkXe+SL8CKLlNBZg5h0JBSdFXnGWKegH2g7Y9BRREgKADA15uL66TOnYgUbm/5fPogTpmFCxSt6/oXjPUxav/8ymLqayyc7eeHxYPRIP79TlRfqn+1//kjwQU/PRnJKfA66tSF+cAyee4hlW5PMAgipd4ms6yw80gOHDw+rt/hKf/5d/GK3/t/4J/54d/YpEEJmXQSdEz95Qg9lwNoBsUThEXJW1KxU9ls3vpu9XpbdXz26SQ7Vx7np3nFD6RenreCIzo0J2lVz6LOfROEDlzUTLFA8m5fDCktFkPDg77+6V9/gooMMPRC2EDDty9NjzLlsZ3IMGHGWw7AAvMGZTmVaBAnbcJ5216T7SZWTHLVwBlHGm/O/kg331TywQsmncUJvJgACiMzx4wcCoQyGkag4D71JeNc7Loy2nRlzV5bAkOTIGLTAMLZnRUIlBQqudYAuwHBVuyelIJQOEmUARi9sid14eUmnr2ANKd01+zNj06fIetd7TS6fvlJQkLZtHercyAGYCChx9dOCetrdnH/FweYDAqVI1mEbYE2JSO1//k23j69/8WXvlr/xAf/JGfFIFxmeLAgBoaDIiLLODR5yYGmKEqYm+Qh1PZ1KNz15g3QXp+oZAPrZeI6dBRyYEhcPsqg6B1P7jzpr7tlm0Wou/SDkuvo/yeJiwUhm9ANRaNcfH3dQrCG5TWmKQOFExjI1KE0fbFDMKmIum9S91XkmR9Yi0vP9rEyYyPAoVJ+z0CCvOxYRNGIAHTGeA8UEwOxFgYwhmM1eVsI8V/apggAgPTIQYCaULeA+i6/gNQPTKrw0X7sAA7Lv3HrHKnekj6z1gEasABw2RJMbjRwGVlxuTIUCdvk4DiQr3nrm7OATBZNRkTAF7fc23th3hfJ6v1QPt8ODDg73MRMCAnlPNPnpLqnzcCA70Xfx/0/aL4Z3dTKLlzVCR88DckfBAxBaey3FouJzBYoHlpnEKVqgCe33lTGvWnP4OrD7wXzDM8mi9I32WLl3KImy7GY319xvciNdqlNOsV2Hs4mg1wnjvVBDSgxh+BJsYst8sCEnzewnQGzrPUljOAQ2wIAJibWM7tjAMXcHCobEO5b9cap6yU1imWRql0FHQBBc7bpMNZa1ymQwUF00GOHQ5FANl7l2aI/P4eDBTD4xYR8Qbm1MWTrL98Mm3vba6ABWGNHFAIGAXW5Vc9m0Bd7LoAhQMqkHD1W53B4rsvyjQfeHq7WIHpsM4IGADcAnGjfnP1u1DfuX5j9n3EVcfYPgNfTNKdpq+y3vFAOk36AEZda4Jw1FUkATqcgY93IewggAlLcDApG+TBfYaAeq03ObETwBAbwNAwRuAPDgD4/XuBojt3deaJv+cFgEAEAvp39Anry5G+oeuBoe1YHqd69cLgT7pMvyYaPvRYO77LdV19/o1lDJqGsEa07QzQBOaE89tv4Jnf/TRe+dBvyFesmAFF8ZIx3lG4i+fsRIprlGPQSXWddS0dKCHW+ageMEyK6J3htyltNEhYrKxDrvdp9ut5U3edP3cleZHWpjv1FOUUtG9XQqUCVMUPVDAALABBMTTmWdp+BQqUkihVIulbInA6oDcuDSCgKTYqBQiIYFvrMDD8TG7wwvJKNPlNqPkrF5uxWX4bpIIF/+GwOs2tAgU6nAF7gQIgbJQDmAUwSIW2zaeXid6z28oTOBUIpEMMAtLU9RXC/gJcn632l3slsq8jTnJ7ae3SNzFImB0Q0HBhzlKnKdU+PKAymf//9t48WpPiuhP83cyv5J6e6fVYNksVludMd7ttjyQQQkBVvULCshAIie4/phFFISSEJA4gS6OxBZYs21osvLVlA1rMIqSiEGO7bQESi2nLvPdqY5fcdnt6xqfd1Aay54/u6TPTtup9eeePiBtxI/JGZn7vvapX9ch7TtX7MjIyMtZ7f3eJSHXOhWxb5cZZ//LYEeIGmFIE+crSQwLs/dgKJeC+OJSZRtplas9N9ygLf/dzGQBAlz8UBGQAIN2pRumwlzTwGfl8J1Wxv0sBsPNHngxbErdtPD8DvEDbOhuVxyH1WT/AwAQE+WC5gZ8PJ0LdgbnTz3cMUqH7lLnGBeJvuv/rDavfBnk3UtChgUkQwskEdcy498hTJfwjVkqBgwCJ/CQ0re3n2yBJPT/kgJRco6Smz7amqIepDDY/G4CAvVUgcRtYQkYDgkzAsBIujZIyDdT0kr4a0FwZJ2krkcxuJ3EqJXwchvACX4EE5g0eqBlAIWj/0fVA/jp3PQQtE4iAQX5DzQvADkLU2qPFxLOzBAa5BpQVJwcCrMYut/7I0ByLcZom4+SrSQTyY4UcJDRTgCkBCOSPrhVrDVcE8NQdddtMgQ2vQMUMXjoaD9xaOuoAgrgIcmuPBvlDwP2AdudBgH1nD7ifmeBW5bSEPjBc8Ku8w4W/0v4Nt9GyP8S2AuJKyZbE6ujm8fzhfbj8D93hRXObtvi2VOgGJ1Lnpjufp/UDDADkgCDxEfr0+YO7ccXD1+C+t305Hv4AWRiZOdbd8H8zRlcdw67LBCe33q/uJ/5McTFMkmutYZpxEwaAiMAhsyCoMqzjlEuWAi00EiAwK5oOhfQwlRwIAAkY0AKHxUIwFBB4AZMLmgZRyIiAYR8bkAsc3equnWDiqg0H7mYAgXxaEEREDizUtbcuRAAgVgXmDUEIJfO98cFqAhrAoHopWiRc7RPAAKix13OxBxhYjDsBAJKHCI223AiDrNIYkFnBWt/4AIbFoGN8EMYC5vg0YXwcSBBAx2AHYJupN+lTPLRGQFwACN7s7y0AzAxsIAcQJD5Igkz96XmWW0gsCq5xbXA/MxlrEVBj6y5UHsNs3wEW+uZNuNUh9CVf8NVToW7WR5Q0DRCqKyax0GRAhanC/OH9eOdj17ljjjdt8enUypuMpebnGiB00DoCBhVagCDzET5xcA+2P3Q1dr19J+bO2OqZH5SFQBgcR6AAmOCAJ6/oDrJaTcoXbP5ezchMIBPTpCTS5nvtv86eGfphJsrSEsEgcQt5WzJLwSxbo4pnpZc0T/eQ2uYjoMDWPqPLQNwK7XuJwGlSYTNVvxMXAgMyCiFpAD+OrgSXuQoWA1dSDhCq8Nu1taq8oBKrQjNN4xXCeRepVYF5gzQEwQUBAJNoZWuN/VCbu2JmrH37pSDB3C2gx8QDghZIU+PSMLcAAaDBwkrGBX5c4kUllhzfjBopSKgqgOAsCRLcG3b9sAcLEnXIDYjECiSnVzLAtUurJgBPE5DghsWDAcvSI9fS3uWuP6C9Bq30LgGv0gAkfv0uQa/f1ZjCvvuZlqDPwcDxAAJIgQrXG4y6Vpg/tBfvfOR9uO+SuzF3xlwCCFqf5w6KbeN4nSgA4SXKNWHQ+gEG4j8EFFOJPt/5g7ux/cGrsOuy+7DtjDkAgcVFpiamPUnLhGxy2Ei1YZCJcXWosGA7AEPfh5m4lW5bIRLddmKXT4VntYAYAjAoJC2TQXWZC0X71ECgR/hwyWrgWzb1P6aZ5tk0Cgx4gcOIAgnINdS0TfpSL92Un3EwVQsgILACDBEs1EFARbBQVZMQr0DejRAsA968nX5zpG1J42SNlOZAa8DaDcqAfDFAUABZyRqQgTMZowScDRiPVnMK45E3oQIwBYd+bliAGaMihGMzKm+xIHZjAwJqEoAgQIAR/MFcgZrGASEBbVUdeFTctqp2ogRBkAL83M3ofmrLYjcVP1ktP02BnpVuzQFkwnGIFl8S8APKHw4Ajq3bwKxD/YpUlsG5v6942H86+Ye2te6no8dgObyFZdtxAyAeKtU31usGGHQFgc0fWMT2r1+Jey/bha1nzKmjHVSQkLuU0vyfyPiS76kD4InyA81iglspAi29q2BFKD9XAA5GWRbIYCtvxzuTOI0NZSFCGtWWyk0eSHMzqTMdsoAdSxDlQqjLLy3zpmH3r2gdaNrCJwqs2HcNkEid3lnEaoqKVgooN0MEDCWwECwJDRRwiFaFCBYA9gJKf1U0aB4BNISWdM+jIiPWVoO2pS+u4wjIgpBv2qAsWAYKIECPg/R5qCUnfzopYRXkhP1U+o44AIXGgzIBCbLXiYg8IEO0Ivh/RDUqggcCLuaDK2XVdCdUpWBN7oklRFlABTAA8MGL+Zobvr6AXGgjGVseIsTz6w4h3fWudrndwr71yedSrMCx4tED38WTV7i/AgoOLmL7Q+/Grst2Ye6MbdGilf2NJZGfhzVADGaKAMHvyOOSsulp3QCD6KOyQMF27LxsFzZvmpOTN5NuCdMjmADdjxgs1CaefJ//sUx3wmoHsGT1iIK7PUlNF4iZ1gECQlldAKQDrBSsG+ZWt1n62GBGrT29uSYq9wwwkAijDrO0WA+0IJoyJ2BAAwEGMPX+BQcuFIgaqHDXoBBzUFcEUhyiCu6FHCy4dGc1cC3ULojUslC7sjLAALEjZcB51gaUtnO2AEAGxDQImAWMHfP+V/mCVcCDhJoITOzOKfNWhJqcsCIwKna/xc3AiZsh7pJqgQSoRiYfjhMrZxu0zfRdix4/e1ELbzHO8r2WwLbeOyRPXp+uZwvPr4hWSRZw7YEBgIUDC9j+wFXYedkubDljDkczNFCSY8GNBYIcM+1VBPcU1eV+wXoCBtTeKjZ/YAHbv74dX33HLmzZOJcwFyAKPEH6gGOKElUMWKBBnonWhuVSH66c5XjyqlSPHCR3lLGce+ZXxTqsF5YAMUHMLFpNByVMxxBE+ZyR+mjBVBJKlmbaNGWBJMJI5t8UDG4cgMibX9yn0cQ9K0fB0fpIFJonAquuCMHKYIGFJgMLRJ6hxE/oWoABIO9LrwMTAuw5QrDnebA1KKND0G+NvnbprMZkGAgIa9z3uwCAmfsdvt99JYmAJXDod55Kf7l+b3zjKwaIAVZWBK5cmj/v2FkHiBzrFkDBHpwFJq9BgjQuBwhtYMBYpbUD9FgACm6AQVq6zV26ar3ce5pm46/d91dTFojwX3xhATse3I6vvH0Xzt84h6NhAxm3niFAWaL8TZk/Mndy60EHrRtgwMpiABCeOLCAKz0o2LxpzjQ5ArGTwzYb9syWxWfYBg0AsLRMcJjUuTAxuw6IOZ406PvqIAN41rZnLqqcrWjuPFv+u70UMByhG/7KDHhH7dRftDTS/DoDBF0WAg0IBAwAMT/g5pk+jnbaMQeW1O/ad/5RuKOia0TAIPeoSsHCrJaFkJc4WQtAG0APmTEKC7jrbC3q/nX5ZrcErFZfJ/VWE136eonZC35JZ/CUOgCCA49iQRCA0Kg4BHEzRKuD3nXi3iPbH72XCOqW6mFABxx3Uktgm6W1ytLX5hkdKi13nQFlHpjTic4TV+p9AGJfTBtg8cA8rn7oSnz50ntx/satOBqsWzp/KruIgCrMR2XB8nPEWaqc9eCJg3vQtVrXDTAQtMYA5gUUvN2BghAMBoO5C2UTr6I2KNB5ZKBmMUaVxFjbGm8vgmOBhLtpWEFdaNrUIjWDVRkK4UYqP2X5rVPXjBcaTApIx8OyJOXzBSgHsA2xEmgLgSWkogbL4V4fLfm8os0uiWmbKIAFZ2XoBguWZQFA4i931xTTVT0qfdXFJBNBwUlyDhCK8QBsWwJyECD9t5L+FQogQPq3cQCNKkLNcg+oG8K0YtTsGLwAsbBdMYy7E/yUAQTpfwGnGiTk/Ki0dhK0YK0RY1lrnmEJn/yxfO1Y+TteZ773eFCf5i9UylYMlcjasRK58MQL87jmG1fizkt24tzTIygo9hU7UBkAgQII8Gvbn5oNAmHh4B5sf+AqnEGbinVaN8BAFIwFDQrOiKCgKzo5J0IchKgxuYS9BxcARP/kNHsur1OJWoPcElrDFlgXkJ4FZa+CAaRIpvGwAA5MYUOxHZbZOkfxs6J3i7ElQMAn6PlSAgRAPygQykFBLrDyr1NOu8Y6tNmBgQAY/MBqwJCDhZjuf1uAAYhCC3n/xx4b0vdpf6vf/m9uAQD6XQG6z7oAQGopKNexztox9ROvVmXWRO593nrgyuRecMAcrQci9AUggPVuBgSmX1JSVjLX3XVb8Fuafw7irPIsfnO8+UqJuqyfZviC+T5vSi5k0qAjB52zyIb3fONK3HHxTpx7uouJK/V9UrYClQIQam+dElNUVUX5uOuyXbj5f7+5WId1AwwaxEbvLIACK0Cs71z3RjHCvYcW8IGHdwAAlgz4prWmLgNeCxN0LNZ8YbXz9pdvldOZuf9WoGkH5K874DkVFlhl5AnxgWTk8X6fkEfdq6zlXapSBzDTYMD9jTG9faBgtSgXYvm4L/lrojSzBRgssGC5IuQegAQ0uPfEjiyNs04t9YTuIx17ASCxAOg2D7GydAGA0pLXAmLKbXCwXJo23AYHQOiURgCAAAQggARTSZEHB85loH8+x3z5vXaRCS8xgJ1Fy+UTQp05CjezkL5i1qIVICtHtqLqIvM501XPofLhixd/FW/YuDWAe6BfTuVWpxqMKVJwsPifXMzCvT6QsWu81g0wmH9hATseaIMCp9FxWIgN2AeTcWsRtIgjg9x3cBHXP7IDX3jrTvyrP7g4THQ9UUqD3Yeku4T9chZkvgjz90+Neubuesvv2meA6A3cMkgvrFpdlASRMJGoxbpK5T5u0WItMKHLG0JDGKe+J1RXlIwFVd7ULGZndpooe5+z9F/Nbgsk+XuAE1JdGm6sa3q9pJmXL6DLunAUqq89YICvizyjx9Lqxrqjb/vmVVm7twFAO1+57FlIgwJpuxxQ5O5TuOfSEdJzd30u+BrE+ZjMHwEIKr2Tv3QoFPIe/Y5cyIf5mqXr3RpAG6gB6TiW+ng5/EBoyLyy4hjrTDTn6zwfC2XsgsZdumi9ronSxpbdOe33D5UP55y2NVE8u+YwZaASxKiY/Dd3GA25QOGFF1zMws63x0D8LmSwboDBjgd89KYVUwAfyRwsBfZ+ZouIgf2HF3HDoztw20U7cc7pWwEoBlQw6ZUGswUC1O8+wW9pV0Aq6Icu3hJjbT+Xg4o25c8PJcoWqTCLRBtVDFjyWNqsBSC0CRzIwYM9bl14oc9a48J63Au8wufBATABOatVxe5348ZtAoArBwpcOvvfvu8rBRi0AOyoR1+9h1gXAGBJj0+yG0Ke7wZXmpH3Bfj1zbM+t8pyAUAKTFW6mm8uXwQEpbmYu2KADMgWQCp78KYtItF33Q/Qi+4YlcCwBb5llSnFZqTPZnVYJR4AtEGDxQ+s58XaFZ9TZfqLo9MUUGggYVnBkrc54yQANYYc80ifJ1Vkuz7ZrYTy892Ksglx7jTk4gpkdwvY7TBiBhYOLeCah67EPZfe6+SjL7RrxNYNMLjn0l3YvHFrjCOACH5O0FHJ9F4ytT95eBE3elDwhtO3BrNew+mkKQIBI22I5p8vYqAbufdpXSVhP0TzSrXiTCvJGMKs/EHzhUpdVEHQu2th2qKhuXvU0tzkGSvIDigseCjQkNV/qGFBeIaUIwfbgIFJFZwiINmuWCkFpUE8ma4iCKSR8Yj3XP4AHKSzqTxeXVQCDIEaTtq/lN2u887K6Gjn3ZQ64yeWKfQtWg4QyO+XwIA817ZquT+l4FqLuGM8u4Q/0D6foW9nRh7fEtPT9+u1r9f9StY8kK57IDW56/iVmhQolWcywFpSKvIgXSlbtpw64jCesv0UqkxtbUh4eGY1yMerA+MFYuN3X3xG3GCtnmWxIrjn9x6cx3u/uQN3XXovNm+ac/KR+0H9ugEGWzZtTTpUQIHV4Qj3DcGtEp48vIgbH9uBW9/iLAV6oJYVVFNY4MAwIGBZAGyfqlWWlS8tC4iL31r4TWJS10wCLVruHuHkEwh+2suBUxVFJuL25kaGEU26qWZXN8BSyEdYYpuJi6bvylak2tFnXRAED0RmxZF/AXAgAT56YgI/7jUw4RhYJydPTz3in4CS8ZvAj3FNYYxJ3ZeLIWChdzNbx21LmJtxHbO+c0YqvXM1QICUY8ValN1bUi+k6Ua9StTrTlS8q+9sDBH2mi/EtPg+vfbzNV9a7ys9C6AKZrZ0HPWaF5qqtS8vdsJdjy+H8eOIaMJY5VtOdT5nQfOlBEUEADNqEJamki8DCkpJDF0zUKGwKAd8LVJls85HKg0uUP59D+/AnZfsxOaNc4kLy02w8uCtG2DAaPvUNA1iRgVQ8IbTt3bbXXrKKiV3BV4BbUBgWQWGbMWaRQMYwhA0M5gFLPRRm0mwZxAcGIT7GFAEDBU5hlE1wJLXLmpyZvDab98TkOC+MIjinvMQ7GVpeGT/zsm65w0E7nf2l4SjMWGDpNdubkzUnNgANx9EkZnw6oCFIQFRuVWoi1Zd6BcC03IwcCJYAiR96FyxKMYW5IBdMrQBQWmrpoCBLiAgIECv93yd94GDoWSBfjQRBKTrXCqq1rtPqiSuyK9Z1x4ZJwBTzwOgLAvM5pZTQO84ifUTPlx7ZjCt4k3JN1V9oK0JysiRUq+ZKPvbla+nrN2H5vH+h69yoGDTnHuMbd5m0boBBqXDUoQqULLdTM3FOA7eBNMCBRYtg//1gYIkr2ElCM+GPN2gYDUAwaxMIgEKs9qAW0zCgwIfxu00hXgaXFW5QBs0HEBCVRGY5dO1DvE7QOCOPw4Agew95xZJlfJAxqHbJNs+YQ2m0jzB4lAT5KsRG2SO1rZ2iMoFNE48c5Txn4iW2AEWNFCoYpBBMr5VRSY4WM096LY2mSa2TcyOtJsJ6I8LkOdLMSqluIBc8OfzQb/Tqqem0pwQxp3HHeRUOhujCxD0gYE8DdDrfwXrGm5dy4ccK4q7vaoqlleB3FZNnzcMvwIJjgRURIAQysvWgMTlkI/VqeH5Abk1I1tO9X1NMU8amwBVDhB3nkg1zWFfrfVC6U+idJ7tO7SA9z98Fe64WIMCV1eZV01PddYNMBg6V8mv9oo5LEIojW7fEQ8KLuoABa6IngrZj0iyjlh3A5Y+QJUDB7WfwKtJApJyWgkoCPcSBtJ+d2nbUjDpC5Mg9ZspMBMBCQ1LekyDmBgrd28KxximFYVI/7qBv0bnGMZjgFMBoE8ZC3wrX6k55WkFARwUBlaCIklzgAFwJ+ptYAC1e840JxOwAZQElOVgYQIbSNb+uaRDpM5NHKOQtowp2gcGLCAwKwjIrQA5ANDav9b883GXZwIo0HXuG/+c1NwLn3r2TFsEV0NpfmVMs4tcBigoAQK9prvWc99abjc6pYrIfTuiin3hwIED+mELvs+T32+VtxL7fYFyF5Km0pbdYi2GVq8nnzVPhTftO+y21N9xsQs0tGiIOFk3wEBTy1rgEVLFXiBm4ECQ+b5Di/igDzQ8twsUoGyK6fQxcZqsJ5OYpRJzlkfKwZwl5i2W+5lJTMoVARHuZ5YEjuBAtMFKq0dSB0HqeaOU5aGq/MIlvaC1cDf6wSBhMhEgRMERy3OMBFVkEEAY3E5wMMuRZzkoiMcEZ2CAYrFRm7S1WotY6os4Z6W7Go59HYQH4qLOd9ZMagIrwMC1HYQ2BCwkVgW0gYLW0AItAxkMsQhYQGAWENDapaLGV96jxzik6ToJQFjxWMvvOJ6iJSNMZRdZTpwBBAEHWrvooWMFCmY9nyOJIQqxA/6vZ6RWPNGw+5TcnwVA2veR3ffXydyMcyqk5Y3umA99Zvyu+/m8lfwWKMhZnrZw9NG6BAYh0EQxuQAOQI7ZUroA9/ktibdftBPnboygoISuSt2rTVGtRylF/aQy6ch1wPuYQSGC3W1tcyYtZwLmMHgcTJ++3YVo9jbDl8XphbFnHgGtCzoPaF2DAOs+JSABSDX/kDCALGbSztNdljMXx8UPoL3vHOobAhkoqCkFBBoMuDyRGaXCZrjAEGJlRUi+GeBKSawIDA8cPMPPLQ0CGCZEvgyaCSxMZH4QF60KAKVuiKHAS9EscQF95wb0gQAL5PksrXFFlmflYxsLYG/LlbFlj1njWSsGf4LT/jU4kG2wNbvIeiKEczFkYFzwbXy2RHq9ltI1yO87kKi0dlcTDKw2EEjz+GtlXcrbnfRA1h3meQZGWonyvPk8k3ksdSNy7oMPPHxVAgooecaoQcecWJfAQChZxABImCghWYB7Di/iukd34IsX78R5G+dSH3BhRKse2MdsT4Z8h5eSBwlomAR/lfurI9gds6bWrgW95c090x2g1mb4hClFzbDxakzj7zUMoM5dCl6Lb92PWmWTdeJQH2XCVAxm4WIKYrqOM6hArd0Kk7oKQqUmwqRywmTiV1EFLxzktxcwWnBUZH+i2NUBZe2yp62c/PZAjdObjQIIwbKQpeWAYXlgwbmvNFhwwY+MKcW8wjy6zsSwKN97HtJXyRJQsvRYwC5hskZaaTzz3xZlw+fHU8ZH3AZ+7IiC4athbgEENM56wN660JDjEUsNMPF+hilcvAzqCkvTxrnPPDhg9o3ya9mZKyLYlzQL5Lu68yBMn4P1Y2ERWC4IkOfTPP56CAjQgEeXiZRKgHEokMxlixXDooHt/kOLSaAhqXyd1JFlXQKD8sBEoUdwwWn7Di5GpLVxzrsa4oG9elC0POs63c2/wD/DZnruL/SGgfgeSoVFA2cujsgmDp4w64l6l7X1zeUhf99fK+1QlzH1lWVvhi8FMDZNNHdGwBAb2/g9MqWdDEOoi5nkWxndNsZoKZhQ3JVQwzFTES6T2rGbKCiilaCiKEi05UBARhAeWtBArzWOiE21t+9zp/qTtWGOVVFndXLBC3cvJAADIHhAt1ywABhR7xUh3yGjQYMflEA6NsZaL0PMuMl3G6QXlgEC3NwoC//eMWxmH0PSjaIqtMsPhxrDCAoYjCmR+60AAlUI1gNXB3fLWRoJ8KGq3PhC68oF1TFjqQImYEyZghvIuRG80K+4B+SH1vRSaiHwf2fYgqy77Hi6A5LWSb3bSYlsyad0SVec5ZRVwANg4xFJ0l883XfIbUm865Kd2HLGNrNemoay3nUFDKhjQHUC+8wLBxdw7Td34O63uU9bumf9YklVuFZhk47eT/yvRj7O6qhNje5aWSz831rlS/yVkAh2VUFGsvUNsE89AwBW2rzF9F0eDvfj85TdQ/Jsfh5CHzCwgiHzYKI+k2JuIegDBAiCPhf+KSCoA3BwvxOB41rtODJzFBr+WgQLJYNWXp2JMAHivmuqABLtuAKLoPGgQQQNI/qsm6oNFpjFf9wNFpjjzogN0kQMm0dC/cDA3zO0NRkfYDYQULLolEGcH6PGj50GczOOnW9EmLX52IUGU+Xa6sdQvp8gIEEAgHZ1gh2wmRJCmpzNPwWwoa4wAbCEBlQ5hWAJAMG7E4j9WRnw7qPUMhjWqa9yDgiGrE+gBAxmF/xDhb50r8vTnkchT+tHaikKtyn9q/PpsvP8RvEmDQmQTGSLrgelSXsPLeJaf3jRnA80tEpPFEwRET3TeN0Ag9wk6NLSiahp4YV4TOSWTfGDEiKIq4QXtHux7nAlDDrkRRcZOEmoeFiIdXbL1ceum3WOg/x2wWnuZQ0ccNAFyyM246fOD9oAKXhIn1d1yUBEXtc+6mIorYONIAI/ugxE4Ay1EAggEG2zqrRQycCAAgIkkhU5OGhbEPoaTFBWBBEwAAjuN3sBI4LGBgzeItCIX5tmAgsAEusCfJlhHnntUn72Nsu4KAUEuuvZQUA6Vr5mOQCwxgtY8ZgBbswExLm0OF6kgAJT5YSp/xss/o3rX2raAAFwDF7S5PhtBkAeIEyJMakYSw1jSvHci4kHCRuAsHMBYhmEfejZwCYDaK9NYPU0/b4DpYBuYa9/l4R9QSb7ZzpuIgruLhqQJZEtVt0qALsPLuCabzj5tbXDUiAubVnHUnTJRS60boBBHiCUTICsExYPLODqh7Zj5zt2YatHWi1AQEroBmYYaTJkFqhyc0qCFI1FmKclqC+5R8nNWT+PmoOJDZVoEpS+Myu0G0igF0i4PGkj+7ZlplpCm6mUTNCOQXfHEIggCSBAhAwZ1oFmGoVLMzWBAGmLgbRLCZy0F0ukGGAAB6TUGgcImCpEi0IbMHA1AYhQZ2Ahfm3UBgvuWlkgZGyhmxSBQ2hVR7MsTaxrN4AVD5BfBzAXekyBtmapE7BRZtkJ5hJgWWPlflaRoYulwI8TZUDBNaL2eSpQVQNw1i0XaOgDfTlstnEBwnBm/2BtaFjFKMAdnMWESZ27hFy98uORXXNjG2dZi67JFhBI884q6Iea8mcV8l0CvirfMl0CljDOqSccrUWTijrfvfvgAt7tP4i09Yy5XrTR+OdZLVwtL806zFjnE5aqbELkpkN/iYUDC7jqge2477JdmFOfnqwRl35cH1TkB0M+FSrllqjLfWeChezailPQheWWjnhF7d1lBriwgEVLENQKSGSgoQ9M5HW0vvpoUa+vkJBoDloDzQMKrfgB7S4IC4inbUHTNBAho9ODcGF/jIjWPmcSOqmw8Q12TwgYgNdA5Z4BCAhHTbBQGZYFCyxIDTVgiLVWcylrRnJIUj7JhalTctkCAHlaEQQ03SDAHB8gGaOWdWAFY5RYeRD7PQEK1cSlNQ248qChWYpjQ7XbDUA+bIDd+HDlhLnbzeC3k1bxADcBCdolBG/hEZcQ0Ab1wMrWINAv5HMB3wcUs2JWTajnAj0X3hZ374oVGAIQ+kjqsCGXLepy98EFXP3gduzy8iu7HSgsSUIwJzUUc/dVd90Ag9xCYAUTzR+Yx/avX4ld7/gqtm3aDPA0MlUg5AwxfjGptVw2zAoDM+pffu7FxSDvQnoKFjzrzPKaJy0mZWhwkT9HSf525LwS+lkZAVTUul6FOrZqmJKe2DmT6TNBk7qXWAdguwuombatAzyNwqSZmmAg1UZdIBhP5TD2xv/pDmQLbay0oAGqulbnvithkwCGo8PBQlVHs7blhlDjLAAByEGCIx18mpOtBUZBoC18vfEAjYzBABDQMS4AVnVsqK79ZLTHJVh3ming+524Asg/59NB7m9tuBkqwLuAEH6zF/oNEbpcQvBp2joIYKY1J+OV/y4Jd/2sDp4D2uBQP5+/pzN+LHsuf9YqI81sJw/dibsK2ABAdCXk5S0cWMCOrztQsO2MrYgal5qnIfA1jimRAgjoXp9C6wYYaEAg15qZzL+wgO0P7sB9l34F2zZuBqbq228KHCQRxUCYWXkXVpx9v/MYUW2OHfXOwq5FzsbDVv62VSE+Z8czOOCQWwwsN0YLWGSVmPXM/S5mY+1JD8DAApFiHWgJHwEACiwwuzwtoTNtCZumEfeDRLorx0qP6ZYT7li73qmqyAj87wgYBoAFZcIuuSGSuAXyqEmNMSsGBHQAWUWlrZxJSUPiAbQ7YFZwxk0U/Pr3DGOSuBgrbxusKvC0CuMBKKDgBX8CEphjP1dN7Hf2sQUBJMh41KiqoASiYWexSYBBqH50D8Gw7Cx7jbniYno+noZQL8V/WXny8vU7OrIU04bcizRbn6waKeFecfZRa2bMH1jE9gd3YNfbd+KC08+N8qsFCqbKWuWsiSTrVRicnzxd/bF+gIFCWYmG0Uyx8MI8tj/0Ltz3ti9j28bzU1AAQHu+Hd8zBH6ORFtlnFjUGvSBICbZLpc8n4ERw9KSCoz0r+WeyYGDtmTk4KVPd+vTREMeCwhon7QSRCTCxtJERcjnVgUlfFgEjuQP5QFopkoYZa3Lt8PlY1cpMAB4rZNagGEIWCAIQIj3NFhwXZYChtC3/re5xbKLrK1/mZulFBDYit0wQVkBBOQAIDyP1RmLqnashAhc1RG4+YP2qfbCHgIEaoRzBahy9Q9uBjmlrlIWBAJomoC22lsSxLoT40Gk2/wMNy073WPVZV4311aWqRsACmmtIe97NrP1bRctlneSUJAtvv3zB3dj+zfe7eTXaecY8kvIzw1MwzpmYqCq4WE/GH5cIvMzaf0AA/83Z/QLLzyBKx66GvddchcuOP08YCpflZ9F1CiSBTHNv06fUZ+2YdLaT+T2XOnuByEOQgSReSa2xkyIBBtjEg3g8kiZuvzemmfVM363NFIBAuratA5ogZMDAtFWhwACAQNKU3V/DG21i7x2ylDggKqWkApgIQMKLVN3o7VYBMGl3RBQQECDhkSPLMwJAOmWPyBbH6nwd/kzAJCZ/ocAAS71P6DGUfU/MPMYkD9WlJsKwBJQ+X5m9pGBNZhzgFCDwaBpE1wJALtz0f04EnMK4HgKUN3a1SDjEYGCG4f2GqJVXENCmSnbmyySNRWyts3e3XNC09rzxTYtw1o8MBCBlqLgnz+0B1c8/F587eI7HShouuROBaBRIJ5B5O1CufUAnbhg/QADS/ObP7CAKx66Gl+7+C5sO/38hNkD3chTa0Em5QM0EJ32ot1ZAMWsiHhZYMUgY4JT3l9iZtXpJFu4olBJ83aAi6z83vGRVybMSTEwdT1oV0Gfn3ppaSZA0NJcQxW1KbugrXpQSlWNYHUU1wCQuBiCoKpiTILWZotWBV9m4oaQOuRBHckYKzBgjEfS/7qN0Wyk2p31dcjfYQ2Q/ltJv+t6hcZU6mcdxoAb6ffG9WXjgUbV9AIETCYuboUYoKYNEIiiZYYITFO0djVIn2frRa/HZa0V3QddQj30YVfe7F5efpJ27HhUd/4ZBH1P2UP7u/jOxgGD+UN78c5H3o+vvfVL2HbaOZ1WavZuhLBWuQKIwVXtLJ1iPRBg2WMtWjfAIAh9oA0KNgoosJiMTQRDc9DMIR+k5Qr8roVQLHP2ZzoByWosxpa2qGI1LLdDS/CTCQjS6G6keQGYyF3K7NNAWgBBC3sMAgSWgBoECpQAC0JJj9HU0lzTNK5VfqqA6VIEC8HGWys/W5UABQAtq4LLllsWgBwwuGeNsZG8RbKFg70jQNZzOy6gZQ0A0r7W1gANFoAZ+lvTFKh9n4nghgcJ4o6A8OXK7TSQnlB5oHABlpaC9QBMbYAg3w0Xhi6MXwCbq4ANthUl9801YWj2qn+KuzVycKABXVZGUo71ruVSh5AuCuhOwV0or+uZ3M1cSB9aJjVTzB/ag3c+dj3uf8vt2Hbq6zvcB/Iqloc9mGcANaiZ+jUOH15QATzF/MHFzv5fN8CA1GKfP7CIK77xbg8Kzm2DAq0FauKM0bZIM5IOk84sAt3IO2gBme9I87VMdUAMhussBxgakW0RVdnmNA2oaiWQXEqaJxM6UfMpAwrrPTEtW5xdY96ntSb3CoFsXTSUMaoxsnaQBFpaUkFcXjgJWJD+Y45AQXzglkUBCH5s9r9ZAQor8p6AxF3Qp4WkpIzcIrCtXQHsTaGGNSDk6bEItIBAtgY6+xiIfTyN4CArIJ1nQfKXiRt3QqHzENQ+aryBc834dmuAAESQoOsk6WmF2/VrV6B9vyTg+wCCAnBAxmOyOb9SvlK4kV76MdK9wC0hbczVAfzDBBvmWA8BGHZfzB/YjcsfvwH3v/k2bDv1DS3Xo/lOkjko382N9SDUbg1418L8wT3Y/tC7cAY22XXEOgIGwijmDyzgim9eo0BB5gvWgED7LHPKI0M9zR95snx/qLbea1LrFvBdwl0vPM7L7vKl5nUyfK1DF7XTnLKJq/ziiTYLpBotFLAQrRUFIFFwVyT5NOWLqQtc9WiyFugqEVWVF27qnaJFMjstv/HaIDdO+PgxdgeTlN+l7xGREnpOiCXabRCUqv8ToLBkBjZqsBDem42Zbmsf5fOIpU+s+dkVIJgDAcmPlYMBoUQACyjQ1gKXKembxDIzkIg5AQcAMoAAOJDg7rQLmH1uu/fmgDfmSwS9Bmu6DNX/LOUIZTxkFlBg9R1XGSjLhb3mMZXFE4yyC6AivLNlDcjeKSb8Qp1aQELkRgdwvPzx63H/m2/FttNeX5RDabp3LTFH61Ilo+jqR6jBaDB/YDeu+OY1uO9tX8ZN93+iWIf1Awy4wfzB3R4U3NkGBcEsrEzD/jnAEN4GPXHkKVz+Rx9y+UtBIIO0+7a2FO4YaJthLKohAt9YpGmgldYabHDRCyAKlOQqLOBcC41fR4pCiTWIKGiwBNG6ClYIqw5oL1pzDnSZSfWzVRX7Teom0eQQMx4AVCBSmrDPR/XEv8uDhEkV5yawPMEmGm5g6rq+3k9O5N4nTHfq2zJFGSwAqSAEIkjrr5VuRHqt5poFApJ0nT8HAyo/gAFuAketA2zqTNgjAwNAOpc9IAjXOXBQ/VcGDVpAK4CQtak1dxvD1GzFcuTuGXUvuMIA5woZCM7S9OXxi7QxWaCkmmtFoW71bQLW4tgmACMrj5UluPSuBDxwk4IHzoBE/jldqUuHvLn/ws/hglNe1xNoiNSV5xYsgiuJWVkOHDhYOLgPVzz8Xtx3ibjXXwauhIUDu3HFwwIKzjNAgcX0m1QYdHTUE0eewuXf+jDuf9Nv4Ccevrqw6JLCk6sSAMi1ei4I6xULf+sdGWN197r9r6ZA6ovkzoQIaSFiaGE8gOlyxnT7zN4AWsAhxJH0BQsl4+tEvDu/nh0gSYrxAKGCq6/0NRFIztv2vnCq4e77+sq4BAGlBd5EFrwCC0IlwZebvaUdVIVxDoLOg4TwPh9QByBG3gewALhP9CBlrMulDtBbBKlDAzaBxAIjFPrYcg3kgYbuAffXArVdYAAoAlqgPDdDW6gy+RKxsU5dgr8/g3smj9GQewWLTJc1psUfZuQNQMFKA7h5CzUmLnNSTgC1Pr+7l4GG6VIChBOlQVvFuABCCiZ9N5ZqXhIZmn2SxeQ9F5z6+n5ARRRkF1vlKLcUA5g/tB/vfORaFYjfXf66AQbvfPg9+NrFd5RBgRFIFqjHtP/Ei0/h8m99BPe/6ddxwalnm3mGAgHrHS3zHNAPBLq0K1XWckytnL9jCFgBoknYoCRKGrBNfWJyr+qEcbPW1sjtEc8ZsxVQFwTutIchB7RtVdyDkhY4EGbOwGTix1uYjerb2o9BsCI0QD1RZySg3c9Z8BzVkzRfPrdmiajOiJtpZLQFM3CYKwosCBGvHBiYJmZLoFgxM3m+Ul9M7HQqWD5MLTUHATrfjEAAcPPHi3G7ztKWnpglCwQEK6M2+Q8N1MyFvygxynJg+r2XyQ8AJPwgmc/TtJ+JCAxvHdFKRSnwFmqOkrbqedCbW8EUQNYWP0AB+CT4VLU3n4ott0TWCShYDrqs12FO+HEMSkSj+JRUyv1eOLQH73z0A1E+ohGNtPiadQMMvvbWOyISGuAWGEopKDgnu1swK2ttZmA8QMtnB8wOCJYJBopAIKsTW0zA6OtWmxF7Kl8sOaoXhhGi5I/6e3LACxDAggCFAALygDp1nzll1rzkFlIECKFC7brlvw2K/mF4IRTnQGUxbqOPCWj51l2x3cCvBRySii3DlCvP5aZ1M+5Eg4YZaeiZAVIfg1qC3cxkCN6S4A/3DQAwIDambAkAoM8SmAXMtYBpTwyABQa6dscIELBAgJ6nOR8o8ABr/YemSL/k7ZNYF0kKAlABaiBaBqtaBd8a8TSmq6yJ4ylVD1Wo1M4RBfZ0mnYVekEcrHyVGiPhYRInJMGQzNl8SEwHxT5bCc0f3ofLH73ObXk8/dzB/GDdAAPnM4kTtBgzQHFQ4QN7KN++5gf2iSP7O0ABAOU5LpEzNc/AAC0yNSVDWAwqqwMU9AGCHIBoK0N2Fm4x0GhJTNjavOdN+3UdzWKeUZD6zaKxMUfNofbH+ioQQFyBmwrWXvK4XcyP/9QAK1IHIAiDYVp55jLRpj5moJqEvqMhJl9/TSXXjwcOSf6SdQeFMZlFOHfRapVToq6tX5Y5OndxdAl9XYYl/CnL3+cG8CbvxK+9DKtOEhjYAgf+lgUKSoBgFjCQA4HC2tfrvi+40MXhaB9+3FFDdR3Pb0DEAyRFaiVYqKrBnhck8TSz9LW35rldIrk53gDHM5LJW9q5BmTJ53PGl6hy5ah884f34fLHrsf9F30R204/N3Vb9/TRugEG/eaXTPhLJwo44CaZBE8cftKDgt/ABadZoACxnFgJ978cMuLfFRjIdJqYB2UicqPMQNrkLJHr2ufs00KwW+LHrkK5rJ/P+4Ib0++alJXTjIyhj0lopB3mqi8rAQoCBqaNAwns6sjeIkDTqTfXey3BR0fLkDPQsZc8XxxqYWV7xcP95LqbEqhkaHxABF1Uq/nJclhtj2aoyu0CD4Gs2JN6AmvtDIogX00wMEDzNwP2+gLQdNlDNH5/PWQ3TNOaHypfK30gqSBBDkqMgATtWjCA+DJBgQUIEmWAm9Z6n2WtW+TWfVzvPPXgQAS7dr/lZKVnsUpp/kKwsyrLdA1lz1vuz0EBigBm2i5ZmjelHQ8CClS++SP7PSj4gncfZMRNp067foBBiYgi4hZwQHVgwC4CTC1CuC2Jl3/rwy46tAQKqto2y2hXgfigpSrav6h4apwH2gcmSUpYCyP2EezhcBUdEZ8BwvB2FfkeqlgjRLwnkboZSqcmInjdhY4iw9CR+UmUfgdppC6/XVnCLOI7ufIahMZ5XeCgLi2w9Hjg8keHkKS7y2E7Hrob3Q5cDZYbYxeEBR5I3Td9zENcFUJG0GoIjEzqnV1nQZPLoaF703Nh0Bl5PrOpv0/oZ2N+TOdCpeaAwNrInwJA4Madf+SDXyP7ssdiZlCgXQ6rAArIAG1aAfAJ7q92K1o7l1S8gbmFtCtQVJd/DMFA5/keluAnsn+H8koWsPbcTUHB+e13DaB1CwxCwFpiCaj8OvNpTCkjptp16r/9Kb+P9A1tUCWMnKoUpAW11wjk8s/FyeIC1txzMWjNCUKtFfiFV6cTOmHYYp2oUg0xRrg3ybPcNO2od7GYAMDUfcAllOXfH9sn2oQDLOS31VHt61/VShMYBg5CU3Lmr1H3LNHvOjgxFJYGJHaDgjoVApTd98+H++6l6bu6qAUoM2uWAAFtSvYBZSGINuTjYCIcBBwKFochwCF5LtxrygBsKJWEPjC74PfXgwR/Dv7kHdTOP2jck/QCJWPfMe4haFrxKO+AItjgQAfI5daccJZG3zdehJRCkrtDcwWgj0puQ58Q34fZwYC1a2RmIGBtX9QC30ozQEArkLRg/o/P2X3HVUksFwCGmrPzR56M7gNvKViOG2vdAgMAgPj6wrUABa/t64AxVJg/tA+XP/5B3P+Tn8e2095QKtT/yU1FRucnMQ/ZoAahrPJlQWvE7huDrbMNasWgtSnYR7w760B8hwYLiSboNUvyz7jyVOSx5X+s4czWvh7aD+nK8QLGiEOITVBtNL6najEN0otcmIfsXvBxBom24PNQVbk+UQGKVE8iKMg/heuvW2AgCAktLDKBouuM/gXZdzZ9sBRoMKC+5xDKsIBDZnEgboB6Q7gnrGymQ2yAeMKoqmdCQ9wLltugsGVsucKfAWX5yQR9JvgToS9j7OvE+bhaVgLMONbZODtLQLQScPGbHQRwBQ4nuZLPP1XuyqUUo2IpHq7lP7DjwDvcWtbWQo83RJfgCtAWQgH/rvlKMC5jPQNI17TcV8I72VLaZxGYFQTMCAAGBZF2CX5zfhRAJBnrIy8/+e3Knj+8PwQazm08P61jl6vdoPUHDPKBygW43l7lVwdx40DBH3rzy+nnZXk0+efDgTQZ6bRgPWjAoR5tS4JYDPIzFQKWSCZVxKVdR5AO1Qgp37vs65EfLevwSxQewQzp85A8nwkTmkQBF7qlZEXINcccDMjvEiAQbUFAwWRDSEc9ifcmE7DXErkSpkMxgFGnKzDACiyAfDCkBgdJZEBrpDvJNjwqgCDjxaqPWYS+DRy6QUMsm7hxgZGABw0DAyNVWqAhFqKOwMBBAX7+Hne5fSzhXxL8iauoUkChPLb57z6i7G94OhH8euy4DRJk/TE5d5m4Fxq4fBMKoJwpjgNVDF46CsIkBtp5F2uwFoqlsJkGZOCUqAj83XjIDhhVvm5oaQ3rexkAcPk6LAF9AGAZMSShfgXBL5QK91J6LvQ7rAZZOWZaV+BsoR7zh/fHLYmbNrfL12hxAEhYP8CgZeIDbPNuLrgbPHFoP975mOvUBGkpod0iS+spxhxU5Tx6a2PIn6SoclTVNbNXTD3cnyXqHUjjGDwQyA88If2sgAG9FVKBBncrgo9AQ10LhjlZgwFAaQ9dgKByFoLoOqjKgKCqW5aDIDjCPQrPMRD+BaWJo5c3cR/3bBPSh7oE/qatUwRUlAoYEVUmcGiBhg5Lg+GeEDCRgwYgzi1Xjpq/M+y+6fXH5sI/pBlm/y7NvwTowvPpWALZeDbJn1UYU2kiufGkWrEsRgIUGrEMcBDiAfB5wY5mCq4FIEwdOBBLX+VP8tNAYOmocy9aAAFAEnsABEUi4RF9VFq7wGxugJLwHyj4Ow+Pkvml3l/249vPxmvquR4IDjxxvcHIX37n/KF9eOcj78PXLrkL2zZujvcSJVWPnfRTsQrrCRhIK1OA0DIFCnkmN39wL654+H342sV3p0iLGUWTDgqDJ8+10toLaubvJ2T3E6bUASYCc5f6agGi8pdMyoFZKOBAimkEX6OyFsS0tr+695Q6oUSLLDAQ7WoQ5kEETDZ4xhMtBIATHJ2AoKoBVPEMBSLn71MCRHTtxkuSBqKIM7QsiZZhVr2cUsNO2CPLEdiYxI8ooaLvEXnwkAEHCzRowBD/dgMGSJsy14T7k1mkgOiq6CNKj7zt3P0R1u8yAUAfkGvSMZQW5kBg+DiqZsraUuOoWxjHEH78CEQ1KoKLHJEA52bJV7ACNY2zDlBVBAhO668AsNPYxQLIEQiE+KHcSthMgYniBytZr9JowA4EHCL4e11I0puqDsL7Yc0tlT9Pn1XAd7kOSs9Y5eZUFWRL633kPgPgjzmeO2PO3AkV4thkMlPHOHpaP8Cg5BcsAQPU7iuM37wG9136Fcxt2mwEGpaZXDlAxL+u9KzJYIyBGgAwOsGFusf6npknBrA5pqAmlAEgEuuDcQBKCxDoGAegP9odaI+X5TvUTEfiB/LdBkkMgXIPhFMURfDXbetAVcMCA1MFBHJgIHkAoEEEC0M1zVTL9H0uXRAYIFARB+Hi8kbgkIOGqlomYJCKc7QW5YFyrd0Us5AW+kCbsSfmf9cDywIAjTFOuimFsUO8XOb4+b/gbOxknNrjVhMwzUGCbFHmBkzeikC1izWoJu67LbJDidm5Ldnlxwb3u5J1W08iSBAg4BtIap26Tmi0CF3+OtWdURL6Ssvv+2AaJ+81+Hs+r6y69gn1wQJ9uOBvb1nMn/NWjNqQLcaz8wf3BPm17Yyt1gvdH1nbRJ6nZ31n0LoBBqzNVyWzodyD+wrj9oeuxq7LdjmkVULDBSbAk++LrzOebYOMDpRWBBH2MwHN95bTDxysb0VwSwhEgMBgYAOCsKDsfhFAACkg0O+dxS/dq0HIuBtgoOQuMGIL2AuYqbIMNE0UKuF3uO+uRfhojTN0ozS3s6Eul6XfEHFqPSByadJFoBZosAQPiFB5E7YGC8GtALQAg7M8qcYUIupDna01UWK+hrWvBe57XACzAjct+IeMmWpxJ0m4cxwzaR6rMXP3o6XAX1eufu43uU/fEFATgaqJq01Vu7ggbgD2vz0QcId7iQuijgLBByvKAVvUE3RK1notNjjju+Fn26zf+4VUynovtxjpdyhBaVoFWoK0x/wfyioISwsodJTT+UzyvnaeljXaAgW5/EoKkLXp+17iUiRmpQ+gYB0BA2RMwwoMk85bOLCA7V+/Evdetgtbz5hDA4CKkaDmz+h3hSGgExqoSQ0ICLGtDZGKpz12WBZY3U+sHB0CIMQaaKGQaY7MDbAhFRrl77kntekgwwRI1N5zroGANj+H9LJ1IAh+dv9EaEx9tRvmRLg0SqiIQAn6thIuU9kDrvp42tHeWrVVtNBatnqF/1gJfQccUuHDCiy4awss9FkX3B8HGuIY9oylAXZbTLcA2ktrd6gVwAIBOQBojZVvqtR6tcYrfFwv9DkrS4Ebj8r3/5TZ/SYCNYzal9X48as8oIN8rEeO+m2mAFcOjHsrQiv4NDTY2b+SszFa7kXfGZ3UPZa5sJZgX50nGePcSqTzIb8Gii7iPvN/eL5HaA8QnkMEv884MJ+natLZ+wsHFrD9gasS+ZW8hfxsJJnD5OdHBAgu38vAYhA/oNM2KwKRqcy/sIAdD2zHznfswuZNc0EjnHXsjvqCLd9iSgML9sCkM/cMdezKWsDKcTLmTB9IBALLvRxQaDCRCQxS2qhVfkuQDDFbWtqEaYbOTNBZICErwdM0HdaBJhUyU2YTDAgQcIAiHig1DaDJX7dbqDsAQBr6Ks10GqT/DXKWgEp9KZ40YOAABKqKZgQLcCmEYGGA5Mnq6X42VqpNhs9XxkB+p6b92UBA0wwDazJGUv18jIC+cXJUi66rxuiod/VbY1QxvELixqf2VoOGZaz87sEq5hUrggNvqRUhrEcVsMjiErLcQX2uoFnWn/6thLp99kMOBCzrgAKIpfLdRauKXfNuoIrWS6tVjlB2mnyQLRYtHlDya6OXXxnFWCOvSFANEKcAoYfWDTCwtIzcN7x4YAE7HtyOr7x9F87fOIfpLAA5I3n2GJ8Qf8yoDRZdguu9Wie59CpN0n89K3UJivnMBiaAxFg7xGedNMJgODP4obV1gBHN0dGV0LYONNwNBgQ4TAHIXu8oePx1fysDOAiaqP/r9oyLZhkBQx0YghJGBFQNezN1v2UB6r4ABp0We5zCzaojWDcnvYsDSIP98iDAVloGArQloGnKIA2AOTbAysfnKNLxOQp3emfdAEvEqImwxGWQwArANQRUTbQi1OTH0oMdYj8uYl3QOxtqRmLdyeNHpJOTXtdpHWStN5XeK+Rbwj2xSbX+JgJT3SyJtSFNOBloWmjg4oEFXP3QdtxzqZdfGklDDY+kUQQJVbCkigWB0aU+rhtgIKZgACnT9xrgwgvSqffi/I1bnUl4Ba9bWuEsXOkk7tsqtVKiDDnoyzx0xeUVgVFHllFFi4oGEl1gIryvZZoujJbSIErxJBYQEAZjaaF97gILECxxY4IBLWi0ENJIv28oj4YmuYx1aB4lQskBgzJYCAJpCsc0ILEIMNwQ7n0pYHBp1m4J18vDKRdNUWbZAEDSOt0BAwAaEMdFC/8IDFQdB8pKOUuwJgcGltj3PxOW4MZgqUILJEyoQl05BaaBAAQ3W5k9QAB8InuXgrf6sFgc7J0Ncb3GNRbAObL1NWBtuWp0afC21QdQlh+9bozxBlKh3zJiHGeeN/vzq1MPS7bsPriAax66Enddei/O27S1lcfNE89bZX36NLE+kdgQvAWha8GuG2Ag5mD3O2X+Cy/M4+rQqXNBG4TKPytNc/uPQQOypNSTvylk6Hps+WtJCelsAgVNJ/yKk0wDgS4BUgQTklfzIKN2Oi1vor62zNFAGwxYJmkrfmDqX2AJoKWGi4BAgwHmdCwbNVGsOZO7q6Y+oWoiaKjJfaVewMKS0l5dGpeBglIeUrCggukswJANRL9bLZKlDVoAwKUbIMBn7AMCpbEAxCqUvrvJBqBvPCp1wUwxqM8raDX8nJm6T+5SRWBmTEBYqhrwNI6DAIS6AyA05OIQAijIQMIUiEAB7qIiILgqC2tZDUNCrbUkiQUh3wX42Con5Oc8KXl+JdQlsNNb8WVVH8ydEQTMsjZy2bLn4AKu/eYO3HHJTpx3+tbkvhSrQSLYg3z2L3aoIDsHhdDViHUDDGQi5trg4oEICjZvnEujyYUGTj7NzJcGSv2+XKWJb6FjC9e3UfWwOizHWpKH2iQH3VD6N4AABQ5SQMFhsrqy2vmTcvJ3d6z2vO8sZjXUNN2nlS4F7TMVRJaFQAMCEUAyjZoODphPtSpcc+i/aUWuXxvGkgcKALAUzNmUmrgLQAFAAha0zSUdY26NcbzXTbqpWvjre9Ln4Z7qd3muz2XTBwTyMXC/+9e15K+I0EzjGKBy9azgBHTdsANx3oLg0jywaxjkAcCkcuZj6fcpuTGW+xogECQOwVkRhO8DEcy5Ovi0rO4rXTc6XyLoC+BO57fGOH+X0GrwJ6CgVOTgyMhUGVwz6TvW6d31kjEbSktNBN57Dy3g2od34I6Ld+K80+faG0U0SIWbUyFgFT4+iuRETASeq4bMrvOxNs8cD6LTiPH+ta7FSCONNNJII50cdOYDZ+G55541Yc26sRj8l5/+bwlC/fyzt+LjT9yET267Be8/6wZTM3R503L6gFID4FW3/j187V88jOsf2YHb37oT523capYVyzTSCmULPXl4ETc+ugO3vmUn3nD61lb+vu1U2m1oRVo/9eJu3PStq3HLm+7B607dAs4g7XQAYKwzqKw/oPL8i7vxM9+6Gr/6pntw9mlbVMCcfzbTUAGEIDkgD3iLf0Ur2HdoEdc9sgNfvHgnzt8016pbPtu7TJS5yVrKv+HRHbj1op045/StLUsBYAeylfzXuRvh29/dg08tvhc/e/4deM0PuhM3tbZachtp0ubOivRv4Dvf3YNP77kWP7f1Tpz1g1tUtHwcJ9kO59IpuCGAGJ8ApGMG/8xThxfxocevwm/5+Wlylx47tU7K5/P+Iwv4yONX49cuvAevP21r6GNgePxGl4XmO9/dg8/sdf3/6h/YvOL+FpdC5d1j335pD35h8Rp86oK7cPYpW1x3qDiQwQGj/h3i3oH/+6Sa/+dtnAvpmvrWgPSR+6vnHrDv4CKuf3QHbrtoJ871/KdvW6fe3QF078J55vAibv7jd+Ozb/wyXnfqlhXzGyDdwfPsi7vx09+6Gr/u54/Lr/NqK1fKh0J68QJ4Sviz7x8jiy/bbksp/VW3/j0cuPG/4s7nb8endt+Mn9vyWbz3zOuzZ7N6KqtrHhNk7TgCgM8/eyt+54H77UpgHQEDWfgM4AszgoJ8UZRIz93rH9mB2/2kKJ0D0if8rUwMBwo++NgOfO7NX8XrTt2CJSWMABsA6IWVL8Q84vq5F3fjY0+8B5++4C782CvPw98spd9bD++Z0ZgkQXHPv7QHn1hwTPFHXnke/ttRf966EkIAOgVRziiBCByePLyID/pFefapW/G9JU4WWr7ochNjl0uGGdh/OAMFuo0VYdrEOUMVUDeEKRg1OzPxBBQEV80MeEbP/vSaZ1/ci08tvhc/v/UuvPoH3ffSm4adaVrM1L79fabtKmtsDgpe60EHs+uXKTsTN+DqWisAo/3gznfs2yh941/1zEtOaP/6m+/B607ZjCUVRj0kgCsH3/l8fvrIIj7yRw5UnnnKlhC74e6ngEDqngcPlrqtIjc/f2nvtfjY+XfiNT+4GQ1zp0/Z6mMgBQOAzGFX/i8sXoPPXHAXzjplSxYYihYoENJzXahB8FAEXrH/kBPat7/Vzc+w9rl/rqeAODyW5H3y0CJufGwHfustO3HmKVvwN0vuTr71Ngdqku7KVvMKKW8R/vALW+/Cj3z/efh/j/bv/ajJxdMIkfKhCA+RuBrhb59945fx6lO24G/9mRD6bJ8lsOI3rm5LUxswACloeOqw659b37IT55y2tdXHegykj1ogTdKNaXfHc7fj03tuxse3fBbvOfP6dvlqEIkorG0H3hgV+w9zk7wouhaYGV989lZ87Imb8Bq8tv1yT+sGGEhX3f7srfiEBQry/ANAgcVc7n7+dgDAbRd5TV4vNKNeQ4CApn0HF/Chx6/C5978Vbz+tC2Jdgr0WwL6tl8989Ju/NwT1+CTc3fh1T+wGUvTdhmlwLikXSpZ1sxRRKH08c134Ee//3z8rWcqEldQ0lwBZAw0Rta7/I6JPntkNz78+Lt8/2zFEiPxnwHRBy9rTvtgu0iDAhnf8DxFAemYhDuEhtlxYyf43YKaMoM9QBCmOQEwJcazLy7iF70meeYp0VIz9ZVrj0OsdGksdADct33///zWuwIo0O3WOxosmjKjFuYv4xMEAOOZI1ETO/uUrSHWJgi4GVyTeTD8lGP5v+ItWaX5nBNVDozVHvyEGBWPZqqKgqXml/ZG0CSALKcq658qERjub96XNYDnXtqNn1+4Brd4TXhZ4Ff9yGMFZH6KUpJ3R+ivAp9psgSt+TMznjqyiA8//i782oX34Me90iDWMMC2iMX09N0WH9H84cdeeV7gD0A5QK+qKJQdxqGJvITDOADPv7Qbn3jiGnz6grvwWg9aa3K7QwTwBiDBvoxpHJclcLSQab7qq6ktZed4Swplfd0Yikk+ayWLHidpz6f33IyPb/4s3vPa600AIWDRPc8JOEjqwW1w8AUvHz99wS343ZeFxQCx0Z/cdgs+8LobO90CRKJFUchXIRXkYeL5Yu5+3iE5ADhvY3tROnG2fNKg4JzTt7ZAgSaLQc4CCs48ZXNxIc8SlNUou+Kf/tVe/NLea/Gz59+BH3vl+fiegAIfEaU1rSm7qHoAWAqMlrHkNQGJrA8LtgKePxzdE2eeshl/O52azDWMYcZc827MF9KTmumq8Q0fzBae5K8nHiAwEAQ/AEzUuE08dJ8y4ztH9uDj89fgVy68B2eK+6Zy4ySHoKYM14OFGO2GLnr+JeeekPEN7ewwuSZakQ4izEAbADx9JPb/WaduScrMhbx1KFzXuSo5KDjbly9Be1HwU8gfLFAc+2eKlKmlmuredv/09Knuu/w8CSC1fj3/4m58fP4a/OqFbfeZnqcABgOBcJucpUDPT03LBQJAtAQ8eXgx9P+rT9mCo9PIR7rcNe49Nu/QfONP/mpP4A///JXn4XuNVhqMIFvfV60AT/9MYgUj4Lkju4Ol8qxTtgSh6c6CcPOH/BzJT9yQPEkaOLEgCCj43Ju/GpSG5ZA142R8AThQ4N0HdmCkfo6K+XL6wjO34hPzN+GTF9yC97/uRvwOXgbAIAcFmnwQsPsNikg2X5VIzTRCDQF3PR/NO5/afZM5EMztQW+Z9rIMgjafzHy200Yzvph/6tXhWhUkwCFodsLURfMD8OyLi/jEE6mmqrUrp/i6vqkq8ppUFoHd4QP/k7/ai1v2Xoubzr8DP/4D54f7FeJzDsHGSG55l47kxtTXy0dzy+L+9uHduPmP341bLrwn+CTrhgLCrzmi+uCj9Qq3NsdKr+Wuh33KPCsxI6Ux1lsr9XSpawEm7u8GhvuoGdz4/sy3rsZvekuQHrch7qCQXrAaPPui01R/6YK7cdapW1pMLifLYpAzS13EM0d246OZzzaW1fmqznzS9ud8TMqvq5gCoQl8f6g6k8FexTKTkH/nLP1TAlKp5SUKfcBZsj7qx/ec07fOJPiHRMlb8xNITdIl14AGBdauDiCCglvedA9e+4ObO3fXyHuH7rBpwC3+kN93faP4cBbDAdiuGyC6bwQU5DEdgGWdlGc1GPZphjuhM6bGsBB03E7eL7T/sBtfALjmrBhTYO600HUWa0nI3+43APjisx4UbLsF12Xy0aJ1Aww+8YRDQte97ka1WOJq8cqdE04ce6zx5m2ZyrrTpZwvP38bPr37Znxi62fx3jNvwKd23xQOFInlpINt+ZaYU+bLPsN+8Vkp8/VE74/WE1UWdtyzFs1SfpGHvdS+jOdedJrqZ9/4ZbzWa0ryga34rGgHfvEF7Tsu/oDgOZ2A3/7ubtziNYFX/4D6dLWnko92KGmf4euUpmqh/IQUM84BAal7+w4t4voskEvnscgyoJTcUvsOLuKnHlPmX3WvpNEB6X7mrjP7nz6yiJ994t349Z9oC+1ZqCTgnz7iQM1vvPkrOKen/JljDCrgqSOL+N/+KC3fuWHK9reJZX0oTKxS/1jtHeJjzoX9U4cX8b8+/i7cdpHbZ+6eRfIXsDU9JGlm9bH30EJrfrJiMCV3ZbCAEtDRlYmlQCxZQ4IBcxJFQq/3hhl/6kGBxR+6eEMXEACisP/OS23Q1wcEukBAPtaitGn+7OqFJJ8mayxLsU/Cf77w1p34V39wscnTzPJCOZQk5lvAv/TcbREUnN0PCoB1BAwEFAB5J0bkzuy00WASzgSc5Qj60vO34pOLN+Pn5z6La8+8Idxq+yAzYeFvJ0zQWMT7Drno1s9n5uvwvMrbAJjIqpBPaiIOomXKfvrIIm72mpiYf7U26j6UyMaz/jVSYeXn00369nf34LN734dPKJ92Hw311xJRAAW3XHgPzjl1S3im/3sBbUCgwYAwgX2HF3DdozvwpUt24vyNc61FViTzXnvQ9h5awPWPxfL1ePbFuQiwY0GQaE/RfYcX8ZHHr45Mq4efz8run5Ly37JzRebTlGLnPXnY+bSt8oN7xSqi4/RlPTRPHnagIyl/Bg2vT8Dv99HpX/Tjmz/fCmqcERTvPbiADzx8Fe645N5QfgNn5gusIoBKDgoKM4I/neHPQGCncDjLgbOy7X9pIVhqzjp1S7DMkATQQiw27ANrEYJocyUi1E3Rt7+7t5M/5P2T8wPA0vb9NRGe+250z5196pZeAFAS/umPOMb7fSD47Xr3ga6bMZ6t+dQh6Pcecvzni5fsxGY/vjm+NQNj8zwZGJB3BkvBBcMsBULrBhhcX2q0gADEzlIuyZbgBiIj+tKzt+EXF27GL87dgve/7obEB5agukyYC8kC1qQnzd5DC7j+0bammsdGiLCoVd0j8IhVyE3Zzx1yTF27J3T50ZTt8pe2F2nztY5beO6l3fikj75+zSmpz7mP+vzcVBGefXE3Pjb/nsRnuxwwoA/iIYr39x9adEz34ntx/qa54uKSsmal3QcX8L6Hd+Dut7kPdgm1T93045aghvhTM1udZe/BBXzw0R340sWRacVn2jSLEiiBmDlTXE2atfxZxkDcQx98bAc+783vXQw7YfYqTwkoVuROpLvukatw19vuDePbdZT4rLT7gMwfd4w74Mbfued8puAuc7xGFBQGB0WEGeHAKoZXMCrCk4d34yOPXx0CnbVri5iBmjBhCnxgEgJl3d8NyC1ZaSc9/9JufGrxvcF9mVPbdRUFfkjr0PxF6fnXb74H556W9n+fK0ffK7lyRGn7QuJe7B9fa8gt4b730ELCf0JeoqJV1UouzeMveVDw6QIo6Jqa6wYY9K2/xIRvqAQ5I/3Cs7fi5xcc0vrAWa5T9WBNgjbHxZeXtkA1rDQBmRSJtUHqxO26Z2UluyLUxR5hiipQaSLACFmbO8zYpV0RTx9ZTAKt8roMJctvCwDPvOQW/ee8ednatggMAwKSXfb4AsC+Q47p3nXJTmw5Y1tSF/tbEHadS7R4wJ1t/pW3u0+jCmntP6bZgl9tAmyNswilO5VQAsrgwpVnD5CVuvfQAm54LGrCyxnbLtp3aAE3GKBY05B+LmVxW/qUpp0J9VIZJSabC5Td/pjaey5Nx3dovUsk/bx4YAHXfONK3HPpvdiyKVqaggEvszYlYAHOQsCIlkx9VkfFztL0wcfc7hvZkruB/Vyo28GJQFQgADseRtMzL+7GJ4Q/nNoGBXkfdfn6LS3/KbEEeUtZe82231Vy5bSAAaLSZloSdcaMhgr0PQfb/EdIu5G75lLXSY+yZf8zM1oKQh1mfuIEpdy030f5ZNad/PlnXCBjV6dOSGIEyu8tBWHvOxg1gc3G4TyyIOtcgFjvSBJdpdykvipM6vzZLhO2JG2oKAixXFiJefY2ZZ616tb3PYl8z7ZvAZ7MzOOlRd8LAvyFtgQQUTh7/K5L78XcprkWALAYSQAiA/jDwoEFXP3gduy6bBfmzjDG1yih1VWJxSAr/4VFvNcLpS2btlqPFLbekrpu10JS9hxcwHUPX4U7L4nzc5YjXfto78EFfOARL7SN+S/UtaS7NCdhund7oRrKKzzfJSSsOeC+cncl7jXGd1ZMYHXrvP/g204FKluAQIQ1kQkWWpH+5AOL4UGf0oTDwV4U17+zPrqyN0hF63Kd9Vp/6vBiEogJlNe6ddGn1Yul6YuGJj+r5Qdoz7M9Xmm765I2f+6KoRkqxHcfXMB7v7kDX1bzUz86KYGLHsQpt29/xp1T8Jk3/jKu74wpKJe3foCBlbgM5O469aP4bNaprUMmClwri6tqkWiS92RMK6Gg2XdTg7Y5TpsfN2+aawmARHho08kA0zWz0/Q++Gi6KEvnP2yYNcoQcdF//q07sVlFX/dG4maCWwt7Leh3HxRNzDHdFggolNP1OxJj/sACrvz6ldh12b3YdsZWlEcxLSGEjmS3FT8GAMwfWMC7H9oehFLZ/ZDVjLuv5XHRhDXTsuaZRUOsCiWhXaLShocSMIia9i7M/VBaviXkgW4LQn69cGABVz2wHfddtgvbWqBvFvTUbgGr8jWoFM2/8cLbuQ8MQCBwIbunrQoi9MSS0oBDHAJQtkAmsS9GM2Wt7z+0iA8/flViqUxa3QG8hrh2EktQYn638+flWnXQ9bDmv1WmRUMCbxcPLODdhiUxqUsIupyRCLj96VvxsT/28uv1ChQYY9ZV23UDDLo0uq78mm57+lbc/McfxS1v+mXc8PoMaeUWhtJ78nT1nHxPe2fHpBDq2PIdKOyvb5kfXfksG33t6pjvSQLdMg6wW5mvW+bfAk8cctSskFg6dKAVYnUA2AzAYiiWO0AvysDUS4BC1avYg9nG/PkDC9j+wA7sevtObNu4GWj6T3TLxyfVolID5vyBRez4uhMa285w5mXdHxrnaWYvZFmXNJWYVhLb0tmW7tv5/OwtrlBeSQkI6+sdu7AtAwVdwt66bnUON3F837ET2zZtBtiP7yx+llKjqMLCgQVs//qVYXylFrnWHMY5dxWpGCo55Q6E8BneBS/0tKUy3+II6HnSniNAeU3ngbZCxVMlOzR3S7gPsQRZz8rzZhVUcp/QnllQZxXS87Nr/oftmTPqVZ3yS5U1xPq3boBBRb18qZNuffpW3PQt16k35p2KNErWutaU81/AawIPbjfNjxZZk7A4oFSYdFyupA5oDPXWXMgXLOCghKRLAIY1cxlAoslY7pVO8272BsukWKHdP10WgU4olZ/S4xs6f2AR2x/0oOCMren37rtIGLO5yT8Ci/mDiy2hRNkz5t5+9bvK+byySljzM5lv1jDOIA97QXHPNOnz3WpNO9fkS9ad9NIYLyXwk/HdtBVgHj7GUhxVNoggwvwLT8TxVZYmyv7XRoNsg1JskmFViJbKzP2ULa7cOmRjnnaPxvW7y3SP+maa1CfYc0uQtvR1ldNT5YQWDyzgXQ+2+cNqkWUJKlGXbClRn/zKy888iy1aN8DAZurDSDr1lwd0av6+IfcWDiwETU9PilndtqUJU5x0WpvMnmkdvNQCBfIgBZ+q5fPMTcz5rgmLLEuEDrQa6kvT1GIKKs/ug1HoaaHRrTUOAwRAKjQu2LTZFjI9ZAkZAQuWUHJ70oz3tMBC/D/9FVu4mM1PSe8FvwMX21BQPMhcWyj/yq/boMBcZaXxKWj+0v/3XfoVD8pmH18gjnEOAudfWHDj+46drv7hQJJw8K2UYGvDyICCJPqLIPQyUGYeBZF3QfZCKzZFr9+tWSCdRTNZggAsHrQ17T7hPVQWzCK0l/OuEv9fTlkWzSq/Sqxe07oDBn1pOf3W07fio75TPzgQFAwtG/Dm5YxpDZCdg0kzxZUw3dJiXThoM3UTWAx5Eacvs0CHXcHuYktBgbIopf4zm5CBTkGQWwpCNyxTePSVH+vk35QPnH5vS7BockLGmp9UeCJ9ukz62b7+n4WsZ9P6d8R0dI1HhysgAQVntH3ms5BlFUrHN3fPNRnQswFCF6Pfc9AJPROU2dOi5376JnP9DhzkIYK9S2gfT/45lPI6Wetr1jK6aKj8sox/Xe9Z18Cgj5YLCoZSaVKslpVqOZOuRJbbcsiiGaJdJvlVvlndK3ld+/KU6l9+fjZf8fyBBVtoo+AamJGKoEDVoZNammdy088fCZRcHtPtki2rBVpLNH9gXtW/ILSHADQiExzo/p87Y+vMFr4+CuW/wwAFnZRCgRJIyPs/r7+1Zi1XU4lK63fW8Szln339zkaryT/XovxZ5NesfbZugMGstFag4EQvXybQsS5/VvParLSwmvXXgtULmiQQbdbyu4LVvLAPoGM55YeyyuCkDQr6AgoKr+gs/1jMf4npKIOatIK+D/oAQgayOvt/wPj1UWf5KwCV1vqV9bVcV41Fq7l++y1BJw//PF7lH2v59bIEBusXFCxHp2kvy1nqvxz0bjGtlZKuxzHtf6o8KLiqXf5Q90GP8JgJdCxDiAwWqsuk4zP/Z6x/Vz+VdpeU+n8lJxiF8o35s0o0tP+XawFZKKzftdPkZ2tJnD8jKCjRyw4YrF9QAAxz8K+k/JXTiV1+n4O1RyitivvgeAiNklBdOWs/9uO7uPqgRo3bcen/B65aZv37x+d4gvoTb/1qsls3goJh9LICBusbFAyhAZrqCV3/41l+yZJyrDXtY1n+4knNFGcrf3Z9+MQY3+WDsxOr/49n+TO4b07I+g+j4wUKgJcRMBhBwVj+iV/+y0VoH4/yZxOwx6f/x/Edy18eHU9QAKzgMKeTiUZQMJY/lj+WP5Y/ln8yln+8QQGwRsCAiH6ViP4PIvoTIvoDIvqH6t7NRPQXRPQfiOgtK33XCArG8sfyx/LH8sfyT8by1wIUAGtnMXgcwI8z86sB/J8AbgYAIvpRAJcD+DEAFwH4PBEN+X6LSSMoGMsfyx/LH8sfyz8Zy18rUACsETBg5j9k5iV/uR/ARv/7HQDuZ+a/Zea/BPAXAM5ZzjtGUDCWP5Y/lj+WP5Z/MpZ/rOXXbz19a+f9EyHG4D0AHvG/TwdwUN075NNmouOBtE7mSTeWP5Y/lj+WP5Z/YpZ/PEDBR7/10c48ZH0UYzWIiP4tgFOMWx9j5gd8no8BOBvAv2RmJqLbAOxn5nv9/bsAPMLMv2eU/z4A7/OX/wzAf1hGNb8fwP+9jOdOdFqv7QLWb9vGdp18tF7bNrbr5KPltO2HmPmV1o1jtl2RmX+i6z4RXQ3gbQAu5IhODgPYpLJt9GlW+b8N4LdXUkcieoaZz15JGScirdd2Aeu3bWO7Tj5ar20b23Xy0Wq3ba12JVwE4GcAvJ2Z/z9160EAlxPR9xHRDwP4JwCeWos6jjTSSCONNNLLkdbqgKPbAHwfgMfJnTu+n5k/wMx/RkS/A+DfA1gCcD0zT9eojiONNNJII430sqM1AQbM/D913PsMgM8cp6qsyBVxAtN6bRewfts2tuvko/XatrFdJx+tatuOWfDhSCONNNJII4108tGJsF1xpJFGGmmkkUY6QehlDwyI6LVEtJ+Ivk1EzxDRsg5UOhGJiG70R0//GRH9ylrXZzWJiD5CRExE37/WdVkt6joq/GQkIrrIH23+F0R001rXZzWIiDYR0R8T0b/36+qn1rpOq0lEVBPR80T0jbWuy2oSEf1DIvo9v77+nIjOW+s6rQYR0Yf9PPxTIvoaEf2d1Sj3ZQ8MAPwKgF9k5tcC+IS/PumJiN4Id5Lka5j5xwD82hpXadWIiDYB+EkAB9a6LqtM5lHhJyP5o8xvB/BWAD8K4J3+yPOTnZYAfISZfxTAuQCuXyftEvopAH++1pU4BvSbAB5l5h8B8BqsgzYS0ekAPgjgbGb+cQA13CcFVkwjMHAfbv/7/vc/AHBkDeuymnQdgFuY+W8BgJn/ao3rs5r0G3DbXddVgEzHUeEnI50D4C+Y+T8y8/cA3A8HVE9qYuYXmfk5//u/wgmYmU9nPRGJiDYCuATAnWtdl9UkIvoHAOYA3AUAzPw9Zv7Pa1qp1aMJgP+OiCYA/i5WSX6NwAD4EIBfJaKDcFr1SaulZfRPAWwloieJaJ6IXr/WFVoNIqJ3ADjMzN9Z67ocY9JHhZ+MtCrHm5/IRESvAnAmgCfXuCqrRZ+DA9zNGtdjtemHAfw1gC97N8mdRPTfr3WlVkrMfBhOZh0A8CKA/8LMf7gaZa/VOQbHlbqOZwZwIYAPM/O/IaL/BQ5Vdp7aeKJQT7smAP4xnLnz9QB+h4j+Rz4JtqH0tOtn4dwIJyXNcFT4EoBdx7NuIw0nIvofAPwbAB9i5v9nreuzUiKitwH4K2Z+loguWOPqrDZNAJwF4EZmfpKIfhPATQB+bm2rtTIion8EZ4X7YQD/GcDvEtGV8kmBldDLAhh0Hc9MRF+F86sBwO/iJDKj9bTrOgC/74HAU0TUwJ2n/dfHq37LpVK7iOh/hlsE3/EHY20E8BwRncPMLx3HKi6blnlU+MlIg483P9mIiDbAgYJdzPz7a12fVaLNAN5ORBcD+DsA/j4R3cvMV65xvVaDDgE4xMxi2fk9OGBwstNPAPhLZv5rACCi3wdwPoAVA4PRleB8Mtv87zcB+L/WsC6rSV8H8EYAIKJ/CuAVOMk/IMLM/46Zf4CZX8XMr4Jb8GedLKCgjzqOCj8Z6WkA/4SIfpiIXgEXFPXgGtdpxUQOkd4F4M+Z+V+vdX1Wi5j5Zmbe6NfV5QC+tU5AATx/OEhE/8wnXQh3uu7JTgcAnEtEf9fPywuxSkGVLwuLQQ9dC+A3ffDG3yB+sfFkp7sB3E1EfwrgewDedZJroC8HMo8KX9sqLY+YeYmIbgDwGFy09N3M/GdrXK3VoM0AdgD4d0T0bZ/2s8z88NpVaaQBdCOAXR6k/kcA717j+qyYvFvk9wA8B+d6fB6rdALiePLhSCONNNJII40UaHQljDTSSCONNNJIgUZgMNJII4000kgjBRqBwUgjjTTSSCONFGgEBiONNNJII400UqARGIw00kgjjTTSSIFGYDDSSCOtKvkvEP4lEf1jf/2P/PWr1rhqI4000gAagcFII420qsTMBwF8AcAtPukWAL/NzP9pzSo10kgjDabxHIORRhpp1ckfG/ws3EFb1wJ4LTMfXdtajTTSSENoPPlwpJFGWnVi5qNE9NMAHgXwkyMoGGmkk4dGV8JII410rOitcJ+D/fG1rshII400nEZgMNJII606EdFrAbwZ7rPfHyaiU9e2RiONNNJQGoHBSCONtKrkv/T2BQAfYuYDAH4VwK+tba1GGmmkoTQCg5FGGmm16VoAB5j5cX/9eQD/nIi2dTwz0kgjnSA07koYaaSRRhpppJECjRaDkUYaaaSRRhop0AgMRhpppJFGGmmkQCMwGGmkkUYaaaSRAo3AYKSRRhpppJFGCjQCg5FGGmmkkUYaKdAIDEYaaaSRRhpppEAjMBhppJFGGmmkkQKNwGCkkUYaaaSRRgr0/wOe1CskuE727QAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.sim = mp.Simulation(cell_size=mp.Vector3(Sx,40),\n", - " boundary_layers=pml_layers,\n", - " k_point=kpoint,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=Air,\n", - " resolution=resolution)\n", - "src = mp.ContinuousSource(frequency=1/1.6,fwidth=fwidth)\n", - "source = [mp.Source(src, component = mp.Ez,\n", - " size = source_size,\n", - " center=source_center)]\n", + "opt.sim = mp.Simulation(\n", + " cell_size=mp.Vector3(Sx, 40),\n", + " boundary_layers=pml_layers,\n", + " k_point=kpoint,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=Air,\n", + " resolution=resolution,\n", + ")\n", + "src = mp.ContinuousSource(frequency=1 / 1.6, fwidth=fwidth)\n", + "source = [mp.Source(src, component=mp.Ez, size=source_size, center=source_center)]\n", "opt.sim.change_sources(source)\n", "\n", "opt.sim.run(until=200)\n", - "plt.figure(figsize=(10,20))\n", + "plt.figure(figsize=(10, 20))\n", "opt.sim.plot2D(fields=mp.Ez)" ] }, diff --git a/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb index 15f9cbf0a..a15e36dea 100644 --- a/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb +++ b/python/examples/adjoint_optimization/.ipynb_checkpoints/Bend Minimax-checkpoint.ipynb @@ -17,17 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using MPI version 3.1, 1 processes\n" - ] - } - ], + "outputs": [], "source": [ "import meep as mp\n", "import meep.adjoint as mpa\n", @@ -38,6 +30,7 @@ "from matplotlib import pyplot as plt\n", "from matplotlib.patches import Circle\n", "from scipy import special, signal\n", + "\n", "mp.quiet(quietval=True)\n", "Si = mp.Medium(index=3.4)\n", "SiO2 = mp.Medium(index=1.44)" @@ -52,17 +45,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20124611797498096\n" - ] - } - ], + "outputs": [], "source": [ "waveguide_width = 0.5\n", "design_region_width = 2.5\n", @@ -75,82 +60,117 @@ "resolution = 30\n", "\n", "nf = 10\n", - "frequencies = 1/np.linspace(1.5,1.6,nf)\n", - "\n", - "minimum_length = 0.09 # minimum length scale (microns)\n", - "eta_i = 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", - "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", - "eta_d = 1-eta_e # dilation design field thresholding point (between 0 and 1)\n", - "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length,eta_e)\n", + "frequencies = 1 / np.linspace(1.5, 1.6, nf)\n", + "\n", + "minimum_length = 0.09 # minimum length scale (microns)\n", + "eta_i = (\n", + " 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n", + ")\n", + "eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n", + "eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n", + "filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)\n", "print(filter_radius)\n", - "design_region_resolution = int(1*resolution)\n", + "design_region_resolution = int(1 * resolution)\n", "\n", - "Sx = 2*pml_size + 2*waveguide_length + design_region_width\n", - "Sy = 2*pml_size + design_region_height + 0.5\n", - "cell_size = mp.Vector3(Sx,Sy)\n", + "Sx = 2 * pml_size + 2 * waveguide_length + design_region_width\n", + "Sy = 2 * pml_size + design_region_height + 0.5\n", + "cell_size = mp.Vector3(Sx, Sy)\n", "\n", "pml_layers = [mp.PML(pml_size)]\n", "\n", - "fcen = 1/1.55\n", + "fcen = 1 / 1.55\n", "width = 0.2\n", "fwidth = width * fcen\n", - "source_center = [-Sx/2 + pml_size + waveguide_length/3,0,0]\n", - "source_size = mp.Vector3(0,Sy,0)\n", - "kpoint = mp.Vector3(1,0,0)\n", - "src = mp.GaussianSource(frequency=fcen,fwidth=fwidth)\n", - "source = [mp.EigenModeSource(src,\n", - " eig_band = 1,\n", - " direction=mp.NO_DIRECTION,\n", - " eig_kpoint=kpoint,\n", - " size = source_size,\n", - " center=source_center)]\n", + "source_center = [-Sx / 2 + pml_size + waveguide_length / 3, 0, 0]\n", + "source_size = mp.Vector3(0, Sy, 0)\n", + "kpoint = mp.Vector3(1, 0, 0)\n", + "src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n", + "source = [\n", + " mp.EigenModeSource(\n", + " src,\n", + " eig_band=1,\n", + " direction=mp.NO_DIRECTION,\n", + " eig_kpoint=kpoint,\n", + " size=source_size,\n", + " center=source_center,\n", + " )\n", + "]\n", "\n", - "Nx = int(design_region_resolution*design_region_width)\n", - "Ny = int(design_region_resolution*design_region_height)\n", + "Nx = int(design_region_resolution * design_region_width)\n", + "Ny = int(design_region_resolution * design_region_height)\n", "\n", - "design_variables = mp.MaterialGrid(mp.Vector3(Nx,Ny),SiO2,Si,grid_type='U_MEAN')\n", - "design_region = mpa.DesignRegion(design_variables,volume=mp.Volume(center=mp.Vector3(), size=mp.Vector3(design_region_width, design_region_height, 0)))\n", + "design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), SiO2, Si, grid_type=\"U_MEAN\")\n", + "design_region = mpa.DesignRegion(\n", + " design_variables,\n", + " volume=mp.Volume(\n", + " center=mp.Vector3(),\n", + " size=mp.Vector3(design_region_width, design_region_height, 0),\n", + " ),\n", + ")\n", "\n", - "x_g = np.linspace(-design_region_width/2,design_region_width/2,Nx)\n", - "y_g = np.linspace(-design_region_height/2,design_region_height/2,Ny)\n", - "X_g, Y_g = np.meshgrid(x_g,y_g,sparse=True,indexing='ij')\n", + "x_g = np.linspace(-design_region_width / 2, design_region_width / 2, Nx)\n", + "y_g = np.linspace(-design_region_height / 2, design_region_height / 2, Ny)\n", + "X_g, Y_g = np.meshgrid(x_g, y_g, sparse=True, indexing=\"ij\")\n", "\n", - "left_wg_mask = (X_g == -design_region_width/2) & (np.abs(Y_g) <= waveguide_width/2)\n", - "top_wg_mask = (Y_g == design_region_width/2) & (np.abs(X_g) <= waveguide_width/2)\n", + "left_wg_mask = (X_g == -design_region_width / 2) & (np.abs(Y_g) <= waveguide_width / 2)\n", + "top_wg_mask = (Y_g == design_region_width / 2) & (np.abs(X_g) <= waveguide_width / 2)\n", "Si_mask = left_wg_mask | top_wg_mask\n", "\n", - "border_mask = ((X_g == -design_region_width/2) | \n", - " (X_g == design_region_width/2) | \n", - " (Y_g == -design_region_height/2) | \n", - " (Y_g == design_region_height/2))\n", + "border_mask = (\n", + " (X_g == -design_region_width / 2)\n", + " | (X_g == design_region_width / 2)\n", + " | (Y_g == -design_region_height / 2)\n", + " | (Y_g == design_region_height / 2)\n", + ")\n", "SiO2_mask = border_mask.copy()\n", "SiO2_mask[Si_mask] = False\n", "\n", - "def mapping(x,eta,beta):\n", - " x = npa.where(Si_mask.flatten(),1,npa.where(SiO2_mask.flatten(),0,x))\n", + "\n", + "def mapping(x, eta, beta):\n", + " x = npa.where(Si_mask.flatten(), 1, npa.where(SiO2_mask.flatten(), 0, x))\n", " # filter\n", - " filtered_field = mpa.conic_filter(x,filter_radius,design_region_width,design_region_height,design_region_resolution)\n", - " \n", + " filtered_field = mpa.conic_filter(\n", + " x,\n", + " filter_radius,\n", + " design_region_width,\n", + " design_region_height,\n", + " design_region_resolution,\n", + " )\n", + "\n", " # projection\n", - " projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n", - " \n", + " projected_field = mpa.tanh_projection(filtered_field, beta, eta)\n", + "\n", " # interpolate to actual materials\n", " return projected_field.flatten()\n", "\n", + "\n", "geometry = [\n", - " mp.Block(center=mp.Vector3(x=-Sx/4), material=Si, size=mp.Vector3(Sx/2, 0.5, 0)), # horizontal waveguide\n", - " mp.Block(center=mp.Vector3(y=Sy/4), material=Si, size=mp.Vector3(0.5, Sy/2, 0)), # vertical waveguide\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables), # design region\n", - " mp.Block(center=design_region.center, size=design_region.size, material=design_variables,\n", - " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi/2), e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi/2))\n", + " mp.Block(\n", + " center=mp.Vector3(x=-Sx / 4), material=Si, size=mp.Vector3(Sx / 2, 0.5, 0)\n", + " ), # horizontal waveguide\n", + " mp.Block(\n", + " center=mp.Vector3(y=Sy / 4), material=Si, size=mp.Vector3(0.5, Sy / 2, 0)\n", + " ), # vertical waveguide\n", + " mp.Block(\n", + " center=design_region.center, size=design_region.size, material=design_variables\n", + " ), # design region\n", + " mp.Block(\n", + " center=design_region.center,\n", + " size=design_region.size,\n", + " material=design_variables,\n", + " e1=mp.Vector3(x=-1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), np.pi / 2),\n", + " ),\n", "]\n", "\n", - "sim = mp.Simulation(cell_size=cell_size,\n", - " boundary_layers=pml_layers,\n", - " geometry=geometry,\n", - " sources=source,\n", - " default_material=SiO2,\n", - " resolution=resolution)" + "sim = mp.Simulation(\n", + " cell_size=cell_size,\n", + " boundary_layers=pml_layers,\n", + " geometry=geometry,\n", + " sources=source,\n", + " default_material=SiO2,\n", + " resolution=resolution,\n", + ")" ] }, { @@ -162,32 +182,47 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mode = 1\n", "\n", - "TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),size=mp.Vector3(y=Sy)),mode)\n", - "TE_top = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,Sx/2 - pml_size - 2*waveguide_length/3,0),size=mp.Vector3(x=Sx)),mode)\n", - "ob_list = [TE0,TE_top]\n", - "\n", - "def J(source,top):\n", - " power = npa.abs(top/source)\n", - " return npa.abs(1-power)" + "TE0 = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(x=-Sx / 2 + pml_size + 2 * waveguide_length / 3),\n", + " size=mp.Vector3(y=Sy),\n", + " ),\n", + " mode,\n", + ")\n", + "TE_top = mpa.EigenmodeCoefficient(\n", + " sim,\n", + " mp.Volume(\n", + " center=mp.Vector3(0, Sx / 2 - pml_size - 2 * waveguide_length / 3, 0),\n", + " size=mp.Vector3(x=Sx),\n", + " ),\n", + " mode,\n", + ")\n", + "ob_list = [TE0, TE_top]\n", + "\n", + "\n", + "def J(source, top):\n", + " power = npa.abs(top / source)\n", + " return npa.abs(1 - power)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opt = mpa.OptimizationProblem(\n", - " simulation = sim,\n", - " objective_functions = J,\n", - " objective_arguments = ob_list,\n", - " design_regions = [design_region],\n", + " simulation=sim,\n", + " objective_functions=J,\n", + " objective_arguments=ob_list,\n", + " design_regions=[design_region],\n", " frequencies=frequencies,\n", ")" ] @@ -201,15 +236,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evaluation_history = []\n", "cur_iter = [0]\n", + "\n", + "\n", "def f(x, grad):\n", - " t = x[0] # \"dummy\" parameter\n", - " v = x[1:] # design parameters\n", + " t = x[0] # \"dummy\" parameter\n", + " v = x[1:] # design parameters\n", " if grad.size > 0:\n", " grad[0] = 1\n", " grad[1:] = 0\n", @@ -235,42 +272,52 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def c(result,x,gradient,eta,beta):\n", - " print(\"Current iteration: {}; current eta: {}, current beta: {}\".format(cur_iter[0],eta,beta))\n", - " \n", - " t = x[0] # dummy parameter\n", - " v = x[1:] # design parameters\n", + "def c(result, x, gradient, eta, beta):\n", + " print(\n", + " \"Current iteration: {}; current eta: {}, current beta: {}\".format(\n", + " cur_iter[0], eta, beta\n", + " )\n", + " )\n", + "\n", + " t = x[0] # dummy parameter\n", + " v = x[1:] # design parameters\n", + "\n", + " f0, dJ_du = opt([mapping(v, eta, beta)])\n", "\n", - " f0, dJ_du = opt([mapping(v,eta,beta)])\n", - " \n", " # Backprop the gradients through our mapping function\n", " my_grad = np.zeros(dJ_du.shape)\n", - " for k in range(opt.nf): \n", - " my_grad[:,k] = tensor_jacobian_product(mapping,0)(v,eta,beta,dJ_du[:,k])\n", + " for k in range(opt.nf):\n", + " my_grad[:, k] = tensor_jacobian_product(mapping, 0)(v, eta, beta, dJ_du[:, k])\n", "\n", " # Assign gradients\n", " if gradient.size > 0:\n", - " gradient[:,0] = -1 # gradient w.r.t. \"t\"\n", - " gradient[:,1:] = my_grad.T # gradient w.r.t. each frequency objective\n", - " \n", + " gradient[:, 0] = -1 # gradient w.r.t. \"t\"\n", + " gradient[:, 1:] = my_grad.T # gradient w.r.t. each frequency objective\n", + "\n", " result[:] = np.real(f0) - t\n", - " \n", + "\n", " # store results\n", " evaluation_history.append(np.real(f0))\n", - " \n", + "\n", " # visualize\n", " plt.figure()\n", " ax = plt.gca()\n", - " opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - " circ = Circle((2,2),minimum_length/2)\n", + " opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + " )\n", + " circ = Circle((2, 2), minimum_length / 2)\n", " ax.add_patch(circ)\n", - " ax.axis('off')\n", + " ax.axis(\"off\")\n", " plt.show()\n", - " \n", + "\n", " cur_iter[0] = cur_iter[0] + 1" ] }, @@ -283,1627 +330,44 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 0; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 1; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 2; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 3; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 4; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 5; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 6; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 7; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 8; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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/Ch/7x+LnfWgjfFPGom+BCl3XiOPGFQilTvDq8nP8CtT9LU2iOTo6qoTOouebUuKW1Xosek++q6urSvxI4GGfOI7XW1zz9tkT0mnAETfeAltyvM6iRVlP3XWL/e5Y9A2om8p1bG5T5UUlSnG8PjR5x6hFhOhZ8LgBJQTPN6RkQaoF5Uy93oEXoud71GlIwfV/JBMjYu94+LsYHuCy/oOS8LMpu1qr9wDQjEXfAFxvTXZpHZtr2Zk1LzXm8E+ggh8MBjsr3bDg1aXnujy73RyaaAwPwc9msz0rz80/vAIP5wqQcGMhl5bHjrgRs8b5WfaeKSX/Ss07Jseib2C73cZqtarEwS4w17H5HnNawiu14bJlZLJ58SjLwaW/L8FfXl7G5eXljpXn5J0KnlfcgRcRETtlPfzUY+f9wbktJexU8CBr6LGFP4xWoteLsgvgAlqv13F1dRVnZ2dxdnYW5+fnlUg4w81xvYo6y9RncTy+l60rCx6i16Qdu/QlwcNbwVRZWHaI/vLyMq6urmKxWFT7iUGHPQxY+vF4vFee4+faShxxc9vrurIktpHlN/A3/R+Zw2gl+i6f3F6vF4vFIj777LP47LPP4uLiIs7OznZEou59XYdd1nDCNWiOnyG4knUvCZ5j3boYPrPyLHjNI6AfAFYe36H3rs8GOhwrYMGroDPRc4kvyweY9ti9b2C73cbl5WU8f/48Pv/881gul3FxcbHjCnMCL2vcKSWtdPVYrYOPRqOdDjtN2MGlV8GzNYX3gSx9SfBoxGHB63x8CH4ymVQDC6y3hhGZF5Od28zjycp03KxT18xkmqkV/Ww2a/XPe2jgwo+I+PDDD+M3v/lNfPzxx3FxcVEJiBN4dY02KnhOZuF3PFBP18kzEPmjR4/SspxaeC6ZIYZHLoLd+UzwGlpwWMF1fy27qYCzJFxduy3/rgKG94PPanjE3pPF30yt6F++fBm/+tWv4re//W0Vw3XphG632/jkk0/i008/rYSRJec4A98keL7gueZciuFh0bMYnpN2bOG18WY+n8dsNouLi4u4vLyMi4uLouAhaI7ldeIOx/Lq1meuOY5Vm2rwOY3vS5Yev2fn34JvT6Olf//99+P999+PiIjJZBKr1epL2bGvkn6/X4n4nXfeie9973txcnKy956I2LNS2XPtqtP559kCGDxFlt37zKXnpJ0Knl35i4uLSvicj8gEz249HhzLI3TAsaroWNg4bj5evK4hgQ6Y+Cz/PhgMiv0OfO55UDI31Ioe8SxYLBavfYfeBFjIn3/+eTx9+jSWy2Vl5bJppBG7STm92DKrrktbwcJrxx2ED+temirLgkf8DqGfn59Xz5GPQC0dQofwtc1XF9Lkc6RCVbFC+LxYZmliDgtZRc7ndTgcVsnJrB3aFr+eWtH3er0dCzeZTKryy0MHF89oNIqrq6vo9XrVhT8ejyMiqoUjIARONulgoC48u8/aeKO3oMrmxusEF4gGJTkI/vz8PM7Pz+PVq1c7pUbuq0dHHbfZquC53VbdcX7geDkGV8+GBY/riasMXPMfDAbVtnFOh8NhOsHJYm9HY/aerR5fKF1htVrFfD7fcVW1pMWizy4+FTxPWOGYmQXPf2fhwbqX6vAQPNx5CB5WHk1F+D/iGFA1YG9DLTyHNPjOOuvMy3llgte2XRUx3ofPrNfr6Pf7e3MfdB8s/HpcsmuAa9zIrEdEJRK46WxxNSYtCR7z3XmlG7jVPG1Vxa4lMm4T5qQdBM8NRdp8w2VCDS/4+zXxlrUb45i1hVhj+YibUIQTdbxdHpQidgWP/VXhlwZds4tF3wK1IDqfXeNqTWiVSmB615lsmWpdvTYTnwqerTyX5xDHa4tt5n3oQpp8fNx2rLe4ytx5zYFwnZ3DRU7A4TmHDNfX11VMn7U929K3w6JvgONXWC5NdHF3WsSN8DmW188gQcdxO8frmWXXeJpn/nGmHq48J+7QWwBBsEsPr0IThdxbz5UBXh5bl8Yu5S90YIT4dbIMCxZ/5+PlzD3vjxN57bHoW8Ju+nA43LHW6GFQUbLV04YbtvQQGpJ7HENrFUAFzxaeRc/uPGrxeiy8T+xt6GQaWFv2Kvihll6X02JPhYWpU2S16sHHz4lDXqOgrnRnciz6FvAkGHbRUUqDi4+LmtE6PFt6TtrxjSgywbPrqi49WmtZ7JeXlzur33BownV4vme9dtxF7JbmWPC8ug4El1l5tvTYDuLybG4899nr+Y/IFzRxBv8wLPoGtOykgjk9Pa1cfHaF8VkWAZfmOHaGldcsOXsOEbt1+GzyDH6yOw8LzzE2W3c8tLsP3wchcRjB6wnw+ncsYPaK+NzgmBCfq+C5Jq9deTi3LHxO5Nm9b4dF34IsA69xuTbK4HOcwdZ7zHF2vHTnGc5oa2ttXS89Z+jxUz2VbD09LgVC0NzDr4uHcJYd+865CC7d4Xj0/PBAgb/rwiJA+wKyTj4Lvh6LvgWZ9eJVaBHX64qzWSZb3V5159WN1eRZNluOe+lhfeFuswC58Sbr8tMW2+12W7nvEDtPNuLYvHTMmoRk1JKz6DmBh3PBn8s68bhByJSx6FvC1l5dcxZNNntOe8/1Dq9Z5r8keLTQwsrz2nZI2HFrbcRuaY6TdpxXQHdhROwkxziO15WCQN1xA3XLs/56nk2HUh4nRXU6bTb5iT9jciz6BthaqpXWGJ1r0dnFryu5slVHjNtk4TlTz0t4sUsP4SA+z+5Vn2XqMdEI353F8LxYCETK1rUk9IjYEbzW1LMJOjwAwqMoWXedbWfKWPQNsFvP4td4HAOCCppFr4MACxy/s+uq2XJeplrX6sNn0LyCn9mClpq0g1vN/fvZLa64Jo9zg3o7jg/wQMYlvyzjjs9mrc7cm8/b1u5HC749Fn0DnADLutW0rxyCU/eW4YuTM/Ncf+csPcfxekMK3GuOLTz2Oevxh3eiE3YgQMw1QCgB4XPnHc4Li1OPjb0VvM7eC98UU7P/bN3x2VK/gsV+OBZ9A4PBYKespXeNyWrNbLEi8nvZQ2gRN24vW3i2tixytrpYpoubYmDd6wTPffDscmvuAM09vEIQC1Az8QDb5Nf1GLmLjrfJP+EpaJIT2zO3o1H0PIqPRqPOTK2FFZpOp/Ho0aN4/PhxPH78uMp2ZyU6bhvlbDVbLo5JWWzcUqvlMYgdr3NdOiK/x1zJndeZbnhkC2eqW4/jQoKQE3B8DtQl1wSeJt4AZ/l1m4oOrKUEotnHi2g0sN1u4xvf+Ea89dZb8fTp03j06FElem1iwSwwrUtrppldeHZ3WfD64CW2WQTcA6CWXefDs4XnCSpZ0o4X/WTBR5RvL4Xj5Ro7ezVZDJ4lPZEHUPFmPRA88Gj+xOQctIjGdDrdu0XRQ6Tf71fLSH3zm9+Mt99+O549e7bTyKLTabnzjd3erG00uzUWix3P+W+c8c46/bLMPFcVtGOwJHhur9XEHYtdV8LR7fJUV/ZsFJ6GmyU98RzZfFQLdBafZv9NmVrRHx8fx09/+tP40Y9+VFmKriRMcMHO5/PYbDY7MTEsJyfBIm7c+2z6q1p0teJ6iywk6PBgOGmoll1vSJG59Npaq3V4WHdusdXSpYpey40czvB3snXnUKhk1SF2tepZVUW7/0xOreifPn0aP/nJT+Ldd9/9svbnjQEXzkcffRS///3v49WrVxER1UXFjSy4uLNZcVoG06y8il3deBYKWzd257MZezyBRwchHoA0jOAGH82q61JfPJEGx8rnRAcADIoYQErnvPT/4KQeLyyilZTs/2BuaLT0Xec73/lORHwh/vl8XmWVtXMuYn+Ja50UkgkMImOxc7wdsTvQaClOBa/lRO0J0IYffmgYgWPSufFZ9YKtuSYqeeDiZCfIBKp/x4AXcbNqEc4DDwBZpt/s4pJdA/1+PyaTSZyenlbVC06slTLRHMvzohNZgo63pRNNWOyl7LyuoacNQixCbbxhd54n0HCTj5YCdbosvoMbi7KprqVuRRWphpBaBuW1ALjvQHMMJsc3sCzAFnI4HMbp6WkMBoNqUUmegaZiLSXvYNVV7LwN3QdcyNmSVrw6Lls6tbwQI9/AUkOLLH6H2PjOOyyuiN3bTtctaMGi1qx/XeMNnwtuL85mKtrSt8M3sGyA559DyLj42HpyDTpi37XXLD0+l80O4yw5W1gWvVq4LJkWsTsjrZS00/id6/A6L57LfocIHgIvJQG1xFkSvfYj6HqC2jdg9rF73wCs7Xg8jtVqFaPRKBaLxY7AMgFn9Xi9uy1/B5euOFmlbj1f6CxA7EtE7MxQ4/1jj4OtOzfdcEKSBx5OkkXsCl5j+JLgs9ZlbsjhB2+D94cnPdWtR2BrX8aib0CtnZbktOmGLT679niuFoytUiY2vsh1+SkdeLihpdQQlDX58JRWWPmI8qxAFXx2bJxtZ+uuCUDAYs+y/Vq5yM6HM/ftsOgb0Nqwuo5amsoGgaxOnSW1cGFnNegseabNMGoxtdWX900TdtqDofvFaEtxk+Cz8EDLiPycvRY05GhtvrRNd+Q1Y9G3JBOpoq6purrYjia39KLWWXs8EHDrrza2sGh4Bhvvjw48bcpnvG0eZFTwvD86WLFAucOOm5uyc5YNillTjr7XlLHoW8DWgy+qpguMu85YrOo9wNryRa3eBSfqImKnHTqLhUvJMN6v7HiwPfxUL6ZUluPtsjCzacg4DvQ88DHw92TnOjsnmTdiylj0LVFxqwXSC5AvWhYZZ77ZumeCV1GqmDOvojQjLfsu/R6gFh2i1vAla63VZJvevEMHFhV9Nlhl59xu/O2x6A+gZOk52cRi1EQVJ870wZY+s1wqcs6UqyUGnIPgfYUAsxV4se3BYLCT7FMrrxaZ423OrJfKithGlszjgaSE5gG4RJkNGOYGi/4WlBJxGnsycPHrEneZ5VW3V6sDmizE/rFXwd9RWok3YjdPgJtL8v7rQKMlNW4k0pKaDiwqag1N+H16HkulPX5u4Zex6O+Iiji76Dirnn2m5GLjp2bhuQSnPe4RNyLnEhxnvbPSH74L7jb3EWhlQgWfNRHpslwcy+u2mwRaCmV0UY660MDcYNHfgtJFlSXuImKnnIb3RUTqwuvvLDgVfKk+DreZBamxtmbT+buy78966TO3Pqufo4f/NucUf8NjvV7HcDjcu49dtn9NIUJXsehbolZEE2Zs5bNMMoRf2jaEigtVk2dZGbBk1TRfoPPNtSxYOtbsu1lIKnj+Wdd1x9+B53XnnQeefv9mdSJufCoJ3+xj0d+CupgzK+8hvtYJNRqDZ3FuFrvyd2G7XPMvibvU6459ydz4rG6On1qeq6ud8/FkZcUmkWKf+Ht1wlKWzDT7WPQHoBeoxt1MVl5CQwrDGf2sDKWC4Mx+VqPOuvnqWl7ZteewoWQ5+ZiyrjjOEcBz4QpAaTDRc6dZef07whad4uy4vhmLvgWZW1q6uFiwdTVl/Rxf3KX+8Sz7z4LiXoCsNZVdZdwumkXPk4N0nQBsl5OFWT89H68KFkm8uhl5er6xHT3fEL1OTbalb8aiP4A68WeoOFWoeqHj/SzgiNgTLgsw8wSyph52j5Fo5LCCLT1PFebtsoehLcK6rez4suaekquv3hTOC3tGiOszS2/KWPS3oJSQAmylWfBalsPnlcxb0DbcTBwaKqgV54Shviezwpql531STyITvE4CKvUaZOLX/AfgjkYcl36+dF7NF1j0LcksUSm5pmTNO1oay8jq+FkCrs5KQry9Xq/Kfte1+GYC0gx/1kjEn9XwROP67KGDQnYsPOhxx6Bj+cOw6F8TLAblkIEim1yi21IBRezeaQbo/IDM+1DxaLJQBzAWvA5Euk3+W6lSoD3/WjlAolAHDIu9PRb9LamzzopmzvE7i5Jj+qzFt070uo3Miur+6Laz6kH2fZqYzMKc0k9+nwqbOwzhtut28b0697/p/JtdLPp7pGSx8ZMtJsSvyTJ+byb4LBuvTT1ZzJxZb53HzxOHSlWHbNCpe0Tk96LTcCJb5EMtOE9o4v0pDZQmx6K/BW1cSZ7Eookwtvb8HqXO7S/tF1tRXsde1+TDgAPBb7fbKjmHAaTt96nLzn8r0SaxpwNiFnKwl1IqdZpdLG5XcbsAAAXuSURBVPo7kiWbmCx7X5fY08Hh0P3IkmIsJHwnfueVbzVOrxN7xG58niUB1SJnn9d4PhukInZv+IFzpl2HtvLtsOjvgMaqbcnc0KxbD9vFQxODGk9nGfC6FW4gfnabM/e8bp/aCD47VxrTq3uP96nHUeoCxCBg4Tdj0R9IdjFrMwrEpJQsPT5XWlACFpq/q9TkU7ev/FkVLcp5/B28z7pd3T89zqb9UWuvmXp+r64XyFOEs3ZjC74ei/6WqDWrc2f5PW23zdtk4WPbJcFn8S17E9m+sQD5b/AC2h4PzzTUv7Onkh2fDiCcqeeWX52+y8IvVTjMLhb9LcjEziLkxJ265Bn8HhZayd3WbLpmsDkjv91uq/JXVinQ5B9ewwCgIuTvxn7qKkF6nvjclJKWGWzBS2IvCd7CL2PR35JSDIyLjV1y/UxG9t7MIpcy+rCyELqWvLD8lWbAsV3eX/YMWERaOsxe14RdZsVLx4bvxE8MXCx0vX9dtu69hV+PRX8AWTzflPiqu/BUeCqmuvfrDDm1tllcnnXpZe/FT/TOa8ac91GX69Zjwv5hW/Bk1OLzwMXeSkns2bLadu3bYdHfAb3Qmi44dY05cw7UErOgeOqsNuno53q9XjV9Fj85WacJuNIEF/4+7DfQG09gH/lYOEnId/rl7WXhQXZLL9zAM7s9tWP69lj0B6Dloyx+1r+XEmDb7XYnW85xPG+DBV+yqtjeYDCIzWYTq9Vqr4bN0081W66Zc+wHb5v3Cy43Z9B5vT0OcbgMp17AaDSK1WqVhgul5bRL7v2hA3CXsegPQEWOi11LVlmMyxc7fmaxLn9XxP5NJDOLCmBVcfdZXRRDF5vIBoCsc09jaxbdZDJJE2ps5dEdqIMVBhCexstNNyxs/U5Y/FJCz5Sx6FvCmXG2QIhNR6PRzoVbZ3lKZSv8rskzfWSWTeNnXjUXC2OgCUaX0M5m6fFx8zFDdGxxx+NxutAmT6SBt6HlN12sQ608i5pd/Uz4rtW3w6JvAVslXHyTyaQSPMTPLnBWJy9Ze36N0RIcW3h1pdmysoVlK59Z/GzteG7i0eNmsUN0sMq8fzoAYeDhxSwx6Oh54zwBRM7Wn9fXZ+Hb0rfDom9AL8TxeFyJBhY+s1aZlVfR83MVvWbpdQDI8gXqpusjWzc/63/X4yjF12yJ2QPBvujgw0tx8feXznW2yq56HTwI2cVvh0XfAo5pcaH2+/1YLpepteLP8Wvq4mfPGU0IatiQiR7C50c2AGSC58Sext0QFAus5FZzUw5/v87603BCQxjtrc/yKXWdeSbHom8AFz4SdhAErD4v7cTvx/O7wgNHU1UgYj+210Egm9WmE2Uy0Wer39bF0ToAoVynFYRDS5T8t8wbcEzfjEXfALuco9EoIqJqGsmWXOaLrfScKVl5/oyWCpu2p5UBFp8OCE0VBM3g84y2UlOO7od6EtlsvMyryTybrIxZmmlnciz6FnANHhZGLWTEvhDv48I7ZBDR/EDWMVj34M/iOzS0YDFmVQrdn2wA4u/TY828mex5tk+a7DQ5vTpLExGdX20ws5KZWJjXYWUO3WYm4pK4G66Bouh4v9oOQqXvzga00razfSk9Ok56Aiz6lhwikjedu+z/mySk1+FZPTAsemM6Rip6Bz/GdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGBa9MR3DojemY1j0xnQMi96YjmHRG9MxLHpjOoZFb0zHsOiN6RgWvTEdw6I3pmNY9MZ0DIvemI5h0RvTMSx6YzqGRW9Mx7DojekYFr0xHcOiN6ZjWPTGdAyL3piOYdEb0zEsemM6xrDh770vZS+MMV8atvTGdAyL3piOYdEb0zEsemM6hkVvTMew6I3pGP8PuSBKGXjv0z4AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 9; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 10; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 11; current eta: 0.5, current beta: 4\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 12; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 13; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 14; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 15; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 16; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 17; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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Sg/CcfTefz/cchuy400KeKockNxHFPLIkmyoCZ2aTCX8YTPoDoMUe/GA1m8tEAZAdN3rETZdZzYBj0mdSnjebgJebfQva4roq7TbTMpjwMCU4x57HgEah3XM5sy/zP8CXkKXdZhmJCiW8yd8cJv0BUAmeFb7ozYsWV6r6qkrNUh4PxMZBeOwlBxLyDc+2PNvxmfMOc2OPvabcspTXCAFX7IHwkPK4PqzS4/jsvIS2wItonfaE3zLq7HxjHyZ9Q/DNx0Tn9Fh44HETZ6GxiN2YuIbnWL3Ga5bwWVy+zFtflX3HGX/sPMRio2HBiJvNLLivHojPCxCfn27IEfFVdEFLhDEvtt3r6uZN8NvBpG8AJbzmwbMkw02/XC5Lq9iwMPAx2F5nomNBqCI8b12FijpOxuFafdZW2I6H45A3xITvgMfSHXAxBpxtqgkx8dknUKYVqdOO56vefr4OXgCaw6RviLLkGX5mtZWf+SbnrjPZ9lhcs8557kyaiF2pC4mL3WmZkNACAJaeGeEvLy8LpyGH6DSXH/n8g8Fg5/i8sWW27RYn7ej15ShHpt6zxmSi3x4mfQ3Y5iyrL4fDi1NTs9xzPg7Hr1Vr4PdZvTwy7uCkQ349Kum4uIY3sYCWERFFL3tECC4vL+Pi4qKIFGDsiN394Hnba9TmQzpzbzuedxZpyK6zLgZZdxz9TlUClJHDpK9BpgZz1RnvDc83Nt+AGsYDCbg9NRYAlex6TKjqIDyXzY5Go4KQ2eaUq9WqiC4sl8uYzWbFdttv376Ni4uLwnnIixjMh5OTk51xWMqz7R9xo1GUed65Kg+fKeH52mUwyW+HRqQ/NBnlIQA3FOrKJ5NJXFxcxNu3b+P8/LyQipPJZC95hcE3PktAOLBAcEh+lW6c3guSwBsP6Y6955X08KjjPDAmHHeTyaQ4n/Pz80K1Z//E8fFxoUmMRqNinEePHu1IeZ6jLnKqpWDhynbK0WvFv8Xn+n8y+Q9DI9K3/aJOp9P4/PPP49WrV/Hll1/GF198EW/evImLi4tCKirp9aZvksCTkYNVbK2Th4QH6UFIVrsjbnrKQbtYLBYxnU4LKX9+fl5IejjwQGKMg+OPx+NCyne73WIRQaYdoA5HkLtJU1DNhYiIvShIFqc3msHqfQ2Wy2V8+eWX8fz583j16lVcXl7GmzdvirCWNomM2FVt2c5lqZd5rnHjKgk0LAe1fjQaFdIXD5XySvjr6+vCcQfN5eLiIi4vL4swHaQ8CH96ehrj8TjG43ExBqQ0zu/6+npPGmsugToeobFoYo8uivgc0Px8E/4wVJJ+Op2mKutDB3uKP/nkk/j444/j008/jYuLi70mFll4CWTPYtEMlYD8OZ7LCA/Ss40Nz72q3Srh2XEHO579EjAhhsNhnJ6expMnT+LJkydxenpamA9IrGEpz555EB7nhvMoSxxSr35myzeN1XsRqEYl6c/Pz+Pjjz+OP/zhD0UjxbYsAHAsff755/HixYsdUqgarnYlh+WyTD328HMuuebGs+qriTcgH/e+Y+cdxuTwIkt4kF4z7yB1saiMx+M4OzsrSA9bnrUIOOVYwuM1S2UQnZOHVMpzmjKOpYtgnbe+LffobVEr6T/66KP46KOPIiJiMBjEcrn8Wib2TYKl5A9+8IN477334uTkZIeMWl2m9neVxzkjNMJiavNiHCa9xsl1C+mIm7x+ZPUhBs+hOXjqlbxQ68fjcTx58iTOzs5iPB7HaDQqPPZYUHgf+8w3gfkgAqBtuzh9F9fekvqvi0rSb7fbmEwmxfvFYvFXn9C3ASxpXr9+HYPBIGazWXGjDgaD6HQ6hRrNWpDa8BE3O7hE7GbR4cE57NzSSgmfpdji7+wDQEiOE2/YfkcsHo0xYM5goamT8hFRRBxw3mWk54WNO/hAymOxhIRv4o3P/m7p3hyVpO90OjEajYr3g8Fgr675oQLE73a7MZvNCkIPh8PC/kY4azAYFKRbrVY74bXValV4uSN2M+mUyHgNMvNrtYO1Ph5zhpoOdf7i4qKINLx9+3YnzIi0XtjaTHjY8WzLD4fDHRMPv2dThhtlqJSHpGfVHloCtqfWpiAAmwlqRmTOUaMctd57lnqcgNIWQEWOuPFAM1GgZkPlBcl7vd5OIQ7HvbOmlSADkyIjOafWsvMM0n2z2RSNLRFp4Dg8nHYsqUGmXq9XeOuV8CcnJzsRAWT2aY69xuW5NoC78uI8Im58IJlqr+81JKqhPRO/Hg7Z1QBkAslB3m63WxB+NBoVWy1xqSyy2rhKDeYA27dwyOlONJDwbOuXdbFlLz0nE4Hw5+fnabYd5gVSnp6eFiE69tbDhMGYuDZcPsvptuykZKJrkQ7mnfXIU1QRviz7z9iHSV8DtldBXqj1w+GwSIiBrauFNJpgAinPEp4JD3JoHDsr0+U8fiY8J95oWA6hxogojstxf0j509PTIvMOCw977Fmd5/AlFhPMVR2Q7HSEA4+vLYNVeT5nNieyfgaW9tUw6RtAVVbNiINXm1VfSL9MtWc7XtX7zGYv63iDXHrWMKDWg+ys0qsdzxpLHeFZKmdVgmrLIyKhHnuW8jiXQ/8P3GKMF5s25pTcBiZ9DfQmYuLqHnHswa9qJMn7zKnjDppA2eYRWaZaVeINl8lyBxwQEim28NSzWs898nlsrjZk0nMUAFqN+imyMF12nSNuQqc4T3j4uVV4Vr5r4lfDpD8AHD/XXWGRmhqxn03GcXkuieUH2+wq3ZXweM2hOexIg1g80mq5NoDPAQk+sN/Pzs4K0nNuPe80o/vVw28Bwmv+QabWc6kvXytVzTnEqSaWNjJhTcOe/HqY9AcC9ipLbdzcuKG5XBSSWhNuykiuWXqA3vjaH19VehCet6BiMsJpd3Z2Fs+ePYunT5/GkydP0oId9tBrWy/4LXCuMHM0JKlqvebYZ6q5OvOQ76A9CDJpb5TDpD8AfBNy51kuIoH9m5Gdya3fY881j6Vk1970kO5cOIPEGy15xSKjhAfpWa3XCj3d2BItvThffzAYFHPXHHvWGthEYcKrdoTrzAlPyIPQhiNW75vDpL8jWPKD/Jpaq4RXomfhKajx7DhTwoPsiMVzEwzumIvGlYjDn5ycxNnZWTx9+jTefffdeOeddwopjzRbtrkhWaFVaA89Vuv5OmSdeDXEqGE3XFNNxOG/c8ORjPhGNUz6A8AShCUVZ9txVxt1ynG3GFXd8czHxE2MG1q793AcHtl2IONms9uNl8Ny4/E4nj59WjyePHkSjx8/LrIN4TDDHHQTDu0WhAWFnZy6Oy5fM40CqEquBTa4NhzWY7Jnkt7Svhwm/YEou2mV/Czly/Zt55tTj6ettNAdF4RnG5499SiI4lRhlvDItENOPQppEJ5DZiEcZkx43dCSw39cHMReek3oYds7u3Zq5rDjkrUCOBF5Tz/b881g0teApQ4AsmaxYv1tnXOOE2s0DMUdcnlTSRTQcK8+lfCc34+wHKfXoiEGPPXaaUd3vGHC8x71nHDEYTkcjxNwWArrdVNJzyW27NPAd7WnflkZs7EPk74Bquq4tbd7pmZmhSGZc47DYSA6pCyr9VCxWeqixRVMCG6JjdRakF3Ta6GFcBiQpTur9OwgxFiI+XO9ANfYa04B72BbRnj+jPMDOBKhUh7fNaph0tcgc9SxF5qlotrvqtLzDQnJBJuUiQ6SZ1tc6W6ymnTD2YKw31EXf3p6utNSC/NlacyJPryBJocAI26q3ljSqx2fLW4cbuNmnXytuRQZ15l9DDzfMu+/UY5a0nOud6/Xa11prarHrA5HxI7dq/nxbLuzNGX1FGQHuSHBQW4mOe98kxW4IBEGKbUgO6Q7YvDssMO5Yk6YCyf6cKEORwQ4L0EbXLJ05u490EpY2leRVbsQcQmuU29vBzfRaIDvfe97hYcbBBoOh4XEQVZcFn7bbDY7e8mBDOqcY9Wd1WkmOxxXbEKAcNy1lptfcC49inmyHWaxeEGrAOm54QakrPYHKEsTZlOBzRUu0ClLyFH7n212fo/fVIU/jV0c1ERjOBwW6t1DBgpnNptN/PCHP4yf/vSnMR6P90pfu91uQeAsXRTSTBNTNN7O20rhwSm0Khkh3TmlluvgkVKrzSwzwis5UZbL84Faz4sMrhOgabKaV4CHVuRFxN6igecslr9er4vx+f+luQ9GOSpJf3JyEr/+9a/jF7/4xU7DxbZgvV7H1dVVLBaLHeJG7N6cvCsN263X19d7zR9ZrWdHmdrOSLDhvHKQJGtOUUV4ZNkp4ZWYmXnBpgTnGDC5ODUYiwPOUTfnRIQBZgK0Jc7xzxaQzDuvWY91CU/GV6gk/dnZWfzqV7+KDz744Ouaz7cGkNx/+ctf4vnz53F+fl5IKEgsEIJJDqLO5/Mihx03JMeYM5JxNl3mmY64yQGAww6bXXBveqjz3BIb5kXErhddw4EaEeBimiyTkM0VVr91N16YB+x0ZK2BF9EqwvMCkaU5sw/KyFEr6duOH//4x9HtduOzzz4rtnyCZIYdzPu44Qbv9/sxm8324tVYIHgfevbGZ3Z7xI0KyzFx3moKnnkk2oDsvLWVqvNswyvhQfaIG2cuJ+CwZObzV+0Bx+L24awx4L3OjzUndubhWmjKb1aVaORwyK4Gx8fHMRqN4t133y3Ub1R6zefz2G5vessjWWU+n++kpbJKrRl2/Mjsds5Q43Je3bSSJbvGyTmkxdVynFqrnXU4BIjXWkvAqcIRu4SHFsQaC84H2YJK0Mwfog4/EDsrUdZmI0YOb2BZAr5xBoNBjMfjQr1FTB4hOE6u0XJalmhs+2rXGU3sYbJrmiuSbngXWY27R9wsMqwac14Aaxrw0CONF/n6IKhWDuKcsEjxYsLZhOqwY9Wcq/+ydOSyNF1tH86lylkGpbELb2BZA7ST4lg2p61yUYrav3rdWNJqCqmSnVVY1MCjcg2NNDnJRjUKLoYpi5mzvc2987hoKCLfLDLLKMSxOdqg5glrDNBeuKJP05uZ8KrS60YflvTNYPW+Brgx+/1+LJfLgnyqPkOCa0opwCEokIFDUfi+5upzrznthV9G9oivqtC0Wg4Snsmp0YHMWaexc3w/S7xRByQvXjgPrrHnOeIc2CTRxTAjvG36w2DSN4CmmqqXWG3mzBzKbmzczKpC4zMunNGdbrQLDXIFoHVgQeLyXO02o5GBzBPOhGfpC3KzT0L75eE8OD1Yt7TC/COiWLCY7BG7DTV04wy26S3lm8GkrwHb1vrA3xl6w+rf9LiqzquEZ+JnO9rAj4BQYFloDio4mxMRu2EvLC7sEWfbnTUCLC5sKrBKzxKeO/7iAZJqIU3Z9cfCweW73KYMczXp62HSNwDfeOqAUocdvpMRn21itnGzJBNup5VtcAFTAmo7j88ah5b/cu2EbqKh5gMvLohYqEMQoceM8PCHwOGIUCJU+4ibvneaacfXKOJmcdL9/Fjzcoy+GUz6A6DEZwmJ9yCFppXi93hmwvNNyx12VHpxDLvT6RQqfWZ3c2wbZFQPeEQUc+dNKbiYBiFJ1R7UTNCyV+7Ug4fW7iObMSKKQhqYAzhOxE2YTvf1K/PaW9pXw6RviEwVV5ub49UR+6FOtk01BJZpDHzzsh+A7XCAC1RY0vP3Od7PC4v6CzjOz+cCE4IfmQ2PTj1IHELSEPfSxzlBOi+Xyz0/Bi+eiGBku//YgXcYTPoDwZKebW2QHkQqU+81dMVSXlX0iHyvusy7zQsBS3UeG4SK2HdOZhEBXkQg3TkGzzkAvIBwKTJSgtGDT/MbsLAwgZn0XFzE8+WFy4Q/DCb9AWDSqvMLvQZg+0KCZeq9NtfI4suag87ttEAGxNUzkvN82WRgUmVqcpZMpKE5lfA4J6j0XAugjTs4Lo8sRo6KIDSK8l0mfTZPNaPamEh2KEz6BmAyMpE0nAZVmItrtFiGJT3b7QCTjfPa4XnHe23Llc1P/QXZBhSZbazSnePw2q0n4sYhyBt6QsprLT8WQ/gAWE3n1+zoi4gdrUjJrtfBxK+GSX8A1CbXXHTYqpD4IHiZMy+ivK02E1y3b9KUXT42J+uw/V62YWZZm2qtsecyW2gbETdaC7z0THi15SHlYQ7gPLNcBA4p4tzYu3zdgNcAAAk+SURBVM+hQ07VNeHrYdI3hJJViY+bVqG2Nr/Gja/f5/g6e8mxCHB6qqbuYo5QhzmRhUnP6asgFlfKcQkwlxJr9R0cbEx69tazWs/JONvtdkcbyRyjWtDD1yirwsscnMY+TPoDURdfZ+nOue/smCsLL7GUzcpKOZElU+s11RXERtcc7vrDG1Ty4sNmBe9bx467iN2kHt69Fw9dWHhxwULF10AXUITwsvRczvXXtN+yxCjjBib9AchuVPW840bF3zXkpVC1mm9sfta0VJXw6pzLJDvH4LkTEC82HIfXFFs4DjEeYuco9UXyjSb4aDovP+N4WQgzy1GIiILwWr5rwjeDSX8gNOymhOeYOKCpuUxeXhQ0rs7fg/aQxa85isDbQyvhOdzFyTY8LkvSrDyWnY9YZJToWQYhhyLL7PDsuuL33JUnIoqFKGu0adu+Gib9LZERPpP2AKup+hm/z1T2iCiIinRV1iayQhSW7GXZa5ySq74ESHsOzYGEnCqs8X3N/VdbW0ORVQ44dtaB1Fg4er1eYXZknYaMcpj0t4CG8DSLruzRJKTEsXw+PrLXOPNPVXtNUdVkG7apVbPQnAAusOEFhjUdDrFlxTksqXnsMkecJhtxFINbcm232x1Jr3M18ath0h8I9eJn3nyos/gMNz0kFaAOLQXn8mfx6KqcAe2iE7Fb3pup9FCfVQrj/NSnwUk/7IA8Pj6OxWJRfMZOPMxDtQoek+fAjUdRervZfLWXQNYa3ISvh0l/B6i6yw9klGn1FxNfTQAsFHitnmv8JkvG4aSWMgeYSnpO72XJqmOyrwJjahMRSHaOv69WNz3/eQHKsv201l+3ooZvAb8fDAY7iUq255vDpL8FWGVXxxOkfcSuGs/980AwJXld4Yiq5TwXHh/fZRs4i3Uz+fT4er68cPEx+DVI3Ov1ij6Cqv4r8dVpyF19uFc+JDrm4xDd7WHS3wIZKTjWnEll3Kjsic9+r695zMwW13ngb1yYkxE2Ox4fR6GLh3rctQiJq/eybjy84GTdgbVpKFT4iBvzQOfLi7FRDpP+QGRhNzyXSXr+TllOfpaXr2RV216JqOW0mgik51AGXXTwfVb9y8JteLATMSuUwXFAes7+4xx/9szzApYlSJnwzWDS3wFl9jVLfcZ6vS7U+zIVPSL2yIGxoC3gGFlEgJtsRMTecTJolAHHYdKrR52f+Tyy6kNkCaptz9EClvK6h5/mCWj2XlkxjpHDpL8FqpxFTN5sUYDDKzMRIqK2RjzL8uPnbD78XPZ5pmmw6s9ER8GNtt/C/KHaQy3vdrtFfgGTHio7Z9hxRZ/W62O+uqh4o4vDYNIfCJasquaWOcBw0+KGVaLw93UslfZsU6sEriM+mwtqB7O5wNB6ACW9SnpIXo4O4KGmD4fltDV3Wd987ZGXJR5Z2lfDpD8ASvjMGVbmYGPys4qvx4fdmqnvZSRXSa/ahdrkvAjwghSxHx7UXntc5ce+CXVQQurCHImInQWP4/QarssI3+ncdMPNqgUt6ZvDpG+IzCPPHmiNc2cSs+7Y6pjThJbM215nalSNV6ZxYLwsU0/Vel7IMGdd+NTjz2YClw/zwlLmJyjrCcB97038apj0t4CmijL5M6mvBC0zCVSdV1JmhGcnIHv1I6q3I9NxeV5c3KJOOz1HzTVgm1tJqKZCJuHVXODjcSMQruizTX8YTPoDkElYJr9+XmbrZ8fV93USK7NdM5u97ByybDwltxKeFzitN2AHHqcEg4wR+VbUTHiV8Jzfz2TXMl6T/jCY9PeEzDuuklulctl3yzzteA0VGg6zKucdoJ54RBI0Zs4qvOa0Z6m46knnfeq4Zh+/562rMhOFr5H24uctubkrj5bzWsWvhknfAGWkgjcaN75KfW6FjdeZx18db3guWwRUNc9+q1BJjtChdtTNFiQcD3n9fN66zRRX+HGewna73Vlk8J7LhDk1GbF9lfCj0SjG43E8fvx4rx0XaxVGOUz6BsgkLSQd6tcjomhwUWYCqB+gzNuO56pQWzbHjPRqgqg9rcUtIDPaUKuNzam2KomzRhqYA8bStFzE6DEOCI9js/0OCc/ttXnXHGflNYNJ3xCsdjLZoSr3+/2dzLEy257V5Ew1z8ieETp7LiN9RnjuM6excQ6ZaUhO23LVxcxxjKwFl25tjXPG8XFcSHNIdu6rj358WWsuI4dJ3xBlhEfCSNbEoczxVxdmUyKXkb4J4aukPAivLba1VJXPHRJepbuq9azG87haPce59RE3O9lkXYCg6nPX3TJJb+KXw6SvgXqoccOzhNf88Ij9wpwydV7H4jH1NdBEA+Cx2BufNaZQD7qeC1fQqcNOU2G1nh8Ljo6XNbRkbUL31uP3bOc7OedwmPQNwaSHw6nX6+1sMxVR7mSrSs5hMKGz57rPGGpSsBMve1R1zNHuPFo9xy25eOHhsavGwzmoRsFluqwFYBHgKIGTc5rBpG8ADpNhwwZeADIJX/a+yVh1r5u8x9hZPkEWh9dYvCb+sMdeH0z2LNKQ5QJkTTB0rOyZF4VsfKMenZqb0i1JYjd9FDZq5oUv+11TVHnmb/O7zMTQfH2OKpSFELV2XV9X2dFljkx1aGIsfmSVf2XzqQpXthjpxTDpG+JQh5z+rg53uVnrSK/vde5NnYtlTsaqeWQLD49Zdi5VTsqyuRh7SC+K1fuG0Jv7rmp807HuC1XzbTL3Q0yNQ8evG6/uvQl/GCzpjYg4nITGdwKW9EY5TOr2wInKhtEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEymPSG0TKY9IbRMpj0htEydGv+3vlaZmEYxtcGS3rDaBlMesNoGUx6w2gZTHrDaBlMesNoGUx6w2gZ/j97MVY84d+JEwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 18; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 19; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 20; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 21; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 22; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 23; current eta: 0.5, current beta: 8\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 24; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 25; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 26; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 27; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 28; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 29; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 30; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 31; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 32; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 33; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 34; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 35; current eta: 0.5, current beta: 16\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 36; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 37; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 38; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 39; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 40; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 41; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 42; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 43; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 44; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 45; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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yYSERE4kElpaW8OTJE2xubmJ2dta437al5Lk7nQ6q1So+fvyI169fY2dnB+Vy2ejs+bs8huyui0Qiphd/WPjABYHElV4SFy6ZvLS9l2FDOhR3Q0k/JmzC80W9SzrLv7srS2/H8czSN5tNnJ2doVAo4P3793j37h329/dRKpWM1JZWXi46FAvNz89je3sbz58/x+rq6lDXXp631Wrh+PjYWPlisWi66WzRDhcXxvOU8srFgfG622wA+XFbNOXzleXSu/IRCnco6T8DJNHdRkMNI7mdrbcJzxJWq9VCpVLB0dERPnz4gN3dXRQKBZydneH8/NzRwMLrAX4e5hGNRjEzM4OtrS188803ePLkCaanp4daeeBmrHatVsPe3h7evXuHo6MjNBoNRy+8vH8SXk7hofpPjvmS7b7y2ZHwUpHn9tz4jOX4MXXvHwZ9Wp8Im+z3ufZudXf5vU149q4Xi0VHpp6EZynLJgld7ampKWxubuKbb77B9vY2FhYWkEgkTCw/zLW/uLhAqVTChw8f8PHjR9RqNRPHS9L7fD4jxJEtxMxxsMQmu/GYbGQi0K6E2G3Hdt6AVQA7KagYDUr6T4Bb/f0+8Y38yu/dCG/H8YVCAXt7ezg8PMTZ2ZkZhyXjeIJS1XQ6jdXVVTx//hxPnz7F0tISUqnUvWIcztI/OTkxNfmLiwvXuJrnkpNvqJmnTr/VaqHZbKLVajmuWR5HkpgZeTtml78jG5iU8A+DuvefCXeRf5gYR36VY56ZqecMvqOjI+zv7zsIzxq5Xa8mMWKxGGZnZ/H48WNsb29jeXnZxPF3lbno2tfrdRweHprZf9TwSytPRSIlvalUyoQNAExo0mg0cH5+brr+mH8gqXnttPa8D7blyqy+nEZkVwMUo0FJ/wmwS2MAHC/hXS+jJL0b4Sm+OTw8xN7eHgqFgpG+uhGe4LiqbDaLtbU1PHnyBKurq8jlcqZtdth1yax9rVbD0dERSqWSY8Am75HkY6ceR21R7MMQodlsolar4fz83HgnVPEBNzV524WXM/bdqiXD9BCK+6GkHwO2e04MS+a5/a106eVc93a7bfT0hUIBHz58wN7eHo6Pj1Gr1Yzu3Nac09UOBoNIpVJYWFjA5uYm1tbWMDU1da/yjtfU6/XMrjknJyeo1WqO2j/PNTExYTT8uVwOU1NTSKfTZoKQ7A1gf79swwXgUDLKY7s9K2LUeQSK4VDSjwk7Frdxl7V3k9Yy9qX45ujoCHt7eyZxVy6XHdNth8XEkUgE+XweKysrWFtbw9zc3K3E3V33dH19bfII3AFXtsCSdNTwZzIZzMzMYHp62rEjDkt+9Xod5+fnJp6Xrb5y2y15DcCNJFdOu3VbYBUPh5J+TEh1mMycj5rBl+48N46oVqs4PT3F4eEh9vf3cXBwgKOjI5TLZUfizk1XPzk5aVR38/PzWFtbw+LiItLp9FCprX1N1NmXy2UcHx8b157/znPRrc9kMpibm8P8/Dymp6eRSqXg9/sNuZvNptkMQ8bywE3dfVi/gRwXLkt8MsxwW2wV90NJPwZsS80Z97YlGva3bvE722PZxVYoFHB6emqm4Mh2UrdSFy3v1NQUlpaWsLKygnw+bxJr97n1dta+UCjg/PzcIfiRJbNkMonp6WksLi5iYWHBnIsLBz0X7n4ju+R4LDfCc0Hjfcp5e/wdec3DPC3FcCjpx4CbLtxNaCN/n7C75ejOswZPwpdKJRMHyykx9qYYdOuj0Siy2SwWFxexurqKubk5sxfdKOIVJt4qlQoODg5QKBTQbDZvafkDgQDi8Tjy+bxZXGZnZ5HJZOD3+01pjzP0SHiKctxcdHoh0sLLnYDlwiqfqRJ+PCjpHwhpFakyo34cgNnwQerHCbf2WNkt9+HDBxwcHDim39gTYkh6uZkGS2azs7NYXV3F0tKSaZsdVo+3r4uxPBefk5MT49oDN4tMOBxGOp3G/Pw8VldXsbi4aBKFLPexAtButx0eilsy0B4wIhOb0sVnc88wQZOSf3Qo6ceA1MRfXFxgMBiYTDRHPdH6Sw9ATryp1+s4PT3F/v4+3r9/j/fv35vmmWazaawjySJjWUl4NtPQraeVHzV5R1K1222cnp5iZ2fHKPCktJfa+ng8jrm5Oayvr2NlZQUzMzNIp9Nm1r3P55y7P0yMI8t+cjafPUxDuvcyJLCfiWJ0KOkfCJKX5bVWqwUAZmSWW2utbGKRE2/YHiu75eQIqmGlMkn4ZDKJfD6P5eVlbGxsGCsfiUTubTvldV1eXqJUKmF3dxdv3rzBwcGBo7TGOD4SiWBmZgarq6tYX1/H4uKiORdws6W2nFNv70LDRCBHanHXH+YdZL5EuvtyBLd9D2rlHwYl/QNB8lITz00rOQZbuqnSesm6NWP43d1dQ/hSqeRoniHhpJ5exvC08NlsFktLS9jY2MDq6ipmZmYQj8dNw8swkFCdTgf1eh17e3v48ccf8f79e1SrVWPlGXOHw2HkcjksLy9jc3MTKysrmJ6eRiKRgN/vd2x5xQ0t5HBOQg4T5Z53nBrM58vrI9lllcTtPhQPw0ik9/qDlS4668+VSsXMifP5fIjH48YyMxFFy+RG+L29PTMA4/T09JYu3U1pJ2vxiUQCuVwOS0tLePToETY2NkyJ7i7lnbSklNvu7e3hhx9+wI8//ojDw0OTjOPfBwIBpFIp04//6NEjzM/PGx0/cCMjlh4Q8xG8F7ryctNMbgoiO/J4nXYeQ+K+xUAxHCOR3usiCFpqWkUq5Y6OjnBxcYFgMGi2i6ayLhgMGssny3JHR0c4ODgwNfhqtWoIQus6LMPNzrlkMmli+I2NDWxsbGB5eRn5fB7xeNy4yhIyFqaFPz8/x97eHr7//nu8ePECOzs7RoFHUOG3uLiIra0tbG1tYWlpCdls1kFWea9srmECD7hZtNiNxw0zOXMfgGm95fvmNr7b/lmJ/3Coe38PaA0rlQrq9TpOTk4cyS4AiMfjJn7tdDpoNpsIBAKmDMbGmWKxiKOjI5ycnKBcLpuSnCxnydgXuLGOnDCbTqcxOztrFHeMraenp02JTmbs7XIhW11rtRr29/fx8uVL/OlPf8Lr169RLpcdROUWXXNzc9je3sZXX32FjY0NTE9Pm8UFcM7FZ23eJr3MC3Bb7Gw2i2QyiUAggF6vZ4aB8LolqXn9ttuvu9Y+HHeSvt1ue3KuOBNyAPD+/Xv88MMPODw8xOnpKc7OznB8fIx6vY5er4doNIpcLmdc04uLC2O5uBtMpVJx7OUuN4mw43cADrdauvP5fB4LCwtYXV3F6uoqlpeXMTs7i2w269iWyp40KxOJ7XYblUoFHz58wKtXr/DixQu8efMGx8fHZsYeAHPemZkZbG9vmwEcs7OzjsoAXW/p0Zyfn5v7A26ku5yqk8lkkMvlkM1mkUgkMDFxsyGlnXyUlpzehNvsPMXouJP0tVoN3333HX766SfzP9kLD5ik7/f7JuF2dnaGSqViFGYATMMJt20aDAZot9umfNVqtVCr1VCpVFCtVh1NJ1Snuan4aN3D4TDi8Tiy2SxmZ2exvLyMlZUVrKysYG5uDlNTU0gkEmZSjRvhSRC688ViEe/fv8erV6/w6tUr7Ozs4OzszMTfwE2n3tTUFJ48eYK/+qu/wtOnT80wTSn4sa08t7tiXoD3xdAklUohm81iamoK2WwWkUgEg8HA5DTkRFy3YZhcvPgZlgNRDMe9lv4Pf/gD/vCHPwCA2eL4SwcJPxgMzGCIwWBwa0tminJCoRBqtRp8Pp/xjqTVY8PJMFeex6N1t5tZlpeXsba2hpWVFSwsLCCXy5mONrm5hq1Yk4m1arWKQqGAn376Cd9//z1++uknI7VlEw/vPRAIIJPJYH19Hd988w2eP39+S8fPc8iy3/n5OSqVijmm3DiTsXwmk0E+n0c+n0c6nTY74PR6PXMf0n1nSEJwx1tO4HET/ijuxp2k5wpM8IX/0iGzxY1GA81m07yMwE3pjAISjoPilkvcAkpqz/mS2uOmeDwZ86ZSKUc9nM0z7GTjdJphG05KbX+z2USxWMTOzg5++OEHY91PT0/RbDYd0l7eVywWw8LCAr766is8e/YMKysryGQyCIfDjsWFZOP90qNpNpuObjpugJFMJs3OOtxSa2JiApeXl7dm45HsstmGSkhujc1F2G1un2I47iS9z+dDLBYzP4dCIYeyygvgiy2tjR1fMmN/cXEB4CaW51bOcvdYtw45ur/RaNQIbTY3N7G5uYn19XXMz88jk8kgFouZyTf3bTYpN6l4/fo1Xrx4gZcvX+LDhw+oVqvGukvC09OYmprC48eP8fz5c6yvrzsIL9164GbSTrPZNCGMdO1Zl49Go0gmk0in0yZrT1EPM/aS6Ny2Sn4mJiaM9Jmktzfq9HqlaRTcm72XK6jcP8zLoDWyJ73w+VxcXJhWUltK66awo7x1enoajx49wrNnz7C9vY3V1VVMTU0hmUyazSDvmhYjG4FarRaKxSL+/Oc/41/+5V/w4sUL7O/v4/z83FUlB/xM/EQigZWVFTx79gzr6+vI5/OIRCKOybkyQdjtdo3KsFqtmr30+J7IGXqJRALJZNIkHYPBoFlM5eIpm2yYsOPv0LMi4eVQEcVo0JLdmJANI7LBRlopuy1UEpXklXPpt7a28PXXX5shlvl83uHK3zWUg1+lrPbt27f49ttv8eLFC3z8+NEQ3o0gPt/PY6ynpqawsbGBx48fY2Zmxmxwac8J4H3JYRmc7CMrAAxZ4vG4EeNwAZMNNiQy43RJfJn/oHvPOQR2Q49a+vuhpB8Ddh87S2tyVJbUycutlwk52mpxcdGUxUj4TCZjLOwo46Fk4o7dci9fvsTLly9HIvzk5CSSySRWV1fx5MkTLCwsmBr6sFn+JGuz2US9XjexPHDTFMQyXSKRQCwWM4QH4GhAkt4RE3tuHhLde7r4w7r4FMOhpB8TckeXcDiMUChkiEG3Vb6Icq871sDT6bRRun311VfY2toyZTG75n4fSMJ2u43j42O8fv3aEL7RaNwZmrFENz09jSdPnmBtbQ3ZbPbOiTvStWeF4vLy0nQcMk9B9V0ikXBssEFPiI05zWbzVsJTWnmZQ5AJVLmNtZJ+NCjpx4CUxHJL5nA4bAgtpbO268khFNls1iTsHj9+jPX1dczMzBhV3UN2YyXhLy4uUC6X8fbtW7x69Qq7u7uoVqt3ZrfpiaTTaaytreHRo0eYm5szgzSHJQrpZksFXr/fNzE8m5A4Gjsej5vOP3okcqRWq9Uylp6E5/mA2zvZ8iN3zVHSjwYl/RigZYzH4yYTza62breLSCTicFNp/dgZl8vlMD8/bybPzM/PI5vNmvh9lMEXhGz1rdVq2N3dxatXr/DTTz/h9PTUIYW1wTbXaDSKxcVFPHnyxJTn7trNlsSjzr7ZbJptpJnl53EzmYwpz3ExYCWDXgJDA84QkAvUsLBC9tzbW2Up7oaS/oGgtWacmkqlTKzKQRp2Z1kgEEAsFjPz4aempjA9PW3q1TK59ZDxzpLw7JZ79eoV/vznP6NQKLjuSiPvg2HG3NwcNjc38eTJE8zMzLhaeVk/l5tqUnI7GAzMQgjAJCjT6bS5R/YjSCtPwlO2K0uI9vx7Xgc9ATk6XEk/OpT0DwStPIUmVMaxBCVbYO2OsmQyiWQyabLYkUgEoVDI7M02KuElAWU//KtXr/Ddd99hd3fXCG9swvP4lPkyjv/666+x+u+72TLudjufHNdN4dLV1RX8fr9D/88FQGbs2ZHHHgBugCEn/fIaGXawg0+qJOXATOnqq3s/GpT0DwS3jEqn00in00ZkEo1GDYlZl2Y8yxBALg6S6PauOMNg18dJ+I8fP+L777/Hv/7rv+Ldu3colUquu9hKMkUiEczOzmJ7ext/+7d/i+3tbczOzpq4W3a6ATBWlcKjarVq1Hf9ft+R0wgGg2YHW97vxMSEg/Byqys52pu5EC6cbj310trzWcjmGy3b3Q0l/QMRCASQSCRuEZ4WPRaLIR6PO9Rn0n231XSjWnZ+tbvlOADju+++w48//ohisejYhsqeuhMKhZBKpTA3N4fHjx8bbf3KygqSyaSZAyCz5rZLX61WUSqVUAJmh1YAABXqSURBVKlU0G63zRARHp/TcHi/Ur8gvQSqFpl3oHpPlui42NjWniSntdeS3ehQ0j8AzEhTWWZbdi4GqVQK6XTaqM+kC38f0e0hEfwqXetms4mzszPs7u7e0tMzvub1suxHme/09DRWVlawtbWF7e1trK+vY25uzuxOw2Ya2a8u5/NXKhXzYWMNk3bS45GLB2NuduIxY29vc8WqiJxUJJV5hLw2Lgrq3o8OJf0DwFjVFpowdifZJeEZ446SkZckly6sJEylUkGhUMDOzg7evHmDt2/folAooFqtOuJihgwk+9TUlBme+ejRI6Ppt6sGwA2pOMiTm1CWy2WUSiUz7afb7ZpkION3kp66BNbd5bHo1rPrkN2K8sPruLq6cmgVpDsvFY/aVz86lPQPAC0aG1/YBhuJRIx7z0TdKM0xhCS5JDqFJxzEIQdqypHZ7Oxz61/P5XJYXFzE+vo6tra2sL6+joWFBUN2CnBkH7uswddqNZydnaFYLJohIPQmmKGXIhwpwGFegVJduZklJ+uwkYYeCQDzdzbh5UANSXy5L4DifijpHwBadEl6WjlJdrehFsNUbbLuLSWpTHSR7HJ/u5OTE1QqFZM5lx2AbjvXPn36FJubm2Y8NkMOSTTg9sANOdOPe+q1Wi1TnmO+gt4Ox1kz6875941Gw3gK1WrVqPdIeOYbKM9lNx0XTJlfoMvP+ry08vxoIu9u3Et6+VJwlpmXYA+JJLllGS6dTpuprg+x7nZb7vn5OcrlMk5PT3FycmI+xWIRpVLJzNWjGMbeQEJub7WxsYFnz57h2bNn2NjYwOzs7J3yXunSM2fAib17e3um/77b7Zp5+7FYzDGWm+8KFw7bUyiVSmZUGJNzFOxQzyCnB/E5yey9tPIyllcrPzp0iMYIyGQySKfTePz4MZ49e2a06Xbm3s16EnaCTm5vVavVcHJy4rDmh4eHt0Z0MTaWL7ndjz8zM2Pq7l999ZXZn57dcvZiJC0kVXbVahWHh4d4//49dnZ2cHh4iHq9burx/X4fkUjEUSGQQiFuhFmpVHB8fIxCoWC8E7r1wM1YMB7Djt1lkk7OxqMnINV7at1Hx4OGaITDYYfl+1IxMTFhSkKrq6v45ptvsL6+jqdPn2J9fR3pdNrxstI1lc0p0kLJPnTpQjcaDZydnWF/fx/v3r3D27dv8fHjRxSLxVuTcu0WU56b1jKZTGJubg5bW1v467/+azx//hzLy8tmVPWwkVr8Kq+J2219+PDBbKbJZhq68fI6SHa2F3PhKBaLODw8xPHxsdmuq9PpYDAYmGSdraSTHock/DAXX4YIoyoZvY47SR+NRvEP//AP+Ju/+RvzUnvFjeKLNTExgXQ6jYWFBZMA424s9pQXTtORG11I68V/o3U/PDzEu3fv8Pr1a7x79w6FQgHlctnReDKM6FJVl0wmsbS05NqeO0q3nl3755bZxWLR9MjzWch7ouCG9Xu2uXKfvuPjY7PPPUU4fE6U+XIaE5+N7K1nf71M1NmVDdm5qIQfDXeSPp1O4+///u/xu9/97i91Pb8qDAYDMwlGZu3lwAwOc5CqMr6UMitOa9hqtVAqlXBwcIC3b9/i9evX2NnZwfHxsWPqzF1kZ92d2fmlpaVb7bmy7n7XpB2ZcGPyrlgsolwuO/rjeV4Kbph042Ih3Xoe4/T01CT/ZDmRXpIkOsOWbrdrxoyR9FKII69b9jcMC6sUt3Gvpfc65HZVTFoBMC4qXWZaPJLdLbFFQuzt7TnceZbB6DHYslNpWZkpj8fjZpeb7e1tbG1tYXNz07Ev/V2El2o2qbRjHoFJO2mVWa3gcA+OBiNxqSMol8s4OzszSTu5kDHMkSEUS4RcRNlqKxdSWZKzn00wGBzaFai4DS3Z3QO+VG5KOX4vLSXbRukNyFi5VCrh8PAQHz9+xN7eHo6Ojhzus7RmPLecwEOJK3e5WV1dxePHjx0bStq73EhI0rgRniU11uEpvCFZOTCEOQLuSsPhHfV63RyD04BttZwcLSZjcy4GHKrBUGHYhhbyucheBnXx74duYHkPZLOKhJTGyu2n+aLLUVCcO884l5lsLhJuLzVfaFp3dq1NTU1hfn7ebGm1urpqtoyWu9XaCUV5zVJL32q1bmnp2UCTTCaNt0fvhVbV5/MZtR2FN9zognvz2fcmtfNSWy9FPPaOt3JEt3w2spOR+gC19KNBN7AcE7YVL5fLZrsrvrC0WOwbZ2cae9DdGkVIWClY4bCO6elpLC0tYW1tzZCds/CphJMjqt3ILnUBHGjJbbcajQZ6vR7C4bCZr0/CyYWE8lh2y3EopuyLlxZauvV274HU1/O67PyIm+iGz4beh9umnQp3qHs/Buxut3q9bva4o3ss3VQ5YUYOf+SLbL+sw7a14qSdpaUlzM7OIpfLmfHYUq9uk53WlElHzqTjbDvZFx8IBIwAyb5fKc+V+/TV63WjspPz9Pl38v7kosbwRR6bY8Ol2s6G3BhECqLUOI0GJf2YkNZOWks2ozCjLz9yv3bZAUew9MQXOplMIp/PY2ZmBgsLC1hcXMTc3Jxjpxs5aJLZcVvtR2kvu9tIcrmvHvMQ9Bhk44tU2DHjTrVdrVZDo9FwlBndyErrTgst43B5vbbKzi20JOnZ5BOJRDR7/wAo6ceAragjmWjNmbHmR1p1to9KzQMXAMaofJm5lx0/09PTZqcbOUZaus+8rk6nYyy67YLTUrOHna3BctAnr5HTeegJyKRdvV53EN4eWW0P7yDZw+Gw6U+ws/gy5HGz8lKfwNkFHOChGA1K+jFB957z3uRUVnuKC194GcdKgQ1HcPFFpq6fQyU5lkvuCc+FhNJoxsR035lYY+97rVZzlMFIdg6/mJycRDQaNTP/mKyj0IhDK5kHIOGlB2NPsAVusvVc0NiRSNLbuYdhFt72FNjKzH4CzdyPDiX9GLBVYVL/zRdcZqzlSywzz4FAwCSjaGFpvbhbbiwWM5Z3MBgYa8vjMsxgJp4dbdxMslarOfaLpxvP8IHklpaTLr4cSim3opbJSFmD57ORz8KuPjBfEIlETPutVPtJMNsvf+biJLfI0sz9w6CkHxPy5ZYkDoVCAGDaS2n9ZDZeZp1JdopeuHGG3DkHgAkVAOeutNzlhYk5uvEkuy3ppVfB8IKusjy/HOdN0RHbfNkpx8YZqS+wCc/7ZfsxvRaODO/3+0aUJIVOMrtPMvOY9E44d5BeiZJ+dCjpx4SUxEprTXdYznqTraN0cenmkvByppwsu5FYMpSgS03rztIZR1HRjafbTYsthUZ2yUuSnQpDad3Pzs7MEA25//wohE+lUsjlcpiamkIulzPink6ng8nJSXQ6HUeHot1DL117ipMymYyjVVhd+9GhpP8E2NNfI5GIiZdpJfl7NuHpyts9+IBzjzdJbPvDrLysEHCRkOcnYbj4yNhaxtcMGZibYHvsyckJjo+PHQ04FN4MG8DJvQFSqRTy+Tzm5uYwMzODbDaLcDiMwWCAdrsNAMZTofCHx5TPj8+aHgNzHLxuJf3oUNKPCZmIk1lpNqjIeJS6darHpKBEWnYmAe1auiyzcfsnWnJ2+cl+cwk5TppE5yw/OamX10sysz326OgIR0dHDg3CsMEVboRnuXF+ft7IhP1+P7rdLgKBgEPRF4lEjDqPz5aQPQe8dtlBqBgdSvoxIF17vuj2LHu/34/r62vH38n5d4xjgZtMvL0/HONzimhssvPvZLJLusJSqspEGkmTy+VMrZ+JR1mJOD8/x+npqSG8PZ5LJiZ5XimaSSaTmJ6exsLCApaXlzE3N2d24vX5fGZxoSeTSCSMB8Fko5xFwK5CTiticlMJ/3Ao6T8BMn61XUw57QVwtpHKwZe0VLSyrVbrVllM1sJtAYwsC/I67Hp4NBo1hJejvjgrjySU7bEcgsF+eMbx9ogu+RykipAuPa18Pp83CTzqA66urhxVi0QiYf5NnosaBlYX2OWnLv14UNJ/JtjNLBTISGvv9/txcXFhYlc5LoqLgJvYRybl7EkzduKMY7NIdLmNFsuAMkvPbjl6HxTelEolnJ2doVwuOxJ3MmHHrzJ8icfjyOVymJ6exuzsrInjuU8AB2/0+33zNywXsk2XC5dd+ZDJRuDGc3IT8SiGQ0n/GSCHUcgyGgUtdkJNDn1gplo2nNhJOkl4O46W3W+06ozZmeHmEEtaSJ5b9hBQwSfr/HTpbfmwJDzDCI7BTqVSyGazyOVyyOVySKVSDoUfJcPy72Vikc8qEAjcCiOYpZcDTIa13iqGQ0n/ibB143TBmYyj1bfr+rJ1Vspnqexja+pdSTNm/OVGmSR7Pp9HNpt1JOtkKCEXKF4rx26zgUYO9gBuJt7IjjmWAaX4hguNrAoAcIQ60jrL3AP79+W21fbEHDmtSJYOFaNBSf8ZQFLKPnrGyHTxbUtpD3KktbLn7gG3J8XaU3SoUMtkMo56OPeFl7VskoYfO5zgV7ndFD0JwFmVsId7MKyQQzxkLwAbgtjAYy8oFC7Rtbe1/Kwq2GVL3dLqYVDSf2bYll/q8W2xiU1mWjKKZ9ykvDKGZzxMC5/L5ZDP55HP55HJZIy1ZdKLFpaWkiRnWZBEYq1f1vV5TtnjLifXyEEWXLy4oAAwm1fIhWfYgshnAcCQn97S5OTkrdKlHDOmyb37oaQfE5K4Mral6+622YV8Mfmxe+rtIRpuyjRJNFp5utRyS2ySjOeVlp0NOfZmklLbTxIHAgFDLGmB+RzssKHdbjsagzqdjmPLahmTk/j2NlXy3+W8fwCOtmC19A+Hkv4TIAnIuJYfOQhCxuTSZZcLhu3u33VOOWSDmW/uoCuHcZLAABzjsWjdz8/PjdpPJsVkLz29D9t7sfePo8tOQsvRV1QeUpBkJ+NYjrQlxvxqk54uvlwwlPSjQ0k/JmxFHrX3sVjMQXb5uzZRbHmsnCbjJi2Vll6eV7rNdKsHg5+HTPK/MztP9RvJblcYZIKQ12AnGqW4xg5b6HJTUUipr5Qcy8EfcnHg9fB7LgRcvBhy0LrbI7UUo0FJ/wmwZafxeNyIZ2R2npZZZvLlUA0pquEiIGfmE25NLcCNVSfZLy8vbxFLDtWwS4HyWuxuQJKeegN+lceVi5nP5zPufTgcRqvVcrQL0yNhfoBdfFyQGKuz7GknNe3JPHZXnuJ+KOnHhHTNZcmKs+JJYBKeXWX2SGhaef6unJkv434p/uHPcmiFVPhJzbp0te0yF7PpstZPr0Faekk4u2TGLL9dUw8EAqZ8KQeMyPieiwkJTu+Dk33kIukmSKK3o112D4OSfgzcVToj6aXVlOQnAdwktJL4bll9+b1MptnDJvl7Uh0oZbayri3Jw2OxU5CQTUAyy09C2zP7OUtAxtv2AiYTgzw2iW8Px5SLiUxi2pNwlfijQUk/BmT8aBOf4hL5uzLm5IspE1MATEggp9XIf6M1lRlut/FU/JnxtiSfvT2UVAja10cNPEt8nOpLF1wm0WxFHK+XZJQeCBcT7n7LBYXHvIvwLB/a04W08eZhUNJ/BtjuJptKrq+vEQ6H0el0EAwGcXV1dcttli+1tH62xZYElhp86e7bQha7BCZ/1y7/8bwMFex8gZQFS3fdTQLLY8vFSi5aMjFHvT9dfJvw9rOlJoFzA7Xb7uFQ0o+JYXE24Myyu9XqbTIQbqU7m8C2pZeEl+Iee2Gw5bw8Pq28LMsxXKAbLuXFtsdgVyN4H7aHQ/C6eS6GCHI2gE14elGxWAy5XA4zMzOYmppCJpMxw0LVtR8dSvoxQTJKC2Z/5N7y/JnxtZwg61bCs0kvh2S4kc629nKstLT+9sJEz4HHtc8tvQuphXez8CSqfQ67EiHvQ04SdnPpWQWIxWLIZDKYnZ01U3jS6bTOvB8DSvoxIK0qyUwhCV1gxsDy4zbWyu5PZy3aPp/0ENwIb1+X/FkSnt/L4RdyoXC7R1mSk4uUnduQ30srLSsYdjJPJhjviuFJeLs/n+U/xehQ0o8JElBuF8WPvaWVTXi7dEcLCTgz8YQkK8HfYeJvMLjZKZcZeBuS/HJxcRO2cOGwF4thRJfXxGqEnNrDUWEygWdfj61D4Hx7En5paclM4clms2bTD83cPwxK+gfCdqHplkq33rbGhJ0tt2NsKeiRbv4wi+12XdJV54IkG1bcXHPZuiqPdd855X1JoRF1C9Fo1PQDyA00OCpMuvlMfvI4HMiRz+eNhV9ZWcHKyorDtdd4/uFQ0o8BO56nu8yEk00eOUcvEokM3caZx6BrK+vu8njDromZdynIYQnMrtPLe3DLBwyz7PbPNtnt4ZvpdNp0+7F+L9WBUoFHj4eaBybt5ufnMT8/j4WFBbNxJ0d96c42D4eS/hMgCUuFGV1aKs+i0ajDvadnION5W7kna9rS2vOc0nuQ10IxDhNkJD3zDbZHYucJhiUl5UIAOCfmyHn+tOycnsOptbTIgLPxx03/z0aiZDJpZgPMzc1hamoK2WzWbJYhd6pV0j8MSvoHQiro6MYOBgPjntpZevnVVsMBNwkrKSkl4aUVs11u25uQpGVikWQn+WWWXCboZEhg34OsQPCcUrYrt8PiVlPcY04OseS1ypFgsp2XlQOO6pZDQTgbgFtuyY0xFA+Hkn4M0LLH43HTceZGIFl3tstzdsLLJrtbvZ5fZQxuk15OqqHghUlFEowkk8SX9Xx5LFtXT2LKfgOSXcbvbKyR8/jk8Axel5Ql87lyGywO9CTR5Zhxte7jwzcsRvx3aL+iC+xynczCy5q4JJQdj0uPQX6VZHcr3dlfh9XopcWXrr5dF5dWX5IcuN37z847qX3nmCx7LzxJTrssaHtCdlsvFxQSXQ4SHXXugAIA4PqQlPRjwiaa/d/dvrchyS1fZLvmfVfyzv5eXpNtsblA2V/d4nw7fpcSY/lhSOLWLOTmqcgSndu5pAckqxluz0ZxL5T0fync80xv4XO+yMNKefZiMOzjVkq0OwFlCOJG8Lvux22h4vf23yvBPxlKesUN3DyQ+xYrSUYl5G8CSnqFwmNwJb3WPBQKj0FJr1B4DEp6hcJjUNIrFB6Dkl6h8BiU9AqFx6CkVyg8BiW9QuExKOkVCo9BSa9QeAxKeoXCY1DSKxQeg5JeofAYlPQKhcegpFcoPAYlvULhMSjpFQqPQUmvUHgMSnqFwmNQ0isUHoOSXqHwGJT0CoXHoKRXKDwGJb1C4TEo6RUKj0FJr1B4DEp6hcJjUNIrFB6Dkl6h8BiU9AqFx6CkVyg8BiW9QuExKOkVCo9BSa9QeAxKeoXCY1DSKxQeg5JeofAYlPQKhcegpFcoPAYlvULhMSjpFQqPQUmvUHgMSnqFwmNQ0isUHoOSXqHwGJT0CoXHoKRXKDwGJb1C4TEo6RUKj0FJr1B4DEp6hcJjUNIrFB6Dkl6h8BiU9AqFx6CkVyg8Bv89/+77i1yFQqH4i0EtvULhMSjpFQqPQUmvUHgMSnqFwmNQ0isUHoOSXqHwGP4/+MCtFIXlnsEAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 46; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 47; current eta: 0.5, current beta: 32\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 48; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 49; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 50; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 51; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 52; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 53; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 54; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 55; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 56; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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XiUSwtLSEv/71r1hYWEAkEkGlUjnxLE8e+4mJCXz22WeYnp7uqA3W4eEhstks1tbW8Le//Q3Pnj0T7bjo+eRkH0pB9ng8cDqdAN7myFMKsix4edFQvn50XblGQI5Y0LHi7+U3+Zhh0feIXKBC51M5Q+5DIQuenHZLS0t48OABnj59ip2dHRSLxVPNeo1Gg4mJCXz66af45JNPEAqFRA77cVAF387ODh48eIDFxUXEYrG252jyZ7hcLng8HjgcDlgsFtGlVz7Ly1aQvAAoswvbvRb0Qa87WRJM57Doz0g70f89dvhsNoudnR0sLi7iu+++w5MnTxCJRFAulzs6xweDQdy7dw9ffvklJiYmWnILjqPRaCCdTmNlZQUPHjzA1tbWsY4zMu1dLpcoLjIYDKjX6y3NOZQlxsC7pB+5LoEcezKyBSUXCvEu3x0s+jMg7/bK8+VZ34jKM/ze3h6ePn2K+/fvY3FxsWPBA8DAwAA+/fRTfPXVV7hy5QqcTuepYiHn3ebmJhYXF7G6unpsl13y2rvdbni9XpG3r9VqhRNOWRMgP1bu3kORD1ocGo3Ge68r/R45UFn03cGiPwPyziMvAB9S8MViEeFwWJj0S0tLYl7daeEqlUoFl8uFa9eu4Te/+Q3m5+fh9XpPdd4BEEkwL1++xNOnT5HJZI79XYrNe71eDAwMiEQfug4VzORyuZaOPnJCjSxkei2p5p58JlT3T5WFLPreYNGfkXY7/VkcS3LH22KxiP39fbx8+RIPHz7Es2fPxA7fSccYg8GAqakpfP7557hx4wYGBwdPTcIharUakskkVldXsba2dmz8X6VSQa/XizbaHo+npcSXimUSiQRSqZQowZVFL+/2ShOfyoPp9+QwKeX2s+i7g0XfI0ozvp3gu30zKgUfi8WwvLyMx48fY3FxEXt7ex2b9DqdDoFAADdv3sSdO3cQCoVaut2eRLPZFBbG1tYWEonEiWd5m82GwcFBjIyMwOPxCEuCOt7EYjHs7+8jlUqJ+ychk09E7rOvPOvLryklRMmlykx3sOjPgCzqD7XDU8PJg4MDvHz5Eo8ePcLCwgLC4fCphTQyVqsVc3NzuHHjRseOO+Lw8BCZTAbr6+uIRCInPqder4ff78f4+DiGhobgcDhElmK1WkUymUQ4HMb+/r4ozmlX8qvsykumfbvjE3XcadeHnzkdFv0Zafem62WHp243xWIR0WgUy8vLePjwIR4/fozNzU3RKqqTc7xGo8Hg4CA++eQTzMzMdJRbL9NoNJBIJLC6unrixBydTgen04mJiYmWSj2qqsvn86JxZjweF8NA5Ew62cSnHZ9+prxf2vmVWY8s/O5g0feIHDMGTp7kctp1KCxHmXYvXrzA48ePsbCwgM3NTeRyuZa009PweDy4dOkSrly5gqGhIRiNxo7vh2Lz8Xgcr1+/Rjweb/t7KtW7Sr3Z2VlMTk7C4/HAaDS2VP9FIhFEIhGxy7cL2dG/5a/l8lt5oVM2EWW6h0X/AWj3Ju70cXLizd7eHp49e4ZHjx7h6dOn2N3dRT6f70rwer0e4+Pj+OSTTzA+Pi523k5pNBrIZrOIRCJiYo8Sct75/X5cvnwZMzMzLYMuKTyXSCSwv78vavwpkw54v8OODMXo5RbXdPyRf58ez0U33cGiPwPt3ojdPFYW/Pb2NhYXF/G3v/0NS0tL2N3dFTXj3Qje5/Nhbm4O8/PzYqZ8N/dWq9UQDoextraGVCrV9nc0Gg2cTiempqYwPz+P8+fPw+12i12eHIHxeFx08qHKObmQRvYxkKkve+zlnHvljt/ue0xnsOh7hN6g7c6nnT6WEm92dnbw5MkT/PWvfxVxeKoX7+RNTaaxw+HA7Owsrl+/Lnb5brzb1HZrbW0NP/7447HJOEajEePj47h27RpmZmYQCAREmI6ScfL5PA4ODhCPx0XNfbtpNLKDDoAQOwmf2mHRY+V8f25/3Rss+h5RxpCBzoSvrIcPh8MitVbOtOu0VpySVSwWCyYmJnD37l1cuXIFbre7K+fd0dERKpUKYrEYVlZWsLKy0ta01+v1GBgYwJUrVzA/P4+hoSHY7XbRBReASBuOxWLIZDKirRU9jzL3Xq5XoGYdNAhTaeYrX0cWffew6HuATHOa2CJPeiXh09fyY4B39eHKTLvFxUWEw+GuOryS4M1mM8bGxnDnzh188sknGB0dFQMqOqXZbCKbzeLly5f48ccfEY/H2x4rHA4HZmZmMD8/j8nJSbhcLpEkQwKtVCoiIadYLLYVp1xoIzcdkZtpKHf5dk032MTvHhZ9D9BuVCwWxWBHeTQUgPcEp9zhI5EInj17hu+++w5Pnz4Vgu80Di/v8MPDw7h9+zbu3buHyclJOByOrnZ56n23tbWFBw8eYHV19b3afMqvHx0dxa1bt3D58mX4/X4xeIKgKbfJZBLJZLKl5FdeDEnslFlH469kC0o+PrUTNwu+N1j0PUDjmtLpNNLpdMv0VGrRDLR6mZX18CT4hYWFrjLt6Lq0ww8NDeH27dv47LPPRPsr2nk7gZpkhMNhPHnyBN9//z3C4fB7v6fVajE4OIj5+XncuHEDIyMjsFgs782rr9fryGQyiMfjLZEH2awnc55aiFOtPYCW3VtZlad8Tdm8742ORN/PL6xyt6Q3djqdFkknJpNJiNbpdIopMPRYqjLLZDLY3d3Fjz/+iO+//x7Pnj3D7u4uisVixyOXZcGPjo7i5s2b+Oyzz3DlyhUEAoGuzHoSfDwex8LCAv7yl79gfX297S7vdDpx9epVfPrpp8KsJ7NcNrmr1SrS6TRisRgKhUJLi2v6LE/FdTqdIouvVquJ0VhKQR9XX3/cz5jj6Uj0nATxbgeqVCqIRqNYWVnB6uoqUqkUzGYz0uk0SqUSAoEAHA6HyCGnY0AymcTOzg6Wl5extLSElZUVxGKxrkx64G24zGKxYGxsDDdv3sTnn3+Oubk5DA4OClO70whCvV5HLBbDwsIC/vSnP+Hx48fI5XLv/S6d4z/77DNRqacsdiFrRg7VKbv50IKl1+vFiGq/3w+73Q6VSiVm3clOvXaf6f+jXc09czps3p8ChdUohTSVSmFlZQU//PADNjY2UK1WYbVaRTw6m82KopNms4l8Pi8GUaytrWFtbU1kqHWTdEOCsVqtOHfuHO7evYt79+5hdnYWgUCgxdQ+DXK2xWIxLC4u4l//9V9x//59JBKJlgWIzvETExP46quvcOvWLYRCIVgslvcWF7IacrmcCNXJFgPt8LRoeb1eDA8PIxgMwmw2i2YbuVyu5bhwnPCVLbSYzjlR9DRvvN9eVGrHBAAbGxtYXl7G/v4+0uk0IpEI3rx5g729PeRyOajVatjtdmQyGfGGdzgcYgZbMpnE/v4+IpEIotEo0ul0V+d3QqvVwufz4dKlS7h9+zZu3ryJixcvwuv1drzD025cLpexu7uLx48f4z/+4z/w8OFDhMNh0UeeRKfVanH+/Hl88cUXuHfvHs6fPw+r1dr2uWghodFamUym5W+kQhmLxYKBgQGMjY1hfHwcgUAAOp0O+Xwe9XodiURCXFvpnacFUpnA02/vz7NyougzmQyePHmClZUVYc71wwtMom82m9jd3cXGxgZisRgSiQTi8XhLQwm5DztVx+l0OtGEgpx9NAmm2zepTqeDzWYTOe63bt3C9evXMTY2Jo4RnQie8uHT6TRev36Nx48f49tvv8WTJ08Qj8ffOz9Ti627d+/i66+/xvT0tJgW2+65yGsfDocRjUZF2q08uMJgMMDlcmF0dBTT09OYmJiAy+VCs9lEMplEJpMRvhB5F5d3edrhaRAmz7LrnlN3+t///vf4/e9/DwCi59nHDr1Rj46OYLFYxOgqavckQ066bDaLRqOBZDIJ4G2CCv0+JZt0KnYyhY1GI7xeL2ZmZnDr1i1cvXoV58+fh9frPXHQpAzt7oVCAbFYDC9evMC3336L+/fv4/Xr12IOnRKXy4Xbt2/j17/+NS5fvgyXy3Ws4GWv/c7ODg4ODlCtVt/rEmy1WhEKhTA1NYXp6WkMDQ3BZDKhVquJgZXKIRjA+1l49JqT40+egMuczomiPzo6QrFYFF93Mj3lY0DeOQqFAgqFQkvSjQyJipxWtGDQm7GX+ek6nQ5+vx+XLl3CtWvXMDc3J6bKkqe7kyqzo6O3PeZyuRw2Njbw3Xff4T//8z/x/PlzRCKRYxdwi8WCqakpfPHFF7h27Ro8Hs+JOfxk2ieTSezu7iKZTLb8zfICNj4+junpaYyNjcHj8UCr1aJYLMJisYgWW7TgykdL+qwceiGnK7PoO+NE0VP5JGEwGDoOLX0sdOIsoh7s5LiSs8q6ETyFxsbHxzE/P4+bN2/iypUrGB4eht1u76pxBO2+qVQKy8vL+Mtf/oI///nPePHixbE59cTQ0BDu3r2L69evY3Bw8ETB08JCufaRSAS5XE4ckcgBabfbMTQ0hPPnz2N0dFRYK/R42ayXz+pKnxKFBWmoJc3JUxbwMMdzqvde3vW6qfjqN2SB9xJK0mq18Hg8mJubw2effYY7d+5gYmICbrcbJpOpqww7up9sNosXL17gm2++wTfffIPNzc1Tp+AYjUZcunQJv/rVrzAyMvJexp0SOW9hb28PqVRKWISUeUedcoeHhzEyMgKfzycWsUaj0WId0c6tXGzps5z+XCqVkMvlRK3CaYM7mLfwq/QBkMXeSwhJpVJhcHAQt2/fxldffYUbN25geHhYOM663cHIibi+vo4//vGP+MMf/oA3b96c6o/RarWiem56elq0yj6Jo6Mj0XSDIhry1BqdTge73S566AWDQdjtdpF2S0U11WoVxWJRlBPTLk/PIf9tJPpKpSJEz5tR57DoPwDKpg7dYDAYMDY2Jrzk1LWWmlh2e06lHTMcDuP+/fv49ttvsba21pEoTCYT5ufnce3aNZGAcxqUkkzNL8vlcksXIZPJJHb5UCgEl8sl/ja5cKlQKIi583LqrvI1pQWCHKuFQkEUKTGdwaL/wHQqfCqWGR8fxxdffIGvvvoKc3NzGBgYEGG4XqAIwvPnz/GnP/0Jq6urHe+CgUAA8/PzmJ6ehtls7igyUK/Xkc1mkUgkkE6nWzz2er0eTqcTQ0NDGBkZgdfrFXX3wLsFqlAoIJ1Oi265J/lCZNGT8LtJcmJY9GdCbgChfNOdJH6DwYBgMIi5uTn86le/ws2bN0WPuW473chQtdzKygr+67/+68RGGErcbjeuXr0qEn462eUpapHJZJBMJlEqlUQWH2UPhkIh0SnX5XKJ9GS5ACmdTiORSIg+eicJmBx98vhqjtV3B4v+DMgdWWXhy9106OcUp6ZstIsXL2J+fh5zc3MYHh6G1Wo9U0vno6O3Y6ho2OT9+/dFzkAnUAOO8fHxjnf5w8NDlEolJJNJ5PN5qNVqmEwmGAwGmM1m+P1+TE1N4fz58yJVmCbWUJ5+JpNBLBbDwcHBe1V5xz2vXHZLHXlY9J3Dou8RebQS8C41lMxbubjEbDbD6XRieHgYFy5cwNzcHKanpzE4OCjmvp1lPBPlve/v7+PRo0e4f/8+3rx503GqL5n1N27cgN/v7+hoQWfxXC6HdDqNWq0Gs9kMt9stuuuMjo5iampKxOSpfLbRaAgLgXwByWQShUKho7wGErtS9Byr7wwWfQ+Qg4qmswIQHmUAonSUxB4MBjE5OSlST4PBIFwuF0wm05kHNpDgqXjm3/7t3/DixYtTQ3OE0+nEnTt3cO/ePYyOjsJisZy6y8vNQDKZDAqFAjQaDXw+H/x+v/ibR0ZGEAqF4PV6xXw7OsfncjnRLTcajSKTyYiQsOwIbLcAyF1zALQkQbHoT4dF3wMGgwEejwehUEjUz1N8GXibUWexWOB0OhEKhXD+/HlMT09jZGREdI39EHPVZcF///33+MMf/oAnT54gkUh09Hgae/Xb3/4W169fPzHVlp6PBF8qlZBKpZBKpVCr1WC322G1WmGz2eD1ehEIBDAwMACn0wmz2QytVtvScSiVSolCpHg8jmKxKGL2dARQ5t4Tyt1d7pzLCTqnw6LvEq1WKzzSo6OjsNvtIlZNb1qdTger1Qq/34/z589jamoKwWBQ7Ha9euZlyKuI9SAAABmiSURBVGkXiUSwuLiIf//3f8e3336L/f39U81jipvfuHEDX3/9NW7evInh4eETh2LQGZ52eNqlyW/g8/lgs9kwMDAgxG61Wlv651E9QjqdRjQaFZWHVGYMvKvGk5N26Pnle5E/yKHH5/rOYNF3iU6ng8/nQygUgs/ng9FoFIKg1FOdTgeXy4Xx8XFMTk4iFAqJQRBnNT9lr/fOzg5++OEH/PGPf8TCwgKi0eiJCTg01PLq1asizfbcuXOiPLddPr9c5FIul5HL5RCPxxGJRHBwcIBisQij0Qin0wmfz4eBgQHY7Xaxu1MCDqXOUpfccDiMcDiMZDIpwnQ0sorMd6qxV8bglbX0ck895nRY9F1iNBrh8XjgdrthtVrfEwl56QcHBzE2NoZgMPhBPPOyWU3TbB89eoTHjx9jdXUViUTivR78BDW0vHjxIubm5jA7O4sLFy4gFAqJar12Y6VIUDRyKxaLYXd3V7QJq9frMJvNcDgcLed5Ejy1vaJr5HI5cQ2qxstms6hWq2Jqjlw+K3voZdqJnnf6zmHRd4FWq4XD4RCmq1arbREaOfAGBgYwNDQEv9/fs+DlNz8JJhqNYnt7G6urq1hcXMTS0hK2t7ffc9qR4I1GI/x+PyYnJ0UBz8WLF+Hz+URVW7uogbzIUEnu9vY2NjY2sL29LbriOBwOjIyMiF53tMPLvRfkOX0HBwfY2toSTUhol6ezuFw9SI+luQLHtcqi3+NmGp3Dou8C6vpCOfHAuxRcKh91OBwIBAIIBAJiCEQ3jSpJbOVyGcViEblcDqlUCuFwGC9fvsTi4iKWl5fF5Jh2YTnq5jM5OYm7d+/i7t27mJ6eFv3oKO/9uNp4ynqj+vjl5WU8f/4c6+vrSCQSODw8FDF3tVoNo9EIs9kMo9HY8vfKyTuRSATr6+tYW1vD1taWyL5rNptiRLVWqxUOPzrTt0PZF5876HTHqaKXX3jqCNNPKHvGWa1WcY6v1+vizWowGGCz2eDz+eDz+URXm04EL5vRpVKppS3X69evsbm5KZxe8Xgc2Wz22De4Wq3G0NAQrl+/jnv37on59G63W2TDnTaBR+6w8/333+OHH37A+vq68NTr9Xq43W4Eg0Fh3cidcUmU5LTb3d3F8vIyXr58iTdv3iCRSKBUKokOPRqNBjqdToieHivfU7v/l+NGZTEnw000OsBqtcLlcmF2dhZXr17F4OCgWADJAWUwGOB0OuHxeOB0OmE0Gk9tdEECIZHt7e1hfX0db968wfb2Nra2trC9vY1oNIp8Pt/y2HYxbJPJhNHRUdy5cwe//vWvRfFOp/kAcm385uamSPRZWVkRPe9ks12umZfNcQphplIpbG1tYXl5GT/++KNoO0YFMmQdUXydjktyN9zjFjeKJMjHK6YzumqiYTQau27o+EuEcsObzSaGh4cxNzcnJsGeO3dOtM+irjpUWUZn205ERmLP5/OIRqNYW1vDkydPsLCwgLW1NSQSiRN76snfU6vVsFqtmJiYwJdffomvv/4as7OzXRfvNJtNlMtlRKNRvHjxAg8fPsTKygqSyaSIClAMnV4nWihoPDV1s0kkEtjZ2cGLFy+E4FOplBA8heZoZyeBy8Mr5Vi8EnLe0eLRzTGq3zlR9GazGf/8z/+M+fl54fTpl3MTvZnJGTYyMoKhoSE4nU6o1WoRwioWi8jn86JfO+3wdA16veS2zhTCSiaTePXqFR4+fNjihS8UCqe+znQ9tVoNp9OJubk5fPnll7h37x4uXLgg0mG7GW1Vr9dFa62lpSW8fv0aqVSqJVMOeBdLV6lUaDQaIoe+VCqhVqshmUxic3MTy8vLWF1dxe7uLjKZjOibR9egJhq0a6vValEnT3kPx5nv8lmexmOx6DvjRNE7nU588cUX+Pzzz/9R9/OzgoRAabfyrmkwGIQDy2w2I5fLidATnWnlwZby9fL5PHZ3d/H8+XM8evRInJk7rYgD3i4oRqMRgUBAdNv59NNPcf78eTgcjq5y+emYQd18X79+jbW1NaTT6Za22HI9AdUc0HALirWnUins7u5ifX0dm5ubiEajIqdeFjzwLrOOymTlBB654eVx90wLAon+QyQ99QOn7vRM6+BFGXJi0ZtVbthIZjCdeeX5d9vb21hYWMCDBw+wtLT03pCJ06AintHRUczPz+Pzzz/HtWvXMDQ01FMSEN0/eeu3traQTCbFwiUnzhiNRjGv7/DwUCwMR0dHyOVy2N/ffy8GryyiUebM0/EAgGglLo+37uT14J2+czhk1wEnCYjKZwGIlsz0QRlpjUZDJNW8evUKT548wdLSEjY3N5HNZjtOKqF+c16vFxcvXsTNmzdx48YNXLhwAX6/v+3kmZOg4wZZH7FYDDs7OyLxRqPRiIm8Wq1W1BRQjL9arSIWiyESiYjRXVQXXyqVjm37rVwAqOqOyoNp1NdJr4vsE9DpdHym7wIeYNkBnXi9KTWWzuMUx5ez2TY3N/Hq1Su8evUK4XC4JSx1GtSFZmRkBLOzs7h+/TquXr2K8fFxOJ3OjodeyPcsJ+BQai1ZHdSplnZ5smqMRqMoGKLCmXw+Lyb80C7d6UImlySTpdRJKI7SnSnM10lLcOYtPMDyjNCbVd7pyKStVCpill0kEhHht2w2K3wFpy2oNM55YGAAU1NTuHbtGm7cuIGpqSmR8ddpH3zg/Uy/QqGAVCqFSCSCWCyGer0Ou90O4F33Y9pVKVxHlkuhUBDz+6gVdSdDPZT3SUcbOt93UlNPRw05x5/pDDbvzwAJgM7qVCqazWZFe+ZMJoNUKoV0Oi3M3tMyyMhpptfrRarr7Ows5ufnMTMzg5GREXg8HhEa7CQfQA6HkbOMxm4lEgnEYjGUSiVYLBaMjIwAQMt1yauezWZF6C2TySCdTqNYLAqxdgpdW+nk7LQLjkqlgtFohM1ma4mYMKfDou8ROfQmF5Ls7u4inU6LSbf5fF60dqa4O5nLZMLKjkISO2X3TUxMYGZmBpcvXxYlutQzXjbnT2o2QUIny4OESyY5ed71er3oxEsptSqVqqWc9vDwUMyez2azKBQKXU/fJatB7uUvT7XpBBqG6fF4xFBNpjNY9D2i3OVjsRii0agw30ulknBKkSdabqQpOwDJfJabbwSDQZw7dw4XLlzA9PQ0hoeHMTAwALPZLJxWSsHTjk67JvWSz2azottsMplEMpkU96hSqWCz2eB2u+HxeODxeGC328WQi6OjI5FdR842Ejw1vuimuo3CfnLqrtznvhP/EXXbpWOPzWZj0XcBi75HZNFTnzja+WiKrTxjjcJo8puT8s4pDOZ0OuH3+xEMBjE2NiYSgrxeLxwOR0umnxynluvPKX+fTPeDgwOEw2Hs7Oxgf38fmUxGOBBNJhO8Xi8sFgvsdjv8fr9obUV+Akq+oSm0uVwOyWQSuVxOTI3tFErIMZlMsFgsorgGQFeDUfV6vah1cLvdImrBdAaLvkfapZ/K45hI6ABaxi2Rt5mEbrPZ4HQ6xU5LBTs+nw9utxs2m03s7nJrKLm5BZ3RSZD7+/vY29tDOBwWHnkal00ORLPZDK/XK0ROHW+odx+JiK5Nc/EODg5EhVy3gjcYDLDb7XA6nWJ3LpfLovNQJ45IunebzQaHwwGr1dpxYRPzFhZ9j8iiox2XdnLajWknk01as9kMq9UKh8MBl8slTGq32y1q0q1Wq0iAIbHXarUWZ1ylUhHOOOo7T51l9/b2RGcbSpCh44XcxVen08HhcMDr9YqON/S8lFlI5n0ymRSLSC6X62r0tlqtFp1yg8EgAoEArFarGMxBzUE6KejS6XTCIpL7DXKEqXNY9GeA3vTklDIajWKyLy0KarUaWq0WJpNJVOt5PB4hNLfbLXYsEpycUUfHBNpxKdefogLxeFz4Eg4ODoQ3nXwJcv4/hdzoPOxyuRAIBEQpMA3KVNbDJ5NJ0fxCnj3fCVqtFmazGT6fD+fOnRM98A0GAwqFAnZ3d1vm2FF/vOOgJiXBYFCUC/Mu3x0s+jNAzjjZXDeZTC1OKcpqs1gscLvd8Hq9wnz3eDxwOBywWCwtyTWyI44iAOSAi8fj4oPCgORUk3PW5Tx32XlITTt9Ph9GR0cxNDQEj8cjjhCyv6BcLreUx25vb4scg06g0V3BYBCzs7O4fPkyxsfH4XK5AACZTAY6nQ6lUgmZTAb5fL5lkIXytT46OoLJZILH4xGtubqd5suw6HuGPPEkaoPBIHZqucEDpYlSNhstDGQVyL3kSOyVSkUkzVBUYH9/HwcHB4jFYkin0yLURmOglCKh3U9emMhZODQ0hKmpKZw7dw6Dg4MiBCjfR7VaRSqVwubmJl6+fIlXr14hFot1lDgDvBP88PAwrly5guvXr4t0YaPRiMPDQzgcDgAQvggSPfku5FAmhflo8aSZAzyeunv4FTsD7fK/5eGM9ObVaDSoVqviHF4qlVAsFkUjyFqtJuLh5XIZmUwG8Xgc4XBYOORisRgymUxLIowytk3+A7o3uj/Z0z04OIhz5861TJ6hTriUqVetVpFOp7Gzs4OXL1/ixYsXCIfDwoN/GmTSDw4O4urVq7h16xZmZ2cxODgoPO2NRgM6nQ71eh2ZTAaJREJYEfJ4K+DdwkVdd+Vuu7zLdw+L/owop7FQequcTkqhLfq5vJuTWU0NLKhFdCQSEYMgyPNODrnjsvlIHPRhNBphtVrhdrsRCAQQCoUwOjqKkZERDA4OYmBgQCS20O5OTTjD4bDoeEOFQZ047+ioQyW/N27cwOXLlzE8PCx6BlLVIQB4PB4MDQ0hlUqJ51Cr1SgWiy2REI1GIzz2NIXnpM46zPGw6M/AcYKn7jEUwqM3MSW10E5O4SatVis82PSzZDIpzHgSu3Kck/Je5J2dmngGg0ExKjoUCsHv98Pj8cBmswnHHYXMKLswGo3i9evXePHiBdbX15FMJoVT8CQoLDcwMIBz585hbm4OFy9eRCgUEg055R4DBoNBDPUcHh5GPp8XTTHkCbZU5UfHIsofOKmzEHM8LPoeUfaIJ8FT3F5OuyWPOQkymUyKN7Cc6ip750ulUks2n/zmlt/ksm9BPreHQiGMjY3h3LlzGB0dRSAQgNvtFl1rKWdf7r5LVsbm5iZWV1exsbGBZDLZkbee8g9sNhuGhoZw4cKFlmm1lDYsRxDIKqDEIDq60OKRz+fFawhA+B2owKlYLHYVSWDewqI/A3IarOxxp51ebvkkv9EpM02j0bS0naJEG3rccYU59Lzy7k5eedo1p6enMTk5iZGREdH6Ws7okxN7qN3VwcEBdnd3sbGxga2tLSQSCdGmWvn3yvdCC47ZbIbH48Hg4CBCoRDcbnfLcyrP30oHo8/nEwVJwNuFhHoQ0mtBr20+nxdtyhqNhujkw5wOi/6MyOa9nKxDgpIdbsDbDDe1Wo1KpdKSi68c3tDOSy6LRp57T2ay1+vF2NiYyNcfHR0VGXeyaU33SeY8Zdrt7e1hZ2cHOzs7QvCU1CPH7mVnoXwPNptNZNyZzWaRuy9PqlH2D6SEIdrxXS6XKFYiiwmAyFWgY1Iul0MulxMFP1xp1zks+jMg77T0QW88eQrLcbs1OdzoaxKCHKoic1gWvByG0+l0sNls8Hg8Yh487fBer/e9szQdQ6gQh5J7IpEIwuEw9vf3kU6nxXmadnEAIhKhrAqkkCS1rSKnJDUV0el0APDeBBt5MaTnoUxBeegl+RsajQZyuZw4/lAOA5n4LPrOYNGfAdrlqNGF2WwWjrl2de5K4cvjlWWnIICWYhT5zCqH4qhXntPpFN75YDAoyk2VsXdZ8FQZSD0AotGoaHVVqVQAvOsBSCE2sj7k+5Fj6NTuilJ2TSYTVCqViMlTHgP9vWTRkM9CjmxQaJP8HLIZT0VNJHxaoJjOYNH3iOxAo2YOLpcLDocDhUKhZRiDXBF33LWUTSWUOzvQak6TSWyxWOByuUQRC42bpigCLSoUfy8UCqL6bn9/H/v7+y0FObRryoMoZHFSREI+fijLjAGIr+ns7ff7RUKNPOuO6gjkTjzpdFpULebzeVHwQw5HErrcRJNF3zks+jOgdER5vV4EAgExUJJEUa1WhfBoEVDG1OlsK+/kxy0EZElQAQ9546kMNp1Oi5bWcgyeBE8mvSx2GkJBz0MmO/0dspORCmOUixo5MqlrkFzDPzw8jFAo1GKFABCtt5Q9CeRkJFqIaMAFCV5OUmI6h0XfIyRG2nEdDgf8fj/y+bzYKUnA9MaVRULXkHPi6aggT2xpV0Em+xIoZl0oFMS0mHw+LzrWUnIQneFJhOQEo7My3bM8m47q/eWdneL18s4vH1soZCkXBlGNQD6fx9DQEFwuF6xWq2igoRxhTX4FOSFJPqLIiU/cELN7WPRnQOm5HhgYECOuqFpOp9O17KZyGE5eOOiMTum8ckgPeJcLIDv7qCiGTHdqzilX6pH5THFt+iBzWa7AoxoCZbEOAHGmJy+6PKBC6aik4wUtAGQd0Iff7xf9+Wk6LjUOlXvmk/Uh1xbIz0NWFve87w4W/RloV8xCefRkqtNuTJ9JMMq8ctrR6bwuPwaAEJY8JFLpRZePBHLuAAmQdkh58ZCfX3k9mg0vn7vlrre02yodlPLCpLRqAKBSqcBut4smGnKt/sHBAXK53HuCV5YxUykzN8bsHhb9GVF68O12e0uevewEozcv/Uw+i8qeehKbLCbZiSZ3jZUdhLJjjX5Gn+Uaf+WuTAuX3GOP8gnkDkFy6Oy09Ff6G1UqlcgHMBqNYjIOjdsm64Q68siNOuUdnl4PjUYj2m3Z7XaRdMSi7xwW/QdCTiu1Wq2iV10ul4PNZkOhUECxWBQON6WpLiexkCktJ7LIST/yvwG0CLndMeA485hMd9rlSWg0nQdonTcnWxmdIC981GM/k8kAgOi+Q9mAdOZvZ9LT60tFRA6HAwMDA6KJJ5v33cGiPwPyzqk0Y8nDTo45pdmtDHuRxdBsNsW/5bRX2WqQE3+UO3e7haRdzj59TfFy+feVuQWy1dArdA3qwEs59JTA085pR8h5CdTth4qHrFarSP5hOoNF3yOySMjBRedn+qBSVfqg35GdYEoTXg7TKbP7lCmtdB/y/dC/TxJ7u79DmXSj/PmHgHwMpVKpJYwoFyfJf6vsp9Dr9XC73RgdHcXExATOnTuHQCAAs9nMu3yXsOjPgByblhNMcrkcstms+CxXzcmhO+XuSamncsYaIQu5neDbofQLnMSHFLf8/BSBkAt95EWRSpBly4UeSyY9FRONjo5idnYWFy9exPDwMFwuFwwGA4fsuoRF3yMkPsp8k3PBqRiEBN9O9O0837Jprdzp6TmVO7r8WXnU+KmhODqlC1PFHfkkyBI6LsmGHutyucRY7uvXr2NychI+n4+75/QIi75H5Eo1ygOXJ9rIYTkKMRkMBtFUknZ12sHlBpby5BvZdG3njFN+LUcHlB77f9SiIPf2p3bfFFoDILoGEfR3yv4NerzL5cLY2BhmZ2dx69YtzMzMiBbaPJ66N1j0PSLnm8tnUmVzCOoEY7VahQef8sXb9cyn8B8l7JATqx3K9Fylk5COHnIbbWrKIRfPKE3rXtHpdDCbzaKnP31QTzvKHpStItkKolAlTcGhQqLJyUnMzMzgwoULCAaDIrGHd/jeYNGfAVlkwLvxyWTWUgcYKn2lajE6x5JJS4KnjDgqU5WTdOTna7dTy+E+chSS4GVnoix6ZfmvfA05vt/Om067sZxKTI0rA4EAgsEgvF6vqK0nwVN6LomdLCSK/5NJTxmO5LgbHR2F3+8XHXB5h+8dFn2P0JvTYrGInHQ5A47O7/RZ9uDLGXVycg/Vo8u578pQXztTXRnflwtj5C4+ckee466nvBb9PST+dgsUTbmlMl8al+VyucS5u9lsio435O8gK4leFwCius9ut8Pn84lyYZpZxyb92VGdcr77eXiEfoaQwOU+dsp4OommXVaenDpL3m25GQfF6dvFzJWxeGUsnXZ5KkMlocmFP8dlBCqvp2zbJYvebDbDbreL0VxUSEO9BaiWgLz2VBpLAzapQw4l5FC2nd1uh8PhEB/UQJSuxWZ9x7R9oVj0Z0C5W9L32u2c7dJf25XXHldTT9eWPyu/Jz+fchQWmdGyk1FuXqH8UOa7y74GeYAHneHJWUdNN5RlwXR/cnmsvNNTlR9ZDFQyrExuYrqCRf9T0EksXf78oZ5TPqvLabvKnHzZIqGjhxw+ozi5Xq8XufNKq4QWAfr+aX+LfG/tegzI12fOBIu+31FaBUpLpF1VWzshKnv7MT9bWPTM6Sgtk7+HJcL8w2DRM0yf0Vb0HPtgmD6DRc8wfQaLnmH6DBY9w/QZLHqG6TNY9AzTZ7DoGabPYNEzTJ/BomeYPoNFzzB9BoueYfoMFj3D9BkseobpM1j0DNNnsOgZps9g0TNMn8GiZ5g+g0XPMH0Gi55h+gwWPcP0GSx6hukzWPQM02ew6Bmmz2DRM0yfwaJnmD6DRc8wfQaLnmH6DBY9w/QZLHqG6TNY9AzTZ7DoGabPYNEzTJ/BomeYPoNFzzB9BoueYfoMFj3D9BkseobpM1j0DNNnsOgZps9g0TNMn8GiZ5g+g0XPMH0Gi55h+gwWPcP0GSx6hukzWPQM02ew6Bmmz2DRM0yfwaJnmD6DRc8wfQaLnmH6DBY9w/QZLHqG6TNY9AzTZ7DoGabP0J7yc9U/5C4YhvmHwTs9w/QZLHqG6TNY9AzTZ7DoGabPYNEzTJ/BomeYPuP/A8D4C3z7Z8bFAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 57; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 58; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 59; current eta: 0.5, current beta: 64\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 60; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 61; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 62; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 63; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 64; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 65; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 66; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 67; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 68; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 69; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 70; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current iteration: 71; current eta: 0.5, current beta: 128\n", - "Starting forward run...\n", - "Starting adjoint run...\n", - "Calculating gradient...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "algorithm = nlopt.LD_MMA\n", - "n = Nx * Ny # number of parameters\n", + "n = Nx * Ny # number of parameters\n", "\n", "# Initial guess\n", "x = np.ones((n,)) * 0.5\n", - "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", - "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", + "x[Si_mask.flatten()] = 1 # set the edges of waveguides to silicon\n", + "x[SiO2_mask.flatten()] = 0 # set the other edges to SiO2\n", "\n", "# lower and upper bounds\n", - "lb = np.zeros((Nx*Ny,))\n", + "lb = np.zeros((Nx * Ny,))\n", "lb[Si_mask.flatten()] = 1\n", - "ub = np.ones((Nx*Ny,))\n", + "ub = np.ones((Nx * Ny,))\n", "ub[SiO2_mask.flatten()] = 0\n", "\n", "# insert dummy parameter bounds and variable\n", - "x = np.insert(x,0,0.5) # our initial guess for the worst error\n", - "lb = np.insert(lb,0,0) # we can't get less than 0 error!\n", - "ub = np.insert(ub,0,1) # we can't get more than 1 error!\n", + "x = np.insert(x, 0, 0.5) # our initial guess for the worst error\n", + "lb = np.insert(lb, 0, 0) # we can't get less than 0 error!\n", + "ub = np.insert(ub, 0, 1) # we can't get more than 1 error!\n", "\n", "cur_beta = 4\n", "beta_scale = 2\n", "num_betas = 6\n", "update_factor = 12\n", "for iters in range(num_betas):\n", - " solver = nlopt.opt(algorithm, n+1)\n", + " solver = nlopt.opt(algorithm, n + 1)\n", " solver.set_lower_bounds(lb)\n", " solver.set_upper_bounds(ub)\n", " solver.set_min_objective(f)\n", " solver.set_maxeval(update_factor)\n", - " solver.add_inequality_mconstraint(lambda r,x,g: c(r,x,g,eta_i,cur_beta), np.array([1e-3]*nf))\n", + " solver.add_inequality_mconstraint(\n", + " lambda r, x, g: c(r, x, g, eta_i, cur_beta), np.array([1e-3] * nf)\n", + " )\n", " x[:] = solver.optimize(x)\n", - " cur_beta = cur_beta*beta_scale" + " cur_beta = cur_beta * beta_scale" ] }, { @@ -1915,35 +379,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "lb = 1-np.min(evaluation_history,axis=1)\n", - "ub = 1-np.max(evaluation_history,axis=1)\n", - "mean = 1-np.mean(evaluation_history,axis=1)\n", + "lb = 1 - np.min(evaluation_history, axis=1)\n", + "ub = 1 - np.max(evaluation_history, axis=1)\n", + "mean = 1 - np.mean(evaluation_history, axis=1)\n", "\n", "num_iters = lb.size\n", "\n", "plt.figure()\n", - "plt.fill_between(np.arange(1,num_iters+1),10*np.log10(lb),10*np.log10(ub),alpha=0.3)\n", - "plt.plot(10*np.log10(mean),'o-')\n", + "plt.fill_between(\n", + " np.arange(1, num_iters + 1), 10 * np.log10(lb), 10 * np.log10(ub), alpha=0.3\n", + ")\n", + "plt.plot(10 * np.log10(mean), \"o-\")\n", "plt.grid(True)\n", - "plt.xlabel('Iteration')\n", - "plt.ylabel('Average Insertion Loss (dB)')\n", + "plt.xlabel(\"Iteration\")\n", + "plt.ylabel(\"Average Insertion Loss (dB)\")\n", "plt.show()" ] }, @@ -1956,30 +409,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "opt.update_design([mapping(x[1:],eta_i,cur_beta)])\n", + "opt.update_design([mapping(x[1:], eta_i, cur_beta)])\n", "plt.figure()\n", "ax = plt.gca()\n", - "opt.plot2D(False,ax=ax,plot_sources_flag=False,plot_monitors_flag=False,plot_boundaries_flag=False)\n", - "circ = Circle((2,2),minimum_length/2)\n", + "opt.plot2D(\n", + " False,\n", + " ax=ax,\n", + " plot_sources_flag=False,\n", + " plot_monitors_flag=False,\n", + " plot_boundaries_flag=False,\n", + ")\n", + "circ = Circle((2, 2), minimum_length / 2)\n", "ax.add_patch(circ)\n", - "ax.axis('off')\n", + "ax.axis(\"off\")\n", "plt.show()" ] }, @@ -1992,70 +438,37 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting forward run...\n" - ] - } - ], + "outputs": [], "source": [ - "f0, dJ_du = opt([mapping(x[1:],eta_i,cur_beta)],need_gradient = False)\n", + "f0, dJ_du = opt([mapping(x[1:], eta_i, cur_beta)], need_gradient=False)\n", "frequencies = opt.frequencies\n", "source_coef, top_coef = opt.get_objective_arguments()\n", "\n", - "top_profile = np.abs(top_coef/source_coef) ** 2" + "top_profile = np.abs(top_coef / source_coef) ** 2" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", - "plt.plot(1/frequencies,top_profile*100,'-o')\n", + "plt.plot(1 / frequencies, top_profile * 100, \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Transmission (%)')\n", - "plt.ylim(98,100)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Transmission (%)\")\n", + "plt.ylim(98, 100)\n", "plt.show()\n", "\n", "plt.figure()\n", - "plt.plot(1/frequencies,10*np.log10(top_profile),'-o')\n", + "plt.plot(1 / frequencies, 10 * np.log10(top_profile), \"-o\")\n", "plt.grid(True)\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel('Insertion Loss (dB)')\n", - "plt.ylim(-0.1,0)\n", + "plt.xlabel(\"Wavelength (microns)\")\n", + "plt.ylabel(\"Insertion Loss (dB)\")\n", + "plt.ylim(-0.1, 0)\n", "plt.show()" ] }, diff --git a/python/examples/antenna-radiation.py b/python/examples/antenna-radiation.py index 9d6ea4cb1..4b5d0b778 100644 --- a/python/examples/antenna-radiation.py +++ b/python/examples/antenna-radiation.py @@ -1,116 +1,116 @@ -import meep as mp import math -import numpy as np + import matplotlib.pyplot as plt +import numpy as np +import meep as mp resolution = 50 # pixels/um sxy = 4 dpml = 1 -cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml) +cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] fcen = 1.0 df = 0.4 src_cmpt = mp.Ez -sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(), - component=src_cmpt)] +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=src_cmpt + ) +] if src_cmpt == mp.Ex: - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)] elif src_cmpt == mp.Ey: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=-1)] + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=-1)] elif src_cmpt == mp.Ez: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=+1)] - -sim = mp.Simulation(cell_size=cell, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers) - -nearfield_box = sim.add_near2far(fcen, 0, 1, - mp.Near2FarRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.Near2FarRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) - -flux_box = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.FluxRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.FluxRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.FluxRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)] + +sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, +) + +nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0)), + mp.Near2FarRegion( + center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1 + ), + mp.Near2FarRegion(center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy)), + mp.Near2FarRegion( + center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1 + ), +) + +flux_box = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0)), + mp.FluxRegion(center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1), + mp.FluxRegion(center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy)), + mp.FluxRegion(center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1), +) sim.run(until_after_sources=mp.stop_when_dft_decayed()) near_flux = mp.get_fluxes(flux_box)[0] # half side length of far-field square box OR radius of far-field circle -r = 1000/fcen +r = 1000 / fcen # resolution of far fields (points/μm) res_ff = 1 -far_flux_box = (nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=r), - size=mp.Vector3(2*r)), - res_ff)[0] - - nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=-r), - size=mp.Vector3(2*r)), - res_ff)[0] + - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(r), - size=mp.Vector3(y=2*r)), - res_ff)[0] - - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(-r), - size=mp.Vector3(y=2*r)), - res_ff)[0]) +far_flux_box = ( + nearfield_box.flux( + mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2 * r)), res_ff + )[0] + - nearfield_box.flux( + mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r)), res_ff + )[0] + + nearfield_box.flux( + mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2 * r)), res_ff + )[0] + - nearfield_box.flux( + mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2 * r)), res_ff + )[0] +) npts = 100 # number of points in [0,2*pi) range of angles -angles = 2*math.pi/npts*np.arange(npts) +angles = 2 * math.pi / npts * np.arange(npts) -E = np.zeros((npts,3),dtype=np.complex128) -H = np.zeros((npts,3),dtype=np.complex128) +E = np.zeros((npts, 3), dtype=np.complex128) +H = np.zeros((npts, 3), dtype=np.complex128) for n in range(npts): - ff = sim.get_farfield(nearfield_box, - mp.Vector3(r*math.cos(angles[n]), - r*math.sin(angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] + ff = sim.get_farfield( + nearfield_box, mp.Vector3(r * math.cos(angles[n]), r * math.sin(angles[n])) + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] -Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) -Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) -Pr = np.sqrt(np.square(Px)+np.square(Py)) +Px = np.real(E[:, 1] * H[:, 2] - E[:, 2] * H[:, 1]) +Py = np.real(E[:, 2] * H[:, 0] - E[:, 0] * H[:, 2]) +Pr = np.sqrt(np.square(Px) + np.square(Py)) # integrate the radial flux over the circle circumference -far_flux_circle = np.sum(Pr)*2*np.pi*r/len(Pr) +far_flux_circle = np.sum(Pr) * 2 * np.pi * r / len(Pr) -print("flux:, {:.6f}, {:.6f}, {:.6f}".format(near_flux,far_flux_box,far_flux_circle)) +print(f"flux:, {near_flux:.6f}, {far_flux_box:.6f}, {far_flux_circle:.6f}") -ax = plt.subplot(111, projection='polar') -ax.plot(angles,Pr/max(Pr),'b-') +ax = plt.subplot(111, projection="polar") +ax.plot(angles, Pr / max(Pr), "b-") ax.set_rmax(1) -ax.set_rticks([0,0.5,1]) +ax.set_rticks([0, 0.5, 1]) ax.grid(True) ax.set_rlabel_position(22) plt.show() diff --git a/python/examples/antenna_pec_ground_plane.py b/python/examples/antenna_pec_ground_plane.py index 6b65db7ad..f16e7ebc8 100644 --- a/python/examples/antenna_pec_ground_plane.py +++ b/python/examples/antenna_pec_ground_plane.py @@ -2,99 +2,100 @@ # positioned a given height above a perfect-electric # conductor (PEC) ground plane and compares the result # to analytic theory. - -import meep as mp import math -import numpy as np + import matplotlib -matplotlib.use('agg') -import matplotlib.pyplot as plt +import numpy as np +import meep as mp + +matplotlib.use("agg") +import matplotlib.pyplot as plt resolution = 200 # pixels/um -n = 1.2 # refractive index of surrounding medium -h = 1.25 # height of antenna (point dipole source) above ground plane -wvl = 0.65 # vacuum wavelength -r = 1000*wvl # radius of far-field circle -npts = 50 # number of points in [0,pi/2) range of angles +n = 1.2 # refractive index of surrounding medium +h = 1.25 # height of antenna (point dipole source) above ground plane +wvl = 0.65 # vacuum wavelength +r = 1000 * wvl # radius of far-field circle +npts = 50 # number of points in [0,pi/2) range of angles -angles = 0.5*math.pi/npts*np.arange(npts) +angles = 0.5 * math.pi / npts * np.arange(npts) -def radial_flux(sim,nearfield_box,r): - E = np.zeros((npts,3),dtype=np.complex128) - H = np.zeros((npts,3),dtype=np.complex128) +def radial_flux(sim, nearfield_box, r): + E = np.zeros((npts, 3), dtype=np.complex128) + H = np.zeros((npts, 3), dtype=np.complex128) for n in range(npts): - ff = sim.get_farfield(nearfield_box, - mp.Vector3(r*math.sin(angles[n]), - r*math.cos(angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] + ff = sim.get_farfield( + nearfield_box, mp.Vector3(r * math.sin(angles[n]), r * math.cos(angles[n])) + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] - Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) # Ey*Hz-Ez*Hy - Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) # Ez*Hx-Ex*Hz - Pr = np.sqrt(np.square(Px)+np.square(Py)) - - return Pr + Px = np.real(E[:, 1] * H[:, 2] - E[:, 2] * H[:, 1]) # Ey*Hz-Ez*Hy + Py = np.real(E[:, 2] * H[:, 0] - E[:, 0] * H[:, 2]) # Ez*Hx-Ex*Hz + return np.sqrt(np.square(Px) + np.square(Py)) def free_space_radiation(src_cmpt): sxy = 4 dpml = 1 - cell_size = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell_size = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] - fcen = 1/wvl - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(), - component=src_cmpt)] + fcen = 1 / wvl + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(), + component=src_cmpt, + ) + ] if src_cmpt == mp.Hz: - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=-1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)] elif src_cmpt == mp.Ez: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)] else: symmetries = [] - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers, - default_material=mp.Medium(index=n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.Near2FarRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + default_material=mp.Medium(index=n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0)), + mp.Near2FarRegion( + center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1 + ), + mp.Near2FarRegion(center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy)), + mp.Near2FarRegion( + center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1 + ), + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = radial_flux(sim,nearfield_box,r) - - return Pr + return radial_flux(sim, nearfield_box, r) def pec_ground_plane_radiation(src_cmpt=mp.Hz): - L = 8.0 # length of non-PML region + L = 8.0 # length of non-PML region dpml = 1.0 # thickness of PML - sxy = dpml+L+dpml - cell_size = mp.Vector3(sxy,sxy,0) + sxy = dpml + L + dpml + cell_size = mp.Vector3(sxy, sxy, 0) boundary_layers = [mp.PML(dpml)] - fcen = 1/wvl + fcen = 1 / wvl # The near-to-far field transformation feature only supports # homogeneous media which means it cannot explicitly take into @@ -105,53 +106,61 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): # equivalent to a PEC boundary condition on one side. # Note: This setup means that the radiation pattern can only # be measured in the top half above the dipole. - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=src_cmpt, - center=mp.Vector3(0,+h)), - mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=src_cmpt, - center=mp.Vector3(0,-h), - amplitude=-1 if src_cmpt==mp.Ez else +1)] - - symmetries = [mp.Mirror(direction=mp.X, - phase=+1 if src_cmpt==mp.Ez else -1), - mp.Mirror(direction=mp.Y, - phase=-1 if src_cmpt==mp.Ez else +1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,2*h), - size=mp.Vector3(2*h,0), - weight=+1), - mp.Near2FarRegion(center=mp.Vector3(0,-2*h), - size=mp.Vector3(2*h,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(h,0), - size=mp.Vector3(0,4*h), - weight=+1), - mp.Near2FarRegion(center=mp.Vector3(-h,0), - size=mp.Vector3(0,4*h), - weight=-1)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=src_cmpt, + center=mp.Vector3(0, +h), + ), + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=src_cmpt, + center=mp.Vector3(0, -h), + amplitude=-1 if src_cmpt == mp.Ez else +1, + ), + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=+1 if src_cmpt == mp.Ez else -1), + mp.Mirror(direction=mp.Y, phase=-1 if src_cmpt == mp.Ez else +1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0, 2 * h), size=mp.Vector3(2 * h, 0), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(0, -2 * h), size=mp.Vector3(2 * h, 0), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(h, 0), size=mp.Vector3(0, 4 * h), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(-h, 0), size=mp.Vector3(0, 4 * h), weight=-1 + ), + ) sim.plot2D() - plt.savefig('antenna_pec_ground_plane.png',bbox_inches='tight') + plt.savefig("antenna_pec_ground_plane.png", bbox_inches="tight") sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = radial_flux(sim,nearfield_box,r) - - return Pr + return radial_flux(sim, nearfield_box, r) -if __name__ == '__main__': +if __name__ == "__main__": src_cmpt = mp.Ez # TM/P: Hz or TE/S: Ez Pr_fsp = free_space_radiation(src_cmpt) Pr_pec = pec_ground_plane_radiation(src_cmpt) @@ -162,22 +171,27 @@ def pec_ground_plane_radiation(src_cmpt=mp.Hz): # as derived in Section 6.2 "Two-Element Array" of # Antenna Theory: Analysis and Design, Fourth Edition # (2016) by C.A. Balanis. - k = 2*np.pi/(wvl/n) # wavevector in free space - Pr_theory = np.zeros(npts,) - for i,ang in enumerate(angles): - Pr_theory[i] = Pr_fsp[i] * 2*np.sin(k*h*np.cos(ang)) + k = 2 * np.pi / (wvl / n) # wavevector in free space + Pr_theory = np.zeros( + npts, + ) + for i, ang in enumerate(angles): + Pr_theory[i] = Pr_fsp[i] * 2 * np.sin(k * h * np.cos(ang)) - Pr_pec_norm = Pr_pec/np.max(Pr_pec) - Pr_theory_norm = (Pr_theory/max(Pr_theory))**2 + Pr_pec_norm = Pr_pec / np.max(Pr_pec) + Pr_theory_norm = (Pr_theory / max(Pr_theory)) ** 2 plt.figure() - plt.plot(np.degrees(angles),Pr_pec_norm,'b-',label='Meep') - plt.plot(np.degrees(angles),Pr_theory_norm,'r-',label='theory') - plt.xlabel('angle (degrees)') - plt.ylabel('radial flux (normalized by maximum flux)') - plt.title('antenna with {}$_z$ polarization above PEC ground plane'.format('E' if src_cmpt==mp.Ez else r'H')) - plt.axis([0,90,0,1.0]) + plt.plot(np.degrees(angles), Pr_pec_norm, "b-", label="Meep") + plt.plot(np.degrees(angles), Pr_theory_norm, "r-", label="theory") + plt.xlabel("angle (degrees)") + plt.ylabel("radial flux (normalized by maximum flux)") + plt.title( + f"antenna with {'E' if src_cmpt==mp.Ez else r'H'}$_z$ polarization above PEC ground plane" + ) + + plt.axis([0, 90, 0, 1.0]) plt.legend() - plt.savefig('radiation_pattern.png',bbox_inches='tight') + plt.savefig("radiation_pattern.png", bbox_inches="tight") - print("norm:, {:.6f}".format(np.linalg.norm(Pr_pec_norm-Pr_theory_norm))) + print(f"norm:, {np.linalg.norm(Pr_pec_norm - Pr_theory_norm):.6f}") diff --git a/python/examples/bend-flux.py b/python/examples/bend-flux.py index 0b788fd3b..f56ab6492 100644 --- a/python/examples/bend-flux.py +++ b/python/examples/bend-flux.py @@ -1,57 +1,68 @@ -# -*- coding: utf-8 -*- - # transmission around a 90-degree waveguide bend in 2d -from __future__ import division +import matplotlib.pyplot as plt +import numpy as np import meep as mp -import numpy as np -import matplotlib.pyplot as plt -resolution = 10 # pixels/um +resolution = 10 # pixels/um sx = 16 # size of cell in X direction sy = 32 # size of cell in Y direction -cell = mp.Vector3(sx,sy,0) +cell = mp.Vector3(sx, sy, 0) dpml = 1.0 pml_layers = [mp.PML(dpml)] pad = 4 # padding distance between waveguide and cell edge -w = 1 # width of waveguide +w = 1 # width of waveguide -wvg_xcen = 0.5*(sx-w-2*pad) # x center of vert. wvg -wvg_ycen = -0.5*(sy-w-2*pad) # y center of horiz. wvg +wvg_xcen = 0.5 * (sx - w - 2 * pad) # x center of vert. wvg +wvg_ycen = -0.5 * (sy - w - 2 * pad) # y center of horiz. wvg -geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), - center=mp.Vector3(0,wvg_ycen,0), - material=mp.Medium(epsilon=12))] +geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, w, mp.inf), + center=mp.Vector3(0, wvg_ycen, 0), + material=mp.Medium(epsilon=12), + ) +] fcen = 0.15 # pulse center frequency -df = 0.1 # pulse width (in frequency) -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(-0.5*sx+dpml,wvg_ycen,0), - size=mp.Vector3(0,w,0))] - -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +df = 0.1 # pulse width (in frequency) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(-0.5 * sx + dpml, wvg_ycen, 0), + size=mp.Vector3(0, w, 0), + ) +] + +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) nfreq = 100 # number of frequencies at which to compute flux # reflected flux -refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0),size=mp.Vector3(0,2*w,0)) -refl = sim.add_flux(fcen,df,nfreq,refl_fr) +refl_fr = mp.FluxRegion( + center=mp.Vector3(-0.5 * sx + dpml + 0.5, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0) +) +refl = sim.add_flux(fcen, df, nfreq, refl_fr) # transmitted flux -tran_fr = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml,wvg_ycen,0),size=mp.Vector3(0,2*w,0)) -tran = sim.add_flux(fcen,df,nfreq,tran_fr) +tran_fr = mp.FluxRegion( + center=mp.Vector3(0.5 * sx - dpml, wvg_ycen, 0), size=mp.Vector3(0, 2 * w, 0) +) +tran = sim.add_flux(fcen, df, nfreq, tran_fr) -pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen) +pt = mp.Vector3(0.5 * sx - dpml - 0.5, wvg_ycen) -sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3)) +sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3)) # for normalization run, save flux fields data for reflection plane straight_refl_data = sim.get_flux_data(refl) @@ -61,25 +72,39 @@ sim.reset_meep() -geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf),center=mp.Vector3(-0.5*pad,wvg_ycen),material=mp.Medium(epsilon=12)), - mp.Block(mp.Vector3(w,sy-pad,mp.inf),center=mp.Vector3(wvg_xcen,0.5*pad),material=mp.Medium(epsilon=12))] - -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +geometry = [ + mp.Block( + mp.Vector3(sx - pad, w, mp.inf), + center=mp.Vector3(-0.5 * pad, wvg_ycen), + material=mp.Medium(epsilon=12), + ), + mp.Block( + mp.Vector3(w, sy - pad, mp.inf), + center=mp.Vector3(wvg_xcen, 0.5 * pad), + material=mp.Medium(epsilon=12), + ), +] + +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) # reflected flux refl = sim.add_flux(fcen, df, nfreq, refl_fr) -tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0),size=mp.Vector3(2*w,0,0)) -tran = sim.add_flux(fcen,df,nfreq,tran_fr) +tran_fr = mp.FluxRegion( + center=mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5, 0), size=mp.Vector3(2 * w, 0, 0) +) +tran = sim.add_flux(fcen, df, nfreq, tran_fr) # for normal run, load negated fields to subtract incident from refl. fields -sim.load_minus_flux_data(refl,straight_refl_data) +sim.load_minus_flux_data(refl, straight_refl_data) -pt = mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5) +pt = mp.Vector3(wvg_xcen, 0.5 * sy - dpml - 0.5) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3)) @@ -92,15 +117,15 @@ Rs = [] Ts = [] for i in range(nfreq): - wl = np.append(wl, 1/flux_freqs[i]) - Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i]) - Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i]) + wl = np.append(wl, 1 / flux_freqs[i]) + Rs = np.append(Rs, -bend_refl_flux[i] / straight_tran_flux[i]) + Ts = np.append(Ts, bend_tran_flux[i] / straight_tran_flux[i]) if mp.am_master(): plt.figure() - plt.plot(wl,Rs,'bo-',label='reflectance') - plt.plot(wl,Ts,'ro-',label='transmittance') - plt.plot(wl,1-Rs-Ts,'go-',label='loss') + plt.plot(wl, Rs, "bo-", label="reflectance") + plt.plot(wl, Ts, "ro-", label="transmittance") + plt.plot(wl, 1 - Rs - Ts, "go-", label="loss") plt.axis([5.0, 10.0, 0, 1]) plt.xlabel("wavelength (μm)") plt.legend(loc="upper right") diff --git a/python/examples/bent-waveguide.py b/python/examples/bent-waveguide.py index 17d465c83..b2d0b4e57 100644 --- a/python/examples/bent-waveguide.py +++ b/python/examples/bent-waveguide.py @@ -1,31 +1,41 @@ -# -*- coding: utf-8 -*- - # From the Meep tutorial: plotting permittivity and fields of a bent waveguide -from __future__ import division - import meep as mp -cell = mp.Vector3(16,16,0) -geometry = [mp.Block(mp.Vector3(12,1,mp.inf), - center=mp.Vector3(-2.5,-3.5), - material=mp.Medium(epsilon=12)), - mp.Block(mp.Vector3(1,12,mp.inf), - center=mp.Vector3(3.5,2), - material=mp.Medium(epsilon=12))] +cell = mp.Vector3(16, 16, 0) +geometry = [ + mp.Block( + mp.Vector3(12, 1, mp.inf), + center=mp.Vector3(-2.5, -3.5), + material=mp.Medium(epsilon=12), + ), + mp.Block( + mp.Vector3(1, 12, mp.inf), + center=mp.Vector3(3.5, 2), + material=mp.Medium(epsilon=12), + ), +] pml_layers = [mp.PML(1.0)] resolution = 10 -sources = [mp.Source(mp.ContinuousSource(wavelength=2*(11**0.5), width=20), - component=mp.Ez, - center=mp.Vector3(-7,-3.5), - size=mp.Vector3(0,1))] +sources = [ + mp.Source( + mp.ContinuousSource(wavelength=2 * (11**0.5), width=20), + component=mp.Ez, + center=mp.Vector3(-7, -3.5), + size=mp.Vector3(0, 1), + ) +] -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) -sim.run(mp.at_beginning(mp.output_epsilon), - mp.to_appended("ez", mp.at_every(0.6, mp.output_efield_z)), - until=200) +sim.run( + mp.at_beginning(mp.output_epsilon), + mp.to_appended("ez", mp.at_every(0.6, mp.output_efield_z)), + until=200, +) diff --git a/python/examples/binary_grating.py b/python/examples/binary_grating.py index dd63e2770..4b0182bc0 100644 --- a/python/examples/binary_grating.py +++ b/python/examples/binary_grating.py @@ -1,52 +1,63 @@ -# -*- coding: utf-8 -*- - -import meep as mp import math -import numpy as np + import matplotlib.pyplot as plt +import numpy as np -resolution = 60 # pixels/μm +import meep as mp + +resolution = 60 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # padding between grating and PML -gp = 10.0 # grating period -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # padding between grating and PML +gp = 10.0 # grating period +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle -sx = dpml+dsub+gh+dpad+dpml +sx = dpml + dsub + gh + dpad + dpml sy = gp -cell_size = mp.Vector3(sx,sy,0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +cell_size = mp.Vector3(sx, sy, 0) +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] -wvl_min = 0.4 # min wavelength -wvl_max = 0.6 # max wavelength -fmin = 1/wvl_max # min frequency -fmax = 1/wvl_min # max frequency -fcen = 0.5*(fmin+fmax) # center frequency -df = fmax-fmin # frequency width +wvl_min = 0.4 # min wavelength +wvl_max = 0.6 # max wavelength +fmin = 1 / wvl_max # min frequency +fmax = 1 / wvl_min # max frequency +fcen = 0.5 * (fmin + fmax) # center frequency +df = fmax - fmin # frequency width -src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0) -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0))] +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ) +] -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) glass = mp.Medium(index=1.5) -symmetries=[mp.Mirror(mp.Y)] +symmetries = [mp.Mirror(mp.Y)] -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources, - symmetries=symmetries) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, + symmetries=symmetries, +) nfreq = 21 -mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) -flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) +mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) +flux_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) @@ -54,25 +65,41 @@ sim.reset_meep() -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +mode_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) freqs = mp.get_eigenmode_freqs(mode_mon) nmode = 10 -res = sim.get_eigenmode_coefficients(mode_mon, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y) +res = sim.get_eigenmode_coefficients( + mode_mon, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y +) coeffs = res.alpha kdom = res.kdom @@ -81,30 +108,36 @@ mode_tran = [] for nm in range(nmode): - for nf in range(nfreq): - mode_wvl.append(1/freqs[nf]) - mode_angle.append(math.degrees(math.acos(kdom[nm*nfreq+nf].x/freqs[nf]))) - tran = abs(coeffs[nm,nf,0])**2/input_flux[nf] - mode_tran.append(0.5*tran if nm != 0 else tran) - print("grating{}:, {:.5f}, {:.2f}, {:.8f}".format(nm,mode_wvl[-1],mode_angle[-1],mode_tran[-1])) - -tran_max = round(max(mode_tran),1) + for nf in range(nfreq): + mode_wvl.append(1 / freqs[nf]) + mode_angle.append(math.degrees(math.acos(kdom[nm * nfreq + nf].x / freqs[nf]))) + tran = abs(coeffs[nm, nf, 0]) ** 2 / input_flux[nf] + mode_tran.append(0.5 * tran if nm != 0 else tran) + print( + "grating{}:, {:.5f}, {:.2f}, {:.8f}".format( + nm, mode_wvl[-1], mode_angle[-1], mode_tran[-1] + ) + ) + +tran_max = round(max(mode_tran), 1) plt.figure() -plt.pcolormesh(np.reshape(mode_wvl,(nmode,nfreq)), - np.reshape(mode_angle,(nmode,nfreq)), - np.reshape(mode_tran,(nmode,nfreq)), - cmap='Blues', - shading='nearest', - vmin=0, - vmax=tran_max) +plt.pcolormesh( + np.reshape(mode_wvl, (nmode, nfreq)), + np.reshape(mode_angle, (nmode, nfreq)), + np.reshape(mode_tran, (nmode, nfreq)), + cmap="Blues", + shading="nearest", + vmin=0, + vmax=tran_max, +) plt.axis([min(mode_wvl), max(mode_wvl), min(mode_angle), max(mode_angle)]) plt.xlabel("wavelength (μm)") plt.ylabel("diffraction angle (degrees)") -plt.xticks([t for t in np.arange(0.4,0.7,0.1)]) -plt.yticks([t for t in range(0,35,5)]) +plt.xticks(list(np.arange(0.4, 0.7, 0.1))) +plt.yticks(list(range(0, 35, 5))) plt.title("transmittance of diffraction orders") cbar = plt.colorbar() -cbar.set_ticks([t for t in np.arange(0,tran_max+0.1,0.1)]) -cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,tran_max+0.1,0.1)]) +cbar.set_ticks(list(np.arange(0, tran_max + 0.1, 0.1))) +cbar.set_ticklabels([f"{t:.1f}" for t in np.arange(0, tran_max + 0.1, 0.1)]) plt.show() diff --git a/python/examples/binary_grating_n2f.py b/python/examples/binary_grating_n2f.py index 8ad39fdbb..77de0bc3a 100644 --- a/python/examples/binary_grating_n2f.py +++ b/python/examples/binary_grating_n2f.py @@ -1,166 +1,227 @@ -# -*- coding: utf-8 -*- - -import meep as mp import math + +import matplotlib.pyplot as plt import numpy as np from numpy import linalg as LA -import matplotlib.pyplot as plt -resolution = 25 # pixels/μm +import meep as mp + +resolution = 25 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # padding between grating and PML -gp = 10.0 # grating period -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # padding between grating and PML +gp = 10.0 # grating period +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle -nperiods = 10 # number of unit cells in finite periodic grating +nperiods = 10 # number of unit cells in finite periodic grating -ff_distance = 1e8 # far-field distance from near-field monitor -ff_angle = 20 # far-field cone angle -ff_npts = 500 # number of far-field points +ff_distance = 1e8 # far-field distance from near-field monitor +ff_angle = 20 # far-field cone angle +ff_npts = 500 # number of far-field points -ff_length = ff_distance*math.tan(math.radians(ff_angle)) -ff_res = ff_npts/ff_length +ff_length = ff_distance * math.tan(math.radians(ff_angle)) +ff_res = ff_npts / ff_length -sx = dpml+dsub+gh+dpad+dpml +sx = dpml + dsub + gh + dpad + dpml cell_size = mp.Vector3(sx) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] symmetries = [mp.Mirror(mp.Y)] -wvl_min = 0.4 # min wavelength -wvl_max = 0.6 # max wavelength -fmin = 1/wvl_max # min frequency -fmax = 1/wvl_min # max frequency -fcen = 0.5*(fmin+fmax) # center frequency -df = fmax-fmin # frequency width +wvl_min = 0.4 # min wavelength +wvl_max = 0.6 # max wavelength +fmin = 1 / wvl_max # min frequency +fmax = 1 / wvl_min # max frequency +fcen = 0.5 * (fmin + fmax) # center frequency +df = fmax - fmin # frequency width -src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt)] +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) +sources = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt) +] k_point = mp.Vector3() glass = mp.Medium(index=1.5) -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, +) nfreq = 21 -n2f_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad) +n2f_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad) n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt)) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9)) -ff_source = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length)) +ff_source = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(ff_distance, 0.5 * ff_length), + size=mp.Vector3(y=ff_length), +) sim.reset_meep() ### unit cell with periodic boundaries sy = gp -cell_size = mp.Vector3(sx,sy) - -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df, is_integrated=True), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))] - -sim = mp.Simulation(resolution=resolution, - split_chunks_evenly=True, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), nperiods=nperiods) +cell_size = mp.Vector3(sx, sy) + +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df, is_integrated=True), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) +] + +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh), + ), +] + +sim = mp.Simulation( + resolution=resolution, + split_chunks_evenly=True, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +n2f_obj = sim.add_near2far( + fcen, + df, + nfreq, + mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), + nperiods=nperiods, +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9)) -ff_unitcell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length)) +ff_unitcell = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(ff_distance, 0.5 * ff_length), + size=mp.Vector3(y=ff_length), +) sim.reset_meep() ### finite periodic grating with flat surface termination extending into PML -num_cells = 2*nperiods+1 -sy = dpml+num_cells*gp+dpml -cell_size = mp.Vector3(sx,sy) +num_cells = 2 * nperiods + 1 +sy = dpml + num_cells * gp + dpml +cell_size = mp.Vector3(sx, sy) pml_layers = [mp.PML(thickness=dpml)] -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df, is_integrated=True), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))] +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df, is_integrated=True), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) +] + +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ) +] for j in range(num_cells): - geometry.append(mp.Block(material=glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+(j+0.5)*gp))) - -sim = mp.Simulation(resolution=resolution, - split_chunks_evenly=True, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -n2f_obj = sim.add_near2far(fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy-2*dpml))) + geometry.append( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3( + -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + dpml + (j + 0.5) * gp + ), + ) + ) + +sim = mp.Simulation( + resolution=resolution, + split_chunks_evenly=True, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +n2f_obj = sim.add_near2far( + fcen, df, nfreq, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy - 2 * dpml)) +) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, n2f_pt, 1e-9)) -ff_supercell = sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(ff_distance,0.5*ff_length), size=mp.Vector3(y=ff_length)) +ff_supercell = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(ff_distance, 0.5 * ff_length), + size=mp.Vector3(y=ff_length), +) -norm_err = LA.norm(ff_unitcell['Ez']-ff_supercell['Ez'])/nperiods -print("error:, {}, {}".format(nperiods,norm_err)) +norm_err = LA.norm(ff_unitcell["Ez"] - ff_supercell["Ez"]) / nperiods +print(f"error:, {nperiods}, {norm_err}") freqs = mp.get_near2far_freqs(n2f_obj) -wvl = np.divide(1,freqs) -ff_lengths = np.linspace(0,ff_length,ff_npts) -angles = [math.degrees(math.atan(f)) for f in ff_lengths/ff_distance] +wvl = np.divide(1, freqs) +ff_lengths = np.linspace(0, ff_length, ff_npts) +angles = [math.degrees(math.atan(f)) for f in ff_lengths / ff_distance] wvl_slice = 0.5 -idx_slice = np.where(np.asarray(freqs) == 1/wvl_slice)[0][0] +idx_slice = np.where(np.asarray(freqs) == 1 / wvl_slice)[0][0] -rel_enh = np.absolute(ff_unitcell['Ez'])**2/np.absolute(ff_source['Ez'])**2 +rel_enh = np.absolute(ff_unitcell["Ez"]) ** 2 / np.absolute(ff_source["Ez"]) ** 2 plt.figure(dpi=150) -plt.subplot(1,2,1) -plt.pcolormesh(wvl,angles,rel_enh,cmap='Blues',shading='flat') -plt.axis([wvl_min,wvl_max,0,ff_angle]) +plt.subplot(1, 2, 1) +plt.pcolormesh(wvl, angles, rel_enh, cmap="Blues", shading="flat") +plt.axis([wvl_min, wvl_max, 0, ff_angle]) plt.xlabel("wavelength (μm)") plt.ylabel("angle (degrees)") -plt.grid(linewidth=0.5,linestyle='--') -plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)]) -plt.yticks([t for t in range(0,ff_angle+1,10)]) +plt.grid(linewidth=0.5, linestyle="--") +plt.xticks([t for t in np.arange(wvl_min, wvl_max + 0.1, 0.1)]) +plt.yticks([t for t in range(0, ff_angle + 1, 10)]) plt.title("far-field spectra") -plt.subplot(1,2,2) -plt.plot(angles,rel_enh[:,idx_slice],'bo-') -plt.xlim(0,ff_angle) +plt.subplot(1, 2, 2) +plt.plot(angles, rel_enh[:, idx_slice], "bo-") +plt.xlim(0, ff_angle) plt.ylim(0) -plt.xticks([t for t in range(0,ff_angle+1,10)]) +plt.xticks([t for t in range(0, ff_angle + 1, 10)]) plt.xlabel("angle (degrees)") plt.ylabel("relative enhancement") -plt.grid(axis='x',linewidth=0.5,linestyle='--') -plt.title("f.-f. spectra @ λ = {:.1} μm".format(wvl_slice)) +plt.grid(axis="x", linewidth=0.5, linestyle="--") +plt.title(f"f.-f. spectra @ λ = {wvl_slice:.1} μm") plt.tight_layout(pad=0.5) plt.show() diff --git a/python/examples/binary_grating_oblique.py b/python/examples/binary_grating_oblique.py index 0214b6318..4b209cda7 100644 --- a/python/examples/binary_grating_oblique.py +++ b/python/examples/binary_grating_oblique.py @@ -1,143 +1,180 @@ -# -*- coding: utf-8 -*- - -import meep as mp -import math import cmath +import math + import numpy as np -resolution = 50 # pixels/μm +import meep as mp -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # length of padding between grating and PML -gp = 10.0 # grating period -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle +resolution = 50 # pixels/μm -sx = dpml+dsub+gh+dpad+dpml +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # length of padding between grating and PML +gp = 10.0 # grating period +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle + +sx = dpml + dsub + gh + dpad + dpml sy = gp -cell_size = mp.Vector3(sx,sy,0) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +cell_size = mp.Vector3(sx, sy, 0) +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] -wvl = 0.5 # center wavelength -fcen = 1/wvl # center frequency -df = 0.05*fcen # frequency width +wvl = 0.5 # center wavelength +fcen = 1 / wvl # center frequency +df = 0.05 * fcen # frequency width ng = 1.5 glass = mp.Medium(index=ng) use_cw_solver = False # CW solver or time stepping? -tol = 1e-6 # CW solver tolerance -max_iters = 2000 # CW solver max iterations -L = 10 # CW solver L +tol = 1e-6 # CW solver tolerance +max_iters = 2000 # CW solver max iterations +L = 10 # CW solver L # rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis theta_in = math.radians(10.7) # k (in source medium) with correct length (plane of incidence: XY) -k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in) +k = mp.Vector3(fcen * ng).rotate(mp.Vector3(z=1), theta_in) symmetries = [] eig_parity = mp.ODD_Z if theta_in == 0: - k = mp.Vector3(0,0,0) - symmetries = [mp.Mirror(mp.Y)] - eig_parity += mp.EVEN_Y - -def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - -src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0) -sources = [mp.Source(mp.ContinuousSource(fcen,fwidth=df) if use_cw_solver else mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(0,sy,0), - amp_func=pw_amp(k,src_pt))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k, - default_material=glass, - sources=sources, - symmetries=symmetries) - -refl_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0) -refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0))) + k = mp.Vector3(0, 0, 0) + symmetries = [mp.Mirror(mp.Y)] + eig_parity += mp.EVEN_Y + + +def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0) +sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df) + if use_cw_solver + else mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + amp_func=pw_amp(k, src_pt), + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k, + default_material=glass, + sources=sources, + symmetries=symmetries, +) + +refl_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0) +refl_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0)) +) if use_cw_solver: - sim.init_sim() - sim.solve_cw(tol, max_iters, L) + sim.init_sim() + sim.solve_cw(tol, max_iters, L) else: - sim.run(until_after_sources=100) + sim.run(until_after_sources=100) input_flux = mp.get_fluxes(refl_flux) input_flux_data = sim.get_flux_data(refl_flux) sim.reset_meep() -geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - -refl_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0,sy,0))) -sim.load_minus_flux_data(refl_flux,input_flux_data) - -tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) -tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k, + sources=sources, + symmetries=symmetries, +) + +refl_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, sy, 0)) +) +sim.load_minus_flux_data(refl_flux, input_flux_data) + +tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) +tran_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) +) if use_cw_solver: - sim.init_sim() - sim.solve_cw(tol, max_iters, L) + sim.init_sim() + sim.solve_cw(tol, max_iters, L) else: - sim.run(until_after_sources=200) + sim.run(until_after_sources=200) -nm_r = np.floor((fcen*ng-k.y)*gp)-np.ceil((-fcen*ng-k.y)*gp) # number of reflected orders +nm_r = np.floor((fcen * ng - k.y) * gp) - np.ceil( + (-fcen * ng - k.y) * gp +) # number of reflected orders if theta_in == 0: - nm_r = nm_r/2 # since eig_parity removes degeneracy in y-direction + nm_r = nm_r / 2 # since eig_parity removes degeneracy in y-direction nm_r = int(nm_r) -res = sim.get_eigenmode_coefficients(refl_flux, range(1,nm_r+1), eig_parity=eig_parity) +res = sim.get_eigenmode_coefficients( + refl_flux, range(1, nm_r + 1), eig_parity=eig_parity +) r_coeffs = res.alpha Rsum = 0 for nm in range(nm_r): - r_kdom = res.kdom[nm] - Rmode = abs(r_coeffs[nm,0,1])**2/input_flux[0] - r_angle = np.sign(r_kdom.y)*math.acos(r_kdom.x/(ng*fcen)) - print("refl:, {:2d}, {:6.2f}, {:.8f}".format(nm,math.degrees(r_angle),Rmode)) - Rsum += Rmode - -nm_t = np.floor((fcen-k.y)*gp)-np.ceil((-fcen-k.y)*gp) # number of transmitted orders + r_kdom = res.kdom[nm] + Rmode = abs(r_coeffs[nm, 0, 1]) ** 2 / input_flux[0] + r_angle = np.sign(r_kdom.y) * math.acos(r_kdom.x / (ng * fcen)) + print(f"refl:, {nm:2d}, {math.degrees(r_angle):6.2f}, {Rmode:.8f}") + Rsum += Rmode + +nm_t = np.floor((fcen - k.y) * gp) - np.ceil( + (-fcen - k.y) * gp +) # number of transmitted orders if theta_in == 0: - nm_t = nm_t/2 # since eig_parity removes degeneracy in y-direction + nm_t = nm_t / 2 # since eig_parity removes degeneracy in y-direction nm_t = int(nm_t) -res = sim.get_eigenmode_coefficients(tran_flux, range(1,nm_t+1), eig_parity=eig_parity) +res = sim.get_eigenmode_coefficients( + tran_flux, range(1, nm_t + 1), eig_parity=eig_parity +) t_coeffs = res.alpha Tsum = 0 for nm in range(nm_t): - t_kdom = res.kdom[nm] - Tmode = abs(t_coeffs[nm,0,0])**2/input_flux[0] - t_angle = np.sign(t_kdom.y)*math.acos(t_kdom.x/fcen) - print("tran:, {:2d}, {:6.2f}, {:.8f}".format(nm,math.degrees(t_angle),Tmode)) - Tsum += Tmode + t_kdom = res.kdom[nm] + Tmode = abs(t_coeffs[nm, 0, 0]) ** 2 / input_flux[0] + t_angle = np.sign(t_kdom.y) * math.acos(t_kdom.x / fcen) + print(f"tran:, {nm:2d}, {math.degrees(t_angle):6.2f}, {Tmode:.8f}") + Tsum += Tmode -print("mode-coeff:, {:11.6f}, {:.6f}, {:.6f}".format(Rsum,Tsum,Rsum+Tsum)) +print(f"mode-coeff:, {Rsum:11.6f}, {Tsum:.6f}, {Rsum + Tsum:.6f}") r_flux = mp.get_fluxes(refl_flux) t_flux = mp.get_fluxes(tran_flux) -Rflux = -r_flux[0]/input_flux[0] -Tflux = t_flux[0]/input_flux[0] -print("poynting-flux:, {:.6f}, {:.6f}, {:.6f}".format(Rflux,Tflux,Rflux+Tflux)) +Rflux = -r_flux[0] / input_flux[0] +Tflux = t_flux[0] / input_flux[0] +print(f"poynting-flux:, {Rflux:.6f}, {Tflux:.6f}, {Rflux + Tflux:.6f}") diff --git a/python/examples/binary_grating_phasemap.py b/python/examples/binary_grating_phasemap.py index f75fdf3ad..50e99b178 100644 --- a/python/examples/binary_grating_phasemap.py +++ b/python/examples/binary_grating_phasemap.py @@ -1,121 +1,174 @@ -# -*- coding: utf-8 -*- +import argparse -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np import numpy.matlib -import argparse -resolution = 60 # pixels/μm +import meep as mp + +resolution = 60 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 3.0 # substrate thickness -dpad = 3.0 # padding between grating and PML +dpml = 1.0 # PML thickness +dsub = 3.0 # substrate thickness +dpad = 3.0 # padding between grating and PML -wvl_min = 0.4 # min wavelength -wvl_max = 0.6 # max wavelength -fmin = 1/wvl_max # min frequency -fmax = 1/wvl_min # max frequency -fcen = 0.5*(fmin+fmax) # center frequency -df = fmax-fmin # frequency width -nfreq = 21 # number of frequency bins +wvl_min = 0.4 # min wavelength +wvl_max = 0.6 # max wavelength +fmin = 1 / wvl_max # min frequency +fmax = 1 / wvl_min # max frequency +fcen = 0.5 * (fmin + fmax) # center frequency +df = fmax - fmin # frequency width +nfreq = 21 # number of frequency bins -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) glass = mp.Medium(index=1.5) -def grating(gp,gh,gdc,oddz): - sx = dpml+dsub+gh+dpad+dpml - sy = gp - - cell_size = mp.Vector3(sx,sy,0) - pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] - - src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0) - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez if oddz else mp.Hz, center=src_pt, size=mp.Vector3(0,sy,0))] - - symmetries=[mp.Mirror(mp.Y, phase=+1 if oddz else -1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources, - symmetries=symmetries) - - mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) - flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=100) - - input_flux = mp.get_fluxes(flux_mon) - - sim.reset_meep() - - geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=300) - - freqs = mp.get_eigenmode_freqs(mode_mon) - res = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y if oddz else mp.EVEN_Z+mp.ODD_Y) - coeffs = res.alpha - - mode_wvl = [1/freqs[nf] for nf in range(nfreq)] - mode_tran = [abs(coeffs[0,nf,0])**2/input_flux[nf] for nf in range(nfreq)] - mode_phase = [np.angle(coeffs[0,nf,0]) for nf in range(nfreq)] - - return mode_wvl, mode_tran, mode_phase - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('-gp', type=float, default=0.35, help='grating periodicity (default: 0.35 μm)') - parser.add_argument('-gh', type=float, default=0.6, help='grating height (default: 0.6 μm)') - parser.add_argument('-oddz', action='store_true', default=False, help='oddz? (default: False)') - args = parser.parse_args() - - gdc = np.arange(0.1,1.0,0.1) - mode_tran = np.empty((gdc.size,nfreq)) - mode_phase = np.empty((gdc.size,nfreq)) - for n in range(gdc.size): - mode_wvl, mode_tran[n,:], mode_phase[n,:] = grating(args.gp,args.gh,gdc[n],args.oddz) - - plt.figure(dpi=150) - - plt.subplot(1,2,1) - plt.pcolormesh(mode_wvl, gdc, mode_tran, cmap='hot_r', shading='gouraud', vmin=0, vmax=mode_tran.max()) - plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) - plt.xlabel("wavelength (μm)") - plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)]) - plt.ylabel("grating duty cycle") - plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)]) - plt.title("transmittance") - cbar = plt.colorbar() - cbar.set_ticks([t for t in np.arange(0,1.2,0.2)]) - cbar.set_ticklabels(["{:.1f}".format(t) for t in np.arange(0,1.2,0.2)]) - - plt.subplot(1,2,2) - plt.pcolormesh(mode_wvl, gdc, mode_phase, cmap='RdBu', shading='gouraud', vmin=mode_phase.min(), vmax=mode_phase.max()) - plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) - plt.xlabel("wavelength (μm)") - plt.xticks([t for t in np.arange(wvl_min,wvl_max+0.1,0.1)]) - plt.ylabel("grating duty cycle") - plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)]) - plt.title("phase (radians)") - cbar = plt.colorbar() - cbar.set_ticks([t for t in range(-3,4)]) - cbar.set_ticklabels(["{:.1f}".format(t) for t in range(-3,4)]) - - plt.tight_layout() - plt.show() + +def grating(gp, gh, gdc, oddz): + sx = dpml + dsub + gh + dpad + dpml + sy = gp + + cell_size = mp.Vector3(sx, sy, 0) + pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] + + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez if oddz else mp.Hz, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ) + ] + + symmetries = [mp.Mirror(mp.Y, phase=+1 if oddz else -1)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, + symmetries=symmetries, + ) + + mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) + flux_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=100) + + input_flux = mp.get_fluxes(flux_mon) + + sim.reset_meep() + + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + mode_mon = sim.add_flux( + fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=300) + + freqs = mp.get_eigenmode_freqs(mode_mon) + res = sim.get_eigenmode_coefficients( + mode_mon, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y if oddz else mp.EVEN_Z + mp.ODD_Y + ) + coeffs = res.alpha + + mode_wvl = [1 / freqs[nf] for nf in range(nfreq)] + mode_tran = [abs(coeffs[0, nf, 0]) ** 2 / input_flux[nf] for nf in range(nfreq)] + mode_phase = [np.angle(coeffs[0, nf, 0]) for nf in range(nfreq)] + + return mode_wvl, mode_tran, mode_phase + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-gp", type=float, default=0.35, help="grating periodicity (default: 0.35 μm)" + ) + parser.add_argument( + "-gh", type=float, default=0.6, help="grating height (default: 0.6 μm)" + ) + parser.add_argument( + "-oddz", action="store_true", default=False, help="oddz? (default: False)" + ) + args = parser.parse_args() + + gdc = np.arange(0.1, 1.0, 0.1) + mode_tran = np.empty((gdc.size, nfreq)) + mode_phase = np.empty((gdc.size, nfreq)) + for n in range(gdc.size): + mode_wvl, mode_tran[n, :], mode_phase[n, :] = grating( + args.gp, args.gh, gdc[n], args.oddz + ) + + plt.figure(dpi=150) + + plt.subplot(1, 2, 1) + plt.pcolormesh( + mode_wvl, + gdc, + mode_tran, + cmap="hot_r", + shading="gouraud", + vmin=0, + vmax=mode_tran.max(), + ) + plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) + plt.xlabel("wavelength (μm)") + plt.xticks(list(np.arange(wvl_min, wvl_max + 0.1, 0.1))) + plt.ylabel("grating duty cycle") + plt.yticks(list(np.arange(gdc[0], gdc[-1] + 0.1, 0.1))) + plt.title("transmittance") + cbar = plt.colorbar() + cbar.set_ticks(list(np.arange(0, 1.2, 0.2))) + cbar.set_ticklabels([f"{t:.1f}" for t in np.arange(0, 1.2, 0.2)]) + + plt.subplot(1, 2, 2) + plt.pcolormesh( + mode_wvl, + gdc, + mode_phase, + cmap="RdBu", + shading="gouraud", + vmin=mode_phase.min(), + vmax=mode_phase.max(), + ) + plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]]) + plt.xlabel("wavelength (μm)") + plt.xticks(list(np.arange(wvl_min, wvl_max + 0.1, 0.1))) + plt.ylabel("grating duty cycle") + plt.yticks(list(np.arange(gdc[0], gdc[-1] + 0.1, 0.1))) + plt.title("phase (radians)") + cbar = plt.colorbar() + cbar.set_ticks(list(range(-3, 4))) + cbar.set_ticklabels([f"{t:.1f}" for t in range(-3, 4)]) + + plt.tight_layout() + plt.show() diff --git a/python/examples/cavity-farfield.py b/python/examples/cavity-farfield.py index 46ee7624e..c643a8105 100644 --- a/python/examples/cavity-farfield.py +++ b/python/examples/cavity-farfield.py @@ -1,34 +1,39 @@ -import meep as mp -import numpy as np import matplotlib -matplotlib.use('agg') -import matplotlib.pyplot as plt +import numpy as np +import meep as mp + +matplotlib.use("agg") +import matplotlib.pyplot as plt -resolution = 20 # pixels/μm +resolution = 20 # pixels/μm -fcen = 0.25 # pulse center frequency -df = 0.2 # pulse width (in frequency) +fcen = 0.25 # pulse center frequency +df = 0.2 # pulse width (in frequency) -eps = 13 # dielectric constant of waveguide -w = 1.2 # width of waveguide -r = 0.36 # radius of holes -d = 1.4 # defect spacing (ordinary spacing = 1) -N = 3 # number of holes on either side of defect +eps = 13 # dielectric constant of waveguide +w = 1.2 # width of waveguide +r = 0.36 # radius of holes +d = 1.4 # defect spacing (ordinary spacing = 1) +N = 3 # number of holes on either side of defect -dpad = 32 # padding between last hole and PML edge -dpml = 0.5/(fcen-0.5*df) # PML thickness (> half the largest wavelength) -sx = 2*(dpad+dpml+N) + d - 1 # size of cell in x direction +dpad = 32 # padding between last hole and PML edge +dpml = 0.5 / (fcen - 0.5 * df) # PML thickness (> half the largest wavelength) +sx = 2 * (dpad + dpml + N) + d - 1 # size of cell in x direction -d1 = 0.2 # y-distance from waveguide edge to near2far surface -d2 = 2.0 # y-distance from near2far surface to far-field line -sy = w + 2*(d1+d2+dpml) # size of cell in y direction (perpendicular to wvg.) +d1 = 0.2 # y-distance from waveguide edge to near2far surface +d2 = 2.0 # y-distance from near2far surface to far-field line +sy = w + 2 * (d1 + d2 + dpml) # size of cell in y direction (perpendicular to wvg.) -cell = mp.Vector3(sx,sy,0) +cell = mp.Vector3(sx, sy, 0) -geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf, w, mp.inf), - material=mp.Medium(epsilon=eps))] +geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=eps), + ) +] for i in range(N): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) @@ -36,29 +41,36 @@ pml_layers = [mp.PML(dpml)] -sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3())] - -symmetries = [mp.Mirror(mp.X, phase=-1), - mp.Mirror(mp.Y, phase=-1)] - -sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers, - resolution=resolution) +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3() + ) +] + +symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)] + +sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + resolution=resolution, +) nearfield = sim.add_near2far( - fcen, 0, 1, - mp.Near2FarRegion(mp.Vector3(0, 0.5*w + d1), - size=mp.Vector3(sx - 2*dpml)), - mp.Near2FarRegion(mp.Vector3(-0.5*sx + dpml, 0.5*w + 0.5*d1), - size=mp.Vector3(0, d1), - weight=-1.0), - mp.Near2FarRegion(mp.Vector3(0.5*sx - dpml, 0.5*w + 0.5*d1), - size=mp.Vector3(0, d1)), + fcen, + 0, + 1, + mp.Near2FarRegion(mp.Vector3(0, 0.5 * w + d1), size=mp.Vector3(sx - 2 * dpml)), + mp.Near2FarRegion( + mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1), + size=mp.Vector3(0, d1), + weight=-1.0, + ), + mp.Near2FarRegion( + mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), size=mp.Vector3(0, d1) + ), ) mon = sim.add_dft_fields( @@ -66,49 +78,57 @@ fcen, 0, 1, - center=mp.Vector3(0, 0.5*w + d1 + d2), - size=mp.Vector3(sx - 2*(dpad+dpml), 0) + center=mp.Vector3(0, 0.5 * w + d1 + d2), + size=mp.Vector3(sx - 2 * (dpad + dpml), 0), ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) sim.plot2D() if mp.am_master(): - plt.savefig(f'cavity_farfield_plot2D_dpad{dpad}_{d1}_{d2}.png',bbox_inches='tight',dpi=150) + plt.savefig( + f"cavity_farfield_plot2D_dpad{dpad}_{d1}_{d2}.png", bbox_inches="tight", dpi=150 + ) Hz_mon = sim.get_dft_array(mon, mp.Hz, 0) -(x,y,z,w) = sim.get_array_metadata(dft_cell=mon) +(x, y, z, w) = sim.get_array_metadata(dft_cell=mon) ff = [] for xc in x: - ff_pt = sim.get_farfield(nearfield, mp.Vector3(xc,y[0])) + ff_pt = sim.get_farfield(nearfield, mp.Vector3(xc, y[0])) ff.append(ff_pt[5]) ff = np.array(ff) if mp.am_master(): plt.figure() - plt.subplot(1,3,1) - plt.plot(np.real(Hz_mon),'bo-',label='DFT') - plt.plot(np.real(ff),'ro-',label='N2F') + plt.subplot(1, 3, 1) + plt.plot(np.real(Hz_mon), "bo-", label="DFT") + plt.plot(np.real(ff), "ro-", label="N2F") plt.legend() - plt.xlabel('$x$ (μm)') - plt.ylabel('real(Hz)') + plt.xlabel("$x$ (μm)") + plt.ylabel("real(Hz)") - plt.subplot(1,3,2) - plt.plot(np.imag(Hz_mon),'bo-',label='DFT') - plt.plot(np.imag(ff),'ro-',label='N2F') + plt.subplot(1, 3, 2) + plt.plot(np.imag(Hz_mon), "bo-", label="DFT") + plt.plot(np.imag(ff), "ro-", label="N2F") plt.legend() - plt.xlabel('$x$ (μm)') - plt.ylabel('imag(Hz)') + plt.xlabel("$x$ (μm)") + plt.ylabel("imag(Hz)") - plt.subplot(1,3,3) - plt.plot(np.abs(Hz_mon),'bo-',label='DFT') - plt.plot(np.abs(ff),'ro-',label='N2F') + plt.subplot(1, 3, 3) + plt.plot(np.abs(Hz_mon), "bo-", label="DFT") + plt.plot(np.abs(ff), "ro-", label="N2F") plt.legend() - plt.xlabel('$x$ (μm)') - plt.ylabel('|Hz|') + plt.xlabel("$x$ (μm)") + plt.ylabel("|Hz|") - plt.suptitle(f'comparison of near2far and actual DFT fields\n dpad={dpad}, d1={d1}, d2={d2}') + plt.suptitle( + f"comparison of near2far and actual DFT fields\n dpad={dpad}, d1={d1}, d2={d2}" + ) plt.subplots_adjust(wspace=0.6) - plt.savefig(f'test_Hz_dft_vs_n2f_res{resolution}_dpad{dpad}_d1{d1}_d2{d2}.png',bbox_inches='tight',dpi=150) + plt.savefig( + f"test_Hz_dft_vs_n2f_res{resolution}_dpad{dpad}_d1{d1}_d2{d2}.png", + bbox_inches="tight", + dpi=150, + ) diff --git a/python/examples/cavity_arrayslice.py b/python/examples/cavity_arrayslice.py index 6fa9e91aa..55c4b1d05 100644 --- a/python/examples/cavity_arrayslice.py +++ b/python/examples/cavity_arrayslice.py @@ -1,6 +1,7 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp # set up the geometry eps = 13 @@ -18,26 +19,23 @@ cell = mp.Vector3(sx, sy, 0) -blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), - material=mp.Medium(epsilon=eps)) +blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps)) geometry = [blk] -for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) - -for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) +geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) +geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / -2 - i)) for i in range(3)) -sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[], - boundary_layers=[mp.PML(dpml)], - resolution=20) +sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[], + boundary_layers=[mp.PML(dpml)], + resolution=20, +) # add sources -sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - mp.Hz, mp.Vector3())] +sim.sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Hz, mp.Vector3())] # run until sources are finished (and no later) sim._run_sources_until(0, []) diff --git a/python/examples/cherenkov-radiation.py b/python/examples/cherenkov-radiation.py index f6f165376..92423d9fd 100644 --- a/python/examples/cherenkov-radiation.py +++ b/python/examples/cherenkov-radiation.py @@ -1,29 +1,40 @@ ## moving point charge with superluminal phase velocity in dielectric media emitting Cherenkov radiation - import meep as mp sx = 60 sy = 60 -cell_size = mp.Vector3(sx,sy,0) +cell_size = mp.Vector3(sx, sy, 0) dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] -v = 0.7 # velocity of point charge +v = 0.7 # velocity of point charge symmetries = [mp.Mirror(direction=mp.Y)] -sim = mp.Simulation(resolution=10, - cell_size=cell_size, - default_material=mp.Medium(index=1.5), - symmetries=symmetries, - boundary_layers=pml_layers) +sim = mp.Simulation( + resolution=10, + cell_size=cell_size, + default_material=mp.Medium(index=1.5), + symmetries=symmetries, + boundary_layers=pml_layers, +) -def move_source(sim): - sim.change_sources([mp.Source(mp.ContinuousSource(frequency=1e-10), - component=mp.Ex, - center=mp.Vector3(-0.5*sx+dpml+v*sim.meep_time()))]) -sim.run(move_source, - mp.at_every(2, mp.output_png(mp.Hz, "-vZc dkbluered -M 1")), - until=sx/v) +def move_source(sim): + sim.change_sources( + [ + mp.Source( + mp.ContinuousSource(frequency=1e-10), + component=mp.Ex, + center=mp.Vector3(-0.5 * sx + dpml + v * sim.meep_time()), + ) + ] + ) + + +sim.run( + move_source, + mp.at_every(2, mp.output_png(mp.Hz, "-vZc dkbluered -M 1")), + until=sx / v, +) diff --git a/python/examples/chirped_pulse.py b/python/examples/chirped_pulse.py index b768b6ab1..493d8bbba 100644 --- a/python/examples/chirped_pulse.py +++ b/python/examples/chirped_pulse.py @@ -1,36 +1,48 @@ ## linear-chirped pulse planewave with higher frequencies at the front (down-chirp) +import numpy as np import meep as mp -import numpy as np resolution = 40 dpml = 2 -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] sx = 40 sy = 6 -cell_size = mp.Vector3(sx+2*dpml,sy) +cell_size = mp.Vector3(sx + 2 * dpml, sy) v0 = 1.0 # pulse center frequency -a = 0.2 # Gaussian envelope half-width +a = 0.2 # Gaussian envelope half-width b = -0.5 # linear chirp rate (positive: up-chirp, negative: down-chirp) -t0 = 15 # peak time - -chirp = lambda t: np.exp(1j*2*np.pi*v0*(t-t0)) * np.exp(-a*(t-t0)**2+1j*b*(t-t0)**2) - -sources = [mp.Source(src=mp.CustomSource(src_func=chirp), - center=mp.Vector3(-0.5*sx), - size=mp.Vector3(y=sy), - component=mp.Ez)] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - k_point=mp.Vector3(), - sources=sources, - symmetries=[mp.Mirror(mp.Y)]) - -sim.run(mp.in_volume(mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx,sy)), - mp.at_every(2.7, mp.output_efield_z)), - until=t0+50) +t0 = 15 # peak time + +chirp = lambda t: np.exp(1j * 2 * np.pi * v0 * (t - t0)) * np.exp( + -a * (t - t0) ** 2 + 1j * b * (t - t0) ** 2 +) + +sources = [ + mp.Source( + src=mp.CustomSource(src_func=chirp), + center=mp.Vector3(-0.5 * sx), + size=mp.Vector3(y=sy), + component=mp.Ez, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + k_point=mp.Vector3(), + sources=sources, + symmetries=[mp.Mirror(mp.Y)], +) + +sim.run( + mp.in_volume( + mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx, sy)), + mp.at_every(2.7, mp.output_efield_z), + ), + until=t0 + 50, +) diff --git a/python/examples/coupler.py b/python/examples/coupler.py index b141681f9..56b321a1f 100644 --- a/python/examples/coupler.py +++ b/python/examples/coupler.py @@ -1,7 +1,8 @@ -import meep as mp import argparse -gdsII_file = 'coupler.gds' +import meep as mp + +gdsII_file = "coupler.gds" CELL_LAYER = 0 PORT1_LAYER = 1 PORT2_LAYER = 2 @@ -17,24 +18,29 @@ t_air = 0.78 dpml = 1 -cell_thickness = dpml+t_oxide+t_Si+t_air+dpml +cell_thickness = dpml + t_oxide + t_Si + t_air + dpml oxide = mp.Medium(epsilon=2.25) silicon = mp.Medium(epsilon=12) -fcen = 1/1.55 -df = 0.2*fcen +fcen = 1 / 1.55 +df = 0.2 * fcen + def main(args): - cell_zmax = 0.5*cell_thickness if args.three_d else 0 - cell_zmin = -0.5*cell_thickness if args.three_d else 0 - si_zmax = 0.5*t_Si if args.three_d else 10 - si_zmin = -0.5*t_Si if args.three_d else -10 + cell_zmax = 0.5 * cell_thickness if args.three_d else 0 + cell_zmin = -0.5 * cell_thickness if args.three_d else 0 + si_zmax = 0.5 * t_Si if args.three_d else 10 + si_zmin = -0.5 * t_Si if args.three_d else -10 # read cell size, volumes for source region and flux monitors, # and coupler geometry from GDSII file - upper_branch = mp.get_GDSII_prisms(silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax) - lower_branch = mp.get_GDSII_prisms(silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax) + upper_branch = mp.get_GDSII_prisms( + silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax + ) + lower_branch = mp.get_GDSII_prisms( + silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax + ) cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax) p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax) @@ -45,14 +51,14 @@ def main(args): # displace upper and lower branches of coupler (as well as source and flux regions) if args.d != default_d: - delta_y = 0.5*(args.d-default_d) + delta_y = 0.5 * (args.d - default_d) delta = mp.Vector3(y=delta_y) p1.center += delta p2.center -= delta p3.center += delta p4.center -= delta src_vol.center += delta - cell.size += 2*delta + cell.size += 2 * delta for np in range(len(lower_branch)): lower_branch[np].center -= delta for nv in range(len(lower_branch[np].vertices)): @@ -62,23 +68,29 @@ def main(args): for nv in range(len(upper_branch[np].vertices)): upper_branch[np].vertices[nv] += delta - geometry = upper_branch+lower_branch + geometry = upper_branch + lower_branch if args.three_d: - oxide_center = mp.Vector3(z=-0.5*t_oxide) - oxide_size = mp.Vector3(cell.size.x,cell.size.y,t_oxide) + oxide_center = mp.Vector3(z=-0.5 * t_oxide) + oxide_size = mp.Vector3(cell.size.x, cell.size.y, t_oxide) oxide_layer = [mp.Block(material=oxide, center=oxide_center, size=oxide_size)] - geometry = geometry+oxide_layer - - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), - volume=src_vol, - eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z)] - - sim = mp.Simulation(resolution=args.res, - cell_size=cell.size, - boundary_layers=[mp.PML(dpml)], - sources=sources, - geometry=geometry) + geometry = geometry + oxide_layer + + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=df), + volume=src_vol, + eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z, + ) + ] + + sim = mp.Simulation( + resolution=args.res, + cell_size=cell.size, + boundary_layers=[mp.PML(dpml)], + sources=sources, + geometry=geometry, + ) mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1)) mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2)) @@ -88,22 +100,44 @@ def main(args): sim.run(until_after_sources=100) # S parameters - p1_coeff = sim.get_eigenmode_coefficients(mode1, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0] - p2_coeff = sim.get_eigenmode_coefficients(mode2, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,1] - p3_coeff = sim.get_eigenmode_coefficients(mode3, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0] - p4_coeff = sim.get_eigenmode_coefficients(mode4, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y+mp.ODD_Z).alpha[0,0,0] + p1_coeff = sim.get_eigenmode_coefficients( + mode1, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 0] + p2_coeff = sim.get_eigenmode_coefficients( + mode2, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 1] + p3_coeff = sim.get_eigenmode_coefficients( + mode3, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 0] + p4_coeff = sim.get_eigenmode_coefficients( + mode4, [1], eig_parity=mp.NO_PARITY if args.three_d else mp.EVEN_Y + mp.ODD_Z + ).alpha[0, 0, 0] # transmittance - p2_trans = abs(p2_coeff)**2/abs(p1_coeff)**2 - p3_trans = abs(p3_coeff)**2/abs(p1_coeff)**2 - p4_trans = abs(p4_coeff)**2/abs(p1_coeff)**2 + p2_trans = abs(p2_coeff) ** 2 / abs(p1_coeff) ** 2 + p3_trans = abs(p3_coeff) ** 2 / abs(p1_coeff) ** 2 + p4_trans = abs(p4_coeff) ** 2 / abs(p1_coeff) ** 2 + + print( + "trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}".format( + args.d, p2_trans, p3_trans, p4_trans + ) + ) - print("trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}".format(args.d,p2_trans,p3_trans,p4_trans)) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-res', type=int, default=50, help='resolution (default: 50 pixels/um)') - parser.add_argument('-d', type=float, default=0.1, help='branch separation (default: 0.1 um)') - parser.add_argument('--three_d', action='store_true', default=False, help='3d calculation? (default: False)') + parser.add_argument( + "-res", type=int, default=50, help="resolution (default: 50 pixels/um)" + ) + parser.add_argument( + "-d", type=float, default=0.1, help="branch separation (default: 0.1 um)" + ) + parser.add_argument( + "--three_d", + action="store_true", + default=False, + help="3d calculation? (default: False)", + ) args = parser.parse_args() main(args) diff --git a/python/examples/cyl-ellipsoid.py b/python/examples/cyl-ellipsoid.py index 3e0d50a34..090e31296 100644 --- a/python/examples/cyl-ellipsoid.py +++ b/python/examples/cyl-ellipsoid.py @@ -1,5 +1,3 @@ -from __future__ import division - import meep as mp @@ -9,7 +7,9 @@ def main(): e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf)) src_cmpt = mp.Hz - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), component=src_cmpt, center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.1), component=src_cmpt, center=mp.Vector3() + ) if src_cmpt == mp.Ez: symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] @@ -17,26 +17,30 @@ def main(): if src_cmpt == mp.Hz: symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)] - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), - geometry=[c, e], - boundary_layers=[mp.PML(1.0)], - sources=[sources], - symmetries=symmetries, - resolution=100) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), + geometry=[c, e], + boundary_layers=[mp.PML(1.0)], + sources=[sources], + symmetries=symmetries, + resolution=100, + ) def print_stuff(sim_obj): v = mp.Vector3(4.13, 3.75, 0) p = sim.get_field_point(src_cmpt, v) - print("t, Ez: {} {}+{}i".format(sim.round_time(), p.real, p.imag)) + print(f"t, Ez: {sim.round_time()} {p.real}+{p.imag}i") - sim.run(mp.at_beginning(mp.output_epsilon), - mp.at_every(0.25, print_stuff), - mp.at_end(print_stuff), - mp.at_end(mp.output_efield_z), - until=23) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.at_every(0.25, print_stuff), + mp.at_end(print_stuff), + mp.at_end(mp.output_efield_z), + until=23, + ) - print("stopped at meep time = {}".format(sim.round_time())) + print(f"stopped at meep time = {sim.round_time()}") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/cylinder_cross_section.py b/python/examples/cylinder_cross_section.py index 73960e545..159614982 100644 --- a/python/examples/cylinder_cross_section.py +++ b/python/examples/cylinder_cross_section.py @@ -1,51 +1,72 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp r = 0.7 # radius of cylinder h = 2.3 # height of cylinder -wvl_min = 2*np.pi*r/10 -wvl_max = 2*np.pi*r/2 +wvl_min = 2 * np.pi * r / 10 +wvl_max = 2 * np.pi * r / 2 -frq_min = 1/wvl_max -frq_max = 1/wvl_min -frq_cen = 0.5*(frq_min+frq_max) -dfrq = frq_max-frq_min +frq_min = 1 / wvl_max +frq_max = 1 / wvl_min +frq_cen = 0.5 * (frq_min + frq_max) +dfrq = frq_max - frq_min nfrq = 100 ## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min) resolution = 25 -dpml = 0.5*wvl_max -dair = 1.0*wvl_max +dpml = 0.5 * wvl_max +dair = 1.0 * wvl_max pml_layers = [mp.PML(thickness=dpml)] -sr = r+dair+dpml -sz = dpml+dair+h+dair+dpml -cell_size = mp.Vector3(sr,0,sz) - -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Er, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr)), - mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Ep, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr), - amplitude=-1j)] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1) - -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r))) -box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h))) +sr = r + dair + dpml +sz = dpml + dair + h + dair + dpml +cell_size = mp.Vector3(sr, 0, sz) + +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Er, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + ), + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Ep, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + amplitude=-1j, + ), +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, +) + +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)), +) +box_r = sim.add_flux( + frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h)) +) sim.run(until_after_sources=10) @@ -59,21 +80,39 @@ sim.reset_meep() n_cyl = 2.0 -geometry = [mp.Block(material=mp.Medium(index=n_cyl), - center=mp.Vector3(0.5*r), - size=mp.Vector3(r,0,h))] - -sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1) - -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r))) -box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h))) +geometry = [ + mp.Block( + material=mp.Medium(index=n_cyl), + center=mp.Vector3(0.5 * r), + size=mp.Vector3(r, 0, h), + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, +) + +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, -0.5 * h), size=mp.Vector3(r)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(0.5 * r, 0, +0.5 * h), size=mp.Vector3(r)), +) +box_r = sim.add_flux( + frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r), size=mp.Vector3(z=h)) +) sim.load_minus_flux_data(box_z1, box_z1_data) sim.load_minus_flux_data(box_z2, box_z2_data) @@ -85,16 +124,16 @@ box_z2_flux = mp.get_fluxes(box_z2) box_r_flux = mp.get_fluxes(box_r) -scatt_flux = np.asarray(box_z1_flux)-np.asarray(box_z2_flux)-np.asarray(box_r_flux) -intensity = np.asarray(box_z1_flux0)/(np.pi*r**2) -scatt_cross_section = np.divide(-scatt_flux,intensity) +scatt_flux = np.asarray(box_z1_flux) - np.asarray(box_z2_flux) - np.asarray(box_r_flux) +intensity = np.asarray(box_z1_flux0) / (np.pi * r**2) +scatt_cross_section = np.divide(-scatt_flux, intensity) if mp.am_master(): plt.figure(dpi=150) - plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_cross_section,'bo-') - plt.grid(True,which="both",ls="-") - plt.xlabel('(cylinder circumference)/wavelength, 2πr/λ') - plt.ylabel('scattering cross section, σ') - plt.title('Scattering Cross Section of a Lossless Dielectric Cylinder') + plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_cross_section, "bo-") + plt.grid(True, which="both", ls="-") + plt.xlabel("(cylinder circumference)/wavelength, 2πr/λ") + plt.ylabel("scattering cross section, σ") + plt.title("Scattering Cross Section of a Lossless Dielectric Cylinder") plt.tight_layout() plt.savefig("cylinder_cross_section.png") diff --git a/python/examples/differential_cross_section.py b/python/examples/differential_cross_section.py index 74c5e14b7..027a2a500 100644 --- a/python/examples/differential_cross_section.py +++ b/python/examples/differential_cross_section.py @@ -1,50 +1,79 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np import PyMieScatt as ps +import meep as mp + r = 1.0 # radius of sphere frq_cen = 1.0 -resolution = 20 # pixels/um +resolution = 20 # pixels/um dpml = 0.5 -dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor +dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor pml_layers = [mp.PML(thickness=dpml)] -s = 2*(dpml+dair+r) -cell_size = mp.Vector3(s,s,s) +s = 2 * (dpml + dair + r) +cell_size = mp.Vector3(s, s, s) # circularly-polarized source with propagation axis along x # is_integrated=True necessary for any planewave source extending into PML -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s,s), - component=mp.Ez), - mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s,s), - component=mp.Ey, - amplitude=1j)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3()) - -box_flux = sim.add_flux(frq_cen, 0, 1, - mp.FluxRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r))) - -nearfield_box = sim.add_near2far(frq_cen, 0, 1, - mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1)) +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s, s), + component=mp.Ez, + ), + mp.Source( + mp.GaussianSource(frq_cen, fwidth=0.2 * frq_cen, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s, s), + component=mp.Ey, + amplitude=1j, + ), +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), +) + +box_flux = sim.add_flux( + frq_cen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r)), +) + +nearfield_box = sim.add_near2far( + frq_cen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1 + ), +) sim.run(until_after_sources=10) @@ -54,49 +83,80 @@ sim.reset_meep() n_sphere = 2.0 -geometry = [mp.Sphere(material=mp.Medium(index=n_sphere), - center=mp.Vector3(), - radius=r)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - geometry=geometry) - -nearfield_box = sim.add_near2far(frq_cen, 0, 1, - mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1), - mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1), - mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1)) +geometry = [ + mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + geometry=geometry, +) + +nearfield_box = sim.add_near2far( + frq_cen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(x=-2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(x=+2 * r), size=mp.Vector3(0, 4 * r, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=-2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(y=+2 * r), size=mp.Vector3(4 * r, 0, 4 * r), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=-2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=+1 + ), + mp.Near2FarRegion( + center=mp.Vector3(z=+2 * r), size=mp.Vector3(4 * r, 4 * r, 0), weight=-1 + ), +) sim.load_minus_near2far_data(nearfield_box, nearfield_box_data) sim.run(until_after_sources=100) -npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle -angles = np.pi/npts*np.arange(npts) +npts = 100 # number of points in [0,pi) range of polar angles to sample far fields along semi-circle +angles = np.pi / npts * np.arange(npts) -ff_r = 10000*r # radius of far-field semi-circle +ff_r = 10000 * r # radius of far-field semi-circle -E = np.zeros((npts,3),dtype=np.complex128) -H = np.zeros((npts,3),dtype=np.complex128) +E = np.zeros((npts, 3), dtype=np.complex128) +H = np.zeros((npts, 3), dtype=np.complex128) for n in range(npts): - ff = sim.get_farfield(nearfield_box, ff_r*mp.Vector3(np.cos(angles[n]),0,np.sin(angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] - -Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1])) -Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2])) -Pz = np.real(np.multiply(E[:,0],H[:,1])-np.multiply(E[:,1],H[:,0])) -Pr = np.sqrt(np.square(Px)+np.square(Py)+np.square(Pz)) - -intensity = input_flux/(4*r)**2 + ff = sim.get_farfield( + nearfield_box, ff_r * mp.Vector3(np.cos(angles[n]), 0, np.sin(angles[n])) + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] + +Px = np.real(np.multiply(E[:, 1], H[:, 2]) - np.multiply(E[:, 2], H[:, 1])) +Py = np.real(np.multiply(E[:, 2], H[:, 0]) - np.multiply(E[:, 0], H[:, 2])) +Pz = np.real(np.multiply(E[:, 0], H[:, 1]) - np.multiply(E[:, 1], H[:, 0])) +Pr = np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz)) + +intensity = input_flux / (4 * r) ** 2 diff_cross_section = ff_r**2 * Pr / intensity -scatt_cross_section_meep = 2*np.pi * np.sum(np.multiply(diff_cross_section,np.sin(angles))) * np.pi/npts -scatt_cross_section_theory = ps.MieQ(n_sphere,1000/frq_cen,2*r*1000,asDict=True,asCrossSection=True)['Csca']*1e-6 # units of um^2 - -print("scatt:, {:.16f} (meep), {:.16f} (theory)".format(scatt_cross_section_meep,scatt_cross_section_theory)) +scatt_cross_section_meep = ( + 2 * np.pi * np.sum(np.multiply(diff_cross_section, np.sin(angles))) * np.pi / npts +) +scatt_cross_section_theory = ( + ps.MieQ(n_sphere, 1000 / frq_cen, 2 * r * 1000, asDict=True, asCrossSection=True)[ + "Csca" + ] + * 1e-6 +) # units of um^2 + +print( + "scatt:, {:.16f} (meep), {:.16f} (theory)".format( + scatt_cross_section_meep, scatt_cross_section_theory + ) +) diff --git a/python/examples/diffracted_planewave.py b/python/examples/diffracted_planewave.py index c9a059db9..572f12443 100644 --- a/python/examples/diffracted_planewave.py +++ b/python/examples/diffracted_planewave.py @@ -1,152 +1,180 @@ ### compute the transmitted diffraction orders of a binary grating using mode decomposition ### based on two different methods: (1) MPB eigensolver and (2) DiffractedPlanewave object. ### Also, verify that the total power in all the orders is equivalent to the Poynting flux. - ### for normal incidence, compute only positive diff. orders (total transmittance <= 0.50) ### for oblique incidence, compute ALL diff. orders (total transmittance <= 1.00) - -import meep as mp -import math import cmath +import math + import numpy as np +import meep as mp + + def binary_grating_diffraction(gp, gh, gdc, theta): - resolution = 50 # pixels/μm + resolution = 50 # pixels/μm - dpml = 1.0 # PML thickness - dsub = 3.0 # substrate thickness - dpad = 3.0 # length of padding between grating and PML - - sx = dpml+dsub+gh+dpad+dpml - sy = gp - - cell_size = mp.Vector3(sx,sy,0) - pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] - - wvl = 0.5 # center wavelength - fcen = 1/wvl # center frequency - df = 0.05*fcen # frequency width - - ng = 1.5 - glass = mp.Medium(index=ng) - - # rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - eig_parity = mp.EVEN_Z - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(fcen*ng).rotate(mp.Vector3(z=1), theta_in) - - symmetries = [] - if theta_in == 0: - k = mp.Vector3() - eig_parity += mp.ODD_Y - symmetries = [mp.Mirror(direction=mp.Y,phase=-1)] - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*sx+dpml,0,0) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Hz, - center=src_pt, - size=mp.Vector3(0,sy,0), - amp_func=pw_amp(k,src_pt))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k, - default_material=glass, - sources=sources, - symmetries=symmetries) - - tran_pt = mp.Vector3(0.5*sx-dpml,0,0) - tran_mon = sim.add_flux(fcen, 0, 1, - mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=50) - - input_flux = mp.get_fluxes(tran_mon) - - sim.reset_meep() - - geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)), - mp.Block(material=glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - - tran_mon = sim.add_mode_monitor(fcen, 0, 1, - mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=100) - - # number of (non-evanescent) transmitted orders - nm_t = np.floor((fcen-k.y)*gp)-np.ceil((-fcen-k.y)*gp) - if theta_in == 0: - nm_t = nm_t/2 - nm_t = int(nm_t)+1 - - bands = range(1,nm_t+1) - - if theta_in == 0: - orders = range(0,nm_t) - else: - orders = range(int(np.ceil((-fcen-k.y)*gp)),int(np.floor((fcen-k.y)*gp))+1) - - eig_sum = 0 - dp_sum = 0 - - for band,order in zip(bands,orders): - res = sim.get_eigenmode_coefficients(tran_mon, [band], eig_parity=eig_parity) - if res is not None: - tran_eig = abs(res.alpha[0,0,0])**2/input_flux[0] - if theta_in == 0: - tran_eig = 0.5*tran_eig - else: - tran_eig = 0 - eig_sum += tran_eig - - res = sim.get_eigenmode_coefficients(tran_mon, mp.DiffractedPlanewave((0,order,0),mp.Vector3(0,1,0),0,1)) - if res is not None: - tran_dp = abs(res.alpha[0,0,0])**2/input_flux[0] - if (theta_in == 0) and (order == 0): - tran_dp = 0.5*tran_dp - else: - tran_dp = 0 - dp_sum += tran_dp + dpml = 1.0 # PML thickness + dsub = 3.0 # substrate thickness + dpad = 3.0 # length of padding between grating and PML + + sx = dpml + dsub + gh + dpad + dpml + sy = gp + + cell_size = mp.Vector3(sx, sy, 0) + pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] + wvl = 0.5 # center wavelength + fcen = 1 / wvl # center frequency + df = 0.05 * fcen # frequency width + + ng = 1.5 + glass = mp.Medium(index=ng) + + # rotation angle of incident planewave; counter clockwise (CCW) about Z axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + eig_parity = mp.EVEN_Z + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(fcen * ng).rotate(mp.Vector3(z=1), theta_in) + + symmetries = [] + if theta_in == 0: + k = mp.Vector3() + eig_parity += mp.ODD_Y + symmetries = [mp.Mirror(direction=mp.Y, phase=-1)] + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * sx + dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=src_pt, + size=mp.Vector3(0, sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k, + default_material=glass, + sources=sources, + symmetries=symmetries, + ) + + tran_pt = mp.Vector3(0.5 * sx - dpml, 0, 0) + tran_mon = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=50) + + input_flux = mp.get_fluxes(tran_mon) + + sim.reset_meep() + + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub), 0, 0), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh, 0, 0), + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + ) + + tran_mon = sim.add_mode_monitor( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run(until_after_sources=100) + + # number of (non-evanescent) transmitted orders + nm_t = np.floor((fcen - k.y) * gp) - np.ceil((-fcen - k.y) * gp) if theta_in == 0: - err = abs(tran_eig-tran_dp)/tran_eig - print("tran:, {:2d}, {:.8f}, {:2d}, {:.8f}, {:.8f}".format(band,tran_eig,order,tran_dp,err)) + nm_t = nm_t / 2 + nm_t = int(nm_t) + 1 + + bands = range(1, nm_t + 1) + + if theta_in == 0: + orders = range(0, nm_t) else: - print("tran:, {:2d}, {:.8f}, {:2d}, {:.8f}".format(band,tran_eig,order,tran_dp)) - - flux = mp.get_fluxes(tran_mon) - t_flux = flux[0]/input_flux[0] - if (theta_in == 0): - t_flux = 0.5*t_flux - - err = abs(dp_sum-t_flux)/t_flux - print("flux:, {:.8f}, {:.8f}, {:.8f}, {:.8f}".format(eig_sum, - dp_sum, - t_flux, - err)) - -if __name__ == '__main__': - binary_grating_diffraction(2.6,0.4,0.3,0) - binary_grating_diffraction(3.7,0.6,0.4,13.5) + orders = range( + int(np.ceil((-fcen - k.y) * gp)), int(np.floor((fcen - k.y) * gp)) + 1 + ) + + eig_sum = 0 + dp_sum = 0 + + for band, order in zip(bands, orders): + res = sim.get_eigenmode_coefficients(tran_mon, [band], eig_parity=eig_parity) + if res is not None: + tran_eig = abs(res.alpha[0, 0, 0]) ** 2 / input_flux[0] + if theta_in == 0: + tran_eig = 0.5 * tran_eig + else: + tran_eig = 0 + eig_sum += tran_eig + + res = sim.get_eigenmode_coefficients( + tran_mon, mp.DiffractedPlanewave((0, order, 0), mp.Vector3(0, 1, 0), 0, 1) + ) + if res is not None: + tran_dp = abs(res.alpha[0, 0, 0]) ** 2 / input_flux[0] + if (theta_in == 0) and (order == 0): + tran_dp = 0.5 * tran_dp + else: + tran_dp = 0 + dp_sum += tran_dp + + if theta_in == 0: + err = abs(tran_eig - tran_dp) / tran_eig + print( + "tran:, {:2d}, {:.8f}, {:2d}, {:.8f}, {:.8f}".format( + band, tran_eig, order, tran_dp, err + ) + ) + else: + print( + "tran:, {:2d}, {:.8f}, {:2d}, {:.8f}".format( + band, tran_eig, order, tran_dp + ) + ) + + flux = mp.get_fluxes(tran_mon) + t_flux = flux[0] / input_flux[0] + if theta_in == 0: + t_flux = 0.5 * t_flux + + err = abs(dp_sum - t_flux) / t_flux + print(f"flux:, {eig_sum:.8f}, {dp_sum:.8f}, {t_flux:.8f}, {err:.8f}") + + +if __name__ == "__main__": + binary_grating_diffraction(2.6, 0.4, 0.3, 0) + binary_grating_diffraction(3.7, 0.6, 0.4, 13.5) diff --git a/python/examples/eps_fit_lorentzian.py b/python/examples/eps_fit_lorentzian.py index 8b825db69..68852eb65 100644 --- a/python/examples/eps_fit_lorentzian.py +++ b/python/examples/eps_fit_lorentzian.py @@ -1,98 +1,109 @@ # fit complex refractive index profile over broad bandwidth imported from # a file to a sum of Lorentzian polarizability terms using gradient-based # optimization via NLopt (nlopt.readthedocs.io) - +import matplotlib import nlopt import numpy as np -import matplotlib -matplotlib.use('agg') + +matplotlib.use("agg") import matplotlib.pyplot as plt + import meep as mp def lorentzfunc(p, x): """Return the complex ε profile given a set of Lorentzian parameters p - (σ_0, ω_0, γ_0, σ_1, ω_1, γ_1, ...) for a set of frequencies x. + (σ_0, ω_0, γ_0, σ_1, ω_1, γ_1, ...) for a set of frequencies x. """ - N = int(len(p)/3) + N = len(p) // 3 y = np.zeros(len(x)) for n in range(N): - A_n = p[3*n+0] - x_n = p[3*n+1] - g_n = p[3*n+2] - y = y + A_n/(np.square(x_n) - np.square(x) - 1j*x*g_n) + A_n = p[3 * n + 0] + x_n = p[3 * n + 1] + g_n = p[3 * n + 2] + y = y + A_n / (np.square(x_n) - np.square(x) - 1j * x * g_n) return y + def lorentzerr(p, x, y, grad): """Return the error (or residual or loss funnction) as the L2 norm - of the difference of ε(p,x) and y over a set of frequencies x as - well as the gradient of this error with respect to each Lorentzian - polarizability parameter in p and saving the result in grad. + of the difference of ε(p,x) and y over a set of frequencies x as + well as the gradient of this error with respect to each Lorentzian + polarizability parameter in p and saving the result in grad. """ - N = int(len(p)/3) + N = len(p) // 3 yp = lorentzfunc(p, x) val = np.sum(np.square(abs(y - yp))) for n in range(N): - A_n = p[3*n+0] - x_n = p[3*n+1] - g_n = p[3*n+2] - d = 1 / (np.square(x_n) - np.square(x) - 1j*x*g_n) + A_n = p[3 * n + 0] + x_n = p[3 * n + 1] + g_n = p[3 * n + 2] + d = 1 / (np.square(x_n) - np.square(x) - 1j * x * g_n) if grad.size > 0: - grad[3*n+0] = 2 * np.real(np.dot(np.conj(yp - y), d)) - grad[3*n+1] = -4 * x_n * A_n * np.real(np.dot(np.conj(yp - y), np.square(d))) - grad[3*n+2] = -2 * A_n * np.imag(np.dot(np.conj(yp - y), x * np.square(d))) + grad[3 * n + 0] = 2 * np.real(np.dot(np.conj(yp - y), d)) + grad[3 * n + 1] = ( + -4 * x_n * A_n * np.real(np.dot(np.conj(yp - y), np.square(d))) + ) + grad[3 * n + 2] = ( + -2 * A_n * np.imag(np.dot(np.conj(yp - y), x * np.square(d))) + ) return val + def lorentzfit(p0, x, y, alg=nlopt.LD_LBFGS, tol=1e-25, maxeval=10000): """Return the optimal Lorentzian polarizability parameters and error - which minimize the error in ε(p0,x) relative to y for an initial - set of Lorentzian polarizability parameters p0 over a set of - frequencies x using the NLopt algorithm alg for a relative - tolerance tol and a maximum number of iterations maxeval. + which minimize the error in ε(p0,x) relative to y for an initial + set of Lorentzian polarizability parameters p0 over a set of + frequencies x using the NLopt algorithm alg for a relative + tolerance tol and a maximum number of iterations maxeval. """ opt = nlopt.opt(alg, len(p0)) opt.set_ftol_rel(tol) opt.set_maxeval(maxeval) opt.set_lower_bounds(np.zeros(len(p0))) - opt.set_upper_bounds(float('inf')*np.ones(len(p0))) + opt.set_upper_bounds(float("inf") * np.ones(len(p0))) opt.set_min_objective(lambda p, grad: lorentzerr(p, x, y, grad)) local_opt = nlopt.opt(nlopt.LD_LBFGS, len(p0)) local_opt.set_ftol_rel(1e-10) local_opt.set_xtol_rel(1e-8) opt.set_local_optimizer(local_opt) - popt = opt.optimize(p0); + popt = opt.optimize(p0) minf = opt.last_optimum_value() return popt, minf # format of input data file is three comma-separated columns: # wavelength (nm), real(n), complex(n) -mydata = np.genfromtxt('mymaterial.csv',delimiter=',') -n = mydata[:,1] + 1j*mydata[:,2] -eps_inf = 1.1 # should be >= 1.0 for stability and chosen such that np.amin(np.real(eps)) is ~1.0 +mydata = np.genfromtxt("mymaterial.csv", delimiter=",") +n = mydata[:, 1] + 1j * mydata[:, 2] +eps_inf = 1.1 # should be >= 1.0 for stability and chosen such that np.amin(np.real(eps)) is ~1.0 eps = np.square(n) - eps_inf -wl = mydata[:,0] -wl_min = 399 # minimum wavelength (units of nm) -wl_max = 701 # maximum wavelength (units of nm) +wl = mydata[:, 0] +wl_min = 399 # minimum wavelength (units of nm) +wl_max = 701 # maximum wavelength (units of nm) start_idx = np.where(wl > wl_min) idx_start = start_idx[0][0] end_idx = np.where(wl < wl_max) -idx_end = end_idx[0][-1]+1 -freqs = 1000/wl # units of 1/μm +idx_end = end_idx[0][-1] + 1 +freqs = 1000 / wl # units of 1/μm freqs_reduced = freqs[idx_start:idx_end] wl_reduced = wl[idx_start:idx_end] eps_reduced = eps[idx_start:idx_end] num_lorentzians = 2 -num_repeat = 30 # number of times to repeat local optimization with random initial values -ps = np.zeros((num_repeat,3*num_lorentzians)) +num_repeat = ( + 30 # number of times to repeat local optimization with random initial values +) +ps = np.zeros((num_repeat, 3 * num_lorentzians)) mins = np.zeros(num_repeat) for m in range(num_repeat): # specify Lorentzian polarizability terms each consisting of three parameters (σ_0, ω_0, γ_0) # with random initial values # note: for the case of no absorption, set γ (every third parameter) to zero - p_rand = [10**(np.random.random()) for _ in range(3*num_lorentzians)] - ps[m,:], mins[m] = lorentzfit(p_rand, freqs_reduced, eps_reduced, nlopt.LD_MMA, 1e-25, 50000) + p_rand = [10 ** (np.random.random()) for _ in range(3 * num_lorentzians)] + ps[m, :], mins[m] = lorentzfit( + p_rand, freqs_reduced, eps_reduced, nlopt.LD_MMA, 1e-25, 50000 + ) print(f"iteration{m}:, {ps[m,:]}, {mins[m]}") # find the best performing set of parameters @@ -104,42 +115,49 @@ def lorentzfit(p0, x, y, alg=nlopt.LD_LBFGS, tol=1e-25, maxeval=10000): E_susceptibilities = [] for n in range(num_lorentzians): - mymaterial_freq = ps[idx_opt][0][3*n+1] - mymaterial_gamma = ps[idx_opt][0][3*n+2] + mymaterial_freq = ps[idx_opt][0][3 * n + 1] + mymaterial_gamma = ps[idx_opt][0][3 * n + 2] if mymaterial_freq == 0: - mymaterial_sigma = ps[idx_opt][0][3*n+0] - E_susceptibilities.append(mp.DrudeSusceptibility(frequency=1.0, - gamma=mymaterial_gamma, - sigma=mymaterial_sigma)) + mymaterial_sigma = ps[idx_opt][0][3 * n + 0] + E_susceptibilities.append( + mp.DrudeSusceptibility( + frequency=1.0, gamma=mymaterial_gamma, sigma=mymaterial_sigma + ) + ) else: - mymaterial_sigma = ps[idx_opt][0][3*n+0] / mymaterial_freq**2 - E_susceptibilities.append(mp.LorentzianSusceptibility(frequency=mymaterial_freq, - gamma=mymaterial_gamma, - sigma=mymaterial_sigma)) + mymaterial_sigma = ps[idx_opt][0][3 * n + 0] / mymaterial_freq**2 + E_susceptibilities.append( + mp.LorentzianSusceptibility( + frequency=mymaterial_freq, + gamma=mymaterial_gamma, + sigma=mymaterial_sigma, + ) + ) -mymaterial = mp.Medium(epsilon=eps_inf, - E_susceptibilities=E_susceptibilities) +mymaterial = mp.Medium(epsilon=eps_inf, E_susceptibilities=E_susceptibilities) # plot the fit and the actual data for comparison mymaterial_eps = [mymaterial.epsilon(f)[0][0] for f in freqs_reduced] -plt.subplot(1,2,1) -plt.plot(wl_reduced,np.real(eps_reduced),'bo-',label='actual') -plt.plot(wl_reduced,np.real(mymaterial_eps),'ro-',label='fit') -plt.xlabel('wavelength (nm)') -plt.ylabel(r'real($\epsilon$)') +plt.subplot(1, 2, 1) +plt.plot(wl_reduced, np.real(eps_reduced), "bo-", label="actual") +plt.plot(wl_reduced, np.real(mymaterial_eps), "ro-", label="fit") +plt.xlabel("wavelength (nm)") +plt.ylabel(r"real($\epsilon$)") plt.legend() -plt.subplot(1,2,2) -plt.plot(wl_reduced,np.imag(eps_reduced),'bo-',label='actual') -plt.plot(wl_reduced,np.imag(mymaterial_eps),'ro-',label='fit') -plt.xlabel('wavelength (nm)') -plt.ylabel(r'imag($\epsilon$)') +plt.subplot(1, 2, 2) +plt.plot(wl_reduced, np.imag(eps_reduced), "bo-", label="actual") +plt.plot(wl_reduced, np.imag(mymaterial_eps), "ro-", label="fit") +plt.xlabel("wavelength (nm)") +plt.ylabel(r"imag($\epsilon$)") plt.legend() -plt.suptitle('Comparison of Actual Material Data and Fit using Drude-Lorentzian Susceptibility') +plt.suptitle( + "Comparison of Actual Material Data and Fit using Drude-Lorentzian Susceptibility" +) plt.subplots_adjust(wspace=0.3) -plt.savefig('eps_fit_sample.png',dpi=150,bbox_inches='tight') +plt.savefig("eps_fit_sample.png", dpi=150, bbox_inches="tight") diff --git a/python/examples/faraday-rotation.py b/python/examples/faraday-rotation.py index 449c18b10..9a607d381 100644 --- a/python/examples/faraday-rotation.py +++ b/python/examples/faraday-rotation.py @@ -1,16 +1,18 @@ # From the Meep tutorial: plotting Faraday rotation of a linearly polarized plane wave - import meep as mp ## Parameters for a gyrotropic Lorentzian medium -epsn = 1.5 # background permittivity -f0 = 1.0 # natural frequency -gamma = 1e-6 # damping rate -sn = 0.1 # sigma parameter -b0 = 0.15 # magnitude of bias vector +epsn = 1.5 # background permittivity +f0 = 1.0 # natural frequency +gamma = 1e-6 # damping rate +sn = 0.1 # sigma parameter +b0 = 0.15 # magnitude of bias vector -susc = [mp.GyrotropicLorentzianSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - bias=mp.Vector3(0, 0, b0))] +susc = [ + mp.GyrotropicLorentzianSusceptibility( + frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0) + ) +] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) ## Set up and run the Meep simulation: @@ -20,50 +22,64 @@ fsrc, src_z = 0.8, -8.5 pml_layers = [mp.PML(thickness=1.0, direction=mp.Z)] -sources = [mp.Source(mp.ContinuousSource(frequency=fsrc), - component=mp.Ex, center=mp.Vector3(0, 0, src_z))] +sources = [ + mp.Source( + mp.ContinuousSource(frequency=fsrc), + component=mp.Ex, + center=mp.Vector3(0, 0, src_z), + ) +] -sim = mp.Simulation(cell_size=cell, geometry=[], sources=sources, - boundary_layers=pml_layers, - default_material=mat, resolution=50) +sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=sources, + boundary_layers=pml_layers, + default_material=mat, + resolution=50, +) sim.run(until=tmax) +import matplotlib.pyplot as plt + ## Plot results: import numpy as np -import matplotlib.pyplot as plt ex_data = sim.get_efield_x().real ey_data = sim.get_efield_y().real -z = np.linspace(-L/2, L/2, len(ex_data)) +z = np.linspace(-L / 2, L / 2, len(ex_data)) plt.figure(1) -plt.plot(z, ex_data, label='Ex') -plt.plot(z, ey_data, label='Ey') -plt.xlim(-L/2, L/2); plt.xlabel('z') +plt.plot(z, ex_data, label="Ex") +plt.plot(z, ey_data, label="Ey") +plt.xlim(-L / 2, L / 2) +plt.xlabel("z") plt.legend() ## Comparison with analytic result: -dfsq = (f0**2 - 1j*fsrc*gamma - fsrc**2) -eperp = epsn + sn * f0**2 * dfsq / (dfsq**2 - (fsrc*b0)**2) -eta = sn * f0**2 * fsrc * b0 / (dfsq**2 - (fsrc*b0)**2) +dfsq = f0**2 - 1j * fsrc * gamma - fsrc**2 +eperp = epsn + sn * f0**2 * dfsq / (dfsq**2 - (fsrc * b0) ** 2) +eta = sn * f0**2 * fsrc * b0 / (dfsq**2 - (fsrc * b0) ** 2) -k_gyro = 2*np.pi*fsrc * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) +k_gyro = 2 * np.pi * fsrc * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) Ex_theory = 0.37 * np.cos(k_gyro * (z - src_z)).real Ey_theory = 0.37 * np.sin(k_gyro * (z - src_z)).real plt.figure(2) -plt.subplot(2,1,1) -plt.plot(z, ex_data, label='Ex (MEEP)') -plt.plot(z, Ex_theory, 'k--') -plt.plot(z, -Ex_theory, 'k--', label='Ex envelope (theory)') -plt.xlim(-L/2, L/2); plt.xlabel('z') -plt.legend(loc='lower right') +plt.subplot(2, 1, 1) +plt.plot(z, ex_data, label="Ex (MEEP)") +plt.plot(z, Ex_theory, "k--") +plt.plot(z, -Ex_theory, "k--", label="Ex envelope (theory)") +plt.xlim(-L / 2, L / 2) +plt.xlabel("z") +plt.legend(loc="lower right") -plt.subplot(2,1,2) -plt.plot(z, ey_data, label='Ey (MEEP)') -plt.plot(z, Ey_theory, 'k--') -plt.plot(z, -Ey_theory, 'k--', label='Ey envelope (theory)') -plt.xlim(-L/2, L/2); plt.xlabel('z') -plt.legend(loc='lower right') +plt.subplot(2, 1, 2) +plt.plot(z, ey_data, label="Ey (MEEP)") +plt.plot(z, Ey_theory, "k--") +plt.plot(z, -Ey_theory, "k--", label="Ey envelope (theory)") +plt.xlim(-L / 2, L / 2) +plt.xlabel("z") +plt.legend(loc="lower right") plt.tight_layout() plt.show() diff --git a/python/examples/finite_grating.py b/python/examples/finite_grating.py index e1cdf1eee..e83b25a19 100644 --- a/python/examples/finite_grating.py +++ b/python/examples/finite_grating.py @@ -1,27 +1,29 @@ -import meep as mp -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp # True: plot the scattered fields in the extended air region adjacent to the grating # False: plot the diffraction spectra based on a 1d cross section of the scattered fields field_profile = True -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 2.0 # substrate thickness -dpad = 1.0 # flat-surface padding -gp = 1.0 # grating periodicity -gh = 0.5 # grating height -gdc = 0.5 # grating duty cycle -num_cells = 5 # number of grating unit cells +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dpad = 1.0 # flat-surface padding +gp = 1.0 # grating periodicity +gh = 0.5 # grating height +gdc = 0.5 # grating duty cycle +num_cells = 5 # number of grating unit cells # air region thickness adjacent to grating dair = 10 if field_profile else dpad -wvl = 0.5 # center wavelength -fcen = 1/wvl # center frequency +wvl = 0.5 # center wavelength +fcen = 1 / wvl # center frequency k_point = mp.Vector3() @@ -29,32 +31,49 @@ pml_layers = [mp.PML(thickness=dpml)] -symmetries=[mp.Mirror(mp.Y)] - -sx = dpml+dsub+gh+dair+dpml -sy = dpml+dpad+num_cells*gp+dpad+dpml -cell_size = mp.Vector3(sx,sy) - -src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen,is_integrated=True), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - -geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dair) -near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml)) +symmetries = [mp.Mirror(mp.Y)] + +sx = dpml + dsub + gh + dair + dpml +sy = dpml + dpad + num_cells * gp + dpad + dpml +cell_size = mp.Vector3(sx, sy) + +src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.2 * fcen, is_integrated=True), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) +] + +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dair) +near_fields = sim.add_dft_fields( + [mp.Ez], + fcen, + 0, + 1, + center=mon_pt, + size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml), +) sim.run(until_after_sources=100) @@ -62,63 +81,86 @@ sim.reset_meep() -for j in range(num_cells): - geometry.append(mp.Block(material=glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+dpml+dpad+(j+0.5)*gp))) - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - -near_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mon_pt, size=mp.Vector3(dair if field_profile else 0,sy-2*dpml)) +geometry.extend( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3( + -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + dpml + dpad + (j + 0.5) * gp + ), + ) + for j in range(num_cells) +) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, +) + +near_fields = sim.add_dft_fields( + [mp.Ez], + fcen, + 0, + 1, + center=mon_pt, + size=mp.Vector3(dair if field_profile else 0, sy - 2 * dpml), +) sim.run(until_after_sources=100) grating_dft = sim.get_dft_array(near_fields, mp.Ez, 0) -scattered_field = grating_dft-flat_dft -scattered_amplitude = np.abs(scattered_field)**2 +scattered_field = grating_dft - flat_dft +scattered_amplitude = np.abs(scattered_field) ** 2 -[x,y,z,w] = sim.get_array_metadata(dft_cell=near_fields) +[x, y, z, w] = sim.get_array_metadata(dft_cell=near_fields) if field_profile: - if mp.am_master(): - plt.figure(dpi=150) - plt.pcolormesh(x,y,np.rot90(scattered_amplitude),cmap='inferno',shading='gouraud',vmin=0,vmax=scattered_amplitude.max()) - plt.gca().set_aspect('equal') - plt.xlabel('x (μm)') - plt.ylabel('y (μm)') - - # ensure that the height of the colobar matches that of the plot - from mpl_toolkits.axes_grid1 import make_axes_locatable - divider = make_axes_locatable(plt.gca()) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(cax=cax) - plt.tight_layout() - plt.show() + if mp.am_master(): + plt.figure(dpi=150) + plt.pcolormesh( + x, + y, + np.rot90(scattered_amplitude), + cmap="inferno", + shading="gouraud", + vmin=0, + vmax=scattered_amplitude.max(), + ) + plt.gca().set_aspect("equal") + plt.xlabel("x (μm)") + plt.ylabel("y (μm)") + + # ensure that the height of the colobar matches that of the plot + from mpl_toolkits.axes_grid1 import make_axes_locatable + + divider = make_axes_locatable(plt.gca()) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(cax=cax) + plt.tight_layout() + plt.show() else: - ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1/resolution)) - FT_scattered_field = np.fft.fftshift(np.fft.fft(scattered_field)) - if mp.am_master(): - plt.figure(dpi=150) - plt.subplots_adjust(hspace=0.3) - - plt.subplot(2,1,1) - plt.plot(y,scattered_amplitude,'bo-') - plt.xlabel("y (μm)") - plt.ylabel("field amplitude") - - plt.subplot(2,1,2) - plt.plot(ky,np.abs(FT_scattered_field)**2,'ro-') - plt.gca().ticklabel_format(axis='y',style='sci',scilimits=(0,0)) - plt.xlabel(r'wavevector k$_y$, 2π (μm)$^{-1}$') - plt.ylabel("Fourier transform") - plt.gca().set_xlim([-3, 3]) - - plt.tight_layout(pad=1.0) - plt.show() + ky = np.fft.fftshift(np.fft.fftfreq(len(scattered_field), 1 / resolution)) + FT_scattered_field = np.fft.fftshift(np.fft.fft(scattered_field)) + if mp.am_master(): + plt.figure(dpi=150) + plt.subplots_adjust(hspace=0.3) + + plt.subplot(2, 1, 1) + plt.plot(y, scattered_amplitude, "bo-") + plt.xlabel("y (μm)") + plt.ylabel("field amplitude") + + plt.subplot(2, 1, 2) + plt.plot(ky, np.abs(FT_scattered_field) ** 2, "ro-") + plt.gca().ticklabel_format(axis="y", style="sci", scilimits=(0, 0)) + plt.xlabel(r"wavevector k$_y$, 2π (μm)$^{-1}$") + plt.ylabel("Fourier transform") + plt.gca().set_xlim([-3, 3]) + + plt.tight_layout(pad=1.0) + plt.show() diff --git a/python/examples/gaussian-beam.py b/python/examples/gaussian-beam.py index bc2747718..42c00f4d4 100644 --- a/python/examples/gaussian-beam.py +++ b/python/examples/gaussian-beam.py @@ -1,42 +1,55 @@ ## launch a Gaussian beam - -import meep as mp import math + import matplotlib -matplotlib.use('agg') + +import meep as mp + +matplotlib.use("agg") import matplotlib.pyplot as plt s = 14 resolution = 50 dpml = 2 -cell_size = mp.Vector3(s,s) +cell_size = mp.Vector3(s, s) boundary_layers = [mp.PML(thickness=dpml)] -beam_x0 = mp.Vector3(0,3.0) # beam focus (relative to source center) +beam_x0 = mp.Vector3(0, 3.0) # beam focus (relative to source center) rot_angle = 0 # CCW rotation angle about z axis (0: +y axis) -beam_kdir = mp.Vector3(0,1,0).rotate(mp.Vector3(0,0,1),math.radians(rot_angle)) # beam propagation direction +beam_kdir = mp.Vector3(0, 1, 0).rotate( + mp.Vector3(0, 0, 1), math.radians(rot_angle) +) # beam propagation direction beam_w0 = 0.8 # beam waist radius -beam_E0 = mp.Vector3(0,0,1) +beam_E0 = mp.Vector3(0, 0, 1) fcen = 1 -sources = [mp.GaussianBeamSource(src=mp.ContinuousSource(fcen), - center=mp.Vector3(0,-0.5*s+dpml+1.0), - size=mp.Vector3(s), - beam_x0=beam_x0, - beam_kdir=beam_kdir, - beam_w0=beam_w0, - beam_E0=beam_E0)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources) +sources = [ + mp.GaussianBeamSource( + src=mp.ContinuousSource(fcen), + center=mp.Vector3(0, -0.5 * s + dpml + 1.0), + size=mp.Vector3(s), + beam_x0=beam_x0, + beam_kdir=beam_kdir, + beam_w0=beam_w0, + beam_E0=beam_E0, + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, +) sim.run(until=20) -sim.plot2D(fields=mp.Ez, - output_plane=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(s-2*dpml,s-2*dpml))) +sim.plot2D( + fields=mp.Ez, + output_plane=mp.Volume( + center=mp.Vector3(), size=mp.Vector3(s - 2 * dpml, s - 2 * dpml) + ), +) -plt.savefig('Ez_angle{}.png'.format(rot_angle),bbox_inches='tight',pad_inches=0) +plt.savefig(f"Ez_angle{rot_angle}.png", bbox_inches="tight", pad_inches=0) diff --git a/python/examples/grating2d_triangular_lattice.py b/python/examples/grating2d_triangular_lattice.py index 6ea00dc7a..44e7bd916 100644 --- a/python/examples/grating2d_triangular_lattice.py +++ b/python/examples/grating2d_triangular_lattice.py @@ -2,18 +2,19 @@ # triangular lattice using a rectangular supercell and verifies # that only the diffraction orders of the actual unit cell # produce non-zero power (up to discretization error) - -import meep as mp import math + import numpy as np +import meep as mp + resolution = 100 # pixels/μm ng = 1.5 glass = mp.Medium(index=ng) wvl = 0.5 # wavelength -fcen = 1/wvl +fcen = 1 / wvl # rectangular supercell sx = 1.0 @@ -25,63 +26,83 @@ hcyl = 0.5 # cylinder height rcyl = 0.1 # cylinder radius -sz = dpml+dsub+hcyl+dair+dpml +sz = dpml + dsub + hcyl + dair + dpml -cell_size = mp.Vector3(sx,sy,sz) +cell_size = mp.Vector3(sx, sy, sz) -boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] +boundary_layers = [mp.PML(thickness=dpml, direction=mp.Z)] # periodic boundary conditions k_point = mp.Vector3() -src_pt = mp.Vector3(0,0,-0.5*sz+dpml) -sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.1*fcen), - size=mp.Vector3(sx,sy,0), - center=src_pt, - component=mp.Ex)] - -substrate = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), - center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), - material=glass)] - -cyl_grating = [mp.Cylinder(center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass)] +src_pt = mp.Vector3(0, 0, -0.5 * sz + dpml) +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.1 * fcen), + size=mp.Vector3(sx, sy, 0), + center=src_pt, + component=mp.Ex, + ) +] + +substrate = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, dpml + dsub), + center=mp.Vector3(0, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + material=glass, + ) +] + +cyl_grating = [ + mp.Cylinder( + center=mp.Vector3(0, 0, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(-0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(-0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3(0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), +] geometry = substrate + cyl_grating -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - geometry=geometry, - boundary_layers=boundary_layers, - k_point=k_point) +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k_point, +) -tran_pt = mp.Vector3(0,0,0.5*sz-dpml) -tran_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=tran_pt, - size=mp.Vector3(sx,sy,0))) +tran_pt = mp.Vector3(0, 0, 0.5 * sz - dpml) +tran_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=tran_pt, size=mp.Vector3(sx, sy, 0)) +) -sim.run(until_after_sources=mp.stop_when_fields_decayed(20,mp.Ex,src_pt,1e-6)) +sim.run(until_after_sources=mp.stop_when_fields_decayed(20, mp.Ex, src_pt, 1e-6)) # diffraction order of unit cell (triangular lattice) mx = 0 @@ -92,14 +113,12 @@ # only even orders should produce nonzero power nx = mx for ny in range(4): - kz2 = fcen**2-(nx/sx)**2-(ny/sy)**2 + kz2 = fcen**2 - (nx / sx) ** 2 - (ny / sy) ** 2 if kz2 > 0: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave((nx,ny,0), - mp.Vector3(0,1,0), - 1, - 0)) + res = sim.get_eigenmode_coefficients( + tran_flux, mp.DiffractedPlanewave((nx, ny, 0), mp.Vector3(0, 1, 0), 1, 0) + ) t_coeffs = res.alpha - tran = abs(t_coeffs[0,0,0])**2 + tran = abs(t_coeffs[0, 0, 0]) ** 2 - print("order:, {}, {}, {:.5f}".format(nx,ny,tran)) + print(f"order:, {nx}, {ny}, {tran:.5f}") diff --git a/python/examples/holey-wvg-bands.py b/python/examples/holey-wvg-bands.py index a04ee390d..3d8f7b447 100644 --- a/python/examples/holey-wvg-bands.py +++ b/python/examples/holey-wvg-bands.py @@ -7,7 +7,6 @@ # J. D. Joannopoulos, "Guided and defect modes in periodic dielectric # waveguides," J. Opt. Soc. Am. B, 12 (7), 1267-1272 (1995). -from __future__ import division import meep as mp @@ -30,31 +29,40 @@ def main(): fcen = 0.25 # pulse center frequency df = 1.5 # pulse freq. width: large df = short impulse - s = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, - center=mp.Vector3(0.1234)) + s = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=mp.Vector3(0.1234), + ) sym = mp.Mirror(direction=mp.Y, phase=-1) - sim = mp.Simulation(cell_size=cell, geometry=[b, c], sources=[s], symmetries=[sym], - boundary_layers=[mp.PML(dpml, direction=mp.Y)], resolution=20) + sim = mp.Simulation( + cell_size=cell, + geometry=[b, c], + sources=[s], + symmetries=[sym], + boundary_layers=[mp.PML(dpml, direction=mp.Y)], + resolution=20, + ) kx = False # if true, do run at specified kx and get fields - k_interp = 19 # # k-points to interpolate, otherwise - if kx: sim.k_point = mp.Vector3(kx) sim.run( mp.at_beginning(mp.output_epsilon), mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(0.1234), fcen, df)), - until_after_sources=300 + until_after_sources=300, ) sim.run(mp.at_every(1 / fcen / 20, mp.output_hfield_z), until=1 / fcen) else: + k_interp = 19 # # k-points to interpolate, otherwise + sim.run_k_points(300, mp.interpolate(k_interp, [mp.Vector3(), mp.Vector3(0.5)])) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/holey-wvg-cavity.py b/python/examples/holey-wvg-cavity.py index 7bedb283d..745b64a41 100644 --- a/python/examples/holey-wvg-cavity.py +++ b/python/examples/holey-wvg-cavity.py @@ -1,95 +1,127 @@ # Meep Tutorial: Hz-polarized transmission and reflection through a cavity # formed by a periodic sequence of holes in a dielectric waveguide, # with a defect formed by a larger spacing between one pair of holes. - # This structure is based on one analyzed in: # S. Fan, J. N. Winn, A. Devenyi, J. C. Chen, R. D. Meade, and # J. D. Joannopoulos, "Guided and defect modes in periodic dielectric # waveguides," J. Opt. Soc. Am. B, 12 (7), 1267-1272 (1995). - -from __future__ import division - import argparse + import meep as mp + def main(args): - resolution = 20 # pixels/um - - eps = 13 # dielectric constant of waveguide - w = 1.2 # width of waveguide - r = 0.36 # radius of holes - d = 1.4 # defect spacing (ordinary spacing = 1) - N = args.N # number of holes on either side of defect + resolution = 20 # pixels/um + + eps = 13 # dielectric constant of waveguide + w = 1.2 # width of waveguide + r = 0.36 # radius of holes + d = 1.4 # defect spacing (ordinary spacing = 1) + N = args.N # number of holes on either side of defect sy = args.sy # size of cell in y direction (perpendicular to wvg.) - pad = 2 # padding between last hole and PML edge - dpml = 1 # PML thickness + pad = 2 # padding between last hole and PML edge + dpml = 1 # PML thickness - sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction + sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction - cell = mp.Vector3(sx,sy,0) + cell = mp.Vector3(sx, sy, 0) - blk = mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=eps)) + blk = mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=eps)) geometry = [blk] for i in range(N): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i))) - geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i)))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i)))) fcen = args.fcen # pulse center frequency - df = args.df # pulse frequency width - nfreq = 500 # number of frequencies at which to compute flux - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[], - boundary_layers=[mp.PML(dpml)], - resolution=20) + df = args.df # pulse frequency width + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[], + boundary_layers=[mp.PML(dpml)], + resolution=20, + ) if args.resonant_modes: - sim.sources.append(mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3())) + sim.sources.append( + mp.Source( + mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3() + ) + ) sim.symmetries.append(mp.Mirror(mp.Y, phase=-1)) sim.symmetries.append(mp.Mirror(mp.X, phase=-1)) - sim.run(mp.at_beginning(mp.output_epsilon), - mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)), - until_after_sources=400) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.after_sources(mp.Harminv(mp.Hz, mp.Vector3(), fcen, df)), + until_after_sources=400, + ) - sim.run(mp.at_every(1/fcen/20, mp.output_hfield_z), until=1/fcen) + sim.run(mp.at_every(1 / fcen / 20, mp.output_hfield_z), until=1 / fcen) else: - sim.sources.append(mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Ey, - center=mp.Vector3(-0.5*sx+dpml), - size=mp.Vector3(0,w))) + sim.sources.append( + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ey, + center=mp.Vector3(-0.5 * sx + dpml), + size=mp.Vector3(0, w), + ) + ) sim.symmetries.append(mp.Mirror(mp.Y, phase=-1)) - freg = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml-0.5), - size=mp.Vector3(0,2*w)) + freg = mp.FluxRegion( + center=mp.Vector3(0.5 * sx - dpml - 0.5), size=mp.Vector3(0, 2 * w) + ) + + nfreq = 500 # number of frequencies at which to compute flux # transmitted flux trans = sim.add_flux(fcen, df, nfreq, freg) vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sx)) - sim.run(mp.at_beginning(mp.output_epsilon), - mp.during_sources(mp.in_volume(vol, mp.to_appended("hz-slice", mp.at_every(0.4, mp.output_hfield_z)))), - until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(0.5*sx-dpml-0.5), 1e-3)) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.during_sources( + mp.in_volume( + vol, + mp.to_appended("hz-slice", mp.at_every(0.4, mp.output_hfield_z)), + ) + ), + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ey, mp.Vector3(0.5 * sx - dpml - 0.5), 1e-3 + ), + ) sim.display_fluxes(trans) # print out the flux spectrum -if __name__ == '__main__': + +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-r', '--resonant_modes', action='store_true', default=False, - help="Compute resonant modes. Default is transmission spectrum.") - parser.add_argument('-N', type=int, default=3, help='number of holes on either side of defect') - parser.add_argument('-sy', type=int, default=6, help='size of cell in y direction (perpendicular to wvg.)') - parser.add_argument('-fcen', type=float, default=0.25, help='pulse center frequency') - parser.add_argument('-df', type=float, default=0.2, help='pulse frequency width') + parser.add_argument( + "-r", + "--resonant_modes", + action="store_true", + default=False, + help="Compute resonant modes. Default is transmission spectrum.", + ) + parser.add_argument( + "-N", type=int, default=3, help="number of holes on either side of defect" + ) + parser.add_argument( + "-sy", + type=int, + default=6, + help="size of cell in y direction (perpendicular to wvg.)", + ) + parser.add_argument( + "-fcen", type=float, default=0.25, help="pulse center frequency" + ) + parser.add_argument("-df", type=float, default=0.2, help="pulse frequency width") args = parser.parse_args() main(args) diff --git a/python/examples/material-dispersion.py b/python/examples/material-dispersion.py index 10acd16cc..ecab6cb87 100644 --- a/python/examples/material-dispersion.py +++ b/python/examples/material-dispersion.py @@ -2,11 +2,8 @@ # simulate homogenous space filled with a dispersive material, and compute # its modes as a function of wavevector k. Since omega/c = k/n, we can # extract the dielectric function epsilon(omega) = (ck/omega)^2. -from __future__ import division - import meep as mp - cell = mp.Vector3() resolution = 20 @@ -18,7 +15,7 @@ susceptibilities = [ mp.LorentzianSusceptibility(frequency=1.1, gamma=1e-5, sigma=0.5), - mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5) + mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5), ] default_material = mp.Medium(epsilon=2.25, E_susceptibilities=susceptibilities) @@ -26,7 +23,9 @@ fcen = 1.0 df = 2.0 -sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3())] +sources = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3()) +] kmin = 0.3 kmax = 2.2 @@ -39,11 +38,11 @@ geometry=[], sources=sources, default_material=default_material, - resolution=resolution + resolution=resolution, ) all_freqs = sim.run_k_points(200, kpts) # a list of lists of frequencies for fs, kx in zip(all_freqs, [v.x for v in kpts]): for f in fs: - print("eps:, {:.6g}, {:.6g}, {:.6g}".format(f.real, f.imag, (kx / f)**2)) + print(f"eps:, {f.real:.6g}, {f.imag:.6g}, {(kx / f) ** 2:.6g}") diff --git a/python/examples/metal-cavity-ldos.py b/python/examples/metal-cavity-ldos.py index b00ed414b..9da8dda72 100644 --- a/python/examples/metal-cavity-ldos.py +++ b/python/examples/metal-cavity-ldos.py @@ -1,41 +1,50 @@ -from __future__ import division - import math -import meep as mp -import numpy as np + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp + def metal_cavity(w): resolution = 50 sxy = 2 dpml = 1 - sxy = sxy+2*dpml - cell = mp.Vector3(sxy,sxy) + sxy += 2 * dpml + cell = mp.Vector3(sxy, sxy) pml_layers = [mp.PML(dpml)] a = 1 t = 0.1 - geometry = [mp.Block(mp.Vector3(a+2*t,a+2*t,mp.inf), material=mp.metal), - mp.Block(mp.Vector3(a,a,mp.inf), material=mp.air)] + geometry = [ + mp.Block(mp.Vector3(a + 2 * t, a + 2 * t, mp.inf), material=mp.metal), + mp.Block(mp.Vector3(a, a, mp.inf), material=mp.air), + ] - geometry.append(mp.Block(center=mp.Vector3(a/2), - size=mp.Vector3(2*t,w,mp.inf), - material=mp.air)) + geometry.append( + mp.Block( + center=mp.Vector3(a / 2), size=mp.Vector3(2 * t, w, mp.inf), material=mp.air + ) + ) - fcen = math.sqrt(0.5)/a + fcen = math.sqrt(0.5) / a df = 0.2 - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3())] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3() + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries, - resolution=resolution) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + resolution=resolution, + ) h = mp.Harminv(mp.Ez, mp.Vector3(), fcen, df) sim.run(mp.after_sources(h), until_after_sources=500) @@ -43,28 +52,29 @@ def metal_cavity(w): m = h.modes[0] f = m.freq Q = m.Q - Vmode = 0.25*a*a + Vmode = 0.25 * a * a ldos_1 = Q / Vmode / (2 * math.pi * f * math.pi * 0.5) sim.reset_meep() - T = 2*Q*(1/f) - sim.run(mp.dft_ldos(f,0,1), until_after_sources=T) + T = 2 * Q * (1 / f) + sim.run(mp.dft_ldos(f, 0, 1), until_after_sources=T) ldos_2 = sim.ldos_data[0] return ldos_1, ldos_2 -ws = np.arange(0.2,0.5,0.1) + +ws = np.arange(0.2, 0.5, 0.1) ldos_1 = np.zeros(len(ws)) ldos_2 = np.zeros(len(ws)) for j in range(len(ws)): ldos_1[j], ldos_2[j] = metal_cavity(ws[j]) - print("ldos:, {}, {}".format(ldos_1[j],ldos_2[2])) + print(f"ldos:, {ldos_1[j]}, {ldos_2[2]}") plt.figure(dpi=150) -plt.semilogy(1/ws,ldos_1,'bo-',label="2Q/(πωV)") -plt.semilogy(1/ws,ldos_2,'rs-',label="LDOS") -plt.xlabel('a/w') -plt.ylabel('2Q/(πωW) or LDOS') +plt.semilogy(1 / ws, ldos_1, "bo-", label="2Q/(πωV)") +plt.semilogy(1 / ws, ldos_2, "rs-", label="LDOS") +plt.xlabel("a/w") +plt.ylabel("2Q/(πωW) or LDOS") plt.show() diff --git a/python/examples/metasurface_lens.py b/python/examples/metasurface_lens.py index 42b56e77d..b9d55fd0f 100644 --- a/python/examples/metasurface_lens.py +++ b/python/examples/metasurface_lens.py @@ -1,182 +1,244 @@ -# -*- coding: utf-8 -*- - -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np +import meep as mp -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 2.0 # substrate thickness -dpad = 2.0 # padding between grating and PML +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dpad = 2.0 # padding between grating and PML -lcen = 0.5 # center wavelength -fcen = 1/lcen # center frequency -df = 0.2*fcen # frequency width +lcen = 0.5 # center wavelength +fcen = 1 / lcen # center frequency +df = 0.2 * fcen # frequency width -focal_length = 200 # focal length of metalens -spot_length = 100 # far field line length -ff_res = 10 # far field resolution (points/μm) +focal_length = 200 # focal length of metalens +spot_length = 100 # far field line length +ff_res = 10 # far field resolution (points/μm) -k_point = mp.Vector3(0,0,0) +k_point = mp.Vector3(0, 0, 0) glass = mp.Medium(index=1.5) -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] - -symmetries=[mp.Mirror(mp.Y)] - -def grating(gp,gh,gdc_list): - sx = dpml+dsub+gh+dpad+dpml - src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) - mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad) - geometry = [mp.Block(material=glass, - size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)))] - - num_cells = len(gdc_list) - if num_cells == 1: - sy = gp - cell_size = mp.Vector3(sx,sy,0) - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - default_material=glass, - sources=sources, - symmetries=symmetries) - - flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))) - - sim.run(until_after_sources=50) - - input_flux = mp.get_fluxes(flux_obj) - - sim.reset_meep() - - geometry.append(mp.Block(material=glass, size=mp.Vector3(gh,gdc_list[0]*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))) - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - flux_obj = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))) - - sim.run(until_after_sources=200) - - freqs = mp.get_eigenmode_freqs(flux_obj) - res = sim.get_eigenmode_coefficients(flux_obj, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y) - coeffs = res.alpha - - mode_tran = abs(coeffs[0,0,0])**2/input_flux[0] - mode_phase = np.angle(coeffs[0,0,0]) - if mode_phase > 0: - mode_phase -= 2*np.pi - - return mode_tran, mode_phase - - else: - sy = num_cells*gp - cell_size = mp.Vector3(sx,sy,0) - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(y=sy))] - - for j in range(num_cells): - geometry.append(mp.Block(material=glass, - size=mp.Vector3(gh,gdc_list[j]*gp,mp.inf), - center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,-0.5*sy+(j+0.5)*gp))) - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy))) - - sim.run(until_after_sources=500) - - return abs(sim.get_farfields(n2f_obj, ff_res, center=mp.Vector3(-0.5*sx+dpml+dsub+gh+focal_length), size=mp.Vector3(spot_length))['Ez'])**2 - - -gp = 0.3 # grating periodicity -gh = 1.8 # grating height -gdc = np.linspace(0.1,0.9,30) # grating duty cycle - -mode_tran = np.empty((gdc.size)) -mode_phase = np.empty((gdc.size)) +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] + +symmetries = [mp.Mirror(mp.Y)] + + +def grating(gp, gh, gdc_list): + sx = dpml + dsub + gh + dpad + dpml + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) + mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad) + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ) + ] + + num_cells = len(gdc_list) + if num_cells == 1: + sy = gp + cell_size = mp.Vector3(sx, sy, 0) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + default_material=glass, + sources=sources, + symmetries=symmetries, + ) + + flux_obj = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)) + ) + + sim.run(until_after_sources=50) + + input_flux = mp.get_fluxes(flux_obj) + + sim.reset_meep() + + geometry.append( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc_list[0] * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh), + ) + ) + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + flux_obj = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy)) + ) + + sim.run(until_after_sources=200) + + freqs = mp.get_eigenmode_freqs(flux_obj) + res = sim.get_eigenmode_coefficients( + flux_obj, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y + ) + coeffs = res.alpha + + mode_tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux[0] + mode_phase = np.angle(coeffs[0, 0, 0]) + if mode_phase > 0: + mode_phase -= 2 * np.pi + + return mode_tran, mode_phase + + else: + sy = num_cells * gp + cell_size = mp.Vector3(sx, sy, 0) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) + ] + + geometry.extend( + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf), + center=mp.Vector3( + -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + (j + 0.5) * gp + ), + ) + for j in range(num_cells) + ) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + n2f_obj = sim.add_near2far( + fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy)) + ) + + sim.run(until_after_sources=500) + + return ( + abs( + sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(-0.5 * sx + dpml + dsub + gh + focal_length), + size=mp.Vector3(spot_length), + )["Ez"] + ) + ** 2 + ) + + +gp = 0.3 # grating periodicity +gh = 1.8 # grating height +gdc = np.linspace(0.1, 0.9, 30) # grating duty cycle + +mode_tran = np.empty(gdc.size) +mode_phase = np.empty(gdc.size) for n in range(gdc.size): - mode_tran[n], mode_phase[n] = grating(gp,gh,[gdc[n]]) + mode_tran[n], mode_phase[n] = grating(gp, gh, [gdc[n]]) plt.figure(dpi=200) -plt.subplot(1,2,1) -plt.plot(gdc, mode_tran, 'bo-') -plt.xlim(gdc[0],gdc[-1]) -plt.xticks([t for t in np.linspace(0.1,0.9,5)]) +plt.subplot(1, 2, 1) +plt.plot(gdc, mode_tran, "bo-") +plt.xlim(gdc[0], gdc[-1]) +plt.xticks(list(np.linspace(0.1, 0.9, 5))) plt.xlabel("grating duty cycle") -plt.ylim(0.96,1.00) -plt.yticks([t for t in np.linspace(0.96,1.00,5)]) +plt.ylim(0.96, 1.00) +plt.yticks(list(np.linspace(0.96, 1.00, 5))) plt.title("transmittance") -plt.subplot(1,2,2) -plt.plot(gdc, mode_phase, 'rs-') +plt.subplot(1, 2, 2) +plt.plot(gdc, mode_phase, "rs-") plt.grid(True) -plt.xlim(gdc[0],gdc[-1]) -plt.xticks([t for t in np.linspace(0.1,0.9,5)]) +plt.xlim(gdc[0], gdc[-1]) +plt.xticks(list(np.linspace(0.1, 0.9, 5))) plt.xlabel("grating duty cycle") -plt.ylim(-2*np.pi,0) -plt.yticks([t for t in np.linspace(-6,0,7)]) +plt.ylim(-2 * np.pi, 0) +plt.yticks(list(np.linspace(-6, 0, 7))) plt.title("phase (radians)") plt.tight_layout(pad=0.5) plt.show() -gdc_new = np.linspace(0.16,0.65,500) +gdc_new = np.linspace(0.16, 0.65, 500) mode_phase_interp = np.interp(gdc_new, gdc, mode_phase) -print("phase-range:, {:.6f}".format(mode_phase_interp.max()-mode_phase_interp.min())) +print(f"phase-range:, {mode_phase_interp.max() - mode_phase_interp.min():.6f}") phase_tol = 1e-2 -num_cells = [100,200,400] -ff_nc = np.empty((spot_length*ff_res,len(num_cells))) +num_cells = [100, 200, 400] +ff_nc = np.empty((spot_length * ff_res, len(num_cells))) for k in range(len(num_cells)): - gdc_list = [] - for j in range(-num_cells[k],num_cells[k]+1): - phase_local = 2*np.pi/lcen * (focal_length-((j*gp)**2 + focal_length**2)**0.5) # local phase at the center of the j'th unit cell - phase_mod = phase_local % (-2*np.pi) # restrict phase to [-2*pi,0] - if phase_mod > mode_phase_interp.max(): - phase_mod = mode_phase_interp.max() - if phase_mod < mode_phase_interp.min(): - phase_mod = mode_phase_interp.min() - idx = np.transpose(np.nonzero(np.logical_and(mode_phase_interp > phase_mod-phase_tol, mode_phase_interp < phase_mod+phase_tol))) - gdc_list.append(gdc_new[idx[0][0]]) - - ff_nc[:,k] = grating(gp,gh,gdc_list) - -x = np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,ff_res*spot_length) + gdc_list = [] + for j in range(-num_cells[k], num_cells[k] + 1): + phase_local = ( + 2 + * np.pi + / lcen + * (focal_length - ((j * gp) ** 2 + focal_length**2) ** 0.5) + ) # local phase at the center of the j'th unit cell + phase_mod = phase_local % (-2 * np.pi) # restrict phase to [-2*pi,0] + if phase_mod > mode_phase_interp.max(): + phase_mod = mode_phase_interp.max() + if phase_mod < mode_phase_interp.min(): + phase_mod = mode_phase_interp.min() + idx = np.transpose( + np.nonzero( + np.logical_and( + mode_phase_interp > phase_mod - phase_tol, + mode_phase_interp < phase_mod + phase_tol, + ) + ) + ) + gdc_list.append(gdc_new[idx[0][0]]) + + ff_nc[:, k] = grating(gp, gh, gdc_list) + +x = np.linspace( + focal_length - 0.5 * spot_length, + focal_length + 0.5 * spot_length, + ff_res * spot_length, +) plt.figure(dpi=200) -plt.semilogy(x,abs(ff_nc[:,0])**2,'bo-',label='num_cells = {}'.format(2*num_cells[0]+1)) -plt.semilogy(x,abs(ff_nc[:,1])**2,'ro-',label='num_cells = {}'.format(2*num_cells[1]+1)) -plt.semilogy(x,abs(ff_nc[:,2])**2,'go-',label='num_cells = {}'.format(2*num_cells[2]+1)) -plt.xlabel('x coordinate (μm)') -plt.ylabel(r'energy density of far-field electric fields, |E$_z$|$^2$') -plt.title('focusing properties of a binary-grating metasurface lens') -plt.legend(loc='upper right') +plt.semilogy(x, abs(ff_nc[:, 0]) ** 2, "bo-", label=f"num_cells = {2*num_cells[0] + 1}") +plt.semilogy(x, abs(ff_nc[:, 1]) ** 2, "ro-", label=f"num_cells = {2*num_cells[1] + 1}") +plt.semilogy(x, abs(ff_nc[:, 2]) ** 2, "go-", label=f"num_cells = {2*num_cells[2] + 1}") +plt.xlabel("x coordinate (μm)") +plt.ylabel(r"energy density of far-field electric fields, |E$_z$|$^2$") +plt.title("focusing properties of a binary-grating metasurface lens") +plt.legend(loc="upper right") plt.tight_layout() plt.show() diff --git a/python/examples/mie_scattering.py b/python/examples/mie_scattering.py index 33a674a1e..86f9887cb 100644 --- a/python/examples/mie_scattering.py +++ b/python/examples/mie_scattering.py @@ -1,52 +1,88 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np import PyMieScatt as ps +import meep as mp + r = 1.0 # radius of sphere -wvl_min = 2*np.pi*r/10 -wvl_max = 2*np.pi*r/2 +wvl_min = 2 * np.pi * r / 10 +wvl_max = 2 * np.pi * r / 2 -frq_min = 1/wvl_max -frq_max = 1/wvl_min -frq_cen = 0.5*(frq_min+frq_max) -dfrq = frq_max-frq_min +frq_min = 1 / wvl_max +frq_max = 1 / wvl_min +frq_cen = 0.5 * (frq_min + frq_max) +dfrq = frq_max - frq_min nfrq = 100 ## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min) resolution = 25 -dpml = 0.5*wvl_max -dair = 0.5*wvl_max +dpml = 0.5 * wvl_max +dair = 0.5 * wvl_max pml_layers = [mp.PML(thickness=dpml)] -symmetries = [mp.Mirror(mp.Y), - mp.Mirror(mp.Z,phase=-1)] +symmetries = [mp.Mirror(mp.Y), mp.Mirror(mp.Z, phase=-1)] -s = 2*(dpml+dair+r) -cell_size = mp.Vector3(s,s,s) +s = 2 * (dpml + dair + r) +cell_size = mp.Vector3(s, s, s) # is_integrated=True necessary for any planewave source extending into PML -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - center=mp.Vector3(-0.5*s+dpml), - size=mp.Vector3(0,s,s), - component=mp.Ez)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - symmetries=symmetries) - -box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r))) -box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r))) -box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r))) -box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r))) -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0))) +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + center=mp.Vector3(-0.5 * s + dpml), + size=mp.Vector3(0, s, s), + component=mp.Ez, + ) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + symmetries=symmetries, +) + +box_x1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_x2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_y1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_y2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)), +) sim.run(until_after_sources=10) @@ -63,24 +99,56 @@ sim.reset_meep() n_sphere = 2.0 -geometry = [mp.Sphere(material=mp.Medium(index=n_sphere), - center=mp.Vector3(), - radius=r)] - -sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=mp.Vector3(), - symmetries=symmetries, - geometry=geometry) - -box_x1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=-r),size=mp.Vector3(0,2*r,2*r))) -box_x2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(x=+r),size=mp.Vector3(0,2*r,2*r))) -box_y1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=-r),size=mp.Vector3(2*r,0,2*r))) -box_y2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(y=+r),size=mp.Vector3(2*r,0,2*r))) -box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=-r),size=mp.Vector3(2*r,2*r,0))) -box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(z=+r),size=mp.Vector3(2*r,2*r,0))) +geometry = [ + mp.Sphere(material=mp.Medium(index=n_sphere), center=mp.Vector3(), radius=r) +] + +sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=mp.Vector3(), + symmetries=symmetries, + geometry=geometry, +) + +box_x1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_x2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r, 2 * r)), +) +box_y1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_y2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0, 2 * r)), +) +box_z1 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=-r), size=mp.Vector3(2 * r, 2 * r, 0)), +) +box_z2 = sim.add_flux( + frq_cen, + dfrq, + nfrq, + mp.FluxRegion(center=mp.Vector3(z=+r), size=mp.Vector3(2 * r, 2 * r, 0)), +) sim.load_minus_flux_data(box_x1, box_x1_data) sim.load_minus_flux_data(box_x2, box_x2_data) @@ -98,20 +166,31 @@ box_z1_flux = mp.get_fluxes(box_z1) box_z2_flux = mp.get_fluxes(box_z2) -scatt_flux = np.asarray(box_x1_flux)-np.asarray(box_x2_flux)+np.asarray(box_y1_flux)-np.asarray(box_y2_flux)+np.asarray(box_z1_flux)-np.asarray(box_z2_flux) -intensity = np.asarray(box_x1_flux0)/(2*r)**2 -scatt_cross_section = np.divide(scatt_flux,intensity) -scatt_eff_meep = scatt_cross_section*-1/(np.pi*r**2) -scatt_eff_theory = [ps.MieQ(n_sphere,1000/f,2*r*1000,asDict=True)['Qsca'] for f in freqs] +scatt_flux = ( + np.asarray(box_x1_flux) + - np.asarray(box_x2_flux) + + np.asarray(box_y1_flux) + - np.asarray(box_y2_flux) + + np.asarray(box_z1_flux) + - np.asarray(box_z2_flux) +) +intensity = np.asarray(box_x1_flux0) / (2 * r) ** 2 +scatt_cross_section = np.divide(scatt_flux, intensity) +scatt_eff_meep = scatt_cross_section * -1 / (np.pi * r**2) +scatt_eff_theory = [ + ps.MieQ(n_sphere, 1000 / f, 2 * r * 1000, asDict=True)["Qsca"] for f in freqs +] if mp.am_master(): plt.figure(dpi=150) - plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_meep,'bo-',label='Meep') - plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_eff_theory,'ro-',label='theory') - plt.grid(True,which="both",ls="-") - plt.xlabel('(sphere circumference)/wavelength, 2πr/λ') - plt.ylabel('scattering efficiency, σ/πr$^{2}$') - plt.legend(loc='upper right') - plt.title('Mie Scattering of a Lossless Dielectric Sphere') + plt.loglog(2 * np.pi * r * np.asarray(freqs), scatt_eff_meep, "bo-", label="Meep") + plt.loglog( + 2 * np.pi * r * np.asarray(freqs), scatt_eff_theory, "ro-", label="theory" + ) + plt.grid(True, which="both", ls="-") + plt.xlabel("(sphere circumference)/wavelength, 2πr/λ") + plt.ylabel("scattering efficiency, σ/πr$^{2}$") + plt.legend(loc="upper right") + plt.title("Mie Scattering of a Lossless Dielectric Sphere") plt.tight_layout() plt.savefig("mie_scattering.png") diff --git a/python/examples/mode-decomposition.py b/python/examples/mode-decomposition.py index 6663c68fd..9cfd58a1b 100644 --- a/python/examples/mode-decomposition.py +++ b/python/examples/mode-decomposition.py @@ -1,30 +1,28 @@ -# -*- coding: utf-8 -*- +import matplotlib.pyplot as plt import meep as mp -import matplotlib.pyplot as plt -resolution = 25 # pixels/μm +resolution = 25 # pixels/μm -w1 = 1.0 # width of waveguide 1 -w2 = 2.0 # width of waveguide 2 -Lw = 10.0 # length of waveguides 1 and 2 +w1 = 1.0 # width of waveguide 1 +w2 = 2.0 # width of waveguide 2 +Lw = 10.0 # length of waveguides 1 and 2 # lengths of waveguide taper Lts = [2**m for m in range(4)] -dair = 3.0 # length of air region -dpml_x = 6.0 # length of PML in x direction -dpml_y = 2.0 # length of PML in y direction +dair = 3.0 # length of air region +dpml_x = 6.0 # length of PML in x direction +dpml_y = 2.0 # length of PML in y direction -sy = dpml_y+dair+w2+dair+dpml_y +sy = dpml_y + dair + w2 + dair + dpml_y Si = mp.Medium(epsilon=12.0) -boundary_layers = [mp.PML(dpml_x,direction=mp.X), - mp.PML(dpml_y,direction=mp.Y)] +boundary_layers = [mp.PML(dpml_x, direction=mp.X), mp.PML(dpml_y, direction=mp.Y)] -lcen = 6.67 # mode wavelength -fcen = 1/lcen # mode frequency +lcen = 6.67 # mode wavelength +fcen = 1 / lcen # mode frequency symmetries = [mp.Mirror(mp.Y)] @@ -32,35 +30,45 @@ R_flux = [] for Lt in Lts: - sx = dpml_x+Lw+Lt+Lw+dpml_x - cell_size = mp.Vector3(sx,sy,0) - - src_pt = mp.Vector3(-0.5*sx+dpml_x+0.2*Lw) - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=src_pt, - size=mp.Vector3(y=sy-2*dpml_y), - eig_match_freq=True, - eig_parity=mp.ODD_Z+mp.EVEN_Y)] + sx = dpml_x + Lw + Lt + Lw + dpml_x + cell_size = mp.Vector3(sx, sy, 0) + + src_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.2 * Lw) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=src_pt, + size=mp.Vector3(y=sy - 2 * dpml_y), + eig_match_freq=True, + eig_parity=mp.ODD_Z + mp.EVEN_Y, + ) + ] # straight waveguide - vertices = [mp.Vector3(-0.5*sx-1,0.5*w1), - mp.Vector3(0.5*sx+1,0.5*w1), - mp.Vector3(0.5*sx+1,-0.5*w1), - mp.Vector3(-0.5*sx-1,-0.5*w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices,height=mp.inf,material=Si)], - sources=sources, - symmetries=symmetries) - - mon_pt = mp.Vector3(-0.5*sx+dpml_x+0.7*Lw) - flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y))) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9)) - - res = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y) + vertices = [ + mp.Vector3(-0.5 * sx - 1, 0.5 * w1), + mp.Vector3(0.5 * sx + 1, 0.5 * w1), + mp.Vector3(0.5 * sx + 1, -0.5 * w1), + mp.Vector3(-0.5 * sx - 1, -0.5 * w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + mon_pt = mp.Vector3(-0.5 * sx + dpml_x + 0.7 * Lw) + flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y)) + ) + + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) + + res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) incident_coeffs = res.alpha incident_flux = mp.get_fluxes(flux) incident_flux_data = sim.get_flux_data(flux) @@ -68,42 +76,55 @@ sim.reset_meep() # linear taper - vertices = [mp.Vector3(-0.5*sx-1,0.5*w1), - mp.Vector3(-0.5*Lt,0.5*w1), - mp.Vector3(0.5*Lt,0.5*w2), - mp.Vector3(0.5*sx+1,0.5*w2), - mp.Vector3(0.5*sx+1,-0.5*w2), - mp.Vector3(0.5*Lt,-0.5*w2), - mp.Vector3(-0.5*Lt,-0.5*w1), - mp.Vector3(-0.5*sx-1,-0.5*w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices,height=mp.inf,material=Si)], - sources=sources, - symmetries=symmetries) - - flux = sim.add_flux(fcen,0,1,mp.FluxRegion(center=mon_pt,size=mp.Vector3(y=sy-2*dpml_y))) - sim.load_minus_flux_data(flux,incident_flux_data) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mon_pt,1e-9)) - - res2 = sim.get_eigenmode_coefficients(flux,[1],eig_parity=mp.ODD_Z+mp.EVEN_Y) + vertices = [ + mp.Vector3(-0.5 * sx - 1, 0.5 * w1), + mp.Vector3(-0.5 * Lt, 0.5 * w1), + mp.Vector3(0.5 * Lt, 0.5 * w2), + mp.Vector3(0.5 * sx + 1, 0.5 * w2), + mp.Vector3(0.5 * sx + 1, -0.5 * w2), + mp.Vector3(0.5 * Lt, -0.5 * w2), + mp.Vector3(-0.5 * Lt, -0.5 * w1), + mp.Vector3(-0.5 * sx - 1, -0.5 * w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy - 2 * dpml_y)) + ) + sim.load_minus_flux_data(flux, incident_flux_data) + + sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mon_pt, 1e-9)) + + res2 = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) taper_coeffs = res2.alpha taper_flux = mp.get_fluxes(flux) - R_coeffs.append(abs(taper_coeffs[0,0,1])**2/abs(incident_coeffs[0,0,0])**2) - R_flux.append(-taper_flux[0]/incident_flux[0]) - print("refl:, {}, {:.8f}, {:.8f}".format(Lt,R_coeffs[-1],R_flux[-1])) + R_coeffs.append( + abs(taper_coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2 + ) + R_flux.append(-taper_flux[0] / incident_flux[0]) + print(f"refl:, {Lt}, {R_coeffs[-1]:.8f}, {R_flux[-1]:.8f}") if mp.am_master(): plt.figure() - plt.loglog(Lts,R_coeffs,'bo-',label='mode decomposition') - plt.loglog(Lts,R_flux,'ro-',label='Poynting flux') - plt.loglog(Lts,[0.005/Lt**2 for Lt in Lts],'k-',label=r'quadratic reference (1/Lt$^2$)') - plt.legend(loc='upper right') - plt.xlabel('taper length Lt (μm)') - plt.ylabel('reflectance') + plt.loglog(Lts, R_coeffs, "bo-", label="mode decomposition") + plt.loglog(Lts, R_flux, "ro-", label="Poynting flux") + plt.loglog( + Lts, + [0.005 / Lt**2 for Lt in Lts], + "k-", + label=r"quadratic reference (1/Lt$^2$)", + ) + plt.legend(loc="upper right") + plt.xlabel("taper length Lt (μm)") + plt.ylabel("reflectance") plt.show() diff --git a/python/examples/mpb_bragg.py b/python/examples/mpb_bragg.py index 8cfd8bf76..d8c503950 100644 --- a/python/examples/mpb_bragg.py +++ b/python/examples/mpb_bragg.py @@ -12,8 +12,13 @@ geometry_lattice = mp.Lattice(size=mp.Vector3(1)) # 1d cell default_material = mp.Medium(index=n_lo) -geometry = mp.Cylinder(material=mp.Medium(index=n_hi), center=mp.Vector3(), axis=mp.Vector3(1), - radius=mp.inf, height=w_hi) +geometry = mp.Cylinder( + material=mp.Medium(index=n_hi), + center=mp.Vector3(), + axis=mp.Vector3(1), + radius=mp.inf, + height=w_hi, +) kx = 0.5 k_points = [mp.Vector3(kx)] @@ -27,12 +32,15 @@ geometry_lattice=geometry_lattice, geometry=[geometry], resolution=resolution, - default_material=default_material + default_material=default_material, ) def main(): - ms.run_tm(mpb.output_hfield_y) # note that TM and TE bands are degenerate, so we only need TM + ms.run_tm( + mpb.output_hfield_y + ) # note that TM and TE bands are degenerate, so we only need TM + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/mpb_bragg_sine.py b/python/examples/mpb_bragg_sine.py index e0a52098b..858ad8d51 100644 --- a/python/examples/mpb_bragg_sine.py +++ b/python/examples/mpb_bragg_sine.py @@ -1,4 +1,5 @@ import math + import meep as mp from meep import mpb @@ -16,8 +17,11 @@ # * (1 + cos(2*pi*x)) # This is periodic, and also has inversion symmetry. def eps_func(p): - return mp.Medium(index=index_min + 0.5 * (index_max - index_min) * - (1 + math.cos(2 * math.pi * p.x))) + return mp.Medium( + index=index_min + + 0.5 * (index_max - index_min) * (1 + math.cos(2 * math.pi * p.x)) + ) + geometry_lattice = mp.Lattice(size=mp.Vector3(1)) # 1d cell @@ -34,7 +38,7 @@ def eps_func(p): k_points=k_points, geometry_lattice=geometry_lattice, resolution=resolution, - default_material=default_material + default_material=default_material, ) @@ -42,5 +46,6 @@ def main(): # the TM and TE bands are degenerate, so we only need TM: ms.run_tm() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_data_analysis.py b/python/examples/mpb_data_analysis.py index 4a338aab5..9d7a27daf 100644 --- a/python/examples/mpb_data_analysis.py +++ b/python/examples/mpb_data_analysis.py @@ -1,10 +1,10 @@ -from __future__ import division - import os import sys + +import matplotlib.pyplot as plt import numpy as np + import meep as mp -import matplotlib.pyplot as plt from meep import mpb examples_dir = os.path.realpath(os.path.dirname(__file__)) @@ -21,8 +21,11 @@ def tri_rods(): def get_efields(tr_ms, band): efields.append(tr_ms.get_efield(band)) - tr_ms.run_tm(mpb.output_at_kpoint(mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, - get_efields)) + tr_ms.run_tm( + mpb.output_at_kpoint( + mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, get_efields + ) + ) # Create an MPBData instance to transform the efields md = mpb.MPBData(rectify=True, resolution=32, periods=3) @@ -36,21 +39,21 @@ def get_efields(tr_ms, band): tr_ms.run_te() eps = tr_ms.get_epsilon() - plt.imshow(eps.T, interpolation='spline36', cmap='binary') - plt.axis('off') + plt.imshow(eps.T, interpolation="spline36", cmap="binary") + plt.axis("off") plt.show() md = mpb.MPBData(rectify=True, resolution=32, periods=3) rectangular_data = md.convert(eps) - plt.imshow(rectangular_data.T, interpolation='spline36', cmap='binary') - plt.axis('off') + plt.imshow(rectangular_data.T, interpolation="spline36", cmap="binary") + plt.axis("off") plt.show() for i, f in enumerate(converted): plt.subplot(331 + i) - plt.contour(rectangular_data.T, cmap='binary') - plt.imshow(np.real(f).T, interpolation='spline36', cmap='RdBu', alpha=0.9) - plt.axis('off') + plt.contour(rectangular_data.T, cmap="binary") + plt.imshow(np.real(f).T, interpolation="spline36", cmap="RdBu", alpha=0.9) + plt.axis("off") plt.show() @@ -72,6 +75,6 @@ def get_dpwr(ms, band): # TODO: Plot -if __name__ == '__main__': +if __name__ == "__main__": tri_rods() diamond() diff --git a/python/examples/mpb_diamond.py b/python/examples/mpb_diamond.py index b97fbdf50..b7aaaa1cb 100644 --- a/python/examples/mpb_diamond.py +++ b/python/examples/mpb_diamond.py @@ -1,4 +1,5 @@ import math + import meep as mp from meep import mpb @@ -10,19 +11,19 @@ basis_size=mp.Vector3(sqrt_half, sqrt_half, sqrt_half), basis1=mp.Vector3(0, 1, 1), basis2=mp.Vector3(1, 0, 1), - basis3=mp.Vector3(1, 1) + basis3=mp.Vector3(1, 1), ) # Corners of the irreducible Brillouin zone for the fcc lattice, # in a canonical order: vlist = [ - mp.Vector3(0, 0.5, 0.5), # X - mp.Vector3(0, 0.625, 0.375), # U - mp.Vector3(0, 0.5, 0), # L - mp.Vector3(0, 0, 0), # Gamma - mp.Vector3(0, 0.5, 0.5), # X - mp.Vector3(0.25, 0.75, 0.5), # W - mp.Vector3(0.375, 0.75, 0.375) # K + mp.Vector3(0, 0.5, 0.5), # X + mp.Vector3(0, 0.625, 0.375), # U + mp.Vector3(0, 0.5, 0), # L + mp.Vector3(0, 0, 0), # Gamma + mp.Vector3(0, 0.5, 0.5), # X + mp.Vector3(0.25, 0.75, 0.5), # W + mp.Vector3(0.375, 0.75, 0.375), # K ] k_points = mp.interpolate(4, vlist) @@ -34,8 +35,10 @@ diel = mp.Medium(epsilon=eps) # A diamond lattice has two "atoms" per unit cell: -geometry = [mp.Sphere(r, center=mp.Vector3(0.125, 0.125, 0.125), material=diel), - mp.Sphere(r, center=mp.Vector3(-0.125, -0.125, -0.125), material=diel)] +geometry = [ + mp.Sphere(r, center=mp.Vector3(0.125, 0.125, 0.125), material=diel), + mp.Sphere(r, center=mp.Vector3(-0.125, -0.125, -0.125), material=diel), +] # (A simple fcc lattice would have only one sphere/object at the origin.) @@ -49,7 +52,7 @@ geometry=geometry, resolution=resolution, num_bands=num_bands, - mesh_size=mesh_size + mesh_size=mesh_size, ) @@ -57,5 +60,6 @@ def main(): # run calculation, outputting electric_field energy density at the U point: ms.run(mpb.output_at_kpoint(mp.Vector3(0, 0.625, 0.375), mpb.output_dpwr)) -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_hole_slab.py b/python/examples/mpb_hole_slab.py index 02442576f..e95faa480 100644 --- a/python/examples/mpb_hole_slab.py +++ b/python/examples/mpb_hole_slab.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb @@ -23,15 +22,20 @@ supercell_h = 4 # height of the supercell # triangular lattice with vertical supercell: -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1, supercell_h), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1, supercell_h), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) geometry = [ - mp.Block(material=mp.Medium(epsilon=loweps), center=mp.Vector3(z=0.25 * supercell_h), - size=mp.Vector3(mp.inf, mp.inf, 0.5 * supercell_h)), + mp.Block( + material=mp.Medium(epsilon=loweps), + center=mp.Vector3(z=0.25 * supercell_h), + size=mp.Vector3(mp.inf, mp.inf, 0.5 * supercell_h), + ), mp.Block(material=mp.Medium(epsilon=eps), size=mp.Vector3(mp.inf, mp.inf, h)), - mp.Cylinder(r, material=mp.air, height=supercell_h) + mp.Cylinder(r, material=mp.air, height=supercell_h), ] # 1st Brillouin zone of a triangular lattice: @@ -40,12 +44,8 @@ K = mp.Vector3(1 / -3, 1 / 3) only_K = False # run with only_K=true to only do this k_point -k_interp = 4 # the number of k points to interpolate -if only_K: - k_points = [K] -else: - k_points = mp.interpolate(k_interp, [Gamma, M, K, Gamma]) - +k_interp = 4 # the number of k points to interpolate +k_points = [K] if only_K else mp.interpolate(k_interp, [Gamma, M, K, Gamma]) resolution = mp.Vector3(32, 32, 16) num_bands = 9 @@ -54,7 +54,7 @@ geometry=geometry, resolution=resolution, num_bands=num_bands, - k_points=k_points + k_points=k_points, ) @@ -69,5 +69,6 @@ def main(): ms.display_eigensolver_stats() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_honey_rods.py b/python/examples/mpb_honey_rods.py index b24c6cc28..f6cd9489e 100644 --- a/python/examples/mpb_honey_rods.py +++ b/python/examples/mpb_honey_rods.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb @@ -13,23 +12,35 @@ eps = 12 # the rod dielectric constant # triangular lattice: -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) # Two rods per unit cell, at the correct positions to form a honeycomb # lattice, and arranged to have inversion symmetry: -geometry = [mp.Cylinder(r, center=mp.Vector3(1 / 6, 1 / 6), height=mp.inf, - material=mp.Medium(epsilon=eps)), - mp.Cylinder(r, center=mp.Vector3(1 / -6, 1 / -6), height=mp.inf, - material=mp.Medium(epsilon=eps))] +geometry = [ + mp.Cylinder( + r, + center=mp.Vector3(1 / 6, 1 / 6), + height=mp.inf, + material=mp.Medium(epsilon=eps), + ), + mp.Cylinder( + r, + center=mp.Vector3(1 / -6, 1 / -6), + height=mp.inf, + material=mp.Medium(epsilon=eps), + ), +] # The k_points list, for the Brillouin zone of a triangular lattice: k_points = [ - mp.Vector3(), # Gamma - mp.Vector3(y=0.5), # M + mp.Vector3(), # Gamma + mp.Vector3(y=0.5), # M mp.Vector3(1 / -3, 1 / 3), # K - mp.Vector3() # Gamma + mp.Vector3(), # Gamma ] k_interp = 4 # number of k_points to interpolate @@ -43,7 +54,7 @@ geometry=geometry, k_points=k_points, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) @@ -51,11 +62,12 @@ def main(): ms.run_tm() ms.run_te() + # Since there is a complete gap, we could instead see it just by using: # run() # The gap is between bands 12 and 13 in this case. (Note that there is # a false gap between bands 2 and 3, which disappears as you increase the # k_point resolution.) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/mpb_line_defect.py b/python/examples/mpb_line_defect.py index fcbb69752..8f1941b64 100644 --- a/python/examples/mpb_line_defect.py +++ b/python/examples/mpb_line_defect.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb @@ -13,9 +12,11 @@ supercell_y = 7 # the (odd) number of lateral supercell periods -geometry_lattice = mp.Lattice(size=mp.Vector3(1, supercell_y), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, supercell_y), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) eps = 12 # the dielectric constant of the rods r = 0.2 # the rod radius in the bulk crystal @@ -29,7 +30,9 @@ geometry += [mp.Cylinder(r, material=mp.air)] Gamma = mp.Vector3() -K_prime = mp.lattice_to_reciprocal(mp.Vector3(0.5), geometry_lattice) # edge of Brillouin zone. +K_prime = mp.lattice_to_reciprocal( + mp.Vector3(0.5), geometry_lattice +) # edge of Brillouin zone. k_points = mp.interpolate(4, [Gamma, K_prime]) # the bigger the supercell, the more bands you need to compute to get @@ -44,7 +47,7 @@ geometry=geometry, k_points=k_points, num_bands=num_bands, - resolution=resolution + resolution=resolution, ) @@ -53,8 +56,12 @@ def main(): # band. (In general, the guided mode in such an air defect may have # exited the gap by the time it reaches the edge of the Brillouin # zone at K_prime.) - ms.run_tm(mpb.output_at_kpoint(k_points[len(k_points) // 2]), ms.fix_efield_phase, - mpb.output_efield_z) + ms.run_tm( + mpb.output_at_kpoint(k_points[len(k_points) // 2]), + ms.fix_efield_phase, + mpb.output_efield_z, + ) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/mpb_sq_rods.py b/python/examples/mpb_sq_rods.py index d78c2fd8e..b752cf03d 100644 --- a/python/examples/mpb_sq_rods.py +++ b/python/examples/mpb_sq_rods.py @@ -1,6 +1,5 @@ -from __future__ import division - import time + import meep as mp from meep import mpb @@ -32,7 +31,7 @@ geometry=geometry, k_points=k_points, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) @@ -41,9 +40,10 @@ def main(): t0 = time.time() ms.run_te() ms.run_tm() - print("total time for both TE and TM bands: {:.2f} seconds".format(time.time() - t0)) + print(f"total time for both TE and TM bands: {time.time() - t0:.2f} seconds") ms.display_eigensolver_stats() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_strip.py b/python/examples/mpb_strip.py index fd918fdcd..b7303c299 100644 --- a/python/examples/mpb_strip.py +++ b/python/examples/mpb_strip.py @@ -1,5 +1,3 @@ -from __future__ import division - import meep as mp from meep import mpb @@ -28,9 +26,14 @@ geometry_lattice = mp.Lattice(size=mp.Vector3(0, sc_y, sc_z)) # define the 2d blocks for the strip and substrate -geometry = [mp.Block(size=mp.Vector3(mp.inf, mp.inf, 0.5 * (sc_z - h)), - center=mp.Vector3(z=0.25 * (sc_z + h)), material=SiO2), - mp.Block(size=mp.Vector3(mp.inf, w, h), material=Si)] +geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, 0.5 * (sc_z - h)), + center=mp.Vector3(z=0.25 * (sc_z + h)), + material=SiO2, + ), + mp.Block(size=mp.Vector3(mp.inf, w, h), material=Si), +] # The k (i.e. beta, i.e. propagation constant) points to look at, in # units of 2*pi/um. We'll look at num_k points from k_min to k_max. @@ -46,7 +49,7 @@ # is the corresponding column in the output if you grep for "freqs:".) num_bands = 4 -filename_prefix = 'strip-' # use this prefix for output files +filename_prefix = "strip-" # use this prefix for output files ms = mpb.ModeSolver( geometry_lattice=geometry_lattice, @@ -54,7 +57,7 @@ k_points=k_points, resolution=resolution, num_bands=num_bands, - filename_prefix=filename_prefix + filename_prefix=filename_prefix, ) @@ -75,9 +78,21 @@ def main(): omega = 1 / 1.55 # frequency corresponding to 1.55um # Output the x component of the Poynting vector for num_bands bands at omega - ms.find_k(mp.NO_PARITY, omega, 1, num_bands, mp.Vector3(1), 1e-3, omega * 3.45, - omega * 0.1, omega * 4, mpb.output_poynting_x, mpb.display_yparities, - mpb.display_group_velocities) - -if __name__ == '__main__': + ms.find_k( + mp.NO_PARITY, + omega, + 1, + num_bands, + mp.Vector3(1), + 1e-3, + omega * 3.45, + omega * 0.1, + omega * 4, + mpb.output_poynting_x, + mpb.display_yparities, + mpb.display_group_velocities, + ) + + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_tri_holes.py b/python/examples/mpb_tri_holes.py index 9c89b8bb8..0365546ff 100644 --- a/python/examples/mpb_tri_holes.py +++ b/python/examples/mpb_tri_holes.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb @@ -11,17 +10,19 @@ # first, define the lattice vectors and k-points for a triangular lattice: -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) kz = 0 # use non-zero kz to consider vertical propagation k_points = [ - mp.Vector3(z=kz), # Gamma - mp.Vector3(0, 0.5, kz), # M + mp.Vector3(z=kz), # Gamma + mp.Vector3(0, 0.5, kz), # M mp.Vector3(1 / -3, 1 / 3, kz), # K - mp.Vector3(z=kz) # Gamma + mp.Vector3(z=kz), # Gamma ] k_interp = 4 @@ -44,7 +45,7 @@ k_points=k_points, default_material=default_material, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) @@ -55,5 +56,6 @@ def main(): else: ms.run() # if kz != 0 there are no purely te and tm bands -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_tri_rods.py b/python/examples/mpb_tri_rods.py index 0052cae88..f1ac47447 100644 --- a/python/examples/mpb_tri_rods.py +++ b/python/examples/mpb_tri_rods.py @@ -1,6 +1,5 @@ -from __future__ import division - import math + import meep as mp from meep import mpb @@ -10,17 +9,19 @@ num_bands = 8 -geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] k_points = [ - mp.Vector3(), # Gamma - mp.Vector3(y=0.5), # M + mp.Vector3(), # Gamma + mp.Vector3(y=0.5), # M mp.Vector3(1 / -3, 1 / 3), # K - mp.Vector3(), # Gamma + mp.Vector3(), # Gamma ] k_points = mp.interpolate(4, k_points) @@ -32,14 +33,18 @@ geometry_lattice=geometry_lattice, k_points=k_points, resolution=resolution, - num_bands=num_bands + num_bands=num_bands, ) def main(): - ms.run_tm(mpb.output_at_kpoint(mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, - mpb.output_efield_z)) + ms.run_tm( + mpb.output_at_kpoint( + mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, mpb.output_efield_z + ) + ) ms.run_te() -if __name__ == '__main__': + +if __name__ == "__main__": main() diff --git a/python/examples/mpb_tutorial.py b/python/examples/mpb_tutorial.py index 7b3ded2b4..7e2df4c34 100644 --- a/python/examples/mpb_tutorial.py +++ b/python/examples/mpb_tutorial.py @@ -1,36 +1,40 @@ -from __future__ import division - import math + +from scipy.optimize import minimize_scalar, ridder + import meep as mp from meep import mpb -from scipy.optimize import minimize_scalar -from scipy.optimize import ridder def print_heading(h): stars = "*" * 10 print("{0} {1} {0}".format(stars, h)) + # Our First Band Structure print_heading("Square lattice of rods in air") num_bands = 8 -k_points = [mp.Vector3(), # Gamma - mp.Vector3(0.5), # X - mp.Vector3(0.5, 0.5), # M - mp.Vector3()] # Gamma +k_points = [ + mp.Vector3(), # Gamma + mp.Vector3(0.5), # X + mp.Vector3(0.5, 0.5), # M + mp.Vector3(), +] # Gamma k_points = mp.interpolate(4, k_points) geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1)) resolution = 32 -ms = mpb.ModeSolver(num_bands=num_bands, - k_points=k_points, - geometry=geometry, - geometry_lattice=geometry_lattice, - resolution=resolution) +ms = mpb.ModeSolver( + num_bands=num_bands, + k_points=k_points, + geometry=geometry, + geometry_lattice=geometry_lattice, + resolution=resolution, +) print_heading("Square lattice of rods: TE bands") ms.run_te() @@ -48,21 +52,25 @@ def print_heading(h): print_heading("Triangular lattice of rods in air") -ms.geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) +ms.geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 1), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), +) -ms.k_points = [mp.Vector3(), # Gamma - mp.Vector3(y=0.5), # M - mp.Vector3(-1 / 3, 1 / 3), # K - mp.Vector3()] # Gamma +ms.k_points = [ + mp.Vector3(), # Gamma + mp.Vector3(y=0.5), # M + mp.Vector3(-1 / 3, 1 / 3), # K + mp.Vector3(), +] # Gamma ms.k_points = mp.interpolate(4, k_points) ms.run_tm() # Maximizing the First TM Gap -print_heading('Maximizing the first TM gap') +print_heading("Maximizing the first TM gap") def first_tm_gap(r): @@ -70,18 +78,21 @@ def first_tm_gap(r): ms.run_tm() return -1 * ms.retrieve_gap(1) + ms.num_bands = 2 ms.mesh_size = 7 -result = minimize_scalar(first_tm_gap, method='bounded', bounds=[0.1, 0.5], options={'xatol': 0.1}) -print("radius at maximum: {}".format(result.x)) -print("gap size at maximum: {}".format(result.fun * -1)) +result = minimize_scalar( + first_tm_gap, method="bounded", bounds=[0.1, 0.5], options={"xatol": 0.1} +) +print(f"radius at maximum: {result.x}") +print(f"gap size at maximum: {result.fun * -1}") ms.mesh_size = 3 # Reset to default value of 3 # A Complete 2D Gap with an Anisotropic Dielectric -print_heading('Anisotropic complete 2d gap') +print_heading("Anisotropic complete 2d gap") ms.geometry = [mp.Cylinder(0.3, material=mp.Medium(epsilon_diag=mp.Vector3(1, 1, 12)))] @@ -91,7 +102,7 @@ def first_tm_gap(r): # Finding a Point-defect State -print_heading('5x5 point defect') +print_heading("5x5 point defect") ms.geometry_lattice = mp.Lattice(size=mp.Vector3(5, 5)) ms.geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] @@ -110,9 +121,9 @@ def first_tm_gap(r): ms.get_dfield(25) # compute the D field for band 25 ms.compute_field_energy() # compute the energy density from D c = mp.Cylinder(1.0, material=mp.air) -print("energy in cylinder: {}".format(ms.compute_energy_in_objects([c]))) +print(f"energy in cylinder: {ms.compute_energy_in_objects([c])}") -print_heading('5x5 point defect, targeted solver') +print_heading("5x5 point defect, targeted solver") ms.num_bands = 1 # only need to compute a single band, now! ms.target_freq = (0.2812 + 0.4174) / 2 @@ -121,7 +132,7 @@ def first_tm_gap(r): # Tuning the Point-defect Mode -print_heading('Tuning the 5x5 point defect') +print_heading("Tuning the 5x5 point defect") old_geometry = ms.geometry # save the 5x5 grid with a missing rod @@ -130,11 +141,12 @@ def rootfun(eps): # add the cylinder of epsilon = eps to the old geometry: ms.geometry = old_geometry + [mp.Cylinder(0.2, material=mp.Medium(epsilon=eps))] ms.run_tm() # solve for the mode (using the targeted solver) - print("epsilon = {} gives freq. = {}".format(eps, ms.get_freqs()[0])) + print(f"epsilon = {eps} gives freq. = {ms.get_freqs()[0]}") return ms.get_freqs()[0] - 0.314159 # return 1st band freq. - 0.314159 + rooteps = ridder(rootfun, 1, 12) -print("root (value of epsilon) is at: {}".format(rooteps)) +print(f"root (value of epsilon) is at: {rooteps}") rootval = rootfun(rooteps) -print("root function at {} = {}".format(rooteps, rootval)) +print(f"root function at {rooteps} = {rootval}") diff --git a/python/examples/multilevel-atom.py b/python/examples/multilevel-atom.py index d3326a3c6..0dc2f8784 100644 --- a/python/examples/multilevel-atom.py +++ b/python/examples/multilevel-atom.py @@ -1,16 +1,15 @@ -from __future__ import division - import math + import meep as mp # This file realizes a 1D, one-sided Fabry-Perot laser, as described in Fig. 2 of Optics Express, Vol. 20, pp. 474-88, 2012. # Cavity definitions resolution = 400 -ncav = 1.5 # cavity refractive index -Lcav = 1 # cavity length -dpad = 1 # padding thickness -dpml = 1 # PML thickness +ncav = 1.5 # cavity refractive index +Lcav = 1 # cavity length +dpad = 1 # padding thickness +dpml = 1 # PML thickness sz = Lcav + dpad + dpml cell_size = mp.Vector3(z=sz) dimensions = 1 @@ -24,15 +23,17 @@ # These different conventions can cause a bit of confusion when comparing against SALT, so here we perform # this transformation explicitly. -omega_a = 40 # omega_a in SALT -freq_21 = omega_a/(2*math.pi) # emission frequency (units of 2πc/a) +omega_a = 40 # omega_a in SALT +freq_21 = omega_a / (2 * math.pi) # emission frequency (units of 2πc/a) -gamma_perp = 4 # HWHM in angular frequency, SALT -gamma_21 = (2*gamma_perp)/(2*math.pi) # FWHM emission linewidth in sec^-1 (units of 2πc/a) +gamma_perp = 4 # HWHM in angular frequency, SALT +gamma_21 = (2 * gamma_perp) / ( + 2 * math.pi +) # FWHM emission linewidth in sec^-1 (units of 2πc/a) # Note that 2*pi*gamma_21 = 2*gamma_perp in SALT. -theta = 1 # theta, the off-diagonal dipole matrix element, in SALT -sigma_21 = 2*theta*theta*omega_a # dipole coupling strength (hbar = 1) +theta = 1 # theta, the off-diagonal dipole matrix element, in SALT +sigma_21 = 2 * theta * theta * omega_a # dipole coupling strength (hbar = 1) # The gain medium in MEEP is allowed to have an arbitrary number of levels, and is not # restricted to a two-level gain medium, as it simulates the populations of every individual @@ -55,32 +56,49 @@ # Gain medium pump and decay rates are specified in units of c/a. -rate_21 = 0.005 # non-radiative rate (units of c/a) -N0 = 37 # initial population density of ground state -Rp = 0.0051 # pumping rate of ground to excited state +rate_21 = 0.005 # non-radiative rate (units of c/a) +N0 = 37 # initial population density of ground state +Rp = 0.0051 # pumping rate of ground to excited state # so for example, these parameters have D_0 (SALT) = 0.0693. # Make the actual medium in MEEP: -transitions = [mp.Transition(1, 2, pumping_rate=Rp, frequency=freq_21, gamma=gamma_21, - sigma_diag=mp.Vector3(sigma_21,0,0)), - mp.Transition(2, 1, transition_rate=rate_21)] +transitions = [ + mp.Transition( + 1, + 2, + pumping_rate=Rp, + frequency=freq_21, + gamma=gamma_21, + sigma_diag=mp.Vector3(sigma_21, 0, 0), + ), + mp.Transition(2, 1, transition_rate=rate_21), +] ml_atom = mp.MultilevelAtom(sigma=1, transitions=transitions, initial_populations=[N0]) two_level = mp.Medium(index=ncav, E_susceptibilities=[ml_atom]) # Specify the cavity geometry: -geometry = [mp.Block(center=mp.Vector3(z=-0.5*sz+0.5*Lcav), - size=mp.Vector3(mp.inf,mp.inf,Lcav), material=two_level)] - -sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - geometry=geometry, - dimensions=dimensions) +geometry = [ + mp.Block( + center=mp.Vector3(z=-0.5 * sz + 0.5 * Lcav), + size=mp.Vector3(mp.inf, mp.inf, Lcav), + material=two_level, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + geometry=geometry, + dimensions=dimensions, +) sim.init_sim() + def field_func(p): - return 1 if p.z==-0.5*sz + 0.5*Lcav else 0 + return 1 if p.z == -0.5 * sz + 0.5 * Lcav else 0 + sim.fields.initialize_field(mp.Ex, field_func) @@ -89,8 +107,12 @@ def field_func(p): # Note that the total number of time steps run is endt*resolution*2. This is the origin of the extra # factor of 2 in the definition of dt in fieldfft_meep.m. + def print_field(sim): - fp = sim.get_field_point(mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad))).real - print("field:, {}, {}".format(sim.meep_time(), fp)) + fp = sim.get_field_point( + mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad)) + ).real + print(f"field:, {sim.meep_time()}, {fp}") + sim.run(mp.after_time(endt - 250, print_field), until=endt) diff --git a/python/examples/oblique-planewave.py b/python/examples/oblique-planewave.py index 4b3b971d4..b5dec9451 100644 --- a/python/examples/oblique-planewave.py +++ b/python/examples/oblique-planewave.py @@ -1,47 +1,53 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -cell_size = mp.Vector3(14,10,0) +cell_size = mp.Vector3(14, 10, 0) -pml_layers = [mp.PML(thickness=2,direction=mp.X)] +pml_layers = [mp.PML(thickness=2, direction=mp.X)] # rotation angle (in degrees) of planewave, counter clockwise (CCW) around z-axis rot_angle = np.radians(0) -fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc) +fsrc = 1.0 # frequency of planewave (wavelength = 1/fsrc) -n = 1.5 # refractive index of homogeneous material +n = 1.5 # refractive index of homogeneous material default_material = mp.Medium(index=n) -k_point = mp.Vector3(fsrc*n).rotate(mp.Vector3(z=1), rot_angle) - -sources = [mp.EigenModeSource(src=mp.ContinuousSource(fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=10), - direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION, - eig_kpoint=k_point, - eig_band=1, - eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, - eig_match_freq=True)] - -sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - k_point=k_point, - default_material=default_material, - symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else []) +k_point = mp.Vector3(fsrc * n).rotate(mp.Vector3(z=1), rot_angle) + +sources = [ + mp.EigenModeSource( + src=mp.ContinuousSource(fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=10), + direction=mp.AUTOMATIC if rot_angle == 0 else mp.NO_DIRECTION, + eig_kpoint=k_point, + eig_band=1, + eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, + eig_match_freq=True, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + k_point=k_point, + default_material=default_material, + symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [], +) sim.run(until=100) -nonpml_vol = mp.Volume(center=mp.Vector3(), size=mp.Vector3(10,10,0)) +nonpml_vol = mp.Volume(center=mp.Vector3(), size=mp.Vector3(10, 10, 0)) -sim.plot2D(fields=mp.Ez, - output_plane=nonpml_vol) +sim.plot2D(fields=mp.Ez, output_plane=nonpml_vol) if mp.am_master(): - plt.axis('off') - plt.savefig('pw.png',bbox_inches='tight') + plt.axis("off") + plt.savefig("pw.png", bbox_inches="tight") diff --git a/python/examples/oblique-source.py b/python/examples/oblique-source.py index 243825dc0..7e5a28da0 100644 --- a/python/examples/oblique-source.py +++ b/python/examples/oblique-source.py @@ -1,67 +1,96 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp -resolution = 50 # pixels/μm +resolution = 50 # pixels/μm -cell_size = mp.Vector3(14,14) +cell_size = mp.Vector3(14, 14) pml_layers = [mp.PML(thickness=2)] # rotation angle (in degrees) of waveguide, counter clockwise (CCW) around z-axis rot_angle = np.radians(20) -w = 1.0 # width of waveguide +w = 1.0 # width of waveguide -geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - e1=mp.Vector3(x=1).rotate(mp.Vector3(z=1), rot_angle), - e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), - material=mp.Medium(epsilon=12))] +geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + e1=mp.Vector3(x=1).rotate(mp.Vector3(z=1), rot_angle), + e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), + material=mp.Medium(epsilon=12), + ) +] -fsrc = 0.15 # frequency of eigenmode or constant-amplitude source -bnum = 1 # band number of eigenmode +fsrc = 0.15 # frequency of eigenmode or constant-amplitude source +bnum = 1 # band number of eigenmode kpoint = mp.Vector3(x=1).rotate(mp.Vector3(z=1), rot_angle) -compute_flux = True # compute flux (True) or plot the field profile (False) +compute_flux = True # compute flux (True) or plot the field profile (False) -eig_src = True # eigenmode (True) or constant-amplitude (False) source +eig_src = True # eigenmode (True) or constant-amplitude (False) source if eig_src: - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc) if compute_flux else mp.ContinuousSource(fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=3*w), - direction=mp.NO_DIRECTION, - eig_kpoint=kpoint, - eig_band=bnum, - eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, - eig_match_freq=True)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc) + if compute_flux + else mp.ContinuousSource(fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=3 * w), + direction=mp.NO_DIRECTION, + eig_kpoint=kpoint, + eig_band=bnum, + eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, + eig_match_freq=True, + ) + ] else: - sources = [mp.Source(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc) if compute_flux else mp.ContinuousSource(fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=3*w), - component=mp.Ez)] + sources = [ + mp.Source( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc) + if compute_flux + else mp.ContinuousSource(fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=3 * w), + component=mp.Ez, + ) + ] -sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else []) +sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)] if rot_angle == 0 else [], +) if compute_flux: - tran = sim.add_flux(fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14))) + tran = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14)) + ) sim.run(until_after_sources=50) - res = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint) - print("flux:, {:.6f}, {:.6f}".format(mp.get_fluxes(tran)[0],abs(res.alpha[0,0,0])**2)) + res = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z if rot_angle == 0 else mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: kpoint, + ) + print( + "flux:, {:.6f}, {:.6f}".format( + mp.get_fluxes(tran)[0], abs(res.alpha[0, 0, 0]) ** 2 + ) + ) else: sim.run(until=100) - sim.plot2D(output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(10,10)), - fields=mp.Ez, - field_parameters={'alpha':0.9}) + sim.plot2D( + output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(10, 10)), + fields=mp.Ez, + field_parameters={"alpha": 0.9}, + ) plt.show() diff --git a/python/examples/parallel-wvgs-force.py b/python/examples/parallel-wvgs-force.py index c7a3e2a42..99200c32d 100644 --- a/python/examples/parallel-wvgs-force.py +++ b/python/examples/parallel-wvgs-force.py @@ -1,96 +1,117 @@ -import meep as mp -import numpy as np import matplotlib.pyplot as plt +import numpy as np + +import meep as mp + +resolution = 40 # pixels/μm -resolution = 40 # pixels/μm - Si = mp.Medium(index=3.45) dpml = 1.0 pml_layers = [mp.PML(dpml)] - + sx = 5 sy = 3 -cell = mp.Vector3(sx+2*dpml,sy+2*dpml,0) +cell = mp.Vector3(sx + 2 * dpml, sy + 2 * dpml, 0) -a = 1.0 # waveguide width/height +a = 1.0 # waveguide width/height k_point = mp.Vector3(z=0.5) -def parallel_waveguide(s,xodd): - geometry = [mp.Block(center=mp.Vector3(-0.5*(s+a)), - size=mp.Vector3(a,a,mp.inf), - material=Si), - mp.Block(center=mp.Vector3(0.5*(s+a)), - size=mp.Vector3(a,a,mp.inf), - material=Si)] - - symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1), - mp.Mirror(mp.Y, phase=-1)] - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - symmetries=symmetries, - k_point=k_point) +def parallel_waveguide(s, xodd): + geometry = [ + mp.Block( + center=mp.Vector3(-0.5 * (s + a)), + size=mp.Vector3(a, a, mp.inf), + material=Si, + ), + mp.Block( + center=mp.Vector3(0.5 * (s + a)), size=mp.Vector3(a, a, mp.inf), material=Si + ), + ] + + symmetries = [mp.Mirror(mp.X, phase=-1 if xodd else 1), mp.Mirror(mp.Y, phase=-1)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + symmetries=symmetries, + k_point=k_point, + ) sim.init_sim() - EigenmodeData = sim.get_eigenmode(0.22, - mp.Z, - mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx,sy)), - 2 if xodd else 1, - k_point, - match_frequency=False, - parity=mp.ODD_Y) + EigenmodeData = sim.get_eigenmode( + 0.22, + mp.Z, + mp.Volume(center=mp.Vector3(), size=mp.Vector3(sx, sy)), + 2 if xodd else 1, + k_point, + match_frequency=False, + parity=mp.ODD_Y, + ) fcen = EigenmodeData.freq - print("freq:, {}, {}, {}".format("xodd" if xodd else "xeven", s, fcen)) + print(f'freq:, {"xodd" if xodd else "xeven"}, {s}, {fcen}') sim.reset_meep() - eig_sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=0.1*fcen), - size=mp.Vector3(sx,sy), - center=mp.Vector3(), - eig_band=2 if xodd else 1, - eig_kpoint=k_point, - eig_match_freq=False, - eig_parity=mp.ODD_Y)] + eig_sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.1 * fcen), + size=mp.Vector3(sx, sy), + center=mp.Vector3(), + eig_band=2 if xodd else 1, + eig_kpoint=k_point, + eig_match_freq=False, + eig_parity=mp.ODD_Y, + ) + ] sim.change_sources(eig_sources) - flux_reg = mp.FluxRegion(direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx,sy)) + flux_reg = mp.FluxRegion( + direction=mp.Z, center=mp.Vector3(), size=mp.Vector3(sx, sy) + ) wvg_flux = sim.add_flux(fcen, 0, 1, flux_reg) - force_reg1 = mp.ForceRegion(mp.Vector3(0.49*s), direction=mp.X, weight=1, size=mp.Vector3(y=sy)) - force_reg2 = mp.ForceRegion(mp.Vector3(0.5*s+1.01*a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy)) + force_reg1 = mp.ForceRegion( + mp.Vector3(0.49 * s), direction=mp.X, weight=1, size=mp.Vector3(y=sy) + ) + force_reg2 = mp.ForceRegion( + mp.Vector3(0.5 * s + 1.01 * a), direction=mp.X, weight=-1, size=mp.Vector3(y=sy) + ) wvg_force = sim.add_force(fcen, 0, 1, force_reg1, force_reg2) sim.run(until_after_sources=1500) flux = mp.get_fluxes(wvg_flux)[0] force = mp.get_forces(wvg_force)[0] - print("data:, {}, {}, {}, {}, {}".format("xodd" if xodd else "xeven", s, flux, force, -force/flux)) + print( + f'data:, {"xodd" if xodd else "xeven"}, {s}, {flux}, {force}, {-force / flux}' + ) sim.reset_meep() return flux, force -s = np.arange(0.05,1.05,0.05) +s = np.arange(0.05, 1.05, 0.05) fluxes_odd = np.zeros(s.size) forces_odd = np.zeros(s.size) fluxes_even = np.zeros(s.size) forces_even = np.zeros(s.size) for k in range(len(s)): - fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k],True) - fluxes_even[k], forces_even[k] = parallel_waveguide(s[k],False) + fluxes_odd[k], forces_odd[k] = parallel_waveguide(s[k], True) + fluxes_even[k], forces_even[k] = parallel_waveguide(s[k], False) plt.figure(dpi=150) -plt.plot(s,-forces_odd/fluxes_odd,'rs',label='anti-symmetric') -plt.plot(s,-forces_even/fluxes_even,'bo',label='symmetric') +plt.plot(s, -forces_odd / fluxes_odd, "rs", label="anti-symmetric") +plt.plot(s, -forces_even / fluxes_even, "bo", label="symmetric") plt.grid(True) -plt.xlabel('waveguide separation s/a') -plt.ylabel('optical force (F/L)(ac/P)') -plt.legend(loc='upper right') +plt.xlabel("waveguide separation s/a") +plt.ylabel("optical force (F/L)(ac/P)") +plt.legend(loc="upper right") plt.show() diff --git a/python/examples/parallel-wvgs-mpb.py b/python/examples/parallel-wvgs-mpb.py index 1436b908c..84420fe26 100644 --- a/python/examples/parallel-wvgs-mpb.py +++ b/python/examples/parallel-wvgs-mpb.py @@ -1,35 +1,43 @@ -# -*- coding: utf-8 -*- +import matplotlib.pyplot as plt +import numpy as np import meep as mp from meep import mpb -import numpy as np -import matplotlib.pyplot as plt resolution = 128 # pixels/μm Si = mp.Medium(index=3.45) syz = 10 -geometry_lattice = mp.Lattice(size=mp.Vector3(0,syz,syz)) +geometry_lattice = mp.Lattice(size=mp.Vector3(0, syz, syz)) k_points = [mp.Vector3(0.5)] a = 1.0 # waveguide width -def parallel_waveguide(s,yodd): - geometry = [mp.Block(center=mp.Vector3(0,-0.5*(s+a),0), - size=mp.Vector3(mp.inf,a,a), - material=Si), - mp.Block(center=mp.Vector3(0,0.5*(s+a),0), - size=mp.Vector3(mp.inf,a,a), - material=Si)] - - ms = mpb.ModeSolver(resolution=resolution, - k_points=k_points, - geometry_lattice=geometry_lattice, - geometry=geometry, - num_bands=1, - tolerance=1e-9) + +def parallel_waveguide(s, yodd): + geometry = [ + mp.Block( + center=mp.Vector3(0, -0.5 * (s + a), 0), + size=mp.Vector3(mp.inf, a, a), + material=Si, + ), + mp.Block( + center=mp.Vector3(0, 0.5 * (s + a), 0), + size=mp.Vector3(mp.inf, a, a), + material=Si, + ), + ] + + ms = mpb.ModeSolver( + resolution=resolution, + k_points=k_points, + geometry_lattice=geometry_lattice, + geometry=geometry, + num_bands=1, + tolerance=1e-9, + ) if yodd: ms.run_yodd_zodd() @@ -37,11 +45,12 @@ def parallel_waveguide(s,yodd): ms.run_yeven_zodd() f = ms.get_freqs()[0] - vg = ms.compute_group_velocity_component(mp.Vector3(1,0,0))[0] + vg = ms.compute_group_velocity_component(mp.Vector3(1, 0, 0))[0] + + return f, vg - return f,vg -ss = np.arange(0.025,1.075,0.05) +ss = np.arange(0.025, 1.075, 0.05) f_odd = np.zeros(len(ss)) vg_odd = np.zeros(len(ss)) @@ -49,26 +58,28 @@ def parallel_waveguide(s,yodd): vg_even = np.zeros(len(ss)) for j in range(len(ss)): - f_odd[j], vg_odd[j] = parallel_waveguide(ss[j],True) - f_even[j], vg_even[j] = parallel_waveguide(ss[j],False) + f_odd[j], vg_odd[j] = parallel_waveguide(ss[j], True) + f_even[j], vg_even[j] = parallel_waveguide(ss[j], False) + +ds = ss[1] - ss[0] + -ds = ss[1]-ss[0] +def compute_force(f, vg): + f_avg = 0.5 * (f[:-1] + f[1:]) + df = f[1:] - f[:-1] + vg_avg = 0.5 * (vg[:-1] + vg[1:]) + return -1 / f_avg * df / ds * 1 / vg_avg -def compute_force(f,vg): - f_avg = 0.5*(f[:-1]+f[1:]) - df = f[1:]-f[:-1] - vg_avg = 0.5*(vg[:-1]+vg[1:]) - return -1/f_avg * df/ds * 1/vg_avg -force_odd = compute_force(f_odd,vg_odd) -force_even = compute_force(f_even,vg_even) +force_odd = compute_force(f_odd, vg_odd) +force_even = compute_force(f_even, vg_even) plt.figure(dpi=200) -plt.plot(ss[:-1],force_odd,'b-',label='anti-symmetric') -plt.plot(ss[:-1],force_even,'r-',label='symmetric') +plt.plot(ss[:-1], force_odd, "b-", label="anti-symmetric") +plt.plot(ss[:-1], force_even, "r-", label="symmetric") plt.xlabel("waveguide separation s/a") plt.ylabel("optical force (F/L)(ac/P)") -plt.legend(loc='upper right') -plt.xticks(np.arange(0,1.2,0.2)) -plt.yticks(np.arange(-1.5,1.0,0.5)) +plt.legend(loc="upper right") +plt.xticks(np.arange(0, 1.2, 0.2)) +plt.yticks(np.arange(-1.5, 1.0, 0.5)) plt.show() diff --git a/python/examples/perturbation_theory.py b/python/examples/perturbation_theory.py index b6a7d5e4f..5782b48ab 100644 --- a/python/examples/perturbation_theory.py +++ b/python/examples/perturbation_theory.py @@ -1,52 +1,63 @@ -import meep as mp -import numpy as np import argparse +import numpy as np + +import meep as mp + def main(args): if args.perpendicular: src_cmpt = mp.Hz - fcen = 0.21 # pulse center frequency + fcen = 0.21 # pulse center frequency else: src_cmpt = mp.Ez - fcen = 0.17 # pulse center frequency + fcen = 0.17 # pulse center frequency - n = 3.4 # index of waveguide - w = 1 # ring width - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 2 # thickness of PML - m = 5 # angular dependence + n = 3.4 # index of waveguide + w = 1 # ring width + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 2 # thickness of PML + m = 5 # angular dependence pml_layers = [mp.PML(dpml)] - sr = r + w + pad + dpml # radial size (cell is from 0 to sr) - dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z) + sr = r + w + pad + dpml # radial size (cell is from 0 to sr) + dimensions = mp.CYLINDRICAL # coordinate system is (r,phi,z) instead of (x,y,z) cell = mp.Vector3(sr) - geometry = [mp.Block(center=mp.Vector3(r + (w / 2)), - size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n))] + geometry = [ + mp.Block( + center=mp.Vector3(r + (w / 2)), + size=mp.Vector3(w, mp.inf, mp.inf), + material=mp.Medium(index=n), + ) + ] # find resonant frequency of unperturbed geometry using broadband source - df = 0.2*fcen # pulse width (in frequency) - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - dimensions=dimensions, - m=m) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + df = 0.2 * fcen # pulse width (in frequency) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ) + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + dimensions=dimensions, + m=m, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_unperturbed = h.modes[0].freq @@ -55,34 +66,68 @@ def main(args): # unperturbed geometry with narrowband source centered at resonant frequency fcen = frq_unperturbed - df = 0.05*fcen - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - dimensions=dimensions, - m=m) + df = 0.05 * fcen + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ) + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + dimensions=dimensions, + m=m, + ) sim.run(until_after_sources=100) deps = 1 - n**2 - deps_inv = 1 - 1/n**2 + deps_inv = 1 - 1 / n**2 if args.perpendicular: - para_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ep, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ep, mp.Vector3(r+w)))**2) - perp_integral = deps_inv*2*np.pi*(-r*abs(sim.get_field_point(mp.Dr, mp.Vector3(r)))**2 + (r+w)*abs(sim.get_field_point(mp.Dr, mp.Vector3(r+w)))**2) + para_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ep, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ep, mp.Vector3(r + w))) ** 2 + ) + ) + perp_integral = ( + deps_inv + * 2 + * np.pi + * ( + -r * abs(sim.get_field_point(mp.Dr, mp.Vector3(r))) ** 2 + + (r + w) * abs(sim.get_field_point(mp.Dr, mp.Vector3(r + w))) ** 2 + ) + ) numerator_integral = para_integral + perp_integral else: - numerator_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ez, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ez, mp.Vector3(r+w)))**2) - - denominator_integral = sim.electric_energy_in_box(center=mp.Vector3(0.5*sr), size=mp.Vector3(sr)) - perturb_theory_dw_dR = -frq_unperturbed * numerator_integral / (4 * denominator_integral) + numerator_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ez, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ez, mp.Vector3(r + w))) ** 2 + ) + ) + + denominator_integral = sim.electric_energy_in_box( + center=mp.Vector3(0.5 * sr), size=mp.Vector3(sr) + ) + perturb_theory_dw_dR = ( + -frq_unperturbed * numerator_integral / (4 * denominator_integral) + ) sim.reset_meep() @@ -90,35 +135,53 @@ def main(args): dr = 0.01 - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=src_cmpt, - center=mp.Vector3(r + dr + 0.1))] - - geometry = [mp.Block(center=mp.Vector3(r + dr + (w / 2)), - size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - dimensions=dimensions, - m=m) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+dr+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + dr + 0.1), + ) + ] + + geometry = [ + mp.Block( + center=mp.Vector3(r + dr + (w / 2)), + size=mp.Vector3(w, mp.inf, mp.inf), + material=mp.Medium(index=n), + ) + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + dimensions=dimensions, + m=m, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + dr + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_perturbed = h.modes[0].freq finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr - print("dwdR:, {} (pert. theory), {} (finite diff.)".format(perturb_theory_dw_dR,finite_diff_dw_dR)) + print( + f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-perpendicular', action='store_true', help='use perpendicular field source (default: parallel field source)') - parser.add_argument('-res', type=int, default=100, help='resolution (default: 100 pixels/um)') + parser.add_argument( + "-perpendicular", + action="store_true", + help="use perpendicular field source (default: parallel field source)", + ) + parser.add_argument( + "-res", type=int, default=100, help="resolution (default: 100 pixels/um)" + ) args = parser.parse_args() main(args) diff --git a/python/examples/perturbation_theory_2d.py b/python/examples/perturbation_theory_2d.py index 13f2d5d2f..08cb3a5ea 100644 --- a/python/examples/perturbation_theory_2d.py +++ b/python/examples/perturbation_theory_2d.py @@ -1,61 +1,73 @@ -import meep as mp -import numpy as np import argparse +import numpy as np + +import meep as mp + def main(args): if args.perpendicular: src_cmpt = mp.Hz - fcen = 0.21 # pulse center frequency + fcen = 0.21 # pulse center frequency else: src_cmpt = mp.Ez - fcen = 0.17 # pulse center frequency + fcen = 0.17 # pulse center frequency - n = 3.4 # index of waveguide - w = 1 # ring width - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 2 # thickness of PML + n = 3.4 # index of waveguide + w = 1 # ring width + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 2 # thickness of PML pml_layers = [mp.PML(dpml)] - sxy = 2*(r+w+pad+dpml) - cell_size = mp.Vector3(sxy,sxy) + sxy = 2 * (r + w + pad + dpml) + cell_size = mp.Vector3(sxy, sxy) - symmetries = [mp.Mirror(mp.X,phase=+1 if args.perpendicular else -1), - mp.Mirror(mp.Y,phase=-1 if args.perpendicular else +1)] + symmetries = [ + mp.Mirror(mp.X, phase=+1 if args.perpendicular else -1), + mp.Mirror(mp.Y, phase=-1 if args.perpendicular else +1), + ] - geometry = [mp.Cylinder(material=mp.Medium(index=n), - radius=r+w, - height=mp.inf, - center=mp.Vector3()), - mp.Cylinder(material=mp.vacuum, - radius=r, - height=mp.inf, - center=mp.Vector3())] + geometry = [ + mp.Cylinder( + material=mp.Medium(index=n), + radius=r + w, + height=mp.inf, + center=mp.Vector3(), + ), + mp.Cylinder(material=mp.vacuum, radius=r, height=mp.inf, center=mp.Vector3()), + ] # find resonant frequency of unperturbed geometry using broadband source - df = 0.2*fcen # pulse width (in frequency) - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - symmetries=symmetries) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + df = 0.2 * fcen # pulse width (in frequency) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + symmetries=symmetries, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_unperturbed = h.modes[0].freq @@ -64,37 +76,73 @@ def main(args): # unperturbed geometry with narrowband source centered at resonant frequency fcen = frq_unperturbed - df = 0.05*fcen - - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - symmetries=symmetries) + df = 0.05 * fcen + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + 0.1), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + symmetries=symmetries, + ) sim.run(until_after_sources=100) deps = 1 - n**2 - deps_inv = 1 - 1/n**2 + deps_inv = 1 - 1 / n**2 if args.perpendicular: - para_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ey, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ey, mp.Vector3(r+w)))**2) - perp_integral = deps_inv*2*np.pi*(-r*abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r)))**2 + (r+w)*abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r+w)))**2) + para_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ey, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ey, mp.Vector3(r + w))) ** 2 + ) + ) + perp_integral = ( + deps_inv + * 2 + * np.pi + * ( + -r * abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r))) ** 2 + + (r + w) * abs(sim.get_field_point(mp.Dy, mp.Vector3(y=r + w))) ** 2 + ) + ) numerator_integral = para_integral + perp_integral else: - numerator_integral = deps*2*np.pi*(r*abs(sim.get_field_point(mp.Ez, mp.Vector3(r)))**2 - (r+w)*abs(sim.get_field_point(mp.Ez, mp.Vector3(r+w)))**2) - - denominator_integral = sim.electric_energy_in_box(center=mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml)) - perturb_theory_dw_dR = -frq_unperturbed * numerator_integral / (8 * denominator_integral) + numerator_integral = ( + deps + * 2 + * np.pi + * ( + r * abs(sim.get_field_point(mp.Ez, mp.Vector3(r))) ** 2 + - (r + w) * abs(sim.get_field_point(mp.Ez, mp.Vector3(r + w))) ** 2 + ) + ) + + denominator_integral = sim.electric_energy_in_box( + center=mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml) + ) + perturb_theory_dw_dR = ( + -frq_unperturbed * numerator_integral / (8 * denominator_integral) + ) # perturbed geometry with narrowband source @@ -102,43 +150,62 @@ def main(args): sim.reset_meep() - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(r+dr+0.1)), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=src_cmpt, - center=mp.Vector3(-(r+dr+0.1)), - amplitude=-1)] - - geometry = [mp.Cylinder(material=mp.Medium(index=n), - radius=r+dr+w, - height=mp.inf, - center=mp.Vector3()), - mp.Cylinder(material=mp.vacuum, - radius=r+dr, - height=mp.inf, - center=mp.Vector3())] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - resolution=args.res, - sources=sources, - symmetries=symmetries) - - h = mp.Harminv(src_cmpt, mp.Vector3(r+dr+0.1), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(r + dr + 0.1), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=src_cmpt, + center=mp.Vector3(-(r + dr + 0.1)), + amplitude=-1, + ), + ] + + geometry = [ + mp.Cylinder( + material=mp.Medium(index=n), + radius=r + dr + w, + height=mp.inf, + center=mp.Vector3(), + ), + mp.Cylinder( + material=mp.vacuum, radius=r + dr, height=mp.inf, center=mp.Vector3() + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + resolution=args.res, + sources=sources, + symmetries=symmetries, + ) + + h = mp.Harminv(src_cmpt, mp.Vector3(r + dr + 0.1), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=100) frq_perturbed = h.modes[0].freq finite_diff_dw_dR = (frq_perturbed - frq_unperturbed) / dr - print("dwdR:, {} (pert. theory), {} (finite diff.)".format(perturb_theory_dw_dR,finite_diff_dw_dR)) + print( + f"dwdR:, {perturb_theory_dw_dR} (pert. theory), {finite_diff_dw_dR} (finite diff.)" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-perpendicular', action='store_true', help='use perpendicular field source (default: parallel field source)') - parser.add_argument('-res', type=int, default=30, help='resolution (default: 30 pixels/um)') + parser.add_argument( + "-perpendicular", + action="store_true", + help="use perpendicular field source (default: parallel field source)", + ) + parser.add_argument( + "-res", type=int, default=30, help="resolution (default: 30 pixels/um)" + ) args = parser.parse_args() main(args) diff --git a/python/examples/phase_in_material.py b/python/examples/phase_in_material.py index 5f629579a..6ccdc0eb0 100644 --- a/python/examples/phase_in_material.py +++ b/python/examples/phase_in_material.py @@ -1,26 +1,25 @@ import meep as mp -cell_size = mp.Vector3(6,6,0) +cell_size = mp.Vector3(6, 6, 0) -geometry1 = [mp.Cylinder(center=mp.Vector3(),radius=1.0,material=mp.Medium(index=3.5))] +geometry1 = [ + mp.Cylinder(center=mp.Vector3(), radius=1.0, material=mp.Medium(index=3.5)) +] -sim1 = mp.Simulation(cell_size=cell_size, - geometry=geometry1, - resolution=20) +sim1 = mp.Simulation(cell_size=cell_size, geometry=geometry1, resolution=20) sim1.init_sim() -geometry2 = [mp.Cylinder(center=mp.Vector3(1,1),radius=1.0,material=mp.Medium(index=3.5))] +geometry2 = [ + mp.Cylinder(center=mp.Vector3(1, 1), radius=1.0, material=mp.Medium(index=3.5)) +] -sim2 = mp.Simulation(cell_size=cell_size, - geometry=geometry2, - resolution=20) +sim2 = mp.Simulation(cell_size=cell_size, geometry=geometry2, resolution=20) sim2.init_sim() -sim1.fields.phase_in_material(sim2.structure,10.0) - -sim1.run(mp.at_beginning(mp.output_epsilon), - mp.at_every(0.5,mp.output_epsilon), - until=10) +sim1.fields.phase_in_material(sim2.structure, 10.0) +sim1.run( + mp.at_beginning(mp.output_epsilon), mp.at_every(0.5, mp.output_epsilon), until=10 +) diff --git a/python/examples/planar_cavity_ldos.py b/python/examples/planar_cavity_ldos.py index ac9fd5cc5..f421c77d8 100644 --- a/python/examples/planar_cavity_ldos.py +++ b/python/examples/planar_cavity_ldos.py @@ -2,14 +2,13 @@ # dielectric cavity with lossless metallic walls. The result is computed in # cylindrical and 3D coordinates and compared with the analytic theory from: # I. Abram et al., IEEE J. Quantum Electronics, Vol. 34, pp. 71-76 (1998). - +import matplotlib +import numpy as np import meep as mp -import numpy as np -import matplotlib -matplotlib.use('agg') -import matplotlib.pyplot as plt +matplotlib.use("agg") +import matplotlib.pyplot as plt # important note: # Meep may round the cell dimensions to an integer number @@ -17,169 +16,196 @@ resolution = 70 # pixels/μm -dpml = 0.5 # thickness of PML -L = 6.0 # length of non-PML region -n = 2.4 # refractive index of surrounding medium -wvl = 1.0 # wavelength (in vacuum) +dpml = 0.5 # thickness of PML +L = 6.0 # length of non-PML region +n = 2.4 # refractive index of surrounding medium +wvl = 1.0 # wavelength (in vacuum) -fcen = 1/wvl +fcen = 1 / wvl def bulk_ldos_cyl(): - sr = L+dpml - sz = L+2*dpml - cell_size = mp.Vector3(sr,0,sz) + sr = L + dpml + sz = L + 2 * dpml + cell_size = mp.Vector3(sr, 0, sz) pml_layers = [mp.PML(dpml)] - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Er, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] def cavity_ldos_cyl(sz): - sr = L+dpml - cell_size = mp.Vector3(sr,0,sz) - - pml_layers = [mp.PML(dpml,direction=mp.R)] - - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + sr = L + dpml + cell_size = mp.Vector3(sr, 0, sz) + + pml_layers = [mp.PML(dpml, direction=mp.R)] + + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Er, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] def bulk_ldos_3D(): - s = L+2*dpml - cell_size = mp.Vector3(s,s,s) + s = L + 2 * dpml + cell_size = mp.Vector3(s, s, s) pml_layers = [mp.PML(dpml)] - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Ex, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] def cavity_ldos_3D(sz): - sxy = L+2*dpml - cell_size = mp.Vector3(sxy,sxy,sz) - - boundary_layers = [mp.PML(dpml,direction=mp.X), - mp.PML(dpml,direction=mp.Y)] - - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=n)) - - sim.run(mp.dft_ldos(fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + sxy = L + 2 * dpml + cell_size = mp.Vector3(sxy, sxy, sz) + + boundary_layers = [mp.PML(dpml, direction=mp.X), mp.PML(dpml, direction=mp.Y)] + + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=n), + ) + + sim.run( + mp.dft_ldos(fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed(20, mp.Ex, mp.Vector3(), 1e-6), + ) return sim.ldos_data[0] -if __name__ == '__main__': +if __name__ == "__main__": ldos_bulk_cyl = bulk_ldos_cyl() ldos_bulk_3D = bulk_ldos_3D() # units of wavelength in medium - cavity_thickness = np.arange(0.50,2.55,0.05) + cavity_thickness = np.arange(0.50, 2.55, 0.05) - gap = cavity_thickness*wvl/n + gap = cavity_thickness * wvl / n ldos_cavity_cyl = np.zeros(len(cavity_thickness)) ldos_cavity_3D = np.zeros(len(cavity_thickness)) - for idx,g in enumerate(gap): + for idx, g in enumerate(gap): ldos_cavity_cyl[idx] = cavity_ldos_cyl(g) ldos_cavity_3D[idx] = cavity_ldos_3D(g) - print("purcell-enh:, {:.3f}, " - "{:.6f} (cyl.), {:.6f} (3D)".format(cavity_thickness[idx], - ldos_cavity_cyl[idx]/ldos_bulk_cyl, - ldos_cavity_3D[idx]/ldos_bulk_3D)) + print( + "purcell-enh:, {:.3f}, " + "{:.6f} (cyl.), {:.6f} (3D)".format( + cavity_thickness[idx], + ldos_cavity_cyl[idx] / ldos_bulk_cyl, + ldos_cavity_3D[idx] / ldos_bulk_3D, + ) + ) # Purcell enhancement factor (relative to bulk medium) pe_meep_cyl = ldos_cavity_cyl / ldos_bulk_cyl pe_meep_3D = ldos_cavity_3D / ldos_bulk_3D # equation 7 of reference - pe_theory = (3*np.fix(cavity_thickness+0.5)/(4*cavity_thickness) + - (4*np.power(np.fix(cavity_thickness+0.5),3) - - np.fix(cavity_thickness+0.5)) / - (16*np.power(cavity_thickness,3))) + pe_theory = 3 * np.fix(cavity_thickness + 0.5) / (4 * cavity_thickness) + ( + 4 * np.power(np.fix(cavity_thickness + 0.5), 3) - np.fix(cavity_thickness + 0.5) + ) / (16 * np.power(cavity_thickness, 3)) if mp.am_master(): - plt.plot(cavity_thickness,pe_meep_3D,'b-',label='Meep (3D)') - plt.plot(cavity_thickness,pe_meep_cyl,'r-',label='Meep (cylin.)') - plt.plot(cavity_thickness,pe_theory,'g-',label='theory') - plt.plot(cavity_thickness,np.ones(len(cavity_thickness)),'k--') - plt.xlabel('cavity thickness, $nL/\lambda$') - plt.ylabel('Purcell enhancement factor') - plt.title("planar point dipole at λ=1.0 μm in a planar cavity\n" - "with n=2.4 and lossless metallic walls") - plt.axis([0.5,2.5,0.4,3.1]) + plt.plot(cavity_thickness, pe_meep_3D, "b-", label="Meep (3D)") + plt.plot(cavity_thickness, pe_meep_cyl, "r-", label="Meep (cylin.)") + plt.plot(cavity_thickness, pe_theory, "g-", label="theory") + plt.plot(cavity_thickness, np.ones(len(cavity_thickness)), "k--") + plt.xlabel(r"cavity thickness, $nL/\lambda$") + plt.ylabel("Purcell enhancement factor") + plt.title( + "planar point dipole at λ=1.0 μm in a planar cavity\n" + "with n=2.4 and lossless metallic walls" + ) + plt.axis([0.5, 2.5, 0.4, 3.1]) plt.legend() - plt.savefig('cavity_purcell_factor_vs_thickness.png', - bbox_inches='tight') + plt.savefig("cavity_purcell_factor_vs_thickness.png", bbox_inches="tight") diff --git a/python/examples/polarization_grating.py b/python/examples/polarization_grating.py index 8cccb1a96..23fbee9a4 100644 --- a/python/examples/polarization_grating.py +++ b/python/examples/polarization_grating.py @@ -1,70 +1,108 @@ -# -*- coding: utf-8 -*- - # polarization grating from C. Oh and M.J. Escuti, Optics Letters, Vol. 33, No. 20, pp. 2287-9, 2008 # note: reference uses z as the propagation direction and y as the out-of-plane direction; this script uses x and z, respectively - -import meep as mp -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np -resolution = 50 # pixels/μm +import meep as mp -dpml = 1.0 # PML thickness -dsub = 1.0 # substrate thickness -dpad = 1.0 # padding thickness +resolution = 50 # pixels/μm -k_point = mp.Vector3(0,0,0) +dpml = 1.0 # PML thickness +dsub = 1.0 # substrate thickness +dpad = 1.0 # padding thickness -pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] +k_point = mp.Vector3(0, 0, 0) + +pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] n_0 = 1.55 delta_n = 0.159 -epsilon_diag = mp.Matrix(mp.Vector3(n_0**2,0,0),mp.Vector3(0,n_0**2,0),mp.Vector3(0,0,(n_0+delta_n)**2)) +epsilon_diag = mp.Matrix( + mp.Vector3(n_0**2, 0, 0), + mp.Vector3(0, n_0**2, 0), + mp.Vector3(0, 0, (n_0 + delta_n) ** 2), +) + +wvl = 0.54 # center wavelength +fcen = 1 / wvl # center frequency -wvl = 0.54 # center wavelength -fcen = 1/wvl # center frequency -def pol_grating(d,ph,gp,nmode): - sx = dpml+dsub+d+d+dpad+dpml +def pol_grating(d, ph, gp, nmode): + sx = dpml + dsub + d + d + dpad + dpml sy = gp - cell_size = mp.Vector3(sx,sy,0) + cell_size = mp.Vector3(sx, sy, 0) # twist angle of nematic director; from equation 1b def phi(p): - xx = p.x-(-0.5*sx+dpml+dsub) + xx = p.x - (-0.5 * sx + dpml + dsub) if (xx >= 0) and (xx <= d): - return math.pi*p.y/gp + ph*xx/d + return math.pi * p.y / gp + ph * xx / d else: - return math.pi*p.y/gp - ph*xx/d + 2*ph + return math.pi * p.y / gp - ph * xx / d + 2 * ph # return the anisotropic permittivity tensor for a uniaxial, twisted nematic liquid crystal def lc_mat(p): # rotation matrix for rotation around x axis - Rx = mp.Matrix(mp.Vector3(1,0,0),mp.Vector3(0,math.cos(phi(p)),math.sin(phi(p))),mp.Vector3(0,-math.sin(phi(p)),math.cos(phi(p)))) + Rx = mp.Matrix( + mp.Vector3(1, 0, 0), + mp.Vector3(0, math.cos(phi(p)), math.sin(phi(p))), + mp.Vector3(0, -math.sin(phi(p)), math.cos(phi(p))), + ) lc_epsilon = Rx * epsilon_diag * Rx.transpose() - lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x,lc_epsilon[1].y,lc_epsilon[2].z) - lc_epsilon_offdiag = mp.Vector3(lc_epsilon[1].x,lc_epsilon[2].x,lc_epsilon[2].y) - return mp.Medium(epsilon_diag=lc_epsilon_diag,epsilon_offdiag=lc_epsilon_offdiag) - - geometry = [mp.Block(center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub)),size=mp.Vector3(dpml+dsub,mp.inf,mp.inf),material=mp.Medium(index=n_0)), - mp.Block(center=mp.Vector3(-0.5*sx+dpml+dsub+d),size=mp.Vector3(2*d,mp.inf,mp.inf),material=lc_mat)] + lc_epsilon_diag = mp.Vector3(lc_epsilon[0].x, lc_epsilon[1].y, lc_epsilon[2].z) + lc_epsilon_offdiag = mp.Vector3( + lc_epsilon[1].x, lc_epsilon[2].x, lc_epsilon[2].y + ) + return mp.Medium( + epsilon_diag=lc_epsilon_diag, epsilon_offdiag=lc_epsilon_offdiag + ) + + geometry = [ + mp.Block( + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + material=mp.Medium(index=n_0), + ), + mp.Block( + center=mp.Vector3(-0.5 * sx + dpml + dsub + d), + size=mp.Vector3(2 * d, mp.inf, mp.inf), + material=lc_mat, + ), + ] # linear-polarized planewave pulse source - src_pt = mp.Vector3(-0.5*sx+dpml+0.3*dsub,0,0) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ez, center=src_pt, size=mp.Vector3(0,sy,0)), - mp.Source(mp.GaussianSource(fcen,fwidth=0.05*fcen), component=mp.Ey, center=src_pt, size=mp.Vector3(0,sy,0))] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - sources=sources, - default_material=mp.Medium(index=n_0)) - - tran_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0) - tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.3 * dsub, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.05 * fcen), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=0.05 * fcen), + component=mp.Ey, + center=src_pt, + size=mp.Vector3(0, sy, 0), + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + sources=sources, + default_material=mp.Medium(index=n_0), + ) + + tran_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad, 0, 0) + tran_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) sim.run(until_after_sources=100) @@ -72,30 +110,37 @@ def lc_mat(p): sim.reset_meep() - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - k_point=k_point, - sources=sources, - geometry=geometry) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + k_point=k_point, + sources=sources, + geometry=geometry, + ) - tran_flux = sim.add_flux(fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0,sy,0))) + tran_flux = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) sim.run(until_after_sources=300) - res1 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.ODD_Z+mp.EVEN_Y) - res2 = sim.get_eigenmode_coefficients(tran_flux, range(1,nmode+1), eig_parity=mp.EVEN_Z+mp.ODD_Y) - angles = [math.degrees(math.acos(kdom.x/fcen)) for kdom in res1.kdom] - - return input_flux[0], angles, res1.alpha[:,0,0], res2.alpha[:,0,0]; + res1 = sim.get_eigenmode_coefficients( + tran_flux, range(1, nmode + 1), eig_parity=mp.ODD_Z + mp.EVEN_Y + ) + res2 = sim.get_eigenmode_coefficients( + tran_flux, range(1, nmode + 1), eig_parity=mp.EVEN_Z + mp.ODD_Y + ) + angles = [math.degrees(math.acos(kdom.x / fcen)) for kdom in res1.kdom] + return input_flux[0], angles, res1.alpha[:, 0, 0], res2.alpha[:, 0, 0] -ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating -ph_twisted = 70 # chiral layer twist angle for bilayer grating -gp = 6.5 # grating period -nmode = 5 # number of mode coefficients to compute -dd = np.arange(0.1,3.5,0.1) # chiral layer thickness +ph_uniaxial = 0 # chiral layer twist angle for uniaxial grating +ph_twisted = 70 # chiral layer twist angle for bilayer grating +gp = 6.5 # grating period +nmode = 5 # number of mode coefficients to compute +dd = np.arange(0.1, 3.5, 0.1) # chiral layer thickness m0_uniaxial = np.zeros(dd.size) m1_uniaxial = np.zeros(dd.size) @@ -106,58 +151,62 @@ def lc_mat(p): ang_twisted = np.zeros(dd.size) for k in range(len(dd)): - input_flux, angles, coeffs1, coeffs2 = pol_grating(0.5*dd[k],math.radians(ph_uniaxial),gp,nmode) - tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux + input_flux, angles, coeffs1, coeffs2 = pol_grating( + 0.5 * dd[k], math.radians(ph_uniaxial), gp, nmode + ) + tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux for m in range(nmode): - print("tran (uniaxial):, {}, {:.2f}, {:.5f}".format(m,angles[m],tran[m])) + print(f"tran (uniaxial):, {m}, {angles[m]:.2f}, {tran[m]:.5f}") m0_uniaxial[k] = tran[0] m1_uniaxial[k] = tran[1] ang_uniaxial[k] = angles[1] - input_flux, angles, coeffs1, coeffs2 = pol_grating(dd[k],math.radians(ph_twisted),gp,nmode) - tran = (abs(coeffs1)**2+abs(coeffs2)**2)/input_flux + input_flux, angles, coeffs1, coeffs2 = pol_grating( + dd[k], math.radians(ph_twisted), gp, nmode + ) + tran = (abs(coeffs1) ** 2 + abs(coeffs2) ** 2) / input_flux for m in range(nmode): - print("tran (twisted):, {}, {:.2f}, {:.5f}".format(m,angles[m],tran[m])) + print(f"tran (twisted):, {m}, {angles[m]:.2f}, {tran[m]:.5f}") m0_twisted[k] = tran[0] m1_twisted[k] = tran[1] ang_twisted[k] = angles[1] cos_angles = [math.cos(math.radians(t)) for t in ang_uniaxial] -tran = m0_uniaxial+2*m1_uniaxial -eff_m0 = m0_uniaxial/tran -eff_m1 = (2*m1_uniaxial/tran)/cos_angles +tran = m0_uniaxial + 2 * m1_uniaxial +eff_m0 = m0_uniaxial / tran +eff_m1 = (2 * m1_uniaxial / tran) / cos_angles -phase = delta_n*dd/wvl -eff_m0_analytic = [math.cos(math.pi*p)**2 for p in phase] -eff_m1_analytic = [math.sin(math.pi*p)**2 for p in phase] +phase = delta_n * dd / wvl +eff_m0_analytic = [math.cos(math.pi * p) ** 2 for p in phase] +eff_m1_analytic = [math.sin(math.pi * p) ** 2 for p in phase] plt.figure(dpi=150) -plt.subplot(1,2,1) -plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)') -plt.plot(phase,eff_m0_analytic,'b--',clip_on=False,label='0th order (analytic)') -plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)') -plt.plot(phase,eff_m1_analytic,'r--',clip_on=False,label='±1 orders (analytic)') +plt.subplot(1, 2, 1) +plt.plot(phase, eff_m0, "bo-", clip_on=False, label="0th order (meep)") +plt.plot(phase, eff_m0_analytic, "b--", clip_on=False, label="0th order (analytic)") +plt.plot(phase, eff_m1, "ro-", clip_on=False, label="±1 orders (meep)") +plt.plot(phase, eff_m1_analytic, "r--", clip_on=False, label="±1 orders (analytic)") plt.axis([0, 1.0, 0, 1]) -plt.xticks([t for t in np.arange(0,1.2,0.2)]) +plt.xticks(list(np.arange(0, 1.2, 0.2))) plt.xlabel("phase delay Δnd/λ") plt.ylabel("diffraction efficiency @ λ = 0.54 μm") -plt.legend(loc='center') +plt.legend(loc="center") plt.title("homogeneous uniaxial grating") cos_angles = [math.cos(math.radians(t)) for t in ang_twisted] -tran = m0_twisted+2*m1_twisted -eff_m0 = m0_twisted/tran -eff_m1 = (2*m1_twisted/tran)/cos_angles +tran = m0_twisted + 2 * m1_twisted +eff_m0 = m0_twisted / tran +eff_m1 = (2 * m1_twisted / tran) / cos_angles -plt.subplot(1,2,2) -plt.plot(phase,eff_m0,'bo-',clip_on=False,label='0th order (meep)') -plt.plot(phase,eff_m1,'ro-',clip_on=False,label='±1 orders (meep)') +plt.subplot(1, 2, 2) +plt.plot(phase, eff_m0, "bo-", clip_on=False, label="0th order (meep)") +plt.plot(phase, eff_m1, "ro-", clip_on=False, label="±1 orders (meep)") plt.axis([0, 1.0, 0, 1]) -plt.xticks([t for t in np.arange(0,1.2,0.2)]) +plt.xticks(list(np.arange(0, 1.2, 0.2))) plt.xlabel("phase delay Δnd/λ") plt.ylabel("diffraction efficiency @ λ = 0.54 μm") -plt.legend(loc='center') +plt.legend(loc="center") plt.title("bilayer twisted-nematic grating") plt.show() diff --git a/python/examples/pw-source.py b/python/examples/pw-source.py index c22d9b227..2a244dc99 100644 --- a/python/examples/pw-source.py +++ b/python/examples/pw-source.py @@ -1,12 +1,10 @@ # This example creates an approximate Ez-polarized planewave in vacuum # propagating at a 45-degree angle, by using a couple of current sources # with amplitude exp(ikx) corresponding to the desired planewave. -from __future__ import division - import cmath import math -import meep as mp +import meep as mp s = 11 # the size of the computational cell, not including PML dpml = 1 # thickness of PML layers @@ -26,12 +24,14 @@ def pw_amp(k, x0): def _pw_amp(x): return cmath.exp(1j * k.dot(x + x0)) + return _pw_amp + fcen = 0.8 # pulse center frequency df = 0.02 # turn-on bandwidth kdir = mp.Vector3(1, 1) # direction of k (length is irrelevant) -n = 1 # refractive index of material containing the source +n = 1 # refractive index of material containing the source k = kdir.unit().scale(2 * math.pi * fcen * n) # k with correct length sources = [ @@ -40,15 +40,15 @@ def _pw_amp(x): component=mp.Ez, center=mp.Vector3(-0.5 * s, 0), size=mp.Vector3(0, s), - amp_func=pw_amp(k, mp.Vector3(x=-0.5 * s)) + amp_func=pw_amp(k, mp.Vector3(x=-0.5 * s)), ), mp.Source( mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(0, -0.5 * s), size=mp.Vector3(s, 0), - amp_func=pw_amp(k, mp.Vector3(y=-0.5 * s)) - ) + amp_func=pw_amp(k, mp.Vector3(y=-0.5 * s)), + ), ] sim = mp.Simulation( diff --git a/python/examples/refl-angular-kz2d.py b/python/examples/refl-angular-kz2d.py index 89bd49d39..1009e047d 100644 --- a/python/examples/refl-angular-kz2d.py +++ b/python/examples/refl-angular-kz2d.py @@ -1,12 +1,14 @@ -import meep as mp import math +import meep as mp + + def refl_planar(theta, kz_2d): resolution = 100 dpml = 1.0 sx = 10 - sx = 10 + 2*dpml + sx = 10 + 2 * dpml cell_size = mp.Vector3(sx) pml_layers = [mp.PML(dpml)] @@ -15,67 +17,94 @@ def refl_planar(theta, kz_2d): # plane of incidence is XZ k = mp.Vector3(z=math.sin(theta)).scale(fcen) - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ey, - center=mp.Vector3(-0.5*sx+dpml))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - kz_2d=kz_2d, - resolution=resolution) - - refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25*sx)) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ey, + center=mp.Vector3(-0.5 * sx + dpml), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + kz_2d=kz_2d, + resolution=resolution, + ) + + refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25 * sx)) refl = sim.add_flux(fcen, 0, 1, refl_fr) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(-0.5*sx+dpml), 1e-9)) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ey, mp.Vector3(-0.5 * sx + dpml), 1e-9 + ) + ) input_flux = mp.get_fluxes(refl) input_data = sim.get_flux_data(refl) sim.reset_meep() # add a block with n=3.5 for the air-dielectric interface - geometry = [mp.Block(size=mp.Vector3(0.5*sx,mp.inf,mp.inf), - center=mp.Vector3(0.25*sx), - material=mp.Medium(index=3.5))] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - kz_2d=kz_2d, - resolution=resolution) + geometry = [ + mp.Block( + size=mp.Vector3(0.5 * sx, mp.inf, mp.inf), + center=mp.Vector3(0.25 * sx), + material=mp.Medium(index=3.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + kz_2d=kz_2d, + resolution=resolution, + ) refl = sim.add_flux(fcen, 0, 1, refl_fr) sim.load_minus_flux_data(refl, input_data) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ey, mp.Vector3(-0.5*sx+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ey, mp.Vector3(-0.5 * sx + dpml), 1e-9 + ) + ) refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - Rmeep = -refl_flux[0]/input_flux[0] - return Rmeep + return -refl_flux[0] / input_flux[0] # rotation angle of source: CCW around Y axis, 0 degrees along +X axis theta_r = math.radians(19.4) -Rmeep_real_imag = refl_planar(theta_r,"real/imag") -Rmeep_complex = refl_planar(theta_r,"complex") -Rmeep_3d = refl_planar(theta_r,"3d") +Rmeep_real_imag = refl_planar(theta_r, "real/imag") +Rmeep_complex = refl_planar(theta_r, "complex") +Rmeep_3d = refl_planar(theta_r, "3d") -n1=1 -n2=3.5 +n1 = 1 +n2 = 3.5 # compute angle of refracted planewave in medium n2 # for incident planewave in medium n1 at angle theta_in -theta_out = lambda theta_in: math.asin(n1*math.sin(theta_in)/n2) +theta_out = lambda theta_in: math.asin(n1 * math.sin(theta_in) / n2) # compute Fresnel reflectance for S-polarization in medium n2 # for incident planewave in medium n1 at angle theta_in -Rfresnel = lambda theta_in: math.fabs((n2*math.cos(theta_out(theta_in))-n1*math.cos(theta_in))/(n2*math.cos(theta_out(theta_in))+n1*math.cos(theta_in)))**2 - -print("refl:, {} (real/imag), {} (complex), {} (3d), {} (analytic)".format(Rmeep_real_imag,Rmeep_complex,Rmeep_3d,Rfresnel(theta_r))) +Rfresnel = ( + lambda theta_in: math.fabs( + (n2 * math.cos(theta_out(theta_in)) - n1 * math.cos(theta_in)) + / (n2 * math.cos(theta_out(theta_in)) + n1 * math.cos(theta_in)) + ) + ** 2 +) + +print( + f"refl:, {Rmeep_real_imag} (real/imag), {Rmeep_complex} (complex), {Rmeep_3d} (3d), {Rfresnel(theta_r)} (analytic)" +) diff --git a/python/examples/refl-angular.py b/python/examples/refl-angular.py index a51791cc9..1f20d3f05 100644 --- a/python/examples/refl-angular.py +++ b/python/examples/refl-angular.py @@ -1,83 +1,110 @@ -import meep as mp import argparse import math +import meep as mp + def main(args): resolution = args.res - dpml = 1.0 # PML thickness - sz = 10 + 2*dpml # size of computational cell (without PMLs) - cell_size = mp.Vector3(0,0,sz) + dpml = 1.0 # PML thickness + sz = 10 + 2 * dpml # size of computational cell (without PMLs) + cell_size = mp.Vector3(0, 0, sz) pml_layers = [mp.PML(dpml)] - wvl_min = 0.4 # min wavelength - wvl_max = 0.8 # max wavelength - fmin = 1/wvl_max # min frequency - fmax = 1/wvl_min # max frequency - fcen = 0.5*(fmin+fmax) # center frequency - df = fmax-fmin # frequency width - nfreq = 50 # number of frequency bins + wvl_min = 0.4 # min wavelength + wvl_max = 0.8 # max wavelength + fmin = 1 / wvl_max # min frequency + fmax = 1 / wvl_min # max frequency + fcen = 0.5 * (fmin + fmax) # center frequency + df = fmax - fmin # frequency width + nfreq = 50 # number of frequency bins # rotation angle (in degrees) of source: CCW around Y axis, 0 degrees along +Z axis theta_r = math.radians(args.theta) # plane of incidence is XZ - k = mp.Vector3(math.sin(theta_r),0,math.cos(theta_r)).scale(fmin) + k = mp.Vector3(math.sin(theta_r), 0, math.cos(theta_r)).scale(fmin) # if normal incidence, force number of dimensions to be 1 - if theta_r == 0: - dimensions = 1 - else: - dimensions = 3 - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ex, - center=mp.Vector3(0,0,-0.5*sz+dpml))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - dimensions=dimensions, - resolution=resolution) - - refl_fr = mp.FluxRegion(center=mp.Vector3(0,0,-0.25*sz)) + dimensions = 1 if theta_r == 0 else 3 + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(0, 0, -0.5 * sz + dpml), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + dimensions=dimensions, + resolution=resolution, + ) + + refl_fr = mp.FluxRegion(center=mp.Vector3(0, 0, -0.25 * sz)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(0,0,-0.5*sz+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(0, 0, -0.5 * sz + dpml), 1e-9 + ) + ) empty_flux = mp.get_fluxes(refl) empty_data = sim.get_flux_data(refl) sim.reset_meep() # add a block with n=3.5 for the air-dielectric interface - geometry = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,0.5*sz), - center=mp.Vector3(0,0,0.25*sz), - material=mp.Medium(index=3.5))] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - k_point=k, - dimensions=dimensions, - resolution=resolution) + geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, 0.5 * sz), + center=mp.Vector3(0, 0, 0.25 * sz), + material=mp.Medium(index=3.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + k_point=k, + dimensions=dimensions, + resolution=resolution, + ) refl = sim.add_flux(fcen, df, nfreq, refl_fr) sim.load_minus_flux_data(refl, empty_data) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(0,0,-0.5*sz+dpml), 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(0, 0, -0.5 * sz + dpml), 1e-9 + ) + ) refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) for i in range(nfreq): - print("refl:, {}, {}, {}, {}".format(k.x,1/freqs[i],math.degrees(math.asin(k.x/freqs[i])),-refl_flux[i]/empty_flux[i])) + print( + f"refl:, {k.x}, {1 / freqs[i]}, {math.degrees(math.asin(k.x/freqs[i]))}, {-refl_flux[i] / empty_flux[i]}" + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-res', type=int, default=200, help='resolution (default: 200 pixels/um)') - parser.add_argument('-theta', type=float, default=0, help='angle of incident planewave (default: 0 degrees)') + parser.add_argument( + "-res", type=int, default=200, help="resolution (default: 200 pixels/um)" + ) + parser.add_argument( + "-theta", + type=float, + default=0, + help="angle of incident planewave (default: 0 degrees)", + ) args = parser.parse_args() main(args) diff --git a/python/examples/refl-quartz.py b/python/examples/refl-quartz.py index 55a61a23a..6427a9256 100644 --- a/python/examples/refl-quartz.py +++ b/python/examples/refl-quartz.py @@ -1,35 +1,43 @@ -# -*- coding: utf-8 -*- - -import meep as mp -from meep.materials import fused_quartz -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np +from meep.materials import fused_quartz + +import meep as mp resolution = 200 # pixels/μm dpml = 1.0 -sz = 10+2*dpml +sz = 10 + 2 * dpml cell_size = mp.Vector3(z=sz) pml_layers = [mp.PML(dpml)] wvl_min = 0.4 wvl_max = 0.8 -fmin = 1/wvl_max -fmax = 1/wvl_min -fcen = 0.5*(fmax+fmin) -df = fmax-fmin +fmin = 1 / wvl_max +fmax = 1 / wvl_min +fcen = 0.5 * (fmax + fmin) +df = fmax - fmin nfreq = 50 -sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), component=mp.Ex, center=mp.Vector3(z=-0.5*sz+dpml))] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=1, - resolution=resolution) - -refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25*sz)) +sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(z=-0.5 * sz + dpml), + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=1, + resolution=resolution, +) + +refl_fr = mp.FluxRegion(center=mp.Vector3(z=-0.25 * sz)) refl = sim.add_flux(fcen, df, nfreq, refl_fr) sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9)) @@ -38,14 +46,22 @@ empty_data = sim.get_flux_data(refl) sim.reset_meep() -geometry = [mp.Block(mp.Vector3(mp.inf,mp.inf,0.5*sz), center=mp.Vector3(z=0.25*sz), material=fused_quartz)] - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - dimensions=1, - resolution=resolution) +geometry = [ + mp.Block( + mp.Vector3(mp.inf, mp.inf, 0.5 * sz), + center=mp.Vector3(z=0.25 * sz), + material=fused_quartz, + ) +] + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + dimensions=1, + resolution=resolution, +) refl = sim.add_flux(fcen, df, nfreq, refl_fr) sim.load_minus_flux_data(refl, empty_data) @@ -53,21 +69,28 @@ sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-9)) refl_flux = mp.get_fluxes(refl) -R_meep = -1*np.divide(refl_flux,empty_flux) +R_meep = -1 * np.divide(refl_flux, empty_flux) freqs = mp.get_flux_freqs(refl) -wvls = np.divide(1,freqs) - -eps_quartz = lambda l: 1+0.6961663*math.pow(l,2)/(pow(l,2)-pow(0.0684043,2))+0.4079426*pow(l,2)/(pow(l,2)-pow(0.1162414,2))+0.8974794*pow(l,2)/(pow(l,2)-pow(9.896161,2)) -R_fresnel = lambda l: math.pow(math.fabs(1-math.sqrt(eps_quartz(l)))/(1+math.sqrt(eps_quartz(l))),2) +wvls = np.divide(1, freqs) + +eps_quartz = ( + lambda l: 1 + + 0.6961663 * math.pow(l, 2) / (pow(l, 2) - pow(0.0684043, 2)) + + 0.4079426 * pow(l, 2) / (pow(l, 2) - pow(0.1162414, 2)) + + 0.8974794 * pow(l, 2) / (pow(l, 2) - pow(9.896161, 2)) +) +R_fresnel = lambda l: math.pow( + math.fabs(1 - math.sqrt(eps_quartz(l))) / (1 + math.sqrt(eps_quartz(l))), 2 +) R_analytic = [R_fresnel(i) for i in wvls] plt.figure() -plt.plot(wvls,R_meep,'bo-',label='meep') -plt.plot(wvls,R_analytic,'rs-',label='analytic') +plt.plot(wvls, R_meep, "bo-", label="meep") +plt.plot(wvls, R_analytic, "rs-", label="analytic") plt.xlabel("wavelength (μm)") plt.ylabel("reflectance") plt.axis([0.4, 0.8, 0.0340, 0.0365]) -plt.xticks([t for t in np.arange(0.4,0.9,0.1)]) -plt.legend(loc='upper right') +plt.xticks(list(np.arange(0.4, 0.9, 0.1))) +plt.legend(loc="upper right") plt.show() diff --git a/python/examples/ring-cyl.py b/python/examples/ring-cyl.py index d4eafd4dd..2d87151ea 100644 --- a/python/examples/ring-cyl.py +++ b/python/examples/ring-cyl.py @@ -1,17 +1,17 @@ # Calculating 2d ring-resonator modes using cylindrical coordinates, # from the Meep tutorial. -from __future__ import division +import argparse import meep as mp -import argparse + def main(args): - n = 3.4 # index of waveguide - w = 1 # width of waveguide - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 32 # thickness of PML + n = 3.4 # index of waveguide + w = 1 # width of waveguide + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 32 # thickness of PML sr = r + w + pad + dpml # radial size (cell is from 0 to sr) dimensions = mp.CYLINDRICAL @@ -21,9 +21,13 @@ def main(args): # is given by exp(i m phi), where m is given by: m = args.m - geometry = [mp.Block(center=mp.Vector3(r + (w / 2)), - size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n))] + geometry = [ + mp.Block( + center=mp.Vector3(r + (w / 2)), + size=mp.Vector3(w, mp.inf, mp.inf), + material=mp.Medium(index=n), + ) + ] pml_layers = [mp.PML(dpml)] resolution = 20 @@ -33,38 +37,58 @@ def main(args): # arbitrary direction. We will only look for Ez-polarized modes. fcen = args.fcen # pulse center frequency - df = args.df # pulse frequency width - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=mp.Vector3(r + 0.1))] + df = args.df # pulse frequency width + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) + ] # note that the r -> -r mirror symmetry is exploited automatically - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - dimensions=dimensions, - m=m) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + dimensions=dimensions, + m=m, + ) - sim.run(mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)), - until_after_sources=200) + sim.run( + mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)), + until_after_sources=200, + ) # Output fields for one period at the end. (If we output # at a single time, we might accidentally catch the Ez field when it is # almost zero and get a distorted view.) We'll append the fields # to a file to get an r-by-t picture. We'll also output from -sr to -sr # instead of from 0 to sr. - sim.run(mp.in_volume(mp.Volume(center=mp.Vector3(), size=mp.Vector3(2 * sr)), - mp.at_beginning(mp.output_epsilon), - mp.to_appended("ez", mp.at_every(1 / fcen / 20, mp.output_efield_z))), - until=1 / fcen) + sim.run( + mp.in_volume( + mp.Volume(center=mp.Vector3(), size=mp.Vector3(2 * sr)), + mp.at_beginning(mp.output_epsilon), + mp.to_appended("ez", mp.at_every(1 / fcen / 20, mp.output_efield_z)), + ), + until=1 / fcen, + ) + -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('-fcen', type=float, default=0.15, help='pulse center frequency') - parser.add_argument('-df', type=float, default=0.1, help='pulse frequency width') - parser.add_argument('-m', type=int, default=3, help='phi (angular) dependence of the fields given by exp(i m phi)') + parser.add_argument( + "-fcen", type=float, default=0.15, help="pulse center frequency" + ) + parser.add_argument("-df", type=float, default=0.1, help="pulse frequency width") + parser.add_argument( + "-m", + type=int, + default=3, + help="phi (angular) dependence of the fields given by exp(i m phi)", + ) args = parser.parse_args() main(args) diff --git a/python/examples/ring-mode-overlap.py b/python/examples/ring-mode-overlap.py index 72182fdd7..593392a88 100644 --- a/python/examples/ring-mode-overlap.py +++ b/python/examples/ring-mode-overlap.py @@ -1,7 +1,6 @@ # Calculating 2d ring-resonator modes, from the Meep tutorial. import meep as mp - n = 3.4 # index of waveguide w = 1 # width of waveguide r = 1 # inner radius of ring @@ -17,7 +16,7 @@ # and the inner (air) cylinder second. geometry = [ mp.Cylinder(radius=r + w, height=mp.inf, material=mp.Medium(index=n)), - mp.Cylinder(radius=r, height=mp.inf, material=mp.air) + mp.Cylinder(radius=r, height=mp.inf, material=mp.air), ] pml_layers = [mp.PML(dpml)] @@ -28,19 +27,26 @@ # arbitrary direction. We will only look for Ez-polarized modes. fcen = 0.118 # pulse center frequency -df = 0.010 # pulse width (in frequency) -sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3(r + 0.1))] +df = 0.010 # pulse width (in frequency) +sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) +] # exploit the mirror symmetry in structure+source: symmetries = [mp.Mirror(mp.Y)] -sim = mp.Simulation(cell_size=cell, - resolution=resolution, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries) +sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, +) h1 = mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df) sim.run(mp.after_sources(h1), until_after_sources=300) @@ -56,5 +62,6 @@ def overlap_integral(r, ez1, ez2): return ez1.conjugate() * ez2 + res = sim.integrate2_field_function(fields2, [mp.Ez], [mp.Ez], overlap_integral) -print("overlap integral of mode at w and 2w: {}".format(abs(res))) +print(f"overlap integral of mode at w and 2w: {abs(res)}") diff --git a/python/examples/ring.py b/python/examples/ring.py index 1c2b2dbd4..4d6042488 100644 --- a/python/examples/ring.py +++ b/python/examples/ring.py @@ -1,47 +1,51 @@ # Calculating 2d ring-resonator modes, from the Meep tutorial. -from __future__ import division - import meep as mp + def main(): - n = 3.4 # index of waveguide - w = 1 # width of waveguide - r = 1 # inner radius of ring - pad = 4 # padding between waveguide and edge of PML - dpml = 2 # thickness of PML - sxy = 2*(r+w+pad+dpml) # cell size + n = 3.4 # index of waveguide + w = 1 # width of waveguide + r = 1 # inner radius of ring + pad = 4 # padding between waveguide and edge of PML + dpml = 2 # thickness of PML + sxy = 2 * (r + w + pad + dpml) # cell size # Create a ring waveguide by two overlapping cylinders - later objects # take precedence over earlier objects, so we put the outer cylinder first. # and the inner (air) cylinder second. - c1 = mp.Cylinder(radius=r+w, material=mp.Medium(index=n)) + c1 = mp.Cylinder(radius=r + w, material=mp.Medium(index=n)) c2 = mp.Cylinder(radius=r) # If we don't want to excite a specific mode symmetry, we can just # put a single point source at some arbitrary place, pointing in some # arbitrary direction. We will only look for Ez-polarized modes. - fcen = 0.15 # pulse center frequency - df = 0.1 # pulse width (in frequency) + fcen = 0.15 # pulse center frequency + df = 0.1 # pulse width (in frequency) - src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r+0.1)) + src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)) - sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy), - geometry=[c1, c2], - sources=[src], - resolution=10, - symmetries=[mp.Mirror(mp.Y)], - boundary_layers=[mp.PML(dpml)]) + sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=[c1, c2], + sources=[src], + resolution=10, + symmetries=[mp.Mirror(mp.Y)], + boundary_layers=[mp.PML(dpml)], + ) - sim.run(mp.at_beginning(mp.output_epsilon), - mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r+0.1), fcen, df)), - until_after_sources=300) + sim.run( + mp.at_beginning(mp.output_epsilon), + mp.after_sources(mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df)), + until_after_sources=300, + ) # Output fields for one period at the end. (If we output # at a single time, we might accidentally catch the Ez field when it is # almost zero and get a distorted view.) - sim.run(mp.at_every(1/fcen/20, mp.output_efield_z), until=1/fcen) + sim.run(mp.at_every(1 / fcen / 20, mp.output_efield_z), until=1 / fcen) + -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/python/examples/ring_gds.py b/python/examples/ring_gds.py index 64e2b99d5..6da93fe4d 100644 --- a/python/examples/ring_gds.py +++ b/python/examples/ring_gds.py @@ -1,103 +1,117 @@ -import numpy as np +import importlib + import gdspy +import numpy as np from matplotlib import pyplot as plt -import importlib + import meep as mp # core and cladding materials -Si = mp.Medium(index=3.4) +Si = mp.Medium(index=3.4) SiO2 = mp.Medium(index=1.4) # layer numbers for GDS file -RING_LAYER = 0 -SOURCE0_LAYER = 1 -SOURCE1_LAYER = 2 -MONITOR_LAYER = 3 +RING_LAYER = 0 +SOURCE0_LAYER = 1 +SOURCE1_LAYER = 2 +MONITOR_LAYER = 3 SIMULATION_LAYER = 4 -resolution = 50 # pixels/μm -dpml = 1 # thickness of PML -zmin = 0 # minimum z value of simulation domain (0 for 2D) -zmax = 0 # maximum z value of simulation domain (0 for 2D) +resolution = 50 # pixels/μm +dpml = 1 # thickness of PML +zmin = 0 # minimum z value of simulation domain (0 for 2D) +zmax = 0 # maximum z value of simulation domain (0 for 2D) + -def create_ring_gds(radius,width): +def create_ring_gds(radius, width): # Reload the library each time to prevent gds library name clashes importlib.reload(gdspy) - ringCell = gdspy.Cell("ring_resonator_r{}_w{}".format(radius,width)) + ringCell = gdspy.Cell(f"ring_resonator_r{radius}_w{width}") # Draw the ring - ringCell.add(gdspy.Round((0,0), - inner_radius=radius-width/2, - radius=radius+width/2, - layer=RING_LAYER)) + ringCell.add( + gdspy.Round( + (0, 0), + inner_radius=radius - width / 2, + radius=radius + width / 2, + layer=RING_LAYER, + ) + ) # Draw the first source - ringCell.add(gdspy.Rectangle((radius-width,0), - (radius+width,0), - SOURCE0_LAYER)) + ringCell.add( + gdspy.Rectangle((radius - width, 0), (radius + width, 0), SOURCE0_LAYER) + ) # Draw the second source - ringCell.add(gdspy.Rectangle((-radius-width,0), - (-radius+width,0), - SOURCE1_LAYER)) + ringCell.add( + gdspy.Rectangle((-radius - width, 0), (-radius + width, 0), SOURCE1_LAYER) + ) # Draw the monitor location - ringCell.add(gdspy.Rectangle((radius-width/2,0), - (radius+width/2,0), - MONITOR_LAYER)) + ringCell.add( + gdspy.Rectangle((radius - width / 2, 0), (radius + width / 2, 0), MONITOR_LAYER) + ) # Draw the simulation domain pad = 2 # padding between waveguide and edge of PML - ringCell.add(gdspy.Rectangle((-radius-width/2-pad,-radius-width/2-pad), - (radius+width/2+pad,radius+width/2+pad), - SIMULATION_LAYER)) - - filename = "ring_r{}_w{}.gds".format(radius,width) + ringCell.add( + gdspy.Rectangle( + (-radius - width / 2 - pad, -radius - width / 2 - pad), + (radius + width / 2 + pad, radius + width / 2 + pad), + SIMULATION_LAYER, + ) + ) + + filename = f"ring_r{radius}_w{width}.gds" gdspy.write_gds(filename, unit=1.0e-6, precision=1.0e-9) return filename -def find_modes(filename,wvl=1.55,bw=0.05): + +def find_modes(filename, wvl=1.55, bw=0.05): # Read in the ring structure - geometry = mp.get_GDSII_prisms(Si,filename,RING_LAYER,-100,100) + geometry = mp.get_GDSII_prisms(Si, filename, RING_LAYER, -100, 100) - cell = mp.GDSII_vol(filename,SIMULATION_LAYER,zmin,zmax) + cell = mp.GDSII_vol(filename, SIMULATION_LAYER, zmin, zmax) - src_vol0 = mp.GDSII_vol(filename,SOURCE0_LAYER,zmin,zmax) - src_vol1 = mp.GDSII_vol(filename,SOURCE1_LAYER,zmin,zmax) + src_vol0 = mp.GDSII_vol(filename, SOURCE0_LAYER, zmin, zmax) + src_vol1 = mp.GDSII_vol(filename, SOURCE1_LAYER, zmin, zmax) - mon_vol = mp.GDSII_vol(filename,MONITOR_LAYER,zmin,zmax) + mon_vol = mp.GDSII_vol(filename, MONITOR_LAYER, zmin, zmax) - fcen = 1/wvl - df = bw*fcen + fcen = 1 / wvl + df = bw * fcen - src = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - volume=src_vol0), - mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - volume=src_vol1, - amplitude=-1)] + src = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, volume=src_vol0), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + volume=src_vol1, + amplitude=-1, + ), + ] - sim = mp.Simulation(cell_size=cell.size, - geometry=geometry, - sources=src, - resolution=resolution, - boundary_layers=[mp.PML(dpml)], - default_material=SiO2) + sim = mp.Simulation( + cell_size=cell.size, + geometry=geometry, + sources=src, + resolution=resolution, + boundary_layers=[mp.PML(dpml)], + default_material=SiO2, + ) - h = mp.Harminv(mp.Hz,mon_vol.center,fcen,df) + h = mp.Harminv(mp.Hz, mon_vol.center, fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=100) + sim.run(mp.after_sources(h), until_after_sources=100) plt.figure() - sim.plot2D(fields=mp.Hz, - eps_parameters={'contour':True}) - plt.savefig('ring_fields.png',bbox_inches='tight',dpi=150) + sim.plot2D(fields=mp.Hz, eps_parameters={"contour": True}) + plt.savefig("ring_fields.png", bbox_inches="tight", dpi=150) - wvl = np.array([1/m.freq for m in h.modes]) + wvl = np.array([1 / m.freq for m in h.modes]) Q = np.array([m.Q for m in h.modes]) sim.reset_meep() @@ -105,8 +119,8 @@ def find_modes(filename,wvl=1.55,bw=0.05): return wvl, Q -if __name__ == '__main__': - filename = create_ring_gds(2.0,0.5) - wvls, Qs = find_modes(filename,1.55,0.05) - for w, Q in zip(wvls,Qs): - print("mode: {}, {}".format(w,Q)) +if __name__ == "__main__": + filename = create_ring_gds(2.0, 0.5) + wvls, Qs = find_modes(filename, 1.55, 0.05) + for w, Q in zip(wvls, Qs): + print(f"mode: {w}, {Q}") diff --git a/python/examples/solve-cw.py b/python/examples/solve-cw.py index b97bbb5c1..76f58dbb4 100644 --- a/python/examples/solve-cw.py +++ b/python/examples/solve-cw.py @@ -1,7 +1,8 @@ -import meep as mp +import matplotlib.pyplot as plt import numpy as np from numpy import linalg as LA -import matplotlib.pyplot as plt + +import meep as mp n = 3.4 w = 1 @@ -9,87 +10,102 @@ pad = 4 dpml = 2 -sxy = 2*(r+w+pad+dpml) -cell_size = mp.Vector3(sxy,sxy) +sxy = 2 * (r + w + pad + dpml) +cell_size = mp.Vector3(sxy, sxy) pml_layers = [mp.PML(dpml)] -nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml)) +nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml)) -geometry = [mp.Cylinder(radius=r+w, material=mp.Medium(index=n)), - mp.Cylinder(radius=r)] +geometry = [ + mp.Cylinder(radius=r + w, material=mp.Medium(index=n)), + mp.Cylinder(radius=r), +] fcen = 0.118 -src = [mp.Source(mp.ContinuousSource(fcen), - component=mp.Ez, - center=mp.Vector3(r+0.1)), - mp.Source(mp.ContinuousSource(fcen), - component=mp.Ez, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - -symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=+1)] - -sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - sources=src, - resolution=10, - force_complex_fields=True, - symmetries=symmetries, - boundary_layers=pml_layers) +src = [ + mp.Source(mp.ContinuousSource(fcen), component=mp.Ez, center=mp.Vector3(r + 0.1)), + mp.Source( + mp.ContinuousSource(fcen), + component=mp.Ez, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), +] + +symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)] + +sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + sources=src, + resolution=10, + force_complex_fields=True, + symmetries=symmetries, + boundary_layers=pml_layers, +) num_tols = 5 -tols = np.power(10, np.arange(-8.0,-8.0-num_tols,-1.0)) -ez_dat = np.zeros((122,122,num_tols), dtype=np.complex_) +tols = np.power(10, np.arange(-8.0, -8.0 - num_tols, -1.0)) +ez_dat = np.zeros((122, 122, num_tols), dtype=np.complex_) for i in range(num_tols): sim.init_sim() sim.solve_cw(tols[i], 10000, 10) - ez_dat[:,:,i] = sim.get_array(vol=nonpml_vol, component=mp.Ez) + ez_dat[:, :, i] = sim.get_array(vol=nonpml_vol, component=mp.Ez) -err_dat = np.zeros(num_tols-1) -for i in range(num_tols-1): - err_dat[i] = LA.norm(ez_dat[:,:,i]-ez_dat[:,:,num_tols-1]) +err_dat = np.zeros(num_tols - 1) +for i in range(num_tols - 1): + err_dat[i] = LA.norm(ez_dat[:, :, i] - ez_dat[:, :, num_tols - 1]) plt.figure(dpi=150) -plt.loglog(tols[:num_tols-1], err_dat, 'bo-'); -plt.xlabel("frequency-domain solver tolerance"); -plt.ylabel("L2 norm of error in fields"); +plt.loglog(tols[: num_tols - 1], err_dat, "bo-") +plt.xlabel("frequency-domain solver tolerance") +plt.ylabel("L2 norm of error in fields") plt.show() eps_data = sim.get_array(vol=nonpml_vol, component=mp.Dielectric) -ez_data = np.real(ez_dat[:,:,num_tols-1]) +ez_data = np.real(ez_dat[:, :, num_tols - 1]) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9) -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.imshow(ez_data.transpose(), interpolation="spline36", cmap="RdBu", alpha=0.9) +plt.axis("off") plt.show() if np.all(np.diff(err_dat) < 0): - print("PASSED solve_cw test: error in the fields is decreasing with increasing resolution") + print( + "PASSED solve_cw test: error in the fields is decreasing with increasing resolution" + ) else: - print("FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution") + print( + "FAILED solve_cw test: error in the fields is NOT decreasing with increasing resolution" + ) sim.reset_meep() df = 0.08 -src = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(-(r+0.1)), - amplitude=-1)] - -sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy), - geometry=geometry, - sources=src, - resolution=10, - symmetries=symmetries, - boundary_layers=pml_layers) +src = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(r + 0.1) + ), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), +] + +sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=geometry, + sources=src, + resolution=10, + symmetries=symmetries, + boundary_layers=pml_layers, +) dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, where=nonpml_vol) @@ -99,7 +115,7 @@ ez_data = np.real(sim.get_dft_array(dft_obj, mp.Ez, 0)) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9) -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.imshow(ez_data.transpose(), interpolation="spline36", cmap="RdBu", alpha=0.9) +plt.axis("off") plt.show() diff --git a/python/examples/stochastic_emitter.py b/python/examples/stochastic_emitter.py index c14e51be4..c6cc377f9 100644 --- a/python/examples/stochastic_emitter.py +++ b/python/examples/stochastic_emitter.py @@ -1,15 +1,30 @@ -import meep as mp -from meep.materials import Ag +import argparse + import numpy as np +from meep.materials import Ag + +import meep as mp -import argparse parser = argparse.ArgumentParser() -parser.add_argument('-res', type=int, default=50, help='resolution (pixels/um)') -parser.add_argument('-nr', type=int, default=20, help='number of random trials (method 1)') -parser.add_argument('-nd', type=int, default=10, help='number of dipoles') -parser.add_argument('-nf', type=int, default=500, help='number of frequencies') -parser.add_argument('-textured', action='store_true', default=False, help='flat (default) or textured surface') -parser.add_argument('-method', type=int, choices=[1,2], default=1, help='type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole') +parser.add_argument("-res", type=int, default=50, help="resolution (pixels/um)") +parser.add_argument( + "-nr", type=int, default=20, help="number of random trials (method 1)" +) +parser.add_argument("-nd", type=int, default=10, help="number of dipoles") +parser.add_argument("-nf", type=int, default=500, help="number of frequencies") +parser.add_argument( + "-textured", + action="store_true", + default=False, + help="flat (default) or textured surface", +) +parser.add_argument( + "-method", + type=int, + choices=[1, 2], + default=1, + help="type of method: (1) random dipoles with nr trials or (2) single dipole with 1 run per dipole", +) args = parser.parse_args() resolution = args.res @@ -22,56 +37,81 @@ dAg = 0.5 sx = 1.1 -sy = dpml+dair+hrod+dsub+dAg +sy = dpml + dair + hrod + dsub + dAg -cell_size = mp.Vector3(sx,sy) +cell_size = mp.Vector3(sx, sy) -pml_layers = [mp.PML(direction=mp.Y, - thickness=dpml, - side=mp.High)] +pml_layers = [mp.PML(direction=mp.Y, thickness=dpml, side=mp.High)] fcen = 1.0 df = 0.2 nfreq = args.nf ndipole = args.nd ntrial = args.nr -run_time = 2*nfreq/df - -geometry = [mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-hrod-0.5*dsub), - size=mp.Vector3(mp.inf,dsub,mp.inf)), - mp.Block(material=Ag, - center=mp.Vector3(0,-0.5*sy+0.5*dAg), - size=mp.Vector3(mp.inf,dAg,mp.inf))] +run_time = 2 * nfreq / df + +geometry = [ + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - hrod - 0.5 * dsub), + size=mp.Vector3(mp.inf, dsub, mp.inf), + ), + mp.Block( + material=Ag, + center=mp.Vector3(0, -0.5 * sy + 0.5 * dAg), + size=mp.Vector3(mp.inf, dAg, mp.inf), + ), +] if args.textured: - geometry.append(mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-0.5*hrod), - size=mp.Vector3(wrod,hrod,mp.inf))) + geometry.append( + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - 0.5 * hrod), + size=mp.Vector3(wrod, hrod, mp.inf), + ) + ) -def compute_flux(m=1,n=0): + +def compute_flux(m=1, n=0): if m == 1: - sources = [] - for n in range(ndipole): - sources.append(mp.Source(mp.CustomSource(src_func=lambda t: np.random.randn()), - component=mp.Ez, - center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))) + sources = [ + mp.Source( + mp.CustomSource(src_func=lambda t: np.random.randn()), + component=mp.Ez, + center=mp.Vector3( + sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub + ), + ) + for n in range(ndipole) + ] + else: - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - k_point=mp.Vector3(), - boundary_layers=pml_layers, - geometry=geometry, - sources=sources) - - flux_mon = sim.add_flux(fcen, - df, - nfreq, - mp.FluxRegion(center=mp.Vector3(0,0.5*sy-dpml),size=mp.Vector3(sx))) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3( + sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub + ), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + k_point=mp.Vector3(), + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + ) + + flux_mon = sim.add_flux( + fcen, + df, + nfreq, + mp.FluxRegion(center=mp.Vector3(0, 0.5 * sy - dpml), size=mp.Vector3(sx)), + ) sim.run(until=run_time) @@ -82,15 +122,18 @@ def compute_flux(m=1,n=0): if args.method == 1: - fluxes = np.zeros((nfreq,ntrial)) + fluxes = np.zeros((nfreq, ntrial)) for t in range(ntrial): - freqs, fluxes[:,t] = compute_flux(m=1) + freqs, fluxes[:, t] = compute_flux(m=1) else: - fluxes = np.zeros((nfreq,ndipole)) + fluxes = np.zeros((nfreq, ndipole)) for d in range(ndipole): - freqs, fluxes[:,d] = compute_flux(m=2,n=d) + freqs, fluxes[:, d] = compute_flux(m=2, n=d) if mp.am_master(): - with open('method{}_{}_res{}_nfreq{}_ndipole{}.npz'.format(args.method,"textured" if args.textured else "flat",resolution,nfreq,ndipole),'wb') as f: - np.savez(f,freqs=freqs,fluxes=fluxes) + with open( + f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_ndipole{ndipole}.npz', + "wb", + ) as f: + np.savez(f, freqs=freqs, fluxes=fluxes) diff --git a/python/examples/stochastic_emitter_line.py b/python/examples/stochastic_emitter_line.py index eef3bac61..5cae674ce 100644 --- a/python/examples/stochastic_emitter_line.py +++ b/python/examples/stochastic_emitter_line.py @@ -1,15 +1,32 @@ -import meep as mp -from meep.materials import Ag +import argparse + import numpy as np +from meep.materials import Ag + +import meep as mp -import argparse parser = argparse.ArgumentParser() -parser.add_argument('-res', type=int, default=50, help='resolution (pixels/um)') -parser.add_argument('-nf', type=int, default=500, help='number of frequencies') -parser.add_argument('-nsrc', type=int, default=15, help='number of line sources with cosine Fourier series amplitude function (method 3)') -parser.add_argument('-textured', action='store_true', default=False, help='flat (default) or textured surface') -parser.add_argument('-method', type=int, choices=[2,3], default=2, - help='type of method: (2) single dipole with 1 run per dipole or (3) line source with cosine Fourier series amplitude function') +parser.add_argument("-res", type=int, default=50, help="resolution (pixels/um)") +parser.add_argument("-nf", type=int, default=500, help="number of frequencies") +parser.add_argument( + "-nsrc", + type=int, + default=15, + help="number of line sources with cosine Fourier series amplitude function (method 3)", +) +parser.add_argument( + "-textured", + action="store_true", + default=False, + help="flat (default) or textured surface", +) +parser.add_argument( + "-method", + type=int, + choices=[2, 3], + default=2, + help="type of method: (2) single dipole with 1 run per dipole or (3) line source with cosine Fourier series amplitude function", +) args = parser.parse_args() resolution = args.res @@ -22,62 +39,89 @@ dAg = 0.4 sx = 1.5 -sy = dpml+dair+hrod+dsub+dAg +sy = dpml + dair + hrod + dsub + dAg -cell_size = mp.Vector3(sx,sy) +cell_size = mp.Vector3(sx, sy) -pml_layers = [mp.PML(direction=mp.Y, - thickness=dpml, - side=mp.High)] +pml_layers = [mp.PML(direction=mp.Y, thickness=dpml, side=mp.High)] fcen = 1.0 df = 0.2 nfreq = args.nf nsrc = args.nsrc -ndipole = int(sx*resolution) -run_time = 2*nfreq/df - -geometry = [mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-hrod-0.5*dsub), - size=mp.Vector3(mp.inf,dsub,mp.inf)), - mp.Block(material=Ag, - center=mp.Vector3(0,-0.5*sy+0.5*dAg), - size=mp.Vector3(mp.inf,dAg,mp.inf))] +ndipole = int(sx * resolution) +run_time = 2 * nfreq / df + +geometry = [ + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - hrod - 0.5 * dsub), + size=mp.Vector3(mp.inf, dsub, mp.inf), + ), + mp.Block( + material=Ag, + center=mp.Vector3(0, -0.5 * sy + 0.5 * dAg), + size=mp.Vector3(mp.inf, dAg, mp.inf), + ), +] if args.textured: - geometry.append(mp.Block(material=mp.Medium(index=3.45), - center=mp.Vector3(0,0.5*sy-dpml-dair-0.5*hrod), - size=mp.Vector3(wrod,hrod,mp.inf))) + geometry.append( + mp.Block( + material=mp.Medium(index=3.45), + center=mp.Vector3(0, 0.5 * sy - dpml - dair - 0.5 * hrod), + size=mp.Vector3(wrod, hrod, mp.inf), + ) + ) + def src_amp_func(n): def _src_amp_func(p): if n == 0: - return 1/np.sqrt(sx) + return 1 / np.sqrt(sx) else: - return np.sqrt(2/sx) * np.cos(n*np.pi*(p.x+0.5*sx)/sx) + return np.sqrt(2 / sx) * np.cos(n * np.pi * (p.x + 0.5 * sx) / sx) + return _src_amp_func -def compute_flux(m,n): + +def compute_flux(m, n): if m == 2: - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(sx*(-0.5+n/ndipole),-0.5*sy+dAg+0.5*dsub))] + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3( + sx * (-0.5 + n / ndipole), -0.5 * sy + dAg + 0.5 * dsub + ), + ) + ] else: - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Ez, - center=mp.Vector3(0,-0.5*sy+dAg+0.5*dsub), - size=mp.Vector3(sx,0), - amp_func=src_amp_func(n))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - k_point=mp.Vector3(), - boundary_layers=pml_layers, - geometry=geometry, - sources=sources) - - flux_mon = sim.add_flux(fcen, df, nfreq, - mp.FluxRegion(center=mp.Vector3(0,0.5*sy-dpml),size=mp.Vector3(sx))) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(0, -0.5 * sy + dAg + 0.5 * dsub), + size=mp.Vector3(sx, 0), + amp_func=src_amp_func(n), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + k_point=mp.Vector3(), + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + ) + + flux_mon = sim.add_flux( + fcen, + df, + nfreq, + mp.FluxRegion(center=mp.Vector3(0, 0.5 * sy - dpml), size=mp.Vector3(sx)), + ) sim.run(until=run_time) @@ -88,20 +132,18 @@ def compute_flux(m,n): if args.method == 2: - fluxes = np.zeros((nfreq,ndipole)) + fluxes = np.zeros((nfreq, ndipole)) for d in range(ndipole): - freqs, fluxes[:,d] = compute_flux(2,d) + freqs, fluxes[:, d] = compute_flux(2, d) else: - fluxes = np.zeros((nfreq,nsrc)) + fluxes = np.zeros((nfreq, nsrc)) for d in range(nsrc): - freqs, fluxes[:,d] = compute_flux(3,d) + freqs, fluxes[:, d] = compute_flux(3, d) if mp.am_master(): - with open('method{}_{}_res{}_nfreq{}_{}{}.npz'.format(args.method, - "textured" if args.textured else "flat", - resolution, - nfreq, - "ndipole" if args.method == 2 else "nsrc", - ndipole if args.method == 2 else nsrc),'wb') as f: - np.savez(f,freqs=freqs,fluxes=fluxes) + with open( + f'method{args.method}_{"textured" if args.textured else "flat"}_res{resolution}_nfreq{nfreq}_{"ndipole" if args.method == 2 else "nsrc"}{ndipole if args.method == 2 else nsrc}.npz', + "wb", + ) as f: + np.savez(f, freqs=freqs, fluxes=fluxes) diff --git a/python/examples/straight-waveguide.py b/python/examples/straight-waveguide.py index b17bd37d4..86d3d9495 100644 --- a/python/examples/straight-waveguide.py +++ b/python/examples/straight-waveguide.py @@ -1,44 +1,48 @@ -# -*- coding: utf-8 -*- - # From the Meep tutorial: plotting permittivity and fields of a straight waveguide -from __future__ import division - import meep as mp -cell = mp.Vector3(16,8,0) +cell = mp.Vector3(16, 8, 0) -geometry = [mp.Block(mp.Vector3(mp.inf,1,mp.inf), - center=mp.Vector3(), - material=mp.Medium(epsilon=12))] +geometry = [ + mp.Block( + mp.Vector3(mp.inf, 1, mp.inf), + center=mp.Vector3(), + material=mp.Medium(epsilon=12), + ) +] -sources = [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-7,0))] +sources = [ + mp.Source( + mp.ContinuousSource(frequency=0.15), component=mp.Ez, center=mp.Vector3(-7, 0) + ) +] pml_layers = [mp.PML(1.0)] resolution = 10 -sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) +sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, +) sim.run(until=200) -import numpy as np import matplotlib.pyplot as plt +import numpy as np eps_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Dielectric) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.axis("off") plt.show() ez_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Ez) plt.figure() -plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary') -plt.imshow(ez_data.transpose(), interpolation='spline36', cmap='RdBu', alpha=0.9) -plt.axis('off') +plt.imshow(eps_data.transpose(), interpolation="spline36", cmap="binary") +plt.imshow(ez_data.transpose(), interpolation="spline36", cmap="RdBu", alpha=0.9) +plt.axis("off") plt.show() diff --git a/python/examples/wvg-src.py b/python/examples/wvg-src.py index b5cbbaf09..de0b7b0f6 100644 --- a/python/examples/wvg-src.py +++ b/python/examples/wvg-src.py @@ -1,8 +1,5 @@ -from __future__ import division - import meep as mp - # Example file illustrating an eigenmode source, generating a waveguide mode # (requires recent MPB version to be installed before Meep is compiled) @@ -10,17 +7,27 @@ # an asymmetrical dielectric waveguide: geometry = [ - mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 1, mp.inf), - material=mp.Medium(epsilon=12)), - mp.Block(center=mp.Vector3(y=0.3), size=mp.Vector3(mp.inf, 0.1, mp.inf), - material=mp.Medium()) + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ), + mp.Block( + center=mp.Vector3(y=0.3), + size=mp.Vector3(mp.inf, 0.1, mp.inf), + material=mp.Medium(), + ), ] # create a transparent source that excites a right-going waveguide mode sources = [ - mp.EigenModeSource(src=mp.ContinuousSource(0.15), size=mp.Vector3(y=6), - center=mp.Vector3(x=-5), component=mp.Dielectric, - eig_parity=mp.ODD_Z) + mp.EigenModeSource( + src=mp.ContinuousSource(0.15), + size=mp.Vector3(y=6), + center=mp.Vector3(x=-5), + component=mp.Dielectric, + eig_parity=mp.ODD_Z, + ) ] pml_layers = [mp.PML(1.0)] @@ -35,20 +42,24 @@ sources=sources, boundary_layers=pml_layers, force_complex_fields=force_complex_fields, - resolution=resolution + resolution=resolution, ) sim.run( mp.at_beginning(mp.output_epsilon), mp.at_end(mp.output_png(mp.Ez, "-a yarg -A $EPS -S3 -Zc dkbluered", rm_h5=False)), - until=200 + until=200, ) -flux1 = sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6))) -flux2 = sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6))) +flux1 = sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6)) +) +flux2 = sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6)) +) # averaged over y region of width 1.8 -print("left-going flux = {}".format(flux1 / -1.8)) +print(f"left-going flux = {flux1 / -1.8}") # averaged over y region of width 1.8 -print("right-going flux = {}".format(flux2 / 1.8)) +print(f"right-going flux = {flux2 / 1.8}") diff --git a/python/examples/zone_plate.py b/python/examples/zone_plate.py index 9defcac9f..17db0e0b9 100644 --- a/python/examples/zone_plate.py +++ b/python/examples/zone_plate.py @@ -1,104 +1,153 @@ -import meep as mp -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np + +import meep as mp -resolution = 25 # pixels/μm +resolution = 25 # pixels/μm -dpml = 1.0 # PML thickness -dsub = 2.0 # substrate thickness -dpad = 2.0 # padding betweeen zone plate and PML -zh = 0.5 # zone-plate height -zN = 25 # number of zones (odd zones: π phase shift, even zones: none) -focal_length = 200 # focal length of zone plate -spot_length = 100 # far-field line length -ff_res = 10 # far-field resolution +dpml = 1.0 # PML thickness +dsub = 2.0 # substrate thickness +dpad = 2.0 # padding betweeen zone plate and PML +zh = 0.5 # zone-plate height +zN = 25 # number of zones (odd zones: π phase shift, even zones: none) +focal_length = 200 # focal length of zone plate +spot_length = 100 # far-field line length +ff_res = 10 # far-field resolution pml_layers = [mp.PML(thickness=dpml)] wvl_cen = 0.5 -frq_cen = 1/wvl_cen -dfrq = 0.2*frq_cen +frq_cen = 1 / wvl_cen +dfrq = 0.2 * frq_cen ## radii of zones ## ref: eq. 7 of http://zoneplate.lbl.gov/theory -r = [math.sqrt(n*wvl_cen*(focal_length+n*wvl_cen/4)) for n in range(1,zN+1)] - -sr = r[-1]+dpad+dpml -sz = dpml+dsub+zh+dpad+dpml -cell_size = mp.Vector3(sr,0,sz) - -sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Er, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr)), - mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True), - component=mp.Ep, - center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml), - size=mp.Vector3(sr), - amplitude=-1j)] +r = [ + math.sqrt(n * wvl_cen * (focal_length + n * wvl_cen / 4)) for n in range(1, zN + 1) +] + +sr = r[-1] + dpad + dpml +sz = dpml + dsub + zh + dpad + dpml +cell_size = mp.Vector3(sr, 0, sz) + +sources = [ + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Er, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + ), + mp.Source( + mp.GaussianSource(frq_cen, fwidth=dfrq, is_integrated=True), + component=mp.Ep, + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + dpml), + size=mp.Vector3(sr), + amplitude=-1j, + ), +] glass = mp.Medium(index=1.5) -geometry = [mp.Block(material=glass, - size=mp.Vector3(sr,0,dpml+dsub), - center=mp.Vector3(0.5*sr,0,-0.5*sz+0.5*(dpml+dsub)))] - -for n in range(zN-1,-1,-1): - geometry.append(mp.Block(material=glass if n % 2 == 0 else mp.vacuum, - size=mp.Vector3(r[n],0,zh), - center=mp.Vector3(0.5*r[n],0,-0.5*sz+dpml+dsub+0.5*zh))) - -sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - sources=sources, - geometry=geometry, - dimensions=mp.CYLINDRICAL, - m=-1) +geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(sr, 0, dpml + dsub), + center=mp.Vector3(0.5 * sr, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + ) +] + +geometry.extend( + mp.Block( + material=glass if n % 2 == 0 else mp.vacuum, + size=mp.Vector3(r[n], 0, zh), + center=mp.Vector3(0.5 * r[n], 0, -0.5 * sz + dpml + dsub + 0.5 * zh), + ) + for n in range(zN - 1, -1, -1) +) + +sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + sources=sources, + geometry=geometry, + dimensions=mp.CYLINDRICAL, + m=-1, +) ## near-field monitor -n2f_obj = sim.add_near2far(frq_cen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0.5*(sr-dpml),0,0.5*sz-dpml), - size=mp.Vector3(sr-dpml)), - mp.Near2FarRegion(center=mp.Vector3(sr-dpml,0,0.5*sz-dpml-0.5*(dsub+zh+dpad)), - size=mp.Vector3(z=dsub+zh+dpad))) +n2f_obj = sim.add_near2far( + frq_cen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0.5 * (sr - dpml), 0, 0.5 * sz - dpml), + size=mp.Vector3(sr - dpml), + ), + mp.Near2FarRegion( + center=mp.Vector3(sr - dpml, 0, 0.5 * sz - dpml - 0.5 * (dsub + zh + dpad)), + size=mp.Vector3(z=dsub + zh + dpad), + ), +) sim.plot2D() if mp.am_master(): - plt.savefig("zone_plate_epsilon.png",bbox_inches='tight',dpi=150) + plt.savefig("zone_plate_epsilon.png", bbox_inches="tight", dpi=150) sim.run(until_after_sources=100) -ff_r = sim.get_farfields(n2f_obj, - ff_res, - center=mp.Vector3(0.5*(sr-dpml),0,-0.5*sz+dpml+dsub+zh+focal_length), - size=mp.Vector3(sr-dpml)) - -ff_z = sim.get_farfields(n2f_obj, - ff_res, - center=mp.Vector3(z=-0.5*sz+dpml+dsub+zh+focal_length), - size=mp.Vector3(z=spot_length)) - -E2_r = np.absolute(ff_r['Ex'])**2+np.absolute(ff_r['Ey'])**2+np.absolute(ff_r['Ez'])**2 -E2_z = np.absolute(ff_z['Ex'])**2+np.absolute(ff_z['Ey'])**2+np.absolute(ff_z['Ez'])**2 +ff_r = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3( + 0.5 * (sr - dpml), 0, -0.5 * sz + dpml + dsub + zh + focal_length + ), + size=mp.Vector3(sr - dpml), +) + +ff_z = sim.get_farfields( + n2f_obj, + ff_res, + center=mp.Vector3(z=-0.5 * sz + dpml + dsub + zh + focal_length), + size=mp.Vector3(z=spot_length), +) + +E2_r = ( + np.absolute(ff_r["Ex"]) ** 2 + + np.absolute(ff_r["Ey"]) ** 2 + + np.absolute(ff_r["Ez"]) ** 2 +) +E2_z = ( + np.absolute(ff_z["Ex"]) ** 2 + + np.absolute(ff_z["Ey"]) ** 2 + + np.absolute(ff_z["Ez"]) ** 2 +) if mp.am_master(): plt.figure(dpi=200) - plt.subplot(1,2,1) - plt.semilogy(np.linspace(0,sr-dpml,len(E2_r)),E2_r,'bo-') - plt.xlim(-2,20) - plt.xticks([t for t in np.arange(0,25,5)]) - plt.grid(True,axis="y",which="both",ls="-") - plt.xlabel(r'$r$ coordinate (μm)') - plt.ylabel(r'energy density of far fields, |E|$^2$') - plt.subplot(1,2,2) - plt.semilogy(np.linspace(focal_length-0.5*spot_length,focal_length+0.5*spot_length,len(E2_z)),E2_z,'bo-') - plt.grid(True,axis="y",which="both",ls="-") - plt.xlabel(r'$z$ coordinate (μm)') - plt.ylabel(r'energy density of far fields, |E|$^2$') - plt.suptitle(r"binary-phase zone plate with focal length $z$ = {} μm".format(focal_length)) + plt.subplot(1, 2, 1) + plt.semilogy(np.linspace(0, sr - dpml, len(E2_r)), E2_r, "bo-") + plt.xlim(-2, 20) + plt.xticks(list(np.arange(0, 25, 5))) + plt.grid(True, axis="y", which="both", ls="-") + plt.xlabel(r"$r$ coordinate (μm)") + plt.ylabel(r"energy density of far fields, |E|$^2$") + plt.subplot(1, 2, 2) + plt.semilogy( + np.linspace( + focal_length - 0.5 * spot_length, + focal_length + 0.5 * spot_length, + len(E2_z), + ), + E2_z, + "bo-", + ) + plt.grid(True, axis="y", which="both", ls="-") + plt.xlabel(r"$z$ coordinate (μm)") + plt.ylabel(r"energy density of far fields, |E|$^2$") + plt.suptitle(f"binary-phase zone plate with focal length $z$ = {focal_length} μm") + plt.tight_layout() plt.savefig("zone_plate_farfields.png") diff --git a/python/geom.py b/python/geom.py index 908337089..0f676e3dc 100755 --- a/python/geom.py +++ b/python/geom.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import functools import math import numbers @@ -9,24 +8,25 @@ from numbers import Number import numpy as np -import meep as mp +import meep as mp -FreqRange = namedtuple('FreqRange', ['min', 'max']) +FreqRange = namedtuple("FreqRange", ["min", "max"]) def check_nonnegative(prop, val): if val >= 0: return val else: - raise ValueError("{} cannot be negative. Got {}".format(prop, val)) + raise ValueError(f"{prop} cannot be negative. Got {val}") + def init_do_averaging(mat_func): - if not hasattr(mat_func, 'do_averaging'): + if not hasattr(mat_func, "do_averaging"): mat_func.do_averaging = False -class Vector3(object): +class Vector3: """ Properties: @@ -113,7 +113,7 @@ def __mul__(self, other): elif isinstance(other, Number): return self.scale(other) else: - raise TypeError("No operation known for 'Vector3 * {}'".format(type(other))) + raise TypeError(f"No operation known for 'Vector3 * {type(other)}'") def __truediv__(self, other): if type(other) is Vector3: @@ -121,7 +121,7 @@ def __truediv__(self, other): elif isinstance(other, Number): return Vector3(self.x / other, self.y / other, self.z / other) else: - raise TypeError("No operation known for 'Vector3 / {}'".format(type(other))) + raise TypeError(f"No operation known for 'Vector3 / {type(other)}'") def __rmul__(self, other): """ @@ -135,7 +135,7 @@ def __rmul__(self, other): if isinstance(other, Number): return self.scale(other) else: - raise TypeError("No operation known for '{} * Vector3'".format(type(other))) + raise TypeError(f"No operation known for '{type(other)} * Vector3'") def __getitem__(self, i): if i == 0: @@ -145,10 +145,10 @@ def __getitem__(self, i): elif i == 2: return self.z else: - raise IndexError("No value at index {}".format(i)) + raise IndexError(f"No value at index {i}") def __repr__(self): - return "Vector3<{}, {}, {}>".format(self.x, self.y, self.z) + return f"Vector3<{self.x}, {self.y}, {self.z}>" def __array__(self): return np.array([self.x, self.y, self.z]) @@ -220,9 +220,11 @@ def close(self, v, tol=1.0e-7): v1.close(v2, [tol]) ``` """ - return (abs(self.x - v.x) <= tol and - abs(self.y - v.y) <= tol and - abs(self.z - v.z) <= tol) + return ( + abs(self.x - v.x) <= tol + and abs(self.y - v.y) <= tol + and abs(self.z - v.z) <= tol + ) def rotate(self, axis, theta): """ @@ -255,7 +257,7 @@ def rotate_reciprocal(self, axis, theta, lat): return cartesian_to_reciprocal(v.rotate(a, theta), lat) -class Medium(object): +class Medium: """ This class is used to specify the materials that geometric objects are made of. It represents an electromagnetic medium which is possibly nonlinear and/or dispersive. @@ -307,32 +309,36 @@ class Medium(object): Dispersive dielectric and magnetic materials, above, are specified via a list of objects that are subclasses of type `Susceptibility`. """ - def __init__(self, epsilon_diag=Vector3(1, 1, 1), - epsilon_offdiag=Vector3(), - mu_diag=Vector3(1, 1, 1), - mu_offdiag=Vector3(), - E_susceptibilities=None, - H_susceptibilities=None, - E_chi2_diag=Vector3(), - E_chi3_diag=Vector3(), - H_chi2_diag=Vector3(), - H_chi3_diag=Vector3(), - D_conductivity_diag=Vector3(), - D_conductivity_offdiag=Vector3(), - B_conductivity_diag=Vector3(), - B_conductivity_offdiag=Vector3(), - epsilon=None, - index=None, - mu=None, - chi2=None, - chi3=None, - D_conductivity=None, - B_conductivity=None, - E_chi2=None, - E_chi3=None, - H_chi2=None, - H_chi3=None, - valid_freq_range=FreqRange(min=-mp.inf, max=mp.inf)): + + def __init__( + self, + epsilon_diag=Vector3(1, 1, 1), + epsilon_offdiag=Vector3(), + mu_diag=Vector3(1, 1, 1), + mu_offdiag=Vector3(), + E_susceptibilities=None, + H_susceptibilities=None, + E_chi2_diag=Vector3(), + E_chi3_diag=Vector3(), + H_chi2_diag=Vector3(), + H_chi3_diag=Vector3(), + D_conductivity_diag=Vector3(), + D_conductivity_offdiag=Vector3(), + B_conductivity_diag=Vector3(), + B_conductivity_offdiag=Vector3(), + epsilon=None, + index=None, + mu=None, + chi2=None, + chi3=None, + D_conductivity=None, + B_conductivity=None, + E_chi2=None, + E_chi3=None, + H_chi2=None, + H_chi3=None, + valid_freq_range=FreqRange(min=-mp.inf, max=mp.inf), + ): """ Creates a `Medium` object. @@ -406,9 +412,13 @@ def __init__(self, epsilon_diag=Vector3(1, 1, 1), mu_diag = Vector3(mu, mu, mu) if D_conductivity: - D_conductivity_diag = Vector3(D_conductivity, D_conductivity, D_conductivity) + D_conductivity_diag = Vector3( + D_conductivity, D_conductivity, D_conductivity + ) if B_conductivity: - B_conductivity_diag = Vector3(B_conductivity, B_conductivity, B_conductivity) + B_conductivity_diag = Vector3( + B_conductivity, B_conductivity, B_conductivity + ) if E_chi2: E_chi2_diag = Vector3(E_chi2, E_chi2, E_chi2) @@ -423,8 +433,8 @@ def __init__(self, epsilon_diag=Vector3(1, 1, 1), self.epsilon_offdiag = Vector3(*epsilon_offdiag) self.mu_diag = Vector3(*mu_diag) self.mu_offdiag = Vector3(*mu_offdiag) - self.E_susceptibilities = E_susceptibilities if E_susceptibilities else [] - self.H_susceptibilities = H_susceptibilities if H_susceptibilities else [] + self.E_susceptibilities = E_susceptibilities or [] + self.H_susceptibilities = H_susceptibilities or [] self.E_chi2_diag = Vector3(chi2, chi2, chi2) if chi2 else Vector3(*E_chi2_diag) self.E_chi3_diag = Vector3(chi3, chi3, chi3) if chi3 else Vector3(*E_chi3_diag) self.H_chi2_diag = Vector3(*H_chi2_diag) @@ -436,7 +446,7 @@ def __init__(self, epsilon_diag=Vector3(1, 1, 1), self.valid_freq_range = valid_freq_range def __repr__(self): - return 'Medium()' + return "Medium()" def transform(self, m): """ @@ -451,12 +461,22 @@ def transform(self, m): left-handed materials, which are [problematic in nondispersive media](FAQ.md#why-does-my-simulation-diverge-if-0). """ - eps = Matrix(mp.Vector3(self.epsilon_diag.x, self.epsilon_offdiag.x, self.epsilon_offdiag.y), - mp.Vector3(self.epsilon_offdiag.x, self.epsilon_diag.y, self.epsilon_offdiag.z), - mp.Vector3(self.epsilon_offdiag.y, self.epsilon_offdiag.z, self.epsilon_diag.z)) - mu = Matrix(mp.Vector3(self.mu_diag.x, self.mu_offdiag.x, self.mu_offdiag.y), - mp.Vector3(self.mu_offdiag.x, self.mu_diag.y, self.mu_offdiag.z), - mp.Vector3(self.mu_offdiag.y, self.mu_offdiag.z, self.mu_diag.z)) + eps = Matrix( + mp.Vector3( + self.epsilon_diag.x, self.epsilon_offdiag.x, self.epsilon_offdiag.y + ), + mp.Vector3( + self.epsilon_offdiag.x, self.epsilon_diag.y, self.epsilon_offdiag.z + ), + mp.Vector3( + self.epsilon_offdiag.y, self.epsilon_offdiag.z, self.epsilon_diag.z + ), + ) + mu = Matrix( + mp.Vector3(self.mu_diag.x, self.mu_offdiag.x, self.mu_offdiag.y), + mp.Vector3(self.mu_offdiag.x, self.mu_diag.y, self.mu_offdiag.z), + mp.Vector3(self.mu_offdiag.y, self.mu_offdiag.z, self.mu_diag.z), + ) new_eps = (m * eps * m.transpose()) / abs(m.determinant()) new_mu = (m * mu * m.transpose()) / abs(m.determinant()) @@ -472,26 +492,48 @@ def transform(self, m): s.transform(m) def rotate(self, axis, theta): - T = get_rotation_matrix(axis,theta) + T = get_rotation_matrix(axis, theta) self.transform(T) - def epsilon(self,freq): + def epsilon(self, freq): """ Returns the medium's permittivity tensor as a 3x3 Numpy array at the specified frequency `freq` which can be either a scalar, list, or Numpy array. In the case of a list/array of N frequency points, a Numpy array of size Nx3x3 is returned. """ - return self._get_epsmu(self.epsilon_diag, self.epsilon_offdiag, self.E_susceptibilities, self.D_conductivity_diag, self.D_conductivity_offdiag, freq) + return self._get_epsmu( + self.epsilon_diag, + self.epsilon_offdiag, + self.E_susceptibilities, + self.D_conductivity_diag, + self.D_conductivity_offdiag, + freq, + ) - def mu(self,freq): + def mu(self, freq): """ Returns the medium's permeability tensor as a 3x3 Numpy array at the specified frequency `freq` which can be either a scalar, list, or Numpy array. In the case of a list/array of N frequency points, a Numpy array of size Nx3x3 is returned. """ - return self._get_epsmu(self.mu_diag, self.mu_offdiag, self.H_susceptibilities, self.B_conductivity_diag, self.B_conductivity_offdiag, freq) - - def _get_epsmu(self, diag, offdiag, susceptibilities, conductivity_diag, conductivity_offdiag, freq): + return self._get_epsmu( + self.mu_diag, + self.mu_offdiag, + self.H_susceptibilities, + self.B_conductivity_diag, + self.B_conductivity_offdiag, + freq, + ) + + def _get_epsmu( + self, + diag, + offdiag, + susceptibilities, + conductivity_diag, + conductivity_offdiag, + freq, + ): # Clean the input if np.isscalar(freq): freqs = np.array(freq)[np.newaxis, np.newaxis, np.newaxis] @@ -501,48 +543,60 @@ def _get_epsmu(self, diag, offdiag, susceptibilities, conductivity_diag, conduct # Check for values outside of allowed ranges if np.min(np.squeeze(freqs)) < self.valid_freq_range.min: - raise ValueError('User specified frequency {} is below the Medium\'s limit, {}.'.format(np.min(np.squeeze(freqs)),self.valid_freq_range.min)) + raise ValueError( + f"User specified frequency {np.min(np.squeeze(freqs))} is below the Medium's limit, {self.valid_freq_range.min}." + ) + if np.max(np.squeeze(freqs)) > self.valid_freq_range.max: - raise ValueError('User specified frequency {} is above the Medium\'s limit, {}.'.format(np.max(np.squeeze(freqs)),self.valid_freq_range.max)) + raise ValueError( + f"User specified frequency {np.max(np.squeeze(freqs))} is above the Medium's limit, {self.valid_freq_range.max}." + ) # Initialize with instantaneous dielectric tensor - epsmu = np.expand_dims(Matrix(diag=diag,offdiag=offdiag),axis=0) + epsmu = np.expand_dims(Matrix(diag=diag, offdiag=offdiag), axis=0) # Iterate through susceptibilities for i_sus in range(len(susceptibilities)): epsmu = epsmu + susceptibilities[i_sus].eval_susceptibility(freqs) # Account for conductivity term (only multiply if nonzero to avoid unnecessary complex numbers) - conductivity = np.expand_dims(Matrix(diag=conductivity_diag,offdiag=conductivity_offdiag),axis=0) + conductivity = np.expand_dims( + Matrix(diag=conductivity_diag, offdiag=conductivity_offdiag), axis=0 + ) if np.count_nonzero(conductivity) > 0: - epsmu = (1 + 1j/(2*np.pi*freqs) * conductivity) * epsmu + epsmu = (1 + 1j / (2 * np.pi * freqs) * conductivity) * epsmu # Convert list matrix to 3D numpy array size [freqs,3,3] return np.squeeze(epsmu) -class MaterialGrid(object): + +class MaterialGrid: """ This class is used to specify materials on a rectilinear grid. A class object is passed as the `material` argument of a [`Block`](#block) geometric object or the `default_material` argument of the [`Simulation`](#Simulation) constructor (similar to a [material function](#medium)). """ + def check_weights(self, w): - if np.amin(w) < 0. or np.amax(w) > 1.: - warnings.warn('The weights parameter of MaterialGrid must be in the range [0,1].') - return np.clip(w, 0., 1.) - else: + if np.amin(w) >= 0.0 and np.amax(w) <= 1.0: return w - - def __init__(self, - grid_size, - medium1, - medium2, - weights=None, - grid_type="U_DEFAULT", - do_averaging=True, - beta=0, - eta=0.5, - damping=0): + warnings.warn( + "The weights parameter of MaterialGrid must be in the range [0,1]." + ) + return np.clip(w, 0.0, 1.0) + + def __init__( + self, + grid_size, + medium1, + medium2, + weights=None, + grid_type="U_DEFAULT", + do_averaging=True, + beta=0, + eta=0.5, + damping=0, + ): """ Creates a `MaterialGrid` object. @@ -593,15 +647,17 @@ def __init__(self, self.grid_size = mp.Vector3(*grid_size) self.medium1 = medium1 self.medium2 = medium2 + def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) - if isclose(self.grid_size.x,0): + return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) + + if isclose(self.grid_size.x, 0): self.grid_size.x = 1 - if isclose(self.grid_size.y,0): + if isclose(self.grid_size.y, 0): self.grid_size.y = 1 - if isclose(self.grid_size.z,0): + if isclose(self.grid_size.z, 0): self.grid_size.z = 1 - self.num_params=int(self.grid_size.x*self.grid_size.y*self.grid_size.z) + self.num_params = int(self.grid_size.x * self.grid_size.y * self.grid_size.z) self.do_averaging = do_averaging self.beta = beta self.eta = eta @@ -609,18 +665,21 @@ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): if weights is None: self.weights = np.zeros((self.num_params,)) elif weights.size != self.num_params: - raise ValueError("weights of shape {} do not match user specified grid dimension: {}".format(weights.size,self.grid_size)) + raise ValueError( + "weights of shape {} do not match user specified grid dimension: {}".format( + weights.size, self.grid_size + ) + ) else: self.weights = self.check_weights(weights).flatten().astype(np.float64) - grid_type_dict = { - "U_MIN":0, - "U_PROD":1, - "U_MEAN":2, - "U_DEFAULT":3 - } + grid_type_dict = {"U_MIN": 0, "U_PROD": 1, "U_MEAN": 2, "U_DEFAULT": 3} if grid_type not in grid_type_dict: - raise ValueError("Invalid grid_type: {}. Must be either U_MIN, U_PROD, U_MEAN, or U_DEFAULT".format(grid_type_dict)) + raise ValueError( + "Invalid grid_type: {}. Must be either U_MIN, U_PROD, U_MEAN, or U_DEFAULT".format( + grid_type_dict + ) + ) self.grid_type = grid_type_dict[grid_type] self.swigobj = None @@ -630,14 +689,19 @@ def update_weights(self, x): Reset the `weights` to `x`. """ if x.size != self.num_params: - raise ValueError("weights of shape {} do not match user specified grid dimension: {}".format(self.weights.size,self.grid_size)) + raise ValueError( + f"weights of shape {self.weights.size} do not match user specified grid dimension: {self.grid_size}" + ) + self.weights[:] = self.check_weights(x).flatten().astype(np.float64) -class Susceptibility(object): + +class Susceptibility: """ Parent class for various dispersive susceptibility terms, parameterized by an anisotropic amplitude $\\sigma$. See [Material Dispersion](Materials.md#material-dispersion). """ + def __init__(self, sigma_diag=Vector3(), sigma_offdiag=Vector3(), sigma=None): """ + **`sigma` [`number`]** — The scale factor $\\sigma$. @@ -650,11 +714,13 @@ def __init__(self, sigma_diag=Vector3(), sigma_offdiag=Vector3(), sigma=None): \\begin{pmatrix} a & u & v \\\\ u & b & w \\\\ v & w & c \\end{pmatrix} """ - self.sigma_diag = Vector3(sigma, sigma, sigma) if sigma else Vector3(*sigma_diag) + self.sigma_diag = ( + Vector3(sigma, sigma, sigma) if sigma else Vector3(*sigma_diag) + ) self.sigma_offdiag = Vector3(*sigma_offdiag) def transform(self, m): - sigma = Matrix(diag=self.sigma_diag,offdiag=self.sigma_offdiag) + sigma = Matrix(diag=self.sigma_diag, offdiag=self.sigma_offdiag) new_sigma = (m * sigma * m.transpose()) / abs(m.determinant()) self.sigma_diag = mp.Vector3(new_sigma.c1.x, new_sigma.c2.y, new_sigma.c3.z) self.sigma_offdiag = mp.Vector3(new_sigma.c2.x, new_sigma.c3.x, new_sigma.c3.y) @@ -666,6 +732,7 @@ class LorentzianSusceptibility(Susceptibility): oscillator) form. See [Material Dispersion](Materials.md#material-dispersion), with the parameters (in addition to $\\sigma$): """ + def __init__(self, frequency=0.0, gamma=0.0, **kwargs): """ + **`frequency` [`number`]** — The resonance frequency $f_n = \\omega_n / 2\\pi$. @@ -676,16 +743,32 @@ def __init__(self, frequency=0.0, gamma=0.0, **kwargs): different `sigma` will appear as a *single* Lorentzian susceptibility term in the preliminary simulation info output. """ - super(LorentzianSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.frequency = frequency self.gamma = gamma - def eval_susceptibility(self,freq): - sigma = np.expand_dims(Matrix(diag=self.sigma_diag,offdiag=self.sigma_offdiag),axis=0) + def eval_susceptibility(self, freq): + sigma = np.expand_dims( + Matrix(diag=self.sigma_diag, offdiag=self.sigma_offdiag), axis=0 + ) if self.gamma == 0: - return self.frequency*self.frequency / (self.frequency*self.frequency - freq*freq) * sigma + return ( + self.frequency + * self.frequency + / (self.frequency * self.frequency - freq * freq) + * sigma + ) else: - return self.frequency*self.frequency / (self.frequency*self.frequency - freq*freq - 1j*self.gamma*freq) * sigma + return ( + self.frequency + * self.frequency + / ( + self.frequency * self.frequency + - freq * freq + - 1j * self.gamma * freq + ) + * sigma + ) class DrudeSusceptibility(Susceptibility): @@ -693,6 +776,7 @@ class DrudeSusceptibility(Susceptibility): Specifies a single dispersive susceptibility of Drude form. See [Material Dispersion](Materials.md#material-dispersion), with the parameters (in addition to $\\sigma$): """ + def __init__(self, frequency=0.0, gamma=0.0, **kwargs): """ + **`frequency` [`number`]** — The frequency scale factor $f_n = \\omega_n / 2\\pi$ @@ -700,16 +784,23 @@ def __init__(self, frequency=0.0, gamma=0.0, **kwargs): + **`gamma` [`number`]** — The loss rate $\\gamma_n / 2\\pi$. """ - super(DrudeSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.frequency = frequency self.gamma = gamma - def eval_susceptibility(self,freq): - sigma = np.expand_dims(Matrix(diag=self.sigma_diag,offdiag=self.sigma_offdiag),axis=0) + def eval_susceptibility(self, freq): + sigma = np.expand_dims( + Matrix(diag=self.sigma_diag, offdiag=self.sigma_offdiag), axis=0 + ) if self.gamma == 0: - return -self.frequency*self.frequency / (freq*(freq)) * sigma + return -self.frequency * self.frequency / (freq * (freq)) * sigma else: - return -self.frequency*self.frequency / (freq*(freq + 1j*self.gamma)) * sigma + return ( + -self.frequency + * self.frequency + / (freq * (freq + 1j * self.gamma)) + * sigma + ) class NoisyLorentzianSusceptibility(LorentzianSusceptibility): @@ -720,6 +811,7 @@ class NoisyLorentzianSusceptibility(LorentzianSusceptibility): `gamma` parameters, but with an additional Gaussian random noise term (uncorrelated in space and time, zero mean) added to the **P** damped-oscillator equation. """ + def __init__(self, noise_amp=0.0, **kwargs): """ + **`noise_amp` [`number`]** — The noise has root-mean square amplitude σ $\\times$ @@ -738,7 +830,7 @@ def __init__(self, noise_amp=0.0, **kwargs): [here](http://doi.org/10.1103/PhysRevB.92.134202) or [here](http://doi.org/10.1103/PhysRevB.88.054305). """ - super(NoisyLorentzianSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.noise_amp = noise_amp @@ -750,6 +842,7 @@ class NoisyDrudeSusceptibility(DrudeSusceptibility): `gamma` parameters, but with an additional Gaussian random noise term (uncorrelated in space and time, zero mean) added to the **P** damped-oscillator equation. """ + def __init__(self, noise_amp=0.0, **kwargs): """ + **`noise_amp` [`number`]** — The noise has root-mean square amplitude σ $\\times$ @@ -768,7 +861,7 @@ def __init__(self, noise_amp=0.0, **kwargs): [here](http://doi.org/10.1103/PhysRevB.92.134202) or [here](http://doi.org/10.1103/PhysRevB.88.054305). """ - super(NoisyDrudeSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.noise_amp = noise_amp @@ -780,13 +873,14 @@ class GyrotropicLorentzianSusceptibility(LorentzianSusceptibility): parameters are `sigma`, `frequency`, and `gamma`, which have the [usual meanings](#susceptibility), and an additional 3-vector `bias`: """ + def __init__(self, bias=Vector3(), **kwargs): """ + **`bias` [`Vector3`]** — The gyrotropy vector. Its direction determines the orientation of the gyrotropic response, and the magnitude is the precession frequency $|\\mathbf{b}_n|/2\\pi$. """ - super(GyrotropicLorentzianSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.bias = bias @@ -798,13 +892,14 @@ class GyrotropicDrudeSusceptibility(DrudeSusceptibility): parameters are `sigma`, `frequency`, and `gamma`, which have the [usual meanings](#susceptibility), and an additional 3-vector `bias`: """ + def __init__(self, bias=Vector3(), **kwargs): """ + **`bias` [`Vector3`]** — The gyrotropy vector. Its direction determines the orientation of the gyrotropic response, and the magnitude is the precession frequency $|\\mathbf{b}_n|/2\\pi$. """ - super(GyrotropicDrudeSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.bias = bias @@ -818,6 +913,7 @@ class GyrotropicSaturatedSusceptibility(Susceptibility): different from the Lorentzian and Drude case. It also takes a 3-vector `bias` parameter and an `alpha` parameter: """ + def __init__(self, bias=Vector3(), frequency=0.0, gamma=0.0, alpha=0.0, **kwargs): """ + **`sigma` [`number`]** — The coupling factor $\\sigma_n / 2\\pi$ between the @@ -842,7 +938,7 @@ def __init__(self, bias=Vector3(), frequency=0.0, gamma=0.0, alpha=0.0, **kwargs the magnitude is ignored; instead, the relevant precession frequencies are determined by the `sigma` and `frequency` parameters. """ - super(GyrotropicSaturatedSusceptibility, self).__init__(**kwargs) + super().__init__(**kwargs) self.frequency = frequency self.gamma = gamma self.bias = bias @@ -858,24 +954,26 @@ class MultilevelAtom(Susceptibility): atomic level. See [Materials/Saturable Gain and Absorption](Materials.md#saturable-gain-and-absorption). """ + def __init__(self, initial_populations=None, transitions=None, **kwargs): - super(MultilevelAtom, self).__init__(**kwargs) - self.initial_populations = initial_populations if initial_populations else [] - self.transitions = transitions if transitions else [] + super().__init__(**kwargs) + self.initial_populations = initial_populations or [] + self.transitions = transitions or [] -class Transition(object): - """ - """ +class Transition: + """ """ - def __init__(self, - from_level, - to_level, - transition_rate=0, - frequency=0, - sigma_diag=Vector3(1, 1, 1), - gamma=0, - pumping_rate=0): + def __init__( + self, + from_level, + to_level, + transition_rate=0, + frequency=0, + sigma_diag=Vector3(1, 1, 1), + gamma=0, + pumping_rate=0, + ): """ Construct a `Transition`. @@ -894,8 +992,8 @@ def __init__(self, + **`pumping_rate` [`number`]** — The pumping rate $f = \\omega / 2\\pi$. Default is 0. """ - self.from_level = check_nonnegative('from_level', from_level) - self.to_level = check_nonnegative('to_level', to_level) + self.from_level = check_nonnegative("from_level", from_level) + self.to_level = check_nonnegative("to_level", to_level) self.transition_rate = transition_rate self.frequency = frequency self.sigma_diag = sigma_diag @@ -903,7 +1001,7 @@ def __init__(self, self.pumping_rate = pumping_rate -class GeometricObject(object): +class GeometricObject: """ This class, and its descendants, are used to specify the solid geometric objects that form the dielectric structure being simulated. @@ -956,6 +1054,7 @@ class GeometricObject(object): geometry = [mp.Prism(vertices, height=1.5, center=mp.Vector3(), material=cSi)] ``` """ + def __init__(self, material=Medium(), center=Vector3(), epsilon_func=None): """ Construct a `GeometricObject`. @@ -1035,7 +1134,7 @@ class Sphere(GeometricObject): def __init__(self, radius, **kwargs): """Constructs a `Sphere`""" self.radius = float(radius) - super(Sphere, self).__init__(**kwargs) + super().__init__(**kwargs) @property def radius(self): @@ -1067,7 +1166,7 @@ def __init__(self, radius, axis=Vector3(0, 0, 1), height=1e20, **kwargs): self.axis = Vector3(*axis) self.radius = float(radius) self.height = float(height) - super(Cylinder, self).__init__(**kwargs) + super().__init__(**kwargs) @property def radius(self): @@ -1090,13 +1189,16 @@ class Wedge(Cylinder): """ Represents a cylindrical wedge. """ - def __init__(self, radius, wedge_angle=2 * math.pi, wedge_start=Vector3(1, 0, 0), **kwargs): + + def __init__( + self, radius, wedge_angle=2 * math.pi, wedge_start=Vector3(1, 0, 0), **kwargs + ): """ Constructs a `Wedge`. """ self.wedge_angle = wedge_angle self.wedge_start = Vector3(*wedge_start) - super(Wedge, self).__init__(radius, **kwargs) + super().__init__(radius, **kwargs) class Cone(Cylinder): @@ -1107,6 +1209,7 @@ class Cone(Cylinder): the radius of the tip is given by the new property, `radius2`. The `center` of a cone is halfway between the two circular ends. """ + def __init__(self, radius, radius2=0, **kwargs): """ Construct a `Cone`. @@ -1116,14 +1219,22 @@ def __init__(self, radius, radius2=0, **kwargs): Radius of the tip of the cone (i.e. the end of the cone pointed to by the `axis` vector). Defaults to zero (a "sharp" cone). """ self.radius2 = radius2 - super(Cone, self).__init__(radius, **kwargs) + super().__init__(radius, **kwargs) class Block(GeometricObject): """ A parallelepiped (i.e., a brick, possibly with non-orthogonal axes). """ - def __init__(self, size, e1=Vector3(1, 0, 0), e2=Vector3(0, 1, 0), e3=Vector3(0, 0, 1), **kwargs): + + def __init__( + self, + size, + e1=Vector3(1, 0, 0), + e2=Vector3(0, 1, 0), + e3=Vector3(0, 0, 1), + **kwargs, + ): """ Construct a `Block`. @@ -1140,7 +1251,7 @@ def __init__(self, size, e1=Vector3(1, 0, 0), e2=Vector3(0, 1, 0), e3=Vector3(0, self.e1 = Vector3(*e1) self.e2 = Vector3(*e2) self.e3 = Vector3(*e3) - super(Block, self).__init__(**kwargs) + super().__init__(**kwargs) class Ellipsoid(Block): @@ -1148,18 +1259,28 @@ class Ellipsoid(Block): An ellipsoid. This is actually a subclass of `Block`, and inherits all the same properties, but defines an ellipsoid inscribed inside the block. """ + def __init__(self, **kwargs): """ Construct an `Ellipsoid`. """ - super(Ellipsoid, self).__init__(**kwargs) + super().__init__(**kwargs) class Prism(GeometricObject): """ Polygonal prism type. """ - def __init__(self, vertices, height, axis=Vector3(z=1), center=None, sidewall_angle=0, **kwargs): + + def __init__( + self, + vertices, + height, + axis=Vector3(z=1), + center=None, + sidewall_angle=0, + **kwargs, + ): """ Construct a `Prism`. @@ -1186,8 +1307,12 @@ def __init__(self, vertices, height, axis=Vector3(z=1), center=None, sidewall_an radians. Default is 0. """ - centroid = sum(vertices, Vector3(0)) * (1.0 / len(vertices)) # centroid of floor polygon - original_center = centroid + (0.5*height)*axis # center as computed from vertices, height, axis + centroid = sum(vertices, Vector3(0)) * ( + 1.0 / len(vertices) + ) # centroid of floor polygon + original_center = ( + centroid + (0.5 * height) * axis + ) # center as computed from vertices, height, axis if center is not None and len(vertices): center = Vector3(*center) # translate vertices to center prism at requested center @@ -1200,10 +1325,10 @@ def __init__(self, vertices, height, axis=Vector3(z=1), center=None, sidewall_an self.axis = axis self.sidewall_angle = sidewall_angle - super(Prism, self).__init__(center=center, **kwargs) + super().__init__(center=center, **kwargs) -class Matrix(object): +class Matrix: """ The `Matrix` class represents a 3x3 matrix with c1, c2, and c3 as its columns. @@ -1240,7 +1365,15 @@ class Matrix(object): Scales the matrix `m` by the number `s`. """ - def __init__(self, c1=Vector3(), c2=Vector3(), c3=Vector3(), diag=Vector3(), offdiag=Vector3()): + + def __init__( + self, + c1=Vector3(), + c2=Vector3(), + c3=Vector3(), + diag=Vector3(), + offdiag=Vector3(), + ): """ Constructs a `Matrix`. """ @@ -1263,13 +1396,13 @@ def __mul__(self, m): elif isinstance(m, Number): return self.scale(m) else: - raise TypeError("No operation known for 'Matrix * {}'".format(type(m))) + raise TypeError(f"No operation known for 'Matrix * {type(m)}'") def __rmul__(self, left_arg): if isinstance(left_arg, Number): return self.scale(left_arg) else: - raise TypeError("No operation known for 'Matrix * {}'".format(type(left_arg))) + raise TypeError(f"No operation known for 'Matrix * {type(left_arg)}'") def __truediv__(self, scalar): return Matrix(self.c1 / scalar, self.c2 / scalar, self.c3 / scalar) @@ -1284,28 +1417,26 @@ def __repr__(self): r0 = self.row(0) r1 = self.row(1) r2 = self.row(2) - return "<<{} {} {}>\n <{} {} {}>\n <{} {} {}>>".format(r0[0], r0[1], r0[2], - r1[0], r1[1], r1[2], - r2[0], r2[1], r2[2]) + return f"<<{r0[0]} {r0[1]} {r0[2]}>\n <{r1[0]} {r1[1]} {r1[2]}>\n <{r2[0]} {r2[1]} {r2[2]}>>" def __array__(self): - return np.array([self.row(0).__array__(), - self.row(1).__array__(), - self.row(2).__array__()]) + return np.array( + [self.row(0).__array__(), self.row(1).__array__(), self.row(2).__array__()] + ) def row(self, i): return Vector3(self.c1[i], self.c2[i], self.c3[i]) def mm_mult(self, m): - c1 = Vector3(self.row(0).dot(m.c1), - self.row(1).dot(m.c1), - self.row(2).dot(m.c1)) - c2 = Vector3(self.row(0).dot(m.c2), - self.row(1).dot(m.c2), - self.row(2).dot(m.c2)) - c3 = Vector3(self.row(0).dot(m.c3), - self.row(1).dot(m.c3), - self.row(2).dot(m.c3)) + c1 = Vector3( + self.row(0).dot(m.c1), self.row(1).dot(m.c1), self.row(2).dot(m.c1) + ) + c2 = Vector3( + self.row(0).dot(m.c2), self.row(1).dot(m.c2), self.row(2).dot(m.c2) + ) + c3 = Vector3( + self.row(0).dot(m.c3), self.row(1).dot(m.c3), self.row(2).dot(m.c3) + ) return Matrix(c1, c2, c3) @@ -1316,16 +1447,20 @@ def scale(self, s): return Matrix(self.c1.scale(s), self.c2.scale(s), self.c3.scale(s)) def determinant(self): - sum1 = sum([ - functools.reduce(operator.mul, [self[x][x] for x in range(3)]), - functools.reduce(operator.mul, [self[0][1], self[1][2], self[2][0]]), - functools.reduce(operator.mul, [self[1][0], self[2][1], self[0][2]]) - ]) - sum2 = sum([ - functools.reduce(operator.mul, [self[0][2], self[1][1], self[2][0]]), - functools.reduce(operator.mul, [self[0][1], self[1][0], self[2][2]]), - functools.reduce(operator.mul, [self[1][2], self[2][1], self[0][0]]) - ]) + sum1 = sum( + [ + functools.reduce(operator.mul, [self[x][x] for x in range(3)]), + functools.reduce(operator.mul, [self[0][1], self[1][2], self[2][0]]), + functools.reduce(operator.mul, [self[1][0], self[2][1], self[0][2]]), + ] + ) + sum2 = sum( + [ + functools.reduce(operator.mul, [self[0][2], self[1][1], self[2][0]]), + functools.reduce(operator.mul, [self[0][1], self[1][0], self[2][2]]), + functools.reduce(operator.mul, [self[1][2], self[2][1], self[0][0]]), + ] + ) return sum1 - sum2 def conj(self): @@ -1360,14 +1495,15 @@ def inverse(self): H = property(getH, None) -class Lattice(object): - - def __init__(self, - size=Vector3(1, 1, 1), - basis_size=Vector3(1, 1, 1), - basis1=Vector3(1, 0, 0), - basis2=Vector3(0, 1, 0), - basis3=Vector3(0, 0, 1)): +class Lattice: + def __init__( + self, + size=Vector3(1, 1, 1), + basis_size=Vector3(1, 1, 1), + basis1=Vector3(1, 0, 0), + basis2=Vector3(0, 1, 0), + basis3=Vector3(0, 0, 1), + ): self.size = Vector3(*size) self.basis_size = Vector3(*basis_size) @@ -1446,10 +1582,7 @@ def reciprocal_to_cartesian(x, lat): m = Matrix(Vector3(s.x), Vector3(y=s.y), Vector3(z=s.z)) Rst = (lat.basis * m).transpose() - if isinstance(x, Vector3): - return Rst.inverse() * x - else: - return (Rst.inverse() * x) * Rst + return Rst.inverse() * x if isinstance(x, Vector3) else (Rst.inverse() * x) * Rst def cartesian_to_reciprocal(x, lat): @@ -1458,10 +1591,7 @@ def cartesian_to_reciprocal(x, lat): m = Matrix(Vector3(s.x), Vector3(y=s.y), Vector3(z=s.z)) Rst = (lat.basis * m).transpose() - if isinstance(x, Vector3): - return Rst * x - else: - return (Rst * x) * Rst.inverse() + return Rst * x if isinstance(x, Vector3) else (Rst * x) * Rst.inverse() def lattice_to_reciprocal(x, lat): @@ -1475,12 +1605,12 @@ def reciprocal_to_lattice(x, lat): def geometric_object_duplicates(shift_vector, min_multiple, max_multiple, go): shift_vector = Vector3(*shift_vector) + def _dup(min_multiple, lst): - if min_multiple <= max_multiple: - shifted = go.shift(shift_vector.scale(min_multiple)) - return _dup(min_multiple + 1, [shifted] + lst) - else: + if min_multiple > max_multiple: return lst + shifted = go.shift(shift_vector.scale(min_multiple)) + return _dup(min_multiple + 1, [shifted] + lst) return _dup(min_multiple, []) @@ -1489,12 +1619,13 @@ def geometric_objects_duplicates(shift_vector, min_multiple, max_multiple, go_li dups = [] shift_vector = Vector3(*shift_vector) for go in go_list: - dups += geometric_object_duplicates(shift_vector, min_multiple, max_multiple, go) + dups += geometric_object_duplicates( + shift_vector, min_multiple, max_multiple, go + ) return dups def geometric_objects_lattice_duplicates(lat, go_list, *usize): - def lat_to_lattice(v): return cartesian_to_lattice(lat.basis * v, lat) @@ -1538,6 +1669,7 @@ def _mem(y=None): fy = f(y) f_memo_tab[y] = fy return fy + return _mem @@ -1550,9 +1682,7 @@ def find_root_deriv(f, tol, x_min, x_max, x_guess=None): f_memo = memoize(f) def lazy(x): - if isinstance(x, numbers.Number): - return x - return x() + return x if isinstance(x, numbers.Number) else x() def pick_bound(which): def _pb(): @@ -1567,6 +1697,7 @@ def _pb(): return x_max else: raise ValueError("failed to bracket the root in find_root_deriv") + return _pb def in_bounds(x, f, df, a, b): @@ -1586,7 +1717,11 @@ def newton(x, a, b, dx): a_prime = x if f < 0 else a b_prime = x if f > 0 else b - if dx != x_max - x_min and dx * (f / df) < 0 and f_memo(lazy(a_prime))[0] * f_memo(lazy(b_prime))[0] > 0: + if ( + dx != x_max - x_min + and dx * (f / df) < 0 + and f_memo(lazy(a_prime))[0] * f_memo(lazy(b_prime))[0] > 0 + ): raise ValueError("failed to bracket the root in find_root_deriv") if isinstance(a, numbers.Number) and isinstance(b, numbers.Number): @@ -1608,8 +1743,12 @@ def newton(x, a, b, dx): if x_guess is None: x_guess = (x_min + x_max) * 0.5 - return newton(x_guess, pick_bound(lambda aa: aa < 0), - pick_bound(lambda aa: aa > 0), x_max - x_min) + return newton( + x_guess, + pick_bound(lambda aa: aa < 0), + pick_bound(lambda aa: aa > 0), + x_max - x_min, + ) def get_rotation_matrix(axis, theta): @@ -1622,6 +1761,8 @@ def get_rotation_matrix(axis, theta): + `theta` [`number`] — The rotation angle (in radians). """ - return Matrix(Vector3(x=1).rotate(axis, theta), - Vector3(y=1).rotate(axis, theta), - Vector3(z=1).rotate(axis, theta)) + return Matrix( + Vector3(x=1).rotate(axis, theta), + Vector3(y=1).rotate(axis, theta), + Vector3(z=1).rotate(axis, theta), + ) diff --git a/python/materials.py b/python/materials.py index 6da4e66e0..604571494 100644 --- a/python/materials.py +++ b/python/materials.py @@ -1,951 +1,1116 @@ -# -*- coding: utf-8 -*- # Materials Library +import numpy as np import meep as mp -import numpy as np # default unit length is 1 μm um_scale = 1.0 # conversion factor for eV to 1/μm [=1/hc] -eV_um_scale = um_scale/1.23984193 +eV_um_scale = um_scale / 1.23984193 -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # crystalline silicon (c-Si) from A. Deinega et al., J. Optical Society of America A, Vol. 28, No. 5, pp. 770-77 (2011) # based on experimental data for intrinsic silicon at T=300K from M.A. Green and M. Keevers, Progress in Photovoltaics, Vol. 3, pp. 189-92 (1995) # wavelength range: 0.4 - 1.0 μm -cSi_range = mp.FreqRange(min=um_scale, max=um_scale/0.4) +cSi_range = mp.FreqRange(min=um_scale, max=um_scale / 0.4) -cSi_frq1 = 3.64/um_scale +cSi_frq1 = 3.64 / um_scale cSi_gam1 = 0 cSi_sig1 = 8 -cSi_frq2 = 2.76/um_scale -cSi_gam2 = 2*0.063/um_scale +cSi_frq2 = 2.76 / um_scale +cSi_gam2 = 2 * 0.063 / um_scale cSi_sig2 = 2.85 -cSi_frq3 = 1.73/um_scale -cSi_gam3 = 2*2.5/um_scale +cSi_frq3 = 1.73 / um_scale +cSi_gam3 = 2 * 2.5 / um_scale cSi_sig3 = -0.107 -cSi_susc = [mp.LorentzianSusceptibility(frequency=cSi_frq1, gamma=cSi_gam1, sigma=cSi_sig1), - mp.LorentzianSusceptibility(frequency=cSi_frq2, gamma=cSi_gam2, sigma=cSi_sig2), - mp.LorentzianSusceptibility(frequency=cSi_frq3, gamma=cSi_gam3, sigma=cSi_sig3)] +cSi_susc = [ + mp.LorentzianSusceptibility(frequency=cSi_frq1, gamma=cSi_gam1, sigma=cSi_sig1), + mp.LorentzianSusceptibility(frequency=cSi_frq2, gamma=cSi_gam2, sigma=cSi_sig2), + mp.LorentzianSusceptibility(frequency=cSi_frq3, gamma=cSi_gam3, sigma=cSi_sig3), +] cSi = mp.Medium(epsilon=1.0, E_susceptibilities=cSi_susc, valid_freq_range=cSi_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # amorphous silicon (a-Si) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 0.83 μm -aSi_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.21) +aSi_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.21) -aSi_frq1 = 1/(0.315481407124682*um_scale) -aSi_gam1 = 1/(0.645751005208333*um_scale) +aSi_frq1 = 1 / (0.315481407124682 * um_scale) +aSi_gam1 = 1 / (0.645751005208333 * um_scale) aSi_sig1 = 14.571 -aSi_susc = [mp.LorentzianSusceptibility(frequency=aSi_frq1, gamma=aSi_gam1, sigma=aSi_sig1)] +aSi_susc = [ + mp.LorentzianSusceptibility(frequency=aSi_frq1, gamma=aSi_gam1, sigma=aSi_sig1) +] aSi = mp.Medium(epsilon=3.109, E_susceptibilities=aSi_susc, valid_freq_range=aSi_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # hydrogenated amorphous silicon (a-Si:H) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 0.83 μm -aSi_H_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.21) +aSi_H_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.21) -aSi_H_frq1 = 1/(0.334189199460916*um_scale) -aSi_H_gam1 = 1/(0.579365387850467*um_scale) +aSi_H_frq1 = 1 / (0.334189199460916 * um_scale) +aSi_H_gam1 = 1 / (0.579365387850467 * um_scale) aSi_H_sig1 = 12.31 -aSi_H_susc = [mp.LorentzianSusceptibility(frequency=aSi_H_frq1, gamma=aSi_H_gam1, sigma=aSi_H_sig1)] +aSi_H_susc = [ + mp.LorentzianSusceptibility( + frequency=aSi_H_frq1, gamma=aSi_H_gam1, sigma=aSi_H_sig1 + ) +] -aSi_H = mp.Medium(epsilon=3.22, E_susceptibilities=aSi_H_susc, valid_freq_range=aSi_H_range) +aSi_H = mp.Medium( + epsilon=3.22, E_susceptibilities=aSi_H_susc, valid_freq_range=aSi_H_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # indium tin oxide (ITO) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 0.83 μm -ITO_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.21) +ITO_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.21) -ITO_frq1 = 1/(0.182329695588235*um_scale) -ITO_gam1 = 1/(1.94637665620094*um_scale) +ITO_frq1 = 1 / (0.182329695588235 * um_scale) +ITO_gam1 = 1 / (1.94637665620094 * um_scale) ITO_sig1 = 2.5 -ITO_susc = [mp.LorentzianSusceptibility(frequency=ITO_frq1, gamma=ITO_gam1, sigma=ITO_sig1)] +ITO_susc = [ + mp.LorentzianSusceptibility(frequency=ITO_frq1, gamma=ITO_gam1, sigma=ITO_sig1) +] ITO = mp.Medium(epsilon=1.0, E_susceptibilities=ITO_susc, valid_freq_range=ITO_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # alumina (Al2O3) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 2.07 μm -Al2O3_range = mp.FreqRange(min=um_scale/2.07, max=um_scale/0.21) +Al2O3_range = mp.FreqRange(min=um_scale / 2.07, max=um_scale / 0.21) -Al2O3_frq1 = 1/(0.101476668030774*um_scale) +Al2O3_frq1 = 1 / (0.101476668030774 * um_scale) Al2O3_gam1 = 0 Al2O3_sig1 = 1.52 -Al2O3_susc = [mp.LorentzianSusceptibility(frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma=Al2O3_sig1)] +Al2O3_susc = [ + mp.LorentzianSusceptibility( + frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma=Al2O3_sig1 + ) +] -Al2O3 = mp.Medium(epsilon=1.0, E_susceptibilities=Al2O3_susc, valid_freq_range=Al2O3_range) +Al2O3 = mp.Medium( + epsilon=1.0, E_susceptibilities=Al2O3_susc, valid_freq_range=Al2O3_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum nitride (AlN) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.26 - 1.65 μm -AlN_range = mp.FreqRange(min=um_scale/1.65, max=um_scale/0.26) +AlN_range = mp.FreqRange(min=um_scale / 1.65, max=um_scale / 0.26) -AlN_frq1 = 1/(0.139058089950651*um_scale) +AlN_frq1 = 1 / (0.139058089950651 * um_scale) AlN_gam1 = 0 AlN_sig1 = 3.306 -AlN_susc = [mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma=AlN_sig1)] +AlN_susc = [ + mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma=AlN_sig1) +] AlN = mp.Medium(epsilon=1.0, E_susceptibilities=AlN_susc, valid_freq_range=AlN_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum arsenide (AlAs) from R.E. Fern and A. Onton, J. Applied Physics, Vol. 42, pp. 3499-500 (1971) # ref: https://refractiveindex.info/?shelf=main&book=AlAs&page=Fern # wavelength range: 0.56 - 2.2 μm -AlAs_range = mp.FreqRange(min=um_scale/2.2, max=um_scale/0.56) +AlAs_range = mp.FreqRange(min=um_scale / 2.2, max=um_scale / 0.56) -AlAs_frq1 = 1/(0.2822*um_scale) +AlAs_frq1 = 1 / (0.2822 * um_scale) AlAs_gam1 = 0 AlAs_sig1 = 6.0840 -AlAs_frq2 = 1/(27.62*um_scale) +AlAs_frq2 = 1 / (27.62 * um_scale) AlAs_gam2 = 0 AlAs_sig2 = 1.900 -AlAs_susc = [mp.LorentzianSusceptibility(frequency=AlAs_frq1, gamma=AlAs_gam1, sigma=AlAs_sig1), - mp.LorentzianSusceptibility(frequency=AlAs_frq2, gamma=AlAs_gam2, sigma=AlAs_sig2)] +AlAs_susc = [ + mp.LorentzianSusceptibility(frequency=AlAs_frq1, gamma=AlAs_gam1, sigma=AlAs_sig1), + mp.LorentzianSusceptibility(frequency=AlAs_frq2, gamma=AlAs_gam2, sigma=AlAs_sig2), +] -AlAs = mp.Medium(epsilon=2.0792, E_susceptibilities=AlAs_susc, valid_freq_range=AlAs_range) +AlAs = mp.Medium( + epsilon=2.0792, E_susceptibilities=AlAs_susc, valid_freq_range=AlAs_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # borosilicate glass (BK7) from SCHOTT Zemax catalog 2017-01-20b # ref: https://refractiveindex.info/?shelf=glass&book=BK7&page=SCHOTT # wavelength range: 0.3 - 2.5 μm -BK7_range = mp.FreqRange(min=um_scale/2.5, max=um_scale/0.3) +BK7_range = mp.FreqRange(min=um_scale / 2.5, max=um_scale / 0.3) -BK7_frq1 = 1/(0.07746417668832478*um_scale) +BK7_frq1 = 1 / (0.07746417668832478 * um_scale) BK7_gam1 = 0 BK7_sig1 = 1.03961212 -BK7_frq2 = 1/(0.14148467902921502*um_scale) +BK7_frq2 = 1 / (0.14148467902921502 * um_scale) BK7_gam2 = 0 BK7_sig2 = 0.231792344 -BK7_frq3 = 1/(10.176475470417055*um_scale) +BK7_frq3 = 1 / (10.176475470417055 * um_scale) BK7_gam3 = 0 BK7_sig3 = 1.01046945 -BK7_susc = [mp.LorentzianSusceptibility(frequency=BK7_frq1, gamma=BK7_gam1, sigma=BK7_sig1), - mp.LorentzianSusceptibility(frequency=BK7_frq2, gamma=BK7_gam2, sigma=BK7_sig2), - mp.LorentzianSusceptibility(frequency=BK7_frq3, gamma=BK7_gam3, sigma=BK7_sig3)] +BK7_susc = [ + mp.LorentzianSusceptibility(frequency=BK7_frq1, gamma=BK7_gam1, sigma=BK7_sig1), + mp.LorentzianSusceptibility(frequency=BK7_frq2, gamma=BK7_gam2, sigma=BK7_sig2), + mp.LorentzianSusceptibility(frequency=BK7_frq3, gamma=BK7_gam3, sigma=BK7_sig3), +] BK7 = mp.Medium(epsilon=1.0, E_susceptibilities=BK7_susc, valid_freq_range=BK7_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # fused quartz (silica) from I.H. Malitson, J. Optical Society of America, Vol. 55, pp. 1205-9 (1965) # ref: https://refractiveindex.info/?shelf=glass&book=fused_silica&page=Malitson # wavelength range: 0.21 - 6.7 μm -fused_quartz_range = mp.FreqRange(min=um_scale/6.7, max=um_scale/0.21) +fused_quartz_range = mp.FreqRange(min=um_scale / 6.7, max=um_scale / 0.21) -fused_quartz_frq1 = 1/(0.0684043*um_scale) +fused_quartz_frq1 = 1 / (0.0684043 * um_scale) fused_quartz_gam1 = 0 fused_quartz_sig1 = 0.696166300 -fused_quartz_frq2 = 1/(0.1162414*um_scale) +fused_quartz_frq2 = 1 / (0.1162414 * um_scale) fused_quartz_gam2 = 0 fused_quartz_sig2 = 0.407942600 -fused_quartz_frq3 = 1/(9.896161*um_scale) +fused_quartz_frq3 = 1 / (9.896161 * um_scale) fused_quartz_gam3 = 0 fused_quartz_sig3 = 0.897479400 -fused_quartz_susc = [mp.LorentzianSusceptibility(frequency=fused_quartz_frq1, gamma=fused_quartz_gam1, sigma=fused_quartz_sig1), - mp.LorentzianSusceptibility(frequency=fused_quartz_frq2, gamma=fused_quartz_gam2, sigma=fused_quartz_sig2), - mp.LorentzianSusceptibility(frequency=fused_quartz_frq3, gamma=fused_quartz_gam3, sigma=fused_quartz_sig3)] - -fused_quartz = mp.Medium(epsilon=1.0, E_susceptibilities=fused_quartz_susc, valid_freq_range=fused_quartz_range) - -#------------------------------------------------------------------ +fused_quartz_susc = [ + mp.LorentzianSusceptibility( + frequency=fused_quartz_frq1, gamma=fused_quartz_gam1, sigma=fused_quartz_sig1 + ), + mp.LorentzianSusceptibility( + frequency=fused_quartz_frq2, gamma=fused_quartz_gam2, sigma=fused_quartz_sig2 + ), + mp.LorentzianSusceptibility( + frequency=fused_quartz_frq3, gamma=fused_quartz_gam3, sigma=fused_quartz_sig3 + ), +] + +fused_quartz = mp.Medium( + epsilon=1.0, + E_susceptibilities=fused_quartz_susc, + valid_freq_range=fused_quartz_range, +) + +# ------------------------------------------------------------------ # gallium arsenide (GaAs) from T. Skauli et al., J. Applied Physics, Vol. 94, pp. 6447-55 (2003) # ref: https://refractiveindex.info/?shelf=main&book=GaAs&page=Skauli # wavelength range: 0.97 - 17 μm -GaAs_range = mp.FreqRange(min=um_scale/17, max=um_scale/0.97) +GaAs_range = mp.FreqRange(min=um_scale / 17, max=um_scale / 0.97) -GaAs_frq1 = 1/(0.4431307*um_scale) +GaAs_frq1 = 1 / (0.4431307 * um_scale) GaAs_gam1 = 0 GaAs_sig1 = 5.466742 -GaAs_frq2 = 1/(0.8746453*um_scale) +GaAs_frq2 = 1 / (0.8746453 * um_scale) GaAs_gam2 = 0 GaAs_sig2 = 0.02429960 -GaAs_frq3 = 1/(36.9166*um_scale) +GaAs_frq3 = 1 / (36.9166 * um_scale) GaAs_gam3 = 0 GaAs_sig3 = 1.957522 -GaAs_susc = [mp.LorentzianSusceptibility(frequency=GaAs_frq1, gamma=GaAs_gam1, sigma=GaAs_sig1), - mp.LorentzianSusceptibility(frequency=GaAs_frq2, gamma=GaAs_gam2, sigma=GaAs_sig2), - mp.LorentzianSusceptibility(frequency=GaAs_frq3, gamma=GaAs_gam3, sigma=GaAs_sig3)] +GaAs_susc = [ + mp.LorentzianSusceptibility(frequency=GaAs_frq1, gamma=GaAs_gam1, sigma=GaAs_sig1), + mp.LorentzianSusceptibility(frequency=GaAs_frq2, gamma=GaAs_gam2, sigma=GaAs_sig2), + mp.LorentzianSusceptibility(frequency=GaAs_frq3, gamma=GaAs_gam3, sigma=GaAs_sig3), +] -GaAs = mp.Medium(epsilon=5.372514, E_susceptibilities=GaAs_susc, valid_freq_range=GaAs_range) +GaAs = mp.Medium( + epsilon=5.372514, E_susceptibilities=GaAs_susc, valid_freq_range=GaAs_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (Si3N4) from H. R. Philipp, J. Electrochemical Society 120, 295-300 (1973) # ref: https://refractiveindex.info/?shelf=main&book=Si3N4&page=Philipp # wavelength range: 0.207 - 1.24 μm -Si3N4_VISNIR_range = mp.FreqRange(min=um_scale/1.24, max=um_scale/0.207) +Si3N4_VISNIR_range = mp.FreqRange(min=um_scale / 1.24, max=um_scale / 0.207) -Si3N4_VISNIR_frq1 = 1/(0.13967*um_scale) +Si3N4_VISNIR_frq1 = 1 / (0.13967 * um_scale) Si3N4_VISNIR_gam1 = 0 Si3N4_VISNIR_sig1 = 2.8939 -Si3N4_VISNIR_susc = [mp.LorentzianSusceptibility(frequency=Si3N4_VISNIR_frq1, gamma=Si3N4_VISNIR_gam1, sigma=Si3N4_VISNIR_sig1)] +Si3N4_VISNIR_susc = [ + mp.LorentzianSusceptibility( + frequency=Si3N4_VISNIR_frq1, gamma=Si3N4_VISNIR_gam1, sigma=Si3N4_VISNIR_sig1 + ) +] -Si3N4_VISNIR = mp.Medium(epsilon=1.0, E_susceptibilities=Si3N4_VISNIR_susc, valid_freq_range=Si3N4_VISNIR_range) +Si3N4_VISNIR = mp.Medium( + epsilon=1.0, + E_susceptibilities=Si3N4_VISNIR_susc, + valid_freq_range=Si3N4_VISNIR_range, +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (Si3N4) from K. Luke, et. al., Optics Letters, Vol. 40, pp. 4823-26 (2015) # ref: https://refractiveindex.info/?shelf=main&book=Si3N4&page=Luke # wavelength range: 0.310 - 5.504 μm -Si3N4_NIR_range = mp.FreqRange(min=um_scale/5.504, max=um_scale/0.310) +Si3N4_NIR_range = mp.FreqRange(min=um_scale / 5.504, max=um_scale / 0.310) -Si3N4_NIR_frq1 = 1/(0.1353406*um_scale) +Si3N4_NIR_frq1 = 1 / (0.1353406 * um_scale) Si3N4_NIR_gam1 = 0 Si3N4_NIR_sig1 = 3.0249 -Si3N4_NIR_frq2 = 1/(1239.842*um_scale) +Si3N4_NIR_frq2 = 1 / (1239.842 * um_scale) Si3N4_NIR_gam2 = 0 Si3N4_NIR_sig2 = 40314 -Si3N4_NIR_susc = [mp.LorentzianSusceptibility(frequency=Si3N4_NIR_frq1, gamma=Si3N4_NIR_gam1, sigma=Si3N4_NIR_sig1), - mp.LorentzianSusceptibility(frequency=Si3N4_NIR_frq2, gamma=Si3N4_NIR_gam2, sigma=Si3N4_NIR_sig2)] +Si3N4_NIR_susc = [ + mp.LorentzianSusceptibility( + frequency=Si3N4_NIR_frq1, gamma=Si3N4_NIR_gam1, sigma=Si3N4_NIR_sig1 + ), + mp.LorentzianSusceptibility( + frequency=Si3N4_NIR_frq2, gamma=Si3N4_NIR_gam2, sigma=Si3N4_NIR_sig2 + ), +] -Si3N4_NIR = mp.Medium(epsilon=1.0, E_susceptibilities=Si3N4_NIR_susc, valid_freq_range=Si3N4_NIR_range) +Si3N4_NIR = mp.Medium( + epsilon=1.0, E_susceptibilities=Si3N4_NIR_susc, valid_freq_range=Si3N4_NIR_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # elemental metals from A.D. Rakic et al., Applied Optics, Vol. 37, No. 22, pp. 5271-83 (1998) # wavelength range: 0.2 - 12.4 μm -metal_range = mp.FreqRange(min=um_scale/12.398, max=um_scale/.24797) +metal_range = mp.FreqRange(min=um_scale / 12.398, max=um_scale / 0.24797) # silver (Ag) -Ag_plasma_frq = 9.01*eV_um_scale +Ag_plasma_frq = 9.01 * eV_um_scale Ag_f0 = 0.845 Ag_frq0 = 1e-10 -Ag_gam0 = 0.048*eV_um_scale -Ag_sig0 = Ag_f0*Ag_plasma_frq**2/Ag_frq0**2 +Ag_gam0 = 0.048 * eV_um_scale +Ag_sig0 = Ag_f0 * Ag_plasma_frq**2 / Ag_frq0**2 Ag_f1 = 0.065 -Ag_frq1 = 0.816*eV_um_scale # 1.519 μm -Ag_gam1 = 3.886*eV_um_scale -Ag_sig1 = Ag_f1*Ag_plasma_frq**2/Ag_frq1**2 +Ag_frq1 = 0.816 * eV_um_scale # 1.519 μm +Ag_gam1 = 3.886 * eV_um_scale +Ag_sig1 = Ag_f1 * Ag_plasma_frq**2 / Ag_frq1**2 Ag_f2 = 0.124 -Ag_frq2 = 4.481*eV_um_scale # 0.273 μm -Ag_gam2 = 0.452*eV_um_scale -Ag_sig2 = Ag_f2*Ag_plasma_frq**2/Ag_frq2**2 +Ag_frq2 = 4.481 * eV_um_scale # 0.273 μm +Ag_gam2 = 0.452 * eV_um_scale +Ag_sig2 = Ag_f2 * Ag_plasma_frq**2 / Ag_frq2**2 Ag_f3 = 0.011 -Ag_frq3 = 8.185*eV_um_scale # 0.152 μm -Ag_gam3 = 0.065*eV_um_scale -Ag_sig3 = Ag_f3*Ag_plasma_frq**2/Ag_frq3**2 +Ag_frq3 = 8.185 * eV_um_scale # 0.152 μm +Ag_gam3 = 0.065 * eV_um_scale +Ag_sig3 = Ag_f3 * Ag_plasma_frq**2 / Ag_frq3**2 Ag_f4 = 0.840 -Ag_frq4 = 9.083*eV_um_scale # 0.137 μm -Ag_gam4 = 0.916*eV_um_scale -Ag_sig4 = Ag_f4*Ag_plasma_frq**2/Ag_frq4**2 +Ag_frq4 = 9.083 * eV_um_scale # 0.137 μm +Ag_gam4 = 0.916 * eV_um_scale +Ag_sig4 = Ag_f4 * Ag_plasma_frq**2 / Ag_frq4**2 Ag_f5 = 5.646 -Ag_frq5 = 20.29*eV_um_scale # 0.061 μm -Ag_gam5 = 2.419*eV_um_scale -Ag_sig5 = Ag_f5*Ag_plasma_frq**2/Ag_frq5**2 - -Ag_susc = [mp.DrudeSusceptibility(frequency=Ag_frq0, gamma=Ag_gam0, sigma=Ag_sig0), - mp.LorentzianSusceptibility(frequency=Ag_frq1, gamma=Ag_gam1, sigma=Ag_sig1), - mp.LorentzianSusceptibility(frequency=Ag_frq2, gamma=Ag_gam2, sigma=Ag_sig2), - mp.LorentzianSusceptibility(frequency=Ag_frq3, gamma=Ag_gam3, sigma=Ag_sig3), - mp.LorentzianSusceptibility(frequency=Ag_frq4, gamma=Ag_gam4, sigma=Ag_sig4), - mp.LorentzianSusceptibility(frequency=Ag_frq5, gamma=Ag_gam5, sigma=Ag_sig5)] +Ag_frq5 = 20.29 * eV_um_scale # 0.061 μm +Ag_gam5 = 2.419 * eV_um_scale +Ag_sig5 = Ag_f5 * Ag_plasma_frq**2 / Ag_frq5**2 + +Ag_susc = [ + mp.DrudeSusceptibility(frequency=Ag_frq0, gamma=Ag_gam0, sigma=Ag_sig0), + mp.LorentzianSusceptibility(frequency=Ag_frq1, gamma=Ag_gam1, sigma=Ag_sig1), + mp.LorentzianSusceptibility(frequency=Ag_frq2, gamma=Ag_gam2, sigma=Ag_sig2), + mp.LorentzianSusceptibility(frequency=Ag_frq3, gamma=Ag_gam3, sigma=Ag_sig3), + mp.LorentzianSusceptibility(frequency=Ag_frq4, gamma=Ag_gam4, sigma=Ag_sig4), + mp.LorentzianSusceptibility(frequency=Ag_frq5, gamma=Ag_gam5, sigma=Ag_sig5), +] Ag = mp.Medium(epsilon=1.0, E_susceptibilities=Ag_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # gold (Au) -metal_range = mp.FreqRange(min=um_scale/6.1992, max=um_scale/.24797) +metal_range = mp.FreqRange(min=um_scale / 6.1992, max=um_scale / 0.24797) -Au_plasma_frq = 9.03*eV_um_scale +Au_plasma_frq = 9.03 * eV_um_scale Au_f0 = 0.760 Au_frq0 = 1e-10 -Au_gam0 = 0.053*eV_um_scale -Au_sig0 = Au_f0*Au_plasma_frq**2/Au_frq0**2 +Au_gam0 = 0.053 * eV_um_scale +Au_sig0 = Au_f0 * Au_plasma_frq**2 / Au_frq0**2 Au_f1 = 0.024 -Au_frq1 = 0.415*eV_um_scale # 2.988 μm -Au_gam1 = 0.241*eV_um_scale -Au_sig1 = Au_f1*Au_plasma_frq**2/Au_frq1**2 +Au_frq1 = 0.415 * eV_um_scale # 2.988 μm +Au_gam1 = 0.241 * eV_um_scale +Au_sig1 = Au_f1 * Au_plasma_frq**2 / Au_frq1**2 Au_f2 = 0.010 -Au_frq2 = 0.830*eV_um_scale # 1.494 μm -Au_gam2 = 0.345*eV_um_scale -Au_sig2 = Au_f2*Au_plasma_frq**2/Au_frq2**2 +Au_frq2 = 0.830 * eV_um_scale # 1.494 μm +Au_gam2 = 0.345 * eV_um_scale +Au_sig2 = Au_f2 * Au_plasma_frq**2 / Au_frq2**2 Au_f3 = 0.071 -Au_frq3 = 2.969*eV_um_scale # 0.418 μm -Au_gam3 = 0.870*eV_um_scale -Au_sig3 = Au_f3*Au_plasma_frq**2/Au_frq3**2 +Au_frq3 = 2.969 * eV_um_scale # 0.418 μm +Au_gam3 = 0.870 * eV_um_scale +Au_sig3 = Au_f3 * Au_plasma_frq**2 / Au_frq3**2 Au_f4 = 0.601 -Au_frq4 = 4.304*eV_um_scale # 0.288 μm -Au_gam4 = 2.494*eV_um_scale -Au_sig4 = Au_f4*Au_plasma_frq**2/Au_frq4**2 +Au_frq4 = 4.304 * eV_um_scale # 0.288 μm +Au_gam4 = 2.494 * eV_um_scale +Au_sig4 = Au_f4 * Au_plasma_frq**2 / Au_frq4**2 Au_f5 = 4.384 -Au_frq5 = 13.32*eV_um_scale # 0.093 μm -Au_gam5 = 2.214*eV_um_scale -Au_sig5 = Au_f5*Au_plasma_frq**2/Au_frq5**2 - -Au_susc = [mp.DrudeSusceptibility(frequency=Au_frq0, gamma=Au_gam0, sigma=Au_sig0), - mp.LorentzianSusceptibility(frequency=Au_frq1, gamma=Au_gam1, sigma=Au_sig1), - mp.LorentzianSusceptibility(frequency=Au_frq2, gamma=Au_gam2, sigma=Au_sig2), - mp.LorentzianSusceptibility(frequency=Au_frq3, gamma=Au_gam3, sigma=Au_sig3), - mp.LorentzianSusceptibility(frequency=Au_frq4, gamma=Au_gam4, sigma=Au_sig4), - mp.LorentzianSusceptibility(frequency=Au_frq5, gamma=Au_gam5, sigma=Au_sig5)] +Au_frq5 = 13.32 * eV_um_scale # 0.093 μm +Au_gam5 = 2.214 * eV_um_scale +Au_sig5 = Au_f5 * Au_plasma_frq**2 / Au_frq5**2 + +Au_susc = [ + mp.DrudeSusceptibility(frequency=Au_frq0, gamma=Au_gam0, sigma=Au_sig0), + mp.LorentzianSusceptibility(frequency=Au_frq1, gamma=Au_gam1, sigma=Au_sig1), + mp.LorentzianSusceptibility(frequency=Au_frq2, gamma=Au_gam2, sigma=Au_sig2), + mp.LorentzianSusceptibility(frequency=Au_frq3, gamma=Au_gam3, sigma=Au_sig3), + mp.LorentzianSusceptibility(frequency=Au_frq4, gamma=Au_gam4, sigma=Au_sig4), + mp.LorentzianSusceptibility(frequency=Au_frq5, gamma=Au_gam5, sigma=Au_sig5), +] Au = mp.Medium(epsilon=1.0, E_susceptibilities=Au_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # copper (Cu) -metal_range = mp.FreqRange(min=um_scale/12.398, max=um_scale/.20664) +metal_range = mp.FreqRange(min=um_scale / 12.398, max=um_scale / 0.20664) -Cu_plasma_frq = 10.83*eV_um_scale +Cu_plasma_frq = 10.83 * eV_um_scale Cu_f0 = 0.575 Cu_frq0 = 1e-10 -Cu_gam0 = 0.030*eV_um_scale -Cu_sig0 = Cu_f0*Cu_plasma_frq**2/Cu_frq0**2 +Cu_gam0 = 0.030 * eV_um_scale +Cu_sig0 = Cu_f0 * Cu_plasma_frq**2 / Cu_frq0**2 Cu_f1 = 0.061 -Cu_frq1 = 0.291*eV_um_scale # 4.261 μm -Cu_gam1 = 0.378*eV_um_scale -Cu_sig1 = Cu_f1*Cu_plasma_frq**2/Cu_frq1**2 +Cu_frq1 = 0.291 * eV_um_scale # 4.261 μm +Cu_gam1 = 0.378 * eV_um_scale +Cu_sig1 = Cu_f1 * Cu_plasma_frq**2 / Cu_frq1**2 Cu_f2 = 0.104 -Cu_frq2 = 2.957*eV_um_scale # 0.419 μm -Cu_gam2 = 1.056*eV_um_scale -Cu_sig2 = Cu_f2*Cu_plasma_frq**2/Cu_frq2**2 +Cu_frq2 = 2.957 * eV_um_scale # 0.419 μm +Cu_gam2 = 1.056 * eV_um_scale +Cu_sig2 = Cu_f2 * Cu_plasma_frq**2 / Cu_frq2**2 Cu_f3 = 0.723 -Cu_frq3 = 5.300*eV_um_scale # 0.234 μm -Cu_gam3 = 3.213*eV_um_scale -Cu_sig3 = Cu_f3*Cu_plasma_frq**2/Cu_frq3**2 +Cu_frq3 = 5.300 * eV_um_scale # 0.234 μm +Cu_gam3 = 3.213 * eV_um_scale +Cu_sig3 = Cu_f3 * Cu_plasma_frq**2 / Cu_frq3**2 Cu_f4 = 0.638 -Cu_frq4 = 11.18*eV_um_scale # 0.111 μm -Cu_gam4 = 4.305*eV_um_scale -Cu_sig4 = Cu_f4*Cu_plasma_frq**2/Cu_frq4**2 - -Cu_susc = [mp.DrudeSusceptibility(frequency=Cu_frq0, gamma=Cu_gam0, sigma=Cu_sig0), - mp.LorentzianSusceptibility(frequency=Cu_frq1, gamma=Cu_gam1, sigma=Cu_sig1), - mp.LorentzianSusceptibility(frequency=Cu_frq2, gamma=Cu_gam2, sigma=Cu_sig2), - mp.LorentzianSusceptibility(frequency=Cu_frq3, gamma=Cu_gam3, sigma=Cu_sig3), - mp.LorentzianSusceptibility(frequency=Cu_frq4, gamma=Cu_gam4, sigma=Cu_sig4)] +Cu_frq4 = 11.18 * eV_um_scale # 0.111 μm +Cu_gam4 = 4.305 * eV_um_scale +Cu_sig4 = Cu_f4 * Cu_plasma_frq**2 / Cu_frq4**2 + +Cu_susc = [ + mp.DrudeSusceptibility(frequency=Cu_frq0, gamma=Cu_gam0, sigma=Cu_sig0), + mp.LorentzianSusceptibility(frequency=Cu_frq1, gamma=Cu_gam1, sigma=Cu_sig1), + mp.LorentzianSusceptibility(frequency=Cu_frq2, gamma=Cu_gam2, sigma=Cu_sig2), + mp.LorentzianSusceptibility(frequency=Cu_frq3, gamma=Cu_gam3, sigma=Cu_sig3), + mp.LorentzianSusceptibility(frequency=Cu_frq4, gamma=Cu_gam4, sigma=Cu_sig4), +] Cu = mp.Medium(epsilon=1.0, E_susceptibilities=Cu_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum (Al) -Al_plasma_frq = 14.98*eV_um_scale +Al_plasma_frq = 14.98 * eV_um_scale Al_f0 = 0.523 Al_frq0 = 1e-10 -Al_gam0 = 0.047*eV_um_scale -Al_sig0 = Al_f0*Al_plasma_frq**2/Al_frq0**2 +Al_gam0 = 0.047 * eV_um_scale +Al_sig0 = Al_f0 * Al_plasma_frq**2 / Al_frq0**2 Al_f1 = 0.227 -Al_frq1 = 0.162*eV_um_scale # 7.654 μm -Al_gam1 = 0.333*eV_um_scale -Al_sig1 = Al_f1*Al_plasma_frq**2/Al_frq1**2 +Al_frq1 = 0.162 * eV_um_scale # 7.654 μm +Al_gam1 = 0.333 * eV_um_scale +Al_sig1 = Al_f1 * Al_plasma_frq**2 / Al_frq1**2 Al_f2 = 0.050 -Al_frq2 = 1.544*eV_um_scale # 0.803 μm -Al_gam2 = 0.312*eV_um_scale -Al_sig2 = Al_f2*Al_plasma_frq**2/Al_frq2**2 +Al_frq2 = 1.544 * eV_um_scale # 0.803 μm +Al_gam2 = 0.312 * eV_um_scale +Al_sig2 = Al_f2 * Al_plasma_frq**2 / Al_frq2**2 Al_f3 = 0.166 -Al_frq3 = 1.808*eV_um_scale # 0.686 μm -Al_gam3 = 1.351*eV_um_scale -Al_sig3 = Al_f3*Al_plasma_frq**2/Al_frq3**2 +Al_frq3 = 1.808 * eV_um_scale # 0.686 μm +Al_gam3 = 1.351 * eV_um_scale +Al_sig3 = Al_f3 * Al_plasma_frq**2 / Al_frq3**2 Al_f4 = 0.030 -Al_frq4 = 3.473*eV_um_scale # 0.357 μm -Al_gam4 = 3.382*eV_um_scale -Al_sig4 = Al_f4*Al_plasma_frq**2/Al_frq4**2 - -Al_susc = [mp.DrudeSusceptibility(frequency=Al_frq0, gamma=Al_gam0, sigma=Al_sig0), - mp.LorentzianSusceptibility(frequency=Al_frq1, gamma=Al_gam1, sigma=Al_sig1), - mp.LorentzianSusceptibility(frequency=Al_frq2, gamma=Al_gam2, sigma=Al_sig2), - mp.LorentzianSusceptibility(frequency=Al_frq3, gamma=Al_gam3, sigma=Al_sig3), - mp.LorentzianSusceptibility(frequency=Al_frq4, gamma=Al_gam4, sigma=Al_sig4)] +Al_frq4 = 3.473 * eV_um_scale # 0.357 μm +Al_gam4 = 3.382 * eV_um_scale +Al_sig4 = Al_f4 * Al_plasma_frq**2 / Al_frq4**2 + +Al_susc = [ + mp.DrudeSusceptibility(frequency=Al_frq0, gamma=Al_gam0, sigma=Al_sig0), + mp.LorentzianSusceptibility(frequency=Al_frq1, gamma=Al_gam1, sigma=Al_sig1), + mp.LorentzianSusceptibility(frequency=Al_frq2, gamma=Al_gam2, sigma=Al_sig2), + mp.LorentzianSusceptibility(frequency=Al_frq3, gamma=Al_gam3, sigma=Al_sig3), + mp.LorentzianSusceptibility(frequency=Al_frq4, gamma=Al_gam4, sigma=Al_sig4), +] Al = mp.Medium(epsilon=1.0, E_susceptibilities=Al_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # beryllium (Be) -Be_plasma_frq = 18.51*eV_um_scale +Be_plasma_frq = 18.51 * eV_um_scale Be_f0 = 0.084 Be_frq0 = 1e-10 -Be_gam0 = 0.035*eV_um_scale -Be_sig0 = Be_f0*Be_plasma_frq**2/Be_frq0**2 +Be_gam0 = 0.035 * eV_um_scale +Be_sig0 = Be_f0 * Be_plasma_frq**2 / Be_frq0**2 Be_f1 = 0.031 -Be_frq1 = 0.100*eV_um_scale # 12.398 μm -Be_gam1 = 1.664*eV_um_scale -Be_sig1 = Be_f1*Be_plasma_frq**2/Be_frq1**2 +Be_frq1 = 0.100 * eV_um_scale # 12.398 μm +Be_gam1 = 1.664 * eV_um_scale +Be_sig1 = Be_f1 * Be_plasma_frq**2 / Be_frq1**2 Be_f2 = 0.140 -Be_frq2 = 1.032*eV_um_scale # 1.201 μm -Be_gam2 = 3.395*eV_um_scale -Be_sig2 = Be_f2*Be_plasma_frq**2/Be_frq2**2 +Be_frq2 = 1.032 * eV_um_scale # 1.201 μm +Be_gam2 = 3.395 * eV_um_scale +Be_sig2 = Be_f2 * Be_plasma_frq**2 / Be_frq2**2 Be_f3 = 0.530 -Be_frq3 = 3.183*eV_um_scale # 0.390 μm -Be_gam3 = 4.454*eV_um_scale -Be_sig3 = Be_f3*Be_plasma_frq**2/Be_frq3**2 +Be_frq3 = 3.183 * eV_um_scale # 0.390 μm +Be_gam3 = 4.454 * eV_um_scale +Be_sig3 = Be_f3 * Be_plasma_frq**2 / Be_frq3**2 Be_f4 = 0.130 -Be_frq4 = 4.604*eV_um_scale # 0.269 μm -Be_gam4 = 1.802*eV_um_scale -Be_sig4 = Be_f4*Be_plasma_frq**2/Be_frq4**2 - -Be_susc = [mp.DrudeSusceptibility(frequency=Be_frq0, gamma=Be_gam0, sigma=Be_sig0), - mp.LorentzianSusceptibility(frequency=Be_frq1, gamma=Be_gam1, sigma=Be_sig1), - mp.LorentzianSusceptibility(frequency=Be_frq2, gamma=Be_gam2, sigma=Be_sig2), - mp.LorentzianSusceptibility(frequency=Be_frq3, gamma=Be_gam3, sigma=Be_sig3), - mp.LorentzianSusceptibility(frequency=Be_frq4, gamma=Be_gam4, sigma=Be_sig4)] +Be_frq4 = 4.604 * eV_um_scale # 0.269 μm +Be_gam4 = 1.802 * eV_um_scale +Be_sig4 = Be_f4 * Be_plasma_frq**2 / Be_frq4**2 + +Be_susc = [ + mp.DrudeSusceptibility(frequency=Be_frq0, gamma=Be_gam0, sigma=Be_sig0), + mp.LorentzianSusceptibility(frequency=Be_frq1, gamma=Be_gam1, sigma=Be_sig1), + mp.LorentzianSusceptibility(frequency=Be_frq2, gamma=Be_gam2, sigma=Be_sig2), + mp.LorentzianSusceptibility(frequency=Be_frq3, gamma=Be_gam3, sigma=Be_sig3), + mp.LorentzianSusceptibility(frequency=Be_frq4, gamma=Be_gam4, sigma=Be_sig4), +] Be = mp.Medium(epsilon=1.0, E_susceptibilities=Be_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # chromium (Cr) -Cr_plasma_frq = 10.75*eV_um_scale +Cr_plasma_frq = 10.75 * eV_um_scale Cr_f0 = 0.168 Cr_frq0 = 1e-10 -Cr_gam0 = 0.047*eV_um_scale -Cr_sig0 = Cr_f0*Cr_plasma_frq**2/Cr_frq0**2 +Cr_gam0 = 0.047 * eV_um_scale +Cr_sig0 = Cr_f0 * Cr_plasma_frq**2 / Cr_frq0**2 Cr_f1 = 0.151 -Cr_frq1 = 0.121*eV_um_scale # 10.247 μm -Cr_gam1 = 3.175*eV_um_scale -Cr_sig1 = Cr_f1*Cr_plasma_frq**2/Cr_frq1**2 +Cr_frq1 = 0.121 * eV_um_scale # 10.247 μm +Cr_gam1 = 3.175 * eV_um_scale +Cr_sig1 = Cr_f1 * Cr_plasma_frq**2 / Cr_frq1**2 Cr_f2 = 0.150 -Cr_frq2 = 0.543*eV_um_scale # 2.283 μm -Cr_gam2 = 1.305*eV_um_scale -Cr_sig2 = Cr_f2*Cr_plasma_frq**2/Cr_frq2**2 +Cr_frq2 = 0.543 * eV_um_scale # 2.283 μm +Cr_gam2 = 1.305 * eV_um_scale +Cr_sig2 = Cr_f2 * Cr_plasma_frq**2 / Cr_frq2**2 Cr_f3 = 1.149 -Cr_frq3 = 1.970*eV_um_scale # 0.629 μm -Cr_gam3 = 2.676*eV_um_scale -Cr_sig3 = Cr_f3*Cr_plasma_frq**2/Cr_frq3**2 +Cr_frq3 = 1.970 * eV_um_scale # 0.629 μm +Cr_gam3 = 2.676 * eV_um_scale +Cr_sig3 = Cr_f3 * Cr_plasma_frq**2 / Cr_frq3**2 Cr_f4 = 0.825 -Cr_frq4 = 8.775*eV_um_scale # 0.141 μm -Cr_gam4 = 1.335*eV_um_scale -Cr_sig4 = Cr_f4*Cr_plasma_frq**2/Cr_frq4**2 - -Cr_susc = [mp.DrudeSusceptibility(frequency=Cr_frq0, gamma=Cr_gam0, sigma=Cr_sig0), - mp.LorentzianSusceptibility(frequency=Cr_frq1, gamma=Cr_gam1, sigma=Cr_sig1), - mp.LorentzianSusceptibility(frequency=Cr_frq2, gamma=Cr_gam2, sigma=Cr_sig2), - mp.LorentzianSusceptibility(frequency=Cr_frq3, gamma=Cr_gam3, sigma=Cr_sig3), - mp.LorentzianSusceptibility(frequency=Cr_frq4, gamma=Cr_gam4, sigma=Cr_sig4)] +Cr_frq4 = 8.775 * eV_um_scale # 0.141 μm +Cr_gam4 = 1.335 * eV_um_scale +Cr_sig4 = Cr_f4 * Cr_plasma_frq**2 / Cr_frq4**2 + +Cr_susc = [ + mp.DrudeSusceptibility(frequency=Cr_frq0, gamma=Cr_gam0, sigma=Cr_sig0), + mp.LorentzianSusceptibility(frequency=Cr_frq1, gamma=Cr_gam1, sigma=Cr_sig1), + mp.LorentzianSusceptibility(frequency=Cr_frq2, gamma=Cr_gam2, sigma=Cr_sig2), + mp.LorentzianSusceptibility(frequency=Cr_frq3, gamma=Cr_gam3, sigma=Cr_sig3), + mp.LorentzianSusceptibility(frequency=Cr_frq4, gamma=Cr_gam4, sigma=Cr_sig4), +] Cr = mp.Medium(epsilon=1.0, E_susceptibilities=Cr_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # nickel (Ni) -Ni_plasma_frq = 15.92*eV_um_scale +Ni_plasma_frq = 15.92 * eV_um_scale Ni_f0 = 0.096 Ni_frq0 = 1e-10 -Ni_gam0 = 0.048*eV_um_scale -Ni_sig0 = Ni_f0*Ni_plasma_frq**2/Ni_frq0**2 +Ni_gam0 = 0.048 * eV_um_scale +Ni_sig0 = Ni_f0 * Ni_plasma_frq**2 / Ni_frq0**2 Ni_f1 = 0.100 -Ni_frq1 = 0.174*eV_um_scale # 7.126 μm -Ni_gam1 = 4.511*eV_um_scale -Ni_sig1 = Ni_f1*Ni_plasma_frq**2/Ni_frq1**2 +Ni_frq1 = 0.174 * eV_um_scale # 7.126 μm +Ni_gam1 = 4.511 * eV_um_scale +Ni_sig1 = Ni_f1 * Ni_plasma_frq**2 / Ni_frq1**2 Ni_f2 = 0.135 -Ni_frq2 = 0.582*eV_um_scale # 2.130 μm -Ni_gam2 = 1.334*eV_um_scale -Ni_sig2 = Ni_f2*Ni_plasma_frq**2/Ni_frq2**2 +Ni_frq2 = 0.582 * eV_um_scale # 2.130 μm +Ni_gam2 = 1.334 * eV_um_scale +Ni_sig2 = Ni_f2 * Ni_plasma_frq**2 / Ni_frq2**2 Ni_f3 = 0.106 -Ni_frq3 = 1.597*eV_um_scale # 0.776 μm -Ni_gam3 = 2.178*eV_um_scale -Ni_sig3 = Ni_f3*Ni_plasma_frq**2/Ni_frq3**2 +Ni_frq3 = 1.597 * eV_um_scale # 0.776 μm +Ni_gam3 = 2.178 * eV_um_scale +Ni_sig3 = Ni_f3 * Ni_plasma_frq**2 / Ni_frq3**2 Ni_f4 = 0.729 -Ni_frq4 = 6.089*eV_um_scale # 0.204 μm -Ni_gam4 = 6.292*eV_um_scale -Ni_sig4 = Ni_f4*Ni_plasma_frq**2/Ni_frq4**2 - -Ni_susc = [mp.DrudeSusceptibility(frequency=Ni_frq0, gamma=Ni_gam0, sigma=Ni_sig0), - mp.LorentzianSusceptibility(frequency=Ni_frq1, gamma=Ni_gam1, sigma=Ni_sig1), - mp.LorentzianSusceptibility(frequency=Ni_frq2, gamma=Ni_gam2, sigma=Ni_sig2), - mp.LorentzianSusceptibility(frequency=Ni_frq3, gamma=Ni_gam3, sigma=Ni_sig3), - mp.LorentzianSusceptibility(frequency=Ni_frq4, gamma=Ni_gam4, sigma=Ni_sig4)] +Ni_frq4 = 6.089 * eV_um_scale # 0.204 μm +Ni_gam4 = 6.292 * eV_um_scale +Ni_sig4 = Ni_f4 * Ni_plasma_frq**2 / Ni_frq4**2 + +Ni_susc = [ + mp.DrudeSusceptibility(frequency=Ni_frq0, gamma=Ni_gam0, sigma=Ni_sig0), + mp.LorentzianSusceptibility(frequency=Ni_frq1, gamma=Ni_gam1, sigma=Ni_sig1), + mp.LorentzianSusceptibility(frequency=Ni_frq2, gamma=Ni_gam2, sigma=Ni_sig2), + mp.LorentzianSusceptibility(frequency=Ni_frq3, gamma=Ni_gam3, sigma=Ni_sig3), + mp.LorentzianSusceptibility(frequency=Ni_frq4, gamma=Ni_gam4, sigma=Ni_sig4), +] Ni = mp.Medium(epsilon=1.0, E_susceptibilities=Ni_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # palladium (Pd) -Pd_plasma_frq = 9.72*eV_um_scale +Pd_plasma_frq = 9.72 * eV_um_scale Pd_f0 = 0.330 Pd_frq0 = 1e-10 -Pd_gam0 = 0.008*eV_um_scale -Pd_sig0 = Pd_f0*Pd_plasma_frq**2/Pd_frq0**2 +Pd_gam0 = 0.008 * eV_um_scale +Pd_sig0 = Pd_f0 * Pd_plasma_frq**2 / Pd_frq0**2 Pd_f1 = 0.649 -Pd_frq1 = 0.336*eV_um_scale # 3.690 μm -Pd_gam1 = 2.950*eV_um_scale -Pd_sig1 = Pd_f1*Pd_plasma_frq**2/Pd_frq1**2 +Pd_frq1 = 0.336 * eV_um_scale # 3.690 μm +Pd_gam1 = 2.950 * eV_um_scale +Pd_sig1 = Pd_f1 * Pd_plasma_frq**2 / Pd_frq1**2 Pd_f2 = 0.121 -Pd_frq2 = 0.501*eV_um_scale # 2.475 μm -Pd_gam2 = 0.555*eV_um_scale -Pd_sig2 = Pd_f2*Pd_plasma_frq**2/Pd_frq2**2 +Pd_frq2 = 0.501 * eV_um_scale # 2.475 μm +Pd_gam2 = 0.555 * eV_um_scale +Pd_sig2 = Pd_f2 * Pd_plasma_frq**2 / Pd_frq2**2 Pd_f3 = 0.638 -Pd_frq3 = 1.659*eV_um_scale # 0.747 μm -Pd_gam3 = 4.621*eV_um_scale -Pd_sig3 = Pd_f3*Pd_plasma_frq**2/Pd_frq3**2 +Pd_frq3 = 1.659 * eV_um_scale # 0.747 μm +Pd_gam3 = 4.621 * eV_um_scale +Pd_sig3 = Pd_f3 * Pd_plasma_frq**2 / Pd_frq3**2 Pd_f4 = 0.453 -Pd_frq4 = 5.715*eV_um_scale # 0.217 μm -Pd_gam4 = 3.236*eV_um_scale -Pd_sig4 = Pd_f4*Pd_plasma_frq**2/Pd_frq4**2 - -Pd_susc = [mp.DrudeSusceptibility(frequency=Pd_frq0, gamma=Pd_gam0, sigma=Pd_sig0), - mp.LorentzianSusceptibility(frequency=Pd_frq1, gamma=Pd_gam1, sigma=Pd_sig1), - mp.LorentzianSusceptibility(frequency=Pd_frq2, gamma=Pd_gam2, sigma=Pd_sig2), - mp.LorentzianSusceptibility(frequency=Pd_frq3, gamma=Pd_gam3, sigma=Pd_sig3), - mp.LorentzianSusceptibility(frequency=Pd_frq4, gamma=Pd_gam4, sigma=Pd_sig4)] +Pd_frq4 = 5.715 * eV_um_scale # 0.217 μm +Pd_gam4 = 3.236 * eV_um_scale +Pd_sig4 = Pd_f4 * Pd_plasma_frq**2 / Pd_frq4**2 + +Pd_susc = [ + mp.DrudeSusceptibility(frequency=Pd_frq0, gamma=Pd_gam0, sigma=Pd_sig0), + mp.LorentzianSusceptibility(frequency=Pd_frq1, gamma=Pd_gam1, sigma=Pd_sig1), + mp.LorentzianSusceptibility(frequency=Pd_frq2, gamma=Pd_gam2, sigma=Pd_sig2), + mp.LorentzianSusceptibility(frequency=Pd_frq3, gamma=Pd_gam3, sigma=Pd_sig3), + mp.LorentzianSusceptibility(frequency=Pd_frq4, gamma=Pd_gam4, sigma=Pd_sig4), +] Pd = mp.Medium(epsilon=1.0, E_susceptibilities=Pd_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # platinum (Pt) -Pt_plasma_frq = 9.59*eV_um_scale +Pt_plasma_frq = 9.59 * eV_um_scale Pt_f0 = 0.333 Pt_frq0 = 1e-10 -Pt_gam0 = 0.080*eV_um_scale -Pt_sig0 = Pt_f0*Pt_plasma_frq**2/Pt_frq0**2 +Pt_gam0 = 0.080 * eV_um_scale +Pt_sig0 = Pt_f0 * Pt_plasma_frq**2 / Pt_frq0**2 Pt_f1 = 0.191 -Pt_frq1 = 0.780*eV_um_scale # 1.590 μm -Pt_gam1 = 0.517*eV_um_scale -Pt_sig1 = Pt_f1*Pt_plasma_frq**2/Pt_frq1**2 +Pt_frq1 = 0.780 * eV_um_scale # 1.590 μm +Pt_gam1 = 0.517 * eV_um_scale +Pt_sig1 = Pt_f1 * Pt_plasma_frq**2 / Pt_frq1**2 Pt_f2 = 0.659 -Pt_frq2 = 1.314*eV_um_scale # 0.944 μm -Pt_gam2 = 1.838*eV_um_scale -Pt_sig2 = Pt_f2*Pt_plasma_frq**2/Pt_frq2**2 +Pt_frq2 = 1.314 * eV_um_scale # 0.944 μm +Pt_gam2 = 1.838 * eV_um_scale +Pt_sig2 = Pt_f2 * Pt_plasma_frq**2 / Pt_frq2**2 Pt_f3 = 0.547 -Pt_frq3 = 3.141*eV_um_scale # 0.395 μm -Pt_gam3 = 3.668*eV_um_scale -Pt_sig3 = Pt_f3*Pt_plasma_frq**2/Pt_frq3**2 +Pt_frq3 = 3.141 * eV_um_scale # 0.395 μm +Pt_gam3 = 3.668 * eV_um_scale +Pt_sig3 = Pt_f3 * Pt_plasma_frq**2 / Pt_frq3**2 Pt_f4 = 3.576 -Pt_frq4 = 9.249*eV_um_scale # 0.134 μm -Pt_gam4 = 8.517*eV_um_scale -Pt_sig4 = Pt_f4*Pt_plasma_frq**2/Pt_frq4**2 - -Pt_susc = [mp.DrudeSusceptibility(frequency=Pt_frq0, gamma=Pt_gam0, sigma=Pt_sig0), - mp.LorentzianSusceptibility(frequency=Pt_frq1, gamma=Pt_gam1, sigma=Pt_sig1), - mp.LorentzianSusceptibility(frequency=Pt_frq2, gamma=Pt_gam2, sigma=Pt_sig2), - mp.LorentzianSusceptibility(frequency=Pt_frq3, gamma=Pt_gam3, sigma=Pt_sig3), - mp.LorentzianSusceptibility(frequency=Pt_frq4, gamma=Pt_gam4, sigma=Pt_sig4)] +Pt_frq4 = 9.249 * eV_um_scale # 0.134 μm +Pt_gam4 = 8.517 * eV_um_scale +Pt_sig4 = Pt_f4 * Pt_plasma_frq**2 / Pt_frq4**2 + +Pt_susc = [ + mp.DrudeSusceptibility(frequency=Pt_frq0, gamma=Pt_gam0, sigma=Pt_sig0), + mp.LorentzianSusceptibility(frequency=Pt_frq1, gamma=Pt_gam1, sigma=Pt_sig1), + mp.LorentzianSusceptibility(frequency=Pt_frq2, gamma=Pt_gam2, sigma=Pt_sig2), + mp.LorentzianSusceptibility(frequency=Pt_frq3, gamma=Pt_gam3, sigma=Pt_sig3), + mp.LorentzianSusceptibility(frequency=Pt_frq4, gamma=Pt_gam4, sigma=Pt_sig4), +] Pt = mp.Medium(epsilon=1.0, E_susceptibilities=Pt_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # titanium (Ti) -Ti_plasma_frq = 7.29*eV_um_scale +Ti_plasma_frq = 7.29 * eV_um_scale Ti_f0 = 0.148 Ti_frq0 = 1e-10 -Ti_gam0 = 0.082*eV_um_scale -Ti_sig0 = Ti_f0*Ti_plasma_frq**2/Ti_frq0**2 +Ti_gam0 = 0.082 * eV_um_scale +Ti_sig0 = Ti_f0 * Ti_plasma_frq**2 / Ti_frq0**2 Ti_f1 = 0.899 -Ti_frq1 = 0.777*eV_um_scale # 1.596 μm -Ti_gam1 = 2.276*eV_um_scale -Ti_sig1 = Ti_f1*Ti_plasma_frq**2/Ti_frq1**2 +Ti_frq1 = 0.777 * eV_um_scale # 1.596 μm +Ti_gam1 = 2.276 * eV_um_scale +Ti_sig1 = Ti_f1 * Ti_plasma_frq**2 / Ti_frq1**2 Ti_f2 = 0.393 -Ti_frq2 = 1.545*eV_um_scale # 0.802 μm -Ti_gam2 = 2.518*eV_um_scale -Ti_sig2 = Ti_f2*Ti_plasma_frq**2/Ti_frq2**2 +Ti_frq2 = 1.545 * eV_um_scale # 0.802 μm +Ti_gam2 = 2.518 * eV_um_scale +Ti_sig2 = Ti_f2 * Ti_plasma_frq**2 / Ti_frq2**2 Ti_f3 = 0.187 -Ti_frq3 = 2.509*eV_um_scale # 0.494 μm -Ti_gam3 = 1.663*eV_um_scale -Ti_sig3 = Ti_f3*Ti_plasma_frq**2/Ti_frq3**2 +Ti_frq3 = 2.509 * eV_um_scale # 0.494 μm +Ti_gam3 = 1.663 * eV_um_scale +Ti_sig3 = Ti_f3 * Ti_plasma_frq**2 / Ti_frq3**2 Ti_f4 = 0.001 -Ti_frq4 = 19.43*eV_um_scale # 0.064 μm -Ti_gam4 = 1.762*eV_um_scale -Ti_sig4 = Ti_f4*Ti_plasma_frq**2/Ti_frq4**2 - -Ti_susc = [mp.DrudeSusceptibility(frequency=Ti_frq0, gamma=Ti_gam0, sigma=Ti_sig0), - mp.LorentzianSusceptibility(frequency=Ti_frq1, gamma=Ti_gam1, sigma=Ti_sig1), - mp.LorentzianSusceptibility(frequency=Ti_frq2, gamma=Ti_gam2, sigma=Ti_sig2), - mp.LorentzianSusceptibility(frequency=Ti_frq3, gamma=Ti_gam3, sigma=Ti_sig3), - mp.LorentzianSusceptibility(frequency=Ti_frq4, gamma=Ti_gam4, sigma=Ti_sig4)] +Ti_frq4 = 19.43 * eV_um_scale # 0.064 μm +Ti_gam4 = 1.762 * eV_um_scale +Ti_sig4 = Ti_f4 * Ti_plasma_frq**2 / Ti_frq4**2 + +Ti_susc = [ + mp.DrudeSusceptibility(frequency=Ti_frq0, gamma=Ti_gam0, sigma=Ti_sig0), + mp.LorentzianSusceptibility(frequency=Ti_frq1, gamma=Ti_gam1, sigma=Ti_sig1), + mp.LorentzianSusceptibility(frequency=Ti_frq2, gamma=Ti_gam2, sigma=Ti_sig2), + mp.LorentzianSusceptibility(frequency=Ti_frq3, gamma=Ti_gam3, sigma=Ti_sig3), + mp.LorentzianSusceptibility(frequency=Ti_frq4, gamma=Ti_gam4, sigma=Ti_sig4), +] Ti = mp.Medium(epsilon=1.0, E_susceptibilities=Ti_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # tungsten (W) -W_plasma_frq = 13.22*eV_um_scale +W_plasma_frq = 13.22 * eV_um_scale W_f0 = 0.206 W_frq0 = 1e-10 -W_gam0 = 0.064*eV_um_scale -W_sig0 = W_f0*W_plasma_frq**2/W_frq0**2 +W_gam0 = 0.064 * eV_um_scale +W_sig0 = W_f0 * W_plasma_frq**2 / W_frq0**2 W_f1 = 0.054 -W_frq1 = 1.004*eV_um_scale # 1.235 μm -W_gam1 = 0.530*eV_um_scale -W_sig1 = W_f1*W_plasma_frq**2/W_frq1**2 +W_frq1 = 1.004 * eV_um_scale # 1.235 μm +W_gam1 = 0.530 * eV_um_scale +W_sig1 = W_f1 * W_plasma_frq**2 / W_frq1**2 W_f2 = 0.166 -W_frq2 = 1.917*eV_um_scale # 0.647 μm -W_gam2 = 1.281*eV_um_scale -W_sig2 = W_f2*W_plasma_frq**2/W_frq2**2 +W_frq2 = 1.917 * eV_um_scale # 0.647 μm +W_gam2 = 1.281 * eV_um_scale +W_sig2 = W_f2 * W_plasma_frq**2 / W_frq2**2 W_f3 = 0.706 -W_frq3 = 3.580*eV_um_scale # 0.346 μm -W_gam3 = 3.332*eV_um_scale -W_sig3 = W_f3*W_plasma_frq**2/W_frq3**2 +W_frq3 = 3.580 * eV_um_scale # 0.346 μm +W_gam3 = 3.332 * eV_um_scale +W_sig3 = W_f3 * W_plasma_frq**2 / W_frq3**2 W_f4 = 2.590 -W_frq4 = 7.498*eV_um_scale # 0.165 μm -W_gam4 = 5.836*eV_um_scale -W_sig4 = W_f4*W_plasma_frq**2/W_frq4**2 - -W_susc = [mp.DrudeSusceptibility(frequency=W_frq0, gamma=W_gam0, sigma=W_sig0), - mp.LorentzianSusceptibility(frequency=W_frq1, gamma=W_gam1, sigma=W_sig1), - mp.LorentzianSusceptibility(frequency=W_frq2, gamma=W_gam2, sigma=W_sig2), - mp.LorentzianSusceptibility(frequency=W_frq3, gamma=W_gam3, sigma=W_sig3), - mp.LorentzianSusceptibility(frequency=W_frq4, gamma=W_gam4, sigma=W_sig4)] +W_frq4 = 7.498 * eV_um_scale # 0.165 μm +W_gam4 = 5.836 * eV_um_scale +W_sig4 = W_f4 * W_plasma_frq**2 / W_frq4**2 + +W_susc = [ + mp.DrudeSusceptibility(frequency=W_frq0, gamma=W_gam0, sigma=W_sig0), + mp.LorentzianSusceptibility(frequency=W_frq1, gamma=W_gam1, sigma=W_sig1), + mp.LorentzianSusceptibility(frequency=W_frq2, gamma=W_gam2, sigma=W_sig2), + mp.LorentzianSusceptibility(frequency=W_frq3, gamma=W_gam3, sigma=W_sig3), + mp.LorentzianSusceptibility(frequency=W_frq4, gamma=W_gam4, sigma=W_sig4), +] W = mp.Medium(epsilon=1.0, E_susceptibilities=W_susc, valid_freq_range=metal_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # metals from D. Barchiesi and T. Grosges, J. Nanophotonics, Vol. 8, 08996 (2015) # wavelength range: 0.4 - 0.8 μm -metal_visible_range = mp.FreqRange(min=um_scale/0.8, max=um_scale/0.4) +metal_visible_range = mp.FreqRange(min=um_scale / 0.8, max=um_scale / 0.4) # gold (Au) # fit to P.B. Johnson and R.W. Christy, Physical Review B, Vol. 6, pp. 4370-9 (1972) -Au_JC_visible_frq0 = 1/(0.139779231751333*um_scale) -Au_JC_visible_gam0 = 1/(1.12834046202759*um_scale) +Au_JC_visible_frq0 = 1 / (0.139779231751333 * um_scale) +Au_JC_visible_gam0 = 1 / (1.12834046202759 * um_scale) Au_JC_visible_sig0 = 1 -Au_JC_visible_frq1 = 1/(0.404064525036786*um_scale) -Au_JC_visible_gam1 = 1/(26.1269913352870*um_scale) +Au_JC_visible_frq1 = 1 / (0.404064525036786 * um_scale) +Au_JC_visible_gam1 = 1 / (26.1269913352870 * um_scale) Au_JC_visible_sig1 = 2.07118534879440 -Au_JC_visible_susc = [mp.DrudeSusceptibility(frequency=Au_JC_visible_frq0, gamma=Au_JC_visible_gam0, sigma=Au_JC_visible_sig0), - mp.LorentzianSusceptibility(frequency=Au_JC_visible_frq1, gamma=Au_JC_visible_gam1, sigma=Au_JC_visible_sig1)] +Au_JC_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Au_JC_visible_frq0, gamma=Au_JC_visible_gam0, sigma=Au_JC_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Au_JC_visible_frq1, gamma=Au_JC_visible_gam1, sigma=Au_JC_visible_sig1 + ), +] Au_JC_visible = mp.Medium(epsilon=6.1599, E_susceptibilities=Au_JC_visible_susc) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # gold (Au) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Au_visible_frq0 = 1/(0.0473629248511456*um_scale) -Au_visible_gam0 = 1/(0.255476199605166*um_scale) +Au_visible_frq0 = 1 / (0.0473629248511456 * um_scale) +Au_visible_gam0 = 1 / (0.255476199605166 * um_scale) Au_visible_sig0 = 1 -Au_visible_frq1 = 1/(0.800619321082804*um_scale) -Au_visible_gam1 = 1/(0.381870287531951*um_scale) +Au_visible_frq1 = 1 / (0.800619321082804 * um_scale) +Au_visible_gam1 = 1 / (0.381870287531951 * um_scale) Au_visible_sig1 = -169.060953137985 -Au_visible_susc = [mp.DrudeSusceptibility(frequency=Au_visible_frq0, gamma=Au_visible_gam0, sigma=Au_visible_sig0), - mp.LorentzianSusceptibility(frequency=Au_visible_frq1, gamma=Au_visible_gam1, sigma=Au_visible_sig1)] - -Au_visible = mp.Medium(epsilon=0.6888, E_susceptibilities=Au_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Au_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Au_visible_frq0, gamma=Au_visible_gam0, sigma=Au_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Au_visible_frq1, gamma=Au_visible_gam1, sigma=Au_visible_sig1 + ), +] + +Au_visible = mp.Medium( + epsilon=0.6888, + E_susceptibilities=Au_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # silver (Au) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Ag_visible_frq0 = 1/(0.142050162130618*um_scale) -Ag_visible_gam0 = 1/(18.0357292925015*um_scale) +Ag_visible_frq0 = 1 / (0.142050162130618 * um_scale) +Ag_visible_gam0 = 1 / (18.0357292925015 * um_scale) Ag_visible_sig0 = 1 -Ag_visible_frq1 = 1/(0.115692151792108*um_scale) -Ag_visible_gam1 = 1/(0.257794324096575*um_scale) +Ag_visible_frq1 = 1 / (0.115692151792108 * um_scale) +Ag_visible_gam1 = 1 / (0.257794324096575 * um_scale) Ag_visible_sig1 = 3.74465275944019 -Ag_visible_susc = [mp.DrudeSusceptibility(frequency=Ag_visible_frq0, gamma=Ag_visible_gam0, sigma=Ag_visible_sig0), - mp.LorentzianSusceptibility(frequency=Ag_visible_frq1, gamma=Ag_visible_gam1, sigma=Ag_visible_sig1)] - -Ag_visible = mp.Medium(epsilon=0.0067526, E_susceptibilities=Ag_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Ag_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Ag_visible_frq0, gamma=Ag_visible_gam0, sigma=Ag_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Ag_visible_frq1, gamma=Ag_visible_gam1, sigma=Ag_visible_sig1 + ), +] + +Ag_visible = mp.Medium( + epsilon=0.0067526, + E_susceptibilities=Ag_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # aluminum (Al) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Al_visible_frq0 = 1/(0.0625841659042985*um_scale) -Al_visible_gam0 = 1/(0.606007002962666*um_scale) +Al_visible_frq0 = 1 / (0.0625841659042985 * um_scale) +Al_visible_gam0 = 1 / (0.606007002962666 * um_scale) Al_visible_sig0 = 1 -Al_visible_frq1 = 1/(0.528191199577075*um_scale) -Al_visible_gam1 = 1/(0.291862527666814*um_scale) +Al_visible_frq1 = 1 / (0.528191199577075 * um_scale) +Al_visible_gam1 = 1 / (0.291862527666814 * um_scale) Al_visible_sig1 = -44.4456675577921 -Al_visible_susc = [mp.DrudeSusceptibility(frequency=Al_visible_frq0, gamma=Al_visible_gam0, sigma=Al_visible_sig0), - mp.LorentzianSusceptibility(frequency=Al_visible_frq1, gamma=Al_visible_gam1, sigma=Al_visible_sig1)] - -Al_visible = mp.Medium(epsilon=0.13313, E_susceptibilities=Al_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Al_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Al_visible_frq0, gamma=Al_visible_gam0, sigma=Al_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Al_visible_frq1, gamma=Al_visible_gam1, sigma=Al_visible_sig1 + ), +] + +Al_visible = mp.Medium( + epsilon=0.13313, + E_susceptibilities=Al_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ # chroimium (Cr) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Cr_visible_frq0 = 1/(0.118410119507342*um_scale) -Cr_visible_gam0 = 1/(0.628596264869804*um_scale) +Cr_visible_frq0 = 1 / (0.118410119507342 * um_scale) +Cr_visible_gam0 = 1 / (0.628596264869804 * um_scale) Cr_visible_sig0 = 1 -Cr_visible_frq1 = 1/(0.565709598452496*um_scale) -Cr_visible_gam1 = 1/(0.731117670900812*um_scale) +Cr_visible_frq1 = 1 / (0.565709598452496 * um_scale) +Cr_visible_gam1 = 1 / (0.731117670900812 * um_scale) Cr_visible_sig1 = 13.2912419951294 -Cr_visible_susc = [mp.DrudeSusceptibility(frequency=Cr_visible_frq0, gamma=Cr_visible_gam0, sigma=Cr_visible_sig0), - mp.LorentzianSusceptibility(frequency=Cr_visible_frq1, gamma=Cr_visible_gam1, sigma=Cr_visible_sig1)] - -Cr_visible = mp.Medium(epsilon=2.7767, E_susceptibilities=Cr_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Cr_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Cr_visible_frq0, gamma=Cr_visible_gam0, sigma=Cr_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Cr_visible_frq1, gamma=Cr_visible_gam1, sigma=Cr_visible_sig1 + ), +] + +Cr_visible = mp.Medium( + epsilon=2.7767, + E_susceptibilities=Cr_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # titanium (Ti) # fit to E.D. Palik, Handbook of Optical Constants, Academic Press (1985) -Ti_visible_frq0 = 1/(0.101331651921602*um_scale) -Ti_visible_gam0 = 1/(0.365743382258719*um_scale) +Ti_visible_frq0 = 1 / (0.101331651921602 * um_scale) +Ti_visible_gam0 = 1 / (0.365743382258719 * um_scale) Ti_visible_sig0 = 1 -Ti_visible_frq1 = 1/(4.56839173979216e-09*um_scale) -Ti_visible_gam1 = 1/(5.86441957443603e-10*um_scale) +Ti_visible_frq1 = 1 / (4.56839173979216e-09 * um_scale) +Ti_visible_gam1 = 1 / (5.86441957443603e-10 * um_scale) Ti_visible_sig1 = 54742662.1963414 -Ti_visible_susc = [mp.DrudeSusceptibility(frequency=Ti_visible_frq0, gamma=Ti_visible_gam0, sigma=Ti_visible_sig0), - mp.LorentzianSusceptibility(frequency=Ti_visible_frq1, gamma=Ti_visible_gam1, sigma=Ti_visible_sig1)] - -Ti_visible = mp.Medium(epsilon=-5.4742e7, E_susceptibilities=Ti_visible_susc, valid_freq_range=metal_visible_range) - -#------------------------------------------------------------------ +Ti_visible_susc = [ + mp.DrudeSusceptibility( + frequency=Ti_visible_frq0, gamma=Ti_visible_gam0, sigma=Ti_visible_sig0 + ), + mp.LorentzianSusceptibility( + frequency=Ti_visible_frq1, gamma=Ti_visible_gam1, sigma=Ti_visible_sig1 + ), +] + +Ti_visible = mp.Medium( + epsilon=-5.4742e7, + E_susceptibilities=Ti_visible_susc, + valid_freq_range=metal_visible_range, +) + +# ------------------------------------------------------------------ # aluminum (Al) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.19 - 0.83 μm -Al_drude_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.19) +Al_drude_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.19) -Al_drude_frq = 1/(0.0789607648707171*um_scale) -Al_drude_gam = 1/(1.78138208333333*um_scale) +Al_drude_frq = 1 / (0.0789607648707171 * um_scale) +Al_drude_gam = 1 / (1.78138208333333 * um_scale) Al_drude_sig = 1 -Al_drude_susc = [mp.DrudeSusceptibility(frequency=Al_drude_frq, gamma=Al_drude_gam, sigma=Al_drude_sig)] +Al_drude_susc = [ + mp.DrudeSusceptibility( + frequency=Al_drude_frq, gamma=Al_drude_gam, sigma=Al_drude_sig + ) +] -Al_drude = mp.Medium(epsilon=1.0, E_susceptibilities=Al_drude_susc, valid_freq_range=Al_drude_range) +Al_drude = mp.Medium( + epsilon=1.0, E_susceptibilities=Al_drude_susc, valid_freq_range=Al_drude_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # cobalt (Co) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.26 - 1.65 μm -Co_range = mp.FreqRange(min=um_scale/1.65, max=um_scale/0.26) +Co_range = mp.FreqRange(min=um_scale / 1.65, max=um_scale / 0.26) -Co_frq = 1/(0.0789607648707171*um_scale) -Co_gam = 1/(0.213802712536644*um_scale) +Co_frq = 1 / (0.0789607648707171 * um_scale) +Co_gam = 1 / (0.213802712536644 * um_scale) Co_sig = 1 Co_susc = [mp.DrudeSusceptibility(frequency=Co_frq, gamma=Co_gam, sigma=Co_sig)] Co = mp.Medium(epsilon=3.694, E_susceptibilities=Co_susc, valid_freq_range=Co_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ ## WARNING: unstable; field divergence may occur # molybdenum (Mo) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.25 - 0.83 μm -Mo_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.25) +Mo_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.25) -Mo_frq = 1/(0.0620790071099539*um_scale) -Mo_gam = 1/(0.148359690080172*um_scale) +Mo_frq = 1 / (0.0620790071099539 * um_scale) +Mo_gam = 1 / (0.148359690080172 * um_scale) Mo_sig = 1 Mo_susc = [mp.DrudeSusceptibility(frequency=Mo_frq, gamma=Mo_gam, sigma=Mo_sig)] Mo = mp.Medium(epsilon=-1.366, E_susceptibilities=Mo_susc, valid_freq_range=Mo_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # nickel chrome (NiCr) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.25 - 0.83 μm -NiCr_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.25) +NiCr_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.25) -NiCr_frq = 1/(0.0868845080588648*um_scale) -NiCr_gam = 1/(0.308418390547264*um_scale) +NiCr_frq = 1 / (0.0868845080588648 * um_scale) +NiCr_gam = 1 / (0.308418390547264 * um_scale) NiCr_sig = 1 NiCr_susc = [mp.DrudeSusceptibility(frequency=NiCr_frq, gamma=NiCr_gam, sigma=NiCr_sig)] NiCr = mp.Medium(epsilon=1.0, E_susceptibilities=NiCr_susc, valid_freq_range=NiCr_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # nickel iron (NiFe) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.25 - 0.83 μm -NiFe_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.25) +NiFe_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.25) -NiFe_frq = 1/(0.0838297450980392*um_scale) -NiFe_gam = 1/(0.259381156903766*um_scale) +NiFe_frq = 1 / (0.0838297450980392 * um_scale) +NiFe_gam = 1 / (0.259381156903766 * um_scale) NiFe_sig = 1 NiFe_susc = [mp.DrudeSusceptibility(frequency=NiFe_frq, gamma=NiFe_gam, sigma=NiFe_sig)] NiFe = mp.Medium(epsilon=1.0, E_susceptibilities=NiFe_susc, valid_freq_range=NiFe_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # titanium (Ti) from Horiba Technical Note 09: Drude Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Drude_Dispersion_Model.pdf # wavelength range: 0.21 - 1.24 μm -Ti_drude_range = mp.FreqRange(min=um_scale/1.24, max=um_scale/0.21) +Ti_drude_range = mp.FreqRange(min=um_scale / 1.24, max=um_scale / 0.21) -Ti_drude_frq = 1/(0.113746966055046*um_scale) -Ti_drude_gam = 1/(0.490056098814229*um_scale) +Ti_drude_frq = 1 / (0.113746966055046 * um_scale) +Ti_drude_gam = 1 / (0.490056098814229 * um_scale) Ti_drude_sig = 1 -Ti_drude_susc = [mp.DrudeSusceptibility(frequency=Ti_drude_frq, gamma=Ti_drude_gam, sigma=Ti_drude_sig)] +Ti_drude_susc = [ + mp.DrudeSusceptibility( + frequency=Ti_drude_frq, gamma=Ti_drude_gam, sigma=Ti_drude_sig + ) +] -Ti_drude = mp.Medium(epsilon=1.0, E_susceptibilities=Ti_drude_susc, valid_freq_range=Ti_drude_range) +Ti_drude = mp.Medium( + epsilon=1.0, E_susceptibilities=Ti_drude_susc, valid_freq_range=Ti_drude_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (SiN), non-stoichiometric, from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.21 - 2.07 μm -SiN_range = mp.FreqRange(min=um_scale/2.07, max=um_scale/0.21) +SiN_range = mp.FreqRange(min=um_scale / 2.07, max=um_scale / 0.21) -SiN_frq1 = 1/(0.190891752117013*um_scale) -SiN_gam1 = 1/(3.11518072864322*um_scale) +SiN_frq1 = 1 / (0.190891752117013 * um_scale) +SiN_gam1 = 1 / (3.11518072864322 * um_scale) SiN_sig1 = 1.2650 -SiN_susc = [mp.LorentzianSusceptibility(frequency=SiN_frq1, gamma=SiN_gam1, sigma=SiN_sig1)] +SiN_susc = [ + mp.LorentzianSusceptibility(frequency=SiN_frq1, gamma=SiN_gam1, sigma=SiN_sig1) +] SiN = mp.Medium(epsilon=2.320, E_susceptibilities=SiN_susc, valid_freq_range=SiN_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon nitride (Si3N4), stoichiometric, from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.23 - 0.83 μm -Si3N4_range = mp.FreqRange(min=um_scale/0.83, max=um_scale/0.23) +Si3N4_range = mp.FreqRange(min=um_scale / 0.83, max=um_scale / 0.23) -Si3N4_frq1 = 1/(0.389153148148148*um_scale) -Si3N4_gam1 = 1/(0.693811936205932*um_scale) +Si3N4_frq1 = 1 / (0.389153148148148 * um_scale) +Si3N4_gam1 = 1 / (0.693811936205932 * um_scale) Si3N4_sig1 = 4.377 -Si3N4_susc = [mp.LorentzianSusceptibility(frequency=Si3N4_frq1, gamma=Si3N4_gam1, sigma=Si3N4_sig1)] +Si3N4_susc = [ + mp.LorentzianSusceptibility( + frequency=Si3N4_frq1, gamma=Si3N4_gam1, sigma=Si3N4_sig1 + ) +] -Si3N4 = mp.Medium(epsilon=1.0, E_susceptibilities=Si3N4_susc, valid_freq_range=Si3N4_range) +Si3N4 = mp.Medium( + epsilon=1.0, E_susceptibilities=Si3N4_susc, valid_freq_range=Si3N4_range +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon dioxide (SiO2) from Horiba Technical Note 08: Lorentz Dispersion Model # ref: http://www.horiba.com/fileadmin/uploads/Scientific/Downloads/OpticalSchool_CN/TN/ellipsometer/Lorentz_Dispersion_Model.pdf # wavelength range: 0.25 - 1.77 μm -SiO2_range = mp.FreqRange(min=um_scale/1.77, max=um_scale/0.25) +SiO2_range = mp.FreqRange(min=um_scale / 1.77, max=um_scale / 0.25) -SiO2_frq1 = 1/(0.103320160833333*um_scale) -SiO2_gam1 = 1/(12.3984193000000*um_scale) +SiO2_frq1 = 1 / (0.103320160833333 * um_scale) +SiO2_gam1 = 1 / (12.3984193000000 * um_scale) SiO2_sig1 = 1.12 -SiO2_susc = [mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma=SiO2_sig1)] +SiO2_susc = [ + mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma=SiO2_sig1) +] SiO2 = mp.Medium(epsilon=1.0, E_susceptibilities=SiO2_susc, valid_freq_range=SiO2_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # indium phosphide (InP) from Handbook of Optics, 2nd edition, Vol. 2, McGraw-Hill (1994) # ref: https://refractiveindex.info/?shelf=main&book=InP&page=Pettit # wavelength range: 0.95 - 10 μm -InP_range = mp.FreqRange(min=um_scale/10, max=um_scale/0.95) +InP_range = mp.FreqRange(min=um_scale / 10, max=um_scale / 0.95) -InP_frq1 = 1/(0.6263*um_scale) +InP_frq1 = 1 / (0.6263 * um_scale) InP_gam1 = 0 InP_sig1 = 2.316 -InP_frq2 = 1/(32.935*um_scale) +InP_frq2 = 1 / (32.935 * um_scale) InP_gam2 = 0 InP_sig2 = 2.765 -InP_susc = [mp.LorentzianSusceptibility(frequency=InP_frq1, gamma=InP_gam1, sigma=InP_sig1), - mp.LorentzianSusceptibility(frequency=InP_frq2, gamma=InP_gam2, sigma=InP_sig2)] +InP_susc = [ + mp.LorentzianSusceptibility(frequency=InP_frq1, gamma=InP_gam1, sigma=InP_sig1), + mp.LorentzianSusceptibility(frequency=InP_frq2, gamma=InP_gam2, sigma=InP_sig2), +] InP = mp.Medium(epsilon=7.255, E_susceptibilities=InP_susc, valid_freq_range=InP_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # germanium (Ge) from N. P. Barnes and M. S. Piltch, J. Optical Society America, Vol. 69, pp. 178-180 (1979) # ref: https://refractiveindex.info/?shelf=main&book=Ge&page=Icenogle # wavelength range: 2.5 - 12 μm -Ge_range = mp.FreqRange(min=um_scale/12, max=um_scale/2.5) +Ge_range = mp.FreqRange(min=um_scale / 12, max=um_scale / 2.5) -Ge_frq1 = 1/(0.6641159*um_scale) +Ge_frq1 = 1 / (0.6641159 * um_scale) Ge_gam1 = 0 Ge_sig1 = 6.7288 -Ge_frq2 = 1/(62.210127*um_scale) +Ge_frq2 = 1 / (62.210127 * um_scale) Ge_gam2 = 0 Ge_sig2 = 0.21307 -Ge_susc = [mp.LorentzianSusceptibility(frequency=Ge_frq1, gamma=Ge_gam1, sigma=Ge_sig1), - mp.LorentzianSusceptibility(frequency=Ge_frq2, gamma=Ge_gam2, sigma=Ge_sig2)] +Ge_susc = [ + mp.LorentzianSusceptibility(frequency=Ge_frq1, gamma=Ge_gam1, sigma=Ge_sig1), + mp.LorentzianSusceptibility(frequency=Ge_frq2, gamma=Ge_gam2, sigma=Ge_sig2), +] Ge = mp.Medium(epsilon=9.28156, E_susceptibilities=Ge_susc, valid_freq_range=Ge_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # silicon (Si) from C. D. Salzberg and J. J. Villa, , J. Optical Society America, Vol. 47, pp. 244-246 (1957) # ref: https://refractiveindex.info/?shelf=main&book=Si&page=Salzberg # wavelength range: 1.36 - 11 μm -Si_range = mp.FreqRange(min=um_scale/11, max=um_scale/1.36) +Si_range = mp.FreqRange(min=um_scale / 11, max=um_scale / 1.36) -Si_frq1 = 1/(0.301516485*um_scale) +Si_frq1 = 1 / (0.301516485 * um_scale) Si_gam1 = 0 Si_sig1 = 10.6684293 -Si_frq2 = 1/(1.13475115*um_scale) +Si_frq2 = 1 / (1.13475115 * um_scale) Si_gam2 = 0 Si_sig2 = 0.0030434748 -Si_frq3 = 1/(1104*um_scale) +Si_frq3 = 1 / (1104 * um_scale) Si_gam3 = 0 Si_sig3 = 1.54133408 -Si_susc = [mp.LorentzianSusceptibility(frequency=Si_frq1, gamma=Si_gam1, sigma=Si_sig1), - mp.LorentzianSusceptibility(frequency=Si_frq2, gamma=Si_gam2, sigma=Si_sig2), - mp.LorentzianSusceptibility(frequency=Si_frq3, gamma=Si_gam3, sigma=Si_sig3)] +Si_susc = [ + mp.LorentzianSusceptibility(frequency=Si_frq1, gamma=Si_gam1, sigma=Si_sig1), + mp.LorentzianSusceptibility(frequency=Si_frq2, gamma=Si_gam2, sigma=Si_sig2), + mp.LorentzianSusceptibility(frequency=Si_frq3, gamma=Si_gam3, sigma=Si_sig3), +] Si = mp.Medium(epsilon=1.0, E_susceptibilities=Si_susc, valid_freq_range=Si_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # poly(methyl methacrylate) (PMMA) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=poly%28methyl_methacrylate%29&page=Sultanova # wavelength range: 0.437 - 1.052 μm -PMMA_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +PMMA_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -PMMA_frq1 = 1/(0.106362587407415*um_scale) +PMMA_frq1 = 1 / (0.106362587407415 * um_scale) PMMA_gam1 = 0 PMMA_sig1 = 1.1819 -PMMA_susc = [mp.LorentzianSusceptibility(frequency=PMMA_frq1, gamma=PMMA_gam1, sigma=PMMA_sig1)] +PMMA_susc = [ + mp.LorentzianSusceptibility(frequency=PMMA_frq1, gamma=PMMA_gam1, sigma=PMMA_sig1) +] PMMA = mp.Medium(epsilon=1.0, E_susceptibilities=PMMA_susc, valid_freq_range=PMMA_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # polycarbonate (PC) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=polycarbonate&page=Sultanova # wavelength range: 0.437 - 1.052 μm -PC_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +PC_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -PC_frq1 = 1/(0.145958898324152*um_scale) +PC_frq1 = 1 / (0.145958898324152 * um_scale) PC_gam1 = 0 PC_sig1 = 1.4182 @@ -953,14 +1118,14 @@ PC = mp.Medium(epsilon=1.0, E_susceptibilities=PC_susc, valid_freq_range=PC_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # polystyrene (PS) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=polystyren&page=Sultanova # wavelength range: 0.437 - 1.052 μm -PS_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +PS_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -PS_frq1 = 1/(0.142182980697410*um_scale) +PS_frq1 = 1 / (0.142182980697410 * um_scale) PS_gam1 = 0 PS_sig1 = 1.4435 @@ -968,22 +1133,24 @@ PS = mp.Medium(epsilon=1.0, E_susceptibilities=PS_susc, valid_freq_range=PS_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # cellulose (CLS) from N. Sultanova et al., Acta Physica Polonica A, Vol. 116, pp. 585-7 (2009) # ref: https://refractiveindex.info/?shelf=organic&book=cellulose&page=Sultanova # wavelength range: 0.437 - 1.052 μm -CLS_range = mp.FreqRange(min=um_scale/1.052, max=um_scale/0.437) +CLS_range = mp.FreqRange(min=um_scale / 1.052, max=um_scale / 0.437) -CLS_frq1 = 1/(0.105294824184287*um_scale) +CLS_frq1 = 1 / (0.105294824184287 * um_scale) CLS_gam1 = 0 CLS_sig1 = 1.124 -CLS_susc = [mp.LorentzianSusceptibility(frequency=CLS_frq1, gamma=CLS_gam1, sigma=CLS_sig1)] +CLS_susc = [ + mp.LorentzianSusceptibility(frequency=CLS_frq1, gamma=CLS_gam1, sigma=CLS_sig1) +] CLS = mp.Medium(epsilon=1.0, E_susceptibilities=CLS_susc, valid_freq_range=CLS_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # barium borate (BaB2O4), beta phase, from G. Tamosauskas et al., Optical Materials Express, Vol. 8, pp. 1410-18 (2018) # ref: https://refractiveindex.info/?shelf=main&book=BaB2O4&page=Tamosauskas-o # ref: https://refractiveindex.info/?shelf=main&book=BaB2O4&page=Tamosauskas-e @@ -991,39 +1158,71 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -BaB2O4_range = mp.FreqRange(min=um_scale/5.2, max=um_scale/0.188) +BaB2O4_range = mp.FreqRange(min=um_scale / 5.2, max=um_scale / 0.188) -BaB2O4_frq1 = 1/(0.06265780079128216*um_scale) +BaB2O4_frq1 = 1 / (0.06265780079128216 * um_scale) BaB2O4_gam1 = 0 BaB2O4_sig1 = 0.90291 -BaB2O4_frq2 = 1/(0.13706202975295528*um_scale) +BaB2O4_frq2 = 1 / (0.13706202975295528 * um_scale) BaB2O4_gam2 = 0 BaB2O4_sig2 = 0.83155 -BaB2O4_frq3 = 1/(7.746612162745725*um_scale) +BaB2O4_frq3 = 1 / (7.746612162745725 * um_scale) BaB2O4_gam3 = 0 BaB2O4_sig3 = 0.76536 -BaB2O4_susc_o = [mp.LorentzianSusceptibility(frequency=BaB2O4_frq1, gamma=BaB2O4_gam1, sigma_diag=BaB2O4_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq2, gamma=BaB2O4_gam2, sigma_diag=BaB2O4_sig2*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq3, gamma=BaB2O4_gam3, sigma_diag=BaB2O4_sig3*mp.Vector3(1,1,0))] - -BaB2O4_frq1 = 1/(0.0845103543951864*um_scale) +BaB2O4_susc_o = [ + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq1, + gamma=BaB2O4_gam1, + sigma_diag=BaB2O4_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq2, + gamma=BaB2O4_gam2, + sigma_diag=BaB2O4_sig2 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq3, + gamma=BaB2O4_gam3, + sigma_diag=BaB2O4_sig3 * mp.Vector3(1, 1, 0), + ), +] + +BaB2O4_frq1 = 1 / (0.0845103543951864 * um_scale) BaB2O4_gam1 = 0 BaB2O4_sig1 = 1.151075 -BaB2O4_frq2 = 1/(0.15029970059850417*um_scale) +BaB2O4_frq2 = 1 / (0.15029970059850417 * um_scale) BaB2O4_gam2 = 0 BaB2O4_sig2 = 0.21803 -BaB2O4_frq3 = 1/(16.217274740226856*um_scale) +BaB2O4_frq3 = 1 / (16.217274740226856 * um_scale) BaB2O4_gam3 = 0 BaB2O4_sig3 = 0.656 -BaB2O4_susc_e = [mp.LorentzianSusceptibility(frequency=BaB2O4_frq1, gamma=BaB2O4_gam1, sigma_diag=BaB2O4_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq2, gamma=BaB2O4_gam2, sigma_diag=BaB2O4_sig2*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=BaB2O4_frq3, gamma=BaB2O4_gam3, sigma_diag=BaB2O4_sig3*mp.Vector3(0,0,1))] - -BaB2O4 = mp.Medium(epsilon=1.0, E_susceptibilities=BaB2O4_susc_o+BaB2O4_susc_e, valid_freq_range=BaB2O4_range) - -#------------------------------------------------------------------ +BaB2O4_susc_e = [ + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq1, + gamma=BaB2O4_gam1, + sigma_diag=BaB2O4_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq2, + gamma=BaB2O4_gam2, + sigma_diag=BaB2O4_sig2 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=BaB2O4_frq3, + gamma=BaB2O4_gam3, + sigma_diag=BaB2O4_sig3 * mp.Vector3(0, 0, 1), + ), +] + +BaB2O4 = mp.Medium( + epsilon=1.0, + E_susceptibilities=BaB2O4_susc_o + BaB2O4_susc_e, + valid_freq_range=BaB2O4_range, +) + +# ------------------------------------------------------------------ # lithium niobate (LiNbO3) from D.E. Zelmon et al., J. Optical Society of America B, Vol. 14, pp. 3319-22 (1997) # ref: https://refractiveindex.info/?shelf=main&book=LiNbO3&page=Zelmon-o # ref: https://refractiveindex.info/?shelf=main&book=LiNbO3&page=Zelmon-e @@ -1031,39 +1230,71 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -LiNbO3_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.4) +LiNbO3_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.4) -LiNbO3_frq1 = 1/(0.13281566172707193*um_scale) +LiNbO3_frq1 = 1 / (0.13281566172707193 * um_scale) LiNbO3_gam1 = 0 LiNbO3_sig1 = 2.6734 -LiNbO3_frq2 = 1/(0.24318717071424636*um_scale) +LiNbO3_frq2 = 1 / (0.24318717071424636 * um_scale) LiNbO3_gam2 = 0 LiNbO3_sig2 = 1.2290 -LiNbO3_frq3 = 1/(21.78531615561271*um_scale) +LiNbO3_frq3 = 1 / (21.78531615561271 * um_scale) LiNbO3_gam3 = 0 LiNbO3_sig3 = 12.614 -LiNbO3_susc_o = [mp.LorentzianSusceptibility(frequency=LiNbO3_frq1, gamma=LiNbO3_gam1, sigma_diag=LiNbO3_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq2, gamma=LiNbO3_gam2, sigma_diag=LiNbO3_sig2*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq3, gamma=LiNbO3_gam3, sigma_diag=LiNbO3_sig3*mp.Vector3(1,1,0))] - -LiNbO3_frq1 = 1/(0.14307340773183533*um_scale) +LiNbO3_susc_o = [ + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq1, + gamma=LiNbO3_gam1, + sigma_diag=LiNbO3_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq2, + gamma=LiNbO3_gam2, + sigma_diag=LiNbO3_sig2 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq3, + gamma=LiNbO3_gam3, + sigma_diag=LiNbO3_sig3 * mp.Vector3(1, 1, 0), + ), +] + +LiNbO3_frq1 = 1 / (0.14307340773183533 * um_scale) LiNbO3_gam1 = 0 LiNbO3_sig1 = 2.9804 -LiNbO3_frq2 = 1/(0.2580697580112788*um_scale) +LiNbO3_frq2 = 1 / (0.2580697580112788 * um_scale) LiNbO3_gam2 = 0 LiNbO3_sig2 = 0.5981 -LiNbO3_frq3 = 1/(20.39803912144498*um_scale) +LiNbO3_frq3 = 1 / (20.39803912144498 * um_scale) LiNbO3_gam3 = 0 LiNbO3_sig3 = 8.9543 -LiNbO3_susc_e = [mp.LorentzianSusceptibility(frequency=LiNbO3_frq1, gamma=LiNbO3_gam1, sigma_diag=LiNbO3_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq2, gamma=LiNbO3_gam2, sigma_diag=LiNbO3_sig2*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=LiNbO3_frq3, gamma=LiNbO3_gam3, sigma_diag=LiNbO3_sig3*mp.Vector3(0,0,1))] - -LiNbO3 = mp.Medium(epsilon=1.0, E_susceptibilities=LiNbO3_susc_o+LiNbO3_susc_e, valid_freq_range=LiNbO3_range) - -#------------------------------------------------------------------ +LiNbO3_susc_e = [ + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq1, + gamma=LiNbO3_gam1, + sigma_diag=LiNbO3_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq2, + gamma=LiNbO3_gam2, + sigma_diag=LiNbO3_sig2 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=LiNbO3_frq3, + gamma=LiNbO3_gam3, + sigma_diag=LiNbO3_sig3 * mp.Vector3(0, 0, 1), + ), +] + +LiNbO3 = mp.Medium( + epsilon=1.0, + E_susceptibilities=LiNbO3_susc_o + LiNbO3_susc_e, + valid_freq_range=LiNbO3_range, +) + +# ------------------------------------------------------------------ # calcium tungstate (CaWO4) from W.L. Bond, J. Applied Physics, Vol. 36, pp. 1674-77 (1965) # ref: https://refractiveindex.info/?shelf=main&book=CaWO4&page=Bond-o # ref: https://refractiveindex.info/?shelf=main&book=CaWO4&page=Bond-e @@ -1071,31 +1302,55 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -CaWO4_range = mp.FreqRange(min=um_scale/4.0, max=um_scale/0.45) +CaWO4_range = mp.FreqRange(min=um_scale / 4.0, max=um_scale / 0.45) -CaWO4_frq1 = 1/(0.1347*um_scale) +CaWO4_frq1 = 1 / (0.1347 * um_scale) CaWO4_gam1 = 0 CaWO4_sig1 = 2.5493 -CaWO4_frq2 = 1/(10.815*um_scale) +CaWO4_frq2 = 1 / (10.815 * um_scale) CaWO4_gam2 = 0 CaWO4_sig2 = 0.9200 -CaWO4_susc_o = [mp.LorentzianSusceptibility(frequency=CaWO4_frq1, gamma=CaWO4_gam1, sigma_diag=CaWO4_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=CaWO4_frq2, gamma=CaWO4_gam2, sigma_diag=CaWO4_sig2*mp.Vector3(1,1,0))] - -CaWO4_frq1 = 1/(0.1379*um_scale) +CaWO4_susc_o = [ + mp.LorentzianSusceptibility( + frequency=CaWO4_frq1, + gamma=CaWO4_gam1, + sigma_diag=CaWO4_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=CaWO4_frq2, + gamma=CaWO4_gam2, + sigma_diag=CaWO4_sig2 * mp.Vector3(1, 1, 0), + ), +] + +CaWO4_frq1 = 1 / (0.1379 * um_scale) CaWO4_gam1 = 0 CaWO4_sig1 = 2.6041 -CaWO4_frq2 = 1/(21.371*um_scale) +CaWO4_frq2 = 1 / (21.371 * um_scale) CaWO4_gam2 = 0 CaWO4_sig2 = 4.1237 -CaWO4_susc_e = [mp.LorentzianSusceptibility(frequency=CaWO4_frq1, gamma=CaWO4_gam1, sigma_diag=CaWO4_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=CaWO4_frq2, gamma=CaWO4_gam2, sigma_diag=CaWO4_sig2*mp.Vector3(0,0,1))] - -CaWO4 = mp.Medium(epsilon=1.0, E_susceptibilities=CaWO4_susc_o+CaWO4_susc_e, valid_freq_range=CaWO4_range) - -#------------------------------------------------------------------ +CaWO4_susc_e = [ + mp.LorentzianSusceptibility( + frequency=CaWO4_frq1, + gamma=CaWO4_gam1, + sigma_diag=CaWO4_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=CaWO4_frq2, + gamma=CaWO4_gam2, + sigma_diag=CaWO4_sig2 * mp.Vector3(0, 0, 1), + ), +] + +CaWO4 = mp.Medium( + epsilon=1.0, + E_susceptibilities=CaWO4_susc_o + CaWO4_susc_e, + valid_freq_range=CaWO4_range, +) + +# ------------------------------------------------------------------ # calcium carbonate (CaCO3) from G. Ghosh, Optics Communication, Vol. 163, pp. 95-102 (1999) # ref: https://refractiveindex.info/?shelf=main&book=CaCO3&page=Ghosh-o # ref: https://refractiveindex.info/?shelf=main&book=CaCO3&page=Ghosh-e @@ -1103,31 +1358,55 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -CaCO3_range = mp.FreqRange(min=um_scale/2.172, max=um_scale/0.204) +CaCO3_range = mp.FreqRange(min=um_scale / 2.172, max=um_scale / 0.204) -CaCO3_frq1 = 1/(0.13940057496294625*um_scale) +CaCO3_frq1 = 1 / (0.13940057496294625 * um_scale) CaCO3_gam1 = 0 CaCO3_sig1 = 0.96464345 -CaCO3_frq2 = 1/(10.954451150103322*um_scale) +CaCO3_frq2 = 1 / (10.954451150103322 * um_scale) CaCO3_gam2 = 0 CaCO3_sig2 = 1.82831454 -CaCO3_susc_o = [mp.LorentzianSusceptibility(frequency=CaCO3_frq1, gamma=CaCO3_gam1, sigma_diag=CaCO3_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=CaCO3_frq2, gamma=CaCO3_gam2, sigma_diag=CaCO3_sig2*mp.Vector3(1,1,0))] - -CaCO3_frq1 = 1/(0.1032906302623815*um_scale) +CaCO3_susc_o = [ + mp.LorentzianSusceptibility( + frequency=CaCO3_frq1, + gamma=CaCO3_gam1, + sigma_diag=CaCO3_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=CaCO3_frq2, + gamma=CaCO3_gam2, + sigma_diag=CaCO3_sig2 * mp.Vector3(1, 1, 0), + ), +] + +CaCO3_frq1 = 1 / (0.1032906302623815 * um_scale) CaCO3_gam1 = 0 CaCO3_sig1 = 0.82427830 -CaCO3_frq2 = 1/(10.954451150103322*um_scale) +CaCO3_frq2 = 1 / (10.954451150103322 * um_scale) CaCO3_gam2 = 0 CaCO3_sig2 = 0.14429128 -CaCO3_susc_e = [mp.LorentzianSusceptibility(frequency=CaCO3_frq1, gamma=CaCO3_gam1, sigma_diag=CaCO3_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=CaCO3_frq2, gamma=CaCO3_gam2, sigma_diag=CaCO3_sig2*mp.Vector3(0,0,1))] - -CaCO3 = mp.Medium(epsilon_diag=mp.Vector3(1.73358749,1.73358749,1.35859695), E_susceptibilities=CaCO3_susc_o+CaCO3_susc_e, valid_freq_range=CaCO3_range) - -#------------------------------------------------------------------ +CaCO3_susc_e = [ + mp.LorentzianSusceptibility( + frequency=CaCO3_frq1, + gamma=CaCO3_gam1, + sigma_diag=CaCO3_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=CaCO3_frq2, + gamma=CaCO3_gam2, + sigma_diag=CaCO3_sig2 * mp.Vector3(0, 0, 1), + ), +] + +CaCO3 = mp.Medium( + epsilon_diag=mp.Vector3(1.73358749, 1.73358749, 1.35859695), + E_susceptibilities=CaCO3_susc_o + CaCO3_susc_e, + valid_freq_range=CaCO3_range, +) + +# ------------------------------------------------------------------ # silicon dioxide (SiO2) from G. Ghosh, Optics Communication, Vol. 163, pp. 95-102 (1999) # ref: https://refractiveindex.info/?shelf=main&book=SiO2&page=Ghosh-o # ref: https://refractiveindex.info/?shelf=main&book=SiO2&page=Ghosh-e @@ -1135,31 +1414,47 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -SiO2_range = mp.FreqRange(min=um_scale/2.0531, max=um_scale/0.198) +SiO2_range = mp.FreqRange(min=um_scale / 2.0531, max=um_scale / 0.198) -SiO2_frq1 = 1/(0.10029257051247614*um_scale) +SiO2_frq1 = 1 / (0.10029257051247614 * um_scale) SiO2_gam1 = 0 SiO2_sig1 = 1.07044083 -SiO2_frq2 = 1/(10*um_scale) +SiO2_frq2 = 1 / (10 * um_scale) SiO2_gam2 = 0 SiO2_sig2 = 1.10202242 -SiO2_susc_o = [mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2*mp.Vector3(1,1,0))] +SiO2_susc_o = [ + mp.LorentzianSusceptibility( + frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1 * mp.Vector3(1, 1, 0) + ), + mp.LorentzianSusceptibility( + frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2 * mp.Vector3(1, 1, 0) + ), +] -SiO2_frq1 = 1/(0.10104546699382412*um_scale) +SiO2_frq1 = 1 / (0.10104546699382412 * um_scale) SiO2_gam1 = 0 SiO2_sig1 = 1.09509924 -SiO2_frq2 = 1/(10*um_scale) +SiO2_frq2 = 1 / (10 * um_scale) SiO2_gam2 = 0 SiO2_sig2 = 1.15662475 -SiO2_susc_e = [mp.LorentzianSusceptibility(frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2*mp.Vector3(0,0,1))] - -SiO2_aniso = mp.Medium(epsilon_diag=mp.Vector3(1.28604141,1.28604141,1.28851804), E_susceptibilities=SiO2_susc_o+SiO2_susc_e, valid_freq_range=SiO2_range) - -#------------------------------------------------------------------ +SiO2_susc_e = [ + mp.LorentzianSusceptibility( + frequency=SiO2_frq1, gamma=SiO2_gam1, sigma_diag=SiO2_sig1 * mp.Vector3(0, 0, 1) + ), + mp.LorentzianSusceptibility( + frequency=SiO2_frq2, gamma=SiO2_gam2, sigma_diag=SiO2_sig2 * mp.Vector3(0, 0, 1) + ), +] + +SiO2_aniso = mp.Medium( + epsilon_diag=mp.Vector3(1.28604141, 1.28604141, 1.28851804), + E_susceptibilities=SiO2_susc_o + SiO2_susc_e, + valid_freq_range=SiO2_range, +) + +# ------------------------------------------------------------------ # gallium nitride (GaN), alpha phase (wurtzite), from A.S. Barker Jr. and M. Ilegems, Physical Review B, Vol. 7, pp. 743-50 (1973) # ref: https://refractiveindex.info/?shelf=main&book=GaN&page=Barker-o # ref: https://refractiveindex.info/?shelf=main&book=GaN&page=Barker-e @@ -1167,27 +1462,41 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -GaN_range = mp.FreqRange(min=um_scale/10.0, max=um_scale/0.35) +GaN_range = mp.FreqRange(min=um_scale / 10.0, max=um_scale / 0.35) -GaN_frq1 = 1/(0.256*um_scale) +GaN_frq1 = 1 / (0.256 * um_scale) GaN_gam1 = 0 GaN_sig1 = 1.75 -GaN_frq2 = 1/(17.86*um_scale) +GaN_frq2 = 1 / (17.86 * um_scale) GaN_gam2 = 0 GaN_sig2 = 4.1 -GaN_susc_o = [mp.LorentzianSusceptibility(frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=GaN_frq2, gamma=GaN_gam2, sigma_diag=GaN_sig2*mp.Vector3(1,1,0))] +GaN_susc_o = [ + mp.LorentzianSusceptibility( + frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1 * mp.Vector3(1, 1, 0) + ), + mp.LorentzianSusceptibility( + frequency=GaN_frq2, gamma=GaN_gam2, sigma_diag=GaN_sig2 * mp.Vector3(1, 1, 0) + ), +] -GaN_frq1 = 1/(18.76*um_scale) +GaN_frq1 = 1 / (18.76 * um_scale) GaN_gam1 = 0 GaN_sig1 = 5.08 -GaN_susc_e = [mp.LorentzianSusceptibility(frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1*mp.Vector3(0,0,1))] +GaN_susc_e = [ + mp.LorentzianSusceptibility( + frequency=GaN_frq1, gamma=GaN_gam1, sigma_diag=GaN_sig1 * mp.Vector3(0, 0, 1) + ) +] -GaN = mp.Medium(epsilon_diag=mp.Vector3(3.6,3.6,5.35), E_susceptibilities=GaN_susc_o+GaN_susc_e, valid_freq_range=GaN_range) +GaN = mp.Medium( + epsilon_diag=mp.Vector3(3.6, 3.6, 5.35), + E_susceptibilities=GaN_susc_o + GaN_susc_e, + valid_freq_range=GaN_range, +) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # aluminum nitride (AlN) from J. Pastrnak and L. Roskovcova, Physica Status Solidi, Vol. 14, K5-8 (1966) # ref: https://refractiveindex.info/?shelf=main&book=AlN&page=Pastrnak-o # ref: https://refractiveindex.info/?shelf=main&book=AlN&page=Pastrnak-e @@ -1195,31 +1504,47 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -AlN_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.22) +AlN_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.22) -AlN_frq1 = 1/(0.1715*um_scale) +AlN_frq1 = 1 / (0.1715 * um_scale) AlN_gam1 = 0 AlN_sig1 = 1.3786 -AlN_frq2 = 1/(15.03*um_scale) +AlN_frq2 = 1 / (15.03 * um_scale) AlN_gam2 = 0 AlN_sig2 = 3.861 -AlN_susc_o = [mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2*mp.Vector3(1,1,0))] +AlN_susc_o = [ + mp.LorentzianSusceptibility( + frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1 * mp.Vector3(1, 1, 0) + ), + mp.LorentzianSusceptibility( + frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2 * mp.Vector3(1, 1, 0) + ), +] -AlN_frq1 = 1/(0.1746*um_scale) +AlN_frq1 = 1 / (0.1746 * um_scale) AlN_gam1 = 0 AlN_sig1 = 1.6173 -AlN_frq2 = 1/(15.03*um_scale) +AlN_frq2 = 1 / (15.03 * um_scale) AlN_gam2 = 0 AlN_sig2 = 4.139 -AlN_susc_e = [mp.LorentzianSusceptibility(frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2*mp.Vector3(0,0,1))] - -AlN_aniso = mp.Medium(epsilon_diag=mp.Vector3(3.1399,3.1399,3.0729), E_susceptibilities=AlN_susc_o+AlN_susc_e, valid_freq_range=AlN_range) - -#------------------------------------------------------------------ +AlN_susc_e = [ + mp.LorentzianSusceptibility( + frequency=AlN_frq1, gamma=AlN_gam1, sigma_diag=AlN_sig1 * mp.Vector3(0, 0, 1) + ), + mp.LorentzianSusceptibility( + frequency=AlN_frq2, gamma=AlN_gam2, sigma_diag=AlN_sig2 * mp.Vector3(0, 0, 1) + ), +] + +AlN_aniso = mp.Medium( + epsilon_diag=mp.Vector3(3.1399, 3.1399, 3.0729), + E_susceptibilities=AlN_susc_o + AlN_susc_e, + valid_freq_range=AlN_range, +) + +# ------------------------------------------------------------------ # alumina/sapphire (Al2O3) from I.H. Malitson and M.J. Dodge, J. Optical Society of America, Vol. 62, pp. 1405 (1972) # ref: https://refractiveindex.info/?shelf=main&book=Al2O3&page=Malitson-o # ref: https://refractiveindex.info/?shelf=main&book=Al2O3&page=Malitson-e @@ -1227,87 +1552,127 @@ ## NOTE: ordinary (o) axes in X and Y, extraordinary (e) axis in Z -Al2O3_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.2) +Al2O3_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.2) -Al2O3_frq1 = 1/(0.0726631*um_scale) +Al2O3_frq1 = 1 / (0.0726631 * um_scale) Al2O3_gam1 = 0 Al2O3_sig1 = 1.4313493 -Al2O3_frq2 = 1/(0.1193242*um_scale) +Al2O3_frq2 = 1 / (0.1193242 * um_scale) Al2O3_gam2 = 0 Al2O3_sig2 = 0.65054713 -Al2O3_frq3 = 1/(18.02825*um_scale) +Al2O3_frq3 = 1 / (18.02825 * um_scale) Al2O3_gam3 = 0 Al2O3_sig3 = 5.3414021 -Al2O3_susc_o = [mp.LorentzianSusceptibility(frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma_diag=Al2O3_sig1*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq2, gamma=Al2O3_gam2, sigma_diag=Al2O3_sig2*mp.Vector3(1,1,0)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq3, gamma=Al2O3_gam3, sigma_diag=Al2O3_sig3*mp.Vector3(1,1,0))] - -Al2O3_frq1 = 1/(0.0740288*um_scale) +Al2O3_susc_o = [ + mp.LorentzianSusceptibility( + frequency=Al2O3_frq1, + gamma=Al2O3_gam1, + sigma_diag=Al2O3_sig1 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq2, + gamma=Al2O3_gam2, + sigma_diag=Al2O3_sig2 * mp.Vector3(1, 1, 0), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq3, + gamma=Al2O3_gam3, + sigma_diag=Al2O3_sig3 * mp.Vector3(1, 1, 0), + ), +] + +Al2O3_frq1 = 1 / (0.0740288 * um_scale) Al2O3_gam1 = 0 Al2O3_sig1 = 1.5039759 -Al2O3_frq2 = 1/(0.1216529*um_scale) +Al2O3_frq2 = 1 / (0.1216529 * um_scale) Al2O3_gam2 = 0 Al2O3_sig2 = 0.55069141 -Al2O3_frq3 = 1/(20.072248*um_scale) +Al2O3_frq3 = 1 / (20.072248 * um_scale) Al2O3_gam3 = 0 Al2O3_sig3 = 6.5927379 -Al2O3_susc_e = [mp.LorentzianSusceptibility(frequency=Al2O3_frq1, gamma=Al2O3_gam1, sigma_diag=Al2O3_sig1*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq2, gamma=Al2O3_gam2, sigma_diag=Al2O3_sig2*mp.Vector3(0,0,1)), - mp.LorentzianSusceptibility(frequency=Al2O3_frq3, gamma=Al2O3_gam3, sigma_diag=Al2O3_sig3*mp.Vector3(0,0,1))] - -Al2O3_aniso = mp.Medium(epsilon=1, E_susceptibilities=Al2O3_susc_o+Al2O3_susc_e, valid_freq_range=Al2O3_range) - -#------------------------------------------------------------------ +Al2O3_susc_e = [ + mp.LorentzianSusceptibility( + frequency=Al2O3_frq1, + gamma=Al2O3_gam1, + sigma_diag=Al2O3_sig1 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq2, + gamma=Al2O3_gam2, + sigma_diag=Al2O3_sig2 * mp.Vector3(0, 0, 1), + ), + mp.LorentzianSusceptibility( + frequency=Al2O3_frq3, + gamma=Al2O3_gam3, + sigma_diag=Al2O3_sig3 * mp.Vector3(0, 0, 1), + ), +] + +Al2O3_aniso = mp.Medium( + epsilon=1, + E_susceptibilities=Al2O3_susc_o + Al2O3_susc_e, + valid_freq_range=Al2O3_range, +) + +# ------------------------------------------------------------------ # yttrium oxide (Y2O3) from Y. Nigara, Japanese J. of Applied Physics, Vol. 7, pp. 404-8 (1968) # ref: https://refractiveindex.info/?shelf=main&book=Y2O3&page=Nigara # wavelength range: 0.25 - 9.6 μm -Y2O3_range = mp.FreqRange(min=um_scale/9.6, max=um_scale/0.25) +Y2O3_range = mp.FreqRange(min=um_scale / 9.6, max=um_scale / 0.25) -Y2O3_frq1 = 1/(0.1387*um_scale) +Y2O3_frq1 = 1 / (0.1387 * um_scale) Y2O3_gam1 = 0 Y2O3_sig1 = 2.578 -Y2O3_frq2 = 1/(22.936*um_scale) +Y2O3_frq2 = 1 / (22.936 * um_scale) Y2O3_gam2 = 0 Y2O3_sig2 = 3.935 -Y2O3_susc = [mp.LorentzianSusceptibility(frequency=Y2O3_frq1, gamma=Y2O3_gam1, sigma=Y2O3_sig1), - mp.LorentzianSusceptibility(frequency=Y2O3_frq2, gamma=Y2O3_gam2, sigma=Y2O3_sig2)] +Y2O3_susc = [ + mp.LorentzianSusceptibility(frequency=Y2O3_frq1, gamma=Y2O3_gam1, sigma=Y2O3_sig1), + mp.LorentzianSusceptibility(frequency=Y2O3_frq2, gamma=Y2O3_gam2, sigma=Y2O3_sig2), +] Y2O3 = mp.Medium(epsilon=1.0, E_susceptibilities=Y2O3_susc, valid_freq_range=Y2O3_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # undoped yttrium aluminum garnet (YAG) from D.E. Zelmon et al., Applied Optics, Vol. 37, 4933-5 (1998) # ref: https://refractiveindex.info/?shelf=main&book=Y3Al5O12&page=Zelmon # wavelength range: 0.4 - 5.0 μm -YAG_range = mp.FreqRange(min=um_scale/5.0, max=um_scale/0.4) +YAG_range = mp.FreqRange(min=um_scale / 5.0, max=um_scale / 0.4) -YAG_frq1 = 1/(0.1088577052853862*um_scale) +YAG_frq1 = 1 / (0.1088577052853862 * um_scale) YAG_gam1 = 0 YAG_sig1 = 2.28200 -YAG_frq2 = 1/(16.814695953242804*um_scale) +YAG_frq2 = 1 / (16.814695953242804 * um_scale) YAG_gam2 = 0 YAG_sig2 = 3.27644 -YAG_susc = [mp.LorentzianSusceptibility(frequency=YAG_frq1, gamma=YAG_gam1, sigma=YAG_sig1), - mp.LorentzianSusceptibility(frequency=YAG_frq2, gamma=YAG_gam2, sigma=YAG_sig2)] +YAG_susc = [ + mp.LorentzianSusceptibility(frequency=YAG_frq1, gamma=YAG_gam1, sigma=YAG_sig1), + mp.LorentzianSusceptibility(frequency=YAG_frq2, gamma=YAG_gam2, sigma=YAG_sig2), +] YAG = mp.Medium(epsilon=1.0, E_susceptibilities=YAG_susc, valid_freq_range=YAG_range) -#------------------------------------------------------------------ +# ------------------------------------------------------------------ # cadmium telluride (CdTe) from D.T.F. Marple, J. Applied Physics, Vol. 35, pp. 539-42 (1964) # ref: https://refractiveindex.info/?shelf=main&book=CdTe&page=Marple # wavelength range: 0.86 - 2.5 μm -CdTe_range = mp.FreqRange(min=um_scale/2.5, max=um_scale/0.86) +CdTe_range = mp.FreqRange(min=um_scale / 2.5, max=um_scale / 0.86) -CdTe_frq1 = 1/(0.6049793384901669*um_scale) +CdTe_frq1 = 1 / (0.6049793384901669 * um_scale) CdTe_gam1 = 0 CdTe_sig1 = 1.53 -CdTe_susc = [mp.LorentzianSusceptibility(frequency=CdTe_frq1, gamma=CdTe_gam1, sigma=CdTe_sig1)] +CdTe_susc = [ + mp.LorentzianSusceptibility(frequency=CdTe_frq1, gamma=CdTe_gam1, sigma=CdTe_sig1) +] -CdTe = mp.Medium(epsilon=5.68, E_susceptibilities=CdTe_susc, valid_freq_range=CdTe_range) +CdTe = mp.Medium( + epsilon=5.68, E_susceptibilities=CdTe_susc, valid_freq_range=CdTe_range +) diff --git a/python/meep-python.hpp b/python/meep-python.hpp index 0e6c6e278..ab8b952af 100644 --- a/python/meep-python.hpp +++ b/python/meep-python.hpp @@ -1,7 +1,7 @@ namespace meep { #ifndef SWIG_PYTHON_THREAD_SCOPED_BLOCK -#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK +#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK #endif // like custom_src_time, but using Python function object, with proper reference counting diff --git a/python/meep.i b/python/meep.i index c629e67a8..66a4703cf 100644 --- a/python/meep.i +++ b/python/meep.i @@ -843,12 +843,12 @@ meep::volume_list *make_volume_list(const meep::volume &v, int c, //-------------------------------------------------- %inline %{ -void _get_gradient(PyObject *grad, double scalegrad, +void _get_gradient(PyObject *grad, double scalegrad, meep::dft_fields *fields_a_0, meep::dft_fields *fields_a_1, meep::dft_fields *fields_a_2, meep::dft_fields *fields_f_0, meep::dft_fields *fields_f_1, meep::dft_fields *fields_f_2, meep::grid_volume *grid_volume, meep::volume *where, PyObject *frequencies, meep_geom::geom_epsilon *geps, double fd_step) { - + // clean the gradient array PyArrayObject *pao_grad = (PyArrayObject *)grad; if (!PyArray_Check(pao_grad)) meep::abort("grad parameter must be numpy array."); @@ -873,7 +873,7 @@ void _get_gradient(PyObject *grad, double scalegrad, // calculate the gradient meep_geom::material_grids_addgradient(grad_c,ng,nf,adjoint_fields,forward_fields,frequencies_c,scalegrad,*grid_volume,*where,geps,fd_step); - + } %} diff --git a/python/mpb_data.py b/python/mpb_data.py index 87ffadd92..608a5f0ee 100644 --- a/python/mpb_data.py +++ b/python/mpb_data.py @@ -1,28 +1,31 @@ -# -*- coding: utf-8 -*- import math + import numpy as np + import meep as mp -from . import map_data -from . import MPBArray +from . import MPBArray, map_data -class MPBData(object): + +class MPBData: TWOPI = 6.2831853071795864769252867665590057683943388 - def __init__(self, - lattice=None, - kpoint=None, - rectify=False, - x=0, - y=0, - z=0, - periods=0, - resolution=0, - phase_angle=0, - pick_nearest=False, - ve=None, - verbose=False): + def __init__( + self, + lattice=None, + kpoint=None, + rectify=False, + x=0, + y=0, + z=0, + periods=0, + resolution=0, + phase_angle=0, + pick_nearest=False, + ve=None, + verbose=False, + ): self.lattice = lattice self.kpoint = kpoint @@ -31,11 +34,7 @@ def __init__(self, if periods: self.multiply_size = [periods, periods, periods] else: - self.multiply_size = [ - x if x else 1, - y if y else 1, - z if z else 1 - ] + self.multiply_size = [x or 1, y or 1, z or 1] self.resolution = resolution self.phase_angle = phase_angle @@ -52,8 +51,10 @@ def __init__(self, self.verbose = verbose self.scaleby = complex(1, 0) - self.phase = complex(math.cos(self.TWOPI * self.phase_angle / 360.0), - math.sin(self.TWOPI * self.phase_angle / 360.0)) + self.phase = complex( + math.cos(self.TWOPI * self.phase_angle / 360.0), + math.sin(self.TWOPI * self.phase_angle / 360.0), + ) self.scaleby *= self.phase def handle_dataset(self, in_arr): @@ -61,7 +62,7 @@ def handle_dataset(self, in_arr): out_dims = [1, 1, 1] rank = len(in_arr.shape) num_ones = 3 - rank - in_dims = [x for x in in_arr.shape] + [1] * num_ones + in_dims = list(in_arr.shape) + [1] * num_ones if np.iscomplexobj(in_arr): in_arr_re = np.real(in_arr) @@ -91,7 +92,7 @@ def handle_dataset(self, in_arr): N *= out_dims[i] if self.verbose: - print("Output data {}x{}x{}".format(out_dims[0], out_dims[1], out_dims[2])) + print(f"Output data {out_dims[0]}x{out_dims[1]}x{out_dims[2]}") out_arr_re = np.zeros(int(N)) @@ -101,16 +102,24 @@ def handle_dataset(self, in_arr): out_arr_im = np.array([]) flat_in_arr_re = in_arr_re.ravel() - flat_in_arr_im = in_arr_im.ravel() if isinstance(in_arr_im, np.ndarray) else np.array([]) - - if self.kpoint: - kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] - else: - kvector = [] + flat_in_arr_im = ( + in_arr_im.ravel() if isinstance(in_arr_im, np.ndarray) else np.array([]) + ) - map_data(flat_in_arr_re, flat_in_arr_im, np.array(in_dims, dtype=np.intc), - out_arr_re, out_arr_im, np.array(out_dims, dtype=np.intc), self.coord_map, - kvector, self.pick_nearest, self.verbose, False) + kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] if self.kpoint else [] + map_data( + flat_in_arr_re, + flat_in_arr_im, + np.array(in_dims, dtype=np.intc), + out_arr_re, + out_arr_im, + np.array(out_dims, dtype=np.intc), + self.coord_map, + kvector, + self.pick_nearest, + self.verbose, + False, + ) if np.iscomplexobj(in_arr): # multiply * scaleby for complex data @@ -131,13 +140,13 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): d_in = [[in_x_re, in_x_im], [in_y_re, in_y_im], [in_z_re, in_z_im]] in_dims = [in_arr.shape[0], in_arr.shape[1], 1] - rank = 2 - if self.verbose: print("Found complex vector dataset...") if self.verbose: fmt = "Input data is rank {}, size {}x{}x{}." + rank = 2 + print(fmt.format(rank, in_dims[0], in_dims[1], in_dims[2])) # rotate vector field according to cart_map @@ -145,9 +154,15 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): fmt1 = "Rotating vectors by matrix [ {:.10g}, {:.10g}, {:.10g}" fmt2 = " {:.10g}, {:.10g}, {:.10g}" fmt3 = " {:.10g}, {:.10g}, {:.10g} ]" - print(fmt1.format(self.cart_map.c1.x, self.cart_map.c2.x, self.cart_map.c3.x)) - print(fmt2.format(self.cart_map.c1.y, self.cart_map.c2.y, self.cart_map.c3.y)) - print(fmt3.format(self.cart_map.c1.z, self.cart_map.c2.z, self.cart_map.c3.z)) + print( + fmt1.format(self.cart_map.c1.x, self.cart_map.c2.x, self.cart_map.c3.x) + ) + print( + fmt2.format(self.cart_map.c1.y, self.cart_map.c2.y, self.cart_map.c3.y) + ) + print( + fmt3.format(self.cart_map.c1.z, self.cart_map.c2.z, self.cart_map.c3.z) + ) N = in_dims[0] * in_dims[1] for ri in range(2): @@ -179,19 +194,25 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): fmt = "Output data {}x{}x{}." print(fmt.format(out_dims[0], out_dims[1], out_dims[2])) - if self.kpoint: - kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] - else: - kvector = [] - + kvector = [self.kpoint.x, self.kpoint.y, self.kpoint.z] if self.kpoint else [] converted = [] for dim in range(3): out_arr_re = np.zeros(int(N)) out_arr_im = np.zeros(int(N)) - map_data(d_in[dim][0].ravel(), d_in[dim][1].ravel(), np.array(in_dims, dtype=np.intc), - out_arr_re, out_arr_im, np.array(out_dims, dtype=np.intc), self.coord_map, - kvector, self.pick_nearest, self.verbose, multiply_bloch_phase) + map_data( + d_in[dim][0].ravel(), + d_in[dim][1].ravel(), + np.array(in_dims, dtype=np.intc), + out_arr_re, + out_arr_im, + np.array(out_dims, dtype=np.intc), + self.coord_map, + kvector, + self.pick_nearest, + self.verbose, + multiply_bloch_phase, + ) # multiply * scaleby complex_out = np.vectorize(complex)(out_arr_re, out_arr_im) @@ -208,15 +229,13 @@ def handle_cvector_dataset(self, in_arr, multiply_bloch_phase): def init_output_lattice(self): cart_map = mp.Matrix( - mp.Vector3(1, 0, 0), - mp.Vector3(0, 1, 0), - mp.Vector3(0, 0, 1) + mp.Vector3(1, 0, 0), mp.Vector3(0, 1, 0), mp.Vector3(0, 0, 1) ) Rin = mp.Matrix( mp.Vector3(*self.lattice[0]), mp.Vector3(*self.lattice[1]), - mp.Vector3(*self.lattice[2]) + mp.Vector3(*self.lattice[2]), ) if self.verbose: @@ -225,9 +244,19 @@ def init_output_lattice(self): fmt = "Read Bloch wavevector ({:.6g}, {:.6g}, {:.6g})" print(fmt.format(self.kpoint.x, self.kpoint.y, self.kpoint.z)) fmt = "Input lattice = ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g})" - print(fmt.format(Rin.c1.x, Rin.c1.y, Rin.c1.z, - Rin.c2.x, Rin.c2.y, Rin.c2.z, - Rin.c3.x, Rin.c3.y, Rin.c3.z)) + print( + fmt.format( + Rin.c1.x, + Rin.c1.y, + Rin.c1.z, + Rin.c2.x, + Rin.c2.y, + Rin.c2.z, + Rin.c3.x, + Rin.c3.y, + Rin.c3.z, + ) + ) Rout = mp.Matrix(Rin.c1, Rin.c2, Rin.c3) @@ -240,11 +269,7 @@ def init_output_lattice(self): # that our first vector (in the direction of ve) smoothly # interpolates between the original lattice vectors. - if self.have_ve: - ve = self.ve.unit() - else: - ve = Rout.c1.unit() - + ve = self.ve.unit() if self.have_ve else Rout.c1.unit() # First, compute c1 in the direction of ve by smoothly # interpolating the old c1/c2/c3 (formula is slightly tricky) V = Rout.c1.cross(Rout.c2).dot(Rout.c3) @@ -255,8 +280,9 @@ def init_output_lattice(self): # Now, orthogonalize c2 and c3 Rout.c2 = Rout.c2 - ve.scale(ve.dot(Rout.c2)) Rout.c3 = Rout.c3 - ve.scale(ve.dot(Rout.c3)) - Rout.c3 = Rout.c3 - Rout.c2.scale(Rout.c2.dot(Rout.c3) / - Rout.c2.dot(Rout.c2)) + Rout.c3 = Rout.c3 - Rout.c2.scale( + Rout.c2.dot(Rout.c3) / Rout.c2.dot(Rout.c2) + ) cart_map.c1 = Rout.c1.unit() cart_map.c2 = Rout.c2.unit() @@ -269,9 +295,19 @@ def init_output_lattice(self): if self.verbose: fmt = "Output lattice = ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g}), ({:.6g}, {:.6g}, {:.6g})" - print(fmt.format(Rout.c1.x, Rout.c1.y, Rout.c1.z, - Rout.c2.x, Rout.c2.y, Rout.c2.z, - Rout.c3.x, Rout.c3.y, Rout.c3.z)) + print( + fmt.format( + Rout.c1.x, + Rout.c1.y, + Rout.c1.z, + Rout.c2.x, + Rout.c2.y, + Rout.c2.z, + Rout.c3.x, + Rout.c3.y, + Rout.c3.z, + ) + ) self.coord_map = Rin.inverse() * Rout self.Rout = Rout @@ -283,11 +319,13 @@ def convert(self, arr, kpoint=None): self.kpoint = arr.kpoint if self.lattice is None: - err = ("Couldn't find 'lattice.' You must do one of the following:\n" + - " 1. Pass the ModeSolver lattice to the MPBData constructor\n" + - " i.e., MPBData(lattice=ms.get_lattice())\n" + - " 2. Create an MPBArray to pass to MPBData.convert()\n" + - " i.e., mpb_arr = MPBArray(arr, ms.get_lattice(), ... ); mpb_data.convert(mpb_arr))") + err = ( + "Couldn't find 'lattice.' You must do one of the following:\n" + + " 1. Pass the ModeSolver lattice to the MPBData constructor\n" + + " i.e., MPBData(lattice=ms.get_lattice())\n" + + " 2. Create an MPBArray to pass to MPBData.convert()\n" + + " i.e., mpb_arr = MPBArray(arr, ms.get_lattice(), ... ); mpb_data.convert(mpb_arr))" + ) raise ValueError(err) if kpoint: diff --git a/python/simulation.py b/python/simulation.py index a3ba603d6..0a4453c0a 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -1,6 +1,3 @@ -# -*- coding: utf-8 -*- -from typing import Callable, List, Tuple, Union, Optional - import functools import math import numbers @@ -10,21 +7,27 @@ import subprocess import sys import warnings -from collections import namedtuple -from collections import OrderedDict +from collections import OrderedDict, namedtuple +from typing import Callable, List, Optional, Tuple, Union try: from collections.abc import Sequence except ImportError: - from collections import Sequence + from collections.abc import Sequence +import meep.visualization as vis import numpy as np +from meep.geom import GeometricObject, Medium, Vector3, init_do_averaging +from meep.source import ( + EigenModeSource, + GaussianBeamSource, + IndexedSource, + Source, + check_positive, +) +from meep.verbosity_mgr import Verbosity import meep as mp -from meep.geom import Vector3, init_do_averaging, GeometricObject, Medium -from meep.source import Source, EigenModeSource, GaussianBeamSource, IndexedSource, check_positive -import meep.visualization as vis -from meep.verbosity_mgr import Verbosity try: basestring @@ -32,13 +35,14 @@ basestring = str try: - from ipywidgets import FloatProgress from IPython.display import display + from ipywidgets import FloatProgress + do_progress = True except ImportError: do_progress = False -verbosity = Verbosity(mp.cvar, 'meep', 1) +verbosity = Verbosity(mp.cvar, "meep", 1) mp.setup() @@ -48,27 +52,40 @@ mp.set_ctl_printf_callback(mp.py_master_printf_wrap) mp.set_mpb_printf_callback(mp.py_master_printf_wrap) -EigCoeffsResult = namedtuple('EigCoeffsResult', ['alpha', 'vgrp', 'kpoints', 'kdom', 'cscale']) -FluxData = namedtuple('FluxData', ['E', 'H']) -ForceData = namedtuple('ForceData', ['offdiag1', 'offdiag2', 'diag']) -NearToFarData = namedtuple('NearToFarData', ['F']) +EigCoeffsResult = namedtuple( + "EigCoeffsResult", ["alpha", "vgrp", "kpoints", "kdom", "cscale"] +) +FluxData = namedtuple("FluxData", ["E", "H"]) +ForceData = namedtuple("ForceData", ["offdiag1", "offdiag2", "diag"]) +NearToFarData = namedtuple("NearToFarData", ["F"]) + def fix_dft_args(args, i): - if len(args) > i+2 and isinstance(args[i],(int,float)) and isinstance(args[i+1],(int,float)) and isinstance(args[i+2],int): + if ( + len(args) > i + 2 + and isinstance(args[i], (int, float)) + and isinstance(args[i + 1], (int, float)) + and isinstance(args[i + 2], int) + ): fcen = args[i] - df = args[i+1] - nfreq = args[i+2] - freq = [fcen] if nfreq == 1 else np.linspace(fcen-0.5*df,fcen+0.5*df,nfreq) - return args[:i] + (freq,) + args[i+3:] + df = args[i + 1] + nfreq = args[i + 2] + freq = ( + [fcen] + if nfreq == 1 + else np.linspace(fcen - 0.5 * df, fcen + 0.5 * df, nfreq) + ) + return args[:i] + (freq,) + args[i + 3 :] elif not isinstance(args[i], (np.ndarray, list)): - raise TypeError("add_dft functions only accept fcen,df,nfreq (3 numbers) or freq (array/list)") + raise TypeError( + "add_dft functions only accept fcen,df,nfreq (3 numbers) or freq (array/list)" + ) else: return args + def get_num_args(func): - if isinstance(func, Harminv): - return 2 - return func.__code__.co_argcount + return 2 if isinstance(func, Harminv) else func.__code__.co_argcount def vec(*args): @@ -95,16 +112,16 @@ def py_v3_to_vec(dims, iterable, is_cylindrical=False): elif dims == 2: if is_cylindrical: return mp.veccyl(v3.x, v3.z) - else: - v = mp.vec(v3.x, v3.y) - v.set_direction(mp.Z, v3.z) # for special_kz handling - return v + v = mp.vec(v3.x, v3.y) + v.set_direction(mp.Z, v3.z) # for special_kz handling + return v elif dims == 3: return mp.vec(v3.x, v3.y, v3.z) else: - raise ValueError("Invalid dimensions in Volume: {}".format(dims)) + raise ValueError(f"Invalid dimensions in Volume: {dims}") + -def bands_to_diffractedplanewave(where,bands): +def bands_to_diffractedplanewave(where, bands): if bands.axis is None: if where.in_direction(mp.X) != 0: axis = np.array([1, 0, 0], dtype=np.float64) @@ -113,8 +130,10 @@ def bands_to_diffractedplanewave(where,bands): elif where.in_direction(mp.Z) != 0: axis = np.array([0, 0, 1], dtype=np.float64) else: - raise ValueError("axis parameter of DiffractedPlanewave must be a non-zero Vector3") - elif isinstance(bands.axis,mp.Vector3): + raise ValueError( + "axis parameter of DiffractedPlanewave must be a non-zero Vector3" + ) + elif isinstance(bands.axis, mp.Vector3): axis = np.array([bands.axis.x, bands.axis.y, bands.axis.z], dtype=np.float64) else: raise TypeError("axis parameter of DiffractedPlanewave must be a Vector3") @@ -122,23 +141,21 @@ def bands_to_diffractedplanewave(where,bands): np.array(bands.g, dtype=np.intc), axis, bands.s * 1.0, - bands.p * 1.0 + bands.p * 1.0, ] return mp.diffractedplanewave(*diffractedplanewave_args) -class DiffractedPlanewave(object): + +class DiffractedPlanewave: """ For mode decomposition or eigenmode source, specify a diffracted planewave in homogeneous media. Should be passed as the `bands` argument of `get_eigenmode_coefficients`, `band_num` of `get_eigenmode`, or `eig_band` of `EigenModeSource`. """ - def __init__(self, - g=None, - axis=None, - s=None, - p=None): + + def __init__(self, g=None, axis=None, s=None, p=None): """ Construct a `DiffractedPlanewave`. - + **`g` [ list of 3 `integer`s ]** — The diffraction order $(m_x,m_y,m_z)$ corresponding to the wavevector $(k_x+2\\pi m_x/\\Lambda_x,k_y+2\\pi m_y/\\Lambda_y,k_z+2\\pi m_z/\\Lambda_z)$. The diffraction order $m_{x,y,z}$ should be non-zero only in the $d$-1 periodic directions of a $d$ dimensional cell of size $(\Lambda_x,\Lambda_y,\Lambda_z)$ (e.g., a plane in 3d) in which the mode monitor or source extends the entire length of the cell. + + **`g` [ list of 3 `integer`s ]** — The diffraction order $(m_x,m_y,m_z)$ corresponding to the wavevector $(k_x+2\\pi m_x/\\Lambda_x,k_y+2\\pi m_y/\\Lambda_y,k_z+2\\pi m_z/\\Lambda_z)$. The diffraction order $m_{x,y,z}$ should be non-zero only in the $d$-1 periodic directions of a $d$ dimensional cell of size $(\\Lambda_x,\\Lambda_y,\\Lambda_z)$ (e.g., a plane in 3d) in which the mode monitor or source extends the entire length of the cell. + **`axis` [ `Vector3` ]** — The plane of incidence for each planewave (used to define the $\\mathcal{S}$ and $\\mathcal{P}$ polarizations below) is defined to be the plane that contains the `axis` vector and the planewave's wavevector. If `None`, `axis` defaults to the first direction that lies in the plane of the monitor or source (e.g., $y$ direction for a $yz$ plane in 3d, either $x$ or $y$ in 2d). @@ -167,10 +184,12 @@ def s(self): def p(self): return self._p + DefaultPMLProfile = lambda u: u * u Vector3Type = Union[Vector3, Tuple[float, ...]] -class PML(object): + +class PML: """ This class is used for specifying the PML absorbing boundary layers around the cell, if any, via the `boundary_layers` input variable. See also [Perfectly Matched @@ -180,12 +199,16 @@ class PML(object): sets up one or more PML layers around the boundaries according to the following properties. """ - def __init__(self, thickness, - direction=mp.ALL, - side=mp.ALL, - R_asymptotic=1e-15, - mean_stretch=1.0, - pml_profile=DefaultPMLProfile): + + def __init__( + self, + thickness, + direction=mp.ALL, + side=mp.ALL, + R_asymptotic=1e-15, + mean_stretch=1.0, + pml_profile=DefaultPMLProfile, + ): """ + **`thickness` [`number`]** — The spatial thickness of the PML layer which extends from the boundary towards the *inside* of the cell. The thinner it is, @@ -236,7 +259,9 @@ def __init__(self, thickness, elif direction == mp.ALL: self.swigobj = mp.pml(thickness, side, R_asymptotic, mean_stretch) else: - self.swigobj = mp.pml(thickness, direction, side, R_asymptotic, mean_stretch) + self.swigobj = mp.pml( + thickness, direction, side, R_asymptotic, mean_stretch + ) @property def R_asymptotic(self): @@ -244,7 +269,7 @@ def R_asymptotic(self): @R_asymptotic.setter def R_asymptotic(self, val): - self._R_asymptotic = check_positive('PML.R_asymptotic', val) + self._R_asymptotic = check_positive("PML.R_asymptotic", val) @property def mean_stretch(self): @@ -255,7 +280,7 @@ def mean_stretch(self, val): if val >= 1: self._mean_stretch = val else: - raise ValueError("PML.mean_stretch must be >= 1. Got {}".format(val)) + raise ValueError(f"PML.mean_stretch must be >= 1. Got {val}") class Absorber(PML): @@ -287,8 +312,7 @@ class Absorber(PML): """ - -class Symmetry(object): +class Symmetry: """ This class is used for the `symmetries` input variable to specify symmetries which must preserve both the structure *and* the sources. Any number of symmetries can be @@ -309,6 +333,7 @@ class Symmetry(object): **`Rotate4`** — A 90° (fourfold) rotational symmetry (a.k.a. $C_4$). `direction` is the axis of the rotation. """ + def __init__(self, direction, phase=1): """ Construct a `Symmetry`. @@ -349,11 +374,10 @@ class Mirror(Symmetry): class Identity(Symmetry): - """ - """ + """ """ -class Volume(object): +class Volume: """ Many Meep functions require you to specify a volume in space, corresponding to the C++ type `meep::volume`. This class creates such a volume object, given the `center` and @@ -365,7 +389,14 @@ class Volume(object): `size` will automatically be computed from this list. """ - def __init__(self, center=Vector3(), size=Vector3(), dims=2, is_cylindrical=False, vertices=[]): + def __init__( + self, + center=Vector3(), + size=Vector3(), + dims=2, + is_cylindrical=False, + vertices=[], + ): """ Construct a Volume. """ @@ -374,16 +405,16 @@ def __init__(self, center=Vector3(), size=Vector3(), dims=2, is_cylindrical=Fals self.size = Vector3(*size) else: vertices = np.array([np.array(i) for i in vertices]) - self.center = Vector3(*np.mean(vertices,axis=0)) - x_list = np.unique(vertices[:,0]) - y_list = np.unique(vertices[:,1]) - z_list = np.unique(vertices[:,2]) + self.center = Vector3(*np.mean(vertices, axis=0)) + x_list = np.unique(vertices[:, 0]) + y_list = np.unique(vertices[:, 1]) + z_list = np.unique(vertices[:, 2]) x_size = 0 if x_list.size == 1 else np.abs(np.diff(x_list)[0]) y_size = 0 if y_list.size == 1 else np.abs(np.diff(y_list)[0]) z_size = 0 if z_list.size == 1 else np.abs(np.diff(z_list)[0]) - self.size = Vector3(x_size,y_size,z_size) + self.size = Vector3(x_size, y_size, z_size) self.dims = dims @@ -396,49 +427,65 @@ def __init__(self, center=Vector3(), size=Vector3(), dims=2, is_cylindrical=Fals self.swigobj = mp.volume(vec1, vec2) def get_vertices(self): - xmin = self.center.x - self.size.x/2 - xmax = self.center.x + self.size.x/2 - ymin = self.center.y - self.size.y/2 - ymax = self.center.y + self.size.y/2 - zmin = self.center.z - self.size.z/2 - zmax = self.center.z + self.size.z/2 + xmin = self.center.x - self.size.x / 2 + xmax = self.center.x + self.size.x / 2 + ymin = self.center.y - self.size.y / 2 + ymax = self.center.y + self.size.y / 2 + zmin = self.center.z - self.size.z / 2 + zmax = self.center.z + self.size.z / 2 # Iterate over and remove duplicates for collapsed dimensions (i.e. min=max)) - return [Vector3(x,y,z) for x in list(set([xmin,xmax])) for y in list(set([ymin,ymax])) for z in list(set([zmin,zmax]))] - + return [ + Vector3(x, y, z) + for x in list({xmin, xmax}) + for y in list({ymin, ymax}) + for z in list({zmin, zmax}) + ] def get_edges(self): vertices = self.get_vertices() edges = [] # Useful for importing weird geometries and the sizes are slightly off - def nearly_equal(a,b,sig_fig=10): - return a==b or (abs(a-b) < 10**(-sig_fig)) + def nearly_equal(a, b, sig_fig=10): + return a == b or (abs(a - b) < 10 ** (-sig_fig)) for iter1 in range(len(vertices)): - for iter2 in range(iter1+1,len(vertices)): - if ((iter1 != iter2) and - nearly_equal((vertices[iter1]-vertices[iter2]).norm(),self.size.x) or - nearly_equal((vertices[iter1]-vertices[iter2]).norm(),self.size.y) or - nearly_equal((vertices[iter1]-vertices[iter2]).norm(),self.size.z)): - edges.append([vertices[iter1],vertices[iter2]]) + for iter2 in range(iter1 + 1, len(vertices)): + if ( + (iter1 != iter2) + and nearly_equal( + (vertices[iter1] - vertices[iter2]).norm(), self.size.x + ) + or nearly_equal( + (vertices[iter1] - vertices[iter2]).norm(), self.size.y + ) + or nearly_equal( + (vertices[iter1] - vertices[iter2]).norm(), self.size.z + ) + ): + edges.append([vertices[iter1], vertices[iter2]]) return edges - def pt_in_volume(self,pt): - xmin = self.center.x - self.size.x/2 - xmax = self.center.x + self.size.x/2 - ymin = self.center.y - self.size.y/2 - ymax = self.center.y + self.size.y/2 - zmin = self.center.z - self.size.z/2 - zmax = self.center.z + self.size.z/2 - - if (pt.x >= xmin and pt.x <= xmax and pt.y >= ymin and pt.y <= ymax and pt.z >= zmin and pt.z <= zmax): - return True - else: - return False + def pt_in_volume(self, pt): + xmin = self.center.x - self.size.x / 2 + xmax = self.center.x + self.size.x / 2 + ymin = self.center.y - self.size.y / 2 + ymax = self.center.y + self.size.y / 2 + zmin = self.center.z - self.size.z / 2 + zmax = self.center.z + self.size.z / 2 + + return ( + pt.x >= xmin + and pt.x <= xmax + and pt.y >= ymin + and pt.y <= ymax + and pt.z >= zmin + and pt.z <= zmax + ) -class FluxRegion(object): +class FluxRegion: """ A `FluxRegion` object is used with [`add_flux`](#flux-spectra) to specify a region in which Meep should accumulate the appropriate Fourier-transformed fields in order to @@ -451,7 +498,15 @@ class FluxRegion(object): example, if you want to compute the outward flux through a box, so that the sides of the box add instead of subtract. """ - def __init__(self, center=None, size=Vector3(), direction=mp.AUTOMATIC, weight=1.0, volume=None): + + def __init__( + self, + center=None, + size=Vector3(), + direction=mp.AUTOMATIC, + weight=1.0, + volume=None, + ): """ Construct a `FluxRegion` object. @@ -533,8 +588,7 @@ class EnergyRegion(FluxRegion): """ -class FieldsRegion(object): - +class FieldsRegion: def __init__(self, where=None, center=None, size=None): if where: self.center = where.center @@ -546,7 +600,7 @@ def __init__(self, where=None, center=None, size=None): self.where = where -class DftObj(object): +class DftObj: """Wrapper around DFT objects that allows delayed initialization of the structure. When splitting the structure into chunks for parallel simulations, we want to know all @@ -569,6 +623,7 @@ class DftObj(object): `Simulation._evaluate_dft_objects` is called, then we initialize the C++ object through swigobj_attr and return the property they requested. """ + def __init__(self, func, args): """Construct a `DftObj`.""" self.func = func @@ -582,106 +637,105 @@ def swigobj_attr(self, attr): @property def save_hdf5(self): - return self.swigobj_attr('save_hdf5') + return self.swigobj_attr("save_hdf5") @property def load_hdf5(self): - return self.swigobj_attr('load_hdf5') + return self.swigobj_attr("load_hdf5") @property def scale_dfts(self): - return self.swigobj_attr('scale_dfts') + return self.swigobj_attr("scale_dfts") @property def remove(self): - return self.swigobj_attr('remove') + return self.swigobj_attr("remove") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") @property def where(self): - return self.swigobj_attr('where') + return self.swigobj_attr("where") class DftFlux(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftFlux`.""" - super(DftFlux, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.regions = args[1] self.num_components = 4 @property def flux(self): - return self.swigobj_attr('flux') + return self.swigobj_attr("flux") @property def E(self): - return self.swigobj_attr('E') + return self.swigobj_attr("E") @property def H(self): - return self.swigobj_attr('H') + return self.swigobj_attr("H") @property def cE(self): - return self.swigobj_attr('cE') + return self.swigobj_attr("cE") @property def cH(self): - return self.swigobj_attr('cH') + return self.swigobj_attr("cH") @property def normal_direction(self): - return self.swigobj_attr('normal_direction') + return self.swigobj_attr("normal_direction") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftForce(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftForce`.""" - super(DftForce, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.regions = args[1] self.num_components = 6 @property def force(self): - return self.swigobj_attr('force') + return self.swigobj_attr("force") @property def offdiag1(self): - return self.swigobj_attr('offdiag1') + return self.swigobj_attr("offdiag1") @property def offdiag2(self): - return self.swigobj_attr('offdiag2') + return self.swigobj_attr("offdiag2") @property def diag(self): - return self.swigobj_attr('diag') + return self.swigobj_attr("diag") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftNear2Far(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftNear2Far`.""" - super(DftNear2Far, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.nperiods = args[1] self.regions = args[2] @@ -689,23 +743,23 @@ def __init__(self, func, args): @property def farfield(self): - return self.swigobj_attr('farfield') + return self.swigobj_attr("farfield") @property def save_farfields(self): - return self.swigobj_attr('save_farfields') + return self.swigobj_attr("save_farfields") @property def F(self): - return self.swigobj_attr('F') + return self.swigobj_attr("F") @property def eps(self): - return self.swigobj_attr('eps') + return self.swigobj_attr("eps") @property def mu(self): - return self.swigobj_attr('mu') + return self.swigobj_attr("mu") def flux(self, direction, where, resolution): """ @@ -715,60 +769,59 @@ def flux(self, direction, where, resolution): FDTD solution) and return its Poynting flux in `direction` as a list. The dataset is a 1d array of `nfreq` dimensions. """ - return self.swigobj_attr('flux')(direction, where.swigobj, resolution) + return self.swigobj_attr("flux")(direction, where.swigobj, resolution) @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftEnergy(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftEnergy`.""" - super(DftEnergy, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[0]) self.regions = args[1] self.num_components = 12 @property def electric(self): - return self.swigobj_attr('electric') + return self.swigobj_attr("electric") @property def magnetic(self): - return self.swigobj_attr('magnetic') + return self.swigobj_attr("magnetic") @property def total(self): - return self.swigobj_attr('total') + return self.swigobj_attr("total") @property def freq(self): - return self.swigobj_attr('freq') + return self.swigobj_attr("freq") + class DftFields(DftObj): - """ - """ + """ """ def __init__(self, func, args): """Construct a `DftFields`.""" - super(DftFields, self).__init__(func, args) + super().__init__(func, args) self.nfreqs = len(args[4]) self.regions = [FieldsRegion(where=args[1], center=args[2], size=args[3])] self.num_components = len(args[0]) @property def chunks(self): - return self.swigobj_attr('chunks') - + return self.swigobj_attr("chunks") -Mode = namedtuple('Mode', ['freq', 'decay', 'Q', 'amp', 'err']) +Mode = namedtuple("Mode", ["freq", "decay", "Q", "amp", "err"]) -class EigenmodeData(object): +class EigenmodeData: def __init__(self, band_num, freq, group_velocity, k, swigobj, kdom): """Construct an `EigenmodeData`.""" self.band_num = band_num @@ -783,7 +836,7 @@ def amplitude(self, point, component): return mp.eigenmode_amplitude(self.swigobj, swig_point, component) -class Harminv(object): +class Harminv: """ Harminv is implemented as a class with a [`__call__`](#Harminv.__call__) method, which allows it to be used as a step function that collects field data from a given @@ -830,6 +883,7 @@ class Harminv(object): # do something with h.modes ``` """ + def __init__(self, c, pt, fcen, df, mxbands=None): """ Construct a Harminv object. @@ -865,7 +919,6 @@ def __call__(self, sim, todo): self.step_func(sim, todo) def _collect_harminv(self): - def _collect1(c, pt): self.t0 = 0 @@ -873,13 +926,17 @@ def _collect2(sim): self.data_dt = sim.meep_time() - self.t0 self.t0 = sim.meep_time() self.data.append(sim.get_field_point(c, pt)) + return _collect2 + return _collect1 def _check_freqs(self, sim): - source_freqs = [(s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) - for s in sim.sources - if hasattr(s.src, 'frequency')] + source_freqs = [ + (s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) + for s in sim.sources + if hasattr(s.src, "frequency") + ] harminv_max = self.fcen + 0.5 * self.df harminv_min = self.fcen - 0.5 * self.df @@ -895,32 +952,40 @@ def _check_freqs(self, sim): warnings.warn(warn_fmt.format(harminv_min, sf_min), RuntimeWarning) def _analyze_harminv(self, sim, maxbands): - harminv_cols = ['frequency', 'imag. freq.', 'Q', '|amp|', 'amplitude', 'error'] - display_run_data(sim, 'harminv', harminv_cols) + harminv_cols = ["frequency", "imag. freq.", "Q", "|amp|", "amplitude", "error"] + display_run_data(sim, "harminv", harminv_cols) self._check_freqs(sim) dt = self.data_dt if self.data_dt is not None else sim.fields.dt - bands = mp.py_do_harminv(self.data, dt, self.fcen - self.df / 2, self.fcen + self.df / 2, maxbands, - self.spectral_density, self.Q_thresh, self.rel_err_thresh, self.err_thresh, - self.rel_amp_thresh, self.amp_thresh) + bands = mp.py_do_harminv( + self.data, + dt, + self.fcen - self.df / 2, + self.fcen + self.df / 2, + maxbands, + self.spectral_density, + self.Q_thresh, + self.rel_err_thresh, + self.err_thresh, + self.rel_amp_thresh, + self.amp_thresh, + ) modes = [] for freq, amp, err in bands: - Q = freq.real / (-2 * freq.imag) if freq.imag != 0 else float('inf') + Q = freq.real / (-2 * freq.imag) if freq.imag != 0 else float("inf") modes.append(Mode(freq.real, freq.imag, Q, amp, err)) - display_run_data(sim, 'harminv', [freq.real, freq.imag, Q, abs(amp), amp, err]) + display_run_data( + sim, "harminv", [freq.real, freq.imag, Q, abs(amp), amp, err] + ) return modes def _harminv(self): - def _harm(sim): - if self.mxbands is None or self.mxbands == 0: - mb = 100 - else: - mb = self.mxbands + mb = 100 if self.mxbands is None or self.mxbands == 0 else self.mxbands self.modes = self._analyze_harminv(sim, mb) f1 = self._collect_harminv() @@ -928,46 +993,49 @@ def _harm(sim): return _combine_step_funcs(at_end(_harm), f1(self.c, self.pt)) -class Simulation(object): +class Simulation: """ The `Simulation` [class](#classes) contains all the attributes that you can set to control various parameters of the Meep computation. """ - def __init__(self, - cell_size: Optional[Vector3Type] = None, - resolution: float = None, - geometry: Optional[List[GeometricObject]] = None, - sources: Optional[List[Source]] = None, - eps_averaging: bool = True, - dimensions: int = 3, - boundary_layers: Optional[List[PML]] = None, - symmetries: Optional[List[Symmetry]] = None, - force_complex_fields: bool = False, - default_material: Medium = mp.Medium(), - m: float = 0, - k_point: Union[Vector3Type, bool] = False, - kz_2d: str = "complex", - extra_materials: Optional[List[Medium]] = None, - material_function: Optional[Callable[[Vector3Type], Medium]] = None, - epsilon_func: Optional[Callable[[Vector3Type], float]] = None, - epsilon_input_file: str = '', - progress_interval: float = 4, - subpixel_tol: float = 1e-4, - subpixel_maxeval: int = 100000, - loop_tile_base_db: int = 0, - loop_tile_base_eh: int = 0, - ensure_periodicity: bool = True, - num_chunks: int = 0, - Courant: float = 0.5, - accurate_fields_near_cylorigin: bool = False, - filename_prefix: Optional[str] = None, - output_volume: Optional[Volume] = None, - output_single_precision: bool = False, - geometry_center: Vector3Type = Vector3(), - force_all_components: bool = False, - split_chunks_evenly: bool = True, - chunk_layout = None, - collect_stats: bool = False): + + def __init__( + self, + cell_size: Optional[Vector3Type] = None, + resolution: float = None, + geometry: Optional[List[GeometricObject]] = None, + sources: Optional[List[Source]] = None, + eps_averaging: bool = True, + dimensions: int = 3, + boundary_layers: Optional[List[PML]] = None, + symmetries: Optional[List[Symmetry]] = None, + force_complex_fields: bool = False, + default_material: Medium = mp.Medium(), + m: float = 0, + k_point: Union[Vector3Type, bool] = False, + kz_2d: str = "complex", + extra_materials: Optional[List[Medium]] = None, + material_function: Optional[Callable[[Vector3Type], Medium]] = None, + epsilon_func: Optional[Callable[[Vector3Type], float]] = None, + epsilon_input_file: str = "", + progress_interval: float = 4, + subpixel_tol: float = 1e-4, + subpixel_maxeval: int = 100000, + loop_tile_base_db: int = 0, + loop_tile_base_eh: int = 0, + ensure_periodicity: bool = True, + num_chunks: int = 0, + Courant: float = 0.5, + accurate_fields_near_cylorigin: bool = False, + filename_prefix: Optional[str] = None, + output_volume: Optional[Volume] = None, + output_single_precision: bool = False, + geometry_center: Vector3Type = Vector3(), + force_all_components: bool = False, + split_chunks_evenly: bool = True, + chunk_layout=None, + collect_stats: bool = False, + ): """ All `Simulation` attributes are described in further detail below. In brackets after each variable is the type of value that it should hold. The classes, complex @@ -1215,7 +1283,11 @@ def __init__(self, self.extra_materials = extra_materials if extra_materials else [] self.default_material = default_material self.epsilon_input_file = epsilon_input_file - self.num_chunks = chunk_layout.numchunks() if isinstance(chunk_layout,mp.BinaryPartition) else num_chunks + self.num_chunks = ( + chunk_layout.numchunks() + if isinstance(chunk_layout, mp.BinaryPartition) + else num_chunks + ) self._num_chunks_original = self.num_chunks self.Courant = Courant self.global_d_conductivity = 0 @@ -1234,7 +1306,7 @@ def __init__(self, self.output_append_h5 = None self.output_single_precision = output_single_precision self.output_volume = output_volume - self.last_eps_filename = '' + self.last_eps_filename = "" self.output_h5_hook = lambda fname: False self.interactive = False self.is_cylindrical = False @@ -1248,7 +1320,7 @@ def __init__(self, self._chunk_layout_original = self.chunk_layout self.collect_stats = collect_stats self.fragment_stats = None - self._output_stats = os.environ.get('MEEP_STATS', None) + self._output_stats = os.environ.get("MEEP_STATS", None) self.load_single_parallel_file = True self.load_structure_file = None @@ -1265,14 +1337,23 @@ def __init__(self, elif kz_2d == "3d": self.special_kz = False else: - raise ValueError("Invalid kz_2d option: {} not in [complex, real/imag, 3d]".format(kz_2d)) + raise ValueError( + "Invalid kz_2d option: {} not in [complex, real/imag, 3d]".format( + kz_2d + ) + ) # To prevent the user from having to specify `dims` and `is_cylindrical` # to Volumes they create, the library will adjust them appropriately based # on the settings in the Simulation instance. This method must be called on # any user-defined Volume before passing it to meep via its `swigobj`. def _fit_volume_to_simulation(self, vol): - return Volume(vol.center, vol.size, dims=self.dimensions, is_cylindrical=self.is_cylindrical) + return Volume( + vol.center, + vol.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ) # Every function that takes a user volume can be specified either by a volume # (a Python Volume or a SWIG-wrapped meep::volume), or a center and a size @@ -1285,8 +1366,12 @@ def _volume_from_kwargs(self, vol=None, center=None, size=None): # A SWIG-wrapped meep::volume return vol elif size is not None and center is not None: - return Volume(center=Vector3(*center), size=Vector3(*size), dims=self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + return Volume( + center=Vector3(*center), + size=Vector3(*size), + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj else: raise ValueError("Need either a Volume, or a size and center") @@ -1306,8 +1391,8 @@ def use_2d(self, k): return self.dimensions def _get_valid_material_frequencies(self): - fmin = float('-inf') - fmax = float('inf') + fmin = float("-inf") + fmax = float("inf") all_materials = [go.material for go in self.geometry] + self.extra_materials all_materials.append(self.default_material) @@ -1324,18 +1409,22 @@ def _get_valid_material_frequencies(self): def _check_material_frequencies(self): min_freq, max_freq = self._get_valid_material_frequencies() - source_freqs = [(s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) - for s in self.sources - if hasattr(s.src, 'frequency')] + source_freqs = [ + (s.src.frequency, 0 if s.src.width == 0 else 1 / s.src.width) + for s in self.sources + if hasattr(s.src, "frequency") + ] dft_freqs = [] for dftf in self.dft_objects: dft_freqs.append(dftf.freq[0]) dft_freqs.append(dftf.freq[-1]) - warn_src = ('Note: your sources include frequencies outside the range of validity of the ' + - 'material models. This is fine as long as you eventually only look at outputs ' + - '(fluxes, resonant modes, etc.) at valid frequencies.') + warn_src = ( + "Note: your sources include frequencies outside the range of validity of the " + + "material models. This is fine as long as you eventually only look at outputs " + + "(fluxes, resonant modes, etc.) at valid frequencies." + ) warn_dft_fmt = "DFT frequency {} is out of material's range of {}-{}" @@ -1345,7 +1434,9 @@ def _check_material_frequencies(self): for dftf in dft_freqs: if dftf > max_freq or dftf < min_freq: - warnings.warn(warn_dft_fmt.format(dftf, min_freq, max_freq), RuntimeWarning) + warnings.warn( + warn_dft_fmt.format(dftf, min_freq, max_freq), RuntimeWarning + ) def _create_grid_volume(self, k): dims = self._infer_dimensions(k) @@ -1356,16 +1447,20 @@ def _create_grid_volume(self, k): self.dimensions = 2 gv = mp.vol2d(self.cell_size.x, self.cell_size.y, self.resolution) elif dims == 3: - gv = mp.vol3d(self.cell_size.x, self.cell_size.y, self.cell_size.z, self.resolution) + gv = mp.vol3d( + self.cell_size.x, self.cell_size.y, self.cell_size.z, self.resolution + ) elif dims == mp.CYLINDRICAL: gv = mp.volcyl(self.cell_size.x, self.cell_size.z, self.resolution) self.dimensions = 2 self.is_cylindrical = True else: - raise ValueError("Unsupported dimentionality: {}".format(dims)) + raise ValueError(f"Unsupported dimentionality: {dims}") gv.center_origin() - gv.shift_origin(py_v3_to_vec(self.dimensions, self.geometry_center, self.is_cylindrical)) + gv.shift_origin( + py_v3_to_vec(self.dimensions, self.geometry_center, self.is_cylindrical) + ) return gv def _create_symmetries(self, gv): @@ -1390,10 +1485,15 @@ def _create_symmetries(self, gv): return sym def _get_dft_volumes(self): - volumes = [self._volume_from_kwargs(vol=r.where if hasattr(r, 'where') else None, - center=r.center, size=r.size) - for dft in self.dft_objects - for r in dft.regions] + volumes = [ + self._volume_from_kwargs( + vol=r.where if hasattr(r, "where") else None, + center=r.center, + size=r.size, + ) + for dft in self.dft_objects + for r in dft.regions + ] return volumes @@ -1412,32 +1512,32 @@ def _boundaries_to_vols_1d(self, boundaries): def _boundaries_to_vols_2d_3d(self, boundaries, cyl=False): side_thickness = OrderedDict() - side_thickness['top'] = 0 - side_thickness['bottom'] = 0 - side_thickness['left'] = 0 - side_thickness['right'] = 0 - side_thickness['near'] = 0 - side_thickness['far'] = 0 + side_thickness["top"] = 0 + side_thickness["bottom"] = 0 + side_thickness["left"] = 0 + side_thickness["right"] = 0 + side_thickness["near"] = 0 + side_thickness["far"] = 0 for bl in boundaries: d = bl.direction s = bl.side if d == mp.X or d == mp.ALL: if s == mp.High or s == mp.ALL: - side_thickness['right'] = bl.thickness + side_thickness["right"] = bl.thickness if s == mp.Low or s == mp.ALL: - side_thickness['left'] = bl.thickness + side_thickness["left"] = bl.thickness if d == mp.Y or d == mp.ALL: if s == mp.High or s == mp.ALL: - side_thickness['top'] = bl.thickness + side_thickness["top"] = bl.thickness if s == mp.Low or s == mp.ALL: - side_thickness['bottom'] = bl.thickness + side_thickness["bottom"] = bl.thickness if self.dimensions == 3: if d == mp.Z or d == mp.ALL: if s == mp.High or s == mp.ALL: - side_thickness['far'] = bl.thickness + side_thickness["far"] = bl.thickness if s == mp.Low or s == mp.ALL: - side_thickness['near'] = bl.thickness + side_thickness["near"] = bl.thickness xmax = self.cell_size.x / 2 ymax = self.cell_size.z / 2 if cyl else self.cell_size.y / 2 @@ -1445,30 +1545,38 @@ def _boundaries_to_vols_2d_3d(self, boundaries, cyl=False): ytot = self.cell_size.z if cyl else self.cell_size.y def get_overlap_0(side, d): - if side == 'top' or side == 'bottom': - ydir = 1 if side == 'top' else -1 - xsz = self.cell_size.x - (side_thickness['left'] + side_thickness['right']) + if side == "top" or side == "bottom": + ydir = 1 if side == "top" else -1 + xsz = self.cell_size.x - ( + side_thickness["left"] + side_thickness["right"] + ) ysz = d - zsz = self.cell_size.z - (side_thickness['near'] + side_thickness['far']) - xcen = xmax - side_thickness['right'] - (xsz / 2) - ycen = ydir*ymax + (-ydir*0.5*d) - zcen = zmax - side_thickness['far'] - (zsz / 2) - elif side == 'left' or side == 'right': - xdir = 1 if side == 'right' else -1 + zsz = self.cell_size.z - ( + side_thickness["near"] + side_thickness["far"] + ) + xcen = xmax - side_thickness["right"] - (xsz / 2) + ycen = ydir * ymax + (-ydir * 0.5 * d) + zcen = zmax - side_thickness["far"] - (zsz / 2) + elif side == "left" or side == "right": + xdir = 1 if side == "right" else -1 xsz = d - ysz = ytot - (side_thickness['top'] + side_thickness['bottom']) - zsz = self.cell_size.z - (side_thickness['near'] + side_thickness['far']) - xcen = xdir*xmax + (-xdir*0.5*d) - ycen = ymax - side_thickness['top'] - (ysz / 2) - zcen = zmax - side_thickness['far'] - (zsz / 2) - elif side == 'near' or side == 'far': - zdir = 1 if side == 'far' else -1 - xsz = self.cell_size.x - (side_thickness['left'] + side_thickness['right']) - ysz = ytot - (side_thickness['top'] + side_thickness['bottom']) + ysz = ytot - (side_thickness["top"] + side_thickness["bottom"]) + zsz = self.cell_size.z - ( + side_thickness["near"] + side_thickness["far"] + ) + xcen = xdir * xmax + (-xdir * 0.5 * d) + ycen = ymax - side_thickness["top"] - (ysz / 2) + zcen = zmax - side_thickness["far"] - (zsz / 2) + elif side == "near" or side == "far": + zdir = 1 if side == "far" else -1 + xsz = self.cell_size.x - ( + side_thickness["left"] + side_thickness["right"] + ) + ysz = ytot - (side_thickness["top"] + side_thickness["bottom"]) zsz = d - xcen = xmax - side_thickness['right'] - (xsz / 2) - ycen = ymax - side_thickness['top'] - (ysz / 2) - zcen = zdir*zmax + (-zdir*0.5*d) + xcen = xmax - side_thickness["right"] - (xsz / 2) + ycen = ymax - side_thickness["top"] - (ysz / 2) + zcen = zdir * zmax + (-zdir * 0.5 * d) if cyl: cen = mp.Vector3(xcen, 0, ycen) @@ -1483,31 +1591,37 @@ def get_overlap_1(side1, side2, d): if side_thickness[side2] == 0: return [] - if side1 == 'top' or side1 == 'bottom': - ydir = 1 if side1 == 'top' else -1 + if side1 == "top" or side1 == "bottom": + ydir = 1 if side1 == "top" else -1 ysz = d - ycen = ydir*ymax + (-ydir*0.5*d) - if side2 == 'left' or side2 == 'right': - xdir = 1 if side2 == 'right' else -1 + ycen = ydir * ymax + (-ydir * 0.5 * d) + if side2 == "left" or side2 == "right": + xdir = 1 if side2 == "right" else -1 xsz = side_thickness[side2] - zsz = self.cell_size.z - (side_thickness['near'] + side_thickness['far']) - xcen = xdir*xmax + (-xdir*0.5*side_thickness[side2]) - zcen = zmax - side_thickness['far'] - (zsz / 2) - elif side2 == 'near' or side2 == 'far': - zdir = 1 if side2 == 'far' else -1 - xsz = self.cell_size.x - (side_thickness['left'] + side_thickness['right']) + zsz = self.cell_size.z - ( + side_thickness["near"] + side_thickness["far"] + ) + xcen = xdir * xmax + (-xdir * 0.5 * side_thickness[side2]) + zcen = zmax - side_thickness["far"] - (zsz / 2) + elif side2 == "near" or side2 == "far": + zdir = 1 if side2 == "far" else -1 + xsz = self.cell_size.x - ( + side_thickness["left"] + side_thickness["right"] + ) zsz = side_thickness[side2] - xcen = xmax - side_thickness['right'] - (xsz / 2) - zcen = zdir*zmax + (-zdir*0.5*side_thickness[side2]) - elif side1 == 'near' or side1 == 'far': - xdir = 1 if side2 == 'right' else -1 - zdir = 1 if side1 == 'far' else -1 + xcen = xmax - side_thickness["right"] - (xsz / 2) + zcen = zdir * zmax + (-zdir * 0.5 * side_thickness[side2]) + elif side1 == "near" or side1 == "far": + xdir = 1 if side2 == "right" else -1 + zdir = 1 if side1 == "far" else -1 xsz = side_thickness[side2] - ysz = self.cell_size.y - (side_thickness['top'] + side_thickness['bottom']) + ysz = self.cell_size.y - ( + side_thickness["top"] + side_thickness["bottom"] + ) zsz = d - xcen = xdir*xmax + (-xdir*0.5*side_thickness[side2]) - ycen = ymax - side_thickness['top'] - (ysz / 2) - zcen = zdir*zmax + (-zdir*0.5*d) + xcen = xdir * xmax + (-xdir * 0.5 * side_thickness[side2]) + ycen = ymax - side_thickness["top"] - (ysz / 2) + zcen = zdir * zmax + (-zdir * 0.5 * d) if cyl: cen = mp.Vector3(xcen, 0, ycen) @@ -1520,15 +1634,15 @@ def get_overlap_1(side1, side2, d): def get_overlap_2(side1, side2, side3, d): if side_thickness[side2] == 0 or side_thickness[side3] == 0: return [] - xdir = 1 if side2 == 'right' else -1 - ydir = 1 if side1 == 'top' else -1 - zdir = 1 if side3 == 'far' else -1 + xdir = 1 if side2 == "right" else -1 + ydir = 1 if side1 == "top" else -1 + zdir = 1 if side3 == "far" else -1 xsz = side_thickness[side2] ysz = d zsz = side_thickness[side3] - xcen = xdir*xmax + (-xdir*0.5*xsz) - ycen = ydir*ymax + (-ydir*0.5*d) - zcen = zdir*zmax + (-zdir*0.5*zsz) + xcen = xdir * xmax + (-xdir * 0.5 * xsz) + ycen = ydir * ymax + (-ydir * 0.5 * d) + zcen = zdir * zmax + (-zdir * 0.5 * zsz) cen = mp.Vector3(xcen, ycen, zcen) sz = mp.Vector3(xsz, ysz, zsz) @@ -1543,19 +1657,19 @@ def get_overlap_2(side1, side2, side3, d): continue v1.append(get_overlap_0(side, thickness)) - if side == 'top' or side == 'bottom': - v2.append(get_overlap_1(side, 'left', thickness)) - v2.append(get_overlap_1(side, 'right', thickness)) + if side == "top" or side == "bottom": + v2.append(get_overlap_1(side, "left", thickness)) + v2.append(get_overlap_1(side, "right", thickness)) if self.dimensions == 3: - v2.append(get_overlap_1(side, 'near', thickness)) - v2.append(get_overlap_1(side, 'far', thickness)) - v3.append(get_overlap_2(side, 'left', 'near', thickness)) - v3.append(get_overlap_2(side, 'right', 'near', thickness)) - v3.append(get_overlap_2(side, 'left', 'far', thickness)) - v3.append(get_overlap_2(side, 'right', 'far', thickness)) - if side == 'near' or side == 'far': - v2.append(get_overlap_1(side, 'left', thickness)) - v2.append(get_overlap_1(side, 'right', thickness)) + v2.append(get_overlap_1(side, "near", thickness)) + v2.append(get_overlap_1(side, "far", thickness)) + v3.append(get_overlap_2(side, "left", "near", thickness)) + v3.append(get_overlap_2(side, "right", "near", thickness)) + v3.append(get_overlap_2(side, "left", "far", thickness)) + v3.append(get_overlap_2(side, "right", "far", thickness)) + if side == "near" or side == "far": + v2.append(get_overlap_1(side, "left", thickness)) + v2.append(get_overlap_1(side, "right", thickness)) return [v for v in v1 if v], [v for v in v2 if v], [v for v in v3 if v] @@ -1573,21 +1687,29 @@ def _boundary_layers_to_vol_list(self, boundaries): if self.dimensions == 1: vols1 = self._boundaries_to_vols_1d(boundaries) else: - vols1, vols2, vols3 = self._boundaries_to_vols_2d_3d(boundaries, self.is_cylindrical) + vols1, vols2, vols3 = self._boundaries_to_vols_2d_3d( + boundaries, self.is_cylindrical + ) return vols1, vols2, vols3 def _make_fragment_lists(self, gv): - def convert_volumes(dft_obj): volumes = [] for r in dft_obj.regions: - volumes.append(self._volume_from_kwargs(vol=r.where if hasattr(r, 'where') else None, - center=r.center, size=r.size)) + volumes.append( + self._volume_from_kwargs( + vol=r.where if hasattr(r, "where") else None, + center=r.center, + size=r.size, + ) + ) return volumes - dft_data_list = [mp.dft_data(o.nfreqs, o.num_components, convert_volumes(o)) - for o in self.dft_objects] + dft_data_list = [ + mp.dft_data(o.nfreqs, o.num_components, convert_volumes(o)) + for o in self.dft_objects + ] pmls = [] absorbers = [] @@ -1598,14 +1720,24 @@ def convert_volumes(dft_obj): absorbers.append(bl) pml_vols1, pml_vols2, pml_vols3 = self._boundary_layers_to_vol_list(pmls) - absorber_vols1, absorber_vols2, absorber_vols3 = self._boundary_layers_to_vol_list(absorbers) + ( + absorber_vols1, + absorber_vols2, + absorber_vols3, + ) = self._boundary_layers_to_vol_list(absorbers) absorber_vols = absorber_vols1 + absorber_vols2 + absorber_vols3 return (dft_data_list, pml_vols1, pml_vols2, pml_vols3, absorber_vols) def _compute_fragment_stats(self, gv): - dft_data_list, pml_vols1, pml_vols2, pml_vols3, absorber_vols = self._make_fragment_lists(gv) + ( + dft_data_list, + pml_vols1, + pml_vols2, + pml_vols3, + absorber_vols, + ) = self._make_fragment_lists(gv) stats = mp.compute_fragment_stats( self.geometry, @@ -1622,7 +1754,7 @@ def _compute_fragment_stats(self, gv): self.subpixel_tol, self.subpixel_maxeval, self.ensure_periodicity, - self.eps_averaging + self.eps_averaging, ) mirror_symmetries = [sym for sym in self.symmetries if isinstance(sym, Mirror)] @@ -1641,8 +1773,8 @@ def _compute_fragment_stats(self, gv): def _init_structure(self, k=False): if verbosity.meep > 0: - print('-' * 11) - print('Initializing structure...') + print("-" * 11) + print("Initializing structure...") gv = self._create_grid_volume(k) sym = self._create_symmetries(gv) @@ -1663,19 +1795,27 @@ def _init_structure(self, k=False): if self.collect_stats and isinstance(self.default_material, mp.Medium): self.fragment_stats = self._compute_fragment_stats(gv) - if self._output_stats and isinstance(self.default_material, mp.Medium) and verbosity.meep > 0: + if ( + self._output_stats + and isinstance(self.default_material, mp.Medium) + and verbosity.meep > 0 + ): stats = self._compute_fragment_stats(gv) - print("FRAGMENT:, aniso_eps:, {}".format(stats.num_anisotropic_eps_pixels)) - print("FRAGMENT:, aniso_mu:, {}".format(stats.num_anisotropic_mu_pixels)) - print("FRAGMENT:, nonlinear:, {}".format(stats.num_nonlinear_pixels)) - print("FRAGMENT:, susceptibility:, {}".format(stats.num_susceptibility_pixels)) - print("FRAGMENT:, conductivity:, {}".format(stats.num_nonzero_conductivity_pixels)) - print("FRAGMENT:, pml_1d:, {}".format(stats.num_1d_pml_pixels)) - print("FRAGMENT:, pml_2d:, {}".format(stats.num_2d_pml_pixels)) - print("FRAGMENT:, pml_3d:, {}".format(stats.num_3d_pml_pixels)) - print("FRAGMENT:, dft:, {}".format(stats.num_dft_pixels)) - print("FRAGMENT:, total_pixels:, {}".format(stats.num_pixels_in_box)) - print("FRAGMENT:, procs:, {}".format(mp.count_processors())) + print(f"FRAGMENT:, aniso_eps:, {stats.num_anisotropic_eps_pixels}") + print(f"FRAGMENT:, aniso_mu:, {stats.num_anisotropic_mu_pixels}") + print(f"FRAGMENT:, nonlinear:, {stats.num_nonlinear_pixels}") + print(f"FRAGMENT:, susceptibility:, {stats.num_susceptibility_pixels}") + print( + "FRAGMENT:, conductivity:, {}".format( + stats.num_nonzero_conductivity_pixels + ) + ) + print(f"FRAGMENT:, pml_1d:, {stats.num_1d_pml_pixels}") + print(f"FRAGMENT:, pml_2d:, {stats.num_2d_pml_pixels}") + print(f"FRAGMENT:, pml_3d:, {stats.num_3d_pml_pixels}") + print(f"FRAGMENT:, dft:, {stats.num_dft_pixels}") + print(f"FRAGMENT:, total_pixels:, {stats.num_pixels_in_box}") + print(f"FRAGMENT:, procs:, {mp.count_processors()}") fragment_vols = self._make_fragment_lists(gv) self.dft_data_list = fragment_vols[0] @@ -1706,10 +1846,15 @@ def _init_structure(self, k=False): absorbers, self.extra_materials, self.split_chunks_evenly, - False if self.chunk_layout and not isinstance(self.chunk_layout,mp.BinaryPartition) else True, + False + if self.chunk_layout + and not isinstance(self.chunk_layout, mp.BinaryPartition) + else True, None, True if self._output_stats is not None else False, - self.chunk_layout if self.chunk_layout and isinstance(self.chunk_layout,mp.BinaryPartition) else None + self.chunk_layout + if self.chunk_layout and isinstance(self.chunk_layout, mp.BinaryPartition) + else None, ) self.geps = mp._set_materials( self.structure, @@ -1728,13 +1873,13 @@ def _init_structure(self, k=False): True, None, False, - None + None, ) if self._output_stats is not None: sys.exit(0) - if self.chunk_layout and not isinstance(self.chunk_layout,mp.BinaryPartition): + if self.chunk_layout and not isinstance(self.chunk_layout, mp.BinaryPartition): self.load_chunk_layout(br, self.chunk_layout) self.set_materials() @@ -1746,7 +1891,8 @@ def _init_structure(self, k=False): if self.load_structure_file: self.load_structure( - self.load_structure_file, self.load_single_parallel_file) + self.load_structure_file, self.load_single_parallel_file + ) def _is_outer_boundary(self, vol, direction, side): @@ -1761,11 +1907,15 @@ def _is_outer_boundary(self, vol, direction, side): # TODO: Support shifted origins def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) + return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) - if side == mp.Low and isclose(vol.get_min_corner().in_direction(direction), -half_cell_size): + if side == mp.Low and isclose( + vol.get_min_corner().in_direction(direction), -half_cell_size + ): return True - if side == mp.High and isclose(vol.get_max_corner().in_direction(direction), half_cell_size): + if side == mp.High and isclose( + vol.get_max_corner().in_direction(direction), half_cell_size + ): return True return False @@ -1810,8 +1960,10 @@ def get_num_pixels(vol, direction, side, target_direction, mult_direction=None): def _get_chunk_communication_areas(self): if self.dimensions == 1 or self.is_cylindrical: - warnings.warn("Can currently only get chunk communication area from 2d or 3d simulations", - RuntimeWarning) + warnings.warn( + "Can currently only get chunk communication area from 2d or 3d simulations", + RuntimeWarning, + ) return if self.structure is None: @@ -1887,7 +2039,7 @@ def set_materials(self, geometry=None, default_material=None): True, self.structure, False, - None + None, ) self.geps = mp._set_materials( self.structure, @@ -1906,7 +2058,7 @@ def set_materials(self, geometry=None, default_material=None): True, None, False, - None + None, ) def dump_structure(self, fname, single_parallel_file=True): @@ -1914,20 +2066,32 @@ def dump_structure(self, fname, single_parallel_file=True): Dumps the structure to the file `fname`. """ if self.structure is None: - raise ValueError("Structure must be initialized before calling dump_structure") + raise ValueError( + "Structure must be initialized before calling dump_structure" + ) self.structure.dump(fname, single_parallel_file) if verbosity.meep > 0: - print("Dumped structure to file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Dumped structure to file: {} ({})".format( + fname, str(single_parallel_file) + ) + ) def load_structure(self, fname, single_parallel_file=True): """ Loads a structure from the file `fname`. """ if self.structure is None: - raise ValueError("Structure must be initialized before loading structure from file '%s'" % fname) + raise ValueError( + "Structure must be initialized before loading structure from file '%s'" + % fname + ) self.structure.load(fname, single_parallel_file) if verbosity.meep > 0: - print("Loaded structure from file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Loaded structure from file: %s (%s)" + % (fname, str(single_parallel_file)) + ) def dump_fields(self, fname, single_parallel_file=True): """ @@ -1937,30 +2101,46 @@ def dump_fields(self, fname, single_parallel_file=True): raise ValueError("Fields must be initialized before calling dump_fields") self.fields.dump(fname, single_parallel_file) if verbosity.meep > 0: - print("Dumped fields to file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Dumped fields to file: {} ({})".format( + fname, str(single_parallel_file) + ) + ) def load_fields(self, fname, single_parallel_file=True): """ Loads a fields from the file `fname`. """ if self.fields is None: - raise ValueError("Fields must be initialized before loading fields from file '%s'" % fname) + raise ValueError( + "Fields must be initialized before loading fields from file '%s'" + % fname + ) self._evaluate_dft_objects() self.fields.load(fname, single_parallel_file) if verbosity.meep > 0: - print("Loaded fields from file: %s (%s)" % (fname, str(single_parallel_file))) + print( + "Loaded fields from file: {} ({})".format( + fname, str(single_parallel_file) + ) + ) def dump_chunk_layout(self, fname): """ Dumps the chunk layout to file `fname`. """ if self.structure is None: - raise ValueError("Structure must be initialized before calling dump_chunk_layout") + raise ValueError( + "Structure must be initialized before calling dump_chunk_layout" + ) self.structure.dump_chunk_layout(fname) def load_chunk_layout(self, br, source): if self.structure is None: - raise ValueError("Structure must be initialized before loading chunk layout from file '%s'" % fname) + raise ValueError( + "Structure must be initialized before loading chunk layout from file '%s'" + % fname + ) if isinstance(source, Simulation): vols = source.structure.get_chunk_volumes() @@ -1979,10 +2159,12 @@ def get_load_dump_dirname(self, dirname, single_parallel_file): else: # When doing a sharded dump (each process to its own file), use # the process rank to get a unique name. - dump_dirname = os.path.join(dirname, 'rank%02d' % mp.my_rank()) + dump_dirname = os.path.join(dirname, "rank%02d" % mp.my_rank()) return dump_dirname - def dump(self, dirname, dump_structure=True, dump_fields=True, single_parallel_file=True): + def dump( + self, dirname, dump_structure=True, dump_fields=True, single_parallel_file=True + ): """ Dumps simulation state. """ @@ -1990,14 +2172,16 @@ def dump(self, dirname, dump_structure=True, dump_fields=True, single_parallel_f os.makedirs(dump_dirname, exist_ok=True) if dump_structure: - structure_dump_filename = os.path.join(dump_dirname, 'structure.h5') + structure_dump_filename = os.path.join(dump_dirname, "structure.h5") self.dump_structure(structure_dump_filename, single_parallel_file) if dump_fields: - fields_dump_filename = os.path.join(dump_dirname, 'fields.h5') + fields_dump_filename = os.path.join(dump_dirname, "fields.h5") self.dump_fields(fields_dump_filename, single_parallel_file) - def load(self, dirname, load_structure=True, load_fields=True, single_parallel_file=True): + def load( + self, dirname, load_structure=True, load_fields=True, single_parallel_file=True + ): """ Loads simulation state. @@ -2008,36 +2192,42 @@ def load(self, dirname, load_structure=True, load_fields=True, single_parallel_f self.load_single_parallel_file = single_parallel_file if load_structure: - load_structure_file = os.path.join(dump_dirname, 'structure.h5') - # If structure is already initialized, load it straight away. - # Otherwise, do a delayed load. - if self.structure: - self.load_structure(load_structure_file, self.load_single_parallel_file) - else: - self.load_structure_file = load_structure_file + load_structure_file = os.path.join(dump_dirname, "structure.h5") + # If structure is already initialized, load it straight away. + # Otherwise, do a delayed load. + if self.structure: + self.load_structure(load_structure_file, self.load_single_parallel_file) + else: + self.load_structure_file = load_structure_file if load_fields: - load_fields_file = os.path.join(dump_dirname, 'fields.h5') - if self.fields: - self.load_fields(load_fields_file, self.load_single_parallel_file) - else: - self.load_fields_file = load_fields_file + load_fields_file = os.path.join(dump_dirname, "fields.h5") + if self.fields: + self.load_fields(load_fields_file, self.load_single_parallel_file) + else: + self.load_fields_file = load_fields_file def init_sim(self): if self._is_initialized: return - materials = [g.material for g in self.geometry if isinstance(g.material, mp.Medium)] + materials = [ + g.material for g in self.geometry if isinstance(g.material, mp.Medium) + ] if isinstance(self.default_material, mp.Medium): materials.append(self.default_material) for med in materials: - if ((med.epsilon_diag.x < 1 and med.epsilon_diag.x > -mp.inf) or - (med.epsilon_diag.y < 1 and med.epsilon_diag.y > -mp.inf) or - (med.epsilon_diag.z < 1 and med.epsilon_diag.z > -mp.inf)): - - eps_warning = ("Epsilon < 1 may require adjusting the Courant parameter. " + - "See the 'Numerical Stability' entry under the 'Materials' " + - "section of the documentation") + if ( + (med.epsilon_diag.x < 1 and med.epsilon_diag.x > -mp.inf) + or (med.epsilon_diag.y < 1 and med.epsilon_diag.y > -mp.inf) + or (med.epsilon_diag.z < 1 and med.epsilon_diag.z > -mp.inf) + ): + + eps_warning = ( + "Epsilon < 1 may require adjusting the Courant parameter. " + + "See the 'Numerical Stability' entry under the 'Materials' " + + "section of the documentation" + ) warnings.warn(eps_warning, RuntimeWarning) if self.structure is None: @@ -2048,7 +2238,8 @@ def init_sim(self): self.m if self.is_cylindrical else 0, self.k_point.z if self.special_kz and self.k_point else 0, not self.accurate_fields_near_cylorigin, - self.loop_tile_base_db, self.loop_tile_base_eh + self.loop_tile_base_db, + self.loop_tile_base_eh, ) if self.force_all_components and self.dimensions != 1: @@ -2061,7 +2252,11 @@ def init_sim(self): print("Meep: using complex fields.") if self.k_point: - v = Vector3(self.k_point.x, self.k_point.y) if self.special_kz else self.k_point + v = ( + Vector3(self.k_point.x, self.k_point.y) + if self.special_kz + else self.k_point + ) self.fields.use_bloch(py_v3_to_vec(self.dimensions, v, self.is_cylindrical)) self.add_sources() @@ -2072,8 +2267,7 @@ def init_sim(self): self._is_initialized = True if self.load_fields_file: - self.load_fields( - self.load_fields_file, self.load_single_parallel_file) + self.load_fields(self.load_fields_file, self.load_single_parallel_file) def using_real_fields(self): cond1 = self.is_cylindrical and self.m != 0 @@ -2098,11 +2292,12 @@ def require_dimensions(self): mp.set_dimensions(self._infer_dimensions(self.k_point)) def has_mu(self): - def _has_mu(medium): if not isinstance(medium, mp.Medium): return False - return medium.mu_diag != mp.Vector3(1, 1, 1) or medium.mu_offdiag != mp.Vector3(0j, 0j, 0j) + return medium.mu_diag != mp.Vector3( + 1, 1, 1 + ) or medium.mu_offdiag != mp.Vector3(0j, 0j, 0j) for go in self.geometry: if _has_mu(go.material): @@ -2120,13 +2315,29 @@ def get_estimated_memory_usage(self): self.init_sim() if self.fragment_stats is None: - self.fragment_stats = self._compute_fragment_stats(self.structure.user_volume) + self.fragment_stats = self._compute_fragment_stats( + self.structure.user_volume + ) - is_complex = (self.k_point and self.k_point != mp.Vector3(0, 0, 0)) or self.force_complex_fields + is_complex = ( + self.k_point and self.k_point != mp.Vector3(0, 0, 0) + ) or self.force_complex_fields realnums_per_grid_point = 1 if self.dimensions == 1 else 3 - E_realnums = self.fragment_stats.num_pixels_in_box * (2 if is_complex else 1) * realnums_per_grid_point - H_realnums = self.fragment_stats.num_pixels_in_box * (2 if is_complex else 1) * realnums_per_grid_point - D_realnums = self.fragment_stats.num_pixels_in_box * (2 if is_complex else 1) * realnums_per_grid_point + E_realnums = ( + self.fragment_stats.num_pixels_in_box + * (2 if is_complex else 1) + * realnums_per_grid_point + ) + H_realnums = ( + self.fragment_stats.num_pixels_in_box + * (2 if is_complex else 1) + * realnums_per_grid_point + ) + D_realnums = ( + self.fragment_stats.num_pixels_in_box + * (2 if is_complex else 1) + * realnums_per_grid_point + ) chi1inv_realnums = self.fragment_stats.num_pixels_in_box * 9 Mu_realnums = 0 @@ -2134,10 +2345,18 @@ def get_estimated_memory_usage(self): Mu_realnums = chi1inv_realnums + H_realnums dft_realnums = self.fragment_stats.num_dft_pixels * 2 - dispersive_realnums = self.fragment_stats.num_susceptibility_pixels * 6 * (2 if is_complex else 1) + dispersive_realnums = ( + self.fragment_stats.num_susceptibility_pixels * 6 * (2 if is_complex else 1) + ) - total_realnums = (E_realnums + H_realnums + D_realnums + Mu_realnums + - dft_realnums + dispersive_realnums) + total_realnums = ( + E_realnums + + H_realnums + + D_realnums + + Mu_realnums + + dft_realnums + + dispersive_realnums + ) total_bytes = total_realnums * mp.get_realnum_size() @@ -2214,7 +2433,7 @@ def get_epsilon_point(self, pt, frequency=0): frequency-independent part of $\\varepsilon$ (the $\\omega\\to\\infty$ limit). """ v3 = py_v3_to_vec(self.dimensions, pt, self.is_cylindrical) - return self.fields.get_eps(v3,frequency) + return self.fields.get_eps(v3, frequency) def get_mu_point(self, pt, frequency=0): """ @@ -2224,7 +2443,7 @@ def get_mu_point(self, pt, frequency=0): frequency-independent part of $\\mu$ (the $\\omega\\to\\infty$ limit). """ v3 = py_v3_to_vec(self.dimensions, pt, self.is_cylindrical) - return self.fields.get_mu(v3,frequency) + return self.fields.get_mu(v3, frequency) def get_epsilon_grid(self, xtics=None, ytics=None, ztics=None, frequency=0): """ @@ -2238,19 +2457,27 @@ def get_epsilon_grid(self, xtics=None, ytics=None, ztics=None, frequency=0): sampling the material geometry to any arbitrary resolution. The return value is a NumPy array with shape equivalent to `numpy.meshgrid(xtics,ytics,ztics)`. Empty dimensions are collapsed. """ - grid_vals = np.squeeze(np.empty((len(xtics), len(ytics), len(ztics)), dtype=np.complex128)) + grid_vals = np.squeeze( + np.empty((len(xtics), len(ytics), len(ztics)), dtype=np.complex128) + ) gv = self._create_grid_volume(False) - mp._get_epsilon_grid(self.geometry, - self.extra_materials, - self.default_material, - self.ensure_periodicity and not not self.k_point, - gv, - self.cell_size, self.geometry_center, - len(xtics), xtics, - len(ytics), ytics, - len(ztics), ztics, - grid_vals, - frequency) + mp._get_epsilon_grid( + self.geometry, + self.extra_materials, + self.default_material, + self.ensure_periodicity and not not self.k_point, + gv, + self.cell_size, + self.geometry_center, + len(xtics), + xtics, + len(ytics), + ytics, + len(ztics), + ztics, + grid_vals, + frequency, + ) return grid_vals def get_filename_prefix(self): @@ -2269,14 +2496,16 @@ def get_filename_prefix(self): elif self.filename_prefix is None: _, filename = os.path.split(sys.argv[0]) - if filename == 'ipykernel_launcher.py' or filename == '__main__.py': - return '' + if filename == "ipykernel_launcher.py" or filename == "__main__.py": + return "" else: - return re.sub(r'\.py$', '', filename) + return re.sub(r"\.py$", "", filename) else: - raise TypeError("Expected a string for filename_prefix, or None for the default.") + raise TypeError( + "Expected a string for filename_prefix, or None for the default." + ) - def use_output_directory(self, dname=''): + def use_output_directory(self, dname=""): """ Output all files into a subdirectory, which is created if necessary. If the optional argument `dname` is specified, that is the name of the directory. If `dname` @@ -2286,17 +2515,17 @@ def use_output_directory(self, dname=''): name is set to `filename_prefix` + "-out" and `filename_prefix` is then reset to `None`. """ if not dname: - dname = self.get_filename_prefix() + '-out' + dname = self.get_filename_prefix() + "-out" - closure = {'trashed': False} + closure = {"trashed": False} def hook(): if verbosity.meep > 0: - print("Meep: using output directory '{}'".format(dname)) + print(f"Meep: using output directory '{dname}'") self.fields.set_output_directory(dname) - if not closure['trashed']: + if not closure["trashed"]: mp.trash_output_directory(dname) - closure['trashed'] = True + closure["trashed"] = True self.init_sim_hooks.append(hook) @@ -2326,34 +2555,46 @@ def stop_cond(sim): cond[i] = stop_cond step_funcs = list(step_funcs) - step_funcs.append(display_progress(t0, t0 + stop_time, self.progress_interval)) + step_funcs.append( + display_progress(t0, t0 + stop_time, self.progress_interval) + ) if do_progress: - self.progress = FloatProgress(value=t0, min=t0, max=t0 + stop_time, description="0% done ") + self.progress = FloatProgress( + value=t0, min=t0, max=t0 + stop_time, description="0% done " + ) display(self.progress) else: - assert callable(cond[i]), "Stopping condition {} is not an integer or a function".format(cond[i]) + assert callable( + cond[i] + ), "Stopping condition {} is not an integer or a function".format( + cond[i] + ) while not any([x(self) for x in cond]): for func in step_funcs: - _eval_step_func(self, func, 'step') + _eval_step_func(self, func, "step") self.fields.step() # Translating the recursive scheme version of run-until into an iterative version # (because python isn't tail-call-optimized) means we need one extra iteration to # be the same as scheme. for func in step_funcs: - _eval_step_func(self, func, 'step') + _eval_step_func(self, func, "step") for func in step_funcs: - _eval_step_func(self, func, 'finish') + _eval_step_func(self, func, "finish") if do_progress and self.progress: self.progress.value = t0 + stop_time self.progress.description = "100% done " if verbosity.meep > 0: - print("run {} finished at t = {} ({} timesteps)".format(self.run_index, self.meep_time(), self.fields.t)) + print( + "run {} finished at t = {} ({} timesteps)".format( + self.run_index, self.meep_time(), self.fields.t + ) + ) self.run_index += 1 def _run_sources_until(self, cond, step_funcs): @@ -2369,8 +2610,10 @@ def _run_sources_until(self, cond, step_funcs): if isinstance(cond[i], numbers.Number): new_conds.append((ts - self.round_time()) + cond[i]) else: + def f(sim): return cond[i](sim) and sim.round_time() >= ts + new_conds.append(f) self._run_until(new_conds, step_funcs) @@ -2392,12 +2635,20 @@ def run_k_point(self, t, k): components = [s.component for s in self.sources] pts = [s.center for s in self.sources] - src_freqs_min = min([s.src.frequency - 1 / s.src.width / 2 if isinstance(s.src, mp.GaussianSource) else mp.inf - for s in self.sources]) + src_freqs_min = min( + s.src.frequency - 1 / s.src.width / 2 + if isinstance(s.src, mp.GaussianSource) + else mp.inf + for s in self.sources + ) fmin = max(0, src_freqs_min) - fmax = max([s.src.frequency + 1 / s.src.width / 2 if isinstance(s.src, mp.GaussianSource) else 0 - for s in self.sources]) + fmax = max( + s.src.frequency + 1 / s.src.width / 2 + if isinstance(s.src, mp.GaussianSource) + else 0 + for s in self.sources + ) if not components or fmin > fmax: raise ValueError("Running with k_points requires a 'GaussianSource' source") @@ -2434,10 +2685,10 @@ def run_k_points(self, t, k_points): freqs = [complex(m.freq, m.decay) for m in harminv.modes] if verbosity.meep > 0: - print("freqs:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end='') - print(', '.join([str(f.real) for f in freqs])) - print("freqs-im:, {}, {}, {}, {}, ".format(k_index, k.x, k.y, k.z), end='') - print(', '.join([str(f.imag) for f in freqs])) + print(f"freqs:, {k_index}, {k.x}, {k.y}, {k.z}, ", end="") + print(", ".join([str(f.real) for f in freqs])) + print(f"freqs-im:, {k_index}, {k.x}, {k.y}, {k.z}, ", end="") + print(", ".join([str(f.imag) for f in freqs])) all_freqs.append(freqs) @@ -2447,7 +2698,9 @@ def set_epsilon(self, eps): if self.fields is None: self.init_sim() - self.structure.set_epsilon(eps, self.eps_averaging, self.subpixel_tol, self.subpixel_maxeval) + self.structure.set_epsilon( + eps, self.eps_averaging, self.subpixel_tol, self.subpixel_maxeval + ) def add_source(self, src): if self.fields is None: @@ -2455,11 +2708,21 @@ def add_source(self, src): if isinstance(src, IndexedSource): self.fields.register_src_time(src.src.swigobj) - self.fields.add_srcdata(src.srcdata, src.src.swigobj, src.num_pts, src.amp_arr, src.needs_boundary_fix) + self.fields.add_srcdata( + src.srcdata, + src.src.swigobj, + src.num_pts, + src.amp_arr, + src.needs_boundary_fix, + ) return - where = Volume(src.center, src.size, dims=self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + where = Volume( + src.center, + src.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj if isinstance(src, EigenModeSource): if src.direction < 0: @@ -2467,8 +2730,12 @@ def add_source(self, src): else: direction = src.direction - eig_vol = Volume(src.eig_lattice_center, src.eig_lattice_size, self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + eig_vol = Volume( + src.eig_lattice_center, + src.eig_lattice_size, + self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj if isinstance(src.eig_band, DiffractedPlanewave): eig_band = 1 @@ -2483,46 +2750,66 @@ def add_source(self, src): where, eig_vol, eig_band, - py_v3_to_vec(self.dimensions, src.eig_kpoint, is_cylindrical=self.is_cylindrical), + py_v3_to_vec( + self.dimensions, src.eig_kpoint, is_cylindrical=self.is_cylindrical + ), src.eig_match_freq, src.eig_parity, src.eig_resolution, src.eig_tolerance, - src.amplitude + src.amplitude, ] - add_eig_src = functools.partial(self.fields.add_eigenmode_source, *add_eig_src_args) + add_eig_src = functools.partial( + self.fields.add_eigenmode_source, *add_eig_src_args + ) if isinstance(src.eig_band, DiffractedPlanewave): add_eig_src(src.amp_func, diffractedplanewave) else: add_eig_src(src.amp_func) - elif isinstance (src, GaussianBeamSource): + elif isinstance(src, GaussianBeamSource): gaussianbeam_args = [ - py_v3_to_vec(self.dimensions, src.beam_x0, is_cylindrical=self.is_cylindrical), - py_v3_to_vec(self.dimensions, src.beam_kdir, is_cylindrical=self.is_cylindrical), + py_v3_to_vec( + self.dimensions, src.beam_x0, is_cylindrical=self.is_cylindrical + ), + py_v3_to_vec( + self.dimensions, src.beam_kdir, is_cylindrical=self.is_cylindrical + ), src.beam_w0, src.src.swigobj.frequency().real, - self.fields.get_eps(py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical)).real, - self.fields.get_mu(py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical)).real, - np.array([src.beam_E0.x, src.beam_E0.y, src.beam_E0.z], dtype=np.complex128) + self.fields.get_eps( + py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical) + ).real, + self.fields.get_mu( + py_v3_to_vec(self.dimensions, src.center, self.is_cylindrical) + ).real, + np.array( + [src.beam_E0.x, src.beam_E0.y, src.beam_E0.z], dtype=np.complex128 + ), ] gaussianbeam = mp.gaussianbeam(*gaussianbeam_args) add_vol_src_args = [src.src.swigobj, where, gaussianbeam] - add_vol_src = functools.partial(self.fields.add_volume_source, *add_vol_src_args) + add_vol_src = functools.partial( + self.fields.add_volume_source, *add_vol_src_args + ) add_vol_src() else: add_vol_src_args = [src.component, src.src.swigobj, where] - add_vol_src = functools.partial(self.fields.add_volume_source, *add_vol_src_args) + add_vol_src = functools.partial( + self.fields.add_volume_source, *add_vol_src_args + ) if src.amp_func_file: - fname_dset = src.amp_func_file.rsplit(':', 1) + fname_dset = src.amp_func_file.rsplit(":", 1) if len(fname_dset) != 2: - err_msg = "Expected a string of the form 'h5filename:dataset'. Got '{}'" + err_msg = ( + "Expected a string of the form 'h5filename:dataset'. Got '{}'" + ) raise ValueError(err_msg.format(src.amp_func_file)) fname, dset = fname_dset - if not fname.endswith('.h5'): - fname += '.h5' + if not fname.endswith(".h5"): + fname += ".h5" add_vol_src(fname, dset, src.amplitude * 1.0) elif src.amp_func: @@ -2534,17 +2821,16 @@ def add_source(self, src): def add_sources(self): if self.fields is None: - self.init_sim() # in case only some processes have IndexedSources + self.init_sim() # in case only some processes have IndexedSources for s in self.sources: self.add_source(s) - self.fields.require_source_components() # needed by IndexedSource objects + self.fields.require_source_components() # needed by IndexedSource objects def _evaluate_dft_objects(self): for dft in self.dft_objects: if dft.swigobj is None: dft.swigobj = dft.func(*dft.args) - def add_dft_fields(self, *args, **kwargs): """ `add_dft_fields(cs, fcen, df, nfreq, freq, where=None, center=None, size=None, yee_grid=False, decimation_factor=0, persist=False)` ##sig @@ -2569,32 +2855,51 @@ def add_dft_fields(self, *args, **kwargs): components = args[0] args = fix_dft_args(args, 1) freq = args[1] - where = kwargs.get('where', None) - center = kwargs.get('center', None) - size = kwargs.get('size', None) - yee_grid = kwargs.get('yee_grid', False) - decimation_factor = kwargs.get('decimation_factor', 0) - persist = kwargs.get('persist', False) + where = kwargs.get("where", None) + center = kwargs.get("center", None) + size = kwargs.get("size", None) + yee_grid = kwargs.get("yee_grid", False) + decimation_factor = kwargs.get("decimation_factor", 0) + persist = kwargs.get("persist", False) center_v3 = Vector3(*center) if center is not None else None size_v3 = Vector3(*size) if size is not None else None use_centered_grid = not yee_grid - dftf = DftFields(self._add_dft_fields, [ - components, where, center_v3, size_v3, freq, use_centered_grid, - decimation_factor,persist - ]) + dftf = DftFields( + self._add_dft_fields, + [ + components, + where, + center_v3, + size_v3, + freq, + use_centered_grid, + decimation_factor, + persist, + ], + ) self.dft_objects.append(dftf) return dftf - def _add_dft_fields(self, components, where, center, size, freq, - use_centered_grid, decimation_factor, persist): + def _add_dft_fields( + self, + components, + where, + center, + size, + freq, + use_centered_grid, + decimation_factor, + persist, + ): if self.fields is None: self.init_sim() try: where = self._volume_from_kwargs(where, center, size) except ValueError: where = self.fields.total_volume() - return self.fields.add_dft_fields(components, where, freq, - use_centered_grid, decimation_factor, persist) + return self.fields.add_dft_fields( + components, where, freq, use_centered_grid, decimation_factor, persist + ) def output_dft(self, dft_fields, fname): """ @@ -2606,9 +2911,11 @@ def output_dft(self, dft_fields, fname): self.init_sim() if not self.dft_objects: - raise RuntimeError('DFT monitor dft_fields must be initialized before calling output_dft') + raise RuntimeError( + "DFT monitor dft_fields must be initialized before calling output_dft" + ) - if hasattr(dft_fields, 'swigobj'): + if hasattr(dft_fields, "swigobj"): dft_fields_swigobj = dft_fields.swigobj else: dft_fields_swigobj = dft_fields @@ -2642,17 +2949,20 @@ def add_near2far(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] near2fars = args[1:] - nperiods = kwargs.get('nperiods', 1) - decimation_factor = kwargs.get('decimation_factor', 0) - n2f = DftNear2Far(self._add_near2far, [freq, nperiods, near2fars, decimation_factor]) + nperiods = kwargs.get("nperiods", 1) + decimation_factor = kwargs.get("decimation_factor", 0) + n2f = DftNear2Far( + self._add_near2far, [freq, nperiods, near2fars, decimation_factor] + ) self.dft_objects.append(n2f) return n2f def _add_near2far(self, freq, nperiods, near2fars, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_near2far, freq, near2fars, - decimation_factor, nperiods) + return self._add_fluxish_stuff( + self.fields.add_dft_near2far, freq, near2fars, decimation_factor, nperiods + ) def add_energy(self, *args, **kwargs): """ @@ -2675,7 +2985,7 @@ def add_energy(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] energys = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) en = DftEnergy(self._add_energy, [freq, energys, decimation_factor]) self.dft_objects.append(en) return en @@ -2683,14 +2993,19 @@ def add_energy(self, *args, **kwargs): def _add_energy(self, freq, energys, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_energy, freq, energys, - decimation_factor) + return self._add_fluxish_stuff( + self.fields.add_dft_energy, freq, energys, decimation_factor + ) def _display_energy(self, name, func, energys): if energys: freqs = get_energy_freqs(energys[0]) if verbosity.meep > 0: - display_csv(self, "{}-energy".format(name), zip(freqs, *[func(f) for f in energys])) + display_csv( + self, + f"{name}-energy", + zip(freqs, *[func(f) for f in energys]), + ) def display_electric_energy(self, *energys): """ @@ -2700,7 +3015,7 @@ def display_electric_energy(self, *energys): All of the energy should be for the same `fcen`/`df`/`nfreq` or `freq`. The first column are the frequencies, and subsequent columns are the energy density spectra. """ - self._display_energy('electric', get_electric_energy, energys) + self._display_energy("electric", get_electric_energy, energys) def display_magnetic_energy(self, *energys): """ @@ -2710,7 +3025,7 @@ def display_magnetic_energy(self, *energys): All of the energy should be for the same `fcen`/`df`/`nfreq` or `freq`. The first column are the frequencies, and subsequent columns are the energy density spectra. """ - self._display_energy('magnetic', get_magnetic_energy, energys) + self._display_energy("magnetic", get_magnetic_energy, energys) def display_total_energy(self, *energys): """ @@ -2720,7 +3035,7 @@ def display_total_energy(self, *energys): be for the same `fcen`/`df`/`nfreq` or `freq`. The first column are the frequencies, and subsequent columns are the energy density spectra. """ - self._display_energy('total', get_total_energy, energys) + self._display_energy("total", get_total_energy, energys) def load_energy(self, fname, energy): """ @@ -2733,7 +3048,7 @@ def load_energy(self, fname, energy): """ if self.fields is None: self.init_sim() - energy.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + energy.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) def save_energy(self, fname, energy): """ @@ -2743,7 +3058,7 @@ def save_energy(self, fname, energy): """ if self.fields is None: self.init_sim() - energy.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + energy.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) def load_minus_energy(self, fname, energy): """ @@ -2764,7 +3079,10 @@ def get_farfield(self, near2far, x): $(E_r^1,E_\\phi^1,E_z^1,H_r^1,H_\\phi^1,H_z^1,E_r^2,E_\\phi^2,E_z^2,H_r^2,H_\\phi^2,H_z^2,...)$ in cylindrical coordinates for the frequencies 1,2,...,`nfreq`. """ - return mp._get_farfield(near2far.swigobj, py_v3_to_vec(self.dimensions, x, is_cylindrical=self.is_cylindrical)) + return mp._get_farfield( + near2far.swigobj, + py_v3_to_vec(self.dimensions, x, is_cylindrical=self.is_cylindrical), + ) def get_farfields(self, near2far, resolution, where=None, center=None, size=None): """ @@ -2791,15 +3109,17 @@ def get_farfields(self, near2far, resolution, where=None, center=None, size=None res_hy = complexarray(result[8], result[9]) res_hz = complexarray(result[10], result[11]) return { - 'Ex': res_ex, - 'Ey': res_ey, - 'Ez': res_ez, - 'Hx': res_hx, - 'Hy': res_hy, - 'Hz': res_hz, + "Ex": res_ex, + "Ey": res_ey, + "Ez": res_ez, + "Hx": res_hx, + "Hy": res_hy, + "Hz": res_hz, } - def output_farfields(self, near2far, fname, resolution, where=None, center=None, size=None): + def output_farfields( + self, near2far, fname, resolution, where=None, center=None, size=None + ): """ Given an HDF5 file name `fname` (does *not* include the `.h5` suffix), a `Volume` given by `where` (may be 0d, 1d, 2d, or 3d), and a `resolution` (in grid points / @@ -2830,7 +3150,7 @@ def load_near2far(self, fname, near2far): """ if self.fields is None: self.init_sim() - near2far.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + near2far.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) def save_near2far(self, fname, near2far): """ @@ -2840,7 +3160,7 @@ def save_near2far(self, fname, near2far): """ if self.fields is None: self.init_sim() - near2far.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + near2far.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) def load_minus_near2far(self, fname, near2far): """ @@ -2900,7 +3220,7 @@ def add_force(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] forces = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) force = DftForce(self._add_force, [freq, forces, decimation_factor]) self.dft_objects.append(force) return force @@ -2908,8 +3228,9 @@ def add_force(self, *args, **kwargs): def _add_force(self, freq, forces, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_force, freq, forces, - decimation_factor) + return self._add_fluxish_stuff( + self.fields.add_dft_force, freq, forces, decimation_factor + ) def display_forces(self, *forces): """ @@ -2921,7 +3242,9 @@ def display_forces(self, *forces): """ force_freqs = get_force_freqs(forces[0]) if verbosity.meep > 0: - display_csv(self, 'force', zip(force_freqs, *[get_forces(f) for f in forces])) + display_csv( + self, "force", zip(force_freqs, *[get_forces(f) for f in forces]) + ) def load_force(self, fname, force): """ @@ -2934,7 +3257,7 @@ def load_force(self, fname, force): """ if self.fields is None: self.init_sim() - force.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + force.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) def save_force(self, fname, force): """ @@ -2944,7 +3267,7 @@ def save_force(self, fname, force): """ if self.fields is None: self.init_sim() - force.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + force.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) def load_minus_force(self, fname, force): """ @@ -2962,9 +3285,11 @@ def get_force_data(self, force): only useful for passing to `load_force_data` below and should be considered opaque. """ - return ForceData(offdiag1=self.get_dft_data(force.offdiag1), - offdiag2=self.get_dft_data(force.offdiag2), - diag=self.get_dft_data(force.diag)) + return ForceData( + offdiag1=self.get_dft_data(force.offdiag1), + offdiag2=self.get_dft_data(force.offdiag2), + diag=self.get_dft_data(force.diag), + ) def load_force_data(self, force, fdata): """ @@ -3010,7 +3335,7 @@ def add_flux(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] fluxes = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) flux = DftFlux(self._add_flux, [freq, fluxes, decimation_factor]) self.dft_objects.append(flux) return flux @@ -3018,8 +3343,9 @@ def add_flux(self, *args, **kwargs): def _add_flux(self, freq, fluxes, decimation_factor): if self.fields is None: self.init_sim() - return self._add_fluxish_stuff(self.fields.add_dft_flux, - freq, fluxes, decimation_factor) + return self._add_fluxish_stuff( + self.fields.add_dft_flux, freq, fluxes, decimation_factor + ) def add_mode_monitor(self, *args, **kwargs): """ @@ -3030,9 +3356,11 @@ def add_mode_monitor(self, *args, **kwargs): args = fix_dft_args(args, 0) freq = args[0] fluxes = args[1:] - decimation_factor = kwargs.get('decimation_factor', 0) + decimation_factor = kwargs.get("decimation_factor", 0) yee_grid = kwargs.get("yee_grid", False) - flux = DftFlux(self._add_mode_monitor, [freq, fluxes, yee_grid, decimation_factor]) + flux = DftFlux( + self._add_mode_monitor, [freq, fluxes, yee_grid, decimation_factor] + ) self.dft_objects.append(flux) return flux @@ -3041,15 +3369,26 @@ def _add_mode_monitor(self, freq, fluxes, yee_grid, decimation_factor): self.init_sim() if len(fluxes) != 1: - raise ValueError("add_mode_monitor expected just one ModeRegion. Got {}".format(len(fluxes))) + raise ValueError( + "add_mode_monitor expected just one ModeRegion. Got {}".format( + len(fluxes) + ) + ) region = fluxes[0] centered_grid = not yee_grid - v = mp.Volume(region.center, region.size, dims=self.dimensions, is_cylindrical=self.is_cylindrical) + v = mp.Volume( + region.center, + region.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ) d0 = region.direction d = self.fields.normal_direction(v.swigobj) if d0 < 0 else d0 - return self.fields.add_mode_monitor(d, v.swigobj, freq, centered_grid, decimation_factor) + return self.fields.add_mode_monitor( + d, v.swigobj, freq, centered_grid, decimation_factor + ) def display_fluxes(self, *fluxes): """ @@ -3060,7 +3399,11 @@ def display_fluxes(self, *fluxes): subsequent columns are the flux spectra. """ if verbosity.meep > 0: - display_csv(self, 'flux', zip(get_flux_freqs(fluxes[0]), *[get_fluxes(f) for f in fluxes])) + display_csv( + self, + "flux", + zip(get_flux_freqs(fluxes[0]), *[get_fluxes(f) for f in fluxes]), + ) def load_flux(self, fname, flux): """ @@ -3074,7 +3417,7 @@ def load_flux(self, fname, flux): if self.fields is None: self.init_sim() - flux.load_hdf5(self.fields, fname, '', self.get_filename_prefix()) + flux.load_hdf5(self.fields, fname, "", self.get_filename_prefix()) load_mode = load_flux @@ -3087,7 +3430,7 @@ def save_flux(self, fname, flux): if self.fields is None: self.init_sim() - flux.save_hdf5(self.fields, fname, '', self.get_filename_prefix()) + flux.save_hdf5(self.fields, fname, "", self.get_filename_prefix()) save_mode = save_flux @@ -3148,7 +3491,7 @@ def flux_in_box(self, d, box=None, center=None, size=None): construct the appropriate volume for you. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using flux_in_box') + raise RuntimeError("Fields must be initialized before using flux_in_box") box = self._volume_from_kwargs(box, center, size) @@ -3167,7 +3510,9 @@ def electric_energy_in_box(self, box=None, center=None, size=None): volume. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using electric_energy_in_box') + raise RuntimeError( + "Fields must be initialized before using electric_energy_in_box" + ) box = self._volume_from_kwargs(box, center, size) @@ -3186,7 +3531,9 @@ def magnetic_energy_in_box(self, box=None, center=None, size=None): volume. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using magnetic_energy_in_box') + raise RuntimeError( + "Fields must be initialized before using magnetic_energy_in_box" + ) box = self._volume_from_kwargs(box, center, size) @@ -3205,7 +3552,9 @@ def field_energy_in_box(self, box=None, center=None, size=None): volume. """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using field_energy_in_box') + raise RuntimeError( + "Fields must be initialized before using field_energy_in_box" + ) box = self._volume_from_kwargs(box, center, size) @@ -3237,7 +3586,9 @@ def modal_volume_in_box(self, box=None, center=None, size=None): via the functions: """ if self.fields is None: - raise RuntimeError('Fields must be initialized before using modal_volume_in_box') + raise RuntimeError( + "Fields must be initialized before using modal_volume_in_box" + ) try: box = self._volume_from_kwargs(box, center, size) @@ -3248,35 +3599,50 @@ def modal_volume_in_box(self, box=None, center=None, size=None): def solve_cw(self, tol=1e-8, maxiters=10000, L=2): if self.fields is None: - raise RuntimeError('Fields must be initialized before using solve_cw') + raise RuntimeError("Fields must be initialized before using solve_cw") self._evaluate_dft_objects() return self.fields.solve_cw(tol, maxiters, L) - def solve_eigfreq(self, tol=1e-7, maxiters=100, - guessfreq=None, cwtol=None, cwmaxiters=10000, L=10): + def solve_eigfreq( + self, tol=1e-7, maxiters=100, guessfreq=None, cwtol=None, cwmaxiters=10000, L=10 + ): if self.fields is None: - raise RuntimeError('Fields must be initialized before using solve_cw') + raise RuntimeError("Fields must be initialized before using solve_cw") if cwtol is None: - cwtol = tol * 1e-3 # solve CW problems much more accurately than eigenvalue tolerance + cwtol = ( + tol * 1e-3 + ) # solve CW problems much more accurately than eigenvalue tolerance self._evaluate_dft_objects() eigfreq = np.array(0, dtype=np.complex128) if guessfreq is None: self.fields.solve_cw(cwtol, cwmaxiters, L, eigfreq, tol, maxiters) else: - self.fields.solve_cw(cwtol, cwmaxiters, guessfreq, L, eigfreq, tol, maxiters) + self.fields.solve_cw( + cwtol, cwmaxiters, guessfreq, L, eigfreq, tol, maxiters + ) return eigfreq.item() - def _add_fluxish_stuff(self, add_dft_stuff, freq, stufflist, decimation_factor, *args): + def _add_fluxish_stuff( + self, add_dft_stuff, freq, stufflist, decimation_factor, *args + ): vol_list = None for s in stufflist: - v = Volume(center=s.center, size=s.size, dims=self.dimensions, - is_cylindrical=self.is_cylindrical) + v = Volume( + center=s.center, + size=s.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ) d0 = s.direction d = self.fields.normal_direction(v.swigobj) if d0 < 0 else d0 c = mp.direction_component(mp.Sx, d) - v2 = Volume(center=s.center, size=s.size, dims=self.dimensions, - is_cylindrical=self.is_cylindrical).swigobj + v2 = Volume( + center=s.center, + size=s.size, + dims=self.dimensions, + is_cylindrical=self.is_cylindrical, + ).swigobj vol_list = mp.make_volume_list(v2, c, s.weight, vol_list) stuff = add_dft_stuff(vol_list, freq, decimation_factor, *args) vol_list.__swig_destroy__(vol_list) @@ -3285,26 +3651,46 @@ def _add_fluxish_stuff(self, add_dft_stuff, freq, stufflist, decimation_factor, def output_component(self, c, h5file=None, frequency=0): if self.fields is None: - raise RuntimeError("Fields must be initialized before calling output_component") + raise RuntimeError( + "Fields must be initialized before calling output_component" + ) - vol = self.fields.total_volume() if self.output_volume is None else self.output_volume + vol = ( + self.fields.total_volume() + if self.output_volume is None + else self.output_volume + ) h5 = self.output_append_h5 if h5file is None else h5file append = h5file is None and self.output_append_h5 is not None - self.fields.output_hdf5(c, vol, h5, append, self.output_single_precision,self.get_filename_prefix(), frequency) + self.fields.output_hdf5( + c, + vol, + h5, + append, + self.output_single_precision, + self.get_filename_prefix(), + frequency, + ) if h5file is None: - nm = self.fields.h5file_name(mp.component_name(c), self.get_filename_prefix(), True) + nm = self.fields.h5file_name( + mp.component_name(c), self.get_filename_prefix(), True + ) if c == mp.Dielectric: self.last_eps_filename = nm self.output_h5_hook(nm) def output_components(self, fname, *components): if self.fields is None: - raise RuntimeError("Fields must be initialized before calling output_component") + raise RuntimeError( + "Fields must be initialized before calling output_component" + ) if self.output_append_h5 is None: - f = self.fields.open_h5file(fname, mp.h5file.WRITE, self.get_filename_prefix(), True) + f = self.fields.open_h5file( + fname, mp.h5file.WRITE, self.get_filename_prefix(), True + ) else: f = None @@ -3314,14 +3700,26 @@ def output_components(self, fname, *components): f.prevent_deadlock() if self.output_append_h5 is None: - self.output_h5_hook(self.fields.h5file_name(fname, self.get_filename_prefix(), True)) + self.output_h5_hook( + self.fields.h5file_name(fname, self.get_filename_prefix(), True) + ) def h5topng(self, rm_h5, option, *step_funcs): - opts = "h5topng {}".format(option) - cmd = re.sub(r'\$EPS', self.last_eps_filename, opts) + opts = f"h5topng {option}" + cmd = re.sub(r"\$EPS", self.last_eps_filename, opts) return convert_h5(rm_h5, cmd, *step_funcs) - def get_array(self, component=None, vol=None, center=None, size=None, cmplx=None, arr=None, frequency=0, snap=False): + def get_array( + self, + component=None, + vol=None, + center=None, + size=None, + cmplx=None, + arr=None, + frequency=0, + snap=False, + ): """ Takes as input a subregion of the cell and the field/material component. The method returns a NumPy array containing values of the field/material at the @@ -3396,23 +3794,31 @@ def get_array(self, component=None, vol=None, center=None, size=None, cmplx=None else: v = self._volume_from_kwargs(vol, center, size) - _, dirs = mp._get_array_slice_dimensions(self.fields, v, dim_sizes, not snap, snap) + _, dirs = mp._get_array_slice_dimensions( + self.fields, v, dim_sizes, not snap, snap + ) dims = [s for s in dim_sizes if s != 0] if cmplx is None: - cmplx = frequency != 0 or (component < mp.Dielectric and not self.fields.is_real) + cmplx = frequency != 0 or ( + component < mp.Dielectric and not self.fields.is_real + ) if arr is not None: if cmplx and not np.iscomplexobj(arr): - raise ValueError("Requested a complex slice, but provided array of type {}.".format(arr.dtype)) + raise ValueError( + "Requested a complex slice, but provided array of type {}.".format( + arr.dtype + ) + ) for a, b in zip(arr.shape, dims): if a != b: fmt = "Expected dimensions {}, but got {}" raise ValueError(fmt.format(dims, arr.shape)) - arr = np.require(arr, requirements=['C', 'W']) + arr = np.require(arr, requirements=["C", "W"]) else: if mp.is_single_precision(): @@ -3444,23 +3850,29 @@ def get_dft_array(self, dft_obj, component, num_freq): `nfreq` parameter to `add_dft_fields`, `add_flux`, etc. """ if not self.dft_objects: - raise RuntimeError('DFT monitor dft_obj must be initialized before calling get_dft_array') + raise RuntimeError( + "DFT monitor dft_obj must be initialized before calling get_dft_array" + ) - if hasattr(dft_obj, 'swigobj'): + if hasattr(dft_obj, "swigobj"): dft_swigobj = dft_obj.swigobj else: dft_swigobj = dft_obj if type(dft_swigobj) is mp.dft_fields: - return mp.get_dft_fields_array(self.fields, dft_swigobj, component, num_freq) + return mp.get_dft_fields_array( + self.fields, dft_swigobj, component, num_freq + ) elif type(dft_swigobj) is mp.dft_flux: return mp.get_dft_flux_array(self.fields, dft_swigobj, component, num_freq) elif type(dft_swigobj) is mp.dft_force: return mp.get_dft_force_array(self.fields, dft_swigobj, component, num_freq) elif type(dft_swigobj) is mp.dft_near2far: - return mp.get_dft_near2far_array(self.fields, dft_swigobj, component, num_freq) + return mp.get_dft_near2far_array( + self.fields, dft_swigobj, component, num_freq + ) else: - raise ValueError("Invalid type of dft object: {}".format(dft_swigobj)) + raise ValueError(f"Invalid type of dft object: {dft_swigobj}") def get_source(self, component, vol=None, center=None, size=None): """ @@ -3476,12 +3888,15 @@ def get_source(self, component, vol=None, center=None, size=None): dim_sizes = np.zeros(3, dtype=np.uintp) mp._get_array_slice_dimensions(self.fields, v, dim_sizes, True, False) dims = [s for s in dim_sizes if s != 0] - arr = np.zeros(dims, dtype=np.complex64 if mp.is_single_precision() else np.complex128) + arr = np.zeros( + dims, dtype=np.complex64 if mp.is_single_precision() else np.complex128 + ) self.fields.get_source_slice(v, component, arr) return arr - def get_array_metadata(self, vol=None, center=None, size=None, dft_cell=None, - return_pw=False): + def get_array_metadata( + self, vol=None, center=None, size=None, dft_cell=None, return_pw=False + ): """ This routine provides geometric information useful for interpreting the arrays returned by `get_array` or `get_dft_array` for the spatial region defined by `vol` @@ -3527,13 +3942,15 @@ def get_array_metadata(self, vol=None, center=None, size=None, dft_cell=None, offset, tics = 0, [] for n in range(3): N = int(xyzw_vector[offset]) - tics.append( xyzw_vector[offset+1:offset+1+N] ) - offset += 1+N - wshape = [len(t) for t in tics if len(t)>1] + tics.append(xyzw_vector[offset + 1 : offset + 1 + N]) + offset += 1 + N + wshape = [len(t) for t in tics if len(t) > 1] weights = np.reshape(xyzw_vector[offset:], wshape) if return_pw: - points=[ mp.Vector3(x,y,z) for x in tics[0] for y in tics[1] for z in tics[2] ] - return points,weights + points = [ + mp.Vector3(x, y, z) for x in tics[0] for y in tics[1] for z in tics[2] + ] + return points, weights return tuple(tics) + (weights,) def get_array_slice_dimensions(self, component, vol=None, center=None, size=None): @@ -3552,14 +3969,25 @@ def get_array_slice_dimensions(self, component, vol=None, center=None, size=None v = self._volume_from_kwargs(vol, center, size) dim_sizes = np.zeros(3, dtype=np.uintp) corners = [] - _,_ = mp._get_array_slice_dimensions(self.fields, v, dim_sizes, False, False, component, corners) - dim_sizes[dim_sizes==0] = 1 + _, _ = mp._get_array_slice_dimensions( + self.fields, v, dim_sizes, False, False, component, corners + ) + dim_sizes[dim_sizes == 0] = 1 min_corner = corners[0] max_corner = corners[1] return dim_sizes, min_corner, max_corner - def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_vol=None, - eig_resolution=0, eig_tolerance=1e-12, kpoint_func=None, direction=mp.AUTOMATIC): + def get_eigenmode_coefficients( + self, + flux, + bands, + eig_parity=mp.NO_PARITY, + eig_vol=None, + eig_resolution=0, + eig_tolerance=1e-12, + kpoint_func=None, + direction=mp.AUTOMATIC, + ): """ Given a flux object and list of band indices `bands` or `DiffractedPlanewave`, return a `namedtuple` with the following fields: @@ -3575,7 +4003,9 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v calculations. """ if self.fields is None: - raise ValueError("Fields must be initialized before calling get_eigenmode_coefficients") + raise ValueError( + "Fields must be initialized before calling get_eigenmode_coefficients" + ) if eig_vol is None: eig_vol = flux.where else: @@ -3584,7 +4014,7 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v direction = flux.normal_direction try: - bands_list_range = isinstance(bands, (list,range)) + bands_list_range = isinstance(bands, (list, range)) except TypeError: bands_list_range = isinstance(bands, list) @@ -3606,7 +4036,7 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v vgrp, kpoint_func, cscale, - direction + direction, ) elif isinstance(bands, DiffractedPlanewave): num_bands = 1 @@ -3627,15 +4057,34 @@ def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_v vgrp, kpoint_func, cscale, - direction + direction, ) else: - raise TypeError("get_eigenmode_coefficients: bands must be either a list or DiffractedPlanewave object") + raise TypeError( + "get_eigenmode_coefficients: bands must be either a list or DiffractedPlanewave object" + ) - return EigCoeffsResult(np.reshape(coeffs, (num_bands, flux.freq.size(), 2)), vgrp, kpoints, kdom, cscale) + return EigCoeffsResult( + np.reshape(coeffs, (num_bands, flux.freq.size(), 2)), + vgrp, + kpoints, + kdom, + cscale, + ) - def get_eigenmode(self, frequency, direction, where, band_num, kpoint, eig_vol=None, match_frequency=True, - parity=mp.NO_PARITY, resolution=0, eigensolver_tol=1e-12): + def get_eigenmode( + self, + frequency, + direction, + where, + band_num, + kpoint, + eig_vol=None, + match_frequency=True, + parity=mp.NO_PARITY, + resolution=0, + eigensolver_tol=1e-12, + ): """ The parameters of this routine are the same as that of `get_eigenmode_coefficients` or `EigenModeSource`, but this function returns an @@ -3668,12 +4117,30 @@ def get_eigenmode(self, frequency, direction, where, band_num, kpoint, eig_vol=N swig_kpoint = mp.vec(kpoint.x, kpoint.y, kpoint.z) kdom = np.zeros(3) - emdata = mp._get_eigenmode(self.fields, frequency, direction, where, eig_vol, band_num, swig_kpoint, - match_frequency, parity, resolution, eigensolver_tol, kdom) + emdata = mp._get_eigenmode( + self.fields, + frequency, + direction, + where, + eig_vol, + band_num, + swig_kpoint, + match_frequency, + parity, + resolution, + eigensolver_tol, + kdom, + ) Gk = mp._get_eigenmode_Gk(emdata) - return EigenmodeData(emdata.band_num, emdata.frequency, emdata.group_velocity, Gk, - emdata, mp.Vector3(kdom[0], kdom[1], kdom[2])) + return EigenmodeData( + emdata.band_num, + emdata.frequency, + emdata.group_velocity, + Gk, + emdata, + mp.Vector3(kdom[0], kdom[1], kdom[2]), + ) def output_field_function(self, name, cs, func, real_only=False, h5file=None): """ @@ -3684,16 +4151,28 @@ def output_field_function(self, name, cs, func, real_only=False, h5file=None): `real_only` is True, only outputs the real part of `func`. """ if self.fields is None: - raise RuntimeError("Fields must be initialized before calling output_field_function") + raise RuntimeError( + "Fields must be initialized before calling output_field_function" + ) ov = self.output_volume if self.output_volume else self.fields.total_volume() h5 = self.output_append_h5 if h5file is None else h5file append = h5file is None and self.output_append_h5 is not None - self.fields.output_hdf5(name, [cs, func], ov, h5, append, self.output_single_precision, - self.get_filename_prefix(), real_only) + self.fields.output_hdf5( + name, + [cs, func], + ov, + h5, + append, + self.output_single_precision, + self.get_filename_prefix(), + real_only, + ) if h5file is None: - self.output_h5_hook(self.fields.h5file_name(name, self.get_filename_prefix(), True)) + self.output_h5_hook( + self.fields.h5file_name(name, self.get_filename_prefix(), True) + ) def _get_field_function_volume(self, where=None, center=None, size=None): try: @@ -3723,7 +4202,9 @@ def integrate_field_function(self, cs, func, where=None, center=None, size=None) where = self._get_field_function_volume(where, center, size) return self.fields.integrate([cs, func], where) - def integrate2_field_function(self, fields2, cs1, cs2, func, where=None, center=None, size=None): + def integrate2_field_function( + self, fields2, cs1, cs2, func, where=None, center=None, size=None + ): """ Similar to `integrate_field_function`, but takes additional parameters `fields2` and `cs2`. `fields2` is a `meep::fields*` object similar to the global `fields` @@ -3785,7 +4266,9 @@ def change_k_point(self, k): self.k_point = k if self.fields: - needs_complex_fields = not (not self.k_point or self.k_point == mp.Vector3()) + needs_complex_fields = not ( + not self.k_point or self.k_point == mp.Vector3() + ) if needs_complex_fields and self.fields.is_real: self.fields = None @@ -3793,7 +4276,9 @@ def change_k_point(self, k): self.init_sim() else: if self.k_point: - self.fields.use_bloch(py_v3_to_vec(self.dimensions, self.k_point, self.is_cylindrical)) + self.fields.use_bloch( + py_v3_to_vec(self.dimensions, self.k_point, self.is_cylindrical) + ) def change_sources(self, new_sources): """ @@ -3831,13 +4316,13 @@ def restart_fields(self): else: self._is_initialized = False self.init_sim() - + def clear_dft_monitors(self): """ Remove all of the dft monitors from the simulation. """ for m in self.dft_objects: - if not (isinstance(m,DftFields) and (m.chunks) and (m.chunks.persist)): + if not (isinstance(m, DftFields) and (m.chunks) and (m.chunks.persist)): m.remove() self.fields.clear_dft_monitors() @@ -3864,8 +4349,8 @@ def run(self, *step_funcs, **kwargs): returns `True`. Like `until` above, `until_after_sources` can take a list of stopping conditions. """ - until = kwargs.pop('until', None) - until_after_sources = kwargs.pop('until_after_sources', None) + until = kwargs.pop("until", None) + until_after_sources = kwargs.pop("until_after_sources", None) if self.fields is None: self.init_sim() @@ -3874,7 +4359,7 @@ def run(self, *step_funcs, **kwargs): self._check_material_frequencies() if kwargs: - raise ValueError("Unrecognized keyword arguments: {}".format(kwargs.keys())) + raise ValueError(f"Unrecognized keyword arguments: {kwargs.keys()}") if until_after_sources is not None: self._run_sources_until(until_after_sources, step_funcs) @@ -3940,26 +4425,26 @@ def output_times(self, fname): for each column and one row per process. """ if self.fields: - if not fname.endswith('.csv'): - fname += '.csv' + if not fname.endswith(".csv"): + fname += ".csv" self.fields.output_times(fname) - def get_epsilon(self,frequency=0,snap=False): - return self.get_array(component=mp.Dielectric,frequency=frequency,snap=snap) + def get_epsilon(self, frequency=0, snap=False): + return self.get_array(component=mp.Dielectric, frequency=frequency, snap=snap) - def get_mu(self,frequency=0,snap=False): - return self.get_array(component=mp.Permeability,frequency=frequency,snap=snap) + def get_mu(self, frequency=0, snap=False): + return self.get_array(component=mp.Permeability, frequency=frequency, snap=snap) - def get_hpwr(self,snap=False): - return self.get_array(component=mp.H_EnergyDensity,snap=snap) + def get_hpwr(self, snap=False): + return self.get_array(component=mp.H_EnergyDensity, snap=snap) - def get_dpwr(self,snap=False): - return self.get_array(component=mp.D_EnergyDensity,snap=snap) + def get_dpwr(self, snap=False): + return self.get_array(component=mp.D_EnergyDensity, snap=snap) - def get_tot_pwr(self,snap=False): - return self.get_array(component=mp.EnergyDensity,snap=snap) + def get_tot_pwr(self, snap=False): + return self.get_array(component=mp.EnergyDensity, snap=snap) - def get_hfield(self,snap=False): + def get_hfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Hr, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Hp, cmplx=not self.fields.is_real, snap=snap) @@ -3970,22 +4455,22 @@ def get_hfield(self,snap=False): z = self.get_array(mp.Hz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_hfield_x(self,snap=False): + def get_hfield_x(self, snap=False): return self.get_array(mp.Hx, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_y(self,snap=False): + def get_hfield_y(self, snap=False): return self.get_array(mp.Hy, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_z(self,snap=False): + def get_hfield_z(self, snap=False): return self.get_array(mp.Hz, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_r(self,snap=False): + def get_hfield_r(self, snap=False): return self.get_array(mp.Hr, cmplx=not self.fields.is_real, snap=snap) - def get_hfield_p(self,snap=False): + def get_hfield_p(self, snap=False): return self.get_array(mp.Hp, cmplx=not self.fields.is_real, snap=snap) - def get_bfield(self,snap=False): + def get_bfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Br, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Bp, cmplx=not self.fields.is_real, snap=snap) @@ -3996,22 +4481,22 @@ def get_bfield(self,snap=False): z = self.get_array(mp.Bz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_bfield_x(self,snap=False): + def get_bfield_x(self, snap=False): return self.get_array(mp.Bx, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_y(self,snap=False): + def get_bfield_y(self, snap=False): return self.get_array(mp.By, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_z(self,snap=False): + def get_bfield_z(self, snap=False): return self.get_array(mp.Bz, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_r(self,snap=False): + def get_bfield_r(self, snap=False): return self.get_array(mp.Br, cmplx=not self.fields.is_real, snap=snap) - def get_bfield_p(self,snap=False): + def get_bfield_p(self, snap=False): return self.get_array(mp.Bp, cmplx=not self.fields.is_real, snap=snap) - def get_efield(self,snap=False): + def get_efield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Er, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Ep, cmplx=not self.fields.is_real, snap=snap) @@ -4022,22 +4507,22 @@ def get_efield(self,snap=False): z = self.get_array(mp.Ez, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_efield_x(self,snap=False): + def get_efield_x(self, snap=False): return self.get_array(mp.Ex, cmplx=not self.fields.is_real, snap=snap) - def get_efield_y(self,snap=False): + def get_efield_y(self, snap=False): return self.get_array(mp.Ey, cmplx=not self.fields.is_real, snap=snap) - def get_efield_z(self,snap=False): + def get_efield_z(self, snap=False): return self.get_array(mp.Ez, cmplx=not self.fields.is_real, snap=snap) - def get_efield_r(self,snap=False): + def get_efield_r(self, snap=False): return self.get_array(mp.Er, cmplx=not self.fields.is_real, snap=snap) - def get_efield_p(self,snap=False): + def get_efield_p(self, snap=False): return self.get_array(mp.Ep, cmplx=not self.fields.is_real, snap=snap) - def get_dfield(self,snap=False): + def get_dfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Dr, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Dp, cmplx=not self.fields.is_real, snap=snap) @@ -4048,22 +4533,22 @@ def get_dfield(self,snap=False): z = self.get_array(mp.Dz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_dfield_x(self,snap=False): + def get_dfield_x(self, snap=False): return self.get_array(mp.Dx, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_y(self,snap=False): + def get_dfield_y(self, snap=False): return self.get_array(mp.Dy, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_z(self,snap=False): + def get_dfield_z(self, snap=False): return self.get_array(mp.Dz, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_r(self,snap=False): + def get_dfield_r(self, snap=False): return self.get_array(mp.Dr, cmplx=not self.fields.is_real, snap=snap) - def get_dfield_p(self,snap=False): + def get_dfield_p(self, snap=False): return self.get_array(mp.Dp, cmplx=not self.fields.is_real, snap=snap) - def get_sfield(self,snap=False): + def get_sfield(self, snap=False): if self.is_cylindrical: r = self.get_array(mp.Sr, cmplx=not self.fields.is_real, snap=snap) p = self.get_array(mp.Sp, cmplx=not self.fields.is_real, snap=snap) @@ -4074,29 +4559,39 @@ def get_sfield(self,snap=False): z = self.get_array(mp.Sz, cmplx=not self.fields.is_real, snap=snap) return np.stack([x, y, z], axis=-1) - def get_sfield_x(self,snap=False): + def get_sfield_x(self, snap=False): return self.get_array(mp.Sx, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_y(self,snap=False): + def get_sfield_y(self, snap=False): return self.get_array(mp.Sy, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_z(self,snap=False): + def get_sfield_z(self, snap=False): return self.get_array(mp.Sz, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_r(self,snap=False): + def get_sfield_r(self, snap=False): return self.get_array(mp.Sr, cmplx=not self.fields.is_real, snap=snap) - def get_sfield_p(self,snap=False): + def get_sfield_p(self, snap=False): return self.get_array(mp.Sp, cmplx=not self.fields.is_real, snap=snap) - - def plot2D(self, ax=None, output_plane=None, fields=None, labels=False, - eps_parameters=None, boundary_parameters=None, - source_parameters=None, monitor_parameters=None, - field_parameters=None, frequency=None, - plot_eps_flag=True, plot_sources_flag=True, - plot_monitors_flag=True, plot_boundaries_flag=True, - **kwargs): + def plot2D( + self, + ax=None, + output_plane=None, + fields=None, + labels=False, + eps_parameters=None, + boundary_parameters=None, + source_parameters=None, + monitor_parameters=None, + field_parameters=None, + frequency=None, + plot_eps_flag=True, + plot_sources_flag=True, + plot_monitors_flag=True, + plot_boundaries_flag=True, + **kwargs, + ): """ Plots a 2D cross section of the simulation domain using `matplotlib`. The plot includes the geometry, boundary layers, sources, and monitors. Fields can also be @@ -4185,22 +4680,24 @@ def plot2D(self, ax=None, output_plane=None, fields=None, labels=False, - `post_process=np.real`: post processing function to apply to fields (must be a function object) """ - return vis.plot2D(self, - ax=ax, - output_plane=output_plane, - fields=fields, - labels=labels, - eps_parameters=eps_parameters, - boundary_parameters=boundary_parameters, - source_parameters=source_parameters, - monitor_parameters=monitor_parameters, - field_parameters=field_parameters, - frequency=frequency, - plot_eps_flag=plot_eps_flag, - plot_sources_flag=plot_sources_flag, - plot_monitors_flag=plot_monitors_flag, - plot_boundaries_flag=plot_boundaries_flag, - **kwargs) + return vis.plot2D( + self, + ax=ax, + output_plane=output_plane, + fields=fields, + labels=labels, + eps_parameters=eps_parameters, + boundary_parameters=boundary_parameters, + source_parameters=source_parameters, + monitor_parameters=monitor_parameters, + field_parameters=field_parameters, + frequency=frequency, + plot_eps_flag=plot_eps_flag, + plot_sources_flag=plot_sources_flag, + plot_monitors_flag=plot_monitors_flag, + plot_boundaries_flag=plot_boundaries_flag, + **kwargs, + ) def plot_fields(self, **kwargs): return vis.plot_fields(self, **kwargs) @@ -4262,20 +4759,26 @@ def _create_boundary_region_from_boundary_layers(boundary_layers, gv): loop_stop_bi = mp.Low while b != loop_stop_bi: - br += mp.boundary_region(*(boundary_region_args + [layer.direction, b])) + br += mp.boundary_region( + *(boundary_region_args + [layer.direction, b]) + ) b = (b + 1) % 2 loop_stop_bi = mp.High else: - br += mp.boundary_region(*(boundary_region_args + [layer.direction, layer.side])) + br += mp.boundary_region( + *(boundary_region_args + [layer.direction, layer.side]) + ) return br # Private step functions + def _combine_step_funcs(*step_funcs): def _combine(sim, todo): for func in step_funcs: _eval_step_func(sim, func, todo) + return _combine @@ -4283,9 +4786,9 @@ def _eval_step_func(sim, func, todo): num_args = get_num_args(func) if num_args != 1 and num_args != 2: - raise ValueError("Step function '{}'' requires 1 or 2 arguments".format(func.__name__)) + raise ValueError(f"Step function '{func.__name__}'' requires 1 or 2 arguments") elif num_args == 1: - if todo == 'step': + if todo == "step": func(sim) elif num_args == 2: func(sim, todo) @@ -4293,24 +4796,28 @@ def _eval_step_func(sim, func, todo): def _when_true_funcs(cond, *step_funcs): def _true(sim, todo): - if todo == 'finish' or cond(sim): + if todo == "finish" or cond(sim): for f in step_funcs: _eval_step_func(sim, f, todo) + return _true # Public step functions + def after_sources(*step_funcs): """ Given zero or more step functions, evaluates them only for times after all of the sources have turned off. """ + def _after_sources(sim, todo): time = sim.fields.last_source_time() if sim.round_time() >= time: for func in step_funcs: _eval_step_func(sim, func, todo) + return _after_sources @@ -4319,11 +4826,13 @@ def after_sources_and_time(t, *step_funcs): Given zero or more step functions, evaluates them only for times after all of the sources have turned off, plus an additional $T$ time units have elapsed. """ + def _after_s_and_t(sim, todo): time = sim.fields.last_source_time() + t - sim.round_time() if sim.round_time() >= time: for func in step_funcs: _eval_step_func(sim, func, todo) + return _after_s_and_t @@ -4332,8 +4841,10 @@ def after_time(t, *step_funcs): Given zero or more step functions, evaluates them only for times after a $T$ time units have elapsed from the start of the run. """ + def _after_t(sim): return sim.round_time() >= t + return _when_true_funcs(_after_t, *step_funcs) @@ -4342,13 +4853,14 @@ def at_beginning(*step_funcs): Given zero or more step functions, evaluates them only once, at the beginning of the run. """ - closure = {'done': False} + closure = {"done": False} def _beg(sim, todo): - if not closure['done']: + if not closure["done"]: for f in step_funcs: _eval_step_func(sim, f, todo) - closure['done'] = True + closure["done"] = True + return _beg @@ -4356,12 +4868,14 @@ def at_end(*step_funcs): """ Given zero or more step functions, evaluates them only once, at the end of the run. """ + def _end(sim, todo): - if todo == 'finish': + if todo == "finish": for func in step_funcs: - _eval_step_func(sim, func, 'step') + _eval_step_func(sim, func, "step") for func in step_funcs: - _eval_step_func(sim, func, 'finish') + _eval_step_func(sim, func, "finish") + return _end @@ -4370,14 +4884,15 @@ def at_every(dt, *step_funcs): Given zero or more step functions, evaluates them at every time interval of $dT$ units (rounded up to the next time step). """ - closure = {'tlast': 0.0} + closure = {"tlast": 0.0} def _every(sim, todo): t = sim.round_time() - if todo == 'finish' or t >= closure['tlast'] + dt + (-0.5 * sim.fields.dt): + if todo == "finish" or t >= closure["tlast"] + dt + (-0.5 * sim.fields.dt): for func in step_funcs: _eval_step_func(sim, func, todo) - closure['tlast'] = t + closure["tlast"] = t + return _every @@ -4386,13 +4901,14 @@ def at_time(t, *step_funcs): Given zero or more step functions, evaluates them only once, after a $T$ time units have elapsed from the start of the run. """ - closure = {'done': False} + closure = {"done": False} def _at_time(sim, todo): - if not closure['done'] or todo == 'finish': + if not closure["done"] or todo == "finish": for f in step_funcs: _eval_step_func(sim, f, todo) - closure['done'] = closure['done'] or todo == 'step' + closure["done"] = closure["done"] or todo == "step" + return after_time(t, _at_time) @@ -4401,8 +4917,10 @@ def before_time(t, *step_funcs): Given zero or more step functions, evaluates them only for times before a $T$ time units have elapsed from the start of the run. """ + def _before_t(sim): return sim.round_time() < t + return _when_true_funcs(_before_t, *step_funcs) @@ -4411,17 +4929,18 @@ def during_sources(*step_funcs): Given zero or more step functions, evaluates them only for times *before* all of the sources have turned off. """ - closure = {'finished': False} + closure = {"finished": False} def _during_sources(sim, todo): time = sim.fields.last_source_time() if sim.round_time() < time: for func in step_funcs: - _eval_step_func(sim, func, 'step') - elif closure['finished'] is False: + _eval_step_func(sim, func, "step") + elif closure["finished"] is False: for func in step_funcs: - _eval_step_func(sim, func, 'finish') - closure['finished'] = True + _eval_step_func(sim, func, "finish") + closure["finished"] = True + return _during_sources @@ -4431,7 +4950,7 @@ def in_volume(v, *step_funcs): output a subset (or a superset) of the cell, corresponding to the `meep::volume* v` (created by the `Volume` function). """ - closure = {'cur_eps': ''} + closure = {"cur_eps": ""} def _in_volume(sim, todo): v_save = sim.output_volume @@ -4439,15 +4958,16 @@ def _in_volume(sim, todo): sim.output_volume = sim._fit_volume_to_simulation(v).swigobj - if closure['cur_eps']: - sim.last_eps_filename = closure['cur_eps'] + if closure["cur_eps"]: + sim.last_eps_filename = closure["cur_eps"] for func in step_funcs: _eval_step_func(sim, func, todo) - closure['cur_eps'] = sim.last_eps_filename + closure["cur_eps"] = sim.last_eps_filename sim.output_volume = v_save if eps_save: sim.last_eps_filename = eps_save + return _in_volume @@ -4467,21 +4987,24 @@ def to_appended(fname, *step_funcs): an `.h5` suffix and the current filename prefix). They append by adding an *extra dimension* to their datasets, corresponding to time. """ - closure = {'h5': None} + closure = {"h5": None} def _to_appended(sim, todo): - if closure['h5'] is None: - closure['h5'] = sim.fields.open_h5file(fname, mp.h5file.WRITE, sim.get_filename_prefix()) + if closure["h5"] is None: + closure["h5"] = sim.fields.open_h5file( + fname, mp.h5file.WRITE, sim.get_filename_prefix() + ) h5save = sim.output_append_h5 - sim.output_append_h5 = closure['h5'] + sim.output_append_h5 = closure["h5"] for func in step_funcs: _eval_step_func(sim, func, todo) - if todo == 'finish': - closure['h5'] = None + if todo == "finish": + closure["h5"] = None sim.output_h5_hook(sim.fields.h5file_name(fname, sim.get_filename_prefix())) sim.output_append_h5 = h5save + return _to_appended @@ -4505,26 +5028,34 @@ def stop_when_fields_decayed(dt=None, c=None, pt=None, decay_by=None): raise ValueError("dt, c, pt, and decay_by are all required.") closure = { - 'max_abs': 0, - 'cur_max': 0, - 't0': 0, + "max_abs": 0, + "cur_max": 0, + "t0": 0, } def _stop(sim): fabs = abs(sim.get_field_point(c, pt)) * abs(sim.get_field_point(c, pt)) - closure['cur_max'] = max(closure['cur_max'], fabs) + closure["cur_max"] = max(closure["cur_max"], fabs) - if sim.round_time() <= dt + closure['t0']: + if sim.round_time() <= dt + closure["t0"]: return False else: - old_cur = closure['cur_max'] - closure['cur_max'] = 0 - closure['t0'] = sim.round_time() - closure['max_abs'] = max(closure['max_abs'], old_cur) - if closure['max_abs'] != 0 and verbosity.meep > 0: + old_cur = closure["cur_max"] + closure["cur_max"] = 0 + closure["t0"] = sim.round_time() + closure["max_abs"] = max(closure["max_abs"], old_cur) + if closure["max_abs"] != 0 and verbosity.meep > 0: fmt = "field decay(t = {}): {} / {} = {}" - print(fmt.format(sim.meep_time(), old_cur, closure['max_abs'], old_cur / closure['max_abs'])) - return old_cur <= closure['max_abs'] * decay_by + print( + fmt.format( + sim.meep_time(), + old_cur, + closure["max_abs"], + old_cur / closure["max_abs"], + ) + ) + return old_cur <= closure["max_abs"] * decay_by + return _stop @@ -4545,22 +5076,30 @@ def stop_when_energy_decayed(dt=None, decay_by=None): raise ValueError("dt and decay_by are all required.") closure = { - 'max_abs': 0, - 't0': 0, + "max_abs": 0, + "t0": 0, } def _stop(sim): - if sim.round_time() <= dt + closure['t0']: + if sim.round_time() <= dt + closure["t0"]: return False else: cell_volume = mp.Volume(center=sim.geometry_center, size=sim.cell_size) cur_abs = abs(sim.field_energy_in_box(box=cell_volume)) - closure['max_abs'] = max(closure['max_abs'], cur_abs) - closure['t0'] = sim.round_time() - if closure['max_abs'] != 0 and verbosity.meep > 0: + closure["max_abs"] = max(closure["max_abs"], cur_abs) + closure["t0"] = sim.round_time() + if closure["max_abs"] != 0 and verbosity.meep > 0: fmt = "energy decay(t = {}): {} / {} = {}" - print(fmt.format(sim.meep_time(), cur_abs, closure['max_abs'], cur_abs / closure['max_abs'])) - return cur_abs <= closure['max_abs'] * decay_by + print( + fmt.format( + sim.meep_time(), + cur_abs, + closure["max_abs"], + cur_abs / closure["max_abs"], + ) + ) + return cur_abs <= closure["max_abs"] * decay_by + return _stop @@ -4570,10 +5109,12 @@ def stop_after_walltime(t): parameter. Stops the simulation after `t` seconds of wall time have passed. """ start = mp.wall_time() + def _stop_after_walltime(sim): if mp.wall_time() - start > t: return True return False + return _stop_after_walltime @@ -4598,6 +5139,7 @@ def _stop(sim): return _stop + def stop_when_dft_decayed(tol=1e-11, minimum_run_time=0, maximum_run_time=None): """ Return a `condition` function, suitable for passing to `Simulation.run` as the `until` @@ -4610,33 +5152,42 @@ def stop_when_dft_decayed(tol=1e-11, minimum_run_time=0, maximum_run_time=None): """ # Record data in closure so that we can persistently edit - closure = {'previous_fields':0, 't0':0, 'dt':0, 'maxchange':0} + closure = {"previous_fields": 0, "t0": 0, "dt": 0, "maxchange": 0} + def _stop(_sim): if _sim.fields.t == 0: - closure['dt'] = max(1/_sim.fields.dft_maxfreq()/_sim.fields.dt,_sim.fields.max_decimation()) + closure["dt"] = max( + 1 / _sim.fields.dft_maxfreq() / _sim.fields.dt, + _sim.fields.max_decimation(), + ) if maximum_run_time and _sim.round_time() > maximum_run_time: return True - elif _sim.fields.t <= closure['dt'] + closure['t0']: + elif _sim.fields.t <= closure["dt"] + closure["t0"]: return False else: - previous_fields = closure['previous_fields'] - current_fields = _sim.fields.dft_norm() - change = np.abs(previous_fields-current_fields) - closure['maxchange'] = max(closure['maxchange'],change) + previous_fields = closure["previous_fields"] + current_fields = _sim.fields.dft_norm() + change = np.abs(previous_fields - current_fields) + closure["maxchange"] = max(closure["maxchange"], change) if previous_fields == 0: - closure['previous_fields'] = current_fields + closure["previous_fields"] = current_fields return False - closure['previous_fields'] = current_fields - closure['t0'] = _sim.fields.t + closure["previous_fields"] = current_fields + closure["t0"] = _sim.fields.t if verbosity.meep > 1: fmt = "DFT fields decay(t = {0:0.2f}): {1:0.4e}" - print(fmt.format(_sim.meep_time(), np.real(change/closure['maxchange']))) - return (change/closure['maxchange']) <= tol and _sim.round_time() >= minimum_run_time + print( + fmt.format(_sim.meep_time(), np.real(change / closure["maxchange"])) + ) + return ( + change / closure["maxchange"] + ) <= tol and _sim.round_time() >= minimum_run_time return _stop + def combine_step_funcs(*step_funcs): """ Given zero or more step functions, return a new step function that on each step calls @@ -4652,11 +5203,13 @@ def synchronized_magnetic(*step_funcs): electric field. See [Synchronizing the Magnetic and Electric Fields](Synchronizing_the_Magnetic_and_Electric_Fields.md). """ + def _sync(sim, todo): sim.fields.synchronize_magnetic_fields() for f in step_funcs: _eval_step_func(sim, f, todo) sim.fields.restore_magnetic_fields() + return _sync @@ -4681,6 +5234,7 @@ def with_prefix(pre, *step_funcs): Given zero or more step functions, modifies any output functions among them to prepend the string `prefix` to the file names (much like `filename_prefix`, above). """ + def _with_prefix(sim, todo): saved_pre = sim.filename_prefix sim.filename_prefix = pre + sim.get_filename_prefix() @@ -4688,6 +5242,7 @@ def _with_prefix(sim, todo): for f in step_funcs: _eval_step_func(sim, f, todo) sim.filename_prefix = saved_pre + return _with_prefix @@ -4698,11 +5253,11 @@ def display_csv(sim, name, data): def display_progress(t0, t, dt): t_0 = mp.wall_time() - closure = {'tlast': mp.wall_time()} + closure = {"tlast": mp.wall_time()} def _disp(sim): t1 = mp.wall_time() - if t1 - closure['tlast'] >= dt: + if t1 - closure["tlast"] >= dt: msg_fmt = "Meep progress: {}/{} = {:.1f}% done in {:.1f}s, {:.1f}s to go" val1 = sim.meep_time() - t0 val2 = val1 / (0.01 * t) @@ -4711,19 +5266,19 @@ def _disp(sim): if do_progress: sim.progress.value = val1 - sim.progress.description = "{}% done ".format(int(val2)) + sim.progress.description = f"{int(val2)}% done " if verbosity.meep > 0: print(msg_fmt.format(val1, t, val2, val3, val4)) - closure['tlast'] = t1 + closure["tlast"] = t1 return _disp def data_to_str(d): if type(d) is complex: - sign = '+' if d.imag >= 0 else '' - return "{}{}{}i".format(d.real, sign, d.imag) + sign = "+" if d.imag >= 0 else "" + return f"{d.real}{sign}{d.imag}i" else: return str(d) @@ -4734,11 +5289,10 @@ def display_run_data(sim, data_name, data): else: data_str = [data_to_str(data)] if verbosity.meep > 0: - print("{}{}:, {}".format(data_name, sim.run_index, ', '.join(data_str))) + print("{}{}:, {}".format(data_name, sim.run_index, ", ".join(data_str))) def convert_h5(rm_h5, convert_cmd, *step_funcs): - def convert(fname): if mp.my_rank() == 0: cmd = convert_cmd.split() @@ -4780,24 +5334,27 @@ def output_png(compnt, options, rm_h5=True): By default, `output_png` deletes the `.h5` file when it is done. To preserve the `.h5` file requires `output_png(component, h5topng_options, rm_h5=False)`. """ - closure = {'maxabs': 0.0} + closure = {"maxabs": 0.0} def _output_png(sim, todo): - if todo == 'step': + if todo == "step": if sim.output_volume is None: ov = sim.fields.total_volume() else: ov = sim.output_volume - closure['maxabs'] = max(closure['maxabs'], - sim.fields.max_abs(compnt, ov)) - convert = sim.h5topng(rm_h5, "-M {} {}".format(closure['maxabs'], options), - lambda sim: sim.output_component(compnt)) + closure["maxabs"] = max(closure["maxabs"], sim.fields.max_abs(compnt, ov)) + convert = sim.h5topng( + rm_h5, + "-M {} {}".format(closure["maxabs"], options), + lambda sim: sim.output_component(compnt), + ) convert(sim, todo) + return _output_png -def output_epsilon(sim=None,*step_func_args,**kwargs): +def output_epsilon(sim=None, *step_func_args, **kwargs): """ Given a frequency `frequency`, (provided as a keyword argument) output $\\varepsilon$ (relative permittivity); for an anisotropic $\\varepsilon$ tensor the output is the [harmonic @@ -4811,11 +5368,11 @@ def output_epsilon(sim=None,*step_func_args,**kwargs): if sim is None: return lambda sim: mp.output_epsilon(sim, *step_func_args, **kwargs) - frequency = kwargs.pop('frequency', 0.0) - sim.output_component(mp.Dielectric,frequency=frequency) + frequency = kwargs.pop("frequency", 0.0) + sim.output_component(mp.Dielectric, frequency=frequency) -def output_mu(sim=None,*step_func_args,**kwargs): +def output_mu(sim=None, *step_func_args, **kwargs): """ Given a frequency `frequency`, (provided as a keyword argument) output $\\mu$ (relative permeability); for an anisotropic $\\mu$ tensor the output is the [harmonic @@ -4829,8 +5386,8 @@ def output_mu(sim=None,*step_func_args,**kwargs): if sim is None: return lambda sim: mp.output_mu(sim, *step_func_args, **kwargs) - frequency = kwargs.pop('frequency', 0.0) - sim.output_component(mp.Permeability,frequency=frequency) + frequency = kwargs.pop("frequency", 0.0) + sim.output_component(mp.Permeability, frequency=frequency) def output_hpwr(sim): @@ -4862,7 +5419,7 @@ def output_hfield(sim): Outputs *all* the components of the field *h*, (magnetic) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('h', mp.Hx, mp.Hy, mp.Hz, mp.Hr, mp.Hp) + sim.output_components("h", mp.Hx, mp.Hy, mp.Hz, mp.Hr, mp.Hp) def output_hfield_x(sim): @@ -4915,7 +5472,7 @@ def output_bfield(sim): Outputs *all* the components of the field *b*, (magnetic) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('b', mp.Bx, mp.By, mp.Bz, mp.Br, mp.Bp) + sim.output_components("b", mp.Bx, mp.By, mp.Bz, mp.Br, mp.Bp) def output_bfield_x(sim): @@ -4971,7 +5528,7 @@ def output_efield(sim): Outputs *all* the components of the field *e*, (electric) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('e', mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep) + sim.output_components("e", mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep) def output_efield_x(sim): @@ -5027,7 +5584,7 @@ def output_dfield(sim): Outputs *all* the components of the field *d*, (displacement) to an HDF5 file. That is, the different components are stored as different datasets within the *same* file. """ - sim.output_components('d', mp.Dx, mp.Dy, mp.Dz, mp.Dr, mp.Dp) + sim.output_components("d", mp.Dx, mp.Dy, mp.Dz, mp.Dr, mp.Dp) def output_dfield_x(sim): @@ -5086,7 +5643,7 @@ def output_poynting(sim): accurately. See [Synchronizing the Magnetic and Electric Fields](Synchronizing_the_Magnetic_and_Electric_Fields.md). """ - sim.output_components('s', mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) + sim.output_components("s", mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) def output_poynting_x(sim): @@ -5117,7 +5674,7 @@ def output_sfield(sim): compute it more accurately. See [Synchronizing the Magnetic and Electric Fields](Synchronizing_the_Magnetic_and_Electric_Fields.md). """ - sim.output_components('s', mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) + sim.output_components("s", mp.Sx, mp.Sy, mp.Sz, mp.Sr, mp.Sp) def output_sfield_x(sim): @@ -5197,17 +5754,19 @@ def dft_ldos(*args, **kwargs): is also stored in the `ldos_data` variable of the `Simulation` object after the `run` is complete. """ - ldos = kwargs.get('ldos', None) + ldos = kwargs.get("ldos", None) if ldos is None: args = fix_dft_args(args, 0) freq = args[0] - if isinstance(freq, (np.ndarray,list)): + if isinstance(freq, (np.ndarray, list)): ldos = mp._dft_ldos(freq) else: - raise TypeError("dft_ldos only accepts freq_min,freq_max,nfreq (3 numbers) or freq (array/list) or ldos (keyword argument)") + raise TypeError( + "dft_ldos only accepts freq_min,freq_max,nfreq (3 numbers) or freq (array/list) or ldos (keyword argument)" + ) def _ldos(sim, todo): - if todo == 'step': + if todo == "step": ldos.update(sim.fields) else: sim.ldos_data = mp._dft_ldos_ldos(ldos) @@ -5215,7 +5774,8 @@ def _ldos(sim, todo): sim.ldos_Jdata = mp._dft_ldos_J(ldos) sim.ldos_scale = ldos.overall_scale() if verbosity.meep > 0: - display_csv(sim, 'ldos', zip(mp.get_ldos_freqs(ldos), sim.ldos_data)) + display_csv(sim, "ldos", zip(mp.get_ldos_freqs(ldos), sim.ldos_data)) + return _ldos @@ -5379,6 +5939,7 @@ def get_center_and_size(vol): size = v3rmax - v3rmin return center, size + def GDSII_layers(fname): """ Returns a list of integer-valued layer indices for the layers present in @@ -5391,6 +5952,7 @@ def GDSII_layers(fname): """ return list(mp.get_GDSII_layers(fname)) + def GDSII_vol(fname, layer, zmin, zmax): """ Returns a `mp.Volume` read from a GDSII file `fname` on layer number `layer` with @@ -5428,6 +5990,7 @@ def complexarray(re, im): z += re return z + def quiet(quietval=True): """ Meep ordinarily prints various diagnostic and progress information to standard output. @@ -5438,52 +6001,61 @@ def quiet(quietval=True): This function is deprecated, please use the [Verbosity](#verbosity) class instead. """ verbosity(int(not quietval)) - warnings.warn("quiet has been deprecated; use the Verbosity class instead", RuntimeWarning) + warnings.warn( + "quiet has been deprecated; use the Verbosity class instead", RuntimeWarning + ) def get_num_groups(): # Lazy import from mpi4py import MPI + comm = MPI.COMM_WORLD return comm.allreduce(int(mp.my_rank() == 0), op=MPI.SUM) + def get_group_masters(): # Lazy import from mpi4py import MPI + comm = MPI.COMM_WORLD num_workers = comm.Get_size() num_groups = mp.get_num_groups # Check if current worker is a group master is_group_master = True if mp.my_rank() == 0 else False - group_master_idx = np.zeros((num_workers,),dtype=np.bool) + group_master_idx = np.zeros((num_workers,), dtype=np.bool) # Formulate send and receive packets - smsg = [np.array([is_group_master]),([1]*num_workers, [0]*num_workers)] - rmsg = [group_master_idx,([1]*num_workers, list(range(num_workers)))] + smsg = [np.array([is_group_master]), ([1] * num_workers, [0] * num_workers)] + rmsg = [group_master_idx, ([1] * num_workers, list(range(num_workers)))] # Send and receive comm.Alltoallv(smsg, rmsg) # get rank of each group master - group_masters = np.arange(num_workers)[group_master_idx] # rank index of each group leader + group_masters = np.arange(num_workers)[ + group_master_idx + ] # rank index of each group leader return group_masters + def merge_subgroup_data(data): # Lazy import from mpi4py import MPI + comm = MPI.COMM_WORLD num_workers = comm.Get_size() num_groups = get_num_groups() # Initialize new input and output datasets - input=np.array(data,copy=True,order='F') - shape=input.shape - size=input.size - out_shape=shape + (num_groups,) - output=np.zeros(out_shape,input.dtype,order='F') + input = np.array(data, copy=True, order="F") + shape = input.shape + size = input.size + out_shape = shape + (num_groups,) + output = np.zeros(out_shape, input.dtype, order="F") # Get group masters group_masters = get_group_masters() @@ -5501,7 +6073,7 @@ def merge_subgroup_data(data): rdsp = [0] * num_workers buf_idx = 0 for grpidx in group_masters: - rdsp[grpidx] = buf_idx # offset group leader worker by size of each count + rdsp[grpidx] = buf_idx # offset group leader worker by size of each count buf_idx += size # Formulate send and receive packets @@ -5513,12 +6085,22 @@ def merge_subgroup_data(data): return output -class BinaryPartition(object): + +class BinaryPartition: """ Binary tree class used for specifying a cell partition of arbitrary sized chunks for use as the `chunk_layout` parameter of the `Simulation` class object. """ - def __init__(self, data=None, split_dir=None, split_pos=None, left=None, right=None, proc_id=None): + + def __init__( + self, + data=None, + split_dir=None, + split_pos=None, + left=None, + right=None, + proc_id=None, + ): """ The constructor accepts three separate groups of arguments: (1) `data`: a list of lists where each list entry is either (a) a node defined as `[ (split_dir,split_pos), left, right ]` for which `split_dir` @@ -5536,18 +6118,26 @@ def __init__(self, data=None, split_dir=None, split_pos=None, left=None, right=N self.left = None self.right = None if data is not None: - if isinstance(data,list) and len(data) == 3: - if isinstance(data[0],tuple) and len(data[0]) == 2: + if isinstance(data, list) and len(data) == 3: + if isinstance(data[0], tuple) and len(data[0]) == 2: self.split_dir = data[0][0] self.split_pos = data[0][1] else: - raise ValueError("expecting 2-tuple (split_dir,split_pos) but got {}".format(data[0])) + raise ValueError( + "expecting 2-tuple (split_dir,split_pos) but got {}".format( + data[0] + ) + ) self.left = BinaryPartition(data=data[1]) self.right = BinaryPartition(data=data[2]) - elif isinstance(data,int): + elif isinstance(data, int): self.proc_id = data else: - raise ValueError("expecting list [(split_dir,split_pos), left, right] or int (proc_id) but got {}".format(data)) + raise ValueError( + "expecting list [(split_dir,split_pos), left, right] or int (proc_id) but got {}".format( + data + ) + ) elif split_dir is not None: self.split_dir = split_dir self.split_pos = split_pos @@ -5558,16 +6148,16 @@ def __init__(self, data=None, split_dir=None, split_pos=None, left=None, right=N def print(self): """Pretty-prints the tree structure of the BinaryPartition object.""" - print(str(self) + " with {} chunks:".format(self.numchunks())) + print(str(self) + f" with {self.numchunks()} chunks:") for line in self._print(is_root=True): print(line) def _print(self, prefix="", is_root=True): # pointers - ptr_l = ' ├L─ ' - ext_l = ' │ ' - ptr_r = ' └R─ ' - ext_r = ' ' + ptr_l = " ├L─ " + ext_l = " │ " + ptr_r = " └R─ " + ext_r = " " if is_root: yield prefix + self._node_info() @@ -5575,28 +6165,23 @@ def _print(self, prefix="", is_root=True): if self.left is not None and self.right is not None: yield prefix + ptr_l + self.left._node_info() if self.left.left is not None and self.left.right is not None: - yield from self.left._print(prefix=prefix+ext_l, is_root=False) + yield from self.left._print(prefix=prefix + ext_l, is_root=False) yield prefix + ptr_r + self.right._node_info() if self.right.left is not None and self.right.right is not None: - yield from self.right._print(prefix=prefix+ext_r, is_root=False) + yield from self.right._print(prefix=prefix + ext_r, is_root=False) def _node_info(self) -> str: if self.proc_id is not None: - return "".format(self.proc_id) + return f"" else: - split_dir_str = { - mp.X: "X", - mp.Y: "Y", - mp.Z: "Z" - }[self.split_dir] - return "".format( - split_dir_str, self.split_pos) - - def _numchunks(self,bp): + split_dir_str = {mp.X: "X", mp.Y: "Y", mp.Z: "Z"}[self.split_dir] + return f"" + + def _numchunks(self, bp): if bp is None: return 0 - return max(self._numchunks(bp.left)+self._numchunks(bp.right), 1) + return max(self._numchunks(bp.left) + self._numchunks(bp.right), 1) def numchunks(self): return self._numchunks(self) diff --git a/python/solver.py b/python/solver.py index 17e3ae17e..106efd456 100644 --- a/python/solver.py +++ b/python/solver.py @@ -1,19 +1,21 @@ -# -*- coding: utf-8 -*- import functools import math -import os import numbers +import os import re import sys import time import h5py import numpy as np -import meep as mp -from . import mode_solver, with_hermitian_epsilon from meep.geom import init_do_averaging from meep.simulation import get_num_args from meep.verbosity_mgr import Verbosity + +import meep as mp + +from . import mode_solver, with_hermitian_epsilon + try: basestring except NameError: @@ -23,10 +25,10 @@ U_PROD = 1 U_MEAN = 2 -verbosity = Verbosity(mp.cvar, 'meep', 1) +verbosity = Verbosity(mp.cvar, "meep", 1) -class MPBArray(np.ndarray): +class MPBArray(np.ndarray): def __new__(cls, input_array, lattice, kpoint=None, bloch_phase=False): # Input array is an already formed ndarray instance # We first cast to be our class type @@ -64,53 +66,54 @@ def __array_finalize__(self, obj): # method sees all creation of default objects - with the # MPBArray.__new__ constructor, but also with # arr.view(MPBArray). - self.lattice = getattr(obj, 'lattice', None) - self.kpoint = getattr(obj, 'kpoint', None) - self.bloch_phase = getattr(obj, 'bloch_phase', False) - - -class ModeSolver(object): - - def __init__(self, - resolution=10, - is_negative_epsilon_ok=False, - eigensolver_flops=0, - eigensolver_flags=68, - use_simple_preconditioner=False, - force_mu=False, - mu_input_file='', - epsilon_input_file='', - mesh_size=3, - target_freq=0.0, - tolerance=1.0e-7, - num_bands=1, - k_points=None, - ensure_periodicity=True, - geometry=None, - geometry_lattice=mp.Lattice(), - geometry_center=mp.Vector3(0, 0, 0), - default_material=mp.Medium(epsilon=1), - dimensions=3, - random_fields=False, - filename_prefix='', - deterministic=False, - verbose=False, - optimize_grid_size=True, - eigensolver_nwork=3, - eigensolver_block_size=-11): + self.lattice = getattr(obj, "lattice", None) + self.kpoint = getattr(obj, "kpoint", None) + self.bloch_phase = getattr(obj, "bloch_phase", False) + + +class ModeSolver: + def __init__( + self, + resolution=10, + is_negative_epsilon_ok=False, + eigensolver_flops=0, + eigensolver_flags=68, + use_simple_preconditioner=False, + force_mu=False, + mu_input_file="", + epsilon_input_file="", + mesh_size=3, + target_freq=0.0, + tolerance=1.0e-7, + num_bands=1, + k_points=None, + ensure_periodicity=True, + geometry=None, + geometry_lattice=mp.Lattice(), + geometry_center=mp.Vector3(0, 0, 0), + default_material=mp.Medium(epsilon=1), + dimensions=3, + random_fields=False, + filename_prefix="", + deterministic=False, + verbose=False, + optimize_grid_size=True, + eigensolver_nwork=3, + eigensolver_block_size=-11, + ): self.mode_solver = None self.resolution = resolution self.eigensolver_flags = eigensolver_flags - self.k_points = k_points if k_points else [] - self.geometry = geometry if geometry else [] + self.k_points = k_points or [] + self.geometry = geometry or [] self.geometry_lattice = geometry_lattice self.geometry_center = mp.Vector3(*geometry_center) self.default_material = default_material self.random_fields = random_fields self.filename_prefix = filename_prefix self.optimize_grid_size = optimize_grid_size - self.parity = '' + self.parity = "" self.iterations = 0 self.all_freqs = None self.freqs = [] @@ -123,7 +126,9 @@ def __init__(self, grid_size = self._adjust_grid_size() - if type(self.default_material) is not mp.Medium and callable(self.default_material): + if type(self.default_material) is not mp.Medium and callable( + self.default_material + ): init_do_averaging(self.default_material) self.default_material.eps = False @@ -170,7 +175,7 @@ def resolution(self, val): self._resolution = [val.x, val.y, val.z] else: t = type(val) - raise TypeError("resolution must be a number or a Vector3: Got {}".format(t)) + raise TypeError(f"resolution must be a number or a Vector3: Got {t}") if self.mode_solver: self.mode_solver.resolution = self._resolution @@ -323,14 +328,14 @@ def allow_negative_epsilon(self): def get_filename_prefix(self): if self.filename_prefix: - return self.filename_prefix + '-' - else: - _, filename = os.path.split(sys.argv[0]) + return f"{self.filename_prefix}-" + _, filename = os.path.split(sys.argv[0]) - if filename == 'ipykernel_launcher.py' or filename == '__main__.py': - return '' - else: - return re.sub(r'\.py$', '', filename) + '-' + return ( + "" + if filename in ["ipykernel_launcher.py", "__main__.py"] + else re.sub(r"\.py$", "", filename) + "-" + ) def get_freqs(self): return self.mode_solver.get_freqs() @@ -362,7 +367,7 @@ def ExH(e, h): flat_res = res.ravel() self.mode_solver.set_curfield_cmplx(flat_res) - self.mode_solver.set_curfield_type('v') + self.mode_solver.set_curfield_type("v") arr = np.reshape(res, dims) return MPBArray(arr, self.get_lattice(), self.current_k) @@ -376,16 +381,16 @@ def get_mu(self): return self.get_curfield_as_array(False) def get_bfield(self, which_band, bloch_phase=True): - return self._get_field('b', which_band, bloch_phase) + return self._get_field("b", which_band, bloch_phase) def get_efield(self, which_band, bloch_phase=True): - return self._get_field('e', which_band, bloch_phase) + return self._get_field("e", which_band, bloch_phase) def get_dfield(self, which_band, bloch_phase=True): - return self._get_field('d', which_band, bloch_phase) + return self._get_field("d", which_band, bloch_phase) def get_hfield(self, which_band, bloch_phase=True): - return self._get_field('h', which_band, bloch_phase) + return self._get_field("h", which_band, bloch_phase) def get_charge_density(self, which_band, bloch_phase=True): self.get_efield(which_band, bloch_phase) @@ -393,15 +398,17 @@ def get_charge_density(self, which_band, bloch_phase=True): def _get_field(self, f, band, bloch_phase): if self.mode_solver is None: - raise ValueError("Must call a run function before attempting to get a field") + raise ValueError( + "Must call a run function before attempting to get a field" + ) - if f == 'b': + if f == "b": self.mode_solver.get_bfield(band) - elif f == 'd': + elif f == "d": self.mode_solver.get_dfield(band) - elif f == 'e': + elif f == "e": self.mode_solver.get_efield(band) - elif f == 'h': + elif f == "h": self.mode_solver.get_hfield(band) dims = self.mode_solver.get_dims() @@ -418,9 +425,9 @@ def _get_field(self, f, band, bloch_phase): self.mode_solver.get_curfield_cmplx(arr) arr = np.reshape(arr, dims) - res = MPBArray(arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase) - - return res + return MPBArray( + arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase + ) def get_curfield_as_array(self, bloch_phase=True): dims = self.mode_solver.get_dims() @@ -428,7 +435,9 @@ def get_curfield_as_array(self, bloch_phase=True): self.mode_solver.get_curfield(arr) arr = np.reshape(arr, dims) - return MPBArray(arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase) + return MPBArray( + arr, self.get_lattice(), self.current_k, bloch_phase=bloch_phase + ) def get_dpwr(self, band): self.get_dfield(band, False) @@ -465,7 +474,7 @@ def get_tot_pwr(self, which_band): tot_pwr = epwr + hpwr self.mode_solver.set_curfield(tot_pwr.ravel()) - self.mode_solver.set_curfield_type('R') + self.mode_solver.set_curfield_type("R") return MPBArray(tot_pwr, self.get_lattice(), self.current_k, bloch_phase=False) @@ -479,13 +488,13 @@ def set_eigenvectors(self, ev, first_band): self.mode_solver.set_eigenvectors(first_band - 1, ev.flatten()) def save_eigenvectors(self, filename): - with h5py.File(filename, 'w') as f: + with h5py.File(filename, "w") as f: ev = self.get_eigenvectors(1, self.num_bands) - f['rawdata'] = ev + f["rawdata"] = ev def load_eigenvectors(self, filename): - with h5py.File(filename, 'r') as f: - ev = f['rawdata'][()] + with h5py.File(filename, "r") as f: + ev = f["rawdata"][()] self.set_eigenvectors(ev, 1) self.mode_solver.curfield_reset() @@ -495,24 +504,22 @@ def load_eigenvectors(self, filename): # Here, we update this data with a new list of band frequencies, and return the new # data. If band-range-data is null or too short, the needed entries will be created. def update_band_range_data(self, brd, freqs, kpoint): - def update_brd(brd, freqs, br_start): if not freqs: return br_start + brd - else: - br = ((mp.inf, -1), (-mp.inf, -1)) if not brd else brd[0] - br_rest = [] if not brd else brd[1:] - newmin = (freqs[0], kpoint) if freqs[0] < br[0][0] else br[0] - newmax = (freqs[0], kpoint) if freqs[0] > br[1][0] else br[1] - new_start = br_start + [(newmin, newmax)] - return update_brd(br_rest, freqs[1:], new_start) + br = brd[0] if brd else ((mp.inf, -1), (-mp.inf, -1)) + br_rest = brd[1:] if brd else [] + newmin = (freqs[0], kpoint) if freqs[0] < br[0][0] else br[0] + newmax = (freqs[0], kpoint) if freqs[0] > br[1][0] else br[1] + new_start = br_start + [(newmin, newmax)] + return update_brd(br_rest, freqs[1:], new_start) return update_brd(brd, freqs, []) def output_band_range_data(self, br_data): if verbosity.mpb > 0: + fmt = "Band {} range: {} at {} to {} at {}" for tup, band in zip(br_data, range(1, len(br_data) + 1)): - fmt = "Band {} range: {} at {} to {} at {}" min_band, max_band = tup min_freq, min_kpoint = min_band max_freq, max_kpoint = max_band @@ -521,27 +528,30 @@ def output_band_range_data(self, br_data): # Output any gaps in the given band ranges, and return a list of the gaps as # a list of (percent, freq-min, freq-max) tuples. def output_gaps(self, br_data): - def ogaps(br_cur, br_rest, i, gaps): if not br_rest: - ordered_gaps = [] gaps = list(reversed(gaps)) - for i in range(0, len(gaps), 3): - ordered_gaps.append((gaps[i + 2], gaps[i + 1], gaps[i])) - return ordered_gaps + return [ + (gaps[i + 2], gaps[i + 1], gaps[i]) for i in range(0, len(gaps), 3) + ] else: br_rest_min_f = br_rest[0][0][0] br_cur_max_f = br_cur[1][0] if br_cur_max_f >= br_rest_min_f: return ogaps(br_rest[0], br_rest[1:], i + 1, gaps) - else: - gap_size = ((200 * (br_rest_min_f - br_cur_max_f)) / - (br_rest_min_f + br_cur_max_f)) - if verbosity.mpb > 0: - fmt = "Gap from band {} ({}) to band {} ({}), {}%" - print(fmt.format(i, br_cur_max_f, i + 1, br_rest_min_f, gap_size)) - return ogaps(br_rest[0], br_rest[1:], i + 1, - [gap_size, br_cur_max_f, br_rest_min_f] + gaps) + gap_size = (200 * (br_rest_min_f - br_cur_max_f)) / ( + br_rest_min_f + br_cur_max_f + ) + if verbosity.mpb > 0: + fmt = "Gap from band {} ({}) to band {} ({}), {}%" + print(fmt.format(i, br_cur_max_f, i + 1, br_rest_min_f, gap_size)) + return ogaps( + br_rest[0], + br_rest[1:], + i + 1, + [gap_size, br_cur_max_f, br_rest_min_f] + gaps, + ) + if not br_data: return [] else: @@ -565,7 +575,6 @@ def retrieve_gap(self, lower_band): # given by index (in 0..num-1), along with the index in L of the first # element of the piece, as a list: [first-index, piece-of-L] def list_split(self, l, num, index): - def list_sub(l, start, length, index, rest): if not l: return list(reversed(rest)) @@ -615,39 +624,36 @@ def output_field_to_file(self, component, fname_prefix): curfield_type = self.mode_solver.get_curfield_type() output_k = self.mode_solver.get_output_k() - if curfield_type in 'Rv': + if curfield_type in "Rv": # Generic scalar/vector field. Don't know k output_k = [0, 0, 0] - if curfield_type in 'dhbecv': + if curfield_type in "dhbecv": self._output_vector_field(curfield_type, fname_prefix, output_k, component) - elif curfield_type == 'C': + elif curfield_type == "C": self._output_complex_scalar_field(fname_prefix, output_k) - elif curfield_type in 'DHBnmR': + elif curfield_type in "DHBnmR": self._output_scalar_field(curfield_type, fname_prefix) else: - raise ValueError("Unkown field type: {}".format(curfield_type)) + raise ValueError(f"Unkown field type: {curfield_type}") self.mode_solver.curfield_reset() def _output_complex_scalar_field(self, fname_prefix, output_k): - curfield_type = 'C' + curfield_type = "C" kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band - fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) + fname = f"{curfield_type}.k{kpoint_index:02d}.b{curfield_band:02d}" description = "{} field, kpoint {}, band {}, freq={:.6g}".format( - curfield_type, - kpoint_index, - curfield_band, - self.freqs[curfield_band - 1] + curfield_type, kpoint_index, curfield_band, self.freqs[curfield_band - 1] ) fname = self._create_fname(fname, fname_prefix, True) if verbosity.mpb > 0: - print("Outputting complex scalar field to {}...".format(fname)) + print(f"Outputting complex scalar field to {fname}...") - with h5py.File(fname, 'w') as f: - f['description'] = description.encode() - f['Bloch wavevector'] = np.array(output_k) + with h5py.File(fname, "w") as f: + f["description"] = description.encode() + f["Bloch wavevector"] = np.array(output_k) self._write_lattice_vectors(f) dims = self.mode_solver.get_dims() @@ -656,35 +662,32 @@ def _output_complex_scalar_field(self, fname_prefix, output_k): reshaped_field = field.reshape(dims) - f['c.r'] = np.real(reshaped_field) - f['c.i'] = np.imag(reshaped_field) + f["c.r"] = np.real(reshaped_field) + f["c.i"] = np.imag(reshaped_field) def _output_vector_field(self, curfield_type, fname_prefix, output_k, component): - components = ['x', 'y', 'z'] + components = ["x", "y", "z"] kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band - fname = "{}.k{:02d}.b{:02d}".format(curfield_type, kpoint_index, curfield_band) + fname = f"{curfield_type}.k{kpoint_index:02d}.b{curfield_band:02d}" if component >= 0: - fname += ".{}".format(components[component]) + fname += f".{components[component]}" description = "{} field, kpoint {}, band {}, freq={:.6g}".format( - curfield_type, - kpoint_index, - curfield_band, - self.freqs[curfield_band - 1] + curfield_type, kpoint_index, curfield_band, self.freqs[curfield_band - 1] ) fname = self._create_fname(fname, fname_prefix, True) if verbosity.mpb > 0: - print("Outputting fields to {}...".format(fname)) + print(f"Outputting fields to {fname}...") - with h5py.File(fname, 'w') as f: - f['description'] = description.encode() - f['Bloch wavevector'] = np.array(output_k) + with h5py.File(fname, "w") as f: + f["description"] = description.encode() + f["Bloch wavevector"] = np.array(output_k) self._write_lattice_vectors(f) - if curfield_type != 'v': + if curfield_type != "v": self.mode_solver.multiply_bloch_phase() for c_idx, c in enumerate(components): @@ -696,70 +699,70 @@ def _output_vector_field(self, curfield_type, fname_prefix, output_k, component) self.mode_solver.get_curfield_cmplx(field) component_field = field[c_idx::3].reshape(dims) - name = "{}.r".format(c) + name = f"{c}.r" f[name] = np.real(component_field) - name = "{}.i".format(c) + name = f"{c}.i" f[name] = np.imag(component_field) def _output_scalar_field(self, curfield_type, fname_prefix): - components = ['x', 'y', 'z'] - - if curfield_type == 'n': - fname = 'epsilon' - description = 'dielectric function, epsilon' - elif curfield_type == 'm': - fname = 'mu' - description = 'permeability mu' + components = ["x", "y", "z"] + + if curfield_type == "n": + fname = "epsilon" + description = "dielectric function, epsilon" + elif curfield_type == "m": + fname = "mu" + description = "permeability mu" else: kpoint_index = self.mode_solver.get_kpoint_index() curfield_band = self.mode_solver.curfield_band - fname = "{}pwr.k{:02d}.b{:02d}".format(curfield_type.lower(), - kpoint_index, curfield_band) + fname = "{}pwr.k{:02d}.b{:02d}".format( + curfield_type.lower(), kpoint_index, curfield_band + ) descr_fmt = "{} field energy density, kpoint {}, band {}, freq={:.6g}" - description = descr_fmt.format(curfield_type, kpoint_index, curfield_band, - self.freqs[curfield_band - 1]) - - parity_suffix = False if curfield_type in 'mn' else True + description = descr_fmt.format( + curfield_type, + kpoint_index, + curfield_band, + self.freqs[curfield_band - 1], + ) + + parity_suffix = curfield_type not in "mn" fname = self._create_fname(fname, fname_prefix, parity_suffix) if verbosity.mpb > 0: - print("Outputting {}...".format(fname)) + print(f"Outputting {fname}...") - with h5py.File(fname, 'w') as f: - f['description'] = description.encode() - self._create_h5_dataset(f, 'data') + with h5py.File(fname, "w") as f: + f["description"] = description.encode() + self._create_h5_dataset(f, "data") self._write_lattice_vectors(f) - if curfield_type == 'n': + if curfield_type == "n": for inv in [False, True]: - inv_str = 'epsilon_inverse' if inv else 'epsilon' + inv_str = "epsilon_inverse" if inv else "epsilon" for c1 in range(3): for c2 in range(c1, 3): self.mode_solver.get_epsilon_tensor(c1, c2, 0, inv) - dataname = "{}.{}{}".format(inv_str, components[c1], - components[c2]) + dataname = f"{inv_str}.{components[c1]}{components[c2]}" self._create_h5_dataset(f, dataname) if with_hermitian_epsilon() and c1 != c2: self.mode_solver.get_epsilon_tensor(c1, c2, 1, inv) - dataname += '.i' + dataname += ".i" self._create_h5_dataset(f, dataname) def _write_lattice_vectors(self, h5file): lattice = np.zeros((3, 3)) self.mode_solver.get_lattice(lattice) - h5file['lattice vectors'] = lattice + h5file["lattice vectors"] = lattice def _create_h5_dataset(self, h5file, key): h5file[key] = self.get_curfield_as_array(False) def _create_fname(self, fname, prefix, parity_suffix): parity_str = self.mode_solver.get_parity_string() - if parity_suffix and parity_str: - suffix = ".{}".format(parity_str) - else: - suffix = '' - - return prefix + fname + suffix + '.h5' + suffix = f".{parity_str}" if parity_suffix and parity_str else "" + return prefix + fname + suffix + ".h5" def compute_field_energy(self): return self.mode_solver.compute_field_energy() @@ -796,8 +799,9 @@ def compute_one_group_velocity(self, which_band): return self.mode_solver.compute_1_group_velocity(which_band) def compute_one_group_velocity_component(self, direction, which_band): - return self.mode_solver.compute_1_group_velocity_component(direction, - which_band) + return self.mode_solver.compute_1_group_velocity_component( + direction, which_band + ) def compute_zparities(self): return self.mode_solver.compute_zparities() @@ -809,12 +813,12 @@ def randomize_fields(self): self.mode_solver.randomize_fields() def display_kpoint_data(self, name, data): - k_index = self.mode_solver.get_kpoint_index() if verbosity.mpb > 0: - print("{}{}:, {}".format(self.parity, name, k_index), end='') + k_index = self.mode_solver.get_kpoint_index() + print(f"{self.parity}{name}:, {k_index}", end="") for d in data: - print(", {}".format(d), end='') + print(f", {d}", end="") print() def display_eigensolver_stats(self): @@ -828,27 +832,29 @@ def display_eigensolver_stats(self): mean_iters = np.mean(self.eigensolver_iters) if verbosity.mpb > 0: fmt = "eigensolver iterations for {} kpoints: {}-{}, mean = {}" - print(fmt.format(num_runs, min_iters, max_iters, mean_iters), end='') + print(fmt.format(num_runs, min_iters, max_iters, mean_iters), end="") sorted_iters = sorted(self.eigensolver_iters) idx1 = num_runs // 2 idx2 = ((num_runs + 1) // 2) - 1 median_iters = 0.5 * (sorted_iters[idx1] + sorted_iters[idx2]) if verbosity.mpb > 0: - print(", median = {}".format(median_iters)) + print(f", median = {median_iters}") mean_flops = self.eigensolver_flops / (num_runs * mean_iters) if verbosity.mpb > 0: - print("mean flops per iteration = {}".format(mean_flops)) + print(f"mean flops per iteration = {mean_flops}") mean_time = self.total_run_time / (mean_iters * num_runs) if verbosity.mpb > 0: - print("mean time per iteration = {} s".format(mean_time)) + print(f"mean time per iteration = {mean_time} s") def _get_grid_size(self): - grid_size = mp.Vector3(self.resolution[0] * self.geometry_lattice.size.x, - self.resolution[1] * self.geometry_lattice.size.y, - self.resolution[2] * self.geometry_lattice.size.z) + grid_size = mp.Vector3( + self.resolution[0] * self.geometry_lattice.size.x, + self.resolution[1] * self.geometry_lattice.size.y, + self.resolution[2] * self.geometry_lattice.size.z, + ) grid_size.x = max(math.ceil(grid_size.x), 1) grid_size.y = max(math.ceil(grid_size.y), 1) @@ -863,13 +869,10 @@ def _optimize_grid_size(self, grid_size): return grid_size def next_factor2357(self, n): - def is_factor2357(n): - def divby(n, p): - if n % p == 0: - return divby(n // p, p) - return n + return divby(n // p, p) if n % p == 0 else n + return divby(divby(divby(divby(n, 2), 3), 5), 7) == 1 if is_factor2357(n): @@ -899,7 +902,7 @@ def run_parity(self, p, reset_fields, *band_functions): if verbosity.mpb > 0: print("Initializing eigensolver data") - print("Computing {} bands with {} tolerance".format(self.num_bands, self.tolerance)) + print(f"Computing {self.num_bands} bands with {self.tolerance} tolerance") self.init_params(p, reset_fields) @@ -907,12 +910,12 @@ def run_parity(self, p, reset_fields, *band_functions): self.load_eigenvectors(reset_fields) if verbosity.mpb > 0: - print("{} k-points".format(len(self.k_points))) + print(f"{len(self.k_points)} k-points") for kp in self.k_points: - print(" {}".format(kp)) + print(f" {kp}") - print("elapsed time for initialization: {}".format(time.time() - init_time)) + print(f"elapsed time for initialization: {time.time() - init_time}") # TODO: Split over multiple processes # k_split = list_split(self.k_points, self.k_split_num, self.k_split_index) @@ -926,10 +929,13 @@ def run_parity(self, p, reset_fields, *band_functions): self.solve_kpoint(k) self.iterations = self.mode_solver.get_iterations() if verbosity.mpb > 0: - print("elapsed time for k point: {}".format(time.time() - solve_kpoint_time)) + print( + f"elapsed time for k point: {time.time() - solve_kpoint_time}" + ) self.all_freqs[i, :] = np.array(self.freqs) - self.band_range_data = self.update_band_range_data(self.band_range_data, - self.freqs, k) + self.band_range_data = self.update_band_range_data( + self.band_range_data, self.freqs, k + ) self.eigensolver_iters += [self.iterations / self.num_bands] for f in band_functions: @@ -942,8 +948,10 @@ def run_parity(self, p, reset_fields, *band_functions): f(self, band) band += 1 else: - raise ValueError("Band function should take 1 or 2 arguments. " - "The first must be a ModeSolver instance") + raise ValueError( + "Band function should take 1 or 2 arguments. " + "The first must be a ModeSolver instance" + ) if len(k_split[1]) > 1: self.output_band_range_data(self.band_range_data) @@ -953,7 +961,7 @@ def run_parity(self, p, reset_fields, *band_functions): end = time.time() - start if verbosity.mpb > 0: - print("total elapsed time for run: {}".format(end)) + print(f"total elapsed time for run: {end}") self.total_run_time += end self.eigensolver_flops = self.mode_solver.get_eigensolver_flops() self.parity = self.mode_solver.get_parity_string() @@ -994,15 +1002,28 @@ def run_yodd_zodd(self, *band_functions): run_tm_yeven = run_yeven_zodd run_tm_yodd = run_yodd_zodd - def find_k(self, p, omega, band_min, band_max, korig_and_kdir, tol, - kmag_guess, kmag_min, kmag_max, *band_funcs): + def find_k( + self, + p, + omega, + band_min, + band_max, + korig_and_kdir, + tol, + kmag_guess, + kmag_min, + kmag_max, + *band_funcs, + ): num_bands_save = self.num_bands kpoints_save = self.k_points nb = band_max - band_min + 1 kdir = korig_and_kdir[1] if type(korig_and_kdir) is list else korig_and_kdir lat = self.geometry_lattice - kdir1 = mp.cartesian_to_reciprocal(mp.reciprocal_to_cartesian(kdir, lat).unit(), lat) + kdir1 = mp.cartesian_to_reciprocal( + mp.reciprocal_to_cartesian(kdir, lat).unit(), lat + ) if type(korig_and_kdir) is list: korig = korig_and_kdir[0] @@ -1019,15 +1040,13 @@ def find_k(self, p, omega, band_min, band_max, korig_and_kdir, tol, bktab = {} def rootfun(b): - def _rootfun(k): # First, look in the cached table tab_val = bktab.get((b, k), None) if tab_val: if verbosity.mpb > 0: - print("find-k {} at {}: {} (cached)".format(b, k, tab_val[0])) + print(f"find-k {b} at {k}: {tab_val[0]} (cached)") return tab_val - # Otherwise, compute bands and cache results else: self.num_bands = b self.k_points = [korig + kdir1.scale(k)] @@ -1035,9 +1054,11 @@ def _rootfun(k): v = self.mode_solver.compute_group_velocity_component(kdir1) # Cache computed values - for _b, _f, _v in zip(range(band_min, b - band_min + 1, 1), - self.freqs[band_min - 1:], - v[band_min - 1:]): + for _b, _f, _v in zip( + range(band_min, b - band_min + 1), + self.freqs[band_min - 1 :], + v[band_min - 1 :], + ): tabval = bktab.get((_b, k0s[_b - band_min]), None) if not tabval or abs(_f - omega) < abs(tabval[0]): @@ -1047,7 +1068,7 @@ def _rootfun(k): fun = self.freqs[-1] - omega if verbosity.mpb > 0: - print("find-k {} at {}: {}".format(b, k, fun)) + print(f"find-k {b} at {k}: {fun}") return (fun, v[-1]) return _rootfun @@ -1058,10 +1079,14 @@ def _rootfun(k): ks = [] for b in range(band_max, band_max - nb, -1): - ks.append(mp.find_root_deriv(rootfun(b), tol, kmag_min, kmag_max, k0s[b - band_min])) + ks.append( + mp.find_root_deriv( + rootfun(b), tol, kmag_min, kmag_max, k0s[b - band_min] + ) + ) if band_funcs: - for b, k in zip(range(1, band_max +1), reversed(ks)): + for b, k in zip(range(1, band_max + 1), reversed(ks)): self.num_bands = b self.k_points = [korig + kdir1.scale(k)] @@ -1076,13 +1101,16 @@ def bfunc(ms, b_prime): self.k_points = kpoints_save ks = list(reversed(ks)) if verbosity.mpb > 0: - print("{}kvals:, {}, {}, {}".format(self.parity, omega, band_min, band_max), end='') + print( + f"{self.parity}kvals:, {omega}, {band_min}, {band_max}", + end="", + ) for k in korig: - print(", {}".format(k), end='') + print(f", {k}", end="") for k in kdir1: - print(", {}".format(k), end='') + print(f", {k}", end="") for k in ks: - print(", {}".format(k), end='') + print(f", {k}", end="") print() return ks @@ -1092,23 +1120,29 @@ def first_brillouin_zone(self, k): Function to convert a k-point k into an equivalent point in the first Brillouin zone (not necessarily the irreducible Brillouin zone) """ + def n(k): return mp.reciprocal_to_cartesian(k, self.geometry_lattice).norm() def try_plus(k, v): - if n(k + v) < n(k): - return try_plus(k + v, v) - else: - return k + return try_plus(k + v, v) if n(k + v) < n(k) else k def _try(k, v): return try_plus(try_plus(k, v), mp.Vector3() - v) try_list = [ - mp.Vector3(1, 0, 0), mp.Vector3(0, 1, 0), mp.Vector3(0, 0, 1), - mp.Vector3(0, 1, 1), mp.Vector3(1, 0, 1), mp.Vector3(1, 1, 0), - mp.Vector3(0, 1, -1), mp.Vector3(1, 0, -1), mp.Vector3(1, -1, 0), - mp.Vector3(1, 1, 1), mp.Vector3(-1, 1, 1), mp.Vector3(1, -1, 1), + mp.Vector3(1, 0, 0), + mp.Vector3(0, 1, 0), + mp.Vector3(0, 0, 1), + mp.Vector3(0, 1, 1), + mp.Vector3(1, 0, 1), + mp.Vector3(1, 1, 0), + mp.Vector3(0, 1, -1), + mp.Vector3(1, 0, -1), + mp.Vector3(1, -1, 0), + mp.Vector3(1, 1, 1), + mp.Vector3(-1, 1, 1), + mp.Vector3(1, -1, 1), mp.Vector3(1, 1, -1), ] @@ -1131,16 +1165,17 @@ def transformed_overlap(self, W, w): def compute_symmetry(self, band, W, w): return self.mode_solver.compute_symmetry(band, W, w) - + def compute_symmetries(self, W, w): - symvals = [] - for band in range(1, self.num_bands+1): - symvals.append(self.mode_solver.compute_symmetry(band, W, w)) - return symvals + return [ + self.mode_solver.compute_symmetry(band, W, w) + for band in range(1, self.num_bands + 1) + ] # Predefined output functions (functions of the band index), for passing to `run` + def output_hfield(ms, which_band): ms.get_hfield(which_band, False) ms.output_field() @@ -1235,7 +1270,7 @@ def output_dpwr(ms, which_band): def output_tot_pwr(ms, which_band): ms.get_tot_pwr(which_band) - ms.output_field_to_file(-1, ms.get_filename_prefix() + 'tot.') + ms.output_field_to_file(-1, f"{ms.get_filename_prefix()}tot.") def output_dpwr_in_objects(output_func, min_energy, objects=[]): @@ -1267,39 +1302,40 @@ def output_charge_density(ms, which_band): def output_poynting(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(-1, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(-1, f"{ms.get_filename_prefix()}flux.") def output_poynting_x(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(0, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(0, f"{ms.get_filename_prefix()}flux.") def output_poynting_y(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(1, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(1, f"{ms.get_filename_prefix()}flux.") def output_poynting_z(ms, which_band): ms.get_poynting(which_band) - ms.output_field_to_file(2, ms.get_filename_prefix() + 'flux.') + ms.output_field_to_file(2, f"{ms.get_filename_prefix()}flux.") def display_yparities(ms): - ms.display_kpoint_data('yparity', ms.mode_solver.compute_yparities()) + ms.display_kpoint_data("yparity", ms.mode_solver.compute_yparities()) def display_zparities(ms): - ms.display_kpoint_data('zparity', ms.mode_solver.compute_zparities()) + ms.display_kpoint_data("zparity", ms.mode_solver.compute_zparities()) def display_group_velocities(ms): - ms.display_kpoint_data('velocity', ms.compute_group_velocities()) + ms.display_kpoint_data("velocity", ms.compute_group_velocities()) # Band functions to pick a canonical phase for the eigenstate of the # given band based upon the spatial representation of the given field + def fix_hfield_phase(ms, which_band): ms.get_hfield(which_band, False) ms.mode_solver.fix_field_phase() @@ -1343,6 +1379,7 @@ def combine_band_functions(*band_funcs): def _combine(ms, which_band): for f in band_funcs: apply_band_func(ms, f, which_band) + return _combine diff --git a/python/source.py b/python/source.py index b50a30acb..bedede024 100644 --- a/python/source.py +++ b/python/source.py @@ -1,17 +1,18 @@ -# -*- coding: utf-8 -*- import warnings -import meep as mp + from meep.geom import Vector3, check_nonnegative +import meep as mp + def check_positive(prop, val): if val > 0: return val else: - raise ValueError("{} must be positive. Got {}".format(prop, val)) + raise ValueError(f"{prop} must be positive. Got {val}") -class Source(object): +class Source: """ The `Source` class is used to specify the current sources via the `Simulation.sources` attribute. Note that all sources in Meep are separable in time and space, i.e. of the @@ -33,8 +34,19 @@ class Source(object): the book [Advances in FDTD Computational Electrodynamics: Photonics and Nanotechnology](https://www.amazon.com/Advances-FDTD-Computational-Electrodynamics-Nanotechnology/dp/1608071707). """ - def __init__(self, src, component, center=None, volume=None, size=Vector3(), amplitude=1.0, amp_func=None, - amp_func_file='', amp_data=None): + + def __init__( + self, + src, + component, + center=None, + volume=None, + size=Vector3(), + amplitude=1.0, + amp_func=None, + amp_func_file="", + amp_data=None, + ): """ Construct a `Source`. @@ -115,11 +127,12 @@ def __init__(self, src, component, center=None, volume=None, size=Vector3(), amp self.amp_data = amp_data -class SourceTime(object): +class SourceTime: """ This is the parent for classes describing the time dependence of sources; it should not be instantiated directly. """ + def __init__(self, is_integrated=False): self.is_integrated = is_integrated @@ -132,9 +145,18 @@ class ContinuousSource(SourceTime): response](FAQ.md#why-doesnt-the-continuous-wave-cw-source-produce-an-exact-single-frequency-response). """ - def __init__(self, frequency=None, start_time=0, end_time=1.0e20, width=0, - fwidth=float('inf'), cutoff=3.0, wavelength=None, - is_integrated=False, **kwargs): + def __init__( + self, + frequency=None, + start_time=0, + end_time=1.0e20, + width=0, + fwidth=float("inf"), + cutoff=3.0, + wavelength=None, + is_integrated=False, + **kwargs, + ): """ Construct a `ContinuousSource`. @@ -171,16 +193,19 @@ def __init__(self, frequency=None, start_time=0, end_time=1.0e20, width=0, """ if frequency is None and wavelength is None: - raise ValueError("Must set either frequency or wavelength in {}.".format(self.__class__.__name__)) + raise ValueError( + f"Must set either frequency or wavelength in {self.__class__.__name__}." + ) - super(ContinuousSource, self).__init__(is_integrated=is_integrated, **kwargs) + super().__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) self.start_time = start_time self.end_time = end_time self.width = max(width, 1 / fwidth) self.cutoff = cutoff - self.swigobj = mp.continuous_src_time(self.frequency, self.width, self.start_time, - self.end_time, self.cutoff) + self.swigobj = mp.continuous_src_time( + self.frequency, self.width, self.start_time, self.end_time, self.cutoff + ) self.swigobj.is_integrated = self.is_integrated @@ -192,8 +217,18 @@ class GaussianSource(SourceTime): (t-t_0)^2/2w^2)$, but the difference between this and a true Gaussian is usually irrelevant. """ - def __init__(self, frequency=None, width=0, fwidth=float('inf'), start_time=0, cutoff=5.0, - is_integrated=False, wavelength=None, **kwargs): + + def __init__( + self, + frequency=None, + width=0, + fwidth=float("inf"), + start_time=0, + cutoff=5.0, + is_integrated=False, + wavelength=None, + **kwargs, + ): """ Construct a `GaussianSource`. @@ -238,16 +273,22 @@ def __init__(self, frequency=None, width=0, fwidth=float('inf'), start_time=0, c `amplitude` or `amp_func` factor that you specified for the source. """ if frequency is None and wavelength is None: - raise ValueError("Must set either frequency or wavelength in {}.".format(self.__class__.__name__)) + raise ValueError( + f"Must set either frequency or wavelength in {self.__class__.__name__}." + ) - super(GaussianSource, self).__init__(is_integrated=is_integrated, **kwargs) + super().__init__(is_integrated=is_integrated, **kwargs) self.frequency = 1 / wavelength if wavelength else float(frequency) self.width = max(width, 1 / fwidth) self.start_time = start_time self.cutoff = cutoff - self.swigobj = mp.gaussian_src_time(self.frequency, self.width, self.start_time, - self.start_time + 2 * self.width * self.cutoff) + self.swigobj = mp.gaussian_src_time( + self.frequency, + self.width, + self.start_time, + self.start_time + 2 * self.width * self.cutoff, + ) self.swigobj.is_integrated = self.is_integrated def fourier_transform(self, freq): @@ -267,8 +308,16 @@ class CustomSource(SourceTime): [`examples/chirped_pulse.py`](https://github.com/NanoComp/meep/blob/master/python/examples/chirped_pulse.py). """ - def __init__(self, src_func, start_time=-1.0e20, end_time=1.0e20, is_integrated=False, - center_frequency=0, fwidth=0, **kwargs): + def __init__( + self, + src_func, + start_time=-1.0e20, + end_time=1.0e20, + is_integrated=False, + center_frequency=0, + fwidth=0, + **kwargs, + ): """ Construct a `CustomSource`. @@ -301,14 +350,15 @@ def __init__(self, src_func, start_time=-1.0e20, end_time=1.0e20, is_integrated= automatically determine the decimation factor of the time-series updates of the DFT fields monitors (if any). """ - super(CustomSource, self).__init__(is_integrated=is_integrated, **kwargs) + super().__init__(is_integrated=is_integrated, **kwargs) self.src_func = src_func self.start_time = start_time self.end_time = end_time self.fwidth = fwidth self.center_frequency = center_frequency - self.swigobj = mp.custom_py_src_time(src_func, start_time, end_time, - center_frequency, fwidth) + self.swigobj = mp.custom_py_src_time( + src_func, start_time, end_time, center_frequency, fwidth + ) self.swigobj.is_integrated = self.is_integrated @@ -364,21 +414,24 @@ class EigenModeSource(Source): The `SourceTime` object (`Source.src`), which specifies the time dependence of the source, can be one of `ContinuousSource`, `GaussianSource` or `CustomSource`. """ - def __init__(self, - src, - center=None, - volume=None, - eig_lattice_size=None, - eig_lattice_center=None, - component=mp.ALL_COMPONENTS, - direction=mp.AUTOMATIC, - eig_band=1, - eig_kpoint=Vector3(), - eig_match_freq=True, - eig_parity=mp.NO_PARITY, - eig_resolution=0, - eig_tolerance=1e-12, - **kwargs): + + def __init__( + self, + src, + center=None, + volume=None, + eig_lattice_size=None, + eig_lattice_center=None, + component=mp.ALL_COMPONENTS, + direction=mp.AUTOMATIC, + eig_band=1, + eig_kpoint=Vector3(), + eig_match_freq=True, + eig_parity=mp.NO_PARITY, + eig_resolution=0, + eig_tolerance=1e-12, + **kwargs, + ): """ Construct an `EigenModeSource`. @@ -460,7 +513,7 @@ def __init__(self, simulation. """ - super(EigenModeSource, self).__init__(src, component, center, volume, **kwargs) + super().__init__(src, component, center, volume, **kwargs) self.eig_lattice_size = eig_lattice_size self.eig_lattice_center = eig_lattice_center self.component = component @@ -478,10 +531,7 @@ def eig_lattice_size(self): @eig_lattice_size.setter def eig_lattice_size(self, val): - if val is None: - self._eig_lattice_size = self.size - else: - self._eig_lattice_size = val + self._eig_lattice_size = self.size if val is None else val @property def eig_lattice_center(self): @@ -489,10 +539,7 @@ def eig_lattice_center(self): @eig_lattice_center.setter def eig_lattice_center(self, val): - if val is None: - self._eig_lattice_center = self.center - else: - self._eig_lattice_center = val + self._eig_lattice_center = self.center if val is None else val @property def component(self): @@ -501,7 +548,10 @@ def component(self): @component.setter def component(self, val): if val != mp.ALL_COMPONENTS: - warnings.warn("EigenModeSource component is not ALL_COMPONENTS (the default), which makes it non-unidirectional.",RuntimeWarning) + warnings.warn( + "EigenModeSource component is not ALL_COMPONENTS (the default), which makes it non-unidirectional.", + RuntimeWarning, + ) self._component = val @property @@ -511,7 +561,7 @@ def eig_band(self): @eig_band.setter def eig_band(self, val): if isinstance(val, int): - self._eig_band = check_positive('EigenModeSource.eig_band', val) + self._eig_band = check_positive("EigenModeSource.eig_band", val) else: self._eig_band = val @@ -521,7 +571,7 @@ def eig_resolution(self): @eig_resolution.setter def eig_resolution(self, val): - self._eig_resolution = check_nonnegative('EigenModeSource.eig_resolution', val) + self._eig_resolution = check_nonnegative("EigenModeSource.eig_resolution", val) @property def eig_tolerance(self): @@ -529,16 +579,17 @@ def eig_tolerance(self): @eig_tolerance.setter def eig_tolerance(self, val): - self._eig_tolerance = check_positive('EigenModeSource.eig_tolerance', val) + self._eig_tolerance = check_positive("EigenModeSource.eig_tolerance", val) - def eig_power(self,freq): + def eig_power(self, freq): """ Returns the total power of the fields from the eigenmode source at frequency `freq`. """ amp = self.amplitude if callable(getattr(self.src, "fourier_transform", None)): - amp *= self.src.fourier_transform(freq) - return abs(amp)**2 + amp *= self.src.fourier_transform(freq) + return abs(amp) ** 2 + class GaussianBeamSource(Source): """ @@ -549,16 +600,18 @@ class GaussianBeamSource(Source): The `SourceTime` object (`Source.src`), which specifies the time dependence of the source, should normally be a narrow-band `ContinuousSource` or `GaussianSource`. (For a `CustomSource`, the beam frequency is determined by the source's `center_frequency` parameter.) """ - def __init__(self, - src, - center=None, - volume=None, - component=mp.ALL_COMPONENTS, - beam_x0=Vector3(), - beam_kdir=Vector3(), - beam_w0=None, - beam_E0=Vector3(), - **kwargs): + def __init__( + self, + src, + center=None, + volume=None, + component=mp.ALL_COMPONENTS, + beam_x0=Vector3(), + beam_kdir=Vector3(), + beam_w0=None, + beam_E0=Vector3(), + **kwargs, + ): """ Construct a `GaussianBeamSource`. @@ -571,7 +624,7 @@ def __init__(self, + **`beam_E0` [`Vector3`]** — The polarization vector of the beam. Elements can be complex valued (i.e., for circular polarization). The polarization vector must be *parallel* to the source region in order to generate a transverse mode. """ - super(GaussianBeamSource, self).__init__(src, component, center, volume, **kwargs) + super().__init__(src, component, center, volume, **kwargs) self._beam_x0 = beam_x0 self._beam_kdir = beam_kdir self._beam_w0 = beam_w0 @@ -593,13 +646,15 @@ def beam_w0(self): def beam_E0(self): return self._beam_E0 + class IndexedSource(Source): """ created a source object using (SWIG-wrapped mp::srcdata*) srcdata. """ + def __init__(self, src, srcdata, amp_arr, needs_boundary_fix=False): self.src = src self.num_pts = len(amp_arr) self.srcdata = srcdata self.amp_arr = amp_arr - self.needs_boundary_fix = needs_boundary_fix \ No newline at end of file + self.needs_boundary_fix = needs_boundary_fix diff --git a/python/tests/test_3rd_harm_1d.py b/python/tests/test_3rd_harm_1d.py index 87e9ee4e7..99a4fe910 100644 --- a/python/tests/test_3rd_harm_1d.py +++ b/python/tests/test_3rd_harm_1d.py @@ -1,9 +1,11 @@ import unittest -import meep as mp + from utils import ApproxComparisonTestCase -class Test3rdHarm1d(ApproxComparisonTestCase): +import meep as mp + +class Test3rdHarm1d(ApproxComparisonTestCase): def setUp(self): self.sz = 100 fcen = 1 / 3.0 @@ -18,23 +20,31 @@ def setUp(self): pml_layers = mp.PML(self.dpml) - sources = mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ex, - center=mp.Vector3(0, 0, (-0.5 * self.sz) + self.dpml), amplitude=self.amp) + sources = mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ex, + center=mp.Vector3(0, 0, (-0.5 * self.sz) + self.dpml), + amplitude=self.amp, + ) nfreq = 400 fmin = fcen / 2.0 fmax = fcen * 4 - self.sim = mp.Simulation(cell_size=cell, - geometry=[], - sources=[sources], - boundary_layers=[pml_layers], - default_material=default_material, - resolution=20, - dimensions=dimensions) + self.sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=[sources], + boundary_layers=[pml_layers], + default_material=default_material, + resolution=20, + dimensions=dimensions, + ) fr = mp.FluxRegion(mp.Vector3(0, 0, (0.5 * self.sz) - self.dpml - 0.5)) - self.trans = self.sim.add_flux(0.5 * (fmin + fmax), fmax - fmin, nfreq, fr, decimation_factor=1) + self.trans = self.sim.add_flux( + 0.5 * (fmin + fmax), fmax - fmin, nfreq, fr, decimation_factor=1 + ) self.trans1 = self.sim.add_flux(fcen, 0, 1, fr, decimation_factor=1) self.trans3 = self.sim.add_flux(3 * fcen, 0, 1, fr, decimation_factor=1) @@ -48,11 +58,16 @@ def test_3rd_harm_1d(self): ) ) - harmonics = [self.k, self.amp, mp.get_fluxes(self.trans1)[0], mp.get_fluxes(self.trans3)[0]] + harmonics = [ + self.k, + self.amp, + mp.get_fluxes(self.trans1)[0], + mp.get_fluxes(self.trans3)[0], + ] tol = 3e-5 if mp.is_single_precision() else 1e-7 self.assertClose(expected_harmonics, harmonics, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_absorber_1d.py b/python/tests/test_absorber_1d.py index b8c9690ce..5a408f1ad 100644 --- a/python/tests/test_absorber_1d.py +++ b/python/tests/test_absorber_1d.py @@ -1,10 +1,11 @@ import unittest -import meep as mp + from meep.materials import Al +import meep as mp -class TestAbsorber(unittest.TestCase): +class TestAbsorber(unittest.TestCase): def setUp(self): resolution = 40 @@ -12,19 +13,30 @@ def setUp(self): absorber_layers = [mp.Absorber(1, direction=mp.Z)] - sources = [mp.Source(src=mp.GaussianSource(1 / 0.803, fwidth=0.1), center=mp.Vector3(), - component=mp.Ex)] - - self.sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - dimensions=1, - default_material=Al, - boundary_layers=absorber_layers, - sources=sources) + sources = [ + mp.Source( + src=mp.GaussianSource(1 / 0.803, fwidth=0.1), + center=mp.Vector3(), + component=mp.Ex, + ) + ] + + self.sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + dimensions=1, + default_material=Al, + boundary_layers=absorber_layers, + sources=sources, + ) def test_absorber(self): - self.sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ex, mp.Vector3(), 1e-6)) + self.sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(), 1e-6 + ) + ) f = self.sim.get_field_point(mp.Ex, mp.Vector3()) self.assertAlmostEqual(f.real, 3.218846961494622e-13, places=6) @@ -33,14 +45,14 @@ def test_absorber_2d(self): source = mp.Source( src=mp.GaussianSource(frequency=0.1, fwidth=0.1), component=mp.Hz, - center=mp.Vector3() + center=mp.Vector3(), ) sim = mp.Simulation( cell_size=mp.Vector3(20, 20, 0), resolution=10, sources=[source], - boundary_layers=[mp.Absorber(5)] + boundary_layers=[mp.Absorber(5)], ) sim.run(until_after_sources=1000) @@ -50,5 +62,5 @@ def test_absorber_2d(self): self.assertAlmostEqual(-4.058476603571745e-11, p.real) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_cyl.py b/python/tests/test_adjoint_cyl.py index ab6a3307d..98bc01d33 100644 --- a/python/tests/test_adjoint_cyl.py +++ b/python/tests/test_adjoint_cyl.py @@ -1,19 +1,22 @@ import meep as mp + try: import meep.adjoint as mpa except: import adjoint as mpa + +import unittest +from enum import Enum + import numpy as np from autograd import numpy as npa from autograd import tensor_jacobian_product -import unittest -from enum import Enum from utils import ApproxComparisonTestCase rng = np.random.RandomState(2) resolution = 20 dimensions = mp.CYLINDRICAL -m=0 +m = 0 Si = mp.Medium(index=3.4) SiO2 = mp.Medium(index=1.44) @@ -23,87 +26,110 @@ dpml = 1.0 boundary_layers = [mp.PML(thickness=dpml)] -design_region_resolution = int(2*resolution) +design_region_resolution = int(2 * resolution) design_r = 5 design_z = 2 -Nr, Nz = int(design_r*design_region_resolution), int(design_z*design_region_resolution) +Nr, Nz = int(design_r * design_region_resolution), int( + design_z * design_region_resolution +) -fcen = 1/1.55 +fcen = 1 / 1.55 width = 0.2 fwidth = width * fcen -source_center = [design_r/2,0,-(sz/2-dpml+design_z/2)/2] -source_size = mp.Vector3(design_r,0,0) -src = mp.GaussianSource(frequency=fcen,fwidth=fwidth) -source = [mp.Source(src,component=mp.Er, - center=source_center, - size=source_size)] +source_center = [design_r / 2, 0, -(sz / 2 - dpml + design_z / 2) / 2] +source_size = mp.Vector3(design_r, 0, 0) +src = mp.GaussianSource(frequency=fcen, fwidth=fwidth) +source = [mp.Source(src, component=mp.Er, center=source_center, size=source_size)] ## random design region -p = 0.5*rng.rand(Nr*Nz) +p = 0.5 * rng.rand(Nr * Nz) ## random epsilon perturbation for design region deps = 1e-5 -dp = deps*rng.rand(Nr*Nz) +dp = deps * rng.rand(Nr * Nz) def forward_simulation(design_params): - matgrid = mp.MaterialGrid(mp.Vector3(Nr,0,Nz), - SiO2, - Si, - weights=design_params.reshape(Nr,1,Nz)) - - geometry = [mp.Block(center=mp.Vector3(design_r/2,0,0), - size=mp.Vector3(design_r,0,design_z), - material=matgrid)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=source, - geometry=geometry, - dimensions=dimensions, - m=m) + matgrid = mp.MaterialGrid( + mp.Vector3(Nr, 0, Nz), SiO2, Si, weights=design_params.reshape(Nr, 1, Nz) + ) + + geometry = [ + mp.Block( + center=mp.Vector3(design_r / 2, 0, 0), + size=mp.Vector3(design_r, 0, design_z), + material=matgrid, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=source, + geometry=geometry, + dimensions=dimensions, + m=m, + ) frequencies = [fcen] - far_x = [mp.Vector3(5,0,20)] + far_x = [mp.Vector3(5, 0, 20)] mode = sim.add_near2far( frequencies, - mp.Near2FarRegion(center=mp.Vector3(design_r/2,0,(sz/2-dpml+design_z/2)/2), - size=mp.Vector3(design_r,0,0),weight=+1)) + mp.Near2FarRegion( + center=mp.Vector3(design_r / 2, 0, (sz / 2 - dpml + design_z / 2) / 2), + size=mp.Vector3(design_r, 0, 0), + weight=+1, + ), + ) sim.run(until_after_sources=1200) Er = sim.get_farfield(mode, far_x[0]) sim.reset_meep() - return abs(Er[0])**2 + return abs(Er[0]) ** 2 def adjoint_solver(design_params): - design_variables = mp.MaterialGrid(mp.Vector3(Nr,0,Nz),SiO2,Si) - design_region = mpa.DesignRegion(design_variables, - volume=mp.Volume(center=mp.Vector3(design_r/2,0,0), - size=mp.Vector3(design_r,0,design_z))) - geometry = [mp.Block(center=design_region.center, - size=design_region.size, - material=design_variables)] - - sim = mp.Simulation(cell_size=cell_size, + design_variables = mp.MaterialGrid(mp.Vector3(Nr, 0, Nz), SiO2, Si) + design_region = mpa.DesignRegion( + design_variables, + volume=mp.Volume( + center=mp.Vector3(design_r / 2, 0, 0), + size=mp.Vector3(design_r, 0, design_z), + ), + ) + geometry = [ + mp.Block( + center=design_region.center, + size=design_region.size, + material=design_variables, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, boundary_layers=boundary_layers, geometry=geometry, sources=source, resolution=resolution, dimensions=dimensions, - m=m) - - far_x = [mp.Vector3(5,0,20)] - NearRegions = [mp.Near2FarRegion(center=mp.Vector3(design_r/2,0,(sz/2-dpml+design_z/2)/2), - size=mp.Vector3(design_r,0,0), - weight=+1)] - FarFields = mpa.Near2FarFields(sim, NearRegions ,far_x) + m=m, + ) + + far_x = [mp.Vector3(5, 0, 20)] + NearRegions = [ + mp.Near2FarRegion( + center=mp.Vector3(design_r / 2, 0, (sz / 2 - dpml + design_z / 2) / 2), + size=mp.Vector3(design_r, 0, 0), + weight=+1, + ) + ] + FarFields = mpa.Near2FarFields(sim, NearRegions, far_x) ob_list = [FarFields] def J(alpha): - return npa.abs(alpha[0,0,0])**2 + return npa.abs(alpha[0, 0, 0]) ** 2 opt = mpa.OptimizationProblem( simulation=sim, @@ -111,9 +137,10 @@ def J(alpha): objective_arguments=ob_list, design_regions=[design_region], fcen=fcen, - df = 0, - nf = 1, - maximum_run_time=1200) + df=0, + nf=1, + maximum_run_time=1200, + ) f, dJ_du = opt([design_params]) sim.reset_meep() @@ -122,7 +149,6 @@ def J(alpha): class TestAdjointSolver(ApproxComparisonTestCase): - def test_adjoint_solver_cyl_n2f_fields(self): print("*** TESTING CYLINDRICAL Near2Far ADJOINT FEATURES ***") adjsol_obj, adjsol_grad = adjoint_solver(p) @@ -131,23 +157,24 @@ def test_adjoint_solver_cyl_n2f_fields(self): S12_unperturbed = forward_simulation(p) ## compare objective results - print("|Er|^2 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-3) + print( + f"|Er|^2 -- adjoint solver: {adjsol_obj}, traditional simulation: {S12_unperturbed}" + ) + + self.assertClose(adjsol_obj, S12_unperturbed, epsilon=1e-3) ## compute perturbed S12 - S12_perturbed = forward_simulation(p+dp) + S12_perturbed = forward_simulation(p + dp) ## compare gradients if adjsol_grad.ndim < 2: - adjsol_grad = np.expand_dims(adjsol_grad,axis=1) - adj_scale = (dp[None,:]@adjsol_grad).flatten() - fd_grad = S12_perturbed-S12_unperturbed - print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) + adjsol_grad = np.expand_dims(adjsol_grad, axis=1) + adj_scale = (dp[None, :] @ adjsol_grad).flatten() + fd_grad = S12_perturbed - S12_unperturbed + print(f"Directional derivative -- adjoint solver: {adj_scale}, FD: {fd_grad}") tol = 0.2 if mp.is_single_precision() else 0.1 - self.assertClose(adj_scale,fd_grad,epsilon=tol) - - + self.assertClose(adj_scale, fd_grad, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_jax.py b/python/tests/test_adjoint_jax.py index 6513369a1..ea007ddc5 100644 --- a/python/tests/test_adjoint_jax.py +++ b/python/tests/test_adjoint_jax.py @@ -1,16 +1,16 @@ import unittest -import parameterized - -from utils import ApproxComparisonTestCase import jax import jax.numpy as jnp -import meep as mp import meep.adjoint as mpa import numpy as onp +import parameterized +from utils import ApproxComparisonTestCase + +import meep as mp # The calculation of finite difference gradients requires that JAX be operated with double precision -jax.config.update('jax_enable_x64', True) +jax.config.update("jax_enable_x64", True) # The step size for the finite difference gradient calculation _FD_STEP = 1e-4 @@ -41,9 +41,13 @@ def build_straight_wg_simulation( # Simulation domain size sx = 2 * pml_width + 2 * wg_length + design_region_shape[0] - sy = 2 * pml_width + 2 * wg_padding + max( - wg_width, - design_region_shape[1], + sy = ( + 2 * pml_width + + 2 * wg_padding + + max( + wg_width, + design_region_shape[1], + ) ) # Mean / center frequency @@ -54,8 +58,7 @@ def build_straight_wg_simulation( sources = [ mp.EigenModeSource( - mp.GaussianSource(frequency=fmean, - fwidth=fmean * gaussian_rel_width), + mp.GaussianSource(frequency=fmean, fwidth=fmean * gaussian_rel_width), eig_band=1, direction=mp.NO_DIRECTION, eig_kpoint=mp.Vector3(1, 0, 0), @@ -63,8 +66,7 @@ def build_straight_wg_simulation( center=[-sx / 2 + pml_width + source_to_pml, 0, 0], ), mp.EigenModeSource( - mp.GaussianSource(frequency=fmean, - fwidth=fmean * gaussian_rel_width), + mp.GaussianSource(frequency=fmean, fwidth=fmean * gaussian_rel_width), eig_band=1, direction=mp.NO_DIRECTION, eig_kpoint=mp.Vector3(-1, 0, 0), @@ -72,12 +74,14 @@ def build_straight_wg_simulation( center=[sx / 2 - pml_width - source_to_pml, 0, 0], ), ] - nx, ny = int(design_region_shape[0]*design_region_resolution), int(design_region_shape[1]*design_region_resolution) + nx, ny = int(design_region_shape[0] * design_region_resolution), int( + design_region_shape[1] * design_region_resolution + ) mat_grid = mp.MaterialGrid( mp.Vector3(nx, ny), sio2, si, - grid_type='U_DEFAULT', + grid_type="U_DEFAULT", ) design_regions = [ @@ -95,19 +99,25 @@ def build_straight_wg_simulation( ] geometry = [ - mp.Block(center=mp.Vector3(x=-design_region_shape[0] / 2 - - wg_length / 2 - pml_width / 2), - material=si, - size=mp.Vector3(wg_length + pml_width, wg_width, - 0)), # left wg - mp.Block(center=mp.Vector3(x=+design_region_shape[0] / 2 + - wg_length / 2 + pml_width / 2), - material=si, - size=mp.Vector3(wg_length + pml_width, wg_width, - 0)), # right wg - mp.Block(center=design_regions[0].center, - size=design_regions[0].size, - material=mat_grid), # design region + mp.Block( + center=mp.Vector3( + x=-design_region_shape[0] / 2 - wg_length / 2 - pml_width / 2 + ), + material=si, + size=mp.Vector3(wg_length + pml_width, wg_width, 0), + ), # left wg + mp.Block( + center=mp.Vector3( + x=+design_region_shape[0] / 2 + wg_length / 2 + pml_width / 2 + ), + material=si, + size=mp.Vector3(wg_length + pml_width, wg_width, 0), + ), # right wg + mp.Block( + center=design_regions[0].center, + size=design_regions[0].size, + material=mat_grid, + ), # design region ] simulation = mp.Simulation( @@ -125,11 +135,14 @@ def build_straight_wg_simulation( monitor_size = mp.Vector3(y=wg_width + 2 * wg_padding) monitors = [ - mpa.EigenmodeCoefficient(simulation, - mp.Volume(center=center, size=monitor_size), - mode=1, - forward=forward) - for center in monitor_centers for forward in [True, False] + mpa.EigenmodeCoefficient( + simulation, + mp.Volume(center=center, size=monitor_size), + mode=1, + forward=forward, + ) + for center in monitor_centers + for forward in [True, False] ] return simulation, sources, monitors, design_regions, frequencies @@ -150,36 +163,74 @@ def test_mode_monitor_helpers(self): self.simulation.run(until=100) monitor_values = mpa.utils.gather_monitor_values(self.monitors) self.assertEqual(monitor_values.dtype, onp.complex128) - self.assertEqual(monitor_values.shape, - (len(self.monitors), len(self.frequencies))) - + self.assertEqual( + monitor_values.shape, (len(self.monitors), len(self.frequencies)) + ) + def test_dist_dft_pointers(self): fwd_design_region_monitors = mpa.utils.install_design_region_monitors( self.simulation, self.design_regions, self.frequencies, ) - self.assertEqual(len(fwd_design_region_monitors[0]),_NUM_DES_REG_MON) - + self.assertEqual(len(fwd_design_region_monitors[0]), _NUM_DES_REG_MON) class WrapperTest(ApproxComparisonTestCase): - @parameterized.parameterized.expand([ - ('1500_1550bw_01relative_gaussian_port1', - onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), 0.1, 0.5, 0), - ('1550_1600bw_02relative_gaussian_port1', - onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), 0.2, 0.5, 0), - ('1500_1600bw_03relative_gaussian_port1', - onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), 0.3, 0.5, 0), - ('1500_1550bw_01relative_gaussian_port2', - onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), 0.1, 0.5, 1), - ('1550_1600bw_02relative_gaussian_port2', - onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), 0.2, 0.5, 1), - ('1500_1600bw_03relative_gaussian_port2', - onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), 0.3, 0.5, 1), - ]) - def test_wrapper_gradients(self, _, frequencies, gaussian_rel_width, - design_variable_fill_value, excite_port_idx): + @parameterized.parameterized.expand( + [ + ( + "1500_1550bw_01relative_gaussian_port1", + onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), + 0.1, + 0.5, + 0, + ), + ( + "1550_1600bw_02relative_gaussian_port1", + onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), + 0.2, + 0.5, + 0, + ), + ( + "1500_1600bw_03relative_gaussian_port1", + onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), + 0.3, + 0.5, + 0, + ), + ( + "1500_1550bw_01relative_gaussian_port2", + onp.linspace(1 / 1.50, 1 / 1.55, 3).tolist(), + 0.1, + 0.5, + 1, + ), + ( + "1550_1600bw_02relative_gaussian_port2", + onp.linspace(1 / 1.55, 1 / 1.60, 3).tolist(), + 0.2, + 0.5, + 1, + ), + ( + "1500_1600bw_03relative_gaussian_port2", + onp.linspace(1 / 1.50, 1 / 1.60, 4).tolist(), + 0.3, + 0.5, + 1, + ), + ] + ) + def test_wrapper_gradients( + self, + _, + frequencies, + gaussian_rel_width, + design_variable_fill_value, + excite_port_idx, + ): """Tests gradient from the JAX-Meep wrapper against finite differences.""" ( simulation, @@ -187,11 +238,13 @@ def test_wrapper_gradients(self, _, frequencies, gaussian_rel_width, monitors, design_regions, frequencies, - ) = build_straight_wg_simulation(frequencies=frequencies, - gaussian_rel_width=gaussian_rel_width) + ) = build_straight_wg_simulation( + frequencies=frequencies, gaussian_rel_width=gaussian_rel_width + ) design_shape = tuple( - int(i) for i in design_regions[0].design_parameters.grid_size)[:2] + int(i) for i in design_regions[0].design_parameters.grid_size + )[:2] x = onp.ones(design_shape) * design_variable_fill_value # Define a loss function @@ -205,16 +258,12 @@ def loss_fn(x, excite_port_idx=0): ) monitor_values = wrapped_meep([x]) s1p, s1m, s2m, s2p = monitor_values - if excite_port_idx == 0: - t = s2m / s1p - else: - t = s1m / s2p - # Mean transmission vs wavelength - t_mean = jnp.mean(jnp.square(jnp.abs(t))) - return t_mean + t = s2m / s1p if excite_port_idx == 0 else s1m / s2p + return jnp.mean(jnp.square(jnp.abs(t))) value, adjoint_grad = jax.value_and_grad(loss_fn)( - x, excite_port_idx=excite_port_idx) + x, excite_port_idx=excite_port_idx + ) projection = [] fd_projection = [] @@ -231,14 +280,14 @@ def loss_fn(x, excite_port_idx=0): x_perturbed = x + random_perturbation_vector # Calculate T(p + dp) - value_perturbed = loss_fn(x_perturbed, - excite_port_idx=excite_port_idx) + value_perturbed = loss_fn(x_perturbed, excite_port_idx=excite_port_idx) projection.append( onp.dot( random_perturbation_vector.ravel(), adjoint_grad.ravel(), - )) + ) + ) fd_projection.append(value_perturbed - value) projection = onp.stack(projection) @@ -252,5 +301,5 @@ def loss_fn(x, excite_port_idx=0): ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index f7db4d152..56cee371a 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -1,17 +1,19 @@ import meep as mp + try: import meep.adjoint as mpa except: import adjoint as mpa + +import unittest +from enum import Enum + import numpy as np from autograd import numpy as npa from autograd import tensor_jacobian_product -import unittest -from enum import Enum from utils import ApproxComparisonTestCase - -MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT LDOS') +MonitorObject = Enum("MonitorObject", "EIGENMODE DFT LDOS") class TestAdjointSolver(ApproxComparisonTestCase): @@ -20,115 +22,161 @@ def setUpClass(cls): cls.resolution = 30 # pixels/μm cls.silicon = mp.Medium(epsilon=12) - cls.sapphire = mp.Medium(epsilon_diag=(10.225,10.225,9.95), - epsilon_offdiag=(-0.825,-0.55*np.sqrt(3/2),0.55*np.sqrt(3/2))) + cls.sapphire = mp.Medium( + epsilon_diag=(10.225, 10.225, 9.95), + epsilon_offdiag=(-0.825, -0.55 * np.sqrt(3 / 2), 0.55 * np.sqrt(3 / 2)), + ) cls.sxy = 5.0 - cls.cell_size = mp.Vector3(cls.sxy,cls.sxy,0) + cls.cell_size = mp.Vector3(cls.sxy, cls.sxy, 0) cls.dpml = 1.0 cls.pml_xy = [mp.PML(thickness=cls.dpml)] - cls.pml_x = [mp.PML(thickness=cls.dpml,direction=mp.X)] + cls.pml_x = [mp.PML(thickness=cls.dpml, direction=mp.X)] - cls.eig_parity = mp.EVEN_Y+mp.ODD_Z + cls.eig_parity = mp.EVEN_Y + mp.ODD_Z - cls.design_region_size = mp.Vector3(1.5,1.5) - cls.design_region_resolution = int(2*cls.resolution) - cls.Nx = int(cls.design_region_size.x*cls.design_region_resolution) - cls.Ny = int(cls.design_region_size.y*cls.design_region_resolution) + cls.design_region_size = mp.Vector3(1.5, 1.5) + cls.design_region_resolution = int(2 * cls.resolution) + cls.Nx = int(cls.design_region_size.x * cls.design_region_resolution) + cls.Ny = int(cls.design_region_size.y * cls.design_region_resolution) # ensure reproducible results rng = np.random.RandomState(9861548) # random design region - cls.p = 0.5*rng.rand(cls.Nx*cls.Ny) + cls.p = 0.5 * rng.rand(cls.Nx * cls.Ny) # random perturbation for design region deps = 1e-5 - cls.dp = deps*rng.rand(cls.Nx*cls.Ny) + cls.dp = deps * rng.rand(cls.Nx * cls.Ny) cls.w = 1.0 - cls.waveguide_geometry = [mp.Block(material=cls.silicon, - center=mp.Vector3(), - size=mp.Vector3(mp.inf,cls.w,mp.inf))] - - cls.fcen = 1/1.55 - cls.df = 0.2*cls.fcen - cls.mode_source = [mp.EigenModeSource(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), - center=mp.Vector3(-0.5*cls.sxy+cls.dpml,0), - size=mp.Vector3(0,cls.sxy-2*cls.dpml), - eig_parity=cls.eig_parity)] - - cls.pt_source = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), - center=mp.Vector3(-0.5*cls.sxy+cls.dpml,0), - size=mp.Vector3(), - component=mp.Ez)] - - cls.line_source = [mp.Source(src=mp.GaussianSource(cls.fcen,fwidth=cls.df), - center=mp.Vector3(-0.85,0), - size=mp.Vector3(0,cls.sxy-2*cls.dpml), - component=mp.Ez)] - - cls.k_point = mp.Vector3(0.23,-0.38) - - - def adjoint_solver(self, design_params, mon_type: MonitorObject, frequencies=None, - mat2=None, need_gradient=True): - matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), - mp.air, - self.silicon if mat2 is None else mat2, - weights=np.ones((self.Nx,self.Ny))) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(self.design_region_size.x, - self.design_region_size.y, - 0))) - - matgrid_geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] + cls.waveguide_geometry = [ + mp.Block( + material=cls.silicon, + center=mp.Vector3(), + size=mp.Vector3(mp.inf, cls.w, mp.inf), + ) + ] + + cls.fcen = 1 / 1.55 + cls.df = 0.2 * cls.fcen + cls.mode_source = [ + mp.EigenModeSource( + src=mp.GaussianSource(cls.fcen, fwidth=cls.df), + center=mp.Vector3(-0.5 * cls.sxy + cls.dpml, 0), + size=mp.Vector3(0, cls.sxy - 2 * cls.dpml), + eig_parity=cls.eig_parity, + ) + ] + + cls.pt_source = [ + mp.Source( + src=mp.GaussianSource(cls.fcen, fwidth=cls.df), + center=mp.Vector3(-0.5 * cls.sxy + cls.dpml, 0), + size=mp.Vector3(), + component=mp.Ez, + ) + ] + + cls.line_source = [ + mp.Source( + src=mp.GaussianSource(cls.fcen, fwidth=cls.df), + center=mp.Vector3(-0.85, 0), + size=mp.Vector3(0, cls.sxy - 2 * cls.dpml), + component=mp.Ez, + ) + ] + + cls.k_point = mp.Vector3(0.23, -0.38) + + def adjoint_solver( + self, + design_params, + mon_type: MonitorObject, + frequencies=None, + mat2=None, + need_gradient=True, + ): + matgrid = mp.MaterialGrid( + mp.Vector3(self.Nx, self.Ny), + mp.air, + self.silicon if mat2 is None else mat2, + weights=np.ones((self.Nx, self.Ny)), + ) + + matgrid_region = mpa.DesignRegion( + matgrid, + volume=mp.Volume( + center=mp.Vector3(), + size=mp.Vector3( + self.design_region_size.x, self.design_region_size.y, 0 + ), + ), + ) + + matgrid_geometry = [ + mp.Block( + center=matgrid_region.center, size=matgrid_region.size, material=matgrid + ) + ] geometry = self.waveguide_geometry + matgrid_geometry - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.pml_xy, - sources=self.mode_source, - geometry=geometry) + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.pml_xy, + sources=self.mode_source, + geometry=geometry, + ) if not frequencies: frequencies = [self.fcen] - if mon_type.name == 'EIGENMODE': - obj_list = [mpa.EigenmodeCoefficient(sim, - mp.Volume(center=mp.Vector3(0.5*self.sxy-self.dpml), - size=mp.Vector3(0,self.sxy-2*self.dpml,0)), - 1, - eig_parity=self.eig_parity)] + if mon_type.name == "EIGENMODE": + obj_list = [ + mpa.EigenmodeCoefficient( + sim, + mp.Volume( + center=mp.Vector3(0.5 * self.sxy - self.dpml), + size=mp.Vector3(0, self.sxy - 2 * self.dpml, 0), + ), + 1, + eig_parity=self.eig_parity, + ) + ] def J(mode_mon): - return npa.power(npa.abs(mode_mon),2) - elif mon_type.name == 'DFT': - obj_list = [mpa.FourierFields(sim, - mp.Volume(center=mp.Vector3(1.25), - size=mp.Vector3(0.25,1,0)), - mp.Ez)] + return npa.power(npa.abs(mode_mon), 2) + + elif mon_type.name == "DFT": + obj_list = [ + mpa.FourierFields( + sim, + mp.Volume(center=mp.Vector3(1.25), size=mp.Vector3(0.25, 1, 0)), + mp.Ez, + ) + ] def J(mode_mon): - return npa.power(npa.abs(mode_mon[:,4,10]),2) - elif mon_type.name == 'LDOS': + return npa.power(npa.abs(mode_mon[:, 4, 10]), 2) + + elif mon_type.name == "LDOS": sim.change_sources(self.line_source) obj_list = [mpa.LDOS(sim)] def J(mode_mon): return npa.array(mode_mon) - opt = mpa.OptimizationProblem(simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies) + opt = mpa.OptimizationProblem( + simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + ) if need_gradient: f, dJ_du = opt([design_params]) @@ -137,47 +185,61 @@ def J(mode_mon): f = opt([design_params], need_gradient=False) return f[0] - - def adjoint_solver_complex_fields(self, design_params, frequencies=None, need_gradient=True): - matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), - mp.air, - self.silicon, - weights=np.ones((self.Nx,self.Ny))) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(self.design_region_size.x, - self.design_region_size.y, - 0))) - - geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - default_material=self.silicon, - k_point=self.k_point, - boundary_layers=self.pml_x, - sources=self.pt_source, - geometry=geometry) + def adjoint_solver_complex_fields( + self, design_params, frequencies=None, need_gradient=True + ): + matgrid = mp.MaterialGrid( + mp.Vector3(self.Nx, self.Ny), + mp.air, + self.silicon, + weights=np.ones((self.Nx, self.Ny)), + ) + + matgrid_region = mpa.DesignRegion( + matgrid, + volume=mp.Volume( + center=mp.Vector3(), + size=mp.Vector3( + self.design_region_size.x, self.design_region_size.y, 0 + ), + ), + ) + + geometry = [ + mp.Block( + center=matgrid_region.center, size=matgrid_region.size, material=matgrid + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + default_material=self.silicon, + k_point=self.k_point, + boundary_layers=self.pml_x, + sources=self.pt_source, + geometry=geometry, + ) if not frequencies: frequencies = [self.fcen] - obj_list = [mpa.FourierFields(sim, - mp.Volume(center=mp.Vector3(0.9), - size=mp.Vector3(0.2,0.5)), - mp.Dz)] + obj_list = [ + mpa.FourierFields( + sim, mp.Volume(center=mp.Vector3(0.9), size=mp.Vector3(0.2, 0.5)), mp.Dz + ) + ] def J(dft_mon): - return npa.power(npa.abs(dft_mon[:,3,9]),2) + return npa.power(npa.abs(dft_mon[:, 3, 9]), 2) - opt = mpa.OptimizationProblem(simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies) + opt = mpa.OptimizationProblem( + simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + ) if need_gradient: f, dJ_du = opt([design_params]) @@ -186,50 +248,69 @@ def J(dft_mon): f = opt([design_params], need_gradient=False) return f[0] - - def adjoint_solver_damping(self, design_params, frequencies=None, mat2=None, need_gradient=True): - matgrid = mp.MaterialGrid(mp.Vector3(self.Nx,self.Ny), - mp.air, - self.silicon if mat2 is None else mat2, - weights=np.ones((self.Nx,self.Ny)), - damping=np.pi*self.fcen) - - matgrid_region = mpa.DesignRegion(matgrid, - volume=mp.Volume(center=mp.Vector3(), - size=mp.Vector3(self.design_region_size.x, - self.design_region_size.y, - 0))) - - matgrid_geometry = [mp.Block(center=matgrid_region.center, - size=matgrid_region.size, - material=matgrid)] + def adjoint_solver_damping( + self, design_params, frequencies=None, mat2=None, need_gradient=True + ): + matgrid = mp.MaterialGrid( + mp.Vector3(self.Nx, self.Ny), + mp.air, + self.silicon if mat2 is None else mat2, + weights=np.ones((self.Nx, self.Ny)), + damping=np.pi * self.fcen, + ) + + matgrid_region = mpa.DesignRegion( + matgrid, + volume=mp.Volume( + center=mp.Vector3(), + size=mp.Vector3( + self.design_region_size.x, self.design_region_size.y, 0 + ), + ), + ) + + matgrid_geometry = [ + mp.Block( + center=matgrid_region.center, size=matgrid_region.size, material=matgrid + ) + ] geometry = self.waveguide_geometry + matgrid_geometry - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.pml_xy, - sources=self.mode_source, - geometry=geometry) + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.pml_xy, + sources=self.mode_source, + geometry=geometry, + ) if not frequencies: frequencies = [self.fcen] - obj_list = [mpa.EigenmodeCoefficient(sim, - mp.Volume(center=mp.Vector3(0.5*self.sxy-self.dpml), - size=mp.Vector3(0,self.sxy-2*self.dpml,0)), - 1, - eig_parity=self.eig_parity)] + obj_list = [ + mpa.EigenmodeCoefficient( + sim, + mp.Volume( + center=mp.Vector3(0.5 * self.sxy - self.dpml), + size=mp.Vector3(0, self.sxy - 2 * self.dpml, 0), + ), + 1, + eig_parity=self.eig_parity, + ) + ] def J(mode_mon): - return npa.power(npa.abs(mode_mon),2) + return npa.power(npa.abs(mode_mon), 2) - opt = mpa.OptimizationProblem(simulation=sim, - objective_functions=J, - objective_arguments=obj_list, - design_regions=[matgrid_region], - frequencies=frequencies, - minimum_run_time=150) + opt = mpa.OptimizationProblem( + simulation=sim, + objective_functions=J, + objective_arguments=obj_list, + design_regions=[matgrid_region], + frequencies=frequencies, + minimum_run_time=150, + ) if need_gradient: f, dJ_du = opt([design_params]) @@ -238,232 +319,257 @@ def J(mode_mon): f = opt([design_params], need_gradient=False) return f[0] + def mapping(self, x, filter_radius, eta, beta): + filtered_field = mpa.conic_filter( + x, + filter_radius, + self.design_region_size.x, + self.design_region_size.y, + self.design_region_resolution, + ) - def mapping(self,x,filter_radius,eta,beta): - filtered_field = mpa.conic_filter(x, - filter_radius, - self.design_region_size.x, - self.design_region_size.y, - self.design_region_resolution) - - projected_field = mpa.tanh_projection(filtered_field,beta,eta) + projected_field = mpa.tanh_projection(filtered_field, beta, eta) return projected_field.flatten() - def test_DFT_fields(self): print("*** TESTING DFT OBJECTIVE ***") # test the single frequency and multi frequency cases - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, - MonitorObject.DFT, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + self.p, MonitorObject.DFT, frequencies + ) # compute objective value for perturbed design - perturbed_val = self.adjoint_solver(self.p+self.dp, - MonitorObject.DFT, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.p + self.dp, MonitorObject.DFT, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) - tol = 0.07 if mp.is_single_precision() else 0.006 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.07 if mp.is_single_precision() else 0.006 + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_eigenmode(self): print("*** TESTING EIGENMODE OBJECTIVE ***") # test the single frequency and multi frequency case - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, - MonitorObject.EIGENMODE, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + self.p, MonitorObject.EIGENMODE, frequencies + ) # compute objective for perturbed design - perturbed_val = self.adjoint_solver(self.p+self.dp, - MonitorObject.EIGENMODE, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.p + self.dp, + MonitorObject.EIGENMODE, + frequencies, + need_gradient=False, + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) - tol = 0.04 if mp.is_single_precision() else 0.01 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.04 if mp.is_single_precision() else 0.01 + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_ldos(self): print("*** TESTING LDOS OBJECTIVE ***") # test the single frequency and multi frequency cases - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(self.p, - MonitorObject.LDOS, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + self.p, MonitorObject.LDOS, frequencies + ) # compute objective for perturbed design - perturbed_val = self.adjoint_solver(self.p+self.dp, - MonitorObject.LDOS, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.p + self.dp, MonitorObject.LDOS, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) - tol = 0.07 if mp.is_single_precision() else 0.006 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.07 if mp.is_single_precision() else 0.006 + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_gradient_backpropagation(self): print("*** TESTING GRADIENT BACKPROPAGATION ***") - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: - # filter/thresholding parameters - filter_radius = 0.21985 - eta = 0.49093 - beta = 4.0698 + # filter/thresholding parameters + filter_radius = 0.21985 + eta = 0.49093 + beta = 4.0698 - mapped_p = self.mapping(self.p,filter_radius,eta,beta) + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: + mapped_p = self.mapping(self.p, filter_radius, eta, beta) # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(mapped_p, - MonitorObject.EIGENMODE, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + mapped_p, MonitorObject.EIGENMODE, frequencies + ) # backpropagate the gradient using vector-Jacobian product if len(frequencies) > 1: unperturbed_grad_backprop = np.zeros(unperturbed_grad.shape) for i in range(len(frequencies)): - unperturbed_grad_backprop[:,i] = tensor_jacobian_product(self.mapping,0)(self.p, - filter_radius, - eta, - beta, - unperturbed_grad[:,i]) + unperturbed_grad_backprop[:, i] = tensor_jacobian_product( + self.mapping, 0 + )(self.p, filter_radius, eta, beta, unperturbed_grad[:, i]) else: - unperturbed_grad_backprop = tensor_jacobian_product(self.mapping,0)(self.p, - filter_radius, - eta, - beta, - unperturbed_grad) + unperturbed_grad_backprop = tensor_jacobian_product(self.mapping, 0)( + self.p, filter_radius, eta, beta, unperturbed_grad + ) # compute objective for perturbed design - perturbed_val = self.adjoint_solver(self.mapping(self.p+self.dp,filter_radius,eta,beta), - MonitorObject.EIGENMODE, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver( + self.mapping(self.p + self.dp, filter_radius, eta, beta), + MonitorObject.EIGENMODE, + frequencies, + need_gradient=False, + ) # compare directional derivative if unperturbed_grad_backprop.ndim < 2: - unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) - tol = 0.025 if mp.is_single_precision() else 0.01 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + unperturbed_grad_backprop = np.expand_dims( + unperturbed_grad_backprop, axis=1 + ) + adj_dd = (self.dp[None, :] @ unperturbed_grad_backprop).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.025 if mp.is_single_precision() else 0.01 + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_complex_fields(self): print("*** TESTING COMPLEX FIELDS ***") - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver_complex_fields(self.p, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver_complex_fields( + self.p, frequencies + ) # compute objective value perturbed design - perturbed_val = self.adjoint_solver_complex_fields(self.p+self.dp, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver_complex_fields( + self.p + self.dp, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) - tol = 0.06 if mp.is_single_precision() else 0.01 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.06 if mp.is_single_precision() else 0.01 + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_damping(self): print("*** TESTING CONDUCTIVITY ***") - for frequencies in [[1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver_damping(self.p, - frequencies) + unperturbed_val, unperturbed_grad = self.adjoint_solver_damping( + self.p, frequencies + ) # compute objective value perturbed design - perturbed_val = self.adjoint_solver_damping(self.p+self.dp, - frequencies, - need_gradient=False) + perturbed_val = self.adjoint_solver_damping( + self.p + self.dp, frequencies, need_gradient=False + ) # compare directional derivative if unperturbed_grad.ndim < 2: - unperturbed_grad = np.expand_dims(unperturbed_grad,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) - tol = 0.06 if mp.is_single_precision() else 0.04 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + unperturbed_grad = np.expand_dims(unperturbed_grad, axis=1) + adj_dd = (self.dp[None, :] @ unperturbed_grad).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.06 if mp.is_single_precision() else 0.04 + self.assertClose(adj_dd, fnd_dd, epsilon=tol) def test_offdiagonal(self): print("*** TESTING ANISOTROPIC ε ***") - filt = lambda x: mpa.conic_filter(x.reshape((self.Nx,self.Ny)), - 0.25, - self.design_region_size.x, - self.design_region_size.y, - self.design_region_resolution).flatten() + filt = lambda x: mpa.conic_filter( + x.reshape((self.Nx, self.Ny)), + 0.25, + self.design_region_size.x, + self.design_region_size.y, + self.design_region_resolution, + ).flatten() # test the single frequency and multi frequency case - for frequencies in [[self.fcen], [1/1.58, self.fcen, 1/1.53]]: + for frequencies in [[self.fcen], [1 / 1.58, self.fcen, 1 / 1.53]]: # compute objective value and its gradient for unperturbed design - unperturbed_val, unperturbed_grad = self.adjoint_solver(filt(self.p), - MonitorObject.EIGENMODE, - frequencies, - self.sapphire) + unperturbed_val, unperturbed_grad = self.adjoint_solver( + filt(self.p), MonitorObject.EIGENMODE, frequencies, self.sapphire + ) # backpropagate the gradient using vector-Jacobian product if len(frequencies) > 1: unperturbed_grad_backprop = np.zeros(unperturbed_grad.shape) for i in range(len(frequencies)): - unperturbed_grad_backprop[:,i] = tensor_jacobian_product(filt,0)(self.p, - unperturbed_grad[:,i]) + unperturbed_grad_backprop[:, i] = tensor_jacobian_product(filt, 0)( + self.p, unperturbed_grad[:, i] + ) else: - unperturbed_grad_backprop = tensor_jacobian_product(filt,0)(self.p,unperturbed_grad) + unperturbed_grad_backprop = tensor_jacobian_product(filt, 0)( + self.p, unperturbed_grad + ) # compute objective value perturbed design - perturbed_val = self.adjoint_solver(filt(self.p+self.dp), - MonitorObject.EIGENMODE, - frequencies, - self.sapphire, - need_gradient=False) + perturbed_val = self.adjoint_solver( + filt(self.p + self.dp), + MonitorObject.EIGENMODE, + frequencies, + self.sapphire, + need_gradient=False, + ) # compare directional derivative if unperturbed_grad_backprop.ndim < 2: - unperturbed_grad_backprop = np.expand_dims(unperturbed_grad_backprop,axis=1) - adj_dd = (self.dp[None,:]@unperturbed_grad_backprop).flatten() - fnd_dd = perturbed_val-unperturbed_val - print("directional derivative:, {} (adjoint solver), {} (finite difference)".format(adj_dd,fnd_dd)) + unperturbed_grad_backprop = np.expand_dims( + unperturbed_grad_backprop, axis=1 + ) + adj_dd = (self.dp[None, :] @ unperturbed_grad_backprop).flatten() + fnd_dd = perturbed_val - unperturbed_val + print( + f"directional derivative:, {adj_dd} (adjoint solver), {fnd_dd} (finite difference)" + ) + tol = 0.1 if mp.is_single_precision() else 0.04 - self.assertClose(adj_dd,fnd_dd,epsilon=tol) + self.assertClose(adj_dd, fnd_dd, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_adjoint_utils.py b/python/tests/test_adjoint_utils.py index 6ed5c684f..7bec9b774 100644 --- a/python/tests/test_adjoint_utils.py +++ b/python/tests/test_adjoint_utils.py @@ -1,48 +1,54 @@ -'''test_adjoint_utils.py +"""test_adjoint_utils.py Check various components of the adjoint solver codebase, like filters, which may not need explicit gradient computation (i.e. forward and adjoint runs). -''' - +""" import meep as mp + try: import meep.adjoint as mpa except: import adjoint as mpa + +import unittest +from enum import Enum + import numpy as np +import parameterized from autograd import numpy as npa from autograd import tensor_jacobian_product -import unittest -from enum import Enum from utils import ApproxComparisonTestCase -import parameterized _TOL = 1e-6 if mp.is_single_precision() else 1e-14 ## ensure reproducible results rng = np.random.RandomState(9861548) + class TestAdjointUtils(ApproxComparisonTestCase): - @parameterized.parameterized.expand([ - ('1.0_1.0_20_conic',1.0, 1.0, 20, 0.24, mpa.conic_filter), - ('1.0_1.0_23_conic',1.0, 1.0, 23, 0.24, mpa.conic_filter), - ('0.887_1.56_conic',0.887, 1.56, 20, 0.24, mpa.conic_filter), - ('0.887_1.56_conic',0.887, 1.56, 31, 0.24, mpa.conic_filter), - ('0.887_1.56_gaussian',0.887, 1.56, 20, 0.24, mpa.gaussian_filter), - ('0.887_1.56_cylindrical',0.887, 1.56, 20, 0.24, mpa.cylindrical_filter) - ]) - def test_filter_offset(self,test_name,Lx,Ly,resolution,radius,filter_func): - '''ensure that the filters are indeed zero-phase''' - print("Testing ",test_name) - Nx, Ny = int(resolution*Lx), int(resolution*Ly) - x = np.random.rand(Nx,Ny) + @parameterized.parameterized.expand( + [ + ("1.0_1.0_20_conic", 1.0, 1.0, 20, 0.24, mpa.conic_filter), + ("1.0_1.0_23_conic", 1.0, 1.0, 23, 0.24, mpa.conic_filter), + ("0.887_1.56_conic", 0.887, 1.56, 20, 0.24, mpa.conic_filter), + ("0.887_1.56_conic", 0.887, 1.56, 31, 0.24, mpa.conic_filter), + ("0.887_1.56_gaussian", 0.887, 1.56, 20, 0.24, mpa.gaussian_filter), + ("0.887_1.56_cylindrical", 0.887, 1.56, 20, 0.24, mpa.cylindrical_filter), + ] + ) + def test_filter_offset(self, test_name, Lx, Ly, resolution, radius, filter_func): + """ensure that the filters are indeed zero-phase""" + print("Testing ", test_name) + Nx, Ny = int(resolution * Lx), int(resolution * Ly) + x = np.random.rand(Nx, Ny) x = x + np.fliplr(x) x = x + np.flipud(x) y = filter_func(x, radius, Lx, Ly, resolution) - self.assertClose(y,np.fliplr(y),epsilon=_TOL) - self.assertClose(y,np.flipud(y),epsilon=_TOL) + self.assertClose(y, np.fliplr(y), epsilon=_TOL) + self.assertClose(y, np.flipud(y), epsilon=_TOL) + -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_antenna_radiation.py b/python/tests/test_antenna_radiation.py index 35a8958d8..501f2869a 100644 --- a/python/tests/test_antenna_radiation.py +++ b/python/tests/test_antenna_radiation.py @@ -1,102 +1,106 @@ -import meep as mp import math -import numpy as np import unittest + +import numpy as np from utils import ApproxComparisonTestCase -class TestAntennaRadiation(ApproxComparisonTestCase): +import meep as mp + +class TestAntennaRadiation(ApproxComparisonTestCase): @classmethod def setUp(cls): cls.resolution = 100 # pixels/μm - cls.h = 1.125 # height of point source above ground plane - cls.n = 1.2 # refractive index of surrounding medium + cls.h = 1.125 # height of point source above ground plane + cls.n = 1.2 # refractive index of surrounding medium cls.src_cmpt = mp.Ez cls.wvl = 0.65 - cls.npts = 50 # number of points in [0,pi/2) range of angles - cls.angles = 0.5*math.pi/cls.npts*np.arange(cls.npts) - cls.r = 1000*cls.wvl # radius of far-field hemicircle + cls.npts = 50 # number of points in [0,pi/2) range of angles + cls.angles = 0.5 * math.pi / cls.npts * np.arange(cls.npts) + cls.r = 1000 * cls.wvl # radius of far-field hemicircle - - def radial_flux(self,sim,nearfield_box,r): - E = np.zeros((self.npts,3),dtype=np.complex128) - H = np.zeros((self.npts,3),dtype=np.complex128) + def radial_flux(self, sim, nearfield_box, r): + E = np.zeros((self.npts, 3), dtype=np.complex128) + H = np.zeros((self.npts, 3), dtype=np.complex128) for n in range(self.npts): - ff = sim.get_farfield(nearfield_box, - mp.Vector3(r*math.sin(self.angles[n]), - r*math.cos(self.angles[n]))) - E[n,:] = [np.conj(ff[j]) for j in range(3)] - H[n,:] = [ff[j+3] for j in range(3)] - - Px = np.real(E[:,1]*H[:,2]-E[:,2]*H[:,1]) # Ey*Hz-Ez*Hy - Py = np.real(E[:,2]*H[:,0]-E[:,0]*H[:,2]) # Ez*Hx-Ex*Hz - Pr = np.sqrt(np.square(Px)+np.square(Py)) - - return Pr + ff = sim.get_farfield( + nearfield_box, + mp.Vector3(r * math.sin(self.angles[n]), r * math.cos(self.angles[n])), + ) + E[n, :] = [np.conj(ff[j]) for j in range(3)] + H[n, :] = [ff[j + 3] for j in range(3)] + Px = np.real(E[:, 1] * H[:, 2] - E[:, 2] * H[:, 1]) # Ey*Hz-Ez*Hy + Py = np.real(E[:, 2] * H[:, 0] - E[:, 0] * H[:, 2]) # Ez*Hx-Ex*Hz + return np.sqrt(np.square(Px) + np.square(Py)) def free_space_radiation(self): sxy = 4 dpml = 1 - cell_size = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell_size = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] - fcen = 1/self.wvl + fcen = 1 / self.wvl - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(), - component=self.src_cmpt)] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(), + component=self.src_cmpt, + ) + ] if self.src_cmpt == mp.Hz: - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=-1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)] elif self.src_cmpt == mp.Ez: - symmetries = [mp.Mirror(mp.X,phase=+1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=+1), mp.Mirror(mp.Y, phase=+1)] else: symmetries = [] - sim = mp.Simulation(cell_size=cell_size, - resolution=self.resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers, - default_material=mp.Medium(index=self.n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,+0.5*sxy), - size=mp.Vector3(sxy,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-0.5*sxy), - size=mp.Vector3(sxy,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(+0.5*sxy,0), - size=mp.Vector3(0,sxy)), - mp.Near2FarRegion(center=mp.Vector3(-0.5*sxy,0), - size=mp.Vector3(0,sxy), - weight=-1)) + sim = mp.Simulation( + cell_size=cell_size, + resolution=self.resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + default_material=mp.Medium(index=self.n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0, +0.5 * sxy), size=mp.Vector3(sxy, 0) + ), + mp.Near2FarRegion( + center=mp.Vector3(0, -0.5 * sxy), size=mp.Vector3(sxy, 0), weight=-1 + ), + mp.Near2FarRegion( + center=mp.Vector3(+0.5 * sxy, 0), size=mp.Vector3(0, sxy) + ), + mp.Near2FarRegion( + center=mp.Vector3(-0.5 * sxy, 0), size=mp.Vector3(0, sxy), weight=-1 + ), + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = self.radial_flux(sim,nearfield_box,self.r) - - return Pr - + return self.radial_flux(sim, nearfield_box, self.r) def pec_ground_plane_radiation(self): - L = 8.0 # length of non-PML region + L = 8.0 # length of non-PML region dpml = 1.0 # thickness of PML - sxy = dpml+L+dpml - cell_size = mp.Vector3(sxy,sxy,0) + sxy = dpml + L + dpml + cell_size = mp.Vector3(sxy, sxy, 0) boundary_layers = [mp.PML(dpml)] - fcen = 1/self.wvl + fcen = 1 / self.wvl # The near-to-far field transformation feature only supports # homogeneous media which means it cannot explicitly take into @@ -107,46 +111,57 @@ def pec_ground_plane_radiation(self): # equivalent to a PEC boundary condition on one side. # Note: This setup means that the radiation pattern can only # be measured in the top half above the dipole. - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=self.src_cmpt, - center=mp.Vector3(0,+self.h)), - mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=self.src_cmpt, - center=mp.Vector3(0,-self.h), - amplitude=-1 if self.src_cmpt==mp.Ez else +1)] - - symmetries = [mp.Mirror(direction=mp.X, - phase=+1 if self.src_cmpt==mp.Ez else -1), - mp.Mirror(direction=mp.Y, - phase=-1 if self.src_cmpt==mp.Ez else +1)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=self.n)) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(center=mp.Vector3(0,2*self.h), - size=mp.Vector3(2*self.h,0)), - mp.Near2FarRegion(center=mp.Vector3(0,-2*self.h), - size=mp.Vector3(2*self.h,0), - weight=-1), - mp.Near2FarRegion(center=mp.Vector3(self.h,0), - size=mp.Vector3(0,4*self.h)), - mp.Near2FarRegion(center=mp.Vector3(-self.h,0), - size=mp.Vector3(0,4*self.h), - weight=-1)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=self.src_cmpt, + center=mp.Vector3(0, +self.h), + ), + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=self.src_cmpt, + center=mp.Vector3(0, -self.h), + amplitude=-1 if self.src_cmpt == mp.Ez else +1, + ), + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=+1 if self.src_cmpt == mp.Ez else -1), + mp.Mirror(direction=mp.Y, phase=-1 if self.src_cmpt == mp.Ez else +1), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=self.n), + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion( + center=mp.Vector3(0, 2 * self.h), size=mp.Vector3(2 * self.h, 0) + ), + mp.Near2FarRegion( + center=mp.Vector3(0, -2 * self.h), + size=mp.Vector3(2 * self.h, 0), + weight=-1, + ), + mp.Near2FarRegion( + center=mp.Vector3(self.h, 0), size=mp.Vector3(0, 4 * self.h) + ), + mp.Near2FarRegion( + center=mp.Vector3(-self.h, 0), size=mp.Vector3(0, 4 * self.h), weight=-1 + ), + ) sim.run(until_after_sources=mp.stop_when_dft_decayed()) - Pr = self.radial_flux(sim,nearfield_box,self.r) - - return Pr - + return self.radial_flux(sim, nearfield_box, self.r) def test_pec_ground_plane(self): """Unit test for near-to-far field transformation and symmetries. @@ -163,18 +178,19 @@ def test_pec_ground_plane(self): Pr_fsp = self.free_space_radiation() Pr_pec = self.pec_ground_plane_radiation() - k = 2*np.pi/(self.wvl/self.n) # wavevector in medium - Pr_theory = np.zeros(self.npts,) - for i,ang in enumerate(self.angles): - Pr_theory[i] = Pr_fsp[i] * 2*np.sin(k*self.h*np.cos(ang)) + k = 2 * np.pi / (self.wvl / self.n) # wavevector in medium + Pr_theory = np.zeros( + self.npts, + ) + for i, ang in enumerate(self.angles): + Pr_theory[i] = Pr_fsp[i] * 2 * np.sin(k * self.h * np.cos(ang)) - Pr_pec_norm = Pr_pec/np.max(Pr_pec) - Pr_theory_norm = (Pr_theory/max(Pr_theory))**2 + Pr_pec_norm = Pr_pec / np.max(Pr_pec) + Pr_theory_norm = (Pr_theory / max(Pr_theory)) ** 2 tol = 0.02 self.assertClose(Pr_pec_norm, Pr_theory_norm, epsilon=tol) - def test_poynting_theorem(self): """Unit test for near-to-far field transformation in 2d. @@ -189,87 +205,102 @@ def test_poynting_theorem(self): resolution = 50 sxy = 4 dpml = 1 - cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = mp.PML(dpml) fcen = 1.0 df = 0.4 - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(), - component=mp.Ez)] - - symmetries = [mp.Mirror(mp.X), - mp.Mirror(mp.Y)] - - sim = mp.Simulation(cell_size=cell, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=[pml_layers]) - - nearfield_box = sim.add_near2far(fcen, - 0, - 1, - mp.Near2FarRegion(mp.Vector3(y=0.5*sxy), - size=mp.Vector3(sxy)), - mp.Near2FarRegion(mp.Vector3(y=-0.5*sxy), - size=mp.Vector3(sxy), - weight=-1), - mp.Near2FarRegion(mp.Vector3(0.5*sxy), - size=mp.Vector3(y=sxy)), - mp.Near2FarRegion(mp.Vector3(-0.5*sxy), - size=mp.Vector3(y=sxy), - weight=-1)) - - flux_box = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(mp.Vector3(y=0.5*sxy), - size=mp.Vector3(sxy)), - mp.FluxRegion(mp.Vector3(y=-0.5*sxy), - size=mp.Vector3(sxy), - weight=-1), - mp.FluxRegion(mp.Vector3(0.5*sxy), - size=mp.Vector3(y=sxy)), - mp.FluxRegion(mp.Vector3(-0.5*sxy), - size=mp.Vector3(y=sxy), - weight=-1)) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-8)) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + center=mp.Vector3(), + component=mp.Ez, + ) + ] + + symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] + + sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=[pml_layers], + ) + + nearfield_box = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)), + mp.Near2FarRegion( + mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1 + ), + mp.Near2FarRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)), + mp.Near2FarRegion( + mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1 + ), + ) + + flux_box = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)), + mp.FluxRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1), + mp.FluxRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)), + mp.FluxRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1), + ) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-8 + ) + ) near_flux = mp.get_fluxes(flux_box)[0] - r = 1000/fcen # radius of far field circle - Pr = self.radial_flux(sim,nearfield_box,r) - far_flux_circle = 4*np.sum(Pr)*0.5*np.pi*r/len(Pr) - - rr = 20/fcen # length of far-field square box - res_far = 20 # resolution of far-field square box - far_flux_square = (nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=0.5*rr), - size=mp.Vector3(rr)), - res_far)[0] - - nearfield_box.flux(mp.Y, - mp.Volume(center=mp.Vector3(y=-0.5*rr), - size=mp.Vector3(rr)), - res_far)[0] + - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(0.5*rr), - size=mp.Vector3(y=rr)), - res_far)[0] - - nearfield_box.flux(mp.X, - mp.Volume(center=mp.Vector3(-0.5*rr), - size=mp.Vector3(y=rr)), - res_far)[0]) - - print("flux:, {:.6f}, {:.6f}, {:.6f}".format(near_flux,far_flux_circle,far_flux_square)) + r = 1000 / fcen # radius of far field circle + Pr = self.radial_flux(sim, nearfield_box, r) + far_flux_circle = 4 * np.sum(Pr) * 0.5 * np.pi * r / len(Pr) + + rr = 20 / fcen # length of far-field square box + res_far = 20 # resolution of far-field square box + far_flux_square = ( + nearfield_box.flux( + mp.Y, + mp.Volume(center=mp.Vector3(y=0.5 * rr), size=mp.Vector3(rr)), + res_far, + )[0] + - nearfield_box.flux( + mp.Y, + mp.Volume(center=mp.Vector3(y=-0.5 * rr), size=mp.Vector3(rr)), + res_far, + )[0] + + nearfield_box.flux( + mp.X, + mp.Volume(center=mp.Vector3(0.5 * rr), size=mp.Vector3(y=rr)), + res_far, + )[0] + - nearfield_box.flux( + mp.X, + mp.Volume(center=mp.Vector3(-0.5 * rr), size=mp.Vector3(y=rr)), + res_far, + )[0] + ) + + print( + "flux:, {:.6f}, {:.6f}, {:.6f}".format( + near_flux, far_flux_circle, far_flux_square + ) + ) self.assertAlmostEqual(near_flux, far_flux_circle, places=2) self.assertAlmostEqual(far_flux_circle, far_flux_square, places=2) self.assertAlmostEqual(far_flux_square, near_flux, places=2) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_array_metadata.py b/python/tests/test_array_metadata.py index 05f1d8e02..2bd98e5ab 100644 --- a/python/tests/test_array_metadata.py +++ b/python/tests/test_array_metadata.py @@ -1,10 +1,12 @@ -import meep as mp import unittest + import numpy as np from utils import ApproxComparisonTestCase -class TestArrayMetadata(ApproxComparisonTestCase): +import meep as mp + +class TestArrayMetadata(ApproxComparisonTestCase): def test_array_metadata(self): resolution = 25 @@ -14,75 +16,101 @@ def test_array_metadata(self): pad = 4 dpml = 2 - sxy = 2*(r+w+pad+dpml) - cell_size = mp.Vector3(sxy,sxy) + sxy = 2 * (r + w + pad + dpml) + cell_size = mp.Vector3(sxy, sxy) - nonpml_vol = mp.Volume(mp.Vector3(), size=mp.Vector3(sxy-2*dpml,sxy-2*dpml)) + nonpml_vol = mp.Volume( + mp.Vector3(), size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml) + ) - geometry = [mp.Cylinder(radius=r+w, material=mp.Medium(index=n)), - mp.Cylinder(radius=r)] + geometry = [ + mp.Cylinder(radius=r + w, material=mp.Medium(index=n)), + mp.Cylinder(radius=r), + ] fcen = 0.118 df = 0.08 - symmetries = [mp.Mirror(mp.X,phase=-1), - mp.Mirror(mp.Y,phase=+1)] + symmetries = [mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=+1)] pml_layers = [mp.PML(dpml)] # CW source - src = [mp.Source(mp.ContinuousSource(fcen,fwidth=df), mp.Ez, mp.Vector3(r+0.1)), - mp.Source(mp.ContinuousSource(fcen,fwidth=df), mp.Ez, mp.Vector3(-(r+0.1)), amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - sources=src, - resolution=resolution, - force_complex_fields=True, - symmetries=symmetries, - boundary_layers=pml_layers) + src = [ + mp.Source(mp.ContinuousSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)), + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + mp.Ez, + mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + sources=src, + resolution=resolution, + force_complex_fields=True, + symmetries=symmetries, + boundary_layers=pml_layers, + ) sim.init_sim() sim.solve_cw(1e-5 if mp.is_single_precision() else 1e-6, 1000, 10) def electric_energy(r, ez, eps): - return np.real(eps * np.conj(ez)*ez) + return np.real(eps * np.conj(ez) * ez) def vec_func(r): - return r.x**2 + 2*r.y**2 - - electric_energy_total = sim.integrate_field_function([mp.Ez,mp.Dielectric],electric_energy,nonpml_vol) - electric_energy_max = sim.max_abs_field_function([mp.Ez,mp.Dielectric],electric_energy,nonpml_vol) - vec_func_total = sim.integrate_field_function([],vec_func,nonpml_vol) + return r.x**2 + 2 * r.y**2 + + electric_energy_total = sim.integrate_field_function( + [mp.Ez, mp.Dielectric], electric_energy, nonpml_vol + ) + electric_energy_max = sim.max_abs_field_function( + [mp.Ez, mp.Dielectric], electric_energy, nonpml_vol + ) + vec_func_total = sim.integrate_field_function([], vec_func, nonpml_vol) cw_modal_volume = (electric_energy_total / electric_energy_max) * vec_func_total sim.reset_meep() # pulsed source - src = [mp.Source(mp.GaussianSource(fcen,fwidth=df), mp.Ez, mp.Vector3(r+0.1)), - mp.Source(mp.GaussianSource(fcen,fwidth=df), mp.Ez, mp.Vector3(-(r+0.1)), amplitude=-1)] - - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - k_point=mp.Vector3(), - sources=src, - resolution=resolution, - symmetries=symmetries, - boundary_layers=pml_layers) + src = [ + mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)), + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + mp.Ez, + mp.Vector3(-(r + 0.1)), + amplitude=-1, + ), + ] + + sim = mp.Simulation( + cell_size=cell_size, + geometry=geometry, + k_point=mp.Vector3(), + sources=src, + resolution=resolution, + symmetries=symmetries, + boundary_layers=pml_layers, + ) dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, where=nonpml_vol) sim.run(until_after_sources=100) Ez = sim.get_dft_array(dft_obj, mp.Ez, 0) - (X,Y,Z,W) = sim.get_array_metadata(dft_cell=dft_obj) + (X, Y, Z, W) = sim.get_array_metadata(dft_cell=dft_obj) Eps = sim.get_array(vol=nonpml_vol, component=mp.Dielectric) - EpsE2 = np.real(Eps*np.conj(Ez)*Ez) + EpsE2 = np.real(Eps * np.conj(Ez) * Ez) xm, ym = np.meshgrid(X, Y) - vec_func_sum = np.sum(W*(xm**2 + 2*ym**2)) - pulse_modal_volume = np.sum(W*EpsE2)/np.max(EpsE2) * vec_func_sum + vec_func_sum = np.sum(W * (xm**2 + 2 * ym**2)) + pulse_modal_volume = np.sum(W * EpsE2) / np.max(EpsE2) * vec_func_sum tol = 5e-2 if mp.is_single_precision() else 1e-2 - self.assertClose(cw_modal_volume/pulse_modal_volume, 1.0, epsilon=tol) + self.assertClose(cw_modal_volume / pulse_modal_volume, 1.0, epsilon=tol) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_bend_flux.py b/python/tests/test_bend_flux.py index 5f3eb3371..83aa10688 100644 --- a/python/tests/test_bend_flux.py +++ b/python/tests/test_bend_flux.py @@ -1,12 +1,13 @@ import os import unittest + import numpy as np -import meep as mp from utils import ApproxComparisonTestCase +import meep as mp + class TestBendFlux(ApproxComparisonTestCase): - def init(self, no_bend=False, gdsii=False): sx = 16 sy = 32 @@ -16,61 +17,91 @@ def init(self, no_bend=False, gdsii=False): wvg_ycen = -0.5 * (sy - w - (2 * pad)) wvg_xcen = 0.5 * (sx - w - (2 * pad)) height = mp.inf - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - gdsii_file = os.path.join(data_dir, 'bend-flux.gds') + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + gdsii_file = os.path.join(data_dir, "bend-flux.gds") if no_bend: if gdsii: - geometry = mp.get_GDSII_prisms(mp.Medium(epsilon=12), gdsii_file, 1, 0, height) + geometry = mp.get_GDSII_prisms( + mp.Medium(epsilon=12), gdsii_file, 1, 0, height + ) else: - no_bend_vertices = [mp.Vector3(-0.5 * sx - 5, wvg_ycen - 0.5 * w), - mp.Vector3(+0.5 * sx + 5, wvg_ycen - 0.5 * w), - mp.Vector3(+0.5 * sx + 5, wvg_ycen + 0.5 * w), - mp.Vector3(-0.5 * sx - 5, wvg_ycen + 0.5 * w)] - - geometry = [mp.Prism(no_bend_vertices, height, material=mp.Medium(epsilon=12))] + no_bend_vertices = [ + mp.Vector3(-0.5 * sx - 5, wvg_ycen - 0.5 * w), + mp.Vector3(+0.5 * sx + 5, wvg_ycen - 0.5 * w), + mp.Vector3(+0.5 * sx + 5, wvg_ycen + 0.5 * w), + mp.Vector3(-0.5 * sx - 5, wvg_ycen + 0.5 * w), + ] + + geometry = [ + mp.Prism(no_bend_vertices, height, material=mp.Medium(epsilon=12)) + ] + elif gdsii: + geometry = mp.get_GDSII_prisms( + mp.Medium(epsilon=12), gdsii_file, 2, 0, height + ) else: - if gdsii: - geometry = mp.get_GDSII_prisms(mp.Medium(epsilon=12), gdsii_file, 2, 0, height) - else: - bend_vertices = [mp.Vector3(-0.5 * sx, wvg_ycen - 0.5 * w), - mp.Vector3(wvg_xcen + 0.5 * w, wvg_ycen - 0.5 * w), - mp.Vector3(wvg_xcen + 0.5 * w, 0.5 * sy), - mp.Vector3(wvg_xcen - 0.5 * w, 0.5 * sy), - mp.Vector3(wvg_xcen - 0.5 * w, wvg_ycen + 0.5 * w), - mp.Vector3(-0.5 * sx, wvg_ycen + 0.5 * w)] + bend_vertices = [ + mp.Vector3(-0.5 * sx, wvg_ycen - 0.5 * w), + mp.Vector3(wvg_xcen + 0.5 * w, wvg_ycen - 0.5 * w), + mp.Vector3(wvg_xcen + 0.5 * w, 0.5 * sy), + mp.Vector3(wvg_xcen - 0.5 * w, 0.5 * sy), + mp.Vector3(wvg_xcen - 0.5 * w, wvg_ycen + 0.5 * w), + mp.Vector3(-0.5 * sx, wvg_ycen + 0.5 * w), + ] - geometry = [mp.Prism(bend_vertices, height, material=mp.Medium(epsilon=12))] + geometry = [mp.Prism(bend_vertices, height, material=mp.Medium(epsilon=12))] fcen = 0.15 df = 0.1 - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3(1 + (-0.5 * sx), wvg_ycen), size=mp.Vector3(0, w))] + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(1 + (-0.5 * sx), wvg_ycen), + size=mp.Vector3(0, w), + ) + ] pml_layers = [mp.PML(1.0)] resolution = 10 nfreq = 100 - self.sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) + self.sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, + ) if no_bend: - fr = mp.FluxRegion(center=mp.Vector3((sx / 2) - 1.5, wvg_ycen), size=mp.Vector3(0, w * 2)) + fr = mp.FluxRegion( + center=mp.Vector3((sx / 2) - 1.5, wvg_ycen), size=mp.Vector3(0, w * 2) + ) else: - fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen, (sy / 2) - 1.5), size=mp.Vector3(w * 2, 0)) + fr = mp.FluxRegion( + center=mp.Vector3(wvg_xcen, (sy / 2) - 1.5), size=mp.Vector3(w * 2, 0) + ) self.trans = self.sim.add_flux(fcen, df, nfreq, fr, decimation_factor=1) - self.trans_decimated = self.sim.add_flux(fcen, df, nfreq, fr, decimation_factor=5) - - refl_fr = mp.FluxRegion(center=mp.Vector3((-0.5 * sx) + 1.5, wvg_ycen), - size=mp.Vector3(0, w * 2)) - self.refl = self.sim.add_flux(np.linspace(fcen-0.5*df,fcen+0.5*df,nfreq), refl_fr, - decimation_factor=1) - self.refl_decimated = self.sim.add_flux(np.linspace(fcen-0.5*df,fcen+0.5*df,nfreq), refl_fr, - decimation_factor=10) + self.trans_decimated = self.sim.add_flux( + fcen, df, nfreq, fr, decimation_factor=5 + ) + + refl_fr = mp.FluxRegion( + center=mp.Vector3((-0.5 * sx) + 1.5, wvg_ycen), size=mp.Vector3(0, w * 2) + ) + self.refl = self.sim.add_flux( + np.linspace(fcen - 0.5 * df, fcen + 0.5 * df, nfreq), + refl_fr, + decimation_factor=1, + ) + self.refl_decimated = self.sim.add_flux( + np.linspace(fcen - 0.5 * df, fcen + 0.5 * df, nfreq), + refl_fr, + decimation_factor=10, + ) if no_bend: self.pt = mp.Vector3((sx / 2) - 1.5, wvg_ycen) @@ -108,13 +139,21 @@ def run_bend_flux(self, from_gdsii_file): (0.119191919192, 0.0254987474079, 0.0252348211592), ] - res = list(zip(mp.get_flux_freqs(self.trans), - mp.get_fluxes(self.trans), - mp.get_fluxes(self.refl))) - - res_decimated = list(zip(mp.get_flux_freqs(self.trans_decimated), - mp.get_fluxes(self.trans_decimated), - mp.get_fluxes(self.refl_decimated))) + res = list( + zip( + mp.get_flux_freqs(self.trans), + mp.get_fluxes(self.trans), + mp.get_fluxes(self.refl), + ) + ) + + res_decimated = list( + zip( + mp.get_flux_freqs(self.trans_decimated), + mp.get_fluxes(self.trans_decimated), + mp.get_fluxes(self.refl_decimated), + ) + ) tol = 1e-6 if mp.is_single_precision() else 1e-8 self.assertClose(np.array(expected), np.array(res[:20]), epsilon=tol) @@ -151,13 +190,21 @@ def run_bend_flux(self, from_gdsii_file): (0.11919191919191931, 0.008646855439680507, -0.005614491919262783), ] - res = list(zip(mp.get_flux_freqs(self.trans), - mp.get_fluxes(self.trans), - mp.get_fluxes(self.refl))) - - res_decimated = list(zip(mp.get_flux_freqs(self.trans_decimated), - mp.get_fluxes(self.trans_decimated), - mp.get_fluxes(self.refl_decimated))) + res = list( + zip( + mp.get_flux_freqs(self.trans), + mp.get_fluxes(self.trans), + mp.get_fluxes(self.refl), + ) + ) + + res_decimated = list( + zip( + mp.get_flux_freqs(self.trans_decimated), + mp.get_fluxes(self.trans_decimated), + mp.get_fluxes(self.refl_decimated), + ) + ) tol = 1e-3 self.assertClose(np.array(expected), np.array(res[:20]), epsilon=tol) @@ -169,5 +216,5 @@ def test_bend_flux(self): self.run_bend_flux(True) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_binary_grating.py b/python/tests/test_binary_grating.py index 51b5a0e69..3f2961dad 100644 --- a/python/tests/test_binary_grating.py +++ b/python/tests/test_binary_grating.py @@ -1,290 +1,338 @@ -# -*- coding: utf-8 -*- - +import cmath +import math import unittest + +import numpy as np import parameterized + import meep as mp -import math -import cmath -import numpy as np class TestEigCoeffs(unittest.TestCase): - @classmethod - def setUpClass(cls): - cls.resolution = 30 # pixels/μm - - cls.dpml = 1.0 # PML thickness - cls.dsub = 1.0 # substrate thickness - cls.dpad = 1.0 # padding thickness between grating and PML - cls.gp = 6.0 # grating period - cls.gh = 0.5 # grating height - cls.gdc = 0.5 # grating duty cycle - - cls.sx = cls.dpml+cls.dsub+cls.gh+cls.dpad+cls.dpml - cls.sy = cls.gp - - cls.cell_size = mp.Vector3(cls.sx,cls.sy,0) - - # replace anisotropic PML with isotropic Absorber to - # attenuate parallel-directed fields of oblique source - cls.abs_layers = [mp.Absorber(thickness=cls.dpml,direction=mp.X)] - - wvl = 0.5 # center wavelength - cls.fcen = 1/wvl # center frequency - cls.df = 0.05*cls.fcen # frequency width - - cls.ng = 1.5 - cls.glass = mp.Medium(index=cls.ng) - - cls.geometry = [mp.Block(material=cls.glass, - size=mp.Vector3(cls.dpml+cls.dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*cls.sx+0.5*(cls.dpml+cls.dsub),0,0)), - mp.Block(material=cls.glass, - size=mp.Vector3(cls.gh,cls.gdc*cls.gp,mp.inf), - center=mp.Vector3(-0.5*cls.sx+cls.dpml+cls.dsub+0.5*cls.gh,0,0))] - - - @parameterized.parameterized.expand([(0,), (10.7,)]) - def test_binary_grating_oblique(self, theta): - # rotation angle of incident planewave - # counterclockwise (CCW) about Z axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(0,0,1), theta_in) - - symmetries = [] - eig_parity = mp.ODD_Z - if theta_in == 0: - symmetries = [mp.Mirror(mp.Y)] - eig_parity += mp.EVEN_Y - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*self.sx+self.dpml,0,0) - sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=self.df), - component=mp.Ez, # S polarization - center=src_pt, - size=mp.Vector3(0,self.sy,0), - amp_func=pw_amp(k,src_pt))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - k_point=k, - default_material=self.glass, - sources=sources, - symmetries=symmetries) - - refl_pt = mp.Vector3(-0.5*self.sx+self.dpml+0.5*self.dsub,0,0) - refl_flux = sim.add_flux(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - input_flux = mp.get_fluxes(refl_flux) - input_flux_data = sim.get_flux_data(refl_flux) - - sim.reset_meep() - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - geometry=self.geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - - refl_flux = sim.add_flux(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.load_minus_flux_data(refl_flux,input_flux_data) - - tran_pt = mp.Vector3(0.5*self.sx-self.dpml-0.5*self.dpad,0,0) - tran_flux = sim.add_flux(self.fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - # number of reflected orders - nm_r = np.floor((self.fcen*self.ng-k.y)*self.gp)-np.ceil((-self.fcen*self.ng-k.y)*self.gp) - if theta_in == 0: - nm_r = nm_r/2 # since eig_parity removes degeneracy in y-direction - nm_r = int(nm_r) - - res = sim.get_eigenmode_coefficients(refl_flux, - range(1,nm_r+1), - eig_parity=eig_parity) - r_coeffs = res.alpha - - Rsum = 0 - for nm in range(nm_r): - Rsum += abs(r_coeffs[nm,0,1])**2/input_flux[0] - - # number of transmitted orders - nm_t = np.floor((self.fcen-k.y)*self.gp)-np.ceil((-self.fcen-k.y)*self.gp) - if theta_in == 0: - nm_t = nm_t/2 # since eig_parity removes degeneracy in y-direction - nm_t = int(nm_t) - - res = sim.get_eigenmode_coefficients(tran_flux, - range(1,nm_t+1), - eig_parity=eig_parity) - t_coeffs = res.alpha - - Tsum = 0 - for nm in range(nm_t): - Tsum += abs(t_coeffs[nm,0,0])**2/input_flux[0] - - r_flux = mp.get_fluxes(refl_flux) - t_flux = mp.get_fluxes(tran_flux) - Rflux = -r_flux[0]/input_flux[0] - Tflux = t_flux[0]/input_flux[0] - - print("refl:, {}, {}".format(Rsum,Rflux)) - print("tran:, {}, {}".format(Tsum,Tflux)) - print("sum:, {}, {}".format(Rsum+Tsum,Rflux+Tflux)) - - self.assertAlmostEqual(Rsum,Rflux,places=2) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) - - - @parameterized.parameterized.expand([(13.2,"real/imag"), - (17.7,"complex"), - (21.2, "3d")]) - def test_binary_grating_special_kz(self, theta, kz_2d): - # rotation angle of incident planewave - # counterclockwise (CCW) about Y axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XZ) - k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(0,1,0), theta_in) - - symmetries = [mp.Mirror(mp.Y)] - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*self.sx+self.dpml,0,0) - sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=self.df), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(0,self.sy,0), - amp_func=pw_amp(k,src_pt))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - k_point=k, - default_material=self.glass, - sources=sources, - symmetries=symmetries, - kz_2d=kz_2d) - - refl_pt = mp.Vector3(-0.5*self.sx+self.dpml+0.5*self.dsub,0,0) - refl_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - input_flux = mp.get_fluxes(refl_flux) - input_flux_data = sim.get_flux_data(refl_flux) - - sim.reset_meep() - - sim = mp.Simulation(resolution=self.resolution, - cell_size=self.cell_size, - boundary_layers=self.abs_layers, - geometry=self.geometry, - k_point=k, - sources=sources, - symmetries=symmetries, - kz_2d=kz_2d) - - refl_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=refl_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.load_minus_flux_data(refl_flux,input_flux_data) - - tran_pt = mp.Vector3(0.5*self.sx-self.dpml-0.5*self.dpad,0,0) - tran_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,self.sy,0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - # number of reflected orders - nm_r = (np.ceil((np.sqrt((self.fcen*self.ng)**2-k.z**2)-k.y)*self.gp) - - np.floor((-np.sqrt((self.fcen*self.ng)**2-k.z**2)-k.y)*self.gp)) - nm_r = int(nm_r/2) - - Rsum = 0 - for nm in range(nm_r): - for S_pol in [False, True]: - res = sim.get_eigenmode_coefficients(refl_flux, - mp.DiffractedPlanewave([0,nm,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + @classmethod + def setUpClass(cls): + cls.resolution = 30 # pixels/μm + + cls.dpml = 1.0 # PML thickness + cls.dsub = 1.0 # substrate thickness + cls.dpad = 1.0 # padding thickness between grating and PML + cls.gp = 6.0 # grating period + cls.gh = 0.5 # grating height + cls.gdc = 0.5 # grating duty cycle + + cls.sx = cls.dpml + cls.dsub + cls.gh + cls.dpad + cls.dpml + cls.sy = cls.gp + + cls.cell_size = mp.Vector3(cls.sx, cls.sy, 0) + + # replace anisotropic PML with isotropic Absorber to + # attenuate parallel-directed fields of oblique source + cls.abs_layers = [mp.Absorber(thickness=cls.dpml, direction=mp.X)] + + wvl = 0.5 # center wavelength + cls.fcen = 1 / wvl # center frequency + cls.df = 0.05 * cls.fcen # frequency width + + cls.ng = 1.5 + cls.glass = mp.Medium(index=cls.ng) + + cls.geometry = [ + mp.Block( + material=cls.glass, + size=mp.Vector3(cls.dpml + cls.dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * cls.sx + 0.5 * (cls.dpml + cls.dsub), 0, 0), + ), + mp.Block( + material=cls.glass, + size=mp.Vector3(cls.gh, cls.gdc * cls.gp, mp.inf), + center=mp.Vector3( + -0.5 * cls.sx + cls.dpml + cls.dsub + 0.5 * cls.gh, 0, 0 + ), + ), + ] + + @parameterized.parameterized.expand([(0,), (10.7,)]) + def test_binary_grating_oblique(self, theta): + # rotation angle of incident planewave + # counterclockwise (CCW) about Z axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(self.fcen * self.ng).rotate(mp.Vector3(0, 0, 1), theta_in) + + symmetries = [] + eig_parity = mp.ODD_Z + if theta_in == 0: + symmetries = [mp.Mirror(mp.Y)] + eig_parity += mp.EVEN_Y + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * self.sx + self.dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + component=mp.Ez, # S polarization + center=src_pt, + size=mp.Vector3(0, self.sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + k_point=k, + default_material=self.glass, + sources=sources, + symmetries=symmetries, + ) + + refl_pt = mp.Vector3(-0.5 * self.sx + self.dpml + 0.5 * self.dsub, 0, 0) + refl_flux = sim.add_flux( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + input_flux = mp.get_fluxes(refl_flux) + input_flux_data = sim.get_flux_data(refl_flux) + + sim.reset_meep() + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + geometry=self.geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + ) + + refl_flux = sim.add_flux( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_pt = mp.Vector3(0.5 * self.sx - self.dpml - 0.5 * self.dpad, 0, 0) + tran_flux = sim.add_flux( + self.fcen, + 0, + 1, + mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + # number of reflected orders + nm_r = np.floor((self.fcen * self.ng - k.y) * self.gp) - np.ceil( + (-self.fcen * self.ng - k.y) * self.gp + ) + if theta_in == 0: + nm_r = nm_r / 2 # since eig_parity removes degeneracy in y-direction + nm_r = int(nm_r) + + res = sim.get_eigenmode_coefficients( + refl_flux, range(1, nm_r + 1), eig_parity=eig_parity + ) r_coeffs = res.alpha - Rmode = abs(r_coeffs[0,0,1])**2/input_flux[0] - print("refl-order:, {}, {}, {}".format("s" if S_pol else "p",nm,Rmode)) - Rsum += Rmode if nm == 0 else 2*Rmode - - # number of transmitted orders - nm_t = (np.ceil((np.sqrt(self.fcen**2-k.z**2)-k.y)*self.gp) - - np.floor((-np.sqrt(self.fcen**2-k.z**2)-k.y)*self.gp)) - nm_t = int(nm_t/2) - - Tsum = 0 - for nm in range(nm_t): - for S_pol in [False, True]: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave([0,nm,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) - t_coeffs = res.alpha - Tmode = abs(t_coeffs[0,0,0])**2/input_flux[0] - print("tran-order:, {}, {}, {}".format("s" if S_pol else "p",nm,Tmode)) - Tsum += Tmode if nm == 0 else 2*Tmode - - r_flux = mp.get_fluxes(refl_flux) - t_flux = mp.get_fluxes(tran_flux) - Rflux = -r_flux[0]/input_flux[0] - Tflux = t_flux[0]/input_flux[0] - - print("refl:, {}, {}".format(Rsum,Rflux)) - print("tran:, {}, {}".format(Tsum,Tflux)) - print("sum:, {}, {}".format(Rsum+Tsum,Rflux+Tflux)) - - self.assertAlmostEqual(Rsum,Rflux,places=2) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) + Rsum = 0 + for nm in range(nm_r): + Rsum += abs(r_coeffs[nm, 0, 1]) ** 2 / input_flux[0] + + # number of transmitted orders + nm_t = np.floor((self.fcen - k.y) * self.gp) - np.ceil( + (-self.fcen - k.y) * self.gp + ) + if theta_in == 0: + nm_t = nm_t / 2 # since eig_parity removes degeneracy in y-direction + nm_t = int(nm_t) + + res = sim.get_eigenmode_coefficients( + tran_flux, range(1, nm_t + 1), eig_parity=eig_parity + ) + t_coeffs = res.alpha -if __name__ == '__main__': - unittest.main() + Tsum = 0 + for nm in range(nm_t): + Tsum += abs(t_coeffs[nm, 0, 0]) ** 2 / input_flux[0] + + r_flux = mp.get_fluxes(refl_flux) + t_flux = mp.get_fluxes(tran_flux) + Rflux = -r_flux[0] / input_flux[0] + Tflux = t_flux[0] / input_flux[0] + + print(f"refl:, {Rsum}, {Rflux}") + print(f"tran:, {Tsum}, {Tflux}") + print(f"sum:, {Rsum + Tsum}, {Rflux + Tflux}") + + self.assertAlmostEqual(Rsum, Rflux, places=2) + self.assertAlmostEqual(Tsum, Tflux, places=2) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=2) + + @parameterized.parameterized.expand( + [(13.2, "real/imag"), (17.7, "complex"), (21.2, "3d")] + ) + def test_binary_grating_special_kz(self, theta, kz_2d): + # rotation angle of incident planewave + # counterclockwise (CCW) about Y axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XZ) + k = mp.Vector3(self.fcen * self.ng).rotate(mp.Vector3(0, 1, 0), theta_in) + + symmetries = [mp.Mirror(mp.Y)] + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * self.sx + self.dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, self.sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + k_point=k, + default_material=self.glass, + sources=sources, + symmetries=symmetries, + kz_2d=kz_2d, + ) + + refl_pt = mp.Vector3(-0.5 * self.sx + self.dpml + 0.5 * self.dsub, 0, 0) + refl_flux = sim.add_mode_monitor( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + input_flux = mp.get_fluxes(refl_flux) + input_flux_data = sim.get_flux_data(refl_flux) + + sim.reset_meep() + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=self.cell_size, + boundary_layers=self.abs_layers, + geometry=self.geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + kz_2d=kz_2d, + ) + + refl_flux = sim.add_mode_monitor( + self.fcen, + 0, + 1, + mp.FluxRegion(center=refl_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_pt = mp.Vector3(0.5 * self.sx - self.dpml - 0.5 * self.dpad, 0, 0) + tran_flux = sim.add_mode_monitor( + self.fcen, + 0, + 1, + mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, self.sy, 0)), + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + # number of reflected orders + nm_r = np.ceil( + (np.sqrt((self.fcen * self.ng) ** 2 - k.z**2) - k.y) * self.gp + ) - np.floor((-np.sqrt((self.fcen * self.ng) ** 2 - k.z**2) - k.y) * self.gp) + nm_r = int(nm_r / 2) + + Rsum = 0 + for nm in range(nm_r): + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + refl_flux, + mp.DiffractedPlanewave( + [0, nm, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) + r_coeffs = res.alpha + Rmode = abs(r_coeffs[0, 0, 1]) ** 2 / input_flux[0] + print( + "refl-order:, {}, {}, {}".format("s" if S_pol else "p", nm, Rmode) + ) + Rsum += Rmode if nm == 0 else 2 * Rmode + + # number of transmitted orders + nm_t = np.ceil((np.sqrt(self.fcen**2 - k.z**2) - k.y) * self.gp) - np.floor( + (-np.sqrt(self.fcen**2 - k.z**2) - k.y) * self.gp + ) + nm_t = int(nm_t / 2) + + Tsum = 0 + for nm in range(nm_t): + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave( + [0, nm, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) + t_coeffs = res.alpha + Tmode = abs(t_coeffs[0, 0, 0]) ** 2 / input_flux[0] + print( + "tran-order:, {}, {}, {}".format("s" if S_pol else "p", nm, Tmode) + ) + Tsum += Tmode if nm == 0 else 2 * Tmode + + r_flux = mp.get_fluxes(refl_flux) + t_flux = mp.get_fluxes(tran_flux) + Rflux = -r_flux[0] / input_flux[0] + Tflux = t_flux[0] / input_flux[0] + + print(f"refl:, {Rsum}, {Rflux}") + print(f"tran:, {Tsum}, {Tflux}") + print(f"sum:, {Rsum + Tsum}, {Rflux + Tflux}") + + self.assertAlmostEqual(Rsum, Rflux, places=2) + self.assertAlmostEqual(Tsum, Tflux, places=2) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=2) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_binary_partition_utils.py b/python/tests/test_binary_partition_utils.py index 5b6f72866..31d1f7996 100644 --- a/python/tests/test_binary_partition_utils.py +++ b/python/tests/test_binary_partition_utils.py @@ -1,18 +1,18 @@ import copy import unittest +import meep.binary_partition_utils as bpu +import numpy as np import parameterized import meep as mp -import meep.binary_partition_utils as bpu -import numpy as np PARTITION_NO_DUPLICATE_PROC_ID = mp.BinaryPartition( - data=[(mp.X, -2.), 0, [(mp.Y, 1.5), [(mp.X, 4.), 1, [(mp.Y, - 0.5), 4, 3]], 2]]) + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] +) PARTITION_DUPLICATE_PROC_ID = mp.BinaryPartition( - data=[(mp.X, -2.), 0, [(mp.Y, 1.5), [(mp.X, 4.), 1, [(mp.Y, - 0.5), 1, 3]], 0]]) + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 1, 3]], 0]] +) # Mocked chunk IDs since we are in a single-core environment CHUNK_OWNERS_NO_DUPLICATE_PROC_ID = np.array([0, 1, 4, 3, 2]) @@ -20,22 +20,25 @@ class BinaryPartitionUtilsTest(unittest.TestCase): - - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, False), - (PARTITION_NO_DUPLICATE_PROC_ID.right, False), - (PARTITION_NO_DUPLICATE_PROC_ID.left, True), - (PARTITION_DUPLICATE_PROC_ID, False), - (PARTITION_DUPLICATE_PROC_ID.right, False), - (PARTITION_DUPLICATE_PROC_ID.left, True), - ]) + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, False), + (PARTITION_NO_DUPLICATE_PROC_ID.right, False), + (PARTITION_NO_DUPLICATE_PROC_ID.left, True), + (PARTITION_DUPLICATE_PROC_ID, False), + (PARTITION_DUPLICATE_PROC_ID.right, False), + (PARTITION_DUPLICATE_PROC_ID.left, True), + ] + ) def test_is_leaf_node(self, partition, expected_leaf_status): self.assertEqual(bpu.is_leaf_node(partition), expected_leaf_status) - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID), - (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID), - ]) + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID), + (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID), + ] + ) def test_enumerate_leaf_nodes(self, partition, chunk_owners): leaf_nodes = list(bpu.enumerate_leaf_nodes(partition)) self.assertEqual(len(leaf_nodes), partition.numchunks()) @@ -44,216 +47,237 @@ def test_enumerate_leaf_nodes(self, partition, chunk_owners): def test_partition_has_duplicate_proc_ids(self): self.assertFalse( - bpu.partition_has_duplicate_proc_ids(PARTITION_NO_DUPLICATE_PROC_ID)) + bpu.partition_has_duplicate_proc_ids(PARTITION_NO_DUPLICATE_PROC_ID) + ) self.assertTrue( - bpu.partition_has_duplicate_proc_ids(PARTITION_DUPLICATE_PROC_ID)) + bpu.partition_has_duplicate_proc_ids(PARTITION_DUPLICATE_PROC_ID) + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ]) + ), + ] + ) def test_get_total_weight(self, partition, weights_by_proc_id): if not bpu.partition_has_duplicate_proc_ids(partition): self.assertEqual( bpu.get_total_weight(partition, weights_by_proc_id), - sum(weights_by_proc_id)) + sum(weights_by_proc_id), + ) self.assertEqual( bpu.get_total_weight(partition.right.left, weights_by_proc_id), - weights_by_proc_id[1] + weights_by_proc_id[4] + weights_by_proc_id[3]) + weights_by_proc_id[1] + weights_by_proc_id[4] + weights_by_proc_id[3], + ) else: with self.assertRaises(ValueError): bpu.get_total_weight(partition, weights_by_proc_id) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, [0, 1, 2, 3, 4], - ), - ]) + ), + ] + ) def test_get_left_right_total_weights(self, partition, weights_by_proc_id): proc_ids = [node.proc_id for node in bpu.enumerate_leaf_nodes(partition)] no_duplicates = len(set(proc_ids)) == len(proc_ids) if no_duplicates: self.assertEqual( bpu.get_left_right_total_weights(partition, weights_by_proc_id), - (bpu.get_total_weight(partition.left, weights_by_proc_id), - bpu.get_total_weight(partition.right, weights_by_proc_id))) + ( + bpu.get_total_weight(partition.left, weights_by_proc_id), + bpu.get_total_weight(partition.right, weights_by_proc_id), + ), + ) else: with self.assertRaises(ValueError): bpu.get_left_right_total_weights(partition, weights_by_proc_id) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, 2, [1500, 2400, 300, 100, 700], - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, 3, [15000, 24000, 3000, 1000, 7000], - ), - ]) + ), + ] + ) def test_pixel_volume(self, partition, dims, expected_pixel_volumes): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() - self.assertEqual([bpu.pixel_volume(vol) for vol in chunk_volumes], - expected_pixel_volumes) + self.assertEqual( + [bpu.pixel_volume(vol) for vol in chunk_volumes], expected_pixel_volumes + ) - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3500), - (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3500), - ]) - def test_get_total_volume_2d(self, partition, chunk_owners, - expected_total_volume): + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3500), + (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3500), + ] + ) + def test_get_total_volume_2d(self, partition, chunk_owners, expected_total_volume): sim = mp.Simulation( - cell_size=mp.Vector3(10.0, 5.0, 0.0), - resolution=10, - chunk_layout=partition) + cell_size=mp.Vector3(10.0, 5.0, 0.0), resolution=10, chunk_layout=partition + ) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() total_volume = sim.cell_size[0] * sim.cell_size[1] * sim.resolution**2 self.assertEqual( - bpu.get_total_volume(partition, chunk_volumes, chunk_owners), - total_volume) + bpu.get_total_volume(partition, chunk_volumes, chunk_owners), total_volume + ) if not bpu.partition_has_duplicate_proc_ids(partition): self.assertEqual( bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners), - expected_total_volume) + expected_total_volume, + ) - @parameterized.parameterized.expand([ - (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 35000), - (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 35000), - ]) - def test_get_total_volume_3d(self, partition, chunk_owners, - expected_total_volume): + @parameterized.parameterized.expand( + [ + (PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 35000), + (PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 35000), + ] + ) + def test_get_total_volume_3d(self, partition, chunk_owners, expected_total_volume): sim = mp.Simulation( - cell_size=mp.Vector3(10.0, 5.0, 1.0), - resolution=10, - chunk_layout=partition) + cell_size=mp.Vector3(10.0, 5.0, 1.0), resolution=10, chunk_layout=partition + ) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() - total_volume = sim.cell_size[0] * sim.cell_size[1] * sim.cell_size[ - 2] * sim.resolution**3 + total_volume = ( + sim.cell_size[0] * sim.cell_size[1] * sim.cell_size[2] * sim.resolution**3 + ) self.assertEqual( - bpu.get_total_volume(partition, chunk_volumes, chunk_owners), - total_volume) + bpu.get_total_volume(partition, chunk_volumes, chunk_owners), total_volume + ) if not bpu.partition_has_duplicate_proc_ids(partition): self.assertEqual( bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners), - expected_total_volume) + expected_total_volume, + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, - ), - ]) + ), + ] + ) def test_get_left_right_total_volumes(self, partition, chunk_owners, dims): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() self.assertEqual( - bpu.get_left_right_total_volumes(partition, chunk_volumes, - chunk_owners), - (bpu.get_total_volume(partition.left, chunk_volumes, chunk_owners), - bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners))) + bpu.get_left_right_total_volumes(partition, chunk_volumes, chunk_owners), + ( + bpu.get_total_volume(partition.left, chunk_volumes, chunk_owners), + bpu.get_total_volume(partition.right, chunk_volumes, chunk_owners), + ), + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, - ), - ]) + ), + ] + ) def test_get_grid_volumes_in_tree(self, partition, chunk_owners, dims): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() - grid_volumes_in_tree = bpu.get_grid_volumes_in_tree(partition, - chunk_volumes, - chunk_owners) + grid_volumes_in_tree = bpu.get_grid_volumes_in_tree( + partition, chunk_volumes, chunk_owners + ) self.assertEqual(set(grid_volumes_in_tree), set(chunk_volumes)) no_duplicates = len(set(chunk_owners)) == len(chunk_owners) - grid_volumes_in_right_expected = chunk_volumes[ - 1:] if no_duplicates else chunk_volumes + grid_volumes_in_right_expected = ( + chunk_volumes[1:] if no_duplicates else chunk_volumes + ) grid_volumes_in_right = bpu.get_grid_volumes_in_tree( - partition.right, chunk_volumes, chunk_owners) + partition.right, chunk_volumes, chunk_owners + ) self.assertEqual( - set(grid_volumes_in_right), set(grid_volumes_in_right_expected)) + set(grid_volumes_in_right), set(grid_volumes_in_right_expected) + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, - { - 0: 1500, - 1: 2400, - 4: 300, - 3: 100, - 2: 700 - }, - ), - ( + {0: 1500, 1: 2400, 4: 300, 3: 100, 2: 700}, + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, @@ -262,20 +286,14 @@ def test_get_grid_volumes_in_tree(self, partition, chunk_owners, dims): 1: 2700, 3: 100, }, - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, - { - 0: 15000, - 1: 24000, - 4: 3000, - 3: 1000, - 2: 7000 - }, - ), - ( + {0: 15000, 1: 24000, 4: 3000, 3: 1000, 2: 7000}, + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, @@ -284,95 +302,110 @@ def test_get_grid_volumes_in_tree(self, partition, chunk_owners, dims): 1: 27000, 3: 1000, }, - ), - ]) - def test_get_total_volume_per_process(self, partition, chunk_owners, dims, - expected_volumes_per_process): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + ), + ] + ) + def test_get_total_volume_per_process( + self, partition, chunk_owners, dims, expected_volumes_per_process + ): + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() volumes_per_process = bpu.get_total_volume_per_process( - partition, chunk_volumes, chunk_owners) + partition, chunk_volumes, chunk_owners + ) self.assertEqual(volumes_per_process, expected_volumes_per_process) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 2, (-5.0, 5.0, -2.5, 2.5, 0.0, 0.0), (-2.0, 5.0, -2.5, 2.5, 0.0, 0.0), - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 2, (-5.0, 5.0, -2.5, 2.5, 0.0, 0.0), (-5.0, 5.0, -2.5, 2.5, 0.0, 0.0), - ), - ( + ), + ( PARTITION_NO_DUPLICATE_PROC_ID, CHUNK_OWNERS_NO_DUPLICATE_PROC_ID, 3, (-5.0, 5.0, -2.5, 2.5, -0.5, 0.5), (-2.0, 5.0, -2.5, 2.5, -0.5, 0.5), - ), - ( + ), + ( PARTITION_DUPLICATE_PROC_ID, CHUNK_OWNERS_DUPLICATE_PROC_ID, 3, (-5.0, 5.0, -2.5, 2.5, -0.5, 0.5), (-5.0, 5.0, -2.5, 2.5, -0.5, 0.5), - ), - ]) - def test_get_box_ranges(self, partition, chunk_owners, dims, - expected_box_ranges, expected_right_box_ranges): - cell_size = mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3( - 10.0, 5.0, 0.0) - sim = mp.Simulation( - cell_size=cell_size, resolution=10, chunk_layout=partition) + ), + ] + ) + def test_get_box_ranges( + self, + partition, + chunk_owners, + dims, + expected_box_ranges, + expected_right_box_ranges, + ): + cell_size = ( + mp.Vector3(10.0, 5.0, 1.0) if dims == 3 else mp.Vector3(10.0, 5.0, 0.0) + ) + sim = mp.Simulation(cell_size=cell_size, resolution=10, chunk_layout=partition) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() self.assertEqual( bpu.get_box_ranges(partition, chunk_volumes, chunk_owners), - expected_box_ranges) + expected_box_ranges, + ) self.assertEqual( bpu.get_box_ranges(partition.right, chunk_volumes, chunk_owners), - expected_right_box_ranges) + expected_right_box_ranges, + ) - @parameterized.parameterized.expand([ - ( + @parameterized.parameterized.expand( + [ + ( copy.deepcopy(PARTITION_NO_DUPLICATE_PROC_ID), copy.deepcopy(PARTITION_NO_DUPLICATE_PROC_ID), True, - ), - ( + ), + ( copy.deepcopy(PARTITION_DUPLICATE_PROC_ID), copy.deepcopy(PARTITION_DUPLICATE_PROC_ID), True, - ), - ( + ), + ( copy.deepcopy(PARTITION_NO_DUPLICATE_PROC_ID), copy.deepcopy(PARTITION_DUPLICATE_PROC_ID), False, - ), - ( + ), + ( mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), True, - ), - ( + ), + ( mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.4), 1, 2]]), mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), False, - ), - ]) + ), + ] + ) def test_partitions_are_equal(self, bp1, bp2, is_equal): self.assertEqual(bpu.partitions_are_equal(bp1, bp2), is_equal) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_cavity_arrayslice.py b/python/tests/test_cavity_arrayslice.py index 42f758b6f..b731a7c4c 100644 --- a/python/tests/test_cavity_arrayslice.py +++ b/python/tests/test_cavity_arrayslice.py @@ -1,15 +1,17 @@ -import meep as mp -from utils import ApproxComparisonTestCase import os import unittest + import numpy as np +from utils import ApproxComparisonTestCase + +import meep as mp class TestCavityArraySlice(ApproxComparisonTestCase): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - expected_1d = np.load(os.path.join(data_dir, 'cavity_arrayslice_1d.npy')) - expected_2d = np.load(os.path.join(data_dir, 'cavity_arrayslice_2d.npy')) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + expected_1d = np.load(os.path.join(data_dir, "cavity_arrayslice_1d.npy")) + expected_2d = np.load(os.path.join(data_dir, "cavity_arrayslice_2d.npy")) def setUp(self): @@ -22,16 +24,14 @@ def setUp(self): cell = mp.Vector3(sx, sy, 0) - blk = mp.Block(size=mp.Vector3(mp.inf, 1.2, mp.inf), - material=mp.Medium(epsilon=13)) + blk = mp.Block( + size=mp.Vector3(mp.inf, 1.2, mp.inf), material=mp.Medium(epsilon=13) + ) geometry = [blk] - for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) - - for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) + geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) + geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / -2 - i)) for i in range(3)) sources = [mp.Source(mp.GaussianSource(0.25, fwidth=0.2), mp.Hz, mp.Vector3())] @@ -40,7 +40,7 @@ def setUp(self): geometry=geometry, sources=sources, boundary_layers=[mp.PML(dpml)], - resolution=20 + resolution=20, ) self.x_min = -0.25 * sx @@ -52,7 +52,9 @@ def setUp(self): self.center_1d = mp.Vector3((self.x_min + self.x_max) / 2) self.size_2d = mp.Vector3(self.x_max - self.x_min, self.y_max - self.y_min) - self.center_2d = mp.Vector3((self.x_min + self.x_max) / 2, (self.y_min + self.y_max) / 2) + self.center_2d = mp.Vector3( + (self.x_min + self.x_max) / 2, (self.y_min + self.y_max) / 2 + ) def test_1d_slice(self): self.sim.run(until_after_sources=0) @@ -70,7 +72,9 @@ def test_2d_slice(self): def test_1d_slice_user_array(self): self.sim.run(until_after_sources=0) - arr = np.zeros(126, dtype=np.float32 if mp.is_single_precision() else np.float64) + arr = np.zeros( + 126, dtype=np.float32 if mp.is_single_precision() else np.float64 + ) vol = mp.Volume(center=self.center_1d, size=self.size_1d) self.sim.get_array(mp.Hz, vol, arr=arr) tol = 1e-5 if mp.is_single_precision() else 1e-8 @@ -78,7 +82,9 @@ def test_1d_slice_user_array(self): def test_2d_slice_user_array(self): self.sim.run(until_after_sources=0) - arr = np.zeros((126, 38), dtype=np.float32 if mp.is_single_precision() else np.float64) + arr = np.zeros( + (126, 38), dtype=np.float32 if mp.is_single_precision() else np.float64 + ) vol = mp.Volume(center=self.center_2d, size=self.size_2d) self.sim.get_array(mp.Hz, vol, arr=arr) tol = 1e-5 if mp.is_single_precision() else 1e-8 @@ -106,16 +112,24 @@ def test_1d_complex_slice(self): self.sim.run(until_after_sources=0) vol = mp.Volume(center=self.center_1d, size=self.size_1d) hl_slice1d = self.sim.get_array(mp.Hz, vol, cmplx=True) - self.assertTrue(hl_slice1d.dtype == np.complex64 if mp.is_single_precision() else np.complex128) + self.assertTrue( + hl_slice1d.dtype == np.complex64 + if mp.is_single_precision() + else np.complex128 + ) self.assertTrue(hl_slice1d.shape[0] == 126) def test_2d_complex_slice(self): self.sim.run(until_after_sources=0) vol = mp.Volume(center=self.center_2d, size=self.size_2d) hl_slice2d = self.sim.get_array(mp.Hz, vol, cmplx=True) - self.assertTrue(hl_slice2d.dtype == np.complex64 if mp.is_single_precision() else np.complex128) + self.assertTrue( + hl_slice2d.dtype == np.complex64 + if mp.is_single_precision() + else np.complex128 + ) self.assertTrue(hl_slice2d.shape[0] == 126 and hl_slice2d.shape[1] == 38) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_cavity_farfield.py b/python/tests/test_cavity_farfield.py index 93de61f01..9d2d8da5c 100644 --- a/python/tests/test_cavity_farfield.py +++ b/python/tests/test_cavity_farfield.py @@ -1,13 +1,15 @@ -import meep as mp -from utils import ApproxComparisonTestCase import os import unittest + import h5py +from utils import ApproxComparisonTestCase + +import meep as mp class TestCavityFarfield(ApproxComparisonTestCase): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) def run_test(self, nfreqs): eps = 13 @@ -22,8 +24,13 @@ def run_test(self, nfreqs): cell = mp.Vector3(sx, sy, 0) - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, w, mp.inf), - material=mp.Medium(epsilon=eps))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=eps), + ) + ] for i in range(N): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) @@ -35,55 +42,66 @@ def run_test(self, nfreqs): fcen = 0.25 df = 0.2 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), component=mp.Hz, center=mp.Vector3() + ) symmetries = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.X, phase=-1)] d1 = 0.2 - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[sources], - symmetries=symmetries, - boundary_layers=[pml_layers], - resolution=resolution) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[sources], + symmetries=symmetries, + boundary_layers=[pml_layers], + resolution=resolution, + ) nearfield = sim.add_near2far( - fcen, 0.1, nfreqs, - mp.Near2FarRegion(mp.Vector3(0, 0.5 * w + d1), - size=mp.Vector3(2 * dpml - sx)), - mp.Near2FarRegion(mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1), - size=mp.Vector3(0, d1), - weight=-1.0), - mp.Near2FarRegion(mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), - size=mp.Vector3(0, d1)), - decimation_factor=1 + fcen, + 0.1, + nfreqs, + mp.Near2FarRegion( + mp.Vector3(0, 0.5 * w + d1), size=mp.Vector3(2 * dpml - sx) + ), + mp.Near2FarRegion( + mp.Vector3(-0.5 * sx + dpml, 0.5 * w + 0.5 * d1), + size=mp.Vector3(0, d1), + weight=-1.0, + ), + mp.Near2FarRegion( + mp.Vector3(0.5 * sx - dpml, 0.5 * w + 0.5 * d1), size=mp.Vector3(0, d1) + ), + decimation_factor=1, ) sim.run(until=200) d2 = 20 h = 4 - vol = mp.Volume(mp.Vector3(0, (0.5 * w) + d2 + (0.5 * h)), size=mp.Vector3(sx - 2 * dpml, h)) + vol = mp.Volume( + mp.Vector3(0, (0.5 * w) + d2 + (0.5 * h)), size=mp.Vector3(sx - 2 * dpml, h) + ) result = sim.get_farfields(nearfield, resolution, where=vol) - fname = 'cavity-farfield.h5' if nfreqs == 1 else 'cavity-farfield-4-freqs.h5' + fname = "cavity-farfield.h5" if nfreqs == 1 else "cavity-farfield-4-freqs.h5" ref_file = os.path.join(self.data_dir, fname) - with h5py.File(ref_file, 'r') as f: + with h5py.File(ref_file, "r") as f: # Get reference data into memory - ref_ex = mp.complexarray(f['ex.r'][()], f['ex.i'][()]) - ref_ey = mp.complexarray(f['ey.r'][()], f['ey.i'][()]) - ref_ez = mp.complexarray(f['ez.r'][()], f['ez.i'][()]) - ref_hx = mp.complexarray(f['hx.r'][()], f['hx.i'][()]) - ref_hy = mp.complexarray(f['hy.r'][()], f['hy.i'][()]) - ref_hz = mp.complexarray(f['hz.r'][()], f['hz.i'][()]) + ref_ex = mp.complexarray(f["ex.r"][()], f["ex.i"][()]) + ref_ey = mp.complexarray(f["ey.r"][()], f["ey.i"][()]) + ref_ez = mp.complexarray(f["ez.r"][()], f["ez.i"][()]) + ref_hx = mp.complexarray(f["hx.r"][()], f["hx.i"][()]) + ref_hy = mp.complexarray(f["hy.r"][()], f["hy.i"][()]) + ref_hz = mp.complexarray(f["hz.r"][()], f["hz.i"][()]) tol = 1e-5 if mp.is_single_precision() else 1e-7 - self.assertClose(ref_ex, result['Ex'], epsilon=tol) - self.assertClose(ref_ey, result['Ey'], epsilon=tol) - self.assertClose(ref_ez, result['Ez'], epsilon=tol) - self.assertClose(ref_hx, result['Hx'], epsilon=tol) - self.assertClose(ref_hy, result['Hy'], epsilon=tol) - self.assertClose(ref_hz, result['Hz'], epsilon=tol) - + self.assertClose(ref_ex, result["Ex"], epsilon=tol) + self.assertClose(ref_ey, result["Ey"], epsilon=tol) + self.assertClose(ref_ez, result["Ez"], epsilon=tol) + self.assertClose(ref_hx, result["Hx"], epsilon=tol) + self.assertClose(ref_hy, result["Hy"], epsilon=tol) + self.assertClose(ref_hz, result["Hz"], epsilon=tol) def test_cavity_farfield(self): self.run_test(nfreqs=1) @@ -92,5 +110,5 @@ def test_cavity_farfield_four_freqs(self): self.run_test(nfreqs=4) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_chunk_balancer.py b/python/tests/test_chunk_balancer.py index 36a85f40c..4c25bd4f8 100644 --- a/python/tests/test_chunk_balancer.py +++ b/python/tests/test_chunk_balancer.py @@ -1,13 +1,12 @@ -import meep as mp -import numpy as np - -import parameterized import unittest import meep.binary_partition_utils as bpu - -from meep.timing_measurements import MeepTimingMeasurements, TIMING_MEASUREMENT_IDS +import numpy as np +import parameterized from meep.chunk_balancer import ChunkBalancer +from meep.timing_measurements import TIMING_MEASUREMENT_IDS, MeepTimingMeasurements + +import meep as mp class MockSimulation(mp.Simulation): @@ -30,17 +29,15 @@ def __init__(self, *args, **kwargs): def _structure_get_chunk_owners(self): # Hacky workaround to make this test work on single-core systems - proc_ids = [] - for leaf in bpu.enumerate_leaf_nodes(self.chunk_layout): - # it seems that grid volumes are enumerated - # in the same order as enumerate_leaf_nodes() - proc_ids.append(leaf.proc_id) + proc_ids = [ + leaf.proc_id for leaf in bpu.enumerate_leaf_nodes(self.chunk_layout) + ] + return np.array(proc_ids) def init_sim(self): super().init_sim() - setattr(self.structure, "get_chunk_owners", - self._structure_get_chunk_owners) + setattr(self.structure, "get_chunk_owners", self._structure_get_chunk_owners) def time_spent_on(self, time_sink: int): # We're going to pretend the amount of time spent is ~volume so that @@ -75,51 +72,49 @@ def time_spent_on(self, time_sink: int): TEST_SIM_1 = lambda: MockSimulation( cell_size=mp.Vector3(10.0, 5.0, 0), resolution=20, - chunk_layout=mp.BinaryPartition(data=[ - (mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2] - ])) + chunk_layout=mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), +) TEST_SIM_2 = lambda: MockSimulation( cell_size=mp.Vector3(10.0, 5.0, 3.0), resolution=10, - chunk_layout=mp.BinaryPartition(data=[ - (mp.X, -2.0), 0, [(mp.Y, 1.0), [(mp.X, 3.0), 1, [(mp.Y, 0.5), 4, 3]], 2] - ])) + chunk_layout=mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.0), [(mp.X, 3.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), +) TEST_SIM_3 = lambda: MockSimulation( cell_size=mp.Vector3(6.0, 4.0, 0), resolution=10, - chunk_layout=mp.BinaryPartition(data=[(mp.X, -2.0), 0, [(mp.X, 2.0), 1, 2]]) + chunk_layout=mp.BinaryPartition(data=[(mp.X, -2.0), 0, [(mp.X, 2.0), 1, 2]]), ) TEST_SIM_4 = lambda: MockSimulation( cell_size=mp.Vector3(6.0, 4.0, 0), resolution=10, chunk_layout=mp.BinaryPartition( - data=[(mp.X, -2.0), 0, - [(mp.X, -0.5), 1, [(mp.X, 1.0), 2, [(mp.X, 2.0), 3, 4]]]])) + data=[(mp.X, -2.0), 0, [(mp.X, -0.5), 1, [(mp.X, 1.0), 2, [(mp.X, 2.0), 3, 4]]]] + ), +) TEST_SIM_DUPLICATE_PROC_ID = lambda: MockSimulation( cell_size=mp.Vector3(10.0, 5.0, 0), resolution=10, - chunk_layout=mp.BinaryPartition(data=[ - (mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 2, 1]], 2] - ])) + chunk_layout=mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 2, 1]], 2]] + ), +) TEST_CHUNK_DATA_1 = { - "chunk_layout": - mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), - "cell_size": - mp.Vector3(6.0, 4.0, 0), + "chunk_layout": mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), + "cell_size": mp.Vector3(6.0, 4.0, 0), "time_stepping": [1.0, 1.0, 1.0], - "new_chunk_layout": - mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), + "new_chunk_layout": mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), } TEST_CHUNK_DATA_2 = { - "chunk_layout": - mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), - "cell_size": - mp.Vector3(6.0, 4.0, 0), + "chunk_layout": mp.BinaryPartition(data=[(mp.X, -2.5), 0, [(mp.X, 2.5), 1, 2]]), + "cell_size": mp.Vector3(6.0, 4.0, 0), "time_stepping": [3.0 - 2.5, 2.5 + 2.5, 3.0 - 2.5], - "new_chunk_layout": - mp.BinaryPartition(data=[(mp.X, -1.0), 0, [(mp.X, 1.0), 1, 2]]), + "new_chunk_layout": mp.BinaryPartition(data=[(mp.X, -1.0), 0, [(mp.X, 1.0), 1, 2]]), } TEST_CHUNK_DATA_3 = { "chunk_layout": mp.BinaryPartition(data=[(mp.X, 2.0), 0, 1]), @@ -134,43 +129,40 @@ def time_spent_on(self, time_sink: int): "new_chunk_layout": mp.BinaryPartition(data=[(mp.X, 0.0), 0, 1]), } TEST_CHUNK_DATA_5 = { - "chunk_layout": - mp.BinaryPartition(data=[( - mp.X, - -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]]), - "cell_size": - mp.Vector3(10.0, 5.0, 0), + "chunk_layout": mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), + "cell_size": mp.Vector3(10.0, 5.0, 0), "time_stepping": [1.0, 1.0, 1.0, 1.0, 1.0], - "new_chunk_layout": - mp.BinaryPartition(data=[( - mp.X, - -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]]), + "new_chunk_layout": mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), } TEST_CHUNK_DATA_6 = { - "chunk_layout": - mp.BinaryPartition(data=[( - mp.X, - -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]]), - "cell_size": - mp.Vector3(10.0, 5.0, 0), + "chunk_layout": mp.BinaryPartition( + data=[(mp.X, -2.0), 0, [(mp.Y, 1.5), [(mp.X, 4.0), 1, [(mp.Y, 0.5), 4, 3]], 2]] + ), + "cell_size": mp.Vector3(10.0, 5.0, 0), "time_stepping": [1500.0, 2400.0, 700.0, 100.0, 300.0], - "new_chunk_layout": - mp.BinaryPartition( - data=[(mp.X, -3.0), 0, - [(mp.Y, 1.25 - ), [(mp.X, -0.3333333333333335), 1, [(mp.Y, - -0.625), 4, 3]], 2]]), + "new_chunk_layout": mp.BinaryPartition( + data=[ + (mp.X, -3.0), + 0, + [(mp.Y, 1.25), [(mp.X, -0.3333333333333335), 1, [(mp.Y, -0.625), 4, 3]], 2], + ] + ), } class MockSimulationTest(unittest.TestCase): - - @parameterized.parameterized.expand([ - (TEST_SIM_1, [6000.0, 9600.0, 2800.0, 400.0, 1200.0]), - (TEST_SIM_2, [45000.0, 52500.0, 31500.0, 3000.0, 18000.0]), - (TEST_SIM_3, [400.0, 1600.0, 400.0]), - (TEST_SIM_4, [400.0, 600.0, 600.0, 400.0, 400.0]), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1, [6000.0, 9600.0, 2800.0, 400.0, 1200.0]), + (TEST_SIM_2, [45000.0, 52500.0, 31500.0, 3000.0, 18000.0]), + (TEST_SIM_3, [400.0, 1600.0, 400.0]), + (TEST_SIM_4, [400.0, 600.0, 600.0, 400.0, 400.0]), + ] + ) def test_time_spent_on(self, test_sim_constructor, expected_stepping_times): test_sim = test_sim_constructor() test_sim.init_sim() @@ -178,20 +170,25 @@ def test_time_spent_on(self, test_sim_constructor, expected_stepping_times): for time_sink in TIMING_MEASUREMENT_IDS.values(): if time_sink == TIMING_MEASUREMENT_IDS["time_stepping"]: self.assertListEqual( - list(test_sim.time_spent_on(time_sink)), expected_stepping_times) + list(test_sim.time_spent_on(time_sink)), expected_stepping_times + ) else: self.assertListEqual( list(test_sim.time_spent_on(time_sink)), - [0.0] * len(expected_stepping_times)) - - @parameterized.parameterized.expand([ - (TEST_SIM_1, [0, 1, 4, 3, 2]), - (TEST_SIM_2, [0, 1, 4, 3, 2]), - (TEST_SIM_3, [0, 1, 2]), - (TEST_SIM_4, [0, 1, 2, 3, 4]), - ]) - def test_structure_get_chunk_owners(self, test_sim_constructor, - expected_chunk_owners): + [0.0] * len(expected_stepping_times), + ) + + @parameterized.parameterized.expand( + [ + (TEST_SIM_1, [0, 1, 4, 3, 2]), + (TEST_SIM_2, [0, 1, 4, 3, 2]), + (TEST_SIM_3, [0, 1, 2]), + (TEST_SIM_4, [0, 1, 2, 3, 4]), + ] + ) + def test_structure_get_chunk_owners( + self, test_sim_constructor, expected_chunk_owners + ): test_sim = test_sim_constructor() test_sim.init_sim() @@ -201,11 +198,12 @@ def test_structure_get_chunk_owners(self, test_sim_constructor, class ChunkBalancerTest(unittest.TestCase): - - @parameterized.parameterized.expand([ - (TEST_SIM_1, False), - (TEST_SIM_DUPLICATE_PROC_ID, True), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1, False), + (TEST_SIM_DUPLICATE_PROC_ID, True), + ] + ) def test_validate_sim(self, test_sim_constructor, should_raise_exception): test_sim = test_sim_constructor() test_sim.init_sim() @@ -218,31 +216,33 @@ def test_validate_sim(self, test_sim_constructor, should_raise_exception): else: chunk_balancer._validate_sim(test_sim) - @parameterized.parameterized.expand([ - (TEST_SIM_1,), - (TEST_SIM_2,), - (TEST_SIM_3,), - (TEST_SIM_4,), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1,), + (TEST_SIM_2,), + (TEST_SIM_3,), + (TEST_SIM_4,), + ] + ) def test_chunk_layout_improvement(self, test_sim_constructor): """Tests that chunk_balancer improves balance after 1 iteration.""" test_sim = test_sim_constructor() test_sim.init_sim() old_timing_measurements = MeepTimingMeasurements.new_from_simulation( - test_sim, -1) + test_sim, -1 + ) chunk_balancer = ChunkBalancer() chunk_balancer.adjust_chunk_layout(test_sim, sensitivity=1.0) new_timing_measurements = MeepTimingMeasurements.new_from_simulation( - test_sim, -1) + test_sim, -1 + ) - old_step_times = np.array( - old_timing_measurements.measurements["time_stepping"]) - new_step_times = np.array( - new_timing_measurements.measurements["time_stepping"]) + old_step_times = np.array(old_timing_measurements.measurements["time_stepping"]) + new_step_times = np.array(new_timing_measurements.measurements["time_stepping"]) old_max_time = np.max(old_step_times) new_max_time = np.max(new_step_times) @@ -253,12 +253,14 @@ def test_chunk_layout_improvement(self, test_sim_constructor): self.assertLess(new_max_time, old_max_time) self.assertGreater(new_min_time, old_min_time) - @parameterized.parameterized.expand([ - (TEST_SIM_1,), - (TEST_SIM_2,), - (TEST_SIM_3,), - (TEST_SIM_4,), - ]) + @parameterized.parameterized.expand( + [ + (TEST_SIM_1,), + (TEST_SIM_2,), + (TEST_SIM_3,), + (TEST_SIM_4,), + ] + ) def test_chunk_layout_convergence(self, test_sim_constructor): """Tests that chunk_balancer converges to load balanced state.""" test_sim = test_sim_constructor() @@ -272,31 +274,35 @@ def test_chunk_layout_convergence(self, test_sim_constructor): chunk_balancer.adjust_chunk_layout(test_sim, sensitivity=0.5) new_timing_measurements = MeepTimingMeasurements.new_from_simulation( - test_sim, -1) + test_sim, -1 + ) new_step_times = np.array( - new_timing_measurements.measurements["time_stepping"]) + new_timing_measurements.measurements["time_stepping"] + ) # Check that new stepping times have converged to close to the mean value tolerance = 0.05 mean_step_time = np.mean(new_step_times) - self.assertTrue( - np.allclose(mean_step_time, new_step_times, rtol=tolerance)) - - @parameterized.parameterized.expand([ - (TEST_CHUNK_DATA_1,), - (TEST_CHUNK_DATA_2,), - (TEST_CHUNK_DATA_3,), - (TEST_CHUNK_DATA_4,), - (TEST_CHUNK_DATA_5,), - (TEST_CHUNK_DATA_6,), - ]) + self.assertTrue(np.allclose(mean_step_time, new_step_times, rtol=tolerance)) + + @parameterized.parameterized.expand( + [ + (TEST_CHUNK_DATA_1,), + (TEST_CHUNK_DATA_2,), + (TEST_CHUNK_DATA_3,), + (TEST_CHUNK_DATA_4,), + (TEST_CHUNK_DATA_5,), + (TEST_CHUNK_DATA_6,), + ] + ) def test_split_pos_adjustment(self, test_chunk_data): chunk_layout = test_chunk_data["chunk_layout"] sim = MockSimulation( cell_size=test_chunk_data["cell_size"], resolution=10, - chunk_layout=chunk_layout) + chunk_layout=chunk_layout, + ) sim.init_sim() chunk_volumes = sim.structure.get_chunk_volumes() chunk_owners = sim.structure.get_chunk_owners() @@ -310,20 +316,23 @@ def test_split_pos_adjustment(self, test_chunk_data): measurements[name] = test_chunk_data["time_stepping"] else: measurements[name] = [0] * num_procs - timing_measurements = MeepTimingMeasurements(measurements, -1, None, None, - None, None, None) + timing_measurements = MeepTimingMeasurements( + measurements, -1, None, None, None, None, None + ) new_chunk_layout = chunk_balancer.compute_new_chunk_layout( timing_measurements, chunk_layout, chunk_volumes, chunk_owners, - sensitivity=1.0) + sensitivity=1.0, + ) expected_chunk_layout = test_chunk_data["new_chunk_layout"] self.assertTrue( - bpu.partitions_are_equal(new_chunk_layout, expected_chunk_layout)) + bpu.partitions_are_equal(new_chunk_layout, expected_chunk_layout) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_chunk_layout.py b/python/tests/test_chunk_layout.py index 91a8082f1..b437025bf 100644 --- a/python/tests/test_chunk_layout.py +++ b/python/tests/test_chunk_layout.py @@ -1,40 +1,41 @@ -import meep as mp import copy import unittest +import meep as mp + -def traverse_tree(bp=None,min_corner=None,max_corner=None): +def traverse_tree(bp=None, min_corner=None, max_corner=None): process_ids = [] chunk_areas = [] - def _traverse_tree(bp=None,min_corner=None,max_corner=None): - if ((min_corner.x > max_corner.x) or (min_corner.y > max_corner.y)): + def _traverse_tree(bp=None, min_corner=None, max_corner=None): + if (min_corner.x > max_corner.x) or (min_corner.y > max_corner.y): raise RuntimeError("min_corner/max_corner have been incorrectly defined.") ## reached a leaf - if (bp.left is None and bp.right is None): + if bp.left is None and bp.right is None: process_ids.append(bp.proc_id) - chunk_area = (max_corner.x-min_corner.x)*(max_corner.y-min_corner.y) + chunk_area = (max_corner.x - min_corner.x) * (max_corner.y - min_corner.y) chunk_areas.append(chunk_area) ## traverse the left branch - if (bp.left is not None): + if bp.left is not None: new_max_corner = copy.deepcopy(max_corner) if bp.split_dir == mp.X: new_max_corner.x = bp.split_pos else: new_max_corner.y = bp.split_pos - _traverse_tree(bp.left,min_corner,new_max_corner) + _traverse_tree(bp.left, min_corner, new_max_corner) ## traverse the right branch - if (bp.right is not None): + if bp.right is not None: new_min_corner = copy.deepcopy(min_corner) if bp.split_dir == mp.X: new_min_corner.x = bp.split_pos else: new_min_corner.y = bp.split_pos - _traverse_tree(bp.right,new_min_corner,max_corner) + _traverse_tree(bp.right, new_min_corner, max_corner) _traverse_tree(bp=bp, min_corner=min_corner, max_corner=max_corner) @@ -42,40 +43,57 @@ def _traverse_tree(bp=None,min_corner=None,max_corner=None): class TestChunkLayoutBinaryPartition(unittest.TestCase): - def test_chunk_layout_binary_partition(self): - chunk_layout = mp.BinaryPartition(data=[ (mp.X,-2.0), 0, [ (mp.Y,1.5), [ (mp.X,3.0), 1, [ (mp.Y,-0.5), 4, 3 ] ], 2 ] ]) + chunk_layout = mp.BinaryPartition( + data=[ + (mp.X, -2.0), + 0, + [(mp.Y, 1.5), [(mp.X, 3.0), 1, [(mp.Y, -0.5), 4, 3]], 2], + ] + ) - cell_size = mp.Vector3(10.0,5.0,0) + cell_size = mp.Vector3(10.0, 5.0, 0) - sim = mp.Simulation(cell_size=cell_size, - resolution=10, - chunk_layout=chunk_layout) + sim = mp.Simulation( + cell_size=cell_size, resolution=10, chunk_layout=chunk_layout + ) sim.init_sim() owners = sim.structure.get_chunk_owners() - areas = [ v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() ] + areas = [ + v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() + ] - process_ids, chunk_areas = traverse_tree(chunk_layout,-0.5*cell_size,0.5*cell_size) + process_ids, chunk_areas = traverse_tree( + chunk_layout, -0.5 * cell_size, 0.5 * cell_size + ) - self.assertListEqual([int(f) for f in owners],[f % mp.count_processors() for f in process_ids]) - self.assertListEqual(areas,chunk_areas) + self.assertListEqual( + [int(f) for f in owners], [f % mp.count_processors() for f in process_ids] + ) + self.assertListEqual(areas, chunk_areas) def test_meep_default_chunk_layout(self): - cell_size = mp.Vector3(10.0,5.0,0) - sim = mp.Simulation(cell_size=cell_size, - resolution=10) + cell_size = mp.Vector3(10.0, 5.0, 0) + sim = mp.Simulation(cell_size=cell_size, resolution=10) sim.init_sim() owners = sim.structure.get_chunk_owners() - areas = [ v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() ] + areas = [ + v.surroundings().full_volume() for v in sim.structure.get_chunk_volumes() + ] chunk_layout = sim.chunk_layout - process_ids, chunk_areas = traverse_tree(chunk_layout,-0.5*cell_size,0.5*cell_size) + process_ids, chunk_areas = traverse_tree( + chunk_layout, -0.5 * cell_size, 0.5 * cell_size + ) + + self.assertListEqual( + [int(f) for f in owners], [f % mp.count_processors() for f in process_ids] + ) + self.assertListEqual(areas, chunk_areas) - self.assertListEqual([int(f) for f in owners],[f % mp.count_processors() for f in process_ids]) - self.assertListEqual(areas,chunk_areas) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_chunks.py b/python/tests/test_chunks.py index 2af803b07..a8cdd61ce 100644 --- a/python/tests/test_chunks.py +++ b/python/tests/test_chunks.py @@ -1,9 +1,9 @@ -import meep as mp import unittest +import meep as mp -class TestChunks(unittest.TestCase): +class TestChunks(unittest.TestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -17,7 +17,7 @@ def test_chunks(self): cell = mp.Vector3(sxy, sxy, 0) fcen = 1.0 # pulse center frequency - df = 0.1 # pulse width (in frequency) + df = 0.1 # pulse width (in frequency) sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3())] @@ -25,51 +25,87 @@ def test_chunks(self): pml_layers = [mp.PML(dpml)] resolution = 10 - sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - sources=sources, - resolution=resolution, - split_chunks_evenly=False) + sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + sources=sources, + resolution=resolution, + split_chunks_evenly=False, + ) sim.use_output_directory(self.temp_dir) - top = mp.FluxRegion(center=mp.Vector3(0,+0.5*sxy-dpml), size=mp.Vector3(sxy-2*dpml,0), weight=+1.0) - bot = mp.FluxRegion(center=mp.Vector3(0,-0.5*sxy+dpml), size=mp.Vector3(sxy-2*dpml,0), weight=-1.0) - rgt = mp.FluxRegion(center=mp.Vector3(+0.5*sxy-dpml,0), size=mp.Vector3(0,sxy-2*dpml), weight=+1.0) - lft = mp.FluxRegion(center=mp.Vector3(-0.5*sxy+dpml,0), size=mp.Vector3(0,sxy-2*dpml), weight=-1.0) + top = mp.FluxRegion( + center=mp.Vector3(0, +0.5 * sxy - dpml), + size=mp.Vector3(sxy - 2 * dpml, 0), + weight=+1.0, + ) + bot = mp.FluxRegion( + center=mp.Vector3(0, -0.5 * sxy + dpml), + size=mp.Vector3(sxy - 2 * dpml, 0), + weight=-1.0, + ) + rgt = mp.FluxRegion( + center=mp.Vector3(+0.5 * sxy - dpml, 0), + size=mp.Vector3(0, sxy - 2 * dpml), + weight=+1.0, + ) + lft = mp.FluxRegion( + center=mp.Vector3(-0.5 * sxy + dpml, 0), + size=mp.Vector3(0, sxy - 2 * dpml), + weight=-1.0, + ) tot_flux = sim.add_flux(fcen, 0, 1, top, bot, rgt, lft, decimation_factor=1) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-5)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-5 + ) + ) - sim.save_flux('tot_flux', tot_flux) + sim.save_flux("tot_flux", tot_flux) sim1 = sim - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(sxy, sxy, mp.inf), - material=mp.Medium(index=3.5)), - mp.Block(center=mp.Vector3(), - size=mp.Vector3(sxy-2*dpml, sxy-2*dpml, mp.inf), - material=mp.air)] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - resolution=resolution, - chunk_layout=sim1) + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(sxy, sxy, mp.inf), + material=mp.Medium(index=3.5), + ), + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(sxy - 2 * dpml, sxy - 2 * dpml, mp.inf), + material=mp.air, + ), + ] + + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + resolution=resolution, + chunk_layout=sim1, + ) sim.use_output_directory(self.temp_dir) tot_flux = sim.add_flux(fcen, 0, 1, top, bot, rgt, lft, decimation_factor=1) - sim.load_minus_flux('tot_flux', tot_flux) + sim.load_minus_flux("tot_flux", tot_flux) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-5)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-5 + ) + ) places = 3 if mp.is_single_precision() else 7 - self.assertAlmostEqual(86.90826609300862, mp.get_fluxes(tot_flux)[0], places=places) + self.assertAlmostEqual( + 86.90826609300862, mp.get_fluxes(tot_flux)[0], places=places + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_conductivity.py b/python/tests/test_conductivity.py index 2aa9a7616..f5018eefc 100644 --- a/python/tests/test_conductivity.py +++ b/python/tests/test_conductivity.py @@ -1,11 +1,13 @@ import unittest + import numpy as np + import meep as mp dB_cm_to_dB_um = 1e-4 -class TestConductivity(unittest.TestCase): +class TestConductivity(unittest.TestCase): def wvg_flux(self, res, att_coeff): """ Computes the Poynting flux in a single-mode waveguide at two @@ -14,87 +16,105 @@ def wvg_flux(self, res, att_coeff): coefficient att_coeff (dB/cm). """ - cell_size = mp.Vector3(14.,14.) + cell_size = mp.Vector3(14.0, 14.0) - pml_layers = [mp.PML(thickness=2.)] + pml_layers = [mp.PML(thickness=2.0)] - w = 1. # width of waveguide + w = 1.0 # width of waveguide - fsrc = 0.15 # frequency (in vacuum) + fsrc = 0.15 # frequency (in vacuum) # note: MPB can only compute modes of lossless material systems. # The imaginary part of ε is ignored and the launched # waveguide mode is therefore inaccurate. For small values # of imag(ε) (which is proportional to att_coeff), this # inaccuracy tends to be insignificant. - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(-5.), - size=mp.Vector3(y=10.), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(-5.0), + size=mp.Vector3(y=10.0), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] # ref: https://en.wikipedia.org/wiki/Mathematical_descriptions_of_opacity # Note that this is the loss of a planewave, which is only approximately # the loss of a waveguide mode. In principle, we could compute the latter # semi-analytically if we wanted to run this unit test to greater accuracy # (e.g. to test convergence with resolution). - n_eff = np.sqrt(12.) + 1j * (1/fsrc) * (dB_cm_to_dB_um * att_coeff) / (4 * np.pi) + n_eff = np.sqrt(12.0) + 1j * (1 / fsrc) * (dB_cm_to_dB_um * att_coeff) / ( + 4 * np.pi + ) eps_eff = n_eff * n_eff # ref: https://meep.readthedocs.io/en/latest/Materials/#conductivity-and-complex sigma_D = 2 * np.pi * fsrc * np.imag(eps_eff) / np.real(eps_eff) - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=np.real(eps_eff), - D_conductivity=sigma_D))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=res, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)]) - - tran1 = sim.add_flux(fsrc, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(x=0.), size=mp.Vector3(y=10.))) - - tran2 = sim.add_flux(fsrc, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(x=5.), size=mp.Vector3(y=10.))) + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=np.real(eps_eff), D_conductivity=sigma_D), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=res, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)], + ) + + tran1 = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=0.0), size=mp.Vector3(y=10.0)) + ) + + tran2 = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5.0), size=mp.Vector3(y=10.0)) + ) sim.run(until_after_sources=20) return mp.get_fluxes(tran1)[0], mp.get_fluxes(tran2)[0] - def test_conductivity(self): - res = 25. # pixels/μm + res = 25.0 # pixels/μm # compute the incident flux for a lossless waveguide - incident_flux1, incident_flux2 = self.wvg_flux(res, 0.) - self.assertAlmostEqual(incident_flux1/incident_flux2, 1., places=2) - print("incident_flux:, {} (measured), 1.0 (expected)".format(incident_flux2/incident_flux1)) + incident_flux1, incident_flux2 = self.wvg_flux(res, 0.0) + self.assertAlmostEqual(incident_flux1 / incident_flux2, 1.0, places=2) + print( + f"incident_flux:, {incident_flux2 / incident_flux1} (measured), 1.0 (expected)" + ) # compute the flux for a lossy waveguide - att_coeff = 37.46 # dB/cm + att_coeff = 37.46 # dB/cm attenuated_flux1, attenuated_flux2 = self.wvg_flux(res, att_coeff) - L1 = 5. + L1 = 5.0 expected_att1 = np.exp(-att_coeff * dB_cm_to_dB_um * L1) - self.assertAlmostEqual(attenuated_flux1/incident_flux2, expected_att1, places=2) - print("flux:, {}, {:.6f} (measured), {:.6f} (expected)".format(L1, - attenuated_flux1/incident_flux2, - expected_att1)) - - L2 = 10. + self.assertAlmostEqual( + attenuated_flux1 / incident_flux2, expected_att1, places=2 + ) + print( + "flux:, {}, {:.6f} (measured), {:.6f} (expected)".format( + L1, attenuated_flux1 / incident_flux2, expected_att1 + ) + ) + + L2 = 10.0 expected_att2 = np.exp(-att_coeff * dB_cm_to_dB_um * L2) - self.assertAlmostEqual(attenuated_flux2/incident_flux2, expected_att2, places=2) - print("flux:, {}, {:.6f} (measured), {:.6f} (expected)".format(L2, - attenuated_flux2/incident_flux2, - expected_att2)) + self.assertAlmostEqual( + attenuated_flux2 / incident_flux2, expected_att2, places=2 + ) + print( + "flux:, {}, {:.6f} (measured), {:.6f} (expected)".format( + L2, attenuated_flux2 / incident_flux2, expected_att2 + ) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_cyl_ellipsoid.py b/python/tests/test_cyl_ellipsoid.py index 330cfb8da..18e6209a4 100644 --- a/python/tests/test_cyl_ellipsoid.py +++ b/python/tests/test_cyl_ellipsoid.py @@ -1,9 +1,12 @@ import unittest + import meep as mp + def dummy_eps(vec): return 1.0 + class TestCylEllipsoid(unittest.TestCase): ref_Ez = -8.29555720049629e-5 @@ -22,9 +25,11 @@ def init(self): c = mp.Cylinder(radius=3, material=mp.Medium(index=3.5)) e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf)) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), - component=self.src_cmpt, - center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.1), + component=self.src_cmpt, + center=mp.Vector3(), + ) if self.src_cmpt == mp.Ez: symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] @@ -32,28 +37,33 @@ def init(self): if self.src_cmpt == mp.Hz: symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)] - self.sim = mp.Simulation(cell_size=mp.Vector3(10, 10), - geometry=[c, e], - boundary_layers=[mp.PML(1.0)], - sources=[sources], - symmetries=symmetries, - resolution=100) + self.sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), + geometry=[c, e], + boundary_layers=[mp.PML(1.0)], + sources=[sources], + symmetries=symmetries, + resolution=100, + ) self.sim.use_output_directory(self.temp_dir) def print_stuff(sim_obj): v = mp.Vector3(4.13, 3.75, 0) p = self.sim.get_field_point(self.src_cmpt, v) - print("t, Ez: {} {}+{}i".format(self.sim.round_time(), p.real, p.imag)) + print(f"t, Ez: {self.sim.round_time()} {p.real}+{p.imag}i") + self.print_stuff = print_stuff def run_simulation(self): - self.sim.run(mp.at_beginning(mp.output_epsilon), - mp.at_every(0.25, self.print_stuff), - mp.at_end(self.print_stuff), - mp.at_end(mp.output_efield_z), - until=23) + self.sim.run( + mp.at_beginning(mp.output_epsilon), + mp.at_every(0.25, self.print_stuff), + mp.at_end(self.print_stuff), + mp.at_end(mp.output_efield_z), + until=23, + ) ref_out_field = self.ref_Ez if self.src_cmpt == mp.Ez else self.ref_Hz out_field = self.sim.fields.get_field(self.src_cmpt, mp.vec(4.13, 3.75)).real @@ -74,5 +84,5 @@ def test_hz_field(self): self.run_simulation() -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_dft_energy.py b/python/tests/test_dft_energy.py index db43946ea..8577c5f52 100644 --- a/python/tests/test_dft_energy.py +++ b/python/tests/test_dft_energy.py @@ -1,4 +1,5 @@ import unittest + import meep as mp # compute group velocity of a waveguide mode using two different methods @@ -7,33 +8,60 @@ class TestDftEnergy(unittest.TestCase): - def test_dft_energy(self): resolution = 20 cell = mp.Vector3(10, 5) - geom = [mp.Block(size=mp.Vector3(mp.inf, 1, mp.inf), material=mp.Medium(epsilon=12))] + geom = [ + mp.Block(size=mp.Vector3(mp.inf, 1, mp.inf), material=mp.Medium(epsilon=12)) + ] pml = [mp.PML(1)] - fsrc = 0.15 - sources = [mp.EigenModeSource(src=mp.GaussianSource(frequency=fsrc, fwidth=0.2*fsrc), - center=mp.Vector3(-3), size=mp.Vector3(y=5), - eig_band=1, eig_parity=mp.ODD_Z+mp.EVEN_Y, - eig_match_freq=True)] - - sim = mp.Simulation(resolution=resolution, cell_size=cell, geometry=geom, - boundary_layers=pml, sources=sources, symmetries=[mp.Mirror(direction=mp.Y)]) - - flux = sim.add_flux(fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), - decimation_factor=1) - energy = sim.add_energy(fsrc, 0, 1, mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), - decimation_factor=1) - energy_decimated = sim.add_energy(fsrc, 0, 1, - mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), - decimation_factor=10) + fsrc = 0.15 + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(frequency=fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(-3), + size=mp.Vector3(y=5), + eig_band=1, + eig_parity=mp.ODD_Z + mp.EVEN_Y, + eig_match_freq=True, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + geometry=geom, + boundary_layers=pml, + sources=sources, + symmetries=[mp.Mirror(direction=mp.Y)], + ) + + flux = sim.add_flux( + fsrc, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), + decimation_factor=1, + ) + energy = sim.add_energy( + fsrc, + 0, + 1, + mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), + decimation_factor=1, + ) + energy_decimated = sim.add_energy( + fsrc, + 0, + 1, + mp.EnergyRegion(center=mp.Vector3(3), size=mp.Vector3(y=5)), + decimation_factor=10, + ) sim.run(until_after_sources=100) - res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y) + res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) mode_vg = res.vgrp[0] - poynting_flux = mp.get_fluxes(flux)[0] + poynting_flux = mp.get_fluxes(flux)[0] e_energy = mp.get_electric_energy(energy)[0] ratio_vg = (0.5 * poynting_flux) / e_energy m_energy = mp.get_magnetic_energy(energy)[0] @@ -48,5 +76,5 @@ def test_dft_energy(self): self.assertAlmostEqual(m_energy, m_energy_decimated, places=1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_dft_fields.py b/python/tests/test_dft_fields.py index 3b190adbe..30de0f038 100644 --- a/python/tests/test_dft_fields.py +++ b/python/tests/test_dft_fields.py @@ -1,12 +1,14 @@ +import os import unittest + import h5py import numpy as np -import meep as mp from utils import ApproxComparisonTestCase -import os -class TestDFTFields(ApproxComparisonTestCase): +import meep as mp + +class TestDFTFields(ApproxComparisonTestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -27,9 +29,10 @@ def init(self): pml_layers = [mp.PML(self.dpml)] geometry = [ - mp.Cylinder(r + w, material=mp.Medium(epsilon=n * n)), + mp.Cylinder(r + w, material=mp.Medium(epsilon=n**2)), mp.Cylinder(r, material=mp.vacuum), ] + self.fcen = 0.118 self.df = 0.1 @@ -43,6 +46,7 @@ def init(self): sources=sources, boundary_layers=pml_layers, ) + def test_use_centered_grid(self): sim = self.init() sim.init_sim() @@ -53,14 +57,20 @@ def test_get_dft_array(self): sim = self.init() sim.init_sim() dft_fields = sim.add_dft_fields([mp.Ez], self.fcen, 0, 1) - fr = mp.FluxRegion(mp.Vector3(), size=mp.Vector3(self.sxy, self.sxy), direction=mp.X) + fr = mp.FluxRegion( + mp.Vector3(), size=mp.Vector3(self.sxy, self.sxy), direction=mp.X + ) dft_flux = sim.add_flux(self.fcen, 0, 1, fr) # volumes with zero thickness in x and y directions to test collapsing # of empty dimensions in DFT array and HDF5 output routines - thin_x_volume = mp.Volume(center=mp.Vector3(0.35*self.sxy), size=mp.Vector3(y=0.8*self.sxy)) + thin_x_volume = mp.Volume( + center=mp.Vector3(0.35 * self.sxy), size=mp.Vector3(y=0.8 * self.sxy) + ) thin_x_flux = sim.add_dft_fields([mp.Ez], self.fcen, 0, 1, where=thin_x_volume) - thin_y_volume = mp.Volume(center=mp.Vector3(y=0.25*self.sxy), size=mp.Vector3(x=self.sxy)) + thin_y_volume = mp.Volume( + center=mp.Vector3(y=0.25 * self.sxy), size=mp.Vector3(x=self.sxy) + ) thin_y_flux = sim.add_flux(self.fcen, 0, 1, mp.FluxRegion(volume=thin_y_volume)) sim.run(until_after_sources=100) @@ -71,14 +81,14 @@ def test_get_dft_array(self): np.testing.assert_equal(thin_x_array.ndim, 1) np.testing.assert_equal(thin_y_array.ndim, 1) - sim.output_dft(thin_x_flux, os.path.join(self.temp_dir, 'thin-x-flux')) - sim.output_dft(thin_y_flux, os.path.join(self.temp_dir, 'thin-y-flux')) + sim.output_dft(thin_x_flux, os.path.join(self.temp_dir, "thin-x-flux")) + sim.output_dft(thin_y_flux, os.path.join(self.temp_dir, "thin-y-flux")) - with h5py.File(os.path.join(self.temp_dir, 'thin-x-flux.h5'), 'r') as thin_x: - thin_x_h5 = mp.complexarray(thin_x['ez_0.r'][()], thin_x['ez_0.i'][()]) + with h5py.File(os.path.join(self.temp_dir, "thin-x-flux.h5"), "r") as thin_x: + thin_x_h5 = mp.complexarray(thin_x["ez_0.r"][()], thin_x["ez_0.i"][()]) - with h5py.File(os.path.join(self.temp_dir, 'thin-y-flux.h5'), 'r') as thin_y: - thin_y_h5 = mp.complexarray(thin_y['ez_0.r'][()], thin_y['ez_0.i'][()]) + with h5py.File(os.path.join(self.temp_dir, "thin-y-flux.h5"), "r") as thin_y: + thin_y_h5 = mp.complexarray(thin_y["ez_0.r"][()], thin_y["ez_0.i"][()]) tol = 1e-6 self.assertClose(thin_x_array, thin_x_h5, epsilon=tol) @@ -88,12 +98,14 @@ def test_get_dft_array(self): fields_arr = sim.get_dft_array(dft_fields, mp.Ez, 0) flux_arr = sim.get_dft_array(dft_flux, mp.Ez, 0) - sim.output_dft(dft_fields, os.path.join(self.temp_dir, 'dft-fields')) - sim.output_dft(dft_flux, os.path.join(self.temp_dir, 'dft-flux')) + sim.output_dft(dft_fields, os.path.join(self.temp_dir, "dft-fields")) + sim.output_dft(dft_flux, os.path.join(self.temp_dir, "dft-flux")) - with h5py.File(os.path.join(self.temp_dir, 'dft-fields.h5'), 'r') as fields, h5py.File(os.path.join(self.temp_dir, 'dft-flux.h5'), 'r') as flux: - exp_fields = mp.complexarray(fields['ez_0.r'][()], fields['ez_0.i'][()]) - exp_flux = mp.complexarray(flux['ez_0.r'][()], flux['ez_0.i'][()]) + with h5py.File( + os.path.join(self.temp_dir, "dft-fields.h5"), "r" + ) as fields, h5py.File(os.path.join(self.temp_dir, "dft-flux.h5"), "r") as flux: + exp_fields = mp.complexarray(fields["ez_0.r"][()], fields["ez_0.i"][()]) + exp_flux = mp.complexarray(flux["ez_0.r"][()], flux["ez_0.i"][()]) tol = 1e-6 self.assertClose(exp_fields, fields_arr, epsilon=tol) @@ -102,13 +114,12 @@ def test_get_dft_array(self): def test_decimated_dft_fields_are_almost_equal_to_undecimated_fields(self): sim = self.init() sim.init_sim() - undecimated_field = sim.add_dft_fields([mp.Ez], self.fcen, 0, 1, - decimation_factor=1) - decimated_field = sim.add_dft_fields([mp.Ez], - self.fcen, - 0, - 1, - decimation_factor=4) + undecimated_field = sim.add_dft_fields( + [mp.Ez], self.fcen, 0, 1, decimation_factor=1 + ) + decimated_field = sim.add_dft_fields( + [mp.Ez], self.fcen, 0, 1, decimation_factor=4 + ) sim.run(until_after_sources=100) @@ -117,5 +128,5 @@ def test_decimated_dft_fields_are_almost_equal_to_undecimated_fields(self): self.assertClose(expected_dft, actual_dft, epsilon=1e-3) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_diffracted_planewave.py b/python/tests/test_diffracted_planewave.py index 7c0d8b3c0..ee5c7ecdc 100644 --- a/python/tests/test_diffracted_planewave.py +++ b/python/tests/test_diffracted_planewave.py @@ -1,148 +1,163 @@ -import unittest -import meep as mp -import math import cmath +import math +import unittest + import numpy as np +import meep as mp # Computes the mode coefficient of the transmitted orders of # a binary grating given an incident planewave and verifies # that the results are the same when using either a band number # or `DiffractedPlanewave` object in `get_eigenmode_coefficients`. -class TestDiffractedPlanewave(unittest.TestCase): - @classmethod - def setUp(cls): - cls.resolution = 50 # pixels/um - cls.dpml = 1.0 # PML thickness - cls.dsub = 3.0 # substrate thickness - cls.dpad = 3.0 # length of padding between grating and PML - - cls.wvl = 0.5 # center wavelength - cls.fcen = 1/cls.wvl # center frequency - - cls.ng = 1.5 - cls.glass = mp.Medium(index=cls.ng) - - cls.pml_layers = [mp.PML(thickness=cls.dpml,direction=mp.X)] - - - def run_binary_grating_diffraction(self,gp,gh,gdc,theta): - sx = self.dpml+self.dsub+gh+self.dpad+self.dpml - sy = gp - cell_size = mp.Vector3(sx,sy,0) - - # rotation angle of incident planewave - # counter clockwise (CCW) about Z axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(self.fcen*self.ng).rotate(mp.Vector3(z=1), theta_in) - - eig_parity = mp.ODD_Z - if theta == 0: - k = mp.Vector3() - eig_parity += mp.EVEN_Y - symmetries = [mp.Mirror(direction=mp.Y)] - else: - symmetries = [] - - def pw_amp(k,x0): - def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) - return _pw_amp - - src_pt = mp.Vector3(-0.5*sx+self.dpml,0,0) - sources = [mp.Source(mp.GaussianSource(self.fcen,fwidth=0.1*self.fcen), - component=mp.Ez, - center=src_pt, - size=mp.Vector3(0,sy,0), - amp_func=pw_amp(k,src_pt))] - - geometry = [mp.Block(material=self.glass, - size=mp.Vector3(self.dpml+self.dsub,mp.inf,mp.inf), - center=mp.Vector3(-0.5*sx+0.5*(self.dpml+self.dsub),0,0)), - mp.Block(material=self.glass, - size=mp.Vector3(gh,gdc*gp,mp.inf), - center=mp.Vector3(-0.5*sx+self.dpml+self.dsub+0.5*gh,0,0))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=self.pml_layers, - geometry=geometry, - k_point=k, - sources=sources, - symmetries=symmetries) - - tran_pt = mp.Vector3(0.5*sx-self.dpml,0,0) - tran_flux = sim.add_mode_monitor(self.fcen, - 0, - 1, - mp.FluxRegion(center=tran_pt, - size=mp.Vector3(0,sy,0))) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(20,mp.Ez,src_pt,1e-6)) - - m_plus = int(np.floor((self.fcen-k.y)*gp)) - m_minus = int(np.ceil((-self.fcen-k.y)*gp)) - - if theta == 0: - orders = range(m_plus) - else: - # ordering of the modes computed by MPB is according to *decreasing* - # values of kx (i.e., closest to propagation direction of 0° or +x) - ms = range(m_minus,m_plus+1) - kx = lambda m: np.power(self.fcen,2) - np.power(k.y+m/gp,2) - kxs = [kx(m) for m in ms] - ids = np.flip(np.argsort(kxs)) - orders = [ms[d] for d in ids] - - for band,order in enumerate(orders): - res = sim.get_eigenmode_coefficients(tran_flux, - [band+1], - eig_parity=eig_parity) - tran_ref = abs(res.alpha[0,0,0])**2 - if theta == 0: - tran_ref = 0.5*tran_ref - vg_ref = res.vgrp[0] - kdom_ref = res.kdom[0] - - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave((0,order,0), - mp.Vector3(0,1,0), - 1, - 0)) - tran_dp = abs(res.alpha[0,0,0])**2 - if (theta == 0) and (order == 0): - tran_dp = 0.5*tran_dp - vg_dp = res.vgrp[0] - kdom_dp = res.kdom[0] - - err = abs(tran_ref-tran_dp)/tran_ref - print("tran:, {:2d} (band), {:2d} (order), " - "{:10.8f} (band num.), {:10.8f} (diff. pw.), " - "{:10.8f} (error)".format(band,order,tran_ref,tran_dp,err)) - - self.assertAlmostEqual(vg_ref,vg_dp,places=4) - self.assertAlmostEqual(tran_ref,tran_dp,places=4) - if theta == 0: - self.assertAlmostEqual(abs(kdom_ref.x),kdom_dp.x,places=5) - self.assertAlmostEqual(abs(kdom_ref.y),kdom_dp.y,places=5) - self.assertAlmostEqual(abs(kdom_ref.z),kdom_dp.z,places=5) - else: - self.assertAlmostEqual(kdom_ref.x,kdom_dp.x,places=5) - self.assertAlmostEqual(kdom_ref.y,kdom_dp.y,places=5) - self.assertAlmostEqual(kdom_ref.z,kdom_dp.z,places=5) - - print("PASSED.") - - def test_diffracted_planewave(self): - self.run_binary_grating_diffraction(2.6,0.4,0.6,0) - self.run_binary_grating_diffraction(2.6,0.4,0.6,13.4) - - # self.run_binary_grating_diffraction(10.0,0.5,0.5,0) - # self.run_binary_grating_diffraction(10.0,0.5,0.5,10.7) - -if __name__ == '__main__': - unittest.main() +class TestDiffractedPlanewave(unittest.TestCase): + @classmethod + def setUp(cls): + cls.resolution = 50 # pixels/um + cls.dpml = 1.0 # PML thickness + cls.dsub = 3.0 # substrate thickness + cls.dpad = 3.0 # length of padding between grating and PML + + cls.wvl = 0.5 # center wavelength + cls.fcen = 1 / cls.wvl # center frequency + + cls.ng = 1.5 + cls.glass = mp.Medium(index=cls.ng) + + cls.pml_layers = [mp.PML(thickness=cls.dpml, direction=mp.X)] + + def run_binary_grating_diffraction(self, gp, gh, gdc, theta): + sx = self.dpml + self.dsub + gh + self.dpad + self.dpml + sy = gp + cell_size = mp.Vector3(sx, sy, 0) + + # rotation angle of incident planewave + # counter clockwise (CCW) about Z axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(self.fcen * self.ng).rotate(mp.Vector3(z=1), theta_in) + + eig_parity = mp.ODD_Z + if theta == 0: + k = mp.Vector3() + eig_parity += mp.EVEN_Y + symmetries = [mp.Mirror(direction=mp.Y)] + else: + symmetries = [] + + def pw_amp(k, x0): + def _pw_amp(x): + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + + return _pw_amp + + src_pt = mp.Vector3(-0.5 * sx + self.dpml, 0, 0) + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=0.1 * self.fcen), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(0, sy, 0), + amp_func=pw_amp(k, src_pt), + ) + ] + + geometry = [ + mp.Block( + material=self.glass, + size=mp.Vector3(self.dpml + self.dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (self.dpml + self.dsub), 0, 0), + ), + mp.Block( + material=self.glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + self.dpml + self.dsub + 0.5 * gh, 0, 0), + ), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=self.pml_layers, + geometry=geometry, + k_point=k, + sources=sources, + symmetries=symmetries, + ) + + tran_pt = mp.Vector3(0.5 * sx - self.dpml, 0, 0) + tran_flux = sim.add_mode_monitor( + self.fcen, 0, 1, mp.FluxRegion(center=tran_pt, size=mp.Vector3(0, sy, 0)) + ) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed(20, mp.Ez, src_pt, 1e-6) + ) + + m_plus = int(np.floor((self.fcen - k.y) * gp)) + m_minus = int(np.ceil((-self.fcen - k.y) * gp)) + + if theta == 0: + orders = range(m_plus) + else: + # ordering of the modes computed by MPB is according to *decreasing* + # values of kx (i.e., closest to propagation direction of 0° or +x) + ms = range(m_minus, m_plus + 1) + kx = lambda m: np.power(self.fcen, 2) - np.power(k.y + m / gp, 2) + kxs = [kx(m) for m in ms] + ids = np.flip(np.argsort(kxs)) + orders = [ms[d] for d in ids] + + for band, order in enumerate(orders): + res = sim.get_eigenmode_coefficients( + tran_flux, [band + 1], eig_parity=eig_parity + ) + tran_ref = abs(res.alpha[0, 0, 0]) ** 2 + if theta == 0: + tran_ref = 0.5 * tran_ref + vg_ref = res.vgrp[0] + kdom_ref = res.kdom[0] + + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave((0, order, 0), mp.Vector3(0, 1, 0), 1, 0), + ) + tran_dp = abs(res.alpha[0, 0, 0]) ** 2 + if (theta == 0) and (order == 0): + tran_dp = 0.5 * tran_dp + vg_dp = res.vgrp[0] + kdom_dp = res.kdom[0] + + err = abs(tran_ref - tran_dp) / tran_ref + print( + "tran:, {:2d} (band), {:2d} (order), " + "{:10.8f} (band num.), {:10.8f} (diff. pw.), " + "{:10.8f} (error)".format(band, order, tran_ref, tran_dp, err) + ) + + self.assertAlmostEqual(vg_ref, vg_dp, places=4) + self.assertAlmostEqual(tran_ref, tran_dp, places=4) + if theta == 0: + self.assertAlmostEqual(abs(kdom_ref.x), kdom_dp.x, places=5) + self.assertAlmostEqual(abs(kdom_ref.y), kdom_dp.y, places=5) + self.assertAlmostEqual(abs(kdom_ref.z), kdom_dp.z, places=5) + else: + self.assertAlmostEqual(kdom_ref.x, kdom_dp.x, places=5) + self.assertAlmostEqual(kdom_ref.y, kdom_dp.y, places=5) + self.assertAlmostEqual(kdom_ref.z, kdom_dp.z, places=5) + + print("PASSED.") + + def test_diffracted_planewave(self): + self.run_binary_grating_diffraction(2.6, 0.4, 0.6, 0) + self.run_binary_grating_diffraction(2.6, 0.4, 0.6, 13.4) + + # self.run_binary_grating_diffraction(10.0,0.5,0.5,0) + # self.run_binary_grating_diffraction(10.0,0.5,0.5,10.7) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_dispersive_eigenmode.py b/python/tests/test_dispersive_eigenmode.py index 5fce1d96d..80b40ed93 100644 --- a/python/tests/test_dispersive_eigenmode.py +++ b/python/tests/test_dispersive_eigenmode.py @@ -1,35 +1,36 @@ - # dispersive_eigenmode.py - Tests the meep eigenmode features (eigenmode source, # eigenmode decomposition, and get_eigenmode) with dispersive materials. # TODO: # * check materials with off diagonal components # * check magnetic profiles # * once imaginary component is supported, check that - -import meep as mp -from utils import ApproxComparisonTestCase +import os import unittest -import numpy as np + import h5py -import os +import numpy as np +from utils import ApproxComparisonTestCase + +import meep as mp + class TestDispersiveEigenmode(ApproxComparisonTestCase): # ----------------------------------------- # # ----------- Helper Functions ------------ # # ----------------------------------------- # # Directly cals the C++ chi1 routine - def call_chi1(self,material,frequency): + def call_chi1(self, material, frequency): - sim = mp.Simulation(cell_size=mp.Vector3(1,1,1), - default_material=material, - resolution=20) + sim = mp.Simulation( + cell_size=mp.Vector3(1, 1, 1), default_material=material, resolution=20 + ) sim.init_sim() - v3 = mp.py_v3_to_vec(sim.dimensions, mp.Vector3(0,0,0), sim.is_cylindrical) - chi1inv = np.zeros((3,3),dtype=np.complex128) - for i, com in enumerate([mp.Ex,mp.Ey,mp.Ez]): - for k, dir in enumerate([mp.X,mp.Y,mp.Z]): - chi1inv[i,k] = sim.structure.get_chi1inv(com,dir,v3,frequency) + v3 = mp.py_v3_to_vec(sim.dimensions, mp.Vector3(0, 0, 0), sim.is_cylindrical) + chi1inv = np.zeros((3, 3), dtype=np.complex128) + for i, com in enumerate([mp.Ex, mp.Ey, mp.Ez]): + for k, dir in enumerate([mp.X, mp.Y, mp.Z]): + chi1inv[i, k] = sim.structure.get_chi1inv(com, dir, v3, frequency) n = np.real(np.sqrt(np.linalg.inv(chi1inv.astype(np.complex128)))) n_actual = np.real(np.sqrt(material.epsilon(frequency).astype(np.complex128))) @@ -45,35 +46,38 @@ def setUpClass(cls): def tearDownClass(cls): mp.delete_directory(cls.temp_dir) - def verify_output_and_slice(self,material,frequency): + def verify_output_and_slice(self, material, frequency): # Since the slice routines average the diagonals, we need to do that too: chi1 = material.epsilon(frequency).astype(np.complex128) chi1inv = np.linalg.inv(chi1) chi1inv = np.diag(chi1inv) N = chi1inv.size - n = np.sqrt(N/np.sum(chi1inv)) - - sim = mp.Simulation(cell_size=mp.Vector3(2,2,2), - default_material=material, - resolution=20, - eps_averaging=False - ) + n = np.sqrt(N / np.sum(chi1inv)) + + sim = mp.Simulation( + cell_size=mp.Vector3(2, 2, 2), + default_material=material, + resolution=20, + eps_averaging=False, + ) sim.use_output_directory(self.temp_dir) sim.init_sim() # Check to make sure the get_slice routine is working with frequency - n_slice = np.sqrt(np.max(sim.get_epsilon(frequency=frequency,snap=True))) - self.assertAlmostEqual(n,n_slice, places=4) + n_slice = np.sqrt(np.max(sim.get_epsilon(frequency=frequency, snap=True))) + self.assertAlmostEqual(n, n_slice, places=4) # Check to make sure h5 output is working with frequency - filename = os.path.join(self.temp_dir, - sim.get_filename_prefix() + '-eps-000000.00.h5') - mp.output_epsilon(sim,frequency=frequency) + filename = os.path.join( + self.temp_dir, f"{sim.get_filename_prefix()}-eps-000000.00.h5" + ) + + mp.output_epsilon(sim, frequency=frequency) n_h5 = 0 mp.all_wait() - with h5py.File(filename, 'r') as f: - n_h5 = np.sqrt(np.max(mp.complexarray(f['eps.r'][()],f['eps.i'][()]))) - self.assertAlmostEqual(n,n_h5, places=4) + with h5py.File(filename, "r") as f: + n_h5 = np.sqrt(np.max(mp.complexarray(f["eps.r"][()], f["eps.i"][()]))) + self.assertAlmostEqual(n, n_h5, places=4) # ----------------------------------------- # # ----------- Test Routines --------------- # @@ -83,76 +87,78 @@ def test_chi1_routine(self): # fields and structure classes by comparing their output to the # python epsilon output. - from meep.materials import Si, Ag, LiNbO3, Au + from meep.materials import Ag, Au, LiNbO3, Si # Check Silicon w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - self.call_chi1(Si,w0) - self.call_chi1(Si,w1) + self.call_chi1(Si, w0) + self.call_chi1(Si, w1) # Check Silver w0 = Ag.valid_freq_range.min w1 = Ag.valid_freq_range.max - self.call_chi1(Ag,w0) - self.call_chi1(Ag,w1) + self.call_chi1(Ag, w0) + self.call_chi1(Ag, w1) # Check Gold w0 = Au.valid_freq_range.min w1 = Au.valid_freq_range.max - self.call_chi1(Au,w0) - self.call_chi1(Au,w1) + self.call_chi1(Au, w0) + self.call_chi1(Au, w1) # Check Lithium Niobate (X,X) w0 = LiNbO3.valid_freq_range.min w1 = LiNbO3.valid_freq_range.max - self.call_chi1(LiNbO3,w0) - self.call_chi1(LiNbO3,w1) + self.call_chi1(LiNbO3, w0) + self.call_chi1(LiNbO3, w1) # Now let's rotate LN import copy + rotLiNbO3 = copy.deepcopy(LiNbO3) - rotLiNbO3.rotate(mp.Vector3(1,1,1),np.radians(34)) - self.call_chi1(rotLiNbO3,w0) - self.call_chi1(rotLiNbO3,w1) + rotLiNbO3.rotate(mp.Vector3(1, 1, 1), np.radians(34)) + self.call_chi1(rotLiNbO3, w0) + self.call_chi1(rotLiNbO3, w1) def test_get_with_dispersion(self): # Checks the get_array_slice and output_fields method # with dispersive materials. - from meep.materials import Si, Ag, LiNbO3, Au + from meep.materials import Ag, Au, LiNbO3, Si # Check Silicon w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - self.verify_output_and_slice(Si,w0) - self.verify_output_and_slice(Si,w1) + self.verify_output_and_slice(Si, w0) + self.verify_output_and_slice(Si, w1) # Check Silver w0 = Ag.valid_freq_range.min w1 = Ag.valid_freq_range.max - self.verify_output_and_slice(Ag,w0) - self.verify_output_and_slice(Ag,w1) + self.verify_output_and_slice(Ag, w0) + self.verify_output_and_slice(Ag, w1) # Check Gold w0 = Au.valid_freq_range.min w1 = Au.valid_freq_range.max - self.verify_output_and_slice(Au,w0) - self.verify_output_and_slice(Au,w1) + self.verify_output_and_slice(Au, w0) + self.verify_output_and_slice(Au, w1) # Check Lithium Niobate w0 = LiNbO3.valid_freq_range.min w1 = LiNbO3.valid_freq_range.max - self.verify_output_and_slice(LiNbO3,w0) - self.verify_output_and_slice(LiNbO3,w1) + self.verify_output_and_slice(LiNbO3, w0) + self.verify_output_and_slice(LiNbO3, w1) # Now let's rotate LN import copy + rotLiNbO3 = copy.deepcopy(LiNbO3) - rotLiNbO3.rotate(mp.Vector3(1,1,1),np.radians(34)) - self.verify_output_and_slice(rotLiNbO3,w0) - self.verify_output_and_slice(rotLiNbO3,w1) + rotLiNbO3.rotate(mp.Vector3(1, 1, 1), np.radians(34)) + self.verify_output_and_slice(rotLiNbO3, w0) + self.verify_output_and_slice(rotLiNbO3, w1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_divide_mpi_processes.py b/python/tests/test_divide_mpi_processes.py index 574f0e834..43ec2ba6d 100644 --- a/python/tests/test_divide_mpi_processes.py +++ b/python/tests/test_divide_mpi_processes.py @@ -1,55 +1,64 @@ import unittest + import meep as mp + @unittest.skipIf(mp.count_processors() < 2, "MPI specific test") class TestDivideParallelProcesses(unittest.TestCase): - - def test_divide_parallel_processes(self): + def test_divide_parallel_processes(self): resolution = 20 sxy = 4 dpml = 1 - cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml) + cell = mp.Vector3(sxy + 2 * dpml, sxy + 2 * dpml) pml_layers = [mp.PML(dpml)] n = mp.divide_parallel_processes(2) - fcen = 1.0/(n+1) - - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(), - component=mp.Ez)] - - symmetries = [mp.Mirror(mp.X), - mp.Mirror(mp.Y)] - - self.sim = mp.Simulation(cell_size=cell, - resolution=resolution, - sources=sources, - symmetries=symmetries, - boundary_layers=pml_layers) - - - flux_box = self.sim.add_flux(fcen, 0, 1, - mp.FluxRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)), - mp.FluxRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1), - mp.FluxRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)), - mp.FluxRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1), - decimation_factor=1) + fcen = 1.0 / (n + 1) + + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(), + component=mp.Ez, + ) + ] + + symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] + + self.sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + sources=sources, + symmetries=symmetries, + boundary_layers=pml_layers, + ) + + flux_box = self.sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(mp.Vector3(y=0.5 * sxy), size=mp.Vector3(sxy)), + mp.FluxRegion(mp.Vector3(y=-0.5 * sxy), size=mp.Vector3(sxy), weight=-1), + mp.FluxRegion(mp.Vector3(0.5 * sxy), size=mp.Vector3(y=sxy)), + mp.FluxRegion(mp.Vector3(-0.5 * sxy), size=mp.Vector3(y=sxy), weight=-1), + decimation_factor=1, + ) self.sim.run(until_after_sources=30) tot_flux = mp.get_fluxes(flux_box)[0] - + tot_fluxes = mp.merge_subgroup_data(tot_flux) - fcens = mp.merge_subgroup_data(fcen) + fcens = mp.merge_subgroup_data(fcen) - self.assertEqual(fcens[0],1) - self.assertEqual(fcens[1],0.5) + self.assertEqual(fcens[0], 1) + self.assertEqual(fcens[1], 0.5) places = 4 if mp.is_single_precision() else 7 self.assertAlmostEqual(tot_fluxes[0], 9.8628728533, places=places) self.assertAlmostEqual(tot_fluxes[1], 19.6537275387, places=places) - -if __name__ == '__main__': - unittest.main() + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_dump_load.py b/python/tests/test_dump_load.py index 744e0c4de..c97712733 100644 --- a/python/tests/test_dump_load.py +++ b/python/tests/test_dump_load.py @@ -4,11 +4,13 @@ import sys import unittest import warnings + import h5py import numpy as np -import meep as mp from utils import ApproxComparisonTestCase +import meep as mp + try: unicode except NameError: @@ -17,40 +19,51 @@ class TestLoadDump(ApproxComparisonTestCase): - fname_base = re.sub(r'\.py$', '', os.path.split(sys.argv[0])[1]) - fname = fname_base + '-ez-000200.00.h5' + fname_base = re.sub(r"\.py$", "", os.path.split(sys.argv[0])[1]) + fname = fname_base + "-ez-000200.00.h5" def setUp(self): - print("Running {}".format(self._testMethodName)) + print(f"Running {self._testMethodName}") @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() - print("Saving temp files to dir: {}".format(cls.temp_dir)) + print(f"Saving temp files to dir: {cls.temp_dir}") @classmethod def tearDownClass(cls): mp.delete_directory(cls.temp_dir) # Tests various combinations of dumping/loading structure & chunk layout in 2d. - def _load_dump_structure_2d(self, chunk_file=False, chunk_sim=False, single_parallel_file=True): + def _load_dump_structure_2d( + self, chunk_file=False, chunk_sim=False, single_parallel_file=True + ): from meep.materials import Al + resolution = 50 cell = mp.Vector3(5, 5) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.2), center=mp.Vector3(), component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.2), center=mp.Vector3(), component=mp.Ez + ) one_by_one = mp.Vector3(1, 1, mp.inf) - geometry = [mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), - mp.Block(material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one)] + geometry = [ + mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), + mp.Block( + material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one + ), + ] pml_layers = [mp.PML(0.5)] symmetries = [mp.Mirror(mp.Y)] - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - symmetries=symmetries, - sources=[sources]) + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + symmetries=symmetries, + sources=[sources], + ) sample_point = mp.Vector3(0.12, -0.29) ref_field_points = [] @@ -61,25 +74,37 @@ def get_ref_field_point(sim): sim1.run(mp.at_every(5, get_ref_field_point), until=50) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_structure') - sim1.dump(dump_dirname, dump_structure=True, dump_fields=False, single_parallel_file=single_parallel_file) + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_structure") + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=False, + single_parallel_file=single_parallel_file, + ) dump_chunk_fname = None chunk_layout = None if chunk_file: - dump_chunk_fname = os.path.join(dump_dirname, 'chunk_layout.h5') + dump_chunk_fname = os.path.join(dump_dirname, "chunk_layout.h5") sim1.dump_chunk_layout(dump_chunk_fname) chunk_layout = dump_chunk_fname if chunk_sim: chunk_layout = sim1 - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - sources=[sources], - symmetries=symmetries, - chunk_layout=chunk_layout) - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + sources=[sources], + symmetries=symmetries, + chunk_layout=chunk_layout, + ) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = [] def get_field_point(sim): @@ -105,25 +130,38 @@ def test_load_dump_chunk_layout_sim_2d(self): self._load_dump_structure_2d(chunk_sim=True) # Tests various combinations of dumping/loading structure & chunk layout in 3d. - def _load_dump_structure_3d(self, chunk_file=False, chunk_sim=False, single_parallel_file=True): + def _load_dump_structure_3d( + self, chunk_file=False, chunk_sim=False, single_parallel_file=True + ): from meep.materials import aSi + resolution = 15 cell = mp.Vector3(2.3, 2.1, 2.7) - sources = mp.Source(src=mp.GaussianSource(2.5, fwidth=0.1), center=mp.Vector3(), component=mp.Hy) + sources = mp.Source( + src=mp.GaussianSource(2.5, fwidth=0.1), center=mp.Vector3(), component=mp.Hy + ) one_by_one_by_one = mp.Vector3(1, 1, 1) - geometry = [mp.Block(material=mp.Medium(index=2.3), center=mp.Vector3(), size=one_by_one_by_one), - mp.Block(material=aSi, center=mp.Vector3(1), size=one_by_one_by_one)] + geometry = [ + mp.Block( + material=mp.Medium(index=2.3), + center=mp.Vector3(), + size=one_by_one_by_one, + ), + mp.Block(material=aSi, center=mp.Vector3(1), size=one_by_one_by_one), + ] default_material = mp.Medium(index=1.4) boundary_layers = [mp.Absorber(0.2)] k_point = mp.Vector3(0.4, -1.3, 0.7) - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - k_point=k_point, - geometry=geometry, - boundary_layers=boundary_layers, - default_material=default_material, - sources=[sources]) + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + k_point=k_point, + geometry=geometry, + boundary_layers=boundary_layers, + default_material=default_material, + sources=[sources], + ) sample_point = mp.Vector3(0.73, -0.33, 0.61) ref_field_points = [] @@ -134,25 +172,37 @@ def get_ref_field_point(sim): sim1.run(mp.at_every(5, get_ref_field_point), until=50) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_structure') - sim1.dump(dump_dirname, dump_structure=True, dump_fields=False, single_parallel_file=single_parallel_file) + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_structure") + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=False, + single_parallel_file=single_parallel_file, + ) dump_chunk_fname = None chunk_layout = None if chunk_file: - dump_chunk_fname = os.path.join(dump_dirname, 'chunk_layout.h5') + dump_chunk_fname = os.path.join(dump_dirname, "chunk_layout.h5") sim1.dump_chunk_layout(dump_chunk_fname) chunk_layout = dump_chunk_fname if chunk_sim: chunk_layout = sim1 - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - sources=[sources], - k_point=k_point, - boundary_layers=boundary_layers, - chunk_layout=chunk_layout) - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + sources=[sources], + k_point=k_point, + boundary_layers=boundary_layers, + chunk_layout=chunk_layout, + ) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = [] def get_field_point(sim): @@ -181,68 +231,103 @@ def test_load_dump_chunk_layout_sim_3d(self): def _load_dump_fields_2d(self, single_parallel_file=True): resolution = 50 cell = mp.Vector3(5, 5) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez + ) one_by_one = mp.Vector3(1, 1, mp.inf) - geometry = [mp.Block(material=mp.Medium(index=3.2), center=mp.Vector3(), size=one_by_one), - mp.Block(material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one)] + geometry = [ + mp.Block( + material=mp.Medium(index=3.2), center=mp.Vector3(), size=one_by_one + ), + mp.Block( + material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one + ), + ] pml_layers = [mp.PML(0.5)] symmetries = [mp.Mirror(mp.Y)] - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - symmetries=symmetries, - sources=[sources]) - - mon_dft = sim1.add_dft_fields([mp.Ez], - 1.1, 0.1, 3, - center=mp.Vector3(1.2,0.4), - size=mp.Vector3(1.5,0.3), - yee_grid=True) + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + symmetries=symmetries, + sources=[sources], + ) + + mon_dft = sim1.add_dft_fields( + [mp.Ez], + 1.1, + 0.1, + 3, + center=mp.Vector3(1.2, 0.4), + size=mp.Vector3(1.5, 0.3), + yee_grid=True, + ) sample_point = mp.Vector3(0.12, -0.29) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_fields') + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_fields") os.makedirs(dump_dirname, exist_ok=True) ref_field_points = {} + def get_ref_field_point(sim): p = sim.get_field_point(mp.Ez, sample_point) ref_field_points[sim.meep_time()] = p.real # First run until t=15 and save structure/fields sim1.run(mp.at_every(1, get_ref_field_point), until=15) - sim1.dump(dump_dirname, dump_structure=True, dump_fields=True, single_parallel_file=single_parallel_file) - + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=True, + single_parallel_file=single_parallel_file, + ) # Then continue running another 5 until t=20 sim1.run(mp.at_every(1, get_ref_field_point), until=5) sim1_mon_dft = sim1.get_dft_array(mon_dft, mp.Ez, 2) # Now create a new simulation and try restoring state. - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - sources=[sources], - symmetries=symmetries, - chunk_layout=sim1) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + sources=[sources], + symmetries=symmetries, + chunk_layout=sim1, + ) # Just restore structure first. - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = {} - mon_dft = sim.add_dft_fields([mp.Ez], - 1.1, 0.1, 3, - center=mp.Vector3(1.2,0.4), - size=mp.Vector3(1.5,0.3), - yee_grid=True) + mon_dft = sim.add_dft_fields( + [mp.Ez], + 1.1, + 0.1, + 3, + center=mp.Vector3(1.2, 0.4), + size=mp.Vector3(1.5, 0.3), + yee_grid=True, + ) def get_field_point(sim): p = sim.get_field_point(mp.Ez, sample_point) field_points[sim.meep_time()] = p.real # Now load the fields (at t=15) and then continue to t=20 - sim.load(dump_dirname, load_structure=False, load_fields=True, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=False, + load_fields=True, + single_parallel_file=single_parallel_file, + ) sim.run(mp.at_every(1, get_field_point), until=5) for t, v in field_points.items(): @@ -262,68 +347,107 @@ def test_load_dump_fields_sharded_2d(self): def _load_dump_fields_3d(self, single_parallel_file=True): resolution = 15 cell = mp.Vector3(2.6, 2.2, 2.3) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Hx) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Hx + ) one_by_one_by_one = mp.Vector3(1, 1, 1) - geometry = [mp.Block(material=mp.Medium(index=2.4), center=mp.Vector3(), size=one_by_one_by_one), - mp.Block(material=mp.Medium(epsilon=7.9), center=mp.Vector3(1), size=one_by_one_by_one)] + geometry = [ + mp.Block( + material=mp.Medium(index=2.4), + center=mp.Vector3(), + size=one_by_one_by_one, + ), + mp.Block( + material=mp.Medium(epsilon=7.9), + center=mp.Vector3(1), + size=one_by_one_by_one, + ), + ] pml_layers = [mp.PML(0.5)] - symmetries = [mp.Mirror(mp.Y,phase=-1),mp.Mirror(mp.Z,phase=-1)] - - sim1 = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - symmetries=symmetries, - sources=[sources]) - - mon_dft = sim1.add_dft_fields([mp.Hx], - 0.9, 0.05, 4, - center=mp.Vector3(0.5,0.4,0.2), - size=mp.Vector3(1.5,0.3,0.2), - yee_grid=True) + symmetries = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.Z, phase=-1)] + + sim1 = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + symmetries=symmetries, + sources=[sources], + ) + + mon_dft = sim1.add_dft_fields( + [mp.Hx], + 0.9, + 0.05, + 4, + center=mp.Vector3(0.5, 0.4, 0.2), + size=mp.Vector3(1.5, 0.3, 0.2), + yee_grid=True, + ) sample_point = mp.Vector3(0.37, -0.42, 0.55) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_fields') + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_fields") os.makedirs(dump_dirname, exist_ok=True) ref_field_points = {} + def get_ref_field_point(sim): p = sim.get_field_point(mp.Hx, sample_point) ref_field_points[sim.meep_time()] = p.real # First run until t=15 and save structure/fields sim1.run(mp.at_every(1, get_ref_field_point), until=15) - sim1.dump(dump_dirname, dump_structure=True, dump_fields=True, single_parallel_file=single_parallel_file) - + sim1.dump( + dump_dirname, + dump_structure=True, + dump_fields=True, + single_parallel_file=single_parallel_file, + ) # Then continue running another 5 until t=20 sim1.run(mp.at_every(1, get_ref_field_point), until=5) sim1_mon_dft = sim1.get_dft_array(mon_dft, mp.Hx, 3) # Now create a new simulation and try restoring state. - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=pml_layers, - sources=[sources], - symmetries=symmetries, - chunk_layout=sim1) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=pml_layers, + sources=[sources], + symmetries=symmetries, + chunk_layout=sim1, + ) # Just restore structure first. - sim.load(dump_dirname, load_structure=True, load_fields=False, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=True, + load_fields=False, + single_parallel_file=single_parallel_file, + ) field_points = {} - mon_dft = sim.add_dft_fields([mp.Hx], - 0.9, 0.05, 4, - center=mp.Vector3(0.5,0.4,0.2), - size=mp.Vector3(1.5,0.3,0.2), - yee_grid=True) + mon_dft = sim.add_dft_fields( + [mp.Hx], + 0.9, + 0.05, + 4, + center=mp.Vector3(0.5, 0.4, 0.2), + size=mp.Vector3(1.5, 0.3, 0.2), + yee_grid=True, + ) def get_field_point(sim): p = sim.get_field_point(mp.Hx, sample_point) field_points[sim.meep_time()] = p.real # Now load the fields (at t=15) and then continue to t=20 - sim.load(dump_dirname, load_structure=False, load_fields=True, single_parallel_file=single_parallel_file) + sim.load( + dump_dirname, + load_structure=False, + load_fields=True, + single_parallel_file=single_parallel_file, + ) sim.run(mp.at_every(1, get_field_point), until=5) for t, v in field_points.items(): @@ -345,27 +469,39 @@ def test_load_dump_fields_sharded_3d(self): def test_dump_fails_for_non_null_polarization_state(self): resolution = 50 cell = mp.Vector3(5, 5) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.4), center=mp.Vector3(), component=mp.Ez + ) one_by_one = mp.Vector3(1, 1, mp.inf) from meep.materials import Al - geometry = [mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), - mp.Block(material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[], - geometry=geometry, - symmetries=[], - sources=[sources]) - dump_dirname = os.path.join(self.temp_dir, 'test_load_dump_fields') + geometry = [ + mp.Block(material=Al, center=mp.Vector3(), size=one_by_one), + mp.Block( + material=mp.Medium(epsilon=13), center=mp.Vector3(1), size=one_by_one + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[], + geometry=geometry, + symmetries=[], + sources=[sources], + ) + + dump_dirname = os.path.join(self.temp_dir, "test_load_dump_fields") os.makedirs(dump_dirname, exist_ok=True) sim.run(until=1) # NOTE: We do not yet support checkpoint/restore when there is a # non-null polarization_state - with self.assertRaisesRegex(RuntimeError, 'meep: non-null polarization_state in fields::dump'): - sim.dump(dump_dirname, dump_structure=True, dump_fields=True) + with self.assertRaisesRegex( + RuntimeError, "meep: non-null polarization_state in fields::dump" + ): + sim.dump(dump_dirname, dump_structure=True, dump_fields=True) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_eigfreq.py b/python/tests/test_eigfreq.py index f73fe73ba..3ceb6ee1d 100644 --- a/python/tests/test_eigfreq.py +++ b/python/tests/test_eigfreq.py @@ -1,42 +1,55 @@ -import meep as mp import unittest -class TestEigfreq(unittest.TestCase): +import meep as mp + - @unittest.skipIf(mp.is_single_precision(), "double-precision floating point specific test") +class TestEigfreq(unittest.TestCase): + @unittest.skipIf( + mp.is_single_precision(), "double-precision floating point specific test" + ) def test_eigfreq(self): - w = 1.2 # width of waveguide - r = 0.36 # radius of holes - d = 1.4 # defect spacing (ordinary spacing = 1) - N = 3 # number of holes on either side of defect - sy = 6 # size of cell in y direction (perpendicular to wvg.) - pad = 2 # padding between last hole and PML edge - dpml = 1 # PML thickness - sx = 2*(pad+dpml+N)+d-1 # size of cell in x direction - - geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf), material=mp.Medium(epsilon=13))] + w = 1.2 # width of waveguide + r = 0.36 # radius of holes + d = 1.4 # defect spacing (ordinary spacing = 1) + N = 3 # number of holes on either side of defect + sy = 6 # size of cell in y direction (perpendicular to wvg.) + pad = 2 # padding between last hole and PML edge + dpml = 1 # PML thickness + sx = 2 * (pad + dpml + N) + d - 1 # size of cell in x direction + + geometry = [ + mp.Block(size=mp.Vector3(mp.inf, w, mp.inf), material=mp.Medium(epsilon=13)) + ] for i in range(N): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d/2+i))) - geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d/2+i)))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) + geometry.append(mp.Cylinder(r, center=mp.Vector3(-(d / 2 + i)))) fcen = 0.25 df = 0.2 - src = [mp.Source(mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3(0), - size=mp.Vector3(0,0))] - - sim = mp.Simulation(cell_size=mp.Vector3(sx,sy), force_complex_fields=True, - geometry=geometry, - boundary_layers=[mp.PML(1.0)], - sources=src, - symmetries=[mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)], - resolution=20) + src = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=mp.Vector3(0), + size=mp.Vector3(0, 0), + ) + ] + + sim = mp.Simulation( + cell_size=mp.Vector3(sx, sy), + force_complex_fields=True, + geometry=geometry, + boundary_layers=[mp.PML(1.0)], + sources=src, + symmetries=[mp.Mirror(mp.X, phase=-1), mp.Mirror(mp.Y, phase=-1)], + resolution=20, + ) sim.init_sim() eigfreq = sim.solve_eigfreq(tol=1e-6) self.assertAlmostEqual(eigfreq.real, 0.23445413142440263, places=5) self.assertAlmostEqual(eigfreq.imag, -0.0003147775697388, places=5) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_faraday_rotation.py b/python/tests/test_faraday_rotation.py index 7c0f337a2..0e9a33695 100644 --- a/python/tests/test_faraday_rotation.py +++ b/python/tests/test_faraday_rotation.py @@ -1,43 +1,61 @@ import unittest + import numpy as np + import meep as mp + ## Farady rotation rate for gyrotropic Lorentzian medium def kgyro_lorentzian(freq, epsn, f0, gamma, sigma, b0): - dfsq = (f0**2 - 1j*freq*gamma - freq**2) - eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq*b0)**2) - eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq*b0)**2) - return 2*np.pi*freq * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) + dfsq = f0**2 - 1j * freq * gamma - freq**2 + eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq * b0) ** 2) + eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq * b0) ** 2) + return 2 * np.pi * freq * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) + ## Farady rotation rate for gyrotropic Drude medium def kgyro_drude(freq, epsn, f0, gamma, sigma, b0): - dfsq = - 1j*freq*gamma - freq**2 - eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq*b0)**2) - eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq*b0)**2) - return 2*np.pi*freq * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) + dfsq = -1j * freq * gamma - freq**2 + eperp = epsn + sigma * f0**2 * dfsq / (dfsq**2 - (freq * b0) ** 2) + eta = sigma * f0**2 * freq * b0 / (dfsq**2 - (freq * b0) ** 2) + return 2 * np.pi * freq * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) + ## Farady rotation rate for Landau-Lifshitz-Gilbert medium def kgyro_llg(freq, epsn, f0, gamma, sigma, alpha): - df1 = f0 - 1j*freq*alpha - df2 = freq + 1j*gamma - eperp = epsn + sigma * df1/(df1**2 - df2**2) + df1 = f0 - 1j * freq * alpha + df2 = freq + 1j * gamma + eperp = epsn + sigma * df1 / (df1**2 - df2**2) eta = sigma * df2 / (df1**2 - df2**2) - return 2*np.pi*freq * np.sqrt(0.5*(eperp - np.sqrt(eperp**2 - eta**2))) + return 2 * np.pi * freq * np.sqrt(0.5 * (eperp - np.sqrt(eperp**2 - eta**2))) + class TestFaradayRotation(unittest.TestCase): ## Simulate a linearly polarized plane wave traveling along the gyrotropy axis. ## Extract Faraday rotation angle by comparing the Ex and Ey amplitudes, and ## compare to a theoretical result corresponding to rotation rate KPRED. ## The default acceptable tolerance TOL is 1.5 degrees. - def check_rotation(self, mat, L, fsrc, zsrc, resolution, tmax, zout, kpred, tol=1.5): + def check_rotation( + self, mat, L, fsrc, zsrc, resolution, tmax, zout, kpred, tol=1.5 + ): cell = mp.Vector3(0, 0, L) pml_layers = [mp.PML(thickness=1.0, direction=mp.Z)] - sources = [mp.Source(mp.ContinuousSource(frequency=fsrc), - component=mp.Ex, center=mp.Vector3(0, 0, zsrc))] - - self.sim = mp.Simulation(cell_size=cell, geometry=[], sources=sources, - boundary_layers=pml_layers, - default_material=mat, resolution=resolution) + sources = [ + mp.Source( + mp.ContinuousSource(frequency=fsrc), + component=mp.Ex, + center=mp.Vector3(0, 0, zsrc), + ) + ] + + self.sim = mp.Simulation( + cell_size=cell, + geometry=[], + sources=sources, + boundary_layers=pml_layers, + default_material=mat, + resolution=resolution, + ) record_Ex, record_Ey, record_t = [], [], [] @@ -46,17 +64,19 @@ def record_ex_ey(sim): record_Ey.append(sim.get_field_point(mp.Ey, mp.Vector3(0, 0, zout))) record_t.append(sim.meep_time()) - self.sim.run(mp.after_time(0.5*tmax, mp.at_every(1e-6, record_ex_ey)), until=tmax) + self.sim.run( + mp.after_time(0.5 * tmax, mp.at_every(1e-6, record_ex_ey)), until=tmax + ) ex_rel = np.amax(abs(np.fft.fft(record_Ex))) ey_rel = np.amax(abs(np.fft.fft(record_Ey))) - result = np.arctan2(ey_rel, ex_rel) * 180/np.pi + result = np.arctan2(ey_rel, ex_rel) * 180 / np.pi Ex_theory = np.abs(np.cos(kpred * (zout - zsrc)).real) Ey_theory = np.abs(np.sin(kpred * (zout - zsrc)).real) - expected = np.arctan2(Ey_theory, Ex_theory) * 180/np.pi + expected = np.arctan2(Ey_theory, Ex_theory) * 180 / np.pi - print("Rotation angle (in degrees): {}, expected {}\n".format(result, expected)) + print(f"Rotation angle (in degrees): {result}, expected {expected}\n") np.testing.assert_allclose(expected, result, atol=tol) def test_faraday_rotation(self): @@ -65,34 +85,47 @@ def test_faraday_rotation(self): resolution = 24 ## Test gyrotropic Lorentzian medium - epsn, f0, gamma, sn, b0 = 1.5, 1.0, 1e-3, 0.1, 0.15 - susc = [mp.GyrotropicLorentzianSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - bias=mp.Vector3(0, 0, b0))] + epsn, f0, gamma, sn, b0 = 1.5, 1.0, 1e-3, 0.1, 0.15 + susc = [ + mp.GyrotropicLorentzianSusceptibility( + frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0) + ) + ] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) k = kgyro_lorentzian(freq, epsn, f0, gamma, sn, b0) - print('=' * 24) + print("=" * 24) print("Testing Faraday rotation for gyrotropic Lorentzian model...") self.check_rotation(mat, L, freq, zsrc, resolution, tmax, zout, k) ## Test gyrotropic Drude medium - susc = [mp.GyrotropicDrudeSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - bias=mp.Vector3(0, 0, b0))] + susc = [ + mp.GyrotropicDrudeSusceptibility( + frequency=f0, gamma=gamma, sigma=sn, bias=mp.Vector3(0, 0, b0) + ) + ] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) k = kgyro_drude(freq, epsn, f0, gamma, sn, b0) - print('=' * 24) + print("=" * 24) print("Testing Faraday rotation for gyrotropic Drude model...") self.check_rotation(mat, L, freq, zsrc, resolution, tmax, zout, k) ## Test Landau-Lifshitz-Gilbert medium alpha = 1e-5 - susc = [mp.GyrotropicSaturatedSusceptibility(frequency=f0, gamma=gamma, sigma=sn, - alpha=alpha, - bias=mp.Vector3(0, 0, 1.0))] + susc = [ + mp.GyrotropicSaturatedSusceptibility( + frequency=f0, + gamma=gamma, + sigma=sn, + alpha=alpha, + bias=mp.Vector3(0, 0, 1.0), + ) + ] mat = mp.Medium(epsilon=epsn, mu=1, E_susceptibilities=susc) k = kgyro_llg(freq, epsn, f0, gamma, sn, alpha) - print('=' * 24) + print("=" * 24) print("Testing Faraday rotation for Landau-Lifshitz-Gilbert model...") self.check_rotation(mat, L, freq, zsrc, resolution, tmax, zout, k) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_field_functions.py b/python/tests/test_field_functions.py index 33cdc41af..00acbe3ae 100644 --- a/python/tests/test_field_functions.py +++ b/python/tests/test_field_functions.py @@ -1,6 +1,7 @@ -import meep as mp import unittest +import meep as mp + def f(r, ex, hz, eps): return (r.x * r.norm() + ex) - (eps * hz) @@ -33,16 +34,19 @@ def init(self): fcen = 1.0 df = 1.0 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), - component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=mp.Ez + ) symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] - return mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[pml_layers], - sources=[sources], - symmetries=symmetries) + return mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[pml_layers], + sources=[sources], + symmetries=symmetries, + ) def init2(self): n = 3.4 @@ -55,7 +59,7 @@ def init2(self): geometry = [ mp.Cylinder(radius=r + w, height=mp.inf, material=mp.Medium(index=n)), - mp.Cylinder(radius=r, height=mp.inf, material=mp.air) + mp.Cylinder(radius=r, height=mp.inf, material=mp.air), ] pml_layers = [mp.PML(dpml)] @@ -63,17 +67,24 @@ def init2(self): fcen = 0.118 df = 0.010 - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3(r + 0.1))] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) + ] symmetries = [mp.Mirror(mp.Y)] - return mp.Simulation(cell_size=cell, - resolution=resolution, - geometry=geometry, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries) + return mp.Simulation( + cell_size=cell, + resolution=resolution, + geometry=geometry, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + ) def test_integrate_field_function(self): sim = self.init() @@ -107,5 +118,6 @@ def test_max_abs_field_function(self): res = sim.max_abs_field_function(self.cs, f, self.vol) self.assertAlmostEqual(res, 0.27593732304637586) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_force.py b/python/tests/test_force.py index 5a3078bb2..a38b4c9ee 100644 --- a/python/tests/test_force.py +++ b/python/tests/test_force.py @@ -1,9 +1,9 @@ import unittest + import meep as mp class TestForce(unittest.TestCase): - def setUp(self): resolution = 20 @@ -11,21 +11,30 @@ def setUp(self): pml_layers = mp.PML(1.0) fcen = 1.0 df = 1.0 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), - component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=mp.Ez + ) - self.sim = mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[pml_layers], - sources=[sources]) + self.sim = mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[pml_layers], + sources=[sources], + ) fr = mp.ForceRegion(mp.Vector3(y=1.27), direction=mp.Y, size=mp.Vector3(4.38)) self.myforce = self.sim.add_force(fcen, 0, 1, fr, decimation_factor=1) - self.myforce_decimated = self.sim.add_force(fcen, 0, 1, fr, decimation_factor=10) + self.myforce_decimated = self.sim.add_force( + fcen, 0, 1, fr, decimation_factor=10 + ) def test_force(self): - self.sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-6)) + self.sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-6 + ) + ) # Test store and load of force as numpy array fdata = self.sim.get_force_data(self.myforce) @@ -37,8 +46,10 @@ def test_force(self): self.assertAlmostEqual(f[0], -0.11039089113393187) places = 6 if mp.is_single_precision() else 7 - self.assertAlmostEqual(f[0], mp.get_forces(self.myforce_decimated)[0], places=places) + self.assertAlmostEqual( + f[0], mp.get_forces(self.myforce_decimated)[0], places=places + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_fragment_stats.py b/python/tests/test_fragment_stats.py index 61055ab22..2bbfe0bb3 100644 --- a/python/tests/test_fragment_stats.py +++ b/python/tests/test_fragment_stats.py @@ -1,29 +1,42 @@ import unittest + import meep as mp -def make_dft_vecs(flx_reg=None, n2f_reg=None, frc_reg=None, fldc=None, flds=None, fldw=None, fld_cmp=None): - dft_vecs = { - 'flux_regions': flx_reg, - 'n2f_regions': n2f_reg, - 'force_regions': frc_reg, - 'fields_center': fldc, - 'fields_size': flds, - 'fields_where': fldw, - 'fields_components': fld_cmp +def make_dft_vecs( + flx_reg=None, + n2f_reg=None, + frc_reg=None, + fldc=None, + flds=None, + fldw=None, + fld_cmp=None, +): + return { + "flux_regions": flx_reg, + "n2f_regions": n2f_reg, + "force_regions": frc_reg, + "fields_center": fldc, + "fields_size": flds, + "fields_where": fldw, + "fields_components": fld_cmp, } - return dft_vecs def make_sim(cell, res, pml, dims, create_gv=True, k_point=False): - sim = mp.Simulation(cell_size=cell, resolution=res, boundary_layers=pml, dimensions=dims, k_point=k_point) + sim = mp.Simulation( + cell_size=cell, + resolution=res, + boundary_layers=pml, + dimensions=dims, + k_point=k_point, + ) if create_gv: sim._create_grid_volume(False) return sim class TestFragmentStats(unittest.TestCase): - def check_stats(self, fragment, a_eps, a_mu, nonlin, susc, cond): self.assertEqual(fragment.num_anisotropic_eps_pixels, a_eps) self.assertEqual(fragment.num_anisotropic_mu_pixels, a_mu) @@ -31,40 +44,65 @@ def check_stats(self, fragment, a_eps, a_mu, nonlin, susc, cond): self.assertEqual(fragment.num_susceptibility_pixels, susc) self.assertEqual(fragment.num_nonzero_conductivity_pixels, cond) - def get_fragment_stats(self, block_size, cell_size, dims, box_center=mp.Vector3(), dft_vecs=None, - def_mat=mp.air, sym=[], geom=None, pml=[]): + def get_fragment_stats( + self, + block_size, + cell_size, + dims, + box_center=mp.Vector3(), + dft_vecs=None, + def_mat=mp.air, + sym=[], + geom=None, + pml=[], + ): mat = mp.Medium( epsilon=12, epsilon_offdiag=mp.Vector3(z=1), mu_offdiag=mp.Vector3(x=20), E_chi2_diag=mp.Vector3(1, 1), H_chi3_diag=mp.Vector3(z=1), - E_susceptibilities=[mp.LorentzianSusceptibility(), mp.NoisyLorentzianSusceptibility()], + E_susceptibilities=[ + mp.LorentzianSusceptibility(), + mp.NoisyLorentzianSusceptibility(), + ], H_susceptibilities=[mp.DrudeSusceptibility()], D_conductivity_diag=mp.Vector3(y=1), - B_conductivity_diag=mp.Vector3(x=1, z=1) + B_conductivity_diag=mp.Vector3(x=1, z=1), ) if geom is None: geom = [mp.Block(size=block_size, center=box_center, material=mat)] - sim = mp.Simulation(cell_size=cell_size, resolution=10, geometry=geom, dimensions=dims, - default_material=def_mat, symmetries=sym, boundary_layers=pml) + sim = mp.Simulation( + cell_size=cell_size, + resolution=10, + geometry=geom, + dimensions=dims, + default_material=def_mat, + symmetries=sym, + boundary_layers=pml, + ) if dft_vecs: - if dft_vecs['flux_regions']: - sim.add_flux(1, 0.5, 5, *dft_vecs['flux_regions']) - if dft_vecs['n2f_regions']: - sim.add_near2far(1, 0.5, 7, *dft_vecs['n2f_regions']) - if dft_vecs['force_regions']: - sim.add_force(1, 0.5, 9, *dft_vecs['force_regions']) - if dft_vecs['fields_components']: - sim.add_dft_fields(dft_vecs['fields_components'], 0, 1, 5, where=dft_vecs['fields_where'], - center=dft_vecs['fields_center'], size=dft_vecs['fields_size']) + if dft_vecs["flux_regions"]: + sim.add_flux(1, 0.5, 5, *dft_vecs["flux_regions"]) + if dft_vecs["n2f_regions"]: + sim.add_near2far(1, 0.5, 7, *dft_vecs["n2f_regions"]) + if dft_vecs["force_regions"]: + sim.add_force(1, 0.5, 9, *dft_vecs["force_regions"]) + if dft_vecs["fields_components"]: + sim.add_dft_fields( + dft_vecs["fields_components"], + 0, + 1, + 5, + where=dft_vecs["fields_where"], + center=dft_vecs["fields_center"], + size=dft_vecs["fields_size"], + ) gv = sim._create_grid_volume(False) - stats = sim._compute_fragment_stats(gv) - - return stats + return sim._compute_fragment_stats(gv) def _test_1d(self, sym, pml=[]): # A z=30 cell, with a size 10 block in the middle. @@ -73,18 +111,22 @@ def _test_1d(self, sym, pml=[]): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(z=-10), size=mp.Vector3(z=10))], [mp.Near2FarRegion(mp.Vector3(), size=mp.Vector3(z=10))], - [mp.ForceRegion(mp.Vector3(z=10), direction=mp.X, size=mp.Vector3(z=10))] + [mp.ForceRegion(mp.Vector3(z=10), direction=mp.X, size=mp.Vector3(z=10))], ) - fs = self.get_fragment_stats(mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs, sym=sym, pml=pml) + fs = self.get_fragment_stats( + mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs, sym=sym, pml=pml + ) sym_factor = 2 if sym else 1 - self.check_stats(fs, - a_eps=300 / sym_factor, - a_mu=300 / sym_factor, - nonlin=300 / sym_factor, - susc=300 / sym_factor, - cond=300 / sym_factor) + self.check_stats( + fs, + a_eps=300 / sym_factor, + a_mu=300 / sym_factor, + nonlin=300 / sym_factor, + susc=300 / sym_factor, + cond=300 / sym_factor, + ) # Check DFT regions self.assertEqual(fs.num_dft_pixels, 40800) @@ -113,11 +155,15 @@ def test_1d_with_partial_fragment(self): # dft_flux with 2 volumes, 1 covering the first 10 units and one covering # half of the second 10 - dft_vecs = make_dft_vecs(flx_reg=[ - mp.FluxRegion(mp.Vector3(z=-9), mp.Vector3(z=8)), - mp.FluxRegion(mp.Vector3(z=-2.5), mp.Vector3(z=5)) - ]) - fs = self.get_fragment_stats(mp.Vector3(z=16), mp.Vector3(z=26), 1, dft_vecs=dft_vecs) + dft_vecs = make_dft_vecs( + flx_reg=[ + mp.FluxRegion(mp.Vector3(z=-9), mp.Vector3(z=8)), + mp.FluxRegion(mp.Vector3(z=-2.5), mp.Vector3(z=5)), + ] + ) + fs = self.get_fragment_stats( + mp.Vector3(z=16), mp.Vector3(z=26), 1, dft_vecs=dft_vecs + ) self.check_stats(fs, a_eps=260, a_mu=260, nonlin=480, susc=480, cond=480) # Check dft stats @@ -127,13 +173,21 @@ def test_1d_dft_fields(self): # A z=30 cell with a block covering the middle 10 units. # dft_fields covering first 10 units - dft_vecs = make_dft_vecs(fldc=mp.Vector3(z=-10), flds=mp.Vector3(z=10), fld_cmp=[mp.X, mp.Y]) - fs = self.get_fragment_stats(mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs) + dft_vecs = make_dft_vecs( + fldc=mp.Vector3(z=-10), flds=mp.Vector3(z=10), fld_cmp=[mp.X, mp.Y] + ) + fs = self.get_fragment_stats( + mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs + ) self.assertEqual(fs.num_dft_pixels, 4000) # Same test with volume instead of center and size - dft_vecs = make_dft_vecs(fldw=mp.Volume(mp.Vector3(z=-10), mp.Vector3(z=10)), fld_cmp=[mp.X, mp.Y]) - fs = self.get_fragment_stats(mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs) + dft_vecs = make_dft_vecs( + fldw=mp.Volume(mp.Vector3(z=-10), mp.Vector3(z=10)), fld_cmp=[mp.X, mp.Y] + ) + fs = self.get_fragment_stats( + mp.Vector3(z=10), mp.Vector3(z=30), 1, dft_vecs=dft_vecs + ) self.assertEqual(fs.num_dft_pixels, 4000) def _test_2d(self, sym, pml=[]): @@ -143,10 +197,20 @@ def _test_2d(self, sym, pml=[]): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(-10, 10), size=mp.Vector3(10, 10))], [mp.Near2FarRegion(mp.Vector3(0, 10), size=mp.Vector3(10, 10))], - [mp.ForceRegion(mp.Vector3(10, 10), direction=mp.X, size=mp.Vector3(10, 10))] + [ + mp.ForceRegion( + mp.Vector3(10, 10), direction=mp.X, size=mp.Vector3(10, 10) + ) + ], + ) + fs = self.get_fragment_stats( + mp.Vector3(10, 10), + mp.Vector3(30, 30), + 2, + dft_vecs=dft_vecs, + sym=sym, + pml=pml, ) - fs = self.get_fragment_stats(mp.Vector3(10, 10), mp.Vector3(30, 30), 2, - dft_vecs=dft_vecs, sym=sym, pml=pml) # Check fragment boxes self.assertEqual(fs.box.low.x, -15) @@ -156,12 +220,14 @@ def _test_2d(self, sym, pml=[]): # Middle fragment contains entire block sym_factor = 4 if sym else 1 - self.check_stats(fs, - a_eps=90000 / sym_factor, - a_mu=90000 / sym_factor, - nonlin=30000 / sym_factor, - susc=30000 / sym_factor, - cond=30000 / sym_factor) + self.check_stats( + fs, + a_eps=90000 / sym_factor, + a_mu=90000 / sym_factor, + nonlin=30000 / sym_factor, + susc=30000 / sym_factor, + cond=30000 / sym_factor, + ) # Check DFT regions self.assertEqual(fs.num_dft_pixels, 2040000) @@ -174,14 +240,17 @@ def test_2d_with_symmetry(self): self._test_2d([mp.Mirror(mp.X), mp.Mirror(mp.Y)]) def test_2d_with_pml_all_sides(self): - self._test_2d([], pml=[mp.PML(1, mp.Y), mp.PML(2, mp.X, mp.Low), mp.PML(3, mp.X, mp.High)]) + self._test_2d( + [], pml=[mp.PML(1, mp.Y), mp.PML(2, mp.X, mp.Low), mp.PML(3, mp.X, mp.High)] + ) self.assertEqual(self.fs.num_1d_pml_pixels, 19000) self.assertEqual(self.fs.num_2d_pml_pixels, 1000) self.assertEqual(self.fs.num_3d_pml_pixels, 0) def test_2d_with_absorbers(self): - fs = self.get_fragment_stats(mp.Vector3(10, 10), mp.Vector3(30, 30), 2, - geom=[], pml=[mp.Absorber(1)]) + fs = self.get_fragment_stats( + mp.Vector3(10, 10), mp.Vector3(30, 30), 2, geom=[], pml=[mp.Absorber(1)] + ) self.assertEqual(fs.num_1d_pml_pixels, 0) self.assertEqual(fs.num_2d_pml_pixels, 0) self.assertEqual(fs.num_3d_pml_pixels, 0) @@ -192,17 +261,29 @@ def test_2d_dft_fields(self): # dft_fields covering 20 by 20 area in center of cell. Test with volume, and center/size cmpts = [mp.Ex, mp.Ey, mp.Ez] - dft_fields_size_center = make_dft_vecs(fldc=mp.Vector3(), flds=mp.Vector3(20, 20), fld_cmp=cmpts) - dft_fields_where = make_dft_vecs(fldw=mp.Volume(mp.Vector3(), mp.Vector3(20, 20)), fld_cmp=cmpts) + dft_fields_size_center = make_dft_vecs( + fldc=mp.Vector3(), flds=mp.Vector3(20, 20), fld_cmp=cmpts + ) + dft_fields_where = make_dft_vecs( + fldw=mp.Volume(mp.Vector3(), mp.Vector3(20, 20)), fld_cmp=cmpts + ) for dft_vec in [dft_fields_size_center, dft_fields_where]: - fs = self.get_fragment_stats(mp.Vector3(10, 10), mp.Vector3(30, 30), 2, dft_vecs=dft_vec) - self.assertEqual(fs.num_dft_pixels, 300000 + 4*75000 + 4*150000) + fs = self.get_fragment_stats( + mp.Vector3(10, 10), mp.Vector3(30, 30), 2, dft_vecs=dft_vec + ) + self.assertEqual(fs.num_dft_pixels, 300000 + 4 * 75000 + 4 * 150000) def test_2d_pml_and_absorber(self): - blayers = [mp.PML(1, mp.Y, mp.High), mp.PML(2, mp.Y, mp.Low), - mp.Absorber(1, mp.X, mp.High), mp.Absorber(3, mp.X, mp.Low)] - fs = self.get_fragment_stats(mp.Vector3(), mp.Vector3(30, 30), 2, pml=blayers, geom=[]) + blayers = [ + mp.PML(1, mp.Y, mp.High), + mp.PML(2, mp.Y, mp.Low), + mp.Absorber(1, mp.X, mp.High), + mp.Absorber(3, mp.X, mp.Low), + ] + fs = self.get_fragment_stats( + mp.Vector3(), mp.Vector3(30, 30), 2, pml=blayers, geom=[] + ) self.assertEqual(fs.num_nonzero_conductivity_pixels, 12000) self.assertEqual(fs.num_1d_pml_pixels, 9000) self.assertEqual(fs.num_2d_pml_pixels, 0) @@ -216,18 +297,32 @@ def _test_3d(self, sym, pml=[]): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(-10, -10, -10), size=mp.Vector3(10, 10, 10))], [mp.Near2FarRegion(mp.Vector3(-10, -10, 0), size=mp.Vector3(10, 10, 10))], - [mp.ForceRegion(mp.Vector3(-10, -10, 10), direction=mp.X, size=mp.Vector3(10, 10, 10))] + [ + mp.ForceRegion( + mp.Vector3(-10, -10, 10), + direction=mp.X, + size=mp.Vector3(10, 10, 10), + ) + ], + ) + fs = self.get_fragment_stats( + mp.Vector3(10, 10, 10), + mp.Vector3(30, 30, 30), + 3, + dft_vecs=dft_vecs, + sym=sym, + pml=pml, ) - fs = self.get_fragment_stats(mp.Vector3(10, 10, 10), mp.Vector3(30, 30, 30), 3, - dft_vecs=dft_vecs, sym=sym, pml=pml) sym_factor = 8 if sym else 1 - self.check_stats(fs, - a_eps=27000000 / sym_factor, - a_mu=27000000 / sym_factor, - nonlin=3000000 / sym_factor, - susc=3000000 / sym_factor, - cond=3000000 / sym_factor) + self.check_stats( + fs, + a_eps=27000000 / sym_factor, + a_mu=27000000 / sym_factor, + nonlin=3000000 / sym_factor, + susc=3000000 / sym_factor, + cond=3000000 / sym_factor, + ) # Check DFT regions self.assertEqual(fs.num_dft_pixels, 102000000) @@ -240,16 +335,25 @@ def test_3d_with_symmetry(self): self._test_3d([mp.Mirror(mp.X), mp.Mirror(mp.Y), mp.Mirror(mp.Z)]) def test_3d_with_pml(self): - self._test_3d([], pml=[mp.PML(1, mp.Y, mp.High), mp.PML(2, mp.Y, mp.Low), mp.PML(3, mp.X), - mp.PML(1, mp.Z, mp.High), mp.PML(2, mp.Z, mp.Low)]) + self._test_3d( + [], + pml=[ + mp.PML(1, mp.Y, mp.High), + mp.PML(2, mp.Y, mp.Low), + mp.PML(3, mp.X), + mp.PML(1, mp.Z, mp.High), + mp.PML(2, mp.Z, mp.Low), + ], + ) self.assertEqual(self.fs.num_1d_pml_pixels, 8262000) self.assertEqual(self.fs.num_2d_pml_pixels, 1188000) self.assertEqual(self.fs.num_3d_pml_pixels, 54000) def test_3d_with_absorbers(self): - fs = self.get_fragment_stats(mp.Vector3(), mp.Vector3(30, 30, 30), 3, - geom=[], pml=[mp.Absorber(1)]) + fs = self.get_fragment_stats( + mp.Vector3(), mp.Vector3(30, 30, 30), 3, geom=[], pml=[mp.Absorber(1)] + ) self.assertEqual(fs.num_1d_pml_pixels, 0) self.assertEqual(fs.num_2d_pml_pixels, 0) self.assertEqual(fs.num_3d_pml_pixels, 0) @@ -262,15 +366,25 @@ def test_cyl(self): dft_vecs = make_dft_vecs( [mp.FluxRegion(mp.Vector3(-10, z=10), size=mp.Vector3(10, z=10))], [mp.Near2FarRegion(mp.Vector3(0, z=10), size=mp.Vector3(10, z=10))], - [mp.ForceRegion(mp.Vector3(10, z=10), direction=mp.X, size=mp.Vector3(10, z=10))] + [ + mp.ForceRegion( + mp.Vector3(10, z=10), direction=mp.X, size=mp.Vector3(10, z=10) + ) + ], + ) + fs = self.get_fragment_stats( + mp.Vector3(10, 0, 10), + mp.Vector3(30, 0, 30), + mp.CYLINDRICAL, + dft_vecs=dft_vecs, ) - fs = self.get_fragment_stats(mp.Vector3(10, 0, 10), mp.Vector3(30, 0, 30), - mp.CYLINDRICAL, dft_vecs=dft_vecs) self.assertEqual(fs.box.low.x, -15) self.assertEqual(fs.box.low.z, -15) self.assertEqual(fs.box.high.x, 15) self.assertEqual(fs.box.high.z, 15) - self.check_stats(fs, a_eps=90000, a_mu=90000, nonlin=30000, susc=30000, cond=30000) + self.check_stats( + fs, a_eps=90000, a_mu=90000, nonlin=30000, susc=30000, cond=30000 + ) self.assertEqual(fs.num_dft_pixels, 2040000) def test_no_geometry(self): @@ -280,13 +394,20 @@ def test_no_geometry(self): mu_offdiag=mp.Vector3(x=20), E_chi2_diag=mp.Vector3(1, 1), H_chi3_diag=mp.Vector3(x=1), - E_susceptibilities=[mp.LorentzianSusceptibility(), mp.NoisyLorentzianSusceptibility()], + E_susceptibilities=[ + mp.LorentzianSusceptibility(), + mp.NoisyLorentzianSusceptibility(), + ], H_susceptibilities=[mp.DrudeSusceptibility()], D_conductivity_diag=mp.Vector3(y=1), - B_conductivity_diag=mp.Vector3(x=1, z=1) + B_conductivity_diag=mp.Vector3(x=1, z=1), + ) + fs = self.get_fragment_stats( + mp.Vector3(), mp.Vector3(10, 10), 2, def_mat=mat, geom=[] + ) + self.check_stats( + fs, a_eps=10000, a_mu=10000, nonlin=30000, susc=30000, cond=30000 ) - fs = self.get_fragment_stats(mp.Vector3(), mp.Vector3(10, 10), 2, def_mat=mat, geom=[]) - self.check_stats(fs, a_eps=10000, a_mu=10000, nonlin=30000, susc=30000, cond=30000) def test_1d_cell_smaller_than_minimum_fragment_size(self): fs = self.get_fragment_stats(mp.Vector3(z=1), mp.Vector3(z=1), 1) @@ -314,7 +435,6 @@ def test_3d_cell_smaller_than_minimum_fragment_size(self): class TestPMLToVolList(unittest.TestCase): - def check1d(self, vol, expected_min, expected_max): min_vec = vol.get_min_corner() max_vec = vol.get_max_corner() @@ -367,7 +487,9 @@ def test_1d_high_side(self): self.check1d(v1[0], 4, 5) def test_1d_two_sides_different_thickness(self): - sim = make_sim(mp.Vector3(z=10), 10, [mp.PML(1, side=mp.High), mp.PML(2, side=mp.Low)], 1) + sim = make_sim( + mp.Vector3(z=10), 10, [mp.PML(1, side=mp.High), mp.PML(2, side=mp.Low)], 1 + ) v1, v2, v3 = sim._boundary_layers_to_vol_list(sim.boundary_layers) self.assertFalse(v2) @@ -401,7 +523,7 @@ def test_2d_all_sides_different_thickness_in_X(self): pmls = [ mp.PML(thickness=1, direction=mp.Y), mp.PML(thickness=3, direction=mp.X, side=mp.High), - mp.PML(thickness=2, direction=mp.X, side=mp.Low) + mp.PML(thickness=2, direction=mp.X, side=mp.Low), ] sim = make_sim(mp.Vector3(10, 10), 10, pmls, 2) v1, v2, v3 = sim._boundary_layers_to_vol_list(sim.boundary_layers) @@ -557,7 +679,6 @@ def test_cylindrical_all_directions_all_sides(self): @unittest.skipIf(mp.count_processors() != 2, "MPI specific test") class TestChunkCommunicationArea(unittest.TestCase): - def test_2d_periodic(self): sim = make_sim(mp.Vector3(10, 6), 10, [mp.PML(1)], 2, k_point=mp.Vector3(1, 1)) max_comm = sim.get_max_chunk_communication_area() @@ -566,7 +687,9 @@ def test_2d_periodic(self): self.assertEqual(avg_comm, 220) def test_3d_periodic(self): - sim = make_sim(mp.Vector3(10, 8, 6), 10, [mp.PML(1)], 3, k_point=mp.Vector3(1, 1, 1)) + sim = make_sim( + mp.Vector3(10, 8, 6), 10, [mp.PML(1)], 3, k_point=mp.Vector3(1, 1, 1) + ) max_comm = sim.get_max_chunk_communication_area() avg_comm = sim.get_avg_chunk_communication_area() self.assertEqual(max_comm, 2360) @@ -587,5 +710,5 @@ def test_3d(self): self.assertEqual(avg_comm, 480) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_gaussianbeam.py b/python/tests/test_gaussianbeam.py index 862543e7e..b3bf83c63 100644 --- a/python/tests/test_gaussianbeam.py +++ b/python/tests/test_gaussianbeam.py @@ -1,60 +1,86 @@ -import unittest -import meep as mp import math +import unittest + import numpy as np -class TestGaussianBeamSource(unittest.TestCase): +import meep as mp + +class TestGaussianBeamSource(unittest.TestCase): def gaussian_beam(self, rot_angle): s = 14 resolution = 25 dpml = 2 - cell_size = mp.Vector3(s,s) + cell_size = mp.Vector3(s, s) boundary_layers = [mp.PML(thickness=dpml)] - beam_x0 = mp.Vector3(0,7.0) # beam focus (relative to source center) - beam_kdir = mp.Vector3(0,1,0).rotate(mp.Vector3(0,0,1),math.radians(rot_angle)) # beam propagation direction + beam_x0 = mp.Vector3(0, 7.0) # beam focus (relative to source center) + beam_kdir = mp.Vector3(0, 1, 0).rotate( + mp.Vector3(0, 0, 1), math.radians(rot_angle) + ) # beam propagation direction beam_w0 = 0.8 # beam waist radius - beam_E0 = mp.Vector3(0,0,1) - beam_x0 = beam_x0.rotate(mp.Vector3(0,0,1),math.radians(rot_angle)) + beam_E0 = mp.Vector3(0, 0, 1) + beam_x0 = beam_x0.rotate(mp.Vector3(0, 0, 1), math.radians(rot_angle)) fcen = 1 src_x = 0 - src_y = -0.5*s+dpml+1.0 - sources = [mp.GaussianBeamSource(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - center=mp.Vector3(src_x,src_y), - size=mp.Vector3(s), - beam_x0=beam_x0, - beam_kdir=beam_kdir, - beam_w0=beam_w0, - beam_E0=beam_E0)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources) - - cell_dft_fields = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=mp.Vector3(), size=mp.Vector3(s-2*dpml,s-2*dpml)) + src_y = -0.5 * s + dpml + 1.0 + sources = [ + mp.GaussianBeamSource( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=mp.Vector3(src_x, src_y), + size=mp.Vector3(s), + beam_x0=beam_x0, + beam_kdir=beam_kdir, + beam_w0=beam_w0, + beam_E0=beam_E0, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + ) + + cell_dft_fields = sim.add_dft_fields( + [mp.Ez], + fcen, + 0, + 1, + center=mp.Vector3(), + size=mp.Vector3(s - 2 * dpml, s - 2 * dpml), + ) sim.run(until_after_sources=50) Ez_cell = sim.get_dft_array(cell_dft_fields, mp.Ez, 0) - [x,y,z,w] = sim.get_array_metadata(dft_cell=cell_dft_fields) + [x, y, z, w] = sim.get_array_metadata(dft_cell=cell_dft_fields) tol = 0.05 - idx_x = np.nonzero((np.squeeze(x) > (src_x+beam_x0.x-tol)) & (np.squeeze(x) < (src_x+beam_x0.x+tol))) - idx_y = np.nonzero((np.squeeze(y) > (src_y+beam_x0.y-tol)) & (np.squeeze(y) < (src_y+beam_x0.y+tol))) + idx_x = np.nonzero( + (np.squeeze(x) > (src_x + beam_x0.x - tol)) + & (np.squeeze(x) < (src_x + beam_x0.x + tol)) + ) + idx_y = np.nonzero( + (np.squeeze(y) > (src_y + beam_x0.y - tol)) + & (np.squeeze(y) < (src_y + beam_x0.y + tol)) + ) - Ez_beam_x0 = Ez_cell[np.squeeze(idx_x)[0],np.squeeze(idx_y)[0]] + Ez_beam_x0 = Ez_cell[np.squeeze(idx_x)[0], np.squeeze(idx_y)[0]] - frac = np.abs(Ez_beam_x0)**2 / np.amax(np.abs(Ez_cell)**2) - print("ratio of the Gaussian beam energy at the focus over the maximum beam energy for the entire cell: {}".format(frac)) + frac = np.abs(Ez_beam_x0) ** 2 / np.amax(np.abs(Ez_cell) ** 2) + print( + f"ratio of the Gaussian beam energy at the focus over the maximum beam energy for the entire cell: {frac}" + ) self.assertGreater(frac, 0.99) def test_gaussian_beam(self): self.gaussian_beam(-40) -if __name__ == '__main__': - unittest.main() + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_geom.py b/python/tests/test_geom.py index 4b2f3a974..7851a1675 100644 --- a/python/tests/test_geom.py +++ b/python/tests/test_geom.py @@ -1,9 +1,11 @@ import math import unittest import warnings + +import meep.geom as gm import numpy as np + import meep as mp -import meep.geom as gm def zeros(): @@ -15,7 +17,6 @@ def ones(): class TestGeom(unittest.TestCase): - def test_geometric_object_duplicates_x(self): rad = 1 s = mp.Sphere(rad) @@ -26,7 +27,7 @@ def test_geometric_object_duplicates_x(self): mp.Sphere(rad, center=mp.Vector3(x=4)), mp.Sphere(rad, center=mp.Vector3(x=3)), mp.Sphere(rad, center=mp.Vector3(x=2)), - mp.Sphere(rad, center=mp.Vector3(x=1)) + mp.Sphere(rad, center=mp.Vector3(x=1)), ] for r, e in zip(res, expected): @@ -71,9 +72,11 @@ def test_geometric_objects_duplicates(self): self.assertEqual(r.center, e.center) def test_geometric_objects_lattice_duplicates(self): - geometry_lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + geometry_lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) eps = 12 r = 0.2 @@ -97,15 +100,16 @@ def test_geometric_objects_lattice_duplicates(self): self.assertEqual(exp.radius, res.radius) def test_cartesian_to_lattice(self): - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) res = mp.cartesian_to_lattice(lattice.basis * mp.Vector3(1), lattice) self.assertEqual(res, mp.Vector3(1)) class TestSphere(unittest.TestCase): - def test_kwargs_passed_to_parent(self): s = gm.Sphere(1.0) self.assertEqual(s.material.epsilon_diag, ones()) @@ -124,9 +128,9 @@ def test_kwargs_passed_to_parent(self): def test_invalid_kwarg_raises_exception(self): with self.assertRaises(TypeError): - gm.Sphere(invalid='This is not allowed') + gm.Sphere(invalid="This is not allowed") with self.assertRaises(TypeError): - gm.Sphere(radius=1.0, oops='Nope') + gm.Sphere(radius=1.0, oops="Nope") def test_non_neg_radius_constructor(self): gm.Sphere(radius=0.0) @@ -186,7 +190,6 @@ def test_info(self): class TestCylinder(unittest.TestCase): - def test_non_neg_height_constructor(self): gm.Cylinder(radius=1.0, height=0.0) gm.Cylinder(radius=1.0, height=1.0) @@ -220,9 +223,9 @@ def test_wrong_type_exception(self): class TestWedge(unittest.TestCase): - def test_default_properties(self): import math + w = gm.Wedge(center=zeros(), radius=2.0, height=4.0, axis=gm.Vector3(0, 0, 1)) self.assertEqual(w.wedge_angle, 8 * math.atan(1)) @@ -232,37 +235,37 @@ def test_contains_point(self): class TestCone(unittest.TestCase): - def test_contains_point(self): c = gm.Cone(center=zeros(), radius=2.0, height=3.0, axis=gm.Vector3(0, 0, 1)) self.assertIn(gm.Vector3(0, 0, 1), c) class TestBlock(unittest.TestCase): - def test_contains_point(self): b = gm.Block(size=ones(), center=zeros()) self.assertIn(zeros(), b) class TestEllipsoid(unittest.TestCase): - def test_contains_point(self): e = gm.Ellipsoid(size=ones(), center=zeros()) self.assertIn(zeros(), e) class TestPrism(unittest.TestCase): - def test_contains_point(self): - vertices = [gm.Vector3(-1, 1), gm.Vector3(1, 1), gm.Vector3(1, -1), gm.Vector3(-1, -1)] + vertices = [ + gm.Vector3(-1, 1), + gm.Vector3(1, 1), + gm.Vector3(1, -1), + gm.Vector3(-1, -1), + ] p = gm.Prism(vertices, height=1) self.assertIn(zeros(), p) self.assertNotIn(gm.Vector3(2, 2), p) class TestMedium(unittest.TestCase): - def test_D_conductivity(self): m = gm.Medium(D_conductivity=2) self.assertEqual(m.D_conductivity_diag.x, 2) @@ -326,50 +329,82 @@ def check_warnings(sim, should_warn=True): for s in invalid_sources: # Check for invalid extra_materials - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=s, extra_materials=[mat]) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=s, + extra_materials=[mat], + ) check_warnings(sim) # Check for invalid geometry materials - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=s, geometry=geom) + sim = mp.Simulation( + cell_size=cell_size, resolution=resolution, sources=s, geometry=geom + ) check_warnings(sim) valid_sources = [ [mp.Source(mp.GaussianSource(15, fwidth=1), mp.Ez, mp.Vector3())], - [mp.Source(mp.ContinuousSource(15, width=5), mp.Ez, mp.Vector3())] + [mp.Source(mp.ContinuousSource(15, width=5), mp.Ez, mp.Vector3())], ] for s in valid_sources: - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=s, extra_materials=[mat]) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=s, + extra_materials=[mat], + ) check_warnings(sim, False) # Check DFT frequencies # Invalid extra_materials - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=valid_sources[0], - extra_materials=[mat]) - fregion = mp.FluxRegion(center=mp.Vector3(0, 1), size=mp.Vector3(2, 2), direction=mp.X) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=valid_sources[0], + extra_materials=[mat], + ) + fregion = mp.FluxRegion( + center=mp.Vector3(0, 1), size=mp.Vector3(2, 2), direction=mp.X + ) sim.add_flux(18, 6, 2, fregion, decimation_factor=1) check_warnings(sim) # Invalid geometry material - sim = mp.Simulation(cell_size=cell_size, resolution=resolution, sources=valid_sources[0], geometry=geom) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + sources=valid_sources[0], + geometry=geom, + ) sim.add_flux(18, 6, 2, fregion, decimation_factor=1) check_warnings(sim) def test_transform(self): - e_sus = [mp.LorentzianSusceptibility(sigma_diag=mp.Vector3(1, 2, 3), - sigma_offdiag=mp.Vector3(12, 13, 14)), - mp.DrudeSusceptibility(sigma_diag=mp.Vector3(1, 2, 3), - sigma_offdiag=mp.Vector3(12, 13, 14))] + e_sus = [ + mp.LorentzianSusceptibility( + sigma_diag=mp.Vector3(1, 2, 3), sigma_offdiag=mp.Vector3(12, 13, 14) + ), + mp.DrudeSusceptibility( + sigma_diag=mp.Vector3(1, 2, 3), sigma_offdiag=mp.Vector3(12, 13, 14) + ), + ] - mat = mp.Medium(epsilon_diag=mp.Vector3(1, 2, 3), epsilon_offdiag=mp.Vector3(12, 13, 14), - E_susceptibilities=e_sus) + mat = mp.Medium( + epsilon_diag=mp.Vector3(1, 2, 3), + epsilon_offdiag=mp.Vector3(12, 13, 14), + E_susceptibilities=e_sus, + ) rot_angle = math.radians(23.9) - rot_matrix = mp.Matrix(mp.Vector3(math.cos(rot_angle), math.sin(rot_angle), 0), - mp.Vector3(-math.sin(rot_angle), math.cos(rot_angle), 0), - mp.Vector3(0, 0, 1)) + rot_matrix = mp.Matrix( + mp.Vector3(math.cos(rot_angle), math.sin(rot_angle), 0), + mp.Vector3(-math.sin(rot_angle), math.cos(rot_angle), 0), + mp.Vector3(0, 0, 1), + ) mat.transform(rot_matrix) expected_diag = mp.Vector3(-7.72552, 10.72552, 3) @@ -380,14 +415,21 @@ def test_transform(self): self.assertEqual(mat.mu_diag, mp.Vector3(1, 1, 1)) self.assertEqual(mat.mu_offdiag, mp.Vector3()) self.assertEqual(len(mat.E_susceptibilities), 2) - self.assertTrue(mat.E_susceptibilities[0].sigma_diag.close(expected_diag, tol=4)) - self.assertTrue(mat.E_susceptibilities[0].sigma_offdiag.close(expected_offdiag, tol=4)) - self.assertTrue(mat.E_susceptibilities[1].sigma_diag.close(expected_diag, tol=4)) - self.assertTrue(mat.E_susceptibilities[1].sigma_offdiag.close(expected_offdiag, tol=4)) + self.assertTrue( + mat.E_susceptibilities[0].sigma_diag.close(expected_diag, tol=4) + ) + self.assertTrue( + mat.E_susceptibilities[0].sigma_offdiag.close(expected_offdiag, tol=4) + ) + self.assertTrue( + mat.E_susceptibilities[1].sigma_diag.close(expected_diag, tol=4) + ) + self.assertTrue( + mat.E_susceptibilities[1].sigma_offdiag.close(expected_offdiag, tol=4) + ) class TestVector3(unittest.TestCase): - def test_use_as_numpy_array(self): v = gm.Vector3(10, 10, 10) res = np.add(v, np.array([10, 10, 10])) @@ -439,14 +481,18 @@ def test_rotate_lattice(self): v = mp.Vector3(2, 2, 2) lattice = mp.Lattice(size=mp.Vector3(1, 1)) res = v.rotate_lattice(axis, 3, lattice) - self.assertTrue(res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563))) + self.assertTrue( + res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563)) + ) def test_rotate_reciprocal(self): axis = mp.Vector3(1) v = mp.Vector3(2, 2, 2) lattice = mp.Lattice(size=mp.Vector3(1, 1)) res = v.rotate_reciprocal(axis, 3, lattice) - self.assertTrue(res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563))) + self.assertTrue( + res.close(mp.Vector3(2.0, -2.262225009320625, -1.6977449770811563)) + ) def test_complex_norm(self): # issue #722 @@ -455,15 +501,18 @@ def test_complex_norm(self): class TestLattice(unittest.TestCase): - def test_basis(self): - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) b = lattice.basis - exp = mp.Matrix(mp.Vector3(0.8660254037844388, 0.5000000000000001), - mp.Vector3(0.8660254037844388, -0.5000000000000001), - mp.Vector3(z=1.0)) + exp = mp.Matrix( + mp.Vector3(0.8660254037844388, 0.5000000000000001), + mp.Vector3(0.8660254037844388, -0.5000000000000001), + mp.Vector3(z=1.0), + ) for e, r in zip([exp.c1, exp.c2, exp.c3], [b.c1, b.c2, b.c3]): self.assertTrue(e.close(r)) @@ -493,16 +542,14 @@ def test_row(self): self.assertEqual(self.identity.row(2), self.identity.c3) def test_mm_mult(self): - m1 = mp.Matrix(mp.Vector3(1, 2, 3), - mp.Vector3(4, 5, 6), - mp.Vector3(7, 8, 9)) - m2 = mp.Matrix(mp.Vector3(9, 8, 7), - mp.Vector3(6, 5, 4), - mp.Vector3(3, 2, 1)) + m1 = mp.Matrix(mp.Vector3(1, 2, 3), mp.Vector3(4, 5, 6), mp.Vector3(7, 8, 9)) + m2 = mp.Matrix(mp.Vector3(9, 8, 7), mp.Vector3(6, 5, 4), mp.Vector3(3, 2, 1)) res = m1 * m2 - exp = mp.Matrix(mp.Vector3(90.0, 114.0, 138.0), - mp.Vector3(54.0, 69.0, 84.0), - mp.Vector3(18.0, 24.0, 30.0)) + exp = mp.Matrix( + mp.Vector3(90.0, 114.0, 138.0), + mp.Vector3(54.0, 69.0, 84.0), + mp.Vector3(18.0, 24.0, 30.0), + ) self.matrix_eq(exp, res) @@ -520,91 +567,105 @@ def test_sub(self): self.assertEqual(result.row(2), zeros()) def test_mv_mult(self): - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) res = lattice.basis * mp.Vector3(1) exp = mp.Vector3(0.8660254037844388, 0.5000000000000001) self.assertTrue(res.close(exp)) def test_scale(self): - m = mp.Matrix(mp.Vector3(90.0, 114.0, 138.0), - mp.Vector3(54.0, 69.0, 84.0), - mp.Vector3(18.0, 24.0, 30.0)) + m = mp.Matrix( + mp.Vector3(90.0, 114.0, 138.0), + mp.Vector3(54.0, 69.0, 84.0), + mp.Vector3(18.0, 24.0, 30.0), + ) res = m.scale(0.5) - exp = mp.Matrix(mp.Vector3(45.0, 57.0, 69.0), - mp.Vector3(27.0, 34.5, 42.0), - mp.Vector3(9.0, 12.0, 15.0)) + exp = mp.Matrix( + mp.Vector3(45.0, 57.0, 69.0), + mp.Vector3(27.0, 34.5, 42.0), + mp.Vector3(9.0, 12.0, 15.0), + ) self.matrix_eq(exp, res) self.matrix_eq(exp, m * 0.5) self.matrix_eq(exp, 0.5 * m) def test_determinant(self): - m = mp.Matrix(mp.Vector3(2), - mp.Vector3(y=2), - mp.Vector3(z=2)) + m = mp.Matrix(mp.Vector3(2), mp.Vector3(y=2), mp.Vector3(z=2)) - m1 = mp.Matrix(mp.Vector3(1, 2, 3), - mp.Vector3(4, 5, 6), - mp.Vector3(7, 8, 9)) + m1 = mp.Matrix(mp.Vector3(1, 2, 3), mp.Vector3(4, 5, 6), mp.Vector3(7, 8, 9)) self.assertEqual(8, m.determinant()) self.assertEqual(0, m1.determinant()) def test_transpose(self): - m = mp.Matrix(mp.Vector3(1, 2, 3), - mp.Vector3(4, 5, 6), - mp.Vector3(7, 8, 9)) - exp = mp.Matrix(mp.Vector3(1, 4, 7), - mp.Vector3(2, 5, 8), - mp.Vector3(3, 6, 9)) + m = mp.Matrix(mp.Vector3(1, 2, 3), mp.Vector3(4, 5, 6), mp.Vector3(7, 8, 9)) + exp = mp.Matrix(mp.Vector3(1, 4, 7), mp.Vector3(2, 5, 8), mp.Vector3(3, 6, 9)) self.matrix_eq(exp, m.transpose()) def test_inverse(self): self.matrix_eq(self.identity, self.identity.inverse()) - lattice = mp.Lattice(size=mp.Vector3(1, 7), - basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5)) + lattice = mp.Lattice( + size=mp.Vector3(1, 7), + basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), + ) res = lattice.basis.inverse() - exp = mp.Matrix(mp.Vector3(0.5773502691896256, 0.5773502691896256, -0.0), - mp.Vector3(0.9999999999999998, -0.9999999999999998, -0.0), - mp.Vector3(-0.0, -0.0, 1.0)) + exp = mp.Matrix( + mp.Vector3(0.5773502691896256, 0.5773502691896256, -0.0), + mp.Vector3(0.9999999999999998, -0.9999999999999998, -0.0), + mp.Vector3(-0.0, -0.0, 1.0), + ) self.matrix_close(exp, res) def test_get_rotation_matrix(self): result = mp.get_rotation_matrix(ones(), 5) - self.assertTrue(result.c1.close(mp.Vector3(0.5224414569754843, -0.3148559165969717, 0.7924144596214877))) - self.assertTrue(result.c2.close(mp.Vector3(0.7924144596214877, 0.5224414569754843, -0.3148559165969717))) - self.assertTrue(result.c3.close(mp.Vector3(-0.3148559165969717, 0.7924144596214877, 0.5224414569754843))) + self.assertTrue( + result.c1.close( + mp.Vector3(0.5224414569754843, -0.3148559165969717, 0.7924144596214877) + ) + ) + self.assertTrue( + result.c2.close( + mp.Vector3(0.7924144596214877, 0.5224414569754843, -0.3148559165969717) + ) + ) + self.assertTrue( + result.c3.close( + mp.Vector3(-0.3148559165969717, 0.7924144596214877, 0.5224414569754843) + ) + ) def test_conj(self): - m = mp.Matrix(mp.Vector3(x=1+1j), mp.Vector3(y=1+1j), mp.Vector3(z=1+1j)) + m = mp.Matrix(mp.Vector3(x=1 + 1j), mp.Vector3(y=1 + 1j), mp.Vector3(z=1 + 1j)) result = m.conj() - self.assertEqual(result.c1, mp.Vector3(x=1-1j)) - self.assertEqual(result.c2, mp.Vector3(y=1-1j)) - self.assertEqual(result.c3, mp.Vector3(z=1-1j)) + self.assertEqual(result.c1, mp.Vector3(x=1 - 1j)) + self.assertEqual(result.c2, mp.Vector3(y=1 - 1j)) + self.assertEqual(result.c3, mp.Vector3(z=1 - 1j)) def test_adjoint(self): - m = mp.Matrix(mp.Vector3(1+1j), mp.Vector3(1+1j), mp.Vector3(1+1j)) + m = mp.Matrix(mp.Vector3(1 + 1j), mp.Vector3(1 + 1j), mp.Vector3(1 + 1j)) getH_result = m.getH() H_result = m.H - self.assertEqual(getH_result.c1, mp.Vector3(1-1j, 1-1j, 1-1j)) + self.assertEqual(getH_result.c1, mp.Vector3(1 - 1j, 1 - 1j, 1 - 1j)) self.assertEqual(getH_result.c2, mp.Vector3()) self.assertEqual(getH_result.c3, mp.Vector3()) np.testing.assert_allclose(getH_result, H_result) def test_to_numpy_array(self): - m = mp.Matrix(mp.Vector3(1+1j), mp.Vector3(1+1j), mp.Vector3(1+1j)) + m = mp.Matrix(mp.Vector3(1 + 1j), mp.Vector3(1 + 1j), mp.Vector3(1 + 1j)) adjoint = m.H m_arr = np.array(m) np_adjoint = m_arr.conj().transpose() np.testing.assert_allclose(adjoint, np_adjoint) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_get_epsilon_grid.py b/python/tests/test_get_epsilon_grid.py index 4e4d80822..b6c5f71ce 100644 --- a/python/tests/test_get_epsilon_grid.py +++ b/python/tests/test_get_epsilon_grid.py @@ -1,77 +1,97 @@ import unittest -import parameterized + +import meep.adjoint as mpa import numpy as np +import parameterized +from meep.materials import Co, SiN + import meep as mp -try: - import meep.adjoint as mpa -except: - import adjoint as mpa -from meep.materials import SiN, Co -class TestGetEpsilonGrid(unittest.TestCase): +class TestGetEpsilonGrid(unittest.TestCase): def setUp(self): resolution = 60 - self.cell_size = mp.Vector3(1.0,1.0,0) + self.cell_size = mp.Vector3(1.0, 1.0, 0) matgrid_resolution = 200 - matgrid_size = mp.Vector3(1.0,1.0,mp.inf) - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) - x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) - y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) - xv, yv = np.meshgrid(x,y) + matgrid_size = mp.Vector3(1.0, 1.0, mp.inf) + Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( + matgrid_size.y * matgrid_resolution + ) + x = np.linspace(-0.5 * matgrid_size.x, 0.5 * matgrid_size.x, Nx) + y = np.linspace(-0.5 * matgrid_size.y, 0.5 * matgrid_size.y, Ny) + xv, yv = np.meshgrid(x, y) rad = 0.201943 w = 0.104283 - weights = np.logical_and(np.sqrt(np.square(xv) + np.square(yv)) > rad, - np.sqrt(np.square(xv) + np.square(yv)) < rad+w, - dtype=np.double) + weights = np.logical_and( + np.sqrt(np.square(xv) + np.square(yv)) > rad, + np.sqrt(np.square(xv) + np.square(yv)) < rad + w, + dtype=np.double, + ) - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=weights, - do_averaging=False, - beta=0, - eta=0.5) + matgrid = mp.MaterialGrid( + mp.Vector3(Nx, Ny), + mp.air, + mp.Medium(index=3.5), + weights=weights, + do_averaging=False, + beta=0, + eta=0.5, + ) - geometry = [mp.Cylinder(center=mp.Vector3(0.35,0.1), - radius=0.1, - height=mp.inf, - material=mp.Medium(index=1.5)), - mp.Block(center=mp.Vector3(-0.15,-0.2), - size=mp.Vector3(0.2,0.24,mp.inf), - material=SiN), - mp.Block(center=mp.Vector3(-0.2,0.2), - size=mp.Vector3(0.4,0.4,mp.inf), - material=matgrid), - mp.Prism(vertices=[mp.Vector3(0.05,0.45), - mp.Vector3(0.32,0.22), - mp.Vector3(0.15,0.10)], - height=0.5, - material=Co)] + geometry = [ + mp.Cylinder( + center=mp.Vector3(0.35, 0.1), + radius=0.1, + height=mp.inf, + material=mp.Medium(index=1.5), + ), + mp.Block( + center=mp.Vector3(-0.15, -0.2), + size=mp.Vector3(0.2, 0.24, mp.inf), + material=SiN, + ), + mp.Block( + center=mp.Vector3(-0.2, 0.2), + size=mp.Vector3(0.4, 0.4, mp.inf), + material=matgrid, + ), + mp.Prism( + vertices=[ + mp.Vector3(0.05, 0.45), + mp.Vector3(0.32, 0.22), + mp.Vector3(0.15, 0.10), + ], + height=0.5, + material=Co, + ), + ] - self.sim = mp.Simulation(resolution=resolution, - cell_size=self.cell_size, - geometry=geometry, - eps_averaging=False) + self.sim = mp.Simulation( + resolution=resolution, + cell_size=self.cell_size, + geometry=geometry, + eps_averaging=False, + ) self.sim.init_sim() - @parameterized.parameterized.expand([ - (mp.Vector3(0.2,0.2), 1.1), - (mp.Vector3(-0.2,0.1), 0.7), - (mp.Vector3(-0.2,-0.25), 0.55), - (mp.Vector3(0.4,0.1), 0) - ]) + @parameterized.parameterized.expand( + [ + (mp.Vector3(0.2, 0.2), 1.1), + (mp.Vector3(-0.2, 0.1), 0.7), + (mp.Vector3(-0.2, -0.25), 0.55), + (mp.Vector3(0.4, 0.1), 0), + ] + ) def test_get_epsilon_grid(self, pt, freq): - eps_grid = self.sim.get_epsilon_grid(np.array([pt.x]), - np.array([pt.y]), - np.array([0]), - freq) + eps_grid = self.sim.get_epsilon_grid( + np.array([pt.x]), np.array([pt.y]), np.array([0]), freq + ) eps_pt = self.sim.get_epsilon_point(pt, freq) - print("eps:, ({},{}), {}, {}".format(pt.x,pt.y,eps_grid,eps_pt)) + print(f"eps:, ({pt.x},{pt.y}), {eps_grid}, {eps_pt}") self.assertAlmostEqual(np.real(eps_grid), np.real(eps_pt), places=6) self.assertAlmostEqual(np.imag(eps_grid), np.imag(eps_pt), places=6) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_get_point.py b/python/tests/test_get_point.py index 154336d92..7884aa9f6 100644 --- a/python/tests/test_get_point.py +++ b/python/tests/test_get_point.py @@ -1,107 +1,121 @@ -import meep as mp +import math import unittest + import numpy as np -import math +import meep as mp -class TestGetPoint(unittest.TestCase): +class TestGetPoint(unittest.TestCase): def test_get_point(self): - sxy = 6 # cell size - dpml = 1 # thickness of PML + sxy = 6 # cell size + dpml = 1 # thickness of PML def sinusoid(p): - r = (p.x**2+p.y**2)**0.5 - return mp.Medium(index=1.0+math.sin(2*math.pi*r)**2) + r = (p.x**2 + p.y**2) ** 0.5 + return mp.Medium(index=1.0 + math.sin(2 * math.pi * r) ** 2) - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(sxy,sxy), - material=sinusoid)] + geometry = [ + mp.Block(center=mp.Vector3(), size=mp.Vector3(sxy, sxy), material=sinusoid) + ] - src = [mp.Source(mp.GaussianSource(1.0, fwidth=0.1), - component=mp.Ez, - center=mp.Vector3())] + src = [ + mp.Source( + mp.GaussianSource(1.0, fwidth=0.1), component=mp.Ez, center=mp.Vector3() + ) + ] - sim = mp.Simulation(cell_size=mp.Vector3(sxy,sxy), - geometry=geometry, - sources=src, - k_point=mp.Vector3(), - resolution=20, - symmetries=[mp.Mirror(mp.X),mp.Mirror(mp.Y)], - boundary_layers=[mp.PML(dpml)]) + sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=geometry, + sources=src, + k_point=mp.Vector3(), + resolution=20, + symmetries=[mp.Mirror(mp.X), mp.Mirror(mp.Y)], + boundary_layers=[mp.PML(dpml)], + ) sim.run(until_after_sources=100) ## reference values for Ez and epsilon from serial run - ez_ref = [ -0.0002065983, - -0.0001954795, - -0.0000453570, - 0.0000311267, - -0.0000121473, - -0.0000410032, - -0.0000341301, - -0.0000275021, - -0.0000397990, - -0.0000351730, - 0.0000079602, - 0.0000227437, - -0.0001092821, - -0.0002202751, - -0.0001408186, - 0.0006325076, - 0.0024890489, - 0.0027476069, - 0.0014815873, - 0.0004714913, - -0.0004332029, - -0.0007101315, - -0.0003818581, - -0.0000748507, - 0.0001408819, - 0.0001119776, - 0.0000395008, - 0.0000078844, - -0.0000010431 ] + ez_ref = [ + -0.0002065983, + -0.0001954795, + -0.0000453570, + 0.0000311267, + -0.0000121473, + -0.0000410032, + -0.0000341301, + -0.0000275021, + -0.0000397990, + -0.0000351730, + 0.0000079602, + 0.0000227437, + -0.0001092821, + -0.0002202751, + -0.0001408186, + 0.0006325076, + 0.0024890489, + 0.0027476069, + 0.0014815873, + 0.0004714913, + -0.0004332029, + -0.0007101315, + -0.0003818581, + -0.0000748507, + 0.0001408819, + 0.0001119776, + 0.0000395008, + 0.0000078844, + -0.0000010431, + ] - eps_ref = [ 1.6458346134, - 1.2752837068, - 1.0974010956, - 1.0398089537, - 1.0465784716, - 1.0779924737, - 1.1059439286, - 1.1135579291, - 1.0971979186, - 1.0653178566, - 1.0391657283, - 1.0513779677, - 1.1466009312, - 1.3882154483, - 1.8496939317, - 2.5617731415, - 3.3788212533, - 3.9019494270, - 3.6743431894, - 2.7285622651, - 1.6635165033, - 1.0891237010, - 1.1485969863, - 1.9498398061, - 3.3100416367, - 3.9038800599, - 2.8471862395, - 1.4742605488, - 1.0370162714 ] + eps_ref = [ + 1.6458346134, + 1.2752837068, + 1.0974010956, + 1.0398089537, + 1.0465784716, + 1.0779924737, + 1.1059439286, + 1.1135579291, + 1.0971979186, + 1.0653178566, + 1.0391657283, + 1.0513779677, + 1.1466009312, + 1.3882154483, + 1.8496939317, + 2.5617731415, + 3.3788212533, + 3.9019494270, + 3.6743431894, + 2.7285622651, + 1.6635165033, + 1.0891237010, + 1.1485969863, + 1.9498398061, + 3.3100416367, + 3.9038800599, + 2.8471862395, + 1.4742605488, + 1.0370162714, + ] - x = np.linspace(-0.865692,2.692867,29) + x = np.linspace(-0.865692, 2.692867, 29) places = 5 if mp.is_single_precision() else 10 for j in range(x.size): - self.assertAlmostEqual(np.real(sim.get_field_point(mp.Ez, mp.Vector3(x[j],-0.394862))), - ez_ref[j], - places=places) - self.assertAlmostEqual(sim.get_epsilon_point(mp.Vector3(x[j],2.967158)), - eps_ref[j], - places=places) + self.assertAlmostEqual( + np.real(sim.get_field_point(mp.Ez, mp.Vector3(x[j], -0.394862))), + ez_ref[j], + places=places, + ) + self.assertAlmostEqual( + sim.get_epsilon_point(mp.Vector3(x[j], 2.967158)), + eps_ref[j], + places=places, + ) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_holey_wvg_bands.py b/python/tests/test_holey_wvg_bands.py index 1e51bef6b..73c6474a9 100644 --- a/python/tests/test_holey_wvg_bands.py +++ b/python/tests/test_holey_wvg_bands.py @@ -1,12 +1,14 @@ -import meep as mp import unittest +import meep as mp -class TestHoleyWvgBands(unittest.TestCase): +class TestHoleyWvgBands(unittest.TestCase): def setUp(self): cell = mp.Vector3(1, 12) - b = mp.Block(size=mp.Vector3(mp.inf, 1.2, mp.inf), material=mp.Medium(epsilon=13)) + b = mp.Block( + size=mp.Vector3(mp.inf, 1.2, mp.inf), material=mp.Medium(epsilon=13) + ) c = mp.Cylinder(0.36) self.fcen = 0.25 @@ -15,7 +17,7 @@ def setUp(self): s = mp.Source( src=mp.GaussianSource(self.fcen, fwidth=self.df), component=mp.Hz, - center=mp.Vector3(0.1234) + center=mp.Vector3(0.1234), ) sym = mp.Mirror(direction=mp.Y, phase=-1) @@ -26,20 +28,22 @@ def setUp(self): sources=[s], symmetries=[sym], boundary_layers=[mp.PML(1, direction=mp.Y)], - resolution=20 + resolution=20, ) def test_run_k_points(self): - all_freqs = self.sim.run_k_points(5, mp.interpolate(19, [mp.Vector3(), mp.Vector3(0.5)])) + all_freqs = self.sim.run_k_points( + 5, mp.interpolate(19, [mp.Vector3(), mp.Vector3(0.5)]) + ) expected = [ (0.1942497850393511, 0.001381460274205755), (0.19782709203322993, -0.0013233828667934015), (0.1927618763491877, 0.001034260690735336), - (0.19335527231544278, 4.6649450258959025e-4) + (0.19335527231544278, 4.6649450258959025e-4), ] - self.assertTrue(any(l for l in all_freqs)) + self.assertTrue(any(all_freqs)) for (r, i), f in zip(expected, all_freqs[17:21][0]): self.assertAlmostEqual(r, f.real) self.assertAlmostEqual(i, f.imag) @@ -71,5 +75,5 @@ def test_fields_at_kx(self): self.assertAlmostEqual(m.decay, i, places=places) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_holey_wvg_cavity.py b/python/tests/test_holey_wvg_cavity.py index a66998d28..29333b579 100644 --- a/python/tests/test_holey_wvg_cavity.py +++ b/python/tests/test_holey_wvg_cavity.py @@ -1,10 +1,11 @@ -import meep as mp -from utils import ApproxComparisonTestCase import unittest +from utils import ApproxComparisonTestCase -class TestHoleyWvgCavity(ApproxComparisonTestCase): +import meep as mp + +class TestHoleyWvgCavity(ApproxComparisonTestCase): def setUp(self): eps = 13 self.w = 1.2 @@ -21,22 +22,23 @@ def setUp(self): cell = mp.Vector3(self.sx, sy, 0) - blk = mp.Block(size=mp.Vector3(mp.inf, self.w, mp.inf), - material=mp.Medium(epsilon=eps)) + blk = mp.Block( + size=mp.Vector3(mp.inf, self.w, mp.inf), material=mp.Medium(epsilon=eps) + ) geometry = [blk] - for i in range(3): - geometry.append(mp.Cylinder(r, center=mp.Vector3(d / 2 + i))) - + geometry.extend(mp.Cylinder(r, center=mp.Vector3(d / 2 + i)) for i in range(3)) for i in range(3): geometry.append(mp.Cylinder(r, center=mp.Vector3(d / -2 - i))) - self.sim = mp.Simulation(cell_size=cell, - geometry=geometry, - sources=[], - boundary_layers=[mp.PML(self.dpml)], - resolution=20) + self.sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + sources=[], + boundary_layers=[mp.PML(self.dpml)], + resolution=20, + ) @classmethod def setUpClass(cls): @@ -47,17 +49,19 @@ def tearDownClass(cls): mp.delete_directory(cls.temp_dir) def test_resonant_modes(self): - self.sim.sources = [mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), - mp.Hz, mp.Vector3())] + self.sim.sources = [ + mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), mp.Hz, mp.Vector3()) + ] - self.sim.symmetries = [mp.Mirror(mp.Y, phase=-1), - mp.Mirror(mp.X, phase=-1)] + self.sim.symmetries = [mp.Mirror(mp.Y, phase=-1), mp.Mirror(mp.X, phase=-1)] self.sim.use_output_directory(self.temp_dir) h = mp.Harminv(mp.Hz, mp.Vector3(), self.fcen, self.df) - self.sim.run(mp.at_beginning(mp.output_epsilon), - mp.after_sources(h), - until_after_sources=400) + self.sim.run( + mp.at_beginning(mp.output_epsilon), + mp.after_sources(h), + until_after_sources=400, + ) expected = [ 0.23445415346009466, @@ -125,21 +129,29 @@ def test_transmission_spectrum(self): ] self.sim.sources = [ - mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), mp.Ey, - mp.Vector3(self.dpml + (-0.5 * self.sx)), size=mp.Vector3(0, self.w)) + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + mp.Ey, + mp.Vector3(self.dpml + (-0.5 * self.sx)), + size=mp.Vector3(0, self.w), + ) ] self.sim.symmetries = [mp.Mirror(mp.Y, phase=-1)] - freg = mp.FluxRegion(center=mp.Vector3((0.5 * self.sx) - self.dpml - 0.5), - size=mp.Vector3(0, 2 * self.w)) + freg = mp.FluxRegion( + center=mp.Vector3((0.5 * self.sx) - self.dpml - 0.5), + size=mp.Vector3(0, 2 * self.w), + ) - trans = self.sim.add_flux(self.fcen, self.df, self.nfreq, freg, - decimation_factor=1) + trans = self.sim.add_flux( + self.fcen, self.df, self.nfreq, freg, decimation_factor=1 + ) self.sim.run( until_after_sources=mp.stop_when_fields_decayed( - 50, mp.Ey, mp.Vector3((0.5 * self.sx) - self.dpml - 0.5, 0), 1e-1) + 50, mp.Ey, mp.Vector3((0.5 * self.sx) - self.dpml - 0.5, 0), 1e-1 + ) ) res = zip(mp.get_flux_freqs(trans), mp.get_fluxes(trans)) @@ -149,6 +161,5 @@ def test_transmission_spectrum(self): self.assertClose(e, r, epsilon=tol) - -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_integrated_source.py b/python/tests/test_integrated_source.py index 61717090b..67246bf07 100644 --- a/python/tests/test_integrated_source.py +++ b/python/tests/test_integrated_source.py @@ -1,24 +1,32 @@ import unittest -import meep as mp + import numpy as np +import meep as mp + # Test that is_integrated=True source correctly generates planewaves # for sources extending into the PML, as in this tutorial: # https://meep.readthedocs.io/en/latest/Perfectly_Matched_Layer/#planewave-sources-extending-into-pml # Regression test for issue #2043. -class TestIntegratedSource(unittest.TestCase): +class TestIntegratedSource(unittest.TestCase): def test_integrated_source(self): - sources = [mp.Source(mp.ContinuousSource(1,is_integrated=True), - center=mp.Vector3(-2), - size=mp.Vector3(y=6), - component=mp.Ez)] - sim = mp.Simulation(resolution=20, - cell_size=(6,6), - boundary_layers=[mp.PML(thickness=1)], - sources=sources, - k_point=mp.Vector3()) + sources = [ + mp.Source( + mp.ContinuousSource(1, is_integrated=True), + center=mp.Vector3(-2), + size=mp.Vector3(y=6), + component=mp.Ez, + ) + ] + sim = mp.Simulation( + resolution=20, + cell_size=(6, 6), + boundary_layers=[mp.PML(thickness=1)], + sources=sources, + k_point=mp.Vector3(), + ) sim.run(until=30) # field in mid-plane should be nearly constant, @@ -26,7 +34,8 @@ def test_integrated_source(self): ez = sim.get_array(mp.Ez, center=mp.Vector3(2), size=mp.Vector3(y=6)) std = np.std(ez) / np.sqrt(np.mean(ez**2)) print("std = ", std) - self.assertAlmostEqual(std, 0.0, places = 4 if mp.is_single_precision() else 8) + self.assertAlmostEqual(std, 0.0, places=4 if mp.is_single_precision() else 8) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_kdom.py b/python/tests/test_kdom.py index 0711831c1..749915459 100644 --- a/python/tests/test_kdom.py +++ b/python/tests/test_kdom.py @@ -1,51 +1,61 @@ +import math import unittest + import meep as mp -import math + class TestKdom(unittest.TestCase): + def run_kdom(self, theta, num_band): + + resolution = 20 # pixels/um + + sx = 5 + sy = 10 + cell_size = mp.Vector3(sx, sy, 0) + + fcen = 1 # center frequency (wavelength = 1 um) + ng = 1.5 + glass = mp.Medium(index=ng) + + # angle of incident planewave; CCW about Y axis, 0 degrees along +X axis + theta_in = math.radians(theta) + + # k (in source medium) with correct length (plane of incidence: XY) + k = mp.Vector3(math.cos(theta_in), math.sin(theta_in), 0).scale(fcen * ng) + + symmetries = [] + eig_parity = mp.ODD_Z + if theta_in == 0: + k = mp.Vector3(0, 0, 0) + eig_parity += mp.EVEN_Y + symmetries = [mp.Mirror(mp.Y)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + k_point=k, + symmetries=symmetries, + default_material=glass, + ) + + sim.init_sim() + + EigenmodeData = sim.get_eigenmode( + fcen, + mp.X, + mp.Volume(center=mp.Vector3(0.3 * sx, 0, 0), size=mp.Vector3(0, sy, 0)), + num_band, + k, + parity=eig_parity, + ) + kdom = EigenmodeData.kdom + + self.assertAlmostEqual(k.y, kdom.y, places=15) + + def test_kdom(self): + self.run_kdom(10.7, 6) + self.run_kdom(22.9, 12) + - def run_kdom(self, theta, num_band): - - resolution = 20 # pixels/um - - sx = 5 - sy = 10 - cell_size = mp.Vector3(sx,sy,0) - - fcen = 1 # center frequency (wavelength = 1 um) - ng = 1.5 - glass = mp.Medium(index=ng) - - # angle of incident planewave; CCW about Y axis, 0 degrees along +X axis - theta_in = math.radians(theta) - - # k (in source medium) with correct length (plane of incidence: XY) - k = mp.Vector3(math.cos(theta_in),math.sin(theta_in),0).scale(fcen*ng) - - symmetries = [] - eig_parity = mp.ODD_Z - if theta_in == 0: - k = mp.Vector3(0,0,0) - eig_parity += mp.EVEN_Y - symmetries = [mp.Mirror(mp.Y)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - k_point=k, - symmetries=symmetries, - default_material=glass) - - sim.init_sim() - - EigenmodeData = sim.get_eigenmode(fcen, mp.X, mp.Volume(center=mp.Vector3(0.3*sx,0,0), size=mp.Vector3(0,sy,0)), - num_band, k, parity=eig_parity) - kdom = EigenmodeData.kdom - - self.assertAlmostEqual(k.y,kdom.y,places=15) - - def test_kdom(self): - self.run_kdom(10.7, 6) - self.run_kdom(22.9, 12) - -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_ldos.py b/python/tests/test_ldos.py index 9807ad6f8..51e9224bc 100644 --- a/python/tests/test_ldos.py +++ b/python/tests/test_ldos.py @@ -1,5 +1,7 @@ import unittest + import numpy as np + import meep as mp # Computes the Purcell enhancement factor of a parallel dipole in a planar @@ -9,184 +11,214 @@ class TestLDOS(unittest.TestCase): - @classmethod def setUp(cls): cls.resolution = 25 # pixels/μm - cls.dpml = 0.5 # thickness of PML - cls.L = 6.0 # length of non-PML region - cls.n = 2.4 # refractive index of surrounding medium - cls.wvl = 1.0 # wavelength (in vacuum) - - cls.fcen = 1/cls.wvl + cls.dpml = 0.5 # thickness of PML + cls.L = 6.0 # length of non-PML region + cls.n = 2.4 # refractive index of surrounding medium + cls.wvl = 1.0 # wavelength (in vacuum) + cls.fcen = 1 / cls.wvl def bulk_ldos_cyl(self): - sr = self.L+self.dpml - sz = self.L+2*self.dpml - cell_size = mp.Vector3(sr,0,sz) + sr = self.L + self.dpml + sz = self.L + 2 * self.dpml + cell_size = mp.Vector3(sr, 0, sz) pml_layers = [mp.PML(self.dpml)] - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(self.fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(self.fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Er, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - - def cavity_ldos_cyl(self,sz): - sr = self.L+self.dpml - cell_size = mp.Vector3(sr,0,sz) - - pml_layers = [mp.PML(self.dpml,direction=mp.R)] - - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Er, - center=mp.Vector3())] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - dimensions=mp.CYLINDRICAL, - m=-1, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(self.fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Er, - mp.Vector3(), - 1e-6)) + def cavity_ldos_cyl(self, sz): + sr = self.L + self.dpml + cell_size = mp.Vector3(sr, 0, sz) + + pml_layers = [mp.PML(self.dpml, direction=mp.R)] + + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Er, + center=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + dimensions=mp.CYLINDRICAL, + m=-1, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(self.fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Er, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - def bulk_ldos_3D(self): - s = self.L+2*self.dpml - cell_size = mp.Vector3(s,s,s) + s = self.L + 2 * self.dpml + cell_size = mp.Vector3(s, s, s) pml_layers = [mp.PML(self.dpml)] - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(self.fcen,0,1), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(self.fcen, 0, 1), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Ex, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - - def cavity_ldos_3D(self,sz): - sxy = self.L+2*self.dpml - cell_size = mp.Vector3(sxy,sxy,sz) - - boundary_layers = [mp.PML(self.dpml,direction=mp.X), - mp.PML(self.dpml,direction=mp.Y)] - - sources = [mp.Source(src=mp.GaussianSource(self.fcen,fwidth=0.2*self.fcen), - component=mp.Ex, - center=mp.Vector3())] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y), - mp.Mirror(direction=mp.Z)] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries, - default_material=mp.Medium(index=self.n)) - - sim.run(mp.dft_ldos(ldos=mp.Ldos(self.fcen,0,1)), - until_after_sources=mp.stop_when_fields_decayed(20, - mp.Ex, - mp.Vector3(), - 1e-6)) + def cavity_ldos_3D(self, sz): + sxy = self.L + 2 * self.dpml + cell_size = mp.Vector3(sxy, sxy, sz) + + boundary_layers = [ + mp.PML(self.dpml, direction=mp.X), + mp.PML(self.dpml, direction=mp.Y), + ] + + sources = [ + mp.Source( + src=mp.GaussianSource(self.fcen, fwidth=0.2 * self.fcen), + component=mp.Ex, + center=mp.Vector3(), + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y), + mp.Mirror(direction=mp.Z), + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + default_material=mp.Medium(index=self.n), + ) + + sim.run( + mp.dft_ldos(ldos=mp.Ldos(self.fcen, 0, 1)), + until_after_sources=mp.stop_when_fields_decayed( + 20, mp.Ex, mp.Vector3(), 1e-6 + ), + ) return sim.ldos_data[0] - - def purcell_enh_theory(self,c): + def purcell_enh_theory(self, c): # equation 7 of reference - return (3*np.fix(c+0.5)/(4*c) + - (4*np.power(np.fix(c+0.5),3) - - np.fix(c+0.5))/(16*np.power(c,3))) - + return 3 * np.fix(c + 0.5) / (4 * c) + ( + 4 * np.power(np.fix(c + 0.5), 3) - np.fix(c + 0.5) + ) / (16 * np.power(c, 3)) def test_ldos_cyl(self): ldos_bulk = self.bulk_ldos_cyl() # not a Van Hove singularity cavity_thickness = 1.63 - gap = cavity_thickness*self.wvl/self.n + gap = cavity_thickness * self.wvl / self.n ldos_cavity = self.cavity_ldos_cyl(gap) # Purcell enhancement factor (relative to bulk medium) - pe_meep = ldos_cavity/ldos_bulk + pe_meep = ldos_cavity / ldos_bulk pe_theory = self.purcell_enh_theory(cavity_thickness) - rel_err = abs(pe_meep-pe_theory)/pe_theory + rel_err = abs(pe_meep - pe_theory) / pe_theory - print("ldos-cyl:, {:.6f} (Meep), {:.6f} (theory), " - "{:.6f} (error)".format(pe_meep,pe_theory,rel_err)) + print( + "ldos-cyl:, {:.6f} (Meep), {:.6f} (theory), " + "{:.6f} (error)".format(pe_meep, pe_theory, rel_err) + ) self.assertAlmostEqual(pe_meep, pe_theory, delta=0.1) - def test_ldos_3D(self): ldos_bulk = self.bulk_ldos_3D() # not a Van Hove singularity cavity_thickness = 0.75 - gap = cavity_thickness*self.wvl/self.n + gap = cavity_thickness * self.wvl / self.n ldos_cavity = self.cavity_ldos_3D(gap) # Purcell enhancement factor (relative to bulk medium) - pe_meep = ldos_cavity/ldos_bulk + pe_meep = ldos_cavity / ldos_bulk pe_theory = self.purcell_enh_theory(cavity_thickness) - rel_err = abs(pe_meep-pe_theory)/pe_theory + rel_err = abs(pe_meep - pe_theory) / pe_theory - print("ldos-3D:, {:.6f} (Meep), {:.6f} (theory), " - "{:.6f} (error)".format(pe_meep,pe_theory,rel_err)) + print( + "ldos-3D:, {:.6f} (Meep), {:.6f} (theory), " + "{:.6f} (error)".format(pe_meep, pe_theory, rel_err) + ) self.assertAlmostEqual(pe_meep, pe_theory, delta=0.1) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_material_dispersion.py b/python/tests/test_material_dispersion.py index 220df72c1..91d2f9c39 100644 --- a/python/tests/test_material_dispersion.py +++ b/python/tests/test_material_dispersion.py @@ -1,14 +1,15 @@ import unittest + import numpy as np + import meep as mp class TestMaterialDispersion(unittest.TestCase): - def test_material_dispersion_with_user_material(self): susceptibilities = [ mp.LorentzianSusceptibility(frequency=1.1, gamma=1e-5, sigma=0.5), - mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5) + mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5), ] def mat_func(p): @@ -18,9 +19,7 @@ def mat_func(p): df = 2.0 sources = mp.Source( - mp.GaussianSource(fcen, fwidth=df), - component=mp.Ez, - center=mp.Vector3() + mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3() ) kmin = 0.3 @@ -35,7 +34,7 @@ def mat_func(p): sources=[sources], material_function=mat_func, default_material=mp.air, - resolution=20 + resolution=20, ) all_freqs = self.sim.run_k_points(200, kpts) @@ -54,5 +53,5 @@ def mat_func(p): np.testing.assert_allclose(expected, res) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_material_grid.py b/python/tests/test_material_grid.py index c50d0dff2..284b5c4ed 100644 --- a/python/tests/test_material_grid.py +++ b/python/tests/test_material_grid.py @@ -1,149 +1,183 @@ import meep as mp + try: import meep.adjoint as mpa except: import adjoint as mpa + +import unittest + import numpy as np from scipy.ndimage import gaussian_filter -import unittest def compute_transmittance(matgrid_symmetry=False): - resolution = 25 - - cell_size = mp.Vector3(6,6,0) - - boundary_layers = [mp.PML(thickness=1.0)] - - matgrid_size = mp.Vector3(2,2,0) - matgrid_resolution = 2*resolution - - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) - - # ensure reproducible results - rng = np.random.RandomState(2069588) - - w = rng.rand(Nx,Ny) - weights = 0.5 * (w + np.fliplr(w)) if not matgrid_symmetry else w - - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=weights, - do_averaging=False, - grid_type='U_MEAN') - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1.0,mp.inf), - material=mp.Medium(index=3.5)), - mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid)] - - if matgrid_symmetry: - geometry.append(mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid, - e2=mp.Vector3(y=-1))) - - eig_parity = mp.ODD_Y + mp.EVEN_Z - - fcen = 0.65 - df = 0.2*fcen - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), - center=mp.Vector3(-2.0,0), - size=mp.Vector3(0,4.0), - eig_parity=eig_parity)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - geometry=geometry) - - mode_mon = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(2.0,0), - size=mp.Vector3(0,4.0))) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - mode_coeff = sim.get_eigenmode_coefficients(mode_mon,[1],eig_parity).alpha[0,:,0][0] - - tran = np.power(np.abs(mode_coeff),2) - print('tran:, {}, {}'.format("sym" if matgrid_symmetry else "nosym", tran)) - - return tran - - -def compute_resonant_mode_2d(res,default_mat=False): - cell_size = mp.Vector3(1,1,0) - - rad = 0.301943 - - fcen = 0.3 - df = 0.2*fcen - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), - component=mp.Hz, - center=mp.Vector3(-0.1057,0.2094,0))] - - k_point = mp.Vector3(0.3892,0.1597,0) - - matgrid_size = mp.Vector3(1,1,0) - matgrid_resolution = 1200 - - # for a fixed resolution, compute the number of grid points - # necessary which are defined on the corners of the voxels - Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) - - x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) - y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) - xv, yv = np.meshgrid(x,y) - weights = np.sqrt(np.square(xv) + np.square(yv)) < rad - filtered_weights = gaussian_filter(weights, - sigma=3.0, - output=np.double) + resolution = 25 - matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), - mp.air, - mp.Medium(index=3.5), - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5) + cell_size = mp.Vector3(6, 6, 0) + + boundary_layers = [mp.PML(thickness=1.0)] + + matgrid_size = mp.Vector3(2, 2, 0) + matgrid_resolution = 2 * resolution + + Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( + matgrid_size.y * matgrid_resolution + ) + + # ensure reproducible results + rng = np.random.RandomState(2069588) + + w = rng.rand(Nx, Ny) + weights = w if matgrid_symmetry else 0.5 * (w + np.fliplr(w)) + + matgrid = mp.MaterialGrid( + mp.Vector3(Nx, Ny), + mp.air, + mp.Medium(index=3.5), + weights=weights, + do_averaging=False, + grid_type="U_MEAN", + ) + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1.0, mp.inf), + material=mp.Medium(index=3.5), + ), + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), + material=matgrid, + ), + ] + + if matgrid_symmetry: + geometry.append( + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), + material=matgrid, + e2=mp.Vector3(y=-1), + ) + ) + + eig_parity = mp.ODD_Y + mp.EVEN_Z + + fcen = 0.65 + df = 0.2 * fcen + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=df), + center=mp.Vector3(-2.0, 0), + size=mp.Vector3(0, 4.0), + eig_parity=eig_parity, + ) + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + geometry=geometry, + ) + + mode_mon = sim.add_flux( + fcen, 0, 1, mp.FluxRegion(center=mp.Vector3(2.0, 0), size=mp.Vector3(0, 4.0)) + ) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + mode_coeff = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity).alpha[ + 0, :, 0 + ][0] + + tran = np.power(np.abs(mode_coeff), 2) + print(f'tran:, {"sym" if matgrid_symmetry else "nosym"}, {tran}') + + return tran + + +def compute_resonant_mode_2d(res, default_mat=False): + cell_size = mp.Vector3(1, 1, 0) + + rad = 0.301943 + + fcen = 0.3 + df = 0.2 * fcen + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Hz, + center=mp.Vector3(-0.1057, 0.2094, 0), + ) + ] + + k_point = mp.Vector3(0.3892, 0.1597, 0) + + matgrid_size = mp.Vector3(1, 1, 0) + matgrid_resolution = 1200 + + # for a fixed resolution, compute the number of grid points + # necessary which are defined on the corners of the voxels + Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( + matgrid_size.y * matgrid_resolution + ) + + x = np.linspace(-0.5 * matgrid_size.x, 0.5 * matgrid_size.x, Nx) + y = np.linspace(-0.5 * matgrid_size.y, 0.5 * matgrid_size.y, Ny) + xv, yv = np.meshgrid(x, y) + weights = np.sqrt(np.square(xv) + np.square(yv)) < rad + filtered_weights = gaussian_filter(weights, sigma=3.0, output=np.double) + + matgrid = mp.MaterialGrid( + mp.Vector3(Nx, Ny), + mp.air, + mp.Medium(index=3.5), + weights=filtered_weights, + do_averaging=True, + beta=1000, + eta=0.5, + ) + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), + material=matgrid, + ) + ] + + sim = mp.Simulation( + resolution=res, + cell_size=cell_size, + default_material=matgrid if default_mat else mp.Medium(), + geometry=[] if default_mat else geometry, + sources=sources, + k_point=k_point, + ) + + h = mp.Harminv(mp.Hz, mp.Vector3(0.3718, -0.2076), fcen, df) + sim.run(mp.after_sources(h), until_after_sources=200) - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), - material=matgrid)] + try: + for m in h.modes: + print(f"harminv:, {res}, {m.freq}, {m.Q}") + freq = h.modes[0].freq + except: + raise RuntimeError("No resonant modes found.") - sim = mp.Simulation(resolution=res, - cell_size=cell_size, - default_material=matgrid if default_mat else mp.Medium(), - geometry=geometry if not default_mat else [], - sources=sources, - k_point=k_point) - - h = mp.Harminv(mp.Hz, mp.Vector3(0.3718,-0.2076), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=200) - - try: - for m in h.modes: - print("harminv:, {}, {}, {}".format(res,m.freq,m.Q)) - freq = h.modes[0].freq - except: - raise RuntimeError("No resonant modes found.") - - return freq + return freq def compute_resonant_mode_3d(use_matgrid=True): resolution = 25 wvl = 1.27 - fcen = 1/wvl - df = 0.02*fcen + fcen = 1 / wvl + df = 0.02 * fcen nSi = 3.45 Si = mp.Medium(index=nSi) @@ -151,59 +185,62 @@ def compute_resonant_mode_3d(use_matgrid=True): SiO2 = mp.Medium(index=nSiO2) s = 1.0 - cell_size = mp.Vector3(s,s,s) + cell_size = mp.Vector3(s, s, s) rad = 0.34 # radius of sphere if use_matgrid: - matgrid_resolution = 2*resolution - N = int(s*matgrid_resolution) - coord = np.linspace(-0.5*s,0.5*s,N) - xv, yv, zv = np.meshgrid(coord,coord,coord) + matgrid_resolution = 2 * resolution + N = int(s * matgrid_resolution) + coord = np.linspace(-0.5 * s, 0.5 * s, N) + xv, yv, zv = np.meshgrid(coord, coord, coord) weights = np.sqrt(np.square(xv) + np.square(yv) + np.square(zv)) < rad - filtered_weights = gaussian_filter(weights, - sigma=4/resolution, - output=np.double) - - matgrid = mp.MaterialGrid(mp.Vector3(N,N,N), - SiO2, - Si, - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5) - - geometry = [mp.Block(center=mp.Vector3(), - size=cell_size, - material=matgrid)] + filtered_weights = gaussian_filter( + weights, sigma=4 / resolution, output=np.double + ) + + matgrid = mp.MaterialGrid( + mp.Vector3(N, N, N), + SiO2, + Si, + weights=filtered_weights, + do_averaging=True, + beta=1000, + eta=0.5, + ) + + geometry = [mp.Block(center=mp.Vector3(), size=cell_size, material=matgrid)] else: - geometry = [mp.Sphere(center=mp.Vector3(), - radius=rad, - material=Si)] + geometry = [mp.Sphere(center=mp.Vector3(), radius=rad, material=Si)] - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df), - size=mp.Vector3(), - center=mp.Vector3(0.13,0.25,0.06), - component=mp.Ez)] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + size=mp.Vector3(), + center=mp.Vector3(0.13, 0.25, 0.06), + component=mp.Ez, + ) + ] - k_point = mp.Vector3(0.23,-0.17,0.35) + k_point = mp.Vector3(0.23, -0.17, 0.35) - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=SiO2, - k_point=k_point, - geometry=geometry) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=SiO2, + k_point=k_point, + geometry=geometry, + ) - h = mp.Harminv(mp.Ez, mp.Vector3(-0.2684,0.1185,0.0187), fcen, df) + h = mp.Harminv(mp.Ez, mp.Vector3(-0.2684, 0.1185, 0.0187), fcen, df) - sim.run(mp.after_sources(h), - until_after_sources=200) + sim.run(mp.after_sources(h), until_after_sources=200) try: for m in h.modes: - print("harminv:, {}, {}, {}".format(resolution,m.freq,m.Q)) + print(f"harminv:, {resolution}, {m.freq}, {m.Q}") freq = h.modes[0].freq except: raise RuntimeError("No resonant modes found.") @@ -212,7 +249,6 @@ def compute_resonant_mode_3d(use_matgrid=True): class TestMaterialGrid(unittest.TestCase): - def test_subpixel_smoothing(self): # "exact" frequency computed using MaterialGrid at resolution = 300 freq_ref = 0.29826813873225283 @@ -227,23 +263,24 @@ def test_subpixel_smoothing(self): # verify that the relative error is decreasing with increasing resolution # and is better than linear convergence because of subpixel smoothing - self.assertLess(abs(freq_matgrid[1]-freq_ref)*(res[1]/res[0]), - abs(freq_matgrid[0]-freq_ref)) + self.assertLess( + abs(freq_matgrid[1] - freq_ref) * (res[1] / res[0]), + abs(freq_matgrid[0] - freq_ref), + ) freq_matgrid_default_mat = compute_resonant_mode_2d(res[0], True) self.assertAlmostEqual(freq_matgrid[0], freq_matgrid_default_mat) - def test_matgrid_3d(self): freq_matgrid = compute_resonant_mode_3d(True) freq_geomobj = compute_resonant_mode_3d(False) self.assertAlmostEqual(freq_matgrid, freq_geomobj, places=2) - def test_symmetry(self): - tran_nosym = compute_transmittance(False) - tran_sym = compute_transmittance(True) - self.assertAlmostEqual(tran_nosym, tran_sym, places=5) + tran_nosym = compute_transmittance(False) + tran_sym = compute_transmittance(True) + self.assertAlmostEqual(tran_nosym, tran_sym, places=5) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_materials_library.py b/python/tests/test_materials_library.py index 1e61af952..9bef924b7 100644 --- a/python/tests/test_materials_library.py +++ b/python/tests/test_materials_library.py @@ -1,36 +1,46 @@ import unittest -from meep.materials import InP, Ge, Si, LiNbO3, SiO2_aniso, Ag, Cr +from meep.materials import Ag, Cr, Ge, InP, LiNbO3, Si, SiO2_aniso -class TestMaterialsLibrary(unittest.TestCase): +class TestMaterialsLibrary(unittest.TestCase): def test_materials_library(self): - self.assertAlmostEqual(InP.epsilon(1/3.3)[0][0], (3.1031)**2, places=2) + self.assertAlmostEqual(InP.epsilon(1 / 3.3)[0][0], (3.1031) ** 2, places=2) - self.assertAlmostEqual(Ge.epsilon(1/6.8)[0][0], (4.0091)**2, places=2) + self.assertAlmostEqual(Ge.epsilon(1 / 6.8)[0][0], (4.0091) ** 2, places=2) - self.assertAlmostEqual(Si.epsilon(1/1.55)[0][0], (3.4777)**2, places=2) + self.assertAlmostEqual(Si.epsilon(1 / 1.55)[0][0], (3.4777) ** 2, places=2) - self.assertAlmostEqual(LiNbO3.epsilon(1/1.55)[0][0], (2.2111)**2, places=2) - self.assertAlmostEqual(LiNbO3.epsilon(1/1.55)[1][1], (2.2111)**2, places=2) - self.assertAlmostEqual(LiNbO3.epsilon(1/1.55)[2][2], (2.1376)**2, places=2) + self.assertAlmostEqual(LiNbO3.epsilon(1 / 1.55)[0][0], (2.2111) ** 2, places=2) + self.assertAlmostEqual(LiNbO3.epsilon(1 / 1.55)[1][1], (2.2111) ** 2, places=2) + self.assertAlmostEqual(LiNbO3.epsilon(1 / 1.55)[2][2], (2.1376) ** 2, places=2) - self.assertAlmostEqual(SiO2_aniso.epsilon(1/1.55)[0][0], (1.5277)**2, places=2) - self.assertEqual(SiO2_aniso.epsilon(1/1.55)[1][0], 0) - self.assertAlmostEqual(SiO2_aniso.epsilon(1/1.55)[1][1], (1.5277)**2, places=2) - self.assertAlmostEqual(SiO2_aniso.epsilon(1/1.55)[2][2], (1.5362)**2, places=2) + self.assertAlmostEqual( + SiO2_aniso.epsilon(1 / 1.55)[0][0], (1.5277) ** 2, places=2 + ) + self.assertEqual(SiO2_aniso.epsilon(1 / 1.55)[1][0], 0) + self.assertAlmostEqual( + SiO2_aniso.epsilon(1 / 1.55)[1][1], (1.5277) ** 2, places=2 + ) + self.assertAlmostEqual( + SiO2_aniso.epsilon(1 / 1.55)[2][2], (1.5362) ** 2, places=2 + ) - self.assertAlmostEqual(Ag.epsilon(1/0.65)[0][0], (0.14623 + 1j*3.9367)**2, places=2) + self.assertAlmostEqual( + Ag.epsilon(1 / 0.65)[0][0], (0.14623 + 1j * 3.9367) ** 2, places=2 + ) - self.assertAlmostEqual(Cr.epsilon(1/0.71)[0][0], (3.8275 + 1j*4.3457)**2, places=2) + self.assertAlmostEqual( + Cr.epsilon(1 / 0.71)[0][0], (3.8275 + 1j * 4.3457) ** 2, places=2 + ) try: - Ag.epsilon(1/0.2)[0][0] + Ag.epsilon(1 / 0.2)[0][0] except ValueError: pass else: raise AssertionError("Ag is not defined at a wavelength of 0.2 μm") -if __name__ == '__main__': - unittest.main() +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_medium_evaluations.py b/python/tests/test_medium_evaluations.py index 0d74bd93c..e3ce7d8ce 100644 --- a/python/tests/test_medium_evaluations.py +++ b/python/tests/test_medium_evaluations.py @@ -1,4 +1,3 @@ - # medium_evaluations.py - Tests the evaluation of material permitivity profiles. # Checks materials with lorentizian, drude, and non uniform diagonals. # The extracted values are compared against actual datapoints pulled from @@ -6,60 +5,61 @@ # TODO: # * check materials with off diagonal components # * check magnetic profiles - import unittest -import meep as mp + import numpy as np +import meep as mp -class TestMediumEvaluations(unittest.TestCase): +class TestMediumEvaluations(unittest.TestCase): def test_medium_evaluations(self): - from meep.materials import Si, Ag, LiNbO3, fused_quartz + from meep.materials import Ag, LiNbO3, Si, fused_quartz # Check that scalars work w0 = LiNbO3.valid_freq_range.min eps = LiNbO3.epsilon(w0) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0])), 2.0508, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0])), 2.0508, places=4) # Check numpy arrays try: w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - eps = Si.epsilon(np.linspace(w0,w1,100)) + eps = Si.epsilon(np.linspace(w0, w1, 100)) except ExceptionType: self.fail("myFunc() raised ExceptionType unexpectedly!") # Check that regions outside of domain don't work - self.assertRaises(ValueError,LiNbO3.epsilon,-1.0) - self.assertRaises(ValueError,LiNbO3.epsilon,10000.0) + self.assertRaises(ValueError, LiNbO3.epsilon, -1.0) + self.assertRaises(ValueError, LiNbO3.epsilon, 10000.0) # Check complex vs non complex numbers - self.assertTrue(np.iscomplex(Ag.epsilon(1.0)[0,0])) - self.assertFalse(np.iscomplex(fused_quartz.epsilon(1.0)[0,0])) + self.assertTrue(np.iscomplex(Ag.epsilon(1.0)[0, 0])) + self.assertFalse(np.iscomplex(fused_quartz.epsilon(1.0)[0, 0])) # Check Silicon w0 = Si.valid_freq_range.min w1 = Si.valid_freq_range.max - eps = Si.epsilon([w0,w1]) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0,0])), 3.4175, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,0,0])), 3.4971, places=4) + eps = Si.epsilon([w0, w1]) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0, 0])), 3.4175, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 0, 0])), 3.4971, places=4) # Check Silver w0 = Ag.valid_freq_range.min w1 = Ag.valid_freq_range.max - eps = Ag.epsilon([w0,w1]) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0,0])), 17.485, places=2) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,0,0])), 0.44265, places=4) + eps = Ag.epsilon([w0, w1]) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0, 0])), 17.485, places=2) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 0, 0])), 0.44265, places=4) # Check Lithium Niobate w0 = LiNbO3.valid_freq_range.min w1 = LiNbO3.valid_freq_range.max - eps = LiNbO3.epsilon([w0,w1]) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,0,0])), 2.0508, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,0,0])), 2.4393, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[0,2,2])), 2.0025, places=4) - self.assertAlmostEqual(np.real(np.sqrt(eps[1,2,2])), 2.3321, places=4) + eps = LiNbO3.epsilon([w0, w1]) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 0, 0])), 2.0508, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 0, 0])), 2.4393, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[0, 2, 2])), 2.0025, places=4) + self.assertAlmostEqual(np.real(np.sqrt(eps[1, 2, 2])), 2.3321, places=4) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_mode_coeffs.py b/python/tests/test_mode_coeffs.py index 076b6e530..42078cb04 100644 --- a/python/tests/test_mode_coeffs.py +++ b/python/tests/test_mode_coeffs.py @@ -1,13 +1,14 @@ -import meep as mp import unittest + import numpy as np +import meep as mp -class TestModeCoeffs(unittest.TestCase): +class TestModeCoeffs(unittest.TestCase): def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): - w = 1 # width of waveguide + w = 1 # width of waveguide L = 10 # length of waveguide Si = mp.Medium(epsilon=12.0) @@ -21,10 +22,12 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): prism_x = sx + 1 prism_y = w / 2 - vertices = [mp.Vector3(-prism_x, prism_y), - mp.Vector3(prism_x, prism_y), - mp.Vector3(prism_x, -prism_y), - mp.Vector3(-prism_x, -prism_y)] + vertices = [ + mp.Vector3(-prism_x, prism_y), + mp.Vector3(prism_x, prism_y), + mp.Vector3(prism_x, -prism_y), + mp.Vector3(-prism_x, -prism_y), + ] geometry = [mp.Prism(vertices, height=mp.inf, material=Si)] @@ -32,25 +35,39 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): # mode frequency fcen = 0.20 # > 0.5/sqrt(11) to have at least 2 modes - df = 0.5*fcen + df = 0.5 * fcen - source = mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=df), - eig_band=mode_num, - size=mp.Vector3(0,sy-2*dpml,0), - center=mp.Vector3(-0.5*sx+dpml,0,0)) + source = mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=df), + eig_band=mode_num, + size=mp.Vector3(0, sy - 2 * dpml, 0), + center=mp.Vector3(-0.5 * sx + dpml, 0, 0), + ) symmetries = [mp.Mirror(mp.Y, phase=1 if mode_num % 2 == 1 else -1)] - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=geometry, - sources=[source], - symmetries=symmetries) - - xm = 0.5*sx - dpml # x-coordinate of monitor - mflux = sim.add_mode_monitor(fcen, df, nf, mp.ModeRegion(center=mp.Vector3(xm,0), size=mp.Vector3(0,sy-2*dpml))) - mode_flux = sim.add_flux(fcen, df, nf, mp.FluxRegion(center=mp.Vector3(xm,0), size=mp.Vector3(0,sy-2*dpml))) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=geometry, + sources=[source], + symmetries=symmetries, + ) + + xm = 0.5 * sx - dpml # x-coordinate of monitor + mflux = sim.add_mode_monitor( + fcen, + df, + nf, + mp.ModeRegion(center=mp.Vector3(xm, 0), size=mp.Vector3(0, sy - 2 * dpml)), + ) + mode_flux = sim.add_flux( + fcen, + df, + nf, + mp.FluxRegion(center=mp.Vector3(xm, 0), size=mp.Vector3(0, sy - 2 * dpml)), + ) # sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(-0.5*sx+dpml,0), 1e-10)) sim.run(until_after_sources=100) @@ -65,27 +82,34 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): # and check that it agrees with the prediction # of the eig_power() class method in EigenmodeSource. ################################################## - if nf>1: - power_observed=mp.get_fluxes(mode_flux) - freqs=mp.get_flux_freqs(mode_flux) - power_expected=[source.eig_power(f) for f in freqs] + if nf > 1: + power_observed = mp.get_fluxes(mode_flux) + freqs = mp.get_flux_freqs(mode_flux) + power_expected = [source.eig_power(f) for f in freqs] return freqs, power_expected, power_observed - modes_to_check = [1, 2] # indices of modes for which to compute expansion coefficients - res = sim.get_eigenmode_coefficients(mflux, modes_to_check, kpoint_func=kpoint_func) + modes_to_check = [ + 1, + 2, + ] # indices of modes for which to compute expansion coefficients + res = sim.get_eigenmode_coefficients( + mflux, modes_to_check, kpoint_func=kpoint_func + ) self.assertTrue(res.kpoints[0].close(mp.Vector3(0.604301, 0, 0))) self.assertTrue(res.kpoints[1].close(mp.Vector3(0.494353, 0, 0), tol=1e-2)) self.assertTrue(res.kdom[0].close(mp.Vector3(0.604301, 0, 0))) self.assertTrue(res.kdom[1].close(mp.Vector3(0.494353, 0, 0), tol=1e-2)) - self.assertAlmostEqual(res.cscale[0],0.50000977,places=5) - self.assertAlmostEqual(res.cscale[1],0.50096888,places=5) + self.assertAlmostEqual(res.cscale[0], 0.50000977, places=5) + self.assertAlmostEqual(res.cscale[1], 0.50096888, places=5) mode_power = mp.get_fluxes(mode_flux)[0] TestPassed = True TOLERANCE = 5.0e-3 - c0 = res.alpha[mode_num - 1, 0, 0] # coefficient of forward-traveling wave for mode #mode_num - for nm in range(1, len(modes_to_check)+1): + c0 = res.alpha[ + mode_num - 1, 0, 0 + ] # coefficient of forward-traveling wave for mode #mode_num + for nm in range(1, len(modes_to_check) + 1): if nm != mode_num: cfrel = np.abs(res.alpha[nm - 1, 0, 0]) / np.abs(c0) cbrel = np.abs(res.alpha[nm - 1, 0, 1]) / np.abs(c0) @@ -95,7 +119,7 @@ def run_mode_coeffs(self, mode_num, kpoint_func, nf=1, resolution=15): self.sim = sim # test 1: coefficient of excited mode >> coeffs of all other modes - self.assertTrue(TestPassed, msg="cfrel: {}, cbrel: {}".format(cfrel, cbrel)) + self.assertTrue(TestPassed, msg=f"cfrel: {cfrel}, cbrel: {cbrel}") # test 2: |mode coeff|^2 = power self.assertAlmostEqual(mode_power / abs(c0**2), 1.0, places=1) @@ -117,165 +141,219 @@ def test_modes(self): hz_at_eval_point = emdata.amplitude(eval_point, mp.Hz) places = 5 if mp.is_single_precision() else 7 - self.assertAlmostEqual(ex_at_eval_point, 0.4887779638178009+0.484240145324284j, places=places) - self.assertAlmostEqual(hz_at_eval_point, 3.4249236584603495-3.455974863884166j, places=places) + self.assertAlmostEqual( + ex_at_eval_point, 0.4887779638178009 + 0.484240145324284j, places=places + ) + self.assertAlmostEqual( + hz_at_eval_point, 3.4249236584603495 - 3.455974863884166j, places=places + ) def test_kpoint_func(self): - def kpoint_func(freq, mode): return mp.Vector3() self.run_mode_coeffs(1, kpoint_func) def test_eigensource_normalization(self): - f, p_exp, p_obs=self.run_mode_coeffs(1, None, nf=51, resolution=15) - #self.assertAlmostEqual(max(p_exp),max(p_obs),places=1) - max_exp, max_obs=max(p_exp), max(p_obs) - self.assertLess(abs(max_exp-max_obs), 0.5*max(abs(max_exp),abs(max_obs))) + f, p_exp, p_obs = self.run_mode_coeffs(1, None, nf=51, resolution=15) + # self.assertAlmostEqual(max(p_exp),max(p_obs),places=1) + max_exp, max_obs = max(p_exp), max(p_obs) + self.assertLess(abs(max_exp - max_obs), 0.5 * max(abs(max_exp), abs(max_obs))) def test_reciprocity_kpoint(self): resolution = 40 sx = 7.0 sy = 5.0 - cell_size = mp.Vector3(sx,sy) + cell_size = mp.Vector3(sx, sy) dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] w = 1.0 - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] fsrc = 0.15 - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(x=-0.5*sx+dpml), - size=mp.Vector3(y=sy), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(x=-0.5 * sx + dpml), + size=mp.Vector3(y=sy), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=symmetries) - - tran = sim.add_mode_monitor(fsrc, 0, 1, - mp.ModeRegion(center=mp.Vector3(x=0.5*sx-dpml), - size=mp.Vector3(y=sy)), - yee_grid=False) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=symmetries, + ) + + tran = sim.add_mode_monitor( + fsrc, + 0, + 1, + mp.ModeRegion(center=mp.Vector3(x=0.5 * sx - dpml), size=mp.Vector3(y=sy)), + yee_grid=False, + ) sim.run(until_after_sources=50) - res_fwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: mp.Vector3(+1,0,0)) - - res_bwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: mp.Vector3(-1,0,0)) - - print("S11:, {}, {}".format(res_fwd.alpha[0,0,1],res_bwd.alpha[0,0,0])) - print("S21:, {}, {}".format(res_fwd.alpha[0,0,0],res_bwd.alpha[0,0,1])) + res_fwd = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: mp.Vector3(+1, 0, 0), + ) + + res_bwd = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: mp.Vector3(-1, 0, 0), + ) + + print(f"S11:, {res_fwd.alpha[0,0,1]}, {res_bwd.alpha[0,0,0]}") + print(f"S21:, {res_fwd.alpha[0,0,0]}, {res_bwd.alpha[0,0,1]}") # |S11|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,1])**2, abs(res_bwd.alpha[0,0,0])**2, places=4) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 1]) ** 2, abs(res_bwd.alpha[0, 0, 0]) ** 2, places=4 + ) # |S21|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,0])**2 / abs(res_bwd.alpha[0,0,1])**2, 1.00, places=2) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 0]) ** 2 / abs(res_bwd.alpha[0, 0, 1]) ** 2, + 1.00, + places=2, + ) def test_reciprocity_monitor(self): resolution = 25 sx = 7.0 sy = 5.0 - cell_size = mp.Vector3(sx,sy) + cell_size = mp.Vector3(sx, sy) dpml = 1.0 pml_layers = [mp.PML(thickness=dpml)] w = 1.0 - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,w,mp.inf), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, w, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] fsrc = 0.15 # source is at the left edge of the waveguide - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(x=-0.5*sx+dpml), - size=mp.Vector3(y=sy), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(x=-0.5 * sx + dpml), + size=mp.Vector3(y=sy), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=symmetries) + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=symmetries, + ) # monitor is at the right edge of the waveguide - tran = sim.add_mode_monitor(fsrc, 0, 1, - mp.ModeRegion(center=mp.Vector3(x=0.5*sx-dpml), - size=mp.Vector3(y=sy)), - yee_grid=False) + tran = sim.add_mode_monitor( + fsrc, + 0, + 1, + mp.ModeRegion(center=mp.Vector3(x=0.5 * sx - dpml), size=mp.Vector3(y=sy)), + yee_grid=False, + ) sim.run(until_after_sources=50) - res_fwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z) + res_fwd = sim.get_eigenmode_coefficients( + tran, [1], eig_parity=mp.EVEN_Y + mp.ODD_Z + ) - print("S11:, {}".format(res_fwd.alpha[0,0,1])) - print("S21:, {}".format(res_fwd.alpha[0,0,0])) + print(f"S11:, {res_fwd.alpha[0,0,1]}") + print(f"S21:, {res_fwd.alpha[0,0,0]}") sim.reset_meep() # source is at the right edge of the waveguide - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(x=0.5*sx-dpml), - size=mp.Vector3(y=sy), - direction=mp.NO_DIRECTION, - eig_kpoint=mp.Vector3(-1,0,0), - eig_parity=mp.EVEN_Y+mp.ODD_Z)] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=symmetries) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(x=0.5 * sx - dpml), + size=mp.Vector3(y=sy), + direction=mp.NO_DIRECTION, + eig_kpoint=mp.Vector3(-1, 0, 0), + eig_parity=mp.EVEN_Y + mp.ODD_Z, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=symmetries, + ) # monitor is at the left edge of the waveguide - tran = sim.add_mode_monitor(fsrc, 0, 1, - mp.ModeRegion(center=mp.Vector3(x=-0.5*sx+dpml), - size=mp.Vector3(y=sy)), - yee_grid=False) + tran = sim.add_mode_monitor( + fsrc, + 0, + 1, + mp.ModeRegion(center=mp.Vector3(x=-0.5 * sx + dpml), size=mp.Vector3(y=sy)), + yee_grid=False, + ) sim.run(until_after_sources=50) - res_bwd = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z) + res_bwd = sim.get_eigenmode_coefficients( + tran, [1], eig_parity=mp.EVEN_Y + mp.ODD_Z + ) - print("S12:, {}".format(res_bwd.alpha[0,0,1])) - print("S22:, {}".format(res_bwd.alpha[0,0,0])) + print(f"S12:, {res_bwd.alpha[0,0,1]}") + print(f"S22:, {res_bwd.alpha[0,0,0]}") # |S21|^2 = |S12|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,0])**2 / abs(res_bwd.alpha[0,0,1])**2, 1.00, places=2) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 0]) ** 2 / abs(res_bwd.alpha[0, 0, 1]) ** 2, + 1.00, + places=2, + ) # |S11|^2 = |S22|^2 - self.assertAlmostEqual(abs(res_fwd.alpha[0,0,1])**2, abs(res_bwd.alpha[0,0,0])**2, places=2) + self.assertAlmostEqual( + abs(res_fwd.alpha[0, 0, 1]) ** 2, abs(res_bwd.alpha[0, 0, 0]) ** 2, places=2 + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_mode_decomposition.py b/python/tests/test_mode_decomposition.py index 6d0262475..54e10cd98 100644 --- a/python/tests/test_mode_decomposition.py +++ b/python/tests/test_mode_decomposition.py @@ -1,12 +1,13 @@ +import cmath +import math import unittest + import numpy as np + import meep as mp -import math -import cmath class TestModeDecomposition(unittest.TestCase): - def test_linear_taper_2d(self): resolution = 10 w1 = 1 @@ -24,142 +25,183 @@ def test_linear_taper_2d(self): symmetries = [mp.Mirror(mp.Y)] Lt = 2 sx = dpml + Lw + Lt + Lw + dpml - cell_size = mp.Vector3(sx,sy,0) + cell_size = mp.Vector3(sx, sy, 0) prism_x = sx + 1 half_Lt = 0.5 * Lt - src_pt = mp.Vector3(-0.5*sx+dpml+0.2*Lw,0,0) - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen, fwidth=0.2*fcen), - center=src_pt, - size=mp.Vector3(0,sy-2*dpml,0), - eig_match_freq=True, - eig_parity=mp.ODD_Z+mp.EVEN_Y)] - - vertices = [mp.Vector3(-prism_x, half_w1), - mp.Vector3(prism_x, half_w1), - mp.Vector3(prism_x, -half_w1), - mp.Vector3(-prism_x, -half_w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], - sources=sources, - symmetries=symmetries) - - mon_pt = mp.Vector3(-0.5*sx+dpml+0.5*Lw,0,0) - flux = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mon_pt, - size=mp.Vector3(0,sy-2*dpml,0))) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9)) - - res = sim.get_eigenmode_coefficients(flux, - [1], - eig_parity=mp.ODD_Z+mp.EVEN_Y) + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.2 * Lw, 0, 0) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + center=src_pt, + size=mp.Vector3(0, sy - 2 * dpml, 0), + eig_match_freq=True, + eig_parity=mp.ODD_Z + mp.EVEN_Y, + ) + ] + + vertices = [ + mp.Vector3(-prism_x, half_w1), + mp.Vector3(prism_x, half_w1), + mp.Vector3(prism_x, -half_w1), + mp.Vector3(-prism_x, -half_w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + mon_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * Lw, 0, 0) + flux = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy - 2 * dpml, 0)), + ) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9) + ) + + res = sim.get_eigenmode_coefficients(flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y) incident_coeffs = res.alpha incident_flux = mp.get_fluxes(flux) incident_flux_data = sim.get_flux_data(flux) sim.reset_meep() - vertices = [mp.Vector3(-prism_x, half_w1), - mp.Vector3(-half_Lt, half_w1), - mp.Vector3(half_Lt, half_w2), - mp.Vector3(prism_x, half_w2), - mp.Vector3(prism_x, -half_w2), - mp.Vector3(half_Lt, -half_w2), - mp.Vector3(-half_Lt, -half_w1), - mp.Vector3(-prism_x, -half_w1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], - sources=sources, - symmetries=symmetries) - - refl_flux = sim.add_flux(fcen, - 0, - 1, - mp.FluxRegion(center=mon_pt, - size=mp.Vector3(0,sy-2*dpml,0))) + vertices = [ + mp.Vector3(-prism_x, half_w1), + mp.Vector3(-half_Lt, half_w1), + mp.Vector3(half_Lt, half_w2), + mp.Vector3(prism_x, half_w2), + mp.Vector3(prism_x, -half_w2), + mp.Vector3(half_Lt, -half_w2), + mp.Vector3(-half_Lt, -half_w1), + mp.Vector3(-prism_x, -half_w1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + geometry=[mp.Prism(vertices, height=mp.inf, material=Si)], + sources=sources, + symmetries=symmetries, + ) + + refl_flux = sim.add_flux( + fcen, + 0, + 1, + mp.FluxRegion(center=mon_pt, size=mp.Vector3(0, sy - 2 * dpml, 0)), + ) sim.load_minus_flux_data(refl_flux, incident_flux_data) - sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9)) + sim.run( + until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, src_pt, 1e-9) + ) - res = sim.get_eigenmode_coefficients(refl_flux, - [1], - eig_parity=mp.ODD_Z+mp.EVEN_Y) + res = sim.get_eigenmode_coefficients( + refl_flux, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y + ) coeffs = res.alpha taper_flux = mp.get_fluxes(refl_flux) - self.assertAlmostEqual(abs(coeffs[0,0,1])**2 / abs(incident_coeffs[0,0,0])**2, - -taper_flux[0] / incident_flux[0], - places=4) + self.assertAlmostEqual( + abs(coeffs[0, 0, 1]) ** 2 / abs(incident_coeffs[0, 0, 0]) ** 2, + -taper_flux[0] / incident_flux[0], + places=4, + ) def test_oblique_waveguide_backward_mode(self): sxy = 12.0 - cell_size = mp.Vector3(sxy,sxy,0) + cell_size = mp.Vector3(sxy, sxy, 0) dpml = 0.6 pml_layers = [mp.PML(thickness=dpml)] - fcen = 1/1.55 + fcen = 1 / 1.55 rot_angle = np.radians(35.0) - kpoint = mp.Vector3(1,0,0).rotate(mp.Vector3(0,0,1), rot_angle) * -1.0 - sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=0.1), - center=mp.Vector3(0.5*sxy-3.4,0,0), - size=mp.Vector3(0,sxy,0), - direction=mp.NO_DIRECTION, - eig_kpoint=kpoint, - eig_band=1, - eig_parity=mp.ODD_Z, - eig_match_freq=True)] - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1,mp.inf), - e1 = mp.Vector3(1,0,0).rotate(mp.Vector3(0,0,1), rot_angle), - e2 = mp.Vector3(0,1,0).rotate(mp.Vector3(0,0,1), rot_angle), - material=mp.Medium(index=3.5))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=20, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry) - - mode = sim.add_mode_monitor(fcen, 0, 1, - mp.FluxRegion(center=mp.Vector3(-0.5*sxy+dpml,0,0), - size=mp.Vector3(0,sxy,0)), - decimation_factor=1) - mode_decimated = sim.add_mode_monitor(fcen, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(-0.5*sxy+dpml,0,0), - size=mp.Vector3(0,sxy,0)), - decimation_factor=10) + kpoint = mp.Vector3(1, 0, 0).rotate(mp.Vector3(0, 0, 1), rot_angle) * -1.0 + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fcen, fwidth=0.1), + center=mp.Vector3(0.5 * sxy - 3.4, 0, 0), + size=mp.Vector3(0, sxy, 0), + direction=mp.NO_DIRECTION, + eig_kpoint=kpoint, + eig_band=1, + eig_parity=mp.ODD_Z, + eig_match_freq=True, + ) + ] + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + e1=mp.Vector3(1, 0, 0).rotate(mp.Vector3(0, 0, 1), rot_angle), + e2=mp.Vector3(0, 1, 0).rotate(mp.Vector3(0, 0, 1), rot_angle), + material=mp.Medium(index=3.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=20, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + ) + + mode = sim.add_mode_monitor( + fcen, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(-0.5 * sxy + dpml, 0, 0), size=mp.Vector3(0, sxy, 0) + ), + decimation_factor=1, + ) + mode_decimated = sim.add_mode_monitor( + fcen, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(-0.5 * sxy + dpml, 0, 0), size=mp.Vector3(0, sxy, 0) + ), + decimation_factor=10, + ) sim.run(until_after_sources=30) flux = mp.get_fluxes(mode)[0] - coeff = sim.get_eigenmode_coefficients(mode,[1], - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint).alpha[0,0,0] + coeff = sim.get_eigenmode_coefficients( + mode, [1], direction=mp.NO_DIRECTION, kpoint_func=lambda f, n: kpoint + ).alpha[0, 0, 0] flux_decimated = mp.get_fluxes(mode_decimated)[0] - coeff_decimated = sim.get_eigenmode_coefficients(mode_decimated,[1], - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint).alpha[0,0,0] - - print("oblique-waveguide-flux:, {:.6f}, {:.6f}".format(-flux, abs(coeff)**2)) - print("oblique-waveguide-flux (decimated):, {:.6f}, {:.6f}".format(-flux_decimated, - abs(coeff_decimated)**2)) + coeff_decimated = sim.get_eigenmode_coefficients( + mode_decimated, + [1], + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: kpoint, + ).alpha[0, 0, 0] + + print(f"oblique-waveguide-flux:, {-flux:.6f}, {abs(coeff) ** 2:.6f}") + print( + "oblique-waveguide-flux (decimated):, {:.6f}, {:.6f}".format( + -flux_decimated, abs(coeff_decimated) ** 2 + ) + ) ## the magnitude of |flux| is 100.008731 and so we check two significant digits of accuracy - self.assertAlmostEqual(-1,abs(coeff)**2/flux,places=2) - self.assertAlmostEqual(flux,flux_decimated,places=3) - self.assertAlmostEqual(coeff,coeff_decimated,places=3) - + self.assertAlmostEqual(-1, abs(coeff) ** 2 / flux, places=2) + self.assertAlmostEqual(flux, flux_decimated, places=3) + self.assertAlmostEqual(coeff, coeff_decimated, places=3) def test_grating_3d(self): """Unit test for mode decomposition in 3d with zero k_point. @@ -179,7 +221,7 @@ def test_grating_3d(self): SiO2 = mp.Medium(index=nSiO2) wvl = 0.5 # wavelength - fcen = 1/wvl + fcen = 1 / wvl dpml = 1.0 # PML thickness dsub = 3.0 # substrate thickness @@ -189,40 +231,46 @@ def test_grating_3d(self): sx = 1.1 sy = 0.8 - sz = dpml+dsub+hcyl+dair+dpml + sz = dpml + dsub + hcyl + dair + dpml - cell_size = mp.Vector3(sx,sy,sz) + cell_size = mp.Vector3(sx, sy, sz) - boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] + boundary_layers = [mp.PML(thickness=dpml, direction=mp.Z)] # periodic boundary conditions k_point = mp.Vector3() src_cmpt = mp.Ex - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.2*fcen), - size=mp.Vector3(sx,sy,0), - center=mp.Vector3(0,0,-0.5*sz+dpml), - component=src_cmpt)] - - symmetries = [mp.Mirror(direction=mp.X,phase=-1), - mp.Mirror(direction=mp.Y,phase=+1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=SiO2, - boundary_layers=boundary_layers, - k_point=k_point, - symmetries=symmetries) - - refl_pt = mp.Vector3(0,0,-0.5*sz+dpml+0.5*dsub) - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) - - stop_cond = mp.stop_when_energy_decayed(20,1e-6) + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.2 * fcen), + size=mp.Vector3(sx, sy, 0), + center=mp.Vector3(0, 0, -0.5 * sz + dpml), + component=src_cmpt, + ) + ] + + symmetries = [ + mp.Mirror(direction=mp.X, phase=-1), + mp.Mirror(direction=mp.Y, phase=+1), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=SiO2, + boundary_layers=boundary_layers, + k_point=k_point, + symmetries=symmetries, + ) + + refl_pt = mp.Vector3(0, 0, -0.5 * sz + dpml + 0.5 * dsub) + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) + + stop_cond = mp.stop_when_energy_decayed(20, 1e-6) sim.run(until_after_sources=stop_cond) input_flux = mp.get_fluxes(refl_flux) @@ -230,34 +278,43 @@ def test_grating_3d(self): sim.reset_meep() - geometry = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), - center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), - material=SiO2), - mp.Cylinder(height=hcyl, - radius=rcyl, - center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), - material=Si)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - geometry=geometry, - boundary_layers=boundary_layers, - k_point=k_point, - symmetries=symmetries) - - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) - sim.load_minus_flux_data(refl_flux,input_flux_data) - - tran_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=mp.Vector3(0,0,0.5*sz-dpml), - size=mp.Vector3(sx,sy,0))) + geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, dpml + dsub), + center=mp.Vector3(0, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + material=SiO2, + ), + mp.Cylinder( + height=hcyl, + radius=rcyl, + center=mp.Vector3(0, 0, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + material=Si, + ), + ] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k_point, + symmetries=symmetries, + ) + + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) + sim.load_minus_flux_data(refl_flux, input_flux_data) + + tran_flux = sim.add_mode_monitor( + fcen, + 0, + 1, + mp.ModeRegion( + center=mp.Vector3(0, 0, 0.5 * sz - dpml), size=mp.Vector3(sx, sy, 0) + ), + ) sim.run(until_after_sources=stop_cond) @@ -265,67 +322,80 @@ def test_grating_3d(self): Rsum = 0 # number of reflected modes/orders in SiO2 in x and y directions (upper bound) - nm_x = int(fcen*nSiO2*sx) + 1 - nm_y = int(fcen*nSiO2*sy) + 1 + nm_x = int(fcen * nSiO2 * sx) + 1 + nm_y = int(fcen * nSiO2 * sy) + 1 for m_x in range(nm_x): for m_y in range(nm_y): - for S_pol in [False,True]: - res = sim.get_eigenmode_coefficients(refl_flux, - mp.DiffractedPlanewave([m_x,m_y,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + refl_flux, + mp.DiffractedPlanewave( + [m_x, m_y, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) r_coeffs = res.alpha - Rmode = abs(r_coeffs[0,0,1])**2/input_flux[0] - print("refl-order:, {}, {}, {}, {:.6f}".format("s" if S_pol else "p",m_x,m_y,Rmode)) + Rmode = abs(r_coeffs[0, 0, 1]) ** 2 / input_flux[0] + print( + "refl-order:, {}, {}, {}, {:.6f}".format( + "s" if S_pol else "p", m_x, m_y, Rmode + ) + ) if m_x == 0 and m_y == 0: Rsum += Rmode elif (m_x != 0 and m_y == 0) or (m_x == 0 and m_y != 0): - Rsum += 2*Rmode + Rsum += 2 * Rmode else: - Rsum += 4*Rmode - + Rsum += 4 * Rmode # sum the Poynting flux in z direction for all transmitted orders Tsum = 0 # number of transmitted modes/orders in air in x and y directions (upper bound) - nm_x = int(fcen*sx) + 1 - nm_y = int(fcen*sy) + 1 + nm_x = int(fcen * sx) + 1 + nm_y = int(fcen * sy) + 1 for m_x in range(nm_x): for m_y in range(nm_y): - for S_pol in [False,True]: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave([m_x,m_y,0], - mp.Vector3(1,0,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + for S_pol in [False, True]: + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave( + [m_x, m_y, 0], + mp.Vector3(1, 0, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) t_coeffs = res.alpha - Tmode = abs(t_coeffs[0,0,0])**2/input_flux[0] - print("tran-order:, {}, {}, {}, {:.6f}".format("s" if S_pol else "p",m_x,m_y,Tmode)) + Tmode = abs(t_coeffs[0, 0, 0]) ** 2 / input_flux[0] + print( + "tran-order:, {}, {}, {}, {:.6f}".format( + "s" if S_pol else "p", m_x, m_y, Tmode + ) + ) if m_x == 0 and m_y == 0: Tsum += Tmode elif (m_x != 0 and m_y == 0) or (m_x == 0 and m_y != 0): - Tsum += 2*Tmode + Tsum += 2 * Tmode else: - Tsum += 4*Tmode - + Tsum += 4 * Tmode r_flux = mp.get_fluxes(refl_flux) t_flux = mp.get_fluxes(tran_flux) - Rflux = -r_flux[0]/input_flux[0] - Tflux = t_flux[0]/input_flux[0] + Rflux = -r_flux[0] / input_flux[0] + Tflux = t_flux[0] / input_flux[0] - print("refl:, {}, {}".format(Rsum,Rflux)) - print("tran:, {}, {}".format(Tsum,Tflux)) - print("sum:, {}, {}".format(Rsum+Tsum,Rflux+Tflux)) + print(f"refl:, {Rsum}, {Rflux}") + print(f"tran:, {Tsum}, {Tflux}") + print(f"sum:, {Rsum + Tsum}, {Rflux + Tflux}") ## to obtain agreement for two decimal digits, ## the resolution must be increased to 200 - self.assertAlmostEqual(Rsum,Rflux,places=1) - self.assertAlmostEqual(Tsum,Tflux,places=2) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=1) - + self.assertAlmostEqual(Rsum, Rflux, places=1) + self.assertAlmostEqual(Tsum, Tflux, places=2) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=1) def test_triangular_lattice_oblique(self): """Unit test for mode decomposition in 3d with nonzero k_point. @@ -342,7 +412,7 @@ def test_triangular_lattice_oblique(self): glass = mp.Medium(index=ng) wvl = 0.5 - fcen = 1/wvl + fcen = 1 / wvl dpml = 1.0 dsub = 2.0 @@ -353,13 +423,13 @@ def test_triangular_lattice_oblique(self): a = 0.6 sx = a - sy = a*np.sqrt(3) + sy = a * np.sqrt(3) - sz = dpml+dsub+hcyl+dair+dpml + sz = dpml + dsub + hcyl + dair + dpml - cell_size = mp.Vector3(sx,sy,sz) + cell_size = mp.Vector3(sx, sy, sz) - boundary_layers = [mp.PML(thickness=dpml,direction=mp.Z)] + boundary_layers = [mp.PML(thickness=dpml, direction=mp.Z)] # plane of incidence is yz # 0° is +z with CCW rotation about x @@ -371,39 +441,44 @@ def test_triangular_lattice_oblique(self): # The planewave source is incident from within the high-index # medium which means ω = c|k|/n where n is the index of medium. # In Meep units (c=1), this implies |k| = nω. - k = mp.Vector3(0,0,ng*fcen).rotate(mp.Vector3(1,0,0),theta) + k = mp.Vector3(0, 0, ng * fcen).rotate(mp.Vector3(1, 0, 0), theta) - def pw_amp(k,x0): + def pw_amp(k, x0): def _pw_amp(x): - return cmath.exp(1j*2*math.pi*k.dot(x+x0)) + return cmath.exp(1j * 2 * math.pi * k.dot(x + x0)) + return _pw_amp - src_pt = mp.Vector3(0,0,-0.5*sz+dpml) - src_cmpt = mp.Ex # S-pol: Ex / P-pol: Ey - sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=0.1*fcen), - size=mp.Vector3(sx,sy,0), - center=src_pt, - component=src_cmpt, - amp_func=pw_amp(k,src_pt))] - - symmetries = [mp.Mirror(direction=mp.X, phase=-1 if src_cmpt==mp.Ex else +1)] - - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=glass, - boundary_layers=boundary_layers, - k_point=k, - symmetries=symmetries) - - refl_pt = mp.Vector3(0,0,-0.5*sz+dpml+0.5*dsub) - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) - - stop_cond = mp.stop_when_fields_decayed(25,src_cmpt,src_pt,1e-6) + src_pt = mp.Vector3(0, 0, -0.5 * sz + dpml) + src_cmpt = mp.Ex # S-pol: Ex / P-pol: Ey + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=0.1 * fcen), + size=mp.Vector3(sx, sy, 0), + center=src_pt, + component=src_cmpt, + amp_func=pw_amp(k, src_pt), + ) + ] + + symmetries = [mp.Mirror(direction=mp.X, phase=-1 if src_cmpt == mp.Ex else +1)] + + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + default_material=glass, + boundary_layers=boundary_layers, + k_point=k, + symmetries=symmetries, + ) + + refl_pt = mp.Vector3(0, 0, -0.5 * sz + dpml + 0.5 * dsub) + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) + + stop_cond = mp.stop_when_fields_decayed(25, src_cmpt, src_pt, 1e-6) sim.run(until_after_sources=stop_cond) input_flux = mp.get_fluxes(refl_flux)[0] @@ -411,55 +486,77 @@ def _pw_amp(x): sim.reset_meep() - substrate = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,dpml+dsub), - center=mp.Vector3(0,0,-0.5*sz+0.5*(dpml+dsub)), - material=glass)] - - grating = [mp.Cylinder(center=mp.Vector3(0,0,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass), - mp.Cylinder(center=mp.Vector3(-0.5*sx,-0.5*sy,-0.5*sz+dpml+dsub+0.5*hcyl), - radius=rcyl, - height=hcyl, - material=glass)] + substrate = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, dpml + dsub), + center=mp.Vector3(0, 0, -0.5 * sz + 0.5 * (dpml + dsub)), + material=glass, + ) + ] + + grating = [ + mp.Cylinder( + center=mp.Vector3(0, 0, -0.5 * sz + dpml + dsub + 0.5 * hcyl), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + 0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + -0.5 * sx, 0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + 0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + mp.Cylinder( + center=mp.Vector3( + -0.5 * sx, -0.5 * sy, -0.5 * sz + dpml + dsub + 0.5 * hcyl + ), + radius=rcyl, + height=hcyl, + material=glass, + ), + ] geometry = substrate + grating - sim = mp.Simulation(resolution=resolution, - cell_size=cell_size, - sources=sources, - geometry=geometry, - boundary_layers=boundary_layers, - k_point=k, - symmetries=symmetries) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + sources=sources, + geometry=geometry, + boundary_layers=boundary_layers, + k_point=k, + symmetries=symmetries, + ) - refl_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=refl_pt, - size=mp.Vector3(sx,sy,0))) + refl_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=refl_pt, size=mp.Vector3(sx, sy, 0)) + ) sim.load_minus_flux_data(refl_flux, input_flux_data) - tran_pt = mp.Vector3(0,0,0.5*sz-dpml) - tran_flux = sim.add_mode_monitor(fcen, - 0, - 1, - mp.ModeRegion(center=tran_pt, - size=mp.Vector3(sx,sy,0))) + tran_pt = mp.Vector3(0, 0, 0.5 * sz - dpml) + tran_flux = sim.add_mode_monitor( + fcen, 0, 1, mp.ModeRegion(center=tran_pt, size=mp.Vector3(sx, sy, 0)) + ) sim.run(until_after_sources=stop_cond) @@ -467,76 +564,107 @@ def _pw_amp(x): Tsum = 0 m = 5 tol = 1e-6 - for nx in range(-m,m+1): - for ny in range(-m,m+1): + for nx in range(-m, m + 1): + for ny in range(-m, m + 1): # convert supercell order to unit cell order mx = nx my = (nx + ny) // 2 # consider only propagating modes in high-index medium - kz2 = (ng*fcen)**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 + kz2 = (ng * fcen) ** 2 - (k.x + nx / sx) ** 2 - (k.y + ny / sy) ** 2 if kz2 > 0: Rpol = 0 for S_pol in [True, False]: - res = sim.get_eigenmode_coefficients(refl_flux, - mp.DiffractedPlanewave((nx,ny,0), - mp.Vector3(0,1,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + res = sim.get_eigenmode_coefficients( + refl_flux, + mp.DiffractedPlanewave( + (nx, ny, 0), + mp.Vector3(0, 1, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) coeffs = res.alpha - refl = abs(coeffs[0,0,1])**2 / input_flux + refl = abs(coeffs[0, 0, 1]) ** 2 / input_flux - pol_str = 'S' if S_pol else 'P' + pol_str = "S" if S_pol else "P" if refl > tol: # determine whether diffracted order is for the unit cell or super cell if (nx + ny) % 2 == 0: Rpol += refl - print("refl:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format(pol_str,mx,my,refl)) + print( + "refl:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format( + pol_str, mx, my, refl + ) + ) else: - print("refl:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format(pol_str,nx,ny,refl)) + print( + "refl:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format( + pol_str, nx, ny, refl + ) + ) Rsum += Rpol # consider only propagating modes in air - kz2 = fcen**2-(k.x+nx/sx)**2-(k.y+ny/sy)**2 + kz2 = fcen**2 - (k.x + nx / sx) ** 2 - (k.y + ny / sy) ** 2 if kz2 > 0: Tpol = 0 for S_pol in [True, False]: - res = sim.get_eigenmode_coefficients(tran_flux, - mp.DiffractedPlanewave((nx,ny,0), - mp.Vector3(0,1,0), - 1 if S_pol else 0, - 0 if S_pol else 1)) + res = sim.get_eigenmode_coefficients( + tran_flux, + mp.DiffractedPlanewave( + (nx, ny, 0), + mp.Vector3(0, 1, 0), + 1 if S_pol else 0, + 0 if S_pol else 1, + ), + ) coeffs = res.alpha - tran = abs(coeffs[0,0,0])**2 / input_flux + tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux - pol_str = 'S' if S_pol else 'P' + pol_str = "S" if S_pol else "P" if tran > tol: # determine whether diffracted order is for the unit cell or super cell if (nx + ny) % 2 == 0: Tpol += tran - print("tran:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format(pol_str,mx,my,tran)) + print( + "tran:, {}, {:2d}, {:2d}, {:.5f}, (unit cell)".format( + pol_str, mx, my, tran + ) + ) else: - print("tran:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format(pol_str,nx,ny,tran)) + print( + "tran:, {}, {:2d}, {:2d}, {:.7f}, (super cell)".format( + pol_str, nx, ny, tran + ) + ) Tsum += Tpol - Rflux = -mp.get_fluxes(refl_flux)[0] / input_flux - err = abs(Rflux-Rsum)/Rflux - print("refl:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Rflux,Rsum,err)) + err = abs(Rflux - Rsum) / Rflux + print( + "refl:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format( + Rflux, Rsum, err + ) + ) Tflux = mp.get_fluxes(tran_flux)[0] / input_flux - err = abs(Tflux-Tsum)/Tflux - print("tran:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format(Tflux,Tsum,err)) + err = abs(Tflux - Tsum) / Tflux + print( + "tran:, {:.6f} (flux), {:.6f} (orders), {:.6f} (error)".format( + Tflux, Tsum, err + ) + ) - self.assertAlmostEqual(Rsum,Rflux,places=3) - self.assertAlmostEqual(Tsum,Tflux,places=3) - self.assertAlmostEqual(Rsum+Tsum,1.00,places=2) + self.assertAlmostEqual(Rsum, Rflux, places=3) + self.assertAlmostEqual(Tsum, Tflux, places=3) + self.assertAlmostEqual(Rsum + Tsum, 1.00, places=2) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_mpb.py b/python/tests/test_mpb.py index e3242cfff..a95e22d05 100644 --- a/python/tests/test_mpb.py +++ b/python/tests/test_mpb.py @@ -8,26 +8,31 @@ import h5py import numpy as np -from scipy.optimize import minimize_scalar -from scipy.optimize import ridder +from scipy.optimize import minimize_scalar, ridder +from utils import compare_arrays + import meep as mp from meep import mpb -from utils import compare_arrays -@unittest.skipIf(os.getenv('MEEP_SKIP_LARGE_TESTS', False), 'skipping large tests') + +@unittest.skipIf(os.getenv("MEEP_SKIP_LARGE_TESTS", False), "skipping large tests") class TestModeSolver(unittest.TestCase): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - examples_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'examples')) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + examples_dir = os.path.abspath( + os.path.join(os.path.dirname(__file__), "..", "examples") + ) sys.path.insert(0, examples_dir) def setUp(self): self.start = time.time() - self.filename_prefix = os.path.join(self.temp_dir, self.id().split('.')[-1] + '-' + str(mp.my_rank())) + self.filename_prefix = os.path.join( + self.temp_dir, self.id().split(".")[-1] + "-" + str(mp.my_rank()) + ) print() print(self.filename_prefix) - print('=' * 24) + print("=" * 24) @classmethod def setUpClass(cls): @@ -35,7 +40,7 @@ def setUpClass(cls): def tearDown(self): end = time.time() - self.start - print("{}: {:.2f}s".format(self.filename_prefix, end)) + print(f"{self.filename_prefix}: {end:.2f}s") @classmethod def tearDownClass(cls): @@ -43,12 +48,7 @@ def tearDownClass(cls): def init_solver(self, geom=True): num_bands = 8 - k_points = [ - mp.Vector3(), - mp.Vector3(0.5), - mp.Vector3(0.5, 0.5), - mp.Vector3() - ] + k_points = [mp.Vector3(), mp.Vector3(0.5), mp.Vector3(0.5, 0.5), mp.Vector3()] geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] if geom else [] k_points = mp.interpolate(4, k_points) @@ -63,7 +63,7 @@ def init_solver(self, geom=True): resolution=resolution, filename_prefix=self.filename_prefix, deterministic=True, - tolerance=1e-12 + tolerance=1e-12, ) def test_attribute_accessors(self): @@ -78,8 +78,8 @@ def test_attribute_accessors(self): self.assertEqual(ms.mode_solver.get_libctl_ensure_periodicity(), True) self.assertEqual(ms.mode_solver.eigensolver_flops, 0) self.assertEqual(ms.mode_solver.negative_epsilon_ok, False) - self.assertEqual(ms.mode_solver.epsilon_input_file, '') - self.assertEqual(ms.mode_solver.mu_input_file, '') + self.assertEqual(ms.mode_solver.epsilon_input_file, "") + self.assertEqual(ms.mode_solver.mu_input_file, "") self.assertEqual(ms.mode_solver.force_mu, False) self.assertEqual(ms.mode_solver.use_simple_preconditioner, False) self.assertEqual(ms.mode_solver.eigensolver_nwork, 3) @@ -95,8 +95,8 @@ def test_attribute_accessors(self): ms.ensure_periodicity = False ms.eigensolver_flops = 20 ms.is_negative_epsilon_ok = True - ms.epsilon_input_file = 'test' - ms.mu_input_file = 'test' + ms.epsilon_input_file = "test" + ms.mu_input_file = "test" ms.force_mu = True ms.use_simple_preconditioner = True ms.eigensolver_nwork = 4 @@ -111,8 +111,8 @@ def test_attribute_accessors(self): self.assertEqual(ms.mode_solver.get_libctl_ensure_periodicity(), False) self.assertEqual(ms.mode_solver.eigensolver_flops, 20) self.assertEqual(ms.mode_solver.negative_epsilon_ok, True) - self.assertEqual(ms.mode_solver.epsilon_input_file, 'test') - self.assertEqual(ms.mode_solver.mu_input_file, 'test') + self.assertEqual(ms.mode_solver.epsilon_input_file, "test") + self.assertEqual(ms.mode_solver.mu_input_file, "test") self.assertEqual(ms.mode_solver.force_mu, True) self.assertEqual(ms.mode_solver.use_simple_preconditioner, True) self.assertEqual(ms.mode_solver.eigensolver_nwork, 4) @@ -129,12 +129,7 @@ def test_resolution(self): ms.resolution = [32, 32, 32] def test_list_split(self): - k_points = [ - mp.Vector3(), - mp.Vector3(0.5), - mp.Vector3(0.5, 0.5), - mp.Vector3() - ] + k_points = [mp.Vector3(), mp.Vector3(0.5), mp.Vector3(0.5, 0.5), mp.Vector3()] k_points = mp.interpolate(4, k_points) ms = mpb.ModeSolver() @@ -167,10 +162,7 @@ def test_list_split(self): def test_first_brillouin_zone(self): ms = self.init_solver() - res = [] - for k in ms.k_points: - res.append(ms.first_brillouin_zone(k)) - + res = [ms.first_brillouin_zone(k) for k in ms.k_points] expected = [ mp.Vector3(0.0, 0.0, 0.0), mp.Vector3(0.10000000000000003, 0.0, 0.0), @@ -199,13 +191,17 @@ def check_band_range_data(self, expected_brd, result, places=3, tol=1e-3): self.assertAlmostEqual(exp[0][0], res[0][0], places=places) # Compare min k msg = "expected {}, got {}" - self.assertTrue(exp[0][1].close(res[0][1], tol=tol), - msg=msg.format(exp[0][1], res[0][1])) + self.assertTrue( + exp[0][1].close(res[0][1], tol=tol), + msg=msg.format(exp[0][1], res[0][1]), + ) # Compare max freqs self.assertAlmostEqual(exp[1][0], res[1][0], places=places) # Compare max k - self.assertTrue(exp[1][1].close(res[1][1], tol=tol), - msg=msg.format(exp[1][1], res[1][1])) + self.assertTrue( + exp[1][1].close(res[1][1], tol=tol), + msg=msg.format(exp[1][1], res[1][1]), + ) def check_freqs(self, expected_freqs, result): for exp, res in zip(expected_freqs, result): @@ -215,13 +211,13 @@ def check_freqs(self, expected_freqs, result): def check_gap_list(self, expected_gap_list, result): self.check_freqs(expected_gap_list, result) - def check_fields_against_h5(self, ref_path, field, suffix=''): - with h5py.File(ref_path, 'r') as ref: + def check_fields_against_h5(self, ref_path, field, suffix=""): + with h5py.File(ref_path, "r") as ref: # Reshape the reference data into a component-wise 1d array like # [x1,y1,z1,x2,y2,z2,etc.] - ref_x = mp.complexarray(ref["x.r{}".format(suffix)][()], ref["x.i{}".format(suffix)][()]) - ref_y = mp.complexarray(ref["y.r{}".format(suffix)][()], ref["y.i{}".format(suffix)][()]) - ref_z = mp.complexarray(ref["z.r{}".format(suffix)][()], ref["z.i{}".format(suffix)][()]) + ref_x = mp.complexarray(ref[f"x.r{suffix}"][()], ref[f"x.i{suffix}"][()]) + ref_y = mp.complexarray(ref[f"y.r{suffix}"][()], ref[f"y.i{suffix}"][()]) + ref_z = mp.complexarray(ref[f"z.r{suffix}"][()], ref[f"z.i{suffix}"][()]) ref_arr = np.zeros(np.prod(field.shape), dtype=np.complex128) ref_arr[0::3] = ref_x.ravel() @@ -232,18 +228,25 @@ def check_fields_against_h5(self, ref_path, field, suffix=''): def compare_h5_files(self, ref_path, res_path, tol=1e-3): with h5py.File(ref_path) as ref: - with h5py.File(res_path, 'r') as res: + with h5py.File(res_path, "r") as res: for k in ref.keys(): - if k == 'description': + if k == "description": self.assertEqual(ref[k][()], res[k][()]) else: compare_arrays(self, ref[k][()], res[k][()], tol=tol) def test_update_band_range_data(self): brd = [] - freqs = [0.0, 1.0000000001231053, 1.0000000001577114, 1.000000000183077, - 1.0000000003647922, 1.4142135627385737, 1.4142135630373556, - 1.4142135634172286] + freqs = [ + 0.0, + 1.0000000001231053, + 1.0000000001577114, + 1.000000000183077, + 1.0000000003647922, + 1.4142135627385737, + 1.4142135630373556, + 1.4142135634172286, + ] kpoint = mp.Vector3() expected = [ @@ -263,27 +266,50 @@ def test_update_band_range_data(self): def test_run_te_no_geometry(self): - expected_freqs = [0.0, 1.0, 1.0000000000000004, 1.0000000000000013, - 1.0000000000000016, 1.4142135623730958, 1.4142135623730965, - 1.414213562373097] + expected_freqs = [ + 0.0, + 1.0, + 1.0000000000000004, + 1.0000000000000013, + 1.0000000000000016, + 1.4142135623730958, + 1.4142135623730965, + 1.414213562373097, + ] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.7071067811865476, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5000000000350678, mp.Vector3(0.5, 0.0, 0.0)), - (1.0000000000464613, mp.Vector3(0.0, 0.0, 0.0))), - ((0.7071067811884221, mp.Vector3(0.5, 0.5, 0.0)), - (1.1180339887555244, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7071067811901163, mp.Vector3(0.5, 0.5, 0.0)), - (1.1313708499266775, mp.Vector3(0.2, 0.2, 0.0))), - ((1.000000000001028, mp.Vector3(0.0, 0.0, 0.0)), - (1.5811388300846416, mp.Vector3(0.5, 0.5, 0.0))), - ((1.1180339887687103, mp.Vector3(0.5, 0.0, 0.0)), - (1.5811388300858549, mp.Vector3(0.5, 0.5, 0.0))), - ((1.2806248475163993, mp.Vector3(0.20000000000000004, 0.0, 0.0)), - (1.5811388300892486, mp.Vector3(0.5, 0.5, 0.0))), - ((1.4142135623752818, mp.Vector3(0.0, 0.0, 0.0)), - (1.8027756376524435, mp.Vector3(0.5, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.7071067811865476, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5000000000350678, mp.Vector3(0.5, 0.0, 0.0)), + (1.0000000000464613, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.7071067811884221, mp.Vector3(0.5, 0.5, 0.0)), + (1.1180339887555244, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7071067811901163, mp.Vector3(0.5, 0.5, 0.0)), + (1.1313708499266775, mp.Vector3(0.2, 0.2, 0.0)), + ), + ( + (1.000000000001028, mp.Vector3(0.0, 0.0, 0.0)), + (1.5811388300846416, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.1180339887687103, mp.Vector3(0.5, 0.0, 0.0)), + (1.5811388300858549, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.2806248475163993, mp.Vector3(0.20000000000000004, 0.0, 0.0)), + (1.5811388300892486, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.4142135623752818, mp.Vector3(0.0, 0.0, 0.0)), + (1.8027756376524435, mp.Vector3(0.5, 0.0, 0.0)), + ), ] ms = self.init_solver(geom=False) @@ -299,27 +325,53 @@ def test_run_te_no_geometry(self): def test_run_te(self): - expected_freqs = [0.0, 0.5527092320101986, 0.7732265593069759, 0.773229883948054, - 0.9229965195855876, 1.0001711176882833, 1.0001720032257042, - 1.092820931747826] + expected_freqs = [ + 0.0, + 0.5527092320101986, + 0.7732265593069759, + 0.773229883948054, + 0.9229965195855876, + 1.0001711176882833, + 1.0001720032257042, + 1.092820931747826, + ] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), - (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), - (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), - (0.80968915516771, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), - (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), - (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), - (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0))), - ((1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), - (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), + (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), + (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.80968915516771, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + (0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), + (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), + (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), + (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), + (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0)), + ), ] expected_gap_list = [ @@ -344,22 +396,41 @@ def test_run_te(self): def test_run_tm(self): expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.28094795352537366, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4171142493246634, mp.Vector3(0.5, 0.0, 0.0)), - (0.5460267793370319, mp.Vector3(0.0, 0.0, 0.0))), - ((0.49619745276546123, mp.Vector3(0.5, 0.5, 0.0)), - (0.5576576362977246, mp.Vector3(0.5, 0.0, 0.0))), - ((0.5520955864542503, mp.Vector3(0.0, 0.0, 0.0)), - (0.7133951516423513, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7413109657068678, mp.Vector3(0.5, 0.0, 0.0)), - (0.8594741069571248, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8295176150251525, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.8783155473463833, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8625159053811312, mp.Vector3(0.5, 0.0, 0.0)), - (0.9511074539064021, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8793510958294801, mp.Vector3(0.5, 0.5, 0.0)), - (1.0825923841450287, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.28094795352537366, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4171142493246634, mp.Vector3(0.5, 0.0, 0.0)), + (0.5460267793370319, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.49619745276546123, mp.Vector3(0.5, 0.5, 0.0)), + (0.5576576362977246, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.5520955864542503, mp.Vector3(0.0, 0.0, 0.0)), + (0.7133951516423513, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7413109657068678, mp.Vector3(0.5, 0.0, 0.0)), + (0.8594741069571248, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + ( + 0.8295176150251525, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.8783155473463833, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.8625159053811312, mp.Vector3(0.5, 0.0, 0.0)), + (0.9511074539064021, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8793510958294801, mp.Vector3(0.5, 0.5, 0.0)), + (1.0825923841450287, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms = self.init_solver() @@ -370,37 +441,37 @@ def test_run_tm(self): def _test_get_field(self, field): ms = self.init_solver() ms.run_te() - get_field_func = getattr(ms, "get_{}field".format(field)) - fix_phase_func = getattr(mpb, "fix_{}field_phase".format(field)) + get_field_func = getattr(ms, f"get_{field}field") + fix_phase_func = getattr(mpb, f"fix_{field}field_phase") fix_phase_func(ms, ms.num_bands) fields = get_field_func(ms.num_bands) - ref_fname = "tutorial-{}.k16.b08.te.h5".format(field) + ref_fname = f"tutorial-{field}.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fname) self.check_fields_against_h5(ref_path, fields) def test_get_bfield(self): - self._test_get_field('b') + self._test_get_field("b") def test_get_efield(self): - self._test_get_field('e') + self._test_get_field("e") def test_get_dfield(self): - self._test_get_field('d') + self._test_get_field("d") def test_get_hfield(self): - self._test_get_field('h') + self._test_get_field("h") def test_output_field_to_file(self): - fname = 'tutorial-epsilon.h5' + fname = "tutorial-epsilon.h5" data_path = os.path.join(self.data_dir, fname) ms = self.init_solver() ms.run_te() ms.output_epsilon() - res_path = self.filename_prefix + '-epsilon.h5' + res_path = f"{self.filename_prefix}-epsilon.h5" self.compare_h5_files(data_path, res_path) def test_compute_field_energy(self): @@ -418,21 +489,21 @@ def test_compute_field_energy(self): self.assertAlmostEqual(pt, 1.330368347216153e-9) expected_fp = mp.Vector3( - 2.582338850993523e-05+6.674311589555886e-12j, - -2.5759589225968237e-05-6.657825513845581e-12j, - 0.0 - 0.0j + 2.582338850993523e-05 + 6.674311589555886e-12j, + -2.5759589225968237e-05 - 6.657825513845581e-12j, + 0.0 - 0.0j, ) expected_bloch_fp = mp.Vector3( 2.5823356723958247e-5 + 6.713243287584132e-12j, -2.575955745071957e-5 - 6.696552990958943e-12j, - -0.0 - 0.0j + -0.0 - 0.0j, ) expected_eps_inv_tensor = mp.Matrix( mp.Vector3(1.0 + 0.0j, 0.0 - 0.0j, 0.0 - 0.0j), mp.Vector3(0.0 + 0.0j, 1.0 + 0.0j, 0.0 - 0.0j), - mp.Vector3(0.0 + 0.0j, 0.0 + 0.0j, 1.0 + 0.0j) + mp.Vector3(0.0 + 0.0j, 0.0 + 0.0j, 1.0 + 0.0j), ) self.assertTrue(expected_fp.close(field_pt)) @@ -443,13 +514,22 @@ def test_compute_field_energy(self): energy_in_dielectric = ms.compute_energy_in_dielectric(0, 1) - expected_energy = [1.0000000000000002, 1.726755206037815e-5, 0.4999827324479414, - 1.7267552060375955e-5, 0.4999827324479377, 0.0, 0.0] + expected_energy = [ + 1.0000000000000002, + 1.726755206037815e-5, + 0.4999827324479414, + 1.7267552060375955e-5, + 0.4999827324479377, + 0.0, + 0.0, + ] expected_energy_in_dielectric = 0.6990769686037558 compare_arrays(self, np.array(expected_energy), np.array(energy)) - self.assertAlmostEqual(expected_energy_in_dielectric, energy_in_dielectric, places=3) + self.assertAlmostEqual( + expected_energy_in_dielectric, energy_in_dielectric, places=3 + ) def test_compute_group_velocity(self): ms = self.init_solver() @@ -459,8 +539,13 @@ def test_compute_group_velocity(self): res3 = ms.compute_one_group_velocity_component(mp.Vector3(0.5, 0.5), 8) expected1 = [ - 0.0, 1.470762578355642e-4, -1.4378185933055663e-4, 1.1897503996483383e-4, - -4.892687048681629e-4, 1.1240346140784176e-4, 1.5842474585356007e-4, + 0.0, + 1.470762578355642e-4, + -1.4378185933055663e-4, + 1.1897503996483383e-4, + -4.892687048681629e-4, + 1.1240346140784176e-4, + 1.5842474585356007e-4, 4.496945573323881e-5, ] @@ -479,23 +564,23 @@ def test_output_efield_z(self): mpb.fix_efield_phase(ms, 8) mpb.output_efield_z(ms, 8) - ref_fname = 'tutorial-e.k16.b08.z.tm.h5' + ref_fname = "tutorial-e.k16.b08.z.tm.h5" ref_path = os.path.join(self.data_dir, ref_fname) - res_path = re.sub('tutorial', ms.filename_prefix, ref_fname) + res_path = re.sub("tutorial", ms.filename_prefix, ref_fname) self.compare_h5_files(ref_path, res_path) def test_output_dpwr_in_objects(self): ms = self.init_solver() ms.run_te(mpb.output_dpwr_in_objects(mpb.output_dfield, 0.85, ms.geometry)) - ref_fname1 = 'tutorial-d.k01.b02.te.h5' - ref_fname2 = 'tutorial-d.k16.b02.te.h5' + ref_fname1 = "tutorial-d.k01.b02.te.h5" + ref_fname2 = "tutorial-d.k16.b02.te.h5" ref_path1 = os.path.join(self.data_dir, ref_fname1) ref_path2 = os.path.join(self.data_dir, ref_fname2) - res_path1 = re.sub('tutorial', ms.filename_prefix, ref_fname1) - res_path2 = re.sub('tutorial', ms.filename_prefix, ref_fname2) + res_path1 = re.sub("tutorial", ms.filename_prefix, ref_fname1) + res_path2 = re.sub("tutorial", ms.filename_prefix, ref_fname2) self.compare_h5_files(ref_path1, res_path1) self.compare_h5_files(ref_path2, res_path2) @@ -503,36 +588,70 @@ def test_output_dpwr_in_objects(self): def test_triangular_lattice(self): expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.2746902258623623, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.44533108084715683, mp.Vector3(0.0, 0.5, 0.0)), - (0.5605181423162835, mp.Vector3(0.0, 0.0, 0.0))), - ((0.4902389149027666, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.5605607947797747, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5932960873585144, mp.Vector3(0.0, 0.0, 0.0)), - (0.7907195974443698, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.790832076332758, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.8374511167537562, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8375948528443267, mp.Vector3(0.0, 0.0, 0.0)), - (0.867200926490345, mp.Vector3(-0.2, 0.39999999999999997, 0.0))), - ((0.8691349955739203, mp.Vector3(-0.13333333333333336, 0.4333333333333333, 0.0)), - (0.9941291022664892, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8992499095547049, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (1.098318352915696, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.2746902258623623, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.44533108084715683, mp.Vector3(0.0, 0.5, 0.0)), + (0.5605181423162835, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.4902389149027666, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.5605607947797747, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5932960873585144, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.7907195974443698, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.790832076332758, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.8374511167537562, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8375948528443267, mp.Vector3(0.0, 0.0, 0.0)), + (0.867200926490345, mp.Vector3(-0.2, 0.39999999999999997, 0.0)), + ), + ( + ( + 0.8691349955739203, + mp.Vector3(-0.13333333333333336, 0.4333333333333333, 0.0), + ), + (0.9941291022664892, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8992499095547049, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (1.098318352915696, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms = self.init_solver() ms.geometry_lattice = mp.Lattice( size=mp.Vector3(1, 1), basis1=mp.Vector3(math.sqrt(3) / 2, 0.5), - basis2=mp.Vector3(math.sqrt(3) / 2, -0.5) + basis2=mp.Vector3(math.sqrt(3) / 2, -0.5), ) k_points = [ mp.Vector3(), mp.Vector3(y=0.5), mp.Vector3(-1 / 3, 1 / 3), - mp.Vector3() + mp.Vector3(), ] ms.k_points = mp.interpolate(4, k_points) @@ -541,7 +660,6 @@ def test_triangular_lattice(self): self.check_band_range_data(expected_brd, ms.band_range_data) def test_maximize_first_tm_gap(self): - def first_tm_gap(r): ms.geometry = [mp.Cylinder(r, material=mp.Medium(epsilon=12))] ms.run_tm() @@ -551,34 +669,53 @@ def first_tm_gap(r): ms.num_bands = 2 ms.mesh_size = 7 - result = minimize_scalar(first_tm_gap, method='bounded', bounds=[0.1, 0.5], options={'xatol': 0.1}) + result = minimize_scalar( + first_tm_gap, method="bounded", bounds=[0.1, 0.5], options={"xatol": 0.1} + ) expected = 39.10325687542367 self.assertAlmostEqual(expected, result.fun * -1, places=2) def test_anisotropic_2d_gap(self): expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.2213165540404889, mp.Vector3(0.5, 0.5, 0.0))), - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.23068427462181276, mp.Vector3(0.5, 0.5, 0.0))), - ((0.21691192680454566, mp.Vector3(0.5, 0.0, 0.0)), - (0.319020283148369, mp.Vector3(0.0, 0.0, 0.0))), - ((0.30110868065428525, mp.Vector3(0.5, 0.0, 0.0)), - (0.3648353129125716, mp.Vector3(0.0, 0.0, 0.0))), - ((0.30701621910773247, mp.Vector3(0.5, 0.5, 0.0)), - (0.3852513546698513, mp.Vector3(0.1, 0.1, 0.0))), - ((0.341835260571013, mp.Vector3(0.5, 0.30000000000000004, 0.0)), - (0.391421048600237, mp.Vector3(0.5, 0.10000000000000003, 0.0))), - ((0.34982139739904294, mp.Vector3(0.5, 0.5, 0.0)), - (0.4075642914057991, mp.Vector3(0.4, 0.0, 0.0))), - ((0.3963465468598276, mp.Vector3(0.1, 0.1, 0.0)), - (0.4772237204606825, mp.Vector3(0.5, 0.5, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.2213165540404889, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.23068427462181276, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.21691192680454566, mp.Vector3(0.5, 0.0, 0.0)), + (0.319020283148369, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.30110868065428525, mp.Vector3(0.5, 0.0, 0.0)), + (0.3648353129125716, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.30701621910773247, mp.Vector3(0.5, 0.5, 0.0)), + (0.3852513546698513, mp.Vector3(0.1, 0.1, 0.0)), + ), + ( + (0.341835260571013, mp.Vector3(0.5, 0.30000000000000004, 0.0)), + (0.391421048600237, mp.Vector3(0.5, 0.10000000000000003, 0.0)), + ), + ( + (0.34982139739904294, mp.Vector3(0.5, 0.5, 0.0)), + (0.4075642914057991, mp.Vector3(0.4, 0.0, 0.0)), + ), + ( + (0.3963465468598276, mp.Vector3(0.1, 0.1, 0.0)), + (0.4772237204606825, mp.Vector3(0.5, 0.5, 0.0)), + ), ] ms = self.init_solver() - ms.geometry = [mp.Cylinder(0.3, material=mp.Medium(epsilon_diag=mp.Vector3(1, 1, 12)))] + ms.geometry = [ + mp.Cylinder(0.3, material=mp.Medium(epsilon_diag=mp.Vector3(1, 1, 12))) + ] ms.default_material = mp.Medium(epsilon_diag=mp.Vector3(12, 12, 1)) ms.num_bands = 8 ms.run() @@ -591,7 +728,9 @@ def test_point_defect_state(self): ms.geometry_lattice = mp.Lattice(size=mp.Vector3(5, 5)) ms.geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=12))] - ms.geometry = mp.geometric_objects_lattice_duplicates(ms.geometry_lattice, ms.geometry) + ms.geometry = mp.geometric_objects_lattice_duplicates( + ms.geometry_lattice, ms.geometry + ) ms.geometry.append(mp.Cylinder(0.2, material=mp.air)) ms.resolution = 16 @@ -616,8 +755,10 @@ def test_point_defect_state(self): ms.run_tm() expected_brd = [ - ((0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0)), - (0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0)), + (0.37730041222979477, mp.Vector3(0.5, 0.5, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -625,7 +766,9 @@ def test_point_defect_state(self): old_geometry = ms.geometry # save the 5x5 grid with a missing rod def rootfun(eps): - ms.geometry = old_geometry + [mp.Cylinder(0.2, material=mp.Medium(epsilon=eps))] + ms.geometry = old_geometry + [ + mp.Cylinder(0.2, material=mp.Medium(epsilon=eps)) + ] ms.run_tm() return ms.get_freqs()[0] - 0.314159 @@ -641,42 +784,60 @@ def test_output_charge_density(self): mpb.fix_efield_phase(ms, 8) mpb.output_charge_density(ms, 8) - ref_fn = 'tutorial-C.k16.b08.te.h5' + ref_fn = "tutorial-C.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('tutorial', ms.filename_prefix, ref_fn) + res_path = re.sub("tutorial", ms.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_bragg_sine(self): from mpb_bragg_sine import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix ms.run_tm() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.19477466366820298, mp.Vector3(0.5, 0.0, 0.0))), - ((0.306403026230844, mp.Vector3(0.5, 0.0, 0.0)), - (0.4687748525867193, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5466257501317459, mp.Vector3(0.0, 0.0, 0.0)), - (0.7316504426541637, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7842615905093812, mp.Vector3(0.5, 0.0, 0.0)), - (0.9893486155437277, mp.Vector3(0.0, 0.0, 0.0))), - ((1.0240548648147831, mp.Vector3(0.0, 0.0, 0.0)), - (1.244098004202588, mp.Vector3(0.5, 0.0, 0.0))), - ((1.266656686185507, mp.Vector3(0.5, 0.0, 0.0)), - (1.4970379696966822, mp.Vector3(0.0, 0.0, 0.0))), - ((1.5115800994652884, mp.Vector3(0.0, 0.0, 0.0)), - (1.7488359039910502, mp.Vector3(0.5, 0.0, 0.0))), - ((1.7581683208483643, mp.Vector3(0.5, 0.0, 0.0)), - (1.9999072007119787, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.19477466366820298, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.306403026230844, mp.Vector3(0.5, 0.0, 0.0)), + (0.4687748525867193, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5466257501317459, mp.Vector3(0.0, 0.0, 0.0)), + (0.7316504426541637, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7842615905093812, mp.Vector3(0.5, 0.0, 0.0)), + (0.9893486155437277, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.0240548648147831, mp.Vector3(0.0, 0.0, 0.0)), + (1.244098004202588, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.266656686185507, mp.Vector3(0.5, 0.0, 0.0)), + (1.4970379696966822, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.5115800994652884, mp.Vector3(0.0, 0.0, 0.0)), + (1.7488359039910502, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.7581683208483643, mp.Vector3(0.5, 0.0, 0.0)), + (1.9999072007119787, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) def test_bragg(self): from mpb_bragg import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix @@ -684,14 +845,15 @@ def test_bragg(self): mpb.fix_hfield_phase(ms, 8) mpb.output_hfield_y(ms, 8) - ref_fn = 'bragg-h.k01.b08.y.tm.h5' + ref_fn = "bragg-h.k01.b08.y.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('bragg', self.filename_prefix, ref_fn) + res_path = re.sub("bragg", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_diamond(self): from mpb_diamond import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -700,46 +862,62 @@ def test_diamond(self): def get_dpwr(ms, band): dpwr.append(ms.get_dpwr(band)) - ms.run(mpb.output_at_kpoint(mp.Vector3(0, 0.625, 0.375), - mpb.fix_dfield_phase, - mpb.output_dpwr, - get_dpwr)) + ms.run( + mpb.output_at_kpoint( + mp.Vector3(0, 0.625, 0.375), + mpb.fix_dfield_phase, + mpb.output_dpwr, + get_dpwr, + ) + ) expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.39455107895905156, mp.Vector3(0.25, 0.75, 0.5))), - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.3967658014080592, mp.Vector3(0.29999999999999993, 0.75, 0.45000000000000007))), - ((0.4423707668172989, mp.Vector3(0.0, 0.5, 0.0)), - (0.5955899630254676, mp.Vector3(0.0, 0.0, 0.0))), - ((0.44355516512198145, mp.Vector3(0.0, 0.5, 0.0)), - (0.5958191312898851, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5030135895148902, mp.Vector3(0.0, 0.6, 0.4)), - (0.5958386856926985, mp.Vector3(0.0, 0.0, 0.0))), - - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.39455107895905156, mp.Vector3(0.25, 0.75, 0.5)), + ), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.3967658014080592, + mp.Vector3(0.29999999999999993, 0.75, 0.45000000000000007), + ), + ), + ( + (0.4423707668172989, mp.Vector3(0.0, 0.5, 0.0)), + (0.5955899630254676, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.44355516512198145, mp.Vector3(0.0, 0.5, 0.0)), + (0.5958191312898851, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5030135895148902, mp.Vector3(0.0, 0.6, 0.4)), + (0.5958386856926985, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) - ref_fn = 'diamond-dpwr.k06.b05.h5' + ref_fn = "diamond-dpwr.k06.b05.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('diamond', self.filename_prefix, ref_fn) + res_path = re.sub("diamond", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) # Test MPBData.convert() md = mpb.MPBData(rectify=True, periods=2, resolution=32) converted_dpwr = [md.convert(d) for d in dpwr] - ref_fn = 'converted-diamond-dpwr.k06.b05.h5' + ref_fn = "converted-diamond-dpwr.k06.b05.h5" ref_path = os.path.join(self.data_dir, ref_fn) - with h5py.File(ref_path, 'r') as f: - expected = f['data-new'][()] + with h5py.File(ref_path, "r") as f: + expected = f["data-new"][()] compare_arrays(self, expected, converted_dpwr[-1]) def test_hole_slab(self): from mpb_hole_slab import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.k_points = [mp.Vector3(1 / -3, 1 / 3)] @@ -749,80 +927,149 @@ def test_hole_slab(self): mpb.fix_hfield_phase(ms, 9) mpb.output_hfield_z(ms, 9) - ref_fn = 'hole-slab-h.k01.b09.z.zeven.h5' + ref_fn = "hole-slab-h.k01.b09.z.zeven.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('hole-slab', self.filename_prefix, ref_fn) + res_path = re.sub("hole-slab", self.filename_prefix, ref_fn) ms.display_eigensolver_stats() self.compare_h5_files(ref_path, res_path) def test_honey_rods(self): from mpb_honey_rods import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 expected_tm_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.3351167660354989, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.3351850759916969, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.42984811237816406, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5751709345431462, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7255897672261712, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6918627724774271, mp.Vector3(0.0, 0.5, 0.0)), - (0.747622077830657, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.7443374497087805, mp.Vector3(-0.06666666666666667, 0.06666666666666667, 0.0)), - (0.7793792212092525, mp.Vector3(0.0, 0.5, 0.0))), - ((0.7852786984418492, mp.Vector3(0.0, 0.0, 0.0)), - (0.8193652861712535, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.7856577771856611, mp.Vector3(0.0, 0.0, 0.0)), - (0.9122560439014182, mp.Vector3(0.0, 0.5, 0.0))), - ((1.0540350508135123, mp.Vector3(0.0, 0.5, 0.0)), - (1.1492769389234725, mp.Vector3(0.0, 0.0, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.3351167660354989, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.3351850759916969, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.42984811237816406, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.5751709345431462, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7255897672261712, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6918627724774271, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.747622077830657, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.7443374497087805, + mp.Vector3(-0.06666666666666667, 0.06666666666666667, 0.0), + ), + (0.7793792212092525, mp.Vector3(0.0, 0.5, 0.0)), + ), + ( + (0.7852786984418492, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.8193652861712535, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.7856577771856611, mp.Vector3(0.0, 0.0, 0.0)), + (0.9122560439014182, mp.Vector3(0.0, 0.5, 0.0)), + ), + ( + (1.0540350508135123, mp.Vector3(0.0, 0.5, 0.0)), + (1.1492769389234725, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms.run_tm() self.check_band_range_data(expected_tm_brd, ms.band_range_data) expected_te_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.5535093489972593, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.5203183590840945, mp.Vector3(0.0, 0.5, 0.0)), - (0.7278447515454929, mp.Vector3(0.0, 0.0, 0.0))), - ((0.576335859651312, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7880878930618354, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8161730293674944, mp.Vector3(0.0, 0.5, 0.0)), - (0.9209611432140968, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8385562359606971, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.9220849425898038, mp.Vector3(0.0, 0.0, 0.0))), - ((1.0168656683915511, mp.Vector3(0.0, 0.0, 0.0)), - (1.1083536673418435, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((1.0184507253059425, mp.Vector3(0.0, 0.0, 0.0)), - (1.159370227370719, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((1.1636719050364361, mp.Vector3(-0.2, 0.2, 0.0)), - (1.2433411839870618, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.5535093489972593, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.5203183590840945, mp.Vector3(0.0, 0.5, 0.0)), + (0.7278447515454929, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.576335859651312, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7880878930618354, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8161730293674944, mp.Vector3(0.0, 0.5, 0.0)), + (0.9209611432140968, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8385562359606971, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.9220849425898038, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.0168656683915511, mp.Vector3(0.0, 0.0, 0.0)), + ( + 1.1083536673418435, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (1.0184507253059425, mp.Vector3(0.0, 0.0, 0.0)), + ( + 1.159370227370719, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (1.1636719050364361, mp.Vector3(-0.2, 0.2, 0.0)), + (1.2433411839870618, mp.Vector3(0.0, 0.0, 0.0)), + ), ] ms.run_te() self.check_band_range_data(expected_te_brd, ms.band_range_data) def test_line_defect(self): - from mpb_line_defect import ms, k_points + from mpb_line_defect import k_points, ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 - ms.run_tm(mpb.output_at_kpoint(k_points[len(k_points) // 2]), - mpb.fix_efield_phase, - mpb.output_efield_z) + ms.run_tm( + mpb.output_at_kpoint(k_points[len(k_points) // 2]), + mpb.fix_efield_phase, + mpb.output_efield_z, + ) - ref_fn = 'line-defect-e.k04.b12.z.tm.h5' + ref_fn = "line-defect-e.k04.b12.z.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('line-defect', self.filename_prefix, ref_fn) + res_path = re.sub("line-defect", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_sq_rods(self): from mpb_sq_rods import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -830,22 +1077,44 @@ def test_sq_rods(self): ms.run_te() expected_te_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.5036058015219026, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4446229134706744, mp.Vector3(0.5, 0.0, 0.0)), - (0.5943440245519593, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5943566394470321, mp.Vector3(0.5, 0.5, 0.0)), - (0.7808428121911926, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6793887413076383, mp.Vector3(0.5, 0.5, 0.0)), - (0.8173893719542897, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.83045738223392, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.9243716830585584, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8957817684117546, mp.Vector3(0.5, 0.5, 0.0)), - (1.0331104139200438, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8957868745330811, mp.Vector3(0.5, 0.5, 0.0)), - (1.0958021492221048, mp.Vector3(0.5, 0.0, 0.0))), - ((1.097416809585406, mp.Vector3(0.5, 0.0, 0.0)), - (1.1280127648119964, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.5036058015219026, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4446229134706744, mp.Vector3(0.5, 0.0, 0.0)), + (0.5943440245519593, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5943566394470321, mp.Vector3(0.5, 0.5, 0.0)), + (0.7808428121911926, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6793887413076383, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.8173893719542897, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + ( + 0.83045738223392, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.9243716830585584, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8957817684117546, mp.Vector3(0.5, 0.5, 0.0)), + (1.0331104139200438, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8957868745330811, mp.Vector3(0.5, 0.5, 0.0)), + (1.0958021492221048, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.097416809585406, mp.Vector3(0.5, 0.0, 0.0)), + (1.1280127648119964, mp.Vector3(0.5, 0.5, 0.0)), + ), ] self.check_band_range_data(expected_te_brd, ms.band_range_data) @@ -853,28 +1122,48 @@ def test_sq_rods(self): ms.run_tm() expected_tm_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.285905779119655, mp.Vector3(0.5, 0.5, 0.0))), - ((0.42065733839975095, mp.Vector3(0.5, 0.0, 0.0)), - (0.5503360754831277, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5029830978365978, mp.Vector3(0.5, 0.5, 0.0)), - (0.5671632878128118, mp.Vector3(0.5, 0.0, 0.0))), - ((0.5613397939889757, mp.Vector3(0.0, 0.0, 0.0)), - (0.7200918204563993, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7472029910480836, mp.Vector3(0.5, 0.0, 0.0)), - (0.874359380500508, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8404509697526803, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.8833173725822788, mp.Vector3(0.5, 0.5, 0.0))), - ((0.8770118718330763, mp.Vector3(0.5, 0.0, 0.0)), - (0.9653253808981632, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8929933495598104, mp.Vector3(0.5, 0.5, 0.0)), - (1.089377682009333, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.285905779119655, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.42065733839975095, mp.Vector3(0.5, 0.0, 0.0)), + (0.5503360754831277, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.5029830978365978, mp.Vector3(0.5, 0.5, 0.0)), + (0.5671632878128118, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.5613397939889757, mp.Vector3(0.0, 0.0, 0.0)), + (0.7200918204563993, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7472029910480836, mp.Vector3(0.5, 0.0, 0.0)), + (0.874359380500508, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + ( + 0.8404509697526803, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.8833173725822788, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.8770118718330763, mp.Vector3(0.5, 0.0, 0.0)), + (0.9653253808981632, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8929933495598104, mp.Vector3(0.5, 0.5, 0.0)), + (1.089377682009333, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_tm_brd, ms.band_range_data) def test_strip(self): from mpb_strip import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -884,8 +1173,18 @@ def test_strip(self): y_parities = ms.mode_solver.compute_yparities() z_parities = ms.mode_solver.compute_zparities() - expected_y_parities = [-0.9997979443175137, 1.0000061871738222, -1.000010781704281, -0.9997880312884855] - expected_z_parities = [0.9992335747085693, -0.9955122771195629, -0.9970929846091117, -0.9945407488966614] + expected_y_parities = [ + -0.9997979443175137, + 1.0000061871738222, + -1.000010781704281, + -0.9997880312884855, + ] + expected_z_parities = [ + 0.9992335747085693, + -0.9955122771195629, + -0.9970929846091117, + -0.9945407488966614, + ] for e, r in zip(expected_y_parities, y_parities): self.assertAlmostEqual(e, r, places=3) @@ -895,28 +1194,40 @@ def test_strip(self): frequency = 1 / 1.55 - kvals = ms.find_k(mp.NO_PARITY, frequency, 1, ms.num_bands, mp.Vector3(1), 1e-3, - frequency * 3.45, frequency * 0.1, frequency * 4, mpb.output_poynting_x, - mpb.display_yparities, mpb.display_group_velocities) + kvals = ms.find_k( + mp.NO_PARITY, + frequency, + 1, + ms.num_bands, + mp.Vector3(1), + 1e-3, + frequency * 3.45, + frequency * 0.1, + frequency * 4, + mpb.output_poynting_x, + mpb.display_yparities, + mpb.display_group_velocities, + ) expected_kvals = [ 1.0395768316060294, 0.9776221778906993, 0.8358057689930384, - 0.788801145849691 + 0.788801145849691, ] for e, r in zip(expected_kvals, kvals): self.assertAlmostEqual(e, r, places=3) - ref_fn = 'strip-flux.v.k01.b04.x.h5' + ref_fn = "strip-flux.v.k01.b04.x.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('strip', self.filename_prefix, ref_fn) + res_path = re.sub("strip", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) def test_tri_holes(self): from mpb_tri_holes import ms + ms.deterministic = True ms.filename_prefix = self.filename_prefix ms.tolerance = 1e-12 @@ -924,23 +1235,56 @@ def test_tri_holes(self): ms.run_te() expected_te_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.2993049473117476, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.4924342823622065, mp.Vector3(0.0, 0.5, 0.0)), - (0.6568362683499375, mp.Vector3(0.0, 0.0, 0.0))), - ((0.5269710506448809, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7156232200212518, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6568031427446027, mp.Vector3(0.0, 0.5, 0.0)), - (0.7578382217502109, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.7383774303752574, mp.Vector3(0.0, 0.0, 0.0)), - (0.7988168792802597, mp.Vector3(0.0, 0.5, 0.0))), - ((0.8259787164701536, mp.Vector3(0.0, 0.0, 0.0)), - (0.9629215441012396, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.8271634538840886, mp.Vector3(0.0, 0.0, 0.0)), - (0.9834563303529568, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.9984200611839882, mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0)), - (1.0411551252079034, mp.Vector3(0.0, 0.0, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.2993049473117476, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.4924342823622065, mp.Vector3(0.0, 0.5, 0.0)), + (0.6568362683499375, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.5269710506448809, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7156232200212518, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6568031427446027, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.7578382217502109, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.7383774303752574, mp.Vector3(0.0, 0.0, 0.0)), + (0.7988168792802597, mp.Vector3(0.0, 0.5, 0.0)), + ), + ( + (0.8259787164701536, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.9629215441012396, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.8271634538840886, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.9834563303529568, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.9984200611839882, + mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0), + ), + (1.0411551252079034, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_te_brd, ms.band_range_data) @@ -948,82 +1292,161 @@ def test_tri_holes(self): ms.run_tm() expected_tm_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.28009156817399916, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.28015523913784407, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.3985126081046686, mp.Vector3(0.0, 0.0, 0.0))), - ((0.4390817228448606, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.49288810189980625, mp.Vector3(0.0, 0.0, 0.0))), - ((0.49336847788268695, mp.Vector3(0.0, 0.0, 0.0)), - (0.5808701365268192, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.581035246804371, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.6824860801372987, mp.Vector3(0.0, 0.0, 0.0))), - ((0.682531744671499, mp.Vector3(0.0, 0.0, 0.0)), - (0.7011061593213783, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.6920145742134771, mp.Vector3(0.0, 0.0, 0.0)), - (0.7841042622393081, mp.Vector3(0.0, 0.4, 0.0))), - ((0.7980077872594108, mp.Vector3(0.0, 0.4, 0.0)), - (0.8982239424823442, mp.Vector3(0.0, 0.0, 0.0))), - + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.28009156817399916, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.28015523913784407, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.3985126081046686, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.4390817228448606, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.49288810189980625, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.49336847788268695, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.5808701365268192, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.581035246804371, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.6824860801372987, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.682531744671499, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.7011061593213783, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.6920145742134771, mp.Vector3(0.0, 0.0, 0.0)), + (0.7841042622393081, mp.Vector3(0.0, 0.4, 0.0)), + ), + ( + (0.7980077872594108, mp.Vector3(0.0, 0.4, 0.0)), + (0.8982239424823442, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_tm_brd, ms.band_range_data) def test_tri_rods(self): from mpb_tri_rods import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix - ms.run_tm(mpb.output_at_kpoint(mp.Vector3(1 / -3, 1 / 3), - mpb.fix_efield_phase, - mpb.output_efield_z)) + ms.run_tm( + mpb.output_at_kpoint( + mp.Vector3(1 / -3, 1 / 3), mpb.fix_efield_phase, mpb.output_efield_z + ) + ) - ref_fn = 'tri-rods-e.k11.b08.z.tm.h5' + ref_fn = "tri-rods-e.k11.b08.z.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('tri-rods', self.filename_prefix, ref_fn) + res_path = re.sub("tri-rods", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) # Test MPBData - with h5py.File(ref_path, 'r') as f: - efield_re = f['z.r'][()] - efield_im = f['z.i'][()] + with h5py.File(ref_path, "r") as f: + efield_re = f["z.r"][()] + efield_im = f["z.i"][()] efield = np.vectorize(complex)(efield_re, efield_im) # rectangularize - md = mpb.MPBData(lattice=ms.get_lattice(), rectify=True, resolution=32, periods=3, verbose=True) + md = mpb.MPBData( + lattice=ms.get_lattice(), + rectify=True, + resolution=32, + periods=3, + verbose=True, + ) new_efield = md.convert(efield, ms.k_points[10]) # check with ref file - ref_fn = 'tri-rods-e.k11.b08.z.tm-r-m3-n32.h5' + ref_fn = "tri-rods-e.k11.b08.z.tm-r-m3-n32.h5" ref_path = os.path.join(self.data_dir, ref_fn) - with h5py.File(ref_path, 'r') as f: - expected_re = f['z.r-new'][()] - expected_im = f['z.i-new'][()] + with h5py.File(ref_path, "r") as f: + expected_re = f["z.r-new"][()] + expected_im = f["z.i-new"][()] expected = np.vectorize(complex)(expected_re, expected_im) compare_arrays(self, expected, new_efield) ms.run_te() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.49123581186757737, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.4730722390280754, mp.Vector3(0.0, 0.5, 0.0)), - (0.5631059378714038, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.5631505198559038, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (0.7939289395839766, mp.Vector3(0.0, 0.0, 0.0))), - ((0.7676614799039024, mp.Vector3(0.0, 0.5, 0.0)), - (0.8214230044191525, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0))), - ((0.865194814441525, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (1.0334130018594276, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8652307994936862, mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0)), - (1.0334230910419813, mp.Vector3(0.0, 0.0, 0.0))), - ((1.021367669109368, mp.Vector3(0.0, 0.5, 0.0)), - (1.115966990757518, mp.Vector3(0.0, 0.0, 0.0))), - ((1.108662658537423, mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0)), - (1.1168107191255379, mp.Vector3(0.0, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + ( + 0.49123581186757737, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + (0.4730722390280754, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.5631059378714038, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.5631505198559038, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (0.7939289395839766, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.7676614799039024, mp.Vector3(0.0, 0.5, 0.0)), + ( + 0.8214230044191525, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + ), + ( + ( + 0.865194814441525, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (1.0334130018594276, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8652307994936862, + mp.Vector3(-0.3333333333333333, 0.3333333333333333, 0.0), + ), + (1.0334230910419813, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.021367669109368, mp.Vector3(0.0, 0.5, 0.0)), + (1.115966990757518, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 1.108662658537423, + mp.Vector3(-0.26666666666666666, 0.26666666666666666, 0.0), + ), + (1.1168107191255379, mp.Vector3(0.0, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1032,11 +1455,11 @@ def test_tri_rods(self): eps = ms.get_epsilon() md = mpb.MPBData(rectify=True, resolution=32, periods=3, verbose=True) new_eps = md.convert(eps) - ref_fn = 'tri-rods-epsilon-r-m3-n32.h5' + ref_fn = "tri-rods-epsilon-r-m3-n32.h5" ref_path = os.path.join(self.data_dir, ref_fn) - with h5py.File(ref_path, 'r') as f: - ref = f['data-new'][()] + with h5py.File(ref_path, "r") as f: + ref = f["data-new"][()] compare_arrays(self, ref, new_eps, tol=1e-3) def test_subpixel_averaging(self): @@ -1045,33 +1468,52 @@ def test_subpixel_averaging(self): ms.output_epsilon() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), - (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), - (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), - (0.80968915516771, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), - (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), - (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), - (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0))), - ((1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), - (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.49683586474489877, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4415884497225449, mp.Vector3(0.5, 0.0, 0.0)), + (0.5931405141160885, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5931535863117832, mp.Vector3(0.5, 0.5, 0.0)), + (0.7732265593069759, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6791690130757013, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.80968915516771, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + (0.8241814443502151, mp.Vector3(0.5, 0.30000000000000004, 0.0)), + (0.9229965195855876, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.8819770916660669, mp.Vector3(0.5, 0.5, 0.0)), + (1.0291597050650205, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8819818134421844, mp.Vector3(0.5, 0.5, 0.0)), + (1.086072932359415, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.0878689635052692, mp.Vector3(0.5, 0.0, 0.0)), + (1.1119173707556929, mp.Vector3(0.5, 0.5, 0.0)), + ), ] expected_gap_list = [ (0.0022038709776893727, 0.5931405141160885, 0.5931535863117832), (1.7739824912427062, 0.80968915516771, 0.8241814443502151), - (0.1652326724344101, 1.086072932359415, 1.0878689635052692) + (0.1652326724344101, 1.086072932359415, 1.0878689635052692), ] - ref_fn = 'subpixel_avg-epsilon.h5' + ref_fn = "subpixel_avg-epsilon.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('subpixel_avg', self.filename_prefix, ref_fn) + res_path = re.sub("subpixel_avg", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1082,36 +1524,55 @@ def test_run_te_with_mu_material(self): ms.geometry = [mp.Cylinder(0.2, material=mp.Medium(mu=5))] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.4165291233037574, mp.Vector3(0.5, 0.5, 0.0))), - ((0.47328232348733695, mp.Vector3(0.5, 0.0, 0.0)), - (0.6699867281290507, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6301802646818523, mp.Vector3(0.5, 0.5, 0.0)), - (0.8037365323032135, mp.Vector3(0.5, 0.0, 0.0))), - ((0.7017932556977557, mp.Vector3(0.5, 0.5, 0.0)), - (0.8863448167711359, mp.Vector3(0.5, 0.10000000000000003, 0.0))), - ((0.9047498485809726, mp.Vector3(0.5, 0.10000000000000003, 0.0)), - (1.0557468193007016, mp.Vector3(0.5, 0.5, 0.0))), - ((1.0077925606103986, mp.Vector3(0.2, 0.2, 0.0)), - (1.1815403744341757, mp.Vector3(0.0, 0.0, 0.0))), - ((1.122424251973878, mp.Vector3(0.20000000000000004, 0.0, 0.0)), - (1.2351567679231688, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((1.2059728636717586, mp.Vector3(0.0, 0.0, 0.0)), - (1.3135062523646421, mp.Vector3(0.30000000000000004, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.4165291233037574, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.47328232348733695, mp.Vector3(0.5, 0.0, 0.0)), + (0.6699867281290507, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6301802646818523, mp.Vector3(0.5, 0.5, 0.0)), + (0.8037365323032135, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.7017932556977557, mp.Vector3(0.5, 0.5, 0.0)), + (0.8863448167711359, mp.Vector3(0.5, 0.10000000000000003, 0.0)), + ), + ( + (0.9047498485809726, mp.Vector3(0.5, 0.10000000000000003, 0.0)), + (1.0557468193007016, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.0077925606103986, mp.Vector3(0.2, 0.2, 0.0)), + (1.1815403744341757, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (1.122424251973878, mp.Vector3(0.20000000000000004, 0.0, 0.0)), + ( + 1.2351567679231688, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + (1.2059728636717586, mp.Vector3(0.0, 0.0, 0.0)), + (1.3135062523646421, mp.Vector3(0.30000000000000004, 0.0, 0.0)), + ), ] ms.run_te() ms.output_mu() self.check_band_range_data(expected_brd, ms.band_range_data) - fname = 'tutorial-mu.h5' + fname = "tutorial-mu.h5" data_path = os.path.join(self.data_dir, fname) - res_path = re.sub('tutorial', self.filename_prefix, fname) + res_path = re.sub("tutorial", self.filename_prefix, fname) self.compare_h5_files(data_path, res_path) mu = ms.get_mu() - with h5py.File(data_path, 'r') as f: - data = f['data'][()] + with h5py.File(data_path, "r") as f: + data = f["data"][()] compare_arrays(self, data, mu) def test_output_tot_pwr(self): @@ -1119,16 +1580,16 @@ def test_output_tot_pwr(self): ms.run_te() mpb.output_tot_pwr(ms, 8) - ref_fname = 'tutorial-tot.rpwr.k16.b08.te.h5' + ref_fname = "tutorial-tot.rpwr.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fname) - res_path = re.sub('tutorial', self.filename_prefix, ref_fname) + res_path = re.sub("tutorial", self.filename_prefix, ref_fname) self.compare_h5_files(ref_path, res_path) # Test get_tot_pwr arr = ms.get_tot_pwr(8) - with h5py.File(ref_path, 'r') as f: - expected = f['data'][()] + with h5py.File(ref_path, "r") as f: + expected = f["data"][()] compare_arrays(self, expected, arr) @@ -1137,19 +1598,19 @@ def test_get_eigenvectors(self): ms.run_te(mpb.fix_hfield_phase) def compare_eigenvectors(ref_fn, start, cols): - with h5py.File(os.path.join(self.data_dir, ref_fn), 'r') as f: - expected = f['rawdata'][()] + with h5py.File(os.path.join(self.data_dir, ref_fn), "r") as f: + expected = f["rawdata"][()] # Reshape the last dimension of 2 reals into one complex expected = np.vectorize(complex)(expected[..., 0], expected[..., 1]) ev = ms.get_eigenvectors(start, cols) np.testing.assert_allclose(expected, ev, rtol=1e-3) # Get all columns - compare_eigenvectors('tutorial-te-eigenvectors.h5', 1, 8) + compare_eigenvectors("tutorial-te-eigenvectors.h5", 1, 8) # Get last column - compare_eigenvectors('tutorial-te-eigenvectors-8-1.h5', 8, 1) + compare_eigenvectors("tutorial-te-eigenvectors-8-1.h5", 8, 1) # Get columns 3,4, and 5 - compare_eigenvectors('tutorial-te-eigenvectors-3-3.h5', 3, 3) + compare_eigenvectors("tutorial-te-eigenvectors-3-3.h5", 3, 3) def test_set_eigenvectors(self): ms = self.init_solver() @@ -1172,7 +1633,7 @@ def set_H_to_zero_and_check(start, num_bands): def test_load_and_save_eigenvectors(self): ms = self.init_solver() ms.run_te() - fn = self.filename_prefix + '.h5' + fn = self.filename_prefix + ".h5" ev = ms.get_eigenvectors(8, 1) zeros = np.zeros(ev.shape, dtype=np.complex128) @@ -1186,6 +1647,7 @@ def test_load_and_save_eigenvectors(self): def test_handle_cvector(self): from mpb_tri_rods import ms + ms.deterministic = True ms.tolerance = 1e-12 ms.filename_prefix = self.filename_prefix @@ -1200,22 +1662,30 @@ def get_efields(ms, band): md = mpb.MPBData(rectify=True, periods=3, resolution=32, verbose=True) result = md.convert(efields[-1]) - ref_fn = 'converted-tri-rods-e.k11.b08.tm.h5' + ref_fn = "converted-tri-rods-e.k11.b08.tm.h5" ref_path = os.path.join(self.data_dir, ref_fn) - self.check_fields_against_h5(ref_path, result.ravel(), suffix='-new') + self.check_fields_against_h5(ref_path, result.ravel(), suffix="-new") def test_epsilon_input_file(self): ms = self.init_solver(geom=False) - eps_fn = 'eps_input_file_test.h5' + eps_fn = "eps_input_file_test.h5" ms.epsilon_input_file = os.path.join(self.data_dir, eps_fn) ms.run_te() - expected_freqs = np.array([ - 0.0, 0.5543986136451342, 0.7613327775255415, 0.7613339178956054, - 0.8940893915924257, 0.998342969572652, 0.9983441882455961, 1.0747466061007138 - ]) + expected_freqs = np.array( + [ + 0.0, + 0.5543986136451342, + 0.7613327775255415, + 0.7613339178956054, + 0.8940893915924257, + 0.998342969572652, + 0.9983441882455961, + 1.0747466061007138, + ] + ) expected_gap_list = [ (3.848610367089048e-5, 0.5781812856814899, 0.5781815082009817), @@ -1224,22 +1694,47 @@ def test_epsilon_input_file(self): ] expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), - (0.4970977843772992, mp.Vector3(0.5, 0.5, 0.0))), - ((0.4402896410505961, mp.Vector3(0.5, 0.0, 0.0)), - (0.5781812856814899, mp.Vector3(0.5, 0.5, 0.0))), - ((0.5781815082009817, mp.Vector3(0.5, 0.5, 0.0)), - (0.761332777525562, mp.Vector3(0.0, 0.0, 0.0))), - ((0.6689126424359774, mp.Vector3(0.5, 0.5, 0.0)), - (0.8051999839699242, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0))), - ((0.8170847453549156, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (0.8940893915924548, mp.Vector3(0.0, 0.0, 0.0))), - ((0.8826671164993868, mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0)), - (1.0014926328155058, mp.Vector3(0.5, 0.0, 0.0))), - ((0.8832199143682116, mp.Vector3(0.5, 0.5, 0.0)), - (1.0580309832489785, mp.Vector3(0.5, 0.0, 0.0))), - ((1.0660233597945266, mp.Vector3(0.2, 0.2, 0.0)), - (1.087345829555555, mp.Vector3(0.5, 0.5, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (0.4970977843772992, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.4402896410505961, mp.Vector3(0.5, 0.0, 0.0)), + (0.5781812856814899, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (0.5781815082009817, mp.Vector3(0.5, 0.5, 0.0)), + (0.761332777525562, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (0.6689126424359774, mp.Vector3(0.5, 0.5, 0.0)), + ( + 0.8051999839699242, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + ), + ( + ( + 0.8170847453549156, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (0.8940893915924548, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + ( + 0.8826671164993868, + mp.Vector3(0.30000000000000004, 0.30000000000000004, 0.0), + ), + (1.0014926328155058, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (0.8832199143682116, mp.Vector3(0.5, 0.5, 0.0)), + (1.0580309832489785, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (1.0660233597945266, mp.Vector3(0.2, 0.2, 0.0)), + (1.087345829555555, mp.Vector3(0.5, 0.5, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1254,78 +1749,265 @@ def test_hermitian_eps(self): ms = self.init_solver() ms.num_bands = 10 ms.geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1)) - ms.k_points = mp.interpolate(2, [mp.Vector3(), mp.Vector3(0.5), - mp.Vector3(0.5, 0.5), mp.Vector3()]) + ms.k_points = mp.interpolate( + 2, [mp.Vector3(), mp.Vector3(0.5), mp.Vector3(0.5, 0.5), mp.Vector3()] + ) if mpb.with_hermitian_epsilon(): mu_offdiag = mp.Vector3(0 + 12.4j, 0j, 0j) else: mu_offdiag = mp.Vector3(1.1 + 0j, 0j, 0j) - mat = mp.Medium(epsilon=15, mu_diag=mp.Vector3(14, 14, 1), mu_offdiag=mu_offdiag) + mat = mp.Medium( + epsilon=15, mu_diag=mp.Vector3(14, 14, 1), mu_offdiag=mu_offdiag + ) ms.geometry = [mp.Cylinder(0.11, material=mat)] ms.run_tm() expected_freqs_with_herm_eps = [ - (0.0, 0.4632939699892961, 0.5786056046494645, 0.6510415824942094, 0.895078332795855, - 0.9065629078750282, 0.9718615669186841, 1.0031527446201098, 1.0142458802909737, 1.0248230445372033), - (0.12937769848279101, 0.4619527556321284, 0.5815596420443466, 0.6509684019890601, - 0.8515085512592131, 0.8991327667388375, 0.9483381392427291, 0.9868373331614826, - 1.0201294704318276, 1.1113539456722876), - (0.240618523732184, 0.45558173972666255, 0.5943681967388351, 0.650900939169996, - 0.7483572681471874, 0.9008690943027022, 0.9516677787271349, 0.9850364677965752, - 1.0516821906747753, 1.1214943845916658), - (0.2922690373273036, 0.4479821144386504, 0.613801526149148, 0.6509434790185421, - 0.6876982556529582, 0.9015193133195613, 0.9570671227792547, 0.9847711283698155, - 1.0828576982954996, 1.1106109238677777), - (0.30027099141005154, 0.46956697245972573, 0.5973756776701357, 0.6743055424064167, - 0.6920274562199666, 0.8979504826615435, 0.9268408824618528, 0.9691495966240021, - 0.9983260085044127, 1.1065083471050117), - (0.31666208802132145, 0.5137036663942733, 0.5882926042080675, 0.6899229118026092, - 0.730249913793744, 0.8458381750994521, 0.8595877992328249, 0.9137388415537298, - 0.9866008233455089, 1.137383764975547), - (0.3251247277636798, 0.5292011796591106, 0.6018330031246529, 0.7028040151334913, - 0.7794097325510528, 0.7819161956650196, 0.8016335408886606, 0.910192351683647, - 0.98598162196522, 1.1535093885340242), - (0.29904860785910004, 0.49821749875617755, 0.5818628214691952, 0.6702162529015839, - 0.792305404698029, 0.8010082951265327, 0.9011331789530838, 0.9347832593312477, - 0.9878915728570912, 1.1274287845362319), - (0.17916974535365005, 0.46423758343427207, 0.5818626159151191, 0.6522567432851836, - 0.8578543711269236, 0.8654932847250656, 0.914866301019591, 0.9836433091978996, - 1.0332068416637614, 1.1280615056475125), - (0.0, 0.46329396998948535, 0.5786056046501502, 0.6510415824943165, 0.8950783327952294, - 0.9065629078748172, 0.9718615669185836, 1.0031527446209287, 1.01424588029229, 1.0248230445379556) + ( + 0.0, + 0.4632939699892961, + 0.5786056046494645, + 0.6510415824942094, + 0.895078332795855, + 0.9065629078750282, + 0.9718615669186841, + 1.0031527446201098, + 1.0142458802909737, + 1.0248230445372033, + ), + ( + 0.12937769848279101, + 0.4619527556321284, + 0.5815596420443466, + 0.6509684019890601, + 0.8515085512592131, + 0.8991327667388375, + 0.9483381392427291, + 0.9868373331614826, + 1.0201294704318276, + 1.1113539456722876, + ), + ( + 0.240618523732184, + 0.45558173972666255, + 0.5943681967388351, + 0.650900939169996, + 0.7483572681471874, + 0.9008690943027022, + 0.9516677787271349, + 0.9850364677965752, + 1.0516821906747753, + 1.1214943845916658, + ), + ( + 0.2922690373273036, + 0.4479821144386504, + 0.613801526149148, + 0.6509434790185421, + 0.6876982556529582, + 0.9015193133195613, + 0.9570671227792547, + 0.9847711283698155, + 1.0828576982954996, + 1.1106109238677777, + ), + ( + 0.30027099141005154, + 0.46956697245972573, + 0.5973756776701357, + 0.6743055424064167, + 0.6920274562199666, + 0.8979504826615435, + 0.9268408824618528, + 0.9691495966240021, + 0.9983260085044127, + 1.1065083471050117, + ), + ( + 0.31666208802132145, + 0.5137036663942733, + 0.5882926042080675, + 0.6899229118026092, + 0.730249913793744, + 0.8458381750994521, + 0.8595877992328249, + 0.9137388415537298, + 0.9866008233455089, + 1.137383764975547, + ), + ( + 0.3251247277636798, + 0.5292011796591106, + 0.6018330031246529, + 0.7028040151334913, + 0.7794097325510528, + 0.7819161956650196, + 0.8016335408886606, + 0.910192351683647, + 0.98598162196522, + 1.1535093885340242, + ), + ( + 0.29904860785910004, + 0.49821749875617755, + 0.5818628214691952, + 0.6702162529015839, + 0.792305404698029, + 0.8010082951265327, + 0.9011331789530838, + 0.9347832593312477, + 0.9878915728570912, + 1.1274287845362319, + ), + ( + 0.17916974535365005, + 0.46423758343427207, + 0.5818626159151191, + 0.6522567432851836, + 0.8578543711269236, + 0.8654932847250656, + 0.914866301019591, + 0.9836433091978996, + 1.0332068416637614, + 1.1280615056475125, + ), + ( + 0.0, + 0.46329396998948535, + 0.5786056046501502, + 0.6510415824943165, + 0.8950783327952294, + 0.9065629078748172, + 0.9718615669185836, + 1.0031527446209287, + 1.01424588029229, + 1.0248230445379556, + ), ] expected_freqs = [ - (0.0, 0.2658849785488965, 0.35685238626885807, 0.3689901366690736, 0.5038968919475888, - 0.5065518587501845, 0.5399110558762593, 0.6356807845993113, 0.6458520209139631, 0.6600163331973673), - (0.1233059084828522, 0.2782923927646106, 0.3573659215347579, 0.36946091907607376, - 0.5037974922871195, 0.5066416041828202, 0.538216765672376, 0.6355701950896693, 0.6455953849366743, - 0.6594309541221774), - (0.1946550854369502, 0.3314575373594674, 0.3616788807546181, 0.3779471264000764, - 0.5026931348465893, 0.5067552606455699, 0.5272272884443048, 0.6333347872001693, - 0.6442148265479308, 0.6532866817588999), - (0.20879926720698877, 0.34897792345617423, 0.3652783351764913, 0.443855894582484, - 0.47790148240242897, 0.506844624322928, 0.5090758743980286, 0.6175265666609957, - 0.6397097609325341, 0.6468898906944409), - (0.21067290783587236, 0.35110317640925215, 0.3649145592788066, 0.4533551114677223, - 0.5005024186371949, 0.507017761099229, 0.5101250893240828, 0.6168213478492276, - 0.6424780509991848, 0.6468935605826212), - (0.214080585764866, 0.353216867533926, 0.3650014330955567, 0.47197667085082984, - 0.5071647195850403, 0.5107899381039265, 0.563894415993932, 0.6150518764874886, - 0.6461781982138443, 0.6483568369087811), - (0.21564852631365375, 0.3536024256611885, 0.3652692866946287, 0.4807484678430083, - 0.5073352894281589, 0.5125343692391049, 0.6079200818245155, 0.6197842747116765, - 0.648907835949042, 0.6503229392874306), - (0.21087876573006833, 0.35463024054783404, 0.3612449317676656, 0.43948603208371123, - 0.5070218413172007, 0.508251630347181, 0.5438146806988851, 0.6205991881007313, - 0.6465891122723096, 0.6511544851425487), - (0.16235265342991828, 0.2958744870280601, 0.3562142355919711, 0.37216060317897104, - 0.5042580244528116, 0.5067350381508423, 0.5373546859406302, 0.6332312603115383, - 0.6459156623195174, 0.6571698237808499), - (0.0, 0.2658849786929068, 0.3568523862640937, 0.36899013667229497, 0.5038968919421172, - 0.506551858753244, 0.5399110559018708, 0.6356807846017583, 0.6458520213315968, 0.6600163321775416), + ( + 0.0, + 0.2658849785488965, + 0.35685238626885807, + 0.3689901366690736, + 0.5038968919475888, + 0.5065518587501845, + 0.5399110558762593, + 0.6356807845993113, + 0.6458520209139631, + 0.6600163331973673, + ), + ( + 0.1233059084828522, + 0.2782923927646106, + 0.3573659215347579, + 0.36946091907607376, + 0.5037974922871195, + 0.5066416041828202, + 0.538216765672376, + 0.6355701950896693, + 0.6455953849366743, + 0.6594309541221774, + ), + ( + 0.1946550854369502, + 0.3314575373594674, + 0.3616788807546181, + 0.3779471264000764, + 0.5026931348465893, + 0.5067552606455699, + 0.5272272884443048, + 0.6333347872001693, + 0.6442148265479308, + 0.6532866817588999, + ), + ( + 0.20879926720698877, + 0.34897792345617423, + 0.3652783351764913, + 0.443855894582484, + 0.47790148240242897, + 0.506844624322928, + 0.5090758743980286, + 0.6175265666609957, + 0.6397097609325341, + 0.6468898906944409, + ), + ( + 0.21067290783587236, + 0.35110317640925215, + 0.3649145592788066, + 0.4533551114677223, + 0.5005024186371949, + 0.507017761099229, + 0.5101250893240828, + 0.6168213478492276, + 0.6424780509991848, + 0.6468935605826212, + ), + ( + 0.214080585764866, + 0.353216867533926, + 0.3650014330955567, + 0.47197667085082984, + 0.5071647195850403, + 0.5107899381039265, + 0.563894415993932, + 0.6150518764874886, + 0.6461781982138443, + 0.6483568369087811, + ), + ( + 0.21564852631365375, + 0.3536024256611885, + 0.3652692866946287, + 0.4807484678430083, + 0.5073352894281589, + 0.5125343692391049, + 0.6079200818245155, + 0.6197842747116765, + 0.648907835949042, + 0.6503229392874306, + ), + ( + 0.21087876573006833, + 0.35463024054783404, + 0.3612449317676656, + 0.43948603208371123, + 0.5070218413172007, + 0.508251630347181, + 0.5438146806988851, + 0.6205991881007313, + 0.6465891122723096, + 0.6511544851425487, + ), + ( + 0.16235265342991828, + 0.2958744870280601, + 0.3562142355919711, + 0.37216060317897104, + 0.5042580244528116, + 0.5067350381508423, + 0.5373546859406302, + 0.6332312603115383, + 0.6459156623195174, + 0.6571698237808499, + ), + ( + 0.0, + 0.2658849786929068, + 0.3568523862640937, + 0.36899013667229497, + 0.5038968919421172, + 0.506551858753244, + 0.5399110559018708, + 0.6356807846017583, + 0.6458520213315968, + 0.6600163321775416, + ), ] if mpb.with_hermitian_epsilon(): @@ -1354,7 +2036,9 @@ def f2(F, eps, r): ms.get_efield(8) field_integral = ms.compute_field_integral(f2) - self.assertAlmostEqual(field_integral, 02.0735366548785272e-18 - 3.0259168624899837e-6j) + self.assertAlmostEqual( + field_integral, 02.0735366548785272e-18 - 3.0259168624899837e-6j + ) def test_multiply_bloch_phase(self): ms = self.init_solver() @@ -1364,7 +2048,7 @@ def test_multiply_bloch_phase(self): efield = ms.get_efield(8, False) bloch_efield = ms.multiply_bloch_phase(efield) - ref_fn = 'tutorial-e.k16.b08.te.h5' + ref_fn = "tutorial-e.k16.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fn) self.check_fields_against_h5(ref_path, bloch_efield) @@ -1394,9 +2078,9 @@ def test_poynting(self): ms.run_te(mpb.output_at_kpoint(mp.Vector3(0.5, 0.5), mpb.output_poynting)) - ref_fn = 'tutorial-flux.v.k11.b08.te.h5' + ref_fn = "tutorial-flux.v.k11.b08.te.h5" ref_path = os.path.join(self.data_dir, ref_fn) - res_path = re.sub('tutorial', self.filename_prefix, ref_fn) + res_path = re.sub("tutorial", self.filename_prefix, ref_fn) self.compare_h5_files(ref_path, res_path) @@ -1407,14 +2091,38 @@ def test_fractional_lattice(self): ms.run_te() expected_brd = [ - ((0.0, mp.Vector3(0.0, 0.0, 0.0)), (2.041241452319313, mp.Vector3(0.5, 0.5, 0.0))), - ((1.4433756729740665, mp.Vector3(0.5, 0.0, 0.0)), (2.886751345948131, mp.Vector3(0.0, 0.0, 0.0))), - ((2.041241452319316, mp.Vector3(0.5, 0.5, 0.0)), (3.2274861218395094, mp.Vector3(0.5, 0.0, 0.0))), - ((2.0412414523193187, mp.Vector3(0.5, 0.5, 0.0)), (3.2659863237109055, mp.Vector3(0.2, 0.2, 0.0))), - ((2.88675134594813, mp.Vector3(0.0, 0.0, 0.0)), (4.564354645876381, mp.Vector3(0.5, 0.5, 0.0))), - ((3.227486121839514, mp.Vector3(0.5, 0.0, 0.0)), (4.564354645876382, mp.Vector3(0.5, 0.5, 0.0))), - ((3.6968455021364752, mp.Vector3(0.20000000000000004, 0.0, 0.0)), (4.564354645876384, mp.Vector3(0.5, 0.5, 0.0))), - ((4.0824829046386295, mp.Vector3(0.0, 0.0, 0.0)), (5.204164998665332, mp.Vector3(0.5, 0.0, 0.0))), + ( + (0.0, mp.Vector3(0.0, 0.0, 0.0)), + (2.041241452319313, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (1.4433756729740665, mp.Vector3(0.5, 0.0, 0.0)), + (2.886751345948131, mp.Vector3(0.0, 0.0, 0.0)), + ), + ( + (2.041241452319316, mp.Vector3(0.5, 0.5, 0.0)), + (3.2274861218395094, mp.Vector3(0.5, 0.0, 0.0)), + ), + ( + (2.0412414523193187, mp.Vector3(0.5, 0.5, 0.0)), + (3.2659863237109055, mp.Vector3(0.2, 0.2, 0.0)), + ), + ( + (2.88675134594813, mp.Vector3(0.0, 0.0, 0.0)), + (4.564354645876381, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (3.227486121839514, mp.Vector3(0.5, 0.0, 0.0)), + (4.564354645876382, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (3.6968455021364752, mp.Vector3(0.20000000000000004, 0.0, 0.0)), + (4.564354645876384, mp.Vector3(0.5, 0.5, 0.0)), + ), + ( + (4.0824829046386295, mp.Vector3(0.0, 0.0, 0.0)), + (5.204164998665332, mp.Vector3(0.5, 0.0, 0.0)), + ), ] self.check_band_range_data(expected_brd, ms.band_range_data) @@ -1422,59 +2130,64 @@ def test_fractional_lattice(self): def test_symmetry_overlap(self): ms = mpb.ModeSolver( num_bands=6, - k_points = [mp.Vector3(0.5, 0.5, 0.5),], - geometry = [mp.Sphere(0.25, material=mp.Medium(epsilon=13))], - geometry_lattice = mp.Lattice(size=mp.Vector3(1, 1, 1)), - resolution = 32, - filename_prefix = self.filename_prefix, - deterministic = True, - tolerance = 1e-12 + k_points=[ + mp.Vector3(0.5, 0.5, 0.5), + ], + geometry=[mp.Sphere(0.25, material=mp.Medium(epsilon=13))], + geometry_lattice=mp.Lattice(size=mp.Vector3(1, 1, 1)), + resolution=32, + filename_prefix=self.filename_prefix, + deterministic=True, + tolerance=1e-12, ) ms.run() # inversion symmetry - Wi = mp.Matrix([-1,0,0], [0,-1,0], [0,0,-1]) - w = mp.Vector3(0,0,0) - symeigs = ms.compute_symmetries(Wi, w) - symeigs_expected = [1,1,1,-1,-1,-1] - for i in range(0,5): + Wi = mp.Matrix([-1, 0, 0], [0, -1, 0], [0, 0, -1]) + w = mp.Vector3(0, 0, 0) + symeigs = ms.compute_symmetries(Wi, w) + symeigs_expected = [1, 1, 1, -1, -1, -1] + for i in range(0, 5): self.assertAlmostEqual(symeigs[i].real, symeigs_expected[i], places=3) self.assertAlmostEqual(symeigs[i].imag, 0, places=3) - + # check that transformed_overlap agrees when called manually, with a # "preloaded" bfield - for i in range(0,5): - ms.get_bfield(i+1, bloch_phase=False) + for i in range(0, 5): + ms.get_bfield(i + 1, bloch_phase=False) symeig_bfield = ms.transformed_overlap(Wi, w) self.assertAlmostEqual(symeigs[i].real, symeig_bfield.real, places=3) self.assertAlmostEqual(symeigs[i].imag, symeig_bfield.imag, places=3) - ms.get_dfield(i+1, bloch_phase=False) + ms.get_dfield(i + 1, bloch_phase=False) symeig_dfield = ms.transformed_overlap(Wi, w) self.assertAlmostEqual(symeigs[i].real, symeig_dfield.real, places=3) self.assertAlmostEqual(symeigs[i].imag, symeig_dfield.imag, places=3) # 2-fold symmetry (across MPI xy-transposition dimensions) - W2 = mp.Matrix([-1,0,0], [0,1,0], [0,0,-1]) + W2 = mp.Matrix([-1, 0, 0], [0, 1, 0], [0, 0, -1]) symeigs = ms.compute_symmetries(W2, w) - self.assertAlmostEqual(sum(symeigs[0:3]), -1+0j, places=3) - self.assertAlmostEqual(sum(symeigs[3:6]), -1+0j, places=3) + self.assertAlmostEqual(sum(symeigs[0:3]), -1 + 0j, places=3) + self.assertAlmostEqual(sum(symeigs[3:6]), -1 + 0j, places=3) - # mirror symmetry: compare with run_zeven & run_zodd - ms.k_points = [mp.Vector3(0,0,0)] # must be at Γ cf. https://github.com/NanoComp/mpb/issues/114 - Wz = mp.Matrix([1,0,0], [0,1,0], [0,0,-1]) + # mirror symmetry: compare with run_zeven & run_zodd + ms.k_points = [ + mp.Vector3(0, 0, 0) + ] # must be at Γ cf. https://github.com/NanoComp/mpb/issues/114 + Wz = mp.Matrix([1, 0, 0], [0, 1, 0], [0, 0, -1]) ms.run_zeven() symeigs_zeven = ms.compute_symmetries(Wz, w) - for i in range(0,5): + for i in range(0, 5): self.assertAlmostEqual(symeigs_zeven[i].real, 1, places=3) self.assertAlmostEqual(symeigs_zeven[i].imag, 0, places=3) ms.run_zodd() symeigs_zodd = ms.compute_symmetries(Wz, w) - for i in range(0,5): + for i in range(0, 5): self.assertAlmostEqual(symeigs_zodd[i].real, -1, places=3) - self.assertAlmostEqual(symeigs_zodd[i].imag, 0, places=3) + self.assertAlmostEqual(symeigs_zodd[i].imag, 0, places=3) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_multilevel_atom.py b/python/tests/test_multilevel_atom.py index 33aa17b39..c0db13e22 100644 --- a/python/tests/test_multilevel_atom.py +++ b/python/tests/test_multilevel_atom.py @@ -1,11 +1,13 @@ -import meep as mp import math import unittest +import meep as mp + class TestMultiLevelAtom(unittest.TestCase): - - @unittest.skipIf(mp.is_single_precision(), "double-precision floating point specific test") + @unittest.skipIf( + mp.is_single_precision(), "double-precision floating point specific test" + ) def test_multilevel_atom(self): resolution = 40 ncav = 1.5 @@ -37,26 +39,37 @@ def test_multilevel_atom(self): pumping_rate=Rp, frequency=freq_21, gamma=gamma_21, - sigma_diag=mp.Vector3(sigma_21, sigma_21, sigma_21) + sigma_diag=mp.Vector3(sigma_21, sigma_21, sigma_21), ) t2 = mp.Transition(2, 1, transition_rate=rate_21) - ml_atom = mp.MultilevelAtom(sigma=1, transitions=[t1, t2], initial_populations=[N0]) + ml_atom = mp.MultilevelAtom( + sigma=1, transitions=[t1, t2], initial_populations=[N0] + ) two_level = mp.Medium(index=ncav, E_susceptibilities=[ml_atom]) - geometry = [mp.Block(center=mp.Vector3(z=(-0.5 * sz) + (0.5 * Lcav)), - size=mp.Vector3(mp.inf, mp.inf, Lcav), material=two_level)] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - geometry=geometry, - dimensions=dimensions) + geometry = [ + mp.Block( + center=mp.Vector3(z=(-0.5 * sz) + (0.5 * Lcav)), + size=mp.Vector3(mp.inf, mp.inf, Lcav), + material=two_level, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + geometry=geometry, + dimensions=dimensions, + ) def field_func(p): return 1 if p.z == (-0.5 * sz) + (0.5 * Lcav) else 0 def check_field(sim): - fp = sim.get_field_point(mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad))).real + fp = sim.get_field_point( + mp.Ex, mp.Vector3(z=(-0.5 * sz) + Lcav + (0.5 * dpad)) + ).real self.assertAlmostEqual(fp, -2.7110969214986387) sim.init_sim() @@ -64,5 +77,5 @@ def check_field(sim): sim.run(mp.at_end(check_field), until=7000) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_n2f_periodic.py b/python/tests/test_n2f_periodic.py index 5cac78d5c..bca8b4384 100644 --- a/python/tests/test_n2f_periodic.py +++ b/python/tests/test_n2f_periodic.py @@ -1,68 +1,100 @@ -import unittest -import meep as mp import math +import unittest + import numpy as np from numpy import linalg as LA -class TestNear2FarPeriodicBoundaries(unittest.TestCase): +import meep as mp + +class TestNear2FarPeriodicBoundaries(unittest.TestCase): def test_nea2far_periodic(self): - dpml = 1.0 # PML thickness - dsub = 3.0 # substrate thickness - dpad = 20.0 # padding between grating and PML - gp = 10.0 # grating period - gh = 0.5 # grating height - gdc = 0.5 # grating duty cycle - - sx = dpml+dsub+gh+dpad+dpml + dpml = 1.0 # PML thickness + dsub = 3.0 # substrate thickness + dpad = 20.0 # padding between grating and PML + gp = 10.0 # grating period + gh = 0.5 # grating height + gdc = 0.5 # grating duty cycle + + sx = dpml + dsub + gh + dpad + dpml sy = gp - pml_layers = [mp.PML(thickness=dpml,direction=mp.X)] + pml_layers = [mp.PML(thickness=dpml, direction=mp.X)] - wvl = 0.5 # center wavelength - fcen = 1/wvl # center frequency - df = 0.05*fcen # frequency width + wvl = 0.5 # center wavelength + fcen = 1 / wvl # center frequency + df = 0.05 * fcen # frequency width - src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub) - sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=src_pt, size=mp.Vector3(y=sy))] + src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub) + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=src_pt, + size=mp.Vector3(y=sy), + ) + ] glass = mp.Medium(index=1.5) - geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub))), - mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh))] - - k_point = mp.Vector3(0,0,0) + geometry = [ + mp.Block( + material=glass, + size=mp.Vector3(dpml + dsub, mp.inf, mp.inf), + center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)), + ), + mp.Block( + material=glass, + size=mp.Vector3(gh, gdc * gp, mp.inf), + center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh), + ), + ] + + k_point = mp.Vector3(0, 0, 0) symmetries = [mp.Mirror(mp.Y)] - n2f_pt = mp.Vector3(-0.5*sx+dpml+dsub+gh+1.0) - dft_pt = mp.Vector3(0.5*sx-dpml) + n2f_pt = mp.Vector3(-0.5 * sx + dpml + dsub + gh + 1.0) + dft_pt = mp.Vector3(0.5 * sx - dpml) - res = [20,25,30] + res = [20, 25, 30] norm = np.empty(3) for j in range(3): - sim = mp.Simulation(resolution=res[j], - cell_size=mp.Vector3(sx,sy), - boundary_layers=pml_layers, - geometry=geometry, - k_point=k_point, - sources=sources, - symmetries=symmetries) - - n2f_obj = sim.add_near2far(fcen, 0, 1, mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), nperiods=10) - dft_obj = sim.add_dft_fields([mp.Ez], fcen, 0, 1, center=dft_pt, size=mp.Vector3(y=sy)) + sim = mp.Simulation( + resolution=res[j], + cell_size=mp.Vector3(sx, sy), + boundary_layers=pml_layers, + geometry=geometry, + k_point=k_point, + sources=sources, + symmetries=symmetries, + ) + + n2f_obj = sim.add_near2far( + fcen, + 0, + 1, + mp.Near2FarRegion(center=n2f_pt, size=mp.Vector3(y=sy)), + nperiods=10, + ) + dft_obj = sim.add_dft_fields( + [mp.Ez], fcen, 0, 1, center=dft_pt, size=mp.Vector3(y=sy) + ) sim.run(until_after_sources=300) - n2f_Ez = sim.get_farfields(n2f_obj, res[j], center=dft_pt, size=mp.Vector3(y=sy)) + n2f_Ez = sim.get_farfields( + n2f_obj, res[j], center=dft_pt, size=mp.Vector3(y=sy) + ) dft_Ez = sim.get_dft_array(dft_obj, mp.Ez, 0) - norm[j] = LA.norm(n2f_Ez['Ez']-dft_Ez[1:-1]) - print("norm:, {}, {:.5f}".format(res[j],norm[j])) + norm[j] = LA.norm(n2f_Ez["Ez"] - dft_Ez[1:-1]) + print(f"norm:, {res[j]}, {norm[j]:.5f}") sim.reset_meep() - self.assertGreater(norm[0],norm[1]) - self.assertGreater(norm[1],norm[2]) + self.assertGreater(norm[0], norm[1]) + self.assertGreater(norm[1], norm[2]) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_oblique_source.py b/python/tests/test_oblique_source.py index edf0042a0..558f8551b 100644 --- a/python/tests/test_oblique_source.py +++ b/python/tests/test_oblique_source.py @@ -1,56 +1,71 @@ -import meep as mp import math import unittest -class TestEigenmodeSource(unittest.TestCase): +import meep as mp + +class TestEigenmodeSource(unittest.TestCase): def test_waveguide_flux(self): - cell_size = mp.Vector3(10,10) + cell_size = mp.Vector3(10, 10) pml_layers = [mp.PML(thickness=2.0)] - rot_angles = range(0,60,20) # rotation angle of waveguide, CCW around z-axis + rot_angles = range(0, 60, 20) # rotation angle of waveguide, CCW around z-axis fluxes = [] coeff_fluxes = [] for t in rot_angles: rot_angle = math.radians(t) - kpoint = mp.Vector3(math.cos(rot_angle),math.sin(rot_angle),0) - sources = [mp.EigenModeSource(src=mp.GaussianSource(1.0,fwidth=0.1), - size=mp.Vector3(y=10), - center=mp.Vector3(x=-3), - direction=mp.NO_DIRECTION, - eig_kpoint=kpoint, - eig_band=1, - eig_parity=mp.ODD_Z, - eig_match_freq=True)] - - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1,mp.inf), - e1 = mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle), - e2 = mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), - material=mp.Medium(index=1.5))] - - sim = mp.Simulation(cell_size=cell_size, - resolution=50, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry) - - tran = sim.add_flux(1.0, 0, 1, mp.FluxRegion(center=mp.Vector3(x=3), size=mp.Vector3(y=10))) + kpoint = mp.Vector3(math.cos(rot_angle), math.sin(rot_angle), 0) + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(1.0, fwidth=0.1), + size=mp.Vector3(y=10), + center=mp.Vector3(x=-3), + direction=mp.NO_DIRECTION, + eig_kpoint=kpoint, + eig_band=1, + eig_parity=mp.ODD_Z, + eig_match_freq=True, + ) + ] + + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + e1=mp.Vector3(1).rotate(mp.Vector3(z=1), rot_angle), + e2=mp.Vector3(y=1).rotate(mp.Vector3(z=1), rot_angle), + material=mp.Medium(index=1.5), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=50, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + ) + + tran = sim.add_flux( + 1.0, 0, 1, mp.FluxRegion(center=mp.Vector3(x=3), size=mp.Vector3(y=10)) + ) sim.run(until_after_sources=100) - res = sim.get_eigenmode_coefficients(tran, - [1], - eig_parity=mp.EVEN_Y+mp.ODD_Z if t == 0 else mp.ODD_Z, - direction=mp.NO_DIRECTION, - kpoint_func=lambda f,n: kpoint) + res = sim.get_eigenmode_coefficients( + tran, + [1], + eig_parity=mp.EVEN_Y + mp.ODD_Z if t == 0 else mp.ODD_Z, + direction=mp.NO_DIRECTION, + kpoint_func=lambda f, n: kpoint, + ) fluxes.append(mp.get_fluxes(tran)[0]) - coeff_fluxes.append(abs(res.alpha[0,0,0])**2) - print("flux:, {:.2f}, {:.6f}".format(t,fluxes[-1])) - print("coef_flux:, {:.2f}, {:.6f}".format(t,coeff_fluxes[-1])) + coeff_fluxes.append(abs(res.alpha[0, 0, 0]) ** 2) + print(f"flux:, {t:.2f}, {fluxes[-1]:.6f}") + print(f"coef_flux:, {t:.2f}, {coeff_fluxes[-1]:.6f}") self.assertAlmostEqual(fluxes[0], fluxes[1], places=0) self.assertAlmostEqual(fluxes[1], fluxes[2], places=0) @@ -61,9 +76,9 @@ def test_waveguide_flux(self): # sadly the above line requires a workaround due to the # following annoying numerical accident: # AssertionError: 100.33815231783535 != 99.81145343586365 within 0 places - f0,f2=fluxes[0],fluxes[2] - self.assertLess( abs(f0-f2), 0.5*max(abs(f0),abs(f2)) ) + f0, f2 = fluxes[0], fluxes[2] + self.assertLess(abs(f0 - f2), 0.5 * max(abs(f0), abs(f2))) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_physical.py b/python/tests/test_physical.py index 08b745a89..f19a98556 100644 --- a/python/tests/test_physical.py +++ b/python/tests/test_physical.py @@ -1,9 +1,9 @@ import unittest + import meep as mp class TestPhysical(unittest.TestCase): - def test_physical(self): a = 10.0 @@ -15,14 +15,22 @@ def test_physical(self): cell_size = mp.Vector3(xmax, ymax) pml_layers = [mp.PML(ymax / 3.0)] - sources = [mp.Source(src=mp.ContinuousSource(w), component=mp.Ez, - center=mp.Vector3(-dx), size=mp.Vector3())] - - sim = mp.Simulation(cell_size=cell_size, - resolution=a, - boundary_layers=pml_layers, - sources=sources, - force_complex_fields=True) + sources = [ + mp.Source( + src=mp.ContinuousSource(w), + component=mp.Ez, + center=mp.Vector3(-dx), + size=mp.Vector3(), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=a, + boundary_layers=pml_layers, + sources=sources, + force_complex_fields=True, + ) sim.init_sim() sim.solve_cw(tol=1e-5 if mp.is_single_precision() else 1e-6) @@ -33,7 +41,7 @@ def test_physical(self): amp2 = sim.get_field_point(mp.Ez, p2) ratio = abs(amp1) / abs(amp2) - ratio = ratio ** 2 # in 2d, decay is ~1/sqrt(r), so square to get 1/r + ratio = ratio**2 # in 2d, decay is ~1/sqrt(r), so square to get 1/r fail_fmt = "Failed: amp1 = ({}, {}), amp2 = ({}, {})\nabs(amp1/amp2){} = {}, too far from 2.0" fail_msg = fail_fmt.format(amp1.real, amp1, amp2.real, amp2, "^2", ratio) @@ -41,5 +49,5 @@ def test_physical(self): self.assertTrue(ratio <= 2.12 and ratio >= 1.88, fail_msg) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_prism.py b/python/tests/test_prism.py index a66f32b5c..1abc472da 100644 --- a/python/tests/test_prism.py +++ b/python/tests/test_prism.py @@ -1,237 +1,256 @@ import os import unittest -import numpy as np -import meep as mp - -class TestPrism(unittest.TestCase): - - def nonconvex_marching_squares(self,idx,npts): - resolution = 25 - - cell = mp.Vector3(10,10) - - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - vertices_file = os.path.join(data_dir, 'nonconvex_prism_vertices{}.npz'.format(idx)) - vertices_obj = np.load(vertices_file) - - ## prism verticies precomputed from analytic "blob" shape using - ## marching squares algorithm of skimage.measure.find_contours - ## ref: https://github.com/NanoComp/meep/pull/1142 - vertices_data = vertices_obj["N{}".format(npts)] - vertices = [mp.Vector3(v[0],v[1],0) for v in vertices_data] - - geometry = [mp.Prism(vertices, - height=mp.inf, - material=mp.Medium(epsilon=12))] - - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - resolution=resolution) - - sim.init_sim() - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) - - print("epsilon-sum:, {} (prism-msq)".format(abs(prism_eps))) - - return prism_eps - - - def convex_marching_squares(self,npts): - resolution = 50 - - cell = mp.Vector3(3,3) - - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - vertices_file = os.path.join(data_dir, 'convex_prism_vertices.npz') - vertices_obj = np.load(vertices_file) - - ## prism vertices precomputed for a circle of radius 1.0 using - ## marching squares algorithm of skimage.measure.find_contours - ## ref: https://github.com/NanoComp/meep/issues/1060 - vertices_data = vertices_obj["N{}".format(npts)] - vertices = [mp.Vector3(v[0],v[1],0) for v in vertices_data] - - geometry = [mp.Prism(vertices, - height=mp.inf, - material=mp.Medium(epsilon=12))] +import numpy as np - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - resolution=resolution) +import meep as mp - sim.init_sim() - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) +class TestPrism(unittest.TestCase): + def nonconvex_marching_squares(self, idx, npts): + resolution = 25 - sim.reset_meep() + cell = mp.Vector3(10, 10) - geometry = [mp.Cylinder(radius=1.0, - center=mp.Vector3(), - height=mp.inf, - material=mp.Medium(epsilon=12))] + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + vertices_file = os.path.join(data_dir, f"nonconvex_prism_vertices{idx}.npz") + vertices_obj = np.load(vertices_file) - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - resolution=resolution) + ## prism verticies precomputed from analytic "blob" shape using + ## marching squares algorithm of skimage.measure.find_contours + ## ref: https://github.com/NanoComp/meep/pull/1142 + vertices_data = vertices_obj[f"N{npts}"] + vertices = [mp.Vector3(v[0], v[1], 0) for v in vertices_data] - sim.init_sim() + geometry = [mp.Prism(vertices, height=mp.inf, material=mp.Medium(epsilon=12))] - cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + sim = mp.Simulation(cell_size=cell, geometry=geometry, resolution=resolution) - print("epsilon-sum:, {} (prism-msq), {} (cylinder), {} (relative error)".format(abs(prism_eps),abs(cyl_eps),abs((prism_eps-cyl_eps)/cyl_eps))) + sim.init_sim() - return abs((prism_eps-cyl_eps)/cyl_eps) + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) + print(f"epsilon-sum:, {abs(prism_eps)} (prism-msq)") - def convex_circle(self,npts,r,sym): - resolution = 50 + return prism_eps - cell = mp.Vector3(3,3) + def convex_marching_squares(self, npts): + resolution = 50 - ### prism vertices computed as equally-spaced points - ### along the circumference of a circle with radius r - angles = 2*np.pi/npts * np.arange(npts) - vertices = [mp.Vector3(r*np.cos(ang),r*np.sin(ang)) for ang in angles] - geometry = [mp.Prism(vertices, - height=mp.inf, - material=mp.Medium(epsilon=12))] + cell = mp.Vector3(3, 3) - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - symmetries=[mp.Mirror(direction=mp.X),mp.Mirror(direction=mp.Y)] if sym else [], - resolution=resolution) + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + vertices_file = os.path.join(data_dir, "convex_prism_vertices.npz") + vertices_obj = np.load(vertices_file) - sim.init_sim() + ## prism vertices precomputed for a circle of radius 1.0 using + ## marching squares algorithm of skimage.measure.find_contours + ## ref: https://github.com/NanoComp/meep/issues/1060 + vertices_data = vertices_obj[f"N{npts}"] + vertices = [mp.Vector3(v[0], v[1], 0) for v in vertices_data] - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + geometry = [mp.Prism(vertices, height=mp.inf, material=mp.Medium(epsilon=12))] - sim.reset_meep() + sim = mp.Simulation(cell_size=cell, geometry=geometry, resolution=resolution) - geometry = [mp.Cylinder(radius=r, - center=mp.Vector3(), - height=mp.inf, - material=mp.Medium(epsilon=12))] + sim.init_sim() - sim = mp.Simulation(cell_size=cell, - geometry=geometry, - symmetries=[mp.Mirror(direction=mp.X),mp.Mirror(direction=mp.Y)] if sym else [], - resolution=resolution) + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - sim.init_sim() + sim.reset_meep() - cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + geometry = [ + mp.Cylinder( + radius=1.0, + center=mp.Vector3(), + height=mp.inf, + material=mp.Medium(epsilon=12), + ) + ] - print("epsilon-sum:, {} (prism-cyl), {} (cylinder), {} (relative error)".format(abs(prism_eps),abs(cyl_eps),abs((prism_eps-cyl_eps)/cyl_eps))) + sim = mp.Simulation(cell_size=cell, geometry=geometry, resolution=resolution) - return abs((prism_eps-cyl_eps)/cyl_eps) + sim.init_sim() - def spiral_gds(self): - data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) - gdsii_file = os.path.join(data_dir, 'spiral.gds') + cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - resolution = 25 - cell_size = mp.Vector3(12,16) - geometry = mp.get_GDSII_prisms(mp.Medium(index=3.5), gdsii_file, 0, 0, mp.inf) + print( + f"epsilon-sum:, {abs(prism_eps)} (prism-msq), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" + ) - sim = mp.Simulation(cell_size=cell_size, - geometry=geometry, - resolution=resolution) + return abs((prism_eps - cyl_eps) / cyl_eps) - sim.init_sim() + def convex_circle(self, npts, r, sym): + resolution = 50 - prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r,eps: eps) + cell = mp.Vector3(3, 3) - print("epsilon-sum:, {} (prism-gds)".format(abs(prism_eps))) + ### prism vertices computed as equally-spaced points + ### along the circumference of a circle with radius r + angles = 2 * np.pi / npts * np.arange(npts) + vertices = [mp.Vector3(r * np.cos(ang), r * np.sin(ang)) for ang in angles] + geometry = [mp.Prism(vertices, height=mp.inf, material=mp.Medium(epsilon=12))] - return prism_eps + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + symmetries=[mp.Mirror(direction=mp.X), mp.Mirror(direction=mp.Y)] + if sym + else [], + resolution=resolution, + ) - # lots of tests, turned off by default since they run too long; - # rename to test_something to run these tests. - def bigtest_prism(self): - print("Testing Non-Convex Prism #1 using marching squares algorithm...") - d1_a = self.nonconvex_marching_squares(1,208) - d1_b = self.nonconvex_marching_squares(1,448) - d1_ref = 458.27922623563074 ## self.nonconvex_marching_squares(1,904) + sim.init_sim() - self.assertLess(abs((d1_b-d1_ref)/d1_ref),abs((d1_a-d1_ref)/d1_ref)) + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - print("Testing Non-Convex Prism #2 using marching squares algorithm...") - d2_a = self.nonconvex_marching_squares(2,128) - d2_b = self.nonconvex_marching_squares(2,256) - d2_ref = 506.79940504342534 ## self.nonconvex_marching_squares(2,516) + sim.reset_meep() - self.assertLess(abs((d2_b-d2_ref)/d2_ref),abs((d2_a-d2_ref)/d2_ref)) + geometry = [ + mp.Cylinder( + radius=r, + center=mp.Vector3(), + height=mp.inf, + material=mp.Medium(epsilon=12), + ) + ] - print("Testing Non-Convex Prism #3 using marching squares algorithm...") - d3_a = self.nonconvex_marching_squares(3,164) - d3_b = self.nonconvex_marching_squares(3,336) - d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) + sim = mp.Simulation( + cell_size=cell, + geometry=geometry, + symmetries=[mp.Mirror(direction=mp.X), mp.Mirror(direction=mp.Y)] + if sym + else [], + resolution=resolution, + ) - self.assertLess(abs((d3_b-d3_ref)/d3_ref),abs((d3_a-d3_ref)/d3_ref)) + sim.init_sim() - print("Testing Convex Prism using marching squares algorithm...") - d = [self.convex_marching_squares(92), - self.convex_marching_squares(192), - self.convex_marching_squares(392)] + cyl_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - self.assertLess(d[1],d[0]) - self.assertLess(d[2],d[1]) + print( + f"epsilon-sum:, {abs(prism_eps)} (prism-cyl), {abs(cyl_eps)} (cylinder), {abs((prism_eps-cyl_eps)/cyl_eps)} (relative error)" + ) - print("Testing Convex Prism #1 using circle formula (without symmetry)...") - r = 1.0458710786934182 - d_nosym = [self.convex_circle(51,r,False), - self.convex_circle(101,r,False), - self.convex_circle(201,r,False)] + return abs((prism_eps - cyl_eps) / cyl_eps) - self.assertLess(d_nosym[1],d_nosym[0]) - self.assertLess(d_nosym[2],d_nosym[1]) + def spiral_gds(self): + data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) + gdsii_file = os.path.join(data_dir, "spiral.gds") - print("Testing Convex Prism #1 using circle formula (with symmetry)...") - d_sym = [self.convex_circle(51,r,True), - self.convex_circle(101,r,True), - self.convex_circle(201,r,True)] + resolution = 25 + cell_size = mp.Vector3(12, 16) + geometry = mp.get_GDSII_prisms(mp.Medium(index=3.5), gdsii_file, 0, 0, mp.inf) - self.assertLess(d_sym[1],d_sym[0]) - self.assertLess(d_sym[2],d_sym[1]) + sim = mp.Simulation( + cell_size=cell_size, geometry=geometry, resolution=resolution + ) - self.assertAlmostEqual(d_nosym[0],d_sym[0],places=3) - self.assertAlmostEqual(d_nosym[1],d_sym[1],places=3) - self.assertAlmostEqual(d_nosym[2],d_sym[2],places=3) + sim.init_sim() - print("Testing Convex Prism #2 using circle formula (without symmetry)...") - r = 1.2896871096581341 - d_nosym = [self.convex_circle(31,r,False), - self.convex_circle(61,r,False), - self.convex_circle(121,r,False)] + prism_eps = sim.integrate_field_function([mp.Dielectric], lambda r, eps: eps) - self.assertLess(d_nosym[1],d_nosym[0]) - self.assertLess(d_nosym[2],d_nosym[1]) + print(f"epsilon-sum:, {abs(prism_eps)} (prism-gds)") - print("Testing Convex Prism #2 using circle formula (with symmetry)...") - d_sym = [self.convex_circle(31,r,True), - self.convex_circle(61,r,True), - self.convex_circle(121,r,True)] + return prism_eps - self.assertLess(d_sym[1],d_sym[0]) - self.assertLess(d_sym[2],d_sym[1]) + # lots of tests, turned off by default since they run too long; + # rename to test_something to run these tests. + def bigtest_prism(self): + print("Testing Non-Convex Prism #1 using marching squares algorithm...") + d1_a = self.nonconvex_marching_squares(1, 208) + d1_b = self.nonconvex_marching_squares(1, 448) + d1_ref = 458.27922623563074 ## self.nonconvex_marching_squares(1,904) + + self.assertLess(abs((d1_b - d1_ref) / d1_ref), abs((d1_a - d1_ref) / d1_ref)) - self.assertAlmostEqual(d_nosym[0],d_sym[0],places=3) - self.assertAlmostEqual(d_nosym[1],d_sym[1],places=3) - self.assertAlmostEqual(d_nosym[2],d_sym[2],places=3) + print("Testing Non-Convex Prism #2 using marching squares algorithm...") + d2_a = self.nonconvex_marching_squares(2, 128) + d2_b = self.nonconvex_marching_squares(2, 256) + d2_ref = 506.79940504342534 ## self.nonconvex_marching_squares(2,516) - print("Testing Non-Convex Prism from GDSII file...") - d = self.spiral_gds() - d_ref = 455.01744881372224 - self.assertAlmostEqual(d,d_ref,places=5) + self.assertLess(abs((d2_b - d2_ref) / d2_ref), abs((d2_a - d2_ref) / d2_ref)) + + print("Testing Non-Convex Prism #3 using marching squares algorithm...") + d3_a = self.nonconvex_marching_squares(3, 164) + d3_b = self.nonconvex_marching_squares(3, 336) + d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) + + self.assertLess(abs((d3_b - d3_ref) / d3_ref), abs((d3_a - d3_ref) / d3_ref)) + + print("Testing Convex Prism using marching squares algorithm...") + d = [ + self.convex_marching_squares(92), + self.convex_marching_squares(192), + self.convex_marching_squares(392), + ] + + self.assertLess(d[1], d[0]) + self.assertLess(d[2], d[1]) + + print("Testing Convex Prism #1 using circle formula (without symmetry)...") + r = 1.0458710786934182 + d_nosym = [ + self.convex_circle(51, r, False), + self.convex_circle(101, r, False), + self.convex_circle(201, r, False), + ] + + self.assertLess(d_nosym[1], d_nosym[0]) + self.assertLess(d_nosym[2], d_nosym[1]) + + print("Testing Convex Prism #1 using circle formula (with symmetry)...") + d_sym = [ + self.convex_circle(51, r, True), + self.convex_circle(101, r, True), + self.convex_circle(201, r, True), + ] + + self.assertLess(d_sym[1], d_sym[0]) + self.assertLess(d_sym[2], d_sym[1]) + + self.assertAlmostEqual(d_nosym[0], d_sym[0], places=3) + self.assertAlmostEqual(d_nosym[1], d_sym[1], places=3) + self.assertAlmostEqual(d_nosym[2], d_sym[2], places=3) + + print("Testing Convex Prism #2 using circle formula (without symmetry)...") + r = 1.2896871096581341 + d_nosym = [ + self.convex_circle(31, r, False), + self.convex_circle(61, r, False), + self.convex_circle(121, r, False), + ] - def test_prism(self): - print("Testing Non-Convex Prism #3 using marching squares algorithm...") - d3_a = self.nonconvex_marching_squares(3,164) - d3_b = self.nonconvex_marching_squares(3,336) - d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) - self.assertLess(abs((d3_b-d3_ref)/d3_ref),abs((d3_a-d3_ref)/d3_ref)) - self.assertLess(abs((d3_b-d3_ref)/d3_ref), 0.02) + self.assertLess(d_nosym[1], d_nosym[0]) + self.assertLess(d_nosym[2], d_nosym[1]) -if __name__ == '__main__': - unittest.main() + print("Testing Convex Prism #2 using circle formula (with symmetry)...") + d_sym = [ + self.convex_circle(31, r, True), + self.convex_circle(61, r, True), + self.convex_circle(121, r, True), + ] + + self.assertLess(d_sym[1], d_sym[0]) + self.assertLess(d_sym[2], d_sym[1]) + + self.assertAlmostEqual(d_nosym[0], d_sym[0], places=3) + self.assertAlmostEqual(d_nosym[1], d_sym[1], places=3) + self.assertAlmostEqual(d_nosym[2], d_sym[2], places=3) + + print("Testing Non-Convex Prism from GDSII file...") + d = self.spiral_gds() + d_ref = 455.01744881372224 + self.assertAlmostEqual(d, d_ref, places=5) + + def test_prism(self): + print("Testing Non-Convex Prism #3 using marching squares algorithm...") + d3_a = self.nonconvex_marching_squares(3, 164) + d3_b = self.nonconvex_marching_squares(3, 336) + d3_ref = 640.0738356076143 ## self.nonconvex_marching_squares(3,672) + self.assertLess(abs((d3_b - d3_ref) / d3_ref), abs((d3_a - d3_ref) / d3_ref)) + self.assertLess(abs((d3_b - d3_ref) / d3_ref), 0.02) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/tests/test_pw_source.py b/python/tests/test_pw_source.py index 797e3c894..e42d61804 100644 --- a/python/tests/test_pw_source.py +++ b/python/tests/test_pw_source.py @@ -1,11 +1,13 @@ -import meep as mp import cmath import math import unittest + from utils import ApproxComparisonTestCase -class TestPwSource(ApproxComparisonTestCase): +import meep as mp + +class TestPwSource(ApproxComparisonTestCase): def setUp(self): s = 11 dpml = 1 @@ -19,6 +21,7 @@ def setUp(self): def pw_amp(k, x0): def _pw_amp(x): return cmath.exp(1j * k.dot(x + x0)) + return _pw_amp fcen = 0.8 @@ -32,22 +35,22 @@ def _pw_amp(x): component=mp.Ez, center=mp.Vector3(-0.5 * s, 0), size=mp.Vector3(0, s), - amp_func=pw_amp(self.k, mp.Vector3(x=-0.5 * s)) + amp_func=pw_amp(self.k, mp.Vector3(x=-0.5 * s)), ), mp.Source( mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3(0, -0.5 * s), size=mp.Vector3(s, 0), - amp_func=pw_amp(self.k, mp.Vector3(y=-0.5 * s)) - ) + amp_func=pw_amp(self.k, mp.Vector3(y=-0.5 * s)), + ), ] self.sim = mp.Simulation( cell_size=cell, sources=sources, boundary_layers=pml_layers, - resolution=resolution + resolution=resolution, ) self.sim.use_output_directory(self.temp_dir) self.s = s @@ -72,7 +75,11 @@ def test_pw_source(self): tol = 1e-4 if mp.is_single_precision() else 1e-9 self.assertClose(pt1 / pt2, 27.557668029008262, epsilon=tol) - self.assertAlmostEqual(cmath.exp(1j * self.k.dot(v1 - v2)), 0.7654030066070924 - 0.6435512702783076j) + self.assertAlmostEqual( + cmath.exp(1j * self.k.dot(v1 - v2)), + 0.7654030066070924 - 0.6435512702783076j, + ) + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_refl_angular.py b/python/tests/test_refl_angular.py index 0397051b4..6e6bad0b6 100644 --- a/python/tests/test_refl_angular.py +++ b/python/tests/test_refl_angular.py @@ -1,12 +1,12 @@ -# -*- coding: utf-8 -*- - +import math import unittest -import parameterized + import numpy as np -import math -import meep as mp +import parameterized from utils import ApproxComparisonTestCase +import meep as mp + class TestReflAngular(ApproxComparisonTestCase): @classmethod @@ -18,76 +18,81 @@ def setUpClass(cls): cls.dpml = 1.0 cls.dz = 7.0 - cls.sz = cls.dz+2*cls.dpml + cls.sz = cls.dz + 2 * cls.dpml cls.wvl_min = 0.4 cls.wvl_max = 0.8 - cls.fmin = 1/cls.wvl_max - cls.fmax = 1/cls.wvl_min - cls.fcen = 0.5*(cls.fmin+cls.fmax) - cls.df = cls.fmax-cls.fmin + cls.fmin = 1 / cls.wvl_max + cls.fmax = 1 / cls.wvl_min + cls.fcen = 0.5 * (cls.fmin + cls.fmax) + cls.df = cls.fmax - cls.fmin cls.nfreq = 11 def refl_angular(self, theta): theta_r = math.radians(theta) # wavevector (in source medium); plane of incidence is XZ - k = mp.Vector3(0,0,1).rotate(mp.Vector3(0,1,0),theta_r).scale(self.n1*self.fmin) - - if theta == 0: - dimensions = 1 - else: - dimensions = 3 + k = ( + mp.Vector3(0, 0, 1) + .rotate(mp.Vector3(0, 1, 0), theta_r) + .scale(self.n1 * self.fmin) + ) + dimensions = 1 if theta == 0 else 3 cell_size = mp.Vector3(z=self.sz) pml_layers = [mp.PML(self.dpml)] - sources = [mp.Source(mp.GaussianSource(self.fcen, fwidth=self.df), - component=mp.Ex, # P polarization - center=mp.Vector3(z=-0.5*self.sz+self.dpml))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - dimensions=dimensions, - default_material=mp.Medium(index=self.n1), - sources=sources, - boundary_layers=pml_layers, - k_point=k) - - mon_pt = -0.5*self.sz+self.dpml+0.25*self.dz + sources = [ + mp.Source( + mp.GaussianSource(self.fcen, fwidth=self.df), + component=mp.Ex, # P polarization + center=mp.Vector3(z=-0.5 * self.sz + self.dpml), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + dimensions=dimensions, + default_material=mp.Medium(index=self.n1), + sources=sources, + boundary_layers=pml_layers, + k_point=k, + ) + + mon_pt = -0.5 * self.sz + self.dpml + 0.25 * self.dz refl_fr = mp.FluxRegion(center=mp.Vector3(z=mon_pt)) - refl = sim.add_flux(self.fcen, - self.df, - self.nfreq, - refl_fr) - - termination_cond = mp.stop_when_fields_decayed(50, - mp.Ex, - mp.Vector3(z=mon_pt), - 1e-9) + refl = sim.add_flux(self.fcen, self.df, self.nfreq, refl_fr) + + termination_cond = mp.stop_when_fields_decayed( + 50, mp.Ex, mp.Vector3(z=mon_pt), 1e-9 + ) sim.run(until_after_sources=termination_cond) empty_data = sim.get_flux_data(refl) empty_flux = mp.get_fluxes(refl) sim.reset_meep() - geometry = [mp.Block(size=mp.Vector3(mp.inf,mp.inf,0.5*self.sz), - center=mp.Vector3(z=0.25*self.sz), - material=mp.Medium(index=self.n2))] - - sim = mp.Simulation(resolution=self.resolution, - cell_size=cell_size, - dimensions=dimensions, - default_material=mp.Medium(index=self.n1), - sources=sources, - boundary_layers=pml_layers, - k_point=k, - geometry=geometry) - - refl = sim.add_flux(self.fcen, - self.df, - self.nfreq, - refl_fr) + geometry = [ + mp.Block( + size=mp.Vector3(mp.inf, mp.inf, 0.5 * self.sz), + center=mp.Vector3(z=0.25 * self.sz), + material=mp.Medium(index=self.n2), + ) + ] + + sim = mp.Simulation( + resolution=self.resolution, + cell_size=cell_size, + dimensions=dimensions, + default_material=mp.Medium(index=self.n1), + sources=sources, + boundary_layers=pml_layers, + k_point=k, + geometry=geometry, + ) + + refl = sim.add_flux(self.fcen, self.df, self.nfreq, refl_fr) sim.load_minus_flux_data(refl, empty_data) sim.run(until_after_sources=termination_cond) @@ -95,41 +100,48 @@ def refl_angular(self, theta): refl_flux = mp.get_fluxes(refl) freqs = mp.get_flux_freqs(refl) - Rs = -np.array(refl_flux)/np.array(empty_flux) - - thetas = [] - for i in range(self.nfreq): - thetas.append(math.asin(k.x/(self.n1*freqs[i]))) + Rs = -np.array(refl_flux) / np.array(empty_flux) + thetas = [math.asin(k.x / (self.n1 * freqs[i])) for i in range(self.nfreq)] return freqs, thetas, Rs @parameterized.parameterized.expand([(0,), (20.6,)]) - def test_refl_angular(self,theta): + def test_refl_angular(self, theta): fmeep, tmeep, Rmeep = self.refl_angular(theta) # angle of refracted planewave in medium n2 for an # incident planewave in medium n1 at angle theta_in - theta_out = lambda theta_in: math.asin(self.n1*math.sin(theta_in)/self.n2) + theta_out = lambda theta_in: math.asin(self.n1 * math.sin(theta_in) / self.n2) # Fresnel reflectance for P polarization in medium n2 for # an incident planewave in medium n1 at angle theta_in - Rfresnel = lambda theta_in: (math.fabs((self.n1*math.cos(theta_out(theta_in))-self.n2*math.cos(theta_in)) / - (self.n1*math.cos(theta_out(theta_in))+self.n2*math.cos(theta_in)))**2) + Rfresnel = lambda theta_in: ( + math.fabs( + (self.n1 * math.cos(theta_out(theta_in)) - self.n2 * math.cos(theta_in)) + / ( + self.n1 * math.cos(theta_out(theta_in)) + + self.n2 * math.cos(theta_in) + ) + ) + ** 2 + ) Ranalytic = np.empty((self.nfreq,)) - print("refl:, wavelength (μm), incident angle (°), reflectance (Meep), reflectance (analytic), error") + print( + "refl:, wavelength (μm), incident angle (°), reflectance (Meep), reflectance (analytic), error" + ) for i in range(self.nfreq): Ranalytic[i] = Rfresnel(tmeep[i]) - err = abs(Rmeep[i]-Ranalytic[i])/Ranalytic[i] - print("refl:, {:4.2f}, {:4.2f}, {:8.6f}, {:8.6f}, {:6.4f}".format(1/fmeep[i], - math.degrees(tmeep[i]), - Rmeep[i], - Ranalytic[i], - err)) + err = abs(Rmeep[i] - Ranalytic[i]) / Ranalytic[i] + print( + "refl:, {:4.2f}, {:4.2f}, {:8.6f}, {:8.6f}, {:6.4f}".format( + 1 / fmeep[i], math.degrees(tmeep[i]), Rmeep[i], Ranalytic[i], err + ) + ) tol = 0.005 if mp.is_single_precision() else 0.004 self.assertClose(Rmeep, Ranalytic, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_ring.py b/python/tests/test_ring.py index 7970f23dc..d4f5eaa9b 100644 --- a/python/tests/test_ring.py +++ b/python/tests/test_ring.py @@ -1,10 +1,11 @@ # Python port of meep/examples/ring.ctl # Calculating 2d ring-resonator modes, from the Meep tutorial. import unittest + import meep as mp -class TestRing(unittest.TestCase): +class TestRing(unittest.TestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -21,7 +22,7 @@ def init(self): dpml = 2 sxy = 2 * (r + w + pad + dpml) - dielectric = mp.Medium(epsilon=n * n) + dielectric = mp.Medium(epsilon=n**2) air = mp.Medium() c1 = mp.Cylinder(r + w, material=dielectric) @@ -32,12 +33,14 @@ def init(self): src = mp.Source(mp.GaussianSource(fcen, fwidth=df), mp.Ez, mp.Vector3(r + 0.1)) - self.sim = mp.Simulation(cell_size=mp.Vector3(sxy, sxy), - geometry=[c1, c2], - sources=[src], - resolution=10, - symmetries=[mp.Mirror(mp.Y)], - boundary_layers=[mp.PML(dpml)]) + self.sim = mp.Simulation( + cell_size=mp.Vector3(sxy, sxy), + geometry=[c1, c2], + sources=[src], + resolution=10, + symmetries=[mp.Mirror(mp.Y)], + boundary_layers=[mp.PML(dpml)], + ) self.sim.use_output_directory(self.temp_dir) self.h = mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df) @@ -48,7 +51,7 @@ def test_harminv(self): self.sim.run( mp.at_beginning(mp.output_epsilon), mp.after_sources(self.h), - until_after_sources=300 + until_after_sources=300, ) m1 = self.h.modes[0] @@ -68,5 +71,5 @@ def test_harminv(self): self.assertAlmostEqual(fp, -0.08185972142450348, places=places) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_ring_cyl.py b/python/tests/test_ring_cyl.py index a6ec6c27d..525ac7c09 100644 --- a/python/tests/test_ring_cyl.py +++ b/python/tests/test_ring_cyl.py @@ -1,9 +1,11 @@ import unittest -import meep as mp + from utils import ApproxComparisonTestCase -class TestRingCyl(ApproxComparisonTestCase): +import meep as mp + +class TestRingCyl(ApproxComparisonTestCase): def setUp(self): n = 3.4 w = 1 @@ -19,7 +21,7 @@ def setUp(self): mp.Block( center=mp.Vector3(self.r + (w / 2)), size=mp.Vector3(w, mp.inf, mp.inf), - material=mp.Medium(index=n) + material=mp.Medium(index=n), ) ] @@ -32,7 +34,7 @@ def setUp(self): mp.Source( src=mp.GaussianSource(self.fcen, fwidth=self.df), component=mp.Ez, - center=mp.Vector3(self.r + 0.1) + center=mp.Vector3(self.r + 0.1), ) ] @@ -44,7 +46,7 @@ def setUp(self): sources=sources, dimensions=dimensions, m=m, - split_chunks_evenly=False + split_chunks_evenly=False, ) def test_ring_cyl(self): @@ -67,5 +69,5 @@ def test_ring_cyl(self): self.assertClose(expected, res, epsilon=tol) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_simulation.py b/python/tests/test_simulation.py index 24d183467..a0c3b3b7a 100644 --- a/python/tests/test_simulation.py +++ b/python/tests/test_simulation.py @@ -4,8 +4,10 @@ import sys import unittest import warnings + import h5py import numpy as np + import meep as mp try: @@ -16,11 +18,11 @@ class TestSimulation(unittest.TestCase): - fname_base = re.sub(r'\.py$', '', os.path.split(sys.argv[0])[1]) - fname = fname_base + '-ez-000200.00.h5' + fname_base = re.sub(r"\.py$", "", os.path.split(sys.argv[0])[1]) + fname = fname_base + "-ez-000200.00.h5" def setUp(self): - print("Running {}".format(self._testMethodName)) + print(f"Running {self._testMethodName}") @classmethod def setUpClass(cls): @@ -33,16 +35,106 @@ def tearDownClass(cls): def test_interpolate_numbers(self): expected = [ - 1.0, 1.0909090909090908, 1.1818181818181819, 1.2727272727272727, 1.3636363636363635, 1.4545454545454546, 1.5454545454545454, 1.6363636363636365, 1.7272727272727273, 1.8181818181818181, 1.9090909090909092, - 2.0, 2.090909090909091, 2.1818181818181817, 2.272727272727273, 2.3636363636363638, 2.4545454545454546, 2.5454545454545454, 2.6363636363636362, 2.727272727272727, 2.8181818181818183, 2.909090909090909, - 3.0, 3.090909090909091, 3.1818181818181817, 3.272727272727273, 3.3636363636363638, 3.4545454545454546, 3.5454545454545454, 3.6363636363636362, 3.727272727272727, 3.8181818181818183, 3.909090909090909, - 4.0, 4.090909090909091, 4.181818181818182, 4.2727272727272725, 4.363636363636363, 4.454545454545454, 4.545454545454546, 4.636363636363637, 4.7272727272727275, 4.818181818181818, 4.909090909090909, - 5.0, 5.090909090909091, 5.181818181818182, 5.2727272727272725, 5.363636363636363, 5.454545454545454, 5.545454545454546, 5.636363636363637, 5.7272727272727275, 5.818181818181818, 5.909090909090909, - 6.0, 6.090909090909091, 6.181818181818182, 6.2727272727272725, 6.363636363636363, 6.454545454545454, 6.545454545454546, 6.636363636363637, 6.7272727272727275, 6.818181818181818, 6.909090909090909, - 7.0, 7.090909090909091, 7.181818181818182, 7.2727272727272725, 7.363636363636363, 7.454545454545454, 7.545454545454546, 7.636363636363637, 7.7272727272727275, 7.818181818181818, 7.909090909090909, - 8.0, 8.090909090909092, 8.181818181818182, 8.272727272727273, 8.363636363636363, 8.454545454545455, 8.545454545454545, 8.636363636363637, 8.727272727272727, 8.818181818181818, 8.909090909090908, - 9.0, 9.090909090909092, 9.181818181818182, 9.272727272727273, 9.363636363636363, 9.454545454545455, 9.545454545454545, 9.636363636363637, 9.727272727272727, 9.818181818181818, 9.909090909090908, - 10.0 + 1.0, + 1.0909090909090908, + 1.1818181818181819, + 1.2727272727272727, + 1.3636363636363635, + 1.4545454545454546, + 1.5454545454545454, + 1.6363636363636365, + 1.7272727272727273, + 1.8181818181818181, + 1.9090909090909092, + 2.0, + 2.090909090909091, + 2.1818181818181817, + 2.272727272727273, + 2.3636363636363638, + 2.4545454545454546, + 2.5454545454545454, + 2.6363636363636362, + 2.727272727272727, + 2.8181818181818183, + 2.909090909090909, + 3.0, + 3.090909090909091, + 3.1818181818181817, + 3.272727272727273, + 3.3636363636363638, + 3.4545454545454546, + 3.5454545454545454, + 3.6363636363636362, + 3.727272727272727, + 3.8181818181818183, + 3.909090909090909, + 4.0, + 4.090909090909091, + 4.181818181818182, + 4.2727272727272725, + 4.363636363636363, + 4.454545454545454, + 4.545454545454546, + 4.636363636363637, + 4.7272727272727275, + 4.818181818181818, + 4.909090909090909, + 5.0, + 5.090909090909091, + 5.181818181818182, + 5.2727272727272725, + 5.363636363636363, + 5.454545454545454, + 5.545454545454546, + 5.636363636363637, + 5.7272727272727275, + 5.818181818181818, + 5.909090909090909, + 6.0, + 6.090909090909091, + 6.181818181818182, + 6.2727272727272725, + 6.363636363636363, + 6.454545454545454, + 6.545454545454546, + 6.636363636363637, + 6.7272727272727275, + 6.818181818181818, + 6.909090909090909, + 7.0, + 7.090909090909091, + 7.181818181818182, + 7.2727272727272725, + 7.363636363636363, + 7.454545454545454, + 7.545454545454546, + 7.636363636363637, + 7.7272727272727275, + 7.818181818181818, + 7.909090909090909, + 8.0, + 8.090909090909092, + 8.181818181818182, + 8.272727272727273, + 8.363636363636363, + 8.454545454545455, + 8.545454545454545, + 8.636363636363637, + 8.727272727272727, + 8.818181818181818, + 8.909090909090908, + 9.0, + 9.090909090909092, + 9.181818181818182, + 9.272727272727273, + 9.363636363636363, + 9.454545454545455, + 9.545454545454545, + 9.636363636363637, + 9.727272727272727, + 9.818181818181818, + 9.909090909090908, + 10.0, ] nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] @@ -73,7 +165,7 @@ def test_interpolate_vectors(self): mp.Vector3(0.42499999999999993), mp.Vector3(0.44999999999999996), mp.Vector3(0.475), - mp.Vector3(0.5) + mp.Vector3(0.5), ] res = mp.interpolate(19, [mp.Vector3(), mp.Vector3(0.5)]) @@ -94,7 +186,7 @@ def test_arith_sequence(self): 0.15240480961923594, 0.15280561122244193, 0.15320641282564793, - 0.15360721442885392 + 0.15360721442885392, ] res = np.linspace(0.15, 0.15 + 0.000400801603206 * 10, num=10, endpoint=False) @@ -112,17 +204,20 @@ def init_simple_simulation(self, **kwargs): fcen = 1.0 df = 1.0 - sources = mp.Source(src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), - component=mp.Ez) + sources = mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), center=mp.Vector3(), component=mp.Ez + ) symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] - return mp.Simulation(resolution=resolution, - cell_size=cell, - boundary_layers=[pml_layers], - sources=[sources], - symmetries=symmetries, - **kwargs) + return mp.Simulation( + resolution=resolution, + cell_size=cell, + boundary_layers=[pml_layers], + sources=[sources], + symmetries=symmetries, + **kwargs, + ) @unittest.skipIf(not mp.with_mpi(), "MPI specific test") def test_mpi(self): @@ -130,7 +225,7 @@ def test_mpi(self): def test_use_output_directory_default(self): sim = self.init_simple_simulation() - output_dir = os.path.join(self.temp_dir, 'simulation-out') + output_dir = os.path.join(self.temp_dir, "simulation-out") sim.use_output_directory(output_dir) sim.run(mp.at_end(mp.output_efield_z), until=200) @@ -141,8 +236,10 @@ def test_at_time(self): sim.use_output_directory(self.temp_dir) sim.run(mp.at_time(100, mp.output_efield_z), until=200) - fname = os.path.join(self.temp_dir, - sim.get_filename_prefix() + '-ez-000100.00.h5') + fname = os.path.join( + self.temp_dir, f"{sim.get_filename_prefix()}-ez-000100.00.h5" + ) + self.assertTrue(os.path.exists(fname)) def test_after_sources_and_time(self): @@ -160,10 +257,15 @@ def _done(sim, todo): def test_with_prefix(self): sim = self.init_simple_simulation() sim.use_output_directory(self.temp_dir) - sim.run(mp.with_prefix('test_prefix-', mp.at_end(mp.output_efield_z)), until=200) + sim.run( + mp.with_prefix("test_prefix-", mp.at_end(mp.output_efield_z)), until=200 + ) + + fname = os.path.join( + self.temp_dir, + (f"test_prefix-{sim.get_filename_prefix()}" + "-ez-000200.00.h5"), + ) - fname = os.path.join(self.temp_dir, 'test_prefix-' + sim.get_filename_prefix() + - '-ez-000200.00.h5') self.assertTrue(os.path.exists(fname)) def test_extra_materials(self): @@ -189,25 +291,29 @@ def test_infer_dimensions(self): def test_in_volume(self): sim = self.init_simple_simulation() sim.use_output_directory(self.temp_dir) - sim.filename_prefix = 'test_in_volume' + sim.filename_prefix = "test_in_volume" vol = mp.Volume(mp.Vector3(), size=mp.Vector3(x=2)) sim.run(mp.at_end(mp.in_volume(vol, mp.output_efield_z)), until=200) - fn = os.path.join(self.temp_dir, 'test_in_volume-ez-000200.00.h5') + fn = os.path.join(self.temp_dir, "test_in_volume-ez-000200.00.h5") self.assertTrue(os.path.exists(fn)) def test_in_point(self): sim = self.init_simple_simulation() sim.use_output_directory(self.temp_dir) - sim.filename_prefix = 'test_in_point' + sim.filename_prefix = "test_in_point" pt = mp.Vector3() sim.run(mp.at_end(mp.in_point(pt, mp.output_efield_z)), until=200) - fn = os.path.join(self.temp_dir, 'test_in_point-ez-000200.00.h5') + fn = os.path.join(self.temp_dir, "test_in_point-ez-000200.00.h5") self.assertTrue(os.path.exists(fn)) def test_epsilon_input_file(self): sim = self.init_simple_simulation() - eps_input_fname = 'cyl-ellipsoid-eps-ref.h5' - eps_input_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'tests')) + eps_input_fname = "cyl-ellipsoid-eps-ref.h5" + eps_input_dir = os.path.abspath( + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "..", "..", "tests" + ) + ) eps_input_path = os.path.join(eps_input_dir, eps_input_fname) sim.epsilon_input_file = eps_input_path @@ -219,19 +325,25 @@ def test_epsilon_input_file(self): # Test unicode file name for Python 2 if sys.version_info[0] == 2: - sim = self.init_simple_simulation(epsilon_input_file=unicode(eps_input_path)) + sim = self.init_simple_simulation( + epsilon_input_file=unicode(eps_input_path) + ) sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) self.assertAlmostEqual(fp, -0.002989654055823199) def test_numpy_epsilon(self): sim = self.init_simple_simulation() - eps_input_fname = 'cyl-ellipsoid-eps-ref.h5' - eps_input_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'tests')) + eps_input_fname = "cyl-ellipsoid-eps-ref.h5" + eps_input_dir = os.path.abspath( + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "..", "..", "tests" + ) + ) eps_input_path = os.path.join(eps_input_dir, eps_input_fname) - with h5py.File(eps_input_path, 'r') as f: - sim.default_material = f['eps'][()] + with h5py.File(eps_input_path, "r") as f: + sim.default_material = f["eps"][()] sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -240,51 +352,68 @@ def test_numpy_epsilon(self): self.assertAlmostEqual(fp, -0.002989654055823199, places=places) def test_set_materials(self): - def change_geom(sim): t = sim.meep_time() fn = t * 0.02 - geom = [mp.Cylinder(radius=3, material=mp.Medium(index=3.5), center=mp.Vector3(fn, fn)), - mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf), center=mp.Vector3(fn, fn))] + geom = [ + mp.Cylinder( + radius=3, material=mp.Medium(index=3.5), center=mp.Vector3(fn, fn) + ), + mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf), center=mp.Vector3(fn, fn)), + ] sim.set_materials(geometry=geom) c = mp.Cylinder(radius=3, material=mp.Medium(index=3.5)) e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf)) - sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), component=mp.Hz, center=mp.Vector3()) + sources = mp.Source( + src=mp.GaussianSource(1, fwidth=0.1), component=mp.Hz, center=mp.Vector3() + ) symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)] - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), - geometry=[c, e], - boundary_layers=[mp.PML(1.0)], - sources=[sources], - symmetries=symmetries, - resolution=16) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), + geometry=[c, e], + boundary_layers=[mp.PML(1.0)], + sources=[sources], + symmetries=symmetries, + resolution=16, + ) - eps = {'arr1': None, 'arr2': None} + eps = {"arr1": None, "arr2": None} def get_arr1(sim): - eps['arr1'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10))) + eps["arr1"] = sim.get_array( + mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)) + ) def get_arr2(sim): - eps['arr2'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10))) - - sim.run(mp.at_time(50, get_arr1), mp.at_time(100, change_geom), - mp.at_end(get_arr2), until=200) + eps["arr2"] = sim.get_array( + mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)) + ) + + sim.run( + mp.at_time(50, get_arr1), + mp.at_time(100, change_geom), + mp.at_end(get_arr2), + until=200, + ) - self.assertFalse(np.array_equal(eps['arr1'], eps['arr2'])) + self.assertFalse(np.array_equal(eps["arr1"], eps["arr2"])) def test_modal_volume_in_box(self): sim = self.init_simple_simulation() sim.run(until=200) vol = sim.fields.total_volume() - self.assertAlmostEqual(sim.fields.modal_volume_in_box(vol), - sim.modal_volume_in_box()) + self.assertAlmostEqual( + sim.fields.modal_volume_in_box(vol), sim.modal_volume_in_box() + ) vol = mp.Volume(mp.Vector3(), size=mp.Vector3(1, 1, 1)) - self.assertAlmostEqual(sim.fields.modal_volume_in_box(vol.swigobj), - sim.modal_volume_in_box(vol)) + self.assertAlmostEqual( + sim.fields.modal_volume_in_box(vol.swigobj), sim.modal_volume_in_box(vol) + ) def test_in_box_volumes(self): sim = self.init_simple_simulation() @@ -307,7 +436,7 @@ def test_get_array_output(self): sim.use_output_directory(self.temp_dir) sim.symmetries = [] sim.geometry = [mp.Cylinder(0.2, material=mp.Medium(index=3))] - sim.filename_prefix = 'test_get_array_output' + sim.filename_prefix = "test_get_array_output" sim.run(until=20) mp.output_epsilon(sim) @@ -320,21 +449,21 @@ def test_get_array_output(self): energy_arr = sim.get_tot_pwr(snap=True) efield_arr = sim.get_efield(snap=True) - fname_fmt = os.path.join(self.temp_dir, 'test_get_array_output-{}-000020.00.h5') + fname_fmt = os.path.join(self.temp_dir, "test_get_array_output-{}-000020.00.h5") - with h5py.File(fname_fmt.format('eps'), 'r') as f: - eps = f['eps'][()] + with h5py.File(fname_fmt.format("eps"), "r") as f: + eps = f["eps"][()] - with h5py.File(fname_fmt.format('ez'), 'r') as f: - efield_z = f['ez'][()] + with h5py.File(fname_fmt.format("ez"), "r") as f: + efield_z = f["ez"][()] - with h5py.File(fname_fmt.format('energy'), 'r') as f: - energy = f['energy'][()] + with h5py.File(fname_fmt.format("energy"), "r") as f: + energy = f["energy"][()] - with h5py.File(fname_fmt.format('e'), 'r') as f: - ex = f['ex'][()] - ey = f['ey'][()] - ez = f['ez'][()] + with h5py.File(fname_fmt.format("e"), "r") as f: + ex = f["ex"][()] + ey = f["ey"][()] + ez = f["ez"][()] efield = np.stack([ex, ey, ez], axis=-1) np.testing.assert_allclose(eps, eps_arr) @@ -346,13 +475,21 @@ def test_synchronized_magnetic(self): # Issue 309 cell = mp.Vector3(16, 8, 0) - geometry = [mp.Block(mp.Vector3(1e20, 1, 1e20), - center=mp.Vector3(0, 0), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + mp.Vector3(1e20, 1, 1e20), + center=mp.Vector3(0, 0), + material=mp.Medium(epsilon=12), + ) + ] - sources = [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-7, 0))] + sources = [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-7, 0), + ) + ] pml_layers = [mp.PML(1.0)] resolution = 10 @@ -362,14 +499,13 @@ def test_synchronized_magnetic(self): boundary_layers=pml_layers, geometry=geometry, sources=sources, - resolution=resolution + resolution=resolution, ) sim.use_output_directory(self.temp_dir) sim.run(mp.synchronized_magnetic(mp.output_bfield_y), until=10) def test_harminv_warnings(self): - def check_warnings(sim, h, should_warn=True): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") @@ -381,21 +517,30 @@ def check_warnings(sim, h, should_warn=True): else: self.assertEqual(len(w), 0) - sources = [mp.Source(src=mp.GaussianSource(1, fwidth=1), center=mp.Vector3(), component=mp.Ez)] - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), resolution=10, sources=sources) + sources = [ + mp.Source( + src=mp.GaussianSource(1, fwidth=1), center=mp.Vector3(), component=mp.Ez + ) + ] + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), resolution=10, sources=sources + ) h = mp.Harminv(mp.Ez, mp.Vector3(), 1.4, 0.5) check_warnings(sim, h) - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), resolution=10, sources=sources) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), resolution=10, sources=sources + ) h = mp.Harminv(mp.Ez, mp.Vector3(), 0.5, 0.5) check_warnings(sim, h) - sim = mp.Simulation(cell_size=mp.Vector3(10, 10), resolution=10, sources=sources) + sim = mp.Simulation( + cell_size=mp.Vector3(10, 10), resolution=10, sources=sources + ) h = mp.Harminv(mp.Ez, mp.Vector3(), 1, 1) check_warnings(sim, h, should_warn=False) def test_vec_constructor(self): - def assert_one(v): self.assertEqual(v.z(), 1) @@ -429,14 +574,16 @@ def check_iterable(one, two, three, four): assert_two(v2) v3 = mp.vec(three) assert_three(v3) - assert_raises(four, (NotImplementedError,TypeError)) + assert_raises(four, (NotImplementedError, TypeError)) check_iterable([1], [1, 2], [1, 2, 3], [1, 2, 3, 4]) check_iterable((1,), (1, 2), (1, 2, 3), (1, 2, 3, 4)) - check_iterable(np.array([1.]), - np.array([1., 2.]), - np.array([1., 2., 3.]), - np.array([1., 2., 3., 4.])) + check_iterable( + np.array([1.0]), + np.array([1.0, 2.0]), + np.array([1.0, 2.0, 3.0]), + np.array([1.0, 2.0, 3.0, 4.0]), + ) with self.assertRaises(TypeError): mp.vec([1, 2], 3) @@ -444,7 +591,9 @@ def check_iterable(one, two, three, four): with self.assertRaises(TypeError): mp.vec(1, [2, 3]) - @unittest.skipIf(mp.is_single_precision(), "double-precision floating point specific test") + @unittest.skipIf( + mp.is_single_precision(), "double-precision floating point specific test" + ) def test_epsilon_warning(self): ## fields blow up using dispersive material ## when compiled using single precision @@ -452,9 +601,11 @@ def test_epsilon_warning(self): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") from meep.materials import Si + self.assertEqual(len(w), 0) from meep.materials import Mo + geom = [mp.Sphere(radius=0.2, material=Mo)] sim = self.init_simple_simulation(geometry=geom) with warnings.catch_warnings(record=True) as w: @@ -464,6 +615,7 @@ def test_epsilon_warning(self): self.assertIn("Epsilon", str(w[0].message)) from meep.materials import SiO2 + geom = [mp.Sphere(radius=0.2, material=SiO2)] sim = self.init_simple_simulation(geometry=geom) with warnings.catch_warnings(record=True) as w: @@ -475,7 +627,7 @@ def test_epsilon_warning(self): def test_get_filename_prefix(self): sim = self.init_simple_simulation() self.assertEqual(sim.get_filename_prefix(), self.fname_base) - sim.filename_prefix = '' + sim.filename_prefix = "" self.assertEqual(sim.get_filename_prefix(), "") sim.filename_prefix = False with self.assertRaises(TypeError): @@ -506,16 +658,32 @@ def test_geometry_center(self): fcen = 0.15 df = 0.1 - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3())] - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 3, mp.inf), - material=mp.Medium(epsilon=12))] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(), + ) + ] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 3, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] def print_field(sim): result.append(sim.get_field_point(mp.Ez, mp.Vector3(2, -1))) - sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml, - sources=sources, geometry=geometry, geometry_center=center) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml, + sources=sources, + geometry=geometry, + geometry_center=center, + ) sim.run(mp.at_end(print_field), until=50) self.assertAlmostEqual(result[0], -0.0599602798684155) @@ -529,29 +697,50 @@ def test_timing_data(self): fcen = 0.15 df = 0.1 - sources = [mp.Source(src=mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, - center=mp.Vector3())] - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 3, mp.inf), - material=mp.Medium(epsilon=12))] + sources = [ + mp.Source( + src=mp.GaussianSource(fcen, fwidth=df), + component=mp.Ez, + center=mp.Vector3(), + ) + ] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 3, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] - sim = mp.Simulation(resolution=resolution, cell_size=cell_size, boundary_layers=pml, - sources=sources, geometry=geometry, geometry_center=center) + sim = mp.Simulation( + resolution=resolution, + cell_size=cell_size, + boundary_layers=pml, + sources=sources, + geometry=geometry, + geometry_center=center, + ) sim.run(until=50) timing_data = sim.get_timing_data() # Non-exhaustive collection of steps where some time should be spent: - EXPECTED_NONZERO_TIMESINKS = (mp.Stepping, mp.Boundaries, - mp.FieldUpdateB, mp.FieldUpdateH, - mp.FieldUpdateD, mp.FieldUpdateE) + EXPECTED_NONZERO_TIMESINKS = ( + mp.Stepping, + mp.Boundaries, + mp.FieldUpdateB, + mp.FieldUpdateH, + mp.FieldUpdateD, + mp.FieldUpdateE, + ) # Due to the problem setup, no time should be spent on these steps: EXPECTED_ZERO_TIMESINKS = (mp.MPBTime, mp.GetFarfieldsTime) - for sink in itertools.chain(EXPECTED_NONZERO_TIMESINKS, - EXPECTED_ZERO_TIMESINKS): + for sink in itertools.chain( + EXPECTED_NONZERO_TIMESINKS, EXPECTED_ZERO_TIMESINKS + ): self.assertIn(sink, timing_data.keys()) self.assertEqual(len(timing_data[sink]), mp.count_processors()) - np.testing.assert_array_equal(sim.time_spent_on(sink), - timing_data[sink]) + np.testing.assert_array_equal(sim.time_spent_on(sink), timing_data[sink]) for sink in EXPECTED_NONZERO_TIMESINKS: for t in timing_data[sink]: @@ -563,34 +752,40 @@ def test_timing_data(self): self.assertGreaterEqual( sum(timing_data[mp.Stepping]), - sum(timing_data[mp.FieldUpdateB]) + - sum(timing_data[mp.FieldUpdateH]) + - sum(timing_data[mp.FieldUpdateD]) + - sum(timing_data[mp.FieldUpdateE]) + - sum(timing_data[mp.FourierTransforming])) + sum(timing_data[mp.FieldUpdateB]) + + sum(timing_data[mp.FieldUpdateH]) + + sum(timing_data[mp.FieldUpdateD]) + + sum(timing_data[mp.FieldUpdateE]) + + sum(timing_data[mp.FourierTransforming]), + ) def test_source_slice(self): sim = self.init_simple_simulation() sim.run(until=1) - vol1d = mp.Volume(center=mp.Vector3(0.1234,0), size=mp.Vector3(0,5.07)) + vol1d = mp.Volume(center=mp.Vector3(0.1234, 0), size=mp.Vector3(0, 5.07)) source_slice = sim.get_source(mp.Ez, vol=vol1d) - x,y,z,w = sim.get_array_metadata(vol=vol1d) + x, y, z, w = sim.get_array_metadata(vol=vol1d) self.assertEqual(source_slice.shape, w.shape) self.assertEqual(np.sum(source_slice), 0) - vol2d = mp.Volume(center=mp.Vector3(-0.541,0.791), size=mp.Vector3(3.5,2.8)) + vol2d = mp.Volume(center=mp.Vector3(-0.541, 0.791), size=mp.Vector3(3.5, 2.8)) source_slice = sim.get_source(mp.Ez, vol=vol2d) - x,y,z,w = sim.get_array_metadata(vol=vol2d) + x, y, z, w = sim.get_array_metadata(vol=vol2d) self.assertEqual(source_slice.shape, w.shape) self.assertNotEqual(np.sum(source_slice), 0) def test_has_mu(self): - def _check(med, expected, default=mp.Medium()): - geometry = [mp.Block(center=mp.Vector3(), size=mp.Vector3(1, 1), material=med)] - sim = mp.Simulation(cell_size=mp.Vector3(5, 5), resolution=10, geometry=geometry, - default_material=default) + geometry = [ + mp.Block(center=mp.Vector3(), size=mp.Vector3(1, 1), material=med) + ] + sim = mp.Simulation( + cell_size=mp.Vector3(5, 5), + resolution=10, + geometry=geometry, + default_material=default, + ) result = sim.has_mu() if expected: @@ -598,7 +793,7 @@ def _check(med, expected, default=mp.Medium()): else: self.assertFalse(result) - print("Estimated memory usage: {}".format(sim.get_estimated_memory_usage())) + print(f"Estimated memory usage: {sim.get_estimated_memory_usage()}") def mat_func(p): return mp.Medium() @@ -633,5 +828,6 @@ def test_iterable_as_v3(self): self.assertAlmostEqual(pt1, expected) self.assertAlmostEqual(pt2, expected) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_source.py b/python/tests/test_source.py index ce397adcc..adecb5a38 100644 --- a/python/tests/test_source.py +++ b/python/tests/test_source.py @@ -1,18 +1,17 @@ import math import os import unittest -import numpy as np -import meep as mp +import numpy as np from meep.geom import Cylinder, Vector3 -from meep.source import EigenModeSource, ContinuousSource, GaussianSource +from meep.source import ContinuousSource, EigenModeSource, GaussianSource +import meep as mp -data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data')) +data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) class TestEigenModeSource(unittest.TestCase): - def test_eig_lattice_defaults(self): src = ContinuousSource(5.0) center = Vector3() @@ -23,19 +22,20 @@ def test_eig_lattice_defaults(self): elc = Vector3(1, 1, 1) els = Vector3(1, 1, 1) - custom_lattice = EigenModeSource(src, center, eig_lattice_center=elc, eig_lattice_size=els) + custom_lattice = EigenModeSource( + src, center, eig_lattice_center=elc, eig_lattice_size=els + ) self.assertEqual(custom_lattice.eig_lattice_size, els) self.assertEqual(custom_lattice.eig_lattice_center, elc) class TestSourceTime(unittest.TestCase): - def test_source_wavelength(self): g_src = GaussianSource(wavelength=10) c_src = ContinuousSource(wavelength=10) - self.assertAlmostEqual(1. / 10., g_src.frequency) - self.assertAlmostEqual(1. / 10., c_src.frequency) + self.assertAlmostEqual(1.0 / 10.0, g_src.frequency) + self.assertAlmostEqual(1.0 / 10.0, c_src.frequency) def test_source_frequency(self): g_src = GaussianSource(10) @@ -52,9 +52,7 @@ def test_source_frequency(self): class TestSourceTypemaps(unittest.TestCase): - def setUp(self): - def dummy_eps(v): return 1.0 @@ -88,7 +86,7 @@ def test_custom_source(self): geometry = [ mp.Cylinder(r + w, material=mp.Medium(index=n)), - mp.Cylinder(r, material=mp.air) + mp.Cylinder(r, material=mp.air), ] boundary_layers = [mp.PML(dpml)] @@ -98,27 +96,34 @@ def test_custom_source(self): # Bump function def my_src_func(t): - if t > 0 and t < 2: - return math.exp(-1 / (1 - ((t - 1)**2))) - return 0j - - sources = [mp.Source(src=mp.CustomSource(src_func=my_src_func, end_time=100), - component=mp.Ez, center=mp.Vector3(r + 0.1))] + return math.exp(-1 / (1 - ((t - 1) ** 2))) if t > 0 and t < 2 else 0j + + sources = [ + mp.Source( + src=mp.CustomSource(src_func=my_src_func, end_time=100), + component=mp.Ez, + center=mp.Vector3(r + 0.1), + ) + ] symmetries = [mp.Mirror(mp.Y)] - sim = mp.Simulation(cell_size=cell, - resolution=resolution, - geometry=geometry, - boundary_layers=boundary_layers, - sources=sources, - symmetries=symmetries) + sim = mp.Simulation( + cell_size=cell, + resolution=resolution, + geometry=geometry, + boundary_layers=boundary_layers, + sources=sources, + symmetries=symmetries, + ) h = mp.Harminv(mp.Ez, mp.Vector3(r + 0.1), fcen, df) sim.run(mp.after_sources(h), until_after_sources=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(1)) - self.assertAlmostEqual(fp, -0.021997617628500023 + 0j, 5 if mp.is_single_precision() else 7) + self.assertAlmostEqual( + fp, -0.021997617628500023 + 0j, 5 if mp.is_single_precision() else 7 + ) def amp_fun(p): @@ -126,7 +131,6 @@ def amp_fun(p): class TestAmpFileFunc(unittest.TestCase): - def create_h5data(self): N = 100 M = 200 @@ -147,18 +151,39 @@ def init_and_run(self, test_type): cen = mp.Vector3(0.1, 0.2) sz = mp.Vector3(0.3, 0.2) - amp_file = os.path.join(data_dir, 'amp_func_file') - amp_file += ':amp_data' - - if test_type == 'file': - sources = [mp.Source(mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=cen, - size=sz, amp_func_file=amp_file)] - elif test_type == 'func': - sources = [mp.Source(mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=cen, - size=sz, amp_func=amp_fun)] - elif test_type == 'arr': - sources = [mp.Source(mp.ContinuousSource(fcen, fwidth=df), component=mp.Ez, center=cen, - size=sz, amp_data=self.amp_data)] + amp_file = os.path.join(data_dir, "amp_func_file") + amp_file += ":amp_data" + + if test_type == "file": + sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + component=mp.Ez, + center=cen, + size=sz, + amp_func_file=amp_file, + ) + ] + elif test_type == "func": + sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + component=mp.Ez, + center=cen, + size=sz, + amp_func=amp_fun, + ) + ] + elif test_type == "arr": + sources = [ + mp.Source( + mp.ContinuousSource(fcen, fwidth=df), + component=mp.Ez, + center=cen, + size=sz, + amp_data=self.amp_data, + ) + ] sim = mp.Simulation(cell_size=cell, resolution=resolution, sources=sources) sim.run(until=200) @@ -166,15 +191,15 @@ def init_and_run(self, test_type): def test_amp_file_func(self): self.create_h5data() - field_point_amp_file = self.init_and_run(test_type='file') - field_point_amp_func = self.init_and_run(test_type='func') - field_point_amp_arr = self.init_and_run(test_type='arr') + field_point_amp_file = self.init_and_run(test_type="file") + field_point_amp_func = self.init_and_run(test_type="func") + field_point_amp_arr = self.init_and_run(test_type="arr") self.assertAlmostEqual(field_point_amp_file, field_point_amp_func, places=4) self.assertAlmostEqual(field_point_amp_arr, field_point_amp_func, places=4) -class TestCustomEigenModeSource(unittest.TestCase): +class TestCustomEigenModeSource(unittest.TestCase): def test_custom_em_source(self): resolution = 20 @@ -183,43 +208,56 @@ def test_custom_em_source(self): sx = 40 sy = 12 - cell_size = mp.Vector3(sx+2*dpml,sy) + cell_size = mp.Vector3(sx + 2 * dpml, sy) v0 = 0.15 # pulse center frequency - a = 0.2*v0 # Gaussian envelope half-width + a = 0.2 * v0 # Gaussian envelope half-width b = -0.1 # linear chirp rate (positive: up-chirp, negative: down-chirp) - t0 = 15 # peak time + t0 = 15 # peak time - chirp = lambda t: np.exp(1j*2*np.pi*v0*(t-t0)) * np.exp(-a*(t-t0)**2+1j*b*(t-t0)**2) + chirp = lambda t: np.exp(1j * 2 * np.pi * v0 * (t - t0)) * np.exp( + -a * (t - t0) ** 2 + 1j * b * (t - t0) ** 2 + ) - geometry = [mp.Block(center=mp.Vector3(0,0,0),size=mp.Vector3(mp.inf,1,mp.inf),material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(0, 0, 0), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] - kx = 0.4 # initial guess for wavevector in x-direction of eigenmode + kx = 0.4 # initial guess for wavevector in x-direction of eigenmode kpoint = mp.Vector3(kx) bnum = 1 - sources = [mp.EigenModeSource(src=mp.CustomSource(src_func=chirp,center_frequency=v0), - center=mp.Vector3(-0.5*sx + dpml + 1), - size=mp.Vector3(y=sy), - eig_kpoint=kpoint, - eig_band=bnum, - eig_parity=mp.EVEN_Y+mp.ODD_Z, - eig_match_freq=True - )] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - resolution=resolution, - k_point=mp.Vector3(), - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)]) - - t = np.linspace(0,50,1000) - sim.run(until=t0+50) + sources = [ + mp.EigenModeSource( + src=mp.CustomSource(src_func=chirp, center_frequency=v0), + center=mp.Vector3(-0.5 * sx + dpml + 1), + size=mp.Vector3(y=sy), + eig_kpoint=kpoint, + eig_band=bnum, + eig_parity=mp.EVEN_Y + mp.ODD_Z, + eig_match_freq=True, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + resolution=resolution, + k_point=mp.Vector3(), + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)], + ) + + t = np.linspace(0, 50, 1000) + sim.run(until=t0 + 50) # For now, just check to make sure the simulation can run and the fields don't blow up. -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_special_kz.py b/python/tests/test_special_kz.py index 8c0e051b2..08ae722d4 100644 --- a/python/tests/test_special_kz.py +++ b/python/tests/test_special_kz.py @@ -1,71 +1,89 @@ -import unittest -import parameterized -import meep as mp import cmath import math +import unittest from time import time +import parameterized + +import meep as mp -class TestSpecialKz(unittest.TestCase): +class TestSpecialKz(unittest.TestCase): def refl_planar(self, theta, kz_2d): resolution = 100 # pixels/um dpml = 1.0 - sx = 3.0 + 2*dpml - sy = 1/resolution - cell_size = mp.Vector3(sx,sy) - pml_layers = [mp.PML(dpml,direction=mp.X)] + sx = 3.0 + 2 * dpml + sy = 1 / resolution + cell_size = mp.Vector3(sx, sy) + pml_layers = [mp.PML(dpml, direction=mp.X)] fcen = 1.0 # plane of incidence is XZ - k_point = mp.Vector3(1,0,0).rotate(mp.Vector3(0,1,0),theta).scale(fcen) - - sources = [mp.Source(mp.GaussianSource(fcen,fwidth=0.2*fcen), - component=mp.Ez, # P-polarization - center=mp.Vector3(-0.5*sx+dpml), - size=mp.Vector3(y=sy))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - sources=sources, - k_point=k_point, - kz_2d=kz_2d, - resolution=resolution) - - refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25*sx), - size=mp.Vector3(y=sy)) - refl = sim.add_flux(fcen,0,1,refl_fr) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mp.Vector3(),1e-9)) + k_point = mp.Vector3(1, 0, 0).rotate(mp.Vector3(0, 1, 0), theta).scale(fcen) + + sources = [ + mp.Source( + mp.GaussianSource(fcen, fwidth=0.2 * fcen), + component=mp.Ez, # P-polarization + center=mp.Vector3(-0.5 * sx + dpml), + size=mp.Vector3(y=sy), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + sources=sources, + k_point=k_point, + kz_2d=kz_2d, + resolution=resolution, + ) + + refl_fr = mp.FluxRegion(center=mp.Vector3(-0.25 * sx), size=mp.Vector3(y=sy)) + refl = sim.add_flux(fcen, 0, 1, refl_fr) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-9 + ) + ) empty_flux = mp.get_fluxes(refl) empty_data = sim.get_flux_data(refl) sim.reset_meep() - geometry = [mp.Block(material=mp.Medium(index=3.5), - size=mp.Vector3(0.5*sx,mp.inf,mp.inf), - center=mp.Vector3(0.25*sx))] - - sim = mp.Simulation(cell_size=cell_size, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - k_point=k_point, - kz_2d=kz_2d, - resolution=resolution) - - refl = sim.add_flux(fcen,0,1,refl_fr) - sim.load_minus_flux_data(refl,empty_data) - - sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,mp.Vector3(),1e-9)) + geometry = [ + mp.Block( + material=mp.Medium(index=3.5), + size=mp.Vector3(0.5 * sx, mp.inf, mp.inf), + center=mp.Vector3(0.25 * sx), + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + k_point=k_point, + kz_2d=kz_2d, + resolution=resolution, + ) + + refl = sim.add_flux(fcen, 0, 1, refl_fr) + sim.load_minus_flux_data(refl, empty_data) + + sim.run( + until_after_sources=mp.stop_when_fields_decayed( + 50, mp.Ez, mp.Vector3(), 1e-9 + ) + ) refl_flux = mp.get_fluxes(refl) - Rmeep = -refl_flux[0]/empty_flux[0] - return Rmeep - + return -refl_flux[0] / empty_flux[0] def test_special_kz(self): n1 = 1 @@ -73,12 +91,17 @@ def test_special_kz(self): # compute angle of refracted planewave in medium n2 # for incident planewave in medium n1 at angle theta_in - theta_out = lambda theta_in: math.asin(n1*math.sin(theta_in)/n2) + theta_out = lambda theta_in: math.asin(n1 * math.sin(theta_in) / n2) # compute Fresnel reflectance for P-polarization in medium n2 # for incident planewave in medium n1 at angle theta_in - Rfresnel = lambda theta_in: math.fabs((n1*math.cos(theta_out(theta_in))-n2*math.cos(theta_in)) - / (n1*math.cos(theta_out(theta_in))+n2*math.cos(theta_in)))**2 + Rfresnel = ( + lambda theta_in: math.fabs( + (n1 * math.cos(theta_out(theta_in)) - n2 * math.cos(theta_in)) + / (n1 * math.cos(theta_out(theta_in)) + n2 * math.cos(theta_in)) + ) + ** 2 + ) # angle of incident planewave; clockwise (CW) about Y axis, 0 degrees along +X axis theta = math.radians(23) @@ -93,74 +116,79 @@ def test_special_kz(self): Rfres = Rfresnel(theta) - self.assertAlmostEqual(Rmeep_complex,Rfres,places=2) - self.assertAlmostEqual(Rmeep_real_imag,Rfres,places=2) - + self.assertAlmostEqual(Rmeep_complex, Rfres, places=2) + self.assertAlmostEqual(Rmeep_real_imag, Rfres, places=2) + # the real/imag algorithm should be faster, but on CI machines performance is too variable # for this to reliably hold - # self.assertLess(t_real_imag,t_complex) - + # self.assertLess(t_real_imag,t_complex) - @parameterized.parameterized.expand([("complex",),("real/imag",)]) + @parameterized.parameterized.expand([("complex",), ("real/imag",)]) def test_eigsrc_kz(self, kz_2d): - resolution = 30 # pixels/um + resolution = 30 # pixels/um - cell_size = mp.Vector3(14,14) + cell_size = mp.Vector3(14, 14) pml_layers = [mp.PML(thickness=2)] - geometry = [mp.Block(center=mp.Vector3(), - size=mp.Vector3(mp.inf,1,mp.inf), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ) + ] fsrc = 0.3 # frequency of eigenmode or constant-amplitude source - bnum = 1 # band number of eigenmode - kz = 0.2 # fixed out-of-plane wavevector component - - sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc), - center=mp.Vector3(), - size=mp.Vector3(y=14), - eig_band=bnum, - eig_parity=mp.EVEN_Y, - eig_match_freq=True)] - - sim = mp.Simulation(cell_size=cell_size, - resolution=resolution, - boundary_layers=pml_layers, - sources=sources, - geometry=geometry, - symmetries=[mp.Mirror(mp.Y)], - k_point=mp.Vector3(z=kz), - kz_2d=kz_2d) - - tran = sim.add_flux(fsrc, - 0, - 1, - mp.FluxRegion(center=mp.Vector3(x=5), - size=mp.Vector3(y=14))) + bnum = 1 # band number of eigenmode + kz = 0.2 # fixed out-of-plane wavevector component + + sources = [ + mp.EigenModeSource( + src=mp.GaussianSource(fsrc, fwidth=0.2 * fsrc), + center=mp.Vector3(), + size=mp.Vector3(y=14), + eig_band=bnum, + eig_parity=mp.EVEN_Y, + eig_match_freq=True, + ) + ] + + sim = mp.Simulation( + cell_size=cell_size, + resolution=resolution, + boundary_layers=pml_layers, + sources=sources, + geometry=geometry, + symmetries=[mp.Mirror(mp.Y)], + k_point=mp.Vector3(z=kz), + kz_2d=kz_2d, + ) + + tran = sim.add_flux( + fsrc, 0, 1, mp.FluxRegion(center=mp.Vector3(x=5), size=mp.Vector3(y=14)) + ) sim.run(until_after_sources=50) - res = sim.get_eigenmode_coefficients(tran, - [1,2], - eig_parity=mp.EVEN_Y) + res = sim.get_eigenmode_coefficients(tran, [1, 2], eig_parity=mp.EVEN_Y) total_flux = mp.get_fluxes(tran)[0] - mode1_flux = abs(res.alpha[0,0,0])**2 - mode2_flux = abs(res.alpha[1,0,0])**2 + mode1_flux = abs(res.alpha[0, 0, 0]) ** 2 + mode2_flux = abs(res.alpha[1, 0, 0]) ** 2 mode1_frac = 0.99 - self.assertGreater(mode1_flux/total_flux, mode1_frac) - self.assertLess(mode2_flux/total_flux, 1-mode1_frac) + self.assertGreater(mode1_flux / total_flux, mode1_frac) + self.assertLess(mode2_flux / total_flux, 1 - mode1_frac) d = 3.5 - ez1 = sim.get_field_point(mp.Ez, mp.Vector3(2.3,-5.7,4.8)) - ez2 = sim.get_field_point(mp.Ez, mp.Vector3(2.3,-5.7,4.8+d)) - ratio_ez = ez2/ez1 - phase_diff = cmath.exp(1j*2*cmath.pi*kz*d) - self.assertAlmostEqual(ratio_ez.real,phase_diff.real,places=10) - self.assertAlmostEqual(ratio_ez.imag,phase_diff.imag,places=10) + ez1 = sim.get_field_point(mp.Ez, mp.Vector3(2.3, -5.7, 4.8)) + ez2 = sim.get_field_point(mp.Ez, mp.Vector3(2.3, -5.7, 4.8 + d)) + ratio_ez = ez2 / ez1 + phase_diff = cmath.exp(1j * 2 * cmath.pi * kz * d) + self.assertAlmostEqual(ratio_ez.real, phase_diff.real, places=10) + self.assertAlmostEqual(ratio_ez.imag, phase_diff.imag, places=10) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_timing_measurements.py b/python/tests/test_timing_measurements.py index 93ec0ca02..e748cbbab 100644 --- a/python/tests/test_timing_measurements.py +++ b/python/tests/test_timing_measurements.py @@ -6,7 +6,6 @@ class TimingTest(unittest.TestCase): - def test_timing_measurements(self): """Tests that timing measurements have expected names and can be updated.""" sim = mp.Simulation( @@ -22,11 +21,15 @@ def test_timing_measurements(self): set(timing_measurements.measurement_names), set(timing.TIMING_MEASUREMENT_IDS.keys()), ) - self.assertTrue(timing_measurements.elapsed_time > 0 or timing_measurements.elapsed_time == -1) + self.assertTrue( + timing_measurements.elapsed_time > 0 + or timing_measurements.elapsed_time == -1 + ) self.assertGreater(timing_measurements.num_time_steps, 0) self.assertGreaterEqual(timing_measurements.comm_efficiency, 0) self.assertGreaterEqual(timing_measurements.comm_efficiency_one_to_one, 0) self.assertGreaterEqual(timing_measurements.comm_efficiency_all_to_all, 0) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_user_defined_material.py b/python/tests/test_user_defined_material.py index 5a363e4dc..54dc746f7 100644 --- a/python/tests/test_user_defined_material.py +++ b/python/tests/test_user_defined_material.py @@ -1,4 +1,5 @@ import unittest + import meep as mp @@ -39,22 +40,28 @@ def my_epsilon_func(p): class TestUserMaterials(unittest.TestCase): - def setUp(self): self.resolution = 10 self.cell = mp.Vector3(10, 10) self.symmetries = [mp.Mirror(mp.X), mp.Mirror(mp.Y)] self.boundary_layers = [mp.PML(1.0)] - self.sources = [mp.Source(src=mp.GaussianSource(0.2, fwidth=0.1), - component=mp.Ez, center=mp.Vector3())] + self.sources = [ + mp.Source( + src=mp.GaussianSource(0.2, fwidth=0.1), + component=mp.Ez, + center=mp.Vector3(), + ) + ] def test_user_material_func(self): - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - boundary_layers=self.boundary_layers, - sources=self.sources, - material_function=my_material_func) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + boundary_layers=self.boundary_layers, + sources=self.sources, + material_function=my_material_func, + ) sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -63,12 +70,14 @@ def test_user_material_func(self): self.assertAlmostEqual(fp, 4.816403627871773e-4, places=places) def test_epsilon_func(self): - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - boundary_layers=self.boundary_layers, - sources=self.sources, - epsilon_func=my_epsilon_func) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + boundary_layers=self.boundary_layers, + sources=self.sources, + epsilon_func=my_epsilon_func, + ) sim.run(until=100) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -78,12 +87,14 @@ def test_epsilon_func(self): def test_geometric_obj_with_user_material(self): geometry = [mp.Cylinder(5, material=my_material_func)] - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - geometry=geometry, - boundary_layers=self.boundary_layers, - sources=self.sources) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + geometry=geometry, + boundary_layers=self.boundary_layers, + sources=self.sources, + ) sim.run(until=200) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) @@ -93,16 +104,19 @@ def test_geometric_obj_with_user_material(self): def test_geometric_obj_with_epsilon_func(self): geometry = [mp.Cylinder(5, epsilon_func=my_epsilon_func)] - sim = mp.Simulation(cell_size=self.cell, - resolution=self.resolution, - symmetries=self.symmetries, - geometry=geometry, - boundary_layers=self.boundary_layers, - sources=self.sources) + sim = mp.Simulation( + cell_size=self.cell, + resolution=self.resolution, + symmetries=self.symmetries, + geometry=geometry, + boundary_layers=self.boundary_layers, + sources=self.sources, + ) sim.run(until=100) fp = sim.get_field_point(mp.Ez, mp.Vector3(x=1)) self.assertAlmostEqual(fp, -7.895783750440999e-4) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_verbosity_mgr.py b/python/tests/test_verbosity_mgr.py index f36688f98..da4fd5b92 100644 --- a/python/tests/test_verbosity_mgr.py +++ b/python/tests/test_verbosity_mgr.py @@ -1,31 +1,31 @@ import unittest + from meep.verbosity_mgr import Verbosity class VerbosityForTest(Verbosity): - """Allows for testing of Verbosity without interfering with the singleton.""" - _instance = None + """Allows for testing of Verbosity without interfering with the singleton.""" + + _instance = None class MyCvar: - def __init__(self): self.verbosity = 1 + def __init__(self): + self.verbosity = 1 class TestVerbosity(unittest.TestCase): - def setUp(self): VerbosityForTest.reset() - self.v1 = VerbosityForTest(name='foo') - self.v2 = VerbosityForTest(MyCvar(), 'bar') - + self.v1 = VerbosityForTest(name="foo") + self.v2 = VerbosityForTest(MyCvar(), "bar") def test_identity(self): # Ensure each verbosity is really the same singleton instance v1, v2 = self.v1, self.v2 self.assertTrue(v1 is v2) self.assertEqual(id(v1), id(v2)) - self.assertEqual(v1.get_all(), [1,1]) - + self.assertEqual(v1.get_all(), [1, 1]) def test_initial_value(self): v1, v2 = self.v1, self.v2 @@ -33,7 +33,6 @@ def test_initial_value(self): v2.set(2) self.assertEqual(v1.get(), 2) - def test_properties(self): v1, v2 = self.v1, self.v2 self.assertEqual(v1.foo, 1) @@ -43,7 +42,6 @@ def test_properties(self): self.assertEqual(v2.foo, 2) self.assertEqual(v2.bar, 3) - def test_operators(self): v1, v2 = self.v1, self.v2 @@ -72,6 +70,5 @@ def test_out_of_range(self): v2.bar = -5 - -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/tests/test_visualization.py b/python/tests/test_visualization.py index 064e50635..627686b0e 100644 --- a/python/tests/test_visualization.py +++ b/python/tests/test_visualization.py @@ -3,116 +3,179 @@ # boundary conditions. Checks for subdomain plots. # # Also tests the animation run function, mp4 output, jshtml output, and git output. - +import os import unittest from subprocess import call -import meep as mp +import matplotlib import numpy as np -import os + +import meep as mp # Make sure we have matplotlib installed -import matplotlib -matplotlib.use('agg') # Set backend for consistency and to pull pixels quickly -from matplotlib import pyplot as plt + +matplotlib.use("agg") # Set backend for consistency and to pull pixels quickly import io +from matplotlib import pyplot as plt + + def hash_figure(fig): buf = io.BytesIO() - fig.savefig(buf, format='raw') + fig.savefig(buf, format="raw") buf.seek(0) data = np.frombuffer(buf.getvalue(), dtype=np.uint8) return np.sum((data > np.mean(data)) + data) + def setup_sim(zDim=0): - cell = mp.Vector3(16,8,zDim) + cell = mp.Vector3(16, 8, zDim) # A simple waveguide - geometry = [mp.Block(mp.Vector3(mp.inf,1,1), - center=mp.Vector3(), - material=mp.Medium(epsilon=12))] + geometry = [ + mp.Block( + mp.Vector3(mp.inf, 1, 1), + center=mp.Vector3(), + material=mp.Medium(epsilon=12), + ) + ] # Add point sources - sources = [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-5,0), - size=mp.Vector3(0,0,2)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(0,2), - size=mp.Vector3(0,0,2)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-1,1), - size=mp.Vector3(0,0,2)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - center=mp.Vector3(-2,-2,1), - size=mp.Vector3(0,0,0)), - ] + sources = [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-5, 0), + size=mp.Vector3(0, 0, 2), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(0, 2), + size=mp.Vector3(0, 0, 2), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-1, 1), + size=mp.Vector3(0, 0, 2), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + center=mp.Vector3(-2, -2, 1), + size=mp.Vector3(0, 0, 0), + ), + ] # Add line sources - sources += [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(0,2,2), - center=mp.Vector3(-6,0)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(0,2,2), - center=mp.Vector3(0,1))] + sources += [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(0, 2, 2), + center=mp.Vector3(-6, 0), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(0, 2, 2), + center=mp.Vector3(0, 1), + ), + ] # Add plane sources - sources += [mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(2,2,2), - center=mp.Vector3(-3,0)), - mp.Source(mp.ContinuousSource(frequency=0.15), - component=mp.Ez, - size=mp.Vector3(2,2,2), - center=mp.Vector3(0,-2))] + sources += [ + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(2, 2, 2), + center=mp.Vector3(-3, 0), + ), + mp.Source( + mp.ContinuousSource(frequency=0.15), + component=mp.Ez, + size=mp.Vector3(2, 2, 2), + center=mp.Vector3(0, -2), + ), + ] # Different pml layers - pml_layers = [mp.PML(2.0,mp.X),mp.PML(1.0,mp.Y,mp.Low),mp.PML(1.5,mp.Y,mp.High)] + pml_layers = [ + mp.PML(2.0, mp.X), + mp.PML(1.0, mp.Y, mp.Low), + mp.PML(1.5, mp.Y, mp.High), + ] if zDim > 0: - pml_layers += [mp.PML(1.5,mp.Z)] + pml_layers += [mp.PML(1.5, mp.Z)] resolution = 10 - sim = mp.Simulation(cell_size=cell, - boundary_layers=pml_layers, - geometry=geometry, - sources=sources, - resolution=resolution) + sim = mp.Simulation( + cell_size=cell, + boundary_layers=pml_layers, + geometry=geometry, + sources=sources, + resolution=resolution, + ) # Line monitor - sim.add_flux(1,0,1,mp.FluxRegion(center=mp.Vector3(5,0,0),size=mp.Vector3(0,4,4), direction=mp.X)) + sim.add_flux( + 1, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(5, 0, 0), size=mp.Vector3(0, 4, 4), direction=mp.X + ), + ) # Plane monitor - sim.add_flux(1,0,1,mp.FluxRegion(center=mp.Vector3(2,0,0),size=mp.Vector3(4,4,4), direction=mp.X)) + sim.add_flux( + 1, + 0, + 1, + mp.FluxRegion( + center=mp.Vector3(2, 0, 0), size=mp.Vector3(4, 4, 4), direction=mp.X + ), + ) return sim + def view_sim(): sim = setup_sim(8) - xy0 = mp.Volume(center=mp.Vector3(0,0,0), size=mp.Vector3(sim.cell_size.x,sim.cell_size.y,0)) - xy1 = mp.Volume(center=mp.Vector3(0,0,1), size=mp.Vector3(sim.cell_size.x,sim.cell_size.y,0)) - yz0 = mp.Volume(center=mp.Vector3(0,0,0), size=mp.Vector3(0,sim.cell_size.y,sim.cell_size.z)) - yz1 = mp.Volume(center=mp.Vector3(1,0,0), size=mp.Vector3(0,sim.cell_size.y,sim.cell_size.z)) - xz0 = mp.Volume(center=mp.Vector3(0,0,0), size=mp.Vector3(sim.cell_size.x,0,sim.cell_size.z)) - xz1 = mp.Volume(center=mp.Vector3(0,1,0), size=mp.Vector3(sim.cell_size.x,0,sim.cell_size.z)) - vols = [xy0,xy1,yz0,yz1,xz0,xz1] - titles = ['xy0','xy1','yz0','yz1','xz0','xz1'] - xlabel = ['x','x','y','y','x','x'] - ylabel = ['y','y','z','z','z','z'] + xy0 = mp.Volume( + center=mp.Vector3(0, 0, 0), size=mp.Vector3(sim.cell_size.x, sim.cell_size.y, 0) + ) + xy1 = mp.Volume( + center=mp.Vector3(0, 0, 1), size=mp.Vector3(sim.cell_size.x, sim.cell_size.y, 0) + ) + yz0 = mp.Volume( + center=mp.Vector3(0, 0, 0), size=mp.Vector3(0, sim.cell_size.y, sim.cell_size.z) + ) + yz1 = mp.Volume( + center=mp.Vector3(1, 0, 0), size=mp.Vector3(0, sim.cell_size.y, sim.cell_size.z) + ) + xz0 = mp.Volume( + center=mp.Vector3(0, 0, 0), size=mp.Vector3(sim.cell_size.x, 0, sim.cell_size.z) + ) + xz1 = mp.Volume( + center=mp.Vector3(0, 1, 0), size=mp.Vector3(sim.cell_size.x, 0, sim.cell_size.z) + ) + vols = [xy0, xy1, yz0, yz1, xz0, xz1] + titles = ["xy0", "xy1", "yz0", "yz1", "xz0", "xz1"] + xlabel = ["x", "x", "y", "y", "x", "x"] + ylabel = ["y", "y", "z", "z", "z", "z"] for k in range(len(vols)): - ax = plt.subplot(2,3,k+1) - sim.plot2D(ax=ax,output_plane=vols[k]) + ax = plt.subplot(2, 3, k + 1) + sim.plot2D(ax=ax, output_plane=vols[k]) ax.set_xlabel(xlabel[k]) ax.set_ylabel(ylabel[k]) ax.set_title(titles[k]) plt.tight_layout() plt.show() -class TestVisualization(unittest.TestCase): + +class TestVisualization(unittest.TestCase): @classmethod def setUpClass(cls): cls.temp_dir = mp.make_output_directory() @@ -129,79 +192,120 @@ def test_plot2D(self): ax = sim.plot2D(ax=ax) if mp.am_master(): hash_figure(f) - #self.assertAlmostEqual(hash_figure(f),10231488) + # self.assertAlmostEqual(hash_figure(f),10231488) # Check plotting of fields after timestepping f = plt.figure() ax = f.gca() sim.run(until=200) - ax = sim.plot2D(ax=ax,fields=mp.Ez) + ax = sim.plot2D(ax=ax, fields=mp.Ez) if mp.am_master(): hash_figure(f) - #self.assertAlmostEqual(hash_figure(f),79786722) + # self.assertAlmostEqual(hash_figure(f),79786722) # Check output_plane feature f = plt.figure() ax = f.gca() - vol = mp.Volume(center=mp.Vector3(),size=mp.Vector3(2,2)) - ax = sim.plot2D(ax=ax,fields=mp.Ez,output_plane=vol) + vol = mp.Volume(center=mp.Vector3(), size=mp.Vector3(2, 2)) + ax = sim.plot2D(ax=ax, fields=mp.Ez, output_plane=vol) if mp.am_master(): hash_figure(f) - #self.assertAlmostEqual(hash_figure(f),68926258) + # self.assertAlmostEqual(hash_figure(f),68926258) - @unittest.skipIf(call(['which', 'ffmpeg']) != 0, "ffmpeg is not installed") + @unittest.skipIf(call(["which", "ffmpeg"]) != 0, "ffmpeg is not installed") def test_animation_output(self): # ------------------------- # # Check over 2D domain # ------------------------- # - sim = setup_sim() # generate 2D simulation + sim = setup_sim() # generate 2D simulation - Animate = mp.Animate2D(sim,fields=mp.Ez, realtime=False, normalize=False) # Check without normalization - Animate_norm = mp.Animate2D(sim, mp.Ez, realtime=False, normalize=True) # Check with normalization + Animate = mp.Animate2D( + sim, fields=mp.Ez, realtime=False, normalize=False + ) # Check without normalization + Animate_norm = mp.Animate2D( + sim, mp.Ez, realtime=False, normalize=True + ) # Check with normalization # test both animation objects during same run - sim.run( - mp.at_every(1,Animate), - mp.at_every(1,Animate_norm), - until=5) + sim.run(mp.at_every(1, Animate), mp.at_every(1, Animate_norm), until=5) # Test outputs - Animate.to_mp4(5,os.path.join(self.temp_dir, 'test_2D.mp4')) # Check mp4 output - Animate.to_gif(150,os.path.join(self.temp_dir, 'test_2D.gif')) # Check gif output - Animate.to_jshtml(10) # Check jshtml output - Animate_norm.to_mp4(5,os.path.join(self.temp_dir, 'test_2D_norm.mp4')) # Check mp4 output - Animate_norm.to_gif(150,os.path.join(self.temp_dir, 'test_2D_norm.gif')) # Check gif output - Animate_norm.to_jshtml(10) # Check jshtml output + Animate.to_mp4( + 5, os.path.join(self.temp_dir, "test_2D.mp4") + ) # Check mp4 output + Animate.to_gif( + 150, os.path.join(self.temp_dir, "test_2D.gif") + ) # Check gif output + Animate.to_jshtml(10) # Check jshtml output + Animate_norm.to_mp4( + 5, os.path.join(self.temp_dir, "test_2D_norm.mp4") + ) # Check mp4 output + Animate_norm.to_gif( + 150, os.path.join(self.temp_dir, "test_2D_norm.gif") + ) # Check gif output + Animate_norm.to_jshtml(10) # Check jshtml output # ------------------------- # # Check over 3D domain # ------------------------- # - sim = setup_sim(5) # generate 2D simulation + sim = setup_sim(5) # generate 2D simulation - Animate_xy = mp.Animate2D(sim,fields=mp.Ey, realtime=False, normalize=True) # Check without normalization - Animate_xz = mp.Animate2D(sim,mp.Ey,realtime=False,normalize=True) # Check with normalization + Animate_xy = mp.Animate2D( + sim, fields=mp.Ey, realtime=False, normalize=True + ) # Check without normalization + Animate_xz = mp.Animate2D( + sim, mp.Ey, realtime=False, normalize=True + ) # Check with normalization # test both animation objects during same run sim.run( - mp.at_every(1,mp.in_volume(mp.Volume(center=mp.Vector3(),size=mp.Vector3(sim.cell_size.x,sim.cell_size.y)),Animate_xy)), - mp.at_every(1,mp.in_volume(mp.Volume(center=mp.Vector3(),size=mp.Vector3(sim.cell_size.x,0,sim.cell_size.z)),Animate_xz)), - until=5) + mp.at_every( + 1, + mp.in_volume( + mp.Volume( + center=mp.Vector3(), + size=mp.Vector3(sim.cell_size.x, sim.cell_size.y), + ), + Animate_xy, + ), + ), + mp.at_every( + 1, + mp.in_volume( + mp.Volume( + center=mp.Vector3(), + size=mp.Vector3(sim.cell_size.x, 0, sim.cell_size.z), + ), + Animate_xz, + ), + ), + until=5, + ) # Test outputs - Animate_xy.to_mp4(5,os.path.join(self.temp_dir, 'test_3D_xy.mp4')) # Check mp4 output - Animate_xy.to_gif(150,os.path.join(self.temp_dir, 'test_3D_xy.gif')) # Check gif output - Animate_xy.to_jshtml(10) # Check jshtml output - Animate_xz.to_mp4(5,os.path.join(self.temp_dir, 'test_3D_xz.mp4')) # Check mp4 output - Animate_xz.to_gif(150,os.path.join(self.temp_dir, 'test_3D_xz.gif')) # Check gif output - Animate_xz.to_jshtml(10) # Check jshtml output - ''' + Animate_xy.to_mp4( + 5, os.path.join(self.temp_dir, "test_3D_xy.mp4") + ) # Check mp4 output + Animate_xy.to_gif( + 150, os.path.join(self.temp_dir, "test_3D_xy.gif") + ) # Check gif output + Animate_xy.to_jshtml(10) # Check jshtml output + Animate_xz.to_mp4( + 5, os.path.join(self.temp_dir, "test_3D_xz.mp4") + ) # Check mp4 output + Animate_xz.to_gif( + 150, os.path.join(self.temp_dir, "test_3D_xz.gif") + ) # Check gif output + Animate_xz.to_jshtml(10) # Check jshtml output + + """ Travis does not play well with Mayavi def test_3D_mayavi(self): sim = setup_sim(4) sim.plot3D() - ''' + """ + -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() - diff --git a/python/tests/test_wvg_src.py b/python/tests/test_wvg_src.py index e4b636398..9fc616f80 100644 --- a/python/tests/test_wvg_src.py +++ b/python/tests/test_wvg_src.py @@ -1,26 +1,35 @@ -import unittest import sys +import unittest import meep as mp class TestWvgSrc(unittest.TestCase): - def setUp(self): cell = mp.Vector3(16, 8) geometry = [ - mp.Block(center=mp.Vector3(), size=mp.Vector3(mp.inf, 1, mp.inf), - material=mp.Medium(epsilon=12)), - mp.Block(center=mp.Vector3(y=0.3), size=mp.Vector3(mp.inf, 0.1, mp.inf), - material=mp.Medium()) + mp.Block( + center=mp.Vector3(), + size=mp.Vector3(mp.inf, 1, mp.inf), + material=mp.Medium(epsilon=12), + ), + mp.Block( + center=mp.Vector3(y=0.3), + size=mp.Vector3(mp.inf, 0.1, mp.inf), + material=mp.Medium(), + ), ] sources = [ - mp.EigenModeSource(src=mp.ContinuousSource(0.15), size=mp.Vector3(y=6), - center=mp.Vector3(x=-5), component=mp.Dielectric, - eig_parity=mp.ODD_Z) + mp.EigenModeSource( + src=mp.ContinuousSource(0.15), + size=mp.Vector3(y=6), + center=mp.Vector3(x=-5), + component=mp.Dielectric, + eig_parity=mp.ODD_Z, + ) ] pml_layers = [mp.PML(1.0)] @@ -31,18 +40,23 @@ def setUp(self): sources=sources, boundary_layers=pml_layers, force_complex_fields=True, - resolution=10 + resolution=10, ) def test_wvg_src(self): self.sim.run(until=200) - flux1 = self.sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6))) - flux2 = self.sim.flux_in_box(mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6))) + flux1 = self.sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(-6.0), size=mp.Vector3(1.8, 6)) + ) + flux2 = self.sim.flux_in_box( + mp.X, mp.Volume(center=mp.Vector3(6.0), size=mp.Vector3(1.8, 6)) + ) self.assertAlmostEqual(flux1, -1.775216564842667e-03) places = 5 if mp.is_single_precision() else 7 self.assertAlmostEqual(flux2, 7.215785537102116, places=places) -if __name__ == '__main__': + +if __name__ == "__main__": unittest.main() diff --git a/python/tests/utils.py b/python/tests/utils.py index a249e8e26..877e0b384 100644 --- a/python/tests/utils.py +++ b/python/tests/utils.py @@ -1,6 +1,8 @@ import unittest + import numpy as np + def compare_arrays(test_instance, exp, res, tol=1e-3): exp_1d = exp.ravel() res_1d = res.ravel() @@ -18,11 +20,11 @@ def compare_arrays(test_instance, exp, res, tol=1e-3): class ApproxComparisonTestCase(unittest.TestCase): """A mixin for adding proper floating point value and vector comparison.""" - def assertClose(self, x, y, epsilon = 1e-2, msg = ''): + def assertClose(self, x, y, epsilon=1e-2, msg=""): """Asserts that two values or vectors satisfy ‖x-y‖ ≤ ε * max(‖x‖, ‖y‖).""" x = np.atleast_1d(x).ravel() y = np.atleast_1d(y).ravel() x_norm = np.linalg.norm(x, ord=np.inf) y_norm = np.linalg.norm(y, ord=np.inf) diff_norm = np.linalg.norm(x - y, ord=np.inf) - self.assertLessEqual(diff_norm, epsilon * np.maximum(x_norm, y_norm), msg) \ No newline at end of file + self.assertLessEqual(diff_norm, epsilon * np.maximum(x_norm, y_norm), msg) diff --git a/python/timing_measurements.py b/python/timing_measurements.py index 08fc068b6..d9aa0c60d 100644 --- a/python/timing_measurements.py +++ b/python/timing_measurements.py @@ -1,34 +1,36 @@ from typing import Dict, List, Optional -import meep as mp + import numpy as np +import meep as mp + # Codes for different Meep time sinks, used by `mp.Simulation.time_spent_on()`. # See # https://meep.readthedocs.io/en/latest/Python_User_Interface/#simulation-time # for more information. TIMING_MEASUREMENT_IDS = { - 'connecting_chunks': mp.Connecting, - 'time_stepping': mp.Stepping, - 'boundaries_copying': mp.Boundaries, - 'mpi_all_to_all': mp.MpiAllTime, - 'mpi_one_to_one': mp.MpiOneTime, - 'field_output': mp.FieldOutput, - 'fourier_transform': mp.FourierTransforming, - 'mpb': mp.MPBTime, - 'near_to_farfield_transform': mp.GetFarfieldsTime, - 'other': mp.Other, - 'field_update_b': mp.FieldUpdateB, - 'field_update_h': mp.FieldUpdateH, - 'field_update_d': mp.FieldUpdateD, - 'field_update_e': mp.FieldUpdateE, - 'boundary_stepping_b': mp.BoundarySteppingB, - 'boundary_stepping_wh': mp.BoundarySteppingWH, - 'boundary_stepping_ph': mp.BoundarySteppingPH, - 'boundary_stepping_h': mp.BoundarySteppingH, - 'boundary_stepping_d': mp.BoundarySteppingD, - 'boundary_stepping_we': mp.BoundarySteppingWE, - 'boundary_stepping_pe': mp.BoundarySteppingPE, - 'boundary_stepping_e': mp.BoundarySteppingE, + "connecting_chunks": mp.Connecting, + "time_stepping": mp.Stepping, + "boundaries_copying": mp.Boundaries, + "mpi_all_to_all": mp.MpiAllTime, + "mpi_one_to_one": mp.MpiOneTime, + "field_output": mp.FieldOutput, + "fourier_transform": mp.FourierTransforming, + "mpb": mp.MPBTime, + "near_to_farfield_transform": mp.GetFarfieldsTime, + "other": mp.Other, + "field_update_b": mp.FieldUpdateB, + "field_update_h": mp.FieldUpdateH, + "field_update_d": mp.FieldUpdateD, + "field_update_e": mp.FieldUpdateE, + "boundary_stepping_b": mp.BoundarySteppingB, + "boundary_stepping_wh": mp.BoundarySteppingWH, + "boundary_stepping_ph": mp.BoundarySteppingPH, + "boundary_stepping_h": mp.BoundarySteppingH, + "boundary_stepping_d": mp.BoundarySteppingD, + "boundary_stepping_we": mp.BoundarySteppingWE, + "boundary_stepping_pe": mp.BoundarySteppingPE, + "boundary_stepping_e": mp.BoundarySteppingE, } @@ -73,14 +75,16 @@ class MeepTimingMeasurements: transforming. This is zero if no simulation work has been performed. """ - def __init__(self, - measurements: Dict[str, List[float]], - elapsed_time: float, - num_time_steps: int, - time_per_step: List[float], - dft_relative_change: List[float], - overlap_relative_change: List[float], - relative_energy: List[float]): + def __init__( + self, + measurements: Dict[str, List[float]], + elapsed_time: float, + num_time_steps: int, + time_per_step: List[float], + dft_relative_change: List[float], + overlap_relative_change: List[float], + relative_energy: List[float], + ): self.measurements = measurements self.elapsed_time = elapsed_time self.num_time_steps = num_time_steps @@ -91,14 +95,14 @@ def __init__(self, @classmethod def new_from_simulation( - cls, - sim: mp.Simulation, - elapsed_time: Optional[float] = -1, - time_per_step: Optional[List[float]] = None, - dft_relative_change: Optional[List[float]] = None, - overlap_relative_change: Optional[List[float]] = None, - relative_energy: Optional[List[float]] = None, - ) -> 'MeepTimingMeasurements': + cls, + sim: mp.Simulation, + elapsed_time: Optional[float] = -1, + time_per_step: Optional[List[float]] = None, + dft_relative_change: Optional[List[float]] = None, + overlap_relative_change: Optional[List[float]] = None, + relative_energy: Optional[List[float]] = None, + ) -> "MeepTimingMeasurements": """Creates a new `MeepTimingMeasurements` from a Meep simulation object. Usage example: @@ -129,16 +133,22 @@ def new_from_simulation( Returns: the resulting `MeepTimingMeasurements` """ - measurements = {} - for name, timing_id in TIMING_MEASUREMENT_IDS.items(): - measurements[name] = sim.time_spent_on(timing_id).tolist() + measurements = { + name: sim.time_spent_on(timing_id).tolist() + for name, timing_id in TIMING_MEASUREMENT_IDS.items() + } + return cls( measurements=measurements, elapsed_time=elapsed_time, num_time_steps=get_current_time_step(sim), time_per_step=time_per_step if time_per_step is not None else [], - dft_relative_change=dft_relative_change if dft_relative_change is not None else [], - overlap_relative_change=overlap_relative_change if overlap_relative_change is not None else [], + dft_relative_change=dft_relative_change + if dft_relative_change is not None + else [], + overlap_relative_change=overlap_relative_change + if overlap_relative_change is not None + else [], relative_energy=relative_energy if relative_energy is not None else [], ) @@ -148,28 +158,36 @@ def measurement_names(self) -> List[str]: @property def comm_efficiency(self) -> float: - computation_time = np.mean(self.measurements['time_stepping']) + np.mean( - self.measurements['fourier_transform']) + computation_time = np.mean(self.measurements["time_stepping"]) + np.mean( + self.measurements["fourier_transform"] + ) if computation_time == 0: return 0 else: - return np.mean(self.measurements['mpi_all_to_all'] + - self.measurements['mpi_one_to_one']) / computation_time + return ( + np.mean( + self.measurements["mpi_all_to_all"] + + self.measurements["mpi_one_to_one"] + ) + / computation_time + ) @property def comm_efficiency_one_to_one(self) -> float: - computation_time = np.mean(self.measurements['time_stepping']) + np.mean( - self.measurements['fourier_transform']) + computation_time = np.mean(self.measurements["time_stepping"]) + np.mean( + self.measurements["fourier_transform"] + ) if computation_time == 0: return 0 else: - return np.mean(self.measurements['mpi_one_to_one']) / computation_time + return np.mean(self.measurements["mpi_one_to_one"]) / computation_time @property def comm_efficiency_all_to_all(self) -> float: - computation_time = np.mean(self.measurements['time_stepping']) + np.mean( - self.measurements['fourier_transform']) + computation_time = np.mean(self.measurements["time_stepping"]) + np.mean( + self.measurements["fourier_transform"] + ) if computation_time == 0: return 0 else: - return np.mean(self.measurements['mpi_all_to_all']) / computation_time + return np.mean(self.measurements["mpi_all_to_all"]) / computation_time diff --git a/python/typemap_utils.cpp b/python/typemap_utils.cpp index 3c1fa1b66..ab8c8a294 100644 --- a/python/typemap_utils.cpp +++ b/python/typemap_utils.cpp @@ -32,7 +32,7 @@ #endif #ifndef SWIG_PYTHON_THREAD_SCOPED_BLOCK -#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK +#define SWIG_PYTHON_THREAD_SCOPED_BLOCK SWIG_PYTHON_THREAD_BEGIN_BLOCK #endif PyObject *py_callback = NULL; @@ -57,9 +57,7 @@ static char *py2_string_as_utf8(PyObject *po) { Py_DECREF(s); return result; } - else { - return NULL; - } + else { return NULL; } } #endif @@ -180,8 +178,7 @@ static PyObject *vec2py(const meep::vec &v, bool newobj = false) { Py_INCREF(py_callback_v3); return py_callback_v3; - } - ; + }; } static void py_user_material_func_wrap(vector3 x, void *user_data, medium_struct *medium) { @@ -463,9 +460,7 @@ static int py_susceptibility_to_susceptibility(PyObject *po, susceptibility_stru std::string class_name = py_class_name_as_string(po); if (class_name.find(std::string("Drude")) != std::string::npos) { s->drude = true; } - else { - s->drude = false; - } + else { s->drude = false; } s->is_file = false; @@ -495,7 +490,7 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { int rval = 1; // specify the type of material grid - PyObject * type = PyObject_GetAttrString(po, "grid_type"); + PyObject *type = PyObject_GetAttrString(po, "grid_type"); long gt_enum = PyInt_AsLong(type); Py_DECREF(type); @@ -504,7 +499,7 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { case 1: md->material_grid_kinds = material_data::U_PROD; break; case 2: md->material_grid_kinds = material_data::U_MEAN; break; case 3: md->material_grid_kinds = material_data::U_DEFAULT; break; - default: meep::abort("Invalid material grid enumeration code: %d.\n", (int) gt_enum); + default: meep::abort("Invalid material grid enumeration code: %d.\n", (int)gt_enum); } // initialize grid size @@ -526,9 +521,7 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { PyObject *po_dp = PyObject_GetAttrString(po, "weights"); PyArrayObject *pao = (PyArrayObject *)po_dp; if (!PyArray_Check(pao)) { meep::abort("MaterialGrid weights failed to init."); } - if (!PyArray_ISCARRAY(pao)) { - meep::abort("Numpy array weights must be C-style contiguous."); - } + if (!PyArray_ISCARRAY(pao)) { meep::abort("Numpy array weights must be C-style contiguous."); } md->weights = new double[PyArray_SIZE(pao)]; memcpy(md->weights, (double *)PyArray_DATA(pao), PyArray_SIZE(pao) * sizeof(double)); @@ -584,7 +577,7 @@ static int pymaterial_to_material(PyObject *po, material_type *mt) { PyObject *py_damping = PyObject_GetAttrString(po, "damping"); double damping = 0; if (py_damping) { damping = PyFloat_AsDouble(py_damping); } - md = make_material_grid(do_averaging,beta,eta,damping); + md = make_material_grid(do_averaging, beta, eta, damping); if (!pymaterial_grid_to_material_grid(po, md)) { return 0; } Py_XDECREF(py_do_averaging); Py_XDECREF(py_beta); @@ -597,10 +590,10 @@ static int pymaterial_to_material(PyObject *po, material_type *mt) { PyErr_Clear(); // clear errors from attributes not present bool do_averaging = false; if (py_do_averaging) { do_averaging = PyObject_IsTrue(py_do_averaging); } - if (eps && PyObject_IsTrue(eps)) { md = make_user_material(py_epsilon_func_wrap, po, do_averaging); } - else { - md = make_user_material(py_user_material_func_wrap, po, do_averaging); + if (eps && PyObject_IsTrue(eps)) { + md = make_user_material(py_epsilon_func_wrap, po, do_averaging); } + else { md = make_user_material(py_user_material_func_wrap, po, do_averaging); } Py_XDECREF(eps); Py_XDECREF(py_do_averaging); } @@ -625,9 +618,7 @@ static int pymaterial_to_material(PyObject *po, material_type *mt) { master_printf("read in %zdx%zdx%zd numpy array for epsilon\n", md->epsilon_dims[0], md->epsilon_dims[1], md->epsilon_dims[2]); } - else { - meep::abort("Expected a Medium, a Material Grid, a function, or a filename"); - } + else { meep::abort("Expected a Medium, a Material Grid, a function, or a filename"); } *mt = md; @@ -775,15 +766,14 @@ static PyObject *material_to_py_material(material_type mat) { return py_mat; } case meep_geom::material_data::MATERIAL_USER: { - PyObject *py_mat = (PyObject *) mat->user_data; + PyObject *py_mat = (PyObject *)mat->user_data; Py_INCREF(py_mat); return py_mat; } default: // Only Medium is supported at this time. meep::abort("Can only convert C++ medium_struct subtype %d to Python", mat->which_subclass); - } - ; + }; } static int pymedium_to_medium(PyObject *po, medium_struct *m) { @@ -1015,24 +1005,12 @@ static int py_gobj_to_gobj(PyObject *po, geometric_object *o) { std::string go_type = py_class_name_as_string(po); if (go_type == "Sphere") { success = pysphere_to_sphere(po, o); } - else if (go_type == "Cylinder") { - success = pycylinder_to_cylinder(po, o); - } - else if (go_type == "Wedge") { - success = pywedge_to_wedge(po, o); - } - else if (go_type == "Cone") { - success = pycone_to_cone(po, o); - } - else if (go_type == "Block") { - success = pyblock_to_block(po, o); - } - else if (go_type == "Ellipsoid") { - success = pyellipsoid_to_ellipsoid(po, o); - } - else if (go_type == "Prism") { - success = pyprism_to_prism(po, o); - } + else if (go_type == "Cylinder") { success = pycylinder_to_cylinder(po, o); } + else if (go_type == "Wedge") { success = pywedge_to_wedge(po, o); } + else if (go_type == "Cone") { success = pycone_to_cone(po, o); } + else if (go_type == "Block") { success = pyblock_to_block(po, o); } + else if (go_type == "Ellipsoid") { success = pyellipsoid_to_ellipsoid(po, o); } + else if (go_type == "Prism") { success = pyprism_to_prism(po, o); } else { meep::abort("Error: %s is not a valid GeometricObject type\n", go_type.c_str()); return 0; @@ -1103,8 +1081,7 @@ static PyObject *gobj_to_py_obj(geometric_object *gobj) { // We currently only have the need to create python Prisms from C++. // Other geometry can be added as needed. meep::abort("Conversion of non-prism geometric_object to Python is not supported"); - } - ; + }; } static PyObject *gobj_list_to_py_list(geometric_object_list *objs) { @@ -1123,13 +1100,13 @@ static PyObject *gobj_list_to_py_list(geometric_object_list *objs) { ; } -void gobj_list_freearg(geometric_object_list* objs) { +void gobj_list_freearg(geometric_object_list *objs) { SWIG_PYTHON_THREAD_SCOPED_BLOCK; - for(int i = 0; i < objs->num_items; ++i) { - material_free((material_data *)objs->items[i].material); - geometric_object_destroy(objs->items[i]); - } - delete[] objs->items; + for (int i = 0; i < objs->num_items; ++i) { + material_free((material_data *)objs->items[i].material); + geometric_object_destroy(objs->items[i]); + } + delete[] objs->items; ; } @@ -1152,8 +1129,7 @@ static std::unique_ptr py_bp_to_bp(PyObject *pybp) { else { bp.reset(new meep::binary_partition( meep::split_plane{direction(PyLong_AsLong(split_dir)), PyFloat_AsDouble(split_pos)}, - py_bp_to_bp(left), - py_bp_to_bp(right))); + py_bp_to_bp(left), py_bp_to_bp(right))); } Py_XDECREF(id); @@ -1169,9 +1145,7 @@ static PyObject *py_binary_partition_object() { SWIG_PYTHON_THREAD_SCOPED_BLOCK; // Return value: Borrowed reference static PyObject *bp_type = NULL; - if (bp_type == NULL) { - bp_type = PyObject_GetAttrString(get_meep_mod(), "BinaryPartition"); - } + if (bp_type == NULL) { bp_type = PyObject_GetAttrString(get_meep_mod(), "BinaryPartition"); } return bp_type; ; } @@ -1180,7 +1154,7 @@ static PyObject *py_binary_partition_object() { static PyObject *bp_to_py_bp(const meep::binary_partition *bp) { SWIG_PYTHON_THREAD_SCOPED_BLOCK; PyObject *bp_class = py_binary_partition_object(); - PyObject *args = PyTuple_New(0); // no numbered arguments to pass + PyObject *args = PyTuple_New(0); // no numbered arguments to pass if (bp->is_leaf()) { // leaf nodes will have proc_id and no other properties int proc_id = bp->get_proc_id(); @@ -1189,19 +1163,18 @@ static PyObject *bp_to_py_bp(const meep::binary_partition *bp) { Py_DECREF(args); Py_DECREF(kwargs); return py_bp; - } else { + } + else { // other nodes will have left, right, split_dir, split_pos PyObject *left = bp_to_py_bp(bp->left_tree()); PyObject *right = bp_to_py_bp(bp->right_tree()); meep::direction split_dir = bp->get_plane().dir; double split_pos = bp->get_plane().pos; - PyObject *kwargs = - Py_BuildValue("{s:O,s:O,s:i,s:d}", "left", left, "right", right, - "split_dir", split_dir, "split_pos", split_pos); + PyObject *kwargs = Py_BuildValue("{s:O,s:O,s:i,s:d}", "left", left, "right", right, "split_dir", + split_dir, "split_pos", split_pos); PyObject *py_bp = PyObject_Call(bp_class, args, kwargs); Py_DECREF(args); Py_DECREF(kwargs); return py_bp; - } - ; + }; } diff --git a/python/verbosity_mgr.py b/python/verbosity_mgr.py index 5b9fcc686..755f8f314 100644 --- a/python/verbosity_mgr.py +++ b/python/verbosity_mgr.py @@ -1,7 +1,4 @@ -# -*- coding: utf-8 -*- - - -class Verbosity(object): +class Verbosity: """ A class to help make accessing and setting the global verbosity level a bit more pythonic. It manages one or more verbosity flags that are located in @@ -53,7 +50,7 @@ def __new__(cls, *args, **kw): # Create the real instance only the first time, and return the same each # time Verbosity is called thereafter. if cls._instance is None: - cls._instance = super(Verbosity, cls).__new__(cls) + cls._instance = super().__new__(cls) cls._instance._init() return cls._instance @@ -63,7 +60,7 @@ def _init(self): instance is created. """ self._master_verbosity = -1 - self._cvars = dict() + self._cvars = {} @classmethod def reset(cls): @@ -83,18 +80,20 @@ def add_verbosity_var(self, cvar=None, name=None, initial_level=1): Add a new verbosity flag to be managed. `cvar` should be some object that has a `verbosity` attribute, such as `meep.cvar` or `mpb.cvar`. """ - if cvar is None or not hasattr(cvar, 'verbosity'): + if cvar is None or not hasattr(cvar, "verbosity"): # If we're not given a module.cvar (e.g., while testing) or if the # cvar does not have a verbosity member (e.g. the lib hasn't been # updated to have a verbosity flag yet) then use a dummy object so # things can still run without it. - class _dummy(): - def __init__(self): self.verbosity = 1 + class _dummy: + def __init__(self): + self.verbosity = 1 + cvar = _dummy() # If a name is not given then manufacture one if name is None: - name = 'cvar_{}'.format(len(self._cvars)) + name = f"cvar_{len(self._cvars)}" self._cvars[name] = cvar # And create a property for so it can be accessed like `verbosity.mpb` @@ -116,7 +115,7 @@ def get_all(self): is mostly intended for debugging this class and won't likely be useful otherwise. """ - return [value.verbosity for value in self._cvars.values() ] + return [value.verbosity for value in self._cvars.values()] def set(self, level): """ @@ -124,7 +123,7 @@ def set(self, level): former value. """ if level < 0 or level > 3: - raise ValueError('Only verbosity levels 0-3 are supported') + raise ValueError("Only verbosity levels 0-3 are supported") old = self._master_verbosity for cvar in self._cvars.values(): cvar.verbosity = level @@ -152,26 +151,39 @@ def __int__(self): return self.get() def __repr__(self): - return "Verbosity: level={}".format(self.get()) + return f"Verbosity: level={self.get()}" # Some comparison operators - def __gt__(self, o): return self.get() > o - def __lt__(self, o): return self.get() < o - def __eq__(self, o): return self.get() == o - def __ne__(self, o): return self.get() != o - def __le__(self, o): return self.get() <= o - def __ge__(self, o): return self.get() >= o + def __gt__(self, o): + return self.get() > o + + def __lt__(self, o): + return self.get() < o + + def __eq__(self, o): + return self.get() == o + + def __ne__(self, o): + return self.get() != o + + def __le__(self, o): + return self.get() <= o + + def __ge__(self, o): + return self.get() >= o def make_property(self, name): """ Add a property to the class with the given name that gets or sets the verbosity in the cvar with that name in self._cvars. """ + def _getter(self, name=name): return self._cvars[name].verbosity + def _setter(self, level, name=name): if level < 0 or level > 3: - raise ValueError('Only verbosity levels 0-3 are supported') + raise ValueError("Only verbosity levels 0-3 are supported") self._cvars[name].verbosity = level setattr(Verbosity, name, property(_getter, _setter)) diff --git a/python/visualization.py b/python/visualization.py index 4d6d1eb10..4f4879e8e 100644 --- a/python/visualization.py +++ b/python/visualization.py @@ -1,13 +1,11 @@ -# -*- coding: utf-8 -*- -from collections import namedtuple import warnings +from collections import namedtuple import numpy as np - -import meep as mp from meep.geom import Vector3, init_do_averaging from meep.source import EigenModeSource, check_positive +import meep as mp # ------------------------------------------------------- # # Visualization @@ -20,68 +18,65 @@ # for the different plotting routines. default_source_parameters = { - 'color':'r', - 'edgecolor':'r', - 'facecolor':'none', - 'hatch':'/', - 'linewidth':2 - } + "color": "r", + "edgecolor": "r", + "facecolor": "none", + "hatch": "/", + "linewidth": 2, +} default_monitor_parameters = { - 'color':'b', - 'edgecolor':'b', - 'facecolor':'none', - 'hatch':'/', - 'linewidth':2 - } + "color": "b", + "edgecolor": "b", + "facecolor": "none", + "hatch": "/", + "linewidth": 2, +} default_field_parameters = { - 'interpolation':'spline36', - 'cmap':'RdBu', - 'alpha':0.8, - 'post_process':np.real - } + "interpolation": "spline36", + "cmap": "RdBu", + "alpha": 0.8, + "post_process": np.real, +} default_eps_parameters = { - 'interpolation':'spline36', - 'cmap':'binary', - 'alpha':1.0, - 'contour':False, - 'contour_linewidth':1, - 'frequency':None, - 'resolution':None - } + "interpolation": "spline36", + "cmap": "binary", + "alpha": 1.0, + "contour": False, + "contour_linewidth": 1, + "frequency": None, + "resolution": None, +} default_boundary_parameters = { - 'color':'g', - 'edgecolor':'g', - 'facecolor':'none', - 'hatch':'/' - } + "color": "g", + "edgecolor": "g", + "facecolor": "none", + "hatch": "/", +} default_volume_parameters = { - 'alpha':1.0, - 'color':'k', - 'linestyle':'-', - 'linewidth':1, - 'marker':'.', - 'edgecolor':'k', - 'facecolor':'none', - 'hatch':'/' - } - -default_label_parameters = { - 'label_color':'r', - 'offset':20, - 'label_alpha':0.3 + "alpha": 1.0, + "color": "k", + "linestyle": "-", + "linewidth": 1, + "marker": ".", + "edgecolor": "k", + "facecolor": "none", + "hatch": "/", } +default_label_parameters = {"label_color": "r", "offset": 20, "label_alpha": 0.3} + # Used to remove the elements of a dictionary (dict_to_filter) that # don't correspond to the keyword arguments of a particular # function (func_with_kwargs.) # Adapted from https://stackoverflow.com/questions/26515595/how-does-one-ignore-unexpected-keyword-arguments-passed-to-a-function/44052550 def filter_dict(dict_to_filter, func_with_kwargs): import inspect + filter_keys = [] try: # Python3 ... @@ -91,12 +86,17 @@ def filter_dict(dict_to_filter, func_with_kwargs): # Python2 ... filter_keys = inspect.getargspec(func_with_kwargs)[0] - filtered_dict = {filter_key:dict_to_filter[filter_key] for filter_key in filter_keys if filter_key in dict_to_filter} - return filtered_dict + return { + filter_key: dict_to_filter[filter_key] + for filter_key in filter_keys + if filter_key in dict_to_filter + } + # ------------------------------------------------------- # # Routines to add legends to plot + def place_label(ax, label_text, x, y, centerx, centery, label_parameters=None): if label_parameters is None: @@ -104,26 +104,25 @@ def place_label(ax, label_text, x, y, centerx, centery, label_parameters=None): else: label_parameters = dict(default_label_parameters, **label_parameters) - offset = label_parameters['offset'] - alpha = label_parameters['label_alpha'] - color = label_parameters['label_color'] - - if x > centerx: - xtext = -offset - else: - xtext = offset - if y > centery: - ytext = -offset - else: - ytext = offset - - ax.annotate(label_text, xy=(x, y), xytext=(xtext, ytext), - textcoords='offset points', ha='center', va='bottom', - bbox=dict(boxstyle='round,pad=0.2', fc=color, alpha=alpha), - arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5', - color=color)) + offset = label_parameters["offset"] + alpha = label_parameters["label_alpha"] + color = label_parameters["label_color"] + + xtext = -offset if x > centerx else offset + ytext = -offset if y > centery else offset + ax.annotate( + label_text, + xy=(x, y), + xytext=(xtext, ytext), + textcoords="offset points", + ha="center", + va="bottom", + bbox=dict(boxstyle="round,pad=0.2", fc=color, alpha=alpha), + arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.5", color=color), + ) return ax + # ------------------------------------------------------- # # Helper functions used to plot volumes on a 2D plane @@ -135,45 +134,56 @@ def intersect_volume_volume(volume1, volume2): # volume2 ............... [volume] # Represent the volumes by an "upper" and "lower" coordinate - U1 = [volume1.center.x+volume1.size.x/2, - volume1.center.y+volume1.size.y/2, - volume1.center.z+volume1.size.z/2] - L1 = [volume1.center.x-volume1.size.x/2, - volume1.center.y-volume1.size.y/2, - volume1.center.z-volume1.size.z/2] - - U2 = [volume2.center.x+volume2.size.x/2, - volume2.center.y+volume2.size.y/2, - volume2.center.z+volume2.size.z/2] - L2 = [volume2.center.x-volume2.size.x/2, - volume2.center.y-volume2.size.y/2, - volume2.center.z-volume2.size.z/2] + U1 = [ + volume1.center.x + volume1.size.x / 2, + volume1.center.y + volume1.size.y / 2, + volume1.center.z + volume1.size.z / 2, + ] + L1 = [ + volume1.center.x - volume1.size.x / 2, + volume1.center.y - volume1.size.y / 2, + volume1.center.z - volume1.size.z / 2, + ] + + U2 = [ + volume2.center.x + volume2.size.x / 2, + volume2.center.y + volume2.size.y / 2, + volume2.center.z + volume2.size.z / 2, + ] + L2 = [ + volume2.center.x - volume2.size.x / 2, + volume2.center.y - volume2.size.y / 2, + volume2.center.z - volume2.size.z / 2, + ] # Evaluate intersection - U = np.min([U1,U2],axis=0) - L = np.max([L1,L2],axis=0) + U = np.min([U1, U2], axis=0) + L = np.max([L1, L2], axis=0) # For single points we have to check manually - if np.all(U-L == 0): - if (not volume1.pt_in_volume(Vector3(*U))) or (not volume2.pt_in_volume(Vector3(*U))): - return [] + if np.all(U - L == 0) and ( + (not volume1.pt_in_volume(Vector3(*U))) + or (not volume2.pt_in_volume(Vector3(*U))) + ): + return [] # Check for two volumes that don't intersect - if np.any(U-L < 0): + if np.any(U - L < 0): return [] # Pull all possible vertices vertices = [] - for x_vals in [L[0],U[0]]: - for y_vals in [L[1],U[1]]: - for z_vals in [L[2],U[2]]: - vertices.append(Vector3(x_vals,y_vals,z_vals)) - + for x_vals in [L[0], U[0]]: + for y_vals in [L[1], U[1]]: + vertices.extend(Vector3(x_vals, y_vals, z_vals) for z_vals in [L[2], U[2]]) # Remove any duplicate points caused by coplanar lines - vertices = [vertices[i] for i, x in enumerate(vertices) if x not in vertices[i+1:]] + vertices = [ + vertices[i] for i, x in enumerate(vertices) if x not in vertices[i + 1 :] + ] return vertices + # All of the 2D plotting routines need an output plane over which to plot. # The user has many options to specify this output plane. They can pass # the output_plane parameter, which is a 2D volume object. They can specify @@ -191,29 +201,37 @@ def get_2D_dimensions(sim, output_plane): plane_center, plane_size = (output_plane.center, output_plane.size) elif sim.output_volume: plane_center, plane_size = mp.get_center_and_size(sim.output_volume) + elif (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: + plane_center, plane_size = ( + sim.geometry_center + mp.Vector3(sim.cell_size.x / 2), + sim.cell_size, + ) else: - if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - plane_center, plane_size = (sim.geometry_center+mp.Vector3(sim.cell_size.x/2), sim.cell_size) - else: - plane_center, plane_size = (sim.geometry_center, sim.cell_size) - plane_volume = Volume(center=plane_center,size=plane_size) + plane_center, plane_size = (sim.geometry_center, sim.cell_size) + plane_volume = Volume(center=plane_center, size=plane_size) if plane_size.x != 0 and plane_size.y != 0 and plane_size.z != 0: raise ValueError("Plane volume must be 2D (a plane).") if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - center = sim.geometry_center+mp.Vector3(sim.cell_size.x/2) - check_volume = mp.Volume(center=center, size = sim.cell_size) + center = sim.geometry_center + mp.Vector3(sim.cell_size.x / 2) + check_volume = mp.Volume(center=center, size=sim.cell_size) else: check_volume = Volume(center=sim.geometry_center, size=sim.cell_size) vertices = intersect_volume_volume(check_volume, plane_volume) if len(vertices) == 0: - raise ValueError("The specified user volume is completely outside of the simulation domain.") + raise ValueError( + "The specified user volume is completely outside of the simulation domain." + ) intersection_vol = Volume(vertices=vertices) - if (intersection_vol.size != plane_volume.size) or (intersection_vol.center != plane_volume.center): - warnings.warn('The specified user volume is larger than the simulation domain and has been truncated.') + if (intersection_vol.size != plane_volume.size) or ( + intersection_vol.center != plane_volume.center + ): + warnings.warn( + "The specified user volume is larger than the simulation domain and has been truncated." + ) sim_center, sim_size = (intersection_vol.center, intersection_vol.size) return sim_center, sim_size @@ -227,19 +245,23 @@ def box_vertices(box_center, box_size, is_cylindrical=False): xmin = 0 xmax = box_size.x else: - xmin = box_center.x - 0.5*box_size.x - xmax = box_center.x + 0.5*box_size.x - ymin = box_center.y - 0.5*box_size.y - ymax = box_center.y + 0.5*box_size.y - zmin = box_center.z - 0.5*box_size.z - zmax = box_center.z + 0.5*box_size.z + xmin = box_center.x - 0.5 * box_size.x + xmax = box_center.x + 0.5 * box_size.x + ymin = box_center.y - 0.5 * box_size.y + ymax = box_center.y + 0.5 * box_size.y + zmin = box_center.z - 0.5 * box_size.z + zmax = box_center.z + 0.5 * box_size.z return xmin, xmax, ymin, ymax, zmin, zmax + # ------------------------------------------------------- # # actual plotting routines -def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, label=None): + +def plot_volume( + sim, ax, volume, output_plane=None, plotting_parameters=None, label=None +): import matplotlib.patches as patches from matplotlib import pyplot as plt from meep.simulation import Volume @@ -258,36 +280,40 @@ def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, la size = volume.size center = volume.center - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(center, - size, - sim.is_cylindrical) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(center, size, sim.is_cylindrical) # Add labels if requested if label is not None and mp.am_master(): if sim_size.x == 0: - ax = place_label(ax, - label, - center.y, - center.z, - sim_center.y, - sim_center.z, - label_parameters=plotting_parameters) + ax = place_label( + ax, + label, + center.y, + center.z, + sim_center.y, + sim_center.z, + label_parameters=plotting_parameters, + ) elif sim_size.y == 0: - ax = place_label(ax, - label, - center.x, - center.z, - sim_center.x, - sim_center.z, - label_parameters=plotting_parameters) + ax = place_label( + ax, + label, + center.x, + center.z, + sim_center.x, + sim_center.z, + label_parameters=plotting_parameters, + ) elif sim_size.z == 0: - ax = place_label(ax, - label, - center.x, - center.y, - sim_center.x, - sim_center.y, - label_parameters=plotting_parameters) + ax = place_label( + ax, + label, + center.x, + center.y, + sim_center.x, + sim_center.y, + label_parameters=plotting_parameters, + ) # Intersect plane with volume intersection = intersect_volume_volume(volume, plane) @@ -296,57 +322,93 @@ def plot_volume(sim, ax, volume, output_plane=None, plotting_parameters=None, la def sort_points(xy): xy = np.squeeze(xy) xy_mean = np.mean(xy, axis=0) - theta = np.arctan2(xy[:,1] - xy_mean[1], xy[:,0] - xy_mean[0]) + theta = np.arctan2(xy[:, 1] - xy_mean[1], xy[:, 0] - xy_mean[0]) return xy[np.argsort(theta, axis=0), :] if mp.am_master(): # Point volume if len(intersection) == 1: - point_args = {key:value for key, value in plotting_parameters.items() if key in ['color','marker','alpha','linewidth']} - if sim_size.y==0: - ax.scatter(center.x,center.z, **point_args) + point_args = { + key: value + for key, value in plotting_parameters.items() + if key in ["color", "marker", "alpha", "linewidth"] + } + if sim_size.y == 0: + ax.scatter(center.x, center.z, **point_args) return ax - elif sim_size.x==0: - ax.scatter(center.y,center.z, **point_args) + elif sim_size.x == 0: + ax.scatter(center.y, center.z, **point_args) return ax - elif sim_size.z==0: - ax.scatter(center.x,center.y, **point_args) + elif sim_size.z == 0: + ax.scatter(center.x, center.y, **point_args) return ax else: return ax # Line volume elif len(intersection) == 2: - line_args = {key:value for key, value in plotting_parameters.items() if key in ['color','linestyle','linewidth','alpha']} + line_args = { + key: value + for key, value in plotting_parameters.items() + if key in ["color", "linestyle", "linewidth", "alpha"] + } # Plot YZ if sim_size.x == 0: - ax.plot([a.y for a in intersection], [a.z for a in intersection], **line_args) + ax.plot( + [a.y for a in intersection], + [a.z for a in intersection], + **line_args, + ) return ax # Plot XZ elif sim_size.y == 0: - ax.plot([a.x for a in intersection], [a.z for a in intersection], **line_args) + ax.plot( + [a.x for a in intersection], + [a.z for a in intersection], + **line_args, + ) return ax # Plot XY elif sim_size.z == 0: - ax.plot([a.x for a in intersection], [a.y for a in intersection], **line_args) + ax.plot( + [a.x for a in intersection], + [a.y for a in intersection], + **line_args, + ) return ax else: return ax # Planar volume elif len(intersection) > 2: - planar_args = {key:value for key, value in plotting_parameters.items() if key in ['edgecolor','linewidth','facecolor','hatch','alpha']} + planar_args = { + key: value + for key, value in plotting_parameters.items() + if key in ["edgecolor", "linewidth", "facecolor", "hatch", "alpha"] + } # Plot YZ if sim_size.x == 0: - ax.add_patch(patches.Polygon(sort_points([[a.y,a.z] for a in intersection]), **planar_args)) + ax.add_patch( + patches.Polygon( + sort_points([[a.y, a.z] for a in intersection]), **planar_args + ) + ) return ax # Plot XZ - elif sim_size.y==0: - ax.add_patch(patches.Polygon(sort_points([[a.x,a.z] for a in intersection]), **planar_args)) + elif sim_size.y == 0: + ax.add_patch( + patches.Polygon( + sort_points([[a.x, a.z] for a in intersection]), **planar_args + ) + ) return ax # Plot XY elif sim_size.z == 0: - ax.add_patch(patches.Polygon(sort_points([[a.x,a.y] for a in intersection]), **planar_args)) + ax.add_patch( + patches.Polygon( + sort_points([[a.x, a.y] for a in intersection]), **planar_args + ) + ) return ax else: return ax @@ -354,6 +416,7 @@ def sort_points(xy): return ax return ax + def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): # consolidate plotting parameters if eps_parameters is None: @@ -363,37 +426,35 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): # Determine a frequency to plot all epsilon if frequency is not None: - warnings.warn("The frequency parameter of plot2D has been deprecated. " - "Use the frequency key of the eps_parameters dictionary instead.") - eps_parameters['frequency'] = frequency - if eps_parameters['frequency'] is None: + warnings.warn( + "The frequency parameter of plot2D has been deprecated. " + "Use the frequency key of the eps_parameters dictionary instead." + ) + eps_parameters["frequency"] = frequency + if eps_parameters["frequency"] is None: try: # Continuous sources - eps_parameters['frequency'] = sim.sources[0].frequency + eps_parameters["frequency"] = sim.sources[0].frequency except: try: # Gaussian sources - eps_parameters['frequency'] = sim.sources[0].src.frequency + eps_parameters["frequency"] = sim.sources[0].src.frequency except: try: # Custom sources - eps_parameters['frequency'] = sim.sources[0].src.center_frequency + eps_parameters["frequency"] = sim.sources[0].src.center_frequency except: # No sources - eps_parameters['frequency'] = 0 + eps_parameters["frequency"] = 0 # Get domain measurements sim_center, sim_size = get_2D_dimensions(sim, output_plane) - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, - sim_size, - sim.is_cylindrical) - - if eps_parameters['resolution']: - grid_resolution = eps_parameters['resolution'] - else: - grid_resolution = sim.resolution + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim_center, sim_size, sim.is_cylindrical + ) + grid_resolution = eps_parameters["resolution"] or sim.resolution Nx = int((xmax - xmin) * grid_resolution + 1) Ny = int((ymax - ymin) * grid_resolution + 1) Nz = int((zmax - zmin) * grid_resolution + 1) @@ -401,38 +462,48 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): if sim_size.x == 0: # Plot y on x axis, z on y axis (YZ plane) extent = [ymin, ymax, zmin, zmax] - xlabel = 'Y' - ylabel = 'Z' + xlabel = "Y" + ylabel = "Z" xtics = np.array([sim_center.x]) ytics = np.linspace(ymin, ymax, Ny) ztics = np.linspace(zmin, zmax, Nz) elif sim_size.y == 0: # Plot x on x axis, z on y axis (XZ plane) extent = [xmin, xmax, zmin, zmax] - if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - xlabel = 'R' - else: - xlabel = "X" - ylabel = 'Z' + xlabel = ( + "R" if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + ) + + ylabel = "Z" xtics = np.linspace(xmin, xmax, Nx) ytics = np.array([sim_center.y]) ztics = np.linspace(zmin, zmax, Nz) elif sim_size.z == 0: # Plot x on x axis, y on y axis (XY plane) extent = [xmin, xmax, ymin, ymax] - xlabel = 'X' - ylabel = 'Y' + xlabel = "X" + ylabel = "Y" xtics = np.linspace(xmin, xmax, Nx) ytics = np.linspace(ymin, ymax, Ny) ztics = np.array([sim_center.z]) else: raise ValueError("A 2D plane has not been specified...") - eps_data = np.rot90(np.real(sim.get_epsilon_grid(xtics, ytics, ztics, eps_parameters['frequency']))) + eps_data = np.rot90( + np.real(sim.get_epsilon_grid(xtics, ytics, ztics, eps_parameters["frequency"])) + ) if mp.am_master(): - if eps_parameters['contour']: - ax.contour(eps_data, 0, levels=np.unique(eps_data), colors='black', origin='upper', extent=extent, linewidths=eps_parameters['contour_linewidth']) + if eps_parameters["contour"]: + ax.contour( + eps_data, + 0, + levels=np.unique(eps_data), + colors="black", + origin="upper", + extent=extent, + linewidths=eps_parameters["contour_linewidth"], + ) else: ax.imshow(eps_data, extent=extent, **filter_dict(eps_parameters, ax.imshow)) ax.set_xlabel(xlabel) @@ -440,6 +511,7 @@ def plot_eps(sim, ax, output_plane=None, eps_parameters=None, frequency=None): return ax + def plot_boundaries(sim, ax, output_plane=None, boundary_parameters=None): # consolidate plotting parameters if boundary_parameters is None: @@ -452,62 +524,59 @@ def get_boundary_volumes(thickness, direction, side): thickness = boundary.thickness - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, - sim.cell_size, - sim.is_cylindrical) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim.geometry_center, sim.cell_size, sim.is_cylindrical + ) if direction == mp.X and side == mp.Low: - return Volume(center=Vector3(xmin+0.5*thickness, - sim.geometry_center.y, - sim.geometry_center.z), - size=Vector3(thickness, - sim.cell_size.y, - sim.cell_size.z)) + return Volume( + center=Vector3( + xmin + 0.5 * thickness, sim.geometry_center.y, sim.geometry_center.z + ), + size=Vector3(thickness, sim.cell_size.y, sim.cell_size.z), + ) elif (direction == mp.X and side == mp.High) or direction == mp.R: - return Volume(center=Vector3(xmax-0.5*thickness, - sim.geometry_center.y, - sim.geometry_center.z), - size=Vector3(thickness, - sim.cell_size.y, - sim.cell_size.z)) + return Volume( + center=Vector3( + xmax - 0.5 * thickness, sim.geometry_center.y, sim.geometry_center.z + ), + size=Vector3(thickness, sim.cell_size.y, sim.cell_size.z), + ) elif direction == mp.Y and side == mp.Low: - return Volume(center=Vector3(sim.geometry_center.x, - ymin+0.5*thickness, - sim.geometry_center.z), - size=Vector3(sim.cell_size.x, - thickness, - sim.cell_size.z)) + return Volume( + center=Vector3( + sim.geometry_center.x, ymin + 0.5 * thickness, sim.geometry_center.z + ), + size=Vector3(sim.cell_size.x, thickness, sim.cell_size.z), + ) elif direction == mp.Y and side == mp.High: - return Volume(center=Vector3(sim.geometry_center.x, - ymax-0.5*thickness, - sim.geometry_center.z), - size=Vector3(sim.cell_size.x, - thickness, - sim.cell_size.z)) + return Volume( + center=Vector3( + sim.geometry_center.x, ymax - 0.5 * thickness, sim.geometry_center.z + ), + size=Vector3(sim.cell_size.x, thickness, sim.cell_size.z), + ) elif direction == mp.Z and side == mp.Low: xcen = sim.geometry_center.x if sim.is_cylindrical: - xcen += 0.5*sim.cell_size.x - return Volume(center=Vector3(xcen, - sim.geometry_center.y, - zmin+0.5*thickness), - size=Vector3(sim.cell_size.x, - sim.cell_size.y, - thickness)) + xcen += 0.5 * sim.cell_size.x + return Volume( + center=Vector3(xcen, sim.geometry_center.y, zmin + 0.5 * thickness), + size=Vector3(sim.cell_size.x, sim.cell_size.y, thickness), + ) elif direction == mp.Z and side == mp.High: xcen = sim.geometry_center.x if sim.is_cylindrical: - xcen += 0.5*sim.cell_size.x - return Volume(center=Vector3(xcen, - sim.geometry_center.y, - zmax-0.5*thickness), - size=Vector3(sim.cell_size.x, - sim.cell_size.y, - thickness)) + xcen += 0.5 * sim.cell_size.x + return Volume( + center=Vector3(xcen, sim.geometry_center.y, zmax - 0.5 * thickness), + size=Vector3(sim.cell_size.x, sim.cell_size.y, thickness), + ) else: raise ValueError("Invalid boundary type") import itertools + for boundary in sim.boundary_layers: # boundary on all four sides if boundary.direction == mp.ALL and boundary.side == mp.ALL: @@ -522,25 +591,40 @@ def get_boundary_volumes(thickness, direction, side): else: raise ValueError("Invalid simulation dimensions") for permutation in itertools.product(dims, [mp.Low, mp.High]): - if ((permutation[0] == mp.X) and (permutation[1] == mp.Low)) and (sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical): + if ((permutation[0] == mp.X) and (permutation[1] == mp.Low)) and ( + sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical + ): continue - vol = get_boundary_volumes(boundary.thickness,*permutation) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters) + vol = get_boundary_volumes(boundary.thickness, *permutation) + ax = plot_volume( + sim, ax, vol, output_plane, plotting_parameters=boundary_parameters + ) # boundary on only two of four sides elif boundary.side == mp.ALL: for side in [mp.Low, mp.High]: - if ((boundary.direction == mp.X) and (side == mp.Low)) and (sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical): + if ((boundary.direction == mp.X) and (side == mp.Low)) and ( + sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical + ): continue - vol = get_boundary_volumes(boundary.thickness,boundary.direction,side) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters) + vol = get_boundary_volumes(boundary.thickness, boundary.direction, side) + ax = plot_volume( + sim, ax, vol, output_plane, plotting_parameters=boundary_parameters + ) # boundary on just one side else: - if ((boundary.direction == mp.X) and (boundary.side == mp.Low)) and (sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical): + if ((boundary.direction == mp.X) and (boundary.side == mp.Low)) and ( + sim.dimensions == mp.CYLINDRICAL or sim.is_cylindrical + ): continue - vol = get_boundary_volumes(boundary.thickness,boundary.direction,boundary.side) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=boundary_parameters) + vol = get_boundary_volumes( + boundary.thickness, boundary.direction, boundary.side + ) + ax = plot_volume( + sim, ax, vol, output_plane, plotting_parameters=boundary_parameters + ) return ax + def plot_sources(sim, ax, output_plane=None, labels=False, source_parameters=None): from meep.simulation import Volume @@ -550,13 +634,21 @@ def plot_sources(sim, ax, output_plane=None, labels=False, source_parameters=Non else: source_parameters = dict(default_source_parameters, **source_parameters) - label = 'source' if labels else None + label = "source" if labels else None for src in sim.sources: - vol = Volume(center=src.center,size=src.size) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=source_parameters,label=label) + vol = Volume(center=src.center, size=src.size) + ax = plot_volume( + sim, + ax, + vol, + output_plane, + plotting_parameters=source_parameters, + label=label, + ) return ax + def plot_monitors(sim, ax, output_plane=None, labels=False, monitor_parameters=None): from meep.simulation import Volume @@ -566,14 +658,22 @@ def plot_monitors(sim, ax, output_plane=None, labels=False, monitor_parameters=N else: monitor_parametesr = dict(default_monitor_parameters, **monitor_parameters) - label = 'monitor' if labels else None + label = "monitor" if labels else None for mon in sim.dft_objects: for reg in mon.regions: - vol = Volume(center=reg.center,size=reg.size) - ax = plot_volume(sim,ax,vol,output_plane,plotting_parameters=monitor_parameters,label=label) + vol = Volume(center=reg.center, size=reg.size) + ax = plot_volume( + sim, + ax, + vol, + output_plane, + plotting_parameters=monitor_parameters, + label=label, + ) return ax + def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=None): if not sim._is_initialized: sim.init_sim() @@ -586,105 +686,148 @@ def plot_fields(sim, ax=None, fields=None, output_plane=None, field_parameters=N else: field_parameters = dict(default_field_parameters, **field_parameters) - # user specifies a field component - if fields in [mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep, mp.Dx, mp.Dy, mp.Dz, mp.Hx, mp.Hy, mp.Hz]: - # Get domain measurements - sim_center, sim_size = get_2D_dimensions(sim, output_plane) + if fields not in [ + mp.Ex, + mp.Ey, + mp.Ez, + mp.Er, + mp.Ep, + mp.Dx, + mp.Dy, + mp.Dz, + mp.Hx, + mp.Hy, + mp.Hz, + ]: + raise ValueError("Please specify a valid field component (mp.Ex, mp.Ey, ...") - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, - sim_size, - sim.is_cylindrical) + # Get domain measurements + sim_center, sim_size = get_2D_dimensions(sim, output_plane) - if sim_size.x == 0: - # Plot y on x axis, z on y axis (YZ plane) - extent = [ymin, ymax, zmin, zmax] - xlabel = 'Y' - ylabel = 'Z' - elif sim_size.y == 0: - # Plot x on x axis, z on y axis (XZ plane) - extent = [xmin, xmax, zmin, zmax] - if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: - xlabel = 'R' - else: - xlabel = "X" - ylabel = 'Z' - elif sim_size.z == 0: - # Plot x on x axis, y on y axis (XY plane) - extent = [xmin, xmax, ymin, ymax] - xlabel = 'X' - ylabel = 'Y' - fields = sim.get_array(center=sim_center, size=sim_size, component=fields) - else: - raise ValueError('Please specify a valid field component (mp.Ex, mp.Ey, ...') + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim_center, sim_size, sim.is_cylindrical + ) + if sim_size.x == 0: + # Plot y on x axis, z on y axis (YZ plane) + extent = [ymin, ymax, zmin, zmax] + xlabel = "Y" + ylabel = "Z" + elif sim_size.y == 0: + # Plot x on x axis, z on y axis (XZ plane) + extent = [xmin, xmax, zmin, zmax] + xlabel = ( + "R" if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical else "X" + ) - fields = field_parameters['post_process'](fields) + ylabel = "Z" + elif sim_size.z == 0: + # Plot x on x axis, y on y axis (XY plane) + extent = [xmin, xmax, ymin, ymax] + xlabel = "X" + ylabel = "Y" + fields = sim.get_array(center=sim_center, size=sim_size, component=fields) + fields = field_parameters["post_process"](fields) if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical: fields = np.flipud(fields) else: fields = np.rot90(fields) - # Either plot the field, or return the array - if ax: - if mp.am_master(): - ax.imshow(fields, extent=extent, **filter_dict(field_parameters,ax.imshow)) - return ax - else: + if not ax: return fields + if mp.am_master(): + ax.imshow(fields, extent=extent, **filter_dict(field_parameters, ax.imshow)) return ax -def plot2D(sim, ax=None, output_plane=None, fields=None, labels=False, - eps_parameters=None, boundary_parameters=None, - source_parameters=None, monitor_parameters=None, - field_parameters=None, frequency=None, - plot_eps_flag=True, plot_sources_flag=True, - plot_monitors_flag=True, plot_boundaries_flag=True): + +def plot2D( + sim, + ax=None, + output_plane=None, + fields=None, + labels=False, + eps_parameters=None, + boundary_parameters=None, + source_parameters=None, + monitor_parameters=None, + field_parameters=None, + frequency=None, + plot_eps_flag=True, + plot_sources_flag=True, + plot_monitors_flag=True, + plot_boundaries_flag=True, +): # Ensure a figure axis exists if ax is None and mp.am_master(): from matplotlib import pyplot as plt + ax = plt.gca() # validate the output plane to ensure proper 2D coordinates from meep.simulation import Volume + sim_center, sim_size = get_2D_dimensions(sim, output_plane) output_plane = Volume(center=sim_center, size=sim_size) # Plot geometry if plot_eps_flag: - ax = plot_eps(sim, ax, output_plane=output_plane, - eps_parameters=eps_parameters, frequency=frequency) + ax = plot_eps( + sim, + ax, + output_plane=output_plane, + eps_parameters=eps_parameters, + frequency=frequency, + ) # Plot boundaries if plot_boundaries_flag: - ax = plot_boundaries(sim, ax, output_plane=output_plane, - boundary_parameters=boundary_parameters) + ax = plot_boundaries( + sim, ax, output_plane=output_plane, boundary_parameters=boundary_parameters + ) # Plot sources if plot_sources_flag: - ax = plot_sources(sim, ax, output_plane=output_plane, - labels=labels, source_parameters=source_parameters) + ax = plot_sources( + sim, + ax, + output_plane=output_plane, + labels=labels, + source_parameters=source_parameters, + ) # Plot monitors if plot_monitors_flag: - ax = plot_monitors(sim, ax, output_plane=output_plane, - labels=labels, monitor_parameters=monitor_parameters) + ax = plot_monitors( + sim, + ax, + output_plane=output_plane, + labels=labels, + monitor_parameters=monitor_parameters, + ) # Plot fields if fields is not None: - ax = plot_fields(sim, ax, fields, output_plane=output_plane, - field_parameters=field_parameters) + ax = plot_fields( + sim, + ax, + fields, + output_plane=output_plane, + field_parameters=field_parameters, + ) return ax + def plot3D(sim): from mayavi import mlab if sim.dimensions < 3: raise ValueError("Simulation must have 3 dimensions to visualize 3D") - xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim.geometry_center, - sim.cell_size) + xmin, xmax, ymin, ymax, zmin, zmax = box_vertices( + sim.geometry_center, sim.cell_size + ) Nx = int(sim.cell_size.x * sim.resolution) + 1 Ny = int(sim.cell_size.y * sim.resolution) + 1 @@ -695,16 +838,16 @@ def plot3D(sim): ztics = np.linspace(zmin, zmax, Nz) eps_data = sim.get_epsilon_grid(xtics, ytics, ztics) - s = mlab.contour3d(eps_data, colormap="YlGnBu") - return s + return mlab.contour3d(eps_data, colormap="YlGnBu") + def visualize_chunks(sim): if sim.structure is None: sim.init_sim() - import matplotlib.pyplot as plt import matplotlib.cm import matplotlib.colors + import matplotlib.pyplot as plt if sim.structure.gv.dim == 2: from mpl_toolkits.mplot3d import Axes3D @@ -739,10 +882,10 @@ def plot_box(box, proc, fig, ax): [points[0], points[3], points[5], points[7]], [points[1], points[4], points[2], points[6]], [points[3], points[4], points[1], points[5]], - [points[0], points[7], points[6], points[2]] + [points[0], points[7], points[6], points[2]], ] - faces = Poly3DCollection(edges, linewidths=1, edgecolors='k') + faces = Poly3DCollection(edges, linewidths=1, edgecolors="k") color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc], alpha=0.2) faces.set_facecolor(color_with_alpha) ax.add_collection3d(faces) @@ -761,11 +904,9 @@ def plot_box(box, proc, fig, ax): points += [low + y_len] points = np.array([np.array(v)[:-1] for v in points]) - edges = [ - [points[0], points[2], points[1], points[3]] - ] + edges = [[points[0], points[2], points[1], points[3]]] - faces = PolyCollection(edges, linewidths=1, edgecolors='k') + faces = PolyCollection(edges, linewidths=1, edgecolors="k") color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc]) faces.set_facecolor(color_with_alpha) ax.add_collection(faces) @@ -775,29 +916,32 @@ def plot_box(box, proc, fig, ax): if mp.am_master(): fig = plt.figure() - ax = fig.add_subplot(111, projection='3d' if sim.structure.gv.dim == 2 else None) + ax = fig.add_subplot( + 111, projection="3d" if sim.structure.gv.dim == 2 else None + ) chunk_colors = matplotlib.cm.rainbow(np.linspace(0, 1, mp.count_processors())) for i, v in enumerate(vols): plot_box(mp.gv2box(v.surroundings()), owners[i], fig, ax) - ax.set_xlabel('x') - ax.set_ylabel('y') - ax.set_aspect('equal') + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_aspect("equal") cell_box = mp.gv2box(sim.structure.gv.surroundings()) if sim.structure.gv.dim == 2: - ax.set_xlim3d(left=cell_box.low.x,right=cell_box.high.x) - ax.set_ylim3d(bottom=cell_box.low.y,top=cell_box.high.y) - ax.set_zlim3d(bottom=cell_box.low.z,top=cell_box.high.z) - ax.set_zlabel('z') + ax.set_xlim3d(left=cell_box.low.x, right=cell_box.high.x) + ax.set_ylim3d(bottom=cell_box.low.y, top=cell_box.high.y) + ax.set_zlim3d(bottom=cell_box.low.z, top=cell_box.high.z) + ax.set_zlabel("z") else: - ax.set_xlim(left=cell_box.low.x,right=cell_box.high.x) - ax.set_ylim(bottom=cell_box.low.y,top=cell_box.high.y) + ax.set_xlim(left=cell_box.low.x, right=cell_box.high.x) + ax.set_ylim(bottom=cell_box.low.y, top=cell_box.high.y) plt.tight_layout() plt.show() + # ------------------------------------------------------- # # Animate2D # ------------------------------------------------------- # @@ -818,7 +962,8 @@ def plot_box(box, proc, fig, ax): # customization_args .. [dict] other customization args # to pass to plot2D() -class Animate2D(object): + +class Animate2D: """ A class used to record the fields during timestepping (i.e., a [`run`](#run-functions) function). The object is initialized prior to timestepping by specifying the @@ -849,8 +994,17 @@ class Animate2D(object): Multiple `Animate2D` objects can be initialized and passed to the run function to track different volume locations (using `mp.in_volume`) or field components. """ - def __init__(self, sim, fields, f=None, realtime=False, normalize=False, - plot_modifiers=None, **customization_args): + + def __init__( + self, + sim, + fields, + f=None, + realtime=False, + normalize=False, + plot_modifiers=None, + **customization_args, + ): """ Construct an `Animate2D` object. @@ -894,6 +1048,7 @@ def mod1(ax): self.ax = self.f.gca() elif mp.am_master(): from matplotlib import pyplot as plt + self.f = plt.figure() self.ax = self.f.gca() else: @@ -908,20 +1063,20 @@ def mod1(ax): self.cumulative_fields = [] self._saved_frames = [] - self.frame_format = 'png' # format in which each frame is saved in memory - self.codec = 'h264' # encoding of mp4 video - self.default_mode = 'loop' # html5 video control mode + self.frame_format = "png" # format in which each frame is saved in memory + self.codec = "h264" # encoding of mp4 video + self.default_mode = "loop" # html5 video control mode self.init = False # Needed for step functions - self.__code__ = namedtuple('gna_hack',['co_argcount']) - self.__code__.co_argcount=2 + self.__code__ = namedtuple("gna_hack", ["co_argcount"]) + self.__code__.co_argcount = 2 - def __call__(self,sim,todo): + def __call__(self, sim, todo): from matplotlib import pyplot as plt - if todo == 'step': + if todo == "step": # Initialize the plot if not self.init: filtered_plot2D = filter_dict(self.customization_args, plot2D) @@ -938,11 +1093,13 @@ def __call__(self,sim,todo): self.init = True else: # Update the plot - filtered_plot_fields= filter_dict(self.customization_args, plot_fields) + filtered_plot_fields = filter_dict(self.customization_args, plot_fields) fields = sim.plot_fields(fields=self.fields, **filtered_plot_fields) if mp.am_master(): self.ax.images[-1].set_data(fields) - self.ax.images[-1].set_clim(vmin=0.8*np.min(fields), vmax=0.8*np.max(fields)) + self.ax.images[-1].set_clim( + vmin=0.8 * np.min(fields), vmax=0.8 * np.max(fields) + ) if self.realtime and mp.am_master(): # Redraw the current figure if requested @@ -957,15 +1114,17 @@ def __call__(self,sim,todo): # to avoid writing to disk. self.grab_frame() return - elif todo == 'finish': + elif todo == "finish": # Normalize the frames, if requested, and export if self.normalize and mp.am_master(): if mp.verbosity.meep > 0: print("Normalizing field data...") - fields = np.array(self.cumulative_fields) / np.max(np.abs(self.cumulative_fields), axis=(0,1,2)) + fields = np.array(self.cumulative_fields) / np.max( + np.abs(self.cumulative_fields), axis=(0, 1, 2) + ) for k in range(len(self.cumulative_fields)): - self.ax.images[-1].set_data(fields[k,:,:]) + self.ax.images[-1].set_data(fields[k, :, :]) self.ax.images[-1].set_clim(vmin=-0.8, vmax=0.8) self.grab_frame() @@ -982,9 +1141,10 @@ def grab_frame(self): # Saves the figures frame to memory. # modified from matplotlib library from io import BytesIO + bin_data = BytesIO() self.f.savefig(bin_data, format=self.frame_format) - #imgdata64 = base64.encodebytes(bin_data.getvalue()).decode('ascii') + # imgdata64 = base64.encodebytes(bin_data.getvalue()).decode('ascii') self._saved_frames.append(bin_data.getvalue()) def _embedded_frames(self, frame_list, frame_format): @@ -992,12 +1152,18 @@ def _embedded_frames(self, frame_list, frame_format): # frame_list should be a list of base64-encoded png files # modified from matplotlib import base64 + template = ' frames[{0}] = "data:image/{1};base64,{2}"\n' return "\n" + "".join( - template.format(i, frame_format, base64.encodebytes(frame_data).decode('ascii').replace('\n', '\\\n')) - for i, frame_data in enumerate(frame_list)) - - def to_jshtml(self,fps): + template.format( + i, + frame_format, + base64.encodebytes(frame_data).decode("ascii").replace("\n", "\\\n"), + ) + for i, frame_data in enumerate(frame_list) + ) + + def to_jshtml(self, fps): """ Outputs an interactable JSHTML animation object that is embeddable in Jupyter notebooks. The object is packaged with controls to manipulate the video's @@ -1009,37 +1175,47 @@ def to_jshtml(self,fps): # Only works with Python3 and matplotlib > 3.1.0 from distutils.version import LooseVersion + import matplotlib + if LooseVersion(matplotlib.__version__) < LooseVersion("3.1.0"): - print('-------------------------------') - print('Warning: JSHTML output is not supported with your current matplotlib build. Consider upgrading to 3.1.0+') - print('-------------------------------') + print("-------------------------------") + print( + "Warning: JSHTML output is not supported with your current matplotlib build. Consider upgrading to 3.1.0+" + ) + print("-------------------------------") return if mp.am_master(): from uuid import uuid4 - from matplotlib._animation_data import (DISPLAY_TEMPLATE, INCLUDED_FRAMES, JS_INCLUDE, STYLE_INCLUDE) + + from matplotlib._animation_data import ( + DISPLAY_TEMPLATE, + INCLUDED_FRAMES, + JS_INCLUDE, + STYLE_INCLUDE, + ) # save the frames to an html file fill_frames = self._embedded_frames(self._saved_frames, self.frame_format) Nframes = len(self._saved_frames) - mode_dict = dict(once_checked='', - loop_checked='', - reflect_checked='') - mode_dict[self.default_mode + '_checked'] = 'checked' + mode_dict = dict(once_checked="", loop_checked="", reflect_checked="") + mode_dict[f"{self.default_mode}_checked"] = "checked" interval = 1000 // fps html_string = "" html_string += JS_INCLUDE html_string += STYLE_INCLUDE - html_string += DISPLAY_TEMPLATE.format(id=uuid4().hex, - Nframes=Nframes, - fill_frames=fill_frames, - interval=interval, - **mode_dict) + html_string += DISPLAY_TEMPLATE.format( + id=uuid4().hex, + Nframes=Nframes, + fill_frames=fill_frames, + interval=interval, + **mode_dict, + ) return JS_Animation(html_string) - def to_gif(self,fps,filename): + def to_gif(self, fps, filename): """ Generates and outputs a GIF file of the animation with the filename, `filename`, and the frame rate, `fps`. Note that GIFs are significantly larger than mp4 videos @@ -1051,32 +1227,43 @@ def to_gif(self,fps,filename): # requires ffmpeg to be installed # modified from the matplotlib library if mp.am_master(): - from subprocess import Popen, PIPE - from io import TextIOWrapper, BytesIO - FFMPEG_BIN = 'ffmpeg' - command = [FFMPEG_BIN, - '-f', 'image2pipe', # force piping of rawvideo - '-vcodec', self.frame_format, # raw input codec - '-s', '%dx%d' % (self.frame_size), - '-r', str(fps), # frame rate in frames per second - '-i', 'pipe:', # The input comes from a pipe - '-vcodec', 'gif', # output gif format - '-r', str(fps), # frame rate in frames per second - '-y', - '-vf', 'pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2', - '-an', filename # output filename + from io import BytesIO, TextIOWrapper + from subprocess import PIPE, Popen + + FFMPEG_BIN = "ffmpeg" + command = [ + FFMPEG_BIN, + "-f", + "image2pipe", # force piping of rawvideo + "-vcodec", + self.frame_format, # raw input codec + "-s", + "%dx%d" % (self.frame_size), + "-r", + str(fps), # frame rate in frames per second + "-i", + "pipe:", # The input comes from a pipe + "-vcodec", + "gif", # output gif format + "-r", + str(fps), # frame rate in frames per second + "-y", + "-vf", + "pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2", + "-an", + filename, # output filename ] if mp.verbosity.meep > 0: print("Generating GIF...") proc = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE) for i in range(len(self._saved_frames)): proc.stdin.write(self._saved_frames[i]) - out, err = proc.communicate() # pipe in images + out, err = proc.communicate() # pipe in images proc.stdin.close() proc.wait() return - def to_mp4(self,fps,filename): + def to_mp4(self, fps, filename): """ Generates and outputs an mp4 video file of the animation with the filename, `filename`, and the frame rate, `fps`. Default encoding is h264 with yuv420p @@ -1086,29 +1273,41 @@ def to_mp4(self,fps,filename): # requires ffmpeg to be installed # modified from the matplotlib library if mp.am_master(): - from subprocess import Popen, PIPE - from io import TextIOWrapper, BytesIO - FFMPEG_BIN = 'ffmpeg' - command = [FFMPEG_BIN, - '-f', 'image2pipe', # force piping of rawvideo - '-vcodec', self.frame_format, # raw input codec - '-s', '%dx%d' % (self.frame_size), - #'-pix_fmt', self.frame_format, - '-r', str(fps), # frame rate in frames per second - '-i', 'pipe:', # The input comes from a pipe - '-vcodec', self.codec, # output mp4 format - '-pix_fmt','yuv420p', - '-r', str(fps), # frame rate in frames per second - '-y', - '-vf', 'pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2', - '-an', filename # output filename + from io import BytesIO, TextIOWrapper + from subprocess import PIPE, Popen + + FFMPEG_BIN = "ffmpeg" + command = [ + FFMPEG_BIN, + "-f", + "image2pipe", # force piping of rawvideo + "-vcodec", + self.frame_format, # raw input codec + "-s", + "%dx%d" % (self.frame_size), + #'-pix_fmt', self.frame_format, + "-r", + str(fps), # frame rate in frames per second + "-i", + "pipe:", # The input comes from a pipe + "-vcodec", + self.codec, # output mp4 format + "-pix_fmt", + "yuv420p", + "-r", + str(fps), # frame rate in frames per second + "-y", + "-vf", + "pad=width=ceil(iw/2)*2:height=ceil(ih/2)*2", + "-an", + filename, # output filename ] if mp.verbosity.meep > 0: print("Generating MP4...") proc = Popen(command, stdin=PIPE, stdout=PIPE, stderr=PIPE) for i in range(len(self._saved_frames)): proc.stdin.write(self._saved_frames[i]) - out, err = proc.communicate() # pipe in images + out, err = proc.communicate() # pipe in images proc.stdin.close() proc.wait() return @@ -1118,19 +1317,23 @@ def reset(self): self.ax = None self.f = None - def set_figure(self,f): + def set_figure(self, f): self.f = f + # ------------------------------------------------------- # # JS_Animation # ------------------------------------------------------- # # A helper class used to make jshtml animations embed # seamlessly within Jupyter notebooks. -class JS_Animation(): - def __init__(self,jshtml): + +class JS_Animation: + def __init__(self, jshtml): self.jshtml = jshtml + def _repr_html_(self): return self.jshtml + def get_jshtml(self): return self.jshtml diff --git a/scheme/casimir.scm b/scheme/casimir.scm index 1f01d183a..5c802e3ca 100644 --- a/scheme/casimir.scm +++ b/scheme/casimir.scm @@ -6,15 +6,15 @@ ; return a list (source-vol mx my mz) ; given the volume integration-vol and n, pick out the appropriate side and mx my mz to use ; sides are ordered by decreasing weight -; weights are w(side, m) = area(side)/total area * 1/(m+1)^4, a rough estimate of the -; contribution to the stress tensor from that side and that m +; weights are w(side, m) = area(side)/total area * 1/(m+1)^4, a rough estimate of the +; contribution to the stress tensor from that side and that m (define (casimir-source-info integration-vol n) (define (modround x n) (modulo (inexact->exact (round x)) n)) - (define (get-src-index n) ;given n, extract out the two values of m for 3-d + (define (get-src-index n) ;given n, extract out the two values of m for 3-d (let* ((s 0) ;sum of diagonals (r 0) ;row intersection (c 0));column intersection - (while (< (+ s r) n) + (while (< (+ s r) n) (set! r (+ r 1)) (set! s (+ s r))) (set! c (- n s)) @@ -43,8 +43,8 @@ (list (vector3- center-vec xshift) (vector3+ center-vec xshift) (vector3- center-vec yshift) (vector3+ center-vec yshift) (vector3- center-vec zshift) (vector3+ center-vec zshift))) - (m-list - (list (vector3 0 m1 m2) (vector3 0 m1 m2) (vector3 m1 0 m2) + (m-list + (list (vector3 0 m1 m2) (vector3 0 m1 m2) (vector3 m1 0 m2) (vector3 m1 0 m2) (vector3 m1 m2 0) (vector3 m1 m2 0))) (orientation-list (list -1 1 -1 1 -1 1)) (size-list @@ -75,7 +75,7 @@ (list (vector3- new-center-vec z-shift) (vector3+ new-center-vec z-shift) (vector3+ new-center-vec r-shift) (vector3- new-center-vec r-shift))) (m-list - (list (vector3 m-dct m-phi 0) (vector3 m-dct m-phi 0) + (list (vector3 m-dct m-phi 0) (vector3 m-dct m-phi 0) (vector3 0 m-phi m-dct) (vector3 0 m-phi m-dct))) (orientation-list (list -1 1 1 -1)) (size-list @@ -91,7 +91,7 @@ (m (/ (- n s) 4)) (x-const-size (vector3 0 sy )) ;sz may be non-zero for quasi-3d systems (y-const-size (vector3 sx 0 )) - (center-list + (center-list (list (vector3- center-vec xshift) (vector3+ center-vec xshift) (vector3- center-vec yshift) (vector3+ center-vec yshift))) (m-list @@ -105,7 +105,7 @@ (print "Casimir.scm: working in 2 dimensions\n") (print " Surface center: "(list-ref center-list s)"\n") (print " Surface size: "(list-ref size-list s)"\n") - (list surface-vol + (list surface-vol (vector3-x surface-m) (vector3-y surface-m) (vector3-z surface-m) (list-ref orientation-list s) 1)))))) @@ -113,7 +113,7 @@ ;n contains both the side number and the harmonic expansion index (define (casimir-force-contrib force-direction integration-vol n Sigma T source-component gt . step-funcs) (define (cos-func X mx my mz source-vol) - (let* + (let* ((min-corner (meep-volume-get-min-corner source-vol)) (max-corner (meep-volume-get-max-corner source-vol)) (size-vec (vector3- max-corner min-corner)) @@ -147,11 +147,11 @@ (begin (set! global-B-conductivity Sigma) (set! global-D-conductivity 0))) - (if (eq? dimensions -2) + (if (eq? dimensions -2) (begin (print "Cylindricals: m = "my" and (nr nz) = ("mx", "mz")\n") (print " surface center = "(meep-volume-center source-vol)"\n") - (print " source size = "(vector3- - (meep-volume-get-max-corner source-vol) + (print " source size = "(vector3- + (meep-volume-get-max-corner source-vol) (meep-volume-get-min-corner source-vol))) (set! m my))) ;set exp(i m phi) field dependence (set! sources @@ -173,13 +173,13 @@ (define (integrate-function) (let* ((f-temp (meep-fields-casimir-stress-dct-integral fields - force-direction + force-direction (meep-component-direction source-component) mx (if (eq? dimensions -2) 0 my) mz ft source-vol))) (set! force-integral (+ force-integral - (imag-part (* (list-ref gt counter) dt + (imag-part (* (list-ref gt counter) dt source-orientation (if (eq? dimensions -2) (* (if (eq? my 0) 1 2) @@ -190,8 +190,8 @@ force-integral))) ;%%%%%%%%%%%%%%%%%%%%% BLOCH PBCS %%%%%%%%%%%%%%%%%%%%%% -;here the source is specified in -;the form exp(i k x), k = pi/L (m + k_red), +;here the source is specified in +;the form exp(i k x), k = pi/L (m + k_red), ;m = (mx,my,mz) are integers (reciprocal lattice vectors ;k_red = (kx,ky,kz) is in the 1st BZ, m an integer ;source-vol is assumed to occupy one entire plane intersecting diff --git a/scheme/examples/3rd-harm-1d.ctl b/scheme/examples/3rd-harm-1d.ctl index 36fb77290..261c6812d 100644 --- a/scheme/examples/3rd-harm-1d.ctl +++ b/scheme/examples/3rd-harm-1d.ctl @@ -39,13 +39,13 @@ (make flux-region (center 0 0 (- (* 0.5 sz) dpml 0.5))))) ; also compute a ''single'' flux point at fcen and 3*fcen -(define trans1 (add-flux fcen 0 1 (make flux-region +(define trans1 (add-flux fcen 0 1 (make flux-region (center 0 0 (- (* 0.5 sz) dpml 0.5))))) -(define trans3 (add-flux (* 3 fcen) 0 1 (make flux-region +(define trans3 (add-flux (* 3 fcen) 0 1 (make flux-region (center 0 0 (- (* 0.5 sz) dpml 0.5))))) (run-sources+ - (stop-when-fields-decayed 50 Ex + (stop-when-fields-decayed 50 Ex (vector3 0 0 (- (* 0.5 sz) dpml 0.5)) 1e-6)) diff --git a/scheme/examples/bend-flux.ctl b/scheme/examples/bend-flux.ctl index 9fed645d6..35fda3e21 100644 --- a/scheme/examples/bend-flux.ctl +++ b/scheme/examples/bend-flux.ctl @@ -16,16 +16,16 @@ (set! geometry (if no-bend? (list - (make block + (make block (center 0 wvg-ycen) (size infinity w infinity) (material (make dielectric (epsilon 12))))) (list - (make block + (make block (center (* -0.5 pad) wvg-ycen) (size (- sx pad) w infinity) (material (make dielectric (epsilon 12)))) - (make block + (make block (center wvg-xcen (* 0.5 pad)) (size w (- sy pad) infinity) (material (make dielectric (epsilon 12))))))) @@ -33,7 +33,7 @@ (define-param fcen 0.15) ; pulse center frequency (define-param df 0.1) ; pulse width (in frequency) (set! sources (list - (make source + (make source (src (make gaussian-src (frequency fcen) (fwidth df))) (component Ez) (center (+ 1 (* -0.5 sx)) wvg-ycen) @@ -52,15 +52,15 @@ (center wvg-xcen (- (/ sy 2) 1.5)) (size (* w 2) 0))))) (define refl ; reflected flux (add-flux fcen df nfreq - (make flux-region + (make flux-region (center (+ (* -0.5 sx) 1.5) wvg-ycen) (size 0 (* w 2))))) ; for normal run, load negated fields to subtract incident from refl. fields (if (not no-bend?) (load-minus-flux "refl-flux" refl)) -(run-sources+ +(run-sources+ (stop-when-fields-decayed 50 Ez - (if no-bend? + (if no-bend? (vector3 (- (/ sx 2) 1.5) wvg-ycen) (vector3 wvg-xcen (- (/ sy 2) 1.5))) 1e-3) diff --git a/scheme/examples/holey-wvg-bands.ctl b/scheme/examples/holey-wvg-bands.ctl index 12a463dfc..8b9a82bd9 100644 --- a/scheme/examples/holey-wvg-bands.ctl +++ b/scheme/examples/holey-wvg-bands.ctl @@ -26,7 +26,7 @@ (set! pml-layers (list (make pml (direction Y) (thickness dpml)))) (set-param! resolution 20) -(define-param fcen 0.25) ; pulse center frequency +(define-param fcen 0.25) ; pulse center frequency (define-param df 1.5) ; pulse freq. width: large df = short impulse (set! sources (list @@ -42,7 +42,7 @@ (if kx (begin (set! k-point (vector3 kx)) - (run-sources+ + (run-sources+ 300 (at-beginning output-epsilon) (after-sources (harminv Hz (vector3 0.1234 0) fcen df))) (run-until (/ 1 fcen) (at-every (/ 1 fcen 20) output-hfield-z))) diff --git a/scheme/examples/holey-wvg-cavity.ctl b/scheme/examples/holey-wvg-cavity.ctl index fe0b09f33..3abc97728 100644 --- a/scheme/examples/holey-wvg-cavity.ctl +++ b/scheme/examples/holey-wvg-cavity.ctl @@ -36,8 +36,8 @@ (set! pml-layers (list (make pml (thickness dpml)))) (set-param! resolution 20) -(define-param fcen 0.25) ; pulse center frequency -(define-param df 0.2) ; pulse width (in frequency) +(define-param fcen 0.25) ; pulse center frequency +(define-param df 0.2) ; pulse width (in frequency) (define-param nfreq 500) ; number of frequencies at which to compute flux @@ -58,7 +58,7 @@ (run-sources+ 400 (at-beginning output-epsilon) (after-sources (harminv Hz (vector3 0) fcen df))) - (run-until (/ 1 fcen) (at-every (/ 1 fcen 20) output-hfield-z)) + (run-until (/ 1 fcen) (at-every (/ 1 fcen 20) output-hfield-z)) ) (begin (set! sources (list @@ -69,12 +69,12 @@ (size 0 w)))) (set! symmetries (list (make mirror-sym (direction Y) (phase -1)))) - + (let ((trans ; transmitted flux (add-flux fcen df nfreq (make flux-region (center (- (* 0.5 sx) dpml 0.5) 0) (size 0 (* w 2)))))) - + (run-sources+ (stop-when-fields-decayed 50 Ey (vector3 (- (* 0.5 sx) dpml 0.5) 0) @@ -83,6 +83,6 @@ (during-sources (in-volume (volume (center 0 0) (size sx 0)) (to-appended "hz-slice" (at-every 0.4 output-hfield-z))))) - + (display-fluxes trans) ; print out the flux spectrum ))) diff --git a/scheme/examples/material-dispersion.ctl b/scheme/examples/material-dispersion.ctl index 42d7fa38f..325662c63 100644 --- a/scheme/examples/material-dispersion.ctl +++ b/scheme/examples/material-dispersion.ctl @@ -14,7 +14,7 @@ (set! default-material (make dielectric (epsilon 2.25) - (polarizations + (polarizations (make polarizability (omega 1.1) (gamma 1e-5) (sigma 0.5)) (make polarizability @@ -35,8 +35,8 @@ (define all-freqs (run-k-points 200 kpts)) ; a list of lists of frequencies (map (lambda (kx fs) - (map (lambda (f) - (print "eps:, " (real-part f) ", " (imag-part f) + (map (lambda (f) + (print "eps:, " (real-part f) ", " (imag-part f) ", " (sqr (/ kx f)) "\n")) fs)) (map vector3-x kpts) all-freqs) diff --git a/scheme/examples/ring-cyl.ctl b/scheme/examples/ring-cyl.ctl index 336448128..658520497 100644 --- a/scheme/examples/ring-cyl.ctl +++ b/scheme/examples/ring-cyl.ctl @@ -27,13 +27,13 @@ ; put a single point source at some arbitrary place, pointing in some ; arbitrary direction. We will only look for Ez-polarized modes. -(define-param fcen 0.15) ; pulse center frequency -(define-param df 0.1) ; pulse width (in frequency) +(define-param fcen 0.15) ; pulse center frequency +(define-param df 0.1) ; pulse width (in frequency) (set! sources (list (make source (src (make gaussian-src (frequency fcen) (fwidth df))) (component Ez) (center (+ r 0.1) 0)))) - + ; note that the r -> -r mirror symmetry is exploited automatically @@ -44,8 +44,8 @@ ; almost zero and get a distorted view.) We'll append the fields ; to a file to get an r-by-t picture. We'll also output from -sr to -sr ; instead of from 0 to sr. -(run-until (/ 1 fcen) +(run-until (/ 1 fcen) (in-volume (volume (center 0) (size (* 2 sr))) (at-beginning output-epsilon) - (to-appended "ez" + (to-appended "ez" (at-every (/ 1 fcen 20) output-efield-z)))) diff --git a/scheme/materials.scm b/scheme/materials.scm index 7436801d9..9a79485fd 100644 --- a/scheme/materials.scm +++ b/scheme/materials.scm @@ -24,7 +24,7 @@ (define cSi-sig3 -0.107) (define cSi (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency cSi-frq1) (gamma cSi-gam1) (sigma cSi-sig1)) (make lorentzian-susceptibility @@ -42,7 +42,7 @@ (define aSi-sig1 14.571) (define aSi (make medium (epsilon 3.109) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency aSi-frq1) (gamma aSi-gam1) (sigma aSi-sig1))))) @@ -56,7 +56,7 @@ (define aSi-H-sig1 12.31) (define aSi-H (make medium (epsilon 3.22) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency aSi-H-frq1) (gamma aSi-H-gam1) (sigma aSi-H-sig1))))) @@ -70,7 +70,7 @@ (define ITO-sig1 2.5) (define ITO (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency ITO-frq1) (gamma ITO-gam1) (sigma ITO-sig1))))) @@ -84,7 +84,7 @@ (define Al2O3-sig1 1.52) (define Al2O3 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Al2O3-frq1) (gamma Al2O3-gam1) (sigma Al2O3-sig1))))) @@ -98,7 +98,7 @@ (define AlN-sig1 3.306) (define AlN (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency AlN-frq1) (gamma AlN-gam1) (sigma AlN-sig1))))) @@ -116,7 +116,7 @@ (define AlAs-sig2 1.900) (define AlAs (make medium (epsilon 2.0792) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency AlAs-frq1) (gamma AlAs-gam1) (sigma AlAs-sig1)) (make lorentzian-susceptibility @@ -138,7 +138,7 @@ (define BK7-sig3 1.01046945) (define BK7 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency BK7-frq1) (gamma BK7-gam1) (sigma BK7-sig1)) (make lorentzian-susceptibility @@ -162,7 +162,7 @@ (define fused-quartz-sig3 0.897479400) (define fused-quartz (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency fused-quartz-frq1) (gamma fused-quartz-gam1) (sigma fused-quartz-sig1)) (make lorentzian-susceptibility @@ -186,7 +186,7 @@ (define GaAs-sig3 1.957522) (define GaAs (make medium (epsilon 5.372514) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency GaAs-frq1) (gamma GaAs-gam1) (sigma GaAs-sig1)) (make lorentzian-susceptibility @@ -204,7 +204,7 @@ (define Si3N4-VISNIR-sig1 2.8939) (define Si3N4-VISNIR (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si3N4-VISNIR-frq1) (gamma Si3N4-VISNIR-gam1) (sigma Si3N4-VISNIR-sig1))))) @@ -221,7 +221,7 @@ (define Si3N4-NIR-sig2 40314) (define Si3N4-NIR (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si3N4-NIR-frq1) (gamma Si3N4-NIR-gam1) (sigma Si3N4-NIR-sig1)) (make lorentzian-susceptibility @@ -758,7 +758,7 @@ (make lorentzian-susceptibility (frequency Au-visible-frq1) (gamma Au-visible-gam1) (sigma Au-visible-sig1))))) -;------------------------------------------------------------------ +;------------------------------------------------------------------ ;; UNSTABLE: field divergence may occur ; silver (Au) @@ -936,7 +936,7 @@ (define SiN-sig1 1.2650) (define SiN (make medium (epsilon 2.320) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency SiN-frq1) (gamma SiN-gam1) (sigma SiN-sig1))))) @@ -950,7 +950,7 @@ (define Si3N4-sig1 4.377) (define Si3N4 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si3N4-frq1) (gamma Si3N4-gam1) (sigma Si3N4-sig1))))) @@ -964,7 +964,7 @@ (define SiO2-sig1 1.12) (define SiO2 (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency SiO2-frq1) (gamma SiO2-gam1) (sigma SiO2-sig1))))) @@ -982,7 +982,7 @@ (define InP-sig2 2.765) (define InP (make medium (epsilon 7.255) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency InP-frq1) (gamma InP-gam1) (sigma InP-sig1)) (make lorentzian-susceptibility @@ -1002,7 +1002,7 @@ (define Ge-sig2 0.21307) (define Ge (make medium (epsilon 9.28156) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Ge-frq1) (gamma Ge-gam1) (sigma Ge-sig1)) (make lorentzian-susceptibility @@ -1026,7 +1026,7 @@ (define Si-sig3 1.54133408) (define Si (make medium (epsilon 9.28156) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency Si-frq1) (gamma Si-gam1) (sigma Si-sig1)) (make lorentzian-susceptibility @@ -1044,7 +1044,7 @@ (define PMMA-sig1 1.1819) (define PMMA (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency PMMA-frq1) (gamma PMMA-gam1) (sigma PMMA-sig1))))) @@ -1058,7 +1058,7 @@ (define PC-sig1 1.4182) (define PC (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency PC-frq1) (gamma PC-gam1) (sigma PC-sig1))))) @@ -1072,7 +1072,7 @@ (define PS-sig1 1.4435) (define PS (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency PS-frq1) (gamma PS-gam1) (sigma PS-sig1))))) @@ -1086,7 +1086,7 @@ (define CLS-sig1 1.124) (define CLS (make medium (epsilon 1.0) - (E-susceptibilities + (E-susceptibilities (make lorentzian-susceptibility (frequency CLS-frq1) (gamma CLS-gam1) (sigma CLS-sig1))))) diff --git a/scheme/meep_op_renames.i b/scheme/meep_op_renames.i index 6000c9dee..d644d11c9 100644 --- a/scheme/meep_op_renames.i +++ b/scheme/meep_op_renames.i @@ -12,4 +12,3 @@ %rename(meep_dft_chunk_subeq) meep::dft_chunk::operator-=; %rename(meep_dft_flux_subeq) meep::dft_flux::operator-=; - diff --git a/src/GDSIIgeom.cpp b/src/GDSIIgeom.cpp index 172965c14..180d95e03 100644 --- a/src/GDSIIgeom.cpp +++ b/src/GDSIIgeom.cpp @@ -152,8 +152,7 @@ geometric_object_list get_GDSII_prisms(material_type material, const char *GDSII } double height = zmax - zmin; vector3 zaxis = {0, 0, 1}; - prisms.items[np] = - make_prism(material, vertices.get(), num_vertices, height, zaxis); + prisms.items[np] = make_prism(material, vertices.get(), num_vertices, height, zaxis); } return prisms; } diff --git a/src/adjust_verbosity.hpp b/src/adjust_verbosity.hpp index 3ca7df896..f4014b367 100644 --- a/src/adjust_verbosity.hpp +++ b/src/adjust_verbosity.hpp @@ -27,27 +27,29 @@ namespace meep { class adjust_mpb_verbosity { public: adjust_mpb_verbosity() { - #if defined(HAVE_MPB) && (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) - old_level = mpb_verbosity; - mpb_verbosity = verbosity - 1; - if (mpb_verbosity < 0) mpb_verbosity = 0; - if (mpb_verbosity > 3) mpb_verbosity = 3; - #else - // avoid warnings - (void)old_level; - #endif +#if defined(HAVE_MPB) && \ + (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) + old_level = mpb_verbosity; + mpb_verbosity = verbosity - 1; + if (mpb_verbosity < 0) mpb_verbosity = 0; + if (mpb_verbosity > 3) mpb_verbosity = 3; +#else + // avoid warnings + (void)old_level; +#endif } ~adjust_mpb_verbosity() { - #if defined(HAVE_MPB) && (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) - mpb_verbosity = old_level; - #endif +#if defined(HAVE_MPB) && \ + (MPB_VERSION_MAJOR > 1 || (MPB_VERSION_MAJOR == 1 && MPB_VERSION_MINOR >= 11)) + mpb_verbosity = old_level; +#endif } private: int old_level; }; -} +} // namespace meep -#endif // ADJUST_VERBOSITY_H +#endif // ADJUST_VERBOSITY_H diff --git a/src/array_slice.cpp b/src/array_slice.cpp index e131cdd75..fe053011f 100644 --- a/src/array_slice.cpp +++ b/src/array_slice.cpp @@ -76,9 +76,7 @@ std::complex cdouble(std::complex z) { return std::complex(real(z), imag(z)); } -std::complex cdouble(std::complex z) { - return z; -} +std::complex cdouble(std::complex z) { return z; } typedef struct { @@ -127,7 +125,8 @@ typedef struct { } array_slice_data; /* passthrough field function equivalent to component_fun in h5fields.cpp */ -static complex default_field_func(const complex *fields, const vec &loc, void *data_) { +static complex default_field_func(const complex *fields, const vec &loc, + void *data_) { (void)loc; // unused (void)data_; // unused return cdouble(fields[0]); @@ -523,8 +522,11 @@ bool increment(size_t *n, size_t *nMax, int rank) { return true; } -// get the dimensions of reduced arrays (i.e., arrays collapsed along empty dimensions of original volume) -void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction dirs[3], size_t stride[3], int &reduced_rank, size_t reduced_dims[3], direction reduced_dirs[3], size_t reduced_stride[3]) { +// get the dimensions of reduced arrays (i.e., arrays collapsed along empty dimensions of original +// volume) +void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction dirs[3], + size_t stride[3], int &reduced_rank, size_t reduced_dims[3], + direction reduced_dirs[3], size_t reduced_stride[3]) { reduced_rank = 0; reduced_dims[0] = reduced_dims[1] = reduced_dims[2] = stride[0] = stride[1] = stride[2] = @@ -534,7 +536,8 @@ void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction d reduced_stride[r] = 0; // degenerate dimension, to be collapsed else { reduced_dirs[reduced_rank] = dirs[r]; - reduced_dims[reduced_rank++] = dims[r]; // reduced_dims is the size of the array after collapsing + reduced_dims[reduced_rank++] = + dims[r]; // reduced_dims is the size of the array after collapsing } } /*--------------------------------------------------------------*/ @@ -545,8 +548,7 @@ void reduce_array_dimensions(volume where, int rank, size_t dims[3], direction d else if (rank == 3) { stride[0] = dims[1] * dims[2]; stride[1] = dims[2]; - if (reduced_rank == 2) - reduced_stride[reduced_stride[0] != 0 ? 0 : 1] = reduced_dims[1]; + if (reduced_rank == 2) reduced_stride[reduced_stride[0] != 0 ? 0 : 1] = reduced_dims[1]; } } @@ -563,10 +565,11 @@ realnum *collapse_array(realnum *array, int *rank, size_t dims[3], direction dir direction reduced_dirs[3]; size_t reduced_grid_size; int reduced_rank; - reduce_array_dimensions(where, full_rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, reduced_stride); + reduce_array_dimensions(where, full_rank, dims, dirs, stride, reduced_rank, reduced_dims, + reduced_dirs, reduced_stride); if (full_rank == 0) return array; - if (reduced_rank==0) { + if (reduced_rank == 0) { *rank = 0; return array; // return array as is for singleton use case } @@ -575,10 +578,11 @@ realnum *collapse_array(realnum *array, int *rank, size_t dims[3], direction dir /*--------------------------------------------------------------*/ /*- allocate reduced array and compress full array into it -*/ /*--------------------------------------------------------------*/ - reduced_grid_size = reduced_dims[0]*reduced_dims[1]*reduced_dims[2]; + reduced_grid_size = reduced_dims[0] * reduced_dims[1] * reduced_dims[2]; size_t reduced_array_size = data_size * reduced_grid_size; realnum *reduced_array = new realnum[reduced_array_size]; - if (!reduced_array) meep::abort("%s:%i: out of memory (%zu)", __FILE__, __LINE__, reduced_array_size); + if (!reduced_array) + meep::abort("%s:%i: out of memory (%zu)", __FILE__, __LINE__, reduced_array_size); memset(reduced_array, 0, reduced_array_size * sizeof(realnum)); size_t n[3] = {0, 0, 0}; @@ -598,8 +602,8 @@ realnum *collapse_array(realnum *array, int *rank, size_t dims[3], direction dir return reduced_array; } -complex *collapse_array(complex *array, int *rank, size_t dims[3], direction dirs[3], - volume where) { +complex *collapse_array(complex *array, int *rank, size_t dims[3], + direction dirs[3], volume where) { return (complex *)collapse_array((realnum *)array, rank, dims, dirs, where, 2); } @@ -624,7 +628,8 @@ void *fields::do_get_array_slice(const volume &where, std::vector com int elem_size = complex_data ? 2 : 1; void *vslice_uncollapsed; - vslice_uncollapsed = memset(new realnum[slice_size * elem_size], 0, slice_size * elem_size * sizeof(realnum)); + vslice_uncollapsed = + memset(new realnum[slice_size * elem_size], 0, slice_size * elem_size * sizeof(realnum)); data.vslice = vslice_uncollapsed; data.snap = snap; @@ -676,14 +681,15 @@ void *fields::do_get_array_slice(const volume &where, std::vector com loop_in_chunks(get_array_slice_chunkloop, (void *)&data, where, Centered, true, snap); if (!snap) { - realnum *slice = collapse_array((realnum *)vslice_uncollapsed, &rank, dims, dirs, where, elem_size); + realnum *slice = + collapse_array((realnum *)vslice_uncollapsed, &rank, dims, dirs, where, elem_size); rank = get_array_slice_dimensions(where, dims, dirs, true, false, 0, &data); slice_size = data.slice_size; - vslice_uncollapsed = (realnum*) slice; + vslice_uncollapsed = (realnum *)slice; } if (vslice) { memcpy(vslice, vslice_uncollapsed, slice_size * elem_size * sizeof(realnum)); - delete[] (realnum*) vslice_uncollapsed; + delete[](realnum *) vslice_uncollapsed; } else vslice = vslice_uncollapsed; @@ -709,9 +715,11 @@ realnum *fields::get_array_slice(const volume &where, std::vector com frequency, snap); } -complex *fields::get_complex_array_slice(const volume &where, std::vector components, - field_function fun, void *fun_data, complex *slice, - double frequency, bool snap) { +complex *fields::get_complex_array_slice(const volume &where, + std::vector components, + field_function fun, void *fun_data, + complex *slice, double frequency, + bool snap) { return (complex *)do_get_array_slice(where, components, fun, 0, fun_data, (void *)slice, frequency, snap); } @@ -730,16 +738,17 @@ realnum *fields::get_array_slice(const volume &where, derived_component c, realn component carray[12]; field_rfunction rfun = derived_component_func(c, gv, nfields, carray); std::vector cs(carray, carray + nfields); - return (realnum *)do_get_array_slice(where, cs, 0, rfun, &nfields, (void *)slice, - frequency, snap); + return (realnum *)do_get_array_slice(where, cs, 0, rfun, &nfields, (void *)slice, frequency, + snap); } -complex *fields::get_complex_array_slice(const volume &where, component c, complex *slice, - double frequency, bool snap) { +complex *fields::get_complex_array_slice(const volume &where, component c, + complex *slice, double frequency, + bool snap) { std::vector components(1); components[0] = c; - return (complex *)do_get_array_slice(where, components, default_field_func, 0, 0, (void *)slice, - frequency, snap); + return (complex *)do_get_array_slice(where, components, default_field_func, 0, 0, + (void *)slice, frequency, snap); } complex *fields::get_source_slice(const volume &where, component source_slice_component, @@ -765,7 +774,7 @@ complex *fields::get_source_slice(const volume &where, component source if (slice) { memcpy(slice, slice_collapsed, 2 * slice_size * sizeof(realnum)); - delete[] (complex*) slice_collapsed; + delete[](complex *) slice_collapsed; } else slice = slice_collapsed; diff --git a/src/boundaries.cpp b/src/boundaries.cpp index dc136c2f1..be4917090 100644 --- a/src/boundaries.cpp +++ b/src/boundaries.cpp @@ -68,13 +68,13 @@ comms_sequence optimize_comms_operations(const std::vector &ope // Assemble send operations. for (const auto &size_pair : send_op_sizes) { int my_chunk_idx = size_pair.first; - const auto& ops_vector = send_ops_by_my_chunk_idx[my_chunk_idx]; + const auto &ops_vector = send_ops_by_my_chunk_idx[my_chunk_idx]; ret.send_ops.insert(std::end(ret.send_ops), std::begin(ops_vector), std::end(ops_vector)); } return ret; } -} // namespace +} // namespace void fields::set_boundary(boundary_side b, direction d, boundary_condition cond) { if (boundaries[b][d] != cond) { @@ -120,9 +120,9 @@ ivec fields::ilattice_vector(direction d) const { switch (d) { case X: return ivec(user_volume.nx() * 2, 0); case Y: return ivec(0, user_volume.ny() * 2); - case Z: // fall-thru - case R: // fall-thru - case P: // fall-thru + case Z: // fall-thru + case R: // fall-thru + case P: // fall-thru case NO_DIRECTION: break; } case D3: @@ -130,8 +130,8 @@ ivec fields::ilattice_vector(direction d) const { case X: return ivec(user_volume.nx() * 2, 0, 0); case Y: return ivec(0, user_volume.ny() * 2, 0); case Z: return ivec(0, 0, user_volume.nz() * 2); - case R: // fall-thru - case P: // fall-thru + case R: // fall-thru + case P: // fall-thru case NO_DIRECTION: break; } } diff --git a/src/casimir.cpp b/src/casimir.cpp index 10756d595..ebc42cf56 100644 --- a/src/casimir.cpp +++ b/src/casimir.cpp @@ -214,8 +214,10 @@ complex fields::casimir_stress_dct_integral(direction dforce, direction if (where.dim != gv.dim) meep::abort("invalid dimesionality in casimir_stress_dct_integral"); if (coordinate_mismatch(gv.dim, dforce) || coordinate_mismatch(gv.dim, dsource)) meep::abort("invalid directions in casimir_stress_dct_integral"); - if (dnormal == NO_DIRECTION) meep::abort("invalid integration surface in casimir_stress_dct_integral"); - if (ft != E_stuff && ft != H_stuff) meep::abort("invalid field type in casimir_stress_dct_integral"); + if (dnormal == NO_DIRECTION) + meep::abort("invalid integration surface in casimir_stress_dct_integral"); + if (ft != E_stuff && ft != H_stuff) + meep::abort("invalid field type in casimir_stress_dct_integral"); if (dforce != dnormal && dsource != dnormal) return 0.0; diff --git a/src/dft.cpp b/src/dft.cpp index d7780e090..802687df9 100644 --- a/src/dft.cpp +++ b/src/dft.cpp @@ -81,11 +81,11 @@ dft_chunk::dft_chunk(fields_chunk *fc_, ivec is_, ivec ie_, vec s0_, vec s1_, ve by 1 pixel in each dimension, while ensuring we don't step outside of the chunk loop itself */ - if(persist){ + if (persist) { is_old = is_; ie_old = ie_; - is = max(is-one_ivec(fc->gv.dim)*2,fc->gv.little_corner()); - ie = min(ie+one_ivec(fc->gv.dim)*2,fc->gv.big_corner()); + is = max(is - one_ivec(fc->gv.dim) * 2, fc->gv.little_corner()); + ie = min(ie + one_ivec(fc->gv.dim) * 2, fc->gv.big_corner()); } if (use_centered_grid) @@ -174,8 +174,8 @@ static void add_dft_chunkloop(fields_chunk *fc, int ichunk, component cgrid, ive dft_chunk *fields::add_dft(component c, const volume &where, const double *freq, size_t Nfreq, bool include_dV_and_interp_weights, complex stored_weight, dft_chunk *chunk_next, bool sqrt_dV_and_interp_weights, - complex extra_weight, bool use_centered_grid, - int vc, int decimation_factor, bool persist) { + complex extra_weight, bool use_centered_grid, int vc, + int decimation_factor, bool persist) { if (coordinate_mismatch(gv.dim, c)) return NULL; /* If you call add_dft before adding sources, it will do nothing @@ -185,7 +185,7 @@ dft_chunk *fields::add_dft(component c, const volume &where, const double *freq, meep::abort("allocate field components (by adding sources) before adding dft objects"); if (!include_dV_and_interp_weights && sqrt_dV_and_interp_weights) meep::abort("include_dV_and_interp_weights must be true for sqrt_dV_and_interp_weights=true in " - "add_dft"); + "add_dft"); dft_chunk_data data; data.persist = persist; @@ -198,13 +198,14 @@ dft_chunk *fields::add_dft(component c, const volume &where, const double *freq, if (s->get_fwidth() == 0) decimation_factor = 1; else - src_freq_max = std::max(src_freq_max, std::abs(s->frequency().real())+0.5*s->get_fwidth()); + src_freq_max = + std::max(src_freq_max, std::abs(s->frequency().real()) + 0.5 * s->get_fwidth()); } double freq_max = 0; for (size_t i = 0; i < Nfreq; ++i) freq_max = std::max(freq_max, std::abs(freq[i])); if ((freq_max > 0) && (src_freq_max > 0)) - decimation_factor = std::max(1, int(std::floor(1/(dt*(freq_max + src_freq_max))))); + decimation_factor = std::max(1, int(std::floor(1 / (dt * (freq_max + src_freq_max))))); else decimation_factor = 1; } @@ -294,7 +295,8 @@ void dft_chunk::update_dft(double time) { else { realnum fr = f[0]; for (int i = 0; i < Nomega; ++i) - dft[Nomega * idx_dft + i] += std::complex{fr * dft_phase[i].real(), fr * dft_phase[i].imag()}; + dft[Nomega * idx_dft + i] += + std::complex{fr * dft_phase[i].real(), fr * dft_phase[i].imag()}; } } } @@ -318,7 +320,7 @@ double fields_chunk::dft_norm2(grid_volume fgv) const { return sum; } -static double sqr(std::complex x) { return (x*std::conj(x)).real(); } +static double sqr(std::complex x) { return (x * std::conj(x)).real(); } double dft_chunk::norm2(grid_volume fgv) const { if (!fc->f[c][0]) return 0.0; @@ -331,22 +333,22 @@ double dft_chunk::norm2(grid_volume fgv) const { and can replicate results with different chunk combinations. */ if (persist) { - grid_volume subgv = fgv.subvolume(is,ie,c); + grid_volume subgv = fgv.subvolume(is, ie, c); LOOP_OVER_IVECS(subgv, is_old, ie_old, idx) { for (size_t i = 0; i < Nomega; ++i) - sum += sqr(dft[Nomega * idx + i]); - } - } + sum += sqr(dft[Nomega * idx + i]); + } + } /* note we place the if outside of the loop to avoid branching. This routine gets called a lot, so let's try to stay efficient (at the expense of uglier code). */ - else{ + else { LOOP_OVER_IVECS(fgv, is, ie, idx) { - idx_dft = IVEC_LOOP_COUNTER; - for (size_t i = 0; i < Nomega; ++i) - sum += sqr(dft[Nomega * idx_dft + i]); + idx_dft = IVEC_LOOP_COUNTER; + for (size_t i = 0; i < Nomega; ++i) + sum += sqr(dft[Nomega * idx_dft + i]); } } @@ -358,8 +360,7 @@ double dft_chunk::norm2(grid_volume fgv) const { int fields::max_decimation() const { int maxdec = 1; for (int i = 0; i < num_chunks; i++) - if (chunks[i]->is_mine()) - maxdec = std::max(maxdec, chunks[i]->max_decimation()); + if (chunks[i]->is_mine()) maxdec = std::max(maxdec, chunks[i]->max_decimation()); return max_to_all(maxdec); } @@ -374,8 +375,7 @@ int fields_chunk::max_decimation() const { double fields::dft_maxfreq() const { double maxfreq = 0; for (int i = 0; i < num_chunks; i++) - if (chunks[i]->is_mine()) - maxfreq = std::max(maxfreq, chunks[i]->dft_maxfreq()); + if (chunks[i]->is_mine()) maxfreq = std::max(maxfreq, chunks[i]->dft_maxfreq()); return max_to_all(maxfreq); } @@ -383,12 +383,12 @@ double fields_chunk::dft_maxfreq() const { double maxomega = 0; for (dft_chunk *cur = dft_chunks; cur; cur = cur->next_in_chunk) maxomega = std::max(maxomega, cur->maxomega()); - return maxomega / (2*meep::pi); + return maxomega / (2 * meep::pi); } double dft_chunk::maxomega() const { double maxomega = 0; - for (const auto& o : omega) + for (const auto &o : omega) maxomega = std::max(maxomega, std::abs(o)); return maxomega; } @@ -414,7 +414,7 @@ void dft_chunk::operator-=(const dft_chunk &chunk) { size_t my_dft_chunks_Ntotal(dft_chunk *dft_chunks, size_t *my_start) { // When writing to a sharded file, we write out only the chunks we own. -size_t n = 0; + size_t n = 0; for (dft_chunk *cur = dft_chunks; cur; cur = cur->next_in_dft) n += cur->N * cur->omega.size() * 2; @@ -431,12 +431,13 @@ size_t dft_chunks_Ntotal(dft_chunk *dft_chunks, size_t *my_start) { } size_t dft_chunks_Ntotal(dft_chunk *dft_chunks, size_t *my_start, bool single_parallel_file) { - return single_parallel_file ? dft_chunks_Ntotal(dft_chunks, my_start) : - my_dft_chunks_Ntotal(dft_chunks, my_start); + return single_parallel_file ? dft_chunks_Ntotal(dft_chunks, my_start) + : my_dft_chunks_Ntotal(dft_chunks, my_start); } // Note: the file must have been created in parallel mode, typically via fields::open_h5file. -void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, bool single_parallel_file) { +void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, + bool single_parallel_file) { size_t istart; size_t n = dft_chunks_Ntotal(dft_chunks, &istart, single_parallel_file); @@ -455,11 +456,13 @@ void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const file->done_writing_chunks(); } -void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, bool single_parallel_file) { +void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, + bool single_parallel_file) { save_dft_hdf5(dft_chunks, component_name(c), file, dprefix, single_parallel_file); } -void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, bool single_parallel_file) { +void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix, + bool single_parallel_file) { size_t istart; size_t n = dft_chunks_Ntotal(dft_chunks, &istart, single_parallel_file); @@ -473,7 +476,7 @@ void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const file->read_size(dataname, &file_rank, &file_dims, 1); if (file_rank != 1 || file_dims != n) meep::abort("incorrect dataset size (%zd vs. %zd) in load_dft_hdf5 %s:%s", file_dims, n, - file->file_name(), dataname); + file->file_name(), dataname); for (dft_chunk *cur = dft_chunks; cur; cur = cur->next_in_dft) { size_t Nchunk = cur->N * cur->omega.size() * 2; @@ -482,7 +485,8 @@ void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const } } -void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, bool single_parallel_file) { +void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix, + bool single_parallel_file) { load_dft_hdf5(dft_chunks, component_name(c), file, dprefix, single_parallel_file); } @@ -602,10 +606,9 @@ dft_flux fields::add_dft_flux(const volume_list *where_, const double *freq, siz } for (int i = 0; i < 2; ++i) { - E = add_dft(cE[i], where->v, freq, Nfreq, true, - where->weight * double(1 - 2 * i), E, false, std::complex(1.0,0), - centered_grid, 0, decimation_factor); - H = add_dft(cH[i], where->v, freq, Nfreq, false, 1.0, H, false, std::complex(1.0,0), + E = add_dft(cE[i], where->v, freq, Nfreq, true, where->weight * double(1 - 2 * i), E, false, + std::complex(1.0, 0), centered_grid, 0, decimation_factor); + H = add_dft(cH[i], where->v, freq, Nfreq, false, 1.0, H, false, std::complex(1.0, 0), centered_grid, 0, decimation_factor); } @@ -702,14 +705,14 @@ dft_energy fields::add_dft_energy(const volume_list *where_, const double *freq, volume_list *where_save = where; while (where) { LOOP_OVER_FIELD_DIRECTIONS(gv.dim, d) { - E = add_dft(direction_component(Ex, d), where->v, freq, Nfreq, true, 1.0, E, - false, 1.0, true, 0, decimation_factor); - D = add_dft(direction_component(Dx, d), where->v, freq, Nfreq, false, 1.0, D, - false, 1.0, true, 0, decimation_factor); - H = add_dft(direction_component(Hx, d), where->v, freq, Nfreq, true, 1.0, H, - false, 1.0, true, 0, decimation_factor); - B = add_dft(direction_component(Bx, d), where->v, freq, Nfreq, false, 1.0, B, - false, 1.0, true, 0, decimation_factor); + E = add_dft(direction_component(Ex, d), where->v, freq, Nfreq, true, 1.0, E, false, 1.0, true, + 0, decimation_factor); + D = add_dft(direction_component(Dx, d), where->v, freq, Nfreq, false, 1.0, D, false, 1.0, + true, 0, decimation_factor); + H = add_dft(direction_component(Hx, d), where->v, freq, Nfreq, true, 1.0, H, false, 1.0, true, + 0, decimation_factor); + B = add_dft(direction_component(Bx, d), where->v, freq, Nfreq, false, 1.0, B, false, 1.0, + true, 0, decimation_factor); } where = where->next; } @@ -794,7 +797,8 @@ direction fields::normal_direction(const volume &where) const { if (d == NO_DIRECTION && gv.dim == D2 && beta != 0 && where_pad.in_direction(X) > 0 && where_pad.in_direction(Y) > 0) d = Z; - if (d == NO_DIRECTION) meep::abort("Could not determine normal direction for given grid_volume."); + if (d == NO_DIRECTION) + meep::abort("Could not determine normal direction for given grid_volume."); } return d; } @@ -808,10 +812,10 @@ dft_flux fields::add_dft_flux(direction d, const volume &where, const double *fr return flux; } - dft_flux fields::add_mode_monitor(direction d, const volume &where, const double *freq, size_t Nfreq, bool centered_grid, int decimation_factor) { - return add_dft_flux(d, where, freq, Nfreq, /*use_symmetry=*/false, centered_grid, decimation_factor); + return add_dft_flux(d, where, freq, Nfreq, /*use_symmetry=*/false, centered_grid, + decimation_factor); } dft_flux fields::add_dft_flux_box(const volume &where, double freq_min, double freq_max, @@ -896,14 +900,17 @@ dft_fields fields::add_dft_fields(component *components, int num_components, con /***************************************************************/ /* chunk-level processing for fields::process_dft_component. */ /***************************************************************/ -complex dft_chunk::process_dft_component(int rank, direction *ds, ivec min_corner, ivec max_corner, - int num_freq, h5file *file, realnum *buffer, int reim, - complex *field_array, void *mode1_data, void *mode2_data, - int ic_conjugate, bool retain_interp_weights, - fields *parent) { - - if ((num_freq < 0) || (num_freq > static_cast(omega.size())-1)) - meep::abort("process_dft_component: frequency index %d is outside the range of the frequency array of size %lu",num_freq,omega.size()); +complex dft_chunk::process_dft_component(int rank, direction *ds, ivec min_corner, + ivec max_corner, int num_freq, h5file *file, + realnum *buffer, int reim, + complex *field_array, void *mode1_data, + void *mode2_data, int ic_conjugate, + bool retain_interp_weights, fields *parent) { + + if ((num_freq < 0) || (num_freq > static_cast(omega.size()) - 1)) + meep::abort("process_dft_component: frequency index %d is outside the range of the frequency " + "array of size %lu", + num_freq, omega.size()); /*****************************************************************/ /* compute the size of the chunk we own and its strides etc. */ @@ -969,14 +976,13 @@ complex dft_chunk::process_dft_component(int rank, direction *ds, ivec m double interp_w = retain_interp_weights ? IVEC_LOOP_WEIGHT(s0i, s1i, e0i, e1i, 1.0) : 1.0; complex dft_val = - (c_conjugate == NO_COMPONENT - ? w - : c_conjugate == Dielectric - ? parent->get_eps(loc) - : c_conjugate == Permeability - ? parent->get_mu(loc) - : complex(dft[omega.size() * (chunk_idx++) + num_freq]) / stored_weight); - if (include_dV_and_interp_weights && dft_val!=0.0) dft_val /= (sqrt_dV_and_interp_weights ? sqrt(w) : w); + (c_conjugate == NO_COMPONENT ? w + : c_conjugate == Dielectric ? parent->get_eps(loc) + : c_conjugate == Permeability + ? parent->get_mu(loc) + : complex(dft[omega.size() * (chunk_idx++) + num_freq]) / stored_weight); + if (include_dV_and_interp_weights && dft_val != 0.0) + dft_val /= (sqrt_dV_and_interp_weights ? sqrt(w) : w); complex mode1val = 0.0, mode2val = 0.0; if (mode1_data) mode1val = eigenmode_amplitude(mode1_data, loc, S.transform(c_conjugate, sn)); @@ -1023,7 +1029,10 @@ complex dft_chunk::process_dft_component(int rank, direction *ds, ivec m } // get variables that are needed by complex fields::process_dft_component -void fields::get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, ivec &min_corner, ivec &max_corner, size_t &array_size, size_t &bufsz, int &rank, direction *ds, size_t *dims, int *array_rank, size_t *array_dims, direction *array_dirs) { +void fields::get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, + ivec &min_corner, ivec &max_corner, size_t &array_size, + size_t &bufsz, int &rank, direction *ds, size_t *dims, + int *array_rank, size_t *array_dims, direction *array_dirs) { /***************************************************************/ /* get statistics on the volume slice **************************/ /***************************************************************/ @@ -1114,11 +1123,13 @@ void fields::get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, /* if where is non-null, only field components inside *where */ /* are processed. */ /***************************************************************/ -complex fields::process_dft_component(dft_chunk **chunklists, int num_chunklists, int num_freq, - component c, const char *HDF5FileName, complex **pfield_array, - int *array_rank, size_t *array_dims, direction *array_dirs, - void *mode1_data, void *mode2_data, component c_conjugate, - bool *first_component, bool retain_interp_weights) { +complex fields::process_dft_component(dft_chunk **chunklists, int num_chunklists, + int num_freq, component c, const char *HDF5FileName, + complex **pfield_array, int *array_rank, + size_t *array_dims, direction *array_dirs, + void *mode1_data, void *mode2_data, + component c_conjugate, bool *first_component, + bool retain_interp_weights) { /***************************************************************/ /***************************************************************/ @@ -1134,7 +1145,8 @@ complex fields::process_dft_component(dft_chunk **chunklists, int num_ch int rank; direction ds[3]; size_t array_size, bufsz, dims[3]; - get_dft_component_dims(chunklists, num_chunklists, c, min_corner, max_corner, array_size, bufsz, rank, ds, dims, array_rank, array_dims, array_dirs); + get_dft_component_dims(chunklists, num_chunklists, c, min_corner, max_corner, array_size, bufsz, + rank, ds, dims, array_rank, array_dims, array_dirs); if (rank == 0) { if (pfield_array) *pfield_array = 0; @@ -1184,7 +1196,7 @@ complex fields::process_dft_component(dft_chunk **chunklists, int num_ch /* repeatedly call sum_to_all to consolidate full field array */ /* on all cores */ /***************************************************************/ -#define BUFSIZE 1 << 20 // use 1M element (16 MB) buffer +#define BUFSIZE 1 << 20 // use 1M element (16 MB) buffer complex *buf = new complex[BUFSIZE]; ptrdiff_t offset = 0; size_t remaining = array_size; @@ -1397,14 +1409,14 @@ void fields::get_overlap(void *mode1_data, void *mode2_data, dft_flux flux, int dft_chunk *chunklists[2]; chunklists[0] = flux.E; chunklists[1] = flux.H; - complex ExHy = process_dft_component(chunklists, 2, num_freq, cE[0], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cH[0]); - complex EyHx = process_dft_component(chunklists, 2, num_freq, cE[1], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cH[1]); - complex HyEx = process_dft_component(chunklists, 2, num_freq, cH[0], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cE[0]); - complex HxEy = process_dft_component(chunklists, 2, num_freq, cH[1], 0, 0, 0, 0, 0, mode1_data, - mode2_data, cE[1]); + complex ExHy = process_dft_component(chunklists, 2, num_freq, cE[0], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cH[0]); + complex EyHx = process_dft_component(chunklists, 2, num_freq, cE[1], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cH[1]); + complex HyEx = process_dft_component(chunklists, 2, num_freq, cH[0], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cE[0]); + complex HxEy = process_dft_component(chunklists, 2, num_freq, cH[1], 0, 0, 0, 0, 0, + mode1_data, mode2_data, cE[1]); overlaps[0] = ExHy - EyHx; overlaps[1] = HyEx - HxEy; } @@ -1421,16 +1433,16 @@ void fields::get_mode_mode_overlap(void *mode1_data, void *mode2_data, dft_flux /* deregister all of the remaining dft monitors from the fields object. Note that this does not -delete the underlying dft_chunks! (useful for +delete the underlying dft_chunks! (useful for adjoint calculations, where we want to keep the chunk data around) */ void fields::clear_dft_monitors() { -for (int i = 0; i < num_chunks; i++) - if (chunks[i]->is_mine() && chunks[i]->dft_chunks) chunks[i]->dft_chunks = NULL; + for (int i = 0; i < num_chunks; i++) + if (chunks[i]->is_mine() && chunks[i]->dft_chunks) chunks[i]->dft_chunks = NULL; } // return the size of the dft monitor -std::vector fields::dft_monitor_size(dft_fields fdft, const volume &where, component c){ +std::vector fields::dft_monitor_size(dft_fields fdft, const volume &where, component c) { ivec min_corner, max_corner; int rank, reduced_rank; direction dirs[3], reduced_dirs[3]; @@ -1438,14 +1450,18 @@ std::vector fields::dft_monitor_size(dft_fields fdft, const volume &wher dft_chunk *chunklists[1]; chunklists[0] = fdft.chunks; - get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, dims); - reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, reduced_stride); + get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, + dims); + reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, + reduced_stride); std::vector reduced_dims_vec = {reduced_dims[0], reduced_dims[1], reduced_dims[2]}; return reduced_dims_vec; } -std::vector dft_fields::fourier_sourcedata(const volume &where, component c, fields &f, const std::complex* dJ){ +std::vector dft_fields::fourier_sourcedata(const volume &where, component c, + fields &f, + const std::complex *dJ) { const size_t Nfreq = freq.size(); ivec min_corner, max_corner; @@ -1455,9 +1471,12 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher dft_chunk *chunklists[1]; chunklists[0] = chunks; - f.get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, dims); - reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, reduced_stride); - size_t reduced_grid_size = reduced_dims[0]*reduced_dims[1]*reduced_dims[2]; // total number of points in the monitor + f.get_dft_component_dims(chunklists, 1, c, min_corner, max_corner, array_size, bufsz, rank, dirs, + dims); + reduce_array_dimensions(where, rank, dims, dirs, stride, reduced_rank, reduced_dims, reduced_dirs, + reduced_stride); + size_t reduced_grid_size = + reduced_dims[0] * reduced_dims[1] * reduced_dims[2]; // total number of points in the monitor std::vector temp; @@ -1467,12 +1486,13 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher std::vector idx_arr; std::vector > amp_arr; - std::complex EH0 = std::complex(0,0); + std::complex EH0 = std::complex(0, 0); component c = component(f->c); direction cd = component_direction(c); sourcedata temp_struct = {c, idx_arr, f->fc->chunk_idx, amp_arr}; - int position_array[3] = {0, 0, 0}; // array indicating the position of a point relative to the minimum corner of the monitor + int position_array[3] = {0, 0, 0}; // array indicating the position of a point relative to the + // minimum corner of the monitor LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { IVEC_LOOP_LOC(f->fc->gv, x0); @@ -1482,38 +1502,48 @@ std::vector dft_fields::fourier_sourcedata(const volume &wher double dJ_weight = 1; // weight for linear interpolation int nd = 0; - LOOP_OVER_DIRECTIONS(f->fc->gv.dim, d){ - if (where.in_direction(d) > 0) position_array[nd++] = int((ix0.in_direction(d)-min_corner.in_direction(d))/2); - else dJ_weight *= (1-abs(x0.in_direction(d)-where.in_direction_min(d))/(f->fc->gv.inva)); // based on distances + LOOP_OVER_DIRECTIONS(f->fc->gv.dim, d) { + if (where.in_direction(d) > 0) + position_array[nd++] = int((ix0.in_direction(d) - min_corner.in_direction(d)) / 2); + else + dJ_weight *= (1 - abs(x0.in_direction(d) - where.in_direction_min(d)) / + (f->fc->gv.inva)); // based on distances } // index when dJ is flattened to a one-dimenional array - size_t idx_1d = (position_array[0]*reduced_dims[1]+position_array[1])*reduced_dims[2]+position_array[2]; + size_t idx_1d = (position_array[0] * reduced_dims[1] + position_array[1]) * reduced_dims[2] + + position_array[2]; - if (f->avg1==0 && f->avg2==0){ // yee_grid = true + if (f->avg1 == 0 && f->avg2 == 0) { // yee_grid = true temp_struct.idx_arr.push_back(idx); for (size_t i = 0; i < Nfreq; ++i) { - EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]; + EH0 = dJ_weight * dJ[reduced_grid_size * i + idx_1d]; if (is_electric(c)) EH0 *= -1; - if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; - if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; + if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) + EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; + if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) + EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); } } - else{ // yee_grid = false - // four or two neighbouring points in the yee lattice are involved in calculating the value at the center of a voxel - ptrdiff_t site_ind[4] = {idx,idx + f->avg1,idx + f->avg2,idx + f->avg1 + f->avg2}; - for (size_t j = 0; j < 4; ++j){ + else { // yee_grid = false + // four or two neighbouring points in the yee lattice are involved in calculating the value + // at the center of a voxel + ptrdiff_t site_ind[4] = {idx, idx + f->avg1, idx + f->avg2, idx + f->avg1 + f->avg2}; + for (size_t j = 0; j < 4; ++j) { temp_struct.idx_arr.push_back(site_ind[j]); for (size_t i = 0; i < Nfreq; ++i) { - EH0 = dJ_weight*dJ[reduced_grid_size*i+idx_1d]*0.25; // split the amplitude of the adjoint source into four parts + EH0 = dJ_weight * dJ[reduced_grid_size * i + idx_1d] * + 0.25; // split the amplitude of the adjoint source into four parts if (is_electric(c)) EH0 *= -1; - if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; - if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; + if (is_D(c) && f->fc->s->chi1inv[c - Dx + Ex][cd]) + EH0 /= -f->fc->s->chi1inv[c - Dx + Ex][cd][idx]; + if (is_B(c) && f->fc->s->chi1inv[c - Bx + Hx][cd]) + EH0 /= f->fc->s->chi1inv[c - Bx + Hx][cd][idx]; EH0 /= f->S.multiplicity(ix0); temp_struct.amp_arr.push_back(EH0); diff --git a/src/dft_ldos.cpp b/src/dft_ldos.cpp index f55363e45..e6f994b0f 100644 --- a/src/dft_ldos.cpp +++ b/src/dft_ldos.cpp @@ -66,8 +66,8 @@ double *dft_ldos::ldos() { // overall scale factor double Jsum_all = sum_to_all(Jsum); saved_overall_scale = 4.0 / pi // from definition of LDOS comparison to power - * -0.5 // power = -1/2 Re[E* J] - / (Jsum_all * Jsum_all); // normalize to unit-integral current + * -0.5 // power = -1/2 Re[E* J] + / (Jsum_all * Jsum_all); // normalize to unit-integral current const size_t Nfreq = freq.size(); double *sum = new double[Nfreq]; @@ -113,34 +113,34 @@ void dft_ldos::update(fields &f) { if (fr && fi) // complex E for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); EJ += complex(fr[idx], fi[idx]) * conj(A); Jsum += abs(A); } else if (fr) { // E is purely real for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); EJ += double(fr[idx]) * conj(A); Jsum += abs(A); } } } - for (const src_vol& sv : f.chunks[ic]->get_sources(B_stuff)) { + for (const src_vol &sv : f.chunks[ic]->get_sources(B_stuff)) { component c = direction_component(Hx, component_direction(sv.c)); realnum *fr = f.chunks[ic]->f[c][0]; realnum *fi = f.chunks[ic]->f[c][1]; if (fr && fi) // complex H for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); HJ += complex(fr[idx], fi[idx]) * conj(A); Jsum += abs(A); } else if (fr) { // H is purely real for (size_t j = 0; j < sv.num_points(); j++) { const ptrdiff_t idx = sv.index_at(j); - const complex& A = sv.amplitude_at(j); + const complex &A = sv.amplitude_at(j); HJ += double(fr[idx]) * conj(A); Jsum += abs(A); } diff --git a/src/fields.cpp b/src/fields.cpp index 966d5e5b1..b9f43f338 100644 --- a/src/fields.cpp +++ b/src/fields.cpp @@ -59,8 +59,8 @@ fields::fields(structure *s, double m, double beta, bool zero_fields_near_cylori typedef fields_chunk *fields_chunk_ptr; chunks = new fields_chunk_ptr[num_chunks]; for (int i = 0; i < num_chunks; i++) - chunks[i] = new fields_chunk(s->chunks[i], outdir, m, beta, - zero_fields_near_cylorigin, i, loop_tile_base_db); + chunks[i] = new fields_chunk(s->chunks[i], outdir, m, beta, zero_fields_near_cylorigin, i, + loop_tile_base_db); FOR_FIELD_TYPES(ft) { typedef realnum *realnum_ptr; comm_blocks[ft] = new realnum_ptr[num_chunks * num_chunks]; @@ -188,9 +188,7 @@ fields_chunk::~fields_chunk() { delete dft_chunks; dft_chunks = nxt; } - FOR_FIELD_TYPES(ft) { - delete[] zeroes[ft]; - } + FOR_FIELD_TYPES(ft) { delete[] zeroes[ft]; } FOR_FIELD_TYPES(ft) { for (polarization_state *cur = pol[ft]; cur;) { polarization_state *p = cur; @@ -229,10 +227,13 @@ void check_tiles(grid_volume gv, const std::vector &gvs) { if (gvs[i].intersect_with(gvs[j], &vol_intersection)) meep::abort("gvs[%zu] intersects with gvs[%zu]\n", i, j); size_t sum = 0; - for (const auto& sub_gv : gvs) { sum += sub_gv.nowned_min(); } + for (const auto &sub_gv : gvs) { + sum += sub_gv.nowned_min(); + } size_t v_grid_points = 1; LOOP_OVER_DIRECTIONS(gv.dim, d) { v_grid_points *= gv.num_direction(d); } - if (sum != v_grid_points) meep::abort("v_grid_points = %zu, sum(tiles) = %zu\n", v_grid_points, sum); + if (sum != v_grid_points) + meep::abort("v_grid_points = %zu, sum(tiles) = %zu\n", v_grid_points, sum); } fields_chunk::fields_chunk(structure_chunk *the_s, const char *od, double m, double beta, @@ -252,9 +253,8 @@ fields_chunk::fields_chunk(structure_chunk *the_s, const char *od, double m, dou if (loop_tile_base_db > 0) { split_into_tiles(gv, &gvs_tiled, loop_tile_base_db); check_tiles(gv, gvs_tiled); - } else { - gvs_tiled.push_back(gv); } + else { gvs_tiled.push_back(gv); } FOR_FIELD_TYPES(ft) { polarization_state *cur = NULL; pol[ft] = NULL; @@ -268,9 +268,7 @@ fields_chunk::fields_chunk(structure_chunk *the_s, const char *od, double m, dou cur->next = p; cur = p; } - else { - pol[ft] = cur = p; - } + else { pol[ft] = cur = p; } } } doing_solve_cw = false; @@ -311,9 +309,7 @@ fields_chunk::fields_chunk(const fields_chunk &thef, int chunkidx) : gv(thef.gv) dt = thef.dt; dft_chunks = NULL; gvs_tiled = thef.gvs_tiled; - FOR_FIELD_TYPES(ft) { - gvs_eh[ft] = thef.gvs_eh[ft]; - } + FOR_FIELD_TYPES(ft) { gvs_eh[ft] = thef.gvs_eh[ft]; } FOR_FIELD_TYPES(ft) { polarization_state *cur = NULL; for (polarization_state *ocur = thef.pol[ft]; ocur; ocur = ocur->next) { @@ -327,9 +323,7 @@ fields_chunk::fields_chunk(const fields_chunk &thef, int chunkidx) : gv(thef.gv) cur->next = p; cur = p; } - else { - pol[ft] = cur = p; - } + else { pol[ft] = cur = p; } } } doing_solve_cw = thef.doing_solve_cw; @@ -505,7 +499,7 @@ void fields::require_source_components() { memset(needed, 0, sizeof(needed)); for (int i = 0; i < num_chunks; i++) { FOR_FIELD_TYPES(ft) { - for (const auto& src : chunks[i]->get_sources(ft)) { + for (const auto &src : chunks[i]->get_sources(ft)) { needed[src.c] = 1; } } @@ -517,8 +511,7 @@ void fields::require_source_components() { bool aniso2d = is_aniso2d(); for (int c = 0; c < NUM_FIELD_COMPONENTS; ++c) - if (allneeded[c]) - _require_component(component(c), aniso2d); + if (allneeded[c]) _require_component(component(c), aniso2d); sync_chunk_connections(); } @@ -546,7 +539,8 @@ bool fields::is_aniso2d() { void fields::_require_component(component c, bool aniso2d) { if (!gv.has_field(c)) - meep::abort("cannot require a %s component in a %s grid", component_name(c), dimension_name(gv.dim)); + meep::abort("cannot require a %s component in a %s grid", component_name(c), + dimension_name(gv.dim)); components_allocated = true; @@ -567,9 +561,8 @@ void fields::_require_component(component c, bool aniso2d) { } void fields_chunk::add_source(field_type ft, src_vol &&src) { - auto it = std::find_if(sources[ft].begin(), sources[ft].end(), [&src](const src_vol &other) { - return src_vol::combinable(src, other); - }); + auto it = std::find_if(sources[ft].begin(), sources[ft].end(), + [&src](const src_vol &other) { return src_vol::combinable(src, other); }); if (it != sources[ft].end()) { it->add_amplitudes_from(src); @@ -580,9 +573,7 @@ void fields_chunk::add_source(field_type ft, src_vol &&src) { } void fields_chunk::remove_sources() { - FOR_FIELD_TYPES(ft) { - sources[ft].clear(); - } + FOR_FIELD_TYPES(ft) { sources[ft].clear(); } } void fields::remove_sources() { @@ -674,7 +665,8 @@ void fields_chunk::use_real_fields() { int fields::phase_in_material(const structure *snew, double time) { if (snew->num_chunks != num_chunks) - meep::abort("Can only phase in similar sets of chunks: %d vs %d\n", snew->num_chunks, num_chunks); + meep::abort("Can only phase in similar sets of chunks: %d vs %d\n", snew->num_chunks, + num_chunks); for (int i = 0; i < num_chunks; i++) if (chunks[i]->is_mine()) chunks[i]->phase_in_material(snew->chunks[i]); phasein_time = (int)(time / dt); @@ -728,7 +720,7 @@ void fields::unset_solve_cw_omega() { chunks[i]->unset_solve_cw_omega(); } -void fields::log(const char* prefix) { +void fields::log(const char *prefix) { master_printf("%sFields State:\n", prefix); master_printf("%s a = %g, dt = %g\n", prefix, a, dt); master_printf("%s m = %g, beta = %g\n", prefix, m, beta); @@ -742,11 +734,8 @@ int mirrorindex(int i, int n) { return i >= n ? 2 * n - 1 - i : (i < 0 ? -1 - i /* map the cell coordinates into the range [0,1]. anything outside [0,1] is *mirror* reflected into [0,1] */ -void map_coordinates(double rx, double ry, double rz, - int nx, int ny, int nz, - int &x1, int &y1, int &z1, - int &x2, int &y2, int &z2, - double &dx, double &dy, double &dz, +void map_coordinates(double rx, double ry, double rz, int nx, int ny, int nz, int &x1, int &y1, + int &z1, int &x2, int &y2, int &z2, double &dx, double &dy, double &dz, bool do_fabs) { /* mirror boundary conditions for r just beyond the boundary */ @@ -775,19 +764,16 @@ void map_coordinates(double rx, double ry, double rz, dy = fabs(dy); dz = fabs(dz); } - } /* linearly interpolate a given point in a 3d grid of data. */ -double linear_interpolate(double rx, double ry, double rz, double *data, - int nx, int ny, int nz, int stride) { +double linear_interpolate(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride) { int x1, y1, z1, x2, y2, z2; double dx, dy, dz; - map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz); + map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz); /* define a macro to give us data(x,y,z) on the grid, in row-major order (the order used by HDF5): */ diff --git a/src/fields_dump.cpp b/src/fields_dump.cpp index fe704a393..34bac631a 100644 --- a/src/fields_dump.cpp +++ b/src/fields_dump.cpp @@ -58,9 +58,7 @@ void fields::dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, for (int c = 0; c < NUM_FIELD_COMPONENTS; ++c) { for (int d = 0; d < 2; ++d) { realnum **f = field_ptr_getter(chunks[i], c, d); - if (*f) { - my_ntot += (num_f_[(chunk_i * NUM_FIELD_COMPONENTS + c) * 2 + d] = ntot); - } + if (*f) { my_ntot += (num_f_[(chunk_i * NUM_FIELD_COMPONENTS + c) * 2 + d] = ntot); } } } } @@ -71,9 +69,8 @@ void fields::dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, if (single_parallel_file) { num_f.resize(num_f_size); sum_to_master(num_f_.data(), num_f.data(), num_f_size); - } else { - num_f = std::move(num_f_); } + else { num_f = std::move(num_f_); } /* determine total dataset size and offset of this process's data */ size_t my_start = 0; @@ -87,12 +84,11 @@ void fields::dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, size_t start[3] = {0, 0, 0}; std::string num_f_name = std::string("num_") + field_name; h5f->create_data(num_f_name.c_str(), 3, dims); - if (am_master() || !single_parallel_file) { - h5f->write_chunk(3, start, dims, num_f.data()); - } + if (am_master() || !single_parallel_file) { h5f->write_chunk(3, start, dims, num_f.data()); } /* write the data */ - h5f->create_data(field_name.c_str(), 1, &ntotal, false /* append_data */, false /* single_precision */); + h5f->create_data(field_name.c_str(), 1, &ntotal, false /* append_data */, + false /* single_precision */); for (int i = 0; i < num_chunks; i++) { if (chunks[i]->is_mine()) { size_t ntot = chunks[i]->gv.ntot(); @@ -123,18 +119,14 @@ void fields::dump(const char *filename, bool single_parallel_file) { file.create_data("t", 1, dims); if (am_master() || !single_parallel_file) file.write_chunk(1, start, dims, _t); - dump_fields_chunk_field( - &file, single_parallel_file, "f", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); - dump_fields_chunk_field( - &file, single_parallel_file, "f_u", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); - dump_fields_chunk_field( - &file, single_parallel_file, "f_w", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); - dump_fields_chunk_field( - &file, single_parallel_file, "f_cond", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f_u", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f_w", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); + dump_fields_chunk_field(&file, single_parallel_file, "f_cond", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); dump_fields_chunk_field( &file, single_parallel_file, "f_w_prev", [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w_prev[c][d]); }); @@ -184,11 +176,11 @@ void fields::load_fields_chunk_field(h5file *h5f, bool single_parallel_file, size_t n = num_f[(chunk_i * NUM_FIELD_COMPONENTS + c) * 2 + d]; realnum **f = field_ptr_getter(chunks[i], c, d); if (n == 0) { - delete[] *f; + delete[] * f; *f = NULL; - } else { - if (n != ntot) - meep::abort("grid size mismatch %zd vs %zd in fields::load", n, ntot); + } + else { + if (n != ntot) meep::abort("grid size mismatch %zd vs %zd in fields::load", n, ntot); // here we need to allocate the fields array for H in the PML region // because of H = B in fields_chunk::alloc_f whereby H is lazily // allocated in fields_chunk::update_eh during the first timestep @@ -214,10 +206,9 @@ void fields::load_fields_chunk_field(h5file *h5f, bool single_parallel_file, /* read the data */ h5f->read_size(field_name.c_str(), &rank, dims, 1); if (rank != 1 || dims[0] != ntotal) { - meep::abort( - "inconsistent data size for '%s' in fields::load (rank, dims[0]): " - "(%d, %zu) != (1, %zu)", - field_name.c_str(), rank, dims[0], ntotal); + meep::abort("inconsistent data size for '%s' in fields::load (rank, dims[0]): " + "(%d, %zu) != (1, %zu)", + field_name.c_str(), rank, dims[0], ntotal); } for (int i = 0; i < num_chunks; i++) { if (chunks[i]->is_mine()) { @@ -258,18 +249,14 @@ void fields::load(const char *filename, bool single_parallel_file) { t = static_cast(_t[0]); calc_sources(time()); - load_fields_chunk_field( - &file, single_parallel_file, "f", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); - load_fields_chunk_field( - &file, single_parallel_file, "f_u", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); - load_fields_chunk_field( - &file, single_parallel_file, "f_w", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); - load_fields_chunk_field( - &file, single_parallel_file, "f_cond", - [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f_u", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_u[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f_w", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w[c][d]); }); + load_fields_chunk_field(&file, single_parallel_file, "f_cond", + [](fields_chunk *chunk, int c, int d) { return &(chunk->f_cond[c][d]); }); load_fields_chunk_field( &file, single_parallel_file, "f_w_prev", [](fields_chunk *chunk, int c, int d) { return &(chunk->f_w_prev[c][d]); }); @@ -284,4 +271,4 @@ void fields::load(const char *filename, bool single_parallel_file) { } } -} // namespace meep +} // namespace meep diff --git a/src/fix_boundary_sources.cpp b/src/fix_boundary_sources.cpp index f31ad4b28..4676d3362 100644 --- a/src/fix_boundary_sources.cpp +++ b/src/fix_boundary_sources.cpp @@ -22,7 +22,7 @@ static MPI_Comm mycomm = MPI_COMM_WORLD; // data structure for sending source information from one chunk to another struct srcpt_info { std::complex A; // amplitude - ptrdiff_t index; // index in chunk's fields array + ptrdiff_t index; // index in chunk's fields array size_t src_time_id; int chunk_idx; int c; // component @@ -32,13 +32,14 @@ struct srcpt_info { // by (processor, src_time_id, chunk_idx, c) struct srcpt_info_compare { fields_chunk **chunks; - bool operator() (srcpt_info a, srcpt_info b) { + bool operator()(srcpt_info a, srcpt_info b) { int aproc = chunks[a.chunk_idx]->n_proc(); int bproc = chunks[b.chunk_idx]->n_proc(); - return (aproc != bproc ? aproc < bproc : - (a.src_time_id != b.src_time_id ? a.src_time_id < b.src_time_id : - (a.chunk_idx != b.chunk_idx ? a.chunk_idx < b.chunk_idx : - a.c < b.c))); + return (aproc != bproc + ? aproc < bproc + : (a.src_time_id != b.src_time_id + ? a.src_time_id < b.src_time_id + : (a.chunk_idx != b.chunk_idx ? a.chunk_idx < b.chunk_idx : a.c < b.c))); } }; @@ -57,14 +58,17 @@ void fields::fix_boundary_sources() { component c = src.c; ivec here = chunks[i]->gv.iloc(c, src.index_at(ipt)); if (!chunks[i]->gv.owns(here) && src.amplitude(ipt) != 0.0) { - if (src.t()->id == 0) abort("bug: fix_boundary_sources called for non-registered source"); + if (src.t()->id == 0) + abort("bug: fix_boundary_sources called for non-registered source"); // find the chunk that owns this point, similar to logic in boundaries.cpp std::complex thephase; if (locate_component_point(&c, &here, &thephase) && !on_metal_boundary(here)) { for (int j = 0; j < num_chunks; j++) if (chunks[j]->gv.owns(here)) { - srcpt_info s = { src.amplitude(ipt)*conj(thephase), chunks[j]->gv.index(c, here), src.t()->id, chunks[j]->chunk_idx, c }; + srcpt_info s = {src.amplitude(ipt) * conj(thephase), + chunks[j]->gv.index(c, here), src.t()->id, chunks[j]->chunk_idx, + c}; boundarysources.push_back(s); break; } @@ -94,78 +98,84 @@ void fields::fix_boundary_sources() { for (size_t idx = 0; idx < boundarysources.size(); ++idx) { int pidx = chunks[boundarysources[idx].chunk_idx]->n_proc(); if (pidx != p0) { - offsets[p*P + p0] = idx0; - numcomm_[p*P + p0] = idx - idx0; + offsets[p * P + p0] = idx0; + numcomm_[p * P + p0] = idx - idx0; p0 = pidx; idx0 = idx; } } - offsets[p*P + p0] = idx0; - numcomm_[p*P + p0] = boundarysources.size() - idx0; + offsets[p * P + p0] = idx0; + numcomm_[p * P + p0] = boundarysources.size() - idx0; // collect the numcomm data from all processes std::vector numcomm(P * P, size_t(0)); - sum_to_all(numcomm_.data(), numcomm.data(), P*P); + sum_to_all(numcomm_.data(), numcomm.data(), P * P); #ifdef HAVE_MPI // declare an MPI datatype mirroring srcpt_info, so that we can send/receive srcpt_info arrays - int srcpt_info_blocklengths[5] = {2,1,1,1,1}; - MPI_Datatype srcpt_info_types[5] = {MPI_DOUBLE, sizeof(ptrdiff_t) == sizeof(int) ? MPI_INT : MPI_LONG_LONG, sizeof(size_t) == sizeof(unsigned) ? MPI_UNSIGNED : MPI_UNSIGNED_LONG_LONG, MPI_INT, MPI_INT}; - MPI_Aint srcpt_info_offsets[5] = { offsetof(srcpt_info,A), offsetof(srcpt_info,index), offsetof(srcpt_info,src_time_id), offsetof(srcpt_info,chunk_idx), offsetof(srcpt_info,c) }; + int srcpt_info_blocklengths[5] = {2, 1, 1, 1, 1}; + MPI_Datatype srcpt_info_types[5] = { + MPI_DOUBLE, sizeof(ptrdiff_t) == sizeof(int) ? MPI_INT : MPI_LONG_LONG, + sizeof(size_t) == sizeof(unsigned) ? MPI_UNSIGNED : MPI_UNSIGNED_LONG_LONG, MPI_INT, MPI_INT}; + MPI_Aint srcpt_info_offsets[5] = {offsetof(srcpt_info, A), offsetof(srcpt_info, index), + offsetof(srcpt_info, src_time_id), + offsetof(srcpt_info, chunk_idx), offsetof(srcpt_info, c)}; MPI_Datatype mpi_srcpt_info; - MPI_Type_create_struct(5, srcpt_info_blocklengths, srcpt_info_offsets, srcpt_info_types, &mpi_srcpt_info); + MPI_Type_create_struct(5, srcpt_info_blocklengths, srcpt_info_offsets, srcpt_info_types, + &mpi_srcpt_info); MPI_Type_commit(&mpi_srcpt_info); #endif -for (int psrc = 0; psrc < P; ++psrc) - for (int pdest = 0; pdest < P; ++pdest) { - size_t N = numcomm[psrc*P + pdest]; - if (N == 0) continue; - if (pdest == p) { - srcpt_info *srcpts; + for (int psrc = 0; psrc < P; ++psrc) + for (int pdest = 0; pdest < P; ++pdest) { + size_t N = numcomm[psrc * P + pdest]; + if (N == 0) continue; + if (pdest == p) { + srcpt_info *srcpts; #ifdef HAVE_MPI - if (psrc != p) { - srcpts = new srcpt_info[N]; - MPI_Status status; - MPI_Recv(srcpts, N, mpi_srcpt_info, psrc, psrc*P + pdest, mycomm, &status); - } - else + if (psrc != p) { + srcpts = new srcpt_info[N]; + MPI_Status status; + MPI_Recv(srcpts, N, mpi_srcpt_info, psrc, psrc * P + pdest, mycomm, &status); + } + else #endif - srcpts = boundarysources.data() + offsets[psrc*P + pdest]; - int chunk_idx = srcpts[0].chunk_idx; - size_t src_time_id = srcpts[0].src_time_id; - int c = srcpts[0].c; - size_t idx0 = 0; - for (size_t idx = 0; idx <= N; ++idx) { - if (idx == N || srcpts[idx].chunk_idx != chunk_idx || srcpts[idx].src_time_id != src_time_id || srcpts[idx].c != c) { - std::vector idx_arr(idx - idx0); - std::vector > amp_arr(idx - idx0); - for (size_t i = idx0; i < idx; ++i) { - idx_arr[i-idx0] = srcpts[i].index; - amp_arr[i-idx0] = srcpts[i].A; - } - sourcedata srcdata = { (component) c, idx_arr, chunk_idx, amp_arr }; - src_time *srctime = lookup_src_time(src_time_id); - if (srctime == NULL) abort("bug: unknown src_time_id (missing registration?)"); - add_srcdata(srcdata, srctime, size_t(0), NULL, false); - if (idx < N) { - chunk_idx = srcpts[idx].chunk_idx; - src_time_id = srcpts[idx].src_time_id; - c = srcpts[idx].c; - idx0 = idx; + srcpts = boundarysources.data() + offsets[psrc * P + pdest]; + int chunk_idx = srcpts[0].chunk_idx; + size_t src_time_id = srcpts[0].src_time_id; + int c = srcpts[0].c; + size_t idx0 = 0; + for (size_t idx = 0; idx <= N; ++idx) { + if (idx == N || srcpts[idx].chunk_idx != chunk_idx || + srcpts[idx].src_time_id != src_time_id || srcpts[idx].c != c) { + std::vector idx_arr(idx - idx0); + std::vector > amp_arr(idx - idx0); + for (size_t i = idx0; i < idx; ++i) { + idx_arr[i - idx0] = srcpts[i].index; + amp_arr[i - idx0] = srcpts[i].A; + } + sourcedata srcdata = {(component)c, idx_arr, chunk_idx, amp_arr}; + src_time *srctime = lookup_src_time(src_time_id); + if (srctime == NULL) abort("bug: unknown src_time_id (missing registration?)"); + add_srcdata(srcdata, srctime, size_t(0), NULL, false); + if (idx < N) { + chunk_idx = srcpts[idx].chunk_idx; + src_time_id = srcpts[idx].src_time_id; + c = srcpts[idx].c; + idx0 = idx; + } } } - } - if (psrc != p) delete[] srcpts; - } + if (psrc != p) delete[] srcpts; + } #ifdef HAVE_MPI - else if (psrc == p) { - srcpt_info *srcpts = boundarysources.data() + offsets[psrc*P + pdest]; - MPI_Send(srcpts, N, mpi_srcpt_info, pdest, psrc*P + pdest, mycomm); - } + else if (psrc == p) { + srcpt_info *srcpts = boundarysources.data() + offsets[psrc * P + pdest]; + MPI_Send(srcpts, N, mpi_srcpt_info, pdest, psrc * P + pdest, mycomm); + } #endif - } + } #ifdef HAVE_MPI MPI_Type_free(&mpi_srcpt_info); @@ -174,4 +184,4 @@ for (int psrc = 0; psrc < P; ++psrc) finished_working(); } -} // namespace meep \ No newline at end of file +} // namespace meep diff --git a/src/h5file.cpp b/src/h5file.cpp index 0271d064d..3987462d9 100644 --- a/src/h5file.cpp +++ b/src/h5file.cpp @@ -23,7 +23,9 @@ #define CHECK(condition, message) \ do { \ - if (!(condition)) { meep::abort("error on line %d of " __FILE__ ": " message "\n", __LINE__); } \ + if (!(condition)) { \ + meep::abort("error on line %d of " __FILE__ ": " message "\n", __LINE__); \ + } \ } while (0) #include "config.h" @@ -311,7 +313,9 @@ void *h5file::read(const char *dataname, int *rank, size_t *dims, int maxrank, close_data_id = false; } else { - if (!dataset_exists(dataname)) { meep::abort("missing dataset in HDF5 file: %s", dataname); } + if (!dataset_exists(dataname)) { + meep::abort("missing dataset in HDF5 file: %s", dataname); + } data_id = H5Dopen(file_id, dataname); } } @@ -323,9 +327,7 @@ void *h5file::read(const char *dataname, int *rank, size_t *dims, int maxrank, data_id = HID(cur_id); close_data_id = false; } - else { - data_id = H5Dopen(file_id, dname); - } + else { data_id = H5Dopen(file_id, dname); } delete[] dname; } space_id = H5Dget_space(data_id); @@ -346,8 +348,8 @@ void *h5file::read(const char *dataname, int *rank, size_t *dims, int maxrank, data = new float[N]; else data = new double[N]; - H5Dread(data_id, single_precision ? H5T_NATIVE_FLOAT : H5T_NATIVE_DOUBLE, - H5S_ALL, H5S_ALL, H5P_DEFAULT, (void *)data); + H5Dread(data_id, single_precision ? H5T_NATIVE_FLOAT : H5T_NATIVE_DOUBLE, H5S_ALL, H5S_ALL, + H5P_DEFAULT, (void *)data); if (close_data_id) H5Dclose(data_id); } @@ -619,8 +621,8 @@ void h5file::create_or_extend_data(const char *dataname, int rank, const size_t that have no data can be skipped). */ static void _write_chunk(hid_t data_id, h5file::extending_s *cur, int rank, - const size_t *chunk_start, const size_t *chunk_dims, - hid_t datatype, void *data) { + const size_t *chunk_start, const size_t *chunk_dims, hid_t datatype, + void *data) { #ifdef HAVE_HDF5 int i; bool do_write = true; @@ -715,7 +717,8 @@ void h5file::done_writing_chunks() { I'm assuming(?) that non-extensible datasets will use different files, etcetera, for different timesteps. All of this hackery goes away if we just use an MPI-compiled version of HDF5. */ - if (parallel && !local && cur_dataname && get_extending(cur_dataname)) prevent_deadlock(); // closes id + if (parallel && !local && cur_dataname && get_extending(cur_dataname)) + prevent_deadlock(); // closes id } void h5file::write(const char *dataname, int rank, const size_t *dims, void *data, @@ -832,14 +835,12 @@ static void _read_chunk(hid_t data_id, int rank, const size_t *chunk_start, void h5file::read_chunk(int rank, const size_t *chunk_start, const size_t *chunk_dims, float *data) { - _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, - H5T_NATIVE_FLOAT, data); + _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, H5T_NATIVE_FLOAT, data); } void h5file::read_chunk(int rank, const size_t *chunk_start, const size_t *chunk_dims, double *data) { - _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, - H5T_NATIVE_DOUBLE, data); + _read_chunk(HID(cur_id), rank, chunk_start, chunk_dims, H5T_NATIVE_DOUBLE, data); } void h5file::read_chunk(int rank, const size_t *chunk_start, const size_t *chunk_dims, diff --git a/src/integrate.cpp b/src/integrate.cpp index 23fe3bb9e..f9efaaf2e 100644 --- a/src/integrate.cpp +++ b/src/integrate.cpp @@ -234,7 +234,7 @@ double fields::max_abs(int num_fvals, const component *components, field_rfuncti } static complex return_the_field(const complex *fields, const vec &loc, - void *integrand_data_) { + void *integrand_data_) { (void)integrand_data_; (void)loc; // unused return cdouble(fields[0]); diff --git a/src/loop_in_chunks.cpp b/src/loop_in_chunks.cpp index 4c86bd939..a831713b7 100644 --- a/src/loop_in_chunks.cpp +++ b/src/loop_in_chunks.cpp @@ -339,8 +339,8 @@ void compute_boundary_weights(grid_volume gv, const volume &where, ivec &is, ive void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, const volume &where, component cgrid, bool use_symmetry, bool snap_empty_dimensions) { if (coordinate_mismatch(gv.dim, cgrid)) - meep::abort("Invalid fields::loop_in_chunks grid type %s for dimensions %s\n", component_name(cgrid), - dimension_name(gv.dim)); + meep::abort("Invalid fields::loop_in_chunks grid type %s for dimensions %s\n", + component_name(cgrid), dimension_name(gv.dim)); if (where.dim != gv.dim) meep::abort("Invalid dimensions %d for WHERE in fields::loop_in_chunks", where.dim); @@ -359,11 +359,15 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con compute_boundary_weights(gv, where, is, ie, snap_empty_dimensions, s0, e0, s1, e1); int original_vol = 1; - LOOP_OVER_DIRECTIONS(gv.dim, d){ + LOOP_OVER_DIRECTIONS(gv.dim, d) { if (boundaries[High][d] == Periodic && boundaries[Low][d] == Periodic) { - original_vol *= (ie.in_direction(d) - is.in_direction(d))/2+1; - } else { - int vol_size_d = (min(user_volume.big_owned_corner(cgrid), ie).in_direction(d) - max(user_volume.little_owned_corner(cgrid),is).in_direction(d))/2+1; + original_vol *= (ie.in_direction(d) - is.in_direction(d)) / 2 + 1; + } + else { + int vol_size_d = (min(user_volume.big_owned_corner(cgrid), ie).in_direction(d) - + max(user_volume.little_owned_corner(cgrid), is).in_direction(d)) / + 2 + + 1; original_vol *= (vol_size_d > 0 ? vol_size_d : 0); } } @@ -390,9 +394,8 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con min_ishift.set_direction( d, int(floor((where.in_direction_min(d) - gvS.in_direction_max(d)) / L.in_direction(d)))); - max_ishift.set_direction( - d, - int(ceil((where.in_direction_max(d) - gvS.in_direction_min(d)) / L.in_direction(d)))); + max_ishift.set_direction(d, int(ceil((where.in_direction_max(d) - gvS.in_direction_min(d)) / + L.in_direction(d)))); } else { min_ishift.set_direction(d, 0); @@ -417,26 +420,31 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con grid_volume gvu(chunks[i]->gv); ivec _iscoS(S.transform(gvu.little_owned_corner(cS), sn)); ivec _iecoS(S.transform(gvu.big_owned_corner(cS), sn)); - ivec iscoS(max(user_volume.little_owned_corner(cgrid), min(_iscoS, _iecoS))), iecoS(max(_iscoS, _iecoS)); // fix ordering due to to transform - - //With symmetry, the upper half of the original chunk is kept and includes one extra pixel. - //When looped over all symmetries, pixels outside the lower boundary "user_volume.little_owned_corner(cgrid)" is excluded. - //isym finds the lower boundary of the upper half chunk. Then in each direction that chunk isn't the upper, - //checked by ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)), - //the end point iecoS will shift to not go beyond isym + ivec iscoS(max(user_volume.little_owned_corner(cgrid), min(_iscoS, _iecoS))), + iecoS(max(_iscoS, _iecoS)); // fix ordering due to to transform + + // With symmetry, the upper half of the original chunk is kept and includes one extra pixel. + // When looped over all symmetries, pixels outside the lower boundary + // "user_volume.little_owned_corner(cgrid)" is excluded. isym finds the lower boundary of + // the upper half chunk. Then in each direction that chunk isn't the upper, checked by + // ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)), the end point iecoS will + // shift to not go beyond isym ivec isym(gvu.dim, INT_MAX); - LOOP_OVER_DIRECTIONS(gvu.dim, d){ - int off_sym_shift = ((gv.iyee_shift(cgrid).in_direction(d) != 0) ? gv.iyee_shift(cgrid).in_direction(d)+2 : 2); + LOOP_OVER_DIRECTIONS(gvu.dim, d) { + int off_sym_shift = ((gv.iyee_shift(cgrid).in_direction(d) != 0) + ? gv.iyee_shift(cgrid).in_direction(d) + 2 + : 2); isym.set_direction(d, S.i_symmetry_point.in_direction(d) - off_sym_shift); } ivec iscS(max(is - shifti, iscoS)); ivec chunk_corner(gvu.little_owned_corner(cgrid)); LOOP_OVER_DIRECTIONS(gv.dim, d) { - if ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)) iecoS.set_direction(d, min(isym, iecoS).in_direction(d)); - } - ivec iecS( min(ie - shifti, iecoS)); - + if ((S.transform(d, sn).d != d) != (S.transform(d, sn).flipped)) + iecoS.set_direction(d, min(isym, iecoS).in_direction(d)); + } + ivec iecS(min(ie - shifti, iecoS)); + if (iscS <= iecS) { // non-empty intersection // Determine weights at chunk looping boundaries: ivec isc(S.transform(iscS, -sn)), iec(S.transform(iecS, -sn)); @@ -501,15 +509,14 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con } if (gv.dim == Dcyl) { dV1 = dV0 * 2 * pi * gv.inva; - dV0 *= 2 * pi * - fabs((S.transform(chunks[i]->gv[isc], sn) + shift).in_direction(R)); + dV0 *= 2 * pi * fabs((S.transform(chunks[i]->gv[isc], sn) + shift).in_direction(R)); } - chunkloop(chunks[i], i, cS, isc, iec, s0c, s1c, e0c, e1c, dV0, dV1, - shifti, ph, S, sn, chunkloop_data); + chunkloop(chunks[i], i, cS, isc, iec, s0c, s1c, e0c, e1c, dV0, dV1, shifti, ph, S, sn, + chunkloop_data); int loop_vol = 1; - LOOP_OVER_DIRECTIONS(gv.dim, d){ - loop_vol *= (iec.in_direction(d) - isc.in_direction(d))/2+1; + LOOP_OVER_DIRECTIONS(gv.dim, d) { + loop_vol *= (iec.in_direction(d) - isc.in_direction(d)) / 2 + 1; } vol_sum += loop_vol; } @@ -525,7 +532,9 @@ void fields::loop_in_chunks(field_chunkloop chunkloop, void *chunkloop_data, con } while (ishift != min_ishift); } int vol_sum_all = sum_to_all(vol_sum); - if (use_symmetry && vol_sum_all != original_vol) master_printf("WARNING vol mismatch:, original_vol %i, looped vol_sum %i \n", original_vol, vol_sum_all); + if (use_symmetry && vol_sum_all != original_vol) + master_printf("WARNING vol mismatch:, original_vol %i, looped vol_sum %i \n", original_vol, + vol_sum_all); } } // namespace meep diff --git a/src/material_data.cpp b/src/material_data.cpp index 774f2a3d3..a24d87cee 100644 --- a/src/material_data.cpp +++ b/src/material_data.cpp @@ -32,24 +32,14 @@ bool transition::operator==(const transition &other) const { bool transition::operator!=(const transition &other) const { return !(*this == other); } -medium_struct::medium_struct(double epsilon) : - epsilon_diag{epsilon, epsilon, epsilon}, - epsilon_offdiag{}, - mu_diag{1, 1, 1}, - mu_offdiag{}, - E_susceptibilities(), H_susceptibilities(), - E_chi2_diag{}, - E_chi3_diag{}, - H_chi2_diag{}, - H_chi3_diag{}, - D_conductivity_diag{}, - B_conductivity_diag{} - {} +medium_struct::medium_struct(double epsilon) + : epsilon_diag{epsilon, epsilon, epsilon}, epsilon_offdiag{}, mu_diag{1, 1, 1}, mu_offdiag{}, + E_susceptibilities(), H_susceptibilities(), E_chi2_diag{}, E_chi3_diag{}, H_chi2_diag{}, + H_chi3_diag{}, D_conductivity_diag{}, B_conductivity_diag{} {} void medium_struct::check_offdiag_im_zero_or_abort() const { - if (epsilon_offdiag.x.im != 0 || epsilon_offdiag.y.im != 0 || - epsilon_offdiag.z.im != 0 || mu_offdiag.x.im != 0 || mu_offdiag.y.im != 0 || - mu_offdiag.z.im != 0) { + if (epsilon_offdiag.x.im != 0 || epsilon_offdiag.y.im != 0 || epsilon_offdiag.z.im != 0 || + mu_offdiag.x.im != 0 || mu_offdiag.y.im != 0 || mu_offdiag.z.im != 0) { meep::abort("Found non-zero imaginary part of epsilon or mu offdiag.\n"); } } @@ -92,4 +82,4 @@ void material_data::copy_from(const material_data &from) { material_type_list::material_type_list() : items(NULL), num_items(0) {} -} // namespace meep_geom +} // namespace meep_geom diff --git a/src/material_data.hpp b/src/material_data.hpp index f81496c29..4e1df20d5 100644 --- a/src/material_data.hpp +++ b/src/material_data.hpp @@ -136,7 +136,7 @@ struct material_data { double beta; double eta; double damping; - bool trivial=true; + bool trivial = true; /* There are several possible scenarios when material grids overlap -- these different scenarios enable different applications. @@ -163,7 +163,7 @@ struct material_data { material_data(); - void copy_from(const material_data& from); + void copy_from(const material_data &from); }; typedef material_data *material_type; diff --git a/src/meep.hpp b/src/meep.hpp index 0dc5b5227..f5a721e81 100644 --- a/src/meep.hpp +++ b/src/meep.hpp @@ -394,7 +394,8 @@ class h5file { // If 'local_' is true, then 'parallel_' *must* be false and assumes that // each process is writing to a local non-shared file and the filename is // unique to the process. - h5file(const char *filename_, access_mode m = READWRITE, bool parallel_ = true, bool local_ = false); + h5file(const char *filename_, access_mode m = READWRITE, bool parallel_ = true, + bool local_ = false); ~h5file(); // closes the files (and any open dataset) bool ok(); @@ -729,8 +730,8 @@ enum in_or_out { Incoming = 0, Outgoing }; const std::initializer_list all_in_or_out{Incoming, Outgoing}; enum connect_phase { CONNECT_PHASE = 0, CONNECT_NEGATE = 1, CONNECT_COPY = 2 }; -const std::initializer_list all_connect_phases{ - CONNECT_PHASE, CONNECT_NEGATE, CONNECT_COPY}; +const std::initializer_list all_connect_phases{CONNECT_PHASE, CONNECT_NEGATE, + CONNECT_COPY}; constexpr int NUM_CONNECT_PHASE_TYPES = 3; // Communication pair(i, j) implies data being sent from i to j. @@ -768,7 +769,7 @@ struct comms_operation { ptrdiff_t my_chunk_idx; ptrdiff_t other_chunk_idx; int other_proc_id; - int pair_idx; // The numeric pair index used in many communications related arrays. + int pair_idx; // The numeric pair index used in many communications related arrays. size_t transfer_size; in_or_out comm_direction; int tag; @@ -787,7 +788,7 @@ struct comms_sequence { // RAII based comms_manager that allows asynchronous send and receive functions to be initiated. // Upon destruction, the comms_manager waits for completion of all enqueued operations. class comms_manager { - public: +public: using receive_callback = std::function; virtual ~comms_manager() {} virtual void send_real_async(const void *buf, size_t count, int dest, int tag) = 0; @@ -799,7 +800,6 @@ class comms_manager { // Factory function for `comms_manager`. std::unique_ptr create_comms_manager(); - class structure { public: structure_chunk **chunks; @@ -867,13 +867,12 @@ class structure { // file after computing their respective offsets into this file. When set to // 'false', each process writes data for the chunks it owns to a separate // (process unique) file. - void dump(const char *filename, bool single_parallel_file=true); - void load(const char *filename, bool single_parallel_file=true); + void dump(const char *filename, bool single_parallel_file = true); + void load(const char *filename, bool single_parallel_file = true); void dump_chunk_layout(const char *filename); void load_chunk_layout(const char *filename, boundary_region &br); - void load_chunk_layout(const std::vector &gvs, - const std::vector &ids, + void load_chunk_layout(const std::vector &gvs, const std::vector &ids, boundary_region &br); // monitor.cpp @@ -908,7 +907,8 @@ class structure { void changing_chunks(); // Helper methods for dumping and loading susceptibilities void set_chiP_from_file(h5file *file, const char *dataset, field_type ft); - void write_susceptibility_params(h5file *file, bool single_parallel_file, const char *dname, int EorH); + void write_susceptibility_params(h5file *file, bool single_parallel_file, const char *dname, + int EorH); std::unique_ptr bp; }; @@ -918,10 +918,8 @@ std::unique_ptr choose_chunkdivision(grid_volume &gv, volume & const symmetry &s); // defined in structure_dump.cpp -void split_by_binarytree(grid_volume gvol, - std::vector &result_gvs, - std::vector &result_ids, - const binary_partition *bp); +void split_by_binarytree(grid_volume gvol, std::vector &result_gvs, + std::vector &result_ids, const binary_partition *bp); class src_vol; class fields; class fields_chunk; @@ -939,8 +937,8 @@ class src_time { // sources are not, but this may change. bool is_integrated; - // a unique ID > 0 can be assigned to a src_time object by fields::register_src_time, - // in order to communicate it from one process to another; otherwise defaults to 0. + // a unique ID > 0 can be assigned to a src_time object by fields::register_src_time, + // in order to communicate it from one process to another; otherwise defaults to 0. size_t id; src_time() { @@ -1212,14 +1210,18 @@ class dft_chunk { int vc; // component descriptor from the original volume - private: - int decimation_factor; +private: + int decimation_factor; }; -void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); -void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); -void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); -void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, bool single_parallel_file=true); +void save_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); +void load_dft_hdf5(dft_chunk *dft_chunks, component c, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); +void save_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); +void load_dft_hdf5(dft_chunk *dft_chunks, const char *name, h5file *file, const char *dprefix = 0, + bool single_parallel_file = true); // dft.cpp (normally created with fields::add_dft_flux) class dft_flux { @@ -1327,7 +1329,6 @@ class dft_force { volume where; }; - struct sourcedata { component near_fd_comp; std::vector idx_arr; @@ -1335,7 +1336,6 @@ struct sourcedata { std::vector > amp_arr; }; - // near2far.cpp (normally created with fields::add_dft_near2far) class dft_near2far { public: @@ -1392,7 +1392,8 @@ class dft_near2far { int periodic_n[2]; double periodic_k[2], period[2]; - std::vector near_sourcedata(const vec &x_0, double* farpt_list, size_t nfar_pts, const std::complex* dJ); + std::vector near_sourcedata(const vec &x_0, double *farpt_list, size_t nfar_pts, + const std::complex *dJ); }; /* Class to compute local-density-of-states spectra: the power spectrum @@ -1412,17 +1413,17 @@ class dft_ldos { } void update(fields &f); // to be called after each timestep - double *ldos(); // returns array of Nomega values (after last timestep) + double *ldos(); // returns array of Nomega values (after last timestep) std::complex *F() const; // returns Fdft std::complex *J() const; // returns Jdft std::vector freq; double overall_scale() const { return saved_overall_scale; } - + private: - std::complex *Fdft; // Nomega array of field * J*(x) DFT values - std::complex *Jdft; // Nomega array of J(t) DFT values - double Jsum; // sum of |J| over all points - double saved_overall_scale; // saved overall scale for adjoint calculation + std::complex *Fdft; // Nomega array of field * J*(x) DFT values + std::complex *Jdft; // Nomega array of J(t) DFT values + double Jsum; // sum of |J| over all points + double saved_overall_scale; // saved overall scale for adjoint calculation }; // dft.cpp (normally created with fields::add_dft_fields) @@ -1431,7 +1432,8 @@ class dft_fields { dft_fields(dft_chunk *chunks, double freq_min, double freq_max, int Nfreq, const volume &where); dft_fields(dft_chunk *chunks, const std::vector &freq_, const volume &where); dft_fields(dft_chunk *chunks, const double *freq_, size_t Nfreq, const volume &where); - std::vector fourier_sourcedata(const volume &where, component c, fields &f, const std::complex* dJ); + std::vector fourier_sourcedata(const volume &where, component c, fields &f, + const std::complex *dJ); void scale_dfts(std::complex scale); void remove(); @@ -1441,7 +1443,6 @@ class dft_fields { volume where; }; - // data for each susceptibility typedef struct polarization_state_s { void *data; // internal polarization data for the susceptibility @@ -1618,7 +1619,7 @@ enum time_sink { BoundarySteppingPE, BoundarySteppingE }; -using time_sink_to_duration_map = std::unordered_map>; +using time_sink_to_duration_map = std::unordered_map >; // RAII-based profiling timer that accumulates wall time from creation until it // is destroyed or the `exit` method is invoked. Not thread-safe. @@ -1633,7 +1634,7 @@ class timing_scope { void exit(); private: - time_sink_to_duration_map *timers; // Not owned by us. + time_sink_to_duration_map *timers; // Not owned by us. time_sink sink; bool active; double t_start; @@ -1685,26 +1686,26 @@ class diffractedplanewave { std::complex get_p() const { return p; }; private: - int g[3]; // diffraction order - double axis[3]; // axis vector - std::complex s; // s polarization amplitude - std::complex p; // p polarization ampiltude + int g[3]; // diffraction order + double axis[3]; // axis vector + std::complex s; // s polarization amplitude + std::complex p; // p polarization ampiltude }; class gaussianbeam { public: - gaussianbeam(const vec &x0, const vec &kdir, double w0, double freq, - double eps, double mu, std::complex E0[3]); + gaussianbeam(const vec &x0, const vec &kdir, double w0, double freq, double eps, double mu, + std::complex E0[3]); void get_fields(std::complex *EH, const vec &x) const; std::complex get_E0(int n) const { return E0[n]; }; private: - vec x0; // beam center - vec kdir; // beam propagation direction - double w0; // beam waist radius - double freq; // beam frequency - double eps, mu; // permittivity/permeability of homogeneous medium + vec x0; // beam center + vec kdir; // beam propagation direction + double w0; // beam waist radius + double freq; // beam frequency + double eps, mu; // permittivity/permeability of homogeneous medium std::complex E0[3]; // polarization vector }; @@ -1749,7 +1750,7 @@ class fields { void remove_susceptibilities(); void remove_fluxes(); void reset(); - void log(const char* prefix = ""); + void log(const char *prefix = ""); // time.cpp std::vector time_spent_on(time_sink sink); @@ -1772,8 +1773,8 @@ class fields { // file after computing their respective offsets into this file. When set to // 'false', each process writes data for the chunks it owns to a separate // (process unique) file. - void dump(const char *filename, bool single_parallel_file=true); - void load(const char *filename, bool single_parallel_file=true); + void dump(const char *filename, bool single_parallel_file = true); + void load(const char *filename, bool single_parallel_file = true); // h5fields.cpp: // low-level function: @@ -1830,15 +1831,13 @@ class fields { // must eventually be caller-deallocated via delete[]. realnum *get_array_slice(const volume &where, std::vector components, field_rfunction rfun, void *fun_data, realnum *slice = 0, - double frequency = 0, - bool snap = false); + double frequency = 0, bool snap = false); std::complex *get_complex_array_slice(const volume &where, std::vector components, field_function fun, void *fun_data, std::complex *slice = 0, - double frequency = 0, - bool snap = false); + double frequency = 0, bool snap = false); // alternative entry points for when you have no field // function, i.e. you want just a single component or @@ -1849,8 +1848,7 @@ class fields { double frequency = 0, bool snap = false); std::complex *get_complex_array_slice(const volume &where, component c, std::complex *slice = 0, - double frequency = 0, - bool snap = false); + double frequency = 0, bool snap = false); // like get_array_slice, but for *sources* instead of fields std::complex *get_source_slice(const volume &where, component source_slice_component, @@ -1861,7 +1859,8 @@ class fields { field_function fun, field_rfunction rfun, void *fun_data, void *vslice, double frequency = 0, bool snap = false); - /* fetch and return coordinates and integration weights of grid points covered by an array slice, */ + /* fetch and return coordinates and integration weights of grid points covered by an array slice, + */ /* packed into a vector with format [NX, xtics[:], NY, ytics[:], NZ, ztics[:], weights[:] ] */ std::vector get_array_metadata(const volume &where); @@ -1903,8 +1902,12 @@ class fields { bool is_aniso2d(); void require_source_components(); void _require_component(component c, bool aniso2d); - void require_component(component c) { _require_component(c, is_aniso2d()); sync_chunk_connections(); } - void add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, std::complex* amp_arr, bool needs_boundary_fix); + void require_component(component c) { + _require_component(c, is_aniso2d()); + sync_chunk_connections(); + } + void add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, + std::complex *amp_arr, bool needs_boundary_fix); void register_src_time(src_time *src); src_time *lookup_src_time(size_t id); @@ -1923,8 +1926,7 @@ class fields { const volume &eig_vol, int band_num, const vec &kpoint, bool match_frequency, int parity, double eig_resolution, double eigensolver_tol, std::complex amp, - std::complex A(const vec &) = 0, - diffractedplanewave *dp = 0); + std::complex A(const vec &) = 0, diffractedplanewave *dp = 0); void get_eigenmode_coefficients(dft_flux flux, const volume &eig_vol, int *bands, int num_bands, int parity, double eig_resolution, double eigensolver_tol, @@ -1994,7 +1996,7 @@ class fields { bool sqrt_dV_and_interp_weights = false, std::complex extra_weight = 1.0, bool use_centered_grid = true, int vc = 0, int decimation_factor = 0, bool persist = false); - dft_chunk *add_dft(component c, const volume &where, const std::vector& freq, + dft_chunk *add_dft(component c, const volume &where, const std::vector &freq, bool include_dV_and_interp_weights = true, std::complex stored_weight = 1.0, dft_chunk *chunk_next = 0, bool sqrt_dV_and_interp_weights = false, @@ -2066,15 +2068,15 @@ class fields { } dft_flux add_mode_monitor(direction d, const volume &where, const std::vector &freq, bool centered_grid = true, int decimation_factor = 0) { - return add_mode_monitor(d, where, freq.data(), freq.size(), centered_grid, - decimation_factor); + return add_mode_monitor(d, where, freq.data(), freq.size(), centered_grid, decimation_factor); } dft_flux add_mode_monitor(direction d, const volume &where, const double *freq, size_t Nfreq, bool centered_grid = true, int decimation_factor = 0); dft_fields add_dft_fields(component *components, int num_components, const volume where, double freq_min, double freq_max, int Nfreq, - bool use_centered_grid = true, int decimation_factor = 0, bool persist = false) { + bool use_centered_grid = true, int decimation_factor = 0, + bool persist = false) { return add_dft_fields(components, num_components, where, linspace(freq_min, freq_max, Nfreq), use_centered_grid, decimation_factor, persist); } @@ -2162,8 +2164,7 @@ class fields { } dft_near2far add_dft_near2far(const volume_list *where, const std::vector &freq, int decimation_factor = 0, int Nperiods = 1) { - return add_dft_near2far(where, freq.data(), freq.size(), decimation_factor, - Nperiods); + return add_dft_near2far(where, freq.data(), freq.size(), decimation_factor, Nperiods); } dft_near2far add_dft_near2far(const volume_list *where, const double *freq, size_t Nfreq, int decimation_factor = 0, int Nperiods = 1); @@ -2183,7 +2184,10 @@ class fields { std::complex get_field(component c, const vec &loc, bool parallel = true) const; double get_field(derived_component c, const vec &loc, bool parallel = true) const; std::vector dft_monitor_size(dft_fields fdft, const volume &where, component c); - void get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, ivec &min_corner, ivec &max_corner, size_t &array_size, size_t &bufsz, int &rank, direction *ds, size_t *dims, int *array_rank=0, size_t *array_dims=0, direction *array_dirs=0); + void get_dft_component_dims(dft_chunk **chunklists, int num_chunklists, component c, + ivec &min_corner, ivec &max_corner, size_t &array_size, size_t &bufsz, + int &rank, direction *ds, size_t *dims, int *array_rank = 0, + size_t *array_dims = 0, direction *array_dirs = 0); // energy_and_flux.cpp void synchronize_magnetic_fields(); @@ -2237,7 +2241,8 @@ class fields { void figure_out_step_plan(); // boundaries.cpp bool chunk_connections_valid; - bool changed_materials; // keep track of whether materials have changed (in case field chunk connections need sync'ing) + bool changed_materials; // keep track of whether materials have changed (in case field chunk + // connections need sync'ing) void find_metals(); void disconnect_chunks(); void connect_chunks(); @@ -2265,9 +2270,9 @@ class fields { // Helper methods for dumping field chunks. using FieldPtrGetter = std::function; void dump_fields_chunk_field(h5file *h5f, bool single_parallel_file, - const std::string& field_name, FieldPtrGetter field_ptr_getter); + const std::string &field_name, FieldPtrGetter field_ptr_getter); void load_fields_chunk_field(h5file *h5f, bool single_parallel_file, - const std::string& field_name, FieldPtrGetter field_ptr_getter); + const std::string &field_name, FieldPtrGetter field_ptr_getter); public: // monitor.cpp @@ -2297,19 +2302,19 @@ class fields { // The sequence of send and receive operations for each field type. comms_sequence comms_sequence_for_field[NUM_FIELD_TYPES]; - size_t get_comm_size(const comms_key& key) const { + size_t get_comm_size(const comms_key &key) const { auto it = comm_sizes.find(key); return (it != comm_sizes.end()) ? it->second : 0; } - size_t comm_size_tot(field_type ft, const chunk_pair& pair) const { + size_t comm_size_tot(field_type ft, const chunk_pair &pair) const { size_t sum = 0; for (auto ip : all_connect_phases) sum += get_comm_size({ft, ip, pair}); return sum; } - int chunk_pair_to_index(const chunk_pair& pair) const { + int chunk_pair_to_index(const chunk_pair &pair) const { return pair.first + num_chunks * pair.second; } }; @@ -2393,8 +2398,8 @@ std::complex eigenmode_amplitude(void *vedata, const vec &p, component c double get_group_velocity(void *vedata); vec get_k(void *vedata); -double linear_interpolate(double rx, double ry, double rz, double *data, - int nx, int ny, int nz, int stride); +double linear_interpolate(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride); // Value class that combines split direction and position. struct split_plane { @@ -2413,17 +2418,17 @@ class binary_partition { // Takes ownership of `left_tree` and `right_tree`. binary_partition(const split_plane &_split_plane, std::unique_ptr &&left_tree, std::unique_ptr &&right_tree); - binary_partition(const binary_partition& other); + binary_partition(const binary_partition &other); bool is_leaf() const; // Returns the leaf node ID iff is_leaf() == true. int get_proc_id() const; // Returns the split plane iff is_leaf() == false. - const split_plane& get_plane() const; + const split_plane &get_plane() const; // Returns a pointer to the left subtree node iff is_leaf() == false. - const binary_partition* left_tree() const; + const binary_partition *left_tree() const; // Returns a pointer to the right subtree node iff is_leaf() == false. - const binary_partition* right_tree() const; + const binary_partition *right_tree() const; private: int proc_id; diff --git a/src/meep/vec.hpp b/src/meep/vec.hpp index 4996b2dc7..461611166 100644 --- a/src/meep/vec.hpp +++ b/src/meep/vec.hpp @@ -148,7 +148,6 @@ component first_field_component(field_type ft); for (meep::direction d = dim == meep::Dcyl ? meep::Z : meep::X; \ d < (dim == meep::Dcyl ? meep::NO_DIRECTION : meep::R); d = meep::direction(d + 1)) - #define LOOP_OVER_IVECS(gv, is, ie, idx) \ for (ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ loop_is3 = (is).yucky_val(2), loop_n1 = ((ie).yucky_val(0) - loop_is1) / 2 + 1, \ @@ -200,7 +199,7 @@ component first_field_component(field_type ft); // the most generic use case where the user // can specify a custom clause #define PLOOP_OVER_IVECS_C(gv, is, ie, idx, clause) \ -for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ + for (ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ loop_is3 = (is).yucky_val(2), loop_n1 = ((ie).yucky_val(0) - loop_is1) / 2 + 1, \ loop_n2 = ((ie).yucky_val(1) - loop_is2) / 2 + 1, \ loop_n3 = ((ie).yucky_val(2) - loop_is3) / 2 + 1, \ @@ -212,13 +211,18 @@ for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), idx0 = (is - (gv).little_corner()).yucky_val(0) / 2 * loop_s1 + \ (is - (gv).little_corner()).yucky_val(1) / 2 * loop_s2 + \ (is - (gv).little_corner()).yucky_val(2) / 2 * loop_s3, \ - dummy_first=0;dummy_first<1;dummy_first++) \ -_Pragma(clause) \ - for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; loop_i1++) \ - for (ptrdiff_t loop_i2 = 0; loop_i2 < loop_n2; loop_i2++) \ - for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; loop_i3++) \ - for (ptrdiff_t idx = idx0 + loop_i1*loop_s1 + loop_i2*loop_s2 + \ - loop_i3*loop_s3, dummy_last=0;dummy_last<1;dummy_last++) + dummy_first = 0; \ + dummy_first < 1; dummy_first++) \ + _Pragma( \ + clause) for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; \ + loop_i1++) for (ptrdiff_t loop_i2 = 0; loop_i2 < loop_n2; \ + loop_i2++) for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; \ + loop_i3++) for (ptrdiff_t idx = \ + idx0 + loop_i1 * loop_s1 + \ + loop_i2 * loop_s2 + \ + loop_i3 * loop_s3, \ + dummy_last = 0; \ + dummy_last < 1; dummy_last++) // For the main timestepping events, we know // we want to do a simple collapse @@ -227,7 +231,7 @@ _Pragma(clause) #define PLOOP_OVER_VOL(gv, c, idx) \ PLOOP_OVER_IVECS(gv, (gv).little_corner() + (gv).iyee_shift(c), \ - (gv).big_corner() + (gv).iyee_shift(c), idx) + (gv).big_corner() + (gv).iyee_shift(c), idx) #define PLOOP_OVER_VOL_OWNED(gv, c, idx) \ PLOOP_OVER_IVECS(gv, (gv).little_owned_corner(c), (gv).big_corner(), idx) @@ -256,7 +260,8 @@ _Pragma(clause) // should only use these macros where that is true! (Basically, // all of this is here to support performance hacks of step_generic.) -#if !defined(__INTEL_COMPILER) && !defined(__clang__) && !defined(_OPENMP) && (defined(__GNUC__) || defined(__GNUG__)) +#if !defined(__INTEL_COMPILER) && !defined(__clang__) && !defined(_OPENMP) && \ + (defined(__GNUC__) || defined(__GNUG__)) #define IVDEP _Pragma("GCC ivdep") #elif defined(_OPENMP) #define IVDEP _Pragma("omp simd") @@ -307,7 +312,7 @@ _Pragma(clause) We can use simd vectorization in addition to the usual par for optimization */ // loop over indices idx from is to ie (inclusive) in gv #define PS1LOOP_OVER_IVECS(gv, is, ie, idx) \ -for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ + for (ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), \ loop_is3 = (is).yucky_val(2), loop_n1 = ((ie).yucky_val(0) - loop_is1) / 2 + 1, \ loop_n2 = ((ie).yucky_val(1) - loop_is2) / 2 + 1, \ loop_n3 = ((ie).yucky_val(2) - loop_is3) / 2 + 1, \ @@ -317,22 +322,25 @@ for(ptrdiff_t loop_is1 = (is).yucky_val(0), loop_is2 = (is).yucky_val(1), idx0 = (is - (gv).little_corner()).yucky_val(0) / 2 * loop_s1 + \ (is - (gv).little_corner()).yucky_val(1) / 2 * loop_s2 + \ (is - (gv).little_corner()).yucky_val(2) / 2 * loop_s3, \ - dummy_first=0;dummy_first<1;dummy_first++) \ -_Pragma("omp parallel for collapse(2)") \ - for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; loop_i1++) \ - for (ptrdiff_t loop_i2 = 0; loop_i2 < loop_n2; loop_i2++) \ - _Pragma("omp simd") \ - for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; loop_i3++) \ - for (ptrdiff_t idx = idx0 + loop_i1 * loop_s1 + loop_i2 * loop_s2 + loop_i3, dummy_last=0;dummy_last<1;dummy_last++) - -#define PS1LOOP_OVER_VOL(gv, c, idx) \ - PS1LOOP_OVER_IVECS(gv, (gv).little_corner() + (gv).iyee_shift(c), \ - (gv).big_corner() + (gv).iyee_shift(c), idx) - -#define PS1LOOP_OVER_VOL_OWNED(gv, c, idx) \ + dummy_first = 0; \ + dummy_first < 1; dummy_first++) \ + _Pragma("omp parallel for collapse(2)") for (ptrdiff_t loop_i1 = 0; loop_i1 < loop_n1; \ + loop_i1++) for (ptrdiff_t loop_i2 = 0; \ + loop_i2 < loop_n2; loop_i2++) \ + _Pragma("omp simd") for (ptrdiff_t loop_i3 = 0; loop_i3 < loop_n3; \ + loop_i3++) for (ptrdiff_t idx = idx0 + loop_i1 * loop_s1 + \ + loop_i2 * loop_s2 + loop_i3, \ + dummy_last = 0; \ + dummy_last < 1; dummy_last++) + +#define PS1LOOP_OVER_VOL(gv, c, idx) \ + PS1LOOP_OVER_IVECS(gv, (gv).little_corner() + (gv).iyee_shift(c), \ + (gv).big_corner() + (gv).iyee_shift(c), idx) + +#define PS1LOOP_OVER_VOL_OWNED(gv, c, idx) \ PS1LOOP_OVER_IVECS(gv, (gv).little_owned_corner(c), (gv).big_corner(), idx) -#define PS1LOOP_OVER_VOL_OWNED0(gv, c, idx) \ +#define PS1LOOP_OVER_VOL_OWNED0(gv, c, idx) \ PS1LOOP_OVER_IVECS(gv, (gv).little_owned_corner0(c), (gv).big_corner(), idx) #define PS1LOOP_OVER_VOL_NOTOWNED(gv, c, idx) \ @@ -349,15 +357,15 @@ _Pragma("omp parallel for collapse(2)") (loop_s3 != 0 && (loop_i3 == 0 || loop_i3 == loop_n3 - 1))) #define IVEC_LOOP_ILOC(gv, iloc) \ - meep::ivec iloc((gv).dim); \ - iloc.set_direction(meep::direction(loop_d1), loop_is1 + 2 * loop_i1); \ - iloc.set_direction(meep::direction(loop_d2), loop_is2 + 2 * loop_i2); \ + meep::ivec iloc((gv).dim); \ + iloc.set_direction(meep::direction(loop_d1), loop_is1 + 2 * loop_i1); \ + iloc.set_direction(meep::direction(loop_d2), loop_is2 + 2 * loop_i2); \ iloc.set_direction(meep::direction(loop_d3), loop_is3 + 2 * loop_i3) #define IVEC_LOOP_LOC(gv, loc) \ - meep::vec loc((gv).dim); \ - loc.set_direction(meep::direction(loop_d1), (0.5 * loop_is1 + loop_i1) * (gv).inva); \ - loc.set_direction(meep::direction(loop_d2), (0.5 * loop_is2 + loop_i2) * (gv).inva); \ + meep::vec loc((gv).dim); \ + loc.set_direction(meep::direction(loop_d1), (0.5 * loop_is1 + loop_i1) * (gv).inva); \ + loc.set_direction(meep::direction(loop_d2), (0.5 * loop_is2 + loop_i2) * (gv).inva); \ loc.set_direction(meep::direction(loop_d3), (0.5 * loop_is3 + loop_i3) * (gv).inva) // integration weight for using LOOP_OVER_IVECS with field::integrate @@ -365,9 +373,8 @@ _Pragma("omp parallel for collapse(2)") ((i > 1 && i < n - 2) \ ? 1.0 \ : (i == 0 ? (s0).in_direction(meep::direction(dir)) \ - : (i == 1 ? (s1).in_direction(meep::direction(dir)) \ - : i == n - 1 \ - ? (e0).in_direction(meep::direction(dir)) \ + : (i == 1 ? (s1).in_direction(meep::direction(dir)) \ + : i == n - 1 ? (e0).in_direction(meep::direction(dir)) \ : (i == n - 2 ? (e1).in_direction(meep::direction(dir)) : 1.0)))) #define IVEC_LOOP_WEIGHT1(s0, s1, e0, e1, k) \ IVEC_LOOP_WEIGHT1x(s0, s1, e0, e1, loop_i##k, loop_n##k, loop_d##k) @@ -412,8 +419,8 @@ inline bool is_electric(component c) { return c < Hx; } inline bool is_magnetic(component c) { return c >= Hx && c < Dx; } inline bool is_D(component c) { return c >= Dx && c < Bx; } inline bool is_B(component c) { return c >= Bx && c < Dielectric; } -inline bool is_E_or_D(component c) {return is_electric(c) || is_D(c); } -inline bool is_H_or_B(component c) {return is_magnetic(c) || is_B(c); } +inline bool is_E_or_D(component c) { return is_electric(c) || is_D(c); } +inline bool is_H_or_B(component c) { return is_magnetic(c) || is_B(c); } inline bool is_derived(int c) { return c >= Sx; } inline bool is_poynting(derived_component c) { return c < EnergyDensity; } inline bool is_energydensity(derived_component c) { return c >= EnergyDensity; } @@ -1149,13 +1156,10 @@ class grid_volume { const char *str(char *buffer = 0, size_t buflen = 0); - std::complex get_split_costs(direction d, int split_point, - bool frag_cost) const; - void tile_split(int &best_split_point, - direction &best_split_direction) const; - void find_best_split(int desired_chunks, bool frag_cost, - int &best_split_point, direction &best_split_direction, - double &left_effort_fraction) const; + std::complex get_split_costs(direction d, int split_point, bool frag_cost) const; + void tile_split(int &best_split_point, direction &best_split_direction) const; + void find_best_split(int desired_chunks, bool frag_cost, int &best_split_point, + direction &best_split_direction, double &left_effort_fraction) const; private: grid_volume(ndim d, double ta, int na, int nb, int nc); @@ -1209,7 +1213,7 @@ class symmetry { volume_list *reduce(const volume_list *gl) const; symmetry operator+(const symmetry &) const; - symmetry operator*(std::complex)const; + symmetry operator*(std::complex) const; symmetry operator-(const symmetry &b) const { return *this + b * (-1.0); } symmetry operator-(void) const { return *this * (-1.0); } void operator=(const symmetry &); diff --git a/src/meep_internals.hpp b/src/meep_internals.hpp index c3461b3b1..b543ed41c 100644 --- a/src/meep_internals.hpp +++ b/src/meep_internals.hpp @@ -33,11 +33,8 @@ static inline int my_round(double x) { return int(floor(fabs(x) + 0.5) * (x < 0 int mirrorindex(int i, int n); /* map the cell coordinates into the range [0,1] */ -void map_coordinates(double rx, double ry, double rz, - int nx, int ny, int nz, - int &x1, int &y1, int &z1, - int &x2, int &y2, int &z2, - double &dx, double &dy, double &dz, +void map_coordinates(double rx, double ry, double rz, int nx, int ny, int nz, int &x1, int &y1, + int &z1, int &x2, int &y2, int &z2, double &dx, double &dy, double &dz, bool do_fabs = true); inline int small_r_metal(int m) { return m - 1; } @@ -55,7 +52,7 @@ class src_vol { // Constructs a new source volume. Takes ownership of `ind` and `amps`. // Requirement: ind.size() == amps.size() src_vol(component cc, src_time *st, std::vector &&ind, - std::vector > &&s, bool fix_boundaries=false); + std::vector > &&s, bool fix_boundaries = false); // Checks whether `a` and `b` are combinable, i.e. have the same indices and point to the same // `src_time` instance, but have potentially different amplitudes. @@ -77,7 +74,7 @@ class src_vol { // It is recommended to use `combinable` before calling this method. void add_amplitudes_from(const src_vol &other); - const component c; // field component the source applies to + const component c; // field component the source applies to bool needs_boundary_fix; // whether fix_boundary_sources needs calling private: src_time *src_t; // Not owned by us. @@ -91,78 +88,79 @@ symmetry r_to_minus_r_symmetry(int m); // functions in step_generic.cpp: -void step_curl(realnum *f, component c, const realnum *g1, const realnum *g2, - ptrdiff_t s1, ptrdiff_t s2, // strides for g1/g2 shift - const grid_volume &gv, const ivec is, const ivec ie, - realnum dtdx, direction dsig, const realnum *sig, - const realnum *kap, const realnum *siginv, realnum *fu, direction dsigu, - const realnum *sigu, const realnum *kapu, const realnum *siginvu, realnum dt, - const realnum *cnd, const realnum *cndinv, realnum *fcnd); - -void step_update_EDHB(realnum *f, component fc, const grid_volume &gv, const ivec is, const ivec ie, const realnum *g, - const realnum *g1, const realnum *g2, const realnum *u, const realnum *u1, - const realnum *u2, ptrdiff_t s, ptrdiff_t s1, ptrdiff_t s2, +void step_curl(realnum *f, component c, const realnum *g1, const realnum *g2, ptrdiff_t s1, + ptrdiff_t s2, // strides for g1/g2 shift + const grid_volume &gv, const ivec is, const ivec ie, realnum dtdx, direction dsig, + const realnum *sig, const realnum *kap, const realnum *siginv, realnum *fu, + direction dsigu, const realnum *sigu, const realnum *kapu, const realnum *siginvu, + realnum dt, const realnum *cnd, const realnum *cndinv, realnum *fcnd); + +void step_update_EDHB(realnum *f, component fc, const grid_volume &gv, const ivec is, const ivec ie, + const realnum *g, const realnum *g1, const realnum *g2, const realnum *u, + const realnum *u1, const realnum *u2, ptrdiff_t s, ptrdiff_t s1, ptrdiff_t s2, const realnum *chi2, const realnum *chi3, realnum *fw, direction dsigw, const realnum *sigw, const realnum *kapw); -void step_beta(realnum *f, component c, const realnum *g, const grid_volume &gv, const ivec is, const ivec ie, realnum betadt, - direction dsig, const realnum *siginv, realnum *fu, direction dsigu, - const realnum *siginvu, const realnum *cndinv, realnum *fcnd); +void step_beta(realnum *f, component c, const realnum *g, const grid_volume &gv, const ivec is, + const ivec ie, realnum betadt, direction dsig, const realnum *siginv, realnum *fu, + direction dsigu, const realnum *siginvu, const realnum *cndinv, realnum *fcnd); // functions in step_generic_stride1.cpp, generated from step_generic.cpp: -void step_curl_stride1(realnum *f, component c, const realnum *g1, const realnum *g2, - ptrdiff_t s1, ptrdiff_t s2, // strides for g1/g2 shift - const grid_volume &gv, const ivec is, const ivec ie, - realnum dtdx, direction dsig, const realnum *sig, - const realnum *kap, const realnum *siginv, realnum *fu, direction dsigu, - const realnum *sigu, const realnum *kapu, const realnum *siginvu, realnum dt, - const realnum *cnd, const realnum *cndinv, realnum *fcnd); - -void step_update_EDHB_stride1(realnum *f, component fc, const grid_volume &gv, const ivec is, const ivec ie, const realnum *g, - const realnum *g1, const realnum *g2, const realnum *u, - const realnum *u1, const realnum *u2, ptrdiff_t s, ptrdiff_t s1, - ptrdiff_t s2, const realnum *chi2, const realnum *chi3, realnum *fw, - direction dsigw, const realnum *sigw, const realnum *kapw); - -void step_beta_stride1(realnum *f, component c, const realnum *g, const grid_volume &gv, const ivec is, const ivec ie, - realnum betadt, direction dsig, const realnum *siginv, realnum *fu, - direction dsigu, const realnum *siginvu, const realnum *cndinv, - realnum *fcnd); +void step_curl_stride1(realnum *f, component c, const realnum *g1, const realnum *g2, ptrdiff_t s1, + ptrdiff_t s2, // strides for g1/g2 shift + const grid_volume &gv, const ivec is, const ivec ie, realnum dtdx, + direction dsig, const realnum *sig, const realnum *kap, + const realnum *siginv, realnum *fu, direction dsigu, const realnum *sigu, + const realnum *kapu, const realnum *siginvu, realnum dt, const realnum *cnd, + const realnum *cndinv, realnum *fcnd); + +void step_update_EDHB_stride1(realnum *f, component fc, const grid_volume &gv, const ivec is, + const ivec ie, const realnum *g, const realnum *g1, const realnum *g2, + const realnum *u, const realnum *u1, const realnum *u2, ptrdiff_t s, + ptrdiff_t s1, ptrdiff_t s2, const realnum *chi2, const realnum *chi3, + realnum *fw, direction dsigw, const realnum *sigw, + const realnum *kapw); + +void step_beta_stride1(realnum *f, component c, const realnum *g, const grid_volume &gv, + const ivec is, const ivec ie, realnum betadt, direction dsig, + const realnum *siginv, realnum *fu, direction dsigu, const realnum *siginvu, + const realnum *cndinv, realnum *fcnd); /* macro wrappers around time-stepping functions: for performance reasons, if the inner loop is stride-1 then we use the stride-1 versions, which allow gcc (and possibly other compilers) to do additional optimizations, especially loop vectorization */ -#define STEP_CURL(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, kapu, \ - siginvu, dt, cnd, cndinv, fcnd) \ +#define STEP_CURL(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, \ + kapu, siginvu, dt, cnd, cndinv, fcnd) \ do { \ if (LOOPS_ARE_STRIDE1(gv)) \ - step_curl_stride1(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, \ - kapu, siginvu, dt, cnd, cndinv, fcnd); \ + step_curl_stride1(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, \ + sigu, kapu, siginvu, dt, cnd, cndinv, fcnd); \ else \ - step_curl(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, kapu, \ - siginvu, dt, cnd, cndinv, fcnd); \ + step_curl(f, c, g1, g2, s1, s2, gv, is, ie, dtdx, dsig, sig, kap, siginv, fu, dsigu, sigu, \ + kapu, siginvu, dt, cnd, cndinv, fcnd); \ } while (0) -#define STEP_UPDATE_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, sigw, \ - kapw) \ +#define STEP_UPDATE_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, \ + dsigw, sigw, kapw) \ do { \ if (LOOPS_ARE_STRIDE1(gv)) \ - step_update_EDHB_stride1(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, \ - sigw, kapw); \ + step_update_EDHB_stride1(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, \ + dsigw, sigw, kapw); \ else \ - step_update_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, sigw, \ - kapw); \ + step_update_EDHB(f, fc, gv, is, ie, g, g1, g2, u, u1, u2, s, s1, s2, chi2, chi3, fw, dsigw, \ + sigw, kapw); \ } while (0) -#define STEP_BETA(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd) \ +#define STEP_BETA(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd) \ do { \ if (LOOPS_ARE_STRIDE1(gv)) \ - step_beta_stride1(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd); \ + step_beta_stride1(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, \ + fcnd); \ else \ - step_beta(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd); \ + step_beta(f, c, g, gv, is, ie, betadt, dsig, siginv, fu, dsigu, siginvu, cndinv, fcnd); \ } while (0) // analytical Green's functions from near2far.cpp, which we might want to expose someday @@ -175,8 +173,11 @@ void greencyl(std::complex *EH, const vec &x, double freq, double eps, d // functions in array_slice.cpp: -complex *collapse_array(complex *array, int *rank, size_t dims[3], direction dirs[3], volume where); +complex *collapse_array(complex *array, int *rank, size_t dims[3], + direction dirs[3], volume where); -void reduce_array_dimensions(volume where, int full_rank, size_t dims[3], direction dirs[3], size_t stride[3], int &reduced_rank, size_t reduced_dims[3], direction reduced_dirs[3], size_t reduced_stride[3]); +void reduce_array_dimensions(volume where, int full_rank, size_t dims[3], direction dirs[3], + size_t stride[3], int &reduced_rank, size_t reduced_dims[3], + direction reduced_dirs[3], size_t reduced_stride[3]); } // namespace meep diff --git a/src/meepgeom.cpp b/src/meepgeom.cpp index 14ef07802..6b83d109b 100644 --- a/src/meepgeom.cpp +++ b/src/meepgeom.cpp @@ -232,9 +232,7 @@ int sym_matrix_positive_definite(symm_matrix *V) { static meep::ndim dim = meep::D3; void set_dimensions(int dims) { if (dims == CYLINDRICAL) { dim = meep::Dcyl; } - else { - dim = meep::ndim(dims - 1); - } + else { dim = meep::ndim(dims - 1); } } vector3 vec_to_vector3(const meep::vec &pt) { @@ -328,12 +326,10 @@ bool is_metal(meep::field_type ft, const material_type *material) { } bool has_offdiag(const medium_struct *material) { - if ((material->epsilon_offdiag.x.re != 0) || /* account for offdiagonal components */ - (material->epsilon_offdiag.y.re != 0) || - (material->epsilon_offdiag.z.re != 0) || - (material->epsilon_offdiag.x.im != 0) || - (material->epsilon_offdiag.y.im != 0) || - (material->epsilon_offdiag.z.im != 0)) + if ((material->epsilon_offdiag.x.re != 0) || /* account for offdiagonal components */ + (material->epsilon_offdiag.y.re != 0) || (material->epsilon_offdiag.z.re != 0) || + (material->epsilon_offdiag.x.im != 0) || (material->epsilon_offdiag.y.im != 0) || + (material->epsilon_offdiag.z.im != 0)) return true; else return false; @@ -362,8 +358,8 @@ vector3 to_geom_object_coords_VJP(vector3 v, const geometric_object *o) { if (size.z != 0.0) v.z /= size.z; return matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v); } - /* case geometric_object::PRISM: - NOT YET IMPLEMENTED */ + /* case geometric_object::PRISM: + NOT YET IMPLEMENTED */ } } @@ -383,9 +379,7 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec double dx, dy, dz; bool signflip_dx = false, signflip_dy = false, signflip_dz = false; - meep::map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz, + meep::map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz, false /* do_fabs */); if (dx != fabs(dx)) { @@ -405,21 +399,23 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec in row-major order: */ #define D(x, y, z) (data[(((x)*ny + (y)) * nz + (z)) * stride]) - double du_dx = (signflip_dx ? -1.0 : 1.0) * - (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + - (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * (1.0 - dz) + - ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + - (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * dz); - double du_dy = (signflip_dy ? -1.0 : 1.0) * - ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * (1.0 - dz) + - (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * dz); + double du_dx = + (signflip_dx ? -1.0 : 1.0) * + (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * + (1.0 - dz) + + ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * + dz); + double du_dy = (signflip_dy ? -1.0 : 1.0) * ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * + (1.0 - dz) + + (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * + dz); double du_dz = (signflip_dz ? -1.0 : 1.0) * - (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + - ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); + (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + + ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); #undef D @@ -439,27 +435,28 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec gradient.set_direction(meep::Z, grad_u_J.z); } else { - gradient.set_direction(meep::X, geometry_lattice.size.x == 0 ? 0 : grad_u.x / geometry_lattice.size.x); - gradient.set_direction(meep::Y, geometry_lattice.size.y == 0 ? 0 : grad_u.y / geometry_lattice.size.y); - gradient.set_direction(meep::Z, geometry_lattice.size.z == 0 ? 0 : grad_u.z / geometry_lattice.size.z); + gradient.set_direction(meep::X, + geometry_lattice.size.x == 0 ? 0 : grad_u.x / geometry_lattice.size.x); + gradient.set_direction(meep::Y, + geometry_lattice.size.y == 0 ? 0 : grad_u.y / geometry_lattice.size.y); + gradient.set_direction(meep::Z, + geometry_lattice.size.z == 0 ? 0 : grad_u.z / geometry_lattice.size.z); } return gradient; } void map_lattice_coordinates(double &px, double &py, double &pz) { - px = geometry_lattice.size.x == 0 ? 0 - : 0.5 + (px - geometry_center.x) / geometry_lattice.size.x; - py = geometry_lattice.size.y == 0 ? 0 - : 0.5 + (py - geometry_center.y) / geometry_lattice.size.y; - pz = geometry_lattice.size.z == 0 ? 0 - : 0.5 + (pz - geometry_center.z) / geometry_lattice.size.z; + px = geometry_lattice.size.x == 0 ? 0 : 0.5 + (px - geometry_center.x) / geometry_lattice.size.x; + py = geometry_lattice.size.y == 0 ? 0 : 0.5 + (py - geometry_center.y) / geometry_lattice.size.y; + pz = geometry_lattice.size.z == 0 ? 0 : 0.5 + (pz - geometry_center.z) / geometry_lattice.size.z; } meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { if (md->material_grid_kinds == material_data::U_MIN || md->material_grid_kinds == material_data::U_PROD) - meep::abort("%s:%i:matgrid_grad does not support overlapping grids with U_MIN or U_PROD\n",__FILE__,__LINE__); + meep::abort("%s:%i:matgrid_grad does not support overlapping grids with U_MIN or U_PROD\n", + __FILE__, __LINE__); meep::vec gradient(zero_vec(dim)); int matgrid_val_count = 0; @@ -467,9 +464,9 @@ meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { // iterate through object tree at current point if (tp) { do { - gradient += material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), - (material_data *)tp->objects[oi].o->material, - tp->objects[oi].o); + gradient += + material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), + (material_data *)tp->objects[oi].o->material, tp->objects[oi].o); if (md->material_grid_kinds == material_data::U_DEFAULT) break; ++matgrid_val_count; tp = geom_tree_search_next(p, tp, &oi); @@ -477,13 +474,14 @@ meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { } // perhaps there is no object tree and the default material is a material grid if (!tp && is_material_grid(default_material)) { - map_lattice_coordinates(p.x,p.y,p.z); - gradient = material_grid_grad(p, (material_data *)default_material, NULL /* geometric_object *o */); + map_lattice_coordinates(p.x, p.y, p.z); + gradient = + material_grid_grad(p, (material_data *)default_material, NULL /* geometric_object *o */); ++matgrid_val_count; } if (md->material_grid_kinds == material_data::U_MEAN) - gradient = gradient * 1.0/matgrid_val_count; + gradient = gradient * 1.0 / matgrid_val_count; return gradient; } @@ -492,17 +490,15 @@ double material_grid_val(vector3 p, material_data *md) { // given the relative location, p, interpolate the material grid point. if (!is_material_grid(md)) { meep::abort("Invalid material grid detected.\n"); } - return meep::linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, - md->grid_size.y, md->grid_size.z, 1); - + return meep::linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, md->grid_size.y, + md->grid_size.z, 1); } static double tanh_projection(double u, double beta, double eta) { if (beta == 0) return u; if (u == eta) return 0.5; // avoid NaN when beta is Inf - double tanh_beta_eta = tanh(beta*eta); - return (tanh_beta_eta + tanh(beta*(u-eta))) / - (tanh_beta_eta + tanh(beta*(1-eta))); + double tanh_beta_eta = tanh(beta * eta); + return (tanh_beta_eta + tanh(beta * (u - eta))) / (tanh_beta_eta + tanh(beta * (1 - eta))); } double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { @@ -527,7 +523,7 @@ double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { } // perhaps there is no object tree and the default material is a material grid if (!tp && is_material_grid(default_material)) { - map_lattice_coordinates(p.x,p.y,p.z); + map_lattice_coordinates(p.x, p.y, p.z); u = material_grid_val(p, (material_data *)default_material); if (matgrid_val_count == 0) udefault = u; if (u < umin) umin = u; @@ -537,11 +533,11 @@ double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { } return (md->material_grid_kinds == material_data::U_MIN - ? umin - : (md->material_grid_kinds == material_data::U_PROD - ? uprod - : (md->material_grid_kinds == material_data::U_MEAN ? usum / matgrid_val_count - : udefault))); + ? umin + : (md->material_grid_kinds == material_data::U_PROD + ? uprod + : (md->material_grid_kinds == material_data::U_MEAN ? usum / matgrid_val_count + : udefault))); } static void cinterp_tensors(vector3 diag_in_1, cvector3 offdiag_in_1, vector3 diag_in_2, cvector3 offdiag_in_2, vector3 *diag_out, cvector3 *offdiag_out, @@ -589,16 +585,14 @@ void epsilon_material_grid(material_data *md, double u) { for (size_t i = 0; i < m1->E_susceptibilities.size(); i++) { // iterate through medium1 sus list first interp_tensors(zero_vec, zero_vec, m1->E_susceptibilities[i].sigma_diag, - m1->E_susceptibilities[i].sigma_offdiag, - &mm->E_susceptibilities[i].sigma_diag, + m1->E_susceptibilities[i].sigma_offdiag, &mm->E_susceptibilities[i].sigma_diag, &mm->E_susceptibilities[i].sigma_offdiag, (1 - u)); } for (size_t i = 0; i < m2->E_susceptibilities.size(); i++) { // iterate through medium2 sus list next size_t j = i + m1->E_susceptibilities.size(); interp_tensors(zero_vec, zero_vec, m2->E_susceptibilities[i].sigma_diag, - m2->E_susceptibilities[i].sigma_offdiag, - &mm->E_susceptibilities[j].sigma_diag, + m2->E_susceptibilities[i].sigma_offdiag, &mm->E_susceptibilities[j].sigma_diag, &mm->E_susceptibilities[j].sigma_offdiag, u); } @@ -624,16 +618,15 @@ void epsilon_material_grid(material_data *md, double u) { // TODO: dampen the lorentzians to improve stability // mm->D_conductivity_diag.x = mm->D_conductivity_diag.y = mm->D_conductivity_diag.z = u*(1-u) * // omega_mean; - md->trivial=false; + md->trivial = false; } - double fake_damping = u*(1-u)*(md->damping); + double fake_damping = u * (1 - u) * (md->damping); mm->D_conductivity_diag.x += fake_damping; mm->D_conductivity_diag.y += fake_damping; mm->D_conductivity_diag.z += fake_damping; // set the trivial flag - if (md->damping != 0) - md->trivial=false; + if (md->damping != 0) md->trivial = false; } // return material of the point p from the file (assumed already read) @@ -665,10 +658,11 @@ geom_epsilon::geom_epsilon(geometric_object_list g, material_type_list mlist, int length = g.num_items; geometry.num_items = length; geometry.items = new geometric_object[length]; - for (int i = 0; i < length; i++){ - geometric_object_copy(&g.items[i],&geometry.items[i]); + for (int i = 0; i < length; i++) { + geometric_object_copy(&g.items[i], &geometry.items[i]); geometry.items[i].material = new material_data(); - static_cast(geometry.items[i].material)->copy_from(*(material_data *)(g.items[i].material)); + static_cast(geometry.items[i].material) + ->copy_from(*(material_data *)(g.items[i].material)); } extra_materials = mlist; @@ -719,10 +713,11 @@ geom_epsilon::geom_epsilon(const geom_epsilon &geps1) { int length = geps1.geometry.num_items; geometry.num_items = length; geometry.items = new geometric_object[length]; - for (int i = 0; i < length; i++){ - geometric_object_copy(&geps1.geometry.items[i],&geometry.items[i]); + for (int i = 0; i < length; i++) { + geometric_object_copy(&geps1.geometry.items[i], &geometry.items[i]); geometry.items[i].material = new material_data(); - static_cast(geometry.items[i].material)->copy_from(*(material_data *)(geps1.geometry.items[i].material)); + static_cast(geometry.items[i].material) + ->copy_from(*(material_data *)(geps1.geometry.items[i].material)); } geometry_tree = geps1.geometry_tree; @@ -731,11 +726,10 @@ geom_epsilon::geom_epsilon(const geom_epsilon &geps1) { current_pol = NULL; FOR_DIRECTIONS(d) FOR_SIDES(b) { cond[d][b].prof = geps1.cond[d][b].prof; } - } geom_epsilon::~geom_epsilon() { int length = geometry.num_items; - for (int i = 0; i < length; i++){ + for (int i = 0; i < length; i++) { material_free((material_type)geometry.items[i].material); geometric_object_destroy(geometry.items[i]); } @@ -780,8 +774,7 @@ void geom_epsilon::set_volume(const meep::volume &v) { unset_volume(); geom_box box = gv2box(v); - if (!restricted_tree) - restricted_tree = create_geom_box_tree0(geometry, box); + if (!restricted_tree) restricted_tree = create_geom_box_tree0(geometry, box); } static void material_epsmu(meep::field_type ft, material_type material, symm_matrix *epsmu, @@ -865,7 +858,7 @@ void geom_epsilon::get_material_pt(material_type &material, const meep::vec &r) tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid - u = tanh_projection(matgrid_val(p, tp, oi, md)+this->u_p, md->beta, md->eta); + u = tanh_projection(matgrid_val(p, tp, oi, md) + this->u_p, md->beta, md->eta); // interpolate material from material grid point epsilon_material_grid(md, u); @@ -1084,15 +1077,14 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma if (!get_front_object(v, geometry_tree, p, &o, shiftby, mat, mat_behind)) { get_material_pt(mat, v.center()); - if (mat && (mat->which_subclass == material_data::MATERIAL_USER || - mat->which_subclass == material_data::MATERIAL_GRID) - && mat->do_averaging) { + if (mat && + (mat->which_subclass == material_data::MATERIAL_USER || + mat->which_subclass == material_data::MATERIAL_GRID) && + mat->do_averaging) { fallback = true; return; } - else { - goto trivial; - } + else { goto trivial; } } /* check for trivial case of only one object/material */ @@ -1203,25 +1195,26 @@ static int eps_ever_negative = 0; static meep::field_type func_ft = meep::E_stuff; struct matgrid_volavg { - meep::ndim dim; // dimensionality of voxel - double rad; // (spherical) voxel radius - double uval; // bilinearly-interpolated weight at voxel center - double ugrad_abs; // magnitude of gradient of uval - double beta; // thresholding bias - double eta; // thresholding erosion/dilation - double eps1; // trace of epsilon tensor from medium 1 - double eps2; // trace of epsilon tensor from medium 2 + meep::ndim dim; // dimensionality of voxel + double rad; // (spherical) voxel radius + double uval; // bilinearly-interpolated weight at voxel center + double ugrad_abs; // magnitude of gradient of uval + double beta; // thresholding bias + double eta; // thresholding erosion/dilation + double eps1; // trace of epsilon tensor from medium 1 + double eps2; // trace of epsilon tensor from medium 2 }; static void get_uproj_w(const matgrid_volavg *mgva, double x0, double &u_proj, double &w) { // use a linear approximation for the material grid weights around the Yee grid point - u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs*x0, mgva->beta, mgva->eta); + u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs * x0, mgva->beta, mgva->eta); if (mgva->dim == meep::D1) - w = 1/(2*mgva->rad); + w = 1 / (2 * mgva->rad); else if (mgva->dim == meep::D2 || mgva->dim == meep::Dcyl) - w = 2*sqrt(mgva->rad*mgva->rad - x0*x0)/(meep::pi * mgva->rad*mgva->rad); + w = 2 * sqrt(mgva->rad * mgva->rad - x0 * x0) / (meep::pi * mgva->rad * mgva->rad); else if (mgva->dim == meep::D3) - w = meep::pi*(mgva->rad*mgva->rad - x0*x0)/(4/3 * meep::pi * mgva->rad*mgva->rad*mgva->rad); + w = meep::pi * (mgva->rad * mgva->rad - x0 * x0) / + (4 / 3 * meep::pi * mgva->rad * mgva->rad * mgva->rad); } #ifdef CTL_HAS_COMPLEX_INTEGRATION @@ -1231,8 +1224,8 @@ static cnumber matgrid_ceps_func(int n, number *x, void *mgva_) { matgrid_volavg *mgva = (matgrid_volavg *)mgva_; get_uproj_w(mgva, x[0], u_proj, w); cnumber ret; - ret.re = (1-u_proj)*mgva->eps1 + u_proj*mgva->eps2; - ret.im = (1-u_proj)/mgva->eps1 + u_proj/mgva->eps2; + ret.re = (1 - u_proj) * mgva->eps1 + u_proj * mgva->eps2; + ret.im = (1 - u_proj) / mgva->eps1 + u_proj / mgva->eps2; return ret * w; } #else @@ -1241,14 +1234,14 @@ static number matgrid_eps_func(int n, number *x, void *mgva_) { double u_proj = 0, w = 0; matgrid_volavg *mgva = (matgrid_volavg *)mgva_; get_uproj_w(mgva, x[0], u_proj, w); - return w * ((1-u_proj)*mgva->eps1 + u_proj*mgva->eps2); + return w * ((1 - u_proj) * mgva->eps1 + u_proj * mgva->eps2); } static number matgrid_inveps_func(int n, number *x, void *mgva_) { (void)n; // unused double u_proj = 0, w = 0; matgrid_volavg *mgva = (matgrid_volavg *)mgva_; get_uproj_w(mgva, x[0], u_proj, w); - return w * ((1-u_proj)/mgva->eps1 + u_proj/mgva->eps2); + return w * ((1 - u_proj) / mgva->eps1 + u_proj / mgva->eps2); } #endif @@ -1329,11 +1322,9 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] int oi; tp = geom_tree_search(p, restricted_tree, &oi); gradient = matgrid_grad(p, tp, oi, md); - uval = matgrid_val(p, tp, oi, md)+this->u_p; - } - else { - gradient = normal_vector(meep::type(c), v); + uval = matgrid_val(p, tp, oi, md) + this->u_p; } + else { gradient = normal_vector(meep::type(c), v); } get_material_pt(material, v.center()); material_epsmu(meep::type(c), material, &chi1p1, &chi1p1_inv); @@ -1368,23 +1359,27 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] mgva.dim = v.dim; mgva.ugrad_abs = meep::abs(gradient); mgva.uval = uval; - mgva.rad = v.diameter()/2; + mgva.rad = v.diameter() / 2; mgva.beta = md->beta; mgva.eta = md->eta; - mgva.eps1 = (md->medium_1.epsilon_diag.x+md->medium_1.epsilon_diag.y+md->medium_1.epsilon_diag.z)/3; - mgva.eps2 = (md->medium_2.epsilon_diag.x+md->medium_2.epsilon_diag.y+md->medium_2.epsilon_diag.z)/3; - xmin[0] = -v.diameter()/2; - xmax[0] = v.diameter()/2; + mgva.eps1 = + (md->medium_1.epsilon_diag.x + md->medium_1.epsilon_diag.y + md->medium_1.epsilon_diag.z) / + 3; + mgva.eps2 = + (md->medium_2.epsilon_diag.x + md->medium_2.epsilon_diag.y + md->medium_2.epsilon_diag.z) / + 3; + xmin[0] = -v.diameter() / 2; + xmax[0] = v.diameter() / 2; #ifdef CTL_HAS_COMPLEX_INTEGRATION - cnumber ret = cadaptive_integration(matgrid_ceps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, - &esterr, &errflag); + cnumber ret = cadaptive_integration(matgrid_ceps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, + maxeval, &esterr, &errflag); meps = ret.re; minveps = ret.im; #else - meps = adaptive_integration(matgrid_eps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, &esterr, - &errflag); - minveps = adaptive_integration(matgrid_inveps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, &esterr, - &errflag); + meps = adaptive_integration(matgrid_eps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, + &esterr, &errflag); + minveps = adaptive_integration(matgrid_inveps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, + maxeval, &esterr, &errflag); #endif } else { @@ -1424,9 +1419,11 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] minveps = ret.im / vol; #else meps = adaptive_integration(eps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / vol; - minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / vol; + &errflag) / + vol; + minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, + &esterr, &errflag) / + vol; #endif } if (eps_ever_negative) // averaging negative eps causes instability @@ -1562,12 +1559,12 @@ static double get_cnd(meep::component c, const medium_struct *m) { } static bool has_conductivity(const material_type &md, meep::component c) { - medium_struct *mm; - if (is_medium(md, &mm) && get_cnd(c, mm)) return true; - if (md->which_subclass == material_data::MATERIAL_GRID && - (get_cnd(c, &md->medium_1) || get_cnd(c, &md->medium_2) || - md->damping != 0)) return true; - return false; + medium_struct *mm; + if (is_medium(md, &mm) && get_cnd(c, mm)) return true; + if (md->which_subclass == material_data::MATERIAL_GRID && + (get_cnd(c, &md->medium_1) || get_cnd(c, &md->medium_2) || md->damping != 0)) + return true; + return false; } bool geom_epsilon::has_conductivity(meep::component c) { @@ -1575,10 +1572,10 @@ bool geom_epsilon::has_conductivity(meep::component c) { if (cond[d][b].prof) return true; } for (int i = 0; i < geometry.num_items; ++i) - if (meep_geom::has_conductivity((material_type) geometry.items[i].material, c)) return true; + if (meep_geom::has_conductivity((material_type)geometry.items[i].material, c)) return true; for (int i = 0; i < extra_materials.num_items; ++i) - if (meep_geom::has_conductivity((material_type) extra_materials.items[i], c)) return true; - return meep_geom::has_conductivity((material_type) default_material, c); + if (meep_geom::has_conductivity((material_type)extra_materials.items[i], c)) return true; + return meep_geom::has_conductivity((material_type)default_material, c); } static meep::vec geometry_edge; // geometry_lattice.size / 2 @@ -1670,8 +1667,8 @@ void geom_epsilon::sigma_row(meep::component c, double sigrow[3], const meep::ve tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid - u = tanh_projection(matgrid_val(p, tp, oi, mat)+this->u_p, mat->beta, mat->eta); - epsilon_material_grid(mat, u); // interpolate material from material grid point + u = tanh_projection(matgrid_val(p, tp, oi, mat) + this->u_p, mat->beta, mat->eta); + epsilon_material_grid(mat, u); // interpolate material from material grid point mat->medium.check_offdiag_im_zero_or_abort(); } @@ -1852,21 +1849,21 @@ void geom_epsilon::add_susceptibilities(meep::field_type ft, meep::structure *s) ss->drude); } else if (gyrotropic) { - meep::gyrotropy_model model = - ss->saturated_gyrotropy - ? meep::GYROTROPIC_SATURATED - : ss->drude ? meep::GYROTROPIC_DRUDE : meep::GYROTROPIC_LORENTZIAN; + meep::gyrotropy_model model = ss->saturated_gyrotropy ? meep::GYROTROPIC_SATURATED + : ss->drude ? meep::GYROTROPIC_DRUDE + : meep::GYROTROPIC_LORENTZIAN; sus = new meep::gyrotropic_susceptibility(meep::vec(ss->bias.x, ss->bias.y, ss->bias.z), ss->frequency, ss->gamma, ss->alpha, model); } - else { - sus = new meep::lorentzian_susceptibility(ss->frequency, ss->gamma, ss->drude); - } + else { sus = new meep::lorentzian_susceptibility(ss->frequency, ss->gamma, ss->drude); } if (meep::verbosity > 0) { master_printf("%s%s susceptibility: frequency=%g, gamma=%g", - noisy ? "noisy " : gyrotropic ? "gyrotropic " : "", + noisy ? "noisy " + : gyrotropic ? "gyrotropic " + : "", ss->saturated_gyrotropy ? "Landau-Lifshitz-Gilbert-type" - : ss->drude ? "drude" : "lorentzian", + : ss->drude ? "drude" + : "lorentzian", ss->frequency, ss->gamma); if (noisy) master_printf(", amp=%g ", ss->noise_amp); if (gyrotropic) { @@ -1937,7 +1934,7 @@ void add_absorbing_layer(absorber_list alist, double thickness, int direction, i /* create a geom_epsilon object that can persist if needed */ -geom_epsilon* make_geom_epsilon(meep::structure *s, geometric_object_list *g, vector3 center, +geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, vector3 center, bool _ensure_periodicity, material_type _default_material, material_type_list extra_materials) { // set global variables in libctlgeom based on data fields in s @@ -2001,16 +1998,15 @@ void set_materials_from_geometry(meep::structure *s, geometric_object_list g, ve absorber_list alist, material_type_list extra_materials) { meep_geom::geom_epsilon *geps = meep_geom::make_geom_epsilon(s, &g, center, _ensure_periodicity, _default_material, extra_materials); - set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, - maxeval, alist); + set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, maxeval, alist); delete geps; } /* from a previously created geom_epsilon object, set the materials as specified */ void set_materials_from_geom_epsilon(meep::structure *s, geom_epsilon *geps, - bool use_anisotropic_averaging, - double tol, int maxeval, absorber_list alist) { + bool use_anisotropic_averaging, double tol, int maxeval, + absorber_list alist) { // store for later use in gradient calculations geps->tol = tol; @@ -2220,8 +2216,7 @@ fragment_stats::fragment_stats(geom_box &bx) } void init_libctl(material_type default_mat, bool ensure_per, meep::grid_volume *gv, - vector3 cell_size, vector3 cell_center, - geometric_object_list *geom_) { + vector3 cell_size, vector3 cell_center, geometric_object_list *geom_) { geom_initialize(); set_default_material(default_mat); ensure_periodicity = ensure_per; @@ -2289,8 +2284,9 @@ void fragment_stats::compute_stats() { for (int i = 0; i < geom.num_items; ++i) { geometric_object *go = &geom.items[i]; - // tolerance and max number of function evaluations of numerical quadrature are increased and decreased - // from default values of 0.0001 and 100000, respectively, to obtain fast, approximate result + // tolerance and max number of function evaluations of numerical quadrature are increased and + // decreased from default values of 0.0001 and 100000, respectively, to obtain fast, approximate + // result double overlap = box_overlap_with_object(box, *go, 0.05 /* tol */, 1000 /* maxeval */); bool anisotropic_pixels_already_added = false; @@ -2486,35 +2482,16 @@ geom_box_tree calculate_tree(const meep::volume &v, geometric_object_list g) { static std::complex cvec_to_value(vector3 diag, cvector3 offdiag, int idx) { std::complex val = std::complex(0, 0); switch (idx) { - case 0: - val = std::complex(diag.x, 0); - break; - case 1: - val = std::complex(offdiag.x.re, offdiag.x.im); - break; - case 2: - val = std::complex(offdiag.y.re, offdiag.y.im); - break; - case 3: - val = std::complex(offdiag.x.re, -offdiag.x.im); - break; - case 4: - val = std::complex(diag.y, 0); - break; - case 5: - val = std::complex(offdiag.z.re, offdiag.z.im); - break; - case 6: - val = std::complex(offdiag.y.re, -offdiag.y.im); - break; - case 7: - val = std::complex(offdiag.z.re, -offdiag.z.im); - break; - case 8: - val = std::complex(diag.z, 0); - break; - default: - meep::abort("Invalid value in switch statement."); + case 0: val = std::complex(diag.x, 0); break; + case 1: val = std::complex(offdiag.x.re, offdiag.x.im); break; + case 2: val = std::complex(offdiag.y.re, offdiag.y.im); break; + case 3: val = std::complex(offdiag.x.re, -offdiag.x.im); break; + case 4: val = std::complex(diag.y, 0); break; + case 5: val = std::complex(offdiag.z.re, offdiag.z.im); break; + case 6: val = std::complex(offdiag.y.re, -offdiag.y.im); break; + case 7: val = std::complex(offdiag.z.re, -offdiag.z.im); break; + case 8: val = std::complex(diag.z, 0); break; + default: meep::abort("Invalid value in switch statement."); } return val; } @@ -2523,46 +2500,27 @@ static std::complex cvec_to_value(vector3 diag, cvector3 offdiag, int id double vec_to_value(vector3 diag, vector3 offdiag, int idx) { double val = 0.0; switch (idx) { - case 0: - val = diag.x; - break; - case 1: - val = offdiag.x; - break; - case 2: - val = offdiag.y; - break; - case 3: - val = offdiag.x; - break; - case 4: - val = diag.y; - break; - case 5: - val = offdiag.z; - break; - case 6: - val = offdiag.y; - break; - case 7: - val = offdiag.z; - break; - case 8: - val = diag.z; - break; - default: - meep::abort("Invalid value in switch statement."); + case 0: val = diag.x; break; + case 1: val = offdiag.x; break; + case 2: val = offdiag.y; break; + case 3: val = offdiag.x; break; + case 4: val = diag.y; break; + case 5: val = offdiag.z; break; + case 6: val = offdiag.y; break; + case 7: val = offdiag.z; break; + case 8: val = diag.z; break; + default: meep::abort("Invalid value in switch statement."); } return val; } void invert_tensor(std::complex t_inv[9], std::complex t[9]) { -#define m(x,y) t[x*3+y] -#define minv(x,y) t_inv[x*3+y] +#define m(x, y) t[x * 3 + y] +#define minv(x, y) t_inv[x * 3 + y] std::complex det = m(0, 0) * (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) - - m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) + - m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0)); + m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) + + m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0)); std::complex invdet = 1.0 / det; minv(0, 0) = (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) * invdet; minv(0, 1) = (m(0, 2) * m(2, 1) - m(0, 1) * m(2, 2)) * invdet; @@ -2577,7 +2535,8 @@ void invert_tensor(std::complex t_inv[9], std::complex t[9]) { #undef minv } -void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, double freq, geom_epsilon *geps) { +void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, double freq, + geom_epsilon *geps) { // locate the proper material material_type md; geps->get_material_pt(md, r); @@ -2585,18 +2544,18 @@ void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, do // loop over all the tensor components for (int i = 0; i < 9; i++) { - std::complex a,b; + std::complex a, b; // compute first part containing conductivity vector3 dummy; dummy.x = dummy.y = dummy.z = 0.0; double conductivityCur = vec_to_value(mm->D_conductivity_diag, dummy, i); - a = std::complex(1.0, conductivityCur / (2*meep::pi*freq)); + a = std::complex(1.0, conductivityCur / (2 * meep::pi * freq)); // compute lorentzian component including the instantaneous ε b = cvec_to_value(mm->epsilon_diag, mm->epsilon_offdiag, i); - for (const auto &mm_susc: mm->E_susceptibilities) { - meep::lorentzian_susceptibility sus = meep::lorentzian_susceptibility( - mm_susc.frequency, mm_susc.gamma, mm_susc.drude); + for (const auto &mm_susc : mm->E_susceptibilities) { + meep::lorentzian_susceptibility sus = + meep::lorentzian_susceptibility(mm_susc.frequency, mm_susc.gamma, mm_susc.drude); double sigma = vec_to_value(mm_susc.sigma_diag, mm_susc.sigma_offdiag, i); b += sus.chi1(freq, sigma); } @@ -2607,63 +2566,67 @@ void get_chi1_tensor_disp(std::complex tensor[9], const meep::vec &r, do } void eff_chi1inv_row_disp(meep::component c, std::complex chi1inv_row[3], - const meep::vec &r, double freq, geom_epsilon *geps) { + const meep::vec &r, double freq, geom_epsilon *geps) { std::complex tensor[9], tensor_inv[9]; get_chi1_tensor_disp(tensor, r, freq, geps); // invert the matrix invert_tensor(tensor_inv, tensor); // get the row we care about - switch (component_direction(c)) { - case meep::X: - case meep::R: - chi1inv_row[0] = tensor_inv[0]; - chi1inv_row[1] = tensor_inv[1]; - chi1inv_row[2] = tensor_inv[2]; - break; - case meep::Y: - case meep::P: - chi1inv_row[0] = tensor_inv[3]; - chi1inv_row[1] = tensor_inv[4]; - chi1inv_row[2] = tensor_inv[5]; - break; - case meep::Z: - chi1inv_row[0] = tensor_inv[6]; - chi1inv_row[1] = tensor_inv[7]; - chi1inv_row[2] = tensor_inv[8]; - break; - case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; - } + switch (component_direction(c)) { + case meep::X: + case meep::R: + chi1inv_row[0] = tensor_inv[0]; + chi1inv_row[1] = tensor_inv[1]; + chi1inv_row[2] = tensor_inv[2]; + break; + case meep::Y: + case meep::P: + chi1inv_row[0] = tensor_inv[3]; + chi1inv_row[1] = tensor_inv[4]; + chi1inv_row[2] = tensor_inv[5]; + break; + case meep::Z: + chi1inv_row[0] = tensor_inv[6]; + chi1inv_row[1] = tensor_inv[7]; + chi1inv_row[2] = tensor_inv[8]; + break; + case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; + } } -std::complex cond_cmp(meep::component c, const meep::vec &r, double freq, geom_epsilon *geps) { +std::complex cond_cmp(meep::component c, const meep::vec &r, double freq, + geom_epsilon *geps) { // locate the proper material material_type md; geps->get_material_pt(md, r); const medium_struct *mm = &(md->medium); // get the row we care about - switch (component_direction(c)) { - case meep::X: - case meep::R: return std::complex(1.0, mm->D_conductivity_diag.x / (2*meep::pi*freq)); - case meep::Y: - case meep::P: return std::complex(1.0, mm->D_conductivity_diag.y / (2*meep::pi*freq)); - case meep::Z: return std::complex(1.0, mm->D_conductivity_diag.z / (2*meep::pi*freq)); - case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); - } -} - -std::complex get_material_gradient( - const meep::vec &r, // current point - const meep::component adjoint_c, // adjoint field component - const meep::component forward_c, // forward field component - std::complex fields_f, // forward field at current point - double freq, // frequency - geom_epsilon *geps, // material - meep::grid_volume &gv, // simulation grid volume - double du, // step size - double *u, // matgrid - int idx // matgrid index + switch (component_direction(c)) { + case meep::X: + case meep::R: + return std::complex(1.0, mm->D_conductivity_diag.x / (2 * meep::pi * freq)); + case meep::Y: + case meep::P: + return std::complex(1.0, mm->D_conductivity_diag.y / (2 * meep::pi * freq)); + case meep::Z: + return std::complex(1.0, mm->D_conductivity_diag.z / (2 * meep::pi * freq)); + case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); + } +} + +std::complex +get_material_gradient(const meep::vec &r, // current point + const meep::component adjoint_c, // adjoint field component + const meep::component forward_c, // forward field component + std::complex fields_f, // forward field at current point + double freq, // frequency + geom_epsilon *geps, // material + meep::grid_volume &gv, // simulation grid volume + double du, // step size + double *u, // matgrid + int idx // matgrid index ) { /*Compute the Aᵤx product from the -λᵀAᵤx calculation. The current adjoint (λ) field component (adjoint_c) @@ -2702,30 +2665,33 @@ std::complex get_material_gradient( const double sd = 1.0; // FIXME: make user-changable? meep::volume v(r); LOOP_OVER_DIRECTIONS(dim, d) { - v.set_direction_min(d, r.in_direction(d) - 0.5*gv.inva*sd); - v.set_direction_max(d, r.in_direction(d) + 0.5*gv.inva*sd); + v.set_direction_min(d, r.in_direction(d) - 0.5 * gv.inva * sd); + v.set_direction_max(d, r.in_direction(d) + 0.5 * gv.inva * sd); } double row_1[3], row_2[3], dA_du[3]; double orig = u[idx]; u[idx] -= du; geps->eff_chi1inv_row(adjoint_c, row_1, v, geps->tol, geps->maxeval); - u[idx] += 2*du; + u[idx] += 2 * du; geps->eff_chi1inv_row(adjoint_c, row_2, v, geps->tol, geps->maxeval); u[idx] = orig; - for (int i=0;i<3;i++) dA_du[i] = (row_1[i] - row_2[i])/(2*du); + for (int i = 0; i < 3; i++) + dA_du[i] = (row_1[i] - row_2[i]) / (2 * du); return dA_du[dir_idx] * fields_f; - } else { + } + else { double orig = u[idx]; std::complex row_1[3], row_2[3], dA_du[3]; u[idx] -= du; - eff_chi1inv_row_disp(adjoint_c,row_1,r,freq,geps); - u[idx] += 2*du; - eff_chi1inv_row_disp(adjoint_c,row_2,r,freq,geps); + eff_chi1inv_row_disp(adjoint_c, row_1, r, freq, geps); + u[idx] += 2 * du; + eff_chi1inv_row_disp(adjoint_c, row_2, r, freq, geps); u[idx] = orig; - for (int i=0;i<3;i++) dA_du[i] = (row_1[i] - row_2[i])/(2*du); - return dA_du[dir_idx] * fields_f * cond_cmp(forward_c,r,freq,geps); + for (int i = 0; i < 3; i++) + dA_du[i] = (row_1[i] - row_2[i]) / (2 * du); + return dA_du[dir_idx] * fields_f * cond_cmp(forward_c, r, freq, geps); } } @@ -2736,27 +2702,24 @@ With the addition of subpixel smoothing, however, the vJp became much more complicated and it is easier to calculate the entire gradient using finite differences (at the cost of slightly less accurate gradients due to floating-point roundoff errors). */ -void add_interpolate_weights(double rx, double ry, double rz, - double *data, int nx, int ny, int nz, int stride, - double scaleby, double *udata, int ukind, double uval, - meep::vec r, geom_epsilon *geps, - meep::component adjoint_c, meep::component forward_c, - std::complex fwd, std::complex adj, - double freq, meep::grid_volume &gv, double du) { +void add_interpolate_weights(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride, double scaleby, double *udata, int ukind, double uval, + meep::vec r, geom_epsilon *geps, meep::component adjoint_c, + meep::component forward_c, std::complex fwd, + std::complex adj, double freq, meep::grid_volume &gv, + double du) { int x1, y1, z1, x2, y2, z2; double dx, dy, dz, u; - meep::map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz); - int x_list[2] = {x1,x2}, y_list[2] = {y1,y2}, z_list[2] = {z1,z2}; + meep::map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz); + int x_list[2] = {x1, x2}, y_list[2] = {y1, y2}, z_list[2] = {z1, z2}; int lx = (x1 == x2) ? 1 : 2; int ly = (y1 == y2) ? 1 : 2; int lz = (z1 == z2) ? 1 : 2; /* define a macro to give us data(x,y,z) on the grid, in row-major order (the order used by HDF5): */ -#define IDX(x,y,z) (((x)*ny + (y)) * nz + (z)) * stride +#define IDX(x, y, z) (((x)*ny + (y)) * nz + (z)) * stride #define D(x, y, z) (data[(((x)*ny + (y)) * nz + (z)) * stride]) #define U(x, y, z) (udata[(((x)*ny + (y)) * nz + (z)) * stride]) @@ -2770,13 +2733,13 @@ in row-major order (the order used by HDF5): */ if (ukind == material_data::U_MIN && u != uval) return; // TODO look into this if (ukind == material_data::U_PROD) scaleby *= uval / u; - for (int xi=0;xi prod = adj*get_material_gradient( - r,adjoint_c,forward_c,fwd,freq,geps,gv,du,udata,u_idx); + for (int xi = 0; xi < lx; xi++) { + for (int yi = 0; yi < ly; yi++) { + for (int zi = 0; zi < lz; zi++) { + int x = x_list[xi], y = y_list[yi], z = z_list[zi]; + int u_idx = IDX(x, y, z); + std::complex prod = adj * get_material_gradient(r, adjoint_c, forward_c, fwd, freq, + geps, gv, du, udata, u_idx); D(x, y, z) += prod.real() * scaleby; } } @@ -2821,7 +2784,9 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge } else if ((tp) && ((mg->material_grid_kinds == material_data::U_MIN) || (mg->material_grid_kinds == material_data::U_PROD))) { - meep::abort("%s:%i:material_grids_addgradient_point does not support overlapping MATERIAL_GRIDs with U_MIN or U_PROD.\n",__FILE__,__LINE__); + meep::abort("%s:%i:material_grids_addgradient_point does not support overlapping " + "MATERIAL_GRIDs with U_MIN or U_PROD.\n", + __FILE__, __LINE__); } // Iterate through grids and add weights as needed @@ -2843,62 +2808,66 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge uval = tanh_projection(matgrid_val(p, tp, oi, mg), mg->beta, mg->eta); do { vector3 pb = to_geom_box_coords(p, &tp->objects[oi]); - add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, - scalegrad, ucur, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, - fwd, adj, freq, gv, tol); + add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, ucur, kind, + uval, vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, + gv, tol); if (kind == material_data::U_DEFAULT) break; tp = geom_tree_search_next(p, tp, &oi); } while (tp && is_material_grid((material_data *)tp->objects[oi].o->material)); } // no object tree -- the whole domain is the material grid if (!tp && is_material_grid(default_material)) { - map_lattice_coordinates(p.x,p.y,p.z); + map_lattice_coordinates(p.x, p.y, p.z); vector3 sz = mg->grid_size; double *vcur = v; double *ucur = mg->weights; uval = tanh_projection(material_grid_val(p, mg), mg->beta, mg->eta); - add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, - scalegrad, ucur, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, - fwd, adj, freq, gv, tol); + add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, ucur, kind, uval, + vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, gv, tol); } } -void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fields_a, std::vector fields_f, - double *frequencies, double scalegrad, meep::grid_volume &gv, - meep::volume &where, geom_epsilon *geps, double du) { +void material_grids_addgradient(double *v, size_t ng, size_t nf, + std::vector fields_a, + std::vector fields_f, double *frequencies, + double scalegrad, meep::grid_volume &gv, meep::volume &where, + geom_epsilon *geps, double du) { /* ------------------------------------------------------------ */ // initialize local gradient array /* ------------------------------------------------------------ */ - double *v_local = new double[ng*nf]; - for (int i = 0; i < ng*nf; i++) { + double *v_local = new double[ng * nf]; + for (int i = 0; i < ng * nf; i++) { v_local[i] = 0; } /* ------------------------------------------------------------ */ // store chunk info in vectors for simplicity /* ------------------------------------------------------------ */ - std::vector> adjoint_dft_chunks; - std::vector> forward_dft_chunks; - for (int i=0;i<3;i++){ - std::vector c_adjoint_dft_chunks; - std::vector c_forward_dft_chunks; + std::vector > adjoint_dft_chunks; + std::vector > forward_dft_chunks; + for (int i = 0; i < 3; i++) { + std::vector c_adjoint_dft_chunks; + std::vector c_forward_dft_chunks; meep::dft_chunk *current_adjoint_chunk = fields_a[i]->chunks; meep::dft_chunk *current_forward_chunk = fields_f[i]->chunks; - while(current_adjoint_chunk) { - if (current_adjoint_chunk->omega.size() != nf) meep::abort("Supplied frequencies %d don't match dft frequencies %d\n",nf,current_adjoint_chunk->omega.size()); + while (current_adjoint_chunk) { + if (current_adjoint_chunk->omega.size() != nf) + meep::abort("Supplied frequencies %d don't match dft frequencies %d\n", nf, + current_adjoint_chunk->omega.size()); c_adjoint_dft_chunks.push_back(current_adjoint_chunk); current_adjoint_chunk = current_adjoint_chunk->next_in_dft; } - while(current_forward_chunk) { - if (current_forward_chunk->omega.size() != nf) meep::abort("Supplied frequencies %d don't match dft frequencies %d\n",nf,current_forward_chunk->omega.size()); + while (current_forward_chunk) { + if (current_forward_chunk->omega.size() != nf) + meep::abort("Supplied frequencies %d don't match dft frequencies %d\n", nf, + current_forward_chunk->omega.size()); c_forward_dft_chunks.push_back(current_forward_chunk); current_forward_chunk = current_forward_chunk->next_in_dft; } if (c_adjoint_dft_chunks.size() != c_forward_dft_chunks.size()) - meep::abort("The number of adjoint chunks (%ld) is not equal to the number of forward chunks (%ld).\n", - c_adjoint_dft_chunks.size(),c_forward_dft_chunks.size()); + meep::abort("The number of adjoint chunks (%ld) is not equal to the number of forward chunks " + "(%ld).\n", + c_adjoint_dft_chunks.size(), c_forward_dft_chunks.size()); adjoint_dft_chunks.push_back(c_adjoint_dft_chunks); forward_dft_chunks.push_back(c_forward_dft_chunks); } @@ -2909,56 +2878,58 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vectorc; - meep::grid_volume gv_adj = gv.subvolume(adj_chunk->is,adj_chunk->ie,adjoint_c); + meep::grid_volume gv_adj = gv.subvolume(adj_chunk->is, adj_chunk->ie, adjoint_c); // loop over forward components - for (int ci_forward=0; ci_forward<3; ci_forward++){ + for (int ci_forward = 0; ci_forward < 3; ci_forward++) { size_t num_f_chunks = forward_dft_chunks[ci_forward].size(); - if ((num_f_chunks == 0) || (cur_chunk>=num_f_chunks)) continue; - meep::dft_chunk* fwd_chunk = forward_dft_chunks[ci_forward][cur_chunk]; + if ((num_f_chunks == 0) || (cur_chunk >= num_f_chunks)) continue; + meep::dft_chunk *fwd_chunk = forward_dft_chunks[ci_forward][cur_chunk]; meep::component forward_c = fwd_chunk->c; - meep::grid_volume gv_fwd = gv.subvolume(fwd_chunk->is,fwd_chunk->ie,forward_c); - + meep::grid_volume gv_fwd = gv.subvolume(fwd_chunk->is, fwd_chunk->ie, forward_c); + // loop over each point of interest - LOOP_OVER_IVECS(gv_adj,adj_chunk->is_old,adj_chunk->ie_old,idx_adj){ - double cyl_scale; + LOOP_OVER_IVECS(gv_adj, adj_chunk->is_old, adj_chunk->ie_old, idx_adj) { + double cyl_scale; IVEC_LOOP_ILOC(gv_adj, ip); IVEC_LOOP_LOC(gv_adj, p); - std::complex adj = adj_chunk->dft[nf*idx_adj+f_i]; + std::complex adj = adj_chunk->dft[nf * idx_adj + f_i]; material_type md; geps->get_material_pt(md, p); /* if we have conductivities (e.g. for damping) then we need to make sure we correctly account for that here */ - if (!md->trivial) adj *= cond_cmp(adjoint_c,p,frequencies[f_i], geps); - + if (!md->trivial) adj *= cond_cmp(adjoint_c, p, frequencies[f_i], geps); + /**************************************/ /* Main Routine */ /**************************************/ - + /********* compute -λᵀAᵤx *************/ /* trivial case, no interpolation/restriction needed */ if (forward_c == adjoint_c) { - std::complex fwd = fwd_chunk->dft[nf*idx_adj+f_i]; - cyl_scale = (gv.dim == meep::Dcyl) ? 2*p.r() : 1; // the pi is already factored in near2far.cpp - material_grids_addgradient_point( - v_local+ng*f_i, vec_to_vector3(p), scalegrad*cyl_scale, geps, - adjoint_c, forward_c, fwd, adj, frequencies[f_i], gv, du); - /* more complicated case requires interpolation/restriction */ - } else if ((md->do_averaging) || /* account for subpixel smoothing */ - (!is_material_grid(md)) || /* account for edge effects of mg */ - (has_offdiag(&(md->medium_1))) || /* account for offdiagonal components */ + std::complex fwd = fwd_chunk->dft[nf * idx_adj + f_i]; + cyl_scale = (gv.dim == meep::Dcyl) ? 2 * p.r() + : 1; // the pi is already factored in near2far.cpp + material_grids_addgradient_point(v_local + ng * f_i, vec_to_vector3(p), + scalegrad * cyl_scale, geps, adjoint_c, forward_c, + fwd, adj, frequencies[f_i], gv, du); + /* more complicated case requires interpolation/restriction */ + } + else if ((md->do_averaging) || /* account for subpixel smoothing */ + (!is_material_grid(md)) || /* account for edge effects of mg */ + (has_offdiag(&(md->medium_1))) || /* account for offdiagonal components */ (has_offdiag(&(md->medium_2)))) { /* we need to restrict the adjoint fields to the two nodes of interest (which requires a factor @@ -2969,35 +2940,40 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fwd_avg, fwd1, fwd2; ptrdiff_t fwd1_idx, fwd2_idx; - //identify the first corner of the forward fields + // identify the first corner of the forward fields meep::ivec fwd_p = ip + gv.iyee_shift(forward_c) - gv.iyee_shift(adjoint_c); - //identify the other three corners - meep::ivec unit_a = unit_ivec(gv.dim,component_direction(adjoint_c)); - meep::ivec unit_f = unit_ivec(gv.dim,component_direction(forward_c)); - meep::ivec fwd_pa = (fwd_p + unit_a*2); - meep::ivec fwd_pf = (fwd_p - unit_f*2); - meep::ivec fwd_paf = (fwd_p + unit_a*2 - unit_f*2); + // identify the other three corners + meep::ivec unit_a = unit_ivec(gv.dim, component_direction(adjoint_c)); + meep::ivec unit_f = unit_ivec(gv.dim, component_direction(forward_c)); + meep::ivec fwd_pa = (fwd_p + unit_a * 2); + meep::ivec fwd_pf = (fwd_p - unit_f * 2); + meep::ivec fwd_paf = (fwd_p + unit_a * 2 - unit_f * 2); // store in vector for convenience std::vector fwd_pl = {fwd_p, fwd_pa}; std::vector fwd_pr = {fwd_pf, fwd_paf}; - //identify the two eps points + // identify the two eps points std::vector ieps = {(fwd_p + fwd_pf) / 2, (fwd_pa + fwd_paf) / 2}; - //operate on the each eps node - #pragma unroll - for (int node=0;node<2;node++) { // two nodes - fwd1_idx = gv_fwd.index(forward_c,fwd_pl[node]); - fwd1 = ((fwd1_idx >= fwd_chunk->N) || (fwd1_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd1_idx+f_i]; - fwd2_idx = gv_fwd.index(forward_c,fwd_pr[node]); - fwd2 = ((fwd2_idx >= fwd_chunk->N) || (fwd2_idx<0)) ? 0 : fwd_chunk->dft[nf*fwd2_idx+f_i]; - fwd_avg = std::complex(0.5,0) * (fwd1 + fwd2); +// operate on the each eps node +#pragma unroll + for (int node = 0; node < 2; node++) { // two nodes + fwd1_idx = gv_fwd.index(forward_c, fwd_pl[node]); + fwd1 = ((fwd1_idx >= fwd_chunk->N) || (fwd1_idx < 0)) + ? 0 + : fwd_chunk->dft[nf * fwd1_idx + f_i]; + fwd2_idx = gv_fwd.index(forward_c, fwd_pr[node]); + fwd2 = ((fwd2_idx >= fwd_chunk->N) || (fwd2_idx < 0)) + ? 0 + : fwd_chunk->dft[nf * fwd2_idx + f_i]; + fwd_avg = std::complex(0.5, 0) * (fwd1 + fwd2); meep::vec eps1 = gv[ieps[node]]; cyl_scale = (gv.dim == meep::Dcyl) ? eps1.r() : 1; - material_grids_addgradient_point( - v_local+ng*f_i, vec_to_vector3(eps1), scalegrad*cyl_scale, geps, - adjoint_c, forward_c, fwd_avg, std::complex(0.5,0)*adj, frequencies[f_i], gv, du); + material_grids_addgradient_point(v_local + ng * f_i, vec_to_vector3(eps1), + scalegrad * cyl_scale, geps, adjoint_c, forward_c, + fwd_avg, std::complex(0.5, 0) * adj, + frequencies[f_i], gv, du); } } /********* compute λᵀbᵤ ***************/ @@ -3012,17 +2988,17 @@ void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector max_val) - max_val = data[i]; + if (data[i] < min_val) min_val = data[i]; + if (data[i] > max_val) max_val = data[i]; } return; } -void get_epsilon_grid(geometric_object_list gobj_list, - material_type_list mlist, - material_type _default_material, - bool _ensure_periodicity, - meep::grid_volume gv, - vector3 cell_size, - vector3 cell_center, - int nx, const double *x, - int ny, const double *y, - int nz, const double *z, - std::complex *grid_vals, - double frequency) { +void get_epsilon_grid(geometric_object_list gobj_list, material_type_list mlist, + material_type _default_material, bool _ensure_periodicity, + meep::grid_volume gv, vector3 cell_size, vector3 cell_center, int nx, + const double *x, int ny, const double *y, int nz, const double *z, + std::complex *grid_vals, double frequency) { double min_val[3], max_val[3]; for (int n = 0; n < 3; ++n) { int ndir = (n == 0) ? nx : ((n == 1) ? ny : nz); @@ -3062,10 +3028,9 @@ void get_epsilon_grid(geometric_object_list gobj_list, const double *adir = (n == 0) ? x : ((n == 1) ? y : z); find_array_min_max(ndir, adir, min_val[n], max_val[n]); } - const meep::volume vol(meep::vec(min_val[0],min_val[1],min_val[2]), - meep::vec(max_val[0],max_val[1],max_val[2])); - init_libctl(_default_material, _ensure_periodicity, &gv, - cell_size, cell_center, &gobj_list); + const meep::volume vol(meep::vec(min_val[0], min_val[1], min_val[2]), + meep::vec(max_val[0], max_val[1], max_val[2])); + init_libctl(_default_material, _ensure_periodicity, &gv, cell_size, cell_center, &gobj_list); dim = gv.dim; geom_epsilon geps(gobj_list, mlist, vol); for (int i = 0; i < nx; ++i) @@ -3074,11 +3039,12 @@ void get_epsilon_grid(geometric_object_list gobj_list, /* obtain the trace of the ε tensor (dispersive or non) for each grid point in row-major order (the order used by NumPy) */ if (frequency == 0) - grid_vals[k + nz*(j + ny*i)] = geps.chi1p1(meep::E_stuff, meep::vec(x[i],y[j],z[k])); + grid_vals[k + nz * (j + ny * i)] = + geps.chi1p1(meep::E_stuff, meep::vec(x[i], y[j], z[k])); else { std::complex tensor[9]; - get_chi1_tensor_disp(tensor, meep::vec(x[i],y[j],z[k]), frequency, &geps); - grid_vals[k + nz*(j + ny*i)] = (tensor[0] + tensor[4] + tensor[8]) / 3.0; + get_chi1_tensor_disp(tensor, meep::vec(x[i], y[j], z[k]), frequency, &geps); + grid_vals[k + nz * (j + ny * i)] = (tensor[0] + tensor[4] + tensor[8]) / 3.0; } } } diff --git a/src/meepgeom.hpp b/src/meepgeom.hpp index ccbb1296e..ef77c74ac 100644 --- a/src/meepgeom.hpp +++ b/src/meepgeom.hpp @@ -177,8 +177,8 @@ class geom_epsilon : public meep::material_function { geom_box_tree restricted_tree; geometric_object_list geometry; cond_profile cond[5][2]; // [direction][side] - double tol=DEFAULT_SUBPIXEL_TOL; - int maxeval=DEFAULT_SUBPIXEL_MAXEVAL; + double tol = DEFAULT_SUBPIXEL_TOL; + int maxeval = DEFAULT_SUBPIXEL_MAXEVAL; geom_epsilon(geometric_object_list g, material_type_list mlist, const meep::volume &v); geom_epsilon(const geom_epsilon &geps1); // copy constructor @@ -207,8 +207,8 @@ class geom_epsilon : public meep::material_function { virtual void eff_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval); - void eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, - const meep::volume &v, double tol, int maxeval, bool &fallback); + void eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, const meep::volume &v, + double tol, int maxeval, bool &fallback); void fallback_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval); @@ -218,17 +218,17 @@ class geom_epsilon : public meep::material_function { void add_susceptibilities(meep::field_type ft, meep::structure *s); void get_material_pt(material_type &material, const meep::vec &r); + private: material_type_list extra_materials; pol *current_pol; }; void set_dimensions(int dims); -geom_epsilon* make_geom_epsilon(meep::structure *s, geometric_object_list *g, - vector3 center = make_vector3(), - bool ensure_periodicity = false, - material_type _default_material = vacuum, - material_type_list extra_materials = material_type_list()); +geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, + vector3 center = make_vector3(), bool ensure_periodicity = false, + material_type _default_material = vacuum, + material_type_list extra_materials = material_type_list()); //, geometric_object_list g, material_type_list extra_materials void set_materials_from_geometry(meep::structure *s, geometric_object_list g, vector3 center = make_vector3(), @@ -239,10 +239,10 @@ void set_materials_from_geometry(meep::structure *s, geometric_object_list g, material_type _default_material = vacuum, absorber_list alist = 0, material_type_list extra_materials = material_type_list()); void set_materials_from_geom_epsilon(meep::structure *s, geom_epsilon *geps, - bool use_anisotropic_averaging = true, - double tol = DEFAULT_SUBPIXEL_TOL, - int maxeval = DEFAULT_SUBPIXEL_MAXEVAL, - absorber_list alist = 0); + bool use_anisotropic_averaging = true, + double tol = DEFAULT_SUBPIXEL_TOL, + int maxeval = DEFAULT_SUBPIXEL_MAXEVAL, + absorber_list alist = 0); material_type make_dielectric(double epsilon); material_type make_user_material(user_material_func user_func, void *user_data, bool do_averaging); @@ -272,21 +272,13 @@ bool is_medium(material_type md, medium_struct **m); bool is_medium(void *md, medium_struct **m); bool is_metal(meep::field_type ft, const material_type *material); geom_box gv2box(const meep::volume &v); -void get_epsilon_grid(geometric_object_list gobj_list, - material_type_list mlist, - material_type _default_material, - bool _ensure_periodicity, - meep::grid_volume gv, - vector3 cell_size, - vector3 cell_center, - int nx, const double *x, - int ny, const double *y, - int nz, const double *z, - std::complex *grid_vals, - double frequency = 0); -void init_libctl(material_type default_mat, bool ensure_per, - meep::grid_volume *gv, vector3 cell_size, vector3 cell_center, - geometric_object_list *geom_list); +void get_epsilon_grid(geometric_object_list gobj_list, material_type_list mlist, + material_type _default_material, bool _ensure_periodicity, + meep::grid_volume gv, vector3 cell_size, vector3 cell_center, int nx, + const double *x, int ny, const double *y, int nz, const double *z, + std::complex *grid_vals, double frequency = 0); +void init_libctl(material_type default_mat, bool ensure_per, meep::grid_volume *gv, + vector3 cell_size, vector3 cell_center, geometric_object_list *geom_list); /***************************************************************/ // material grid functions @@ -297,10 +289,11 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md); double material_grid_val(vector3 p, material_data *md); geom_box_tree calculate_tree(const meep::volume &v, geometric_object_list g); -void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fields_a, - std::vectorfields_f, - double *frequencies, double scalegrad, meep::grid_volume &gv, - meep::volume &where, geom_epsilon *geps, double du = 1e-6); +void material_grids_addgradient(double *v, size_t ng, size_t nf, + std::vector fields_a, + std::vector fields_f, double *frequencies, + double scalegrad, meep::grid_volume &gv, meep::volume &where, + geom_epsilon *geps, double du = 1e-6); /***************************************************************/ /* routines in GDSIIgeom.cc ************************************/ diff --git a/src/monitor.cpp b/src/monitor.cpp index 8b355defe..84439f65a 100644 --- a/src/monitor.cpp +++ b/src/monitor.cpp @@ -178,7 +178,9 @@ complex fields::get_chi1inv(component c, direction d, const ivec &origlo 0); return parallel ? sum_to_all(val) : val; } - return d == component_direction(c) && (parallel || am_master()) ? 1.0 : 0; // default to vacuum outside computational cell + return d == component_direction(c) && (parallel || am_master()) + ? 1.0 + : 0; // default to vacuum outside computational cell } complex fields_chunk::get_chi1inv(component c, direction d, const ivec &iloc, diff --git a/src/mpb.cpp b/src/mpb.cpp index db63ccf9a..b337c667d 100644 --- a/src/mpb.cpp +++ b/src/mpb.cpp @@ -298,9 +298,9 @@ void special_kz_phasefix(eigenmode_data *edata, bool phase_flip) { complex *H = (complex *)edata->fft_data_H; complex im(0, phase_flip ? -1 : 1); for (size_t i = 0; i < n; ++i) { - E[3*i + 2] *= im; // Ez - H[3*i + 0] *= im; // Hx - H[3*i + 1] *= im; // Hy + E[3 * i + 2] *= im; // Ez + H[3 * i + 0] *= im; // Hx + H[3 * i + 1] *= im; // Hy } } @@ -352,8 +352,7 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c // special case: 2d cell in x and y with non-zero kz if ((v.dim == D3) && (float(v.in_direction(Z)) == float(1 / a)) && - (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && - (real(k[Z]) != 0)) + (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && (real(k[Z]) != 0)) empty_dim[2] = true; if (resolution <= 0.0) resolution = 2 * gv.a; // default to twice resolution @@ -533,7 +532,7 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c if (am_master()) { set_random_seed(314159); for (int i = 0; i < H.n * H.p; ++i) { - ASSIGN_SCALAR(H.data[i], uniform_random(-1,1), uniform_random(-1,1)); + ASSIGN_SCALAR(H.data[i], uniform_random(-1, 1), uniform_random(-1, 1)); } restore_random_seed(); } @@ -604,17 +603,15 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c k[i] = dot_product(R[i], kdir) * kmatch; // kdir*kmatch in reciprocal basis if (gv.dim == D2) k[2] = beta; } - else { - k[d - X] = kmatch * R[d - X][d - X]; - } + else { k[d - X] = kmatch * R[d - X][d - X]; } update_maxwell_data_k(mdata, k, G[0], G[1], G[2]); } } while (match_frequency && fabs(sqrt(eigvals[band_num - 1]) - frequency) > frequency * match_tol); if (dp) { - scalar_complex s = {real(dp->get_s()),imag(dp->get_s())}; - scalar_complex p = {real(dp->get_p()),imag(dp->get_p())}; + scalar_complex s = {real(dp->get_s()), imag(dp->get_s())}; + scalar_complex p = {real(dp->get_p()), imag(dp->get_p())}; // compute sum of (kparallel+G)^2 in all the periodic directions double k2sum = 0, ktmp = 0; @@ -622,14 +619,15 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c LOOP_OVER_DIRECTIONS(v.dim, dd) { m = dp->get_g()[dd - X]; if (eig_vol.in_direction(dd) != 0) { - ktmp = kpoint.in_direction(dd) + m/eig_vol.in_direction(dd); - k2sum += ktmp*ktmp; + ktmp = kpoint.in_direction(dd) + m / eig_vol.in_direction(dd); + k2sum += ktmp * ktmp; } } if (((v.dim == D3) && (float(v.in_direction(Z)) == float(1 / a)) && - (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && (real(k[Z]) != 0)) || + (boundaries[High][Z] == Periodic && boundaries[Low][Z] == Periodic) && + (real(k[Z]) != 0)) || ((v.dim == D2) && (real(k[Z]) != 0))) { - k2sum += k[Z]*k[Z]; + k2sum += k[Z] * k[Z]; } // compute kperp (if it is non evanescent) OR @@ -647,23 +645,23 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c if (match_frequency) { vec cen = eig_vol.center(); double nn = sqrt(real(get_eps(cen, frequency)) * real(get_mu(cen, frequency))); - double k2 = frequency*frequency*nn*nn - k2sum; + double k2 = frequency * frequency * nn * nn - k2sum; if (k2 < 0) { master_printf("WARNING: diffraction order for g=(%d,%d,%d) is evanescent!\n", - dp->get_g()[0],dp->get_g()[1],dp->get_g()[2]); + dp->get_g()[0], dp->get_g()[1], dp->get_g()[2]); return NULL; } else if (k2 > 0) k[dd - X] = sqrt(k2); } else - frequency = sqrt(kpoint.in_direction(dd)*kpoint.in_direction(dd) + k2sum); + frequency = sqrt(kpoint.in_direction(dd) * kpoint.in_direction(dd) + k2sum); } if (am_master()) { update_maxwell_data_k(mdata, k, G[0], G[1], G[2]); maxwell_set_planewave(mdata, H, band_num, dp->get_g(), s, p, dp->get_axis()); - eigvals[band_num - 1] = frequency*frequency; + eigvals[band_num - 1] = frequency * frequency; evectmatrix_resize(&W[0], 1, 0); evectmatrix_resize(&W[1], 1, 0); for (int i = 0; i < H.n; ++i) @@ -751,7 +749,8 @@ void *fields::get_eigenmode(double frequency, direction d, const volume where, c // so we need to divide the E-field amplitudes by -frequency; we also take this // opportunity to rescale the overall E and H amplitudes to yield unit power flux. double scale = -1.0 / frequency, factor = 2.0 / sqrt(fabs(vgrp)); - complex *efield = (complex *)fft_data_E, *hfield = (complex *)(mdata->fft_data); + complex *efield = (complex *)fft_data_E, + *hfield = (complex *)(mdata->fft_data); for (int n = 0; n < NFFT; ++n) { efield[n] *= factor * scale; hfield[n] *= factor; @@ -824,8 +823,7 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d const volume &where, const volume &eig_vol, int band_num, const vec &kpoint, bool match_frequency, int parity, double resolution, double eigensolver_tol, complex amp, - complex A(const vec &), - diffractedplanewave *dp) { + complex A(const vec &), diffractedplanewave *dp) { /*--------------------------------------------------------------*/ /* step 1: call MPB to compute the eigenmode */ /*--------------------------------------------------------------*/ @@ -833,10 +831,9 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d double frequency = real(src.frequency()); am_now_working_on(MPBTime); - global_eigenmode_data = - (eigenmode_data *)get_eigenmode(frequency, d, where, eig_vol, band_num, kpoint, - match_frequency, parity, resolution, eigensolver_tol, - NULL, NULL, dp); + global_eigenmode_data = (eigenmode_data *)get_eigenmode( + frequency, d, where, eig_vol, band_num, kpoint, match_frequency, parity, resolution, + eigensolver_tol, NULL, NULL, dp); finished_working(); if (is_real && beta != 0) special_kz_phasefix(global_eigenmode_data, true /* phase_flip */); @@ -849,8 +846,9 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d global_eigenmode_data->center -= where.center(); if (!global_eigenmode_data) - meep::abort("eigenmode solver failed to find the requested mode; you may need to supply a better " - "guess for k"); + meep::abort( + "eigenmode solver failed to find the requested mode; you may need to supply a better " + "guess for k"); global_eigenmode_data->amp_func = A ? A : default_amp_func; @@ -868,9 +866,10 @@ void fields::add_eigenmode_source(component c0, const src_time &src, direction d component cE[3] = {Ex, Ey, Ez}, cH[3] = {Hx, Hy, Hz}; int n = (d == X ? 0 : (d == Y ? 1 : 2)); if (d == NO_DIRECTION) { - n = where.in_direction(X) == 0 - ? 0 - : where.in_direction(Y) == 0 ? 1 : where.in_direction(Z) == 0 ? 2 : -1; + n = where.in_direction(X) == 0 ? 0 + : where.in_direction(Y) == 0 ? 1 + : where.in_direction(Z) == 0 ? 2 + : -1; if (n == -1) meep::abort( "can't determine source direction for non-empty source volume with NO_DIRECTION source"); @@ -926,7 +925,7 @@ void fields::get_eigenmode_coefficients(dft_flux flux, const volume &eig_vol, in bool match_frequency = true; if (flux.use_symmetry && S.multiplicity() > 1 && parity == 0) meep::abort("flux regions for eigenmode projection with symmetry should be created by " - "add_mode_monitor()"); + "add_mode_monitor()"); vec kpoint(0.0, 0.0, 0.0); // default guess @@ -955,7 +954,8 @@ void fields::get_eigenmode_coefficients(dft_flux flux, const volume &eig_vol, in continue; } - if (is_real && beta != 0) special_kz_phasefix((eigenmode_data *) mode_data, false /* phase_flip */); + if (is_real && beta != 0) + special_kz_phasefix((eigenmode_data *)mode_data, false /* phase_flip */); double vg = get_group_velocity(mode_data); vec kfound = get_k(mode_data); diff --git a/src/multilevel-atom.cpp b/src/multilevel-atom.cpp index df9c2213f..a838d36e8 100644 --- a/src/multilevel-atom.cpp +++ b/src/multilevel-atom.cpp @@ -159,7 +159,8 @@ void multilevel_susceptibility::init_internal_data(realnum *W[NUM_FIELD_COMPONEN for (int i = 0; i < L; ++i) for (int j = 0; j < L; ++j) d->GammaInv[i * L + j] = (i == j) + Gamma[i * L + j] * dt / 2; - if (!invert(d->GammaInv, L)) meep::abort("multilevel_susceptibility: I + Gamma*dt/2 matrix singular"); + if (!invert(d->GammaInv, L)) + meep::abort("multilevel_susceptibility: I + Gamma*dt/2 matrix singular"); realnum *P = d->data + L * L; realnum *P_prev = P + ntot; diff --git a/src/mympi.cpp b/src/mympi.cpp index 501e987bf..57da0ad73 100644 --- a/src/mympi.cpp +++ b/src/mympi.cpp @@ -161,25 +161,24 @@ int verbosity = 1; // defined in meep.h code based on github.com/JuliaLang/julia/blob/master/src/processor_x86.cpp#L1087-L1104, which is free software under the GPL-compatible "MIT license" */ -static void _set_zero_subnormals(bool iszero) -{ +static void _set_zero_subnormals(bool iszero) { #if HAVE_IMMINTRIN_H - unsigned int flags = 0x00008040; // assume a non-ancient processor with SSE2, supporting both FTZ and DAZ flags - unsigned int state = _mm_getcsr(); - if (iszero) - state |= flags; - else - state &= ~flags; - _mm_setcsr(state); + unsigned int flags = + 0x00008040; // assume a non-ancient processor with SSE2, supporting both FTZ and DAZ flags + unsigned int state = _mm_getcsr(); + if (iszero) + state |= flags; + else + state &= ~flags; + _mm_setcsr(state); #else - (void) iszero; // unused + (void)iszero; // unused #endif } -void set_zero_subnormals(bool iszero) -{ +void set_zero_subnormals(bool iszero) { int n = omp_get_num_threads(); #ifdef _OPENMP -#pragma omp parallel for schedule(static,1) +#pragma omp parallel for schedule(static, 1) #endif for (int i = 0; i < n; ++i) _set_zero_subnormals(iszero); // This has to be done in every thread for OpenMP. @@ -188,8 +187,7 @@ void set_zero_subnormals(bool iszero) void setup() { set_zero_subnormals(true); #ifdef _OPENMP - if (getenv("OMP_NUM_THREADS") == NULL) - omp_set_num_threads(1); + if (getenv("OMP_NUM_THREADS") == NULL) omp_set_num_threads(1); #endif } @@ -199,7 +197,7 @@ initialize::initialize(int &argc, char **&argv) { int provided; MPI_Init_thread(&argc, &argv, MPI_THREAD_FUNNELED, &provided); if (provided < MPI_THREAD_FUNNELED && omp_get_num_threads() > 1) - abort("MPI does not support multi-threaded execution"); + abort("MPI does not support multi-threaded execution"); #else MPI_Init(&argc, &argv); #endif @@ -257,7 +255,7 @@ double wall_time(void) { fprintf(stderr, "meep: %s", error_msg.c_str()); if (fmt[strlen(fmt) - 1] != '\n') fputc('\n', stderr); // force newline MPI_Abort(MPI_COMM_WORLD, 1); - std::abort(); // Unreachable but MPI_Abort does not have the noreturn attribute. + std::abort(); // Unreachable but MPI_Abort does not have the noreturn attribute. #else throw runtime_error("meep: " + error_msg); #endif @@ -686,8 +684,8 @@ meep_printf_callback_func set_meep_printf_stderr_callback(meep_printf_callback_f return old_func; } -static void _do_master_printf(FILE* output, meep_printf_callback_func callback, - const char *fmt, va_list ap) { +static void _do_master_printf(FILE *output, meep_printf_callback_func callback, const char *fmt, + va_list ap) { if (am_really_master()) { if (callback) { char *s; diff --git a/src/near2far.cpp b/src/near2far.cpp index 92dacdf0f..ff8f5daf5 100644 --- a/src/near2far.cpp +++ b/src/near2far.cpp @@ -43,7 +43,7 @@ dft_near2far::dft_near2far(dft_chunk *F_, double fmin, double fmax, int Nf, doub } } -dft_near2far::dft_near2far(dft_chunk *F_, const std::vector& freq_, double eps_, double mu_, +dft_near2far::dft_near2far(dft_chunk *F_, const std::vector &freq_, double eps_, double mu_, const volume &where_, const direction periodic_d_[2], const int periodic_n_[2], const double periodic_k_[2], const double period_[2]) @@ -151,8 +151,7 @@ void green3d(std::complex *EH, const vec &x, double freq, double eps, do vec p = zero_vec(rhat.dim); p.set_direction(component_direction(c0), 1); double pdotrhat = p & rhat; - vec rhatcrossp = vec(rhat.y() * p.z() - rhat.z() * p.y(), - rhat.z() * p.x() - rhat.x() * p.z(), + vec rhatcrossp = vec(rhat.y() * p.z() - rhat.z() * p.y(), rhat.z() * p.x() - rhat.x() * p.z(), rhat.x() * p.y() - rhat.y() * p.x()); /* compute the various scalar quantities in the point source formulae */ @@ -632,7 +631,8 @@ dft_near2far fields::add_dft_near2far(const volume_list *where, const double *fr double s = j == 0 ? 1 : -1; /* sign of n x c */ if (is_electric(c)) s = -s; - F = add_dft(c, w->v, freq, Nfreq, true, s * w->weight, F, false, 1.0, false, c0, decimation_factor); + F = add_dft(c, w->v, freq, Nfreq, true, s * w->weight, F, false, 1.0, false, c0, + decimation_factor); } } } @@ -641,8 +641,10 @@ dft_near2far fields::add_dft_near2far(const volume_list *where, const double *fr period); } -//Modified from farfield_lowlevel -std::vector dft_near2far::near_sourcedata(const vec &x_0, double* farpt_list, size_t nfar_pts, const std::complex* dJ) { +// Modified from farfield_lowlevel +std::vector dft_near2far::near_sourcedata(const vec &x_0, double *farpt_list, + size_t nfar_pts, + const std::complex *dJ) { if (x_0.dim != D3 && x_0.dim != D2 && x_0.dim != Dcyl) meep::abort("only 2d or 3d or cylindrical far-field computation is supported"); greenfunc green = x_0.dim == D2 ? green2d : green3d; @@ -657,49 +659,49 @@ std::vector dft_near2far::near_sourcedata(const vec &x_0, dou component c0 = component(f->vc); /* equivalent source component */ vec rshift(f->shift * (0.5 * f->fc->gv.inva)); - std::complex EH6[6]; - size_t idx_dft = 0; - sourcedata temp_struct = {component(f->c), idx_arr, f->fc->chunk_idx, amp_arr}; - - LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { - IVEC_LOOP_ILOC(f->fc->gv, ix0); - IVEC_LOOP_LOC(f->fc->gv, x0); - x0 = f->S.transform(x0, f->sn) + rshift; - vec xs(x0); - double w; - w = IVEC_LOOP_WEIGHT(f->s0, f->s1, f->e0, f->e1, f->dV0 + f->dV1 * loop_i2); - - temp_struct.idx_arr.push_back(idx); - for (size_t i = 0; i < Nfreq; ++i) { - std::complex EH0 = std::complex(0,0); - for (int i0 = -periodic_n[0]; i0 <= periodic_n[0]; ++i0) { - if (periodic_d[0] != NO_DIRECTION) - xs.set_direction(periodic_d[0], x0.in_direction(periodic_d[0]) + i0 * period[0]); - double phase0 = i0 * periodic_k[0]; - for (int i1 = -periodic_n[1]; i1 <= periodic_n[1]; ++i1) { - if (periodic_d[1] != NO_DIRECTION) - xs.set_direction(periodic_d[1], x0.in_direction(periodic_d[1]) + i1 * period[1]); - double phase = phase0 + i1 * periodic_k[1]; - std::complex cphase = std::polar(1.0, phase); - for (size_t ipt = 0; ipt < nfar_pts; ++ipt){ - vec x = vec(farpt_list[3*ipt], farpt_list[3*ipt+1], farpt_list[3*ipt+2]); - if (x_0.dim == Dcyl) - greencyl(EH6, x, freq[i], eps, mu, xs, c0, w, f->fc->m, 1e-3); - else - green(EH6, x, freq[i], eps, mu, xs, c0, w); - for (int j = 0; j < 6; ++j) - EH0 += EH6[j] * cphase * (f->stored_weight) * dJ[6*Nfreq*ipt + 6*i + j]; - } + std::complex EH6[6]; + size_t idx_dft = 0; + sourcedata temp_struct = {component(f->c), idx_arr, f->fc->chunk_idx, amp_arr}; + + LOOP_OVER_IVECS(f->fc->gv, f->is, f->ie, idx) { + IVEC_LOOP_ILOC(f->fc->gv, ix0); + IVEC_LOOP_LOC(f->fc->gv, x0); + x0 = f->S.transform(x0, f->sn) + rshift; + vec xs(x0); + double w; + w = IVEC_LOOP_WEIGHT(f->s0, f->s1, f->e0, f->e1, f->dV0 + f->dV1 * loop_i2); + + temp_struct.idx_arr.push_back(idx); + for (size_t i = 0; i < Nfreq; ++i) { + std::complex EH0 = std::complex(0, 0); + for (int i0 = -periodic_n[0]; i0 <= periodic_n[0]; ++i0) { + if (periodic_d[0] != NO_DIRECTION) + xs.set_direction(periodic_d[0], x0.in_direction(periodic_d[0]) + i0 * period[0]); + double phase0 = i0 * periodic_k[0]; + for (int i1 = -periodic_n[1]; i1 <= periodic_n[1]; ++i1) { + if (periodic_d[1] != NO_DIRECTION) + xs.set_direction(periodic_d[1], x0.in_direction(periodic_d[1]) + i1 * period[1]); + double phase = phase0 + i1 * periodic_k[1]; + std::complex cphase = std::polar(1.0, phase); + for (size_t ipt = 0; ipt < nfar_pts; ++ipt) { + vec x = vec(farpt_list[3 * ipt], farpt_list[3 * ipt + 1], farpt_list[3 * ipt + 2]); + if (x_0.dim == Dcyl) + greencyl(EH6, x, freq[i], eps, mu, xs, c0, w, f->fc->m, 1e-3); + else + green(EH6, x, freq[i], eps, mu, xs, c0, w); + for (int j = 0; j < 6; ++j) + EH0 += EH6[j] * cphase * (f->stored_weight) * dJ[6 * Nfreq * ipt + 6 * i + j]; + } } } idx_dft++; - if (is_electric(temp_struct.near_fd_comp)) - EH0 *= -1; + if (is_electric(temp_struct.near_fd_comp)) EH0 *= -1; EH0 /= f->S.multiplicity(ix0); - if (x_0.dim == Dcyl){ + if (x_0.dim == Dcyl) { if (xs.r() != 0) EH0 /= xs.r(); - //Somehow, a factor of (2pi)r for r of the near2far region was double counted. - //2pi is canceled out by the integral over design region, where an extra factor of 2pi*r' (r' of the design region) is needed. See meepgeom.cpp + // Somehow, a factor of (2pi)r for r of the near2far region was double counted. + // 2pi is canceled out by the integral over design region, where an extra factor of 2pi*r' + // (r' of the design region) is needed. See meepgeom.cpp } temp_struct.amp_arr.push_back(EH0); } diff --git a/src/output_directory.cpp b/src/output_directory.cpp index caec99187..0b207cced 100644 --- a/src/output_directory.cpp +++ b/src/output_directory.cpp @@ -109,9 +109,7 @@ const char *make_output_directory(const char *exename, const char *jobname) { snprintf(sourcename, buflen, "%s.cpp", stripped_name); if (jobname != NULL) { snprintf(basename, buflen, "%s", jobname); } - else { - snprintf(basename, buflen, "%s", stripped_name); - } + else { snprintf(basename, buflen, "%s", stripped_name); } static char outdirname[buflen]; snprintf(outdirname, buflen, "%s-out", basename); diff --git a/src/random.cpp b/src/random.cpp index 928ca6738..74a4d7236 100644 --- a/src/random.cpp +++ b/src/random.cpp @@ -65,9 +65,7 @@ double gaussian_random(double mean, double stddev) { s = v1 * v1 + v2 * v2; } while (s >= 1.0); if (s == 0) { return mean; } - else { - return mean + v1 * sqrt(-2 * log(s) / s) * stddev; - } + else { return mean + v1 * sqrt(-2 * log(s) / s) * stddev; } } } // namespace meep diff --git a/src/sources.cpp b/src/sources.cpp index cc1dc91d3..61a55cde8 100644 --- a/src/sources.cpp +++ b/src/sources.cpp @@ -254,7 +254,7 @@ static void src_vol_chunkloop(fields_chunk *fc, int ichunk, component c, ivec is size_t npts = 1; LOOP_OVER_DIRECTIONS(is.dim, d) { npts *= (ie.in_direction(d) - is.in_direction(d)) / 2 + 1; } std::vector index_array(npts); - std::vector> amps_array(npts); + std::vector > amps_array(npts); complex amp = data->amp * conj(shift_phase); @@ -283,29 +283,33 @@ static void src_vol_chunkloop(fields_chunk *fc, int ichunk, component c, ivec is index_array[idx_vol++] = idx; } - if (idx_vol > npts) meep::abort("add_volume_source: computed wrong npts (%zd vs. %zd)", npts, idx_vol); + if (idx_vol > npts) + meep::abort("add_volume_source: computed wrong npts (%zd vs. %zd)", npts, idx_vol); field_type ft = is_magnetic(c) ? B_stuff : D_stuff; fc->add_source(ft, src_vol(c, data->src, std::move(index_array), std::move(amps_array))); } -void fields::add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, std::complex* amp_arr, bool needs_boundary_fix){ +void fields::add_srcdata(struct sourcedata cur_data, src_time *src, size_t n, + std::complex *amp_arr, bool needs_boundary_fix) { if (n == 0) { - n = cur_data.idx_arr.size(); - assert(amp_arr == NULL); - amp_arr = cur_data.amp_arr.data(); + n = cur_data.idx_arr.size(); + assert(amp_arr == NULL); + amp_arr = cur_data.amp_arr.data(); } assert(n == cur_data.idx_arr.size()); sources = src->add_to(sources, &src); std::vector index_arr(cur_data.idx_arr); - std::vector> amplitudes(amp_arr, amp_arr+n); + std::vector > amplitudes(amp_arr, amp_arr + n); component c = cur_data.near_fd_comp; field_type ft = is_magnetic(c) ? B_stuff : D_stuff; - if (0 > cur_data.fc_idx or cur_data.fc_idx >= num_chunks) meep::abort("fields chunk index out of range"); + if (0 > cur_data.fc_idx or cur_data.fc_idx >= num_chunks) + meep::abort("fields chunk index out of range"); fields_chunk *fc = chunks[cur_data.fc_idx]; if (!fc->is_mine()) meep::abort("wrong fields chunk"); - fc->add_source(ft, src_vol(c, src, std::move(index_arr), std::move(amplitudes), needs_boundary_fix)); + fc->add_source(ft, + src_vol(c, src, std::move(index_arr), std::move(amplitudes), needs_boundary_fix)); // We can't do require_component(c) since that only works if all processes are adding // srcdata for the same components in the same order, which may not be true. // ... instead, the caller should call fields::require_source_components() @@ -402,13 +406,15 @@ void fields::add_volume_source(component c, const src_time &src, const volume &w std::string dataset_im = std::string(dataset) + ".im"; size_t re_dims[] = {1, 1, 1}; - realnum *real_data = (realnum *)eps_file.read(dataset_re.c_str(), &rank, re_dims, 3, sizeof(realnum) == sizeof(float)); + realnum *real_data = (realnum *)eps_file.read(dataset_re.c_str(), &rank, re_dims, 3, + sizeof(realnum) == sizeof(float)); if (verbosity > 0) master_printf("read in %zdx%zdx%zd amplitude function file \"%s:%s\"\n", re_dims[0], re_dims[1], re_dims[2], filename, dataset_re.c_str()); size_t im_dims[] = {1, 1, 1}; - realnum *imag_data = (realnum *)eps_file.read(dataset_im.c_str(), &rank, im_dims, 3, sizeof(realnum) == sizeof(float)); + realnum *imag_data = (realnum *)eps_file.read(dataset_im.c_str(), &rank, im_dims, 3, + sizeof(realnum) == sizeof(float)); if (verbosity > 0) master_printf("read in %zdx%zdx%zd amplitude function file \"%s:%s\"\n", im_dims[0], im_dims[1], im_dims[2], filename, dataset_im.c_str()); @@ -466,8 +472,8 @@ void fields::add_volume_source(component c, const src_time &src, const volume &w /* add_volume_source only if certain conditions are met */ /***************************************************************/ void fields::add_volume_source_check(component c, const src_time &src, const volume &where, - complex A(const vec &), complex amp, component c0, - direction d, int has_tm, int has_te) { + complex A(const vec &), complex amp, + component c0, direction d, int has_tm, int has_te) { if (!gv.has_field(c)) return; if (c0 != Centered && c0 != c) return; if (component_direction(c) == d) return; @@ -486,13 +492,13 @@ static std::complex gaussianbeam_ampfunc(const vec &p) { std::complex EH[6]; global_gaussianbeam_object->get_fields(EH, p); switch (global_gaussianbeam_component) { - case Ex: return EH[0]; - case Ey: return EH[1]; - case Ez: return EH[2]; - case Hx: return EH[3]; - case Hy: return EH[4]; - case Hz: return EH[5]; - default: meep::abort("invalid component in gaussianbeam_ampfunc"); + case Ex: return EH[0]; + case Ey: return EH[1]; + case Ez: return EH[2]; + case Hx: return EH[3]; + case Hy: return EH[4]; + case Hz: return EH[5]; + default: meep::abort("invalid component in gaussianbeam_ampfunc"); } } @@ -502,9 +508,10 @@ void fields::add_volume_source(const src_time &src, const volume &where, gaussia component cE[3] = {Ex, Ey, Ez}, cH[3] = {Hx, Hy, Hz}; int n = (d == X ? 0 : (d == Y ? 1 : 2)); if (d == NO_DIRECTION) { - n = where.in_direction(X) == 0 - ? 0 - : where.in_direction(Y) == 0 ? 1 : where.in_direction(Z) == 0 ? 2 : -1; + n = where.in_direction(X) == 0 ? 0 + : where.in_direction(Y) == 0 ? 1 + : where.in_direction(Z) == 0 ? 2 + : -1; if (n == -1) meep::abort( "can't determine source direction for non-empty source volume with NO_DIRECTION source"); @@ -515,22 +522,22 @@ void fields::add_volume_source(const src_time &src, const volume &where, gaussia int np2 = (n + 2) % 3; // Kx = -Hy, Ky = Hx (for d==Z) global_gaussianbeam_component = cH[np1]; - add_volume_source_check(cE[np2], src, where, gaussianbeam_ampfunc, +1.0, cE[np2], d, - has_tm, has_te); + add_volume_source_check(cE[np2], src, where, gaussianbeam_ampfunc, +1.0, cE[np2], d, has_tm, + has_te); global_gaussianbeam_component = cH[np2]; - add_volume_source_check(cE[np1], src, where, gaussianbeam_ampfunc, -1.0, cE[np1], d, - has_tm, has_te); + add_volume_source_check(cE[np1], src, where, gaussianbeam_ampfunc, -1.0, cE[np1], d, has_tm, + has_te); // Nx = +Ey, Ny = -Ex (for d==Z) global_gaussianbeam_component = cE[np1]; - add_volume_source_check(cH[np2], src, where, gaussianbeam_ampfunc, -1.0, cH[np2], d, - has_tm, has_te); + add_volume_source_check(cH[np2], src, where, gaussianbeam_ampfunc, -1.0, cH[np2], d, has_tm, + has_te); global_gaussianbeam_component = cE[np2]; - add_volume_source_check(cH[np1], src, where, gaussianbeam_ampfunc, +1.0, cH[np1], d, - has_tm, has_te); + add_volume_source_check(cH[np1], src, where, gaussianbeam_ampfunc, +1.0, cH[np1], d, has_tm, + has_te); } -gaussianbeam::gaussianbeam(const vec &x0_, const vec &kdir_, double w0_, double freq_, - double eps_, double mu_, std::complex E0_[3]) { +gaussianbeam::gaussianbeam(const vec &x0_, const vec &kdir_, double w0_, double freq_, double eps_, + double mu_, std::complex E0_[3]) { if (x0_.dim == Dcyl) meep::abort("wrong dimensionality in gaussianbeam"); x0 = x0_; @@ -596,61 +603,64 @@ gaussianbeam::gaussianbeam(const vec &x0_, const vec &kdir_, double w0_, double void gaussianbeam::get_fields(std::complex *EH, const vec &x) const { double n = sqrt(eps * mu); double k = 2 * pi * freq * n; - double ZR = sqrt(mu/eps); + double ZR = sqrt(mu / eps); double z0 = k * w0 * w0 / 2; - double kz0 = k*z0; + double kz0 = k * z0; vec xrel = x - x0; vec zhat = kdir / abs(kdir); - double rho = abs(vec(zhat.y()*xrel.z()-zhat.z()*xrel.y(), - zhat.z()*xrel.x()-zhat.x()*xrel.z(), - zhat.x()*xrel.y()-zhat.y()*xrel.x())); + double rho = + abs(vec(zhat.y() * xrel.z() - zhat.z() * xrel.y(), zhat.z() * xrel.x() - zhat.x() * xrel.z(), + zhat.x() * xrel.y() - zhat.y() * xrel.x())); double zhatdotxrel = zhat & xrel; bool UseRescaledFG = false; - complex zc = complex(zhatdotxrel,-z0); - complex Rsq = rho*rho + zc*zc; + complex zc = complex(zhatdotxrel, -z0); + complex Rsq = rho * rho + zc * zc; complex R = sqrt(Rsq); - complex kR = k*R, kR2 = kR*kR, kR3 = kR2*kR; - complex f,g,fmgbRsq; + complex kR = k * R, kR2 = kR * kR, kR3 = kR2 * kR; + complex f, g, fmgbRsq; if (abs(kR) > 1e-4) { complex coskR, sinkR; if (fabs(imag(kR)) > 30.0) { UseRescaledFG = true; - complex ExpI = exp(complex(0,1.0)*real(kR)); - complex ExpPlus = exp(imag(kR)-kz0); - complex ExpMinus = exp(-(imag(kR)+kz0)); - coskR = 0.5*(ExpI*ExpMinus + conj(ExpI)*ExpPlus); - sinkR = -0.5*complex(0,1.0)*(ExpI*ExpMinus - conj(ExpI)*ExpPlus); - } else { + complex ExpI = exp(complex(0, 1.0) * real(kR)); + complex ExpPlus = exp(imag(kR) - kz0); + complex ExpMinus = exp(-(imag(kR) + kz0)); + coskR = 0.5 * (ExpI * ExpMinus + conj(ExpI) * ExpPlus); + sinkR = -0.5 * complex(0, 1.0) * (ExpI * ExpMinus - conj(ExpI) * ExpPlus); + } + else { coskR = cos(kR); sinkR = sin(kR); } - f = -3.0 * (coskR/kR2 - sinkR/kR3); - g = 1.5 * (sinkR/kR + coskR/kR2 - sinkR/kR3); - fmgbRsq = (f-g)/Rsq; - } else { - complex kR4 = kR2*kR2; + f = -3.0 * (coskR / kR2 - sinkR / kR3); + g = 1.5 * (sinkR / kR + coskR / kR2 - sinkR / kR3); + fmgbRsq = (f - g) / Rsq; + } + else { + complex kR4 = kR2 * kR2; // Taylor series expansion for small R - f = kR4/280.0 - kR2/10.0 + 1.0; - g = 3.0*kR4/280.0 - kR2/5.0 + 1.0; - fmgbRsq = (kR4/5040.0 - kR2/140.0 + 0.1)*(k*k); + f = kR4 / 280.0 - kR2 / 10.0 + 1.0; + g = 3.0 * kR4 / 280.0 - kR2 / 5.0 + 1.0; + fmgbRsq = (kR4 / 5040.0 - kR2 / 140.0 + 0.1) * (k * k); } - complex i2fk = 0.5*complex(0,1)*f*k; + complex i2fk = 0.5 * complex(0, 1) * f * k; complex E[3], H[3]; - for (int j=0; j<3; ++j) { - E[j] = complex(0,0); - H[j] = complex(0,0); + for (int j = 0; j < 3; ++j) { + E[j] = complex(0, 0); + H[j] = complex(0, 0); } - double rnorm = sqrt(real(E0[0])*real(E0[0])+real(E0[1])*real(E0[1])+real(E0[2])*real(E0[2])); + double rnorm = + sqrt(real(E0[0]) * real(E0[0]) + real(E0[1]) * real(E0[1]) + real(E0[2]) * real(E0[2])); if (rnorm > 1e-13) { - vec xhat = vec(real(E0[0]),real(E0[1]),real(E0[2])) / rnorm; - vec yhat = vec(zhat.y()*xhat.z()-zhat.z()*xhat.y(), - zhat.z()*xhat.x()-zhat.x()*xhat.z(), - zhat.x()*xhat.y()-zhat.y()*xhat.x()); + vec xhat = vec(real(E0[0]), real(E0[1]), real(E0[2])) / rnorm; + vec yhat = + vec(zhat.y() * xhat.z() - zhat.z() * xhat.y(), zhat.z() * xhat.x() - zhat.x() * xhat.z(), + zhat.x() * xhat.y() - zhat.y() * xhat.x()); double xhatdotxrel = xhat & xrel; double yhatdotxrel = yhat & xrel; @@ -669,12 +679,13 @@ void gaussianbeam::get_fields(std::complex *EH, const vec &x) const { H[2] += rnorm * (gb_Hx * xhat.z() + gb_Hy * yhat.z() + gb_Hz * zhat.z()); } - double inorm = sqrt(imag(E0[0])*imag(E0[0])+imag(E0[1])*imag(E0[1])+imag(E0[2])*imag(E0[2])); + double inorm = + sqrt(imag(E0[0]) * imag(E0[0]) + imag(E0[1]) * imag(E0[1]) + imag(E0[2]) * imag(E0[2])); if (inorm > 1e-13) { - vec xhat = vec(imag(E0[0]),imag(E0[1]),imag(E0[2])) / inorm; - vec yhat = vec(zhat.y()*xhat.z()-zhat.z()*xhat.y(), - zhat.z()*xhat.x()-zhat.x()*xhat.z(), - zhat.x()*xhat.y()-zhat.y()*xhat.x()); + vec xhat = vec(imag(E0[0]), imag(E0[1]), imag(E0[2])) / inorm; + vec yhat = + vec(zhat.y() * xhat.z() - zhat.z() * xhat.y(), zhat.z() * xhat.x() - zhat.x() * xhat.z(), + zhat.x() * xhat.y() - zhat.y() * xhat.x()); double xhatdotxrel = xhat & xrel; double yhatdotxrel = yhat & xrel; @@ -685,29 +696,36 @@ void gaussianbeam::get_fields(std::complex *EH, const vec &x) const { complex gb_Hy = g + fmgbRsq * yhatdotxrel * yhatdotxrel + i2fk * zc; complex gb_Hz = fmgbRsq * yhatdotxrel * zc - i2fk * yhatdotxrel; - E[0] += complex(0,1.0) * inorm * (gb_Ex * xhat.x() + gb_Ey * yhat.x() + gb_Ez * zhat.x()); - E[1] += complex(0,1.0) * inorm * (gb_Ex * xhat.y() + gb_Ey * yhat.y() + gb_Ez * zhat.y()); - E[2] += complex(0,1.0) * inorm * (gb_Ex * xhat.z() + gb_Ey * yhat.z() + gb_Ez * zhat.z()); - H[0] += complex(0,1.0) * inorm * (gb_Hx * xhat.x() + gb_Hy * yhat.x() + gb_Hz * zhat.x()); - H[1] += complex(0,1.0) * inorm * (gb_Hx * xhat.y() + gb_Hy * yhat.y() + gb_Hz * zhat.y()); - H[2] += complex(0,1.0) * inorm * (gb_Hx * xhat.z() + gb_Hy * yhat.z() + gb_Hz * zhat.z()); + E[0] += + complex(0, 1.0) * inorm * (gb_Ex * xhat.x() + gb_Ey * yhat.x() + gb_Ez * zhat.x()); + E[1] += + complex(0, 1.0) * inorm * (gb_Ex * xhat.y() + gb_Ey * yhat.y() + gb_Ez * zhat.y()); + E[2] += + complex(0, 1.0) * inorm * (gb_Ex * xhat.z() + gb_Ey * yhat.z() + gb_Ez * zhat.z()); + H[0] += + complex(0, 1.0) * inorm * (gb_Hx * xhat.x() + gb_Hy * yhat.x() + gb_Hz * zhat.x()); + H[1] += + complex(0, 1.0) * inorm * (gb_Hx * xhat.y() + gb_Hy * yhat.y() + gb_Hz * zhat.y()); + H[2] += + complex(0, 1.0) * inorm * (gb_Hx * xhat.z() + gb_Hy * yhat.z() + gb_Hz * zhat.z()); } double Eorig; if (UseRescaledFG) - Eorig = 3.0/(2*kz0*kz0*kz0) * (kz0*(kz0-1) + 0.5*(1.0-exp(-2.0*kz0))); + Eorig = 3.0 / (2 * kz0 * kz0 * kz0) * (kz0 * (kz0 - 1) + 0.5 * (1.0 - exp(-2.0 * kz0))); else - Eorig = 3.0/(2*kz0*kz0*kz0) * (exp(kz0)*kz0*(kz0-1) + sinh(kz0)); + Eorig = 3.0 / (2 * kz0 * kz0 * kz0) * (exp(kz0) * kz0 * (kz0 - 1) + sinh(kz0)); EH[0] = E[0] / Eorig; EH[1] = E[1] / Eorig; EH[2] = E[2] / Eorig; - EH[3] = H[0] / (Eorig*ZR); - EH[4] = H[1] / (Eorig*ZR); - EH[5] = H[2] / (Eorig*ZR); + EH[3] = H[0] / (Eorig * ZR); + EH[4] = H[1] / (Eorig * ZR); + EH[5] = H[2] / (Eorig * ZR); } -diffractedplanewave::diffractedplanewave(int g_[3], double axis_[3], std::complex s_, std::complex p_) { +diffractedplanewave::diffractedplanewave(int g_[3], double axis_[3], std::complex s_, + std::complex p_) { for (int j = 0; j < 3; ++j) { g[j] = g_[j]; axis[j] = axis_[j]; diff --git a/src/step_db.cpp b/src/step_db.cpp index 1f08c7802..9841297a7 100644 --- a/src/step_db.cpp +++ b/src/step_db.cpp @@ -44,7 +44,7 @@ void fields::step_db(field_type ft) { bool fields_chunk::step_db(field_type ft) { bool allocated_u = false; - for (const auto& sub_gv : gvs_tiled) { + for (const auto &sub_gv : gvs_tiled) { DOCMP FOR_FT_COMPONENTS(ft, cc) { if (f[cc][cmp]) { const component c_p = plus_component[cc], c_m = minus_component[cc]; @@ -116,12 +116,10 @@ bool fields_chunk::step_db(field_type ft) { default: meep::abort("bug - non-cylindrical field component in Dcyl"); } - STEP_CURL(the_f, cc, f_p, f_m, stride_p, stride_m, - gv, sub_gv.little_owned_corner0(cc), sub_gv.big_corner(), - Courant, dsig, s->sig[dsig], - s->kap[dsig], s->siginv[dsig], f_u[cc][cmp], dsigu, s->sig[dsigu], s->kap[dsigu], - s->siginv[dsigu], dt, s->conductivity[cc][d_c], s->condinv[cc][d_c], - f_cond[cc][cmp]); + STEP_CURL(the_f, cc, f_p, f_m, stride_p, stride_m, gv, sub_gv.little_owned_corner0(cc), + sub_gv.big_corner(), Courant, dsig, s->sig[dsig], s->kap[dsig], s->siginv[dsig], + f_u[cc][cmp], dsigu, s->sig[dsigu], s->kap[dsigu], s->siginv[dsigu], dt, + s->conductivity[cc][d_c], s->condinv[cc][d_c], f_cond[cc][cmp]); } } } @@ -149,10 +147,10 @@ bool fields_chunk::step_db(field_type ft) { const direction dsigu0 = cycle_direction(gv.dim, d_c, 2); const direction dsigu = s->sigsize[dsigu0] > 1 ? dsigu0 : NO_DIRECTION; const realnum betadt = 2 * pi * beta * dt * (d_c == X ? +1 : -1) * - (f[c_g][1 - cmp] ? (ft == D_stuff ? -1 : +1) * (2 * cmp - 1) : 1); - STEP_BETA(the_f, cc, g, gv, gv.little_owned_corner0(cc), gv.big_corner(), - betadt, dsig, s->siginv[dsig], f_u[cc][cmp], dsigu, - s->siginv[dsigu], s->condinv[cc][d_c], f_cond[cc][cmp]); + (f[c_g][1 - cmp] ? (ft == D_stuff ? -1 : +1) * (2 * cmp - 1) : 1); + STEP_BETA(the_f, cc, g, gv, gv.little_owned_corner0(cc), gv.big_corner(), betadt, dsig, + s->siginv[dsig], f_u[cc][cmp], dsigu, s->siginv[dsigu], s->condinv[cc][d_c], + f_cond[cc][cmp]); } // in cylindrical coordinates, we now have to add the i*m/r terms... */ @@ -336,7 +334,7 @@ bool fields_chunk::step_db(field_type ft) { for (int iz = (ft == D_stuff); iz < nz + (ft == D_stuff); ++iz) { realnum df; realnum dfcnd = (sd * Courant) * (f_p[iz] - f_p[iz - sd] - f_m_mult * f_m[iz]) * - (cndinv ? cndinv[iz] : 1); + (cndinv ? cndinv[iz] : 1); if (fcnd) fcnd[iz] += dfcnd; the_f[iz] += (df = dfcnd * (siginv ? siginv[dk + 2 * (dsig == Z) * iz] : 1)); if (fu) fu[iz] += siginvu[dku + 2 * (dsigu == Z) * iz] * df; diff --git a/src/step_generic.cpp b/src/step_generic.cpp index 454d55a7a..aa49bdf92 100644 --- a/src/step_generic.cpp +++ b/src/step_generic.cpp @@ -12,7 +12,7 @@ KSTRIDE_DEF defines the relevant strides etc. and goes outside the LOOP, wheras KDEF defines the k index and goes inside the LOOP. */ #define KSTRIDE_DEF(dsig, k, is, gv) \ - const int k##0 = is.in_direction(dsig) - gv.little_corner().in_direction(dsig); \ + const int k##0 = is.in_direction(dsig) - gv.little_corner().in_direction(dsig); \ const int s##k##1 = gv.yucky_direction(0) == dsig ? 2 : 0; \ const int s##k##2 = gv.yucky_direction(1) == dsig ? 2 : 0; \ const int s##k##3 = gv.yucky_direction(2) == dsig ? 2 : 0 @@ -63,13 +63,13 @@ namespace meep { df/dt = dfu/dt - sigma_u * f and fu replaces f in the equations above (fu += dt curl g etcetera). */ -void step_curl(RPR f, component c, const RPR g1, const RPR g2, - ptrdiff_t s1, ptrdiff_t s2, // strides for g1/g2 shift - const grid_volume &gv, const ivec is, const ivec ie, - realnum dtdx, direction dsig, const RPR sig, const RPR kap, - const RPR siginv, RPR fu, direction dsigu, const RPR sigu, const RPR kapu, - const RPR siginvu, realnum dt, const RPR cnd, const RPR cndinv, RPR fcnd) { - (void) c; // currently unused +void step_curl(RPR f, component c, const RPR g1, const RPR g2, ptrdiff_t s1, + ptrdiff_t s2, // strides for g1/g2 shift + const grid_volume &gv, const ivec is, const ivec ie, realnum dtdx, direction dsig, + const RPR sig, const RPR kap, const RPR siginv, RPR fu, direction dsigu, + const RPR sigu, const RPR kapu, const RPR siginvu, realnum dt, const RPR cnd, + const RPR cndinv, RPR fcnd) { + (void)c; // currently unused if (!g1) { // swap g1 and g2 SWAP(const RPR, g1, g2); SWAP(ptrdiff_t, s1, s2); @@ -253,10 +253,10 @@ void step_curl(RPR f, component c, const RPR g1, const RPR g2, and/or PML). This is used in 2d calculations to add an exp(i beta z) time dependence, which gives an additional i \beta \hat{z} \times cross-product in the curl equations. */ -void step_beta(RPR f, component c, const RPR g, const grid_volume &gv, const ivec is, const ivec ie, realnum betadt, - direction dsig, const RPR siginv, RPR fu, direction dsigu, const RPR siginvu, - const RPR cndinv, RPR fcnd) { - (void) c; // currently unused +void step_beta(RPR f, component c, const RPR g, const grid_volume &gv, const ivec is, const ivec ie, + realnum betadt, direction dsig, const RPR siginv, RPR fu, direction dsigu, + const RPR siginvu, const RPR cndinv, RPR fcnd) { + (void)c; // currently unused if (!g) return; if (dsig != NO_DIRECTION) { // PML in f update KSTRIDE_DEF(dsig, k, is, gv); @@ -364,11 +364,11 @@ inline realnum calc_nonlinear_u(const realnum Dsqr, const realnum Di, const real */ -void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, const ivec ie, const RPR g, const RPR g1, - const RPR g2, const RPR u, const RPR u1, const RPR u2, ptrdiff_t s, - ptrdiff_t s1, ptrdiff_t s2, const RPR chi2, const RPR chi3, RPR fw, - direction dsigw, const RPR sigw, const RPR kapw) { - (void) fc; // currently unused +void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, const ivec ie, + const RPR g, const RPR g1, const RPR g2, const RPR u, const RPR u1, + const RPR u2, ptrdiff_t s, ptrdiff_t s1, ptrdiff_t s2, const RPR chi2, + const RPR chi3, RPR fw, direction dsigw, const RPR sigw, const RPR kapw) { + (void)fc; // currently unused if (!f) return; if ((!g1 && g2) || (g1 && g2 && !u1 && u2)) { /* swap g1 and g2 */ @@ -468,9 +468,7 @@ void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, f[i] += (kapwkw + sigwkw) * fw[i] - (kapwkw - sigwkw) * fwprev; } } - else if (g2) { - meep::abort("bug - didn't swap off-diagonal terms!?"); - } + else if (g2) { meep::abort("bug - didn't swap off-diagonal terms!?"); } else { PLOOP_OVER_IVECS(gv, is, ie, i) { realnum gs = g[i]; @@ -565,9 +563,7 @@ void step_update_EDHB(RPR f, component fc, const grid_volume &gv, const ivec is, calc_nonlinear_u(gs * gs + 0.0625 * (g1s * g1s), gs, us, chi2[i], chi3[i]); } } - else if (g2) { - meep::abort("bug - didn't swap off-diagonal terms!?"); - } + else if (g2) { meep::abort("bug - didn't swap off-diagonal terms!?"); } else { PLOOP_OVER_IVECS(gv, is, ie, i) { realnum gs = g[i]; diff --git a/src/stress.cpp b/src/stress.cpp index 8cc1a6e2d..bed26cd0b 100644 --- a/src/stress.cpp +++ b/src/stress.cpp @@ -93,8 +93,7 @@ static void stress_sum(size_t Nfreq, double *F, const dft_chunk *F1, const dft_c complex extra_weight(real(curF1->extra_weight), imag(curF1->extra_weight)); for (size_t k = 0; k < curF1->N; ++k) for (size_t i = 0; i < Nfreq; ++i) - F[i] += real(extra_weight * - complex(curF1->dft[k * Nfreq + i]) * + F[i] += real(extra_weight * complex(curF1->dft[k * Nfreq + i]) * conj(complex(curF2->dft[k * Nfreq + i]))); } } diff --git a/src/structure.cpp b/src/structure.cpp index 9edd5a512..e05c830e0 100644 --- a/src/structure.cpp +++ b/src/structure.cpp @@ -30,7 +30,6 @@ using namespace std; namespace meep { - typedef structure_chunk *structure_chunk_ptr; structure::structure(const grid_volume &thegv, material_function &eps, const boundary_region &br, @@ -64,10 +63,10 @@ structure::structure(const grid_volume &thegv, double eps(const vec &), const bo } } -static std::unique_ptr split_by_cost(int n, grid_volume gvol, - bool fragment_cost, int &proc_id) { - if (n == 1) return std::unique_ptr(new binary_partition(proc_id++ - % count_processors())); +static std::unique_ptr split_by_cost(int n, grid_volume gvol, bool fragment_cost, + int &proc_id) { + if (n == 1) + return std::unique_ptr(new binary_partition(proc_id++ % count_processors())); int best_split_point; direction best_split_direction; @@ -88,17 +87,18 @@ static std::unique_ptr split_by_cost(int n, grid_volume gvol, split_plane optimal_plane{best_split_direction, best_split_position}; grid_volume left_gvol = gvol.split_at_fraction(false, best_split_point, best_split_direction); grid_volume right_gvol = gvol.split_at_fraction(true, best_split_point, best_split_direction); - return std::unique_ptr( - new binary_partition(optimal_plane, - /*left=*/split_by_cost(num_left, left_gvol, fragment_cost, proc_id), - /*right=*/split_by_cost(n - num_left, right_gvol, fragment_cost, proc_id))); + return std::unique_ptr(new binary_partition( + optimal_plane, + /*left=*/split_by_cost(num_left, left_gvol, fragment_cost, proc_id), + /*right=*/split_by_cost(n - num_left, right_gvol, fragment_cost, proc_id))); } void structure::choose_chunkdivision(const grid_volume &thegv, int desired_num_chunks, const boundary_region &br, const symmetry &s, const binary_partition *_bp) { - if (thegv.dim == Dcyl && thegv.get_origin().r() < 0) meep::abort("r < 0 origins are not supported"); + if (thegv.dim == Dcyl && thegv.get_origin().r() < 0) + meep::abort("r < 0 origins are not supported"); user_volume = thegv; gv = thegv; @@ -107,11 +107,8 @@ void structure::choose_chunkdivision(const grid_volume &thegv, int desired_num_c a = gv.a; dt = Courant / a; - if (_bp) { - bp.reset(new binary_partition(*_bp)); - } else { - bp = meep::choose_chunkdivision(gv, v, desired_num_chunks, s); - } + if (_bp) { bp.reset(new binary_partition(*_bp)); } + else { bp = meep::choose_chunkdivision(gv, v, desired_num_chunks, s); } // create the chunks: std::vector chunk_volumes; @@ -149,7 +146,6 @@ void structure::choose_chunkdivision(const grid_volume &thegv, int desired_num_c chunks[i]->cost = chunks[i]->gv.get_cost(); } } - } std::unique_ptr choose_chunkdivision(grid_volume &gv, volume &v, @@ -165,8 +161,7 @@ std::unique_ptr choose_chunkdivision(grid_volume &gv, volume & const direction d = (direction)dd; break_this[dd] = false; for (int n = 0; n < S.multiplicity(); n++) - if (has_direction(gv.dim, d) && - (S.transform(d, n).d != d || S.transform(d, n).flipped)) { + if (has_direction(gv.dim, d) && (S.transform(d, n).d != d || S.transform(d, n).flipped)) { if (gv.num_direction(d) & 1 && !break_this[d] && verbosity > 0) master_printf("Padding %s to even number of grid points.\n", direction_name(d)); break_this[dd] = true; @@ -291,7 +286,8 @@ void structure::check_chunks() { } size_t v_grid_points = 1; LOOP_OVER_DIRECTIONS(gv.dim, d) { v_grid_points *= gv.num_direction(d); } - if (sum != v_grid_points) meep::abort("v_grid_points = %zd, sum(chunks) = %zd\n", v_grid_points, sum); + if (sum != v_grid_points) + meep::abort("v_grid_points = %zd, sum(chunks) = %zd\n", v_grid_points, sum); } void structure::add_to_effort_volumes(const grid_volume &new_effort_volume, double extra_effort) { @@ -339,8 +335,7 @@ void structure::add_to_effort_volumes(const grid_volume &new_effort_volume, doub structure::structure(const structure &s) : num_chunks{s.num_chunks}, shared_chunks{false}, gv(s.gv), user_volume(s.user_volume), a{s.a}, Courant{s.Courant}, dt{s.dt}, v(s.v), S(s.S), - outdir(s.outdir), num_effort_volumes{s.num_effort_volumes}, - bp(new binary_partition(*s.bp)) { + outdir(s.outdir), num_effort_volumes{s.num_effort_volumes}, bp(new binary_partition(*s.bp)) { chunks = new structure_chunk_ptr[num_chunks]; for (int i = 0; i < num_chunks; i++) { chunks[i] = new structure_chunk(s.chunks[i]); @@ -517,11 +512,9 @@ void structure::use_pml(direction d, boundary_side b, double dx) { pml_volume.set_num_direction(d, int(dx * user_volume.a + 1 + 0.5)); // FIXME: exact value? const int v_to_user_shift = (gv.big_corner().in_direction(d) - user_volume.big_corner().in_direction(d)) / 2; - if (b == Low){ - pml_volume.set_origin(d, user_volume.little_corner().in_direction(d)); - } - - if (b == High){ + if (b == Low) { pml_volume.set_origin(d, user_volume.little_corner().in_direction(d)); } + + if (b == High) { pml_volume.set_origin(d, user_volume.big_corner().in_direction(d) - pml_volume.num_direction(d) * 2); pml_volume.set_num_direction(d, pml_volume.num_direction(d) + v_to_user_shift); @@ -715,9 +708,7 @@ structure_chunk::structure_chunk(const structure_chunk *o) : v(o->v) { cur->next = ocur->clone(); cur = cur->next; } - else { - chiP[ft] = cur = ocur->clone(); - } + else { chiP[ft] = cur = ocur->clone(); } cur->next = NULL; } } @@ -736,18 +727,14 @@ structure_chunk::structure_chunk(const structure_chunk *o) : v(o->v) { for (size_t i = 0; i < gv.ntot(); i++) chi3[c][i] = o->chi3[c][i]; } - else { - chi3[c] = NULL; - } + else { chi3[c] = NULL; } if (is_mine() && o->chi2[c]) { chi2[c] = new realnum[gv.ntot()]; if (chi2[c] == NULL) meep::abort("Out of memory!\n"); for (size_t i = 0; i < gv.ntot(); i++) chi2[c][i] = o->chi2[c][i]; } - else { - chi2[c] = NULL; - } + else { chi2[c] = NULL; } } FOR_COMPONENTS(c) FOR_DIRECTIONS(d) { trivial_chi1inv[c][d] = true; } FOR_COMPONENTS(c) FOR_DIRECTIONS(d) { diff --git a/src/structure_dump.cpp b/src/structure_dump.cpp index e0db507d7..fadda6fbb 100644 --- a/src/structure_dump.cpp +++ b/src/structure_dump.cpp @@ -33,8 +33,7 @@ namespace meep { // Write the parameters required to reconstruct the susceptibility (id, noise_amp (for noisy), // omega_0, gamma, no_omega_0_denominator) -void structure::write_susceptibility_params(h5file *file, - bool single_parallel_file, +void structure::write_susceptibility_params(h5file *file, bool single_parallel_file, const char *dname, int EorH) { // Get number of susceptibility params from first chunk, since all chunks will have // the same susceptibility list. @@ -88,7 +87,8 @@ void structure::dump_chunk_layout(const char *filename) { } void structure::dump(const char *filename, bool single_parallel_file) { - if (verbosity > 0) printf("creating epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); + if (verbosity > 0) + printf("creating epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); /* * make/save a num_chunks x NUM_FIELD_COMPONENTS x 5 array counting @@ -122,9 +122,8 @@ void structure::dump(const char *filename, bool single_parallel_file) { if (single_parallel_file) { num_chi1inv.resize(num_chi1inv_size); sum_to_master(num_chi1inv_.data(), num_chi1inv.data(), num_chi1inv_size); - } else { - num_chi1inv = std::move(num_chi1inv_); } + else { num_chi1inv = std::move(num_chi1inv_); } // determine total dataset size and offset of this process's data size_t my_start = 0; @@ -407,9 +406,7 @@ susceptibility *make_sus_list_from_params(h5file *file, int rank, size_t dims[3] } if (sus->next) sus = sus->next; } - else { - meep::abort("Invalid number of susceptibility parameters in structure::load"); - } + else { meep::abort("Invalid number of susceptibility parameters in structure::load"); } } return res; } @@ -437,7 +434,8 @@ binary_partition::binary_partition(const split_plane &_split_plane, if (!left || !right) { meep::abort("Binary partition tree is required to be full"); } } -binary_partition::binary_partition(const binary_partition& other) : proc_id(other.proc_id), plane(other.plane) { +binary_partition::binary_partition(const binary_partition &other) + : proc_id(other.proc_id), plane(other.plane) { if (!other.is_leaf()) { left.reset(new binary_partition(*other.left)); right.reset(new binary_partition(*other.right)); @@ -476,10 +474,10 @@ void split_by_binarytree(grid_volume gvol, std::vector &result_gvs, } const auto &plane = bp->get_plane(); - int split_point = static_cast( - (plane.pos - gvol.surroundings().in_direction_min(plane.dir)) / - gvol.surroundings().in_direction(plane.dir) * gvol.num_direction(plane.dir) + - 0.5); + int split_point = static_cast((plane.pos - gvol.surroundings().in_direction_min(plane.dir)) / + gvol.surroundings().in_direction(plane.dir) * + gvol.num_direction(plane.dir) + + 0.5); // traverse left branch grid_volume left_gvol = gvol.split_at_fraction(false, split_point, plane.dir); split_by_binarytree(left_gvol, result_gvs, result_ids, bp->left_tree()); @@ -542,8 +540,7 @@ void structure::load_chunk_layout(const char *filename, boundary_region &br) { delete[] nums; } -void structure::load_chunk_layout(const std::vector &gvs, - const std::vector &ids, +void structure::load_chunk_layout(const std::vector &gvs, const std::vector &ids, boundary_region &br) { if (gvs.size() != size_t(num_chunks)) meep::abort("load_chunk_layout: wrong number of chunks."); // Recreate the chunks with the new grid_volumes @@ -558,7 +555,8 @@ void structure::load_chunk_layout(const std::vector &gvs, void structure::load(const char *filename, bool single_parallel_file) { h5file file(filename, h5file::READONLY, single_parallel_file, !single_parallel_file); - if (verbosity > 0) printf("reading epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); + if (verbosity > 0) + printf("reading epsilon from file \"%s\" (%d)...\n", filename, single_parallel_file); /* * make/save a num_chunks x NUM_FIELD_COMPONENTS x 5 array counting @@ -623,10 +621,9 @@ void structure::load(const char *filename, bool single_parallel_file) { // read the data file.read_size("chi1inv", &rank, dims, 1); if (rank != 1 || dims[0] != ntotal) { - meep::abort( - "inconsistent data size for chi1inv in structure::load (rank, dims[0]): " - "(%d, %zu) != (1, %zu)", - rank, dims[0], ntotal); + meep::abort("inconsistent data size for chi1inv in structure::load (rank, dims[0]): " + "(%d, %zu) != (1, %zu)", + rank, dims[0], ntotal); } for (int i = 0; i < num_chunks; i++) if (chunks[i]->is_mine()) { @@ -671,7 +668,9 @@ void structure::load(const char *filename, bool single_parallel_file) { int rank = 0; size_t dims[] = {0, 0, 0}; file.read_size("num_sigmas", &rank, dims, 1); - if (dims[0] != (size_t)num_chunks * 2) { meep::abort("inconsistent data size for num_sigmas in structure::load"); } + if (dims[0] != (size_t)num_chunks * 2) { + meep::abort("inconsistent data size for num_sigmas in structure::load"); + } if (am_master() || !single_parallel_file) { size_t start = 0; size_t count = num_chunks; diff --git a/src/support/mt19937ar.c b/src/support/mt19937ar.c index f5b5f286d..a532aa887 100644 --- a/src/support/mt19937ar.c +++ b/src/support/mt19937ar.c @@ -54,133 +54,131 @@ #define UPPER_MASK 0x80000000UL /* most significant w-r bits */ #define LOWER_MASK 0x7fffffffUL /* least significant r bits */ -static unsigned long mt[N]; /* the array for the state vector */ -static int mti=N+1; /* mti==N+1 means mt[N] is not initialized */ +static unsigned long mt[N]; /* the array for the state vector */ +static int mti = N + 1; /* mti==N+1 means mt[N] is not initialized */ static unsigned long mt_save[N]; /* save state from before latest genrand */ /* initializes mt[N] with a seed */ -void meep_mt_init_genrand(unsigned long s) -{ - int i; - for (i=0; i> 30)) + mti); - /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */ - /* In the previous versions, MSBs of the seed affect */ - /* only MSBs of the array mt[]. */ - /* 2002/01/09 modified by Makoto Matsumoto */ - mt[mti] &= 0xffffffffUL; - /* for >32 bit machines */ - } +void meep_mt_init_genrand(unsigned long s) { + int i; + for (i = 0; i < N; ++i) + mt_save[i] = mt[i]; + mt[0] = s & 0xffffffffUL; + for (mti = 1; mti < N; mti++) { + mt[mti] = (1812433253UL * (mt[mti - 1] ^ (mt[mti - 1] >> 30)) + mti); + /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */ + /* In the previous versions, MSBs of the seed affect */ + /* only MSBs of the array mt[]. */ + /* 2002/01/09 modified by Makoto Matsumoto */ + mt[mti] &= 0xffffffffUL; + /* for >32 bit machines */ + } } /* restores state to what it was before most recent call to genrand */ void meep_mt_restore_genrand() { - int i; - for (i=0; ikey_length ? N : key_length); - for (; k; k--) { - mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL)) - + init_key[j] + j; /* non linear */ - mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ - i++; j++; - if (i>=N) { mt[0] = mt[N-1]; i=1; } - if (j>=key_length) j=0; +void meep_mt_init_by_array(unsigned long init_key[], int key_length) { + int i, j, k; + meep_mt_init_genrand(19650218UL); + i = 1; + j = 0; + k = (N > key_length ? N : key_length); + for (; k; k--) { + mt[i] = + (mt[i] ^ ((mt[i - 1] ^ (mt[i - 1] >> 30)) * 1664525UL)) + init_key[j] + j; /* non linear */ + mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ + i++; + j++; + if (i >= N) { + mt[0] = mt[N - 1]; + i = 1; } - for (k=N-1; k; k--) { - mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL)) - - i; /* non linear */ - mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ - i++; - if (i>=N) { mt[0] = mt[N-1]; i=1; } + if (j >= key_length) j = 0; + } + for (k = N - 1; k; k--) { + mt[i] = (mt[i] ^ ((mt[i - 1] ^ (mt[i - 1] >> 30)) * 1566083941UL)) - i; /* non linear */ + mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ + i++; + if (i >= N) { + mt[0] = mt[N - 1]; + i = 1; } + } - mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */ + mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */ } /* generates a random number on [0,0xffffffff]-interval */ -unsigned long meep_mt_genrand_int32(void) -{ - unsigned long y; - static unsigned long mag01[2]={0x0UL, MATRIX_A}; - /* mag01[x] = x * MATRIX_A for x=0,1 */ - - if (mti >= N) { /* generate N words at one time */ - int kk; - - if (mti == N+1) /* if init_genrand() has not been called, */ - meep_mt_init_genrand(5489UL); /* a default initial seed is used */ - - for (kk=0;kk> 1) ^ mag01[y & 0x1UL]; - } - for (;kk> 1) ^ mag01[y & 0x1UL]; - } - y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK); - mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1UL]; - - mti = 0; +unsigned long meep_mt_genrand_int32(void) { + unsigned long y; + static unsigned long mag01[2] = {0x0UL, MATRIX_A}; + /* mag01[x] = x * MATRIX_A for x=0,1 */ + + if (mti >= N) { /* generate N words at one time */ + int kk; + + if (mti == N + 1) /* if init_genrand() has not been called, */ + meep_mt_init_genrand(5489UL); /* a default initial seed is used */ + + for (kk = 0; kk < N - M; kk++) { + y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK); + mt[kk] = mt[kk + M] ^ (y >> 1) ^ mag01[y & 0x1UL]; } + for (; kk < N - 1; kk++) { + y = (mt[kk] & UPPER_MASK) | (mt[kk + 1] & LOWER_MASK); + mt[kk] = mt[kk + (M - N)] ^ (y >> 1) ^ mag01[y & 0x1UL]; + } + y = (mt[N - 1] & UPPER_MASK) | (mt[0] & LOWER_MASK); + mt[N - 1] = mt[M - 1] ^ (y >> 1) ^ mag01[y & 0x1UL]; + + mti = 0; + } - y = mt[mti++]; + y = mt[mti++]; - /* Tempering */ - y ^= (y >> 11); - y ^= (y << 7) & 0x9d2c5680UL; - y ^= (y << 15) & 0xefc60000UL; - y ^= (y >> 18); + /* Tempering */ + y ^= (y >> 11); + y ^= (y << 7) & 0x9d2c5680UL; + y ^= (y << 15) & 0xefc60000UL; + y ^= (y >> 18); - return y; + return y; } /* generates a random number on [0,0x7fffffff]-interval */ -long meep_mt_genrand_int31(void) -{ - return (long)(meep_mt_genrand_int32()>>1); -} +long meep_mt_genrand_int31(void) { return (long)(meep_mt_genrand_int32() >> 1); } /* generates a random number on [0,1]-real-interval */ -double meep_mt_genrand_real1(void) -{ - return meep_mt_genrand_int32()*(1.0/4294967295.0); - /* divided by 2^32-1 */ +double meep_mt_genrand_real1(void) { + return meep_mt_genrand_int32() * (1.0 / 4294967295.0); + /* divided by 2^32-1 */ } /* generates a random number on [0,1)-real-interval */ -double meep_mt_genrand_real2(void) -{ - return meep_mt_genrand_int32()*(1.0/4294967296.0); - /* divided by 2^32 */ +double meep_mt_genrand_real2(void) { + return meep_mt_genrand_int32() * (1.0 / 4294967296.0); + /* divided by 2^32 */ } /* generates a random number on (0,1)-real-interval */ -double meep_mt_genrand_real3(void) -{ - return (((double)meep_mt_genrand_int32()) + 0.5)*(1.0/4294967296.0); - /* divided by 2^32 */ +double meep_mt_genrand_real3(void) { + return (((double)meep_mt_genrand_int32()) + 0.5) * (1.0 / 4294967296.0); + /* divided by 2^32 */ } /* generates a random number on [0,1) with 53-bit resolution*/ -double meep_mt_genrand_res53(void) -{ - unsigned long a=meep_mt_genrand_int32()>>5, b=meep_mt_genrand_int32()>>6; - return(a*67108864.0+b)*(1.0/9007199254740992.0); +double meep_mt_genrand_res53(void) { + unsigned long a = meep_mt_genrand_int32() >> 5, b = meep_mt_genrand_int32() >> 6; + return (a * 67108864.0 + b) * (1.0 / 9007199254740992.0); } /* These real versions are due to Isaku Wada, 2002/01/09 added */ diff --git a/src/susceptibility.cpp b/src/susceptibility.cpp index d160dcdb2..e0abe05ad 100644 --- a/src/susceptibility.cpp +++ b/src/susceptibility.cpp @@ -347,8 +347,9 @@ void noisy_lorentzian_susceptibility::dump_params(h5file *h5f, size_t *start) { *start += num_params; } -gyrotropic_susceptibility::gyrotropic_susceptibility(const vec &bias, realnum omega_0, realnum gamma, - realnum alpha, gyrotropy_model model) +gyrotropic_susceptibility::gyrotropic_susceptibility(const vec &bias, realnum omega_0, + realnum gamma, realnum alpha, + gyrotropy_model model) : omega_0(omega_0), gamma(gamma), alpha(alpha), model(model) { // Precalculate g_{ij} = sum_k epsilon_{ijk} b_k, used in update_P. // Ignore |b| for Landau-Lifshitz-Gilbert gyrotropy model. @@ -619,8 +620,8 @@ void gyrotropic_susceptibility::dump_params(h5file *h5f, size_t *start) { size_t num_params = 9; size_t params_dims[1] = {num_params}; realnum bias[] = {gyro_tensor[Y][Z], gyro_tensor[Z][X], gyro_tensor[X][Y]}; - realnum params_data[] = {8, (realnum)get_id(), bias[X], bias[Y], bias[Z], omega_0, gamma, - alpha, (realnum)model}; + realnum params_data[] = {8, (realnum)get_id(), bias[X], bias[Y], bias[Z], omega_0, gamma, + alpha, (realnum)model}; h5f->write_chunk(1, start, params_dims, params_data); *start += num_params; } diff --git a/src/update_eh.cpp b/src/update_eh.cpp index 0a28c21af..76cc43d51 100644 --- a/src/update_eh.cpp +++ b/src/update_eh.cpp @@ -45,9 +45,8 @@ void fields::update_eh(field_type ft, bool skip_w_components) { if (loop_tile_base_eh > 0 && is_aniso) { split_into_tiles(chunks[i]->gv, &chunks[i]->gvs_eh[ft], loop_tile_base_eh); check_tiles(chunks[i]->gv, chunks[i]->gvs_eh[ft]); - } else { - chunks[i]->gvs_eh[ft].push_back(chunks[i]->gv); } + else { chunks[i]->gvs_eh[ft].push_back(chunks[i]->gv); } } for (int i = 0; i < num_chunks; i++) @@ -189,8 +188,8 @@ bool fields_chunk::update_eh(field_type ft, bool skip_w_components) { } if (f[ec][cmp] != f[dc][cmp]) - STEP_UPDATE_EDHB(f[ec][cmp], ec, gv, gvs_eh[ft][i].little_owned_corner0(ec), gvs_eh[ft][i].big_corner(), - dmp[dc][cmp], dmp[dc_1][cmp], dmp[dc_2][cmp], + STEP_UPDATE_EDHB(f[ec][cmp], ec, gv, gvs_eh[ft][i].little_owned_corner0(ec), + gvs_eh[ft][i].big_corner(), dmp[dc][cmp], dmp[dc_1][cmp], dmp[dc_2][cmp], s->chi1inv[ec][d_ec], dmp[dc_1][cmp] ? s->chi1inv[ec][d_1] : NULL, dmp[dc_2][cmp] ? s->chi1inv[ec][d_2] : NULL, s_ec, s_1, s_2, s->chi2[ec], s->chi3[ec], f_w[ec][cmp], dsigw, s->sig[dsigw], s->kap[dsigw]); diff --git a/src/vec.cpp b/src/vec.cpp index c64a4a74f..ff7024ace 100644 --- a/src/vec.cpp +++ b/src/vec.cpp @@ -454,12 +454,8 @@ bool grid_volume::owns(const ivec &p) const { return o.x() > 0 && o.x() <= nx() * 2 && o.y() > 0 && o.y() <= ny() * 2 && o.z() > 0 && o.z() <= nz() * 2; } - else if (dim == D2) { - return o.x() > 0 && o.x() <= nx() * 2 && o.y() > 0 && o.y() <= ny() * 2; - } - else if (dim == D1) { - return o.z() > 0 && o.z() <= nz() * 2; - } + else if (dim == D2) { return o.x() > 0 && o.x() <= nx() * 2 && o.y() > 0 && o.y() <= ny() * 2; } + else if (dim == D1) { return o.z() > 0 && o.z() <= nz() * 2; } else { meep::abort("Unsupported dimension in owns.\n"); return false; @@ -683,9 +679,7 @@ double grid_volume::rmin() const { const double qinva = 0.25 * inva; if (dim == Dcyl) { if (origin.r() == 0.0) { return 0.0; } - else { - return origin.r() + qinva; - } + else { return origin.r() + qinva; } } meep::abort("No rmin in these dimensions.\n"); return 0.0; // This is never reached. @@ -814,7 +808,7 @@ bool grid_volume::intersect_with(const grid_volume &vol_in, grid_volume *interse } if (initial_points != final_points) meep::abort("intersect_with: initial_points != final_points, %zd, %zd\n", initial_points, - final_points); + final_points); } return true; } @@ -858,11 +852,11 @@ ivec grid_volume::iloc(component c, ptrdiff_t ind) const { } size_t grid_volume::surface_area() const { - switch(dim) { - case Dcyl: return 2*(nr()+nz()); + switch (dim) { + case Dcyl: return 2 * (nr() + nz()); case D1: return 2; - case D2: return 2*(nx()+ny()); - case D3: return 2*(nx()*ny()+nx()*nz()+ny()*nz()); + case D2: return 2 * (nx() + ny()); + case D3: return 2 * (nx() * ny() + nx() * nz() + ny() * nz()); } return 0; // This is never reached. } @@ -909,15 +903,15 @@ vec grid_volume::dz() const { grid_volume volone(double zsize, double a) { if (!isinteger(zsize * a)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); return grid_volume(D1, a, 0, 0, (int)(zsize * a + 0.5)); } grid_volume voltwo(double xsize, double ysize, double a) { if (!isinteger(xsize * a) || !isinteger(ysize * a)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); return grid_volume(D2, a, (xsize == 0) ? 1 : (int)(xsize * a + 0.5), (ysize == 0) ? 1 : (int)(ysize * a + 0.5), 0); } @@ -928,8 +922,8 @@ grid_volume vol2d(double xsize, double ysize, double a) { return voltwo(xsize, y grid_volume vol3d(double xsize, double ysize, double zsize, double a) { if (!isinteger(xsize * a) || !isinteger(ysize * a) || !isinteger(zsize * a)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); return grid_volume(D3, a, (xsize == 0) ? 1 : (int)(xsize * a + 0.5), (ysize == 0) ? 1 : (int)(ysize * a + 0.5), (zsize == 0) ? 1 : (int)(zsize * a + 0.5)); @@ -937,8 +931,8 @@ grid_volume vol3d(double xsize, double ysize, double zsize, double a) { grid_volume volcyl(double rsize, double zsize, double a) { if (!isinteger(rsize) || !isinteger(zsize)) - master_printf_stderr( - "Warning: grid volume is not an integer number of pixels; cell size will be rounded to nearest pixel.\n"); + master_printf_stderr("Warning: grid volume is not an integer number of pixels; cell size will " + "be rounded to nearest pixel.\n"); if (zsize == 0.0) return grid_volume(Dcyl, a, (int)(rsize * a + 0.5), 0, 1); @@ -978,26 +972,27 @@ static double cost_diff(int desired_chunks, std::complex costs) { return right_cost - left_cost; } -void grid_volume::tile_split(int &best_split_point, - direction &best_split_direction) const { +void grid_volume::tile_split(int &best_split_point, direction &best_split_direction) const { const size_t ntot_thresh = 10; if (ntot() < ntot_thresh) { best_split_point = 0; best_split_direction = NO_DIRECTION; - } else if (nx() > 1) { + } + else if (nx() > 1) { best_split_point = nx() / 2; best_split_direction = X; - } else if (ny() > 1) { + } + else if (ny() > 1) { best_split_point = ny() / 2; best_split_direction = Y; - } else { + } + else { best_split_point = nz() / 2; best_split_direction = Z; } } -void grid_volume::find_best_split(int desired_chunks, bool fragment_cost, - int &best_split_point, +void grid_volume::find_best_split(int desired_chunks, bool fragment_cost, int &best_split_point, direction &best_split_direction, double &left_effort_fraction) const { if (size_t(desired_chunks) > nowned_min()) { @@ -1037,9 +1032,11 @@ void grid_volume::find_best_split(int desired_chunks, bool fragment_cost, std::complex costs = get_split_costs(d, split_point, fragment_cost); double left_cost = real(costs), right_cost = imag(costs); double total_cost = left_cost + right_cost; - double split_measure = std::max(left_cost / (desired_chunks / 2), right_cost / (desired_chunks - (desired_chunks / 2))); + double split_measure = std::max(left_cost / (desired_chunks / 2), + right_cost / (desired_chunks - (desired_chunks / 2))); // Give a 30% preference to the longest axis, as a heuristic to prefer lower communication costs - // when the split_measure is somewhat close. TODO: use a data-driven communication cost function. + // when the split_measure is somewhat close. TODO: use a data-driven communication cost + // function. if (d == longest_axis) split_measure *= 0.7; if (split_measure < best_split_measure) { best_split_measure = split_measure; @@ -1072,8 +1069,8 @@ grid_volume grid_volume::split_at_fraction(bool side_high, int split_pt, int spl // Halve the grid_volume for symmetry exploitation...must contain icenter! grid_volume grid_volume::halve(direction d) const { grid_volume retval(*this); - retval.set_num_direction(d, 1+(big_corner().in_direction(d)-icenter().in_direction(d)) / 2); - retval.set_origin(d, icenter().in_direction(d)-2); + retval.set_num_direction(d, 1 + (big_corner().in_direction(d) - icenter().in_direction(d)) / 2); + retval.set_origin(d, icenter().in_direction(d) - 2); return retval; } @@ -1089,7 +1086,6 @@ void grid_volume::pad_self(direction d) { shift_origin(d, -2); } - ivec grid_volume::icenter() const { /* Find the center of the user's cell. This will be used as the symmetry point, and therefore icenter-io must be *even* @@ -1218,9 +1214,8 @@ int symmetry::multiplicity() const { int symmetry::multiplicity(ivec &x) const { int m = multiplicity(); - for (int n=1; ntransform(sd.d, nrest) * ph; } - else { - return sd * ph; - } + else { return sd * ph; } } } @@ -1600,8 +1593,7 @@ const char *volume::str(char *buffer, size_t buflen) { buffer = sbuf; buflen = sizeof(sbuf); } - snprintf(buffer, buflen, "min_corner:%s, max_corner:%s", - min_corner.str(), max_corner.str()); + snprintf(buffer, buflen, "min_corner:%s, max_corner:%s", min_corner.str(), max_corner.str()); return buffer; } @@ -1613,23 +1605,21 @@ const char *grid_volume::str(char *buffer, size_t buflen) { buflen = sizeof(sbuf); } - written += snprintf(buffer+written, buflen-written, + written += snprintf(buffer + written, buflen - written, "grid_volume {\n dim:%s, a:%f, inva:%f, num:{%d, %d, %d}\n", dimension_name(dim), a, inva, num[0], num[1], num[2]); // Adapted from the print() method LOOP_OVER_DIRECTIONS(dim, d) { - written += snprintf(buffer+written, buflen-written, - " %s =%5g - %5g (%5g) \t", direction_name(d), origin.in_direction(d), - origin.in_direction(d) + num_direction(d) / a, num_direction(d) / a); - if (buflen-written <=0) - break; + written += snprintf(buffer + written, buflen - written, " %s =%5g - %5g (%5g) \t", + direction_name(d), origin.in_direction(d), + origin.in_direction(d) + num_direction(d) / a, num_direction(d) / a); + if (buflen - written <= 0) break; } - snprintf(buffer+written, buflen-written, "\n}"); + snprintf(buffer + written, buflen - written, "\n}"); return buffer; } - /********************************************************************/ /********************************************************************/ /********************************************************************/ @@ -1645,13 +1635,14 @@ grid_volume grid_volume::subvolume(ivec is, ivec ie, component c) { void grid_volume::init_subvolume(ivec is, ivec ie, component c) { ivec origin(dim, 0); - for (int i=0;i<3;i++) num[i] = 0; + for (int i = 0; i < 3; i++) + num[i] = 0; LOOP_OVER_DIRECTIONS(dim, d) { - num[(int)d] = (ie - is).in_direction(d)/2; - origin.set_direction(d, is.in_direction(d)-iyee_shift(c).in_direction(d)); + num[(int)d] = (ie - is).in_direction(d) / 2; + origin.set_direction(d, is.in_direction(d) - iyee_shift(c).in_direction(d)); } num_changed(); - //center_origin(); + // center_origin(); set_origin(origin); } diff --git a/tests/aniso_disp.cpp b/tests/aniso_disp.cpp index 4ae352c09..f17d2a5e3 100644 --- a/tests/aniso_disp.cpp +++ b/tests/aniso_disp.cpp @@ -117,7 +117,7 @@ int main(int argc, char **argv) { } double T2 = 200; int iT2 = T2 / f.dt; - std::vector> vals(iT2); + std::vector > vals(iT2); while (f.t - iT < iT2) { if ((f.t - iT) % (iT2 / 10) == 0) master_printf("%g%% done with harminv\n", (f.t - iT) * 100.0 / iT2); diff --git a/tests/array-slice-ll.cpp b/tests/array-slice-ll.cpp index c98f4a741..5cea412ef 100644 --- a/tests/array-slice-ll.cpp +++ b/tests/array-slice-ll.cpp @@ -30,7 +30,8 @@ vector3 v3(double x, double y = 0.0, double z = 0.0) { } // passthrough field function -std::complex default_field_function(const std::complex *fields, const vec &loc, void *data_) { +std::complex default_field_function(const std::complex *fields, const vec &loc, + void *data_) { (void)loc; // unused (void)data_; // unused return fields[0]; @@ -39,7 +40,7 @@ std::complex default_field_function(const std::complex *fields, /***************************************************************/ /***************************************************************/ /***************************************************************/ -const double RELTOL = sizeof(realnum) == sizeof(float) ? 1.0e-4: 1.0e-6; +const double RELTOL = sizeof(realnum) == sizeof(float) ? 1.0e-4 : 1.0e-6; double Compare(realnum *d1, realnum *d2, int N, const char *Name) { double Norm1 = 0.0, Norm2 = 0.0, NormDelta = 0.0; @@ -137,9 +138,7 @@ int main(int argc, char *argv[]) { symmetry sym = use_symmetry ? -mirror(Y, gv) : identity(); structure the_structure(gv, dummy_eps, pml(dpml), sym); meep_geom::material_type vacuum = meep_geom::vacuum; - auto material_deleter = [](meep_geom::material_data *m) { - meep_geom::material_free(m); - }; + auto material_deleter = [](meep_geom::material_data *m) { meep_geom::material_free(m); }; std::unique_ptr dielectric( meep_geom::make_dielectric(eps), material_deleter); geometric_object objects[7]; @@ -204,17 +203,17 @@ int main(int argc, char *argv[]) { // read 1D and 2D array-slice data from HDF5 file // h5file *file = f.open_h5file(H5FILENAME, h5file::READONLY); - std::unique_ptr rdata(static_cast( + std::unique_ptr rdata(static_cast( file->read("hz.r", &rank, dims1D, 1, sizeof(realnum) == sizeof(float)))); if (rank != 1 || dims1D[0] != NX) meep::abort("failed to read 1D data(hz.r) from file %s.h5", H5FILENAME); - std::unique_ptr idata(static_cast( + std::unique_ptr idata(static_cast( file->read("hz.i", &rank, dims1D, 1, sizeof(realnum) == sizeof(float)))); if (rank != 1 || dims1D[0] != NX) meep::abort("failed to read 1D data(hz.i) from file %s.h5", H5FILENAME); - std::vector> file_slice1d; + std::vector > file_slice1d; for (size_t n = 0; n < dims1D[0]; n++) file_slice1d.emplace_back(rdata[n], idata[n]); @@ -230,16 +229,18 @@ int main(int argc, char *argv[]) { // rank = f.get_array_slice_dimensions(v1d, dims1D, dirs1D, true, false); if (rank != 1 || dims1D[0] != NX) meep::abort("incorrect dimensions for 1D slice"); - std::unique_ptr []> slice1d(f.get_complex_array_slice(v1d, Hz, 0, 0, true)); - std::vector> slice1d_realnum; + std::unique_ptr[]> slice1d( + f.get_complex_array_slice(v1d, Hz, 0, 0, true)); + std::vector > slice1d_realnum; for (int i = 0; i < NX; ++i) slice1d_realnum.emplace_back(slice1d[i]); double RelErr1D = Compare(slice1d_realnum.data(), file_slice1d.data(), NX, "Hz_1d"); master_printf("1D: rel error %e\n", RelErr1D); rank = f.get_array_slice_dimensions(v2d, dims2D, dirs2D, true, false); - if (rank != 2 || dims2D[0] != NX || dims2D[1] != NY) meep::abort("incorrect dimensions for 2D slice"); - std::unique_ptr slice2d(f.get_array_slice(v2d, Sy, 0, 0, true)); + if (rank != 2 || dims2D[0] != NX || dims2D[1] != NY) + meep::abort("incorrect dimensions for 2D slice"); + std::unique_ptr slice2d(f.get_array_slice(v2d, Sy, 0, 0, true)); std::unique_ptr slice2d_realnum(new realnum[NX * NY]); for (int i = 0; i < NX * NY; ++i) slice2d_realnum[i] = static_cast(slice2d[i]); diff --git a/tests/bragg_transmission.cpp b/tests/bragg_transmission.cpp index 7830b2031..3cf2c42ab 100644 --- a/tests/bragg_transmission.cpp +++ b/tests/bragg_transmission.cpp @@ -224,11 +224,11 @@ void doit(bool use_hdf5) { if (errT > maxerrT) maxerrT = errT; if (errR > maxerrR) maxerrR = errR; if (errT * sqr(freq_min / (freq_min + i * dfreq)) > 0.01) - meep::abort("large error %g at freq = %g: T = %g instead of %g\n", errT, freq_min + i * dfreq, T[i], - T0[i]); + meep::abort("large error %g at freq = %g: T = %g instead of %g\n", errT, freq_min + i * dfreq, + T[i], T0[i]); if (errR * sqr(freq_min / (freq_min + i * dfreq)) > 0.01) - meep::abort("large error %g at freq = %g: R = %g instead of %g\n", errR, freq_min + i * dfreq, R[i], - R0[i]); + meep::abort("large error %g at freq = %g: R = %g instead of %g\n", errR, freq_min + i * dfreq, + R[i], R0[i]); } master_printf("Done (max. err in T = %e, in R = %e)\n", maxerrT, maxerrR); diff --git a/tests/convergence_cyl_waveguide.cpp b/tests/convergence_cyl_waveguide.cpp index 992631d5a..3c94de0ce 100644 --- a/tests/convergence_cyl_waveguide.cpp +++ b/tests/convergence_cyl_waveguide.cpp @@ -48,7 +48,7 @@ void test_convergence_without_averaging() { while (f.time() < f.last_source_time()) f.step(); int t_harminv_max = 2500; // try increasing this in case of failure - std::vector> mon_data(t_harminv_max); + std::vector > mon_data(t_harminv_max); int t = 0; monitor_point mp; while (t < t_harminv_max) { @@ -58,10 +58,10 @@ void test_convergence_without_averaging() { t++; } const int maxbands = 10; - std::vector> amps(maxbands); + std::vector > amps(maxbands); std::vector freq_re(maxbands), freq_im(maxbands), errors(maxbands); - int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, amps.data(), - freq_re.data(), freq_im.data(), errors.data()); + int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, + amps.data(), freq_re.data(), freq_im.data(), errors.data()); double w = 0.0; for (int jf = 0; jf < nfreq; jf++) if (abs(freq_re[jf] - w0) < abs(w - w0)) w = freq_re[jf]; @@ -117,7 +117,7 @@ void test_convergence_with_averaging() { while (f.time() < f.last_source_time()) f.step(); int t_harminv_max = 2500; // try increasing this in case of failure - std::vector> mon_data(t_harminv_max); + std::vector > mon_data(t_harminv_max); int t = 0; monitor_point mp; while (t < t_harminv_max) { @@ -127,10 +127,10 @@ void test_convergence_with_averaging() { t++; } const int maxbands = 10; - std::vector> amps(maxbands); + std::vector > amps(maxbands); std::vector freq_re(maxbands), freq_im(maxbands), errors(maxbands); - int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, amps.data(), - freq_re.data(), freq_im.data(), errors.data()); + int nfreq = do_harminv(mon_data.data(), t_harminv_max - 1, f.dt, 0.10, 0.50, maxbands, + amps.data(), freq_re.data(), freq_im.data(), errors.data()); double w = 0.0; for (int jf = 0; jf < nfreq; jf++) if (abs(freq_re[jf] - w0) < abs(w - w0)) w = freq_re[jf]; diff --git a/tests/cyl-ellipsoid-ll.cpp b/tests/cyl-ellipsoid-ll.cpp index a6fe6f471..5741aa5ef 100644 --- a/tests/cyl-ellipsoid-ll.cpp +++ b/tests/cyl-ellipsoid-ll.cpp @@ -33,8 +33,8 @@ bool compare_hdf5_datasets(const char *file1, const char *name1, const char *fil int rank1; std::vector dims1(expected_rank); - std::unique_ptr data1; - std::unique_ptr data2; + std::unique_ptr data1; + std::unique_ptr data2; data1.reset(static_cast( f1.read(name1, &rank1, dims1.data(), expected_rank, sizeof(realnum) == sizeof(float)))); if (!data1) return false; @@ -129,9 +129,7 @@ int main(int argc, char *argv[]) { // (make ellipsoid (center 0 0 0) (size 1 2 infinity) // (material air)))) double n = 3.5; // index of refraction - auto material_deleter = [](meep_geom::material_data *m) { - meep_geom::material_free(m); - }; + auto material_deleter = [](meep_geom::material_data *m) { meep_geom::material_free(m); }; std::unique_ptr dielectric( meep_geom::make_dielectric(n * n), material_deleter); geometric_object objects[2]; diff --git a/tests/cyl-ellipsoid.ctl b/tests/cyl-ellipsoid.ctl index 907deaf61..727f482cb 100644 --- a/tests/cyl-ellipsoid.ctl +++ b/tests/cyl-ellipsoid.ctl @@ -27,7 +27,7 @@ (run-until 23 (at-beginning output-epsilon) (at-every 0.25 print-stuff) - (at-end print-stuff) + (at-end print-stuff) (at-end output-efield-z)) (print "stopped at meep time = " (meep-round-time) ) diff --git a/tests/cylindrical.cpp b/tests/cylindrical.cpp index 831fcd74e..150120fb6 100644 --- a/tests/cylindrical.cpp +++ b/tests/cylindrical.cpp @@ -32,9 +32,7 @@ int compare(double a, double b, const char *n, double eps = 4e-15) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p, double eps = 4e-8) { diff --git a/tests/dft-fields.cpp b/tests/dft-fields.cpp index 6e1e44746..074528ae9 100644 --- a/tests/dft-fields.cpp +++ b/tests/dft-fields.cpp @@ -33,8 +33,8 @@ double dummy_eps(const vec &) { return 1.0; } /***************************************************************/ /***************************************************************/ /***************************************************************/ -void Run(bool Pulse, double resolution, std::complex **field_array = 0, int *array_rank = 0, - size_t *array_dims = 0) { +void Run(bool Pulse, double resolution, std::complex **field_array = 0, + int *array_rank = 0, size_t *array_dims = 0) { /***************************************************************/ /* initialize geometry */ /***************************************************************/ @@ -102,16 +102,18 @@ void Run(bool Pulse, double resolution, std::complex **field_arra /***************************************************************/ /* return L2 norm of error normalized by average of L2 norms */ /***************************************************************/ -double compare_array_to_dataset(std::complex *field_array, int array_rank, size_t *array_dims, - const char *file, const char *name) { +double compare_array_to_dataset(std::complex *field_array, int array_rank, + size_t *array_dims, const char *file, const char *name) { int file_rank; size_t file_dims[3]; h5file f(file, h5file::READONLY, false); char dataname[100]; snprintf(dataname, 100, "%s.r", name); - double *rdata = (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); + double *rdata = + (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); snprintf(dataname, 100, "%s.i", name); - double *idata = (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); + double *idata = + (double *)f.read(dataname, &file_rank, file_dims, 2, false /* single_precision */); if (!rdata || !idata) return -1.0; if (file_rank != array_rank) return -1.0; for (int n = 0; n < file_rank; n++) @@ -148,9 +150,11 @@ double compare_complex_hdf5_datasets(const char *file1, const char *name1, const int rank1; size_t *dims1 = new size_t[expected_rank]; snprintf(dataname, 100, "%s.r", name1); - double *rdata1 = (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); + double *rdata1 = + (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); snprintf(dataname, 100, "%s.i", name1); - double *idata1 = (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); + double *idata1 = + (double *)f1.read(dataname, &rank1, dims1, expected_rank, false /* single_precision */); if (!rdata1 || !idata1) return -1.0; // read dataset 2 @@ -158,9 +162,11 @@ double compare_complex_hdf5_datasets(const char *file1, const char *name1, const int rank2; size_t *dims2 = new size_t[expected_rank]; snprintf(dataname, 100, "%s.r", name2); - double *rdata2 = (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); + double *rdata2 = + (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); snprintf(dataname, 100, "%s.i", name2); - double *idata2 = (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); + double *idata2 = + (double *)f2.read(dataname, &rank2, dims2, expected_rank, false /* single_precision */); if (!rdata2 || !idata2) return -1.0; // check same size diff --git a/tests/dump_load.cpp b/tests/dump_load.cpp index befa65443..b6edbdd08 100644 --- a/tests/dump_load.cpp +++ b/tests/dump_load.cpp @@ -27,7 +27,8 @@ double one(const vec &) { return 1.0; } double targets(const vec &pt) { const double r = sqrt(pt.x() * pt.x() + pt.y() * pt.y()); double dr = r; - while (dr > 1) dr -= 1; + while (dr > 1) + dr -= 1; if (dr > 0.7001) return 12.0; return 1.0; } @@ -41,12 +42,10 @@ static const double tol = 1e-9, thresh = 1e-15; int compare(double a, double b, const char *n) { if (fabs(a - b) > fabs(b) * tol && fabs(b) > thresh) { master_printf("%s differs by\t%g out of\t%g\n", n, a - b, b); - master_printf("This gives a fractional error of %g\n", - fabs(a - b) / fabs(b)); + master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; - } else { - return 1; } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { @@ -58,12 +57,11 @@ int compare_point(fields &f1, fields &f2, const vec &p) { if (f1.gv.has_field(c)) { complex v1 = m_test.get_component(c), v2 = m1.get_component(c); if (abs(v1 - v2) > tol * abs(v2) && abs(v2) > thresh) { - master_printf("%s differs: %g %g out of %g %g\n", component_name(c), - real(v2 - v1), imag(v2 - v1), real(v2), imag(v2)); - master_printf("This comes out to a fractional error of %g\n", - abs(v1 - v2) / abs(v2)); - master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), - p.y(), p.z(), f1.time()); + master_printf("%s differs: %g %g out of %g %g\n", component_name(c), real(v2 - v1), + imag(v2 - v1), real(v2), imag(v2)); + master_printf("This comes out to a fractional error of %g\n", abs(v1 - v2) / abs(v2)); + master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), p.y(), p.z(), + f1.time()); return 0; } } @@ -80,12 +78,11 @@ int approx_point(fields &f1, fields &f2, const vec &p) { if (f1.gv.has_field(c)) { complex v1 = m_test.get_component(c), v2 = m1.get_component(c); if (abs(v1 - v2) > tol * abs(v2) && abs(v2) > thresh) { - master_printf("%s differs: %g %g out of %g %g\n", component_name(c), - real(v2 - v1), imag(v2 - v1), real(v2), imag(v2)); - master_printf("This comes out to a fractional error of %g\n", - abs(v1 - v2) / abs(v2)); - master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), - p.y(), p.z(), f1.time()); + master_printf("%s differs: %g %g out of %g %g\n", component_name(c), real(v2 - v1), + imag(v2 - v1), real(v2), imag(v2)); + master_printf("This comes out to a fractional error of %g\n", abs(v1 - v2) / abs(v2)); + master_printf("Right now I'm looking at %g %g %g, time %g\n", p.x(), p.y(), p.z(), + f1.time()); return 0; } } @@ -126,40 +123,34 @@ int test_metal(double eps(const vec &), int splitting, const char *tmpdir) { grid_volume gv = vol3d(1.5, 0.5, 1.0, a); structure s(gv, eps, no_pml(), identity(), splitting); - std::string filename_prefix = - std::string(tmpdir) + "/test_metal_" + std::to_string(splitting); - std::string structure_filename = - structure_dump(&s, filename_prefix, "original"); + std::string filename_prefix = std::string(tmpdir) + "/test_metal_" + std::to_string(splitting); + std::string structure_filename = structure_dump(&s, filename_prefix, "original"); master_printf("Metal test using %d chunks...\n", splitting); fields f(&s); f.add_point_source(Ez, 0.8, 0.6, 0.0, 4.0, vec(1.299, 0.299, 0.401), 1.0); - while (f.time() < ttot) f.step(); + while (f.time() < ttot) + f.step(); - std::string fields_filename = - fields_dump(&f, filename_prefix, "original"); + std::string fields_filename = fields_dump(&f, filename_prefix, "original"); structure s_load(gv, eps, no_pml(), identity(), splitting); structure_load(&s_load, structure_filename); fields f_load(&s_load); - f_load.add_point_source(Ez, 0.8, 0.6, 0.0, 4.0, vec(1.299, 0.299, 0.401), - 1.0); + f_load.add_point_source(Ez, 0.8, 0.6, 0.0, 4.0, vec(1.299, 0.299, 0.401), 1.0); fields_load(&f_load, fields_filename); if (!compare_point(f, f_load, vec(0.5, 0.5, 0.01))) return 0; if (!compare_point(f, f_load, vec(0.46, 0.33, 0.33))) return 0; if (!compare_point(f, f_load, vec(1.301, 0.301, 0.399))) return 0; - if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) - return 0; + if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) return 0; if (!compare(f.electric_energy_in_box(gv.surroundings()), - f_load.electric_energy_in_box(gv.surroundings()), - "electric energy")) + f_load.electric_energy_in_box(gv.surroundings()), "electric energy")) return 0; if (!compare(f.magnetic_energy_in_box(gv.surroundings()), - f_load.magnetic_energy_in_box(gv.surroundings()), - "magnetic energy")) + f_load.magnetic_energy_in_box(gv.surroundings()), "magnetic energy")) return 0; return 1; } @@ -171,20 +162,18 @@ int test_periodic(double eps(const vec &), int splitting, const char *tmpdir) { grid_volume gv = vol3d(1.5, 0.5, 1.0, a); structure s(gv, eps, no_pml(), identity(), splitting); - std::string filename_prefix = - std::string(tmpdir) + "/test_periodic_" + std::to_string(splitting); - std::string structure_filename = - structure_dump(&s, filename_prefix, "original"); + std::string filename_prefix = std::string(tmpdir) + "/test_periodic_" + std::to_string(splitting); + std::string structure_filename = structure_dump(&s, filename_prefix, "original"); master_printf("Periodic test using %d chunks...\n", splitting); fields f(&s); f.use_bloch(vec(0.1, 0.7, 0.3)); f.add_point_source(Ez, 0.7, 2.5, 0.0, 4.0, vec(0.3, 0.25, 0.5), 1.0); - while (f.time() < ttot) f.step(); + while (f.time() < ttot) + f.step(); - std::string fields_filename = - fields_dump(&f, filename_prefix, "original"); + std::string fields_filename = fields_dump(&f, filename_prefix, "original"); structure s_load(gv, eps, no_pml(), identity(), splitting); structure_load(&s_load, structure_filename); @@ -197,15 +186,12 @@ int test_periodic(double eps(const vec &), int splitting, const char *tmpdir) { if (!compare_point(f, f_load, vec(0.5, 0.01, 0.5))) return 0; if (!compare_point(f, f_load, vec(0.46, 0.33, 0.2))) return 0; if (!compare_point(f, f_load, vec(1.0, 0.25, 0.301))) return 0; - if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) - return 0; + if (!compare(f.field_energy(), f_load.field_energy(), " total energy")) return 0; if (!compare(f.electric_energy_in_box(gv.surroundings()), - f_load.electric_energy_in_box(gv.surroundings()), - "electric energy")) + f_load.electric_energy_in_box(gv.surroundings()), "electric energy")) return 0; if (!compare(f.magnetic_energy_in_box(gv.surroundings()), - f_load.magnetic_energy_in_box(gv.surroundings()), - "magnetic energy")) + f_load.magnetic_energy_in_box(gv.surroundings()), "magnetic energy")) return 0; return 1; @@ -219,12 +205,10 @@ int main(int argc, char **argv) { master_printf("Testing 3D dump/load: temp_dir = %s...\n", temp_dir.get()); for (int s = 2; s < 7; s++) - if (!test_periodic(targets, s, temp_dir.get())) - abort("error in test_periodic targets\n"); + if (!test_periodic(targets, s, temp_dir.get())) abort("error in test_periodic targets\n"); for (int s = 2; s < 8; s++) - if (!test_metal(one, s, temp_dir.get())) - abort("error in test_metal vacuum\n"); + if (!test_metal(one, s, temp_dir.get())) abort("error in test_metal vacuum\n"); delete_directory(temp_dir.get()); return 0; diff --git a/tests/h5test.cpp b/tests/h5test.cpp index 17e9ff2be..84640fd63 100644 --- a/tests/h5test.cpp +++ b/tests/h5test.cpp @@ -121,11 +121,13 @@ bool check_2d(double eps(const vec &), double a, int splitting, symfunc Sf, doub snprintf(dataname, 256, "%s%s", component_name(file_c), reim ? ".i" : (real_fields ? "" : ".r")); - realnum *h5data = (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); + realnum *h5data = + (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); file->prevent_deadlock(); // hackery if (!h5data) meep::abort("failed to read dataset %s:%s\n", name, dataname); if (rank != expected_rank) - meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, dataname); + meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, + dataname); if (expected_rank == 1 && file_gv.in_direction_min(X) == file_gv.in_direction_max(X)) { dims[1] = dims[0]; dims[0] = 1; @@ -234,11 +236,13 @@ bool check_3d(double eps(const vec &), double a, int splitting, symfunc Sf, comp snprintf(dataname, 256, "%s%s", component_name(file_c), reim ? ".i" : (real_fields ? "" : ".r")); - realnum *h5data = (realnum *)file->read(dataname, &rank, dims, 3, sizeof(realnum) == sizeof(float)); + realnum *h5data = + (realnum *)file->read(dataname, &rank, dims, 3, sizeof(realnum) == sizeof(float)); file->prevent_deadlock(); // hackery if (!h5data) meep::abort("failed to read dataset %s:%s\n", name, dataname); if (rank != expected_rank) - meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, dataname); + meep::abort("incorrect rank (%d instead of %d) in %s:%s\n", rank, expected_rank, name, + dataname); vec loc(loc0.dim); for (size_t i0 = 0; i0 < dims[0]; ++i0) { for (size_t i1 = 0; i1 < dims[1]; ++i1) { @@ -341,7 +345,8 @@ bool check_2d_monitor(double eps(const vec &), double a, int splitting, symfunc snprintf(dataname, 256, "%s%s", component_name(file_c), reim ? ".i" : (real_fields ? "" : ".r")); - realnum *h5data = (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); + realnum *h5data = + (realnum *)file->read(dataname, &rank, dims, 2, sizeof(realnum) == sizeof(float)); file->prevent_deadlock(); // hackery if (!h5data) meep::abort("failed to read dataset %s:%s\n", file->file_name(), dataname); if (rank != 1) meep::abort("monitor-point data is not one-dimensional"); diff --git a/tests/harmonics.cpp b/tests/harmonics.cpp index 9deb62035..ac176d26a 100644 --- a/tests/harmonics.cpp +++ b/tests/harmonics.cpp @@ -45,12 +45,12 @@ void harmonics(double freq, double chi2, double chi3, double J, double &A2, doub f.add_point_source(Ex, src, vec(-0.5 * sz + dpml), J); vec fpt(0.5 * sz - dpml - 0.5); - dft_flux d1 = f.add_dft_flux(Z, volume(fpt), freq, freq, 1, true, true, - 1 /* decimation_factor */); - dft_flux d2 = f.add_dft_flux(Z, volume(fpt), 2 * freq, 2 * freq, 1, true, true, - 1 /* decimation_factor */); - dft_flux d3 = f.add_dft_flux(Z, volume(fpt), 3 * freq, 3 * freq, 1, true, true, - 1 /* decimation_factor */); + dft_flux d1 = + f.add_dft_flux(Z, volume(fpt), freq, freq, 1, true, true, 1 /* decimation_factor */); + dft_flux d2 = + f.add_dft_flux(Z, volume(fpt), 2 * freq, 2 * freq, 1, true, true, 1 /* decimation_factor */); + dft_flux d3 = + f.add_dft_flux(Z, volume(fpt), 3 * freq, 3 * freq, 1, true, true, 1 /* decimation_factor */); double emax = 0; @@ -106,7 +106,8 @@ int main(int argc, char **argv) { if (different(a2, 9.80330e-07, thresh, "2nd harmonic mismatches known val")) return 1; if (sizeof(realnum) == sizeof(float)) { if (different(a3, 9.99349e-07, thresh, "3rd harmonic mismatches known val")) return 1; - } else { + } + else { if (different(a3, 9.97747e-07, thresh, "3rd harmonic mismatches known val")) return 1; } @@ -130,7 +131,8 @@ int main(int argc, char **argv) { harmonics(freq, 0.0, 1e-4, 1.0, a2_2, a3_2); if (sizeof(realnum) == sizeof(float)) { if (different(a3, a3_2, 0.0017, "chi2 has too big effect on 3rd harmonic")) return 1; - } else { + } + else { if (different(a3, a3_2, 0.001, "chi2 has too big effect on 3rd harmonic")) return 1; } diff --git a/tests/integrate.cpp b/tests/integrate.cpp index def80d451..cbf92cc18 100644 --- a/tests/integrate.cpp +++ b/tests/integrate.cpp @@ -271,7 +271,7 @@ void check_loop_vol(const grid_volume &gv, component c) { meep::abort("FAILED: LOOP_OVER_VOL has %zd iterations instead of ntot=%zd\n", count, gv.ntot()); if (count_owned != gv.nowned(c)) meep::abort("FAILED: LOOP_OVER_VOL has %zd owned points instead of nowned=%zd\n", count_owned, - gv.nowned(c)); + gv.nowned(c)); if (min_i != 0) meep::abort("FAILED: LOOP_OVER_VOL has minimum index %td instead of 0\n", min_i); if (size_t(max_i) != gv.ntot() - 1) meep::abort("FAILED: LOOP_OVER_VOL has max index %td instead of ntot-1\n", max_i); @@ -284,12 +284,15 @@ void check_loop_vol(const grid_volume &gv, component c) { vec here0(gv[ihere0]); if (ihere0 != ihere) meep::abort("FAILED: wrong LOOP_OVER_VOL_OWNED iloc at i=%td\n", i); if (abs(here0 - here) > 1e-13) - meep::abort("FAILED: wrong LOOP_OVER_VOL_OWNED loc (err = %g) at i=%td\n", abs(here0 - here), i); - if (!gv.owns(ihere)) meep::abort("FAILED: LOOP_OVER_VOL_OWNED includes non-owned at i=%td\n", i); + meep::abort("FAILED: wrong LOOP_OVER_VOL_OWNED loc (err = %g) at i=%td\n", abs(here0 - here), + i); + if (!gv.owns(ihere)) + meep::abort("FAILED: LOOP_OVER_VOL_OWNED includes non-owned at i=%td\n", i); ++count; } if (count != count_owned) - meep::abort("FAILED: LOOP_OVER_VOL_OWNED has %zd iterations instead of %zd\n", count, count_owned); + meep::abort("FAILED: LOOP_OVER_VOL_OWNED has %zd iterations instead of %zd\n", count, + count_owned); count = 0; LOOP_OVER_VOL_NOTOWNED(gv, c, i) { @@ -299,7 +302,8 @@ void check_loop_vol(const grid_volume &gv, component c) { vec here0(gv[ihere0]); if (ihere0 != ihere) meep::abort("FAILED: wrong LOOP_OVER_VOL_NOTOWNED iloc at i=%td\n", i); if (abs(here0 - here) > 1e-13) - meep::abort("FAILED: wrong LOOP_OVER_VOL_NOTOWNED loc (err = %g) at i=%td\n", abs(here0 - here), i); + meep::abort("FAILED: wrong LOOP_OVER_VOL_NOTOWNED loc (err = %g) at i=%td\n", + abs(here0 - here), i); if (gv.owns(ihere)) meep::abort("FAILED: LOOP_OVER_VOL_NOTOWNED includes owned at i=%td\n", i); if (ihere < vmin || ihere > vmax) meep::abort("FAILED: LOOP_OVER_VOL_NOTOWNED outside V at i=%td\n", i); @@ -307,7 +311,7 @@ void check_loop_vol(const grid_volume &gv, component c) { } if (count != gv.ntot() - count_owned) meep::abort("FAILED: LOOP_OVER_VOL_NOTOWNED has %zd iterations instead of %zd\n", count, - gv.ntot() - count_owned); + gv.ntot() - count_owned); master_printf("...PASSED.\n"); } @@ -323,12 +327,11 @@ int main(int argc, char **argv) { const grid_volume v1d = vol1d(sz[0], a); const grid_volume vcyl = volcyl(sz[0], sz[1], a); - srand(0); // use fixed random sequence - check_splitsym(v3d, 0, identity(), "identity"); - check_splitsym(v3d, 0, mirror(X, v3d), "mirrorx"); - return 0; + check_splitsym(v3d, 0, identity(), "identity"); + check_splitsym(v3d, 0, mirror(X, v3d), "mirrorx"); + return 0; for (int splitting = 0; splitting < 5; ++splitting) { check_splitsym(v3d, splitting, identity(), "identity"); diff --git a/tests/known_results.cpp b/tests/known_results.cpp index 773e0346a..c8a3b77ba 100644 --- a/tests/known_results.cpp +++ b/tests/known_results.cpp @@ -45,9 +45,7 @@ void compare(double b, double a, const char *n) { if (fabs(a - b) > fabs(b) * thresh || b != b) { meep::abort("Failed %s (%g instead of %g, relerr %0.2g)\n", n, a, b, fabs(a - b) / fabs(b)); } - else { - master_printf("Passed %s\n", n); - } + else { master_printf("Passed %s\n", n); } } static double dpml = 1.0; diff --git a/tests/near2far.cpp b/tests/near2far.cpp index 164a8e15e..b1a2367af 100644 --- a/tests/near2far.cpp +++ b/tests/near2far.cpp @@ -252,7 +252,7 @@ int main(int argc, char **argv) { const double a2d = argc > 1 ? atof(argv[1]) : 20, a3d = argc > 1 ? a2d : 10; - if ((sizeof(realnum) == sizeof(double)) && !check_cyl(5.0, 10.0, 20.0)) return 1; + if ((sizeof(realnum) == sizeof(double)) && !check_cyl(5.0, 10.0, 20.0)) return 1; // NOTE: see hack above -- we require sources to be odd in Z and even in X or vice-versa if (!check_2d_3d(D3, 4, a3d, Ez, Hx, false)) return 1; diff --git a/tests/one_dimensional.cpp b/tests/one_dimensional.cpp index 14558e63d..844b39c53 100644 --- a/tests/one_dimensional.cpp +++ b/tests/one_dimensional.cpp @@ -37,9 +37,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { diff --git a/tests/physical.cpp b/tests/physical.cpp index 938c088a3..5a2c9835b 100644 --- a/tests/physical.cpp +++ b/tests/physical.cpp @@ -54,8 +54,9 @@ int radiating_2D(const double xmax) { master_printf("Ratio is %g from (%g %g) and (%g %g)\n", ratio, real(amp1), imag(amp1), real(amp2), imag(amp2)); if (ratio > 2.12 || ratio < 1.88) - meep::abort("Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2)^2 = %g, too far from 2.0\n", - real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); + meep::abort( + "Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2)^2 = %g, too far from 2.0\n", + real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); return 1; } @@ -90,8 +91,9 @@ int radiating_3D(const double xmax) { master_printf("Ratio is %g from (%g %g) and (%g %g)\n", ratio, real(amp1), imag(amp1), real(amp2), imag(amp2)); if (ratio > 2.12 || ratio < 1.88) - meep::abort("Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2) = %g, too far from 2.0\n", - real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); + meep::abort( + "Failed: amp1 = (%g, %g), amp2 = (%g, %g)\n abs(amp1/amp2) = %g, too far from 2.0\n", + real(amp1), imag(amp1), real(amp2), imag(amp2), ratio); return 1; } diff --git a/tests/pml.cpp b/tests/pml.cpp index 6c3e1a8b4..7906d7d35 100644 --- a/tests/pml.cpp +++ b/tests/pml.cpp @@ -335,9 +335,12 @@ int main(int argc, char **argv) { if (sizeof(realnum) == sizeof(double)) { if (pml1d_scaling(one)) meep::abort("pml doesn't scale properly with length."); } - if (pmlcyl_scaling(one, 0)) meep::abort("m=0 cylindrical pml doesn't scale properly with length."); - if (pmlcyl_scaling(one, 1)) meep::abort("m=1 cylindrical pml doesn't scale properly with length."); - if (pmlcyl_scaling(one, 2)) meep::abort("m=2 cylindrical pml doesn't scale properly with length."); + if (pmlcyl_scaling(one, 0)) + meep::abort("m=0 cylindrical pml doesn't scale properly with length."); + if (pmlcyl_scaling(one, 1)) + meep::abort("m=1 cylindrical pml doesn't scale properly with length."); + if (pmlcyl_scaling(one, 2)) + meep::abort("m=2 cylindrical pml doesn't scale properly with length."); return 0; } diff --git a/tests/ring-ll.cpp b/tests/ring-ll.cpp index a478d6728..06f70b1ec 100644 --- a/tests/ring-ll.cpp +++ b/tests/ring-ll.cpp @@ -73,11 +73,9 @@ int main(int argc, char *argv[]) { // (radius (+ r w)) (material (make dielectric (index n)))) // (make cylinder (center 0 0) (height infinity) // (radius r) (material air)))) - auto material_deleter = [](meep_geom::material_data *m) { - meep_geom::material_free(m); - }; + auto material_deleter = [](meep_geom::material_data *m) { meep_geom::material_free(m); }; std::unique_ptr dielectric( - meep_geom::make_dielectric(n*n), material_deleter); + meep_geom::make_dielectric(n * n), material_deleter); geometric_object objects[2]; vector3 v3zero = {0.0, 0.0, 0.0}; vector3 zaxis = {0.0, 0.0, 1.0}; @@ -107,7 +105,7 @@ int main(int argc, char *argv[]) { double T = 300.0; double stop_time = f.round_time() + T; - std::vector> fieldData; + std::vector > fieldData; vec eval_pt(r + 0.1, 0.0); while (f.round_time() < stop_time) { f.step(); @@ -136,15 +134,15 @@ int main(int argc, char *argv[]) { int ref_bands = 4; double ref_freq_re[4] = {1.1807e-01, 1.4470e-01, 1.4715e-01, 1.7525e-01}; double ref_freq_im[4] = {-7.5657e-04, -8.9843e-04, -2.2172e-04, -5.0267e-05}; - std::complex ref_amp[4] = {std::complex(-6.40e-03,-2.81e-03), - std::complex(-1.42e-04,+6.78e-04), - std::complex(+3.99e-02,+4.09e-02), - std::complex(-1.98e-03,-1.43e-02)}; + std::complex ref_amp[4] = { + std::complex(-6.40e-03, -2.81e-03), std::complex(-1.42e-04, +6.78e-04), + std::complex(+3.99e-02, +4.09e-02), std::complex(-1.98e-03, -1.43e-02)}; if (bands != ref_bands) meep::abort("harminv found only %i/%i bands\n", bands, ref_bands); for (int nb = 0; nb < bands; nb++) if ((fabs(freq_re[nb] - ref_freq_re[nb]) > 1.0e-2 * fabs(ref_freq_re[nb]) || fabs(freq_im[nb] - ref_freq_im[nb]) > 1.0e-2 * fabs(ref_freq_im[nb]) || - abs(amp[nb] - ref_amp[nb]) > 1.0e-2 * abs(ref_amp[nb])) && (err[nb] < err_tol)) + abs(amp[nb] - ref_amp[nb]) > 1.0e-2 * abs(ref_amp[nb])) && + (err[nb] < err_tol)) meep::abort("harminv band %i disagrees with ref: {re f, im f, re A, im A}={%e,%e,%e,%e}!= " "{%e,%e,%e,%e}\n", nb, freq_re[nb], freq_im[nb], real(amp[nb]), imag(amp[nb]), ref_freq_re[nb], diff --git a/tests/symmetry.cpp b/tests/symmetry.cpp index 4997c38ba..4e8f6a6dd 100644 --- a/tests/symmetry.cpp +++ b/tests/symmetry.cpp @@ -56,9 +56,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { @@ -978,7 +976,8 @@ int main(int argc, char **argv) { if (!nonlinear_ex(vol1d(1.0, 30.0), one)) meep::abort("error in 1D nonlinear vacuum\n"); if (sizeof(realnum) == sizeof(double)) { - if (!nonlinear_ex(vol3d(1.0, 1.2, 0.8, 10.0), one)) meep::abort("error in 3D nonlinear vacuum\n"); + if (!nonlinear_ex(vol3d(1.0, 1.2, 0.8, 10.0), one)) + meep::abort("error in 3D nonlinear vacuum\n"); } // disable for parallel runs due to a bug in splitting cylindrical cell @@ -986,7 +985,8 @@ int main(int argc, char **argv) { if ((count_processors() == 1) && (!test_cyl_metal_mirror_nonlinear(one))) meep::abort("error in test_cyl_metal_mirror nonlinear vacuum\n"); - if (!exact_metal_rot4z_nonlinear(one)) meep::abort("error in exact_metal_rot4z nonlinear vacuum\n"); + if (!exact_metal_rot4z_nonlinear(one)) + meep::abort("error in exact_metal_rot4z nonlinear vacuum\n"); if (!exact_metal_rot4z_nonlinear(rods_2d)) meep::abort("error in exact_metal_rot4z nonlinear rods_2d\n"); diff --git a/tests/three_d.cpp b/tests/three_d.cpp index 428635ab6..3155ac028 100644 --- a/tests/three_d.cpp +++ b/tests/three_d.cpp @@ -45,9 +45,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { diff --git a/tests/two_dimensional.cpp b/tests/two_dimensional.cpp index f6f62c0aa..cab79303a 100644 --- a/tests/two_dimensional.cpp +++ b/tests/two_dimensional.cpp @@ -45,9 +45,7 @@ int compare(double a, double b, const char *n) { master_printf("This gives a fractional error of %g\n", fabs(a - b) / fabs(b)); return 0; } - else { - return 1; - } + else { return 1; } } int compare_point(fields &f1, fields &f2, const vec &p) { @@ -221,9 +219,7 @@ int test_pml(double eps(const vec &), int splitting) { new_energy / last_energy); return 0; } - else { - master_printf("Got newE/oldE of %g\n", new_energy / last_energy); - } + else { master_printf("Got newE/oldE of %g\n", new_energy / last_energy); } field_energy_check_time += deltaT; } } @@ -266,9 +262,7 @@ int test_pml_tm(double eps(const vec &), int splitting) { new_energy / last_energy); return 0; } - else { - master_printf("Got newE/oldE of %g\n", new_energy / last_energy); - } + else { master_printf("Got newE/oldE of %g\n", new_energy / last_energy); } field_energy_check_time += deltaT; } } @@ -313,9 +307,7 @@ int test_pml_te(double eps(const vec &), int splitting) { new_energy / last_energy); return 0; } - else { - master_printf("Got newE/oldE of %g\n", new_energy / last_energy); - } + else { master_printf("Got newE/oldE of %g\n", new_energy / last_energy); } field_energy_check_time += deltaT; } } diff --git a/tests/user-defined-material.cpp b/tests/user-defined-material.cpp index 681357e43..0080ad2da 100644 --- a/tests/user-defined-material.cpp +++ b/tests/user-defined-material.cpp @@ -54,13 +54,15 @@ bool compare_hdf5_datasets(const char *file1, const char *name1, const char *fil h5file f1(file1, h5file::READONLY, false); int rank1; size_t *dims1 = new size_t[expected_rank]; - realnum *data1 = (realnum *)f1.read(name1, &rank1, dims1, expected_rank, sizeof(realnum) == sizeof(float)); + realnum *data1 = + (realnum *)f1.read(name1, &rank1, dims1, expected_rank, sizeof(realnum) == sizeof(float)); if (!data1) return false; h5file f2(file2, h5file::READONLY, false); int rank2; size_t *dims2 = new size_t[expected_rank]; - realnum *data2 = (realnum *)f2.read(name2, &rank2, dims2, expected_rank, sizeof(realnum) == sizeof(float)); + realnum *data2 = + (realnum *)f2.read(name2, &rank2, dims2, expected_rank, sizeof(realnum) == sizeof(float)); if (!data2) return false; if (rank1 != expected_rank || rank2 != expected_rank) return false; From ff413bffbad73311ba641a6cbd9510848e1a3df5 Mon Sep 17 00:00:00 2001 From: Alex-Kaylor Date: Fri, 29 Jul 2022 16:36:07 -0400 Subject: [PATCH 22/26] Update build from source instructions (#2169) * Update build from source instructions Update broken installation link for nlopt installation in ubuntu and add a disclaimer for users on ubuntu 20.02. * Update doc/docs/Build_From_Source.md Co-authored-by: Steven G. Johnson --- doc/docs/Build_From_Source.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/docs/Build_From_Source.md b/doc/docs/Build_From_Source.md index d3d3525b6..33ba7fb8f 100644 --- a/doc/docs/Build_From_Source.md +++ b/doc/docs/Build_From_Source.md @@ -292,6 +292,7 @@ chmod +x build-meep.sh #### Ubuntu 16.04 and 18.04 There are a few differences in building for 16.04 and 18.04, so be sure to read the script and adjust appropriately. + (Ubuntu 20.04 will not work without a few changes.) ```bash #!/bin/bash @@ -371,7 +372,7 @@ pip3 install --user matplotlib>3.0.0 pip3 install --user ffmpeg cd ~/install -git clone git://github.com/stevengj/nlopt.git +git clone https://github.com/stevengj/nlopt.git cd nlopt/ cmake -DPYTHON_EXECUTABLE=/usr/bin/python3 && make && sudo make install From 4868c0ebce36d62082e7bd453a03dd164cc4172b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 29 Jul 2022 16:48:22 -0400 Subject: [PATCH 23/26] Bump mkdocs from 1.2.3 to 1.3.1 (#2171) Bumps [mkdocs](https://github.com/mkdocs/mkdocs) from 1.2.3 to 1.3.1. - [Release notes](https://github.com/mkdocs/mkdocs/releases) - [Commits](https://github.com/mkdocs/mkdocs/compare/1.2.3...1.3.1) --- updated-dependencies: - dependency-name: mkdocs dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- doc/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/requirements.txt b/doc/requirements.txt index c3f3d78ae..43beef07f 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -1,3 +1,3 @@ -mkdocs==1.2.3 +mkdocs==1.3.1 python-markdown-math mkdocs-material From 55f909d11ce671ca762999d5eae55e5c8e9304f2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 29 Jul 2022 16:48:30 -0400 Subject: [PATCH 24/26] Bump codecov/codecov-action from 1 to 3 (#2170) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 1 to 3. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/master/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v1...v3) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-ci.yml b/.github/workflows/build-ci.yml index 72ff0c8a0..233860c61 100644 --- a/.github/workflows/build-ci.yml +++ b/.github/workflows/build-ci.yml @@ -188,7 +188,7 @@ jobs: - name: Upload coverage to Codecov if: ${{ matrix.enable-mpi == false && matrix.python-version == 3.7 }} - uses: codecov/codecov-action@v1 + uses: codecov/codecov-action@v3 with: files: ${{ github.workspace }}/python/coverage.xml From fabd531b8d6cbb8a930787fad8560507663f2737 Mon Sep 17 00:00:00 2001 From: "Steven G. Johnson" Date: Fri, 29 Jul 2022 16:55:17 -0400 Subject: [PATCH 25/26] approximate material-grid smoothing --- libpympb/pympb.cpp | 7 +- python/adjoint/filters.py | 20 + python/adjoint/utils.py | 2 +- python/meep.i | 4 +- python/requirements.txt | 1 + python/tests/test_material_grid.py | 446 +++++++------- python/typemap_utils.cpp | 12 +- src/material_data.cpp | 6 +- src/material_data.hpp | 8 +- src/meepgeom.cpp | 912 ++++++++++++++++++----------- src/meepgeom.hpp | 69 ++- src/support/Makefile.am | 2 +- 12 files changed, 879 insertions(+), 610 deletions(-) diff --git a/libpympb/pympb.cpp b/libpympb/pympb.cpp index c6033cb99..5bee3941d 100644 --- a/libpympb/pympb.cpp +++ b/libpympb/pympb.cpp @@ -369,12 +369,9 @@ int mode_solver::mean_epsilon(symmetric_matrix *meps, symmetric_matrix *meps_inv bool o1_is_var = o1 && meep_geom::is_variable(o1->material); bool o2_is_var = o2 && meep_geom::is_variable(o2->material); - bool default_is_var_or_file = - meep_geom::is_variable(default_material) || meep_geom::is_file(default_material); + bool default_is_var = meep_geom::is_variable(default_material); - if (o1_is_var || o2_is_var || - (default_is_var_or_file && - (!o1 || !o2 || meep_geom::is_file(o1->material) || meep_geom::is_file(o2->material)))) { + if (o1_is_var || o2_is_var || (default_is_var && (!o1 || !o2))) { return 0; /* arbitrary material functions are non-analyzable */ } diff --git a/python/adjoint/filters.py b/python/adjoint/filters.py index 262f37937..c095ad7d0 100644 --- a/python/adjoint/filters.py +++ b/python/adjoint/filters.py @@ -6,6 +6,10 @@ from scipy import signal, special import meep as mp +from scipy import special +from scipy import signal +import skfmm +from scipy import interpolate def _proper_pad(x, n): @@ -874,3 +878,19 @@ def gray_indicator(x): density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. """ return npa.mean(4 * x.flatten() * (1 - x.flatten())) * 100 + +def make_sdf(data): + ''' + Assume the input, data, is the desired output shape + (i.e. 1d, 2d, or 3d) and that it's values are between + 0 and 1. + ''' + # create signed distance function + sd = skfmm.distance(data- 0.5 , dx = 1) + + # interpolate zero-levelset onto 0.5-levelset + x = [np.min(sd.flatten()), 0, np.max(sd.flatten())] + y = [0, 0.5, 1] + f = interpolate.interp1d(x, y, kind='linear') + + return f(sd) diff --git a/python/adjoint/utils.py b/python/adjoint/utils.py index fb37d2b0b..9683348e1 100644 --- a/python/adjoint/utils.py +++ b/python/adjoint/utils.py @@ -20,7 +20,7 @@ _Z_AXIS = 1 # default finite difference step size when calculating Aᵤ -FD_DEFAULT = 1e-3 +FD_DEFAULT = 1e-6 class DesignRegion: diff --git a/python/meep.i b/python/meep.i index 66a4703cf..0cae141dd 100644 --- a/python/meep.i +++ b/python/meep.i @@ -1504,8 +1504,6 @@ void _get_gradient(PyObject *grad, double scalegrad, %ignore material_type_equal; %ignore is_variable; %ignore is_variable; -%ignore is_file; -%ignore is_file; %ignore is_medium; %ignore is_medium; %ignore is_metal; @@ -1986,7 +1984,7 @@ meep_geom::geom_epsilon* _set_materials(meep::structure * s, geps = existing_geps; } else { geps = meep_geom::make_geom_epsilon(s, &gobj_list, center, _ensure_periodicity, _default_material, - extra_materials); + extra_materials, use_anisotropic_averaging); } if (set_materials) { meep_geom::set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, diff --git a/python/requirements.txt b/python/requirements.txt index 772aac158..c43d34869 100644 --- a/python/requirements.txt +++ b/python/requirements.txt @@ -7,3 +7,4 @@ numpy parameterized pytest scipy +scikit-fmm diff --git a/python/tests/test_material_grid.py b/python/tests/test_material_grid.py index 284b5c4ed..d64b38348 100644 --- a/python/tests/test_material_grid.py +++ b/python/tests/test_material_grid.py @@ -1,5 +1,13 @@ -import meep as mp +''' +Checks that a material grid works with symmetries, and that the +new subpixel smoothing feature is accurate. + +TODO: + Create a 3D test that works well with the new smoothing +''' +import meep as mp +from meep import mpb try: import meep.adjoint as mpa except: @@ -8,258 +16,204 @@ import unittest import numpy as np -from scipy.ndimage import gaussian_filter +import unittest +rad = 0.301943 +k_point = mp.Vector3(0.3892,0.1597,0) +Si = mp.Medium(index=3.5) +cell_size = mp.Vector3(1,1,0) def compute_transmittance(matgrid_symmetry=False): - resolution = 25 - - cell_size = mp.Vector3(6, 6, 0) - - boundary_layers = [mp.PML(thickness=1.0)] - - matgrid_size = mp.Vector3(2, 2, 0) - matgrid_resolution = 2 * resolution - - Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( - matgrid_size.y * matgrid_resolution - ) - - # ensure reproducible results - rng = np.random.RandomState(2069588) - - w = rng.rand(Nx, Ny) - weights = w if matgrid_symmetry else 0.5 * (w + np.fliplr(w)) - - matgrid = mp.MaterialGrid( - mp.Vector3(Nx, Ny), - mp.air, - mp.Medium(index=3.5), - weights=weights, - do_averaging=False, - grid_type="U_MEAN", - ) - - geometry = [ - mp.Block( - center=mp.Vector3(), - size=mp.Vector3(mp.inf, 1.0, mp.inf), - material=mp.Medium(index=3.5), - ), - mp.Block( - center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), - material=matgrid, - ), - ] - - if matgrid_symmetry: - geometry.append( - mp.Block( - center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), - material=matgrid, - e2=mp.Vector3(y=-1), - ) - ) - - eig_parity = mp.ODD_Y + mp.EVEN_Z - - fcen = 0.65 - df = 0.2 * fcen - sources = [ - mp.EigenModeSource( - src=mp.GaussianSource(fcen, fwidth=df), - center=mp.Vector3(-2.0, 0), - size=mp.Vector3(0, 4.0), - eig_parity=eig_parity, - ) - ] - - sim = mp.Simulation( - resolution=resolution, - cell_size=cell_size, - boundary_layers=boundary_layers, - sources=sources, - geometry=geometry, - ) - - mode_mon = sim.add_flux( - fcen, 0, 1, mp.FluxRegion(center=mp.Vector3(2.0, 0), size=mp.Vector3(0, 4.0)) - ) - - sim.run(until_after_sources=mp.stop_when_dft_decayed()) - - mode_coeff = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity).alpha[ - 0, :, 0 - ][0] - - tran = np.power(np.abs(mode_coeff), 2) - print(f'tran:, {"sym" if matgrid_symmetry else "nosym"}, {tran}') - - return tran - - -def compute_resonant_mode_2d(res, default_mat=False): - cell_size = mp.Vector3(1, 1, 0) - - rad = 0.301943 - - fcen = 0.3 - df = 0.2 * fcen - sources = [ - mp.Source( - mp.GaussianSource(fcen, fwidth=df), - component=mp.Hz, - center=mp.Vector3(-0.1057, 0.2094, 0), - ) - ] - - k_point = mp.Vector3(0.3892, 0.1597, 0) - - matgrid_size = mp.Vector3(1, 1, 0) - matgrid_resolution = 1200 - - # for a fixed resolution, compute the number of grid points - # necessary which are defined on the corners of the voxels - Nx, Ny = int(matgrid_size.x * matgrid_resolution), int( - matgrid_size.y * matgrid_resolution - ) - - x = np.linspace(-0.5 * matgrid_size.x, 0.5 * matgrid_size.x, Nx) - y = np.linspace(-0.5 * matgrid_size.y, 0.5 * matgrid_size.y, Ny) - xv, yv = np.meshgrid(x, y) - weights = np.sqrt(np.square(xv) + np.square(yv)) < rad - filtered_weights = gaussian_filter(weights, sigma=3.0, output=np.double) - - matgrid = mp.MaterialGrid( - mp.Vector3(Nx, Ny), - mp.air, - mp.Medium(index=3.5), - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5, - ) - - geometry = [ - mp.Block( - center=mp.Vector3(), - size=mp.Vector3(matgrid_size.x, matgrid_size.y, 0), - material=matgrid, - ) - ] - - sim = mp.Simulation( - resolution=res, - cell_size=cell_size, - default_material=matgrid if default_mat else mp.Medium(), - geometry=[] if default_mat else geometry, - sources=sources, - k_point=k_point, - ) - - h = mp.Harminv(mp.Hz, mp.Vector3(0.3718, -0.2076), fcen, df) - sim.run(mp.after_sources(h), until_after_sources=200) - - try: - for m in h.modes: - print(f"harminv:, {res}, {m.freq}, {m.Q}") - freq = h.modes[0].freq - except: - raise RuntimeError("No resonant modes found.") - - return freq - - -def compute_resonant_mode_3d(use_matgrid=True): - resolution = 25 - - wvl = 1.27 - fcen = 1 / wvl - df = 0.02 * fcen - - nSi = 3.45 - Si = mp.Medium(index=nSi) - nSiO2 = 1.45 - SiO2 = mp.Medium(index=nSiO2) - - s = 1.0 - cell_size = mp.Vector3(s, s, s) - - rad = 0.34 # radius of sphere - - if use_matgrid: - matgrid_resolution = 2 * resolution - N = int(s * matgrid_resolution) - coord = np.linspace(-0.5 * s, 0.5 * s, N) - xv, yv, zv = np.meshgrid(coord, coord, coord) - - weights = np.sqrt(np.square(xv) + np.square(yv) + np.square(zv)) < rad - filtered_weights = gaussian_filter( - weights, sigma=4 / resolution, output=np.double - ) - - matgrid = mp.MaterialGrid( - mp.Vector3(N, N, N), - SiO2, - Si, - weights=filtered_weights, - do_averaging=True, - beta=1000, - eta=0.5, - ) - - geometry = [mp.Block(center=mp.Vector3(), size=cell_size, material=matgrid)] - else: - geometry = [mp.Sphere(center=mp.Vector3(), radius=rad, material=Si)] - - sources = [ - mp.Source( - src=mp.GaussianSource(fcen, fwidth=df), - size=mp.Vector3(), - center=mp.Vector3(0.13, 0.25, 0.06), - component=mp.Ez, - ) - ] - - k_point = mp.Vector3(0.23, -0.17, 0.35) - - sim = mp.Simulation( - resolution=resolution, - cell_size=cell_size, - sources=sources, - default_material=SiO2, - k_point=k_point, - geometry=geometry, - ) - - h = mp.Harminv(mp.Ez, mp.Vector3(-0.2684, 0.1185, 0.0187), fcen, df) - - sim.run(mp.after_sources(h), until_after_sources=200) - - try: - for m in h.modes: - print(f"harminv:, {resolution}, {m.freq}, {m.Q}") - freq = h.modes[0].freq - except: - raise RuntimeError("No resonant modes found.") - - return freq - + resolution = 25 + + cell_size = mp.Vector3(6,6,0) + + boundary_layers = [mp.PML(thickness=1.0)] + + matgrid_size = mp.Vector3(2,2,0) + matgrid_resolution = 2*resolution + + Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) + + # ensure reproducible results + rng = np.random.RandomState(2069588) + + w = rng.rand(Nx,Ny) + # for subpixel smoothing, the underlying design grid must be smoothly varying + w = mpa.tanh_projection(rng.rand(Nx,Ny),1e5,0.5) + w = mpa.conic_filter(w,0.25,matgrid_size.x,matgrid_size.y,matgrid_resolution) + weights = 0.5 * (w + np.fliplr(w)) if not matgrid_symmetry else w + + matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), + mp.air, + mp.Medium(index=3.5), + weights=weights, + beta=np.inf, + grid_type='U_MEAN') + + geometry = [mp.Block(center=mp.Vector3(), + size=mp.Vector3(mp.inf,1.0,mp.inf), + material=mp.Medium(index=3.5)), + mp.Block(center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), + material=matgrid)] + + if matgrid_symmetry: + geometry.append(mp.Block(center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), + material=matgrid, + e2=mp.Vector3(y=-1))) + + eig_parity = mp.ODD_Y + mp.EVEN_Z + + fcen = 0.65 + df = 0.2*fcen + sources = [mp.EigenModeSource(src=mp.GaussianSource(fcen,fwidth=df), + center=mp.Vector3(-2.0,0), + size=mp.Vector3(0,4.0), + eig_parity=eig_parity)] + + sim = mp.Simulation(resolution=resolution, + cell_size=cell_size, + boundary_layers=boundary_layers, + sources=sources, + geometry=geometry) + + mode_mon = sim.add_flux(fcen, + 0, + 1, + mp.FluxRegion(center=mp.Vector3(2.0,0), + size=mp.Vector3(0,4.0))) + + sim.run(until_after_sources=mp.stop_when_dft_decayed()) + + mode_coeff = sim.get_eigenmode_coefficients(mode_mon,[1],eig_parity).alpha[0,:,0][0] + + tran = np.power(np.abs(mode_coeff),2) + print('tran:, {}, {}'.format("sym" if matgrid_symmetry else "nosym", tran)) + + return tran + +def compute_resonant_mode_2d(res,radius=rad,default_mat=False,cylinder=False,cylinder_matgrid=True,do_averaging=True): + fcen = 0.3 + df = 0.2*fcen + sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df), + component=mp.Hz, + center=mp.Vector3(-0.1057,0.2094,0))] + + matgrid_size = mp.Vector3(1,1,0) + matgrid_resolution = 1200 + + # for a fixed resolution, compute the number of grid points + # necessary which are defined on the corners of the voxels + Nx, Ny = int(matgrid_size.x*matgrid_resolution), int(matgrid_size.y*matgrid_resolution) + + x = np.linspace(-0.5*matgrid_size.x,0.5*matgrid_size.x,Nx) + y = np.linspace(-0.5*matgrid_size.y,0.5*matgrid_size.y,Ny) + xv, yv = np.meshgrid(x,y) + weights = mpa.make_sdf(np.sqrt(np.square(xv) + np.square(yv)) < radius) + if cylinder: + weights = 0.0*weights+1.0 + + matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny), + mp.air, + Si, + weights=weights, + beta=np.inf, + eta=0.5) + + # Use a cylinder object, not a block object + if cylinder: + # within the cylinder, use a (uniform) material grid + if cylinder_matgrid: + geometry = [mp.Cylinder(center=mp.Vector3(), + radius=radius, + material=matgrid)] + # within the cylinder, just use a normal medium + else: + geometry = [mp.Cylinder(center=mp.Vector3(), + radius=radius, + material=Si)] + # use a block object + else: + geometry = [mp.Block(center=mp.Vector3(), + size=mp.Vector3(matgrid_size.x,matgrid_size.y,0), + material=matgrid)] + + sim = mp.Simulation(resolution=res, + eps_averaging=do_averaging, + cell_size=cell_size, + default_material=matgrid if default_mat else mp.Medium(), + geometry=geometry if not default_mat else [], + sources=sources, + k_point=k_point) + + h = mp.Harminv(mp.Hz, mp.Vector3(0.3718,-0.2076), fcen, df) + sim.run(mp.after_sources(h), + until_after_sources=300) + + try: + for m in h.modes: + print("harminv:, {}, {}, {}".format(res,m.freq,m.Q)) + freq = h.modes[0].freq + except: + raise RuntimeError("No resonant modes found.") + + return freq + +def compute_resonant_mode_mpb(resolution=512): + geometry = [mp.Cylinder(rad, material=Si)] + geometry_lattice = mp.Lattice(cell_size) + + ms = mpb.ModeSolver(num_bands=1, + k_points=[mp.cartesian_to_reciprocal(k_point, geometry_lattice)], + geometry=geometry, + geometry_lattice=geometry_lattice, + resolution=resolution) + + + ms.run_te() + return ms.freqs[0] class TestMaterialGrid(unittest.TestCase): def test_subpixel_smoothing(self): - # "exact" frequency computed using MaterialGrid at resolution = 300 - freq_ref = 0.29826813873225283 - - res = [25, 50] + res = 25 + + def subpixel_test_matrix(radius,do_averaging): + freq_cylinder = compute_resonant_mode_2d(res, radius, default_mat=False, cylinder=True, cylinder_matgrid=False, do_averaging=do_averaging) + freq_matgrid = compute_resonant_mode_2d(res, radius, default_mat=False, cylinder=False, do_averaging=do_averaging) + freq_matgrid_cylinder = compute_resonant_mode_2d(res, radius, default_mat=False, cylinder=True, cylinder_matgrid=True, do_averaging=do_averaging) + return [freq_cylinder,freq_matgrid,freq_matgrid_cylinder] + + # when smoothing is off, all three tests should be identical to machine precision + no_smoothing = subpixel_test_matrix(rad,False) + self.assertEqual(no_smoothing[0], no_smoothing[1]) + self.assertEqual(no_smoothing[1], no_smoothing[2]) + + # when we slightly perturb the radius, the results should be the same as before. + no_smoothing_perturbed = subpixel_test_matrix(rad+0.01/res,False) + self.assertEqual(no_smoothing, no_smoothing_perturbed) + + # when smoothing is on, the simple material results should be different from the matgrid results + smoothing = subpixel_test_matrix(rad,True) + self.assertNotEqual(smoothing[0], smoothing[1]) + self.assertAlmostEqual(smoothing[1], smoothing[2], 3) + + # when we slighty perturb the radius, the results should all be different from before. + smoothing_perturbed = subpixel_test_matrix(rad+0.01/res,True) + self.assertNotEqual(smoothing, smoothing_perturbed) + + # "exact" frequency computed using MPB + freq_ref = compute_resonant_mode_mpb() + print(freq_ref) + res = [50, 100] freq_matgrid = [] for r in res: - freq_matgrid.append(compute_resonant_mode_2d(r)) + freq_matgrid.append(compute_resonant_mode_2d(r,cylinder=False)) # verify that the frequency of the resonant mode is # approximately equal to the reference value self.assertAlmostEqual(freq_ref, freq_matgrid[-1], 2) + print("results: ",freq_ref,freq_matgrid) # verify that the relative error is decreasing with increasing resolution # and is better than linear convergence because of subpixel smoothing @@ -268,14 +222,10 @@ def test_subpixel_smoothing(self): abs(freq_matgrid[0] - freq_ref), ) - freq_matgrid_default_mat = compute_resonant_mode_2d(res[0], True) + # ensure that a material grid as a default material works + freq_matgrid_default_mat = compute_resonant_mode_2d(res[0], rad, True) self.assertAlmostEqual(freq_matgrid[0], freq_matgrid_default_mat) - def test_matgrid_3d(self): - freq_matgrid = compute_resonant_mode_3d(True) - freq_geomobj = compute_resonant_mode_3d(False) - self.assertAlmostEqual(freq_matgrid, freq_geomobj, places=2) - def test_symmetry(self): tran_nosym = compute_transmittance(False) tran_sym = compute_transmittance(True) diff --git a/python/typemap_utils.cpp b/python/typemap_utils.cpp index ab8c8a294..f18764b26 100644 --- a/python/typemap_utils.cpp +++ b/python/typemap_utils.cpp @@ -521,9 +521,15 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { PyObject *po_dp = PyObject_GetAttrString(po, "weights"); PyArrayObject *pao = (PyArrayObject *)po_dp; if (!PyArray_Check(pao)) { meep::abort("MaterialGrid weights failed to init."); } - if (!PyArray_ISCARRAY(pao)) { meep::abort("Numpy array weights must be C-style contiguous."); } - md->weights = new double[PyArray_SIZE(pao)]; - memcpy(md->weights, (double *)PyArray_DATA(pao), PyArray_SIZE(pao) * sizeof(double)); + if (!PyArray_ISCARRAY(pao)) { + meep::abort("Numpy array weights must be C-style contiguous."); + } + md->weights.clear(); + for (size_t i=0;iweights.push_back(((double *)PyArray_DATA(pao))[i]); + } + //md->weights = new double[PyArray_SIZE(pao)]; + //memcpy(md->weights, (double *)PyArray_DATA(pao), PyArray_SIZE(pao) * sizeof(double)); // if needed, combine sus structs to main object PyObject *py_e_sus_m1 = PyObject_GetAttrString(po_medium1, "E_susceptibilities"); diff --git a/src/material_data.cpp b/src/material_data.cpp index a24d87cee..7848aae83 100644 --- a/src/material_data.cpp +++ b/src/material_data.cpp @@ -66,10 +66,10 @@ void material_data::copy_from(const material_data &from) { } grid_size = from.grid_size; - if (from.weights) { + if (from.weights.size() != 0) { size_t N = from.grid_size.x * from.grid_size.y * from.grid_size.z; - weights = new double[N]; - std::copy_n(from.weights, N, weights); + weights.clear(); + weights.insert(weights.end(), from.weights.begin(), from.weights.end()); } medium_1 = from.medium_1; diff --git a/src/material_data.hpp b/src/material_data.hpp index 4e1df20d5..ac6d57e24 100644 --- a/src/material_data.hpp +++ b/src/material_data.hpp @@ -25,6 +25,7 @@ #include #include +#include "support/dual.hpp" #include "meep.hpp" @@ -122,6 +123,9 @@ struct material_data { // these fields used only if which_subclass==MATERIAL_USER user_material_func user_func; void *user_data; + + // used for any variable material (USER, FILE, or GRID): + // indicates whether we should use the fallback subpixel averaging via quadrature bool do_averaging; // these fields used only if which_subclass==MATERIAL_FILE @@ -130,7 +134,7 @@ struct material_data { // these fields used only if which_subclass==MATERIAL_GRID vector3 grid_size; - double *weights; + std::vector weights; medium_struct medium_1; medium_struct medium_2; double beta; @@ -184,7 +188,7 @@ material_type make_user_material(user_material_func user_func, void *user_data); material_type make_file_material(char *epsilon_input_file); material_type make_material_grid(bool do_averaging, double beta, double eta, double damping); void read_epsilon_file(const char *eps_input_file); -void update_weights(material_type matgrid, double *weights); +void update_weights(material_type matgrid, duals::duald *weights); }; // namespace meep_geom diff --git a/src/meepgeom.cpp b/src/meepgeom.cpp index 6b83d109b..660b15423 100644 --- a/src/meepgeom.cpp +++ b/src/meepgeom.cpp @@ -14,11 +14,15 @@ % Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. */ +#include #include #include #include "meepgeom.hpp" #include "meep_internals.hpp" +namespace dlit = duals::literals; +using namespace duals::literals; + namespace meep_geom { #define master_printf meep::master_printf @@ -121,9 +125,6 @@ void material_free(material_type m) { // object so will assume that the caller keeps track of its lifetime. delete[] m->epsilon_data; m->epsilon_data = NULL; - - delete[] m->weights; - m->weights = NULL; delete m; } @@ -148,9 +149,9 @@ bool material_type_equal(const material_type m1, const material_type m2) { /* rotate A by a unitary (real) rotation matrix R: RAR = transpose(R) * A * R */ -void sym_matrix_rotate(symm_matrix *RAR, const symm_matrix *A_, const double R[3][3]) { +void sym_matrix_rotate(symm_matrix *RAR, const symm_matrix *A_, const duals::duald R[3][3]) { int i, j; - double A[3][3], AR[3][3]; + duals::duald A[3][3], AR[3][3]; A[0][0] = A_->m00; A[1][1] = A_->m11; A[2][2] = A_->m22; @@ -173,8 +174,8 @@ void sym_matrix_rotate(symm_matrix *RAR, const symm_matrix *A_, const double R[3 /* Set Vinv = inverse of V, where both V and Vinv are real-symmetric matrices.*/ void sym_matrix_invert(symm_matrix *Vinv, const symm_matrix *V) { - double m00 = V->m00, m11 = V->m11, m22 = V->m22; - double m01 = V->m01, m02 = V->m02, m12 = V->m12; + duals::duald m00 = V->m00, m11 = V->m11, m22 = V->m22; + duals::duald m01 = V->m01, m02 = V->m02, m12 = V->m12; if (m01 == 0.0 && m02 == 0.0 && m12 == 0.0) { /* optimize common case of a diagonal matrix: */ @@ -184,13 +185,13 @@ void sym_matrix_invert(symm_matrix *Vinv, const symm_matrix *V) { Vinv->m01 = Vinv->m02 = Vinv->m12 = 0.0; } else { - double detinv; + duals::duald detinv; /* compute the determinant: */ detinv = m00 * m11 * m22 - m02 * m11 * m02 + 2.0 * m01 * m12 * m02 - m01 * m01 * m22 - m12 * m12 * m00; - if (detinv == 0.0) meep::abort("singular 3x3 matrix"); + if (detinv.rpart() == 0.0) meep::abort("singular 3x3 matrix"); detinv = 1.0 / detinv; @@ -206,18 +207,19 @@ void sym_matrix_invert(symm_matrix *Vinv, const symm_matrix *V) { /* Returns whether or not V is positive-definite. */ int sym_matrix_positive_definite(symm_matrix *V) { - double det2, det3; - double m00 = V->m00, m11 = V->m11, m22 = V->m22; + duals::duald det2, det3; + duals::duald m00 = V->m00, m11 = V->m11, m22 = V->m22; #if defined(WITH_HERMITIAN_EPSILON) - scalar_complex m01 = V->m01, m02 = V->m02, m12 = V->m12; + // FIXME + scalar_complex m01 = V->m01.rpart(), m02 = V->m02.rpart(), m12 = V->m12.rpart(); det2 = m00 * m11 - CSCALAR_NORMSQR(m01); det3 = det2 * m22 - m11 * CSCALAR_NORMSQR(m02) - CSCALAR_NORMSQR(m12) * m00 + 2.0 * ((m01.re * m12.re - m01.im * m12.im) * m02.re + (m01.re * m12.im + m01.im * m12.re) * m02.im); #else /* real matrix */ - double m01 = V->m01, m02 = V->m02, m12 = V->m12; + duals::duald m01 = V->m01, m02 = V->m02, m12 = V->m12; det2 = m00 * m11 - m01 * m01; det3 = det2 * m22 - m02 * m11 * m02 + 2.0 * m01 * m12 * m02 - m12 * m12 * m00; @@ -285,15 +287,14 @@ bool is_material_grid(material_type mt) { } bool is_material_grid(void *md) { return is_material_grid((material_type)md); } +// return whether mt is spatially varying bool is_variable(material_type mt) { - return (mt->which_subclass == material_data::MATERIAL_USER) || - (mt->which_subclass == material_data::MATERIAL_GRID); + return (mt && ((mt->which_subclass == material_data::MATERIAL_USER) || + (mt->which_subclass == material_data::MATERIAL_GRID) || + (mt->which_subclass == material_data::MATERIAL_FILE))); } bool is_variable(void *md) { return is_variable((material_type)md); } -bool is_file(material_type md) { return (md->which_subclass == material_data::MATERIAL_FILE); } -bool is_file(void *md) { return is_file((material_type)md); } - bool is_medium(material_type md, medium_struct **m) { if (md->which_subclass == material_data::MEDIUM) { *m = &(md->medium); @@ -304,24 +305,29 @@ bool is_medium(material_type md, medium_struct **m) { bool is_medium(void *md, medium_struct **m) { return is_medium((material_type)md, m); } +// note: is_metal assumes that eval_material_pt has already been called for variable materials bool is_metal(meep::field_type ft, const material_type *material) { material_data *md = *material; if (ft == meep::E_stuff) switch (md->which_subclass) { case material_data::MEDIUM: + case material_data::MATERIAL_USER: + case material_data::MATERIAL_FILE: case material_data::MATERIAL_GRID: return (md->medium.epsilon_diag.x < 0 || md->medium.epsilon_diag.y < 0 || md->medium.epsilon_diag.z < 0); case material_data::PERFECT_METAL: return true; - default: meep::abort("unknown material type"); return false; + default: meep::abort("unknown material type in is_metal"); return false; } else switch (md->which_subclass) { case material_data::MEDIUM: + case material_data::MATERIAL_USER: + case material_data::MATERIAL_FILE: case material_data::MATERIAL_GRID: return (md->medium.mu_diag.x < 0 || md->medium.mu_diag.y < 0 || md->medium.mu_diag.z < 0); case material_data::PERFECT_METAL: return false; // is an electric conductor, but not a magnetic conductor - default: meep::abort("unknown material type"); return false; + default: meep::abort("unknown material type in is_metal"); return false; } } @@ -337,37 +343,50 @@ bool has_offdiag(const medium_struct *material) { // computes the vector-Jacobian product of the gradient of the matgrid_val function v // with the Jacobian of the to_geom_box_coords function for geometric_object o -vector3 to_geom_object_coords_VJP(vector3 v, const geometric_object *o) { +cvector3 to_geom_object_coords_VJP(cvector3 v, const geometric_object *o) { if (!o) { meep::abort("must pass a geometric_object to to_geom_object_coords_VJP.\n"); } switch (o->which_subclass) { default: { - vector3 po = {0, 0, 0}; + cvector3 po = cvector_zero(); return po; } case geometric_object::SPHERE: { number radius = o->subclass.sphere_data->radius; - return vector3_scale(0.5 / radius, v); + return cvector3_scale(0.5 / radius, v); } /* case geometric_object::CYLINDER: NOT YET IMPLEMENTED */ case geometric_object::BLOCK: { + /* In order to leverage the underlying libctl infrastructure + *and* the dual library that makes computing derivatives so easy, + me perform a bit of a trick: we use the *complex* libctl vector, + storing the real and dual parts of the dual library and *manually* + propagate the dual through existing libraries as needed. + */ + vector3 v_real = cvector3_re(v); // real part + vector3 v_imag = cvector3_im(v); // "dual" part + vector3 size = o->subclass.block_data->size; - if (size.x != 0.0) v.x /= size.x; - if (size.y != 0.0) v.y /= size.y; - if (size.z != 0.0) v.z /= size.z; - return matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v); + if (size.x != 0.0) {v_real.x /= size.x; v_imag.x /= size.x;} + if (size.y != 0.0) {v_real.y /= size.y; v_imag.y /= size.y;} + if (size.z != 0.0) {v_real.z /= size.z; v_imag.z /= size.z;} + v_real = matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v_real); + v_imag = matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v_imag); + return make_cvector3(v_real,v_imag); } /* case geometric_object::PRISM: NOT YET IMPLEMENTED */ } } -meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_object *o) { - if (!is_material_grid(md)) { meep::abort("Invalid material grid detected.\n"); } +cvector3 material_grid_grad(vector3 p, material_data *md, const geometric_object *o) { + /* computes the actual spatial gradient at point `p` + for the specified material grid `md`. */ + + if (!is_material_grid(md)) {meep::abort("Invalid material grid detected.\n"); } - meep::vec gradient(zero_vec(dim)); - double *data = md->weights; + cvector3 gradient = cvector_zero(); int nx = md->grid_size.x; int ny = md->grid_size.y; int nz = md->grid_size.z; @@ -397,25 +416,23 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec /* define a macro to give us data(x,y,z) on the grid, in row-major order: */ -#define D(x, y, z) (data[(((x)*ny + (y)) * nz + (z)) * stride]) - - double du_dx = - (signflip_dx ? -1.0 : 1.0) * - (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * - (1.0 - dz) + - ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * - dz); - double du_dy = (signflip_dy ? -1.0 : 1.0) * ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * - (1.0 - dz) + - (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * - dz); - double du_dz = (signflip_dz ? -1.0 : 1.0) * - (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + - ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); +#define D(x, y, z) ((md->weights)[(((x)*ny + (y)) * nz + (z)) * stride]) + + duals::duald du_dx = (signflip_dx ? -1.0 : 1.0) * + (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + + (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * (1.0 - dz) + + ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + + (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * dz); + duals::duald du_dy = (signflip_dy ? -1.0 : 1.0) * + ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * (1.0 - dz) + + (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * dz); + duals::duald du_dz = (signflip_dz ? -1.0 : 1.0) * + (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + + ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); #undef D @@ -424,25 +441,23 @@ meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_objec // respect to r2 where g(r2) is the to_geom_object_coords function (in libctl/utils/geom.c). // computing this quantity involves using the chain rule and thus the vector-Jacobian product // ∇u J where J is the Jacobian matrix of g. - vector3 grad_u; - grad_u.x = du_dx * nx; - grad_u.y = du_dy * ny; - grad_u.z = du_dz * nz; + vector3 g_real = make_vector3(nx*du_dx.rpart(),ny*du_dy.rpart(),nz*du_dz.rpart()); + vector3 g_imag = make_vector3(nx*du_dx.dpart(),ny*du_dy.dpart(),nz*du_dz.dpart()); + cvector3 grad_u = make_cvector3(g_real,g_imag); + if (o != NULL) { - vector3 grad_u_J = to_geom_object_coords_VJP(grad_u, o); - gradient.set_direction(meep::X, grad_u_J.x); - gradient.set_direction(meep::Y, grad_u_J.y); - gradient.set_direction(meep::Z, grad_u_J.z); - } - else { - gradient.set_direction(meep::X, - geometry_lattice.size.x == 0 ? 0 : grad_u.x / geometry_lattice.size.x); - gradient.set_direction(meep::Y, - geometry_lattice.size.y == 0 ? 0 : grad_u.y / geometry_lattice.size.y); - gradient.set_direction(meep::Z, - geometry_lattice.size.z == 0 ? 0 : grad_u.z / geometry_lattice.size.z); + gradient = to_geom_object_coords_VJP(grad_u, o); + } else { + g_real = make_vector3( + geometry_lattice.size.x == 0 ? 0 : nx*du_dx.rpart()/ geometry_lattice.size.x, + geometry_lattice.size.y == 0 ? 0 : ny*du_dy.rpart()/ geometry_lattice.size.y, + geometry_lattice.size.z == 0 ? 0 : nz*du_dz.rpart()/ geometry_lattice.size.z); + g_imag = make_vector3( + geometry_lattice.size.x == 0 ? 0 : nx*du_dx.dpart()/ geometry_lattice.size.x, + geometry_lattice.size.y == 0 ? 0 : ny*du_dy.dpart()/ geometry_lattice.size.y, + geometry_lattice.size.z == 0 ? 0 : nz*du_dz.dpart()/ geometry_lattice.size.z); + gradient = make_cvector3(g_real,g_imag); } - return gradient; } @@ -452,21 +467,29 @@ void map_lattice_coordinates(double &px, double &py, double &pz) { pz = geometry_lattice.size.z == 0 ? 0 : 0.5 + (pz - geometry_center.z) / geometry_lattice.size.z; } -meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { +cvector3 matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { + /* loops through all the material grids at a current point + and computes the final *spatial* gradient (w.r.t. x,y,z) + after all appropriate transformations (e.g. due to + overlapping grids). Calls the helper function, `material_grid_grad`, + which is what actually computes the spatial gradient. + */ + + // check for proper overlapping grids if (md->material_grid_kinds == material_data::U_MIN || md->material_grid_kinds == material_data::U_PROD) meep::abort("%s:%i:matgrid_grad does not support overlapping grids with U_MIN or U_PROD\n", __FILE__, __LINE__); - meep::vec gradient(zero_vec(dim)); + cvector3 gradient = cvector_zero(); int matgrid_val_count = 0; // iterate through object tree at current point if (tp) { do { - gradient += - material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), - (material_data *)tp->objects[oi].o->material, tp->objects[oi].o); + gradient = cvector_add(gradient,material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), + (material_data *)tp->objects[oi].o->material, + tp->objects[oi].o)); if (md->material_grid_kinds == material_data::U_DEFAULT) break; ++matgrid_val_count; tp = geom_tree_search_next(p, tp, &oi); @@ -480,29 +503,54 @@ meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { ++matgrid_val_count; } + // compensate for overlapping grids if (md->material_grid_kinds == material_data::U_MEAN) - gradient = gradient * 1.0 / matgrid_val_count; + gradient = cvector3_scale(1.0/matgrid_val_count,gradient); return gradient; } +duals::duald dual_linear_interpolate(double rx, double ry, double rz, std::vector &data, + int nx, int ny, int nz, int stride) { + + int x1, y1, z1, x2, y2, z2; + double dx, dy, dz; -double material_grid_val(vector3 p, material_data *md) { + meep::map_coordinates(rx, ry, rz, nx, ny, nz, + x1, y1, z1, x2, y2, z2, + dx, dy, dz); + + /* define a macro to give us data(x,y,z) on the grid, + in row-major order (the order used by HDF5): */ +#define D(x, y, z) (data[(((x)*ny + (y)) * nz + (z)) * stride]) + + return (((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) * + (1.0 - dz) + + ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy) * + dz); + +#undef D +} +duals::duald material_grid_val(vector3 p, material_data *md) { // given the relative location, p, interpolate the material grid point. - if (!is_material_grid(md)) { meep::abort("Invalid material grid detected.\n"); } - return meep::linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, md->grid_size.y, - md->grid_size.z, 1); + if (!is_material_grid(md)) { abort(); } + return dual_linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, + md->grid_size.y, md->grid_size.z, 1); + } -static double tanh_projection(double u, double beta, double eta) { +static duals::duald tanh_projection(duals::duald u, double beta, double eta) { if (beta == 0) return u; if (u == eta) return 0.5; // avoid NaN when beta is Inf - double tanh_beta_eta = tanh(beta * eta); - return (tanh_beta_eta + tanh(beta * (u - eta))) / (tanh_beta_eta + tanh(beta * (1 - eta))); + duals::duald tanh_beta_eta = tanh(beta*eta); + return (tanh_beta_eta + tanh(beta*(u-eta))) / + (tanh_beta_eta + tanh(beta*(1-eta))); } -double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { - double uprod = 1.0, umin = 1.0, usum = 0.0, udefault = 0.0, u; +duals::duald matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { + duals::duald uprod = 1.0, umin = 1.0, usum = 0.0, udefault = 0.0, u; int matgrid_val_count = 0; // iterate through object tree at current point @@ -569,7 +617,7 @@ static void interp_tensors(vector3 diag_in_1, vector3 offdiag_in_1, vector3 diag void epsilon_material_grid(material_data *md, double u) { // NOTE: assume p lies on normalized grid within (0,1) - if (!(md->weights)) meep::abort("material params were not initialized!"); + if (md->weights.size()==0) meep::abort("material params were not initialized!"); medium_struct *mm = &(md->medium); medium_struct *m1 = &(md->medium_1); @@ -807,7 +855,7 @@ static void material_epsmu(meep::field_type ft, material_type material, symm_mat epsmu_inv->m01 = epsmu_inv->m02 = epsmu_inv->m12 = 0.0; break; - default: meep::abort("unknown material type"); + default: meep::abort("unknown material type in epsmu"); } else switch (md->which_subclass) { @@ -835,7 +883,7 @@ static void material_epsmu(meep::field_type ft, material_type material, symm_mat epsmu_inv->m01 = epsmu_inv->m02 = epsmu_inv->m12 = 0.0; break; - default: meep::abort("unknown material type"); + default: meep::abort("unknown material type in epsmu"); } } @@ -847,26 +895,31 @@ void geom_epsilon::get_material_pt(material_type &material, const meep::vec &r) boolean inobject; material = (material_type)material_of_unshifted_point_in_tree_inobject(p, restricted_tree, &inobject); - material_data *md = material; + eval_material_pt(material, p); +} - switch (md->which_subclass) { +// evaluate the material at the given point p if necessary — this is needed if +// the material is variable (a grid, function, or file); otherwise it is a no-op. +void geom_epsilon::eval_material_pt(material_type &material, vector3 p) { + switch (material->which_subclass) { // material grid: interpolate onto user specified material grid to get properties at r - case material_data::MATERIAL_GRID: - double u; + case material_data::MATERIAL_GRID: { + duals::duald u; int oi; geom_box_tree tp; tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid - u = tanh_projection(matgrid_val(p, tp, oi, md) + this->u_p, md->beta, md->eta); + u = tanh_projection(matgrid_val(p, tp, oi, material), material->beta, material->eta); // interpolate material from material grid point - epsilon_material_grid(md, u); + epsilon_material_grid(material, u.rpart()); return; + } // material read from file: interpolate to get properties at r case material_data::MATERIAL_FILE: - if (md->epsilon_data) - epsilon_file_material(md, p); + if (material->epsilon_data) + epsilon_file_material(material, p); else material = (material_type)default_material; return; @@ -877,16 +930,16 @@ void geom_epsilon::get_material_pt(material_type &material, const meep::vec &r) // the user's function only needs to fill in whatever is // different from vacuum. case material_data::MATERIAL_USER: - md->medium = medium_struct(); - md->user_func(p, md->user_data, &(md->medium)); - md->medium.check_offdiag_im_zero_or_abort(); + material->medium = medium_struct(); + material->user_func(p, material->user_data, &(material->medium)); + material->medium.check_offdiag_im_zero_or_abort(); return; // position-independent material or metal: there is nothing to do case material_data::MEDIUM: case material_data::PERFECT_METAL: return; - default: meep::abort("unknown material type"); + default: meep::abort("unknown material type in eval_material_pt"); }; } @@ -907,20 +960,21 @@ double geom_epsilon::chi1p1(meep::field_type ft, const meep::vec &r) { material_epsmu(ft, material, &chi1p1, &chi1p1_inv); material_gc(material); - return (chi1p1.m00 + chi1p1.m11 + chi1p1.m22) / 3; + return (chi1p1.m00.rpart() + chi1p1.m11.rpart() + chi1p1.m22.rpart()) / 3; } /* Find frontmost object in v, along with the constant material behind it. - Returns false if material behind the object is not constant. + Returns false if more than two objects & materials intersect the pixel. Requires moderately horrifying logic to figure things out properly, stolen from MPB. */ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, vector3 &pcenter, const geometric_object **o_front, vector3 &shiftby_front, - material_type &mat_front, material_type &mat_behind) { + material_type &mat_front, material_type &mat_behind, + vector3 &p_front, vector3 &p_behind) { vector3 p; const geometric_object *o1 = 0, *o2 = 0; - vector3 shiftby1 = {0, 0, 0}, shiftby2 = {0, 0, 0}; + vector3 shiftby1 = {0, 0, 0}, shiftby2 = {0, 0, 0}, p1 = {0,0,0}, p2 = {0,0,0}; geom_box pixel; material_type mat1 = vacuum, mat2 = vacuum; int id1 = -1, id2 = -1; @@ -982,6 +1036,7 @@ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, shiftby1 = shiftby; id1 = id; mat1 = mat; + p1 = q; } else if (id2 == -1 || ((id >= id1 && id >= id2) && (id1 == id2 || material_type_equal(mat1, mat2)))) { @@ -989,10 +1044,12 @@ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, shiftby2 = shiftby; id2 = id; mat2 = mat; + p2 = q; } else if (!(id1 < id2 && (id1 == id || material_type_equal(mat1, mat))) && - !(id2 < id1 && (id2 == id || material_type_equal(mat2, mat)))) + !(id2 < id1 && (id2 == id || material_type_equal(mat2, mat)))){ return false; + } } // CHECK(id1 > -1, "bug in object_of_point_in_tree?"); @@ -1000,130 +1057,126 @@ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, id2 = id1; o2 = o1; mat2 = mat1; + p2 = p1; shiftby2 = shiftby1; } - if ((o1 && is_variable(o1->material)) || (o2 && is_variable(o2->material)) || - ((is_variable(default_material) || is_file(default_material)) && - (!o1 || is_file(o1->material) || !o2 || is_file(o2->material)))) - return false; - if (id1 >= id2) { *o_front = o1; shiftby_front = shiftby1; mat_front = mat1; - if (id1 == id2) + p_front = p1; + if (id1 == id2) { mat_behind = mat1; - else + p_behind = p1; + } + else { mat_behind = mat2; + p_behind = p2; + } } if (id2 > id1) { *o_front = o2; shiftby_front = shiftby2; mat_front = mat2; + p_front = p2; mat_behind = mat1; + p_behind = p1; } return true; } void geom_epsilon::eff_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval) { + /* for speed reasons, we need to "wrap" the + actual eff_chi1inv_row_grad function below, since + the original eff_chi1inv_row is a virtual function + and doesn't play well with default arguments */ + eff_chi1inv_row_grad(c,chi1inv_row,v,tol,maxeval,false); +} +void geom_epsilon::eff_chi1inv_row_grad(meep::component c, double chi1inv_row[3], const meep::volume &v, + double tol, int maxeval, bool needs_grad) { symm_matrix meps_inv; bool fallback; eff_chi1inv_matrix(c, &meps_inv, v, tol, maxeval, fallback); if (fallback) { fallback_chi1inv_row(c, chi1inv_row, v, tol, maxeval); } else { - switch (component_direction(c)) { + /* for gradient calculations, we need to return + the dual part of the calcuation, which corresponds + to the first derivative w.r.t. u_i */ + if (needs_grad) { + switch (component_direction(c)) { case meep::X: case meep::R: - chi1inv_row[0] = meps_inv.m00; - chi1inv_row[1] = meps_inv.m01; - chi1inv_row[2] = meps_inv.m02; + chi1inv_row[0] = meps_inv.m00.dpart(); + chi1inv_row[1] = meps_inv.m01.dpart(); + chi1inv_row[2] = meps_inv.m02.dpart(); break; case meep::Y: case meep::P: - chi1inv_row[0] = meps_inv.m01; - chi1inv_row[1] = meps_inv.m11; - chi1inv_row[2] = meps_inv.m12; + chi1inv_row[0] = meps_inv.m01.dpart(); + chi1inv_row[1] = meps_inv.m11.dpart(); + chi1inv_row[2] = meps_inv.m12.dpart(); break; case meep::Z: - chi1inv_row[0] = meps_inv.m02; - chi1inv_row[1] = meps_inv.m12; - chi1inv_row[2] = meps_inv.m22; + chi1inv_row[0] = meps_inv.m02.dpart(); + chi1inv_row[1] = meps_inv.m12.dpart(); + chi1inv_row[2] = meps_inv.m22.dpart(); break; case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; + } + } else { // no gradient needed; standard epsilon evaluations + switch (component_direction(c)) { + case meep::X: + case meep::R: + chi1inv_row[0] = meps_inv.m00.rpart(); + chi1inv_row[1] = meps_inv.m01.rpart(); + chi1inv_row[2] = meps_inv.m02.rpart(); + break; + case meep::Y: + case meep::P: + chi1inv_row[0] = meps_inv.m01.rpart(); + chi1inv_row[1] = meps_inv.m11.rpart(); + chi1inv_row[2] = meps_inv.m12.rpart(); + break; + case meep::Z: + chi1inv_row[0] = meps_inv.m02.rpart(); + chi1inv_row[1] = meps_inv.m12.rpart(); + chi1inv_row[2] = meps_inv.m22.rpart(); + break; + case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; + } } } } -void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, - const meep::volume &v, double tol, int maxeval, - bool &fallback) { - const geometric_object *o; - material_type mat, mat_behind; - symm_matrix meps; - vector3 p, shiftby, normal; - fallback = false; - - if (maxeval == 0) { - noavg: - get_material_pt(mat, v.center()); - trivial: - material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); - material_gc(mat); - return; - } - - if (!get_front_object(v, geometry_tree, p, &o, shiftby, mat, mat_behind)) { - get_material_pt(mat, v.center()); - if (mat && - (mat->which_subclass == material_data::MATERIAL_USER || - mat->which_subclass == material_data::MATERIAL_GRID) && - mat->do_averaging) { - fallback = true; - return; - } - else { goto trivial; } - } - - /* check for trivial case of only one object/material */ - if (material_type_equal(mat, mat_behind)) goto trivial; +void kottke_algorithm(meep::component c, symm_matrix *chi1inv_matrix, symm_matrix &meps, + symm_matrix &eps2, cvector3 &cnormal, duals::duald fill){ + /* Ref: " Perturbation theory for anisotropic dielectric interfaces, + and application to subpixel smoothing of discretized numerical methods", + https://math.mit.edu/~stevenj/papers/KottkeFa08.pdf + */ - // it doesn't make sense to average metals (electric or magnetic) - if (is_metal(meep::type(c), &mat) || is_metal(meep::type(c), &mat_behind)) goto noavg; - - normal = unit_vector3(normal_to_fixed_object(vector3_minus(p, shiftby), *o)); - if (normal.x == 0 && normal.y == 0 && normal.z == 0) - goto noavg; // couldn't get normal vector for this point, punt - geom_box pixel = gv2box(v); - pixel.low = vector3_minus(pixel.low, shiftby); - pixel.high = vector3_minus(pixel.high, shiftby); - - double fill = box_overlap_with_object(pixel, *o, tol, maxeval); - - material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); - symm_matrix eps2, epsinv2; symm_matrix eps1, delta; - double Rot[3][3]; - material_epsmu(meep::type(c), mat_behind, &eps2, &epsinv2); + duals::duald Rot[3][3]; eps1 = meps; - Rot[0][0] = normal.x; - Rot[1][0] = normal.y; - Rot[2][0] = normal.z; - if (fabs(normal.x) > 1e-2 || fabs(normal.y) > 1e-2) { - Rot[0][2] = normal.y; - Rot[1][2] = -normal.x; + Rot[0][0] = cnormal.x.re + 1_e*cnormal.x.im; + Rot[1][0] = cnormal.y.re + 1_e*cnormal.y.im; + Rot[2][0] = cnormal.z.re + 1_e*cnormal.z.im; + if (fabs(cnormal.x.re) > 1e-2 || fabs(cnormal.y.re) > 1e-2) { + Rot[0][2] = cnormal.y.re + 1_e*cnormal.y.im; + Rot[1][2] = -(cnormal.x.re + 1_e*cnormal.x.im); Rot[2][2] = 0; } else { /* n is not parallel to z direction, use (x x n) instead */ Rot[0][2] = 0; - Rot[1][2] = -normal.z; - Rot[2][2] = normal.y; + Rot[1][2] = -(cnormal.z.re + 1_e*cnormal.z.im); + Rot[2][2] = cnormal.y.re + 1_e*cnormal.y.im; } { /* normalize second column */ - double s = Rot[0][2] * Rot[0][2] + Rot[1][2] * Rot[1][2] + Rot[2][2] * Rot[2][2]; + duals::duald s = Rot[0][2] * Rot[0][2] + Rot[1][2] * Rot[1][2] + Rot[2][2] * Rot[2][2]; s = 1.0 / sqrt(s); Rot[0][2] *= s; Rot[1][2] *= s; @@ -1172,7 +1225,7 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma #define SWAP(a, b) \ { \ - double xxx = a; \ + duals::duald xxx = a; \ a = b; \ b = xxx; \ } @@ -1191,6 +1244,240 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma sym_matrix_invert(chi1inv_matrix, &meps); } +duals::duald get_material_grid_fill(meep::ndim dim, duals::duald d, double r, duals::duald u, double eta){ + /* The fill fraction should describe the amount of u=1 material in the current pixel. + + Occasionally, the distance to the nearest interface is outside the current + sphere, which means we don't need to do any averaging (the fill is 0 or 1). Again, + we don't know if that means we are in void or solid, however, until we look + at u. To make things easy, we'll assume the cap is solid until the end, when we + can verify. + */ + duals::duald rel_fill; + if (abs(d) >= abs(r)){ + return -1.0; // garbage fill + } else { + if (dim == meep::D1) + return (r-d)/(2*r); + else if (dim == meep::D2 || dim == meep::Dcyl) + return (1/(r*r*meep::pi)) * (r*r*acos(d/r)-d*sqrt(r*r-d*d)); + else if (dim == meep::D3) + return (((r-d)*(r-d))/(4*meep::pi*r*r*r))*(2*r+d); + } + + return rel_fill; +} + +duals::duald normalize_dual(cvector3 &cv){ + duals::duald d[3], norm; + // compute the norm, u_0' + d[0] = cv.x.re + 1_e*cv.x.im; + d[1] = cv.y.re + 1_e*cv.y.im; + d[2] = cv.z.re + 1_e*cv.z.im; + + norm = sqrt(abs(d[0])*abs(d[0]) + abs(d[1])*abs(d[1]) + abs(d[2])*abs(d[2])); + + // normalize the normal vector + d[0] = d[0] / norm; + d[1] = d[1] / norm; + d[2] = d[2] / norm; + cv.x.re = d[0].rpart(); cv.x.im = d[0].dpart(); + cv.y.re = d[1].rpart(); cv.y.im = d[1].dpart(); + cv.z.re = d[2].rpart(); cv.z.im = d[2].dpart(); + + return norm; +} + +duals::duald get_distance(cvector3 &cv, duals::duald uval, double eta){ + duals::duald d[3], norm; + d[0] = cv.x.re + 1_e*cv.x.im; + d[1] = cv.y.re + 1_e*cv.y.im; + d[2] = cv.z.re + 1_e*cv.z.im; + + norm = sqrt(abs(d[0])*abs(d[0]) + abs(d[1])*abs(d[1]) + abs(d[2])*abs(d[2])); + return (eta - uval) / norm; +} + +void dual_interpolate_mat(material_type mat,symm_matrix &eps, duals::duald u) { + duals::duald u_bar = tanh_projection(u, mat->beta, mat->eta); // projection + if (std::isnan(u_bar.dpart())) u_bar = u_bar.rpart(); // when beta=inf, we need to correct when gradient breaks down + eps.m00 = mat->medium_1.epsilon_diag.x + u_bar * (mat->medium_2.epsilon_diag.x-mat->medium_1.epsilon_diag.x); + eps.m11 = mat->medium_1.epsilon_diag.y + u_bar * (mat->medium_2.epsilon_diag.y-mat->medium_1.epsilon_diag.y); + eps.m22 = mat->medium_1.epsilon_diag.z + u_bar * (mat->medium_2.epsilon_diag.z-mat->medium_1.epsilon_diag.z); + eps.m01 = mat->medium_1.epsilon_offdiag.x.re + u_bar * (mat->medium_2.epsilon_offdiag.x.re-mat->medium_1.epsilon_offdiag.x.re); + eps.m02 = mat->medium_1.epsilon_offdiag.y.re + u_bar * (mat->medium_2.epsilon_offdiag.y.re-mat->medium_1.epsilon_offdiag.y.re); + eps.m12 = mat->medium_1.epsilon_offdiag.z.re + u_bar * (mat->medium_2.epsilon_offdiag.z.re-mat->medium_1.epsilon_offdiag.z.re); +} + +void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, + const meep::volume &v, double tol, int maxeval, + bool &fallback) { + const geometric_object *o; + material_type mat, mat_behind; + vector3 p_mat, p_mat_behind, p, shiftby; + symm_matrix meps, eps2, epsinv2; + cvector3 normal; + duals::duald fill, uval; + int oi; + geom_box_tree tp; + fallback = false; + + /* first let's check if our pixel has more than a single + interface (i.e. a corner) in which case we abandon any + smoothing efforts*/ + if (!get_front_object(v, geometry_tree, p, &o, shiftby, mat, mat_behind, p_mat, p_mat_behind)) { + noavg: + get_material_pt(mat, v.center()); + trivial: + material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); + material_gc(mat); + return; + } + + /* we may have entered this routine (e.g. because of a material grid + backprop routine) but don't want to actually do any averaging */ + if (maxeval == 0){ + get_material_pt(mat, v.center()); + if (is_material_grid(mat)){ + tp = geom_tree_search(vec_to_vector3(v.center()), restricted_tree, &oi); + uval = matgrid_val(vec_to_vector3(v.center()), tp, oi, mat); + mgavg: + dual_interpolate_mat(mat,meps,uval); + sym_matrix_invert(chi1inv_matrix, &meps); + //get_material_pt(mat, v.center()); + //material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); + material_gc(mat); + return; + }else{ + goto trivial; + } + } + + // For variable materials with do_averaging == true, switch over to slow fallback integration method. + // For material grids, however, we have to do some more logic first... + if ((is_variable(mat) && mat->do_averaging) || (is_variable(mat_behind) && mat_behind->do_averaging)) { + if ((!is_material_grid(mat)) && (!is_material_grid(mat_behind))){ + fallback = true; + return; + } + } + + /* ------------------------------------------- */ + // One material + /* ------------------------------------------- */ + /* check for case of only one object/material + in the case of the material grid, make sure we don't need + to do any interface averaging within. + */ + if (material_type_equal(mat, mat_behind)) { + /* if we have a material grid, we need to calculate the fill fraction, + normal vector, etc. we also need to determine if we really need + to do any averaging, or if the current pixel is smooth enough + to just evaluate. + */ + if (is_material_grid(mat)){ + // if we have a damping term and β≠∞, we can't smooth + if ((mat->damping != 0) && (mat->eta != std::numeric_limits::infinity())) goto noavg; + + tp = geom_tree_search(p_mat, restricted_tree, &oi); + uval = matgrid_val(p_mat, tp, oi, mat); + + normal = matgrid_grad(p_mat, tp, oi, mat); + if (cvector3_re(normal).x == 0 && cvector3_re(normal).y == 0 && cvector3_re(normal).z == 0) + /* couldn't get normal vector for this point; no averaging is needed, + but we still need to interpolate onto our grid using duals */ + goto mgavg; + duals::duald u_prime = normalize_dual(normal); + duals::duald d = (mat->eta - uval) / u_prime; + + double r = v.diameter()/2; + fill = get_material_grid_fill(v.dim,d,r,uval,mat->eta); + if (fill.rpart() < 0){ + /* we are far enough away from an interface that + we don't need any averaging -- but we still need + to interpolate from the material grid */ + goto mgavg; + } else{ + /* we have a material grid interface within our pixel. + we therefore need to set the two materials used for + averaging to our corresponding solid and void materials. + */ + symm_matrix eps_0, eps_plus, eps_minus; + dual_interpolate_mat(mat,eps_0,uval); + dual_interpolate_mat(mat,eps_plus,uval+r*u_prime); + dual_interpolate_mat(mat,eps_minus,uval-r*u_prime); + + if (d > 0) { + // Case 1: d > 0. Then let ε̃₊ = ε₊ and let ε̃₋ = [ε₀d + ε₋(Δx/2 – d)] / (Δx/2). + meps = eps_plus; + eps2 = (eps_0*d + eps_minus*(r-d)) / (r); + }else { + // Case 2: d < 0. Then let ε̃₋ = ε₋ and let ε̃₊ = [-ε₀d + ε₊(Δx/2 + d)] / (Δx/2). + eps2 = eps_minus; + meps = (-eps_0*d + eps_plus*(r+d)) / (r); + } + } + } else if(is_variable(mat)) { + // no averaging is needed (not a material grid) + eval_material_pt(mat, vec_to_vector3(v.center())); + goto trivial; + // materials are non variable and uniform -- no need to average + } else { + goto trivial; + } + /* ------------------------------------------- */ + // Two different materials (perhaps a m.g. and a geom object) + /* ------------------------------------------- */ + } else { + + vector3 normal_re = unit_vector3(normal_to_fixed_object(vector3_minus(p, shiftby), *o)); + if (normal_re.x == 0 && normal_re.y == 0 && normal_re.z == 0) + goto noavg; // couldn't get normal vector for this point, punt + normal = make_cvector3(normal_re,make_vector3()); + geom_box pixel = gv2box(v); + pixel.low = vector3_minus(pixel.low, shiftby); + pixel.high = vector3_minus(pixel.high, shiftby); + fill = duals::duald(box_overlap_with_object(pixel, *o, tol, maxeval),0); + /* Evaluate materials in case they are variable. This allows us to do fast subpixel averaging + at the boundary of an object with a variable material, while remaining accurate enough if the + material is continuous over the pixel. (We make a first-order error by averaging as if the material + were constant, but only in a boundary layer of thickness 1/resolution, so the net effect should + still be second-order.) */ + if (is_variable(mat)){ + eval_material_pt(mat, p_mat); + eval_material_pt(mat_behind, p_mat_behind); + if (material_type_equal(mat, mat_behind)) goto trivial; + } + + /* to properly determine the tensors of a material + that has a material grid, we need to be careful. + Specifically, we need to include the effects of u_i + in the dual part of the material tensor */ + if (is_material_grid(mat)){ + tp = geom_tree_search(p_mat, restricted_tree, &oi); + duals::duald uval = matgrid_val(p_mat, tp, oi, mat); + dual_interpolate_mat(mat,meps,uval); + sym_matrix_invert(chi1inv_matrix, &meps); + } else { + material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); + } + if (is_material_grid(mat_behind)){ + tp = geom_tree_search(p_mat_behind, restricted_tree, &oi); + duals::duald uval = matgrid_val(p_mat_behind, tp, oi, mat); + dual_interpolate_mat(mat_behind,eps2,uval); + sym_matrix_invert(&epsinv2, &eps2); // not really needed + } else { + material_epsmu(meep::type(c), mat_behind, &eps2, &epsinv2); + } + } + + // it doesn't make sense to average metals (electric or magnetic) + if (is_metal(meep::type(c), &mat) || is_metal(meep::type(c), &mat_behind)) goto noavg; + + kottke_algorithm(c,chi1inv_matrix,meps,eps2,normal,fill); + material_gc(mat); +} + static int eps_ever_negative = 0; static meep::field_type func_ft = meep::E_stuff; @@ -1207,7 +1494,7 @@ struct matgrid_volavg { static void get_uproj_w(const matgrid_volavg *mgva, double x0, double &u_proj, double &w) { // use a linear approximation for the material grid weights around the Yee grid point - u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs * x0, mgva->beta, mgva->eta); + //u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs*x0, mgva->beta, mgva->eta); if (mgva->dim == meep::D1) w = 1 / (2 * mgva->rad); else if (mgva->dim == meep::D2 || mgva->dim == meep::Dcyl) @@ -1314,18 +1601,7 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] material = (material_type)material_of_unshifted_point_in_tree_inobject(p, restricted_tree, &inobject); material_data *md = material; - meep::vec gradient(zero_vec(v.dim)); - double uval = 0; - - if (md->which_subclass == material_data::MATERIAL_GRID) { - geom_box_tree tp; - int oi; - tp = geom_tree_search(p, restricted_tree, &oi); - gradient = matgrid_grad(p, tp, oi, md); - uval = matgrid_val(p, tp, oi, md) + this->u_p; - } - else { gradient = normal_vector(meep::type(c), v); } - + meep::vec gradient = normal_vector(meep::type(c), v); get_material_pt(material, v.center()); material_epsmu(meep::type(c), material, &chi1p1, &chi1p1_inv); material_gc(material); @@ -1333,19 +1609,19 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] chi1p1.m11 != chi1p1.m22 || chi1p1.m00 != chi1p1.m22 || meep::abs(gradient) < 1e-8) { int rownum = meep::component_direction(c) % 3; if (rownum == 0) { - chi1inv_row[0] = chi1p1_inv.m00; - chi1inv_row[1] = chi1p1_inv.m01; - chi1inv_row[2] = chi1p1_inv.m02; + chi1inv_row[0] = chi1p1_inv.m00.rpart(); + chi1inv_row[1] = chi1p1_inv.m01.rpart(); + chi1inv_row[2] = chi1p1_inv.m02.rpart(); } else if (rownum == 1) { - chi1inv_row[0] = chi1p1_inv.m01; - chi1inv_row[1] = chi1p1_inv.m11; - chi1inv_row[2] = chi1p1_inv.m12; + chi1inv_row[0] = chi1p1_inv.m01.rpart(); + chi1inv_row[1] = chi1p1_inv.m11.rpart(); + chi1inv_row[2] = chi1p1_inv.m12.rpart(); } else { - chi1inv_row[0] = chi1p1_inv.m02; - chi1inv_row[1] = chi1p1_inv.m12; - chi1inv_row[2] = chi1p1_inv.m22; + chi1inv_row[0] = chi1p1_inv.m02.rpart(); + chi1inv_row[1] = chi1p1_inv.m12.rpart(); + chi1inv_row[2] = chi1p1_inv.m22.rpart(); } return; } @@ -1353,79 +1629,47 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] integer errflag; double meps, minveps; - if (md->which_subclass == material_data::MATERIAL_GRID) { - number xmin[1], xmax[1]; - matgrid_volavg mgva; - mgva.dim = v.dim; - mgva.ugrad_abs = meep::abs(gradient); - mgva.uval = uval; - mgva.rad = v.diameter() / 2; - mgva.beta = md->beta; - mgva.eta = md->eta; - mgva.eps1 = - (md->medium_1.epsilon_diag.x + md->medium_1.epsilon_diag.y + md->medium_1.epsilon_diag.z) / - 3; - mgva.eps2 = - (md->medium_2.epsilon_diag.x + md->medium_2.epsilon_diag.y + md->medium_2.epsilon_diag.z) / - 3; - xmin[0] = -v.diameter() / 2; - xmax[0] = v.diameter() / 2; -#ifdef CTL_HAS_COMPLEX_INTEGRATION - cnumber ret = cadaptive_integration(matgrid_ceps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, - maxeval, &esterr, &errflag); - meps = ret.re; - minveps = ret.im; -#else - meps = adaptive_integration(matgrid_eps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, maxeval, - &esterr, &errflag); - minveps = adaptive_integration(matgrid_inveps_func, xmin, xmax, 1, (void *)&mgva, 0, tol, - maxeval, &esterr, &errflag); -#endif + integer n; + number xmin[3], xmax[3]; + vector3 gvmin, gvmax; + gvmin = vec_to_vector3(v.get_min_corner()); + gvmax = vec_to_vector3(v.get_max_corner()); + xmin[0] = gvmin.x; + xmax[0] = gvmax.x; + if (dim == meep::Dcyl) { + xmin[1] = gvmin.z; + xmin[2] = gvmin.y; + xmax[1] = gvmax.z; + xmax[2] = gvmax.y; } else { - integer n; - number xmin[3], xmax[3]; - vector3 gvmin, gvmax; - gvmin = vec_to_vector3(v.get_min_corner()); - gvmax = vec_to_vector3(v.get_max_corner()); - xmin[0] = gvmin.x; - xmax[0] = gvmax.x; - if (dim == meep::Dcyl) { - xmin[1] = gvmin.z; - xmin[2] = gvmin.y; - xmax[1] = gvmax.z; - xmax[2] = gvmax.y; - } - else { - xmin[1] = gvmin.y; - xmin[2] = gvmin.z; - xmax[1] = gvmax.y; - xmax[2] = gvmax.z; - } - if (xmin[2] == xmax[2]) - n = xmin[1] == xmax[1] ? 1 : 2; - else - n = 3; - double vol = 1; - for (int i = 0; i < n; ++i) - vol *= xmax[i] - xmin[i]; - if (dim == meep::Dcyl) vol *= (xmin[0] + xmax[0]) * 0.5; - eps_ever_negative = 0; - func_ft = meep::type(c); + xmin[1] = gvmin.y; + xmin[2] = gvmin.z; + xmax[1] = gvmax.y; + xmax[2] = gvmax.z; + } + if (xmin[2] == xmax[2]) + n = xmin[1] == xmax[1] ? 1 : 2; + else + n = 3; + double vol = 1; + for (int i = 0; i < n; ++i) + vol *= xmax[i] - xmin[i]; + if (dim == meep::Dcyl) vol *= (xmin[0] + xmax[0]) * 0.5; + eps_ever_negative = 0; + func_ft = meep::type(c); #ifdef CTL_HAS_COMPLEX_INTEGRATION - cnumber ret = cadaptive_integration(ceps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, - &esterr, &errflag); - meps = ret.re / vol; - minveps = ret.im / vol; + cnumber ret = cadaptive_integration(ceps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, + &esterr, &errflag); + meps = ret.re / vol; + minveps = ret.im / vol; #else - meps = adaptive_integration(eps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / - vol; - minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, - &esterr, &errflag) / - vol; + meps = adaptive_integration(eps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, + &errflag) / vol; + minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, + &errflag) / vol; #endif - } + if (eps_ever_negative) // averaging negative eps causes instability minveps = 1.0 / (meps = eps(v.center())); { @@ -1667,8 +1911,8 @@ void geom_epsilon::sigma_row(meep::component c, double sigrow[3], const meep::ve tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid - u = tanh_projection(matgrid_val(p, tp, oi, mat) + this->u_p, mat->beta, mat->eta); - epsilon_material_grid(mat, u); // interpolate material from material grid point + u = tanh_projection(matgrid_val(p, tp, oi, mat), mat->beta, mat->eta).rpart(); + epsilon_material_grid(mat, u); // interpolate material from material grid point mat->medium.check_offdiag_im_zero_or_abort(); } @@ -1936,7 +2180,7 @@ void add_absorbing_layer(absorber_list alist, double thickness, int direction, i if needed */ geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, vector3 center, bool _ensure_periodicity, material_type _default_material, - material_type_list extra_materials) { + material_type_list extra_materials, bool _use_anisotropic_averaging) { // set global variables in libctlgeom based on data fields in s geom_initialize(); geometry_center = center; @@ -1987,6 +2231,7 @@ geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, ve } geom_epsilon *geps = new geom_epsilon(*g, extra_materials, gv.pad().surroundings()); + geps->use_anisotropic_averaging = _use_anisotropic_averaging; return geps; } @@ -1997,8 +2242,10 @@ void set_materials_from_geometry(meep::structure *s, geometric_object_list g, ve bool _ensure_periodicity, material_type _default_material, absorber_list alist, material_type_list extra_materials) { meep_geom::geom_epsilon *geps = meep_geom::make_geom_epsilon(s, &g, center, _ensure_periodicity, - _default_material, extra_materials); - set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, maxeval, alist); + _default_material, extra_materials, + use_anisotropic_averaging); + set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, + maxeval, alist); delete geps; } @@ -2060,6 +2307,7 @@ material_type make_user_material(user_material_func user_func, void *user_data, material_type make_file_material(const char *eps_input_file) { material_data *md = new material_data(); md->which_subclass = material_data::MATERIAL_FILE; + md->do_averaging = false; md->epsilon_dims[0] = md->epsilon_dims[1] = md->epsilon_dims[2] = 1; if (eps_input_file && eps_input_file[0]) { // file specified @@ -2095,7 +2343,9 @@ material_type make_material_grid(bool do_averaging, double beta, double eta, dou void update_weights(material_type matgrid, double *weights) { size_t N = matgrid->grid_size.x * matgrid->grid_size.y * matgrid->grid_size.z; - memcpy(matgrid->weights, weights, N * sizeof(double)); + //memcpy(matgrid->weights, weights, N * sizeof(double)); + for (size_t i=0;iweights.push_back(weights[i]); } /******************************************************************************/ @@ -2603,30 +2853,27 @@ std::complex cond_cmp(meep::component c, const meep::vec &r, double freq const medium_struct *mm = &(md->medium); // get the row we care about - switch (component_direction(c)) { - case meep::X: - case meep::R: - return std::complex(1.0, mm->D_conductivity_diag.x / (2 * meep::pi * freq)); - case meep::Y: - case meep::P: - return std::complex(1.0, mm->D_conductivity_diag.y / (2 * meep::pi * freq)); - case meep::Z: - return std::complex(1.0, mm->D_conductivity_diag.z / (2 * meep::pi * freq)); - case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); - } -} - -std::complex -get_material_gradient(const meep::vec &r, // current point - const meep::component adjoint_c, // adjoint field component - const meep::component forward_c, // forward field component - std::complex fields_f, // forward field at current point - double freq, // frequency - geom_epsilon *geps, // material - meep::grid_volume &gv, // simulation grid volume - double du, // step size - double *u, // matgrid - int idx // matgrid index + switch (component_direction(c)) { + case meep::X: + case meep::R: return std::complex(1.0, mm->D_conductivity_diag.x / (2*meep::pi*freq)); + case meep::Y: + case meep::P: return std::complex(1.0, mm->D_conductivity_diag.y / (2*meep::pi*freq)); + case meep::Z: return std::complex(1.0, mm->D_conductivity_diag.z / (2*meep::pi*freq)); + case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); + } +} + +std::complex get_material_gradient( + const meep::vec &r, // current point + const meep::component adjoint_c, // adjoint field component + const meep::component forward_c, // forward field component + std::complex fields_f, // forward field at current point + double freq, // frequency + geom_epsilon *geps, // material + meep::grid_volume &gv, // simulation grid volume + double du, // step size + std::vector &u, // matgrid + int idx // matgrid index ) { /*Compute the Aᵤx product from the -λᵀAᵤx calculation. The current adjoint (λ) field component (adjoint_c) @@ -2668,26 +2915,26 @@ get_material_gradient(const meep::vec &r, // current point v.set_direction_min(d, r.in_direction(d) - 0.5 * gv.inva * sd); v.set_direction_max(d, r.in_direction(d) + 0.5 * gv.inva * sd); } - double row_1[3], row_2[3], dA_du[3]; - double orig = u[idx]; - u[idx] -= du; - geps->eff_chi1inv_row(adjoint_c, row_1, v, geps->tol, geps->maxeval); - u[idx] += 2 * du; - geps->eff_chi1inv_row(adjoint_c, row_2, v, geps->tol, geps->maxeval); - u[idx] = orig; - - for (int i = 0; i < 3; i++) - dA_du[i] = (row_1[i] - row_2[i]) / (2 * du); - return dA_du[dir_idx] * fields_f; - } - else { - double orig = u[idx]; + double dA_du[3]; + double maxevals = geps->use_anisotropic_averaging ? geps->maxeval : 0; // punt if no smoothing + /* we can calculate the gradient w.r.t. u_i using + a dual method. We just need to "flag" the index + within our weights array with a dual value + of "1", and the duals library will take care of the + rest. */ + u[idx] += 1_e; // add dual component + geps->eff_chi1inv_row_grad(adjoint_c, dA_du, v, geps->tol, maxevals, true); + u[idx] -= 1_e; // remove dual component + + return -dA_du[dir_idx] * fields_f; // note the minus sign! (from Steven's adjoint notes) + + } else { std::complex row_1[3], row_2[3], dA_du[3]; u[idx] -= du; - eff_chi1inv_row_disp(adjoint_c, row_1, r, freq, geps); - u[idx] += 2 * du; - eff_chi1inv_row_disp(adjoint_c, row_2, r, freq, geps); - u[idx] = orig; + eff_chi1inv_row_disp(adjoint_c,row_1,r,freq,geps); + u[idx] += 2*du; + eff_chi1inv_row_disp(adjoint_c,row_2,r,freq,geps); + u[idx] -= du; for (int i = 0; i < 3; i++) dA_du[i] = (row_1[i] - row_2[i]) / (2 * du); @@ -2702,14 +2949,16 @@ With the addition of subpixel smoothing, however, the vJp became much more complicated and it is easier to calculate the entire gradient using finite differences (at the cost of slightly less accurate gradients due to floating-point roundoff errors). */ -void add_interpolate_weights(double rx, double ry, double rz, double *data, int nx, int ny, int nz, - int stride, double scaleby, double *udata, int ukind, double uval, - meep::vec r, geom_epsilon *geps, meep::component adjoint_c, - meep::component forward_c, std::complex fwd, - std::complex adj, double freq, meep::grid_volume &gv, - double du) { +void add_interpolate_weights(double rx, double ry, double rz, + double *data, int nx, int ny, int nz, int stride, + double scaleby, std::vector &udata, int ukind, duals::duald uval, + meep::vec r, geom_epsilon *geps, + meep::component adjoint_c, meep::component forward_c, + std::complex fwd, std::complex adj, + double freq, meep::grid_volume &gv, double du) { int x1, y1, z1, x2, y2, z2; - double dx, dy, dz, u; + double dx, dy, dz; + duals::duald u; meep::map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz); int x_list[2] = {x1, x2}, y_list[2] = {y1, y2}, z_list[2] = {z1, z2}; @@ -2731,7 +2980,7 @@ in row-major order (the order used by HDF5): */ dz); if (ukind == material_data::U_MIN && u != uval) return; // TODO look into this - if (ukind == material_data::U_PROD) scaleby *= uval / u; + if (ukind == material_data::U_PROD) scaleby *= uval.rpart() / u.rpart(); for (int xi = 0; xi < lx; xi++) { for (int yi = 0; yi < ly; yi++) { @@ -2757,7 +3006,7 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge geom_box_tree tp; int oi, ois; material_data *mg, *mg_sum; - double uval; + duals::duald uval; int kind; tp = geom_tree_search(p, geps->geometry_tree, &oi); @@ -2804,13 +3053,13 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge it's up to the user to only have one unique design grid in this volume.*/ vector3 sz = mg->grid_size; double *vcur = v; - double *ucur = mg->weights; uval = tanh_projection(matgrid_val(p, tp, oi, mg), mg->beta, mg->eta); do { vector3 pb = to_geom_box_coords(p, &tp->objects[oi]); - add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, ucur, kind, - uval, vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, - gv, tol); + add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, + scalegrad, mg->weights, kind, uval, + vector3_to_vec(p), geps, adjoint_c, forward_c, + fwd, adj, freq, gv, tol); if (kind == material_data::U_DEFAULT) break; tp = geom_tree_search_next(p, tp, &oi); } while (tp && is_material_grid((material_data *)tp->objects[oi].o->material)); @@ -2820,10 +3069,11 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge map_lattice_coordinates(p.x, p.y, p.z); vector3 sz = mg->grid_size; double *vcur = v; - double *ucur = mg->weights; uval = tanh_projection(material_grid_val(p, mg), mg->beta, mg->eta); - add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, ucur, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, gv, tol); + add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, + scalegrad, mg->weights, kind, uval, + vector3_to_vec(p), geps, adjoint_c, forward_c, + fwd, adj, freq, gv, tol); } } diff --git a/src/meepgeom.hpp b/src/meepgeom.hpp index ef77c74ac..3b5014cb4 100644 --- a/src/meepgeom.hpp +++ b/src/meepgeom.hpp @@ -153,9 +153,50 @@ inline vector3 make_vector3(double x = 0.0, double y = 0.0, double z = 0.0) { return v; } -typedef struct { - double m00, m01, m02, m11, m12, m22; -} symm_matrix; +inline cvector3 cvector3_scale(number s, cvector3 v) { + return make_cvector3(vector3_scale(s,cvector3_re(v)),vector3_scale(s,cvector3_im(v))); +} + +inline cvector3 cvector_zero(){ + return make_cvector3(make_vector3(),make_vector3()); +} + +inline cvector3 cvector_add(cvector3 cv1, cvector3 cv2){ + return make_cvector3(vector3_plus(cvector3_re(cv1),cvector3_re(cv2)),vector3_plus(cvector3_im(cv1),cvector3_im(cv2))); +} + +struct symm_matrix{ + duals::duald m00, m01, m02, m11, m12, m22; + struct symm_matrix& operator+=(const symm_matrix& rhs) { + m00 += rhs.m00; m11 += rhs.m11; m22 += rhs.m22; + m01 += rhs.m01; m02 += rhs.m02; m12 += rhs.m12; + return *this; + } + struct symm_matrix& operator+=(const duals::duald& k) { + m00 += k; m11 += k; m22 += k; + return *this; + } + struct symm_matrix& operator+(const symm_matrix& rhs) { + m00 += rhs.m00; m11 += rhs.m11; m22 += rhs.m22; + m01 += rhs.m01; m02 += rhs.m02; m12 += rhs.m12; + return *this; + } + struct symm_matrix& operator*( const duals::duald& k) { + m00 *= k; m11 *= k; m22 *= k; + m01 *= k; m02 *= k; m12 *= k; + return *this; + } + struct symm_matrix& operator/( const duals::duald& k) { + m00 /= k; m11 /= k; m22 /= k; + m01 /= k; m02 /= k; m12 /= k; + return *this; + } + struct symm_matrix& operator-() { + m00 = -m00; m11 = -m11; m22 = -m22; + m01 = -m01; m02 = -m02; m12 = -m12; + return *this; + } +} ; struct pol { meep_geom::susceptibility user_s; @@ -173,6 +214,7 @@ class geom_epsilon : public meep::material_function { public: double u_p = 0; + bool use_anisotropic_averaging; geom_box_tree geometry_tree; geom_box_tree restricted_tree; geometric_object_list geometry; @@ -206,6 +248,8 @@ class geom_epsilon : public meep::material_function { virtual double chi1p1(meep::field_type ft, const meep::vec &r); virtual void eff_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval); + void eff_chi1inv_row_grad(meep::component c, double chi1inv_row[3], const meep::volume &v, + double tol, int maxeval, bool needs_grad); void eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, const meep::volume &v, double tol, int maxeval, bool &fallback); @@ -221,14 +265,17 @@ class geom_epsilon : public meep::material_function { private: material_type_list extra_materials; + void eval_material_pt(material_type &material, vector3 p); pol *current_pol; }; void set_dimensions(int dims); -geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, - vector3 center = make_vector3(), bool ensure_periodicity = false, - material_type _default_material = vacuum, - material_type_list extra_materials = material_type_list()); +geom_epsilon* make_geom_epsilon(meep::structure *s, geometric_object_list *g, + vector3 center = make_vector3(), + bool ensure_periodicity = false, + material_type _default_material = vacuum, + material_type_list extra_materials = material_type_list(), + bool use_anisotropic_averaging = false); //, geometric_object_list g, material_type_list extra_materials void set_materials_from_geometry(meep::structure *s, geometric_object_list g, vector3 center = make_vector3(), @@ -266,8 +313,6 @@ bool is_material_grid(material_type mt); bool is_material_grid(void *md); bool is_variable(material_type mt); bool is_variable(void *md); -bool is_file(material_type md); -bool is_file(void *md); bool is_medium(material_type md, medium_struct **m); bool is_medium(void *md, medium_struct **m); bool is_metal(meep::field_type ft, const material_type *material); @@ -284,10 +329,8 @@ void init_libctl(material_type default_mat, bool ensure_per, meep::grid_volume * // material grid functions /***************************************************************/ void update_weights(material_type matgrid, double *weights); -meep::vec matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md); -meep::vec material_grid_grad(vector3 p, material_data *md, const geometric_object *o); -double matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md); -double material_grid_val(vector3 p, material_data *md); +duals::duald matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md); +duals::duald material_grid_val(vector3 p, material_data *md); geom_box_tree calculate_tree(const meep::volume &v, geometric_object_list g); void material_grids_addgradient(double *v, size_t ng, size_t nf, std::vector fields_a, diff --git a/src/support/Makefile.am b/src/support/Makefile.am index f65ba7ddb..603b0c461 100644 --- a/src/support/Makefile.am +++ b/src/support/Makefile.am @@ -1,3 +1,3 @@ noinst_LTLIBRARIES = libsupport.la -libsupport_la_SOURCES = mt19937ar.c meep_mt.h +libsupport_la_SOURCES = mt19937ar.c meep_mt.h dual.hpp From 9793cf5c138042f66f58a586cfed1f2a0bd9aa65 Mon Sep 17 00:00:00 2001 From: "Steven G. Johnson" Date: Fri, 29 Jul 2022 17:04:21 -0400 Subject: [PATCH 26/26] reformat --- python/typemap_utils.cpp | 10 +- src/meepgeom.cpp | 506 ++++++++++++++++++++------------------- src/meepgeom.hpp | 106 ++++---- 3 files changed, 333 insertions(+), 289 deletions(-) diff --git a/python/typemap_utils.cpp b/python/typemap_utils.cpp index f18764b26..ee3e45c2f 100644 --- a/python/typemap_utils.cpp +++ b/python/typemap_utils.cpp @@ -521,15 +521,13 @@ static int pymaterial_grid_to_material_grid(PyObject *po, material_data *md) { PyObject *po_dp = PyObject_GetAttrString(po, "weights"); PyArrayObject *pao = (PyArrayObject *)po_dp; if (!PyArray_Check(pao)) { meep::abort("MaterialGrid weights failed to init."); } - if (!PyArray_ISCARRAY(pao)) { - meep::abort("Numpy array weights must be C-style contiguous."); - } + if (!PyArray_ISCARRAY(pao)) { meep::abort("Numpy array weights must be C-style contiguous."); } md->weights.clear(); - for (size_t i=0;iweights.push_back(((double *)PyArray_DATA(pao))[i]); } - //md->weights = new double[PyArray_SIZE(pao)]; - //memcpy(md->weights, (double *)PyArray_DATA(pao), PyArray_SIZE(pao) * sizeof(double)); + // md->weights = new double[PyArray_SIZE(pao)]; + // memcpy(md->weights, (double *)PyArray_DATA(pao), PyArray_SIZE(pao) * sizeof(double)); // if needed, combine sus structs to main object PyObject *py_e_sus_m1 = PyObject_GetAttrString(po_medium1, "E_susceptibilities"); diff --git a/src/meepgeom.cpp b/src/meepgeom.cpp index 660b15423..09513597b 100644 --- a/src/meepgeom.cpp +++ b/src/meepgeom.cpp @@ -14,7 +14,7 @@ % Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. */ -#include +#include #include #include #include "meepgeom.hpp" @@ -289,7 +289,7 @@ bool is_material_grid(void *md) { return is_material_grid((material_type)md); } // return whether mt is spatially varying bool is_variable(material_type mt) { - return (mt && ((mt->which_subclass == material_data::MATERIAL_USER) || + return (mt && ((mt->which_subclass == material_data::MATERIAL_USER) || (mt->which_subclass == material_data::MATERIAL_GRID) || (mt->which_subclass == material_data::MATERIAL_FILE))); } @@ -368,12 +368,21 @@ cvector3 to_geom_object_coords_VJP(cvector3 v, const geometric_object *o) { vector3 v_imag = cvector3_im(v); // "dual" part vector3 size = o->subclass.block_data->size; - if (size.x != 0.0) {v_real.x /= size.x; v_imag.x /= size.x;} - if (size.y != 0.0) {v_real.y /= size.y; v_imag.y /= size.y;} - if (size.z != 0.0) {v_real.z /= size.z; v_imag.z /= size.z;} - v_real = matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v_real); - v_imag = matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v_imag); - return make_cvector3(v_real,v_imag); + if (size.x != 0.0) { + v_real.x /= size.x; + v_imag.x /= size.x; + } + if (size.y != 0.0) { + v_real.y /= size.y; + v_imag.y /= size.y; + } + if (size.z != 0.0) { + v_real.z /= size.z; + v_imag.z /= size.z; + } + v_real = matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v_real); + v_imag = matrix3x3_transpose_vector3_mult(o->subclass.block_data->projection_matrix, v_imag); + return make_cvector3(v_real, v_imag); } /* case geometric_object::PRISM: NOT YET IMPLEMENTED */ @@ -381,10 +390,10 @@ cvector3 to_geom_object_coords_VJP(cvector3 v, const geometric_object *o) { } cvector3 material_grid_grad(vector3 p, material_data *md, const geometric_object *o) { - /* computes the actual spatial gradient at point `p` + /* computes the actual spatial gradient at point `p` for the specified material grid `md`. */ - - if (!is_material_grid(md)) {meep::abort("Invalid material grid detected.\n"); } + + if (!is_material_grid(md)) { meep::abort("Invalid material grid detected.\n"); } cvector3 gradient = cvector_zero(); int nx = md->grid_size.x; @@ -418,21 +427,24 @@ cvector3 material_grid_grad(vector3 p, material_data *md, const geometric_object in row-major order: */ #define D(x, y, z) ((md->weights)[(((x)*ny + (y)) * nz + (z)) * stride]) - duals::duald du_dx = (signflip_dx ? -1.0 : 1.0) * - (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + - (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * (1.0 - dz) + - ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + - (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * dz); - duals::duald du_dy = (signflip_dy ? -1.0 : 1.0) * - ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * (1.0 - dz) + - (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * dz); + duals::duald du_dx = + (signflip_dx ? -1.0 : 1.0) * + (((-D(x1, y1, z1) + D(x2, y1, z1)) * (1.0 - dy) + (-D(x1, y2, z1) + D(x2, y2, z1)) * dy) * + (1.0 - dz) + + ((-D(x1, y1, z2) + D(x2, y1, z2)) * (1.0 - dy) + (-D(x1, y2, z2) + D(x2, y2, z2)) * dy) * + dz); + duals::duald du_dy = + (signflip_dy ? -1.0 : 1.0) * ((-(D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx)) * + (1.0 - dz) + + (-(D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx)) * + dz); duals::duald du_dz = (signflip_dz ? -1.0 : 1.0) * - (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + - (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + - ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + - (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); + (-((D(x1, y1, z1) * (1.0 - dx) + D(x2, y1, z1) * dx) * (1.0 - dy) + + (D(x1, y2, z1) * (1.0 - dx) + D(x2, y2, z1) * dx) * dy) + + ((D(x1, y1, z2) * (1.0 - dx) + D(x2, y1, z2) * dx) * (1.0 - dy) + + (D(x1, y2, z2) * (1.0 - dx) + D(x2, y2, z2) * dx) * dy)); #undef D @@ -441,22 +453,21 @@ cvector3 material_grid_grad(vector3 p, material_data *md, const geometric_object // respect to r2 where g(r2) is the to_geom_object_coords function (in libctl/utils/geom.c). // computing this quantity involves using the chain rule and thus the vector-Jacobian product // ∇u J where J is the Jacobian matrix of g. - vector3 g_real = make_vector3(nx*du_dx.rpart(),ny*du_dy.rpart(),nz*du_dz.rpart()); - vector3 g_imag = make_vector3(nx*du_dx.dpart(),ny*du_dy.dpart(),nz*du_dz.dpart()); - cvector3 grad_u = make_cvector3(g_real,g_imag); + vector3 g_real = make_vector3(nx * du_dx.rpart(), ny * du_dy.rpart(), nz * du_dz.rpart()); + vector3 g_imag = make_vector3(nx * du_dx.dpart(), ny * du_dy.dpart(), nz * du_dz.dpart()); + cvector3 grad_u = make_cvector3(g_real, g_imag); - if (o != NULL) { - gradient = to_geom_object_coords_VJP(grad_u, o); - } else { + if (o != NULL) { gradient = to_geom_object_coords_VJP(grad_u, o); } + else { g_real = make_vector3( - geometry_lattice.size.x == 0 ? 0 : nx*du_dx.rpart()/ geometry_lattice.size.x, - geometry_lattice.size.y == 0 ? 0 : ny*du_dy.rpart()/ geometry_lattice.size.y, - geometry_lattice.size.z == 0 ? 0 : nz*du_dz.rpart()/ geometry_lattice.size.z); + geometry_lattice.size.x == 0 ? 0 : nx * du_dx.rpart() / geometry_lattice.size.x, + geometry_lattice.size.y == 0 ? 0 : ny * du_dy.rpart() / geometry_lattice.size.y, + geometry_lattice.size.z == 0 ? 0 : nz * du_dz.rpart() / geometry_lattice.size.z); g_imag = make_vector3( - geometry_lattice.size.x == 0 ? 0 : nx*du_dx.dpart()/ geometry_lattice.size.x, - geometry_lattice.size.y == 0 ? 0 : ny*du_dy.dpart()/ geometry_lattice.size.y, - geometry_lattice.size.z == 0 ? 0 : nz*du_dz.dpart()/ geometry_lattice.size.z); - gradient = make_cvector3(g_real,g_imag); + geometry_lattice.size.x == 0 ? 0 : nx * du_dx.dpart() / geometry_lattice.size.x, + geometry_lattice.size.y == 0 ? 0 : ny * du_dy.dpart() / geometry_lattice.size.y, + geometry_lattice.size.z == 0 ? 0 : nz * du_dz.dpart() / geometry_lattice.size.z); + gradient = make_cvector3(g_real, g_imag); } return gradient; } @@ -470,7 +481,7 @@ void map_lattice_coordinates(double &px, double &py, double &pz) { cvector3 matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { /* loops through all the material grids at a current point and computes the final *spatial* gradient (w.r.t. x,y,z) - after all appropriate transformations (e.g. due to + after all appropriate transformations (e.g. due to overlapping grids). Calls the helper function, `material_grid_grad`, which is what actually computes the spatial gradient. */ @@ -487,9 +498,10 @@ cvector3 matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { // iterate through object tree at current point if (tp) { do { - gradient = cvector_add(gradient,material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), - (material_data *)tp->objects[oi].o->material, - tp->objects[oi].o)); + gradient = + cvector_add(gradient, material_grid_grad(to_geom_box_coords(p, &tp->objects[oi]), + (material_data *)tp->objects[oi].o->material, + tp->objects[oi].o)); if (md->material_grid_kinds == material_data::U_DEFAULT) break; ++matgrid_val_count; tp = geom_tree_search_next(p, tp, &oi); @@ -505,19 +517,18 @@ cvector3 matgrid_grad(vector3 p, geom_box_tree tp, int oi, material_data *md) { // compensate for overlapping grids if (md->material_grid_kinds == material_data::U_MEAN) - gradient = cvector3_scale(1.0/matgrid_val_count,gradient); + gradient = cvector3_scale(1.0 / matgrid_val_count, gradient); return gradient; } -duals::duald dual_linear_interpolate(double rx, double ry, double rz, std::vector &data, - int nx, int ny, int nz, int stride) { +duals::duald dual_linear_interpolate(double rx, double ry, double rz, + std::vector &data, int nx, int ny, int nz, + int stride) { int x1, y1, z1, x2, y2, z2; double dx, dy, dz; - meep::map_coordinates(rx, ry, rz, nx, ny, nz, - x1, y1, z1, x2, y2, z2, - dx, dy, dz); + meep::map_coordinates(rx, ry, rz, nx, ny, nz, x1, y1, z1, x2, y2, z2, dx, dy, dz); /* define a macro to give us data(x,y,z) on the grid, in row-major order (the order used by HDF5): */ @@ -536,17 +547,15 @@ duals::duald material_grid_val(vector3 p, material_data *md) { // given the relative location, p, interpolate the material grid point. if (!is_material_grid(md)) { abort(); } - return dual_linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, - md->grid_size.y, md->grid_size.z, 1); - + return dual_linear_interpolate(p.x, p.y, p.z, md->weights, md->grid_size.x, md->grid_size.y, + md->grid_size.z, 1); } static duals::duald tanh_projection(duals::duald u, double beta, double eta) { if (beta == 0) return u; if (u == eta) return 0.5; // avoid NaN when beta is Inf - duals::duald tanh_beta_eta = tanh(beta*eta); - return (tanh_beta_eta + tanh(beta*(u-eta))) / - (tanh_beta_eta + tanh(beta*(1-eta))); + duals::duald tanh_beta_eta = tanh(beta * eta); + return (tanh_beta_eta + tanh(beta * (u - eta))) / (tanh_beta_eta + tanh(beta * (1 - eta))); } duals::duald matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md) { @@ -617,7 +626,7 @@ static void interp_tensors(vector3 diag_in_1, vector3 offdiag_in_1, vector3 diag void epsilon_material_grid(material_data *md, double u) { // NOTE: assume p lies on normalized grid within (0,1) - if (md->weights.size()==0) meep::abort("material params were not initialized!"); + if (md->weights.size() == 0) meep::abort("material params were not initialized!"); medium_struct *mm = &(md->medium); medium_struct *m1 = &(md->medium_1); @@ -970,11 +979,11 @@ double geom_epsilon::chi1p1(meep::field_type ft, const meep::vec &r) { stolen from MPB. */ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, vector3 &pcenter, const geometric_object **o_front, vector3 &shiftby_front, - material_type &mat_front, material_type &mat_behind, - vector3 &p_front, vector3 &p_behind) { + material_type &mat_front, material_type &mat_behind, vector3 &p_front, + vector3 &p_behind) { vector3 p; const geometric_object *o1 = 0, *o2 = 0; - vector3 shiftby1 = {0, 0, 0}, shiftby2 = {0, 0, 0}, p1 = {0,0,0}, p2 = {0,0,0}; + vector3 shiftby1 = {0, 0, 0}, shiftby2 = {0, 0, 0}, p1 = {0, 0, 0}, p2 = {0, 0, 0}; geom_box pixel; material_type mat1 = vacuum, mat2 = vacuum; int id1 = -1, id2 = -1; @@ -1047,9 +1056,9 @@ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, p2 = q; } else if (!(id1 < id2 && (id1 == id || material_type_equal(mat1, mat))) && - !(id2 < id1 && (id2 == id || material_type_equal(mat2, mat)))){ + !(id2 < id1 && (id2 == id || material_type_equal(mat2, mat)))) { return false; - } + } } // CHECK(id1 > -1, "bug in object_of_point_in_tree?"); @@ -1088,14 +1097,15 @@ static bool get_front_object(const meep::volume &v, geom_box_tree geometry_tree, void geom_epsilon::eff_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval) { - /* for speed reasons, we need to "wrap" the + /* for speed reasons, we need to "wrap" the actual eff_chi1inv_row_grad function below, since the original eff_chi1inv_row is a virtual function and doesn't play well with default arguments */ - eff_chi1inv_row_grad(c,chi1inv_row,v,tol,maxeval,false); + eff_chi1inv_row_grad(c, chi1inv_row, v, tol, maxeval, false); } -void geom_epsilon::eff_chi1inv_row_grad(meep::component c, double chi1inv_row[3], const meep::volume &v, - double tol, int maxeval, bool needs_grad) { +void geom_epsilon::eff_chi1inv_row_grad(meep::component c, double chi1inv_row[3], + const meep::volume &v, double tol, int maxeval, + bool needs_grad) { symm_matrix meps_inv; bool fallback; eff_chi1inv_matrix(c, &meps_inv, v, tol, maxeval, fallback); @@ -1103,57 +1113,58 @@ void geom_epsilon::eff_chi1inv_row_grad(meep::component c, double chi1inv_row[3] if (fallback) { fallback_chi1inv_row(c, chi1inv_row, v, tol, maxeval); } else { /* for gradient calculations, we need to return - the dual part of the calcuation, which corresponds + the dual part of the calcuation, which corresponds to the first derivative w.r.t. u_i */ if (needs_grad) { switch (component_direction(c)) { - case meep::X: - case meep::R: + case meep::X: + case meep::R: chi1inv_row[0] = meps_inv.m00.dpart(); chi1inv_row[1] = meps_inv.m01.dpart(); chi1inv_row[2] = meps_inv.m02.dpart(); - break; - case meep::Y: - case meep::P: - chi1inv_row[0] = meps_inv.m01.dpart(); - chi1inv_row[1] = meps_inv.m11.dpart(); - chi1inv_row[2] = meps_inv.m12.dpart(); - break; - case meep::Z: - chi1inv_row[0] = meps_inv.m02.dpart(); - chi1inv_row[1] = meps_inv.m12.dpart(); - chi1inv_row[2] = meps_inv.m22.dpart(); - break; - case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; + break; + case meep::Y: + case meep::P: + chi1inv_row[0] = meps_inv.m01.dpart(); + chi1inv_row[1] = meps_inv.m11.dpart(); + chi1inv_row[2] = meps_inv.m12.dpart(); + break; + case meep::Z: + chi1inv_row[0] = meps_inv.m02.dpart(); + chi1inv_row[1] = meps_inv.m12.dpart(); + chi1inv_row[2] = meps_inv.m22.dpart(); + break; + case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; } - } else { // no gradient needed; standard epsilon evaluations + } + else { // no gradient needed; standard epsilon evaluations switch (component_direction(c)) { - case meep::X: - case meep::R: + case meep::X: + case meep::R: chi1inv_row[0] = meps_inv.m00.rpart(); chi1inv_row[1] = meps_inv.m01.rpart(); chi1inv_row[2] = meps_inv.m02.rpart(); - break; - case meep::Y: - case meep::P: - chi1inv_row[0] = meps_inv.m01.rpart(); - chi1inv_row[1] = meps_inv.m11.rpart(); - chi1inv_row[2] = meps_inv.m12.rpart(); - break; - case meep::Z: - chi1inv_row[0] = meps_inv.m02.rpart(); - chi1inv_row[1] = meps_inv.m12.rpart(); - chi1inv_row[2] = meps_inv.m22.rpart(); - break; - case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; + break; + case meep::Y: + case meep::P: + chi1inv_row[0] = meps_inv.m01.rpart(); + chi1inv_row[1] = meps_inv.m11.rpart(); + chi1inv_row[2] = meps_inv.m12.rpart(); + break; + case meep::Z: + chi1inv_row[0] = meps_inv.m02.rpart(); + chi1inv_row[1] = meps_inv.m12.rpart(); + chi1inv_row[2] = meps_inv.m22.rpart(); + break; + case meep::NO_DIRECTION: chi1inv_row[0] = chi1inv_row[1] = chi1inv_row[2] = 0; break; } } } } -void kottke_algorithm(meep::component c, symm_matrix *chi1inv_matrix, symm_matrix &meps, - symm_matrix &eps2, cvector3 &cnormal, duals::duald fill){ - /* Ref: " Perturbation theory for anisotropic dielectric interfaces, +void kottke_algorithm(meep::component c, symm_matrix *chi1inv_matrix, symm_matrix &meps, + symm_matrix &eps2, cvector3 &cnormal, duals::duald fill) { + /* Ref: " Perturbation theory for anisotropic dielectric interfaces, and application to subpixel smoothing of discretized numerical methods", https://math.mit.edu/~stevenj/papers/KottkeFa08.pdf */ @@ -1162,18 +1173,18 @@ void kottke_algorithm(meep::component c, symm_matrix *chi1inv_matrix, symm_matri duals::duald Rot[3][3]; eps1 = meps; - Rot[0][0] = cnormal.x.re + 1_e*cnormal.x.im; - Rot[1][0] = cnormal.y.re + 1_e*cnormal.y.im; - Rot[2][0] = cnormal.z.re + 1_e*cnormal.z.im; + Rot[0][0] = cnormal.x.re + 1_e * cnormal.x.im; + Rot[1][0] = cnormal.y.re + 1_e * cnormal.y.im; + Rot[2][0] = cnormal.z.re + 1_e * cnormal.z.im; if (fabs(cnormal.x.re) > 1e-2 || fabs(cnormal.y.re) > 1e-2) { - Rot[0][2] = cnormal.y.re + 1_e*cnormal.y.im; - Rot[1][2] = -(cnormal.x.re + 1_e*cnormal.x.im); + Rot[0][2] = cnormal.y.re + 1_e * cnormal.y.im; + Rot[1][2] = -(cnormal.x.re + 1_e * cnormal.x.im); Rot[2][2] = 0; } else { /* n is not parallel to z direction, use (x x n) instead */ Rot[0][2] = 0; - Rot[1][2] = -(cnormal.z.re + 1_e*cnormal.z.im); - Rot[2][2] = cnormal.y.re + 1_e*cnormal.y.im; + Rot[1][2] = -(cnormal.z.re + 1_e * cnormal.z.im); + Rot[2][2] = cnormal.y.re + 1_e * cnormal.y.im; } { /* normalize second column */ duals::duald s = Rot[0][2] * Rot[0][2] + Rot[1][2] * Rot[1][2] + Rot[2][2] * Rot[2][2]; @@ -1244,9 +1255,10 @@ void kottke_algorithm(meep::component c, symm_matrix *chi1inv_matrix, symm_matri sym_matrix_invert(chi1inv_matrix, &meps); } -duals::duald get_material_grid_fill(meep::ndim dim, duals::duald d, double r, duals::duald u, double eta){ +duals::duald get_material_grid_fill(meep::ndim dim, duals::duald d, double r, duals::duald u, + double eta) { /* The fill fraction should describe the amount of u=1 material in the current pixel. - + Occasionally, the distance to the nearest interface is outside the current sphere, which means we don't need to do any averaging (the fill is 0 or 1). Again, we don't know if that means we are in void or solid, however, until we look @@ -1254,59 +1266,70 @@ duals::duald get_material_grid_fill(meep::ndim dim, duals::duald d, double r, du can verify. */ duals::duald rel_fill; - if (abs(d) >= abs(r)){ + if (abs(d) >= abs(r)) { return -1.0; // garbage fill - } else { + } + else { if (dim == meep::D1) - return (r-d)/(2*r); + return (r - d) / (2 * r); else if (dim == meep::D2 || dim == meep::Dcyl) - return (1/(r*r*meep::pi)) * (r*r*acos(d/r)-d*sqrt(r*r-d*d)); + return (1 / (r * r * meep::pi)) * (r * r * acos(d / r) - d * sqrt(r * r - d * d)); else if (dim == meep::D3) - return (((r-d)*(r-d))/(4*meep::pi*r*r*r))*(2*r+d); + return (((r - d) * (r - d)) / (4 * meep::pi * r * r * r)) * (2 * r + d); } - + return rel_fill; } -duals::duald normalize_dual(cvector3 &cv){ +duals::duald normalize_dual(cvector3 &cv) { duals::duald d[3], norm; // compute the norm, u_0' - d[0] = cv.x.re + 1_e*cv.x.im; - d[1] = cv.y.re + 1_e*cv.y.im; - d[2] = cv.z.re + 1_e*cv.z.im; + d[0] = cv.x.re + 1_e * cv.x.im; + d[1] = cv.y.re + 1_e * cv.y.im; + d[2] = cv.z.re + 1_e * cv.z.im; - norm = sqrt(abs(d[0])*abs(d[0]) + abs(d[1])*abs(d[1]) + abs(d[2])*abs(d[2])); + norm = sqrt(abs(d[0]) * abs(d[0]) + abs(d[1]) * abs(d[1]) + abs(d[2]) * abs(d[2])); // normalize the normal vector d[0] = d[0] / norm; d[1] = d[1] / norm; d[2] = d[2] / norm; - cv.x.re = d[0].rpart(); cv.x.im = d[0].dpart(); - cv.y.re = d[1].rpart(); cv.y.im = d[1].dpart(); - cv.z.re = d[2].rpart(); cv.z.im = d[2].dpart(); + cv.x.re = d[0].rpart(); + cv.x.im = d[0].dpart(); + cv.y.re = d[1].rpart(); + cv.y.im = d[1].dpart(); + cv.z.re = d[2].rpart(); + cv.z.im = d[2].dpart(); return norm; } -duals::duald get_distance(cvector3 &cv, duals::duald uval, double eta){ +duals::duald get_distance(cvector3 &cv, duals::duald uval, double eta) { duals::duald d[3], norm; - d[0] = cv.x.re + 1_e*cv.x.im; - d[1] = cv.y.re + 1_e*cv.y.im; - d[2] = cv.z.re + 1_e*cv.z.im; + d[0] = cv.x.re + 1_e * cv.x.im; + d[1] = cv.y.re + 1_e * cv.y.im; + d[2] = cv.z.re + 1_e * cv.z.im; - norm = sqrt(abs(d[0])*abs(d[0]) + abs(d[1])*abs(d[1]) + abs(d[2])*abs(d[2])); + norm = sqrt(abs(d[0]) * abs(d[0]) + abs(d[1]) * abs(d[1]) + abs(d[2]) * abs(d[2])); return (eta - uval) / norm; } -void dual_interpolate_mat(material_type mat,symm_matrix &eps, duals::duald u) { +void dual_interpolate_mat(material_type mat, symm_matrix &eps, duals::duald u) { duals::duald u_bar = tanh_projection(u, mat->beta, mat->eta); // projection - if (std::isnan(u_bar.dpart())) u_bar = u_bar.rpart(); // when beta=inf, we need to correct when gradient breaks down - eps.m00 = mat->medium_1.epsilon_diag.x + u_bar * (mat->medium_2.epsilon_diag.x-mat->medium_1.epsilon_diag.x); - eps.m11 = mat->medium_1.epsilon_diag.y + u_bar * (mat->medium_2.epsilon_diag.y-mat->medium_1.epsilon_diag.y); - eps.m22 = mat->medium_1.epsilon_diag.z + u_bar * (mat->medium_2.epsilon_diag.z-mat->medium_1.epsilon_diag.z); - eps.m01 = mat->medium_1.epsilon_offdiag.x.re + u_bar * (mat->medium_2.epsilon_offdiag.x.re-mat->medium_1.epsilon_offdiag.x.re); - eps.m02 = mat->medium_1.epsilon_offdiag.y.re + u_bar * (mat->medium_2.epsilon_offdiag.y.re-mat->medium_1.epsilon_offdiag.y.re); - eps.m12 = mat->medium_1.epsilon_offdiag.z.re + u_bar * (mat->medium_2.epsilon_offdiag.z.re-mat->medium_1.epsilon_offdiag.z.re); + if (std::isnan(u_bar.dpart())) + u_bar = u_bar.rpart(); // when beta=inf, we need to correct when gradient breaks down + eps.m00 = mat->medium_1.epsilon_diag.x + + u_bar * (mat->medium_2.epsilon_diag.x - mat->medium_1.epsilon_diag.x); + eps.m11 = mat->medium_1.epsilon_diag.y + + u_bar * (mat->medium_2.epsilon_diag.y - mat->medium_1.epsilon_diag.y); + eps.m22 = mat->medium_1.epsilon_diag.z + + u_bar * (mat->medium_2.epsilon_diag.z - mat->medium_1.epsilon_diag.z); + eps.m01 = mat->medium_1.epsilon_offdiag.x.re + + u_bar * (mat->medium_2.epsilon_offdiag.x.re - mat->medium_1.epsilon_offdiag.x.re); + eps.m02 = mat->medium_1.epsilon_offdiag.y.re + + u_bar * (mat->medium_2.epsilon_offdiag.y.re - mat->medium_1.epsilon_offdiag.y.re); + eps.m12 = mat->medium_1.epsilon_offdiag.z.re + + u_bar * (mat->medium_2.epsilon_offdiag.z.re - mat->medium_1.epsilon_offdiag.z.re); } void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, @@ -1336,27 +1359,27 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma /* we may have entered this routine (e.g. because of a material grid backprop routine) but don't want to actually do any averaging */ - if (maxeval == 0){ + if (maxeval == 0) { get_material_pt(mat, v.center()); - if (is_material_grid(mat)){ + if (is_material_grid(mat)) { tp = geom_tree_search(vec_to_vector3(v.center()), restricted_tree, &oi); uval = matgrid_val(vec_to_vector3(v.center()), tp, oi, mat); mgavg: - dual_interpolate_mat(mat,meps,uval); + dual_interpolate_mat(mat, meps, uval); sym_matrix_invert(chi1inv_matrix, &meps); - //get_material_pt(mat, v.center()); - //material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); + // get_material_pt(mat, v.center()); + // material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); material_gc(mat); return; - }else{ - goto trivial; } + else { goto trivial; } } - // For variable materials with do_averaging == true, switch over to slow fallback integration method. - // For material grids, however, we have to do some more logic first... - if ((is_variable(mat) && mat->do_averaging) || (is_variable(mat_behind) && mat_behind->do_averaging)) { - if ((!is_material_grid(mat)) && (!is_material_grid(mat_behind))){ + // For variable materials with do_averaging == true, switch over to slow fallback integration + // method. For material grids, however, we have to do some more logic first... + if ((is_variable(mat) && mat->do_averaging) || + (is_variable(mat_behind) && mat_behind->do_averaging)) { + if ((!is_material_grid(mat)) && (!is_material_grid(mat_behind))) { fallback = true; return; } @@ -1375,13 +1398,13 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma to do any averaging, or if the current pixel is smooth enough to just evaluate. */ - if (is_material_grid(mat)){ + if (is_material_grid(mat)) { // if we have a damping term and β≠∞, we can't smooth if ((mat->damping != 0) && (mat->eta != std::numeric_limits::infinity())) goto noavg; tp = geom_tree_search(p_mat, restricted_tree, &oi); uval = matgrid_val(p_mat, tp, oi, mat); - + normal = matgrid_grad(p_mat, tp, oi, mat); if (cvector3_re(normal).x == 0 && cvector3_re(normal).y == 0 && cvector3_re(normal).z == 0) /* couldn't get normal vector for this point; no averaging is needed, @@ -1389,92 +1412,93 @@ void geom_epsilon::eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_ma goto mgavg; duals::duald u_prime = normalize_dual(normal); duals::duald d = (mat->eta - uval) / u_prime; - - double r = v.diameter()/2; - fill = get_material_grid_fill(v.dim,d,r,uval,mat->eta); - if (fill.rpart() < 0){ + + double r = v.diameter() / 2; + fill = get_material_grid_fill(v.dim, d, r, uval, mat->eta); + if (fill.rpart() < 0) { /* we are far enough away from an interface that we don't need any averaging -- but we still need to interpolate from the material grid */ goto mgavg; - } else{ + } + else { /* we have a material grid interface within our pixel. we therefore need to set the two materials used for averaging to our corresponding solid and void materials. */ symm_matrix eps_0, eps_plus, eps_minus; - dual_interpolate_mat(mat,eps_0,uval); - dual_interpolate_mat(mat,eps_plus,uval+r*u_prime); - dual_interpolate_mat(mat,eps_minus,uval-r*u_prime); - + dual_interpolate_mat(mat, eps_0, uval); + dual_interpolate_mat(mat, eps_plus, uval + r * u_prime); + dual_interpolate_mat(mat, eps_minus, uval - r * u_prime); + if (d > 0) { // Case 1: d > 0. Then let ε̃₊ = ε₊ and let ε̃₋ = [ε₀d + ε₋(Δx/2 – d)] / (Δx/2). meps = eps_plus; - eps2 = (eps_0*d + eps_minus*(r-d)) / (r); - }else { + eps2 = (eps_0 * d + eps_minus * (r - d)) / (r); + } + else { // Case 2: d < 0. Then let ε̃₋ = ε₋ and let ε̃₊ = [-ε₀d + ε₊(Δx/2 + d)] / (Δx/2). eps2 = eps_minus; - meps = (-eps_0*d + eps_plus*(r+d)) / (r); + meps = (-eps_0 * d + eps_plus * (r + d)) / (r); } } - } else if(is_variable(mat)) { + } + else if (is_variable(mat)) { // no averaging is needed (not a material grid) eval_material_pt(mat, vec_to_vector3(v.center())); goto trivial; - // materials are non variable and uniform -- no need to average - } else { - goto trivial; + // materials are non variable and uniform -- no need to average } - /* ------------------------------------------- */ - // Two different materials (perhaps a m.g. and a geom object) - /* ------------------------------------------- */ - } else { - + else { goto trivial; } + /* ------------------------------------------- */ + // Two different materials (perhaps a m.g. and a geom object) + /* ------------------------------------------- */ + } + else { + vector3 normal_re = unit_vector3(normal_to_fixed_object(vector3_minus(p, shiftby), *o)); if (normal_re.x == 0 && normal_re.y == 0 && normal_re.z == 0) goto noavg; // couldn't get normal vector for this point, punt - normal = make_cvector3(normal_re,make_vector3()); + normal = make_cvector3(normal_re, make_vector3()); geom_box pixel = gv2box(v); pixel.low = vector3_minus(pixel.low, shiftby); pixel.high = vector3_minus(pixel.high, shiftby); - fill = duals::duald(box_overlap_with_object(pixel, *o, tol, maxeval),0); + fill = duals::duald(box_overlap_with_object(pixel, *o, tol, maxeval), 0); /* Evaluate materials in case they are variable. This allows us to do fast subpixel averaging at the boundary of an object with a variable material, while remaining accurate enough if the - material is continuous over the pixel. (We make a first-order error by averaging as if the material - were constant, but only in a boundary layer of thickness 1/resolution, so the net effect should - still be second-order.) */ - if (is_variable(mat)){ + material is continuous over the pixel. (We make a first-order error by averaging as if the + material were constant, but only in a boundary layer of thickness 1/resolution, so the net + effect should still be second-order.) */ + if (is_variable(mat)) { eval_material_pt(mat, p_mat); eval_material_pt(mat_behind, p_mat_behind); if (material_type_equal(mat, mat_behind)) goto trivial; } - - /* to properly determine the tensors of a material + + /* to properly determine the tensors of a material that has a material grid, we need to be careful. Specifically, we need to include the effects of u_i in the dual part of the material tensor */ - if (is_material_grid(mat)){ + if (is_material_grid(mat)) { tp = geom_tree_search(p_mat, restricted_tree, &oi); duals::duald uval = matgrid_val(p_mat, tp, oi, mat); - dual_interpolate_mat(mat,meps,uval); - sym_matrix_invert(chi1inv_matrix, &meps); - } else { - material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); + dual_interpolate_mat(mat, meps, uval); + sym_matrix_invert(chi1inv_matrix, &meps); } - if (is_material_grid(mat_behind)){ + else { material_epsmu(meep::type(c), mat, &meps, chi1inv_matrix); } + if (is_material_grid(mat_behind)) { tp = geom_tree_search(p_mat_behind, restricted_tree, &oi); duals::duald uval = matgrid_val(p_mat_behind, tp, oi, mat); - dual_interpolate_mat(mat_behind,eps2,uval); + dual_interpolate_mat(mat_behind, eps2, uval); sym_matrix_invert(&epsinv2, &eps2); // not really needed - } else { - material_epsmu(meep::type(c), mat_behind, &eps2, &epsinv2); } + else { material_epsmu(meep::type(c), mat_behind, &eps2, &epsinv2); } } // it doesn't make sense to average metals (electric or magnetic) if (is_metal(meep::type(c), &mat) || is_metal(meep::type(c), &mat_behind)) goto noavg; - kottke_algorithm(c,chi1inv_matrix,meps,eps2,normal,fill); + kottke_algorithm(c, chi1inv_matrix, meps, eps2, normal, fill); material_gc(mat); } @@ -1494,7 +1518,7 @@ struct matgrid_volavg { static void get_uproj_w(const matgrid_volavg *mgva, double x0, double &u_proj, double &w) { // use a linear approximation for the material grid weights around the Yee grid point - //u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs*x0, mgva->beta, mgva->eta); + // u_proj = tanh_projection(mgva->uval + mgva->ugrad_abs*x0, mgva->beta, mgva->eta); if (mgva->dim == meep::D1) w = 1 / (2 * mgva->rad); else if (mgva->dim == meep::D2 || mgva->dim == meep::Dcyl) @@ -1665,9 +1689,11 @@ void geom_epsilon::fallback_chi1inv_row(meep::component c, double chi1inv_row[3] minveps = ret.im / vol; #else meps = adaptive_integration(eps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / vol; + &errflag) / + vol; minveps = adaptive_integration(inveps_func, xmin, xmax, n, (void *)this, 0, tol, maxeval, &esterr, - &errflag) / vol; + &errflag) / + vol; #endif if (eps_ever_negative) // averaging negative eps causes instability @@ -1912,7 +1938,7 @@ void geom_epsilon::sigma_row(meep::component c, double sigrow[3], const meep::ve tp = geom_tree_search(p, restricted_tree, &oi); // interpolate and project onto material grid u = tanh_projection(matgrid_val(p, tp, oi, mat), mat->beta, mat->eta).rpart(); - epsilon_material_grid(mat, u); // interpolate material from material grid point + epsilon_material_grid(mat, u); // interpolate material from material grid point mat->medium.check_offdiag_im_zero_or_abort(); } @@ -2180,7 +2206,8 @@ void add_absorbing_layer(absorber_list alist, double thickness, int direction, i if needed */ geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, vector3 center, bool _ensure_periodicity, material_type _default_material, - material_type_list extra_materials, bool _use_anisotropic_averaging) { + material_type_list extra_materials, + bool _use_anisotropic_averaging) { // set global variables in libctlgeom based on data fields in s geom_initialize(); geometry_center = center; @@ -2241,11 +2268,10 @@ void set_materials_from_geometry(meep::structure *s, geometric_object_list g, ve bool use_anisotropic_averaging, double tol, int maxeval, bool _ensure_periodicity, material_type _default_material, absorber_list alist, material_type_list extra_materials) { - meep_geom::geom_epsilon *geps = meep_geom::make_geom_epsilon(s, &g, center, _ensure_periodicity, - _default_material, extra_materials, - use_anisotropic_averaging); - set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, - maxeval, alist); + meep_geom::geom_epsilon *geps = + meep_geom::make_geom_epsilon(s, &g, center, _ensure_periodicity, _default_material, + extra_materials, use_anisotropic_averaging); + set_materials_from_geom_epsilon(s, geps, use_anisotropic_averaging, tol, maxeval, alist); delete geps; } @@ -2343,8 +2369,8 @@ material_type make_material_grid(bool do_averaging, double beta, double eta, dou void update_weights(material_type matgrid, double *weights) { size_t N = matgrid->grid_size.x * matgrid->grid_size.y * matgrid->grid_size.z; - //memcpy(matgrid->weights, weights, N * sizeof(double)); - for (size_t i=0;iweights, weights, N * sizeof(double)); + for (size_t i = 0; i < N; i++) matgrid->weights.push_back(weights[i]); } @@ -2853,27 +2879,30 @@ std::complex cond_cmp(meep::component c, const meep::vec &r, double freq const medium_struct *mm = &(md->medium); // get the row we care about - switch (component_direction(c)) { - case meep::X: - case meep::R: return std::complex(1.0, mm->D_conductivity_diag.x / (2*meep::pi*freq)); - case meep::Y: - case meep::P: return std::complex(1.0, mm->D_conductivity_diag.y / (2*meep::pi*freq)); - case meep::Z: return std::complex(1.0, mm->D_conductivity_diag.z / (2*meep::pi*freq)); - case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); - } -} - -std::complex get_material_gradient( - const meep::vec &r, // current point - const meep::component adjoint_c, // adjoint field component - const meep::component forward_c, // forward field component - std::complex fields_f, // forward field at current point - double freq, // frequency - geom_epsilon *geps, // material - meep::grid_volume &gv, // simulation grid volume - double du, // step size - std::vector &u, // matgrid - int idx // matgrid index + switch (component_direction(c)) { + case meep::X: + case meep::R: + return std::complex(1.0, mm->D_conductivity_diag.x / (2 * meep::pi * freq)); + case meep::Y: + case meep::P: + return std::complex(1.0, mm->D_conductivity_diag.y / (2 * meep::pi * freq)); + case meep::Z: + return std::complex(1.0, mm->D_conductivity_diag.z / (2 * meep::pi * freq)); + case meep::NO_DIRECTION: meep::abort("Invalid adjoint field component"); + } +} + +std::complex +get_material_gradient(const meep::vec &r, // current point + const meep::component adjoint_c, // adjoint field component + const meep::component forward_c, // forward field component + std::complex fields_f, // forward field at current point + double freq, // frequency + geom_epsilon *geps, // material + meep::grid_volume &gv, // simulation grid volume + double du, // step size + std::vector &u, // matgrid + int idx // matgrid index ) { /*Compute the Aᵤx product from the -λᵀAᵤx calculation. The current adjoint (λ) field component (adjoint_c) @@ -2925,15 +2954,15 @@ std::complex get_material_gradient( u[idx] += 1_e; // add dual component geps->eff_chi1inv_row_grad(adjoint_c, dA_du, v, geps->tol, maxevals, true); u[idx] -= 1_e; // remove dual component - + return -dA_du[dir_idx] * fields_f; // note the minus sign! (from Steven's adjoint notes) - - } else { + } + else { std::complex row_1[3], row_2[3], dA_du[3]; u[idx] -= du; - eff_chi1inv_row_disp(adjoint_c,row_1,r,freq,geps); - u[idx] += 2*du; - eff_chi1inv_row_disp(adjoint_c,row_2,r,freq,geps); + eff_chi1inv_row_disp(adjoint_c, row_1, r, freq, geps); + u[idx] += 2 * du; + eff_chi1inv_row_disp(adjoint_c, row_2, r, freq, geps); u[idx] -= du; for (int i = 0; i < 3; i++) @@ -2949,13 +2978,12 @@ With the addition of subpixel smoothing, however, the vJp became much more complicated and it is easier to calculate the entire gradient using finite differences (at the cost of slightly less accurate gradients due to floating-point roundoff errors). */ -void add_interpolate_weights(double rx, double ry, double rz, - double *data, int nx, int ny, int nz, int stride, - double scaleby, std::vector &udata, int ukind, duals::duald uval, - meep::vec r, geom_epsilon *geps, +void add_interpolate_weights(double rx, double ry, double rz, double *data, int nx, int ny, int nz, + int stride, double scaleby, std::vector &udata, + int ukind, duals::duald uval, meep::vec r, geom_epsilon *geps, meep::component adjoint_c, meep::component forward_c, - std::complex fwd, std::complex adj, - double freq, meep::grid_volume &gv, double du) { + std::complex fwd, std::complex adj, double freq, + meep::grid_volume &gv, double du) { int x1, y1, z1, x2, y2, z2; double dx, dy, dz; duals::duald u; @@ -3056,10 +3084,9 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge uval = tanh_projection(matgrid_val(p, tp, oi, mg), mg->beta, mg->eta); do { vector3 pb = to_geom_box_coords(p, &tp->objects[oi]); - add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, - scalegrad, mg->weights, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, - fwd, adj, freq, gv, tol); + add_interpolate_weights(pb.x, pb.y, pb.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, mg->weights, + kind, uval, vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, + freq, gv, tol); if (kind == material_data::U_DEFAULT) break; tp = geom_tree_search_next(p, tp, &oi); } while (tp && is_material_grid((material_data *)tp->objects[oi].o->material)); @@ -3070,10 +3097,9 @@ void material_grids_addgradient_point(double *v, vector3 p, double scalegrad, ge vector3 sz = mg->grid_size; double *vcur = v; uval = tanh_projection(material_grid_val(p, mg), mg->beta, mg->eta); - add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, - scalegrad, mg->weights, kind, uval, - vector3_to_vec(p), geps, adjoint_c, forward_c, - fwd, adj, freq, gv, tol); + add_interpolate_weights(p.x, p.y, p.z, vcur, sz.x, sz.y, sz.z, 1, scalegrad, mg->weights, kind, + uval, vector3_to_vec(p), geps, adjoint_c, forward_c, fwd, adj, freq, gv, + tol); } } diff --git a/src/meepgeom.hpp b/src/meepgeom.hpp index 3b5014cb4..00bc8e50c 100644 --- a/src/meepgeom.hpp +++ b/src/meepgeom.hpp @@ -154,49 +154,70 @@ inline vector3 make_vector3(double x = 0.0, double y = 0.0, double z = 0.0) { } inline cvector3 cvector3_scale(number s, cvector3 v) { - return make_cvector3(vector3_scale(s,cvector3_re(v)),vector3_scale(s,cvector3_im(v))); + return make_cvector3(vector3_scale(s, cvector3_re(v)), vector3_scale(s, cvector3_im(v))); } -inline cvector3 cvector_zero(){ - return make_cvector3(make_vector3(),make_vector3()); -} +inline cvector3 cvector_zero() { return make_cvector3(make_vector3(), make_vector3()); } -inline cvector3 cvector_add(cvector3 cv1, cvector3 cv2){ - return make_cvector3(vector3_plus(cvector3_re(cv1),cvector3_re(cv2)),vector3_plus(cvector3_im(cv1),cvector3_im(cv2))); +inline cvector3 cvector_add(cvector3 cv1, cvector3 cv2) { + return make_cvector3(vector3_plus(cvector3_re(cv1), cvector3_re(cv2)), + vector3_plus(cvector3_im(cv1), cvector3_im(cv2))); } -struct symm_matrix{ +struct symm_matrix { duals::duald m00, m01, m02, m11, m12, m22; - struct symm_matrix& operator+=(const symm_matrix& rhs) { - m00 += rhs.m00; m11 += rhs.m11; m22 += rhs.m22; - m01 += rhs.m01; m02 += rhs.m02; m12 += rhs.m12; - return *this; + struct symm_matrix &operator+=(const symm_matrix &rhs) { + m00 += rhs.m00; + m11 += rhs.m11; + m22 += rhs.m22; + m01 += rhs.m01; + m02 += rhs.m02; + m12 += rhs.m12; + return *this; + } + struct symm_matrix &operator+=(const duals::duald &k) { + m00 += k; + m11 += k; + m22 += k; + return *this; + } + struct symm_matrix &operator+(const symm_matrix &rhs) { + m00 += rhs.m00; + m11 += rhs.m11; + m22 += rhs.m22; + m01 += rhs.m01; + m02 += rhs.m02; + m12 += rhs.m12; + return *this; + } + struct symm_matrix &operator*(const duals::duald &k) { + m00 *= k; + m11 *= k; + m22 *= k; + m01 *= k; + m02 *= k; + m12 *= k; + return *this; + } + struct symm_matrix &operator/(const duals::duald &k) { + m00 /= k; + m11 /= k; + m22 /= k; + m01 /= k; + m02 /= k; + m12 /= k; + return *this; } - struct symm_matrix& operator+=(const duals::duald& k) { - m00 += k; m11 += k; m22 += k; - return *this; - } - struct symm_matrix& operator+(const symm_matrix& rhs) { - m00 += rhs.m00; m11 += rhs.m11; m22 += rhs.m22; - m01 += rhs.m01; m02 += rhs.m02; m12 += rhs.m12; - return *this; - } - struct symm_matrix& operator*( const duals::duald& k) { - m00 *= k; m11 *= k; m22 *= k; - m01 *= k; m02 *= k; m12 *= k; - return *this; - } - struct symm_matrix& operator/( const duals::duald& k) { - m00 /= k; m11 /= k; m22 /= k; - m01 /= k; m02 /= k; m12 /= k; - return *this; - } - struct symm_matrix& operator-() { - m00 = -m00; m11 = -m11; m22 = -m22; - m01 = -m01; m02 = -m02; m12 = -m12; - return *this; - } -} ; + struct symm_matrix &operator-() { + m00 = -m00; + m11 = -m11; + m22 = -m22; + m01 = -m01; + m02 = -m02; + m12 = -m12; + return *this; + } +}; struct pol { meep_geom::susceptibility user_s; @@ -249,7 +270,7 @@ class geom_epsilon : public meep::material_function { virtual void eff_chi1inv_row(meep::component c, double chi1inv_row[3], const meep::volume &v, double tol, int maxeval); void eff_chi1inv_row_grad(meep::component c, double chi1inv_row[3], const meep::volume &v, - double tol, int maxeval, bool needs_grad); + double tol, int maxeval, bool needs_grad); void eff_chi1inv_matrix(meep::component c, symm_matrix *chi1inv_matrix, const meep::volume &v, double tol, int maxeval, bool &fallback); @@ -270,12 +291,11 @@ class geom_epsilon : public meep::material_function { }; void set_dimensions(int dims); -geom_epsilon* make_geom_epsilon(meep::structure *s, geometric_object_list *g, - vector3 center = make_vector3(), - bool ensure_periodicity = false, - material_type _default_material = vacuum, - material_type_list extra_materials = material_type_list(), - bool use_anisotropic_averaging = false); +geom_epsilon *make_geom_epsilon(meep::structure *s, geometric_object_list *g, + vector3 center = make_vector3(), bool ensure_periodicity = false, + material_type _default_material = vacuum, + material_type_list extra_materials = material_type_list(), + bool use_anisotropic_averaging = false); //, geometric_object_list g, material_type_list extra_materials void set_materials_from_geometry(meep::structure *s, geometric_object_list g, vector3 center = make_vector3(),